From de36144051f7a8516e28dc109e58f5d55a583687 Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Tue, 16 Jan 2024 10:25:15 -0500 Subject: [PATCH 01/26] Create __init__.py (#502) --- docs/examples/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/examples/__init__.py diff --git a/docs/examples/__init__.py b/docs/examples/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/docs/examples/__init__.py @@ -0,0 +1 @@ + From aef8c9b0244dc88416fa0628c456e29398181c43 Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Wed, 17 Jan 2024 12:28:29 -0500 Subject: [PATCH 02/26] Setup experiment configurations (#504) * added scaffolding for experiment configs * added experiment configs and template files * remove experiment configs --- docs/examples/__init__.py | 1 - .../robust_paper/datasets/1/config.json | 13 +++ .../examples/robust_paper/datasets/1/data.pkl | Bin 0 -> 765 bytes docs/examples/robust_paper/functionals.py | 0 docs/examples/robust_paper/models.py | 8 ++ .../robust_paper/scripts/create_datasets.py | 46 ++++++++++ .../scripts/create_experiment_configs.py | 82 ++++++++++++++++++ docs/examples/robust_paper/scripts/statics.py | 7 ++ 8 files changed, 156 insertions(+), 1 deletion(-) delete mode 100644 docs/examples/__init__.py create mode 100644 docs/examples/robust_paper/datasets/1/config.json create mode 100644 docs/examples/robust_paper/datasets/1/data.pkl create mode 100644 docs/examples/robust_paper/functionals.py create mode 100644 docs/examples/robust_paper/models.py create mode 100644 docs/examples/robust_paper/scripts/create_datasets.py create mode 100644 docs/examples/robust_paper/scripts/create_experiment_configs.py create mode 100644 docs/examples/robust_paper/scripts/statics.py diff --git a/docs/examples/__init__.py b/docs/examples/__init__.py deleted file mode 100644 index 8b137891..00000000 --- a/docs/examples/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/docs/examples/robust_paper/datasets/1/config.json b/docs/examples/robust_paper/datasets/1/config.json new file mode 100644 index 00000000..36f0bd9d --- /dev/null +++ b/docs/examples/robust_paper/datasets/1/config.json @@ -0,0 +1,13 @@ +{ + "dataset_configs": { + "dataset_name": "dataset_1", + "dataset_description": "Dataset 1", + "dataset_path": "./data/1.csv", + "seed": 1 + }, + "model_configs": { + "model": "causal_GLM", + "link_function": "normal", + "p": 200 + } +} \ No newline at end of file diff --git a/docs/examples/robust_paper/datasets/1/data.pkl b/docs/examples/robust_paper/datasets/1/data.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e797ba12efae7c1fe0546b77f2105f0cdb7b0bf6 GIT binary patch literal 765 zcmdUsyH3L}6ozx7m3k3~jg2u#sA@|obgdAJxwCjdR+|_S6yn-(Dix_xmsWtXVKyFs zS7T*EV&J$JBoI6R|KKRcUyhHzjGU)8%a~@U+|0x(D@i82ph8j^Mv2N+iJ;vyjC>+# zoG?N9b@d2nd4?tM=v+~it&oUyd~zw+4e1UfO@L}|nuam59tzRN^Zxt#^~=dxWn>+` z+OQ6};o=oIxhI%p9*anyiX>z)Hvzz9*oi_ZBT8uOhn<*PcensWIp|T6gts)|_K-U+ zUV>s0Fd_L8vak(rVJvwLIoQ!XSbW9v$VpQ;)5UoW&qn5kH@!mP0Fd)pt-wvZ3`ng} zt?%qso2_PTt5vV^SzoW0avSB2BzI1s+XXj%h0RVS2R(G)`qRu68+tk&J(-T4U1!0o zu{u-8Ko+tm3fahsJ|C_5)^NoDGA?X=0F9G8|2%if3Cc- j-D*~wjc>}WzmzXvP`*(7Q~8(Du|nop-3QBaWohLTmZ{B) literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/functionals.py b/docs/examples/robust_paper/functionals.py new file mode 100644 index 00000000..e69de29b diff --git a/docs/examples/robust_paper/models.py b/docs/examples/robust_paper/models.py new file mode 100644 index 00000000..922c09f0 --- /dev/null +++ b/docs/examples/robust_paper/models.py @@ -0,0 +1,8 @@ +class causal_GLM: + pass + +class kernel_ridge: + pass + +class neural_network: + pass \ No newline at end of file diff --git a/docs/examples/robust_paper/scripts/create_datasets.py b/docs/examples/robust_paper/scripts/create_datasets.py new file mode 100644 index 00000000..96df8e01 --- /dev/null +++ b/docs/examples/robust_paper/scripts/create_datasets.py @@ -0,0 +1,46 @@ +import os +import json +import pickle +import torch +from docs.examples.robust_paper.scripts.statics import MODELS, LINK_FUNCTIONS, EXPERIMENT_CATEGORIES, DATASET_PATHS, FUNCTIONALS, ESTIMATORS, INFLUENCE_ESTIMATORS +from docs.examples.robust_paper.models import causal_GLM, kernel_ridge, neural_network + + +MODELS_DICT = {"causal_GLM": causal_GLM, "kernel_ridge": kernel_ridge, "neural_network": neural_network} + + +def main(): + # EXPERIMENT 1 - Influence function approximation experiment + # for model_str in MODELS: + # model = MODELS_DICT[model_str](*model_args, **model_kwargs) + # data = model() + + # # TODO: save the data. + + # # TODO: save the data config. + + if not os.path.exists("docs/examples/robust_paper/datasets/1"): + os.makedirs("docs/examples/robust_paper/datasets/1") + json.dump(example_json, open("docs/examples/robust_paper/datasets/1/config.json", "w"), indent=4) + + data = {"X": torch.tensor([[1, 2, 3], [4, 5, 6]]), "y": torch.tensor([1, 2])} + + pickle.dump(data, open("docs/examples/robust_paper/datasets/1/data.pkl", "wb")) + + +example_json = { + "dataset_configs": { + "dataset_name": "dataset_1", + "dataset_description": "Dataset 1", + "dataset_path": "./data/1.csv", + "seed": 1, + }, + "model_configs": { + "model": "causal_GLM", + "link_function": "normal", + "p": 200, + }, +} + +if __name__ == "__main__": + main() diff --git a/docs/examples/robust_paper/scripts/create_experiment_configs.py b/docs/examples/robust_paper/scripts/create_experiment_configs.py new file mode 100644 index 00000000..81a5c0b6 --- /dev/null +++ b/docs/examples/robust_paper/scripts/create_experiment_configs.py @@ -0,0 +1,82 @@ +import os +import json +from docs.examples.robust_paper.scripts.statics import MODELS, LINK_FUNCTIONS, EXPERIMENT_CATEGORIES, FUNCTIONALS, DATASET_PATHS, ESTIMATORS, INFLUENCE_ESTIMATORS + +def main(): + # EXPERIMENT 1 - Influence function approximation experiment + experiment_category = "influence_approx" + assert experiment_category in EXPERIMENT_CATEGORIES + + experiment_id = 0 + for model_str in MODELS: + for link_function_str in LINK_FUNCTIONS: + for functional_str in FUNCTIONALS: + for estimator_str in ESTIMATORS: + for influence_estimator_str in INFLUENCE_ESTIMATORS: + for dataset_path in DATASET_PATHS: + experiment_id += 1 + example_json = { + "experiment_configs": { + "experiment_name": f"{experiment_category}_{experiment_id}", + "experiment_description": "Influence function approximation experiment", + "dataset_path": dataset_path, + "results_path": f"./results/{experiment_id}/", + "seed": 1, + }, + "model_configs": { + "model": model_str, + "link_function": link_function_str, + }, + "functional_configs": { + "functional": functional_str, + "num_monte_carlo": 1000, + }, + "estimator_configs": { + "estimator": estimator_str, + }, + "influence_estimator_configs": { + "influence_estimator": influence_estimator_str, + "num_samples_outer": 1000, + "num_samples_inner": 1000, + "cg_iters": None, + "residual_tol": 1e-4, + } + } + + if not os.path.exists(f"docs/examples/robust_paper/experiments/{experiment_id}"): + os.makedirs(f"docs/examples/robust_paper/experiments/{experiment_id}") + json.dump(example_json, open(f"docs/examples/robust_paper/experiments/{experiment_id}/config.json", "w"), indent=4) + + # json.dump(example_json, open("docs/examples/robust_paper/experiments/example_1.json", "w"), indent=4) + + +example_json = { + "experiment_configs": { + "experiment_name": "influence_approx_1", + "experiment_description": "Influence function approximation experiment", + "dataset_path": "./data/1.csv", + "results_path": "./results/1/", + "seed": 1, + }, + "model_configs": { + "model": "causal_GLM", + "link_function": "normal", + }, + "functional_configs": { + "functional": "ATE", + "num_monte_carlo": 1000, + }, + "estimator_configs": { + "estimator": "plug_in", + }, + "influence_estimator_configs": { + "influence_estimator": "monte_carlo", + "num_samples_outer": 1000, + "num_samples_inner": 1000, + "cg_iters": None, + "residual_tol": 1e-4, + } +} + +if __name__ == "__main__": + main() diff --git a/docs/examples/robust_paper/scripts/statics.py b/docs/examples/robust_paper/scripts/statics.py new file mode 100644 index 00000000..eab8d778 --- /dev/null +++ b/docs/examples/robust_paper/scripts/statics.py @@ -0,0 +1,7 @@ +MODELS = ["causal_GLM", "kernel_ridge", "neural_network"] +LINK_FUNCTIONS = ["normal", "bernoulli"] +EXPERIMENT_CATEGORIES = ["influence_approx", "estimator_approx", "capstone"] +DATASET_PATHS = [f"docs/examples/robust_paper/datasets/{i}" for i in range(1, 5)] +FUNCTIONALS = ["ATE", "ESD", "CATE"] +ESTIMATORS = ["plug_in", "tmle", "one_step", "double_ml"] +INFLUENCE_ESTIMATORS = ["monte_carlo", "analytical", "finite_difference"] \ No newline at end of file From ee60ad4bfdc896c9e45ba991194885c7a19ce400 Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Thu, 18 Jan 2024 11:24:49 -0500 Subject: [PATCH 03/26] More experiment configs (#505) * ate functional * causal glm * all combos of data config * additive data config * simulator for causalglm refactored * uncomitted changes * major refactor * more refactoring needed but expected density done now * added missing files --------- Co-authored-by: Raj Agrawal --- docs/examples/robust_paper/analytic_eif.py | 46 ++++ .../robust_paper/datasets/1/config.json | 13 -- .../examples/robust_paper/datasets/1/data.pkl | Bin 765 -> 0 bytes docs/examples/robust_paper/functionals.py | 62 +++++ docs/examples/robust_paper/models.py | 189 ++++++++++++++- .../robust_paper/scripts/create_datasets.py | 221 +++++++++++++++--- .../scripts/create_experiment_configs.py | 159 +++++++------ .../robust_paper/scripts/influence_approx.py | 89 +++++++ docs/examples/robust_paper/scripts/statics.py | 55 ++++- docs/examples/robust_paper/utils.py | 114 +++++++++ 10 files changed, 826 insertions(+), 122 deletions(-) create mode 100644 docs/examples/robust_paper/analytic_eif.py delete mode 100644 docs/examples/robust_paper/datasets/1/config.json delete mode 100644 docs/examples/robust_paper/datasets/1/data.pkl create mode 100644 docs/examples/robust_paper/scripts/influence_approx.py create mode 100644 docs/examples/robust_paper/utils.py diff --git a/docs/examples/robust_paper/analytic_eif.py b/docs/examples/robust_paper/analytic_eif.py new file mode 100644 index 00000000..bf16ba7a --- /dev/null +++ b/docs/examples/robust_paper/analytic_eif.py @@ -0,0 +1,46 @@ +import torch +from typing import Tuple +from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood + + +def analytic_eif_expected_density(test_data, plug_in, model, *args, **kwargs): + log_marginal_prob_at_points = BatchedNMCLogMarginalLikelihood(model, num_samples=1)( + test_data, *args, **kwargs + ) + analytic_eif_at_test_pts = 2 * (torch.exp(log_marginal_prob_at_points) - plug_in) + analytic_correction = analytic_eif_at_test_pts.mean() + return analytic_correction, analytic_eif_at_test_pts + + +def analytic_eif_ate_causal_glm( + test_data, point_estimates +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Computes the analytic EIF for the ATE for a ``CausalGLM`` model. + + :param test_data: Dictionary containing test data with keys "X", "A", and "Y" + :param point_estimates: Estimated parameters of the model with keys "propensity_weights", + "outcome_weights", "treatment_weight", and "intercept" + :type point_estimates: _type_ + :return: Tuple of the analytic EIF averaged over test, + and the analytic EIF evaluated pointwise at each test point + :rtype: Tuple[torch.Tensor, torch.Tensor] + """ + assert "propensity_weights" in point_estimates, "propensity_weights not found" + assert "outcome_weights" in point_estimates, "outcome_weights not found" + assert "treatment_weight" in point_estimates, "treatment_weight not found" + assert "intercept" in point_estimates, "treatment_weight not found" + assert test_data.keys() == {"X", "A", "Y"}, "test_data has unexpected keys" + + X = test_data["X"] + A = test_data["A"] + Y = test_data["Y"] + pi_X = torch.sigmoid(X.mv(point_estimates["propensity_weights"])) + mu_X = ( + X.mv(point_estimates["outcome_weights"]) + + A * point_estimates["treatment_weight"] + + point_estimates["intercept"] + ) + analytic_eif_at_test_pts = (A / pi_X - (1 - A) / (1 - pi_X)) * (Y - mu_X) + analytic_correction = analytic_eif_at_test_pts.mean() + return analytic_correction, analytic_eif_at_test_pts diff --git a/docs/examples/robust_paper/datasets/1/config.json b/docs/examples/robust_paper/datasets/1/config.json deleted file mode 100644 index 36f0bd9d..00000000 --- a/docs/examples/robust_paper/datasets/1/config.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "dataset_configs": { - "dataset_name": "dataset_1", - "dataset_description": "Dataset 1", - "dataset_path": "./data/1.csv", - "seed": 1 - }, - "model_configs": { - "model": "causal_GLM", - "link_function": "normal", - "p": 200 - } -} \ No newline at end of file diff --git a/docs/examples/robust_paper/datasets/1/data.pkl b/docs/examples/robust_paper/datasets/1/data.pkl deleted file mode 100644 index e797ba12efae7c1fe0546b77f2105f0cdb7b0bf6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 765 zcmdUsyH3L}6ozx7m3k3~jg2u#sA@|obgdAJxwCjdR+|_S6yn-(Dix_xmsWtXVKyFs zS7T*EV&J$JBoI6R|KKRcUyhHzjGU)8%a~@U+|0x(D@i82ph8j^Mv2N+iJ;vyjC>+# zoG?N9b@d2nd4?tM=v+~it&oUyd~zw+4e1UfO@L}|nuam59tzRN^Zxt#^~=dxWn>+` z+OQ6};o=oIxhI%p9*anyiX>z)Hvzz9*oi_ZBT8uOhn<*PcensWIp|T6gts)|_K-U+ zUV>s0Fd_L8vak(rVJvwLIoQ!XSbW9v$VpQ;)5UoW&qn5kH@!mP0Fd)pt-wvZ3`ng} zt?%qso2_PTt5vV^SzoW0avSB2BzI1s+XXj%h0RVS2R(G)`qRu68+tk&J(-T4U1!0o zu{u-8Ko+tm3fahsJ|C_5)^NoDGA?X=0F9G8|2%if3Cc- j-D*~wjc>}WzmzXvP`*(7Q~8(Du|nop-3QBaWohLTmZ{B) diff --git a/docs/examples/robust_paper/functionals.py b/docs/examples/robust_paper/functionals.py index e69de29b..19130c73 100644 --- a/docs/examples/robust_paper/functionals.py +++ b/docs/examples/robust_paper/functionals.py @@ -0,0 +1,62 @@ +from typing import Callable +import math +import torch +import pyro +from chirho.counterfactual.handlers import MultiWorldCounterfactual +from chirho.indexed.ops import IndexSet, gather +from chirho.interventional.handlers import do +from chirho.robust.handlers.predictive import PredictiveFunctional +from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood + +pyro.settings.set(module_local_params=True) + + +class ATEFunctional(torch.nn.Module): + def __init__( + self, model: Callable, *, treatment_name: str = "A", num_monte_carlo: int = 1000 + ): + super().__init__() + self.model = model + self.num_monte_carlo = num_monte_carlo + self.treatment_name = treatment_name + + def forward(self, *args, **kwargs) -> torch.Tensor: + """ + Computes the average treatment effect (ATE) of the model. Assumes that the treatment + is binary and that the model returns the target response. + + :return: average treatment effect estimated using Monte Carlo + :rtype: torch.Tensor + """ + with MultiWorldCounterfactual(): + with pyro.plate( + "monte_carlo_functional", size=self.num_monte_carlo, dim=-2 + ): + treatment_dict = { + self.treatment_name: (torch.tensor(0.0), torch.tensor(1.0)) + } + with do(actions=treatment_dict): + Ys = self.model(*args, **kwargs) + Y0 = gather(Ys, IndexSet(A={1}), event_dim=0) + Y1 = gather(Ys, IndexSet(A={2}), event_dim=0) + # TODO: if response is scalar, we do we need to average over dim=-1? + ate = (Y1 - Y0).mean(dim=-2, keepdim=True).mean(dim=-1, keepdim=True).squeeze() + return pyro.deterministic("ATE", ate) + + +class ExpectedDensity(torch.nn.Module): + def __init__(self, model, *, num_monte_carlo: int = 10000): + super().__init__() + self.model = model + self.log_marginal_prob = BatchedNMCLogMarginalLikelihood(model, num_samples=1) + self.num_monte_carlo = num_monte_carlo + + def forward(self, *args, **kwargs): + with pyro.plate("monte_carlo_functional", self.num_monte_carlo): + points = PredictiveFunctional(self.model)(*args, **kwargs) + + log_marginal_prob_at_points = self.log_marginal_prob(points, *args, **kwargs) + return torch.exp( + torch.logsumexp(log_marginal_prob_at_points, dim=0) + - math.log(self.num_monte_carlo) + ) diff --git a/docs/examples/robust_paper/models.py b/docs/examples/robust_paper/models.py index 922c09f0..e015ea69 100644 --- a/docs/examples/robust_paper/models.py +++ b/docs/examples/robust_paper/models.py @@ -1,8 +1,191 @@ -class causal_GLM: - pass +from typing import Callable, Optional, Dict +import torch +import math +import pyro +import pyro.distributions as dist + +pyro.settings.set(module_local_params=True) + + +class CausalGLM(pyro.nn.PyroModule): + def __init__( + self, + p: int, + link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0), + prior_scale: Optional[float] = None, + ): + super().__init__() + self.p = p + self.link_fn = link_fn + if prior_scale is None: + self.prior_scale = 1 / math.sqrt(self.p) + else: + self.prior_scale = prior_scale + + self.observed_sites = ["X", "A", "Y"] + + def sample_outcome_weights(self): + return pyro.sample( + "outcome_weights", + dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1), + ) + + def sample_intercept(self): + return pyro.sample("intercept", dist.Normal(0.0, 1.0)) + + def sample_propensity_weights(self): + return pyro.sample( + "propensity_weights", + dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1), + ) + + def sample_treatment_weight(self): + return pyro.sample("treatment_weight", dist.Normal(0.0, 1.0)) + + def sample_covariate_loc_scale(self): + return torch.zeros(self.p), torch.ones(self.p) + + def forward(self): + intercept = self.sample_intercept() + outcome_weights = self.sample_outcome_weights() + propensity_weights = self.sample_propensity_weights() + tau = self.sample_treatment_weight() + x_loc, x_scale = self.sample_covariate_loc_scale() + X = pyro.sample("X", dist.Normal(x_loc, x_scale).to_event(1)) + A = pyro.sample( + "A", + dist.Bernoulli( + logits=torch.einsum("...i,...i->...", X, propensity_weights) + ), + ) + + return pyro.sample( + "Y", + self.link_fn( + torch.einsum("...i,...i->...", X, outcome_weights) + A * tau + intercept + ), + ) + + +class ConditionedCausalGLM(CausalGLM): + def __init__( + self, + data: Dict[str, torch.Tensor], + *, + link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0), + prior_scale: Optional[float] = None, + ): + p = X.shape[1] + super().__init__(p, link_fn, prior_scale) + self.X = data["X"] + self.A = data["A"] + self.Y = data["Y"] + + def forward(self): + intercept = self.sample_intercept() + outcome_weights = self.sample_outcome_weights() + propensity_weights = self.sample_propensity_weights() + tau = self.sample_treatment_weight() + x_loc, x_scale = self.sample_covariate_loc_scale() + with pyro.plate("__train__", size=self.X.shape[0], dim=-1): + X = pyro.sample("X", dist.Normal(x_loc, x_scale).to_event(1), obs=self.X) + A = pyro.sample( + "A", + dist.Bernoulli( + logits=torch.einsum("ni,i->n", self.X, propensity_weights) + ), + obs=self.A, + ) + pyro.sample( + "Y", + self.link_fn( + torch.einsum("ni,i->n", X, outcome_weights) + A * tau + intercept + ), + obs=self.Y, + ) + + +class DataGeneratorCausalGLM(CausalGLM): + def __init__( + self, + p: int, + alpha: int, + beta: int, + link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0), + treatment_weight: float = 0.0, + ): + super().__init__(p, link_fn) + self.alpha = alpha # sparsity of propensity weights + self.beta = beta # sparsity of outcome weights + self.treatment_weight = treatment_weight + + def sample_outcome_weights(self): + outcome_weights = 1 / math.sqrt(self.beta) * torch.ones(self.p) + outcome_weights[self.beta :] = 0.0 + return outcome_weights + + def sample_propensity_weights(self): + propensity_weights = 1 / math.sqrt(self.alpha) * torch.ones(self.p) + propensity_weights[self.alpha :] = 0.0 + return propensity_weights + + def sample_treatment_weight(self): + return torch.tensor(self.treatment_weight) + + def sample_intercept(self): + return torch.tensor(0.0) + + +class MultivariateNormalModel(pyro.nn.PyroModule): + def __init__(self, p: int): + super().__init__() + self.p = p + self.observed_sites = ["x"] + + def sample_mean(self): + return pyro.sample("mu", dist.Normal(0.0, 1.0).expand((self.p,)).to_event(1)) + + def sample_scale_tril(self): + if self.p > 1: + return pyro.sample("scale_tril", dist.LKJCholesky(self.p)) + else: + return pyro.sample( + "scale_tril", dist.HalfNormal(1.0).expand((self.p, self.p)).to_event(1) + ) + + def forward(self) -> torch.Tensor: + mu = self.sample_mean() + scale_tril = self.sample_scale_tril() + return pyro.sample("x", dist.MultivariateNormal(loc=mu, scale_tril=scale_tril)) + + +class ConditionedMultivariateNormalModel(MultivariateNormalModel): + def __init__(self, data: Dict[str, torch.Tensor], *, p: int): + super().__init__(p) + self.x = data["x"] + + def forward(self): + mu = self.sample_mean() + scale_tril = self.sample_scale_tril() + with pyro.plate("__train__", size=self.x.shape[0], dim=-1): + pyro.sample( + "x", + dist.MultivariateNormal(loc=mu, scale_tril=scale_tril), + obs=self.x, + ) + + +class DataGeneratorMultivariateNormalModel(MultivariateNormalModel): + def sample_mean(self): + return torch.zeros(self.p) + + def sample_scale_tril(self): + return torch.eye(self.p) + class kernel_ridge: pass + class neural_network: - pass \ No newline at end of file + pass diff --git a/docs/examples/robust_paper/scripts/create_datasets.py b/docs/examples/robust_paper/scripts/create_datasets.py index 96df8e01..e3a206b5 100644 --- a/docs/examples/robust_paper/scripts/create_datasets.py +++ b/docs/examples/robust_paper/scripts/create_datasets.py @@ -1,46 +1,207 @@ import os +import math import json import pickle -import torch -from docs.examples.robust_paper.scripts.statics import MODELS, LINK_FUNCTIONS, EXPERIMENT_CATEGORIES, DATASET_PATHS, FUNCTIONALS, ESTIMATORS, INFLUENCE_ESTIMATORS -from docs.examples.robust_paper.models import causal_GLM, kernel_ridge, neural_network +import pyro +from pyro.infer import Predictive +from docs.examples.robust_paper.scripts.statics import ( + LINK_FUNCTIONS_DICT, + MODELS, +) +from docs.examples.robust_paper.utils import uuid_from_config -MODELS_DICT = {"causal_GLM": causal_GLM, "kernel_ridge": kernel_ridge, "neural_network": neural_network} +def save_config_and_data( + data_generator, config_dict, overwrite=False, **data_generator_kwargs +): + # Create unique data uuid based on config + config_uuid = uuid_from_config(config_dict) -def main(): - # EXPERIMENT 1 - Influence function approximation experiment - # for model_str in MODELS: - # model = MODELS_DICT[model_str](*model_args, **model_kwargs) - # data = model() + # Check if dataset already exists + if ( + os.path.exists(f"docs/examples/robust_paper/datasets/{config_uuid}/data.pkl") + and not overwrite + ): + print(f"Dataset with uuid {config_uuid} already exists. Skipping.") + return - # # TODO: save the data. + # Create directory for dataset if it doesn't exist + if not os.path.exists(f"docs/examples/robust_paper/datasets/{config_uuid}"): + os.makedirs(f"docs/examples/robust_paper/datasets/{config_uuid}") - # # TODO: save the data config. + # Save config + json.dump( + config_dict, + open( + f"docs/examples/robust_paper/datasets/{config_uuid}/config.json", + "w", + ), + indent=4, + ) - if not os.path.exists("docs/examples/robust_paper/datasets/1"): - os.makedirs("docs/examples/robust_paper/datasets/1") - json.dump(example_json, open("docs/examples/robust_paper/datasets/1/config.json", "w"), indent=4) + # Simulate data + seed = config_dict["dataset_configs"]["seed"] + N = config_dict["model_configs"]["N"] + with pyro.poutine.seed(rng_seed=seed): + model = data_generator(**data_generator_kwargs) + D_train = Predictive( + model, + num_samples=N, + return_sites=model.observed_sites, + )() + D_test = Predictive( + model, + num_samples=N, + return_sites=model.observed_sites, + )() - data = {"X": torch.tensor([[1, 2, 3], [4, 5, 6]]), "y": torch.tensor([1, 2])} + # Save data + pickle.dump( + { + "train": D_train, + "test": D_test, + }, + open( + f"docs/examples/robust_paper/datasets/{config_uuid}/data.pkl", + "wb", + ), + ) + print(f"Saved dataset with uuid {config_uuid}.") - pickle.dump(data, open("docs/examples/robust_paper/datasets/1/data.pkl", "wb")) +def simulate_causal_glm_data( + seed, + link_function_str, + p, + N, + sparsity_level, + treatment_weight, + overwrite=False, +): + model_str = "CausalGLM" + data_generator = MODELS[model_str]["data_generator"] + link_fn = LINK_FUNCTIONS_DICT[link_function_str] + alpha = math.ceil(sparsity_level * p) + beta = math.ceil(sparsity_level * p) + misc_kwargs = dict( + alpha=alpha, + beta=beta, + treatment_weight=treatment_weight, + sparsity_level=sparsity_level, + ) + config_dict = { + "dataset_configs": { + "seed": seed, + }, + "model_configs": { + "model_str": model_str, + "link_function_str": link_function_str, + "p": p, + "N": N, + }, + "misc": misc_kwargs, + } + data_generator_kwargs = { + "p": p, + "link_fn": link_fn, + "alpha": alpha, + "beta": beta, + "treatment_weight": treatment_weight, + } + save_config_and_data( + data_generator, config_dict, overwrite=overwrite, **data_generator_kwargs + ) + + +def simulate_multivariate_normal( + seed, + p, + N, + overwrite=False, +): + model_str = "MultivariateNormalModel" + data_generator = MODELS[model_str]["data_generator"] + misc_kwargs = dict() + config_dict = { + "dataset_configs": { + "seed": seed, + }, + "model_configs": { + "model_str": model_str, + "p": p, + "N": N, + }, + "misc": misc_kwargs, + } + data_generator_kwargs = { + "p": p, + } + save_config_and_data( + data_generator, config_dict, overwrite=overwrite, **data_generator_kwargs + ) + + +def simulate_kernel_ridge_data(): + pass + + +def simulate_neural_network_data(): + pass + + +def main_causal_glm(num_datasets_per_config=100, overwrite=False): + num_datasets_simulated = 0 + # Effect of increasing dimensionality + for link_function_str in LINK_FUNCTIONS_DICT.keys(): + for p in [1, 10, 100, 200, 500, 1000]: + for N in [500]: + for sparsity_level in [0.25]: + for treatment_weight in [0.0, 1.0]: + for seed in range(num_datasets_per_config): + kwargs = { + "seed": seed, + "link_function_str": link_function_str, + "p": p, + "N": N, + "sparsity_level": sparsity_level, + "treatment_weight": treatment_weight, + "overwrite": overwrite, + } + simulate_causal_glm_data(**kwargs) + num_datasets_simulated += 1 + + # We can keep adding more configurations on the fly. Due to the `overwrite` flag, + # we can run this script multiple times and it will only simulate the datasets that + # *don't* already exist. + + print(f"Simulated {num_datasets_simulated} datasets.") + + +def main_multivariate_normal(num_datasets_per_config=100, overwrite=False): + num_datasets_simulated = 0 + for p in [1, 2, 3]: + for N in [50]: + for seed in range(num_datasets_per_config): + kwargs = { + "seed": seed, + "p": p, + "N": N, + "overwrite": overwrite, + } + simulate_multivariate_normal(**kwargs) + num_datasets_simulated += 1 + print(f"Simulated {num_datasets_simulated} datasets.") + + +def main_kernel_ridge(): + pass + + +def main_neural_network(): + pass -example_json = { - "dataset_configs": { - "dataset_name": "dataset_1", - "dataset_description": "Dataset 1", - "dataset_path": "./data/1.csv", - "seed": 1, - }, - "model_configs": { - "model": "causal_GLM", - "link_function": "normal", - "p": 200, - }, -} if __name__ == "__main__": - main() + main_causal_glm(5) + main_multivariate_normal(100) diff --git a/docs/examples/robust_paper/scripts/create_experiment_configs.py b/docs/examples/robust_paper/scripts/create_experiment_configs.py index 81a5c0b6..74e4a977 100644 --- a/docs/examples/robust_paper/scripts/create_experiment_configs.py +++ b/docs/examples/robust_paper/scripts/create_experiment_configs.py @@ -1,82 +1,99 @@ import os import json -from docs.examples.robust_paper.scripts.statics import MODELS, LINK_FUNCTIONS, EXPERIMENT_CATEGORIES, FUNCTIONALS, DATASET_PATHS, ESTIMATORS, INFLUENCE_ESTIMATORS +from docs.examples.robust_paper.scripts.create_datasets import uuid_from_config +from docs.examples.robust_paper.utils import get_valid_uuids -def main(): - # EXPERIMENT 1 - Influence function approximation experiment - experiment_category = "influence_approx" - assert experiment_category in EXPERIMENT_CATEGORIES - experiment_id = 0 - for model_str in MODELS: - for link_function_str in LINK_FUNCTIONS: - for functional_str in FUNCTIONALS: - for estimator_str in ESTIMATORS: - for influence_estimator_str in INFLUENCE_ESTIMATORS: - for dataset_path in DATASET_PATHS: - experiment_id += 1 - example_json = { - "experiment_configs": { - "experiment_name": f"{experiment_category}_{experiment_id}", - "experiment_description": "Influence function approximation experiment", - "dataset_path": dataset_path, - "results_path": f"./results/{experiment_id}/", - "seed": 1, - }, - "model_configs": { - "model": model_str, - "link_function": link_function_str, - }, - "functional_configs": { - "functional": functional_str, - "num_monte_carlo": 1000, - }, - "estimator_configs": { - "estimator": estimator_str, - }, - "influence_estimator_configs": { - "influence_estimator": influence_estimator_str, - "num_samples_outer": 1000, - "num_samples_inner": 1000, - "cg_iters": None, - "residual_tol": 1e-4, - } - } +def save_experiment_config(experiment_config): + experiment_uuid = uuid_from_config(experiment_config) + experiment_config["experiment_uuid"] = str(experiment_uuid) - if not os.path.exists(f"docs/examples/robust_paper/experiments/{experiment_id}"): - os.makedirs(f"docs/examples/robust_paper/experiments/{experiment_id}") - json.dump(example_json, open(f"docs/examples/robust_paper/experiments/{experiment_id}/config.json", "w"), indent=4) + experiment_config[ + "results_path" + ] = f"docs/examples/robust_paper/experiments/{experiment_uuid}" - # json.dump(example_json, open("docs/examples/robust_paper/experiments/example_1.json", "w"), indent=4) + if not os.path.exists(f"docs/examples/robust_paper/experiments/{experiment_uuid}"): + os.makedirs(f"docs/examples/robust_paper/experiments/{experiment_uuid}") + json.dump( + experiment_config, + open( + f"docs/examples/robust_paper/experiments/{experiment_uuid}/config.json", + "w", + ), + indent=4, + ) -example_json = { - "experiment_configs": { - "experiment_name": "influence_approx_1", - "experiment_description": "Influence function approximation experiment", - "dataset_path": "./data/1.csv", - "results_path": "./results/1/", - "seed": 1, - }, - "model_configs": { - "model": "causal_GLM", - "link_function": "normal", - }, - "functional_configs": { - "functional": "ATE", - "num_monte_carlo": 1000, - }, - "estimator_configs": { - "estimator": "plug_in", - }, - "influence_estimator_configs": { - "influence_estimator": "monte_carlo", - "num_samples_outer": 1000, - "num_samples_inner": 1000, - "cg_iters": None, - "residual_tol": 1e-4, +def influence_approx_experiment_ate(): + valid_configs = [] + for seed in range(25): + for p in [1, 10, 100, 200, 500]: + causal_glm_config_constraints = { + "dataset_configs": { + "seed": seed, + }, + "model_configs": { + "model_str": "CausalGLM", + "link_function_str": "normal", + "N": 500, + "p": p, + }, + "misc": { + "sparsity_level": 0.25, + "treatment_weight": 0.0, + }, + } + valid_configs.append(causal_glm_config_constraints) + + valid_data_uuids = get_valid_uuids(valid_configs) + + for uuid in valid_data_uuids: + for num_samples_outer in [1000, 10000, 100000]: + experiment_config = { + "experiment_description": "Influence function approximation experiment", + "data_uuid": uuid, + "functional_str": "average_treatment_effect", + "functional_kwargs": { + "num_monte_carlo": 10000, + }, + "monte_carlo_influence_estimator_kwargs": { + "num_samples_outer": num_samples_outer, + "num_samples_inner": 1, + "cg_iters": None, + "residual_tol": 1e-4, + }, + } + save_experiment_config(experiment_config) + + +def influence_approx_experiment_expected_density(): + valid_configs = [] + mult_normal_config_constraints = { + "model_configs": { + "model_str": "MultivariateNormalModel", + }, } -} + valid_configs.append(mult_normal_config_constraints) + valid_data_uuids = get_valid_uuids(valid_configs) + for uuid in valid_data_uuids: + for num_samples_outer in [1000, 10000, 100000]: + experiment_config = { + "experiment_description": "Influence function approximation experiment", + "data_uuid": uuid, + "functional_str": "expected_density", + "functional_kwargs": { + "num_monte_carlo": 10000, + }, + "monte_carlo_influence_estimator_kwargs": { + "num_samples_outer": num_samples_outer, + "num_samples_inner": 1, + "cg_iters": None, + "residual_tol": 1e-4, + }, + } + save_experiment_config(experiment_config) + if __name__ == "__main__": - main() + influence_approx_experiment_ate() + influence_approx_experiment_expected_density() diff --git a/docs/examples/robust_paper/scripts/influence_approx.py b/docs/examples/robust_paper/scripts/influence_approx.py new file mode 100644 index 00000000..1c308b0e --- /dev/null +++ b/docs/examples/robust_paper/scripts/influence_approx.py @@ -0,0 +1,89 @@ +import functools +import pickle +from chirho.robust.handlers.predictive import PredictiveModel +from chirho.robust.handlers.estimators import one_step_corrected_estimator +from docs.examples.robust_paper.scripts.statics import ( + LINK_FUNCTIONS_DICT, + FUNCTIONALS_DICT, + MODELS, + ALL_DATA_CONFIGS, + ALL_EXP_CONFIGS, +) +from docs.examples.robust_paper.utils import get_mle_params_and_guide +from docs.examples.robust_paper.analytic_eif import ( + analytic_eif_expected_density, + analytic_eif_ate_causal_glm, +) + + +def run_experiment(exp_config): + data_config = ALL_DATA_CONFIGS[exp_config["data_uuid"]] + data = pickle.load( + open( + f"docs/examples/robust_paper/datasets/{exp_config['data_uuid']}/data.pkl", + "rb", + ) + ) + D_train = data["train"] + D_test = data["test"] + + model_str = data_config["model_configs"]["model_str"] + if model_str == "MultivariateNormalModel": + model_kwargs = { + "p": data_config["model_configs"]["p"], + } + elif model_str == "CausalGLM": + model_kwargs = { + "p": data_config["model_configs"]["p"], + "link_fn": LINK_FUNCTIONS_DICT[ + data_config["model_configs"]["link_function_str"] + ], + } + else: + raise NotImplementedError + model = MODELS[model_str]["model"](**model_kwargs) + conditioned_model = MODELS[model_str]["conditioned_model"](D_train, **model_kwargs) + + functional_class = FUNCTIONALS_DICT[exp_config["functional_str"]] + functional = functools.partial(functional_class, **exp_config["functional_kwargs"]) + + one_step_estimator = one_step_corrected_estimator( + functional, D_test, **exp_config["monte_carlo_influence_estimator_kwargs"] + ) + + # Run inference + theta_hat, mle_guide = get_mle_params_and_guide(conditioned_model) + plug_in_est = functional(PredictiveModel(model, mle_guide))() + automated_monte_carlo_estimator = one_step_estimator( + PredictiveModel(model, mle_guide) + )() + + if model_str == "CausalGLM": + # TODO + pass + elif model_str == "MultivariateNormalModel": + analytic_correction, analytic_eif_at_test_pts = analytic_eif_expected_density( + D_test, plug_in_est, PredictiveModel(model, mle_guide) + ) + else: + raise NotImplementedError + + # Save results + results = { + "theta_hat": theta_hat, + "automated_monte_carlo_estimator": automated_monte_carlo_estimator, + "analytic_correction": analytic_correction, + "analytic_eif_at_test_pts": analytic_eif_at_test_pts, + "analytic_one_step_estimator": analytic_correction + plug_in_est, + "plug_in_est": plug_in_est, + "experiment_uuid": exp_config["experiment_uuid"], + } + + +if __name__ == "__main__": + # expected density + exp_config1 = ALL_EXP_CONFIGS["b175a477-1b1a-581b-68b2-d374e292a8e7"] + print(exp_config1) + run_experiment(exp_config1) + + # exp_config2 = "" diff --git a/docs/examples/robust_paper/scripts/statics.py b/docs/examples/robust_paper/scripts/statics.py index eab8d778..a0838820 100644 --- a/docs/examples/robust_paper/scripts/statics.py +++ b/docs/examples/robust_paper/scripts/statics.py @@ -1,7 +1,52 @@ -MODELS = ["causal_GLM", "kernel_ridge", "neural_network"] -LINK_FUNCTIONS = ["normal", "bernoulli"] +import os +import json +import pyro.distributions as dist +from docs.examples.robust_paper.models import * +from docs.examples.robust_paper.functionals import * + + +MODELS = { + "CausalGLM": { + "data_generator": DataGeneratorCausalGLM, + "model": CausalGLM, + "conditioned_model": ConditionedCausalGLM, + }, + "MultivariateNormalModel": { + "data_generator": DataGeneratorMultivariateNormalModel, + "model": MultivariateNormalModel, + "conditioned_model": ConditionedMultivariateNormalModel, + }, +} + +LINK_FUNCTIONS_DICT = { + "normal": lambda mu: dist.Normal(mu, 1.0), + "bernoulli": lambda mu: dist.Bernoulli(logits=mu), +} + +FUNCTIONALS_DICT = { + "average_treatment_effect": ATEFunctional, + "expected_density": ExpectedDensity, +} + EXPERIMENT_CATEGORIES = ["influence_approx", "estimator_approx", "capstone"] -DATASET_PATHS = [f"docs/examples/robust_paper/datasets/{i}" for i in range(1, 5)] -FUNCTIONALS = ["ATE", "ESD", "CATE"] ESTIMATORS = ["plug_in", "tmle", "one_step", "double_ml"] -INFLUENCE_ESTIMATORS = ["monte_carlo", "analytical", "finite_difference"] \ No newline at end of file +INFLUENCE_ESTIMATORS = ["monte_carlo_eif", "analytical_eif", "finite_difference_eif"] +ALL_DATA_UUIDS = [ + d for d in os.listdir("docs/examples/robust_paper/datasets/") if d != ".DS_Store" +] +ALL_DATA_CONFIGS = { + uuid: json.load( + open(f"docs/examples/robust_paper/datasets/{uuid}/config.json", "r") + ) + for uuid in ALL_DATA_UUIDS +} + +ALL_EXP_UUIDS = [ + d for d in os.listdir("docs/examples/robust_paper/experiments/") if d != ".DS_Store" +] +ALL_EXP_CONFIGS = { + uuid: json.load( + open(f"docs/examples/robust_paper/experiments/{uuid}/config.json", "r") + ) + for uuid in ALL_EXP_UUIDS +} diff --git a/docs/examples/robust_paper/utils.py b/docs/examples/robust_paper/utils.py new file mode 100644 index 00000000..4ab0dab7 --- /dev/null +++ b/docs/examples/robust_paper/utils.py @@ -0,0 +1,114 @@ +from typing import Dict, List +import hashlib +import uuid +import json +import torch +import pyro + +from chirho.robust.internals.utils import ParamDict +from docs.examples.robust_paper.scripts.statics import ALL_DATA_CONFIGS, ALL_DATA_UUIDS + + +pyro.settings.set(module_local_params=True) + + +def uuid_from_config(config_dict): + serialized_config = json.dumps(config_dict, sort_keys=True) + hash_object = hashlib.sha1(serialized_config.encode()) + hash_digest = hash_object.hexdigest() + return uuid.UUID(hash_digest[:32]) + + +def is_subset(superset: Dict, subset: Dict) -> bool: + """ + Checks if a dictionary is a subset of another dictionary. + Source: https://stackoverflow.com/questions/49419486/ + """ + for key, value in subset.items(): + if key not in superset: + return False + + if isinstance(value, dict): + if not is_subset(superset[key], value): + return False + + elif isinstance(value, str): + if value not in superset[key]: + return False + + elif isinstance(value, list): + if not set(value) <= set(superset[key]): + return False + elif isinstance(value, set): + if not value <= superset[key]: + return False + + else: + if not value == superset[key]: + return False + + return True + + +def any_is_subset(superset: Dict, subset: List[Dict]) -> bool: + """ + Checks if any dictionary in a list of dictionaries is a subset of another dictionary. + """ + for sub in subset: + if is_subset(superset, sub): + return True + return False + + +def get_valid_uuids(valid_configs: List[Dict]) -> List[str]: + """ + Gets the valid uuids for a given set of configs. + """ + valid_uuids = [] + for uuid in ALL_DATA_UUIDS: + if any_is_subset(ALL_DATA_CONFIGS[uuid], valid_configs): + valid_uuids.append(uuid) + return valid_uuids + + +class MLEGuide(torch.nn.Module): + """ + Helper class to create a trivial guide that returns the maximum likelihood estimate + """ + + def __init__(self, mle_est: ParamDict): + super().__init__() + self.names = list(mle_est.keys()) + for name, value in mle_est.items(): + setattr(self, name + "_param", torch.nn.Parameter(value)) + + def forward(self, *args, **kwargs): + for name in self.names: + value = getattr(self, name + "_param") + pyro.sample( + name, pyro.distributions.Delta(value, event_dim=len(value.shape)) + ) + + +def get_mle_params_and_guide(conditioned_model, n_iters=2000, lr=0.03): + """ + Returns the maximum likelihood estimate of the parameters of a model. + """ + guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model) + elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train) + + # initialize parameters + elbo() + adam = torch.optim.Adam(elbo.parameters(), lr=lr) + + # Do gradient steps + for _ in range(n_iters): + adam.zero_grad() + loss = elbo() + loss.backward() + adam.step() + + theta_hat = { + k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items() + } + return theta_hat, MLEGuide(theta_hat) From 47e0e861a98bdd57d3dbac7b208dceb85cd21c72 Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Thu, 18 Jan 2024 14:18:54 -0500 Subject: [PATCH 04/26] ignore experiments and datasets folder --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 13598e90..046bb388 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,10 @@ # C extensions *.so +# Data and experiment folders +docs/examples/robust_paper/datasets/ +docs/examples/robust_paper/experiments/ + # Packages *.egg *.egg-info From 3c3562ef93a1a69ea3a42b4a85f468d3a0164d6b Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Thu, 18 Jan 2024 14:37:47 -0500 Subject: [PATCH 05/26] ignore experiments and datasets folder (#507) --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 13598e90..046bb388 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,10 @@ # C extensions *.so +# Data and experiment folders +docs/examples/robust_paper/datasets/ +docs/examples/robust_paper/experiments/ + # Packages *.egg *.egg-info From 325d4b063661ed2b71bc10f408396db940926f94 Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Fri, 19 Jan 2024 12:19:33 -0500 Subject: [PATCH 06/26] Refactor of `influence_approx.py` (#509) * ignore experiments and datasets folder * finished up influence approx script * clean up --- docs/examples/robust_paper/models.py | 3 +- .../scripts/create_experiment_configs.py | 72 +++---- .../robust_paper/scripts/influence_approx.py | 186 +++++++++++++++--- docs/examples/robust_paper/utils.py | 22 ++- 4 files changed, 218 insertions(+), 65 deletions(-) diff --git a/docs/examples/robust_paper/models.py b/docs/examples/robust_paper/models.py index e015ea69..c1346892 100644 --- a/docs/examples/robust_paper/models.py +++ b/docs/examples/robust_paper/models.py @@ -72,10 +72,11 @@ def __init__( self, data: Dict[str, torch.Tensor], *, + p: int, link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0), prior_scale: Optional[float] = None, ): - p = X.shape[1] + assert data["X"].shape[1] == p super().__init__(p, link_fn, prior_scale) self.X = data["X"] self.A = data["A"] diff --git a/docs/examples/robust_paper/scripts/create_experiment_configs.py b/docs/examples/robust_paper/scripts/create_experiment_configs.py index 74e4a977..67410f3c 100644 --- a/docs/examples/robust_paper/scripts/create_experiment_configs.py +++ b/docs/examples/robust_paper/scripts/create_experiment_configs.py @@ -1,7 +1,8 @@ import os import json from docs.examples.robust_paper.scripts.create_datasets import uuid_from_config -from docs.examples.robust_paper.utils import get_valid_uuids +from docs.examples.robust_paper.utils import get_valid_data_uuids +from docs.examples.robust_paper.scripts.statics import ALL_DATA_CONFIGS def save_experiment_config(experiment_config): @@ -45,25 +46,25 @@ def influence_approx_experiment_ate(): } valid_configs.append(causal_glm_config_constraints) - valid_data_uuids = get_valid_uuids(valid_configs) + valid_data_uuids = get_valid_data_uuids(valid_configs) for uuid in valid_data_uuids: - for num_samples_outer in [1000, 10000, 100000]: - experiment_config = { - "experiment_description": "Influence function approximation experiment", - "data_uuid": uuid, - "functional_str": "average_treatment_effect", - "functional_kwargs": { - "num_monte_carlo": 10000, - }, - "monte_carlo_influence_estimator_kwargs": { - "num_samples_outer": num_samples_outer, - "num_samples_inner": 1, - "cg_iters": None, - "residual_tol": 1e-4, - }, - } - save_experiment_config(experiment_config) + experiment_config = { + "experiment_description": "Influence function approximation experiment", + "data_uuid": uuid, + "functional_str": "average_treatment_effect", + "functional_kwargs": { + "num_monte_carlo": 10000, + }, + "monte_carlo_influence_estimator_kwargs": { + "num_samples_outer": [1000, 10000, 50000, 100000], + "num_samples_inner": 1, + "cg_iters": None, + "residual_tol": 1e-4, + }, + "data_config": ALL_DATA_CONFIGS[uuid], + } + save_experiment_config(experiment_config) def influence_approx_experiment_expected_density(): @@ -74,24 +75,25 @@ def influence_approx_experiment_expected_density(): }, } valid_configs.append(mult_normal_config_constraints) - valid_data_uuids = get_valid_uuids(valid_configs) + valid_data_uuids = get_valid_data_uuids(valid_configs) for uuid in valid_data_uuids: - for num_samples_outer in [1000, 10000, 100000]: - experiment_config = { - "experiment_description": "Influence function approximation experiment", - "data_uuid": uuid, - "functional_str": "expected_density", - "functional_kwargs": { - "num_monte_carlo": 10000, - }, - "monte_carlo_influence_estimator_kwargs": { - "num_samples_outer": num_samples_outer, - "num_samples_inner": 1, - "cg_iters": None, - "residual_tol": 1e-4, - }, - } - save_experiment_config(experiment_config) + data_config = ALL_DATA_CONFIGS[uuid] + experiment_config = { + "experiment_description": "Influence function approximation experiment", + "data_uuid": uuid, + "functional_str": "expected_density", + "functional_kwargs": { + "num_monte_carlo": 10000, + }, + "monte_carlo_influence_estimator_kwargs": { + "num_samples_outer": [1000, 10000, 50000, 100000], + "num_samples_inner": 1, + "cg_iters": None, + "residual_tol": 1e-4, + }, + "data_config": data_config, + } + save_experiment_config(experiment_config) if __name__ == "__main__": diff --git a/docs/examples/robust_paper/scripts/influence_approx.py b/docs/examples/robust_paper/scripts/influence_approx.py index 1c308b0e..b59d2560 100644 --- a/docs/examples/robust_paper/scripts/influence_approx.py +++ b/docs/examples/robust_paper/scripts/influence_approx.py @@ -1,15 +1,17 @@ import functools +import time +import torch import pickle from chirho.robust.handlers.predictive import PredictiveModel from chirho.robust.handlers.estimators import one_step_corrected_estimator +from chirho.robust.ops import influence_fn from docs.examples.robust_paper.scripts.statics import ( LINK_FUNCTIONS_DICT, FUNCTIONALS_DICT, MODELS, - ALL_DATA_CONFIGS, ALL_EXP_CONFIGS, ) -from docs.examples.robust_paper.utils import get_mle_params_and_guide +from docs.examples.robust_paper.utils import get_mle_params_and_guide, MLEGuide from docs.examples.robust_paper.analytic_eif import ( analytic_eif_expected_density, analytic_eif_ate_causal_glm, @@ -17,7 +19,12 @@ def run_experiment(exp_config): - data_config = ALL_DATA_CONFIGS[exp_config["data_uuid"]] + # Results dict + results = dict() + results["experiment_uuid"] = exp_config["experiment_uuid"] + + # Load in data + data_config = exp_config["data_config"] data = pickle.load( open( f"docs/examples/robust_paper/datasets/{exp_config['data_uuid']}/data.pkl", @@ -27,6 +34,9 @@ def run_experiment(exp_config): D_train = data["train"] D_test = data["test"] + print(f"=== Running experiment {exp_config['experiment_uuid']} ===") + + # Load in model model_str = data_config["model_configs"]["model_str"] if model_str == "MultivariateNormalModel": model_kwargs = { @@ -44,46 +54,170 @@ def run_experiment(exp_config): model = MODELS[model_str]["model"](**model_kwargs) conditioned_model = MODELS[model_str]["conditioned_model"](D_train, **model_kwargs) + # Load in functional functional_class = FUNCTIONALS_DICT[exp_config["functional_str"]] functional = functools.partial(functional_class, **exp_config["functional_kwargs"]) - one_step_estimator = one_step_corrected_estimator( - functional, D_test, **exp_config["monte_carlo_influence_estimator_kwargs"] - ) - - # Run inference + # Fit MLE + mle_start_time = time.time() theta_hat, mle_guide = get_mle_params_and_guide(conditioned_model) + mle_end_time = time.time() + mle_time_min = (mle_end_time - mle_start_time) / 60.0 + results["theta_hat"] = theta_hat + results["mle_time_min"] = mle_time_min + + # Get plug-in estimate + plug_in_start_time = time.time() plug_in_est = functional(PredictiveModel(model, mle_guide))() - automated_monte_carlo_estimator = one_step_estimator( - PredictiveModel(model, mle_guide) - )() + plug_in_end_time = time.time() + plug_in_time_min = (plug_in_end_time - plug_in_start_time) / 60.0 + results["plug_in_est"] = plug_in_est + results["plug_in_time_min"] = plug_in_time_min + + #### Monte Carlo EIF #### + monte_eif_all_kwargs = exp_config["monte_carlo_influence_estimator_kwargs"] + num_samples_inner = monte_eif_all_kwargs["num_samples_inner"] + cg_iters = monte_eif_all_kwargs["cg_iters"] + residual_tol = monte_eif_all_kwargs["residual_tol"] + num_monte_carlo_outer_grid = monte_eif_all_kwargs["num_samples_outer"] + all_monte_carlo_eif_results = [] + + # Keeps erroring out due to https://github.com/BasisResearch/chirho/issues/483 + for num_monte_carlo_outer in num_monte_carlo_outer_grid: + monte_carlo_eif_results = dict() + # Hack to avoid https://github.com/BasisResearch/chirho/issues/483 + theta_hat = { + k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items() + } + mle_guide = MLEGuide(theta_hat) + + print(f"Running monte carlo eif with {num_monte_carlo_outer} samples") + monte_carlo_eif_results["num_monte_carlo_outer"] = num_monte_carlo_outer + monte_kwargs = { + "num_samples_outer": num_monte_carlo_outer, + "num_samples_inner": num_samples_inner, + "cg_iters": cg_iters, + "residual_tol": residual_tol, + } + + # One step estimator + monte_one_step_start = time.time() + one_step_estimator = one_step_corrected_estimator( + functional, D_test, **monte_kwargs + ) + automated_monte_carlo_estimate = one_step_estimator( + PredictiveModel(model, mle_guide) + )() + monte_one_step_end = time.time() + monte_one_step_time_min = (monte_one_step_end - monte_one_step_start) / 60.0 + + monte_carlo_eif_results["wall_time"] = monte_one_step_time_min + monte_carlo_eif_results["correction"] = ( + automated_monte_carlo_estimate - plug_in_est + ) + monte_carlo_eif_results["corrected_estimate"] = automated_monte_carlo_estimate + + # Hack to avoid https://github.com/BasisResearch/chirho/issues/483 + theta_hat = { + k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items() + } + mle_guide = MLEGuide(theta_hat) + # Pointwise EIF + monte_eif_pointwise_start = time.time() + monte_eif = influence_fn(functional, D_test, **monte_kwargs) + monte_eif_at_test_pts = monte_eif(PredictiveModel(model, mle_guide))() + monte_eif_pointwise_end = time.time() + monte_eif_pointwise_time_min = ( + monte_eif_pointwise_end - monte_eif_pointwise_start + ) / 60.0 + + monte_carlo_eif_results["pointwise_wall_time"] = monte_eif_pointwise_time_min + monte_carlo_eif_results["pointwise_eif"] = monte_eif_at_test_pts + all_monte_carlo_eif_results.append(monte_carlo_eif_results) + + results["all_monte_carlo_eif_results"] = all_monte_carlo_eif_results + + ### Finite Difference EIF ### + # TODO: Andy to add here + + ### Analytic EIF ### if model_str == "CausalGLM": - # TODO - pass + analytic_time_start = time.time() + analytic_correction, analytic_eif_at_test_pts = analytic_eif_ate_causal_glm( + D_test, theta_hat + ) + analytic_time_end = time.time() + analytic_time_min = (analytic_time_end - analytic_time_start) / 60.0 elif model_str == "MultivariateNormalModel": + analytic_time_start = time.time() analytic_correction, analytic_eif_at_test_pts = analytic_eif_expected_density( D_test, plug_in_est, PredictiveModel(model, mle_guide) ) + analytic_time_end = time.time() + analytic_time_min = (analytic_time_end - analytic_time_start) / 60.0 else: raise NotImplementedError + # Can't pickle _to_functional_tensor so convert to vanilla torch tensor + analytic_eif_results = dict() + analytic_eif_results["wall_time"] = analytic_time_min + analytic_eif_results["correction"] = torch.tensor(analytic_correction.item()) + analytic_eif_results["corrected_estimate"] = ( + plug_in_est + analytic_correction.item() + ) + analytic_eif_results["pointwise_wall_time"] = analytic_time_min + analytic_eif_results["pointwise_eif"] = torch.tensor( + [e.item() for e in analytic_eif_at_test_pts] + ) + results["analytic_eif_results"] = analytic_eif_results + # Save results - results = { - "theta_hat": theta_hat, - "automated_monte_carlo_estimator": automated_monte_carlo_estimator, - "analytic_correction": analytic_correction, - "analytic_eif_at_test_pts": analytic_eif_at_test_pts, - "analytic_one_step_estimator": analytic_correction + plug_in_est, - "plug_in_est": plug_in_est, - "experiment_uuid": exp_config["experiment_uuid"], - } + pickle.dump( + results, + open( + f"docs/examples/robust_paper/experiments/{exp_config['experiment_uuid']}/results.pkl", + "wb", + ), + ) + return results if __name__ == "__main__": + from docs.examples.robust_paper.utils import get_valid_exp_uuids + # expected density - exp_config1 = ALL_EXP_CONFIGS["b175a477-1b1a-581b-68b2-d374e292a8e7"] - print(exp_config1) - run_experiment(exp_config1) + expected_density_config = { + "experiment_description": "Influence function approximation experiment", + "functional_str": "expected_density", + "data_config": { + "model_configs": { + "model_str": "MultivariateNormalModel", + }, + }, + } + + # Run all expected density experiments + exp_uuids_for_density = get_valid_exp_uuids([expected_density_config]) + for exp_uuid in exp_uuids_for_density: + exp_config = ALL_EXP_CONFIGS[exp_uuid] + print(run_experiment(exp_config)) + break + + # Run all ATE experiments + ate_config = { + "experiment_description": "Influence function approximation experiment", + "functional_str": "average_treatment_effect", + "data_config": { + "model_configs": { + "model_str": "CausalGLM", + }, + }, + } - # exp_config2 = "" + # Run all ate experiments + exp_uuids_for_ate = get_valid_exp_uuids([ate_config]) + for exp_uuid in exp_uuids_for_ate: + exp_config = ALL_EXP_CONFIGS[exp_uuid] + print(run_experiment(exp_config)) + break diff --git a/docs/examples/robust_paper/utils.py b/docs/examples/robust_paper/utils.py index 4ab0dab7..43dbf11d 100644 --- a/docs/examples/robust_paper/utils.py +++ b/docs/examples/robust_paper/utils.py @@ -6,7 +6,12 @@ import pyro from chirho.robust.internals.utils import ParamDict -from docs.examples.robust_paper.scripts.statics import ALL_DATA_CONFIGS, ALL_DATA_UUIDS +from docs.examples.robust_paper.scripts.statics import ( + ALL_DATA_CONFIGS, + ALL_DATA_UUIDS, + ALL_EXP_UUIDS, + ALL_EXP_CONFIGS, +) pyro.settings.set(module_local_params=True) @@ -60,9 +65,9 @@ def any_is_subset(superset: Dict, subset: List[Dict]) -> bool: return False -def get_valid_uuids(valid_configs: List[Dict]) -> List[str]: +def get_valid_data_uuids(valid_configs: List[Dict]) -> List[str]: """ - Gets the valid uuids for a given set of configs. + Gets the valid data uuids for a given set of configs. """ valid_uuids = [] for uuid in ALL_DATA_UUIDS: @@ -71,6 +76,17 @@ def get_valid_uuids(valid_configs: List[Dict]) -> List[str]: return valid_uuids +def get_valid_exp_uuids(valid_configs: List[Dict]) -> List[str]: + """ + Gets the valid experiment uuids for a given set of configs. + """ + valid_uuids = [] + for uuid in ALL_EXP_UUIDS: + if any_is_subset(ALL_EXP_CONFIGS[uuid], valid_configs): + valid_uuids.append(uuid) + return valid_uuids + + class MLEGuide(torch.nn.Module): """ Helper class to create a trivial guide that returns the maximum likelihood estimate From 72452d10b380aec334df09ec5ec27e0d17f5ed95 Mon Sep 17 00:00:00 2001 From: Andy Zane Date: Fri, 19 Jan 2024 09:32:32 -0800 Subject: [PATCH 07/26] Finite Difference Baseline (#508) * added robust folder * uncommited scratch work for log prob * untested variational log prob * uncomitted changes * uncomitted changes * pair coding w/ eli * added tests w/ Eli * eif * linting * moving test autograd to internals and deleted old utils file * sketch influence implementation * fix more args * ops file * file * format * lint * clean up influence and tests * make tests more generic * guess max plate nesting * linearize * rename file * tensor flatten * predictive eif * jvp type * reorganize files * shrink test case * move guess_max_plate_nesting * move cg solver to linearze * type alias * test_ops * basic cg tests * remove failing test case * format * move paramdict up * remove obsolete test files * add empty handlers * add chirho.robust to docs * fix memory leak in tests * make typing compatible with python 3.8 * typing_extensions * add branch to ci * predictive * remove imprecise annotation * Added more tests for `linearize` and `make_empirical_fisher_vp` (#405) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * removed missing import * fixed failing test with seeding * addressing Eli's comments * Add upper bound on number of CG steps (#404) * upper bound on cg_iters * address comment * fixed test for non-symmetric matrix (#437) * Make `NMCLogPredictiveLikelihood` seeded (#408) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * seeded NMC implementation * linting * removed missing import * changed to eli's seedmessenger suggestion * added failing edge case * explicitly add max plate argument * added warning message * fixed linting error and test failure case from too many cg iters * eli's contextlib seeding strategy * removed seedmessenger from test * randomness should be shared across calls * switched back to different * Use Hessian formulation of Fisher information in `make_empirical_fisher_vp` (#430) * hessian vector product formulation for fisher * ignoring small type error * fixed linting error * Add new `SimpleModel` and `SimpleGuide` (#440) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * seeded NMC implementation * linting * removed missing import * changed to eli's seedmessenger suggestion * added failing edge case * explicitly add max plate argument * added warning message * fixed linting error and test failure case from too many cg iters * eli's contextlib seeding strategy * removed seedmessenger from test * randomness should be shared across calls * uncomitted change before branch switch * switched back to different * added revised simple model and guide * added multiple link functions in test * linting * Batching in `linearize` and `influence` (#465) * batching in linearize and influence * addressing eli's review * added optimization for pointwise false case * fixing lint error * batched cg (#466) * One step correction implemented (#467) * one step correction * increased tolerance * fixing lint issue * Replace some `torch.vmap` usage with a hand-vectorized `BatchedNMCLogPredictiveLikelihood` (#473) * sketch batched nmc lpd * nits * fix type * format * comment * comment * comment * typo * typo * add condition to help guarantee idempotence * simplify edge case * simplify plate_name * simplify batchedobservation logic * factorize * simplify batched * reorder * comment * remove plate_names * types * formatting and type * move unbind to utils * remove max_plate_nesting arg from get_traces * comment * nit * move get_importance_traces to utils * fix types * generic obs type * lint * format * handle observe in batchedobservations * event dim * move batching handlers to utils * replace 2/3 vmaps, tests pass * remove dead code * format * name args * lint * shuffle code * try an extra optimization in batchedlatents * add another optimization * undo changes to test * remove inplace adds * add performance test showing speedup * document internal helpers * batch latents test * move batch handlers to predictive * add bind_leftmost_dim, document PredictiveFunctional and PredictiveModel * use bind_leftmost_dim in log prob * Added documentation for `chirho.robust` (#470) * documentation * documentation clean up w/ eli * fix lint issue * Make functional argument to influence_fn required (#487) * Make functional argument required * estimator * docstring * Remove guide argument from `influence_fn` and `linearize` (#489) * Make functional argument required * estimator * docstring * Remove guide, make tests pass * rename internals.predictive to internals.nmc * expose handlers.predictive * expose handlers.predictive * docstrings * fix doc build * fix equation * docstring import --------- Co-authored-by: Sam Witty * Make influence_fn a higher-order Functional (#492) * make influence a functional * fix test * multiple arguments * doc * docstring * docstring * Add full corrected one step estimator (#476) * added scaffolding to one step estimator * kept signature the same as one_step_correction * lint * refactored test to include multiple estimators * typo * revise error * added dict handling * remove assert * more informative error message * replace dispatch with pytree flatten and unflatten * revert arg for influence_function_estimator * docs and lint * lingering influence_fn * fixed missing return * rename * lint * add *model to appease the linter * add abstractions and simple temp scratch to test with squared unit normal functional with perturbation. * removes old scratch notebook * gets squared density running under abstraction that couples functionals and models * gets quad and mc approximations to match, vectorization hacky. * adds plotting and comparative to analytic. * adds scratch experiment comparing squared density analytic vs fd approx across various epsilon lambdas * fixes dataset splitting, breaks analytic eif * unfixes an incorrect fix, working now. * refactors finite difference machinery to fit experimental specs. * switches to existing rng seed context manager. * reverts back to what turns out to be a slightly different seeding context. --------- Co-authored-by: Raj Agrawal Co-authored-by: Eli Co-authored-by: Sam Witty Co-authored-by: Raj Agrawal Co-authored-by: eb8680 --- .../finite_difference_eif/__init__.py | 0 .../finite_difference_eif/abstractions.py | 174 +++++++++++++++++ .../finite_difference_eif/distributions.py | 34 ++++ .../finite_difference_eif/mixins.py | 56 ++++++ .../scripts/fd_influence_approx.py | 178 ++++++++++++++++++ docs/examples/robust_paper/utils.py | 11 ++ 6 files changed, 453 insertions(+) create mode 100644 docs/examples/robust_paper/finite_difference_eif/__init__.py create mode 100644 docs/examples/robust_paper/finite_difference_eif/abstractions.py create mode 100644 docs/examples/robust_paper/finite_difference_eif/distributions.py create mode 100644 docs/examples/robust_paper/finite_difference_eif/mixins.py create mode 100644 docs/examples/robust_paper/scripts/fd_influence_approx.py diff --git a/docs/examples/robust_paper/finite_difference_eif/__init__.py b/docs/examples/robust_paper/finite_difference_eif/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docs/examples/robust_paper/finite_difference_eif/abstractions.py b/docs/examples/robust_paper/finite_difference_eif/abstractions.py new file mode 100644 index 00000000..64dbb82b --- /dev/null +++ b/docs/examples/robust_paper/finite_difference_eif/abstractions.py @@ -0,0 +1,174 @@ +import torch +import pyro +import pyro.distributions as dist +from typing import Dict, Optional +from contextlib import contextmanager +from chirho.robust.ops import Point, T +import numpy as np + + +class ModelWithMarginalDensity(torch.nn.Module): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def density(self, *args, **kwargs): + # TODO this can probably default to using BatchedNMCLogMarginalLikelihood applied to self, + # but providing here to avail of analytic densities. Or have a constructor that takes a + # regular model and puts the marginal density here. + raise NotImplementedError() + + def forward(self, *args, **kwargs): + raise NotImplementedError() + + +class PrefixMessenger(pyro.poutine.messenger.Messenger): + + def __init__(self, prefix: str): + self.prefix = prefix + + def _pyro_sample(self, msg) -> None: + msg["name"] = f"{self.prefix}{msg['name']}" + + +class FDModelFunctionalDensity(ModelWithMarginalDensity): + """ + This class serves to couple the forward sampling model, density, and functional. Finite differencing + operates in the space of densities, and therefore requires of its functionals that they "know about" + the causal structure of the generative model. Thus, the three components are coupled together here. + + """ + + model: ModelWithMarginalDensity + + # TODO These managers are weird but lets you define a valid model at init time and then temporarily + # modify the perturbation later, eg. in the influence function approximatoin. + # TODO pull out boilerplate + @contextmanager + def set_eps(self, eps): + original_eps = self._eps + self._eps = eps + try: + yield + finally: + self._eps = original_eps + + @contextmanager + def set_lambda(self, lambda_): + original_lambda = self._lambda + self._lambda = lambda_ + try: + yield + finally: + self._lambda = original_lambda + + @contextmanager + def set_kernel_point(self, kernel_point: Dict): + original_kernel_point = self._kernel_point + self._kernel_point = kernel_point + try: + yield + finally: + self._kernel_point = original_kernel_point + + @property + def kernel(self) -> ModelWithMarginalDensity: + # TODO implementation of a kernel could be brought up to this level. User would need to pass a kernel type + # that's parameterized by the kernel point and lambda. + """ + Inheritors should construct the kernel here as a function of self._kernel_point and self._lambda. + :return: + """ + raise NotImplementedError() + + def __init__(self, default_kernel_point: Dict, *args, default_eps=0., default_lambda=0.1, **kwargs): + super().__init__(*args, **kwargs) + self._eps = default_eps + self._lambda = default_lambda + self._kernel_point = default_kernel_point + # TODO don't assume .shape[-1] + self.ndims = np.sum([v.shape[-1] for v in self._kernel_point.values()]) + + @property + def mixture_weights(self): + return torch.tensor([1. - self._eps, self._eps]) + + def density(self, model_kwargs: Dict, kernel_kwargs: Dict): + mpart = self.mixture_weights[0] * self.model.density(**model_kwargs) + kpart = self.mixture_weights[1] * self.kernel.density(**kernel_kwargs) + return mpart + kpart + + def forward(self, model_kwargs: Optional[Dict] = None, kernel_kwargs: Optional[Dict] = None): + # _from_kernel = pyro.sample('_mixture_assignment', dist.Categorical(self.mixture_weights)) + # + # if _from_kernel: + # return self.kernel(**(kernel_kwargs or dict())) + # else: + # return self.model(**(model_kwargs or dict())) + + _from_kernel = pyro.sample('_mixture_assignment', dist.Categorical(self.mixture_weights)) + + kernel_mask = _from_kernel.bool() # Convert to boolean mask + + # Apply the respective functions using the masks + with PrefixMessenger('kernel_'), pyro.poutine.trace() as kernel_tr: + kernel_result = self.kernel(**(kernel_kwargs or dict())) + with PrefixMessenger('model_'), pyro.poutine.trace() as model_tr: + model_result = self.model(**(model_kwargs or dict())) + + # FIXME to make log likelihoods work properly, the log likelihoods need to be masked/not added + # for particular elements. See e.g. MaskedMixture for a non-general example of how to do this (it + # uses torch distributions instead of arbitrary probabilistic programs. + # https://docs.pyro.ai/en/stable/distributions.html?highlight=MaskedMixture#maskedmixture + # FIXME ideally the trace would have elements of the same name as well here. + + # FIXME where isn't shape agnostic. + + # Use masks to select the appropriate result for each sample + result = torch.where(kernel_mask[:, None], kernel_result, model_result) + + return result + + def functional(self, *args, **kwargs): + # TODO update docstring to this being build_functional instead of just functional + """ + The functional target for this model. This is tightly coupled to a particular + pyro model because finite differencing operates in the space of densities, and + automatically exploit any structure of the pyro model the functional + is being evaluated with respect to. As such, the functional must be implemented + with the specific structure of coupled pyro model in mind. + :param args: + :param kwargs: + :return: An estimate of the functional for ths model. + """ + raise NotImplementedError() + + +# TODO move this to chirho/robust/ops.py and resolve signature mismatches? Maybe. The problem is that the ops +# signature (rightly) decouples models and functionals, whereas for finite differencing they must be coupled +# because the functional (in many cases) must know about the causal structure of the model. +def fd_influence_fn(fd_coupling: FDModelFunctionalDensity, points: Point[T], eps: float, lambda_: float): + + def _influence_fn(*args, **kwargs): + + # Length of first value in points mappping. + len_points = len(list(points.values())[0]) + eif_vals = [] + for i in range(len_points): + kernel_point = {k: v[i] for k, v in points.items()} + + # Evaluate the original functional. + psi_p = fd_coupling.functional(*args, **kwargs) + + # Evaluate the functional of the perturbation. + with (fd_coupling.set_eps(eps), + fd_coupling.set_lambda(lambda_), + fd_coupling.set_kernel_point(kernel_point)): + psi_p_eps = fd_coupling.functional(*args, **kwargs) + + # Record the finite difference. + eif_vals.append((psi_p_eps - psi_p) / eps) + return eif_vals + + return _influence_fn + + diff --git a/docs/examples/robust_paper/finite_difference_eif/distributions.py b/docs/examples/robust_paper/finite_difference_eif/distributions.py new file mode 100644 index 00000000..f9564526 --- /dev/null +++ b/docs/examples/robust_paper/finite_difference_eif/distributions.py @@ -0,0 +1,34 @@ +from .abstractions import ModelWithMarginalDensity, FDModelFunctionalDensity +from scipy.stats import multivariate_normal +import pyro +import pyro.distributions as dist + + +class MultivariateNormalwDensity(ModelWithMarginalDensity): + + def __init__(self, mean, scale_tril, *args, **kwargs): + super().__init__(*args, **kwargs) + + self.mean = mean + self.scale_tril = scale_tril + + # Convert scale_tril to a covariance matrix. + self.cov = scale_tril @ scale_tril.T + + def density(self, x): + return multivariate_normal.pdf(x, mean=self.mean, cov=self.cov) + + def forward(self): + return pyro.sample("x", dist.MultivariateNormal(self.mean, scale_tril=self.scale_tril)) + + +class PerturbableNormal(FDModelFunctionalDensity): + + def __init__(self, *args, mean, scale_tril, **kwargs): + super().__init__(*args, **kwargs) + + self.ndims = mean.shape[-1] + self.model = MultivariateNormalwDensity( + mean=mean, + scale_tril=scale_tril + ) diff --git a/docs/examples/robust_paper/finite_difference_eif/mixins.py b/docs/examples/robust_paper/finite_difference_eif/mixins.py new file mode 100644 index 00000000..22c0adab --- /dev/null +++ b/docs/examples/robust_paper/finite_difference_eif/mixins.py @@ -0,0 +1,56 @@ +from .abstractions import FDModelFunctionalDensity +import numpy as np +from scipy.integrate import nquad +import torch +import pyro +from .distributions import MultivariateNormalwDensity + + +class ExpectedDensityQuadFunctional(FDModelFunctionalDensity): + """ + Compute the squared normal density using quadrature. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def functional(self): + def integrand(*args): + # TODO agnostic to kwarg names. + model_kwargs = kernel_kwargs = dict(x=np.array(args)) + return self.density(model_kwargs, kernel_kwargs) ** 2 + + ndim = self._kernel_point['x'].shape[-1] + + return nquad(integrand, [[-np.inf, np.inf]] * ndim)[0] + + +class ExpectedDensityMCFunctional(FDModelFunctionalDensity): + """ + Compute the squared normal density using Monte Carlo. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def functional(self, nmc=1000): + # TODO agnostic to kwarg names + with pyro.plate('samples', nmc): + points = self() + return torch.mean(self.density(model_kwargs=dict(x=points), kernel_kwargs=dict(x=points))) + + +class NormalKernel(FDModelFunctionalDensity): + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @property + def kernel(self): + # TODO agnostic to names. + mean = self._kernel_point['x'] + cov = torch.eye(self.ndims) * self._lambda + return MultivariateNormalwDensity( + mean=mean, + scale_tril=torch.linalg.cholesky(cov) + ) diff --git a/docs/examples/robust_paper/scripts/fd_influence_approx.py b/docs/examples/robust_paper/scripts/fd_influence_approx.py new file mode 100644 index 00000000..2fd3580d --- /dev/null +++ b/docs/examples/robust_paper/scripts/fd_influence_approx.py @@ -0,0 +1,178 @@ +from typing import List, Dict, Tuple +import pyro +import pyro.distributions as dist + +# TODO move these into __init__.py of finite_difference_eif for single import. +from docs.examples.robust_paper.finite_difference_eif.mixins import ( + NormalKernel, + ExpectedDensityMCFunctional, + ExpectedDensityQuadFunctional +) +from docs.examples.robust_paper.finite_difference_eif.abstractions import ( + fd_influence_fn, + FDModelFunctionalDensity +) +from docs.examples.robust_paper.finite_difference_eif.distributions import ( + PerturbableNormal +) +import torch +from itertools import product +from chirho.robust.ops import Point, T +from docs.examples.robust_paper.utils import rng_seed_context +import time +import numpy as np + + +# Couple together perturbation kernels, perturbable models and functionals. +class ExpectedNormalDensityQuadFunctional( + NormalKernel, + PerturbableNormal, + ExpectedDensityQuadFunctional, +): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + +class ExpectedNormalDensityMCFunctional( + NormalKernel, + PerturbableNormal, + ExpectedDensityMCFunctional, +): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + +def compute_fd_correction_sqd_mvn_quad(*, theta_hat: Point[T], **kwargs) -> List[Dict]: + mean = theta_hat['mean'] + scale_tril = theta_hat['scale_tril'] + + fd_coupling = ExpectedNormalDensityQuadFunctional( + # TODO agnostic to names + default_kernel_point=dict(x=mean), + mean=mean, + scale_tril=scale_tril + ) + + return compute_fd_correction(fd_coupling, **kwargs) + + +def compute_fd_correction_sqd_mvn_mc(*, theta_hat: Point[T], **kwargs) -> List[Dict]: + mean = theta_hat['mean'] + scale_tril = theta_hat['scale_tril'] + + fd_coupling = ExpectedNormalDensityMCFunctional( + # TODO agnostic to names + default_kernel_point=dict(x=mean), + mean=mean, + scale_tril=scale_tril + ) + + return compute_fd_correction(fd_coupling, **kwargs) + + +def compute_fd_correction( + fd_coupling: FDModelFunctionalDensity, + test_data: Point[T], + lambdas: List[float], + epss: List[float], + num_samples_scaling: int, + seed: int, +) -> List[Dict]: + epslam = product(epss, lambdas) + + results = list() + + for eps, lambda_ in epslam: + result = dict() + + with rng_seed_context(seed): + st = time.time() + + # TODO HACK nmc depends on eps but only applies when fd_coupling.functional takes that argument. + # Better ways to abstract this. + functional_kwargs = dict() + if isinstance(fd_coupling, ExpectedDensityMCFunctional): + functional_kwargs['nmc'] = int(num_samples_scaling / eps) + + pointwise = fd_influence_fn( + fd_coupling=fd_coupling, + points=test_data, + eps=eps, + lambda_=lambda_ + )(**functional_kwargs) + + result['wall_time'] = time.time() - st + + result['eps'] = eps + result['lambda'] = lambda_ + result['pointwise'] = [ + y if isinstance(y, float) else y.item() for y in pointwise + ] + result['correction'] = np.mean(pointwise) + + results.append(result) + + return results + + +if __name__ == "__main__": + + import matplotlib.pyplot as plt + import json + + def smoke_test(): + # Recommended values for experiments are commented out. + fd_kwargs = dict( + # lambdas=[0.1, 0.01, 0.001], + # epss=[0.1, 0.01, 0.001, 0.0001], + # num_samples_scaling=100, + # seed=0 + lambdas=[0.001], + epss=[0.01, 0.001], + num_samples_scaling=10000, + seed=0 + ) + + # Runtime + for ndim in [1, 2]: + theta_hat = th = dict( + mean=torch.zeros(ndim), + scale_tril=torch.linalg.cholesky(torch.eye(ndim)) + ) + + test_data = dict( + x=dist.MultivariateNormal(loc=th['mean'], scale_tril=th['scale_tril']).sample((20,)) + ) + + mc_correction = compute_fd_correction_sqd_mvn_mc( + theta_hat=theta_hat, + test_data=test_data, + **fd_kwargs + ) + + quad_correction = compute_fd_correction_sqd_mvn_quad( + theta_hat=theta_hat, + test_data=test_data, + **fd_kwargs + ) + + # print("MC Correction") + # print(json.dumps(mc_correction, indent=2)) + # print("Quad Correction") + # print(json.dumps(quad_correction, indent=2)) + + # Plot the quad correction results against the MC correction results. Fix aspect ratio. + for mc, qu in zip(mc_correction, quad_correction): + plt.figure() + plt.suptitle(f"D={ndim}, eps={mc['eps']}, lambda={mc['lambda']}") + plt.plot(mc['pointwise'], qu['pointwise'], 'o') + plt.xlabel("MC Correction") + plt.ylabel("Quad Correction") + # Set xlim and ylim to the same range. + xymin = min(min(mc['pointwise']), min(qu['pointwise'])) - 0.1 + xymax = max(max(mc['pointwise']), max(qu['pointwise'])) + 0.1 + plt.xlim(xymin, xymax) + plt.ylim(xymin, xymax) + plt.show() + + smoke_test() diff --git a/docs/examples/robust_paper/utils.py b/docs/examples/robust_paper/utils.py index 43dbf11d..bec1d6e0 100644 --- a/docs/examples/robust_paper/utils.py +++ b/docs/examples/robust_paper/utils.py @@ -4,6 +4,7 @@ import json import torch import pyro +from contextlib import contextmanager from chirho.robust.internals.utils import ParamDict from docs.examples.robust_paper.scripts.statics import ( @@ -128,3 +129,13 @@ def get_mle_params_and_guide(conditioned_model, n_iters=2000, lr=0.03): k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items() } return theta_hat, MLEGuide(theta_hat) + + +@contextmanager +def rng_seed_context(seed: int): + og_rng_state = pyro.util.get_rng_state() + pyro.util.set_rng_seed(seed) + try: + yield + finally: + pyro.util.set_rng_state(og_rng_state) \ No newline at end of file From bd63526f9d8efbbab38a787c0336a3f97c9ebf37 Mon Sep 17 00:00:00 2001 From: Andy Zane Date: Wed, 24 Jan 2024 12:48:12 -0800 Subject: [PATCH 08/26] Integrates FD for Squared Density Into Experiment (#510) * added robust folder * uncommited scratch work for log prob * untested variational log prob * uncomitted changes * uncomitted changes * pair coding w/ eli * added tests w/ Eli * eif * linting * moving test autograd to internals and deleted old utils file * sketch influence implementation * fix more args * ops file * file * format * lint * clean up influence and tests * make tests more generic * guess max plate nesting * linearize * rename file * tensor flatten * predictive eif * jvp type * reorganize files * shrink test case * move guess_max_plate_nesting * move cg solver to linearze * type alias * test_ops * basic cg tests * remove failing test case * format * move paramdict up * remove obsolete test files * add empty handlers * add chirho.robust to docs * fix memory leak in tests * make typing compatible with python 3.8 * typing_extensions * add branch to ci * predictive * remove imprecise annotation * Added more tests for `linearize` and `make_empirical_fisher_vp` (#405) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * removed missing import * fixed failing test with seeding * addressing Eli's comments * Add upper bound on number of CG steps (#404) * upper bound on cg_iters * address comment * fixed test for non-symmetric matrix (#437) * Make `NMCLogPredictiveLikelihood` seeded (#408) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * seeded NMC implementation * linting * removed missing import * changed to eli's seedmessenger suggestion * added failing edge case * explicitly add max plate argument * added warning message * fixed linting error and test failure case from too many cg iters * eli's contextlib seeding strategy * removed seedmessenger from test * randomness should be shared across calls * switched back to different * Use Hessian formulation of Fisher information in `make_empirical_fisher_vp` (#430) * hessian vector product formulation for fisher * ignoring small type error * fixed linting error * Add new `SimpleModel` and `SimpleGuide` (#440) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * seeded NMC implementation * linting * removed missing import * changed to eli's seedmessenger suggestion * added failing edge case * explicitly add max plate argument * added warning message * fixed linting error and test failure case from too many cg iters * eli's contextlib seeding strategy * removed seedmessenger from test * randomness should be shared across calls * uncomitted change before branch switch * switched back to different * added revised simple model and guide * added multiple link functions in test * linting * Batching in `linearize` and `influence` (#465) * batching in linearize and influence * addressing eli's review * added optimization for pointwise false case * fixing lint error * batched cg (#466) * One step correction implemented (#467) * one step correction * increased tolerance * fixing lint issue * Replace some `torch.vmap` usage with a hand-vectorized `BatchedNMCLogPredictiveLikelihood` (#473) * sketch batched nmc lpd * nits * fix type * format * comment * comment * comment * typo * typo * add condition to help guarantee idempotence * simplify edge case * simplify plate_name * simplify batchedobservation logic * factorize * simplify batched * reorder * comment * remove plate_names * types * formatting and type * move unbind to utils * remove max_plate_nesting arg from get_traces * comment * nit * move get_importance_traces to utils * fix types * generic obs type * lint * format * handle observe in batchedobservations * event dim * move batching handlers to utils * replace 2/3 vmaps, tests pass * remove dead code * format * name args * lint * shuffle code * try an extra optimization in batchedlatents * add another optimization * undo changes to test * remove inplace adds * add performance test showing speedup * document internal helpers * batch latents test * move batch handlers to predictive * add bind_leftmost_dim, document PredictiveFunctional and PredictiveModel * use bind_leftmost_dim in log prob * Added documentation for `chirho.robust` (#470) * documentation * documentation clean up w/ eli * fix lint issue * Make functional argument to influence_fn required (#487) * Make functional argument required * estimator * docstring * Remove guide argument from `influence_fn` and `linearize` (#489) * Make functional argument required * estimator * docstring * Remove guide, make tests pass * rename internals.predictive to internals.nmc * expose handlers.predictive * expose handlers.predictive * docstrings * fix doc build * fix equation * docstring import --------- Co-authored-by: Sam Witty * Make influence_fn a higher-order Functional (#492) * make influence a functional * fix test * multiple arguments * doc * docstring * docstring * Add full corrected one step estimator (#476) * added scaffolding to one step estimator * kept signature the same as one_step_correction * lint * refactored test to include multiple estimators * typo * revise error * added dict handling * remove assert * more informative error message * replace dispatch with pytree flatten and unflatten * revert arg for influence_function_estimator * docs and lint * lingering influence_fn * fixed missing return * rename * lint * add *model to appease the linter * add abstractions and simple temp scratch to test with squared unit normal functional with perturbation. * removes old scratch notebook * gets squared density running under abstraction that couples functionals and models * gets quad and mc approximations to match, vectorization hacky. * adds plotting and comparative to analytic. * adds scratch experiment comparing squared density analytic vs fd approx across various epsilon lambdas * fixes dataset splitting, breaks analytic eif * unfixes an incorrect fix, working now. * refactors finite difference machinery to fit experimental specs. * switches to existing rng seed context manager. * reverts back to what turns out to be a slightly different seeding context. * gets fd integrated into experiment exec and running. * adds perturbable normal model to statics listing * switches back to mean not mu * lines up mean mu loc naming correctly. --------- Co-authored-by: Raj Agrawal Co-authored-by: Eli Co-authored-by: Sam Witty Co-authored-by: Raj Agrawal Co-authored-by: eb8680 --- .../scripts/create_experiment_configs.py | 6 +++++ .../scripts/fd_influence_approx.py | 12 ++++----- .../robust_paper/scripts/influence_approx.py | 25 +++++++++++++++++-- docs/examples/robust_paper/scripts/statics.py | 2 ++ 4 files changed, 37 insertions(+), 8 deletions(-) diff --git a/docs/examples/robust_paper/scripts/create_experiment_configs.py b/docs/examples/robust_paper/scripts/create_experiment_configs.py index 67410f3c..96581994 100644 --- a/docs/examples/robust_paper/scripts/create_experiment_configs.py +++ b/docs/examples/robust_paper/scripts/create_experiment_configs.py @@ -91,6 +91,12 @@ def influence_approx_experiment_expected_density(): "cg_iters": None, "residual_tol": 1e-4, }, + "fd_influence_estimator_kwargs": { + "lambdas": [0.1, 0.01, 0.001], + "epss": [0.1, 0.01, 0.001, 0.0001], + "num_samples_scaling": 100, + "seed": 0, + }, "data_config": data_config, } save_experiment_config(experiment_config) diff --git a/docs/examples/robust_paper/scripts/fd_influence_approx.py b/docs/examples/robust_paper/scripts/fd_influence_approx.py index 2fd3580d..d499389b 100644 --- a/docs/examples/robust_paper/scripts/fd_influence_approx.py +++ b/docs/examples/robust_paper/scripts/fd_influence_approx.py @@ -43,8 +43,8 @@ def __init__(self, *args, **kwargs): def compute_fd_correction_sqd_mvn_quad(*, theta_hat: Point[T], **kwargs) -> List[Dict]: - mean = theta_hat['mean'] - scale_tril = theta_hat['scale_tril'] + mean = theta_hat['mu'].detach() + scale_tril = theta_hat['scale_tril'].detach() fd_coupling = ExpectedNormalDensityQuadFunctional( # TODO agnostic to names @@ -57,8 +57,8 @@ def compute_fd_correction_sqd_mvn_quad(*, theta_hat: Point[T], **kwargs) -> List def compute_fd_correction_sqd_mvn_mc(*, theta_hat: Point[T], **kwargs) -> List[Dict]: - mean = theta_hat['mean'] - scale_tril = theta_hat['scale_tril'] + mean = theta_hat['mu'].detach() + scale_tril = theta_hat['scale_tril'].detach() fd_coupling = ExpectedNormalDensityMCFunctional( # TODO agnostic to names @@ -136,12 +136,12 @@ def smoke_test(): # Runtime for ndim in [1, 2]: theta_hat = th = dict( - mean=torch.zeros(ndim), + mu=torch.zeros(ndim), scale_tril=torch.linalg.cholesky(torch.eye(ndim)) ) test_data = dict( - x=dist.MultivariateNormal(loc=th['mean'], scale_tril=th['scale_tril']).sample((20,)) + x=dist.MultivariateNormal(loc=th['mu'], scale_tril=th['scale_tril']).sample((20,)) ) mc_correction = compute_fd_correction_sqd_mvn_mc( diff --git a/docs/examples/robust_paper/scripts/influence_approx.py b/docs/examples/robust_paper/scripts/influence_approx.py index b59d2560..7881438a 100644 --- a/docs/examples/robust_paper/scripts/influence_approx.py +++ b/docs/examples/robust_paper/scripts/influence_approx.py @@ -16,6 +16,10 @@ analytic_eif_expected_density, analytic_eif_ate_causal_glm, ) +from fd_influence_approx import ( + compute_fd_correction_sqd_mvn_mc, + compute_fd_correction_sqd_mvn_quad +) def run_experiment(exp_config): @@ -55,7 +59,8 @@ def run_experiment(exp_config): conditioned_model = MODELS[model_str]["conditioned_model"](D_train, **model_kwargs) # Load in functional - functional_class = FUNCTIONALS_DICT[exp_config["functional_str"]] + functional_str = exp_config["functional_str"] + functional_class = FUNCTIONALS_DICT[functional_str] functional = functools.partial(functional_class, **exp_config["functional_kwargs"]) # Fit MLE @@ -139,7 +144,23 @@ def run_experiment(exp_config): results["all_monte_carlo_eif_results"] = all_monte_carlo_eif_results ### Finite Difference EIF ### - # TODO: Andy to add here + if model_str == "MultivariateNormalModel" and functional_str == "expected_density": + fd_kwargs = exp_config["fd_influence_estimator_kwargs"] + fd_mc_eif_results = compute_fd_correction_sqd_mvn_mc( + theta_hat=theta_hat, + test_data=D_test, + **fd_kwargs + ) + results["fd_mc_eif_results"] = fd_mc_eif_results + + # Maybe run this for 2d if you're having too good of a day, and need it to get worse. + if theta_hat["mu"].shape[-1] == 1: + fd_quad_eif_results = compute_fd_correction_sqd_mvn_quad( + theta_hat=theta_hat, + test_data=D_test, + **fd_kwargs + ) + results["fd_quad_eif_results"] = fd_quad_eif_results ### Analytic EIF ### if model_str == "CausalGLM": diff --git a/docs/examples/robust_paper/scripts/statics.py b/docs/examples/robust_paper/scripts/statics.py index a0838820..32aae1b5 100644 --- a/docs/examples/robust_paper/scripts/statics.py +++ b/docs/examples/robust_paper/scripts/statics.py @@ -3,6 +3,7 @@ import pyro.distributions as dist from docs.examples.robust_paper.models import * from docs.examples.robust_paper.functionals import * +from docs.examples.robust_paper.finite_difference_eif.distributions import PerturbableNormal MODELS = { @@ -15,6 +16,7 @@ "data_generator": DataGeneratorMultivariateNormalModel, "model": MultivariateNormalModel, "conditioned_model": ConditionedMultivariateNormalModel, + "fd_perturbable_model": PerturbableNormal }, } From 85353096dfb68f484cf9b5296067025b43f1d060 Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Thu, 25 Jan 2024 04:02:48 -0500 Subject: [PATCH 09/26] initial experiments --- .../notebooks/error_vs_dimension.ipynb | 549 ++++++++++++++ .../notebooks/expected_density.ipynb | 711 ++++++++++++++++++ .../figures/error_rate_causal_glm_vs_dim.png | Bin 0 -> 50821 bytes .../figures/expected_density_gateaux_grid.png | Bin 0 -> 212084 bytes ..._eif_samples_vs_error_expected_density.png | Bin 0 -> 28477 bytes .../figures/runtime_causal_glm_vs_dim.png | Bin 0 -> 37721 bytes .../toy_normal_influence_functions.pdf | Bin 0 -> 17034 bytes .../toy_normal_monte_carlo_eif_known_var.pdf | Bin 0 -> 15533 bytes ...toy_normal_monte_carlo_eif_unknown_var.pdf | Bin 0 -> 20671 bytes .../scripts/fd_influence_approx.py | 85 ++- 10 files changed, 1305 insertions(+), 40 deletions(-) create mode 100644 docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb create mode 100644 docs/examples/robust_paper/notebooks/expected_density.ipynb create mode 100644 docs/examples/robust_paper/notebooks/figures/error_rate_causal_glm_vs_dim.png create mode 100644 docs/examples/robust_paper/notebooks/figures/expected_density_gateaux_grid.png create mode 100644 docs/examples/robust_paper/notebooks/figures/monte_carlo_eif_samples_vs_error_expected_density.png create mode 100644 docs/examples/robust_paper/notebooks/figures/runtime_causal_glm_vs_dim.png create mode 100644 docs/examples/robust_paper/notebooks/figures/toy_normal_influence_functions.pdf create mode 100644 docs/examples/robust_paper/notebooks/figures/toy_normal_monte_carlo_eif_known_var.pdf create mode 100644 docs/examples/robust_paper/notebooks/figures/toy_normal_monte_carlo_eif_unknown_var.pdf diff --git a/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb b/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb new file mode 100644 index 00000000..4543cd5b --- /dev/null +++ b/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Callable, Optional, Tuple\n", + "from fractions import Fraction\n", + "\n", + "import functools\n", + "import torch\n", + "import math\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "from pyro.infer import Predictive\n", + "import pyro.contrib.gp as gp\n", + "\n", + "from chirho.counterfactual.handlers import MultiWorldCounterfactual\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.interventional.handlers import do\n", + "from chirho.robust.internals.utils import ParamDict\n", + "from chirho.robust.handlers.estimators import one_step_corrected_estimator \n", + "from chirho.robust.ops import influence_fn\n", + "from chirho.robust.handlers.predictive import PredictiveModel, PredictiveFunctional\n", + "from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood\n", + "\n", + "\n", + "pyro.settings.set(module_local_params=True)\n", + "\n", + "sns.set_style(\"white\")\n", + "\n", + "pyro.set_rng_seed(32891) # for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class CausalGLM(pyro.nn.PyroModule):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " super().__init__()\n", + " self.p = p\n", + " self.link_fn = link_fn\n", + " if prior_scale is None:\n", + " self.prior_scale = 1 / math.sqrt(self.p)\n", + " else:\n", + " self.prior_scale = prior_scale\n", + "\n", + " def sample_outcome_weights(self):\n", + " return pyro.sample(\n", + " \"outcome_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_intercept(self):\n", + " return pyro.sample(\"intercept\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_propensity_weights(self):\n", + " return pyro.sample(\n", + " \"propensity_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_treatment_weight(self):\n", + " return pyro.sample(\"treatment_weight\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_covariate_loc_scale(self):\n", + " return torch.zeros(self.p), torch.ones(self.p)\n", + "\n", + " def forward(self):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1))\n", + " A = pyro.sample(\n", + " \"A\",\n", + " dist.Bernoulli(\n", + " logits=torch.einsum(\"...i,...i->...\", X, propensity_weights)\n", + " ),\n", + " )\n", + "\n", + " return pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"...i,...i->...\", X, outcome_weights) + A * tau + intercept\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class ConditionedCausalGLM(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " X: torch.Tensor,\n", + " A: torch.Tensor,\n", + " Y: torch.Tensor,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " p = X.shape[1]\n", + " super().__init__(p, link_fn, prior_scale)\n", + " self.X = X\n", + " self.A = A\n", + " self.Y = Y\n", + "\n", + " def forward(self):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " with pyro.plate(\"__train__\", size=self.X.shape[0], dim=-1):\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1), obs=self.X)\n", + " A = pyro.sample(\n", + " \"A\",\n", + " dist.Bernoulli(\n", + " logits=torch.einsum(\"ni,i->n\", self.X, propensity_weights)\n", + " ),\n", + " obs=self.A,\n", + " )\n", + " pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"ni,i->n\", X, outcome_weights)\n", + " + A * tau\n", + " + intercept\n", + " ),\n", + " obs=self.Y,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class GroundTruthModel(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " alpha: int,\n", + " beta: int,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " treatment_weight: float = 0.0,\n", + " ):\n", + " super().__init__(p, link_fn)\n", + " self.alpha = alpha # sparsity of propensity weights\n", + " self.beta = beta # sparsity of outcome weights\n", + " self.treatment_weight = treatment_weight\n", + "\n", + " def sample_outcome_weights(self):\n", + " outcome_weights = 1 / math.sqrt(self.beta) * torch.ones(self.p)\n", + " outcome_weights[self.beta :] = 0.0\n", + " return outcome_weights\n", + "\n", + " def sample_propensity_weights(self):\n", + " propensity_weights = 1 / math.sqrt(self.alpha) * torch.ones(self.p)\n", + " propensity_weights[self.alpha :] = 0.0\n", + " return propensity_weights\n", + "\n", + " def sample_treatment_weight(self):\n", + " return torch.tensor(self.treatment_weight)\n", + "\n", + " def sample_intercept(self):\n", + " return torch.tensor(0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "simulated_datasets = []\n", + "\n", + "# Data configuration\n", + "alpha = 50\n", + "beta = 50\n", + "N_train = 500\n", + "N_test = 50\n", + "p_grid = [1, 10, 25, 50, 100, 150, 200, 250, 300, 500]\n", + "\n", + "for p_dim in p_grid:\n", + " true_model = GroundTruthModel(p_dim, alpha, beta)\n", + "\n", + " # Generate data\n", + " D_train = Predictive(\n", + " true_model, num_samples=N_train, return_sites=[\"X\", \"A\", \"Y\"]\n", + " )()\n", + " D_test = Predictive(\n", + " true_model, num_samples=N_test, return_sites=[\"X\", \"A\", \"Y\"]\n", + " )()\n", + " simulated_datasets.append((D_train, D_test))\n", + "\n", + "N_datasets = len(simulated_datasets)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "fitted_params = []\n", + "model_fitting_time = []\n", + "for i in range(N_datasets):\n", + " # Generate data\n", + " D_train = simulated_datasets[i][0]\n", + "\n", + " start = time.time()\n", + " # Fit model using maximum likelihood\n", + " conditioned_model = ConditionedCausalGLM(\n", + " X=D_train[\"X\"], A=D_train[\"A\"], Y=D_train[\"Y\"]\n", + " )\n", + " \n", + " guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model)\n", + " elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train)\n", + "\n", + " # initialize parameters\n", + " elbo()\n", + " adam = torch.optim.Adam(elbo.parameters(), lr=0.03)\n", + "\n", + " # Do gradient steps\n", + " for _ in range(2000):\n", + " adam.zero_grad()\n", + " loss = elbo()\n", + " loss.backward()\n", + " adam.step()\n", + "\n", + " model_fitting_time.append(time.time() - start)\n", + "\n", + " theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items()\n", + " }\n", + " fitted_params.append(theta_hat)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "10\n", + "25\n", + "50\n", + "100\n", + "150\n", + "200\n", + "250\n", + "300\n", + "500\n" + ] + } + ], + "source": [ + "class ATEFunctional(torch.nn.Module):\n", + " def __init__(self, model: Callable, *, num_monte_carlo: int = 100):\n", + " super().__init__()\n", + " self.model = model\n", + " self.num_monte_carlo = num_monte_carlo\n", + " \n", + " def forward(self, *args, **kwargs):\n", + " with MultiWorldCounterfactual():\n", + " with pyro.plate(\"monte_carlo_functional\", size=self.num_monte_carlo, dim=-2):\n", + " with do(actions=dict(A=(torch.tensor(0.0), torch.tensor(1.0)))):\n", + " Ys = self.model(*args, **kwargs)\n", + " Y0 = gather(Ys, IndexSet(A={1}), event_dim=0)\n", + " Y1 = gather(Ys, IndexSet(A={2}), event_dim=0)\n", + " ate = (Y1 - Y0).mean(dim=-2, keepdim=True).mean(dim=-1, keepdim=True).squeeze()\n", + " return pyro.deterministic(\"ATE\", ate)\n", + " \n", + "# Closed form expression\n", + "def closed_form_doubly_robust_ate_correction(X_test, theta) -> Tuple[torch.Tensor, torch.Tensor]:\n", + " X = X_test[\"X\"]\n", + " A = X_test[\"A\"]\n", + " Y = X_test[\"Y\"]\n", + " pi_X = torch.sigmoid(X.mv(theta[\"propensity_weights\"]))\n", + " mu_X = (\n", + " X.mv(theta[\"outcome_weights\"])\n", + " + A * theta[\"treatment_weight\"]\n", + " + theta[\"intercept\"]\n", + " )\n", + " analytic_eif_at_test_pts = (A / pi_X - (1 - A) / (1 - pi_X)) * (Y - mu_X)\n", + " analytic_correction = analytic_eif_at_test_pts.mean()\n", + " return analytic_correction, analytic_eif_at_test_pts\n", + "\n", + "# Helper class to create a trivial guide that returns the maximum likelihood estimate\n", + "class MLEGuide(torch.nn.Module):\n", + " def __init__(self, mle_est: ParamDict):\n", + " super().__init__()\n", + " self.names = list(mle_est.keys())\n", + " for name, value in mle_est.items():\n", + " setattr(self, name + \"_param\", torch.nn.Parameter(value))\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " for name in self.names:\n", + " value = getattr(self, name + \"_param\")\n", + " pyro.sample(\n", + " name, pyro.distributions.Delta(value, event_dim=len(value.shape))\n", + " )\n", + "\n", + "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", + "plug_in_ates = []\n", + "analytic_corrections = []\n", + "automated_monte_carlo_corrections = []\n", + "automated_monte_carlo_at_test = []\n", + "automated_monte_carlo_time = []\n", + "analytic_at_test = []\n", + "for i, p in enumerate(p_grid):\n", + " print(p)\n", + " theta_hat = fitted_params[i]\n", + " D_test = simulated_datasets[i][1]\n", + " mle_guide = MLEGuide(theta_hat)\n", + " functional = functools.partial(ATEFunctional, num_monte_carlo=10000)\n", + " ate_plug_in = functional(\n", + " PredictiveModel(CausalGLM(p), mle_guide)\n", + " )()\n", + " analytic_correction, analytic_eif_at_test_pts = closed_form_doubly_robust_ate_correction(D_test, theta_hat)\n", + "\n", + " start = time.time()\n", + " monte_eif = influence_fn(functional, D_test, num_samples_outer=10000, num_samples_inner=1)\n", + " monte_eif_at_test_pts = monte_eif(PredictiveModel(CausalGLM(p), mle_guide))()\n", + " end = time.time()\n", + " automated_monte_carlo_time.append(end - start)\n", + " automated_monte_carlo_at_test.append(monte_eif_at_test_pts)\n", + " analytic_at_test.append(analytic_eif_at_test_pts)\n", + "\n", + " plug_in_ates.append(ate_plug_in.detach().item())\n", + " analytic_corrections.append(ate_plug_in.detach().item() + analytic_correction.detach().item())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def median_rel_error(x, y):\n", + " x = torch.tensor(x)\n", + " y = torch.tensor(y)\n", + " return torch.median(torch.abs(x - y) / torch.abs(y))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/1n/rv21b_n10gx0tp5_zz33z7qc0000gn/T/ipykernel_55710/3392044420.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " x = torch.tensor(x)\n", + "/var/folders/1n/rv21b_n10gx0tp5_zz33z7qc0000gn/T/ipykernel_55710/3392044420.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " y = torch.tensor(y)\n" + ] + } + ], + "source": [ + "monte_eif_errors = [median_rel_error(monte_eif_at_test_pts, analytic_eif_at_test_pts) for monte_eif_at_test_pts, analytic_eif_at_test_pts in zip(automated_monte_carlo_at_test, analytic_at_test)]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "model_dimension_grid = [sum([v.numel() for k, v in param.items()]) for param in fitted_params]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Fraction(int(round(8*obs_slope)),8).numerator" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "obs_slope, obs_intercept = np.polyfit(\n", + " np.log10(model_dimension_grid), \n", + " np.log10(monte_eif_errors), 1)\n", + "\n", + "theory_slope = 1/2\n", + "theory_intercept = (np.log10(monte_eif_errors) - theory_slope * np.log10(model_dimension_grid)).mean()\n", + "\n", + "# Plot results\n", + "plt.plot(\n", + " model_dimension_grid, \n", + " monte_eif_errors, \n", + " label='Monte Carlo EIF (10k Monte Carlo samples)', \n", + " color='#154c79',\n", + " marker='o'\n", + ")\n", + "obs_rate_num = Fraction(int(round(8*obs_slope)),8).numerator\n", + "obs_rate_denom = Fraction(int(round(8*obs_slope)),8).denominator\n", + "plt.plot(model_dimension_grid, \n", + " 10**obs_intercept * np.power(model_dimension_grid, obs_slope), \n", + " label='Est. Error Rate: O($p^{3/8}$)',\n", + " color='red',\n", + " linestyle='--'\n", + ")\n", + "plt.plot(model_dimension_grid, \n", + " 10**theory_intercept * np.power(model_dimension_grid, theory_slope), \n", + " label='Our Theory: O($p^{1/2}$)',\n", + " color='green',\n", + " linestyle='--'\n", + ")\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.ylabel('Median Relative Error', fontsize=18)\n", + "plt.xlabel('Model Dimension (p)', fontsize=18)\n", + "sns.despine()\n", + "plt.legend(fontsize=13, frameon=False)\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/error_rate_causal_glm_vs_dim.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results\n", + "plt.plot(\n", + " model_dimension_grid, \n", + " automated_monte_carlo_time, \n", + " label='Monte Carlo EIF (10k Monte Carlo samples)', \n", + " color='#154c79',\n", + " marker='o'\n", + ")\n", + "plt.plot(\n", + " model_dimension_grid, \n", + " model_fitting_time, \n", + " label='Model Fitting Time', \n", + " color='#f95d6a',\n", + " marker='o'\n", + "\n", + ")\n", + "# plt.xscale('log')\n", + "# plt.yscale('log')\n", + "plt.ylabel('Runtime (s)', fontsize=18)\n", + "plt.xlabel('Model Dimension', fontsize=18)\n", + "sns.despine()\n", + "plt.legend(fontsize=13, frameon=False)\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/runtime_causal_glm_vs_dim.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "basis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/robust_paper/notebooks/expected_density.ipynb b/docs/examples/robust_paper/notebooks/expected_density.ipynb new file mode 100644 index 00000000..56e8a5af --- /dev/null +++ b/docs/examples/robust_paper/notebooks/expected_density.ipynb @@ -0,0 +1,711 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import time\n", + "import math\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "from pyro.infer import Predictive\n", + "from chirho.robust.internals.utils import ParamDict\n", + "\n", + "pyro.settings.set(module_local_params=True)\n", + "sns.set_style(\"white\")\n", + "pyro.set_rng_seed(321) # for reproducibility" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class ToyNormal(pyro.nn.PyroModule):\n", + " def forward(self):\n", + " mu = pyro.sample(\"mu\", dist.Normal(0.0, 1.0))\n", + " sd = pyro.sample(\"sd\", dist.HalfNormal(1.0))\n", + " return pyro.sample(\n", + " \"Y\",\n", + " dist.Normal(mu, scale=sd),\n", + " )\n", + "\n", + "\n", + "class ToyNormalKnownSD(pyro.nn.PyroModule):\n", + " def __init__(self, sd_true):\n", + " super().__init__()\n", + " self.sd_true = sd_true\n", + " def forward(self):\n", + " mu = pyro.sample(\"mu\", dist.Normal(0.0, 1.0))\n", + " sd = pyro.sample(\"sd\", dist.HalfNormal(1.0))\n", + " return pyro.sample(\n", + " \"Y\",\n", + " dist.Normal(mu, scale=self.sd_true),\n", + " )\n", + "\n", + "class GroundTruthToyNormal(pyro.nn.PyroModule):\n", + " def __init__(self, mu_true, sd_true):\n", + " super().__init__()\n", + " self.mu_true = mu_true\n", + " self.sd_true = sd_true\n", + " def forward(self):\n", + " return pyro.sample(\n", + " \"Y\",\n", + " dist.Normal(self.mu_true, scale=self.sd_true),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "normal_pdf = lambda x, mu, sd: torch.exp(-0.5 * ((x - mu) / sd) ** 2) / (sd * math.sqrt(2.0 * math.pi))\n", + "normal_pdf_partial_sd = torch.func.grad(normal_pdf, argnums=2)\n", + "\n", + "\n", + "def fisher_eif_analytic(Y, mu, sd, known_sd):\n", + " assert isinstance(mu, float)\n", + " assert isinstance(sd, float)\n", + " if known_sd:\n", + " return torch.zeros(Y.shape[0])\n", + " else:\n", + " sd_torch = torch.tensor(sd, requires_grad=True)\n", + " z_monte = sd * torch.randn(10000) + mu\n", + " grad_functional = 2 * torch.tensor([normal_pdf_partial_sd(z, mu, sd_torch) for z in z_monte]).mean()\n", + " inverse_fisher = sd ** 2 / 2\n", + " score = ((Y - mu) ** 2) / (sd ** 3) - 1/sd\n", + " return grad_functional * inverse_fisher * score\n", + "\n", + "\n", + "def kennedy_if_analytic(Y, mu, sd):\n", + " assert isinstance(mu, float)\n", + " assert isinstance(sd, float)\n", + " pdf_at_y = normal_pdf(Y, mu, sd)\n", + " z_monte = sd * torch.randn(100000)\n", + " expected_density = torch.tensor([normal_pdf(z, mu, sd) for z in z_monte]).mean()\n", + " return 2 * (pdf_at_y - expected_density)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analytic Influence Function & Efficient Influence Function" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "N_pts = 500\n", + "mu_true = 0.0\n", + "sd_true = 1.0\n", + "true_model = GroundTruthToyNormal(mu_true, sd_true)\n", + "D_pts = Predictive(true_model, num_samples=N_pts, return_sites=[\"Y\"])()\n", + "Y_pts = D_pts[\"Y\"]\n", + "Y_pts = torch.sort(Y_pts).values\n", + "\n", + "fisher_pointwise = fisher_eif_analytic(Y_pts, mu_true, sd_true, False)\n", + "kennedy_pointwise = kennedy_if_analytic(Y_pts, mu_true, sd_true)\n", + "tangent_fn_pointwise = kennedy_pointwise - fisher_pointwise" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "plt.plot(Y_pts, torch.zeros(Y_pts.shape[0]), label=\"Efficient Influence (Known SD)\")\n", + "plt.plot(Y_pts, fisher_pointwise, label=\"Efficient Influence (Unknown SD)\")\n", + "plt.plot(Y_pts, kennedy_pointwise, label=\"Nonparametric Influence\")\n", + "plt.plot(Y_pts, tangent_fn_pointwise, label=\"Tangent Nuisance Function\")\n", + "plt.xlabel('Value of x', fontsize=18)\n", + "plt.ylabel('Influence Function at x', fontsize=18)\n", + "plt.legend(fontsize=13)\n", + "sns.despine()\n", + "plt.savefig(\"figures/toy_normal_influence_functions.pdf\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor(0.0004)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This should be close to zero since the tangent nuisance function is orthogonal to the efficient influence function\n", + "fisher_pointwise.dot(tangent_fn_pointwise) / N_pts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare Analytic w/ Automated Influnce Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import functools\n", + "from chirho.robust.ops import influence_fn\n", + "from chirho.robust.handlers.predictive import PredictiveModel, PredictiveFunctional\n", + "from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "class MLEGuide(torch.nn.Module):\n", + " def __init__(self, mle_est: ParamDict):\n", + " super().__init__()\n", + " self.names = list(mle_est.keys())\n", + " for name, value in mle_est.items():\n", + " setattr(self, name + \"_param\", torch.nn.Parameter(value))\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " for name in self.names:\n", + " value = getattr(self, name + \"_param\")\n", + " pyro.sample(\n", + " name, pyro.distributions.Delta(value, event_dim=len(value.shape))\n", + " )\n", + " \n", + "class ExpectedDensity(torch.nn.Module):\n", + " def __init__(self, model, *, num_monte_carlo: int = 10000):\n", + " super().__init__()\n", + " self.model = model\n", + " self.log_marginal_prob = BatchedNMCLogMarginalLikelihood(model, num_samples=1)\n", + " self.num_monte_carlo = num_monte_carlo\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " with pyro.plate(\"monte_carlo_functional\", self.num_monte_carlo):\n", + " points = PredictiveFunctional(self.model)(*args, **kwargs)\n", + "\n", + " log_marginal_prob_at_points = self.log_marginal_prob(points, *args, **kwargs)\n", + " return torch.exp(\n", + " torch.logsumexp(log_marginal_prob_at_points, dim=0)\n", + " - math.log(self.num_monte_carlo)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "functional = functools.partial(ExpectedDensity, num_monte_carlo=10000)\n", + "\n", + "theta_true = {\n", + " \"mu\": torch.tensor(mu_true, requires_grad=True), \n", + " \"sd\": torch.tensor(sd_true, requires_grad=True)\n", + "}\n", + "\n", + "model = ToyNormal()\n", + "guide = MLEGuide(theta_true)\n", + "\n", + "monte_eif = influence_fn(\n", + " functional, {'Y': Y_pts}, num_samples_outer=50000, num_samples_inner=1\n", + ")(PredictiveModel(model, guide))()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uknown Mean, Unknown SD" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(Y_pts, fisher_pointwise, label='Analytic EIF', color='black')\n", + "plt.scatter(Y_pts, monte_eif.detach().numpy(), label='Monte Carlo EIF', alpha=.5)\n", + "plt.xlabel('Value of x', fontsize=18)\n", + "plt.ylabel('Influence Function at x', fontsize=18)\n", + "plt.legend(fontsize=13)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/toy_normal_monte_carlo_eif_unknown_var.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uknown Mean, Known SD" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "functional = functools.partial(ExpectedDensity, num_monte_carlo=10000)\n", + "\n", + "theta_true = {\n", + " \"mu\": torch.tensor(mu_true, requires_grad=True), \n", + "}\n", + "\n", + "model = ToyNormalKnownSD(sd_true)\n", + "guide = MLEGuide(theta_true)\n", + "\n", + "monte_eif = influence_fn(\n", + " functional, {'Y': Y_pts}, num_samples_outer=50000, num_samples_inner=1\n", + ")(PredictiveModel(model, guide))()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(Y_pts, torch.zeros(Y_pts.shape[0]), label='Analytic EIF', color='black')\n", + "plt.scatter(Y_pts, monte_eif.detach().numpy(), label='Monte Carlo EIF', alpha=.5)\n", + "plt.xlabel('Value of x', fontsize=18)\n", + "plt.ylabel('Influence Function at x', fontsize=18)\n", + "plt.legend(fontsize=13)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig(\"figures/toy_normal_monte_carlo_eif_known_var.pdf\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Finite-Difference Smoothed Gateaux Approach" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../scripts/\")\n", + "from fd_influence_approx import (\n", + " compute_fd_correction_sqd_mvn_mc,\n", + " compute_fd_correction_sqd_mvn_quad\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/raj/Desktop/causal_pyro/docs/examples/robust_paper/finite_difference_eif/distributions.py:16: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/TensorShape.cpp:3618.)\n", + " self.cov = scale_tril @ scale_tril.T\n" + ] + } + ], + "source": [ + "theta_true = {\n", + " \"mu\": torch.tensor(mu_true, requires_grad=True).unsqueeze(0), \n", + " \"scale_tril\": torch.tensor(sd_true, requires_grad=True).unsqueeze(0)\n", + "}\n", + "\n", + "\n", + "fd_quad_kwargs = {\n", + " \"lambdas\": [.05, .01, .005],\n", + " \"epss\": [.05, .01, .005],\n", + " \"num_samples_scaling\": 10,\n", + " \"seed\": 0,\n", + "}\n", + "\n", + "fd_quad_eif_results = compute_fd_correction_sqd_mvn_quad(\n", + " theta_hat=theta_true,\n", + " test_data={'x': Y_pts.unsqueeze(-1)},\n", + " **fd_quad_kwargs\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot 3 x 3 grid of results\n", + "fig, axes = plt.subplots(3, 3, figsize=(12, 12))\n", + "for iter, result in enumerate(fd_quad_eif_results):\n", + " i = iter // 3\n", + " j = iter % 3\n", + " epsilon = fd_quad_kwargs[\"epss\"][i]\n", + " lam = fd_quad_kwargs[\"lambdas\"][j]\n", + " axes[i, j].scatter(Y_pts, result['pointwise'], label='FD Smoothed Gateaux', color='#eab676')\n", + " axes[i, j].plot(Y_pts, kennedy_pointwise, label='Nonparametric Influence', color='black')\n", + " axes[i, j].set_xlabel('Value of x', fontsize=15)\n", + " axes[i, j].set_ylabel('Influence Function at x', fontsize=15)\n", + " axes[i, j].set_title(f\"$\\epsilon$={epsilon}, $\\lambda$={lam}\", fontsize=15)\n", + " axes[i, j].legend(frameon=False)\n", + " sns.despine()\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(\"./figures/expected_density_gateaux_grid.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Runtime vs. Quality" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'fd_quad_eif_results' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/raj/Desktop/causal_pyro/docs/examples/robust_paper/notebooks/expected_density.ipynb Cell 22\u001b[0m line \u001b[0;36m6\n\u001b[1;32m 3\u001b[0m y \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mtensor(y)\n\u001b[1;32m 4\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39mmedian(torch\u001b[39m.\u001b[39mabs(x \u001b[39m-\u001b[39m y) \u001b[39m/\u001b[39m torch\u001b[39m.\u001b[39mabs(y))\n\u001b[0;32m----> 6\u001b[0m fd_quad_rel_mae \u001b[39m=\u001b[39m [median_rel_error(result[\u001b[39m'\u001b[39m\u001b[39mpointwise\u001b[39m\u001b[39m'\u001b[39m], kennedy_pointwise) \u001b[39mfor\u001b[39;00m result \u001b[39min\u001b[39;00m fd_quad_eif_results]\n\u001b[1;32m 7\u001b[0m fd_quad_time \u001b[39m=\u001b[39m [result[\u001b[39m'\u001b[39m\u001b[39mwall_time\u001b[39m\u001b[39m'\u001b[39m] \u001b[39mfor\u001b[39;00m result \u001b[39min\u001b[39;00m fd_quad_eif_results]\n", + "\u001b[0;31mNameError\u001b[0m: name 'fd_quad_eif_results' is not defined" + ] + } + ], + "source": [ + "def median_rel_error(x, y):\n", + " x = torch.tensor(x)\n", + " y = torch.tensor(y)\n", + " return torch.median(torch.abs(x - y) / torch.abs(y))\n", + "\n", + "fd_quad_rel_mae = [median_rel_error(result['pointwise'], kennedy_pointwise) for result in fd_quad_eif_results]\n", + "fd_quad_time = [result['wall_time'] for result in fd_quad_eif_results]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/1n/rv21b_n10gx0tp5_zz33z7qc0000gn/T/ipykernel_53328/3498049407.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " x = torch.tensor(x)\n", + "/var/folders/1n/rv21b_n10gx0tp5_zz33z7qc0000gn/T/ipykernel_53328/3498049407.py:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " y = torch.tensor(y)\n" + ] + } + ], + "source": [ + "monte_eif_size = []\n", + "monte_eif_errors = []\n", + "monte_eif_runtimes = []\n", + "for n_monte in [10, 100, 1000, 10000, 100000]:\n", + " start = time.time()\n", + " functional = functools.partial(ExpectedDensity, num_monte_carlo=n_monte)\n", + " theta_true = {\n", + " \"mu\": torch.tensor(mu_true, requires_grad=True), \n", + " \"sd\": torch.tensor(sd_true, requires_grad=True)\n", + " }\n", + "\n", + " model = ToyNormal()\n", + " guide = MLEGuide(theta_true)\n", + " monte_eif = influence_fn(\n", + " functional, {'Y': Y_pts}, num_samples_outer=n_monte, num_samples_inner=1\n", + " )(PredictiveModel(model, guide))()\n", + " end = time.time()\n", + " monte_eif_runtimes.append(end - start)\n", + " monte_eif_errors.append(median_rel_error(monte_eif, fisher_pointwise))\n", + " monte_eif_size.append(n_monte)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'fd_quad_time' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/raj/Desktop/causal_pyro/docs/examples/robust_paper/notebooks/expected_density.ipynb Cell 24\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m plt\u001b[39m.\u001b[39mscatter(monte_eif_runtimes, monte_eif_errors, label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mMonte Carlo EIF\u001b[39m\u001b[39m'\u001b[39m, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m plt\u001b[39m.\u001b[39mscatter(fd_quad_time, fd_quad_rel_mae, label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mFD Smoothed Gateaux\u001b[39m\u001b[39m'\u001b[39m, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m#eab676\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 3\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mWall Time (s)\u001b[39m\u001b[39m'\u001b[39m, fontsize\u001b[39m=\u001b[39m\u001b[39m18\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[39m.\u001b[39mylabel(\u001b[39m'\u001b[39m\u001b[39mMedian Relative Error\u001b[39m\u001b[39m'\u001b[39m, fontsize\u001b[39m=\u001b[39m\u001b[39m18\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'fd_quad_time' is not defined" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plt.scatter(monte_eif_runtimes, monte_eif_errors, label='Monte Carlo EIF', color='blue')\n", + "# plt.scatter(fd_quad_time, fd_quad_rel_mae, label='FD Smoothed Gateaux', color='#eab676')\n", + "# plt.xlabel('Wall Time (s)', fontsize=18)\n", + "# plt.ylabel('Median Relative Error', fontsize=18)\n", + "# # plt.xscale('log')\n", + "# # plt.yscale('log')\n", + "# plt.legend(fontsize=12)\n", + "# sns.despine()\n", + "# plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(\n", + " monte_eif_size, \n", + " monte_eif_errors, \n", + " label='Monte Carlo EIF', \n", + " color='#154c79',\n", + " marker='o'\n", + ")\n", + "plt.xscale('log')\n", + "plt.yscale('log')\n", + "plt.ylabel('Median Relative Error', fontsize=18)\n", + "plt.xlabel('Number of Monte Carlo EIF Samples', fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig(\"./figures/monte_carlo_eif_samples_vs_error_expected_density.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "base_dist = dist.Normal(torch.tensor(0.0), torch.tensor(1.0))\n", + "dist_y = dist.TransformedDistribution(base_dist, [pyro.distributions.transforms.ExpTransform()])" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'TransformedDistribution' object has no attribute 'params'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/raj/Desktop/causal_pyro/docs/examples/robust_paper/notebooks/expected_density.ipynb Cell 27\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m dist_y\u001b[39m.\u001b[39;49mparams\n", + "\u001b[0;31mAttributeError\u001b[0m: 'TransformedDistribution' object has no attribute 'params'" + ] + } + ], + "source": [ + "dist_y." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "# class NormalizingFlowModel(pyro.nn.PyroModule):\n", + "# def __init__(self):\n", + "# self.base_dist = dist.Normal(0, 1)\n", + "# self.exp_transform = dist.transforms.ExpTransform()\n", + "\n", + "# def forward():\n", + "# return pyro.sample(\"Y\", dist.TransformedDistribution(self.base_dist, [self.exp_transform]))\n", + "\n", + "base_dist = dist.Normal(torch.zeros(10), torch.ones(10))\n", + "\n", + "\n", + "class NormalizingFlowLikelihood(torch.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " # self.base_dist = torch.distributions.normal.Normal(torch.tensor([0]), torch.tensor([1]))\n", + " self.base_dist = dist.Normal(torch.tensor(0.0), torch.tensor(1.0)).expand([1])\n", + " self.arn = pyro.nn.AutoRegressiveNN(1, [40], param_dims=[16]*3)\n", + " self.transform = dist.transforms.NeuralAutoregressive(self.arn, hidden_units=16)\n", + " # self.transform = dist.transforms.ExpTransform()\n", + "\n", + " def forward(self, x):\n", + " return dist.TransformedDistribution(self.base_dist, [self.transform]).log_prob(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/pyro/nn/auto_reg_nn.py:179: UserWarning: ConditionalAutoRegressiveNN input_dim = 1. Consider using an affine transformation instead.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "NotImplementedError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/raj/Desktop/causal_pyro/docs/examples/robust_paper/notebooks/expected_density.ipynb Cell 29\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m NormalizingFlowLikelihood()(torch\u001b[39m.\u001b[39;49mtensor([[\u001b[39m1\u001b[39;49m]]))\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", + "\u001b[1;32m/Users/raj/Desktop/causal_pyro/docs/examples/robust_paper/notebooks/expected_density.ipynb Cell 29\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 21\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[0;32m---> 22\u001b[0m \u001b[39mreturn\u001b[39;00m dist\u001b[39m.\u001b[39;49mTransformedDistribution(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbase_dist, [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransform])\u001b[39m.\u001b[39;49mlog_prob(x)\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/torch/distributions/transformed_distribution.py:168\u001b[0m, in \u001b[0;36mTransformedDistribution.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 166\u001b[0m y \u001b[39m=\u001b[39m value\n\u001b[1;32m 167\u001b[0m \u001b[39mfor\u001b[39;00m transform \u001b[39min\u001b[39;00m \u001b[39mreversed\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms):\n\u001b[0;32m--> 168\u001b[0m x \u001b[39m=\u001b[39m transform\u001b[39m.\u001b[39;49minv(y)\n\u001b[1;32m 169\u001b[0m event_dim \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m transform\u001b[39m.\u001b[39mdomain\u001b[39m.\u001b[39mevent_dim \u001b[39m-\u001b[39m transform\u001b[39m.\u001b[39mcodomain\u001b[39m.\u001b[39mevent_dim\n\u001b[1;32m 170\u001b[0m log_prob \u001b[39m=\u001b[39m log_prob \u001b[39m-\u001b[39m _sum_rightmost(\n\u001b[1;32m 171\u001b[0m transform\u001b[39m.\u001b[39mlog_abs_det_jacobian(x, y),\n\u001b[1;32m 172\u001b[0m event_dim \u001b[39m-\u001b[39m transform\u001b[39m.\u001b[39mdomain\u001b[39m.\u001b[39mevent_dim,\n\u001b[1;32m 173\u001b[0m )\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/torch/distributions/transforms.py:262\u001b[0m, in \u001b[0;36m_InverseTransform.__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[1;32m 261\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inv \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inv\u001b[39m.\u001b[39;49m_inv_call(x)\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/torch/distributions/transforms.py:173\u001b[0m, in \u001b[0;36mTransform._inv_call\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m y_old:\n\u001b[1;32m 172\u001b[0m \u001b[39mreturn\u001b[39;00m x_old\n\u001b[0;32m--> 173\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inverse(y)\n\u001b[1;32m 174\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cached_x_y \u001b[39m=\u001b[39m x, y\n\u001b[1;32m 175\u001b[0m \u001b[39mreturn\u001b[39;00m x\n", + "File \u001b[0;32m/opt/homebrew/anaconda3/envs/basis/lib/python3.10/site-packages/torch/distributions/transforms.py:187\u001b[0m, in \u001b[0;36mTransform._inverse\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_inverse\u001b[39m(\u001b[39mself\u001b[39m, y):\n\u001b[1;32m 184\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 185\u001b[0m \u001b[39m Abstract method to compute inverse transformation.\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 187\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m\n", + "\u001b[0;31mNotImplementedError\u001b[0m: " + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], + "source": [ + "NormalizingFlowLikelihood()(torch.tensor([[1]]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "basis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/robust_paper/notebooks/figures/error_rate_causal_glm_vs_dim.png b/docs/examples/robust_paper/notebooks/figures/error_rate_causal_glm_vs_dim.png new file mode 100644 index 0000000000000000000000000000000000000000..8e404f55bcfa89223943e2e7c9a03b2d30e112e0 GIT binary patch literal 50821 zcmd43bySw$7d`j_(jXGjAq~J(kUU*E!`l}-QC??bJ5T5JG0jO zKWqL_frZa=MKNZ6s9fArLg(r$10X1oBKFkj@iHQ6VLll)ZUJ7o~SNEz65te1gJ0O9%LK zq4grZOIEOI8NUNXe98RDI*C5J+XSv09N@1M*I(Z1CkskIlTKf`rU$}bEY!0ZOFG%J zI&Xx%m&bx7{qjN>A=+q*(h(wz0KOr!m4v4G`?D^I|1ZA4MGMJV+T0AxqLCyD0Yf1b zEtG`DMSFO7;Ns$1Iy_7t%X}J^a7Pk_&))%>hW-Eai{mwtl}PO%ZG}K|^B4%y-$A!m zT~5+e6r%W{J(u12px6H@ALeO}UnGLZ@ZVGn4jS$ClzlO6J3>3|W@tfGXPeD_zF$uP zh4u71S3=eei$kuTjAfg-Q3ZF~id1I|XgQ2HBnegb{@a>knCiKC;pY4MBczkIxuKM6 z)XmXGC1<7A$_jzl{~JV%Tq&9#8fq8a@6Z#~-ioqu@e{+}@ngF8%#slQr{U=J{Fw?- z?*C}JJx|%COcI#cq^l5!Mg4y!@k$!A>+7f@jnSvDYOz`9pNEcWQ)&n{%)O)%K6L`i~*1YsY;5J;6gr9&2Jjt(OVjyOpT}&0OCcn^P;rEJh&e z*@wR|_xW80{C&!8+5Q6uZ_8i}rcLHO4yrsOeFO5C_Cn)eFu(F)hs*UL*w zEmh`|rKP13J2p>2e(Xp5i@#;=H}49zwT{Uvd@;Y;XjhDP-%8J7xFJtF7}SwVgY>9z z9mv}I=V>p%wn1_Oa*w4RghfbU!;1bLmLT@^*hlIlE3-c>FffQn$ZaZ%xQj0quQ)vp z2){C-5^#1k7d@jw%y?StkuVs!=N~+5Z7&3-Bcq$p?RN*)KjevIvq|Dyiu3ViI9vZv zdwDSLw>>KKeOR>*Q3%`j?;4FGBO&W`M5EI;l)v!NbLyO$G9#yCjSF)B9s<2P*9wvh!_%!}(#5}E3XdMZE(!L*TFqkm0zc??hSk89gspZT_a2SxJ zzL~s(QiS|l<>=R@Sg0Xc86|mO_Q~bN{-*b*0ytzGv(3A3!o;lqZFYX& z06Y(ES@yH;G!p%d>qHkgx2_T)9GtB&L?kR9943Ae60R3^n-SWxlA{030m+Nh>uhE= zbV6Ny6@5!6k>2pv*e=Gl%xHI!P&nK-Mt@=d${fwle#=uV^Khx62G9R4v_eDgA>-<= z(day4Ir&UIgScqv8&eGN8{39A?zbFg`^Y#b0hYb{{@j~$Ohh)*_K)Gh#6oYOWJt=ki(U#r%vFh*EEPN+%Me15Mmg zo0_Sv>-R{War~N6PMu>z>{j8LO@;G4t6ix3`ihD+&Vka24mdWf{{?GI)dxqKSXnu} zo95AB8hkLq_O6cv7H-K8Pw;T zW1D|&=^zetBQN4mlNj=l&`O0oA2Q;slE8EtkXBZ>Y@KVM-(uvx~*Yv`2(66A7D*toKBA} z*IMo6GadKYWJF7IR-A)ZWCnUt+y7~I^yQd!v+Zw;K zs-J_?5uAp)GZ+t7gNZ*`n5SOhb|;|Ay)YGy{nMGnkwsJL#DGFl%&-YP)98^WH(7l4 zGWs`z&eKt&KopZ#u`z`*jE`Th?zLo1GBnT*^kkP_O!sAT7JL5y;s13zWM79luz4tM zX>nq(wV;`N_mb)P=3sjaWz1ct;D3Wb9DF7Bz;z2Rq$|Y!d(vn2-g=bTn!JnAo~q8% ztUVHh1WA<|=C#4$Le$%e>fFUD^AGt7!hz{oJ^zg(7$p}^WmFGUjxUY1bWTv|^~=oz z-bmJyOG;Rm!;Sv-^tHk96nuZG$t*R8e7ao%76I}Lrl;F(*7LnyW7By1I+@I z58O4rXv*W(LHo`vvwjfhA&gOzfFr@*I3t@5SUkn zge?ADVR`;L=QdrJE)N&!zX3V)Uf*egxo z*45pTVCrwCb9!q%=XxI89#4Xi*0uKgYZ`Gl-AI|0>WFFrRgbxxBn4C${NGg!8XPl2 zea>eLVh=vt3qsr(pN0F(cjiSXrz49koiCDd$GLLIX0HAH3Hwi_XPD&egQ&D2PFS|r zUJLcNLtE|SY7O2kS!N7)tl1)>L@l34_*6WXFl67AtgPc8_~b{ET6(<}8>EV(iJ`O4 zYI^u8iqYcouhL)b&82Qu5>}9{lxbh+e^G1bJaJ`1`OGFtXf0pFMNYIE!&zWCCa%@^ zS;#NLP-;xGUM%Sy?x^f5C?V24F3-@H12`^6C;A=7e>&|(DhS(UMIIdbhi*nijXquy+z>2f+zjCyQQ4+%D36IB-@F(1vNNISOU|3G;d~{iK6<#` zOEOdQNgj)F!Mz7*6PfUrjewqze@ z)588xBX1MG&ARE*!mQ6d&w78%n~tXOD`UQTe}HIz)oi@7-UW*mV>4l))~)Pf$#8>e$d@%->Ki}`Y zKGwS0%c#zYBy&1(6YF(1x3(Yhr51@7&;$gQx|>$>f;k~4cfSyj5DhPXu-n#w04QG(fc06 zu8Dk+%i0Nhe$f9t9Wqg(&uQ<`VEFbuYT=8~j!=(tzG~vtNzbI7o77%6 z&)NgqZ_a1B6WPqA8Y|87D#VEEHa7OVyU0JOf)jc8IGZJ_Ef*;s7cx{~EB5+jZ|Z9+ zG^zi+s2@vAce={(&h6?GbUSnzTg~b=lUx~lwWvIn_+3SyV<4lxNygxfZEC)9Zp$Wh zi6(OM!%xla0)CWCns3QgT%t7+x2Ll)ip;-u^ik}* zHwI;-U}aoV=KrJsQM2`(!}legjZ?QKOc2MDF;-DPb}-&|nT_M>Ke6aFk=A>uPw0kH-I@4H%^t<7(;FmpBxoSp^wQfZHT3fY%CC%# z_8F-`;bV15WILSqBug{5*6$qSm*Fq*kfr+H@y!+wOe4;^O*`;7yA1;9d(>Rc*rYRa zE5DLlHPk81aAe01(uX$|GjQH(>U?;01a-18&VfOQvO6Jkd7u-;y|d?f4ZGrS_PXFg zd#x&z8UVGBn{{M)c4wo98IPEf*q2G7vOEF2QIrvEOlY@x@8f6aT_==GRSq zjJ=jDSZ~^xG6dP86q>~A(`rkCNR9PLSRN?c*(!d&*)gB{+p613HI!{|m!wEfSLb#r%|}CgSs69HZH<#3{GNG~utY zA2!aBju!fpL6xO??@^XH`fO-?JkI0#B%0sTO_PJ}sVRDvCC}d1$jqrn$LaI1ep8V# z3@wc+LmrV@cl_m{q9$XQ#-Ug-$yN(3^8@Kr8O+&vRe|T>ycG8NJ%-}Lo)g$yvX7H> zE>~WUS)y5^$tM>V7alif!9znIP!z9^N}k}^NaLlaT+RGE*UcSG?a2Cy>?4l~u2|}b z!}+p#oy@F+L_$(TjvcG}Jc`m}iMiaz!XKJz@?=huAI!eeitJTKh3-2B3+0jfkc)@4 z^!A48wg((v?5k^XFo5wR5Y61%a8hE&FKwNEPry$&abI6#;bf1roA>)fvdv}60Lsl6 zeQLTu_qm{YYe5$fku+-1LQS!kAGwMOBQ8#rObJx61`}RVBGPVZHH$^7yGkV zOj;@5-$mM;ZOL0$T9#E*w0Cq+%OPJs@95zNQ zEUKd0@V*b|yI&9cIjw$_zwVLop!;Naj{7bw97VSupZSfybRx#vn>9&o?Z|x3q$hy) zI;awZiVNdoEy%g2ZV z+??k+*y`rF7gi~S?^}E#Fk5_K;NL4yKcP`Yyna@N-_RrJBy!AZFkL5Kzw#*?zlVwP<e(8p`eMYodlj ztNMAbe`RyCI{=k{%V`gnNvq-2t5*;%eW{pW0Ucf`5&c;k zjZM5bWM>Zv(Ac&s%Up;1x%LC&d?n*m0!KJE+e5aN34(Lc9#@iNyS>HKHgqk-x*R%NcIMsDXjIlV8QG6s7O zSj9Nbk05b4&`$fqqT=WnTD&}O)9=pk_IA0!;#;zq_{>|~hijSqy3$ZOMiELslYm?E zncdzL3z%Hu8}pA}*O*e`;$Bu5^@AE=yER0r>BIycolUDPTUE|dp{v>JXkpeET*WXU zF7u$F(K3v_GvS{n*SbJlZQ1VL%b}H+6FTB#$Dl*+AOaFYiB=<-TpF)I@3;NrNyEXk z)CSknlbdt%|DK9R>`Qz$JsJ4TUu{LD4c&*^bw|{@EKAT2&gPVAYr8c=h}jTHMOB`O zJ|ai=L_Ic+njT$!u$dO)w4 zr^12F?n`<@2S(o7-tLEj$J*hN8W;!L+^YazJ1$ke0^&(3+iW9+t>l0LEpZv8p?X-B&!klHBKU*o2*a#ueMAslPy> z!r`IA1&<7G$ka@`(aljjGbPXy_z1+qAWS8Ln`dN?Qvy!s zERhQyZEa%-#i)O%kLX_X1k1jI)I08WfzyE2Py(U*A@HrNP8kUrll!vEht@9zV ztgI}r>&Y`@WaK)>-Q3;-aAdbyChJ8!B!%j^boNf}*$Y32TA@?LM0%0X);G{YWkO9Q zO_yhFBPHRO5npF;5g69=1&nF#Gl5;^RF}BdpUl z{qKYUjQX>0Q#my)ZDU-%WOAC%dEW|>&I+$dmdt-%fA#;uJ3HN3<-)8618U0h5qg1!9uI@}UzYhkb&YsHe zG$OaNAl5-9Zn*`Im7yGX2#Sd+1F2Sk#0J>L=fu3Y-rn9$ds9L;XWLNBoSfZrcLY+S zPt}O-W#}52B3v)-r8nv#(aKH;`qu6)Iw5{QK^{r5mKSJ^)?@QX(s@0rBKaQO_i{Y< zNN|M(ZD>mP-Hvv?cRTBsv2wOD)zlC_*V)D zsZ>7J3k{@F-{?Vs1F7^YH8TZ!S9HymQy?=3`>SM4?(*rJTdP?w-_~Dl*JAIVw?7hH zTMK*^X+JtfYwH#W$~&BEnyn6x!+VOx``2&pSZyn%tcBpD*v|BDDUr4B@6P9b9E}gs zy>ph1qWJLLB*9&m@q1(>l@jKAqtHG;f_!sx-SuqRnr6dghC~n!^FN%*sZ?wP-rfM# zraG1rf;(1M_dE-SHd+=_5NtF%emG+?&@iHdgxkl?QkFXkB8gQJ@O6#!I;iEEgHDg< ziNG<(MOM_eGcLQ{a0Cj_D#_Vf`MamFxjLMmh!hPk9bo!^X1T%c+GzI1&21`|t(#GE zZEdLQPl~I$9M#Q^@g>CBYvF;dTbmF*!?ksz(4-n#olSiomXws)99udSNk~Y?0!6VWvv=SnoGU&F6mp2cbef7epWJncrL5YPqA&N?nduzS1fNs8$%2*{+Jek7tzW z3d3Vz39hXkiXs(p!21ja&2^(ck6xYj%eN_wnC_ipwltJw6`Up&52BEq9Gbd0vPBWY z)q>AQlLUNc>&M?c%ii`_B?4~k^BK$e6_fqU-Yd9gip=#yj=k7-5167qbHEru)&HUN8cS?>zTUY3 zpqno9mtr94{bk2{o|?nH#bO2|-plxJW3o9tz96e5S{DAc(0{o;SIaYK`qrq^c{XVO z^>%`Wn>!-Vk+`s(4MyZQl2S$F1|!Gp;@m z&~z)&$I!{{2b#7ecsX$cid(qohpM+bA9+DUN8eGiUjOBxNVC_{4pNPkZ!|hCe%lvB z+nqym9@+Vh&i42|yh0P@@7c05ShVpVkj{4}cgt%jYynt_AQ2b<2q=~t3lsz!oWn+) zmXsoz5aF7Dm$);^CArOh zXqB<;YUj)v(<{isl}P;%IlEH5d-wV7hcdl8tsxwCd{kuT6sQdA)7Q9oJ7?X^621=H z4?G@MHXHrPfz8d$jL9`*UZIMN* z)xf1(@X^ruN_+;4=Y6C7dDeZ$8<$&n`6e4#K+nA$cB+#&NK#@HZ$}a%EAn!piAqgl zOW|@mM^DWw)E^Av$Lfi@uDA3&x)n_K^h7{G&bGVwV_{P3JGX6b{_eicIj~5*3dg87 zcYd(O_0)yoB&eq?b!NEP3YAXB6#KMII@$@u2?`Pt8OW!&xD<<->qI;B663IrH!%@Y+qKPmll6H%zjtN z#);pjJ6_#41Kt;u#v3S$7TpUYX+VZOZM}r~UJ(A!@I5e}*2Tr7#*%bWpB%>1jkD>@A4AW{E=S%vRQ^6Oc$V*OIuUr$Z*`9sI+28)>{|Ui=?rPj>9&S!Ik0(_O z_)(B9Hj_Pi{E)Ah7Xzv|ArX=4FB{U$!Sv0ZSZaWgsk#I}^MDZuS}hRS8ow?vq}cR1 zyYvM)IeEMJ5|;n3;oV&T0WDLTZIE&Z@GAZ z3B{V)B=qh8^cVPoZn0;5#he6+Fql)dit6o$^E~^pjSZDm4;cAyi&?40P$nv6N6yYl z=%%5v%`o$Gb4M2!aFrw+i?`oHJ^lE2d4GWBCcD=C;NSoSm)WP>uvZ~d81~_66|JqU zO>bNa>|z3ozIekbhH%iDLF)w@AY_By=jaVvTgKiW5cg;jyQ_1^?FHnE;lke;vmNPp zLkq(I#T>ilb}=_xRn=qyMXhFn%G>qCN0} zxx2E}7|`Bh9X7@gkW zU1?2cTp!v-{NV<*V2TJD$I>#GM+I|4#G7aMd_UD4RwVM2w@CP*EoV$V-QeT59X6X= z-zOzenR!TErjWBs%j9I$+R!BM$=_o4q=Up)s9Ld8UTX~$4V=@>LF0}f^t+qg67+7+ zFm$f>#=o`SK*`O`H2~ce6bu}*`2_uw0AV@+2-9oF2U!SQWubVwk#Y=I)>u0@H)gf3 z?f3gF?@A`te7&iRjQ9t(0@hnPM0qx?kM(F2D2DY*gmfaS7-4TRC)43#b9Z+)by+)>a1ez(;9%XPoynw&G9CLB z!VW(Qkw0gAMZhPSJJBoBHUy&MMU9kO?=Ww9WXSp{^;JdRG z6ckLk1sm{ntrOmvTUt#-@B_lQ)cWdSYtfKYcCB%KbQVotGkbb@@1GHXPWL0^Lxk&y zZdvHG0+1g2j0}Y=eb>u+FDvegv_E4xM1+G>@aMdu6aus`XeQ1zYiU4Y^Ofq&( z#z}mg!*&~q_Z|AG>!(kj7W_*>Mo0m(0SyB~PK4}jVP#c(`T$Z}t?l{A_xmqzez{&N z(#v6gj69%mZ|-V;*Br@iXVwuVB7*P{eid_1YB} z{YIY()rUAoP!}2Rp#8SlU5S|Zf5o;l@+DI_#TGTM=pKG=#M0JC5s^dF)zfKftFyQD zQg|M#V7Po`yRXqK@ZgioDX(1g=c`t@sq%LUT{Bd^Tpru?<=sQ)ED@j!dOMj)HJ@2( z!HU4#i|%V@6XqXyfm~M$G&h}@O4F*I}nf`{u(67XH@SSBK==w~EU#nIbAnK_q} zZz$i4EE4BZrMK5{7JmUpPF}jbK$5jpgt`v_C0pE@TDmz(ircgC4#^i7(C54D^YzR| zD7bh~8KA`7krn}t9O6rw$tA1S+y03$82hqS>eBUM0M*yEA=>~4+I<& zYI^!GaAHP_HFS;$zrxxW{l8w4+A0^0ks3A^+#X`qU%Hg&X2`a zPY6m0dx}&eqdbK^`hHVmB3t`Lzs*sG6K7-zE*^fVox?jqB%^N7RHaiFZuCN8444gCFBoeb^q{0R08!v>b(~Jod()xaF3LVVD=TpIv4x*vHdb(Hc3NqM&$9w zTm96a`1Cgk-0GNH9`|o{c6Yin)jnJ?0J-bTLV>pR20dToCn4}Q1yvfH`-Xx8hU7C1 zqxPZ1BQqP72G(u$K0wXfyzF4YJCxvXF4Jh!cI?_P&5303@=!qgE_IDb4PYH$$cGi2D`1>~#YvqV7m1Nm-!-dJn z89QfW%5Ptn&I(4u_&Gu{qT4g1@wmK+>FIS&3`#3P6=wk`v`C#JBA{cl;`e+vX-a_b zJbZp4TVUm(HyXqP9K63J+CN4*$!c(g8YmoZH`vgAfelxr*6Ih(>_8)`=*&@lV%Qrr`o$_C5!d!& zf(>q9Iz+8#cpe!Fdw;C>&QhV4#&yrqVWwIl_20|3hRt!fF%PzcAnc0Vo z4{Phg)apK|ORK>zc%A4?CaMEvKv|R8GGvTN`N>-nRJrC?&-qlkK`52`+m#wz4dQM| zHP%eI?cXQio14A(iAb5}D9cefBREc|tef!m$2hYtG(}4n)SLI~tLcj~BgPZh6T}VD zT@KiH3oHjbdj9HlmxSQFDod?pIzY`+6q22Xeu8$nJkT_oEG7baXL0fIwi@t8QCK$% z&d6!>@^D(87*-j!|6&BoL+PkO`~LYZ;aySz%;CU+vAiO6p}o>XS^_^`>#dbm(KF&E zs4$I&_~~R>psNv=ysS@af+`g7fQ=d!IM!c@gTTSVd)xEW9Hz$40_GUqy){$UA-UQY zo=HG2ej%nmwl22ywG*AfC@XMRIGYnG+bNNzdx_)neA^D4$jpOS=e7C51mnwHiodiy zy)gbZYTHF`PvsYf$FTa#a5f1h9svOvO>REoKax z;GRu#49vUw`scHNR7d$P_e_%LWBDeqG6`&YS=2X&BjsMd_eSW4X3sCB?dod zTK>>t)y8=DwZZ>~2(GIGMb+f^2JnlW>>94MA^Ee!hYgW%5?d|GjsV^^EsYr2+fpiA z;To(}tL=yyaq~4+!RF6#{DZZr4)@|qIRl&tX0v2$O9w@Mdzg-aNy`pP#8=uVRJ29N zQ#RuHRC36F*wX3VaJMAnCZ)(dL6>~w8=~~rl_QZWJMYpK)$9xzs&Rcey-bR20WEaC z^M*snZO@`M(NHE{*UAVr6kDkEvm}vLCB1a`G*=Vdb@|K*0Ax<9pH`PKU#y;oWR>dF zJI&S{gum8fENBrU{OdyK8A>%i+~)Tr2T{*Z5U-;U86ZidP`-}6ByOUDP^ca#Ka63a zx!1KXhOgEn!B6hFsIPB1Fww(TT2r;rqyCAIx}=kd`Nq?4aNc#GKkty4g*D^T=&$%F zbkpP~^HX$R!_Ah^ZLs+f8cL>FFmE5p3>+Wd^{I~-Co3+4V&DQ>KUe!=t z?;~xK1>XGjThi&hkupOC&Jpz_JD}u0p&^PSPd}W4p@I9|58Y7e=Aj+W6|*r;g2}$T z=n1N?5fG452``8z5wkNi>Q9ixCHRvoqm2yq>d05yOTp@k4h~6m-JQ#4bM#L;tW*;7Qf3o^*C)EwxegrG(subEaN1w+Zq8Yn8gcD5WmmRV zN-Q!73-MPa@_Kfs95*-sbu#CD9gTLtk3NY@fNyts?QUl{5cMTGCWRm_v^q`({31C2*E>*zKYL8O+LoLuJ!`aU4Oi~;k|Fjoibc^h*&Lis?8DCBBC zvU$nyLs}-5yZo8s@q?7dou3|W3FL6Tq4qxstD!~oXU6F>n}L+Kg(_vxV)8?yqauJ( zs!~IJlC=Q;zqi<&9!VllZq$#Df?|N5^@40)&HV_&o+BNyH0ae2dMOEzB0R2KJ=SX8 zp4-ehMB+2C>zGPT5-aL12@c8K^JxEax!O77CT+?dRez$64X#ht6A}`bZC6AYwVR}a zF(}CfKK=OMo5*IYXJ+;X*mtMO4MQ30aXd!eEtS|m4CsLEzE zkP@6lvo~8K_athFh5&SFelsu^CjNa1*bK;tV!fuf zQB6fjZ_Gr2WICxOrPuQRgG>_U*<{kVa_$9gn&HX#&%`U;+h_G%Kmi2pQ0LAJ_!}Jy z>vZiSfP3Nmb+`o+G(WCu~DlW7PfXs3aWel3oqBnpp&X!!)lf+7t*ajVG0;6V~N}Q`amVb@)V$73Q z;7OkANs9D-K}1!w-cQYRGBsPO>w(iJ6Bff=n7z6VtKZcUL%F7awX?yp=2@m;v1K2` zyKSg!{`98dQekw)1@GX{Kqjzj3S|+Y-3S8RM+lM4by z2WPcfyY;Q{0vx)Jg*rnSLJ7&qnE3ceg2^0~F4rfN;LZae3hSNre`wT}{|yNP)0G{O zJ#Kpf7*D>Bkxtp7O_e+)`@y%E-0M8;>yNpi$#2YLO~QX>FB2>Sb7<=0&o_Vr1e8SA zQ;KkQj^y&XEOu%Z267qDJ46l_8Xf1WKpE!=b$;xPnSX>nx{R6tP6vTt^7qhrvY8ED z{Xg|GQQopYd;H)%f~Q(hIn6rr(lUN*7%`eEA4^QW$$L5y85MQCE1cj-b<@_?mX?v} zN#)^O{}b2F9%r$*3a)ba`N05MBD8uV^97mZtm6i@r?DyWLgem*kG9$;xs@P{#AE^# zl8Ek!sxzzn!2#$Q%}le6pFMThyT7ec`GR4v$o5CJ5thJN`JJYD8^FQNo(=3LUzxPE z;x(U)&o)nRxqtq-;9+hY?$}Q?kYIw?@l`6|fSQPgrj%>*wgfT2ja0hmO`L9AKxRpN zm((IcbO+FXCJH`9g4G%MsZ58@ZW3iCe)HwU%DXwwW8Y$Li&uc&sCeC(Fj8PULwn}& z$bYu`?)P;C?oT1*tY_QtjD{7B_?R%5gnL9W+`^>KxSL=#e%a_^JrUiyW@hjG;80Db z%PGM^j4I>wgv>!>3w!JXhfuh;_Gb{paiQx*r`c8sxOh~KD#7jTFy+RZX|9k6OC^+n zg#hAq&d<8|uJ^*(uYh$(!0Yb!LZkb1y|Z-(hc$2*SxlD?mm5l{RhsxVHZ~U5N!dNY zq^7lHOu^X2_@lE_0)KlWy~8km74_^?9#Q~FOf)>jw@9cZ zm?De1y*#WipJaSa$mLUC&+|GCGs}xxbg_dx8F@^{6fU+L0^Hm zdE>Y>i&LxxyJXsrfmP^=gNpy&;B_l7xziebq7U}N684bG+N)jN*ywt=zkTa*#ms5F zAo_h6@}X3xwPOpY?GEBa6E>iF{$~ZZuSu_XIC=?Y85&q4)!36>!W2dTkBn{7+n}P_ zdbYi`I~y;b*M*6T{kw8zl$nKDa!11dMOl;%qK=>-1W5S0i%p&y4KC>6;(l2)D(dQ^ zm8ObnRc2zqONQ|i_y~sHZk{^h)%PV{9`MT#$Q;gorHWHL{yq7P;plSFM2We?n>OjW zmc)awG9*FG{O8=kOEUjqQ|KQb3*Ne4(BEF{uXY4uK7)nLq(O3Xb5kl(iv#+Xe7%!p zSeU`&J5tdF*3f4O6_4)e!W+c!em1X z!v`bz@CB$3Nsx~PA5rDxbLvS+)tCTkCt2`3view(*5)f+)k-11kf3B`97mV)4P@@>LP$Kw(MUi@0EaT?NpK1>AXS8uO4W)l zq(QQn`@yiaxAT*4;ye7^HTCp}x_*Tjfo>8rN9CMgRmyk_2dKUS9dDuLnml9?M_41g0DZx~ZQ zLm{<~jk`^ti(p}0Lqw-fHmT~!@>erBa|woKF+TN zu(ec!J_`?y@YN1q22gklXOjY9LkWvg4=2hR5dmRoG*9m8szXCX8G$q=B+nYcXUv6; zk(IuwgU;okIYIkdraKYv)DYQw;{d7oC>=MDCQ>oLq6%(=?TA0-KYydW#Gq zPH5FUJ|HXqG_gKcQ*WOrisIq4!js;r_)g^a2QBO4ff@u{)gTS_dnrs22Uu?a(Z2&2 z2Ef0-$Z=HE3x2P=tNG2{?d{=a@5f(HxDRpV&@%6>(>XeoI9ws&IJ$W#R(ZwnYz?yk z!rolI=KO4J#f3|i8KIY3f>mh!@$eyejfT~+$q>2f8mTBgPKlh4{C9ErDl=t4o^np3 zK3u!?KVl#;>U%`I&uN>RO9A*MhCFaA9z&Rd#Mp{&T`o@_(zi7kw&JQFXj%YFHI)cm z5wHRRL*N#cpWp|T7vCeoR}Q7TVLkxz6v&v8C)8hpw4ni2-z72I9FE5ZM?gR@jUkH= zz2aeWnY&oXV8V_>w8J%2W;LDteEYUo!xvb(Zyfhm-^c0nV9>~a=3#W+`c*;hEHDqN z^PyI#uaE~*u^o#wi}s7e2k!efKokh9vycT!nV3mJ(t@{DSO08!R&53IckPO&2O13q zEP78KtS2)Pfkbh(1MB9->)sq>l_pU zUVV8Xt?Hi8i>9S(YSi~fo`~fQhxATVQ;8l>>nHPLIq>?v(yk~8C4XjT8Y2XD(~obo z{Znf-G2mW*k%(|egVg={1yoONVgWDd4r}&smbW*yh`WyYy&q%)n zRe5e6lp9<&Ljb)Hz6Ciz`=d>*|983~ADa z`BPC-rAzRC5iAb!Yo1)X$$Z@#AeW)9>w=r9*$OPnB;p}iIu;g%Pe^(tkk|XL>xqS7 z&Mc5rU9LuoX+y|J>gN=Ql?`5{l<*Vm`g?8Rr;`>nIJ6$%FjM>dT&I5HAV`KOZZ zaF*C#o17lQE1E9{qp3&8D7nJXDR(7OjoHs>e^wZSEYWuO_3kY8qWaLgxT!g0RrVcm zy@0rXhzH(D=EpJI9|mzHFIHw9ZfK7Wn;)|j@>PIB3LXum15yug4V~_mG(#_KY={Gg zXDv{4f!QT3)f#Kfrwuy?=*20X^(F#pii785ys%n!Si@ z3zhN}41g;Rp!C1?#;f94xkVPYZGMQj^G~U94A)q$tdfKz;lZ>Ig$YjTHHN*fL;`|F zjRK4s&fJ3iqhSk@ON`8uiwv0ikHXC*(9R=_3%2ihHlwL_$LXTQc=ms82S!bb&nF229>tEu5wzFo)J zN65%A>@XbUOK_`2)@W=c{$1YWR?(Duk}2Myb#D$42F@h5 zx!iF1#4v%AiR@Yt%l{4MzuV>Zg2DAZ-~T3eyVN{<+FRg{#(!>D;C}r?M%Lr2`PH>gTx7$;TbP z{@=fY#KZ9^m7Kv!c2=IhtF<|##Ln#kRgdL3wwcde=fZ-^`o=By*{RTmrk;- zP$Cs`hDeGb1GEgl?{(GN)9SqbGE~*F^ucYw=E+qElp*F~_4|(Ps}V{|hGzAe%E*F` z{*+P~Cc_f(v>|OP1)+FO2Dspg=~TiDbMXxqXcS%qzaIFDxE=8Vfls&N@!S6D!7195 zN@AIw0elz=ro~Tt1N#jS04R7m8`4_~dAMrKB=1C=_p@!c& zg5?oNA22uNxfYWlm2%a(sD|82e=!x}B2zsbsD?Q>We8O?N`ZUE0Gv*T-n zFz&^p%vlk$!u(cnF#@!br^cxQ*EwxWTlogBt6z{EQ;w8$0fxQwpy{gRF!T~~E^^BQ< z;tL3NyOTA^Gy(63k`gAsZgv3EHrS%!3S&9oC-tQFe^Ix*N+JqwJUsLN0*nQa=IVk( zW+GVl_CnPe`KBeE{j^3VZQ){#VJ)xAQOy(M-;rg$EO2gcdlogZXSv%fpw=c_0Y(3t zgV*75XDAN4`9zpxES2rendw4%QL5MY8Z282wd)qIkEcf@v`IY3z1G)N*33$3AO z2L?b;6>F}NN+3JUlNHKWV75x(xGqqHKbQ;VxE`g!-$+UKYiEV;N+rax0X}$(MT1kZ0tUhzkB69HrsdTu^?~l$rnIEBpby2 zg_>CIgZWrpxFo{Ah7G`4m&XUbH|7&-purC#e2by3J{xojye0+!Ap#FZU}-7y#}cig zr=)7y2UO_9hMN}eOpE+&4*ta6)IXKMdh`}KAwk2c} zVf)6R`e(q2C#NIy1=;lQHen0-y9gZ}ofcrL@GUE|t(*iUp3U{d0I=fiIa2XYm6inrsz**^z@)J>Uw0{YC zu1fgyDGtDDxx+{W8Vb_X2uPFX?Ki++=90!Ryh+Vj7V#xaf z8&D0jQGDbT@Ks|ny4OAs(60gmF_q`?&R8(ZHO$r8W3c6AO{Q??kJsP17Ec>M#}Q;8 z1MWNkDygNVMMbk<*o2w&{aW}m$YX8k1d+BE7xutMrt)S69eH%?kn1kW4<69-F2};R zS9_{k!{lAPFU5i-xKF|Te}GHNSSm7&ij$5b#Q?lb049Ba4ti#5tebhtFPGclLE#n$ zYvcHLUAWxVNx`)~Xq^Kl2HL23MlaMRZ?ydxq3`0r-KfqnRfXKd^6olkQtIhEmTjgN z;8ik?sKp2Xo)26-WA?D^Uh9dK$D*7u7Zw%b-aES|n?CPZ7V=-j(~>w5F%%2dkB1z&r#nRPd{w-Ax3xH|^IArb`1HA0hk{v;)yH=I=* zISxrYXHJ|IDcqnDV4%BuG*7-6KdtY-igMx zkU_;43fo|0Q60LM@qN(`moWzAmt^nWiJbZ&TN(|gk`^mfap$QqR`nmJhgK;LYrJ-H z`;ecPC+s|LA*7eL*H&3oH3TTx32Z)I?>AtJLF2>#YT^0#rjd8|O7?PGy`C{e=F-qz zaoR9skW>1G<;FJ3l==DC2X;=IENCB7_2at>mM$sAuqKX8rn*kAdB(jt9G-Pm;mjFt zR~7ul7d5G5hR2qBYpQ-Nbc7?# ziIm)zUYLNxSNHM+`G1h-bAGL0)UILDj@J6c42RDh{W~wUI@U|@ov+EN) zGVW9xYv26o)IKrlm83>bnVZaa4@~6`+g-Q5Rbv&=MMA|Z?Wwb1cGJw_-Ny({WGoF7 zG6W%6BRTSvo7CmkCxavmK3k|CH?OY)_|{(HA?G;t#}v>diT3sDvujf&Ejatjib7sW z_u-bXVQw=Bd=az%<4Y0OL{#@v?0&>_;>N1|E(%gQx{>x7-_FY;O^9PRAd&lyx=RTQ)qENlw+sjKW=Z+4vF)Q<tFEoT=%>w93$9d3NO2b9X|*Y#hFO4=d21($3X_3B#?s z(Z?^Otbsn!LU$aH?D2}FaG z-ML0OVW#bo9V5cd_+0tKc!JBn%I4I}*dwU4t#re_4DS94sLV-tyqS&1Tm`bUSL;p> zUOanw>3B-3=4&S#ZhXuY-|ULoV?#_nF6yo3^smkJPB`kXIA5zl$drNZhrNP zTqt9%&Z;^2tOWBaNx^23Tuj~b3%V`GUep)@_PCIAMBVDXn1NH#ArQ`n#3FeJ1(d5| zlntqz>#|yeEhuB2V8Hy6-b!lTji64CCa1}jC>3dG+un&C zBrE~aneUr8K3y(0WLCfnrF6D)cq!4dog!6U?vZbe?oqL78}59(GKYbaT-18^s#C!2 zk01YMkP&e3*gNAH_AYX5yz)3fGNe$Uqc0DAq2KO&o95jsVR}wZP0X{aLvI+(ew)~z z&3|!5xuQvHGz+qcd+%yrlBk1E@Q?7o;rJ@}`(6~of#91TD88j>Toq{jlBcnR`0?$-6Y#~vs2WH zbhuGXSZwg_x0q2j6W{RvJ90nUg|A)CUoB|1vr%1?{X>4bTjstFEQN5!C{}jkb6*aH z_0qZZDCs1@-!k_(2M2#lBz)`)Wf*;)u`InQTqa*cgPA<&0Zi~J3wY}j0ByltSz+bI9GQB|$ z>KzEkubEvM{q=l+IM5>$rqrA5Y( zD!q3Imdk5>J%LiEF6#apY*+d>l*}I>gEgZCTqgsmEZq@hzlDV!W+sFdPIq_EGuJo7 z-~YOCiz0;0O@-tk43g48v74XQ6xb?~1;L`C{kKccl6_ZqP<%rOS!2R!x@#wO`H0iT zwGY7DSX&x-tVZI+Kt_>A(0~{b*e%w{->QLadGLVKoWfe3m2RXs&A8mLy1a#XR^u19 zwI+f`^+LW^%$2wQP9-lGDM}Ncd^kzCEog0&jEpsEp~m-8ItNH@s=L7VMTJY!E{ZEHvGO0g3)+( zX==h;x$EwB&r!G68ZtHmMnBRD4b(051$|hV6w-={I2har&}Z)jRrGZj3-pe6WCb1+ zCjdqF)lr_~rr*8dZr16Z9v!VQugRpA)!v={YPP7cH+s(;<%zC`)XY$ z*iF-RDaR#R`45h+zmtAFQc@!K=j$JBMxDklcgMV33AY9Jrx%-U3ci36n+^aQxHrZN zy6zX#wvGIMe2=gPAsmAbR_S+AdB%5Y2+#}cy7c1ok5@ge*D?QSHP*T}^XJl&f$wz> zbvA@gG#v@}bLJ0~jPTPS$CG4gBD&{^0qGyA2XwRHgh|F3%*XKb%*sm=D`WQ7v~R4b zy`$Pr{y0Nqead5cZK~75ql$ej_%7ZPv`ihbFUIF)9eWl|1)V(E7rw*~CyBtcQ*XAe zZn5ul&g@UXx{FI~m>A?*M-vlUuIFXmao5Er9));pK6G6%TKK$iU~;A*HH(;YypmbW zp&icDF+HoAsQ707L3`u9ZQ+?(7oRplC0tI3USO=tx`88*Ce~{38q%?*xX=U3j1}G|5~NU6d7ob9HTY6 zsSazLz&tYbXX=1{-rmZ<71#RaGzSEDC>mMwRGeN(~!OOcVNP%t?l#3(6!h4CKN6aZfZeuSc-lfSiCGOKx zC7o5=m*m@0ZExS6Z!Y@nGDF&BKeR5H$HwZIZC>@5l|(|$;hjgvrp6yM_8NO}?hyk+ zITicH$Je_d=?sP9naj(QB=YzU^dITM6O*iAJl~!kzA@cz^B^f+%T~&nT2^*{QGr>~ zpVkkBOKu_PNUm63Hq}nNIcvaxqJ$U-q0?&5RV_GN7#DXKfPY?o{!J^Zpo|O=khox$ zfC`;gFHiXYg^4|9`ft{=Oq9G9d%T14kYcce*!|%Z7X`tNfVaEj??y+gJ6JPAthzfB zwx*!w*zX6w$?sf3i==0LeLXr?LJ#xfwK&1t*tv5jq*9MhnAa1n^%2!KfuW(a77CVZ;BVy0-$ZrwI1n zo8h?A9;^TJrX^)tT9xxdMSD0gP4`GUt$ckv?l9Uu-*u?-0yjfr9J$TTHfW%{&>U00UTLLiRNR-w5jB50CIbTe?G1i`k{TM!(|Fh)h4ni?UU zfia-jo$gw*Hoo@)zjY)(aE=Q8V7zYo9<@9%q!1+)6%z}}%(V&LvX+ze*p%su#KNFO z9Kgb1AmaD=Yn5U_Y`p6Ot9=$$DXcp_>Fe_RXgKxdS%#z3zGs^v412aTjvHQ!{tR;< zj+Mq+1YMv3Gl*S#_5@21vm7Xco8lEiQ>iH^4s&sx19lVog>UzGdt7&gF_7q>l0B?{ zd(V#au+J~_|8j*tV7KFNQHr&0>(Zd zq;~@vzPvAAjEs!RVBbQ3YRR?vc4}WfpB$ysN_&%2owes#2~wle)a2ub9t>3L6C$$$ zr&rfx)gKHD^jbzZ?Zh~qL-Gn=?~>{L>Z&SV;8_|Uuci1I6J%TPz6yajg3KojZ;K}k zf*;0G>B6zk_UzfCSKvTRNg0r^M=^ncjjdC+i2rl0am{Nj>4sx_3Y^X4?+#Y;}A7k@~GbHAo>%GO~v{3HO9rhhFS2X6|Z((reeQg+7z>e6pWk1I9F7BPxDB z&!~USUiYEirlmEJVNi6Vk}8_p13s^x9shj|sOu8G8eY+NS)XKHv@+%!2^6??pjdsN zZ-@_bC(Ig-Q4sVr_r{i_#w403nPfM;&%BVcUj6v*Q@|ygW5kfk{7u7Y((uY%I|fTc zO5Ou9z2BNk^yA!^K!A}(eHy6L1!#Q}z>skuqXiYx_k{UpyS2&?-yZkJTSJYNTIfJV z&67@y&U5{I!YqBo<&n+0S7=Xeu3zo*uNpiXa6M6S?~@@<$G1kL0iTWlYdKj1ph{Is z)|7FYJ|xu5LwTHBt-Lniz;urbxu=+AtU^$ARo`fM8^6@#c=d|-Fsa4!0`4~vwKMZq z>*8JT%Lu(C5+Nin__g*lPHxUDh+x2-muE4&V%0$g@ot7uXMgvX(L#O<+ly3}(vRo7 zwHk_J`IEAjC8}H-XyxzrK3cSe*OEQobHR@-)+!}3m)pAab$&aE zP|t}EwEt36US1QjHrNC=pTL!zMN=V*eT%n(LyH&el=}L5FQK7?F8`7O(YH{;Gt$rN zr2;9<5GU4aNTvcHTw3{N6zq_vvo&uG5&~X&>)1-#r{y zXa#4pH_x5Xq>J*_Q^dx{8~-;Jy91s)#$utLbaXAlN?KETr%Dc6UW-1tRXsr)EX|s~ z*vm|kGVA!aze3}aQ{3fEz)j_O*3^(+{Jd3y_@I#rPz^gLU`B8G;@^>X2izYB zE2;k}_EwGgocrL6bXD3piscUy&aRk(&5qTsEKH|u_uBdN50~u3gm0FO%igbX2Jo{m z|N8XgPa$2kqz!n5E3NcJk?eVLvu_o$$$Y4JVL%lR3JkrF9wR?L|D#8bz!T>~N^@Ob z4u#W=My_?Y1i8?;OgaX5ESKfkKIIN@_!Hyr?{I*ImB)j`+%P6F&CZbIgK3)okJn~* zdOmcYNys@%y2;keTrjUutBQxGDW$)|6&fsXnpIyOHzi06IH&5V6uWmDLCqQ^>+&L; zUrQrdgIoW8%m@TPes@Sb$c5wREEY7x?);Bhc?-M>t z3R0b8ntoiN+?3CcCJIhuI@vy|o5PfhLGOBLwJ|5ff7~5KgTx~Xz8hyD)yrueT9-`_ zyI;G%`9Tk{e~zK5Lhm;S=oc){P(;K^r*1%@7-_jZd9%gS9Q$-HJ}jGTX{ACz7&3e3 z_Qvva!`*Jv8>g3KyCv7hg-Pz;`}OUgDcwn{)?kU&qgOmjd~=S33Cu$fKl9jLiagz_ zD+hfehGUpB&_FvMT2H=XM~3@`GsqN=apoWtA=cf?Zcza44EuSCuEa8IvgV|7)+orT9N-K95S&-{qP9K*+Bt z=#D()RNMpZ&GgdJFmkb%w>Rh&(Ng!{f&@V@E4sntIO6tE%aYx^ZkOD~^S|8;>3p6T z>gxz*X{fhET>jWY5=UQWUG_smA z$+h(}f4wm=V%9Q|chn(LFvli(avwNI_5b_zvCwIT4D<+qwTcMle7ZRT&d$lkSr0Cd zT*>Ijx>>$!q9oveFZZR-*&F*j0S>mV=mbhj21_msOGX zR}3z#kM6uDbacN-HAx8D8L}5g3E8V#^jaV5cu;TV{+9eN6_U_@h%*E=O~^2OXv0aeH+b(-#nLg%gPHu zO0Cq=!Utd#QGEL3rURZ!>3jJATLbg28^bZ_8OM#`G_l@MB58Ic^K3AR3NM)8+^u~f zh$;XRgtOVVf4wcLgvUD$I=+%G4LiYjafOvla4?? z{q2xX?FqTvFwM&>*NsbLXXTUCkFqB*P`!f{6a~X$;%swA*OL~yED7mknyd_+yh{Rh z8@FZar$W~i4OG0eCxR4QUTdk+FbMq=S|qTT3A#_0plEfm6f9v`-%+wxZYLXK_0Q&y zOrnklh1iO*1LjIn7e>22W&RgCLf@mh-no7IQ%u&i^)SK!>&O08HpI!JT zd?#|TT~&^I*_w@T$GbmSySUax`u;@`v#GpGqr>l`B(vtV)x_DAQ7z++MYubsY%eu_ z1445Yoy!KdzG8T1Q0(fTl9Ga|xi70x%}QjL&gGj0U>+Aax$LNl!s2SN(TQ z%>2cv9`*@Kux-sh8Len6k3rp|71-wF*VlYpnCLHWQ8-ITE z>J!BOEReoom8G%<7l3PzN*coW^@}}97BP%Gs`>uup%@$6M7XH|ZOC$*GKh+bx@&JA zIP(PpxW2KmT2=q_qz_7uv5kt|dUG7=S$}=FWwUZbK4UWMn2LtEJ}aoqAd48k*vh!Q zU`KZL)U&V6f%%Vho6?O!w}5y}$Lq1s}5g(wuv-1@>4O_Z#)gJWmK4D>WlJ^E0FL+SI!A9q2imrI& zTn1m#RlI;Pwi%$*|-Nj#XSzk|LUE6c0*cg@rffEwsOA7DU+iP{CQue(YWJTEs3~ zQ=+mE^{d7-Jfe|2T-$yibkQlU6V zXagTsD&|0;NzFbiQCVZ$+0mX7j|97v>)CG+^{7}Ro}y?01Y+Nrn@TUB{S>2a3|@fk z;ole&Hi6qZUh?Y1+IYta%-hP53gX?~fY?fTKnSLUM~Eq^Cz5RwHYC3P1&AD*+S)?w z^m@TEtVc^OVK=1w`NAMT9aMPR03OUtj_JZ9W1lex%Ym<7zv2_@g^DA`riU&01HoT? ziD@ZsbL{2$J>QqfbQ$Pcp^vsPyeC|a56a*m<7XJlvQPD{@wv+XCKW}&cDf17I zy8rK7V65xJNK?G#do|M9bG)JPmp}&=(8=`Lz5nRB3!5YtUkKDR$Zgg2SB2y|OkCf0 zF7mjH>+;h}5kluzyxmdKi`x8QId<$=C)}7P>{O#9&OndDDrN5{@eaEIPjY3t_iTBC z@AAu zeHV@aQ+O_<{|YDg$bH_^Wb}_5?CnjkG`lPds>BJF>wwQIfraN{gq^ADW5j7_TwJ~g~B~Q5R}O_+bG_DOmNXG;UZqs-fp{qjpIWPAnc5OmAN*Rh^ZWm%uiK z{wrYd0l-9~QcqH15>JG=94p;mHd*G;SLjEYV$W=|?kT>E)4{8g$%QRo_U~shZN*>5 z$=^gb4K9+iC<1DrQ*lJZ0nkg{LAylHe=QJ{>gm&`O2Z(IQvk*mL&O3hzyioU zNOno`-n*#|o)q}bl8(`H=ADk?;G12>Cg8pHiILzI*k3BdaUO?}aNp{&;cEs34nbIL zb$n{Vz>iqp`>#>)Z$!R)*#r@*MZ-ZRrVOM5q@M#Kk5YJdLlXKHZ$xYt5QAE<>cG($ zMrE%cja3G-)?%acslVNhY*Y2)hhV3BszDJm-zFrucOgKWs+^jb`0-vn{=H7t5gJ1MX@(O)9A?K! zy${z?oTbC>-G7sFvhD$amk^*nOAEb}v)m3P` zFQV|SL9CLu=WBlT_r~&^YNRAx{TuJ*#Ox|^>Wj}OX--Kh<;r%0=SIQl#`#I}rN#Q1 zhPA94`RRzAgD-z)sVBUxhZB4=jIs@r1lfPPjYxL;hsR4dC)~?8=f7nMh_TX*gR z=I5_CcI~2|@XrZqm)U{<4l2O?+a6cT3AwLLhzAyD>Gy%Plb(|k{F#&fbmf-2zkrCyG&+OHk#?pDg`jRu z0@}mv+lg#%O%&$nf1z?UEr+@O6tPHw(EY%l%Hn_-c&br&#-Xw<4y#L3A@FMhQ_6m< zH3)Fb6-crfjStE=3B&Ok_F_Wq9UZo4yMROoCnfQCtk00gUM@w3xP&y$c*<1*9?3+U zLU1%PGpmd}cB(~RUw^bMjUA0S!7Bqv+Xq0!-|_K4ydLMOqN2hZX;Q>_eng4W3j7Kb zQUrkEN6VRq0@Y({LmWkr2_)!EmF!&&F$k0=pqzsq_zcAAaQ`>y{&Jrmjo&F&>E8pbk(<;y#3!Wz@qy|x^(Z+;uzF8pwZ*LLCYze+Fkba$U_ z3go_JDIRsTmYt4qHrSrw$a+WGj)kN2@}%pJKbL%kUkj{nf}gFE@_O!G?m2G$A)?N= zcr%2$?lsfFN|()7@OdF(2O=Au)w%T>a`y%>1lZHGiE9k*9|e&FhKo<}SV^QD#+9&P z|EmBGo($BE{fmeNOKHw)2XZXifv9cdZ)(KaE?5*df6*3hg5KPTA9D3Kp zD_YNYlc2yvug+8W(D+WPM=HPkToF}b8=x97>G4H1`lbinkm*?kAIr0347PH8%*RR& zt=9ip|7OK+p-48D9kMCjWJ6d7uWgRgO>w0onWE0%Hrmb&A2&K46|^*^ylr3d^NE-s zWk0evbp7E;KEmdqWOJ1t#+gYd%xQ<2Pu^in4l47eR4n)~e+f&Kc={ok2}mfE zHxUfmySlPaRQYW!wG$&toI2W%#vr7ib>;?tf{>l(vpV*bQ^1m1HKuwh8s(rGM44EE z^Vmb>Bk_+PpG`3Nbob^&y=>2Q?R&^Je~R5MAr{UoE(Y6ACdIfLp?CslEz<15`#Yki zB*WCgq8dS!f|@$W{&!0Y9dI`?GBOU<2J|F>HHJ>>M5MzW2~|)N6cn^Yg^WJ#cacj( zbJIGR^{rd}7<6KeVXHy!cJ*jTTLl01QEYK%D_T7f9^TvyPFTJ&@w zY$0MFQA&Qvz8?Ye-3q-NZXh^c*RZ$mHSYCbuwR_t9)3}pOu~xUb>^{DoDQST-nSG@ z8|57)Z-G33PpS|Y3#r)-8=EPMOHaMFcCv4ht|wyVMxt&r<))un-q<)@AXaBkgqLrq>==?;#LRdsdauQ$A|Bx|BQ$h05TAPnfi$9aIYE_aPd>(9=}STS=! z%O2#QtBl(pK7IPc5#&|QJcMlMcFA( zg^C5cQ;_1oDp&E#e)4nltXt0W$w0~i+2!8g*@~cKKx?ybcH?TA0V^jbClDM3*h)y; z|3{AkO!sr!=E%}ry%G;nAf^P~^S0=SOz7$>ObqNtn;{V3`M%W?!62kpjg-lRKF`6y z0faCzb8~Z;4WRU+N9~z}jZQ@8?{+0xkA`)(6nlq@7i50}DkHou!LSDcK}EO;V_I|P zcU~x7Vhq_J>S*npJ4|RR;=qVT410uyfyidS!sBaz^(l)xa$Cj0V(VjcEn8>r$axTj z^cz_hqBugVAI4cFW&e^MgIOMGIXS; zq)0nU(|z3P(=wdtCuC0lcOToU#M!NBmecwlbaREUMF|Hmq5}I=w{d;hW~k|xhYK*O z)1Tv+<&Gj;Ew8O9G8N&nqakF0rcDRdR0LcM`!i4zL)64DAqL8GFU|-p!U=Bucb$+Z*}7WU1rzdawIAYit9Poy5bED5hcp;N*b zZp~0Yp*xBgho;6D?Uji8#9QN=H_xCwl(g;JzW=CZX+y)Y%GqPRx!Eyt9&Dy@jR6Oa z37D@d}2pQD<0QFm6`9lavarjh^XT+Jp?O2cR`V_(8gVj zijHOyzCnVSjDMa=%C&xw8^7hw_RpPKnw#s#rXFaBi8*Q2+4%1AK|r*T_`Za9E++Ab zNZJCrIe|E|icE($nnD}eq0Xg7=m~IQF>n-p*AR+4FXNn$vUDAr(yyruSEn{XwsZ1M z`(AF+`Ptcv3}c*q>tSZFAEvdLoc?tOEftZRQEjxK?9$$#<#@7SW^0UJS`V_&Wvna|I12b{mZjk*XTmHp~!;}Pr5gr^mvX!BdkFQrAUD*a30pr{3 z6Rvq_9$M5MGTDy(T$eZh&gs2Eh#?9;P@om- zYCTQeImuT)GN6GV;$dhi&`l96RWF)eksNblV^IIP^RTqF5ywLAZDN0X(#;JpB)elbzo2sXUdya z3sDJ4$xwj#jEWUIsTe(rieyii)%l^LcR?}u4QE&fotG&z+_tpmmsK>0KP}pPf9j8H zeTJ`Fni@oz^6F|{4)v|$HKgtEJAv9kPBdokX9`!89vz4X3CwYX)JanEyz8EG z!(a$WNJ(X4ulpH8vOvr2Nkv7)QJiB~g=3+EZ9VgC#=Rk;$Crdy+0s%^qC9bNLp0b) z!&V0&KHKPAoKh$Ua)`pp>W5#qaah7Cj(ON_-s*(h(s^;502L(e{v`o`J%A-9=C4@) zD~T8PGmBd*PjqG*%Wf$o*iQZV+8DQY^I@p0w2a3d5!X^Kum9go!b zG%43OXed1_FEPAkXJ@j|$Y+Ern#uTa^FIY^=U5v4&k`l<)AqWEGR32Sk}jaQD3K($ zMB73P)rj)|npUm;UBcp>QPe@zySaV0yDn@ zwOgO>QiNQ&F{jKXaG_@6%?E0};pYTf4%PkIB-!4^%|qSzQ-Q;^&-v*31t*s?b8~rM zQ}p}GeSVb&fD*E9tMkOr5Y}aYnuu3IJ!J|AN92ecyuWcQdElV9BZC5Wrns2;~TP8l>PoAZjmuOA~i}^B2EGfJrEhjL@%8 zu}Tque-nZUc6$dPTSDZD1ll8DYiwdtj+jV*G*+FTcc9{jkfR%HN>#@r_7)S`iuL+ z5#)gcZ^OYKK*590`4ENNxxnWgsHKtYG29>uNq`GCQB2xCS#kmD=?#P)V}(IPyFol8 zX#eCYoAMCVV9p+ZoY;(hpHS$-daxtQ_wU~uXpb3_f1pXAMM>LVSXNFy)%zSjYss}S zB$_3JYC^I^Y-BuffR)S;jNWejbOCh~r*+LPTL0@XF8$h`@ptSa>%`iER;2J@^~I?j z5YpByJMN@YxqLe1eT{W^G%dGz`n`>Ve+p#FATw)<9urA7{pB=~-+_xEVA52ED@<%> zrnzBogq!ce2RJziv+fN}({Sz*c~deL^`*kx?nulr6fN6}k~zLId8!d;DfuUxqTTZU>xp_*|11Lzwz zCQp?Dx=%!szSC12%^-NaT>IFRCC(IqH=)eouB08myS3J{RUO7dgOwkbZSb>eIlWWaNl<}I^kH~C# zY_9m?A97et5MvJrmuG&Y`4g8Ayge@ffe>(c0))e+Pu5CfBT{zt4&cl9{3o;l>6M$& zqI^|Ixi(kD?r|4$(&!21oV&g9#1i0Fs$U2m#A68jeCbD9Sx-sN+WIZT5)gWboJI7! zZwKlywM?G=<+^~=PV^Xn4DZjr>N}gr)wn4FKLG+EV!><$p(0Kr8C3@XQ4^kUSeqjO z7oN`a{(rpkfSl_0emmIN-(RIH%kBFBSE=g$gB31xWf{X&?+XlGPk-k7{esMWLOWw^ z=aaWj#XkyO^4U_ZalIgCe%7yi5$2!QKQDW@EU_~D$`-r3EPhs<@6nDRqccH9<9blIZuNC=0c9bTTj^ zD5@mwkf6B%a=?3Z=2$>32?_146M3-WbC~*b8IQ*H?p-Cjo!087=>>PUw%*;4a@~^6 z%QG44EV?zSmcOn4U{I24=xvtY;dQUZhH>dqGHFlnz3Oxfj_Hnyp25J8nwoUC0%r~L zp#A)ZL^btCU!ScpRMw-4UkW}lt240W8?J2p?W=&UdrUyvl+mqgUNPoWH)e*5zy{j! z2Qf_&z(;Ry@9LiOelnKG9l(K3{>aX@oQ!5eENxgUe6$kOvI;5{XceXBJxPLV3 zjuHF!7p^6IhrFtf;mbTmmIL;8*-qa0@VQRhz###|5#Up+Bk%Q&lRKxe1f2=Tjvl@E zdOjQj6moKMO#GjjZj)X(UrA!7CQk-^iQQ0XZ3OO$I$>7YX82KSp*mUk*}mnW5SzPl zqrpdY&JP-ezcg?pHA7YMg6>v@n>j;tnSclOvKTJfL--K^Sd-!{e(>8Dwa4P?Rz^=fzC z%->jio5jV$b0z-{#WiJz%G6ixXTECuBF~ z%o^(+YG!f?7*IXlIcSl_{@!NAVP>*y&TE*SUoTMdhirQRXYhvBHWJ3hrZ@TVQNsMs zX1s^N_VFtmkESaHTtr-?_%=R1`$7!vRs>WF6j}0{-ARAfPnKG|S9Y?n-rRI`G8S2g zGSaFH&Kxr3)B4O3^E~R2uQ^GBVWsiS>yZ^PO9{Gjoi=j*AgP3g6wWc0A{)f3@e;CM zKwsz`y)mmcaemu49#tw@P{UX%u9r_%vQ9QRxwSA`QSO;&8ti-TWS_vI>4f^xpD|T` ztH%TkJbb#7e&)V?{p&;d>;<>Q_|l+^tvh_t4w+TQYK?%thV z^EosxD5MfS@3tjdUiGnUhE+zp=$LOsG=#hdtyks{lXR!}9+l@N>-fuIj{iu9+Y1%R zc*7(P+%0nXRulFwT)HyYJKyeC?U%vy_wBqsQ_S_7V{4=%dWk34BOul)Xk6h~-m+^?kBsuoZ)J72#6mtM$^ETOsUoqs z-r_SDwyKf(Jn>H-WOIk_#oy7TNI1kRe6_S8=9_rWX*hzIe)s4K3$t5yNL$UU;Wa3j zT%{Znah@S%lhYrl-(aMA{!c!nK`vCD=J4zzOs<)6i3yr>pX6jdP;!X*Fyq0k+&zv) zMjXH_fg;IbqoYF7BP081_n@<*V+J*>#!26ZaC(koVTOvK#j-B33e=Fj7O!i_E~=iT zsp-kDwrfwx>^J&(o@8IxGrvN;7Z>J2?1tT`PMSW+vgu7y9_dnmplsF;wI(4jN99ok z2xS*FwJCBBUnfCkMNYc%L6fg|W$56(ef!{h(2s#b26rd!{{f(`k)TA2LD#+WG97#=kwCnNF)`qI69BwZ zGE<`k#V=kWWbzjl6?LEwK=QxBKV+WNP3(h;2^p5PZTE!OKAMux?2 z-`~0^Q@I$cSS#i1f2`!Lon1VdLaC*20iEMlhFAMTh7-8?`)l}YD%WQR zFQJocARYr4#rXVyj7|PU>C9X=J7;3TMUV@zM8sGC$Y*F&R2^a{xNQ`u&xwbo2_W!S z28z6IU%&Q7Ka&f>NHEK&VmCU$hY(7j2~^4GftLXaYf>1*ZiJvEB^spX7r9aN#bMUg znS*lpTT@dgAX7D2-!|3*9BCkxC(twOJYn{p$KJY?>RPH-=jzAUjft3^W&5e5^74us zMkZzJ4gt$+c{{9M@7dOv@Q%8cXCcm&uiO7obsl;MwN$;)hCGH7#pC%X&QXKRmvsH9n71M6qt`rDz4C=9`@i9Db6U3*20WOcU3^{F(b19Nw&s9=f}ea`DF)eS z2XA))`R#{!2gKk2hdt8rysFboo<3LOabae6f1f(7U&O2u)o3}JJN55Wlv|X)e?Z*L z4xdk!B_W2bGNx7jB_e7^Uy8inCOs7Nd_Z-;ImUICf&`SwMw>&orWY2tA%a4yl%%vN z`mZ&m7Jx5N*voCL+dXkZx%#cXUa0oj0lyUUABEuXElciVpEx+(@~G|VI^ z>VI&EGad}i6LW-!NYZt)`(|0Z@%!a7wu^O+H=o%_^UXItMoS`$ktc{;XEB8xX^bPt zO@+=2yVXK`TYi7A1W55%td`6y4DHxV#Q{UYo)2Re3s}iwf~;gGeH#9UFwVm_*@M0V z2xu9e#|isCj&Y+RE^f`<0|O=}ZT=_&O2ft8@puAtJ}PSFcSCL5uVj4xv~d38Ge zytciRL&xaM;#oX_hUW9Zp(EZ4%$Dx+^P^XAauV!f^79E32@(QF27~i|=x`pUen2&0 zOi_hPaA|-78yOoD{Xa%5n9PXj_LCB*2GOzOOF#{CT3SJ^%>j%lY-&Zodjy2M+xrm? zs>q#oVa#N2^%QUDHoVrU3i&8k!tOR7>d-|aw_b{=D`7N6@K4YvjsMOMJ0HMs)711l zGD(Z8AgEej5%~xj2^ygCs;Yj_Bf&Q$kP(aNEjv5lgiq0~tAQpeYJBP9#XckgY*Al% z?`EM`4PJUt>)(+;TZrKmW+xIrM^RtEElcX^8on*U2|;55^#UraeN0R>AQ~}=-KIzH zoq#@2#$_=O&z;!WV2tGBj2ajifIPX|6Th&Rfq^h%Bn2odv7i51IeJ>?bMcP24<1@) zjta6JXq$j`%dd69X6D)U^jQB8EgqL1cS>7Zq9Qf_P_;4I^rK3gX+YXfZfgC)$YZra zmZJ6{k2j&qC`Bg}x@gfB<8_Hhh&qqn&ReBddYK6DEDwxt&Lw@tu1I2Fk^I zT+gB-|6F5wefVkDHz$rQNl0m#yG7@;ELl7cE#iZL7rq64r_Y0H76TO9PIL1+*N z*5I&ZvPSYUZiY+NFF1)#xvaF5L@ngkSW`T%7Ogi0P(lKG_sx{>D&ijNQ>WPY`9J2K zDgo!5bT2Dw5KhdQGiL}X%)x`NHLV^$evAVFN(~Y9RSSynYs%nU0dE8p`$#^HxLDnF zYh#Ivi%YH0$riJTWwd9c_h%H5Os;UP;{oLr6tsM~ZwYn)!A=Awp1cp8yDq@nOHNM9 z(pontLk%yGX+MpUp%4;+CfogYv8>aBGugMDDfuKNUj{{-JU+PuJ2Vf4Ci(Nph93^c zJh)S_M6;mh5oD=ZZh5cg_lef2mATJLOpK>{%a|-bYL;0(9zF4Ev4V0z(6u`A-#E_h zyN+*(Os+Q8*1`V%6o4ix!L3D$eFL{#IQFq$P*9`g>K0>#N5^K2jSP24wM!^I&0AJ-N!V-WHx4eGaxWWQ`X>fM7*mrTC z0Ai>6`R&4$6&hR~o(_R=Lo{&gJ^>m3r8aZV#2K57%n;nPiS&3Hk zF_-6#3rc>ghRGV~P9ju{u@N;!bVtka+2*;0_JJe_QE zamyiZ@mA2yo$gWhl$}2o+fVFXTijU6)8xCK`R>wKD=e-(Lago_N)a)mW-c8w{(1W+ zrk209@1XSNggBHN)lRSCNrQ;D92%#bcM$#mLG1VsSw=CQ0!d7!TCc z)O7YmwB356tB~|LBR4k*=Y{F^+L6o8wxFkRlrr#JSKe2;E+EM?`J42IVKM9r;uZfH zl!^{%)$*Njd_0n$&C{#;O+<}x`s!Gk@Rt_9hT`s!i{Z-N%@x`r6)$~$^|%>`gvvrJ zrT{>Ks4R$48FmB#7eY$n>gw9es`x^``CpN3kCVXZ({3CcV$ zbRyUxUNs6?y+DnIz}ZpYcntoYxNsu`5rSA(pFOOvCdF`?NDzUEi#W^=>f0?Oo{v+z zVQyYUpmC_ynq9Zh{y<#ToLWg5t9kT(@w>xY9uaXl!8+T83^bR<+dytUJA=W)(X$iw zO@-H9y0Lrn?&2b?OOu!Xo7M2QRdCmyYfIPp!iC+w6mxF-yDf{x>CJQ$O@K%B+g5(< z>`^K6vzsKbs=Cs}TLK3TIG0zxE1LSj{%UJVq-pfW(J}FBr<8K7?%sW|ux_ew=Z*kK zsK`^{sJ~ip(?VZ=KXK71zUg030{EWr-aB|Xv=pc5$^@oq45xiu>W>o~ZNj(>{};Ro zqlxeIpkxsAK!VNn@81WjnCH*WA=!VCjd?YE!^jB!5I;1=3oxZbT&uBH9S5GKj4ee} zxZtx%b>U7zS~j+N;>O4>Fr&|yn3#OXd9__lt1laJEhwq%8G=o+BO@6P&B^A=({DfS ztKdF2%k4U|<7WzGRmit*zI-;U|J3K_ww@gxd)vyw=;WDp`;&QJ;gu-rl>>HR@}Hvm z>d-#lvzaCTC*pW(!)4A_yeKCvWVH576j=#|LL z(S^Lxv*2!IWMZk89nfH8XIEUb-B|8}L>{`$l zN1!=lWc1dd>vftbels0!fl6G)!|FFIVg*p)l!CO{B8&QFp{%|>80p*=lLyT;u#mxZ zI*E9ENhI_>2AKZA!AJP`_|C5Z6DJ`vh%wl`V1EK@?z%dfL=PJ86_MhA_wRYFqTt1p zKrSpNX}_y7@Zmx6pCQpvH#&|lNEJIj$EN)k`%76$)Zfr$<9pRFxuK2#3HOeBlrhE! zNG#?*1`_S8a2j*qCi6{hI<3y!#`=S>%r%iZn$G4@iD{xW|OLC}ggEwVcsF8f* zUWtS9c-Ilf{m%5Kp|2R7+*0N)M@L5vd{kU%oh(_^uhP+;Az%9E6mx(86o z)y)mo__3(^qF@9qE-wDNyZgj*5;Lwhj=dPeV75CmJ6nctcJ}O9q9wtw^z+=XX*S-j zA4LmJHAtdj7{{BUwT2)k9jznwcQQC`n4S!wPRH!#jC9+Z)HaPdmTRB1AA*v0G4UA? zXo5sQZ`o%yBz6oO_dkFBpys5*T{kFEFDNS~;343XHbWJpxq}@8x^u~z_b{QfB69-o z0muTQlszv&<--;#*ll#!>lKG&Zg+gmSrpF5 z06e_BycN&)IX5NIEU%U=&j_3E8F^nT@A7$ zp=@P^lv$FQ?Cs|LoITHb9MAh6@A1BWJn#L_UH9#BUDxk-{=Vn;`&p;J1L@CJ3`vaL z9S?R$gadIP=)R20HYaehM1Ka8QuG<)75sV}E4vR-&)2X1r}-3K$l;@*_=A8U0(LnpA+J>p>XG9on&{iMuTo z*bd|%{@>rYqaBb^E2C8G!C4F&gDJhDb#d=YeFhzV@Tf0ZIep7`SbIxDd_ecB zpyWzPn%RPeKIgH9I{(<8by45jBDe5*wDc6djBJlcNX~mxFSj;`3wsPudwg93)`K}L42TFhQbNUHI zH-AsPpxd>($%lBvLwC{A)RI0$o!T-{(+&Fm^!E;#HS_d6gC#HaX&%b{rn8l8=1SAq z1^anP`b=)m*`>@sljiyYioFY)h^4PrX+ZTw|qw|@<7+0=F_0VF?j(><;X4c~H3D@k|DU}$Pd|6pcR;q1;!LBI~_ zTv`w1U6AnKkc9ir+44)RXDtJ@o|HCy4gD#n@!GtrYs0J&pYr}Kzg`{~ zi*EPg5#<|@(7{!ay)5&GO^4@J(%zdIYz4-LVh`IGRmH1Pyx(w^Jadqk?Oa%kS4rtN zzwer`{%acdmXvp-c&hJRma5v>b-dm6*0v;BWdoz_Z#9-`dag*=k4w@wa+f)Flc zWQPFR1kN8A1nlVH!_eYK$9hLQszx;Tym)q5&!;)S;#=kj*RH^8YnnVc3_}*Qs3+;9 z6p@7@>utwH_qaZPtbff)XIe%^CQ9+cd^t8fn>E@ji?y#Ry|5|pOq$n(E)D}jLCzJO;Y$()~k2+=>XJvPAs-lMr*6W8v|9Fn0SNkLJdCq)N%t{0+b?a&tqoZ`P6;TYK2Y$A^r`=>H36 zyVoNzAP~D1@Dn3Qg2cDIX5?;6q{;!a# zq<-2l07i&k9s6R7*^vycrjQ`Y%gf(@u;tnV)%eaSsXV(84yf%-;Rb-QjH|KbIvhY0 zTDXNWYd{s7)09@L^k$KSpK{f5Ec7o9+o5(NjxLu_~vlxkFf;@Y68-82Vw67-+x z&D|<5p3M<#ipn9uFKrFfW{;K4m0n-zqrAU5JgeA3i!-`8_2tM71$y8^m$9Qm?Qjnm zQT>av0(d=&t|TOWmoSvPJeTcT9hNzg38nnW#$>hZzLvLUSR z>YRkEIwdjrWK3m`&&-&jxKVTV^6=dH@nJRK$1tM>7?3c#5%o6og;H(Zn`AHhV`lTP z@)j_5<6Q49w}rM{f2s1(=?d|w89hq=ku=YWH*rE=(kQ-xfu?|!i8>b&_dN=m4x82Q z1Uz&X@*iktjR2N0^9D=BGXYVsNFTc z5s*jlQOEkU1B*Cx|8Bh7wFinQ{%%av0P~uvs$cf_JOwB zi}g~jSjeJ{3##L1W#;DRH*Mek2}6(e?yXxf<&uO~BGZE;U5|l@MH250)|w!GK$#-0 zkC|I87glw2!~)_#@4gicgZ8i;Tz#N5xJ5)n+Ke{;y*P7CejcpOF25~MdTC{-ni+Xs z43}trv5C5^7fctR&O8xSbla}DOh29RNS_9VhW|4H2y%P}G<)jJ(>23r7Jm8(6rwSH zfPJSPC)9CB+CRK97LQZYG^FP|h?Wg%mx6)A=jm^z?nl!zv$M-uT8?z|O{R`0 z1B{0S7(hJN=>;@p#&`*^Uedf0;~VB&B2z(ZR^qn0nA>~YvBm$y7)TsF5H}1-rs9J@ z$WV7 zdnk@+k)VsA{QBWgIS4BJ8iWCuG+^oJ>1fh~khY9wnZSWSteSErCMJl?m@HU4y^zDP zB_t%UL75L7+JJOC6jCeT4_RU!M&OD!@Y$*=I?cT@#Cu3CwsgvjgN8bsp;}s-z^te@ zON)pw;|vG#mwb_ z>7UL;1Bd8*ltXz>F{f3zm>=}ySYbcjLpL@y2BE>b?Ca1-QZQ%KYA&?ESA@8wb?_Yi zqkc1akF<0gaL{^~+b9GTKn)>qEdWY69F{lLasfuC>yV^*+_{sV;R2EZWspEc8Sq?f z+Ps;-DMU2Y(!vVZhXNSX3-<3W=ZTFN@jgK5SJOvU4h#wG$6O0++u%t#Bq;a<4Lkw& z;sCm)EIjJ$lL7*e03!^Xv58l=ZQt&XXgth7A|_( z_3@_{$<9Kso!1!}X;peV9PD7LKUF?|Wwo$frpS!VtJW}=mDA>&5##&HPy>&)<)nU% zHZBP;=WJUvE4jy-Ur{c)ugyL4>C!g8GsX@ZEsy1Glo>x8rBNy3%PJ56fD#&5XN$J} zezWgIhAE%+o#J-lVaA~mC&Tt_IcT7};7n|W6ofPs!&-YgZkLdd7EVFeN@}t=Z;`B* z=QNXgn5JpU`O%|Cx>;sSkRqozP7N+%^%6HXYSooNaqM|muTQNli!q#F)12!G(a;)h z+#Sfn!@f{b-`N?5Ret%|tAo+27QkMNIMZ{LE`+Q)#QE%8!hRLcO$$QPi#=HZ4+>i% zZAQ;h23^f+s(EX5(!puQB1G62wiY*xaqkTe)!0desB>|C^(pdfRgRNIjzHq)$CK! zBR*Qxrf+iKsaMEgX8;5RvW0>QS*hu4RXY-)7d^B4tu)Ra^bcBfC@_C&mL<%W>cm$a z9r5eyYvTgPYt@nB5^p%hD4w$K-x%^oNxxoMeq#KvX_9X4SCJX^ecDUU6f}I)Ghb;1 zjUPTAX`y?+ZB5`t*A)HqeCNfn3SyarDFHiqLgoXAeIe|D?yI*bGF0XICHE5`@&yG2 zb=3!Y2FL4hKE&?v~K?H*_B_X`*F&f76mbM*xF0jN!L_)DdP?`-8_2PkaKw^ z=YPUoyEYFO&VaEKKr2Gl4nF9~>|^lI0wy6-`iX$;)DA;9U+_5<3=AIGwqcKe)v(#A zBJ?y&B%a_Q;4JT%n^-Q_g-=cB(uY;;?d?S7g4U0yypULmQC>T10m~7%Spq;*t9Nlc zKlBmSDABRhY~M1INIdF_iYd`+_KJzIx#J(^HoM_g1#pU-@FqafvVLm99wjn;= zZMQ_FoTXG@+4mjy#J9Mvb1yl<(3q$rQO-a1qqJ-rMeOitR!3%m{hl1$#-NpmIuJ$M zf4g`v^mK5R8$&`(csfFegRHW5aJY1J{=$a=03=AQ{8$;Zi&zA3U~@12+Blv3YYp&i zI4fe1bc52;Qe-=q;Crj$783{!?bxrf^&Or9^F-&1hD$HqXf?>9ePJrF zz*i%x3nzqJe{PHaL@uP6nG%Mds3TS!c#11Wr%2JpvjStL+SGZ}IUH3y(_Q{3`UsYInBc3s z_6etN&&#b!I+U{Vaw97%4NXl=U+S(5t-tEL2PPkX@l!+ux*naz{QXMf&^CtWFJy;w zct4_KIqM#@w$4OL)(Pnyc4bM>8*siKu1Gs(`&*Z=gyH;y%O*H;?GQ9D;IxyhhdUqL zhT#v-LKL47Q^MmAj$(rYyvk)7dvh{>jk9cEUh~OvYEQ>2FTp_zPDrS}qPlQFv(&Q= zgvPaw{4h9K2KUUZB*Z@`rkw{4Y{1EiaD2dE_*vcbsRo3wiO`)PB|K=o(wB#-!%{9$ z0%t~k{V7R_3^n%|YgDSGzNftlIh_SO0!3Zuk!ii;*RKgA&KKw=hb@mUQ<*P_f`bmJR2n0v>!1B@#+b$meM)*wLm6JyLCeoy! zFNC5f3C07*2ekhQ6G=8pym7i=ic7ME!)F|X^@uA$lZlH9+5ZGuNR&}R9GJNA-mo4P7ZWaS#P<; z+mipnXcdefG1n@&pRva`_)FHybxp>Un5NfN@1Nm!uMLU^Ec20#ZC}(-_o|2c>6#?t zB&tuX!!f8xj;h`5SLyjEy1CmlmnM`q=+@0?p*{Sw->y^CYdQ<}y(0jP{JG2A{RY)k zJ^p-=)b|(OUut)n^#5E|gEu_kNMiQZj&-7$D%-FjR3z=y@~@b`Z(5{e7yS3tG`o;& zPkzmR{Rh4~1zx1vLLr^nP9%j~>$suKW?rX|92l}{R0=-8?d|}1hr~A#y$rh=_tB&Cv6)une|yi2rHXr_+aXtQwLkM6@^4w@LnJ=*zx%`3G7OS; zV`5?oOBlIZ`~m|ju+^iH`wWRbp4`VH4?QY3leSsJd=p#;rWoZ>a{BpM0H#wUB_&5k zM`c*bnYxDobYoa~kAg)!EOvHwE?&Fk<8F+8KMqFdJzP=51B+A%N53*ntFtWJtb{}- zmTu7IL~d(PS5>9_cKR9m=&xUO>zL?*j=)FL{rx+o7|4a7uyCXPfzK=VtwK>7RrfPr ze~K|GDhel-SA#N$L=pukyLozLZ^@GSdJ-Cq)<_-`-V}@1->W9X4*6tu`gm+p2NJ=8D8)fM^$#2vvm(fAT^D20O(+quU55$W{c%^NWS2|%x+ zlC(7cgB*9UHi(z+%uAw~1u#$1L?Z!ceQJ9cW8MX95K)K?eEGGl)^I< zgX$z5C)Yw`f~Hy(h!KrFlBK}z^6cD6fy^Dknp#Zxf+C?scRU`B_%n|12)Nl@-tf5c zEJnE-wzlLHa^1yu=gBE^M*^w1A_pj(7XASbLD47G)IEn@*)trDZp9pAGrfmv#C3xeZYUQ6zt$+_zpoV%d(q zOBw@)L0}MFkVKW;32(cQh(d{Aim!8BdHEVAC#S-NKN5J-=olC(5HEmRI3yv$b!*RD z?bkt6JvAcbR;^CE7qY`A^$GJ*oV2(v}z8!wilFF z*rz{^3Z@mENn2j%e;P>av)Gpj_JPsT3wvv9pZ!0gMNrX&OGXq_0|`+)CO5(d1^~Z+ zSQk6{Zb9OB^Jh7FHPhplW%~p1H1w>5rf2uA7e|-iC=q8=`*Q`xJv^D&%h0&W!KZ8w zpF1X0;@`#e3(k-_qN%_FY=G$QLl5W$GwnK2Ff#{(93*fn&>!eA#xl+uKO3QVFpg9J z1i(~;KkvXm{C!ZfQizD_tpr|X|(Q%$Sb;>b7sUQ%YvJd>^^#~#ht$w+X2Rz~_ zICc3GC&1!|T0r!yUM^C=()4I2kW}e!-A{?^rx!SBM?PD)h%9hn!Sx>2J#zK z_wzSH?XT89%QqsF0Rq$~cm$eK&&QzO`i`R{h=$WS_O_3oUr9v;t*7jh%o!`(91!Fg z(96NZ{+^^+A#e-UaDpDeF$xs^g-Bi3Q$+>}ww6#TgsJ?f%Zv_>w5X-X$OI;N^LsH3 z*mmvghV~^JsEB3^pewutk{viOG!U<|vwvYsM+s(RWGn_fJqqF%Ma}6X#I>l<*jaZ` zNPmxIfFlpcIlI^*ZKGQzMn_3{It998O$ZXvWWziSXHFmpKOvjJrjtcHBS=%a3)w1h zFq3a=Yg=1?lgvYar+e|H&a*$fa4#7l(!e@KnM?r?=sZz#gqc@s6&hRk9OYnxhM9Kn z!Gi&i!z78tY`;8Mt+tWD)|OrdU{50KI{s*%<*;)P$iAc&8racNBUs^9PGZT z-tDrW)GBF3a%~#(Hx@vIkKzL*fp&DC7^2W2$Hncu&d`qClpuLfDW_bn3MLyXY?{m! z|6nD;=DG^if#d|zb4q{0g&?OBLR(y8ScQG(H4u43)K|mQ18G2UOG^aq{%e>nA-N%P z3?b1D)iY#{qF`9985IOLRPp(9FuE@^3LJnO*pa#O_|N$mAelfte?r9!zwJFF0>UP) zN+igHZQf*d63RE593qWWVJovOI>IAz({K;!%aUy%+xo|kX{O1A(Yy4Uw+6N3@ z4lx2+5ya!gcIW)U|Av=t2Z^r2dlAzWlp@w>JYbg$)S_NSql1jDAFtBh<6^=My%|bS zXJ}SQ6+rx4_bUR;`GFW*W$1!P_&2xwWY)n*nml{7xo463JStIIo#ZqbITt; z894rBind{h^psgymyyFl_B(O`AP7LoQ%2YaG#yQUA3i9Z-2ts$5j)6rYdyNsN?JUG zyik9l%;Hx{mlXnazX7lYkqO-Iw%IXTnR4AE@7WzOwqUbh;LXY=$T|M~n-^u+#!%z7W1 znwZ_unVsCxhxiuwaHq$RUza6y1|k5IR=I_I7Cdk$z!Yw7Zou6>({G?}34yjJlvkTg z=(3WpVH)TS#E#HE+nkJhnBw~sk*#Q5v|#xd&cULyu=<8X_zTEkaaRf3Ph$6&`v#x} z#MP;&sv3AzMh`#_%8xC7b*)Fhyif#`Uw*+}=ULLmtiWJJ!Q1mG@2_&GkECEhm z{rSsKqaNKK&6I8lkWzOMzO&dx)j%0Q7ThR)CIjB-qbZUg6xLCQ*4pv~_VLFYR$^!) zFbZpJp8%RifNQv25`|eF6c@|D65)$;U2tWZz9pmoB>{${XMrr`svCCM+l9Trq1Cs{ zw3%)ClzV#r5%bUTaHE6#fUQCtJ5#h&AV`1}P1*`GO(@0^xP%M*Q0t#n2V0ELH#$DHHo z5Up-w2ozrNy=?DW%u_?Ou{F~?v=mxQ#?4QBd+cfB!-CF_VL>e?&Zg;_^zot+aj8X% zT*5LXsR*F?F&>SMjr|Se8*YohLP(10DRGPEI=^~zTBCFM?bB1qcCI_+UUX1+EmJIl zE3&Z3Cgy2O-AD^G!!lr zc=xlryIbpe<8Dr2KcVgt2qF85#9kQ_rLnDu8)2W3%)Uo`;o>i&GiL`fsmu!J62?0cnrAIbwZu^T&4ylUzv z4-6oY5R{8_gSe<48hI?9;Heb{0)r>+)F zmkm!VHe7V`IA`u8Xs_EGFR1iib{EO^>>-P)&g}UeG$$JhT*gf|VlSfPSa$fPq?q~| z=7OX?07^wv^z zgtLP$(kM1*Cbntf3z)S)qT^^QIg&Dsnr)0*_|36q(#)`t*wFC&GmikXe{AapeP@=% z@uC-`?rQE6LvRC{*M>7P_Z&G&QR-F z6Y^Y^d$W|R9PvIdiYbqYOCcEpqag(bheYwzQ^F@Eu3K+h7qqR46vHS|3{i5(iSm$$ z0&b`ry}7)!AV5SRKqd$&kGnF4GrRFrP|TX1F=4+yDR|loMrjhv0f-q8DjHb^&~dd; z24D_Lfpf0)w5;I~B*9|poEy&VibOOw$&^OS0rssx%%w8_>`IN2ILHUt$$kZmE1Dl+9%edL^M=!fCalQx#MVI4?9lMxXV<~9flGy@g09_h!~)mS599*r+Asv_ zLZS(^FFCl$!^sG-aC>qL!8*P^>F!N^9KXH0`h&U54P3JT_3+OTos4or34 zEK-9pWfq^e^QhfpoCv|i#YOyt=u$B~tiRrz28J5LBDMA>cEK#EZY$LQ&&mg6;*2MO zujH-4(cZ=hPug3a5dH-<{0b&M96zvW>sA%`E6iK7coA$`Ut2Si44$YFu{DT^P%<;y zhXO-+TYpkv6HW!OK3@Wl@BCC>iNteYJz zVIVhY7NLj46$md$@6$l(1c+TXa7c=Es90?T?+1)P4FlcR0*B0LDSkLk$s~xzctBk- zH0&I5n_!|uQ}Y8C*t|0t2*F|8F*Hn@n3+AWg<}>@fD~eYMPoGvFIHBP;XtqeTd9p) z84tL{pV~|T=#l5hz;=g*xEiQel(dVZuM1Qa7twfPf85Bx5O^}a4Y(-k{~%@2T|eHy0Y3r$ z;8Vs#D%QD&$0|sjBaC8AxW+f3e}yDYAD9M-F(kZ{#DMh)1qFq445rBRj_EyDBipn3 zU%xLGlZh@;j4}57Kt!SA$OJ!v!m%}8Hn+)VoZqRilVDiDizokUnE1aznU`smHw{f> T#S7C@@Q<>Bx_qka#k>Cl!{7KW literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/figures/expected_density_gateaux_grid.png b/docs/examples/robust_paper/notebooks/figures/expected_density_gateaux_grid.png new file mode 100644 index 0000000000000000000000000000000000000000..c5263e6db2691d7b006b48f09b0b3fa18fe504c3 GIT binary patch literal 212084 zcmdSBbySwo*Dd-|(%mfvNQ0mP64FRZivj{7ASKfOy`zn!Xi9$o6?#oDOxh1ZSyFb?2JI3DZ zA`swe;7Fa~;aI1){KRVewd_hp^s|S9-^=6863(o5qVL#^=RSI7*iI#&Qn60Y!yZZ> zdJ^#o{nO>|zeEH&9DZ*Q(~-r^dA=5C;OaWr+um!7TS(l&3NinE;8oLgb=o}{nK^|ZWp%-pYNnxJQ@COCfAj&e&fY_2W?Ib6>3U|$h0M* zsm>p}(D%ZJ>rn~8&U|N1uQ2oc^GN|I9OLQdH7AexjYL=X28~*>G&5gpPSurr(FTxc zV9a4mFJEOYXzs0g?U;tkHB&;xtsgMg9+Pe}TH>~xmH9Ry;Z0+s41>5A&C0+xY52La z5-m}jx|iQ6@Hx&aD_na|x6wMcE#j+E<#`~tF;P|Mx?$K6e+xfTEhTS9nE7ug@(v0c zDSZ!qnpC@We>ZD?8yg#3P{7Au!zC4*knVN)vA0)OKqI_?57W5z(lNY7^P|O9EoyN$ z1M_opbA8StCC;aikJRk`QMxTG4?B01CLI*IBOCibU%#!?YA|A>Cmp-XKJmta)7}Ox zO90L6GOU-~Xj8N+r~mo6Ke70~??=8eG9)DHgjxoNMRX3ss;}ih*+O*ON{Bl9r7VNt z{rkApC+l~^m?fBywUpY9^Szk5Cwg(ZBjj~hpuO<8%HtMlDoWSzWoD-N-imY(ku0p& zeySGj@7!F7N5rR3SH>z_i5c&@{CM6Wb$D`;e*XijI!Dy6-@mEm67$Z*t_BE6v{Bdp zRIEn%eg1qcj6s;J=HmSBB_f6!ZG%!c_NpI=1018gE?-Mtzm-UA!*Y#?`_3I|HL*Pk z2M34lY;DfnwNYx(uB#Ff%&@!7vS)<$D*+J9t?&Q}0@pY@ZdxLuF z7)~qLG2P3UQMZ*ERp4{xp{1qu_C$LFuDfk-YcB{|;>jiJI=} z^JA;OfBzz1iA2rw$`*SDp$H6=a>8vwGcz;gg}#-&UmYDXy%{Q29c}b9J`X1=-8K6h zX?!Ggw6vza#2D5e?JT6}G_Qn%4h{)1rmMNm&@z2?Fd3wigQXrN^O}%e=c?SV z1N;|faLkQI52{IiM{Q%FL{CMt zGZO=Kyt|~iB9^6vm-XX0^{Qi+Sc}*1?QMrm(a74`d+8e|Cnp$w!$#c$i4B|h+Y2ea z&3uF zjNz9E=~CTy=3{SJMfWdgaYUh>IO&|%4RUgF%8CUkvr5C>ZEdwMr~6)<_vagxJK<_( zsy5XJ;**k-Yg+xxBN-kZK0IE@?`bF>eVF8{o7b@W7Wk;nxnQ~cDt1iz7Q9L z+o+?es=CKp%j|nfYEa=Kd+(lK&k&@Yo0OD^@P&1sktnBO|N0rIR$re4I zu;9XBeAB`I`Pgvw>`|#L8fv7(hI2$1cIVHZKehG@9=nSVR=hZ(@K950Yn`XgqF+-} zQ7PX0w69*h+MEtz)~T7O_V)bz<;(O)vDGcIw@B2)BR6j))vN9|<<5V0abc9(jKCzS5re<^dN?rNmyl@VbM4vx)6 za-9qAn9Ep#o7IvP~^;qzJ`#mn;bo+~D24eY)(NR+p7e~Z>&z<*H zV3TB@Kj(N&K>KQQ^F`0^o>WPU-IYP&xw$#L?@#ZC?=XmYP^=8*%e%P=$%WCd99j)m zxbnKJjRe9aFkKxgyftApSuKXLCwt53jU0{tHr8F;PU-*>bO??5s2wz`e;a|@wd3VY z{EEahLp3S%1DhILK8#4wZJhf0diBST!@|M{#%g?-GgXtaZT+pnp>ojV=;q7i=zJrg zr+-sfDY`oLlbn!Fuom{cr_z0AdFam{jnnORUex)>Aonf2EhqHw1k< zH^e>j=nS9KK{of~lb7UCeeC|)=v6K*0&HTY+?_#b9DAaV#IV;P|DB|aqoSgwD>ka7 zH8jX&wzyaq>xCDD>ESetLegfn-y4b&^l%>02XK`TfV& z-A?vx8k?KZQF(?{&-;Z@mw9=KCw)%1-+%bU2!**sCYpvCHc@ z@`n%dzea96iQhJwy*J$SUKd~SK-M9Z& zKvC&(^1=f{Ln01FIUJToiZ)8XlU!+!Aqm!ZV@BVjq@-LNE@Ff0vg~;SKE*&WhX+VA zT0IZIsqz0eIP>YA7)lCKs^g6=G3>pc&Zu4As_7--Ngmuy%RXE(+)e>y?PtyC%&0Zje3xL)+j$?`yKA{3b#-oNj3aW zZ;hmp8;0L+x3?Jtl$pM=Sg40@&T*Cn<84)8i^4666+bduka`U>qAIsiu^j@heWj$^ z^6zcGX9$eb0-}F5Mr~Z=K`5F?N=`*_spdmDlFpdEP z*L~wEl|*)Q_d>05hg1qD%T-pMD*bd!e@j*2=PQ!B@87?_jgJp4o%Ep>7ZV~m#g@I7 zgoTA~aC3j7ta0CY_SDR58sKf=0$)f%+oUjY_wchXM#IDXH!8k2ur9F5C?nT3oMQEY zBL2}8aTfPj!|+oB=8^SNcrj}p-$|)WGvV8nuc(ghP8Lod;o3981>4-(%G!#b-cbDV zy?N!1?O3hv`3|$!RueUxYQZQ?h-db(nC<8{pXCn!8&#E^8H1NU=l$EdUy~>NZhPLN zM#1?q=R6+e5T3tp_zqV1Wi*Npe!9;k#ienBLOtvG0Oifo@xj5;l`(sspXYUmgnj26U2_qW3V^~cC8OhI&zbtIU=qund)u|jyk-SClcLk4 z4OHdC`QWvHSPEL&keL}13JMCX8kgB-!hoQlhRMmA<6?Yjp34FPcl7pg6+vs z#Y+BbmyX`f27}$$DikSlZg#-z6o5e^H7tnQ08UHG{m$&O+Dh-_{TP zz9^*cgCmgnP*Zfyq#l!GpfZjdBK>boT4kfx~jx^0;(I*CBZmr@$be3x?i4Qi+aI{zeklgOY^T~PgFoz=xO>S##G#_BVOBPdqmNY^QlPQIEP$63HV$Xr^1zP%}%D(;^j|gGWf%1o$|-+I2o`2|Ez!YWm*>C$NkE zL|30II(D)adz}VBYfVxZFwfpLJoav zB)V0Ct%5g+$Y}!|F&o{!FD)vyP1t|i(F!X?Bgq}7dQJWw$Ct=gQ1WiAPycof3)!rwYr8Q@S06>MDZsSXv=QoZ>@DxO5 z?%v+@2ro7wg0e|p=4*_P;tVZ0d7W-NCZU{{yJtxj*iCxz{kgJiQ1$m^AxOF<4}L62 z=S1L#oM5si?QWBKcgJC8mI!>gE1=!et0uBY37l^`Q0 zM>+#&eJDE;WJJMxnW|G?!0uAr|MxDl=$GhsVQ9&jflq7>0e#3 zI=zb5Gz1xr;x=ER-4Hnp)NUcYavN^cXQEIIU-WkJy{ij$(^*F=4>I~v)ncg1-bTNC zcU3-KjP=SDbkF_Ox|0oE8{s3bJyNgluWY_h8$Fb|a~;m;GTymOH{m0&Lx*p5bIs)o8ol9Vm9y0=CEQd6f#3|D)+FyC-o}@$x56{dvuHV z+|;Y0zGpniM;2E(iao*>d#O&;9?L4=JW}Dz(h9}Gr+U)*{yNI9X(eK|#U?W@;!6S< zWEfw{QZ;mss8rLZD(R$j0`i;1ry^mmavw^(l;2VxFUp%hppX67WU2Zlzt1?^qw%uV zcqp0oXQpfr+M{sfS8`i*v7?z!#UEN^(Ei0+RyW4gQ;i^qv~0`H78X#2ON zUk-&+nEbt75lWpHMYB!o9-|@25r>b7$4$#tWLGZ1);HpLR9&hpAF?KB*@`_nav5vo zm6rK%IrrFu$ICl#TuXFu+;y@kjJC^v;iYc1LSxdY#+#Pze{UgWdshOJnOx*=m55R4 zboT*DVTgFjsm%9pg2=y&$S2yBHgkFrzGF0YKd{Okuc@t3NZ0(2G6I_VmlnpikE@a*mF^{PCIljM~iOAcol zaW``rx)R*ayg9YT-k`=T9un>UtbZws;KsdgG??hqs8x%rLizEa2Mv)&sQsn9te$pA6sPf#Ji!quh>Jo2Q z>do9*%1oKb$*-s>t&*0Ooph22 z_wk0eTJP)t8m~kldvt?xr@~RU5J%IW>__&amWk!@_`ZQ`=VDeu6r_GXaZw}n4sN0#3VfuGJhSXYVjrXu^oHfx=giHy93PW861@e{O*dZFiH0KhrbmO@f zV{5-=-KHa)qMX3mZWe>!-MjJg$FNQVOXIc7l-lgCU;QE@)xrk}e04dZSR>oJx>}I! z`=Ue*e>G>AUdwCvqjKLkgzVznab+r~7dy=?rp0>qvkat9wSdx00_?75Ed1BUXZW2o zksRG(FJiI7gCitm(wjhr+VqsE{ot(n=6(Ak>pL{S6FS0mIUS>uY&rZ9LitVZ6Fb^C zD{koEm?ByWZT?-A;BRoY(NbxCieIu3=v7@^Ef*>5xzEL_niw1$jB^TfPoVO%gv{i| z;)1b{NUSMxa`b$0zA7qq1rJRuW5`+**xx3rB106)Yj+mS;)ll$_P?7vG2g%tWnyxjH-)RGm*~S^lguKg zdGjV&Q&ZE88#liCtTl(z;}8=E)ztWYJKS3toc+0w+yGYt4)qWn%G^E!kG)B>tzzB; zM=fuNIU0(&izcjA-r84ldqO@X?E!|-(|M}d%(YDJ|Kz!$2*Z3dUHeF3!{t#N@$ib# z7c$xqQC=gBcp@3A=;I#&c?|{R|G_#fm$2ON#PhGcVN5)E>t|+`GHxL!oZ|ETY)X&) z)sR4Z>Q9xG48W%8l{>M+p6PMA9ITHYwn&`61n7UMH&ZP?sE5qF$w70wHPpY`QB-c% z@(tP5x^LA;wSRn8?fvyXOGobA^&k_EnYFNIj*^T@r%$`kAwzLWDndiNpc zRgupcxZ;W8-FbJe^VaOpij)w@-gm!VJ?iksq*CM{cMV$>hs9PdM;kryweUoA;Ar?e zh0mMTxBSjf$^IFeuh%XCX>P0FEbvg>%fDK{Mv$!c^wI9?x0dL8;b*(G{zKtP6u$op zVC#rmzL+SnNvL`)^EenzCZFdv{Z3PSwzdi`hDfSu*Grcw_IIs+4{L;Zr-lb5nzp$! zHyxySF^}C7!WI;BM94<0*l)4~l==TjpTr9-b)I%HMHdN^k(`&DKMb|1n5Lw&Y#=2C zYDIk^8QUo%Axx~JqVuiCNhK3mDJrmH(ll)TfyewEOgu{(som($iC(bK&{k1Y^l9wDaXZRyqcl;;=Xx*VN+2Dp_|Z6F+uU8b1XSm4+>q7o7mM3AqXzg&IPFreAJy!&j{GK&`=h(T-I zBx=*TPD9u$VgJXHo8F;ZGdlXV_R-NrPqqQT?Dr5MeX<=}=i_XnF9ho`$uXpTrh1>_ z-zu%_6NyGD@jJHA#8QfQeYd=KF)Z6;a%FPwRDSGRQ}w~|?GtOd!YAzA!I z*YVG?T`!%sz(IH}Wsa(+;={ShqCtw^m>#Uv_t@2vRJbq+%BF2Ly!jxHj*7Ms!A-tX z!*S0wCHk05D!5oHu0C1t?jMy9wGT{wV==;p-U~n0E=l9C@F*vECJXBQQoKc+i-8nxFW;va+ETpnxb zcGG}jhIcE3e3nX@+#s#o4GSfadaO1#{${l<*@wtgb7nHhn=<F5B%{y_C;5_9SyLrE%WAJ9UC%6-F;NNhHG7 zCegN^>>DkY419e+!y36noSJ<#?RxyDgc3xrEW74?BsQ*c@fr!Fuif?R|1KaB3*2lx(F+|EcNehnZHk4(*{V5yoX>{EW=y0FwILnPr}Ny@&S%f(*RPNQOK`9CM=`unI{5 zDF0=Na^*!&Z2o(aUFd7m3Jo1j%sKqpK*60kE$cK`CY9f z5~(3vpKS3dm6g2oo+o?D{WO9WBy@rnegF+khYE~ALy`mHT*P@fAX5Ao=U~0i&de(o z>V)nG`PkW|ED@Ffy=HrnMWv$QS7M?)Q-#ANjBjzWpBooH3!S?)*S(@LsYYtTjl4Is z=;Un9e*N4@UtH8?$FKEGTd|?(5@I@KZ!>YaL(f?kANKdEX0gQoS*7wXP3JDTq+EUP zV#ftlC(wG-B+dkZQBLb!A|WGtrJ5v?29niVg=>hkc)k_sn+nL`aJN@2t^YZe5jN_y zT>LkKl3G75OXE!zmG@8UB6`yX-ltA<3AAwJuNF)z1Xp}NNh;M-n!qi@g0CxOQk0I< z@Q&>&u&7rv9S>d z9nH-_8MXe9qEC+{8MMFnZ8Id-S=ab7zJ#!9yc^73vc2v;O7yzydJk*C`F?3lT54*o zbw`QKC^92AVO;m@b<#eK?w?9ZO8nMCd|qG%K=}ROI_0g-?IPTd#7w6st^tTCGTU&8( zaB4GD5;g&R)J;xKuG(v4QGit0@Y8Xj``e>CV^yBi7?%j{DJtUa6zBYIIM*^TP1EFM zahCo0N=RUC@0wp>Nv>!v{%4K2aQSOIvMoIwg>&YE>PZGyIAFu)I}?IJL-9_}Hhfz^ zZBnxk)V@;>3Q8lWu>}y5h^GQX^a{P0Ycg68+ZT35+G{z+Ril?H;|emD_biWdOVLm@ z8&x>T3!Tk>G@VVXAa;s+bz_Yvw=WN5ED@Xh7_#pKG6Iek>ZcmnD+PbE#zsGbw?D<%TYadD9NV$H9Y}a!MpLxB4?? z3$cia)fBww_~dqO2p5fH%(AS@{=m&OPw}Qg^sfq+wSwpU9LkA8q#M;IJcb@i*FS#z z_yk%;xaSxmuIpS#L`D6~Tm~|a5IfrzGdXx|Rm-zK#mnKQSLNTbXoKLQCD+d8=Nsn3 zmRgMl58zysf!ThH^{7Px)+5?A3%Xwmupvm)U|%<<{85&U zWkN{r$-_gGqubjn?b?EXu5ZR+&wR7bTiH^kfHC)1W^DjlKzFf~?g}<;?cg9a^r5n_ z$;f?IP8J>h+jZ0sAgVn`IUXPqgSSgIns$OWV=bhLJubGBQhaeG@Y5PeLPSe+<)eK{ z3M2cSf^a#hvGA|tdf+?&*G*HNfp%a420Ho+2zal<=^!2FCYDA+Xw}!xxj$^=D`k&} z(=hik`E+^+jT`PQ6c~v-3C*uQ#;b7MC>b6Kp5;`#@c)?+;@aL4o1D!o;)DZqFgA+Ipq!$xu+W(< zT~a{-$7OX0&DYnL0Y1y@wTZS8MQH)>WQL9v($ju;E2O!udW{UDo|QG`K-9xJWvakyBEU2M4a_>laaYmLvAIdwr& zV7YaR(yr$0Hh2oUE8QJC5`}vg+pa5cAmeler?Pr9}Zkru|GK?t>PYL~MkE z5vpa_pd4s5={P$%;CEiudchE6*_&b997gL*V{C);r%;M8QAn+UOz+Fb$ObtvQ~J7d z!S&ns4j|J4MzvXgIPd1J@OkZ$DVtRJ;CB4Y@|w$^NNmupfL$;1I=Ts#i-5J!Ql!Ri z?(8(wobT~+J^YRd9rGbXG{60fOd-$x;2RJwod&(!hOUfC@_~wPsHR`i@NMH}dg;$h zPrqbqCd5`R#%m|zC{eKf0uk-uAp$x-q6(ksC)~Dq`{9E>*aGMZ{|}Ufkk7&c6Z-i2 z1irnu=~N-uf9pu88lH6}hp(<$|F9XQQi|rv8bztZ3fW)zN6x!V2U0&j%cZ{x&Gi
" ] @@ -454,7 +454,7 @@ "obs_rate_denom = Fraction(int(round(8*obs_slope)),8).denominator\n", "plt.plot(model_dimension_grid, \n", " 10**obs_intercept * np.power(model_dimension_grid, obs_slope), \n", - " label='Est. Error Rate: O($p^{3/8}$)',\n", + " label='Est. Error Rate: O($p^{7/16}$)',\n", " color='red',\n", " linestyle='--'\n", ")\n", diff --git a/docs/examples/robust_paper/notebooks/figures/error_rate_causal_glm_vs_dim.png b/docs/examples/robust_paper/notebooks/figures/error_rate_causal_glm_vs_dim.png index 8e404f55bcfa89223943e2e7c9a03b2d30e112e0..bc93cb57af9781fb1184a4028d4f02f76a41fe8c 100644 GIT binary patch delta 17291 zcmb_^WmJ`IyX`_OLO=us5fD^BK^g%`0Yyqeq`MpG=0T;DQfVZmq@|@ny1QGtyQI#w z-fxd@?7hd{=g&F*V!&EY-1impnseTI1z`RR!2IlXp@BRX;r)O(P*C&^^S?de3HCB0 zl%UJK_`oD+=zxzpfleoFuCOo+yU;sb!H?SL1Dy(S(hv51m3tp)S1m zOfT~2l%*>2lO#1&vtEh7;GFX$_X79Am`#V|LWfQ4F?Yq`+406seNY&|aG_2;wnDa= zn3mR*skCv(>#^~1B)70of379;asZ~a6-9LvhpAk?ffS8&tfZ1sM7H7E!EE)_iHdK8 z55%WdSEHwK$G-d0$zE7por*)IGIF%qBGlaMEnhrUTxqLIxsj%)5d5L6FY|Z-A2qQt z62bJMOy4Opj=-y(XQN!WHSFfA` z_+Z|u?c66hC31^EA^UGQ&iPnFLy!N4A)d~#sN4Oc8_y?Zn}fM+7YSr#Wxes9`(7|z z8!5D1?4qhQG+aOmc_XD~etY5E;d<-w;R-(CjT?y>zgqD1yQuNitFoOGHkhiaKU4j* zN;GitYFp@PHKw(Qo;oGpu-TcG`cCmYw`9RSC3~!9{dH8ysmfqZLre4N51IM)6c2*$ zKYwUe@vpD1XDa1V*J1^D5s>=%`_DFgxd$sc+Lf?4J3>;8>@0Mye61WBQc~|xOc1&h z9TQ_^W!0SfF2&70EW8-^nh$*Ob957kqRw*s1JjP4%eo zlgWb&mc$wz1LI?DRh-;`!^Y_2XeCI!?;vtvk=I*M6Z|~&A8Ni5G^Qs z(QNp_2>0ifkazm6Q>ORw&t=WMKi#*#O!J0Zb+Cgs~tV(H8=fJ>CLqo$-yOppx?r$Pg0~I_WO!(Q^KaH-J z?=tU||52Lwf~`ROL+Ly9Mrh=xa0h1T*azC?veEjKdnuwB&N&kw352f7`j+pLRI3!4 zB*@Sq%&e^6op$Db{rc7Q%WB3CgQ3;~`JDgRsktYIRy#TJ%%9Pp?y7 z>AR?U2|ZI%8v5pa7xyuXZ1Aqz9`XGL#<4Rzh@P*3It4A#`8t-3{_V|Hcg=>u4$YbF zu@94s>V{)Iaduu5Y?mDjgyM?i7GW#DH#sL>+?2xcwMj!M&nDj!)ml|*7wJGTGHS&) z>zsc&_Irl$;g{nAHENJTO?Ns)*mdTd{!wUD`kchhn*76EcsMEz9t>Pu+_q*ek!Lo@ z0-oy$%gJHlyqy>JW;^HRgo~5LY=T0DGHLnOCDD}Nh_Aj`C694N7#0lAMa@cFaXsF} z#6$?Zz~)20sN;u=GBj38VG{6^5~tA20jywe?3`evh{ zESZ)K88%i=XzanNSQ=LO*Y~p!UME6dI?tO7UAw4kjcb=P4+Gta+NVu@9$KhHJDD%+ z94r+d+K#fIRQhraVTsd}1{$=suwW&QWV89OlIZAI-&9M-RxiW}59cx4EhDY{%e+v0LbYhqN)daKQyp!!#CbN-(kG{TF#lskyyAwrw*A5oD6Cs!1H#NOvU|=BOcjZca z5zw-Ca8KmfT|N(L8<%~f*h!Zinuti_beGo9-b^;Z?E}#^O8+5_3zHg^?2Z{257W}> z)Y7X5M}rdN(sG$JKOvT>rM*84YGO2A`hK4uE3q-wfZJdyDh{%@cEs{YB#VdAsTMJy z1_lOjNm)I49M*pt_omn$^SYmK*(`MEZ~dufH}3JQs}r%9tc-E76!N|Bf>1C)Q=Z7t zu{w}EYVb{Zb^4q02Z)novck08h@W(zyHMVv$TXkSyzJH|4T!V(y(fMSL?hHa3JoS#G10 zRlh~gRI}>rFFiVQ8w1I6VhU#Uc%A0vBI5X58cJ*yh6+t@B_t$36(=(_T`^l@qwUaF zsH>}EHtD0w)~FC)=!jYBO~p4UjDj7?bv?2!G9PV#&iX?kOQqBt7AYt{e}{|~L7knQ zAqD26Vb)Bixuiryb+BCd1wpCOaodBM$n>8-Ld6#2j=PK3_f`fC_g9BY@4No?#QIfQ z+LNKkWHPsv-|W?de;2eZB(7}3;W>dw?(k{Q}9JWr05tJ zq^KhMtIC0QxPK=I`%G{J8g|C!T2Auce5?{;a5RaW2Z$%PSbyJN?7oNenQ)ZZul*_8 zs7$n7>S0j&=@G>L*O#2fK3<0IXMTQde}AYK&aFQyE5{AFII>+$SDg~X_6sK5RbNk+ z**}iv((anIoSZyR9Q8Aym)~9Kz?Yo%tnOu+-u!XQFbfiLAEBOf*{C=` zaUo~r+L+CPf`TY*ZS6KBg5{pnc4~k8Gfac_sQN6`Vl-Up%^dxyjTsmi$o(~QW3q~p zjg4)j*fP%GsI?f;tp9Vqp@2*NG~^R8J-llJpbIQ~d|Dcs7lR$`?E&z5F}}XOoPp}T z?_UsBf0xYiHXn^w;kb%-W_c9yklDGwAW#6)I&E4g+(dm7F`E-(%6D=tm|~W-+LCw5 zAac1GFb$|iZq5{iPsy)0-xgV5IT;HrucySuu)Mt7@$7^}>%Yg%t3N}LFZ zr}nE&v~jNQQ&VXnR#MGJi^eKkIBVxnR8&;Bl>B!@MMdwBlaoHRrGuSl?{U}kDQaA; z7rWJGf^eu7nI%|GR&qUkDwm_d2lr_hDKxcPW912l7&o4%^}T-Ym*bXB)Xe3J7pJDC zVBrK)wg5rl*x2mY&a7+n3_6Kl*M7(bCHaU?BwJm zuiTOf8>i%wn!I{hN@TS%kH<(Wj{D!{c&g+da+};MwLe}YGIO$srpauZ(+bcqoJW54 zok8txEH?edch=I^7>m!n`67sd@6XxkvGbs^va+_0&V}l_k2j#IYb|yqggjOaLvU^} z{O2O+`4M@Yv!GV^F>Ky>jnWZJMfZH6_926dI$NRE{CU4O3F<$0ir+ablF+MJ? z9^|5Yy8Opn-Nx@SbdV%G@U6zt(QmI}o>o{_4cndkn%_C@;MrIVb35G|bUj`x-d!L6 z2G6pyyY$+AeXO}BSt1uHEJPPS(%9A;w6B=$SCvfq)4hCru84_=UA%aa*>O|L{c!rq z?ep`oXsIZ6ZFISZDpNZo_xg2-?Ghn+x0Q1JaefeCUuuuy(A}7*I62;^8X6g~{!@nq zA2J32U%G$&`gOWc@f68OA?R3}o11rOXfA7NYQo|m?KK~+Jk+c#&prkG&E>Fu2j?ck z%lx=9hYdnfQrJ4Fp8A-tUxmcQv7w$p?MjrPW9Hzv#9lnXX)*S4WgzR}n{*;g%p#7J_21V8R84sI5 zn^v9gb=2<4AbzO$b6Z>X_{iS&ni}+;RdsV!X4WN!Mnp6%_h+a0AlaP{PMCCeH zIPd*wWvbGHJU~H!EQN+PLL%RP_z+7~byTrZV2y|Yz$|PL;qBYv2<&KXak2E^6KGeV z;`I?Mx`&hJXQ`?h5OFEeaex6_NZ1VgElR4Y1Omu7Qy@N!_isR&Whi9z+&_t5D%{=O z?W@R!QN!NezVrws8gRQF)?#VAj1zY3MZoQ)$JADD-)cc)3wkJ2>Zk~RjiRKKD=Q9p-Df1g)vC&x@#sR;M-HP$A*4i)@ZKCRWtT(e_SGE^3Q zv4MDnmutyiPKySAYnz+Mk0jFihf1iG%(>9E4?&QWg;5rd3?Z8;X&G#6Q(F+>C@w)Y6(-eb`rCW zMOjJ2m`RI|=BjA2-0KoGF$HP6RIx11I7P|XObR~dt`sRUG~(ErY2cag$f(w}y9(u)M`A00g72xsnnw7(D`Ie< z0V;+TxebpSMcG5s|a1Mv+De~7n%DF`W+ot zp8l9Oln1R<&6GwBWBR)rbCeTpBb8h!Ol9Wn?dPiDK8%``ZBSOW4h{w%L(25;%^NlP z8aI0C4EG)?=b}VV>PO}4py`|nO zfWX2S)mxf_X?wFE+6+6Qx#csJSlSVh0pdsUcNrN2`}+FW*J^+NMqPEca(>9}MFy+x zx><82gie-Pm1RB*C?XH`{u&{oOFfuM5qF+moN1`(&xjhdu=r`6JkMQuISGb#veK|1 ze7CkBH}~0bm5uE~V`>4jioo;S9TdBp=<7w<5;gvZyL9g%Prgnk4hr^4Abme%??p#P zD^yP^j(KWF{pisrfwRL8&{p(~ zf4V9BX=c1};|5^V+9a`HD3JJom7XWaOl@pPtEt_Yn3!0?zC97*<#h?&CUqP9wPB0_ zTr;(}=nGXFuorUw>C@XV%)S5cVYt|mvi=jXMDWlOPdHPRk52ukT(@I;m}>*V!^0V` zcP?`)J_^lLEBOkLixp5%sr{NbAOm1<{s4iuI-KvbJ=Z!jGn4pDk2D^dKHTW-Tnn+f z_A5C#LP)$sKxPaYUrRO53gr( zgt+ACk$t4q9j|tHCp9W?Rc0|ZH-Zt;LPMf5rj5&5E1Ifl9`|%BimY}zFhj&Iy}oR# zXMMhZl+l%VRpr-P?Ua>Cw*z%RX3(m2`qSTBymTqoeDq0XR@Tx;Aw2~JMJbB~lAWD> z=c%o)hX)FlV5u+d1|anM-rissdJG<7w2AZP)D90v0?vaM`w$d_-_X#&Zq(&*w6m}> z-`Q#mK)e=)4uB(h_G>B)L6n`4%v;;rI)F(cqN3ygML=LT)cf5U{$-VDktDBoYc6A|L?61X1M5^2g2v9d@f0sMOr?N8TkRL9f&PVqI zbZ+RdaIoq>YxMeI(Pub3pqrE~FVCn-7?*Zx{5MeBh0cp0$MWQGYpl#62u8OHQKLif zTn4b$%I@|#3DI0u@1ai;`zm=g8M9vg8UNG)Pveg3X**|Bu6t`Z8Sjwe zT5xIT#Q7sRbd90?6Ee+~ERd19o9g+TpApzOy^q1cH^`sbY5|(}puxygDNMKE5_GqN zzKcixv<|Wim;4jKWhyMKX9)_mK=5FBRGr{W4b8B2b@q`SN2t#scf zl4?bMqSA!MY8kB0CMdg$-GqgG=scUA!E4L`4=wWI1wwfJ`ZFm^es#JGPzIVkFff6% zu1pZV{`;;T?i5*5K8pvZMYSkpO)MX9Nf{;-(*PsZ$=S$sC>oiMru4$NnG)ZJrhH`t z5{c5;eCEZf8)QQ8v4d8N2i`x7rO+&<+@!vUt`hFFmRfShiWXo6FJM}H<4d8b5KQ%vDjP0@7GZ2SD36!<3flglQn`b zGFRp2y?>p`-nr90jb);#*tAA~T)u)kzu|%;3f2^a^%|Lt#C6*oY%PjWhZ)o#F4xBH zu5>X^wjDeGveWnc!KZf`6lg$k?Dud5O?;R*Zo}y z*g%6)<7_v^vwmUXlXPwROfI}wT0-B7sNxCkR&DvmR(;4h3H+Qc*Ij1r_P$);>#Q`$ z0d#^%S7a9Lw&%qr@EwsjGJMN;c{(vkF};Ikfyg}TEBJBub+ipy6e;(UkSn79qKvUg z;4N-9r!}`7+fm@WViZhPknK--WU4-xZ#c>Kf_(Ub5jLM|%5JL3QP1T-1+?gpBMdyG z&EDCY%a?Gext@}EBwX`mO>dA)VLbC%VL6bYBa&Pm3`Ei}dBhTYV4-9^fXB0?pyiua zYvVTiG&(i&M|f;*`&Z}wy$5f+)JaXzmw&EKxR`=dI7R`<|G1G#f0v z2t^vGA=|Z)&}(E*X3eXGe9eL>s;L*9T5R~a|6*xWIbm!b<;2Y#tV_G*SuhDu;9aWD zeeoQPS5+>d$>$%rMMVZppQ(WEMSK7!>iaVkm&Qui-2U!9%TlmeAL9;V z((C|ARVm-#T7UZ7gkkOYcnnaBiyA)O-g(eqLDsagw(fb1#CWFMbq+RVMb2D7SF_nu zy96B=Uc54xX#!wtFcPAZ zfkDX*Da2A;cem`pQx1+$SS0jVG2wPFu7+?MxbM*n&H{S5!)@&eOzND9D{_9y33H|; zHK*kSFYHy1IvdQ-Ktm}1dP`MVSy=+&3bkp{)g<4=(^r;bCZ$_a#g-(dRU`+I0#?h? zQVETm6dSn~4T554pMC(c`2G7g@Cq-YM1(V7Hg1dJ@KY_e*g8F!B)g0J{=OiU-3-0@ zEkrv&u9BN^`}-TMHyPEMfa+E%wjfVQNm&{wD=FCmV(G^9>t9TB|Fu3s%f{QU`FZw9 zp$wy-gbeqz#x+fS^4BN`J@hnWcO4B(oRrkkGNg5iR=r+iI>^}8-VUsUgSWZ!{_017 z*C4fYBac^xa_ht3kk+w1Ni6f-b!zgx#~KI&$?T{&3rwWa0znWTkZxn z8io%56;i!4?Cc@awLUA(q@w;LHGpw?*XZxxzb{n=tg|&BP1}u)xdS$bb+tQp?)!iRraehmPo6wEJUY@}aTM~6rp3~t zvgP0B4H$efzoRLzF!O;D^X+t;QK_Ux{k$+$lu(fS+EDQ6b5G(2U+Ne+ODtaXn%M#QPmNKMQ#n%obM$krIyP%Yl+$ zH637hB>$rt6qDc->ph@3amk+edwO2v<>e*ev1fpp-(Yj9#?#YtapcnF%dC1$1OTm( zc2HMHIW6ww=H>!Z99>;qEjifCGYkn>~8!5mG)-NkPBdeX;fz^hP2;}tTTfWC$? z6a@rzcvDjo!oD#LWZ7)-q&#scza zbGlxOYFMg6)JPq8u|Iwv|zT{`|@7 zJ9`dHMW#kY6y$z|LNuuuWG(P+p!uSZu0&CMfXs~$N2H+WJqrEQ$$#t*ftCx*pT9qQ z=o(t#_)nZ3yaqF*C1^FGpdo-Jg~I(Wl%n6qk7C{%jSxPk#|Laye?-Rfpag5foSgb9 z2K7TejSz;6mR-JzW)7@t*N|k#tv^Gfqp7B3H*b22C+mQYa1DSQM02;%^s zKPlVgu3bTAXXoXSz8p8AWA>wmCWW_AIIsN}>a&9EDM#w) z*kC3xE@}>hC0TIXz9LiZXa%ELx{JB7u~?x=zin{@jJ3b)R&GKP{eg8hE?VnKz$ANO zcK68>B<$BOE*)Ln$ftJcj7OM1^YU~cNEy^iLnOi(mp;kwR>T3fGc`B&Ttws|T#Op( zCs0Q{Fanwpca%4CeEjt3cSi?aGV5A<%zvlx`~w3A91;4a@JN? zA(6J}b^Cj05I4}+zFw5Miuv{8Pl7{^ZAE9xmG%#HS0cVe>=f<{h9DE~4^ob|rncDH z{34kVN&L%pUvk~KYpfIWy;n*H7dtAS9oy!#I)~0dHLNfnePi61JKboTvsRV6u>Sf~ zcjmUDQQL6$dBF3Giu5O(6cZ>xKF(vTNClXznORtn>4^%L95b?8x2~KFu7r>$ODuGF z{((ZWM3@X5+sZ^|`!`MurE*6WzgtWLWkFD-#@9O2-}DNssPMG6fDmVg6<>KUGe6$| za>8&GA^?l@=U@_9fa^l`-BaCIlFAlybuFJL!KkgRmCfichf2CMU>q!Jvy*v>XfMvr ziU6^l-={!n{{9p)e{jNw;7>c?jH`Wg>X^wwA74AsR@#hKr+Cn}@gLqpU7IMH8BJT4 zFAmOir{?>w?AP12NR=#fSr!j{RKbcN37KBZKwqJpGxO&7&3|Uj(ApwAJR36R^W<62 z#CF@_uesLnRG^qvC#&LsCfR|iprxyu_)VTWV`4T6QK~u693FA`$j9`6xR|eBmI+ zK-Xr0#$I850TUB~4&C<8>yzY{u`vsP1o90EN`-gkCLH~im!FTxUZDzfl4SlmT2ysC z{EXhp5p@tO$N7P&$E#Mgtk`Rr*<0*JK1d6kh5&i!6CZyU?Q5a2YD*uiph=bpfAzX8 zAz2Ew_dp$5zLJW{^5Z+r*S5B{1lRYlaR8yDd*DP6y}{Nz`Fn{&p-)Tn*L#0h1It_pJg$zZX)E*|$L-lFmau=5Uef;l{y$!) z;z1b(?%xYq4*Qz>$(AU{glVe9>-0gXQiq_0@2M0cNTJ3-l>85wcTkLNd#3>LVi4T9 z6K!ymtzLE&3v2oVK7YK(N5ULpQ~UachGiXMA(BA7E0>yJi%Iizucm%~h4J=qL^~>c zt;uulEc(}5V$|SGXs&V(lDdTT0_4*4z})6f&JzZ2(p_k(2r|6)moL|EF{)`nVGUtI zG$|-4DH#~fB)G=M#t#4ZF_CbX5P?Zy7Rca}-CpU^iGq`AuseVyjTx}O-`yo)AX6VI zWRd_TUH~xR=;XA7{ZPXK`t|0)0q`GqmX;f~Uc`TuR8`Rk#{FpS7WC!M^VjX;ANCy1 z1PK@c=azDbAjd^2iW9MX1zkijTdkWf`QF2aAAyXLO?^cU6uYFE83Q18n)~-XA#mW? z2RSRVzL5j&KnL1a6t=b&%s?n8##I8RB*m6@f`Tb@^-b#2*E8t$lCG}_Y!47SyK;{N z)Ton5B=|HHL3^G8cSc7H57yPI)gV-cgb+PK6tn$7Zp6Hd`%+Pj*{JL8pP3n;Nf-Q> zD&t{`&}1|lF5b*~bCVg5!G@;FO>OAl(0A}b8js}@= z0&%c{hDVNk&bxP+nLn?Om!%#vf|_*`CIdj1hb`DcgC#VG`AEUDGS#RvEAPeBzk zGBS!vyJs!;k1a4jehv-BFD+ehhv^|}2D(TH%%4@-&Z zW`A)irPL(S#p_j*#eXi(xEC19i~|oKJDZ%8vIZ(v&M*OKNmr2>K2KMW%AT zH?WC>6cn$3gk<61;82o`j||@GN)X<@gJ@B0W2If_@Y#>8tZVWTus)u+hhorC(cP{K z6qUXq_9o3pKA*Z#K{8}k+Sm2>&`<s#mz|L?JIAO*M;5j{o1~HpiO47i!U!kz-`XCL`TuiFQIZt!fMwPwX|J)Yb4kn zzU)cB1nf?nvBqdDcCy6h?t)Bgc3srB!J^LEAOBn-0(}J%(uYUZ{$zrs_hpX%wsvRB zp{P9(;B^LRltuVw2l5yvUGToUE*0P#GHEtMZSY;09@y^3PewnTXOpkw#Te<5!c0Gj z{1JG{=9uH4hl(wq<_p)77tpKV1yNv8v>^8fe) z@_+f;|M;g_vAdRnSqhk+KY!Mmdil@G5h*Jvfmt0@<`+xkQ z?wkjr0&G>@c*4G}Odx-Q?r|BPLh}6Vlp9Pq3BrWm1z#d!V$)C(4W_E!`QPUFba?3a zp`IEmp1>nK{Fa7>M$-o?Psc(SVOQ7Iwsv-AGIA<)rhj{R9 zLZE^}Z@tZMa|wg!0u(s+EFWVWSt4Z>6&-Eu8i4ST{BBQytN-VgwGjU-x(z>JGywak z{!gxdw)KlS#6Ei10_m~$mzA!t&pnZMXga>gV!Q=CFWvkwb^)BSUmiQGk7=(C<+|?m zD*}mM-8Fp`k<~(Xnfo*}tN<>+Rw(o6+8{nHtN^e|$`#HW(y_cXt*tm7V7L3Twq`!{ z!?WqU)M29o1Zc4G62NYP(m~2_(@Uj=;yIPg`I#HgA4lL`G=||31sSRfQq#pNS9JCC zM4;nEvglrJXxQfbOt2ku{m&i|GP@Dzk3r-mZe&CUR^_pBC${!&uzkS1(gD>D3g7~a zX1cZB*SxXhd|gE<*@Vht-=B?txhO+)o5y}tYq>8?GM1OSeH-cmbnZ5IF0cwF$|Z~Y zlX2n+2nb+4eHrJv<(-w4HNkQdy&`RG4DGCIDP zyPl5dc@u!ab+FIOGz74j-2!nL@bWfu-pK+QFM=6lDJ&J}6u_$d$E$3N>ZNs{b{s)} z1eT+_@FCit{w`Z4K}f?3iDt4}>hZdK?Y0)UCl+NijG<+nz`8N06!-!iR8udte@N@)Oh6SKlX%>LrV3$PG*fy|Ds zArRB(@AeizpHBc{K|V*LLhwF+Npnlfr)8u+<8_I(zOc{NwBv(~owedg$Q)uI$Rc%* z$yxQTf^QZ8=9iF=5Zz;9Y9^q_MKA_Iphtlk6tZMKrRjcjDOv8LpPz(=m%&np-fPvxVt5@@2d+mSR`QxgH>TDkPX3tI!H0L{FBp`s(6muwmi#9mgo-5V(4$L(( z5aUto#zL@NXsZ)or#9v=h)aXEh=j76XfNDs(-#Os8OviTVQ(-lRzr0L<6kuhneYU1 z@B#qH>X7%BaDE}@l@sO%Yom=2B}+g}z=I|~_qz!(_!aodM7gw{0=GBUn=GqN8$>DH zlFCwGCN~5S55nzBOBZwPPS|2BJUqBH3%lt6nmhvY?)&0mPAFB)o4iXfIr)Np3)@S` za$^#_I%qoh+12CGwSrru8N@HG7jHs)tvyBhJCN+uCrU3|NepK8Kd!?+Mn-VPh-{ee z0xOXZ7m$>a0$<(&&{b+>_5lFPtzebFl)77~>Gle?6)bjX>Sa%0x@`S94%Efg{{B3q z87$NXn5zKQ1Ke2vgaYWBrhP#@1Kh6QTzd=6hMJoC2V5`g4{A;7^4jsSO|vZKwcJ0z3uyM$Us^326jV9gM-a(DtVew`QQzU-p0+kup38 zsdWUSv&^H=wphMzpFb1WuMRx}dJ;Ikc&LPE^AIt;!eW3vRQCdt{!egOC?aUi6#+t{ zrm4vn&_hy*xf3i8+J^?cYUr2MJ~ z;0y{hgv{7acfj$8AqeSRRHO>h(Ek2@>0qL0(7>J&H#awn&AjBe69*fcF4QHIh^Xk! z=Cts*J-D@mvlQ6umgT+i$fJ;y!3-#M3~Ij!y6#KjQ}DjLB?@*yh~^ZaZc6pR^V0(a zHp{~*kRDJFw#mw1;oa{A5)M3KzTdu$()*DEA=Ci{1S5jWiW)lmE4KBH`ugXbnseZz zY64$I&srjI3E=G)9Gpp!p(EjSWCm|I%G2B10J&Z32LKgQ5WLpVL**YqJWR(pOmsiC zv9;}ckcK8%fU$5c&VoEbRh^%?gw3Gy=Aw+RkRTY1R#$P8axM={oUH-h={+j?;)OMc zohY>Tq!|31iP??!n3)MYAS5XS+$ABdb|*A|mKtyw0}q7N!8)Q2m>U$`;E9>N)!`cu zV$fx-;^4G0?~j4A1=pyB=Digz4rqbF#N-hKqeL-CKl{#|PC)JU)So^Yxw!Ktv;~^O zpv!R)^eQltT@Mux)$Y`V`5vR4NJ5B+o%J#-(C_+sGzhWYoO;H~$7d&ybOrBDyi%@? zEr_VK<~izRmyHm@o918RrN>L{xE=*(Ir5tdAwmf+$2qiO?tNVX*h+ z|B!zK`S$`kE}L0+Z$Y!tN5Thr1&^jN={$3Jr6 z(psU5>`U5a*KiyBA2ap`ipqLo)$0`N!@Fq}FXxbfX{EcRQ z01Wb8BgVc21qL7hPibd|pN6FGUVV)0^`2Isks840`hXdUd(X*-~=SIrC33 zm7PbfeV#|`OP@Hk`4$%1d9M`2t@!gJIVw#5*gy!pJole$LLh_Mjsm}da_K3~`2+hA z$}N8*WU*kaaORkGlo4RuIIDS{JaC3ky;%7SG(dmq=EKegzSGIw)m%;+ICQecuu-TU z5Jbsv(qpU&i30$I9@cWIkTY=*{J%XhzW{zq{^qxoBb{{_7QK}xq%GH&XujrrT7afT zn%(Q34SGzLL&cNswvV*_SHBGE;l>kq`4-()IyRevN~sI_7N`hpRL^NY)$puGDJgg||S``N@uSss6|s^}Ty+44c4;otzyb8(>0$#(A^gO(O`+&@3Kw z24*DlaY7wH>z<@VIKTo!adEz}y880v%PCMfN>zWsu{d;agF176fZ;{3(c2a1E0HxNzDR3CvK;*@npq1*bZu%mOgl z9s+Dc1|Nu$O0nL#eY*v~!V(m*uoS~)f547#SSfZQO&M5LNDh<+-&sdm4-!8XCPqbVS(WL|i4%L|;1KY3>bkyPy1;5aFSt^M$DjP|VU$ZsLS_VSmlYtfKFG z5p-gZnsaPIbMjD>K0`9+rl&Eg!A=45>#N=s`}sCfnpe?R1tBDCdn*9REfuLkp@O~b zHQ$-4!K9IjN-B%_OAYv8oQMuk+X8gxS33nC?}-L7gVZkAn-MwM8SMr#FX;Q)U;yTI z`x^qZJFE6@Pc=RRgMtNc&J+SJVQeqF3jMD;%W~*v!n$%-P6lY5@HTJ+umSL_Epbn)P!@th0D+1Jhdw;!Mb&s5)LwygMJg(`rLRvOLAND&`4Gk} zz-)T(N#%6c_Qzlfg|x_ni5-o|!RG><-8lO>L?Wbx(~NFEj8Q<+pwAYfKLENCz^hqE zJGk=~IIRPy6r4Bn^amU;B?02D2UrooX-Pq^kQon!wGNb&?6mxRwtPdR(|zWbW-{8h z11%{hnx|`GB6f50?Nf(!Nf@G>{_dt_FAsvl6rcZ4aKpa2xw({LOYuP?a4zViX#zPq z4*Z?~H6NGSE`81V)CC{(05=ZUr6r)N!O0^qFu#XibfI;-F;}K~2M22OFcftt+_b#B(J+alEi+?dV{aWC5Pf|kdsv^8 z$R6PY87%_?Z|ESh&px}P}0fs=L+r)&p*gMI@Ve{ZtHEvTnqx?V(R zvkr`NPXv(D-5bd1-YvA{28iENvG{a$g)$6i7;hH-8$0D=tX>HDom!FGOy z$-4{86q2d;TEx)y+lB^ju+vzBOZue({uYyz zOae<&9~=TGp716pd69s0n5b6(5i9F~mRevJnRV*WGwc_GaTpZg|4X<6`bl+bEB)y4 zKKLqtYCsbcFigzJ)CtX!!(a#;ugq;Y*#}I)D%hk%>6>AWkk-<=`zBpJ0T48IktWqP zI8)JlbTW(r=o_q0R&_#CwK1zgVB&;hPHd2z?48CpzuyI756Jig4(DAXpfduBiycye z`3>KI7=;G&ASaOWXDIu=7lbdU+qV}+RGjlwhA5JUgRtld{LsY5!3PNymF26QC%q6jTg7I{jaeVK0c>{r`d4p zUIt};|7*{)jBGb{SU#_@s$I6w*6EMhNHc9q9&Nn@p zi#=%+g7(3Kc?LS#+P{M6Wd;gORGzD<&(JkBWjmGG%Nh7yW4^_qptcYt+N2?^ikGzK zN4KdwbLt)^H(M(ClB$(JOqq) z7JKtehPhe`@+@EbwWPj7Hm1bzDR`=Xe!}+j@#%S``p4~oVZbNTQMs(%qoXxeJJz=Y z@nh;-e|fiahdhVr$?|osoC+O;t*1`DU99wFY_`ch3br?`BC%%5d#g?LJJhj?@z&?Y z`|vcM^77cF!tOn@6w#r6JoM|=FCL4Ti)FTJeYOm*2e~e+!y*hNBlnq@B%`)N?y8Vr zV`GZzec*^PGcHtAmRS3znQu4f*+Hz z*dOhX2z6U5<~}w0sh>o6mxEi#dIE9;Rc5~@;!%ITLX)m%L@;r%HOFf{fpN#bDH*UG z!YKbFYP*7W@@wPV^BCl#jIb+m@^9=}SMe-{tfMowBq^Ge zIdH>u&h?6jNBMFLQT6++*?03D)QL$+1rl)~A=mKn@vT-zcxx=VJRN@+D>BGmptz}Z z;%KMCwiB(bSnroRtH-FAPZ1X!{n6b3)2>mkqxCwxE^aSbWoyVx*z=0W#Q^{EEwbvx z-t;6{23A(qc!?l{&T65{^}^-ry7q}3%hA;wWldz&+^QvKs*caBuAc5}*44VC==r%@ za80r7>aDzB>WOQZXk21ZN8CnJdx{dHdcW6hN;qW~{yrG^44>fSu%=QiAt4dXXYD1Q z`dsf&$yMZ$x%v8$+pX=p_nyaWJDm#FHl+7&+!Ro%xbGf>R4IC9g)eXi)ggChvE8Z6 zO{RXwg_*JGciNaFG;DvkN!FhpLTrDL`ZGiJ7&DgR_?fA{VS1x{rA&7_6yuzWTDQGc-zuwQa#Im=!^iTG;0S33V>uo1XM_BW! zDeP2X;f>{WsRgc--t{}l4!DfluuRk(7P@z?1Q0kOdoiWh=5?1kbn0wtyD#F83rK6# z{Aw}c^iNL}o@mZ!z!<*ncKZ`_D3=+WGB!m7FSST|tAdk!i-GlJb$eV5_xQs!Iaa;j z$Z$}Q<*`lHVQv$HdfDnB?h>2*1{S5T+DzMDGQ8kL5&w4K01*P}&5v1CC)W`Ki4}Bk zOG`r-WB6_L;k1(7I4#1r)z+qIjp8xka<v62HwiD#afco5MsGy6kbpLw@|4 z&d1iE`1|qP%QN!$GFs0XGJJLiBs)UPF^2_N?Y8$uS=j?b{9D4=bT067tXW>a&iSSm zsrS9XZawBsKqZ&_^s2P9w1t?!AY(?jua6JIs~=>SZ*b{O)q1W8tSyvPhDHE-GM86W z3>KMD0gQ_z$jT;&oTs>X^PP=N{m6*w+`GNi(Ih~Ex5I_Lm##7C9+px$?^q{6K#0aN1uu{DB@U+_K$V@*U>sCPp)){&@3` zR9l>IdnW9}rAylI1WHOux{J}u8?W^r>*~_p7WbQhMY5Xj_@G{49}peg0&uFFtBa#j zWQy*aLZ&KjZ_d_iYt7G}lQDt9zP;&+dD?ZC)XVKYq@>(QSIl~zGd?~Zr`H^cwO)1l z*I(2fzq8WGAz1Q`!|pPYU@}}dR5)GlThrMYKq+W1E9l;Tlawaw%Q@L(Vv-B&`rqiS zO#AR&WquwyOppJ&5;wkYwU%02Tc@e5`|H@od;h5TylE8v*^cC|mz(K#5*!&vJwG-U zsyrRz7od6ws;n znOq$$JKk)*&tWp8P-fN<{|Jx#!8uY=(kH&x>&7Y^80C^ZY)+v^^Fl9!eqx_RrmCuH z{_fo*EYb2D36VO-^} z(I=qS;912c%M`)(?JWPSZ}5!FjD4KrCJu)5o@$5LEu;0FojW`{;S1fVsZUQh7W*=q zTO!y?Ea&irPme9VoR<1BYTDZFH3#l6z5K3eY~0*~q{$Zyt5^L^mrElPa^wLHM8m+~ zu{GD;ogj*Rfq+t4MC9Wx89hDy$>Gv{5mC`R+I!TSHzY8)xih*0rPr7g-A=QD-$)f#`)-q<^T)54(>6&wfYeS;6 zGNI}*-PQc{-N?_84gckdqgZaiKo_=&UmjDC>K%Fsp~sm8?Y?2{NiYs22} z*9a&Do?7xh)YQCH<8~RI1)rav-wlua7iRI2`Y0TOzEcU!*B8 z)?+GTG4-{z=6kDZu!=ruX^gJ8r1}HdTB=pfF>v%(*Z^8;ySflUGqW|TtUHe%-6?Xx z{T{{LOT2*;BYwt}L6M z=HHm5bKSXa?7Y3bAP?s!*)p!Ag!jplCm0_p;nU`Jc%4+XxE_Lurd2Z2ln^ zwCA|w`Vr16+uH$F0YO2d@yfp{?NQ1N{^`P1R8HFgHp2wM$JM_>m=^}~=$(#s|I~St zE{~K1iFuP~>FJ34*mc4!b_E_h@;yw6>NYa8}jT z)$`x|hw$35#QZ1Oa*B$9StNFW*~S=G zki_pGJT_x-k+bcXrD7}&fimbQey2kzwOo7V#C85qj@bE)Ji#^e2192;Hf8PW@@>|9Yxb$ zW#l*My`Fp493`;?2O0+tPxs{bAeiCBM`*xUU%q^){`xEmF}JdsIa;ggFI*cf<9-&! z^=IPer|soI`8ne>xwQSmrK~iyGOj2tlV8=<7+L?`rOqU5AiR2WZ84bO`x+-1Aq_II z0@Ol6l+4WOMM70_DUw)MZr=2TbMxrYBjS6iWH)X^DVTiv^a+kdad9!)0c6T5;Mb(1 zxO@d2kll%?sgEKFn$UL~Pyg=G($dP7+r6WppxB4e4a<5%96F0U?J?|8aamb+skN~J z)3ulBuk`->4oM?a0xaEK9hI`P+viu*#0}7pIHU9>S^($Xy(@%?&LfqAcJCh*ys^h+ zAU`G}4HCCuKwiq8_?^`?A%HJa=dpP-v z+&Ep~m+_|s_TJq(+?~6Qjk$}T#%%CYNo;hDR13 z))dW2S>@O=@02un*!~LhTEXz=%+P9!+6MjU6XE)YX}Xf9HpRO}zFHTYqb;QE(<`l3 zE*XhHt9v;V^2JjDp-&BRag5UefuDo zE8h@R=eI?XJnM=K4}bdbAqJXV5>!e{oUm{&g*_l51-}g*z?tc2Y2C_jQ2~ycJuV0G z7m6RlqLl3dh%^6|h@3oP@?5sv#uR+=#`^(#D$Crw(vv)D?mi7nry~=p~ zBK_4}RW&sf_5ip#?wN!FZ374$EVI48ySv+yuILX)fvzm#hJQ11l@ksX2GAJ(LB`;Q zn&nQ4Sc0skfkDGKa@qFXF%2d(9Za>Qq{J`|QNoJOaDe z^=JZb1Zhm{V^%6y;4=_C!Nkj`@K>I)kdoTh)_<*7Yo&@jU1RH9Xk}XBekJs@$i&<{ zbmv&_$=kOq2?+_%v_gQAb(h(iO-)ZL7n@VVvrf!31u=RtSloCPPz`5Fex=NMiR7W1 zL)(KyfUWJd@i_Qtp52D_%5UNiA3iK2qh(=iI`tEai{DM+f`cDJ^Dh;Qi=5Nc))vXr zZrS#F?0q@k`*-rM+vns0dj;NU7HWgE;B`NNXI43Bhmb-x!zo|9Fa zDscX5-_p`zMdGJh5FSo;YfG?1g!tS8_b1LCHu%@nxC8&bx%gsx>okyh zDY1`&%+4k2%@2dAa{1`X2c(ukB0r+1T41`iFx$jlWV1E;>#*g{T~V0jztH?vg(&EB z_fI^tVkWEk#6zfv{9!mc1r@xJY}%1*S_?bH3!<%&oH@C<+JK<7m7$Fd_4S)eSykH5 zi8Edo6Oq0TqsEA0opaT~`vlLr^^v;-&9L-ZCpU~s>9k}NR2b>MZRAHET9(< zh=%fb0gMCha%1~bfe1Za-P+~Bya-mUGw_s&wrgVy#Ft=|J^cLYySv$6KB_r-z{&Xs z*0a8@&J`%&B(TVsn49|g`p~C{u3r7%>Utgv3u}3}$X~?uQp(e1tBO!FA>c5VazKAu z9jowmcX#KyinPxgXDTo$7rrG=68G1iX$-Jh?C}J~rK71?4fF|!D1qMZ;GfOSxZV9H z^M+l?IMC@;%WR&hgcDIwwL>+hDd*AQlCa!jV)BB%bA^no5k|TBp0sX<(Y{t6T+(~8 zIoe`?=pBy$m2L@9y5YT$S;W&c$xv@E5=RIsNZw;c5a+l6hbOs5zfCcP$9b-~18hS& z%%@`xK5}WA7F9OV%focG*X)TOuCA^wte);q`@diyX3?mO{?i;by|&g4-6SVFJ01!O zIM7E~(ISl50X|s^w zT~Hce6F4h?wO`&j=eW_??h`ZJ9x*W-evR>S|4ikN@R6{@uO-DDq89_Gq2Yk!915)% zYw=t*Fu$Qm%;EZ4)hSvc2%U6zd=c5_#uaE5%z;5#4fs3fs)dg+Od<9vj^&lNrBb%s zVJETG-;bu8`$YH8;0yDlaomeTh1^Ls7%2F5tHKMO@t592~)K z>{Zn+Y61dnFZKKLSuf6W;!|6&lb_wEw0Mg*u`;}VFqduMfA}s1kzBkp!e`r7X{;|Y zllPR4GP+UQy)KwjQE~XZ8qOTMf5wQP{bx7@+T(@NtNpJo34+S$q5AN{#8q zukI3XRXXNUo_^ZghvxG(ZmYU8s{sOz2VcTG?{BQ+eWG&mGio>txv$p*NNyz8{bW4h z5=h5WrxItvhdB|$c=f{8G0In&3gc(m&c`+g8%+K5{J~pv0aHe>9InY76r1Nfm4*Dm z%=doB-x5cBT$KZ?+Gx>l5lx4qX=-4KpZYwlf2(y0=gs)biP47_4)JFeYtk8HSqlag zu9NNQ^(Y?M9SY#*(YWrPvky8Q0cOZ1}DBTIP}_UxfG>qi@}*S^NV>A zmHW4}0zhQMaT;PR(ct&NOM$(o{@mC~i$!c^yJP zEWw0a`}2ckBXY!@j+1$TUjY_IMEBt;nF5_f3jI47vT_~P7%pq=rZ_2#M=@2+xA*+} zV@DSyn63$`^!^;*vXnSM7ee5%J;wDp{i$Hnt?@@A&K5UPL?l$TDavF{T4(h5?KTDQ zyJ_kjPnB&C3k!lk0|j5eKLGTlFJFR@>S*EF_0gs^**odqbd^1O{fr0m)~Ij$zrE1o zN~s=nB$pDHSDCw!R=wa@>~D=nf5nZri9e z_+;fjpS=d2{dN0P+&iI7fl$}G8;et7+aV8l+BfOgBwVc}aLeiTQV^rNcc{qBA9@!^EZU=`*1R{euh4WSV`pRRL-xJy`#FDTh@-?fZZ&Y* zA;&i~ehfBexqrfBr@!{QCLf)qiXI6DLlzbmG66eQnBsJRD)0>h;Q!Nde<-m@hO#sP z)ThZNoca0lrw5BIQgODJ=UjeC57A+W8e!MR5 z`W=$DmN&V)EZzO}S%Lqjj~@q0t(n2B92g!pE~=6e2_mg%cX(hnCIB;0?oPqx-kt%# zTv{PL9i7GGuk)Z&hG-uZzng}!E?L~4QXxxSaCnMPMArw#xzA=E_jM2|To=)6*dr<@ zjnOX~5Z;|VWlSk|mTN4~p4 zXllfafr4>YL3o3D%J&Zn3`D;oH0Wv2m)k;(XJ4&t#iIKU%CR|!dvCqhz;XJfz!c*- z0%8R#H@D1Atnz<~pvXTJw7n3_Bl6o~x6VIq_qU%?X!WGxKW#LOI?vO-?<3O#uarV#sGv{S1kZbSTUUTQOncw21iI`C zO{nAhOJ+&|yDxehaRToHWrPks4;3KBu+KNj%gdiiN=kOpf%d9Z<7VVQ4&A>kMJh}# zh8cW1u`+5=H}l=)L523U*14@-MGY;`Zs7#U4C!pI?zdQn-rUNelT^MDwFc&1j6uiCI>y5A*v?bqYkFvVs0xf?UW<;0< zVyEi7mjp85n<%}0J+-nDleUCIof}K*a?s)W4VOb{)Wz&%-%SFW9?N{g{@hLadj{PJ z53Li#WS1aw-J-$P1$+Dnka}RSBg+j@iWiEP4{rqns=!CkKez1N&`5;LxaS z2)b5J;nvoc4xDwgYT*#G>$i|4CMJ?`y(M#Yc3u#Q`*0TfOg3_QLcH?v<3|sWa+{iP zWa%FQYewSH^%hFqdoS}mn&*+693hx7&wGV9IEYi^nqZiec>1)uw>RYU?;0{vYVG|Z z>B&{l2x#0Cv(y7XXRxq%kfmNh1Nzs0%7>v-ye%>OVW79OkIoQOcXWIKb;ul~431&2 z?%aH^TErEam`F}b!Xup7C1jC1vh_dk1k?9)7j~x|s}@*sJSBn|Ul3nzqiUJdmaWOD}w8UCPSY;d??Lz0#^e75tWm2Es?qBghWKB;sVuH-``IP+7Jd5 zQ}>`CqH)Kq%P{`^YHVa^okPp#lqY@t;AJ}jW2C2veZq=b zVWw#gO!f+EKZ{yPH+sF2qKmDFFsb^S${d;WbuG`f$=}4LmSkjlf9P`g<>t!X^D0eN zuJ&6ae*xkYu>KM30*tipGj`&ldy$rggh|_E*ROx&p<`!K`1a*b6BUSot_wYBLAyB- z5#-<=^^IwR6+a99B_0{83%D7{JcXc&1LpX_EDLtMrLzC+FwCIe{6`3g2t&db%6kLYd{9AFYeXd;lERYAP+H!_ruW zvO%!Ew(&C+g>!sM`d7yXtx*l_>e_OR9`BKRqhuW~>JRnKv)krz>{(|Fg*ctvHKGdJ zWT*o)I@`YA=QZxQ6$&@oFIKUTNcq?CGp$Cu@HOtF^6F3`c@Vfu5{8zlo z*Euv#PyR|r^8|pFc2i19Y9;wM`Hs+8sOL?XOKVnCMM(gJTF5oV3SKN+*rwN_=5js=Tw!PQpe+shI8l6Wm_-3 zazpUQ6Z3rI;;0}YP*_~4jG)R53_=Pz#6R()NE=np)s=uRDyFI$jo1uB!*~zu#i1ei<(uB`uHanU;5MB(*gCCO zs;{qqp!MtA$jAtqmH|45M#kQ6>BFcmpML~33G!MZ=&fcIv2>Fvm5vAeqbI#~{d>uv zs)1^d8hL@9hlk7s{tyq_t_+ERZ5ii$$OwX&Q>qd>Gen<|8|ha>5L*65QB%Rnx`~^g zc&|BMa;f1a=uTGPufMx7SzC7l!^8EG)Smc0lPe{S4tP?y)ah6Ut~5u3;)@@`}NNSP{Uhd1*33? z5IU5H+V8DCOAv8|#Muo{y6S3bL{*+{=<~x#@b@R^?Ci9&xBtrYqoeNEuaEGw8^EmP z3*QbBOAUM|eL4+$+>@mtJbK2%)=4Ik!{}pH*8ToWweHNWtN$l~YP=8m?-VL_3CxxY zpmF)>u6|1;8+bKtB{{s!W%p9|w+{0UM&woI*=&P}&hm495g`aLx+4_|4qX%^Sf)UI zOHHMRxdvE07Fc%B!ot26YgW5p)z;QdOi$OqlS{!aqe2@nv_EyYqptT4Cnk`8GL&+T z_IYra7(yh!zsmUSb&c5&=7aAx@FR3B4Go{dSQNo+MhU06aeW5`a5yn)Aj0ja0YtNa zg9@TwRl%8EwXvYkyDEtaOuP=eKe({UZC0-1;NalhM)ikTEZ`gDLA z^X?+!KF|Q}sTO}p^isN*K;<>_!o*~0}gW!9*$dUqMGhmma(mhMdJYZ>KkmQ3VEdYi{eP16}Lzb}3vJ&bphTdH~ z)!*OWXN4pUIN~XmD`U}CQ%Efi9Ek{K?W>9LL6p_1EQ}I$GW3Oce>O?*h%dC5?%Wy8 zaA#XIrh+uq0Kg12q@QLkfd%q*v@|4=(|GaD!C5po0KU{M78bl4H*ScCh_F6*(DmFP zNrvBcH5o!|fyX!jxKI1ONUaDuMcjY2_Gh&wH9XkJbE8}GEpm+gsy>5GB4z5+l$f$m z8o}MASDm)sM2PKRFQp?nJR2LI-cu{B4ZI_@P2ycqAqb5YO`X7c6A==Mq)3Laa&h&k zNXN$pZGbqJi=Ta^paRpPB6CAd$~3BO zg|TuV95cHs!z1PIg8&A)3yc*j>QR3EZl>`nBo-l>l=2!3L|^RxR!ib1rat%J!g_^n z16g%4?X}(8B!)r4YB22Gnomt5BYS#s4^<0Vg79d<4w!jZfCf&iexg6VMiI)6g@cL@ z;{fSlC42MAp@j&V==y9IMC(!_y)czVYJtSrpg*}{76`v%Bv<2>d&euVFaO7-9vw_n zPj=JKZ}8?91LB^Z32!DNV~ALqI`@SMr$-=`|M!ba57B?yXruLRbd8SeoiZrG3T6zj zc(?oD&fWb%6U_70u}X#tRKt1vT9;;C^>>;wa<`Nt^QXp7tr3T9pRT%A_{1mnsHk#r zV+{CJ9UfjVM^?t^pQwMITJ_X6@ZZa^ZsWxNKluxCoB2bzJ4(O+Q+FuPkRtUZ8hLqn z;!9#*{e#Y}3ko176fPhLFYywh>Jaf`=*8g?5qgI_V!2;$Na;pOrqJ;59meFDq5tf$ zw~@)~0sIGD!}C7GAJG37A!ZH4prYIV{1@bZ{?UK+lZ{h93f_-VQBjpOS7`dZ{rwxD z^#i%%}K8NjFy*fW2Z1hjXIfPldI`uanX`m0kbAdABk z^AnJ3tqM7%w6(Q$a;JS9Yra152yCREv#n9@tgLkZ9`4wC*U6eGlF*Q%9{kcvat9hh`K)j&)cf7!NAq!z0GH1B3$OqriPd^h!Hbwt1kJy&>*IlsVDF{iG4iOAU8FqfczqOP z#F-ngH51?!TY+;>w+r1x%g9J#p(~|e_`5>dFW8ayhucQa-5DQeQje2Uk3DnVUxjer z=4EDP<`_hWB~}=D9zY`|_qJQW8PeE(wRDe7Y5&__6eJ=dQm=H3(2knTNlQx;5+S5f z4Em49b^7s5!!{_al0OjR<=9?df5E}Q zq4H>zAI=*N0Rhs`-0Th8xWvyvZE@$I?c#>%W|yrv;lmfVpy>y~gnP8RvM^k9A1o4G z(1i~1r94!n{yly9=4gOtJfxN3#F8zbTMQvx&3}9SfBzTcfA=yPf>N;f3kwT}5YXUS zJKn)mowpkf6hzN5_zh&s>tG~=q12%100c&N2ysE%ZeC~0s(O`mdNiNppAG3RXw#@V z0&}N3blR9XyT6=O5XuV`2rK{H-;Z#M3yZEXgJl5kF#YGx&e)sN{<*d0jhE(wI^6EVb z7MT%%Eu`@`27~dR8d?-X_dX5Oj>3lEeljw$jvO6v$Yr^}kwyy)Ui`GPzOm5*a)h$b zgarcjb?GfFEurASS-ZAQ(9jnzUfBCRE%yfk?MZrHy(3kI zD){%0qY@A-lE9LG{NxFw0D|=9uF=p-oMCuO*&qMtEX2c#LGT6G>>iv2T>b3k)&)rg zP^2SP4`46Nz9P?KBq81O?PWR%Dmj6M1QaP*`Bs8l+CZffKkRW5q@_3vJ1~Hg=Z-@rM!lxMJ0n#{ zTvp|w7n;uiIfl<_zB=#@B9&k9*q|+1tJdQx2jt#RnJ~{l1}wM;eKh1C9VeuAq>hU9 zP(f&ih*^PB^np@`NC-WsRKRtChCKnD#t>qY<4$`w{V4^z-Y-KLl0l6CT}puugg9$3 zyg|0Zo~Z)UORqvR<}(KXga^mSR(rvkQ4rlTGVsm;fV&3Oo1=*WhNM^3CEj|po-0R7GI*zY-7{b z@eUo8K+K+nPj%d%bcg-C0znU`WZ;A|NnuadAP) zeG|$*7qTO8gxz3@f)ROW7c6&F&BhoT8-vaRiHbh;C3quAzBT(S^u9(LEYrp=vfFpyXwCb6 zlNLfQ3E&M=erWzV zRktY+?XxCB`NR!>A@fJUYjF->!ujA2DL$1@3oHfP0zgyCt?EtCgt9@w_aeFb8*UFl zwxGJdKMX1$Tepc2k~?Um;{oTW)GKtv!oVcky3PgyQbSi)AP_Pj4wRrtkn`KH#ILGF zf#eDLC^8KJ>E(f(Pty(l-MM;F7~nZEQ>tj*YS^qwEKLEucyQ+I*})tgYyfyv<0HHy znQgnK9(qshJ9{WRepmT>OL$-OQUIv-^^j%&ZeluK88cP*2mV>9$n+*aqBeXOdcO62p`?3l}8NS74%r5lRF(Evk=CSa{U8gWb7=63zU4-3Gs#X!k>x z00IR7eiE{>S0ESC17{FPtA!A?3#hPqJol(AaDl5fV#8Tvgh(kMF1)8uT_uE!%t6or zoC!_*zVi&+Dy-_kL2`f|kZ22zVCMe?Q3VVSY$=3$TBZo~Z--C?hVKC%$xEtqig0-95gmRvHXc*{70N`BzU5u?w&@>6>$_tA4(| zzPlUWUL@rUzJe41pseUoDG8iAz+wyy4GoCnp1Z}6k_m#0P%xuB*#q6DUg}w3h)k`n z;(sIh3fq|KmGsz?5V(vSTq{{Agvv8E8?1~C)+n(!?;;t3FoAwDO+2>%@j_UjhNh;4 zj5>S}{vbYb?O%i%82(o>+kTMNR~$pomTL>~2x~upwXIRxaU+Kjcn*kl*7o(0xWvcD zTj|)1f}D=|26JQS3aB+8A~^$YATa^z>Fo4p*W2!qz_6B_LIuuZH2)x)9Rr-Me;>JHtX_K2XpMpis9BH^aRV zzAT56F@Li4SV{byN1+%jh`2t7-B-Xj>3Tea;FEnn8 ziJAVRF4F;&5Nk)N21HdC4rWDYs?We~^+ULI;gyg;4Qtag_!U?&AWI}lVj$b{27#VyRXH01 z{?GGkd&7o=Ek!SxxQ)6V&#erDUjkP*c%e^43i^XZAC@kw=i4ufXY0#PH+T;4^ydzID;0}xzWlev@SYF~g z;OGxy3{yf_TC05}hU_i0wG`0# z^5K4Io!!rT!>MoZEh1O0T{;gM?*uUJq4u^Vbs5CNx-RC`)vhQY_)X1W_dgU>xi2_n z0@8y$^Za|Vl4!Sl#49b|DCZ6&BvC($$A;kY3dIoIJxH)a#H z+uAN`V<#Bks3LG*z5Jg{@&t^GXr@yzOoD1}Hpn$@yHh__k0T4C#7(hrBvnuhXjxLP`g8IXu03Fc zj@L!uVdkDQKO&~#4mv06;g}2wO^C=tsM!-J30x+~Hyc06DEbL>C;6E`UzSEz9?}YR zRty=}b&W@&pDtWxc#(JxdOcsN@%w|#J8+flSwRI5p75xst>c3&4hPKM@?Z^M5sbG| zei@BG4=z3<)=gE|Bq<-vShC?bv2n*x23%mqw_Qm(v^g zJrq#&aKrfEFfy#8r3GPA8kns4hEYoxRvvDr=F3m$VnZKYG2Sn1Zv!4?O*aJn7g)ab z+qcqSmRV7zr{VX}R*B5qK`X1>@cRc~D|qEjn+BJmF<}Z#N4-*04ZPHgz~N?fKDNWW zE_|C7vswqp#b<_(u!;&#IkXc6on@vp4(^hbn;8>T))bzWF+nVg|opo#m7=~)=- zEPb0S-Xj>pz=N;(LM;k=bsq3r2ext0zIEGBy&Jvs1~@>x1{#074FZ|beB(z|=_mxt zGVs_elL4dceD&~?J!LI8b!QfTPj8nCW%#Bdsr2yVjAPxvtlrFRFAW!E?GpGcCzd#! z&gMTgvxg6YTWJy-yn>dqcrZ6&;0{GHZ{`d3CtD_#moEj}XhLBz0)<7u9*`3JAa@FS zMC1Cq)03n7Xs`sN3)ItxjoFy*ybWgqW7zZUD)fJ8wwLY*T+#+BTMqO1YS&%_(p>}N9BNy`q?qD(u=!X_ z%xGL5u8_kf?2NsudIr~D_TdYU@!7`v`d+4{rbaI?0(FI{0@tKB3+!zeB-7fEX=sZ1 zf>UNvey)waAYRbKK{vohKO6jAtK|VU`D+QNX9IYy2h-rW3l|8lUzdbC97aR=jDq&t z9c`_9U%_1J&Nq_hG97tpXvhEu9i5rr+rl_`HU=kNBw+%`WZlo7AArDuKYkuGa_HPw zz>h#`Ac|mMFjw@-P5%j?h!>+E@Ch+5;-7G@1Smu&@D7M)#ibpb<~9SOn@f_C}<9*u=Ld4D;teSf`%0Ntnkxi|#9h6oo{W|RDd5+Op1M=8KFDrt5Z z{6f$Wp24g)Q2ss-*%iDC_gv925%}`LbMfGi*F~njaB&l8&Lpg{(b!jz5V&C1fI|@y z6Mu%54wG^sq$w6iWX{7(MoKCUn}=!zHiDO_bA4%eldN|@40=qN3(XH`@^8?_B7~gm zy__JJp$Tjhy5(g80)Oea;a;RS1eyUWun7C@`2ex2xB-^vbSUECXapI1fSo*0epoVS zk?r}bkmUeFG63~}2j9CkO8nDw8^h3P06yV8{00~fExB8nQ0<>TpMwx}n0Aal^(G;Z zS_s&wfa;M56Ao%-@h@0D5fc;3F-1oCh6+uhW?#38%$)y^H!OmgMJZbo3kIUe)HrAR zwXM6#xu0poymXDMORpT>61#;eT_7%R{}-C_e|Ai*~fBE`TYze}pt^7E4 Rd<^`N5PSYOQ$+LQ{|7WlDir_# From da3d5d51504178473fd790b0541959e5023f40c7 Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Thu, 25 Jan 2024 16:24:52 -0500 Subject: [PATCH 11/26] Implement TMLE estimator using Influence Functions (#484) * added robust folder * uncommited scratch work for log prob * untested variational log prob * uncomitted changes * uncomitted changes * pair coding w/ eli * added tests w/ Eli * eif * linting * moving test autograd to internals and deleted old utils file * sketch influence implementation * fix more args * ops file * file * format * lint * clean up influence and tests * make tests more generic * guess max plate nesting * linearize * rename file * tensor flatten * predictive eif * jvp type * reorganize files * shrink test case * move guess_max_plate_nesting * move cg solver to linearze * type alias * test_ops * basic cg tests * remove failing test case * format * move paramdict up * remove obsolete test files * add empty handlers * add chirho.robust to docs * fix memory leak in tests * make typing compatible with python 3.8 * typing_extensions * add branch to ci * predictive * remove imprecise annotation * Added more tests for `linearize` and `make_empirical_fisher_vp` (#405) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * removed missing import * fixed failing test with seeding * addressing Eli's comments * Add upper bound on number of CG steps (#404) * upper bound on cg_iters * address comment * fixed test for non-symmetric matrix (#437) * Make `NMCLogPredictiveLikelihood` seeded (#408) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * seeded NMC implementation * linting * removed missing import * changed to eli's seedmessenger suggestion * added failing edge case * explicitly add max plate argument * added warning message * fixed linting error and test failure case from too many cg iters * eli's contextlib seeding strategy * removed seedmessenger from test * randomness should be shared across calls * switched back to different * Use Hessian formulation of Fisher information in `make_empirical_fisher_vp` (#430) * hessian vector product formulation for fisher * ignoring small type error * fixed linting error * Add new `SimpleModel` and `SimpleGuide` (#440) * initial test against analytic fisher vp (pair coded w/ sam) * linting * added check against analytic ate * added vmap and grad smoke tests * added missing init * linting and consolidated fisher tests to one file * fixed types * fixing linting errors * trying to fix type error for python 3.8 * fixing test errors * added patch to test to prevent from failing when denom is small * composition issue * seeded NMC implementation * linting * removed missing import * changed to eli's seedmessenger suggestion * added failing edge case * explicitly add max plate argument * added warning message * fixed linting error and test failure case from too many cg iters * eli's contextlib seeding strategy * removed seedmessenger from test * randomness should be shared across calls * uncomitted change before branch switch * switched back to different * added revised simple model and guide * added multiple link functions in test * linting * Batching in `linearize` and `influence` (#465) * batching in linearize and influence * addressing eli's review * added optimization for pointwise false case * fixing lint error * batched cg (#466) * One step correction implemented (#467) * one step correction * increased tolerance * fixing lint issue * Replace some `torch.vmap` usage with a hand-vectorized `BatchedNMCLogPredictiveLikelihood` (#473) * sketch batched nmc lpd * nits * fix type * format * comment * comment * comment * typo * typo * add condition to help guarantee idempotence * simplify edge case * simplify plate_name * simplify batchedobservation logic * factorize * simplify batched * reorder * comment * remove plate_names * types * formatting and type * move unbind to utils * remove max_plate_nesting arg from get_traces * comment * nit * move get_importance_traces to utils * fix types * generic obs type * lint * format * handle observe in batchedobservations * event dim * move batching handlers to utils * replace 2/3 vmaps, tests pass * remove dead code * format * name args * lint * shuffle code * try an extra optimization in batchedlatents * add another optimization * undo changes to test * remove inplace adds * add performance test showing speedup * document internal helpers * batch latents test * move batch handlers to predictive * add bind_leftmost_dim, document PredictiveFunctional and PredictiveModel * use bind_leftmost_dim in log prob * Added documentation for `chirho.robust` (#470) * documentation * documentation clean up w/ eli * fix lint issue * progress on tmle * placeholder test * more progress on TMLE * more progress, still need to refactor * progress on variational tmle * Make functional argument to influence_fn required (#487) * Make functional argument required * estimator * docstring * Remove guide argument from `influence_fn` and `linearize` (#489) * Make functional argument required * estimator * docstring * Remove guide, make tests pass * rename internals.predictive to internals.nmc * expose handlers.predictive * expose handlers.predictive * docstrings * fix doc build * fix equation * docstring import --------- Co-authored-by: Sam Witty * more progress on tmle * really resolved merge conflicts * more progress, still a bit stuck on functional tensors * Make influence_fn a higher-order Functional (#492) * make influence a functional * fix test * multiple arguments * doc * docstring * docstring * update tmle signature and remove unused imports * make tmle signature consistent with one-step * lint * progress * pair program still issues * debugging still * Add full corrected one step estimator (#476) * added scaffolding to one step estimator * kept signature the same as one_step_correction * lint * refactored test to include multiple estimators * typo * revise error * added dict handling * remove assert * more informative error message * replace dispatch with pytree flatten and unflatten * revert arg for influence_function_estimator * docs and lint * lingering influence_fn * fixed missing return * rename * lint * add *model to appease the linter * more attempts * added scipy optimize :( * more progress * more progress * working end-to-end tmle * remove comment * revert changes * update tests and defaults * lint * playing with tmle performance * more tweaks * pulled out influence computation and changed loss * finally got tmle working * revert test * added placeholder for passing in influence_fn_estimator * analytic influence for example * lint * fix estimator * fix tests * lint * notebook * bump notebook * use torchopt * add torchopt * rerun tmle notebook with effect = 1 * lint --------- Co-authored-by: Raj Agrawal Co-authored-by: Eli Co-authored-by: Raj Agrawal Co-authored-by: eb8680 --- chirho/robust/handlers/estimators.py | 214 +++++- docs/source/tmle.ipynb | 930 +++++++++++++++++++++++++++ setup.py | 4 +- tests/robust/test_handlers.py | 27 +- 4 files changed, 1170 insertions(+), 5 deletions(-) create mode 100644 docs/source/tmle.ipynb diff --git a/chirho/robust/handlers/estimators.py b/chirho/robust/handlers/estimators.py index eb6e8d6e..779b108e 100644 --- a/chirho/robust/handlers/estimators.py +++ b/chirho/robust/handlers/estimators.py @@ -1,8 +1,13 @@ +import copy +import warnings from typing import Any, Callable, TypeVar import torch +import torchopt from typing_extensions import ParamSpec +from chirho.robust.handlers.predictive import PredictiveFunctional +from chirho.robust.internals.utils import make_functional_call from chirho.robust.ops import Functional, Point, influence_fn P = ParamSpec("P") @@ -10,9 +15,216 @@ T = TypeVar("T") +def tmle_scipy_optimize_wrapper( + packed_influence, log_jitter: float = 1e-6 +) -> torch.Tensor: + import numpy as np + import scipy + from scipy.optimize import LinearConstraint + + # Turn things into numpy. This makes us sad... :( + D = packed_influence.detach().numpy() + + N, L = D.shape[0], D.shape[1] + + def loss(epsilon): + correction = 1 + D.dot(epsilon) + + return -np.sum(np.log(np.maximum(correction, log_jitter))) + + positive_density_constraint = LinearConstraint( + D, -1 * np.ones(N), np.inf * np.ones(N) + ) + + epsilon_solve = scipy.optimize.minimize( + loss, np.zeros(L, dtype=D.dtype), constraints=positive_density_constraint + ) + + if not epsilon_solve.success: + warnings.warn("TMLE optimization did not converge.", RuntimeWarning) + + # Convert epsilon back to torch. This makes us happy... :) + packed_epsilon = torch.tensor(epsilon_solve.x, dtype=packed_influence.dtype) + + return packed_epsilon + + +# TODO: revert influence_estimator to influence_fn and use handlers for influence_fn +def tmle( + functional: Functional[P, S], + test_point: Point, + learning_rate: float = 1e-5, + n_grad_steps: int = 100, + n_tmle_steps: int = 1, + num_nmc_samples: int = 1000, + num_grad_samples: int = 1000, + log_jitter: float = 1e-6, + verbose: bool = False, + influence_estimator: Callable[ + [Functional[P, S], Point[T]], Functional[P, S] + ] = influence_fn, + **influence_kwargs, +) -> Functional[P, S]: + from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood + + def _solve_epsilon(prev_model: torch.nn.Module, *args, **kwargs) -> torch.Tensor: + # find epsilon that minimizes the corrected density on test data + + influence_at_test = influence_estimator( + functional, test_point, **influence_kwargs + )(prev_model)(*args, **kwargs) + + flat_influence_at_test, _ = torch.utils._pytree.tree_flatten(influence_at_test) + + N = flat_influence_at_test[0].shape[0] + + packed_influence_at_test = torch.concatenate( + [i.reshape(N, -1) for i in flat_influence_at_test] + ) + + packed_epsilon = tmle_scipy_optimize_wrapper(packed_influence_at_test) + + return packed_epsilon + + def _solve_model_projection( + packed_epsilon: torch.Tensor, + prev_model: torch.nn.Module, + *args, + **kwargs, + ) -> torch.nn.Module: + prev_params, functional_model = make_functional_call( + PredictiveFunctional(prev_model, num_samples=num_grad_samples) + ) + prev_params = {k: v.detach() for k, v in prev_params.items()} + + # Sample data from the model. Note that we only sample once during projection. + data = { + k: v + for k, v in functional_model(prev_params, *args, **kwargs).items() + if k in test_point + } + + batched_log_prob: torch.nn.Module = BatchedNMCLogMarginalLikelihood( + prev_model, num_samples=num_nmc_samples + ) + + _, log_p_phi = make_functional_call(batched_log_prob) + + influence_at_data = influence_estimator(functional, data, **influence_kwargs)( + prev_model + )(*args, **kwargs) + flat_influence_at_data, _ = torch.utils._pytree.tree_flatten(influence_at_data) + N_x = flat_influence_at_data[0].shape[0] + + packed_influence_at_data = torch.concatenate( + [i.reshape(N_x, -1) for i in flat_influence_at_data] + ).detach() + + log_likelihood_correction = torch.log( + torch.maximum( + 1 + packed_influence_at_data.mv(packed_epsilon), + torch.tensor(log_jitter), + ) + ).detach() + if verbose: + influence_at_test = influence_estimator( + functional, test_point, **influence_kwargs + )(prev_model)(*args, **kwargs) + flat_influence_at_test, _ = torch.utils._pytree.tree_flatten( + influence_at_test + ) + N = flat_influence_at_test[0].shape[0] + + packed_influence_at_test = torch.concatenate( + [i.reshape(N, -1) for i in flat_influence_at_test] + ).detach() + + log_likelihood_correction_at_test = torch.log( + torch.maximum( + 1 + packed_influence_at_test.mv(packed_epsilon), + torch.tensor(log_jitter), + ) + ) + + print("previous log prob at test", log_p_phi(prev_params, test_point).sum()) + print( + "new log prob at test", + ( + log_p_phi(prev_params, test_point) + + log_likelihood_correction_at_test + ).sum(), + ) + + log_p_epsilon_at_data = ( + log_likelihood_correction + log_p_phi(prev_params, data) + ).detach() + + def loss(new_params): + log_p_phi_at_data = log_p_phi(new_params, data) + return torch.sum((log_p_phi_at_data - log_p_epsilon_at_data) ** 2) + + grad_fn = torch.func.grad(loss) + + new_params = { + k: v.clone().detach().requires_grad_(True) for k, v in prev_params.items() + } + + optimizer = torchopt.adam(lr=learning_rate) + + optimizer_state = optimizer.init(new_params) + + for i in range(n_grad_steps): + grad = grad_fn(new_params) + if verbose and i % 100 == 0: + print(f"inner_iteration_{i}_loss", loss(new_params)) + for parameter_name, parameter in prev_model.named_parameters(): + parameter.data = new_params[f"model.{parameter_name}"] + + estimate = functional(prev_model)(*args, **kwargs) + assert isinstance(estimate, torch.Tensor) + print( + f"inner_iteration_{i}_estimate", + estimate.detach().item(), + ) + updates, optimizer_state = optimizer.update( + grad, optimizer_state, inplace=False + ) + new_params = torchopt.apply_updates(new_params, updates) + + for parameter_name, parameter in prev_model.named_parameters(): + parameter.data = new_params[f"model.{parameter_name}"] + + return prev_model + + def _corrected_functional(*models: Callable[P, Any]) -> Callable[P, S]: + assert len(models) == 1 + model = models[0] + + assert isinstance(model, torch.nn.Module) + + def _estimator(*args, **kwargs) -> S: + tmle_model = copy.deepcopy(model) + + for _ in range(n_tmle_steps): + packed_epsilon = _solve_epsilon(tmle_model, *args, **kwargs) + + tmle_model = _solve_model_projection( + packed_epsilon, tmle_model, *args, **kwargs + ) + return functional(tmle_model)(*args, **kwargs) + + return _estimator + + return _corrected_functional + + +# TODO: revert influence_estimator to influence_fn and use handlers for influence_fn def one_step_corrected_estimator( functional: Functional[P, S], *test_points: Point[T], + influence_estimator: Callable[ + [Functional[P, S], Point[T]], Functional[P, S] + ] = influence_fn, **influence_kwargs, ) -> Functional[P, S]: """ @@ -30,7 +242,7 @@ def one_step_corrected_estimator( """ influence_kwargs_one_step = influence_kwargs.copy() influence_kwargs_one_step["pointwise_influence"] = False - eif_fn = influence_fn(functional, *test_points, **influence_kwargs_one_step) + eif_fn = influence_estimator(functional, *test_points, **influence_kwargs_one_step) def _corrected_functional(*model: Callable[P, Any]) -> Callable[P, S]: plug_in_estimator = functional(*model) diff --git a/docs/source/tmle.ipynb b/docs/source/tmle.ipynb new file mode 100644 index 00000000..b9fe30e4 --- /dev/null +++ b/docs/source/tmle.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Automated doubly robust estimation with ChiRho - TMLE Version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline\n", + "\n", + "- [Setup](#setup)\n", + "\n", + "- [Overview: Systematically adjusting for observed confounding](#overview:-systematically-adjusting-for-observed-confounding)\n", + " - [Task: Treatment effect estimation with observational data](#task:-treatment-effect-estimation-with-observational-data)\n", + " - [Challenge: Confounding](#challenge:-confounding)\n", + " - [Assumptions: All confounders observed](#assumptions:-all-confounders-observed)\n", + " - [Intuition: Statistically adjusting for confounding](#intuition:-statistically-adjusting-for-confounding)\n", + "\n", + "- [Causal Probabilistic Program](#causal-probabilistic-program)\n", + " - [Model description](#model-description)\n", + " - [Generating data](#generating-data)\n", + " - [Fit parameters via maximum likelihood](#fit-parameters-via-maximum-likelihood)\n", + "\n", + "- [Causal Query: average treatment effect (ATE)](#causal-query:-average-treatment-effect-\\(ATE\\))\n", + " - [Defining the target functional](#defining-the-target-functional)\n", + " - [Closed form doubly robust correction](#closed-form-doubly-robust-correction)\n", + " - [Computing automated doubly robust correction via Monte Carlo](#computing-automated-doubly-robust-correction-via-monte-carlo)\n", + " - [Results](#results)\n", + "\n", + "- [References](#references)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we install the necessary Pytorch, Pyro, and ChiRho dependencies for this example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NOTE: Redirects are currently not supported in Windows or MacOs.\n" + ] + } + ], + "source": [ + "from typing import Callable, Optional, Tuple\n", + "\n", + "import functools\n", + "import torch\n", + "import math\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "from pyro.infer import Predictive\n", + "import pyro.contrib.gp as gp\n", + "\n", + "from chirho.counterfactual.handlers import MultiWorldCounterfactual\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.interventional.handlers import do\n", + "from chirho.robust.internals.utils import ParamDict\n", + "from chirho.robust.handlers.estimators import one_step_corrected_estimator, tmle\n", + "from chirho.robust.handlers.predictive import PredictiveModel \n", + "from chirho.robust.ops import influence_fn\n", + "\n", + "pyro.settings.set(module_local_params=True)\n", + "\n", + "sns.set_style(\"white\")\n", + "\n", + "pyro.set_rng_seed(321) # for reproducibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this tutorial, we will use ChiRho to estimate the average treatment effect (ATE) from observational data. We will use a simple example to illustrate the basic concepts of doubly robust estimation and how ChiRho can be used to automate the process for more general summaries of interest. \n", + "\n", + "There are five main steps to our doubly robust estimation procedure but only the last step is different from a standard probabilistic programming workflow:\n", + "1. Write model of interest\n", + " - Define probabilistic model of interest using Pyro\n", + "2. Feed in data\n", + " - Observed data used to train the model\n", + "3. Run inference\n", + " - Use Pyro's rich inference library to fit the model to the data\n", + "4. Define target functional\n", + " - This is the model summary of interest (e.g. average treatment effect)\n", + "5. Compute robust estimate\n", + " - Use ChiRho to compute the doubly robust estimate of the target functional\n", + " - Importantly, this step is automated and does not require refitting the model for each new functional\n", + "\n", + "\n", + "Our proposed automated robust inference pipeline is summarized in the figure below.\n", + "\n", + "![fig1](figures/robust_pipeline.png)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Probabilistic Program\n", + "\n", + "### Model Description\n", + "In this example, we will focus on a cannonical model `CausalGLM` consisting of three types of variables: binary treatment (`A`), confounders (`X`), and response (`Y`). For simplicitly, we assume that the response is generated from a generalized linear model with link function $g$. The model is described by the following generative process:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "X &\\sim \\text{Normal}(0, I_p) \\\\\n", + "A &\\sim \\text{Bernoulli}(\\pi(X)) \\\\\n", + "\\mu &= \\beta_0 + \\beta_1^T X + \\tau A \\\\\n", + "Y &\\sim \\text{ExponentialFamily}(\\text{mean} = g^{-1}(\\mu))\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where $p$ denotes the number of confounders, $\\pi(X)$ is the probability of treatment conditional on confounders $X$, $\\beta_0$ is the intercept, $\\beta_1$ is the confounder effect, and $\\tau$ is the treatment effect." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class CausalGLM(pyro.nn.PyroModule):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " N: int,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " include_prior: bool = True,\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " super().__init__()\n", + " self.p = p\n", + " self.N = N\n", + " self.link_fn = link_fn\n", + " self.include_prior = include_prior\n", + " if prior_scale is None:\n", + " self.prior_scale = 1 / math.sqrt(self.p)\n", + " else:\n", + " self.prior_scale = prior_scale\n", + "\n", + " def sample_outcome_weights(self):\n", + " return pyro.sample(\n", + " \"outcome_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_intercept(self):\n", + " return pyro.sample(\"intercept\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_propensity_weights(self):\n", + " return pyro.sample(\n", + " \"propensity_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_treatment_weight(self):\n", + " return pyro.sample(\"treatment_weight\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_covariate_loc_scale(self):\n", + " return torch.zeros(self.p), torch.ones(self.p)\n", + " \n", + " def generate_datum(self, x_loc, x_scale, propensity_weights, outcome_weights, tau, intercept):\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1))\n", + " A = pyro.sample(\n", + " \"A\",\n", + " dist.Bernoulli(\n", + " logits=torch.einsum(\"...i,...i->...\", X, propensity_weights)\n", + " ),\n", + " )\n", + " return pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"...i,...i->...\", X, outcome_weights) + A * tau + intercept\n", + " ),\n", + " )\n", + "\n", + " def forward(self):\n", + " with pyro.poutine.mask(mask=self.include_prior):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " with pyro.plate(\"plate\", self.N, dim=-1):\n", + " return self.generate_datum(x_loc, x_scale, propensity_weights, outcome_weights, tau, intercept)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will condition on both treatment and confounders to estimate the causal effect of treatment on the outcome. We will use the following causal probabilistic program to do so:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class ConditionedCausalGLM(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " X: torch.Tensor,\n", + " A: torch.Tensor,\n", + " Y: torch.Tensor,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " include_prior: bool = True,\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " p = X.shape[1]\n", + " N = X.shape[0]\n", + " super().__init__(p, N, link_fn, include_prior, prior_scale)\n", + " self.X = X\n", + " self.A = A\n", + " self.Y = Y\n", + "\n", + " def forward(self):\n", + " with pyro.condition(data={\"X\": self.X, \"A\": self.A, \"Y\": self.Y}):\n", + " return super().forward()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cluster_plate\n", + "\n", + "plate\n", + "\n", + "\n", + "\n", + "intercept\n", + "\n", + "intercept\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "intercept->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "outcome_weights\n", + "\n", + "outcome_weights\n", + "\n", + "\n", + "\n", + "outcome_weights->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "propensity_weights\n", + "\n", + "propensity_weights\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "propensity_weights->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "treatment_weight\n", + "\n", + "treatment_weight\n", + "\n", + "\n", + "\n", + "treatment_weight->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "X->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "distribution_description_node\n", + "intercept ~ Normal\n", + "outcome_weights ~ Normal\n", + "propensity_weights ~ Normal\n", + "treatment_weight ~ Normal\n", + "X ~ Normal\n", + "A ~ Bernoulli\n", + "Y ~ Normal\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Visualize the model\n", + "pyro.render_model(\n", + " ConditionedCausalGLM(torch.zeros(1, 1), torch.zeros(1), torch.zeros(1)),\n", + " render_params=True, \n", + " render_distributions=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating data\n", + "\n", + "For evaluation, we generate `N_datasets` datasets, each with `N` samples. We compare vanilla estimates of the target functional with the double robust estimates of the target functional across the `N_sims` datasets. We use a similar data generating process as in Kennedy (2022)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class GroundTruthModel(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " N: int,\n", + " alpha: int,\n", + " beta: int,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " treatment_weight: float = 0.0,\n", + " ):\n", + " super().__init__(p, N, link_fn)\n", + " self.alpha = alpha # sparsity of propensity weights\n", + " self.beta = beta # sparsity of outcome weights\n", + " self.treatment_weight = treatment_weight\n", + "\n", + " def sample_outcome_weights(self):\n", + " outcome_weights = 1 / math.sqrt(self.beta) * torch.ones(self.p)\n", + " outcome_weights[self.beta :] = 0.0\n", + " return outcome_weights\n", + "\n", + " def sample_propensity_weights(self):\n", + " propensity_weights = 1 / math.sqrt(self.alpha) * torch.ones(self.p)\n", + " propensity_weights[self.alpha :] = 0.0\n", + " return propensity_weights\n", + "\n", + " def sample_treatment_weight(self):\n", + " return torch.tensor(self.treatment_weight)\n", + "\n", + " def sample_intercept(self):\n", + " return torch.tensor(0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "N_datasets = 50\n", + "simulated_datasets = []\n", + "\n", + "# Data configuration\n", + "p = 200\n", + "alpha = 50\n", + "beta = 50\n", + "N_train = 500\n", + "N_test = 500\n", + "treatment_weight = 1.0\n", + "\n", + "true_model = GroundTruthModel(p, N_train+N_test, alpha, beta, treatment_weight=treatment_weight)\n", + "prior_model = CausalGLM(p, N_train+N_test)\n", + "\n", + "# Generate data\n", + "D = Predictive(true_model, num_samples=N_datasets, return_sites=[\"X\", \"A\", \"Y\"], parallel=True)()\n", + "D_train = {k: v[:, :N_train] for k, v in D.items()}\n", + "D_test = {k: v[:, N_train:] for k, v in D.items()}\n", + "\n", + "# D_train : (N_datasets, N_train, p)\n", + "# D_test : (N_datasets, N_test, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fit parameters via maximum likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "tensor(143220.2969, grad_fn=)\n", + "tensor(142401.8281, grad_fn=)\n", + "tensor(142398.5000, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.5000, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.5312, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.5000, grad_fn=)\n", + "tensor(142398.6094, grad_fn=)\n", + "tensor(142398.4844, grad_fn=)\n", + "tensor(142398.6094, grad_fn=)\n", + "tensor(142398.6562, grad_fn=)\n", + "1\n", + "tensor(142864.0625, grad_fn=)\n", + "tensor(142156.9531, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.7969, grad_fn=)\n", + "tensor(142155.7812, grad_fn=)\n", + "tensor(142155.8281, grad_fn=)\n", + "tensor(142155.8125, grad_fn=)\n", + "tensor(142155.8906, grad_fn=)\n", + "tensor(142155.9688, grad_fn=)\n", + "tensor(142155.7969, grad_fn=)\n", + "2\n", + "tensor(143104.5469, grad_fn=)\n", + "tensor(142385.0469, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.3750, grad_fn=)\n", + "tensor(142384.3906, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.5156, grad_fn=)\n", + "tensor(142384.3750, grad_fn=)\n", + "tensor(142384.3906, grad_fn=)\n", + "tensor(142384.3594, grad_fn=)\n", + "tensor(142384.4062, grad_fn=)\n", + "tensor(142384.4375, grad_fn=)\n", + "tensor(142384.4062, grad_fn=)\n", + "tensor(142384.3906, grad_fn=)\n", + "3\n", + "tensor(142825.3125, grad_fn=)\n", + "tensor(142005.4062, grad_fn=)\n", + "tensor(142000.4844, grad_fn=)\n", + "tensor(142000.4688, grad_fn=)\n", + "tensor(142000.4688, grad_fn=)\n", + "tensor(142000.4688, grad_fn=)\n", + "tensor(142000.4688, grad_fn=)\n", + "tensor(142000.4688, grad_fn=)\n", + "tensor(142000.5000, grad_fn=)\n", + "tensor(142000.4844, grad_fn=)\n", + "tensor(142000.4844, grad_fn=)\n", + "tensor(142000.5000, grad_fn=)\n", + "tensor(142000.5156, grad_fn=)\n", + "tensor(142000.4844, grad_fn=)\n", + "tensor(142000.5156, grad_fn=)\n", + "tensor(142000.4844, grad_fn=)\n", + "tensor(142000.7188, grad_fn=)\n", + "tensor(142000.5938, grad_fn=)\n", + "tensor(142000.6094, grad_fn=)\n", + "tensor(142000.5000, grad_fn=)\n", + "4\n", + "tensor(143287.3281, grad_fn=)\n", + "tensor(142508.1094, grad_fn=)\n", + "tensor(142506.4531, grad_fn=)\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[13], line 20\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m2000\u001b[39m):\n\u001b[1;32m 19\u001b[0m adam\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 20\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43melbo\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m j \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m100\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28mprint\u001b[39m(loss)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/pyro/infer/elbo.py:25\u001b[0m, in \u001b[0;36mELBOModule.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43melbo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdifferentiable_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mguide\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/pyro/infer/trace_elbo.py:121\u001b[0m, in \u001b[0;36mTrace_ELBO.differentiable_loss\u001b[0;34m(self, model, guide, *args, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[1;32m 120\u001b[0m surrogate_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model_trace, guide_trace \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_traces(model, guide, args, kwargs):\n\u001b[1;32m 122\u001b[0m loss_particle, surrogate_loss_particle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_differentiable_loss_particle(\n\u001b[1;32m 123\u001b[0m model_trace, guide_trace\n\u001b[1;32m 124\u001b[0m )\n\u001b[1;32m 125\u001b[0m surrogate_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m surrogate_loss_particle \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_particles\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/pyro/infer/elbo.py:237\u001b[0m, in \u001b[0;36mELBO._get_traces\u001b[0;34m(self, model, guide, args, kwargs)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_particles):\n\u001b[0;32m--> 237\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mguide\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/pyro/infer/trace_elbo.py:57\u001b[0m, in \u001b[0;36mTrace_ELBO._get_trace\u001b[0;34m(self, model, guide, args, kwargs)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, model, guide, args, kwargs):\n\u001b[1;32m 53\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124;03m Returns a single trace from the guide, and the model that is run\u001b[39;00m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;124;03m against it.\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m model_trace, guide_trace \u001b[38;5;241m=\u001b[39m \u001b[43mget_importance_trace\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mflat\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_plate_nesting\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mguide\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_validation_enabled():\n\u001b[1;32m 61\u001b[0m check_if_enumerated(guide_trace)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/pyro/infer/enum.py:75\u001b[0m, in \u001b[0;36mget_importance_trace\u001b[0;34m(graph_type, max_plate_nesting, model, guide, args, kwargs, detach)\u001b[0m\n\u001b[1;32m 72\u001b[0m guide_trace \u001b[38;5;241m=\u001b[39m prune_subsample_sites(guide_trace)\n\u001b[1;32m 73\u001b[0m model_trace \u001b[38;5;241m=\u001b[39m prune_subsample_sites(model_trace)\n\u001b[0;32m---> 75\u001b[0m \u001b[43mmodel_trace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_log_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 76\u001b[0m guide_trace\u001b[38;5;241m.\u001b[39mcompute_score_parts()\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_validation_enabled():\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/pyro/poutine/trace_struct.py:230\u001b[0m, in \u001b[0;36mTrace.compute_log_prob\u001b[0;34m(self, site_filter)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlog_prob\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m site:\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 230\u001b[0m log_p \u001b[38;5;241m=\u001b[39m \u001b[43msite\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[43m \u001b[49m\u001b[43msite\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msite\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43margs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msite\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mkwargs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 234\u001b[0m _, exc_value, traceback \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/distributions/independent.py:99\u001b[0m, in \u001b[0;36mIndependent.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_prob\u001b[39m(\u001b[38;5;28mself\u001b[39m, value):\n\u001b[0;32m---> 99\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_dist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _sum_rightmost(log_prob, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreinterpreted_batch_ndims)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/distributions/normal.py:83\u001b[0m, in \u001b[0;36mNormal.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m 81\u001b[0m var \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 82\u001b[0m log_scale \u001b[38;5;241m=\u001b[39m math\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale, Real) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale\u001b[38;5;241m.\u001b[39mlog()\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m-\u001b[39m(\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m) \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m var) \u001b[38;5;241m-\u001b[39m log_scale \u001b[38;5;241m-\u001b[39m math\u001b[38;5;241m.\u001b[39mlog(math\u001b[38;5;241m.\u001b[39msqrt(\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m math\u001b[38;5;241m.\u001b[39mpi))\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_tensor.py:34\u001b[0m, in \u001b[0;36m_handle_torch_function_and_wrap_type_error_to_not_implemented..wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_handle_torch_function_and_wrap_type_error_to_not_implemented\u001b[39m(f):\n\u001b[1;32m 32\u001b[0m assigned \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mWRAPPER_ASSIGNMENTS\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f, assigned\u001b[38;5;241m=\u001b[39massigned)\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 37\u001b[0m \u001b[38;5;66;03m# See https://github.com/pytorch/pytorch/issues/75462\u001b[39;00m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function(args):\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "fitted_params = []\n", + "for i in range(N_datasets):\n", + " print(i)\n", + "\n", + " # Fit model using maximum likelihood\n", + " conditioned_model = ConditionedCausalGLM(\n", + " X=D_train[\"X\"][i], A=D_train[\"A\"][i], Y=D_train[\"Y\"][i]\n", + " )\n", + " \n", + " guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model)\n", + " elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train)\n", + "\n", + " # initialize parameters\n", + " elbo()\n", + " adam = torch.optim.Adam(elbo.parameters(), lr=0.03)\n", + "\n", + " # Do gradient steps\n", + " for j in range(2000):\n", + " adam.zero_grad()\n", + " loss = elbo()\n", + " if j % 100 == 0:\n", + " print(loss)\n", + " loss.backward()\n", + " adam.step()\n", + "\n", + " theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items()\n", + " }\n", + " fitted_params.append(theta_hat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Query: Sample Average treatment effect (ATE)\n", + "\n", + "The average treatment effect summarizes, on average, how much the treatment changes the response, $ATE = \\mathbb{E}[Y|do(A=1)] - \\mathbb{E}[Y|do(A=0)]$. The `do` notation indicates that the expectations are taken according to *intervened* versions of the model, with $A$ set to a particular value. Note from our [tutorial](tutorial_i.ipynb) that this is different from conditioning on $A$ in the original `causal_model`, which assumes $X$ and $T$ are dependent.\n", + "\n", + "\n", + "To implement this query in ChiRho, we define the `SATEFunctional` class which take in a `model` and `guide` and returns the average treatment effect by simulating from the posterior predictive distribution of the model and guide." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining the target functional" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SATEFunctional(torch.nn.Module):\n", + " def __init__(self, model: Callable, *, num_monte_carlo: int = 100):\n", + " super().__init__()\n", + " self.model = model\n", + " self.num_monte_carlo = num_monte_carlo\n", + " \n", + " def forward(self, *args, **kwargs):\n", + " with MultiWorldCounterfactual():\n", + " with do(actions=dict(A=(torch.tensor(0.0), torch.tensor(1.0)))):\n", + " Ys = self.model(*args, **kwargs)\n", + " Y0 = gather(Ys, IndexSet(A={1}), event_dim=0)\n", + " Y1 = gather(Ys, IndexSet(A={2}), event_dim=0)\n", + " sate = (Y1 - Y0).mean(dim=-1, keepdim=True).squeeze()\n", + " return pyro.deterministic(\"SATE\", sate)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SATE = SATEFunctional(true_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Closed form doubly robust correction\n", + "\n", + "For the average treatment effect functional, there exists a closed-form analytical formula for the doubly robust correction. This formula is derived in Kennedy (2022) and is implemented below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Any\n", + "from chirho.robust.ops import Functional, Point, P, S, T\n", + "\n", + "def SATECausalGLM_analytic_influence(functional: Functional[P, S], \n", + " point: Point[T], \n", + " pointwise_influence: bool = True,\n", + " **kwargs) -> Functional[P, S]:\n", + " # assert isinstance(functional, SATEFunctional)\n", + " def new_functional(model: Callable[P, Any]) -> Callable[P, S]:\n", + " assert isinstance(model.model, CausalGLM)\n", + " theta = dict(model.guide.named_parameters())\n", + " def new_model(*args, **kwargs):\n", + " X = point[\"X\"]\n", + " A = point[\"A\"]\n", + " Y = point[\"Y\"]\n", + " \n", + " pi_X = torch.sigmoid(torch.einsum(\"...i,...i->...\", X, theta[\"propensity_weights_param\"]))\n", + " mu_X = (\n", + " torch.einsum(\"...i,...i->...\", X, theta[\"outcome_weights_param\"])\n", + " + A * theta[\"treatment_weight_param\"]\n", + " + theta[\"intercept_param\"]\n", + " )\n", + " analytic_eif_at_pts = (A / pi_X - (1 - A) / (1 - pi_X)) * (Y - mu_X)\n", + " if pointwise_influence:\n", + " return analytic_eif_at_pts\n", + " else:\n", + " return analytic_eif_at_pts.mean()\n", + "\n", + "\n", + " return new_model\n", + " return new_functional\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # Closed form expression\n", + "# def closed_form_doubly_robust_ate_correction(X_test, theta) -> Tuple[torch.Tensor, torch.Tensor]:\n", + "# X = X_test[\"X\"]\n", + "# A = X_test[\"A\"]\n", + "# Y = X_test[\"Y\"]\n", + "# pi_X = torch.sigmoid(X.mv(theta[\"propensity_weights\"]))\n", + "# mu_X = (\n", + "# X.mv(theta[\"outcome_weights\"])\n", + "# + A * theta[\"treatment_weight\"]\n", + "# + theta[\"intercept\"]\n", + "# )\n", + "# analytic_eif_at_test_pts = (A / pi_X - (1 - A) / (1 - pi_X)) * (Y - mu_X)\n", + "# analytic_correction = analytic_eif_at_test_pts.mean()\n", + "# return analytic_correction, analytic_eif_at_test_pts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing automated doubly robust correction via Monte Carlo\n", + "\n", + "While the doubly robust correction term is known in closed-form for the average treatment effect functional, our `one_step_correction` function in `ChiRho` works for a wide class of other functionals. We focus on the average treatment effect functional here so that we have a ground truth to compare `one_step_correction` against." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tracemalloc\n", + "\n", + "tracemalloc.start()\n", + "\n", + "# Helper class to create a trivial guide that returns the maximum likelihood estimate\n", + "class MLEGuide(torch.nn.Module):\n", + " def __init__(self, mle_est: ParamDict):\n", + " super().__init__()\n", + " self.names = list(mle_est.keys())\n", + " for name, value in mle_est.items():\n", + " setattr(self, name + \"_param\", torch.nn.Parameter(value))\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " for name in self.names:\n", + " value = getattr(self, name + \"_param\")\n", + " pyro.sample(\n", + " name, pyro.distributions.Delta(value, event_dim=len(value.shape))\n", + " )\n", + "\n", + "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", + "# estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator}\n", + "estimators = {\"one_step\": one_step_corrected_estimator}\n", + "estimator_kwargs = {\"tmle\": {\"learning_rate\":5e-5,\n", + " \"n_grad_steps\":500,\n", + " \"n_tmle_steps\":1,\n", + " \"num_nmc_samples\":1000,\n", + " \"num_grad_samples\":N_test}, \"one_step\": {}}\n", + "# influences = {\"analytic\": SATECausalGLM_analytic_influence, \"monte_carlo\": influence_fn}\n", + "influences = {\"analytic\": SATECausalGLM_analytic_influence}\n", + "\n", + "estimates = {f\"{influence}-{estimator}\": torch.zeros(N_datasets) for influence in influences.keys() for estimator in estimators.keys()}\n", + "estimates[\"plug-in-mle\"] = torch.zeros(N_datasets)\n", + "estimates[\"plug-in-prior\"] = torch.zeros(N_datasets)\n", + "estimates[\"plug-in-truth\"] = torch.zeros(N_datasets)\n", + "\n", + "functional = functools.partial(SATEFunctional, num_monte_carlo=10000)\n", + "\n", + "for i in range(N_datasets):\n", + " print(\"plug-in-prior\", i)\n", + " estimates[\"plug-in-prior\"][i] = functional(prior_model)().item()\n", + "\n", + " print(\"plug-in-truth\", i)\n", + " estimates[\"plug-in-truth\"][i] = functional(true_model)().item()\n", + "\n", + " # D_test = simulated_datasets[i][1]\n", + " theta_hat = fitted_params[i]\n", + " mle_guide = MLEGuide(theta_hat)\n", + "\n", + " model = PredictiveModel(CausalGLM(p, N_test), mle_guide)\n", + " \n", + " print(\"plug-in-mle\", i)\n", + " estimates[\"plug-in-mle\"][i] = functional(model)().detach().item()\n", + "\n", + " for estimator_str, estimator in estimators.items():\n", + " for influence_str, influence in influences.items():\n", + " if estimator_str == \"tmle\" and influence_str == \"monte_carlo\":\n", + " continue\n", + "\n", + " print(estimator_str, influence_str, i)\n", + " \n", + " estimate = estimator(\n", + " functional, \n", + " {\"X\": D_test[\"X\"][i], \"A\": D_test[\"A\"][i], \"Y\": D_test[\"Y\"][i]},\n", + " num_samples_outer=max(10000, 100 * p), \n", + " num_samples_inner=1,\n", + " influence_estimator=influence,\n", + " **estimator_kwargs[estimator_str]\n", + " )(model)()\n", + "\n", + " estimates[f\"{influence_str}-{estimator_str}\"][i] = estimate.detach().item()\n", + "\n", + " print(tracemalloc.get_traced_memory())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = pd.DataFrame(estimates)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The true treatment effect is 0, so a mean estimate closer to zero is better\n", + "results.describe().round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the results\n", + "fig, ax = plt.subplots()\n", + "\n", + "for col in results.columns:\n", + " sns.kdeplot(results[col], ax=ax, label=col)\n", + "\n", + "ax.axvline(treatment_weight, color=\"black\", label=\"True ATE\", linestyle=\"--\")\n", + "ax.set_yticks([])\n", + "sns.despine()\n", + "ax.legend(loc=\"upper right\")\n", + "ax.set_xlabel(\"ATE Estimate\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plt.scatter(\n", + "# results['automated_monte_carlo_correction'],\n", + "# results['analytic_correction'],\n", + "# )\n", + "# plt.plot(np.linspace(-.2, .5), np.linspace(-.2, .5), color=\"black\", linestyle=\"dashed\")\n", + "# plt.xlabel(\"DR-Monte Carlo\")\n", + "# plt.ylabel(\"DR-Analytic\")\n", + "# sns.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "Kennedy, Edward. \"Towards optimal doubly robust estimation of heterogeneous causal effects\", 2022. https://arxiv.org/abs/2004.14497." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "basis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/setup.py b/setup.py index 47c6dcd3..0b425f72 100644 --- a/setup.py +++ b/setup.py @@ -25,6 +25,7 @@ ] DYNAMICAL_REQUIRE = ["torchdiffeq"] +ROBUST_REQUIRE = ["torchopt"] setup( name="chirho", @@ -45,8 +46,9 @@ ], extras_require={ "dynamical": DYNAMICAL_REQUIRE, + "robust": ROBUST_REQUIRE, "extras": EXTRAS_REQUIRE, - "test": EXTRAS_REQUIRE + DYNAMICAL_REQUIRE + "test": EXTRAS_REQUIRE + DYNAMICAL_REQUIRE + ROBUST_REQUIRE + [ "pytest", "pytest-cov", diff --git a/tests/robust/test_handlers.py b/tests/robust/test_handlers.py index e4301528..9cd55e74 100644 --- a/tests/robust/test_handlers.py +++ b/tests/robust/test_handlers.py @@ -1,3 +1,4 @@ +import copy import functools from typing import Callable, List, Mapping, Optional, Set, Tuple, TypeVar @@ -6,7 +7,7 @@ import torch from typing_extensions import ParamSpec -from chirho.robust.handlers.estimators import one_step_corrected_estimator +from chirho.robust.handlers.estimators import one_step_corrected_estimator, tmle from chirho.robust.handlers.predictive import PredictiveFunctional, PredictiveModel from .robust_fixtures import SimpleGuide, SimpleModel @@ -42,7 +43,7 @@ @pytest.mark.parametrize("num_samples_outer,num_samples_inner", [(10, None), (10, 100)]) @pytest.mark.parametrize("cg_iters", [None, 1, 10]) @pytest.mark.parametrize("num_predictive_samples", [1, 5]) -@pytest.mark.parametrize("estimation_method", [one_step_corrected_estimator]) +@pytest.mark.parametrize("estimation_method", [one_step_corrected_estimator, tmle]) def test_estimator_smoke( model, guide, @@ -66,6 +67,20 @@ def test_estimator_smoke( )().items() } + predictive_model = PredictiveModel(model, guide) + + prev_params = copy.deepcopy(dict(predictive_model.named_parameters())) + + if estimation_method == tmle: + estimator_kwargs = { + "n_tmle_steps": 1, + "n_grad_steps": 2, + "num_nmc_samples": 10, + "num_grad_samples": 10, + } + else: + estimator_kwargs = {} + estimator = estimation_method( functools.partial(PredictiveFunctional, num_samples=num_predictive_samples), test_datum, @@ -73,7 +88,8 @@ def test_estimator_smoke( num_samples_outer=num_samples_outer, num_samples_inner=num_samples_inner, cg_iters=cg_iters, - )(PredictiveModel(model, guide)) + **estimator_kwargs, + )(predictive_model) estimate_on_test: Mapping[str, torch.Tensor] = estimator() assert len(estimate_on_test) > 0 @@ -83,3 +99,8 @@ def test_estimator_smoke( assert not torch.isclose( v, torch.zeros_like(v) ).all(), f"{estimation_method} estimator for {k} was zero" + + # Assert estimator doesn't have side effects on model parameters. + new_params = dict(predictive_model.named_parameters()) + for k, v in prev_params.items(): + assert torch.allclose(v, new_params[k]), f"{k} was updated" From a3310350f26adac852fa083442c6f6cea2fdaf27 Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Fri, 26 Jan 2024 01:50:36 -0500 Subject: [PATCH 12/26] quality experiment --- .../notebooks/quality_vs_estimators.ipynb | 1275 +++++++++++++++++ 1 file changed, 1275 insertions(+) create mode 100644 docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb new file mode 100644 index 00000000..e66a2ef4 --- /dev/null +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -0,0 +1,1275 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Automated doubly robust estimation with ChiRho" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline\n", + "\n", + "- [Setup](#setup)\n", + "\n", + "- [Overview: Systematically adjusting for observed confounding](#overview:-systematically-adjusting-for-observed-confounding)\n", + " - [Task: Treatment effect estimation with observational data](#task:-treatment-effect-estimation-with-observational-data)\n", + " - [Challenge: Confounding](#challenge:-confounding)\n", + " - [Assumptions: All confounders observed](#assumptions:-all-confounders-observed)\n", + " - [Intuition: Statistically adjusting for confounding](#intuition:-statistically-adjusting-for-confounding)\n", + "\n", + "- [Causal Probabilistic Program](#causal-probabilistic-program)\n", + " - [Model description](#model-description)\n", + " - [Generating data](#generating-data)\n", + " - [Fit parameters via maximum likelihood](#fit-parameters-via-maximum-likelihood)\n", + "\n", + "- [Causal Query: average treatment effect (ATE)](#causal-query:-average-treatment-effect-\\(ATE\\))\n", + " - [Defining the target functional](#defining-the-target-functional)\n", + " - [Closed form doubly robust correction](#closed-form-doubly-robust-correction)\n", + " - [Computing automated doubly robust estimators via Monte Carlo](#computing-automated-doubly-robust-estimators-via-monte-carlo)\n", + " - [Results](#results)\n", + "\n", + "- [References](#references)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we install the necessary Pytorch, Pyro, and ChiRho dependencies for this example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-01-26 00:49:14,946] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" + ] + } + ], + "source": [ + "from typing import Callable, Optional, Tuple\n", + "\n", + "import functools\n", + "import torch\n", + "import math\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "from pyro.infer import Predictive\n", + "import pyro.contrib.gp as gp\n", + "\n", + "from chirho.counterfactual.handlers import MultiWorldCounterfactual\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.interventional.handlers import do\n", + "from chirho.robust.internals.utils import ParamDict\n", + "from chirho.robust.handlers.estimators import one_step_corrected_estimator, tmle\n", + "from chirho.robust.ops import influence_fn\n", + "from chirho.robust.handlers.predictive import PredictiveModel, PredictiveFunctional\n", + "from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood\n", + "\n", + "\n", + "pyro.settings.set(module_local_params=True)\n", + "\n", + "sns.set_style(\"white\")\n", + "\n", + "pyro.set_rng_seed(321) # for reproducibility" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "In this tutorial, we will use ChiRho to estimate the average treatment effect (ATE) from observational data. We will use a simple example to illustrate the basic concepts of doubly robust estimation and how ChiRho can be used to automate the process for more general summaries of interest. \n", + "\n", + "There are five main steps to our doubly robust estimation procedure but only the last step is different from a standard probabilistic programming workflow:\n", + "1. Write model of interest\n", + " - Define probabilistic model of interest using Pyro\n", + "2. Feed in data\n", + " - Observed data used to train the model\n", + "3. Run inference\n", + " - Use Pyro's rich inference library to fit the model to the data\n", + "4. Define target functional\n", + " - This is the model summary of interest (e.g. average treatment effect)\n", + "5. Compute robust estimate\n", + " - Use ChiRho to compute the doubly robust estimate of the target functional\n", + " - Importantly, this step is automated and does not require refitting the model for each new functional" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Probabilistic Program\n", + "\n", + "### Model Description\n", + "In this example, we will focus on a cannonical model `CausalGLM` consisting of three types of variables: binary treatment (`A`), confounders (`X`), and response (`Y`). For simplicitly, we assume that the response is generated from a generalized linear model with link function $g$. The model is described by the following generative process:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "X &\\sim \\text{Normal}(0, I_p) \\\\\n", + "A &\\sim \\text{Bernoulli}(\\pi(X)) \\\\\n", + "\\mu &= \\beta_0 + \\beta_1^T X + \\tau A \\\\\n", + "Y &\\sim \\text{ExponentialFamily}(\\text{mean} = g^{-1}(\\mu))\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where $p$ denotes the number of confounders, $\\pi(X)$ is the probability of treatment conditional on confounders $X$, $\\beta_0$ is the intercept, $\\beta_1$ is the confounder effect, and $\\tau$ is the treatment effect." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class CausalGLM(pyro.nn.PyroModule):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " super().__init__()\n", + " self.p = p\n", + " self.link_fn = link_fn\n", + " if prior_scale is None:\n", + " self.prior_scale = 1 / math.sqrt(self.p)\n", + " else:\n", + " self.prior_scale = prior_scale\n", + "\n", + " def sample_outcome_weights(self):\n", + " return pyro.sample(\n", + " \"outcome_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_intercept(self):\n", + " return pyro.sample(\"intercept\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_propensity_weights(self):\n", + " return pyro.sample(\n", + " \"propensity_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_treatment_weight(self):\n", + " return pyro.sample(\"treatment_weight\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_covariate_loc_scale(self):\n", + " return torch.zeros(self.p), torch.ones(self.p)\n", + "\n", + " def forward(self):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1))\n", + " A = pyro.sample(\n", + " \"A\",\n", + " dist.Bernoulli(\n", + " logits=torch.einsum(\"...i,...i->...\", X, propensity_weights)\n", + " ),\n", + " )\n", + "\n", + " return pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"...i,...i->...\", X, outcome_weights) + A * tau + intercept\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will condition on both treatment and confounders to estimate the causal effect of treatment on the outcome. We will use the following causal probabilistic program to do so:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class ConditionedCausalGLM(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " X: torch.Tensor,\n", + " A: torch.Tensor,\n", + " Y: torch.Tensor,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " p = X.shape[1]\n", + " super().__init__(p, link_fn, prior_scale)\n", + " self.X = X\n", + " self.A = A\n", + " self.Y = Y\n", + "\n", + " def forward(self):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " with pyro.plate(\"__train__\", size=self.X.shape[0], dim=-1):\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1), obs=self.X)\n", + " A = pyro.sample(\n", + " \"A\",\n", + " dist.Bernoulli(\n", + " logits=torch.einsum(\"ni,i->n\", self.X, propensity_weights)\n", + " ),\n", + " obs=self.A,\n", + " )\n", + " pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"ni,i->n\", X, outcome_weights)\n", + " + A * tau\n", + " + intercept\n", + " ),\n", + " obs=self.Y,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cluster___train__\n", + "\n", + "__train__\n", + "\n", + "\n", + "\n", + "intercept\n", + "\n", + "intercept\n", + "\n", + "\n", + "\n", + "Y\n", + "\n", + "Y\n", + "\n", + "\n", + "\n", + "intercept->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "outcome_weights\n", + "\n", + "outcome_weights\n", + "\n", + "\n", + "\n", + "outcome_weights->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "propensity_weights\n", + "\n", + "propensity_weights\n", + "\n", + "\n", + "\n", + "A\n", + "\n", + "A\n", + "\n", + "\n", + "\n", + "propensity_weights->A\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "treatment_weight\n", + "\n", + "treatment_weight\n", + "\n", + "\n", + "\n", + "treatment_weight->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "X\n", + "\n", + "X\n", + "\n", + "\n", + "\n", + "X->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "A->Y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "distribution_description_node\n", + "intercept ~ Normal\n", + "outcome_weights ~ Normal\n", + "propensity_weights ~ Normal\n", + "treatment_weight ~ Normal\n", + "X ~ Normal\n", + "A ~ Bernoulli\n", + "Y ~ Normal\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Visualize the model\n", + "pyro.render_model(\n", + " ConditionedCausalGLM(torch.zeros(1, 1), torch.zeros(1), torch.zeros(1)),\n", + " render_params=True, \n", + " render_distributions=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating data\n", + "\n", + "For evaluation, we generate `N_datasets` datasets, each with `N` samples. We compare vanilla estimates of the target functional with the double robust estimates of the target functional across the `N_sims` datasets. We use a similar data generating process as in Kennedy (2022)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class GroundTruthModel(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " alpha: int,\n", + " beta: int,\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " treatment_weight: float = 0.0,\n", + " ):\n", + " super().__init__(p, link_fn)\n", + " self.alpha = alpha # sparsity of propensity weights\n", + " self.beta = beta # sparsity of outcome weights\n", + " self.treatment_weight = treatment_weight\n", + "\n", + " def sample_outcome_weights(self):\n", + " outcome_weights = 1 / math.sqrt(self.beta) * torch.ones(self.p)\n", + " outcome_weights[self.beta :] = 0.0\n", + " return outcome_weights\n", + "\n", + " def sample_propensity_weights(self):\n", + " propensity_weights = 1 / math.sqrt(self.alpha) * torch.ones(self.p)\n", + " propensity_weights[self.alpha :] = 0.0\n", + " return propensity_weights\n", + "\n", + " def sample_treatment_weight(self):\n", + " return torch.tensor(self.treatment_weight)\n", + "\n", + " def sample_intercept(self):\n", + " return torch.tensor(0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "N_datasets = 10\n", + "simulated_datasets = []\n", + "\n", + "# Data configuration\n", + "p = 2\n", + "alpha = 50\n", + "beta = 50\n", + "N_train = 500\n", + "N_test = 500\n", + "\n", + "true_model = GroundTruthModel(p, alpha, beta)\n", + "\n", + "for _ in range(N_datasets):\n", + " # Generate data\n", + " D_train = Predictive(\n", + " true_model, num_samples=N_train, return_sites=[\"X\", \"A\", \"Y\"]\n", + " )()\n", + " D_test = Predictive(\n", + " true_model, num_samples=N_test, return_sites=[\"X\", \"A\", \"Y\"]\n", + " )()\n", + " simulated_datasets.append((D_train, D_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fit parameters via maximum likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fitted_params = []\n", + "for i in range(N_datasets):\n", + " # Generate data\n", + " D_train = simulated_datasets[i][0]\n", + "\n", + " # Fit model using maximum likelihood\n", + " conditioned_model = ConditionedCausalGLM(\n", + " X=D_train[\"X\"], A=D_train[\"A\"], Y=D_train[\"Y\"]\n", + " )\n", + " \n", + " guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model)\n", + " elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train)\n", + "\n", + " # initialize parameters\n", + " elbo()\n", + " adam = torch.optim.Adam(elbo.parameters(), lr=0.03)\n", + "\n", + " # Do gradient steps\n", + " for _ in range(2000):\n", + " adam.zero_grad()\n", + " loss = elbo()\n", + " loss.backward()\n", + " adam.step()\n", + "\n", + " theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items()\n", + " }\n", + " fitted_params.append(theta_hat)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Query: Average treatment effect (ATE)\n", + "\n", + "The average treatment effect summarizes, on average, how much the treatment changes the response, $ATE = \\mathbb{E}[Y|do(A=1)] - \\mathbb{E}[Y|do(A=0)]$. The `do` notation indicates that the expectations are taken according to *intervened* versions of the model, with $A$ set to a particular value. Note from our [tutorial](tutorial_i.ipynb) that this is different from conditioning on $A$ in the original `causal_model`, which assumes $X$ and $T$ are dependent.\n", + "\n", + "\n", + "To implement this query in ChiRho, we define the `ATEFunctional` class which take in a `model` and `guide` and returns the average treatment effect by simulating from the posterior predictive distribution of the model and guide." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining the target functional" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class ATEFunctional(torch.nn.Module):\n", + " def __init__(self, model: Callable, *, num_monte_carlo: int = 100):\n", + " super().__init__()\n", + " self.model = model\n", + " self.num_monte_carlo = num_monte_carlo\n", + " \n", + " def forward(self, *args, **kwargs):\n", + " with MultiWorldCounterfactual():\n", + " with pyro.plate(\"monte_carlo_functional\", size=self.num_monte_carlo, dim=-2):\n", + " with do(actions=dict(A=(torch.tensor(0.0), torch.tensor(1.0)))):\n", + " Ys = self.model(*args, **kwargs)\n", + " Y0 = gather(Ys, IndexSet(A={1}), event_dim=0)\n", + " Y1 = gather(Ys, IndexSet(A={2}), event_dim=0)\n", + " ate = (Y1 - Y0).mean(dim=-2, keepdim=True).mean(dim=-1, keepdim=True).squeeze()\n", + " return pyro.deterministic(\"ATE\", ate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Closed form doubly robust correction\n", + "\n", + "For the average treatment effect functional, there exists a closed-form analytical formula for the doubly robust correction. This formula is derived in Kennedy (2022) and is implemented below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Any\n", + "from chirho.robust.ops import Functional, Point, P, S, T\n", + "\n", + "def ate_causal_glm_analytic_influence(\n", + " functional: Functional[P, S], \n", + " point: Point[T], \n", + " pointwise_influence: bool = True,\n", + " **kwargs\n", + ") -> Functional[P, S]:\n", + " def _ate_influence_functional(model: Callable[P, Any]) -> Callable[P, S]:\n", + " assert isinstance(model.model, CausalGLM)\n", + " theta = dict(model.guide.named_parameters())\n", + " def correction(*args, **kwargs):\n", + " X = point[\"X\"]\n", + " A = point[\"A\"]\n", + " Y = point[\"Y\"]\n", + " \n", + " pi_X = torch.sigmoid(torch.einsum(\"...i,...i->...\", X, theta[\"propensity_weights_param\"]))\n", + " mu_X = (\n", + " torch.einsum(\"...i,...i->...\", X, theta[\"outcome_weights_param\"])\n", + " + A * theta[\"treatment_weight_param\"]\n", + " + theta[\"intercept_param\"]\n", + " )\n", + " analytic_eif_at_pts = (A / pi_X - (1 - A) / (1 - pi_X)) * (Y - mu_X)\n", + " if pointwise_influence:\n", + " return analytic_eif_at_pts\n", + " else:\n", + " return analytic_eif_at_pts.mean()\n", + " return correction\n", + " \n", + " return _ate_influence_functional" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing automated doubly robust estimators via Monte Carlo\n", + "\n", + "While the doubly robust correction term is known in closed-form for the average treatment effect functional, our `one_step_correction` and `tmle` function in `ChiRho` works for a wide class of other functionals. We focus on the average treatment effect functional here so that we have a ground truth to compare `one_step_correction` and `tmle` against. We also compare against DoubleML estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper class to create a trivial guide that returns the maximum likelihood estimate\n", + "class MLEGuide(torch.nn.Module):\n", + " def __init__(self, mle_est: ParamDict):\n", + " super().__init__()\n", + " self.names = list(mle_est.keys())\n", + " for name, value in mle_est.items():\n", + " setattr(self, name + \"_param\", torch.nn.Parameter(value))\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " for name in self.names:\n", + " value = getattr(self, name + \"_param\")\n", + " pyro.sample(\n", + " name, pyro.distributions.Delta(value, event_dim=len(value.shape))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "plug-in-mle-from-model 0\n", + "tmle analytic_eif 0\n", + "tmle monte_carlo_eif 0\n", + "one_step analytic_eif 0\n", + "one_step monte_carlo_eif 0\n", + "0.0\n", + "(1936348, 13798027)\n", + "plug-in-mle-from-model 1\n", + "tmle analytic_eif 1\n", + "tmle monte_carlo_eif 1\n", + "one_step analytic_eif 1\n", + "one_step monte_carlo_eif 1\n", + "0.0\n", + "(2033412, 13798027)\n", + "plug-in-mle-from-model 2\n", + "tmle analytic_eif 2\n", + "tmle monte_carlo_eif 2\n", + "one_step analytic_eif 2\n", + "one_step monte_carlo_eif 2\n", + "0.0\n", + "(2079867, 13798027)\n", + "plug-in-mle-from-model 3\n", + "tmle analytic_eif 3\n", + "tmle monte_carlo_eif 3\n", + "one_step analytic_eif 3\n", + "one_step monte_carlo_eif 3\n", + "0.0\n", + "(2095378, 13798027)\n", + "plug-in-mle-from-model 4\n", + "tmle analytic_eif 4\n", + "tmle monte_carlo_eif 4\n", + "one_step analytic_eif 4\n", + "one_step monte_carlo_eif 4\n", + "0.0\n", + "(2111200, 13798027)\n", + "plug-in-mle-from-model 5\n", + "tmle analytic_eif 5\n", + "tmle monte_carlo_eif 5\n", + "one_step analytic_eif 5\n", + "one_step monte_carlo_eif 5\n", + "0.0\n", + "(2125888, 13798027)\n", + "plug-in-mle-from-model 6\n", + "tmle analytic_eif 6\n", + "tmle monte_carlo_eif 6\n", + "one_step analytic_eif 6\n", + "one_step monte_carlo_eif 6\n", + "0.0\n", + "(2143966, 13798027)\n", + "plug-in-mle-from-model 7\n", + "tmle analytic_eif 7\n", + "tmle monte_carlo_eif 7\n", + "one_step analytic_eif 7\n", + "one_step monte_carlo_eif 7\n", + "0.0\n", + "(2162399, 13798027)\n", + "plug-in-mle-from-model 8\n", + "tmle analytic_eif 8\n", + "tmle monte_carlo_eif 8\n", + "one_step analytic_eif 8\n", + "one_step monte_carlo_eif 8\n", + "0.0\n", + "(2178117, 13798027)\n", + "plug-in-mle-from-model 9\n", + "tmle analytic_eif 9\n", + "tmle monte_carlo_eif 9\n", + "one_step analytic_eif 9\n", + "one_step monte_carlo_eif 9\n", + "0.0\n", + "(2191290, 13798027)\n" + ] + } + ], + "source": [ + "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", + "\n", + "import tracemalloc\n", + "tracemalloc.start()\n", + "\n", + "# Estimators to compare\n", + "estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", + "estimator_kwargs = {\n", + " \"tmle\": {\n", + " \"learning_rate\": 5e-5,\n", + " \"n_grad_steps\": 500,\n", + " \"n_tmle_steps\": 1,\n", + " \"num_nmc_samples\": 1, # Since we're using point estimate\n", + " \"num_grad_samples\": N_test\n", + " }, \n", + " \"one_step\": {}\n", + "}\n", + "\n", + "# Influence functions\n", + "influences = {\"analytic_eif\": ate_causal_glm_analytic_influence, \"monte_carlo_eif\": influence_fn}\n", + "\n", + "# Cache the results\n", + "estimates = {f\"{influence}-{estimator}\": torch.zeros(N_datasets) for influence in influences.keys() for estimator in estimators.keys()}\n", + "estimates[\"plug-in-mle-from-model\"] = torch.zeros(N_datasets)\n", + "estimates[\"plug-in-mle-from-test\"] = torch.zeros(N_datasets)\n", + "\n", + "# ATE functional of interest\n", + "functional = functools.partial(ATEFunctional, num_monte_carlo=10000)\n", + "\n", + "for i in range(N_datasets):\n", + " D_test = simulated_datasets[i][1]\n", + " theta_hat = fitted_params[i]\n", + " # Weird memory leak issue hack fix\n", + " theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", + " }\n", + " mle_guide = MLEGuide(theta_hat)\n", + " model = PredictiveModel(CausalGLM(p), mle_guide)\n", + " \n", + " print(\"plug-in-mle-from-model\", i)\n", + " estimates[\"plug-in-mle-from-model\"][i] = functional(model)().detach().item()\n", + "\n", + " mu_X = (\n", + " torch.einsum(\"...i,...i->...\", D_test[\"X\"], theta_hat[\"outcome_weights\"])\n", + " + D_test[\"A\"] * theta_hat[\"treatment_weight\"]\n", + " + theta_hat[\"intercept\"]\n", + " )\n", + " \n", + " mu_X_treat = mu_X[D_test[\"A\"] == 1]\n", + " mu_X_control = mu_X[D_test[\"A\"] == 0]\n", + " \n", + " # Used for DoubleML later on\n", + " estimates[\"plug-in-mle-from-test\"] = (mu_X_treat.mean() - mu_X_control.mean()).detach().item()\n", + "\n", + " for estimator_str, estimator in estimators.items():\n", + " if estimator_str != 'double_ml':\n", + " for influence_str, influence in influences.items():\n", + " print(estimator_str, influence_str, i)\n", + " \n", + " estimate = estimator(\n", + " functional, \n", + " D_test,\n", + " num_samples_outer=max(10000, 100 * p), \n", + " num_samples_inner=1,\n", + " influence_estimator=influence,\n", + " **estimator_kwargs[estimator_str]\n", + " )(model)()\n", + "\n", + " estimates[f\"{influence_str}-{estimator_str}\"][i] = estimate.detach().item()\n", + " \n", + " # Compute DoubleML estimate (see Proposition in our paper for this trick to reduce one step to DoubleML)\n", + " if 'one_step' in estimators.keys():\n", + " for influence_str, influence in influences.items():\n", + " eif_correction = estimates[f\"{influence_str}-one_step\"][i] - estimates[\"plug-in-mle-from-model\"][i]\n", + " double_ml = estimates[\"plug-in-mle-from-test\"] + eif_correction\n", + " estimates[f\"{influence_str}-double_ml\"][i] = double_ml.item()\n", + " \n", + " # Check memory usage\n", + " print(torch.cuda.memory_allocated() / 1e9)\n", + " print(tracemalloc.get_traced_memory())\n", + " del theta_hat # Free up memory (weird memory leak issue)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
analytic_eif-tmleanalytic_eif-one_stepanalytic_eif-double_mlmonte_carlo_eif-tmlemonte_carlo_eif-one_stepmonte_carlo_eif-double_mlplug-in-mle-from-modelplug-in-mle-from-test
count10.0010.0010.0010.0010.0010.0010.0010.00
mean0.020.010.060.010.010.050.01-0.08
std0.060.110.130.060.090.110.080.00
min-0.08-0.15-0.13-0.08-0.12-0.11-0.10-0.08
25%-0.03-0.07-0.04-0.04-0.08-0.03-0.07-0.08
50%0.030.010.030.020.010.040.04-0.08
75%0.060.090.160.060.060.140.07-0.08
max0.100.210.260.100.160.210.11-0.08
\n", + "
" + ], + "text/plain": [ + " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", + "count 10.00 10.00 10.00 \n", + "mean 0.02 0.01 0.06 \n", + "std 0.06 0.11 0.13 \n", + "min -0.08 -0.15 -0.13 \n", + "25% -0.03 -0.07 -0.04 \n", + "50% 0.03 0.01 0.03 \n", + "75% 0.06 0.09 0.16 \n", + "max 0.10 0.21 0.26 \n", + "\n", + " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", + "count 10.00 10.00 \n", + "mean 0.01 0.01 \n", + "std 0.06 0.09 \n", + "min -0.08 -0.12 \n", + "25% -0.04 -0.08 \n", + "50% 0.02 0.01 \n", + "75% 0.06 0.06 \n", + "max 0.10 0.16 \n", + "\n", + " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", + "count 10.00 10.00 \n", + "mean 0.05 0.01 \n", + "std 0.11 0.08 \n", + "min -0.11 -0.10 \n", + "25% -0.03 -0.07 \n", + "50% 0.04 0.04 \n", + "75% 0.14 0.07 \n", + "max 0.21 0.11 \n", + "\n", + " plug-in-mle-from-test \n", + "count 10.00 \n", + "mean -0.08 \n", + "std 0.00 \n", + "min -0.08 \n", + "25% -0.08 \n", + "50% -0.08 \n", + "75% -0.08 \n", + "max -0.08 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The true treatment effect is 0, so a mean estimate closer to zero is better\n", + "results = pd.DataFrame(estimates)\n", + "results.describe().round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1hT1xvA8W9YMgVBFFRERMWBA8GNWnHGLdrWnxZbqbbWVttqnbXubZ3YqrW4rbbuiQP3qHvgAFEEFSfgQEB2fn9cEgkEZIShPZ/nyZNwx7nnhpGXM94jUygUCgRBEARBEIT3nk5RV0AQBEEQBEHQDhHYCYIgCIIgfCBEYCcIgiAIgvCBEIGdIAiCIAjCB0IEdoIgCIIgCB8IEdgJgiAIgiB8IERgJwiCIAiC8IEQgZ0gCIIgCMIHotgGdgqFgpiYGET+ZEEQBEEQhJwptoFdbGwsrq6uxMbGFnVVBEEoYLGxschkMmQymfidFwRByIdiG9gJgiAIgiAIuSMCO0EQBEEQhA+ECOwEQRAEQRA+ECKwEwRBEARB+ECIwE4QBEEQBOEDIQI7QRAEQRCED4ReUVdAEARByLmUlBSSkpKKuhqCIGiJvr4+urq6WitPBHaCIAjvAYVCwZMnT3j58mVRV0UQBC2zsLDAxsYGmUyW77JEYCcIgvAeUAZ1ZcqUwdjYWCsfAIIgFC2FQkFcXBzPnj0DwNbWNt9lisBOEAShmEtJSVEFdVZWVkVdHUEQtMjIyAiAZ8+eUaZMmXx3y4rJE4IgCMWcckydsbFxEddEEISCoPzd1sb4WRHYCYIgvCdE96sgfJi0+bstAjtBEARBEIQPhBhjJwiCIBS6mJgYmjVrhomJCUePHsXAwECr5Xt5eVG+fHlmzpz5zmOTkpJYv349X3zxBQA+Pj5s27aNw4cP5+naTk5OWe5zcHBg3759Guvo5eXFuXPnNJ7Xr18/fv755yzLPXz4MJs2beLnn3+mdevW2dbvu+++o0ePHqrjtm7dSq1atTIdJ5fLuXv3LmvWrKFRo0Y5el9ycu+xsbH07NmTFStWUK5cuWzrKuSeCOwEQRCEQrdnzx6srKyIjIzk4MGDdOrUqcjqsnv3bmbMmKEK7Ly9venbt2++yhw7diwdO3bMtP1dA+PlcrnGAE45wF6T6OhoJk2axKpVq7C1teXkyZOqfStWrGDv3r1s3rxZtc3Y2JgXL14AUg61ffv2ZQrsgoKCCA0NzbauWXnXvZuYmDBgwADGjRvHihUr8nQNIWsisBMEQRAK3ZYtW3B3d+fp06ds3LixSAM7hUKh9rWJiQkmJib5KtPMzAxra+tcn2doaJjr81atWoWzszMODg4AaucbGxujq6ubqUxlYNekSRP27dvH8OHD1fbv3bsXNzc3zp8/n+t7yMm9d+/enblz53L69GmaNm2a62sIWRNj7ARBEN5jsbFZP+Ljc37smzd5Pza3QkJCuHr1Ks2aNaNDhw6cO3eOkJAQ1X4vLy9mzZrF2LFjcXNzo379+owaNYrY2FjVMYcPH6Z37964uLhQu3ZtevXqxenTpzVer3v37owZM0Zt2/Hjx3F2dsbX11e1z8nJibNnz+Lj44OHh4fq2OfPnzNq1CgaNWqEq6srAwcOJCwsLH9vgpYkJCSwfv36PAfGcrmc+/fvc+PGDbXtfn5+GlvdtEVPT4+2bdvi6+tbYNf4rxKBnSAI/3nr10PFijB/PiQnF3VtcsfUNOtHz57qx5Ypk/Wxcrn6sZUqZX1sixb5q/PmzZsxNjamRYsWtGnTBgMDAzZs2KB2zNq1ayldujSbNm1i6tSp7N27l1WrVgFw/fp1vv32W9q1a8fOnTvZtGkTVlZW/PTTTyQmJma6nqenJ/v37yc+XaS7Y8cOWrVqRd++fRk7diwAJ0+exMXFRe3c5ORkvL29CQ4O5rfffuOff/5BV1cXb29vkovBD8uFCxeIjo6mZcuWeTq/fPny1KlTRzXuDyAgIIDo6GiaNWumrWpq5OHhwdmzZ9UCdiH/RGAnCMJ/TkoKJCS8/frhQ3jwAIYNg44dQUNsIGhJcnIyu3btolWrVhgZGWFmZkbLli3ZsWMHb9I1BTo6OjJs2DAcHBzo2LEjLVq04NKlS4A0VmvcuHF4e3tjZ2dH9erV6devH1FRUURFRWW6ZteuXUlKSsLf3x+QJm74+/vj6emJoaEhZmZmgNSFmXESx5kzZwgMDGTu3Lm4ubnh6OjIlClTaNeuXbbLu02YMAEXF5dMj/Xr12f7/uzatSvTOd7e3lkef+XKFSpUqJCvrmO5XK4W2Pn5+dG+ffs8J8rN6b07OTmRlJREYGBgnusuZCbG2AmC8J8zbhzcugX//AN6evDVV2BuDj/9BAcPwoABsHo1vA9p42Jist6X8XM5bdUijXQy/JufXU9jxmNz49ixY0RERKh183Xs2JGDBw+yZ88eevXqBUiBXXpmZmZER0cDUKNGDczNzVm+fDmhoaGEhYWpgoOUlJRM17SwsMDDw4Pt27fTuXNn/Pz8MDMzo3nz5u+s761btyhZsiSVK1dWbbO2tmb06NHZnjd06FDatWuXabulpWW253l4ePDTTz+pbTM0NMzy+MjIyHeW+S5yuZzZs2dz48YNatasiZ+fH7NmzcpzeTm9d+XXEREReb6WkJkI7ARB+E85dgxmzQKFAk6flroVLSzg66+l7sdOnWDtWujWLXNXZnGUm4aagjo2N7Zu3QpIH/4Zbdy4URXYZZf+5Pz583h7e9OyZUvc3Nzo1KkTb9684dtvv83ynJ49ezJo0CAiIyPZuXMnXbt2RU/v3R+Benp6eUoea2Vlhb29fa7PMzExydV5MpmM1NTUXF8nPVtbW+rVq8e+fftISEggOTmZBg0a8OjRozyVl9N7V9Y7v0toCepEV6wgCP8ZyclS65xCAd7emceKtW8PyjH2P/yQfWuYkHvPnz/n2LFjeHp6sn37drVHr169uHbtWqZB/Jr4+vrSqFEjFi9ezBdffEGzZs14/PgxkHmGq5K7uzvW1tZs2rSJixcv4unpqdqXXeBWpUoVXr16xb1799Tuo0GDBly8eDGnt15gypYty/Pnz/NdTocOHThw4AB+fn7I5XJ08tMsm0PKbvO8zB4WsiZa7ARB+M9YtQqCg6F0aWmihCZjxsC6dVJX5NKlUvesoB07duwgOTmZAQMGZOpqHTRoENu2bcs0iUITW1tb/P39uXDhAjY2Npw9e5aFCxcCaJw8AaCjo0P37t1ZunQpzs7OVKlSRbVPuU7n9evX1baDlA7E2dmZkSNHMnbsWIyNjfn111+xsrKidu3aWdbx9evXWXYxWllZaS1wqlOnDvPnz+fly5dYWFjkuRy5XM7MmTPZunVrtjNV4+PjOX78eKbttWvXplSpUkDO7/3mzZuUKFGC6tWr57neQmYisBME4T8hMREmTZJejx0LJUtqPs7YGBYulCZUpOWrFbRk69atNG3aNFNQB2BnZ0fbtm3Zs2cPlSpVyracoUOHEhkZyaBBgwCpVW369OmMGDGCgIAAjeWDNDt26dKlaq11AI0bN6Zu3br07t2bOXPmqO3T0dHh999/Z+bMmXz55ZcANGrUCF9f32y7i6dPn8706dM17jt58qTWWqkaNGiAubk5Z8+epX379nkup2zZstSvX58nT55Qr169LI+Liopi4MCBmbavXLlSlY8up/d+5swZmjZtmm3yZSH3ZIqs2q2LWExMDK6urly8eBFTU9Oiro4gCAUoNjZW9XseExOT7+Swmvz1F/TtC7a2cPcuZDMevdiJj48nNDQUBweHbAfSC9k7f/48AwcO5MSJE6qZsB+CBQsWEBgYyLJly4q6KjmWkJBAixYtWLBgAU2aNCnq6hQ5bf6OizF2giD8J/zxh/T8zTfvV1An5F9ISAh+fn5MnTqVHj16fFBBHUD//v25fv26WpLn4m7btm1Ur15dBHUFQAR2giD8J2zaBNOmgYZepCwtXQr168PVqwVXL6HghYWFMWbMGMzNzfnxxx+LujpaZ25uzsSJEzN1IxdXsbGxrFixgqlTpxZ1VT5IoitWEIQiVxhdsXnRqxds2QI//gjz5hVdPURXrCB82ERXrCAIQiFQTp5Yt+79W2pMEIT/JhHYCYLwQfPzgzZtYPPm3J/bvj1YWkJEBJw6pf26CYIgaJsI7ARB+KCtXw+HDsGJE7k/V18funSRXm/bpt16CYIgFAQR2AmC8MFKTITdu6XXn3yStzJ69JCet22TVqwQBEEozkRgJwjCB+vYMXj1CsqWhcaN81ZGu3ZS0uL79+HKFa1WTxAEQevEyhOCIHywtm+Xnrt2hbyuM25kBB9/LNaNFQTh/SBa7ARB+CApFLBjh/S6e/f8lbVqlTT5wsUlv7X6b/Pw8MDJyYmVK1dq3D9+/HicnJzw8fHR6nWPHDnCnTt38l1OUFAQI0eOpEWLFtSpU4d27doxb948oqOj8122h4dHvu5769atODk5Zfn4Iy1Dd3h4OE5OTpw9e1bt66wegYGB2V53yJAhHDx4EB8fn2zLcXJyIjw8XHVcF+Xg1QyuXLmCk5MTHh4eOX5vcnrvBw4cYPDgwbl6X99HosVOEIQP0rVr0nqvRkaQ7jNCKGL6+vrs27eP/v37q21PTk7mwIEDyGQyrV7v4cOHDBo0iDVr1lClSpU8l3Pw4EGGDRtG586dWbRoEVZWVty6dYvZs2dz4sQJ1q5dWyxyrp48eVLj9nflhvTx8cFFw38upUqVyvKcvXv3EhUVRdu2bWnatCm9e/dW7evVqxcdO3bE29tbtc3S0hKQfgaCg4O5e/culStXzlRmXn8G3nXv7dq1Y82aNezcuZOuXbvm6RrvAxHYCYLwQUpIgNatwcxMO0uIKRRw5w7o6YGDQ/7L+69q0qQJJ06c4PHjx9ja2qq2nzlzBmNjY60vCK+NHPyRkZGMHj2avn37Mnr0aNV2Ozs7nJyckMvlrF27lm+++Sbf18ova2vrPJ1nbm6eq3NTUlKYN2+e6v0wMTFRCx51dXUxNjbWWGaZMmUwMjJi3759ai1oCoWCffv24ebmxqNHj3J9Dzmpf//+/Zk2bRodO3ZET+/DDIFEV6wgCB+kBg3A3x+2btVOeT//DNWqwfz52invv6pOnTqUK1eOffv2qW3fu3cvcrk8U2vN5cuX6devH66urjRq1IixY8fy6tUr1X4PDw/++OMPhgwZgouLC40aNWL69OkkJycTHh5O69atAejXr5+qOy8kJISBAwfi4uKCu7s7w4cPJyIiIss679q1izdv3jBo0KBM++zs7Fi9ejWfpE27VigU/Pnnn8jlcpydnXF1deXrr7/mwYMHqnOcnJyYP38+rVq1olmzZty9ezdTue+676J24MABXr16RYsWLfJ0focOHfDz81PbdvHiRVJTU2nQoIE2qqhR8+bNiY6OZv/+/QV2jaImAjtBED5o2urZU/ZSHTumnfK0QaGA2NiieeSnIUwul6sFdomJifj7+9OpUye14wICAvDy8qJKlSr8/fffLFq0iICAALy9vUlNTVUd5+PjQ4MGDdi2bRtDhgxhzZo17N69G1tbWzZt2qQ6xtvbm6dPn9KnTx/s7OzYvHkzS5cuJSYmht69exMXF6exvteuXcPBwQELCwuN+11dXbGysgJg9erVLFu2jBEjRrB//35+//13QkNDmTlzpto5yvv57bffMnVH5vS+i5K/vz/NmjXDwMAgT+d37NhR1R2rtGfPHjp06ICOTsGFJgYGBjRt2pTDhw8X2DWK2ofZDikIwn/akyegowNlykhfKxSwdy8sXw5hYdL27t1h4EApCXFONG8uPV+7Bs+fSytSFCWFAtzd4fTporl+s2ZS0ue8BM5yuRxfX19Vd+ypU6coVaoUNWvWVDtuxYoVODk5MX78eACqVKnC3Llz6dq1KydOnKBly5aA1ArTr18/ACpVqsTmzZu5dOkS3bt3V43rMjc3x8TEhOXLl1OmTBlVmQALFiygcePG7Nu3D09Pz0z1ffXqFSVLlszRvVWsWJGZM2eqBv+XL18euVzOnj171I7r1q0btWvX1lhGTu9bE03j5ACOHz+OmZlZlucNHDgQ3QxTx8ePH08PZSLHDK5evarxvcopR0dHqlWrpuqOTUlJ4cCBAyxevDjLsXLvktN7d3JyYtsHnHFcBHaCIHxwFiyAWbNgzBjp8dlnsHOn+jEHD8Kff0qzXTM0mGhkYwNOTnDrlrS8WBaT+gqVlucZFBpnZ2fs7OxUkyj27t1L586dMx0XHBxMs2bN1LY5OTlRsmRJbt26pQpwHB0d1Y4xMzMjKSlJ47Vv3rxJSEhIpiAgISGBkJAQjeeUKlUqx2O+PDw8uHr1KosWLeLevXuEhIRw+/ZtypYtq3acvb19lmXk9L412a7M8ZPBuyZPTJ06lbp166ptU7ZCahIZGakKmvNK2R07ePBgzp07R4kSJXBxcclzYJfTe7e0tCQyMjJP13gfiMBOEIQPzvHj0nOlStIEivPnoUQJGDJEmiF74wbMmAGXL0tfnzwJFSq8u9yWLaXA7tixog/sZDKpxSyL3sMCZ2ycv8BS2R3bp08fDh06pOoyTU+hUGicIZmamop+uqZWTd2BWU2aSE1NpXHjxkyYMCHTvqxatFxcXNizZw8vXrzQOEt01qxZlChRgh9++IHly5fj4+ODp6cnDRs2xMvLi0OHDmVqsTPMZkZPTu9bk+wCxuyULVs2V+fKZLJ8dwt37NiRRYsWcffuXfbu3UvHjh3zVV5O65+amlqg3b1F7cO9M0EQ/pNiY6VADqQ8dufPg5UVHD0Kc+aAXA4//QQBAVC1Kty7JyUwTkx8d9nKceLFZZydTAYmJkXzyG9roVwu5+rVq2zevBk7O7tMrW4A1apV48KFC2rbgoKCiImJ0Xi85vdIvaJVq1YlJCQEW1tb7O3tsbe3x9zcnOnTpxMcHJxlXU1MTFi2bFmmfWFhYfz111+qbswlS5bw3XffMXHiRD799FPq1atHWFhYrmbnauO+C1qZMmV4/vx5vspwcHCgevXq+Pn5ceDAgUxjLAvK8+fP8zx7+H0gWuwEQfignDkDyclQqpQ0rk5fX1rnNeOSYuXLS92xrq5Sy92UKdIjO8pxdleuSC1lxsYFcgv/CTVq1MDe3p558+bx9ddfazzmiy++oG/fvkyePJm+ffsSFRXF5MmTqVmzJk2aNMnRdYzTvknBwcHUrFmTPn368PfffzNs2DC+/fZbZDIZc+bM4ebNm1StWlVjGZaWlkyYMIFRo0YRHR1N7969sbCw4OrVq8ybN4+qVauq8rUpxwx6eHigo6PDjh07OHDgAKVLl87xe5Of+85qdq+BgQHm5uY5rsO71K1blxs3buS7HLlczvLly7G2tqZGjRpZHnfv3j2OK5vi05QoUYJGjRqpvs7pvd+4cYN69erlr+LFmAjsBEH4oChb05RLgE2f/jYgy8jeHpYsgU8+kbpm+/SBbD5bsLODiROhbt28L1EmvCWXy1myZEmWXXAuLi4sX76chQsX0r17d0xNTWnTpg3Dhw9/Z5ekUqlSpejZsyezZ8/m3r17jBs3jnXr1jF37lz69OmDrq4u9erVY/Xq1dmOKevSpQs2Njb4+voyePBgoqOjKVeuHN27d+fLL79UjeOaPXs2kydPpmfPnpiYmFC3bl0mTZrExIkTCQ8Pp0IO+vzzc9/u7u5Zbvf19X3ntXOqTZs2jBs3jqSkpBx/LzTp2LEj8+fP54svvsj2uF27drFr1y61bWXLllUL9nJy70lJSVy6dImpU6fmuc7FnUyhjeyNBSAmJgZXV1cuXrxYLLJ5C4JQcGJjY1W/5zExMe8c6J2djz56G9y5ucG//0pJhbPTrZs0uaJbt7fryxYn8fHxhIaG4uDgkO3YLEEoLMnJybRv354RI0bQoUOHoq5Oju3du5e5c+eyf//+YpWgWJu/42KMnSAIH4z4ePX0Hz4+7w7qAGbOlNKj7NhRdOlDBOF9oqenx9ChQ1m1alVRVyVX1qxZw5AhQ4pVUKdtIrATBOGDoVCAcuhM9+6Zx9VlpUYNUPYEzZ6d/bGJiXDggHRc8ezvEITC0a1bNywtLTOtIlJc+fn5YWFhQffu3Yu6KgVKdMUKglDktNUVGxIiLfuVmirNes0i/6tGQUFSgCeTSa+rVdN8XFwcmJtLEzTCwqRxegVNdMUKwodNdMUKgiBosGCBFNTJ5bkL6gCqV4fOnaVWuIULsz7O2BicnaXXly7luaqCIAgFQgR2giB8EKKi4I8/pNfDhuWtjO+/l57Xr4c3b7I+rn596VkEdoIgFDcisBME4YMwb540/k0mk2bG5oWHh7RaxatXsHVr1seJwE4QhOJKBHaCILz3FApYvVp67eiYs5mwmujoQP/+0uvsUn6JwE4QhOJKBHaCILz3zp2Dhw+l1/lcblI1O/bo0bdlZlSnjtQy+OQJPH6cv+sJgiBokwjsBEF4723Y8PZ1y5b5K6tiRWjWTGoF1LAuPSCtlVq9uvT68uX8XU8QBEGbRGAnCMJ7LSUF/v777dfplo7Ms08/lZ7Tl5vR0qXSmrFt2+b/eoIgCNoiAjtBEN5rJ05IXaIA5cpB+fL5L7NXL6mr9cwZuH9f8zEtWkhrxuZjmcz/tJiYGOrWrUvTpk1JTEzUevleXl6MHj06R8cmJSWpraDg4+ODh4dHnq/t5OSU5SP98lsZ6+jl5ZXledOmTcv2mocPH+abb75R23b37l1GjRpF8+bNcXZ2xsPDgwkTJvDgwYM831tuPX36lF9++UVVB3d3d0aOHMn9DL9YR44c4c6dO1q77pMnT5DL5cQoF43+DxGBnSAI77WNG9++zulKE+9iawvK9cR37tROmYK6PXv2YGVlRUxMDAcPHizSuuzevZsZM2aovvb29mbz5s35KnPs2LGcPHky0+Ovv/7K9jy5XK7xvO+VuXg0iI6OZtKkSYwcOVK17dSpU3h6ehIXF8e8efPYv38/06dP5/Hjx/To0YOzZ8/m6/5yIjExkX79+vHkyRMWLlzI/v37WbBgAU+ePOF///sfz58/B+Dhw4cMGjSIqKgorV3bxsaGDh06MHPmTK2V+b4QgZ0gCO+tpCRQfv7OnQujRmmv7C5dpOdduzTvVyjgt9/A2xvSPp+EXNiyZQvu7u40adKEjemj8yKQcQEmExMTLC0t81WmmZkZ1tbWmR7vKtfQ0FDjedmtwLRq1SqcnZ1xcHAApEBv2LBhdO/eHR8fHxo0aED58uVp3Lgxy5Yto0WLFgwfPpzo6Oh83eO7nDp1irCwMObMmUP9+vUpX748bm5u/Pbbb0RHR7N7924g8/uvLf369WPHjh2EhoYWSPnFlQjsBEF4bx06JCUmLlMGhg6Fhg21V3bXrtLzkSOg6fNPJpNy561cKSZQ5FZISAhXr16lWbNmdOjQgXPnzhESEqLa7+XlxaxZsxg7dixubm7Ur1+fUaNGERsbqzrm8OHD9O7dGxcXF2rXrk2vXr04ffq0xut1796dMWPGqG07fvw4zs7O+Pr6qvY5OTlx9uzZTF2xz58/Z9SoUTRq1AhXV1cGDhxIWFiYFt+RvEtISGD9+vV06tRJtW3Hjh28fv2aoUOHZjpeJpMxYsQIIiMj2bNnDwCjR49mxIgRzJo1iyZNmlC3bl0GDx5MRESE6rynT5/y448/4ubmRqNGjRg0aNA73wMdHSnEOHr0qNp2MzMzdu7cSbdu3QgPD6d169aAFIj5+PgA0s/IwIEDcXFxwd3dneHDh6vVx8vLi+nTpzNy5Ejq1atHixYt+OOPP9SCxFKlStGwYUNWrlyZg3fywyECO0EQ3lvKhp6PP8577rqsODlJ68UmJcH+/ZqPKRb57GJjs37Ex+f82IxLbeTm2FzavHkzxsbGtGjRgjZt2mBgYMCG9FObgbVr11K6dGk2bdrE1KlT2bt3r2oc3PXr1/n2229p164dO3fuZNOmTVhZWfHTTz9pHK/n6enJ/v37iU/3fuzYsYNWrVrRt29fxo4dC8DJkydxcXFROzc5ORlvb2+Cg4P57bff+Oeff9DV1cXb25vk5OR8vQ/acOHCBaKjo2mZbjr45cuXqVSpUpatg7a2ttjb23Mp3Q+un58fL1++ZN26dSxevJiLFy8yf/58AOLi4vDy8iIlJYV169axdu1aSpUqxSeffMLTp0+zrFuTJk1wdnZm1KhRtG/fnokTJ7J7926eP3+Og4MD5ubm2Nrasilt+rmPjw/e3t48ffqUPn36YGdnx+bNm1m6dCkxMTH07t2buLg4Vfl//fUXRkZGbNmyhR9//JHffvuN5cuXq9XBw8ODw4cP5/6NfY+JwE4QhPdScjLs2CG9fvIE9u3T/jWUjSAHDmjeXywCO1PTrB89e6ofW6ZM1sfK5erHVqqU9bEtWuS5usnJyezatYtWrVphZGSEmZkZLVu2ZMeOHbxJFzA6OjoybNgwHBwc6NixIy1atFAFIrq6uowbNw5vb2/s7OyoXr06/fr1IyoqSuM4ra5du5KUlIS/vz8gTdzw9/fH09MTQ0NDzMzMALC2tsbAwEDt3DNnzhAYGMjcuXNxc3PD0dGRKVOm0K5dO16+fJnlfU6YMAEXF5dMj/Xr12f7/uzatSvTOd7e3lkef+XKFSpUqICJiYlq28uXL7GwsMj2OqVKlVKNcQMwNTVl8uTJODo60rx5c7p168bFixcBaTzkixcvmDt3LtWrV6datWpMmzYNU1NT/vnnnyyvYWBgwF9//cWoUaMoWbIkf//9N8OHD6d58+ZMnjyZpKQkdHV1VQGoubk5JiYmbNiwgTJlyjB+/HgcHR1xdnZmwYIFREZGsi/dL3rlypWZOHEijo6O9OjRAy8vL9asWaPWaufk5ERERASP/0MJJ7X8P64gCELhOH0aXr4EIyPYsgVKlYJ0Ew61ok0bmD8f0uKBTOrVk56vXtXudT9kx44dIyIigo7pMkl37NiRgwcPsmfPHnr16gVIgV16ZmZmqjFhNWrUwNzcnOXLlxMaGkpYWBiBgYEApKSkZLqmhYUFHh4ebN++nc6dO+Pn54eZmRnNmzd/Z31v3bpFyZIlqVy5smqbtbX1O2fcDh06lHbt2mXa/q4xdh4eHvz0009q2wwNDbM8PjIyMlOZFhYW3L59O9vrREdHU6FCBdXX9vb26Keb4m1mZkZSUhIAN2/eJCYmhoYZxjokJCQQEhLCo0eP1LqCQWo1BChRogTe3t54e3sTHR3NuXPn2LFjB+vXr8fIyIgRI0ZkqtvNmzcJCQnJ1HqqvJ5Sw4YNkclkqq/r1avH8uXLefHiheo9KVWqFAARERHY2tpm+558KERgJwjCeylt3DUWFlLPoKur9q/RooXUxRsWBnfvQrrPdgCcnaXn27chIQFKlNB+Hd4pu3QOurrqXz97lvWxOhk6cLIbP5Xx2FzYmrYIr6bxXxs3blQFdhlbztI7f/483t7etGzZEjc3Nzp16sSbN2/49ttvszynZ8+eDBo0iMjISHbu3EnXrl3Ry0H/vZ6enlrwkFNWVlbY29vn+jwTE5NcnSeTyUhNTVXb5urqyr59+4iKisLKyirTOU+fPiU0NJS+ffuqtmX3fqempuLg4MCSJUsy7TM2NqZUqVJs3749075NmzaRkpJC7969AShZsiRt2rShTZs2/PDDDxw7dkxjYJeamkrjxo2ZMGFCpn3K1lUg0/dP2VKnm+7nXvne6Gb8XfiAia5YQRDeS8rAThnXFERgZ2r6NoXKoUOZ91eoACVLSt3CwcHav36OmJhk/cjY0pPdsUZGeT82h54/f86xY8fw9PRk+/btao9evXpx7do1bty48c5yfH19adSoEYsXL+aLL76gWbNmqq62rGZYuru7Y21tzaZNm7h48SKenp6qfdkFblWqVOHVq1fcu3dP7T4aNGig6qosSmXLllXrUgXo0qULlpaW/PrrrxrP+fXXXzE3N6dz5845uka1atV49OgRZmZm2NvbY29vT/ny5Zk7dy7nz59HT09PtV35ALhz5w6LFi3SmEvO1NRUFXRmfP+rVq1KSEiIaiygvb095ubmTJ8+neB0v2jXrl1TO+/SpUtUqFABc3Nz1Tble2NtbZ2je/0QiMBOEIT3TkgIBAZKrWmvX0sNU7VrF8y10ibsaeyOlcnettppMbfqB2vHjh0kJyczYMAAqlWrpvYYNGgQurq6mSZRaGJra8utW7e4cOEC4eHhbNmyhYULFwJkmexYR0eH7t27s3TpUpydnalSpYpqn7GxMSBNyojPMOFEOQFg5MiRXL16ldu3bzNmzBisrKyonc0P3evXr4mIiND4yNjClh916tTh4cOHauP9SpYsybx58zh48CCDBw/mwoULPH78mAsXLvDNN9/g7+/P3Llz1QKg7HTt2hVzc3O+++47rly5QkhICGPGjOHYsWNUrVo1y/P69++Pjo4OXl5e+Pv7Ex4ezrVr11i6dCk7d+5k0KBBwNv3Pzg4mNevX9OnTx9ev37NsGHDCAwMJCgoiOHDhxMQEKB2vQsXLrBo0SJCQ0PZvHkz69evZ8CAAWp1uHnzJuXKlaNMmTI5fUvfeyKwEwThvZOWpUG1XmutWpkbp7SlTRvp+fBh0PR5vH69lA6lR4+Cuf6HZOvWrTRt2jTT+DkAOzs72rZty549e965WsDQoUOpV68egwYNonv37mzatInp06djaGhIQEBAlud5enoSHx+v1loH0LhxY+rWrUvv3r05cuSI2j4dHR1+//13ypUrx5dffsn//vc/9PT08PX1zbb7cvr06bi7u2t8aDMRb4MGDTA3N8+UcLhhw4Zs3boVKysrRo4cSbt27RgxYgSlS5dm27ZtNGvWLMfXMDMzY926dVhZWTFgwAB69erFw4cP8fX1zTaws7GxYdOmTdSqVYvp06cjl8vp378/58+fx9fXlyZNmgDSOLiePXsye/ZsFi5ciJ2dHevWrePNmzf06dOHzz77DJlMxurVq9W6llu3bs3t27fp1q0bS5cuZfTo0fzvf/9Tq8OZM2dU6VT+K2SKgsoMmE8xMTG4urpy8eLFbBMzCoLw/ouNjVX9nsfExKjN8NOkXTs4eFAKuvz9oX9/WLGiYOqWmAiWllKWj8uX306YKEzx8fGEhobi4OCQ7UB6IXvnz59n4MCBnDhxQm2s1vtuwYIFBAYGsmzZsqKuSqHx8vKifPny2a4s8ezZM1q3bs2uXbuoVKlS4VUuD7T5Oy5a7ARBeK+8fg3KfKfKyQrKtCMFwcAAlCnCNI2zE4q/kJAQ/Pz8mDp1Kj169PiggjqQujyvX7+uNmNUkHIhdunSpdgHddomAjtBEN4rBw9KSYOrVpUmUDx8CH36FOw1lT05mgK7xET47jto1QrS5U4VipGwsDDGjBmDubk5P/74Y1FXR+vMzc2ZOHEic+bMKeqqFBuPHz/mwIED70xL8yES6U4EQXivKJMFK/PplitX8NdUjrM7dkwKKtOl+0JfH/7+GyIjpQkdBTE7V8if1q1bc+XKlaKuRoFq27Ytbdu2LepqFJq1a9dmu9/W1pb9WS0Z84ETLXaCILxXDh6UngvzM8zZWUqAHBcHGcfmp58Zm4NMHYIgCAVKBHaCILw37t6VHnp6cPMmdOkC27YV/HV1dCBtAh+nTmXeX6uW9Hz9esHXRRAEITsisBME4b2hzCXXpIkUYO3eDffvF861mzaVnk+fzrxP2WInAjtBEIqaCOwEQXhvKLth27SBtPXgC3RGbHo5CexEV6wgCEVNBHaCILwXUlLezkp1c4PwcOl1YeWVa9BAWuHiwQPpkZ6yK/b+fSlZsSAIQlERgZ0gCO+FS5fgxQswN3+7tn3VqlBYKclMTaFuXen1v/+q7ytVSpqda2PzNuAUBEEoCiKwEwThvaDshm3VCpRrfysDrcKSXXdsUBA8fgw1axZunQRBENITgZ0gCO8F5cSJtm3h6lXpdWEv75VdYPeBLWZQIDw8PHBycmLlypUa948fPx4nJyd8fHy0et0jR45w586dfJcTFBTEyJEjadGiBXXq1KFdu3bMmzePaC30v3t4eOTrvrdu3YqTk1OWjz/++AOA8PBwnJycVGvLKr/O6hEYGJjtdYcMGcJB5X9dafbs2cNnn32Gq6srLi4ueHp6snr1apKSkvJ8f7l18uRJ+vXrh6urK3Xr1qVLly788ccfanWIi4tj/fr1Wr3uypUrmTZtmlbLzC2RoFgQhGLvzZu3aUbatIGzZ6Wu0cIO7JTrpl++LOW0MzYu3Ot/CPT19dm3bx/9+/dX256cnMyBAweQyWRavd7Dhw8ZNGgQa9asoUqVKnku5+DBgwwbNozOnTuzaNEirKysuHXrFrNnz+bEiROsXbu2WKxrfvLkSY3b37X+so+PDy4uLpm2lypVKstz9u7dS1RUlFpi5F9++YXdu3fzzTffMHHiRPT09Lhw4QI+Pj74+fnh6+v7zrrk1+nTpxk0aBBDhw5lwoQJ6Ovrc+nSJWbMmEFoaCgzZswAYMWKFWzdupW+fftq7dqfffYZnTt3pn379ri5uWmt3NwQgZ0gCMXe2bPS0l3lyknj6lavhpUrITW1cOthZwfly0vLmF24AC1avN135460tFhiIhw+XLj1ep80adKEEydO8PjxY2xtbVXbz5w5g7GxMUZGRlq9nkKhyHcZkZGRjB49mr59+6otUWVnZ4eTkxNyuZy1a9fyzTff5Pta+WVtbZ2n88zNzXN1bkpKCvPmzVN7P7Zv386WLVtYt24d9dNNV69UqRLNmzena9euzJ49m0mTJuWpjjm1ceNG3N3d+eqrr1TbKlasSHx8PBMnTmTMmDGULFlSKz8bGenr69O3b1/mzp3Lhg0btF5+ToiuWEEQir2jR6Xnli2llR5AShqsV8j/mspkWXfHGhvD/v1w/LgU3BUKhQJiY4vmkccPxTp16lCuXDn27duntn3v3r3I5fJMLXaXL19Wdak1atSIsWPH8urVK9V+Dw8P/vjjD4YMGYKLiwuNGjVi+vTpJCcnEx4eTuu0hX779eun6uoMCQlh4MCBuLi44O7uzvDhw4mIiMiyzrt27eLNmzcMGjQo0z47OztWr17NJ598kvYtUfDnn38il8txdnbG1dWVr7/+mgfpplI7OTkxf/58WrVqRbNmzbh7926mct9130XtwIEDvHr1ihbp/rtZs2YNLVu2VAvqlMqWLcvnn3/O1q1bef36NSC9D//88w/9+/enTp06NG/enGXLlqmdd+TIETw9PalTpw5t27ZlwYIFJL7jF0wmk3Hr1i2ePHmitr1bt27s2bMHY2NjfHx8WLx4MQ8fPsTJyYnwtFlPW7ZsQS6XU6dOHeRyOatXryY17T9IZbf1rl276Nq1K3Xq1OGTTz7hkjL3UpoOHTpw5cqVIlvGTgR2giAUe8eOSc8ffZTneEJrGjeWns+fV99uayt1D6ekQEhIIVREoQB3d+miRfFo3jzP3wy5XK4W2CUmJuLv70+nTp3UjgsICMDLy4sqVarw999/s2jRIgICAvD29lZ92ILUjdigQQO2bdvGkCFDWLNmDbt378bW1pZNmzapjvH29ubp06f06dMHOzs7Nm/ezNKlS4mJiaF3797ExcVprO+1a9dwcHDAwsJC435XV1esrKwAWL16NcuWLWPEiBHs37+f33//ndDQUGbOnKl2jvJ+fvvtNypXrpyn+y5K/v7+NGvWDAMDAwDi4+MJDAzUGNQpNWnShMTERK6ny+Q9e/Zsunfvzo4dO+jZsyfz5s3jwoULABw/fpzvv/+ejz/+mN27dzNhwgT8/PwYMWJEtnX74osviIqKonXr1vTr14/Fixdz9uxZ9PX1cXR0RE9PD29vb7y9vbGxseHkyZPY2try999/M2vWLL799lv27NnDDz/8wPLly/n111/Vyp82bRpfffUV27Zto3LlyvTv318tcC9Tpgw1a9bkcBE13YvAThCEYi0+/m16kZYtYfhwqF4d3rEGeIFxdZWe0z57VGQyqV4gzZAtFFoej1ZY5HI5V69e5fHjxwCcOnWKUqVKUTPDlOIVK1bg5OTE+PHjqVKlCo0aNWLu3Llcv36dEydOqI5r3rw5/fr1o1KlSnz22WdUr16dS5cuoauri6WlJSB1NZqYmLBhwwbKlCnD+PHjcXR0xNnZmQULFhAZGZmpFVHp1atXlCxZMkf3VrFiRWbOnImHhwfly5enUaNGyOVybt26pXZct27dqF27NvU0DBTN6X1r4uLiovGhbCXLirIFM/1jWzbr9V29epVq1aqpvn716hWpqalZBr/wdrze8+fPVdt69OhBt27dcHBw4IcffsDc3JyLFy8CsHTpUnr16sX//vc/KlasiLu7O5MmTWLfvn2qFras3oNt27bRs2dP7t27h4+PD/369aNVq1b4p83CMjExwdjYGF1dXaytrdHV1eX333/n66+/pnPnztjZ2dG+fXt+/PFH1q1bR0JCgqp85TGOjo5MmTKFUqVK8c8//6jVwcnJiavKWV6FTIyxEwShWDt7FhISpBxx1arBxYuQ4TOyULm4SPHU/fsQEQHphyU5OUkBX6HUTyaDEyekWRxFwdg4z4Gls7MzdnZ2qkkUe/fupXPnzpmOCw4OpplyxkoaJycnSpYsya1bt2jZsiUAjo6OaseYmZllOQPz5s2bhISEZJookJCQQEgWTa2lSpXi0aNHObo3Dw8Prl69yqJFi7h37x4hISHcvn2bsmXLqh1nb2+fZRk5vW9Ntm/frnH7uyYsTJ06lboZ8gcpWyE1iYyMVAXNgCqgi4mJyfIc5ezh9BMyMn7vTE1NVd+7mzdvEhAQoBZgKsfFhYSEsHv3brWu2y5dujB58mRVucrXYWFhnDx5kjVr1vD999+rZhCn9/z5c548ecLChQtZvHixantqaioJCQmEh4dTokQJABo2bKjar6+vT61atQgODlYrz9LSUgR2giAImii7YZWfZcq/lYWdw06pZEkpwLx1SwoyO3R4u69IWuwKeIZhQVF2x/bp04dDhw6pukzTUygUGmfJpqamoq+vr/pa2R2Y8VxNUlNTady4MRMmTMi0zyyLnDUuLi7s2bOHFy9eaJwlOmvWLEqUKKHquvPx8cHT05OGDRvi5eXFoUOH2LNnj9o5hoaGGq+lrHtO7luT7ALG7JQtWzZX58pkMrVu4RIlSlC7dm3Onj2bacaz0pkzZzAwMKCWcqkWsv/epaamMmDAAHr06JHpGGtra+rWrYtcLldtMzU1JS4ujvnz59OzZ0+qp/1CVqpUiUqVKtG5c2datGjByZMnMwV2ynsZM2YMTZUDadOxtbXl2bNnAOhlGNybmpqKjo56B2hKSkqmbYVFdMUKglCsKSdOfPQR3LsHr16BgcHbIKooKLMYZOyOVX5WFGWL4vtC2R27efNm7OzsMrXcAFSrVk013kopKCiImJgYjcdrkjFAqlq1KiEhIdja2mJvb4+9vT3m5uZMnz49U6tL+rqamJhkGtgPUmvQX3/9hW7acihLlizhu+++Y+LEiXz66afUq1ePsLCwXM3A1MZ9F7QyZcqodakCeHt7c+zYMf7NuDQL8PTpU1atWkXXrl0xNzfP0TWqVq3K3bt3Vd8ne3t7nj59yuzZs4mNjcXCwkJtn5WVFYaGhuzcuZONGzdmKs/IyAg9PT1VS2T6nw0rKyusrKy4f/++Wpk3btxgwYIFauVcU2ZIRxofeuPGDbVgFeDFixeUKVMmR/epbSKwEwSh2EpIUB9fp5xkVquWFNwVlawCu+rVpeXFskn9JaSpUaMG9vb2zJs3L9OkCaUvvviCoKAgJk+eTEhICOfOneOnn36iZs2aNGnSJEfXMU5LNhgcHMzr16/p06cPr1+/ZtiwYQQGBhIUFMTw4cMJCAigatWqGsuwtLRkwoQJrFmzhrFjxxIQEMD9+/fZtWsX/fv3p2rVqnh7ewNSy86pU6e4c+cOd+/eZf78+Rw4cOCdMzm1dd8REREaH9qeUVu3bl1u3Lihtq1jx4707duXQYMGsWzZMkJCQnjw4AHbt2+nd+/e2NraMmbMmBxfY+DAgRw4cAAfHx9CQ0P5999/GTNmDNHR0VmmZtHR0eGnn35iw4YNTJgwgYCAAMLDwzl9+jTffvstNjY2dEhrZjc2NubVq1eEhoaSnJzMgAEDWLt2LWvXruX+/fv4+/szadIkDAwM1FoWFyxYwNGjRwkJCWHs2LG8efNGNSta6caNG5m6tguL6IoVBKHYOndOmjxRpowUNP39t7S9qLphlbKaQOHsDFFR7+2chkInl8tZsmQJHTt21LjfxcWF5cuXs3DhQrp3746pqSlt2rRh+PDh7+ySVCpVqhQ9e/Zk9uzZ3Lt3j3HjxrFu3Trmzp1Lnz590NXVpV69eqxevTrbMWVdunTBxsYGX19fBg8eTHR0NOXKlaN79+58+eWXqjFss2fPZvLkyfTs2RMTExPq1q3LpEmTmDhxIuHh4VSoUOGddc7Pfbu7u2e53dfX953Xzqk2bdowbtw4kpKS1Oo0btw4GjduzLp161ixYgWJiYlUqlQJLy8vPvvsM41dr1np0KED8+fPZ9myZSxbtgxzc3NatWr1zlmxH3/8MaVLl2bNmjUMHDiQ2NhYSpcuTevWrZk9e7aqG7xdu3b8888/dO3alXXr1uHt7U2JEiVYu3Yts2bNwsrKCk9PT3788Ue18nv37s2MGTN48uQJdevWZe3atWqtc1FRUdy+fTvTTOjCIlMURIY+LYiJicHV1ZWLFy8Wi2zegiAUnNjYWNXveUxMjOpDcupU+OUX+Phj+Ocf6NEDtm+HBQvg+++Lrr4xMdJYO4VCWh/WxqZgrxcfH09oaCgODg7Zjs0ShMKSnJxM+/btGTFihKoF7EOnzIu4Zs0aGjVqlOVxy5cv5+jRo7larkybv+OiK1YQhGIr/fg6kCYt1K0L2aTKKhSmplCjhvQ6LTNDJsXzX2ZB0A49PT2GDh3KqlWriroqxUpiYiIbN27khx9+KLI6iMBOEIRiKTHx7eoOyhmxs2ZJ4+yaNy+yaqlk1R3755/SsmejRhV+nQShMHXr1g1LS8ss8//9FylX32jQoEGR1UGMsRMEoVg6fx7evIHSpSFD3tpiwc1NSpKcscUuJUVaNzbDuHJB+CD9/vvvRV2FQlOhQoVMiaYzGjBgQCHVJmuixU4QhGIp4/qwL19CcnJR1khdVi12ypQnhZbLThAEIR0R2AmCUCylXx8WpKXETE1hyZIiq5KaevVAR0eaPJF+UQJlYBcWJqVrEQRBKEwisBMEodhJSoJTp6TXysDuyhUpUMqwMlORMTGRJnPA29UwQJoha2ICqalScCcIglCYRGAnCEKxc+GCtASqlZU0vi4pCa5fl/ZpWDO9yCjz6aUP7GQyqFJFen3nTuHXSRCE/zYR2AmCUOykH1+noyMt0ZWYCGZmUKlSUdZMnTLIzLjWtzKwu327UKsjCIIgZsUKglD8KMfXKdOcKJcSq1tXCvSKC00tdiDl2bt/HywsCr1KgiD8xxWjP5GCIAhSt+vJk9Lr9OProOiXEstIWZ9bt6TULEpjx0rLoX3xRZFU670QExND3bp1adq0aa7WUc0pLy8vRo8enaNjk5KS1BLt+vj44OHhkedrOzk5ZflIv0pDxjp6eXlled60adOyvebhw4f55ptvANi6davaudWrV6d+/fr873//w9/fP8/3lRXl9bLj4eGBj49PjstUvhdTp07VuP+PP/7AyclJ9f6Fh4fj5OTE2bNnsyxz0KBBHDx4MMd1eF+JFjtBEIqVy5chNhYsLaW1V+Fti1hxGl8HYGsr5dmLjJTy1rm5FXWN3h979uzBysqKyMhIDh48SKdOnYqsLrt372bGjBl8kRaJe3t707dv33yVOXbsWI1r4Orq6mZ7nlwu5+eff8603cjIKMtzoqOjmTRpUqZVIE6m/YeUmprKixcv2LlzJ9999x1Tpkzh448/zsFdFC19fX3279/Pzz//jCzDAsx79+7NtO1dRo8eTb9+/WjQoAEWH3BzugjsBEEoVpStdS1avO127dwZzM2hYcOiq5cmMpnUanfokNSqmDGwS06W7qE4dR8XF1u2bMHd3Z2nT5+ycePGIg3sMi6ZbmJiolqvOK/MzMywtrbO9XmGhoa5Pm/VqlU4Ozvj4OCgtj19OWXLlqV69eokJiYyc+ZM2rVrh7m5ea7rV5gaNWrE6dOnuXjxIm7pfrlCQ0MJCwujZi4zl1eqVIk6deqwcuVKfvzxR21Xt9gQf24EQShWjh+XnpXj6wC+/x42b4Y6dYqmTtnJapydqysYG0NISOHXqbgLCQnh6tWrNGvWjA4dOnDu3DlC0r1RXl5ezJo1i7Fjx+Lm5kb9+vUZNWoUsbGxqmMOHz5M7969cXFxoXbt2vTq1YvTyjXoMujevTtjxoxR23b8+HGcnZ3x9fVV7VN25WXsin3+/DmjRo2iUaNGuLq6MnDgQMKKSS6bhIQE1q9fn+PA+PPPPycmJoajyhlKwNGjR/nkk09wcXHB3d2dmTNnkpAuCaOTkxNbt25VK0dT1+qmTZto0aIF9erVY+jQoTx//jzLely6dIm+fftSp04dPvroIyZNmkRMTIzaMdbW1ri5uWVasmzv3r189NFHeQq+5XI5GzZsID4+Ptfnvi9EYCcIQrHy77/Ss3J8XXGX1czY5GRpvGBBz4yNTYzN8hGfHJ/jY98kvcnzsbm1efNmjI2NadGiBW3atMHAwIANGzaoHbN27VpKly7Npk2bmDp1Knv37lV1NV6/fp1vv/2Wdu3asXPnTjZt2oSVlRU//fSTxvF6np6e7N+/X+3DfMeOHbRq1Yq+ffsyduxYQOq6dHFxUTs3OTkZb29vgoOD+e233/jnn3/Q1dXF29ub5GKwFMqFCxeIjo6mZfr/hLJhZ2eHkZERQWlLo/j7+/PNN9/QsmVLtmzZwpQpU/Dz8+Onn37KdV3WrFnDggULWLduHU+fPsXb2ztTayhAUFAQX3zxBc2aNWPnzp38+uuv3LhxQ+Pxcrmc/fv3q2338/PLcwtvy5YtiY6O5kLGJWM+IKIrVhCEYiU2VppNWru29HVwMBgYgL291PVZ3Chb7AICQKF4W8cqVaRtBZ3LznSGaZb7OlbtyJ4+e1Rfl/m1DHFJcRqPbWnfkqNfHFV9XWlhJSLjIjUe61bOjfMDz+epvsnJyezatYtWrVqpxo21bNmSHTt2MHz4cNU2R0dHhg0bBoCDgwN79uzh0qVLgDRObdy4cWrj4Pr164e3tzdRUVHY2tqqXbNr167MmTMHf39/OnfuTExMDP7+/ixYsABDQ0PMzMwANHaBnjlzhsDAQPz8/KhcuTIAU6ZMwdfXl5cvX1K6dGmN9zlhwgSmTJmSaftPP/2U7fi9Xbt2sX//frVtLi4urFixQuPxV65coUKFCrlqvTIzM+P169cALFu2jLZt2/Ltt98CULlyZRQKBd988w0hISE4OjrmuNw5c+ZQvXp1AGbNmkX79u35999/adq0qdpxvr6+NGnShMGDBwNSF+ncuXNp06YN586do1GjRqpj27dvz9SpU1XdscHBwTx+/JiWLVuyZs2aHNdNydTUlAoVKnD16lXc3d1zff77QAR2giAUO82bg3KM+bhxsGkTzJ0LaZ/zxUr16qCvD69ewb17b/PsVa0qPYtcduqOHTtGRESE2sSCjh07cvDgQfbs2UOvXr0AMgUUZmZmREdHA1CjRg3Mzc1Zvny5arxVYGAgACkpKZmuaWFhgYeHB9u3b6dz5874+flhZmZG8+bN31nfW7duUbJkSVVQB1IA+K4Zt0OHDqVdu3aZtltaWmZ7noeHR6bWMkNDwyyPj4yMfGeZGcXExKiC2eDg4EytXw0aNACke89pYGdiYqIK6kAK1szNzQkODs4U2N28eZN79+5lah0FqZs+fWBnZWVFgwYN2LdvH25ubuzdu5e2bdtiYGCQs5vVwNLSkshIzf+0fAhEYCcIQrHTqtXb18pUJ8oWvOLGwEBaHePqVemhDOwKa/WJmDExWe7T1VGfgfnsp2dZHqsjUx+ZE/Z9WI6PzQ3lWK2hQ4dm2rdx40ZVYJfdB/f58+fx9vamZcuWuLm50alTJ968eaNqddKkZ8+eDBo0iMjISHbu3EnXrl3R03v3R6Cenl6uZ1+CFJDY29vn+jwTE5NcnSeTyUhNTc3x8Xfv3iUuLk418UChUGS6P2VwnP79ydhFmpSUpPa1ptm+qampGr+PqampdOnShUGDBmXapylI7dixI4sXL2bs2LH4+flpnDWcGykpKe+cnfw+E2PsBEEodpTj616/fhsYFbccdulpmkBRWIGdiYFJlg9DPcMcH2ukb5TnY3Pq+fPnHDt2DE9PT7Zv36726NWrF9euXePGjRvvLMfX15dGjRqxePFi1Vitx48fA5kDECV3d3esra3ZtGkTFy9exNPTU7Uvu8CtSpUqvHr1inv37qndR4MGDbh48WJOb73AlC1bNttJChn99ddfmJqa0irtv6dq1aplug/l+DNla52+vr6q6xakFr+M14yOjub+/fuqr2/dusXr16+pplxQOZ2qVaty+/Zt7O3tVY+UlBRmzJih+j6m17ZtW54/f87GjRt59epVphbA3Hrx4kWeZiy/L0SLnSAIxYqFxdtA6do1adxauXJQpkyRVitbmgI7ZVdsaKg0iUJfv/DrVdzs2LGD5ORkBgwYkKmLb9CgQWzbti3TJApNbG1t8ff358KFC9jY2HD27FkWLlwIkGWyYx0dHbp3787SpUtxdnamijLyBoyNjQFpUkb67QBNmjTB2dmZkSNHMnbsWIyNjfn111+xsrKidjbNyK9fvyYiIkLjPisrK3S0lAOnTp06zJ8/n5cvX2bKzaa8fmpqKs+fP2fv3r2sX7+eKVOmYGoqjc388ssv+fHHH/ntt9/o2LEjYWFhTJkyhVatWqm+Ry4uLvz99980aNAAfX19FixYkKm1U0dHhx9++IHx48cD0hjDhg0bqqUpUVLmCRw/fjz9+vUjNjaWSZMmERsbSyUNawZaWlrSqFEj5s6dS6dOnbJtaQ0ICFCb0QtQpkwZVTfxixcvePToEXWL83+K+SQCO0EQipVmzd7mfVN2wxa3xMQZKT/f0zc22dpCkyZQsSLExECpUkVTt+Jk69atNG3aVOO4LTs7O9q2bcuePXs0frinN3ToUCIjI1VdeVWqVGH69OmMGDGCgICALMeFeXp6snTpUrXWOoDGjRtTt25devfuzZw5c9T26ejo8PvvvzNz5ky+/PJLQMqv5uvrm2138fTp05k+fbrGfSdPntRai1GDBg0wNzfn7NmztG/fXm2fcnKAjo4OVlZW1KhRg+XLl6tNGpDL5aSkpLBs2TKWLFmCpaUlnTt3VusqnzhxIpMmTaJ3795YWlrSv39/4uLUJ+FYWlrSrVs3Bg8ezJs3b2jVqhXjxo3TWOd69erx559/snDhQjw9PTEyMqJx48aMGjUqy/dULpdz6tSpd86G/fXXXzNt69Kli2r72bNnMTc31xhwfihkiqzarYtYTEwMrq6uXLx4UfWfhSAIH6bY2FjV7/nMmTGMGiXN8PvqK1i+XFqi6x0rKhWpx4+lVkUdHWlWbzZj3fMkPj6e0NBQHBwcsh1IL2Tv/PnzDBw4kBMnTqgmD3wIFixYQGBgIMuWLSvqqhR7AwcOxNnZme+//76oq6JGm7/jYoydIAhFLv047PTpuN6XFjsbG2kJtNRUSEsPJhQjISEh+Pn5MXXqVHr06PFBBXUA/fv35/r162pJnoXM7ty5w40bN1RLx32oRGAnCEKRu3z57etatd6+HjVKeqTLflAsyWRv17W9fl19X2qqlApFKDphYWGMGTMGc3PzD3IpKXNzcyZOnJipG1lQN2fOHMaPH1/sl1LLLzHGThCEInfixNvX6ceU9+wpPYqKQqEg6to1np45w+sHDzAwM8PS2Rm71q3RLVFC7VhnZ2k5tPSB3d9/w+efg4cH7N1byJUXVFq3bs0VZfPvB6pt27a0bdu2qKtRrP1XuqpFYCcIQpFTrg9bnLwICuLS7Nk8PXs20z4Dc3NcfvqJyj16qFJlaGqxK1MGEhIKPuWJIAiCkgjsBEEoUklJb9eHTe/4cWmfq6uUAqUwhe3dy9lx40hJSEBHX5/yH32EhZMTiS9f8uDQIeIeP+bsL7/w5PRpGk+fjq6BgcbALn3Kk+RkyEE+XEEQhHwRf2YEQShSFy5AnIblS6dMAX9/aVbsgAGFV5/bGzdyPm2NT9vmzWnwyy+Yli+v2u8yciRBq1YRsGgR9/z8SE1Optmvv1KrlvTn9N49iI6GkiWlmbKGhhAfL23PxbKbgiAIeSImTwiCUKSOHs28TaEomhmxDw4e5PzUqQA4eXnR8rff1II6AB1dXWp++SUtfvsNHX19Hhw8yMWZM7G0lAI5gJs3047VeRvMiTVjBUEoDCKwEwShSGkK7B49gshI0NVVnyVbkF7ducPp0aNBoaDKxx9Tf9QodLJZT7KcuzvN5s4F4PaGDTw4eDDb7lgxzk4QhMIgAjtBEIpMUhKcPJl5u7K1rnp1MMrbsqS5khwfz6kRI0iJj8emaVPcfvklRwu/27VuTY3+/QE4M3489apISzilD+wKa81YQRAEEIGdIAhF6OxZaXydpaX69sLuhg3w8eFlcDCGVlY0mTEj25a6jOp+/z2WtWqRFB2N8zOpBS99YNe0KfTqBfXra7vWgiAImYnAThCEInPwoPT80Ufq2wszsHsRGMittWsBaDRlCkalS+fqfB19fRr88gvIZOje3IWT8UW1wK5HD9i0Cfr102at308eHh44OTmxcuVKjfvHjx+Pk5MTPj4+Wr3ukSNHuKOFJtOgoCBGjhxJixYtqFOnDu3atWPevHlER0fnu2wPD4983ffWrVtxcnLK8vHHH38AEB4ejpOTE2fT0vgov87qERgYmO11hwwZwsG0X2QvLy+1c2vVqoW7uzsjRozg0aNHeb63rHh5eTF69Ogs9589exYnJyfCw8NzVF769+JG+oWf05HL5Wrvn4+PDx4eHlmW+eTJE+RyOTExMTmqgzaIwE4QhCKjDOwy/l0srMBOkZrKuSlTUKSkULF9e8qnX88sF6xq18YxLZNy37KzefpUQUSENmv64dDX12ffvn2ZticnJ3PgwIEcdYHnxsOHDxk0aBBRUVH5KufgwYN8/PHH6OrqsmjRIvbs2cPIkSPZt28fn3/+eaF+cGfn5MmTGh+fffZZtuf5+PhoPK+qcpCoBnv37iUqKkotMbJcLledu3//fubMmcO9e/f49NNPefLkidbusyBl9TMaFBREaGhorsqysbGhQ4cOzJw5U1vVeycR2AmCUCRevYJz56TXGQO7DRukNCdubgVbh3t79xJ19Sp6xsbUHzUqX2XVHToUXSMjHI2uU8/0uGpmLEjLij18KKU9+a9r0qQJV69e5fHjx2rbz5w5g7GxMba2tlq9nkKhyHcZkZGRjB49mr59+zJjxgzq1auHnZ0dbdq0wdfXl9u3b7M2rdW3qFlbW2t8GBsbZ3ueubm5xvP0ski+mJKSwrx58/D29lbbbmhoqDq3QoUKNGnSBF9fX3R0dJg3b57W7rMgNWnSRGNgt3fvXtzy8EepX79+7NixI9dBYV6JwE4QhCJx9CikpEizRitWVN/n5iblrivIxMQpiYlcXbQIgJoDBmBctmy+yjO0sqJa794AeFr/RuDNtwFFnTpQoYI0pvC/rk6dOpQrVy7TB+fevXuRy+WZWuwuX75Mv379cHV1pVGjRowdO5ZX6Rbf9fDw4I8//mDIkCG4uLjQqFEjpk+fTnJyMuHh4bRu3RqQPlyVXZ0hISEMHDgQFxcX3N3dGT58OBHZNLHu2rWLN2/eMGjQoEz77OzsWL16NZ988gkgBZJ//vkncrkcZ2dnXF1d+frrr3nw4IHqHCcnJ+bPn0+rVq1o1qwZd+/ezVTuu+67qB04cIBXr17RokWLdx5rZmaGp6cnBw4cIDExEYD4+HgWLFhA69atqV27Nt27d8ff3191jrJrOT1NXatxcXEMHz6cevXq0bx5c1atWpVlMK9QKFi+fDmtW7embt26dOvWjZ07d2Y6Ti6Xc//+/UzdsX5+fnTs2PGd95tRqVKlaNiwYZZDELRNBHaCIBQJZTdsmzZFc/3bf/9N7MOHGFlbU93LSytl1vD2JkXXCEejGzxMN91XmQpPw+d3vigUCmITY4vkkZ+WMLlcrhbYJSYm4u/vT6dOndSOCwgIwMvLiypVqvD333+zaNEiAgIC8Pb2JjU1VXWcj48PDRo0YNu2bQwZMoQ1a9awe/dubG1t2bRpk+oYb29vnj59Sp8+fbCzs2Pz5s0sXbqUmJgYevfuTZymTNnAtWvXcHBwwCKL/zRcXV2xsrICYPXq1SxbtowRI0awf/9+fv/9d0JDQzN1xSnv57fffqNy5cp5uu+i5O/vT7NmzTAwMMjR8dWqVePNmzeEhYUBMGzYMLZv387PP//Mzp07adOmDd999x2HDh3KVT32799PqVKl2LJlCyNGjGDhwoWsXr1a47Hz58/nr7/+Yty4cezatYt+/foxceJE1q9fr3Zc+fLlqVOnjtrPaEBAANHR0TRr1ixX9VPy8PDg8OHDeTo3t8TKE4IgFAllYJdx3fLVq6XEvp07S61cBSE5Pp6by5cD4Dx4MHrv6KbKKUNLSxR1e8GltZgFrgOaA6D83A4J0cplACmoc1/pzukHp7VXaC40s2vGif4n8jQmTi6X4+vry+PHj7G1teXUqVOUKlWKmjVrqh23YsUKnJycGD9+PABVqlRh7ty5dO3alRMnTtAybUxk8+bN6Zc2O6VSpUps3ryZS5cu0b17dyzTplybm5tjYmLC8uXLKVOmjKpMgAULFtC4cWP27duHp6dnpvq+evWKkiVL5ujeKlasyMyZM1UD6suXL49cLmfPnj1qx3Xr1o3atWtrLCOn962Ji4uLxu3Hjx/HzMwsy/MGDhyIbobZ4OPHj6dHjx4aj7969arG9yoryvfv9evXhISEcOjQIZYuXUqrVq0A+O6777h16xZLly5VtbLmRM2aNRk3bhwAjo6OhISEsGLFCr744gu14+Li4li1ahWzZ89WXbNixYo8fPgQX19f+vbtq3a8XC5nw4YNDB8+HJBa69q3b5/pPcopJycnIiIiVD/zBUkEdoIgFLr79yE4WArg0v7GqixaBLduSd2zBRXYhWzZQnxUFMa2tlTu3l2rZVf6uC/3L66jQsJJXoWEYO7oqArstN1iJ0O7Ew0Ki7OzM3Z2duzbt4/+/fuzd+9eOnfunOm44ODgTC0kTk5OlCxZklu3bqkCHMcMa7WZmZmRlJSk8do3b94kJCQkUwCUkJBASBaRd6lSpXI8q9PDw4OrV6+yaNEi7t27R0hICLdv36Zshq5+e3v7LMvI6X1rsn37do3bTUxMsq331KlTqVu3rto2ZSukJpGRkaqgOSdev34NSN+bW7duAVJLZ3pubm7MTUv6nVMZy6hTpw5Lly7NNFP5zp07JCQkMGrUKMaMGaPanpycTGJiIvEZBsDK5XJmz57NjRs3qFmzJn5+fsyaNStXdUuvVKlSAERERIjAThCED49yKE3DhtI4utjYt/vS/uaT4e+11qQkJhK4YgUANb/8Et0cdiXlVJ0Wdmx77UGDkoe4tnI97lPHF0hgJ5PJONH/BHFJmrsPC5qxvnG+ZrAqu2P79OnDoUOHVF2m6SkUCo3XSE1NRV9fX/W1pu7ArLqKU1NTady4MRMmTMi0L6sWLRcXF/bs2cOLFy9UH9DpzZo1ixIlSvDDDz+wfPlyfHx88PT0pGHDhnh5eXHo0KFMLXaGhoYar6Wse07uW5PsAsbslC1bNlfnymSyXHUL37hxAyMjIxwcHLJMPZOampppskb69yI5OTnTOTo66iPKUlNTkclkmd4n5c/DggULMnV9Q+afIVtbW+rVq8e+fftISEggOTmZBg0a5Dlti/K9ymuLX26IMXaCIBS6Awek56zG15UvD/mcy5Cle3v2EPfkCUbW1jjmoisppyws4IJMSi3xYO8ukmJjC67FTibDxMCkSB75TUsil8u5evUqmzdvxs7OLlOrG0jjsi5cuKC2LSgoiJiYGI3HZ/UepVe1alVCQkKwtbXF3t4ee3t7zM3NmT59OsHBwVnW1cTEhGXLlmXaFxYWxl9//aX6wF6yZAnfffcdEydO5NNPP6VevXqEhYXlakyiNu67oJUpU4bnz5/n6NjY2Fi2b9+OXC5HX1+fatWqAXDx4kW14y5cuECVtKValIGZsqUP4N69e5nKzjjB4eLFi1SoUAGjDEvWVK5cGT09PR49eqT6vtvb23Ps2DHVrN2MOnTowIEDB/Dz80Mul2s8JqeU75W1tXWey8gpEdgJglCokpNh/37pdYcOmo8pqNY6hUJB0Jo1ADh99hm6JUoUyHUMqjTgSUJFFAlx3D9wQBXYRURAus+p/7QaNWpgb2/PvHnzMk2aUPriiy8ICgpi8uTJhISEcO7cOX766Sdq1qxJkyZNcnQdZZqP4OBgXr9+TZ8+fXj9+jXDhg0jMDCQoKAghg8fTkBAQJY52ywtLZkwYQJr1qxh7NixBAQEcP/+fXbt2kX//v2pWrWqKu2HcszgnTt3uHv3LvPnz1ebDZoT+bnviIgIjQ9tz6itW7euxiS+8fHxqms+evSIkydPMnDgQBQKBT/88AMgjRls2bIlkyZN4siRI4SGhrJ48WIOHTqkeh/r1auHjo4OCxYs4MGDBxw9epQVaS3t6V26dIk5c+YQEhLCpk2b+Ouvvxg8eHCm48zMzOjduzcLFixg+/btPHjwgG3btjFnzhxKZ5GUXC6Xc+/ePbZu3Zrlz6jyno8fP57p8eLFC9UxN2/epFy5cpQpUybb91UbRFesIAiF6t9/4eVLaRmxxo01H1NQgd2Tf//lZXAwekZGVPn444K5CFCzloxjAZ58WnYBd7dswbFHD/r1k1ohsxj69Z8kl8tZsmRJlikkXFxcWL58OQsXLqR79+6YmprSpk0bhg8f/s4uSaVSpUrRs2dPZs+ezb179xg3bhzr1q1j7ty59OnTB11dXerVq8fq1auzHVPWpUsXbGxs8PX1ZfDgwURHR1OuXDm6d+/Ol19+qRrDNnv2bCZPnkzPnj0xMTGhbt26TJo0iYkTJxIeHk6FHAwczc99u7u7Z7nd19f3ndfOqTZt2jBu3DiSkpLU6uTn54efnx8AJUqUwMbGhhYtWjBv3jy1cYbz589n3rx5jBs3jujoaKpWrYqPj48q2bGdnR2TJ09m6dKl/PPPP9SqVYuxY8fyzTffqNXj448/JiwsjB49emBpacnw4cOznNQxZswYLC0tWbRoEc+ePcPGxobvvvuOr776SuPxZcuWpX79+jx58oR62WRLj4qKYuDAgZm2r1y5kqZNmwJSnsbcTArJD5lCG9kbC0BMTAyurq5cvHgRU1PToq6OIAhaMno0zJoFffqAMstAbGxsut/zGHbvNiGbf5Dz7MigQTw+cYJqffviNnas9i+Q5vff4efvn+FTrTU6pNJp1y7MNYzryan4+HhCQ0NxcHDIdmyWIBSW5ORk2rdvz4gRI+iQVdO7AMCzZ89o3bo1u3btolKlShqP0ebvuOiKFQShUCnHkGuYBKlSEC12MQ8e8Dgtt5xThtQG2lajBrxMLsPtFCndSdiuXQV6PUEobHp6egwdOpRVq1YVdVWKvbVr19KlS5csgzptE4GdIAiF5t49uH5dSnPSvr3mY/z8wMZG+9e+s3kzKBTYNG2KWR5nDuZUjRrSs/8TKXq95+dHSoqCR4+0P4FCEIpKt27dsLS01Lj8liB5/PgxBw4cYPTo0YV2TRHYCYJQaJStdU2bSmPsNGneXPvXTUlM5O62bQBUTVv6qSCVLQulSsHF6I+QlTAi5sEDVs+4Rvny8N13BX55QSg0v//+u+iKzYatrS379+/PcYJrbRCBnSAIhUYZ2BXE+LnshB8+THxUFEbW1pT/6KMCv55MJrXaJSiMUVSRMjBbPdkLiBY7QRAKlgjsBEEoFHFxoFwqMbvA7ulT7V/7zt9/A+DYsyc6OZxNmV/K7thwc2nGZ+LVfchIJTQUislyn4IgfIBEYCcIQqHw94f4eGmpMGdn9X0vX759nY8coBq9unuXp+fOIdPRwbFnT+0Wng3lsqcXXjRD39SUpOcRVDMNIDERHj4stGoIgvAfIwI7QRAKhXLFKE9PqasyvfQJ6LWdmP1O2oVtmzfHpFw57RaeDWWL3c0gA8qlre3Zury0lprojhUEoaCIwE4QhAKXkAA7d0qve/XKvP/8+YK5bkpiImFpFy6MSRPpKQO74GAo10pKTFrbwB9QiMBOEIQCIwI7QRAK3MGDEB0N5cqBphWRCiqwe3zyJAkvX2JYujS2BTHdNhsVK4KxsbTSxBtbd3QMDCiZ8gC7EsEisBMEocCIwE4QhAKn7Ibt2TPzGDqFouACu7DduwGw79gRnbRF2guLjg5Ury69Dg4zwTZtaaHvOxwirWdWEARB60RgJwhCgUpMhB07pNealme9exeePy+A675+zcOjRwFw6NJF+xfIAWV3bGAgqjQrzsYnadOmSKpT5Dw8PHByclI9atSogZubG15eXly4cEHtOB8fnwKtS3h4OE5OTpw9ezbPZSjvZ+XKlRr3jx8/HicnJ9W9nD17FicnJ8LDw7Ms08vLS+09yviIiIjIc32F/wa9oq6AIAgfNn9/ePVKWk0irdFKzYMHULo0REZq97oP/P1JSUigZOXKlFJGWIUsfWBXrr/UFfz82jUSXr6khIVFkdSpqHl7e+Pt7Q2AQqHg5cuXzJs3jwEDBrBv3z5sCmLZEQ1sbW05efIk5ubm+SpHX1+fffv20b9/f7XtycnJHDhwAFnGmUI5IJfL+fnnnzXus7KyylM9hf8O0WInCEKBSj8bVlNv6EcfQWio9q+rXJ/VoUuXPH24aoMy5cnNm2BsY4N51aooUlM5tuo08fFFUqUiZ2xsjLW1NdbW1pQpU4Zq1aoxadIk3rx5w4EDBwqtHrq6ulhbW2NgYJCvcpo0acLVq1d5/Pix2vYzZ85gbGyMra1trss0NDRUvUcZHzrazgckfHDET4ggCAUmNha2bJFeZzcpVdtxV9yTJzw9dw4A+8Je5iIdZYtdUJCUlLicuzsAW+ecIDg4/+UrFAqS4+KK5KFQKPJ/A2n09KTOI01B1tatW3FyclLblrFLMyUlhfnz5+Pu7k7dunUZMmQI06ZNw8vLK8trZuyK9fLyYtasWYwdOxY3Nzfq16/PqFGjiI2NzbbuderUoVy5cpnWS927dy9yubzI/qkQ/rtEV6wgCAVm82Z4/RocHaFFi8z7FQrtB3UAYXv3gkKBtasrpuXLa/8COeToCHp6UoD78CHYursTuHIldUxPEXInlTp18v6/tUKh4OBnnxF55Yr2KpwL1i4utFm7Nt+By9OnT5k+fTrGxsa00PRDkgO//vor27ZtY/LkyTg6OvLXX3+xdu1aGjRokKty1q5di7e3N5s2bSIwMJBRo0ZRsWJFvv3222zPk8vlat2xiYmJ+Pv7s2rVKvz8/PJ0T4KQVyKwEwShwPj6Ss/9+2sO4M6elfLatW2r3esqu2Erde6s3YJzSV9fCu5u3ZIerVrUJ1nHCAu9KB5cCALPmvkq/31sDVq2bBkrVqwApHFoiYmJODo6smDBAsrlIYH0mzdv+OuvvxgzZgzt2rUD4JdffuHy5cu5LsvR0ZFhw4YB4ODgwJ49e7h06dI7z5PL5fj6+vL48WNsbW05deoUpUqVombNvH1/d+3axf79+zNtb9WqFfPmzctTmcJ/hwjsBEEoEMHBcOKElPbj8881H3PqlNSSpc31YV8GB/MyOBgdPT0qpn3QFyUnJymoCwqCNm0MiCvTmJJPjhAbcALIe2Ank8los3YtKW/eaK+yuaBrZJSnwLJ3796qLlIdHR0sLCwwMzPLcz1CQkKIj4+nXr16attdXV0JCgoCYMCAAVxMt7zJpEmTqF+/fqayHB0d1b42MzMjOjr6nXVwdnbGzs5O1Wq3d+9eOufjnwoPDw9++umnTNuNjY3zXKbw3yECO0EQCoQyA0T79lChguZjjh+Xnt3dQVs9VsrcdeVatiwWM0+Vw8Nu3ZKeDWs1hydHMHx0Evg6X2XLZDL03rMPe3Nzc+zt7XN9nkKhUAWSycnJqu3K8XnZjfmbNm0a8elmq1hZWfEy/QLFafIzkULZHdunTx8OHTrEJuWsoTwwMTHJ03skCCAmTwiCUACSk2H1aun1l19qPiY1VWrRA2jWTDvXVaSmErZnD1D03bBKyiTFysCufEsp7Yl1/FUSc9Aa9F+nr68PwOvXr1Xb7t27p3ptb2+PoaEhVzKMNQwICFC9Llu2LPb29qqHqamp1uspl8u5evUqmzdvxs7OLlPrnyAUFtFiJwiC1u3ZA48fS/npssoNfPMmvHgBJiZQt652rvvswgXinjxB38yM8sVkeYeMLXbV3MpxJaEy5Uvc5dGpf6kkb190lXsP1KtXDx0dHRYsWED//v0JCQlRjdEDMDIywsvLi0WLFmFtbY2joyNbtmzhypUrNGzYsNDqWaNGDezt7Zk3bx5ff519S+z58+e5m2FduYoVK1KpUiUA4uPjs0xEXLJkSUqUKKGVOgsfJhHYCYKgdQsWSM/e3pBV75ayG7ZpU2mSgTYou2ErtmuHbjH58FMGdvfvS7NjK1QAvWrucO8uj06eEoHdO9jZ2TF58mSWLl3KP//8Q61atRg7dizffPON6pjvv/+epKQkxo0bx5s3b2jVqhWtW7cmISGhUOsql8tZsmQJHTt2zPa40aNHZ9o2aNAgfvzxRwD8/PyynE07b948OhVhCh+h+JMptJmMSItiYmJwdXXl4sWLBdJsLghCwbh8GerXl5IRh4aCnZ3m43r3hr//hsmTYdiwWNXveUxMDCYmJrm+bkpCAltbtiTp9Wtar1xJ2UJsrXkXKytp2bTLl6FePXh04gRHBw3C1M6Orhnyn2kSHx9PaGgoDg4OGBoaFnyF3zMHDx7E1dUVS0tL1TZvb29sbGyYPn16EdZMEHJGm7/jYoydIAhaNWeO9PzJJ1kHdSB1vzZsKK08oQ0Pjx0j6fVrjG1sKOPmpp1CtSTjODvr+vWR6eoS8+ABsY8eFV3FPhC+vr4MHz6cwMBAHjx4wKpVqzhz5gxdu3Yt6qoJQqETgZ0gCFoTFAQbN0qvR4zI/tgxY6Q8ds2ba+faym7YSp06IStmyy5lHGcXm2SCgX0tAJ6eP19Etfpw/Prrr5iYmPDFF1/QuXNndu3axcKFC2ncuHFRV00QCp0YYycIgtZMny6tJtG1K7i4FN51E16+5NGxYwBUymq2RhFSBnZpadVYvhwunmtA19IBPDt3jsrduhVd5T4AFSpUYPHixUVdDUEoForXv7WCILy3AgJg3Trp9S+/ZH/szZsQE6O9a98/cIDU5GQsnJywqFpVewVrScauWEdHuBkrjQEULXaCIGiTCOwEQdCKkSOl1rqPP4Z3DXHr3BksLKSVJ7RB2Q3rUAxb6+Bti11wsPQeVa4MwXEupCj0iH34kJiHD4u2goIgfDDyHdhllWtHEIT/jp07Yf9+KW3JjBnZH3vnjjRbVkcH6tTJ/7VjHj4k4uJFkMmwf0eaiaJSubI0SzgmBh49kr5OUJgQ8sYZgGfnzhVxDQVB+FDkO7Br1aoVgwYN4sCBAyQlJWmjToIgvEdev4Zvv5VeDxsmdTNmR7m2ebNmkI8lQlXupa00UbZhQ4zLls1/gQXAwEAK5kAaZ1eypJQCRdUdKwI7QRC0JN+BXUpKCkePHuX777+nefPmTJs2jcDAQG3UTRCE98CwYRAeDg4OMH78u49Xpm1rr4W8vAqFgtBdu4DiOWkivYzj7CpXhsC4BoAU2BXTlKKCILxn8h3YHTlyhB9++IFKlSrx8uVL1q5di6enJz169GDt2rW8ePFCG/UUBKEY2rQJ/vwTZDLw9YV3rUefmAhHjkivO3TI//VfBAYSffcuuiVKYNemTf4LLEAZU54ox9kpdPSIe/KE2PDwoqucIAgfjHynO7GxsWHQoEEMGjSIq1evsnXrVvbt20dgYCDTp09n9uzZtGrVCk9PT1q0aIFOMcsvJQhC3gQEQP/+0utRo6BVq3efc+qUtKxW2bLaGV+nnDRRvlUrDLTRr1uAMqY88fICd3cjTI7UIS7oEk/PncM0u4zO77nRo0ezbdu2bI+5pYx6C1Hv3r25fPky27dvp0aNGgCcPXuWfv36ZXvejBkzaNiwIa1bt87yGHd3d3x9fbVaX0F4F63msatbty5169bl559/5vDhw+zYsYPTp09z4MABDh48iJWVFd27d6dHjx44vmsgjiAIxVZYGHTqJAVprVtLy4LlhHJ8Xbt20uSJ/EhNSSEsbXxdpc6d81dYIcjYYqdc7vNqagNupAV2jj17Fk3lCsHPP//M8OHDVV+7u7szduzYd66rWpBCQ0O5fPkyDg4ObNiwgclpP8guLi6cPHlSddy0adN48uQJPj4+qm1mZmZERkYC4OPjg4uGxI0GWS2ULAgFqEASFBsYGNChQwc6dOhAfHw8GzduZNGiRURFReHr64uvry/169enf//+tCnm3SeCIKi7fVsKzMLDpXFjmzZJs2Fz4quvoHx5qF07//V4evYs8ZGRGJibY9usWf4LLGDKMXb378ObN2BkJH1dtmFDbixbRsSlS0VXuUJgZmaGWYZWVTMzM6ytrYuoRrBlyxYcHBz4+OOPWbx4MSNHjsTU1BQDAwO1ehkaGqKvr59lXc3NzYv0PgQhvQLrFw0PD2fp0qX06dOHWbNmERcXh0KhwMnJCSsrKy5evMiQIUMYOHAgcXFxBVUNQRC06MgRaNJEarGrUgUOHYJSpXJ+fuXKMGSIdtaHDUubNGHfoQO670HLSOnS0nulUEjBcUqKtKTa4Vu1kenqEvvoEXFPnhR1NYvU1q1b8fDwYNq0abi5uTFo0CDOnj2Lk5MT4enGIIaHh+Pk5MTZs2dV27Zs2YJcLqdOnTrI5XJWr15NampqltdKSUlhx44dNGvWjPbt2xMXF8fOnTsL9P4EoTBotcXu9evX+Pn5sWPHDi6l/fepUCgwNzenS5cu9OrVi+rVq5OSksKhQ4eYPHkyJ0+eZNq0aUybNk2bVREEQYvi4qTu1tmzpcDEzQ1275bGyhWF5DdveODvDxT/2bBKMpnUHXvmjDTOrmZNKeVLSooJO7o6EXPnJhGXL2Mvl+e4TIVCUWT/GBsbGyOTybRe7sOHD3n69Cnbtm0jPj6e58+fv/Ocv//+m7lz5zJ+/Hjq1q3LzZs3mTJlCk+fPmXkyJEazzlx4gTPnj2jffv2VKhQgXr16rFx40b69Omj7VsShEKV78AuJSWFY8eOsWPHDo4ePUpiYiIKhQIdHR2aNGlCz549adOmjdpYA11dXdq1a4exsTEDBgzA399fBHaCUAy9eSPNdp09Gx48kLYNGAALF757BmxG06dDmTLQs2fuWvk0CT9yhOS4OEwqVKB0vXr5K6wQVa8uBXa3boGeHtjbw927gJ0L5DKwUygUuLu7c/r06YKtdBaaNWvGiRMnCiS4Gzx4MHZpE0nSt8pl5ffff+frr7+mc9pYSzs7O2JiYpg0aRLff/89JUqUyHTO1q1bKVOmDG5py6R06tSJadOmcfnyZY3j5bIzcOBAdHV1M22fN28erXIyq0gQtCjfgZ27uzsvX75U5WCys7OjR48eeHp6YmNjk+25ldMydmr6hRAEoei8egVLlsD8+fDsmbTNzg4WL4auXXNfXlwcTJ0qBYr16+c/sFMtIda5c4EEFgVFU8qTu3fhpakLeqwn4vLlXJX3Pt17blSqVCnHxz5//pwnT56wcOFCFi9erNqemppKQkIC4eHhmSbrPX/+nMOHD/O///1PlalBLpczY8YMNmzYkOvAburUqdStWzfTdjHuTigK+Q7sXrx4gaGhIe3ataNnz540atQox+cmJCTwySefUFsbI6kFQci3iAipNW7xYim4A6hUSVoHtn9/MDTMW7kHDkhBnb095PIzM5M3kZE8Tpux+D7Mhk1PU2AHcC/FBUfg5a1bJMXGom9i8s6yZDIZJ06c+OC6YkGarJBR+gTOycnJqtfKcXRjxoyhadOmmc6ztbXNtG3Xrl0kJSWxbt061q9fr1bWvn37GDt2LBYWFjmub9myZbG3t8/x8YJQkPId2E2aNIlOnTphamqa63MdHBxU08sFQSg6MTFSd+vcuVLrGkCNGjBmDPTunfNZr1lRpi/r0UMaa5Yf9/bsQZGSglWdOpR0cMhfYYUsfS47heJtYBf8xIbatrbEPX5M1LVr2DRunKPyZDIZJjkIAt9n+mk/fDExMapt9+7dU722srLCysqK+/fv87///U+1fe/evRw8eJBZs2ZlKnPr1q1Uq1aNuXPnqm2/dOkSEyZMYNu2bfRXJmkUhPdMvmfFRkREcODAgRwdu2TJEkaMGJHfSwqCoCUKBaxfD9WqwZQpUlDn5gZbt8L161IS3fwGdfHxsH279NrTM99V5u6OHQBU7tYt/4UVMkdH0NWVAunHj98GdnfvgnVaU+aHnvYkt6pVq4aJiQlLlizh3r17nD9/nvnz56taC2UyGQMGDGDt2rWsXbuW+/fv4+/vz6RJkzAwMMiUS+7GjRsEBQXx2WefUa1aNbXHJ598gr29PRs3bszVEm+vXr0iIiIi00OZ504QClO+W+wWL16Mq6srnjn4i33w4EFCQ0Pze0lBELQgJkaaCPH339LXlStLrXaenvlvVUtvzx6IjpbG6OU33dyLoCBe3rqFjr4+FbWxJlkhK1FCWlP3zh2pO1YtsPvWhXt79+Z6nN2HztTUlF9//ZW5c+fSqVMnHBwcGDNmDAMGDFAd4+3tTYkSJVi7di2zZs3CysoKT09Pfvzxx0zlbd26lZIlS9JVw2BRHR0dPv/8cyZPnsyZM2do0qRJjuo4ZMgQjdsNDAy4du1aDu9UELRDpsjFvyUPHz7k33//Vds2btw47O3tGThwYJbnKRQKHj16xPLlyzE2NubcuXPvvFZMTAyurq5cvHgxT928giBk7dEjaeWDK1ek2Znjx0vj6DRMHsy3nj2lFsCRI0FDrxgAsbGxqt/zmJiYLLsXL82eTdDq1di1bUvzBQu0X9lC0LmzFOwuWQJ9+8KqVVKA17RyIH69eqFnYkKvf/9FJ92ksvj4eEJDQ3FwcNA4/kwQhPebNn/Hc9ViZ2VlhY+PD8+U0+SQmsHv37/PL7/88s7zFQpFjv8DEgShYDx8KK3revu2lH5k2zbQMOZcKxQK6SGTQX7Tg6UmJ7+dDZuXqbnFhJOTFNgFBYGZmZSwGSA1pRp6JiYkx8by6vZtSimXqhAEQciFXAV2hoaG/PTTT8yfP1+17dGjRxgYGFC6dOksz9PR0cHY2JiaNWtmmSxSEISC9+oVtG8vBXWVKsHhw1LXYEGRyaTWuocPoVy5/JX1+PRp4qOiKGFpSbnmzbVTwSKgjNcyrnevo6tL6bp1eXL6NBGXL4vAThCEPMn1GLsuXbrQJV2m9+rVq1O7dm21KeOCIBQ/KSnwv//BjRtSkHXkiBTcFYby5fNfRmjapIlKHTuik98ZHUUoY8qT27fh4kVpYoW1i4sqsKuWboanIAhCTuV7Vux3332Xo4kTgiAUrTlzwM9PWnx+586CD+ru3ZMWvNeGhJcvCT98GACH93A2bHrKwC4sTJox/OefUsC9bh1Y168PQKSYQCEIQh5pJbDr2bOnNuoiCEIBuXgRlMNgFy8GV9eCv+bYsVLwmCFVWJ6E7txJamIipWrUoFSNGvkvsAiVKQMWFtLYw9u31WfGWtWuDTIZsY8e8SYiokjrKQjC+ylXXbHKGbH169dXrb2XcZZsTogJFIJQeJKTYeBA6blXL2kFiYL24IGURkWhAA+P/JWlUCi4s2kTAFU+/vi9X0ZLJpNa7c6ezZzyRN/EBPMqVXh1+zZR165RIcObl5vcaoIgvD+0+budq8Cuf//+6OjosGfPHhzSRlz3798/V39oZTIZN2/ezF0tBUHIs99+g8uXpVaixYu1m6MuKwsXSmP6WrXKwxJiqanSI20Nz4hLl4i+exc9IyMqdeqk/coWgfSBXe/e0ra7d6VAuHSdOry6fZvIgABVYKdcfSEuLg4jI6OiqrYgCAVEuTSgvhbGD+d68oRyXb70chNpiv84BaHwvHgBkyZJr2fNgrJlC/6aT57A779Lr3/6CYiKgsBAaVDZ/fvS4LvwcGl7VBQ8fw4vX74toGRJKfq0sIDSpbljbAyAfZky6O/ZA7VrS0tl6OU7v3qRSb+0WMWKUgwbHy+9d1Z16hCyZQtRV6+qjtfV1cXCwkKVaqog12kVBKHwKBQK4uLiePbsGRYWFuimy1+ZV7n6yxgUFJSjbYIgFA/Tp0vBnbMzfPllAV8sJgYuXeL4hBvMfnODxiVv4tr/BqTLe5ljCgW8eEHCq1fcr1YNdHSocuSINPsDwNgYmjeHdu2kBWjfszVj06c80deXVuW4d09qtatVpw4AUdevk5qSokpUbGNjA6CWR1QQhA+DhYWF6nc8v97ff3kFQcjWkydS1ytIrXVa+EfwrdRUqRXu7FnpceaMtLhsaiqfKI+JTnsA2NtLg8ns7aUmqooVoXRpsLICS0spuqlWTTo2LExaAuP5c0L/+YfUTZsoZWWFZdOm0jWuX5eCyP37pcfw4dJskC+/lLIgm5tr8UYLRvqUJwqF9NYoA7smjR3RMzYmOS6O6JAQLNLeF5lMhq2tLWXKlCEpKakIay8Igjbp6+trpaVOqUADu/j4eE6fPk1qaipubm5YWFgU5OUEQUhn7lype69JE5DL81mYQiEtcHrwoPQ4ckTKdpzBm9IVOP6qLtEVatFrfE1kzrWk5ql3LQsYG/v2denSYGKComxZQi5dAqDK4MHIlIPRUlPh5k2pHrt3w9Gj0rTfixdhxAhppsiPP0rBYzFVpYrU/RodDU+fSjOIf/wRGjSQEhVbOTvz9Nw5IgMCVIGdkq6urlY/BARB+LBoJbB7+vQpS5YsoVy5cnz11VcAhISE0L9/fyLSpuwbGRkxdepUOnbsqI1LCoKQjagoaS1SgHHj8jhhIjUVzp2DLVuk5SPu3lXfb2wMbm7QuDE0agSNGmFUvjz1IyAhAWQV8ncPT8+e5VVICLpGRlTq3PntDh0dqW/Z2VmKhiIi4K+/4I8/pIBvwQJpxsigQfDzz4UzsDCXSpSQUsHcvSuNs2vTRn2/Vd26PD13jqiAAKr06lUkdRQE4f2U78Du+fPnfPLJJzx79oyPPvpItX38+PE8e/YMmUyGiYkJMTExjBw5EicnJxwdHfN7WUEQsrF8udQIVq9eHlrrAgOlAjZtkiY5KOnrQ7NmUhTSti3Ur6+awJCS8rar19paK7dA4KpVADj26IF+di1+1tbw/fcwdKjUijdzptSi6OMDq1ZJs0eGDCl2ky2qV5cCu1u3IN2fTkCaGQsQGRBQ+BUTBOG9lu8ExatXr+bp06dUrFiRTz/9FIB79+5x8eJFdHV12bBhAxcuXOCrr74iOTmZVWl/rAVBKBhJSVKDFcAPP+SwtS4xEf75R8pPUrMmzJ8vBXWmptKyCFu2SLMwjhyRWsEaNlQFSs+eSTHe7t3au4dXd+7w+MQJkMlw8vLK2UkymTSZ4vBh8PeXWhNfv4Zhw6QxeFeuaK+CWpB+nF1srPT2L1okbbOqXRuQ3oekmJgiqqEgCO+jfAd2x48fR09PD19fX1WL3dGjRwEpkXG9evUAGDJkCCVLluTMmTP5vaQgCNnYvl2KycqUeZsjLUuJiVKfrYMDfPqpNF5NRwe6dpUKUnZzenqCiUmm0x8/hvbtISBAip8SE7VzD0Fr1gBQoXVrzPIyVq51a2lSxx9/SJMzAgKkAWxTp0rNi8VA+pQnb95Ib//330vjIo2srTEpVw4UCqKuXy/aigqC8F7Jd2D34MEDKlWqRIUKbwfUnD59GplMRtOmTVXb9PX1qVChgpiqLwgF7I8/pOevvpLGcmmUnAwrV0ozUQcPhkePwMZGWncsLAx27IBu3cDQMMvrHD8uDa+7ckUKInfvBgOD/Nc/PiqK0F27AKjx+ed5L0hHR5pIERgopURJTpbur0MHacZCEUuf8sTKCszMpK/DwqRnq7p1AYgS3bGCIORCvgO7lJQUDNL9NU9OTub8+fMANGzYUO3YN2/eiKSaglCAQkOlXkiZLJu8dUeOSEl+vb2lHBs2NlJelLAwmDxZSqqWhZQUKaD7+GNo2VLKN1ylCpw+/TZbSX7d3ryZ1MRErGrXpnSul63QoEwZqSt51Sppwoe/v9R3fPly/svOB2WLXViYNNkk/dJiAKXTumPFODtBEHIj36OJy5cvz8OHD0lKSkJfX5/z588TFxeHqampqhsWpJmzDx48wC6bDw1BEPJnxQrpuU0badalmhcvpEF3ad2cWFnBmDEoBn3D4J+MedJbWgDizRup5U35sLUFX1/plMRE6NjxbXaSAQOktColS2rvHkI2b0YGVP/iC+39IyiTweefS92xvXpJrXjNm8PGjZB+xm0hKltWSrn36pWUSaZyZbh69W1gl77FTqFQiH+KBUHIkXwHdrVr12b79u38+uuv9OjRgwULFiCTyWjZsqUq11JUVBQjRowgJSWFJk2a5LvSgiBkplDAunXSa2/vDDv9/eGLL+DhQxQyGdF9B2PuMxUsLJABJ09KeX81Sb+og5ERfPaZFOANGyZlHNG2hBcvsKpQAbuMOUC0oWZNqXmxVy84dEjqbl64EL77TvvXegeZTGq1O3dO6o7N2GJnWaMGMj094qOiiHv8WBpzJwiC8A75DuwGDhzIvn37WLNmDWvWrEGhUKCnp8fAgQMBuHDhAl988QUpKSmYmZnhnekTRxAEbTh3TurWMzGR5j4AUt/p5MkwZQooFCRXrso4uzUs3dWYMB+wSDts8mRp2JmFhRS8JSVJwVtiovR1ekuXFvy91Bw4EJ2CSk9iYSEtTfbNN1JT5JAhUh/2r7/mMeFf3mUX2OmWKIFF1aq8CAwk6to1EdgJgpAj+f7LWblyZVasWMGMGTO4desW9vb2jBgxguppI4PLlClDcnIy1apVY/78+WqTLARB0J6//5aeu3aVhpIRHS1Ni01bX/XFp1/T7Mw8Ao8Zo6cH589L6ehAmltQXBjb2FC5e/eCvYi+vpSrr0oVGDMG5s2TothFiwo1uFNOoAgKklZDAynGVLJydpYCu+vXqdi+faHVSxCE95dW/iV2cXHhn3/+0bivQoUKbN++XRXoCYKgfampUh40SEtxcv8+dOok9a8aGfFowjLq/upFZCQ4OkpzCdKGcBUL6XO11ezfH11tTK99F5kMRo+WEhwPHPh2Yd1CDO7S57Jr0EDKMFO16tv9ls7OsGkTz0XKE0EQcqjAU7Hr6OiIoE4QCtipU/DwoTQYv4N9IDRpo0phErFqN40HuhIZKU0G3bdPe6tDaIsybx2Ag6ofuZB8+aUUyA0YUOjBXfrAztJSGvKXnlXaIMbnN2+iSE1FppPvRAaCIHzgtBrYvXnzhtevX5OSkoJCocjyuHJirIggaNXGjdLz0BZXULRtR0hSEhE1a/KqRk2CRszAW7cE8dUr8PHA2pSIaQbWDtkXWIjinj7l1vr1qq919PULvxLKsb/K4E5PT+qeLeDgrkoVKd3eq1fSGEcbG/X95lWqoGtoSFJMDK/v3aOkQ/H5vgmCUDxpJbA7duwYCxYsICgo6J3HymQybt68qY3LCoKAlHd382ZoqHeCjlf7sa10aVKVAUngTawBa2OAK9zy2c0tH2nJqqq9e1OpU6eiCaTSuTRnDikJCUVaB+BtcPfll7BgAZQrByNGFOglDQ2ltDTKNWODgqQFM1q1klZt09HTo1T16kReuULU9esisBME4Z3yHdhduHCBwYMHk5qamm0rnVJOjhEEIedOnAD3hD/4xHEBd3WNAbCoUoXyrVtjUa0aGJhw73YcloTx7Px5np4/T9S1a0Rdu8a1JUtwGTYMu3btiiRP2uPTp7nv51d8uhi9vaXms2HDYORIqFBBWiu3ADk5vQ3szpyRFgSZOlUK7EAaZ6cM7By6dCnQugiC8P7Ld2D3559/kpKSgpOTE9999x2VK1fGMJtliARB0J7UlBQCp/5Mz/K7SEGGlUyGi48PZVq1UjvO3iPtxddfEx8VRcjWrdxau5bY8HBODhuGTdOmuI0dW6gtQslxcZyfPBmAqp98AhMnFtq1s/Xjj/DgAcyfLyU1trGRmtAKSPXq0sTloKC3OQOVKU8g3Tg7MYFCEIQcyPe/yZcvX6ZEiRL4+vrStm1bHB0dKV++fLYPQRDyLzUpiVM//ojFk13IFAqc4pJpe/AgZVq1Yts2KS2bph5OQysrag0cSNf9+3H+5ht0DAx4cvo0e7t3J+C330hJTCyU+l/69VdiHjzA2MYG50GDCuWaOfbrr9K6aUlJ0L07XLtWYJdKP4EiYy47eBvYvQgMJDUpqcDqIQjChyHfgd2bN29wdHSkdOnS2qiPIAg5oFAoODt+PA8OHUInNZU64S+ptmU3Ora2JCRIK4eNGAFLlmRdhp6REXW++45OO3ZQrkULUpOTuf777+zv3ZsXgYEFWv/wI0e4k5Z4r/G0aeibmhbo9XJNR0daeq1FCykfYOfOEBFRIJd6V2BnZm+PvqkpKQkJvLpzp0DqIAjChyPfgV25cuWIiorSRl0EQciha4sXE7pzJzKFgibhj/mjyt+Y1ZcSoPn6SmnsypeHr756d1lmFSvS8vffafbrr5SwsODlrVvs6927wFrvosPC+Hf0aACcvLywadxY69fQCkPDt4nl7t+HTz+VZqpomTIbVGio9D0DqSdY+dbLdHSwrFULgCjRHSsIwjvkO7Dr0KEDz549499//9VGfQRBeIeHx45xPW1dr4aPHzM9ZhYV+zYHpK7XGTOk48aMSVuBIgdkMhn2cjkdd+zArk0bFAXUehf/4gXHhwwhKSYG6/r1qTdsWK7OT0lNISI2gpDnIdyMuMn1Z9e58/wOj14/IjGlALqQS5WSgjtTUzhypEBmyZYtCyVLSkmmo6Ol75lCAffuvT1G2R0rAjtBEN4l35Mnvv76aw4dOsTIkSOZMGECLVq0wKAwssYLwn9Q7OPH/DtqFADVoqI4Hu3JH3xFYCdp/7p1EB4uZer48svcl29UujTuCxZwf98+Lkydqmq9qzVwILW++ipfK0Ikvn7N0a+/JvruXYxtbHCfNy/L8lJSU7j27BrnHp7j4qOLBEUFcTvqNk9jn5KqSM3yGhaGFlSyqERVy6o4l3GmUflGNCzfkFJGpfJcb2rWlLplPT2lNCguLtCvX97Ly0Amk1rtzp2D4GCpO/b6dak7VrkKhaWYQCEIQg7JFPnMPzJ8+HCio6M5ceIEMpkMXV1dzM3N0c8iN5ZMJuPIkSPvLDcmJgZXV1cuXryIaXEbfyMIRUChUHD06695fOoUlm/e0FBhSrmwc5SvbIhy6FXt2nDjhjT2f/jw/F0vPiqK81Om8ODgQQAsqlWj8fTpWNaokeuy4p484eg33/AyOJgSpUrRZs0azJUDyoDY2FjV73mPNT04+ugoL+JfaCxLhgwTAxNK6JZAJpORkJxAXFIcKYqULK/vZOVEU7umdKzakfaO7TErYZbre2DCBJg8GUqUgJMnwc0t92VkoV8/WLsWpk0DDw8wMoJq1aRngNhHj9jRti0yXV0+PncOPZF5QBCELOS7xW7Pnj2q1wqFguTk5GzH3BVFrixB+BCE7tzJ41On0ElNpUlUFHPb7iYhzJBOnaRWnwMHpKDO1FRaQCG/DK2saL5gAff37+f8lCm8DA5m/6efUr1fP2oOGEAJC4sclfPw2DHOjh9PfGQkhlZWtFq2TBXUpSpS8b/rz++nflcdvy1oGxhAyRIlaVi+IQ3KNaCWdS2qWlWlonlFShuXRk9H/U9XqiKVl/Evefz6MXdf3CU4KphLTy5xNvwsIS9CuBV1i1tRt1h5ZSUGuga0qtSKbk7d6FGjBzamGZZ7yMqECXD5MuzaBb16Sa9L5aMlMJ30EyjGjs2839jWFkMrK+Kjonh56xali9NCv4IgFCv5DuxmKAf0CIJQYBJevuTS9OkA1I6IoOSs2ayaXhOAjh2lY2xspHijYkVpzVhtqdi+PWXc3Dg/dSoPDhwgcOVKbv/zD5W7d8fR0xMLJ6dM/7ApFAqeX7/OjT//JNzfH5CWx2r5+++Yli9PfHI8Ky+vZO6/cwl5EQLphseNajaKbrW70aB8g0wBXFZ0ZDpYGlliaWRJrTK11PZFxEZw7uE5joQdYeetndx+fpv9IfvZH7KfIX5DkFeV413Pm07VOmGgm01Xs46O1KxWv77UT/rll7Bli1aWHVNOoLh1S/N+mUyGZa1aPDp+nKjr10VgJwhClvLdFVtQRFesILx1YepUgjdswDw+HrmTEyEL9lDNSYaBAbx4oT5JQqEouCVOHx49SoCPDy/SLR9obGODpbMzJra26OjpEffkCZEBAcQ+fCgdIJNRvV8/6gwdSqq+DksvLGX6iek8jX0KgHkJc/o49WGJp5SbJSYmBhMTkwKpv0Kh4FbULXbe2snWwK2cfXhWta+0cWm86njxbYNvcbR0zLqQCxegaVMpx52PD3z3Xb7rdf261I1ubi4lKl67FuLipEZCpYDffuP6779TqWtXmop/qAVByIII7AShmHt55w5+3bujUCjwiIjA5vJllu0uz6BB0LIlHD1auPVRpKby5N9/ufPPPzw6cSLLdV51S5TArl07ag0YQElHRzbf3MzoQ6O5+0JK0mZX0o6RzUbSv15/SEL1e16QgV1GQZFBrLy8kjUBa3gS8wSQxvB1q96NYY2H4V7RXfPwkUWL4PvvwcAA/v1XasXLh/j4t7Nh//0XmjSRxtfFxr4N0h8eO8axwYMpWbkynXftytf1BEH4cOW7K1YpMTGRrVu3cuTIEe7evcvr1685c+YMz58/Z86cOXz55ZdUqVJFW5cThP+Mq9OmoVAoqBAdjc2sWVC+PIcOSfs8PKRgYPZs6NoV8jCvIddkOjrYNmuGbbNmJL95Q+SVK7y8fZs3EREoUlIwsramZOXKlG3QAD1jY25H3eaT9R04EHIAABtTGya2nEh/l/6qrs/YpNiCr7gG1UtXZ1bbWUxrPY19d/bx2/nf2HdnH9uDtrM9aDuutq6MbDaSnjV6oquj+/bEIUPg8GHYsQM++QQuXZJyluSRoaG0nNjdu1JLna4uvHkDjx9LM5zhbcqT6NBQkmJiil9SZ0EQigWttNiFhobyzTffcO/ePZTFyWQyAgMDuX79Or169aJEiRLMnTuXNm3a5KhM0WInCBB59SoH+vRBplDQydqakkePkqqQUbYsREZKkzN1dd+28EREQCE1dr1TSmoKC84s4OfDP5OQkkAJ3RKMdh/NiKYjMDFQr2T6WbGF2WKnyc2Imyw8s5A1AWuIT44HoEbpGvzS4hc+qfXJ2wDv+XMp9cn9+9CnD6xfn6/rduworRm7bBnMmiUFecePQ/Pmb4/Z3qYNcY8f03rFCso2apSv6wmC8GHKd4Li169f8+WXXxIWFoatrS39+/enYsWKqv1mZmZUrlyZhIQEfvjhB4KDg/N7SUH4zwgYNw4Ah9evKfnHHyCTce2aFNSZmECDBrBqlXRsz57FJ6gLexnGR6s/4qeDP5GQkkDbym25Pvg6Ez+amCmoK25qWtdkWZdlPPjxARNbTsTC0ILAyED6bO2D8xJn1gesJyU1BSwtYcMGaVLFX3/Bxo35um76mbGOaUP8Mq4gJhIVC4LwLvkO7FatWsWjR4/46KOP8PPzY9SoUWrrxtrb27Nr1y7atGlDcnIyK1euzO8lBeE/IeLff3ly9y46CgXOvXurPvkPH5b2N28OKSlv4wlv7yKqaAbbArfhssyFk/dPYmpgyh+d/2D/Z/upYqnloRh37sDBg9LM1IyPXbukAWr5UNq4NBM+mkDY92FMaTWFUoalCIoM4rNtn1Hz95qsvbqWlMaN4OefpRO++UZaCyyP0s+MVY5aEYGdIAi5le8xdgcOHEBPT49p06ZRokQJjcfo6uoyefJkjh8/ztmzZzUeIwiCupu//AJApZQUTCdPVm1Xjq9r3VqKX169gkqVpIkURSk5NZnR/qOZ++9cABqVb8SGnhtwKOWQ+8IiI+HKFbh9W4pu7tyBsDBpLJtuWlfo5MnS9NGs3L//tglz2jTYvFkahOjsLDV1NmyYo7ww5obmjGsxjqGNhrL43GLm/juX4Khg+m3vx8xTM5n+6WS67nNDdv4CfPGFFGzq5P5/ZmWLXVCQNHYSICRE/RjVChQ3buS6fEEQ/hvyHdiFh4dTtWpVrKyssj3O0tISBwcH7t69m99LCsIH7+XRozx8/BgUCmoOG6ZagiApCY4dk47x8JAmTYC0Pn0eYgmtef7mOb039+bgXWmViuFNhjOj9Qz0dTWvQJMtJydQpkrJKDwc7O2l146OUpBmbp45v0t8vHry4Fu3pEDxyhX141xcoG1bGD36ncmGS5YoydjmYxnScAi/nf+NOafncDPiJt0396Lp5y7MfFWC5ocPw8KF8OOPubpleBvYhYZKuQghc4udZU0pd2Hsw4fEv3iBoZYSJAuC8OHI9+SJ+vXrU6ZMGfbt26fa1qdPHy5fvkxghsXDu3TpwqNHj7h48eI7yxWTJ4T/sn/d3Ql98QK7EiVofvGiKnD5918phVqpUlKMU7YsxMRI64w2aFA0dQ17GUb7de0JjgrGWN+Y1d1X06tmr6xPePxYik5PnJBmf/j7E2ts/HbyBGACUn9kjRrSs/Lh7q6etC+n7t2DgAC4eROuXoWzZ6XZCSBNSX3xQnoGqbXQyuqdyQBfxr9kzqk5zD8znzfJbwDoFAwzjutTe+9FKTFdLigUYGEB0dHS99nQUFo3NuNk210dO/L63j0+WraMcu7uubqGIAgfvny32Nnb23P79m0iIiKwtrbO8rjHjx8TEhJCdeVAEkEQNHpz5Aj3nj8HmYwaP/+sFmAoW+s++khqhNLVhQoVtLpsaa4EPA2gw7oOPI55TEXziuz63y7qlK2jftCbN1IQd+CA9Lh2TX3/qVNSq5nSvn3SNN98pA/JxN5eenTp8nbbkydSv/ajR2+DOoD27aWcI4MGSQMXzTSvK2thaMG01tP4tuG3TD42mT8v/cmeainsrZrEZws/YvKMM1SyrprjKspkUqvd+fNSlTw9NR9nWasWr+/d4/mNGyKwEwQhk3x33ignRUyePJmsGv8SExP5+eefpQSrysEjgiBodPvnn0mVyShtbEzpnj3V9p08KT03by71Ij57Bv7+BbfSRHaOhR2jxcoWPI55jHMZZ057n34b1KX/W7B+vRQszZ0rBXUymZTQ94cfpHFvLVqoF+zurt2gLis2NtC3L4wY8XZbeLg0yC0oSKqfnR2MGiW90VkoZ1aOpZ2XcvPbm3xcuQsKGay1e47T7zX4Yd8PRMZF5rhK6WfGZkU5geK5mEAhCIIG+Q7sPv/8c8qVK4e/vz8ff/wxK1asICoqCoBjx47x559/0qVLF06fPk3p0qXx8vLKd6UF4UOVcvgwd9J+f5wGD1bbl5oqNW7B29xmBgZvg4HCtC1wG+3XtedVwiuaV2zO8S+OU/5pHMyZA82awR9/vD24bVspQPryS/j7bynZ3sWLMH++lKPF0rLwbyArFSpIXcW//w7VqkkzU2bPlvpEx46VumyzUM2qGv947eRc+Ul43IVEUlh4diGOixyZdXIWb5LevPPy6WfGbtoEgwe/nQWtpJxAESUmUAiCoIFWEhSHhITwzTffcP/+fY3L7ygUCqytrVmyZAnOaX+U3kWMsRP+cxQK7jVqxKnYWIwMDOh27hw6+m8nH1y7BnXqSBM9Hz2SegiLoqVuw7UNfLbtM1IVqXQr35oN9xtgtGWnNH5NqW1bqdtV6R0L2BanBMUqqamwezdMmSKtDwuwZg28659ThQJFt64cvLmbUV2NuGIuBXR2Je2Y5jGNvnX6oiPT/D/15s3w8cfQqJE0L8TXFyZNgvHj3x6TFBvLpkaNQKGgx9GjGGUzBEYQhP8ercyjc3R0ZMeOHYwZMwY3NzfMzc3R1dXF1NSU2rVr8/3337Nnz54cB3WC8J+0dy8hT58C4Pjxx2pBHbzthm3SBEaOlCaFbt5cuFXcGrgVr21epCpS6f+gNJu/PoTRlJlSUKenB+3aSa1dGfNVFkUEml86OtI6befOwfbt0K+f1HWrFBen+TyZDNnSZbSLsuDigjes1vuYCiUr8CD6Af2298P1D1f87/prPDV9i13lytLrjDNj9U1MME/bKdKeCIKQkdbWijUyMuLzzz/n888/11aRgvDfkZpKzNixPElrtaqs4fdIGdg1ayYtO/XkCRRaY/br1+zePY/ed6aRokjh87qf8+fpR+joHAF5e/jf//7P3l2HN3m1ARz+JXUFalBa3Iu7u7sOGG4bjGEbso1vbOgYgw3bgMHGgA139+FeGFqsOLRIS1vqlvf74zRJSy1p02Lnvi6upMkr56VN8uTI80Dr1mJZ5/tGpYL27cU/rdBQKF9ePDZ1avKSH3nzwty5qPv2pc/krXzkfYq5IfuYfnw6F59epOnfTWlRtAU/NfmJsrn1q2eLFhWnCw4GNzfx2OuBHYjh2JA7dwi8ehWPBg1MfsmSJL27MhXYxcTE4O3tzZkzZ/D39yc4OBiVSoWjoyNFihShcuXKVKlSJcXhWUmSElm3jrv+/uDqSp4qVbD38Ei2ybFj4jZPHhHU2dlBw4ZZ2CZFEb1Vixez7/RKOneKJtYcupfpzp/t/kRdylfMj/sQhwK3bBEJ5+bMgV27REmxypWTbtO7N6xbBzt3YjPoM74+cYJBlQYx5cgUFngvYI/vHvbd2Ue/8v2Y3HAyHo4eWFuLZNP37ukP83qSYhArY+9t3Sp77CRJSiZDgV1sbCwrVqxgyZIlhISE6B5XFCVZEOfm5sann37Kxx9/jPpNZlCVpLeVRoNmyhTuJPR2Ffn442SbPHwoqlWZmUHCaC2NGkEqxV4yJyQE/vlHLIC4fJkjBaBDL4gxh45u9VnRYQVmarM3s2rjbdG7twhoBw2CW7egRg3Rczd2rD5TtEolulZLlxYB8uzZuIwdy9yWcxlefTjfHPyGDT4bWHpxKauvrmZ0zdGMqz2OkiUduHdPP9L7/LnIbZd4obBzogoUKb3vSpL04TI60goLC2PgwIHMmjWL4OBgFEXBzs6O4sWLU6lSJcqUKUOBAgUwMzNDURSePXvG1KlT+eSTT4hIbU6KJH3Itm/H/+FDIi0ssMqRA88UUgJph2ErVdKXFGvZMgvacugQeHjAsGFw+TI+Hpa072NBpAW0LtaaNZ/uy1g1ifdRixYi6fFHH0FcnKhe0b590pWzHh5i9S/AhAkijQpQ1Kko6z9az8kBJ6mVrxaRcZFMPTaVIvOKEFV2AahjefhQ3xn6eq9dzhIlUJmZERUYSMTTp9lwsZIkvSuMDuxGjhzJ2bNnUavV9OjRg61bt+Lt7c3WrVtZtWoV69evZ8+ePVy4cIF//vmH9u3bo1KpOHnyJOPGjcuKa5Ckd5eiwPTp3E3orSvYrh1mlpbJNtMOw1atCidPivstWpjo/C9e6H+uXFn0OHl58XT2FFp9mZsQs1hq56vNhq4bsDRL3rYPmpOTSOGyZInoPt2xA0aPTrpNv37ilxUdDZ9+KlbbJqiZrybH+x9nU9dNFHMqxouIFxyy/RyGluHw0y0UKSqSFty/n/SQ5tbW5ChaFJALKCRJSsqowO7QoUOcOHECe3t7li9fznfffUeJVIZjLC0tqVKlCjNmzGDZsmXY2Nhw8OBBTp8+bZKGS9J74fBhIr29eZxQ3aDIawmJtbQ9dnZ2EB8vRkELFcrEeTUaMU+salURdGizHjk6wvnzhF84Q1u7rTwIfUQxp2Js6b4Fa3PrNA/5wVKpxJDsqVNQvz7MmJH8+UWLxC/v2DGRwyTJ0yo6lurItaHX+LXlr+S0cAWXW5wv1pH43vU4cP0MHTsmP612ODZQJiqWJCkRowK7bdu2oVKpdGlNDFWtWjVGjx6Noihs377d6EZK0nvrhx94kCMHikqFc9my5CyWvARVSAhoO2WaNxdJa/v1y+D54uPFhP4KFaBjR5Eo+MaNJLP144sUpsfmnnj7eeNs48yunrtwsXXJ4AnF3NuXkS+5H3yfGwE3uPzsMrcDb+MX6kdUXFSGj/vWqVgRDh9Oupjk7FlxW6CAmIMHIldNCsOnFmYWfF7tc7z7+MLR8RBrzbnnx2mytgbdNnTjzsuk47FOiebZSZIkaRkV2Pn4+GBpaUm7du2MPlHHjh0xMzPj8uXLRu8rSe8lb284cIAHOXIAUDBxHdNEzp0THWqFCkHjxvDbb2I6l1EURSS9K1sWunUT2Y4dHOCbb8Q4nzZpGjB632i23dyGlZkV2z7eRlGnogafJjQ6lJeRL3U/H7p3CPvp9jj/5EyhuYUo9Vspyi8qT/Ffi+PxiwcTD09MdozD9w8THhNu5AW+hRYtEpmGx48X///Dh4uh7uBgUa4sFYU9HHG6OA3m36Z9/v6oULHu2jpK/SZKlAVGiMokzqVLA6K0mAnyzEuS9J4wKrB78eIF+fLlw8LC+MnTtra2eHp64u/vb/S+kvRemj6dUAsLAm1sUKnV5G/ePMXNTp0StzVqZOJcO3aISf7Xr4tcc99/LwK6H35I0sP0x4U/mHtmLgArOq6gVr5a6R76adhTZp+aTeMVjXH+yZm//tMnJ3aycSIiViyasjG3IZd1Ltzs3HCwdECtUlMgR4Fkx2uzqg05Z+Sk9tLafH/oe877nX83A5dXr8Tt9OkwZIi4v2SJWNq8dq1Ik5IClQpKlQJeedIieildX17EPbw5sZpYXYmyn078hHXh/KgtLIh59Yrwx4+z55okSXrrGZXuJDo6OlPlvXLkyMFj+QYkSSLA2rSJBy5iiDN39erYuKQ83Kmdlpo7t5hrV62aqBGbrshIsLER91u3hjp1RI6U0aOT5s5IcPbJWT7f9TkAUxpOoWvprqkeWqNo2HFrB4vPL2aP7x7ilXjdc/eC9cO6pVxLcXv4bTwdPZPN0VMUBY2i4XUeDh48iX7CyUcnOfnoJJOPTsbT0ZNfW/5K+5Ltk23/1ho3DnLlgsGDReqYoCD4+2/44guYNUuMqV+9mmKW6VKlRF1gX19YO68csIftPvv59shYLj27xFcHvuK3c7/xQ/5iqO74E3j1Kvb58mX/NUqS9NYxqscuPj4eMzOzDJ/M3NwcjSb5G7kkfXB+/hmAB56eABRo1SrFzRRFH9idOQN164qKXWkKDxdBRcmS4j6Ila5Hj4rCoykEdc/Dn9N5XWdi4mNoX6I94+uOT/XwkbGRlFtYjvZr2rPz9k7ilXhqetZkTvM53Bp2i19b/arb1tLMkqJORVNceKFSqUQ+vNfcGHaDuyPu8kfbP+hUqhN2FnY8fvWY/Dny67bxC/UjJCok2b5vnU8+EXMaLSxg/Xpo2xbGjBFZiB88ED2nKfDyErcPHkDCSD2Flaac//Q8yzssx9PRk4chDzmAmF/334mUe/8kSfrwyIzBkpTdAgJg5UqCrawIiYlBbWFBviZNUtzU1xdevgRra7h0STyWQpo7vW3bRFQwc6bIapx4sVIqSWzjNHF039Cdx68eU9y5OMs7LE+1SD2AjYUNRZ2K4mjlyJiaY7jx+Q1ODjzJyBojKeacfPGHsVQqFYVyFWJgpYFs7LqRgHEB7Ou1jwp5Kui2+d+//8PjFw+G7hyKzwufTJ8zS3XpAjt3ilWx+/eL8mvz54vn5swRC1heU6qUuPXxEWXGQPwtmKnN6FO+D7eG3WJ64+n4J5Qd8z6+ndarWnM78HbWX48kSW81GdhJUnZbvBiiorifkCoob716WKbQiwb6+XXFiolKBC4ukLAYMqmHD6FDB5Eg9+FD0SO0Ywd0755uc8YfHM+h+4ews7Bjc7fN5LDOkeR5nxc+NP+nOY9CHukem9dyHg9HPWRms5mUcMnaChTW5tY0LdJUV10hXhPPlWdXCI8NZ6H3QkovKE3jFY3ZdXvX2zsXr2lTOHBALFhp3BjatBEBnkYjevXi4pJsrg3sbt/Wr2u5nShms7Gw4es6X7Ng2GYACgVZs/vWLsosLMOEfyfo5jVKkvThMbqkWGhoKOfOncvQyUJDQzO0nyS9N2JjYcECFOCBnR2EhKQ6DAv6YdiENHc0bKivWAWIsdq//oKRIyEsDMzNxVDfhAlga5tuczb4bGDmyZkA/NX+L7xcvXTPRcdF8+PxH5l2bBqxmlgmHZnEH+3+AEgyLJrdzNRmnPvkHIfvH2b+2flsvbmVf+/9y7/3/qWSeyWmNJxCq2Kp/5++MTVqiNQyefOKn2fPhj174L//YN48+PJL3ab58olfnzaYh6SBnVbBMlU4Z22NTVQUXXI1Yn3wv0w9NpW/L//N3BZzaVeinSw3JkkfGKMDu9u3b9OnT5+saIskvf82boQnTwjw8CA8JARzW1s86tdPdXNtYBccLG5THIbdvFkEdbVr62uTGuBe0D0GbhsIwJiaY/io9Ee653xe+PDxxo+5/EykJ2pTvA3f1095PtiboFKpaFioIQ0LNeRhyEPmnZnHIu9FXPC/wLkn597OwA70QR2IodmGDWHTJhGId+okeloRwXupUmKUVrtQ5ubN5IdTm5uTq2RJAi5e5MdCI+le7HNG7RnFg5AHdFjbgfYl2rOg9QLyOuRNvrMkSe8lo4diFUXJ1D9J+qDNmQPAo6pVAfBo2BBz7crV14SHi1KkoO+t0QV22kVIKpWoZPDLL3DkiMFBXWx8LD029eBV9CtqetZkepPpgHh9Lzm/hCqLq3D52WVcbV1Z22Ut27pvI1+Ot3PVZf4c+ZnVbBb3R91nQr0JjKg+QvfciYcnWH1ldYqrb98oRRELKTZtgjx5RNfc0KH6CiDoh2NjYsRtQEDKh9ImKg66do1OpTpx/fPrfFPnGyzUFmy9uRWv37z488Kf8v1Xkj4QRvXYHdRWH5ckyXhnzsCZMyiWljxKyHGWv1mzVDf39haFIlxdRTlXDw8oViAGRo0TqTOWLxcburmJFBpGmHRkEqcfn8bRypFVnVdhrhZvBb+d+43hu4cD0KxIM5Z3WE4e+zwZuNjs52LrwuSGk3U/K4rC6H2jOfPkDNOOTWNyw8l0LNnx7RiaVKlg2DCxUvnpU5HbbvdusXK2q0gzow3sQkJEUOfsnPKhdImKEypQ2Fna8UPjH/i4zMcM3DaQc37nGLR9EKuvrmZJ2yUUypWZWnSSJL3tjArsPDw8sqodkvT+mysS/wa1a0e4jw9m1ta4166d6uZnzojbunXhq68g+MZTVE0+0heOHTVKlLEy0qF7h/jh2A8ALG6zmII5C+qe61u+L39c+IOeZXsyutboNFfHvu3iNHG0Kd6GGwE3uPbiGp3XdaZinopMbjiZ1sVav/kAr3NnWLpU1IeLT8gDOGqUqN3r6KgL7G7eTD2oA3DSBnY3bqCJi0NtLt7Wy+Yuy8mBJ5lzeg4TDk3g4L2DlFtUjvkt59O3fN83f/2SJGWJd/ddW5LeJU+eiN4Y4FFCPdi8deqkOgwLopQYiDn31eJP0ezrSiKoc3QUaU0yENQFRATQa3MvFBQGVhxItzLdeBjyUDdM52DlwLlPzjG29th3OqgDUXv123rfcm/kPb6t+y32lvb89/Q/2q5uS80/a3L84fE33UTo21fkFtTy99flttPmsrt+XT/ynhLHQoUwt7UlPjKSV3fvJnnOXG3OmFpjuPLZFermr0tYTBj9t/an+8buBEUGmfpqJEl6C7zb79yS9K5YvFiktKhTh8dXrwLg2bRpmrt4ewMotPP/HerXFx/6Xl4i4kulrmxaFEVh4LaB+IX6UcK5BHNbzGWv7168fvPSrYwFERC9T3LZ5GJKoyncG3mPcbXGYWNuw5knZ5Kkb3mjJkyA/v31P8+bBxcvUqSIyGscESHqA7dqlXJyapVareu1C0wYjn1dUaeiHOp7iGmNpmGmMmPdtXWUX1Seow+OZsUVSZL0BsnATpKyWmysqBEKvOrenZA7d1Cbm+NRr16qu7x4IUq5zmIMJWYPEcfo0kUsky1ePEPNWPrfUrbd3IalmSVruqxh0/VNtFndhvDYcA7cPUC8Jj79g7zDXGxdmNF0BvdG3mNao2lJSqZtu7mNk49OvpmGqVRiNXPTpmBlJbrnhg7FXK0hoXOX//4TU/BOptJE3XBswpeGlJipzRhfdzwnB56kSK4iPHr1iIbLGzLt6LS3b3GJJEkZJgM7ScpqO3aI3jZXVx4llOTLXaNGqkmJQV+MwNuxMdFYsrrCj6I0lTahnZEeBD/gi71igcXUhlM5++Qsfbb0IU4TR8+yPdnRY0eK5b3eR7ntczO+7njd9UbERjB4x2BqL61Nq5Wt8Pbzzv5GWVjAhg1w+LCoHXvqFCxdqptnp13QmlLKE0gU2KXSY5dYNY9q/Df4P/qW74tG0fDtoW/pvK4zr6JfmeBCJEl602RgJ0lZbdEicTtgAI8OHQJItYSYlnZ+3RG7VhTFl5eDvkq1JFh6NIqGAdsGEBoTSq18tbAws2DwjsEADKs6jBUdV2BpZpmhY78PImMjaV2sNWYqM3b77qbqkqp0WNNBl8Mv2zg6igmVkxNW9o4dS9UCzwHQ5na/dStJRhQdZ23Kk5s3idfmR0mDg5UDyzosY0nbJViaWbLlxhaq/1GdmwGpRI6SJL0zZGAnSVnp7l3Ytw+A8A4dxFCZSoVHw4Ypb//wITRowOPDvoCoE/uYfNSqlfEmLDy3kH/v/YuNuQ0NCzTU9dyNqTmGeS3nvfOLJDLL2daZP9r9wY1hN+hdrjdqlZqtN7dSflF5uq7vyp2Xd7K3QcOHQ4ECEBzMpxtEOhw/PxHXv3oFz58n38U+Xz4sHB3RxMQQ4utr8KkGVRrE0X5H8XDw4EbADaouqcq2m9tMdSWSJL0BH/Y7uiRltcWLxW3z5jxO+MB1rVQJG22dqMSuXYOaNeHIEfoe/wSA6GhRoKBs2Yyd3velL+MOjANgRpMZONk6AfC/uv/jp6Y/yZQXiRR1KsqKjiu4+tlV3fy79T7rCY4Kzt6GmJtDQnWfXA8vMZ6p3LghYj0QvXavU6lU+nx2acyzS0l1z+qc//Q8dfPXJTQmlA5rOvDr2V8zdQmSJL05MrCTpKwSHS3ylAEMHszjf/8FwDOlumDXronyUn5+xBb3olvMCt3Ia40a4rPeWPGaePpt6UdEbAQNCzbk82qf82XNLzk54CRTGk6RQV0qSrmWYm2XtVwacokZTWZQOW9l3XNzT8/lwN0DWV/FYfJkSBhencJ3FAw6r602lu48u9RWxqYlt31uDvY5yODKg1FQGL57OF8f+FouqpCkd5AM7CQpq2zeLJa35s1LTP36PE9YEeH5+jCsj4+oFfbiBVSsyIHvjvGYfOTIIZ7O6DDsvDPzOPHoBDbmNsxtMVc35FozX00Z1BmgXO5yjKs9TvezX6gfY/ePpenfTSm/qDzLLi4jOi466xpw4ABYWKBGYT/NyGcbiKOjKDWXEu08O0MWUKTEwsyCha0XMrXhVABmnJhB3y19iYlPf86eJElvDxnYSVJW+f13cTtoEP5nzqDExeFYuDAO2jE1ENlnGzUSE6cqVIADBzh5QwyX2tqKTTIS2N0Luse3h74FRAWGPlv6EBgRmImLkdQqNUOqDMHOwo4rz6/Qf2t/PGd78sWeL7jy7IrpT5g7N8yaBYAzL5l+vT3BgfGMHJny5toeu+Dbt4mPzljAqVKp+F+9//FX+78wU5nxz+V/aL2qNaHRoRk6niRJ2U8GdpKUFW7cEKkr1GoYNIgnhw8D4FG/ftLtvvwSnj2D8uVFD42TU0JiYvj2W/FUgwbGnVpRFIbsHEJEbATmanNiNbG427vjaJV6ehUpfXns8zCv5TweffGIGU1m4OnoSUBEAHPOzKHconKsvLzS9CcdNownruUB8Lh3AtVfS1Pd1NbdHSsnJ5S4OIJSG681UL8K/djRYwd2FnYcuHuAFitbyHQokvSOkIGdJGUF7dy6Vq3QuLvjd1Rk+E+2Gvaff6B3bxHUOTujKOgCuypVwM0NrK2NO/U/l/9h3519qFARp4mjat6qrP9o/XtXUeJNyWWTi3G1x3Fv5D12fLyDTqU6YW9pT4uiLXTbbLu5jeUXl/My8mXmTqZWc37ECuK1b9V586a6qUqlMihRsaFaFG3Bob6HyGmdk5OPTtJyZUsZ3EnSO0AGdpJkanFxImADGDCAgIsXiXn1CsscOXApX148r+XsDCtWQMIq2QcPICBA5KstV874U78If6FLZ6KgUChnIXb22ImdpV1mr0p6jbnanNbFW7Ox60b8R/vjbOuse27myZn029oPt5luNFnRhAXnFnDn5Z0MLbrwaFmOuYjx16fdRlK+eCSvlYTVyew8u9dV9ajKgd4HdMFdi39kz50kve1kYCdJprZ/v6g04ewMrVvrhmHz1quHOioKatcWxT9ToO2ts7GBNm3g0iXjTv3F3i8IjBRz6ews7Nj+8XZc7VwzeiWSgewt7XX3FUWheZHmlM9dnnglnoP3DvL5rs8pOr8oBeYUYOTuVCbJpaJUKZjIJJ6Qlzzhd+hy+weip/8CKSQizszK2NRUzluZg30Okss6F6cen5LBnSS95WRgJ0mmtny5uP34Y7C05MmRIwB41Kkj6r2ePQsTJ0Jg8sUM2ooTYWFidNbOiI62Pb57WHlFP89rTZc1lHYrndGrkDJIpVLxbb1vuTjkIr7DfZnZdCZ189fFQm3Bo1ePeBDyQLetoij03tybqUensuv2Lp6FPUt2PFtbyF3UgVHMAWA80yj1x2gYNy7ZttrA7tWdO8RFRJjsmiq5V+JAnwO64K7lypZExJru+JIkmY5KyfKETBkTFhZG5cqVOX/+PPb29unvIElvg+BgyJNH5LDz9ibUxYXtrVqhMjenc9myWK5cKT6p//0XqldPtnvjxuIpEPPrnj41rJJYeEw4pReU5kHIAwZWHEj9AvXpXb63aa8tC4WHh+te52FhYdgZE9G+I8Jjwjn56CS2FrbUzl8bEDV8C84tmGQ7d3t3SrqUpIRzCVoVa0XbEm3p2BG2bFHwdmpO5Zf79RuvWwcffZRk/80NGxL5/DlNVqzArXJlTOk///9otKIRwVHBtCnehs3dNmOuzkCSRUmSsozssZMkU1q7VgR1pUtDpUq6YVi3nDlFUGdmJj6MUwjqNBr9UCyINCeGppubenQqD0IekD9Hfua0mPNOBXUfCjtLO5oWaaoL6rSP/dLsF3qW7Ukpl1KoUOEf5s+h+4dYdH4Rxx8eBxJyFTv403DgLZr2UvFZa5hZCzbO6Mt/53cSEhWiO6ZuAYUJh2O1KrpXZMfHO7A2t2bHrR0M3j4465M1S5JkFPlVS5JMSTsM27cvqFT6YVjtKsXFi6F16xR39fUVtUDVahHkGZq/ztvPmxknZgAwt8XcJPO9pLebi60LX9T8QvdzWEwYPi98uBlwkxsBN2hcuDGQENg5+RJq94ADReGAbo9I2NEGdsC3db9lSqMpOJcpw5NDhzh7bCt+dT0pnKsw+RzzYaY2M0mba+evzdoua+m4tiNLLy7F3cGdqY2mmuTYkiRlngzsJMlUbt2CU6dEZNarF7Hh4bxIqDaRNyxMJKYbMCDV3bW9dcYEdhqNhtarWqOgYG1uTdPCTU1xJdIbYm9pTzWPalTzqJbk8dKlAf+KWK8+jMbGhyG5viIgVyh3nVXcyanwwg48HT0BfY/d8yuX6LtCBIbmanMK5ixI4VyFKeFcgm6luyXpOTRWuxLtWNR6EZ/u+JRpx6bhbu/O59U+z/DxJEkyHRnYSZKprFghbps3B3d3nh48iCYuDnsHBxzbtoVJk9LcXbtwIi4OLC3BkOlRA7cN5Hn4cwD+7vC3TGvynipeHMw1DkTdrI+HR31sgp1Yeag7WJhDbCyhLo6oPhV59JwSUp7kDbWirH1xbkbeJyY+Bt+Xvvi+9GXfnX2UcC6hC+x8X/ryu/fvNC7cmHoF6mFrYWtQmz6p/An+Yf58f/h7hu8eTr4c+WhXol3W/AdIkmQwGdhJkiloNPrArm9fAPyPHQMgb9u2MH58uhPmtD12ZcqAh0f6iYkP3TvEskvLAGhRpAVdSnfJcPOlt5ulJZQoAdeuwaJF0KZ1V2iyWKy08fLCYds2cBel6qxz5cI+Xz7CHj1ib82/cKtRHb9QP+4G3eVO0B2uPr9Kg4INdMc++uAos07NYtapWViZWVGvQD2aFWlG8yLNKeNWJs26whPqTcAv1I/fz/9Oz009OT3wtFyJLUlvmFw8IUmmcOgQPHoEOXJAu3Yov/2mqzaRt169dIO6uDi4cEHcX7cO9uxJ+3RBkUF0WNsBEPnqNnXblNkrkN5yCR1x+Pgg/p7mzwdzc/HArVtJtnUuWxaAwMuXMVObkS9HPuoXrM+AigP4pfkvSYKvUi6lGFhxIPkc8xEdH83+u/sZu38s5RaVo+j8opz3O59qm1QqFfNbzqdBwQaExYTRfk37zFfbkCQpU2RgJ0mmoF000b07/PEHIaNHE/HsGWZWVrhVrZru7jduQEQE2NuLYbe0KIpCx7UddUliV3RcgY2FTWavQHrLaQM77TocpZQXjBghfhgxQqzGPngQli/HOaFsScDly+ket2a+mvzR7g8ejHqAz1AfZjefTYuiLbA2t+ZRyCMK5yqs2/b6i+sERwUn2d/CzIL1H62nYM6C3Am6Q7cN3YjTxCFJ0pshAztJyqzQUNi4UdyvXBm+/BK/hJxsblWrYm5AsVft/LqyZUVGlLTEaeLwfekLQONCjelUqlOGmy69O7SB3dmz4n61asD334u8ib6+Irhr2hQGD8YlIQ9g4OXLBqcjUalUlHItxagao9jdczcBYwPY33s/uWxy6bYZsG0Anr94MnzXcN3fIIjVvVu7b8XOwo4Ddw8wdt9Yk123JEnGkYGdJGXWhg2iu61IEZgyBeLi8C8sejny1qtn0CG08+tOnYImTdLedvONzTwJfYK1uTVL2y/NTMuld4g2sLt3T8y1u3wZ4mwdYdYs8cTff0ODBhAdTa4JE1BbWBAdFET448cZOp+dpR31C9bX/fwq+hWh0aGEx4bz67lfKT6/OB+t/4jLz0SvYLnc5VjRUcwznXNmDssvLs/wtUqSlHEysJOkzNIOw1pYwKNHxBYtyvOoKADy1qlj0CESJybOmzflbSJiIwiNDmXsftEb8nXtr8mfI3+Gmy29WwoVEjWEY2LAykrc3rsH9OgBdetCZKQYy8+dGzMfH3Il9BQbMhxrCEcrR658doX9vffTqlgrFBQ2+Gyg/KLydFrbiRsBN+hUqhPf1/8egCE7h3Dl2RWTnFuSJMPJwE6SMuPePThyRExmv3EDLC15OnYsSnw89vnz41CgQLqHiImBixf1P6eWv27wjsGUWViGhyEPyZ8jP2Nry+GuD4mZGXh5ifvu7uL2+nXE396vv4oNtm+HL0TCY+f79wHTBXYghmubFG7Czh47ufLZFbqW7ooKFZtvbObcEzGf4Lv639GyaEui4qLouqErYTFhJju/JEnpk4GdJGWGNsWJpaW4/eUX/J48AQwfhr16VQR3WikFdquurOKfy//wMOQhADObzjQ435j0/tAOxzo4iFsfn4QnypWDzxMSBC9bBiNH4hIZCUDg+dRXtWaqLW5lWNtlLVc+u8KXNb6kZ7meAKhVar6v/z15HfJyI+AGn++SiYslKTvJwE6SMkpR9IHdzz/DDz+gfPYZftr8dQYOw2oXTgA4OiZUGUjkfvB9Ptv5me7negXq8ZFX0sLv0odBG9hpNOL2+vVET06aBG5uoufYzQ3n/GKYPujmTeITf3MwsdJupfm5+c+oVeLjJDQ6lM7rOmNnYYdapWbFpRUsu7gsy84vSVJSMrCTpIw6fhzu3hXzmvr1g2++IcTXl0gj0pxA0vl1NWokXRUbr4mn9+beutQmapWauS3mppk0Vnp/aQO7kBBxmySwy5kTZoiawUyfjv3PP2NlY4NGoyHoxo1sa+OV51cIjw3n9svbuhW5n+/6HJ8XPunsKUmSKcjATpIy6nsxSZyPPoKE9BLa3rrc1asblOYEkgZ2rw/DTj8+neMPj+t6Qz6p9AkV8lTIVLOld5c2sPP3Fz27CXmI9fr0gZo1ISwM1bJlOFcTNWcDTTjPLj218tXi5rCbdC/THQUR2EXERtB2dVsiYiOyrR2S9KGSgZ0kZcSNG6LaBIg0Jwm0gZ27gcOwkZFwJWHhYK9eosys1pnHZ5h4eCIAGkVDDqscTGk4JdNNl95dHh6iuEl8PKxaBX/++doGarVYSKFSwerVOCdMxgs4dw6+/VaUOMkGbnZurO68mo1dN+Ji6wLA3aC7tFrZyuC8epIkZYwM7CTJWBoNdOwo7ltZwVixOjU2LIwX//0HQN66dQ061KVL4kPazU1M16tRQ/+cjYUNpVxKYWVmBcCkBpNwtXM13XVI7xyVSt9rdyW1TCKVKsGQIQC47NwJQOD+/TBtGvzwQza0Uq9TqU74DPWhukd1AI48OML+O/uztQ2S9KGRgZ0kGWv2bNFjB/Dpp7oVsU9PnUKJi8OhYEEc8huWX067cKJq1eTlZMvlLkfDQg2Jjo+mlEsphlYdaqorkN5h2uHXy5fFd4zw8BQ2mjoVnJ1xTpiEF6ZSEWVmBpMni9IV2cjVzpVTA09Rv4BIdjxg2wCCIoOytQ2S9CGRgZ0kGePKFfjmG/3Po0bp7hq7Ghb08+ty5xalPgFi42MBuBlwk4XeCwGY02IOFmYWGW+39N4oX17cbt8u0p58+WUKGzk5wfTpWGo0OMaKv6fA5s1F93CvXqlEg1lHpVKxs8dOijkV40noE4btGsbg7YNZf219trZDkj4EMrCTJENFR4sPxYQPSurVg4TSYYqi4Hf8OADuBg7Dgr7HbulSUZksMjaSyosrM+nwJMbuH0ucJo42xdvQrEgzk16K9O7SBnZ+fqKSXZKVsYkNHAhVquCcEMQFNmgAnp5w+zaMGZMtbU3MztKOvzv+jVqlZtXVVSy+sJiuG7ry04mf5Lw7STIhGdhJkqF27hTjX9p8JH376p4KvnVLpDmxtia3gWlOQkP1I7oAtWvDxMMTufL8CvPOzmP7re2Yqcz4qclPprwK6R1XtqwYttemPLl2TaRUTEathnnzdImKA86dE8mLARYtgh07sqW9iVX3rM74OuMBsDYXq8a/OvAVn+38jDhN9izskKT3nQzsJMlQnTrB3LliOMvGBrp00T3ld/QoINKcmFlZGXS4//7TfyC7u8NTszPMOiUKujvbOAPwaeVPKeVayoQXIb3r7O31C7FVKnj5Ep49S2XjmjVxbtgQgMBr11AaNNCVHGPy5FQiwqz1Xf3vqOReiai4KLxcRY2038//TtvVbQmNDs329kjS+0YGdpJkDO24V+fOokxEAv+EYVhj5tclrjhRvXYUA7cPQKNoqJ2vNrdf3sbe0l5XUF2SEtMOxzqL+D/1FbJAzjlzMNNoiAVe/fqrWBn7zTewf3/yFTvZwMLMgr87/o2lmSU+L3z4ssaX2JjbsMd3D3X/qsvjV4+zvU2S9D6RgZ0kpWfNGnj4EKKixH1IMgwbExpqdJoTSJqYOLTSZHxe+OBm66arB/t17a/JbZ878+2X3jvawM7GRtxevZr6tup8+XDKkweAwNmzxRzRH34QCfHeEC9XLybUmwDAissr2NJtC252blx9fpX//P97Y+2SpPeBDOwkKS1Xrohs/qVLwx9/QHCwmICeMLwFCWlO4uNxLFQI+3z5DD60rscu7zkOxYhSUC2KteDRq0d4OHjwRc0vTHgh0vtEG9hpV1Kn1WMH4JKQ+TogOjppLjtFgSVL4M6dLGhl2sbVHke53OUIiAhg+eXlnB54mo1dN9K2RNtsb4skvU9kYCdJqYmLgwEDRA9H48awe7d4vE+fJAVdtcOwhlabAAgK0n+WmuW+haW5JZ1KdmLrja0ATGs0DVsLW9Nch/Te0QZ2gYHQpk3SxNYpca5UCYAAW1v45Rfw9RVPTJwocjH26pVtVSm0LM0s+bPdn2KV7JVVXHtxjfYl2+uefxD8gKvP0+iKlCQpRTKwk6TU/PKLGC/NmVN8AO7dKx7v00e3SeI0JxkZhs2bF5Z92ZPLQy7jbOtMSHQIFfJUoFe5Xia6COl9lD+/+LOMj4cpU0RslhbXihUBCLG2JjYuTp/8bsAAMVf09Olsr0oBUCVvFUbXHA3AZzs/41X0KwCevHpCoxWNaLCsgRyalSQjycBOklJy6xZ89524P3s2HDwoPkVr1IASJXSb6dKc2NjgVqWKwYfXDsPWqSM6SwD+uvgXALOazsJMbZbKnpIk1jxoe+0uXUp/extXV+w8PVGAAHt7kd14714oUAAWLBAbvYGqFAATG0ykSK4iPH71mK8PfA2ArYUtLrYuBEYG0mhFI84+yf52SdK7SgZ2kvQ6RYHBg8UEpmbNRA+dNv9Xv35JNvVPqDaRu1o1g9OcAJzxjoFunXCtLHr7vj74NXGaOFoVa0Xjwo1NcRXSe04b2F28CE+ewPPnaW+v7bV7UV+U9mLUKDHNoEcP6N5dfHHp2RPCwrKszSmxtbBlSdslACz0XsiJhyfIZZOL/b33UztfbYKjgmmyookM7iTJQDKwk6TXrV4Nhw+LJYeLFolPzqtXwcoKunVLsqmu2kTt2kad4t+4qVBqM8siOnPw7r9sur4JtUotkxFLBtMGduvWifU8ixenvb02sAvIlQtcXUV27F9/Fd1/CxZAvnxi7t0X2b9op2GhhgyqOAiAITuHEBsfi6OVI3t67aFBwQaExoTS4p8Wcs6dJBlABnaS9LoOHeDrr0Uh9UKFYPly8Xj79mJiU4LYsLAMpTnZe/kCYRXFfCb7Y/P5378iE/+gioMo7VbaJJcgvf+0gV1wsLhNK+UJJArsfHzQTJsmHpw4UWQ3zpVL/J2rVKK+XeKSKNnkxyY/4mLrwtXnV5lzeg4A9pb2bP94O9U9qhMUFUTTv5vi+9I329smSe8SGdhJ0utsbWH6dDHBPCYGVq0SjyfKXQcJaU7i4nAoUACH/PkNOnRMfAxDdvcHdTxc60LJknDmyRnsLOyY1HCSqa9Eeo+VLg3m5qJeLKSf8iRH0aJYODgQFxFBcK1aULkyvHoF//uf2KBhQ5g5E44cgZIls7bxKXC2dWZm05kATDwykQfBDwAR3O3quYuybmXJZZ0LG3ObbG+bJL1LZGAnSVoPH4p5Ront2gUBAZAnj5hvl4hfwvw6dyN666Yfm879qMsQ4Qx7f+Gau5gsPq72OPLY58lc+6UPirW1CO60bt3S57VLiUqtxiWhm+/FxYswb554YulSOH9e3B89WqzoeUP6lu9LvQL1iIiNYMSeEbrHnWyc2Nd7H0f7H8XD0eONtU+S3gUysJMkgMhIkauuVi24d0//uHYYtlcv0T2SIEmaEwM/CC89vcTUY1PFD7t+hdLrCYi/h7u9uy7lgyQZIyE9HVZWIg3dzZtpb+9SoQIAAf/9J/7We/YUi4WGD09eN/bGDVi/3vSNToNKpWJh64WYq83ZdnObLq8jQB77PLjYuuh+3nlrJyFRIdnaPkl6F8jATpJA5PDy9YVHj8DJSTz24gXs2CHuvzYMG3L7tkhzYmWFW9WqBp1ixaUVxGniML/dEe40hfpTAJjaaCp2lnYmuxTpw1G5sri1Tchlbeg8O+3cUGbMADs7OHUKVq7Ub3jlioga+/QBHx8TtzptXq5ejKk5BoDhu4cTFpN8le7yi8tpu7otHdZ2IDoujW5KSfoAycBOkm7cEB9wAPPn62torl4tukEqV4YyZZLsoh2GdatWDXNra4NOM6vZLH6quZy4rQug3jSwDqasW1n6lu+b/s6SlAJtj512CPby5bS3dylXDpWZGRFPnxLu7w8eHvo5dl99pU91UqYM1K8v6iP37Jn2GG8WmFB/AgVzFuTRq0dMPjI52fPl85TH3tKew/cPM2DbAJTXexsl6QMmAzvpw6YoMGyYyOfVujV06qR/TjsM2zd54KUN7IxZDatSqfAI6AMW4VDtV0AEezIZsZRR5cqBWi0WUAwenGwaaDLmtrbkSlgYEXDxonjwiy+gcGHw89NXn9CujnVxEel+tMm6s4mthS3zW84HYM7pOdwMSDrGXCFPBTZ03YC52pxVV1bxv3//l63tk6S3mQzspA/b+vWiqoSVlZhMrlKJx69ehQsXwMICPv44yS5J0pykM78uNj6WiYcnEhwVDCRUnGjyDZjF0jBfc5oVSeeTWJLSYGenX8Dati00apT+Pi6vD8daW4vyeQA//6wvYuzuDktE4mBmzhS5HbNRm+JtaFWsFbGaWEbuGZmsV65ZkWa6xMbTj0/nd+/fs7V9kvS2koGd9OEKC9PXzPzmG9FroaXtrWvTRvRaJPL09GmUuDjs8+fHoUCBNE/x04mfmHRkEnX/qotG0XDw1ikovR4Vaua2nmnKq5E+UNrh2AsXDNteN88u8Q7t2onuvpgY/WsCRE7HQYNEz3bv3hAUZJpGG2hO8zlYmlmy985ett3cluz5fhX6MbH+RACG7hrKzls7s7V9kvQ2koGd9OF68UL0ShQuDOPG6R+Pi4O//xb3MzEMe/X5VSYdEbnpvqr9FfFxKq7lFZPCOxXqT9ncZU1wEdKHTruA4vRp0amWeFF3SlwTIsHgmzeJCQ0VD6pUMGeOWPm9bRvs26ffYfZsKFoUHj+GuXNN3v60FHMuplsxPmrvKCJjI5Nt81397+hfoT8aRcPxh8eztX2S9DaSgZ304SpUSHwa/vuvKB+mtW+fyMbv4gItWybZRVEU/LVpTtII7OI0cfTf2p9YTSxti7elZ9mezNu/CY3nSYixpWPO5BPCJSkjtD12hw+LHMNr1qS9va2bG/b586NoNEl77UqVEvNNQV9HFsDeXqyYnTgRvv3WxK1P3/i64/Fw8OB+8H1mnkzey61Sqfi9ze+s/2g905tMz/b2SdLbRgZ20ofNzAxeH07VDsP27AmWlkmeCvH1JeLp03TTnMw6OQtvP29yWudkUZtFxGpimXHhK/HkyTGYReQ15VVIH7CE1HS6ChTaNRFpyZ3wt/vc2zvpE99/L77QXL8u6sdqVasmnkuUyzG72FvaM6vZLEDMpdNWpEjMwsyCLl5ddD/HxscSERuRbW2UpLeJDOykD8/GjSLFQ3h48ueCgmBrQlLUNIZh3apWTTXNic8LH74//D0g5gjldcjLwnMLeRF/B8Jyw8mx1KplmkuRJEdHKFZM/7N2TURa3KpUAVII7HLmBG0d2e+/F9MVXhcdLRZTZGMKlG6lu1G/QH2i4qIYvS/tZN5BkUG0XNmSXpt6oVE02dRCSXp7yMBO+rCEhsKIESKtw8KFyZ9fvVp8YJUrp+8KScTfgPl1Xx/4mpj4GFoVa0Wf8n0Iigxi8tGEoddDk3F2sMfA0rKSZBDtPDsQeba1U+dSow3sXl67RuzrX3AGDhR/+yEhMGFC8p3bthVzUsePz1yjjaBSqZjfcj5mKjM2Xt/IwbsHU932VuAtjj08xuYbm/nuUPamaZGkt4EM7KQPy5QpIl9X4cL6+USJLV0qbvv316c+SRAbHq6bk5RWfdi/2v/FwIoD+b3N76hUKn449gMvI1/C89Lw3wBq1zbZ1UgSAAlxGtbWYgHrlStpb2+XNy92Hh4o8fH6fHZaZmb6OrKLFycf29W+bn75BXbvzmzTDVY2d1mGVh0KiIoUsfGxKW5X3bO6Lg3KtGPTWHl5ZYrbSdL7SgZ20ofDx0es8APxwfX6UOrly6IYuoWFmF/3Gv8TJ9AkpDlxTCPNibOtM3+0+wNPR0/uBd1j3tmED8l9M0Fjnm4SWUkyVrVq4lab6s2g4diEbr5kw7EAdetCt27igCNHJq0j266dPrjr2xf8/TPRcuNMajAJF1sXrgdcZ/7Z+alu16d8H76qLea0Dtw2kDOPz2RXEyXpjZOBnfRh0FaYiIuD9u1FlYnX/fWXuG3bFlxdkz3td+QIAB4NGiR7Ll4Tz67bu5IlUR3/73hi4mMopm4Cvi0A8ZkpSaZUqZLoaNNOezNkAYV28c/zc+dS3uCnn8Rq8aNHRSLvxGbOhPLlxRy83r1Bkz1z2XLZ5GJ6Y7HydeLhiTwNe5rqtj80/oF2JdoRHR9N+zXtefzqcba0UZLeNBnYSR+GtWvh0CHRSzdnTvLnY2Lgn3/E/QEDkj2tiY/nydGjAHjUr5/s+bln5tJ6VWv6b+2ve+zM4zOsuboGFSpyX54JqHBwgNKlTXFBkqRnZ6cvZzx4MAwfnv4+2nl2gVeuEBcVlXyD/PlF/ViAMWP0y25BvI7WrAFbW1G5RVtrORsMqDiAKnmrEBoTyviDqc/zU6vU/NPxH8q6leVZ+DP6bO6TbW2UpDdJBnbS+0+j0U8CHz8eChZMvs2OHRAQIBIWN2+e7OnAK1eIfvkSCwcH3RCW1q3AW7palXXyixJjiqLoVu/1Ld+XuycrACL3q5ksDStlAe1wbK5cYu1Peuzz5cMmd240cXEEXrqU8kZjx4oA79Ej0UuXWMmSMD9hOPTnn+HVq4w33ghqlVpXR/avi39x9snZVLd1sHJga/etVPOoxtwW2ZtcWZLeFBnYSe8/tVr0KgwdKj6oUqIdhu3TJ8VcXdphWPc6dVBbWOgej9fEM2DrAKLiomhauCkDKw4EYPONzZx4dAIbcxuGlJiCn584rPbDV5JMTfu3dTb1OCcJlUql67V7ltI8OxA9crNEDjlmzICHD5M+378/TJ4siiA7Omag1RlTw7MGvcv1BmDE7hFppjUplKsQpweelpVepA+GDOykD0P+/PDbb8kXTICY/L1rl7jfv3/y54EnCQXQXx+GnX92PicencDB0oE/2v2BSqUiJj6Grw6IIazRNUdz75InIKYk2dqa5nIk6XXawO7MGfj9d0jIzJOm3Np8dqnNswPo0gXq14fIyORfjFQq0RteqFAGW51xPzb5EXtLe848OcM/l/9Jc1tVohXuJx6e4NgDA/5zJOkdJQM76f2lKIZVRv/7bzFcW6sWlCiR7OlwPz+Cb91CpVYnyV93O/C2bo7PrGazyJ9DJKdb5L0I35e+uNm5Ma72OPbuFdvfv59tc8ylD5CXl/jiEB4OQ4boyx2nRbuAIuDSJeJTSzisUokasWo1rFsnFlOkZs8eWLIkA603Xl6HvHxbV5Q4++rAV4RGp5O8Dzhw9wANlzek87rOPAp5lNVNlKQ3QgZ20vtr7VqRufXTT1PfRlGS5q5LweNDhwBwqVgRq5w5E3ZTGLhtIJFxkTQu1JhPKn0CQHBUMJOOTAJgcoPJOFg56D4HnZzEZ6MkZQVz86SJitPqhNNyKFgQGzc3NDExvEgrR0r58vrX0YgREB+ffJvTp6FVKzHl4fRp4xqfQaNqjKKoU1Gehj1l2rFp6W5fK18tSruV5kXECzqu7UhkbGQ2tFKSspf8mJHeT6Gh8OWX4n5aZR5On4abN0VXR9euKW6iS3OSaBhWpVIxqcEkyrqV1Q3BArpkxKVcSjGw0kCiokRPHYgC7ZKUlapX19+/ckWMnqZFpVKRp0YNAJ6mF4xNmSJKjl26BH/8kfLJu3QRKYW6doXAQOManwFW5lb80uwXAGafno3vS980t7e1sGVzt8042zhz3v88Q3YOSZaiSJLedTKwk95PkyaJuXNFi4pUDanR9tZ16ZLi5O/Y8HCeJcxGfz1/XcNCDbk05BIFcxYE4H7wfeaeESvvZjadibnanPPn9cOvKaXOkyRT0s6zMzcXnWqG5LPLU7MmAE9PnUp7QxcX8boCUWs5KCjp8yqVCPiKFROraHv1ypa5B22Kt6F5kebExMfw5d4v092+YM6CrPtoHWYqM1ZcWpFmomNJehfJwE56/1y9qs9VN39+ygsmQExGWrtW3E8hdx3A05Mn0cTGYp8vH46FCxOnieN+8H3d84knZY8/KJIRNyrUiFbFWgGwf7/+WLKUmJTVtIGddqTUkOHY3Ak9di+vXSMmJCTtjT/7TEzmCwzUB3mJOTrChg3iNbdnj6jJnMVUKhVzWszBXG3O9lvb2eO7J919GhVqxKxmYrXvl3u/5NC9Q1ndTEnKNjKwk94v2goT8fHQsSO0aJH6ths3iiHbwoWhXr0UN9Gthm3QAJVKxYzjMyizoAzLLi5Lst3ZJ2dZfXU1KlT83OxnXcCnXTjh5gbOzpm9OElKW/78kCePvgKYIYGdrZsbOYoUAUXR9U6nysJC/6Xp119Fmb7XlSsHCxaI+99/D//+a3D7M6qkS0mGVxNZmUftGUVMfEy6+4ysPpJe5XoRr8Sz9trarG6iJGUbGdhJ75fVq+HIEVEKSVsXNjXa3HX9+4thpNcoGo2+2kSDBlx6eolJRyYRHhuOuVqf605RFMbsE8O9fcr3oUKeCgmP64uxy/x1UnZQqZL2DBsS2AHkNnQ4FqBpU1GWLz4eRo1KWkdWq39/8U+j0X+7yWLf1/8eNzs3bgbe5Nezv6a7vUqlYnGbxSxpu4SFrRdmQwslKXvIwE56v9jaii6Lb7+FAgVS3+72bTh8WHwS9km51JCu2oS9PTkrlKXPlj7EamLpWLIjPcv21G235cYWjj08hrW5NVMbTdU9/vChGO1N4xSSZHK1aonb6tXh5EnD9smTsOoi3QUUWj//DJaWYq7B9u0pb/Prr6LGbDaVG8thnYMfGomh30lHJvEs7Fm6+9hY2DCo0iBdD7uiKHIxhfTOk4Gd9H7p0EGsch09Ou3tFi8Wty1apLpqVjsM616nDtNO/sjlZ5dxsXVhUZtFug+CqLgoxuwXvXWja47G09FTt7+286NyZfjoowxfkSQZRdtjd/u2KC9mCLeqVVGZmRH64AHhfn7p71CkiH7V+ZdfQko58GxtxaIkrWwImPpX7E9l98q8in6lK/NnqPCYcLpv7M5Cb9l7J73bZGAnvX8cHcHKKvXno6Nh2TJxf/DgVDfTBnbR5fIx/fh0ABa2XoibnZtumzmn53A36C7u9u58XefrJPtrA7uEUS5JyhYVK4q1Cy9fiu84hrB0cMC5rCi5ZXCv3fjxorbynTv6eXepCQyEZs1ED14WUqvUzGs5D4Cl/y3lvN95g/ddc3UN666tY+SekRx/eDyrmihJWU4GdtK7T6OBzp1Fqn1DegU2b4aAAPDwSDUHSeiDB6LahJkZ4wIWE6/E83GZj+nipe+B8A/11yVF1ZY3Sky7IjZxbjFJymqWlvo5nV98ARMnGrafLp+dIfPsABwc9MOsU6eK9EKp+f13OHAA+vWDy5cNO34G1cpXi55le6KgMHz3cIOHVgdUHEC30t2I08TRZV0Xnrx6kqXtlKSsIgM76d23fDls2iRSMTx9mv72v/8ubgcOFAm/UvDo4EEAXKtUpmXFTng6evJrq6QTsv/37/8Iiwmjmkc1epXrleS5yEi4cUPcT+vzTpKygnae3Z494s/dkNhGl8/u9GkUQ/PP9ewpvrmEhcHXX6e+3bhx0KQJRESI6RIvXxp2/Aya0WQGdhZ2nHp8ipVXVhq0j0ql4s92f1LWrSzPwp/RZX0XouNSKbMmSW8xGdhJ77aXL8WHBsB334mhobTcvCkWTajVMGhQqps9OnAAgPxNmzG10VRufH4DJxsn3fPeft78dVGsqp3bYi5qVdKX0uHD+g/TTp2MuiJJyrTEK2OfPoV799Lfx7lcOcxtbIh++ZIgQ8dw1WqYJ4Y+WbECzpxJeTtzc1izBgoVEo3p3l1UqMgiHo4ejK8r6jh/deArwmLCDNrPztKOzd02k9M6J6cfn2bE7hFZ1kZJyioysJPebf/7nxhW9fISqRfSo1000aoV5MuX4iYRz58TeOkSAJ6NGwPiDV9LURRG7hkJQK9yvajhWSPZMdasEbe2tuKzTJKyk7bHTuvEifT3MbO01PXa+WkLHBuiWjUxxAqijmxqvX3OzrBli3hR7N8v5uhloS9rfknhXIXxC/Xjh2OGJ0ou4lSE1Z1FTsrFFxaz5PySLGylJJmeDOykd9e5c/ph1d9+E5OL0hIVZdCiiccJw7B+7ioeqpMPGa25uoaTj05ia2HLj41/TPEY2s/FsmVTTJEnSVnKyQlKldL/bGjak7x16wJGBnYgKkzY28PZs2Kua2rKldPnj5w5U/8NKAtYm1vr6sj+fOpn7ry8Y/C+LYq2YGqjqeS2y00p11Lp7yBJbxEZ2Envpvh4GDpUjHf26gWv1XFN0aZNYujW0xNatkx1M+8tYk7OEbdnBEYkLWQeERvBuANi6PebOt/g4eiRbP+YGJHDDqBtW8MuR5JMLXGvnSE9dgDuCYFd4OXLRAcHG34yd3eYMEHc//prUdElNV27im0KFYLSpQ0/Rwa0K9GOpoWbEhMfw+h96aRAes03db7hymdXqJO/Tha1TpKyhgzspHfTkSPg7S1Sm8ycadg+2t69QYPAzCzFTe4+8iH+2l0AqrbvRe38SQu8/nTiJx6/ekyBHAUYXTPlD4rjx/WjUT16GNY0STK1xFXyrl4FQ+I0O3d3chYvjqLR4H/cyJQfI0dC0aJiUt+0aWlvO3UqXLggurSzkLaOrJnKjK03t7Lvzj6j9nW1c9X9fCvwFrHxsVnRTEkyKRnYSe+mRo3g2DExZy5PnvS3v3xZjI+amYnVsCmI18Tz47wBmCkqXrio+abLT0mefxjykJ9OiMdmNp2JjYVNisdZtUrc2tjI+XXSm5O4EztXLrh717D98iZEhH7Hjhl3Qisr+EUMfTJ7Nvj6pr6tmRnkzKn/+eJFiM2aoMnL1Yth1YYBoo5sRoKzzdc3U+n3Skb3+knSmyADO+ndVacOdOtm2La/JqQq6dhRDMWm4JdTv+BwUWTdL9+2KxZmFkme/+rAV0TGRVKvQL0k+exep03c3727YU2TpKyQPz8ULizur1gBlSoZtp82sPM/fhxNfLxxJ23TBpo3F/MRRo40LM/KX3+JBRiff55l1SkmNpiIi60L1wOu89u534ze30xtRnhsOPPPzmfFpRVZ0EJJMh0Z2EnvlsuXDcvdkNjLl/DPP+L+8OEpbvKf/39M2TuBss/E6tfybbsmef7og6OsuboGFSrmNJ+jKyn2uvh4/UT1zz83rpmSZGraXrsjRwzfx6V8eSwcHIgODubl1avGnVClgrlzwcICdu2CrVsNOKGLSH2yZIm+x8/EclrnZFojMTw88fBEXoS/MGr/diXa8X397wEYvGOwURUtJCm7ycBOenfExoqFEl5esHOn4fstXSoyBpcvDwmTw1/naOVIx/DSWMarcShUiJzFi+tPGx/L57tElPZp5U+p6F4x1VNduQIhISIpf/nyhjdRkrJCw4bi9tAh0RlmSIeY2twc94REeEavjgUoUQLGjhX3R46E8PC0t2/bVh/QjR1rWDCYAQMrDqRinoqERIcYXUcW4Lv639GmeBui4qLotK6T0cGhJGUXGdhJ746ffxaRk52d4XW64uNFKhQQvXWp9LQVcSpCvwhRh6lAixZJeuTmn53P1edXcbZx5ofGaefD0qbJK1ky1aIWkpRttD1258+LCnoXLhi2nzbtyZOMBHYg8ksWKCCWh6e3kAJEAPjZZyLy7NHD8IYawUxtpqsj+8eFP7jgb9w51Co1f3f8m2JOxXgY8pDuG7sTp8m6JMuSlFEysJPeDXfuwKRJ4v7s2WL4xhA7d8L9+yKxVwpLVJ+HPwcgJiSEZwljqAVatNA9/+TVE74/LIZgZjSZkaT6REp27RK3FhZpbiZJ2cLTUyxUVRRR2u7wYcP20wZ2QT4+RL7IQM+Ura0YkgWYNUtfXy81KpWoYNGsmSg71rYtPDF9rdY6+evwcZmPUVAYsXsEGsXA0mkJclrnZHO3zdhZ2PHvvX9l8mLprSQDO+ntpyji23xUFDRuLIZjDaUtdzRokFimmsjNgJsUnVeUr/Z/xf39+9DExZGzeHFyFC2q22bM/jGExYRRw7MG/Sv2T/NU8fHw6JG437mz4U2UpKyUeHXsv/8ato+1szPOCalIHh86lLETt2sHrVuLKRTDhqU/DmxuDuvWiakWfn5izl0W+KnpT9hZ2HHi0QmWX1xu9P6l3UqzvMNyvqzxJZ9U/iQLWihJmSMDO+ntt2qVKEFkbQ2LFhleysHHBw4eFPUshw5N8lRUXBTdNnQjNCaUs35nebxX5LcqkChx8cG7B1lzdQ1qlZrfWv2WrB7s67Zt0+evGzDA8MuTpKyknWcHIuOPoVlF8jVpAsCj/fszdmJtL5y1tXgdrluX/j45cohe9hkz4PvvM3bedHg6euoWQozdPzZZEnJDdPbqzM/Nf8ZcLedbSG8fGdhJb7eXL+GLL8T9CRPEuJKhtClO2rcX830SGb13NJeeXcLV1pW/Gv7Gs4Ti5fkThmFj4mMYtlvkvvqsymdUck8/V8TyhC//zs5JU3RJ0puUuMcuLEzMtzOEZ9OmADw7e5aYkJCMnbxwYfjmG3H/yy/TrkihVbAgjBun/wKXBSlQRtUYRRm3MgRGBvL1ga8zdayY+Bi+P/R9hgJEScoKMrCT3m52dqK3rVIlGDPG8P0CA/WR1mspTjb6bGSB9wIAVnRcQeypKyjx8TiVLo1D/vyAyGl3I+AGrrauTG001aBTatOc1KhheDMlKavlzSsW82gZOhzrWKCAqEIRF8djQyfnpWTcOChSRAyvTpxo3L7h4WJeg/ZLmolYmFmwsPVCAP747w9OPjKwmG4KBm4byOSjk+myvousTCG9FWRgJ73drKzEh8GZM2Bpafh+CxaISdgVKybpsrgbdJeB20TliXG1xtGiaAse7N4N6Idh77y8w6QjYqHGzKYzyWmdM93ThYeDdo65MVMAJSk7NG+uv29oYAfgmTAc+/jAgYyf3NpaH5jNnStyURpqzRrYvBlGjDBsKNcIdfLXoX8FMW/2s52fZXiF69e1v8be0p7D9w8zfPdwlCxKsixJhpKBnfR2iooS2eu1jMkdEhkJ8+eL+4mGdGLjY+m8rjMh0SHU8KzB1EZTiXj2jOfe3gDkb94cRVEYsnMIUXFRNCrUiD7l+xh0ym3bxK1KBV1SL0ohSW+ENrCzsDCuR1k7z87/xAli08tHl5YWLaBTJ7HC6NNPxa0hBgzQV6To3du4qNQAPzX9CScbJy4/u8z8M/MzdIzSbqVZ3Xk1KlT8fv53FpxbYNI2SpKxZGAnvZ3Gjxdlhi5eNH7f5ctF91nBgkmiLAszC8bWGkuhnIVY/9F6LMwsuL99OygKrpUrY5c3L/9c/ocDdw9gbW7N721+T7XCxOu0Cfo7dZL566S3T/36ovM7NhZ69jR8v5zFi2OfLx/x0dH4Hz+euUbMmweOjqL3fYGBwY+2kkWXLuKLXocO8N9/mWtHIi62LsxoMgOA7w5/x+NXjzN0nDbF2+iOM3LPSPbfyeCCE0kyARnYSW+fkydhzhy4dEkk3zJGfLzImwVisvZrUVaPsj24MewGno6eKIrC3YQs94Xbt+dF+Au+2CsWanxX7zuKOhm+UOPgQXHbpo1xzZWk7GBrCwklYNmzx/D9VCqVfnVsZoZjQWRI/vFHcX/8eH1uoPSYmYmSgA0bisUXLVvC3buZa0siAyoOoFa+WoTFhPH5rs8zPJQ6ptYY+pTvQ7wST9cNXbkVeMtkbZQkY8jATnq7REWJ4RdFgb59xZu4MTZvFsmMnZx0OUcuPr3I07Cnuk0szcRcvZdXr/Lq7l3MrKzI37w5o/eNJjAykLJuZRlTy/CFGk+fwrlz4n7jxsY1V5Kyi3Y4dtcu0WlmqHwJq2OfHDlCfOLpERkxeDDUqiWW5w4daviKVysr8douXx6ePRNd4xrjkgunRq1Ss7jNYizUFmy7uY0NPhsydByVSsXiNoup6VkTjaLJcO+fJGWWDOykt8vEiXDzJuTJIypMGENR4KefxP3PPwc7O56FPaP1qtZU+r0SV58nLWiu7a3zbNyYw89O8fflv1GhYknbJViYGV46YuJE8Rnj7Az58hnXZEnKLtqCKgcOiNjK0AwmzmXLYuPmRlx4OP4nTmSuEWq1SDxsYQE7dsAGI4KoHDlg924xReP338WxTKS0W2m+qSPSsgzbPYyXkS8zdBwrcys2d9vM6YGnaVSokcnaJ0nGkIGd9PY4dw5mzhT3Fy2CXLmM2//oUXEMa2sYNow4TRzdN3bHL9SPHNY5KJBDn8suPiaGBwn1v/K2bs6QnUMAGFZtGNU9DaxDmyBhUS0lShjXXEnKTl5eYjQUxBcRQ/MOq9Rq8jdrBsCDnTtN0xBtbrvhwyEoyPB93d3h9GnDa0UbYXzd8ZRyKcXz8OeM3Tc2w8fJbZ+bUq6ldD8/efVErpSVspUM7KS3Q0yMGDrVaODjj0VSYWPNEJOX6dcP3NwYu28sh+8fxt7Snk1dN+Fg5aDb1O/oUWJCQrBxdWV2+FbuBt3F09GTaY0MKFieSGSkfqpQ797GN1mSsotKpe+1A1HgwVAF27YFRHmxTK2O1Ro/XiTXe/ZMrFw3RuIFTd7eYpVtXMZSlSRmZW7FH+3+QIWKpReXcvDuwUwf88j9I5RdWJapRw3LhSlJpiADO+ntEBwseuhcXfX1XY1x9qzoOjMzg9GjWXJ+CXPOzAFgWftlSb5BA9xLGIZV1yvPfG+RY+vPdn8mCf4MsXSpGAFWqaB/2qVkJemNSzxlddcuw6epOZUujUPBgsRHRWV+EQWIOXPaWrB//AFHjhh/jLAwaNVKHGfgQJPMuauVrxZDq4ryg5/u+JSI2IhMHe9GwA2CooL47vB3rLy8MtPtkyRDyMBOeju4ucGhQ3DsGLi4GL//JJFQmF69OGz+mKG7xJvzpAaT6OzVOcmmUS9f8uToUQB+UG0H4NNKn9KsSDOjT/vPP+I2f37xWSVJb7NmzfR/p8+fG545RKVSUTBhyff9HTtM05g6dcRiChC9blFRxu1vbw+LF4svcytWwLBhJik/9kPjH/B09ORu0F2+P5S5erWDqwxmTE2xEGvAtgEcfXA00+2TpPTIwE56sxJ/yzYzy9hEtTNnRPeDmRnK//7HpCOTxPy6Mt2ZUG9Css3vbtmCEhdHqKc9Z9X3KJCjALOazTL6tIoCFy6I+61bG99sScpuDg6QkL0EEC8bQxVKCOyenT5NpLbMSmb9+KOYN3frFnyfgSCqQwcR1KlUsHChGNbNZHDnaOXIotaLAPjl9C+ZKjcGMKPpDDqX6kxMfAwd1nTgZsDNTB1PktIjAzvpzRo4EEaOFJPVMkrbW9e7N6pixdjWfRvjao1jabulyRIMKxoNvgmlidbkEXmmlrZfavQQLIjcddrsD198kfHmS1J2Sjx91ZjAzj5fPlwqVEDRaHQLjzItZ06xUApE/slTp4w/Ro8eoudOe4zJkzPdrNbFW9OnfB80ioa+W/oSHpPxeYVqlZq/O/5NdY/qBEUF0WJlC/xC/TLdRklKjQzspDdn40ZYtkzUkTSmfmRip0/r59Z9+y0ADlYOzGg6AxsLm2SbPz19mrBHj4iyUDiVP4TPq36e4bQEPj7i1s0Nihqey1iS3qi2bfXrD6ZPN25f7XDsPVMNxwK0awd9+oje+379RI1nYw0aJJKag8g/tHp1pps1t8VcPB098X3py1cHvsrUsWwsbNj28TaKOhXlfvB9phyZkun2SVJqZGAnvRl+fmJeDcDXX2c8fcGkSSjAqJElmPl0U7ppBXzXrgXgSIEgPFwL8WOTHzN2XkSBDBCdjpL0rsiTB2rWFPevXTNu3/wtWqAyNyfIx4eQO3dM16g5cyBvXjEkm/AFzWgjR4pItVkzMUSbSTmtc/JX+78A+O3cbxy4m7lFI252buzttZchlYcwu4WROTolyQgysJOyn0YjUpu8fAmVKmVsbg2I3ro9e5hVR8VcRx/GHRiHt593qptHPH/Oo39FCoPDRYJZ1n4Z9pb2GTp1VJQ+XUS7dhk6hCS9Mdq4Z8sW4/azzpWLvHXqAHB382bTNShXLrE6FkSQd+xYxo7z9dfihWmTvLc+I5oUbsLQKmIh1oCtAwiJMjCrcyoK5yrMwjYLsTa31j2mUUxTQUOStGRgJ2W/WbNg716RSPiff8DS0vhjKApMmMDf5WBcE9FL90uzX6jqUTXVXc6v/AM0CjdcIujXdhx1C9TN6BUwaZLItpA3r0iEL0nvEm1gd+iQqG8cGmr4vkU6i1Xmd7dsyXyJscRathTd34oihmTDwjJ2HG19aEURgZ6x482v+anpTxTJVYRHrx4xcs/ITB0rMY2iYcy+MXyy7ROZwFgyKRnYSdnrxAmRnBRg7lwoVSrt7VOzdy977x1gQMJE8NE1R/NFzdRXMMTEROGzZhUAj6q5MKF+8tWyxvj9d3Hr5WXSykaSlC2KFRN/u/HxooPLmGTFeevVwyZ3bqKDgnhkaPkKQ/38s6jLd/cujBqVuWMdPiySlo8fn6ngzs7SjuUdlqNCxfJLy9noszFz7Upwwf8Cs0/PZunFpYw/ON4kx5QkkIGdlN1CQkT+qR494JNPMnaMuDi8p31O524QZwY9yvbgp6Y/pbnL7N8+xyFMIdQqngljV2GuNs/YuYHr1/VVkEaMyPBhJOmN+vhj/f316w3fT21uTtEuXQB0K8xNJkcO+Ptvsbrjzz/FAquMatgQpiZUfBg/Xkz5yGDPWO38tfmqtlhAMXDbQO4H3894uxJUyVuF39uIb4g/nviRaUeNq3ojSamRgZ2UvVq1EllRFy1KWhrICEFLf6NVzbuEW0KTfPX5q/1fqFWp/ykfuX+EiC3/AuDYoh6F3Ypl6LxaPyast7C0FJcjSe+ixIHdzp3GjXwW6dwZlZkZz729CfH1NW3D6tcXQ6ggvvw9fpzxY/3vf/oX7OTJMHp0hoO7yQ0nU92jOiHRIfTY2IPY+NiMtyvBoEqDmNlU1Mf+9tC3/Hzy50wfU5JkYCdlj8R56goVEplSMyIsjFzf/8ikQ1DVLD+bem7H0iz1OXovwl8w/ve+lAiwRWOmosMXmftWrCj6Cee1aoksK5L0LipSRL8YPTrauJx2trlz41G/PgC+xnT3GWriRKhSRXSN9+kjxowz6quvYP58cX/2bBgyJEPHszCzYHXn1eSwysGpx6eYeHhixtuUyJhaY5jSUKQ/GbN/DL+e/dUkx5U+XDKwk7Le8eNQuDDs2ZP5Y82aBU+f8llQEU6OuZ5mYuE4TRzdN3anygXxJl6gTWtsXF0zdfpTp+DVK3F/pOnmUUvSG9Gjh/6+sfFZ0a5dAbi7bRtxmUkwnhJLS1i1CuzsxAqPWcZXhkli2DBR2FmtFrVlM5IIGSiUqxBL2ooat9OPT890ChStb+t9y//q/g+AUXtG4fvSxL2g0gdFBnZS1vLzg27d4OlTWJnxItgvwl/Q859OBPyaMJfuxx8xt7ZNc59v//2Wq1eOUfWJCP7KDsjgnL5EZopREywsZBkx6d3Xtat+8c/27cYNx7rXro2dhwexr17xwBRf2l5XrBjMmyfuf/utSG+UGf37i8TFixeLOrUZ9FHpjxhceTAKCr039+Z5+PPMtSvBlIZT+Lr21/zd8W+KOsmM51LGycBOyjpRUdCxowjuvLxELccMeBn5kqZ/N2XVnc30bhEpsqsmpFxIzabrm5hxYgYtbjmjVlS416lDThOUhzh/Xtw2biyCO0l6l+XJI/6WtfefPjV8X5VaTbGEXrsby5dnTcqO/v1F9BkXJ24DAzN3vK5dRZUKrceP9SuhjDC7+WxKu5bmadhTem7qSZwmLnPtAlQqFdObTOfjsvrJj1FxUZk+rvThkYGdlDUURVSWOHtWJB/dtk2shjVScFQwzf5uxqVnl8gdBnP2AL/8kubCi5sBN+m3pR920WqaPBRDr6X698/oleiEh+s/A77KXIUhSXpraIdjLS3FvDtjFP3oI8xtbQm5fRu/jCYVTotKJYZOixWDR4+gVy+R4NwUAgOhaVOoV8/oBRo2Fjas7bIWWwtbDtw9kCXpSvxD/Sm3sBwLzi0w+bGl95sM7KSs8fPPIm2BmZmYvGPsJwbwKvoVLf5pwXn/87hGmfHvcijRZTDUqJHqPmExYXRa14nQmFA+fVEJs5h4cpUsSe6MlixLZMsWMVRVuLBYuCdJ74POncV3rtu34cgR4/a1zJGDoh99BMD1P//MgtYBjo6wYYNIaL5nD/zwg2mO+/y5mDB79apYCXX9ulG7l3YrrSs5NvPkTNZeXWuadiX45/I/3H55m893fc6sk5mcYyh9UGRgJ5nesWP6Lq05c/RjPUYIiAig8YrGnHlyBidsOPBXPF4qtzQTjcZr4umxsQc+L3wobJGX6pdEr57XJ5+gymBqFa2QEH3Foz59MpypRZLeOg4O0LOnuD9vnr4GsqFK9umD2tyc597eBFy6ZPoGApQrp5/K8f338O+/mT9mqVLiYkuUEL2BtWuLBOpG6Fq6qy6/3YBtA7j01HTXP6bWGMbXET2BY/ePZeLhibJChWQQGdhJpletmoh+PvkEPv88Q4fov7U/3n7euFjl4sDSOMo9Q6QqyJUr1X3G7BvD9lvbsTKzYr6mJ/Hh4eQsXpz8zZpl8EL0fvpJJLIH6N0704eTpLfK4MHidvNm6NLFuGwgtnnyULBNGwB8li7NgtYl6NdP1JjWaEQSvocPM3/MAgVEMFejhphn0bixWI1rhGmNptG8SHMiYiPouLYjgRGZnAeYQKVSMa3xNKY1EimaJh2ZxLj942RwJ6VLBnaS6VlZidQCCxZkuGtrbou5VMtbjWOnSlHxYSw0aZI0o+prfjv7G3POzAFgeeNFhG09BEDZYcNQZbLml0aj7ywoVkwMxUrS+6RiRahUSdz394eDB43bXzuH9fHBg7y6d8/ErUvk11+hQgUxjNq+vZj4mlnOznDggCigGx0tui+NCFDN1Gas6ryKwrkKcy/4Hh9v/Ngkiym0xtcdz5zmcwCYdWoWn27/1KTHl94/MrCTTCMkRGR4137VV6n0xbgNFB6jf5MunKswp21HUHLbSREoLlyYapC46/YuRuwRtb1+aPQDxU8EEBcRQS4vLzwbNcrY9SSyZ48sISa9/z77TH9/8WLj9s1RtCgeDRqAouCjnbOQFWxsxGRXNze4eBH69jXNYgo7O1G+bOxY0YtnZC4jJxsntnTbgq2FLfvv7ufznZ+btGdtZI2RLGm7BLVKzYlHJwiLMSIvjfTBkYGdlHlRUeLb7jffiESgGXD68WkKzyvM9pvbxQOPHqHSHuvbbyGVVCWXnl6i24ZuaBQNAyoMYGTJQdxavRqAcsOGZXpuHYhKRCBWDfbtm+nDSdJbqXt3Ed8AbNoEDx4Yt3/pTz8F4N62baYvM5ZYgQKigRYWIhjTvkAzS60Wcy4uXoTcufWPG5jcr2zusqzstBIVKhZfWMz046nPB86IQZUGsbX7Vnb13EVO65wmPbb0fpGBnZQ58fFi0tnhw2IWdsKbuzHWX1tPw+UNeR7+nNmnZ6PEx4sIKjhYzNdLJbfI3aC7tFrVirCYMBoVasSiNou49vvvxEdF4VyuHHnr1cvctQH378OZM+J+164Zr4QmSW87e3uRNg5EtiJtFS5DuZQvj2ejRigaDZe0iYWzSu3a+m7FSZNg3TrTHTtnTv395cvFIotz5wzatUPJDsxvKf7j/vfv/1hxaYXp2gW0Kd6GgjkL6pt3cTk+L3xMeg7p3ScDOynjFEWMTW7YILqztmwRk3UM3l1h2tFpdN3Qlai4KFoXa822j7ehmj1blBGytYV//kkxE/DjV49pvKIxfqF+lHYtzYaPNhB+5x6+a0XKgQqjRpmkty7xItz//S/Th5Okt9qXX+orUSxaBKGhxu1ffuRIVGo1jw8e5MXFiyZvXxL9+sHo0eJ+375Gr2hNV3y8WNX/+DHUrWvwvLvPq33OuFrjABi4bSD77+w3bbsS7L69m/5b+1N7aW2O3DcyT430XpOBnZQxiiIiHe0Cib//BiPms0XHRdN3S1++PfQtAKOqj2Jr963Y+/jC+IRkn3PmiNUKr3ke/pwmK5pwP/g+RZ2KcqDPAXJa5+T89OkoGg35mjY1Sd662Fh9FbQKFaBkyUwfUpLeaoUKQUJaOiIjRX5xY+QoWpRCHToAcGn27KxfwTljBrRtK6aDtGkD166Z7thmZiKxX7t2YlHFwIFiImJMTLq7Tm8ynY/LiEUUndd15uLTi6ZrV4KqHlWpma8mwVHBNPm7CQvOLZArZiVABnZSRn33nb47a/58MU5poMjYSJr83YS/L/+NmcqMha0XMrvFbMyiY0Qa/NhYseItcemfBEGRQTT7uxk3A2+SzzEfB3ofII99Hh7t38+zs2cxs7Ki4tixJrnEuDjREQlitEeSPgTjRGcTKlWqU1vTVHboUNSWljz39s6aahSJmZnBmjWizGBwMDRvbpo0KFqOjiIHzOTJ4j9k0SJo0EDkvUuDWqXmr/Z/0aBgA0JjQmn6d1OuPr9qunYBLrYuHOh9gG6luxGniePzXZ8zcNtAWYZMkoGdlEF16ojVqvPnG52rztrcmrJuZXG0cmRXz10MqTJEPDFqlMj+niePyAb82lBqaHQorVa1EuXF7HJzsM9BCuQsQFxUFP/NnAmItAv2Hh6muEKWLROrYQsWNHqRnCS9sypVEtmF4uNF9T5j2bm7UyIh4/HFWbOIN6CHK1NsbWHHDjEX7skTaNEi8zVlE1OrYcIE2L4dcuSAU6egcmURSKbBytyKzd02U9m9si7huqnnw9lY2LC682pmNp0pgsmLf1H3r7o8Ckk78JTebzKwkzKmeXNRg8jAVbCKoujSmahUKua1nMeFTy/QrEhC8uClS8VkaJVKTFh2cUmyf2BEII1XNOb049M42Tixv/d+ijmLYVqfP/8k3M8P2zx58Bo40CSX9/ixvkNy9GjRMSBJHwrteqXFi0WZ5zgj06aV/uQTrJycCLlzhxvLlpm8fck4OcHeveDpKb4ctm4tyoWZUuvW4O0tgrpPP026yCIVOa1zsq/3Pirmqcjz8Oc0Wt6IGwE3TNoslUrFmFpj2NtrL842znj7ebP3zl6TnkN6t8jATjKMRiO+td66pX8sXz6Ddg2LCaP35t60XtVal1jTXG1OEaeE+rHe3jB0qLg/eTK8VinCL9SP+svqc87vHM42zuzrtY+yucsCEHTjBj4Jq+MqjhmDua1tJi5SiI6GsmXFaIubW4ojwpL0XmvcWHTKR0WJWRFGFmPAMkcOKiVEh1cXLSLU2NwpGZEvn0g6mSuXWMrevLnpg7uiRcUijYkT9Y/dvQt37qS6i/aLaPnc5XkW/oxGyxtxK/BWqttnVJPCTfD+1JsfGv3AwIqm+YIrvZtkYCelLypKZGOfOlWM0RiR7d3nhQ/VllRj5ZWVHH94nJOPXitE+eSJ+OSIjhaTn7ULJxLcC7pH3b/qcu3FNfI65OVo/6NUzlsZgPiYGE6NH48mLg7Pxo3J36JFpi8VRL1M7SjLuHGi9rgkfUhUKrEuQWv8eIPWDCRRsHVr8tSqRXx0NOemTMmeif2lS4sqErlywenTWRPcWVnpk69HR4vVJhUqwJIlYlFZCpxtnTnQ5wBl3criH+ZPw+UNufLsimnbBRTMWZBv6n6jywgQGBFI1/VdeRCcDYG19NaQgZ2UtsBAaNpUTFA2N4dp0/RZTNOgKAqLvBdRdUlVrgdcJ69DXg73O0y9Aolyy4WHixVnfn7g5SVSmyQq/+Xzwoc6f9XhbtBdCucqzPH+x/Fy9dI9f23xYoJv3sQqZ06qfvedSdKbvHwpaoyDmDedOBu/JH1IatXSzy198gT++su4/VUqFVUnTMDMyoqnp05xf+dO0zcyJZUqZX1wp/XqlXg/DAsTw7OtW4v3sxS42LpwsM9ByriVwS/Uj7p/1c3yNCUj94xkvc96yi8qz5qra7L0XNLbQwZ2Uuru3BHv7sePi0nDe/aIZMTpeBjykGb/NOOznZ8RERtBk8JN+G/wf9TJX0e/UWwsdOsGFy6I+XQ7dohzJNh3Zx+1/qyly1N3vP9xCuUqpHv+pY8P1xKGYKtMmIDNa3PyMmrSJJHmAUTBCxOM7ErSO2vGDP0apvHjReVAYzjkz0+ZIWJx1IXp04l49szELUxFpUqi4K02uGvSBF68MP15XF1Fzs1Zs0RP3u7dUKaMGLtOoffO1c6VI/2OUCd/HUKiQ2j2TzM2+GwwfbsSTGowiRqeNQiJDuHjjR/Td0tfXkVnUZArvT2Ut1RoaKhSvHhxJTQ09E035cN06pSiuLgoCihK/vyKcvWqwbs2WNZAYSKKzVQbZe7puUq8Jj7pBhqNovTtK45tba0oJ04kekqjzDk1R1FPUitMRKn9Z20lIDwgye4xoaHK9tatlZVeXsqxL77IzFUmceeOoqjVolkuLooSFmayQ0vpCAsLUwAFUMLkf/xbRftSBUX5/HPj94+LjlZ2demirPTyUvb37avEx8WZvI2punBBUZydReOLFlUUX9+sO9e1a4pSpYr+P6tDB0UJCUlx04iYCKXjmo4KE1FUE1XK/DPzs6xZMXExyoR/J+jeUz1/8VS23tiaZeeT3jwZ2Ekp69hRvDlVqqQofn5G7Xr56WWlwbIGys2Am8mf1GgUZcQIcWwzM0XZvl33VHRctDJo6yCFiShMROm/pb8SFRv12u4a5eiIEcpKLy9lU8OGSmRgYIYuL6VmNW+uf09eudIkh5UMJAO7t9fz54ri4CBeFyqViJWMFXL/vrK2ShVlpZeXcunXX03fyLTcuKEoBQuKC3B1VZSzZ7PuXDExijJ5sqKYmytKzZqKEh+f6qZx8XHK0B1Dde93I3aNUGLiYrKsaUfvH1WKzC2iO9+S80uy7FzSmyUDOyllAQGKMniwoqTz/x8dF638eOxH5bt/v0v/mBqNonzxhf4TYtky3VN+r/yUen/VU5iIop6kVn4++bOi0WiSHeLakiXKSi8vZXW5csqLixeNvqzUBAcrSq5comkVK4qmStlHBnZvt6VL9S/bNWsydox727crK728lJWlSytPT582bQPT4+8vXtigKLa2irJjR9ae78oVRfHx0f8cFpZiQKnRaJSpR6bqgq06S+sofq+M+yJtjPCYcOWr/V8pBWYXUEKiUu5NlN59MrCThEuXFOU7A4KzRPbc3qMUn19cYSKK+WRz5Xbg7dQ3jo9XlGHD9F1iS/TfFrff3K64/OSiMBHFcbqjsuvWrhQP4X/ypLKqTBllpZeXcmvtWqPamp4jR/QfXOfPm/TQkgFkYPd202gUpU4d8Rpp3jzjX3xOffutstLLS9lYr54S9uSJaRuZnlevFKVpU/0LfcqUNHvUTGrMGHHOTz4RQeZrtlzfojhOd1SYiJJnVh7l6P2jWdqciJgI3f14Tbzy2Y7PlNOPsjnYlrKMXDzxoVMU+P13qF5d5JDTFkdNw/3g+3Rc25EWK1twK/AWue1y82e7Pymcq3DKO8TGiiLdv/6qL8szaBBRcVGM2D2CtqvbEhARQIU8FTgz6Awti7VMdojgW7c4/uWXKBoNhTt2pKi2oKUJvHolmgfwySdi3rUkSXoqFfz5p1gfsHcvzJ4tKlMYq8o335CjWDGiAgI4/NlnxGTVatWUODiIRVqDB4v3vQkToEOHdCtIZJqiiOX2iiJSohQrJrILREToNmlfsj3en3hTxq0MT8Oe0nB5Q3459QsaRZMlTbKxsNHdX3t1LQu9F1Ljzxr02NhDpkZ5H7zpyDI1sscuG/j6KkqjRvpetJYtFeXFi1Q3D4wIVIbvGq5YTrFUmIhiNslM+WLPF0pwZHDq5wgO1n9LNjNTlH/+URRFUa4+u6qUW1hONwQxaveoZPPptF49eKBsrFdPWenlpezt0UOJjYzM1GUntmaNopQvL5pXsGCqc52lLCZ77N4NCxaI14parSjFimVsgVHYkyfKpgYNdIsp4qKjTd/Q9CxdqihWVvpFFZcuZf05jx1TlGrV9O+3np6KsmJFkl7DsOgwpcfGHrr3xQbLGih3X97N0mY9Cnmk9NvST1FNVClMRLGaYqV8tf+rtN/XpbeaDOw+RHFxijJrlqLY2Ig3GBsbRfn553SHJQLCAxSHHxwUJqI0Wt5IufosnZWyt28rSpky+nkt27crETERyvgD4xXzyeYKE1Fcf3JVdt7ameohwp89U7Y0a6as9PJSdnbooEQHm+7N5upVRbG01I/MHM3a0Q8pDTKwezdoNIrSvr0+NunRI2PDsi+vX1fWVq2qrPTyUo6PGZO9K2W1vL0VpUABcSGWlooyY4Z4b8xK8fGKsmqVyDSg/U+cNCnJJhqNRll4bqFiO81WYSKK3TQ7ZeG5hSnOOTalC34XlEbLG+mCSucZzsq0o9OU2PjYLD2vZHoysPsQde2qf1Np1CjVFAAB4QHKkvNLkryh/PXfX8qBOwfSP8fatfqldO7uinL+vLLn9h6l8NzCujeOdqvbKf6hyeebaIX7+ys72rZVVnp5KVubN1cinj83+lJTExysf08HRRk71mSHljJABnbvjuBgRcmbV//amT49Y8fxO3FCWVWunD64i8m6FaGpevFCUdq21V9M7driC2lWi4gQ/3G5cyvK48f6xwMDdV+w77y8o1tQxkSUJiuaKL6BWZiuRRFB5fab25WSv5ZUmIhSbUm1LA8oJdOTgd2HaP9+RcmZU1H++CPFr9s3A24qQ7YPUWym2ihMRNl/Z7/hx46MVJShQ/VvlHXqKHevn1S6re+me4Py/MVT2Xx9c5qHCb59W9ncqJEurUnoo0dGXmTqYmL0o8PajC5vYjRI0pOB3bvlwgX9SCaITqiMeLBnjy64O/TZZyadZmEwjUYMzWq/iNraKsrs2eKNIqu9fo42bRTFy0u8N0dGKvGaeGXOqTm692LLKZbKuH3jsnxFa2x8rPLPpX+Uf+/+q3ssODJY+fbgt8qTV9m86EUymgzs3ndBQaI76uefkz7+2pCmRqNRDt87rLRd1VY314KJKBUXVTSsh05RFOXiRX1KAVCefjNCGbZjqGIx2UKXxuSLPV8or6JepXmYZ97eyvoaNZSVXl7K9tatTbp6Li5OUbp3138g5c6d4iI1KZvJwO7ds2ePPqG3Wq0oBw9m7DiPjxxR1lSsqJtzZ8rpFka5f19RGjTQvzl4eWX8ojLi+XPxhVt7fldXkang6VPlZsBNpdnfzXTvy24z3ZQl55cocfHZN4StTctiPtlc+WjdR8qR+0dkb95bSgZ276sXL8TcDScn8Sbh4KAoL1+muOnT0KeK129eujcNJqK0XdVWOXTvkGEv3NBQRfnyS7E4ApQg91zKhCUfK3bT7HTHa/Z3M+WCX9qZTTUajXJz1SpldYUKuoUSUUFBGbj41A0Zon/ftLHJnjnTUvpkYPduWrZM/3oyM1OUe/cydpynZ8/q5txtadZMCUycAy47xccryuLF+qo7oChdumRtxYrEgoIUZeZMRcmXT39+S0tF6dNH0Zw/r+y4uUOXYoqJKGUWlFFWX1mdLQHeXt+9Sp2ldZJ8TpRZUEZZcHZBul/WpewlA7v3zZ07ou6PdmEEKErp0oqyc6du2DUmLka5/PSybheNRqMUn19csZlqowzZPkS58eKGYefSaBRl82bdm9CdXCgjhhdV7Kba6l741ZZUUw7eTf9bb3RIiHJ05EiRwNTLSzkyfLgSGxGR7n7GiIxUlOrVxX+Jubn4L5HeDjKwe3fNnq1/q+nVK+MjmC99fHQLpVZXqKD4btxo0nYa15iXIu+mtkvSzExRBgwQ76/ZITZWLNnXvmGBovz1l6IoIin8Lyd/UXJMz6F7ny02r5iy9MLSLK1coXXR/6Ly6bZPdYs7tAstouPkfJa3hQzs3id//KF/IwJFqVxZvDnExSlRsVHK9pvblf5b+itOM5yUnD/mTJKk8j///wxf3q7RKMrevYpSo4aiAeVofpRO/WwV9US17oVedkFZZZPPJoN6/J4cPapsadJEV1Hi+vLlJu3i12jEnOQmTfTlaXfvNtnhJROQgd27bcUK8WUJFKV+fdETHpuBxZTRwcHKoc8+03/BGzZMCTOypKFJXbqkKC1aJO2WHDAgaVWJrHbqVPIqQPPmKS9bN1Ym/dJecfoxl+59N//s/MoPR39QnoY+zfJmBUUGKbNPzVaKzy+udFvfLclzU49MVY49OJatQ8WSnkpRFCX7s+elLywsjMqVK3P+/Hns7e3fdHPeTr6+EBMDXl7i5zt3oEQJaNoUxo0jqHp5Dtw7yOYbm9lxawehMaG6Xd3s3Njbay8V8lQw/HyKAgcOwKRJPLx6ghXlYXkFFb5O+j+hFkVb8GWNL2lSuAkqlSrNw4X7+XH+xx95fPAgAPb58lF71iycy5QxvE3pOHsW+veHkBB48gRsbWH7dmjUyGSnkEwgPDxc9zoPCwvDzs7uDbdIMtbOndC9O4SFgbk5lC4Nu3eDu7txx1E0Gnz++IPLv/2GEheHua0t5UaMoPjHH6M2N8+axqfn9GmYOFFkZ9Zq3BiGDYM2bcQFZ6eyZeHqVQBCbc34vWsRZhV+yjONSPhsrjanQ8kODK48mEaFGqFWZV0tAkVRCIsJw8HKAYBrz69RZqF4D3exdaFVsVa0Ld6WZkWa4WjlmGXtkPRkYPeuuXkTtm6FLVvg1Cno2BE2bQIgJj4GzYP7WBcuDsBPJ37iqwNf6XbN65CXTiU70alUJ+oWqIu52sA3o6AgWL6cJ8vns938Luu94FAhUBLiNjsLO7qX6c4XNb6gtFvpdA8X+eIF15ct4/aaNcRHRaEyM6NE796UHToUCxN9oMfGwqRJ8MMPIh4FKFwYNm6EChVMcgrJhGRg9364eRNatxbfMQEsLcVr8IsvQG1kbBF8+zZnJ04k4OJFABwKFKDM0KEUaNkStZmZaRtuqFOnYMYM8e1Qk1AVIn9+6NULevQQ0Wx2uHUL1q2D9evh8mUAIs1hXWn4vXEOTjmG6Db1dPSkS6kudPHqQs18NbM0yAO4GXCTacemsf3WdoKjgnWPW6gtqF+wPuPrjKdhoYZZ2oYPnQzs3gXHj4s3kq1bxTtngkA7Fac7VuNUv8acenKaM4/PsLD1QnqX7w3AxacX6bGxB62LtaazV2eqeVQz/EUdHU38vj1c2raEXXf3srVIHN4eSTdpWLAh/Sr0o1OpTthbpv87enX/PrdWrsR3wwY0MTEAuFauTNUJE8hZrJhh7UqHooiYd+RIePRI/3jz5rB6NeTKZZLTSCYmA7v3R0SE6CVft07/WIECsHAhtGghypMZStFohadzygAAHWxJREFU8F2/nsvz5hGdUPrLsXBhSvbpQ4FWrUz2RdBo9++L0oh//AGBgfrHy5YV3Zbt2okgz5iLzahbt0SAt2kTXLgAXbpw5dfvWHx+MX9f/puQaH2Q527vTseSHWletDkNCjbI0h602PhYTjw6wY5bO9h+azu3Am8BsLPHTloVawWAt583++/sp1a+WlRyr6Tr9ZMyRwZ2b5tXr0QXe61a+scaNoTDhwF47GTO1x+74p1Hw834Z8l2H1J5CAvbLMzQqWOf+nF57wqOnF3HkVdXOOoRR7C+pCAqVNRwr0o7r450L9OdgjkLpn/M8HAeHTjA3U2beO7trXvcpUIFygwZgnudOukO2Rrq0CEYPhyuXdM/Zmcn6loOHGh8j4GUfWRg9/45fBg+/hiePtU/VrCg+J7q4ZHaXimLDQ/n1qpVXF+6VFdf1tzOjoJt2lCoXTtcypVD9SZe4FFR4pvk6tVi3Dk2Vv9cvnzQsqWIZuvWBReXrG/P06diLLxoUdG8G1fZ16EsG7xgawl4Za3f1Aw11V0r0qRUa+oVqEdVj6pZGujdCrzFHt899KvQT3ee8QfHM/34dEB8vpR0KUlVj6pUca9CJfdKVMlbBStzqyxr0/tKBnZviqKAnx9cvw7//QfnzxP333nuB/hyyxluLZzCrSg/bgXeouFTa/53OSe0a0dg/Wq4LCqkO0xx5+LU9KxJTc+a1MpXi9JupQ3qlQuLDuXm1cNc+m833ndPcD78NpccI4l+bXTWQWNBA7dqtKvRl7Yl2pHbPnc6l6UQ9vAhT0+d4vG///Ls7Fk0CW92KrUa9zp1KNm3L7mrVzdJQBcXJ2LhNWvg55/h7l3xuEoFPXvCL7+Aq2umTyNlMRnYvZ9iY2HmTJg+XcQbANbW0LmzeH3mzg0VKxresRUbFobv+vX4rl9P6AN9sXobV1c8GjXCo149XCtVwtLxDczlevlS9Jpt3Cii2qiopM+XKAF16ogv7RUqiB49qywOWp4/h5Ur4dgxok8c5UCOQHYUhwOFwdc56aYqVJRSu1HNqSxVi9SjbMl6lM5dFicbpyxr3kafjay+upqzT87y6NWjZM/fHXGXQrnE592+O/t4EPyAIk5FKJKrCJ6Onpip39CQ/FtOBnZZKSZGjAc+fEjs/TsENK3NY1UYj189JueKtTT8cS0g5kaUHAZ+DhCXwt9p08JN2dd7n+7nX079QgnnEtTwrIGzrXPyHRAB1vPw5zwMfsDDR1d5cPcCD/xvcCPEl+vxz3hkFZXifjli1NRWF6BBqVY0qNebinkrpzkXLyooiOCbNwm6cYPAK1d4cf48kS9eJNnGoUABCrVvT+H27bHNkye9/7U0BQaKnrkNG8StrS08fiwCPAAzM+jWTcyvS/jSKr0DZGD3fouOhrlz4c8/xchhYtbWUK4cNGsmRjG9vNIP9BRF4dmZM9zZtIknhw8TFx6uf1KlIlepUrhWqECuUqXIVaoUOYoUwczS0vQXlpqICDhyBHbtgoMHxRf415mbQ6lSYvi2aFEoUkT8K1oU3NxMP4yrKGIqz+nTcOEC96+d4EDPmhzMEcjpx6e5H3w/xd3coy0prbhQtHAVCpaqRcGcBSlo50FBB0/cnAuYbMTlWdgzzvmdw9vPm3N+57gdeJsbw27oOiq6bejGumv68X0LtQUFcxakcK7C5HPMx7yW87CxEENMj0IeYaY2w83OzfC55O8RGdgZQlEgIgIlJISnz+/w6uVTQkOe8yo0gFelChNqCa+iX5Hv2iPa/XMOnj8n/sUz6rR5QYAtBNiSZEgToLVVWXb875qY0V+hAjnKbOcV0diY21DUqSjFnYtT3Lk4xZyKUTZ3WYo6FSU0OpSwmDBCo18REvKcgBcPePHyEQEh/rwIe0ZAeAAvIgPwiw/moVkY0WpNmpflGg5lIuypbFOYysUbULlBD4oUrZpij19seDiP9u0j9OFDwh4/JuzxY8KfPCEq8fySBGpzc5zLlcOjfn08GjXCsVChDL/4Y2Ph22/F/OD//oNnyUefAfHB0L+/6AWQPXTvHhnYfRgUBc6dg2XLxBy8FN4+UKvFXNjmzcWC00KFwNNTPJbSn0V8TAzPzpzh8cGDPDt7NklPnpZKrcY2Tx7s8+XDPl8+HPLlwzZPHqxdXLBxdcWhYMGsXZARGAgnT8KJE2Kp/qVLoocvNfb2YmKiuzvkyaP/5+4uhnRz5Ej6z8Ym04HgsyO7OLv8B85E3OI/iwCuuSg8yJn2PtaxkD/Cgtzx1riq7XGzyImbtTOuFWrhVqIyTjZOOEQrOPq/xMHJHYdcebDP6YaZnb3RK4lnnZzFwXsHuRt0l3tB94jV6Ie9zdXmRH8brfvs+mj9R2zw2YBapcbNzg1XW1dy2eTCycYJJ2snFrVZhIWZBQDnnpzjRcQL7C3tsbOww87SDjsLO/GzpR2WZtn4hcBEPvjALiouig5rOhAZF0lUXBSRsQm3CT+3jC3Iiu/+g/h44lVg/n3qx2pp6cWu8T66nx2/gdBEPe1q1Lg7uOPp6EndvDWZ2fhHXVf8xacXcbF14fKzy3x36DuCooIIjQ4lNCaUqLiUe9fSo1IgbyjkD4H8cbbkt3SjeM4ilMpXkZKVm+Ncua7BQwHnJk/m9tq1KT5nny8fuUqUEN+SK1fGuWxZzK2tU9w2I3LnFiMKiVlbi2/23bqJYZ0iRUx2OukNkIHdh0dRxCLTP/6AY8fEegRtz/vrrKxgyRLo3Tv940Y8f87zc+d4ee0aQdev8/LGDWIT5uWlJk+tWjRassT4i8goRRFDDZcugY+PWEbs6ytuHz7UL+U3lLk55MwpgrzPPoPRozPXvrg48PMj1NcHH99T+Phf5p6XO/fNw7gffJ/7T67xOO6lLjOCsexiwCEGHGLVdCnYih9GbRdPnDsHY8eK4Zg0AtV4TTxPQp9w5+Ud7gbd5VX0K76o+YXu+Q5rOrDj1g7ilfhk+1qoLYj+NlrX2dBpbSc239ic6rkixkfoegLH7hvLnjt7sDa3xsrMCitzKzqW7MiwasMy8t+QZd7aPkptvBmmnZiRReI18ey/sT/V519Y5CZMUcTXSJWKHGEKKlQ4xJthrzHHMW9B7J3ccbB0oKyFB2FLx4CzM7i68nfcbeyc3XG2c8HZxpkc1jmSzAkIi43VTbYtai/GDXde3cl/D/9L0gY14luImcoMByyxD4rEIQacI1W4xFvgjC0uKjucLXLg7JiH3E75yJenOHnzeWGZr6D4lmfzWpfha+dPj0vDhgQ9e4aVkxP2Hh7Yurtj7+mJvadnspVpUXFx+gk1JjB2rEibUKaMeL9zdRXf4hO/7rP4z0TKYuHh4agTJr+HhYXxln7flEysXDmYN0/cVxQxTPvvv3D+vHjs8WMR58TGigUXBr3ObW1xqV8fl/r1E46rEBUYSPjjx4Q+eUL4kyeEP35M5IsXRAUGEvXyJWoXlyz/rEkmVy5o0ED8Syw6Gh48EIk3nz3T/3v+XCyOePlSTCzW/lMUkXrl5Uvxb/NmGDw48+1zckJVrQ6lq9UhWRIXRSEm5CVPHlzhsd9NXgQ8ICDYn4DwZwREvuRFQTdeWMQQHBVMWMgLwkIDeWWhEJ/w8RepgkgreG4Fa4LO8E1oqAi07t8XQ8WJh9ZTa56ZE06uTlR1rQokjRX+afMP8Zp4AiIC8A/zJygyiKCoIIIig4iOjyY80fHdrdyp6FSR8NhwwmPCiYiNIDwmnDglDhUq4qLiCIsWx/Z96ovPE58k7SjpUDJb/3bs7OzSHQF7a3vsnj59Sv2EF6YkSZIkSdKHzpBRzLc2sNNoNDx//tyg6FSSJEmSJOl990732EmSJEmSJEnGkSlbJUmSJEmS3hMysJMkSZIkSXpPyMBOkiRJkiTpPSEDO0mSJEmSpPeEDOwkSZIkSZLeEzKwkyRJkiRJek+8U4FdREQE33zzDdWrV6dy5cqMGzcuSQbp1Dx//pxatWqxadOmbGillFnG/p737t1L+/btqVSpEo0aNeLXX39Fo0m7Tq70ZgQGBjJ06FCqVKlC9erVmTZtGnGp1JE6cuQIbdu2pUKFCrRs2ZJDhw5lc2ulzDDmd7169WqaN29OxYoVad68OStXrszm1koZZczvWevWrVuUL1+eM2fOZFMrPyzvVGA3ZcoU/P392bt3L/v27cPf359Zs2aluY9Go2HMmDEEBQVlUyulzDLm93z16lXGjRvHqFGj8Pb2ZsmSJWzatIlly5Zlb6Mlg4waNQpbW1uOHTvGhg0bOHXqVIq/q/v37zN8+HBGjhyJt7c3w4cPZ9SoUTx79iz7Gy1liKG/6wMHDvDLL78wY8YMLly4wI8//sicOXPYu3dv9jdaMpqhv2etyMhIRo8eTVRUxmqgS+l7ZwK7yMhItm/fzogRI8iZMyfOzs6MGTOGTZs2ERkZmep+v/32G3ny5MHd3T0bWytllLG/5ydPntC9e3caNmyIWq2mSJEiNG3alHPnzr2B1ktpefDgAWfPnmXs2LHY2NiQL18+hg4dmmLvzObNm6lSpQpNmjTB3NycVq1aUbVqVdauXfsGWi4Zy5jf9bNnz/jkk0+oUKECKpWKihUrUr16dfkafgcY83vWmjRpEk2aNMnGVn54zN90AxKLiopK9Rt5ZGQksbGxFC9eXPdYkSJFiIqK4v79+5QqVSrZPqdPn2bnzp1s3LiRtm3bZlm7JeOY8vfcvHlzmjdvnuTYhw8flr/vt9Dt27fJmTMnuXPn1j1WpEgR/Pz8ePXqFY6OjrrHfX19k/wNABQtWpQbN25kW3uljDPmd92zZ88k+wYGBnLu3Dm++eabbGuvlDHG/J4BtmzZwoMHD5g2bRoLFizI7uZ+MN6qwO7SpUv06dMnxedGjhwJgK2tre4xGxsbgBTnXwUGBjJ+/HjmzZuHnZ1dFrRWyihT/p4TCwsLY+TIkVhbW9OvXz/TNFYymfDwcN3vUkv7c0RERJIPgZS2tba2JiIiIusbKmWaMb/rxF68eMHgwYMpU6YMbdq0yfJ2SpljzO/5zp07zJ49m9WrV2NmZpat7fzQvFWBXfXq1bl582aKz/n4+DB37lwiIyN1gZp2aM7e3j7JtoqiMG7cOHr37k2ZMmWyttGS0Uz1e07s7t27jBgxAmdnZ1asWJHmttKbYWtrm2w4Xfvz61++bGxsks3BiYqKkl/S3hHG/K61Ll68yMiRI6lSpQrTp0/H3Pyt+niSUmDo7zk6OpovvviC8ePHkzdv3mxt44fonZljV6hQISwsLPD19dU9dufOHSwsLChYsGCSbf39/Tl79iy//fYbVapUoUqVKvj5+TFp0iQGDx6czS2XjGHM71nryJEjfPTRR9StW5c///yTHDlyZFNrJWMUK1aM4OBgAgICdI/duXOHPHny4ODgkGTb4sWLc/v27SSP+fr6UqxYsWxpq5Q5xvyuATZs2EC/fv3o27cvP//8M5aWltnZXCmDDP09X7lyhfv37/O///1P95kMMGTIECZOnJjdzX7/Ke+QMWPGKL169VICAwOVwMBApVevXspXX31l0L4NGzZUNm7cmMUtlEzBmN/zf//9p5QuXVpZv359NrdSyoiPP/5Y+eKLL5TQ0FDl4cOHSuvWrZV58+Yl287X11cpW7assnPnTiU2NlbZuXOnUrZsWeXu3btvoNVSRhj6u96zZ49SunRp5ejRo2+glVJmGfp7fl3x4sWV06dPZ0MLPzzvTI8dwPfff0/BggVp27YtLVq0wNPTk++++073fOvWrVm0aNEbbKFkCsb8nhctWkRcXBzTpk2jYsWKun+DBg16U82X0jBv3jzi4uJo3LgxXbt2pW7dugwdOhSAihUrsm3bNkBMwP7tt9/4/fffqVq1KgsWLGD+/PkUKlToTTZfMoKhv+tff/2V+Ph4RowYkeQ1nPg1L729DP09S9lHpSiK8qYbIUmSJEmSJGXeO9VjJ0mSJEmSJKVOBnaSJEmSJEnvCRnYSZIkSZIkvSdkYCdJkiRJkvSekIGdJEmSJEnSe0IGdpIkSZIkSe8JGdhJkiRJkiS9J2RgJ0mSJEmS9J6QVZYlSUpix44djB49GoDu3bszadKkJM9//fXXbN682ejjDhs2jOHDhwNQokQJo/Z1cHDA29vboG3PnDlDnz59jDp+48aNWbBggVH7pCQyMpLAwEA8PT11j23atIlvvvmG3Llzc/To0UyfIyvdvn1b1uOVpHecDOwkSUpi48aNuvvbtm1j7Nix2Nvb6x4rWLAglSpVSrbfrVu3CAsLw9nZmQIFCiR73t3dPdljBQsWxMnJKd022dnZGdr8JMqUKWNQQfmiRYtm6PiJbd++nZkzZzJ8+HA++uijTB8vO927d4+pU6cSERHB6tWr33RzJEnKBBnYSZKk4+fnx+nTp8mZMycFCxbk4sWLbN++nY8//li3zZAhQxgyZEiyfXv37s3Zs2epV68eP/74o0HnGzx4MJ06dTJZ+183d+7cJL1nWWn27Nk8e/Ys2eNNmzalfPnyWFhYZEs7MmLHjh0cP348xYBdkqR3i5xjJ0mSzqZNm9BoNFSsWJFGjRoBsGbNmjfcqnebg4MDRYoUIX/+/G+6KZIkfQBkYCdJEgCKoujmztWrV4+WLVsCcOPGDS5evPgGWyZJkiQZSgZ2kiQBcPr0aR4/foxaraZx48bkz5+fcuXKAXyQ86527tzJwIEDadiwIWXKlKFmzZoMHDiQbdu2odFodNvNnz+fEiVK8OTJEwC+/fZbSpQowfz58wHRC1qiRAnq1auX5Pi9e/emRIkSnDx5ksuXL/PZZ59RvXp1KlSoQMeOHdmyZYtu2/Xr19OpUycqVKhA5cqVGTBgQKrB9qtXr1i8eDE9e/akevXqlC5dmipVqtCpUyfmz59PSEiIbtvHjx9TokQJfv31VwAuXLhAiRIldL21WvHx8WzevJk+ffpQrVo1ypQpQ6NGjZgwYQL379/P6H+xJElZQM6xkyQJ0C+aqFatGrlz5wagTZs2XL58md27dzN+/Hhy5MjxJpuYbaZPn86yZcsA8PDwoESJEjx//pzjx4/r/v3000+AWBRSqVIlrl69SkxMDAUKFMDZ2TnFxSIp2bNnDxs3bsTS0pKCBQvi5+eHj48PX331FREREVy4cIHt27fj7OxMoUKFuH37NidOnODcuXOsX7+ekiVL6o51//59+vXrh7+/P+bm5uTPnx8PDw+ePHnCtWvXuHbtGjt37mTjxo3Y2dlhZWVFpUqV8Pf3x9/fH3t7e4oXL46rq6vumOHh4QwbNoyTJ08CkDt3bjw9Pbl//z7r1q1j27ZtzJw5k2bNmpnof1+SpMyQPXaSJBEaGsr+/fsBaNeune7xNm3aYG5uTnR0dIZSnLyL7ty5w7Jly7CysmLFihX8+++/bNy4kWPHjjFjxgzUajVbt27V9Zh16dKF1atX64KhTz75hNWrV9OlSxeDzrd27VoaN27M0aNH2bx5M0ePHqVOnToATJkyhX379vHTTz9x8uRJNm/ezN69e3F3dycmJobly5cnOdaECRPw9/enQoUKHDp0iN27d7Np0yZOnz6ta/u9e/d0vYGurq6sXr2azp07A1C8eHFWr17NvHnzkhzz5MmTFCtWjPXr13P06FE2bdrEqVOnGDJkCFFRUYwZM4Zbt25l5r9dkiQTkT12kiSxY8cOoqKisLKyonnz5rrHnZ2dqVmzJseOHWPNmjX069fPpOf95ptv+Oabb9LdbsWKFVSvXt3o4zdu3Nig7W7evJnsfqFChZKds0OHDly+fJmQkBBiYmKMbk9KcubMyY8//oitrS0AVlZW9O/fn+PHj6PRaBg0aBDt27fXbe/h4UGXLl2YP38+165d0z0eGBjI7du3AREQurm56Z5TqVR06NCBzZs3c/r06STXm5YbN26wc+dObGxs+PPPP3U9udp2fvHFFzx48IDdu3ezYMEC5syZk5n/CkmSTEAGdpIksWnTJgAaNWqUJGcdiB68Y8eOce/ePU6fPk2NGjVMdl5D89g5ODhk6PiG5rFLTJuD78aNG8yYMYNu3bpRsGBB3fPfffddhtqSmurVq+uCOi0PDw/d/fr16yfbRxu0hYWF6R5zdnbm9OnTREVFYW1tnWyf+Ph43e82KirKoLZpe3ETD8+/rn379uzevZujR48SHx+PmZmZQceWJClryMBOkj5wvr6+XL58GUg6DKvVtGlTbG1tdclrTRnYvY157EqXLk3btm3Zvn07S5cuZenSpXh4eFCzZk3q1KlD3bp1kwW/mZEnT55kjyXOeZdS4Gtunvpbt7W1Nf7+/ly6dImHDx/y6NEj7ty5w/Xr14mIiABIsvgjLdoewKtXrybJZZhYdHQ0IObiPXv2jLx58xp0bEmSsoYM7CTpA7dhwwbd/c8++yzNbQ8ePEhAQAAuLi5Z3aw3aubMmdSoUYP169dz6dIlnjx5woYNG9iwYQNWVlZ07dqVcePGGd0bmBIbG5s0n1epVAYf6+7du/z0008cOXIkSfBmb29PlSpVeP78OTdu3DD4eKGhoYAY5g0MDEx3+1evXsnATpLeMBnYSdIHLDY2lm3btgHg6OiYapChKArPnz8nNjaWDRs2pFh54n2iUqno0qULXbp04eXLl5w5c4azZ89y5MgRnjx5wt9//w2I1CZvi8DAQHr16kVgYCB58+ala9eueHl5UbhwYTw9PVGpVIwePdqowE779zBgwAC++uqrrGq6JEkmJAM7SfqAHTlyRNcTs3TpUsqWLZvqtm3btuXWrVusW7eOTz/9FLX6/VxUHxYWxv3797G1taVw4cI4OTnRsmVLWrZsiUajYfLkyaxevZqtW7e+VYHdxo0bCQwMJGfOnGzcuDHFIdyUSp6lpVChQoB+SDYlQUFB3L17F3d3d9zd3Y3qYZQkyfTez3dmSZIMoh2GLV68eJpBHUD37t0BePLkCUePHs3ytr0p8+bNo3PnzsyYMSPZc2q1mpo1awJiMUJi2oBGUZSsb2QKHj9+DEDevHlTDOp8fX11KVoMbXvDhg0BOHXqFHfu3EnxvD///DM9evSgd+/eBs/dkyQp68jATpI+UC9evODYsWMAujxmaWnfvr1u9eb7XD+2Xbt2qFQqDh8+zJIlS4iNjdU95+fnx6JFi4Dkq1W1/zfaChTZrXDhwoBYzbt3717d44qicPToUQYNGqS7lsjIyCT72tnZAfD8+XPi4uJ0j1epUoW6desSFxfHJ598woULF3TPxcTEsGDBAtavXw+I/H1yRawkvXlyKFaSPlBbtmwhLi4OCwuLFFfDvs7e3p62bduydu1ajhw5gp+fX6Ynyv/++++6wCA9Q4YMSTH1R1pGjhxp8AKHefPm4erqSpkyZRg1ahSzZ89m1qxZLF68GE9PTyIjI3n06BFxcXHkz5+fr7/+Osn+Xl5e3Lp1iz/++IOjR4/StGlThg4dalR7M6NLly6sWrWKBw8eMGLECDw8PMiVKxf+/v4EBgZiYWHB/9u7QxUFgjiO479tIqIYFIPRsi8gNkXUV5ANstFq0ofwEcyLuIjJbjPYBAU1iKBBZJNFMHgX5A7u5GDhToS57ydPmPhlZ/Y/+Xxes9ns4UjWtm1J9yit1WpKp9Pq9/uyLEvdblfNZlPz+VyO4yibzSqRSGi/3+t8PkuSXNf9/KIL4LUIO+Cf+nhJolwuh5olJ0mO42gwGOh2u8n3fbVarV/tYbfbhX5rNMxfmd8tFovQaz/Gdkj3iMzlcvJ9X8vlUpvNRpFIRLZtq1qtqtFoPMye63Q6ulwumk6n2m63Px5dPkssFtNwOFSv19NkMtHhcFAQBMpkMiqVSnJdV9FoVJVKRavV6kuYFwoFtdtteZ6n0+mk6/WqIAiUSqWUTCbleZ5Go5HG47HW67WOx6Pi8biKxaLq9XroQdAAns96e9WFEAAAAPwp7tgBAAAYgrADAAAwBGEHAABgCMIOAADAEIQdAACAIQg7AAAAQxB2AAAAhiDsAAAADEHYAQAAGIKwAwAAMARhBwAAYAjCDgAAwBCEHQAAgCEIOwAAAEO8A2eqXMUVOw4UAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the results\n", + "fig, ax = plt.subplots()\n", + "\n", + "# TMLE\n", + "sns.kdeplot(\n", + " estimates['analytic_eif-tmle'], \n", + " label=\"Analytic EIF (TMLE)\",\n", + " ax=ax,\n", + " color='blue',\n", + " linestyle='--'\n", + ")\n", + "\n", + "sns.kdeplot(\n", + " estimates['monte_carlo_eif-tmle'], \n", + " label=\"Monte Carlo EIF (TMLE)\",\n", + " ax=ax,\n", + " color='blue'\n", + ")\n", + "\n", + "# One-step\n", + "sns.kdeplot(\n", + " estimates['analytic_eif-one_step'], \n", + " label=\"Analytic EIF (One-Step)\",\n", + " ax=ax,\n", + " color='red',\n", + " linestyle='--'\n", + ")\n", + "\n", + "sns.kdeplot(\n", + " estimates['monte_carlo_eif-one_step'], \n", + " label=\"Monte Carlo EIF (One-Step)\",\n", + " ax=ax,\n", + " color='red'\n", + ")\n", + "\n", + "# DoubleML\n", + "sns.kdeplot(\n", + " estimates['analytic_eif-double_ml'], \n", + " label=\"Analytic EIF (DoubleML)\",\n", + " ax=ax,\n", + " color='green',\n", + " linestyle='--'\n", + ")\n", + "\n", + "sns.kdeplot(\n", + " estimates['monte_carlo_eif-double_ml'], \n", + " label=\"Monte Carlo EIF (DoubleML)\",\n", + " ax=ax,\n", + " color='green'\n", + ")\n", + "\n", + "# Plug-in MLE\n", + "sns.kdeplot(\n", + " estimates['plug-in-mle-from-model'], \n", + " label=\"Plug-in MLE\",\n", + " ax=ax,\n", + " color='brown'\n", + ")\n", + "\n", + "ax.axvline(0, color=\"black\", label=\"True ATE\", linestyle=\"solid\")\n", + "ax.set_yticks([])\n", + "sns.despine()\n", + "ax.set_xlabel(\"ATE Estimate\", fontsize=18)\n", + "ax.set_ylabel(\"Density\", fontsize=18)\n", + "\n", + "ax.legend(loc=\"upper right\", fontsize=11)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/causal_glm_performance_vs_estimator.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Double ML\n", + "plt.scatter(\n", + " estimates['monte_carlo_eif-double_ml'],\n", + " estimates['analytic_eif-double_ml'],\n", + " color='green',\n", + ")\n", + "\n", + "# Plot y=x line for min and max values\n", + "min_val = min(\n", + " estimates['monte_carlo_eif-double_ml'].min(),\n", + " estimates['analytic_eif-double_ml'].min()\n", + ")\n", + "max_val = max(\n", + " estimates['monte_carlo_eif-double_ml'].max(),\n", + " estimates['analytic_eif-double_ml'].max()\n", + ")\n", + "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", + "plt.xlabel(\"Monte Carlo EIF (DoubleML)\", fontsize=18)\n", + "plt.ylabel(\"Analytic EIF (DoubleML)\", fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/double_convergence_causal_glm.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TMLE\n", + "plt.scatter(\n", + " estimates['monte_carlo_eif-tmle'],\n", + " estimates['analytic_eif-tmle'],\n", + " color='blue',\n", + ")\n", + "\n", + "# Plot y=x line for min and max values\n", + "min_val = min(\n", + " estimates['monte_carlo_eif-tmle'].min(),\n", + " estimates['analytic_eif-tmle'].min()\n", + ")\n", + "max_val = max(\n", + " estimates['monte_carlo_eif-tmle'].max(),\n", + " estimates['analytic_eif-tmle'].max()\n", + ")\n", + "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", + "plt.xlabel(\"Monte Carlo EIF (TMLE)\", fontsize=18)\n", + "plt.ylabel(\"Analytic EIF (TMLE)\", fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/tmle_convergence_causal_glm.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACAzUlEQVR4nO3deVxN+f8H8NdtoSJiQllLKcOgFMko+zZkSZYs2Y1lyNgZs2BsgxFjjBlbZE1qLBMZ+y4qyyAtilKSknZt5/dHv+5XKm63W3fp9Xw8enA/53POed/TpXefVSQIggAiIiIiUnpq8g6AiIiIiGSDiR0RERGRimBiR0RERKQimNgRERERqQgmdkREREQqgokdERERkYpgYkdERESkIpjYEREREakIJnYyJAgCUlNTwTWfiYiISB6Y2MlQWloarKyskJaWJu9QiIiIqBJiYkdERESkIpjYEREREakIJnZEREREKoKJHREREZGKYGJHREREpCKY2BERERGpCCZ2RERERCqCiR0RERGRimBiR0RERKQimNgRERERqQgmdkREREQqgokdERERkYrQkHcAREREpCJyc4ErV4DYWMDQELCzA9TV5R1VpcLEjoiIiMrO2xtwdQWio/9X1rAhsGkT4Ogov7gqGXbFEhERUdl4ewNOToWTOgB48SK/3NtbPnFVQkzsiIiISHq5ufktdYJQ9FhB2ezZ+fWo3DGxIyIiIulduVK0pe59ggBEReXXU0G+vr549OiRvMMQY2JHRERE0ouNlW09JaOuro4RI0YgPT1d3qEA4OQJIiIiKgtDQ9nWUwLZ2dnQ1NQEAPTu3Ru5ubnQ0FCMlIotdkRERCQ9O7v82a8iUfHHRSKgUaP8eirAy8sLpqamePr0qbjsq6++QpUqVeQY1f8ofWKXkJCA6dOnw9raGjY2Nli5ciVycnKKrXvw4EH07t0blpaW6N27N/bv31/o+Pbt22Fvbw8LCwuMGTOm0DeNiIiIiqGunr+kCVA0uSt47eam9OvZZWZmYsaMGRg6dCieP3+OdevWyTukYil9Yjd79mzo6OjgypUr8PLywo0bN+Du7l6k3tmzZ/Hrr79i7dq1CAwMxJo1a+Dm5gY/Pz8AgI+PDzw8PLBz507cunULLVu2xKxZsyAUN8uHiIiI/sfREfDyAho0KFzesGF+uZKvYxcaGoqOHTti69atAICFCxdi8+bNco6qeEqd2D179gz+/v6YP38+tLW10ahRI0yfPr1ISxwAxMXFYfLkybCwsIBIJIKlpSVsbGxw+/ZtAICnpydGjhyJZs2aoWrVqpg7dy5iYmJw69atin5bREREysfREYiMBC5cAA4cyP8zIkLpk7pDhw7BysoKQUFB0NfXh6+vL9asWSMeY6doFGOkn5RCQ0Ohp6eHevXqictMTEwQExOD5ORk1KhRQ1w+atSoQucmJCTg9u3bWLx4MQAgLCwMkydPFh/X1NSEkZERgoOD0aFDh3J+J0RERCpAXR3o0kXeUciMp6cnnJ2dAQB2dnY4ePAgGnzYKqlglLrFLi0tDdra2oXKCl5/bNpxfHw8Jk+ejC+++AL9+/cv8VpaWloKM32ZiIiIKtbAgQPRrl07LF26FOfPn1f4pA5Q8sROR0cHGRkZhcoKXlerVq3Yc+7evQsnJycYGxvjjz/+EE9P1tbWRmZmZqG6mZmZJV6HiIiIVM8///wjnoRZtWpVXL16FStWrFCY5Uw+RakTu2bNmiEpKQmvX78Wl4WHh8PAwAC6urpF6nt5eWHcuHEYO3YsNmzYUGhqcrNmzRAaGip+nZ2djcjISJiZmZXvmyAiIiK5S0tLw/jx49G/f38sW7ZMXK4oy5hISqkTOyMjI1hZWWHVqlVITU1FVFQUtm7dCicnpyJ1/fz88NNPP+G3337DhAkTihwfMmQI9u3bh+DgYLx79w4bNmyAvr4+rK2tK+KtEBERkZw8fPgQ7du3h7u7O9TU1FC1alV5hyQ1kaDk63m8fv0ay5cvx61bt6CmpoZBgwZh3rx5UFdXh6WlJZYtW4YBAwbAwcEBYWFh0NLSKnS+g4MDli9fDkEQsHv3buzfvx+JiYlo1aoVli1bBmNjY4ljSU1NhZWVFQICAlC9enVZv1UiIiKSoYKf/d988w0yMjJgaGiIAwcOoIsSTwBR+sROkTCxIyIiUg4pKSmYNm2aeIm0Xr16wcPDA3Xr1pVzZGWj1F2xRERERNKIjo6Gj48P1NXVsWrVKpw6dUrpkzpAydexIyIiIpLG559/jt27d6N+/fro1KmTvMORGbbYERERkcpLTk7GqFGjcPXqVXHZsGHDVCqpA9hiR0RERCouMDAQw4YNQ3h4OG7cuIEnT54o7JZgZcUWOyIiIlJJgiBgy5YtsLW1RXh4OBo3boz9+/erbFIHsMWOiIiIVFBSUhImTpwIb29vAPnbg+3atQu1a9eWc2Tli4kdERERqZS4uDjY2toiIiICmpqaWLduHWbNmgWRSCTv0ModEzsiIiJSKXXr1oWlpSUA4PDhw2jXrp2cI6o4TOyIiIhI6SUmJkJDQwM1atSASCTCrl27IAgC9PT05B1aheLkCSIiIlJq169fh4WFBSZPnoyCDbVq1qxZ6ZI6gIkdERERKam8vDz88ssvsLe3R1RUFAIDA/H69Wt5hyVXTOyIiIhI6cTHx6N///5YuHAhcnNzMWLECAQEBKBOnTryDk2uOMaOiIiIlMrly5fh7OyMmJgYaGlpYfPmzZg0aVKlmPX6KUzsiIiISGlkZWVhzJgxiImJgbm5OTw9PdG6dWt5h6Uw2BVLRERESqNKlSrw8PDA2LFjcefOHSZ1H2CLHRERESm0CxcuICEhAU5OTgAAe3t72NvbyzkqxcQWOyIiIlJIubm5+Omnn9C9e3eMGzcOISEh8g5J4bHFjoiIiBRObGwsRo4ciYsXLwIAhg8fjoYNG8o3KCXAxI6IiIgUypkzZzB69GjEx8ejWrVq2LZtG0aPHi3vsJQCu2KJiIhIIQiCgO+//x59+vRBfHw8WrdujYCAACZ1pcDEjoiIiBSCSCRCdnY2BEHA1KlTcfPmTZibm8s7LKXCrlgiIiKSq6ysLFSpUgUAsGLFCnTp0gV9+vSRc1TKiS12REREJBfZ2dlYsGAB7O3tkZWVBQDQ1NRkUlcGbLEjIiKiCvf8+XOMGDECN27cAACcPHkSjo6Oco5K+bHFjoiIiCrU8ePHYWFhgRs3bqBmzZrw8vJiUicjTOyIiIioQmRlZeHbb7/FwIED8ebNG7Rr1w5BQUEYMmSIvENTGUzsiIiIqEJMnz4dbm5uAIBvv/0WV69ehbGxsXyDUjFM7IiIiKhCLFq0CEZGRjh27Bh+/fVX8UxYkh1OniAiIqJykZmZiXPnzqFfv34AAFNTU4SEhEBTU1POkakuttgRERGRzIWGhqJjx45wcHDA2bNnxeVM6soXEzsiIiKSqUOHDsHKygpBQUH47LPPIAiCvEOqNJjYERERkUxkZGTg66+/hrOzM1JSUmBvb4+7d++iZ8+e8g6t0mBiR0RERGUWHBwMGxsb/PXXXxCJRFi6dCnOnTuHBg0ayDu0SoWTJ4iIiKjMbty4gQcPHqBu3brYv38/evToIe+QKiUmdkRERFRm48aNQ0JCAkaNGgVDQ0N5h1NpsSuWiIiISu3hw4fo27cvEhISAAAikQjz5s1jUidnTOyIiIhIYoIgYPfu3WjXrh1Onz6N+fPnyzskeg+7YomIiEgiqampmDZtGvbt2wcA6NWrF9asWSPnqOh9MknsEhMTER0djYSEBLx9+xZaWlrQ19dH06ZNUbt2bVncgoiIiOTo/v37GDp0KEJCQqCuro4VK1Zg4cKFUFNj558ikTqxu3//Pnx9fXHt2jWEhYWVWK9x48bo0qULBgwYgJYtW0p7OyIiIpKTM2fOYMCAAXj37h0aNGiAQ4cOoVOnTvIOi4ohEkq5HPSVK1ewZcsW3L9/HwDEq0mrqamhevXq0NbWRnJyMjIyMgrfSCRChw4dMGPGDFhbW8sofMWSmpoKKysrBAQEoHr16vIOh4iISCbevHmDtm3bomXLlnB3d4e+vr68Q6ISSJzYRUVFYdmyZbh27RoEQUDTpk1hZ2cHKysrmJmZoUmTJhCJROL67969w8uXLxEYGIiAgABcu3YNsbGxEIlE6NixI37++WeVmznDxI6IiFRFeHg4mjZtKv7Z/uLFCxgaGrLrVcFJlNgdOXIEq1atQnZ2NgYOHIgRI0agVatWpbqRIAi4fPkyDh8+jEuXLkFbWxtLliyBo6Oj1MEDQEJCAr7//nv4+/tDXV0dAwYMwMKFC6GhUXIvs5+fH3755RecO3dOXJaXlwcrKysIglAoQb127Rp0dHQkioWJHRERKTtBEPD7779j7ty52Lx5M77++mt5h0SlIFHa/f3336NLly44ffo0Vq5cWeqkDsjviu3cuTO2bt2K48ePw9raGt99912pr/Oh2bNnQ0dHB1euXIGXlxdu3LgBd3f3YutmZ2dj+/btmDNnTpENicPCwpCdnQ1/f38EBQWJvyRN6oiIiJRdUlISnJycMHPmTGRlZeHixYtFfl6SYpNo8sS+fftkOi7OxMQE27Ztg7+/f5mu8+zZM/j7++Py5cvQ1tZGo0aNMH36dKxbtw6TJk0qUn/ChAmoWrUqJk+ejOPHjxc69uDBA5ibm6NKlSpliomIiEgZ3b59G8OHD0dERAQ0NTWxbt06zJo1q1AvFik+iVrsymuyQ/v27ct0fmhoKPT09FCvXj1xmYmJCWJiYpCcnFyk/rp167Bjxw40bty4yLEHDx7g3bt3GDJkCDp06IBRo0YhMDCwTPEREREpOkEQ4Obmhi+//BIREREwNjbGtWvX4OrqyqROCSn1CMi0tDRoa2sXKit4nZ6eXqS+gYFBidfS0tJC69atsXXrVly8eBHdunXDxIkTERUVJdugiYiIFMj9+/cxd+5cZGdnw8nJCUFBQWjXrp28wyIplXmB4tTUVBw8eBDnzp1DZGQkUlNTUatWLTRr1gx9+/bFoEGDoKmpKYtYi9DR0SmyrErB62rVqpXqWosWLSr0euLEifD29salS5cwevTosgVKRESkoNq0aYOVK1eiRo0amDZtGlvplFyZEjt/f3/MmTMHCQkJhQZXxsfHIz4+XjyRYfPmzTAxMSlzsB9q1qwZkpKS8Pr1a/GaOuHh4TAwMICurm6prrVx40b07t0bLVq0EJdlZWWhatWqMo2ZiIhInvLy8vDrr7/CwcEB5ubmAIo2bpDykjqxe/78OaZPn47U1FQ0bNgQgwcPhrm5OapVq4aUlBQ8fvwYx44dQ3h4OKZMmQJvb2/UrFlTlrHDyMgIVlZWWLVqFZYvX443b95g69atcHJyKvW1QkJCcOfOHbi5uaFmzZr466+/kJqaip49e8o0ZiIiInmJj4/H2LFjcerUKXh4eMDf358NGCpG6jF227dvR2pqKnr06AFfX1/MmDEDPXr0gK2tLXr16gVXV1f4+vrCzs4OMTEx2Lt3ryzjFtu8eTNycnLQvXt3DBs2DHZ2dpg+fToAwNLSssjs15KsXr0ajRs3xsCBA2FjYwN/f3/s3r0benp65RI3ERFRRbp8+TIsLCxw6tQpaGlp4ZtvvuFKECqo1FuKFejevTsSExNx+fLlj3Z7JiYmokuXLmjYsCF8fX2lDlQZcIFiIiJSNLm5uVi9ejV+/PFH5OXlwdzcHJ6enmjdurW8Q6NyIHVXbHx8PMzMzD45lq127dpo1qwZwsPDpb0VERERSSEpKQlDhw7F2bNnAQBjxozB1q1b2figwqRO7OrWrYu4uDiJ6iYlJaFWrVrS3oqIiIikUK1aNaSnp0NbWxtbt27FuHHj5B0SlTOpx9j1798f8fHxnxw7d/bsWbx48QJ9+vSR9lZEREQkodzcXGRnZwMANDU1cfDgQdy5c4dJXSUhdWI3ffp02NraYs2aNVi3bl2R1rv09HTs378f8+fPh5mZGaZNm4a8vLwiX0RERCQbMTEx6NGjBxYvXiwua9y4caGlvEi1ST15YvDgwcjJyUFoaKh4McO6deuiZs2ayMjIQGxsLHJzcz9+c5EIjx49kub2ComTJ4iISF7OnDmD0aNHIz4+Hrq6uggJCfnojkukmqQeY/f48WPx3wtyw7i4OInH3REREVHZ5eTk4Mcff8Tq1ashCALatGkDT09PJnWVlNSJXXmtS0dERESSiY6OhrOzM65evQoAmDp1Kn799dci+6hT5SF1Yte+fXtZxkFERESlkJ2dDXt7e0REREBXVxc7duzAsGHD5B0WyZnUkydKkpaWJutLEhER0Qc0NTWxatUqtG3bFoGBgUzqCIAMEruEhARs2LAB/fv3xxdffIF27doByF/AeMyYMbh27VqZgyQiIiLg2bNnuHnzpvj1iBEjcOvWLZiamsoxKlIkZUrsAgMD4eDggB07diAsLAw5OTniiRQxMTG4ffs2Jk+ejH379skkWCIiosrq2LFjsLS0xKBBgwpNVNTQkHpUFakgqRO7+Ph4TJs2DYmJiWjdujW+//77Qr8x1KtXD7a2tsjLy8PKlSsREBAgk4CJiIgqk6ysLMyePRuDBg3Cmzdv0LhxY7x7907eYZGCkjqx27lzJ96+fQsnJyccPnwYo0aNQo0aNcTHDQwMsHv3bowYMQKCILDVjoiIqJSePn2KL7/8Eps2bQIAzJkzB1evXkXjxo3lHBkpKqkTu4sXL0JLSwuLFi36aL358+dDW1ubLXZERESlcPToUVhaWuLOnTuoVasWjh8/jg0bNqBKlSryDo0UmNSJXWxsLExMTD65w0K1atVgbGyMxMREaW9FRERU6fj4+CA5ORm2tra4e/cuHBwc5B0SKQGpR1xqamri7du3EtXNyMjgYolERESl8Mcff6BVq1aYM2cONDU15R0OKQmpW+xMTEwQExODZ8+efbReWFgYIiIiYGJiIu2tiIiIVN6hQ4cwZswY8eoSurq6WLhwIZM6KhWpE7uvvvoKeXl5WLp0aYmzcxITEzF//nyIRCL07t1b6iCJiIhUVUZGBr7++ms4Oztj3759OHTokLxDIiUmdVess7MzfHx8cPv2bfTu3Rvdu3dHbGwsAGD//v0ICwuDr68v3r59C2NjYzg7O8ssaCIiIlUQHByMYcOG4cGDBxCJRPjuu+8wdOhQeYdFSkwkFLT5SiE+Ph4zZ87E3bt3IRKJihwXBAFmZmb4/fff0ahRozIFqgxSU1NhZWWFgICAT04qISKiys3DwwPTpk1DWloa6tati3379qFnz57yDouUXJmWq65Tpw4OHTqEc+fO4ezZswgJCUFqaiq0tbVhbGyMrl274quvvuKq2ERERO/57rvvsGrVKgBA165dsX//fhgaGso5KlIFZWqxo8LYYkdERJK4desWOnfujMWLF2Pp0qVQV1eXd0ikIqSePOHi4oKVK1dKVHfmzJno1auXtLciIiJSaoIgIDQ0VPzaxsYGT58+xY8//sikjmRK6sTO398fjx49kqhuWFgYXr58Ke2tiIiIlFZqaipcXFzQunVrPHjwQFxev359OUZFqkqiwW/h4eH4888/i5RHRkZiwYIFJZ4nCAJiYmIQERGBOnXqSB8lERGRErp//z6GDh2KkJAQqKurw9/fH61atZJ3WKTCJErsTExMEBkZifv374vLRCIREhIScPz4cYluNGjQIKkCJCIiUjaCIOCvv/6Cq6sr3r17hwYNGuDQoUPo1KmTvEMjFSfxdNXvv/8eBw4cEL/28fGBvr4+7OzsSjxHJBJBR0cHLVq0YGJHRESVQnJyMqZMmYLDhw8DyF/Qf8+ePdDX15dzZFQZSD0rtnnz5rCyssL+/ftlHZPS4qxYIqJKJjcXuHIFiI0FDA0BOzu4/fYbvv32W2hoaGD16tWYM2cO1NSkHtJOVCpSLzC3d+9e6OrqyjIWIiIi5eHtDbi6AtHR/ytr2BAzf/0Vd8eOxddffw1bW1v5xUeVEtexkyG22BERVRLe3oCTEyAISAKwGsAyAFoFuzB5eQGOjvKLjyqtUrfYpaWlITQ0FF988UWRHSWuXr2KAwcOICIiArVr10aXLl0wcuRIVKtWTWYBExERyVVubn5LnSDAH8BwAJEA0gBsEQRAJAJmzwYGDgS4Rh1VsFJ1+m/ZsgV2dnYYOXIk4uLiCh37888/MXnyZFy4cAEREREICAjAr7/+CicnJ0S/30xNRESkzK5cgRAdjY0AOiE/qTMGMLbguCAAUVH5Y++IKpjELXbLly/HwYMHUdBzm5KSIj4WGBgINzc3CIKA6tWrY9SoUahTpw58fX0RGBiIWbNm4ejRoxAVNFETEREpqcTQUIwHULDYlxOAHQBqflgxNrYiwyICIGFi999//+HgwYNQU1PD9OnTMWzYsEILDhckdSKRCNu2bYO1tTUAYNSoUZg2bRouXbqE48ePY+DAgeXzLoiIiCpAUFAQBn3/PZ4DqApgI4CpAIpttjA0rMjQiABI2BX7999/AwCWLFmCGTNmFErqEhMTcefOHYhEInz55ZfipA7IX8du/vz5EAQBfn5+so2ciIiogtWuXRspWVlopqGBmwCmoZikTiQCGjUCPrLOK1F5kSixu3XrFrS0tDB06NAix27evIm8vDwAQK9evYocNzExQb169fD48eMyhkpERFTxMjIyxH9v0qQJTp8+jQB3d1iIRPlJ3PsKXru5ceIEyYVEid2rV69gZGSEKlWqFDl2+/Zt8d9LWq+nbt26SEhIkDJEIiIi+bhy5QrMzMzwzz//iMvat28P3VGj8pc0adCg8AkNG3KpE5IriRK79PR0VK1atdhjd+7cAZCfvDVq1KjYOpmZmSWeT0REpGjy8vKwcuVKdOnSBdHR0Vi9ejWKLPvq6AhERgIXLgAHDuT/GRHBpI7kSqLJE7Vq1cKbN2+KlL9+/RphYWEQiUSwsbEp9tzs7GxER0dzjzwiIlIKcXFxGDNmDP79918AgIuLC37//ffiV3ZQVwe6dKnYAIk+QqIWOzMzM0RFRSE+Pr5QuZ+fn/g3mM6dOxd77uXLl5GRkYHmzZuXMVQiIqLydf78eVhYWODff/+Fjo4Odu/ejT179nA3IVIaEiV2PXr0QF5eHpYvX47s7GwAwNu3b+Hu7g4AqFatGrp161bkvNTUVGzYsAEikQh2nB1EREQK7OHDh+jZsydevnyJli1b4vbt2xg3bpy8wyIqFYkSuyFDhqBx48Y4e/YsevfujRkzZqBfv36Ijo6GSCTC1KlToa2tLa6flZWFS5cuYdSoUXj69CkMDAzKbQ27hIQETJ8+HdbW1rCxscHKlSuRk5Pz0XP8/PzQvXv3IuXbt2+Hvb09LCwsMGbMGDx9+rRcYiYiIsXTsmVLTJw4ERMnToS/vz9atGgh75CISk2ixE5TUxM7duxA48aNERMTg3PnzuH169cQBAH9+/fHxIkTC9Xv1asXpk6diidPnkBbWxtr1qwpt8kTs2fPho6ODq5cuQIvLy/cuHFD3JL4oezsbGzfvh1z5swpMgjWx8cHHh4e2LlzJ27duoWWLVti1qxZRQfLEhGRyjh79myhLTL/+OMP7NixAzo6OnKMikh6Eu8V27hxY5w8eRK//PILxowZgwkTJmD37t1Yt25dkQGlNWrUgCAIsLS0xJ49e0qcWFFWz549g7+/P+bPnw9tbW00atQI06dPx/79+4utP2HCBNy6dQuTJ08ucszT0xMjR45Es2bNULVqVcydOxcxMTG4detWucRORETyk5OTg++++w69evXCmDFjxOuxqnPtOVJyEu8VC+S33A0YMAADBgz4aL01a9agRo0aaNiwYZmC+5TQ0FDo6emhXr164jITExPExMQgOTkZNWrUKFR/3bp1MDAwgLe3d5FrhYWFFUr4NDU1YWRkhODgYHTo0KH83gQREVWo6OhoODs74+rVqwDyf27k5OQUu1YrkbIpVWInqYoal5CWllZobB8A8ev09PQiiZ2BgUGprqWlpYX09HQZRUtERPLm6+sLFxcXJCQkQFdXFzt27MCwYcPkHRaRzEjcFSupn3/+GWPHjpX1ZYulo6NTaKsX4H9bv1SrVq1U19LW1kZmZmahsszMzFJfh4iIFE92djYWLFiAfv36ISEhAW3btkVQUBCTOlI5Mk/sHj16BH9/f1lftljNmjVDUlISXr9+LS4LDw+HgYEBdHV1S32t0NBQ8evs7GxERkbCzMxMZvESEZF8ZGZm4u+//wYAzJw5E9evX4eJiYl8gyIqBzJP7CqSkZERrKyssGrVKqSmpiIqKgpbt26Fk5NTqa81ZMgQ7Nu3D8HBwXj37h02bNgAfX19WFtbl0PkRERUkXR1deHp6YmjR49i8+bN3OaSVJZSJ3YAsHnzZuTk5KB79+4YNmwY7OzsMH36dACApaUljh8/LtF1nJycMG7cOMyYMQMdOnTAo0eP8Oeff0JTU7M8wyciUi25ucDFi8DBg/l/5ubKJYysrCx8++232LRpk7jMwsICjtzHlVScSJDxQm0jR45EUFAQHj9+LMvLKoXU1FRYWVkhICCA288QUeXj7Q24ugLR0f8ra9gQ2LQJqMCE6unTpxg+fDju3LmDKlWqIDw8vNxXaSBSFErfYkdERArA2xtwciqc1AHAixf55cUsM1UevLy8YGlpiTt37qBWrVrw8vJiUkeViswTO0EQuFsDEVFlkpub31JX3P/9BWWzZ5drt2xmZiZmzJiBoUOHIjk5GR07dsTdu3fh4OBQbvckUkQyX8fu4MGDsr4kEREpsitXirbUvU8QgKio/Hpdusj89jk5OejcubN4RYaFCxdixYoVHCNNlZLMEjtBEBAXF4fk5GTxEiGCIBTZboyIiFRMbKxs65WShoYGhg4diqdPn8LDwwN9+vQpl/sQKYMyd8WGhYVhzpw5aNeuHbp27YpBgwYBAGJjY9GrVy8cOXKkrLcgIiJFZmgo23oSyMjIQGRkpPj1nDlz8PDhQyZ1VOmVKbH7999/4eTkhFOnTiE1NbXQ+LrY2FhERUXhhx9+wNq1a2USLBERKSA7u/zZryX10IhEQKNG+fVkIDg4GDY2Nujbty/S0tIAAGpqaqhbt65Mrk+kzKRO7J49e4b58+cjMzMTffr0wbZt2wrtEdu0aVM4OTlBEAS4u7vj4sWLsoiXiIgUjbp6/pImQNHkruC1m1t+vTLy8PCAtbU1Hjx4gMTERISFhZX5mkSqROrEbufOncjMzMTUqVOxceNGdOnSBVpaWuLjenp6+PnnnzFr1iwIgoBDhw7JJGAiIlJAjo6AlxfQoEHh8oYN88vLuI5dWloaxo8fDxcXF6SlpaFbt264e/cu2rRpU6brEqkaqRO7a9euoXr16uJdHkoyadIk1KhRAw8ePJD2VkREpAwcHYHISODCBeDAgfw/IyLKnNQ9fPgQ7du3h7u7O9TU1LBs2TKcOXMGhjIcs0ekKqSeFfvq1SuYm5ujSpUqH61XpUoVNGrUCE+ePJH2VkREpCzU1WW+pMnChQvx6NEjGBoa4sCBA+hSDkumEKkKqVvsdHR08Pr1a4nqvn37FtWqVZP2VkREVIlt374dzs7OuHv3LpM6ok+QOrEzNzdHXFwc/vvvv4/WCwoKQnR0NMzNzaW9FRERVSL37t3D6tWrxa8LWuo465Xo06RO7AYPHgxBELBkyRLEx8cXW+fp06eYN28eRCIRt3UhIqKPEgQBf/75J2xsbLBkyRL4+PjIOyQipSP1GLuBAwfi+PHjuHHjBnr16gUbGxs8e/YMAPDLL78gLCwM169fR05ODiwsLOBYxsGzRESkupKTkzFlyhQcPnwYAPDVV1/BTkbr3hFVJiJBKG7XZsmkp6dj6dKl8PX1LXxRkUi8ULGdnR3WrVsHPT29MgWqDFJTU2FlZYWAgABUr15d3uEQESmFwMBADBs2DOHh4dDQ0MDq1asxZ84cqKmVeXMkokqnTIldgeDgYJw9exYhISFITU2FtrY2jI2N0bVrV1hZWckiTqXAxI6IqHR27NiBGTNmICsrC40bN8ahQ4dga2sr77CIlJbUXbHva968OZo3by6LSxERUSWir6+PrKwsDBw4ELt27ULt2rXlHRKRUpNJYkdERCSp9PR06OjoAAAGDRqEixcvwt7eHqKS9polIomVKbETBAHnz59HYGAgUlJSkJOTg5J6dkUiEVatWlWW2xERkRITBAFubm5Yt24d/P390bBhQwBA586d5RwZkeqQOrFLT0/HpEmTEBQUJC4rLqkrmEjBxI6IlE5uLnDlChAbCxgaAnZ2MtnIvjJKTEzEuHHjcOLECQDArl278MMPP8g5KiLVI3Vit3PnTgQGBgLIX6y4adOm0NLSkllgRERy5e0NuLoC0dH/K2vYENi0qcx7n1Y2169fx4gRIxAVFYUqVapg48aNmDZtmrzDIlJJUid2p06dgkgkwg8//ABnZ2dZxkREJF/e3oCTE/BhL8SLF/nlXl5M7iSQl5eH9evXY8mSJcjNzYWpqSk8PT1haWkp79CIVJbUiwRFR0fDwMCASR0RqZbc3PyWuuLGCxeUzZ6dX48+asuWLVi4cCFyc3Ph7OyMwMBAJnVE5UzqxE5bWxs1a9aUZSxERPJ35Urh7tcPCQIQFZVfjz5q0qRJaNeuHf766y/s378furq68g6JSOVJndi1adMGkZGRSE1NlWU8RETyFRsr23qVSF5eHvbv34+8vDwAgI6ODm7evInJkydzKROiCiJ1Yjdp0iS8e/cOa9askWU8RETyZWgo23qVRFxcHPr06YPRo0dj7dq14nJuC0ZUsaSePFGnTh2MGzcO7u7uePjwITp37ox69epBU1OzxHOcnJykvR0RUcWws8uf/friRfHj7ESi/OPcoF7s/PnzGDVqFF6+fAltbW3Ur19f3iERVVpS7xXbvHnzQmvUSeLx48fS3EppcK9YIhVRMCsWKJzcFfxfx1mxAIDc3FysWLECy5cvhyAIaNmyJTw9PdGiRQt5h0ZUaUndYsffyIhIZTk65idvxa1j5+bGpA5AbGwsRo4ciYsXLwIAJkyYgN9++028VRgRyYfULXZUFFvsiFQMd54oUVBQEDp06ABNTU1s27YNo0ePlndIRIQy7hVLRKTS1NWBLl3kHYVCsrS0hIeHB9q0aQNzc3N5h0NE/69MiV1iYiJCQkKQmJgIAKhVqxbMzc1Ru3ZtmQRHRESKITo6GuPHj8fq1athbW0NABg2bJicoyKiD0mV2B0/fhz79u3Df//9h+J6clu2bInRo0dj0KBBZY2PiIjkzNfXFy4uLkhISMDr168RGBjIdemIFFSpFhhKSEjA119/jYULF+L+/fvIy8uDIAhFvv777z8sXrwYkyZNQkJCQnnFTkRE5Sg7OxsLFixAv379kJCQgLZt2+LIkSNM6ogUmMQtdmlpaXBxccHTp08hCAIsLCzQq1cvfP7559DX14e6ujrevHmDx48f4+zZs7h58yauXbuGkSNHwtvbG9WqVSvP90FERDL07NkzjBgxAjdv3gQAzJw5E+vWrUPVqlXlHBkRfYzEs2JdXV3h5+eHunXrYvny5ejyiQHFt2/fxvz58xEXF4evvvoKGzZskEW8Co2zYolIFYSEhKBDhw548+YNatasiV27dsGRS7wQKQWJumKDgoLg5+eHGjVqYPfu3Z9M6gCgXbt22Lt3L/T09ODr64v79++XNVYiIqoApqam6NChA9q3b4+goCAmdURKRKLEzsfHByKRCNOnT4eJiYnEF2/cuDFmz54NQRDw999/SxsjERGVs4iICKSlpQHI39/1wIEDuHLlCoyNjeUcGRGVhkSJXUBAADQ0NKSa2j5gwABUqVIFt2/fLvW5RERU/o4ePQpLS0t888034jI9PT1UqVJFjlERkTQkSuxiYmJQv359qbaK0dbWRpMmTRD9/rY8REQkd5mZmfjmm2/g5OSEt2/fIiQkRNxqR0TKSaLELi8vD7q6ulLfpGrVqsjJyZH6fCIikq2wsDB07NgRv//+OwBg4cKFuHjxIlcwIFJyEi13Urt2bcTExEh9k9jYWNSqVUvq84mISHYOHTqEKVOmICUlBfr6+vDw8ECfPn3kHRYRyYBELXbNmzfHmzdvEBgYWOob3L17FwkJCWjRokWpz5VEQkICpk+fDmtra9jY2GDlypUltg5eunQJDg4OsLCwQN++fXHhwgXxsby8PFhaWsLCwgKWlpbir/T09HKJm4hIHt6+fYtZs2YhJSUF9vb2uHv3LpM6IhUiUWLXtWtXCIKAjRs3lurieXl52LBhA0QiEXr27ClVgJ8ye/Zs6Ojo4MqVK/Dy8sKNGzfg7u5epF5kZCRmzpwJV1dX3LlzBzNnzsTs2bMRFxcHIL9bIjs7G/7+/ggKChJ/STOukIhIUdWsWRMeHh5YunQpzp07hwYNGsg7JCKSIYkSu0GDBsHAwAB37tzB8uXLi90f9kM5OTlYunQpbt++DUNDQwwYMKDMwX7o2bNn8Pf3x/z586GtrY1GjRph+vTp2L9/f5G6Pj4+sLa2Ro8ePaChoYGvvvoK7dq1w+HDhwEADx48gLm5OWeBEZHK8fDwgI+Pj/h17969sWLFCmhoSLVdOBEpMIkSuypVquDnn3+GmpoaDh48iBEjRuDMmTPFzp569+4d/Pz8MHToUPj4+KBq1apwc3ODpqamzIMPDQ2Fnp4e6tWrJy4zMTFBTEwMkpOTC9UNCwuDmZlZoTJTU1MEBwcDyE/s3r17hyFDhqBDhw4YNWqUVF3PRESKIi0tDRMmTICLiwvGjRuHqKgoeYdEROVM4l/XOnXqhJUrV+KHH37AvXv34OrqCpFIhIYNG0JPTw+ampp4+/Ytnj17hpycHAiCAG1tbaxatQqtW7cul+DT0tKgra1dqKzgdXp6OmrUqPHRulpaWuIxdFpaWmjdujVcXV1Rs2ZN7N+/HxMnTsTx48fRqFGjcomfiKi8PHz4EMOGDcOjR4+gpqaGuXPnon79+vIOi4jKWana4QcNGoRmzZrhl19+wa1btyAIAp4/f47nz58XqWtjY4MVK1agcePGMgv2Qzo6OsjIyChUVvD6wyn72trayMzMLFSWmZkprrdo0aJCxyZOnAhvb29cunQJo0ePlnXoRETlQhAE7N69G9988w0yMjJgaGiIAwcOSLQVJBEpv1IPsGjZsiX27NmDsLAwXL9+HY8fP8abN2+QlZUFPT09NG/eHF26dCnS7VkemjVrhqSkJLx+/Rr6+voAgPDwcBgYGBRZd8/MzAwPHz4sVBYWFoYvvvgCALBx40b07t270OzdrKwsVK1atZzfBRGRbOTl5WHcuHHw8PAAAPTq1QseHh6oW7eunCMjoooi9chZU1NTmJqayjKWUjMyMoKVlRVWrVqF5cuX482bN9i6dSucnJyK1B0wYAB2794NX19f9OrVC2fOnIG/vz++++47AEBISAju3LkDNzc31KxZE3/99RdSU1PLbTYvEZGsqampQU9PD+rq6lixYgUWLlwINTWJhlITkYoQCZJMcVVgr1+/xvLly3Hr1i2oqalh0KBBmDdvHtTV1WFpaYlly5aJZ+ReuXIF69evx/Pnz9GgQQPMnz8fnTt3BgAkJSVh7dq1uHTpEjIyMtCqVSssWbIEzZs3lziW1NRUWFlZISAgANWrVy+X90tE9D5BEJCWlib+P+fdu3e4d+8e2rdvL+fIiEgeJErsJkyYgCVLlsi0he7hw4dYu3Yt9u7dK7NryhsTOyKqSMnJyZg8eTJiY2Nx/vx5Ll9CRJItdxIdHY2BAwfi+++/L9PWYgAQHByM+fPnY+jQoYiPjy/TtYiIKqvAwEC0bdsWnp6euHHjBm7cuCHvkIhIAUiU2B07dgxDhgyBl5cXevbsialTp8LX1xcpKSkS3eTly5fw9PTE0KFDMXjwYJw8eRLOzs6FFswkIqJPEwQBW7Zsga2tLcLDw9G4cWNcvnwZdnZ28g6NiBRAqcbYBQYGYs2aNbh//z5EIhHU1dVhYmKCZs2aoUmTJtDV1YW2tjaSk5Px5s0bxMXFISgoCLGxsQDy/0Nq164dXF1dYW1tXW5vSl7YFUtE5SkpKUm8FBMADBw4ELt27ULt2rXlHBkRKQqpJk9cunQJe/fuxY0bN5CXl5d/IZGoSL2CS2toaKBTp06YOHEi2rVrV8aQFRcTOyIqTw4ODjh58iQ0NTWxbt06zJo1q9j/e4mo8irTrNi3b9/i1q1buHXrFqKjo5GQkIDk5GRUrVoV+vr6MDY2Rtu2bdGxY8dK8RslEzsiKk+PHj3C8OHDsWvXLpX+JZmIpKf0y50oEiZ2RCRLiYmJuHjxIhwdHcVleXl5XJuOiErE/x2IiBTQ9evXYWFhgWHDhuHq1aviciZ1RPQx/B+CiEiB5OXl4ZdffoG9vT2ioqJgbGzMHgAikhhXsyQiUhDx8fEYO3YsTp06BQBwdnbGn3/+WWTvayKikjCxIyJSAJcvX4azszNiYmKgpaWFzZs3Y9KkSZz1SkSlwsSOiEgB3L9/HzExMTA3N4enpydat24t75CISAkxsSMikhNBEMQtcjNmzAAAjBs3jmPqiEhqnDxBRCQH58+fh729Pd6+fQsgf5H3b775hkkdEZUJEzsiogqUm5uLn376CT169MDVq1excuVKeYdERCpEoq7YvXv3ol69eujdu3d5x0NEpLJiYmIwatQoXLx4EQAwceJE/PTTT3KNiYhUi0QtdqtWrcLevXtLPB4TE4OEhASZBUVEpGrOnDkDCwsLXLx4EdWqVcO+ffuwY8cO6OjoyDs0IlIhMumK7datG1xdXWVxKSIilbNv3z706dMH8fHxaN26NQICAjBq1Ch5h0VEKkhmY+y45SwRUfF69+4NQ0NDTJ06FTdv3oS5ubm8QyIiFcXlToiIysH9+/fFa9HVqVMH9+/fx2effSbnqIhI1XFWLBGRDGVnZ2PBggVo06YNPDw8xOVM6oioIrDFjohIRp49e4YRI0bg5s2bAICHDx/+72BuLnDlChAbCxgaAnZ2gLq6nCIlIlXFxI6ISAaOHTuG8ePH482bN6hZsyZ27doFR0fH/IPe3oCrKxAd/b8TGjYENm0CCuoQEckAu2KJiMogKysL3377LQYNGoQ3b96gXbt2CAoKKpzUOTkVTuoA4MWL/HJv74oPmohUFhM7IqIyuHnzJtzc3AAAc+bMwdWrV2FsbJx/MDc3v6WuuFUDCspmz86vR0QkAxJ3xYaEhMDFxUXq4yKRCHv27ClddERECs7e3h4rV65Eq1at4ODgUPjglStFW+reJwhAVFR+vS5dyjVOIqocJE7sUlJS4O/vL/VxkUhUusiIiBRQZmYmli5dihkzZohb5pYsWVJ85dhYyS4qaT0iok+QKLEbPHhwecdBRKTwQkNDMXz4cAQFBeHq1au4fv061NQ+MqLF0FCyC0taj4joE0QCt4yQmdTUVFhZWSEgIADVq1eXdzhEJEOHDh3ClClTkJKSAn19fezduxd9+/b9+Em5uYCRUf5EieL+qxWJ8mfHRkRw6RMikglOniAi+oiMjAx8/fXXcHZ2RkpKCuzs7HD37t1PJ3VAfrK2aVP+3z8cjlLw2s2NSR0RyQwTOyKiEkRHR6NDhw7466+/IBKJsHTpUpw/fx4NGjSQ/CKOjoCXF/DhOQ0b5pdzHTsikiGJxtjFxMTI5Gb169eXyXWIiCpCnTp1oKGhgbp162Lfvn3o2bOndBdydAQGDuTOE0RU7iQaY/f555+X/UYiER49elTm6ygyjrEjUn7p6emoUqUKNDTyf++NiIiAlpYWDDnBgYiUgERdsYIgyOSLiEiRPXz4EO3atcPy5cvFZcbGxkzqiEhpSNQVu3fv3vKOg4hIbgRBgLu7O2bMmIGMjAwkJydj/vz50NXVlXdoRESlIlFi1759+/KOg4hILlJTUzFt2jTs27cPANCrVy94eHgwqSMipcRZsURUad27dw9WVlbYt28f1NXVsXr1apw6dQp169aVd2hERFKReEsxIiJVkpqaim7duiExMRENGzbEwYMH0alTJ3mHRURUJhK12K1evVrcTSGNkSNHokWLFlKfT0Qka9WrV8e6devQr18/BAUFMakjIpUgUWK3Z88enDp1qsTj3bt3x7fffvvRa3BWLBHJW2BgIPz9/cWvx48fjxMnTkBfX79o5dxc4OJF4ODB/D9zcyssTiIiaclkjN2LFy/w6tUrWVyKiEjmBEHAli1bYGtrCycnJyQkJADIX19T9OFWXwDg7Z2/x2vXrsDIkfl/GhnllxMRKTBOniAilZaUlIShQ4di5syZyMrKQtu2baGm9pH/+ry9AScnIDq6cPmLF/nlTO6ISIEpfWKXkJCA6dOnw9raGjY2Nli5ciVycnKKrXvp0iU4ODjAwsICffv2xYULFwod3759O+zt7WFhYYExY8bg6dOnFfEWiKic3L59G23btsXRo0ehqakJNzc3+Pj4oFatWsWfkJsLuLoCxQ0dKSibPZvdskSksJQ+sZs9ezZ0dHRw5coVeHl54caNG3B3dy9SLzIyEjNnzoSrqyvu3LmDmTNnYvbs2YiLiwMA+Pj4wMPDAzt37sStW7fQsmVLzJo1i2MDiZSQIAhwc3PDl19+iYiICDRt2hTXr1+Hq6tr8V2vBa5cKdpSV/jCQFRUfj0iIgWk1Inds2fP4O/vj/nz50NbWxuNGjXC9OnTsX///iJ1fXx8YG1tjR49ekBDQwNfffUV2rVrh8OHDwMAPD09MXLkSDRr1gxVq1bF3LlzERMTg1u3blX02yIiGbh8+TKys7Ph5OSEwMBAWFtbf/qk2FjJLi5pPSKiCqbUiV1oaCj09PRQr149cZmJiQliYmKQnJxcqG5YWBjMzMwKlZmamiI4OLjY45qamjAyMhIfJyLFV9DCLhKJsHPnTuzcuROenp6oWbOmZBeQdE9Y7h1LRApKqRO7tLQ0aGtrFyoreJ2env7JulpaWuJ6nzpORIorLy8Pa9euhYuLizi5q1WrFiZMmPDxrtcP2dkBDRsCJZ0jEgGNGuXXIyJSQEqd2Ono6CAjI6NQWcHratWqFSrX1tZGZmZmobLMzExxvU8dJyLFFB8fj/79+2PRokXYt28fzp49K/3F1NWBTZvy//5hclfw2s0tvx4RkQJS6sSuWbNmSEpKwuvXr8Vl4eHhMDAwKLKBt5mZGUJDQwuVhYWFoVmzZuJrvX88OzsbkZGRRbpviUhxXL58GRYWFjh16hS0tLTw119/oUePHmW7qKMj4OUFNGhQuLxhw/xyR8eyXZ+IqBxJvFdsYGAgPv/882KPiUSijx4vL0ZGRrCyssKqVauwfPlyvHnzBlu3boWTk1ORugMGDMDu3bvh6+uLXr164cyZM/D398d3330HABgyZAh+++032Nvbw9jYGBs3boS+vr5kA66JqELl5uZi9erV+PHHH5GXl4fmzZvD09MTrVq1ks0NHB2BgQPzZ7/GxuaPqbOzY0sdESk8kSDBeh7Nmzcv+41EIjx+/LjM1/nQ69evsXz5cty6dQtqamoYNGgQ5s2bB3V1dVhaWmLZsmUYMGAAAODKlStYv349nj9/jgYNGmD+/Pno3LkzgPxB17t378b+/fuRmJiIVq1aYdmyZTA2NpY4ltTUVFhZWSEgIADVq1eX+Xslonxjx47F3r17AQAuLi74/fff+W+OiAgSJnY+Pj4yudngwYNlch1FxcSOqGIULDa+efNmjBs3Tt7hEBEpDIkSO5IMEzui8pGbm4v//vsPbdq0EZe9efOm5B0kiIgqKaWePEFEqi8mJgY9evTAl19+iZCQEHE5kzoioqKY2BGRwjpz5gwsLCxw8eJFACiU2BERUVFM7IhI4eTk5GDJkiXo3bs34uPj0aZNGwQGBqJ///7yDo2ISKFJvNwJEVFFiI6OhrOzM65evQoAmDp1Kn799dciO8MQEVFRTOyISKFs374dV69eha6uLnbs2IFhw4bJOyQiIqXBxI6IFMrSpUsRFxeHefPmwdTUVN7hEBEpFY6xIyK5evbsGaZPn46srCwAgKamJrZt28akjohICjJJ7FJTU4uUPXjwANHR0bK4PBGpqGPHjsHS0hJ//PEHli1bJu9wiIiUXpkSu9TUVMybNw+dOnUqktxt27YNvXr1wpw5c5CcnFymIIlItWRlZWH27NkYNGgQ3rx5g3bt2mHSpEnyDouISOlJndilpqbC2dkZJ0+eRGZmJqKiogodz83NRV5eHk6dOoXx48cjOzu7zMESkfJ7+vQpvvzyS2zatAkAMGfOHFy9erVU+zITEVHxpE7sdu7cidDQUDRp0gQHDx7E559/Xuj4tm3b8Pfff8PExASPHj2Ch4dHmYMlIuX277//wtLSEnfu3EGtWrVw/PhxbNiwAVWqVJF3aEREKkHqxO7ff/+FhoYGduzYAUtLy2LrNG/eHJs3b4aamhpOnDghdZBEpBqMjY0hCAI6duyIu3fvwsHBQd4hERGpFKmXO4mKikLTpk3RqFGjj9Zr2rQpGjdujIiICGlvRURK7O3bt6hZsyYAwNTUFJcuXcIXX3wBTU1NOUdGRKR6pG6xq1KlCgRBkKhu1apVIRKJpL0VESmpgwcPokmTJjh//ry4zNLSkkkdEVE5kTqxa9y4McLDw4tMmvhQXFwcQkNDP9myR0SqIyMjA1OmTMHIkSPx9u1bbNu2Td4hERFVClIndn369EFeXh7mzp2LxMTEYuu8ffsWc+fORV5eHnr27Cl1kESkPIKDg2FjY4Pt27dDJBJh6dKlOHDggLzDIiKqFESCpP2pH0hNTcXgwYMRHR2NatWqoUePHmjevDl0dHSQlpaGkJAQnD9/Hm/fvkX9+vVx7Ngx6Orqyjp+hZKamgorKysEBASgevXq8g6HqMLt3bsX06ZNQ3p6OurWrYt9+/bxlzoiogokdWIHAOHh4fj2228REhJS7Bg6QRDQpEkTbN26FSYmJmUKVBkwsaPK7NKlS+jSpQsAoGvXrti/fz8MDQ3lGxQRUSVTpsQOALKzs/Hvv//iwoULeP78OZKSkqCtrQ0jIyN07twZ/fr1qzRrVDGxo8pMEASMHTsWpqam+O6776Curi7vkIiIKp0yJ3b0P0zsqDIRBAEHDhxA3759Ubt2bXEZZ8ATEclPmfaKJaLKKTU1FS4uLhg9ejTGjx8vXvqISR0RkXxJtECxl5cXgPyZsAUtUQVlpeHk5FTqc4hIsdy7dw/Dhg1DSEgI1NXV0aFDB7bUEREpCIm6Yps3bw6RSARfX1/xRt0FZaXx+PFj6aJUEuyKJVUmCAL++usvuLq64t27d2jQoAEOHTqETp06yTs0IiL6fxK12NWvXz+/soZGkTIiUn3JycmYPHkyPD09AQBfffUV9uzZA319fTlHRkRE75MosXt/O6CPlRGRasrJycHNmzehoaGB1atXY86cOVBT4xBdIiJFI1FiV1Zv377Fixcv0KJFi4q4HRHJwPsTImrXro0jR44gNzcXtra2co6MiIhKIvWv3J9//jlGjx4tUd0JEyZgypQp0t6KiCpYUlISnJycsGPHDnFZ+/btmdQRESk4qRM7QRAgyRJ46enpePXqFZKTk6W9FRFVIH9/f1haWsLb2xvz5s1DUlKSvEMiIiIJSdQVGxYWhsmTJxdJ5B48eCDeQqg4giAgOTkZmZmZMDIyKkucRFTOBEGAm5sbFi5ciOzsbBgbG+Pw4cPQ09OTd2hERCQhiRI7U1NTtG3bFv/880+h8qysLLx8+fKT56upqWHatGnSRUhE5S4xMRHjx4/H8ePHAQBDhgzBjh07mNQRESkZiSdPLFq0SLxelSAIWLJkCYyMjPD111+XeI5IJEK1atVgbm6Oxo0blz1aIpK59PR0WFtbIyIiAlWqVMHGjRsxbdo0LjhMRKSEJE7s6tSpg8GDB4tfL1myBJ999lmhMiJSPjo6Ohg7diz27dsHT09PWFpayjskIiKSkkQ7TxTn1atXqFu3rqzjUWrceYKUxevXr5GSkiLeSSY3Nxfp6enQ1dWVc2RERFQWUs+K7datG6ZOnYozZ84gOztbljERUTm6fPky2rRpg8GDByMzMxMAoK6uzqSOiEgFSJ3Y5ebm4uLFi3B1dYWdnR1Wrlyp8nvBEimzvLw8rFy5El27dkVMTAzevXuHuLg4eYdFREQyJHVX7MuXL/H333/j2LFjiIiIyL+YSITmzZvD0dER/fv3R61atWQarKJjVyyVWm4ucOUKEBsLGBoCdnaAurrMbxMXF4cxY8bg33//BQC4uLjg999/5+eUiEjFSJ3Yve/evXvw9vbG6dOn8fbtW4hEImhoaKBr165wdHSEvb19pdhXkokdlYq3N+DqCkRH/6+sYUNg0ybA0VFmtzl//jxGjRqFly9fQkdHB7///jvGjRsns+sTEZHikEliVyArKwvnz5/HsWPHcP36dbx79w4ikQifffYZBg0ahMGDB8PExERWt1M4TOxIYt7egJMT8OE/v4IlRry8ZJLcCYIAOzs7XLt2DS1btoSnpyf3bCYiUmEyTezel5mZiUOHDmHz5s3IyMgQl7dt2xbjx49Hjx49yuO2csXEjiSSmwsYGRVuqXufSJTfchcRIZNu2WfPnuHXX3/F6tWroaOjU+brERGR4pJ5YhcdHY2TJ0/izJkzePz4sXgbsubNm+P169d4/fo1RCIROnXqhE2bNqnUDxomdiSRixeBrl0/Xe/CBeAjW/aV5N9//8Xt27exZMmSUp9LRETKTeIFij8mJSUFp06dwrFjxxAYGAggvwuoZs2acHBwgJOTE5o3b47c3FycO3cOy5cvx9WrV7Fy5UqsXLlSFiEQKY/YWNnW+385OTn48ccfsXr1agiCABsbG3Tv3l2KAImISFlJndjl5ubi0qVLOHbsGC5evIisrCwIggA1NTXY2tpiyJAh6NGjB6pUqSI+R11dHb169YKOjg4mTZqEs2fPlimxS09Px4oVK3D+/Hnk5OSge/fu+PHHH1GtWrVi69+7dw8///wzwsLCUKtWLUybNg1Dhw4VH+/bty9iYmIKTfTw8vJS6XGBJAeGhrKth/yW8pEjR+LKlSsAgKlTp+LLL7+UJjoiIlJiUid2nTp1QlJSkrirtVGjRhg8eDAcHR1hYGDw0XObNm0KID/RK4sVK1YgNjYWfn5+yM3NxezZs7F+/Xr8+OOPReq+ffsWU6ZMwaxZszB8+HDcvn0bM2bMgLm5OVq3bo3U1FRERETg3LlzaNCgQZniIvooO7v8MXQvXhSdPAH8b4ydnZ1El/P19YWLiwsSEhKgq6uLHTt2YNiwYTIOmoiIlIHUid2bN2+gpaWFXr16YciQIbCxsZH43Hfv3mHYsGFo1aqVtLdHRkYGTpw4gb1790JPTw8AMG/ePLi4uGDBggXQ1tYuVP/MmTPQ09PDqFGjAAC2trZwcHDA/v370bp1a/z333/Q09NjUkflT109f0kTJ6f8JO795K5gVqybm0QTJ5YtW4affvoJQP7EJE9PT7YwExFVYlIndsuWLUO/fv2kmiRgbGyM5cuXf7JeZmZmiSvjZ2RkIDs7G2ZmZuIyExMTZGZmIjIyEp9//nmh+qGhoYXqAoCpqSm8vLwAAA8ePIC2tjZGjx6N0NBQNGjQADNnzkRXSQa5E5WWo2P+kibFrWPn5ibxUifNmjUDAMycORPr1q1D1apVyyFYIiJSFlIndsOHD5dlHMW6d+8eXFxcij3m6uoKAIVm1Ra00qWlpRWpn5aWVqQVT0tLC+np6QDyd81o1aoV5syZg/r16+P06dOYOXMm9u3bBwsLC1m8HaLCHB2BgQNLvfPEmzdvxLu6jBw5Es2bN0fbtm0rImIiIlJwEiV2N27ckMnNbG1tS1XfxsYGT548KfbYo0ePsGnTJmRkZIgnSxSsl1dcK6K2tjZSUlIKlWVmZorPnTRpUqFjAwYMwMmTJ+Hn58fEjsqPurrES5pkZWVhwYIF8PT0RFBQEOrVqwcATOqIiEhMosRu/PjxEBWM/ZGSSCTCo0ePynSN9xkbG0NTUxNhYWFo06YNACA8PByampowMjIqUt/MzAzXrl0rVBYWFibuytq5cydatGhRKPnMyspi1xYphKdPn2L48OG4c+cOAODEiRNFfhkhIiKSeANXQRDK9JWXlyfTwLW1tdG3b1+sX78eiYmJSExMxPr169G/f39oaWkVqd+zZ0+8fv0a7u7uyM7Oxs2bN3HixAkMGTIEABAbG4tly5YhKioKOTk58PLyQlBQEAYPHizTuIlKy8vLC5aWlrhz5w5q1aqF48ePM6kjIqJilduWYhUhNTUVa9euxfnz55GdnY3u3bvj+++/F4+769evHxwcHDB16lQA+RMkVq5ciZCQENSuXRvTp0+H4/8PUs/KysL69etx6tQppKSkwNTUFPPnzy/VbF/uPEGylJmZiblz52Lr1q0AgI4dO+LgwYNo3LixnCMjIiJFpdSJnaJhYkey9P333+Pnn38GACxcuBArVqyApqamnKMiIiJFJnFXbFm9fPmyom5FpBIWLFiAjh074tSpU1izZg2TOiIi+qQy7RWbkZEBHx8fhISEIDMzs8g4utzcXGRkZODly5cICQnBf//9V6ZgiVRZRkYG3N3dMXXqVIhEIujq6uLq1atlnrhERESVh9SJXXJyMkaMGIGIiIgixwRBKPTDiL29RB8XHByMoUOH4r///kNWVpZ4nUYmdUREVBpSd8Xu3bsXT58+hUgkgo2NDbp37w5BENC8eXP0798f1tbW4r1gbWxscPbsWZkFTaRK9u7dCysrK/z333+oW7cuWrRoIe+QiIhISUndYnf+/HmIRCKsWbMGAwYMQG5uLtq1a4e6deti/fr1APLXiZs0aRICAwORmZkps6CJVEFaWhq++eYbuLu7AwC6deuGffv2wdDQUL6BERGR0pK6xS4qKgp6enoYMGAAAEBdXR2ff/45AgMDxXVMTU2xfPlyZGdnY8+ePWWPlkhFPHz4EO3bt4e7uzvU1NSwbNkynDlzhkkdERGVidSJXUZGBurXr1+ozMTEBKmpqXjx4oW4zN7eHp999hn8/f2lj5JIxSQnJyMkJASGhoY4d+4cfvjhB/HQBSIiImlJndhVr169SPdqw4YNAeRvf/Q+Q0NDxMXFSXsrIpXw/iQiW1tbHDp0CHfv3kUXCfeKJSIi+hSpEztTU1M8e/YMCQkJ4rImTZpAEAQ8fvy4UN3ExETO7qNK7d69e2jbtm2hJX+GDBmCunXryjEqIiJSNVIndvb29sjJycE333yD8PBwAECbNm2gpqaGQ4cO4e3btwCAM2fOICYmRtyaR1SZCIKAP//8EzY2Nrh79y7mzJkj75CIiEiFSZ3YOTs7w8DAAEFBQXBwcEBWVhYMDAxgb2+PmJgY9O7dG46Ojvj2228hEonQrVs3WcZNpPAK1nqcOnUq3r17h379+uHAgQPyDouIiFSY1Imdrq4u9uzZg3bt2qFmzZqoUqUKgPw9LWvXro2kpCQ8evQIubm5aNy4MSZPniyzoIkUXWBgINq2bQtPT09oaGhg3bp1OH78OPT19eUdGhERqTCRIINtIRITE1G7du1Cr728vBAdHY2mTZvCyckJ1atXL+ttFF5qaiqsrKwQEBBQKd4vFe/WrVuwt7dHVlYWmjRpgkOHDqFDhw7yDouIiCoBmSR2lI+JHQH5eyT36NEDNWvWxO7du1GrVi15h0RERJWE1DtPENH/BAUF4fPPP4eWlhbU1dVx/PhxVK9enbPBiYioQpUpsUtPT4eXlxcCAwORkpKCnJwclNQAKBKJuPsEqRxBEODm5oaFCxdi8uTJ+P333wHkj0ElIiKqaFIndomJiXB2dsbz588BoMSErgBbLkjVJCYmYty4cThx4gQA4NWrV8jJyYGGBhvCiYhIPqT+CfTnn3/i2bNnUFdXh729PUxMTKClpSXL2IgU1vXr1zFixAhERUWhSpUq2LhxI6ZNm8ZfYIiISK6kTuzOnTsHkUiELVu2oGvXrrKMiUhh5eXlYf369ViyZAlyc3NhamoKT09PWFpayjs0IiIi6dexi4uLQ+PGjZnUUaXy8uVLrF69Grm5uXB2dkZgYCCTOiIiUhhSt9jVqFFDvCgxUWVRv359uLu749WrV5g0aRK7XomISKFIndhZW1vj3LlzSEhIwGeffSbLmIgURm5uLtasWYO2bduib9++AICBAwfKOSoiIqLiSd0VO23aNADA0qVLkZWVJbOAiBRFXFwc+vTpg6VLl8LFxQWJiYnyDomIiOijpG6xe/36NZydneHh4YFu3brB1tYW9erVg6amZonnuLq6Sns7ogp1/vx5jBo1Ci9fvoS2tjbWrVtXaNs8IiIiRST1lmLNmzeHSCQSr1/3sbFGgiBAJBLh8ePH0kWpJLilmPLLzcrC8smTscLDA4IgoGXLlvD09ESLFi3kHRoREdEnSd1i165du1LV5yBzUnSZhw6h77hxuPjuHQBgAoDf3ryBTnAwwMSOiIiUgNSJnYeHh8R1Y2NjceTIEWlvRVT+vL2hNXIkTAQBtwFsAzAaAGJjAScnwMsLcHSUb4xERESfIHVXrCQuXbqEQ4cO4fLlyxAEAY8ePSqvWykEdsUqn5ycHKQlJ6NmmzZAdDTSAUQDMHu/kkgENGwIREQA6uryCZSIiEgCMt/UMjExEV5eXvD09MSLFy8A/G+MHZEiiY6OxsiRI6GTmQnf6GioAdDBB0kdAAgCEBUFXLkCdOlS4XESERFJSmaJnb+/Pw4ePIizZ88iJydHPKlCW1sbDg4OGDlypKxuRVRmvr6+cHFxQUJCAnS1tREM4JOj6GJjKyAyIiIi6ZUpsUtJSYG3tzcOHz6MiIgIABAndM2aNcOIESMwcOBAdkuSwsjOzsZ3332HdevWAQDatm2Lw3PmwHT06E+fbGhYztERERGVjVSJ3f3793Hw4EGcOnUK7969EydzOjo6SE9PR7169XDixAmZBkpUVs+ePcOIESNw8+ZNAMDMmTOxbt06VNXQABYtAl68yO92/VDBGDs7uwqOmIiIqHQkTuzS09Nx4sQJHDp0CMHBwQDyW+c0NDRga2uLAQMGoEePHrC0tOR4OlI4giBg2LBh8Pf3R82aNbFr1y44vj/LddOm/NmvIlHh5K7gs+zmxokTRESk8CRK7JYtW4bjx48jPT1d3DrXunVr9O/fH/379+eK/KTwRCIRtm3bBldXV+zZswfGxsaFKzg65i9p4uoKREf/r7xhw/ykjkudEBGREpBouZOCXSbatGmDbt26oW/fvmjUqFGJdQ0MDHDx4kVZx6rwuNyJYnn69Cnu3LmDYcOGics+OUM7Nzd/9mtsbP6YOjs7ttQREZHSKNUYu8jISAQFBaFmzZro1q0b6tSpU15xEZXJ0aNHMWHCBGRkZKBp06awtrYGIMEOKOrqXNKEiIiUlpokldavXw9bW1skJyfjwoUL+Omnn9ClSxeMHz8ef//9N9LS0so7TiKJZGZm4ptvvoGTkxOSk5PRrl071K1bV95hERERVYhS7TwRGxuLo0eP4u+//0b0/49DEolEqFq1Krp37w4HBwdMnTqVXbHsipWL0NBQDB8+HEFBQQCARYsWYfny5dDU1JRzZERERBVD6i3Fbt68CS8vL5w9exaZmZniLi5BEFCzZk3s3r0bLSrZxulM7OTn8OHDmDx5MlJSUqCvrw8PDw/06dNH3mERERFVKKkXKO7QoQM6dOiA1NRUnDhxAj4+Prh//z4AIDk5GUOGDIGZmRmcnJzg4OAAPT09WcVMVMSzZ8+QkpICe3t7HDhwAA0aNJB3SERERBVO6ha74oSHh+PIkSM4ceIEEhIS8m8gEkFTUxPdunWDm5ubrG6lkNhiV7Hy8vKgpqYm/ruHhwdGjRoFDQ2Zb4FMRESkFCSaPCEpExMTLFq0CJcuXcKWLVvQtWtXqKurIysrC35+frK8FYD8RZMXL14MGxsbWFlZYcGCBRJN5AgKCkKrVq2KlPv4+KBnz56wsLCAo6OjeKwWKR4PDw/Y2tqKv99qamoYO3YskzoiIqrUZJrYFdDQ0ECPHj3wxx9/4OLFi5g7dy6aNm0q8/usWLECsbGx8PPzw5kzZxAbG4v169eXWF8QBHh5eWHChAnIysoqdOzWrVtYsWIF1qxZg9u3b2PAgAGYNm0aMjIyZB43SS8tLQ0TJkyAi4sL/P398ccff8g7JCIiIoVRLond+/T19TF58mT8888/Mr1uRkYGTpw4gVmzZkFPTw+fffYZ5s2bB29v7xKTsSVLluDIkSOYNWtWkWNHjhxBv379YGVlBU1NTYwbNw61atWCr6+vTOMm6T18+BDt27fH7t27oaamhmXLluHbb7+Vd1hEREQKQ6H7rTIzMxEXF1fssYyMDGRnZ8PMzExcZmJigszMTERGRuLzzz8vco6rqysMDAxw69atIsfCwsIwZMiQQmWmpqbifXFJfgRBwO7du/HNN98gIyMDhoaGOHDgALpwIWEiIqJCFDqxu3fvHlxcXIo95urqCgDQ0dERl2lrawNAiePsDAwMSrxXWlqa+PwCWlpaSE9PL1XMJHvr16/HggULAAC9evWCh4cHFx0mIiIqRrl3xZaFjY0Nnjx5UuxXQWvN+92uBX+XZkaqtrY2MjMzC5VlZmaiWrVq0r8BkolRo0bB0NAQq1atwqlTp5jUERERlUChE7uPMTY2hqamJsLCwsRl4eHh0NTUhJGRUamv16xZM4SGhhYqCwsLQ7NmzcoaKpWSIAi4evWq+HX9+vUREhKCxYsXi5c3ISIioqKU9qektrY2+vbti/Xr1yMxMRGJiYlYv349+vfvDy0trVJfz8nJCSdOnMDNmzeRnZ0Nd3d3JCQkoGfPnuUQPZUkOTkZI0aMgJ2dHY4ePSou57qAREREn6a0iR0A/PjjjzAyMoKDgwP69OmDhg0b4ocffhAf79evH7Zt2ybRtWxtbfHjjz/ip59+Qvv27fHPP/9g+/bt3DGjAgUEBKBt27bw9PSEhoZGiRNniIiIqHgy3XmisuPOE9IRBAFbtmzBvHnzkJWVhSZNmuDQoUPo0KGDvEMjIiJSKgo9K5ZU35s3bzBx4kT4+PgAAAYNGoRdu3ahVq1aco6MiIhI+Sh1Vywpv2vXrsHHxweamprYtGkTvL29mdQRERFJiS12JFf9+/fHqlWr0KNHD7Rr107e4RARESk1tthRhUpMTMTYsWPx4sULcdnixYuZ1BEREckAW+yowly/fh0jRoxAVFQUXr58CT8/P3mHREREpFLYYkflLi8vD2vXroW9vT2ioqJgamqKNWvWyDssIiIilcMWOypX8fHxGDt2LE6dOgUAcHZ2xp9//gldXV05R0ZERKR6mNhRuXn06BF69uyJmJgYaGlpYfPmzZg0aRJEIpG8QyMiIlJJTOyo3BgZGUFPTw81atSAp6cnWrVqJe+QiIiIVBoTO5Kp169fo3bt2lBTU4OOjg7++ecf6OvrcycOIiKiCsDJEyQz58+fxxdffIF169aJy4yMjJjUERERVRAmdlRmubm5+PHHH9GjRw/ExcXh4MGDyM7OlndYRERElQ4TOyqTmJgY9OjRA8uXL4cgCJg4cSKuX78OTU1NeYdGRERU6XCMHUntzJkzGD16NOLj41GtWjX8+eefGDVqlLzDIiIiqrSY2JFU4uLiMHDgQGRmZqJ169bw9PSEubm5vMMiIiKq1JjYkVTq1auHdevW4eHDh/j111+hra0t75CIiIgqPSZ2JDFfX18YGBigbdu2AIBvvvlGzhERERHR+zh5gj4pOzsbCxYsQL9+/TBs2DAkJyfLOyQiIiIqBlvs6KOePXuGESNG4ObNmwCAr776ClWrVpVzVCXIzQWuXAFiYwFDQ8DODlBXl3dUREREFYaJHZXo2LFjGDduHJKSklCzZk3s2rULjo6O8g6reN7egKsrEB39v7KGDYFNmwBFjZmIiEjG2BVLRWRnZ2P27NkYNGgQkpKS0L59ewQFBSl2UufkVDipA4AXL/LLvb3lExcREVEFY2JHRairqyM4OBgAMGfOHFy5cgXGxsZyjqoEubn5LXWCUPRYQdns2fn1iIiIVBy7YkksLy8PampqUFNTg4eHB/z9/dGvXz95h/VxV64Ubal7nyAAUVH59bp0qbCwiIiI5IEtdoTMzEzMmDEDkydPFpfVqVNH8ZM6IH+ihCzrERERKTG22FVyoaGhGD58OIKCggDkr01naWkp56hKwdBQtvWIiIiUGFvsKrFDhw6hbdu2CAoKgr6+Pnx9fZUrqQPylzRp2BAQiYo/LhIBjRrl1yMiIlJxTOwqoYyMDHz99ddwdnZGamoq7OzscPfuXfTt21feoZWeunr+kiZA0eSu4LWbG9ezIyKiSoGJXSXk4OCAv/76CyKRCEuXLsX58+fRoEEDeYclPUdHwMsL+PA9NGyYX66oy7QQERHJGMfYVUJz587Fw4cP4eHhgR49esg7HNlwdAQGDuTOE0REVKmJBKG4BcBIGqmpqbCyskJAQACqV68u73DE0tLS8OjRI7Rr165QWbVq1eQYFREREckau2JV3MOHD9G+fXv06tULkZGR4nImdURERKqHiZ2KEgQBu3btQrt27fDo0SNoa2sjLi5O3mERERFROWJip4JSU1MxZswYTJw4ERkZGejVqxfu3r0LGxsbeYdGRERE5YiJnYq5d+8erKyssH//fqirq2PVqlU4deoU6tatK+/QiIiIqJxxVqyK2b17N0JCQtCgQQMcOnQInTp1kndIREREVEGY2KmYNWvWQE1NDUuWLIG+vr68wyEiIqIKxK5YJRcYGIjx48cjJycHAKClpYVff/2VSR0REVElxBY7JSUIAn7//XfMnTsXWVlZaNGiBebPn1++N83N5QLARERECoyJnRJKSkrCxIkT4e3tDQAYNGgQJk2aVL439fYGXF2B6Oj/lTVsmL9PK7fsIiIiUgjsilUy/v7+sLS0hLe3NzQ1NbFp0yZ4e3ujVq1a5XdTb2/AyalwUgcAL17kl/9/gklERETyxcROiXh4eKBTp06IjIxE06ZNcf36dcyaNQsikaj8bpqbm99SV9zOcwVls2fn1yMiIiK5YmKnRCwtLaGhoQEnJycEBgbC2tq6/G965UrRlrr3CQIQFZVfj4iIiORKqcfYpaenY8WKFTh//jxycnLQvXt3/Pjjj5/cBzUoKAguLi548OBBofK+ffsiJiYGamr/y3e9vLxgYmJSLvGX1hdffIHAwECYm5uXbyvd+2JjZVuPiIiIyo1St9itWLECsbGx8PPzw5kzZxAbG4v169eXWF8QBHh5eWHChAnIysoqdCw1NRURERHw9fVFUFCQ+EtRkroCzZs3r7ikDsif/SrLekRERFRulDaxy8jIwIkTJzBr1izo6enhs88+w7x58+Dt7Y2MjIxiz1myZAmOHDmCWbNmFTn233//QU9PDw0aNCjv0JWLnV3+7NeSkkmRCGjUKL8eERERyZVCd8VmZmYiLi6u2GMZGRnIzs6GmZmZuMzExASZmZmIjIzE559/XuQcV1dXGBgY4NatW0WOPXjwANra2hg9ejRCQ0PRoEEDzJw5E127dpXdG1JG6ur5S5o4OeUnce9PoihI9tzcuJ4dERGRAlDoxO7evXtwcXEp9pirqysAQEdHR1ymra0NAEhLSyv2HAMDgxLvJRKJ0KpVK8yZMwf169fH6dOnMXPmTOzbtw8WFhZSvgMV4egIeHkVv46dmxvXsSMiIlIQCp3Y2djY4MmTJ8Uee/ToETZt2oSMjAzxZImCLtjq1auX+l4fLvA7YMAAnDx5En5+fkzsgPzkbeBA7jxBRESkwBQ6sfsYY2NjaGpqIiwsDG3atAEAhIeHQ1NTE0ZGRqW+3s6dO9GiRQvY2tqKy7KyslC1alVZhaz81NWBLl3kHQURERGVQGknT2hra6Nv375Yv349EhMTkZiYiPXr16N///7Q0tIq9fViY2OxbNkyREVFIScnB15eXggKCsLgwYPLIXoiIiIi2VPaFjsA+PHHH7F27Vo4ODggOzsb3bt3x/fffy8+3q9fPzg4OGDq1KmfvNaCBQugpqaGkSNHIiUlBaampvjrr7/QpEmT8nwLRERERDIjEoTi9ooiaaSmpsLKygoBAQFSjfMjIiIiKgul7YolIiIiosKY2BERERGpCCZ2RERERCqCiR0RERGRimBiR0RERKQimNgRERERqQgmdkREREQqQqkXKFY0BUsCpqamyjkSIiIiUjXVqlWDSCT6aB0mdjKUlpYGAOjcubOcIyEiIiJVI8kGCNx5Qoby8vLw6tUriTJqIiIiotKQJL9gYkdERESkIjh5goiIiEhFMLEjIiIiUhFM7IiIiIhUBBM7IiIiIhXBxI6IiIhIRTCxIyIiIlIRTOzkID09HYsXL4aNjQ2srKywYMEC8eLGHxMUFIRWrVoVKe/bty/atGkDS0tL8Vd4eHh5hF5uZP1MfHx80LNnT1hYWMDR0RFBQUHlEXa5Ku0zuXfvHoYOHQpLS0t069YNR44cKXRcWT8nCQkJmD59OqytrWFjY4OVK1ciJyen2LqXLl2Cg4MDLCws0LdvX1y4cKHQ8e3bt8Pe3h4WFhYYM2YMnj59WhFvQeZk9Uzy8vJgaWkJCwuLQp+L9PT0inorMlOaZ1LAz88P3bt3L1KuKp8TQHbPpbJ+Vg4ePIjevXvD0tISvXv3xv79+wsdV8jPikAVbtGiRcLYsWOFN2/eCK9fvxZGjx4t/PTTTyXWz8vLE44cOSJYWFgIZmZmhY6lpKQI5ubmQnR0dHmHXa5k+Uxu3rwpWFpaCnfu3BGysrKE3bt3CzY2NkJ6enp5vw2ZKs0zSUpKEtq3by/s27dPyM7OFq5fvy5YWloK9+7dEwRBuT8no0ePFubOnSukp6cLz58/F/r16yds3769SL2IiAihVatWwr///itkZ2cL//zzj9C6dWvh5cuXgiAIgre3t2BnZyeEhIQImZmZwurVq4V+/foJeXl5Ff2WykxWz+TJkydCy5YthXfv3lX0W5A5SZ+JIAhCVlaW8NdffwktWrQQunbtWuiYKn1OBEF2z6Uyflb+/fdfwdraWggKChLy8vKEwMBAwdraWjh9+rQgCIr7WWFiV8HS09OFli1bCgEBAeKyu3fvCq1bty4x8Vi0aJEwbNgwYdeuXUWSmBs3bgg2NjblGnN5k/UzmTt3rrB06dJCZX369BG8vLxkH3w5Ke0z8fT0FHr16lWo7IcffhAWLFggCILyfk4iIyMFMzMzcSIiCILwzz//CF26dClS99dffxXGjx9fqGzixInCpk2bBEEQhBEjRgh//PGH+FhWVpZgaWkp3Lhxo5yiLx+yfCZeXl6Co6Nj+QZcAUrzTAQh/wf7xIkThY0bNxZJYFTlcyIIsn0ulfGzsm/fPuHPP/8sVDZjxgxhxYoVgiAo7meFXbHlIDMzE8+ePSvxKzs7G2ZmZuL6JiYmyMzMRGRkZLHXc3V1xeHDh9GiRYsixx48eABtbW2MHj0aNjY2cHR0LNL9pAgq8pmEhYUVuhYAmJqaIjg4WKbvqaxk+UxCQ0M/+p6V5XPyodDQUOjp6aFevXriMhMTE8TExCA5OblQ3U993z88rqmpCSMjI4X7XHyKLJ/JgwcP8O7dOwwZMgQdOnTAqFGjEBgYWP5vQsZK80wAYN26ddixYwcaN25c5JiqfE4A2T6XyvhZGTVqFKZMmSJ+nZCQgNu3b+OLL74AoLifFQ253l1F3bt3Dy4uLsUec3V1BQDo6OiIy7S1tQGgxPFTBgYGJd5LJBKhVatWmDNnDurXr4/Tp09j5syZ2LdvHywsLKR8B7JXkc8kLS1NfH4BLS0thRsLIstn8qn3rCyfkw8V974KXqenp6NGjRofrfv+M1CWz8WnyPKZaGlpoXXr1nB1dUXNmjWxf/9+TJw4EcePH0ejRo3K+Z3ITmmeCaAa/39IQpbPpbJ+VgrEx8fj66+/xhdffIH+/fuXeC1F+KwwsSsHNjY2ePLkSbHHHj16hE2bNiEjIwPVqlUDAGRkZAAAqlevXup7TZo0qdDrAQMG4OTJk/Dz81OoH9gV+Uy0tbWRmZlZqCwzMxO1atUq9bXKkyyfiba2NlJSUgqVZWZmis9Vls/Jh3R0dMTvu0DB64L3VqCk73tBvU8dVxayfCaLFi0qdGzixInw9vbGpUuXMHr0aFmHXm5K80w+RVU+J4Bsn0tl/qzcvXsXrq6usLa2xurVq6GhkZ86KepnhV2xFczY2BiampoICwsTl4WHh4ubcEtr586duHHjRqGyrKwsVK1atayhVhhZP5NmzZohNDS0UFlYWBiaNWtW1lArTGmfiZmZ2Uffs7J+Tpo1a4akpCS8fv1aXBYeHg4DAwPo6uoWqvupZ/Dh5yI7OxuRkZFFuioVnSyfycaNG/Ho0aNCx5Xhc/Gh0jwTSa6lCp8TQLbPpbJ+Vry8vDBu3DiMHTsWGzZsQJUqVQpdSxE/K0zsKpi2tjb69u2L9evXIzExEYmJiVi/fj369+8PLS2tUl8vNjYWy5YtQ1RUFHJycuDl5YWgoCAMHjy4HKIvH7J+Jk5OTjhx4gRu3ryJ7OxsuLu7IyEhAT179iyH6MtHaZ9Jz5498fr1a7i7uyM7Oxs3b97EiRMnMGTIEADK+zkxMjKClZUVVq1ahdTUVERFRWHr1q1wcnIqUnfAgAHw9/eHr68vcnJy4OvrC39/fwwcOBAAMGTIEOzbtw/BwcF49+4dNmzYAH19fVhbW1f02yoTWT6TkJAQrFy5EvHx8cjKysKWLVuQmpqqVP9WgNI9k09Rlc8JINvnUhk/K35+fvjpp5/w22+/YcKECUWOK+xnRa5TNyqplJQUYenSpULHjh2Fdu3aCYsWLRLS0tLEx7/66qtCM20K3Lx5s8gM0Hfv3gkrV64UOnXqJLRp00YYMmSIcPPmzXJ/D7Imy2ciCILw999/C7179xYsLCwEJycn4e7du+Uaf3ko7TO5f/++MHz4cMHS0lLo3r27cPToUfExZf6cxMfHCzNnzhTat28vdOjQQVizZo2Qk5MjCIIgWFhYCMeOHRPXvXz5sjBgwADBwsJC6Nevn3Dx4kXxsby8PGHnzp1Ct27dBAsLC2HMmDHC06dPK/z9yIKsnsmbN2+ERYsWCba2tuJn8vjx4wp/P7JQmmdS4OjRo0Vmf6rS50QQZPdcKuNnpX///kLz5s0FCwuLQl/ff/+9IAiK+1kRCYIgyDe1JCIiIiJZYFcsERERkYpgYkdERESkIpjYEREREakIJnZEREREKoKJHREREZGKYGJHREREpCKY2BERERGpCCZ2RERERCpCQ94BECmS6OhodO/eXfx6zJgxWLp06SfP27lzJ3755RcAQL169XD58uVyi1FSoaGhctkf9+rVqzh58iSCgoLw8uVL5ObmQl9fH61bt8aAAQPQo0ePCo1n0aJF8PHxgYODA9avX1+u9xozZgz8/f1Ldc7ff/+Nzz//XPy6IN727dvDw8OjUF1zc/NSXfv27duoUaNGqc65cuUKJk+ejLVr14q3HvtQVFQUvLy8cP36dURHRyM5ORnVq1eHiYkJ7O3tMXToUHz22Weluq8iiIqKwqFDh3D9+nXExMQgLS0Nenp6MDY2hp2dHYYPH46aNWuWeH5SUhKys7NRp06dCoy6sMWLF+P8+fM4ceIE6tatK7c4SH6Y2BF9hJ+fH7777juIRKKP1vP19a2giD7t1atXWLt2Le7cuYNLly5V6H3nz5+PmzdvAgCqVq2K+vXrQ1NTE9HR0fDz84Ofnx/at28PNzc3pfzBL6nPPvsMTZo0kaiujo5Oqa9vZGSE2rVrf7Keurp6qa779u1bLF68GK1atcKAAQOKHM/NzcWGDRuwd+9eZGdnQyQSoVGjRqhfvz5evnyJgIAABAQEYNu2bVi6dKlUe5LKy+HDh/Hzzz8jKysL2traaNy4MbS0tPDmzRvcvn0b/v7+2LlzJ9atWwd7e/si57u7u2Pr1q1wc3OTa2I3d+5c+Pn5YfHixdi5c6fc4iD5YWJHVAINDQ28evUKAQEBH93UOSoqCv/9918FRvZxBS1m9erVq7B7hoeHY/To0UhMTISRkRFmzZqFPn36iBOLnJwcHDt2DBs2bIC/vz/Gjh2LQ4cOoXr16hUWY0Wyt7fHmjVryu36X3/9NRwdHWV+3Q0bNiA+Ph5ubm5FfpnJycnBtGnTcPnyZWhoaGDixIkYO3Zsoc9ZWFgY/vjjD5w8eRLfffcdQkJCsGTJEpnHKWs3btzATz/9BJFIhJ9++glDhgxBlSpVxMdjYmKwfPlyXLhwATNnzoSXl1eR1vDVq1dXdNjF0tfXx+TJk+Hm5oaTJ0+if//+8g6JKhjH2BGVoEOHDgCA06dPf7ReQWtdixYtyj0mRZSVlYU5c+YgMTERLVq0wOHDh9GvX79CrUUaGhoYMmQI3N3doaWlhdDQULi5uckvaCoiNDQUR44cQYcOHYr9RWbDhg24fPkyqlSpgh07dmDBggVFfnkwNTXFhg0bsHjxYgDAnj17FKo1uyR//vkn8vLyMGHCBDg7OxdK6gCgfv36cHNzg6mpKTIzM/HXX3/JKVLJjBkzBrq6uli/fj2ysrLkHQ5VMCZ2RCXo06cPAODMmTMQBKHEer6+vlBTU0Pfvn0rKjSF4u7ujuDgYKipqWHdunXQ09Mrsa6ZmRnGjBkDADhy5AhSU1MrKEr6lC1btiAvLw/Dhw8vciwqKgp79+4FAMyfPx+2trYfvda4cePQu3dvAMBPP/2k8N/nBw8eAADatGlTYh0tLS1x9/T9+/crJC5pVa9eHQ4ODoiNjYWXl5e8w6EKxq5YohJYW1ujTp06iIuLQ2BgIKysrIrUefr0KYKDg9GhQwfo6+t/9HoPHjzA3r17cfv2bbx+/Ro6OjowNzfHwIEDMXjw4CLjoQoG4m/fvh1169bFH3/8gdu3byM5ORn16tVD9+7dMXXq1EJjrd4fXB8XFyd+/eTJk0LXPnv2LDw9PfHgwQOkpKSgVq1aaN++PSZMmICWLVuW6jkdOXIEANC1a1eYmpp+sv6YMWNgZmYGKyurIl2xycnJOHToEC5duoSwsDCkpqaKxzt17doVLi4uRQavF7zHa9euYc2aNTh37hzU1NTQsmVL7Nq166Ox5ObmwtvbG8ePH0dwcDAyMjKgr6+Pdu3aYdy4caV+FsoqLi4OZ8+eRbVq1Yqd3HLgwAHk5ORAT0+v2MSvON9++y38/Pzw9u1bHDt2DKNGjQIA3Lp1Cy4uLmjTpg32798PDw8P/P3333j27Bk0NTXRsmVLjBkzpsRJNq9fv8auXbtw8eJFvHjxAmpqamjatCn69euHUaNGoWrVqqV+/5qamgCACxcuoGfPniXWGz58OLp06VJofGjBZJcC48ePB5DfNft+d3lUVBR27tyJa9eu4eXLl6hatSrMzMwwePBgODo6Fvn3X3DdxYsXw87ODhs3bsTt27eRlZWFJk2aYPDgwRgxYkSJ73fQoEE4cOAA9u3bh5EjR5b6mZDyYosdUQnU1NTErQ4ldccWdDP169fvo9favn07hg0bhuPHjyMlJQXm5uaoXr06/P398d1332HcuHFISUkp9tzLly/DyckJZ8+eRa1atWBoaIjo6Gjs2bMHI0aMKNQa0rZtWxgZGQHI/2HVtm1btG3bVnw8JycH8+bNw4wZM3Dp0iWIRCKYm5sjKysLJ0+exNChQ7Fv3z6Jn1FUVBSeP38OAPjyyy8lOqdevXoYMGAAGjRoUKg8MjISAwYMwIYNG3D37l3Url0b5ubmUFdXx8OHD7FlyxYMHz4caWlpxV535syZOHnyJBo1agRtbW3UqVMHGhol/+6ampqKUaNGYenSpfD394euri7Mzc2RkpKC48ePw8nJCbt375bwSSi306dPIycnBx07dizSDQnkj0EDABsbG4kTJ2NjY/E4tOIm8WRnZ4tn37569QomJibIzc3FzZs3MWPGDBw8eLDIOQEBAejXrx927tyJ58+fiyduPHz4EGvXrsWwYcMQHx9fmrcOAOjUqRMA4OjRo5g2bRouXbpUbBemnp4ezM3NC/0SZ2RkVOjfmJmZGdq2bVso+fv333/Rv39/HDx4EK9evULTpk1Ru3ZtBAQEYOnSpZg4cWKJn+snT55g6NChOHv2LOrWrQsDAwM8fvwYq1atwvjx40v8f6NVq1bQ09NDeHg4goODS/1MSHkxsSP6iILu1ZK6Y0+dOgVNTU306tWrxGv4+flh/fr1yMvLw/Tp03Hjxg0cPXoU58+fx549e6Cvrw9/f38sWLCg2PM9PDzw5Zdf4sKFC/jnn3/w77//YuvWrVBXV8ezZ88KdbUcPHgQX3/9NQCgdu3aOHjwYKEfkJs2bcKJEydgYGCAHTt24Pr16zh69CiuX7+OpUuXQiQS4eeff8a1a9ckej5Pnz4V/720S3F86Pvvv0dsbCwsLCxw4cIFnDp1Ct7e3rh58ybWrl0LNTU1RERE4O+//y72/P/++w8eHh44fvw4Ll++jO+///6j95s3bx6CgoJQp04d7N27F+fPn8fRo0dx48YNTJ8+HXl5eVizZg3OnDlTpvelDAoSt+JapfPy8hASEgKg9N/jgmVcPmwxBoBHjx7h3r17WL9+PW7evAlvb29cvnxZ3M3r5uaGnJwccf24uDhMnz4dSUlJGDZsGK5fv46TJ0/in3/+wZkzZ9CmTRsEBwdj9uzZpYoRAObMmSOeyXr+/HlMmTJF3Gr722+/4datW8jOzi723KlTpxb6N7Z48WIcPHgQnTt3BgAEBwdjzpw5ePfuHaZNmwZ/f38cO3YMZ86cgY+PD4yMjMSTN4rj7e0NPT09+Pj44MSJEzh16hQOHToEfX19BAQEYN26dcWep6amBktLSwCQ+N8zqQYmdkQfYWVlhXr16uHly5cICgoqdOzJkycICwtDx44dPzqubOPGjQDyu3FcXV0LtYh06NABW7ZsAZD/A+XOnTtFzv/ss8+wefPmQmtSde/eXbzkQmBgoETvJSEhAe7u7gCArVu3ws7OTnxMXV0dY8aMwbhx4yAIgsQTG96+fSv+uyTLb3wsttDQUADAihUrCr1XkUiEQYMGoX379gCKTxKA/CS8Xbt2APJ/qH3se3L37l1cuHABALB582bY2NiIj1WpUgWurq7iLkdp1r7z8fGBubn5J79+++23Ul8byE8ePnbdgnGMksjLyxOvvVfcuodv375Fbm4uAHz0mRanoGUrMTGx2OOzZs2Cg4OD+LWuri7mz58PIH9NuIiICPGxnTt3IikpCd26dcOKFSsKrc/XuHFjbN26FdWrV5dqmZ/69evDy8sL3bp1E5dlZmbixo0b2LJlC1xcXGBra4vly5fjzZs3pbr2b7/9hqysLIwePRqzZ88u1OLZokULbN68Gerq6jhx4gTCwsKKnK+mpoatW7cWWuvQ0tISa9euBZA/FCIuLq7Ye5uZmQGAeAkiqhyY2BF9hEgkKrE7tqAb9quvvirx/MjISPEPp7FjxxZbx9LSUvyb9blz54oct7W1Lbb7y8TEBABK7Ir5UEH3kqmpaYljxwoWpL1//z4SEhI+ec3312B7v3WltD777DPcvHkT9+7dE/8wel9ubq54PF5mZmax1yiutakkBUld69atC3WjvW/ChAkAgGfPnolbrCT12WefibvBP/ZlaGhYqusWKOj+K+mruGdYkqSkJHE3YHFr773fJVlcN+3HFIwbK2nyUdeuXYuUFXyugfwxlwXOnj0LAMWurwfkJ5EFwwEKvr+lYWBggD/++AN+fn6YO3cuOnToAC0tLfHxlJQU7N+/H3379pW4azMrK0u8WHlJcZubm6N58+YQBKHYuDt06IDmzZsXKe/UqRMaNmyIvLy8Et+vsbExgPwhE1R5cPIE0Sf07dsXe/fuFS/6WbC+1+nTp1G1atWP7qRQ0FWpra1d6AfWh7744gsEBQUVaqEoUNJ6dAU/dCRNqApaxF6+fAlnZ+di67z/A/jp06efXET4/YVYS9uSURwtLS3Exsbi3r17eP78OaKiohAeHo7Hjx8jPT0dQH4L06di+ZSC78vHJkcYGRmhevXqSE1NRURERKmSJWVax+79BF5XV7fI8Zo1a0IkEkEQBCQlJZXq2gWfiZJac4v7bL+fTBW0FKalpeHFixcA8lubC2bofqigzvtDBErLyMgIU6ZMwZQpU5CVlYX79+/j2rVrOHbsGF68eIE3b95g2rRp8PPz+2SiGxkZKU6Mly1bVmL9mJiYEuNu3bp1idc3NzdHdHQ0IiMjiz1e8P0sqcWUVBMTO6JPsLS0hKGhIWJjY3H37l1YWlri4cOHiIyMRO/evT+6yG7BxIZPLcRbrVo1ACh2AHXBjL2yKmjZS01Nlaj79v3WkpI0btwY6urqyM3NRWhoaKEuzY95/PgxmjVrVmhyw9OnT/HLL7/g0qVLhZK36tWrw9raGq9evfpoS8n7CcGnFHxfiktk3letWjWkpqaWOLBdFbzfna6trV3kuJaWFpo0aYLIyMhSD8J//PgxAJSYFH/qs13wi8b7E4QkaT0t+KxfunQJ27ZtK7bO1KlTxePgSlKlShVYW1vD2toaM2bMwIYNG7Br1y7ExMTg4sWLHx1b+34cACRaxLy41vePbWFW0GJe0r/VTx0n1cTEjugTCrpj3d3dcfr0aVhaWkrUDQv8L2H71DpeBf/xFtQvDwU/tHv37o3NmzfL5Jq1atVC27Ztcfv2bVy7dg2jR4/+5DlxcXEYPHgwtLW1sWHDBnTr1g0JCQkYPXo0EhISUL9+fQwbNgwtWrRA06ZN0bBhQ4hEIsydO1dms/sKnvOnurELjpfn90Xe3u/mT05OLrbls2vXrti9ezeuXr2KzMxMiZLo6Oho8ferS5cuZYrx/YTzxIkTEreeJiQklPhLTEFL5ZYtW3DixAl06NABy5YtK/FaGhoamD9/Pnx9ffHy5ctiW9c/9P5QhcDAQKk+RwUt1cUp+H+lpJb1gqRdmiVgSHlxjB2RBApmx/r5+UEQBJw6dQo6Ojqf/IHVtGlTAEBGRgbCw8NLrFfw27yk+4tKo2C8TUGXbHEyMjLg7++PqKgocTfYpxQs9XLp0iWJWlMOHDgAQRCQnZ0t7mY6evQoEhISoKenJ15yonPnzmjUqJG467ukAeLSKPi+PHz4sMQ64eHh4h+q5fl9kTdJutOdnZ2hqamJpKSkErtBP7R161bk5uZCV1dXPHZTWjVq1BBPxChugkGBJ0+e4PHjx+KExtHREU+ePCn2q6ArOy8vD5GRkTh//nyJM18LqKmpiZMoSSYLNWrUSDzO8GNx379/H0+ePCm2Zfhj/14LEueS1o8s+H6q8r7MVBQTOyIJWFhYoEGDBoiNjcX+/fvx4sULdO/e/ZMtF8bGxuKEas+ePcXWCQwMFK9kX9zm4qWlppb/z/rDAeudO3eGuro6nj59WuLyB+7u7hgzZgwGDhyIjIwMie43dOhQmJmZITc3F0uWLPnoOKz79++LFw0eMWKE+Id1dHQ0gPzZicX9wAwLC8Pdu3cBQOKE82MKBu3fv3+/xBadghnEBgYGZV7KRZHVq1dP3LL08uXLYus0adIEM2fOBJA/i7hgQkBJDh8+jKNHjwIAfvjhh092eUui4Jeoffv2FTvOMiUlBWPHjsWgQYNK/LdWnH79+kFNTQ2vXr0qsdu2QFhYGJ48eQJNTc0i6zYW/ALy/r+76tWri2dzl5QQR0VFYeTIkRgwYECx62Vevny52LX5Lly4gNjYWFSpUqXQbN73FfwyVPCLDFUOTOyIJFQwO/bXX38F8OlFiQu4uroCyP9ht3nz5kKzDG/duoVZs2YBAOzs7NCxY8cyx/n+uJr3u4AbNGiAoUOHAshft+v8+fPiY3l5eThy5Ih46ZVRo0Z9clxgAQ0NDaxcuRK6urp48OABhg8fjtOnTxdKwN69e4cDBw5g3LhxyMrKgpmZGebMmSM+XvCDJzg4GH5+fuJyQRBw+fJlTJo0SdyaImnC+TGWlpbi8VWzZs3CrVu3xMeysrKwefNmeHp6AgAWLFgg/qGtikQikXhW9odL+rxv8uTJ6N27N7KzszFt2jSsX7++SMIRFRWF7777Dj/88AMAiBMWWZgyZQp0dHQQEBCA+fPnF5oQ8OLFC0yZMgVv3ryBrq6ueJcLSZiYmIhnrG/ZsgXz588v0rqWnZ2Nf//9FxMmTEBOTg7GjRuH+vXrF6pT8O+uYCJEgZkzZ0JdXR0nT57E6tWrC7XKhYSEYMqUKcjOzkaDBg0KLf1SID09HdOnT0dsbKy47NatW+L9eKdMmVJi4lzwS0tpZoyT8uMYOyIJ9e3bF7t27UJaWhpq1qwpXq1ekvOeP3+OjRs34vfff8eePXtgbGyMxMRE8Sy+9u3bY926dTJJIMzNzaGmpobMzEz06dMHdevWxc6dO1GrVi0sWbIEcXFxuHDhAqZNm4a6deuiXr16ePHihfgHZe/evUu9yGvr1q2xf/9+TJ06FZGRkXB1dYWOjg4aNWoENTU1PH36FO/evQMAdOzYEb/++muh8UdOTk44cOAAnj17hlmzZqFBgwaoVasWYmNjkZCQAE1NTbRv3x7+/v4y65L95ZdfMHXqVAQFBcHFxQUNGjRA7dq1ERERgdTUVKirq2P27NkSJ/Dvu3z5cokzjz/UuXNnTJ06tdT3kCV7e3tcu3YNAQEBJdZRU1PDpk2b8Mcff+D333/H9u3bsWPHDjRp0gQ1atRAQkKC+POsra2N7777TvyLhCw0adIEbm5u+Pbbb3Hy5En4+fnB1NQU2dnZiIyMRE5ODnR0dPDXX3+Vuutx4cKFUFdXh7u7O44fP47jx4+jTp06qFu3LnJychAVFYX09HSIRCKMGTMGc+fOLXKNFi1a4Pbt21i+fDkOHjyIkSNHwsnJCVZWVlixYgV+/PFHuLu749ChQzAxMUFaWhqePXsGQRCgr6+PnTt3Fjtr1sjICI8fP0aPHj1gZmaG9PR08SzY/v37ixck/1B2dra4J+BTk0RItTCxI5JQ69at0bBhQ0RHR6Nnz56lmq369ddfw9bWFnv27MGdO3cQHByMGjVqwNbWFoMGDcKAAQPEXahl1aRJE6xevRp//PEHXrx4gdzcXLx48QK1atVC1apV8ccff4h3dfjvv//w+PFjVKtWDTY2NnB0dJQ6FnNzc5w6dQo+Pj44f/48goOD8fTpU4hEItSpUwetW7fGoEGDih2XWL16dXh5eWH79u24cOECoqOj8fr1axgYGKBLly4YO3YsdHR00KNHDwQHByMmJqZIi0lp6enpwcPDAz4+Pjh+/DiePHmC+Ph41KtXD3369MGoUaPQokULqa6dkJAg0TqAgGKM3/vqq6+wbt063L17FykpKSW2AIlEIkyfPh0ODg44cuQIrl27hsjISERFRUFXVxfW1tbo3LkzhgwZUi7jujp37ox//vkH7u7uuHLlCiIiIpCbm4sGDRrgyy+/xIQJE9CoUaNSX1ckEmH+/PkYMmQIjh07hlu3buHFixcIDQ2FpqYmDAwMYGtri8GDB6NVq1bFXmPVqlX46aefxMsWvb8EyZAhQ2BhYYE9e/bg+vXrCA0NhUgkgomJCbp06YIJEyaU+LxatWqF9evXY/PmzQgICICGhgbat28PZ2fnj07e8vf3R2ZmJpo1ayb155iUk0goaeVIIiKqNBYvXgxvb2/88MMPperKpPKxaNEi+Pj4wMHBQardT1xdXXH69GmsXbsWgwYNkn2ApLA4xo6IiDB16lSoq6uLxxaS8kpMTMS5c+fQpEmTYsftkWpjYkdERGjSpAkGDx6M4OBgbhqv5Pbs2YPs7Gx888034uVWqPJgYkdERADyJxEYGBjgl19+KXHrNlJsL1++hLu7O7p27SqzGcmkXJjYERERgPyFgFetWoUnT57Ax8dH3uGQFDZu3AhtbW38/PPP8g6F5ISTJ4iIiIhUBFvsiIiIiFQEEzsiIiIiFcHEjoiIiEhFMLEjIiIiUhFM7IiIiIhUBBM7IiIiIhXBxI6IiIhIRTCxIyIiIlIRTOyIiIiIVMT/AcKsdU00/ETnAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# One-step\n", + "plt.scatter(\n", + " estimates['monte_carlo_eif-one_step'],\n", + " estimates['analytic_eif-one_step'],\n", + " color='red',\n", + ")\n", + "\n", + "# Plot y=x line for min and max values\n", + "min_val = min(\n", + " estimates['monte_carlo_eif-one_step'].min(),\n", + " estimates['analytic_eif-one_step'].min()\n", + ")\n", + "max_val = max(\n", + " estimates['monte_carlo_eif-one_step'].max(),\n", + " estimates['analytic_eif-one_step'].max()\n", + ")\n", + "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", + "plt.xlabel(\"Monte Carlo EIF (One-Step)\", fontsize=18)\n", + "plt.ylabel(\"Analytic EIF (One-Step)\", fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/one_step_convergence_causal_glm.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "from pympler.tracker import SummaryTracker\n", + "tracker = SummaryTracker()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " types | # objects | total size\n", + "=================================================== | =========== | ============\n", + " list | 38180 | 3.26 MB\n", + " str | 39575 | 2.85 MB\n", + " int | 7653 | 205.02 KB\n", + " pandas.core.series.Series | 24 | 7.35 KB\n", + " type | 14 | 5.58 KB\n", + " pandas.core.internals.blocks.NumericBlock | 24 | 2.06 KB\n", + " wrapper_descriptor | 28 | 1.97 KB\n", + " bytes | 14 | 1.71 KB\n", + " pandas._libs.internals.BlockPlacement | 24 | 1.69 KB\n", + " slice | 24 | 1.31 KB\n", + " pandas.core.flags.Flags | 24 | 1.12 KB\n", + " pandas.core.internals.managers.SingleBlockManager | 24 | 1.12 KB\n", + " re.Pattern | 1 | 752 B\n", + " collections.deque | 1 | 624 B\n", + " code | -1 | 540 B\n" + ] + } + ], + "source": [ + "tracker.print_diff()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "Kennedy, Edward. \"Towards optimal doubly robust estimation of heterogeneous causal effects\", 2022. https://arxiv.org/abs/2004.14497." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "basis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From eac68e6da3663d6ffb404dadad17637797ba1b21 Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Fri, 26 Jan 2024 14:46:10 -0500 Subject: [PATCH 13/26] still memory leak --- chirho/robust/handlers/estimators.py | 4 + .../notebooks/quality_vs_estimators.ipynb | 103 +++--------------- 2 files changed, 19 insertions(+), 88 deletions(-) diff --git a/chirho/robust/handlers/estimators.py b/chirho/robust/handlers/estimators.py index 779b108e..8c382e44 100644 --- a/chirho/robust/handlers/estimators.py +++ b/chirho/robust/handlers/estimators.py @@ -98,6 +98,10 @@ def _solve_model_projection( prev_params = {k: v.detach() for k, v in prev_params.items()} # Sample data from the model. Note that we only sample once during projection. + with torch.no_grad(): + data: Point[T] = functional_model(prev_params, *args, **kwargs) + data = {k: v.detach() for k, v in data.items() if k in test_point} + data = { k: v for k, v in functional_model(prev_params, *args, **kwargs).items() diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index e66a2ef4..6d3de071 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -59,7 +59,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-26 00:49:14,946] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" + "[2024-01-26 14:43:12,708] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" ] } ], @@ -294,7 +294,7 @@ "Y\n", "\n", "\n", - "\n", + "\n", "intercept->Y\n", "\n", "\n", @@ -306,7 +306,7 @@ "outcome_weights\n", "\n", "\n", - "\n", + "\n", "outcome_weights->Y\n", "\n", "\n", @@ -348,13 +348,13 @@ "X\n", "\n", "\n", - "\n", + "\n", "X->Y\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "A->Y\n", "\n", "\n", @@ -374,7 +374,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -562,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -632,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -644,80 +644,57 @@ "tmle monte_carlo_eif 0\n", "one_step analytic_eif 0\n", "one_step monte_carlo_eif 0\n", - "0.0\n", - "(1936348, 13798027)\n", "plug-in-mle-from-model 1\n", "tmle analytic_eif 1\n", "tmle monte_carlo_eif 1\n", "one_step analytic_eif 1\n", "one_step monte_carlo_eif 1\n", - "0.0\n", - "(2033412, 13798027)\n", "plug-in-mle-from-model 2\n", "tmle analytic_eif 2\n", "tmle monte_carlo_eif 2\n", "one_step analytic_eif 2\n", "one_step monte_carlo_eif 2\n", - "0.0\n", - "(2079867, 13798027)\n", "plug-in-mle-from-model 3\n", "tmle analytic_eif 3\n", "tmle monte_carlo_eif 3\n", "one_step analytic_eif 3\n", "one_step monte_carlo_eif 3\n", - "0.0\n", - "(2095378, 13798027)\n", "plug-in-mle-from-model 4\n", "tmle analytic_eif 4\n", "tmle monte_carlo_eif 4\n", "one_step analytic_eif 4\n", "one_step monte_carlo_eif 4\n", - "0.0\n", - "(2111200, 13798027)\n", "plug-in-mle-from-model 5\n", "tmle analytic_eif 5\n", "tmle monte_carlo_eif 5\n", "one_step analytic_eif 5\n", "one_step monte_carlo_eif 5\n", - "0.0\n", - "(2125888, 13798027)\n", "plug-in-mle-from-model 6\n", "tmle analytic_eif 6\n", "tmle monte_carlo_eif 6\n", "one_step analytic_eif 6\n", "one_step monte_carlo_eif 6\n", - "0.0\n", - "(2143966, 13798027)\n", "plug-in-mle-from-model 7\n", "tmle analytic_eif 7\n", "tmle monte_carlo_eif 7\n", "one_step analytic_eif 7\n", "one_step monte_carlo_eif 7\n", - "0.0\n", - "(2162399, 13798027)\n", "plug-in-mle-from-model 8\n", "tmle analytic_eif 8\n", "tmle monte_carlo_eif 8\n", "one_step analytic_eif 8\n", "one_step monte_carlo_eif 8\n", - "0.0\n", - "(2178117, 13798027)\n", "plug-in-mle-from-model 9\n", "tmle analytic_eif 9\n", "tmle monte_carlo_eif 9\n", "one_step analytic_eif 9\n", - "one_step monte_carlo_eif 9\n", - "0.0\n", - "(2191290, 13798027)\n" + "one_step monte_carlo_eif 9\n" ] } ], "source": [ "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", "\n", - "import tracemalloc\n", - "tracemalloc.start()\n", - "\n", "# Estimators to compare\n", "estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", "estimator_kwargs = {\n", @@ -745,9 +722,8 @@ "for i in range(N_datasets):\n", " D_test = simulated_datasets[i][1]\n", " theta_hat = fitted_params[i]\n", - " # Weird memory leak issue hack fix\n", - " theta_hat = {\n", - " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", + " theta_hat = theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", " }\n", " mle_guide = MLEGuide(theta_hat)\n", " model = PredictiveModel(CausalGLM(p), mle_guide)\n", @@ -779,22 +755,16 @@ " num_samples_inner=1,\n", " influence_estimator=influence,\n", " **estimator_kwargs[estimator_str]\n", - " )(model)()\n", + " )(PredictiveModel(CausalGLM(p), mle_guide))()\n", "\n", " estimates[f\"{influence_str}-{estimator_str}\"][i] = estimate.detach().item()\n", - " \n", + " \n", " # Compute DoubleML estimate (see Proposition in our paper for this trick to reduce one step to DoubleML)\n", " if 'one_step' in estimators.keys():\n", " for influence_str, influence in influences.items():\n", " eif_correction = estimates[f\"{influence_str}-one_step\"][i] - estimates[\"plug-in-mle-from-model\"][i]\n", " double_ml = estimates[\"plug-in-mle-from-test\"] + eif_correction\n", - " estimates[f\"{influence_str}-double_ml\"][i] = double_ml.item()\n", - " \n", - " # Check memory usage\n", - " print(torch.cuda.memory_allocated() / 1e9)\n", - " print(tracemalloc.get_traced_memory())\n", - " del theta_hat # Free up memory (weird memory leak issue)\n", - " \n", + " estimates[f\"{influence_str}-double_ml\"][i] = double_ml.item() \n", " " ] }, @@ -1192,49 +1162,6 @@ "plt.savefig('./figures/one_step_convergence_causal_glm.png')" ] }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "from pympler.tracker import SummaryTracker\n", - "tracker = SummaryTracker()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " types | # objects | total size\n", - "=================================================== | =========== | ============\n", - " list | 38180 | 3.26 MB\n", - " str | 39575 | 2.85 MB\n", - " int | 7653 | 205.02 KB\n", - " pandas.core.series.Series | 24 | 7.35 KB\n", - " type | 14 | 5.58 KB\n", - " pandas.core.internals.blocks.NumericBlock | 24 | 2.06 KB\n", - " wrapper_descriptor | 28 | 1.97 KB\n", - " bytes | 14 | 1.71 KB\n", - " pandas._libs.internals.BlockPlacement | 24 | 1.69 KB\n", - " slice | 24 | 1.31 KB\n", - " pandas.core.flags.Flags | 24 | 1.12 KB\n", - " pandas.core.internals.managers.SingleBlockManager | 24 | 1.12 KB\n", - " re.Pattern | 1 | 752 B\n", - " collections.deque | 1 | 624 B\n", - " code | -1 | 540 B\n" - ] - } - ], - "source": [ - "tracker.print_diff()" - ] - }, { "cell_type": "markdown", "metadata": {}, From 2a932280a303c9272435d5d8e13258d2f19851cf Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Sun, 28 Jan 2024 15:18:10 -0500 Subject: [PATCH 14/26] hacky workaround for memory issues --- .../notebooks/quality_vs_estimators.ipynb | 375 +++++++++--------- 1 file changed, 183 insertions(+), 192 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index 6d3de071..73319ebc 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -59,7 +59,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2024-01-26 14:43:12,708] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" + "NOTE: Redirects are currently not supported in Windows or MacOs.\n" ] } ], @@ -91,9 +91,7 @@ "\n", "pyro.settings.set(module_local_params=True)\n", "\n", - "sns.set_style(\"white\")\n", - "\n", - "pyro.set_rng_seed(321) # for reproducibility" + "sns.set_style(\"white\")" ] }, { @@ -269,13 +267,13 @@ "\n", "\n", - "\n", "\n", "\n", "\n", - "\n", + "\n", "\n", "cluster___train__\n", "\n", @@ -294,7 +292,7 @@ "Y\n", "\n", "\n", - "\n", + "\n", "intercept->Y\n", "\n", "\n", @@ -306,7 +304,7 @@ "outcome_weights\n", "\n", "\n", - "\n", + "\n", "outcome_weights->Y\n", "\n", "\n", @@ -336,7 +334,7 @@ "treatment_weight\n", "\n", "\n", - "\n", + "\n", "treatment_weight->Y\n", "\n", "\n", @@ -348,13 +346,13 @@ "X\n", "\n", "\n", - "\n", + "\n", "X->Y\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "A->Y\n", "\n", "\n", @@ -374,7 +372,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -439,15 +437,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "N_datasets = 10\n", - "simulated_datasets = []\n", - "\n", "# Data configuration\n", - "p = 2\n", + "p = 200\n", "alpha = 50\n", "beta = 50\n", "N_train = 500\n", @@ -455,7 +450,7 @@ "\n", "true_model = GroundTruthModel(p, alpha, beta)\n", "\n", - "for _ in range(N_datasets):\n", + "def generate_data(N_train, N_test):\n", " # Generate data\n", " D_train = Predictive(\n", " true_model, num_samples=N_train, return_sites=[\"X\", \"A\", \"Y\"]\n", @@ -463,7 +458,7 @@ " D_test = Predictive(\n", " true_model, num_samples=N_test, return_sites=[\"X\", \"A\", \"Y\"]\n", " )()\n", - " simulated_datasets.append((D_train, D_test))" + " return D_train, D_test" ] }, { @@ -475,15 +470,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "fitted_params = []\n", - "for i in range(N_datasets):\n", - " # Generate data\n", - " D_train = simulated_datasets[i][0]\n", - "\n", + "def MLE(D_train, D_test):\n", " # Fit model using maximum likelihood\n", " conditioned_model = ConditionedCausalGLM(\n", " X=D_train[\"X\"], A=D_train[\"A\"], Y=D_train[\"Y\"]\n", @@ -506,7 +497,7 @@ " theta_hat = {\n", " k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items()\n", " }\n", - " fitted_params.append(theta_hat)" + " return theta_hat" ] }, { @@ -530,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -562,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -610,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -632,68 +623,33 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Dataset 0\n", "plug-in-mle-from-model 0\n", "tmle analytic_eif 0\n", "tmle monte_carlo_eif 0\n", "one_step analytic_eif 0\n", "one_step monte_carlo_eif 0\n", + "Dataset 1\n", "plug-in-mle-from-model 1\n", "tmle analytic_eif 1\n", - "tmle monte_carlo_eif 1\n", - "one_step analytic_eif 1\n", - "one_step monte_carlo_eif 1\n", - "plug-in-mle-from-model 2\n", - "tmle analytic_eif 2\n", - "tmle monte_carlo_eif 2\n", - "one_step analytic_eif 2\n", - "one_step monte_carlo_eif 2\n", - "plug-in-mle-from-model 3\n", - "tmle analytic_eif 3\n", - "tmle monte_carlo_eif 3\n", - "one_step analytic_eif 3\n", - "one_step monte_carlo_eif 3\n", - "plug-in-mle-from-model 4\n", - "tmle analytic_eif 4\n", - "tmle monte_carlo_eif 4\n", - "one_step analytic_eif 4\n", - "one_step monte_carlo_eif 4\n", - "plug-in-mle-from-model 5\n", - "tmle analytic_eif 5\n", - "tmle monte_carlo_eif 5\n", - "one_step analytic_eif 5\n", - "one_step monte_carlo_eif 5\n", - "plug-in-mle-from-model 6\n", - "tmle analytic_eif 6\n", - "tmle monte_carlo_eif 6\n", - "one_step analytic_eif 6\n", - "one_step monte_carlo_eif 6\n", - "plug-in-mle-from-model 7\n", - "tmle analytic_eif 7\n", - "tmle monte_carlo_eif 7\n", - "one_step analytic_eif 7\n", - "one_step monte_carlo_eif 7\n", - "plug-in-mle-from-model 8\n", - "tmle analytic_eif 8\n", - "tmle monte_carlo_eif 8\n", - "one_step analytic_eif 8\n", - "one_step monte_carlo_eif 8\n", - "plug-in-mle-from-model 9\n", - "tmle analytic_eif 9\n", - "tmle monte_carlo_eif 9\n", - "one_step analytic_eif 9\n", - "one_step monte_carlo_eif 9\n" + "tmle monte_carlo_eif 1\n" ] } ], "source": [ + "import json\n", + "import os\n", + "\n", "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", + "N_datasets = 25\n", + "\n", "\n", "# Estimators to compare\n", "estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", @@ -712,24 +668,35 @@ "influences = {\"analytic_eif\": ate_causal_glm_analytic_influence, \"monte_carlo_eif\": influence_fn}\n", "\n", "# Cache the results\n", - "estimates = {f\"{influence}-{estimator}\": torch.zeros(N_datasets) for influence in influences.keys() for estimator in estimators.keys()}\n", - "estimates[\"plug-in-mle-from-model\"] = torch.zeros(N_datasets)\n", - "estimates[\"plug-in-mle-from-test\"] = torch.zeros(N_datasets)\n", + "RESULTS_PATH = \"../results/ate_causal_glm.json\"\n", + "\n", + "if os.path.exists(RESULTS_PATH):\n", + " with open(RESULTS_PATH, \"r\") as f:\n", + " estimates = json.load(f)\n", + " i_start = len(estimates[\"plug-in-mle-from-model\"]) \n", + "else:\n", + " estimates = {f\"{influence}-{estimator}\": [] for influence in influences.keys() for estimator in estimators.keys()}\n", + " estimates[\"plug-in-mle-from-model\"] = []\n", + " estimates[\"plug-in-mle-from-test\"] = []\n", + " i_start = 0\n", "\n", "# ATE functional of interest\n", "functional = functools.partial(ATEFunctional, num_monte_carlo=10000)\n", "\n", - "for i in range(N_datasets):\n", - " D_test = simulated_datasets[i][1]\n", - " theta_hat = fitted_params[i]\n", - " theta_hat = theta_hat = {\n", + "for i in range(i_start, N_datasets):\n", + " pyro.set_rng_seed(i) # for reproducibility\n", + " print(\"Dataset\", i)\n", + " D_train, D_test = generate_data(N_train, N_test)\n", + " theta_hat = MLE(D_train, D_test)\n", + "\n", + " theta_hat = {\n", " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", " }\n", " mle_guide = MLEGuide(theta_hat)\n", " model = PredictiveModel(CausalGLM(p), mle_guide)\n", " \n", " print(\"plug-in-mle-from-model\", i)\n", - " estimates[\"plug-in-mle-from-model\"][i] = functional(model)().detach().item()\n", + " estimates[\"plug-in-mle-from-model\"].append(functional(model)().detach().item())\n", "\n", " mu_X = (\n", " torch.einsum(\"...i,...i->...\", D_test[\"X\"], theta_hat[\"outcome_weights\"])\n", @@ -741,13 +708,12 @@ " mu_X_control = mu_X[D_test[\"A\"] == 0]\n", " \n", " # Used for DoubleML later on\n", - " estimates[\"plug-in-mle-from-test\"] = (mu_X_treat.mean() - mu_X_control.mean()).detach().item()\n", + " estimates[\"plug-in-mle-from-test\"].append((mu_X_treat.mean() - mu_X_control.mean()).detach().item())\n", "\n", " for estimator_str, estimator in estimators.items():\n", " if estimator_str != 'double_ml':\n", " for influence_str, influence in influences.items():\n", " print(estimator_str, influence_str, i)\n", - " \n", " estimate = estimator(\n", " functional, \n", " D_test,\n", @@ -757,20 +723,22 @@ " **estimator_kwargs[estimator_str]\n", " )(PredictiveModel(CausalGLM(p), mle_guide))()\n", "\n", - " estimates[f\"{influence_str}-{estimator_str}\"][i] = estimate.detach().item()\n", + " estimates[f\"{influence_str}-{estimator_str}\"].append(estimate.detach().item())\n", " \n", " # Compute DoubleML estimate (see Proposition in our paper for this trick to reduce one step to DoubleML)\n", " if 'one_step' in estimators.keys():\n", " for influence_str, influence in influences.items():\n", " eif_correction = estimates[f\"{influence_str}-one_step\"][i] - estimates[\"plug-in-mle-from-model\"][i]\n", - " double_ml = estimates[\"plug-in-mle-from-test\"] + eif_correction\n", - " estimates[f\"{influence_str}-double_ml\"][i] = double_ml.item() \n", - " " + " double_ml = estimates[\"plug-in-mle-from-test\"][i] + eif_correction\n", + " estimates[f\"{influence_str}-double_ml\"].append(double_ml)\n", + "\n", + " with open(RESULTS_PATH, \"w\") as f:\n", + " json.dump(estimates, f, indent=4)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -818,80 +786,80 @@ " \n", " \n", " mean\n", - " 0.02\n", - " 0.01\n", - " 0.06\n", - " 0.01\n", - " 0.01\n", - " 0.05\n", - " 0.01\n", - " -0.08\n", + " -0.01\n", + " -0.01\n", + " 0.03\n", + " -0.01\n", + " -0.01\n", + " 0.04\n", + " -0.01\n", + " 0.03\n", " \n", " \n", " std\n", " 0.06\n", - " 0.11\n", - " 0.13\n", + " 0.09\n", + " 0.10\n", " 0.06\n", + " 0.08\n", " 0.09\n", - " 0.11\n", " 0.08\n", - " 0.00\n", + " 0.06\n", " \n", " \n", " min\n", " -0.08\n", - " -0.15\n", - " -0.13\n", - " -0.08\n", " -0.12\n", + " -0.09\n", + " -0.07\n", " -0.11\n", - " -0.10\n", - " -0.08\n", + " -0.05\n", + " -0.12\n", + " -0.06\n", " \n", " \n", " 25%\n", - " -0.03\n", - " -0.07\n", - " -0.04\n", + " -0.05\n", + " -0.09\n", + " -0.05\n", + " -0.06\n", + " -0.06\n", " -0.04\n", - " -0.08\n", - " -0.03\n", " -0.07\n", - " -0.08\n", + " 0.00\n", " \n", " \n", " 50%\n", - " 0.03\n", + " -0.01\n", + " -0.03\n", " 0.01\n", - " 0.03\n", + " -0.01\n", + " -0.02\n", + " 0.02\n", + " -0.03\n", " 0.02\n", - " 0.01\n", - " 0.04\n", - " 0.04\n", - " -0.08\n", " \n", " \n", " 75%\n", - " 0.06\n", - " 0.09\n", - " 0.16\n", - " 0.06\n", - " 0.06\n", - " 0.14\n", - " 0.07\n", - " -0.08\n", + " 0.01\n", + " 0.03\n", + " 0.10\n", + " 0.01\n", + " 0.01\n", + " 0.08\n", + " 0.03\n", + " 0.05\n", " \n", " \n", " max\n", - " 0.10\n", - " 0.21\n", - " 0.26\n", - " 0.10\n", - " 0.16\n", + " 0.13\n", + " 0.14\n", " 0.21\n", - " 0.11\n", - " -0.08\n", + " 0.13\n", + " 0.13\n", + " 0.19\n", + " 0.15\n", + " 0.17\n", " \n", " \n", "\n", @@ -900,46 +868,46 @@ "text/plain": [ " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", "count 10.00 10.00 10.00 \n", - "mean 0.02 0.01 0.06 \n", - "std 0.06 0.11 0.13 \n", - "min -0.08 -0.15 -0.13 \n", - "25% -0.03 -0.07 -0.04 \n", - "50% 0.03 0.01 0.03 \n", - "75% 0.06 0.09 0.16 \n", - "max 0.10 0.21 0.26 \n", + "mean -0.01 -0.01 0.03 \n", + "std 0.06 0.09 0.10 \n", + "min -0.08 -0.12 -0.09 \n", + "25% -0.05 -0.09 -0.05 \n", + "50% -0.01 -0.03 0.01 \n", + "75% 0.01 0.03 0.10 \n", + "max 0.13 0.14 0.21 \n", "\n", " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", "count 10.00 10.00 \n", - "mean 0.01 0.01 \n", - "std 0.06 0.09 \n", - "min -0.08 -0.12 \n", - "25% -0.04 -0.08 \n", - "50% 0.02 0.01 \n", - "75% 0.06 0.06 \n", - "max 0.10 0.16 \n", + "mean -0.01 -0.01 \n", + "std 0.06 0.08 \n", + "min -0.07 -0.11 \n", + "25% -0.06 -0.06 \n", + "50% -0.01 -0.02 \n", + "75% 0.01 0.01 \n", + "max 0.13 0.13 \n", "\n", " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", "count 10.00 10.00 \n", - "mean 0.05 0.01 \n", - "std 0.11 0.08 \n", - "min -0.11 -0.10 \n", - "25% -0.03 -0.07 \n", - "50% 0.04 0.04 \n", - "75% 0.14 0.07 \n", - "max 0.21 0.11 \n", + "mean 0.04 -0.01 \n", + "std 0.09 0.08 \n", + "min -0.05 -0.12 \n", + "25% -0.04 -0.07 \n", + "50% 0.02 -0.03 \n", + "75% 0.08 0.03 \n", + "max 0.19 0.15 \n", "\n", " plug-in-mle-from-test \n", "count 10.00 \n", - "mean -0.08 \n", - "std 0.00 \n", - "min -0.08 \n", - "25% -0.08 \n", - "50% -0.08 \n", - "75% -0.08 \n", - "max -0.08 " + "mean 0.03 \n", + "std 0.06 \n", + "min -0.06 \n", + "25% 0.00 \n", + "50% 0.02 \n", + "75% 0.05 \n", + "max 0.17 " ] }, - "execution_count": 18, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -952,12 +920,46 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1041,12 +1043,23 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 14, "metadata": {}, "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'min'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 10\u001b[0m\n\u001b[1;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[1;32m 3\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonte_carlo_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 4\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 5\u001b[0m color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Plot y=x line for min and max values\u001b[39;00m\n\u001b[1;32m 9\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(\n\u001b[0;32m---> 10\u001b[0m \u001b[43mestimates\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmonte_carlo_eif-double_ml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m(),\n\u001b[1;32m 11\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmin()\n\u001b[1;32m 12\u001b[0m )\n\u001b[1;32m 13\u001b[0m max_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\n\u001b[1;32m 14\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonte_carlo_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmax(),\n\u001b[1;32m 15\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot([min_val, max_val], [min_val, max_val], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'min'" + ] + }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1082,20 +1095,9 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# TMLE\n", "plt.scatter(\n", @@ -1123,20 +1125,9 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# One-step\n", "plt.scatter(\n", @@ -1193,7 +1184,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.4" }, "orig_nbformat": 4 }, From 111db712233f4855e07dc9fbf6d9d22a42c6dae2 Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Sun, 28 Jan 2024 17:18:37 -0500 Subject: [PATCH 15/26] ran experiment and results --- .../causal_glm_performance_vs_estimator.png | Bin 0 -> 74895 bytes .../figures/double_convergence_causal_glm.png | Bin 0 -> 31480 bytes .../one_step_convergence_causal_glm.png | Bin 0 -> 31563 bytes .../figures/tmle_convergence_causal_glm.png | Bin 0 -> 27155 bytes .../notebooks/quality_vs_estimators.ipynb | 309 +++++++++--------- .../robust_paper/results/ate_causal_glm.json | 218 ++++++++++++ 6 files changed, 379 insertions(+), 148 deletions(-) create mode 100644 docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png create mode 100644 docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png create mode 100644 docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png create mode 100644 docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png create mode 100644 docs/examples/robust_paper/results/ate_causal_glm.json diff --git a/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png b/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png new file mode 100644 index 0000000000000000000000000000000000000000..3debf29817851b69ab2a1e016e91ec0fccfb5b5a GIT binary patch literal 74895 zcmce;1yogi*DtyO6$KPULO?+gkZz>Gpg~DNTBN%>4UiN;xP^haC;==MM6uKu0g*I{d z68wa-dt?Uw!(%J*(pJGj-_}9fS`YP1+t$+5!q(L2^<8^CYa1gAb9N>!CN_q%U5RsW4ICh? ztL2i~!@qEY{qYr6$;(vwrpbF5eF?^P#Hkr_*+WC2-wFv{+<4ujY4&1O^vaD&(SV=v z>+3<&V>7Lv+}Jzzx1EIc$F}2b#^$Su_scsKY5kJ#28&bT`3h6eK3rH-MZp)y_Y!9m z1dsmtI^?ytB8m!mMXJnzh15{eo^-CYkj{&-uplHVz~_bMpdp6~al^ZL(>Ew+pyU>wFJ%M^2ZykhmQZK@{lA}__C1YXeZvP_r_Cv^JQ28n z)4^n@C>eB@^u1&AJff2XqJ|i zKZ{Hx{$5fnVqafhlG)CVj^_hY;*=rkjg5^LJez~a6|xjF`E4-UD6b>;OE*L3431^T z0N=D=_j(LF5s%aVb2?wdOY?>0Ia-2h%`#Dve{XLvKD#c?-|K;O;l_;{9A-oK?Ck6x z@9=piC28latuHQ0?5~Xy3pnqP&WnnO6uKSTAMLN>*1f-Sv&`dMaCsoVbIWdhv{Ecv zDfhR)c(Mq#Ql5HLT0ne!{79{bP`jN%nzVMEC)(m#rG7if8oV&(q1i1CLs@UOjwtpI z*0ZgwhFy1ec6WK4wkW@S`=)9$H8o}U`^%Nd$;sQiwmw_4ZMBnmf`qlcouk4|#_w1+*B)b+g`)FI-W{s1r)YLjRZ-Elut=D- zOG`^r(JVC`WaG78@d*nf@a%Wi(b2)VdbMY?XLK~q?RYO$9tF2T>fNQQ+%|L4gzI;0 z_602`9z}CmbZksESXo=QZgEnoJZ*wIt8$P3Hksi20m0jpA#b1P>K=(nOV7=ViWzvA z+h4wRZEos^KiuiiZ)U^RM>|GOo;=~07Z4EG+}zaOS?De+FYnHfyRY97EAiyXTm6py zT(!lKa`wEuyxv^32=~(?tHZ5X#R0#dpk_ER_~6EY7GqV!xVX543@__`r%IJWVK6Z< zjZ``WUAalk0_WI1GF)o0{T-8Bw$|N^gwwcjKEb1-Ctb$oOmqf4h)m!$d@x5RCq0wu zhzKH&(_Ny@c!8dPAB~Nc2OE?8FIsbRbFCa44BDePo$Rklup!-M?0s@we(jTL=T}Bo zu3ej$x$ybHe8+x98f8 zdeW|XdwZLXRjHVCB#ZbP{wcV}=dh-Ga-#`ugLFkZ3inIm`(a~ z?mc`M-tzfDdPR6r(mjm|TkZLdIMr!)cX!*x-(LIU9tlZLKgg#^6F-mVkIK5W-WI{U zI9@AcWo4D4Q5oKst!x8TCV0Bj6I*+uysl8ExuCeXv9FI)ZJG3;sZU_wRZLv6-hewi z))&OY#9&E>sJ%{;@F!&Cc0I6+*L+Ix3HnyZjm^!Cv*WHxu2`W2e)oN*U(pw@l0K3B zv^Miu+z)l-%9WLo@|!X;G68{sh2Oq4H8fxd2?@Q{(c#?Y=jVrYcm3nXkB%-b3f0bb z9dUe^mzSY1ix!4T=x1BQQp`syc2)*IC)%C~R##V-yB$BA*E-es@czB!*@=_$&cd6h zC}OD7h2>=(sM)OR>+u3EJ^=x`XD5ffSugyfIZfE0GMD;urPS5cEB1L_H(nkY8IivO zJ*so#RHfWnFN;&lZTCgRHnV!!qY~pjwh{LGigX04LnS)nHEz*`)h_$L;lh}%M-95< zE9H{lk_*n%o}abtl~6|gE$qc48Wu+u)1eE_&Ma0Gt|x^B=xcw_5?s;Wyy>dojOD%f z@nc2!ONx6bw@wF0v8t9HhEY4`M`4!gW!;KKPv}9=uXPkzGI#S~nV=|Bz92y!r#~BwFm#ItkKX7pu0fsaIaQ!&|52@jaVR5YIP{g}vu=Bxh=Q8M{u5+%0rz z1U>;LR$_3lT-NrI?o|&D4{GViM^Lhv%K2n&M?3duXncs-^}o1iSXfvfEBA}U=Z*b+ z16XlUAp>=HUC5EO-JxXtE-dUtc>m(!;w-mLIV}G_gM(HZ6OW<7tnBTZy1QA2v0mw~ zYvdLz&+e#s%>@2*cu67QFBS3X#BtMg@`>53%(KPc*RP}WTI+<)FJmXcSi+M@5S;x} zr~};wsn6Vo;o+F=`Ofs^re`fdLTBQz{G<3Bm`;!P)3qDCN2*20G+ql@9`FT!VH11ZGQf@0uh0D0O0|6H=U7A{3>wuZd?y~IU4OOg!E@m%V`DTB8 zyc62?HsgJ@IPq=j>O19^P(Sbx9qJBD^R`r;`Qzw23*nmEG*Ztv9Yg_3;y!HN{W)J z?$jCVlWBCd;_I*mq_b}E>a+jg2LX$x5z3b6pIwd6~ zi`fuIY+M{I49yBQZ!fRSnU>Fr(=c^-><81JSA@Y8`3&WFUn&|JB}{_)uC7m+3h8Nl z>p%R7jG9<$PWOhR=cB6k>6Pc!Xhvnt>A=)=$bNY*BVOey4$CWbBhIa|Necb z?~W>`?Sqw~&NA0SwyPu@;x-!4!l881#e&FGr(xL54rqBm?J$vu$OroRUJ(=&`~|aK z(Cz4zsi`z{w1X*sX0}k)>T8tHO`>%}N9uZdKEua=%S&L~mld|^25o~z1e!JPIZcGOe1t>907_lQ!+Qd& zmF3~X{t|945-lyQa0Zonct1bIk-^Kg?( z)7E{MDr5u}Xwu2xA0LMCU#~52KU4e8E2Q~svEuoDsl&T<%Ll-PKM`}9$N8Cv~tI*zIQYxd+g75}~wzXdbpP`}JE?35VRozo_3y*eP^);p@=R;&hP6>z|{Q8*R+x z%n7i1IGNPT>Yy^pZKFjK2;Kj^dZBG($8;OQ^bP)lv@k#=!(e!PmDQ=U$_gHZl+YB7N%{NJm1+B-HokP92_~MVz`ZFh16h-upmq%@pw$ zRy%njyudS%^xi|lrGA1_H=FvO!BbY_f@MzZ&B+pfQS&eYjo!?B3B31oOnra-nPM*1 z>X7djyb?7P1j~n@zlURl#BfdS7zGz}XDhF*WPU@>Q^UM+rMxKp@4J*EufVg5$qZ){ z(Hb8~e2PhtKtFVQ*1Ku@gg?i^&2;Wr>g-f z+1QQ)xGXCx`{HtNaDcL09}|F)$L)4h+`qi@F)=X_C!f!_m*(1S?z#{vDYlX3A3wZY z_ScN&?AAtj+E3vIdiwkOEBTjkRdc~=eTRw5MwjBgE^=!S`=xx$@a)dZL;Fh@ZkBdRgVjYHx3^UTGibv^{6FH6sD2PWkRqI$&nD_D!z79*wWM zSIo`LGn>pzO@C_?nG7)1H#8tH=~*;K613}R8v&QSF930I?(izI>mO&$ASejdW%Q5( z?O>6C%r_5Az`@Xn^7BYVA&j7P-3m#1N<4O%^W3 z06d8s^#CDnK6t*dG5NIh%jJfH?M@+9PEJ66a5ExEPjL}U4_sqmxRhDT{V-L3q`Uiu zfq_9)Rn?yYZH)6a36)Is`mr%VwdI_goUg?XSy_XQu?zBRqTI@=~I668~sS(u{ zsqm!w5fjJvLuVkbytY*G;@H^K;WxAL+FD_GVM{x^RMWw)a6}msSD08Z)KkBn#R_@E z!$IZ#1#}yIZ#tcwl+RT`$w40sAQK?r;^IOeFuaq5xcCGNrDEegEKm3wqYkBaLrstP0lt8v5D(zP@USt#QF7@fa z>>eq%5i4qfa)W`;^Hom@j#Fm0td!+8ZaZEP2V99V5uNkvqZd#j?2R1zg8Z_Sb5wb0{V zq_}VAFw0%ucQYU$;A=?@RJD4w^9!lN(P|eixGZAKxWmRIBqYC! zjTElk;SDtypz#qWD)@5n)kBDqx?auV@ocM;%~dY0jF&geNAU*QR=J=0&Jg&alaXy4 zuUC9dGE(5SqHsy&sIIEo*xa1#P7#AK77aaK+r&f)SWL-Wu4)Mu5fM?EbQJodFuE6! zLLP!ZI%c7(S8aFjc@`|;kZ@eSZaxT7h7Gig3cKYrU%sNRU+aJU3P^B2@@3O&HB^s4 zVG}WZv6^l|s+vr|`AY~F0|QQ*bknXh0UNhC{`f{V$tojLMb@|Gbdw{1%xy)rNY>jfA zo_w}@gS6>H#3DZ?VE9$2;gt;dG@EU^ar6z?u&h#f%>ff^XC>9 zSE(tzXH>O=nwlD*%ObnLdOxK8Z;D7Lp@zS;51V8XJo51H@xMv+8nhSDRJ?vJ4N;QF z=sD>tFDl1_j=S~LPLe8n*yVF?ToW*02LbLS=rd= z0ORF%*^}0hlWvLSv0>rjG5|1qaxfjlp!O{QbdqU+u`cV?&2Z2^)h?y|Bsx0N9Qk3v zEBWINs=8lZ>I*OaPAw`{0ww9sozbMHn1sxlzo2%Z731EqKDa1QwDkZ~59e=AkHtU| z@OyB6f`Los>Fc`@GfTC$HZgHm@;iRft(y{O$@0MK);`9%yE{8MSwXRZEa07zqC-bd z=A-LdWYB3Gs8Q`42CI)_KK)eC2wOE*l@soUm8E4}OUso94<114jTCUPui#vVzRbeT zuJgl>AhY8lCT5XQ4>bma}vHZl!Cv zC5DUB?CLIM$P>vx5>88N>#5`QTA5PU)=&4IE)3+8Yk8b^MF6as zS^MGkyh_0;fh3C^mB6%{`+QyV0esG_mX7yVb*xllUb>WK z?LcIQjL`Vo+;_>ghfcH8#Bh^Pz`{gPM)xsAfjr!(Deq;lp+4Y4$y|?@o_Da z9QAVR+k&nyw|Uw3$;in&;UY(|>WF^RV+zO!9m(*Ck`WdbM%1H@c!A?4thtcbo5aMK zP6_uZD6oV@j@9qoLo+qKOM%X$?s;<1AMxp~GsXw6?+G4HQbe6Ro=F5#QB#Zdu-;24 zWA&vJ0>L&N+F<~>P&^R1Wz|^eB8zcB;7)4rZN&W_oLnH0S`lN=(7LOa-3=9@r_Xpn z?~!Np3S~CrGw-rL*U1UWHzheM2Zt`KT2N3#2a`tAz-sEf zGJbycDEDZX#f7pj9ic1W&e)diewGGO~<2R8gWMCuo8kG zl}l_+_R?-8B%S$as5e#tqaU8E5A_s#uSzc2MAPUeyOYcZV2F*kB zkqjpR?fMIsuHBBzT^_4+z$GGzunjstUH4dQU07Ufpj|vXJZrPg`T&LtjI@F$yAh7PsmIFCvXv&LF+SivFt{W|`6G6;(m}_- zp!xFy8E(r7)E!>it|$hO$?FFOA^^W*uo(TO1oyzEcpkcirHzdgrzUg?oPn!=IH2pN zktpvQ%lHEcFEH+-J2^e&)Pb`w*qUjvUG5JA9P= zSFfgO)w&m(4u&LnoDtL0(}Thr$z?%?fq6~0`Qx2i#Kcx}zlcE&s)P5jULAr9^aXhL z$;ozSz-=D3fj!0?AZYUZKE7|-B7SUpU$vvh_DnbUiue_%Ki|wq0m4iJ)2IRUvlvd#Y=35EFN6h973efy+u1GZvjE1I-lG4!|DD`mmvoS zB7t70r#`?IP0A0Gi0N{KNv8b7LWYS^qMp>I;~77{OPZPr(8xz1<`FS#et|bDHX9Cy zPIVIxFPR5i4QK(lK$V-?+XJBmvqE_=I*k2SrCa6n2lGX!y|T*6BHKmz+|BkFuIb6i z?{6<+zXUW1jH~${W|wM>1`1{jPzz5$EIpu~3!a~BtJ^8hy$4mK&}HAeO5Ty^p~YAL zXsA!UaU?j$fBy7=Pj7j8WcR!pD~y=i^6t3X{vBXBQ~+rcHLINih}pe;@U7bZGb#8* zs?kCh=J)fbct$gW2+RQ(%IbP#~*-y-Cn-N}O3I54HBC^=|+3Dd@ zcK)9tgP?F6xJ5r-3|RkzsNWn7>p!+U&ypL!2sM8tVgA}Y{%mqJSD;ieb7#JWEJn5P z^^uLuOJ(+Z_uc@Hnu3xpEM)EP?{{=`oS2^%Gd0c8kN@xi6A=l4GsCz>l)G}9c~rr- zZ~9JkP5I8K^b*A5-$o8a&;lh|_;p7wnBJJXG ztjN?2I>2>e;+B)c?M$GZ&^vVJenlbH5Xd36>k4L!E~ngid9t6bL93K5naj?vk#sl% zQl6od8{Pl%<;z=el8brWJv|Mrt$uKSOqvh{7aXDIfD(=RvIw5VJnXB|fqpCc)~5wiW^m@^|86jGFT^7lb@$EioVXj0Xn^9{IF}(Ql8rtasPAx%B60WK%tb z0f2~`5xgr1qbV4Gu;@%}&qHrx)HTuc(lR_u^_L#m5(h zPtPfd^$7S-CM-?R`k7xT@2kSzUgyG`WlA@eDS9oD3)op%>{ z(s4m)0@!(fblKU(rRddnbn0i(??F)orN1+@>e8jb;{^td3J#!uFn#p&!cy%(HP2)V z2ZK5&Kc5*)IHQ^%z_QeIbbijeOGZts}?^4 z>k0R#Yr#yfB)aFJ{26aHx9VbpRXmJL8|p%3cTZ5N!oC4_=Gu!~0(7WaDX;sw-UI z$e0)thKT&yb9iT%KBTHH)Zk$lblg5WUKeVw8u;uE9-|ndcQnNdy7>hM2ZxswaYKYe zqC5R=g-2h;G)nK$*DsM@p+f?oy2bkXLPtl3YV*(F`_LYw!f-+euU)$q%jfX1uI>>) zwFcy5M)HSJ?W(*a-sY#s)qe zD8k6ZXVXAW16&1bj@^FcB_a?b$NrZQ0>@s=I|eFN?yybjFQR$kmu-L!ZH=Ft>&C9jaHqPmX-$X z3NGgx;Jqy1K_gun$R&HI6>uJyGGBOAOU>gZD{^4=NB}#dD+heq2_#$6)|P|ZZRZ98 zumN&_HH?TcTum%#josbBP{S}jBmgZDv%ErqOagi}Q$tw=XrcumRIaS7M1pvRSi+!X zBUcdGr%8x1*kVreaSjJNJu-t|^|0ad&B9%sgPxWO+KcPPcTB}BMPHakhUHms@j8$o z2wVW*-U;)j99mg|l<{DIJ^ga&- z1vZUgW&ghU@?)x5n zv%@Xf@~w9;(09h_qsP2h=*^t)SCp65=1j>Y;HE06NY#e zXu;~$Yk)7Jcx?3Z)}ly%KIsZcZYeRjPKrG@S6`xfOP1c#KB1%{ z>ir0&a$ig@Tz>iiyq3Faza_u=gST_Mzi#mJ(>>K`NR@yfmnffUz3cqH?lC!MBuJ-M zg;or>ID^M_0d!WyX}}kYd4nY;M3C|VlU$VjpTYPfY4YUNey(m#{_~5KCXb@rxZ5#Cut>#kpe-9^^0P&73$m8@RqByA# z)xt3OA&`=?^d)uZe>mS!h1W z%Q&0<`iCDh)h=KbHHUMg&^>|~iP?cds~Ps^5W_&}3E<)3xpDJm1I!7eLxzWkmv2>W z@?N@h3FkKVC(zSoD(qMv;D^YF`cnGidtbYopCV7d&9yOD)K#1*duD3->d?A7XQt!M zW|=ldC==tzcSco8<+0#}%OY=nijUqIJlU}Q?<_48#8m#bEiLt&|IA+hgQaya@eWr$ zE?YJU9UxXJNWUn+WQEqVl2F28;E`lNodd)JNs=#`6LD!Zt0Li&OaTQ4r%`_OIPJKf z6YQ$@US8c`=d}L;qXaBlr|58IOiav6xMaVefUe-;Vm2OcK>BMFQ2yl7lHZ39Vp;A@ z0Nj%RbCp$8{7!pD3hd%QRQao9>J$uUFrUOts$d!;gcX=A5(W4V$u?Y1xO0=UvtF>+ zL4!pSUSE_qnT;LF6B5V)n$<&C15msbkj>oXVvF&3zyUUysMF7(f9vrZiZJ}bb}+lQmPbubO>u*J3BpvI2Z-GIc2eE;s-(6u0{cH~3w&zye+afs&5n4TF#vblNf; zJyL#07I3D2fK&%W9MNaaj+U80LIBGE3t1Nb2xas<@oyI6&mjy4RGkLQ#=cl)kO|)+w{a{sh8H&%q%n0{PTeyh&Gc`Q;1HM0UsccluI9ibr&vOGX7EF z2`FGFz4A3+2}7k833O2i#p%t{h^a6-8E;*2OZSS2QHvIH zZp|er5#g8x38TYyUaQIx@!s2)!9vCt`93tnJIZN%@d(gUH1x2F?h+GaI64gN#u*CznP2Ov}VN?$U~YiHYGj8TE7g!uN-d14)uiT8a!T*KTu| z#|jNRdn}K)vr1pF@FEkP&z@54^moG$$;rw4@4LT<50>a1+sp2?m?+Y5TcM9_B*mvk zM(e9x$%Ts#(WRB~+61um3!$wk_6vC?Cf<#HXqLsP1SvrzP;9?SvJNpm)Qc>|?r0rt z?WC~Ps1P6^pa||NUcm)j12}hjIj}EpNQUYzjX~!DEoiO6FiP zq*jXQuk$gUoSgi=?V(^g3-N-??Pwwqvp#A6t}fZ!ZBSh@6ti&HleKE-TrFaf)f7a9 zDv`H?zTtkn%GK^csHS5J)E!Eec>g822?| z%Ig;tfDZV6Yroa)yFU>{k#-y5E%r+_rw?Ts`<}?ilx^Ct;giqt%Az6|-aP%#s5ZZj zOMFs0ko@H0#wMDD#ir8ka)gI-(F_3{0lsHW&q}gA1}GXapcfmdm(WTui)+c@Lnq+O z?=%1TE9aS{WHLY_=pLMXJlNR&90Q=gA)+i8jA^M=83^?Q!wm3wG^ilKusFKGrGd`m zv|hc>ZM$$C7)8ITPV8ekH{>F-$uqDgL+DpUHCsNjc%++3GBgn?5`0|h7v81Opi$}Q z>I&!LYxT1q4N_52u>yOla9EEw{s;sMVB7b$HYs*}bMxnliuf=Wnj!rN><9IvB?s0$ z5HMtQLI|BL3_auHb_};wvXXiV3CuH4X>fdmgF>O9C-1AGzgE?$_%b05I|c}sjyzPl z1) z#LUcl5K5Jllzznv@Qp@0z!(G-v=DHN;yl>HgT+RlMQL9azP?EAesB*^T?({QL?(o( zsz*D`!L`PhPYbo5^#yS|kT~wM!0(~_?kABz;s6GfN>}z|zsMF97x$T)pMewz;D9I~ zuE5b=s;Qa86%`dVfNu}c7*N$?jfEiY1AT%sye+hr6y&i#Lqpd4YZ|!%7#J9UxRUw> zX?1SFyu6HqGm~37_B(nT3K-%BVVTFja1r~;IfI~X;Os!2)!^F7P}oU@lTC1YqWqgT zZ)A@iS`exRmpW`!RrMS9VokJsK2$YQk$crkbK}xrjeu^c^N-STzYD=S&B9K%d?|~1 zGvqKpE&-wOm}9gS1!_OyC?nefU_skJ;J)eO9V7(;k@n7BJ83PaOW;I9d^P?5O3g~w|0rkVoVnp2mp88 z%*)o#*N!2%ZbZgxQ@&mXkOe-Lst(*R#DYN#GlWDSCkIyyItZe-AsQjDfvB1$kPQDo zRsy7WDrh@GX9w?mZ$7{yBa1h0A%Q3ye3&-ym63CcVA7ZZjqC*EAH?m4BIyE2_0fqr zz_S|=uly%P`wIFS60!%8AIaE*XzLBVEL*6l0)R^Y4rT^ z{}mU#3Cza-4=y^u)aYpdZe4y6ZL~a}nH<{EQ9HtWyV3IZbw)J;gB2Lowj zc~$4hp;5DlNZClH8Nw;Oxw1zw!rXp!#^N^3U*ak^QPFTApJV;yuz*ldk z!UxcrO;nDdqk`(j#c?qm`-0>>3VwcdaF&!HOK8U^SqOH@6Eyrs;F)xla7W!|m6MZx zMsN{-cB!v;9y~i5y*59d25&vHbBA$)?y`i-aUB$Kd~Sc2y!!8C?dwU$NOHpYgcEB*}PJzT=$|J1#Qw+h#^9Y2_pygN^!tuk?s;JvZ1SMCWNs_e^ygwJV=PpqDSbwZmjxm2A1}Z?fTdqdireJv+O6qQ0q&w`IYy8<>R{!=n zDB|T%H{LeAEsriu;|=?D!je5})t-DC1G&{(6Q}zkMI$4Pz4*PWpSQZM z-X@{L6MXdY+`3Y@FGG$ZO84A7SQ@eaL*BlDkbnqw2{t$5#R4Tpxn8GM{MBD$D{OXj zwhMRn+I6pHGX&#WdHNIjerj>vy(_ZXtDa)5x6muW?~$8Oi6~39NK8@W!}|+2rZJ5w zJ$HALl$nDsgpe*QeouQwLCC@^SwB;VelT^BL6ad~COXu?Ojj(3Flc$;sjcmp`cp&` zQu(U@H3X7U&=2%-$mq-!7ZgjI-@)9-3U%BaXGGeJ;-+BSh{OS@rbs0~wzuuHAXd z=dg9=49B5pxrInx*A{6#m-JWfi_XMlO3C2%=e}dp6F&YnFmypLhTjJx!2jLh_K)o= zH-9rlL=G$FT#k=V{sQScCl3LMNlLImL4WB2iaj+wZR6$v0X(3@A(`cJa&o34Wpdlj zFe`9=>6fR&Y6B7zvbI@^A6Tjt0XjbWs2vQgpBojhDzw2WM1c$>Y+?p!Eyv^4Qb-kD zhA@9eznflT%oy{oOGubWwjS=~%i`N71=h-_J5pJZZ$3>kSL@H;1hYA{ohrtO2*9rdPkdFR`=E{m!ViwLcW{R_1>t z+>xpWDF^WIh#`cBY*4FpKLv-^I?Loob2AdP{5{~O07Ln|-NncpCbbPR65=uR=4&ND zUSty-rEPF-_#F+tb|5=0pl&py7}j_|SYI2j?F6@*L+783ffwNofw%#GEpI}P4nKaF zTloI{g_^S?6TsDCDWZW8ff1$q!AJ$LyP>TO8(t28qQB=HBP5Ja~hi}`-4YJ6t#Ba^Yzb*<=vazgYhwU5Gn}! zl#qK0*^uvDT`XY)mj7*D8-=8r*!6$Cg4FrSXk`Sz_UojiZMr2Qf)~NoK{iDKg)|P# z4vYVfn2E$S&Beyq9LkLPkQ;3P&&mODcRup$C5IetznZ?5~m}k7NKz zfEXNPk~C$1s7XxluC&0cmH{clE3?M*Zzj`Hy3HjNB&YNvOuiH-ARIcZqQi9AVp z(i1zJXo}Gh!2Ma^&5iKne@}IQ1n^I)qZur(Jm27L5K_7lAES^|H2^cjWCa!F-*O#` zzb|?M5bB1M8loc4al$S*BxHj`VIb}VxIXLb z6}U0n85YU;b@xV}8;}r9C(!~$5xh8={eO0H-YS9jO?R;%Vp303C9JrkQgw8Gi;V1J zGR+BGXxJ_G=;k@FKM~C;$M2=%?rqH~;+93FrSpjT1RJmszgb4`@I7onWP#IEw1Il) zz$ABl)U5oh0o+)?h=^F^y4CVP)hYz2igYZu+_vUAB;Da?q-tuz(rw{*fahQ!fp_%< zgqFdf6qdif(4$;be?~CBQsp;u?9SAa>y8_bIL6vPHYrqBunc&4V`b}qrXIvI*nF*R zXEN~omZTj7pf5MW###Fn=uEjY^MP~AM|EP@W;n#EWkUBv#S4ymE5xvO7TK7~pjs@>9uE<8Q&R>|NP3P2YrzYJ=Mn&k zGlZ~>%ic;72vkuu;{$T^A7!=@C)^(+;jiS!s#vccPY!Vg6URng_Bf@=d8hI?6;G*d zb=ZrPn-uHS%Uc;&FDH1!&(4-hMld}=8!1;P)(f9ux^eUVZ-MmubTc<+{ynP-n_b1B z8&66F%flASRYWwtO{WA2O4^x2K8)ld3u9X}uS&!CcmnJb?<%)3Kw2WC6fR?7Ne)O} zs>dq(FF}vNoH#`Y`p3q`ey#fJs4Va7?0gL^ynT@M_6a}=d8*nBIr`s=qb{lgE~7)4 zW7E3{DbB7_jlLWb)91s>Opc%pBHQ<1kDNvIE+G(Mh#s>&D`$JBYF}NI;Y>gJwYq-6A(c932Vdt1Q=&&rV1OvB& zr#uG#u}~leVyyBCQuqE4AOqP_bsMz(f|8P1=$YI+JVr?P0?d)fv|q3aA)}2*E{Kq+ zC-;kPX(o+UbD=#Um!F9LjVxkkmix;n~??LLdX}OGq$$hGB`& zR8S;KIszfZ1uM-Ui!uPje9)@?edBEoZRl-?zv|}-z(!n1qc?z`09mpyB_2YsZ$SQr zjUq`WCQ8T9w2ENxBgqgX2m_J?VmLx5i;Xxm1`8Wos;508Jw1XdVl@UfcH-V5|kX=&2mO60rQFFro=ZlDJTS!!(e1LP10e>8HemflPi#YWF~{(jzWT0mD7^6_5*uBk z66>O6^R-MAp##M0j9`lt-7Ie66E7?z!4CterhAnatlFBRC3?t{c*5p&WXA?#sq76J zsDnm7I&lWqXkvX`=HEgkZAc;nlp%S$5f)a~@6F90U`I|Dq5pam>kLh%e@6eg>vgztP=TN5x0Yldt_u}p0d2TIXMVzK-5L0rVY?+SlQTK zgWV1yWDG<^S>Txi;B8&H>}7^XEbv3vSvCdgA+izg{B*x$25EY*y>Dl!4;#$bqrKI( z47rrwp-uqeLuusRf{KCUbtJa^W1ba3OTB)Bj4Thfj?jazT|t=o5(4Z=D3~b1!V9XgJ5gLRa*yPkck%L- zbFSw@$T3&l2(}0$N5)_us+P!y{`~nX->gS8N3DWGDq?2`zpW{-Ejh>H)v>(@kBgEV z9^TiQy;g#IpM!&wt6ZZhtqCxbQ&Pg94r#i(yW#D+ip+)|ri%CzO1^%boF~%q0wTyZ zNCKyVFs1G$DQR{U><(`QebAQCj}W#kGl5Xxl`wGgQSWXNo5}@Ft<4Qjx8wJm>x&9L zzE0aDoP$0XC-dap?$2aoY|nmPV`VLHb+UeDw)=8_O}4gX!IR6GW27eTP#|0RgK?j* z*3{{7(Sr!sz5E~3DnF@Ee+@TS6jFtrKW@N=e+cvRNDI;~;pdJ!N^DZ5qTeEVpq?Yk zLh#5l?U|R7{l;D0RPz^-7g;{HF!~euH?NI)5%Ff9?2#(w(uIocYNEQkDToJuL zgw%KpPo9dzYApUv-qX}RR1&gDTR7%3ls!@*flP%S6-N#!*F;IQVgB|`W(N1on;6X> zA78T5SGYlTZM_zE>Cf@j?4&>Qdx}W;s`nRyEd?C%x{T09Mnz1uNOo;XxUHW2)HT5l zBJ;gO7xD|t{-@&Nm*Lc5gW5qmHsoHCD1^?Muv^koghQ_3l4H#R52t*Saq7D|Ohh)j zeqbec9SjR$?~QkuMxE@eH=-Kva#V0_Y0Q9?|crhG4f4N5Y7G;?~-ZheKEEXwtHydj5lE$JLPt& z*4h|B>$uSQqbRm|$^G2{i#5S?`Ny^H8}kXtQ|)RN)rYE=F12Vn6JsI|MLP5cCB-WS zYEv@Q(_xHOU7)E~|67qk(qjy{CA|a7U~Q|7#Jv$h#>VOH6yhc{^Kqt79v7E|o(J2> zp^WslzlKUglJp(+b4LCA{kshMAlk#yl_oa)ZHo2oU6TQL2Rw-mgoRovd{V2^3xk(i zRjt?D93&uO@}cPO?ryPxn-VnXvVWJGdo;KA3hDWA0!x5Cp_|*wN+rkCwy?G_vkV@q zCp7P#dVk5!&Z-QF<-`1ub2S}wpap!;Td=rj3F@NQMFc{mB5dguoQVn zMItNYml^5U@JKlE@`K2p+4$ScPs~UZ_F=zq`M`Wj^8bm^mVX zdA_Sa`@mtv$6|YKW$m|D3b|`tUx|sYW1so5T|l4+&eY!O#_7J1R;4s?hJx70&~npr zBnvfj2jPh3@^qfr%Kpxji!_v8ej!xa-4u>5oX9;H)!>O1Gq!`aQy^;M1yws=i7*#! zYTKZyK6qKC)c?n=w7UYq02e8T(j3Xxwu>F+OMZo{_V4q#W$nVFxv z?h!NRX$`237u?Gjc6=U7gA;9ZZ?p~(vCeL@)hnptu1S*Mixg|~*-0u!$Sp?)lW4Lg2=z4d}oJ`w`mz%YHHwt zF|j=N`Tx)w4HJUF*gpLC?vvdENu^#&_LC!n2Jc{1Zz9^umkZKg_<=LZnTXtpB_hGX zz{E=30L1R2`X`Wt%Ehl|`gmQto#e*;{&IdYU-PL>^Pl0O2m3)}B6_bL!J`ZooHy>a z6EY^Nu8)NpccH-3qvs#ux{ZkXS6(3LI>wB`@fP33@UEp=R;KlcB`wh7D ziB5|l8-}$XKYZA*7IH+rc~iclDS>qro}mSi`U3FDIXXRd`;?JybX!6o-~|~@@OVT& z?w337(gEH}0la|3Sn@RZK=pql>{r{i0;$ZXl;geK-5)=GP>nXg_8`xHw7Gi71e9o| zJ6y;--%o3R;fL^e6zE;Os>l6m7BITL!=qUs#;W;kol#(HCq8rh^DpE%QGhsLzodS& z#tWc^hFyth5Xgx>j)LeD{IRq{CrqM$ZH;1M6w|y5G2cm%Q<y);vhT5wxt z{_MRjXuN@Q<;Rq9WBjHe+%I+FyugZa`3fsRga zS#G$L{0XuV#e<~86SBpZFJFEX)}}uRp*D61dBoQoC5LFBqoX_Sk2|%#X(^r{mU#%^O&zv6LCSiLYkxz1=_L#|b0hJ<}I(kp| z4kpbq8leo>+Q@(ee%MQL{;%gPJU;b$5b-nc(o1;WfHp|E;EY57Op}GpArR>>f``Ef zou9BP=WFT#;4Qyv)=^kmDznWZ_aHJAc|1_`GR|+K;m?tgKOtmF9L{D>j66mqhf%AB ze{F3Io{*84pWk_+eC~k73*SQkgt_kkj6x$2F~awe2eE;xcpnO{{GR4Fq$_VEe?x^c z#^m6nF+5QYg)K0}1+c$ge4$I^vk{-Zbdy;h#7tWn&CLr8gX;%*ipsya3!rwJg3>2v zTB2{&bH<{f5+B7{A*=dn+B?ZCv{oE~~%LHPp%cylYf z)e?sNzdgd?|5x5^#OuH2-Pkgo%>W>lmc}9f5^SuM^yDH?0x5VqG;v8uBx`{@T|~9S zSPnMfBAcQBXLFdqVnZIDcWK$Qp2+kUZkos)wcj+$5zV2FC9# zSB(4c0`>ecfV$O{iv=ZW3b?w7T$8>U^8rU$Z$)Y0&yD8^-nnX%OJCRj2WM{?Rb?Lj z{enn|D3VG`gGh%;Dj`fhrXP+gu3PK{=N~Iy2y(zRB!e{)wJOV z;m!2CJk%iNdDjGw0@5x3wO(e#1Bb(QK^%^>?M|~EouEhW?(G?ZI7RMJ)T<6}C3xsV zQA;3J!Ch3DK4b=;7o6ic`0XaNlE{9t8c5HJD@8r_rVzMQKHd{o6U!{Ztn`BC1Q$#6 zsm&)BvYD1ptniJB^tQlpYCb_VYJ=|Ee>acDt9J4p1127{0;NXcHaYs1O=_3FpowCG!yHTw7cmYMl@2)f#B9qDa>(ShC zZR3VqKyJAFQ_jEQm4-(kbN?T}F<8<^;e7zL4|Q&J^(Vv?pE_-kPuDoM9{nPbfXW91 z$4)@xWr7AfGGwb`3aJ7fc;dmV1`J1p>Kn_%#QAFMo`$!U) z5GtC25Py<^*Joskw){AS7$iKi@U&-+w}2tzFdhbI76{!HmBJ1>0)Q(XWF#&UWRkqv z*^T_!#+@A-2);apybx3e)7u~2?OYwoLT=H>e}%?E!b}zX9lB`v-taux&bOc=i>*X) zST^TJ?;$|s527Kj9xuUt_|9Ri1#3c*7SmfMCldsJB;o@CrKl~J=Vv4_UloDD03|*# zWbx?PsxCWnGx^($Hs3iqPExT;{eIR^Dc8{g3u|s|87*1p92-y&@wc}zXXS|MKlQ{C zw4?ZFSEXz7N791}G$rKx$$ozsRq=R#px~@qEjB6s>61bCF8n(U$CSqrDt`Q30nu_+Ewwp3Jdwpc~BJb+t>OoFCA{|JS~SBX@`P}<`0 zcs!Z~%L^3$Y@lfzMsrZ3*3r`93!4H@i3j-g#rln@GD^W=Vf0T@!UZ51o}gd=eTlz7 zApz|&2;^$WjUW>(@PUsdLJ}xui!z~p`#Wf9vi2A9oXDhjHoO*EM&^YkT7CfmCeBxm zqr29SLDX?u8iJ5%!&ov%0wzxtb`FJ*lms9QD)-fbvLO29Z9MYlh;?IUXGeyGA&dob z!UF8qp=A7Qe-NxdB!$4m{z5xWUtH9MIOw7+h{-dJ0_6@=Yl#GnS(Cmi;oyh1x5-TW zCX$))E>#bF$)4b`s8Wv=an`#ww})_8k^ehcBW-*Yj}rW%Lf9iegRgZ7m40J11GcK< ztMx~0YlAu$6J{o%pGL}W)e8!WN=ms@-q)C< zBr*^dm*LK9=IK0;z>R5dZx4!OAN1UQLUMO?rd|Mc<~ks(BB`qom9Qw{>`%8g`v1k< z%KnDmfk4jaC~|GHyR3MzXHF;~&A)b2YTva)djwEITo)d{t8HmbOiUhZp}r{nUS7Tk zZtZT?>l96wT)QZ<1+n{5N>{PJ=FiRDqv(spc+vA=F|N{416uFN_@ zpPwkN43I20R6kjrh<-pNywLmL9_0^{b{qxwJx;{XP>iip-c=jI$=GjdyAWvJB8xurS2Z=bzomwLgHom zHw*MJfGttT`{8h14YU9*ijc#aWH_(UK6!EyAd0z<~0d%f!U>a(9V1$D*r_!hK zznF&Qu@{k1V4)kneH&v%Zl;F>D$LU@i7c%Q_zP55XXJGj;L{}8e%(Oj;aogBYx%BT z|7@x;BJY)mDqZP^qLyi6eSDW~N|U9Yk%QXp!5++w0tmrx7N~GO(og+~dH2cnv{T)6 zL;l_S_%|9aq6EiBR0MNJmK2oR3Y4-ipmFIAy4qTYIxf_mAw|3k`C9^Lo5C9TK;Q!_ zxPood(?TlaN}cuS=;$DjDS-D1XJ=DrQtAL#2Asb=i1Gqd))*-ZO|_1VeFlxH45Gaa z7kjcuNfi!6a3jHlO%D80QshI50@*Sf$W9}hdEkN|c`Wc(;=l3UPtoU|Q0+_X?9mBN z`rzm&pX2WH^Rt@%K5W5|sf>b8no2?mStCQ8FSCZWJ2=-XuuS{;`F(=~Ib;jk1~v48 z;hW1;E_e-4G%!t!;3!}@+aUDo5PJ4~Ajnp`>@y&bteAJm4Iu4Tunh63FgO}MP4 zNZtReF(gdH?|WSY}L7fBC9 z9_o>?e%L=3p*`OY_8{oFjsI_83{aMA930-lrw_Ho;VNr=r1A^NUU;y3IQR;|x$#NnROaQEl(XX1(a3+&~bjJ?NEad+LC*PqQSrTOF8n_u? z+6qxex#U8E1R8(v*%~5IJ{lU|Up2!4@QK}DEM$-kymjP5$|IAWAQFX-|Bl0jtKZ{%U3{6F+tkiugrOI} zyr&%$szp{U<))cL)3w_z(TpuzSeY$hXFtS~;^Xhb{+JI4r2tzad}&X7Dymm-hPf}| zS@+T`I=c^88@#3IV`lCT27qaUjBE)nEck8X`R$on;FKtzgy3{W;5?cTYrWN`@qt+4 zQ29hYI!at{`mpTriCGGzS2&fJ@{1cw+Euw)X~NHI1rRqo20$o;R|-DYuwn`39pK{xJOr7n;9`jcDMtxg%@7KUIjU=n zVGv$1tCWw%0s$IIYx7>u6!chy(-NU^ z{p{+MZqs*Lqv@_pw4;Cb#yDwdPxfTRg+zgoSgcQSiv}y@39}7V)rZX&IeC&JvT*>( zlOEL{dbp%rk@-(x`%}!Prc>+2U!jFdpKZPIrRPoH-ZqS$jEXc7frOS2uG#kpnb&g9 zmu*u0%Jd!m?%kB&=(M)Z5H?cCVcZB0$BItyL520I){6iB6Gi6^qg3Ig?F4RA=Z(wb z(h_~D%uMRA^tB7|9A%~L5O&1}_jf>HqrBS16uF?TJGs8X*{-9F8BeEH+SW#f)_x$v zX8w08(7(x&ykRh((W%<|OZ!NpcM5rH8l|)yllf2X72F^9bOy{&WiYf*MPpS}^2Q6)e zTEQQyP_npx_P_2`ybNy}Z1G)znqjX3z;Z>EMVfY zXAtzb#cRQ^VXLGx_=EdvHDmCyYa3NN&z@D;hEp4srHYBvtF$`|3^r#xkky{8O7|}f z!?muji9GRcA1$Pici_3Wu(u;m2Tl?Qy)*wwQ8cV_gh6PL7vDVVt(LkNeSHnO@OLlH zwtqNxZ&Z1Da7rGbe}>@XlqB*j>JN{#Qq%7zKg@0Jb#zKHJ%+9iGkif0BYtx6tGFmaj3BfBEvVBmU!<{2D#&UYnJ={IiC9QcYpIw2;a}(%$?)i z#WvFW1{4IX+M3K^`&zU87cb%U+0+P|?3wx_)o3;FfM0>dyB!^52M{!PB$03nsPv&> z+@6B~iq`(;{6*@$AlQN-ncq@RKH9B1D=8`}nz#}Ocp7Ng!kdKx&Gv=80Kdxd*{qnr zq-Vx@G+i_pHN>KTk%pg;@IH>Z9$>d<{*}HF8@l>EP0`ysSJIfS^W(3J)6wQ4Xr=im z$LGgGfKT}GS;n=_oq0GI?Z(`K#65>R+U|;XKJtBuf{k|mwteG@RfneV-^FWORN7G0 z^V7L^>0El@294~3Q8)C~Iq&)cmy8?Q97u9F)X?`O?tpj#5+S0f=zaJRk_QHcF`$Oj zEg;Fa0)6vm+HSe-5z3hwp*7Ufb|j}sw9Go$-%%PaE4Tjs1PQ`_^N&P}WRA>WL9`;q zBaJbu6k-a}#N@AxI`?_%-TBJ}bKAQ+w9O#^eUJ&7-4) zZf0%=|-UX>C;27UCuWC-DW$Ai4;BFQNgF3%om&H{INJwUh|2Ah%|yUx%mJxX#er zcnuzuiTgcn?$U=%buP~au++6t2{N-zJ(@{7!rXqX-7G*cA(l{*3JM;c$oRXk{mIA2 zM)5&TbD-bU2ix6c% z2IZh$1FQOj?iomE!9C;2u2%CQoRQ%R3JkT@q3L2oHUyj@>_gZvGV)O=tEo$R9DfNL zfdI}xo%2JzM(2l9X@%;~3)Ls>t+%IpraxjvmwP@^!w=qF8=)Bj-ash((U9cMW|htI zZE_K|vu|ry6+}BHl2o>{*0hg`>jLd5$V59(O)w;sp1_}(LBh5whvCN0uLUuDmcWT6 z(Bce%UKO91xTnD9#n^bVfP+Llz=;FN8bTH*01XIDIb*XlaA`RKB7v-80->-Sz}wK+ zQx7Mf`HUFaEMuvVW^23Mg$#yBr$wb&!PIB6U-Exw}{W+(*9L5 zvkofTgO1n7BGv9sEA~hzNV_LA%Tb0lG@=jxWCc3A6TMA+m?P6X*Sv6AHM=}a@s_HB zNKu3{G5mwY6DNf_%;sjB*`k`_WN2NiMIRa|SZ!JU+;##nmA%oLcmEK_fe0L(_5aroGIoT3e{jIcGTLK6+ zQ{@`ZUq#PFnY{JgAF_yxxG{7$ylso;%-rhUnx>&t46GnhV!LT%5r4**np6(j2T3-EdW$2O2-SOA}HZSzJCf| z*^TtrhuWFq*hqv_2_PhX0xp)P#0$cSJjY-5CaKZ^7PFi1GuaD>6pDoY05Bc|#yfoV zVxa1lJ5Vu8!>iRcJk0gSO(vKhdFG)Uy_KGm!=$TxQ>H2T$wNY5&PM4o(nFWO3s+!x z;b^9QN_Oo`{sE&}1xs~JOOEW^cADk9Y=rr*bT_9roEKXHuj`+!@P9@_u2^cnzW7a7 zKetHgBgZD`-WQUJ?8o*`(NnVF|g;kuHg&Z-Y} zLFf<~esM8tr#4|28-fNEtj)-lJy@_31_spd&ATtw9Q+JBmZFhHKriCfT_2x?-8lWZ zQ~T-eC^3Z;e75Q3KL5SE5_T;zNlCFV(r=cUXBJs$fDU{QOaYs}D$(7weBvR+Y_(jAd|+Q&}--Q8VV` zVN(EyZfeo55HzX#9yFo^k^Y(e-#M44`x%b&_tp@l=Gz9%&&M8q$FG&}*{= zsU&3zK!^_~>q8VCLO5oWP;9f-n+?up$|R(LW)HGeLk~rt|K=^x&=88E#sFAl`j-7t zaAuHyJ;L{^9+6|G`m!-_6xuvUFRy zfl)DSY0-?)ctrl7+g0BtJy;GoUPGI8r^SD+SBGM;!Cx{_F4-FqLcsj$*Mu{c#ib;iIzCv-xh zu`g0>E5_81k1cBXg0)J2YI&hGWa>_>{A+g?)YC|SMY47Ouoj*ev;mU;q$WANyrS7>U`qb+kvE{M9 z59z-a6)bGxiV7q-dWWLLV1>+1f2+ONKcPw$7kd7@xvk01j(90wsSM+ge0``wQ=`&q zs{2Rz$`iLO*23}Pj`~Oe6m}zhJNkoLPAyxX;$uz%3Z>~J2#{x7%=I$%lJ1X$Z9VqCaVQeB9kER2Al=_PMB2sRmoBz`>SJ2oKVt>{50N6?3(_W-f`m! z-0PzmZyC52tm8{pD%H~BIMdI;(TrHc+hu>QqD}k`xwq|N+y3c(^9c9iCEH`omhvXi zP}aw0JVr0aWM9XyL&+WLP25$v9&SUXr|luVSSb-B|DLaH&Qa|l_v^i2Lil6c+ zo6dH7NeKD1rV+lvzQk88FA6A6;U=bM<+`j=YDvEt`<`XdawRk)^&uxVrw(p?chB!Q z7k5m2>1LW&giS~d4D59-MT_(U=BVOj3N zlsfm5(DP$Q)=zZ$2>GBT^HnvGND(_Vp|^>ZA}Dt|3#nVcc~{PO|K}gh(k5<4hs`0&+f0NIHDQzM zVY=x+FLRhns7JkPO#T7!OV`rxWfL?--t9pfkrh+ANyfvZiRO-N`Z+}O3h3aYFWO&6<6CJGWU1fU8+XcQx(^ss2J4P{}%AXtk^FFL4m@bHXE~LAYDog&j2!o3C{rXOaYI> zD#ZT4ih+xEBQlK%eT;p`Jlg|8>~@l)EFB7xo$FvTSVX>thew@=R0}FgEiYY%8enpR zsu8m`-{R$!W~pzd*L9dr0kizdfTRfRd(Lto*0ah5UlsUc=T*K(aI$=?XjTa=-iFaF zQG()1gjNV)|80gywvVD#QC+UL&~N?jd+jfJ74 zl$kB79F>23TvPh*?C@{)q}fQi*+}TI+sR7WMY$n5iuJ3cfShQREv)r5X4gSo4Nz#}uM?t}}`2^>q}^NUeq zF_7fd*AMeesF~053F)uV+#xgyS63XEMZ@q+A^`#39Ov7gcu1Dpa?Sq(MB`+iD>VQx z&`+tpbMkxld^BISukYa-87WD)tgB{75K-qU_&?zG>*V236cFcRyOYRsFLl&u=3eT* z&~<*vz<{#xKS_|i_~Ps~vWnZcSl;W4zH_>gDSB|apJ2Zk{(yEk*6jcwI(8bVmUtw@ z9k~oe9CV~-r-RnYM!F%{1RxqCBik_A2Am_1SS<;_Ie-WS^;b_gTp67(w%S^3VTihf z?5R3^QGg9}7qNSPjciHgpZ-g7AKKX20e=Dc5a&A% zz`X{z(B0qzA@62zA_AWh3k>25!dJ?8h#)DejL3cxG+o>CN~Xgbkjg9AS7|ttVe+c1 z>~g#w7L%4)h(Hh^uyNbJn5(Uu8a)5W_Yt!-PQRr+B=cKk?RnDp?FTf3O%Rp;gHJ&+ z&sSEq@I%E(X8OuM&uuKzeWIG$k4dKNc~q32VK!bgp_sVNO(pCXKZd!-S|`U6xt5KY ztTid|Dz0H6!S*io;e>7GHL0edv8BI{-j-+R=I@4&XNT)X=BC{<6mBlozuht$(i0NK z;tb&20nIODvd2E{po1PJ2d0wvf~Gx(9One>1mrj?xVPGZX$#}5kn}#Vv7q5%3w;@w zJ`kFcLc#bd^)1-aE{_wlIc!dB_Vxj0pF(a1eg2ls^L_!EAwGHtSjrjT#j1A44LFxP zf-ZLJte52M@B0zkjh)o&tT!twGetPwHuM1gDYH`kg|7`d2?|TSfH$3l|8&S=@`RfT zynqPlXw~!cq2zO_~0)XV_RK|{J8p>K2>b)Jh$ABUz0M3BrBwZ@|>-0@|c{#$TBrus_s}{Hy9d1Ix zZl7gkKcfs9?7jW^MZJ$KH{dK4LT{aDTI+aKEsf>G~`^v$oY1 zTB0@Fw8z1?T(YuxN5%26a_ZibJ*7S)bzDc_oDRNlx3BD zZD{z;7uela-4`9mqUY;+zqXq%vcxz!Ifh2J%2^(Mbs~OAEZ!!8mqusa@tK#sM>}U` zSj+2r%ftNFSAY1d*=h84=DN^O2nkyZTWwTxtRj$w9SAbC10+uZ1agRw-G+Jvt)O5s zkOT9WuOT)EqdkxX#uyg0TNY#%ch0D?Gu*_?C&x$EIe>KkVNV?OU}t9zrq_^?lw@Vm z!bXRjD~9BDwpKv)4ucwMrtIR;gnXlj=jgp>B!oBB)d#;2RsS$c({9i?IISr2l?dUQ zVFZy(A&d-8>*DQfy7?Cj?>Y1GAE~7YJ^Jb$&DiJym!h2sQ^UU4M~dYpGu_h18J1sG|P?!vp-d`;u@peuH@9{Vfr9 zjmwWaZ(#L>4hocFx}jCYBV(XrNPd@=GT5^|*w;VO|0OEx8^s%@+NQ-KwF>UabquXl ze$OX0w?Wg&w3FC9#q-i)3i)MO_{P}Szx26X=&0BHb+gKAsO&)=ArIa-6jd1K0?Eb2 z#l`628`Y5VM^;!TGz!ir>Yjar#OjsX0To{dUrae`+}d%==AqZ^k|&PqF}JOw2(jP; zb-%iJ3LQewC5eFp4XBxzwF-y}K?NcNu6j%U@1j})o}pEHdQ5lklEUSY^1OASec?1oI8^4d-yI5Z^0~7^ z4pW7h?s#l-C(EW3-F{V$I}1(6f7S%|g>*w=EZ|Riwc=@L+4Q<)u?01MMKPkkKgCS3 z67|mV9lCbL43gZy`UD})Hato)1yy%KTWRzxVMBLALHIcLo~)O4;K2zbIHiCBUOMFh ztO(qdj&d?O7DW90c*Q=pMK%%0ZprbQSQ>=ZCw%y-`_6%hpM8c7=5_*UJv}eZvO-o$ zikgir9Aq|CpF_yl&~tEn>SAsngt=S^fL`~{s*Nxd_(=>WF}d5^B&jgyF>bk94keP1 z5I#qwo(qk@UiDlnu=3+0|4c+gwS;J)I(!%_A~!R5|2RAbMpFLNHX9a?3G50ggn1Bi zzc~*=Lw8QD+-~nb>Z#FSkLSU*n)dxwINy4Aw$5ab`ga8mrEstPE|*y07|U|W#z0V+ zic6g-f%FJwW}MPz`G*hLIK%H<(Y|)Fr9jzQnm)}v@C0Q;kyr3PLZ z+T^xlwb>2W{kT^u*q?SDQD$v;7sGWiEF?uXcji^z6w5qa5Rqi0>AmjTy0~l}%1S zL?sazMuGga&R9GeEcPiNIU|*Gk>ij2NP1(<+GC41l2q`557nb*vn3!IZX~P95c}Y$W z#92|g2VbCbNsI!2^t|Q(0WABe!mhy$VZuWuLosX`iLh0`RH`gvqMNYpnL=d^ioD2y z2l95A5cYxAY4~e1FErC1* zgiMd30ju|{*WLGjf^~vp=;#qqF4U6^#)QqtLsS8$ks>~R{$MeMn%iNwfX@iru)bB1 zR9Hfwuy0>B#@QYx+P!U()O?I{{^x!wO0wQrh!uMGLpVFt)gJ2J@h5MY`k4$ZW$xUr zc-HZ{5tTKi;qP=6GQV27y0OuO&@u@!e;gfklJm|S>UfEM9BOIQcO_F4 zGSU}o!_SD&sRean9DM$c)tK{H;#I^xek^g>cmRs-2<%@F8IBitws&-}cDkH8($aQQ z_)@jncctIo^O9?J|9D0yQt0dA>s;c;2A9JP4y->g>XW?iJ}kbRF;QH(2jthJTxlBoVhLpH8aeD8I2bi3lsjJ#O9Jr(4G!$V}U%Sw@G&FNTjjWieD8ep2Gg z&|3EG%opvj-L{Ahpv21LFwbW)z%npr+AQ7?l0LEbd5QVG`m;Zc=Kw83rg+^U#f}`R z11Hn{b`t%aH^DXmuoD4-;o~-`KBbVS_!?KvDKMTM2)jFfxr6`_a@dmm$m$0pE31Aa z6&e6l5B40eOg@-QdAVGNak)BYUvgVQJrDHY-j-<@Iv25U0Urpf3_`~MIAqC1hryZ1 zeVf+L&n)=vaBACeKRg!}7NFA#ut(;q@KJhDidne9aN2#En5n%6J z3i-lH+i2SNVxY;JkBr|iujg$Z3A;E>^p4HRX4Po>?GeKho7WXGbRV;$r4#i3O)(Sh zcJT^l1gum4ay`y{^K#M&)p%+4(Ssjubnup6oS!YK^=*V%lugo8bFoFir)SRUNPi$Z z_iioUIHTtl=PoC#PeszooO~R)!Sb;@1ax|Bb#r4O+Q=(yx~ZsfQupi6-+K3W?R}r^ zip$HrxE*?_qN2%#jBgeuEpPekvJ%qEcOBvJA1p`0^tUG|f!lGsJZcOfi+}-*ZZ4M&F zZA4thlRvKecXx$%Ww~pA{gY$%nrQC*d7DIlghf-MMRDNM@3z-gf{o2^@I!=0c{gVJg+iPVlBry9wI+?vwduYsE+5+Mon zr{#{LsD+;?0_j@>)9F4|#;8{(Xl%OpdIQZLqdSd^<~K_IBh8fL<5NB8>KYJXWEM`f z1FS2Tf#?iQ$^*y|CvHNHVbDIfA=iw{hFUI-jqAewWZN+|eSOm4Qb2?-IOn zEhYB$=O4c0U7!*fEZ{HR|HmI+-S(uW#=%%8x=ubor9c&QwKwlA`@HU5?=mgi10fLq zgzi&REkD`qzI!RTXI|}JFg=yeN@ZK)C*ouK#YRM zLCaqGnUJJE?UiToVoNKE$R!=+`N6&5z(ia!aXXl`veS5-(|FYji8W}V^@1v%Qdijbky0sK2BlTqCsV`^4i7l_xjNw)Vc}ED zH`i8^wN)ncCmCX|CUuQX8nDJsBaDDc<1bnDygK948yOcq-xk!^HB<1<=>2$i)_4u^ zXLj7ueDN{pnclM*Mr}95sDm9zKR_tD-P=YWO}7@wm8;rGZvY1E;ZV%(B3x_w_!mxp zm#(dd`<T%h;4`l?1W8nnWV5=R5Yv#5*K2^MMka0dfj*gsc9QdP2<80Jpn&3jsG008L@bKkl|42eDyX2dh{V2s?SOfKO zkhJF(#AdCNX`@B=NKzPl1l>MQs8vTZ@& zo-S_6zj#Ym0TW9mvtBNh%q18fn$KTVR3I}aMy`ThY&R$u7bT+lwXJhR_#<4dbS@H^ zZXG;6q)3q|&JA@VfDKXFl*>3EywTz9z@d7B^I;u|?R5w@XIm(`oA*s-68#noybi$^+u_4;l?U{IvTer6{E9 z4;Bnpd`sUf&kRW3i>N-|V}rGDlub=Q?fr8E9phg~e5r6Fl)&2$d|})jWQub^qDd_M zdLahy8W=G2d6`nd`0ID5m%7(80Bp!bdu@A3==X|IYzO1!Ve7|Jg~{rS*u|G&U^@3yX$p4+FEbV7Pnl#~sW>Vk>%iS>>@h z)?Mk{Ho7)ExQ>Dtn3+`o8;-erAQLyU^ap8dK~ar+fUtXLHw*e<5#m2x;Gq* z=3fMbeDo%{G&69C#}g4*ci+Q)NlaWr{3@i#M;qH6Kd7RVH#*5w|M{=+@y^LfEfud{ zphQT1ZX5imNcl2YcmNJh@IxDzzm-vbfq%AYlxrH0phx3mwk;W-VWjkfHw zen(wf@6Uf>w=xn^9|TATv@y2r&z+*lB*ymMRic-;FQF6^9K5i**a;?oR$8D?Kh8Jl z7s{5W=hn`9;)3I)=r;Q%_rl|cf7)%Q23>#r%jL=}9fX)4kd9W2ipu|;gQKwdXC>m> zk7lP?pT@>_tyQ+PgfVu!57LVhaicm(@JrhHsDASbaS# z*wIplbs#f!S8n^k5-W^Fx5&?)gXxn+r(^?<_H1+*DbWwKUlG;Uua)k2C4#s;2deC@X^Fkd1eacbpMjzEtB;|RzJ%g%yjbo>5J;HY z2i{p@^b?n8?v4cwKISFGkLvgDqZd(D`c&r4rqj5&P4M6Wtv=X}_~Ko?RC&{C9$Xho zdCSvkB@CigZnz({z*sC;2JTU%{qXGw&a_!L!d6e#eGbZ`r^f})Em`^;yLXX8LSlh34f&)!QfThqN=JdP zz4(>x?{=W4r}}5=G^ToudxVuV64QLQh!gz$DV-@i+|Yd_chn*k2DDr#7k)+>%S&7H zt*4BZR`>*2OuXhnN#tB~moB!p!G@-c%M?#OdF|yiGp1p2!We0#1zOrzeKKBUXgd~6 z34D;tvn!C6kx|z1vQdi<;M)J)>)z<;Zdg~gl=fI=n#hqTH-iH=r)``*ru=UIaxJUB zkI-{B!umDWTO*1M0*BLNE7uN3jQ(a!IgO8t#5*LGKl(vYy(_w;161oQ&@14~;J?rO z^(n-cV>@);n3%i-hA>F{jIXEnWN8qgatbJ%JcJ(Jj%_84!JRYp(VcfF3H{UFTSD1q z^Au59dhyhy%;xgLmfY#*$NTbQIyvIlC#RInD!xg8NvL_eSB^JG4vR_Avx>&HM6TCkNjP`e4NTs3 zygn|Sy7Z=XfY zVOjn59pcRU_uoBoEmTKzUR}-~4$H-Po%qD@TN^@M?XqS0%p-+CK58Us)=Qsh*=(q) z#QEUR!=5BkFx$mGqvYzmWJl)ss*?Q{%awp*aNNU(&!T1fsv5m;ns!`UOF2tjTK`Qg zf+;TdgvUqVjT4iZR#d^Z+|p5A)oJBsRZIobO@#Ebw+DZ+PW3OZGtvhKs;J^9b!D82 zGa52-V`q=O++V#=W;ipKrF+Hz4jFu3Mg|7nfG>Nj=fNxDdcZ_|>*ctInw}vX8DDE@ z?I9=I_}`CekwQgW|7p+_tb7+ab+);tva?Sxpl_W(2F`h7p$6I@R3LoEH)#cWQ*mKd zO_BB|J!zu5ckl7vC&q9)UM_TIF0FuG!7d=AtOB3fJzStb@pZ6Fxmej1A+L5p@rV^p zz(DSYb-gsNJt_ATa0PU&*QqdY(-t8+VM10gQk{d6y}6ejdgz z6HYVDrD}y2Bv05m_yeiD2nT>vP^73Q`HFqw)@#wJCVuN%#x}DfR||*X6m5B?&AphP z(=_jhR;^FxztiO$KlY-$Om@jzR&r4?cD)=6!Nz2Szy&H6{W8h6zH`Zz+kLXX^87xT zp&1Pi8|%->{@m?dc{1KM=r?2?fnhLH;8NC5l--0h;K6@-`SNArIK&VE_Ko2U zBrv)TVyo4iufirTw@Z*L~;w=r(+tPY3_adTxh zw;@g+z0(^hPFcMyWLoGjGEx~(dgNXEp}8mS^pXB8C?{u#WxC26yfxBWRik^z6{UUk zpwniF2<|RrSHrF-!j>m*ink~Zc!KUac}bn3FP$#Tks!rAWs{tf>Y+}!(KNWAN#}m! zEX|M%qf8us!2E%#;qoE8wmR+(k=}FO7kIB|y7<6c{>-8l48pR73<3N;0T4`r`XtYm zzBefDw!nghX<&OVZcD$BNV%E0NHfU%)QuRfai>t0rH|;8d}()-WR2o%(S~(3FryhD zY5U>L$xIl|J$yk?De9X}%v)OuAu+?sSu@Jkr(YCh(AOUmGPO^`U-&QlfM1VyJbmg} zH!vk3Cle4 zG|XHKwkUtTn~4D>3Q8!CkpN3jGDRUq2(YsHN_lu5kO+U$umDE8JDRaEx?Ei1H`k8d z+x~fRXO6uwNUJefv-RJeZ~!j%+k8|um?>GHr1ZqaMQOXarD@nVFz^lEPMrR-)hOza zhwVDyS3O)_i^SOm%c-e!d9qT^wI~~`yB)YMEtW*x4XB-7rEnDrfv~huppWWYup}Fi zvNjm@4(2+l>UP`6UKbWk zkk!RS&olN}W{4|0%PpV?A5YmMqiax_4!A?n4WZ>yq~EyG7K;RlBPVEqrI!uodO;N} zuP5{pMyGc9F+z&dtzs7m2?q}GeDa~yztCyQ%M6xeN`-AR=YRWYS5;TOyNB`^idGw# ze|%*L044lk^(*TTYV@IKT-H-@S)AtssfIu7^HxU{hH7l|xKX^lb7cF>dkTV7bZXqN z-7MTX_+1VVbl7Xya?@O~yB4?+NH*U;jFJ)5JyCRl~bzAf=_hg;V7t<|lu62W%r`Y4?u`HRC zcx~0y-riH^u)3Bf%{sSUR2q-e@K#p`o%U5(HDgkVek^t@7_56LqfYEoqgafP?vXKC zqrYdHJnEnvx$Px#C~Tw=VLUhNGo?@+7EyawpmV#ebGq|)u1s)HTnGsc20O~!xzX&( zqq`&U7S=dBJX{%Ek4m-%&Y zHsvcB7BJmfX(Um(VUA1cr=9Z5q;l9-F7YOKRZ^Sf1HNO5aW9XzXq25#IQ;xxw+UKU zh{b%63}@(lHj&cJuTd33mW0M&U)Iz_E@Y) zd0(N{srqC=qUJmX-z7u2si_9drM_Vcuy<^CCS`E5u0gKPeNoG)HwXGe`kPPN?ok_U`DCpj22m zgXOVR^q-yWb}v*;8o45ti*ZXClUtXc4``pT-KTP*3Kwxpws!UWm=%^)AQvl0!|mFe z?h?DKYV$?Z@TFnuCE5jsVowqA@yOQxz^NCjL5BFAjU1n;1r!!8U~D1a<~Btl)c(7} zx2xt#$=opQ56aPcoGC+WgW@>U$e7CEaJDRWt)=}j^Oqf)z?p_=@BQR2pPOyLLPRh> zENKM56}w*YEuyxDp)CJ#NIcf+Mat!kwrLJJ%>N-25y4aV3qvHmp4$PCtM_-fv4^VRvJ`wf%ZF zf!oF6W}jb4$&L~?w*=aNiXk;tV;Z?X6o6~<6z(}K26Xi|9!+X-RV46y`5oj+w0^m= z8=U!9=Hb!7X4kq>Va~qBjUBt)?xhPMrY50+HM->YDL?Q%{B@t#_P$RQ-&2Iu_X@hQ z?|5LG%tgF_6C2Q&O`vUh7luUHu8)&s@Z&YkJZ0#L51IA+*d*GT9Z~PreldHCNZdrF zaL9&J`)LsIR6KzhnjH4&$I3JT11tbKT41du3qchA= zd{lhKkWLkTZSZh|F6x34fd*tzP2`A17{me9i#8Z#4%iK$gICHO4+MxiOgEA>ZEggW zlzj3=6+3e}v6vmZVVvF#`40z14wZ}L%+^lX zKL&r69_F?W($XLtG{t%Ib) z=6`Rxn>?14xe3oS_xY-&f<*Gdh)3G;@DNt@S5_c-eT^Vzrs0jdK0!li%u8+QV7BgimGE|1j?;H>>gmY_d+GaCMNXo( z+j=1hzlxH6itE=hB*J_HfUUBu|Wy9o>#YpW3YZOnB!&-@d z(NcS@baxD(^Nm0tINQZ!WoU<3?}L+cB}DETzrq*XCAD4Q6;U-=Z>%anw`}_D+91{HjOW%Zf-wbZ&S|_io*C^p zD7_;5BC~4`@e%YK2(;h8Z`_)x?@`j#{UpA}vD}Bo&YnSqY2t*kJx$ODZ+g&k5)`s> z)B5AhN5hSA5jZ%y1|yC|Mu zcG~~(+Xn}_hfG2Fi=AN~8h*A(9ieZd8K{0#LZ>7lrs^1666+c@;mW+iYWR1RGi3Sc z^=GQ{3n}^W#Hrxgc5Q*%3LajKSI$42ZP}M73rmVC)BW)gsL@ZnVdR5gg~hlDl=ES% zTS#c=ZI$Gg%36btD2()ooCJxM<4uAdNU4hA++MOk!+dd&z%8C895y+b==wAeRTh#- zFk8wHQq}WVzEXF`3*+2cLLRKm>a!mI^BGlj-gx{Fj(Oj?D_{WoT)@2*w33>#Tj>6k zdhZL?)XSR|g(uJN4KWY#6ivmTei$Jvn{oMGylmyUvYOOL1e;ROveMxXfDM+ zf=;en*EM7lnIcy?yZ8ryU@Z=D8pyUU$7<+h0PPDoQVy!|f6vEZ>Xu6ULuTex@|zO( z9zIDSF=24XF|Vn5qxcEWud)orm(SaMc&}a5{LyyQF-Xde+m7zLIXwpRhH?w|$=@6N zYkbJ~2z{1DRp$_ZU#dm7T`o)IBIMlAPOeq&eD_%u!ddE@K^Lhq$KrwW!=J zY%FiiiSWTPV`6EvLe&7KxzCRSHcLytojSi~sWkV`{6%UkY@@17DA^K@!jX9u6iB&Y zxzW3g<`rk}Ayl_XD)dteML!A>u(G{$M5p8 zy{da%Q!V{S^CKc!HIrWN zG|wBRrdYT{y1dU)B)Nx6s4lK*^U;#IF^5N*|7hzt{kwk8nk*9)bL2Bqswg6_T(gaL8PfQ&%$1G zwVwVZm`7pLq8r1!8{l})zCMpm z4R@<|b>@3-u3js{3PA>GYE4aWi2`_bf{8FP-f=JdrK9ttc7hI3lri5)_M{t9E55=qjlI(jKmSuZ;rcI8smob~w+{8a=&FB<*|{Kee#l6kj>o zlXrOJPf+wt9@)b20pnmC?~@Q)?{*iX@nkWR`$6RE{v^g{oKsWc#X_}gzo!IWWB#rx z;b5k@k@+QD)Yw=87LGQdg?|e|RY#wq5?(mQt@9L&(v3H;z9(k4^1SD5nkXz*r8P}? zXa?SErFv!WrT~ezrlg52!`|&z{2rumo9mx>+S3&jOj zgp(S{WT8r+v;nCRJRs!+_#wg47y?^aAG4WhwXjWp05!!eX9X7Me@Yl zWpAC6rpA8;J1_MMn3DTkIFxhCsvfpATOt^;vdZ~6$!VQquZ8Tc?)Fv zRu+ayldT%?Pg4X+&mTX3{sAm;fiSY3I8eMX znf7+@G%Lz^x7M>dSm6qPyBa@bEl93R7Fb_WQj z<*-fGLuIBKrwsfieV~8wm9)S;-3F*e)ipIYXT#4Mnwo~MNP$C^_}u|7cniOD>3^e| zjNk9t6NUZ|hB0t>dolXfK@N|qHySE*h^_TqL!aa{%%sNSbxzlp5nG)K>5^c5bE18T zCk|mp^*rKHT4G6N)p}oEYwJKG&cA5%bom}6{9f`=sU}*}y%e>sko;Cx*Mn-0fcZFk zp>X0?Mkl6q$A3TLpb=!L<{Q_z6x)=*Zav@9UEaV<9F8Od_>}kT%K-HS9wKX}H2f04 zx}ppiWXUQhWcOhzy-^^!86F`V_PZE)yO)UD_JP95Dc@A20K@y766 z0>5#wE&R`(=Q_Oo^VfrU_h9*^;V>ihJrL%=u0G$8t1hb&9-Q^G~#T>#tvo zEx(K~Dk(M3y{8DXzG`D&e*E+(I(!5uQ#4*!2LURxrGNsYa2nn_IeQpi4rx= z^XH@js@2TQF`MC7g)jR@JolN9$7JO}jsev8=1iJ;s~1l3)GprfaM4a%r@bT_)Etd??3D~LUpV#I;x6e!8~JM_}6~lzLO{`)RBW~Tb(ce#gA$S zy9U_1%fiy`0b7|Av_b(3mzjwPIq>1`>K{OlxPb-~xX%OwZ}pAM%$mmUA9;b_WU@gP z_pSFawMiG-+)^vaMWCRIg+{FjBfpmO)gn=YxK0$!4 zNdOY~fcrlz9Qciw7eDOWisgbYuX+OO)XotD_^ljwvj@kEcn70%F0-SfOekcqr;B$B|F*6Ll z;;j4(Sp(ao1<^?ghOFFqtKRT2FaqY$K#sUaeN!X7kT_`|c=@Bn;51cRCi8?1Mdtus znqiN>7&ukMVZN8Mvqa#y0zEzZnR2|ku1j+8KrRKj8>Ku_+BrA3j?ZwC(->W{wcrN_ zkUo7NwvDvF67iN6`<~Kf_pO0dAG?lPY{XXOqhtyGv}CX5vF~2$`qN4*i2LS(z{6u| zt>MwjvI54O8*4u0%7SHI1b>JV&TnkV=c{H06*Z9StrI~AW}RTp;wGoyq`WrI3Fq-J z3Y7$;SI;>nxDQlj98PJ)9F|jT-oPFVrXT@sN-%G^HZb%s9RvtI{&M{%2DtWSo}DNE zQH>!!nNpFaLmVN88;O!f=zJ^1-0B6H0Wg>P;|nAZOK6)ILlSmmWkG9ToV|=mLNal4 z1zej;s;UZPd?xA2d5u(&(U!Y&KmWSlziwG2#I|rXPkO|P$FN;s#G8HJ`q0VLXr{fG zIpV5u;2V4m!WlmbIFsv?s;&bkS|DIwSqE*5 zjlV-FI5jm%sn2+L*0$6dT$oT^Fd_kOpRrs5)?VJ%wESFA0$hbyzZ5d)Q%N(F(||2B z44{_Fk)Q(juFV)^H53in?#Y_^&ruSXWKP)H;-@Cqj&!BI%ZV03ibSRD^;YPSDYDx! zs}kX?Y~ldK)u)!gMTjVd|uK#LtpS)VVL+!#999R_UGVCmJsc9Z$^6!`{0PV3Ch z@)a;z1MxpEfN8>vwnNcDgEK{FgiNyo9pd&Mcw2`KA%99UU%4D%4prHw#WtsJdb!~- z2{_cmS^CCInS4r~kmp8-=Kid7!Q^q88v^t);izJJDjEHJeaKk-e>;-sEhlK-SZcmL zw~G%rLknWp(>&fS^H736L|+UGmM)5pD>qXA;%aXHfWB+E8|xypSUoPMeMYt>7)UKH zhT1_R3x2~^UT)s&>U>!!dhDMMu-I2n|G;>x!1q9t5DDn_0L}x#$zKA5wixIR4Gawn zSkOU$+WXT15>bG|7aK@oOG7RFpG*nYIY&PQ8PCK7!t5tyuGzBQ0w2f8yFeKM6R{HDU`3bUG4rQB$7f%JeX_s z-F^A#>-lp@ss!PC@e}Wk+2mxsvn#i6XSC3a^$6W1S3ht&U7FC(1t|~vCYjqwDt|a`!I~@ zSr0{95PX233lxN8skvON=s*@X05DGA^&whImYfXC!l4UaUYhi9&5aVm%^+nhwGcg^ zl)VjK+jm2gvyqE4>l%TEAj&+>j;V9d6t=#`29}6FIMHjgqqKwaY0Jgl3qb_+>*Tcv z-y$C(%08^Ni);(|-LcF$IDT?|tY-WaT%3Apzpc^GO3b6H)2iNF&!0RsW+f5`A}w_5 z%&4*zcK&(*JCWhx7{y$n>f&<`<(Z}R4TRm@KD3idf#C`d;lSNpiw`m*?q2r4k>Bx- zJ>&YfBlspQVtu+5TzUYOZ}--_&B|-ZTU`z**5yG>Hu3Ois0>UV>BA(D=TvaFcX= z3}T%e9QHP{{)1j5b5YxRdQiWA49fGzXMDXei1xZZtw`}{j^^2PkL|!XP8L7G?kYLI zWK|fyP?I#IdvT$!i-AfrPgoM#*G&#l~}hLs3ha$LuRe+1C0TxX;yjjAJ4Za0%;V@s{ofl zYzq>SojELr`S5T1@Z~Nvk@7Y&7u3t6wjm7J+*bWlU*XI?5PkCLE41Y>w#`%(^em6W zT={~gkPz(pmR#s&!aiqe*0X9h4pzeDd{h-1_w%e>SfFcBQD)}4VL}cKEXmTKh_b@%T7g3cTmSr7gYXBk|~05oHXS!|!zYEAH#z-DdotRTJ9 zE5q(L>yVYpe!)YCVicRcD-y$P8=E=W3JsgseCdt7;`8mmRy7CJW2^V}!tJR$lW?j8 z<99nfLkL8gB8~o=vPzuKyPpch+%*q;rOg?1m-y2bH7&O9H%>>yg|lDj>vOkE8mMG5 zu!fNiz+s}ITJ$0MGIHGDJl?8cu7T$TK7rgng5@lQdB17Kw0J9E;_mkUsB(7??|4{0gZ&@nL&^$5U zY;0A&@-bqu;baw2wc`m|PJUAvEX4_U7QSln8y z_kFpmGYO&!>Ri|+K{oFIu*DqSE3X`5Ja@=esF&jG3h$h40=V`kmlIR~H0_YOf(9d0M(=>W3k zXDhPD|AuGyh7rf|#G+xwE9`jndGzs!Drm93q;eMw;d90IIY!E_eQnyJOHz2jKmJ>= zDJ;41M@rl$*&6MA`hYx|>l{qA@UWWXRAOtA`hL;BpmKRV-o~ec_@WHK=W2}$qV0Go zTj@=RKrTqUyZV_=3&){=?*-{pFUZL4(8B$|#+D}r`Ne)jc@{5I6yDE1-(E*y!;+L4 z$+lTJ@l7GM5;4D5LEyUZ_SGwolQOVp4Mm2Y&0bh(IOOX`mD0!?ocG;%-%}pU-#M z#OYPT_&Klm6WfX+8ry=IBB|l}Y%K%4_6?^Plvt&N1M#N`NKjZ=3x}Ju8Q(1ZRE`be zGeEq<%Le=?4x0&rD8X9PF+2e^Y+Mwva)4#oo@|{s* z=SQ+|T+_oaVqoP|MA3v&>#~(=eM@|F{xwlurV$lQ$G;|OBp3GT;mCkO4o(M3SY2Zo znECl@7=}TkP?MP$u)UgrjOF3n1z*9fIT>R^_Sp%vDrT=SDUg5gfn_1Opcz}$?sv@Q zC|_gwFD0APDXEx`mP#_px7|~TV{{F|7QMqwDoj{gJzBe=>?|EKMg$vAJTf6oj#gU* z0Unw(yk4?g|3BLua2!vi2oPXSRj6In{%(&DP1uk=s$cfJYC6Ha zWfN5ewJ2BzX@>1)n$!3F-%drS5lCxUjVDJ+NE{v%&Z+!8FywOyD=w zP&A~$$;t-n)306ZpNxqV>UloCkICeLhT0*c#bIo0J!F@^av1d5>s7IG*syrs%|pem zU+R8$2|+Y0cQn*~>sJ59!axkq%gp@^8ie&DYfhA?l~NOUN$x_FRaJc2G@)iq$T_)K zyn&;*a5Fa(OMQ_$ud^>H zZTr=}bG08%GD|H^83+v8Cv1n%u<)k~_mR!s_#&0jt8YAn2qL6oCQ^M(R*;7++`{Q6 z;1Yy-Us@U*f^dDEc|RkJQr~&?Y{CImr7BLph+IhNVf&9#k3W=*ggG4rJF>h13E1G2 zvr_hYc#oalw#;i0ULWv2(+3Q_S0dhnweNkWj+W{F^uon}r7HrvfsnqV_ZJW1ej<(U zY8FdUN*a-H{HWMo{Ffd~uKnchQ?rvjs4ME8TU)5cj=@#e5T#I#F?l8JuKodk74;)D zY9%gcnXTf4DtGyRm@$FBV3Ye%s+~m&+ZPiG^8j5p<&Qp{X;79-ESuUwH98T=^%V6-5*u zh=JZGop!@a%Vme1LV&Tw>GZLffew7!t|p<7uXc5-ouwXHCU|nPV=VgSN~Po|zE4LH z)S*Z+ZGFxfU#>gRT4yj0fWQ!xXLNlBr8^w~T5x!rLS0SRwB)jsRAIX7lSONL`!TPl zOIgUVTIcEnJ9Cp82MEkG)(*GSwB_+TT@SYonODGtC1bw7jika5CI_2aJVFM=ASq&tSA2y*6M!2_md|guq(pS}%vY3B6g3vh`le))xJ6xiDiJ%s z-}86RbM2lQ zgSGjlw_ZyWMK3+%W$$|vk zygxSwOgl7NYYo*R6=T=*a!3t-%~f%3AiqMf7^7_H@pyd#GsZG##dgZcDgT(;0mK%i zL`n+_PUr$ipgq3`3P>ax*_PCJo{}`9Oa@ z6VS1;W?(Yvb5bIcCP6X*M}*6z%k{fk<6m@2O0uv?&7b^By=gOuoXBY`oB!sRrVmau zZ6XY$)y>Fj!|yEUa9kZnT>AhffGqrIXzin`^+OV7jbLi2+Q9p@Y7W4RelsF+9I5q2 zEh}>AlUf#RT`_US;Vuh_fMYS%o@Ebmg?!A&Gnt=8hEF1G;gnx8lAQVil&RcUWf~zd z80SZvE_ou%>1x?o_cI_1?&T}KO`Ycu>8CmwloS+d#=$}@dTw+sMx++!5X*0C(;8uk zZDhYl8gT*vgYkT61=mIo#NIJ5ZgwRA`%eWp*IFYRCbbUt0IYFpPmH0Cwe&$J3BJBXD$lb9e9CFcYS#`lG>W;&V>sh}(E6+#5q8o7+}~ z0=<}pUI+;nscgY~4ew0_Jpp|?o(BRE8@awi^{TjCVkX4>Nqi)mxn);3Ui?P3dY+sQ zwYixeK<6G$w_YhrY^+N=^4~by8Eg?+>sF}a3N1%x`F}^YTPjU6Fe1Xv=x?VR(1lVg zS-Gg4IAxnN?6ncq03y{Lv@@%0Ajw-S%WP-yXA*ok)9?yZ5Tij>sN>v7v!yFo@dGh|04 zimF}(EV^yU#5lN_W-Fs6-J;=JU7{AX;*eEEoj0sQGfk$8^b$nxC@C%WbQ^#(@QXt! zwcekHi|YpMJY=V*zd-?9+@j1-L>>!|iER-(A7-=2>bN|#h z&hKR`ELz*DClOT1OlJkcna0PuV(1p18^EiQd1NEL$JCgOTxDjM;m5B5PZ3ud&%IP* z0!?R|u<^uafisAynV;4#!i)4OUQ+pH_5PC7WlJ+7y#*cN+2=T{l559^&wuNqLBw|)zo(5_hi7~se@a#eAG54oWFYJ>I zo-U0i>v-N2w$-R0++k9o_vRKUD52vBM%4f>I;1jm@C3yQOv})Re^I^iqOwz$nUOc_ zpZA#Yl&BK70@&Ug)1O&m$&o(XN*ZNl!o%A}L9h>S!##{}%WH%P9|2)wqB3nrgPj{G zS0p4ZP7!WtF}g8Wr%W|{e3h^sEfATAJl`+Uc7nn+(!Z3`c>(J65NF$Vk z%G0swiq8LOe-eE>Y#nevg+@jK^6fJ|-5-4r-QyK1V-gbWqu=XelST5)K;at|QG_D) zUUoXUmP|IHki{gb8;S<1F9>YbRU{z*7cye_T8%|ZqprfZ6>BHC@J8ncJ=oOv@ndkm zn-pY|SAAsc8c5qIt|cSe-0Vy$xIxbf2oONmKPCp81Q-S-%10xg({ia+5K!P@&0|0L z+3_RfZm1hXhgZk`LEn-5aVjypL6FlXtC$p41iksljd1^9dF%ORNfnEh9E-sO*e?rH zHNPW@N4zf7mg;=VBv#Wu^Vx|NS>*db)UAYw2)ET@qH)$co(mz0BS%}6(hhED40b6T=GGTH*&6M` z+U4BP|DWsg+`Log~k^+WfgPqGkz?EZp`8UUJ6eWnw03 z$CkM%?~hS_M@>%~Jp3rMiISiUbFYGm}pN8nfk*x4TAeaf!%!gkiJu{TnlG zH9-+Y3Q0#2PMH%TJpnY2_q=sX7Nh(BB2%MOb+LNPm`v#}Ek-uf?!pwyMdqy=ceaG+; z@p`pG>gpnIZEX7rWWwRTPzZ>=6+GG88NU5k@xk$m^X#FfdadiK|Mpbo`m0=ZyJo0pMn;AbICcOET!8I%<)US#SrUDn5HRA~=~zb|n)UAH(y#bC zsi69GnfFoeO4me53`>A%o94khe@Lj)va+rFnbiV&k|<^_%X#!L`?J32Wz3f$*|Uk8zq zu|vnj3Gm1PY0UqFnLb!$1)#9==gWw!{3*0{w7$kkJJz`Ve(wf*6 ztdldJmS#Kmt+KEF#L%62UsO(1xvc!I9|uN@mA2+-h&86hz4VF<2^vqr4{tud`K$Mv zQ(fWHSo3j4N{{Bo9XW?+&xG`xQZxa$SwzL7;mrfEmRh9b!+(`}K#W_!4<2{z3HMqQ zoBAA|6HnaJMkSp2{-C}eUvvjTErP5eftBaac2+C**X7KVOTipDnh9cxTU&eblO6g{ zRfjo^>!p&*v{r*f0orupmp6dg>(=N61yc`6H#hp^Xzr&c4B(Ud_h+vwR9KV1O9eGF z6jknb5W)Zlha8?&XYXIU-kAOV^903CW+6XmJFK6#J=!4TyYS1}t(TV7Vof&bh5J>l zRB(BsQ+LD1)|MWm$eW%7ou`bHc{2hcoHC7X$&8=?U{TN{EMIJ(zx?#6c%UD*JD^8v zj5+CVL^s9t&uEkQm1(uSLC|2q&>|7Sf7`FUr4)_!-+-XVqNjtnf;w}bwrec`83e_j zNLo*?H&>%%BKifuvq|}l2d|0d!2*5%0PqxOjrnDAI$U9H@Lo$5va8(`GpTxh9tt3x z+q#~orIcMra92?^R>*X0kzI+EzKoid)E1%6ke*K; zT}s*y9+*=3j?xYYpTa(f*ER6Lti;|59i9-n4 zi;gKmX?`+udEkMtKd*yLWz!N9t2j7-&#rf-409X3W=3?MydAfn>9Qb}-KIF0q6M}% z4f9T)bw`tZZDZa3-@EWC+as(dIJPxc$glQ|y?>3;@(g40w z_!bW+-P%z~t{T8IR73x}T-8?BOE?*kk_26z+_dnkjsE$a8|Q zZVCyL%VTY+0>bDY=wAs%AXic8C4O+W`qA@O;czj$y?3+eLB*83O}@_Te@)V5X?ae@ zmUgQbvRP_j5-BNeJISY1eR`y$y9Qha-}z;!PM9ps$OcQ2(55r^aEXw$60SHbzH|&{W6kKL} zsQJ5FaDq;DQ^>f>=QwrhhwTK6gA~nnaGO6r9;7D!2?pg?r#_#*kWi$k<1^HFjKP(k zpEwvXnw6c;Z+mk1TsYqA)DZ8@g^f#~czZW!2$FL-ZL9PA*-*0$DR!EkZe<29X;~&Y zcb4F<}qvdd^h7J*pm;_U2i3Z7&v@tV}yO2ktJv z+kf9dt+0L$-<-JbXWW$ZB=*?pwUdGWu(XNbSpu5dztV~7T1~0nTp%yn#oynKmj`v_ z%R;e$;!iHXh6IH!`rkujl`?6RCfjt_RuFe3D%iMow*$cj{RP|}kZeIP%b6(!{&ST7_S8QqBWU>zM z%BoQ=0wHL=7$Y(7)zK(8?)@>s=LyN4fQ$Ws zeS=iWXM6m{8>9vC>ovj^V-`#!fC`u6729dqB;hYC$_!$Z-tNQ?fxG0A)PEnY2F|Wl z*N=PT*hqWd^+Le;^1H_`?H9ZL2sEbnV6sck8vsIW+3zzRzP2+@oL}v0G`r8XWwu-iB8iHFsS>g4C!!tKxYAzS^FQ^q2P-sYA-jI5g2V|VdYj8-($?^9uO>;(8gq=MI z4&{rHJZf%fj(_&mz`m-qhkxLSIRMadwW?-3%Pl{#hp%)M81fb%T#xVq&zY!%*xYOi zGciAin~p$;qH1Vd8rHYHsH|z5jP%XL*EKC$3tQvxCwIH6qb}L~jj$uf?spH)_IQk! zsjxcOchmAS$XO6IKj9>W(9+rVeszFkwG5xTk`-CXXlZ67Lw96pgfxpIo}1$T3-)lk z95a;uBhyCYjS&cVfNKuN4#YaI*klMN7L5>DLBL-t*!y3z1yj<}ZHeD8*d%aceTPUR z=r9M@Zh$XRs8_K-)0kH^sn2QosbBiPZ$l}}uvdG3pYLwdFp7B*pvA0FsHDOu(1gDj z{dgPO$|MMiC+8CCCW@MONxbxJRk{FA;k<0;?|KT zEn}a;@@XM1{lC}T&uyx8^<>Lrtx6hsj33O^fr7;&*o%$7=QWBkD1hM7qRR=G6)~~+ zJ57@tzwU%f6Z##_HW%A$atZEiF2w*S%BO5u#8)_sv4jLvzR~KsspU=ZJ6XBP(`b@+ zH)OWvWFJHGX_H-7b|{;b>xdr zY{Qj|{oY8Zu70xU2wnaPmnA^7KilzkU~D;e2IvGu-~WPMDk%$g@STM3Qz(upjx|Zh zqejg=>nJP3 zcc8AddlX{7Qm|&YKPRX*{D%AV?ESw%7TFdTN@}EBc1hH zT6}N_b2Hs}BKvheS1v{yxikYYqos%B3LfB6;qdy++KK=A2M73@#Pym`b+FV~$%lsi zJZILgm3%KMBrIJd&-Bvy@A@}@t9uifcVJ&g>$5TLKm8O+W0wg*hJH9U`*xYezMiJP zc6SVSK1D+1;Mu#fxbjWl7c(UndcP{J?bU7>)gK8cG4g`ATD>T8 z1yY{h?|iS5Qj_|pR8&TtKlP7*y{^so_@I9u+;v-NY5dli8ek`>E^3lKmoL@(JwY63 z2y(OPlk%Aw0uF_IHNtzSM`RO;cm0048|2;0o+TW@zq3IKi`UG4W#KpxiMVc6rW6TG zYMPCWsL6mc8#NIn?TYqGTG~&#_5)j*GLQ`{Rjg`ya_%QQ_>bLMh#L3XI)KrEu4@lRSUlZdI<09Wey)>Gh`_Mqd-bY6u7?hx^ zW?8pA%$q|EUw@7qL*y{$%9dhaDgf8{%tg6M2UG6F{M`0}M8lBmLG^*qyKN3;dUc~- z>8N(tnme&;4yQ3(PN*#;r3+-zwu%QE;r#AusPXMH+7ed4m%VpPV_VyIge?aAT#=X| zS~xgqea7rrwL^#(DJ{1U8<`iH*6cX($c{@IrU>& z5a1>)KTwoACm>UNNC#s0Z#d<}m>YZOHAYbv2N*D>`db!3I5*5L1WBNDsMj_&?C-xn zISu0e;|=nu3_6Lc4-QVKxZwjSRMHJ5gq7^&^-h;=>Vk15=gchs(Bp<*_yL}rYs5v^K-0avbI_m`UMP-GF;^7()3dYaqtS? zqOt7FN?v*=9~ppYc=_KnI1TG19DkHiP|JAaPZCUb-^RYGGet;?Zi=F+Fw~iQQaXF~ znkv7r{8c{TM{Roa|O1ziogZ=uSnA>ei6zT#p5 zsIcp=u|0M^%G{y}Cr?Fy^%h3YI)r<;zcgQO+BzzG5zw)dm#aZ=o8VUF<*2Ae)D*Bk zFF3@?$e8r@P}bPlnqeqote)S39l|eyMLUiDW%!$ zEDoM@gz@g1{iq>8PT^zLv17h5xVB|`u47wL?8f}=caHvit>g-mb(E~eWp?3akYwRX zJoNiD)j>Do$_uggexzTjppjhY*GsN|T%-8)M(l=xHcN`IX3l+3NtqLeAPgDQzom=b zv!pFh(kty^Az%CduGR1T7Xe8M@%c&`-@BtplCp#un8=yNb{!rW@x?l-g1g=|NX4bZ z5o{NY6-mooW!MH~WSrDSKl^YO{Y+R%2`U_^hXJ&HeTzd;{c9FSJGRa$4D!~h-?+;) z-@G`__t$%?4rat&w*ikV3zPVhu^k)+V=2HAlKZ#={JG-zd;eYpv|b&+(Jt1oU+hHLJQSD=C%AVI+(GEa)Hjy3lmc){YRE{>oVUnSd$kVU6)?EdmUn#<*G` z*q6A6PzVOcr4WeAghRCP<)P$XOup>=Sw;$EB>5MR&^4j0*mv)L>-{-8RyKnPcA`_( zb)%;+Q@E^lGJC>(Wx&oCU{|So^5&Bdz4O$GvcA<%em*z%<-V9*yzXlUmB+8QI-WIG z70W1%%ue;9btlemEN58UhH5*XcIX?bQ#c7pyIpCN*4K#}RkgExD<^dz93ZWEb<)*} zJRT%3ga!VBT&^r;zwY_3BcilEwD17(T~B_H3xxhmi}SC1w>_3bO%hR4#qOvsfFe7N z_T#*cA%KUEtoo`=pv(uigUXt=>2!zf41; zdUl}+K}C}M*VnHWTP@B2sht(}E;Sj=vSwny{VZ@igBpQc(cbNInwJJKkDoH70GQc5?~7mEX4;*mw%!H82e8&wq{Iq_YjlA8$=0)@g>erC2H);_4)=SFp(^lMb~^=g9k&4Pd!QkS8O3_l8cnIYuaqs*`U z*CB<$@>-f|7lB1rqk?qb_tq@n*fGYzZ^6U;bA;fgJfvQUCP`xh^6q#0ScRp@Rx?IcuJnqrMuP>OhwyUF$!L(TR~2yaNyVg=QiHN3}93ZF~-5xj*cvb zH5S%&m;?=Qa$XYpp@Jt>Mb#01r$w2RDxf|IUgd`4kQ6jqeWztOzMuXR>T<2FiJKz? z=hEz`Q(~n+Sd(7GgU-UubHP4SX1Hk!Xch*(zBb+8LFY&sEII(4H0};E@fx#mGlBc< z?|2r!+nM!|vyxJx*e5`mj`japzuc+y+32UBU2hOZVO*2ZTfx@})P*c&bVEacw{h=t zuP|6k4U{I}p#UNM?L)}tz<|AY*Lz)A`3mbyj$JQvac#*vb$H^@({BSCCxkb3M7x~`w>Z!u}N5LA@8VBdyOK;Y@m&2r~6dIX2rpPcB< z44#0U+~wEsq4QUI)^aSPc%Gm zGcQcUZ1zr#Ln9X-ZWx`@NE09ofIiqrZvgci6rhYr`x$N3n(*durTKk=Z|#6bxn={X z@POUT#|7!A22flvJO|H`B*Tln&6RFek>3&)fl`Evip9%48Z%MgcxgEt0^EUjZuJH~ zk#UDY7)mKRh`tMuS7|6TUpS~RZY$$>LlolRjg6D&y4}8-FiGIkd|*&T^4Vns?V_1H z9;Cp)O(3F8XwCOZo{~>pB`2Ona$Oa3_m_# zEfFFKxQ=x$D~28~gcMG$9UYIKQt(gO_5Tp8(Yr2~t)h5HO~xO)-QNd0?%Vjc1lzRi z-RYi?J#2gw&pn1ckurzTP$|PMP-PTKhJn?QvYfnu*!?m$Z^S6x>;WB8Cgj?)YqxGqF;u(Rk5? z9VJlVjXLG_8^Cr{_Gq@;dTv63R1lfPzFxz@0>MGtUC?-B!uD8$RUiOC0?eG|`CWhB zN(_bc7x&jcl(G;~ExB=sxE?W>ro`8EKEDw4Nafloe1G#Y5CJ*zWAEyFE94j&9DRKm z1<)c*{KUnTp5r(}OE3N@yGqY+PEx6&Z|Kg0!qNo zG+WjGrYpF!X&>IbJ2p$L=JLi-~0@}lQM%az`!d0n1q2z(wcAL zqHrOd>Z!~*Uh;qFcV899TONywQ;Uj%z>nmzi+{qyqjRijX)N?_(f7GH?io^6X{rxP zp6V~QrNjENev1q!(CDH^okgqbS1C05MRi%jd{IfTbu36F+`Bh>GQ`~8nn%3Rd@fNg zFPryxxIlpGUsh{f<*^NwXn6&NTl8suc70(XAl8l+r&r_(nJf$NnyEtJcZ^Onyo}uy zWkJso!(e4)YPmhFZwe7yCEVw=jTEmj3PD~6kI9P3NyOR_^Hw!FU&1Us7v<@nYC{v& z^?#vXy-M$7E(u5}e$bRXy#UrkRsbqhTDM;@38jry^fRGMioijjd#D;_aKeSlHL-~V z_&yMiz8ZeD`RmZ?w@Q0NXP3xfRqObNMCFSUz8Ae9B(|%@(VuNl^&?&3pP(S9e0^FC zCstknbs&4dgIv10pb{N7JYXk8lmwaEJ!1;t5b6p1i)k2Rlnh3rzc-o)7gzJdpP)EE zrqxwp=x5*ZDoq4=GcfX%kHu}$fNNC9Q)ck1UG7YGM4Y|d?|ZXvT=rZ*?xQg;EGuNr?C|>)~7luv!)2C!Su%J#DW76WreVCku^%ZQLI?V{=ba|u$ zg5~BrRiNIRaj%jyF)|6_4P_i405t{T>1iQ5pu)KP{_&JqAQR`mxaQ<6bqt3fzLlBKP*Z`2Y_WHZVYGj*EMSF62sWI3SwF$@&s726#Syx?XvD(0$aAsQ!Q3 zB8b1xBZ85fqre?Q0_?pPj78C(n&5ZQPk4)RdMO~Q94qTCi#YjuEKpevG{w)SC4T?z z*DeiU#A)QUV+L|?J0eamv3+;pGrrb8F6(w1iao+eGiAcU< zKJLZ};%kV)0hr(cWS2M2^#%#)*QxtCaR{b!HNzfc-~Vy;4-E8>JTbD;vw~vP{t^>V zsbPmTyp0vZ01j;2I%X!zwQe+SNQGy1rX_-#0%F;l9m1?ilyC7lO>qGsHW|gx$1GNy z0&H{u+}{RWJnWx;AG&--)zxtpc26mZJL$@TXKxXB+)Xen;@#hz4QiP1=iR8ivp4@r zxbpk$JTH!vbI-gKM-;7h2kxJ9fu4KjZ&lCYC$|F>UL$5-N>D}w?bw~Kw@wyeEqRnC z$L2h~B4pIR6=aaLQ>)UY!!@iiav_tBV&LM8_nM!Bd(p5fODJbKkVXWFb$GG2?mhQ1 zFak$LRXmxvNFk=;89?#U*yTSj@?nxArm@Fd2(I9&96vs=ZXc?FjWSQn*$5k)Fy#`c zG6uKb$<;KdBjL>K|Dgl2)t~9HJh0`5tiZvH#$gC=Xb3z>j&6EH+(Xae4&nw(d(?qV zAMp07ygH~jzOB(!;y}UT@$1#n?t_u^><#Tem6FfvT)O99-fTJ3TwNi8di=r!@EDGbK-?^ zkGaK<#5rgha+oV6EN%B05~9JXs@E)@%@pFky7bz|+#ghbvQ>Dh^g4>IiHR`_k|FU- z-8CNy*(qjW68H!r#i0W>cn=h7f)E9Y?@#`L&t&>}w(o?QY5ZBV`}5LcQ3s2aG<&)Y z=J~^cVOPW%7I`ALzDid8oBkW8vLfbh`&cN{)JUnR9K{F*bL}1qpI5eWgm9pgCQ=>t z2~m1Q&xGz8x==77{hx8n%@q|PuV2fIcFV%M(QhGtccJ-a|sZxCBS}>Pcd% zN7&N_Q{?U6=eNH*nj_(j49SKZrvE9lTpsm>;ZkD*49C{B3rP|jG7P}$4F(-a16}ps zKlu1l1mJYGJy0uk`N-x7rvg}T9Q#1`pVPbC`SshkdH{%|sL4l)dqvNbvpD@sD{{EZ zY1I%90(z)N@elno2%zzr7ePZ}*4FF#5Kz?UVFRT8bPVE%fP8T~w`<&nV*lHqI9Nvq zi@y%3fRjv{_;4v^UF_q>@O81Y#m@!G-{V;f4Nb036<{Y5zh~tFT7X7CGH=SwRbLMh zwKu~7szl!XN3sap#B|2*1jMhw+Qw^|6Mj3=H5#goFW98x#_xC0$F?-BLs%e#6|R~q zo17w_M$X;dzQZ>o_XG?cIZSRzWXVFf&L@x9Pn)oQ_a z|I{=qP=QSN9SN7gn2=J1REhR(QJ_TbJsP8gmX`&%9;nM4?- zq{h4sym``g*bmSbLCGVaC-CJWltm`f%bJ+HW)S!rMg!93FweL+#^YnX$Lpg*c0EbI z4q##*iRii#%OplM4^uhLL_XXNxHi_pH3bB4A1TwpJtLX1^hX)2qtY<=68VD)^QEHV zdsLV-r0i{8p_m1bbL^sB9e?{8azpd)pb7fY^%HIc*H*bMECTFd@71qXjcUOxVIp=B zk)uz-a=)-y$Jl+}149$vj-hGD$V=|B-VGpE{0H%rQGd{w%n>3p&E~>c>*Q6T6hVD6)x&>yJalB2o zNoiRQz>2LTN`LVSeD4-THd-v!$Q3EGF|(OHXMFY>yYN^^5}p66?JdKq`o5@9#6VF& zk&u>dX{12}q!Ex7knTo0RZ>FW&?Vj7Eh^F=-CZKx4R@a3|9zk5e!HKpA3S(C?z8t= zYsQ*ujxoRxRn$v_MNi|odJ1=)_Ol*!Dd&f5g|uon$-v1eZ2fmhSWv`X7GK98Zb4zU zQ9JO~T(y!&rvdcSI&-^zMTBP*Unh(?Ed1QkV5#H%4ze;~J3QPV>8*DeqeHSuWF(&X z5hAkdo3Hcs%GYtmHU)~lh<1sh?i6dDn^c*1gfNB9hS&T*{+L>{zt3-Fww}v9!EJZc zJB~Woiq(Z&aRDmkk1*WUGN%Z<>J-!jT!o|P#RWEdhRJc>G#blqOijUR9vYt<3%I(LbgJT> zQ~;tgL(*^U*|^etiKET8Q@(8!n-4Sm5kG!186;(J>}n*8qNMhyf29ltA7(AEi@OaX ze}Fqn8042E3&(h`ZZP`%@tVDDN7Bl$*~7J2IG!Ml>|Qds+?bvC>HF<(1eIP&?+x&! zl5$sq=Pv-B%uz7*6;54ll*~<`3d>Nx5y<1NO-hp37ipP>Zn*_=iF{uy%A5iv(0T#| zx>nai?Z%v;I?nh5hhES9kmcY=1cB?>K}NE%02Vve>A6Kqk7CM)%i!WVFEQBb7*)tA zs=>tK_Geats{^tN~&xYMQ{MY>aheL}= zUBS>-3+HNwv*l=9a73fga4ksB!0^Lya{*dIquJjm(j$BFfQnI=<5RobEV4JNi(r1< zPd$gZ;j3GURROSeiubk7bX#5R5DOATY4q$aJ|bo^&=EdKsQVCzWaEJT8pW1U)J4KD zwDud=b*mv@!nGJ1dCAn06G`AINHZcStA!PPMZj-ZrO$N2Y0KZ^_KKEhd@db*kN z?x*9m>{>(Ztr_1a5lvYgVFOA%W}TIpTKk-W!rhKg#wu&f7-m9hPh=gErqRP~|HEKY$-FrJ=r|G& z?uFZbqzVg(ER1Zz-2tSDP{p8AOYQ9q%<;a1%fTL4ge($hB?TaQ{kQF4t+u)^1rr}1 zgbXVzOox=TyQ~;<0n-7cyr)$ zPQNou&R$7q>#H;;+a#mNmrKUhYN<=B!}=bKujg`6({6BctghzI!Y53W8a6LM+Z#7< z%j6U_Vkcbbq0cEXjJFS2N+CH`$POz}(JTj+Eo1>P-8aIY3`|w@rK$-*Na`Q0(MuCb z#jP1CgZvY{oxbRiKXI(q$!c$QRg*x-BQ!iLB zowR)cSoHMnte15nP!m(jJ{Tlk?O~Ux%%3Tz(VnhLH88Zj7?f9#YVXzuNsH;0Bs`4( z#k68f53M;{NN{8<$O-NXO&{l~TSQ*GfUwR$8+0P(yf3S8jEF4M^d0i8ukW`ny&)Zw zW?-n0>SE7dW}f>y{hxoX(gPtxdNRg$w5V4ag9~zc5qg6N;Bss0#W!uX{7EH314@E@A{1W^ z+bJI|&XeHYK5*POQdF{~+S{E8TXy)WhYYUhyqCpvN}?I^Z9NHoZ>BzJwS-CT{0=er z)wcMykYKp(!%fi?N!PlU=kns$l)T8X3Qe(!b%&n7<`i64>bibK@l4%UCes9EWsvtJ za>+8T_H+@xWd7Y9r+-oqLd=j%Eu6%vzhg0hC|EKG^*BkDxh+ld#dI?}6&g7RYNFV~ zjUiF{8A_@Li7Zr$q2}Hej4QPVHDW`b!ED_=5>_pL8ONB(psZj~oif(8pA?)jOt_>fbV`hCoSI z#$IoFXUb(Cy(n^XL^fDPMv{(oaUO+V;9--w39b8alA3DnaK+_6h5{m@0AAaV>(^K2 zJQP$^B(r->!Ire~d4eRAD zzvEODRKKPALqe2ncI=^TK_HmPh*2#A_-Z}5GLxm^svtqcfXeJ zj%FIk#y4Mbp{zDvC!Zv|V0xdFQNhyKoi};e1i<-# zsGULm~S79ASEs&JH-5gf+EteK37T*9@AoYtUI{!#FuD-qD^aMO7@Uq8Fa7f+&Us$n! zIGQ$@=474vFqMM!vFDX~LGyW03j;f{vb;Zrz;VJfPS_5q>w%!u>i;2OCs`*-GNq4IS=JW^Th`H&zfpg z9nQQyI=U8UWhu2=lFF)zmuiboJK7$()MTuuq?B@Ce=Q4}hcsdVx(=3%I_y-%7EGbL zH@ubezmKZEyNSxJk|&xkFL+~aV7u8q`x{4%=pHFtYu7G6=ElkUzMtvd8e-DWC^<`V z8ol$`#y*a=!^Z(dnol_|VE$X7CSg@(HsKOumh6)rGVz5O-}|B@g26!XamHiUfPwJ? z0TA-A48kAJ!XeE2onY!(HP@b5!nJTi(o*3NN<)Qn%OD;#*n2hk$-4atKYwffFgkOd zYB84b{jr?z)OA5}5mD3+NFJl1fAr|Dvt%g%@5v@ECB?t(MT7XqEts>~*~~I=k`5 zgaozgOQ*5wzLH`%aF|kdWy&H7J%VG z?1B*qT(Ss!5+2m5Dsl>@aH3|9#DBA@##_6!wN+kjOPWB-XQEUX;*v=Ycq|Hb&y&CtqjF>YB&LVDP%XMODDEtB`wc(5+g}Hz5kk|o*Ikd7>Oxx zUoVEC0E?zic?=$kkB^Aio9tHtx*sKOOLuaCn!xX)7Xh*%%iYAEY&McI_%7TPIcPj4 zPL&#og2$aX>t}2Rv!>$dF!v4y=N2h45O%I_sl0(!;~^PJwe_lsx831xD!@d%bI=|L z*?hma1Je#EMfX3trM4#C0~|Ptx=nKKQr(xUQAF=k>f`QFGABunwMaqyeZWuUesLr9 z2#t(4&^Vmy={*3Dn1|8R$M0`hZIAzKv|j~KNOsG9-+HyzaT3ct(YRs0vY}U~jLi*+ z>D6HiCX4NOTFrl~ug~U2GmJ6^;1tWl?3lG#*VB{Su81}bx8#|Hh3G9}QS6di!`-=W zcUDvU=B}BW?|P~FAA8>zA`#FSy4kcJI)sMc$AbRuO#P5h@J2gFz18>md*SGs^gLj$ zW%Ki-ooFIH#C22se)7kl_3Ovn+}&usA|oO3$c`obHQ^>AKKMZ|$U~sO8P$Rs=#eiC z#oZwjn_o4q7z8>%Xs2qZq^K9qxvYO{pL^#ntI70Ob)w!Mqg6OT-rH`TzqY3@&wC4?eM}Ib#kC%*$i)hE zB{yrFcdC31Gd6PaJUtC9`;5o6%%8xWay%x*+-{^95xP?2aQS-|9c%hKijD`>!Lwjd zpkJr-_0!v4i_7148e#l2^Au4tD-jqN+holPRmPLLbGE`pvg?!iWK7A?3?QJqKW+Y@ zI?-pkE--q0$>66kwNOF9v-%Amp{qysmC}!_o9MD8eR7_5;5(O=Rc0|N(McvCESdwI>inaHcD;}}0=!~DCo#KWWh6c9WYkc8L;=f@rmA)xQdIeX(SN_7UX!uyl7F4wj&WvWa`A)r*8Q z{AtTq!&fx~9>WhtqAbBhXtcMpaClsRHbnJsscrg6dFkWZ>*5!-0wBx^O{I!WFK?PX z#7~YR+02jfLhw{nRIxvG{T1FkRq*=U^jls5duD2v8fB3nEM06EH=09%#g3B0 zf;R0R?`7J@Sf0TgJmr-PPmkDefq?m*7cG-~)u^Fv({&YHTXX1YbqJ`+8+t;*62FJB ze43f^m-hPKoGR1@FaIpgPF+#27Z)2(P1xQRe?avc_%||dYidGmQKi6JkSAyr>RGa6 z)=l@WuKwrLZdp&=rN=20JiYY+z{`uAbw0-(?Fl%|S^(P;ct+R+v}Ir>#Y3&7TB zpmOql^Lxy6lY4t(Pk@FcZN&LJTEDRJQ;}EqNvKq1T$XXdpGrtSLG7myUrNT?7%n92 znf1W)-WR&~-ZOmr%Q=ADSj$=@#5te(iubeFPKyqdqmSfX79BewwOHcX*=1&5OuLIm zzl3p)FDYk#P&vw~e`l)hwzl@2O*V?JQSqID7PSGq>8}@ez5Vg<=|0jN9o-p!8F=fP za;3^!$D#+q?ivkEuXfVqNELoo`ccVt^?W5u>wrG1KLXb*(adQ+y1q!}>L<RvL%UkEi2{VEepn$d&dTGD<>pdco%1c(;i#SF1=bH)*4B!@1XbVf2 zmXqvg9qf;sJ-fbxWOm@ea?JRy?>|iV8;~{PFLk@~SVbwtW4pT9d?6CEsjHJWpUwOG zZRrr3nu@FlF-ZR{4t}azF({tG^RrUy3yO(=2XEE$=iYzL*ZW^(M1qjH+3?&a%;5j* zFezypNrQHW5nniZ#3r>&$XTd45v%fL^U*a9pZTI}CM&_sJHiQE$g9q&9NImH_eBEZOsaaZd?J^w^LVJe6*^)wAoRJH_=)eSt%R4 z($_k$9liBCipbUg2A z)e~6Br(l?m5?JLI7Ot_laVhgUibh8NzKYRa2;I8L9FF}_=j`%hbGFcY;C!$;C-tb9 zu3YDMl>TMxA#~70eog)kq=`mV6%W24o&oI?j*yTL6)S81K$cpv96L3pn9nX9M&e|pJA15V{>uEV-u{Dp=VE^$WV$3Z z&A@>FV$5>#0qJMigl_$m$57%|0D#?!YC#U|?ygH%hm^ePQPWA5tW1sR1mRk&vLFJXBxA4n{p+;adb z*RfK)*`*E^n{WMcZu%WqB5-(UXenE7-PA(;T6UhH_0! z;0;*nHUKr3-^N-GYkvV$oNar9|80&MhrweIufVOS`09R2gq}w zZvA{a#BrkDxYveS6SH8A(0VTJ)v(K<8R#$AY`mDgE5Ir_P6_SbB{)B8T&}5StY#RA zo8G3JHF|E>2_Bw%`X8@p_)dK*vuQquq$MG8aevL#u1mhXtcEYfl@8B@xFu?e14wTC%YO$HoD5Mnq|t0x z|LOU(nc*-2!3#CKuvvKu<--^RdgQ*R?OyUtA|D z#?+?PhtqqmuVTz)MneewjM_S*bX!ovg^zkiLfv1^^Fk`z5v5vcwLkMu!mont^IDc)>hMfLUl!u z=LzcZKng}fM-eD^eKZd#k5N^P1DRKzH2SffgD|JrEn+?bD`p)G=0?hq>tmtahnBb4 z?Az72Rf=R*bv#=pTF6ELtQK&Zs-hyom8K__o~WVt1CT<{fxbTI#IUk;g@FSd)`dmlIe6w@NMMucA1{nT))e*xkIvGLW# zRNJY1qJ;l=d1+ZW1t^nLg%E({G)#m+s{xTE(KwKdK4jpc1V5d8eL%?}$T6$GS-7eG z!YFA*ccUstGgYxI;sSDP$+y)wM8G>rwy%?wnD?nglEIo4GI#>+j zUfJ1-h}7*NaMpZa3pj&RX}td)=8p+yL&LJ6A0z3H*KT&r*Vtn7m($z(fMEhDn2sH) zilUJTk!XUz{&d{|N-}pIqu`uR&QBd3MWi2zHvaN^m{r{mSOk#)$zyf2@XKU=by2iR zjh{yZjc$YH9auoxO~b>VNGvbV+B2MQsP%0r6$-KCo4JIHO6f58iJMN8o8OV?Z*_olcu}c6K=0$~Z7xZ@)d;NC@p^v%mF!2i&_QZqnET zvXl|JtgtzR<3(n827e}g$xpYRbfjos7k@K0iHbu$e zEHRHGpI55w3%3`J@?$*!g@VSp$3%D*+WOM>aS`9>*bJr6SQw-gw4GbN(HzbD*ZLkt zGbv#c5|$S0G+&QUGf;LoZP6z9KTx^otSt(GS?Cm5od}} zl9qb<_}=5rCtd=nEX1ux_L_#{t%heF1cl;pbuM{79I&&}FPt}C-`6lB7ZG{z(M79{ zR!U>yXINkNTg1yxzjjAPg34@CT{bw#H44+Z;|;&!7plrD=;@(ztE4MDzufgNtFYBy zo;^E^Qr?_oAe*dC7=14pxTw7+klB-B30LR#T+1nFDy#dh&(0awnb(!yZKAaXDP3#$ ziL)3!o2dKGythGngYvbM$nb)=n^I02>m8#9mbOMt2RqnCyY{L#wdxN_v z=$_vqddJIy7B(0KAN};ZJT_((&{9Zt?c59vTWw*V+mk@l$QTwQJLwb1teqJ(j<(7c(Fmv`aM=*(9Xo6kjEa?!ag_3Ft$ieRr3SAT)-F|k!cPC(>K!S7 zYIy3vUjs{{)Mq(!5%;ATO>*PLnx-FxY|m&ZU|s2(`G}{}S~um=T3H3zl`dVqNfIgs z;oYaG@gyXAjjzzeYTbE`OV!ubI%`Qi-$ZkmWu%bUU1g^rWKOBb>AZsr9xQKeTCXR4 zAMvVE`M8ZHbRgJQM{msY zvM!Co$|3;W^Ds)ms4#P?F@Gb4SJ~@Blh?nuR1|#E34=F2Q#S4&FGZ4nC8Mjqa9=Ps z{4sG)O0VvO(X3L#aQC!Ts{~Reu`pbT<#Q{3##{575SjOWm*|jW)hwT?8G&K!#tmr^XWU8o0mMLGzwMjjaSL4Pd3)K zJl>emsx7sMbGSuyYPWvHlsVntc(xr&-OQyB`kAETSPi=4E^H3aE(>(QDA){tVpGxQ zKEg9Je6WV15s^bH2K!3e@r#Xemt#_%G+NpCr%kf;4ho2XfHvMtm3(Cpr>(bKN882M zd(*sj8CWp5SFr~UV)xJ$5!JI?+}_KbRCEb1ghq0ysDfh=PYowRO_3`FeNX|oW>{3E zsJN)OMm-byED(K19x8LgE^$C>(2t+G{~0RXHk@^l)cAs{S64y6=D%ji$=1@6_F%8Y z(ermX1zSUs*IE^IN;Jz;VM6qhVW$E#bRH%Z1j~JcUi4e+Q-`5* z-!ea;2k(eciqbBQ1P_v)?N!2 z>q(FlcV38y_6%fk#BqY3}CfJ5o?vLk3r;FuD#m4KafB8;1Z7L%C9zK+R6@$@a|9WG#g3jrP zkDXJwwhD?sS>~#jPJ&sH`;|fLa$>wqrNlaLnQX$_0ZXNis$u_ zlH<%&oOREv&)iL^nA=*2zXq?OH!YUbmu>#ezl#el3y@Nf3KcO{RK#M^sF*ug8|3EW z)0{|5v;X@D+BQEQu(7vRsT4x!N>!p6t5J}_A1i&ep_D%lT;BZs=r|;6h>q}ZWNY+X zXB6lh-S>|Abo1&7^&9A0D6w^<=E3(odD&gu>@{nn!(41&HH%wp58ovxCl_||i;Bv( z+Nq&uBfb2vI^nI^54`E7Z#tWviJ|;G%-Np0L%w1~yA;49k)Cp1eVY5)k${~pMk5^zzxRs)7V=MU@RAVifrRmH?bwOg;F z=TJ0T->d51q_^waYJ!t0zO}j1laUn}`^Nwk_7k^fssR%KXU_6aOK`4R8w|GOj@H^5-Og-TEsrGVDETa%MtCZRK))cx5ApA)Xo9SjWE6ftVvaoP&-FsqE++U@S#!3Y{c{dD9zQc*$8XZ(2F%WCD=xF1zTwXab8~e!OQx5{pAL?# zfi3_^ykHyBrkhz+HZ(|k1-$Z=OVD!iObdmS)OlCzDJQzwQ2Vv#3+o_;vgT{t#YOQj z+{x`JpK{~YWB>GY_2eIm--dsj9WJbq3dCQt!uXbDT4^Mwkp7x&ko+@I`PpJpJgC>_ z&RZ1~XD(BbWyj5&u`()>hXn7nP_@wl*MO{X={$XQ9|Si8<-EaG6FwB8| z!FLVIhj(|+@Ln{xcQPHteUY)15RuHOJQ!}@4d_g=pp{kPm$ zGAx9X=$oQKsiAa(f5<|#B_{dy1QrpaUo9zLv|Dgw7-qwH5zFZBI}wqQhs6MVmwq0U z_J|0t`!r`ivDrTP$zvLBY}Wt)WTIp_CHH1)2~ts0`9ZI2DRh+Lr9HRd-YB_x`pBN! zf0VGdt57?uP*oFumjQi#p1k{NRHruEufpOUwBS4tULSEus;hTOb$2Pj71Hibqy^3$?$ zVxY;@P2;WU%XFy_00+I_tgK|@Z4S6ehW3Y+9Gwly-zp&*w*$6&f$IJZbb251ug@E3 z)_LB{ys3sgFxZM$rom*C>JxkOY@^)flucB0sZo9qB%xUWw;$Tk-rhJnt#yI&fS6wK zRaMoQR!|x=)X4m6bjz5kja+A@<4-*KF6^Q4f+i@6X?Hngu5KXYm4a5y<1I-zT9nUq zclkT3bBp@&(tbM<^APxQj1u8FW54|j7SDPGF z9N~;IAit3TN*MqcS>gFT+#3b50Z;!4Ap|SW5p~SM<&lXGuCExbFi5cRQHi;@{^UG{ zy6}~|M@!fJJXJhAf3r#(6t64%A3yd4tHLme1Rp?G>*S3ayZ5+&}Z_kZ7A zEHRBdsdr}GW*r&%VKJ?mr}0oZPxyWcOMD%1rt8Y%sffPF$C1b9nBTMB@g(M{^c=3L z)v_IJZH=v6O-;R-9E;tFlVWUz2eNoL*7=)|a2&rPvZkVjzm0pGdwzROUbZ^^tz({y zoe^8=ZYp5VLOXh>7uWtuf-K($Pd0=3|6x;8L3$Tx{IdP!F@Xl~R>1ZGv^v1%bHz12 zEW`n2pMnebcNm`PzvFGfXw$T6p>Fc#&w>%d}^J~~GTTXBKx8G8` zL~nPZBs5Ip-@9i(N%=J{HXwpsR24;IwewwIVP9*F(K~so--Q)-4Cty(N!GWXD>A_W zhT%(mS~zd8rRL>{cNH3?;NryfZ6KZB4QVSk`Ad#cR-x|fzUTuT@npMXm>yGrkvCDO zyp8O~fb${HHB!rRmK6gA`>|(6uB802*S9k;2npxMl#8De38SGs{w?ILW zXoz(U|IH^t7x)!E`MT!O2OlURxBoG?SoJ6z9sznx4vi2&k=Q3%N4Mq%rlmQ~Djx%A zw#5EIE?o#y=?(w$d-nr-Rb}aE6Ac*$26ZQ^8YYL$6iOlxluf0c*V82eKwYu$rOrrp zS2f-5ZZW3?t<@^OpaAi};5{B!gs|3r+f6kd-UV217*X`|YPnb#I1CGKYuTPxn$0Wb z<1#mGb$2Q)s70Z_Rl!Htw*%4ptw#i$Se~GDyHGDggXu;W85yZyJQVH zEvQMcAyqyS!qdd-6Z&;xt+7XB%CmJji@=bZ2`xvxgZvNrORKdRQw@X*!cv z`?)rl*cYg)0)N;tue_O5UTuoTcuR+*!^>b|267)VGMlxC_9$(T$+h%e~uiqQwO6{tPd)BEN6E|Kh#2=90Yvl-vk?jwo7FVee86y$UN zlodFi5TK71e<^Fvq1CNYVbG2+f86`-iwWjavnmT^I%l=nmY+u$BwUZ*eyIC7_=-X@ zFqE3&dFa83Gf?cj%%>7yeT?U@tpb_W2Nt!+{c5Xadp_(*q*Yy|Ncto%ifFh z_vrg)aR)D6(fM{KypUg;sx7zMTasM<+n+Awk3o!RzB*%=)!&|J%&)K*SBw*ItFi;x zM#I4@S*djK5JaQfar~&s{k?JH6uO&e7;izoH3PosmFY-9T-k^95&kwR8XAVp$!b-j z?%32@cka-GmRIdSkrp2n56?KN3LYu?`I%OutFq~zZ$4!<^CDE-+-m9#&ZPt2Ufx^= zAuHUHI_EcIqt9s~Y2;NdE-nxt3m@0fxgf2p)BO4KtxTNreqSD;J(6GNio&B;3 zmr29s>f)5&bRQP&AdK(j7(O)I?yvOlIBg~J$Uawr1xv{5Kx^H6>6qLm;oi=2HUJwv zKJWA6ZFch!Mx;2SeZ0B_UFW(JC@|GIZHGohMR`ub^mc7n9HuomT3K1a+ORu2Fs;yF zG9M{u86S@$;53V!+|UF47a9`~JM4;K_WEnP_kzL;R!rR4i=Pv;VT!>4p`mxa>9q!( zStchZqfFJ>(~LTzFf%hZG&CGk>+9)_HMp=BH|z!B7gX|t`c6%Ky}r45{_M)ynxv~M zudS^uf>}p!v=mlH?*}hTOiWZ%)NUhdTdziI~eK z3M}N7KdG;Bm9j%?WtpPk+3(-KpKiPO+Z`6g#(23uh==cjvL~%75SkEMaq7;Y`E=Vb8*HgR>wSn=~c3qkX}RCC95hgA1uDf&CQK{5)0OWRIms8EH~(g@F&rPBk%IAechqys_JUy zp;Al|)o9wz_3TA3TA6r`dtwSelaFEcA+Q0%%>w;=A~Q2{w}U2SXDvG=dhK}P6ZUeW zF>qTpx*j?o4y)eC(P>{%Ib&*SD&xDFN7mNXkz-m!xo>l^j<;uN7#QxGo9{ZpBK$owLxP7F!{0Vj@5DS> z<#xQaESVoe13C3H2}EtKq)A7VWIds@RW z{o5SxurT=|%bA%OLoQS`c#OT}u3%CjZ7Xy#~+h>E@+7Qk7na>a2 zK71Z=*?JiDiLrH~t!G7#BgZ1_GB>}>L6pe>4 zm&n|7f{KTSmzJImV&T-!pZAK4B;bKQq2B}iA5b}|wV6k)cihwiA9Xg$z{?w7QNe+7 zcChwJL|R3Vk#37l9CEAu2~$w3kiwCCt%?7 zT0cHLJzXBK;Ptv{U^8fcl&O#|-Wl+i0s&4^^ZNpFqME^+R?oHFj;EcN3~HC@I^CFQ z6w%(ifhPyqyH0RmQGO2z=^@8ZkxS&(z47G>CO9`R^Hdno*QOH{xon_HxIB<4jayRV zyf&_@xODx$lsdoNko&*=>4lHy*3iJ6eX5 zI{h2UMLIl`A<#6IIA&5UBFdIaq8}=43nT60L@#4;*q%v(g^($6+z~c3^j1$#&#os# ztK8@zQj{}_UU}iW&&@Co3?(c;_^zy^OH&%&MrMK${z#W`c?L^94#XvUdwb82rU`zP zzwZ)Mo?n7*AR!?+J8k3vIcTY5#hxCMsvL;506FirBOzp!WxUF5M{xRu2FK@XyoFYhe4^1c$?pTS1gpDs=o z!)nuPvzE@cn`!9j>HCh5Lg5*bPb@4fy7k)9OH2o;F^G8)#rn+Q5>jYocVOc~Pr;{O@zy-#FNPXpB2KFe-y#iUb!B~kd@=fr=P^#?1{)?cY zq}*vE^@4()e1DcKApwU8GBwBNvUrNLazR0DuMKdY$gdZ=Ja~d)+?$vR!*TYv7$Zdd zC!|MlEV>`B4*!l65`sHmZE0x<+h>!e#9}8i|8H3S#tXqs%4SMMp+K$;Y83C@ua6YE z@3f%R)YWMWh?eg{wpISV1r;SvIbR$pZtHgZ91`wr^IVIZg5ncQ z$in=5xb>j4r^RGd$Z)J zWJgynnZKhmiedNocvMKm#)d^+=#nEmJX}pvQz>2iA@aMOow;KGECiv(Vx}PuF21<5 z^f@Vs7?hT2AXRK{mjJce-TnP^5ZXO!laZ4%0(&EZE@5v73-im%VsKRkm4f%61lb+W z`9-_Y6@P6wMuIX+SR6SLB+%|N4tW{Mr#lY^tVv-kR8&+lva^LE4x}ZQR(cW`)k;wK z`1oLEe!=QgYjovA!z6(N-?*G%vyoz*;;`=_Pgy@^O2_<5%0dQA_=!#LSAa;8_37R+ z(zIdUQR29%fkDJA3X5tCDH}kvzTp4qF2uQ3Tm++tZ6SW0uwJ| zU_c2g#SpeO7SnZ6oaQ4o8)K}Eo7Gm+1F!KIp!N&C4_^?Z9>EtgYSkfl>{pgtyCIEw zlPyQAQuMCF=O!9xQ$KtiMzZXSr~M34HrNa%5WcYD@9laX4y4J!NGs+?jw(c?jm^!q z>H2EVdVha3xTQtNrWiG=g0oXRCvsLjAg@B^R@eJAVqb7!JtmL~d4kC2{SN?b9NjM% z%~NLR=sXP6j8_S*8(vCC+(pLk7#+`fe^O89#269qQvex2J|Re5!#1oZ^AwGo+<9m2 zCD=)8fBI{eg+Q8M$C)4hP@)-Bx57!9OL)C``c0o|*E@zl*kyA)FllIPJUkZyrPJ4C zna?IFOw*mN9Ny-u$TkCXZI%DHpT`N>!2Zka;r3*<+l2ze)BCSyvp}4@lS}I@x3T*7 zPwAN`H&s8ZTkO!O>F|Wfna9l2lOKquk%bhM&%R>*k`(9R6 zJwM5xnwd$4tyW}9*oGkym4_pmVsuvGSX_JsW{)gtK)exCz`i&inV@wCl1;s;-+G&s}_(f)}~3;#v{0Cb$%aWnX*R70cdu z{@N%}@ErQ}vHhd^yq9pN2z5E}1L%;#5m&&KQjob9V#M+Lw~-l9?U#Y+R)A;lqc+l+aD~4-O9PM&+i~*77ay-}LqM)!Od@*{pIa^v55)cp= zs7L$x`8n9xMexOR7*v#$d^w$1YMP(&bSD8Weq)@@tW%$qe zH*RkAKjurw!vi1%oT=Bkka_l~{dT}C>~Bv?s5*;-!@^dza#)3gm}9y!Vde#C&jb%P%cujf#osT63Tem-xH8tKe9#1%tvXDCk*@y|L>|Y4|gLbY0ohG#7k_ zLHRfbJA3;4ylzr*a$a?{DtslVUI(@6xw^VWP(FO{ASXL}wYdl9dkO^#>Q}^cx=MtG zhQ{8%RvN#KQz-Wn~Rb z&B4>t+O+;0baeEkU1y-gpib#KE^?Y(TFMw$tH8j-{NUknIY>3=)jDruqObo-T3UKy zdU|+zI!Q4X(b?HKG&GcPb>)eJiI2roL4%#TG zj%B+Jcyw%ZQ+s=*$;rvOhK8Zb%SKSD86F$U+1lD_Ahf!9!e6E8Ie>zK!h$Yj0xwVf zQ0}6jynl-N3_-`9Oz*S%slZCuC8CBa2e6z_Ut zh9yNYMNky2mxB%eMysXk4gO0fz{oaWv(Juz{m%aG)CT7OUvHlPZx0vQeeV8yJ$&{o zRb8&SWU=h7fB@gUx@u~>|9OF`kN-}!MfQW?_z+HC1W6)X=(m%U3;GfzpMQ6{;CO! zp@0Dc|7=@RILs7-e~hces95~>H6CVJK|#TkQ_hqEolb9E!V@w3x+An?@~TJblo0;x zAGn)Y1e zTNnPnKIi}AN36AZ#wvg>Nwr>5!DIL|O5mI%?^5BV+S_&3tSihJzG9Lv`-ltqX>zo2 zuF2JImbv}u(FLrx?5q3sjFi+ink3oRkhK`&#OUU>WggO88H~WFRtz! z>MWC|YP!3nJUl#<&)yNfq$nNole+98JNu%AdK!%Pg11KS95KXcZL~PkjBb z{d&hX%fFw&?`Gi9-?HItON%cCblq)ZGTpwoVfy^%GQWYf#tK4B87t?#dz!iK*N-n+ zR=X$sct}K9NO$e-?%OeNp_4PW#LwRD)qdWqN1N8JUCZ|V`K6;$EB0lXrm3D$z25mJ z1U{XYe{Lu`q8fMq)gGTUcoiFuSj2|EmYrXlAaO>yWX_*gaV4jwvf6i#KDg#TXi{EY z?$mkNIobGAhJvNE@9ejxdSEklW}J;2+wFKZ*njQr6~4E^e$I7paPZx`_mG_`JC&ZE z?ld*tH>f*J`?GplDG%M_CIWWM-{E`1|45?M#hMP5!z?w*&88kZ`M5W82UUgCb+_BQ zU+<~f&wq%W|K5jJH)+(nC+C#YTz`G~b0Cy#j?0P)2vPj{Np=T5tyMItt$WHmb7H{<)g)x+4GswcvABL>q_oM5wX|d zyBU@&Q5meUTSgXShDQDR`7OsX&t&Su&DXwf9tgG-Z@0NNlBPJjuenPUdHq`+wis0& zw0NoT^yzew;XtF6YHCT+EAM8k zJTNEe)s5Z9<}Ke-)6;XizGJc4>>e4r$4N9x-_y4W8TB6I;of}A?!d&`WCJGC&89ed zqE+(^1upM?wXE~iD_3kodEm&Y{))J+_uo`MOerBD8Oxt7K5Owb*WvuT-M4O7ct~a? zTCw+(^iavlEN}H3`YN-V-Q9Z9($iy{>yGq&eyYFD#Dwc5en+A8rMV*#BDC3bRuGSO zxk}xe8{*(*=~m(w@$tsTahW$sAr~5Ht#{k98KyOMw}J$}e*M~c-AgneAYdGyG%z{* z?DCZ>O6`G4OeM@D&nZ1G4;i4JOX~sq?CDe zs9y7VecQGp-}ui0IeS`Ub4szUU%x)GtW1M)dfRk**YiS=kZ&LDKYp7KSyLg&oSu=9 zh$K+g(jvHVlygBr!LE)Hsbzb*Vv<(<;Go9u%Y^s#HyrD{9ahy^@%i1;`#4FLUGMWz zFMj$H&LP$G1gO9$#ni@WGoamgYkbj@hXmy?Jx_?Vlf4 zxVgF2bahF-y6JQD*s)_A);4;Bdw867?OJ%{%9WE_hWFujRR)g8raelX>l+^vWANyd zib#^85a$_9k0`|J934{BQiAH!nIkh%l4hmKD=PY)>i=dWNN6#t&5bNS zRz?i;_irlRrZqG?yeRQ`(N^K%;bEl6>#Y@9UhNET@1oW+e1L+2LVk`FGO^{MhbI;c z47KDmcXZsxhL`!gUUlTo>XTJe`l(YF=FCtFrFXiyEtDGTu3DZby)y3EMRR#&W$vF} zKhz_4Aj_w8eMU0>Z%jj$g9R775$;>OHX#eId1 zY5pBdwih=_;YZ6VE1UW)R#oNu@#BZOme%9InW>2ZzYj09&LXOirW#PGHOBt|?uE`6!=MYry)!Efw4mH*VZ$e*U~6VczmN_oEISa^AV~q9%=n z;*-)n?0E1yYe+~)zC{jO&+V{dkJ8kXckq*K{XYKwxpH|`m1|eIpRuXwnx`2n`-X<{ zhp#ji`-@V%JUr@7HB{5N6{0uybl;5?UT``1^Nx5i4JLzqUcK0O8r9z3-dTNMd7{*c zBMZzkdjImd9eMh+xNhM4+=ptBz%FrpIM3t=eI0w9WRj}Xhosh!BEN3a%{}%T(zfhL zt)e)nt2-XA{`~gw#P@fZu06Mc)AfRvgnS+4q%4&|cx3};2Pwr-aElbdUWLnUr!cx4)j^hlra_{u7 zUlYR@Gy5N@-KmcgQFd~3%N;(hbl876cRGHh(hXG3Is`M-+t(L)$)@yfZLMLQwe@0e zeMLGwdx@NehG5+h-ZhuD6bnd66>C2kU$ANxi?x;?6IHw0(<8lN|3}8^)l5$^R$jvA zYX147*lDvPtlV>JUc3+8FGrWo$tpk9V+7D z#Rcsp_Q#a03wM=YiAz!;C@@zL2dA!L?>i@NZyp&LnbEF_6Mg0zI+;jrlqlQPLcbOI zT?mJHUC!DBP5ZCixAJ{a_;B2rQW_kDQTFn*Etr{-AtS4iIZJ+lI* zr!4DBb`6L zJA1F6Ut~sx^aZoiQOY(Y>eHzjN?x@1q@;CjZVC?7A<^~q419LZj>l(qbzYV$v?(nq zd_~d)8%I<`)&8+;qhoU^3TL3}PPhzW@Qh0QCk0RHR^Z57KABbV+@h+zEjjDHXY6!$ zk5;v>%nJWKv#aM;MYK05+*q|+?4hcAR zvhjUykFhm#go_7#|q=bEL^*YY}MMGPtMk2 zHw8pQqDioS{qQP|GIqBqU~*zGSl&MV>nMJifdGr9cc;LUCr^;N4NlD8u<6dhFaWrQ z??`Jgv9Yz+y*dOWC1)Q3$+|pXSV%WuD1ro1Q&Ul!;~cWPTXyW2|MtnbV^{}60hZ3r z&YF&nw7$}_$s9V50~@aGd@8-zu3X^s>CHaf zb$3@QC>AhK!X+go@y$H0uC5!>*5G;koSmJ;mbh`KI^O1?#8!rA-m0#4#?k3(%+EKG zr2bmAJFchtzyLY*(a}Z)+`-dR8mCmYAGx~oDP2J!x2;2%n;?OdD}#^Hu;AkP+pa4u z^X`m5sk3^fXrYm+O<}XG6$ifJ8{$!;j3-A<&6$16k_x-TvD-iSus%_#si_wpofld- zp&Lesi!QqG*=l09(zU2C4H_)Mclf(pyI>?X%P`^w-6^Hqk$+pdpy?|a zIZk; zQY8-6amdD=m7110LP@mgu{*y_LZ7{P^G4{-DJ-7Jd<6xTjEs!(;Lr0Zr^b_uY_GU+ z;&Zj^#0$2^GWTLZHeYM2xhJ)$_DeSAO}coMu>wLneZ_v&xhmVxRjcoOBI*6|+WKei7UFlKvygz4 zlJ6(UxIRdhEB7DdqMRm&T5QW)qIVq2UUIRXrcS4?KFPFLMa8Is57Bw!RhcXMe6#e3 zH*egq(jxPgdDmRbJXkn>zCiu zxHAhITR#GkgOjsXCVV;)DDgbdFKJ8z0(I^m#`k(7SjAR_YNH|@12{6vTqT7Zm;XQ^ zisO>b>vBJ4vc3fUh^jdl&{JyK+UB6CVE~yn;|KXyc^UuDe6USbR+a*mP!tjpLN$s< zqH;m+cSiMCqd0OWpP>NBTWgN;^#Q>Ip<_}I;zU`X+1S{Sm_x%fcVwSuE$MyTYk_Li zcfUj?yiB8-FKn1M`tmwAAYZGmyOngT1oc`m?U5gf`80Y0?Ss?WLc+obHK!drj^Md- z5d9>dEA3HHBBi$V8baH(Im?iPmp7V0uSMZG7X0~b*`qWQGk4m!{no8p%YFNp5+!w_ zHecK*r=ufIk)(@NEd22`Z?+jfgZij-&8vg&y65w|$lHDW{n^9E^UrR6w3yf4-ag=C zmpJddWejwYWggGw1BbKWH(1~QfLLoh7az}K#O;V8+BY_q1i+Prx+PH(uf&wfUW0@p zspHQQ85xP)q9w1~(u}1BVA=fT&wFuVNqeB}+Z*9B8v1+YmzJG~wSp371hCi|Cnu+p z=AAAsOroNqlOMb1p<`#DeX;U8fF$C-Xyr<&*9U*9AS=@-RF=bc?%Y8X#u;CurY46s z8x3*u^0FpN{fLi`pL71{!-uiVTkvx~2?z->nVXw;-3k__)ZH8BEZ%yBQD<#oWL-#2 zy?H2XG*lJ-dsW0}^UhtnVn}fAAL|)vD-d3TtZT7~#jxfG@4WFZ9ggxUD!jno_3g#m zTWY@oS49p8 z<#cr<{-Fxi<$XK(XmIHD9sSCEAL3BkBrFT8^1jJ4nTS&jojoc$j~j1oK+NSzF|B6m z6&Z?tkZata@o!Gy+E2F~J6r}EQwhe$Qc-T-a#K50HW!2{`H5bxsHjK}1ZwawGccy~ zruAeEDmEx6E#i_sD2-lL&4h!8o4XFDl>kqG5y!IPd37?g$+2Mb#d?y`jOFp8Z!uN- zN9$7pvIb0?7uYsN|CO-4pz_fE(auOt;rX%%NF;3|wDcyMmsu3#yDtzRMM7z4YqI6d zv7am)F~)HQ%~xdu%061=dfi@es(H?Z&g(&ceCE|L7+SmWEpigYcnxdDMMW>%lcJ6^Ds zPzu*Po{`fS@ZqHhGaC;@G1jfiL0i^&b!U>&9#d3E?;Z1vw)^KTd^Z@cXmxF&1d}oQ zCf4L|)pdK5-tvT7&5x0$l4SJx`1$!s*e_bNNPvZ=Bb< z&d#1R&QBRJa*-^jE}R1KqdKk|S9aKAEM8HQRees4wt-9P%2S)>r^VXnS#TmLAUnm0 zFWu3LHqAFMumSC5Ghk)IXPnH#M~|{2HW7?|Z=1w5e9S?!uAQIO*K}g(c=d;4OU<90 zTxd6J9N7EeO!c>qKjqZlO>lLHL~o=xL*^cSV46~6LXDO9?x58e1TU6LxqvT2f;1;w z&%nKeI@HAsFTWws8!oj`tm#YKx$s>;wGFpcA~aGWk}2B*+jB0N+wgFTMU||wcmA+D zAtE`##qm*hyA&uWi>KEzTK&Mo&{b$C9rLF326oo)8%RW4`kuMHt?V$PP~pA7VI1L_ z&Q3Am`DP2pZ>imx79!mrp7qbSa9S6=dbekUZ~45NE@LeI1NV4=Pw1MOnm-NJQTCtp z@8rAk#agXzqo(>BxPb@_%*{({K2J_gI(c}+{`@v}EEq|nm8sC~run+U&2RD?K!9#3 zv5WP&^J}iSxVZY#rE4y1pYQNIEOqDZKUBz&?P_|Uz;*7<=4Jsk$J>irD`I0~<5YZc zj9r9W<9mL_UEPzZ^y}x?9m1@oq@+a7{Q5b7EwI&hUm4Mwpr~NUZo!;jFHP@U4 zjS=GgUc)g_0*?u8a?|Ja+R#wF+f)7AdLfe>zh{1`f#DeMJ1XOXM`~_a^lC#N!kmD4 zUte`}3tIMkf)=M2oOtB)VfQ(o<-4EQmiv~D3k4p1Yp1WYsIaDNpJMevht;cB_YM!o zq6}txb(Claf6BESl2`I!?53SG$+Nw>gZIbe@S$thG|%enWh-^4j*{i*yyDJ{gMPHA zXfe>NGir$Ys%hlpiGq;$4BL^fBGGuwWoKrfc;C-&H?)B$K|*z%%JDa!{I!dRmv`co z&mCt{YyA3IkfS32lEuWtvJ4Ng4ZXasjJKHtCQVKTH2t-l|9+y>q1(4tf%lEJ$XPoW z{BdI7K6$&`{P}Evv<>|F(-G)r(%#Q77$@}u<69nydtL0@`z;&{_~pBS(F?naCnhGu zmhRwTV!Bdw>A=5oy`Q+K(r>F9a?bc=E!R9Yr`jDDZp|y-`;IR(G<1CW``*x>A8x8I zUw-t`rAw`|EZFkbp@+VIewPzC(%xAasC@g!du8xkl5A{jN(Biq#;*#z;vLp^d{B-{ zP8OIPeIbl6%(p5K0x&%MrxpyY$QSZpRJ=;j)zT6<@O88nY*`CvJ58S+NkX$>N%ykW z;Ou4LNyXHiD9Y*OHIL4Uy$hsQhOn$yv4YpioMAOFI9j~DQ*5P1d!6!=%CH6t;D_`x zXR_Cym=C;WgxYU(hdpIJI+`bEb*{E8Pcv2kSYBV|AS@vfhaCges+_2!t?h(uOMWzH za_5{ipe8czY6Q*bhYx}R0u=f#+pSwuEBDdqjoY7yt>o9|HFigXWNc?&S2?UD^Y znw29*j%Wvs%X)T{Jm!qBlEXL7{QCAf*;rxWLxujc9jOY`y@rPK+pc?>R3(rY_(PUy zkseyR2ZZ%m-9WB2M|dI=6Zz2Hf=fyQ-!P|Y2a>WbHd6Q*$Q*SXmeAk7zD<371(FBp z6$qSBxVfLw1|O8r6lG#+`sn&|yC(xcK6wM2oSZSoj@2Pjo6#9f{rKV1UbOXJoWPAU zu`$bh7nPQl z284e9G`OoZm-7gp6c;rGa;T+hzt3oQm8k3eL~HX}BzQAx*U5U{;Z{W+@g*#vSdvZh zroi&3ANcBhM8B15@MC!JIo-hIC)9j; zs|yK0fJxaC4?(;-71>wK1rT^1u$AGd7(&G-Cf;j0z0~i^duwXb83lhx2SEpB!EgF< zABWf-Tg1i0oN6LyvyG6dT@0W+X-sUd?GlU_Z8s1W7AE`*VDZ$)sc|XEV#o z%PTBcFaShXjzk_Ynz#MNp6+egZ@(f@5t@*s4G`x*5(#K4nJ6%Fm%&hhw0o-l>grDZ zWj;Oe=teqUzU)WUP;1{P+XVtHKh$)?h6c0<8lG(njh><-6V{?iY|EH%s@8zjy6pK} zNpn}TY=;h@XhZJ$69U4*hqhkb;f$Z_((Rx~+S|rbqJcbnCVc+<`O9TxwdYps*M5@3 zK{sd8**7W%ap5vBxn7+NMUK5!dr>5E|NZMIlJe4N!9l~e^|F3`e&gfg z23xkwJ*DQDhz91J?gBLApifq#k^4IOg4xv6v}{kQ&{>w%467U6+!aSJe!3W>5>Dh; z&841f!mh$72D>UXvzJ^#i@fu|^>5W67SZLPql&zKUF(dN7aR6J_re7_>MxcVzzz)9 zA^hp!kKv1!VYI?~fZ0~dIHMvs_04O3u#U)G6QFL64$QqUcYNQMBUQy!JGd2;>d?l% z?sh4zM~*}*V)=d~UkH?s#S38>nWR;Re)fKvc=Uo@f=P_YKr~sl>QP5{vi=%bi&Z9i zoNhQ@F71X`MunYEGa3lfCN6$@fo69n(O%^@;xj!W z=Ol}dG{!W_Sai@rXQqS899{P~Jh+&K#B6{hL*z6CMaA>`UcJ;F*n6S5BmH!G!ENTiIkgmH-?5&R8m6-JY%cnrTzDAWlAfL(MU|A4P}FHVHnb%SMI%Flj}`H= z7>u@4dFS?R~rWdfpLgvOek6|J>bh4b--CGnx1mP zzW^11pkKA`V;85qe2(3_%2$~*mjf;=32V+F=IaLErvJ62EzD4VGM>#(E@jc%E;t#kw zn3OM&y@%Dt3f()DfYpcCczZ`iE`59@$}Ji-pKe)=bgsGkN!3R znC8;6Z%}i;U#P6iw<(nd(8-E@B`jzmz8R}V3I&M6yXb41%l$xeGOW&fC{PzJUffyQ zyAdU21gIw8p;{O05RC%!e#lBG8VneYbKz9mZGixS1b@#QP?$geZhQNdZjwHDg;{7l zJw3Z#-B3BgFT;1*jE#cwv~x<{QrKcURTVINd~7d_81nMWfX_HILa~HX`?K8Tot?I! z6Ahx;{%<&_EO5rg8#YAaW%#~U)oivJ2<6$`RyLx9b=gS?qt~dZF(6V|Sy>s8w*YbC zkOlT;v!}4dNKm3G+pKy&>+0&}Zz+}l1Yq5~d9#Mk2JkIbAtAcRfYGnRe@juM7NATM z79BK&@d0!@?5GEC2eC~|Kqn+JC?^~?+sc4NJUl#_b|NYJHh*8-3tFB~A21N)Tjs?T z`XHmZ8$4;x1S|m{BgEB-1?J7Aj{0B&7@I7p__(;c=+b=Ow=Jr_e_uKAFCZpR0qL3^ zLPbSIR(@XSNG?xJ1;tCPmgc32_5k{!IwGH(PvA8|i=A2S z!w0y9Z6xw5VQ8gSg~oV1yU>SL@9lCn98p=4GoX>85o1CkK(t`VJ{{1r5Lfi1K%OiG zB?Xp<$;ZdXs?civa4?`^P*4z|NwxRB%ix?yhN&jSaBXPGBr|q zXZ&4e_>yVCR6hg>#H$=U7s56q^4;@%3+4@HG!3n+PX2lGQXGyX{h1iRu6_i!nC7lz zFyPsA0WHczK?vN(+~KaKCNtX7uFXRc6McH^D~;95Ht*lqr_H3P~>Pnt@-6k z31|Y*H%5)tuI)$QP46&dSS3DiTe2839N4LPNJU4$qf#&KOpm-T4BfhwE|ii_yNpg2 z=_Fml^+^A`x}V2SoFIZI2l9jrwW1KxOXY>ic7Y$czQrKJ$z zNnwe|MtTv`_LU{O2XsTA2tI%yO^4f|u~?x2EVBDSb0^{)c)15;8X;->2L=d{iqh=_ zzyP4ERFP?>x(0j-W5GH&dwAf}X-;{c|Hgsw(qu9^3jw6Dqe-m3p<(d*W2h@_1)JG{ zL=F`f7n6Vk;aE8IiQ5i6Ga&+q7)$vE1l0eXQc|A_!Glt2pvIVQq?EWdeOt||=kwrv zP^wZ(xgi+VycTdvP)sZa0Hq(0hl87Y^C)XqmLYR_c{#x^^XJcZc@51nt{|&M>+-OH z-hrHqLhPX!<#wQQ%9C2mkc7DlN%dxa=B+Hn)1 zDgIAKH}fv&x*ax;aE=}-u(q`Kw3`@C+RxUPNZ(T5eeA-q27nMKtE(QoSrWt=vgqiY zIn)Z8WaB|?Eng+iwxaW&#@Q*P=TE%hl&pclA9G8=t-18wvfD&`VH6@l0%T=*U0sP% z$2&)W7zzp&t(xj(zP6`ZV)x5y7B8}#(O^{CMUKct{GxiOX4>H=O!Q=_@=DwN7QriF zfXU8S$6!?sq@W7a-`M@?GHgvao^yltpx|lj8?^=7v0BM}KwdEA!on!4xe>yY>aVfb zm((iSreD>ct?(|hw$Cy7PI+7rXvfplotb8Sj_JSQQPfs;uovO@*22zsCv4J0F~DVLf3msKL9DW&TD84~c#)WLLRvEHI$ zekAoIKLz&2JiW)t? z<}9n3LWa<{ItY&vqN;Wgz}M)NA$go7a2DD?EvzV}rxw?Ocp;nxGH^e{u;2_(V1?FO zOPB0WIm4>SqApGROhvx-=i9|^{^P{?ax!sy`Zo1x!PugD+Bk9_wyqZ7w|8jB_D#q2 z-LK}p4*kpvva9wGyJ-jrB&7>PLBtEU=*ZKm4y)P!O06hd5!z4t z_Wn6HWaaXZFG^@Pt){qpAl$_*+x;>N79#gmELYG6G*)4Mnlkle?o5B;HruelkFXe} zj*b(0Hyz|#S1D$&2#JcaqxEz-s!yqFY95Y`X3fsgIhPq@oO<`}UG;#WO%2KOV6|(@ zw^%bQDOHfT?No?d_LZD^`xrJ9etXg_6Wt=(FvX_Dgp_WFhC~Vw;8$JH-s&6o|304X76T2p*$mh;$bo@Q}3T&y*X}sD@fxAk9ZUU-LrfN zUFn#K2zBDbi3ybF)T!$T!Vn7!3sR_-EM4jcMQrH(`_+z)nSDtyp9oDq-nRxejMYWk zP_WYpqYjNqe$^^z6z1}{Yp#uC%k9heyythn`}5+5OSnP&5ZbK&+DyOf`nohw?W*)FQ9p zRoh*)U#H-3%+N}RBeoU(dHvY%$9?bK9R+gnxU581dWu#dCp)|M?b{=exdfkP>Z@4` zqVqricaBhKLc+&4mqr5pBp7%VT#hs<>9{%lF|g;$I!rl zI(zP13z{_T6)WoM>vNks?$_7%qQM8d&Hu5hf|(*>_l~A?i`L_i5aXMhH(@3E!52{x z=m5_HE}c<{i&5f<-PHKS7SVGAD4Ot{s42}YEw$`2p%Hj-4%{i~8X9;g+VSJZ3Htz$ zXLskIR7*<>b%kB?rpGfj;)M(dm@{}xX!AchTN7_vtnnbu8lI$ga4;G@H3FG=`weeG zb86a42wKbL@kQtmNpJV78+|Yb0IR(aO*8gCm$r8W;*`*WD5ATbT}Tj9%x6JYRqzam zyNp=83?Op%4Gb{v-Mbe)42DR;t^>6FP_YQck$)p-@`a^*TibI>RE&`eD{UjEuZY0!n2p|GlD8>Os-@{^+EzGDYnNr?-- z4C=~(^+XMtH}5ZcwZr^0xuLBB*(Y+Gcet>ju^BQBw6fI@v}Vb^4)xk%0_WLzsQEm} z%jmJ4JUzK$8J2IxiKp{_*3&|zzhacAuPOq99yR}QkYkdPY>JL-vLb%s@z0yM!YP5& zIjmsPU?+{)sw!x$P8=|vCn1gq%CB|8*BNizxE9?LHkda6|Mx)$oWrmpM@4kW2l*#% z;62NA+HU@~ZiZDEv3V9e1Rizr@ks(NzeXw51z`iU7wEFJ(dGu2mB0BSA0aNGGn|^z zMHwPB@#RIkeKeGPR}>M%4BjgY8s+S};JTyyM-vh@l<31hWx2P3k(+=_z^|sJ)-Zqr z?1=VD<{TjIkT*X*8O# znOQyZ3#0gGOk>7btxr9U^A&d{=>0aF$!1r8Z5CViN9T5TdJJ5t=X%Lidq-KX*CW3b4Spz$w2r z9u}>X{4W9)FDyGHf`yFLfxEiAyBNGCM2&!_hN6%GH3)x(y^Y=XYZ`~AxuNn{?R_PK@qW|CQ;|?R`CtoxH9SvyE~+%REzsj{zn{s#x!T4?borjHO_JPvd`Hfmv(FGVK&66gcz#OdT#V8( z>ynhTr5V6e?Sy>H1q9PBUkhAT2}tGt?=91RS^U2J_afC=`LB=(`MimTeu z!h!`BFj!LRUhRtseV7?Oql17w3ZgCtzks5^l}fG(we|Z5WH+0(6vo;qdSQ@%`-4k6 zx0w%@M;92yo{G@f|51`O<>BGssOK&aK8Q~TCYOfAf5jN@ZtPYNkgXE&eRy1vNCzeF zKEA$FI$2JfKF=;DRhr0WFInBB^$C_A;!@Jw(x5+`JVGuRhjC9fQx$gqswA=p{q@v9l0HR|?E)cMv0FjVI!KW( z0`NdIk1EMA%3Un0Kw%gp#w^ZC@~Zmvh?|%8mvxlp+(lo^Nfe;til*xRZ;VUCO&HtG z*I$j;y?eK5_}*9+lt|?-fmX3sBi5fIW`VwUG+Lv~qq9_e&u765M-xynZ`7M(3DXO25A-)Chx2hCzzPr@&V!h~pwTX`3TPgn1r zTjF3yEVuK5!PyX76%lJ}H(PHma=I7K``KCMy6eRiMdGEz&bflyn*v`iJ_A1!jAp5V z=$J37U*BXl6_m*$WP4`YUGzeaQ$_6kD$AbB>FG&=oq|6{hcsYjXSGwVhQ`KhilfRQ z1wekxsb4M)oCR#LrN2YiT2t3j3gaEj1|ap~oo;-(s(8gT`}dp-kCl0ZcOG=% z6x*=Rl{;#7j0M^X(mP9Xbz2*r5Y3Y8k6++Cm!^JT5rSnC>sk2z6GP+_J_O0Pad4xo zPIo6dzTiaWlLk!SW?QPtgoT9E(Tz_1{K-tsObzfiL8opz5EET>OTc1lO>u8bqpVcH z#?y}P71i)&*hG>Ivga^aKTnXFGv_e4QMkvLDl03e2GbnX!GW}ts9m^nLyr%R{*6p> zbc=#Z^H#u&zV~gDA$Up{Pa;vX`f&Uju<3HCr>cE+>pU8`rN4gu&fv4~9q?MOWxOQL zTm{|00N3A>&&-K_MLE5BkOD}pXl7={sJflbwOxAMzlr-F2lCixEMzqa5)F!9W*cv(0>r7 zm^Y+Zdy6=yiP8&g@r&}!;^N{~L-HV**3I4B880EN1LQbO`)y^`hwU9;JxJkD>`Kf* z(#jq!5GMkHSH9&xAo_3fYOV+|d!ey42WF-7+R&Xl-%u63cdfKgzf zW?h)@*h4Ia*&vC(FuIfQ1kY+;bNbwAFmsc@21HhE<=R5Qm^g z6CC8@6Zd869zEiQh|^i_mjil^-pWN#jAlQ^7^cvH3C^2$jGRPBqUqXx*%uy>S0F9s zZXrGg7$eB60%bt@r;!nEB3j!ZajxuSHDh>Q-|<+&XzH)O36UfE;3HjOQ!RK7CCzuV zQzKqRUwY4;J!e#p#uzITi>Z|oiXn+W5OEY9iXW{EPdMD>^>YpAmZwqCd8BlS4Vj97 zGVfJRLfWd6DIML;NGA$`AVit_FBn!SWDAkfhg$Qv@tWp*3;D^B4)bU3f`To-;S<_y zX-SR%x|Tt%NTT&Yo78{>1UzkQm=|4%`EFfZOTp#^qylm0nTk&TA!EVp zgpJo*xzcZ_Sq@&EEiE5TteJnpP&IAfL5loT|1q`X)2A1~XkuJP!U3iQ0cfO$K}~_3 zEkkdLdN})0A>cO0F?3Q-0K%PvVPJX*y+8aNSi>1K;#BV;ej?s@zrJnX^h!A8h!z~& zppeinFz@NP6$^I6s}jcE#zXiVm>^3c3oGnONYVM{w(vBA_uGwfb-MQS11lLE^~8 z0F>aIIl0;&(bM0Nnc?;F@e24d94ALx)BOCRa#3}$pny}QnhobPtA z3_4kv4cXb*%{lA%L%((JC%_afw8&Fd%-&&mE@ge=(wUs&*(ijy0+9BI#86yRM4r&@ zgno}Gf)YBu$V*FOhWpbi${3@B?+B6~}CYOT+&DS?c4F9aO*v~XEIe9oUQ)bWW+X)zv%Rcb!BT@N@0|wqJ zy`XUlQ#-5ib%0(PzyV}Sl&*m(Ao@E+LUQ`kBQ=m7n{^lH>QowcD7_Dc8(t!t%>bG~ z(e|5sz{U*-6~EpZnxwX`!LX#E05OzK;~d+~i_e!7*9%gQii#rBGw`G=eYzLCR$M?a zY3CS)a)NQXUO8ff*_od5I}2J5abJgq00oJHd4PCLfsh|R_usT7FE_WQww6{~TMNwA z9-zbU{ER|QW<8-ztw9Bko0NcTh#iM)IH<$7iHQ>q9D$D_7%ffgRD|p$82gebolGAB zMDhds<-|P_PKg6m&x5YR0P=O~vuDR{?W+2t8KP&7U=hj_SZMfFyB6x6*bLB{*<^J@ zZ2q(fq63U|%Ox8%%(7;qT}#*5yQ8cGgSvcZkMjRUx~!9j9fa%pj~_R zWQ5V|yY247a!_{j<~poj^X;$$nB_>wbcf`#QSoYovd|66(kKvz9WDi>Z67lAf|-<+ zm5uWxCCnIBEgqvQ!Vo#=4=xtG686ApSUioQOQu{vfz7!OkpM6$=Gx)>*B<~YHQ;Zi zpIZT610=33h-nMCzmpo_Ibk+~$tIKJdpa-l7_O*@?8$`}FI)h%BMJky(C4oVD++UF zp;LWF%Y!E9AVrbik6}!Z5N-6<2qAN(;K?CD%vPLG9`6RrZ(kYk@LM3{MvN?>G+^jR zb@)SUehiUZprRS=_ko+>^4@nF50YOA!>$vW;^xXgFXUK#llu^hkefCSjpSJ_x|by5 z*ot2)Xpj9NV}4djv%W7hq81)sSHbw0Y5ABb5zZe}0h-&*(Z>i&kUmrIFO`0}bDqJ3 zE|g5BG&UCI-rO?p3DbqRULZ9ikf(j4qea7(ATN0Y0>#sjDKT(G12HmZ{=L`p9MPClU%M{4I-%WM}U(gO6@GqyK3b0^YruZ#lCMISrHje}=_5kN9Y1nYep2~(?k1>S1CfEIK(?NN1s zRu7|>D=AThx0!ra!27lWJMR_nGrMnv?4rAGGzP?>3{mYo?1?k*BC zVf5)MS4u%Z((-JBorhW7x7Q$ayq3Li@p&sTs-UO>zIZaZ3t5pyq1&FNuN8y{d|(Vl zH!}BDfiEA&wmIWzF(SBG(g$Q9vAhI~_1N3z%@JpoO%(wfG6jfU7W&;Ec$gbzy0(Dz zc$|#|?kggo>`CedaDZc5V{9Db^0OTK-H(YNOqbrn2tn_gH!RRlVLvL&S)A=ZfLcl> zb@3O<&1Y;r2h(<7^nCbAILK7$!Gj0IbORO=^EloF8y7zuHe$=X(_OASz%Wx8M$!ps z3Q3d*x0XZKL0xOW52mfFdl$1BSlTDwy1+ryL26UkeuDvKl^Do0?wBbpfKU60o(}Wt zSGTo4zu|H7KYhVe3A40h=n>8-QhCS(CF#G>NN5o2Wo+zio+f-PDFno*iFJU$4>cSp z=subJMxI!M3^ySmB_&1NiRc=qP+R=^>tk21>4oqBTddU7)Ko8y5HZ_1@7cqLaf!Uo zVedM?(Es|A78wTL`yM`ipSv~iV1qsnhtng1Sn8A*1f_1H*W6Z ztDF3I5Mn!nv5JfkoLzZfF34m<$RVZ^sg}V292mpr=h!6uUg8h^3L#SMs@yoXeG1Ao zYX31<8dz~;)kK%RL!lUjcFB3%fd0Hrx<830w{&P zA#$HU^gZ3WfD3st$u=c+hL};!0vn=z11>R_P$R31hqGelAV^SI15`CLP`S(JEeCP- zsXXdO^@@^GBnS*}MS^aCXpqy_h&ZDQUe|Gki$*(qM(>oW{k9?qpS%*9HPBu);A&xd zidkn;cTZNPz(p1dyX>ulzqSo`fswH)b&x`DEl&<5YLlF{cDZe!#dnd(#l{7%zJ)RU zef31L#HGg9#3lh`i*?)LH;P#>q7%_uusQlaN0+9mr5^t;lgj2G9x!#W2?iGLu5v-> zskP_}1Tl)*DpBAavr+ZsPrJ6yRvzFl$gJ&?O#LJb>#kN!o6U!XH7LTN6a7XOTrf?9 zl|3o_&k5#o_&~i;sIZHa0hnapzPqd>vBLJQZ1mhp8a}4jkPIcrrmmmfJj8O*l79sg z!^Hg`n4K~K%X1L4LY~8s)%28w%Py-GA|J`3e*ijee*Rch|DVBTYu%e$inql*RMY`p zeZiEk_-6tjGCwz8?>@~$HWJr<;9qHNk{U;XbjIvf5Got!qJvX??9-rp;(m8vlA+MD za0i>AfXSDO3f*@9bLKhi((^ZU{QC7M{Hcl2(b0b5yehcWBJ$>?$ePG(QJBK*Kg7SP=G*IV$D*yNUEJj^ z5H4`f$>E_lyRd1spkzL%gp)C^mp6TqaNh^>1BV|#_(>~51O!uFdT3Jj>g(rJ8eF@e zL0Nhbqz!aPm^I8m(~vuktOCGT*upMJ{)D)}#Q-!+kN@R$Hq&hWnr+)8TwGkpa3c8D zb)!Ntjqzj#3;!bS;YN%eA2pjlV2gEVU1$-`rKqUrjK6U3^FRI4RkcF$jo$pidKp?$2BTVs_>+q8a4@~-9rKQCC7WjS+2GU?li$|SCn|L0LS-ySM zG8iWLC=zS8r$3q6Rqf-&D4ht9mwObB2EH|#G~O?+xD(gXeUkN0(>=MJFwk4Lr8o{B z#XR-UR{`3KRLKaW?kDPrGcVjqhtQ7T;(#bn#*kd`5%QjtnW^a^ zj2E?*@voYqU|EQXi?fs*!K4*t<{~jb8iRq21hVQVJV#Nh7AB%5^5K89Ia!lYL1avc zdGp@AZ@+%|GL>A-d>`_8lfb-r%4qA_OrR_P|Kf5Dcu6G?+G$>yo@A(q_z}^F>xKQ8 z0~y9r@)9h5EQSWC%&ir$(v3>w_$w1G9CxfB$B^k-%$`ky{Uai9fCs)fT1bo9_r8r2 zC!8z^p?%1dH#0TbQ#}vfH7WuePag_eT)ScRr#Hx)Q{#2~&~@2~%?bB*00|68KSMRz z6Qj%Bi9!q>`z~Up1;#PrntuECEhJ1{;weGbR|`4n+t_PNdqg7l!qa3~fnUT0NLG4Oj=!hMa@I9@D6p`hj&Cc1B_&n9v4+G&3_36CcmO6##JWTBv?) zA9Sdh1STSrp3(2__5Yj1X|0N%cHU|(rOd@<@FDf=+4x19FF1kkz{tqL z7KJ3Rc<|iFEm>sg*93StA?Oh&w$$dkQd)V07>9N)dnyG>0l%F3BWI z&4AlI30DVtKXTU%O^)Vr$_(r3mc5aQ76G3-SV{t0h{TDq`N_-eq_IL5@8QNdn67qz zbPDE&gTyina-j?@f%b|Oq9eM0NNj=RpTBQYw11SSPAPn_=K@a(L;6Q?PXYRBHtIXu z9ZAqsjmM{^f{4tojUz_Y)YUO#gw}vGr(wleI}Izg&Gab#%QSTUSYX=m*&j)Va}Aq5 z07f0+xgiiMV5tlMfMe5kfdas3PD@Km+$RzPY03p#p*W+csH?LL9v@@}4Eqz5jq^;# z^>O&(f<5R2pB}=Q#mv(t*yupwfF2iKwrp8mWy$H-)A{efrNpj>(+Tu6XcQMr#*h(x z5^InoaVig{0bo zIP0oDAd2L-&eHOD{x>+uh~ta$kFNQb^%m*?ygi!P(PO=|}aSnj!PWEcF}54d@rYWy5Fh!jX`Fzq%Qf2|aYg)vHS}_o>YA zk`;|A(N2`nPlTQ=i}4&R*S#z5kBDb7_37Go8)Xp??I3O-k0gO=a=|?(FsRQbMFFKc z3(Uax+xH$lGA(*_iG|sz0e%HSLJ+ScEcRr!1xy2RlL)4)#i#t~zq4(Bmu8(3F%*zZ zAs%je`v>bN+U0(d!ExQfVW7M^m?xeMlEKON{6{sID!gF(Zm--fOT34eLSW}N&`=J8 zA5Tof=)-~_lAqO_qRD4p&j(~B7eiqyUP_v>E2mO&?h zjLS6s+m(u~2k4E4tS1hRHUghRoE-L5`_4mAIird@I`W$=JSg0=v4nVpFvCLbY|+rE zhvEp;o{WE51s`Y?LJl8#en}MLIF8f{*I$TCUbqxzTPdq$vC_oggE60oJ2OAH5eQTk z$3fT}?!gJ&_x9~Q@#_i_=-h0#ZM%yBVT_mzs?8@f|pDAn}`gi(Pey2&RJ!(NU6-Bv36dBV1R-5z=j5QZDbYUqssx8nq@F3~h0WaaL(5 zKbiC%>EH+acBOKKs?eSje;025$igK76&d>IOe=o`s7&f!)YnhI?N(Nsa4&_$cgy$G zR0+Av1d$CItOof=WNA37p#cPq*#qa*ZdUZFfR#Bb1oIj2!qz-^z~LA=VU*%9aNw2b z8YV8spFU|nRfApzpSr=oV)&c4MQ6PN^1#rNoT8#pi%}@h6lDOtch&bd!XS2f$sHEx zpUb`37sY}FLUZg39-fR8B76z+G#Lm@arR;}obSRRfUu@VXkmLXB@cYj4;t&m@>FAz zt4W`W37o=PVFhKZn^yiuI>Wt1wl}=R=bLBP<$jUhdym4!ZkUhw{X~-74whTgb~S?g z82}C8ueA?+UHo@FSiNo?JGyZQ?D>U0=I(YNOJMAZMA@PvM`F0NjttPC5YGvo@csK5 zKDVRQ`v=MOW>EwN424_uFr}x#cI*NuNleC&LyO4x!FI}e0S+6k|N3*g1Tf1KWHf}B zZT86Eb-t{;0>v%mCB3>4*y^!nV`{Ov7-cB(sy8 z7}f9vJvr$mH7dw$RFHzymkzM-qLV-mDQ2J){y*4>g&ix=q!L2`8hBu6W(pS<67n&o z&8*GcDSvu%!@)|NKiqjjJ2>-wCqN34A#rBmM9sN`h^H&D-Jp&~DSdqw_G5ApnbilW z3-!bmdAZzgfDPUZT)6|qH)3>;W2r%@UPsF#on>0So@JGXe*YLzyXN_vi&#=2s1UuL zO!LFwxMevx;xf##Gzzo&gPz|Hq){}%^@8Y4ta3*lx-G#Fn>^@A7?kRe0M*@$`(9ng zTT!3CER8KSa~+hl@n zD|oTTRsZ=gZ1LbQ=sKc;%{EbFl8vBttjNMA54Wxu|K;xLZ!8;`SCH5GGSX#Cdo`A# z0R%6uLao965lo9wgPf2r@5%YX-B;WYkn{GMUfJj5v48fdT9F0qHlEdvga?hJ72>AG zt(3`Q6%AWg2rS%siQLK)du)W7yLx*&4HrQqn_LZ-0e2n+hX5#KO6pv8@gtkFTEF~v z(3m%!;juh1$` zCgSewqtKr_QEf|zH68bxVQi9%B6EPquF<4U-@v6bbW4!r?MpI4KXH=37g)ui(-j1# z=1SP1)+K{WC=4q7Hy_|w#lZa}_y7}HGr^`^Dtmdw9iBvF-+KVo8l?BZaLO}9a=y-_ zK2q)dj~fU7tKYBUN!Z`lH{sAJCt9OK=@9sVB7xiSqI!FsG%-qv zD`^mK*X(eu9^ZeZ_Q(V~q4xIH-MtHti8~AmJ}%Kx!kr^zq!Bf9;?tlhAp!aW*8(!b}>x)z&|0O=2XxyYr(O#^W`QvcCaF2_e zilRj@(zUnGg+m1e;7GqMnNA-al}%L&*LfZGO$ru}ynw|REXC(qtt(I>S%O?3LHKS! z4jLtz>BtDp2{JhT&5Pw<Yz`1~-};~4lYzUAS#-iQVD*S-Z_J`dq>u`&bY6GP^IX@t&YW{S=Q@APHFHh% z`+b+s=l>Vz@yQ45!r!dx<>ikguyp1R>}Umly>Q_pveiojshj6lF1lIO{k5r~Q+{#! zxv6~#9dAo|Wtq8gC zpPUVWc_YwdM}8w4dxDxVXJM~Mn{*?rAj9{h(8tbutaX}EoQc_^$e6QX4D|zNn)vGN zTTS{+8z}~s99q(2V{E1$rWZB`N|@Ds_Oao(A-wiMh6$}~@*%unwvPHG zE>QjHyJOfuLYEV3JC1l&RaJ34a&&a8Fxzi;q0S688AZGOn7yC_*nfQU=ed{@KKWhR zW%=mSOcIC#_WvVio{NxRi<0M>9MIO(YzH;xdX%M9Wn}nSG-dC)4$U|=vTCj%^+a*W zr9`1hGZCA*<4?kkCUNV1iS{Tmr8>a->yEuqSv-FtHhAoLQHx8vhCZz{mIY7#)uAWX zc!C9qOvPc8CQcOiNc!$^IWa`pI}As78(kG8Jde`TCXdwR6?WsI%Hb>Ek`IGY@g2wH zu_X7Ij!|`kfn)?JMQ0&uM?kW6RL5VeT9xv9MSP1>RGnEKNLqKkf(j?`!dPQx?+2ND z8?q;%)xD~%-2mH#O9^vIc27)lZ(Nt-UxiooHaX6}lw`|#vG z7Cr@Wy$vo17LJn}j0&2Z(pN8+P5;b!&6<&>rlxmh7-uAmton;!^9pNX;aUn$gC!!~ zhkCoNq+V@>P8Pkhv%mdMTg+HK_;p{b` zem3_=arAyLS`qyXy07au?7m>1Y7h791`exUJes(-9ZYq8Nml4Te;15c`aSe=E|Xxr zf)j&1?MFCa+#*vOv3RXcALEN4gkOQIXVYddWEXTq$~I$6QQYL7Qfh_gfxudkn1#_! z?SRFXIuRr`RH8h=#680pr%*ebGs9RGxpmVo6HDH~QL^G2E$jxCC7df3$y*o?-VVNq zI*I(@(AGUY8hu3!rK0G2#K@@sh!Ku$HTDHTnxZ#VT0(FR z)W4$@yInW9KITTzJKSHyGa=-8mtP7Gj7~V+C!&JA<@x8iQ@>XQa6kzWhKMx@gd6Aj zB`^r&*rRJKVcey%0i&Li*p&+ch#Jr7Sq6?0+vx?WS6Vsf0B_Ed(9>w7^0b_%krEZzPcM+9@UU4oh%~<72@CFH=!fb=I za)~eAjt=%R64B)rN8yzoIEhp+(yfJ*dwqI(dU=sB^t)5nYM3}aZm^xsVX?<0_JIbc z0yGI7Rz)5{Ond>_!!x?M)U;5hmry)o+<5u=b<^TnIhWbL;lVAm+da^(;e@LuuJD*b zIKK|CYsecK68DAx*uG!Wr*GFkPI(VHJf+Mc(wal;`w`3g5XHp3yP4}K|Kn`(%*x&H z(F6mQb|goL1P$Iw%=uWOeQX5}wlv6wTWIF1#|b0^!cYd`P*t1}CeR1SG5gt--KnEG z-Y~_t3ZAxNU;EhtB+Y1MFV&RgBZTYU_$INL`3y-Blg6?*d&a^=z+yyN_T(r5o+><71L~xLCOD|<| zrRAtmv1}@lsM3u8U`5C&APbYtJiFwLaE=8w1Bw^dU!m4%e(L56k?&wny0 z_=VSfGoq5K+2`Mn4H^Zp>LDa@=_Hz%9LsVcM#+=p4i3{iX}wO$S?+I7d2#H8aGnAu zI5G(PipirW?WLEblprPdkDhg_FdK9>`so`UmzuV=cFtfe|BjtHNh%BTQsx(+V2_qgu|k8%urn&^Bd(~Iwst8n zj1U)v1rEdMX~XZh8(gA+O$$XD*y1+~eG!75REIW6o>be8*RzgeFh~I01|Qudx{xf3 zWJ|pEg$73z%R9Sz^=g(J>Jm~bHv@aBa_T4w`KRfvr38o#0LBa)aD7%UOfc~c!$DIZ zcQVzFxi*?}C8R9QtX&rFFidy!3oC)IdlQz|L)#-A5LB(a;>oxuOK6d-qE>D$M@1H_ zB(8v-e=Y%{3r4K$(^SuvZe^|ro`8u!Fxz7r5!tvF%N^yk!GN&Mn@v7*`mxYz@{8h3 zU3hr|h4sRo&7#pr=#WV?N%digh5&&geiM(gHE*ByhBhDvidgBTWtQU18I(cS9%QT^ zo&%W)d(d#|2uB7h?tcs)Zoi-|re;f<9UbgeRX!=|5{R1}&}2v0e%I^zh#eq%zyH2` z@#&PYFp&0h$N)=9OE(a=9`T}tZ62LWFJ`nb?1B$k)R-oPf;eNLLn3Ua8(t;U6j z(J5+*>~pvwk?mg4k}CyOlIk9uXCs{-E%|s5_z2EzF=P^L4%;vMbyZJI&7bN6y!RRU z7mI5S`(Mb{BZC@;i-ZIyh^nk3b{nBAGLkr&a|fSRR)Hb{@|3dTa~aj}ie{+hnoMfk zQewyvhT@1l0>NGK9^r8dFbxHRttBO>vkY*J4&!P`U=zt}9eWY81)2~TI_s`75{8=mU+h2qa)VZ}t2 zpQasfDe>)8(XN1H$&M9BB&XNc4)`SkbF~lATX%1A0P6PsP3=86x259X15G~jYk40e z1UVfTw_bnFnRfOr_CD=Un8$1G1-Lf4=QJAVj9l%<$~Tg?@{%I&y{NdCq)l?Ksx+*g zo>@g?3D-=57dc^Z;7-UHlF(3mTXoy0H~q$%v$kgp^LMG+|3P4Eql{~5{`pps@y;(L z)lI>Cx#3i$1-38@+!|zb&WOwrRGZ5&Zt+uIGCY z5NGwRrFcXHtSD4whOhgqd*5K-o*CRlNKjDZZ7NM%X3u`e%D{)kJ0^cHLIE9Gl(Ka( zxs8k-O%;I->IMXJkHHUTxwg`Sq>zti_0w1BaR1>yGK+s(nYM)5e0NnP@`BiMmHZWM z!&#&`D@y&h{jh{dK_uZ(mG#gN6vg_b8J)1coLC~DLVrA_aHY?^;B*=xlqnA77R7Ft zPn@h?t(FL@L)aH^oQ;ErWZ3HOHp=Zx@EcLMR6XI1u|ad{vima=Bbwb{{HrKJ0P6~S zpe8l+`Hkq?@7!-3T)$Vv=v&gl(4t=}i-(K@#&leDOw0F0b~feXlTrG)i(=w%tE*}L zsO-9PaO3O5nM+r@)0=6dgja8?I)>|^N3V$mEy)*%te5ZKw=Dc-p6{xbCt?Pt7;f_F zRwfCp!< zqYa5|pB$xmh8AOB(V~E}`$wCa9>0Ye#@_+XOfCne3K}?L~F>; z&~$g|m!5Ob@!NkQWaJ^RYqVvjPpG?NzH*mui#V`us|SHhCZ2 z^X-*-(>-_6-ER4RwvU@lNl6JT^!JN(nBlUa^U_E^B>RW}yx%qYiv9=2ru5t8K@^T{ zI)3B8<|{jHubsQ6&~mX>yU$}=c78cP*f3I3<5>rRUMsOltG-2dOjwfNzS^nh4(Zy* zN7i1>0J{+L7sI0Cc-8^33Hw(@VnFJ5j!E7^jsapN#zvAdQ?1Bg0m#ss_&cEnq5`*q zwk)bjWc`dXkRe3fm*;vEWUl;b+>w>hPGwE8i>p>|)92A08)z}kGIQSd`}ZH_h*RvF zW?x;bV+M#_lw>gucKTYK5beR=qmCd9=Vu0FlS&vee>v7Ie@>ec*lUUw>EEIo#_C$?qa!rtB@hp|LZN-ZThp|J@XJK-hyiWDF>Yr><6aA;X zXz#0pzbPlZKmc42_2T-72-}#yw!@6KQyr|UdvHwQd1$6QQ#B?0hm83Ckqg%a7SBB+ zW>G@!N!d@%#nDGYY+rT)TEn zu)x+9b5Y$1n1%Q*=~85F&deN#6y{Fae!GIf_6^$t2E0Ip^Ex`}epcqYx7s1Gg?DOe zYE%>{lVm;5G|A=YYo*l-R}cFfyNXdOgTFV>*EjY(OHtnSU~r>RtJgG9V$UN>@&R%o z!@UpDw|K_PPZhGpBV-&kTkqAvRhS^5+nbO$6wLF7j~YL-c_Mgkw!kPeVO`Wv)h1}ETVbnEq#cvtv6USZ20hrEGAMujeIi=zqiS* zW|JW9hOVor^eJq}nWWQm+L6GAy*`hvlgSqxYPtL)X3HQ+8V$apnIzrXE>xzxy!Na_ z%K8f)_r#k^3c?xdH&JL2CTT-Ftf&a5z%7-ra%A=54^0FUh_n};sW@qG6VZaPu}yW` zB*KbyV`%*ea2av;5=sMRCn_X8D3r2q-Rc1W7X&_h`DvNT#Q2S=i#zrv$rh&!;RirL zWLn#2!Z^n7NrIIam5~2Q3LERIHkeo;BL$$U3Cm4Py;%&CNF|}WqF>oS(!1Al@^*Gk z+34GiLEs9~Obid|%Iuyy9iiJ`Gw&7pGzWXa#xxOU(Fo7^xR=mA>1o$S#k*2^5i-T~ zi5DrV4alzRV`BOXhXftcR_#we=4IudeM$Zx%zi1bY3+sJ%q+9SNQZ|lyl&TQZnuN% zb-oxQQ=Evzy6_EnKKC1~6zJc_tp99M&%#eHq9AXd((*j-qqF&F_pB=O*cg zO5rmSLa|DSpD%p)z4u}RU&Of~_kVEv^a5K#LqBu#)4w&X3Vw6fNv0&S2)wYo-neO# zFd?P0jBZV8B%B6710GHFHbmNOaLpgS!C*o^`mQ!FF$_&mB)9~y;>a3Rph;sD^g!e*Jlth!Z< z0i@cn1Vg)-5#YzQJ%Uqo{>(0u6Xcn~sTBgt`}a*NWFi^lEg@&Ry15OdRT;RitI>7B zdU(oqJ-ZRPS`_hGgnQvWky1oeJuj#8;Oh2-7Up{Px)hfGiO~%@IXZQ~K{aTh5>>?8 zUx;x;;ls%FnoS_!M147w+way@XLlnt=lvu@rzb+u)hEF*rnfdW>~ z(69siQ?d8OA6IHAWDA!VO?kDbk~sj}BFDkeGuLz#u>zG57+k4Yb&L1Wa|5j0o80Mv zhC$vir2-L~VC#zPxSD+23kXiFSkFhj4n6ZZvX!k%lh^#4KSxr`8s?6jnWK)uGv#h9 zr%ZWK@LnSaal-Y+9C`O^gep6Kc6dGY5fiOsdu)#F)XG!Wuy-fc<@^k7Qs&b^au zY^piZS2XI?HaN%cg(+`~Np-`R_ZitwVnQHKzG`OPgg2wyE^aU1Y%&YGtzZw23FI H=B@t^v_=Bp literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png new file mode 100644 index 0000000000000000000000000000000000000000..07a5fa164d773ea83e6728d7f08ec853302cf5bf GIT binary patch literal 31563 zcmbrmc{G*n`#yYc^N@MW5N%UQp%Nie+pI(?qzEZQgpv%KGB!vel_C@>Ljy^LGK40i zlm-$FRECrx@gA3+_qV?9`u*1X{_#F*eb#3^Pwl<$`?{|4Jdg7@j`K=fx7M7GM~a7{ zC_YOIhCM~GFe!@G&BcNLqSgAg7yq|1*wit2{cf+|ecSeIr`Bu>4%oFjc$c@k{NC+* zg1mS8FI%F$WT~3`&fwsHAYFBJzyI|Ymh9fMLw(_vz9@VNcYuXc5Jd@WBmbin80LFZ zR9J;2!`LA-^ZUDfj_Y?wa}4S2V&hDbW-|?Z5#7x(c2)WsJEwEeOS}5=&X;z+MF(#B zoGas($rcNIxuNKae_%JIqAD+c{Ul%0#I!}Kt!I4dp_;#UmZ(2&I{VW=@=*Lf{St;C z8;yZ~9DKfb>rr(4)261(B>&~&91$b@YmR*!x4e*$kln&sw&Mv22?Zy&u~?7~G;7cl z@#m`BGbmyFdD>DNDiMEfHTVDX;~4_Rg2yv5xQqpF%JSf&DGM^FPYMecavwW(%)-j5 zZSMl|`#Em^e}Cuy{V`enIF0Z#ic*E56#=w_1nTl8P8Sy{BErvn!?)RY%QVHED~I0{A2(UOdU&MI`sVJgc~p0Qf6~d5qGh8> z;>XA)r1%E~rVRd0v)B{4l}@`_)c2iHzPrm{xnHMfadB}%Qj+~vZ7nUad+M5+vwQdM zQi;0yFBVT*7O~__#iC{N-0baz8;(kIYHMrPJ$zXE@}+oTVPVcY*LSmfobn?mgnep? zc}DBT)?3$4xJq2(?=ISWtNY8Bgf(YZ7}HbSK0Q7?KGqer&BH^%S+2i9iyS0}r4^Md ztQ-a36$On2OZW6yh0pvQv#_-_e{x3aTF~dU!Ts%)C;#{g&#s*6%F$mmEhSIkM7HSY zX#A~*HPNiY59Ok=?JrvSe6{}fAVPifmn{?F;21gS{j2}Qm$r+%aRPGumi}kg22&lg zr?F2S@40pBR!I#v3adW)s&)ldSo^)F+hmNjp&yRO^3S4e4ot_*h5l)+;$;>Vs3sqNF*ZuRE;lpTFPKsI; zHp;wpOZ)PME0^BYv@;cEr9n0JJglfR~=MX-8mg!h=N-bQ+=A#*mGgebSKZ5Ma8 z<#88py_Zx{HOUherlh?_^>)@=5^M9$>+g@bcVJ9a?v~FBo?E-S;tn1>D4yFkXEwGR zE_1~&zrVRvd;dO7!R&~Un_KqC?GyU_V^wX#P9zp-78VvYLmz52_w-sQDl6w~xU#We z-G!L)+BTgZEXp`cPg1O2?A^FKa*1ozk!`hc^A#1@CMPE|^!6?BdYnOv;QQs8`7gSk z42^N#y~`{uopHT!on>PYi_4Z5oxghO4{ILyz02aF{j1K-2M-@|`n@UN;G0!H_g`TA zo%&1DzIC-pjye?osN~O|D`f|a1?AJMK79BP{pQy0Q~kXHlwyoLS0{gtjr z78VvafB)j|0mvZ1$qqcPI=yVCMHPi7EZi&-3ZCCjOiXOfdS_oKsY*5UqDg%S*aS!FFnSY6^UrcDe{~i6e zl_9*yH?MAbi~Q)SPOhGrzSB6SmiX_WhixcX;(te`(R1k(_I>B`OMLST!V{#le7f5U z*MI4%u5KPvYpUSQz|*!rsyD4_P}g@cc=u<1y_AuJM4G?c+_{N%xn>{g5=AH`jg`ag z=cgwH4u$r1IZImqov{9kvys=BdT(suyb_nf5su?csVX1w0S!&f`;)&0u0_p6Do0JU zG_h$cUd*>_$J0ZpsUn%@&RyI7C_UU&L|mMMlatfsUf2d!A?&!FcyTc^OK#l_ua^TG zH;xRL7O;*#Qm@WlpTCxii>qdQ_~Y%?Tq<}EPbf}Z#lFFLckkUB?tif;eC*x60=ry} zuwVUY1HPZ0o;%#z>&a(kiLiB=jatzO#p9d;^SZyZ=4Pz$lcH)mJEe9!&9pPGQ%ZlT z#B6G6dUa=?`U=0!Lr5FBfgc+}r^Yq+e(y~4WeNS#GG9?i$y>)B2d>%7Rqji><;7Dx zG0d*08ClneUt;$5_AS}QtfSxGY{H3O*;(R;lo}$dm&1)Y#U@e{!V=}Fd;iPt)H$5!A?d4S`6wzM; z9q}b4yVISF?2kS@N=Zpe@30;fOOII^B@w2rm;=s`c8@$tmHF8IuWokfA9XA^X zN6uuFd9CJzNY>Lew6kZTiH$s}e)PI9NJ z*qSBJTSZY%pFY(I8!bpz0KCYFIc{|^!!XLg#l=NXSeRw==FKjs)<1sy=;SYAq;i?8 z&0m^o;_gnzaS;*}q=JHi{Nh)m=(rz>i*p|z`k)gvtxwg|*0K(Cl$cM9^p(jtHRY$y zX6U>>>5DhV{rvo__U&7l_wV1oD)W^{HRNl(y+(evY^gclY}s;4Ixb>8$o7&7n@$(65@ zzp(wIs0Q+K;Tlg*MYE$)u_7vKDT-r`B-7ULufrodq1lpTvG~}$RY_h?&XBzEKhnU< zkBd3}WsRK8mB9bE9PDv5EWOF(O;6Za@ zk=MTunksX#kSv#MFv&%A_x2u&7m$1K^5w=m(RRoF`y0X{A_wmuc*=D#r_0HJvqPXK-`7Z@jW=p z6jRfq!R^K64I0aqJ>TQ$ur5cL2_%36v0%y8y9p|`=X*PgHx@a6X)hF+92-nV+BrOz z1#8>KP-S1@0tCzKe?gOwFmiRhxOw8wpO5%X)IOHTnZNty%w5Xpb+dd&YLDr?rdVEy zQm-dcIOHtH2iaICgm}i;v$gH*qPx4QOs%Y}UOrV4zUlp(18W_Vn!4VucHQB+ItD-i z9ZBHRr%#>y@xa4FsCMe=@@n5^I<8)2Zn#2i>M-eAtDO4b&tUb#Q?8MT&SgG{k_)`8 zQiK5o)=iB*x2*^rrn4Cvozo5J?&)DMH#dLv=1pxw1BY-TXTtgElTk1)omuTKzCxUM!pe|KpQOF{7(X&}^%k0Nl+tj@(0Ux_Mt zhZFIkuaBMD`TDvN3ImDl5Qe#PokxbM>C9uSqOAO&qeCL!Hmd;{Bn90JFE4NVl}!Rz_y%CsZ98`s94p4J-ZZas zK9JMWaq3i9U{I>cfkE{@GwKUZ{vF|cnz`z5@s>L*l+VkniRQ`kE||pfICEtzH$AD# z97MvDs+ykk1k z;NF2iWNjIp0E(4kPRrIKd3HI+sH*S@VdNVIlH}E^S2cFF$hVic%6WNtg^vA@jfsf~ z80t3GkC+tw*l_eM0flh1^|!r0DG*@ti^!WatFam2J+3~t3_Z&XHz;CZKHSeh4Xx`Ti0=kgq-rdb77gk{P zQmgGP+83fZ*@@`NMWxzbe#P+`9lIZoT}yhs3U&BXp`)*@bj-nn1Zp@sJG;Lr_xtiZ zKe>E0eX)=h?W+Ano}8Q1;hk$*3!nlzmaHdZGDA`dj9*`bGorZLjj}xFRUN`z-d@+8|!@F%$`q==ic1e z%2KlRp8eLd%e{qD4TZI7nOG%)TI&x>cmSq_{+$d-{|s1j&9`HTO_qV|@87>OG`6vL zE_Et5#7Wg%ZRab{c%Ds_;Kx=z`MGoFW@!0}SML3u1d#3VG;^iYz}et~1$UU_DIY(! z(T|*RT=~}JqLtW5b=P?d7cK;PWjd%n(UVVP6PTwjxG0bQafur``r|G>-?#Ny9ZhGF z=#Ar-O+r}FDAenNX^S?E?2z0(zh9~*J6pc5OKksk2M34f4tAijuUBP!zI#t9l3;c3 zsY@j1OVy!Z7VoW&o73tzlc&d6L;c+R#~cMZ=j#CLIbH9I1B*A8yFJd(q^KP`c6{qB z<1O=f(PpLQFqIf7>S9^D5D(PG4opu#xWPoP2>vQ7V9BxJb=b6yn{l_{jR;}(h}%MA}!&opy7{cGZWanj`V zT_Vp}%J&(yvkiTFeS;tq^!Qmj?tFcok7{{x7EG}pPf3YJT;%}X=!Eo(B61a72UXIV z58HlrXjvWT*YB`jFJ~;_3}wb()I5EwU#twzWix)p! zaqZf*41)+=MPcr+-`^I_?w9VO1q--Pa~go=DQf*Co2IB6d53x3wzE%d@?R{S$W~gO zKKm9S)GB@bQnYz4)seER4*X_x6mJn4?kfF>nlRMcBqFmaOq83Oo2-z&zJ489A&bvw z{QpP$;V4#H}NPHm6a@1<-S4w-Me=?sDC?IR9zje zJ@iM~1YA;6SJaqtSk@Qr><8F*g8ucxM!C?KYiMbie(x-6y&be8d}{ciZjgl*!4CE+ zSSdipT$KLt{wpQf`T0HlYUkNZVzbNHO7mtJG56-i#u{W~&B&=xgfZ>-vtxbBea-Tg z#Jd#(yo#=0Nef4ATjBpUs_3@@%#jd0!QA7~kNI@Be?~D1g&5g_*NQ3hWWroH-6#Q!Nw|5)9uRo$;kqpWxnX65~zf`ycNa=IRb)%9r%Mz zvArc587m|wm&_xkxo) zMIeZ5rVC!73&Th%x#)hHKVw%CP-s|=pNgXOo;BL#=XcHfxw4~^lh{Sy zquo+CTg{2ck~lZR@Ah@gU$sgWG5rwyT1%PlCck*IlO^<{)YG%t0##L2&AlM-_BDU@ ze}6X--)SPq28{3Vs&pG6^+>;UR(rQQ5W}lmyXCHAi3p{vtqlHpVq|$TPOb-No6?&% z+xIvy*mL)=gbu!61&Cr|YbyXeRd;;;>UF54eLs_WZ9#pa(F@qKhlQFWy<(1tvQ5qH zpw9v0L)%8Nsb8|#fi>b*>gjzPD5(a5lU26KsG;$-EmYmjvNE8XqDTQjLusGhtG;4| zIO*cC|A2reo8;uPSk5EGfGhynu^~AVS>Rw>Tbl+>(4pAag(XFf*I4j4o+IYE=6ymn zfQS#!W1-$Q{eFuVmQQVxPqApZvE$jUXK!A=zTorXG8Yezec1t=fur0!JTdX{oIvsi z@wuMSug`5JE%FQXS;i-S`4Vccv2nqhppFt(WM$-;l~q+%X{tEkcC#NZ6Jeua=k@+$ zY5<>l@OoZ&5L-M5h$KmA>CfYDk4kG(jj8&+z5_CE@-AKaG#Q4U_B(v^X#J~e9^@2K zU*6x-10iL1FDzuSxtT+-stOZeE8+0rM|=N1IjbEy(#wqu>wL@q+(+MKOP30Yi#NQp zM^LB{%m?5M=zS0s0VU_qKPENje3nt3mTHvJn>TM1mM<3t9CIqp(g`$i62Ii}@Yu?# z-=I7!EG-c_d=B49V)fg#YZs}LfQlcR(-w_?I=ee#$}TZyWK$5jVJxD??)Unho}Lti zx>M@+W(CDOeR?iAD&Iqp0v)fpv*Qs#QvCZm2aq+#n>;Dz((TRNAYwtn866g1d{bs_ z$~d|tFltB86@DOO;_DkK%4^OXGKiRzN6i86{qbRPJjj&m5n*Av;7j5`A$&mC7Hlk% z0%IWGtYVB;7uDPMg8{^T4YXKanQln`5ZeOPYYtmkckbMoU^qR_iq`~=R43`IFX->& zuUa%}*KZEC=5hki%5$;UtXXp{blBaWWj>u=6ecYZZqA^ueO@G z9%_^lP-(ret?lFC;b>7NA9D-U$;!;B%F2>UHBxO|o=)|vlBB3)J@og_&fl%(7M$Ju zZmXC)gWAU}s6~t971>%kI?NInKKUu8{7CK(8&edtTE*~e=cO3t9!g9C;G5RwS!m>V~imG63e!uJhO<%J#3&hG?u z2H8MJQgVT_40d8lNkzp2Xt#O&WkI26Zpw_M@u$(P1g>A4=6OoGx1V_b561OlvY|T? zo(`&A`CdeM%~hux8i75Gsb3wg0RaIu2#A!EC-Y(p5)&+*le~$_ebu#kUTwU9Ie5`^ zExB()d!^IdKyoVMSJBiH#4n}oe*{VHBatJ39j=!h1;PY{q=`=7ar0)6wmi#(y?c(fBzBB!N2#8C*=k7S@}~Jgx7pFqd;43+qR9SprFvF z1t6Ap@#1^~1A~VhE>+=k!3#Uy@Z$4&n#m2mjn5P~{fwxva16oESTll1D?+-(D}Q;h?(HlWm2qP|?%|MOE$ z9q7aQ_@hVpKtl`8o5%0!>KZ=UYJOXLZwNcboa2MPk0u;1nFtE}&?uM2BV`G}Fv9fC zscY8)%Ne%Auj=X)A9f%WK4@zb=96Ag2NX+qy4mwuhkdlW;k5=8Xf=um;Y3m6uV$2~^fBqa#+MY*cf5rxL(a)-m zp$YDkke23!N_a$Cn;)Gd7ayOgv-6qqT6){u92I8N%wO%dZ{G%>SBL!M+$JjoYv`Tc3-Cf+9h=Yd{=aSz6v$5(+#`;)&1PoupafR`}_B! zpPyxUK<5L*ssq!_m~cUO{3NLnXyJZWmt0(2oMZW}xyR%T`9U@U0OT!0pXquxG#Wrv zfe4;qzaHT22%75q8PeY#efF$03TnCEn}FZnoYE#wgTx|Hl%inXlb;p&ptmU%i!VSi z5&XaiJQU%nL2|Mh8XBUY%VGU$KRr2XhgOvyRg8sqBF*=z11og*{WwxuszFZHDRtKd zkcZcwP(pU^6=NLV1kuRrM@>(tG$hL<7p~7|p-5}J`IbM3F^kve=|UXug=ry|AHu;& zcyZ4eYxe;0Mx)U*w6#0->Ohm6t+&c!W#*uleq^@i%>KWTk7fY<<9fv4~zxEJ; z|8v*a#OEvz1ZBm6v0?(7Q*#%Y{L;xK*u%E%WTfB;6r6a85Fqz z;3NDfmiM*!^m`R9BSIZnJb3V+rD(JE<1<>kP#B>tpi^4Wd26@2vm8TFrC<3tB)smH z3#%)_Czghdeu+jK{Sn2LVxm3mfoxY1{8f1AvSqDz_Xkysl={(gdKS`6-tL`9ICjkJ zhSw8x`n*V6SGU{|2Q7{!JLhdy!gaiJ`}60uK=MRNMYFfu`dl^oMpa%Dz;X{r6)m5m z-yjvO|JC19e!!LgPST=H#YYHDj>0)KJZy|p%@gHHSJki^psLh$bR_fFo_mmMo=mJ2 z>({SuFWw@J>=TE?s$1#6`;w-ZV{fUNHu#%Q;MsoJYzB*?6K($d*hSnQ2i09SONz(o zQK~GZ3=AP45i*>CCqRL_OYg{d62*yd=~Ddu{xz1)!)1dtuf=G{>(dDLAI+L zLyMW^)LPb-Qx-f?hIIS6&z{bw=VaclY)-xY&nloAe*Je>anAx{BROh4Esu49fV@+y z8Q-0x#05KYifIly5tGXaKu0|x!v1Z(rB;f;0k_+pJxdK{rh=gDTJK++PjI=BAREcOl}4`B z7M5tDq`cI&B)QHUm=dD8W(>R(+KS!j&1cctBc20)ep*A3-WWO6&E1{uC4tcPfPG*s z2Rn_bVk;G8dix*U@Opy{BUagmB^2q+LQ$AR^}KoW+~1#pbe$m21uZZvEDTL??BMj@ z(Dy~sll2Xs?p=9RZ~F7TaGD7^TW((7)kqx~Q&25TRoS1OXAh6)le2!+5dZ3xBSZ=| zk|VeJw%uJh`FHxx)cyGSbHtyaHq~iDw@(E90cN!Ml)5_U(X7OyqV`AJ`yVWjH4_c@ z1*aQcq^0@vEGz4m<2ucKVj^}aAV605zMS(c%h252JtyOq5?yt!Xvi-EeGhF=JX_*Z zYmOY5Gc{Bz@a668We5tQloCQ7Y4~Yp%L}p4X&Id$j-la{s5#u*M$GfJA=A*Bsuf9 z68K~7Xoq5#xw&RXACQDRGaV89?UfKZQb5gZ#U+939Zsvz?Kkl6cqD!&ujikE=6S1b z=zp9bg50hud_Uo@#(Pt3(O3%gt!`&4CD=qN>i+%vT>Sh`$F$(DDX@62w|V2&>Koe+ zzqsjcJn-hLqpUsxlf3b(8#@FbTeC|`OZ&w$80;jU<(lzn?lxzf)UH2MDUiQG%(p-3 zc|%Oh%UjQ*bwdf;fcA1v(in91I`DZgVWihyOt9cnH1Yp@Q;3q+)D(ih0LY(t`}Xbf z+WB<$LWimYD{gsjFx_(d`0*Z1*{ccK`uc;qq4i4dW#FYy+fpeS^yx7qR6Z1g*z4OL z`RJ+9?V}6M8i6>zq=DaOjfj}IxjZ{HWQgRfdk6MC&Ri)1y_f)18|CMF9T|D_)`tVA zg2P{073vxqSSU3$wRI5X9IiUreJf5VSpK|TD5dCft8 zzs<`luFSXN!Sm-LCj>;CNHJGp#`DWQK(P%(_uISt%G-)Sk2!J%GDx_hSFGu(Pl7|% zoSPL0hP%q<$fFkr<#ivb9F1DLcI|&@3Vn}|*oKr2s-doqw7Sro!jC1orax5_CO#${ zcVj@6q_(1l06uv;%?&Is+r{?9$Y+Ds0#??k`ulq`w0{98`knlT6Bz6A*fc;upn`Ej zIpi=FY(eH7{nE;cx_Rj0MWxYSzrel5gP@@1CdIxBl)Zd>VxQ zL){tzspC((TNC%6YTIfK~{Vw0*e0pkP7x@7FsDpeNvrUwYw~U~vz9 z;O=*o?4U*<@$=&>mEO3qsAXVlRd^y)VbS5?VR9(Xtnj;RH6{h`odu_8t*5GOeQD?~ zFaerA?F$&DruSdHk{lcCQXd)%p|`D4Jr(`BI?528sPfuO$AaC$$TAPmWJ0y&m(j`g z0iQgRZ@uG@>ZKw>wQaczwtgQhX$?U54xs_bMZ~}&_5hsDA!%)YjR^(S4|qmaICF5e z+fFC{{WBI1bWrSCT}-~I(p)tyd8%*uaL%m9?HmUfo?Iv}$NN zWpsi*8AmJHUSP+%9c;^}@0vAh;1WAndN-*;SSW3IeL-jlIvirud0Q1Bou<0s0V>@P z*dmCM4&Nh0QhtMLYxcZOm!)~`+I0kSMw+WFBQGe)ZrBG{`0D0c{MayaH#d1~O8#$y z#Dej4w*`Z-CByjLG`(ZcI$~pEn@d#aZ7ZFZY>k+n+7F9cZq&>)#oT%?jI?(8tp^K4 z?f0{lf)@bht_AnN`?nKfih{B-$5AP*S=Qv;#~ymu_p_THD5W%7zpAK6==3u)3@`Ov4&fmC!jp4Un_bsHWB^&fo4oy$OFU*|@Sp%KOkHX zdUwyof;$;m5W@Lob={_?CQwG;+0zXY77;2;ZssOYY6oKq;*S*w>d}~_j#1T@P$;(* zk$-g|3KwU_$ThWr#hxaz+IYWKsIQEA(^MxN^a_U#BeueckusEVM^{CDr(1qmDv z$4w5TPL1Wu873wyTLIItn_MvzFbk>qDklt~?KTb8C5In-=8y&<2Puqv4sIRdG|IjExOsp3aRgB=Ri(fFj68v$Cm z?cS}vaTAHe1fDgj?7RGe)F;(a8qHZ&n=noTR0%wUjzm-tNaz7KlM_ai`u8QGEzLnP zcYl8Zeo@g+b;fNO^KB4d3~W};%}>?T`GgQ(LAmH8p&{E=e<2`DawDAw|a!J98G8CfLV{-JXfnRc ze@v7^EBWo!HE@L{_%z{WJ(^A^;UuWKw=f*e;EU$rH4btL#DSI}JtT6{N3iq|{kBAJ zB?dUdnJE#_koRG^BK%QkXs8<+*2{6=O!Nze>jV-RF?b{Q#^dwJ*}y_=yy=)&;GjCP zq?>TEh$1w6hZyu|82A9l2pDJ>3^0$bH1DoKtLNn~bb zGA8os>bBG{@~+P&;zy~~CMTIIQA_5n3OlrL{iOss1)(&noV4hb`+msa03U--X|-}{ z)9&-NqwzU<$S67h;UpklU%x0hzknTzhUy7==6FhadfeP)Ufmt8QL%V17${w?R=WzP z{17afV*?Jl^_qt{x;NS>l?7OwYq4+>P=rVTE>QN>a7@x>zj1HWL94a-=zgC9LVJ1cAC zB4zrMo_(@(`ZddoBPrDazh5op7@A|Wn7X63_=Em|a;wiY5h1a(W?#Z*97Ewwn9D0< z*A(!<E# zn&(k(x~d=eb=KsPYO|K8QqZ`bH{YY2Y|167j$5YC{U?h3|d^GPC1GA67 z?(OYOtf8RuX~fZ71JrH@Say}~*RSZc=a$RK!K7&s+K zNlCX44`K;ppPm2EQk34d2D=FoKzJEH0%D;1{z1Y{p;_+!_>mP#H^KJ~io($kLwXnf zZy23Vh|PGVH3@QJae|3&%D{-B6tniJD+tN|jS_~c;*MMjzRjpQ@^8T8s5hUP6{E5d zr$1tH)EIz@Op*Xrn50#BgquZa6I6;s8wlQ*14vZ= z!0$Q##TmSqMP%37P5RhMvG}GxTp-`!@nXY3FlvDB_|V;*efJa0|A5^_%BkC5r)k4n z1d+t^&ci$V1|0;>Bv^^UEC!au9SETD@nZ%+IKFEMa;vZ2ayq@tZY8b#<@*${S&oj5 zHBfE$KLKxwg9yUf9AGJ}p=P&^k{-(#4Y6ULM(4g6?!7tS$4MHooP%059cz1Rp7 zZR@SwuA%$)&xcJh;?HmLVKA0?C_xD~&HC6{-n@}U6@2t-0w=2HN=a4o&v}=BD4zfS zB3N|-d)SC!%(GDthhx!hgOA+?+z+!AVd5a(UN2v*KyOn` z(~j;{(bt#3Vy}O64_(DI&nDr%#`k|<*e{qmur~jopxsv?XaztD%l+R8bPS7lA9PS9rJQmDwgx&U#vi22GW4u=!xw@`^Sy`uFR3jDdjd-a)tk?U7(0et zYUP9tHs$GIBruh%@EEwqdmtP)r>;s!Sg?cCvvYetJYXl9Fcd)z4UHN&8Z`Vmm;1$= zG90cFf9<)Gr%xvWqHnt&!w$)?VDqgdFeSEJ-);>eo8j;+kS8M{xGmXmIsscg7m%9} zC8({Z!5)bTq9C+c5;HwCdoCyyyWUlbVM5@=Ud%q}9>l20*F$LQwjmodvy1?KfDm4S zC?YSH@?&9}R1Kh34iv_f1}Z5QBjM*03bL_rvu0dy=L^81$mb$9mqk=M%m=`fERxs;4RL80wJ^X44`S=_*yQ6YQ=XKY5Z3?LB3Lxgi~`H7-#Yj_UjPV(u0}#xQ~eYAcxA26RL>Wrc<83Txm&YfA!z zaO-VML5Cc3?V4t~y6Z7GXuUzuFxORrzIH$UY= zyrs|qVKejm6Ars^+wZQI$eImc%Xq zwd@~(7hYw!(z=EQ^?EdpV38u6h%iE_srnW97rF;e^vh&e(IL1PRpiXeFH;s zKKx9_Bi=^u9^-%BFBpQP@pU-4ACb+8kENtU9j7*V0(yEj&?E zkRpi-WA^*x;lGj94X%MbnwYrAfDTAs;-`a^w)Cv%O<6ef8SPtY5URwW4jJU4x*u;!%Hl;X(AdH+?(!A^gP@6@8z{I%(AxrMi*L-6N<96jc}Bx1OAoP^|kR zyL_hai_5X_(|Ns_8oq$srN4hfu`9)dt~yOuP)Lh{ZPDOqmSHl17Hg(q)!V!|d-JWk z7=v=Wec~4e+}drxfubu$N+Hll@a!Y)i9?6j!hb%|#(yFV1eBE&jwa8gr2dtcmsbqE zzeMzANy)p~x4Q?`)dx>>d=fA+82SA9AwrKKCEqVviWY?oEWX771wtf&MkaVpE?mz6 zHa2whix2@8Xn(Td0+iJ9IZ1va?ofPuE!vWqshs{F$cLW68R@ zx|+w2=TxK7{Zak6Z_{sf%H6#;QI16QQK7$TZqQQ3F$IA0(Qta%jv-7=lEFq8-SNs_ zUR_f_>&ptw88JcEva+sk zibktF4`a1r8H@!xKU4{fPfXB`4^C-mYd_mlqyg>J;pVh7RTu8pn|{8uOwJ&j3rLc5 z<&=hZo1)t0@;Oj@P48%G#|R3E>E)bfvzJ5>4IS!^F0WJ(PVULL@jujykUA)%EdXic z3kVH}jYju!CPyWzW=1=vw_Kk=Pbs1GPE_MaD)_L#$+a4M^j#xAWVoXkK!cYvCc=>uvp}l-77ic3Xvw>n#e17#CJ%L9BMamis+s_^zx>l$ZEbo54G*f3#)K4tL` z*of`7K?5CYY-|j7Edg1^Euvh|xeBalrSAs8UQ5g^qEV4r!-QT+;3siaP{h6bM3E_{ z9GMmVpbjy3I4M}T5mb)=tk~2@$~{C3ri8>@!-q|aOHwQpF_W_zBB~u^=bT{Mye+1$ z%T&3fN+LcF3>+r>&;KJS?)$ax+n{=uYE;y@ytK6NE%N!1WO4fzc4sc%*+P-o%QD|n zj)+|NWWw zO7Z81>g4o%Oqp5fp{~HBmJ4d${avvmYVCe|MIEM(W%uc2M!T*$n>UhF35-idl);!1 z?(4c}z$R(an9MgTYlIW0e)b9l;5@1HFX>*grP6I`d{_th7Sl3yceU(>Pj>uxX_Yoo z5+NabE^6*+dVk9WRx;Cq_K(;F+H6KR8_kyoSx+;sKJSKPXa`*kLNXZTSoCmkOO?9a zXJKP!Pkha8H~g`mM?Y$b+>@V>(ZZ)?#{ce{_<8$9)t^3z?tzk@OB9xc_*@v^_>ZN7 z^(}Fv6FG@hVvU6JC-RW>=@2WiF&ZKL4&cM&F`1yZ_Z#!dl|>w|`OE6wC(VKgPw?qf>$%{GpOY1|ltiSLx_tkAVD5inRDO=Rb^%|xF!dC+Xvrt5?gI~(h zUug&CI1C_IE=aQq{PA|BjDDyfj-Dy(gQ%OJ&&f;)Ry-Fg0sgl6cPH#Q5~P15iU8It zNz6V4oygGzHC#^k5Rz>yo(l~1T#SreNRLsX)3>Pjm^$KxQ9BpqhZYqTX#%ywES>`w z4>K$3b~6nO_)|NQ3^|fH5uDQwhC5AQK550&Tt;F*H9C7hd~dUKM~-y{;ZO(6-Pg>1z&WfdArqP44N)XRivlD zsKOET_m>35V@uI6PW>J0x~vrCW8e$=Jvg~pfeHJLxDpd|Y0@6KkPlq)-fi&%lSoAI zc;gG`uwtuE=R9@ufB0lr5j2y%HAoU157Vv8B`Fps^Cd(#;S8@oZx}_IA0p=fYk0Ko zUeIQ#fJSOW)Eg$RL-fBXO`h%%^BB5*V?W;Qe0g>5^xrX8n={KTGiRiQgnG&q=sCix z`?l0TgK=*#c2EUz#E&uAINUWrO`PC@<@(Fl#8UlRVIhHU;BHKu-;b*zR4dS>=aM0c z-@oIq{m@$vg37~X4jVK1U0iy7r}k{VJU^w;RwgiT;rObH_7m;bOG>749aPQoki51z zuc1(+%24oLlF%c07r*J|=XdnKd>0cPop5pGuU~x^w=%z3X7>y?r?GcYJ%j394sExW zX#^KcC9^(;6tkRfuyk zj^Rieg%%Ok=GaNyMZHe7x8K)3;Ts=%6H8AS{pqpBm`<0a`>>3oj{yu<0M>jHB$s|` zUGV0&9l}d#ir0vja_5`!#TYN;GK!o$idjmpV?m3WE#=izRx=!W5_H)?UhgGL7c4h?tE9Ng=4LRnT~pUD=Qx2(ItaJht;drO^lHYj%o7*ajIBsnEBH&T8;6k-zX2Pr8I+ItX;!W1>TwMK zJH!S~3W%PKL?JQ0K;uM1<8msU~3pp1?GrGRM%l;zpL&V34I;%`x`~ld3mJ(M(oAdf}kufnDCqN`K zSmQ7sFBNh!m6(TNWLgf`x}wN;`*wB;ilkHe04Q{G2L}TG#dvYC1QBx3G;b`~dK4B& zQl~rltwY>T&3;}4<}Z*QL9y5H+K)k{e9F9sxJDUb*E7-(BCq|U0-aB7S(k6ET$GjF^ zRqr7pl4{bGyg(bZvKGBG880C1oA!QqFlK8n3&at#yP?A@h$RBVAcrz+n+e81*@HD( z^_c@a-*JhF+^`0!__#WRZNW?#g1H`cT>b6!|Xy_fHIdLT{7?nHs+9RWZ6%QL3Q(gp z$fGdT@e=xxp{Q%eYkXk;{ttLh9Xw3AF0(QzHru)!8Sl%$z@n(Y{lCA>MMfdEC?YX2 z@(hq%*TRoVX`p#d_q^af2tPd`hEO|m?`{MIL_ncKPhur^F9@ump`jWwZ3Ox2rr#U! z|7BGh9vKl{#+DO~)($QzcTfW4?h#yj0ba0aOE12`2i4~-7!x$;+y7SI?VsX~0nCnp zQ|VgeCf>EQQ))P<#3y%5W+e+n@-1c$VnF8aT)7leGf#v<*h))xI2+$r7UZOV+h0r)a?=1NkBq!*Zx&q>Z*=iE8s^A9-?d$C?;(}T9G-PTj0&Z^OsM40 z(1tsKjB8>G5;5saxv*OFD{-+PrzRm!Un$eT$U({y`vIPif zio=wPOBrw-kRehaxsNBkQ@+g7#)biMne;al6OtVqD+h^{k8EI054np0XI>|KTm#rP zEjI$%hAB8PqN)&!-c9+$5qB>y4oqeeR~V4o=T6+8lM@cZ1;Bwc-~>|MYUJydJNtHH zy5sO%X}e+&MSwXpL;wyJG3{VZ7)FC~B9YKle~%`G9fXJeBM3BNRff%@J|S^|Ee24? zP?AR2=+3?`LEz+xAyr81ILt;PeUDnp{WcSoAGiJ7e|tNqwQ#)(v9Rf2Tn!1Y?no*Z zv#~cWW6=H^%jg@nke&1kXoPq_@ z05S&QlA4;DBWsm}(Us+3Juq5xQTLAeQt~tyGbWcY5c@0ULq6=5Ps6kkB_w3@tDkw8 zH@ieVG8@%F*W^2Qwh0i+)AOqiLsNsA%}dfFgKj5&d`WQz<&%zyojZFMqw0{BR_t%w z^fdF7#8B6y1I0`e8yYBKUQ|jo!B(eXII3z8De4t$fWHU3lF@JaRQ@s9#!|5Ie`FSj zfBK~t&50sB9YPi78JVa$nDt|Qg zAU1|jdFI^tYtvMc@%l=VB1Uaz;5@FVsBnYronjIy39F4sEAQ6b#a7WfjVY7WeN75z zak$ZE-)sN}3q9Wi0x`KJi%dwN3m5wQ>BW^R)6VCYnBYrgI;c+veW`je48x9(hxUCs zhW-@dW^S0BaCzo}m+(PJ>=J4G^E8vD8zQVd4>9I`NP(HBNpK1rY8=3FfNwMk9bz4> znb>c6YQvS@DD^MSCbJ{lrw#%8s@^BG!gen&cf>SdlZkl~tR5KeE7cyGHcb@2Kcn?? z_Z17_O~sz8jPQYBe8Bl0Dmm_cc>tRy+i4pPEG{X%m`iE3p^A&)yBP7_A}2TT&~WBt z$KL~4X=2tjP$&wwWq+p%dOKkXCD#EEmK_<{u{v@UNuU!(qrYyHd<_ce8wN?_;Dd&~ z49L*2%ts84w-$g~Vg-cjsFQzSW{(vQ0>X-0nVBaJNI_~kbd6|2_th|6eXuF@?2l@C z4{mqkG5hCQBm-oSE^o^6BfR^fYnM8B;_?bOIaJnW8Sqg7K|#2n?jW2-SjV>k5*lJn zA8O-UZ+frKG>DMG=FFl+xO8VLG-H1^mwX}a24lxe+Cx+c8igZ^+q2Zsgk|?N0&^`e zw4n-H;K(If9!aG&pg51BF2P3S_$UPrVP~vC%QL~mbq21@z=SLhM%G8ay z0TKZQ6}*m1bakcFx85bUgN-=DZ3xPvq`m+@U?KLF&17N{my?jEBtt!jkLCf`VL$+g zh!{0*02P8UNJ0A=wDl{XaGBti2Qobb0xug3Oed;% zLIR_X?Ksu=Y~;oB=LZ>daxGjdHqs_jM+m!y`<2LFX;|28zXaF}N>D5Sm8e)WFkn2& z-!cLoRWInUI3|$;M`|PZBN_$L#0;x?Q$`F6RVZyvSr_8=fou#6kZXy^hf<XFsfj z4>uE-jS2i$<$lssHp&%*S7VR@We{*N={EW)FbHW*v5y7+IuOqW)vn z8bS~&xr+mb^P;p!jqm~-AA)ztB~aSC-@|3dp`p?3rj9Rd(xjc(4|E$Im12^? zH{t__V4SXiWy$G@sWnk*#DWgL*yKvQ37501Ffi`f0q82jpx3su|Z+fX5xBOwb;Mi2!BNmuj$b8h4kmAyOh z2zje3IM$h?hw%VYOx#FG}HlO!tV ziPX5eVLNwr6wA&tjGTkn;mnmGJch2fS!y$-cw?qMwihxTF5f5LU4Z)~9OpP(cHl-! zD2dw(kf0A93k#c-j>%1txbxLG$QUTrF7dt=v!00!#DyJkfc4GKhL0vJc*V=x2?PW1 zX+s@O1QYak#`sr1`vE&+&^tmx_u|*r)TiL8s9PnB6F-0cyDN&E4hIX})6}K^gI5eQ z-)``_5|)o#3`#7K8unse2Oe49qh4dC>SNj>CjruKI^?8sceh=%CIc66$bH0}BXuZ9 z0odwY-vJsm{om^7tXxS3A2Am@yCIpGS7Qr!n_h?w{bcod`8e!dvsL7|5OU3_g{iRd zx^}TvKEPcODPJbiIL4A>Dr6_S3+#UdDZNlwBE0vnHcDP&O*GKB6+6DffDk5-ia z@4mB~D1(U;R(*FO6zILa|5s(_9+&go|M4$sl1ZW%wV1;?5oX9K9h^y&v#HRq%HfJ( zq8uVkPOA`0B_vZ$MWZBAa){Z4km6ElEk%;L^n1SB?%!Yc{rLUv{k2Exy1s|c=Y4p+ zU$58a<0=Ck9)#g8%nqr2r9I?wa=e)92%i_1qVVLEe?4Z;gLX|SuF>)Jpe4q9oCN+5zyWw)ta*l+bjy>V-&G>5V}&=QQNOl zCTt>}6CFQS#3Tu=U^@l3)a`JPs6y|A8yT`JkpCg?{ae|MrPmH7|3J&h59md)KjBR+NS%FoSP^xa^lpZc_2Zj zC$HGi6s8pv=|Uzu77HH89lwhCZe7MdrqG3rCdL1aS@e`pSe8<9fVg7$2v{&K!N(Nt zS{H}Sl8qX7?UM^fFONPJrTZq*LX;20%Rh!MkB!f`m`LZ4iMOhNXX`W{q}lJdEmZeV z?}{>nBlulR6xU29gi!H`%}qch zx~$*~I9#Wb#*H0oAPP{cITxqP5biZo*p|eew)8${^}kjhj;c6jzjW!zI|<(mjwbbX z+_qcycvOX#v-3{DE-vTi&sz1dK1Jwx(F~>S`MFWdk@|LT*QT!1{QCOtDAN|r^^EnY z7tQ4>;QfXn+l&j0Gq(-Y471YN&>M2YWvU638^N1ntUlYT#CJ9~9|L!7Ot^J2@PaN0 zBlrIIEF62fLF&jJIvYBJ*F+OpGU&Vd8Ck0ndsiImtg|68F;Ns!a;F5dQ#?GX__n}* zKdB!<*$*U*jwoL?xv<^KwG3wBPLdVW&q;YLFptSVPtec6(r~uNrPeJsbY{5Z3^#Q_ z)3~NiO^$a)bSmnzrAwXX_@YqYcfk!#pG{>s8k?AeWu2uTZDay3DY!pBJ9J0J#iPp7 zkM*nn;avXa9_l(haJ8Ellr6e<#Iun%`upc!8ajDA%>!qfm$9jZ5u z8FSd!X19gzn>37}qNyNqNXoX?sOY5ooe(%q+x0<1q9W)fn4UX(U1Y^l2tNgSoLFypczOnc?j9eE4H>VA7i$me6EG;|)cdg=djwd3BHn(7Sjv0u z+{sbNrpaB)EpDzE`>R7(O*QfB{hw!Y@%8{`Z#8tNQ)s&Z@fQ;z{o!HbNckrocYi)j z-QU8!kIS8!uVHz@uDT3-mR*rMBE7=fLhumW&35kFckH6k|GLlinw3v|H9zGY7v_C0 zBlerX)f308Z1-+$2x2kp#dbnFVDNv4@;2hrJ-TtdE4(0u;x>9v9r5$eCVXJy`mrm> zgK;6QtmwaF#Uy+N1KCOtw#xG>4BHMj?Dsd>bw+=cRLfB39*%7JuXwAkr37@=5%`s# z9J#*J-1P6Oc)50hlhZYT8$H}R|9qU%mOAc_rDt88Syh_)`q$70=BIp*NQnApRNLsu zUD$zu*J6IV6;h5_gCg#?g=+QT!nhUf{ZAiDhZ@8`Tm|yL;vcmp&5L>?yzlnQ!TkNLX$G{Oty3b=}D#oBK_yjJI~KA22`+ zp&bu5ZM66P|2X{rd*^*pPQd7VQLdt^R6m!=D0p6N+F}H4QU)uvZ5+X=rp?->T@9jy zi^9a`8g|8{fvOieQX_*VFciaGBZg@45AJs4?kaKbz{ko|Y;HiV{&P`ngdrE=d(&>>>=B4`t%O? z9kj&9?*SnKNFAY6kkjW^3}GZP>v&F~wyOG-A!)%m+Fo0jrM-Ifk})RJn%dcJfQUPQ zHV`<^*f?py*S;^uOGAwN?nuJfS)=Bq;^S&6>}S!nY<1kd;DSIlW-;~TiH?x3FO%d! z3~fTHI~f`Vf{lrxtsJ^ajKYQ$w&kqUoEzWK-oo_y@Py}^fgx_+AKyr7T}!Rt>lS)$7M z@ z^78UNbFb7;okIdRcu=C+7o*5m6&gp4RpVWLO((@bGRv6&BexjGTTuBddC0Ib!F z>S}7~@Rb0zjrtL?axM5F%@qbu-*xLXW#9h&0_Re=1{^$i5beDQ9PXGJx~6DwWDz-& zETLBJJ#h<8gTArxHu|FU-+t?xTPNIW$Rk;-#LAbqItjIv|IMP(#5p(0yDX_)Ctb0- zXMed9Z$x}6ekk;gji#Zt>-@r#+$)wC@FkOntpkn@UvrRw2ULJB!S8=0OI*gA9=d!6 zmDgRGUwh494O3Dj0P@u#f?RE~DFTE)%gYO2ya@HteNSqAkh*4o*xk@C;3_aF<@4iI zoTQ|i$vSS8HVn=i`*~tRG^0LVq)2>MUxT5KyV?#yRu{@NOnRgY;9pRV$JsZ`Dv5_x zEVCF~IBgdHhTMF;tHlVW0IKo&zEz+i;zBOxNC58ifibA0gU=V1mTu*Vdq9~Gv;k3h zKzr*s!ignZfCer|*dGWzY9ll8XUs_;f^LCDHjP$3a+IqR@{2m;I^!Q zAd;S$sV(Pg$Y7lvL;iG^-&naaxB86kaSCLaKHB&7A%HeKJJC!SgC4u;V>5?B=ao%gP^pEn8ni7fi-fe&r0G3!!UK7|4qh zq5~-c!5YS@k)F#B!@D_|l%(CcA^JYBAGNQ2tsl`zRxuS#Au)s5t9&>#ehC9&_km#3 z>h+~b3m2LZ^I$sWpR>N>CDUDaGkkH!840#%i&)@UY+G^jXHwLLRk;P#inzLqCBAT4iM9sa3g|k@r0>ig|>C#*oICyX~ML1Owe0ef9PCs)_ ziJYX6TXiGy#0j79hIy1Y3jt8WyHzrEDB;QYtzBCQDn~unobcvIdHii|WC(_uFFvf$ zz;ciWDiA06RSt51cIhGVKq_OEChaaIl1REmvO*0S-7q0;Iogl6O)}3eUVJZ|>R=;+ zMUdTHWka0~3|vnqK+Efay_>)#a;a?O=ICnKOILY}uf;a>{RaVU8QjQ<=A0O7VUr27 z03Qle%s*PW3_*1yK#ihs$(!Vq2-8?Nv>9OJqUpyp)l>#^^7%}VJSd7-Bx3dsD`t8W zR=c^8<_&Wm7<4!N0Cg3VU_G_C^b7;h%+zZ%%NUMHOU0_zY0=^`UZW~M4Lz+$DMa>3 zQI!QSyK{j*?qF9y+MH8Vp56s%Cc?>=Z)IpYkDEjpFdeNJ#qo^T+;Q#A3@#oRnc~^^ zGca(isMi&zVlLj=()EraP%N*jIe#SzUu-pY0Qp)T-2hA6|E!NcQfW7 zpgF>+F?K_R%E-34V)nXWfki4wY;8JbTcAyL9MV7_4pXO4r-{FlY=y|Or|UG;)0C>x zuE>6^x>^YZhF(O;_J0jB(KWw}dVLe&V8OA{KPYcK&Ba;NsKl3YAVlTp1>&}J*|JH) z1do-%m+DPlD>}VXKc+LG{??;s?7ld64QHn)&uC_DSEXQpB>FrOMbn+WeVb(eR86u%i~Yag@P#8eh)bmP@cE;vcWCo7x?ImN=?6|F9_*=W{oju+OIvRA;xn=#lDegs7x zB#7D3e`&Du5Vuy4i!Ao92PQ;*31sy~p-y$RyeN^mxdBrn$F)_6TgwVmlQ zT?BJDXMi0as*kQ7cFZ+?wlzUD;`vZj$JCsVFKWFQQHGaZs0qQ3L3s^yuGX1u#}XQ2 zqlwHrNTp7(tsA^3>+#1IZ1nqsG~+IE522~~?-av1;{axy%EZdAWHfdzqLwr7#PdNblil@JLe7JTV+ zARX#WX!Tt{@K5H=dp=}hvh(;|-0ruvm)}Lp(IJeDa{3lU@0>N%Hna874L#-rw@Ica zqhfsV;HAd&cLoGE&P{()r@f@*;J&+QX4^6eeNLSwtS53?<=avh0&*9~Q~`chEiO&3 zI<(5I>d^}iFE8n^m^9&$9P2zLtk)k+{A}-9;|Z8tOq9VZqN$y}zNx8O+#1WY9;!|z zw@bb1G_m9QRP7B7-6XV=s=H<6&Ei$IBlg$q>CrCy#EG6%LR$zavf?;FZoGX9ar*reN+9}3UY1btK7NQf?IX0H5}6atFM{`7`NYG`MsG@*xXq9qf^80 zU8~rU_TmVehf&!^N?l;~Gvg1x?2ctPbe0>Z$2^B=B?i%FOb&m$c(ERGbrS5L++BNW z+HR>gd%EXd*I1OSpFfa%>ySGQ=;0;zZnOgtCPjP9SB%%anID znt@8YxaodQhiX@w3_aRmeQtTfck6Q7nnOs>5mO~z_$hXkF(m`gsdmIi?T-f^sHZib zP}yqc-8m-hY#)OsVd|O3%m&FPxJot2?u&r2mvNWFWrxmJaXg zRcSyZdx&eJWc#U8r;-&#%;uPXEAIWu+e<#ouDm#?puNu;|J9eN*?o=2lw({b%p)NK z5s0+MCUX*0Xayp^pM%>yuiIK$cx6k=AssuxxGS?B9K0)6N+vOb&iCIbQsd(pKnol0DT`WUyvWWv@PSuB=w!sDi@shmp z=BX}Tr#SwQ^Kwd0P)c;caftlo99|?j4<%xl%U>xymrn~UTAM zQ^}1RH<%r{LxvZoy7VY{Kc|V&M$Y`>o%ct|*OqmD6FivHr?7muxh&QuvB4!+Yw)=b z`-5xFbUS!qLJ?w{&>zVUQl|j|Ua)H?&A4S->&!8_roW0Q|NQj_^!8a> z4INtRuWsG`=}LUw7-S1&IKi`vbcvD~(JArt%$@xicQUXYN}l3Sx?RPCP7$9ymwgLA zTHU%DmrbSW$zB_c`&#Uw8+}u>oyNb3dElqtp~K!Xon3RYmg9aEJiF+~z~eKM;#IL0 zKpR>8`A<4?r8Z$v=C#d_!9zPC!*8ZgOnq5!?A-?E`qN2tSH^kk(;(X;sRCJZPiV+f z{dROBZ2M9ff226Iw-oj^&twxGb;-Z(33hXR+Tg!{6YL(RM$RfS1{|WQj=@~Y=6C(ak)c~p`oo)+Sc!HCI>j4th`bXq{%xx3?VIKGTc1+NXx@gUmGN_g{@2dC8sFa)^66nCMikXdsLcrHwK3K?l?DV}q?ZYDfOb8`YP0d1V|_NHUZqA4cS z?*L`6m}p|IL%vq)X}DE)viQT1Wqjgmu_FS#hb1MBu%`|CPOHD<;$7k_ua>G?8Y@ok zGP_|`0GigDfhndGLaUfDw#W>y)}2}xaKL%a1-XxlYA!<^;cmGu#r&q;YKxx{ESWW) z<3q`XsC*NcqUFTaX4#G2Y`|BBAY4Kg_%1IY1Uia3Ayc45ppBSr9i=U!<)t_koLI>I zl!2RJP>MEOI&K@^Pq2p{F6{~PNb;kle-v-~%$>~Tw4lLExICzN86MeSm*tG7DkMK~ z3ntV$%$(U#k#nHltM|B_{$+;>)(wXN2y-4wBe_2~&corN6GS0W1aVb`?7)}_+SXWP z5%aSs{h!goQ@6$taOoB7AZhc!`anKv`sIfTd{z7s2A{C~9quS{nzTD>gO1Sfkr{wL#plNf!GT06elslp zUChuQSCd8DJPT`DbEuiw0;8(@KkZ@Qn6;*i3JeP~{M&P}#=iWvhGy;#BO;m4Q)XZ> zIWk41mY+z`PB}2m>4{Mo30XYpSrT1ws6Y|O0hqj@0Ei<%(1}Alp96m3u(bgzdCK@! zP|m{U9ix5|X+0nE98RqrJa{oVWPUD>1Ke8z()O|*Xr&8_i??uTf;I{Y%tDinS8V7Q z8#}^(yo0MP{$9_CBo9Zr>mEluBCw4R0|n+F8HzJ zqdE|5*iM2RmaKhmHEQ6Y)RzKEhYqmrSq&VT{uzA*eGcLM!% zCA=*U-s$71Id9)D>_gF3y2J z7rhy$7k0~9gGHRUmP`rv{ozu-8zLfOv?=dFka!v>%nzZ*-+`_Q^K(#MY!a2t^fW&k z5uZS8Cm=?#f}wC{`?h6sAKv1aePZU9a)b|#_5sk1nNGRHnY&Kj zfx`E5{I5l<=Q=3l+Kn46xxTF3-N*IDN4v6FtU;g`bc?aape{O~8c%t*Yxy*WeL{}w z)M>wQo}t0oAY39QnR{Y-$e@w_cVON(MX{Ygj-c+Y=hUppGd6EFvzBv!$DXQ4tJ9OB zpL_Bhs@f5Jjw)MbpbS`ze46(U^ZWbP0Cxo3SoQvO7v5=N`O?waa`qtZC4Jb)K3nVu zZ4&zu7!9BOJ^ueM4r%`PBOLi&`8cK)G7cr_cE{E2R4k3{94^`zQIsy)Sy@=xS(xeXus5=?HM6`T#3#Zh z$g{)L&d$nKoS*;lzh1y+X=B2_SEtJZAF|R)PQ#W$S$mQEP`#9jHKS0>JmqDMoO66K z)a;=BsB5u!td{EBxu%yH^0r0{GD@p$yFY8obe$?cDO7C{QePVvDYos>V^!mf>UB!I z2hxn=t=c~DY-v0C>DrE;R_-28MCCo>gghtQC0QkVn|j2j{#@HI+MStjQ6N;2O9p=m z&C*3PDUA4&$iG~g{C9U0We+<$dxzmh%5l8l$zAHFKKK)OhO!aAwH93EiQlrHIze@s zyuD-({^ZMS+0)T+JuGb7 z-0z{}%V+AN<;gX&Po0U^cIG{LZQ>#KlqkjB-Mz4|ur4#s;>=pI9iQEVEw4ljnF~b! z9$fX5m5)~8LD3>DaZyt;(WZE z2lr#cwsPgl^o-D5)pwbNOq(+gxXn%$JeZAppnc@fB?@1d|aAK6~gkyzvK`dRE(9VNT=K8qN2{{8KTOzV?k41AN=e=V)${6|B<{U%>o zTwPs7Z8}%PCz)`pXy}UmqkiN(y=<`CwkR; zIxoe#r%8mFVn8Xe8YaSuXgZy^0#`;=%2h-h7PISppyDlcdMTJX&)|=vqa$VX>!Fd;dG2oF_Jbh{~62HEytnye~I<5W^Kidj!OixdbmW#An4c6^H zc%`YgH^7Et>Do(vt>3U=W`3f=tl|CM-O3Ts2pfxr_qX|UGDS1rv&b8erRPw~vg=#_ zXKu>mV+OHX>DuoNHxZ`N!(co#oy!^2+(>ytGyY+^oK5dXWm z84V_pZbd)dsD&Od-@I08{#{gQ$)^#<)4{U5K-Q{J1`-am0~t61tw zZN)TCa@DepnOC`g4b+<8*PW%oet7f4s{=J-`7~>BZ`%J{xPj6WQUCo0MS6mvFEx_6 z6oUCaH8wU@8g)7MQz2jZv%GQs)sHKoU9(l@^yvp5A|L$?c$GVT zVd3o!8P31S1Q#5qoe@AHC{s8#mX(yY#LT@aF9a@q-$NnC7*Ue{ajn!~+iQ-0KBHgry{S^J z>x^0J!d1x`L$(9v`{uhimXct=Wv;;6QL=v2>zGz7EZp<)lPbBjrrNBvWjjCrgmnq? z-)PZ_*U2gps*t$h=eIV})n=WC$BNZ7qe<^quBG$v@XCL3bZG;UEE-cUe^0m&`G{Lh zUobR8pnV0G$`^b2;uCi%QI+wWC-?pRwCawZKbeJ$>ECLlaY;x#!Qm4$uhC`SH5W6p zHE`w8V;6f)9ooF_)mc(7{T0RBW`)bcL`**1T!~Q9bsal?{rdIM(b2Ep2KaT~TVD&{ z&qkF;xG~KJDbWkNS@tBXDSd9Y1z_r|I^9WcB|Eqmxs-fQ0)4@-|p=-rD$twhX@*- zOd6$Y>t9tJ8@AY}C+B`Yy14p9Xya{zt}hg(ZQDv+Msj4MPTU*)^~w8_doJWkY!|natNcBZw`kn|{gtqJO_@!cdZPYs z9yNS??aDQ4Vw58#2kR1PJR=UfXr|gL_(ihW4PA;Dav6K>RCZyayMLxs-bROV_x}Cv zVm6P?Fw5L$r^3a*zf$p5i9MC*IM%U|N`Q~={=0Vq2q%TUs+jHP5|M?6lk`8W(sl0R zWZ$)GY;;twCQf5=sBz0jkCA~E?>F4E*ds0Xe1E+mc7dWg*w{@}&Iz{e`bIh1{T9Ta|DjPti-NWQJUy zkIz$?+v__kB3o_Bs;dL*>eR0^rTfPX^;O5>``f}DS_*1ww|IDXq*}IK>A8cvW#dN* zT~&<2#BkO~l6fs3t8?#8*LTi%@a*63kEqjPReO2*n$+Ta;Oz~&15iEbRJl?g4ir%~?>W8W z_%{XT!QtVE!Ra>%xhGq5-FO2fFa3D^Dbb)0H``C3}T5FZ*ZqSN*$-G{eKgGAB-mXL2M)X>XwbUEBlM@@Ckc#IkzTs^?dlGt<*K z7VTQxrgTpDv4#(-%gL<(21t>8MXu>qWEg4COG+1 zVWGg4CQ;_OeUh3Q?`p)Taqa|ycPl~JH`0g@b(N==ucLfrKG?mn=UzPmo(?YJ|k4?=K5(td*4WXFtB@+{<+$mMt9k^3q^_+qQfv z#EYoMf&}f2Ygowf4qtxq({U-`hk%eADPnP)DOV}n=dYVp3vO9BdhA$VV;Vm$&jgt^ z`&W&YYQp*PR35r~B+9YwFw1e2YE%33c21#xF%ZE3%bwt?Iv||m!HIk z&XT}as&6$_PbRG-*fH_N;P5h1ubv*c;dA$HA+Sb$vWdB`O38bCI6zZ-Uth^K%j|+` zWvOtBM`L9t<>d5GuM{IC!*MmX)1&P?k);hJFgSLn+x9RZadqN^(LS~QSw!HmH>>mK zf=BXK9(FN4M_M)h_VkDD+LfW+0FK~l7HfP0OK%Hv z4)80-jvb!GtS4s1$(Vg}10l#iR&o2@J)=S&MuN?6dU@@?_+c56 zy3@HJnl@kvpF4L>PRt;;0Q^nIMc@sEubzlnUzcTbdx%m&GdeqvkZfAXPN&A{^hl|g za>@NRc;+AI1pfsgk4{c@&W+^7Vq@>~DUAy#Q3F?5KSjRW?>MSQiEtg4@2-ecMrvTu ze$;t&2|F=-Pcc_Rev!c)I)XUy%+21Z3s_PWwtp%v7BVQJr(Mgc*#5!G+S>YJ`Qroi z4QCWMl9!J2VCQ6C^<@7K-yD}IoAFNh($p)fJoGL5O@Ig!Z=f7iK0QM7d$?I)tmBi@ zc**YVIsu~lmsm^PH{7%1uODg}*^c`u$QV4j*&$yZaW_t8p4c_mv~mmo(F~h!*<$IL z4U+)@zDxU8cmmAK?Auc+ocp35KOXr>&idd}8YJeI(_RnW^8V8D9OqCeXQxm`M(VxgS~xy=5h~p0(Re%RTt*FYB9oX+w2?+|LhkIH;pVKy zocq38-Zq{Z=Udw3l3Rf7l`qfiBGEukO9T9-|EvE8!z<3HL!ZrJ#jMpSckkSR!t>=! z@D`-gpu$3#7fNA8QGSv*l~7t98T>4|r}$X-_WaDmq39Rg<>dzRRa>813P{JC?6&V{ zVp&hwwQHBz_m_-HkAEQ~DP z$|um$jM_(ZdhwsTZx&{VHaa0KrM8JKhuCA`+r0V9>rKWy*)T^_YTlM>TfpPw! zWw`4As>H;^yW2$WKRf1qHc|gLMe@(23Z2>`aVe?ZhVzH6$t|T0nJVj!!j;ay2TR&N zde>(;@P~<5rhd^(Pwnhq#j7QrxvJ!qFv;~CYrqi_@)outxiYXx-i?jjjeJu!)~?KP zn6Mvz?T@DKTQ8Rn)Fw<+DeyB+SI}lEOU`gi^i;$?sX6U0Ax$!?jaR-jlcdDVsV$qh zADiT8y6AsIjW?~0-+uApMZz)hf8#~lEZaJsZ!-xH>0LC$VvkYN6tqfp9W-fg@BX=H zSEZr!Xy5rgn(y}#PAC25zP{QPSO`Ki66HysG{3(w< zDsoNC+C#fC$uaJ+w=y-|vuWSC{7;{}j(V+1G%nxz<>B5N-rlk`%KHM28HrJT4>hXY z&^sz^Lw*v@xV)yzIqogJp@LOZ^nw{9E*LP24mJfC| zfr@^6qsIUBiP#Na-_>lcqf{d~QxQe8w^csdP~t{g4;zDzSUh#Iov>&f9sOQRg__$F zCQ?y114$%sx3V#y1xn@XbFV%CSULg8z12#WV|XEDi|v!Oy?TKE7j^eLhXfK-My}MK zU#Y}SkA7Ucf9pN);~O{td*^Rgm)NY;Gc|pAeicFJ=fC2NIt53vEn60y%h2}TEL4l` z9nLi3^d45OGU0-hlIg}+Ljfb}6DQtkrX>AQ0=5?g|4#jS(Hy!9WRrCpH%7-$WMo)) zxDQ>Ek(HgO(X!lc)h505z$L@TqsML1Ek>3Fgfr(nUIQU5w7vgvMvN}Qc5xYOEULi8 z(jZ=z!_FH;L@rVms{o5M0X(a)xdDhvhipUaK}eR|-2wJ_ZCxLfJK{Z}U449GjVO zBU*ZS=ctwsL>_c`fj??r^Pb2}~z{~}D0;k`bo@q!iFIG`L zd>jDTc4&td>brkg$rsVX=3=(p^!OTN2=4T(tciM)NYs)x?YTp8g{pDuDS@D~fFtTz z_PpCfu5{vKnCNLwKH7I2P|>mfRjg^BJu{hlM`Nm zRB8gmAu;}WS8Co%IX=T56vqT)CzOrS3aQ1iqI=siprion0P0mH8puXC_wNOeG4NH; zdW141SF|Mie);q`P`s6z+F}E>a)g9D7KcYF3MJsgv!h;n)Zab*#m^NWTgO9b;oiCP z8Y)BLm1gl&i~0{ZX;d%I#;n4zPy5xCd>n_I!|lW_M<>panJl%cE89)Js*l*egFrak zltIrX`2@(405;8@LiSVne96L1O--hi&zGad#^AQg!Vd)lK3WYoiMeQ_h+A0ne0~3s zEyQK{F`Pfa79wgARd>&r*r_@KNOy z^)-)T%0bJBU*_6*?%^cG0{R1Lg-W_*78&~*o=K%{0}|qWHUa?YDK?4B<%7Uq+Dpfd zm0psJ{t{n_7VevuCpOr;jXKbnN;xbjXXOr~(jX(~vQ3#+eS0EY__TiA5zXSMpdM~W z;r3XV*{#RmwtV&GXuQg9u+Ej3p*l2C9!QhF!M_nf5ih^|#N7UNEp<>d>W>zGHgjBb>!R`nDC7YpEm{df>Shash z-K$=>(r94wy8^v@Q(;2Ragra!*mh2mwoK^H{>Q&!bpNli*`@`VIG$3fcaT?W;*Ozl zqm2!8oBM49wTVK~7gk6m+tJ#)hBIIbGWeDHq)(5pLO0%kt$#{1M6507qmX$G&+M-O z@116}w|&$f>DUv9mk|j_TaDBUUF~B4r@~9!oXjYN^(ECwER?xl1F}GEkgDz;7{F_H zTThQ(JQ>6zk8q_BnUIf?|DVu^>kAlNetC?X}BametzIi2du5I;*d8`}Xa5 zKwnDs?*{Y1#x&)2<-&rbA2g1Wz2syBcGYg9r#%Qv-&gyosA#<1n{6Bnq5~Q<0+3nM zs?F`fhRvH#KthF>$^gV@s;aP=(Vrd-HY6^{VaN?T$k#%$wi|A$hib5*p57Bs_olbE z$;>YeoiUQl=AC3}L3J(%G8bvj)Igf$q&EQC)>{~)gV7S`N*v|l!l`TZz4{XgC$ z+x4CGlJaNSS6fbf|F2+GvU&Bd$w`ib2ZNB-PbV3c7!K`LdMqT7cyJmV{nY8x>mc`} zXt}Yo|K7~B;-mL5^}4*`eX2M6stanOup1f2P>n!yo`de> z{_#Vte1bj}_x$b7^%;DWAYQGY&!6R>^zjq}b3&YsMLG{MC@3hndF$3VLht9$&}C+! zX<)+r_=c=nUUvP<1eH}7o}AIHN@UzsHWX(Dh_r0^at+ozgee5%d;8A6R-Zb`t}mpK zyIjJ$Lk6Y6s9aMp8rdQ}Bg4A)+fKlTWztvzfGnkhRMEOQ&Ou(XfuxCnhRDS9Prt?# zV@5`;x(p$siq;%Ot`%zzq?y+X&eaNSy{8C875W?@?F_6EL?aMjqEFB0%49sweu}rW zH4(B{n(=mK)&CT~LIf^SquO7DqO15slu=-ccJtn|%N!i0&Mj``)k=*!cKwZyPm0b` zi6JK@&;3swx+j!-TMw{f>*jWEX7v6%&fA8a9Y-@wBY87geB@PCmqiflD99gI^JnLK zv$=nMckv?u#Dvx1H1MmwRRF)=r}i~-s4D5wkA0*K0R3;TcDg{T$Kqiez8LIp{h>r3 z(YpnPCofOR%{HW2DkDAOv=BWLJz0|sAIPz}e|~GZFU*(_fCzMxg;#%95{DJSk}i5Ws29UYH$l1>N+_sI@E!;M7!A;8^?F zW^Ef(KgaG+lNBpgw3Y0RG#qU!AeY>f>vrf^LLNFA5L|iyd173qhU0Xyf>8XE07K$} zIM_SRY3r0L7=gO%=4Lu0C(?p;%-?a93S{D(x2QPsz?-N@ER?ytDPKR zDgICRmXsYq@4T{j8dJegpL_RGcp|TMec6Pz>roW5IJI{^qS~o6dZewCtXDvdUeje} zCX4Bj$mkG{bPA%c9TW@_nj8q<+cVdd&_9*CqIWM``k#EhN96@QHYP`@A758+iAtaZ z5;7rHSOZQTp1U}+)*uF}Htz96U0f?Uy#uOAXJRp{LgHnuQ_-k{U-gjmp}|IC^;7_F zNu;3Mvgic))cO3N(bFY;3?~f>3m&uuy3taiYeUw*_zwyMP?fQe=XSW`Bl-%PPM94K zD`nz7tI7+-XK10{=fw+70jL$OH-Cf3v6ZXlW)lC+uemnUh`S`{qiLau( z{2qVae8BEV*MPP*JufL(DE52yePfblEaA2vtSbP42TbdG!-5V@V_FOa>G~ z{8&mD$Pf_prM`wAGI2VE@*`$O4~W1D5N75)HC&F6fMhTcb?((!hWL{*EJ~yu>hv8| z*c4KQ%kRNwi0*mN6zI;%A2noTmVU2<1WiLrkGok;kyx1iN#qa20x6y)dkMM1&KWnXx>7=BJe>^HQ6(&Mv>E)z~eJiDCbqP`9hp=3Tuxfxc}OYi?!lT_JB90U@f2B2Ae{YdPa1) zHr%}RxUpWv&Q2JL-wT8saL;x^*;yrg($do0R)eU^WA8g9`E<+Fvo>)LitO(kZT~2I z>AMf4kyV}!El%DD-*9|Y<3a~}z;5CXXo4KhY%XHU1(8TsH+B049398Lm|#)sj%EL$ zeMCrvLi=bOe$F1y^Y=Xy-QD%M%+34DYc?DM;c9i8+D`g$=mfpE+?bl0LFypkHB;^V zmvsA=3vQ;qynnwTV!te8|Htukw4-<~m;}2Z#?K4fc_n?Cgu5YgDZWz{N+Ej!%I40DG9kE@NcO8lAWiuT%c)%BNKm zA$gOAcGU8hfY5vElXrt}QR^Xp5h4Xe+~@P*q)imywsK&)r1|WfQ8z9jlqhOyru9|x zt;{PunZu=R_<_`+p)yOliXzEB+;{%YNbcNy66yw&}UBk1aVn z1^SAL-kWvXhyIAu5OSYxrvvrKN8j$YukWo(zCOSDcKiCsZH_*$KYFQun0GgPx1?l5 zvPp#|>spin~5uL1e47_9YW@jnL}v1LYwR+Nmbm= z2Wojksyd^;hvCfTQLoio;?8;&r9zalA1QoLrk-TD5;4~e$c1M5UGjBx27VejpeNrA z#VDy})m)8RM40GVq}(MMdmwNI4NmqN6%rkDD5X|C-D)SrPijHbcQfyY($Z3AsBH4G zK{QW?S$ySmH~bfPBdk3Axi=(tu0G9@4IG0=sOjnH0>BnUcbRX5X{D-mu4jaVzW80^ zo!&LFh}JLjr6=NA78fpuy-%|==CY4ALM?eB>Gl|;ob>ix-Q1ELKG@Y|im1rg@GDWv zdvjg$>HIG=Hq#xQ4Ss2ecI11wPCb!>BAe{~oc`Ta%NIVKS2PWzWuCNr4L9?a`aIi` z3uR6p>U>Z!T7Jgh6p$wu7WPWTMz@sZONT+twxNPRVC~G@oGcPG1T@Qx7BMh~3GN_H zH%Gj=78b6{+jw*?Etm=$4{E*&H<=#&?yyH*{Gh%}O$9%@(=n*tup#g^ZZbNKwus|& zyLa-uGb5vUNpD0BvzY@p3II~!6ERzgUjCNtlKGYEd-y(TJ<-pJZoO4Sb)* zA5DLaxw&!b)0PW{Sf9dApT;Jul{Y~5L=qlx(a~y(PcbG7szZz#M}JTK9;%F@14lIk zCda}&MlOhTQ6eupL0;yj>2^-0lg-`zTZ(y6%~#)h_quv3qre|1-{N%+p^2q{3lnrb zj5jFtmrqWKc;~pdY!PY*+sp10Nt_kK{s@SQh;F}E%VQv*RpDTvt5FWF!&5A@VIki> z%{;kr>D7eR(xO|?{WV+xCLWT3!D4^LCNhIQ*G(zwe|DGy2X>ng^@F1uwNe1(AP%v-9LDDw8iokUGdZDng~0McYp z_im?FJOAw~wRe1W5e1)PyVRe_4bPrEBgh9{sc+D$-$Y0W1ws>#J3^F~O|U?Hl$yG@ zTQNi$sMc}dZ5m7x?@E5zWvR*xmgs%yuxA01NSe1QyN|xC;T%iq#!Z{N(4IeX~=j5wVN_Q)W!O z&9i8B2tb`EnjNQM_KO1J#}V6#Vq0{7dnxE7ffYVVNCkjY&4ina-mEk=#EstFekkbX z&1JBAl|i>0`}OOpof$j(!KeaW_Kxqm9)GR^2P;rWdo6dipZm@h{^7dkkcJcoP?=O* zkUaM2WD29R`4i~*^z;VC*rzbWSnmU|v}n$Jgfc>PI?Mht2?vN-ETEGt^Ibmfb z0QB+#5PGm7rPOA=Wz zx`hd7`P+Y-CnHHd_JCl51|#f&CnMZv&2Ft_JB1Pj-;^-;(EIs{)NR+UwT*y|mRw+i z$ufQ&aNKQ#R;NbZ!Ynr$4nKo0xIY8xD0?Cn2)~yK`iX`^cTX@$vYge5lq5(Xb2|^a zlXp3_-nnz>SMV(Dn7H`;4efcbx%U{eBTDIQ@2U;UBy_4*ylZmXR5!enXf4 z^5vcu=K)@1J(ZjJ&?m`ZIdPw*1gSXA4Sk-wQ+Cxk!0`m|54Pq-VxM2kJoeopWIud2 zbpvBbU))wo)br=0Y68r5p_>A#$K}#uDjWa{C|=DZpeOzM#=GM(Ngv=^L~ELG7NB4% z&@966H}a^D<|go}@fp;9Ha!HR#n;zVqm7n%U10IRfnG?PXJJh32qClClT`{VhwRIel4VaEuw>Z8eRQ9LH`T=0$wy^FOZQ5fkNxdH z!xKe%?Dy|eDM}Ej(Tk^rXcMQAT=W>toZatjdrsj3m8GRASFT)91df5QA2XNfQajg} zu|aaN$#p&t#TOi?p*lnbMPoPNfoNM1dJeR6_Khmo#jmnsOEe@saF)>(-$~9Y^f-DJ z(FdUOFBURNyt$EFv`A{EpJ znF73}-%jmj|617ZLe;1VCPCac_*mi(lYrHfeygz0C*LixrrQ`93yA5dHeTn|xmWAa zK!ACFgc^w8;KJvN!UgkZEj0ofYDnI!EEDlj4iilTTBbLbjR zXtfXboMEr$Uk=X#B-IS-pR`yy#j(eLWJ2gvDROOybREBaW${ku4!|G&-0ADkm;kI< zU#q@dB^`Sz+`?IBi5R>H2GUQ2YK(Zdl)M(Eb;lrekYMwjqA0+HlS;qIJ&j3Sw|53MWKu&SS-K zZ~s%en>u&lXJ6lK2nlG?cZ0_frVLJ1|DZ-UvpUwqmA)C4QR1&5r2&?pwm@aci@o2T zEq!LasKxrv$jf4A*uA)*_CGk#wz063K`jf|4p_StnOB2bQ-;T-Z^1$K-jWw8e=biF zI;TjhYase|R!B2M&4P);38h)^@J)`a6x4cdZ_fqzMk~0y=kLw$ns!Z33I#@S1EG@+s($ zNkC(`xG+ESJUJg!Oj-C< zdx6xSz0|G#yJStBB41raY@m6*e%m(R;9#|&_`}fka2|@G>v?*6(*ZR`p>al;o}Cp0 zP@12gC)zdqa-DGF{Dit3gC3GA!1_a8t%C}+o6u#cR}g_|3D@`p>yI*a0+*?g=bm%y z5@I$Po*6iNe6YXcNTRzBvBJNF%30->17+jbE&7tGl-1D1&@>8_aI(46a|)Gh&>EX) z3Nv9){4VwqBYXa4>W;{nYH?Z1&(E*x z)b;2+zp23KbgPS(YU1R<{F9x{vMP1+*%!a`>So5C;v9u6KZc(`0D)s`4;K;{rzt(L z*}O70mz{mD5jSN)4J8;Z5c#WGsZE$K;mC*%O?xn_o0)7JezE;S3kdTzPXrG!&fM9! zSAR*Tz{PSO2gv&+nx5!-#IH=E%gY-+_)JGNb;KCpcbqM%_P+Lf`b|Wp}NV%8j82lwTU@R>O-2Jnw>(f`4&5aXd zV`qR{OWNZaXdDxuu5W-V4Be2wjaw=a5fR9pUXhV($`WHTcft?ZkfD(1jjXJvARzP1 zd8|cW6|Qoz)ID%Z2!t)q=YV8uQJ>@$={|d#h&gc0LV!WX>N!pWxez#1>yvI;V7Nto zI5yI~1B{IHo;UJohmiD!^*3>5y8r_POBh|g3;dby;AJDwLs9W=mm@-%SwN41T$`e{ zm-z){kN)Nj8$Kdim7vyBO-v1er@cB8bqgL(w84`~rNdz(u8G$vMRW5D93DK4#Rx%P zP>l5(Hx{CX60bCxvK#lE`^;)|TaGJEGv&+r1p-nf7p4uydn!iSUc;0WC$iDkNunce&3EiIDe_AFW56>>A%OT4O)lB%gMr zna9NvO3AR{53T$YQU1U#6F7Z|mm3elHh{(!)P!#!f)P&L%nhlR!!9Kh5LYBjdV5sk zn4qYosLI#B(8}+KBOY>?y^DcA_MUluGpZD)k~`_r+NbbVid<=W*VOSAWf4WBxT1o; zUi{*#SDGpNC;%ZfJn}M0ZD<3;Aa1mCT|==yoB6acRRY`E9e^8Hh0sxrMY@ofC=-&H z?Tun~zYO$Iad?UajXs|P3hVt9p2TayrJ9`V`2bp2#sHxN0W?7d-1Ok znde{QT6u8z<;i3Wcq;{knxtr;@NWf=mR_GiHws5XT6dpJ;VEQE8C3M!_wTKFf*!FC-_)a> zOVGHqqoXJSrweB-iNyuD9W7^>!Vj+xdupXxz^x*HF)Gl?>y@U_;3E(Bs@wvOU*-u= z>x=%VHSDpl#G8X5h~|*=vE_oR|358@)GN*XWOq5}5ah&d0Th^Kv9qhc|0Fh5%&N^Z zrko^wFdrEJYB;~ry1R})QN=nT!TaspqPp1{=iEHmOZ{l1> z%qO$Fft3Zz3vGnM+y(Nn#Rw-)#~`FM8FZrizQ5W49*=GTd%#ZBHJ-@E%W#@WYn^~z z%J;WgTRcG!t^oy5kD`xmgMPQd$WtG~CRl)d{QT&4fJZ>qxpu(3=JX3iW*B5)PV_>t z!O?U=F(LCqD9Bte1VVC!$K8NWH(L&B3W^WdT1&p30P)+K>*{hS`}+F+gpsDGwl*02 zFyAKIuq2>gX$yvB5-(AB&PJ~w&O7Wd5n~}!(TrBgvYmM@vl8PI1%R(`eGx7ctm|BY zH7-eBn4eI_5RS3NsXi#UnuMe8(f@P z+6}M&r6ccA`_3&ES-Ikn!-r44r{h_KEg2T${35QG7o)ox_^O5l}W+7)A7f_)0c`WN1 z#GtqW(65AtP}>UhhF40sHQWqq%-bNY8mE2)fn){<qYz`2>x6vf1t*^k$c z4k+^mym`bOTqBfr?V_jC9+`0Qa`?w?Lv!@0dwAZXlvET@e<=O)vgpC=cQ27WJV>`y zpTUD~Bd>VS)2G`Vot(s;%b>ioBaDHbQg{}4*cWY<9D?#YM^}t8jcz%;vaSdeMeZ84 z>d-@?#y>)w!WSzFLmN3O-Dbzfmq|&jSV5UM$>&FDzGm|3bh`kzs<3h*0~z9diwkF9 z7F_0u^B0+(E@~d3$x%Cp1Zq}4E^bosq2?`Zcg2OzLA<4+4)HgSeMH!)k!f_d)j)d1XF~t{hix!mjHQ5aYGKj`p*N1gc)ls`3O%_9!d;BmmmfrjNj6n zh9dxa!Idlb*#?5&3zc{T4=6T-a_o@a32`+cso(Nh*|6$Uucw>|f}x6p@~5>p`=WS% z!ChQmz8Y=&@F)eEvNR{}waUy3`L1Ppo{YPmnyXDL14${-LJXj*7}B1KvXNwA!ygc= zo#XrnU~2*Zp61&tmEAZPve2wqwCnQ(Q-yA4=#&Q8b1TTm3>FT*DZkR18@}_{t#7H9 zwU9v#MoN9_o*jR3^hn5FhgM zFp)Zb9p*I{?;yvK;*AAUd3ky#I(|L9Rc9al{>q_Yz8i_jSBa(#vvf0X1=YPb+kjLT zvcPP04yRBYvLX}?3y5)9M8WBU#6wU;TmCf)CIr40oNvBR^_8Q&{goe$FjHBDh~<^V z22`Qn^js>Jq!yBgkz$t7(k1~^qJ-1vt#pUj84D4LOhqB9nCsRU|7TXI{m%3IqG0GW z#<`ddS}=j(k<3LzoyW~q0mf9v#@B^>-=Cn7PrRx>oGCYM+|WSnN4$^$mi4ocnq(kj z5H}p0Ts4_be2yLen^{U_UO*5*oBz7@hK07)aQU-ZbXE<*Xmb0L;S!QFkg5sxMYDq{ z#N{}Ot*pViifQEG-sUV3cn6EoE++;Qm|WW*udY=Xn-Jpvm1G<1 z^Z_l@?#A4VQ56`3710@Ff%gfMQ#-}Q8>$-B5_As}#hCoSTxOQSe%$8Hw{NG>jZi`< zMXE3W!3ot}6pXdL`r|A=JNs9zQmRo1-Y5yAZ9rZKdITLN;DGst5^Ly^g@;D6kD*F~ zVn)N`a-Wan+v=plw4*@j#3P~q=}sl8$;n{8%l9&6Z0=%#FxRaF%L;{1K`ZOTx%H2c zZB+xiTJ@oTti~0w#Ta+Bcw1WURYPURhm=6T6iGd# zvw+1wXTdCflRqfh+QiwT-}D%iP}evG1Rg*bnDAOyc>^2WiFEx8gF6_iYHA2h6<;ph zV~x`j;r3etURP>QunaO(%6amk6)bk_>@`M#FxwL3gvvsO;2^@3Z8^XMqR9rJXAFm3 zYPM_YUW&LM4jlA$G3UuWLdIo!I47EUaDk35akv~985k<{SsHKnmzVKM^?jJ1l&t zBVIKr>tfe4G9E#yB8Lv61jH$g2JccuRRMF1O|<@bf;bXD9QU4nN+CCf2mzuj`uiE@ zQ`4?*C|N@$MW?2E5I7Q0r{I!(2%!UW`?v>hj7G&dSy#(B8?=SuMQrU%HcJ9frq1VHu!z0g%ko^E0gxRu~dV6%)NE!jo zL`0P#qYkOR5+y{O0B^8cc1)G;6cntgQCzbqwg!l)2R(+nTMyhJ@C$|5 za0vDQ(w}zh33ni3aNeKa+d$BFW6B8_TO{{?73k@SJx%0DVi^PYwuD0GiDo7Zg1>Ak#JaC)P9+1Kz1z+&{l)-m<=_G3Q5CIVxq2KxE|UQ+qv zqUf`f*XqD}d-3C~%6Au!5jS@AR?f>Oc3CviDg-~4+Immpkn*{64~=Y!zdWL~A9{SF z#BN2ek=X`ThJ}Sy*Ko)dA7O0w8ujwC%W|TNA-liI?MBdL<6#mcgv&zw2M;FJDeLWU zbUN}z_1l}^83R@4K3Ia*LfZL^wxhh9z=1-iszz8Ick>^&GYAf(>U)$HKyM^K=8yH%t!OU ze+z12(3EZsb6he^7E9Q=A0+oW8P6R>gTWe+$)y}&;l#(zel*#02%h?fhdriFCInx> zmVSSu<_8U!AOJMCOA+`GGvMkmin~R+eT>$$8CRH@JTY@Cgc)63$M)s0rIM5Y&PS7v zQwO#Lfvp%iBzeXO96lCFwNU575w2p-?P4ERwxoS;XwGp7MhsXFH5@?5lJbmv`(C0o zTkKq%Y3`#Znrox}`eJxCqqqgok*Pyd{s7sARjb+LDiDKx1bvbjF5isO^#L`ip=OKmi zB7Q4+_z1FP;y%cJI?as=TiDqA5-e>2qUiGRY?2u$0ejEAeDHG>=IO1eA#Zw@paN|d zw|fds7>5T%z#LC}KR$KWxUNMX3zUu zeH--tQ^1V%XsMU;Xr^xf-j0zRW_XN>90Y>3?H8TC^hDR;keC$`TcB>Y(v zO7o+SmF63gCN9K~y-@YqFuZJUC&qLS-nRe1w~gPz#iBSb;$mmOC6k6wLna4m$*H1g z8yWF~@9vnn%~h|y>hff_HMcr<3Yd9km{o5ix{R3FdLP456!n*C39Pu-7x1=`Y9Va; zEx*!;syoM<=07I;gxRJhY!!8I?e|}>N|&J5&eY;QcNtZDxipcmai$=m*jO2r%*-6A zjc-_qY}~24$}_+WiUzu_<7k)B2%tF&-xxYb%UQHF`n7eQ87auBktsCWJ(QC#nCWSC z-F}^4SeUaUlde!6FmsA(Yk1w5D)v~iXF0x|h$SevNi12IE5hJ{t=@V3Yrv+*%h>xG z(ye!)8yW>m1-@PV{!-L+p29`E00ktM`%idd=2H~Cr2BjzH!?|ZB%b|+F>2x%8-tOU zA^_S?ayZ7g+uOe2B^+5BDV)skVGxfh^yig$xWRu0H%VTy!2pTus!uiYPnDS}W=KZ( zt|l;%45Pn3&rJxV*J6CMg}34cNrwlcPZp;j#`nBIX?|HudQ{I0po>h5Q zqrd9@eb;hnIxE9zF6nx$D|Vshxl~gFQg>fxNFto(mU>Mq-@XkZ|^DP zkEF^ip08P4TSp}k@>~bgOnLUIUo8j3vCL2Wsh`q_3eLMO%!q31 z`c;6_!6e=TmRDGR1`BtOlrPxtiZbjk{CD{*YmKt1WHRjP6-%a~*!`}GF zNyi4^M&)d)s|PCVbEh)zzr^59Av55xXU>O;e`20ELZJ_*v3Lw;xGr0k?@{XzuU?s} zkT5;tzpI}-LE<1;^WVC3@6K}CPM4mVCUhT!%NPlrf@9(d+7eS&ED}4I@2|4-20Wdt z@0}54*}%<6mkR5t;WLtRal1h-m+N859gkK(?5|SKu}7{e34g!o$&HLm6c-H-w{hLV z`uuIuJfD1nyS&-Dt8?94-h3wyuyXRI*5eDZ5%K4i=K1EoO(YlXAi?|G$WAggJWA9h zF$>K~9KR!?3_LmWE12kwEK-;i#Kl_oyFJIV_6x(@rjK?}2)L108<&{hNtKd&p@58b3a zui4G??~7P?mxa?{{^2g}!VBqZyzR!?Dp*E5&wXSw^bo;SUhLx>dnMGe*{AfS;DMT%XP}{ZwUCD6?cfr$SO5nE@I?Irz}f>1WZqg+|&R>^$}o&s0hB zfc~FikfN`E!eo;T^lXJ0G!&T%AYJmj2~f~jXh)!VlrzCk849zuO^*5Vx)sT ziYh3i`5Uh1cMn+>b+cDXffmOhq6d4;vG9VcWb3FQK&Yx0+X;0g{ zN1~6OE_gUu*W7&gM=cRs0sqNd=8^?)_E51uzSG&+xudU7p0u#A@>27oH0T<>yZXw8 zjoFP2Jg^kciJKvL&DG=6e!+>HX&{3t_|ZzE{jl)XFsYzHaS9}CNabDKLwBK*_f^qn z`rn`B^up8w-WV8Xl@JDSW)N_W=XIU+51kUg=m>-w&O?~%J%D$Dv`6zsjTpe73cD{E zzQVH=wTi-WU^szWwr2QhX&$bm_rE_Is!;_C{T40-j45$uEoa)ek@T%pCI^qp$_kl& z+W`v_@%Ex(rr4$gJjRwmOmqERZ&E_+$9D+?k2;s(hqFVxsAy0dAtHi2DA2~?KySB? z*BpKnIzr}zg24xOAKJWr{qvDe5;9a+YcQoMJQ@?kLKc^eQ7V1R$_2&fBCC;DrCf0P zwTTQ@k9U>?_23_cSJxE?POwTY&QCzT@+RY3BbkYJE;7jc144msRMY{E+*18W=0%wC@Zp)xk<;MmoWKn*T^ zdwQ*;wzhV%*`ZY<;thNWW;B`EH4F^j~pH#BYiIvL#ad3 zzBVKpc$Z%)gQ@7$nH=Y8y3`*MdRe2$`H>5<6jl{E^ zsxoX%BxgD|gz3J)#0%Q4{3;0=&}boNrb?W8odzb)gKRb*c z=Y%n!5Mi_Rh_lkFo@D}mtxa`V4hcr3f&w1yFtf=`{UY)0TMkdj(yfJcEt%^Dl&IRAy2}Bx0LuO;X}b#SU$80V&=wp zY-#meY0$p&Uy_2aIHOPSuy4Y}$>|K{b?q86GBb_)vtdj%1TG>u8-gA6Y3x1}+OzNB z?%D&F%|mXr`*8loI7+^qazKsfqm_=T{EW{RJnv$Bz@kpz*bt)EaApJ-upfpn@}SY& zneJ`yQf<B~P2ucQv z%tw&&vGMT^xX(zn45ARLF%0H~aPfS>s1mXIK{;R}wtubv)!Mno^?dJf{FfYUbZp{5 zd8low;~GvXQrIzTBK@`y6)`2+iqb@-ayfIX9dwFR6hpTSsnPwSi>6GG=^`ym(uJb< z(T_UMcjs~bIR77i=lA&izTeOHeRY&T$Hh%I z_mJ}RPjLpKOP%cW@u)Pd7gpcbt>T($_#L|P*62mwT;N`d)JYS<0MeMe(SpW7SVZ%C zZh|Pf=PRLUMc=Zs|E(%3H`kB0!{YTQ>y+`g9AZfcV|Bl!?kkGaCqd(iyJCc4IFwzz^2r}SPu5Xo>7>zJu##(rXB_*{4 zS8acIbp$Ge%kX~L@YRw-!VWK~um~HHbW@L*z3M|t^#e8o*+nUZM9rB%F)rcZ*R|f^ zlb*ItWv_#vGrjp4DzeD9ATGLCp%!BMK-I(YH}Q(eJ;t(UP$w72#SnQowA(~5MXEv~NSnYI&_u>TORHP50AEn2lmakv z@I?)bIHW+f zog0!$9&baZ>CT`EJXwQ&)vO_@C)(=*$7Rr#{1>Bz*rceS1QgLDy<1EV2vj$KcIowv z>nQ_O<&js01jyYhLld5lQ?$qT(~$X;!SQ3=JNY$IGEk ztI&Hm%UqkpR8pV?Car|;o2!F(8v2tzj5!%_$;7Q8At#{4E)M-ba@ZRZ_f5>!4Q7o;+mXqQZ{(3$1e`v>zQ8klu2|Wh_iLqWlfpb zM41Tb)x~ex9Ck#kjOgng+`WDcBYHTE){V%VvKdB3$Yzi8b_s*Hl+?w7<%VRx4xJm?Ss6IEKu zAEh(0(SB*p3iAAv39oz6({IIxu7F|qc6kHyF2NDki}q^OqlOF9i{{*%7Tvom`eAd9 zLF}V*fAll+Xi0UPXGmpU3h~l;RfiJ38zqgqyFHo}}9>9^#A@v&<+SF>~H91UFMgBf# zS4_$a?tfH6o8Fq-8CLKhn|{x;6IlFDNy@ zyWAl*W$2n+pd6BKa;;4kuqV=A^#lK$(O1Koi*5UJt zOd*bAw!~5n zyr8tO{eu#)6AkF34E^q|u9FA=Mc5x69&T0@<6QNnbHwjQdj=^fL*J48==Z?Z)u+?@ z4bS+U=2*yg$v2V7S27e>$4CceSlzy(?{&+q#()V)i+Tea)#jtp%nxXcN*lSiD15zz zkIm-(;=-b7`7fSE6gxTuxh(44a2u3Ebn{OOgH#LcmwcyVJT>23FK)Jkgyn4Sw5QLR zsIy#i{ST0Mdtpn{JQT21`$r%;Z%>t`C#Y7_&hDlAqN2-_&!qYVW>!VUXEv)JY*G!M zes$5~%zbbL-3)0)E~Dn0Zdzt(TU|S4X6VlS?nk9Wzui7ipC>YW1G43HwhtIi&{N7b z9Q!09BjV>~-Lv?a-oIBcthNl9HeUfLKjJ>UIa{iCoZ~)h-1Nm=IuBbf3dAJFe+5C& zd$`~Dj(icg6@exQvrX$s?-}xTd-2EqM)}iK$MRpRnnGPRTQ#_O9Y4KFZ4qK)9IQOI zwlJQ(iXvdc-(%P~-}u$l^Jh|527bLOY*nOuXLBG~NJPo{f#y*`!=UgG~*>u+_OQ_~Sz*+h`C8iO2sQX9mY zil}@Dl5NVoA6MS2VThKN%jk_yZ=U`~^O%k35*3sr<+iOQQh;Ia6{h3tI+GY#p}LwS583I>n~BY*bhS#kWPYE{<$xv)=l zPSw2|P6WTy3x5_9Mjh8$$N!d^4W)6-53;?PXPE1WEjfo{hhkCDM z;*g+2Cg`Y^$iRmTu@O&3er*~f@%8XN9HyN0%u%AxkRiXT%SRDw=2;95bxy~O zmI9MLOavs4Z|tVcbphTxp!pEEQ8H6#N#cLsP;vMNz%{lI7Y|b&*Z2{JHtytswcU1O z3DGvNj;2GH(yc_!1?t5lf1e70tanhC&_-eza#>J@0Fcuz0dfK&O(WA?ly<$@8vs3e)52fMBa4LD+*x)2%kh#`|{bwL; zrN-NT1x{JGchjJkUQ~V_c`n^Dt}D#=Wty%@u9w7a(E`M;V-Vc)1(u`q)l2wX2^16H z7`T*=_92Ny+#ySjCJHz}FTd>@X<%S5c<^9(W~r%N9eVif7}t~V zlo!1lqF8JQr4L^}CHvROP7$Jr%mW|ni_DZ={Uo{8HB#t8Sk zW!(k_`SiZBLy~Y7o=xP>1v1&*n5u2Y?&NyEO w7*qZ|aR1M%JpSMBB>Eq&;lD35dNV&Ra;ma@?B?CPph;o-sr|z93)b!VHw}<7?*IS* literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index 73319ebc..99df7747 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -292,7 +292,7 @@ "Y\n", "\n", "\n", - "\n", + "\n", "intercept->Y\n", "\n", "\n", @@ -304,7 +304,7 @@ "outcome_weights\n", "\n", "\n", - "\n", + "\n", "outcome_weights->Y\n", "\n", "\n", @@ -334,7 +334,7 @@ "treatment_weight\n", "\n", "\n", - "\n", + "\n", "treatment_weight->Y\n", "\n", "\n", @@ -346,13 +346,13 @@ "X\n", "\n", "\n", - "\n", + "\n", "X->Y\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "A->Y\n", "\n", "\n", @@ -372,7 +372,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -437,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -470,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -521,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -553,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -601,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -623,23 +623,25 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Dataset 0\n", - "plug-in-mle-from-model 0\n", - "tmle analytic_eif 0\n", - "tmle monte_carlo_eif 0\n", - "one_step analytic_eif 0\n", - "one_step monte_carlo_eif 0\n", - "Dataset 1\n", - "plug-in-mle-from-model 1\n", - "tmle analytic_eif 1\n", - "tmle monte_carlo_eif 1\n" + "Dataset 23\n", + "plug-in-mle-from-model 23\n", + "tmle analytic_eif 23\n", + "tmle monte_carlo_eif 23\n", + "one_step analytic_eif 23\n", + "one_step monte_carlo_eif 23\n", + "Dataset 24\n", + "plug-in-mle-from-model 24\n", + "tmle analytic_eif 24\n", + "tmle monte_carlo_eif 24\n", + "one_step analytic_eif 24\n", + "one_step monte_carlo_eif 24\n" ] } ], @@ -738,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -775,91 +777,91 @@ " \n", " \n", " count\n", - " 10.00\n", - " 10.00\n", - " 10.00\n", - " 10.00\n", - " 10.00\n", - " 10.00\n", - " 10.00\n", - " 10.00\n", + " 25.00\n", + " 25.00\n", + " 25.00\n", + " 25.00\n", + " 25.00\n", + " 25.00\n", + " 25.00\n", + " 25.00\n", " \n", " \n", " mean\n", - " -0.01\n", - " -0.01\n", - " 0.03\n", - " -0.01\n", - " -0.01\n", - " 0.04\n", - " -0.01\n", - " 0.03\n", + " 0.32\n", + " 0.22\n", + " 0.70\n", + " 0.32\n", + " 0.21\n", + " 0.69\n", + " 0.32\n", + " 0.81\n", " \n", " \n", " std\n", - " 0.06\n", - " 0.09\n", + " 0.12\n", + " 0.11\n", + " 0.14\n", + " 0.12\n", " 0.10\n", - " 0.06\n", - " 0.08\n", - " 0.09\n", - " 0.08\n", - " 0.06\n", + " 0.13\n", + " 0.13\n", + " 0.14\n", " \n", " \n", " min\n", - " -0.08\n", - " -0.12\n", - " -0.09\n", - " -0.07\n", - " -0.11\n", - " -0.05\n", - " -0.12\n", - " -0.06\n", + " 0.14\n", + " 0.01\n", + " 0.48\n", + " 0.13\n", + " 0.02\n", + " 0.50\n", + " 0.11\n", + " 0.56\n", " \n", " \n", " 25%\n", - " -0.05\n", - " -0.09\n", - " -0.05\n", - " -0.06\n", - " -0.06\n", - " -0.04\n", - " -0.07\n", - " 0.00\n", + " 0.20\n", + " 0.14\n", + " 0.57\n", + " 0.21\n", + " 0.14\n", + " 0.56\n", + " 0.22\n", + " 0.71\n", " \n", " \n", " 50%\n", - " -0.01\n", - " -0.03\n", - " 0.01\n", - " -0.01\n", - " -0.02\n", - " 0.02\n", - " -0.03\n", - " 0.02\n", + " 0.32\n", + " 0.22\n", + " 0.70\n", + " 0.33\n", + " 0.19\n", + " 0.71\n", + " 0.33\n", + " 0.83\n", " \n", " \n", " 75%\n", - " 0.01\n", - " 0.03\n", - " 0.10\n", - " 0.01\n", - " 0.01\n", - " 0.08\n", - " 0.03\n", - " 0.05\n", + " 0.40\n", + " 0.29\n", + " 0.81\n", + " 0.38\n", + " 0.28\n", + " 0.79\n", + " 0.40\n", + " 0.90\n", " \n", " \n", " max\n", - " 0.13\n", - " 0.14\n", - " 0.21\n", - " 0.13\n", - " 0.13\n", - " 0.19\n", - " 0.15\n", - " 0.17\n", + " 0.57\n", + " 0.44\n", + " 0.93\n", + " 0.57\n", + " 0.41\n", + " 0.92\n", + " 0.60\n", + " 1.04\n", " \n", " \n", "\n", @@ -867,44 +869,44 @@ ], "text/plain": [ " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", - "count 10.00 10.00 10.00 \n", - "mean -0.01 -0.01 0.03 \n", - "std 0.06 0.09 0.10 \n", - "min -0.08 -0.12 -0.09 \n", - "25% -0.05 -0.09 -0.05 \n", - "50% -0.01 -0.03 0.01 \n", - "75% 0.01 0.03 0.10 \n", - "max 0.13 0.14 0.21 \n", + "count 25.00 25.00 25.00 \n", + "mean 0.32 0.22 0.70 \n", + "std 0.12 0.11 0.14 \n", + "min 0.14 0.01 0.48 \n", + "25% 0.20 0.14 0.57 \n", + "50% 0.32 0.22 0.70 \n", + "75% 0.40 0.29 0.81 \n", + "max 0.57 0.44 0.93 \n", "\n", " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", - "count 10.00 10.00 \n", - "mean -0.01 -0.01 \n", - "std 0.06 0.08 \n", - "min -0.07 -0.11 \n", - "25% -0.06 -0.06 \n", - "50% -0.01 -0.02 \n", - "75% 0.01 0.01 \n", - "max 0.13 0.13 \n", + "count 25.00 25.00 \n", + "mean 0.32 0.21 \n", + "std 0.12 0.10 \n", + "min 0.13 0.02 \n", + "25% 0.21 0.14 \n", + "50% 0.33 0.19 \n", + "75% 0.38 0.28 \n", + "max 0.57 0.41 \n", "\n", " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", - "count 10.00 10.00 \n", - "mean 0.04 -0.01 \n", - "std 0.09 0.08 \n", - "min -0.05 -0.12 \n", - "25% -0.04 -0.07 \n", - "50% 0.02 -0.03 \n", - "75% 0.08 0.03 \n", - "max 0.19 0.15 \n", + "count 25.00 25.00 \n", + "mean 0.69 0.32 \n", + "std 0.13 0.13 \n", + "min 0.50 0.11 \n", + "25% 0.56 0.22 \n", + "50% 0.71 0.33 \n", + "75% 0.79 0.40 \n", + "max 0.92 0.60 \n", "\n", " plug-in-mle-from-test \n", - "count 10.00 \n", - "mean 0.03 \n", - "std 0.06 \n", - "min -0.06 \n", - "25% 0.00 \n", - "50% 0.02 \n", - "75% 0.05 \n", - "max 0.17 " + "count 25.00 \n", + "mean 0.81 \n", + "std 0.14 \n", + "min 0.56 \n", + "25% 0.71 \n", + "50% 0.83 \n", + "75% 0.90 \n", + "max 1.04 " ] }, "execution_count": 12, @@ -959,7 +961,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -1043,23 +1045,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ - { - "ename": "AttributeError", - "evalue": "'list' object has no attribute 'min'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[14], line 10\u001b[0m\n\u001b[1;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[1;32m 3\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonte_carlo_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 4\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 5\u001b[0m color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Plot y=x line for min and max values\u001b[39;00m\n\u001b[1;32m 9\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(\n\u001b[0;32m---> 10\u001b[0m \u001b[43mestimates\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmonte_carlo_eif-double_ml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmin\u001b[49m(),\n\u001b[1;32m 11\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmin()\n\u001b[1;32m 12\u001b[0m )\n\u001b[1;32m 13\u001b[0m max_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\n\u001b[1;32m 14\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonte_carlo_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmax(),\n\u001b[1;32m 15\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-double_ml\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmax()\n\u001b[1;32m 16\u001b[0m )\n\u001b[1;32m 17\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot([min_val, max_val], [min_val, max_val], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblack\u001b[39m\u001b[38;5;124m'\u001b[39m, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'min'" - ] - }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAHWCAYAAAD6oMSKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB590lEQVR4nO3deVzN2f8H8Ne9Je32JSoxSIhJMZayZAtDNI2RbQzD2DMGM8yMwRj7zMgy1kG2TBKGIfsSYxnZKVuhUonSorTc+/n90a/7dbW43e7W7fV8PDwe7uee+/m8c916dc7nnCMSBEEAEREREZV5Ym0XQERERESqwWBHREREpCcY7IiIiIj0BIMdERERkZ5gsCMiIiLSEwx2RERERHqCwY6IiIhITzDYEREREekJBru3CIKA9PR0cM1mIiIiKosY7N7y+vVrODs74/Xr19ouhYiIiKjEGOyIiIiI9ASDHREREZGeYLAjIiIi0hMMdkRERER6gsGOiIiISE8w2BERERHpCQY7IiIiIj3BYEdERESkJxjsiIiIiPQEgx0RERGRnmCwIyIiItITDHZEREREesJQ2wUQERER6QKJVILQp6GIS4uDlYUV3GzdYCA20HZZJcJgR0REROVecHgwfEN8EZMaIztmbWkNPw8/eDl4abGykuFQLBEREZVrweHB8A70lgt1ABCbGgvvQG8EhwdrqbKSY7AjIiKicksilcA3xBcChALP5R+bEjIFEqlE06UphcGOiIiIyq3Qp6EFeureJkBAdGo0Qp+GFnhOKpUiMDBQneWVGIMdERERlVtxaXFKtxOLxTA0NMTly5dVXZbSOHmCiIiIyi0rC6sStYuOjsbTp0/RoUMHAICXlxcyMzPVVl9JsceOiIiIyi03WzdYW1pDBFGhz4sggo2lDdxs3bBnzx60aNECXl5eSEhIkLUxMTHRVLnvxWBHRERE5ZaB2AB+Hn4AUCDc5T9e1HERxo0dB29vb7x69Qp2dnZ48+aNxmtVBIMdERERlWteDl4IGhiEupZ15Y5bW1pjSfMlmDdkHjZs2ACRSIRZs2bh3LlzqFevnpaqLR7vsSMiIqJyz8vBC572nrKdJ2qb18bVvVcxa/AsZGdno27duti+fTs6d+6s7VKLxWBHREREhLxh2c52nWWPt97aiuzsbPTv3x8bN25EtWrVtFecghjsiIiIiP5fbm4uDA3z4tGKFSvg7u6OoUOHQiQqfHKFruE9dkRERKQzJFIJTj8+jYBbATj9+LTGdnx48+YNfH194enpCalUCgCwsLDAsGHDykyoA9hjR0RERGogkUpk96tZWVjBzdYNBmKDYl8THB4M3xBfuZ0grC2t4efhBy8HL7XVevfuXQwaNAi3bt0CAJw9e1bn76UrCoMdERERqZQyAS04PBjegd4F9myNTY2Fd6A3ggYGqTzcCYKAdevW4euvv8abN29Qo0YNbNmypcyGOoBDsURERKRC+QHt3f1X8wNacHhwgddIpBL4hvgWCHUAZMemhExR6bDsy5cv4eXlhXHjxuHNmzfo0aMHbt68id69e6vsGtrAYEdEREQqoWxAC30aWiAIvvva6NRohD4NVVmtn3zyCfbt24cKFSrgt99+w+HDh1G7dm2VnV9bGOyIiIhIJZQNaHFpcQqdX9F2iliyZAkcHR1x6dIlfP311xCL9SMS6cdXQURERFqnbECzsrBS6HWKtivMw4cPsXv3btnjNm3a4Pr163ByclL6nLqIwY6IiIhUQtHg9SDpgdxjN1s3WFtaF9irNZ8IIthY2sDN1q3ENQmCgK1bt8LJyQnDhg3DzZs3Zc/pSy/d2/TvKyIiIiKtcLN1Q12Luu9tt+HqBrn77AzEBvDz8AOAAuEu//Fyj+XvXS7lXSkpKRg6dCg+//xzpKeno02bNqhcuXKJzlHWMNgRERGRShiIDTDGecx728WkxhS4z87LwQtBA4NQ11I+GFpbWiu11MmFCxfg5OSEnTt3wsDAAD///DNOnToFW1vbEp2nrOE6dkRERKQyjao2UqhdYffjeTl4wdPes8QLG79r0aJF+OGHHyCRSGBnZ4edO3eiXbt2JTpHWcVgR0RERCpT2okQBmIDdLbrXKoaBEGARCKBj48P1qxZg0qVKpXqfGUJgx0RERGpTP5EiNjU2ELXsxNBBGtLa6UmQhQnPT0d5ubmAIAZM2agZcuW6NWrV5na51UVdP4eu6ysLMyaNQsuLi5wdXXFpk2bimx77tw59OvXD05OThgxYgQiIyM1WCkRERGpayJEUV6/fo0xY8agXbt2yMzMzKvBwAC9e/cud6EOKAPBbsmSJbh9+zb8/f3x008/YdWqVQgJCSnQ7sGDB/jqq6/QtWtX7NmzB02bNsXnn3+O169fa6FqIiKi8kvVEyGKcu3aNTg7O2PDhg24c+cOjh07ppLzlmUiQRAK9pPqiIyMDLRt2xYbNmzARx99BAD4448/cOHCBWzbtk2u7bx583D//n1s374dQN74ep8+fTB8+HAMGjRIoeulp6fD2dkZYWFhsu5cIiIiUo5EKin1RIjCSKVS+Pn54bvvvkN2djbq1KmDbdu2wd3dXQVVl206fY9dREQEcnNz5VaFdnZ2xtq1ayGVSuUWFoyOjkaLFi1kj0UiERo3bozr168rHOyIiIhIdVQxEeJd8fHxGDFiBI4cOQIA8PT0xJ9//olq1aqp9DpllU4PxSYmJqJKlSowMjKSHatevTqysrLw6tUrubbVq1dHQkKC3LH4+HgkJydrolQiIiLSgEmTJuHIkSMwNjbGmjVrsHfvXoa6t+h0sMvMzJQLdQBkj7Ozs+WO9+rVC0eOHMGpU6eQm5uLvXv34tatW8jJydFYvURERKRev/32Gzp37oywsDCMHTu2XE6QKI5OD8VWrFixQIDLf2xsbCx3vGPHjpgwYQImTZoEiUSCjz76CJ6enkhPT9dYvURERKRad+/exaFDhzBt2jQAgI2NDU6dOqXlqnSXTvfY1apVC8nJycjNzZUdS0xMhLGxMSwtLQu0HzduHK5evYpz585hy5YteP36NerWff+edURERKRbBEHAunXr4OLigunTp+PgwYPaLqlM0Olg5+DgAENDQ1y/fl12LCwsDI6OjnITJwDg4MGD+OWXX2BkZIRq1arhzZs3uHTpkmw2LREREZUNL1++hJeXF8aOHYvMzEz06NEDLi4u2i6rTNDpYGdiYoL+/ftjzpw5uHnzJo4fP45NmzZh+PDhAPJ67968eQMAsLOzw65du3D06FE8fvwY33zzDaysrNCxY0dtfglERERUAqdOnULLli2xb98+VKhQAb/++isOHz6M2rVra7u0MkGngx0AzJw5E82aNcPnn3+OuXPnYtKkSejRowcAwNXVFYcOHQIANG/eHHPmzMGiRYvg5ZW38OG6desK9OwRERGRblq4cCG6du2K2NhY2Nvb49KlS5g6dSp/lpeATi9QrGlcoJiIiEh7goKC8Omnn+LLL7/E8uXLYWZmpu2SyhydnhVLRERE+u3Zs2eoU6cOAMDb2xtXrlyBs7Ozlqsqu9i3SURERBqXkpKCoUOHomXLloiLi5MdZ6grHQY7IiIi0qgLFy7AyckJO3bsQHJyMs6cOaPtkvQGgx0RERFphEQiwfz58+Hm5oaoqCjY2dkhNDSUe7qrEO+xIyIiIrWLjo7G0KFDcfbsWQCAj48P1qxZg0qVKmm5Mv3CHjsiIiJSu6VLl+Ls2bMwNzeHv78/duzYwVCnBuyxIyIiIrVbuHAhXrx4gXnz5qFhw4baLkdvsceOiIiIVO7atWuYMGECpFIpAMDMzAw7d+5kqFMzBjsiIiJSGalUit9//x1t27bFH3/8gbVr12q7pHKFQ7FERESkEvHx8RgxYgSOHDkCAPD09MRnn32m5arKF/bYERERUakdOnQILVu2xJEjR2BsbIw1a9Zg7969qFatmrZLK1fYY0dERESlsmTJEnz77bcAAEdHR+zatQtNmzbVclXlE3vsiIiIqFTc3d1hZGSEyZMn4/Llywx1WqSSHrukpCTExMTg5cuXSElJgbGxMapXr44GDRqgatWqqrgEERER6QhBEHDnzh00b94cAODi4oJ79+7Bzs5Ou4WR8sHu5s2bOHToEM6fP4+HDx8W2c7W1hadO3dGv3790KxZM2UvR0RERDrg5cuXGD16NP755x9cvHgRTk5OAMBQpyNKHOxCQ0OxatUq3Lx5E0BeagcAsVgMc3NzmJiYIDU1FZmZmQCAJ0+ewN/fH1u3bkXbtm0xYcIEuLi4qPBLICIiIk04ffo0hg4ditjYWFSoUAG3bt2SBTvSDQoHu+joaMydOxfnz5+HIAho0KAB3Nzc4OzsjMaNG6NevXoQiUSy9llZWYiPj8fVq1cRFhaG8+fP48KFC7h48SLat2+P+fPnw8rKSi1fFBEREalOTk4O5syZg4ULF0IQBNjb2yMgIIChTgeJhPwut2Ls3r0bCxYsQE5ODjw9PTFo0CA4OjqW6EKCIODs2bP466+/cObMGZiYmGDWrFnw8vJSunhVS09Ph7OzM8LCwmBubq7tcoiIiLQuMjISgwcPxqVLlwAAo0aNgp+fH8zMzLRcGRVGoWDXpEkT9OrVC9988w2sra1LfdFHjx5h6dKlOHPmDMLDw0t9PlVhsCMiIpK3bNkyTJ8+HZUrV8b69evx6aefarskKoZCwe7KlStquS/u0qVL+Oijj1R+XmUx2BEREcmTSqX47rvvMHHiRNja2mq7HHoPhdaxU9dkB10KdURERARcvHgRffr0QUZGBoC8yZFLlixhqCsjNLJAcXp6OtLT0zVxKSIiIlKCRCLB/Pnz4erqikOHDmH+/PnaLomUoPYtxZKTk9GuXTuIxWLcvXtX3ZcjIiKiEoqOjsbQoUNx9uxZAICPj49sizAqWzS2pZgCt/IRERGRhu3ZswctW7bE2bNnYW5uDn9/f+zYsQOVKlXSdmmkBLX32BEREZFuWrFiBXx9fQEArVu3xs6dO9GwYUMtV0WlobEeOyIiItItn3zyCWrUqIHvvvsO58+fZ6jTA+yxIyIiKiekUilOnTqFrl27AgDq1q2L+/fvo3LlytotjFSGPXZERETlQHx8PHr37o1u3bph//79suMMdfqFwY6IiEjPHT58GC1btsSRI0dgbGyM5ORkbZdEasJgR0REpKfevHmDKVOmoHfv3nj+/DkcHR1x5coVjBgxQtulkZoodI/df//9p/QF0tLSlH4tERERKSc8PBw+Pj64ceMGAGDy5MlYvHgxjI2NtVwZqZNCwW7YsGEQiUTqroWIiIhUJCIiAjdu3ED16tWxZcsW9OnTR9slkQYoPCuWCwwTERHpNkEQZB0xAwYMwKpVq+Dl5QUrKystV0aaolCwO3HihLrrICIiolI4deoUpk6din/++Qd16tQBAEyYMEHLVZGmKRTs6tatq+46iIiISAk5OTmYM2cOFi5cCEEQMHv2bGzcuFHbZZGWcIFiIiKiMurRo0cYPHgwLl++DAAYNWoUli9frt2iSKsUCnbR0dEquZiNjY1KzkNERFTebd++HePHj0daWhoqV66M9evX49NPP9V2WaRlCgW77t27l3pWrEgkwt27d0t1DiIiIgI2bdqEUaNGAQBcXV2xY8cO2Nraarkq0gUlWqBYEASl/0ilUnV9DUREROXKZ599hubNm2PevHk4deoUQx3JlGi5E5FIBAcHB/Tp0wddunRBxYoV1VkbERERAZBIJAgICMDgwYMhFothZmaGsLAwGBkZabs00jEKBbugoCAcOnQIISEhuHv3LsLDw/HHH3+ga9eu6NOnDzp06ABDQ87DICIiUrXo6GgMGzYMZ86cQXx8PKZNmwYADHVUKJFQwpWHr1+/jn/++QdHjhzB8+fPIRKJYGlpiR49eqB3795o27Ztmd2lIj09Hc7OzggLC4O5ubm2yyEionIuODgYX375JZKTk2Fubo41a9Zg6NCh2i6LdFiJg10+QRAQFhaGgwcP4tixY3j58iVEIhGqVasGDw8P9O7dG61atVJ1vWrFYEdERLrg9evXmDp1KtavXw8AaN26NXbu3ImGDRtquTLSdUoHu7dJpVJcunQJ//zzD44dO4aUlBSIRCJYWVmhV69e6N27N5o1a6aKetWKwY6IiLTt1q1bGDhwICIiIiASifDtt99i7ty5HHolhagk2L0tNzcX58+fx+HDh3Hy5EmkpaUBAOrVq4eQkBBVXkrlGOyIiEjbrl69irZt26JGjRrYtm0b3N3dtV0SlSEqn/FgaGiITp06oUGDBqhXrx42btyI169f48mTJ6q+FBERkV7Izs6W9ci1atUKQUFBaN++PapXr67lyqisUWmwi46OxuHDhxESEoLw8HAAeffiWVpaomvXrqq8FBERkV44fPgwxowZg/3798vuTe/Xr5+Wq6KyqtTB7unTpwgJCSkQ5iwsLODu7o5evXqhQ4cOqFChQqmLJSIi0hdZWVn49ttv4efnBwBYsGABgoKCtFwVlXVKBbsnT57IwlxERASAvDBnZmYmC3Ourq680ZOIiKgQ4eHh8PHxwY0bNwAAkydPxuLFi7VcFekDhYPd48ePZWHu3r17APLCnKmpKbp06YJevXqhY8eODHNERERFEAQB69evx9dff43MzExUr14dW7ZsQZ8+fbRdGukJhYKdp6cn7t+/DyDvP6WJiQk6d+6MXr16oVOnTtxajIiISAH79u3D2LFjAQDdu3eHv78/rKystFwV6ROFljtp0qQJgLwZr23btkXnzp1hbGxc4ot5e3uXvEIN4nInRETlh0QqQejTUMSlxcHKwgputm4wEBuo9ZpSqRT9+vVDly5d8PXXX0MsFqv1elT+KBzsVLFNWP7kCl3FYEdEVD4EhwfDN8QXMakxsmPWltbw8/CDl4OXyq6Tk5ODFStWYOzYsTAzMwOQN/JVVrfeJN2n0FBsnTp11F0HERGRRgSHB8M70BsC5Ps1YlNj4R3ojaCBQSoJd5GRkRg8eDAuXbqEe/fuybYHY6gjdVIo2J08eVLddRAREamdRCqBb4hvgVAHAAIEiCDClJAp8LT3LNWw7Pbt2zF+/HikpaWhcuXK6N69e2nKJlKYyneeICIi0lWhT0Plhl/fJUBAdGo0Qp+GorNdZ4XO+fa9epawxM5FO7Fz504AgKurK3bs2AFbW1tVlE/0XioLdoIgICEhAampqWjcuLGqTktERKQycWlxKm0nd69ePIBdAF4BYgMx5vw0BzNnzoShIftQSHNKPR3n4cOHmDp1Klq3bo0uXbqgf//+AIC4uDh0794du3fvLu0liIiIVMLKQrGlRRRpl3+vnqwH0BRAFoBKgPRzKZp5N2OoI40rVbA7duwYvL29cfjwYaSnp0MQBORPso2Li0N0dDRmz57N1bSJiEgnuNm6wdrSGiIUPoFBBBFsLG3gZutW7Hlk9+plvnWvniWAIQDGAiLbvHv1JFKJ6oonUoDSwe7JkyeYPn063rx5Aw8PD6xduxZNmzaVPd+gQQN4e3tDEARs2bIFp0+fVkW9RERUTkmkEpx+fBoBtwJw+vFppUKTgdgAfh55e7O+G+7yHy/3WP7eiROhT0MRczEGWAHg7ZW8rAGYyN+rR6RJSge7P//8E2/evMHYsWPx+++/F1i0uHLlypg/fz4mT54MQRCwa9culRRMRETlT3B4MOz87NDFvwsGBw9GF/8usPOzQ3B4cInP5eXghaCBQahrWVfuuLWltUJLnbx+/Ro/T/8ZCASQCeBK0W0VvVePSFWUHvw/f/48zM3NMX78+GLbffnll9iyZQtu3bql7KWIiKgcU8e6c14OXvC09yzxzhPXr1+Hj48PIiIi8g50ANCl6PaK3tNHpCpKB7vnz5/D3t4eRkZGxbYzMjKCjY0N7t27p+yliIionFLnunMGYgOFlzSRSqVYsWIFvv32W2RnZ8PKygo5njl4WftlobWJIIK1pfV779UjUjWlh2JNTU3x4sULhdqmpKTItlIhIiL9pIp74N5VknXn1Onff//F119/jezsbPTr1w83b97EusnrAJTuXj0iVVM62Nnb2yMhIQG3b98utt21a9cQExMDe3t7ZS9FREQ6TpX3wL1N1evOKcvV1RVTp07F6tWrsW/fPlSvXr3U9+oRqYPSwW7AgAEQBAGzZs1CYmJioW0iIyMxbdo0iEQi9O3bV+kiiYhIdxVYz+3/5d8DV5pwp8p150oiKysLM2fOREzM/76mX3/9FePHj5fb69XLwQuPfR/j1OensNNrJ059fgpRvlEMdaQ1IiF/4bkSkkqlGDVqFC5cuAATExN89NFHuHXrFpKSkvDFF1/g4cOH+Pfff5Gbm4sPP/wQO3bsgIGBbndJp6enw9nZGWFhYTA3N9d2OUREOk8ilcDOz67I4dL8e82ifKOUGpbMP39samyx97Ipe/7ChIeHw8fHBzdu3ECXLl1w4sQJuTBHpMuU7rETi8VYvXo1evfujczMTJw+fRovX76EIAjYvHkzzp49i9zcXLi6umLt2rU6H+qIiKjk1H0PnKrWnVOEIAhYv349nJ2dcePGDVSvXh1Tp05lqKMypVR7nZiamuK3337DmDFjcPz4cdy/fx/p6ekwMTFB/fr10aVLFzg7O5eqwKysLMydOxdHjx6FsbExRo4ciZEjRxba9tixY/jtt98QHx+PJk2a4IcffkCzZs1KdX0iIiqaJu6By7+XTbYn6/+ztrTGco/lKhn2TEpKwujRoxEcnDds3L17d/j7+8PKisuVUNmikk3smjRpgiZNmqjiVAUsWbIEt2/fhr+/P549e4Zvv/0WderUgYeHh1y7Bw8e4JtvvsG8efPQqlUrbNmyBV999RWOHTsGExMTtdRGRFTeaeoeOGXXnVNEeHg4evTogZiYGFSoUAELFy7E119/DbG41NupE2mcTu9OnJGRgd27d2PDhg1o1qwZmjVrhgcPHmDHjh0Fgt358+fRsGFD9O/fHwAwdepU7NixAw8fPoSjo6MWqici0n/5e6++7x44VaznVpJ150qiXr16sLS0ROPGjREQEIBWrVqp/BpEmqJQsJsxY0apLyQSibB48eISvSYiIgK5ublwcnKSHXN2dsbatWshlUrlfpuqXLkyHj58iLCwMDg5OSE4OBjm5uawtbUtde1ERFS4/HvgvAO9IYJILtzp8npu0dHRqFu3LsRiMUxNTXHgwAHUrFmTE+eozFMo2P39998QiURQZgJt/uuUCXaJiYmoUqWK3O4W1atXR1ZWFl69eoWqVavKjvfu3RsnT57E4MGDYWBgALFYjHXr1qFSpUolrpmIiBSniXvgVGn79u0YP348vv/+e3z77bcAgAYNGmi5KiLVUCjY9e/fXyuzgjIzMwtsWZb/ODs7W+54cnIyEhMTMXv2bLRs2RIBAQGYOXMm9u7di2rVqmmsZiKi8kid98CpSmpqKiZMmIDt27cDAI4cOYLp06fzXjrSKwoFu0WLFqm7jkJVrFixQIDLf2xsbCx3fNmyZWjcuDGGDBkCAPj555/Rq1cv7NmzB2PGjNFMwURE5Zi67oFThYsXL2Lw4MGIioqCWCzGTz/9hFmzZjHUkd7R6f/RtWrVQnJyMnJzc2XHEhMTYWxsDEtLS7m2d+7ckZuZKxaL0aRJEzx79kxj9RIRkW6RSCRYsGABXF1dERUVhXr16uHs2bOYPXs2DA11ev4gkVJU8r/66tWrOHPmDB49eoTMzExUqlQJjRs3hru7Oxo3bqz0eR0cHGBoaIjr16/DxcUFABAWFgZHR8cCv2XVrFkTjx49kjsWFRXFGbFEROXYgwcPMHfuXEgkEgwaNAhr1qxB5cqVtV0WkdqUKtjFxcVhxowZuHLlCgDITa44fPgw/Pz80Lt3b8ydO1epmUYmJibo378/5syZgwULFuD58+fYtGkTFi5cCCCv987CwgLGxsYYOHAgvvvuOzRv3hxOTk7YvXs3nj17hgEDBpTmSyQiojKsSZMmWL58OUxNTTF8+HDuIkF6T+m9YtPS0tC/f388e/YMYrEYLi4usLe3h5mZGdLS0nD37l1cu3YNQN4SJVu2bFGq2zszMxNz5szB0aNHYW5ujlGjRmHEiBEAAHt7eyxcuBBeXnkzrnbv3o1NmzYhPj4eDg4O+P7770u08wT3iiUiKtsyMjIwbdo0jBw5UjbSQ1SeKB3sli9fjrVr16Jhw4ZYuXIl6tevX6DNnTt3MHHiRMTHx2PmzJkYPnx4qQtWJwY7IqKy6/r16/Dx8UFERATs7e1x+/Zt3kdH5Y7SkyeOHj0KAwMDrF69utBQBwDNmjXD6tWrIQgC9u7dq3SRRERERREEAcuXL8dHH32EiIgIWFlZYfXq1Qx1VC4p/b8+JiYGjRo1Qr169Ypt17RpUzRq1AhRUVHKXoqIiKhQCQkJ+OKLL3D48GEAQL9+/fDnn3+ievXqWq6MSDuUDnaWlpbIyspSuP27684RERGVRlRUFNq2bYvnz5/D2NgYv/32G8aOHcsJElSuKT0U27FjRzx+/BhXr14ttt29e/fw8OFDtG/fXtlLERERFVCvXj04OTmhefPm+O+//zBu3DiGOir3lJ48kZiYCG9vb+Tm5mLZsmVo165dgTYRERGYOHEi0tPTsXv3btjY2JS6YHXi5AkiIt12N/wunkif4JX0FawsrNDUrCkszC1gYmKi7dKIdIJCwS5/m653vXjxAk+ePIFIJIKdnR2aNm0KMzMzZGRkIDIyEhERERAEAW3btoWdnR1++uknlX8BqsRgR0SkmwRBwPifx2Pd/HUQmguAZ95xa0tr+Hn4wcvBS7sFEukIhYLd21t1KX0hkQjh4eGlPo86MdgREemepKQk9B7UG5eOXco70ADAYACGgAh5Q69BA4MY7oig4OSJiRMnqrsOIiKiAs6cOYOhQ4ciJiYm767wrgDaQXaHuAABIogwJWQKPO09YSA20GK1RNrHYEdERDonJycHc+fOxYIFC/K2q6wKwBtAnYJtBQiITo1G6NNQdLbrrOFKiXSL0rNiiYiI1CUpKQnr16+HIAjoPKAz8BUKDXVvi0uL00BlRLpN6XXsnj17VuLX1Knznk8lERERgFq1amHr1q1ITU1FzTY1cdr/9HtfY2Vhpf7CiHSc0sGua9euJWovEolw9+5dZS9HRER6LDU1FRMmTICnpye8vb0BAB4eHgAAiVQCa0trxKbGQkDB+X4iiGBtaQ03WzeN1kyki5QeihUEQaE/AFCzZk3UqFFDZUUTEZH+uHjxIj788ENs374d48aNw+vXr+WeNxAbwM/DD8D/ZsHmy3+83GM5J04QoRQ9didOnCjyuczMTDx//hxHjx7F7t270bVrV8yePVvZSxERkR6SSCRYvHgxZs+eDYlEgnr16mHHjh0wMzMr0NbLwQtBA4PgG+KLmNQY2XFrS2ss91jOpU6I/p/Swa5u3brFPt+wYUO0b98e9evXx6JFi+Ds7Iw+ffooezkiItIjMTExGDZsGE6fPg0AGDRoENasWYPKlSsX+RovBy942nsi9Gko4tLiYGVhBTdbN/bUEb1F6S3FFJWbmwtXV1fUr18fAQEB6rxUqXGBYiIi9UtMTESTJk2QlJQEMzMzrF69GsOHD+c+r0QqoHSPncIXMDRE3bp1cf/+fXVfioiIyoAaNWpg8ODBuHjxInbu3IlGjRppuyQivaH2YJeVlZW3YjgREZVbN27cQNWqVWFjYwMAWLp0KcRiMYyMjLRcGZF+UesCxUlJSfj++++RkpICBwcHdV6KiIh0kCAI8PPzQ5s2bTBs2DBIJBIAgLGxMUMdkRoo3WPXuXPnIp8TBAHZ2dlISUmBIAgQiUTw8fFR9lJERFQGJSQk4IsvvsDhw4cBAJUqVUJGRgYsLCy0XBmR/lI62MXHxyt2AUNDfPnll5wRS0RUjoSEhGDEiBFISEiAsbExfv31V4wbN44TJIjUTOlgt3DhwmKfNzAwQJUqVdCyZUtYWloqexkiIipDsrKyMHPmTPz+++8AgObNmyMgIADNmzfXcmVE5YPSwW7AgAGqrIOIiPSAVCrF0aNHAQATJ07EkiVLYGJiouWqiMoPlc6KTUlJwevXr2FmZoZKlSqp8tRERKSj8reQFIvFMDExQUBAAB4/foy+fftquzSicqfUwS42NhZr167FyZMnkZSUJDtuaWmJTp06YeLEibC1tS3tZYiISAclJSVhzJgxcHZ2xsyZMwEAjo6OcHR01HJlROVTqXaeuHz5MiZOnIi0tDQUdhqRSAQzMzOsXLkS7dq1K1WhmsCdJ4iIFHfmzBkMHToUMTExMDU1xePHj1GjRg1tl0VUrindY5eYmIhJkyYhNTUVjRs3xrBhw9CsWTOYm5sjJSUFt2/fxs6dO/HgwQNMnToVf//9Nz/wRER6ICcnB/PmzcMvv/wCQRDQqFEjBAQE8Hs8kQ5QOtht2rQJKSkpcHd3h5+fHypUqCD3vKOjI7y9vTF58mScPn0aAQEBmDx5cqkLJiIi7YmMjMSQIUNw8eJFAMDIkSPh5+fHUQ4iHaH0zhNnzpyBoaEh5s+fXyDU5atQoQLmz58PAwMDHD9+XOkiiYhI+16/fo22bdvi4sWLqFSpEv766y/8+eefDHVEOkTpYPfs2TM0btwYVatWLbZdtWrV0LhxY8TGxip7KSIi0gFmZmb48ccf0aFDB9y4cQMDBw7UdklE9A6lg51IJEJOTo5CbXNyciCVSpW9FBERacnly5cRFhYmezxx4kScPn0a9erV02JVRFQUpYOdnZ0dIiMj39sTFxMTg0ePHvGbABFRGSKRSLBw4UJ06NABn332GdLS0gDk/VJvaKjSJVCJSIWUDnZdu3aFRCLB9OnTZR/4d6WlpWHatGkQBAHdunVTukgiItKcmJgYdO/eHbNmzUJubi5cXFw46kJURii9jl1qair69OmDFy9eoFq1ahgwYACaNWsGCwsLpKWl4c6dO9i7dy9evHiBmjVr4uDBgzq/ZyzXsSOi8m7fvn0YNWoUkpKSYGZmhtWrV2P48OEQiURquZ5EKkHo01DEpcXBysIKbrZuMBAbqOVaROVBqRYojoiIwOjRo5GYmFjoh14QBNSqVQtr166Fg4NDqQrVBAY7ItJ3RQWp7OxsTJ48GevWrQMAuLi4YOfOnWjUqJHaagkOD4ZviC9iUmNkx6wtreHn4QcvBy+1XZdIn5XqRokmTZogJCQEO3bswKlTpxAZGSnbK7Z+/fpwd3eHj48PLCwsVFUvEREpqbggNaDJADx9+hQikQgzZszAvHnzYGRkpNZavAO9IUC+byE2NRbegd4IGhjEcEekhFL12Okb9tgRkb4qNEgJAHIBUQURggYGwbWaK27duoWuXbuqtRaJVAI7Pzu5gPk2EUSwtrRGlG8Uh2WJSkjpyRNERFQ2SKQS+Ib4yoe6dAA7ABzMezglZAqqVa+m9lAHAKFPQ4sMdQAgQEB0ajRCn4aqvRYifaPUUGx2djauXLmCS5cuIS4uDq9evYJIJIKlpSU++OADtGrVCq1bt1bbzbZERKS4AkHqAYB9AF4DMACETgKikRekOtt1Vns9cWlxKm1HRP9TomCXk5ODrVu3YsOGDUhJSSm2bc2aNTFmzBj4+PhALGbHIBGRtsgCUi6A4wAu/v8TNQF8AqDqO+3UzMrCSqXtiOh/FA526enpGD9+PP777z/k35Znbm6OunXrwszMDDk5OUhLS0NMTAxyc3ORkJCA+fPn4+TJk1i5ciVMTU3V9kUQEVHRrCysgEQAewDE///BNgC6A6jwTjsNcLN1g7WlNWJTYwtMngD+d4+dm62bRuoh0icKT54YNWoUzp8/DwMDA3z22Wf47LPPYG9vX6BddnY2bt68id27d+PAgQMQBAFdu3bFqlWrVF68qnHyBBHpo6zsLJhZmUGSJAFMAPQH8Na3b21MVsifzAFALtyJkHcLD2fFEilHoTHSU6dO4fz58zA3N4e/vz9mz55daKgDACMjI7i4uGDx4sXYsmULTExMcOLECVy8eLHQ9kREpF4VjSpi5sKZQAMA41Ag1AHAco/lGp2B6uXghaCBQahrWVfuuLWlNUMdUSkoFOz+/vtviEQizJw5Ey4uLgqfvE2bNvjmm28gCAIOHDigdJFERFQyZ8+exd9//y17/POYnxF0IAjW1tZy7bQZpLwcvPDY9zFOfX4KO7124tTnpxDlG8VQR1QKCg3F9uzZE/Hx8bhy5QoqVKjwvuZyMjIy0Lp1azRo0EDnwx2HYomorMvJycG8efPwyy+/wNLSEjdu3EC9evVkz3MLLyL9ptDkicTERNjY2JQ41AGAqakprK2tERfHaetEROoUGRmJIUOGyG598fLyQrVq1eTaGIgNNLKkCRFph0JDsVlZWaXqwapUqRIyMzOVfj0RERVv586d+PDDD3Hx4kVUqlQJu3btwqZNmzj6QFTOKNRjJ5FIYGCgfFe9oaEhpFKp0q8nIqLCSaVSfPHFF9i6dSsAoEOHDtixY4fc8CsRlR9cOZiIqAwTi8WoUqUKxGIx5syZg9OnTzPUEZVjSm0pRkRE2iORSJCSkoKqVfO2jFi0aBEGDx6MNm3aaLkyItI2hYNdWloa/vvvP6UukpaWptTriIhIXmxsLIYNG4acnBycOnUKhoaGMDY2ZqgjIgAlCHYPHjzA8OHD1VkLEREVY9++fRg1ahSSkpJgZmaGW7duwcnJSdtlEZEOUfgeO0EQSvWHiIiUk5GRgbFjx2LAgAFISkqCs7Mzrl27xlBHRAUo1GN34sQJdddBRESFuHHjBnx8fBAeHg4AmDFjBn7++WcYGRlpuTIi0kUKBbu6deu+vxEREamUIAgYPXo0wsPDYWVlha1bt6Jbt27aLouIdBiXOyEi0lEikQibN2/Gp59+ihs3bjDUEdF7KRTsRo4ciYcPH6r0wnfu3OFkDCKid4SEhGDFihWyx82aNUNgYCBq1KihxaqIqKxQKNjFxsbC09MTP/74I549e1aqC0ZERGD69On49NNPkZiYWKpzERHpi6ysLEydOhW9evXC1KlTlV5eiojKN4Xusdu3bx8WLlyI3bt3Izg4GG5ubujXrx/c3NxgYWHx3tfHx8fj7Nmz2L17N27fvg0AGDx4MKZPn1666omI9EBERAR8fHxw/fp1AMDYsWPRvHlz7RZFRGWSSCjBWiRXr17FokWLcPPmTYhEIhgYGOCDDz5Ao0aNUK9ePVhYWMDExASpqalITk5GQkICrl27hri4OAB5NwK3bt0avr6+cHFxUdsXpaz09HQ4OzsjLCyMG2cTkdoJgoCNGzfC19cXmZmZqFatGjZv3oy+fftquzQiKqNKFOzynTlzBlu3bsWFCxcglUrzTiQSFWiXf2pDQ0O4urpi1KhRaN26dSlLVh8GOyLSpGHDhmH79u0AgG7dusHf3x916tTRclVEVJYptVdsp06d0KlTJ6SkpODSpUu4dOkSYmJi8PLlS6SmpqJixYqoXr066tevj1atWqF9+/ayPQ2JiChPu3btsGvXLixYsADffPMNxGIuVEBEpaNUj52+Yo8dEalTTk4OYmNjYWdnByBvVOP+/fuwt7dX6PUSqQShT0MRlxYHKwsruNm6wUBsoMaKiaisUarHjoiISiYyMhJDhgxBQkICrl+/DktLS4hEIoVDXXB4MHxDfBGTGiM7Zm1pDT8PP3g5eKmrbCIqY9jvT0SkZjt37sSHH36IixcvIikpSbY6gKKCw4PhHegtF+oAIDY1Ft6B3ggOD1ZluURUhjHYERGpSWpqKoYPH44hQ4YgLS0NHTp0wPXr19G+fXuFzyGRSuAb4gsBBe+ayT82JWQKJFKJyuomorKLwY6ISA0uX74MJycnbNu2DWKxGHPmzMHp06dl99cpKvRpaIGeurcJEBCdGo3Qp6GlrJiI9AHvsSMiUoNFixYhMjIStra22LFjB1xdXZU6T1xanErbEZF+Y7AjIlKDdevWoVatWli4cCEqV66s9HmsLKxU2o6I9JvOD8VmZWVh1qxZcHFxgaurKzZt2lRou2HDhsHe3r7An5kzZ2q4YiIqj/bt2wdfX1/Z4xo1amDNmjWlCnUA4GbrBmtLa4hQcBF4ABBBBBtLG7jZupXqOkSkH3S+x27JkiW4ffs2/P398ezZM3z77beoU6cOPDw85NqtXLkSOTk5ssc3btzAlClTMHjwYE2XTERaoK013jIyMvDNN99g7dq1AICuXbuiX79+Kju/gdgAfh5+8A70hggiuUkU+WFvucdyrmdHRAAUDHZbt25FrVq10LNnT3XXIycjIwO7d+/Ghg0b0KxZMzRr1gwPHjzAjh07CgS7t38rlkgk+P333/Hll1/C0dFRozUTkeZpa423GzduwMfHB+Hh4QCA6dOnF/jepApeDl4IGhhU6Ne43GM517EjIhmFgt2CBQvg7OxcZLB79uwZKlasiGrVqqm0uIiICOTm5sLJyUl2zNnZGWvXroVUKi1y+53g4GCkpKRg9OjRKq2HiHRP/hpv7y4Hkr/GW9DAIJUHH0EQsHLlSkyfPh3Z2dmoXbs2tm3bhm7duqn0Om/zcvCCp70nd54gomKpZCjW3d0dLi4uss2sVSUxMRFVqlSBkZGR7Fj16tWRlZWFV69eFbr/rCAI2LhxI4YPHw4zMzOV1kNEuuV9a7yJIMKUkCnwtPdUaQAaNWoUNm/eDAD4+OOPsWnTJtSoUUNl5y+KgdgAne06q/06RFR2qWzyhDq2nM3MzJQLdQBkj7Ozswt9zaVLlxAfH4+BAweqvB4i0i3aWuNt0KBBMDExwapVq/D3339rJNQRESlCpydPVKxYsUCAy39sbGxc6GuOHDmCjh07lnomGhHpPk2t8ZaVlYXbt2/D2dkZANCjRw88fvwYNWvWLNV5iYhUTaeXO6lVqxaSk5ORm5srO5aYmAhjY2NYWloW+prQ0FB07dpVUyUSkRZpYo23e/fuoV27dnB3d0dUVJTsOEMdEekinQ52Dg4OMDQ0xPXr12XHwsLC4OjoWOjEiaSkJERHR8t+qyYi/abONd7y79dt1aoVrl27hgoVKuDp06elLZmISK10OtiZmJigf//+mDNnDm7evInjx49j06ZNGD58OIC83rs3b97I2j948AAVK1aEtbW1tkomIg3KX+MNQIFwV5o13pKTkzFw4ECMHj0aGRkZ6NatG27evIlOnTqppnAiIjXR6WAHADNnzkSzZs3w+eefY+7cuZg0aRJ69OgBAHB1dcWhQ4dkbV++fAlLS0uIRIX/9k5E+id/jbe6lnXljltbWiu11MnZs2fRsmVLBAUFwdDQEEuWLMGRI0dQp04dVZZNRKQWOj15AsjrtVu8eDEWL15c4Ll79+7JPe7duzd69+6tqdKISEeoco23/fv3Izo6Go0aNcLOnTvh4uKihoqJiNRD4WB3//592RCoMs+LRCL4+/uXrDoiIgWVZo03QRBkPf0LFiyAhYUFpk2bBnNzcxVWSESkfiJBgQXomjRpUvoLiUSybXd0VXp6OpydnREWFsZv6ETlxM6dO7F9+3b8/fffMDTU+UEMIqJiKfRdbMCAAequg4hIo1JTUzFx4kRs27YNALBp0yaMGTNGy1UREZWOQsFu4cKF6q6DiEhjLl++DB8fH0RGRkIsFmP27NkYOXKktssiIio1jjsQUbkhkUiwZMkSzJ49G7m5ubC1tcWOHTvg6uqq7dKIiFSCwY5IyyRSiUpmc9L7TZ48GX/88QcAYODAgVi3bh23HyQivaJQsHv27JlKLsZ1oIjkBYcHwzfEV24je2tLa/h5+JV4/TV6v4kTJ2L37t1YvHgxRowYwTUviUjvKDQr1sHBofQXEolw9+7dUp9HnTgrljQpODwY3oHeECD/EczfMUGZxXVJXkZGBk6dOoU+ffrIHTM1NdViVURE6qPQzhOCIKjkDxHlkUgl8A3xLRDqAMiOTQmZAolUounSlCKRSnD68WkE3ArA6cendaLumzdvonXr1ujXrx/Onz8vO85QR0T6TKGh2K1bt6q7DqJyJfRpqNzw67sECIhOjUbo01ClF93VFF0bThYEAStXrsSMGTOQlZWF2rVrIycnR+N1EBFpg0LBrk2bNuqug6hciUuLU2k7bSlqODk2NRbegd4aH05+/vw5vvjiC9ke0h9//DE2bdqEGjVqaKwGIiJtUmgolohUy8rCSqXttEHXhpOPHj2KFi1a4NChQ6hYsSJWrVqFv//+m6GOiMoVBjsiLXCzdYO1pbVsosS7RBDBxtIGbrZuGq5McSUZTtaEqKgoJCQkoFmzZvjvv/8wYcIEznolonJHoWC3cOFCbN++XemLDB48GE2bNlX69UT6xkBsAD8PPwAoEO7yHy/3WK7T69npwnCyRPK/3sAxY8Zg3bp1+O+//+Do6Ki2axIR6TKFgp2/vz8OHz5c5PNdu3bF119/Xew5OCuWSJ6XgxeCBgahrmVduePWltZlYqkTbQ4nC4KAjRs3omXLlkhJSQGQt6TSmDFjYGJiovLrERGVFSrZeSI2Nha1a9dWxamIyhUvBy942nuWyZ0n8oeTY1NjC73PTgQRrC2tVT6cnJycjDFjxiAoKAgA8Mcff2DmzJkqvYaqcFcRItI0bilGpGUGYgOdX9KkMPnDyd6B3hBBJBfu1DWcHBoaiiFDhiA6OhqGhob45ZdfMG3aNJWdX5V0bRkYIiofOHmCiJSmqeHk3NxczJ49G507d0Z0dDQaNmyIf//9FzNmzIBYrHvfxvKXgXl3ckn+MjDB4cFaqoyI9B177IioVDQxnPzTTz9hwYIFAIARI0ZgxYoVsLCwUNn5Vel9y8CIIMKUkCnwtPfksCwRqRyDHRGVmrqHk7/++mvs3bsXs2fPxqBBg9R2HVXQp11FiKjs0b0xDCIq99LS0rBu3TrZbPrq1avj1q1bOh/qAN1YBoaIyi/22BGRTrl8+TIGDx6MR48ewdTUFMOGDQMAGBiUjWFLfdhVhIjKLvbYEZFOkEgkWLRoETp06IBHjx7B1tYW9evX13ZZJaYPu4oQUdnFYEdEWhcbG4vu3btj5syZyM3NxcCBA3Hjxg24urpqu7QS04ddRYio7BIJCmwJ0aRJE5XsuRgeHl7qc6hTeno6nJ2dERYWBnNzc22XQ1QuHD58GEOHDkVSUhLMzMywcuVKjBgxoszv81rYOnY2ljZY7rGc69gRkdoofI9dabcEK+vfpIlIPSpUqICkpCQ4Oztj586daNy4sbZLUomyvKsIEZVdCgW7hQsXqrsOIipHXr9+DTMzMwBAt27dcPDgQXTv3h1GRkZarky1yuquIkRUdik0FFtecCiWSL0EQcDKlSsxf/58XLx4EQ0aNNB2SUREeoWTJ4hII54/f46PP/4Yvr6+SExMxIYNG7RdEhGR3uE6dkSkdkeOHMHnn3+OhIQEVKxYEcuWLcOECRO0XRYRkd5hsCMitcnKysKsWbPw22+/AQCaNWuGgIAAODo6arkyIiL9xKFYIlKb1atXy0Ld+PHj8d9//zHUERGpEXvsiEhtJk6ciKNHj2LcuHHw9PTUdjlERHqPPXZEpDLJycmYPXs2cnNzAQBGRkYICQlhqCMi0hD22BGRSoSGhmLIkCGIjo4GAMybN0/LFRERlT8q6bFLT08vcOzWrVuIiYkppDUR6ZPc3FzMnj0bnTt3RnR0NBo2bIi+fftquywionKpVMEuPT0d06ZNg6ura4Fwt3btWvTo0QNTp05FampqqYokIt0UFRWFjh074ueff4ZUKsWIESNw9epVtG7dWtulERGVS0oHu/T0dPj4+ODgwYN48+aNbPgln0QigVQqxeHDh/HFF18gJyen1MUSke44fPgwPvzwQ1y4cAGWlpYICAjA5s2bYWFhoe3SiIjKLaWD3Z9//okHDx6gXr16CAgIgIODg9zza9euxb59+/DBBx/g7t272LZtW6mLJSLd0aBBA+Tm5qJ9+/a4ceMGBg0apO2SiIjKPaWD3bFjx2BoaIiNGzfCycmp0DZNmjTBihUrIBaLceDAAaWLJCLdEB8fL/u7vb09zp49izNnzsDG1ganH59GwK0AnH58GhKpRItVEhGVX0oHu+joaDRo0AA2NjbFtmvQoAFsbW0RFRWl7KWISMukUikWL14MOzs7hIaGyo47Ozvj7wd/w87PDl38u2Bw8GB08e8COz87BIcHa7FiIqLySelgZ2RkBEEQFG4rEomUvRQRaVFsbCy6d++O7777DllZWdizZ4/sueDwYHgHeiMmVX4GfGxqLLwDvRnuiIg0TOlgZ2tri0ePHhWYNPGuhIQEPHz48L09e0Ske/bv34+WLVvi5MmTMDMzw6ZNm/D7778DACRSCXxDfCGg4C94+cemhEzhsCwRkQYpHew8PDwglUrxzTffICkpqdA2KSkp+OabbyCVStG9e3eliyQizcrMzMT48ePRv39/vHz5Eq1atcLVq1fxxRdfyHrfQ5+GFuipe5sAAdGp0Qh9GlpkGyIiUi2ld57w8fFBYGAgbt26hR49eqBbt25o0qQJTE1N8fr1a9y/fx8nT55ESkoK6tSpgxEjRqiwbCJSp3379mHNmjUAgGnTpuGXX36BkZGRXJu4tDiFzqVoOyIiKj2lg525uTnWrl2LKVOm4MGDB9i/fz/2798v10YQBNSrVw9//PEH17YiKkMGDRqE0NBQ9O/fHz169Ci0jZWFlULnUrQdERGVnkhQdAZEEXJycnDs2DGcOnUKT58+xatXr2BiYgI7Ozt06tQJffr0KfCbvq5KT0+Hs7MzwsLCYG5uru1yiDTm+fPnmDVrFpYuXYoqVaoo9BqJVAI7PzvEpsYWep+dCCJYW1ojyjcKBmIDVZdMRESFULrHLl+FChXQu3dv9O7dWxX1EJGGHT16FMOHD0dCQgKysrIUXkzcQGwAPw8/eAd6QwSRXLgTIe8+vOUeyxnqiIg0qFR7xRJR2ZWVlYVp06ahZ8+eSEhIQLNmzTBjxowSncPLwQtBA4NQ17Ku3HFrS2sEDQyCl4OXKksmIqL3UGgoNigoCEDeTNj8Icr8YyXh7e1d4tdoEodiqby4d+8efHx8cO3aNQDAhAkTsHTpUpiYmCh1PolUgtCnoYhLi4OVhRXcbN3YU0dEpAUKBbsmTZpAJBLh0KFDqF+/vtyxkggPD1euSg1hsKPy4Pjx4/D09ERGRgaqVauGTZs2oV+/ftoui4iIVEChe+zq1KmT19jQsMAxIipbnJycUKVKFbRr1w5bt27lZ5mISI+UelasPmGPHemru3fvwsHBQdbL/vjxY9ja2kIs5m22RET6RCPf1VNSUnD37l1NXIqI3pKbm4uffvoJjo6O2Lp1q+y4nZ0dQx0RkR5S+ju7g4MDhg4dqlDbkSNHYsyYMcpeioiUEBUVhY4dO2LevHmQSqX477//tF0SERGpmdLBThAEKDKKm5GRgefPnyM1NVXZSxFRCQUEBODDDz/EhQsXYGlpiYCAAKxatUrbZRERkZopNHni4cOHGD16dIEgd+vWLXTu3LnI1wmCgJSUFGRlZcHOzq40dRKRAtLS0jBx4kTZsGv79u2xY8cOfv6IiMoJhYJdw4YN0apVK/zzzz9yx7OzsxEfH//e14vFYowbN065ColIYdeuXcO2bdsgFovxww8/4Mcff5SbzU5ERPpN4VmxiYmJOHfuHIC8nrhZs2bBzs4OX331VdEnF4lgZmYGe3t72NraqqZiNeKsWNIHv/76K9q0aQM3Nzdtl0JERBqm9HInTZo0gbOzM3bs2KHqmrSGwY7KmtjYWIwfPx6//vorGjZsqO1yiIhIy5Qeozl79ixq1qypylqIqAT279+PUaNG4eXLl0hNTcWpU6e0XRIREWmZ0rNi3d3dMXbsWBw9ehQ5OTmqrImIipGZmYkJEyagf//+ePnyJZycnLB27Vptl0VERDpA6R47iUSC06dP48yZM6hUqRL69u0LLy8vODg4qLI+InrLrVu3MGjQINmC39988w1++eUXVKxYUcuVERGRLlD6Hrv4+Hjs27cP+/btw+PHj/NOJhKhSZMm8PLywscff4wqVaqosla14z12pMv+/fdfuLu7IysrC7Vq1cLWrVvRo0cPbZdFREQ6RCV7xd64cQPBwcEICQlBSkoKRCIRDA0N0aVLF3h5eaFjx45lYvsiBjvSZdnZ2ejQoQNq1aqFTZs28R5XIiIqQCXBLl92djZOnjyJ/fv3499//0VWVhZEIhGqVauG/v37Y8CAAfjggw9UdTmVY7AjXXP+/Hm0adMGFSpUAAAkJSWhSpUqEIlEWq6MiIh0kUqD3dsyMzPx119/YcWKFcjMzJQdb9WqFb744gt069ZNHZctFQY70hXZ2dn4/vvvsWzZMsycORMLFizQdklERFQGqHxJ+piYGBw8eBBHjx5FeHi4bBuyJk2a4MWLFwgLC8PVq1fh6uoKPz8/mJqaqroEojLt/v378PHxwdWrVwHkbRMmCAJ76YiI6L1UcuNbeno6AgMDMWTIEHTv3h1+fn64e/cuLC0tMXToUNkkizNnzmDFihWoVq0azp07h19++eW9587KysKsWbPg4uICV1dXbNq0qci29+7dg4+PD1q0aIG+ffvi4sWLqvjyqIyRSCU4/fg0Am4F4PTj05BIJdouSSGCIGDTpk1wcnLC1atXUa1aNezfvx8rV65kqCMiIoWUarmTM2fOYP/+/Th9+jSys7MhCALEYjHatWuHTz75BN26dYORkZHsNQYGBujRowdMTU3x5Zdf4vjx4+8Nd0uWLMHt27fh7++PZ8+e4dtvv0WdOnXg4eEh1y4tLQ0jR46Eu7s7Fi1ahP3792PixIk4cuQIqlWrpuyXSWVMcHgwfEN8EZMaIztmbWkNPw8/eDl4abGy4iUnJ2Ps2LEIDAwEkLdO5NatW1G3bl0tV0ZERGWJ0sHO1dUVr169kg212tjYYMCAAfDy8kLt2rWLfW2DBg0AALm5ucW2y8jIwO7du7FhwwY0a9YMzZo1w4MHD7Bjx44CwW7v3r0wNTXFnDlzYGBggMmTJ+PMmTO4ffs2OnXqpOyXSWVIcHgwvAO9IUD+ttHY1Fh4B3ojaGCQzoa7Fy9e4J9//oGhoSF++eUXTJs2rUzMJCciIt2idLBLTk6GsbExevTogU8++QQfffSRwq/NysrCwIED0bx582LbRUREIDc3F05OTrJjzs7OWLt2LaRSqdwPvsuXL6Nr164wMDCQHduzZ08JviIqyyRSCXxDfAuEOgAQIEAEEaaETIGnvScMxAaFnEHz3r5vrlGjRvD394etrS1at26t5cqIiKisUrpLYO7cuTh37hyWLFlSolAHAPXr18e8efMwcODAYtslJiaiSpUqcsO51atXR1ZWFl69eiXXNjo6GlWrVsWPP/6IDh06YODAgQgLCytRXVR2hT4NlRt+fZcAAdGp0Qh9GqrBqor2+PFjdOzYEWfOnJEd++STTxjqiIioVJQOdp999pnalwTJzMyUC3UAZI+zs7PljmdkZGD9+vWoUaMGNmzYgNatW2PUqFGIi4tTa42kG+LSFHufFW2nTrt27ULLli1x7tw5TJgwAVKpVNslERGRnlBoKPbChQsquVi7du1K1L5ixYoFAlz+Y2NjY7njBgYGcHBwwOTJkwEATZs2xfnz57F//36MHTu2FFVTWWBlYaXSduqQlpaGSZMmwd/fH0De52Hnzp28l46IiFRGoWD3xRdflHq5BZFIJNu4XFG1atVCcnIycnNzYWiYV2piYiKMjY1haWkp17ZGjRqySRn57Ozs2GNXTrjZusHa0hqxqbGF3mcnggjWltZws3XTQnXAf//9h8GDB+Phw4cQi8X44Ycf8OOPP8r+XxMREamCwl0FgiCU6o8yw00ODg4wNDTE9evXZcfCwsLg6OhYoJfjww8/xL179+SORUZGcrmIcsJAbAA/Dz8AeSHubfmPl3ss18rEiVu3bqF9+/Z4+PAhbGxscPr0acydO5ehjoiIVE6hnywRERHqrqNQJiYm6N+/P+bMmYMFCxbg+fPn2LRpExYuXAggr/fOwsICxsbGGDRoELZv346VK1eiX79+2LdvH6Kjo+Hp6amV2knzvBy8EDQwqNB17JZ7LNfaUifNmzfHgAEDAADr1q1DlSpVtFIHERHpP7XtFasqmZmZmDNnDo4ePQpzc3OMGjUKI0aMAADY29tj4cKF8PLK+4EdFhaGX375BQ8ePMAHH3yA77//vkSzDLlXrH6QSCUIfRqKuLQ4WFlYwc3WTeM9df/88w/atWuHqlWrAshb4sfIyIg7SBARkVppLNjFx8e/d+FibWOwo9LKzMzEtGnT8Mcff8Db2xuBgYEMc0REpDGlusknMzMTe/fuxf379/HmzZsC99FJJBJkZmYiPj4e9+/fx+3bt0tVLJEuu3XrFgYNGiSbJFSvXj1IJBLeS0dERBqj9E+c1NRUDBo0CFFRUQWee3tF/fzHRPpKEASsXr0a06ZNQ1ZWFmrVqoWtW7eiR48e2i6NiIjKGaUX0Nq6dSsiIyMhEonw0UcfoWvXrhAEAU2aNMHHH38MFxcX2fZeH330EY4fP66yool0xYsXL9C3b19MmjQJWVlZ6NOnD27evMlQR0REWqF0j93JkychEomwaNEi9OvXDxKJBK1bt0bNmjWxbNkyAMDDhw/x5Zdf4urVq3jz5o3KiibSFQYGBrh58yYqVqyIpUuXYuLEibynjoiItEbpHrvo6GhUrlwZ/fr1A/C/nR+uXr0qa9OwYUPMmzcPOTk5stX2icq6nJwc2e0FVapUQWBgIC5fvoxJkyYx1BERkVYpHewyMzNRp04duWMffPAB0tPTERsbKzvWsWNHVKtWDZcvX1a+SiIdcf/+fbRt2xabN2+WHWvbti1atGihxaqIiIjyKB3szM3NCwyvWltbA8jb8eFtVlZWSEhIUPZSRFonCAI2bdoEJycnXL16FXPnzi2wjzEREZG2KR3sGjZsiCdPnuDly5eyY/Xq1YMgCAgPD5drm5SUxCEqKrOSk5MxaNAgjBo1ChkZGXB3d8e///4LIyMjbZdGREQkR+lg17FjR+Tm5mLixIl49OgRAMDR0REAsGvXLqSkpAAAjh49imfPnsl684jKknPnzuHDDz9EYGAgDA0NsWjRIhw9epR7EBMRkU5Selasj48Pdu7ciWvXrqFv3764fv066tSpg9atW+PKlSvo2bMn6tSpg3v37kEkEsHd3V2VdROpXXR0NNzd3ZGTk4MPPvgAAQEBJdqijoiISNOU7rGzsLCAv78/WrdujUqVKsmGpebNm4cqVarg1atXuHv3LiQSCWxtbTF69GiVFU2kCTY2NpgxYwaGDx+Oa9euMdQREZHOU8lesS9fvkS1atVkj5OSkhAUFISYmBg0aNAA3t7eZWLvVe4VS3/99RecnJzQuHFjAIBUKoVYrPjvPxKpBKFPQxGXFgcrCyu42brBQGygrnKJiIjkqCTY6QsGu/IrLS0NkydPxpYtW+Ds7KzU5Ijg8GD4hvgiJjVGdsza0hp+Hn7wcvBSdclEREQFKD0US6Qv/vvvP7Rq1QpbtmyBWCxGnz59StRLB+SFOu9Ab7lQBwCxqbHwDvRGcHiwKksmIiIqlNKTJwAgIyMDQUFBuHr1KtLS0pCbm4uiOgBFIhF3nyCdIpVKsXTpUvzwww/Izc2FjY0NduzYATc3txKdRyKVwDfEFwIK/t8XIEAEEaaETIGnvSeHZYmISK2UDnZJSUnw8fHB06dPAaDIQJeP69iRLklKSsKnn36KkydPAgC8vb2xfv16VKlSpcTnCn0aWqCn7m0CBESnRiP0aSg623VWtmQiIqL3UjrYrVu3Dk+ePIGBgQE6duyIDz74AMbGxqqsjUhtLCwskJ6eDlNTU6xYsQIjR45U+pePuLQ4lbYjIiJSltLB7sSJExCJRFi1ahW6dOmiypqI1CIzMxMGBgYwMjJChQoVEBAQgJycHNjb25fqvFYWViptR0REpCylJ08kJCTA1taWoY7KhFu3bqF169aYPXu27FiDBg1KHeoAwM3WDdaW1hCh8B4/EUSwsbSBm23J7t0jIiIqKaWDnaWlJffKJJ0nCAJWrVqF1q1b486dO9i2bRtSU1NVeg0DsQH8PPwAoEC4y3+83GM5J04QEZHaKR3sXFxcEBUVhZcvX6qyHiKVSUxMRL9+/TBp0iRkZWWhT58+uHbtGiwtLVV+LS8HLwQNDEJdS/k9ZK0trRE0MIjr2BERkUYovUBxREQEvL294ebmBj8/P73oveMCxfrj2LFjGD58OOLj41GxYkUsXboUEydOVPvsbO48QURE2qR0sDt37hzOnDmDbdu2oXr16mjXrh1q1aqFChUqFPkaX19fpQvVBAY7/ZCcnAw7OzukpqaiadOmCAgIQIsWLbRdFhERkdopHeyaNGkCkUgkW7+uuJ4QQRAgEokQHh6uXJUawmCnP7Zu3YqLFy9i2bJlMDU11XY5REREGqH0cietW7cuUXsuUEzqIggCNm/ejPr168tmaQ8fPhzDhw/XcmVERESapXSw27Ztm8Jt4+PjERgYqOyliIr06tUrfPXVVwgMDESdOnVw+/ZtpXaPICIi0gel2iv2fc6cOYNdu3YhNDQUUqkUkydPVuflqJw5d+4chgwZgqdPn8LQ0BCTJk1Sy4xXIiKiskLlwS4pKQlBQUEIDAxEbGwsgP/dY0ekCrm5ufj5558xf/58SKVSfPDBB9i5cyfatGmj7dKIiIi0SmXB7tKlS9i1axeOHz+O3Nxc2aQKExMT9O3bF4MHD1bVpagcS09PR8+ePfHvv/8CyLuXbtWqVbCwsNByZURERNpXqmCXlpaG4OBg/PXXX4iKigIAWaBr1KgRBg0aBE9PT84wJZUxMzODjY0NLC0tsWbNGv7CQERE9Balgt3NmzcREBCAw4cPIysrSxbmTE1NkZGRgVq1auHAgQMqLZTKr/T0dOTk5KBKlSoQiURYu3YtkpOTUb9+fW2XRkREpFMUDnYZGRk4cOAA/vrrL9l6dIIgwMDAAO3bt0e/fv3QrVs3ODk58X46UpkrV65g8ODBaNasGYKDgyESiVC5cmVUrlxZ26URERHpHIWC3Zw5c3DgwAFkZGTIeudatGiBjz/+GB9//DGqVq2q1iKp/JFKpVi2bBm+//575Obm4s2bN4iLi0OdOnVkbbh9FxERkTyFgt2uXbsgEonQsmVLuLu7o1evXrCxsVF3bVROPXv2DMOHD8eJEycAAN7e3li/fr3c+nTB4cHwDfFFTGqM7Ji1pTX8PPzg5eCl8ZqJiIh0gbgkjR8/foxr167h33//RWJiorpqonLswIEDaNGiBU6cOAFTU1Ns3LgRgYGBBUKdd6C3XKgDgNjUWHgHeiM4PFjTZRMREekEhfaKPXjwIIKDg3Hx4kVIpVKIRCKIxWK0adMGnp6e6N69O8zMzADk7SFbu3ZtnD59Wt21qxz3itWuN2/eoEmTJnjy5AmcnJwQEBAAe3t7uTYSqQR2fnYFQl0+EUSwtrRGlG8Uh2WJiKjcUSjY5YuLi8OePXuwb98+xMTk/WAViUQwNjaGu7s7+vbti7FjxzLYkdLOnz+Pffv2Yf78+ahYsWKB508/Po0u/l3ee55Tn59CZ7vOaqiQiIhId5Uo2L3t4sWLCAoKwvHjx/HmzRvZTFhBEFCpUiVs3rwZTZs2VWmx6sZgp1mCIGD16tUwMzPDF198odBrAm4FYHDw+9eu2+m1Ez6OPqUtkYiIqExReoHitm3bom3btkhPT5cN1d68eRMAkJqaik8++QT29vb45JNP0LdvXy5PQXISExMxcuRIHDx4ECYmJujSpQvs7Oze+zorCyuFzq9oOyIiIn2idI9dYR4+fIigoCAcOHAAL1++zLuASIQKFSrA3d0dy5cvV9Wl1II9dppx7NgxDB8+HPHx8TAyMsLSpUsxadIkhdY/zL/HLjY1FgIK/tflPXZERFSelWhW7Ps0bNgQ3333Hc6cOYNVq1ahS5cuMDAwQHZ2No4cOaLKS1EZlJ2djenTp6NHjx6Ij49H06ZN8d9//2Hy5MkKL2ptIDaAn4cfgLwQ97b8x8s9ljPUERFRuaTSHrvCvHjxAnv37sXevXtx6NAhdV6q1Nhjpz45OTlwdXXF5cuXAQBjx47Fr7/+ClNTU6XOV9g6djaWNljusZzr2BERUbml9mBXljDYqddPP/2EVatW4c8//0T//v1LfT7uPEFERCSPwe4tDHaq9erVK7x69Uo2KSI3NxeJiYmwsuLEBiIiInVQ6T12VPZIpBKcfnwaAbcCcPrxaUikEpWc99y5c2jZsiW8vLyQlZUFADA0NGSoIyIiUiOllzuhsk8d+63m5uZi/vz5+PnnnyGVSmFoaIjY2Fg0aNBAVWUTERFREdhjV06pY7/VJ0+eoHPnzpg7dy6kUimGDx+O69evM9QRERFpCINdOSSRSuAb4lvoOnD5x6aETCnRsOxff/2Fli1b4vz587C0tMSOHTvg7+8PCwsLldVNRERExWOwK4dCn4YW6Kl7mwAB0anRCH0aqtD5JBIJ/Pz8kJKSgrZt2+L69esYPPj9234RERGRavEeu3IoLi1Ope0MDAywY8cObNu2DbNmzYKhIf9bERERaQN77Mqh0u63KpVKsXTpUnz//feyY/Xr18fs2bMZ6oiIiLSIP4XLITdbN1hbWr93v1U3W7cCzz179gzDhw/HiRMnAADe3t5wcnJSe81ERET0fuyxK4eU3W/1wIEDaNGiBU6cOAFjE2OM/mk0XlV+pbK174iIiKh0GOzKKS8HLwQNDEJdy7pyx60trRE0MEhuHbvMzExMnDgR/fr1w8uXL1GhbgW8GfkGG0Qb4L7VHXZ+dkotj0JERESqxS3F3lIetxR7336rgiDA1dUV//77b96BdgC6Qm4QP7+X791AWJLrEBERUenxHrtyzkBsgM52nYt8XiQSYfz48Xj06BEknhK8qPOiQBsBAkQQYUrIFHjaexYIbOrY4YKIiIgK4lAsFZCYmIgrV67IHg8ZMgQbj24sNNTlK2rtO3XscEFERESFY7AjOceOHUOLFi3Qr18/JCYmyo6nidIUev3ba9+pY4cLIiIiKhqDHQEAsrOzMX36dPTo0QPx8fGoXLkykpOTZc8rs/adqne4ICIiouIx2BHu37+P9u3bY9myZQCAsWPH4sqVK2jcuLGsTf7ad+8uj5JPBBFsLG3k1r5T9Q4XREREVDwGu3JMEARs3rwZrVq1QlhYGKpWrYq9e/dizZo1MDU1lWurzNp3pd3hgoiIiEqGwa6cO378OF6/fo0uXbrg5s2b6N+/f5FtS7L2HaBcLx8REREpj+vYvaW8rGMnlUohFudl+tTUVPj7+2P8+PEwMFBsXbmSrEmXPysWgNwkCkXWviMiIqKSYbB7i74Hu9zcXMyfPx83b97Enj17IBIV3pOmaoWtY2djaYPlHssZ6oiIiFSIwe4t+hzsnjx5giFDhuD8+fMA8pY16datm8auz50niIiI1I87T5QDf/31F7766iukpKTA0tISa9as0WioA96/wwURERGVHoOdHktPT8ekSZOwZcsWAEDbtm2xc+dO1K9fX7uFERERkVpwVqwe++STT7BlyxaIxWL8+OOPOHv2LEMdERGRHtP5YJeVlYVZs2bBxcUFrq6u2LRpU5Ftx40bB3t7e7k/p06d0mC1umXOnDmws7PDqVOnMG/ePFSoUEHbJREREZEa6fxQ7JIlS3D79m34+/vj2bNn+Pbbb1GnTh14eHgUaPvo0SMsXboU7dq1kx2rVKmSJsvVqri4OISFheHjjz8GALRr1w73799noCMiIiondDrYZWRkYPfu3diwYQOaNWuGZs2a4cGDB9ixY0eBYJednY2YmBg4OjqiRo0aWqpYew4cOICRI0ciPT0dYWFhaNq0KQAw1BEREZUjOj0UGxERgdzcXDg5OcmOOTs748aNG5BKpXJtIyMjIRKJYGNjo+kytSozMxMTJ05Ev3798OLFCzRp0gSGhjqd14mIiEhNdDrYJSYmokqVKjAyMpIdq169OrKysvDq1Su5tpGRkTA3N8eMGTPg6uoKb29vnDlzRsMVa9bt27fRpk0brF69GgDQe3hvLN61GB80/EDLlREREZE26HSwy8zMlAt1AGSPs7Oz5Y5HRkbizZs3cHV1xcaNG9GpUyeMGzcOt27d0li9mrRmzRq4uLjg9u3bEFuIgaHAoQaH0HNXT9j52SE4PFjbJRIREZGG6fSYXcWKFQsEuPzHxsbGcsfHjx+PYcOGySZLNGnSBHfu3EFgYCAcHR01U7AGPX/+HFlZWUBDQNpfCry1UUZsaiy8A725DysREVE5o9M9drVq1UJycjJyc3NlxxITE2FsbAxLS0u5tmKxuMAM2AYNGiAhIUEjtWpCVlaW7O/fzfwOVYdWBYZALtQBgIC8XeKmhEyBRCrRYIVERESkTTod7BwcHGBoaIjr16/LjoWFhcHR0RFisXzp3333HWbOnCl3LCIiAg0aNNBEqWqVnZ2Nb7/9Fu3atcObN28AABeeXUBSwyRAVPhrBAiITo1G6NNQDVZKRERE2qTTwc7ExAT9+/fHnDlzcPPmTRw/fhybNm3C8OHDAeT13uUHHXd3dxw4cAD79u3DkydPsGrVKoSFhWHo0KHa/BJK7cGDB2jfvj2WLFmCa9eu4cCBAwCAuLQ4hV6vaDsiIiIq+3Q62AHAzJkz0axZM3z++eeYO3cuJk2ahB49egAAXF1dcejQIQBAjx498NNPP2HNmjX4+OOPcfLkSWzcuBHW1tbaLF9pgiBgy5YtcHJyQlhYGKpWrYrg4GB8+umnAAArCyuFzqNoOyIiIir7RIIgCNouQlekp6fD2dkZYWFhMDc3f/8L1OTVq1cYO3Ys/vrrLwBA586dsW3bNrmQKpFKYOdnh9jUWNk9dW8TQQRrS2tE+UbBQGygsdqJiIhIe3S+x648yg91BgYGWLBgAY4fP16g59FAbAA/Dz8AeSHubfmPl3ssZ6gjIiIqRxjsdNCiRYvQqlUrnD9/HjNnzoSBQeHhzMvBC0EDg1DXsq7ccWtLay51QkREVA5xKPYt2hqKffLkCQ4dOoRx48bJjgmCAJGoiCmv75BIJQh9Goq4tDhYWVjBzdaNPXVERETlkE4vUFwe/PXXX/jqq6+QkpKC+vXrw8PDAwAUDnVA3rBsZ7vOhT7H0EdERFR+MNhpSXp6OiZPnozNmzcDANq2bYvGjRur9BrB4cHwDfFFTGqM7Ji1pTX8PPw4TEtERKSHeI+dFly5cgWtWrXC5s2bIRKJ8MMPP+Ds2bMqXUw5ODwY3oHecqEO+N92Y9xLloiISP8w2GnY6tWr0b59ezx48ADW1tY4deoUfv75Z1SoUEFl15BIJfAN8S10GRRuN0ZERKS/GOw0rFq1asjJycEnn3yCGzduoFOnTiq/RujT0AI9dW/jdmNERET6iffYadigQYNQu3ZtdOrUqUQTJEqC240RERGVTwx2WtC5c2e1np/bjREREZVPHIrVQ262brC2tC6wI0U+EUSwsbSBm62bhisjIiIidWKw00PcboyIiKh8YrDTU9xujIiIqPzhlmJv0daWYurEnSeIiIjKD06e0HPFbTdGRERE+oVDsURERER6gsGOiIiISE8w2BERERHpCQY7IiIiIj3BYEdERESkJxjsiIiIiPQEgx0RERGRnmCwIyIiItITDHZEREREeoLBjoiIiEhPcEuxt+Rvm5uenq7lSoiIiIjkmZmZQSQSFduGwe4tr1+/BgB06tRJy5UQERERyQsLC4O5uXmxbURCfjcVQSqV4vnz5wolYiIiIiJNUiSfMNgRERER6QlOniAiIiLSEwx2RERERHqCwY6IiIhITzDYEREREekJBjsiIiIiPcFgR0RERKQnGOx0XFZWFmbNmgUXFxe4urpi06ZNRbYdN24c7O3t5f6cOnVKg9XSu0ry/t27dw8+Pj5o0aIF+vbti4sXL2qwUnqXou/dsGHDCnzu7O3tMXPmTA1XTG8ryWfv2LFj6NWrF5ycnODj44M7d+5osFJ6V0neu3PnzqFfv35wcnLCiBEjEBkZqcFKdZRAOm3evHlC3759hdu3bwtHjx4VnJychMOHDxfatnv37sL+/fuF58+fy/5kZWVpuGJ6m6LvX2pqqtC+fXvhhx9+EB4/fiz4+fkJzs7OwosXL7RQNQmC4u9dcnKy3Gfu2LFjQrNmzYSbN29qoWrKp+j7d//+fcHR0VHYu3ev8OTJE2Hu3LlChw4dhIyMDC1UTYJQsveuadOmwvLly4VHjx4JixcvFlxdXYX09HQtVK07GOx02OvXrwVHR0fh4sWLsmOrV68Whg4dWqBtVlaW4ODgIERGRmqyRCpGSd4/f39/oVu3bkJubq7smJeXl3D69GmN1ErySvLevS03N1fo3bu38Pvvv6u5QipOSd6/zZs3CwMGDJA9TktLExo3bsxgriUlee/mzp0rDBkyRPZYKpUKvXr1EgICAjRSq67iUKwOi4iIQG5uLpycnGTHnJ2dcePGDUilUrm2kZGREIlEsLGx0XSZVISSvH+XL19G165dYWBgIDu2Z88e7lusJSV5794WHByMlJQUjB49WhNlUhFK8v5VrlwZDx8+RFhYGKRSKYKDg2Fubg5bW1tNl00o2XsXHR2NFi1ayB6LRCI0btwY169f11S5OonBToclJiaiSpUqMDIykh2rXr06srKy8OrVK7m2kZGRMDc3x4wZM+Dq6gpvb2+cOXNGwxXT20ry/kVHR6Nq1ar48ccf0aFDBwwcOBBhYWEarpjyleS9yycIAjZu3Ijhw4fDzMxMQ5VSYUry/vXu3RudO3fG4MGD0bx5cyxZsgQrVqxApUqVNFw1ASV776pXr46EhAS5Y/Hx8UhOTtZEqTqLwU6HZWZmyv3nBiB7nJ2dLXc8MjISb968gaurKzZu3IhOnTph3LhxuHXrlsbqJXklef8yMjKwfv161KhRAxs2bEDr1q0xatQoxMXFaaxe+p+SvHf5Ll26hPj4eAwcOFDt9VHxSvL+JScnIzExEbNnz0ZgYCA8PT0xc+ZMvHz5UmP10v+U5L3r1asXjhw5glOnTiE3Nxd79+7FrVu3kJOTo7F6dZGhtgugolWsWLHAf+T8x8bGxnLHx48fj2HDhsl+y2zSpAnu3LmDwMBAODo6aqZgklOS98/AwAAODg6YPHkyAKBp06Y4f/489u/fj7Fjx2qmYJIpyXuX78iRI+jYsSMqV66s7vLoPUry/i1btgyNGzfGkCFDAAA///wzevXqhT179mDMmDGaKZhkSvLedezYERMmTMCkSZMgkUjw0UcfwdPTE+np6RqrVxexx06H1apVC8nJycjNzZUdS0xMhLGxMSwtLeXaisXiAkMHDRo0KNBNTZpTkvevRo0aaNCggdwxOzs79thpSUneu3yhoaHo2rWrpkqkYpTk/btz5w6aNGkieywWi9GkSRM8e/ZMY/XS/5T0szdu3DhcvXoV586dw5YtW/D69WvUrVtXkyXrHAY7Hebg4ABDQ0O5G0HDwsLg6OgIsVj+rfvuu+8KrJsVERFRICyQ5pTk/fvwww9x7949uWORkZHl/huUtpTkvQOApKQkREdHw9nZWYNVUlFK8v7VrFkTjx49kjsWFRUFa2trTZRK7yjJe3fw4EH88ssvMDIyQrVq1fDmzRtcunQJH330kYar1i0MdjrMxMQE/fv3x5w5c3Dz5k0cP34cmzZtwvDhwwHk/Rbz5s0bAIC7uzsOHDiAffv24cmTJ1i1ahXCwsIwdOhQbX4J5VpJ3r9Bgwbh3r17WLlyJZ48eQI/Pz9ER0fD09NTm19CuVWS9w4AHjx4gIoVKzIM6IiSvH8DBw5EYGCg7HvnsmXL8OzZMwwYMECbX0K5VZL3zs7ODrt27cLRo0fx+PFjfPPNN7CyskLHjh21+SVon7bXW6HiZWRkCDNmzBA+/PBDwdXVVdi8ebPsucaNGwt79uyRPQ4MDBR69OghNG/eXBgwYIBw+fJlLVRMbyvJ+3flyhVhwIABQvPmzQVPT0++f1pWkvfun3/+ETp06KCFKqkoJf3e6eHhIXz44YeCj4+PcPv2bS1UTPlK8t4FBQUJXbp0EZycnITx48cLCQkJWqhYt4gEQRC0HS6JiIiIqPQ4FEtERESkJxjsiIiIiPQEgx0RERGRnmCwIyIiItITDHZEREREeoLBjoiIiEhPMNgRERER6QkGOyIiIiI9YajtAojULSYmRm5z9mHDhuGHH3547+v+/PNPLFmyBEDextRnz55VW42KevDgARo1aqTx6547dw4HDx7EtWvXEB8fD4lEgurVq6NFixbo168funXrptF6vvvuO+zduxd9+/bFsmXL1HqtYcOG4fLlyyV6zb59++Dg4CB7nF9vmzZtsG3bNrm29vb2JTr3f//9V+hm6MUJDQ3F6NGjsWjRIvTv3x8AsHLlSqxatarQ9kZGRjA1NUWdOnXQvn17fPrpp7CzsyvRNdXF3d0dsbGxmD9/Pj799FOFX5f/77x582a0b9++VDVcunRJtsUVkPf+fvHFF+993bx587Bjxw4AQKtWrRAQECB7Lv/9qFu3Lk6ePKlwLbNmzcKJEydw4MAB1KxZswRfBekrBjsqd44cOYLvv/8eIpGo2HaHDh3SUEXv9/z5cyxevBhXrlzBmTNnNHbdhIQETJ8+HZcuXQIAVKxYEXXq1EGFChUQExODI0eO4MiRI2jTpg2WL1+OatWqaaw2TatWrRrq1aunUFtTU9MSn9/Ozg5Vq1Z9bzsDA4MSnTclJQUzZ86Eo6NjoXsPGxkZoXnz5nLHsrOzkZSUhPDwcNy9exdbt27FtGnT8Pnnn5fo2uVFSEjIe4OdRCLBkSNHVH7tqVOnIiQkBDNnzsSff/6p8vNT2cNgR+WKoaEhnj9/jrCwMLi4uBTZLjo6Grdv39ZgZcXL7zGrVauWxq756NEjDB06FElJSbCzs8PkyZPh4eEhCxa5ubnYv38/fv31V1y+fBmff/45du3aBXNzc43VqEkdO3bEokWL1Hb+r776Cl5eXio/76+//orExEQsX7680F9matSoIddz9Lb4+Hj8/vvv2LdvHxYsWAAjIyP4+PiovMayzNDQEDdu3EBcXBysrKyKbHf58mW8ePFC5devXr06Ro8ejeXLl+PgwYP4+OOPVX4NKlt4jx2VK23btgWQ9xt2cfJ765o2bar2mnRRdnY2pk6diqSkJDRt2hR//fUX+vTpI9dbZGhoiE8++QRbtmyBsbExHjx4gOXLl2uvaCrgwYMH2L17N9q2bVvsLzJFqV27NhYvXoyBAwcCABYsWIC4uDhVl1mmtW3bFoIgvLc37vDhwwDU8z1l2LBhsLCwwLJly5Cdna3y81PZwmBH5YqHhwcA4OjRoxAEoch2hw4dglgsRq9evTRVmk7ZsmULIiIiIBaLsXTpUlSuXLnIto0bN8awYcMAALt370Z6erqGqqT3WbVqFaRSKT777LNSnWfWrFmoWrUqsrOzsW7dOhVVpx/yv6cU98tibm4ujh49CktLS7i5uam8BnNzc/Tt2xdxcXEICgpS+fmpbGGwo3LFxcUFNWrUQEJCAq5evVpom8jISERERKBNmzaoXr16see7desWpk+fjs6dO6N58+Zo06YNhg0bhqCgIEgkkgLthw0bBnt7e5w9exYRERHw9fVF+/bt0bx5c3Tt2hULFixAUlKS3Gvs7e0xc+ZMAHn3vNnb2xd6w/3x48cxZswYtGvXDs2bN4ebmxu++eYb3LlzR9F/HpnAwEAAQJcuXdCwYcP3th82bBiWLl2Kw4cPFxiKTU1Nxfr16zFkyBB89NFHaNasGVxcXODl5YWVK1ciJSWlwPnyv8YXL15g2rRpcHJygrOzM4YPH47c3Nxia5FIJNi9ezeGDRuG1q1bo3nz5ujcuTOmT5+u1L9FWZWQkIDjx4/DzMys1JNbTExM0K9fPwDAiRMnCm2j7Gfh999/L/R8K1euhL29veyXhsKEhoZi6NChcHJygouLC4YPH67UvbEvXrzAkiVL0Lt3b7Rs2RJOTk745JNPsGnTJmRlZRX72m7duqFChQq4fv064uPjC21z4cIFJCcno3v37qhQoUKJ61NE/qSY7du3q+X8VHYw2FG5IhaL0bNnTwBF/4ad/4OhT58+xZ5rw4YNGDhwIP7++2+kpaXB3t4e5ubmuHz5Mr7//nuMGDECaWlphb727Nmz8Pb2xvHjx1GlShVYWVkhJiYG/v7+GDRokFyvV6tWrWQzEitUqIBWrVqhVatWsudzc3Mxbdo0TJgwAWfOnIFIJIK9vT2ys7Nx8OBBfPrppyX6Zh8dHY3o6GgAQIcOHRR6Ta1atdCvXz/UqVNH7vjjx4/Rr18//Prrr7h+/TqqVq0Ke3t7GBgY4M6dO1i1ahU+++wzvH79utDzTpo0CQcPHoSNjQ1MTExQo0YNGBoWfWtweno6hgwZgh9++AGXL1+GhYUF7O3tkZaWhr///hve3t7YsmWLYv8QZVxISAhyc3PRvn17GBkZlfp8zs7OAPIm8kRFRck9V5rPgrL27duHL7/8Ejdv3kT9+vVhamqKS5cu4euvv5b9IqSIsLAw9OnTB3/++SeePn0KGxsb1KlTB3fu3JENQycmJhb5egsLC7i6ukIQhFJ/TykNR0dHVK5cGY8ePUJERITarkO6j8GOyp384dWihmMPHz6MChUqoEePHkWe48iRI1i2bBmkUinGjx+PCxcuYM+ePTh58iT8/f1RvXp1XL58GTNmzCj09du2bUOHDh1w6tQp/PPPPzh27Bj++OMPGBgY4MmTJ3LDKQEBAfjqq68AAFWrVkVAQIDcze5+fn44cOAAateujY0bN+Lff//Fnj178O+//+KHH36ASCTC/Pnzcf78eYX+fSIjI2V/L+lSHO/68ccfERcXhw8//BCnTp3C4cOHERwcjIsXL2Lx4sUQi8WIiorCvn37Cn397du3sW3bNvz99984e/Ysfvzxx2KvN23aNFy7dg01atTA1q1bcfLkSezZswcXLlzA+PHjIZVKsXDhQhw9erRUX1dZcOHCBQD/C2SlZW1tLfv72/fZlfazoKwrV66gc+fOOHPmDIKDg3H27Fn88ssvMDQ0RHBwsEJDkgkJCRg/fjxevXqFgQMH4t9//8XBgwfxzz//4OjRo2jZsiUiIiIwZcqUYs9T3HBsdnY2jh8/jmrVqsnu8VUHsVgMJycnAFD4s076icGOyh1nZ2fUrFkT8fHxuHbtmtxz9+7dw8OHD9G+ffti7yvLHz767LPP4OvrK9cj0rZtW9n6YCdPnsSVK1cKvL5atWpYsWKF3LpTXbt2RceOHQGgyGHid7148ULWA/XHH3/I3b9jYGCAYcOGYcSIERAEQeGJDW8PjSqy/EZxtT148AAA8PPPP8t9rSKRCP3790ebNm0A5P27F6ZXr15o3bo1gLwfXMW9J9evX8epU6cAACtWrMBHH30ke87IyAi+vr6ye82UWftu7969siHi4v6sXLmyxOcGgJkzZxZ73uKGJN8llUpla++pat1DMzMz2d9fvXol+3tpPwvKsrW1xYoVK1ClShXZMW9vb4wePRoAsH79+vee488//8SrV6/g7u6On3/+WW59QFtbW/zxxx8wNzd/7zJD3bp1g5GREa5fv46EhAS5586dO4fU1FT07NmzxEvVlFTjxo0BABcvXlTrdUi3MdhRuSMSiYr8DTt/yKR3795Fvv7x48eyoaii1vVycnKS/fZc2D1J7dq1Q8WKFQsc/+CDDwBA4WGrs2fPIjs7Gw0bNkSzZs0KbZO/dtnNmzfx8uXL957z7TXY3nc/W3GqV6+Oixcv4saNG7IfOG+TSCSy+/HevHlT6DlK0tuUH+patGghN1T9tpEjRwIAnjx5gvv37yt8biAvjOcPgxf3p7glL4pjZ2dX7HkL+zcsyqtXr2TD24quvfc+OTk5BY6p4rOgLG9v70I/Q/kzeJ88eSLX+1yY48ePA4Ds/sF3Va9eXXY7Qv7/r8KYm5vDzc2t0OFYTQzD5qtfvz4AyG6loPKJ69hRudSrVy9s3boVR44cwcyZM2Xrex0+fBgVK1Ys9mbz/B8WJiYmsiBWmObNm+PatWsF7kcCUOR6dMbGxgAUD1T5PWLx8fFFri/29nBzZGTkexcRrlGjhuzvycnJCtVRHGNjY8TGxuLWrVt4+vQpoqOj8ejRI4SHhyMjIwNAXg/T+2p5n/z3paiAC+SFJ3Nzc6SnpyMqKqpEYaksrWP3doC3sLBQyTnf/mWjUqVKAFTzWVBWUcuG1KlTBxYWFkhLS0NkZCQaNGhQaLvXr18jNjYWQF5v99atWwttl9/mfSGxV69eOHHiBEJCQmQhNysrCydPnkTt2rVVNiRenPz3+t0JWFS+MNhRueTk5AQrKyvExcXh+vXrcHJywp07d/DkyRP07Nmz2EV28yc2vG8h3vyhq8ImBqhqZlz+D9v09HSFhm9TU1Pf28bW1hZisRhSqRQPHjyQG9IsTnh4OBo1aiQ3uSEyMhI//fRTgS25zM3N4eLigufPnxd7o3d+0FVE/vvyviBjZmaG9PT0Iids6IO3h9NNTExUcs5Hjx7J/p4f4lTxWVDW20PDhT2XlpaGzMzMItu8PUFJkd7b9/Wiu7u7o2LFirh27RoSEhJQq1YtnDlzBq9fv8Znn3323p1uVCG/t12RzznpLwY7KpdEIhF69uyJLVu2ICQkBE5OTgoNwwL/+4HyvvXa8r+5FvcDqLTyf2j37NkTK1asUMk5q1SpAmdnZ/z33384f/48hg4d+t7XJCQkYMCAATAxMcGvv/4Kd3d3vHz5EkOHDsXLly9Rp04dDBw4EE2bNkWDBg1gbW0NkUiEb775RmUz+PL/nd/3Azj/eXW+L9r29hBlampqiXo+i5L/i0OdOnVQu3ZtAOr9LOT35irzfP57XNyeum8H3gMHDpSo97YwZmZm6NixI44dO4YjR47ILb3yvu8pqpIf6Asboqbyg/fYUbn17uzYw4cPw8zMDJ07dy72dflDO5mZmXK9GO/K35JMVfc4FSb/npr8IdnCZGZm4vLly4iOji50PbHC5N8PdObMmWLPnW/nzp0QBAE5OTlo0aIFAGDPnj14+fIlKleujD179mDcuHHo1KkTbGxsZL0X795oXhr570txa9U9evRIFgjU+b5om6qH09PT02U7J7x9r1hpPgv5EwmK2inh+fPnxdZU1NDokydPZD2Dxc3qtrS0lK1T+fDhwyLb3bt3D+Hh4YWut/iu/O8pISEhyMjIwOnTp1GvXj04Ojq+97WqkP9e6/OezfR+DHZUbn344YeoU6cOnj17hh07diA2Nhbu7u7vHf6rX7++LFD5+/sX2ubq1au4efMmAMhmupaGWJz3UX13eZZOnTrBwMAAkZGRRS5xsGXLFgwbNgyenp7FDk297dNPP0Xjxo0hkUgwc+ZMuVmQ77p58yY2bdoEABg0aJDsh2VMTAyAvB6ewmbXPnz4ENevXwcAhQNncbp06SKrp6hh6fwZxLVr1y71Ui66rFatWrJhuaIWzS2JBQsWICMjA6ampnKzc0vzWcifzVpYQHv9+rVsuZai7Nu3r9D/N9u2bQOQdw9efs9iUfJ/idu+fXuh93mmpaVh+PDh6N+/f5Ff39u6dOkCY2NjXLt2DUFBQcjMzNTo7jX5vygVdV8hlQ8MdlSu5c+O/e233wAoPnPN19cXAPDXX39hxYoVcr0Oly5dwuTJkwEAbm5uaN++fanrzP8hnZKSIjfsVbduXXz66acAgKlTp+LkyZOy56RSKXbv3i1bbmLIkCHvvRcqn6GhIX755RdYWFjg1q1b+OyzzxASEiL3gzQrKws7d+7EiBEjkJ2djcaNG2Pq1Kmy5/N/uERERMjtoykIAs6ePYsvv/xSNtNS0cBZHCcnJ3Tq1AkAMHnyZFy6dEn2XHZ2NlasWCHbUWPGjBkauedJW0QikWwm6rtL+pREVFQUvvnmG+zZswdA3rqE7078UfazkD+ZIDQ0VG5dwefPn2Py5MnvncF9584dfP/993ITcLZs2SJbjPt9a88BwJgxY2BqaoqwsDBMnz5dbtJBbGwsxowZg1evXsHCwgJDhgx57/lMTU3RqVMnSKVS2fJCJZ0NK5VKkZSUVOyfooa+83+h0cREDdJdvMeOyrVevXph06ZNeP36NSpVqgRXV1eFX/f06VP8/vvvWL16Nfz9/VG/fn0kJSXJZtG1adMGS5cuVUmAsLe3h1gsRlZWFjw8PFCzZk38+eefqFKlCmbNmoWEhAScOnUK48aNQ82aNVGrVi3ExsbKflD17NlToR90b2vRogV27NiBsWPH4vHjx/D19YWpqSlsbGwgFosRGRkp226pffv2+O233+SWSvH29sbOnTvx5MkTTJ48GXXr1kWVKlUQFxeHly9fokKFCmjTpg0uX76ssiHZJUuWYOzYsbh27RqGDx+OunXromrVqoiKikJ6ejoMDAwwZcoUpZaeOHv2bJEzj9/VqVMnjB07tsTXUKWOHTvi/PnzCAsLK7ZdYmJiga8rMzMTL168kO24ULFiRcyaNavQWbvKfhYGDBiA7du3IyoqCpMmTYKtrS1MTU3x6NEjGBgYYOzYsVi7dm2Rdffs2RN79+7F0aNHUb9+fcTHx+PFixcQiUSYPn26LOQXp169eli+fDm+/vprHDx4EEeOHEHDhg2Rk5ODx48fIzc3F6ampli/fr3Cw5seHh44cuQIXr9+jUaNGpX43r24uDi0a9eu2DZdu3bFH3/8IXcsJydH1jOqyNdO+ovBjsq1Fi1awNraGjExMSXex/Grr75Cu3bt4O/vjytXriAiIgKWlpZo164d+vfvj379+smGUEurXr16WLhwIdasWYPY2FhIJBLExsaiSpUqqFixItasWSPb1eH27dsIDw+HmZkZPvroI3h5eSldi729PQ4fPoy9e/fi5MmTiIiIQGRkJEQiEWrUqIEWLVqgf//+hd6XaG5ujqCgIGzYsAGnTp1CTEwMXrx4gdq1a6Nz5874/PPPYWpqim7duiEiIgLPnj0rsCVZSVWuXBnbtm3D3r178ffff+PevXtITExErVq14OHhgSFDhhS5TMb7vHz5UqF1AAHduH+vT58+WLp0Ka5fv460tLQiZwtnZ2cXGLquUKECLCws0KpVK7Rv3x6ffvppscOaynwWzMzM8Ndff2H9+vU4duwYnj17hkqVKqFnz56YMGEC4uPjiw12I0eOhIeHB/788088fPgQRkZG6Ny5M0aPHg0XFxeF/506deqEf/75B1u2bEFoaCiioqIgkUhQt25ddOjQASNHjoSNjY3C5+vSpQtMTU2RkZGhsUkTAHD58mW8efMGjRo1Uvr/OOkHkVDYnkpERFTmzZw5E8HBwZg9e7ZCQ4lUdvn6+iIkJASLFy9G//79tV0OaRHvsSMi0lNjx46FgYGB7N5C0k9JSUk4ceIE6tWrh759+2q7HNIyBjsiIj1Vr149DBgwABEREdwYXo/5+/sjJycHEydOVPt+tKT7OBRLRKTHUlNT0bdvX1SuXBl79+5V2X2fpBvi4+PRs2dPtGvXrth7Eqn84CeciEiPWVpaYsGCBbh37x727t2r7XJIxX7//XeYmJhg/vz52i6FdAR77IiIiIj0BHvsiIiIiPQEgx0RERGRnmCwIyIiItITDHZEREREeoLBjoiIiEhPMNgRERER6QkGOyIiIiI9wWBHREREpCcY7IiIiIj0xP8BwxsVZspGuvMAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -1078,12 +1069,12 @@ "\n", "# Plot y=x line for min and max values\n", "min_val = min(\n", - " estimates['monte_carlo_eif-double_ml'].min(),\n", - " estimates['analytic_eif-double_ml'].min()\n", + " min(estimates['monte_carlo_eif-double_ml']),\n", + " min(estimates['analytic_eif-double_ml'])\n", ")\n", "max_val = max(\n", - " estimates['monte_carlo_eif-double_ml'].max(),\n", - " estimates['analytic_eif-double_ml'].max()\n", + " max(estimates['monte_carlo_eif-double_ml']),\n", + " max(estimates['analytic_eif-double_ml'])\n", ")\n", "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", "plt.xlabel(\"Monte Carlo EIF (DoubleML)\", fontsize=18)\n", @@ -1095,9 +1086,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# TMLE\n", "plt.scatter(\n", @@ -1108,12 +1110,12 @@ "\n", "# Plot y=x line for min and max values\n", "min_val = min(\n", - " estimates['monte_carlo_eif-tmle'].min(),\n", - " estimates['analytic_eif-tmle'].min()\n", + " min(estimates['monte_carlo_eif-tmle']),\n", + " min(estimates['analytic_eif-tmle'])\n", ")\n", "max_val = max(\n", - " estimates['monte_carlo_eif-tmle'].max(),\n", - " estimates['analytic_eif-tmle'].max()\n", + " max(estimates['monte_carlo_eif-tmle']),\n", + " max(estimates['analytic_eif-tmle'])\n", ")\n", "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", "plt.xlabel(\"Monte Carlo EIF (TMLE)\", fontsize=18)\n", @@ -1125,9 +1127,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# One-step\n", "plt.scatter(\n", @@ -1138,12 +1151,12 @@ "\n", "# Plot y=x line for min and max values\n", "min_val = min(\n", - " estimates['monte_carlo_eif-one_step'].min(),\n", - " estimates['analytic_eif-one_step'].min()\n", + " min(estimates['monte_carlo_eif-one_step']),\n", + " min(estimates['analytic_eif-one_step'])\n", ")\n", "max_val = max(\n", - " estimates['monte_carlo_eif-one_step'].max(),\n", - " estimates['analytic_eif-one_step'].max()\n", + " max(estimates['monte_carlo_eif-one_step']),\n", + " max(estimates['analytic_eif-one_step'])\n", ")\n", "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", "plt.xlabel(\"Monte Carlo EIF (One-Step)\", fontsize=18)\n", diff --git a/docs/examples/robust_paper/results/ate_causal_glm.json b/docs/examples/robust_paper/results/ate_causal_glm.json new file mode 100644 index 00000000..c0ae9be5 --- /dev/null +++ b/docs/examples/robust_paper/results/ate_causal_glm.json @@ -0,0 +1,218 @@ +{ + "analytic_eif-tmle": [ + 0.29480472207069397, + 0.154845729470253, + 0.1780671328306198, + 0.309093177318573, + 0.458683580160141, + 0.14270278811454773, + 0.3192795217037201, + 0.34946882724761963, + 0.369506299495697, + 0.3466206192970276, + 0.2951693832874298, + 0.512150228023529, + 0.425812304019928, + 0.19491614401340485, + 0.4425966441631317, + 0.4880734384059906, + 0.23327100276947021, + 0.24540312588214874, + 0.3959514796733856, + 0.572983980178833, + 0.36296698451042175, + 0.1563234180212021, + 0.15700078010559082, + 0.19665764272212982, + 0.3422546982765198 + ], + "analytic_eif-one_step": [ + 0.34353700280189514, + 0.13584259152412415, + 0.36127716302871704, + 0.15113501250743866, + 0.27882128953933716, + 0.1423613578081131, + 0.06405952572822571, + 0.3712005615234375, + 0.13835936784744263, + 0.16611404716968536, + 0.24185031652450562, + 0.28594306111335754, + 0.1216767430305481, + 0.06503324210643768, + 0.15733659267425537, + 0.26941221952438354, + 0.22228166460990906, + 0.1440175473690033, + 0.4367375075817108, + 0.26493746042251587, + 0.41136711835861206, + 0.010259732604026794, + 0.2545619308948517, + 0.11344790458679199, + 0.30539238452911377 + ], + "analytic_eif-double_ml": [ + 0.6361349821090698, + 0.5309189856052399, + 0.9158359318971634, + 0.7087532132863998, + 0.8173158764839172, + 0.6751835569739342, + 0.4823194742202759, + 0.9330247044563293, + 0.7448698878288269, + 0.5200318247079849, + 0.8224777281284332, + 0.699287623167038, + 0.5718091130256653, + 0.5177016705274582, + 0.5459288358688354, + 0.8066826462745667, + 0.7433740049600601, + 0.6001838147640228, + 0.9106434285640717, + 0.6786685585975647, + 0.9169524013996124, + 0.4951963275671005, + 0.8120758682489395, + 0.6665371954441071, + 0.781407356262207 + ], + "monte_carlo_eif-tmle": [ + 0.3155955672264099, + 0.12997934222221375, + 0.20883847773075104, + 0.3270210325717926, + 0.49054190516471863, + 0.1453614979982376, + 0.33265239000320435, + 0.3326042890548706, + 0.33840954303741455, + 0.37711814045906067, + 0.28450721502304077, + 0.5187819600105286, + 0.4033525288105011, + 0.18305093050003052, + 0.42360472679138184, + 0.46743232011795044, + 0.2725902497768402, + 0.24055863916873932, + 0.3730815052986145, + 0.5657938718795776, + 0.34321627020835876, + 0.1875062733888626, + 0.1575453281402588, + 0.20332394540309906, + 0.3585251569747925 + ], + "monte_carlo_eif-one_step": [ + 0.35389941930770874, + 0.12066487222909927, + 0.3272823095321655, + 0.16992847621440887, + 0.28082412481307983, + 0.11357730627059937, + 0.09033170342445374, + 0.36031511425971985, + 0.16861987113952637, + 0.17305417358875275, + 0.22208906710147858, + 0.2634490728378296, + 0.1412336826324463, + 0.08644792437553406, + 0.16948935389518738, + 0.226078063249588, + 0.22199749946594238, + 0.1070089042186737, + 0.40926358103752136, + 0.1891499161720276, + 0.3888532817363739, + 0.01757611334323883, + 0.22711563110351562, + 0.15685753524303436, + 0.3101191520690918 + ], + "monte_carlo_eif-double_ml": [ + 0.6464973986148834, + 0.515741266310215, + 0.8818410784006119, + 0.7275466769933701, + 0.8193187117576599, + 0.6463995054364204, + 0.5085916519165039, + 0.9221392571926117, + 0.7751303911209106, + 0.5269719511270523, + 0.8027164787054062, + 0.67679363489151, + 0.5913660526275635, + 0.5391163527965546, + 0.5580815970897675, + 0.7633484899997711, + 0.7430898398160934, + 0.5631751716136932, + 0.8831695020198822, + 0.6028810143470764, + 0.8944385647773743, + 0.5025127083063126, + 0.7846295684576035, + 0.7099468261003494, + 0.7861341238021851 + ], + "plug-in-mle-from-model": [ + 0.29303088784217834, + 0.16591405868530273, + 0.1615283340215683, + 0.3347514867782593, + 0.48925507068634033, + 0.10964994877576828, + 0.3111332952976227, + 0.3290335536003113, + 0.34723520278930664, + 0.3917902112007141, + 0.331586092710495, + 0.5071767568588257, + 0.4482594132423401, + 0.20763325691223145, + 0.4439792037010193, + 0.4998875856399536, + 0.2433425337076187, + 0.2581843137741089, + 0.38689935207366943, + 0.5970532894134521, + 0.3297704756259918, + 0.1574830412864685, + 0.14008547365665436, + 0.22331777215003967, + 0.3969348669052124 + ], + "plug-in-mle-from-test": [ + 0.585628867149353, + 0.5609904527664185, + 0.7160871028900146, + 0.8923696875572205, + 1.0277496576309204, + 0.6424721479415894, + 0.7293932437896729, + 0.8908576965332031, + 0.9537457227706909, + 0.7457079887390137, + 0.9122135043144226, + 0.9205213189125061, + 0.8983917832374573, + 0.660301685333252, + 0.8325714468955994, + 1.0371580123901367, + 0.7644348740577698, + 0.7143505811691284, + 0.8608052730560303, + 1.010784387588501, + 0.8353557586669922, + 0.6424196362495422, + 0.6975994110107422, + 0.7764070630073547, + 0.8729498386383057 + ] +} \ No newline at end of file From eb10112fe20defac1462a52c6443c5cf19c1b19f Mon Sep 17 00:00:00 2001 From: Sam Witty Date: Sun, 28 Jan 2024 19:52:15 -0500 Subject: [PATCH 16/26] ran experiment more --- .../causal_glm_performance_vs_estimator.png | Bin 74895 -> 73681 bytes .../figures/double_convergence_causal_glm.png | Bin 31480 -> 33425 bytes .../one_step_convergence_causal_glm.png | Bin 31563 -> 33991 bytes .../figures/tmle_convergence_causal_glm.png | Bin 27155 -> 29159 bytes .../notebooks/quality_vs_estimators.ipynb | 2 +- .../robust_paper/results/ate_causal_glm.json | 216 +++++++++++++++++- 6 files changed, 209 insertions(+), 9 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png b/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png index 3debf29817851b69ab2a1e016e91ec0fccfb5b5a..a4c178847a8aafc056d5b42c11a585b974a9db3a 100644 GIT binary patch literal 73681 zcmce;1yq*%x-a^HqSA_hC?JTSASET8B9elLbf+p z*1lu!d-l2ao-@Y1YYfMl)A*_sY`H&QZt4041YiXJuw-XJ)KR>0n@EYiw!3#>C0Q%0T(X&d$n~ zhnd;@|NH=xrHv6YoyLMEd=pG7aTQw>3SS5LgZ51@!x)8zLP>}UD>%ikCpan-4h?xt zr8~-r3O~!%ewLs5hLHYF<;&C_(SEnDF@}C(fju$xG4&Q-9t`VxIJ_llAXX#b7`*)K zzAhRmWi@l<6vb}_1_p7hR`(>M#Mo+Eqs8`Ln~B`7QxvlXu3bj=7NWdcNkq^H|Mj)sbpOBmumUZHT#jORNQjqe z&R{9+zkc>*qA09%)Pn~PdVgr}65PD`mr*j3l7;1luZXuu%Jr@Cf1V=pBYXzy-w)>( z{J;B)i~W018t&h}|Mt%xJ9qzk*RM%l)6>(#!NryOd*`23@WJv^%~{-;YpSoWUsfZ8 z_4_qEGBUHg{81!@HgXU9pS65MTd=ECXea|aA-VBEgz|a>xk+(cS{iktfIF5~c=)SK z_2;x+$l{~5G>CdhYM<9}_=^&rvQeE~5C{k0atZOr!jGm8rd9;e|0ogN_RG^12JD z(Y=$|Lj=dtiKgiP*{W|pua)Ks$#K(dbd~(4=cc;y&z3H{fA{Wkg~NtldOFQR*~I$c z;WE*#t&NS2Je}V$Ps~Scmmbj2?2K8~vRI6Bp$yw&gq@tYYEO5iQgTU&iN$g?t9Qqz zQlH}wW~-#W60@|l{Pp{{{asF@PBKmBg-E`xN}bSLW>mtoDgZ5MF{vXqA6!lR6|=hoRMgaL|E|79;o#tK+00){eI7i>vy}NNLtI?ES!uA!X|j6g`)hx+OINl# zIH$H7ZfOy&-0|_b|E~)YLF%wE`mEGq!s&4Po?5BJKo6wD7(Tzk!q8<>;6p^Z7{tId7Eb`O(9n;@-Z#oj-r{7g{4#=isRt+S>eSq@wa{79?Tgt!-`Z zGcW|)W;1;3aq84D?s~Af(@1LB4Hpx(y#6YMn~;Kn!p_u3f@#=SGA=6=1w|4S`PSUr z@1wmX=l%6qLk1ce-?^sX=iP3; zDmMj=uTfF`VT=_@_Kkc!+4k|qsIBQ>yQ{^Ei4>2sswIalO}BE#&GApZz8J7t8L~+N zu&#ynYl=DCZBdM=Pe0(TPgF(+3!cYxeC90lyzoE;k#YYzJ=%-qb7nC}ct*z;KX7A?iZf$2b-yX|W?zhv}(12W%-rnBf zGVA+mL%B=mXD3w$ldcV(_a(i;AIj?L>AkJ2bPWyK>Q1>d@cs4Na!+bp{>_Ikrq|b{ zQ^f)$C8ZPoEcd)cR$!ImGEz>k8e`eItGL&P(F)XP1@6|I;PKe|@H+mXPZE-=d-*65 z9^n@3VtadgUSZ+ywl-`}Pfz(*uQ>Lqs;Xih>!5dbb_S5J_LR<800XMFws3>J+W$ia_ zKE8eX^oK~*Rs6fuEG(f7{XEjxWMu;283#WglIcAi{{pnJ8wz1>_K&tv~+ z>cFTYj-x#2XuYs&H{Nrhl_-wgIBbi1?`eu$cdnKo9PYH+*w?OUPyh6OCSWAIRV@+z zfY0MJ=6Ud4q9awGrGF0flJy!f&TVuFiRl$g!BbR@YF*0H?M~jX-K7N#qb`xRSB|3; zDJg47&f;+~SSw{#>M1{bn%0)Gnf=12CDlb(>!evGI@I^c&C8bx&(+}wL1p;ul zO|cdnmb<+}Lar<3s5T_q=XvO zuz7tBPIy*sjij9&J2Wa-8^2(Ic1HORp5GHftvI<*-$?z zv6%S&5+x%oZL<*_&H;t32`(cE`&Wdu*{7cs7FYO3~a4l18tLjE}1UnsQPE`MmDHkDOW%&xDi>tp04 zA289cUukgvetq8G$q~cnRJokMVmt&reSfn<{#-vkUi-o$uh`diiIO5ZK!~ziMnQoP zwnYDMd*<-u#FX*ASitn|LYs4ibzz~yhAMIrfB9nH=CmLq=dqivdwW%AGe9%;mX=#6 z+`_?JEm?hiYUY~5FNRtk2_$zoE!yE$e+mvB3c$d?m|0nAfm?&^vX=h-?c2AC=+9q< zO#Fluen3f?+|F9CAR4!%@(iQlsWQ~bDyL(@#g6cj+G8w}fnNTu1`Ln$+nO$z#Ed@H zbIAs61>uF)#w##LIVCMBI}>z%qOOxSTt3#_nyjXcl>90kEAsn2Zt=cM0#D?W#|fhn z>qn;e`1mrr6#`*lVP$9<{ zN&zM=Zo0)pg=!P-9rlkD9*5q@p@3?EpDy3Bl;jx%M=&TT$hj4&mejkTdX`I<-s)DG z70zvLcFz3%@G?`Op&;$(@DORG0fB+Byp9%$d%C*1c8x4hye1tVrJwcRt}XiuPDDIBSOnkrT&D=1tt zCE%UhK3wg}sZn9ql75QTpQRK$S?yZrxHY*{p|R8+``akVy^%h*%IDK3Oz08@Jzt(x zU7Vks?sW+kSx#v#S^xfkZ!}fojzb}!ZdwI>$84%50ru6DP~x_&tu1t(!{fDFXcste zu8_JXD{w@ub$f4BNLNQEMM8`Yv#Popo{MQHS1ZGS<*qnCAK$l^saK$;^|!9wBcXd> z(;7in-WW{I`*nnAxjKp05zo?ci>i%*mITXQ(_zO(TtozYW3+f~w!#04jP&i0n3!+Q zNjC`y@)$Z$$-83eWc@kx2FFm>EAPH%vY3iu_3i7!_K?0^XmzhWp3eN_P<+7biFsH= zoz8FVAtoKnY}&N6G^mb}Z?E7!W@GCsox9*KfZzeK1v5FUqQExQ0b@UBSi2$7i_Ik^-u;(Ri74q~y^hx)kJW59fOsv@CXf zcHU{Yl~uK?M6B@R!ofS4S}wDIJ;iZ~QQh5NRshY`w#*8x_2s?3eua>5`YESN1w-He z?uNDAMt3_lJ~t4)?9*BwNv{;c@A?Fq!Qz0D)<7v2%?o(S7CX=g@DDa|p9e(*1^CXd<L`;ktswJ8Wf2TGJA<(7{wOqMYrK4-`J3p$cq| z3!i!mNQgJfFV~zYI)3>ef@YIh^g1g|u?YeF->MLf(fmR+E&>znv7vNN=gcfsu3vk^jC9lj+rzvxMsDspY-YkUKFb=q z9M3Z%U0q4hqAwwdc>cGf#wa{uDshz^2>lrHhjPM2?jbBkj{62yvQP+l`SAL;mq(^j zU8$x@nOash1qw#bWXbrh9c)PjNhoJ$>R7%J7T9?%#gmf1veh2uXu0BI5j-_Bj$Js?tgY#CoUL}SV=mmqj!)(0|#X~i$gY{55U4)b=2q68VnT(G%=j4Dn@kFVy z@yh(5JSivfg`4kuv{o7(g+_%?-&YL5&c0VRx0@OzpMAmb>1`YvBjx1e99+_IIEPkk zWoxVV3{&Zmp!MwKQp+i(eP^GyZ;zH!gO?|(c{evV|EvuK1Lg|}36Wd3b#-;UwN?Wa zA@K8OwM2g{!$u`#WfBVu3xJp}*6cOg;5hA8Z+^C{X$igmTsdF&SENo~gG);yx|F&m*W&|7{Z zrls0EV1}hOHQ0UqJx-|0rIVAVNdh!XaY7;3z{T(=1lsG#7ERLSCA76E{RlQ+lH9%9 z*`N7pq{#SYASox)t~2_jOR)m(JkU+3;6^n~P9}clv@CejL)972{Tr}S>oeQ|HlxmE zGEYsJ2_kyM>6w{O(<;?s6X&}KP@J8cTLDG`bx}vU#1HHK^C117UTBBM$0m*6m2&aK zFD?P_@9nQ?>x63Oe=4|L<+?Rd$w|s-p%3NI`B8G@4TQy@4mO45Li+!vT26)W=9Y5y zXWl1@n5?A`3!p8xUHZK-W_47M*1oW8sYUr7FOu*lziZl8+Q{suQORVp(C^?^K)4F@ zh`62F^J8c#*zFx1Qh!`%dM@3gqVj>lx($40ZzUtiq%k2O!P?%wp{eNufZaXMiv!Pw z8bxbsD=XSwYhWY3JWqhqrb|V$y6&5y7#JCg3ALX-eG2r{F0l|$Ld@sSws89=L-f9d zo-2O9BmV+~gwO3Tulqpm`jNZ4yGoumFd>|udDMJDM5vu&rt2s&9_k!^{(F1IMWT<{ zBXrU3(f=;P8P0#I>gp1vn12vUdryDXG0pKsW4 z=lJj1b07!CVko~}#>1UC*M9&K*2#(`3Y=9LQ=AqZmago?in4a+V)R<5w8Pb63dYz?i;DQv`l zdwrx(Ay+d2+M#I&uv2oVLvUg~hy~v1DYrEuX4aIJlM7L-ijI!H1!~04k&#%y!3c_H z(x`Zsl9KXZvwd)oSV~H&#B9VTHT8a>QP+~V{GhmmQrp395a!pPXWX(0MY3G z{#wd3(cfPxafx=>eGPb%*3#jgF8wVAWudjfoZ?3PtglQ|_wVmbXk9-(*y@7Ej0K9g z)RolK-i`zM%+jNB=a=2CASm&`DSlQV_DmGfzs%0odei?D*co7K|3nj)M922;Eyq*A z(zXsCiF@KZT0ArJG*eW!WEI=T#!uuP-Fg?xYQ!C4;szxl1I}$YOmM(r8ee}-s3PF% zH{k#hA2(Dn9^y=%C7-!GUe0E_)Y(zoH9D%g!~y~cC=y;qU5UmFej`T!O!Es0bVrMf zvo$KCU^iLJh9C3s^E+=84{u$T6_`(*H$ho6B5EJ2oB(<4GZ-OALf zI3z!T+LQyM1j39ohXx)k;f1c|X4)OIz1;vSH0;YZr&`8ko(7}EZ;$_`Yb|B`GMJvS{-aoh#1_Du6K`q!eKhd z;2@76-#4R0bchb{_;H@{=F~5^c5Egf06rLcgb%<>uDIpy#X}?hh${u8Bn7POlHNas zGF?ApWfc*MW(VL57fVTcO_s>G)@I*K6403af_0h4crg1mlp?o_vjgYzZP|FPP-w@y zpr~CZAovBX@DRw8@njWuVqzjjMbNXaMCU-l>zkU`pnO0tK7#*@2@lW36>~7}+ zT`?uA16gd2o9e*C+B1?oTA{a;d0q&(?yu|grb)1y4h9Tqy5gc>eL55Q+cw5Z;pgB< zMKRoa{5Yhjs0h85v!Q$o5PX6ZteYT%kQGJk{oUFdN*@|rQ_a~(m!lLg8rxpEZuAn5)0RCp)1w`8a;Kw^| z8&TDz1r`aZw~kZ3vI3zvVU({kjuuHX7pI2vwN2?co}$sEpr%iqZ(OCNxS2EYhy+mgTauvzGx~^kv9;{-9U*3Md6pe4hCeTW?MBgL>5g_xtGlWXl

&s`nAP=Gt{Nbz-;tDF~5?qz`B*EHdRwb@}qK}7% zPQ_VSSv&#)np0mCFroJJd)<+HNKL)07T>FL=2Cp8--J2Wr$gz{!0KGnz-q9dVwT`( zp4E_iX1~d;TPrS=4n&H*=sjimcE|1~c3T(gY{s6cVkY*gs)J?nnToP8lp9$ZYB@nc zodo5EGRCLvvGO-m3zEldDDR zS^+L!iv;I@!h9P&J<2Fz`Z%_nTbu0eN=K^OL&fE$?0QjPV6`GY*+fq$pGO|yM|ybf zo_XnG%00WSMAwUrv9NUf={DwjC%?;6?qzG@%EXCFP$ap9(G;9+tJh!V1G?e+_{kGP zs6HS<3Qv6@^aI?wyFX@mtIb5E=Q>t$vdJqsjO`|hbcPvvC2ES5cbMBdO)4jEeWu^y zQ*-VPX}y(9KjE{XFY`jtDd%1ewN)7{y{eJk@>TXjze46<@elN##l`KtuHOCQzhOpX z1%y8fjieOR;Rj3Hequ%fh|qaZmcf#Y+vYMT7VmLMzgoU*CA@Ko_FF}LLtX8Kz2frUemAM^pL{Rw7|f$@r_}$Bo8dl3tY7lp-xspa$d;{*RFJYdu#mVX zEi;g4^yZq4;T-Tk2YM6xbFAcXbCZ5rE8DgU^}Y<#uAS|WFu^aWG@J$h5JLeVh6)&?E{El9W z>Tp%L!oxhh=f-@`of-dOdvc;Ua1OpGN?E%kSDsI$OUd7#aQW-!a6Va`UNmadrQLje z!+mg2-gn`rki<*4@^6Nk#XL5r?r>=Dum4Ku>U^gZ&t@M_n#FI;JZACF<#uKoytuUg7i|E?W zosJ3=q*W1{kcbq>&G!d3f*nzewvO}4wReALMZ0BSn9=`LpPkii3CoVDy`>c~Xw)UC z_)GUXAvFz=nzBBf32R%FgywQu+OH(Xb!FpfOA6paDj2U`%_MpH(fmO6sTB>`CmH<% z%SwmQxkr)xKXA1N86?)|m?YEF#67l!6`zQm9f$44hQ8#Zzpt`6Wie5d)k*z8lE_#r zxbwTiD-s@u2)^l0pX57CM{pSkgn?)$&w?~2JRquw8Y>;TJ5`E*ej+P>`c;}P*7Wx6 zx;AF1v90Q>EG)exW*F@~O$B-LL>G?JUiwF--N|pvgNU#)-C4vJa1At$S|h3aA6@5* z218A?)ItVmfjIMw?g|P@Mz(VP7a`m5u&~3wLs}fUM(r_mPTWtvguL_hompIrTG~bU zm~-p$#zw-DHMG9Yg9`9HwCj8@RP_KQ>p>kr(B=2n^vC-fZ6K0<3dyRCMgwzW=Imgq zsjn|QicxJAT)Qggy^jF_{p~Na22P<`h(3E3Ic%S+Roe*;8JGn$4xN5}$brN!=4r!- ze}#6s$!mPpNUCk{+Ggl<`99s(?UtJ{Y|rn6&!ObP~*f}4RG2qnU zrbmkVeDmJ9xw)NlV$Bo2c2*bX?$E@W03@^P)t>E>GS{B6K6&yaTeCV2>aAgCd<%FC zNG;-&`o=2kAu1-u3f&%j4y5;T+bv(;TkMzvfNFSpxRYjZ<&IXFl{YZnkob6Y-60DA z3s5l^jmkG(5{c-WpoCiQ-%FnBM6=&z)FA!cYkJBC9~(r$;t6|e*vZ+nH^EMCmt~|v9EvyI6pXK zT-V`mQGlS4Ub$7Z7ZlZy4{EU8#%QFHmU|?Sdte`Tain%Gc>wW_kfhOa|4Zc8p3w(C z2YRph6@ZxVp%vh6KMrcsw*US`6#f8G&L5)ZUx|ox;G*ysJoJi*H3+;o$&I+qYDU7v z9eHZ+`qu~T5_RE3Mv~+mg{hj}8mj&Fh>2Q$mpAnKwHHiu-u7!I^zXSp)p{mfl$g)$ zI!;*&J`VX)FPd|9<;wIHb1}a~pO}klSU3%HxsY7%yLa!bfXaY{ED6#Ex8o)m@Tj;m zIP`}{M=a)}?0kHD%l(-*!6E6kS=t!Re=Gd<%FO2Gi_cHY5e9m(+^jAsD~p_AU~wja zmJ=03%*@PD3@X1`Bk3{m@tZn20`v0n)S0ILM>|pO%Vz8B_I%4tJTfk?;NW0GGvFEk zZCR~n>(%$b2Z;fZ2S6o-5gVyzFoj^^R?SI}tl(J$FqXa&b2SiaUxsiM7S$($j$`bCJ+};%L7@rfB|2b?oF41HOF&wbaeT%TnG2B zXntpZfAIr_hc>{>vdT(;WfY*tr`VGN$1@rF(eY$NM*bP7d&#TT8*xRQnYU$PP{DT% zXuKn?Ou5}Re$DGBsR+V-+K%ocuZr0Vp)EtCu~5GsZ%9hoUSPg$Vs7bGcg5$O%@sz)z}zFs#5F0f3P*R z#9wY%mdNeAOAQyitc^kyv3+1^^uh|CJ3jz8+R)!0;lKNoRUStt@D9gtx$Q&5SZN9( z*8``;D@`A?b>dsMZaHj@w*ab_5Es`5w}5)#l?!4Wa&dFBfZhBakGvHsBjQqFVq&se zjFSM5hr`FdxlhjP@c3UML@bvz)kKB89zayLg9*pWU>vQjWf?G@&U~N%lKyjQDhYo3 zoe`rX>+NY_#@ooA=Ib?Koz*Bl0B!Y~OEZ>s39}e(Nr}AHRPPr9sZ5RWcbM|BIwR;# zJ6{x{^RB0**RAyRI=sNxK6f`}EG6#@f-p#zN?}=r9o=dG4%ui)dQWqxG8cC!0Z~K# zr<=}T@eQ(shHkIrcBg&%qgsy^Wt*M7t7+31FpwR4Fm5(@U#L~H%hcjvvw=V*Q=!0d zOA|~57B;ruot=1aQ^1>)=oox82GSG=Wnd(?16)TKS~M$#>iYWnAxMC?S@o|0vQI&* zd@}AZkVaOj7U5%v;~CAYb%pxY%716~GDRt-mwdfQTSKBnq|b(4ifre$wgl zHDhcm|DPDW%@o7ZU~%QQWfXXOd!qrK6n*~Ov=MRbkQe~yL{PYHK^$SAN=vQ8tPV~P z_#}PbUfu`Aq6IG1CqKVgIRX4)2M34s$?DG#y9kSmYXy1Ec`XM~FX*!rbI_^%)7C3~ z01pHI2+B=Qv8mG67YUN^-HoSe_@_KC_?2=s7u-r7{RIKA>TG|E-E24nXkQ@cdWF_= zVvM)Jk^UukaSCX;^;KtU&_|?d+R=GAZb=GS7}Ujaj2f0dF{ciE^!U1_bNbH=ZpRm_ z66pXum}HkI2NbD5D~`T9V?Jdfk>zxq6AdnqGH zcPtI_2@?}4fDH4YtRW2>gn;CF>EeZ=xAJ7 z8JAG&w=?Lo+s!jF5;8J0)YR`l9{fx<@|~{#hsNhiv74B@yu6d17w*U(h%dDQHEn?# zi)g0M>GZPIKeCeouUZ10T>j%~7=6X7j2Ec5;sDTPYuiR_ViCy>!c<^ge$%XWN!*e! z5To_-`Tk4<{oOn9F&lJoyCVHj8^`FP_^|f#foCt0%r4Hu#O#;lQV4&pZ)#~+XllG~ z|46S?R^Y^f+AwTS?%AG+b8n1_vOC-44U&zk@2kC7T)#@u?bt?)BO|>$Amw6uS1@W3 z3nMQ+obkbjp@R>{V^TRZ=d{#{{8V7n-1SB44@C?w#928%u={Ehy6$P$s&f zrZy%*ZltX(BqsKLP#L&0VyuFu%QMvkdc_AwevOuxXWedV)4)mouiDfdsNKju>+QOh zfK9$Sn3E;3ovBjz0AxlTAn=G(19I-F0_yc6B2Z^^SNk)|_YIV0ZO+XV9uZyPJ{Mg0 zfd1^I6tx06*llvfA@o$d+!9~eLQLXmBg15iiy~{+$II^hRWluwc5y$Q< z`amKwv~Ehv1Ji`#?^WC{EpT=gNl0$@ER0#QRVk()`0oSa6{@xR&`FTZ^G5;^Oj`OR%@ad5EcY&n(BbDY6su>LH; z5p{UbL?PiHobmLk1gFQo^eP@Y)eTWO8zl1s83D-JSlQbTs6xr}^Y#}0p?_TidZ9IF zgRnq|)&ZbDKVRwnX#XMwevABkcOR|UFqd2U@{$3)3)an z?xI#!kHN!d24%EiV1VfP^Cmt6NEgshQBe)a_m2Dq1F(F23LJPh=>6F$h3_H4XSFfP zahKnv9+0fl-vOm^_md|eq|sSn#z5~RprGgiG$an+x2~@4Kk@;PB3X1ESF@~s26iC` z-Ls(6zx(jP_Gs6Dm`S}EpdhzcJ58uY?nA5kIfPQ-&1Fz(Jy*a9x)XWk-drvnY+ zYG+$Tlsj6XcUNMh$#Y)br~4t_ehRgm+`9AV@0F+)ahZjr_UTnc%W7YhpyA|r_B#S( zpReGG2L>%RR@jq}{>Ek&Kba@7nYlt7K?vvaE1ZY&*$xx{l;;31Mk*aGYhu$;wu|j4 z;6GIDw$fMq>6MD-vPP6SwDKI05hPv(Mifrh4taN}r52oI5IV;Y5m&I4K3Ch!tR2kG-M}nhYqxnF)_;N#3Y*vq z;Vh(&LevvUE>h>hZtecUX5OB3-O4u~djh}0Z%_fji{>N&f@WE@a|I$S+JMzmf2`{K zes4YBd;|WbK9S#58DI}{vzB%&5;@c?Firjb<%bZon=B0tje(t=onzv3NpO;ruk?HI zfOb7K8-X)XnKh8CXgNw<4JxtAL|MLSj(lVe!=p&mjZG#E&qFKYjfL0APmS6`zSbUp z-!IF}Wj{YZ8J#OFF@JS2T2+3`X=z!^%26ydR7OGa?Y!98_HR&U!obR>Ppb|7>9&>S z5^MQ-*mie>=|bbMVEIh^qk5y1Kl)TyW$2$B2C+bx0MxzFN#vimm(u?TMLk&q+EFk*~-q zE{?n;v$%&wxssItq8rSn&#K!hF;oRN;gpj8tOxO731@Y^y1V=iC5Jnx5*y^16hKizz|s78?SNUr>R z3^G{Pu)`zh@88eNjg^dCI3IZZIG2HhO>8&w{RgS@@cB4i2t$Tf1aUxieeN#B0O=bGvdiT6{yxTgXzq z+{PC{YZdmUn(lvJyZgVIWq;G{1!Z<&)j>WIerYOpc7-e=&DYN&yl*Iw>#u3`eU-P= z1To?CM$lahS=qB@eG8^Y50!HHFidKU$4T!ZUS&{E6ieLPkY|1 zCp6cS%F=q5L>YAl0RF&2+owv~4@S{lY=+WC{U&tYJX9i*-#5EckG79ZSWru`#aV z;yy24)3;}H(Q5yqu!*N>s>exWRQ`&oGNjq@2`?W!-23x~Mc|Ck$0yBw z_g{h>Hqw`_(9v1#eJz4Zl-|~vfW1Js&C5?duPFOGcr~7?(pjz8XQ>EpVMogon2E1( zu}OdE8V)gCbc=oH2xDw z|Lpvuxl@|bF{(-W{z0i0IeAcOsOXwtJ^f%<-T4wvY+tq1GA5;u?6nHcZsnB zHW_cGvy)WX6ugI>vDK1uKjPC9^}dM762T^yIk~#dEiN_!p|Rh*_;>22!HWdh>B zRsc42lOBSfg+u~3Cn_QH_X0El1Z2Yh8U#v9OC9q!K@gcJD=%Lt2kwJ*otXIj=g*`| ztd#^UxqGSrP9wMM!Jd{0nm@$jwVFodR_$Uz7Vh`&-)|uVTi|i#0zqXzH&5S%KP!C! zAdUd6?S(6iRBnJ}60)*G?GOi;9n$h->{+uIFAIfmc*HWdzd!9|#&353G3I>ccuUTk zwy`^Z9_hS715U-LfV--sCJWo8U5ATStYq~IS=g@M?ZQjz96ElV5HtSi@9P58VJKCT4o0|a+u-UIFfD0)K*V^AL@#jw=r0e@L z*EEBEkY8H51YC$gwdf;g4|(9VLuy4zZug7zV*BlcIYmXqqr0V`KJwW)-T2-j`9BV&YzaSa2t>$*T+hOfmQB;ny9C*U+;^33n45Zi0FbqkbW+Z!0#HxIqCEk97V1B zzrkulvWke?y_Q>hMH@Z?w6s#x|6MRCXAr2MyYRU_@V5wfEv>IZomqIua$|EMyyP=y z*J`EOT_T8M6plJkKa{HW4z2oSV-T}M=M{W$c*($ql{hF!p)0XKO_~r3!=81jFx32o z={zD3Q6nf9R4~3Me`!PTbUbx*bi83USUxAX$ zLh95I*aL#J;b89dN-^iVpEztzR=06ZxxM;wkmV13uO~;1^MOp9DCE__R@NEL(?LKT zBzkW~$Mz@^T#$0Ju(IlcDhsY(T78#>0wSE8HZN$I^oK+h8qqX78tXGIhiu~*Zv5@5 zBzLeNI``?;$jPaIdtBtZdrcUvEfk#q<;n)q92TD?YH-P9DfUL|JC!LYzLa|*mzDnE zt~*1+;fx(5vY}&>@z{x*p8h*9bgWh5cEk$Vj%1J)ka-3`GX&)1IU_LA5(t0_STS<` zA+3=QItGZUu%drj1B??M>-aP_+ySQjuK@c`P*Xr$y?NtCXhu6+#``dq0EglJPz{*o z`Jfz#89Gcu0v7?m9%sir5QPW&);8Y~Uha8Ovq%DQx+rF?7hQ?`JG;BHKZ1O=yDBZ+ zZ<3IRo0%~{Y^*21e6;8jT9_VdL_X* zC<%|<4z0j}hF=OFfTsHodX*Hp>ttl@!2F6D;ncQ*q>-POrwuhETfIDNd)o?POV_Zm zFKL5;09mT(&CQ_TV0?(6iAJhq3Ohu@5X0WAKl3c4i6K*42J+RQJ(A1-A*lT8{W@6+ zq=+81Y@TCC_^*!NHZ%YG@RdhPvjMF2jr7^BnbD2)s5nRsf`gR@M-H^fMi91R*^LQq-@YB^{NK-OK>rEvfA zvZkQ=cr^?BnHg|xU^Ji=t~s~oIUgickT?*`m%$VLCuwzlz8=c6a?zU`mbGV3uHunR zS07By0o$Fe+^Xs0xqzRB9`*YVK`7>L`Awmw(Ccj)yV{l&242!p@*8{|31pC}B4!c|4Ar?GU^M zIN0lFzqh-q3kf7BP;9#O*C1Qc3MKV1q!HjkdCkv4;zA!b8Nvui0Pzloi75P=3XliT z4$8B`So8Dqhnw$_cBOzqf`WTXYemL8~r>i>!${r>%Q zDJ4t;vq=l&8>?b>QY9Cm2YN z4SS!huCXyPc=Z1Ldl>YB@tJ0P!PB3lmQ{5?R)L9q)vEPC%BI<5RV++5bR22HFcBho zz@yj0$b)dP$-r=pdu0?3-3uab1n}@kcoqx}E>QZJ)+l}=QL#9FVsu1M21Rnb$~pWN>m^-M z4wE!QA5^6uW8+1%J&*+3BDX{0;v~hzKX&SYUfK9No@jXQ=l@MuY#dkoDn_-X2DFYf z2-@OIBa=iE6~%)wW&{1t^!3vQJwRC`#Cle9yT@idAv@yfxo0IzRoQ~v;9$7V^C7#e z8jPF$t*I`Ug=9ZLfD7nxXm7^LShNgXlp)&0<$lZ##>V1=LT@)-%6+)o)L-E_EjSuCIRwmBFNO_hbx|EwFpqAD26MJOQi# zd%yt^u{aibdit=iFy$&I78un@R{6>ajY15{(?@Ux`U;I?5o1>hEn@Bz+}cc{A=){p zQebzX+0ZoV>+3`8b$gt}{iQ$}%*guxu{+!nG*-h4DlKNb*o5(-=4@F*H+ED=fm@+x z)5XqJ2K8%7xstiz_wPGLWq#sbIsEd7E_=1VuE0Wr(1gZ2uTt&-`1ueBa;Rvuk)y)IaUFTx0|r}RyH=j8X7RbI5IOgKSEk;w)4{5+*=rR zh5TY?F*8WnppU$PykwTr-k_T04q!nxmp^dUjemo-gdJ(EN^qbt+Mlh$rX=qN5){mk zb@!9Bcfv!ChKM(D8=4e$EnZdf(UM45XC^CduDd_7qrCOzBBB{0KCC4Z$ZkAxcD_V9CIKP zraA8|Lg;{anFnMzuX)A5;9w&d!v*bT56m(GSZ`4Dz!wm>INb|YlvZ>o9kMh=1&3!5=J0@$I z@%FKaiG*Y_*>`XTtDs4qPF|r~cO9+vX?H+EjJ$^h552FvA3GP=ke487@#Esy0)`9lu8yN{uyzR=jZi7+DoT- zE+=;#bWx0&p(_8v52^w3bIt!9Jw31K_xs zV!c=+x5uD@P~pbv>UY4xL#4E@eITsE0pcU{?_=VOH*QDis<2tpg1d>XPeZXH+ z`|e}C;785;%`qC2$$$TNOrW%q7*ZAc>UQ{8Z@ z-yLq&eaTDys>OLfv?wqhjdGvj3RziYYAM271Z!Ad}d_hbRMNXD%t!LcQlGP^Ole#1q}MgT>m&S{c#P7rx{wK=Kc4SA}Mr;6B@(S;=%iJ~0A z0Be&eUwCu3xkYFJ6Un(~-sKCyn!Q8%R-I~w`AG5uED9e7JuNxNG_67eS zH$WXf&o_Pk3bFM+Z+&1v^rl@ZdatOc>$e`#$r05P9B$QKzst1Z&a7~8RbD=gyeY`I z<5kwc@oN;@rMS;(lAkE4@Q`;pJi0Y~)q6!^WS7Quji`mt6j!3h{C% z3lk!24(_TJ`*2Q0RE#%_S3I*3z5L$Lu)qKJS<6tip37Cz-?Xg~!M3m<*ZHo3H1~+v zMp02et!RI{JBh(p)Zyaz5cT!z9naj^Ewude^+4HbSw^jPeibabBpI=|j? z0dE%Ica8WEE;crh5XP>Bi}S6E-H4vCG2X5@NP)+1*>jo={{)bUf;lwb!F+JUQ-Lr- z);ScK5z$J`?ws>h^l2jD;%LD-q!xG%WaHQ|fyx8O$NdRp9R}Ch_S{-pTHs|9UdZ&e z^7~ewZ*Z*Ro9*RI1|xh{P_4;N#U!lOW9BE#`^8`F%l2MoJ5k@i7ox~qbdR4U^rKWX z>RXO1M)&WxV`HUrhSr~oGOiQDKm=W=!2pwa;-5ET`*nr{x}J3))afWD;%7YKswOZhS4jai5(mk1_qH!EMNi|A=1=XsVv*#@^UjM324pD&6F?=2CEba zUcv5Mu;8xGshvs?%abRIFw662V@%1EkB6tC+bPk5i0CXXp;%EO)IF5|WhVe<)kw*c%!o)e= zS)f=(31;$vD&-d!4+Px4bH^me-p(!!#xNMv%R*o)*eoacB5d02xEmoj2J%QqTS+o|-^Jh|M3bY?hdT)E} ztAEinPyc%MSzw|T1qnu7#ndZeSTwjDZ$Y`DWxaRro;r+I!O&GNxOA|h&)>ZHW>{NV z%IV?ZA^rIYF1#QBdA$W3tfGi0By04hn-ZiN7*4~5k~sr~a)Qtm;LL=XP>3Y1VG5!r?LK}O?= zoX-*RYQm!gXD7M+KkNJ1vpPPvTjFMff8_p}_NB&7;^sw$>;bcvNJ_UdnnnjRCPp4v zNK91BQrH)1y6y4#Kl4;wj99#moAtkb`M^FR={RUbwkl}R|6?2tAOi{#=fjwDAS={OOjHZ)EpU}jx8rX^&^-p?n84Kx!NaXO*{lR< zJ>P2Pmq-iZtAF|OWw^wg6w(UwaC6)sBf_&}F;)@?h<^v-wzk08;JfpC9IrgAIoagR zaR$*9nQKSM6uUt#cOD$vKU-6^a=R_a-wK`|zMaSFXDTZDNCL|%T7ok1S$=e7*Yf4o zq_$2FJpmmgx6A#4{h;oo>D{&cI~5Ur>{NL)ksS zQGM9!!6mN8`(BIo9a|=05s7KGo7hau+b5>ArkX=qib>6B;t956Ll#}WbhrE-JPu2L z1J4LPgr1?{``Fmq5Rn2VzE^jZ;*IY7zuz8$0;B&F++y_#yH8*m=o%R{0jg>NwH58# zs|-AF+V#&*k9cyqLDP8x#TCTEo$YN(cJ>Gecpy@JhGwShn#yq-y=$|=9G zp#uIBWHyC>>A(vy;Oh-q34s^c1~RLa1i-OaIywhw^?_2rD7=Jbx2lw0=|^|k^(jw~ zAW*~fZriVm)dCPNhKr17Qy5WYNXy8$ zNk}N_THfkG121OsY5kOq8vK@`#f{aH$lvSI95Q|amRsJR_XqoknS>$(qh+|aVEuCY zr4jVu`dL;%^_TbJv3-8o?9NNT92|$>ISL}B^Yx!@f|>gmrg%_yTHLGIU>t8yAKUT2 zdoM}*f=C36@TNl%gngQunencz6@+`t-pODdK>_vk3n<0*_72&kq9XCgI^Y zu;YIDaj0cr1}(w1+)2MV{XdmZa%#`8nXBN{T#Mi*!K*I(-(JOMWi3<{c^N{~bgkS) zlt>6!xaSIlqR*wq_+7+DYKf9Xc7A`5_tf&brPZz%mPHRiSb@vf*wvMu2H!c%{YHn$ z=x4u0S$`NFZqz@~AdFzaWV^XLZuBPE+}-7P=2gy+WMtSM9Ep^I$0d~9{ISasZ^HV* zxldbla0su&ki=;lnE3`~;x@i-M3Khl)yo}yrs;^L#)CGyT8TofSBHQDdmhcq5`KbZ?6`xC;m=jF`^z$iBE$1y>Ey(O_GABxY2-PDofEr~B@> zxqwt5#Ns85@Id)1;Q`^2^Og}8o^j5F8|Cs(GhT$uRR-ndwZDH*S4T^Pl^7R`HVv=H z8o`RadZn>(_=!2DS5||;`I%A|`fFb$P=HIC!Cj%YaXdfjwx6GSkNxrzX%gs8W892vRv0J{R9q`9EU(gn~_#I}n$ zb`I7E07mP4z;tA;3CG3AeLk)gugzgyk6W)kS5BxH}ib%x(L;-kO4M9x7-D0J~6XwN%EA4_WH-w1Hl7?-8G z)6m@WeT{#jq2di28O5XZL%XkiOKgJDdcx1;3InU;!W+s(2DS!nm`GUJINf|9l$M!F zot!oxr4_7hfTkF29G!c*xLI+1qZj0m|G^vJ)^XW;`Y(KM9uYaN!FnF-+)%YxH02q8uu=886WPtwK}1T=Ya{6&L~qP{IKqvbMb) zQ&eP|Cj|7uwy!WPH8sVc{izgeZ1Lb@0q!*Qp%-kkXIKN(cd1EaP9EDch!0BK|E?jE0(Mv3?ZVFhF#ciIo7Z~Q)$4LXcMpb>z4ya3=a zsx;{WQq0E`MM;38|6J1=HWZ>7g|o!x;IkHBM?T2sc>iM&NJTK{BDmb_AmJS#AuVjd zy*)L^L+Stbw;#Z>2fVbm=Ktx6n68C+J4X=|px9YeaSAYm)@DT%m7h7+e3mMpB>B96 zv7DM`S$uVI@@5LYn^%@?=$0w9e0Y>b^qT*nJFNH%1X%Pu^if*z({==sCO4Mt_Im2p z9eYapz6+c&HB`B}rXSbe?Oy9v)6_{#eOIrzJwJ2p@O%Rv85WZU-v&tma1e)IKRgB7 zr_Ai01Z3UGobC!k-W?^#01==ZV2f4Yn*cXD8xU>|g}ftV=>bON8h^C6m;ov+5YjJO zToD@{8To#2um&wZ;3tBaSy}MKlE|E_rv%&QK(5rN7f3((Zu7NVB#n<5S}+@zg!k z5Ud1>M)*FZ&rw$%^+z0Vu3Va7pP6c`)^gJ1bf$5prlWpeV20(U$>wSj$^8)5i{v^@ zrwFg>s@q!J%|f{oS=A@VenEZQM%O~KvP!Kcxt62K=H<32fWT$$x1Rp?|5@&7e7opR z7ERVfrLu7R4_x%BYDnwvsk$oM&i6f~Qc?Hd)>SUl)m?lo8vjD%CNRTKX*HaDo$>?& zHlpEX0`#mOBoh#=Wl@4YlT$Cl$HU_^;IT3UZQxa7<5};LKWR8=I~}(ebt~#(K>b@-^Lj8)H9Le^F$_~c( z_~DzWisY$+5Wguo5rA?ssbY#GP(aRfE@^xgES78o&u3ga$y6ddE(Hc$__2g*Ufs4X zCR5|7={154h1Ly`j}#x1CQ*b()Zh@OZ-`M?0Xdv~vp+5EL3%^^3kc7AUncFwX!5iY?~vJ4 zO|?QD?TGg@Wu=27VBHTw{8MXc> zl_`Y7%zs-sEA>%UyY%$O(5sb|#X@2dv>$c|cYgm=6Rz*=VT_mktE9jDL(xo`*4oP& z1^?AUOdOKcV%k7Iw{0tX^nQ;7B(J74=;u}2s9aOo9p2R~V!zH#k!BXaK^!huAd9F$ zEZz|m#K#+$m*n{DW=_=?J7|qhU#K?~KHe4PsySKNdXKsRk)+){tV6eR<}X)E73gwC z2{>bQ{*_f?Rt;q4;fuTIcOktmeX724;KN*N*%Y^%rhA#aX@$vVf?D*7skhNH2!Abj z;~o-66sM;5+)3yW4yOExJo3RKDp6lQ@;d8m(9i#s^k)6?h2*8u-RIAYOYa46NwO-v zn1249JcE3gRwG3F%KMjJj7h^I;hT$pU^Ldqpr@wCTFhouAkf)6Q)2Ry)fR2j{C8LM z$mXlF**gqs8rl^bjt0JaIr}Ef4{#oMBLTT-gr*qS?~MM`OC_gY==jU-zTQqBT>iUl_N!P#cn!YQVZYo}n7^1WoZ|-P&CDPE_J@??EU*u;AA%6#d12Kpl`W{AEcs))|*YKZfvcO2gxlU-q z0z)dufMCVPH#J$xTga24?Jzm@%Qf?JdMPP^By+=`+iX>9Yb$;6wU=I_e5ohN(iXyK z`<{q@eR-Z&W|-@1C;`VMCk4T_$?S@Cw}Q|VO(tEI{`=bF5YVyV@HHlvd^7zaysE#} z@N2fAJT|;LtmjZsi^&;Ybtv+xrKEzeqvw}2;W>uK{?akaW-Iblv1W zBD+lW+9c|I#e<&)qFNGsQe=4X?0)CggQvprAm2A{ze*2`=lb+b`{_ zZ1V0q3$z-ye?1~iBv5@yoBhmyD=_((b+!iTc+lU~fRrwG^M9hV5D_2u0U&M7xgT70 z760$qS-P0fPVmRQuOSvH0Ca%qn~=ePEBF74&f3K}^4kYO!yEL}sJx^-5Xm!DlPzhH8DVy5Xf0wSD_AlHB=d3oz(J?1(wVT)L4Oe2SCYy z{fn$*sm_ul>gM=^!H4wcrD(P1b>2DH-iKuri<|4It;fQ4X^}Ld7a8&;A9M2(4J|a( zj9AXSwv;Z@!(wJ(g9PT$4=UO^WjpoZD(27FInt^bYu1p<8NAQLD)a)@C9@8fk_;1r>uBMbx01+)pB$!r?b(C`62ghO^)tty4#+d3kXsX3qj`RP{*_7hG-FW2)pVx2Z|Z_0og;Ebs z0M8~t<#X`o93jR17#veoVn_Zf1+4EI0Ygkb0F+elpFuz)D0C3>ADe%ENi;~E9{U>H z!uvOkI6@{LicN%qu{bq+142{}rI}*JCEWo~@tsaGSrmB%5fm>2s{K|I=4V{n@xEiU2 z?4pb+L~ngHzh!&r(j{<{Wr1`9VM~$h>{0~HM36_{of!mqMhNKlHrxM3C^sy1f;f#{ z_I?gzu}K}>_gUTERtGG39+XOjPk51d*)pkj1j{=w-!$%!%Rxp9+E1!DoUqjpS9-+cA8xJEkC@idIWt zk`m2<7MLfVw}m|9G5jBV88O&)HZr)sRePL@Xxbzaj9+o{d4-Q18fP5Iss0=AcI^O*9x`; z;z|d!2a!1;rpkLqe1PUoNqi1HKm8|M6OyeV-M2&as7fb+L0TO|U;=^@AK2JA5D>7q z*eUe`iOewyLY6;6{wcKZ$QA?Qln+R8-c)+zAOWxl3|bio*8*&~iw&8Nkk4mLBxESy zQ%FCcl#zhH1Gv8d@{8H**+K2&l6&|#nsD7WTTnIYIa4J(b{ z=FQ?g?~fO>{mVa&d_-4q~ww1(#M+< ziD~f^+$`*bqdV~nX&$>wNxC6#+{N1akq|$GRNnxaL(o+$AckmwT9E6;FBb`YfMuT} z?t^lgD!UX*#qaPgkqLf{q!OyyfNh|eDS&|jaT!9|0}j>$in(&jTQ78+TwP;9jU)T_ zkQ`DB0o6D6$psb6BpB_%_}T*)yUzztP|S*o;%GcC|8LwHb^$ONfTYW2^!mX6RPQpa zzWGnk+BjuD?wwz|raV9JGqQy22_nS@DUi$}5G?Kd6S)RZOZw)CH*-fQNWl>++3uVm zvT&X-NTZM#bT}!I1PXYuRL5imz%`3a|+Oa`7TjqBShr;HN}`aW@Zl?ga^c9r zLIVqambH^AkwZCkg57x*q&OUe=)0R&KYxwht1acArcrCKy{S-W{gy1b@vc;z z6P8?GdqvswHW|q()faZh-Q2uK%WB=^lZIPT8uyinVrjE)K}85F|55f0d2xu} z{Xdm`c5hC7nEN~C=jQGJ3WS1#yi}wVK*Wdy^5MV)xP5%kk2BVT9HIt6G1mYT-+Mpq zqyNZ(z(;OtW77>+^D$(S68s5GOo7Kp$;;E$)QtOFSX_w|zut_kp~6C}5u8kg7TiG9 zYWO|fnTZ(9_4%fwGe)qbfk(1ws!K|Mg|+QqM&w(2FCcyT+xmca5o)2yEnMgwo~sfz z{>qbA)#hh)f4C*U&K<)4ZS7O(xs9FGqo?h3vnd!Pe8`WgtqIlVYm&Mem2q)?`Wnm8&-ML{cCx18?+muW1Jke&af`s=m1! z*IG?7=!9O{f_)cocZaV_EiJzQ=-ZJ(uuvcgkbJdV*^fZrih!#D7{_?XGOLA|0kD`O z;ZlCt{)-kci8Y{0&H6L9Y7b>qe&rlh#r~lf`cnpML1BU+Cq;y}3N8)~$lc|E(mUb5 z$9!9K%Ed2FjU#MyXQTdkxxtIz^Q-qkX=pLO@S4XPF^Yg05DrrtV22=y8i*hzBa~s* zt(?Q}VLZs8^l+Evm4}CJ=W$9cyX=vtI>8Oex656mlsMK|GqR8U_XLK%MK8=4P0|-y z^kVq$+Vr)jK1VIx@HQQrG`K?co?K8&w6t357GR^9G*4How&Zv#%uKRP(zlcUstk8c zVkkr~aSc*vVa90s9c)w6P$#7)Df`CwV?KoA6OLsYxIO~=j9OY+09aD0s##vP0+j#( zeQeqRZWAKO`@cATH%t#k`}=W0ga>;**Y!52-5`8mf|Cz$$In$wA6KEoa|CJ*LTMra ztiH@m*TVX>wI0mY4&fiVXCA>!H1sbN|b7{kcq~#4RdQjA%X5L^fqy_@XA{`>w=^$wFmyzU;Zon%Lmy$ zkWWY%eLMI&4v^f4LlfAsL@?E?ZEcw{o~$=F0bLKCgfZBK5VXFDbRFPHf^QnJ`a??K zRY0!P*h|`l$oF5BDHEnhaDHYYnrOSe+0+3b%&$=`VL!;cubx z#=CfN%C6(`qt&GVMa4&>YyhpL=C~_0^>ce zN&@`~4D1*dc?qio(UC*&pEpFdBmDqC*FC`EK(c4v=)FUr4WQ%#mCHF0WgKHi%)l}t z7q=N;jMsgtZ+9fLw%&X5&DojBqVvI^JdQ4kn&(t(?!Z@%gMVS$~aS8iz556SQQpyl}Ne9IY>F-VmUvRHdiv4d22 z0z5!Kcb-`omuZ5KoLbNZ8|jtcR)ahCDe|5Go<0Cx*Vf5N6-X}#M%I5*V+!O!_r$~{ zsFpv1Zw$!ue>h&{IIGp3=xpj%V7Wm>5Ud+AOq@;52Mo`Mi%E0-I!xKUtYb7Oa%iZrb@Y68x zy-~5TZ>Oiz;Pp8ri$VL61zLM3vsaMX3=X6<UF8 zsrZqJ21sxa#L_E6)c`WXwITkFVd1Rae@P_>N6EPsDJ%*6p3VOR$18(e2BUmP)jHWh zoh;%t>kI4jOP~g)*F%0f!xtyl+CZ*q(5aFaW%Hq zLzj=2vAEugN$TmLjT1rVd~{pjB!G_U^H~4K54rcv8>Poc^6_uCajT$6q zpxri+U6;&|DHE_6{uF9`m=oqB-vvGuCRoovL|Va_3y5xsk+ll}SBfy!Hko?Zm&VGS2@cCO_R!;Hc$&}{b>)ZjhUqNA@2#-ZTVMf#ZyWqLK*6zq& zjo8?g{%tF!mZ*F-S%vb++d2mF1CaGQ3@0iSK@Z<3aUK1~w7hL_Hi(Jzj50^x9NFYS zU6-(0z7ZaVQq@eAsuSN4ub6)2UsW&dOjg|2v%mf)upo+HdFwqJqu0RlO5w79t-(#^ zAA^*C3G@e|)e~-(7$p@BGD5YJs9wFk+hRU5m$@(w5I>KW_dU`|%}N0=A(axNOKB7( zxLepIUEtGs1=6B-J!f^61$W5$=H8 z&mCvUOK%(;K+sAG8sM@t^<9fwHGEDdI3!Pa*!E|5xtchk06o z={yakFCd=;{NszikE9?`p3vT)Ru^@R9M<(EzU5%2iDx)))9~FHc5&7{yUztfd_Zlr zx6suS{X0zwS557eXMTOv(BH*M8lKz4!VY1%SC%C2l5)!Z^7J1bX4OJ}Wh^LqQX-Q` zCcl6p4gBd+sBVczpl#to52pyA*MQgAl?e+7UhnFwx4OkW_yD@=uf0nwGKQ_++uK{D z2`E@gIFeGUTQ{;n{}}l1rzfxycgG0WFDS;Ic}cRjxcSl35%>M`uBDbl`b%AFvggB0($HQ*W;`fZ%|`lTYPGPLYnDT?kp z^+|=78XX$tWE5ItDOwXp!-ihw&D^O7?qcSk*DiJ$9xjj z#UV9%t0LvFLi|E5UeqOA6Iy_KVf~|CU2kNS(W-rwCU*4F;)tJ}EwcXN-a>%)162cb z>MJrrZ2L!0YExdF$82`cYKaa>QB%WcdH){91f3?Q6b5*jbjVzL%ywPauvuPjaBitJ zV*|6p8^^}>AlLFT)Z8KQ8;Xe>8P#8m3)EF@%$RAmF9Mh>!e?NvZ?1W9$XJZt$JR&d z+Up$rtMH+a`lMQ&gRkL_bPqoEKk@F|qFY;xO&CI=>Gkp6JxMI+@0+jvpxfIEkv+fI zp8#25$@T)jKWo`|eC+82g8T1p$}^jv+iJXua_YQiYfn}`$>GC#`jf%R6}Gaeq>`B2 zH}x*{wWwdYy`e^ms@})SP$57iYa2_(09b<>DLVwx3=a|Gs0}$Sfj7$?F|mPlgM>tL zHR7w)xj8x61duQ0K9;IU;K-Z-^wiYwJt$~e|(lV1@7Hz^H^c2r%kFlnp1U( zX@qbG(O1z63JWQeqix)*Dx=G>Iw01z*QJwf!=!Z~iOH+2e`LL|4s)m40KLjq(s zi_V*OHaHc^iu)$!@rkfus9_8zWIp^o#c&dE(w*QpBi_Lfb-f@s zn63C+yim6y>i6c=e^yQ&o=pK~{_VZU3y}uGDV0;=A387hUCyM*DnwUAX-CgIzKbcJ z_AL0m1|BvJTPx;VTg%z{c-OZM1>ftC&Gbt&2OnP`^i93A4|`^IH<cUfxJf6DyETu?oNcBCZR_(QhHXBdtCpFKwx|?TE zU?7e0S52xe;0_V_=ur;9zS#$#Eu^HRVt6VFw$X3?*uy9vu6}}o-Loqapn|IF>o~U@ zG<2TATbEf05Ymd^laZm$reL`%+e&zYmuNKqF_k6f?-O;@Nx)vmy6D}73#5T3F_s(U zJjIU1-y*4@Zw}|>?doN_c8T>zHYeUKvK&I$$Ldwxy4Ga_XH_1S?H1E#?;(9) zoT~_YbPc&K`m;5B)4LUVMIJ;@qN=E?6F?=$C42&;`<**?It5Pk%`YK_X($XJvD_sd z_rirqK>e6=-*%Hb%1n;HZ0e|ZU^gC6Ud*Rl-h07)jiCUuIt;| zE9oA8*}*rBgN--ePG=Y-F%1DKmS5NP=mGUnPDpJW(qn$tP9ZiG`MkKdkBr~ZgD^(Z zD(Z0D%HF1gE7!);;|W~cS*gVtB?2++kf)YS|(Q49@ zSVI?&m_$w(u92qVAeVY`aj|N{H@P1rm;3$9y6w^r)_SRYVt;lMnp&C~DqlEvU%U{u zlPSpN^SxZZ>%IO9xxf8a3kWTeiOOx()j>+PLDPY}m`CW6^>?`} zcbk6Bum+oCnONI6btf5HPMCMOM(kOZK{ktiHQKmt~N$m=sJJ3!zAr0GDWupMlU2}8y>1i)o| z3j}Gz7Gz2Thyqdzx-iH|Lx3ab8o+>QneiGc}f&Wox>>d`XZKU4w(Fox#9UtubO0Vzih5#(6X_jAMjmxAt7*F8SIMk4BB zDbA0UyRZ-_5mXN#4j}}L5P@c=%JhzDqyK|@0qg}8vphhLEg+2Wd2#?<1+qj-)Qm;} z@|b{#B<6mq!Ebt__8^W?lE^P`Vc~(RF?RQ?p(cEJ*_#}TnhaCSLseOMq^9wI-+5=LUde2L{`|1LW_8*Jm%fzkluGROp%1^#`nBfBMK482)BGT<$c@2Qi%HGEXWB9tjmc?# z{yG5H+_yh=Q`DHDXBxXu>uy)P|kmXm(Qg_Dwn zM|SU2)cO1OOcV1Ry%#YWCT$e?&*jjKzzGY8UQh3%Ac*4`3}6=sOA1Ho4_K@P=nAO* zdfDpE8;T~ALi5fPg3YA1XtuKN-D9AkF~up~U?omdPuu(hK#NQ755T-)Dk^-peDB6gRpp-dvp*PiqKC`9R3*x6 z`laeG9Y-E`e5n0AP!p?1`1BZc(HGi6CFm1)a$p`6_mGBVC+` z&$BBX-Y%)l~9Zx>Gp(3X21u~0kh(q!DHTY7;>>EHimo@iL7B?-ZW?(Y`)?8dsf9u73UAQ0Ab98`_fis>FMnpow z@BI{4ehfgBK!%RY&fJG41;GZ8gAfL}4PDo+1@HkbF+gfY0NFaoy@!axkijL_Pa#Pe zM!Af)Nbz4YWqgN3Y*SLrW16&zrjlnEgB`_p=QDfD{qsNi-dN|qEBu+ij@%Aa$^uJf zh3j{T(Dev6jMOUbjRN{OWsKonJTg7vWPNVtfh~0K?an=v+~AqWbxz9>i4bcit1>-H z{j`OjKVD>M5LiJ~y+zZqw)dab6t15xaHB#b2GVW0j+_8H=`>q?1-7~^fJl%-ySlPc zSi_Kx1EWU~K`iiWK@KnROCAE%v~C8BK*f9qpvs`N_T>}vSmN9>zhodpu}xr*B=WzK zDJ4kvKjhUe;SdzmJCK18d@EwY}q*PWz>I+{WI=pQV{`* ziS6AQK{qkaLVhPQP(7Yq?VU;yR#GQxpmmvk5QVq(bFL=&@(UHCmyu#0QB-ww!P9qG zA$ut9P`NECqXh^sa&>JO&8b5e5hP3W>9CyX;~ zA3F(~xh!+(O>P6&z0cO)K_;TXI?Zy!EI7`%WZ4`8JIu`+k!RO>8#y&nNn*6hNs&yc zJE|(~r;aB0X~GOzc^Osbo3x)vuj=gPO3sORRElMB(?0gN%FHDlJ)WsMwdDepd^N!*bAH z*1Pl4R(D}uI_)%;3_9`T^$ZLE2MmVJe)T#`mJ|`7s5gv~J z;h%5BdXea2v{pQu2um7N6T?VTG^>*9vOG3RPa>hq53D4J*jHi@~$^0xGz#k_rE zjt*A?mK_x|^{ucb)zFuMAmaJ1hPINa;9gYCAT2SXNXNy55Y6zf9Nj-#m`8vGbL#0YRKblhM}%PU4`qo4cbC{u0n zDY7bA0rVJ$}upWQ3*4*=5H}_lFn;%*1K28iVK}=UsL120lH@C)NpGH`|lrN z)JSlM;B^sR$(8j4#_}vB|DP75W=5!Km*%87jIdHFzAx~S!lBa?lWbGVczp{+y)MD| z=?53(C9H_>z7J39P9ID0t->hJx-PXnRMg2gS+uRqA%_8ue3en%+HYc%aszdVqq910%c+W{dR#i4 zi8i*Hy3XXGEDW*eEt(qYtOx%#Y#F4mMebez#UWx;7NBo%__2TuLl~?du<6-dgz*U? z>CbV4j8Xdfg?cPpZPh-GkZF1+DF{7wx zQ-Qr|?B)Gsj2`=`jy&rk$(R7Kz4g!X2Wt<8hHAWze!Z3!6EX|aWlgHi&7dctM^p7BqV0Z@E0Uh{&IJmh908<7v zi4%-f1G8RnfU}|G`u=+Z)2ku35X4&|LiL%ED1B|QeXcEdlj)*|0U>GLY;

u@dcIXSyk<x9&!Z~B5Izx6}scJ46vF^$rs$%({z5j2l7@8&zUXp-qVYFk2KqyV^t zMnyg+7NsXwW$~^+KT+CSgsxC;z#avE15$zjBX19sAcPs<fEu%;q{K+a_up}T-UHkz zv(0k444gli(ExNKBUvF@aspfhNazv>%%M16Ni(+W0%>q)XsC9c)WCnz*5H)~gT=mt zH@o23kNv{>^w`0z@NwNt5e=bA>3i&4!dE0+#_%DJWNxuZu(7t=Z`Y*#9ll+|BbX*J zq{bW-fx~mGq>depPt49MBa>hBf_=U<6dv8y-bU%XFRkNA6$qW+MnI>be@p^iLL+2_ z_te;<FQ9;5|5N55&@>3J!eh==mSYQQT?m<o1||LkMAk)Kr0E87<DlIiV1H0zlz=P= zsX$=voe!8<CtFR!<#bYGa0Fn+Arz3cFjxW3g8-0hLx~C+dWaDkfDC73iW2+0OCk78 z5vwQYt1@8{I5Le|y?{|nP!RorGAx4dCwpJhr|u`R-C+E3<>%j92!)3Uovp;^^jo^2 z>E?FGXwROIfL@I=BdV@OJ7=!jaqish(KM@19PE?|A^5(XZ2}Ro-G5INEnX|;Qdy42 zYHm=roIWlu<SM5rzvxBxAS=oAO+S>r$?IdPG`5iW#_V|hmKlLjbH7L;B6iRtqLw;B zuV(b|1vmV<R!LM@h+=cd0O|uaA2M#+Y3>yh6`#d2@R`VL4tT*^QV!>5e}Rd458<wm z3=`4bE)4}X3~;ZQJ7ObE<xp3`IQ;PgD6VU<LogT*Vq66watz#*2hxr33yL#saFSF@ z7n1(Nu3RaU$7+uvy#i5Fs*BUb#Y*1Wyd*Ge`*$Bv-0+CAg!Rp=!P1nzcs88ziAxZ* z{d;;#ZNBzZB5gB_RW^S4!0>>Wb*W?QJt5U2%uKBLzphSJcr;@H=P#yRpcqCD$ibU! z7RV?JkYZ7p<JSwMcZUK%Cqls3xubu(zq1Pk^#Yj!`GK?w8cl}GN&%4?)ZAEMz(rnS zV1V%4+j#`b$Zh~iu)$Sh_UHHS)?lWV3i+K11mERt835pX6Y{0+89lmzf2a+Y8sJ-q z0|Q<>(yw#O;~1h~kUC<E;QjgKBYCRrpgdtS5gjX**-$#;tk_s}lU>u92#xRseaTj# z3_T749N(P_lK!e*VL=9sOCQ*9?z|)2uXh<+&u&$uFChN)DVbK2qlClS-A3z^vV>5) zw=UV`8zHvVtXwJsgLd(B)CGYlpRxO~Q=Vx~^eBGr1x*y>t=s=T+@hwoQ<CILQ-}2m z8P{FInv2=AkiP<{BS5Im#1YZoNCZx=&bFWUW6)03%@2>kuQlrBBW?N#=fQ4j4CW0m zOJ5+SH!0RE1#$_dJN!W>s_Swk+Ip?79(o6+5g1h^H$EnLd~(1RYjpa{)F&@TQe~>a zl{!0n@~~m{klB$hPbLBeq{yD9S^^6C%FlJKg<MEz*-Z0Z89wOuIHZ{{Y?GMqJByB0 z{v7SfQgG@PIjFwhYz+mCa48(U`+QakSPDSHsH$p=bDz1{TSpisU(d_TTg1#vN;0HV z`6b7D>$Sm}>0m;1f`1$^A{UlH=f4U^@ytRKh`T=CT_fVP{}I@n`cP2>23f;ujf;w6 z1W_IE>9PS|;RJsN{O&Qha5WSt<n7nC4V|yhRb&iZl_h3kLeogMq*`Y1S-ItiY`)IF ziXwVU(%+^`B&}MGWo~}FC#E{Y^Xzmpzrf(Lhna<PbE}3cIeC<NW&Gd_&BHldslTV5 z+jg&aUPx%qXiMdtQ8%S|D6**tzV7{kd4<EV!pIlMTKHs)qK2n+9<DALH_b51T?T|Q z6BDOJzrsnDy$4sk4Wvv$%q0>e><|n+BBaPjW)wo6K`zjwt8g`O<Oe@%;D;IY;O`2D zW6C90gR$&gs`}-9$p4y$*#_aCK@I6`P+i1CD)mG%ptvE=qH||f1LBN<r!L;x&6{$B zqUZRDENIFnE89%YQJPwH3!hC*<ZeDNpfJ;^_u#EcNh$fMPgrg+Vmp763$+ZI7P2(@ zh}^7O;Y%H9_$`0UgI%O(wt0r~mZ;`F&NF}k3Gw=YgjO7)w01T~IMG}HQue(9wH}<T zZ^7d^_wTrls!@|SaJQ3QxiSLq78q5h&A@#72s~nN%<xW0L#n|Si`B#RE>gT&{oVd^ zu&<v@eJ{BJMFzBt2U6N#y4hb*0BTM4(#30>)GlD^QeO<(#&C1wn9jfd(~w%qgm$PJ z_3>T(+4Rn*s}-;PhAcb(d|5J`G`m8-{84&<DCa}Kt;neg4hW46#pPGJClq!?N+v*? z|3|8EBaS<kmC1YSOzO}6rbZ5cj0*@0u_$Fdqr`Org)JaMnQ#BR{9NZU&kcihkm(8F z!qTZ<Jp}$*4JdSzMmHKz5ch(J0m{k8pn}_c{Sn?QL<(X6Yz{*t2M1$-z=}rZw86*F zdhqVx-8G+PxI8Kc;>a)K9Y);DZC|mu1REHUjlwxpui9TR?E;$Y{#yH?JV~+tw)}c( z?H5%wHK*FfkX)L8Yf8%MgM^)LLiSf<gD$Z!$-CIkw25bCsiiq<YB2%b_KS!*%&er_ zah4BEvoEMle%LLFQVU<$o(kW;LoH$8O}G>7@j=cbRrf7(wms47aI=wDpF;cFVyMPH zT&jQg?d&*#E=dd=C7a{T3Wu&sI<DFPu0xg*qMkGsQm|)|5n%u}NHboup--Vm0qEB5 ziNU1Tq8q#N8jPGw>Zih%ZiEG3!i0s(gczXWq=S!1$;ruG*2&Ky2P_N#d^n&s;aumd zrhf23PFHuPF^?yVkuoIRhf^XTJykZ0ZfCa0sQk{4AKamXSMB_;1OJ$q<;(EeH&8H_ zwX-^ZFhirXG#Q1;X&rt(O(F}*?cW@okkc6q3A7#?k<JOd%YeG)bFYogU_>-<=J||7 zzq_V;lzIZQD4P+C5tYBhm4vqsmM}s1L?b)E3LwKX5VceC4Ty1OazxS5K1k8evXtN^ z=<Y6F$BDqy&P(mHKk^)`b6gtWIT>$j6AgT6%wIuVP+sn<-_@1OrBCuMnz6z4?GV3$ zU(u-4#rDw@+4IUt0vM2U74`NhQUBcdjeM3D4BqrswL)3-arNAr3{-gP572>!p@0z+ zreg4rQV_I!$!l*fj!0tyi9@|J3tMQ}wCQCzQ;duA4Y6@k1}~bN<VPUf)1VQPWfsG2 zmKZ3jzC#mYWoR?h!e73@Y&y$BNuBNR$IxV<3r`NZ@)b*A(&F!{{7%0-?b&2R^3I=~ zvGDL!Y&@+g6in|!tEyYPP%VQ-`er#-7^J#!#_JQl5c1|<Jo<R;K*KY|=KI!^TFf~i zNd|YB?>6>}xQia1pNPYD=4v)tNxwC^?;m|KrQs>n+0jY`I6e39yKT8<p~7M#b!KKF zG@jq*2Lla6*1ukLnL@ldgg-0%11L(=)!bZo0ADvFxll`~i@c8s&PGC$Q50k+@IKG# z(tTa~Apdb`siT+DB?JF;K1Tc9+TcIb>!W5YDh_OX@Vyus;Gh4!9!+x9#>Q2n{p|0M zLE}x9Yo#ArHFh94Jmon<s*#39xx$CgbG4wdmKK`n4~wKHM}Z9ICmf<dE<Z~>?Qlq~ z?CO%xeXf*!psxp{TNn+-AS(~IYMRj0{ix<Cm=EiBa(|x;Z;+|?%mEn`1cxm$5%xmX z?KwEuc%~lf0lEt6xz!lvvf;96^fv*{1KKbcznk%G9AkTC1btz{G2J|?p^KL2YJ#59 zK%d)tWg+Xx2sJkA?b+E_>aSmuG2x%>>3@5cmA8*P-A}4&+x~4O_7HE-bUa{rDDqOJ zDfrE4Q*G-I5z$4kmobToq=SDr9@EL$COW(u1z%;Ykm-9_6UH(g$enLhwCLnc<DShl ztK!>#wsSXJE*6BPX9RLI0kd;;DrO`!%vJVS0U3OxFb(2~kk$hPLf3DJfrLjiS-hL| z`y!-aO<H7oTvn{P0mhgjH1|)IR0beIi(-AKP&t)3U$%2v*>mH(98PIM8bYpVtUnu{ zU+kvgy!c8qsrFF>orx|@lI&t%Ur7qKksF~EFmd61*bi)&N<VN@m^Pq>!X9BK(oJ7v zxTvOPN_73TuYAm3{R*!iKe}IAwhN_Hn3$NUe^2%1Z`Cig`g6BEikaiVyOmtWn`ZmT z@kCo*UW$KD`H9^Zz9{j9Oe__O6YbLO_dk@qTYjQJO-ub*e0F@C6fz>Uh-Q+v-<mTB z_EMxKmT9ue2wJ94x+jR&44OP!E2<H$eg85MQyw1_8AzZtFcJ_yX6=-DbR_3hw$R1) zAm*W)e_bQr`@0Rc{fr`pLg@`|Tr@+e>gB%CDGCIe!cY6fXoQ~++i{ELO1|BO9cn0T zhLV;l_EUURFB9-F(fO_90i!^oN~w;qIF5k0be6OjTZ)hTQU|D-QA&J0x09k`pRn+< z5c$>9UL!A;3kgz|Ncx_n$n{KDCH4j=|GGy<wGU|ci3Tk&G+~MfR5NlV=!rKW_jc_* zJ&{Yidj5x{YN+|_cD@WLDJnWT`6E~Vx!)G%N`FG~WxCji6!M0R;+UAQrEP9jMYB(B z_+XH&GPCE4sjFL`$dLT=p@Ss7s#U>?kwhoIFJz%!C`0WwwlvYI$rnL@`|dP)J@@%A zIwlPkQW%n=2y}*8#z<LOYG((Qc*yy6@<q*J!-qy;c3fF))<Qbu`Kw}cPpW_Z@XrDi z5Z~5z4PEzVAT=BF7|i4M|9n?LhMXZiv>eDQVdJneA9#IQTFQa+aG1gsHc6(SYl=sd zHe;WIva!aPC%;$65%%b7O05VT@s${Pu25Z#ROR>r2d(m7>Te_OY$l{GR6d^6g89c& zYy!HIdsn3&oGPQwryS+wixriu9XxM;_TG9prgMLEbO(KYD9GDyRls>Ss<9AaPtQ%h zJPmmL=54Fxx6ibmdaXEA)fUV9?Qajg_eyXFU^=H-`4mR*uU#5ou53UD2h-+Fh0kF6 zm$L<BdU`r58ykY`lQWw$V?St1)KUy`!zeX!416Zp7`5#6T+~F?oGOPP{AQ9pPHNTt zAYZJZP&8e6B^{N=4JWfL>EzZ918-o|=-bk!|Jt^--nS7N5)Nz$IK94$)tMxzGKi)R zFX#r8ZyDT0NO~~{)@%=*xDqy?m$iUN^3btEDr+~0Op)(10bOnfBQ{EyA`5C0)7cwh z!zzFK*>~ni?@y@Qv2+<2gt4*=E;ilgbpfANv<y8_{T<G)sUox&Y6lr`20y-|NaQky zOb0!$XN=kwoU4ivSGfzycBSn+IqB51N_HD5B%HPwoWFj1@}89!$E|Q+??(RSmXD{0 zhxez*#5dw=(#=X|vCn4LJ{IFwProqbd2VB?imI#aG3uq1zldL;jMZ^YJSG!cqw)83 zjF;(TqsF4@`)Ha_t+uD6U#^sSE3z3|n9-lIXf`;s;`-t;t}Ek->3h7OOlM7#a8N@* zq(gi|g9waESxHi1z_5Z8$}mpq57z^%j-U~U0R=LI9t)^YqfL{Vl<gi9UvAxffU`d$ z_Ue9nv-_7b_X$j9(I}X0T2l3MtiaV+Z+bx>aH9vmImA3oH(QKqQ6r<rZarJ1(EDqL zkMA9U-#PS#tF^_3n?^K8_B)^2uS7|O&HD0R;HQbRytMJQEvL<C`#nXNdqz_^buMcf zA3P!CQzB5<+1Xj2E!YH{uG4~U-BHTH*7jjXM`%lC7rXSM<GVFg`ncZ4PX)!=Er(GT z1TSH0e(1PGdy8>p*Yx_#3|r{NlY96C4@xvv_L_Qc$hCh=T`@C>r+vn(Wt7e}d*n2U zO=oOe>+y;GqX0%(+~CCKs~=11a#M|TG&j&2QR>x<`Hb}L^wHYUpYOsPn55kc>BY~4 zHIODM9B`rUjoPJapfE;(BZB)=3rt}4-hSg78c8X)JykQu_T(y6U}<cu$v3LbaosCr zm#RYFT#>Z7UGuEhMu<+K>9zVAiilhuHB<F4|Nevat{H>TzqQm=tH+8OH>mv*kA1(h zmAd|{K7M`tf?jXH-qBrUEL$4GB;bDT_hu%HrC=|gtlKn7$uY5}WO6W=4%E$Y_V~b# zYXTbyaZG(*Z<7i~Am5_f$|M7t%a)VJvZ=b4uZ69NsZfLJRm~-w0-an;qM*vLXj!R} z#?x6jy&eg-NC|y5EBfUa%KZh`XQv;;n&^&$Xmn7<`R49Rp)UsXxs}lphZQVmpEhm@ zw<t^#&zUd!NS{bJu8qE+%w|pFhBpTdh!(&EFbshR2{(m&47s8SWSXOG`2)xaMc`p? zZ*M1869dEKub;<cduJBB?XMBx7Zjcz9eceS2=_mGJP_9&9v@41zh%u_qbi<GsbYJz zclFoG+vfwKmmR2aGqbrHaY?fKFg{9zWc$A5*%vC)ao4*r^l>3i{wHoZz1D!^-S!0K zs~bT-{8036^j}OGSWxDCgC16D+!yOu!@%v+3wnDb=?Y3z?VKCoMiutpZTctw3i=}- z@Q}kC^1Hw09Y-!j8eIPJGcmPw)bVZP<rX{TNX}T?@mrb#tO3|q(tpJ;bW2o7>Mzqi zaT1Muvp1BXbgQ$H=Y6Upp>RFQvX#A&L+8Qjx?1lAqOjTzL~Vz|Xw-$-t8Nx6H(!f+ zI(nL&L>DTaM?O{GeCHLGY;YaEKP*hlOvwN~h{S`*S;*{eAZeA_mli<bVEM&Y0y*y? zfBZn}a#C?{_gf`nwy+4ZCB9o^B3o_kTJ58ciKD-M+MZv;NJ6CtrE$x9JW#4Kv#CIf z!Q3bq+??!5LNc)cV;lR7X5KAvW7=y4`AeMV{J{mQypmUw$1;NmghJ*T`zNQRl&D)I z8)Roz>F`2cE}KUB(oY${D8U|QuZhRDmDd>ii16CASL0L>7l?Y8X~aCU)33+`Q@cpy z^hiiwcIDs*Uifn)bgSg%=`Wk@s)rAotI!%x(<raIbDLRK7*YPbOv3QU@zi%^YB)>r z+oFoLgTt1mU%Nto;1#uD)#4aeN?U2kt&Vu7>zKExZrcV3$k~4DyDJ`eDTVg!S=3+O z>W`+qcx>hC!Wx9jp_WsF>g=KD6>68424z-*eLQkSf+O+Gei{_v9xJt!q)IUjfetMh zE|Cw$BL)4tx(eQxlf_um7qqv}ef>m2xii~8(f>H-(AOiiCa7?8b=2$nZ%&ks=>1w% zjgGW{2JY$A`2xr9UcUqx>LyKSQk-C(vD*2_ZvG9}^*ud3NFNSh?za;bwE&n+KW9{K zlQhH?Q{F!hxnZb?#xzCiPVNswx*G9J7*akLmX;XVaBki-!0$~&(MAR{NU)}#3;ANH z?9CObTYMJU+2>h1)8Ms43nteV-6P6EsiUp4stE6b2jdS+70VG$-ESs|SALt-Rw1pL z+%;Wc<4RrO;NUnaH<q{R_)*Ghci(!yt~KiMv|**2`}e)M3FaCTC^VjxAM_EG-M)Rj zb?+l@x@3)jLi9KxmDPK8eUI(g62g_(%a<B_IAScH^u2JaPvyPQfFiw>VaRRrnNZs_ zIXSKSTmSt{xpj=o#FkOYMS=-fQr>^@@VD>{d^xfFB-jQthkvLAfqB~U{)s(oqQ}oT z&rgbGcmCNy)UVBWuYdOVB-4EVLu$YSr)Za~#lJi68+k>wwJ%5aGGC|76eWTglW$+J zbH`&<-ZH;_)4?vRRg&7$;hRy_D}QfhY0>n%3bA*%0#^I*S0@#lPJ8^x4HKUGPP){$ zHSI}X&bJ&Mpsq2C>!^_ZDYac_e7M_;ZZwNyD{sy!!GT696b8*dhH1V+_y61l`Q0nA zi@Qn1&jb$pX|o>=59^-w-Vs;2gui^eg#Ajr$>VbL+h=)$xgpydI9Ee?$mEo2zGP~U zlFLJq=g8AuY*K}cS2Hu0KM`MPZgeKslk{JnXs%IO6M9^J?>VMbX@jlFTWf9=eF^co z8{^UilCEPf?cL|hXz#@yDLn~e$cdHgPmeXf+jh3bt-__omSJj!f_b?Kxw%)60cL<= zKnOOutZc{WnJOBi$uqXR*{a5V`PUx#;u39-qEa}YObjitW7i--2j49ovd7$T<<^Bm zu0W+k1sAnC`PU_U&o}riJ{i3tZC~~5L3#0vvy$XCNt9i$vTKpIVu|r5i4Y7ED$6^j zH^hl`lfRDf;9CU+Nr<@4^uWLldL8~Ws>o+OGBVEu^0SmwVrj!@<IDR`_N|@1GOJJA z74b|6DN%QLqCk1?SLbFvm_(j0atpAAF)_j7eC2eBl{LyQL&S_XdUGs=jHX=Ot(T2Q zCT_~DW__UamW-iz_T*m8!V`=;0zVi9%LP%ek!h-whk75yT4*;{I^0b*c$US60kTK1 zI)9|s;Z5WC*3QvL$!)Uk9XZp{7q68e6-=Ei5xe@X<o;IE>B-qW2(r+BR)v;#daIbY ze+Lhb^z`)TA&kGtYZ;1A^jLfjPq5Kc)PaG#3i-5$2C!-le0~<0Za8pq_8H1s>{&Rk z@xSSEZ?88T*=H?Rge!gyVtQai=A38Q3cDJ~>Lgo@h#jVb1NFJ%mouCjUaYx62`Z?; zncYv4e%q{#k+eLm0aTTbZ87rlKwC5?#-nX>93YtbDSFN<=)A1du0&w-m>%`f>2Kug zuGC8*uABZ?`57NNlMN`~zz0W=5^w~)0OZZXIBQc=Z&QZysL4r190yR*@p=!qOe<0q z7Z&R1F>VZBa;vI+<^M9SB7#iM(0BBf2$VXn8YADm`=ejT`)-_Q_X}rZWV-4368?>v z{wFC6bQL%4NWLvDd$!~TK3zz2dEe^q+3=Q&{P*<U<J=kIs<fHA!@9%{iyQ9iUwozr zw35}Y*9~KyEk7iIU~DESsT>Huq^vU1h#;VRRnu~6<p}3af{@X=VUTJd2NU)4huQN_ zJ!7suq!TO1*1j%#60jZC*Ij4BJ2?I=<g}3*_wIHoHLgkf>s|#wmE!)khL|tO_~O;p zc9whFFJWOt%feLFy~T3r;O1W2d(=TnV<fntsDfG_`<5Rng3d-QG+yLucN+af8C<s( z$*9b!JW4*T9>=fM?>7YB(oo~x8vcP_4~Y=J)`^*oDR6SNRf2=1K;6^4`Gj?YoYrNb zY5d}{<P}-s_*^ASlZ_<eb1#3c(&_BqZ-`Qw46^ZT;0b?UH>l*?#$=G?OEBDGGV^C{ zOnp<`{&Qsqt|9E@hMAY;bec3aB{hQ?e=z2p+`pg|hUqibP;6UYhbJ)+4nBL$x#Y+C zvV6_76OYgZ0rm?yxmq6*P^G^Vmpub;q-XBZ{bn1g9nH1=8#M8k_=OfWJ^@+#xS-&8 z!?heqj$iU|SQk&eN_sFzC_30M`+Io2=s9MT`S@KBl^~jMu}n;E!)fq&Sutnut>E4l zj2O};<c*QM8IAiMj{~NO;f2vdMFVCcq5k*k8v_$XHalC+W<))4>^hOMXJMWlFiMBC zf0cOe?bUa`JYuT;Kc22Ks;Vy9UO+%v8c9*gOLs{qAf3|9r5kAp0g+BYy1Tm@q@=sM z6r@9>dHa6vjrY%iKhC*l@4eRAbIvtaSlEKGQW{8o)Jy0X__zA{L|ENj2+3Ty_I?E! zffG6M+(mlDYTacD6nJA9^j7`Yh%y7_BTbD?<Q>$>>=_mpPiEE4bOLI0CmB5!$85*^ zej=rQzUIPuLq<aX<)E!Tq4t0a6wQxYS~Nvo-~5$wV~Wh=tLl{g9dC4J&i$r3GxO1N zepI*8>TB`A`tn5_p*@UPR!*pMs~>|!3bu>Vt{&R#*D-$|@bU34Pelod^nyuVRd#lp z=D#^Mvc^bHisD9fYDW4nA&r?&RI)14X0uP&7#YLhrl3F(HVG`f7Pr;s+FbUD0I2^9 zR7$^ETUC>vs|Q&cJ#;M<6~S7cC(@DA_n9lFAwiyCGti>}a~ungnre=?0Oi*RTui;@ zi3p_(nNuO&Vu<dq_!1AMRSB~hlY^IVncmbr^KCwIvD}`K+6f+{>n?r)B0F-=_Kzck zZ0BflR0e6}BxZzGFcb+u_`hQng^fVUh8qkEx@oV0`T*{l4={W)>(}Lbv`G`!=P+O+ zx4`Oh*JyEHqPy_jjZ({n+_S_v=7MtCLTBuN{X1V^au7&}T*H$5u9&NzbuQkqYQBh6 zD^$Zs_%kzMZjN7Pod7*0?7dju+h)in7+_>JQ07mr9;$>&If%2JTEeIiwQxX$9{~~G z(UD_J=pVU8xo(!@&S3a+&Op!#PkQY3@gp^$L57Fr;Iz*u*~7dnUS}L*J{>=XGXfQ^ zOZs^nS!**`v%aQ!)}<YTqwmo#P*&xWUfN~w89}ja->Q|oi-ShTBH`jbBQ}RVEt{|i zFZ2CIdxx<d@ty3>Rj%}oy{+Fl{i)YG@X^Yx>}D(uR!G{i&9?#~AkG-i>Ru@>zv&wJ zo?;b9FHIY%h1UtUY6arg^PjJxfXW305F6j^_0~a%tO65x!C>`Bg#u&H4EO7)7_=;3 zAz-M<+Fknv!W%D5n{ztfg$)={wfDWNS;yy)%|GZR8ewQJK7Y=(<l@teQ^H*7oNYR% zJ>z*#s*c>$?#bI6j)@_u?!I>T7>TKeVtcp5mUZ}KnPO!IyGV|+6dmlxOUJY9BE=7) z1Of3h13gS=>p%_<6al}$V6@KB(WCO;7IoUf_ZJ_4!3RH>9!M>1m<&i1^FOiNIfD3| zrArMTrG=10BUpAa?_~A0%Pe0LP*GK>HIeZNXv;l8!@%!tLV$AH1jkmeHeWbrV<92S zCIUx1Gs<+d&RdE#Rs-e%D0?{4vb)i%1^xwya|Lxjg3RPfA}vBV@DK$yDvd2`k4tuW zGI3yB2uf9+vljgT=7q-;^aY?&QxUa?36S+aA-|8{7~yUDyv{&MP;Ne(sYS0?qu(Ju zsOLDMiKCPk@*?ob$%#>w5}L2k&y4puT^(O5u-<H~W2)4An6Rq-+8a<rq0lrwSV73j z<>|NJZJzUDwwi|tqhV)~h=6dmAwD-|kO^QCIn+7PD;yqelsXvGpJHV~h7zcS^bSx_ zlJ6Z!v05Jl?(TU>+AC6*XtZ-#O&U~;X}SJB$LkCu?!6qRt|AW#;I^Xi$o-&_X(aa1 z=C{JseW2ZXA%)=2*GBsh{RO>b3Eo4>jz0%qYn{faX)`j{+>V&J=WNVd^{&N5stLjh z>RxfYj;VA{3YLxW1G@<97hvddn!)S|xMl$Gd;@<-dWBiUg^tF4sAGt7veH8(_(U|{ z-r9**G+yrSe%R{?EiI*@n0ZP8lT)@@ZHp!K13~@}+g0g~^tW&4J>8?zQin(Ic*0Ho zq!-bch7NG@sPnv8JNOj$3fcj`Wh$w0(c3pMm?Qo70;1j9k)Xr%B3=GSOG^treuyFT z9f$|;dYCkg&uFnh6tU5*BGJL7kUin!K2fqJJL8H_;f^<@R-h#p42uV1Z(pVMiQG^( zRSE?an;_>2giBprb-=caN!l98pv61F0Q(WvD%3jR@Fs0=LkW>EU=?#GY{kIg^#<OH zp)RTac8S-`UIDNyHwA1wCWXLvAnU-RV*SGbG>~Bc>4G;e;*yiYhl2wl7Y_h@EKN3u zI)A6*>qRU%(m(Cn9(~#ruN>4l8#ny<9<RjHZ35YT*Au+edX`u=!^Lz_wnE+8xO5?e z;N^CTHtH~qh3{z}ehv?a`&$Ud*F7@X|M-RGRk?+vo~DCr$n>=Bd{AL)`<XQKcsK?k zASiea#;_^p$q<6R40uBZ81_mE0S1>_;Lkh2X8=q@;Q(?XD7ex^vgJ<h4$#Zr{vFow z<)=md(cSxbDj(*#<-05MCT(%hy;RrZ#_}mv0FLmp27iC~kzAtb;y4B*d_v?VD(Y+Z zItxGB0CW>NnrGIDLgm?<RpwXcJsA)N2CkCj@Y&BhHguoR#w!#=I%}D9m3Ce%Q!Go5 z%KTFLz7xbLr6O%oA17)xTRH&VDG7*Fn6+y&-vyw9lMlqnXh2;COn}S*SHWdPlOPdu zR;VIjbAQ*_+gqA|jzw4l{499>I=@E=6T#llDl0NrD@|Zs!1Sjg<3{A={l@&;Hm&ZN zq@=~F?g4PVZ?Nx<q{zQ5D=j-6J1l2GvqtIwx0{PlN=Y(F(b|WBGuC)>O+u5gIw6`| zdc(Xb4x)f~P)O1wx-c7}qN1jMzOjV66#={FPM{{^1BRo3BHYMJ(El+rGn1N-AoEHL zjReEQAkd4BLCR(d2KyP@8m>Q)Su{(|&rk7ll!rU2cpA$_Gv58&jQvB4wZ>1uj^Re= z&w5*iK!h*Qc~4u`l5Tb5yein<c%GbvpdW6;`$8hcPwF))@}wR-;stZ3_E#Og@LcFt z&2mIPJU9sVt|Wis9=*G30W7H}EV#f_FqIlJd0=Ov05tvE4X~ojSCWR^C{^};Pfo5r zR&j6h@;aF}>kn9n`qw*ZA_Dui3jJ*4XX5zFDfZfL!ddhTZ=BgtDnEPkxyXFwaIb7! zE3nnENllZE3ZZ7<B2Xl}IdZz-Z*AUCgQVklqzl*Gju*I6h4oV>VV3enPgNCY5!Qz) zM@T-$@AUtMDm^IoV@82TRBA`3iq>~wVPQB!BxCLKZ3OU_A&X)RRe7-h=BaH45bGNP z8KI_DQXQ@eRQ~hB^LS?bTWBqWR3;=<+E!vI->k!HfS80qrgM2%Pt0YEa>CqhgVmlw zAUK9Cfwg;g;w8dz-OzW4l`XiFFDI4k&*4{@hC$KRQ@5O+HsO8&Ru>s=_sg4e3FxGn z_Ad+$uD@VykV%ax6eR0#Td5WI2Ivsae=?=N-@nul6qi;}t1>YF21IZEEjz)7w1K|q zYzA}y{ekJ~K>#zF07KzGf)7+Lkx(XpPaFc8|54yAAkA6I^FsPle`3-n#9#$fdc{z0 zvG!+9+fO0pQ`)4lKO9F^R%+jbmNEDAr!?;}|LpCpmCG>Fv{?Nz4pDPz@q1OT`MECW z>&27RrAtbP@ThS38r!PoXK@w9^TCl*x4)9sc{?_#?rWqiex^dA($I9GPGA<F2D)#u zW~|^-xPdIA;7Cu#-Ne)smy~o6^!8{0qW}Q%!e>JO7uP1fAJH;3rA*4vW1YjyzM+V+ z385h`5D=m%$z%uXtak~qeouV~myL?^1pbn<FefMfM3M>g`Aomw?%hdbZ1QJ+A#Z7p zPfzn_bf9j#zO!9^(}fIpqv?4u$i}JFPUxq5r%zNd*L9cqfmvG8taoy6IqOQ=`y8X8 zU1snfY~|>aHD-!t6iL1LVJWJS3Nh3F#+iMA5t1Po4$TLG7Wj~76OvRb+}B8;u_X_b zo^YEGP_-a|69_l-1(O3Pc`%D=1wCg~&3Z;gxR~dFR4vm$#Qo7*S%c+ro*nX%V}G4& zJv24r`~HMvFAu+#jau^4$-FQCN7>)lMK|!&)%hc}H%TBdYZ2V24!<^@Fc%>ffq*LN z%OATLW+H!QdjNAhj}`GP=Wd4Cn#y05Ik2oY7s5<pa@&IxAn-ma0Mf*JzAgoT9Xs&d zB9JfwQ6GmaP<3(V@S)6LZF6fF=4kyhP1ah!egAQ#$i=$qMTL>KS)BS4%QbzPLtNZ^ z_LNTv0Z3CehO#b5jG;fs$ktv+4rED+;#}%4VRYtR#twhBEwUhxp#G2$Qj%8kQL8b6 zn5URLj;3fo3%?VXNx=PbA0H&Blmr3LTC2?rbPoT$!AcS&d4bBO^Uq8)O}sTX5iYi@ zHB0~Fj;S$H(@>d_DJ@53i3T%eU{20Y34~(x0IeF}s(1JM%H=3LE&@wBfz|0Y`&!3M zJzZy@erTz3YH`Etci@+IkIna%yEEelbMff*6Gal!*G<^R_C8d;`rMufl^Z`$xVvL$ zW`vv(fu)FS98|3m-Wg>~K|yf}9RCFbZj8F4Wdt!4Aj$;p`^;742fbm@ajA0eG)Z3I zi7p_z2mQ_<cF{yc>a~s2M!;I>oGb5Y{HRo-{%~uXns#bAhwU3Yk}^Un3lLR&9!1D{ zli66+<}x~D)%LMmtAdd*t!y?Pqk#oZG7v~)X)u3-95q(^W9kJCPDsaiPzoS`;blDw z3)g;B23hZrC$Uy!Rj&VTMXevRUXCmylHeksp}|6`P_SOG&^#dsNYPT0n*KCM)G7FP z56Qj*t4XiaDhg@2N{r^zEw+JdJ~baz@wdz-;ho<7!0;#ySU1H(;o7pv+0wbV(4&du zD44zNSl(CJ)KGp2fUK&zmdk0ol6saGpX-ByZXK*&YASiH_92qU>f$14Tj<Pf9$vPQ z*c5>xCq2-lC$bouC-~fDBoJR+CH_6j)98-`C^LrPQvK!{qX6mU5#b8h0KY`~J7kHu z{ud0g$uIxI7@~3Ic$@(7n%W}!d~pBD!1XqUwfVDA{Ju0!|G?~k=7Z@>lNz9Vj2SaQ z5{WafMz2xj&}~D1{(O_w>+c`4HW1!m*1Hp}PIno)xxZL8w<M-si)tn^pcxxoUS>0J zb!JPPoDBgr`n3+y;qvl{-3+>KaGA~sUjNq-{Ga2dCsKbUNoE~L6zUaPmp1g0Z@9RJ z{^kbpR<4Rd;xqKHMUS=H5CAL&T{A&sD6+qHWbebwRG_Fj@PGasT@)4Noq`$NX%p%; z=K3w4yV7toTln9V7L7PIk4u6j0RP+m34Zz%XUr#(zs0XXe{nFhhH>1g*6Z@8IfG{X zZtaC3$~1+qdd^PI2>#dppeX=Jsgp8}_NDDuW$(~)ORGpLF$HAC#>N7#@Dn5vb?9%i zF_dm9Ym8wC4~=gG>D#-a54$xr(ld=toF&ENk8Z4Kf??r|jIztCZDf=_%bP|Q2WYW$ zAwvz;^y*I*n$^7Q*-nlZ<GO)M$K60|?t95C)h5R(T|q(mlsTSOlumGCG#V^_-ILeL z?$)WBtY)XqG#5SGy)uyo0!uZh@Z;08`nDruE%(>ry>V~n!9|3%-d*|d8STJkC@lge z4$kkQf^=wPLCheTgx@mlJOnVzT3QZAsq;9PU*|`%Ar}kZfBC+zhpdLh#*!d4+NLy? zO^QeNMMy-Pr117K8HyoZoekwXk-rf-wV4f{&yft7Z;tcGkt80f^AWhf2i(%;-mdf< zsb11Y3lvatHwrcq51q^Y`fD>@Gwo3FHh~nzqg<igCXVQ?Oy{d|t;(d=8JhiOI!ZNC zubnA>n-pa=i65c1o+wt7h?VH?HYUF4C=GA_K#`#(?(3*_Z~0ql>in0XL?9m}hudiK znccchwZDx!TJhW5M~PI3G#ea!4Us$KiF@7B^Uz4NRrpK2OtYLiX)gHvtHc$19&l@f z<bU+lVYQ`Yq?a8Tr;#SaSwU<Z8Grdg8t2FOjkzf@KfhT8f`uuq4DiT<gzB@(bV7_Q zM=Z!qE;oYmmW-pi0EXt>M)}1*OM^8mXrhMx?!IAn(9OxeN1V@NT9ttm{5G3KE?5-B zD<K{tRK>BN=3gyKtF-)#N$Q_rU!;FVJcG`wt>=tV2~D5N{6Q-w|C$g>4A9jll26XT zexJscj$!(fg)a&~<;`F*Xay#kp+cku@~~jy&~OZk-b+FLB=Rz(IgUT$l97)s9gB6n z9b?z$o0mr`#WcQwM4iU{;vqjG$Tlwa?$9#9T{VuBdvmtvgSg$A!r?|Y)s+Y8OB}Yx z9xpD7tLEFea7|%E-zWi}uR%He-FMKKfIUJX)K@swtS~D32G0~pYvDnIET1ZBFMl~m zo}=U_V*GTR+nqs~HZVJy9lAe3@;Z+w6u?i=dB*mB>8a4H<DG%go6AH?gS?Nv!ouUZ zAtb_!HS|E;JSMDj(puchMy9DuuoX&WoE&MipI5dl)%12Gl>1ZO4kON8YmC6=)aD*r z1=6w2ryyCh1i8Nu)<F-jPhR1RN(+S|_D7}I@miT&%067ZYCXr+ZGy=_6luATqd9#s zwhkL4=2$zyUB@*C9dpMpk_uI0t|enDBL%Yh#w)QDmAs-6MDs7NzI?3xxIM*^Q7z&q z0-5rZ>mOY_@=O1q5>*geP?cxDkYsvMx`2;G`p!NO54fV(ImDP4aUgu3&Y1|#=MyVY z=?*k&({-$tx)a?_4m|at-%QDz(WZH1y7%(Vk|&$MOo*e65hWzS>l{gu56C=DRs5x0 zMp}-5z#u>|N0n)3<f_1PY)5E)t6(9BAfh2sAHMn{pPu(Q28L|j*f@$9AOk)4JjN$0 zXdx*qb}F1q($#!`R{G=ULg?*oc!@b&i72%*xDNA9St4ddbf9iS1J~g=y4-i;7IH&B zil(uav;0Yz9qyZVXYHp@R=pe6KRq;IJOzZ6^0xJ`ut3U*&IX|7P8C|*AmbKX*)vis z#2>7%ig}+hMi2&7Q2hdVs0kXZYB%siNhKjWk%JS1*4NMP88IokXuOkJj1zR=)JNju zuW$w=q~bCR8+)9lIDI|`Q6|;uQ<m<=J4gJ^eb39xu~C+bb9z&{(}8kD$W`Nc|F8&q zMy}jYLff~{jD{NCv=SE-!_%VPz9$Y-0rJ)4;i~07!2`!iDt?@gg|%y0TeA;Mho7+^ zn3Fsb+s+U~Z#{<gfNJ+)vE3Uv>_jU#i)mwLC|j9>iy-@mq;F@nk^S=M$b_Dr%vzYJ zZ|=68j)gfA0Dwgn2H#G}`lc>?<OfDyd*Qu~(|tu2@nk(C_7VP#R~1%i%xP<Giv<iU zXr!^=>-;Si>HHV)blG%WX@(WCa}F+zTS!3^dw3C|nSH@nYAoj7#@8wz<jed3zb)Xg z{gi7+FT}Q+qRm}ho4Gwr!dlUUy2Wp6a`S4V69?jT_LIJ~<K7#RZg<ATqn#2IsiU$M zjUrY#vxt{LsR-Juqh^Tg^XrVP)gt9$m?Y_ZV+4Nu5I-}Fy+xft<YXI79cWV(6FP0u z>qIRdcyi*mtw$&h&erG8n``pQnc7D%^2Y~ovRbzw7&T|(4P9$7$!Rh*gCmVG?g2v~ z38yZnJBP0J#Kspf$xs2+?Vt0nbUn97(-qP$N|tbZ>weD-&J^W3iou_Y`S%g3PN2g% z20>|b1_Fh^;z;5F1?jiOzeUB(Y(Z%!1E7WLYFc}t&NM+ajPB?lI{L7~*)o`;qrR~1 zQg&U<D{g40xESufCtNi9yITb5#AAOhso4z*0mTCzy-oovLQh2_)9r;eW7UNs;Me9Y z4;xe6lWIPi|M&C%dyOL|DwrP;XkN3JK#Upv3W?rZC4mQ)HUFpRc*Y0_u7^-f|K4|) zXkgmX`rd22F7Mm34CBLfU&W>RPM<N92fIhNT$8+K+xR_ze5Ns_n`m`(O1HVo<^Pc& z14+}DtQSrJ2@|oJOcN>sFC2_J7z$-!+4I_!Pcw|}3}~uv|M_WYy%m|T?K@lo*%4|~ zYx5efBbP>*)NEJD!Ii~5-H7Mpz7L_VpqF!N921g`;SsqOd5_ZBAk@(-^GXCdlpR1% zy!n_Wa%|_F@YzKU&L2_!ROB8wQ~5y6i}s39cKYJ!Q?`BD0(t)geLSqFK*aHWJ04_E zsi5+h1Q!u1<vFsctHXp0pV?>8%F3O8I6K>4e$GW|mnI;gEjO%}v44xS78?`y?n6dC zW}%=jxFNHHsz^ZN*rsnyR3)|`mY(;6eKpW9c+3*a^!}4PqGw@N*!f8_a0h;|*6K{j z`i?27yGDnwMV(pOcD3nA$75GrrFaaB0g7S&rv=0-B}z|B{Q*534S+cs{z1|A%m^ey zRa9Ii<3)M#hsBr(e-c0Oqf+gI_wu#z9plyc?@kdd0cXpPL5y)P^orfO`WJ$9_8R!` zJp=QF<tG?D9<VxwY+g!3jp5JN_eaS#Tx$HdS)bJ6<K(!0VOeD`qdojk_qRv^mQO~= z_->}ytj%j-wkUSKcpC93Op8xPhtEfU_(IrocDgfl?p_`F`f%PJoZYFPCAdg`u-PaZ z>Z^HlmSb*e)wSPqks|~r`+8f4+2g}k83(?~VmY`|j$H?6>v^)kfhQijCISuN#mdU` z4ZX%+kEgba!7o>FakEV|kju-*%Tneu_>{dmu1>nY{C*C^D?siF$c3%bgH)T(pwNM& z6}!TAC$m6Vm?C6XWa9qv1P!7dRQBuJpD2i<+Os0BXtYcdtx#ApyH(+^%`ZH*z#&jh zoMvTP?iQE7e$oFZRJ(05CG%o`wuq=N^DQi8sb#F*Hu~GQ$5eD~>rLG}^qW$q>~lFn zeOI&R-=x2}3x29O{qY_Yy`x`ZF_;LNa_foOnJsn~q5&3ebZP{@5;&@J2(l1+-&;sS z<>4QBHUMLN3#mIW1U7DXNhI2h=MX{qiv&ZIs)@S)kr0E5iai~_2NN?6ljMcA<5(*0 zMs6PALENkbSALzJ)3GW+z%jNLswiWJx~@#?*M?YL{6+ani@O*PE>f&96d{bx*62@t z)DXteawU+YI0QkGim&q#>FEhZ&{<f>HVaBZ-2j;Ji${#z$w?p!fb|z?ZzkFxc)9CI z+;c@zd|-aU%BqZlB8NYCTn`O4II4D-b31-NvA+O-E6H-IE+7%=Rt`eA%A7*wTUyPF z4_7r4TDX$dx4#$Y_;;Juzb}H_SzM_G$WT=fI)~w*)AaeD$LrfLTacrvz7i+sOk_2? zJcUHok;Z;6?HyHMddnn4(`}OeouJWCqK*wv41sEIXDe9UBh72fGzA4>aH+=1IL*V$ zd`7&IKfd~l@=*Q|Zp$<K4p){o090ur7dL)%!t(C9H?JBOTK-eF{JggcqLVfQ1<FA6 z7ysVbS#_HfF7{hmw*w&Xbx_1Xc+o4b%gHugOHg(3Ou&VsExN|MgeVYbOFY$wWt$wb z485(&%9w=@7ZDt+rl9H>I#`|8I<SyN9D&ciX?1D6uy`3tva*OOhFYL$8{~k0<=9l4 z<ZmBu>*uvCo~5Ud(WK(xi<X;k*KOww2}hv(nZdvP$Jl3XHd7Mj^Pz2(U^JN<dF*_M zSr5P7R0tjYuh1X-1R_b7hCktGq{db5c`@cbJ)hIHuL&N?vx8teO>qoRW+w*eUvy!* z((z8a(O3JrWywXBeTdAykmqP<G{56t^OL+rM;to!=`Hl}VV^&{`@}w^leZ|~kMdS3 zm_)nTF0JT|oPK)~TBegYUin*X;o}?r-&<oJR>}nGwRMJ2nBo`!8@E$2-r-*RD~C^0 z(1G?8otpRE(_$tOrrc`DdhjEScOS6OzwFOR#5^Ohn3YvXLl^SsTzm|f6`AveV5gNr zeiC)U{w_PgQet2AAr_mQ7*+{+5G*zjmtd|AbWP#s1>!*T>@|!<R}kQL$M;;MMqN^Q zF&1(kCJ<Y}UCCHp6exq@)IiE->R&UsQ{x%FDO1DH)gAU25b`wKSC_)VN6Y+>M%TB@ z>1N7S#5Y|?ePj671C%;-Y@|E{A`1(ZRx@p5f=97WL@RQ&Ey{*lFtb!Wrd=!K`->?+ z#7nhFg-ihPk?A)%2<;H)hr2uqfU5P_VmLpN%5*l;1wf(?pygUE^}BT6L%(HVhlTDV zB4JXo)cf2a9JgC*m(RZd13hLUVYZ?vQ{V19)!PtfRR+$zYJMZkQ#DapGg>S3r_(X9 z$n6x;6kUOGt-3Y(SrxYvmCd^^nvV166bp0WbR_B2eP_V7&&*H?;&b&j99eas(g@aK zPbHTE8tCnySV*vywJ^dd>H48AVT_m9s3rJ?sxPsu`7m)Lak67_o?hUIshaRssuIB; z5MH7o&*=jMfJr(KtTG?wlAzew^hhB8lVk~lb~)Z<Ja;}|s2>=peAgzD=&~Q$5U)Uw zE^cnx)fchZE~iK5^YzXThD<H|k`c3cqgcCCWxyC98%}Rwa@K(grSy&4;T|Q+QZY@S z6s!*CUHd8%x9;=EIRfEvA~KVLEUBX*bPBOWZ&9nqH#%RdR#5T@gx&{WsA2U2HO2ai z`oL3P#<4OQVIfK~MzVL%umNb5rzu9}_ci!cP_2lln+&^uaWFZWj2fO5oIsFF%Ft&e zGMm}&tsPD!cW^@$5vkFiRdTG1_fLeVWWD`hJeGTa$b-bl$UcDjf=iX@x&Tj%*FpU2 ze7PP5aP-=rFhKY@Ywpx#5cR%f=dw2pD6fkMid6B`+)75TPk&!ueT&+|9Y^x*Eg&ud zSQ{ZLIKRK#Zn%2-{bOSj)6ue{Mu+P*ZEVET_u@$rMYEmo36Q<KI+vD86IePtF4zt$ zKG-`-j3ke@HeHP>S7q<UJ%C~HuDT0GZ+?gHjBS2fBJlE_V~KkiCj*r~`aN|Ln9ctQ zz-iSCwW|5xD{_c*Q_Z;^%H3BJ?fR#&BfT8$&5?dKBKrG-34%opXE@Rm%{J{eK407> z(|ZsOMUJ%%TNO(t&eWG0!b`r0AFkT&o#d*#03^PRH_xp4Y-)tyP*LIss~{Tb-uEEj zUV{iPNm(`j<eC4~`0~+OGB>~Ld(6uOw9Mz+)@H@+t(L&hsfcJW7&rUd^^J=|N#QfC zJywXm<u&?;7Wq7YmU{`hp}hDtPVKz6SKJH6%y&@U$5mPPZ(1<-)dLl15z{x2N%cd) zO;6C}3;s)LPB)CqtO7cge;$<bDUJt;e6yuVTC95Q3ybekcwimMuav_iX&)ZO>q_RS zPCcHUE^>kz^D29vSk6WS*B2d3DAa7?;7>|ge&Z}vr<oUHsk+VpL=e)0cmds}V4ugs zzgk6&IW>>^MfWF<cPcSbcz<R%_f0{=Eyf3O12RNkot*r>RdWD~CS_3?`lFa-?4BFE zk0M&?HGf3O^jUdOt*kI?HOQGDnL~-+g5(LRITu>!QjW?P_X6b<d6;m%qiT%m8~E-6 zGZluDk&oL@%}0Zcu}Fhy#>3x<QJ-1y@O*>~D`bDbudeADk*5iac@Bz@qm>dn^3JU& z|F)dSCqC>CtQ+z1^GgHQqXUB2O7%xlZXPuwDVmn0<BT4nu1K<`K_Kj$pPl%A+yW9I zrrCRp>?d-Bx96HWCWc#o2KUIgArm*|zXtkMQh2gyhIfW4o#=I2^0gUQSjNq^T^*Vb zI%j257TRa%*7#S;baFs-&n%F<M9fl%o3Hw1rc^Z+WMI42w%zHM#vBaNc}egn$L}a^ zGm;81GY?Duy@*W>ap$rduG-oxW1;ue^erjbTWgdhzS<#H>#nl8Y1r$ls$$YH$vFOO zjf&c;U5B{doQlP?(kpnl%6=qCBT;!<?3E#vmjVABO|DpGqXY|y*_|8Ro9;vLW^Fd^ zjhYW7?bb(6qUq$fpFER=?`E~3u3WRc>c>mRk75;mueLN>i16^wGt75U;?XjldCTs! zCyKP`p@JGHh7TdvP)QnT97XsxE;YcZXhj5WwBNqLOrMs`9x(gD#o_#6PbyWm{&%&t zrB<J?G0EK(8XjKP<?cJmPNW`(m9z^aB~{vJJ)C3WzlB9n(GOLc)zR#n9^*e&n-pr| zyYD6C<(iggY)Zw10tAKje4$p_#`BV^?t$CNM4NS1?wwc{=<-FT#p5EyBPWfGrz3h< zqz6uaJmwjlFT{Gb5U=k`Z2smLxkns9)g3F~fgj(P`}pW0Au2gK24lxtBgrE3F9=Mu z%XnHf&b**y3;@#oB8nIqAp&@)`Id&Y)DU*x3L<uk4+2ypus``LBztrz#A(#GP^fGs z`Th(q*yn39w|s-zE{ejaoEco|q584xootldu2cWf@gnF;|0S}<+Q3k&ODNt<3`Aw% z`U*)T=1|<RpxN7iAD1Y9)Lz8Wqa)a_0A&154Shei_koc0=L9tUk43b{)ec=<u&2Jr zWr7`%Ac_zkK;g=bbRPw;<>#cC8s)=2?O9}e9%GaQZK*r233D0H8OK6+M?s%NW4f2# zPB6`07sx#vQ9#XEFNssdJq)QA1y=$lV(@6SKs!Tob9OX~L+vpA&){SGYTg_IF%l>2 ztJK5fWKYlboc@qcySdUcp3zY_rEFhF$|C&<fYx-iX%OEpKQibaRQZABj030xhP$I7 zmj`IXES-l-u!#Bh5yKt}wOmqdX#-_b1qcrIli!zhmFRrq%`5=?|5dHTaGD`ZPk^5@ z35%Eq!dAM$CaPNSoX(ZsL-jj!cxbRM5Okg(J)_k?{&r*}H^mN0MuZd4iD-K2P&6Hb zmZ^HDq9P}e;d#O4^2lS0&cpN1OY>u*2vzcJ+S`n@h_)8Ns%GeNJ2Mfb<#;F95l5;m z^_pmOf;t3G8X6vt`w3!aj7kQ^O_h4<(EarN`nJY|=<CCX!*J2vE-!9=MIwD4`XT)f zW^UW>NwJEw?lI_sEiu5In>Q&9px09;SE8-9dK&~EpRmYB46K0UOZaKE9X#EFEkt#S z*8EPNOyeT`z*`xR5OBLw$skOTK@_Dh@jR-*9>-<>ofY)rm^B_*ZnTCY)xJOh_Uo$2 zDR-sOXd*Fha%*O`0%p(LJ}$Q`pbXz6UGPnULrlE8s&}Bnhfo^Zf*4HxsDTJTGWnGC z6}OH1e<Pm<wueoOONQ5P{Htb?XVYdafInRCX9qTM@!(leq}x!!Q_utwz0x_^wxF|g zt{ZKku<GQ314Pa6Q!GIb3h-8EfpGtm;Zz6|J@rkIQopk9SdQg~BuMcH8#WNl0NJwH z2%g>#vkSM~wZ1xS`g+!Rf%9R`_4OC(VF*`;4R`-;h0dP7z!%+nXg@mT2I-E0fk~iF z>d=9v=qCu*gcaw%vm`LCE<Sv`9eysQNc`$#f0}(iuR;&$_Tnj2v)|a%SgiL3%LK8o zK3`q}`RF@Ap(ZXKp5Yi}jlScrjU)(Y5t;nivYwk0O1Ke1-_!mc3G@KO8zYGcb*2D& zUs*+7>r$1y9gD4>l4-(->!3hZjeH*yssLZG#a1o3Dz4gel4hS2URJ)t>cq4+XfEVp zkizLoYTm8G;0VIr3?!uEN6i5+Eiubhr^=ureZE}Vw0<bhH1DH;`Y?oQ%6V95%=3EZ zuEFPVrPd(J)s~!5gN~jvHU;!(ypk&lCt31h0>AqpYI*FxH2+Fk;w8Rlk^7F`1C|_` zVHlBFhqDF4^eDw+Lc?vtXYYv}`l22->~S^>^S@RT2KFc&l(&S)r^|sJwr*3U&ui^$ z(TyLx!iRb+xky2|O&A(gg9Au$RKxMjx4_|~g!~%~VLCvTH)GSWm+C|ASKE4Lzu=1^ zB4x!@FhTS-N%|1<n`xlz1}!Mm)Mic7Hz$7~)5WS`q1c4So=1!o!DV%I3?KM?+V_OF zsvTNf2L$XVy(6$lF**SQf8{(^0F`_t9RM~*(Ur{-T})@OngmaLs?CkqT7JX=>*MaL zfkv}Hr1gp15OxGibdKA+yjviCz37&FpB(d+(-~?1LLgEyj1{tWd3Px?MkSzHs#-V| z62lfHWY#>^qQ+Q@jh(yJD*dC$sh6@u)%Buu2A`FlRWw|r&Gj~U5La3S9v`8?fqCsW zmxMX{IFtl&t|e;zP&ELgU5zU#5u}!=c6Cin#k7r0-t+M%B^#A!0F<SMz;qehf&u*v z7?i03*Nn$EYpay{9?}Q>y&(-nezT*IgH6W7Ij%=cmzsIi-JrC%ir+#Qe=U%IkeY2L z8lx%&;#p`=c4xB-tiwR0iksk(I~;*CN2g(ZdG?uT_+wb;mt70?W8fDT4+)GHY16;! z_Bwf6Ww80r7)B@A(R^Q7U(GUYtp7=(6c3-Asl8$w9>>s!*q%Qz)c^TLVVN(>Q9R}X zPP`FDZJX>)N;p6M!p>|kL2R_6ut^71lKE7A-)u)9mITuGT#jzf7krw|sKhnjhY5&M zbs?gHkFEiqQ=_7aXog#kfA}I~$KWOcKrQ2qw1=*4RUcy<GhD7<!S%!awrs?Hpejct z1ip&yjm@C#uRD%0+g`o^%mC@}K3qz<Kg*(bEZlj~?&A3b`QvslpPgS5y^0ZtE>i^( zPG$P6R^mjl&x6b|Gc(_I7j`O3cpQ+~2OpN}RhnVlo%lB%uJ1|b1!5sIwtKo&ff`i4 zqRwNavE%;Cl}CTR5XSX?Ka({=(=!2|zUNB`AvD;Gk2CBIQKUZ)Vef1Rs4BG_&#F{O z($itM`p@cKmZ;+52ZR=x+OD2{f=qv`@XskT{zkc!i{`v<21Vnvrv#p)m!|wAMU~n& zb-588i3hVKW~|M~&L2Q7Y8C;R8|QeIS@|ogeL_&yZG*RG4Y4oE&t`H&U*j@5i_+R^ z<VqTjaHKb%$y-~+88eNRcf}f$9Ah!ih#~Ggy9=lukqxVOI%Py09CBs8K$}N)dzm%# z(|ukkk7INkoy3t-d_|O|=owf%p;pr&an8ZaxB13I<d4K;7{UwG5LL^$@3INv3Rc#P z8j#n@n8yL&M!YUsWdL{;OxX?uprpG7&(LoZ7jsciIC2^#s6$}nvGC1_!U4)iyMwjI z@hTb9rF9hlq66I|ve&$t0L22DNPx-~QpD4&#DNnZWX|viv#|R>5Ju+<L*rAv7^{f0 zM3kD!e~I>8=BXIQ`nOjvw-6^NQsXa`6a^o@7Ov~IsMtdJRXs9)S61W2ii*}E($R*{ zDCJD7uhlU^z6HMg9pj7x@CAJ<YAX!y`dd!Y5v!i1lr?pJsB|+Np2DGL#;Ug?ybU~K zO<?JYAbWXRElPE;cR%62@IFg2OcHF^BLl6+imye=NT5_Yuu*1f)#k2RsowF+;DEjk zWo)Q`oT$bYP(oXAUx_EZ6JrFkUc(w1e0bU88G-!zLSRKqPYimiZ}ET#-ea^kZOiNK z(`TU@#-JSR67a(b;kjkQW|0O(UaiFJk3Dzo1DN1z_9R6s2VD}O@~kW<A~rS^qM~BS zm9O6EeH)rbrdcwP{Z{UcNAtiG4q<v7w_Ga2<>3MFFY0w8sUTvmEK~9u?h%J!4Gl?6 z3b_k<lC6b(X8Fv((0X?~^nb|MSu9%Sg#%DPEp_?JbNVvR-(##3u;O)k2x$W;uw<`% z5+q*SjSRKXwTyx60x}y=A@(*az#gMm)%|M5tS=nF-u~w>!9YeGx2Bd3nK_aq@N_JC zwrcl}hm;Gry=<6l^?cixOmy;!Tr`qa5e9*WQr9fs@@_4fI)95u0b=ZU>(!=RyS~h? z?A<qj_GhN%=T2TWTP*|y6&NJ2S>l!IUUzc#Y5?6~XJ<1~80{NR&qTJj2f#!BOhv*E zy3&FKBhO?yVZ2WeIC3FbHe`bU2|I-C(~`;Nq{-WlMcV*uy;!fe!DaNlx_-MqyQyiz z1Xt@X5q_2kM0L5oU<SM?VrPvOr}01g^Iv9K4(~iP{ruK9U#{|+kNwav2+2J`!0WX1 zET{s)69~#InWhbOBpK?_(KAYH*47vAIrzA(f`B-w7Rcg4t9<KJUFEGr(0sReBR`Dg zP&Q23vwzw*iOY$h!H4g%qX|g~JWx*Li>wQ}2HWPm->fK1I=qt&ACRdm4*<<BAm?vY z-!^=mw30W#ygc|+Sa7p*!n#W+q#vo@ryjovUW}CPEt8$o0m<Ekh^Lp6Mr{b2oJUOV zt;NRD7d!6etf_r~%WUOT6zED8B;beS#=NETo%FA%@;h2Fx^kGRI$Y|lPcbcC@24VO z=@3}^<!kx3iN}`7@zdU0l4d_FF=W>RUk!cSp&r6XkmiBSA%v}VLy0T!w4ir8PNPS& z^T6ig?UH@gJ4R3d(hvq_5juEZdDKU)4mN+=2blqWr10ROpS7Dx67YkwU|PfYt3rOD z*?)`ota~UI4|**AOagbvZa5WfD1rFqHBq{*#WK~bgC;xb)5yq^RZrMj-}`Cg)S4Df z_1W3wcK2`KW=U|%omXApDeCsc>x<Nog7NRTwB82^i5SX25HL{BH-cdIrRTrp`^)wG zF^I)^4UX`%e|`Tbh>WwM1tGn(19Jnj15-LWNa7jdl*+V2N}&RzECxIyDEe*9N;nz> z0U`=_2)<!u7>C7)=B0ZWtik_cvItKS_{;ftaaTGuc<s1`zzay^Oq{$ez=2$yu177) zbVvY>j<v!kX?NQqE+=*n9!Mk#rn*YCvQ_NFzwR}H_$-0p;KJ=mlJ!X0FOx&hvy{>4 zDompOC#KAYHr3z1NakO<(0D@yiI~Sh;uYR0Rip&f7QEf{X713wo`u`f^GfvYuy0hs zt7)EKg3Q;NjCXLMQzZ*7fCStLT-*wpf}XcIa@9l*Z`MUr6j3Crj)E%@w0Wh4T3BE1 z?B~*?`Nq*2hM@RA`%*EdTJ3POOi*8=Z&RS|?x8i|GoeqRCpO%=N%>R%<3Hbsc>YQq zbzqpll;!uq(U-oy$lRd>X|7?{gxuT8Nlo^=a{`DNd2$!HhQBSDs6H6xnEhI&%*$7# z3j&F%vuu3uVW(fV3X8A4z4qkLJ2b;VEp~(+flOpTcR*h1KxF*@8JQ(j@@3x2He!hq zK`2zDzICoTAWDSjHZro(%AfNgP2$YMr5mM2Uk54(zJem)E2RcxjHY_!<t!LO<=a~y zOk+4=fuIjs0pDfv>O@R>&`ZfkE((#4h<2+4go(^b5bcEYNZCngNVD)tD$uXfy~kw6 z043Z`UXZE9DFz09-78egXAN$LU%r056`t<uL-0-K_S`htHyLF>a6gkS)X^XyS*yb+ z2AZtkkG^n35EEQ(>7%?5JI8`P|H_MJ#Z_YWE!<{UUAt@ty00C0+R9#NeQvjcNJaHi zj}imYoq1KRCs7lU)En?MbGQ2A#c9@-Z!su)%tn~1?<y}Up=w_Ov3kK2X{DO{LJF-s zbf9iu3o6IPU+MAxgT>+Y-Pd9}ok#w_=O4u`l7Nxvv8a_Q!706>HVsPAg=gudaqWez zw3RDVgz6><sQHny(|14@4^)yqP7+?={mSs^aqS7dleGoKj(!^xUSEViDgs<b74iO6 z|6|<yz>o0sf4H<UNjvrAo8az+06ZN@=0s(`6$71x%mfS<$2e&MZ{MR}`b_$*H?lx< z!C1Sn-WPFppL7s>`0p@WR`$<8&QnslGQ36o{|D#4$5N*L7eX)B9~0BFSZ-ML<YdzI z8^r7?n06PCm?}S`AxDd}^`IWg?-e>|t_~xfYkMl%0zEu&JmYZt`koXE=tky6yhtur z@M`>6A6wA86(V~#qh%WO>pUi!w9Pr3pL;cKiM8)vQ-EqLf>tb{5bSzv_^wYb`alhZ z`Y?{v+h%jg9WFk5W_bCmIwn@S!yVn1Hc0zhJYC?6FE$8FYR$pwT#-~`B1f!(I+&b| z-Ps?)!f)YZp_?-@pmYP<py;|aN1|4dj9RDcJtRb#@wtzg6F?W!KL>&96~Gj6xbQ8G z99l%4q)G{YxIh_?kL<`|9c-zkL4t+K^MZl&V)<vO%ocV#xe)YhY%ng%N-gZ`3&J4q zFMXkJ%FneC9`Ep5Tjjz|e+>=}z<|D_Zwcv-mN3GT)BFBeqD?&8g$$b2<6HAGC0e2) zXRDpL&<(7hoJ=;$l0ENo26XH0*_fOJ=D`?K_*EkH%6($e&D>2x0lQR({t{L5SJYXA zSP+CSZ8kZcS-x$|c4@T%RT&zBSq5~l!DSsHG<2bkYyxSgwaml~(TPb<MZKeLKuKym z!G=|=5j1B&eB1BXL-RfNm5#Jxbl#iXAGZK3o!4Bb+}ot9*Cp7gd0$2%@hEYBy*I81 zUw>k-@zBk$9d}nLZvGDW+^xS=?&}7^+5Dm5mzTk(KEFA2a7h^csQCA?uo4!O>-El7 zhRXdO&F*y9P2Hm+(sUnGz0-;~T5DbPRFs1W<4y$^mTJ#O4}8HfkrypGu~IJ{7-9x2 z^yX{`NQr4BA=x*_ww_TK4cppNG1u@lAQwCReYA2;We}?~(5^_O{B>-Y10P?yg+|@5 zvUmdB+Iqf0ahV>!Q{|_U444h4{BvTF6lEvgO^dGCYpQxm^OI}dDYcc$BWYt(7kviA zVm{#|Y$jd%Q%4Z!;iYoEfoKS7ytDhtc6{{)UdriNJJaeVjMkK7NL1|3U@Jqn;4x{` z4++*byU~44#mlrZ)6pUEqY?B;lYLVT3oLuX_X*E#4TYH4VzTEYwN&pwHxel?(XF6c z=;3%4tObU=y<PeH59Oq@CFF|l7If>x?QFGnH-5~v*@&F~yj#`Bb3_wDY*G@T>P{I+ z={3Fg3PNX}GJH6^G`uH*N>4_Ask_w88uVG2|6NfjJSe*hd49>r*t=Y)0dfV4>l&r& zgp{VgkE6Mvr#^vrVDMa$dS0wdl3+iA>mDK@V`!Y|<<ZWBb`%HHfdF#<=b~FC=b=Bo zj{EKcpJu1giAl?OMNj*+4nkh`r@Fr-)8~bfHrh-dTotmKbzHWXHryK8hJYLuD#;LM z`o||mCa*t{{o0-^{_Fk(5>&NP>M4++O;FYSs_K{P?h$aQ{<%Zz1iBJI_nxKgy6KP- zL4ck{FVlwKUli>(t)8E@aY=YEU1>3&tlyp0wO(uUIJ)o^Roc|m5ASj#J(KN83+3+x z3=W)j%{47xcRkc>TFx}#f$ziqkupPG`y&!&<Ak)Nyp_hcK%#C@TDr?sjGUgHPbA9! zP(iRiEse<v!~@fbPfks@a-x(#*NWx^`^(suAFDrRTSt`t>GKHMo7(VFki>I2+Kt^p zdoXVKs98!w_&zFGh(y)?8NUp6g{?al7U{2x3^Dv;I=Q_!$PrAoFOot)M)>iZhw1TU zi;<DTS?(?d{L}9)FHpuIB2|If{KR2G*20`V^g<@@drFFC&vPIJp%DB*pK+r3Q}9id zZmGaji8|{+ne6PlF}0E(Jp()kQY46-IY&OgAs#lsCk-t<ro6}YwDts?PuC=;ELtH+ z=P7a2R8{hw8Dmn^f6o^YezX<)XaDaA)*6ot62Vr;A-~uB^vztc+QYIcG~kFF^{F_$ zBwRo+x+Z|rnd#v_9eg0tc8;CuM(L+em7l)Fela%&TEeX)Y2)Z-R*KK<Zru>vy|?yh z%xHdDU$?mLzZ7UY(#J{2MTNsb4reT<`3xsxA~2tMwlH1&iYeT*mi6P*G>S%d<;X|I z4`-1`!xZPWG|trMur+mSS&(XGLXX-H4mV<tTlo?%7$8yVH(_HULHl!R*m6)cI+sLo zy?@O>4cOJ9v&K!C;bepDSA&>T@0J`o@gISj=Q%XodV6Zz-*aH#=}Gct{$*AV01I4I z@Luzm>&n@Sm#72jVbWw(j;8V9AmLBug6j3!qARDUQ078I`=pTfIIvELROw$Dh%KXy zXbNus_5c)>HV%3@dpUe0NanjBX<qYhQ6F+pdMGuQ$7wo85UDRxY!<BY98mjvN295@ z`}!+jT0+R7FT(mCt8Wh3|3Y!PV_zShp3TCHkrG(Lnvf0`wP>CUq^723ilJ6@e0)W0 zueYsqf$K;Hc}~7js&5mJInf#PXgwTo(Kl~Vy{G>X?fkcxRPf?A={)J*f6_h)?|y>x zl~c&(v&1U<<&Z2IG6=LqPB2Qwrl*xVepk_=hQG<@TdbH^49dAZLnFJP;oS)$jNcx1 zh$P9A>6eG{-2N%t_!60U9P7#x0fYJSaP;!PTC^nSfl=YZ<bKI?2`bN_IHRH4KZ)V= zB-7H5%+djWU7{*ZUC(}jCX4pvTUyeHK&_~eY|z#Xl+LDSJPWl<ggBt)Mb0bert(?7 zZ<kN$iP1|>2NyVG6B-(a{R>OTyAMlK<rDy5J8E$<6(}MYl83}mENMBMQEhFo%#}Le zM16TBIUQO$u7BM`-|$yob-XXO(Tzg=3|}3Z4WPWF37u(_)Mj&N7O&VxH)nH&M}Eki z`-x<|{^Ea2KrND<`z4+wphvt=C2!rk<DRZwQP}cZoezTD6IB1~XK{K#z%UpLhnrm< z1K>$Kbp)Az6wb|sv%bUOmzuKOO9~renj_xnG^Q@y6`PY?P~g@FnLGV5;d%W!I=R@< zP^E~Ywo3`fy+wgGtUz5@LS2EV&HCT3JR(*EHnYDZc$4_5kt)zbc;M&K!4wCL0sVjX zEA7(esRU<X8zg;?dbvoKm#{`n)$fIm^q}&hCdIlq2vPUzSKOB#ka_DL=$B?d@Q+nX zWwXT(0p+tmDrT9WKF>Efil%r0m-U*l=}17}0%c9j?P%XhR~kOA;>q>V7((Kzu>cTk zaMmwq>K-z2K4?Q*z&q@?ozXl&4MQAL6pDLn|8)L>UJ`S*!eH&?27^ai)x+BN>%)+6 zt~j5)aRhR5CcHqC*G<+k6;FwWES(D%5i$^RIu{mdg&ok$x3;BL%+rxGC4Sk!vTJ}p z!`8N$G?W0i-8{_w^YPt97bbThq1U00Y;zBXi-c!oN*uqcSOO&OwTRgM8aN7D#scZF z#(y5_4ENW7bmnJpRR3ZZ!kF2JgImH%hv~vMy7aqQa-x=ymmdE6I4F{mPjQHMg3j9M zWh-Y{vFQ6(DOi`Jn%zt37%^6T3=Ru(C(Xbu1pt3v$Z3NLG=zZXjYs>811JaKmJ=}W za60htj)pZNpCWxv=Eu{&`x}4M9NZe_7ds%|mKu=Or?QZ=b#$BsreL@n2uz-z1s;i6 zFZ}PDx)rG^9vkOh(}raDfrg#86>m^GjqN(JX~Sq`*H4V%kYZ)hGCUu{0YVLQ>>QT+ zG+U=bXbG0*o@5^PQ(@mr2?$?(mth1);aaT%JHtoc;ZsBsXYXYIG1KBPX<W*ocU3N< z_f2P)fAn`64<!FDOhkbu2(PtXGl|sbE{}`$b14?9HX6SKhD+k_wS^l|mF&0KN_Ce{ zt0S^yB~Cr%lU1RC>@u{$EjfbcD8oqW1!5vq4|g6A-2>fV*)TYi)b}X(@L7>s+Km<- zgfEYf%ZT(pFTfJyLV{nM-hHZ^tUflQXVK%BdE8%A`}!)5VrYzso?DPmFd|V;_)Lj( z_isKvXnksJGujk)L1cOfXxHR69&BIgbde2M+RsRe%1<?ee$sggK%(!S^Op?rrsn23 zu$Mk{xG?NmM+V$7yc6@zWs}v#|GNwY_!K|dy~hipEPYp2FrOnnJP=_FjNhlIRPh&i z@GdqaY&{NE!|&Xp`7sgdIAYj&zm7R=#~~aXhJUT3UQ_7l*9BsA^JikS%r-pVq_%2# z-rggnqU<$u&j?yQXn}yYKwT(6e_{$yw!%ku>=4#6pODH%wz!<{GH|2??lm5WRcSHN zHU^xkhI;*9BfLqT#PTI#?s;PBYq!Eg8fs#ufmcxpZ)H8Cv=nyFr;ZWwk7v$OXO@!n zS1Vzn*hH$TuFoIohh0j}g~}EhoXYua)WxWhxX!vVk+(hrotz*B4f1xaK~6sR|N8{5 zK+Fm_O!l6s)R;_27de2#_*VQ$sV4O$m_mK@+xZZEBt?fQH~i}p&;WpBIFnTFTPT<o zM;rKhw%D{vi3Wqs*?@nM50R~U_QglZRdK}}KjoaBhcC!P;YS;w^Y!h_s(0li17^ty zGX83eKR`X;2$RU=4X*FM`nLX~l9mZ=ORbYDM6XnDK0>F^2HuV`kQyvOYLJ#Y0ghZO zdN0_<o$vavrXIec5n=7!jHWSo2&uRK4Ia42Gn40YmRg>x$oPqzTMb&U@vulItEVa( z>dn_P2VQQ8coO|MbG8PO4nVcWe9aTDaa(;4#J?2%&$4++VO?Ktqw1q`JwLJR^1tV$ zl#h(*J~8bMQHad1mNkvX)Or_GOyOR!<!Kef5Qhhj-tpw*`kX=ULz?%8H811E4kz`> zB#B?64-wAK%6#;i6N#wzmgcgvq=?vB)@-&oG@@ocV`@}DvuB>4H6YHGV*vz*mC5JP z_F9xGItNS}><dFv&GByE{A*qg=-tF=CTWQ*a9J~0d&J!({0R&UFb#N8fk0D0egEf? zxhruhKdbb=7x<lLc3p^#E*MtR&*ZI9KqFWxfBq7YjpI~a0hz|5gZ*3_iymki3XUYg zc;|d%179Q{-kJ>v!}Kx9`L?;<=?C-t?cQPD3njZd4WfO=$n!VdBQ!K3d9zS{Lt^b; zBVk7Du^<3<<8vZj4Fxgx&y2<i&0WIbNl}_06zd<e&yrLHWay;-M57sg%4c8`8<sNx z9da&e!uJH#HEL*#9>@BQ92^d3e|M6k|IWATaj7C+9K4=|#1958J#T0NLT>N5%a4bb z+`2Y;yB}Jj8f$!fs)QUwO&(ERz3-eZf=#NthMC&R2kjYrDMP|a#h&WFS8Tt|S1q-p zj}xt1u|Dtj6@gs2J~l83)(pF^4R>=sue5+55o6IX3(Ds`&5Hp9<4%%3j(z*<{5eu> zHOM=$-sTQy)7fzcMAiF3d5c$j;a-Pl1S3hZGYTPGCqsv|qU#yCjUz_H?ajOWS(wiV z>6AZI)psDaYi#Vtgz%e$Vl~UZ4=oVP^BY{ndw$_Z-JQ%_6>FvYx2;nOs^;#le{1?U z*u1U&b#+hp=S)a`GMnMtCA_~+e-AA%LDWpQtYb3^FRc6Zi!SnYXyQrMTzBSMBAnNP zv;l?7?<_45qHxI=Nqr*8asU0>Gd_)`;Z!CFc%-<dLPrNh*OMN|Kd+-1EZxu<@;4;^ z>h&sTcimSW<sYurY*~hObn{TT>XuXm)b_=JWY(VuXVbOMwzx&jeCH$%e(wdMY2lT> zMT<r<w9%lxzMCz>pTf7bl>k(@gw0B*Fe3&~Af~5l;;^z_UFv)^C{c$|KP|GE_jch0 zN?ZVX4Ga(D0{4?eHOquYn(l;X+a>hT#Q8>Cj|<->CuV#HcF<Vu{BZN(f~EaxUT^Dv zTKlSiDBGx83_!{x6c9{0q#G<ix<k5KS~@I{M!G?VmKeG_B}W>hq(QoyIeWhUKlkV2 zc%zQa@Xix^uU%`AOqa(UmC3Fbq$zencUtQ!(z&JC_OXg$%xn&{tHZU5opk2LN?AMk z(wa${Lz%7I|LbZ&P5iS<COx0_90v*`iCq>*3ek%R1_pjU+CA$R?=45Z29j}^l|ndJ zA?JjSW=0E~h+r{{sa@8CPK0Be!YlISEWilfmYT1dg0A}wvOWvt(@eLl3yYA-{!<R7 z-+ZnFCw<-)-u<lgSlvwzigCI3DsgZvaiJvkOCUkCd@J@l8zk!_U4%;i(L3o*=9$DQ z;ys4jMkh#Fht|nmb>Bv;9DSt@K>X&i@be^)3JsitX<Mu>V}5m0COr?z9|cKcb$yE+ zyc?bJ_Ig9`AO*M=7vL>)7CGK-Z*|(f&wN)-2zz+gApBPxhe^<|-~`2B<}igW*6&^m zsxD!jbLvnqMwY-kw2Rw=`}gZ-k*#rs?N&35LvzBPJbt-}Ob6QInSkK`dnz*+Lmx)k z=$Z|_p%m~K<$UZi%w%g{<f1;p{qAK)M<cWqa=HJ!WH1z3!g=l5hYT5Js&FHk-4N7c z%@i5qrB1B9{2>Zmvgzs+LH2T{ZeOBr-}Lx5<_u3%<?%HyUE&Y&j2z`>#rjsb_PB|s zlazd=k;wen7dh%%ubg!*4vn33#{7pRyF^0K%|$=DVkhj1I&Vonh48T}VcG0xSFTXR zJbcu|w0X49V75I=VQV?{=NFUZi~aS!ugsb)g+QNqUsx;uLLr)opLsZuJ2DGX=5tS( z$O=>cLBrTpV)Ubt;E}7rhSnQdZt*M=o`X#_ydGD=JTdXDFFgLxk+S|RO>ynBEcG)) z*j-#+mkvFcK=%6_eC0C*A$Kwa7_tUp2)7rKPd{NEX|wK*_nYz3&)4rux63Aekc?`n z!&gKg{0Q(a1^Y6F)Jn*asR-MzzTohvV9=K^DKhP*(s{SfP|jg9H?#a=njhU)Z)1jC z<3RLRoSJ0rYDBm)v-aLI%A{Pg{bMppxs5MG_pH1wNMO)gMSv+gvfqredtD?Zx{kRr z$}6y)R8Y)Aqh|X8xK*lpHpP(j?qP{<`Rh3)T46k7p(B`LtBchDREt^<mK6qE>TT+= zSy@$HR!3?sHdnv59A4kM+A@mnmfk+BwKG&{3Wqt};W*23<fSd6zY%adH_=$HE>{@h zzFvGg+0cnU<??C`q<^FR7=5Uy4Vp<~HhE))33GCd%7jEzX+HK}aVW6U+xHv7^hBw9 z+&w2fcr2tTeN-K0|GD7k*>$n|X`4-TMe>e(G;sFkSCOr`)5Ce7qRh7HJpZN-`|N0L zYj5D^m-La9iV7|1Ygv3Q&r-Y?-1dr!TF#*ewDzV``lSLf%Tp}_nOM4wGlM`f^yx=v z-QLe4Y32OVsJCafJWM2f{H0$o%4vlGw=VF=Cvb(RLsD9Aycl)cdnXNwZt2R#F`ZU- zOE2-b^Bjn8n9lEnLG90MdFY#roOKiUqe(Sq4zukC?C;|&zA|P%$1LL=IX}tH6pu&j zKOqIk@#*}m<$+sNQSNHBbA;C}jS~r@V!FF?O|9tp^aNfbJSd^7SI;izg{~oFqJlm( zrE*cHCyUoM35^zL^h{J5)cpJy5kZH`VG$Wiq9r+$PjvpZa%W+PFHz1?QdRNxc#3!O z=v+IHU@@eVyKx!y_;Ep7ruzM|_-%BExB{`pQQ6;V+Kbk{wy+*D9uBLqXK`1fUjc}a z(<r%FI!NV79L;pg`wbU4D4MZd$UP;CX2E^{CFznf1f%T`L*KKptXrIB-!;q>*{ULK z{TEDQ92gdfDVkdO>yV{hd0TSNjILEwG^9%vn1;q7lt+;DpQhS;%tyQy44epxloUtV zoS1lSP^$`XW=H;0&wGp(ytqNmdA7NXj)kxkYdxoohvqA|@@KG$hCGGJ!eD>}`C%aC z1lwg($YuW4&`XJ0ucXyV?`}(=r4M8cxb3qm%Evf%qUL*M`xkdFAQ>l_=uNL=hEw{V zy`MulTXKKruRa#@JD>JubwRI>&-NJJoz}qT6KHOye&svmk77LwH3}_T?jIQAv!)z5 z+)xn^%#I|7;wOh&AiFO0+R+Es_SP@i)|V#L4_o*tbMNz#dIl{=nsGHpGD)Y>rJMei z2C)K+y1;T@Do`Qh+?QS1J{UK6J~`hp%PPG(Y<rL!i(TU>FgiN1uMZl9Z6kb7pJLN; z4B`l~8RBG`qT1j#S$GNpdsVAluo&S|mF*>qVWxvFNm3`qb5fN$<VzSrSJBR~XI>%a zaO?RCU0<Z@{@1BVEieE16M$H7rDeV^h#9Oo+)bDIyQy1K|Ha41B;fM0Y43;H_cVdN zbua$;PeM(3;2;N?HC$c(=b>c!;Cx=hvBk6+I0JQdBmY9qZos_R^YZ>;I>E{z@<mTT z&T`o2Yv5Rz$oZ3DW%tE<VQ0rDP5tSI(icoGkOOw~<@uU<`pxP^uPKolDo?y8t2*Y9 zxv}qENX^I0VFtOeG|jSNv}#Lwz%=0EHM3>_aFXTdZ7&VywZ0Bpvtpacjw~wv42>hJ z&c3}Np)b8#>P>C-`0t;By2UO5+0Xk3Wl0&BFg8sP4%F%6us?n?Cm$p`#J?bQQKmc9 ziy_DIp?~WKX0%EoqJ_EzI+}6=NElzQf?AZpQ*08^^$7_#D<-EXh8bvE2z>oc4=O0b zzonR?tUKa5JiPz9V*$(IC55B0*4~B2WZN^B!Xz#N$EveK&%LnGi<2BWH=O`bigR&$ zxooak#w(M0ur>3Yj`H}-K^7!A=>OG(NnV3G7V_N7*rj>KXtmhYqhoHZrif#j@AUD( zfb$F8KRxfnT|6rIKT485ywgx*b!e?#sW>RPI?&F^{VzuMwOU+f@51%Hoa3W8ttyMI zLc>RcEw62z$v8fy_uD{^WlLte(oiV_t#$JA&%WDvZ6Zf^u2axdd8H!s#){4k->)+S zB$}E;|5JT_6OBt~s8@d`Nl+`2)+fb!$#y43mzV!NgXGlM?{MX7zzyY1bQ0+G{v_;2 z*Z0j&b(DUBdNtDn9TA-@QI&9!4;;oFcqL_@Gvoj&R9)GBrvGV_npeK$Dg%BH1ujYi zX<>$C_v1$kl~6!bDcAdCm!^t_&(+nV!sjK+_z{xzY%=Wy`hz%^8L+Vop1b8qmQtPJ zLHh*MxG&Un7m(+feGQTH2HuO(6>j^D$W->=HsxJ@THIt0#aBISbLJOQ?pTSs1t0!O zMEqhS?v6}*S=9HZz-ak)6|N%$)=^XFJsRNa#sb#aM@M6NFp@6@iSp31<7+v+d5Rob z9nzS`X4bkU9`Yge@m1Vc8svM+58JLpV=dVS9;>bn;JVA%7{1eO{!@?f1u&0`X>J2+ zePv7M>mk*JM0+UNop^A;L=-#IBeepKE05anfJ(kuS4tFOD)J2XiyHO4w2dr<k_2v| z;8HF%^m)uA(1Nqb<S#xT39c;_%JD@aJfjXEk=s9qq)M!?yNm_t9Vg-12$=1bgd2<j zz+FPRo%*=O7)16+^cM4DEa-50`j?zr*|Y!ai=CmI*H%-6&udMSAJXG}`qa?)f>X<B zjV}6Qgk(98%gm9d>24g~u0>=5YsFQS>uyK1NM(r<lPf@$*{IrJKJeY!JC=8Q2APhC z=bGu^k%8tzZNllisdCmN?~8kRwL*RFhee)i>QKZ{2~=W8O0-H+N4L0XoRmF*8~}^A z)6DXIDptj4Wx<C3`yd;7U(1^Ea}14au1SR=TLhlo+qAxhwqS$fTxe<2v6$o!BaFt% z^gbmLMYfkam@*9K1tuhz6>xqzY$EvSOG^?#FOVtkASP3Xci}4{=SMRIvsUsRWI!ZR zP)^1?u{yF;k2ID7CQNDNTi$5Ttpn~!DgjmH2B7B+%Xe7RTm2`g_2!ypidTF|g8gEK zGAGFzV}bD=>7SGGrw<>0wBD_@1Hx1+=%YLvT%t7*;tp)w$K`+oV^nHXKm=xuYjp-M zs_jLIQQFm{h98BZ(qD_&y9TRYDGU37jSK4VoXRIB0c;nFD^A<Pq8NG`DE*vKT(CkR z0;1%gGL=81)1nM&q+Rze*PQ0xrE_Mbr*C!ECUAI95}b&o!3?~v3!x774aMe4RUoEu zT}Nl4&h{g-oUnu<J=GyycIqcjY;K`S|LZCOwgS7{LKpuU>nu-$4nr;3LegKwAWHi$ ze~V6P3+WOV#8+dW0Y5CY=Qdh%Psd%xNO^b|u_l$HY9VgoXmMZ_nMmpJr?ffM&d_Po zx8T{)hJYP92N}S?rMPl1yjTv?F$2T(d4=6AOWGo=7X&i#SE2sY1S+#6H6rwQIvxro zSIPeoM7=}_Ug=2)OhffvcUor;-m%S538eBy2TPS&r0LZ6M$@Lu565x*gvLN&7AF=f z%F-~Fq3UmmQQfKDBAxk+)j@}r_9Fq%<Dqup=>t2k-4_!2t8aQKFh&{CakYTyc%7%P zcayc=Zya*&sN{cbk?rdQWEC|QBv>DjHJ=cE^*#Il{0q-yWoOHjtKQWVgmLLx{<e5+ z`SDXzfp0UA-j^J)sun)-IQlW_eW>jh*yUZH#|$a&6X|K!%p;`DLyf$<=yC_z?quc= zu+&FGSc&In%iANm%DK8J-J0d2C*2(X&78QT_jhy*`@fgVO$Bxy;O$%4mt%zviw@@y zoxVA}b!bXAtrkf=6sZNZR~34L6@9=OxsaYvx=Q4Ac1tWonK`kV4_q{`KYN#3yKukj zjAk~lyPY%PJk!M*SihvW5|!OGzaNayEVD_fgI3;9AhThu_B5{x457XeOc6*w@7phq zb8zs?G-MRLuQpoL>R}iAb|B+-C5PZXanQZT<Y&-o5fxDA?3{T@?frMC=xNoUvQy2` zqg%d&3gP%2fYP`gkNhr|)rGt)p@yK}bZFf0)Ubwm-0`UN1=Su8u%TL2miT-hdtp^y zqurX7j*D?LKwBTw9z<y8zGKu!r1hzVlJDhw**hrLnT1n~@2$)VW26D<c5TX)PoyU~ zqIS2Jw(a+3rE`~kJz%m0!}l~T`p)XLeP*AZ>Sn3s?(}qJvHAWt)8vn_cXwM2E|CYv zPH2@fw6rz-{(+b!`Vd^qp>2X{tJmb&d;50HbB>5*k)@nY!p9h9&I0rq?+!Q;0*mcv zso}BJi0N@>Ly(1Fz$+9rBHvvbCf}C1>nZMV%RG1$^TS|XQ2{oA$z=vxVfTjKmfnEh z_U)G`5zS%QN|!K*p2T8kc9d<jYRIuSkaaO-g)T~V?8GXUQ`IrAFY12@7j)VUvQ7_m zHGnVbgUD{s@=nic4)VK~XNA{&vEVB7m$2I#og{2j&mLXY(rUpEMb=sT`~pKasm;#Q z3JzO(#I?zvsl9&Pd&QXbgd{w8*q85oY0FEz@YA5UfXM8^KI;<2?#S<)hvK@toq;{A zqe=BI(U`=B%4`(X2gQIVw6u&MdaeNKqeIuz=Z`MjMQ^2?2lN-EBj)Fc$CWbE(m;QR z4oJ#woCz+Gu_#}d|9<}VPeyRK*J?C^E`Z(0j~=)(MJrv4D-h9xWyeh>trrgNKJ;|x zFBgWD1g0hol7XbNGDB6Rh_sBwdi0*TsGTG6p<J?6WkokcG250$M|1&Vj}N?$9-{f& zYIXQ7*2_CZh{);C0Ng1Pgthxt_y1jGin0))K6~3I-A5<=;$Df_xA6EVjlZByH%y04 zM)ORw9eXj384>82ml^nEOXeb3*TzlB<<(xMP1Z>KI-I`p`)Ek55UC#a?c14DMcr-) zoeEu)j->T&gihb?P#qFTg?>N&j{BJ6tu5uxJZ2^xCY*38K|0d@p%v*v9<v8k0ovNF z@r~)Daw7gLe<^sRBhIGX7|{qiD2rv-)QZY)%*Pb(F4{q@v~S5%26||9%=hu7d|4V* zExS4$7*}|ak6~Q<xOYXZoEj=f+*3OCZg@%PWodP-*!1Qo{A?p*0>zm;Z2}DThw;X( zncIb(;yM8c+oNLCFIkC32wE)`@~ylV0`BhOA|l5%ZL-Ny_qHIHAwJIc4lj^kZt0as zG4v+G&>H5t8LnIl#JhGA&9k;Hy1o1G)dQhPM|75g?aU{2mJNMllO`DK@)pU}!1g`| zp&38KV0*|5`<-UpIVuL_o6YyR$ir5hHd)&GuTx+D@#7ekhwkPm0W1kTP%*0cMw<yX zh_2K4H;NAlA)WC~?p^ahUxZ;_yV<d()b-a?6|0qR`QFyl9>0T*gjW%0E`8wJG+Hv! z<TY5SHzQ8pW7>!Tee>PL9zM`908x93uc^rZhIe9;n9FvT5}qDQ$;fck^}?~^<>Mn+ z*^W^~E;hJt>O(dYY20O-qfPN4N3GR1chB)?iP~k>-{6LJ5hiC8-(xMNY)=d&B@<{& z1S{0N#t{*tQJ1EpXE_oub}KIKcSi7=Zi_)W7abT$7CAlJMV}{jbQlbUO;+opsKQ+D zndnYP^~4(xZBPll&Ph#3Sg8vlgz`0Uc3JTqiJa6_v!3!YmO<okuYf_Vo|@XW_rJd{ zwzfaky0@n927p9+?H!u$s7?g%!8l$lkO3tU!Tb9X>=p4YQ5S(HcicFL1@i85P$zU0 zzd%jx*VwJL7ef6VE8c}?=J29^psCy^{Z;ga=p@xLBFqqrMD+OFS+ua|_Gd3&BBjVR zt77ec`fXS=+O<EKtovRbXTG?yFrWLjZO*M00SK~bb5LR;S~3%|-3wtNR0^@56y3$% zrC*@-Nix3go#mKz+&wLJFGo~V#^J=vrsUJPPPd1J1<cMo-0)s`%I5nFS-~s%wp3DK z%1tVWdC)@UiW-r~!9jpxUOb}2;_1|=b~S@!u}Q$}g36W_Bcmv^Xo$k(TF}sBapqsX z?{sBl0sRF(5Z#H#DfI>>8Hf@~TB?Kjh=Bfz!cnBcD+L4sKiPZt*DYh93;genGgdpP zSSJY_+9C^;S)Q8GFhcTKIrdW%aipoLki&@GM=%J-t1Y?#r<~4UQ&dyb>22P5NL41; zY5HWP3xEgbJU+1;pvJSb6dkJ^$c=ITeN{dt?1$A|er3$!81F9kNdnn0T>t`-^oW`x z#LA|LdTiyRm~I*52;<yHs9Nl&**%|#e(x9F@B@UWkLdb3`YgVh+HWHej%#Ko-g7fQ z=;qq!J3FHS>@R8%HHXP7b>v*c3&A|Z3zbX6=?baUw5D7Zbe#iojGc*mv)j&6j!KHe z#&cq#_e69QEQV_icWcmf2<4w?bVE6ek_(*%)5F7#y?ZMeOF==(k!*&<#^Ys|@6;j? zEs4?H(xUz$+}cG7k4621neXcK59M_JX{S>9&+JYhd~R1-sukAFD;|GKMJZ%#B>z<R zG)RAj{nXavyFJUnhAy3T#)PsM3x&D#-OosDO<s)^YX~*SmmLFk788!qAFkpsbiUj5 z3UvE+RUQ`LblO|D`{doYszk>--wLN5&P#V*voJ>K3>?b9!XV-h3dtJVkxj(J>$Doi zBT=_wrI#HM3xW^+Zgh;Ed;J?Bo7T8E7O(L~cP(@sNANbwO;!c$j2Z^?SC=Dz@|AyM zJ&$hM%hDpQyW<vWd$azLE7#Gv^MK(`HMK~3CE}-#X7e9v)zIxo+$8cJL6Qm5a<D0k z0V4YEa1fAlDfafJ=(20POII{KO<xZ<bhLp@3en|dKntAJyVu2B2%(WXjb%4>?pMb> zl6syc8ghwg1p?;>mHVqOZm&vm1?B14<F<l(?~2Pos0gpHCqFqOW7XLSBZin}B=Dj9 zxC23}$c|surL3WNHkS@7)nx6CIVmdKzi~9EttEdE1^J_kzD?KE9*TcB&LaFtE^~S6 z4{2nQgc#edeRAR8=k?omd0?XU#rGvNbeHMZq#!^%RNd<_Fw*O*r{XYa(e)}3Z=?I$ z#Jam7V^`(af%fKNmXW@Dyd%Z-fV9Ob3nhGqi*WORdaeZzq@lIi3o;5zOYdG0)c>ST zb5&sqBK@tcn^KR(MPID{G3cmZP}vbNmQ^MFDsmbmRq2Ds+24@KkrQsr@H6y$O3%+1 zufQl)IM;a{>tt}FIq||_%kH3ZbB={pE2A%0Ot5bmPk4QYK1!QG^O07^^L4@!x=w9^ zzYAR%`E;)eS#4}Y`P_#e#jskyYx}FgT<yKx38sP<LuwgqfdM3&!rT%JLbHl<k>AnS z^q)zI6s<&xRboHwbe^b5>Jo(OAnESiL+tn5JdNW(<h*bAaJIy4<+7>T^iyRw#eLUg z2B+M_*gt=soNK3i&E}8eW_wx&L+7-2;H47ZcC*qplR6-^p(cae7dZjZLto#i^{2lX zO_xw=q@32dX9M3q7M0oPKHYBeL5FCEZ(y!K9l?cc;c2(5K0d}oETF!LY@Sv=n`<C6 zEH7wYy50#KeNmuQNFb(N*FoA4ZqY;DNB-^GgSwSf$G<C}z5N^%f$)CNF@G50wlSF& z*!;fiIOw?}5np@C0<L|1T=bP6!p}FDS@ZR0Kjo@C0?qaU<7P}JP}bCgXhQ8zr7ANf z`O)|RkAP0H%9%{c#*EW|23cNy<UN|^f0V7Z*@%pa!{aawAZ_?^)h<_!wNMrD1AE6h zgz}?JJxQqu`ndh>!QPB`Y)J{9MxL)t-7TW8OQ_Gq#b(df#>@XIwNeSRP2t^n6YBrM z(nbynuRn&jW};c=9!I%l8pyJ_Eto+^BG&J8H!@5-Sw;1J)9nI6w%CgIj~Z_^VpaU9 z+;JK&#~fdXlhqm4b|n7Wa!~?3F<(T@Mb*l<=k8)ae_jIO=-5AskSVKx`9sV|K3Ca; zwFFLyzNPDmHwe0*Q5I#SmrdN!w?r3FE~{EMb{GuUuV`(L-LOkH#ruRVuBsdDf|D&j zJJ<Szn1W&;cc##AQ`nCcSrhTHsfoB&ELAJ#ZdQ=qN5oUj<5>c><I&&jyzl-@k`YP< z2CI|_9VQl%c%Krm(q-HGQ>|PwS}BS|9(bhldTe}uhTVF=p=sP7Eg@;(Hmi*qS>5lf zDtIp$@m3~?`O_s49J(>S#T@&G9TE?oLQX2+HnCA>?39O+^tzN3;{?{8!GSK`cO9gf zEczf}`$k)xL_`pW7m28XSP17i!)Cor$FKBRd&c$X{-qS*wi^p0JT=x@xBqRohBB^< ztyp|jKlt*MvGZ?Z23qj?b%S`DmrdRxPGjt%c6MyS+==m>4rduP=N7&@6Al@fYh$|` zuIa+G;(UiAx}Avc4;E5zh$5fnX)@{)kj|faLs!-NmEou9htsveDbyPS6H(MR&RkHe z!F(r_#xos0l6m(^BX5*@n+fKRtS%Vc;(*Gq#J6w+olX6_pByHpw32FBf*Jq3@bpwA z1B#Y<c!E2o!&vJ%%g$CD1K8~lE1^A%$gGQ;H#hWFTk`_Y&KLMf@@vAyN*Wh``j1`+ zTd(tq#ypO=T>Ph4T;Wd{*SYvlWr;{%33O<hz{3{`P>t=YuBM=-@K3pYzH!)z6v?1J zILHX#?TVf}N1GV9R1`)I{D;i1-$S${^Xf<IDlv@jxz!e(Ojx8(q4tAPsE53>^1B5& zcawD=6g$3^)KHDzKFA<QARb3qMbWAWW?$7MVDX#_mZIask(Y53V$!&}S<gBweueE0 zn~-+UDxdw;PoI<tHgycK3_~dO(e9gJ<Rm_Af8rM-XcTCj`9f84{(hy_8)4a|O!1`g z65@-@+Jhz-!=2%dGp{peESTV@5$||N!-WGhC|(<%O&YSWu&CK3UcXR2=fIF&F3@%1 ziQby~IPHD|diUDT&RcIkKndX<CKM*b<JX^y{V|>X>%u6R8|JZ2Zu&U!9-pE#6VCG3 z9h2@4RnCNc{Y&#V>&$Nb5IKMCv{B@AF<Cv}a^^DkQ#f2%9h&kg+n08>dqXS3?5|+i z(ArFy?cx|F1h-E1ohOHS8qQH*n%CymX{Kac3XcN`EqT}KLL)YSXuv|AxKMfMbh9~I zX*RaDR-GKbg<N)b@kM*czt%U6SLGw(C6_}*?9GQ5uo68y%@6lSTyU_%l9+!EF=^6J zS=!uiSZewe#(>Q#bW>EpjJNn9bShnz2U;EIa>0^suf6Vl<0K*4i&-V8AhEOKmXxs9 zSz&C@<-Z%(k#+cUZT=GUhv9MboxRJB&#WoO%bV4d_G|9&_{+)O&`-%H;pQgS@wi1J z%cRaiBg7JNXWAQ;K9mz2VfgdlpJ;7nauXzZcF%hi<02t9G%@E8M<tkqDXi(`lo~_R zR?Y7<Y$&Qbj&z8_yc@^vFKN;wjn}@iXL%)sMHog~rn@y*&`}e3L1p|=8jG2pUc$o< zl}K;#?W4Ib>xVq%nqwM|y)gAV%EqpD8nid-QTy*#f13tk*IHh`_YzEE<DZVW6mMVW zX_AE=;2W$rCMbz?eVJ{`H^W}XQaAc`{L(Z1-K&fDQ^XZbZbPaJ5^yzRi!zwjA=$Db zvBaOC%#IS7Sw+G$gk3lEgkS$crgQT>ZeH%jv90K>B>ZIBvI3uEEG<X;`H?{dCS$rV z?n{skvB1N@S@d3me9^V@1+qunHr!MWHwe=XeuXKoOlc3_hzxiF6s<m)q&v+Ysd>HT zDz;sS%E>VS(+?uw{-$!#k49UnC(IXw#}UUrcJV52y@Vd(FF^!sQ}qxMKtvG6g-rkx zT#M0R4+Bsvx-9YSggz!oi7lCHO+E;X)vcMWl}v0kTQv!Q17zIq5IX1!tnO^J8}5vZ zf}cNKb6dKvwaTOaPx)wD{EalHkoB_s_F+fki=>M5CQL;Yh@N*~<lPFAT5+Bx2A1!- zavvA-+MoMv*Ic|m<tSZMCU<+AvG+~+4S&*+G;{$fr2T0?-M}IG1oLA;*M_mBcE5aW z<}s8Vu$~?vG{n1ZbazwWrGqNR2iG8Qdm<=dM*FTBq98m|l)d$ZN51u_-r38n((-ns zgDK|dEI6$f&@-o?6#Z@IPi|obPzYpM9&iZ^xzvDkoZIl41TX<Q5-j_EO%<<e$8>w= z?P)sx9LD=Fn5#Td??lT%8*xIlzoJ-60KNF>+EGsI4{HeGut>`jDWTK;VC~kG8EzZj ziEWM#M^2DcfYuQ+hVf$Yy-H<dzQ!*cdIg0EAhm}X_uHlJJRSc2g3cS1SgXli@ieSK zOOEygs(H67tzbH#aDTOVwiR2GC@uUU>}c)Cf&GIWEVRy13^d^U&^tjQUl`WBgVPR$ zl^`3gWL2-cLHxH2xxeL))_MAR+)v9^Tv`KuXGf^JWh&7sDE%HDb~(H_-<pfOs>Q=< zOeeL{=~S|O$t<5}MqTz!Axc4GNG{{w_r;Qbi_k?gdul1>AxIX@JO?7&X<F4L9y-CQ zf$n+_0K(4prQrdQYg3(pXkZPo;!1RURHrWig}R#w{c11rF|z>k^f{1>_jMvk^3FBx z%-?w}6zZnz_^h_Pw>`ylFhGH_){Ap`iQ+>_AoZO;vVVKZY%T+=dXYJD&t16laU7QL zHPKsRaz<%Km9{S&SCWO9!|QACrhx$>Y_HVDrqX5ix3SSTr#asv|B%Ilbg3uFB60@m zcUKAvk9%c`5B1gxC$TE3GQNe+e$lb1(#;`Zl<$eX1P#$!G2if^-+w%=++QMiMy!(v z({})632h6<hk#*$_|etAH3D0hco%;U-w3#Pl8M|%<!I#y#8^oJg9r;3$9EPA+_)yH zqR?Ix6iVCwfbTY@*J9{eS8i{jH{<T6?)Dj*EeUhcC;wv$mK3>Cg9EGBi2WyWJ2%`o zwSMp~Wy$4=dluykw-lC9tDc|84+#^VXA@2E7clFxoBf?%4YYiY#iEikt5y1Q^yFB7 zbLL}S4rBbOqo&k?5>!X3ksBsmt<0EgW&|7Urdx7mDlIIu!bmO4C6X&!@3&i_yTC8) znrv(0EC}xPwj|RRXf60t(3%}sasVUC_k3~CXv*@1bq5;-{13O6@B1MGU1Ky=5~Q&< zO+ZRvR!lK`h4#@)#~~9VzqeJc96toc#^Q$WVrQum@jH`>gbF#KFOA=zU;K3||J~S2 zCwum>>};dVw7ZAJx9@axkL$XBrOd62obpqm&*O{w6YXIB{v&?nVFs4$(aFntuV;E6 z)ccyJrmX4%l&{@iboy&QYiyuo*fDHMwo;fJl3cQF^9p#hU2}U4mGk6>q!lDdVZ5=> zu)rbn2=6)!47ku*T1HAneOG+S2_luP0bg2`joS5THSGQ3wVHxIg`%2p+}Y)C;!Oy) z0R_~9Z4$~i_C=4Hxp9-YiyP*4`prs5D=YOkH$q@xHE?lr?^71oIdX2O<!Z+KbJlEn z&A<bX!$<u7&6$=+3uYXZM)YE7u7sR9-zguO1hSUpZ#j!QV@Q)Bop~U(R=YIk<+6vX zW3$VT1=yERlb;a!9wyc%ZA<K0V9XDiltALe1<`kBFKg_|2T+4b3=xunmeznJGM-e$ zQm2TAYBFp=sT{bqIzjDHGPP(|XD`F`5jn!>Y+YTCBkO6F0}8I&#uPj3hG*^>(hobH z@t#ou(*v)Y`{x{<9OvUZac>iTb!)sf8Ga<iCL`Qup`<{(?S7pi8i)+=Yuyuue~zql zpX9vpu@$=D=dlh9$5$n`62-`A2vjIF=i$J~xn@i7J=s(!{+KgW(ud&~M?GO59cAPS z98f7Sx}>$FBl-SR0Z@e=%l5lw$HcBksO=xv#gpHB!-XBCX%W@_W|2`&fnGSMDlyYD zD#vWk?axuB-9hg1QC993rN;t6<aW<Dl#I<epZpn*&|Ceb#%;q^;(m3QJ}hmj#-D{A z&yk;fcD5qPS-L}8aJb-MNOF;JRgHba$KJZ6m9_CP7baj50s{P-RMfIA9=s#r`F0%p zG>|dD5H`OQyNr)tUfdEB6WbE&fpRTJNyD1=g_|u|o=FD<1wJ`Z<?MSs%~kfi{P`cZ za_qm3^X5Q~nekcftHE(CL32mP%E#z;Oe)MDK>qE!!%{EW6Y)eM{E37j-`4k9L^{Kz z!PR|`>J+psW5m21t{fd1Nc#N0KLX=WN46T~#U~OAP_KOY->aSXpdI+X7o#{}Ea<;~ zWiW;2-2ZudFHFw;_pjNB^v-zS5p)<4juP_$U(j3F20@tAdQ_41!-o%p?y`OQ{`~<J zv^++$Xj?RzF+b+uh&Y^b4<%*R`2FJ*?qY9}JIVo-$kz;iGw)Bs1BtLjV7Vs^Qob+h z>glvG(edHZm23F;2;F)wtMRg)PhlE53OW>@fBky%>C-1(m#y#EkCfFEbW#cmo{5D} zN`QjS$_SDgB%BNM(*DtPt`-}2k-8mj$jHmzd@LR^4@x`m+1j8=w6{KfScf+4oHH$c zp|6v&b1<ewC&>r84V3Ayf}JzXU!y?DjPX9dqr4Y1M^z_N+_{rlVg|XxgK?Xvt{7HE z&=pd;eC0~ieE}zRZZnS4y#cu>5EY`&luc6PwwcPkij7TA!EHqcn%NdV`$1!U0R*NM zS+pv?dw{yGWVO?J<RC_1tt_#rPS_NctU}Ihm34N!l+qc+s1{idG{okk1wZT$*51PZ z32Lh2b`=7>6Uik0_mNj{udCBZ?h_yEgU-An$U|p(Y&GKk_daUxGX?Xhn#$W`WSK7q zqm@QSmd=iMCH3^a7uhdJVx)ySX;VB9B{tJK5>ir14=uVnfBllzab1Wyy-1-)rt}0T zR*Yl{dU2D9RZ3wAIB(=K>DEoSxDWkQk&I!{-f-LagiR2sQ|q=M01WQb3^uj<(Qge) z(%`E+efI3xw0EMe*RiPUP6sVPVzuLnV!Gu>egK4##G(oi@qkEOs0Y&Yty1KO>B-(o zrvpkTW^=R{C7k1NIO%K>szfB}`i@W)sgnn12ch-k1Dt}3vt7A#_rr|<*-Q>9_p|Nx zs3Z`Td!h=t#QiuPn`Cy-*O*(MpEpVeEhR}avn){q6)6{&ivD_U?{qjfhvy<nR3Zr^ z>03xS*HbH&T>BwxkKIKXL~5$}SW)`6I;7M60|Fk=&}1|m!)DHRvDP!$P82+M1S<at zK1crhFJ8Q8GsLw1fKDZLRhwKQ?+bWOuv9PbUd3}+ioJQ`_w-d+7NSNN#0Y2C%cohk zC_6ej{`c!(-M)Q077}PFUMJa4o0VnYw1Of`U!EpUiALV`=*~h{*WRBc_@WaN6BV{I zmq7$o6gg6C(25TdOW7KwKhvAbvM}$cdT&X}s^c-e3x}42_+p>lCZ)&6IL`?y8d3)Y zA7Wx+wqrrg3Ja{jFK5bz>1k@vES_5j<!KmZ0VeP|i;r*vxw)gEf5yPl(o%47X6A*7 z35~zMzw%FI`svD2i=oa#7iVYZrsig5Qe-;G!hZJKVw1Xd->+Ybz0qD`L3i~FU6`}! zkVFk2j@kPC#pf*T>az4#Dx&bE{QRzw^H_I->h-UK_3`c9UBmt~2~L|St>E9x2s)%( zim>Nsou`1D;PzFGJW@tRMiW%B42WwEUYj&XhFZkSBsJKkAOClk*hesG;~?>?ws{f} z9gXvC^GbU`N>tQ-3hMM<4sv#8rV$j#dkzWI)!p3O>PD_?oX1bBPN3b^C5vGvTqmpE z2f{-}Vf27tt&7{Tq7gORptVCO<Q5sD3v(XZ*?I6-AR1nOal)9KoO}j1yTocNd@0#I z1QeSBTP@9DS77)2f`b=QLxl}G!k-e}eFk0Gw0EefZ3<naigu31&c1gxn>y%6RgyN` zp_SblRFa$AuXCPq#l1<*;^gGi=81)i`_*08)WU*+wf>A9Gzj$@KVcsVm6sG27c*`> z)USAFW22kpuqlXSqVHTayh%m|<DZ5A^6qh%=ozal)|p=N@qWk7&VIC<8hUgz7uvPw zz91{=x(Om&|3;7~OJpt1b2xaT<u=qgYDklb3cF~$(WZc%6O38LNFphuie1_oGfnBM zcD9LT)$Ju<t<j77AXR{Q`Ujm||2_THg7>S#UwnMv;S+dl*ga1i4Gax~wX1!t_2uMH z)xtDSpZb9MPcmHRZTMZW*6n?9OCG6>jSVc&?-fUN&A{?kR#r?02L~W>Mas*=!oo6L zSW!`NS$oM?LP3Z6<|bP+**6d#CtJp0mcl>%^TpTq_N`m`($<7+A@{o)K3?fsi)}#E zI1361;a$7-%Q@F=|DSOcb^#p{wK9--PspugyDTb-WdGmL+{ykbX>es4Lug8ms7kVM z0zr;edi~VY)X~X_{ob;|Tp^g5+0|9|5i|^NC2!BQ-??|M113sTXxdqZkh;{Cma870 zeL|^(z%J^2v=Nty=bSm(o}&x6>=z%<9&$fbB7!EGa0~%!n3kTteYT4-G&XJp)wtmD z@^YWl(p<2{!4y0uU<GnDN;{9PY=PO{Jy}I+@>+D!ce-bq4`g81Rp?YX;)#feNPYPk z04EYe%z>?dZMqPg#}Y-Vc5+lZY7uf&aEVp&Z$JMK_E>zUhiBS$>+g%}RD!?Cr@a!8 zT}Q)tnrRssmkX@M*fFod(ud^aFcuaTwzRc{$HrzU?zN&C8$W{X61#CH@yl1QFhaWE z!MN&Z;6E7|8DaefLErH3@FUO&#h4dek8Ny_?%vy&)a=o1%!RMmoh+09+sgLt59gdG zyvf)p<E_3fg}b}^PnDeLP23hTcCZOt-q^(qo$jVBez)M5Y!AqZ*zNyQf#xakM~@!e z1ymeg|LHAQjC<TxcR_ef9~_2Oty`2$-LWqyT4L1Z;S*G%4pEC}#bl8L0+}9iP763V zop3^<01wdRX_l*I$fa24CZ*8@z}zvJaa+*60E8d^3n;YHZ#mTSo2DyhXvDMI&Av{D z1(y7J{eC1gzTDf)QOcC9(@><9O_ayEPMLKT2Z!MmbXLfw2<N?QiB<s@2%1&3tEa|b zkQhX*z2NT5t5E&JVvy|9NdZ_u2e(I&oXa8&ZufIO$K`MMw=`A3qJi)`U9H=`Vm$Y| zpFxy-ihLk&j(;l$02aqiF<<>@t?<Hjapg14fbek>rvHIcQ3MP51nU0-j%r|({6FZV z$cGW!z`U4dk70TJ@5TRjw56VN|HBiBr_&(e9s5lGG8U+aEy42lIHRjK>$<?}2cFf; zwTCVZsp(>Wx1OvLb;}I|3gHk+`oT)v8&uup^M?A4tHUr|XG*%l<{XinEbHOnQTN_b zEmys9z9VA$;K1Z?WAcV4kpupX8>|tl-=!2?VJ1-`{9*vSQ}8Jq4&&Jtf9QIp2vtGq zB!NjKahZUtEFmG$I5|04fn%!!n1#g_b7jGmtcV!e+uJi(9UXI83|`aJ97hWx0UF-e zJ9Ev`u8xM=ybBhb-EsN%S1)0yDiDnY?K3`eb8{xG3VC1$lwdHypw5-LeBFBB1;Dj+ za7cwu{_^EZ0mz^N&NCM@1uGXs!IR%$2=21y$E&Z)gV}d<J@-|9<R8L8-vKj8rg`Rk zvfWO-TiO;k04nGx@ZbcLn|-OcaNQf*+S(r33HpK2D|FtRs-`&ze-h<oPY4@!SPHIp z&rJxtzT^5Ik`Jwcz%{J0vN${YvL~Jk<B<RScoVKH3)BhWY3_=|t-NJOIDl}WC_WUM zqQD#?K#J^%M1M2zD8a`${%yGQ=<(yiq9TN5xwUehMpRoc1&~SF=>vi^?2ctCu^0*v z3nuR?d#9P~zQH#P`eiyup5#N=V}){SZ3F^=bp({NS(@c6AgX!ifrbjaQ3_z@ju0A} zk+b1-aBE;rclxDRF(?7fD=?6KRxh<6d2kz=g<~+wF;HTz1iFL00JhvwX@d0h{$LQJ zz%^jr0nqae2okygx;kFTOsN0?>+J~u+5d*~x=<o$!iGY_cKG@Eh5I89KujJsdj}ld zJ>AfJ?dlzXfuO1z_0=`*sYDSLB&cCo*rhMbhJKQOO~wMG9rOFQJh#;-bRyJ>V>~`# zpF!Q(4#>{T-M~6KJA)}?d^R?=waMy{qg{v}t{B)$6`S<%zW+xINL@KcmB`uouv*rw zG69Tkk;kz;D9&c<)bfuYb(gy1IHcn_{Yy%K0$-pFps*cuS;1<40mx!AEqt9?xEpLq zwi+^dyE0#^vWWvF_{!0db9i_dc7g<~Gb}H!=Lu(~T#5<kSOVCkq@|@@TU#?5&b<fn zsjtPw=hoJw0d;Tf?Q#C+#^J8f^77sVky4wp^Ycpk1$>OXgR>!>BAk>g<W4$}!z!U@ z7r_iSeNqLqhc$s0^t(6uqHWIe^guLcC_FCi_<6dnWNIK8>rOkhH&Cv&14thObOgQu zLIELA)guCg1VH$m)kg|;qiY0YESxklhV3E7Gfh5(_t?|qM>N5i0LU?ftDCD;89rI< zJOCjTmhkBs2ZSHppgbsJYx|5*E#GJ$Lq<7YD=s&e39vNE1GNu3cKiN)8F+oH&=41b z6us5}5=vg%hP$T=T`|FVfq;hKE`DqLjC(~sQ}#Y7i`FktP2LeQg-<NC95GqxPhXp= zO(0=ZEdX2y=UH4#49BMaoO=nC;vE~K9sYzP{J*b11g^T{*RO`Dsbq0#&q0@o$;l*; z?kzM$QGv~Gf!$Eb9Z3RVQIM>@K<zG~Qsm%p2cDh5H)9-h)z{a@KCm3jQurGWG0ziD z&PY(EjEIU-nRnS}>*)zb)}7#si;H8ye%<9TA%rLapz|(#Ne;aRthr+^FE3cDFMz!L z2QuX-LfczgZ&6WQ_iO3v>gsX<y<dG8N5m{1eE9sS=jPHxr9-Z6Jr#%<E_TNmTHWJr zIbKY#u?I(oIlge#j$!Gj7Sq2y!L%aNN%U*qtMZ00Cmgv*?MK=u<bGg#ykmT@IStw= z?*iAWeDba=&(7{VN9m68e{j8LaW`4OIRtzco5zvuPc@`<4H|Hz+jsSibTCA1^QZ<C z3RN);Qni;u*`7E<8&8`d6%`d`XJUU%)6>$nAK4^?g(+l>66Q6ze#M?JZ}@P@YP8@Z zY(5C5eOAq39(0fjH{!#X&+HFdTebiQKAuh1;|RI!HT3m`!8L(pSI&j{Snie40{vI8 zJ?(I5;sl&o05r6sP@$mW*bnE3-)T+#frQsxVPRqHkcGT^F6CFp{+vuvF9E=NEh=ia zGcN&mU+nekt7NP?^J`<p0F*AxC$!j$_>#7QIb6uWj#Go+;z1ie2v*<G)t!DRRQ^OF z`ae&I|EqeZU5d5VaoVB$-ME;R7A1Vk#p_ZkuuGXT3EWKDRgnOuZ<3I-78!S`ms#Bd zBuz-c9prz9mbCKywlyUs<%WB`SStOVgLajpf2hbsEJR2P0vm8u!H)U)`_I5u`gqkr zSco+{H|HN3ibm~5gok4`4Ks%LRHBnsKp=t3av1CHzkhBhqt^<=1|SLB+iNmbWbCYs zm&OG7CCuS@_{!IIb(k3gzQE0lmRgc2C@7qPmHI$;tf<)1-o85@A$OaaIuwayZT2Nf zeeoIR>eZ`%H#bA!cr|^&6P3gw+Wcyo;u#H~GLF$vBD`A~g8>$bEk{@kyL#a{K}WY{ zsS<3%Q$fK*03BOR1gwDkxd{l;($Z`fI!P$+-v>ziT`^q}yiCGWt$QjReu~Eq9#{n< zz{5`&E@{<y@NI8zS5#IOdK~XmJKdjtmtA$54Z_%Z|M2S)zI?gLqFwbGtaP?krG1zN z6Ikk?qGNMriT*2Ko$g`5@vF7j!M)r5K~z+<(EZT5xIH5p0+44eTY4EX395gEgT)Lf zuf#<}DE$?_nU`+i7Zh~0VPxbmzhl~mfGjB85}(2y@&j0-_1+?IB5GFO*PkC~LR^O@ z7D|=(XOl&u{~Y5npQMhCsvoS4U6If7zk3CT@L8{_c8OWoTSFx!^u;->`PxKf1R(9C zwKc0bPmG24MxRoHN>KhxDO>I}1M^FlE`5lttuY^<nX?mB7_D{Z0S8sH?JNOd%Aq>g zZY<bGz?E%t7_DMB70ZtNm;eJjSkR%du`!#&qBLZq@cmA{8kef5hf4~KVFP&U1FQ@0 z8XX;-cz>{&1fe%3kpu=B1C785khzrxy2lB*m$7|3VudtQwWUPlsq6Xv2!W7-Aplhn zZ0-lyS&Wc;9s0r@fNU>Z3XHm@kWdnY#*ju($x&UHbe`@xJi;L&x4i%=OZ2%j7(-Do z_^!tbF@eOJ&37@jo|Ne?<Y{US{#YIzA1f3Zh(f9YPH=l$8z#eMxBUCZW$niO)e+p+ zJAaBz<!Wkb)GKTsLf-`jY?JAFgu&f;W<UQ1yi>OPkISG1{^aS?OIzI>sAmQ(*8<P; z!=I1@W9|UEMxg;VmNsb6Dl-e;oCHB~RvK{cfOvM>C~O3k2_agg-_t1aiSvO9Or|W! ziPkH=J2yK_@-#}-i;VEVX)c1}8XX^3s`v7A+gok~JVG$Ik-%ww8)A&BxVR3-I}7ek zaq)r0#T;Ewih-yGoY?)>Vq%b>`WhN~BUEtb8fb-chJ=JTZcZhE5wbf!c|R94$^Gun zGeG(m$4k_hi`m)vh=t|4oSYnkYVI!)^jR|`G1SYg?}LW`D0oFpO^x4qqZ={;1lqQm z;ZNS15%WEN^*=aVE9^7Je~>%;V%qxtH7xjbZKs@S9$_YibqUMl>P;-Y|6hNg0m8XA U$uA1oFh4{>RQ7fLOTAD32L@W$Gynhq literal 74895 zcmce;1yogi*DtyO6$KPULO?+gkZz>Gpg~DNTBN%>4UiN;x<k4Jq(eXukdl(_?ru19 z`Mlrx?iY8Ad(Iu_jPd$D#AfZa*P8!1fAwF!vd_h?Vv%5>P^haC;==MM6uKu0g*I{d z68wa-dt?Uw!(%J*(pJGj-_}9fS`YP1+t$+5!q(L2^<8^CYa1gAb9N>!CN_q<hPJkr zHoVNtX8-dAOcvG#%(NP_9&nH=mf|WlC={+X@((RTDBTE!hC)dQKUQ>%U5RsW4ICh? ztL2i~!@qEY{qYr6$;(vwrpbF5eF?^P#Hkr_*+WC2-wFv{+<4ujY4&1O^vaD&(SV=v z>+3<&V>7Lv+}Jzzx1EIc$F}2b#^$Su_scsKY5kJ#28&bT`3h6eK3rH-MZp)y_Y!9m z1dsmtI^?ytB8m!mMX<OZu{>Jnzh15{eo^-CYkj{&-uplHVz~_bM<Q}QK0c4+@HPDZ zc|T!!Ls~_wSGu~CyUPO)*x2L;lJT)A?%$WoRLoGMJB*h?K9Br+8bz$ce?E;b<^2Ed z$!aH>pdp6~al^ZL(>Ew+pyU>wFJ%M^2ZykhmQZK@{lA}__C1YXeZvP_r_Cv^JQ28n z<pCunoO-RHk5lDm2RXl>)4<QXu9s{nXDQN3KYO;l^X>^n@C>eB@^u1&AJff2XqJ|i zKZ{Hx{$5fnVqafhlG)CVj^_hY;*=rkjg5^LJez~a6|xjF`E4-UD6b>;OE*L3431^T z0N=D=_j(LF5s%aVb2?wdOY?>0Ia-2h%`#Dve{XLvKD#c?-|K;O;l_;{9A-oK?Ck6x z@9=piC28latuHQ0?5~Xy3pnqP&WnnO6uKSTAMLN>*1f-Sv&`dMaCsoVbIWdhv{Ecv zDfhR)c(Mq#Ql5HLT0ne!{79{bP`jN%nzVMEC)(m#rG7if8oV&(q1i1CLs@UOjwtpI z*0ZgwhFy1ec6WK4wkW@S`=)9$H8o}U`^%Nd$;sQiwmw_4ZMB<jEi2VR&O1K)V=h6t zYNfBu&Eu1DLPj3bdH$`?>nmf`q<nmQ@%&EpZ!j<yx{}^?=&F<$6V%kykX*F<{Y8S3 zntIN#lS(RlVs0+nxJuX;k2sH%*Oun#hilI`<AalJ=R58w<*Its)(TNmQ^)c<$=jIs z=cw?z9(;m7GHhaCU?_9kq(C`4JKyHE^c<^pF<EswIk2LjpkNE-6d1EzPnC*DgQK}_ zw{y9k?hXuAcvG7EtGF({H5>lcouk4|#_w1+*B)b+g`)FI-W{s1r)YLjRZ-Elut=D- zOG`^r(JVC`WaG78@d*nf@a%Wi(b2)VdbMY?XLK~q?RYO$9tF2T>fNQQ+%|L4gzI;0 z_602`9z}CmbZksESXo=QZgEnoJZ*wIt8$P3Hksi20m0jpA#b1P>K=(nOV7=ViWzvA z+h4wRZEos^KiuiiZ)U^RM>|GOo;=~07Z4EG+}zaOS?De+FYnHfyRY97EAiyXTm6py zT(!lKa`wEuyxv^32=~(?tHZ5X#R0#dpk_ER_~6EY7GqV!xVX543@__`r%IJWVK6Z< zjZ``WUAalk0_WI1GF)o0{T-8Bw$|N^gwwcjKEb1-Ctb$oOmqf4h)m!$d@x5RCq0wu zhzKH&(_Ny@c!8dPAB~Nc2OE?8FIsbRbFCa44BDePo$Rklup!-M?0s@we(jTL=T}Bo zu3ej$x$ybHe8+<X;Sm4Gm~;<E7n4rL7j&oP`ODV`=+lHsowj4!PKPaOwT+D>x98f8 zdeW|XdwZLXRjHVCB#ZbP{wcV}=dh-Ga<FOor{Il7r9GPEL>-#`ugLFkZ3inIm`(a~ z?mc`M-tzfDdPR6r(mjm|TkZLdIMr!)cX!*x-(LIU9tlZLKgg#^6F-mVkIK5W-WI{U zI9@AcWo4D4Q5oKst!x8TCV0Bj6I*+uysl8ExuCeXv9FI)ZJG3;sZU_wRZLv6-hewi z))&OY#9&E>sJ%{;@F!&Cc0I6+*L+Ix3HnyZjm^!Cv*WHxu2`W2e)oN*U(pw@l0K3B zv^Miu+z)l-%9WLo@|!X;G68{sh2Oq4H8fxd2?@Q{(c#?Y=jVrYcm3nXkB%-b3f0bb z9dUe^mzSY1ix!4T=x1BQQp`syc2)*IC)%C~R##V-yB$BA*E-es@czB!*@=_$&cd6h zC}OD7h2>=(sM)OR>+u3EJ^=x`XD5ffSugyfIZfE0GMD;urPS5cEB1L_H(nkY8IivO zJ*so#RHfWnFN;&lZTCgRHnV!!qY~pjwh{LGigX04LnS)nHEz*`)h_$L;lh}%M-95< zE9H{lk_*n%o}abtl~6|gE$qc48Wu+u)1eE_&Ma0Gt|x^B=xcw_5?s;Wyy>dojOD%f z@nc2!ONx6bw@wF0v8t9HhEY4`M`4<eww{dSSg-tHee(2a34Q?dH|v?7H}+sTm70&D z<W&-2g>!gW!;KKPv}9=uXPkzGI#S~nV=|Bz92y!r#~BwFm#I<t5bidtkIDJ@582uD zFS3=~QmP(5eyo`DQtqY}rYv05Eam+8$%-g;1Nrplaf;Kal3}fu8VQ1K#Uq`ctkA^` z4Gll?HA1bkI&Qp*7xMTR9DD;VkHJWp6~E)gUA1Gqy|KQiW|?^YmY#=(cu1RGEPC}u z_6!H-)$qfG-&BX{>tkKX7pu0fsaIaQ!&|52@jaVR5YIP{g}vu=Bxh=Q8M{u5+%0rz z1U>;LR$_3lT-NrI?o|&D4{GViM^Lhv%K2n&M?3duXncs-^}o1iSXfvfEBA}U=Z*b+ z16XlUAp>=HUC5EO-JxXtE-dUtc>m(!;w-mLIV}G_gM(HZ6OW<7tnBTZy1QA2v0mw~ zYvdLz&+e#s%>@2*cu67QFBS3X#BtMg@`>53%(KPc*RP}WTI+<)FJmXcSi+M@5S;x} zr~};wsn6Vo;o+F=`Ofs^re`fdLTBQz{G<3Bm`;!P)3qDCN2*<j#N)cN(-hL5H#RkW zb>20G+ql@9`FT!VH11ZGQf@0uh0D0O0|6H=U7A{3>wuZd?y~IU4OOg!E@m%V`DTB8 zyc62?HsgJ@IPq=j><pOw*onehyng89?w5UiyHYR%NW#uWun1oD(P^Wp7hkS$AX0h^ z6Q0(7Z3G%2`hC%WL>O19^P(Sbx9qJBD^R`r;`Qzw23*nmEG*Ztv9Yg_3;y!HN{W)J z<IYueAtWR$HtO-7ZHws1dO^>?$jCVlWBCd;_I*mq_b}E>a+jg2LX$x5z3b6pIwd6~ zi`fuIY+M{I49yBQZ!fRSnU>Fr(=c^-><81JSA@Y8`3&WFUn&|JB}{_)uC7m+3h8Nl z>p%R7jG9<$PWOhR=cB6k>6P<jznQ9JqUJD=ZmDU|L(T1kC;iN8cm0_Fadnk&^3Ncl zdV_@T;~w+;G?Emw)CWgLFeyJPwvs>c!Xhvnt>A=)=$bNY*BVOey4$CWbBhIa|Necb z?~W>`?Sqw~&NA0SwyPu@;x-!4!l881#e&FGr(xL54rqBm?J$vu$OroRUJ(=&`~|aK z(Cz4zsi`z{w1X*sX0}k)>T8tHO`>%}N9uZdKEua=%S&L~mld|^25o~z<trsLt57Bl z-ccu5V*$5VDf$ke#d37AyR4_nBsBH(un)6Iu?0y=bUxHX`TF=ILUqOpxF~(|YrzEI z$GL7JEPO%ViTHw|thn_1XigFR^#Lt^dFH6emYW%>1e!JPIZcGOe1t>907_lQ!+Qd& zmF3~X{t|945-lyQa0Zonct1bIk-<n-or14lTUpe~65*cQ<QEVaEYuN;*1GI>^Kg?( z)7E{MDr5u}Xwu2xA0LMCU#~52KU4e8E2Q~svEuoDsl&T<%Ll-PKM`}9$N8Cv<RM0| zY>~tI*zIQYxd+g75}~wzXdbpP`}JE?35VRozo_3y*eP^);p@=R;&hP6>z|{Q8*R+x z%n7i1IGNPT>Yy^<id}o>pZKFjK2;Kj^dZBG($8;OQ^bP)lv<GA)ML}Axu;PVVoPh! zH1YXCk_!8cV~2gFsw}ZEs+z+~)Y6_LoWlB+H;U-Qu`d0-$PkQg_67STMT?A59)6J9 zk*>@k#=!(e!PmDQ=U$_gHZl+YB7N%{NJm1+B-HokP92_~MVz`ZFh16h-upmq%@pw$ zRy%njyudS%^xi|lrGA1_H=FvO!BbY_f@MzZ&B+pfQS&eYjo!?B3B31oOnra-nPM*1 z>X7djyb?7P1j~n@zlURl#BfdS7zGz}XDhF*WPU@>Q^UM+rMxKp@4J*EufVg5$qZ){ z(Hb8~e2Ph<f(MZ29j1+e%s;1k1vyo<Z$8a6ZZ;M&_fuw0`}5kX-Kat+ywlSk06@vn ztmbXshJi!?-!MSADd)SuR4^YY3!Zk%)2wbSvzj&>tKtFVQ*1Ku@gg?i^&2;Wr>g-f z+1QQ)xGXCx`{HtNaDcL09}|F)$L)4h+`qi@F)=X_C!f!_m*(1S?z#{vDYlX3A3wZY z_ScN&?AAtj+E3vIdiwkOEBTjkRdc~=eTRw5MwjBgE^=!S`=xx<l`C(gA|7dxS2%Qz zR%2d$OC?p;DO2nt)orLr5lpFLX!!H&^q81U*QehR8WS2CT81@A#4!o<ID?s=!8vNB zAG2O$>$@a)dZL;Fh@ZkBdRgVjYHx3^UTGibv^{6FH6sD2PWkRqI$&nD_D!z79*wWM zSIo`LGn>pzO@C_?nG7)1H#8tH=~*;K613}R8v&QSF930I?(izI>mO&$ASejdW%Q5( z?O>6C%r_5Az`@Xn^<kVJ93Q7=dlJ3?ibFfL^5-5|3}>7BYVA&j7P-3m#1N<4O%^W3 z06d8s^#CDnK6t*dG5NIh%jJfH?M@+9PEJ66a5ExEPjL}U4_sqmxRhDT{V-L3q`Uiu zfq_9)Rn?yYZH)6a36)Is`mr%VwdI_goUg?XSy_X<u!-0XU*OsSk72y{QcH_mL7|6Z zpW#IoK4ANm;nE;n!GlNac{EtZd#fpr-(5oHZrlDR;=>Qu?zBRqTI@=~I668~sS(u{ zsqm!w5fjJvLuVkbytY*G;@H^K;WxAL+FD_GVM{x^RMWw)a6}msSD08Z)KkBn#R_@E z!$IZ#1#}yIZ#tcwl+RT`$w40sAQK?r;^IOeFuaq5xcCGNrDEegEKm3wqY<C)-@ikj zLx-0jaE)Cb2Y`TtvOWy&*RNj}hZ1lS&?&xx>kBaLrstP0lt8v5D(zP@USt#QF7@fa z>>eq%5i4qfa)W`;^Hom@j#Fm0td!+8ZaZE<efO?sK><rNhfzrG?kb%4E}!Mzib_aG zh+$;UmOAmo<m6*VM{cMnC;Py+KCr}oL>P2V99V5uNkvqZd#j?2R1zg8Z_Sb5wb0{V zq_}VAFw<BtH#?iUJ4sGN)cpM&=Hj2ilzw<M6D#0;D8r`w+Ot+b*a&}W@V<JVl~s=2 zqB~iH!}QOmI6en9_Y=p5rhn@4G%A7XzFN*sUY81I&;}024}=f;`Q*$@qebm$lOF*+ z(EUW{nZl{t4#%BB=dzjdskWm@FoEhiI<9%zE%#4OPd7lZ`~l*LKsYGzUvYfOChg3% zC-p#iR4Qy~3Fwu6^}8*Hz>0%ucQYU$;A=?@RJD4w^9!lN(P|eixGZAKxWmRIBqYC! zjTElk;SDtypz#qWD)@5n)kBDqx?auV@ocM;%~dY0jF&geNAU*QR=J=0&Jg&alaXy4 zuUC9dGE(5SqHsy&sIIEo*xa1#P7#AK77aaK+r&f)SWL-Wu4)Mu5fM?EbQJodFuE6! zLLP!ZI%c7(S8aFjc@`|;kZ@eSZaxT7h7Gig3cKYrU%sNRU+aJU3P^B2@@3O&HB^s4 zVG}WZv6^l|s+vr|`AY~F0|QQ*bknX<hU^=QnnUk!Mm2QVJoR#pTn#vUs{QJa(OU4} z@pJ#;sRPvWI9{*JOrwfr(STdQtyH{jN46DTw!IGFQrUKXKQE(uku|Zh@~g(}_!f^f zB_cj}@l?O8;R^l-oI?W~tbjE_=NU5PKQuC)^mI()Y3f@tfmdg)lb6D=5-D+8X4QRN zQ-N7?^YVW6ICsBvm820kgS^T)>h0UNhC{`f{V$<z3kwP+hDuCGL^349=u~Y0i~9rx z$!rfP=c!K~?Jj4Adj0wH^6k5KJ?cP-KQBgd0Ks9>tojLMb@|Gbdw{1%xy)rNY>jfA zo<D!S`1ccH3klV7G>_w}@gS6>H#3DZ?VE9$2;gt;dG@EU^ar6z?u&h#f%>ff^XC>9 zSE(tzXH>O=nwlD*%ObnLdOxK8Z;D7Lp@zS;51V8XJo51H@xMv+8nhSDRJ?vJ4N;QF z=sD>tFDl<ab8knmN#0QCmx#I~8Z2O!(E~>1_j=S~LPLe8n*yVF?ToW*02LbLS=rd= z0ORF%*^}0hlWvLSv0>rjG5|1qaxfjlp!O{QbdqU+u`cV?&2Z2^)h?y|Bsx0N9Qk3v zEBWINs=8lZ>I*OaPAw`{0ww9sozbMHn1sxlzo2%Z731EqKDa1QwDkZ~59e=AkHtU| z@OyB6f`Los>Fc`@GfTC$HZgHm@;iRft(y{O$@0MK);`9%yE{8MSwXRZEa07zqC-bd z=A-LdWYB3Gs8Q`42CI)_KK)eC2wOE*l@soUm8E4}OUso94<114jTCUPui#vVzRbeT zuJgl>AhY8lCT5XQ4>b<X`#?STf$PEK2kDrHfza?2O3gKZYlfwo&Wx0;SEVUt%JV#z z%v8+81D0$6t@Of$3tx;bc3?xTh{7u0npM4c(FiRRU#7Re)?w@oPL16>ma}vHZl!Cv zC5DUB?CLIM$P>vx5>88N>#5`QTA5PU)=&4IE)3+8Yk8b<o*wPq=ims1I~@%i6$nCn z=5mHkEtFt}N)a{uRtMTez#Tp%1^j~;i<(KOMQ%R6^zsk6xs0%AXZ@LL?ov>^MF6as zS^MGkyh_0<G*08*iR%3^jJogN-@uC|7Z-iw<H`8!SMEZSg`uy1RNd0zmLAcro-FJu z-OQ?gNm*GL9RtIrDhRN<(CN-iK%2>;fh3C^mB6%{`+QyV0esG_mX7yVb*xllUb>WK z?LcIQjL`Vo+;_>ghfcH8#Bh^Pz<BAE{px!_aU{^x)I8Z}z+J33gBovuuK26o3wyCY zmvnA!uGoAunpLN%s+%CIsj2A_&^(}o%}^szF!TX*PQaB)w-~PhKCh6kDFE#<BF#nl z84wyEAUMdhBH{FlVAhh)Q|GCwu0{X{bq7c$PQM~qG2m#we*MY-2nsrm!BDXgj8V_8 zdTljwbFm|(V!WCk2^m!j0MT=4@)aRx0>`{gPM)xsAfjr!(Deq;lp+4Y4$y|?@o_Da z9QAVR+k&nyw|Uw3$;in&;UY(|>WF^RV+zO!9m(*Ck`WdbM%1H@c!A?4thtcbo5aMK zP6_uZD6oV@j@9qoLo+qKOM%X$?s;<1AMxp~GsXw6?+G4HQbe6Ro=F5#QB#Zdu-;24 zWA&vJ0>L&N+F<~>P&^R1Wz|^eB8zcB;7)4rZN&W_oLnH0S`lN=(7LOa-3=9@r_Xpn z?~!Np3S~CrGw-rL*U1UWHzheM2Zt`KT2N3#2a<JtOH2l~y2Z!|Zp|{+f>`tAz-sEf z<IcuUE#;ge(oK_+TEM<vGP<f=9ALr7_i;01fS8Dql4`r%$IOuP^VTDY&8K%bMsifn zG?jBvXS+o2y|E$+0Pf<mTC?Y|<RfUztamKxwRbTrXd*xKrMJoLG4tysZDH?*O1yBE zDDnRyUt0u$|M1UN_S$W4ys$LTdA=;gg&ap53k75lpSgE!yeZJGC-|ubhUK`RxMVB5 z0Kw&Nk-xiYhc52ke4c4Eg#W93a#JTbkO7hYeXk)O$ua30YR=&#f#m*u!#+v=h}l=j z-zDEiid1l<=BmTGU$ko=>GJbycDEDZX#f7pj9ic1W&e)diewGGO~<2R8gWMCuo8kG zl}l_+_R?-<WFH7|hS5F#;g>8B%S$as5e#tqaU8E5A_s#uSzc2MAPUeyOYcZV2F*kB zkqjpR?fMIsuHBBzT^_4+z$GGzunjstUH4dQU07U<TC2E9`cT(#l+&c21%^OwA%aSr zcML~HM-eFph^pS&NI5?U&uaVOcg)ACS_~50yeljDK$)rsM!MLOev^bGD=LKs3*^gr z__o}B6o_>fpj|vXJZrPg`T&LtjI@F$yAh7PsmIFCvXv&LF+SivFt{W|`6G6;(m}_- zp!xFy8E(r7)E!>it|$hO$?FFOA^^W*uo(TO1oyzEcpkcirHzdgrzUg?oPn!=IH2pN zktpvQ%lHEcFEH+-J2^e&)Pb`w*qUjvUG5JA9P<i}4e&sv#+3&kV108lCVYN|5xM>= zSFfgO)w&m(4u&LnoDtL0(}Thr$z?%?fq6~0`Qx2i#Kcx}zlcE&s)P5jULAr9^aXhL z$;ozSz-=D3fj!0?AZYUZKE7|-B7SUpU$vvh_DnbUiue_%Ki|wq0m4iJ)<fjebgaZ= z8r6J81_tY@bkME$_V!dX4UCPWXYHV=rsy<(e5s;>2IRUvlvd#Y=35EFN6<vlp_p;V z_<>h973efy+u1G<nFF99E-wCRZ)Fhtnm`=Aw(wLyX0hB>ZvjE1I-lG4!|DD`mmvoS zB7t70r#`?IP0A0Gi0N{KNv8b7LWYS^qMp>I;~77{OPZPr(8xz1<`FS#et|bDHX9Cy zPIVIxFPR5i4QK(lK$V-?+XJBmvqE_=I*k2SrCa6n2lGX!y|T*6BHKmz+|BkFuIb6i z?{6<+zXUW1jH~${W|wM>1`1{jPzz5$EIpu~3!a~BtJ^8hy$4mK&}HAeO5Ty^p~YAL zXsA!UaU?j$fBy7=Pj7j8WcR!pD~y=i^6t3X{vBXBQ~+rcHLINih}pe;@U7bZGb#8* zs?kCh=J)fbct<Qx3a4hOB!T_<*fYQwJZ?uEpbDWUf-(tu4!R{wbGTSRpj9%e7QZ^$ zSybI}bawvgdT0$ol{fs@Y^Yd!xj$ER0JOX`U^DPPDs~IKU{M6sy%{<kF1S~9)Sszv z`*xz})o#MI1vw3=`?Qk6qG=wDjk@mUwzhlo`tQcwkFP2yD1a659PAx%!E`}mSG9o& zAPks)u-xVWm{|t!<;qYAwkMP!i%t`cH!gVxfB}^v{i}tAh0^i-JbfkLcL1!xhNAB! z+27tyffZYIes&C;GZYZ7CGghUw{I8ERl6RBz!JI%lmlk9O0_em!`jGXkBm_J=J6-e z+kEyoQc_at8XBBB^PLIJeSHKewpbGb*~&q9RFVkcY5xN_01=pKPxnaS<Qakby@sOp zlw~{KUH<U-^G)Oz^h!C%*oSkxNk9;mXL^Uvz7d8!SWLfj)wt$5;@V&hAhHvzoye%D z$+@{S-*+PwcJ#2+CzkrMafsR8>$gW2+RQ(%IbP#~*-y-Cn-N}O3I54HBC^=|+3Dd@ zcK)9tgP?F6xJ5r-3|RkzsNWn7>p!+U&ypL!2sM8tVgA}Y{%mqJSD;ieb7#JWEJn5P z^^uLuOJ(+Z_uc@Hnu3xpEM)EP?{{=`oS2^%Gd0c8kN@xi6A=l4GsCz>l)G}9c~rr- zZ~9<v*{%+S!FZMfEatqoA|Wd)8$GbIw>JkP5I8K^b*A5-$o8a&;lh|_;p7wnBJJXG ztjN?2I>2>e;+B)c?M$GZ&^vVJenlbH5Xd36>k4L!E~ngid9t6bL93K5naj?vk#sl% zQl6od8{Pl%<;z=el8brWJv|Mrt$uKSOqvh{7aXDIfD(=RvIw5VJnXB|f<j7`j$(tl zhyo%ao2|r1$fW)gmhfQ2<7_Dt6BAcAH)7x=P&**xMB@sbd`f#30|m_X=p|)eo(bT1 zkf>qpCc)~5wiW^m@^|86jGFT^7lb@$EioVXj0Xn^9{IF}(Ql8rtasPAx%B60WK%tb z0f2~`5<jX)KL^0X#KlTRUs${%6(I?2afQcz<yKNslI_YLIre^#0YBd6sfQv#P}kj& z_q4!fx^%I*fZt9`yaEE&1U&=sQU>xgr1qbV4Gu;@%}&qHrx)HTuc(lR_u^_L#m5(h zPtPfd^$7S-CM<sMWyFAk=_el;LHB~L!44#QiLf`Za)^gO16q!%&clZf-+%a!UMVVv z4@P7`VPSW`i#&A|laF3r7n!wcE(nXR!hBGmDgn_P@AmCq;~wYjJ~!@jOhaeY1ODMD zi!OA$!Y&B^$b{T~k$aqchYqL$QV|?^XL*1*%oSl^P!LAc-4`%1k*<bnPeBBXk2Q08 zg(W3af`akTUtgJ+#H_i<_XYz(Shz`eX&}#c{-Ok$>-?R`k7xT@2kSzUgyG`WlA<M4 zTDWT-C)}sy&iOn(e_a`d<d2(23n@YJal=(kMWSfZZEl(N=ZD9~>@eDS9oD3)op%>{ z(s4m)0@!(fblKU(rRddnbn0i(??F)orN1+@>e8jb;{^td3J#!uFn#p&!cy%(HP2)V z2ZK5&Kc5*)IHQ^%z_QeIbbijeOGZt<ZHO=^si?ZktaQ?z$3-v2!bJl$`rz=;sHt~- z5OE=3nQ~qcL{K`oK(=cmQE-!OR{j)n5Y&P60_PU1zh^E<wYHn^OkN((Iseo>s}?^4 z>k0R#Yr#yfB)aF<YkvS0`V@#$#oy3R*gLKtbh|s3Atd=-l*-YELaVv?=tx};b3(_X z9a<R6FpkKOx4$zsHU=wDP=m`{(*0yp3|40gs5*$32zX`&xZ$Iv08LIPjo)SWp8iiG zr1f!M){Anx<?wJ3M(`sX93035T_Xq8I@woNSDp4$tyRE<^6~YZOiyr^<P?Iov0i%~ z52X+DEI89dF<U7R>J{26aHx9VbpRXmJL8|p%3cTZ5N!oC4_=Gu!~0(7WaDX;sw-UI z$e0)thKT&yb9iT%KBTHH)Zk$lblg5WUKeVw8u;uE9-|ndcQnNdy7>hM2ZxswaYKYe zqC5R=g-2h;G)nK$*DsM@p+f?oy2bkXLPtl3YV*(F`_LYw!f-+euU)$q%jfX1uI>>) zwFc<g99|(k{%u7C1&Pom7_@3)0UKx=8D;VS=8%Nua_`=~Oh0)T2MF$gb7}@kc?iuV zlGlz74h|rMT7$TuzTOdLN!I<+q7NDx8VcpLckbK)&kku>y5M)HSJ?W(*a-sY#s)qe zD8k6ZXVXAW16&1bj@^FcB_a?b$NrZQ0>@s=I|eFN?yyb<lWrQM97sSUKoezmSW^eZ zH?c+jAt&cWWHJbgin7@*Ai_-j&SDSv*K^QKk#Vn9YA#p!TBQA~(qUa6E-6eCf5322 zFfzY<UC}JJ7NLKfR`#zeNbk91&C>jFQR$kmu-L!ZH=Ft>&<GPj4Fe~bz2TR<ClD{$ zueuVKaBkIAuGh2$-xou$;HM$F&=Am!uHU@*6ttt_rr~d9{?*k2;A>C9jaHqPmX-$X z3NGgx;Jqy1K_gun$R&HI6>uJyGGBOAOU>gZD{^4=NB}#dD+heq2_#$6)|P|ZZRZ98 zumN&_HH?TcTum%#josbBP{S}jBmgZDv%ErqOagi}Q$tw=XrcumRIaS7M1pvRSi+!X zBUcdGr%8x1*kVreaSjJNJu-t|^|0ad&B9%sgPxWO+KcPPcTB}BMPHakhUHms@j8$o z2wVW*<F6*`y%14Nl`U4#Em~=bdHlz%un^@w!fj6<pUX>-U;)j99mg|l<{DIJ^ga&- z1v<ONUT6Ci0=l}<%KG-9WUsu`ZfXYUvbUc@CdLFDXQ_k5*(j)~y+3`r4zua~=CZkl zMEJ(;u3k$B6{J>ZUgW&ghU@?<KwrH`M)lr}ot-ammZ1Lfj`Bbkngiirx-01^vA)Q1 z^vMA!GjHuGo`fpDD@~|S7}nCjKnX`?;n%knc0X(eiNKWM0_?B}-t6YokDc}LT5(#B zyLiUnUdPZ87L*l1u*MsQSUxeV_9WTxv`LCGKgIsMKLhPz$!buA_YkS^8wtvgk!iSh z$S~!#TVjEl*6{zlj9fABS5uel<zuhl;D7-soK2u<B^v$)+#pQ?-_*w#qpiU=+G85( zy$~01D;PjmPpyZChJSFn?zfz0a+4dR##uGH{c6!6Pw^0i#*Rt-dXO=f$zx#+`+F?c zpFFjr_eYqg-$jIZnlWkO2VGP_D=x*fi_7}_;6MCQqrUzwr7*ut%BlRDCrxNbK8ce2 z??|y;3~{uz#ZOhj`WyW~SmWO%AadtixhvNL)gJBJygfhpqR8$q|J}j@=f{u9>)x5n zv%@Xf@~<?PPeGOq7e%}!k=y_LvIYI}@2d#24RL)6ST|YX3Hr%)kfY`;22WW-{JM!( z2gep=)*`nUs6t{V09^U|V`nSpKi@u4)^hWLZx9~Zw@k)kjcAWEjtTHfHuDi~fWRLC zRwLyLEdy4?jH88&Oy}*<QkX(uGcA&Vu7ZMS43Jf#=Lv$jyS&x+X=rE~mOp~n0?XVm zaCNLY1`Xp9B6ffcx&WvGj!g}JJG>w9;(09h_qsP2h=*^t)SCp65=1j>Y;HE06NY#e zXu;~$Yk)7Jcx?3Z)}ly%KIsZcZYeRjPKrG@S6`xfOP1c#<Md*8_p8^*YEJT>KB1%{ z>ir0&a$ig@Tz>iiyq3Faza_u=gST_Mzi#mJ(>>K`NR@yfmnffUz3cqH?lC!MBuJ-M zg;or>ID^M_0d!WyX}}kYd4nY;M3C|VlU$Vjp<XLKP+SDh^`t#(sH^j2H|QvE+<Xn- zqZwR;trIt>TYPfY4YUNey(m#{_~5KCXb@rxZ5#Cut>#kpe-9^^0P&73$m8@RqByA# z)xt3O<GGEUSN-qlB5OSwH9|6e&z|%o5o`^q3y);4?=0TmQb6HcO_C;dJW?!Hi5I-{ z&D^U!CduAxw3UuF7Pz0yRO1zxrs7d-dK}XpXNL~}DMZd}McfmSv#vr2ek6-FIz+T) z+n8&61NeA(r&d-1K;;9Gjpn*pGYsIOl8)9^Iw_NzH*czxS>A&`=?^d)uZe>mS!h1W z%Q&0<`iCDh)h=KbHHUMg&^>|~iP?cds~Ps^5W_&}3E<)3xpDJm1I!7eLxzWkmv2>W z@?N@h3FkKVC(zSoD(qMv;D^YF`cnGidtbYopCV7d&9yOD)K#1*duD3->d?A7XQt!M zW|=ldC==tzcSco8<+0#}%OY=nijUqIJlU}Q?<_48#8m#bEiLt&|IA+hgQaya@eWr$ zE?YJU9UxXJNWUn+WQEqVl2F28;E`lNodd)JNs=#`6LD!Zt0Li&OaTQ4r%`_OIPJKf z6YQ$@US8c`=d}L;qXaBlr|58IOiav6xMaVefUe-;Vm2OcK>BMFQ2yl7lHZ39Vp;A@ z0Nj%RbCp$8{7!pD3hd%QRQao9>J$uUFrUOts$d!;gcX=A5(W4V$u?Y1xO0=UvtF>+ zL4!pSUSE_qnT;LF6B5V)n$<&C15msbkj>oXVvF&3zyUUysMF7(f9vrZiZJ}bb<T_8 zXgD|*WMq;S`m!H{R^bYQ8fZ1vc;RUOLt8|W+<nJA9Mw|q7%n?{he8oKVYSCLHlugi zqxICT>}+lQmPbubO>u*J3B<HF+F|iG6-Z&?z$XrN2eed*4Q~K6*9Dst(vQY-KrVfN z^CM8J3F27r<sVpo0Iy6(D+sPLb%3<8wa|SZ#Ebu){-#xdo`DNimE^#kA)rw(r%_24 z4i64k0H^Okx(2jgFd7MAVfLwR!#Y4#Z#a{NB+!~7yYY&diSO??jDBB$2nh<j%mY}T znE<9LOhAo;<~j|DnCL>pvI2Z-GIc2eE;s-(6u0{cH~3w&zye+afs&5n4TF#vblNf; zJyL#07I3D2fK&%W9MNaaj+U80LIBGE3t1Nb2x<R{<oQBYN9~)bHdKe}PPZ6jMq^}z zPD2qhZN29Bxyd<jV>as<@oyI6&mjy4RGkLQ#=cl)kO|<LPQkcVao5`ZJ6os+OHfTs z^{c~{%Y+M430bOWC#McSn<o|qoVI|p5K>)<dFXXt?ENqo<I1Y`xS*xY&E=z=?#zWB zG3p<~+GM~cDYKk>+w{a{sh8H&%q%n0{PTeyh&Gc`Q;1HM0UsccluI9ibr&vOGX7EF z<mku(IoaEH?riSur2&BfP<+kJ&COJcz`OJxR}kQ^9E1<4W#XmbE+LLEn1eGQT4v^i z7}wX=7btp=I%5DfoB)d>2`FGFz4A3+2}7k833O2i#p%t{h^a6-8E;*2OZSS2QHvIH zZp|er5#g8x38TYyUaQIx@!s2)!9vCt`93tnJIZN%@d(gUH1x2F?h+GaI6<U`LvKP( z&=XY3u54vy0D(^-Q?#+Mfk;q@VXjjs<qw}?a(NjE;6Wr+ACME$#$a(<L%0m}{}dfu zARdgUoJjXaG#CJpU}SC%8YC2IH(Wx(jmHoYJholx^#wGx0mTnCVFListbdq%;+ZKO z3X#{lrzf8}p1f>4gN#u*CznP2Ov}VN?$U~YiHYGj8TE7g!uN-d14)uiT8a!T*KTu| z#|jNRdn}K)vr1pF@FEkP&z@54^moG$$;rw4@4LT<50>a1+sp2?m?+Y5TcM9_B*mvk zM(e9x$%Ts#(WRB~+61um3!$wk_6vC?Cf<#HXqLsP1SvrzP;9?SvJNpm)Qc>|?r0rt z?WC~Ps1P6^pa||NUcm)j12}hjIj}EpNQUYzjX~<D99(n=P*H&83D^@>!DEoiO6FiP zq*jXQuk$gUoSgi=?V(^g3-N-??Pwwqvp#A6t}fZ!ZBSh@6ti&HleKE-TrFaf)f7a9 zDv`H?zTtkn%GK^<H=+r8DQL;Bq5h+7Kvn8*Bm#OB1`?goTs-KofZaD*9wZpDr73K! z46?&W{0YPbT1~J%KP{~<xU@*Z+ZT@tW!Re$JXZxM0+x<s>csHS5J)E!Eec>g822?| z%Ig;tfDZV6Yroa)yFU>{k#-y5E%r+_rw?Ts`<}?ilx^Ct;giqt%Az6|-aP%#s5ZZj zOMFs0ko@H0#wMDD#ir8ka)gI-(F_3{0lsHW&q}gA1}GXapcfmdm(WTui)+c@Lnq+O z?=%1TE9aS{WHLY_=pLMXJlNR&90Q=gA)+i8jA^M=83^?Q!wm3wG^ilKusFKGrGd`m zv|hc>ZM$$C7)8ITPV8ekH{>F-$uqDgL+DpUHCsNjc%++3GBgn?5`0|h7v81Opi$}Q z>I&!LYxT1q4N_52u>yOla9EEw{s;sMVB7b$HYs*}bMxnliuf=Wnj!rN><9IvB?s0$ z5HMtQLI|BL3_auHb_};wvXXiV3CuH4X>fdmgF>O9C-1AGzgE?$_%b05I|c}sjyzPl z1)<jrKRno$@$tQZw(4<$R_)@yeVFN!Ib0vd9qcjS`lG~T;|I~y;r=-GJ6$EnUXHmb zzq}!I#yvdwD&(~v02UauIzR-$u21`V;f*++qd?sm!dHcbzwih#YRb@MLMj&~IyX1> z#LUcl5K5Jllzznv@Qp@0z!(G-v=DHN;yl>HgT+RlMQL9azP?EAesB*^T?({QL?(o( zsz*D`!L`PhPYbo5^#yS|kT~wM!0(~_?kABz;s6GfN>}z|zsMF97x$T)pMewz;D9I~ zuE5b=s;Qa86%`dVfNu}c7*N$?jfEiY1AT%sye+hr6y&i#Lqpd4YZ|!%7#J9UxRUw> zX?1SFyu6HqGm~37_B(nT3K-%BVVTFja1r~;IfI~X;Os!2)!^F7P}oU@lTC1YqWqgT zZ)A@iS`exRmpW`!RrMS9VokJsK2$YQk$crkbK}xrjeu^c^N-STzYD=S&B9K%d?|~1 zGvqKpE&-wOm}9gS1!_OyC?nefU_skJ;J)eO9V7(;k@n7BJ83PaOW;I9d^P?5O<ee@ zB&{Y1V*<Dv9pc-<be#b-0;13d=!Yz(e^`urGrmJw0Q0g`H>3g~w|0rkVoVnp2mp88 z%*)o#*N!2%ZbZgxQ@&mXkOe-Lst(*R#DYN#GlWDSCkIyyItZe-AsQjDfvB1$kPQDo zRsy7WDrh@GX9w?mZ$7{yBa1h0A%Q3ye3&-ym63CcVA7ZZjqC*EAH?m4BIyE2_0fqr zz_S|=uly%P`wIFS60!%8AIaE*XzLBVE<Dv5c=~Mt=TP8N8?zBwcz5nR%h#+fo`E~o zSb{1Sfq0_)4$v3kALn;?&_7dO5m`U7`}wn7E?0GHyVAkS*JN7s#@z^_-s}e_`JD;= zy~DqnSm!=zd3ZH^5P3X5+g6ky1N#J~oBfYG71FUrRkVa0^uq8pU>L*6l0)R^Y4rT^ z{}mU#3Cza-4=y^u)aYpdZe4y6ZL~<T4x}lB>a}nH<{EQ9HtWyV3IZbw)J;gB2Lowj zc~$4hp;5DlNZClH8<ZRsSb>Nw;Oxw1zw!rXp!#^N^3U*ak^QPF<bEE*b}886@&&NR zuNW?wX?Kg^Z;Z8PNA%E!KpMD;U??z63fxbf!SIlRDZSX2O$1G{u>TApJV;yuz*ldk z!UxcrO;nDdqk`(j#c?qm`-0>>3VwcdaF&!HOK8U^SqOH@6Eyrs;F)xla7W!|m6MZx zMsN{-cB!v;9y~i5y*59d25&vHbBA$)<t#<H&=0saOcu_L&VI`bTJa78;*WK|B!|*J zVB*mc?byf0R=_tgDIA|`=NXqter9W1j0A|OczIO^lEo=WU?Wi|zzI4KUiwOC4x7M^ zB>?y`i-aUB$Kd~Sc2<YEdXDFs$IB2Sk+9U<mqGk~$_}G#PnxeeDHGEtiXwUi2Gt8; zbiUqYV`EOr>y!!8C?dwU$NOHpYgcEB*}PJzT=$|J1#Qw+h#^9Y2_pygN^!tuk?s<j zi+*Kg+z-u$8XqLMCY#6=|D~cyAB8gIo|ZXi&5wI%53+5#4t~uskmlv~Np=Viw_L)n z`o8+ftaleB6Z>;JvZ1SMCWNs_e^ygwJV=PpqDSbwZmjxm2A1}Z?fTdqdireJ<p+df z?uWBPAU)*0pi8@ZR|iEU;?G6p89@2~i{D`tD}U&*9<hNzH%6`$ksN!f_S0M3EG{mo zV$)4e-c&ot;HSCg(myiaYUfIxuo<mLKGQPSHoJOX)W>v+O6qQ0q&w`IYy8<>R{!=n zDB|T%H{LeAEsri<Mf4s<Hc3>u;|=?D!je5})t-DC1G&{(6Q}zkMI$4Pz4*PWpSQZM z-X@{L6MXdY+`3Y@FGG$ZO84A7SQ@eaL*BlDkbnqw2{t$5#R4Tpxn8GM{MBD$D{OXj zwhMRn+I6pHGX&#WdHNIjerj>vy(_ZXtDa)5x6muW?~$8Oi6~39NK8@W!}|+2rZJ5w zJ$HALl$nDsgpe*QeouQwLCC@^SwB;VelT^BL6ad~COXu?Ojj(3Flc$;sjcmp`cp&` zQu(U@H3X7U&=2%-$mq-!7ZgjI-@)9-3U<p&TswB!rQ>%BaXGGeJ;-+BSh<MWb~$`` z*aQ*~C*1Y*ua5VsDnBBp!WM*_3TDZ1ss6LoSKk&4Z^&@Dd^6k*C!(ah%&r<n*J9<v z^P_`lFvMv~cWvd+@2B|7f=B!b9JKp7=km$vnC~$C=d+dPN9~YQMTiqJmUzEJpthFt z0gj%W6n!gU;_B{sS7hL&-&a|BIj8MvuldAuvyDNma*p~HnS>{OS@rbs0~wzuuHAXd z=dg9=49B5pxrInx*A{6#m-JWfi_XMlO3C2%=e}dp6F&YnFmypLhTjJx!2jLh_K)o= zH-9rlL=G$FT#k=V{sQScCl3LMNlLImL4WB2iaj+wZR6$v0X(3@A(`cJa&o34Wpdlj zFe`9=>6fR&Y6B7zvbI@^A6Tjt0XjbWs2vQgpBojhDzw2WM1c$>Y+?p!Eyv^4Qb-kD zhA@9eznflT%oy{oOGubWwjS=~%i`N71=<akR#x8!28d7)$A^IR?@?x6-e`zqZUCE< zZj%lDWcT-iV{_}Fi_2a=IZPKv5wlTR<ZESxF5DQY;5<0E90+4Ib~RH8)tT^N!?K{@ zXJMpTsb3zsUh9neVST_YmPb1zkYgx|qY4=qg#0I92Z)5cM-=yATyCM4|Lh|K*_GT= z;xlz{WMpKD0I<`yK#;OFp*8B3nZxLSKFQGspwg+r1TiW=vVvsOKtLF+f;VE0dp0%- zBT;5(jmQTE3k3tP|8so{5rbPKBuF%J=$lzKC-A*(2Kuj%Q^p||jD?9^1lIBhev#~5 zMvy2Uo94*wJsL(YGy3dk+9PVn?-ZE+Ksufw{(RlT^3=Iw&&C%2bTA}jB1p)ZAwHS1 z`ONJ=d(5e^C)ve;g#+JvXawsstXPQmiGpTqI8w$M!)4K*$<S(Y#}Nn#_`)6C@qp)G zI;8V5jLV9)_V#kL+ril^=Ba!$B}TYcYpW!CT7G`MiW+cD<AJ<(03k+@kq6BC0K5XR z{RnbIF9S3OysV@N106jB@Lu@-GHfve{yrU~esHe(=Wi#xj!((!`cm~tx%F#lx%VR1 zCi+SbTqm>h-_J5pJZZ$3>kSL@H;1hYA{ohrtO2*9rdPkdFR`=E{m!ViwLcW{R_1>t z+>xpWDF^WIh#`cBY*4FpKLv-^I?Loob2AdP{5{~O07Ln|-NncpCbbPR65=uR=4&ND zUSty-rEPF-_#F+tb|5=0pl&py7}j_|SYI2j?F6@*L+783ffwNofw%#GEpI}P4nKaF zTloI{g_^S?6TsDCDWZW8ff1$q!AJ$LyP>TO8(t28<YlEjqeMO@9wenNN=Qh=I<B5i z!esrb$J#$IfMnNln7oON5lQ`O@9w#AzciDf!(L87T~wq`zr~@~8?=)xY{d*zf-A)@ zO`dy11dmE~EhZk(sIWB;>qQB=HBP5Ja~hi}`-4YJ6t#Ba^Yzb*<=vazgYhwU5Gn}! zl#qK0*^uvDT`XY)mj7*D8-=8r*!6$Cg4FrSXk`Sz_UojiZMr2Qf)~NoK{iDKg)|P# z4vYVfn<GJqdDmYBHpp+V5@1Rr9~LBTz!!YTJ}4e{a_e`H8v<9)H#^%TZ7zFy2Fx}r zpFiKh%Id0guz(#|z(QT$0`#%zd)C0-0@4j)q9XDRtUiQyTl6f}7wfeVB8f-X4%Z00 z^boA0!^QN3gSE=_;<>2E$S&Beyq9LkLPkQ;3P&&mODcRup$C5IetznZ?5~m}k7NKz zfEX<GeQuTvy}p=EO<j)17eUn;LDXtse(<snYhtmdzVFoAF=^-WwM#-mo9fC53K|tY z<YJaA>NPk~C$1s7XxluC&0cmH{clE3?M*Zzj`Hy3HjNB&YNvOuiH-ARIcZqQi9AVp z(i1zJXo}Gh!2Ma^&5iKne@}IQ1n^I)qZur(Jm27L5K_7lAES^|H2^cjWCa!F-*O#` zzb|?M5bB1M8loc4al$S*BxHj`VIb}Vx<N;RP$%dEtnBO!y}gRlY;JM?p6TFZ>IXLb z6}U0n85YU;b@xV}8;}r9C(!~$5xh8={eO0H-YS9jO?R;%Vp303C9JrkQgw8Gi;V1J zGR+BGXxJ_G=;k@FKM~C;$M2=%?rqH~;+93FrSpjT1RJmszgb4`@I7onWP#IEw1Il) zz$ABl)U5oh0o+)?h=^F^y4CVP)hYz2igYZu+_vUAB;Da?q-tuz(rw{*fahQ!fp_%< zgqFdf6qdif(4$;be?~CBQsp;u?9SAa>y8_bIL6vPHYrqBunc&4V`b}qrXIvI*nF*R zXEN~omZTj7pf5MW###Fn=uE<v|LhaxBtJtz%L5nkK2qN1`E+5*PPx;NuJEE(D+6#0 zgsBMPCP;7)LbV7mq9aNsfm4FM7zi>jY^MP~AM|EP@W;n#EWk<!L9Ev=KN~h4^(nZc zJ$nb2`akUxmDlUl4@KdTkod*LX?VM&QS$TO231%JR=+3&KX&`7e`;;+4O`rVkSyE{ z_G$-^$&2y1pl`sHK>UBv#S4ymE5xvO7TK7~pjs@>9uE<8Q&R>|NP3P2YrzYJ=Mn&k zGlZ~>%ic;72vkuu;{$T^A7!=@C)^(+;jiS!s#vccPY!Vg6URng_Bf@=d8hI?6;G*d zb=ZrPn-uHS%Uc;&FDH1!&(4-hMld}=8!1;P)(f9ux^eUVZ-MmubTc<+{ynP-n_b1B z8&66F%flASRYWwtO{WA2O4^x2K8)ld3u9X}uS&!CcmnJb?<%)3Kw2WC6fR?7Ne)O} zs>dq(FF}vNoH#`Y`p3q`ey#fJs4Va7?0gL^ynT@M_6a}=d8*nBIr`s=qb{lgE~7)4 zW7E3{DbB7_jlLWb)91s>Opc%pBHQ<1kDNvIE+G(Mh#s>&D`$JBYF}NI<D-S5)pVez zji#i=qQVX#r~?(ZB<JphXdZ4K9G~;EchBR5t;LmEv~PotKlPQ<1PeQsX|Y#gq1fY` zv9(*=nuz^=ku9zc#<X-NCo4zG-bl{I&Y}W1Qlj9pZ)|V(@IsWD$K#Cu@e<BC&cS9g z`Ql*F1JGAfVPhb&DF&n_^h|c=K19w0K}{)N^BLTCB<&Hapx1S~5<vtNc2YV#7)iqA zm6hADKmFI3b38mnpcAtCYyut-8-T_zH8&>;Y>gJwYq-6A(c932Vdt1Q=&&rV1OvB& zr#uG#u}~leVyyBCQuqE4AOqP_bsMz(f|8P1=$YI+JVr?P0?d)fv|q3aA)}2*E{Kq+ zC-;kPX(o<K<+VS_Z$PRiBNbt{NtH80ZjrK!_I5yW(1YExr~Wsd{p#D&ac_Sjk?GMm zK9SC;U&%@+nK(Qu?XMfpI9YG>+UbD=#Um!F9LjVxkkmix;n~??LLdX}OGq$$hGB`& zR8S;KIszfZ1uM-Ui!uPje9)@?edBEoZRl-?zv|}-z(!n1qc?z`09mpyB_2YsZ$SQr zjUq`WCQ8T9w2ENxBgqgX2m_J?VmLx5i;Xxm1`8Wos;508Jw1XdVl@Ufc<Dhv4;(2Y zm6G0)^G7d7Au!PhoRZsW>H<ioy)@N8krAU8E;zDTh)JWO5rmn=(Mm4Z=mGH<1&Gcg zkuqOg^<qOgy3|7us;5D|L2Q|kDyPpty&+n$p-A{BO!5Z^EiYkX0Vpr2PTO<HUOh;z zbpw>-V5|kX=&2mO60rQFFro=ZlDJTS!!(e1LP10e>8HemflPi#YWF~{(jzWT<oi6I z!}~1=Orwusp+A5AWYMHga_6bS@niPv3;nrZ25(Gx(IiKBO_YyTPR>0mD7^6_5*uBk z66>O6^R-MAp##M0j9`lt-7Ie66E7?z!4CterhAnatlFBRC3?t{c*5p&WXA?#sq76J zsDnm7I&lWqXkvX`=HEgkZAc;nlp%S$5f)a~@6F90U`I|Dq<A5nX9Vy@%XL%4o9!b+ zBigOtR>5pam>kLh%e@6eg>vgztP=TN5x0Yldt_u}p0d2TIXMVzK-5L0rVY?+SlQTK zgWV1yWDG<^S>Txi;B8&H>}7^XEbv3vSvCdgA+izg{B*x$25EY*y>Dl!4;#$bqrKI( z47rrwp-uqeLuusRf{KCUbtJ<XBtU)o{fw7ZF8Knq*eP(`AyLiNu&)CHF|!GXOv+&t zG^=PxqP>a^W1ba3OTB)Bj4Thfj?jazT|t=o5(4Z=D3~b1!V9XgJ5gLRa*yPkck%L- zbFSw@$T3&l2(}0$N5)_us+P!y{`~nX->gS8N3DWGDq?2`zpW{-Ejh>H)v>(@kBgEV z9^TiQy;g#IpM!&wt6ZZhtqCxbQ&Pg94r#i(yW#D+ip+)|ri%CzO1^%boF~%q0wTyZ zNCKyVFs1G$DQR{U><(`QebAQCj}W#kGl5Xxl`wGgQSWXNo5}@Ft<4Qjx8wJm>x&9L zzE0aDoP$0XC-dap?$2aoY|nmPV`VLHb+UeDw)=8_O}4gX!IR6GW27eTP#|0RgK?j* z*3{{7(Sr!sz5E~3DnF@Ee+@TS6jFtrKW@N=e+cvRNDI;~;pdJ!N^DZ5qTeEVpq?Yk zLh#5l?U|R7{l;D0RPz^-7g;{HF!~euH?NI)5%Ff9?2#(w(uIocYNEQkDToJu<HZZS zOO^UjMa<4Na3GwDr)haT-qM${w%l?O9UOMZ_3?th`Ep=th8HZc*RbX?E8@gMeEs|& zE&l<0t7TRExJ3ETx1Xq^AoW9$p;apg&9c4VQV693JlLYQsuP}x1ETig%VZ8&{2L>L zgw%KpPo9dzYApUv-qX}RR1&gDTR7%3ls!@*flP%S6-N#!*F;IQVgB|`W(N1on;6X> zA78T5SGYlTZM_zE>Cf@j?4&>Qdx}W;s`nRyEd?C%x{T09Mnz1uNOo;XxUHW2)HT5l zBJ;gO7xD|t{-@&Nm*Lc5gW5qmHsoHCD1^?Muv^koghQ_3l4H#R52t*Saq7D|Ohh)j zeqbec9SjR$?~QkuMxE@eH=-Kva#V0_Y<E66%vR$Xbc`f^^5ngp#kPL10=v`KE-s-X zni6y4S9e=mZ}ZcrH}?0}-#z9&-LfVb8U5bz{N;6-xYjDyzY+qE*UU|M3W*QO)%2N) zR{{dPaJLrZMwR(Gu%5-#!>0Q9?|crhG4f4N5Y7G;?~-ZheKEEXwtHydj5lE$JLPt& z*4h|B>$uSQqbRm|$^G2{i#5S?`Ny^H8}kXtQ|)RN)rYE=F12Vn6JsI|MLP5cCB-WS zYEv@Q(_xHOU7)E~|67qk(qjy{CA|a7U~Q|7#Jv$h#>VOH6yhc{^Kqt79v7E|o(J2> zp^WslzlKUglJp(+b4LCA{kshMAlk#yl_oa)ZHo2oU6TQL2Rw-mgoRovd{V2^3xk(i zRjt?D93&uO@}cPO?ryPxn-VnXvVWJGdo;KA3hDWA0!x5Cp_|*wN+rkCwy?G_vkV@q zCp7P#dVk5!&Z-QF<-`1ub2S}wpap!;Td=rj3F@NQMFc{mB5dguo<k&4L;CGIk>QVn zMItNYml^5U@JKlE@`K2p+4$ScPs~UZ_F<A&9fkyvHz;fQe!O>=zq`M`Wj^8bm^mVX zdA_Sa`@mtv$6|YKW$m|D3b|`tUx|sYW1so5T|l4+&eY!O#_7J1R;4s?hJx70&~npr zBnvfj2jPh3@^qfr%Kpxji!_v8ej!xa-4u>5oX9;H)!>O1Gq!`aQy^;M1yws=i7*#! zY<dzzM$utWe__>TKZyK6qKC)c?n=w7UYq02e8T(j3Xxwu>F+OMZo{_V4q#W$nVFxv z?h!NRX$`237u?Gjc6=U7gA;9ZZ?p~(vCe<iV~5nYwMDcL7p0{ok4L?E+`kluPyNm8 zosW<159Yg%B0J=D9r>L@)hnptu1S*Mixg|~*-0u!$Sp?)<C1Oc=ctT$=xO(|vm~nf z6NW;BAe-(53kUoChYSHk19?{G3vy)e#2^O@^fHHq>lW4Lg2=z4d}oJ`w`mz%YHHwt zF|j=N`Tx)w4HJUF*gpLC?vvdENu^#&_LC!n2Jc{1Zz9^umkZKg_<=LZnTXtpB_hGX zz{E=30L1R2`X`Wt%Ehl|`gmQto#e*;{&IdYU-PL>^Pl0O2m3)}B6_bL!J`ZooHy>a z6EY^Nu8)NpccH-3qv<JQ?Zd=P>s#ux{ZkXS6(3LI>wB`@fP33@UEp=R;KlcB`wh7D ziB5|l8-}$XKYZA*7IH+rc~iclDS>qro}mSi`U3FDIXXRd`;?JybX!6o-~|~@@OVT& z?w337(gEH}0la|3Sn@RZK=pql>{r{i0;$ZXl;geK-5)=GP>nXg_8`xHw7Gi71e9o| zJ6y;--%o3R;fL^e6zE;Os>l6m7BITL!=qUs#;W;kol#(HCq8rh^DpE%QGhsLzodS& z#tWc^hFyth5Xgx>j)LeD{IRq{CrqM$ZH;1M6w|y5G2cm%Q<<NGJmmtaawTVPGEDyD zldG4bU%U3&bXrtyVSP#@Ir%;2?TMmJwlTW9sHPyIP9g@+hi3SB2M3m>y);vhT5wxt z<UKf=dT7r;ZIQ8Hy1F90O(kV`bh>{_MRjXuN@Q<;Rq9WBjHe+%I+FyugZa`3fsRga zS#G$L{0XuV#e<~86SBpZFJFEX)}}uRp*D61dBoQoC5LFBqoX_Sk2<iju)Km~C6WjM zleiE*I0T2Jb#--_Bx2c-hdIr4=WEsC3@`$gZnuMh``n=&PzB=H1@bmvZ$K0$XA~SV zWFrX#kCCS(@QdfK8KFIe_MFn2Hgm8!t)jFC@}~q4!9&oFY^178;o*1^le0sdn+sDR zIVfkxh3D}n^}pr6?7y{i!^CV@9nMj?AMiGe?oFw~QQP`BQOgfMcm_?~hm;WR{dO+( zNFj1hW4Ghq`VE(I#=4AV>|%#X(^r{mU#%^O&zv6LCSiLYkxz1=_L#|b0hJ<}I(kp| z4kpbq8leo>+Q@(ee%MQL{;%gPJU;b$5b-nc(o1;WfHp|E;EY57Op}GpArR>>f``Ef zou9BP=WFT#;4Qyv)=^kmDznWZ_aHJAc|1_`GR|+K;m?tgKOtmF9L{D>j66mqhf%AB ze{F3Io{*84pWk_+eC~k73*SQkgt_kkj6x$2F~awe2eE;xcpnO{{GR4Fq$_VEe?x^c z#^m6nF+5QYg)K0}1+c$ge4$I^vk{-Zbdy;h#7tWn&CLr8gX;%*ipsya3!rwJg3>2v zTB2{&<h)kN#53bEs~;MSitLiThI>bH<{f5+B7{A*=dn+B?ZCv{oE~~%LHPp%cylYf z)e?sNzdgd?|5x5^#OuH2-Pkgo%>W>lmc}9f5^SuM^yDH?0x5VqG;v8uBx`{@T|~9S zSPnMfBAcQBXLFdqVnZI<!Kn7Fv7zA|XsD?}`_J}sO`v8kV`Ha)6m1b}qOJWHU^cRT z!2<L&^!R2u`2-Fm0(f|Fs3bdFkDGDdOjQTDsUXrpzK0Yl5;jgo=G}%ra0MI<RuvBH z=YSsb1LS6f_U)~$DFE!htudPaK7CD_*LGo}>DcWK$Qp2+kUZkos)wcj+$5zV2FC9# zSB(4c0`>ecfV$O{iv=ZW3b?w7T$8>U^8rU$Z$)Y0&yD8^-nnX%OJCRj2WM{?Rb?Lj z{enn|D3VG`gGh%;Dj<zC(g*?)(nzP&CZ(jiML<A8x{*#nkVaa%`&@heXFX?~bDmev zyqGn!W|j-~zVBar<8%FJFeAuU<w@lYS^tQ+@|wqpj7$(3iap)22-lNOMpT|s?r*cz zO@$?ftQ?ffbFtosBJR;!(oMkChh~w+@x$M?G9+vw2L%8ZNCp+K^747N;l_oG4#CBE zccY}S?A;Izvfl&dft2bv*Ny*u2@TQ|l~&WYut-+m3gL3FdJjzL)~+rDyu-+DC9-+C zJyQ=SMH!VD2z&-YQs51+2bNC(VF1NT%q9pLLOdS%t{~+`xU<=Aj6DVkv#?@0PT%tl ztS8V%iGj|M$wq@?7g(flnZkaVaB*Yp-#>`fhrXP+gu3PK{=N~Iy2y(zRB!e{)wJOV z;m!2CJk%iNdDjGw0@5x3wO(e#1Bb(QK^%^>?M|~EouEhW?(G?ZI7RMJ)T<6}C3xsV zQA;3J!Ch3DK4b=;7o6ic`0XaNlE{9t8c5HJD@8r_rVzMQKHd{o6U!{Ztn`BC1Q$#6 zsm&)BvYD1ptniJB^tQlpYCb_VYJ=|Ee<myD*J~Uv&RKS@HqA!zr;4<T5vG==l08&j zZ@so>>acDt9J4p1127{0;NXcHaYs1O=_3FpowCG!yHTw7cmYMl@2)f#B9qDa>(ShC zZR3VqKyJAFQ_jEQm4-(kbN?T}F<8<^;e7zL4|Q&J^(Vv?pE_-kPuDoM9{nPbfXW91 z$4)@xWr7AfGGwb`3aJ7fc;dmV1`J1p>K<UV-Xwf}DuBzPn%~4f>n_%#QAFMo`$!U) z5GtC25Py<^*Joskw){AS7$iKi@U&-+w}2tzFdhbI76{!HmBJ1>0)Q(XWF#&UWRkqv z*^T_!#+@A-2);apybx3e)7u~2?OYwoLT=H>e}%?E!b}zX9lB`v-taux&bOc=i>*X) zST^TJ?;$|s527Kj9xuUt_|9Ri1#3c*7SmfMCldsJB;o@CrKl~J=Vv4_UloDD03|*# zWbx?PsxCWnGx^($Hs3iqPExT;{eIR^Dc8{g3u|s|87*1p92-y&@wc}zXXS|MKlQ{C zw4?ZFSEXz7N791}G$rKx$$ozsRq=R#px~@qEjB6s>61bCF8n(U$CSqr<nyy?h51k2 zpLB*JNXPiCf?kU|rp>Dt`Q30nu_+Ewwp3Jdwpc~BJb+t>OoFCA{|JS~SBX@`P}<`0 zcs!Z~%L^3$Y@lfzMsrZ3*3r`93!4H@i3j-g#rln@GD^W=Vf0T@!UZ51o}gd=eTlz7 zApz|&2;^$WjUW>(@PUsdLJ}xui!z~p`#Wf9vi2A9oXDhjHoO*EM&^YkT7CfmCeBxm zqr29SLDX?u8iJ5%!&ov%0wzxtb`FJ*lms9QD)-fbvLO29Z9MYlh;?IUXGeyGA&dob z!UF8qp=A7Qe-NxdB!$4m{z5xWUtH9MIOw7+h{-dJ0_6@=Yl#GnS(Cmi;oyh1x5-TW zCX$))E>#bF$)4b`s8Wv=an`#ww})_8k^ehcBW-*Yj}rW%Lf9iegRgZ7m40J11GcK< ztMx~0Y<i&{muI1*OC86vAyywY#t`C!ctSy_{b_+K0}m+%66q)ak;oX3OwA(vKF-+F z!2DfeR;X?TSv8U$_RK+Q@r%5)tCfj_5*&9%>lAu$6J{o%pGL}W)e8!WN=ms@-q)C< zBr*^dm*LK9=IK0;z>R5dZx4!OAN1UQLUMO?rd|Mc<~ks(BB`qom9Qw{>`%8g`v1k< z%KnDmfk4jaC~|GHyR3MzXHF;~&A)b2YTva)djwEITo)d{t8HmbOiUhZp}r{nUS7Tk zZtZ<CvPB5R16QOH>T?>l96wT)QZ<1+n{5N>{PJ=FiRDqv(spc+vA=F|N{416uFN_@ zpPwkN43I20R6kjrh<-pNywLmL9_0^{b{qxwJx;{XP>iip-c=jI<!T_$E`suj<EnR5 z{Lk&h&a5=N=4#u8spO~WeBE#%#IGSjA3iB#>$=GjdyAWvJB8xurS2Z=bzomwLgHom zHw*MJfGttT`{8h14YU9*ijc#aWH_(UK6!E<b_-CPk;OQutiorQr@h|~UL8cvkc!;8 z4mgoeR~=?zgBN|S^{&WUP#_N=jtHBC9r%u{?Q+W<9xJ?1CG2)kmH{Xoc!LlMr-b&N zJWc1uF)|IUQem!!5|)`%_D@Eui_52kh!9K}R`>yAd0z<~0d%f!U>a(9V1$D*r_!hK zznF&Qu@{k1V4)kneH&v%Zl;F>D$LU@i7c%Q_zP55XXJGj;L{}8e%(Oj;aogBYx%BT z|7@x;BJY)mDqZP^qLyi6eSDW~N|U9Yk%QXp!5++w0tmrx7N~GO(og+~dH2cnv{T)6 zL;l_S_%|9aq6EiBR0MNJmK2oR3Y4-ipmFIAy4qTYIxf_mAw|3k`C9^Lo5C9TK;Q!_ zxPood(?TlaN}cuS=;$DjDS-D1XJ=DrQtAL#2Asb=i1Gqd))*-ZO|_1VeFlxH45Gaa z7kjcuNfi!6a3jHlO%D80QshI50@*Sf$W9}hdEkN|c`Wc(;=l3UPtoU|Q0+_X?9mBN z`rzm&pX2WH^Rt@%K5W5|sf>b8no2?mStCQ8FSCZWJ2=-XuuS{;`F(=~Ib;jk1~v48 z;hW1;E_e-4G%!t!;3!}@+aUDo5PJ4~Ajnp`>@y&bteAJm4Iu4Tunh63FgO}<Ij*ZB zDUk5~Af3k7KkVosa4oe@2_PsW+U#^OLIkc0GRO7Ky9?;R{e0yJ<iU5#06zhrc>MP4 zNZtReF<PkZyDtR!EONP(9N=K~?JCj+t!laAHa`xImFJ2qU9KvP-$d4y%Hf17CAmOA zDwMQH*s{SMZ|E|AXQi-M(ugtFF;?W4B0|slIcSU8oIQLJmvs>(gdH?|WSY}L7fBC9 z9_o>?e%L=3p*`OY_8{oFjsI_83{aMA930-lrw_Ho;VNr=r1A^NUU;y3IQR;|x<Ixc zKsVzA;vJ+!+F)VWIXdcq-HX5oVu*qe_lJKsY4S$BiH^<*lqo2KCYl|;QP}#^+DhAJ zlsrk8CW{o9!N152u7Gk>$#NnROaQEl(XX1(a3+&~bjJ?NEad+LC*PqQSrTOF8n_u? z+6qxex#U8E1R8(v*%~5IJ{lU|Up2!4@QK}DEM$-kymjP5$|IA<I}An6cksp|v47Ch zZyFegfIDqYQt$t5m_Vmp{mcFS803#?*cPNJ`R-l5Nk2Tyyz~eMADOPO;P_Mc0B?d$ zyz+0N95jC5$I!fb`!;*oBRnWbiikEt<%=4D=1qDTQA<|%Z0im`7w2?qXjS-b1a8}4 zG7i!Io!}lCB^jdF+N{bLZItAEuNA@Dvu)>WAQFX-|Bl0jtKZ{%U3{6F+tkiugrOI} zyr&%$szp{U<))cL)3w_z(TpuzSeY$hXFtS~;^Xhb{+JI4r2tzad}&X7Dymm-hPf}| zS@+T`I=c^88@#3IV`lCT27qaUjBE)nEck8X`R$on;FKtzgy3{W;5?cTYrWN`@qt+4 zQ29hYI!at{`mpTriCGGzS2&fJ@{1cw+Euw)X~NH<Yqw0(-e~3Cjy&GBUx}lFjl}Z| zyXFJY%I2m7f)p>I1rRqo20$o;R|-DYuwn`39pK{xJOr7n;9`jcDMtxg%@7KUIjU=n z<feZlm8p55B%1Q|iRJzKuJvEu$7T)|<j`CG!SK4muF~+7Z=0@-*LpoztxTU{oQ!%u zF+s^|K^)~uw<36^$ePRDc`pM0EqS`Qa{L->VGv$1tCWw%0s$IIYx7>u6!chy(-NU^ z{p{+MZqs*Lqv@_pw4;Cb#yDwdPxfTRg+zgoSgcQSiv}y@39}7V)rZX&IeC&JvT*>( zlOEL{dbp%rk@-(x`%}!Prc>+2U!jFdpKZPIrRPoH-ZqS$jEXc7frOS2uG#kpnb&g9 zmu*u0%Jd!m?%kB&=(M)Z5H?cCVcZB0$BItyL520I){6iB6Gi6^qg3Ig?F4RA=Z(wb z(h_~D%uMRA^tB7|9A%~L5O&1}_jf>HqrBS16uF?TJGs8X*{-9F8BeEH+SW#f)_x$v zX8w08(7(x&ykRh((W%<|OZ!Np<yT?m!F;xK3C82ci1&15=}D4X^D{z9cXoPv6WWBm z!Yj(Tts(K7`Q*C{dJ<nVf}o}ryJ@&fnjpmas&@N#BRj%k+%ITsEZyoS_5VnC{QucX zirOgpM0Xyu33}auc;pJV$vb>cM5rH8l|)yllf2X72F^9bOy{&WiYf*MPpS}^2Q6)e zTEQQyP_npx_P_2`ybNy}Z1G)znq<o~m3M`O1Xua9JBl?vNRpEEfA>jX3z;Z>EMVfY zXAtzb#cRQ^VXLGx_=EdvHDmCyYa3NN&z@D;hEp4srHYBvtF$`|3^r#xkky{8O7|}f z!?muji9GRcA1$Pici_3Wu(u;m2Tl?Qy)*wwQ8cV_gh6PL7vDVVt(LkNeSHnO@OLlH zw<Uh+JA7wKgOy^6zeGW9LI#hjGojSTe`CDeziP%y-z|&Fcy2MGeV<vy!3y2s0SAf- zCT1q3&|f2`-Ey58)xU?U8zJT*PFt(0;vl&)M<GA0YDMkq(Hyzp=esM}bk5fvkthpG z=<9!La7e_N(tbSs_ot+#+6{B-wU*s+niqW4bHWy%Z7v)7Pj+9}gB^Y9F?#QeUPkd) z?GaP-zR2R+>tqNxZ&Z1Da7rGbe}>@XlqB*j>JN{#Qq%7zKg@0Jb#zKHJ<rVri)OF7 zR^a3mQN|&kDC)4ke4(3r8^s!zDqP;Rz4ZP2_7(>%+9iGkif0BY<qX)7k?!ja7~WJ~ zejuj1XyR$Xvt>tx6tGFmaj3BfBEvVBmU!<{2D#&UYnJ={IiC9QcYpIw2;a}(%$?)i z#WvFW1{4IX+M3K^`&zU87cb%U+0+P|?3wx_)o3;FfM0>dyB!^52M{!PB$03nsPv&> z+@6B~iq`(;{6*@$AlQN-ncq@RKH9B1D=8`}nz#}Ocp7Ng!kdKx&Gv=80Kdxd*{qnr zq-Vx@G+i_pHN>KTk%pg;@IH>Z9$>d<{*}HF8@l>EP0`ysSJIfS^W(3J)6wQ4Xr=im z$LGgGfKT}GS;n=_oq0GI?Z(`K#65>R+U|;XKJtBuf{k|mwteG@RfneV-^FWORN7G0 z^V7L^>0El@294~3Q8)C~Iq&)cmy8?Q97u9F)X?`O?tpj#5+S0f=zaJRk_QHcF`$Oj zEg;Fa0)6vm+HSe-5z3hwp*7Ufb|j}sw9Go$-%%PaE4Tjs1PQ`_^N&P}WRA>WL9`;q zBaJbu6k-a}#N<P8&-qGA6MA-jjgvS6Hm=>@AxI`?_%-TBJ}bKAQ+w9O#^eUJ&7-4) zZf<VSFNG!rAs`6k(u7lBN4S&y&NhA=I8TrYi157AcpHKO|KChr<&oSeWsV?t{dRPP z*(b>0%=|-UX>C;27UCuWC-DW$Ai4;BFQNgF3%om&H{INJwUh|2Ah%|yUx%mJxX#er zcnuzuiTgcn?$U=%buP~au++6t2{N-zJ(@{7!rXqX-7G*cA(l{*3JM;c$oRXk{mIA2 zM)5&T<j<JTe+~{yS6APGMJEGiI%Lf=nnB(WD)Vyj91o{08n$j69UQ>bD-bU2ix6c% z2IZh$1FQOj?iomE!9C;2u2%CQoRQ%R3JkT@q3L2oHUyj@>_gZvGV)O=tEo$R9DfNL zfdI}xo%2JzM(2l9X@%;~3)Ls>t+%IpraxjvmwP@^!w=qF8=)Bj-ash((U9cMW|htI zZE_K|vu|ry6+}BHl2o>{*0hg`>jLd5$V59(O)w;sp1_}(LBh5whvCN0uLUuDmcWT6 z(Bce%UKO91xTnD9#n^bVfP+Llz=;FN8bTH*01XIDIb*XlaA`RKB7v-80->-Sz}wK+ zQx7<Yc<Fz!G<d!?H0o>Mf`HUFaEMuvVW^23Mg$#yBr$wb&!PIB6U-Exw}{W+(*9L5 zvkofTgO1n7BGv9sEA~hzNV_LA%Tb0lG@=jxWCc3A6TMA+m?P6X*Sv6AHM=}a@s_HB zNKu3{G5mwY6DNf_%;sjB<Kp6+M<;((cZZ2U%5UrI1HvgkWSQh4tOMZpXV53?Rlk^p z!|ep9-M~D|fwLI6-TQznR4#kl#k#!&-T~bH;keiaohC^0*s~cks6PWTt}P@&!8JfZ zvZ(>*`k`_WN2NiMIRa|SZ!JU+;##nmA%oLcmEK_fe0L(_5aroGIoT3e{jIcGTLK6+ zQ{@`ZUq#PFnY{JgAF_yxxG{7$ylso;%-rhUnx>&t46GnhV<D;#Iq_c1k{p@w<Q5PM zG}4{)y}lx_nnOi%V9@fB%uyb4y>!LT%5r4**np6(j2T3-EdW$2O2-SOA}HZSzJCf| z*^TtrhuWFq*hqv_2_PhX0xp)P#0$cSJjY-5CaKZ^7PFi1GuaD>6pDoY05Bc|#yfoV zVxa1lJ5Vu8!>iRcJk0gSO(vKhdFG)Uy_KGm!=$TxQ>H2T$wNY5&PM4o(nFWO3s+!x z;b^9QN_Oo`{sE&}1xs~JOOEW^cADk9Y=rr*bT_9roEKXHuj`+!@P9@_u2^cnzW7a7 zKetHgBgZD`-W<wOMs+8Ei!GXp3XmC$fi7s%{YFX>QUJ?8o*`(NnVF|g;kuHg&Z-Y} zLFf<~esM8tr#4|28-fNEtj)-lJy@_31_spd&ATtw9Q+JBmZFhHKriCfT_2x?-8lWZ zQ~T-eC^3Z;e75Q3KL5SE5_T;zNlCFV(r=cUXBJs$f<tdF$IfQ2huKb&iTF8SM9hLB zW8^AcY$_#hATC<ESl40O%V0SfaCnuBPS$($#l`(h{(<b7xB^BJAm7-sg91ZCLTD8) zMQ$=_6qCc%G7&0XPsW#^?Ysavr<3#Z5x@nIYw`kZ{U!2|3DmHnkoinlYT+L<-zwpB z7w=_nhM0tOgM3m`_pJ~AybpvK2_@zaH1zM05C_PIk&coJ3vMEWT!Tox&X2{MozKX= znEh5HN59F)4s-^++t-(p+=Ue6*c%cy98<NJOFbJ}&k)50Kqvv=8ZxW{&kCD<L+=q` zPAIaB3qKb~&L%+FMpkN|w~l1)l-Nf7ueeS#wDq}?-C(YWsuBE_fB`1;26!Q4!K0S7 z$jHd#$G(e4X=cNb;Ym;!k!I5;9rEH&3;$U0$zFzflktn!FUu#1EA-^bx6me0Hvg&I zg)`y7ZCu+lZpKZ$MwAM3Yz-Sr)_>DU{QOaYs}D$(7weBvR+Y_(jAd|+Q&}--Q8VV` zVN(EyZf<F*9qy>eo55HzX#9yFo^k^Y(e-#M44`x%b&_tp@l=Gz9%&&M8q$FG&}*{= zsU&3zK!^_~>q8VCLO5oWP;9f-n+?up$|R(LW)HGeLk~rt|K=^x&=88E#sFAl`j-7t zaAuH<V=$LdfR%9<4G9|)#B94tj0_2vhUs@N+OQ=!Iq<4@O%*!M{buV6EzJf+CtjE5 z+8O`)!moc4!rs;~Gm}7awDU${ZAw$=!)EtIq9Bbhm-579HlnQ5MBdosy8aeZ_s)FH z=Pw3$6cj~pjg~-!hmS$^CL9zndO<Fo-Py$wLDwTGL~w{Qel$Ljgc5S<4GXNrze@e^ zyONNgPc!b%gm;uHy)6bso$TI5_9eafiKo=Lec{5v>yJ;L{^9+6|G`m!-_6xuvUFRy zfl)DSY0-?)ctrl7+g0BtJy;GoUPGI8r^SD+SBGM;!Cx{_F4-FqLcsj<SzFU6A;2Dg z)LA}IqrANReQ|ZDMRQp^s)~+|en;@^DZ8N_-aV4%FO+W}Rm_(uqz+`iaY15M*b?Zv z#5^{ecxEJ<EUv8c33yWTb9+mFSNz-11)bOqHgPyPGd;AsixnXXvwH-X&TqBmY8}S7 zB}MuJi`=Fh9GuM_W2^^UBVWEKP|V*74<Yba;%ddD!g_wRO>$*Mu{c#ib;iIzCv-xh zu`g0>E5_81k1cBXg0)J2YI&hGWa>_>{A<hm2pJl9@)7oo`1gpLzu(Q<T?%6VVRw2< zzxUjiDTK*472_TeNsh%XJ$}l6Cy2QLk@l<{W1m+jN`&!T=+LPy=?o_-a(QnVHPCo1 zd~wPDdjb(aVPdkjO+rlG>+g?)YC|SMY47Ouoj*ev;mU;q$WANyrS7>U`qb+kvE{M9 z59z-a6)bGxiV7q-dWWLLV1>+1f2+ONKcPw$7kd7@xvk01j(90wsSM+ge0``wQ=`&q zs{2Rz$`iLO*23}Pj`~Oe6m}zhJNkoLPAyxX;$uz%3Z>~J2#{x7%=I$%lJ1X$Z<hxG zHPp0DmdS<;Xmgsanr~!8tZjr)Hpa5#G-@cj3wp^VODXG+-=U;b^*EmXiak=2rg$@g zD)3Jgca_N~({8nSEQi|qefX|Xi5wp*D2_43(@Hu#juelp3-yqmTNI^~5PtEzxy+BA z?E~BB_Y%ZMe>9VqCaVQeB9kER2Al=_PMB2sRmoBz`>SJ2oKVt>{50N6?3(_W-f`m! z-0PzmZyC52tm8{pD%H~BIMdI;(TrHc+hu>QqD}k`xwq|N+y3c(^9c9iCEH`omhvXi zP}aw0JVr0aWM9XyL&+WLP25$v9&SUXr|luV<M(>SSb-B|DLaH&Qa|l_v^i2Lil6c+ zo6dH7NeKD1rV+lvzQk88FA6A6;U=bM<+`j=YDvEt`<`XdawRk)^&uxVrw(p?chB!Q z7k5m2>1LW&giS~d4D59<DY<@eNz!zES{ZnLnM0FB%8c$EfGZKSke9~r$k*KWV}_IU z(3;Uty!%4W7V9YiY7?u0jy4PEl@X1@rm|2iGrdN=leAgCh6X&mC>-MT_(U=BVOj3N zlsfm5(DP$Q)=zZ$2>GBT^Hnv<mx4E&W=2yJk9bSDD!Y$TKhv`onCTPX(Bk07iWiI2 z_m{7l{-7@tSDx!4K!*%sB=|rOD^DAQnOwkUq~9QvE(753!q6mXZ=WMecT&Epw8JT! zJvOt$cWhQC)mF>GND(_Vp|^>ZA}Dt|3#nVcc~{PO|K}gh(k5<4hs`0&+f0NIHDQzM zVY=x+FLRhns7JkPO#T7!OV`rxWfL?--t9p<ziqB?FXQr0m~J23&No*fVzgj9IOK}N zI>fkrh+ANyfvZiRO-N`Z+}O3h3aYFWO&6<6CJGWU1fU8+XcQx(^ss2J4P{}%A<Psk zOF{O(@9XGJLu-_P#Mz-0eFqy`+EXvn??Z1|X4B8e%;=PlA9ucdW{SMhlg7rr5cU)- z9Y`}$seg7jD!lm5(2V)}D?D3|1hAx^xT&|H5qlr&Ams<Np^@saO@^@&#7L=l)CDyU zOH6v+b6B&$e2|epPkf#9dS#vWK-E>Xtk^FFL4m@bHXE~LAYDog&j2!o3C{rXOaYI> zD#ZT4ih+xEBQlK%eT;p`Jlg|8>~@l)EFB7xo$FvTSVX>thew@=R0}FgEiYY%8enpR zsu8m`-{R$!W~pzd*L9dr0kizdfTRfRd(Lto*0ah5UlsUc=T*K(aI$=?XjTa=-iFaF zQG()1gj<bu14SfNii+PjE1TQ<(P=Lop3VNni<Oa(*a-f8|4({4-UaJxh5)HxK^Hx& z8nBO$4OTeL+MevZftqYHgrXHhMNvTDh2a9>NV)|80gywvVD#QC+UL&~N?jd+jfJ74 zl$kB79F>23TvPh*?C@{)q}fQi*+}TI+sR7WMY$n5iuJ3cfShQREv)r5X4gS<bi~_F z*O%<}8+r|dzrN18ZuGURaJmKrsIHAfoNp{InpK9o9oHx|)NK_-NBu)W5Q``q%xq73 z-*jum^zBnNz;Z0Z7aQb(-(#~q?<aJ3@#he23=4C&0>o4Nz#}uM?t}}`2^>q}^NUeq zF_7fd*AMeesF~053F)uV+#xgyS63XEMZ@q+A^`#39Ov7gcu1Dpa?Sq(MB`+iD>VQx z&`+tpbMkxld^BISukYa-87WD)tgB{75K-qU_&?zG>*V236cFcRyOYRsFLl&u=3eT* z&~<*vz<{#xKS_|i_~Ps~vWnZcSl;W4zH_>gDSB|apJ2Zk{(yEk*6jcwI(8bVmUtw@ z9k~oe9CV~-r-RnYM!F%{1RxqCBik_A2Am_1SS<;_Ie-WS^;b_gTp67(w%S^3VTihf z?5R3^QGg9}7qNSPjciHgpZ-g7A<q~vw(fJ=bHhNh7Qa^v0Iz~PCmjpR4`&BBq9aFq z04m~F=)~k?8Q_N*6q0{J{9$Bjc4~^8fFN419#eXr5u%p+6+|$7>KKX20e=Dc5a&A% zz`X{z(B0qzA@62zA_AWh3k>25!dJ?8h#)DejL3cxG+o>CN~Xgbkjg9AS7|ttVe+c1 z>~g#w7L%4)h(Hh^uyNbJn5(Uu8a)5W_Yt!-PQRr+B=cKk?RnDp?FTf3O%Rp;gHJ&+ z&sSEq@I%E(X8OuM&uuKzeWIG$k4dKNc~q32VK!bgp_sVNO(pCXKZd!-S|`U6xt5KY ztTid|Dz0H6!S*io;e>7GHL0edv8BI{-j-+R=I@4&XNT)X=BC{<6mBlozuht$(i0NK z;tb&20nIODvd2E{po1PJ2d0wvf~Gx(9One>1mrj?xVPGZX$#}5kn}#Vv7q5%3w;@w zJ`kFcLc#bd^)1-aE{_wlIc!dB_Vxj0pF(a1eg2ls^L_!EAwGHtSjrjT#j1A44LFxP zf-ZLJte52M@B0zkjh)o&tT!twGetPwHuM1gDYH`kg|7`d2?|TSfH$3l|8&S=@`RfT zyn<zjXU&UBe`#qJX_cftyiB+sZ}8p{*BcmXLITHcFNY6rZ}TKi5SZ96924-^TC_Cd zVE(u|c&4<g1ey#fSbbad>qPlXw~!cq2zO_~0)XV<g3)BcqyiL40Ip=alrKB%t+!=i zM*MbiPO~IqWRCia_;3G!avFHZcObJaqoYF}oSB}N7YNw!nVA_dC`e(_khdOumeA-7 zDJWn@;;X>_RK|{J8p>K2>b)Jh$ABUz0M3BrBwZ@|>-0@|c{#$TBrus_s}{Hy9d1Ix zZl7gkKcfs9?7jW^MZJ$K<N3jvZuv{quX^7^4>H{dK4LT{aDTI+aKEsf>G~`^v$oY1 zTB0@Fw8z1?T(YuxN5%26a_ZibJ<eL+5BKoMn+{FKUGDGVF>*7S)bzDc_oDRNlx3BD zZD{z;7uela-4`9mqUY;+zqXq%vcxz!Ifh2J%2^(Mbs~OAEZ!!8mqusa@tK#sM>}U` zSj+2r%ftNFSAY1d*=h84=DN^O2nkyZTWwTxtRj$w9SAbC10+uZ1agRw-G+Jvt)O5s zkOT9WuOT)EqdkxX#uyg0TNY#%ch0D?Gu*_?C&x$EIe>KkVNV?OU}t9zrq_^?lw@Vm z!bXRjD~9BDwpKv)4ucwMrtIR;gnXlj=jgp>B!oBB)d#;2RsS$c({9i?IISr2l?dUQ zVFZy(A&d-8>*DQfy7?Cj?>Y1GAE~7YJ^Jb$&DiJym!h2sQ^UU4M~dYpGu<Z`UyJ4q z5zl$%I4X}X!BA%I;G1B_(oy_D_90G_(WG>_h18J1sG|P?!vp-d`;u@peuH@9{Vfr9 zjmwWaZ(#L>4hocFx}jCYBV(XrNPd@=GT5^|*w;VO|0OEx8^s%@+NQ-KwF>UabquXl ze$OX0w?Wg&w3FC9#q-i)3i)MO_{P}Szx26X=&0BHb+gKAsO&)=ArIa-6jd1K0?Eb2 z#l`628`Y5VM^;!TGz!ir>Yjar#OjsX0To{dUrae`+}d%==AqZ^k|&PqF}JOw2(jP; zb-%iJ3LQewC5eFp4XBxzwF-y}K?NcNu6j%U@1j})o}pEHdQ5lklEUS<ZStqo=C0eZ zpT&KHt*OvQ<4_k%i)OfYxq6QnAj*K@794PZygh>Y^1OASec?1oI8^4d-yI5Z^0~7^ z4pW7h?s#l-C(EW3-F{V$I}1(6f7S%|g>*w=EZ|Riwc=@L+4Q<)u?01MMKPkkKgCS3 z67|mV9lCbL43gZy`UD})Hato)1yy%KTWRzxVMBLALHIcLo~)O4;K2zbIHiCBUOMFh ztO(qdj&d?O7DW90c*Q=pMK%%0ZprbQSQ>=ZCw%y-`_6%hpM8c7=5_*UJv}eZvO-o$ zikgir9Aq|CpF_yl&~tEn>SAsngt=S^fL`~{s*Nxd_(=>WF}d5^B&jgyF>bk94keP1 z5I#qwo(qk@UiDlnu=3+0|4c+gwS;J)I(!%_A~!R5|2RAbMpFLNHX9a?3G50ggn1Bi zzc~*=Lw8QD+-~nb>Z#FSkLSU*n)dxwINy4Aw$5ab`ga8mrEstPE|*y07|U|W#z0V+ zic6g-f%FJwW}MPz`G*hLIK%H<(Y|)Fr9jzQnm)}v<ma(B&*f!D@<;RWWD;Vb9T4xA zeKS=;#Av$T(N6*^B;6+!HK$KTrTeSuj8~~MO!;BXP|BF{aEYUn7<K05=gRvkp~)H= zb$%!u;`#FDJ{}=qZ&+`Ex%OdL7rAw}9|Y8HrhnHoYe~atKW!}$&Q>@C0Q;kyr3PLZ z+T^xlwb>2W{kT^u*q?SDQD$v;7sGWiEF?uXcji^z6w5qa<llQP_=7u;T0mX}c#i|A zJPmXd1Sq6}8J<74xu|yDTFBJa)U(~Dgy7zX<`r5=Xt{83c*j0^&i=8ncu+R38Z3R3 zLq@qK1`!K#()&SQexb~I;v}q%PZtnO)juNaxFK3@veVfUE>5Rqi0>AmjTy0~l}%1S zL?sazMuGga&R9GeEcPiNIU|*Gk>ij2NP1<I3LPGs#}xTg`11yI1PIEycWmbWp?g4Q zR`z;-Pq9FCwctQXIf{dqCnxbeug*W6Z>(<+GC41l2q`557nb*vn3!IZX~P95c}Y$W z#92|g2VbCbNsI!2^t|Q(0WABe!mhy$VZuWuLosX`iLh0`RH`gvqMNYpnL=d^ioD2y z2l95A5cYx<V4<s(QRkTlBB&Y*D!}D*h2qMJa7_U!SB_=R+rDq$Pu>AY4~e1FErC1* zgiMd30ju|{*WLGjf^~vp=;#qqF4U6^#)QqtLsS8$ks>~R{$MeMn%iNwfX@iru)bB1 zR9Hfwuy0>B#@QYx+P!U()O?I{{^x!wO0wQrh!uMGLpVFt)gJ2J@h5MY`k4$ZW$xUr zc-HZ{5tTKi;qP=6GQV27y0OuO&@u@!e;gf<!f~IDjq_Ny4O<#pyp8^mVI@PQYDJMu zSQPI!uFB!pJy9`qR)N#YTRpWXzmq0a+H++p@gG6F17^D|by9i#g<vCcuEQ_phUA#< z%t#pUuI^?J73THa*rxWyw<ApCFcz5{rS4S-G(to)8T@0IMfKGY<{aLJ=#s+LJ;%$f z`ntG+<K6!O8|ioR*+=<457%u10_X}7-_48cb%WOYdFz>klJm|S>UfEM9BOIQcO_F4 zGSU}o!_SD&sRean9DM$c)tK{H;#I^xek^g>cmRs-2<%@F8IBitws&-}cDkH8($aQQ z_)@jncctIo^O9?J|9D0yQt0dA>s;c;2A9JP4y->g>XW?iJ}kbRF;QH(2j<vS@$rPc z$6W>thJTxlBoVhLpH8aeD8I2bi3lsjJ#O9Jr(4G!$V}U%Sw@G&FNTjjWieD8ep2Gg z&|3EG%opvj-L{Ahpv21LFwbW)z%npr+AQ7?l0LEbd5QVG`m;Zc=Kw83rg+^U#f}`R z11Hn{b`t%aH^DXmuoD4-;o~-`KBbVS_!?KvDKMTM2)jFfxr6`_a@dmm$m$0pE31Aa z6&e6l5B40eOg@-QdAVGNak)BYUvgVQJrDHY-j-<@Iv25U0Urpf3_`~MIAqC1hryZ1 ze<rSH*2SN|rfVE>Vf+L&n)=vaBACeKRg!}7NFA#ut(;q@KJhDidne9aN2#En5n%6J z3i-lH+i2SNVxY;JkBr|iujg$Z3A;E>^p4HRX4Po>?GeKho7WXGbRV;$r4#i3O)(Sh zcJT^l1gum4ay`y{^K#M&)p%+4(Ssjubnup6oS!YK^=*V%lugo8bFoFir)SRUNPi$Z z_iioUIHTtl=PoC#PeszooO~R)!Sb;@1ax|Bb#r4O+Q=(yx~ZsfQupi6-+K3W?R}r^ zip$HrxE<gcpjr9tWrxI`^=cyuQm`q=;L(%X3%FRl8D&HQuzgN=wcDGUpW8D9`@%GU zvELh#&{l*Y2c(duF<KwZ)a{gw!X-ra{n!pwI)_I%FUTD?|K1fI<ojHtNgLBRoWso) zef)3M>*?_qN2%#jBgeuEpPekvJ%qEcOBvJA1p`0^tUG|f!lGsJZcOfi+}-*ZZ4M&F zZA4thlRvKecXx$%Ww~pA{gY$%nrQC*d7DIlghf-MMRDNM@3z-gf{o2^@I!=0c<y7A zK+=5Ss^|QsFgBea8#&yApN81MZ<Qc(Dk~*J{nrJHKXsb=Au}og8i9Gt*U0T|s`Q+T zcKQz6#|Nb2sd}`+oCLci%pBZYtz&J(c*d>{gVJg+iPVlBry9wI+?vwduYsE+5+Mon zr{#{LsD+;?0_j@>)9F4|#;8{(Xl%OpdIQZLqdSd^<~K_IBh8fL<5NB8>KYJXWEM`f z1FS2Tf#?iQ$^*y|CvHNHVbDIfA=iw{hFUI-jqA<TIs4)>ewWZN+|eSOm4Qb2?-IOn zEhYB$=O4c0U7!*fEZ{HR|HmI+-S(uW#=%%8x=ubor9c&QwKwlA`@HU5?=mgi10fLq zgzi&RE<Ll6-pj(&nGHGydb_1AB6;~2h4|{K-@bhMVwIN3BpBh+4=Q8gLceiukK$OS zr4Lg!dD{I5-xqKyxemArfz7U_0&^rYS>kD`qzI!RTXI|}JFg=yeN@ZK)C*ouK#YRM zLCaqGnUJJE?UiToVoNKE$R!=+`N6&5z(ia!aXXl`veS5-(|FYji8<SqJGsW)gU-ux z(`}bI{;_I+Ln}~`u&xcwSC$y9mX`6=cSv)7XnyhKx3m0f)RM1%#D@#*sL@j<7)|yE z7QMdlSFGgqi!A-w_G^G($>W}V^@1v%Qdijbky0sK2BlTqCsV`^4i7l_xjNw)Vc}ED zH`i8^wN)ncCmCX|CUuQX8nDJsBaDDc<1bnDygK948yOcq-xk!^HB<1<=>2$i)_4u^ zXLj7ueDN{pnclM*Mr}95sDm9zKR_tD-P=YWO}7@wm8;rGZvY1E;ZV%(B3x_w_!mxp zm#(dd`<<NI_smj(AtVpWGJXlk-BI4Gmp)lpQd_Sp|Ml%-;^IdD*S$z5VNNX?v%k}c zhGr-#KG5lOyA3;~JLW-gfS&Of_RzYWSzt~<@Pif14vY@uw&Pg4{yG*<{j2HFK=t0= zTN<f4^Nl;#)z^E%rPy7%QshPC1!`Z2`55>T%h;4`l?1W8nnWV5=R5Yv#5*K2^M<qF zGz3q*&kv>Mka0dfj*gsc9QdP2<80Jpn&3jsG008L@bKkl|42eDyX2dh{V2s?SOfKO zkhJF(#AdCNX`@B=NKzPl1l>MQ<?#uUJ6!Cm69S3x-PDt(SKC#y;wAcIWb>s8vTZ@& zo-S_6zj#Ym0TW9mvtBNh%q18fn$KTVR3I}aMy`ThY&R$u7bT+lwXJhR_#<4dbS@H^ zZXG;6q)3q|&JA@VfDKXFl*>3EywTz9z@d<X-Bw$_ut2%xWlspoF6fI_IM)#*oa~BH zSFaT_U`HpX^`Mm>7B^I;u|?R5w@XIm(`oA*s-68#noybi$^+u_4;l?U{IvTer6{E9 z4;Bnpd`sUf&kRW3i>N-|V}rGDlub=Q?fr8E9phg~e5r6Fl)&2$d|})jWQub^qDd_M zdLahy8W=G2d6`nd`0ID5m%7(<PSG+BaxMS~vcr=0aVTqqm=j)fmBr3zyaxynNb3%B z$Ea)G&WS4h<={{pE!C{yx%|P$2h#tr4gY)J1n&J;{XF6YDwr_Ut(E<Ju-OXt?)6E^ zw<P{j*IycS>80Bp!bdu@A3==X|I<GZxxvpr9g!Q5`$X&MjUB;dmpxuZo{0vtjJ)C2 zmY+A^O#d~ks61J-Q1<qr|1ik`4_2&9Ed9i0-#-_^(;!$jz)u*e_6Ki~TI2<e9X&Q3 zIH$i}tNX=q5=N=&)G|;y>YzO1!Ve7<XKsnP*u{ie;X19?fzqS}`}GdmD|X!$NH{Uf zx_?Z4p6a02O93<6yYPqRP6Da?N@}g94_3U69yn~X-JuxC^jAcYr|7n4tE0zdy-RS= z!_Ff&G*Xcc`a36|0V@g>|Jg~{rS*u|G&U^@3yX$p4+FEbV7Pnl#~sW>Vk>%iS>>@h z)?Mk{Ho7)ExQ>Dt<m=lYLj_-SC~hbxCl52nuE#{P;w+x2fGOy-n#w4Ls;*Ds1#ATV z@xfin(Au26$U91hc3q9&X6v|!yImx?7Y&Sg<W+$XVJ;wif2bvdFJ6CUCvl9*le^~_ z{R{AEl;?*8xV(CdP`fLx>n3+`o8;-erAQLyU^ap8dK~ar+fUtXLHw*e<5#m2x;Gq* z=3fMbeDo%{G&69C#}g4*ci+Q)NlaWr{3@i#M;qH6Kd7RVH#*5w|M{=+@y^LfEfud{ zphQT1ZX5imNcl2YcmNJh@IxDzzm-vbfq%AYlxrH0phx<US{jce>3mwk;W-VWjkfHw zen(wf@6Uf>w=xn^9|TATv@y2r&z+*lB*ymMRic-;FQF6^9K5i**a;?oR$8D?Kh8Jl z7s{5W=hn`9;)3I)=r;Q%_rl|cf7)%Q23>#r%jL=}9fX)4kd9W2ipu|;gQKwdXC>m> zk7lP?pT@>_tyQ+PgfVu!57LV<ov(%cwD}gW)xJgCS<X$P`UO483TIOA8WTQPlA;(* zb!M-Bj#Pt^589_qnhS(9f)rg+iLiyX6_#O1<Lm{c-&y2c>haicm(@JrhHsDASbaS# z*wIplbs#f!S8n^k5-W^Fx5&?)gXxn+r(^?<_H<E0VUP<%nHhn)i)<w64sy}Z*DxAm z->1+*DbWwKUlG;Uua)k2C4#s;2deC@X^Fkd1eacbpMjzEtB;|RzJ%g%yjbo>5J;HY z2i{p@^b?n8?v4cwKISFGkLvgDqZd(D`c&r4rqj5&P4M6Wtv=X}_~Ko?RC&{C9$Xho zdCSvkB@CigZnz({z*sC;2JTU%{qXGw&a_!L!d6e#eGbZ`r^f})Em`^;<!0tr*<x%$ zHEk=M!i+4!x3)=VdJlT5Emc4((IcxzF4hEz3UfkL$9u0{OGmZ=c<R+-FVYynh56RN zZcyiALHsfAfWUVKbk^ixL{g11fRrJ!EI$F#eD3k>yLXX8LSlh34f&)!QfThqN=JdP zz4(>x?{=W4r}}5=G^ToudxVuV64QLQh!gz$DV-@i+|Yd_chn*k2DDr#7k)+>%S&7H zt*4BZR`>*2OuXhnN#tB~moB!p!G@-c%M?#OdF|yiGp1p2!We0#1zOrzeKKBUXgd~6 z34D;tvn!C6kx|z1vQdi<;M)J)>)z<;Zdg~gl=fI=n#hqTH-iH=r)``*ru=UIaxJUB zkI-{B!umDWTO*1M0*BLNE7uN3jQ(a!IgO8t#5*LGKl(vYy(_w;161oQ&@14~;J?rO z^(n-cV>@);n3%i-hA>F{jIXEnWN8qgatbJ%JcJ(Jj%_84!JRYp(VcfF3H{UFTSD1q z^Au59dhyhy%;xgLmfY#*$NTbQIyvIlC#R<K8oKpb4wq6mVq!{|m=1b`S`<;v2Tb?l z6O}W{nZ&8SzxmkzR;H^t3G>InD!xg8NvL_eSB^JG4vR_Avx>&HM6TCkNjP`e4NTs3 zygn|Sy7<cgZtPMLt3P&$xgP7gDim*9%#L}lK9Sbbj~lmhGaV(s5p7vXe&rd&6!|w^ zPzeR_a6RD6<-;(5dVLSq85pMrf$&Udjsha~1f+K)zXL{Q8-V`r@-QHq!|v2>Z=XfY zVOjn59pcRU_uoBoEmTKzUR}-~4$H-Po%qD@TN^@M?XqS0%p-+CK58Us)=Qsh*=(q) z#QEUR!=5BkFx$mGqvYzmWJl)ss*?Q{%awp*aNNU(&!T1fsv5m;ns!`UOF2tjTK`Qg zf+;TdgvUqVjT4iZR#d^Z+|p5A)oJBsRZIobO@#Ebw+DZ+PW3OZGtvhKs;J^9b!D82 zGa52-V`q=O++V#=W;ipKrF+Hz4jFu3Mg|7nfG>Nj=fNxDdcZ_|>*ctInw}vX8DDE@ z?I9=I_}`CekwQgW|7p+_tb7+ab+);tva?Sxpl_W(2F`h7p$6I@R3LoEH)#cWQ*mKd zO_BB|J!zu5ckl7vC&q9)UM_TIF0FuG!7d=AtOB3fJzStb@pZ6Fxmej1A+L5p@rV^p z<GbP+D31OV(=^sp^!3Ee-{h-AJMlr$uee{j8urKKwQ?4mxB@D*g3%dF8PJDVsJJ;G zlM0&fw@2?Z0`>z(DSYb-gsNJt_ATa0PU&*QqdY(-t8+VM10gQk{d6y}6ejdgz<mSa zeLO~hg^OX=7yhGZ8ATHmsZ`dXR$tTKwmOK)Adla{&P&E0tW0ZVwV()bFRy{ru&7U> z6HYVDrD}y2Bv05m_yeiD2nT>vP^73Q`HFqw)@#wJCVuN%#x}DfR||*X6m5B?&AphP z(=_jhR;^FxztiO$KlY-$Om@jzR&r4?cD)=6!Nz2Szy&H6{W8h6zH`Zz+kLXX^87xT zp&1Pi8|%->{<ywh>@m?dc{1KM=r?2?fnhLH;8NC5l--0h;K6@-`SNArIK&VE_Ko2U zBr<Iaa9+Rn3P#g$TizdaBZ|uw!+|F>v)TVyo4iufirTw@Z*L~;w=r(+tPY3_adTxh zw;@g+z0(^hPFcMyWLoGjGEx~(dgNXEp}8mS^pXB8C?{u#WxC26yfxBWRik^z6{UUk zpwniF2<|RrSHrF-!j>m*ink~Zc!KUac}bn3FP$#Tks!rAWs{tf>Y+}!(KNWAN#}m! zEX|M%qf8us!2E%#;qoE8wmR+(k=}FO7kIB|y7<6c{>-8l48pR73<3N;0T4`r`XtYm zzBefDw!nghX<&OVZcD$BNV%E0NHfU%)QuRfai>t0rH|;8d}()-WR2o%(S~(3FryhD zY5U>L$xIl|J$yk?De9X}%v)OuAu+?sSu@Jkr(YCh(AOUmGPO^`U-&QlfM1VyJbmg} zH!<x;$Kw<G)6IS<aUx+NrM#y*fp);QSa13TapI@#hL6ES^W8)zXB&0rNqj_JmuHCi zI^s-Ol7|!aivfce;yET_ZTC^ESar=KgL)TmLZrK;T4qPm*k*J?kE-p%2Qs!^Ii4<! zvS+`|g%W|$73Bs0!;-)q&G7-73MbylA9NU7bki9dEyd`3{xMEW1NMDsXa>vk3Cle4 zG|XHKwkUtTn~4D>3Q8!CkpN3jGDRUq2(YsHN_lu5kO+U$umDE8JDRaEx?Ei1H`k8d z+x~fRXO6uwNUJefv-RJeZ~!j%+k8|um?>GHr1ZqaMQOXarD@nVFz^lEPMrR-)hOza zhwVDyS3O)_i^SOm%c-e!d9qT^wI~~`yB)YMEtW*x4XB-7rEnDrfv~huppWWYup}Fi zvNjm@4(<LbKc2uN0gN-;w*!7c$!=H4a{GxsX$lu@SD!9J^#xLsN>2+l>UP`6UKbWk zkk!RS&olN}W{4|0%PpV?A5YmMqiax_4!A?n4WZ>yq~EyG7K;RlBPVEqrI!uodO;N} zuP5{pMyGc9F+z&dtzs7m2?q}GeDa~yztCyQ%M6xeN`-AR=YRWYS5;TOyNB`^idGw# ze|%*L044lk^(*TTYV@IKT-H-@S)AtssfIu7^HxU{hH7l|xKX^lb7cF>dkTV7bZXqN z-7MTX_+1VVbl7Xya?@O~yB4?+N<H<&RkL+{0i|TCT7`wIS)2rE4p)ARb*a?CLa9oH zFC$Z9tLQQVEO0yc2#xR6Y*{0+pBUm}4nl+&ektVWLI9{k-Fgws%yyC5{6^r^zcn^i z%2S|#DJ6-8n&Qd<Pu<3-=?ybhVU#`1p2m-|pXN9I&9XC$xHyYeSp^skT%TmUyI37X zn76;<E0$I)5PvuNOHKS7@b=e>H*U;jFJ)5JyCRl~bzAf=_hg;V7<Y}xE8X~+O)Q_B zpC@CH-7Vz*7unhHzNQp`(a1Bm5-n1Gu%PU_g}HX@@(dzN%N=9gpNVKS6lXF7O=$*p zuC^{LJZG(r`A{s1iyyKI_a-yQ(D`?~<&J_ucpXr)dIB6dVC$xX6m{kS%`O<F77jBS z<~BAgSP1Co!wjiWvsAd&MkINveV^mG{^rQ=xNWoDg5L6B>t<|lu62W%r`Y4?u`HRC zcx~0y-riH^u)3Bf%{sSUR2q-e@K#p`o%U5(HDgkVek^t@7_56LqfYEoqgafP?vXKC zqrYdHJnEnvx$Px#C~Tw=VLUhNGo?@+7EyawpmV#ebGq|)u1s)HTnGsc20O~!xzX&( zqq`&U7S=dBJ<ob~a{70`q*9TFnVk4RRDpWo;gBfeCZ2BYO8)~Z?cb>X{%Ek4m-%&Y zHsvcB7BJmfX(Um(VUA1cr=9Z5q;l9-F7YOKRZ^Sf1HNO5aW9XzXq25#IQ;xxw+UKU zh{b%63}@(lHj&cJuTd<n{qx6<h}Ig5jUd^OEO<z2eytzPI{j>33mW0M&U)Iz_E@Y) zd0(N{srqC=qUJmX-z7u2si_9drM_<iMX-L$tc9RupFHc&b?2IhRF@oQmLpVvIDVi( z)VmuTN(5vJXlOE<M5G@u(%oEGxLx&l4kf@Z=%q%*q@rx4H5Ft@Q&TY#A_XMF@f;U! znv;={ZsZ!r5UH|c;I6J>Vcuy<^CCS`E5u0gKPeNoG)HwXGe`kPPN?ok_U`DCpj22m zgXOVR^q-yWb}v*;8o45ti*ZXClUtXc4``pT-KTP*3Kwxpws!UWm=%^)AQvl0!|mFe z?h?DKYV$?Z@TFnuCE5jsVowqA@yOQxz^NCjL5BFAjU1n;1r!!8U~D1a<~Btl)c(7} zx2xt#$=opQ56aPcoGC+WgW@>U$e7CEaJDRWt)=}j^Oqf)z?p_=@BQR2pPOyLLPRh> zENKM56}w*YEuyxDp)CJ#NIcf<hXX6<skdafs4~9g#0@gOKf^UEQs(qmFPnyQM*od; zzFlcdb4}ay`Gb1UhvMHp)DhV(Mza5BZ8o0&-3;yc9T~ZZlnRQC&aa)*GZc*4d4lIf zqv~~&%W5;fuQ2Qtdf&)|FqdgcDu3mWzPST;gt~)aS@=b8C6uoAUnOye(J?2ju3vxG z8d5U%nN-xp(2Be2>+Mat!kwrLJJ%>N-25y4aV3qvHmp4$PCtM_-fv4^VRvJ`wf%ZF zf!oF6W}jb4$&L~?w*=aNiXk;tV;Z?X6o6~<6z(}K26Xi|9!+X-RV46y`5oj+w0^m= z8=U!9=Hb!7X4kq>Va~qBjUBt)?xhPMrY50+HM->YDL?Q%{B@t#_P$RQ-&2Iu_X@hQ z?|5LG%tgF_6C2Q&O`vUh7luUHu8)&s@Z&YkJZ0#L51IA+*d*GT9Z~PreldHCNZdrF zaL9&J`)<ihw)F26!kerXueDnTCfYammz#&O4C`N@E74-T80&oTR}Rf-mHH)VV7r=c zO(x}bN0~8$!0}$sf=h%I#~FOxg#O1L>LsIR6KzhnjH4&$I3JT11tbKT41du3qchA= zd{lhKkWLkTZSZh|F6x34fd*tzP2`A17{me9i#8Z#4%iK$gICHO4+MxiOgEA>ZEggW zlzj3=6+3e}v6<iY+nka%l)|bkE<Sk`Z@@_wg1h;rb>vmZVVvF#`40z14wZ}L%+^lX zKL&r<Z2f-t<(9*U_EB3%jO9)M?pwO$qCc)WnbBDj6m)FwB>69_F?W($XLtG{t%Ib) z=6`Rxn>?14xe3oS_xY-&f<*Gdh)3G;<gs~@hEj&1Y}02QE@6B`4*cBkq6R}^AvIMw zPe%D>@DNt@S5_c-eT^Vzrs0jdK0!li%u8+QV7BgimGE|1j?;H>>gmY_d+GaCMNXo( z+j=1hzlxH6itE=hB*J_Hf<Gi%7GdR8?3x7yQ<LACS66iH9dSt|#6nme=j%2ciACr- zTRq&~^t|hB?dI9@cR8BMwJ!T)VQsZ54NXF9ls~X>UUBu|Wy9o>#YpW3YZOnB!&-@d z(NcS@baxD(^Nm0tINQZ!WoU<n@F=a6FrnjjU+}kL<%fUqgHiTQfeI<qdxH-biuF4u zn|*3(GPw}(_bwW*5r?lth;&J;tTH)aQnjJUk6N+X4~8B+#P0@0V^~d0zEo?d^kUw5 zUD><3?}L+cB}DETzrq*XCAD4Q6;U-=Z>%anw`}_D+91{HjOW%Zf-wbZ&S|_io*C^p zD7_;5BC~4`@e%YK2(;h8Z`_)x?@`j#{UpA}vD}Bo&YnSqY2t*kJx$ODZ+g&k5)`s> z)B5AhN5hSA<x2|glHqOreSKe#h}`s?QQh6CS#FsO^w+;qYD+_39e>5jZ%y1|yC|Mu zcG~~(+Xn}_hfG2Fi=AN~8h*A(9ieZd8K{0#LZ>7lrs^1666+c@;mW+iYWR1RGi3Sc z^=GQ{3n}^W#Hrxgc5Q*%3LajKSI$42ZP}M73rmVC)BW)gsL@ZnVdR5gg~hlDl=ES% zTS#c=ZI$Gg%36btD2()ooCJxM<4uAdNU4hA++MOk!+dd&z%8C895y+b==wAeRTh#- zFk8wHQq}WVzEXF`3*+2cLLRKm>a!mI^BGlj-gx{Fj(Oj?D_{WoT)@2*w33>#Tj>6k zdhZL?)XSR|g(uJN4KWY#6ivmTe<UH4W>i$Jvn{oMGylmyUvYOOL1e;ROveMxXfDM+ zf=;en*EM7lnIcy?yZ8ryU@Z=D8pyUU$7<+h0PPDoQVy!|f6vEZ>Xu6ULuTex@|zO( z9zIDSF=24XF|Vn5qxcEWud)orm(SaMc&}a5{LyyQF-Xde+m7zLIXwpRhH?w|$=@6N zYkbJ~2z{1DRp<Y%#2AdoV_?sg2fn4n9@-dgG$IXkS+x~8K2Koo%lqa!y6%3sNJpR4 zKa=8;wMq6{YXTqNIzDLMa^JL6S<{y3055J!uMGKlH__dhgRCb2(W$9}SIW!EkdueO z^+msu?gj?hkHdxavA#>$_ZU#dm7T`o)IBIMlAPOeq&eD_%u!ddE@K^Lhq$KrwW!=J zY%FiiiSWTPV`6EvLe&7KxzCRSHcLytojSi~sWkV`{6%UkY@@17DA^K@!jX9u6iB&Y zxzW3g<`rk}A<L=naPGU$p1hKy&c0Tq_E;mf`RXI;$>yl_XD)dteML!A>u(G{$M5p8 zy{da%Q!V<!cVgq3hE^gkU&c7zFt_lUj02^ZUGKK&VXBaAfu@btD_>{S^CKc!HIrWN zG|wBRrdYT{y1dU)B)Nx6s4lK*^U;#IF^5N*|7hzt{kwk8nk*<i_eG5dFsENB#oX-d zn@k7^wbb8B^TyScsY^eQ{#sKOjF4yOHp4SU!6d`H5ZeD-R`+{PVoXQ3HFG~Ce#i); zzp|hBn*z<f_%>9)bL2Bqsw<NY<#;{*i?TipXjnx5kl)QAyc>g6_T(gaL8PfQ&%$1G zwVwVZm`7pLq8r1!8{l<KWM+W##Cy6n1ixLoN}O+URzFC*L=TtD+96A~Jve)&UUoH{ zVw99)E^?rJH=x=at`!`!mm?n(7ROZ@?Ku8Y?_&(O{oR+6q3pD#GVPdu!1|!l&#IzE zsn-4d4I?y7S6k+R3g`aeW$A8?va{EQns#H?J`)t8rlyhWzrq5hOT|L*_V>})zCMpm z4R@<|b>@3-u3js{3PA>GYE4aWi2`_bf{8FP-f=JdrK9ttc7hI3lri5)_<d~AIipeL zT$9|g#pOFF7{4fDOs^xq(Vn$j=aI@{XOxKtFno-BBw4mK`AqoF^#k)*Eq3gExFv<L ziP1$8qDMdTe(>M{t9E55=qjlI(jKmSuZ;rcI8smob~w+{8a=&FB<*|{Kee#l6kj>o zlXrOJPf+wt9@)b20pnmC?~@Q)?{*iX@nkWR`$6RE{v^g{oKsWc#X_}gzo!IWWB#rx z;b5k@k@+QD)Yw=87LGQdg?|e|RY#wq5?(mQt@9L&(v3H;z9(k4^1SD5nkXz*r8P}? zXa?SErFv!WrT~ezrlg52!`|&z{2rumo9mx>+S3&jO<bv{{FyW1|6=K?0;1r$?a<xb zNVn2RcQ;7q5Yiwaq0-$A64KouEg=GmbPGs{bR(Vj%y<7cebAXXxzE}w5PX2S6u^1= zpB}FyfW?jmpeUX$NSpzT6Xv}D5Yi^c49Y~)(%mJJm3JD7z2&-^ck9yxs!{S*k@y$Y zUV(p4+?-5JD=G+5QwZbLbNeAH;PECx0&o}l4N*}JX#7#naW7obpVlUVc`AsCpk6<F z=Yxo*;9XthU)3jZpMn=x*Ct%DrukoFd~GjtZtOH9wF}7(Br=mO6biNYL_60FO%6>j zgp(S{WT8r+v;nCRJRs!+_#wg47y<fI2&_A9AHXRFtOz5{&ppio9=rgWYl+Uwz=*Vr zn3%;sDlA6Fr|TKIC*-9ZDc#u-JbJ?jxyGglZ|>?^aAG4WhwXjWp05!!eX9X7Me@Yl zWpAC6rpA8;J1_MMn3DTkIFxhCsvfpATOt^;vdZ~6$!VQquZ8Tc?)<eYJ<h}K6%_0K zI;|By$Wa9wa_@KEuwg!+Z}KfDQABv&^*Ocev(PX(GiwvrXeNMw1jZK@V*q@z&p>Fv zRu+ayldT%?Pg4X+&mTX3{sAm;fiSY<KVzH2g&WfPD0W-t#|kt-Wo0|qNWa>3I8eMX znf7+@G%Lz^x7M><lHq}b$aAi^I8wJhjErMO(4bYFL&P(1Z#K-yvc8gYjNc*gW2&s1 zdRL~j8(0f?QFua2!qWqtDzzW-j_y)J&k?iq?Z=93Q>dSm6qPyBa@bEl93R7Fb_WQj z<*-fGLuIBKrwsfieV~8wm9)S;-3F*e)ipIYXT#4Mnwo~MNP$C^_}u|7cniOD>3^e| zjNk9t6NUZ|hB0t>dolXfK@N|qHySE*h^_TqL!aa{%%sNSbxzlp5nG)K>5^c5bE18T zCk|mp^*rKHT4G6N)p}oEYwJKG&cA5%bom}6{9f`=sU}*}y%e>sko;Cx*Mn-0fcZFk zp>X0?Mkl6q$A3TLpb=!L<{Q_z6x)=*Zav@9UEaV<9F8Od_>}kT%K-HS9wKX}H2f04 zx}ppiWXUQhWcOhzy-^^!86F`V_PZE)yO)UD_JP95Dc@A20K@y7<yWQlo3;jQ+)<k3 z?-nDZcu*`XBDzUV<Gz36F^iJC)1_FtwHyu*uPKScMY8a*8FC)P<~Xc|jOPR%6(>66 z0>5#wE&R`(=Q_Oo^VfrU_h9*^;V>ihJrL%=u0G$8t<zUmb)@q?lmZSEuqsn}21zBG znKc9~6Wt2k;*t`gJ%bv(m_sYndSzu6qZ%p1+ZC|}z9NKjM@8pJcuJ~)^7?8HQq6bF zS*Z*;Rr+O(NS^!3M3<K@Y@!WT8xSgWgW7~-%4cKCXMLx8+b>1hb&9-Q^G~#T>#tvo zEx(K~Dk(M3y{8DXzG`D&e*E+(I(<Mk!^TgP!?v?8znzz+B^h-Cs$9SsRRdn<kGzc} zWMo6Y7^i}jnIlyU9zkxquB$5%6|PA*FakTuCITB;@lUBk-vA@By@#z?bxBO%c{fWq z+wrO^X-f`gos~;CCf@MH{=fxFZx}`)dckXr^4wfx?3Ko_*wE0Y_g_H!4$f|WHXU7^ z=zK|`mU5`<odlye=St?5iZWT$ZWLeb3~As13319No5vq*#ZFeq=0c_(-(Tl_(955& zk(Wn?WdZ^MQBC&DAe+a|R8My|)Uct@&_>!5uQ#4*!2LURxrGNsYa2nn_IeQpi4rx= z^XH@js@2TQF`MC7g)jR@JolN9$7JO}jsev8=1iJ;s~1l3)GprfaM4a%r@bT_<r=pI zDLVa+c=w0%CG2oY>)Etd??3D~LUpV#I;x6e!8~JM_}6~lzLO{`)RBW~Tb(ce#gA$S zy9U_1%fiy`0b7|Av_b(3mzjwPIq>1`>K{OlxPb-~xX%OwZ}pAM%$mmUA9;b_WU@gP z_pSFawMiG-+)^vaMWCRI<m;;oh_1^LaarZ7QCp1)g?Y?Is|dR;HD#W1o@_j0;}E7b zBfOn~;o%po*1Oti;^?Gp&;gk;K5GY_g%6H`kS@ooIoG^5KFE-iEcL?lcbpsIa7q;g z$vTW#x-Qb`$zes!^pJmJM%~6{+AZLF?;ISA<N<B~a9L>g+{FjBfpmO)gn=YxK0$!4 zNdOY~fcrlz9Qciw7eDOWisgbYuX+OO)XotD_^ljwvj@kEcn70%F0-SfOekcqr;<io z9?DgDIaIDcElAs2m<paOHiz$qXxM1HzT2ibp%y}AM?v|6fDkojTKxCh>B$B|F*6Ll z;;j4(Sp(ao1<^?ghOFFqtKRT2FaqY$K#sUaeN!X7kT_`|c=@Bn;51cRCi8?1Mdtus znqiN>7&ukMVZN8Mvqa#y0zEzZnR2|ku1j+8KrRKj8>Ku_+BrA3j?ZwC(->W{wcrN_ zkUo7NwvDvF67iN6`<~Kf_pO0dAG?lPY{XXOqhtyGv}CX5vF~2$`qN4*i2LS(z{6u| zt>MwjvI54O8*4u0%7SHI1b>JV&TnkV=c{H06*Z9StrI~AW}RTp;wGoyq`WrI3Fq-J z3Y7$;SI;>nxDQlj98PJ)9F|jT-oPFVrXT@sN-%G^HZb%s9RvtI{&M{%2DtWSo}DNE zQH>!!nNpFaLmVN88;O!f=zJ^1-0B6H0Wg>P;|nAZOK6)ILlSmmWkG9ToV|=mLNal4 z1zej;s;UZPd?xA2d5u(&(U!Y&KmWSlziwG2#I|rXPkO|P$FN;s#G8HJ`q0VLXr{fG zIp<H_ebjAj#|tSyD}VKwxJ6l8??nefW>V5u;2V4m!WlmbIFsv?s;&bkS|DIwSqE*5 zjlV-FI5jm%sn2+L*0$6dT$oT^Fd_kOpRrs5)?VJ%wESFA0$hbyzZ5d)Q%N(F(||2B z44{_Fk)Q(juFV)^H53in?#Y_^&ruSXWKP)H;-@Cqj&!BI%ZV03ibSRD^;YPSDYDx! zs}kX?Y<W>~ldK)u)!gMTjVd|uK#LtpS)VVL+!#999R_UGVCmJsc9Z$^6!`{0PV3Ch z@)a;z1MxpEfN8>vwnNcDgEK{FgiNyo9pd&Mcw2`KA%99UU%4D%4prHw#WtsJdb!~- z2{_cmS^CCInS4r~kmp8-=Kid7!Q^q88v^t);izJJDjEHJeaKk-e>;-sEhlK-SZcmL zw~G%rLknWp(>&fS^H736L|+UGmM)5pD>qXA;%aXHfWB+E8|xypSUoPMeMYt>7)UKH zhT1_R3x2~^UT)s&>U>!!dhDMMu-I2n|G;>x!1q9t5DDn_0L}x#$zKA5wixIR4Gawn zSkOU$+WXT15>bG|7aK@oOG7RFpG*nYIY&PQ8P<I&C*Va8e&_)wBJ0&*$YpIdWb*!5 z*gG}Fr$^}{W;W#=gt>CK7!t5tyuGzBQ0w2f8yFeKM6R{HDU`3bUG4rQB$7f%JeX_s z-F^A#>-lp@ss!PC@e}Wk+2mxs<U-KW`^EQ`;0gPVB=D8Ko)VT)l;MkLgzH7ex;G&5 ze#U_xMD#H1&)01OaIG2`H3A8IJy5v=3H2a|u7MfU0%rvI5^b2h1L)G)Vgl3|%@n1c zs}lMQZYLJA&65v3Kp|<YYN54m;eaLO*Dq8YjI|$lct|apf^Kg=(hR4Qbp&}p4|f!6 zq@c<&`C}@w)_k5=v@bW!8Ir$$9b1eSm|fHyZ=$X+tN;(CpHoaqKOqT+5Ma8Q0hBes zQg8%V4&VGu(SYTQbpsN~;#8xJitSj=5U|~Fb91u*h#KX)HFRU`=%_fNzIU|9O~DJ4 zz11A+-hplqjZdM20!x}zgreDk8H)j1YrACwJTEjkypNGFM&N1q6g?l25hMLHsqpZi zPN(4L8=`H!I;7`+XR<!fNfM<R-T!ql2%BHH{$PsdlhHpy=5HEV4=)v!Nq|ZZ&L07C zefgu6xJ6!@*vARQ4LGO)mjht*U<or+h8agi7uGl6R^BCR2XozSZ&<m(k1)V_tOrlf zzV!+V$Fe*&v)g7vjPjbI!6%q5Ph#m99tq9jm&3|ePg{shWq-Y+G(;9^@`bciE#$Zx zp;g%H#BK_akv+zEKTN)u>vn#i6XSC3a^$6W1S3ht&U7FC(1t|~vCYjqwDt|a`!I~@ zSr0{95PX233lxN8skvON=s*@X05DGA^&whImYfXC!l4UaUYhi9&5aVm%^+nhwGcg^ zl)VjK+jm2gvyqE4>l%TEAj&+>j;V9d6t=#`29}6FIMHjgqqKwaY0Jgl3qb_+>*Tcv z-y$C(%08^Ni);(|-LcF$IDT?|tY-WaT%3Apzpc^GO3b6H)2iNF&!0RsW+f5`A}w_5 z%&4*zcK&(*JCWhx7{y$n>f&<`<(Z}R4TRm@KD3idf#C`d;lSNpiw`m*?q2r4k>Bx- zJ>&YfBl<iSabl&5SP9|k7)M<lm(nQ{%cLetirMWHT0U8ZGZ!Dv5FU!_f6o#jEr(~1 zlSh5;ccu>spQVtu+5TzUYOZ}--_&B|-ZTU`z**5yG>Hu3Ois0>UV>BA(D=TvaFcX= z3}T%e9QHP{{)1j5b5YxRdQiWA49fGzXMDXei1xZZtw`}{j^^2PkL|!XP8L7G?kYLI zWK|fy<A_UmQBgS(qKu3%DIXjOC%mHq^Oq)%0p_QhkeO;-HWB^7;enWd=h3yJB*xu# z>P?I#IdvT$!i-AfrPgoM#*G&#l~}hLs3ha$LuRe+1C0TxX;yjjAJ4Za0%;V@s{ofl zYzq>SojELr`S5T1@Z~Nvk@7Y&7u3t6wjm7J+*bWlU*XI?5PkCLE41Y>w#`%(^em6W zT={~gkPz(pmR#s&!aiqe*0X9h4pzeDd{h-1_w%e>SfFcBQD)}4<noIuI23I4I2wC7 zOwPdCtVm=V<$HP_y3!>VL}cKEXmTKh_b@%T7g<B8War-~;?({IVdWhTxnN5q*@xT6 zR%=~Cr}3i=H}_ztyac(J{P*9&+h%KGgjH1TH1#|Dyf-s+dLVSS%+2(SgImo4b-VLD z!q;g?oJpy+#Rx17nEG|mB_An!Z~m<xwuYUnC`SkD_vu0Qu+w(UW7ZC_oEIaHVqT?B zi#9cfG3OEP#}JdXXk^k_Lv`7njj!_?{P6I^axc?~Q&)B3Ns=d`;4T<K#4zAwJ@mZV zRhKJ^hG1S~Y_Kwxeq||I!zUnk>3cTmSr7gYXBk|~05oHXS!|!zYEAH#z-DdotRTJ9 zE5q(L>yVYpe!)YCVicRcD-y$P8=E=W3JsgseCdt7;`8mmRy7CJW2^V}!tJR$lW?j8 z<99nfLkL8gB8~o=vPzuKyPpch+%*q;rOg?1m-y2bH7&O9H%>>yg|lDj>vOkE8mMG5 zu!fNiz+s}ITJ$0MGIHGDJl?8cu7T$TK7rgng<N4Ki5hwOdrVxa-n+8nK7zLG6*3BD zg_JyVZHP<+dc^S$JguAxy%;x>5@lQdB17Kw0J9E;_mkUsB(7??|4{0gZ&@nL&^$5U zY;0A&@-bqu;baw2wc`m|P<Y*RdMgjrhAPe%@4wbiVHoUXVYQIcqTbigMs9njFBHI* zMv8{Kw$JYF#<aD_zwRphQtIIreJ)6Pc(lkACneAL4A)_fa3w>JUAvEX4_U7QSln8y z_kFpmGYO&!>Ri|+K<OP-qfm_Od@YW^Xp=mcMm}AvO#thyfbQHf@mqS8Y#aL~hLXtd z@eqJJFOj}gR2C72+v)JaDq0&!eVmnhGHMBJ3(Rh_m-#WXII&fsZu_$5?=zsJE&V2B zs{qxn4J8?UUUfF315}oU`&WZl1-b}@MwaW9ohM^RC!u#o4!@6IH}1@Pf4qGZZCYYw zHI8)^{q#>{oFIu*DqSE3X`5Ja@=esF&jG3h$h40=V`kmlIR~H0_YOf(9d0M(=>W3k zXDhPD|AuGyh7rf|#G+xwE9`jndGzs!Drm93q;eMw;d90IIY!E_eQnyJOHz2jKmJ>= zDJ;41M@rl$*&6MA`hYx|>l{qA@UWWXRAOtA`hL;BpmKRV-o~ec_@WHK=W2}$qV0Go zTj@=RKrTqUyZV_=3&){=?*-{pFUZL4(8B$|#+D}r`Ne)jc@{5I6yDE1-(E*y!;+L4 z$+lTJ@l7GM5<m(B<TO9O@ba8-aAQxwrDjIt@CH6NBixT8|9JnIj0~56evU#PXW;VD zi!Yjp|D7lQO*>;4D5LEyUZ_SGwolQOVp4Mm2Y&0bh(IOOX`mD0!?ocG;%-%}pU-#M z#OYPT_&Klm6WfX+8ry=IBB|l}Y%K%4_6?^Plvt&N1M#N`NKjZ=3x}Ju8Q(1ZRE`be z<!)(lyAucvm)?7c_s|J|d~C60Reu+yXOk}jI~IB^E$Qg&Y!42xw0c(qNQNh_HMI|1 zgXf~HUlDFP4NtoKQGxtk{2axB@e9lw<^@f3ZW@)=ke04zrJ@g1C?MzY+mx-6G92u; zQVR;=g?D<X+y|96wl?t&@4jLyH=}ZKBE(+1Ise4x?2xXbf-ZA+xwig{3lq441VYy1 z3<Ftp`rW6QP;nw2zTUxUmb4>GeEq<%Le=?4x0&rD8X9PF+2e^Y+Mwva)4#oo@|{s* z=SQ+|T+_oaVqoP|MA3v&>#~(=eM@|F{xwlurV$lQ$G;|OBp3GT;mCkO4o(M3SY2Zo znECl@7=}TkP?MP$u)UgrjOF3n1z*9fIT>R^_Sp%vDrT=SDUg5gfn_1Opcz}$?sv@Q zC|_gwFD0APDXEx`mP#_px7|~TV{{F|7QMqwDoj{gJzBe=>?|EKMg$vAJTf6oj#gU* z0Un<I8B&b=82HBvk^>w(yk4?g|3BLua2!vi2oPXSRj6In{%(&DP1uk=s$cfJYC6Ha zWfN5ewJ2BzX@>1)n$!3F-%drS5lCxUjVDJ+N<v1Nz{H}JlA>E{v%&Z+!8FywOyD=w zP&A~$$;t-n)306ZpNxqV>UloCkICeLhT0*c#bIo0J!F@^av1d5>s7IG*syrs%|pem zU+R8$2|+Y0cQn*~>sJ59!axkq%gp@^8ie&DYfhA?l~NOUN$x_FRaJc2G@)iq$T_)K zyn&;*a5F<aqLUn-cBZAnw@e%a7e8~~R4~d7%n}Jl?vM+*M*B%(cp>a(OMQ_$ud^>H zZTr=}bG08%GD|H^83+v8Cv1n%u<)k~_mR!s_#&0jt8YAn2qL6oCQ^M(R*;7++`{Q6 z;1Yy-Us@U*f^dDEc|RkJQr~&?Y{CImr7BLph+IhNVf&9#k3W=*ggG4rJF>h13E1G2 zvr_hYc#oalw#;i0ULWv2(+3Q_S0dhnweNkWj+W{F^uon}r7HrvfsnqV_ZJW1ej<(U zY8FdUN*a-H{HWMo{Ffd~uKnchQ?rvjs4ME8TU)5cj=@#e5T#I#F?l8JuKodk74;)D zY9%gcnXTf4DtGy<X}Z!tj6VAa`Q&SV1ukilH{%i(HjXMaQX!Bw*6Ch+0xNLQc!o?u zs=qWQdoT51^p1Sv-SosAqJIYaxS^MD{jpfI6%`<U+8`-N_!<{dM$oy}6q^iHe7G&t z5#?0#JPsU~>Rm@$FBV3Ye%s+~m&+ZPiG^8j5p<&Qp{X;79-ESuUwH98T=^%V6-5*u zh=JZGop!@a%Vme1LV&Tw>GZLffew7!t|p<7uXc5-ouwXHCU|nPV=VgSN~Po|zE4LH z)S*Z+ZGFxfU#>gRT4yj0fWQ!xXLNlBr8^w~T5x!rLS0SRwB)jsRAIX7lSONL`!TPl zOIgUVTIcEnJ9Cp82MEkG)(*GSwB_+TT@SYonODGtC1bw7jika5CI_2aJVFM<d}rqZ zC4we-2rqAesmRV|O75fDfvRZ$JDVa>=ASq&tS<o)qNkzY7Yjc~K)B*8$0#k8+ui%n z5b=-@Pexsk+uLoiqHASj>A2y*6M!2_md|guq(pS}%vY3B6g3vh`le))xJ6xiDiJ%s z-}86RbM2lQ<CrDjs$W9JVA0_jBojeMzHp4NrV7<JeA36?nNu@)frx?u_dP*87}VM> zgSGjlw_ZyWMK3+%W$$|vk<GeIRE4T1I|ct&)Yeq_rYWlSr^JP_oTmqJTH81IfL2|g zonEqHbg(Ccv9!oWB?*3|ho;t)V1o!S%^Y4e<%o;FNc_9hI@A}hGUROYV6N1=Kql-> zygxSwOgl7NYYo*R6=T=*a!3t-%~f%3AiqMf7^7_H@pyd#GsZG##dgZcDgT(;0mK%i zL`n+<S|##)<r9IJYRG`Ow7xm_+~Z=iYGjXpc&b!Mne@MNxLMfycD}!;TU+NDH(uXW z^!af~aZ53KJP*8q_JrI$2j6Rb!Kc;^6|d6f>_PUr$ipgq3`3P>ax*_PCJo{}`9Oa@ z6VS1;W?(Yvb5bIcCP6X*M}*6z%k{fk<6m@2O0uv?&7b^By=gOuoXBY`oB!sRrVmau zZ6XY$)y>Fj!|yEUa9kZnT>AhffGqrIXzin`^+OV7jbLi2+Q9p@Y7W4RelsF+9I5q2 zEh}>AlUf#RT`_US;Vuh_fMYS%o@Ebmg?!A&Gnt=8hEF1G;gnx8lAQVil&RcUWf~zd z80SZvE_ou%>1x?o_cI_1?&T}KO`Ycu>8CmwloS+d#=$}@dTw+sMx++!5X*0C(;8uk zZDhYl8gT*vgYkT61=mIo#NIJ5ZgwRA`%eWp*IFYRC<dbq6=3`<`Zdc{*%^YP3IeV; zgw>bbUt0IYFpPmH0Cwe&$J3BJBXD$lb9e9CFcYS#`lG>W;&V>sh}(E6+#5q8o7+}~ z0=<}pUI+;nscgY~4ew0_Jpp|?o(BRE8@awi^{TjCVkX4>Nqi)mxn);3Ui?P3dY+sQ zwYixeK<6G$w_YhrY^+N=^4~by8Eg?+>sF}a3N1%x`F}^YTPjU6Fe1Xv=x?VR(1lVg zS-Gg4IAxnN?6ncq0<q;8L!m&0qE5&U<izA~u&q3pt%s{KWw32QdldLdfXeUoP*3k< zD;xT7cRbLnri|`+$ksRw9w;Af&UE(|i4s$!7)oCQQUJI<)I;-Vg}5@2i6~z5x<b&= zn-WG^KKmx)2f~)Slwd@S8imYxojR4OSmae7(ht?){G_Id10m`qkT%eiO066l#Rc!P zM+l@jb5}=19m!1ALvna(r*0?tZ*LacFV%8TiP$6+QLBv@SAHVd!y~)?j%1RF-%ieq z|2dYnYog;hvOm>3y{Lv@@%0Ajw-S%WP-yXA*ok)9?yZ5Tij>sN>v7v!yFo@dGh|04 zimF}(EV^yU#5lN_W-Fs6-J;=JU7{AX;*eEEoj0sQGfk$8^b$nxC@C%WbQ^#(@QXt! zwcekHi|YpMJY=V*zd-?9+@j1-L>>!|iER-<P6dPF!Cvdhy&X*k8w>(A7-=2>bN|#h z&hKR`ELz*DClOT1OlJkcna0PuV(1p18^EiQd1NEL$JCgOTxDjM;m5B5PZ3ud&%IP* z0!?R|u<^uafisAynV;4#!i)4OUQ+pH_5PC7WlJ+7y#*cN<n%OKOAN*VGBC%n8e_K- zFJvL36L7#)&a;+j@9A>+2=T{l559^&wuNqLBw|)zo(5_hi7~se@a#eAG54oWFYJ>I zo-U0i>v-N2w$-R0++k9o_vRKUD52vBM%4f>I;1jm@C3yQOv})Re^I^iqOwz$nUOc_ zpZA#Yl&BK70@&Ug)1O&m$&o(XN*ZNl!o%A}L9h>S!##{}%WH%P9|2)wqB3nrgPj{G zS0p4ZP7!WtF}g8Wr%W|{e3h^sEfATAJl`+Uc7nn+(!Z3`c<sz4^M#btYBNS(U+=<j zjI#3*JnrYWHA+rtik`79FUSKoGX^*;9Gm$a<?GfjDO&9}W(UD|N5A}Uyn*qg)=Y{< zw7Fs&vZ$!6X+~6(5X)50V&$mPWXD7Y|F5O&`_K3~YX_JbqR-~Mj}U2SQa-6Omx~mx z0Lp@XXG<azVy2jJ=4?B!ZUubM0{NE?+r<24$H^=fC~FdUmC{5vFJ}GI+HUvP_<_@0 zy@(I0O$=iu)+5+%H*yyU%ruC)Ik#`?HY^Tst(05OQ+-Nzb1x0@Vqli>>(J65NF$Vk z%G0swiq8LOe-eE>Y#nevg+@jK^6fJ|-5-4r-QyK1V-gbWqu=XelST5)K;at|QG_D) zUUoXUmP|IHki{gb8;S<1F9>YbRU{z*7cye_T8%|ZqprfZ6>BHC@J8ncJ=oOv@ndkm zn-pY|SAAsc8c5qIt|cSe-0Vy$xIxbf2oONmKPCp81Q-S-%10xg({ia+5K!P@&0|0L z+3_RfZm1hXhgZk`LEn-5aVjypL6FlXtC$p41iksljd1^9dF%ORNfnEh9E-sO*e?rH zHNPW@N4zf7mg;=VBv#Wu^Vx|NS>*db)UAYw2)ET@q<Wg(WFw;mMxdE~&5QRZK?aYv zRZ^c`uwwv5`+oO=@1GN~cZxA_{8?T2Lm`-_G{JF;;VRzGcj^7H1+aaD{R;D6T=kn^ z7_+d(!^Ybro)T|ZuSS+|LX|vlzE;Y#>H)$co(mz0BS%}6(hhED40b6T=GGTH*&6M` z<AZ1J)2Ls&hldEiBUynvm{u^EXJG#JyB(Jj6DW*g;!_FTuCB`7halTEHKMMxO|pRf zeY1YFrhdE#J@Omlt5?UjeO$N!-4%m=0i{z|!LorzV6}7o;SU-&&ogl|Pxra1*Hbz) zB9#;UhJ1l0^NKea+HzyUz>+U4BP|DWsg+`Log~k^+WfgPqGkz?EZp`8UUJ6eWnw03 z$CkM%?~hS_M@>%~Jp3rMiISiUbF<ojx{LE&C4l`f*fQ`u9lTe`VrC78{dQ5$fuqOA z!hL;`hbnXPA3m9h81G!QCj}^L7So;QjS#>YGm}pN8nfk*x4TAeaf!%!gkiJu{TnlG zH9-+Y3Q0#2PMH%TJpnY2_q=sX7Nh<T^^L8}pa#jrAfa9|iz=o?zQY+D`x)<eRr_YQ zwp<1{b9`C3^nz?r=7q?!i%c;h!>(BB2%MOb+LNPm`v#}Ek-uf?!pwyMdqy=ceaG+; z@p`pG>gpnIZEX7rWWwRTPzZ>=6+GG88NU5k@xk$m^X#FfdadiK|Mp<vg^^-o!@JfU z^cGJ2_&pG^1Y3tj!2M%KrxV9w0ud3``rQO_P4**V3fCXnh*5B`^&e6Dcr1ldFNcxx zN#GeA8*>bo`m0=ZyJo0pMn;AbICcOET!8I%<)US#SrUDn5HRA~=~zb|n)UAH(y#bC zsi69GnfFoeO4me53`>A%o94khe@Lj)va+rFnbiV&k|<^_%X#!L`?<Y^0)@gS*V8Zl zow9{8u&Gb{XrBr9ySwWei7y4Ad`z)es+|{t$VPX}KHXCid$c^@e&xa)aG2}8mhDR2 z+0IhtBrmnG`iMb6VTp~J;%UEj^G~WOEE-7eO^T2KS0eQ2*Qvp!G6RlP;40*x<`69s zYwdTg$L-<sR=<yNR)S~a69F8rbBce#o&~1{4e;WR284295i#vGdU{O9ka#f<!q0|n zGnE4?9{iAxkDOjRbb+tOB%P`!5Z-U+e$V0}pd_iTORL;A#L-X~SMaYMN-2QKO`=s# z23A!WNcic_Snd`EO7UQEa6j8T_&M359zg5;xh_kEANd-qSJ^p>J32Wz3f$*|Uk8zq zu|vnj3Gm1PY0UqFn<w9Hh1bR^Tk}3BaG=?-kaV6(J)+#d(eQVdWSh^#C`EU)RQNK% zU2@n!J^WSbx|r4hF5uzSd&I?7lBM_Im{Ksx&O%fFYHHyZ{Aun=sTG&C9BVrs#c`e^ z1}deY{|ZTq((^jKqRl~KttS%m0^W*>Lb!$1)#9==gWw!{3*0{w7$kkJJz`Ve(wf*6 ztdldJmS#Kmt+KEF#L%62UsO(1xvc!I9|uN@mA2+-h&86hz4VF<2^vqr4{tud`K$Mv zQ(fWHSo3j4N{{Bo9XW?+&xG`xQZxa$SwzL7;mrfEmRh9b!+(`}K#W_!4<2{z3HMqQ zoBAA|6HnaJMkSp2{-C}eUvvjTErP5eftBaac2+C**X7KVOTipDnh9cxTU&eblO6g{ zRfjo^>!p&*v{r*f0orupmp6dg>(=N61yc`6H#hp^Xzr&c4B(Ud_h+vwR9KV1O9eGF z6jknb5W)Zlha8?&XYXIU-kAOV^903CW+6XmJFK6#J=!4TyYS1}t(TV7Vof&bh5J>l zRB(BsQ+LD1)|MWm$eW%7ou`bHc{2hcoHC7X$&8=?U{TN{EMIJ(zx?#6c%UD*JD^8v zj5+CVL^s9t&uEkQm1(uSLC|2q&>|7Sf7`FUr4)_!-+-XVqNjtnf;w}bwrec`83e_j zNLo*?H&>%%BKifuvq|}l2d|0d!2*5%0PqxOjrnDAI$U9H@Lo$5va8(`GpTxh9tt3x z+q#~<sggcHF%RS!0fB2wYRt$XTKuSr9pldDfN~c)7~w8@-{#-h86sju5s%dWT2i|O zjK<$nfJ)^yP20VskffwkeY^Pi?us>orIcMra92?^R>*X0kzI+EzKoid)E1%6ke*K; zT}s*y9+<K1x_Z}KTwesd^xfy2B=EYfUx)s!>*=3j?xYYpTa(f*ER6Lti;|59i9-n4 zi;gKmX?`+udEkMtKd*yLWz!N9t2j7-&#rf-409X3W=3?MydAfn>9Qb}-KIF0q6M}% z4<ytTrzSUHYii}|KX-c@zSIhW#-zpi=K^c2?PO~z(P!!RpNK*;GNdqGaBFd7a0G#? z=k&5vI)<3Vh*N5Rj_l$KzLL6UQ;@!iS&$yL2_=8sR1RpuC02}DuYB}P=h4u{1drFh zr=W1?mDI{EVejVEd03rQqdW5%t|aK-uHtzx{%OBzR_yP(8XgF#Z;gN}T+4k`g?@qS zSW7llr^Y5&09a&2**8a>f9T)bw`tZZDZa3-@EWC+as(dIJPxc$glQ|y?>3;@(g40w z_!bW+-P%<dkn6*9*Y+G4r3Go>z~t{T8IR73x}T-8?BOE?*kk_26z+_dnkjsE$a8|Q zZVCyL%VTY+0>bDY=wAs%AXic8C4O+W`qA@O;czj$y?3+eLB*83O}@_Te@)V5X?ae@ z<U**c^Y~gv;TGSvlLf9(P+hZc95?_A^^^qjtLdJ$f$;700N%><b{-TT+$3BUX-R=# z%YQR>mUgQbvRP_j5-BNeJISY1eR`y$y9Qha-}z;!PM9ps$OcQ2<d}<z2On_sIO<IB z9(RKf+%}L6M1(Y(%q1Z3o7T8dXp>(55r^aEXw$60SHbz<pZ}(05Rg^>H|&{W6kKL} zsQJ5FaDq;DQ^>f>=QwrhhwTK6gA~nnaGO6r9;7D!2?pg?r#_#*kWi$k<1^HFjKP(k zpEwvXnw6c;Z+mk1TsYqA)DZ8@g^f#~czZW!2$FL-ZL9PA*-*0$DR!EkZe<29X;~&Y zcb4<SlF=kWUU1*~iq1H!AKY9=E_E0>F<}qvdd^h7J*pm;_U2i3Z7&v@tV}yO2ktJv z+kf9dt+0L$-<-JbXWW$ZB=*?pwUdGWu(XNbSpu5dztV~7T1~0nTp%yn#oynKmj`v_ z%R;e$;!iHXh6IH!`rkuj<Tg^Tq^NzSv)vVqQD;#5!O+b&+#v{=z{UIb#Xd{9kZ;|G zPfXTItf1>l`?6RCfjt_RuFe3D%iMow*$cj{RP|}kZeIP%b6(!{&ST7_S8QqBWU>zM z%BoQ=0w<MKRX?U|Hy24fB_idvNni)W-}h@gk>HL=7$Y(7)zK(8?)@>s=LyN4fQ$Ws zeS=iWXM6m{8>9vC>ovj^V-`#!fC`u6729dqB;hYC$_!$Z-tNQ?fxG0A)PEnY2F|Wl z*N=PT*hqWd^+Le;^1H_`?H9ZL2sEbnV6sck8vsIW+3zzRzP2+@oL}v0G`r8XWw<Bs z>u-iB8iHFsS>g4C!!tKxYAzS^FQ^q2P-sYA-jI5g2V|VdYj8-($?^9uO>;(8gq=MI z4&{rHJZf%fj(_&mz`m-qhkxLSIRMadwW?-3%Pl{#hp%)M81fb%T#xVq&zY!%*xYOi zGciAin~p$;qH1Vd8rHYHsH|z5jP%XL*EKC$3tQvxCwIH6qb}L~jj$uf?spH)_IQk! zsjxcOchmAS$XO6IKj9>W(9+rVeszFkwG5xTk`-CXXlZ67Lw96pgfxpIo}1$T3-)lk z95a;uBhyCYjS&cVfNKuN4#YaI*klMN7L5>DLBL-t*!y3z1yj<}ZHeD8*d%aceTPUR z=r9M@Zh$XRs8_K-)0kH^sn2QosbBiPZ$l}}uvdG3pYLwdFp7B*pvA0FsHDOu(1gDj z{dgPO<Ovxjb?{nga639mq|$o#0=?~W&j?Z}qz^Ze@i@!vlzCllZIfZ~a8u@n$lmH4 zqcdA+&BTP?;yC|}MwZT3R(SqEm9b2@zjSbmJOp)Z^Ur`}Pf%bid^$gDAMklfbeMLK zw(Qbz+m=6xWY0uxjlMGZ!qPH~Mr)IZasm1Jg5o+zxVL#e6d^PDBYb8V90RkS9#j}L zGSb3;6ogx`HIwHR6sqtlmIg60^(i|K{J5nL`cgOn8E^HW4h-8SNzVG{ptr=3{@*%~ zxhkrPY3uJtO*0Oht5I`T2E5j@=iB%q>$|MMiC+8CCCW@MONxbxJRk{FA;k<0;?|KT zEn}a;@@XM1{lC}T&uyx8^<>Lrtx6hsj33O^fr7;&*o%$7=QWBkD1hM7qRR=G6)~~+ zJ57@tzwU%f6Z##_HW%A$atZEiF2w*S%BO5u#8)_sv4jLvzR~KsspU=ZJ6XBP(`b@+ zH)OWvWFJHGX_H-7<wH2<r<MxYKTf65D*q%E7hgN%JgZ#jTl4YjTXMxX{`n2zMRCA` z*aMv1s~V!sgONH?7e{v0=?43hNJQaRHOgrRr*FYEkhRREw2*Il@fPSfk}{KiwDdZ+ zUm8}_{1SQdoW}FMmo5)0$_89R#XzEpFq$1Bp`r2LLg+ZJ(~p?@-E~}E_b=ljz$O!A z9|8x6`rx*Aa=_d*2QHkDj+q0fI)=yTL6cukD#*A6RL$CHL`<VrZ0GdYUd7l1qY_)) z4+UtM=Hv$2m;ZeTpFKRNStg6Zoo(G97t<2LUeZ-WhT@6X79W{<gnLnZ>b|{;b>xdr zY{Qj|{oY8Zu70xU2wnaPmnA^7KilzkU~D;e2IvGu-~WPMDk%$g@STM3Qz(upjx|Zh zq<jTMe&)OPwe4yt2C$bkHI=b(!;FL43M4rxQ4+ApA-em&KNcy-k(9Iw>ejg=>nJP3 zcc8AddlX{7Qm|&YKPRX*{D%AV?ES<k!V$yj*OkRjZ)cYb`*o>w%7TFdTN@}EBc1hH zT6}N_b2Hs}BKvheS1v{yxikYYqos%B3LfB6;qdy++KK=A2M73@#Pym`b+FV~$%lsi zJZILgm3%KMBrIJd&-Bvy@A@}@t9uifcVJ&g>$5TLKm8O+W0wg*hJH9U`*xYezMiJP zc6SVSK1D+1;Mu#fxbj<Xt8bag?7dbsFu>Wl7c(UndcP{J?bU7>)gK8cG4g`ATD>T8 z1yY{h?|iS5Qj_|pR8&TtKlP7*y{^so_@I9u+;v-NY5dli8ek`>E^3lKmoL@(JwY63 z2y(OPlk%Aw0uF_IHNtzSM`RO;cm0048|2;0o+TW@zq3IKi`UG4W#KpxiMVc6rW6TG zYMPCWsL6mc8#NIn?TYqGTG~&#_5)j*GLQ`{Rjg`ya_%Q<!yX%QR^S=+PhWosI~-e; zz}LzsF8s%VM~>Q_>bLMh#L3XI)KrEu4@lRSUlZdI<09Wey)>Gh`_Mqd-bY6u7?hx^ zW?8pA%$q|EUw@7qL*y{$%9dhaDgf8{%tg6M2UG6F{M`0}M8lBmLG^*qyKN3;dUc~- z>8N(tnme&;4yQ3(PN*#;r3+-zwu%QE;r#AusPXMH+7ed4m%VpPV_VyIge?aAT#=X| zS~xgqea7rrwL^#<L2Y8<CrRIkGoUt_k(S@u%=Hds;p+vSAXlEF;vecgyqP!0sDR+9 zsr*Co6mW0>(DJ{1<V&}63u}aD<PTsJMjW#Hzre=d2V|{>U8<`iH*6cX($c{@IrU>& z5a1>)KTwoACm>UNNC#s0Z#d<}m>YZOHAYbv2N*D>`db!3I5*5L1WBNDsMj_&?C-xn zISu0e;|=nu3_6Lc4-QVKxZwjSRMHJ5gq7^&^-<a$w%!7E?!SWopf?LFCx}&v;VrYr z=LJZr()#2m+u`qp3Ak#NrxqvHEJ@Ws^(WU==cMDfSi&WnTm8aqII&(eL4l-#(R`Oe zDw4*se!>h;=>Vk15=gchs(Bp<*_yL}rYs5v^K-0avbI_m`UMP-GF;^7()3dYaqtS? zqOt7FN?v*=9~ppYc=_KnI1TG19DkHiP|JAaPZCUb-^RYGGet;?Zi=F+Fw~iQQaXF~ znkv7r{<bo+N@SvhU!l<^I9df9vg87M?{i>8c{TM{Roa|O1ziogZ=uSnA>ei6zT#p5 zsIcp=u|0M^%G{y}Cr?Fy^%h3YI)r<;zcgQO+BzzG5zw)dm#aZ=o8VUF<*2Ae)D*Bk zFF3@?$e8r@P}bPlnqeqote)S39l|eyMLUiDW%<kLo2!QUq#NE39hM*N-Zq?5h!6)F z@6V?sv0VIO7*yDX9JMz`bPjp{JMeDs;BHmI=LLwG(z+%gs474C8;#r{apQB*N)rBQ zc6Cjj-YX55Ib4mh83vi|4PC~9hEL2xK_MZJVm@A1?f@mI*wl+L-qnJ5*3DAA;{NdM zv*T+P%h-70VP)sVwLcp&dJb%@0#5ZgZ<4iRTz$=f=qU<daC<*1;S)MRr86pc7A>!$ zEDoM@gz@g1{iq>8PT^zLv17h5xVB|`u47wL?8f}=caHvit>g-mb(E~eWp?3akYwRX zJoNiD)j>Do$_uggexzTjppjhY*GsN|T%-8)M(l=xHcN`IX3l+3NtqLeAPgDQzom=b zv!pFh(kty^Az%CduGR1T7Xe8M@%c&`-@BtplCp#un8=yNb{!rW@x?l-g1g=|NX4bZ z5o{NY6-mooW!MH~WSrDSKl^YO{Y+R%2`U_^hXJ&HeTzd;{c9FSJGRa$4D!~h-?+;) z-@G`__t$%?4rat&w*ikV3zPVhu^k)+V=2HAlKZ#={JG-zd;eYpv|b&+(Jt1oU+h<r z@*p-g_TR4>HLJQSD=C%AVI+(GEa)Hjy3lmc){YRE{>oVUnSd$kVU6)?EdmUn#<*G` z*q6A6PzVOcr4WeAghRCP<)P$XOup>=Sw;$EB>5MR&^4j0*mv)L>-{-8RyKnPcA`_( zb)%;+Q@E^lGJC>(Wx&oCU{|So^5&Bdz4O$GvcA<%em*z%<-V9*yzXlUmB+8QI-WIG z70W1%%ue;9btlemEN58UhH5*XcIX?bQ#c7pyIpCN*4K#}RkgExD<^dz93ZWEb<)*} zJRT%3ga!VBT&^r;zwY_3BcilEwD17(T~B_H3xxhmi}SC1w>_3bO%hR4#qOvsfFe7N z_T#*cA%KUEtoo`=p<bjdx4kd{vgHo(KEx+^K7i}!==0zOh{yQ9+u&-&#dTfgu?fQ< ztZ4MyaNqWEz5~dReNtbD#!GzMA6)f={NyOTu4S9niBqAl?FCe;-#XL@J0k2q&wB2f zFWu%UskRJ`Vu4oZUE`e;L$b*M*dIPq<*A4Q+g$kB<T2S@6g5@N#JT%w*}?J%j&lZ! zGw{C0V%)kYYjJ}8+VhzBZGQY8hlXH3-{?(~x0S_m5Ey}ap;fC-t~a8P{o0z8zKIf4 zaI2$(^ux708Qhsr?EFF&Ka`X7xRcBKd)eLw5bKCwK<Aq{s~>v(uigUXt=>2!zf41; zdUl}+K}C}M*VnHWTP@B2sht(}E;Sj=vSwny{VZ@igB<nu%_IP8O-zF2tA?6-z|Hcx zY6Y8!Ek2ZryVVz;0Kj!h>pQc<v`_ei43E_{YEy8O0sokkEG^5Oofq&}J}(~}^oxId zO#7<<I8LBm_2B#6%(>(cbNInwJJKkDoH70GQc5?~7mEX4;*mw%!H82e8&<tOp%FmZ zF2^P8_<aNN8I!*kxE^-5Ep-xnBpm8q`*1`3?=En){b9=)P%3TBHW-*-8!mXr5I2YS zp^9L`aY=6nf&Ami!H)UBob!TrKU&~Y>wq{Iq_YjlA8<g6aRQoemQ`>$<xDMCmxs<m zKfa@^_@bn9aC;gP%KXd_{4(w}+S7~-K;qFj(70}|xw}_lknaP0VJ)nFq^UnKH)TN- zdQOUf&$q>=0)@g>erC2H);_4)=SFp(^lMb~^=g9k&4Pd!QkS8O3_l8cnIYuaqs*`U z*CB<$@>-f|7lB1rqk?qb_tq@n*fGYz<q3Hw4NobIIERfrGd6~0Zv6kJY6h~|2OIv1 zj}<s#0-{oHi!)&0G;8K!GuD0wjcUytE*xJFLZ<r_7dOdsc+DaO{oBhh+tL1Ry_(+T z<sDE%z%%iE?8V?TvjCJorx*z=$vjC+=q6*Vu$RRYDCRv%4Q}J8VipI(!DhkxaFjFP zl#;5xIB%x`G|vE=Z#*3we4hXuIf{mZxn}MQm@H2%Z>Z^6U;bA;fgJfvQUC<a)x<rX zbeyLvfXqJKa!4f9Cvc?;qa&lZdK$F!d$6z5;`v1yIbRq*J;(hvpsXGovOfI0Aal49 z*f>P`xh^6q#0ScRp@Rx?IcuJnqrMuP>OhwyU<Xe;f#u}t460^%;OVhHV!p+v0fh%c zFqHQmQ406~w5pJVn1$h?Ej|0ZROz)pfI<VB67?QszcQLIwdjTtKu3=tjWR6dXZ&Ts z-(!f&Z<f^b?Gf(I4{`<SR67$=)d@7u(;o?#;r~dpa`Vkdrz<J5Yw#0euK~|0q_M|Y zHRPLrBY8kHGisaNe+_<gvV_D_PS4t>F$!L(TR~2yaNyVg=QiHN3}93ZF~-5xj*cvb zH5S%&m;?=Qa$XYpp@Jt>Mb#01r$w2RDxf|IUgd`4kQ6jqeWztOzMuXR>T<2FiJKz? z=hEz`Q(~n+Sd(7GgU-UubHP4SX1Hk!Xch*(zBb+8LFY&sEII(4H0};E@fx#mGlBc< z?|2r!+nM!|vyxJx*e5`mj`japzuc+y+32UBU2hOZVO*2ZTfx@})P*c&bVEacw{h=t zuP|6k4U{I}p#UNM?L)}tz<|AY*Lz)A`3mbyj$JQva<PyX^iHqgd~R$SoK6m!AOKfZ zaq#>c#*vb$H^@({BSCCxkb3M7x~`w>Z!u}N5LA@8VBdyOK;Y@m&2r~6dIX2rpPcB< z44#0U+~wEsq4QUI)^aS<bWifV!CQMW2oQijqvH;qPAbzXC=AGb3@@0xDGB->Pc%Gm zGcQcUZ1zr#Ln9X-ZWx`@NE09ofIiqrZvgci6rhYr`x$N3n(*durTKk=Z|#6bxn={X z@POUT#|7!A22flvJO|H`B*Tln&6RFek>3&)fl`Evip9%48Z%MgcxgEt0^EUjZuJH~ zk#UDY7)mKRh`tMuS7|6TUpS~RZY$$>LlolRjg6D&y4}8-FiGIkd|*&T^4Vns?V_1H z9;Cp)O(3F8XwCOZo{~><e{~MOhcU+a;|gY%;&&6xF|qpkNg%@>pB`2Ona$Oa3_m_# zEfFFKxQ=x$D~28~gcMG$9UYIKQt(gO_5Tp8(Yr2~t)h5HO~xO)-QNd0?%Vjc1lzRi z-RYi?J#2gw&pn1c<ltc17cZ`ImzS}QH=xu=pBuOlIa-}V+Js<8w~N<)a$0hqSFX`# zSttV&KM}cX%-C}}mF%=*Zko6Ei2)H0!x6dSe4y^7ht<6<$thUzEBR94huh;a)(k=~ zrMAc9C7kWv-nRPHnOWEE@jqFgYb4WjoFD%Rw^ABYwH@btMqBF*F*U(-{%qXx_e0?6 z#=u=W;>kurzTP$|PMP-PTKhJn?QvYfnu*!?m$Z^S6x>;WB8Cgj?)YqxGqF;u(Rk5? z9VJlVjXLG_8^Cr{_Gq@;dTv63R1lfPzFxz@0>MGtUC?-B!uD8$RUiOC0?eG|`CWhB zN(_bc7x&jcl(G;~ExB=sxE?W>ro`8EKEDw4Nafloe1G#Y5CJ*zWAEyFE94j&9DRKm z1<)c*{KUnTp5r(}OE3N@yGqY+PEx6&Z|<jF9oqF(`;+=98`4m?QZzqo@s>Kg0!qNo zG+WjGrYpF!X&>IbJ2p$L=J<Vo?^f|DhF(FI;H>Li-~0@}lQM%az`!d0n1q2z(wcAL zqHrOd>Z!~*Uh;qFcV899TONywQ;Uj%z>nmzi+{qyqjRijX)N?_(f7GH?io^6X{rxP zp6V~QrNjENev1q!(CDH^okgqbS1C05MRi%jd{IfTbu36F+`Bh>GQ`~8nn%3Rd@fNg zFPryxxIlpGUsh{f<*^NwXn6&NTl8suc70(XAl8l+r&r_(nJf$NnyEtJcZ^Onyo}uy zWkJso!(e4)YPmhFZwe7yCEVw=jTEmj3PD~6kI9P3NyOR_^Hw!FU&1Us7v<@nYC{v& z^?#vXy-M$7E(u5}e$bRXy#UrkRsbqhTDM;@38jry^fRGMioijjd#D;_aKeSlHL-~V z_&yMiz8ZeD`RmZ?w@Q0NXP3xfRqObNMCFSUz8Ae9B(|%@(VuNl^&?&3pP(S9e0^FC zCstknbs&4dgIv10pb{N7JYXk8lmwaEJ!1;t5b6p1i)k2Rlnh3rzc-o)7gzJdpP)EE zrqxwp=x5*ZDoq4=GcfX%kHu}$fNNC9Q)ck1UG7YGM4Y|d?|ZXvT=rZ*?<SUcMy^KC zbu6IYa=H`>xQg;EGuNr?C|>)~7luv!)2C!Su%J#DW76WreVCku^%ZQLI?V{=ba|u$ zg5~BrRiNIRaj%jyF)|6_4P_i405t{T>1iQ5pu)KP{_&JqAQR`mxaQ<6bqt3f<VWbT z5=tc^+iKNhtNkdKOcAFib0xQ2acRoWC2uOLMW|<rhlMM%hduP9ZrT}LZ)iYj(w*b| zB*c9a2>zLlBKP*Z`2Y_WHZVYGj*EMSF62sWI3SwF$@&s726#Syx?XvD(0$aAsQ!Q3 zB8b1xBZ85fqre?Q0_?pPj78C(n&5ZQPk4)RdMO~Q94qTCi#YjuEKpevG{w)SC4T?z z*DeiU#A)QUV+L|?J0eamv3+;pGrrb8F6(w1iao+eGiAcU<XcAj4Ds%NwwaOD9Qo>< zKJLZ};%kV)0hr(cWS2M2^#%#)*QxtCaR{b!HNzfc-~Vy;4-E8>JTbD;vw~vP{t^>V zsbPmTyp0vZ01j;2I%X!zwQe+SNQGy1rX_-#0%F;l9m1?ilyC7lO>qGsHW|gx$1GNy z0&H{u+}{RWJnWx;AG&--)zxtpc26mZJL$@TXKxXB+)Xen;@#hz4QiP1=iR8ivp4@r zxbpk$JTH!vbI-gKM-;7h2kxJ9fu4KjZ&lCYC$|F>UL$5-N>D}w?bw~Kw@wyeEqRnC z$L2h~B4pIR6=aaLQ>)UY!!@iiav_tBV&LM8_nM!Bd(p5fODJbKkVXWFb$GG2?mhQ1 zFak$LRXmxvNFk=;89?#U*yTSj@?nxArm@Fd2(I9&96vs=ZXc?FjWSQn*$5k)Fy#`c zG6uKb$<;KdBjL>K|Dgl2)t~9HJh0`5tiZvH#$gC=Xb3z>j&6EH+(Xae4&nw(d(?qV zAMp07ygH~jzOB(!;y}UT@$1#n?t_u^><#T<oJatGyMg)p`sOQ+#?v0)Pco^=zS$j& z^S#n9x3D8-g#+VN*Ns1%Pb&o4l5=x>em6FfvT)O99-fTJ3TwNi8di=r!@EDGbK-?^ zkGaK<#5rgha+oV6EN%B05~9JXs@E)@%@pFky7bz|+#ghbvQ>Dh^g4>IiHR`_k|FU- z-8CNy*(qjW68H!r#i0W>cn=h7f)E9Y?@#`L&t&>}w(o?QY5ZBV`}5LcQ3s2aG<&)Y z=J~^cVOPW%7I`ALzDid8oBkW8vLfbh`&cN{)JUnR9K{F*bL}1qpI5eWgm9pgCQ=>t z2~m1Q&xGz8x==77{hx8n%@q|PuV2fIcFV%M(QhGtccJ-a<UD%Kv>|sZxCBS}>Pcd% zN7&N_Q{?U6=eNH*nj_(j49SKZrvE9lTpsm>;ZkD*49C{B3rP|jG7P}$4F(-a16}ps zKlu1l1mJYGJy0uk`N-x7rvg}T9Q#1`pVPbC`SshkdH{%|sL4l)dqvNbvpD@sD{{EZ zY1I%90(z)N@elno2%zzr7ePZ}*4FF#5Kz?UVFRT8bPVE%fP8T~w`<&nV*lHqI9Nvq zi@y%3fRjv{_;4v^UF_q>@O81Y#m@!G-{V;f4Nb036<{Y5zh~tFT7X7CGH=SwRbLMh zwKu~7szl!XN3sap#B|2*1jMhw+Qw^|6Mj3=H5#goFW98x#_xC0$F?-BLs%e#6|R~q zo17w_M$X;dzQZ>o_XG?cIZSRzWXVFf&<!-n4XTJL*0T7+TrAMmK)1>L@x9Pn)oQ_a z|I{=qP=QSN9SN7gn2=J1REhR(QJ_TbJsP8gmX`&<Cv?3l)OFnnF~&Lh%A%6#=%{)d zRiV(w6cEs@DnQH*Zs7WZotWcDJZtMn9V#)l@WfwB!?zMjF4TnSvus9sW_V}MpB5hO zXgjK6;wa=crbEhSp_rqGODEo6H|nAZKHQOi=FRh!=UBeKKOqw(4fJSwO>%9;nM4?- zq{h4sym``g*bmSbLCGVaC-CJWltm`f%bJ+HW)S!rMg!93FweL+#^YnX$Lpg*c0EbI z4q##*iRii#%OplM4^uhLL_XXNxHi_pH3bB4A1TwpJtLX1^hX)2qtY<=68VD)^QEHV zdsLV-r0i{8p_m1bbL^sB9e?{8azpd)pb7fY^%HIc*H*bMECTFd@71qXjcUOxVIp=B zk)uz-a=)-y$Jl+}149$vj-hGD$V=|B-VGpE<bZL2{ynPcnVUK@?K8*$7M4v~CMlj@ z=No$OLz+N!nS&wbI6u&yK{a6<d^=0c4G+RwaQOE8Uwiz-Rb~M2uKW3=mY$heg~r8R zf4+hssp<839X30=Or-DO;(Y>{0H%rQGd{w%n>3p&E~>c>*Q6T6hVD6)x&>yJalB2o zNoiRQz>2LTN`LVSeD4-THd-v!$Q3EGF|(OHXMFY>yYN^^5}p66?JdKq`o5@9#6VF& zk&u>dX{12}q!Ex7knTo0RZ>FW&?Vj7Eh^F=-CZKx4R@a3|9zk5e!HKpA3S(C?z8t= zYsQ*ujxoRxRn$v_MNi|odJ1=)_Ol*!Dd&f5g|uon$-v1eZ2fmhSWv`X7GK98Zb4zU zQ9JO~T(y!&rvdcSI&-^zMTBP*Unh(?Ed1QkV5#H%4ze;~J3QPV>8*DeqeHSuWF(&X z5hAkdo3Hcs%GYtmHU)~lh<1sh?i6dDn^c*1gfNB9hS&T*{+L>{zt3-Fww}v9!EJZc zJB~Woiq(Z&aRDmkk1*WUGN%Z<>J-!jT!o|P#RWEdhRJc>G#blqOijUR9vYt<3%I<A z7E}uTpaC7EUC-|+7-{XE#^;+$^ve7mosKygo)nt6dAvavv_eik_b2rt%w>(LbgJT> zQ~;tgL(*^U*|^etiKET8Q@(8!n-4Sm5kG!186;(J>}n*8qNMhyf29ltA7(AEi@OaX ze}Fqn8042E3&(h`ZZP`%@tVDDN7Bl$*~7J2IG!Ml>|Qds+?bvC>HF<(1eIP&?+x&! zl5$sq=Pv-B%uz7*6;54ll*~<`3d>Nx5y<1NO-hp37ipP>Zn*_=iF{uy%A5iv(0T#| zx>nai?Z%v;I?nh5hhES9kmcY=1cB?>K}NE%02Vve>A6Kqk7CM)%i!WVFEQBb7*)tA zs=>tK_Geats<MkJu|Gyjp{7XSc)JE;Dxc%msKj9V!+&orhJ%3FuIb%|wB+L^lN9oi zeQ7CXC~+e*50v5x&)dMoEb;EH+oxkt82U{8gk<{`Z~wk>{^tN~&xYMQ{MY>aheL}= zUBS>-3+HNwv*l=9a73fga4ksB!0^Lya{*dIquJjm(j$BFfQnI=<5RobEV4JNi(r1< zPd$gZ;j3GURROSeiubk7bX#5R5DOATY4q$aJ|bo^&=EdKsQVCzWaEJT8pW1U)J4KD zwD<ikkCV1k@(DTLne)%w0~|yUju`vU*7PYUC@lwEaax=>ud=b*mv@!nGJ1dCAn<qr zasmbng((*o_8e<#(X4WcW>06G`AINHZcStA!PPMZj-ZrO$N2Y0KZ^_KKEhd@db*kN z?x*9m>{>(Ztr_1a5lvYgVFOA%W}TIpTKk-W!rhKg#wu&f7-m9hPh=gErqRP~|HE<d zY0Y38_Zas#TJ-z9%qXH~$EBrJe&H0Yb@#;jn4sa5Jie&^dyVbaM8>KY$-FrJ=r|G& z?uFZbqzVg(ER1Zz-2tSDP{p8AOYQ9q%<;a1%fTL4ge($hB?TaQ{kQF4t+u)^1rr}1 zgbXVzOox=TyQ~;<0n-7cyr)$<pH7_n%?I*h6y)jX=4<$B_O<<w4<eqRy~4zD5V{L> zPQNou&R$7q>#H;;+a#mNmrKUhYN<=B!}=bKujg`6({6BctghzI!Y53W8a6LM+Z#7< z%j6U_Vkcbbq0cEXjJFS2N+CH`$POz}(JTj+Eo1>P-8aIY3`|w@rK$-*Na`Q0(MuCb z#jP1CgZvY{oxbRiKXI(q$!c<mw5V)Yk}ogp6o(2!O<C>$QRg*x-B<qITW^b>Q!iLB zowR)cSoHMnte15nP!m(jJ{Tlk?O~Ux%%3Tz(VnhLH88Zj7?f9#YVXzuNsH;0Bs`4( z#k68f53M;{NN{8<$O-NXO&{l~TSQ*GfUwR$8+0P(yf3S8jEF4M^d0i8ukW`ny&)Zw zW?-n0>SE7dW}f>y<kL)}?j)COu$6n1sNLnvhlxJxK5!ieljK+w2ZOh=s*=qbBA18i zhB`=>{hxoX(gPtxdNRg$w5V4ag<k`1C^XFO{lqMFv?E`+a6ye`DlZ6+!0IEEZ}2IM zV)7Tkv}MWgUgLe5<_(=)wzL%}r&NLJNYA0$hC#M-(MG!9&rDNSG-sUrn7l@uoPjgS zdjlfJ0kfd?-)*3}^o-^G@R9dJu%ndZy;ay$+EQ)=cut_*W&I00VZ`PR8IsG?zXej| zwoDeq70?n$WfOu3Zii%virp|D{<zVE>9~zc5qg6N;Bss0#W!uX{7EH314@E@A{1W^ z+bJI|&XeHYK5*POQdF{~+S{E8TXy)WhYYUhyqCpvN}?I^Z9NHoZ>BzJwS-CT{0=er z)wcMykYKp(!%fi?N!PlU=kns$l)T8X3Qe(!b%&n7<`i64>bibK@l4%UCes9EWsvtJ za>+8T_H+@xWd7Y9r+-oqLd=j%Eu6%vzhg0hC|EKG^*BkDxh+ld#dI?}6&g7RYNFV~ zjUiF{8A_@Li7Zr$q2}Hej4QPVHDW`b!ED_=5>_<M^G=<<jd5~;scg)lBFhRT`k*xH ztDN&8rJTY76eKgJ5c`wPs85e6{x^Q>pL8ONB(psZj~oif(8pA?)jOt_>fbV`hCoSI z#$IoFXUb(Cy(n^XL^fDPMv{(oaUO+V;9--w39b8alA3DnaK+_6h5{m@0AAaV>(^K2 zJQP$^B(r->!Ire~d4e<Nwmxlui0@d{5;AU?yX~;Mr3V?nZKQd4TS@#KD}6)Z?>RAD zzvEODRKKPALqe2<S5)N9qY*i|6C?Sn8fOIus(*L5gNYL~{WTOF?}cD4hp~(J@9h_* ztY^D-3y_I!YMY#*LOztma~jK<b};`>ncI=^TK_HmPh*2#A_-Z}5GLxm^svtqcfXeJ zj%FIk#y4Mbp{zDvC!Zv|V0xdFQNhyK<p6yi7Wc;X{tz_7c(cN02zH(jIwDBHYBq1( z#y_|$Yu-5Qj-yy&2B6>oi};e1<Co&?5?yG5yXjY?lXNrZeuD(14)H}007|qP>i<-# zsG<PCW={0vo<*TW+<;!tn%vfc-k)1TjgtHNzwQEi|BOfVoP;jxubG)YOV@ouK({ca z>ULm~S79ASEs&JH-5gf+EteK37T*9@AoYtUI{!#FuD-qD^aMO7@Uq8Fa7f+&Us$n! zIGQ$@=474vFqMM!vFDX~LGyW03j;f<lYGj`R!3w`Vc~Ay^D=o!)?oY*GchfXd=+u% zw!8HNSV0@gf9#$WbQzy*0_ppW_kd{x^sk8qg@Y<ZSIjF##VgQ$f&6blq_aKJPNt_s zm;6goGRAg;{#OB@@Txeyu(!~97trXgjxGv|HH^<I7%;@(VS#cm)Ssjts0A-p&R7@! zJYZL+ae1_c6748>{vb;Zrz;VJfPS_5q>w%!u>i;2OCs`*-GNq4IS=JW^Th`H&zfpg z9nQQyI=U8UWhu2=lFF)zmuiboJK7$()MTuuq?B@Ce=Q4}hcsdVx(=3%I_y-%7EGbL zH@ubezmKZEyNSxJk|&xkFL+~aV7u8q`x{4%=pHFtYu7G6=ElkUzMtvd8e-DWC^<`V z8ol$`#y*a=!^Z(dnol_|VE$X7CSg@(HsKOumh6)rGVz5O-}|B@g26!XamHiUfPwJ? z0TA-A48kAJ!XeE2onY!(HP@b5!nJTi(o*3NN<)Qn%OD;#*n2hk$-4atKYwffFgkOd zYB84b{jr?z)OA5}5mD3+NFJl1fAr|Dvt%g%@5v@ECB?t(<W36FsaG_)f8A-N?ag&+ zMe$5BP+ZK6pUU~PNy<PCM4ADU@aU?I^byJP71S_7<Do(A_GHm*f%hoA!}+Td+3UiI zY^{&(ArO{(`PYF`L3&rUrzj1x^B(*7kjax*y3M0*Ol$7g#>MT7XqEts>~*~~I=k`5 zgaozgOQ*5wzLH`%aF|<JeXSw$0B-J8e!toeQOSF4gZ)a(iw9dpPLf@nUfpR@nG0=# zLeOfa?rKU0RZEWSI4nrFT=6;4&yM}qNA0cUr9Sov;jO2*qdGc@^BF>kdWy&H7J%VG z?1B*qT(Ss!5+2m5Dsl>@aH3|9#DBA@##_6!wN+kjOPWB-XQEUX;*v=Ycq|Hb&y&<S zpso=$X9^&gTr!AEFk%EXp2$syU{Y(A*@>CtqjF>YB&LV<xwkr|C4%s*tKAavmh&Lm z2aLZq!gGH2C66g~Lh9mKL&b6?YLZx>DP%XMODDEtB`wc(5+g}Hz5kk|o*Ikd7>Oxx zUoVEC0E?zic?=$kkB^Aio9tHtx*sKOOLuaCn!xX)7Xh*%%iYAEY&McI_%7TPIcPj4 zPL&#og2$aX>t}2Rv!>$dF!v4y=N2h45O%I_sl0(!;~^PJwe_lsx831xD!@d%bI=|L z*?hma1Je#EMfX3trM4#C0~|Ptx=nKKQr(xUQAF=k>f`QFGABunwMaqyeZWuUesLr9 z2#t(4&^Vmy={*3Dn1|8R$M0`hZIAzKv|j~KNOsG9-+HyzaT3ct(YRs0vY}U~jLi*+ z>D6HiCX4NOTFrl~ug~U2GmJ6^;1tWl?3lG#*VB{Su81}bx8#|Hh3G9}QS6di!`-=W zcUDvU=B}BW?|P~FAA8>zA`#FSy4kcJI)sMc$AbRuO#P5h@J2gFz18>md*SGs^gLj$ zW%Ki-ooFIH#C22se)7kl_3Ovn+}&usA|oO3$c`obHQ^>AKKMZ|$U~sO8P$Rs=#eiC z#oZwjn_o4q7z8>%Xs2qZq^K9qxvYO{pL^#ntI70Ob)w!Mqg6OT-rH`Tzq<SwM)*SR zFMewqVPXr`$1u!`Z7;vc)YRJJYUgbM`U;bmMv7SH*>Y3@&wC4?eM}Ib#kC%*$i)hE zB{yrFcdC31Gd6PaJUtC9`;5o6%%8xWay%x*+-{^95xP?2aQS-|9c%hKijD`>!Lwjd zpkJr-_0!v4i_7148e#l2^Au4tD-jqN+holPRmPLLbGE`pvg?!iWK7A?3?QJqKW+Y@ zI?-pkE--q0$>66kwNOF9v-%Amp{qysmC}!_o9MD8eR7_5;5(O=Rc0<?3e5pAf@Eqc zk>|N(McvCESdwI>inaHcD;}}0=!~DC<HrZPhk;-mNAbW^VwNPV-nE7DzGw7OpT|iO zPtTzyOt?Nne0Ib+L8r7my|_@^jQVG3ShchxdD9Va1;zxw9{K(b331erytjOjw146J zj$2mf@EoKg`Y}9t7dXB!Gv1k-OrF)?LI>o#KWWh6c9WYkc8L;=f@rmA<fBoBmt$k! z7h2OOyv`k2nJ8DEJv!f8UOO0+hh}V>)xQdIeX(SN_7UX!uyl7F4wj&WvWa`A)r*8Q z{AtTq!&fx~9>WhtqAbBhXtcMpaClsRHbnJsscrg6dFkWZ>*5!-0wBx^O{I!WFK?PX z#7~<AAm>YR+02jfLhw{nRIxvG{T1FkRq*=U^jls5duD2v8fB3nEM06EH=09%#g3B0 zf;R0R?`7J@Sf0TgJmr-PPmkDefq?m*7cG-~)u^Fv({&YHTXX1YbqJ`+8+t;*62FJB ze43f^m-hPKoGR1@FaIpgPF+#27Z)2(P1xQRe?avc_%||dYidGmQKi6JkSAyr>RGa6 z)=l@WuKwrLZdp&=rN=20JiYY+<K>z{`uAbw0-(?Fl%|S^(P;ct+R+v}Ir>#Y3&7TB zpmOql^Lxy6lY4t(Pk@FcZN&LJTEDRJQ;}EqNvKq1T$XXdpGrtSLG7myUrNT?7%n92 znf1W)-WR&~-ZOmr%Q=ADSj$=@#5te(iubeFPKyqdqmSfX79BewwOHcX*=1&5OuLIm zzl3p)FDYk#P&vw~e`l)hwzl@2O*V?JQSqID7PSGq>8}@ez5Vg<=|0jN9o-p!8F=fP za;3^!$D#+q?ivkEuXfVqNELoo`ccVt^?W5u>wrG1KLXb*(ad<NyxR#14HI{BhSO%n zoLX=R@4xa7I-Tadppp4@Ro;hcqj%;2?nwWJG!<1`%K4=X9PYjgZR);0x-R~{m=~Sj znR4^`*8AV7_(XTZ6-BcEnn0D5uPUbW+Vl2=abJ+fvjMf<UU9xn-&%Xg2lrZ16{+I? zk_UT>Q+y1q!}>L<<BxXdqeSOZXdejH5{QQGmA#Gg_s3{cl69}3GJY_4Gw7jNrLN!6 zRP93EimtPBz>RvL%UkEi2{VEepn$d&dTGD<>pdco%1c(;i#SF1=bH)*4B!@1XbVf2 zmXqvg9qf;sJ-fbxWOm@ea?JRy?>|iV8;~{PFLk@~SVbwtW4pT9d?6CEsjHJWpUwOG zZRrr3nu@FlF-ZR{4t}azF({tG^RrUy3yO(=2XEE$=iYzL*ZW^(M1qjH+3?&a%;5j* zFezypNrQHW5nniZ#3r>&$XTd45v%fL^U*a9pZTI}CM&_sJHiQE<V4X*tURE4^~fIM zWk5m9w|ivI(mZ)m76vl!T;qvm8?DO=WonET0W4tbctCoJ$FUpOBcvE#tv@t;w`dDT zAn7Wh3s!s6s9{Ni#uyLjIi9i6Ma(n!32|65x^Tcsi8$Sp1`go|$3vzuYbSpxYTWVZ zm9aKG#LX_+B_!tB>$g9q&9NImH_eBEZOsaaZd?J^w^LVJe6*^)wAoRJH_=)eSt%R4 z($_k$9li<WuwP3LMc7QF_URmsOTTS&{RHio1NlrWrt)h}eEku|$8z`4>BCipbUg2A z)e~6Br(l?m5?JLI7Ot_laVhgUibh8NzKYRa2;I8L9FF}_=j`%hbGFcY;C!$;C-tb9 zu3YDMl>TMxA#~70eog)kq=`mV6%W24o&oI?j*yTL6)S81K$cpv<s`OPrXuzmgx6I^ zqxG!d&!&fjB&XPtl9EWNJ4MBav|+QTPu2~o7n;pYWlc`jR1|I3uN*4UoLz&IKBgUP z<kD(vQlw}oqSv?|mn|G*M*eU>96L3pn9nX9M&e|pJA15V{>uEV-u{Dp=VE^$WV$3Z z&A@>FV$5>#0qJMigl_$m$57%|0D#?!YC#U|?ygH%hm^ePQPWA<p7rHfXPHqCBH)#O zw{=d&FT3R}oYqqYW8XE}K}Q+4zoo#AM}N=so>5tW1sR1mRk&vLFJXBxA4n{p+;adb z*RfK)*`*E^n{WMcZu%WqB5-(UXenE7-PA(;T6UhH_<Q5VeGVF$)GrLTr@NlB(y>0! z;0;*nHUKr3-^N-GYkvV$oNar9|80&MhrweIufVOS`09R2g<!^vdA^KW(;Wt6_<-R) ztFe8gvO_eqAdx9mVnxi&eOI9G#ca3M{kwNk|C(vlr%B2iUw1J-X)ydEwh@Kk|EHYO zcJXrm#>q<ii-IOa8)KKQJSIA0V+pDbSK&q214f8T1?8-L3Gr1aw3DZAZ-1oqJv>}w zZvA{a#BrkDxYveS6SH8A(0VTJ)v(K<8R#$AY`mDgE5Ir_P6_SbB{)B8T&}5StY#RA zo8G3JHF|E>2_Bw%`X8@p<gfoE$vc^w*L~D|zVyWQ@ezwrpI?<FaCfFk;S5e18)#Hw z$@Z9W0!IVyX|`<F^rRjTy!dZ7r9*#!2|GK_UN7IfI3b-NF=_PH_B#|V<%NH)iJBex zq2c>_)dK*vuQquq$MG8aevL#u1mhXtcEYfl@<zw`ylNPeZgUm#Vs1w_b+yDQN#XuZ zXwxwXNC0u3AGdieh+ZE3dDG97lWwi!JbybqNktJA4y50wSEVLe@^FPri;B^(67sSN zgr|1-N5qCpv=I=;%^21hK4hYgqydVhw87)t?7i)VQC(vibUov46m#?PI1`yW7#z3w zgf8DAUL0-S=fQbjU}0%`4YJ_GLL|tO1p9wo%>8B@xFu?e14wTC%YO$HoD5Mnq|t0x z|LOU(nc*-2!3#CK<s*$|cTu$=;`I*sSSF(wp~O#mb>uvvKu<--^RdgQ*R?OyUtA|D z#?+?PhtqqmuVTz)MneewjM_S*bX!o<L-C^#!l2tSez9b9BsV4fDd-^zfQDF&4xbjy z)Js8`MV-~|Hf-8)t11F=h8N10|Iza%*;Tc#yzc!{J~T8njZO6#PrIGTJT@Nv^88D` zE$@(uN(Cf!@$%3{hXWO>vg^zkiLfv1^^Fk`z5v5vcwLkMu!mont^IDc)>hMfLUl!u z=LzcZKng}fM-eD^eKZd#k5N^P1DRKzH2SffgD|JrEn+?bD`p)G=0?hq>tmtahnBb4 z?Az72Rf=R*bv#=pTF6ELtQK&Zs-hyom8K__o~WVt1CT<{fxbTI<v8Ma7H$=k#`2-- zEim@B0OqnXee|F2H)$eH^B>#IUk;g@FSd)`dmlIe6w@NMMucA1{nT))e*xkIvGLW# zRNJY1qJ;l=d1+ZW1t^nLg%E({G)#m+s{xTE(KwKdK4jpc1V5d8eL%?}$T6$GS-7eG z!YFA*ccUstGgYxI;sSDP$+<St*1OeDX!f=y>y)wM8G>rwy%?wnD?nglEIo4GI#>+j zUfJ1-h}7*NaMpZa3pj&RX}td)=8p+yL&LJ6A0z3H*KT&r*Vtn7m($z(fMEhDn2sH) zilUJTk!XUz{&d{|N-}pIqu`uR&QBd3MWi2zHvaN^m{r{mSOk#)$zyf2@XKU=by2iR zjh{yZjc$YH9auoxO~b>VNGvbV+B2MQsP%0r6$-KCo4J<G^)ARWp@|;zm|`3Lr)&=_ z^D4=>IHO6f58iJMN8o8OV?Z*_olcu}c6K=0$~Z7xZ@)d;NC@p^v%mF!2i&_QZqnET zvX<Kjx+y*<x#phXQPt)q$HGT0B<$j`+=l2fNoQjam4p@9mGB$QIGU5saB%3&Vu}lA zvuV*uw=ZMk{EbmXHe3R(AKh0sGg#^Nx${u;r*gCBj?P-Ib(2rvlh&NX{`6K~&3hu5 zSn~5NbWY_)fsDd(Q<s-(<$AY=>l|JtgtzR<3(n827e}g$xpYRbfjos7k@K0iHbu$e zEHRHGpI55w3%3`J@?$*!g@VSp$3%D*+WOM>aS`9>*bJr6SQw-gw4GbN(HzbD*ZLkt zGbv#c5|$S0G+&QUGf;LoZP6z9KTx<T)X`BN%2R$$EG(vg*qp>^otSt(GS?Cm5od}} zl9qb<_}=5rCtd=nEX1ux_L_#{t%heF1cl;pbuM{79I&&}FPt}C-`6lB7ZG{z(M79{ zR!U>yXINkNTg1yxzjjAPg34@CT{bw#H44+Z;|;&!7plrD=;@(ztE4MDzufgNtFYBy zo;^E^Qr?_oAe*dC7=14pxTw7+klB-B30LR#T+1nFDy#dh&(0awnb(!yZKAaXDP3#$ ziL)3!o2dKGythGngYvb<eNgPYL&CGy?B$Fud2qVl8Q+@}1^xM|Do+10J}2=+cHNjN zA*<dWEzeHN$auOu6oK`-<?AP@DC>M$nb)=n^I02>m8#9mbOMt2RqnCyY{L#wdxN_v z=$_vqddJIy7B(0KAN};ZJT_((&{9Zt?c5<dJ5()?HMQWfVBz1KsKi<RRY-OA6a|H1 zM^g0V+k0X>9vTWw*V+mk@l$QTwQJLwb1teqJ(j<(7c(Fmv`aM=*(9Xo6<x0fHN9?} zPK=Bq4hjyg8+2r(f(MLkNtgA8#Msr;_rMHoDfr*Hr+-Wvo1pLPjPmoKqYGklDVuLw zT#gaT*kSEAtL!}Vx>k<nvdsH0(=h$>jEa?!ag_3Ft$ieRr3SAT)-F|k!cPC(>K!S7 zYIy3vUjs{{)Mq(!5%;ATO>*PLnx-FxY|m&ZU|s2(`G}{}S~um=T3H3zl`dVqNfIgs z;oYaG@gyXAjjzzeYTbE`OV!ubI%`Qi-$ZkmWu%bUU1g^rWKOBb>AZsr9xQKeTCXR4 zAMvVE`M8ZHbRgJQ<fWp^8+3_x)Y(VlsluVr^D2ya9SpH1wVzh1SUX6uu`Z2>M{msY zvM!Co$|3;W^Ds)ms4#P?F@Gb4SJ~@Blh?nuR1|#E34=F2Q#S4&FGZ4nC8Mjqa9=Ps z{4sG)O0VvO(X3L#aQC!Ts<yb3PUdwkcYc~3_d$toAqNGY6M={xlLoES+aa%68b-$B zO}Aqi{e*jgfsZBcPCI?tBJck#u7Fe!j)=4;C3QQ@E3=1ydfYQ%HI4AbVL&jR`og1u zduFg=pF*xoujK-FrzJirrAV)7g|~_bn6i|2a*G{1kq`=&o)Ldv_&#2)7h4os@AS}@ zGcGP@Z?igRVaecWZ>{~Reu`pbT<#Q{3##{575<m04g3y?%Kw5(H$fz~{0ZZmR3`0U zb{BrLVaT7FH_LG!`e+l12v;Un+*3aP_ND(6jWOW0=9oCE0Hl4DpL3&^mn44*Z_q1a z8;%zJn04wS*~L%c%>SjOWm*|jW)hwT?8G&K!#tmr^XWU8o0mMLGzwMjjaSL4Pd3)K zJl>emsx7sMbGSuyYPWvHlsVntc(xr&-OQyB`kAETSPi=4E^H3aE(>(QDA){tVpGxQ zKEg9Je6WV15s^bH2K!3e@r#Xemt#_%G+NpCr%kf;4ho2XfHvMtm3(Cpr>(bKN882M zd(*sj8CWp5SFr~UV)xJ$5!JI?+}_KbRCEb1ghq0ysDfh=PYowRO_3`FeNX|oW>{3E zsJN)OMm-byED(K19x8LgE^$C>(2t+G{~0RXHk@^l)cAs{S64y6=D%ji$=1@6_F%8Y z<QZ+}NTEwupNy<>(ermX1zSUs*IE^IN;Jz;VM6qhVW$E#bRH%Z1j~JcUi4e+Q-`5* z-!ea;2k(eciqbBQ1P_v)?<R-!SlI3YwNdmlMi~Wd4`l@+q6f2I^?yJ8cM6AkNsV<L z%UukeI$q0#MbA;8?nGs9?r1nUR(i%fiF9eBSEsN04XSsIjOaUOy}ms2D=dsiI>N!2 z>q(FlcV38y_<qW_8nw#BpUv$)|CJ%niDM%tjW$hs8_rKw&&an9oM_aY3n_Ti6_UMX zc1{m>6%fk#BqY3}CfJ5o?vLk3r;FuD#m4KafB8;1Z7L%C9zK+R6@$@a|9WG#g3jrP zkDXJwwhD?sS>~#jPJ<DC52G%XvgzS`nl>&sH`;|fLa$>wqrNlaLnQX$_0ZXNis$u_ zlH<%&oOREv&)iL^nA=*2zXq?OH!YUbmu>#ezl#el3y@Nf3KcO{RK#M^sF*ug8|3EW z)0{|5v;X@D+BQEQu(7vRsT4x!N>!p6t5J}_A1i&ep_D%lT;BZs=r|;6h>q}ZWNY+X zXB6lh-S>|Abo1&7^&9A0D6w^<=E3(odD&gu>@{nn!(41&HH%wp58ovxCl_||i;Bv( z+Nq&uBfb2vI^nI^54`E7Z#tWviJ|;G%-Np0<C%sK3UB=RCUVp0C|0tW_tGxpF5;Qs zDswYq1?tQ0$DZ2)sf22O8h+Jqv-TKoy-UTvyVK3iVx6oGtv2fFCC;qfiT6f}0xba^ z{H$}PQ7>L%w1~yA;49k)Cp1eVY5)k${~pMk5^zzxRs)7V=MU@RAVifrRmH?bwOg;F z=TJ0T->d51q_^waYJ!t0zO}j1laUn}`^Nwk_7k^fssR%KXU_6aOK`4R8w|GOj@H<! z>^5-Og-TEsrGVDETa%MtCZRK))cx5ApA)Xo9SjWE6ftVvaoP&-FsqE++U<WWmb{iv zi@xjQGc{B0KUwee#O<`qGo{fVt?8%#BDh9C6abG6G{EW=nD&luT3PM1;_VD6b=1^L zLm@J)-;CR7l?b>@S#!3Y{c{dD9zQc*$8XZ(2F%WCD=xF1zTwXab8~e!OQx5{pAL?# zfi3_^ykHyBrkhz+HZ(|k1-$Z=OVD!iObdmS)OlCzDJQzwQ2Vv#3+o_;vgT{t#YOQj z+{x`JpK{~YWB>GY_2eIm--dsj9WJbq3dCQt!uXbDT4^Mwkp7x&ko+@I`PpJpJgC>_ z&RZ1~XD(BbWyj5&u`()>hXn7nP_@wl*MO{X={$XQ9|Si8<-<m;tpO_d>EaG6FwB8| z!FLVIhj(|+@Ln{xcQP<YcF6RT>HteUY)15RuHOJQ!}@4d_g=<L6PbmDxgk1kJ5l#Z zVt(49B^p_*^G~0A5uQEqHtV<><B@K^tuv(-%&y0cTf;=j+5^I9p8v;#6d>pp{kPm$ zGAx9X=$oQKsiAa(f5<|#B_{dy1QrpaUo9zLv|Dgw7-qwH5zFZBI}wqQhs6MVmwq0U z_J|0t`!r`ivDrTP$zvLBY}Wt)WTIp_CHH1)2~ts0`9ZI2DRh+Lr9HRd-YB_x`pBN! zf0VGdt57?uP*oFumjQi#p1k{NRHruEufpOUwBS4tULSEus;hTOb$2Pj71Hibq<c<e zY3VyURG3hs(-pnYS@_g`8J+RnUEtd;`ao)MV6uy|wvAa@YO|3OO)!G@M|(nQspTf6 z0E?r%p`kB`xH@x~eKt?xtr;!~ftRruW@|rVN;)~H!I!N3BLFppJ`gZI%Xt>y^3$?$ zVxY;@P2;WU%XFy_00+I_tgK|@Z4S6ehW3Y+9Gwly-zp&*w*$6&f$IJZbb251ug@E3 z)_LB{ys3sgFxZM$rom*C>JxkOY@^)flucB0sZo9qB%xUWw;$Tk-rhJnt#yI&fS6wK zRaMoQR!|x=)X4m6bjz5kja+A@<4-*KF6^Q4f+i@6X?Hngu5KXYm4a5y<1I-zT9nUq zclkT3bBp@&(tbM<<NfaI^x;P_xNT6!f$r+p&}&C#OEz>^APxQj1u8FW54|j7SDPGF z9N~;IAit3TN*MqcS>gFT+#3b50Z;!4Ap|SW5p~SM<&lXGuCExbFi5cRQHi;@{^UG{ zy6}~|M@!fJJXJhAf3r#(6t64%A3yd4tHLme1Rp?G>*S3a<p0!4$(>yZ5+&}Z_kZ7A zEHRBdsdr}GW*r&%VKJ?mr}0oZPxyWcOMD%1rt8Y%sffPF$C1b9nBTMB@g(M{^c=3L z)v_IJZH=v6O-;R-9E;tFlVWUz2eNoL*7=)|a2&rPvZkVjzm0pGdwzROUbZ^^tz({y zoe^8=ZYp5VLOXh>7uWtuf-K($Pd0=3|6x;8L3$Tx{IdP!F@Xl~R>1ZGv^v1%bHz12 zEW`n2pMnebcNm`PzvFGfXw$T<K;S0l32~amk(>6p>Fc#&w>%d}^J~~GTTXBKx8G8` zL~nPZBs5Ip-@9i(N%=J{HXwpsR24;IwewwIVP9*F(K~so--Q)-4Cty(N!GWXD>A_W zhT%(mS~zd8rRL>{cNH3?;NryfZ6KZB4QVSk`Ad#cR-x|fzUTuT@npMXm>yGrkvCDO zyp8O~fb${HHB!rRmK6gA`>|(6uB802*S9k;2npxMl#8De38SGs{w?ILW<dP$<L6OC zyqV1bFfD{;h3+kjF_qTyxnB!ZSej3)s!F7#iT+sH(G8EPaeln8czy24#q}n8fS8~r zsZC&!zBkwBZT0<(G)uu=+m~VU8^M*4Pq;!u)%bYzHs`+SOmOolxh+ZX6L7i)8`G3> zXoz(U|IH^t7x)!E`MT!O2OlURxBoG?SoJ6z9sznx4vi2&k=Q3%N4Mq%rlmQ~Djx%A zw#5EIE?o#y=?(w$d-nr-Rb}aE6Ac*$26ZQ^8YYL$6iOlxluf0c*V82eKwYu$rOrrp zS2f-5ZZW3?t<@^OpaAi};5{B!gs|3r+f6kd-UV217*X`|YPnb#I1CGKYuTPxn$0Wb z<1#mGb$2Q)s70Z_Rl!Htw*%4ptw#i$Se~GDyHGDggXu;W85yZyJQ<Hbh~BGHp*>VH zEvQMcAyqyS!qdd-<cn?_Y>6Z&;xt+7XB%CmJji@=bZ2`xvxgZvNrORKdRQw@X*!cv z`?)rl*cYg)0)N;tue_O5UTuoTcuR+*!^>b|267)VGMlxC_9$(T$+<s2pB5;Z{&*c; zzku4gXl*-tUqD+>h%e~uiqQwO6{tPd)BEN6E|Kh#2=90Yvl-vk?jwo7FVee86y$UN zlodFi5TK71e<^Fvq1CNYVbG2+f86`-iwWjavnmT^I%l=nmY+u$BwUZ*eyIC7_=-X@ zFqE3&dFa83Gf?cj%%>7yeT?<weg%^twVxxqas1Kkud#{op`5vEI+58IDX721c6-JW zrW(8=LH|o+@zp))yA+Iq!R;9mfRBe0x%8eZv~S`efT{u3Q3^(~;GJJK@h%*LUVSs6 zO(DEWAxXb`4+Nz!5)%_iYKgK%#Zb1VYG1APCNWw1iIRs)P+|qPx+Dgh1lplgYA`u% z&v-C~ss8W%430NO<lb0;|9vc?EgOYX@qa&KNtz;A2mkjEWF{^JSAkC<SfP*kze|cL z{s#ER|E@2I58XWf_rt-8YrseT17wDzC#x)x;wQ82`wu7lgWvKwZ4v4J4o+1sH#*pw zPKaVqjqlg!icC)KMI&_=?uuqw`;&Tef3=T!2bgn;SnQT0;yFxz?9BfJeX9t3CZ=TU z<-^HpYf@05$%1P<AmaWCa@-010}HPewE2jut!I&M6A5`vJ;24a4%$1@YW7S5ji!Mb zTm3s2L_iqV)@@?Cn@(Fi4dSG0lhw>U@tpb_W2Nt!+{c5Xadp_(*q*Yy|Ncto%ifFh z_vrg)aR)D6(fM{KypUg;sx7zMTasM<+n+Awk3o!RzB*%=)!&|J%&)K*SBw*ItFi;x zM#I4@S*djK5JaQfar~&s{k?JH6uO&e7;izoH3PosmFY-9T-k^95&kwR8XAVp$!b-j z?%32@cka-GmRIdSkrp2n56?KN3LYu?`I%OutFq~zZ$4!<^CDE-+-m9#&ZPt2Ufx^= zAuHUHI_EcIqt9s~Y2;NdE-nxt3m@0fxgf2p)BO4KtxT<Y$4*d^&A5ez#vpWcl0J|o zc7J)M(e3~IzpdXPg#X<rXynq9k&$&G#U?X7&X1&|q=If2JQoC=J36QX?)|?8<LpKH zTVC%ZJ|`8GV(r4_MyqMwO!LvAQ<8dv4y;W16v5gmkBi;5&IFK}S0wy$zoVlA`4x7J zH$dIV*l?=G)+JP{)-H3PQ1gXY{xCFofqI_G8mN^Ojn)+?AV#f63e-D6o66qOZ6(ea zvE6juyX1)bI%scVO|99pk<F<4DXc9qw@X~uavl8bf7Q|1Ibco5Wf5Bj=Og7~8dJ2L z=EujgUJM{?GdwFNBl8wHG9Ygg+d8r!AuR08X7cBm;oNs0%uB}3+29MhjdkOHv%!in zsG&hY<ZNsapg_1*U1`$)ac9Q{q$d(qPeO_L_CQ2!b=;~~w>NreqSD;J(6GNio&B;3 zmr29s>f)5&bRQP&AdK(j7(O)I?yvOlIBg~J$Uawr1xv{5Kx^H6>6qLm;oi=2HUJwv zKJWA6ZFch!Mx;2SeZ0B_UFW(JC@|GIZHGohMR`ub^mc7n9HuomT3K1a+ORu2Fs;yF zG9M{u86S@$;53V!+|UF47a9`~JM4;K_WEnP_kzL;R!rR4i=Pv;VT!>4p`mxa>9q!( zStchZqfFJ>(~LTzFf%hZG&CGk>+9)_HMp=BH|z!B7gX|t`c6%Ky}r45{_M)ynxv~M zudS^uf>}p!v=mlH?*}hTOiWZ%)NUhdTd<v!;$rKf^E<b0*&O^;_#KR|&*k>ziI~eK z3M}N7KdG;Bm9j%?WtpPk+3(-KpKiPO+Z`6g#(23uh==cjvL<pRO2=B+!n7W)kA{J= z_zoyK=|P&PQMdw=*ET#njFp9VKiPe+Lxh;di+wM<q&Owli(Sg#77G(o$k(r5-LDlD z6;bRwN<OTE*z5Ia4Lk}uJZLb9z=VVRx2NIp@$u`GA3y38e+<1W9<Ah1LX0xgl$Mq@ z>~%75SkEMaq7;Y`E=Vb8*HgR>wSn=~c3q<?A6K1%xrhNJBvP-7mq`7*{XQYn;XE8L zGHN!q0gEGC8o2H0X0MdLe@z5Ib62H6t!1v|6H;6jDdU~cNQHBnpvy0_o1j2W;l0|I zV*KOtz0)l>kX}RCC95hgA1uDf&CQK{5)0OWRIms8EH~(g@F&rPBk%IAechqys_JUy zp;Al|)o9wz_3TA3TA6r`dtwSelaFEcA+Q0%%>w;=A~Q2{w}U2SXDvG=dhK}P6ZUeW zF>qTpx*j?o4y)eC(P<X+$0RknJUeXI`Eh@RwdN^U*w&<Vb8BB;Saz~2I#MD#vB~4S z%=OSLOD0Z_<dZb*-ix2yvHxDA19OGtM8*8s;ri*;m!Kd$FicS9Tvw{EtFVD#*=v-C zO>>{%Ib&*SD&xDFN7mNXkz-m!xo>l^j<;uN7#QxGo9{ZpBK$owLxP7F!{0Vj@5DS> z<#xQaESVoe1<u*W+3VkPZLlE(O^<ps?S{G2{Eyq9XL->3C3H2}EtKq)A7VWIds@RW z{o5SxurT=|%bA%OLoQS`c#OT}u3%CjZ7<D2mBsCCc~Q~N>Xy#~+h>E@+7Qk7na>a2 z<SJ4jRUOv`vkxcbQUql|XMNve(6mw`OD?Gs{4U7hd3C<?P=N6&sjBK#M!3Nu0MkOO z(x*#CQYQ+y;fg7sq*gnxbmK!9(vyq)f+OZ~3G2@JXxvyKf0$9bAyz7iK5l$sK}=Vt z9{g!J=&z0xLO=9PPy%Fj-mDz$w)n7{O1s?%YH2IACK%}GX&RMgD=d{`Fcn2$ol{_i zqg9|~wwy;CvlMpG<IFE}nV|5GNZ1>K71Z=*?JiDiLrH~t!G7#BgZ1_GB>}>L6pe>4 zm&n|7f{KTSmzJImV&T-!pZAK4B;bKQq2B}iA5b}|wV6k)cihwiA9Xg$z{?w7QNe+7 zcChwJL<CjG^OVZR$H)D2IVK}3YoOX%M_Taq7e&PAHdr7H2L}Qa-~Jtoy?z}t{(SC7 zljG6R(GWPjQPO}f0^kn*_u=E2SXTWS&r4qW)m|z}%Fkdl;Uy(3cQ8pTI<nuhf~ABI za0cNpsFXmc5D^hMIhwQ%1Xi@ae^R5}(OYp(6Ly_r4`2MG)njW7E+}AJ&M$6K)Tpw^ ztuP%FkK;6t*7lcBz(&TH&O{!2Y_Z4jyq=z(E>|R3Vk#37l9CEAu2~$w3kiwCCt%?7 zT0cHLJzXBK;Ptv{U^8fcl&O#|-Wl+i0s&4^^ZNpFqME^+R?oHFj;EcN3~HC@I^CFQ z6w%(ifhPyqyH0RmQGO2z=^@8ZkxS&(z47G>CO9`R^Hdno*QOH{xon_HxIB<4jayRV zyf<g8aAkxY;@{|*b=C8RPRh|j<$tM_s3J9O6y5v(n@+U=uKNGvT){|M-T!4=Y<_^? z`(NVJi4;me=4ztkbo&3_A0CP35+)DFw&t>&_@xODx$lsdoNko&*=>4lHy*3iJ6eX5 zI{h2UMLIl`A<#6IIA&5UBFdIaq8}=43nT60L@#4;*q%v(g^($6+z~c3^j1$#&#os# ztK8@zQj{}_UU}iW&&@Co3?(c;_^zy^OH&%&MrMK${z#W`c?L^94#XvUdwb82rU`zP zzwZ)Mo?n7*AR!?+J8k3vIcTY5<S!wdTnSm)+1b$-oYtC*m5^0cRb9KhgOd%?wO#u% z+eL8@No-{(Zurq2P64&i?hd$Tq#409>#hxCMsvL;506FirBOzp!Wx<zE7h;5t)1W8 z?175+SdA?O<Uhlt`#<h8gSTT|s@*=>UF5M{xRu2FK@XyoFYhe4^1c$?pTS1gpDs=o z!)nuPvzE@cn`!9j>HCh5Lg5*bPb@4fy7k)9OH2o;F^G8)#rn+Q5>jYocVO<Y+v7RS z|Cm*Bn2!WoP1oHhaX+=Yuc=%{2gBOb)HDQ&KLc43lo4#f$T^j4q76zb-drEb1%%wu z?KuePiF{7IAEPB<ikEDGSXPw0BL?o-_WZ~aq!%HP%?8bE4y&oB-(*I7!TAP)7wWJ3 zU{1hg(f&l9zXkH5@e<vekTH2pK|IgZtbRT}Kab*m(El3dHwH!ll{`<Q+DZ!)l@aT^ zFvs%Kbsu=_mhR#`e>c~Pr;{O@zy-#FNPXpB2KFe-y#iUb!B~kd@=fr=P^#?1{)?cY zq}*vE^@4()e1DcKApwU8GBwBNvUrNLazR0DuMKdY$gdZ=Ja~d)+?$vR!*TYv7$Zdd zC!|MlEV>`B4*!l65`sHmZE0x<+h>!e#9}8i|8H3S#tXqs%4SMMp+K$;Y83C@ua6YE z@3f%R)YWMWh?eg{wpISV1r;SvIbR$pZtHgZ91`w<Ymfb&7v3gTrXZ>r^IVIZg5ncQ z$in=5xb>j4r^RGd$Z)<YBr#vAt6zYW_q$p<U#D4*7z6^lH<9NjtkvC}oo|re-Ga>J zWJgynnZKhmiedNocvMKm#)d^+=#nEmJX}pvQz>2iA@aMOow;KGECiv(Vx}PuF21<5 z^f@Vs7?hT2AXRK{mjJce-TnP^5ZXO!laZ4%0(&EZE@5v73-im%VsKRkm4f%61lb+W z`9-_Y6@P6wMuIX+SR6SLB+%|N4tW{Mr#lY^tVv-kR8&+lva^LE4x}ZQR(cW`)k;wK z`1oLEe!=QgYjovA!z6(N-?*G%vyoz*;;`=_Pgy@^O2_<5%0dQA_=!#LSAa;8_37R+ z(zIdUQR29%fkDJA3<HRAa<ShxUaF6V+>X5tCDH}kvzTp4qF2uQ3Tm++tZ6SW0uwJ| zU_c2g#SpeO7SnZ6oaQ4o8)K}Eo7Gm+1F!KIp!N&C4_^?Z9>EtgYSkfl>{pgtyCIEw zlPyQAQuMCF=O!9xQ$KtiMzZXSr~M34HrNa%5WcYD@9laX4y4J!NGs+?jw(c?jm^!q z>H2EVdVha3xTQtNrWiG=g0oXRCvsLjAg@B^R@eJAVqb7!JtmL~d4kC2{SN?b9NjM% z%~NLR<MD@h=X)P7AS{fAf+D^BXvsezfsoy(`+I-?W8@H0%f|nLRS(9?f4))vfP`Po z%N=Chp0euQf;GJiGkmrQ7o~Xi4CPU%K1lf<o`V|rE58TGF(Tq~WE|c1csg37r4Ok+ zByTL>=sXP6j8_S*8(vCC+(pLk7#+`fe^O89#269qQvex2J|Re5!#1oZ^AwGo+<9m2 zCD=)8fBI{eg+Q8M$C)4hP@)-Bx57!9OL)C``c0o|*E@zl*kyA)FllIPJUkZyrPJ4C zna?IFOw*mN9Ny-u$TkCXZI%DHpT`N>!2Zka;r3*<+l2ze)BCSyvp}4@lS}I@x3T*7 zPw<?v;6&}_zTe;=cnFKj*VQ6WR8RZA9lYJ+M~^`58_#=cO@|m#@L&D?;b1D!Oqxb- zp@HEa3YW<rZ<t7=XI|+`>AN`H&s8ZTkO!O>F|Wfna9l2lOKquk%bhM&%R>*k`(9R6 zJwM5xnwd$4t<!Vu1_=`rT3uaT$Z#U_Rf{7cB2d5P2QKUWB@Y7Q?aaI#7kO~tAUuRF zE+(b`b0H#71u{y^|HZ)CcAnEka9HktOY5DsS0G?JySm0tFn(c!J<~*$Wf#~jkJlAH zxW598${^)@6<uTFwj71@ST0MA@boXI^K)}Me=g~x^8SS3U}OJ+rPK+VB|A$eN5?F~ zuIQ;ow;0GNaL#tY1aAZc29i-zuLy<7OGqFq8j3RiIp_Y!xVR3;1W;^jZR!5tD`5M= zy+pu!;2QRlZYjnCA~G24?=gk=BSONi-NiO!w1JHs4L^S(?03-~MKHJe0{tO)p4T!m z58y5hAw~=p>yW}9*oGkym4_pmVs<DWw*8HhUHrgXjlRu7x=JMZv1pp{_0{EBdoPSU zNMbAav|&7D@%HvcLGCyZbV_gXrL%K{A<}|}<>uvGSX_JsW{)gtK)exCz`i<ezAklG zQv^?;Q$PfuUeza0o_qqkfZ6DM@gZ?Gr_Ns?Uxn%WSM=U6xa}v{*rE{fb0A=clSg9( z{&Gk3TElwTlYlWsho=2?!J*Te2b}L=)QPxlzAsg&2Z80YxS!a7{%)Lc3eZZ93Y&4n z^PdZgN;9=UjJS~l*=|#>&inV@wCl1;s;-+G&s}_(f)}~3;#v{0Cb$%aWnX*R70cdu z{@N%}@ErQ}vHhd^yq9pN2z5E}1L%;#5m&&KQjob9V#M+Lw~-l9?U#<B#woW|$9h$l znd0TsewXC(Gl%5;OTnI=o`{U}bP6pkEsv9_@p1LT!^4i%eUFK$8y6%u9(hx)&CkDt z)Hnj6N+xb>Y+R)A;lqc+l+aD~4-O9PM&+i~*77ay-}LqM)!Od@<l@Vhh#A$@rpAP7 z;+I#|ot;vW<NubEl9Hx&M@Ln~$R9t%z*t#{kjrkaug=Mlh(O3t8>*{pIa^v55)cp= zs7L$x`8n9xMexOR7*v#$d^<e+AUS^JuXC8svV&7{>w$1YMP(&bSD8Weq)@@tW%$qe zH*RkAKjurw!vi1%oT=Bkka_l~{dT}C>~Bv?s5*;-!@^dza#)3gm}9y!<KqcVFFoOz zO<i3ze1wIqsx{XfG)mQEq78G?(nOS$lxC-Ow6qFMIP%`J<Xm2QxHM{qgoa-AJ70^d zs>Vde#C&jb%P%cujf#osT63Tem-xH8tKe9#1%tvXDCk*@y|L>|Y4|gLbY0ohG#7k_ zLHRfbJA3;4ylzr*a$a?{DtslVUI(@6xw^VWP(FO{ASXL}wYdl9dkO^#>Q}^cx=MtG zhQ{8%RvN<?pP%z3+S=OK82-!0=htQrUc7kWvQ3pBkMl1KlE=NKqr#*>#KQz-Wn~Rb z&B4>t+O+;0baeEkU1y-gpib#KE^?Y(TFMw$tH8j-{NUknIY>3=)jDruqObo-T3UKy zdU|+zI!Q4X(b?HKG&GcPb>)eJi<?(jsSLZLpti-@py{(SS6LYuT5NQ>I2roL4%#TG zj%B+Jcyw%ZQ+s=*$;rvOhK8Zb%SKSD86F$U+1lD_Ahf!9!e6E8Ie>zK!h$Yj0xwVf zQ0}6jynl-N3<bqo_pUd*<wu5s`v2oEnr?93RdQC(2!<~~L6H=b6D<;X|MC9;n0bFg diff --git a/docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png index c3fa9a2da57c4f063a69244139e0b8259893d0c0..dfecd0b1c87982818784c27c240d377635b1bfc7 100644 GIT binary patch literal 33425 zcmagG2RPUL`!@bD8cIrJ6e)d>vO=<=?#RffG)QJfiV)eeY0HRYRaV^%nUPs4eUhvS ziR{RhjI96p((m^?-{13pj^lA09d~zlf8Ouc`+8m1d7bBZy#f#GsI6JGaTSF^S)-w@ za+E@$_oGng+E*^ezwEC2){XzjIjibB>)Bg5yP93Jq-dKtJJ{Mg+uE3Kak0GUWMh9p zN^Fmqq{x=D&dv@_^5Wul|LX_D>@Ql23mf(L;v!5A>IO~}%33q>hwg!Lh7E<{zo4PA z|Cn3CP^+u{=bpvNu^)8b0z9vDs(-uw;9=qJh7YP1=QORl9;KV-ms%aDs(WUCuI2rw zqgDPftJTD^KfT)PAnwq?$he;+`8%KUM_r2w{|dLgPah5Z^!hkI^UXet-+p+kRQtLa zR{*}${X0xYC@T1(S_{*$v$C=Zn?_TD@EvvuO^Pc1eHE90A3wg9g{<F)Z%5D{WV%a! zkuHVtFutAYcZ7aBzJ0WO`~U0L%cZ%l@3*j^UN>t}AfLn<f0+LE^_J)MT|GT-hFYEv z@siIC(Efj3=zrcvR0dmlQ<MH?-#JH@;cszPExrpgH^huybniPx?z_-1Nt<%U+?;FA z1)j=qKHZ$5x)imsU%&KrS(AGhKTGe{`I_m^)JRRC(~o-F*<UrvQl{mU_qTS&GDHO8 ze!6|!CF#E9o#$M;cVb=H-1wn8cF*lv-rnHzc~^VyK#sBmt`ebGPHUsHczbvMmHO7n zD)BPL^6%f3i(ZjWpy`cwuOv$rU}2oZv$RNs$#QL4?S=cq4xUccr0vLk)W)K=Le2BW zt({BnAVRTbF+kae&gbgY8~T}rFA5}cX0?h3JX>~Uf78$_$3sPC%<zp>T4(9r?_Rq_ zT9G~ZjnXt<os!>dPZ6Vph)9Tn>u{3-Gx>13UN<RRiHrK=_xVB@8B>v7Hz%DNF^}+r z&+J<A9~<PnZ8O8Ysk5w9<h*UsJz~6k`HzYkHa9T|wa&osaO<(oH*)nSPjt`h_EQVr zto=Lh+Vkgo+S*PrY{6|LpAIQF+u&l*)86hE6~$KMGIZavDn_o}+NyhIDLhs^-68O( zDc8s<LD@IS>Bq;h(TcSUOjmU3>gt-mB#U+z37g_Bg=N1Ux4-cyc&DHit<?V2)%f5| zL;pUnidl#iH9Qz4s69G4**o4-^=05q&<6agt?E(;b{mATgnbxPV%wsz{;d1nlv(EZ zMuuYVSsC%tX?`UorEkUVY_6_Tig`@O|Ngv9+wT&CufD>cPyZ|MV)$q0`}&1LcE&3l zYLfQ4xx0@IJ^yYl8Rzz^de2N(xblM|Dc7}UX*z-x5|P949FN|;Q!g$qUb$-3tLEnB zwhppntFmXQ>nbE}?>e)5ael-%Ff=snRNlF{i3VNg!KOMZ?{0QGdOTf(*UP$>5n;o; zIYTpoQGxTbQ*4_y9r|ACJu_T1vi;=K)A_$+6aQ|yRDLlr7NLx*(KcTm8#p7HUtU>p zMpB?`j4EEcaVhLAz7;yHP*zs{Gtqy*%S(|Wa45#&-Oa7;@-qyfOZ%4UI6Z2%oSCcK zpJ4^1Dc{a8>GVMC`oG_stCC<YUHEez4<hT(PT8>InCb7IpJ#Ub_wQ01hQ97j@?CI0 z@mQa3-}4KtxA)j@zP0mYzP)MlzHLispqx!t@nz74o!gH+ShqO)eNn&2#jaFqVc*gU zucYvKPq2n=+GF+OV`A~#c$KVd_jhUGMITGD6CcVtb}}A{5y>jO(3~ycIW_#Ip`jt{ zNDuQ;f;oDZ>-x*NnO|cQ6Xbygzn6XgW9+l}chSk0(>q8a@-uvXVVB#eX_9*{H~+S6 zHn>LGq1b#^eL0<_4`*NLJO7(ADJe<hbiq0`HMKV1MQ<7VHZ45i5bvolJ#H?tAEuP1 z+ZozC21GwtNevGV<7aPvd1Bb2AW~Gfv`M=?ubY|5#B<9!Y{4Ch*3$USE(P-VLj*uV zVj`W}{O?Aaueri|Jtp4$`Z4|W@<Ea&sV0U06?jh!p9{*%Z=~Q3HhT{467XF-x|ASi z-X`t!933C;Y_TuvLE^A&diJtyYmeqqmZjDNhll@Y&{a-5{^$hHUXSR1k3Y(G>^kT% zHEcIhdr{yCNgEN0L-lEgn0e)+FAaR5tIg2mD_vahM540(@$RDd(i>J!lJS^0iw9Dx zyRSO)^HHXLsaIiSne=}>LoNx^(<L6autP2{U$e1EAwhBf(tC}};8F6r-QSR5Xi`*I znC1S<O2C)<E-MK+8XI$k>EvKDd7mn3YI>Q*dr7WEwi5cAtxk`8%{#BSIQQ$Zeipso zh1M5!#qLhC%WvJVS*4Hnb}_FyeosTLjUIw(|Gj^%O?OA>B5FiQT)CcgkNLZPjdx`1 zHnIt7l}j)yDk|#do@K?GoMhQq)A0AToLZ|mO1W+}I`^xFjh#KUyXK(u(y|u5zqXq5 z!Ku6*6zq6>QyDwCS?=FC)xVFm^V^F{!PrFP^Tmvd&z3yxu9^J1*IMx+P24;@vQ1yo zU-(w=600`Ed$crt>2(jjwUv=_AGg4hRbo{fU0sDFByQ3%EN@cC_0m31UioXWkIBk4 z?Juu1=i6<fFsxwdew0~y|JcK$E@2{zfA6~KO(mc2hkUyh2NxHswqkb?{b&1VCM<s= zt?5m!O+$gl?;Rs0Q=2dS{FLP?yDaGMVyF!^=iG=j_6VGwcB5!UZ-4pf+Ui?#&HhUj zn08`pY|QIVe@5=P&q3m4bCH>43t!u|Zg9Ip&w(JZb?UDdxX!}+{>7yMN?$|9THU?B zqZ3p9W&rhk{T!<`+VL`$tuHRIi=K+kEd3MIQx&WC^t7DkjT7xQOAl{5oD#0o8M>#9 za@Voz?HpEB`fP3a$0LE)mTvELue|H9R>!Mr+)`G|Ncl&{W2-D$R<{2hXk=ny>cEnb zMZd2ae6-Zd{oLn&RhzRda~>HYl(HBqBxIfY)`f1|O@G(@S1%%+-PP4~cDW!e+lIct zchLuFuVQkjMb5EPC1~j1lbJe|VZ8mVMSIq4lF7APNh)8(Q=_7{tHq1Ue|~<{oONae z!i1u^aB){W)z9DG-)mv!2T}*0`m`ve{N+nJYinyHtAlQCvZy$eguR|I_}E>(3tmXn zZw`r<sQl`!wqphEQ1>?v+k>iiGnke0;nD0>s6Q2TFLzb``JQdL`pSy6g3{8_|6E(; z*j`R|o_>4b$(Z%!zkbw6+0;|>?Z1baaeeytc(BXR*N&bZRV?kpBPq)TqiA211O=}j ziWST9oVsxN^5v#dZv~R06kG=H;vHxuDaP$M{%>m0BH&ALNs0UH&~x{`dp9EOoRGrX zyr+!9cohyN%3r!IZ4-IP?G&<%_%G$oa1%eitsPsxoPR3zlvKZwKW)ulr&^0#<76HA z8`8Ba{(A>SM3yPOve3UiAbFM+DWH`of2+uKq#kJl)nQ-P+fb<q<_ZZ^h)n=BtC3vO z^s~&1XL{q^JTvBFWpL*wjoq(d;U#<*<}BFCA3WHOkUE%YTrxT~W`_Lo*r+H5-#%RI zHs<_>i<>(zHkNb${{6BF3f6rCY)Bkp3u<#?27_1jck*=!u3v5(Gih?iPNV&yUIv%+ z*>%7ujO~R#lFsygh>PBSgx~NFTT9i1So`zuKJj?)_3PG^M+@soCd|>@))JI=?kkTq z@uAt5%^$=oi<2<>=gw53tmEZ{`B|soZ-vKNo*3j@=T{9H{WFlMhngg)b$egndV!TI zSH5$2jJPShCf62X;%5@sz4n>$rcDwYPQ`MDf2O9UtWm0>#f<K}3FiFO>iS)Hrs|C3 z-eq&h#m&yG#zSB8W^lzPW!kA$jZcjfCu$FnJg7L`enmJR(3Q{F*!YXh*(jPyj#YJ2 zk*ocSgWSjs7gNfe`~%ayN32a9l;#xuf6({$wbk{`hi}>ww)5muiF2Q}Be*a8`f+Ho zYGS3Si``ds8`1A2NXf6`?MpSX%-<Zf<K*Bt2)x5)gkRu^6*GE&Gz8Bv>Ab7+zIIER zB{s&yU~?4S$`-SzA8LHwsommha}2lkp-!PZ=H!L<^_^YCDVET83R@M}r22~w?krpH zR%CyRL|mJT@UHl35`sui*;dsX1$C1W(tc<Ro5iNIBOUG^`0~Wj!J&P$qoVnXZUnz- zB-Jx3D+@V8y5$dw5$yumA)nvesvWJhXr#L%(eblWv*_oL--la4a=r_S*fLfv`F0d$ z9@$!t-1u&9m%XcHqc+`acycsN6D4SOUEZUUnSyZ~$XT>QJiI({&EA4a!QK*Pn$e7> zT<B{0lo-@jRBGJ{GRU{(7B3#9-*e$xd2@3dDP3C+T&M0B5_C5#Ra#wO(~ypIS$XH| z#_KPKTZ>SeA0}=i;kOYxLu9Bd`kdJ3^+v9j9P?uQvZ8|ueJj+@x^2ftjVgC0cNgNl zy{fFdv1!kR+WV@jQRl8?W@bi~c?o_O@G~<rBXG+Pmu1_$`6I%SKp4Grt*!F%cQ)_! zXp+-J#(n(j=}0wqnUt-MmDK7I&g_}E>V!#!P*?8}t=J&<&Oo6Dhwsso%G-?nk3>(4 z$W*-;W0c{Sa{wz8hq~9+)`ndJ;Q4(NkTBbA%p5iF_Fhky7s302(Sf)C=+a_5H0&SV zeqx<(o*>g#pSGIv`QPJRO<89Uyz=q<G2gb-2>nIqmw=d2_uX}_Q~CMLUHf;?vC)aI zb&s3(9kxb5P4qXcKoz39`Gbv=ca0s@>m@4vH#}RM^a<h+eGl|@kza%+uv8?w4+zHu z6|S~rT-fXO2(>`uRBpiaHJiR})5?m{67ut1oOexE2n`5$H&H@zbH2r)H&^Kn`ByR7 zZQeA;RaI(~{-<Es;dHirpBdKUjkJoSnLoZn$)t~cdG9L2V&Z~HI&-r5$htQ=+0x4r zTCXJ#Bz6AJ=K(doaHTI%`&^`(UY@Thxqg24NzWUCF46srqf_5~pIh?a!_3db@3FV~ zR^a&9xa8}p9ag4Y{*!AoFJ8lKVPOsxMF6397a-$FqL!p<$D`pb{`FD8YpT_ClI74v z)*(_YX=*x2iZpa>P3Z3^HG8Lcq^v$Xvp*a;U?ESZ=(|v?P4D33ltXhOjqr<PpFe>S z+tlzR`}^zD;`VyV=qk;mmQ+NZ-rm5}zAaoavd5?#2*2XSTI+sp+#|hH?}rYgIEM9M zsz%RD={`R?`P}JPq@kR}Z@TMd7Jtf6-wnOi;3t>0>FMrOO*t6q1~kxA?4A>;tZ_W1 zsvN2BXJe)=I+XrT57|_M*t3q?l9i^<Y5sr;OY$fS3k%Zc<-2#cV7J?j)Kb^JbEYqF z?mvoJdSKr^%8j*rz3(EndCm01I``LK0}35S6V}z8x>pca%6D0qbHC2a{R;4>(7As- z0KpDFeE6Oyvn%I50s>nCKR-5nPSTdCWSoPe<2{rL#mS~KfFC<*k~XgY&&1!Nz=LkC zeQti<9fi2iv1|Fqj~~5$e?G~)S+S|jOk?wzD8|}X63i%u`g!LzY?85U2VB5I0>!FL z4Y#@vW>ud3@;L6viz-$EjH!DH0IN}}&R}Cwa?jRX2HHECp~I`7Cg>ia%~tODrm`}} z>4(mVOyg<<X2GQa4!i(V)!G!bwFG}}l(D_dYt#}=pi|a!ptRaDU*F4DuI!4_Djs{g zN$ZZZhF)o;O>+#TZKs%67-)zbm!VTKc3<(v87aHZecFPrqN1Ahq)9QiM?9qiq91#3 z*q_gLF5+XNLW=^G)^*V$_B#4sUU}!cC!U=8^x(*qk<!^P)CVWrcS4^r!ny`2I4^8; z_xUd&Xj|&jwKu(w6}SKKj;j1_l5rG`4=Ja9ev~jCIeO~j4bE6-AcyAG5@3@ga!<xy zW2!#$zfTT)ooOraY?0eR)6Ev3Q*<5Xp`)YIh%Aj1I8<NgWbHB0_p)NGvKEkbc8VwI z8iY~G_q4fPiIcLXm$>xvSd5s_xgQUx%Gn0=Zj*xs=j%Cnc$leFZvoBdXM)}!1wLOs zj^DH3k-K;mO=PzHcO}Z(5bg>v9%u1;W$%yDyY+tHK40%T(RXCa*8ZKw#j-#t_dyTj z+J86JNG7fE|Lhr%je0*O9zqxo@!sh^6Q@I&o#?N~I#cO)wX7I9DdkLM*hF956&7Ck zK6P1qxHGWGGVG%j?Gb95%>bQ&tKWP)qTv(UFDZeDWLWPqoe%sApl}pn!K_?aSt%sd z`h)A1KYn|VosUm<q^*SP86adyY>`P8ja7ak0)F|jZPnQt)aOf0S-$hsu#r{uoj0g* z!bk2i-js-nIT@~Xt5~=}uN<#AZz<EvwEb_y(%n~=^5slx^qhuH6?U0TgP`Htt=Ny5 z^XU3Rc;q4j0#<6qid7>Sn2+2{rBp>k?IXG4-_cQrgJC?h%ga_%&bH)>uI3QA>+$=i zOPJQGY+G&0nu>Pt8&_}K&_mNbIH-wuX}R51&6u>h+x)faztNBXL1}excF_7&3i_bo zxxI-_vQlVD$~J_VSXX6uB~7DC+}xyje9!3XKsK_~ZCmm~5r1bS<7B<2B{;>6UpF?! za+-KW2qk+-D!f4M%MxDPwlKYHnsxnV^O9b$As>dN#-5HO{wzU5Af%|6uyOafXAT|z ze9g6qYV8P04X&QLW8d_Qj*pM;%TuF6OGKV&^I5O4PF2zfDU1}S{OrY24E$fHsWIJ) zHRuALESlt<J5De@BKX#v##8eK#!(dS;>oY)QD|?V&i+DE)^K+}!@z9bxz8xfE5NTP zxQZF(+h$o#Az#Uy3ER`f*?wbx{W+VM^Jf^|0=64w&d$yf&=(_aa-rl)w&fwbg2bV* zAd9O=5hG=bNx+&CUegXrcuUkbWM5Tx)1pZGf{qeBV@ozR3Tr&m|G|LW!~*_GUYxrj zl;n1KpfQsa=x)<ges1dqMu*wSA=GTCi{0;zwd_UnTToEofabu^MI3oTigTM-`lD78 z-$hk|R#7RCmB=3a?lUVIpTb7l7=MAj@M6!Z&6_u~i=CGJ?1H@Zwz@hrMJ@d6-<x@O zR5J9JTbF<0=<p}k!cCsBNNS_tr9c71@&xKqciPI+>leE@J~b}6^E4k}lfBu08<xL? zu!$!*Pimz)XjZzL@_q{~IfSjyT;^LAsZ5iyy+iSEb90y(w`AF@c>7$@UeC#O>(^ht zapQ()5hsFz8$_g)OES|<DwDL?t78=^nbsa-Z#Ye!nZH>@5>~i`s^pt=Cet#Xc?sq# zd%k-`J$v@dq9sB8QqV~g?+pOLXb^0hk+s-OK|LCNv^A^qSGASP5vUvf_F)`I38dS@ z-lJ<e{X)y1T$T4bG%Mcoys&K7#cuV)o}KPtv}f%7IsPb&NuI-U3x5X0(Z9Vtuy*fj zq@n_!dHEWpSrH+fg!UTcMJf{Z3O=u!%T{x=_uY08xa1iW67ssLDj2m8RLu}?7}~nX zHa6MpqmSkGvTT-D|1LCpar~WzqM>K@P6tk%F!|~O<6`lGev~6ejuij?VhB1^&{0h= zDsPK3fo4kH9!U>^Q_&Q^R;rKxWDvCbyGdq^q+L<&V!f7-KY|CGp`^>8{{55`VUtot z<+)LYaODNJ^dzB3!zM+#+?6aWR;XS)3N9gl<)jm>zO$QTCA1CAb(Z;u!C^%H!lmQ7 zstanIb+e3<nb^?R0Y4@_&G8($H2>$4v0KNoxp_97S-0*8HA2XtWz160N14xOlqg%e zLQcKrPs-^-FD_aQa?{r=tG6-EkWMZH4s~`OxjoE<lJXA%WF-^Ry~FqZ(TbM|MJvU< zVnuxN-5utfr^><P7HXana-dvQxhf&xuxf3z_ZFiXE&C(Z!N>vaot^)nr8NL0gPX6a zo#!t!N@pu?H1XYAl^Cxjs5CcPA)>6NqB0&nA$We|f_tn6uTa_}yF03h{%Dc)o}Ajv z!^5*3MFJ2a)%;D+o%skaVE`udaxrJms2$WF)+1ET)FIt$t1-LU=LIp&0@D1_G>`q$ zi@W{MW*qu0r#wHwwoNU};?-3qQno2*w|AcL5ANPdO3CEVqZew}bxtvCvh{bCU%&dW z9k;N{u}Vej_Id&Tm^Is^Ueu^NGN^+F5HFcnPcBA5f76xMXxwm0nKqTnL|yZh5u4}N zT=?{_*PTN3pIz=v{Szd+LF1_8+v+!=+^yEB`t(-lxtKRf2Q*~pcD1*c7mbuwp<4@# zii)W`v=A%g`zA0jumd?F6&ypZT}zCdQ*Ub1Bm&~D>qr}zCQ0YM!#ZfguS5g|s9U); zPJOKXql6|UVN9W-*IFj0)^lroa`Ok9vId%<uT@SQ%1%89T%>w!Feao^A1VG77O5;O zk!{E50Jvn^`r=lUknVb3UhRiR(;gT;7fV!dO$P&K;4wKnQdaZbcd?p~Ob#9N_Qhk% z^)htQfan6h@+$izlzPvOcLs~sKRPK6K=2*_jA`Y{yDEX}`+puH+}WfV60K52;?0#U zU#3^Hu%x5Udj0mTB(#yzKR+KSI!gElh&C(Iq5J0t10|0XxOU%tZT^_uSHkyBa-j!# zf9#(P8#bsuI`L$Bvc;Y-8<gqsD)E_t%(CgRN<OwcZ(v<nm%-bY>~5HGp_yefF);x* zar5Z{X+E05)3!+89Q;%IA!&T<^GTB<NOoS{-g!wztEllhbO<;Ju)%<td<d_zV1r9b zOGCJJT?X6N)!)xlerNZ2E+sEnM@L8Q;cc?Aw+jkn2XZK&zMIgtgF*}ZngmTkQ2UO) z38MJtmnT&yN>WqH@NUBXcb*chyL6JX(OJkB83OgaUS5Ju!u<70LP87p9yM)t&Z<C5 zqI>r2c64?fIW<Ztz!Mr@dE}h~j-(u9WoLg4?r!Y&@9M*R=qAxYUdR6%X5JDvNxFTT z>q1+x2G}t+qcX3Vi}z>6jb5nWMM_TJg78#=07(y5<HLr&xT}-@xaNs_M!#M9?@WT~ z5eJZ^;+eoAIgYmPfAWj%<=bt3C{dMlbrD4VXzpHn*R0~Y7g9H9bzlprZ)(oY&X9|q zm4=0eYCD$ztr1v{s?5g8S&5&JgCc-9t#&BGR#HaCiRAIF{4E2Ri(S>-H4@BLA8x-y zB35y5kU0MDv5sKz(!=9I<Kpo?LRYU|9W7^Adl>!8v6kIuYyDPicJsq7u`UP)B}3Ji z5XlVY5WUmqfvxJ^pBBrt*FzfdePgpnfRgv0b==%>9pEOY_61%uk|e*MZ+yCp>>01c z`Kf4eleUi$f!&*Bc<BI&w+adhwhoQxHr1zqNI?6yt?tjuXU`<ix&{RYABvpZb}+;g zTVoCp_90Or79|`RD33Y>NHwD1{YP5+j<gwA9YTH*@^r(_6WeFl3c~>uz&oN}<8~Qo zOFUPELVOXrAOSxeN@&5tqoVZghw^~mlD_awR-<PU@Nk#qdv^3(S}5cNPCr-xGIEDE zbFfCtr5z02i2Yvw;K;Uxg<1Q9Azb%DH}lh;+0c^>pqFK?dN6!*wTbWiE!+6^c2i@I zegVx}JKjJyOz2P|RRZD=Ve@9EMyydjKEz2?zKEm#)%7Ut!^0m@{675oJ%9wmoLM}+ z3a{gy_v~ccB0pB~q@2gZgG)BySFSJsWtxJul0Nt89a%!-xTg%$z^Ou>^B_+_hH^yF zt`-$3tl20{Hol6gs^ia3!lXH3zJfNU10RGO7vE8ul9EDhhr|~cm>)@=VfPN)pl|Q! zc!}&XI;pdL%`@9($<pc0)g+*iS>4fPKLmt-F?*DEA8W)n8g!@GVTHN5IW7n*NJ}Lp z2iDW1BoirLkbeeHM$l|ft7>REr0ctwOu*Kw)#B|VBWue6rC%XC2_H=j0Ix&aBw4cB z4INf~$N{`kqx=i4@4>-?cwVY)$gZF%1PoS)@e40i757(!8I`tKkL348f_bxBI~|AU zsjIQEu_XuCXigIYK<wR2jhQCa=+v-Po|#oNm3TgnRF2Tfw%OXZnp4bTItgmbS4l?D z1sMQguhFY9tzDbpySOmPqDIxtv7v7QCS%Uq6Q>0cr731B`X41$@I{1QPFC^;T}pQs zdDkUOlj@5YxZSqqjbN1Ufi$QisiuU(zOsBxCukW)Mn=-Mg77yRd8RVH2Ee2Y3Qy38 zs+yWp&-)EuGEHjG<q`6S6aoU+A{0N`%KYlM!8iU<VFbDrA)fKpu`$ih$Y@g<<j*`u z#lc&;j&wNwas^*I2x_!pcHEco+())<-O9`@c~#UfpCa)$vF`+Qwrea<8Q)ekNbduc zLHPK;>?oK~dtczwP75LkL8fF|R&fwr0-)WXz~LTN%htSswcOeDXSWJ)J4xz)e?A?Y zIVqXE3NECVAj|K0gBqFHEI=0$7`T;6eZAQzI6z%23r*(x7}4PP_)Qcv*1OQL0T(xs zsP&aoTIK~D1V*<U;@Sg~G9MjRfNU2luH{nJA5$SX;~5xpnwj^3l)g0=*u5@3jN&rb zwCl|jbHx@JnHUfjW?bOo0|6nI&^4_`%%t=Kl64i>2fFQeLS*-75rcYs9=izE4T(6q zR3|aP4do?|L0=`6g5qe}S9|YtsaG6$mvNd<GARREf>8(~VF#dUop>41&r`g-yuy0v z%*e}c@D;Q4Y#Z3vDyGM}X0Y?<&VG8pid1+XwE?|fYCB|)rc4uMLnR&HCyG;X9C%}B z>gu;5kDonR!iy~YWvZ*I%b?6>F9q4P6T-}W?5oLDCsp408<hjqbs}vM{s-C6($y6h z<_a(jIwUpq>BW;S4woD)-=;|Qoy_DTOS<z!h-xs0BeEQtPs_@)s;^&Jq)t)3Gp}5; zrU!M|3i`q6GM`qD);Djip&9>gqI%#$jw%{HpjO6Z%Q^-JZATnHa<3r`q{!sU_ENrW zN6Izb{?uj6^y_Vy<D`Y!CQ*jJ<^_`y2)68ExJ;ZDxk)7V5F)o`57^!Vs-_A%6?0F; z4F?`vpvW>zGjy2lu<d+Rxcb=BW6dnALF(sh<(KhraJ)s+O?DiB4T)CpJe`As9}o+& zZlhbUL%XrMAz;?gT!pUMu%l^dtI~mVXA|<ZGTr%X=zF8(%h6pc!&uTKkzX!u!p0)Z z9PWf@?BGR?f}&L!0mICN?v`*JF`|Y<gFN##B#X#S=Gz|H4Hs}ZjxBC77Bc%(6S>xd zzSW<9DvvGVbVa(5KY-W<At52cp;Kpah&B#b_7=4mZ0b_PWwBzdfbcUKiglRB`BXC8 z6CINK!xmY6Dd_GaYb7|Q!>)E`u54$Rn3-A}u@mAi3_?l5!ySHp!3aAe{#?<;p7%%a zKmQy!U`P6^CO=E3%K3rWe<}_Tkp-9fFB3)jE5SZW0o^t~-$S!;5*@vr2SFZOl=ouQ zSb+z}HXtO{qaox)_&c7Ht~yfnuS-P3b@KA>uL1T4*8#VH4q$6L%f!4)Q>a)v!Xdjd zWLRqbX9o6&Zx!iGcTqMnbP_e4EX1kQt9aO>DF@dq^}Ytt7EYBVoiA5P7mPP=__Mb; zT<tWUnayHaLCnQ#p5uyWO`!t?fgMj^I;A4{Aw7QI3+Mh<;8op*@*1am<Lx8&epBG* zzf9W63W>h2d5@M7ortDm!&^~PIVMYEzn{M!vGMVRyL#LIahuTiO<l)I^TrnZ^XCuB zYuPjFn$2Lr_M=5T+w^QJ91`HcFCVzUk_C{3c3BgI6A)S6-zTSC_$o_3w_JtcnqQK{ zZ(qG9847apvh{T(XFe_y<~OkV^-t}~moJM)U#-4+?V6cnTx795*YaCCj(1|ar2EYO z$sd$Ae6TJzfSnpcUvsbH9+%_t$IC7+c<1aGWi0sY+?D&?CUiWo+f2rYVdY_H=k)aS z#EP_5X?ly8n3$}*d?)%VY&&lJBcSqoHd=Ck`u?uI_UXlV1s;vtf0ns%aV<~3C}=ml zj<zDZN2GmD1^E2vO_-^)w3yH9WWE$H55Kv3tHZJf%Pxpr_|T_xAYMUboG$(;Oal?c z1Aumlvx7Ou9_vTI;dIXFhPskf4lA)t5$p`h6b;f>q5CgE!6+t9PR?_UPqm?moJB6p z9-w!Ug0Db*|2l|q@GKMppMit!z+XQ-HEL-y3sje(jjwDcC05y2iI5w}NSb+eSYdZS zPvW9rVUaQML3_u|{{K&dN5_RCYb<RTB_t&y5dh;r(r4U5a&n}=;HLnQd@J#c1)=uO zKmP;(+x692+(%;dnryaa7dQSUW1=ck4|72GNxUD(OgBqBr|3z-Qu@=k4Is+udpTG% zOJO>%;ldw8dJPK;Q~do&kNk0SbEDdDZrW4@`OqNSf(f1JYYAo&hd+{ilhWFqE`u+o zh2zzbrYb`>IsJ6uMfACACw_jeB9F~N1N#S;1fZQi?iWyQH;}Y$qc`+$^XbtJ7$tsz zg{=j0_~4v;hV(NfAoQAFFm2ni#gB+V1e_9FF<8hSIcw>$wGHj?b()3w-1Jz#&I09B zzAbHMX7Ob+uE(bfC39MX1G>*cPIXM~*Fo;!>Q)ZW`6b_>)WD0p66ezk5pNn?#N$lt z?T?UYqvrzI_n|jl%cn$?dPwN5*GGmLGx@Lx(8raRE@=2ypFN=Nrhb0_p^IEjb6D>N zPPENu)=u7KFd#Ei+`1+yuQaxChg3j-orMRdgT6l3g$ozhcO2VZF~uQn%yaVONik!s zj#@`p1K;!5zZEZEdnNUe6Em~cZLUocF-n`gk0!8`y8dh`Fq0SvH5mMs#YVBWw+Et& zePVJ2?mFBh?0MO_f=|KmqLWB%JxuED|H;L{KNVFx;`>^;gE4$`UDMjf^yc^6Xq_Ks zTv*_6IF|oLjNe@iN>fgANXnUB&TR^Ea;$uO$^EmcD&r}^to`xa8c!1~e>Mf|X+LTD zz0AH~|0lL1?B^oKxHqxVl8=QNs8Bb7g#lA_O?3rFR?i2H4`aQ@T22ZZ2pH9rlo{QB zcxJ`j?WXkd^aoxZlQ;V*`(!~i)wI^2oS(m$TIW1icG~DP(J<jLiz>-Lr&jQyVEx9; zcLwG6u?gaPHYZDr?hhF1*l+DVx>En=fa|i23{RQF8}7(+L|hg)#AFJ+VM$LL*}zK6 zeB6*VCEys7SPXhhTZRcHQVXl8I8tn{3fIjGu6G&y*r&9uZu!mH+QV;|c^G2-tto1E z*L{J1uRqb8BVtbU5L5eDS0%}F$3+OgW3_sh^(CQ9<V+&N5B7DCbS@CK^TcB!!gryY zVAA-$VaqLp?g^b;!RiY!X^ax9Gv3ktt1Q4@c+x7}UNrkf6zBTLa`HyC_NCmvPtw?T zuU~5@fCX2bS9fSe?Brg08ba=Xb-hp}3y=`{LFfA^3rnGq5{SG}+Uur<S}Ng9le^fx zl0tX`{Gie!ou}_>KV=25pGGh|I{wJ2N>-8z)j}gmFw1A&gUFJ=<y<nh`q_4SY@5m# z7p9cq@Kh=B`0Y0E<T<ju8H<_-zxy@ORlBTRgzP;4<cUQDIpx98v{hiCGSE`)dz&7H zS5_5O<fn`j0siw{vHP$8CP&cE{cm!FI3K3kJj2n;?{XbJy#N*%H?W*!m5e@B>Yki8 z6Lwav<LNhO7M+kqOzTrMNUIH5Xlvc9jW%y^z&?(j6qZdgA#6fAO_CgdQqjHXkr3-+ zL=3=Tv5~?Qt~kCDjhrIzx_Z3}TH{X-^=^QcIa=az>22sHHGTct)m?a%6&wxE5ecJ< z4VfHqabatKL_wi{X@vK}0`5$W$TH8_k-oMF|IigETPcystQ!0cOs1eDy#GveAS?8O zaxhrM55Az$*fSIYKqNR@>6W)|-gNZ$+m!Ueev6NdOb|Kx!r4YAL3aP)!|OnhOwZ1C z4Gt3jTn>9?K(|T$ozgt5$`ei+#i!qXezUuJ_f?KvyLJ)a1u0wto&l}c*7dA~Cg~Y| zCm-vR)=(d*bzxy4_k3d*xRr*dMhcoSB5}R*a5I>LUllRPz6zfJy~NqtEykWhdB;Xp zuU?&^6(>oczsD1=neo@JUpxHj)dv1KU$W42COiv*3rrGMp?m<&dV!FS-f?_Klu2Gd zw`+bvY4v+&Td)%#M=U^jQ<(Xb63~fTP7grl)&XY;@^P%qbEFRX39$j@+cp!~)6v;E z71kX^P#8qHLx*+rvHq@C6%|Ckj1@PrLcesn__l<%IoER0Q@Q0eHQ~M0@fpqb|ASDO z$14Lu7(*Xc3$q!T%-4XW2BltdJ@0Q-4aSP`&*)pN+LiIUI3%GM?Z|iUDPA8RAL6Kc zdRoqBx?=?(`K^1I+j4d@BTC<m3*JQZWx)$TT3qy;gg2A*nAlESxcMKG(_HRCafDje zgYBCFRNp_GsUE?vv3_sW06G-G7hqPL39AJs%bIeqfx6+F_v#ZbMd`Hp^->*}ZU+nF zzd#j&tj**HSCyoFo8rGGp4_=-ZIO2UUI06@Ov|!w_YSNe-W!1Jra~tnP#jd?J!~fQ zw>ofzYzONNNi)?T#|rOTu+G`e17t)X+!X6LId4Si{WA^L3tS63CugjR-CADx*EhEw zc$~@0!}H)|rg2Q8NRax1b!KZWuHTE&Yc(EC%N@0O`Q^Of!}akCr8%c64y&wM9v|yZ z+EB32Dd^?$eWWSynpM(hfhhT>GD5CgzI^su0eZ&3sayhc07nI1xcr&0*|_IIf@oHt z`a)z~f6Ipg&ndBc_wE%G6)}+B?ZdE@lKdamt;$xmj5Qp87kxs^)KmDxV=j;>mYuHy zQ7Myw;3cg;@|>T!4VDQ`BZhs83-boWZo5e4gm$Hstdt0tUW+cy(}#(lpF*S%^kfat zVi7}h`kRY+%F5;=Kk?^=v_l%x9dqY?XtteynPJi)3f)^c(krg7aqo?@hRbU`Sf~6j zSrqdaEGmVm$PbXpu5_k%)1S$qDyU;|d+d{B`~m`6Md4c|(g*r#2lU27%jsRYxxC3Q z3n+u8)Nn$r7LRQ$DN-a!HHe6afGZMQ<kg&<oRSKV24ax+(3_1xPof_Mtoel?A#_iS zlwqHArrw>JrnwKj78&U|9%TXSJnrM$Wq~ALAB_|kt=U()d2u)AM<clF{9W7JZ!ob5 z>hH<5yu>K35Pp{}VvcyT)nzn~MfO;?^T@_HTGT(*(Be|udZguX(m&<{8?W<6`u5Ov zgJE{}PooB3I&)C`uH0=C>0gty=TWwO&qCT;eSMx?V`2lAeHhl>+__SomA06>Tw={B zhVA@uV5Rl?XEaYMc&+iEdViPX_53>Ge4kbBQ2k1(!sXaDs*aF<fciON4mCf6uHJ3d z!)fTo`Dbh^b-oIwMT+Eae3|n}f~U!C3va8!jv2NH70Ht24hwTL#}Kw~QGJRT12M8H zj(0zpLq24ZZg?k{IKNUZ9lenJ?K+ER;y-X}0BDqho$u`Fxn)l8*70gt5WDdGJD&WJ zKP-Ahj4C9i2XksjI};EZ`lj%)RK(tG_gsE@r{&)}kakLiT4d3}?f}~vrVy&0Rt5)H z1XDNdS-AB@gNHa!kQBhPcYdx5e)r?3?m~Uey#wS|1~%>+631Jh?i6xl=92J3F55Mb zV{rQCB__?(wpjmrks|!&dx(lLo*9ymwuz1Hz~RF;f0v+XCjI_K<+j_k0kzVzcRo2r zYGxyZFlGT~I*+|oRh+1Z{IdX2>-<zV;|u5NJ=d$%E^ImV{DL;_QO^5M?r#s^`G)zB z74Ma9c1)|N*s78>PKu4w-bB;*MP*@VT4V1$&Ea}~<S<ehJP~RTiW(L}nDg0KX)GJ@ zIf|k=@iLK7KQ(yj%Ga9;d<!f+0A3QBq%tfM>Wfmfy=z%%yg<q7Nw;p{s#ytXtEoEe zA1NkuH(u7!BEpsa1i<us1l-buGDBkJPX59h1fl0hX4`BliQF65wS67S@pH$_Doqr~ zx;r?i&m`+E{hU$E7s_S@7iJTa?^<Q{l5tSbj8FWtE`na9WRqBZ%g0wmrF%*SCVRtw z`8t4R?^zz*6@JZk^w0Usj9D#*#NMs^g$6Ktq76ZfxYa+Uo!;H}E+8OP&Bd-jzrbND z5tGX1CpYQnFfD4i#D5a480(kD(>wvWIXTc+6XS@%kK2|Ns%PEKW)D!bZ-bK2p1K}P z3<04V-#{H&S)L&0<OcX)w=pL`{ld+DMhDQ&gx{oAh-?z`;+Qpk6}_G5L*Mmaqav4G z4(<Cu>0JZ83lfk8$)hU2NmXS~BYvL>Rk!Nr_0_NjAs4$%cifP>^phQ)BWp0S#k0dj zU;0fzPQSJ2D{niddhj5kSM~7W!!I~}gVjGuRmMx@b#$i18h4WMmAha!m&z+Km!)iA z&B}zYic{?LGYtyubKQGwysB<{wJ;v=z${+)B)HO##W$#=?t3>ecM#=0`&%KP5o{F( zJ`iC&R8>`pr`%(5kZIripWlSHM$@@MQYrFwbA%cztLJ-X`Ui1mSZURfhd!|ID!v9n zWP)}Ikf4aRi}IygJjHrd%TH!=)`)}{ED$y<S)kWekyNYY(O*vnJ=lGODBY;Fg)rMj zdScb6!a`@{^rjcq2yokp^GC*?WmDgnyZkWKUL*3hDt#G!C7ZFwr3v$)&OG3@mmc9T zh|X$Fv4`Kv`NXC;<hl{|L+0n4tTel0%MXH$1yxl>DO=QC(4m+_+?<o64}aNX94l=2 z)b{?Yq({1gOTvB;{>Ug2#HA6`T}4damhYg5(~EAU@>4a;W9p=?<qRy{f8^g6Oy)%5 zhTUrK?uWa2OT$!fz_$^YJp#&pT;?2=Wulp+&3<j&hR3#U<bKW1FDC$04ZKPQE`ml} z;pBpdEBD#@?99D@rXVw}LE{nyv-eXU%Q&_7Io&<nnLj0D6(e;`w6e0b<|@-3&OHNh zua?%Kg=8OqLkynKNcPchEpWVoK3K|o&Uq!nf=;QCW?)>8mf4~B*%ANO`1srsL}dyx zc4Wq~<;&{<ZXg&20el=V=B8h3t+=bIFYc9ap14V|qR*4L?S_vrW0WIWsscwvgT@d3 zNFgwx1a=X*q_e9Fd6NYNGZm0!VsHJPX8LNP&gRJ^{}-JzMcX`;zqot)BnOZFKK)}{ zQZ64%--dDTSS89|7(+IF6tPC9>m;rsltkR_^BiD!7$uY5wO0KdvuWVHk#c2QV@jr! z(+{l;*Ek{+51=MB9n(n+odrLX0~V;w{Rb~m8oTF11TV(jN}X)q`yUN!uHa#in=pKt zOEtU8TtH(+NJf|-URy{Wft87cN7gST#XIWX5cX>y1Fs%aq3+FPLRx%vk+Ef-cGGg> zr^Nu@^yHHBjbe&>_i`(Fx!wPhGO4q$PxYQE)t)$y3}U`WZzt87yKTdHnfRIZD{Ghb ztNS<=`!xf=PbRSW#4+|$D&zdPk4VdAN0|$FXQX9--A4$<2fVQykmg_W?al|^v^z*H zE&jfKV>vo~JuqJu-=*TT9+ryp&)j&Du^-8{3Mxq<{1b3_wS)OS12X%z7EQ=t(n5IE zP)^edwse)0yGx6F93vTPD&E!m@<%E+d2GyuX8@cquei84q_JxJZv4(D<=_yG2oBb) zJy8)fE!&y#9vP;)Am(M<bzB1f4-ba=QwyRbHzfXtQ}{hJZsD-2t)<@a>jgCRP2d^1 zqqf=oDjF4Q!*BU9dII2=vv;72*$<wW>Mc^_=Wmg*UvDFm6!R5ABGaOTa?Vt;V7lOz z6kRa9CZzDmM8w(Dg31}4?h5bv@dFJM7r{^2H8Pd)%@?g!2eXKnqu9xxIjKSomZrbG z9Nd<LmIs#SiD9Kwjba6iPmq~bLb?2bM5vklMO-2H5UzeqlhSaP@$l<>b93{h`LOSc z3oG^^^%7GA0HRL)7s)4Hi!WZ~rr&}BcZp15#2>PIKM9TWnV>I>3*BgQb!29$o_5yr z^WV?$Q+@rm{!TXU?sM#rx~yLZutNS3dh??%#0c)SC?U3Th{KSkU!yG-K9##2A9V&z zuEy6j;J;A>1PX_ThKRI(k^fi(y+E^Y%h{F9?627NB$+msTS7P%gln<dy8nM&jucR| zS_I;Yoh~TfWM8}=99uuSvimN{-jm!ATk}fWfOufUvqVag<ITo<t9o&Xrqi6XEQYRw zNxU+K`w(oo{XY*_4+aHTB-<XMY}>Xi%JvQ!C9`}V{qpnY-3>LNK|$u2Ht@nMgS2ha zO>p%W=cXMwMD%Y`=|qfP<o;f)wvs}}A&kz~D|;YRWz|N*k*C_ymgkq})BF3;MK4o) zR~!ALC!{qu=+FSU|B##+!JBZ;7<>Odj%bJR%;NqOnPLOY^|rvVYvtOtHJ^T6xpE~i zIJg26WggXV00F=g_NUVu?I)Q=;WT#Jsy83hEfn+c>uh7wwd*%J5%b{AxP8(u?U-CX z%J3}oYWK+gcIAC}vtQF*UVJMyxA@d#bz;^|{>a@>gz%_@%>+Z+34nuXMy0x0NZ9Zx z!3<YLHpN*FnUYtpUTudx{C?Lxc%^hf&+fqh+T>;cLioBzfB)8Y{uHggKia71{G;f{ z^p*aZ{_F0p9A&JX`o389+(`cK`!a(m4*l8>w2!18MV>r@4XrYQ(UC@sSY3u@MFvEC zAaOHKvGVdJcH17V8fa>2vKVPbkA~`dAJv<CSeIHxdpG<wga}EuzU|nr*uI>3#jt^$ z85yuNyq;lOGT`a-c7&ODPW~CWM(pbu^Ex|c0NUsw6u_KYjuwkJ4C~m3N}w25!XD+| z;9!a(%RQX(pUtLc4PM4ssMhy)`|~fm^~AN&_=Pi*`t9ub`l&;199*lSXBK4)60<Kk zJEW#~sx6n<tgbc{c4rb)7e7AgM@@1D1m}Xn!nChX@t0$n#)*IJ1CYg`=dJ+}`?!%T zF7^Ugb6Oi47neF7RPtM_x{t-9);!P^1csouW8=ClajMVgd%WeD*p5?zV+X~xn}Y(n z#bjESoi1=NtM|-44MH?3VJ2?#|864dF|kI0Dfw?3sqw2ByV429Xt+iGenfC<9$Lrg zKYy~(<x-#y9V~KT8o~lLRHlSizpsMXe5l!(@fmvf1VwjAT5tHQ5u`$}KdEv@Zzlk( z6VgFYC*d9Pb~9WH(4Ss5Hgc+Sttoc;5tcU(t`4b3Jw~%8pj*hG)o2#oJ}I@tiHtdx z+fvpn*flnhO462yNY7?zy1}**1w3}~`1_qm&wHC?h!}Y5r14fIr6lB1s@{AEpK=m5 z;P?lr8VL+4!E>OEpIP#sp8xVVE5F2LXd_lLZlfJLEmJb5NCs04pv>xVr=SB4l8KfQ z&pk-Dk4wuSPjW>?w8^zN2ZV&ABGrSbJ$zq<>4nRXCTKWf?|N?ky%+NcwqJ7t(WL{c zA18KYFg`YqhW|r86cDx*jcl~+rrtzk8|c=#!yX<dX{i~>I|C(ZPm?qqqb%wm5>XF! z04;14gbvuBh;!nhQBl5V(MJx5c<Vv&5q%vo_hj2J`9*k<7)gtdxHX08GC{#L3=;x` z%K6sX+FyH*0anKkQrlK&QdYAVCI!dyfzrQ542*!_w<Rs#Q$2K(Zw08g#fVzdaqc)4 zP`TMN1b;*nHjA4(<tuSECfP<LJTWQT43`q*V=a8$zdtn7pR=A-_(8TMql9wZO49{v zInJqt$n7z_S(E>o#Kx(JJ)#*P4`8yfJxW)Jj+k=D5CzC0!{p>m$~$2yxD99xOX{-q zE(@pDZ>!1+fnk8AH6?eD$#lVLsdVmrlTs(LdrW6G@nfCv#}Cwt2HGD%y1fTVZqBh@ zOChE<qMD;kW4JhCEdaG8wx98taft`4^<)eH{TQ23RRD3&rO)!DFiQBgpdKdnUee5I zh4ZWdYS-inmEQG*j|Hv4-K6R!E5QwN1l=5lvkrhuds4%q$oXz0!^iq!d$)8~4Xp+q zWl5oi^Au#nJEIh#3~Hmw_$tHX;P~TzwM1G{t(mFmtJeTI6Jvc*+ap$k(KjErE}Zh3 zc88})Fh0TE!{cbW7B@Q0YVcc6zE{ygRni4Al*pnKV^LzZ-K~Ei7#ZLE^Ym#$nk_$; za}q7Zr%V$*_)OutL7i8AU|jOzxB8ptgZvGVdU$~W7D?K=^t1qkAen^ryd+LPw^&#( z#TRJxH}U=Zqx0)gD>bTA<U25+V@S;P1&(KWYm)7~Yg$`d@64sitdEPF@H+87?5oQ# zFV<QEhiprh4ZVJukN5Jmn-iYqqcC>?80JDt<c|65<Y*_Fvn{e;+rp^&DZKDWghNJ& zfC*^Cq9tSZ`nYeuw*IbpkLmtlsn&k!Otvd?E9z{Fg}zz8=z_G-I*&YJhaDizd}U%# zI4~%v6FLcO;V>viXwg0t7Br`Yp5OGIQ#`qIRyZ^}O(??q)f}U7b@4RP1!gnISRNUH zq)kTEUGlQ=J`vO3Jsb7DcJwg%q3ulUtTeN@wq;&0Ho;YTO@-+@j4Rtu7f8TEp*^Nl zAwlLR!0955V&dQ{QLb`V7qmZ{>gTYrD^R!L94&F=EAvTc-k-x1x-zfS<cY)X%}p~D zL!Vaqeep7O`j~OSP_^*e$hOux`#jHJvAh?le+(Xag#@S%NA+uOfk*Bhs6veF$k+Xb z0tpg7x_2H_E{uEp(4?9YQm&-5zK>0{uDFKd07zpHJttr&{w#H8l)O%-fA-qLX%-to zq?Q{AIz#pIf8OR1jCnzYy5ae5{q*Pq6$he1X_2|wgR;A-Q$-&sM60U2p3HV;|K__m zYXb1Fni?`#f)PhNATf3ez46o%WCJ2Duc)|!0%x7(8R4~c?<TPw?3v|(bwb+*Cl;;G zW<*P0KX_FVc2c68+&o-ZfvH}tsu)q5MltVr0};LVf9KXdsU2)Q*qeV8;<b5KN;|H1 zBQ};3?EbUQ+lF-X$|GAP^+M!7+kAE~-F<BGsn)yho5wP&8t#niT)0~I*JN5-nWFn5 z{e#n)EIZbS%VIEMPN4<f1e02B&o<TZk`Ey_(`27)WS(24Qjw+_yV7gdjLqkX&AWdX zeOG8!8EH%@6V%wJC;zWlNdryp_&InaZHrgKoA!}V;V5@b(Dn?KPEo(V*Xoa@YIGTy z?jDs|x8~UL&ALj3ANs;hSABi=S4P-Ywt0Sc#ng}VzmLOMYb%oj(+?b6XB(gL0^`tr zTh9kI8CnG$5z^c!&X7ZQNTcja`LBI5sWi(|YSnZhYq;Xg{d+&OCC6(HA*@3iDZm7Q zcobqK|Ck^bRqIb93Jy8k0cO1n@c#Kf#njHOx!cWH^?>K*;Pv2c%`$i<gt|>veqi{3 zBL?^m3-#3RFfa;_{X9ElL>_N_6N)a35#;$WJmWLd!<ppqQ~31h)3Oklh~X7TFoav` zIxJ4>`1tIro~n-LhJ@WT$T<Fp{+8bgKUV!HWp$rZyB{fB%<)*9Z=h)|sHhB9z?Crk z8RZvK>F*)eH09c`l`Z_)iK9h?2w=yykKTDQ45~>zcwb27iAH7t-Q2=<1qI3)U!gVQ z<6eVUs#-33#yO(ybU=}x<#M?~4}Xrhz2`ojK(DVpY}z2)o=bFM@GTPOzlgv?{-iN} z_xZN~lZVi-IqiscZeV}b&4IB?t=kg*7(<uxnR6qJuZ8WY&ytrCzJz^?*6Dfoa~|bS zflOeRKq)74Rk+0;pe?_2brIBtj*@BBDsz}3i7A=5UEtYGSb2zA1D_Tb5liqb3Z_&{ ziAhShnN91c=Bk`i?aeXE?qmP>z-&wedN9(X$TFvV&vIYi50`4+=SQ5Fx&QR&<XSLJ z=x;uHj+9j9oo~bowR#^d+{}739p?@V!uv<m4T3(79b*S}JcE}%G2E)~#<O0pOFWOh zroTBN{xfvi*QGFV+^M!Af(N8Qs6K{-K$T!72H(~fyLA=E7}5NsIdr_z7@kM&C-y8N z3=rq#adiWX?hp2$vXWo`y~MzDNe0ML7o_3QVxhFIj>s16gwMQ%U+aLNT$`O&6+k_~ zAt4R&Zw7jM4+3N3&eIW058DhMoe-q}blP%9swZ9g{qs2H%R<3;AnrS0MSk;gYftE` z&F4NTGjrQ=eV9<PbXHjR^yNKngE4LL3;;ZCgIG+#zK*?S(;clnur_VB4NV;mn$d-k zYw5z~28}BYKXMT@o}J#!b4Z~+`%;w!Tajm)n_@ywGgrb|W!?6|?(iFINCmLmw5MuB z^`YFSr)vVe(XiOoRSqiS(eS`YSiD;Y@6m_6pvDE?(AEv@`tPlWPFds~#3&X!kBlhe z{3ktKo5Tcl!KGn-yRXs83)5G&)~xLaOiosxGAQEsw@!$kzyDT0YdONaN{}x1LhDz- z8dh2`MsA2(7l4Z4?(u;W3H=uZn@y@C4J%#6tKwc^35uq)%rNtJ8+4}<Y$jjwG~+Cn zhK?L!lD0;koS&ciP7HuR!tL<P8`_ts&Tbv*f3c<Ka^J%zU3#HP(B?8lwCOfok4Rf| z-u#UH*a{XNG9I5G>qx(6&mJPu;mHfudkYv%W#ontA`si>Wi;*;-dT)a9lZ;KYXGb` zbfYle9|oc*ml>F@_e_6=r#~&{!Qo(>`cJL-PRtvpgG7!NHRMp=?tKixugW1|=Pyse z_cO*1(|>$G=IwGkBY4nLRRdeTMudIzUHHX!{@w(!F&n6zH>g~(SazZ2p8>GouTR*X zTCr}uPm`!gX#&uyBiM54z#PsM!BGRJk?P35!JKV)V@u?D9$5!JoF1XSN8$5>BMk5; zMCrZ%?h$jzi<bD~QH~2@p98Uf{l*P)_7DvJiQOfhy0XuTIcS;8744uA+7WgFki^jt zk>d-{ZpA#E%?r~7O%es8*vzjVPVnyjmlGaCm$u&uB0wIHsVL%g#MGxmd^H(gN1A;T z(;D2p3i_KRNFdbWjehtx5xI7pczpZbkG{u70AONhdiU6Uy$Txl%33Aj#=c!`bt?Dl zet=92#>i915YEhxWNRc3*gz|nuKCwpl1%3Ck<z8keR?3$_yN=6Spt}MB4gpe8|k>z zzE^8s;qG!k)t--+zw~ny^q?bZ(?I_Ue)m>TSWl`pB860Rz6ox|WG-Z}6XOWb!Z184 z#@dNW*>?&qZg5RhY3WZMPmtwFLPmCI#`shbNo;U^6K(#P@%hjHZh-4M>&!6(voP$s zeS?Du7N;6Nf8OAS3L)^qI8Ac{7Rk11SoLDG85iu<D|B^r3E73fLk7xo)yHqzz^kc` z(bJDfd#jM;9yj9Y{LQ$CarsCKGVb|vTH}uao{5o`%r4!(f4}crp%f_@81BMRC;xQ~ zSdl!DTDZXd2<av<k|Q=KWLtu!SnWq@G~}d#zPswi<A52XU<S!NctSz~jvQDE;JKGf zH_uvO%JFo0dXXPC%R8`UqzynVH3w8b_T<zpsL!JySVsF&!jGa4L$44-v$zi&kUwZl zZArx314s-422i@Hp%%mKb*-d=jA#?@EIbO47-0dHT#azEOk_c>nF6mt*o<xfa0CKQ zFZc+Dg(;RSI}xT`;^e^m1@urg_}2ouH3YZChjkaA!9>E(DJ=!@h}|Us?HC7dbaZuH z$;okYakkU@U(#f>Q0Io0*`8RF(TRyvWKGKJ%F3>G)5bHo<j?|Y%lh&`Vj#l{A!~rn z(Gq+Z^ClUQ=6nd^M64wv7Z_KPFnh&_&$u;Q8cpFi4fiRj{xH5l?MTz!j7n+WO*0Gm z4?c{r$x4fJPB7+0=3?636c;8=44h9h{%%`19AytwilPNvCH@DA2$|I-^arY5I`AyK zT%0Rbtbn}n@Q5-NagXf_CdMs0b_5~Zvk~KX^DwY&OWS`iiWc3`un(a@tP#X72NOps zdO{vd<Y0`g7J{~l9Xtm*Fm@2jJvJ1%f5_t(kxHP%-|WvkVq5ipJs#}GSrI~mfEFU7 zY_H+&=<R)rfl~lBqp+nZ)kNeN<1(K__@FNif0OfLXHyRT@QIF@g+&nOP9f$8c7x>w zw>CgMPZ|JKP%IA4&bh`Izcjc$8$F07g%4+qaZwkF;B5-3G3O(Y_>2ZQ`^6om=w{#~ zau5&lO*~p^3R6lnY`BG7juAKptoEsg0z5!^o~ba%Kea%S#Pn1LLIx(tdTa{fJVead zH81-+DI-{S>M_iVY<TVP)(IQtKmPp(dCsOjl?h%s8T1?X@uJB%7&-GtT9C%`SE8ZP z_;3^q6B~v@>w%}ksHcbp6bJ^{L<&F)l!(>5fWV&6qd51);uca01`>FqXs=;F(1EGd zcIND(zP>){>M_yG5#{SU@gA|gx&{WS&}vixQMtoVNKP<<(}5N<O`Rv*tmcE^|2*qs zm!Ww;SQv2tV{_`k7DW6Y$ZKPxqtEhb+9}ZpiG0(B<3o7Y=^%}oW9I1Cg^yA-?pwQi zFP_D~#WpOd^@rQLAP)J#wzCH3<bY0Tv<=p+Co)lde$@j&H*#hL5i<tzuoMEC3-ue* z7-75nG8}&bfKKuioVsvM=Eu?=9DBGP{$_YiKYED5#E(snBoz6;?%Jvi58!>ee;$n+ zTrPW=u32c|h>%U!wC#%~B*C^TFDBsT9r|jwf^k%Xr6I~LKH>;D_@Wq>CsYeGV(0uA zgNG(%N;*0^gRMo49-`Z~6OKbjxM2)w>mp{mRT`mMrb!t(1-g80!C@gmf!%u;{@b3M z-{QXs$DJU1aRY3i10_eBfUlv>^t^o4wzOK!F9Cr9ShrD#<113D;j1Q2N$G2OE)Ha2 z0ZRrcc+eHw2L@JC=lserk3(Tt&fE^`I5CVS;N%}5-;3b%aB^CdXqLuXS$SD<uAU!N z<8IGynBi>m=`4WjrETi3Vm*$es<T3dd4v9&46+1GHJy(>ih&b_<_YRef}XG?#{m$? z8An7aPt&{w+xOw1sp**+%YtMSDMI$4#yPsU^unP-^cD(irxJEwrHf10IC@qrTSf%2 zFg|4+6f$z4&GnD=pEb5Lix=|d|Ml@!SjF#zt;!!>>yC~N5(vn9bx48IBamx}74$ze z<sKM~Z(YS(Q3O^rMJ<qs)B5tq_u=OmDP{%)H;6sk&)%3gy)AX|{@XSXE@biqENh0_ zSZ9A}P4;Rob)39%P&jY3KFGd}pk)tSXQt`@5M+aY?)mYl=6{$>+bp=2lOb310=cxO z6A4^t<R}<nnI{Z{ib*dTLc+sPauyuhbnP%n&HYebG(##QH3{;=DcxW1L@@9OLMVmI z#Zyh5%@~y1G{3mCQ+aVt3g<T=>*EBD+X*cD$Fs;PXC(XFLD}l)?tYCosrRiT{s?9w znory$ry&XTsMEW_{X*s!vA3DltXa0;yXJ0yD01HzC>L^o7ZDtdie!l32$ExxMH|1@ zNc+M_{1>F^lCU9@fxR~gM~h)3*vq05TMyB(a&yx#ZSg+YcY(<dK3M!$4tXTk$pTxq zFsho3SORd+1!nC{WHDFfw|mU9-G2p_gee8KySnSEvf?0ZwfFX34-Q^!0_`HZOuzfJ z*qNn$W5v`?oX1d)V5I`AdwXYMgSc@5{DEY^2r*~(Ig#A~#gRCmkw|g!#x#y0B8D_9 zy;i+UA#d^D<Dc{t+*BdE<4gvw4I4<blKXl5dycuH9LWdlaV<HN$Ym%fBO~L>Y+yAx zB8}j2B*D&+k#L{_9122?kkK$aw5Dt}SoX$}G@`b94#P?NAj{*#8<i7JPiq`I7JVr< z6OK&9h3PKx2RQnR)&3w-sHKnq$bn0zPxH7Af9t@zzzg5qJo(>DVEcbCfhql_XcrEn zU0A@qfS*K*t%;Y>Uzf51#^U@C?bkn%GeNbFLj+-H@9L^RiO_p8!igg(>vBitnVCP{ zNERABefND4>WF{m&Ce^~pSX@}F^UAfo`b_G__oS=gTjjf0DT>>l#>7_$G(Vwqpt4! zNJjGqX8JK=!5?XYV_p&fX0e$CVTnSSk;3D``TPz|c-8+`*_nX#oWJY8qA`|XWGnlq ztZkB|ESWS^l&Op%Wo;p4E0RLSPL@KH$kH-Qi54?avK2{EjmT1#QrS{eqUF4vX6Bsp zKiB{FyUv;Gn(Lb3yL~>N_xpK2&;8u@{XqA`e&oFNi~UNk$d~+kj}75*!I{i}iY13Y zZenBt{+;Eo)KkzGnpW-93R4z81Pa%5-u!iI^FvE$!%88@`}0^`TRQZyA|pccxl?=o z9c!bu4;t|gyTgkQ(KdivZ(h5Wy9PSg`kM}xt9l*#M-Cn%j47LAj)scK_c6VgalvyC zxn?mRn{VCynx1HwWMd-bJNuKDAzSqhv0E3fBo^U&*htQG-a!tR$Dh!+YkIZFQp|4x ze!0o%AhI4H+^a=JVK_?+@cg(`zYJX;E|1^SlnvB=6!2)E;%!4tWRfj^35)>h?fZ=L zzj)CnDD1F(ukla|oIS&i7gLI~33oGBs8|WS__w4u3EvWvsQ#TeWF-6KI8z^eF<W2L z&u{^umS?;IvpOV{x5X!q4embO*amLVwr%yPZc`PsIiVh4zXsH%j{6?l6uniVpXU;& z$v$alkxBI;r-rp3kA6<nI+El!Z_m1eS=mZf=hO!bkoE-WPZBnJnFn=UXjB{CODQ&? z1c{(lc=Ir^t>3e2TR$M@!p3Ylt}-l&RHvl&bDhzh9;Iuzf&KR2q)&z>!Xi^j9nugj zKB*M1Khz7ny2T2&Hlt#5JD+ublI*>Yj)UDuFXB%}j<W-UPp8RCY~ScB4OGqpq5mGC zM`cY78rHAOW^_L$_e{g3<U#lGgZ|)JWeJc`jw6d3WoWqkUA>{n5tGCrKT~VA->+bC z-pHp$YpQXB0nD^kmmzL0Ie9V1+o{xaW!+!g^T=0K=!T0GEEOl8moTspn@V1L11*>2 zmZhm{RpZu}{dddQ%HM4h2xrQWsWQ6K&Xgnab-p&aL>jGOZ`aGV7=&p0=){&h{EJ#> z*o$)6Y{Q9`KgMk)M*-Xv?sLnpzOspTwv;?bt?uH%4Oi;kf55xs_7yXd_cpyX{pNT2 zJ2M5yy0RvQgH^nkMj2{q4Np;BKGY%GV(Dm7^^BhGN&#9Ik*iZn)DyuKK7IQ1w7fhx z+<}y2NNr%@Dt>LCVRuEudjDgC({L77wv6-cv^$B7hx9bRTxw<!Vu+#BLmkJHj+%af zgDB4ZyAR*N<#}Ls+S~X2_w@Y)NE#X(IQ9MCMr7jyHSSienOLiKx)suuz${9MzW+}< zKr`>6`#0B)5e|g+tso?5sF>(`QVc4p|I_56Sv)Rsr_)C4-hPJT7b~>=nx->1wIKXc z9BL2Ph4c%h+Tuvu<6O4xuhVe?=D<f_H1wO_O8jTLI(7P={<Drd5PS*A=~Svq@G8sf zo3mg$pw)=rqw*Ki<`XB%^Bp+KP2&zk`h75@f03DX5cd3dr1@o7Dzc^XUklo4TGe0E zug-q@4&58+=~WLG$L7S$8Y%R8Os+U_Ca&5tr1{6Uo;&Vf!ShT^5KaQmz<cf0ymv?t zMWc5326w8T;pD^PtCIY?&Kc2eBiWdq>+ON>OrKndNN8*9+ti}4@Zk|YD{#)jmU>~C z3oD(t7ygYa47^Uf^BcXXL)#k_(S6XQc<8yxVn7T0A?YI@HSa3*&Qu-mz_KMdcU!zy zw*TAm*gSCjtb1L^KmtM|^z{~<^-@#yNG|AKT^0CSi?Wx`(+4;hKf4<@;mxDp_oO~* zuv}=mFs}Bn7}W{=g!J!W!Wr>pF2)vU(ODOzg|9VUH#){$u<lj#-6gl8OJk2;`S#eV zSHgvLJ~L@f_6mpZ`B~@sSSps<1U=8HJ-Ez#k<o~;EEy#7!oFYOUFsC#Jz@b=e%xT* z=O!G?ozke);F8$<H4hG|T(-a2HSMr1mRT8czJLfx!v&y1>h@3V-kaZVm}fmN_{<Qw zl}Q_U{lFn<hx4jH5cQBDfbOhjnTy}3WC68Hp0!CE68>A*$NWCcHzBH}UbkCrl`lUY zqH-jicPmT|mJSpeT*dMtZTqYUy9=Mqmo~^{WO!RBcpUfdy3{HQsB|U;b2Zplj3K}Y z9#~Uz^FgmgdlFcT|Fqr6RgQn(t8qrHHM+%V%tS7fai&GWen;~m8Fp+%!nryn7S1c( z4HhcQ?NvQ__uqW3b07Vk!tc7A>(GD7_U9M9!u5tlxU%RpTv!C%EGj(REX=2SdKv(V zo**n^4S0Zh=vndpM*F71Cs$$V_AehWR(&&fB+#cM!^n^70N1YVlt;D+3$4jx(vfBa zx{rp^O}ewc>><u1mXvY1_kLdT*E%4%x0w<rdJhqvpcz#VN3mCbo}%Kq{<5;*v{|Ao zgXl4a%Jn&u`Uq%8?s@~%X7!6*BgI^n%57?H4W2<#T?;n4CWh^0sJ0|BVt6l~RB=&- zjTa2QEBPBNz11IcCdF%y%QQPKpRDbzqv-!{h%x@Vb_p~>!DM^f<nyhA#Us?r<IdP8 znb0}LXLzm{TypICY{b?OKd)>}Kbl`s;Qu4vkW0o5x_$em)aXjU3(~5`j~~D3=(>B` zj@|R`mpF)~_)*lOD%&Gr;u+W9w(6oMvDviBUP+acivIgR`#;WB+i^gOi3yi4>D`(J zK5G=tgyf#_u-OLxd`3=bn&}(In*4r>2Hw%`b)A1}G2O$f%;A&Qf>?TXX}|1Tq`rl# z4j-8PRtfyVYJa_A{(YJl+s}kSjR&j0d)|GP*a3*JfQk<O(D>bHhkHpWw9)yS8I=uf z-fbz|yr8LR_Vhb#h}!|be3!jTq3hq}u>*czruX5VW{8yF@HlC4KtrYM7z!2ZPOzW* zO}%#V{BZCV_qfh2*0iXuShVp!5rI9cO2qJ0#d}H1vwwL=Y?KNh@xNRoPMoJDX}+pH zA5&L3?9HOjdm=;m3am|-K6~qvT3j>VUq92aB${LvdOEjJdcvS|c!0)M+aaJ4T3CW& z%crSL4Z2YFrh00lzu{mlG5vMKaNkeMq*c|kS1)hZ+qO^JhFfR#ALgPA`efF4uvA5a zD*uiz(p~|d7iaQFp##OcWLJU1;);A7ir?EhO~+d(1I7z`gGSV~IlYgbF;4n;bPy`A ze#(=Nj%lp{&0~9~0dXj+a7mIpR@KOJpaND@SOa~tv|^LPSzNlGVg2xeL|kMsLL>nL z2)`H`YSg;8(RTRh9`|N;JG;B1`otNJRJUx%aJ-80Z*8Pch>LPq&FJP_wO^p=(J2ZZ zHdY#b=DU4Ie;xT#fB(^6abQ~3Z0aOupjM5d6r6Y39DT67AC^PoCnb=*$YbfYY~2F$ z;&$z}DdsErG+Dl~f42YHDaQ)2*~C0*UuYNm^D|6(UHAGudGSBSWk2<hVFU`h6e0(W zo}{2{n}kE|w{5gqoN4;v>~pNe9|x9CUOBk7@YIVJ_xB92sMpdu`3uqQClv{_-4zp} zklnY=DAMVxu&tH7!cvu6$$p8UlPyXWXWeOYUSnT|wYyklCjET$k#cC*jLd}2>fhD< zHr2%4xVH1dh^rw}BF85xvYoD*sQayQ)XvLQUDp4*%3%??I$g?R>Y^UX;eVpz_|dL^ zr6#Qxe{>Dhs2eu#DqUaDkchhIkNNes&EH|Iv~Us?t{D4%YTDJkYtVgZg5M8GG0ku! zzD|*q+ZfsM?j5ZX`_oC&CVM_XiZub0Kltyzl7sNRdEa2|!qD(v4W4og68@$sO_bZi zId|(+%ez5t2mX3-xWnbTy%8hi5Hwo>HJaD_ALcp8E&12uAj$^*VK8I2#cqH91P=|a zzu)!)1Fe+=|NOFFA@Ugt83oU>yI1arPv<s`uFc{Uk1Z9$)Pla#F`rH_6MnMi7H!@c z=%9Z%?4PL+!nFNA{mjeuI%0qb(TAXrN9cjlvTyz))-DDn0cEKaeWh~~l4eaDmr0*N zpI{VXu*if=4ldKA_7g)cM$~%r)OR!sOx?p4VJnQ=s(vR$zshR`XCA;-3vg4348))n zdby9a)x%y$rq78kR`$R?2!aN;QqLMaf58H8jt@9`E<4;Qp>9J6GN&LAi&&ft=C+@B zXk>V)N5Ut{SUf%^w>PcUov<R26Fi$<>p?k|7C@fgLM49=aMtnBIPmFokat&@OlhCz zKuBsoDby#THojel4kM8eED#(xot)mEf4ig7Os5t6_S=>3_7!GVa@cHd)LDWHJzB)$ z=kVShZF*!hZytkrM-Ggb1)K%x0H3?4`PDiY*Prd!rHj+nUp>P7eRor4>H*WtBX`aH zgnnojlBMpz6|1%x)n0(lR+&3lSB-pPI5r#sZigvCddMeKVnCuw-$+lq`6Y==C)e;8 z_3-hKv0*2FShnnJ4z`#l>B$&&J~EH8Gi^!HC3o@L$(a*MEqhgU%<5%<=Scu2avyf_ zx)R52-V=FnB)w|XbHgd3qVHp+y^H>Pu~~G|%!%V}_&`5b!;<PyWqDMXv&SayNxY%Y zx=&Zi?#G5zuQWB?B(4gYc@xve-H=PA!b+%K>Fb-!oRAI+0Hh677SCP=iS;DuiP&G; zSlZJenU8fu!L~hnMz&Lb7VnESn?LwciQ^wh)7KbJx~3;S8)}IBkAW%0_Ow(Ot{l2k zxm7E?mbUWzszYFgkS3~wm0LgJLNFG<Xg0H4@53g4-ylTfCuo;|i`&I=B~wbj-*+y% za`u1YvJSX<yOVy^Ov>yK@K5X-OxZE~J%_a|*k5S-(f2cMmaYcwWHHZQ>#iRxLUv4O zvQ91YNa0ph(vhx&c@X}Pd%P9^edeCNKoth$m0=~RxnB&G#6gmWwwHD7ynb%&vRgTY z8Wa8^xVDpq*6bl@|9BRUDSPpQv5Vz&Z-d|Z?DY*RrFX#b1elu;7_{E0I6k3%Z*%HO ze}MiKSykcX%*@Q*$4dvR)%Db-#mQ-Njgl6$qL_PV$0HK{9ZO^=;t0-3@uH?(aGbQO z=IgOPb8%vEOxG^7tdUz)?vZ>$hOgYy0o$gbsBr<z%8-oWXs>xChUE=C6)!QlrSG$D zC86ORFWYMw85!jmyZTkHbaQh%@tSN=SiM4z5Z_iYJ|UZg=D&AA<x6L^IxW=B*fEb_ zV8&C%H$?Dk$J9RduqWlX@jNR=zsk_=&Euqf{EnQ|^-XSjbagXsx_HA|<#w$uJ>n&~ z383ISwEgz<4gP*l4Th)e5fR3)Zn+#ont4xwmJGWtT^EpBdNen+Mc^07l83+y4P4s) zA7Lj}i8%JoZ?HTM!%x~?Wbk<~p+j4b>Xv!arM#@njQt_@mI`dtxvaB#5;#~9V_cN( zM98ld=Px3#Esh6o+Uri`*9wOJuhgP<T82(`D)x|)NSKC1Nv0!wU;*V|CXd&+hgM;I zzliLy5$juN)Ix&PnSRPfQ#{1&?L({Ws7twDY(h=zS*==a)yPwYkYS<!#QI0pMKspf z^1q&0Po6la`#HYxNcg&4yW0i1UwNCd22dAS<C*b#G2J!E^H_H)v<e>&KDmCtB!5-0 z`<78HVh{@XG?MJfP;JGfANh81#VmVBxC6XDaim|Mzm3wNo%bLP4j2$+y3_T)6r)XL zjZ87=e=4?X&z_+g0fT;JSD_8w$Y=tCoJ4kJ`PP<dUwSH@nUi{l=2PN2eKpnt+RvLv z5IQ-Cqu7}<XAC>Ko(T%bUe4O4zrI;|J}f`d_G>IFalTtI>Q!>=UryyXn)+=@yd)+p z`~faNxrqOD7>+SiM4s5UiYyLn&k>po2$@pr)RICmA?K6gSCm-r!Yf4OI>1w5^}K(2 zrKVnG#V(@8zif9b$kzSt=bJZ6zm_N_N<~vmxd`0FvVm%2ii+q0l=7QDB3C;)$K=f3 zfb5(nPmX|i2UBH<C)#Z9k}jvGoibr5kzf|9)vgPoBodQY+>Vxk_eD+#Hlc{4SUKyl zKdGpp$?Boy?k=GLQ{XV_OGJ1<=r@3Yw^&>>@@rXYhkt#U%h&Is*{cS>SD#*q3xHf^ z`;Miv`E^a0lbnQSJdwiCA1!lPd@_Ms$V|;gNv{*Pd+2{|c#`7d)%?+%zzchhS$&sx zTM<%dT)KK<wN5wL0N>q3_%`7vRQgTr{n(35@sEFB8YcPmNqX;F_UzfSoaUC#rr_5R z)rNLj0DO~PD}=OrQB*mL45#CuZw-_scm7{YxcdKRM9bs66#9wFstLa;I(92&+t6SR zyj)A$ajXo?*}F{jv$*hUr++kBS3inLC*yb5egP6ai^Vp!WY_4bz}^r<5>3e8fPToy z%KF;WCMQm5Oi6aa4s%&NhmOrW;7+v*V)PDAw&?Rim8HO^b<KW@)$iZmyc^$yBOUxW zeb1?L=k^t2{XV|$72!`Pi!A5`?SZN1*TRq=1X>Q<8$k#jQ&+qX#-z)y*>A*Q8UvzW zkV?GI9WPYG``!`U+5KTchD|%+Vf8j{)q4FUNaJoHwJGE-S~Jcn_=%EzVNSo-K0(HN z(Ci@NkMz7zh1ZW@{IaTrd9oqaq>b2YUxwUrNN=7JT(o<ugz9vBTkb0C&oCODAeJ@I z!$KA`%!Xbsb_P^JN<Y%9gA-zb=QSp$^U#S)fCGisUy$C8p2)!k=SmX9K8dyDnW#NG zdG(gCig)j<fZ_ApO3TV1y%Zu6frBq$)G_-HG%|XuNefSH?88J0P<*qaWNdWJgsv`f zZT&yJzVG<$w|kwH7LGVP^Y<l3vcB}j5Cbo6sg{6o3twg*tpC3CvCgY$iVD{pY$OEz z_WX<V(>&O>J^i&-26g-cibctcFsLAo{Fw-?==5SiH36_WrnMO@6(Jsm`Bt?iD|jC4 za`YMLVR}J<;f?vX%}Jk1VdvaDU)n^c?aHG^k6vasFkk2Ewr#PlQp;$}1yI;*s%~9N zq*oEy4UucEARjvRhr|tSOnUNdL>@f;<@7Ds;(_xok{v9E@^-j(4kxU*&x-LQRyC_h zUfm0~7OzE!tvs8if#0U02eu2;8WtlMI)7t-4}s)K)(DkWAo8C;poOn?A_r$I9F(88 z77K96r&!}+vp?r<;B{`f`0oILSune4|Fkpq!IE>(9BUy~<ubD8wcdvWG$Hd;Cx)E# zp?*}3D=pg31c)Ew9L+)ht=!zAv%++}!&Jr(dVj&IqWxt8$<TJ{X|i;LBaAsk@wA0W zw;(AYDNW~3gz!&(9rb{fB#vS&{b=lo3*`iB3p=5L&@}XZbt)?EW2lr3BsVHp1@WH? zPL@@*%1j+}s=-DL(@g2^V$;t38nNdJ#)zE8au-R;T$$`q7}fH}Hp$c+JOX9mU~yXQ za~GBt4vR9q3bonPYHvC7@u3w5bfK}yqJ_=JXY43&v&vn^C@i|hjA|RfyAX^^)-10B zpOqWLy*^sjyF-T#7zuRHh|c)%{=M0%7ne!y#eZoLq)C?X34Wo-L`W3|ru5l!kEp9y zh6yojZ1eaAP?S$}Z)Z|N1`c|PEE&IxEc^hn#Osq1XHj9YVPRpRbkU(G4axYRHRlCU zSoQ%04tC3J5k!!{*z8J!BkY)=LT+M$$DLT+fr%G$ahZR*1pF*|7(fx56Q<j>mgb7f z0%Vu3o{D?wc}wTiZeq^u7*<>05=OIoUPDPgL;AtY4N#@NV&CkJja?d<|BicnK*&3i z9b!cIoMxO%B%=ekZToif>OzDon7Msd^X^F|Q8Uo}1JD>z{+r(3#T{wk?0@zt_gY+1 zDShQzK|CWUqix2JT=ZW1(YkL*(F1Mc@vwM;U&UW)s9`)zMYar_K>$kSM`|q?nAi}a zI~Ay+rVs)tWTtD}ZgKsC4}zoNCW?`vb1(auo?M9RBL3ZFE$ra<uagjkF!Ou<e6>Un zTu>iyH}Tx!;ZYfaz;=;{x@Fx!8I?zfXd%W7QLm2@A9Cp7Hgn!SQ^*N+FX4b>KEkaa zj@9wBtW2uUPoxC0YyrJi#bx&*fsw`@rsAMU)~vzMP@CCzdV)R6!{r1@<6aG%8K=Ru zQBl7qv6X9Ko8;AgU%Z?^2Er%>nGBIBDfRaDR&3oPP`HU@Dbc>Vj`=h#rEenX3UG8d zT~<kg!9fc?x3yh^QD9EI%TyUrM&_~=ju@wj3_i;*ufTq$bA>Kn&HYQ$^Ve!R9N2}W z5lJgrx*-rn-Cj?Uu0+4NNAfKfbzd!ptw?g%t`VR7$(hNfJ=vscaPMTG5+~dO3hCR2 zNA7_zBu|NyquQ~eiDS<jSy(}`_b|#ZXI8U#<5O3y;8-=y8^tnxZroJYL4n%MiO{sY z>)ULY=}^CHl2$kgMoE);s<d-ShBCNko9Wq96=z5)>=2fXlsUR7EJsDN)(2cZ2`6N+ zb(6Uy{9x>JM4~FE7Qnqw1c<$6HI48fgg8>g{XWOG#%k;Bv@bra31Vdj8OevXT{Kku zq)HaA7^8Ek!HkLFAj;0Yf1q1E9{HD>1}vFb^6HgqLzB;aMdxat@8acbl-4ztHkHgZ zLO4J!s!Z&KrI0tg%Nji2Wds3uM>!5)!b@a*%$M;V1QGU<O3$9R!8~l&TDj(32CsF4 z8o{AC^L}3!c1SCv%!;`UDLT?D?5MJ?FNtUX70)@EPNo&Cw*3^MclnrU_fuv@#s~`^ zv4G01v&;#(WjDv2=fnCmHh-tfI3vJGi)p|A#@kWJ^zNHhUjD@)Wz9XK6V)6V{q5!y zv!aPhf=h0h7w)q$J;gSze9+`TJ2ysnwt5Puc<RT*kSYDim?!h_H*CrtYe?>Sj6wry z=^uYKPLTjW>{UF3KD@)&j~HQ);35ikacFxm_~8an-QTS&qiTv=!qx^oa}k7d+`ZDs z5na~EOqRuOO=jKOrDc6$Q+3nQ!sJB_P0{Og9j9}vL~f4O_1x?am6gvncblyslkpaf zdm(yFzE12U324UFYttxKK5#KORfam(MMzjeeJH<*u-_@AmhA3ZEMj4pggjFjr}h&N zgm{()#59-{GFf%gregI7!jbK8kc$~RYQqdY(v6gZY#a=PrML7&Moh3P!CeRgE0FO$ zR46~HBT5k-n{XHDiu8Y!Zu7+*Vw6;zSuTaMuIa>7uR0|*^~@ieOVOX-XG*R9;jH}X zUm$Im*QlwfrNn8hr-^QE@WZ-TNQsKe7!N{UGw7Ym1e4q)!Rw~JP5skiLwNd=Cu8rG z_ODfoEasZ?7gc}E*O`(@L9ow0KCRZox$e^^jW1&z3(on<E<0I}A%aK0m1|!#$OHk0 zujbW#InWcN03qwaHwMeBrZn0889lSp<qMl8TsK*%@I&K0kQW45A}UoiUYDFW?jbJ~ zdF1JxFSU)Q5qO*}9|Y!Mugn+hb4=;HcmCK^7;Q@BQ<JsS-AJ=7&W(GNlJ}Ux-Xea6 z2lRvJru8&^JktU~c%4f3?zhN(Mlm}Dfj}vJ9hr5~JWA{O;FuY?^Xoj!2X)-CNh6a} z9qF+Iz2o77b?+%AkIW%%g<Br=uF%zl^QGkWA!YB(K?iJ4(Ipa^Ax@JNjn?ce$44S> z$Dgh5ypO)@>5TL73FbqB-@tQ8ZE74%e9d<p1v1HjZcVT`Y?&67Ugfg3X`()T>V8dC z@L&o3U<?7AlXA`x;zz*%;PhK~I?Z3Zhe7V()o#K1lb3AV7_&;rZr#916DLM4emH8( zm@!!+IH7Nl>adsv1X(zE_;5NV&o;E`ZI)pEf+Zq;Am+PmJXWoW<h&w1)x4+tI%w0V zv0CeO7HN(_6V+E=Uq5ZvfVwLwd7r2z;xmR#JvwGl!}`f_E;}nW-@f2wVU$!$T?Eh3 z>ReVzkC@zH-9z(#^@YnLB6^>iH_r&hVyh$U)h{*fgoINDQ+vlLXO^v9b16GwE|Y_+ zbfKmR%}TPfufM*Wmp9BIFZF$s@#@^uUM_W$MkV%{Hq)u@StH$XeYl&4$HWuVfU7Fm zX7L<!BEx$*R#=qOisEm}A-ahJN1FTm-EElK(!hh;67~JYuNWK97Q;eBU)W~v$Xcak zH?A~c-H)>hv+o#p^SS8dYQW8#pnhu5x%yv3z)HibonuAqFHWALhE>dJ^tT;7tgi6- zYJs-8e`95ZFc;DDSW%o(b8NTB4X<9hBW8w5Q*mT^#N52@;{$vir}mK3OU!TCNs(*9 zoGb<j`1I?|2CYdM1+L#rU!k>lotEvDjP1L3%ZNFVmKUsX%QzSP^TP`(7oW~PFD^4% z=Hd760G6k8nRHZm)nqr$)Gy+A4_*b&%(>S$N$aopPM^_eZ}VHVS5>k3;^%vhtk<b= ze!ZxkMf}>lYwa1Q%v)T|1XGIvKQ}+~LpC9SFsGtdh7V12c!L-?-zW@#*6iF9&`&)2 z%;K0&=~6dR6W2=i2Vx8p5=vy!%R_1({!)@{WE_^E9ZbL~?kz{39Pz|iYCb;}K4T(7 zUnVRd_lenlpQdP_yAb|sDvCPHeNu3^OZ)b%i!Rk3j1g@veL{iB({ejN6LKQ<iyy+h zLFatNQhWXxGpidCBh<j1-hNIlqBtN8YNmIdZT#_@)~yv)Ci<i85m_Svw(Z_`*A0d< zkSH#FU#{mLPB%PxT=DkL`;YvM@@A!xUc~e3YD3ALVPPX%UVY-p&6#zx&%P<N)Csr< z-7C@GQb3k~H${iR@jIH$P+zEaYRdMKgDFx!6|DRfOaNR4m2^?()Jr}j(c1|rahT4M zv|Hc_xBF;+$GXjfBUbLQS;mCaq&-d}--}~m--glE9El227;!93KVj`p@v859TUCMZ zHVHm+T+gVIt#xb{70chbm15PW;z#x>ws)_6pwQr$cinEw3xI4ZkC>p%e<_lVJ7xrm zz9j1CnjL<Aex}CD@Y9q<1A3-O{Yn&-vh4MjpgtxPMBDF}4{x`T%&U^~c$QaC<jA3x zyP~27;KZab7lsx|T;Au|Zf;r}(s4(ZRRJBA-VD+5VPaPPk>6_%8r|9kUi9JCkXSK& z?|b~ri-sPD^uMIcE$G})RrS!XIxk3{REIfF2^pO)hHuE{RNOT}Mthw}Ec3QY{GuR( ztafzMwW3KHn^I$UBC8Y9`s7c~cM6h>`(72sI8k-UR4IA5&yoAYI33i#@MR>rb~2!- z&vPtn-v!O_T`~XK_F`>n(G_9hKZtw<BwOZSFki|2LIqevPkCT-d@GgVD9cy|it8sA z7M~@tk~aPbbJ^_p8T2IxQ=5q`fAc_HbSEIq8JyFSH!@G)7zwn*F_!e}4hfiq88{e! zTX8Skyc50_mk~sWpAoiQxYG7Su_W2v%2)fV4@JXAcrX25>(*-cN_W+<ze$cKxGXrK z_#8@br=qo{U=hDpk_5LAU4()#lm;<J8>0wy<)x(guoOes#312nqgS~_TC4z`*G@Z< zU;CQf!=S9}Wqr>735gyyAvgBN({F*E1AQGtibP_yMrk5ueXH?H-1j6Rw4h=$YN<-f zfaRFvi7=1MM(`d$i0iOMpTUSD#f(y|mB>sG7yEHi{9=k`K8+zi*B8g8I5#-;9nOHX zxd06^6EoT_aR;fnH4>S3g(;bAQ8W)HsR|GSjDC`Ln*6|pDEXK}k?|0HuPtt?J3HA7 z&WWviUc}^X;PaWL_j%{yUW+WV@9QY<R2d)e1vYj|0yerveP63m5o3v3oLC}FGN$XG z;NDEWD-*F~R`QVxS*Jw<0?C>oB||`}7;?F3%NCKD1AJb^Q`%{)(+m3Ka^MR!5)}K{ z+qXx`XRsE8(=RMRQq`8NT1lnOK|w}-jJD9nw~s~^tlWpP3Kx_8GP;I5x|52^Rl%5X z=#@$raOODI>xoYo$}}*fy~P8Q{0BQ+Fo4ReE<^$Z1TP>^;1g*LNDW$Xhlg-ve%;;# z$<iEFcK*&QS{J(O03y#&3Otnj{nDP~kky$--fjy65o;AjgR23zJ%h72Wu?*ZVMT4l z(ucBZ9xH~a(i`A9|9E=ZASr2OvV>@?U6n?;J<yZy<Jiom`48JePku5ftPJ8UZwwVO z203dmSUPn#%dLBS!4#l*@g-6l;U=cP<?CjXP>Iz8#(IY5>srWg2@dY9@;vj_-_UH* zSIao!!;h}ZC0J90(EBJfzu!5s*n#cAc!T#$DiC`KpRMXi{`r_9ckavDWoSvxg&OOs zD7N<pGHF}9#NhxrRV>r!CT>YKe&U0|lI;ja|6FHz#uw7g8>}a_;jYuphmF+o-WTcj zgK3*XfET>)2Y74vZYE{rM_9aD2EkPB1k2{gs}S=+4?a5R0_DQb68J<+6j<L(ihA$> zNI)b7kTBy?CIr||!DO3%l>M7$9-FpNB`ItEf2yB<*Z2OXPc4?u=nwT|pamqz|LxoB a`%Kxh-ZZJI>jwq?G5^8J^o+^eU;YD^Mqz#c literal 31480 zcmbq*c|6wJ`u6uR&to!Ve9%CqqL4y8N+=OgnTN_yhA2daQpi||+Jy{-3~4l^2n|vp zp+bffQ7Dy4datE@_Bp@d^S*z)`|~-UbI$fm-?i>_-`9Oz*S%slZCuC8CBa2e6z_Ut zh9yNYMNky2mxB%eMysXk4gO0fz{oaWv(Juz{m%aG)CT7OUvHlPZx0vQeeV8yJ$&{o zRb8&SWU=h7fB@gUx@u~>|9OF`kN-}!MfQW?_z+HC<E?usir<<1hnBB@(SxEw+}AS< zt%A;e=nl5q@clRYct^G3hp%~7F3AEXTo$`OoO@)Kg#0V9BV{{zcBG3R()fBh#l7f| z_h|*5HD49)ExEG!#_BwwJR^^aZ__hFuYOg$9O*i6>1W6)X=(m%U3;GfzpMQ6{;CO! zp@0Dc|7=@RILs7-e~hces95~>H6CVJK|#TkQ_hqEolb9E!V@w3x+An?@~TJblo0;x zA<kxiztUCeC}aFN*Zu$X=F0+6va*VbtWmPND(8@QTBuWV`yc<c+}GE)=C9>Gn)Y1e zTNnPnKIi}AN36AZ#wvg>Nwr>5!DIL|O5mI%?^5BV+S_&3tSihJzG9Lv`-ltqX>zo2 zuF2JImbv}u(FLrx?5q3sjFi+ink3oRkhK`&<lu}rnt9Z6ia8=bcVW;}u&k_t!J5?A zL$e<+B!$m+h;$H;eG{VkuCuMCAo-#Dd({n^e^zpgBV_K$OM_$fOe_oqBiW&?n@Zlj z@z<H2p0?VW@%Quh2%eL)@Bf@@xhNno&~LC&r0kK9$bv}n>#OUU>WggO88H~WFRtz! z>MWC|YP!3nJUl#<E}8Rq)5xZU9KD*lbg`L=Y^1`|cZ$h1t!+Y2wmj*q+OM|lhBxQ_ z{rl_h-BWs^p{~Bkrb6-V><&)yNfq$nNole+98JNu%AdK!%Pg11KS95KXcZL~PkjBb z{d&hX%fFw&?`Gi9-?HItON%cCblq)ZGTpwoVfy^%GQWYf#tK4B87t?#dz!iK*N-n+ zR=X$sct}K9NO$e-?%OeNp_4PW#LwRD)qdWqN1N8JUCZ|V`K6;$EB0lXrm3D$z25mJ z1U{XYe{Lu`q8fMq)gGTUcoiFuSj2|EmYrXlAaO>yWX_*gaV4jwvf6i#KDg#TXi{EY z?$mkNIobGAhJvNE@9ejxdSEklW}J;2+wFKZ*njQr6~4E^e$I7paPZx`_mG_`JC&ZE z?ld*tH>f*J`?GplDG%M_CIWWM-{E`1|45?M#hMP5!z?w*&88kZ`M5W82UUgCb+_BQ zU+<~f&wq%W|K5jJH)+(nC+C#YTz`G~b0Cy#j?0P)2vPj{Np=T5tyMItt$WHmb7<O# zKQc<z3F|WTW2BU<X<MN+_x786iZ}J~JBg}enF?$-CLah7?><Z3pFE=VCzdMi%1{ku z1(BPLKJXp<@?NB?yIbSnbP#!zl?T4o7jM7We9>H{<)g)x+4GswcvABL>q_oM5wX|d zyBU@&Q5meUTSgXShDQDR`7OsX&t&Su&DXwf9tgG-Z@0NNlBPJjuenPUdHq`+wis0& zw0No<muH%G_sJ8!G&RS29gg9=1=srT<;ML?#g9z6m#X>T^yzew;XtF6YHCT+EAM8k zJTNEe)s5Z9<}Ke-)6;XizGJc4>>e4r$4N9x-_y4W8TB6I;of}A?!d&`WCJGC&89ed zqE+(^1upM?wXE~iD_3kodEm&Y{))J+_uo`MOerBD8Oxt7K5Owb*WvuT-M4O7ct~a? zTCw+(^iavlEN}H3`YN-V-Q9Z9($iy{>yGq&eyYFD#Dwc5en+A8rMV*#BDC3bRuGSO zxk}xe8{*(*=~m(w@$tsTahW$sAr~5Ht#{k98KyOMw}J$}e*M~c-AgneAYdGyG%z{* z?DCZ>O6`G4OeM@D&nZ1G4;i4JOX~<S+`oN)`HOOq1GM39Dh{{A^ry#}-Ij>sq?CDe zs9y7VecQGp-}ui0<e98Ej}&uAwcXyt`j9L~@<X@r$;tYc*S#$AOj0fFOK-W(=GxT8 z)6IgZb5h@hL<ku>IeS`Ub4szUU%x)GtW1M)dfRk**YiS=kZ&LDKYp7KSyLg&oSu=9 zh$K+g(jvHV<HpWg!FzAN%g_&(#vUK7u3ot|Q8M=OWp%ABCiv-vXPQ$om;3ZMjdqsp zdVPCkYisKjujUrR%vE7xGcy<M*lMud{ZG!V7@wG^HP`=j`1vIpd3AO5j$nS7RrNNG zVbOcKE1k!Bs&};)iLd%Sz4un|=e5k%!l(smWeWn{y!-KGgyiyj3G@8$6D9Qz+MOKP zqA6r3;D58>lygBr!LE)Hsbzb*Vv<(<;Go9u%Y^s#HyrD{9ahy^@%i1;`#4FLUGMWz zFMj<F`}wsYMo?ZoXuOx+t%j<>$H&LP$G1gO9$#ni@WGoamgYkbj@hXmy?Jx_?Vlf4 zxVgF2bahF-y6JQD*s)_A);4;Bdw867?OJ%{%9WE_hWFujRR)g8raelX>l+^vWANyd zib#^85a$_9k0`|J934{BQiAH!n<s9kr;Ei)>Ikh%l4hmKD=PY)>i=dWNN6#t&5bNS zRz?i;_irlRrZqG?yeRQ`(N^K%;bEl6>#Y@9UhNET@1oW+e1L+2LVk`FGO^{MhbI;c z47KDmcXZsxhL`!gUUlTo>XTJe`l(YF=FCtFrFXiyEtDGTu3DZby)y3EMRR#&W$vF} zKhz<sq?|roa3h2qg6ju=epntb+{#*8TPwDFkK}@oIffM@OeJ;|{v}yypKTo-9fyN1 zZak;%*UvD^&=X&BnnxyVQn0J5EBW~G>_4Aj_w8eMU0>Z%jj$g9R775$;>OHX#eId1 zY5pBdwih=_;YZ6VE1UW)R#oNu@#BZOme%9InW>2ZzYj09&LXOirW#PGH<dc{9T38L zS-hZgFtwLD@~>OBt|?uE`6!=MYry)!Efw4mH*VZ$e*U~6VczmN_oEISa^AV~q9%=n z;*-)n?0E1yYe+~)zC{jO&+V{dkJ8kXckq*K{XYKwxpH|`m1|eIpRuXwnx`2n`-X<{ zhp#ji`-@V%JUr@7HB{5N6{0uybl;5?UT``1^Nx5i4JLzqUcK0O8r9z3-dTNMd7{*c zBMZzkdjImd9eMh+xNhM4+=ptBz%FrpIM3t=eI0w9WRj}Xhosh!BEN3a%{}%T(zfhL zt)e)nt2-XA{`~gw#P@fZu06Mc)AfRvgnS+4q<sDT?_%v}l*+biHC+{Z%Yr6c%Byy& zsaG8Q^}|jj8Yj$Z8UDu6T^YE>%4&|cx3};2Pwr-aElbdUWLnUr!cx4)j^hlra_{u7 zUlYR@Gy5N@-KmcgQFd~3%N;(hbl876cRGHh(hXG3Is`M-+t(L)$)@yfZLMLQwe@0e zeMLGwdx@NehG5+h-ZhuD6bnd66>C2kU$ANxi?x;?6IHw0(<8lN|3}8^)l5$^R$jvA zYX147*lDvPtlV>JUc3<R>+8FG<Hp)en*`YSrHh7Jl{6D)Q9}a0z2>rWo$tpk9V+7D z#Rcsp_Q#a03wM=YiAz!;C@@zL2dA!L?>i@NZyp&LnbEF_6Mg0zI+;jrlqlQPLcbOI zT?mJHUC!DBP5Z<x&fKK(7c_hs>CixAJ{a_;B2rQW_kDQTFn*Etr{-AtS4iIZJ+lI* z<EZ}36!S=X@w0&S+x}p!+$9<s8e%&B3OF2G6iRm8&djh2eT6Z9evi;mewnpc3E#;f zi=mMb*AFkRci!}o@odXKKiqmvdOJb1#sR<;4L4sO<fblPy;}FNyQ-yV>r!4DBb`6L zJA1F6Ut~sx^aZoiQOY(Y>eHzjN?x@1q@;CjZVC?7A<^~q419LZj>l(qbzYV$v?(nq zd_~d)8%I<`)&8+;qhoU^3TL3}PPhzW@Qh0QCk0RHR^Z57KABbV+@h+zEjjDHXY6!$ zk5;v>%nJWKv#aM;MY<x;T*{g1kS|=W_u^~ye@`F6@2tiCQDQTV>K05+*q|+?4hcAR zv<EXP6a_RGpOE0}<P?#ctB{+QN24-Ug>hjUykFhm#go_7#|q=bEL^*YY}MMGPtMk2 zHw8pQqDioS{qQP|GIqBqU~*zGSl&MV>nMJifdGr9cc;LUCr^;N4NlD8u<6dhFaWrQ z??`Jgv9Yz+y*dOWC1)Q3$+|pXSV%WuD1ro1Q&Ul!;~cWPTXyW2|MtnbV^{}60hZ3r z&YF&nw7$}_$s9V50~@aGd@8-zu3X^s>C<HUdTU8GuqpDw;_ZF+q_6bO%w3c)@>Haf zb$3@QC>AhK!X+go@y$H0uC5!>*5G;koSmJ;mbh`KI^O1?#8!rA-m0#4#?k3(%+EKG zr2bmAJFchtzyLY*(a}Z)+`-dR8mCmYAGx~oDP2J!x2;2%n;?OdD}#^Hu;AkP+pa4u z^X`m5sk3^fXrYm+O<}XG6$ifJ8{$!;j3-A<&6$16k_x-TvD-iSus%_#si_wpofld- zp&L<n=9<N+yE0e;TAHf;>esi!QqG*=l09(zU2C4H_)Mclf(pyI<Hw^;o}9CxI|GM{ zOYX$O%TBdS+B!OY<9&6umz^Rnn9@^rxha<)KFk8lAZ=MN2TA%FgOj9_h?p1-o_Wi7 zfGnA*ppX!y8~buTndfY;P4%6UBMaY*2W}~}RzAC8Ki|E3_ekMJ{RXBG*^suD-eNp^ z_)<3@s1K*5iBefW&jj5_XnE%L6&ia>>?X%P`^w-6^Hqk$+pdpy?|<EQH=3I#Tx8Dd z$+?zpr+R<**~OOP?aL_xMCy2NEwfeeHtVa=DJEsCXvK_88|v$`%rjRF^;ECmlhn>a zI<lG2Al204y!^VvEbB{Kv_mErPJa24lAN41eB3x;77|^1fQ+pX_}&T`nO)7{R>Zk; zQY8-6amdD=m7110LP@mgu{*y_LZ7{P^G4{-DJ-7Jd<6xTjEs!(;Lr0Zr^b_uY_GU+ z;&Zj^#0$2^GWTLZ<x-66i=y#btA(1HbAWXikI!h81&(Z){P4==nPSS3lb&<cMg8Z~ z=}NCy3^!?Z@<?h6Aj2N1s#?+1)TC)A?qP{fLd9+<I)sm5?E5k4feg<3d-^kf)8of9 zN_fG>HeYM2xhJ)$_DeSAO}coMu>wLneZ_v&xhmVxRjcoOBI*6|+WKei7UFlKvygz4 zlJ6(UxIRdhEB7DdqMRm&T5QW)qIVq2UUIRXrcS4?KFPFLMa8Is57Bw!RhcXMe6#e3 zH*egq(jxPgdDmRbJXkn<C7(yhj~YcLDcDIfRS@c`+FxqbAq$A%gmOjB16A|zAt!SG zVT9<BOW9Wn+qez6qc-*H`&Zb-m!bd~kA!`Fv2#~THtXA`nMptzOQ$U&O{#g~8T82F zVwE3XKO8HuugdlxY;<|P76JI3;M6Z)8j|JK$fla$tR3#{bpr13sMJ(q$C3N>>zCiu zxHAhITR#GkgOjsXCVV;)DDgbdFKJ8z0(I^m#`k(7SjAR_YNH|@12{6vTqT7Zm;XQ^ zisO>b>vBJ4vc3fUh^jdl&{JyK+UB6CVE~yn;|KXyc^UuDe6USbR+a*mP!tjpLN$s< zqH;m+cSiMCqd0OWpP>NBTWgN;^#Q>Ip<_}I;zU`X+1S{Sm_x%fcVwSuE$MyTYk_Li zcfUj?yiB8-FKn1M`tmwAAYZGmyOngT1oc`m?U5gf`80Y0?Ss?WLc+obHK!drj^Md- z5d9>dEA3HHBBi$V8baH(Im?iPmp7V0uSMZG7X0~b*`qWQGk4m!{no8p%YFNp5+!w_ zHecK*r=ufIk)(@NEd22`Z?+jfgZij-&8vg&y65w|$lHDW{n^9E^UrR6w3yf4-ag=C zmpJddWejwYWggGw1BbKWH(1~QfLLoh7az}K#O;V8+BY_q1i+Prx+PH(uf&wfUW0@p zspHQQ85xP)q9w1~(u}1BVA=fT&wFuVNqeB}+Z*9B8v1+YmzJG~wSp371hCi|Cnu+p z=AAAsOroNqlOMb1p<`#DeX;U8fF$C-Xyr<&*9U*9AS=@-RF=bc?%Y8X#u;CurY46s z8x3*u^0FpN{fLi`pL71{!-uiVTkvx~2?z->nVXw;-3k__)ZH8BEZ%yBQD<#oWL-#2 zy?H2XG*lJ-dsW0}^UhtnVn}fAAL|)vD-d3TtZT7~#jxfG@4WFZ9ggxUD!jno_3g#m zTW<O|BK?Wj#xiI9_18RUy&y{Vr+#T<B&{-FIEri-%1)P1VoXdv2LXb91NUfD$m9?W zg*5&2X^(QH!XcAeJZ8@>Y@oS49p8<n1#e^u3;X5bc)zYLN8zT;JXPj3jNFxO{^Dq> z<#cr<{-Fxi<$XK(XmIHD9sSCEAL3BkBrFT8^1jJ4nTS&jojoc$j~j1oK+NSzF|B6m z6&Z?tkZata@o!Gy+E2F~J6r}EQwhe$Qc-T-a#K50HW!2{`H5bxsHjK}1ZwawGccy~ zruAeEDmEx6E#i_sD2-lL&4h!8o4XFDl>kqG5y!IPd37?g$+2Mb#d?y`jOFp8Z!uN- zN9$7pvIb0?7uYsN|CO-4pz_fE(auOt;rX%%NF;3|wDcyMmsu3#yDtzRMM7z4YqI6d zv7am)F~)HQ%~xdu%061=dfi@es(H?Z&g(&<h*{QIl#CMO?C!3(_B%r{yOmnV!Q|rV znsV~w;rScR<bT?NyrL*{PEzH4u!$?DD>ceCE|L7+SmWEpigYcnxdDMMW>%lcJ6^Ds zPzu*Po{`fS@ZqHhGaC;@G1jfiL0i^&b!U>&9#d3E?;Z1vw)^KTd^Z@cXmxF&1d}oQ zCf4L|)pdK5-tvT7&5x0$l4SJx`1$!s*e_bNNPvZ=Bb<<vlY@*Dsk8U3Z{S`O17R=> z&d#1R&QBRJa*-^jE}R1KqdKk|S9aKAEM8HQRees4wt-9P%2S)>r^VXnS#TmLAUnm0 zFWu3LHqAFMumSC5Ghk)IXPnH#M~|{2HW7?|Z=1w5e9S?!uAQIO*K}g(c=d;4OU<90 zTxd6J9N7EeO!c>qKjqZlO>lLHL~o=xL*^cSV46~6LXDO9?x58e1TU6LxqvT2f;1;w z&%nKeI@HAsFTWws8!oj`tm#YKx$s>;wGFpcA~aGWk}2B*+jB0N+wgFTMU||wcmA+D zAtE`##qm*hyA&uWi>KEzTK&Mo&{b$C9rLF326oo)8%RW4`kuMHt?V$PP~pA7VI1L_ z&Q3Am`DP2pZ>imx79!mrp7qbSa9S6=dbekUZ~45NE@LeI1NV4=Pw1MOnm-NJQTCtp z@8rAk#agXzqo(>BxPb@_%*{({K2J_gI(c}+{`@v}EEq|nm8sC~run+U&2RD?K!9#3 zv5WP&^J}iSxVZY#rE4y1pYQNIEOqDZKUBz&?P_|Uz;*7<=4Jsk$J>irD`I0~<5YZc zj9r9W<9mL_UEPzZ^y}x?9m1@oq@+a7{Q5b7EwI&hUm4Mwpr~NUZo!<Dkid&@^aG?~ zdRlexmo8mi-r3bv{w?xfK9Atu_@1hIhnuCjXV0D`1)*r`RW|(Eb$}73D^DdG+cS70 znA!Ob%W|ZzJg}_4zyFiog2jt@P)d(7=!SO={;DT?kSbWwUiUNetVT#4XQWHXD;r6G zX%gLZ`QN|2ejr_GINxQ-s}Akn-91r1!nra0phQ*I{=Ug~7X?3xJQ-i-9>;jFHP@U4 zjS=GgUc)g_0*?u8a?|Ja+R#wF+f)7AdLfe>zh{1`f#DeMJ1XOXM`~_a^lC#N!kmD4 zUte`}3tIMkf)=M2oOtB)VfQ(o<-4EQmiv~D3k4p1Yp1WYsIaDNpJMevht;cB_YM!o zq6}txb(Claf6BESl2`I!?53SG$+Nw>gZIbe@S$thG|%enWh-^4j*{i*yyDJ{gMPHA zXfe>NGir$Ys%hlpiGq;$4BL^fBGGuwWoKrfc;C-&H?)B$K|*z%%JDa!{I!dRmv`co z&mCt{YyA3IkfS32lEuWtvJ4Ng4ZXasjJKHtCQVKTH2t-l|9+y>q1(4tf%lEJ$XPoW z{BdI7K6$&`{P}Evv<>|F(-G)r(%#Q77$@}u<69nydtL0@`z;&{_~pBS(F?naCnhGu zmhRwTV!Bdw>A=5oy`Q+K(r>F9a?bc=E!R9Yr`jDDZp|y-`;IR(G<1CW``*x>A8x8I zUw-t`rAw`|EZFkbp@+VIewPzC(%xAasC@g!du8xkl5A{jN(Biq#;*#z;vLp^d{B-{ zP8OIPeIbl6%(p5K0x&%MrxpyY$QSZpRJ=;j)zT6<@O88nY*`CvJ58S+NkX$>N%ykW z;Ou4LNyXHiD9Y*OHIL4Uy$hsQhOn$yv4YpioMAOFI9j~DQ*5P1d!6!=%CH6t;D_`x zXR_Cym=C;WgxYU(hdpIJI+`bEb*{E8Pcv2kSYBV|AS@vfhaCges+_2!t?h(uOMWzH za_5{ipe8czY6Q*bhYx}R0u=f#+pSwuEBDdqjoY7yt>o9|H<q>FigXWNc?&S2?UD^Y znw29*j%Wvs%X)T{Jm!qBlEXL7{QCAf*;rxWLxujc9jOY`y@rPK+pc?>R3(rY_(PUy zkseyR2ZZ%m-9WB2M|dI=6Zz2Hf=fyQ-!P|Y2a>WbHd6Q*$Q*SXmeAk7zD<371(FBp z6$qSBxVfLw1|O8r6lG#+`sn&|yC(xcK6wM2oSZSoj@2Pjo6#9f{rKV1UbOXJoWPAU zu`$<Dl<p73DlS^Y2{2!e&qwc`B;61%zErzCN$HjldkxOpv90S;tV`^4Y?>bh7nPQl z284e9G`OoZm-7gp6c;rGa;T+hzt3oQm8k3eL~HX}BzQAx*U5U{;Z{W+@g*#vSdvZh zroi&3ANcBhM8B15@MC!JIo-hI<m3qKKtox1xi(0Jy0$i}VL<sYrN@j@CV9JFURwnI zwuJC?`Q9lH-Edmvkc$MQr4vD~4uE$`U%HboAn;LlmvOd`#f#sQIIn%9FRlim+XbV$ z3B8}DM=Oh}sw#f%`q)x0FE4IDMo?<ZiHV6+1Q2}Zix-1Gii=UN@usrYvhi`>C)9j; zs|yK0fJxaC4?(;-71>wK1rT^1u$AGd7(&G-Cf;j0z0~i^duwXb83lhx2SEpB!EgF< zABWf-Tg1i0oN6LyvyG6dT@0W+X-sUd?GlU_Z8s1W7AE`*VDZ$)s<Gy5BW}X?16Bsj zOzW`mNiKXMih@445l6AirGZOKLc*m!MzHJop=-zU56~7YSm27!Ca*=9NJ>c|XEV#o z%PTBcFaShXjzk_Ynz#MNp6+egZ@(f@5t@*s4G`x*5(#K4nJ6%Fm%&hhw0o-l>grDZ zWj;Oe=teqUzU)WUP;1{P+XVtHKh$)?h6c0<8lG(njh><-6V{?iY|EH%s@8zjy6pK} zNpn}TY=;h@XhZJ$69U4*hqhkb;f$Z_((Rx~+S|rbqJcbnCVc+<`O9TxwdYps*M5@3 zK{sd8**7W%ap5vBxn7+NMUK5!dr>5E<xQW=^qULlbf)B$)4LktM5cyvk_03qxRx$m ziYj!ZFKH;8@k@cRc5OBQYrf;1gOTq5czM9Et_=z*pMC3UZ|}vVG7zjoHQxyjpGKVK zZ@qfqh_bxW3Y4x37cLl^o2z~LK34i_9pIA$!jzEm4!446VPRp@IADLcWr6B-ZK}N+ z&*}D|7cSrTVO!+8Zjf6fLxAL+Epi?e2*^6HRfG=>|NZMIlJe4N!9l~e^|F3`e&gfg z23xkwJ*DQDhz91J?gBLApifq#k^4IOg4xv6v}{kQ&{>w%467U6+!aSJe!3W>5>Dh; z&841f!mh$72D>UXvzJ^#i@fu|^>5W67SZLPql&zKUF(dN7aR6J_re7_>MxcVzzz)9 zA^hp!kKv1!VYI?~fZ0~dIHMvs_04O3u#U)G6QFL64$QqUcYNQMBUQy!JGd2;>d?l% z?sh4zM~*}*V)=d~UkH?s#S38>nWR;Re)fKvc=Uo@f=P_YKr~sl>QP5{vi=%bi&Z9i zoNhQ@F71X`MunYEGa3lfCN6$@<?-ry5nngOdy4)WUC#^1)s-kZmGA8Wl_%R@Zs}5C zoQyg!Emv=f7xUFHM+AS|%laR<I{U3jY~w16oixzm1tzHX;HfA>fo69n(O%^@;xj!W z=Ol}dG{!W_Sai@rXQqS899{P~Jh+&K#B6{hL*z6CMaA>`UcJ;F*n6S5BmH!G!EN<y zf8#c)6P>TiIkgmH-?5&R8m6-JY%cnrTzDAWlAfL(MU|A4P}FHVHnb%SMI%Flj}`H= z7>u@4dFS<ZGb@Lfm#m99R;SER23T{8OIuWCA+AV01f*Fgf|Dnve^3wfdCugmzty^# z@OGcXv-g$lY<!tGELH(p=mmdryZtmTFRu>?R~rWdfpLgvOek6|J>bh4b--CGnx1mP zzW^11pkKA`V;85qe2(3_%2$<j*bB5W0;lH`Xzy|Mj1(zRE@$Dfpdrw|KuH+?4#038 z1i-H5EZST(2Of5IcESTS2>~*mjf;=32V+F=IaLErvJ62EzD4VGM>#(E@jc%E;t#kw zn3OM&y@%Dt3f()DfYpcCczZ`iE`59@$}Ji-pKe)=bgsGk<s9G)^(9O0B0SYy>N!3R znC8;6Z%}i;U#P6iw<(nd(8-E@B`jzmz8R}V3I&M6yXb41%l$xeGOW&fC{PzJUffyQ zyAdU21gIw8p;{O05RC%!e#lBG8VneYbKz9mZGixS1b@#QP?$geZhQNdZjwHDg;{7l zJw3Z#-B3BgFT;1*jE#cwv~x<{QrKcURTVINd~7d_81nMWfX_HILa~HX`?K8Tot?I! z6Ahx;{%<&_EO5rg8#YAaW%#~U)oivJ2<6$`RyLx9b=gS?qt~dZF(6V|Sy>s8w*YbC zkOlT;v!}4dNKm3G+pKy&>+0&}Zz+}l1Yq5~d9#Mk2JkIbAtAcRfYGnRe@juM7NATM z79BK&@d0!@?5GEC2eC~|Kqn+JC?^~?+sc4NJUl#_b|NYJHh*8-3tFB~A21N)Tjs?T z`XHmZ8$4;x1S|m{BgEB-1?J7Aj{0B&7@I7p__(;c=+b=Ow=Jr_e_uKAFCZpR0qL3^ zLPbSIR(@XSNG?xJ1;tCPm<zp;0CuVd^7p=d`|!hJ{9|qlKS))!QTJ$7K;4lqH3bVN zCNA#$ywDnYYIXX}Eu0@oJ`4DFI^bNhW(~re1w2kG>gc32_5k{!IwGH(PvA8|i=A2S z!w0y9Z6xw5VQ8gSg~oV1yU>SL@9lCn98p=4GoX>85o1CkK(t`VJ{{1r5Lfi1K%OiG zB?Xp<$;ZdXs?civa4?`^P*4z|NwxRB<p~W7(*U_bjyqk88ze#V`89rkDpU<*xZV#R z9y<tUFEML7X8?wdz=g%Omt}3H9$6GvD&l`q*h9ed!?Q(3>%ix?yhN&jSaBXPGBr|q zXZ&4e_>yVCR6hg>#H$=U7s56q^4;@%3+4@HG!3n+PX2lGQXGyX{h1iRu6_i!nC7lz zFyPsA0WHczK?vN(+~KaKCNt<nvnj>X7uFXRc6McH^D~;95Ht*lqr_H3P~>Pnt@-6k z31|Y*H%5)tuI)$QP46&dSS3DiTe2839N4LPNJU4$qf#&KOpm-T4BfhwE|ii_yNpg2 z=_Fml^+^A`x}V2SoFIZI<OuZ8vwf*$%7X{SU}>2l9jrwW1KxOXY>ic7Y$czQrKJ$z zNnwe|MtTv`_LU{O2XsTA2tI%yO^4f|u~?x2EVBDSb0^{)c)15;8X;->2L=d{iqh=_ zzyP4ERFP?>x(0j-W5GH&dwAf}X-;{c|Hgsw(qu9^3jw6Dqe-m3p<(d*W2h@_1)JG{ zL=F`f7n6Vk;aE8IiQ5i6Ga&+q7)$vE1l0eXQc|A_!Glt2pvIVQq?EWdeOt||=kwrv zP^wZ(xgi+VycTdvP)sZa0Hq(0hl87Y^C)XqmLYR_c{#x^^XJcZc@51nt{|&M>+-OH z-hr<?$QVm^KB=Gn{`vgHix*qB{o$eP>HqLhPX!<#wQQ%9C2mkc7DlN%dxa=B+Hn)1 zDgIAKH}fv&x*ax;aE=}-u(q`Kw3`@C+RxUPNZ(T5eeA-q27nMKtE(QoSrWt=vgqiY zIn)Z8WaB|?Eng+iwxaW&#@Q*P=TE%hl&pclA9G8=t-18wvfD&`VH6@l0%T=*U0sP% z$2&)W7zzp&t(xj(zP6`ZV)x5y7B8}#(O^{CMUKct{GxiOX4>H=O!Q=_@=DwN7QriF zfXU8S$6!?sq@W7a-`M@?GHgvao^yltpx|lj8?^=7v0BM}KwdEA!on!4xe>yY>aVfb zm((iSreD>ct?(|hw$Cy7PI+7rXvfplotb8Sj_JSQQPf<vY%PQoXugLL=Om9TSt5j@ z5|fb75XH((l2g&P>s;uovO@*22zsCv4J0F~DVLf3msKL9DW&TD84~c#)WLLRvEHI$ zekAo<pRM^t<5ra~0@eX}XIPemOzyHP_od;~PK{oDI{sp3X6?Ou^E-kOByiWvC1n;~ zi`4=w0VD0akUvzdC*Id!$*lzQcIMVSXyUjMB<@5xQ4#xZhk3cQ8a>IKLz&2JiW)t? z<}9n3LWa<{ItY&vqN;Wgz}M)NA$go7a2DD?EvzV}rxw?Ocp;nxGH^e{u;2_(V1?FO zOPB0WIm4>SqApGROhvx-=i9|^{^P{?ax!sy`Zo1x!PugD+Bk9_wyqZ7w|8jB_D#q2 z-LK}p4*kpvva9wGyJ-jrB&7>PLBtEU=*<L=J{{K|;zx4J`>ZKm4y)P!O06hd5!z4t z_Wn6HWaaXZFG^@Pt){qpAl$_*+x;>N79#gmELYG6G*)4Mnlkle?o5B;HruelkFXe} zj*b(0Hyz|#S1D$&2#JcaqxEz-s!yqFY95Y`X3fsgIhPq@oO<`}UG;#WO%2KOV6|(@ zw^%bQDOHfT?No?d_LZD^`x<n?d7groEc3X~vYAYW;7K}hVhMI4tA%pLwQsB^0p`f7 z%MZnv^e`<v@h~fA^Cwj%<mLy1pM>rJ9etXg_6Wt=(FvX_Dgp_WFhC~Vw;8$J<q}H; zEm-AKO)P+DjPoo>H-s&6o|304X76T2p*$mh;$bo@Q}3T&y*X}sD@fxAk9ZUU-LrfN zUFn#K2zBDbi3ybF)T!$T!Vn7!3sR_-EM4jcMQrH(`_+z)nSDtyp9oDq-nRxejMYWk zP_WYpqYjNqe$^^z6z1}<pUc1}XxYz2ambTkUdXY!U`7BA9sqY2^rR$zA|v+o_C}0! zl%jpGNI6-~I{xP2iCJWA6o5r1#3GbGN`3EJvuP+W&Ci}a^L1xfIsDP!O2<!t*0zGF z4i*%s8pQH}2RVXV>{Yp#uC%k9heyythn`}5+5OSnP&5ZbK&+DyOf`nohw?W*)FQ9p zRoh*)U#H-3%+N}RBeoU(dHvY%$9?bK9R+gnxU581dWu#dCp)|M?b{=exdfkP>Z@4` zqVqr<VO#ZUax+qN>icaBhKLc+&4mqr5pBp7<c*@kzGE>%VT#hs<>9{%lF|g;$I!rl zI(zP13z{_T6)WoM>vNks?$_7%qQM8d&Hu5hf|(*>_l~A?i`L_i5aXMhH(@3E!52{x z=m5_HE}c<{i&5f<-PHKS7SVGAD4Ot{s42}YEw$`2p%Hj-4%{i~8X9;g+VSJZ3Htz$ zXLskIR7*<>b%kB?rpGfj;)M(dm@{}xX!AchTN7_vtnnbu8lI$ga4;G@H3FG=`weeG zb86a42wKbL@kQtmNpJV78+|Yb0IR(aO*8gCm$r8W;*`*WD5ATbT}Tj9%x6JYRqzam zyNp=83?Op%4Gb{v-Mbe)42DR;t^>6FP_<Bp*1EXRi2zR$I8r0n#?`2~N;W0&0M*%` z2X}Q>YQck$)p-@`a^*TibI>RE&`eD{UjEuZY0!n2p|GlD8>Os-@{^+EzGDYnNr?-- z4C=~(^+XMtH}5ZcwZr^0xuLBB*(Y+Gcet>ju^BQBw6fI@v}Vb^4)xk%0_WLzsQEm} z%jmJ4JUzK$8J2IxiKp{_*3&|zzhacAuPOq99yR}QkYkdPY>JL-vLb%s@z0yM!YP5& zIjmsPU?+{)sw!x$P8=|vCn1gq%CB|8*BNizxE9?LHkda6|Mx)$oWrmpM@4kW2l*#% z;62NA+HU@~ZiZDEv3V9e1Rizr@ks(NzeXw51z`iU7w<CdXhFQ6&p&-rr7_Wu(*-6M z=bsc2wlHeYAm4Wd)+Ib%ZDS)Bk&929*c@9TJS)jCa>EFJ(dGu2mB0BSA0aNGGn|^z zMHwPB@#RIkeKeGPR}>M%4BjgY8s+S};JTyyM-vh@l<31hWx2P3k(+=_z^|sJ)-Zqr z<czYJhsW=oHGpy(Cm$#aqyDjgroCUqu<}cNH1|Iph(Nm&u&CaJ5eogN$vgNHxS+Gg z-G{7S4-}-~_CO#o&5R+DRiO3Ba`R@g`-IP=B8UJDk4?6{<GMm-9~&RovkjPHTiue+ zZYsYU$lAfIkR1x5at};1f$!T45n=U37dOG5NKPK{Cjyw}K(P&)8ka-Ad0Lse{d4;b zv-w1LNhC@L8UoMa(=l!4o4ww2L%2i(MdVZp2}r)<+k6euL53%XXE+W()v;qwj^VQE zr=a<+*4Ea3GmpyD4^M1NRR)(Np{S~=h7|Iku`#N;y1GC8&>?1=VD<{TjIkT*X*8O# znOQyZ3#0gGOk>7btxr9U^A&d{=>0aF$!1r8Z5CV<G2WrD=WoBMdc)^+BEhgBnfohd zo7jXEkpdQF1x4JQ4GpZI|Kc`0Za~#K>iN9T5TdJJ5t=X%Lidq-KX*CW3b4Spz$w2r z9u}>X{4W9)FDyGHf`yFLfxEiAyBNGCM2&!_hN6%GH3)x(y^Y=XYZ`~AxuNn{<JcO8 z$T5=zk@+rv^&w;Ajk6m}XoIJD+F^G)RGc`u0QiT<Cud{V;6CGFW@avOsFnbMbaNZ5 zG#2YqtJcdRs-K8V`b&?DjJ*4P249*Dr93Yqcpbw%v$FkxDSk3CMQ?8}MGd{Ux*$U@ zI0|?>?R_PK@qW|CQ;|?R`CtoxH9SvyE~+%REzsj{zn{s<k_G13V6P~Y!dBp#a0}W_ z4!owmetw1^(fYMXSr8pmrCz>#x!T4?borjHO_JPvd`Hfmv(FGVK&66gcz#OdT#V8( z>ynhTr5V6e?Sy><j5v(8FZG5Soqk<;Kdrf@hJs{CjDmh6?J8%r{g@!G+=Hi;a=XcP zJM8BczZo1Eiwb{tTU*;*g>H1q9PBUkhAT2}tGt?=91RS^U2J_afC=`LB=(`MimTeu z!h!`BFj!LRUhRtseV7?Oql17w3ZgCtzks5^l}fG(we|Z5WH+0(6vo;qdSQ@%`-4k6 zx0w%@M;92yo{G@f|51`O<>BGssOK&aK8Q~TCYOfAf5jN@ZtPYNkgXE&eRy1vNCzeF zKEA$FI$2JfKF=;DRhr0WFInBB^$C_A;!@Jw(<N55??V)XRW2paA(@t}HSGM-3o9K= zKivZUPD(QOFp;G<+IX}-5N0uK*?SqMf%xvBE2%GAHn(gwo&GqPL(#6N6m4(boeD;F zr)oiw39QdJ5-^Z{PslVjC9aGW`$lVkOL*<O{a;w1+0GIQ-#Jg)H;d@v9(Je{P?--S z9WqlSJW*^s%a!Q#37mJ;ZP$#6Uq|yj4j=5-3|m_<kjZdWAA;860jxkzb$ZPv=%EGH z2~3s?SSNBYQ&U9<>x5+`JVGuRhjC9fQx$gqswA=p{q@v9l0HR|?E)cMv0FjVI!KW( z0`NdIk1EMA%3Un0Kw%gp#w^ZC@~Zmvh?|%8mvxlp+(lo^Nfe;til*xRZ;VUCO&HtG z*I$j;y?eK5_}*9+lt|?-fmX3sBi5fIW`VwUG+Lv~qq9<rO|HafEFvs5v1Y;Tkmrha zL82%QBRnRzgkxkmvZv*;z5m<XfZT~kZT8z@(Nkdxg6;OrFu%7CPcO0f20D;v?2}`! zWdP_mNrEb(@7~<6+KZ|lyOlkqojYW1Zrg{StNy7Z^~dI2GTl0IZ!QZB&da+H1yW!` z2eU$7x^(jg>_e&u765M-xynZ`7M(3DXO25A-)Chx2hCzzPr@&V!h~pwTX`3TPgn1r zTjF3yEVuK5!PyX76%lJ}H(PHma=I7K``KCMy6eRiMdGEz&bflyn*v`iJ_A1!jAp5V z=$J37U*BXl6_m*$WP4`YUGzeaQ$_6kD$AbB>FG&=oq|6{hcsYjXSGwVhQ`KhilfRQ z1wekxsb4M)oCR#LrN2YiT2t3j3gaEj1|<UNpM#=D;+9lPJ8d1hck1EHoj^al-uBMH zmE@Bkq7;l~K(W{Wn@Lg<KkPim$3CO})WKA2->ap~oo;-(s(8gT`}dp-kCl0ZcOG=% z6x*=Rl{;#7j0M^X(mP9Xbz2*r5Y3Y8k6++Cm!^JT5rSnC>sk2z6GP+_J_O0Pad4xo zPIo6dzTiaWlLk!SW?QPtgoT9E(Tz_1{K-tsObzfiL8opz5EET>OTc1lO>u8bqpVcH z#?y}P71i)&*hG>Ivga^aKTnXFGv_e4QMkvLDl03e2GbnX!GW}ts9m^nLyr%R{*6p> zbc=#Z^H#u&zV~gDA$Up{Pa;vX`f&Uju<3HCr>cE+>pU8`rN4gu&fv4~9q?MOWxOQL zTm{|00N3A>&&-K_MLE5BkOD}pXl7={sJflbwOxAMzl<mIst&~(s{nHtD_V6Y{DL&@ z3L7QSxQI3R%Fmed^bem_Gep+B7+D}kZ(QT=zXTjdsqISQYC?Ch?=*k7!3$gIqcKDF z7~aWVGKLT#z*$=VAP=|`9+_2=6k4ZMm8QyfRn>r-F2lCixEMzqa5)F!9W*cv(0>r7 zm^Y+Zdy6=yiP8&g@r&}!;^N{~L-HV**3I4B880EN1LQbO`)y^`hwU9;JxJkD>`Kf* z(#jq!5GMkHSH9&xAo_3fYOV+|d!e<I`_mQKUiTEiXvS7daXj{tGQb=RI^o(tEJCq~ z7Te`(+bdGP`MhQ1N~9`TuLj2pG5j)SN}%g8o+-v=>y42WF-7+R&Xl-%u63cdfKgzf zW?h)@*h4Ia<HEcyHTKf2kAGfW6>*&vC(FuIfQ1kY+;bNbwAFmsc@21HhE<=R5Qm^g z6CC8@6Zd869zEiQh|^i_mjil^-pWN#jAlQ^7^cvH3C^2$jGRPBqUqXx*%uy>S0F9s zZXrGg7$eB60%bt@r;!nEB3j!ZajxuSHDh>Q-|<+&XzH)O36UfE;3HjOQ!RK7CCzuV zQzKqRUwY4;J!e#p#uzITi>Z|oiXn+W5OEY9iXW{EPdMD>^>YpAmZwqCd8BlS4Vj97 zGVfJRLfWd6DIML;NGA$`AVit_FBn!SWDAkfhg$Qv@tWp*3;D^B4)bU3f`To-;S<_y zX-SR%x|Tt%NTT&Yo78{>1UzkQm=|4%`EFfZOTp#^qy<H%LoDf}=>lm0nTk&TA!EVp zgpJo*xzcZ_Sq@&EEiE5TteJnpP&IAfL5loT|1q`X)2A1~XkuJP!U3iQ0cfO$K}~_3 zEkkdLdN})0A>cO0F?3Q-0K%PvVPJX*y+8aNSi>1K;#BV;ej?s@zrJnX^h!A8h!z~& zppeinFz@NP6$^I6s}jcE#zXiVm><a?2D&)(37PM7h+hJGCzopCH{NSVNLC35m^o8b zFvbu+2Pn_Gt&Kwe9++cWQek$;T1c>^3c3oGnONYVM{w(vBA_uGwfb-MQS11lLE^~8 z0F>aIIl0;&(bM0Nnc?;F@e24d94ALx)BOCRa#3}$pny<TZCq^3LOkXs>}QnhobPtA z3_4kv4cXb*%{lA%L%((JC%_afw8&Fd%-&&mE@ge=(wUs&*(ijy0+9BI#86yRM4r&@ zgno}Gf)YBu$V*FOhWpb<W_BX~6MF$@2n<FoJ^1_Aek#ID^^i@LD9f7}BlX$A%uzlF z-|FxMUAm+U3)1cHZ|6Z9co)18T`2iKAfnO3t|80J1*OXQ?qN>i${<q#Kx6|zon&L@ z6n_UF>3@B?+B6~}CYOT+&DS?c4F9aO*v~XEIe9oUQ)bWW+X)zv%Rcb!BT@N@0|wqJ zy`XUlQ#-5ib%0(PzyV}Sl&*m(Ao@E+LUQ`kBQ=m7n{^lH>QowcD7_Dc8(t!t%>bG~ z(e|5sz{U*-6~EpZnxwX`!LX#E05OzK;~d+~i_e!7*9%gQii#rBGw`G=eYzLCR$M?a zY3CS)a)NQXUO8ff*_od5I}2J5abJgq00oJHd4PCLfsh|R_usT7FE_WQww6{~TMNwA z9-zbU{ER|QW<8-ztw9Bko0NcTh#iM)IH<$7iHQ>q9D$D_7%ffgRD|p$82gebolGAB zMDhds<-|P_PKg6m&x5YR0P=O~vuDR{?W+2t8KP&7U=hj_SZMfFyB6x6*bLB{*<^J@ zZ2q(fq63U|%Ox8%%(7;qT}#*5yQ8cGgSvcZkMjRUx~!<bWLC53I1CjmCS9zfV`KcX z2j2Q@?AZ0BtRE`%!iOK8AF;2=RRO@|N6sYN2t}r-7H_}7106}RGULDV^WNX(=}PjT zgD`UdRv-$Cp95mT<bIqy1`UWml+B=(ZNbK~5l~dongU+(5{!eXybh>9j9fa%pj~_R zWQ5V|yY247a!_{j<~poj^X;$$nB_>wbcf`#QSoYovd|66(kKvz9WDi>Z67lAf|-<+ zm5uWxCCnIBEgqvQ!Vo#=4=xtG686ApSUioQOQu{vfz7!OkpM6$=Gx)>*B<~YHQ;Zi zpIZT610=33h-nMCzmpo_Ibk+~$tIKJdpa-l7_O*@?8$`}FI)h%BMJky(C4oVD++UF zp;LWF%Y!E9AVrbik6}!Z5N-6<2qAN(;K?CD%vPLG9`6RrZ(kYk@LM3{MvN?>G+^jR zb@)SUehiUZprRS=_ko+>^4@nF50YOA!>$vW;^xXgFXUK#llu^hkefCSjpSJ_x|by5 z*ot2)Xpj9NV}4djv%W7hq81)sSHbw0Y5ABb5zZe}0h-&*(Z>i&kUmrIFO`0}bDqJ3 zE|g5BG&UCI-rO?p3DbqRULZ9ikf(j4qea7(ATN0Y0>#sjDKT(G12HmZ<haCr%6M=C zrn!hC-!c4`2r-2t@R5CDU-IK@=(LsCZ*MXcve3ZMqey#<t}7XQ4lIGQNm%I+;~eW~ ze}5Dxoj%~a2Te_LKDT1F1MM-;s32()Nuvf52kCvb3sK4;zPpERM7SgL8hXnt_&t30 z?>{=L`p9MPClU%M{4I-%W<Y!Nra2xogl8C&5M;PLJCPQT$sO1q%aBGOKPvoW!=xi( z6>M}U(gO6@GqyK3b0^YruZ#lCMISrHje}=_5kN9Y1nYep2~(?k1>S1CfEIK(?NN1s zRu7|>D=AThx0!ra!27lWJMR_nGrMnv?4rAGGzP<V#PuV22xcCm>?>3{mYo?1?k*BC zVf5)MS4u%Z((-JBorhW7x7Q$ayq3Li@p&sTs-UO>zIZaZ3t5pyq1&FNuN8y{d|(Vl zH!}BDfiEA&wmIWzF(SBG(g$Q9vAhI~_1N3z%@JpoO%(wfG6jfU7W&;Ec$gbzy0(Dz zc$|#|?kggo>`CedaDZc5V{9Db^0OTK-H(YNOqbrn2tn_gH!RRlVLvL&S)A=ZfLcl> zb@3O<&1Y;r2h(<7^nCbAILK7$!Gj0IbORO=^EloF8y7zuHe$=X(_OASz%Wx8M$!ps z3Q3d*x0XZKL0xOW52mfFdl$1BSlTDwy1+ryL26UkeuDvKl^Do0?wBbpfKU60o(}Wt zSGTo4zu|H7KYhVe3A40h=n>8-QhCS(CF#G>NN5o2Wo+zio+f-PDFno*iFJU$4>cSp z=subJMxI!M3^ySmB_&1NiRc=qP+R=^>tk21>4oqBTddU7)Ko8y5HZ_1@7cqLaf!Uo z<h&wlKooSE=x@Mu07bfN!poo^a5*?^b%fMt<!0`b^(k?#|9050DJ$Dz5D_#qyS~0Y z^<u=EkZx$;2!YSA>VedM?(Es|A78wTL`yM`ipSv~iV1qsnhtng1Sn8A*1f_1H*W6Z ztDF3I5Mn!nv5JfkoLzZfF34m<$RVZ^sg}V292mpr=h!6uUg8h^3L#SMs@yoXeG1Ao zYX31<8dz~;)kK%RL!lUj<qOkB29Az0WVoKJ9n81+4@x(n>cFB3%fd0Hrx<830w{&P zA#$HU^gZ3WfD3st$u=c+hL};!0vn=z11>R_P$R31hqGelAV^SI15`CLP`S(JEeCP- zsXXdO^@@^GBnS*}MS^aCXpqy_h&ZDQUe|Gki$*(qM(>oW{k9?qpS%*9HPBu);A&xd zidkn;cTZNPz(p1dyX>ulzqSo`fswH)b&x`DEl&<5YLlF{cDZe!#dnd(#l{7%zJ)RU zef31L#HGg9#3lh`i*?)LH;P#>q7%_uusQlaN0+9mr5^t;lgj2G9x!#W2?iGLu5v-> zskP_}1Tl)*DpBAavr+ZsPrJ6yRvzFl$gJ&?O#LJb>#kN!o6U!XH7LTN6a7XOTrf?9 zl|3o_&k5#o_&~i;sIZHa0hnapzPqd>vBLJQZ1mhp8a}4jkPIcrrmmmfJj8O*l79sg z!^Hg`n4K~K%X1L4LY~8s)%28w%Py-GA|J`3e*ijee*Rch|DVBTYu%e$inql*RMY`p zeZiEk_-6tjGCwz8?>@~$HWJr<;9qHNk{U;XbjIvf5Got!qJvX??9-rp;(m8vlA+MD za0i>AfXSDO3f*@9bLKhi((^ZU{QC7M{Hcl2(b0b5yehcWBJ$>?<q@Nozm?3|p?0}3 zJ-`WVIlqRTSh`Q0Iz<vcD82jR0BD{T{h7-^5xcZoxB-n2@JU83qq#*%Sth`iOr6PA zX*Ji^vtVdj3}9~`COME42npVKCmbyn9ir8Rl$Yy&kqbEVALu^~q-@GdnmdKmiFvOk zl9@&U@eu@~X~~XKy5Rap*2Sk#q5khS%HK}>$ePG(QJBK*Kg7SP=G*IV$D*yNUEJj^ z5H4`f$>E_lyRd1spkzL%gp)C^mp6TqaNh^>1BV|#_(>~51O!uFdT3Jj>g(rJ8eF@e zL0Nhbqz!aPm^I8m(~vuktOCGT*upMJ{)D)}#Q-!+kN@R$Hq&hWnr+)8TwGkpa3c8D zb)!Ntjqzj#3;!bS;YN%eA2pjlV2gEVU1$-`rKqUrjK6U3^FRI4RkcF$jo$pid<b2{ zvjw!Bd+}lpjE|i+cF)7ycfoKgCj9E{IYIywV2?NiMS7h~*kNc4Fk=wM7h%FBrKCh& zT?w0PS@*Y1MoE}>Kp?$2BTVs_>+q8a4@~-9rKQCC7WjS+2GU?li$|SCn|L0LS-ySM zG8iWLC=zS8r$3q6Rqf-&D4ht9mwObB2EH|#G~O?+xD(gXeUkN0(>=MJFwk4Lr8o{B z#XR-<oHiQM3)&X}JJa>UR{`3KRLKaW?kDPrGcVjqhtQ7T;(#bn#*kd`5%QjtnW^a^ zj2E?*@voYqU|EQXi?fs*!K4*t<{~jb8iRq21hVQVJV#Nh7AB%5^5K89Ia!lYL1avc zdGp@AZ@+%|GL>A-d>`_8lfb-r%4qA_OrR_P|Kf5Dcu6G?+G$>yo@A(q_z}^F>xKQ8 z0~y9r@)9h5EQSWC%&ir$(v3>w_$w1G9CxfB$B^k-%$`ky{Uai9fCs)fT1bo9_r8r2 zC!8z^p?%1dH#0TbQ#}vfH7WuePag_eT)ScRr#Hx)Q{#2~&~@2~%?bB*00|68KSMRz z6Qj%Bi9!q>`z~Up1;#PrntuECEhJ1{;weGbR|`4n+t_PNdqg7l!qa3~fn<xqNDNvs zq2o04^2e@U$DFanR#{K(tk_si_#+6xYE@uq)FQPkd?wWD(iUF$QtnkB_y()B6Q;s7 zT<?+fO}%>UT0NLG4Oj=!hMa@I9@D6p`hj&Cc1B_&n9v4+G&3_36CcmO6##JWTBv?) zA9Sdh1STSr<KUEO6pD~>p3(2__5Yj1X|0N%cHU|(rOd@<@FDf=+4x19FF1kkz{tqL z7KJ3Rc<|iFEm>sg*93<N?pg7@87Nu>StA?Oh&w$$dkQd)V07>9N)dnyG>0l%F3BWI z&4AlI30DVtKXTU%O^)Vr$_(r3mc5aQ76G3-SV{t0h{TDq`N_-eq_IL5@8QNdn67qz zbPDE&gTyina-j?@f%b|Oq9eM0NNj=RpTBQYw11SSPAPn_=K@a(L;6Q?PXYRBHtIXu z9ZAqsjmM{^f{4tojUz_Y)YUO#gw}vGr(wleI}Izg&Gab#%QSTUSYX=m*&j)Va}Aq5 z07f0+xgiiMV5tlMfMe5kfdas3PD@Km+$RzPY03p#p*W+csH?LL9v@@}4Eqz5jq^;# z^>O&(f<5R2pB}=Q#mv(t*yupwfF2iKwrp8mWy$H-)A{efrNpj>(+Tu6XcQMr#*h(x z5^Inoa<toFD7J*Pgj_*{Vx;h(iq!)B^ahnaZ|hrS<hltLG-+qfobgw)Qu>Vig{0bo zIP0oDAd2L-&eHOD{x>+uh~ta$kFNQb^%m*<FuZfH=^%VC7n_=%rYN+*&Q9{jx3MH- z1bHBI>?ygi!P(PO=|}aSnj!PWEcF}54d@rYWy5Fh!jX`Fzq%Qf2|aYg)vHS}_o>YA zk`;|A(N2`nPlTQ=i}4&R*S#z5kBDb7_37Go8)Xp??I3O-k0gO=a=|?(FsRQbMFFKc z3(Uax+xH$lGA(*_iG|sz0e%HSLJ+ScEcRr!1xy2RlL)4)#i#t~zq4(Bmu8(3F%*zZ zAs%je`v>bN+U0(d!ExQfVW7M^m?xeMlEKON{6{sID!gF(Zm--fOT34eLSW}N&`=J8 zA5Tof<uKf?s1$k@%)Qu-W1)M@%y@7$7xBDPy;u~|Q1&8yz`B59bCFRMjL)^U${u62 zOD9hR*u%la6@|h1lZ!UD*)v%?5%>=)-~_lAqO_qRD4p&j(~B7eiqyUP_v>E2mO&?h zjLS6s+m(u~2k4E4tS1hRHUghRoE-L5`_4mAIird@I`W$=JSg0=v4nVpFvCLbY|+rE zhvEp;o{WE51s`Y?LJl8#en}MLIF8f{*I$TCUbqxzTPdq$vC_oggE60oJ2OAH5eQTk z$3fT}?!gJ&_x9~Q@#_i_=-h0#ZM%yBVT_mzs?8<IRzQX7sSM=4efu`?2%~2dIsX*% zc);hxEx;$Ox3K+#@gwj=Bps4UgK`NWgV;{kcqL}p&Fm$mRy15KBLaP}9xZjA?Nf6B zmZ71cELgQ?2WR(>@f|pDAn}`gi(Pey2&RJ!(NU6-Bv36dBV<Fp{<98t3;ifueV~f( zjX{1f@^VCUOn~+p0B~4~Hp58yI0pq<k4zyTYe}O>1R-5z=j5QZDbYUqssx8nq@<qa zi(P|nhTv5@JG*9#_TbUuw$4kr3mXDS$7l6|g(zT=NQ@sXEFSycT_B#Nr?ERl$4`A_ zGSASfMX*eemRD7EWBi$zlahKt0;reuIAhL8B&hebPoJvUOnv_E&XD{nZ)95xJ{+LQ zpjCwpfLyzQ$3TJs0!4$&y?ps{;!g#&NnQEIQ3CBB%(ncF51zHv5n3Zale2?FxEurv zN$BHy_tp+e&QfzDc719`A(N$VlkHMI+l|2`#zG^i3Gh7u^~eSEa!h>F3~h0WaaL(5 zKbiC%>EH+acBOKKs?eSje;025$igK76&d>IOe=o`s7&f!)YnhI?N(Nsa4&_$cgy$G zR0+Av1d$CItOof=WNA37p#cPq*#qa*ZdUZFfR#Bb1oIj2!qz-^z~LA=VU*%9aNw2b z8YV8spFU|nRfApzpSr=oV)&c4MQ6PN^1#rNoT8#pi%}@h6lDOtch&bd!XS2f$sHEx zpUb`37sY}FLUZg39-fR8B76z+G#Lm@arR;}obSRRfUu@VXkmLXB@cYj4;t&m@>FAz zt4W`W37o=PVFhKZn^yiuI>Wt1wl}=R=bLBP<$jUhdym4!ZkUhw{X~-74whTgb~S?g z82}C8ueA?+UHo@FSiNo?JGyZQ?D>U0=I(YNOJMAZMA@PvM`F0NjttPC5YGvo@csK5 zKDVRQ`v=MOW>EwN424_uFr}x#cI*NuNleC&LyO4x!FI}e0S+6k|N3*g1Tf1KWHf}B zZT86Eb-t{;0>v%mCB3>4*y^!nV`{Ov7-c<zJ0<R-Voad_bpaKVcUyWt6hFF>B(sy8 z7}f9vJvr$mH7dw$RFHzymkzM-qLV-mDQ2J){y*4>g&ix=q!L2`8hBu6W(pS<67n&o z&8*GcDSvu%!@)|NKiqjjJ2>-wCqN34A#rBmM9sN`h^H&D-Jp&~DSdqw_G5ApnbilW z3-!bmdAZzgfDPUZT)6|qH)3>;W2r%@UPsF#on>0So@JGXe*YLzyXN_vi&#=2s1UuL zO!LFwxMevx;xf##Gzzo&gPz|Hq){}%^@8Y4ta3*lx-G#Fn>^@A7?kRe0M*@$`(9ng zTT<TR(n1;qf0)rqb8H{@z<SgJ!rlzM|8z%!0KD3{227iN{0bOEhQUQMIO`66MQFB( zd`W(-g?L?N6U*Nf-01wzVcqRI??OHTjH1r4Y|PN(q)73E^QX}Zcamfu$8AZph2IZW zA~iCQ%KSdPSwA#y$hFDbb-ePuS4%q|R2@2QS2-T_-;`xc|Ce6lb3z|Ph#9m20|@7Q zt5Tt!#E@eN#}j;Cyk+w884*&MhnsX5h4+hlxy~nSuNAqGSt83pZnnDfd*-jOtd$Xa zHc4zse&#uK(0B2ZFl?T&mNr|me@n{yEI_5uObn6|FEc^er~l$nd+hjDcU=U<sgN$A zEwX3Xo{Fe*O8X=cM%mfUqUs{+fhvHT3xo7vhV|CHvFPb|n*5#X%+_TC9AsCdm}N!Y zL%t#EK0zOtp=p?KTkoIZ@cZM-$ihxP+#M1Vca)a=DHY?~n6=M>!3CER8KSa~+hl@n zD|oTTRsZ=gZ1LbQ=sKc;%{EbFl8vBttjNMA54Wxu|K;xLZ!8;`SCH5GGSX#Cdo`A# z0R%6uLao965lo9wgPf2r@5%YX-B;WYkn{GMUfJj5v48fdT9F0qHlEdvga?hJ72>AG zt(3`Q6%AWg2rS%siQLK)du)W7yLx*&4HrQqn_LZ-0e2n+hX5#KO6pv8@gtkFTEF~v z(3m%!;juh<of)@a<WGuG+!ig&PJq927u!Z)K!IGehkGpdoEF~hFhXrRv&|)Xgp0I~ zbP-o-F~f*20#=*`GR6UlgnIF)nlPZdzs({n#+ll42G`n9T%${G|KLIwgnHWm=>1$` zCgSewqtKr_QEf|zH68bxVQi9%B6EPquF<4U-@v6bbW4!r?MpI4KXH=37g)ui(-j1# z=1SP1)+K{WC=4q7Hy_|w#lZa}_y7}HGr^`^Dtmdw9iBvF-+KVo8l?BZaLO}9a=y-_ zK2q)dj~fU7tKYBUN!Z`lH{sAJCt9O<IydNP6%e2aGY~29%oM?lI4J5<_%x3)+z^NH zqB7hyMMx<)r`G{AOu(9qE6SGIP)!dW7~nRq(a}-DTk%>K=@9sVB7xiSqI!FsG%-qv zD`^mK*X(eu9^ZeZ_Q(V~q4xIH-MtHti8~AmJ}%Kx!kr^zq!Bf9;?tlhAp<Ha2USNf zw3iZx3;x9Zcc)$I6JGELFwXCIa+bJ?Fs9Rs0bb7%+iI-UAqY1vs0dWVD)}Ey4?ikS zWwo%A@guZ?ad^mQt*xZVYWOqkjPVx&XkbaWjEi81R)_V!kNW?7OQ~)#1Votr$UJ-Q zd`l-W-|{}^$(P;S7`fo!;A{?vMf_W_ovb=o3r^u3SBOs1moHyZ6V21rX;8Fm%zh9M z@v*0x8~h8gyIgeA3Yq9<1?r1j8xa~I8MJfY_@CQveKYMz{tj1(AcjgXzPOWM&V5{u zKrjThC2wz@rALYQ9i)14L*h11>!aW*8(!b}>x)z&|0O=2XxyYr(O#^W`QvcCaF2_e zilRj@(zUnGg+m1e;7GqMnNA-al}%L&*LfZGO$ru}ynw|REXC(qtt(I>S%O?3LHKS! z4jLtz>BtDp2{JhT&5Pw<<CT9d!*t&O58Z$8X*h9xxIK;VX=~%fXyDV-7YJ08m-hk? z6AlgY4sql@Ol@s3G~UnNq;2xVv^0arR{_J8L^DJ@u~3-d5gq<wv=S9NM(F~!i{^ys zbK*WC7iN;X48XOng~CdsP_VT_r_?Eu)5x6oWegw^afY<5Fcq+1kYU3qM;G|o(VSm~ zK8T0M*nDC{1Vy@Aidrgsm2Q7=wvV)EP!MD>Yz`1~-};~4lYzUAS#-iQVD*S<LZe7k z#p)<WZ~aH7U33qs^?wSrFFi3v_<u?}^RS-tc8`ChtYd5~NE1D@3y~3q5sgun&?Xg= zLAFtmq(vb{VQeuo5~U^~VaifcqGB>-Z_J`dq>u`&bY6GP^IX@t&YW{S=Q@APHFHh% z`+b+s=l<OH`+mRQckJnS*E7nJ!%?pi4j<kiXd_SOyo<SS+$hwu?RMzAeGn=9s1G4& zP@dvEO~4d@`&@om7&GPNk98jXt9Gn3(m6XMwWP-o)|_gHTUOXDd#zW52=R}C^5O11 zrJL%P0~)4O2YvT<-Bvw4rOd0p=W@A=hsTmHO253%a8HHqgktDZ-}ZF+hID7YYqge} zCa@4+<SNda;^N{&{U$yY$|1@p<nUXL)UohGAOPs?*3-zNH(m0g;@6jGf%FDl8XB_T z3UBzPARG5jtI?zqM^`8j1{JY)0W&@jxU*VT^vugx&+1HR+jd@WbliR5X}4i5i2egV z&23V+k>>Vz@yQ45!r!dx<>ikguyp1R>}Umly>Q_pveiojshj6lF1lIO{k5r~Q+{#! zxv6~#9dAo|Wtq<dp%hB*D8HAk$G3cvbE)+s4BPV&H{)%!BHa)EafP@y<KxwBmUe`_ zPCozm_x=MUv;QXnPJ(ROP-RKSZ+e0wPqgf%q;@#Fh1jhZ!pUzlYtE&_9<4dwt#6`K zW5YWAu8$?sx5i^vi|Y9Q?(hBgzT6}lJAZcYv9Sr?WI|q`H*+C0C5ZITi-vx1|5ieH z#7och;AeUZJ}?W+-c}B<n$}aRY%<gtK<<Ile&hPV+HVDZu&Rz*FtVeh2LHYnx#?!2 z)_?1~AsM_Zl~Eyw(_QC58ObB7ih=4M;)Fsz5KGUM!fRI_llVG~HyPSv1J|4=JOE8b z!p@WMXKpzS&vh{WplsTN*RBwMu)QU`RGAb6_#w&`N+pn|Foh5V-Em%Qsb_b77>8gC zpPUVWc_YwdM}8w4dxDxVXJM~Mn{*?rAj9{h(8tbutaX}EoQc_^$e6QX4D|zNn)vGN zTTS{+8z}~s99q(2V{E1$rWZB`<?QXuqS68;jt@O*G>N|@Ds_Oao(<!8W5V=<oj4SG z(h@R6f7AN6k23r?Y<fB#ckVwXxY-#3&lXys1K2#Chbm5QYjZM+Bz)VvMT-nv(^gIP zUdAtH15EiG*dHBj1}C8tCs2jy&Q5kwb%l9(li;7Fx3aFEynJa!5Ausask=@;y5Lv! z%P+t50MY+PGmTl}6FWN1jH+8fhMP_ALE}51h%mCWAz4em^nQ@e7k1=!9med<FS8;G zQUiobz2g#nvh%s9j{nBFY^0mx-y-m~VDz|hdN1Qc{@mv>A-wiMh6$}~@*%unwvPHG zE>QjHyJOfuLYEV3JC1l&RaJ34a&&a8Fxzi;q0S688AZGOn7yC_*nfQU=ed{@KKWhR zW%=mSOcIC#_WvVio{NxRi<0M>9MIO(YzH;xdX%M9Wn}nSG-dC)4$U|=vTCj%^+a*W zr9`1hGZCA*<4?kkCUNV1iS{Tmr8>a->yEuqSv-FtHhAoLQHx8vhCZz{mIY7#)uAWX zc!C9qOvPc8CQcOiNc!$^IWa`pI}As78(kG8Jde`TCXdwR6?WsI%Hb>Ek`IGY@g2wH zu_X7Ij!|`kfn)?JMQ0&uM?kW6RL5VeT9xv9MSP1>RGnEKNLqKkf(j?`!dPQx?+2ND z8?q;%)xD~%-2mH#O9^vIc27)lZ(Nt-UxiooHa<nPrBJD56?!$jT}}Wzn)IN?3Ojzg zYI|H(iq_Rf<<Y#d$uodwfy=H*@KXAU|26rT$(oT$j5eD{U6n&1f?}j6EpU3Lq$e{C z3j+3u*RnXo(GP(?d!!qSLEs<Om*{Jh5Q)!h*<7e)BIJCdKBW^E3i!B@E~S8TNQ1?D zLv&tv5~bdq<9<@@WYphF3XBh!MB1$MosDzg5U1LaiPL!|)W9)N<81OibQb&b={~Az zYB8i36~?VQrHV#8xmDt$Q})Kzyo_qBhEng^wj`fBm_bYukX$;xq}}0CXwY`3Fg@=& zPcdCYZ!`uoqTz=uNJQAzJ8UA03vebiD@vOW6noxEt@RRwTS%9}X_Tg>X6}lw`|#vG z7Cr@Wy$vo17LJn}j0&2Z(pN8+P5;b!&6<&>rlxmh7-uAmton;!^9pNX;aUn$gC!!~ zhk<mrmYhg?PW7<}75@L^#r$@KH61IAv;UXe>CoNq+V@>P8Pkhv%mdMTg+HK_;p{b` zem3_=arAyLS`qyXy07au?7m>1Y7h791`exUJes(-9ZYq8Nml4Te;15c`aSe=E|Xxr zf)j&1?MFCa+#*vOv3RXcALEN4gkOQIXVYddWEXTq$~I$6QQYL7Qfh_gfxudkn1#_! z?SRFXIuRr`RH8h=#680pr%*ebGs9RGxpmVo6HDH~QL^G2E$jxCC7df3$y*o?-VVNq zI*I(@(AGUY8hu3!rK0G2#K@@sh!Ku$HTDHT<xriv*-nnVnY#b4Be#4>nxZ#VT0(FR z)W4$@yInW9KITTzJKSHyGa=-8mtP7Gj7~V+C!&JA<@x8iQ@>XQa6kzWhKMx@gd6Aj zB`^r&*rRJKVcey%0i&Li*p&+ch#Jr7Sq6?0+vx?WS6V<?5i-&v?`avp(m51qkp-!z zP8r=xZGjw**haI`V*L2=;<+U~&Ngk^DGCkuFfy#C{ueP@hW`i#yB&YZlH1eAg{7wY zo_d*RjBODxcs4@lSER(64ZfBw1tK{l&$e;6O#IQ9v%Agxsw%Cyf!IBZiz&ybisEwi zn=YIZIV&xzCLyjyM4hNtWDZm#uq{X6M9`gxYKI*ozETFLh*h-MNrCsOMf;I|S~%FB z6SpFyE}{sAa&_!X6Lz{D5ZTTFvnv92Vgoh_vr8;qwn#uD$cV)T=VmBwx<`^m_#T6e zQ^C`WTiK;cmx!PsmLwdEj2pGEQo?=zx^iWrkdfH4{x1h5B_wQSof+!nQ<g&T#(u4p z&<i<+oH!3m&i#16(ul0AEUUJM5S}5Zwa%;~BzHrrPuLSndi8gW_Ctp#yYAhF-V1G8 zypktP$_p%vz{H!Ua>sf0B_Ed(9>w7^0b_%krEZzPcM+9@UU4oh%~<72@CFH=!fb=I za)~eAjt=%R64B)rN8yzoIEhp+(yfJ*dwqI(dU=sB^t)5nYM3}aZm^xsVX?<0_JIbc z0yGI7Rz)5{Ond>_!!x?M)U;5hmry)o+<5u=b<^TnIhWbL;lVAm+da^(;e@LuuJD*b zIKK|CYsecK68DAx*uG!Wr*GFkPI(VHJf+Mc(wal;`w`3g5XHp3yP4}K|Kn`(%*x&H z(F6mQb|goL1P$Iw%=uWOeQX5}wlv6wTWIF1#|b0^!cYd`P*t1}CeR1SG5gt--KnEG z-Y~_t3ZAxNU;EhtB+Y1MFV&RgBZTYU_$INL`3y-<D=IoE;zpu6{%YL?N@OIL;zwFx zoHXS=Zg5r;CP1q`wd}rmtC7sg{md3%zqM7+N^amNv2d6iHalaj1xWtbrsqG;bX&v^ z#NNf+-27zlyR|!y9_!_EF+u&c?E>Blg6?*d&a^=z+yyN_T(r5o+><71L~xLCOD|<| zrRAtmv1}@lsM3u8U`5C&APbYtJiFwLaE=8w1Bw^dU!m4<LDFwB|G~7Vfm;BpX{CLO zy4c@e(oFe9S~i;~<MhUkb$@0-%kq1yP(q#$_2)nRr}Y)bzTE9P5p4RysF1x@ug(=; zJXE-vZw^kPsnJ!rk-F4rILv_?wGxh`xyz$HpBA!v*DkWhcEZ?~=>%e(L56k?&wny0 z_=VSfGoq5K+2`Mn4H^Zp>LDa@=_Hz%9LsVcM#+=p4i3{iX}wO$S?+I7d2#H8aGnAu zI5G(PipirW?WLEblprPdkDhg_FdK9>`so`UmzuV=cFtfe|BjtHNh%BTQsx(+V2_<R z-)}Co4M3?_z%^+GaS7Ge3=RsV5f;bbSE=j{2?Ji}bFW;v^4PzCtp^ktLIx?#%bXL* zhCcdr=!7N=EJ~<ho={3OXFPF0dEBBMP|$S;DUjxxB#>qgu|k8%urn&^Bd(~Iwst8n zj1U)v1rEdMX~XZh8(gA+O$$XD*y1+~eG!75REIW6o>be8*RzgeFh~I01|Qudx{xf3 zWJ|pEg$73z%R9Sz^=g(J>Jm~bHv@aBa_T4w`KRfvr38o#0LBa)aD7%UOfc~c!$DIZ zcQVzFxi*?}C8R9QtX&rFFidy!3oC)IdlQz|L)#-A5LB(a;>oxuOK6d-qE>D$M@1H_ zB(8v-e=Y%{3r4K$(^SuvZe^|ro`8u!Fxz7r5!tvF%N^yk!GN&Mn@v7*`mxYz@{8h3 zU3hr|h4sRo&7#pr=#WV?N%digh5&&geiM(gHE*ByhBhDvidgBTWtQU18I(cS9%QT^ zo&%W)d(d#|2uB7h?tcs)Zoi-|re;f<9UbgeRX!=|5{R1}&}2v0e%I^zh#eq%zyH2` z@#&PYFp&0h$N)=9OE(a=9`T}tZ62LWFJ`nb?1<W>B$k)R-oPf;eNLLn3Ua8(t;U6j z(J5+*>~pvwk?mg4k}CyOlIk9uXCs{-E%|s5_z2EzF=P^L4%;vMbyZJI&7bN6y!RRU z7mI5S`(Mb{BZC@;i-ZIyh^nk3b{nBAGLkr&a|fSRR)Hb{@|3dTa~aj}ie{+hnoMfk zQewyvhT@1l0>NGK9^r8dFbxHRtt<rP_{OwfHgKM()VGv4x{yGIXJ(H6_+-k#uw7}K zj)EZ3<`zmYSny@>BO>vkY*J4&!P`U=zt}9eWY81)2~TI_s`75{8=mU+h2qa)VZ}t2 zpQasfDe>)8(XN1H$&M9BB&XNc4)`SkbF~lATX%1A0P6PsP3=86x259X15G~jYk40e z1UVfTw_bnFnRfOr_CD=Un8$1G1-Lf4=QJAVj9l%<$~Tg?@{%I&y{NdCq)l?Ksx+*g zo>@g?3D-=57dc^Z;7-UHlF(3mTXoy0H~q$%v$kgp^LMG+|3P4Eql{~5{`pps@y;(L z)lI>C<ny<g0q#=K@Y<BC@7p-t2oU32(M%{OsmLTl4u1;Y1HXj$UY(tGrM9hm@kc`W zk7nhCWVzu%%M}8tV`$N!BPOqdFl9frI>x#3i$1-38@+!|zb&WOwrRGZ5&Zt+uIGCY z5NGwRrFcXHtSD4whOhgqd*5K-o*CRlNKjDZZ7NM%X3u`e%D{)kJ0^cHLIE9Gl(Ka( zxs8k-O%;I->IMXJkHHUTxwg`Sq>zti_0w1BaR1>yGK+s(nYM)5e0NnP@`BiMmHZWM z!&#&`D@y&h{jh{dK_uZ(mG#gN6vg_b8J)1coLC~DLVrA_aHY?^;B*=xlqnA77R7Ft zPn@h?t(FL@L)aH^oQ;ErWZ3HOHp=Zx@EcLMR6XI1u|ad{vima=Bbwb{{HrKJ0P6~S zpe8l+`Hkq?@7!-3T)$Vv=v&gl(4t=}i-(K@#&leDOw0F0b~feXlTrG)i(=w%tE*}L zsO-9PaO3O5nM+r@)0=6dgja8?I)>|^N3V$mEy)*%te5ZKw=Dc-p6{xbCt?Pt7;f_F zRwf<MmfN)Yh4U_39;X@g%ELOCC&F4W^STjO&{YF@y7jz#+X&4;UXUwXOXHQt>Cp!< zqYa5|pB$xmh8AOB(V~E}`$wCa9>0<E<I=lF<Ld*+=XO>Ye#@_+XOfCne3K}?L~F>; z&~$g|m!5O<q)VsWwHF_2Z47<BV*wWS<On!NYnj+bCip7FT^jZE^<!OY|C(!a)IL$= z^C&`M+1aS@^XDxQQtp7c#b_rfJPT`~2yZxY#8A1P^c0$6e(i;*m7O~8mMRyr0sc-$ zI$TD6A;>b@!NkQWaJ<ih$~uEx-RY*j`ws9*xOy-EK1}ZVM2szoXlQGaRkVf`{BUT- zZ|n0c-4^;!_3E}%e_lb?G8`-Pe6<Y*4%Aq?9l|oK1d<v|%Z}WMbb;*A4L!K?;$2^$ zuogE)*Exy3c?nRCnDk)im9=(SKeO<ty7-e>^RYqVvjPpG?NzH*mui#V`us|SHhCZ2 z^X-*-(>-_6-ER4RwvU@lNl6JT^!JN(nBlUa^U_E^B>RW}yx%qYiv9=2ru5t8K@^T{ zI)3B8<|{jHubsQ6&~mX>yU$}=c78cP*f3I3<5>rRUMsOltG-2dOjwfNzS^nh4(Zy* zN7i1>0J{+L7sI0Cc-8^33Hw(@VnFJ5j!E7^jsapN#zvAdQ?1Bg0m#ss_&cEnq5`*q zwk)bjWc`dXkRe3fm*;vEWUl;b+>w>hPGwE8i>p>|)92A08)z}kGIQSd`}ZH_h*RvF zW?x;bV+M#_<vl(?KT~@vJHq++y^;%<%*7C|W%2++5{{+}nEF-mtSh^EHrH14F?f-S zr$^PAbt7I8$jE*+vg_3A)1u7c#el(aXI<H{fd=<_9l!8+Ys0l?oeFGe5=#~iembUT ze!gwNnt0;5=9(9MMZn*$0|FK4J-1QhP+|qxr7m$0pIgAiNksgw=zV#1@n4q}xcKpf z!D>lw>gucKTYK5beR=qmCd9=Vu0FlS&vee>v7Ie@>ec*lUU<rC_x8-)_nbAQF=(6G zuO6RTSU4Q7ZJ>w>EEIo#_C$?qa!rtB@hp|LZN-ZThp|J@XJK-hyiWDF>Yr><6aA;X zXz#0pzbPlZKmc42_2T-72-}#yw!@6KQyr|UdvHwQd1$6QQ#B?0hm83Ckqg%a7SBB+ zW>G@!<IP;O9VhjIB*6Rk&k1$&sA@zvbrUcE_c#$zFTVG*N%9Wh)V7L9k?UiK(;eBh zhROG@APUZAvnYqIUP|dlZ^7Zt?zw(z3bsBYao)Rj%l^~HS&!i_vzj#x%(hD+4Yr0x z!RF_kwzadb{*bZXX$LNP+dO*jZ+=deTG;X_AWQolpM5bTA>N!d@%#nDGYY+rT)TEn zu)x+9b5Y$1n1%Q*=~85F&deN#6y{Fae!GIf_6^$t2E0Ip^Ex`}epcqYx7s1Gg?DOe zYE%>{lVm;5G|A=YYo*l-R}cFfyNXdOgTFV>*EjY(OHtnSU~r>RtJgG9V$UN>@&R%o z!@UpDw|K_PPZhGpBV<OQ<Wy2su*p6`5@a*Z3nK0*#K~d<Nr@&HDT<n(){K3++0N7R z$gQ!7>-&kTkqAvRhS^5+nbO$<trMVsTSW#g0W6qAQwGa%tuZ<Z&~(<I`KO0l!{1-{ z;9};T8Qz3M8R~Nhz^={L$(8l*Re#zQtm&g~4X9?6lrIMJQ+_ayztOu=;uZ|_<l#F# z>6wLF7j~YL-c_Mgkw!kPeVO`Wv)h1}ETVbnEq#cvtv6USZ20hrEGAMujeIi=zqiS* zW|JW9hOVor^eJq}nWWQm+L6GAy*`hvlgSqxYPtL)X3HQ+8V$apnIzrXE>xzxy!Na_ z%K8f)_r#k^3c?xdH&JL2CTT-Ftf&a5z%7-ra%A=54^0FUh_n};sW@qG6VZaPu}yW` zB*KbyV`%*ea2av;5=sMRCn_X8D3r2q-Rc1W7X&_h`DvNT#Q2S=i#zrv$rh&!;RirL zWLn#2!Z^n7NrIIam5~2Q3LERIHkeo;BL$$U3Cm4Py;%&CNF|}WqF>oS(!1Al@^*Gk z+34GiLEs9~Obid|%Iuyy9iiJ`Gw&7pGzWXa#xxOU(Fo7^xR=mA>1o$S#k*2^5i-T~ zi5DrV4alzRV`BOXhXftcR_#we=4IudeM$Zx%zi1bY3+sJ%q+9SNQZ|lyl&TQZnuN% zb-oxQQ=Evzy6<Oe)sf>_EnKKC1~6zJc_tp99M&%#eHq9AXd((*j-qqF&F_pB=O*cg zO5rmSLa|DSpD%p)z4u}RU&Of~_kVEv^a5K#LqBu#)4w&X3Vw6fNv0&S2)wYo-neO# zFd?P0jBZV8B<rH(i|;1S)g?8^=5==0&``rvfKyl=w%kAeQzLyDOTT^p{{6)i7TtiV zpI=>%B6710GHFHbmNOaLpgS!C*o^`mQ!FF$_&mB)9~y;>a3Rph;sD^g!e*Jlth!Z< z0i@cn1Vg)-5#YzQJ%Uqo{>(0u6Xcn~sTBgt`}a*NWFi^lEg@&Ry15OdRT;RitI>7B zdU(oqJ-ZRPS`_hGgnQvWky1oeJuj#8;Oh2-7Up{Px)hfGiO~%@IXZQ~K{aTh5>>?8 zUx;x;;ls%F<Z#uW7WFOpkV>noS_!M147w+way@XLlnt=lvu@rzb+u)hEF*rnfdW>~ z(69siQ?d8OA6IHAWDA!VO?kDbk~sj}BFDkeGuLz#u>zG57+k4Yb&L1Wa|5j0o80Mv zhC$vir2-L~VC#zPxSD+23kXiFSkFhj4n6ZZvX!k%lh^#4KSxr`8s?6jnWK)uGv#h9 zr%ZWK@<C>LnSaal-Y+9C`O^gep6Kc6dGY5fiOsdu)#F)XG!Wuy-fc<@^k7Qs&b^au zY^<Ogww{iEoSmNHooGJ-MBorRR&-6F^YtgESI?TxU}R*nr*ONGSHYXPy~=XHX~D`s zC_c>piZS2XI?HaN%cg(+`~Np-`R_ZitwVnQHKzG`OPgg2wyE^aU1Y%&YGtzZw23FI H=B@t^v_=Bp diff --git a/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png index 07a5fa164d773ea83e6728d7f08ec853302cf5bf..8cf9c4d5558874b0604a75ec284a7d3e8c345bb0 100644 GIT binary patch literal 33991 zcmb5W2RN4h|2BRbB@&^~K*=mCNo6)DvPuKVOd&gaYZ(!_jmS(Q4SQ#XBrBs5Wrwmy zMn?bhs_*mrJ<tDn{>T4#KF9GnKA)rR`?{~|{eHb)uk&@D=j#eMqkd`=-F7+>iL^;s zNkNlDqVyq=D0(-n$DbT*`8tUI9C1+8b~tBq&B58|##NG<k%O(Jjf17R@h+#UH|))A ztPhAB5)l{LW#-^uYcC}#YV}_q5V5&oDk`Y=-5VF7wN=uwCy^M9h<_=b$)}o=NOvTZ z6^@;Ei5>su>~Vj1N9}a8@~vvlEG_?6C#x;fbUL4J%{`fRNik5ws^z)Ji^a$HTAz1V z1?W7<`^dWIS?QywV?jzhWSMe#_2#s9>gw|gt8T6}u1%{l&5pqe>GmO`lG}WZc5&mM zXkWeSG=Kk3P=z+p-``(}?+Nu81qB73u(Ol`_yD<1fMN?16H}c@IO!h#-bDN~NfCc9 zzKz%C?*)RGkK(UGRZsr^e*JBw7(FEw)qeUtc_;7{rYu@sA3K-DsofJ36EYr)V*mZ8 z|Cbm3zq&6wrEwDOU%_&(%uc6?I~Jc4&CZ&&Zx|atC;ePP@$W;J?9|s6a}ZcuT6KP@ zKpQ2sRYpcrG+pB4i3(yt_Pe+qOH~gIllR%D)lgenqRF2wA)v?SvwQdMAol&YI`b^r zGL23QHO1|?($$u#(UxbS^;|1MJx0>1<F&I5-O0VUh#<Gj*RQjt>}L~lcDgU<*-I-Y z(DvS;WjXA%ayULd-eYZPST#W|Zqp7)1@nd8N-DR73ALgd-?ekDZ4xb+qMR(5Whk)e zQNQ~PTfy^X?CZ9?;Ynp*in6jHr#kG~Ed%e>gF2H&8-IvU(9_ZN7xZ{t8GILd{?(-j zHhq_yWuD8~W-UnqR$4E$kIj#L*(U2I@l@sEwRaD8hl}Vv8mJ9PJw8Z0s%5%fj@4_0 z?3|pc%F3Io%M1LYp5(pXgei|W{@h|@WK>vGq!Uu~`t<>ePeS;7X3nx`>bG)l`No(- zwQB(p^7RizHCkMVdtA3g_<ENUZSUx4z5Jh*D*I+x8XrGDn##&b$?_24r>{AWdV71% ze6K(C>7~xA+Ss@_T3Xt^iEpn%JAeQFO_ulel3N}-5br*5MBnq*g_6?JbBB!)LCQ|| zw?}I&KT>Uxw-G;9M;>yL%=K8hf#nXy#SdA3&E#~xaU=VOr={>WULX8eC|SPZ_hAVM ziPq`F#9h~#<JBr}t&2Wj%Ha63{jo-Z++~%a2?OH7M)UJSnxdNz(&oy<i`QKHMdFh4 zQ2MjB`lCIK#d)&3rl#iZsSwUDgPnrAapf=BR|Cl-I-Jr{QYWRXZEQ5YzmM1^ZnELR z^Yca>nMQ?$g_+ksKHn0kg}Br7_oETgU0)M;N=`rN&Qjt1pCnE>4|Zo~=b?t^+jS<! z)&5K?zdjpaCp!5)Yta6%W-#n~bC-6*hFwBJ^fWZ~i)(9Zhoz**(#{h~RvTG`l<@0& zIPC50&NAWvE1mq_M={>XTd%&$*GTN@`z>-ygDl5SoDkG6IlPfgBw0C(PdP;TE>^ta zeZ;<j-rkDPJ*QsQVoB33zPgmWZWrmG#izl!A8nz*dWE((`!&49H{*B*RdsVdlDL_! z*JxlRF=iniJvBYOTS6jye!R<mZluK_dgYjuDk&~D)~u&Y_Q|P`x3#r?YB7@gohL54 z&5zMWxl;*WnbIqE=);vg7QQ)OF7t>^JD1u&I;!&8X<T!bmq<NQ%&e@}R+n?l+oI&X z<@ELSw+Niswe`vA?O{d=TH?}H8yOg`;m1~{Yq<s+qQm4oe}8zPB}&?R@#VXYtSi%t z&apjRT`@LgOTIa#&HX=rzJC|G2S1`-GP#wV-T(D#>F(}seDS~${b2lD^05$3+5UkL z`Jt~x_AhmE_}(jBaf=jTsR&OdG9b_F((+{akJhIfYlAtc3`ak``r%Y~NNO8@#8x6e zxU0mX4qC7xb?uaP3c9s!;|xA+`fQM~wQsK>J`g%+yF738dw#qg%S>VRC7T!LdW)bY zk0X`J`k#MODutBO*gO1f9hrAy7{A7~)!)Cii7!$8%c~^SD6yLenk<_hxo-E#Qu@4+ z0rPDO5dpaK>VDku%6ym2r&`C+Pv7yBIEYV;&kLUTm%jF5=e^fFzJC2W((;5xPEPLA zu=tIPo-py+7ep$jz`3d&%_wi$VN-I<&~O*7!E4i9lE!iI-nqXyKl)9)th=qf{gvtd znvSln*^gQVS&pL@Pe<&H=zRS2aKugG`RHxF7TS6I{*ije&u^f=|4n~Qp!tWyW7qv6 zuD4SWm1A4H5j*~l$bZ2*kC4;C<j)WR>Th!9=FOX*tS-y{9@%rf{aK}oXz86glhfbd zX(qT4rG@8&D{2CgP3p1R>#1pIs0?jvn(IZ&Uk*+h{H;AaRfkb6u=3bSKNf!Vso@0g z6elHf^506Nynx?c?D?~H3hQ~q^S4CJy{%W;Qq*#-x+L>XsDw$-5aq2kZ}f9U8=^<j z`s;?H2*Jf4sR;sS5|8dZ73q}rpN~}Drpd7zIJuL9Bl?Iv-@c14&&;sV{36PrQn94# zjEVKP;+lmXZ;gn(7c`|~9gfKo6_K32vbxZt8hg0qccDog+45WQg*pSuXa6p_4pAfP zE;(7cz=>>h{`vU;EqzzsysFa=|5jI}N6!DRpvklNto|*rb^j)cK>X^Xd~5@;X8$8< z_|%N?ltftJ(F*>pyZI*^hHXb~@c0^?$ud@q6w<jB6QhfLnfT9hy+oe9!oJ>fdA_Er zOSdlJ{(o*R{DoGA=1cAUBq_(A)p&M0XJ?J?&%{~(J@F&k$!=MF?^Q?Q_i<Y<Ya>>^ z#Q$4zNB8-$%Erd~ih-<5n>SY=DV&o|rdRtLjmeLurfkdBep_rmc)hwNkj>7{&S<zP z&f!NZZ{CUe5C679^KM3lXh!*(l(f?rDbeC{dPD4yFd5gG<Qpd2{u31!JeJ1`#yYZY z#~!io|M8<PNii^d<>}^sH}{-f+DWLu`ddHZB>8gDjWiDRlq609m?$&(F>(!!7xeIP zb5r{4JaQw`W!jk6JMHM-o8x)m@4sPfq5RL;axc$o4N;ZI1Devwsv7@Fy=A&Vxq60v zX-A>m9@2Pc-ay`GeU<Oe6#i|r(5F|rr|xe*Nc?SZa4>PpTlZZ=4Bc#J5N<p7H`qJA zqN*=04unuVyZCCm%0rQ-I7MkYBeegmxn;Ue&NY+9M-jVE_-1<l@#6J15Bhfq>gcO_ zs{>eMW*ZKW+SB!7fFy9r$i+96bN_9#@(qLvG6<2^@QC@GS~Cxw1c}5Fs?e2hwd3IR zc0+_dyKiOIm0C?{o&cJ^iG^$!!X<wVU_ip+Q_w!GbRtBsU#FVmWzR`V1o)Z6P`L~@ zajdMY6nZX8E=+u@YH49IH8p)7DXg-#vJ^I&PsDhG$mPPLSnrMk8!nRP?>VEN?P<8? zo`^kRi`nLsBLf3f#g3yLc@}Kg>cA|$G|i`Gt;w`jUHMOu)AH$$;RuqWA3kJTo*&<X z+|v**8`Bggo$91SKFU`m7L=@Sf-eFBad2^QRg^GqsAy^mCN_R>uo{_xmVqJI!#*tF z_pi}eq``AIzsbI#LI9VD*;>>9d^E-BFzz%>1ZYS@Zt0G5x2vD#)6-9#4wE-1pC);F zdM@<`iV}B-W0G&FgXp}2mE5}bd?G?KN|n!I`N4zD*#D--C;X=7=dXQE*Sq}MX;WBT zbxyNf{bvJjwU#7BtC=^Je=(zpll#KN_ZJyu1lHPv<B-M?+;KFCrJ~1c`TqVZZ>JWg zuK*dIdMKiYK)7@(-H3fXV!ijFftL~1;P8<n`;DtN$jHbrvuI|YA+m7v1s9jYS89W= zqT(Tq9`IPSeH*kbtkWo96OR<d%kyc>qNQ`pTpm*tyr;vJZtNOvP8ja8DZh%V#mcyf zf650Q^_Z?<BdR6fLMyN*P$Y*{*G(##?5p*WO10M-A1NX+4s3l$oZ`A54^L0m)nA{# zWL=>l>AiNk3Oq7c7sem4hecOuj{CvgjclC`-SID8?8~{<n4K<gukpVD>*Z3nh!FX; zDC3%Y(^%G%<`&9AJ0#2uWm9bQ5s*>B24%5ctG|&PjyA;}Sz243{L%8{RI<4qxr|qe zLN#4+$FH9s1Ai?6lzA<WAHpx6`1t%n+NHd`rp@tJvCwCkD?WTUgQz5q(YlSSs*wb& z6epXvrR;YYIYUDHRiOe2YNcP^dx|$da)Rzyf|TRE%uF#<!I!mPzkNI6HYY&Ttxo+y zTZ*tc1ytR0QmXg^hcvA679s5iM3~^Y1()g*$xk;spfajN3K`7~HC`@u2&kwyO2E^l zneVPYo<1Z{j!w3Ta(mwD>T2CXQ7#<gJWhKjCq5pYJFlH5Pwo}>?0d8B#!y4uDK5|I zwbf-o&EyTYX&6)N2Hq-r-A80@!x^B~EwFy-Iy*$#BB~#Iz_jVXA)D^@O{(OGMt9P} zWKVOTXxTe#!I2w-8vr_af&YM#ohG{*pPhX|+yeGoqy5+I+qd`ddi(scGcz+w9$*u> z+>5o9u<Uri<-HR6_Wq6ow!KHk&mp8JFPD48A>yK0g)ae+?j5z`_1>1C=1%&vG|aVr z({=?sGJ+}bgdfBCm)fC7mQ(0G)Z=C9o?m!z*n3R|N3XWNeqZHAaYPU+Z%dAhlY>K$ zg9qw9qO{5_1j)^=znazyVMP7#C6>ER=~IS*22yCD>ntCVBsDd4G7kAI%SWUpRBx>6 zrChU0JmZtO_B_ik<brOuK$M<YoUi?WFzXpPV-wxCh+K~H@gco=^TyUz%x`jXGUUmV zJw^7z!R?=#nnX_D(#f~{qFs>2f}eZGDeG3vpWyMPJ?-4Tlp873^66!CkeZ(lmhgU9 z7z=j$K6yn4XF}X-#U<OM?hVoii$}=+4u`ipIfWWD5m%e)tJ=dMjiv_&M{UXzq1XBv zkWCX+!(!%;$-B+-lRX)OqAW!2!|maWpj*3_r-zIwXiz4jm7%XTB^&$yfsW1Z_hG|G zL_!Ak&b;ycw1`1j1Ay{0nh<mX8jFE=H05FSIH?+Z(9X%}8fuYhoYcYhbAoj{v6QTP z&-?E<WL^DY{M&11grh;y1d%VEUwri*7_XAez@xIa^0qlZC<!g4$+u!hAJ=zFv(56R zIB6v_@Ap}L$-awdY2lN8NFZYx4eP~^k5AB~Xe0!OgfPC+El{s{#*@eEwy$tRHCp^G zqMb-r79p1%B&|BDr6)_O0K4mIxx6i3bn$PrTF1}L{o^CbqwAm5g(bit!HX}oulwyW z_eZeNAkc2F-^6~%dM68?YVS~EOlUkK9i1^wIsFdFAiAx4#SpMKAxSI5d98L|S=_(p zRB$rR2_o{&*(WETB7)v_<kDm!Q19HmYd1fpGwOBoF5Omcjd&R#M*H#3gKw&;t}wnc z>7OkBlYc=;IfVcN${I}x@)|_hLXY~lY`qT`{LR4^+L24&fK2zwyI?Q^U8Da($zSMD zf|Mt2_F)&NjLXsP64z)Tw>W8M_3=!eP1tB02oGI-byTu`^NFuT*e@C}QUA-iW=yE@ zr@uerd6oTRv+c>`-G=yOM)Y_}fNHM4K5A`~^C)!8p$;8Me&<|U<;Pfu4(#;T(?GuG zHZU@pA~89Pe%d50Eq!C~-OYjm=b#<?{blM>Uib)G_mnlFgLC;Fq2)3ZZTjq7Dgy~A zBN?seSZCh8Bi8#+n*|qt|86aDwRf8vAr96n;522AFC{cnA0D45nQfBB5!7b8rG1gw z;I-3zDjJ5~@$p7~raifP{D3eiU0q#U3TzB0DJkDYAK+KKw?%pU0S`y^(nVW*bPzTA ze1?8pv)ppfjqmSu5=1W-if!61u?s~OZP$7m8ynqjeLP5&8z8(*=`U6eX(ygtyL^Bs zZ5OAE98;8%IwUAofVB&qCpqJ!9Q%=u9;0;P%3Z%bLb(+emxdFFrpwnT)3)#UzpI@9 z%wY%QyauiUm6_;h*M5&?D8&hJ(;Br=+XC+3G|$xWCscP@qu)j~{R?$@I639L8kS~< z5q|98UyK1CA5HQr8@C<tK{<Z{plBDVg5{^V`aZl5-9&y#Db~Q3Y5Vr;YBK;THAr(V z^IuGH<kY{v<JpsG&S(fcjeFNG>0D1kGcY)4Qr`t+Zn6RGMD53q5r{~gkUXodi**Sd zL24{VwY0R-^-JG>uYY*O%6a1J5fS|o{}(S_2-}Dfox<Haccvy@PtMHLiw+DA*GBEX z0-#oxh!b75GE<Mpn!-vk;~%RSbo*kK9b&3{EUzx*Wq%(hQqU_R1!d^Vrl`l!RXo^z zB3E0Jd|z#t<TTzH0VHbMTS0Lt@46x?Sdr8Cc2Xd#Fwr=^)GY|cil0qU-B#>0e*N$L zCrhJcbf33Grzv~u_H81?AmDSYze(kt2%~Qpa;I2cUOwP5Wr$ABSeUfRQziE7%wx(P z??1A<Dv`P0UxnSBr({Lbn`>M{2NXkc>ATA|H8+<Gnh9vv#6ePnBrIvu-H@UdbJ%`} z9vvVyiMWMFl2-2l+GDLH(=HOQr%n*DlyBA5nrnWJxSO#t^3{hILzT$F6b%A@n<Qab zuazjG?i*H+a6%}ePSqyuILM+bBDosXl4Gh-wloyulE)$AA||YxpZjm6azTB3cy@M= z+{)Am;;KaDBez;gF}w!oMyeS2IFFS<dHfbxz=q?R!zyz5UE;CZE>pb}$UEQfvgs?~ z9I&W3P`a$GxOy%2`3cmD>J?X`x^Ee9^)<5n_45MuRta<xi@Q64D*90L>9<SxqbH*H z074KoR{=0s^N4s|19KdyxVZQRS|AS(kC(OBRIqv10D-29PXfRFyMjbBI23|kneJB{ z8y{a<nQcbqa4FW=Ah=D7Tt+1iMvPTd-_o-LYYxWDy#3kPpkbVkY}00Sw4}shDfteK zjWwWb3+fe11hR<^eKAc?8-Jic+L1w-4hV@R`jhoU(9st@iVEUBQ??yNEa`f;lN2AH z-IH0M5vlvF)cs+J>+B`2$)55AWM%~^K60mJF&!3RiRk_FOOkP5T3uXk25ywFqWe}H zU0z%m71FhGQNOUbI2$)MnuSJS?^2jVZ3?X+3QyYQqHJMNY%<wJU;jfI%i@xvoY(hz z;K9fhXk%tR>DshpT{(q>$}-Na9GY9rN85`WNkAYFj%VYSL0qs)m~AG&FN&Qh;3T-? z53B`XWdd|u4%2hcwKO_p|7BHQ^Bl(4{-?yTwm9G->bpf*SXtj!!t=KSNZNXkRg0*E z;KvGo6>)hj-V{8SLK>P8WfRpOM8<S+b=_~;wDWYhKxjL0(iLomo1V#fYlzcN{W@4+ z(aKZ#FvQ>Aurt@(14#RhudimV*;!U0?V~t#EL=HCbjq5Pi{Bp_0B0}@jVhs(7<x|N zx+tR{1Be`pr${xdyhR*KWRLv-6Op0@=|6|DL^6N**VM7@(yro|Q%G}5$OpDaM(ueD znwQXBfU3>Gg~!Imh&4fRN;&iR_@`mvx&#o3pi<E}?(hNKLqNcMo1Uge2QAESnO1|l z{{HXU?y=@sy-p>NB3KMAq^k7{JD)LZQr2RYrLhHtNh0`c^fS_$GtTG<88~J9>rDC) zLV0}NhUrv?k&o!B46A%M1%zBi(wDry{a66==5xl^{&BI>o0*vj<TUMWtnJ#Gtc=s3 zRF{w}9iUEH81M1}hQkd9&wTAJbqA|OO1$vm;@Nm-N5`kOeQ$^sCW|<V0db|Ws&UuX zmfL6_eV+~&oPJK@GBc?B^W%%!Lc4*HFFE{@LXY>6_u@z)W3jNYv1MOt-2S}?)k^zM zH;%Abv*`I}n+djjj7XaOLwywWk(DePw@}zO_hw%m8oq*741D3+Z{Ok&3;j4RVDd6J z<&_t(3}ONT{s2dc%U^*?DeTEw*hjN28T1lwXK^*lw^dY~c`8%tyO7);8<cywtm^KY zLbj-AYYR&Xh&>=cjzqWCfDVCzg5qp&PqAa5{cw|!c=R)1!rWmwuN8KAdHGk7%uJ!- zMm7nFnxgy~@yS4$i%UzPyeh13gE{KK{`tklsV!)1Rz4W8rk;+(TUJ)KxV(Hystokh z=vRM${8R1|!5q>X7#LE}<pz`zbu61W^nl4J$M4dOeS7_oQEu^QU4n@~z?!vnOVT=k zE03k`4`px*KBx!#ZMtQMe0mJsT~aI?JNr8z4Y6odiF?EE_W@b+ys0wklGM(wUO5=> z+JWazMn;As#T8U!fWb{5R@<Ln*jHn`Sy>R<i_TUFxvnGEoTyg)U|&)|GUAc&;Ot_C zBz1N5PxFr-Kep^Ci$iqUqgA@MMesP<c*_}XL=x513?&<zS?88w#{=GucKf+BhN=-1 zcL@Q*rhN62+e_>TnB2CM1t#TvswbI0W&gn$o*Vrf0?FZ&RCkKn4nEbW&E^s3!}!z? zC+mHUdO;n4-7${c6BS9letrMML~ubt!P=h9Ov)leCOX&d>8{2%n?<><x9&X?@98L& zlIpB`heXha4dVf>;pa&bR-NHswap8)#7lU1eGK(KWO=Q+Vckyx6__Fa<~}v(%(tqC zqCn86i&K3-OR|CmYWoS|lC0xWSd=Q7@9Ca!?T4ZNDZz(P#I}G%v?Egd*w`YG;`bX< zGcYK3@?PN4Q@TOPZs7fA^$@X%3IWW5cG59~Do1X}w>rSMz!zR;GC^D7vR}U@Rv1NW zo4l9Ilau$kWn^MopQ_}TwQxf)DY{d;i>&@so68v`p;d1N$Xu|uGp{<%l)ZP<B7O29 z2_5!#G@85cH4(jH+rf8CsEt38mE;%Dgf)K2F`b^9Qw`_}@K<spx4(H$tGa97h3D1b zg6H1F%Sy|-&$p*@k_DeCG4=utG@|HPIhBGrIT^%$3|)$ON9Jw>{QcRT2y?XJAdC_k z`=Kc8Fl&D&6CTr{AQVh3jzb2nSI0SxlKrslE}N2RU$T5%ZtHi|&$KLjwkwNvGP1Ho zZol@TMcEHdYoLbBAZR)ky%5WmE#bG$qRy3cbj4`RIsKgLdET>_*+3EWG-V91dIYFA zRXxtQK2mt6yw~MG83m=vBR=`eTeegqj@}ab05}FhQUe=G@C^IobKHJi2Uw5#d)Zqq zE>T)@tfHJtE;lKMGBg(2bvQMmna3$NgUA)XN{5m?fKGFZ@TG7x8v1n!@5)>CC|!QG z3lhz$y?qX+y|eQ$SolwL6B=|IHuQp_`3|_FS|_Hc!1Gu}rni;1qj)Q;sHAtP9aW@g z$RqYh0j4XoQRwDA6gyfFSOwyZc~`zLXnYAA(X0A?N>UFd<mi@`m%~I2<dE&I%c97u zDk=Tt2)x%;LOVlt913ROkUCT5u@ug5K|z7}J)zzNcr*~%>OjVPsZ*oOa0WO8_ca2v zpf4%<*Pg4!%S2O9tv5?gcy~^dheCp$N;hH$J=nJJ6^c)_II*F@lij5|fb&14>sf@k z2l^}R4G`HMbP|x=&fb0iJ+zQc_8rrBSH7hyEXsbk#^r32fX4I)U@?J!`0rUyo;>;0 zfjrB8@cNbn?={!t0o1vySUJx}pl`wsn5yF(XrqTh+=b+FmHI10zO*N8s_I3L4pQ&! zwa-FmIgm@y_vwUS4F*O=lmHf9)dERP{rYtk8YZDi4%5#g8K77EBE$(i#{qbYDvkhT zmvVUa3!Awvd?lcIw~iaBIZnDZXxjmB<}{34a@8i{(<p{S2MOIunNa)y43pCJif&xp zxpU`#@WlEGaE#~=6gx{^yZ!n}P;8-1R%oxGyXNgJ3-nL6{~z_h)s+IGrwVolGM&oz zcM7dEG5kK>YfJp7n|pY8c;1(kgYWKxFbYzq(6)~fL2h>|V{&j%0Z@>TX0N;r>O+^@ z5GVZ*R4(OEcLf@kK0G%T{Yk7rp{)^}_Io-c{NfzXWha872T%46q7v~%G~XfZw7a;o za(-ykqYc?6X~|CLUN}M~00`&PG;4l);MNFX6N`ih1Ne@7woK0@7Yo*B=IsufDSTE8 z*#A<#CGAn%wN8O`oG>AwCGQ58Jk+Kk{}gZ>+5K#phbsy~l^>Vavq24>o2tIAB8MrF zlKPQJu2%R^lq^rYCZV~#4@75=DsJ(5+ktChR-F=$B+TC+Ia@j|n=t(^Qe9pZN>8r! z*TQ3Y*FR1zETn?O0rPR;k7ytdnc#HBI~4%|w@H|tc{xs~Z9uDsoyMa6Hdx!Vk+h>B zB@#ItB9_{?twQU;nEB|QzewSsfaCA@$b%LaPe%&XmAWrjx&pY@9i8YY*T4#C=UYbq z>Of+|qg1a)O9r(#4t!nBi*bAwnZxhjZ&U-mq(MDIhhfrNakH~oulg-M4Z<T7hZxCg zTDyw_0x$9(gi14_-BOZb<K%2Yw`Ydz2%MP=#`)#WgZFm6(5lL}+RPc>x%-~U21zxN zw8Myx5j#*ArMLGVv#!e!WEtgGHiNxHH~aT?m20urXtLIUA>ko1c(ZJ6Xl;nA^}Ed@ zpEf;`T*t<?STLX9&l9hvMR7_&;X|qh7q;j&crYS&A?gv{eusKI!jPr2+!w5ox1Ql? z&?~-?d(>Fc#{3F>o$~+M8qCU-*%5mIq(OX};0ae_Y+CJqJE&?Ll2~;&`650wmFc?3 z9eo!qun$OGxz)KgfxJakzOXzo2ZT`cVLNX0Re40?2=Y19WoG8q;}ez5ljT=<Pgk_3 zO`rY*>aPJyt5ae~@!h~>_l#q_iFkB|`?PThS%oR!{Sa+p4YXYXVxR}uEhe^^pPwIe z&IvHugm4wC%eRpbJf?fUuZxqaY%A9^{*fgO+y*)z6}lV=7;XbNmh6dcE6$1g^`BmS zNDkO^i>=Osd{xxY2SnysAeh185`_8IzP_RO@{!*x^i{FcWy>PD7Buto5LO^mFOED3 zG5e565p&4qZFe{4sZ*zBk9<zAnJk;O;^#hVHn|hY=TV&W70&c^_RJ%r8>O9Z47}ZR zsldAN^<<ffnVEp=%;0VT0f8IKNs0r`J&XA)p#tQmG`8p-c1k(;(=c*wW@SC8`lNv` zEC4bB&#4XE+%w4vB2E4eUFNH;Ix=tSqh{lhgh(l2&J5UFZQ^!4?bMT#Lr1){(iM7M zm_<`{JPRcrEf#UPe%(5P-(ldAvy^4kn=#edoCLL-XC057-O1Sgk${a9zEpp9$aDH2 zhx?+ppK3VJtl)jCwDd^6dR#+YcUbu;p}PKX!Am4=GBrZf`}Yrilp5-zpx<mA9anAC zK_exXM6sG)E;5$04!p6~ZmvU-<bC+?QEhg@w*t<uqOMMG-oni#>Pn!9U7WZgub~3@ z9r!H+ucfP~NAHjbQMg~c5{Y@9OQ#K<W6IXPZ!laS)mT}Af}V;>jP%;ueJoh*QT5Xs z-<iuOj^1F<3p6pt-9W)qM{e&ZcEq=%f1(9eD$cXgeII#l$@$q*a01sVg|nec9r<S# zi}GztDe5s~*?1XOOIKy=)5~y?)c(H8`4U&xQWdt9l~tX|5v3kb_u9$+R_UVLl2_Wq z#UQKo0$l+?cFts@bg>I+tAC#!sPzC%Bw^J;Devw5{-J1daQT|%3_tng7YT~7t^!fQ z8?@t4Q{=;~AT%?Dnp}z-?%l1U=2hV<ihU23(FKN|B~V)X>2D+E?MFU5cE}qIQ@VDg zZFeuc1+5UOQ2}ZLSoqDp7VgIv_#jQ}k8i6JH`hiNvTxn{G<3laP$UN?CqaU`5-M{> z+3)ME{j8xTrfrJ5fd1~C9}txTn{Q5Vw8E6&C`5~;d?YW&PJeUmnFcD!1Y|t@P$X_} zG(c%42V7T{agC+x5jeHhF*44|l}PSQ%?4irGQuuHhs9}PFphE^`(h2-;cx+?u0lng zFW~mR!U`iS3Y1C9q}wRtzRdH`jTWTln>(~Y+YjDrZED(?pR?!l#}`qH3l9T?o!OW| zO(KNcp>8YDsSJ&GBb%Y8OU-Mt7$5x{;8Gm=?;}^i*pX0D36c&ehsMWaY33}H%bDS3 zoBFO~_C|^=p%S7g<K05`Sz%ERUR^FC!yRIarWGAc<$_L5>w#T9W~OdkH=ME2l9DPU zv|o*H?ruB+!&Qr3HfnhZD8*cxo^I9x6#}jEsHwai?*{fFd_3|iQ&c2^N0|QbIDE2Y z&qSD2-fMq<o<N?@6eW;J38s_4rod<*?F`vZhRfH6aG3)C&y9CI>?*M7;&x(U`ciaY zXppg?^cG>$q76rPhh66a`9pjay-$*dN0^fCI26k|Ze{zwjw7hDMSuj5D08l<q5dkP zD+f7;R1*(DgivNcsKHhyN_-u~32X&WFN1C=15;>2-P^9xZNA3<Js`Y*7a}4E0K%-h zq^Rf^-30MF{K_XPKssC<czb^fGz8J|6*8Zl!vfb~TOXq;UuadE^l>Kn)E<R*=%%v6 zga8YI)eZ@(?ar@V=<B)*?QRpL1N|ks$C8m9I=-<Pmjk4RH$&CuiBp=}jPBp>3*>P$ zFhg)@1Rq$UB1r4m;bwk(0h9*=7gs_xK^a$E>nbJE8Fbi8^i+`|dhd~`pV*jv(W|~O zo;tZsxmypKu>nXAP@9J6BSCnir>9TLWdOqvzyb*w@bfQsc9VzS1V^sq&)RayT&oI< zDz}Mr2L0{r+Mi-SKi8}1xI#l73Or^O<Nl$L#Bvmf7VwTaZ}<x6k-A3*_t7xyR3Zq2 zYVJ_lFDNw649eyHEcg5&xeUiS6Z%23%&h9lgzFEotC{Px@$Bb7wuqI0WR?JZ>Kot! z?`_?85_OivBOR3%-nzG<`4JY}+|1szUnhixgn|ea4t$(u;!z5(wIx%~M`4-norXko za-f0U!_ECJVxLx$O{}o4BuX-~XPLjh3@-(3^zGYJ2d*_3&J5Ny#2nIaFA6jf$LF!2 z%^zQAv3L#uONNRXlqn>DV5ZHvQp+&WU8;g-qHuv0Z2s~2)|4pL2jB%GMvnL(CvbzD zM*#3Heg%La981s7r;$4dj3PtOtOU`I-Tweu6`GW#r+Qt2(opyabtV8GyRT84%ud2B zr}{{O7P|OG(UJ%MzDfWUY<D~y9owtKrlzLgiJ(m5i1bg+R^ay;?<z=D3gzA@<MIFj zC*Ha0oxsY<`ZhJe>piq-flIm19NCW}tJ%PQ)c#T@3~-Cgufa)#1kV12c6|lr1QNvS z2;%Z6;pn}OLsy5%LLP2Xqi{m`w)|Qc3b7&w3?w-Gw?wx}=2aozLcVzhX~|{a{-KfX zQfWp`nckw|_yKfiCG(vYX&jIIlbMt|Hx?rLJ!TsZ5jhLZiZ~=@^d$oD&;hleDhhpl z?MzS&EUc`)M(l)of31NXwnOU|pLe01x$9T^x@4nITpS1V;wo4d(1`56F{re2CwZ=? z)Zau5Khe<-dk10iB78WV5DDBopbdrZy|6c}Ky`oTMr{Snh=**TGc`V`37>savRuoe z_^_&~stCdfP6iDj@-8keo`IYd9*iPM5yRud3e&_Dcs9VEk&z#ez3O5PiDH|RVEa4& z>`djwg42JK+sD5i)~Pw~w#bOre<o(^zQ5l}=tbZYzo2%yEi3S-ORgFuq0tE6r-iz; zg{dy~O;w|;cZ=uAL-h8{ixaP3QvaSCtp$Y#EKCEBTvcPE@Y5$-Lml{90*`{k**5-l zn`_5{;4$Ayl7WGg!`M4~))wtBlEUdV1s;7+cJmY8VgNk9h_kB49zKR_XXU1by7Z$n zk3B((u6@1N;==T)Blg+u9b%}e3Cy7ZO6wntP*35Y=URLYMtBM->%O6gh18E7Ybmlf zU0Gcnu;c>q2ubUi-fKxh??ivNKVPrZ?I6ggB=l>5+v|{bcxv`?lezD)-k;B;n4?ms zRJ<vFbDEKh48z)N>@k2+B?rnvsIi1ij}U?&X%OTUoPYx$1E;0_pxmyYXgf@NjRpkD zbQ-?|LFGM;e!q+}3LY4Tof;Y%a(`CrA*43>ZrYKQEA2A13n$G4iunp$5NK6fG7QSi zEVkbBPZs=ZW|hmk5JS7TZ6v6OKXcv6A&seT&W-joiVFP?1}{|jF>n|on_=IP0W%8< z2+0Yygmc9X=FsErBG;hR;m4UKS`VCs^i}B+(3^0#8Ua&)4VG?NUbk)?;dUZC7Nu^# zNN7XW!>?il<s!5bewTQdl=g!z?k(S1TE053TQa)&`qoVU<VW6G6gFb?O{5e3_Duv@ zR-Spw%l$*2tVQwp`Gr7qz7aUs2Ox?PE#Qx~RCw<ka$|yYXVhqDXtqmQDkaEyBKgCO z>HLo$;Z}2Sa&|UF7fKXO(*=XnA$S2zR+n+&q7K<`uA|#h@$uuq{h}8XcogsES~pI8 zdbQNknb$$J$>;E&f}pDtuoK>Gd|x49{WS^{s67m7&`WAzZ=RZ+9l-y7E%#0UQ`iT3 zBwGCHvkNa+5iu|G49$;&J^eqhC->(cVJRM?$3!u~@g$U4vi=Kyr5l9y4{r$JwItD- zbv!U#`@L_1<w}&a0QU?}jMm1+Fn&f*90YTlUnW8NpRLB7_6XgOhY#6MpTCD`$UO#= zhqQJ;r=NvMnXXD~5$wS>DF*?32Yy!{kRs8NqUH1=BDUX8&*f2olZ>rAd0$iJ8k4ej zt6tF}K}Rdk359TQa6FN%{qkiS+*y0I(i@Nubc!9)&`v3bfxD;!x}hLCL&A5u?_$Hs zV0+q7+%eUZcgl%ZiyOlQvnH5?xQ#>Ml!0aBKmLlBwKzX^4h}rPTCiR(F4`hXDb-%q z(}Vw5DO6igf%*BdQn$k$T?ZapXh||~sb{^p`^UYts;XpF7bR6=@7XuS!m<8J9?7q% z;beG!`0!!v;Tn>QyZbpDKBbLENs+?3Zy`&=PGQ$iP1q>{&LFtw;Ew|PXyVoTy{5u$ z;4u2*OkR~ti|(&7W6LI<@{Ejy4s|7+&i_?um2Tk|KGf9(V4DxP&6$DzA$mb1le~#t z{gac;2qKcpc;06M9bpLw4-bk~-@w2LxKT8xw}LKV=49x~uO#yhWk|lvaRvz;4?+qO z$=lVt8y}zJSA{!ny6&V_dX%4Mf+{6s@~8U6P6xd|W@h;FoFR{rQha-R4Nrz}HUhvM z0}Fwm4>_3jLOY8a9+R4Z9*rt&`Bju^!tIP>dH^K=NOS9?dm2Nc65r7Gbtn8zc83)x z(9T{i;h8TK)-Q<y`$m{<@TIEampZPJDRB>kHp+<TpU>v!mJI!mJDDf!cfkhx@T~=r z>(~Nf?VbOud3L)Fb6E8K9*%bpzB^fRTjg`QUBXN5W^Us>Kdd)sV@TkxUyS=&(y~YF z9jni9tAxbF?0|Wk@YNx3a$t_V+`oL}vSPeq^~qMfZS2711@@+cMm?$9)-V2k)K$~I zhmF(PYpwI3z1`5s`-FP~VUq~#C3Gp*AGz&O*V_V@oO?2X{`aAH#DaA}o00-eCqL+v z0?)6Sf>hxYr~M5aD}D8%g5dD5M1<@3kL>4Xp{~IR_#(72AV%aPxWB!EnhoG719aw} zpMi~!Ur_c&WYKLeub0%mmx2w7j#tWgMlHSBIM^7mZ|1$Rn5fXrKSS)7p1sTQ=eZ3J zV+RhHF9XMR=Mzk!xo=zdEI5c?Ga_WEv+jdyankH2?DW)9q&A<AQwGiYf7XUNz0_ik z7g!Hvh=>dgpC8mw&{WE9qG+eQ!le9gY@DTFyt658=dal(`)p@=PE`4@2{-RpuW(4* zIPF(~WpvTI*>m<ZA7}Zw^HUU2|GgA)GPx`3&U$1ReQ^>-k996Z;Ya8J`rDIUYtcqW z1<p`wlXCqvRDF%qg?c>MI`ht@v0;{h=sG+z70(Xc(MO$+eNeN<Xsj)jQT~s#=<ltD zB4@<bGDN)g1u807Dmdb!<L<^riaae=Vb?}2s_nb-aC``jW3I$iM~yP9oImYTf`2jz z90>VvUEwL~@MWGn0ekT+P7JiPm0ew3zYB5rh*MGE{Tt|Q%Pb|i@Y#QT#a3UxCBx*t zhQ{UNyvYLgH}nT+Q#BG&z|s>oB_P*JA#8ikkCYePZh18%d}dc4abM({MDk(RrLW~v z&<qss`nf%d&20s^4BAj2D8I0OEl<L;O|mi#FI)?(@74r18i?xM`jXSw{DS!HY#z*( zz#jlvL3M7HFN{`0qdED`E^a}=^vCV|TG^d@L$him%_R#@9z9BetZfSBl9X%KLWw$y zy34vsiy+ZB-TU4cUIb%tW}TNAAgPqgi|VT3?-{U;y7#|%MAIy~Bxh}HF6Ue4eLMYW z%64|@#^tVLy%Ki&*63;r!f*`olEc8j0Q~{`fot@H<I>3~V5dTs;*lLpp*%_sjD{%7 zmfQT`o08mqTJ1r{J@MO1c9&121_+O>WZ(pV+*JV()PYokCQ`js<zBus!MVWth~a^k z!ULN_=PFpMvI2~@Nsaf&|5V?;j*e$+JFUQ&<?lmBcH&3jChM<aly6t^fz>2{ld?%j zf?8;Xnpo+`tJ8ID3wd+BHO}Q<UFgd}phV&1Wkbf;x%0fJfr2<ED7?_c9Qvon-=~}8 zUF9g(rjzbLWrWGUwbb1i2EAtBYAdEv7|g>)Lh{iX=P8r+rzf(6<_aFXn9Y|QY!Jp5 zaLFVK%|C%~aQV!foGZ|~<|e*%<!78w;JFX1)B*05r|0WG2AzLNiktj~IrCJy8js1e zvg-1xdOZEQjVA*Rfu)41BX+%A0F8~n1&qb83MT=*)y-alr^N>i!XqgGa^2Ix)1MYa z_8xI(JsA=#b$)D^CZlZol6C;InB(3Pk~Y_1h9#B!{`q2Ms)}(C14nhcb<+mHydOh5 z1Ye|0{b2<K<4?~*j~E47O)F3Li<zp=HJ=*piBsu@gRO^-)dbY++|Q5o#WSDzjDOyb zmAasqpvZH<e5>tFuueP?I&L2>z7lWVACwl{Gtx4(G;F6YvY~@6H$PT4iTW77`U6=l zk*>E^FD}%hktQ4zJmWJn;t%7@JKJMdC}c{PPX;g#nLPJcIxIR&)lSh#`MRRmh39oa zf$)?3@#j~OU=zW-weqPjcDB9n7FKS1J^830_O(mP*D%(4nPXH@>)Y3@t*4_1Nw74m zk5k!Y^deGGLGu_IU^MaBkV6%E3Q=E>fq7eo1C`dZ>N0MR@;eKxQIhyZTw^Njh{vIu zT0W1DzAN`$pm-$n<=rRghskdvUC|f<Lw|rm{cH4d?V<;`MpsNE7?=8{aNSeNWp2C8 zsuucF-sfk9)UF+39NL?<<i=k4E9nomL~2^p%K#dm&fnb&_c*~*5*(?CNx=BAoGoc0 z`a69BD>fzWwI#Xi$ed^78*bu_?0#4HwfJh+@;(3RupuyVond!4bEllf|7u?mYk{oO zRm+q1hJDAM%f>y>VGZG_J$>2T-M;TfVacB|)?5Bc&p(I^bCc(*Qzr}pm_vQ7AqSyZ zGzQxX*IS^B+9N~C-_E1gtUo+edCzJhqAfNdsbOuv&rQBXSmn(%BxaXhCT%O;1pN&v zz*ETdv4`#66z7}@w~#ca)O-D?oNvK;=Os|9=21iji-~{KZ2GJH!#Fy~w6jmvLp>zs z-@x+C0;{DSuh3{Y4%xU0XOP?cp&w1I1>5)sD{XCd5SY9@9<!U!6QgYk#<T<QZd`kC zrw~(B2ZUEe3=WV*JVE;qy&h06F~5Y?A>4^9uQVl=@2_-P6Z)?C=P=z$$I)wRKl>^5 z^-C7I-Os0AjwZ-hh}>cO%G{TH{U8)%nglFa+(>_OfB5e=+cpEGt3|%*UE`4RBnhqm zy}SflKJD<;dI%!y^Rz#X&CZBVip`jW{$S>9kaet!2&xee66PjfQa#CCFz^g^pp;a< zgUo$EXUb(KZ_v@xSAYA)0cHd?=IG8oe-j3R2QTwx$y+^os8>deYs4s89*As(B`uZt z^uGE(SAK@FgsSp@Q}Q+9`4Qr;l)LtgPGl!Ljf~%srG4wdZ;lk)3RJUn->4g0x}1Jb zJWwembYyog!nso%gw>zRuitJ>q{;l%b0ULMp5&TvolTVXi>|g}v?31+kze3Q)qT60 z+c<A6!P|wSkZO=rM&&kLEmy`m{pxXQQUf=4s2LO3=Oa4TKMgYU2D|p1x|_O#C)YO~ zzvZuxBre8H<|EufzZY0vUCMH*-rz*F)$;6#X3g*;DR(XSSi*(Ua22vSMqWeDUu9np zE#V0|2hbUnZY94C^*nG~y3y7C;oRA4Orc5yx%`~aO*2Yn=)j@W^z<a4b}3l*_E9_( zsr>XR(<N(Zv?efM-{&Oa-Yy2DQ$^`K1M-_8CPtuguZP!kmu|i#hJ`|5v)OS@L1Bhf zje$`M*RPvvy~=0^?rzV?phOt@2$v36w6`5jAal_BG8{T|2&_>RNWF7nnoO>j@KK^P ziIy#L<FbjsnLEMaVeEmX?cUb?)~nYJ{2gf|qJ8fYwuu;7CH%LG(*rJUZZoi59zeq; zY1y&)={N9Q1kE+lk+lbhJQ*D!J%SXm{PcJ`cl1%2`7f(zUZdgBA;HPpUvZP_{ELg@ z<#AH+>7ShmGna4}L>*O;FTOrhR8&vsqd74nM^Ls&3IPfRhamT(b%x)4Cu}@46@>bl zW6B6L>fi2!euL=dfv8*DUScAX7=R&8z_b^~c1qR&KbMJ|9ox5`#v*KkRR9iy16&^0 zM+*d)bAH8OGVa}wtPjkE9x&(;D^^iaq14>?=wKkajv3stJp^3^y+4rbp&$1R4PBER znwV$;04B!j+VioC#5d2TPs1*)@J(02c(cl+-qG6Npr2o(F-t&#+WsC`cTHt&myxt1 zBv&#BetLTPWYBIe^<GE9h>K#s<Hr(ao<K8cmwU;=7t05kRDDVot=0c&RN(sP&!4AZ z8Gac8{-Yj68G6#OTa){^_xoQ>_!j4TY*X)xtM!XF=0>729>+;xf;3!>r6R%WcK}>V zn&|uq{tLYD9$9&e;a*I?902<{TqDlmySo-)2>LJWet$gS;)1TGidGCPu<O4cNm&q~ zXJ02l;c@fE(U^`DFSAsf#VuFcsHd9c|5PLI5%hHH=JBw-uzf)CB1%7&l(y^?Z1+h# zZjrU1ms{fPM?VR7y9{iysML{u?NBtY$YbU9Tx)`RN@i;7=9{jJJtvOF$+#Mq`8xeX z?mR2mcn9+)g1Y%a$ZJ7)dH`~QzyeT1Ri|X>*hEy19`$jQV~X;-i$1pw%u)C}5!Ns& z!DA;5mI2g%La+NhC879{2wlc@>UmPpP2P&;qv@%givPV#5S5vG6l>s7s@c+(x~h#t zx(##$VPlmY8Xs4Ks+=@821<kw#R%&`q3v$K8}+GG1s(>Cx$cM>nU!TB+btuM)03rb z>KsVoZ((GFG)UOL!)v%SUc+5P3_0bbib=uO1PLJ?@iZ_nPz7o}O4?ZjSeCk>^d=-_ z%3dhry=d@_06jhpBOdbTw5xs7mzQ<jC-**59V6vai9DF<me#di#kksjn9LV3O~cH@ zR7u0-QGrEBvLZ+hxy4>8g2V(N#q5{>iv3*P^$Wz>mo4^hB=#2ti-ORWK~+&T=&H%O z&H1C05o7722}C^FiJnn-i7T+fF(e`)GA5+`q0@bs;js30-joG(BCvU+V_0u7Ah2;H z2rC*y1q^B&Lzoh-9~dQwIa)9gcL^skW(G0Sq?mrWh!F4}AOFC87JiAl2xVeSgoHi} zrTScMb2^l7quIElaN##Y{;Y?`<4N3rDf@b2_#Ai+meJ0cPj$y>6HNppm4Lo3DS|U1 z=FnjO?8P^p0Gb9oYJ<x?t!noKAML=;pZ7ql5N-;BT*1l`GvlCR`}+HjL(?#a;WYe` z7YtMVMHh5c;cQ;&z0DYfVNMb@-WX1MkfOVbxZzwjAzQjrm~Qz=5l&y3eW9yOej-x; z_0{@Bz1Q0AG!JpYz_XGTfi>YSwwXJouD}zgNy(0xI`YI_zuSQ-4esNQR3FI8AC`I( zvF{Iu_jBhDV)Wf!%T}EQHa2<=ycmc$2k3^Li()9EJhJ-T4WZp|#fL|ArLq$jvFci{ z7Q?rg8&k2NS64%0W>xnZt-nl2=9$OdJ9^ra7{QA2w9p!21K`OcyrSY)-z!K(ZiZb@ zwPE}m-ol_f+kdGe%Y=4C+F^%*H%a#9j*8JQIXS&+p0fv^#GG18rfv}2!W7ybHlnx- zCaMDl6_vZ+?`KlpZ6`dofzfZrp?=Sz&4xT;KYD5}+zh#OtATbXPP(Oek4J07z9GrZ zBA|S=ww)D+F}c|KvXIruWASm(f@n&2acb=PVoJY1o-2}5!oAd)w!1xMlvmQPibsci z2xL3BQ-~Wfs006lJA~z*$V=D1>GODQWIVL6qPVcsDAwRzwkp|O*Ta{{Iy9I;%A|{G zDzD@6DfcePxNGE-kx}#+1G}v}F?>EyM2LBq5UnnVq;2Ki-WU(yne8Y#utuw6c{<!D z_J|$T3`HXaSK@e>72Osec(J|4Ek2Vu6A;iFaRFbW%?V83;YYCUE~!KNXyxi2+EBH= z?qd&^caYu0PLukR6uqJif$~Xlb-Kw)Z*0!wnvp@%g$@#nhEx6o*zqiL8MuoUxFgw{ z(bMAqFflXV3Jg>+wh<*4@$N37qbj2;ix<0DRI#JLhN_pjjrNb626_QSPCL(cISmw+ z?E)?P;aQtG?LeuWe)$MWbq(BOoIhOP2l$*}5HjllRRdn!D5xnW*7vv9$nSQ4qnIe2 zNmC(q*mh*IP4#efo<-xnKxs#DewY2<+OkO)^cN;UJgOq+(sclcl&}5XD7G+mAJ!3A zV!Pc&Uq#uNi4(^dCKSS($?QJ5<;jH#@->}{6ekAn2>DW5(Tb4@83!e!7meO+E*8?a z)bC~Ds(%<${`MkGrirrsOe(}OZu3WK^%WRgro{^jU}-mbp^_qn_)SsNB0e<Ix{gns zy7#8h6oc5Gp45bOtiL8h>SK>gPs(~^=WaCP-;6kJN}=sV!zO65boaqA0g7-4w{b_; z)EJx>ms+UBC=!O68clOd2d4V`0(2Ei9QRd=*8(fI`YAM11gXQFN@O-pPENv%3A0(p z%nUa(hx_tKm^dS&+2`P4=ZV2CF7IU6Dka>O$CD!-oHd8)LtlxsjfqMChb!<^0pt2% zbg8$m?-)v_mFo_sfEt7sAu7T#O6VRRl4-eQqIdD}2AuoP_Z>;d0q>E9RA9U+a$gW3 z__M02+XVM-Sc!=lN=sL1CfE0{O~Jvi9$thLgiFhz)2hs-;?cvb&vPNG`946N;gWUR zA}T5huvGzZ3kMoB=?4rvU~+<R0%YZ=<i(BjtJ#xyWl_L~(D|DX?Jm@UEV#X~gY0>B zW&$S7*74HpT#>CJ^^xX|8U9M^LoI0&6%j<hlt!4Jgl{|sR!k_&pwbheGdYZWpwQC# zJV?J=Ih=Gl$oP^+=o`Yl3ia%zyM^~uB@H2ZnV@|9X34j@joDCdst738b+%+#3J*<8 z-<7Y-Yy>LabNFzFTL}_$3DP0>7w)IW0}k=}FuxjkpAjxV@n@Oquym@nW+4nk;8RZm zo1&;*!_Yvk<rne!ut=esnAI*@l%52(+B*&#1;tY($`OpfU`BQcI^JQ7emgD~661t` z71+wVbtVKy0P9!87(Cz3O4BLSXOc3v7zQ_osN-yLNRZPOgz!QNf&c~ceE<aZVuaB> z8^*-bkWNjs>mo_(*RRjUL<{b^1~a{+S?k@AA1g6~lmv<h6Sg_7v)3?oqk<u;`*?c@ z`Z%>4cftiulX%HfwzhTw-rFKfqT~xKK8yVP^vXgw#{pmpq^@7xjf}$TU)$q_e!q;D zN1)XWT{R@U_6UOxh~+R%vishI6o@&gL?jNZ)-9ltOU+pY1*s?pCa{$eFJu8ZEfGPw zr$GL&-3`RxGhc?gQ1NoFoWy<-iV=V@;mLyui1a|-dyQI;#@&6Sb1QNul5@QlUYn3( zPKn{C6A*kYIQoTkvUxb99EkYjR=ij3W(|Nvv0>xJoU2OQ`4Lfne`9+>?7*Xn$9+KJ zA0T>J*!+e<E=Ip+t<+0nW+vO3h3yqMr(38!^f<GIpxl7;u!Cg84CanXT@rRcXv5I+ z`vc7~SLz=qSk_;>`yk`;{^mVF@V1H36Vf(;N6wttj0iM@4oAF&!ERv_lK;uK60=n% zx1OpPjcF4@{3eEyaa4<Np}_Y#N`FvG9nsHRzf!bMtG{3akEC5g-o_0Z3}GT9BsU+| z7c~$;&y~1LSNELT2)U7Z$0YSkr$vUKGC8QRyIaO#;~(KEC7~)vkFzCj%)Y{S4}UTT zu;1Gdu4Y1H$sJ_um%sB$)*GWQxeAOFlV5Ub`6wjJK2-31W>VhI$vO16D|M8__lc$M zQ`qcpLc~c;<wpTd!XTz{Ib*f=23_|x+_)JCISkWi_VXpmyV{4<6}un1^mMmjKueqS z&BbMK8~ieYE6#<#U>m}e>}{ylkDylZ8#H8XGI~KB<gb+SAX#yja-3Y$t(XV?9r2}} z?XEXCIsfq9y-QcQ!=~cni!6TK``fb|erO_*3&Wq-3>)$NkPwZTu?VYeZzkI7JpS+x zWP2o@qXlnIFREB@OC<!<hqCdPgVafC`J6)96}`anZEY-K8Ufm%Ap!bAGp>w!`aS7- zum1NTwx1>qfR7>z`I?MYNKbySYT60Nh`9p}bQ*#Qp7Yxn7#QplYyyRcEaT3jdKHI0 zr|kuSG$6D&t`ohmnoNWxM}5r0OO8JY%Nnqi4s5)5J;FUVebr?K{wh{`oO9O>84CD` zuK|F-&g+YQ30w)&-+l?`tjKFs3U0nW^rRTYYZRmiA2eiNpHVu$okUo0z@Y|qD!I?& z*Tu7bsPBT-nTp=@G9Ku?y@rkM(@@hoOD`ad?Gk23k!<!ys|smn`rx#^EqNrD?&+8C za?i7*M%)HTQtui8;PDy*A7mDY@9<Xd#84YZ5Tcz2P5XAOC;lG9;w<wvJ_2W`(IFFK z5MU@mOTX_`L3g&-Vja55v8Ul=&D<k0UrR=72_>nok2Y8rlj$Nc^CX<Pb?`-C{s2Qa z!hL;x=sW!gv&?i)<1l>3r+Ugfk2s8MMr%M!^tHwkxZ<e_n?-3oVub}Y&hhan2WH*e zBSozD(QNt}wQ(K=6FoE3Xz}eCW-j8PgTpMg+ad;1+cc((W+0<k{MZ;8^j3@>nll2` zaS_ujgx&&0n8+d1Dr*%bdkYi-q6%UUL^nLujlV2otRpcq_hA|^Z$Mu^!2K8&=rOEZ z*rycjEaTANqT*s^%!8mlzN{rAGP*4S6@<|r=t-gvQ@&^leW8f6Ff$)V$1y!U{TO+T z_VpRdWrkB)#M*qFW&5^rsXoiO=c8?=zki1$1-b{*aHDU`yr)UTV24sINZ|MPcN~JL zHB_P!PD;G@fiQ01(!@)zU@koUwMJ-}fsL@@2QZ&iso*hbAzmhP9a)0_QRwL2Kz|lI zlSnd*pEY4$4`3X0es~N)tlX`l&GX6yW5fty_@I!qYjFrqL#Ix|&bNwA5N}MX(aSv| z7<dxKg>zZvjJ;smG{EQ)jzu(@G>I6bBnjyihXd|WOkvQO82Gc8OjL4n8fw_~^K(Ws znq^DbixAekc)VF7J^fxoDZHOPVPMQgY+cHgHtI}}1b9`;9Vj6vp*x6|GNF<~o9{!_ z%f0@Qj**d(F#MxlqrX;Z_dI_5I6KBc5R56Peei!DYO===mJ%^q<|rRJ0+AZTJ>k2@ zKpF9_2eig1`bPtFB+%PMlfe=YM)4m%lo1B+AW)+VUe}8Z%VSXH@fDMO<$u;<Kvba0 zDwcb#qLuvbX$xZ7Fj~%2YKNqyf7U95O(@jQ?r8)zVtN=fE8Fk6A19F2ai4_W<kRHR z`OpuFa4nI|dAsy0G@7WWXJg9|1%1QAr~bLi&n4ww+(<{4gx5e|^ZjtLD8#P3r6<e+ za5yIA8WcMS5^o-ZHy7qg?0qElHx1p&x;FUoM2np#oABN+yy<`nMuUSG(|erv&h>d< zI)fqJ`ZcHlJq#5<l+v{|td5H~`ad9WkTcx>*FlW`vKp`zowJ8yC5THt9&aGPL5Uw+ zRv8+COapjH1PY90#2f<}v}7mk&>H8zp>_!vf{*Rrd&=0%pEZ8@5)E`k_^a@Pd-4)s zq#)_<_1PzR25*Lt!`qEOa1d(<@#ggSgCx_x+rqpF_WxtYjtS}J2cfqH(L@Cq#5mR+ z5RtiLhmx(WZMf^j$l5e13Ip+%Ae7gE=-Ye#87XQwSMFPz5SBO|XS2k9n1S#kppa4c z+qWnh|0G=lY&)G~>N++u64g5zP(6qvNxx0}4jfB;6BE{YgO3L_0HdYR2NB5>ZwVl5 zgs^FGV)8GB|0)m~ADkngxklrk|JOUltYf-W#4}(k{_slo(0?id-Uo289~RGTz6dcO z`77elQFu;FM&*5f`U+lzAnenr;GK`bTmDD-Ke&1;^3F!akX&u<{-WVUrx_Di!p{)` z5O504{=M8l;+VLZf_6f2By3P$$nqq3y3s#3!=8mf0eXzO6JDMm4rx}gh4n-4BUGv? znF~_iKmAG}Wm^9>^1<f6uPy>WB;J9rcRi*|jol^3#p}VG+}rX7F8sq#<G$ZN)ZGhT zzaeyXfEi{V5PHCc#U>%gT91f|j<gGEzACwV)DROJH@PtL4*Bl_F*dc0thVtOBY03e zF$OK>1sq9j`?lU{@_AELS+j0sWyAV0iu&5fjTKiC!7SKLCtV-L%#^Q58z=Zt8cJf6 z`#DcwvYxc_tpu)GAE}CG{PBA?o~I_}NQqZh_*hEOz~Q3nfaz_#;p77pY@&`KJEE4V z1Pz0NZI9gTUtL$|7BP8~@#f}TBz6h~ofBT;m*`G`E+UJ=(0mzc5aF_4Gq7}Aqpzwk z5x2vfh3+>w9kUIGuS^pI6tA6Le#@cp`BZT^@r=X$0_%^~+wtYyUvg>09=j*;rEJbs zGF}B8JU8|w3{&l4n-*DJX#8jN%)~L>Ikakzu}AS;2qJ>7b?(9^W-*f6IWy0_pKim3 zM3BUU>jqq5X0is?bMM8+OLhFxmBL)7Q&d|hG5A%NX<5c*dznDUC~{wl?D+uz5T`s( zPdrc+KxQId4@b<!!R$<yb$6OQ-OK-zL<s}F;JJ#bJ$c75=&S66tj`{XnfOff6L{MQ zUi(r+X~vMqBMLta?j19{s$+NS5;|$`oV?!-VV--Ne)c?RqN^YTTobT1A42bC;0fZD z9BnC^KohAnb*b<<E==eV5R14X<m9(de6EO$SlVRV<-`*G0Cw@fKsa7Zhv|G#zz(L0 zM~e}n2Jw*IKfqh{D@8eb5sN`M3;(RlGNRnjbIL@0Eqb!WW&gQJun;DiupWDQcxY#v z&|>BId3dTH&0R$Y08Rp=X9JogOGoKYCEVTO&leesfVyC&;ev`Fv?R2jBN{H(xeQ5k ztam29J=T-vy^Z^N%qvK|<<S_)n7FS`MIOFJ#5>dQo+85eX$}vNwYBx$CGI*tF|h|| ze~38`5@8X-J1go?)#v!N=PA70e!nO$ccpk2E;v-P>s;#f{X}_Hf|?4DhD%#)prgy4 zpTB_DKj58ZcZq*jmS($zltMf3=BL|)yHeWO`dU-$w&ky_Fzh!QT((G9S!q!2&%Sz^ z64P!~%COS`!=k*wM4yOaM0j)2!na0d1yl>**5H{YOj-B<(lFsn1;<tG)nC3$s_lX} z>UW1Aleo9}>uHMGVs;)g3=N?B5Yz>Vs@z#pu+|ZDF5Fw6x`&}fzl~ct_GW(JATg=U z|M!(UN?_m+R;`K0s0b8|!$!<ksXdbD`+rJ16R;lhw(n;bVU!`pax;}IQCYHQOj496 zGs-q?CR-?k78*mOLPd)$lxS#3%2tUMvb35)v<r8VvZRHc&&hqi?|VPb{l3TZ9PfS1 zaoopD|E{k8b^U(l?>xWf_xnBX>lNDr1W0oz$Z0pr^akceBJ4mNiA&m{m5RnMk`o^5 z9anbkhPr?Bh>RV_&Mm%{PA*|}#aVqzOV{X76AeTd%k$6nc6@x%hC)TodK|AF4XW&M za$5HC6bQO6sa=tZw3lsy=hei@#w|JrHYs+~t5M*Pc=A?3D}MD)(U^~9%vp6+<+kWw z;#c)X>{AA&oPXfDm(4zyuu@jR)3rTQc(S9qXVe<rj@02#k~|I(`$nOs5~ruPcj{iu zOqg`#kAe%GRBg>#LnPbWYvhWqkml?)GcJ6)2)7?HlC-;bJCkg^_51v3>0&7LupN!~ zgSQXqor_-MxNfF`MFCVQNsQ&hz~l|Q9@DV25VKuTsXiq6XaDXu!q_)j6vyoYUb(dj zL)6Xb9fJ&;Qra;oD#Jk3u{;+xrEzBA;85k{2U14Us>mR?n$kwFUA?S#k)KwS+l3if zx+V!Lz8$`(<E7P~3gW??CQnu0$|`TA?$&x)cX-0+(=3zR$Fup>FhmN>u-E$Ak)MaO zGO_Zqd4t+55R9t`7-Rd9gnuyAUsS$ZzHl7R)IcJSub%VTW0*2X9S&&BH|Zo=E%Qsk z>yrV_c;vAVIVCVWzBq(BsrLF*_xV+Z1(`)lI!`7$%rkQCmz;_SU%(xJ*b!q^k6fP3 zIc$ue%Icbn77KeR98LT?C)o7zUgI5{x_=uXE@P^>daE8W9lLZ{N|i2EGy%Zk<jilo z_8lkY9m+{t*MAZ1=o}HW=);&5``;yt%ZCo|+6s(JOc2tApqo-?(_RUoSsRfSJN3GF zNiW#-x6{VQL>zyxkVqGbDWCBe+^I^@?Wv&$J!=`WjdYt}`XOTRgOrp-S5I!Ly?txe z{yD}7?ESZ%d^oh^-OMwdj#ca?^1_0vm%smNHMRS(>yo2CzsK+}RWt49RzBiOy1u3e z*+-}I4q5$4(!5B?ssr`2{}7um%Vixc0~V7tvtY#NiX2j3h|<zSbg`72G{{v!-D)vx zIQ~*BOW^;ELPNhCVw-Vp3Ahh0e404xVb4a>^H+qq^eAat68e_{(c5>2A4oAJTxd#A z2Z-V{7&PKiXq9va*}^*Dk^{da)nE7T4NFRntr^4pU7WVmy6l*)b}iSr0e%ZSH9}w> zoQb%(m^qhx)gJw)O$y|8(s>>~RaVhz?XSkW?ZPJ<$iM}+q1}rx<NkxLv*_j99`8TZ zct4Cl@`-dbq}`^nFL@1$@UL;V<P&Nm0F!PgrO5IFoBPg_PmwjI?}IryZ;uI|D8s>1 zEd=idT7XdK`a^4?@Hp^zAg6#)>t$c4DG%?lOyQp6dgoY0D1YrwsWZ<P{~!b9AAP2( z&EC5D;nept4=iiC+4<wSMT(`R3ec@tNJ9YV!f{da!iVl|@RG8|BNHpA0zA9+OHON- zkYGM>5>L+<UAuBLh|rr=2Zov~O6=p=D*1<Ft&<N7%Z>^RATs%{pQ(7vz*e<A`S>W} z?aij<&2?>td4^8?(9$RLdQ#J$8^)nYGRW=5h0^GOiNmugn7qe&KczPA9+*G&@H6ED z0VDn|rpZ#4kZD?k8+H-uHHWZ;cJ4b~QETnsP2$9vSG4JO{ol%*j%=!4;JlO6XV6&j zZT<JvOYR8T%~Y%MxDpZ)5M`HBy$?cj_w9+<hd5|eIRYq<Zkl)31T<87{a5FvkBg2i z%YpsIU9K95zc2Z^*sc0vV=9{1o1I5y(=G)5?s2jjwCZ+{)^`Iu-gT#HkdL5;G&nT; zS3}}uB)oem%w!9aHn!B;ha1j0(Kf^+Cv4SMlMHS?;7_QmZ~kjfIyxn!CueUOB#gZ4 z#=hVboZVbpP5sB&fm<4$^%5t}KNnwvSFa}=Dppm?nDdu*Mp@k7x^====i(>gPnCOM zeXv5~zh^w>#^V^`zvo++$Hb#k2B(*sb1q1eP~3Wnh7ZpD5(fTA`hV%P-{eWwk90LZ zKQKv#LUL>JBy){rteKdo^_O~4xU7w+=yrZe*DhWB<`<qST&jWjQw6So!ZD@uP=|2{ zmKH%FW^dkjx>Vmg<8f~BsH}#S4~!-g-v_<4ygExER&3>d`)Vuq^%{f{*e9ceqChyg zRiozSwk%i8s+^tfzd!J`ax!4A3buko9q^eq*zx^?zC8DF(bWyZa$|_`(|h;Kyv>91 zx^ziT&>aP!*b*)pQ{O22$}8w~r2>Z$jp3a}oyUK*LjL0YHk%lP*X;SY1aBKv(PN@c zim<;O)4AJD$3p82<+t;{dwo_8Ix+aBJtU67;<v?B?N{Tyz24dR1eJns|H*wgV#O+r zdR=k=>(^b}-@WLrAZ!g0C*z)0%8vwn$G_}t?yQj~D`$P&8{F&i<*9j*+0lm@&iKp< zsDIdn?|5Wm(Q(oWK{Tn{t=wjDNEqNc&{6yUZTY4kp2<|stzh)fXc&hU76O3=g!h=m zQ<i=agp@q*)$mbZAa9e_!dW0M7O%!uo3L?v1dkebOrsU4vSfb<QM&u_w81cRw={xy zWEr8OPC1U~(|`5rmu?dpVzq&mejc&JBzr|6M^7EF22HYQ)H0Y!L<lL^TS))9zUv>g z33-iBWL;ecYcZr)9V~`s$l|vLFL71mp~=}lNLkBNX;znlKOdc4Oe+CAp^dfJnV+8X z8=1G|Mt#;`_ss`g6)b+tIbpoISD!w2Kxj;$rkwRz0&P>oqyh3F?*pVB*}^ye#l-ud z=r0czPA?~Cn$+|vR<t-kGsN?Q_DVm?-Sr{4x>3|Wa`u>(PcPN7>o(olui1GjmN#%G zI2cOd4^a8n*43SWcAhaHRuX1a5+<L2b@<*nBO-;Q^d!laq-o@s`_NRaefMJSg!M1> zZTkUNAtS?M)BhHg@t^G(W?ffO)fQQ7J2yACq{hDw6I9`q%A7&KR0gTj6s=dpUfQW) zTDA%o<8hg=N1)P(u}s}9`ptuS2YGqso#vz%wXV{n%8ouD;1OzeemB5mvgh(_PT89a z*CeLVgVMB!RFcK$c?VtNu3e_5J7{-Nr2Wr*qn7#7<p<iTE~WK8XzZ!B+J57{X4_@p z2k9_ADQ+(yHp3<M(BtE>soIAREE!$Get4c(UsO&E->*4$8xTV-Xz17TQdjf6LS!rY z0ww%({i}!mZ1pckyuba){>h_{f$T~K4H~fWIGSQ83F@B94FWekXe}nzG3~xsBLDx) zBlsVU4fy9YgmDor3d@h!s44(bMLMhTI0pHn3TYFVvA^W)eueZ>=oktJQpO$lURI^| zy{~Sx8XqSG+Po|aSjJ`*(xia{d_Z`k>M;%8P!0)ZCWfM#V%7ck2P@q3{I3XMdb}9M z0C+?i`t^~gDwvve#B#G~MH78!@8Ashw#Tj!)iZB5kw}jPdEAK-Kq?mQYKZFc#{v85 z9m!<DBX`5j$^+{|T4Ao8dr@6S|J0T(TcioOrZ}m`E^SAyF&3qe^dnzNF=e6s_@es2 zny0{pD7c1^D4hZmqL$Zd_r0mYkGh9haJkgmeok1r(B=Cq=B&OXa5I+IzbXB(93|c; zUbn;Hi5oMe{PlGR^T-!T-%x`iV^ixBW5+?5sC^Zi)rZTP-q6fD`Y~w@$<JtvE?00u z^r4Tbi*wsDe1iU>g=o+3HI_9c^&7V~S^N@xsdf8MnD5o${e%Pg7+PCf%V(4u*R4?X z%E22U9C$vRQ_n*4&c(q4YYNdcTAy7IcL)2@$oFPOMn;`x9CqPW@Qq9Qawl|0qMRs3 z?Qo#=x_0dv{mSThyCy*99~FY1Ss&W4KJUVwkm;>S0Lh&D_%n%wAwk9$=jjxhZG#5g z6e5o=-W;ba6P6qEo-jb>oK(dObhHRVqg0Z&C#=CpIGQ^QGrC1_=ZI0u_($a?E0yb# zIGDvt!s~r4uUG6dRY@95{VnqXwtqg{$IC`;{{>J><ZK0%x+E+IRe20!>3r-97#L}# zxVZ*%9Y1mzRs7ZR?Pxkkg=Mamzdie#we8paD}pLkHnK=pxDmZD)s0?w8$ewM3>@(0 zx)r0pA6U~bnf9PCi%^%%l$XTK(}^shi|y;|hoga-+u`M=(~2$;;k~~gV=r>PhsHPq z;XV!mra2*b+qm=gZQH(~-BL$aBho%(`D}gxiw$INAcpoccZO=HS0onxW7Y(%5Z6_7 zhh*QSmHCZS_w4O^{klH?<@=TSXY=hcGc&hu-@X>4TGO%fw?lipwB&DHMrJ&7`@u@J zio`Y|f+GnxxzRB%lG`PbgW{0U_>rT5e|F-H8!_+la>HrYixmK@e`m+w$L+wR1~I+f zal&+>a$^$Ri+`H#^zhZKHoo2D^1m&B4KTeBixmVdW~ICG_H0ksh=*52R#=!wY^7;E z{}+#M*Q}}Ov1i<t{R+Qoi-Ii4xK&koc6RorIFGA~fA!y2Kl^jw!+K#Q3#>$8u=LLK z(cYVj|DiAY(MzcmeObo-#P^Dui?IpzBj4c=oEh^m_Yc0+zjp@aWd+&RMyj(Awr$%c zz5#%b_NbYpStTNRIF>zp^pg6TPgIDBhVnJN!Rz7|-`_6@T2FqjLZ6#pJEf7vkSLiP z8rx;!wR`neqjhQy#m0`}=?j0E@O}aKX9aG&PmV_psr4upuU?oEVte)e?W~(g#o|Fs zGfa9F3RF$Bt5l@h3%OKlY3tcPD%TY~DlD|XipNW-ck8KqLJGt)KS$ewwhU2OJ85ZB z%Z5>x|5rvB@;-!BK+jryOJ^2JCOE-`{i&I9Gjuxptifzr@g;FuLIpuLMx0`3Dw}1j z&GY4PA?AlBJ}6fQcpPpAJK~~RQq3X!aCvh8mTdv%tD2+fXXucK_t-lzPQ9Y(K411= zv(<jBg+W|6!sx85^lLOY%t1L2(k)t)Yv1jda<Wq7S8<#{pFVXTK1A{^=ek-+KF{%? zqtr$rMSC2LL2xpojD2!98|%WjCF&3i1$w~_BFY(&O<9b4e330zNF^RJX|r>L-Qd}W zch&i5jM`Px^mC%?1`H3ok=ZW))aDJP`zZJyJ@!m^&wW<2P;Brg@ZUq}Gt^?s^&f5) zT?6EErm<smOWfV5sVxRQ*lzsnr`rKaGJPc+3%7J&C_f6nJhYTSF__O0!OGdYhHKZJ zw^6M$S0<S2NunZ2RXR(0>7@E6>q4(~wRSJQHW(N1NfyDs>G*!Uz6R^ku_Bo6OZ8>$ z5z|y(bL!WRQ5s1#g=7PMz{jebN23t4EQ9m$*J-QRNGEKn9w_PmrB?a;fwr&7;`Zs< zWi3gLPcG-2UJ&<(;5g+(x%7?29u7bwPcefsz5mAIC->rsSnq_B)k!nam#pFo4i&3^ zh7f%W+_gM>?-X5D<#Gta*#aI%XZ-{#(EP}CR%{=7ih9(1zpnrLgN$)bWd=hH4!aOB zV&}A0ZK;nYlQpC_d7`-im6OGgx(5gL)m$Qws8VL^o*TVj3_`9H+~a5O*p=s%+I)Dy z(YyB)tqEmxvHn{2=)%cb>{V2Wj@n4_!a8+7)sO8&mbQ#Hb>qvCMGNKRqxwvH)s{Ui z<W+PMk9nI)>Ja+hAH4LhiO$XkU8kzWH8eEjU=mza&L))BMEd3;GyPAgpq>hEe=QXQ zzj)R1D&FFL5ZSbvCV;vaa52GLdB<3Kpgawg1R>sWA<R(!6SeLb@JgLanWK#fxP9V$ zwlYo$TeoEjM9QHPKP5huO}H3)%x|I`p#Z3?ucotP@aWi-5x{v70BX3T>_O2vqcFXo zAXrdDplUopE^lp_0@#uY5H7v~f(~FQ(DtdGmUw<a#}cX&gsVEz2)g65Q2S!X*Ik$c z80>i0Hsi@Iz41;xhhywEV=U&?;zrAY*QAR0BW>VIALXh3r^GdYgUnVp)YcKO{c4Zh znyaPh1YK9$3dFeJp$BtMZk)GC`7-)|QY4%fIX%mEEHbk2+vG1%fPw;b1}jc;m%5r& zdFzZL=Z1>A2T3@@<pB;8-Q1emF0Loj5K<2ZXb$zMX4X!O{+H*fRc3`{k+;EL03Hv( zRG+oKfg%e-@L!Qji}Nb$amWw#W~M^Ol9nhmBXQLIW8M%zXvDgL5Wd2+Re0i9-39U3 zrr@Yic^^}--e?-se^N;n_z8Wy)QwxX<UF4rb9<o|G^3IiTW%5U;wz$B*wEPM%p>UG zJKQCxzl7IQ4<~2A;guOOWSVQ4=z;B80$k<%wqUZp><S&e1xq?h?8o{3REG|`(?SA- zJ-`zRURAKQD*6(}5a$~<3^qNuoiNQ4^R7ESm|rI+kXk|tH{_4#Bn%^JVyUonC}Xgl zyUUR)8bO2+=QkgYj|YfEgX;%q2D?TwUSU^-UB@m^<T$Lir8N3zw+^3K(}>@d#e`SO z#!sA>ckUPcNR!Z;B*2@o)5dq%xC5>w6^g9x4&rGV6CeBT^G~$X9@)+G;dBrybFj#+ z%ycRh(`urjziXJ>Bq|A>D|0gZsi$dK?0?XC!HIEhABH2J|BlP;{m>>0v{4-9STE(2 zrWJ&o-E{-SfEw=%Z(@F%l9w+-BVBLV)sZIAe)s{KJ#y%kAS%aLV7#y$Lh&F|5*xCa z{^?>-1|y>v^KaCcXBC&?zn+mMw^f$7*G^ZEu&<P|D~9^L3c7yz$>1e3qq1}N(+YNC z-H6pBSt#BRYkVDt%{x7nYbksr+3KjIWO7_neFVu3K=QVNC2m@G)^XaATepUeIe^B= z@0RDt>ql~6wM!-$%xk{>7X6L_$GD-dqx3&ux+rjT4pE+O`wYN=Xg~%r2Bu8V<5Hsu z#|B#a50-f*9EswD1$UCM8*cl@N^(NR0opXc!Qr{(xs9(L>c}==F<jnI)f1}tDfWSw zKJY6Bq34!b%@IdG(%n>$obhW*WMIN6xFisH4kMyAIN13^#h~9fge?a^BA<{Orw9B3 z?c)x&E4I!ghf)HdU_3;8*LdO4$O+j>K!5r`KCvD=6S264e6dHjtvd>9@gE^Hjo<jX zuM9A1u%rk%NqcCnM(M&O<!oQkM2im?a2Hq91x&QGlR$a*t`Cn)GHEDIxc6Y!Jptk2 zup2egT{rLycti%!S57RBh-EenMxOFwE-Kot7cz*%6zX%!xE0@G6x_iY%8)c>#U=;Z zC6QFdcwKRC&?PHA4j#l0N2R9;Tiupc`KrQrKhSq;2M1wcK!+d0MFv9;WkdtN&`QQy zCA_)WSts5NsXQZEj3XM58BXje0HAe$<t@%3MFe6ABCri43Xy5ZiHDm+8%l+_TRv+Q zNM9YG%ofeML`e{4Or%WF#7foSd+rhl*zm8S)Ld$nw$}#-T!BMgq3Gh`lE1_^#OpV$ zl!g3Y&-(@ID({t|&|6CVIcd`yPg<}6I6_JsVb8z(>r5~FT6TSS0<M@HWieQu8xEHk z#%NNPvPSuH!A6P01e8uoYp2~}eg?+}{_Vn)0rZW~UfhN^cb|7iV3h$E+$r&77Qmeq zCW;Bg-@~nfv(akP>nqR8Hq0i!lGV&P<+Hw-Yr|a(0?&2AA(a?z#($^H@SGe~M)k2* z{HXT~-JU|bkPZ^2g5LOXkW8oE7+9GV$C{DW35V={nCF4R=8vFmzD>zyi7^RDm@S}E zt=spxSBS_E=+f!lez)|a%Dr_J3LLY&YTBM52;^^e&e1YNQ>{ZXWJgSXf|DWnDU0S2 zqj(7PV=<@kcE;LPk4IXGOtOsDRLqE}<8fQyND&)EglhT&NA?rmL+kU5%Y$itn}-tK zZvqhYNZgft-`QlPrK#s;S@NdI^nQ*|tEE~mf%#?w7lFSS$TE<re4I%Q@AAwj_|1Ha z3q&DKoL*Iw5K0+6H^Qj9I8;$vZ35S{Yu*}8)Ih2mj9U@AA?nLYKx(&n_fku}NE;y& zib5QltKQ{fWZm;<NqMa4g-8)bfh_Pa)vDOjBjj>_mop>h=yj;qG8ix(ZZ<6XOulXg zFc{`2&uPy3du^imC@5Dup#`z#jU#@Ep!>R%=8V8gh@1S`6R|u88LYws+nr%w#>plF ze`;N~k_uaYa+@}5|D<Z(NI4-{EQxrw7bqX;Cy2cS&-E6Y9##AWg7mU1eN4q*M_99r zML#YsHTg{*2+R74uWY-{B445$xXJwy*Sb-nk<V=-F4b{1sJ+~9vO`eYkyn?z1|gsh zYopd&{Ls9p@@4;Jp3)j2E7c_t#X83a?F*UO`K}|)I3FPHtZX9QX*00`Qqo=F<<e%8 zmL$J6acgsJ2QjZB$ckH*;_mMEhB;^r+jwc!qiH<M=)~k=kNSR4;{~kCIXV%lt&zSR zfT#?lps-f;<f+vnL=snTk_%aHlwM^l44E@^`SNAUUdIh2*nu#K^E-)33Nk-DNrq4q zkxabhgfVnG3Z)b}f<o=*%|Z8=IeOay9AO9+-6$W#8p!|XM25DpC(G#<_-!aEZ24TT zpFGKZG%2ys<l!t52Wk}0hF)20H0)1t(4xuIGb(aEhj8>sO^$~i=L(#mjj!CB&q5#% z^nX7d{1ktqA0RCQxfFFC&4Y3H<mP@SAhl;9@1T{yO@k7D##xgmAPL+z9=fe6?Gp*r zo(;jjtZLDK&tyZiRG_-`gS=)Hb{Cg07+i5(H)uq<ln|><b5I}Nn#99x`^$Itj15;6 z#NCVa#T~xm%Icdh{%%-AD-^n&r)6>83rp8KglCoNw-oKiIY{zv!4k8jG3<FIvIwq6 zbQX@8W<t`VaH(a}PXA+#CCME#%EW0z<)_c`*MvmI&#m@~!i)B<HVRI$tz|`hgb5;^ zE!lC`n_f?p{Dql3o1xaw9=SMWK7D7Nnpa}tWB?LrJ<QF#gszX=(0|4Bt5>e*Fd8R$ zQt$_owVa}B>}Cal@qW;mRmphRiXj@XoK-7c+F0LxQv;!}Z+QdI+l9}{Zun}Qa4bhy z`6(iaY(q0kkJb3tMvoZfsk+niXgrCX5Fa7z_^vA)9#;&wh``I`aDn>;ucv{DV-%lZ zz)o7sxTe$ZgYyH4Y|(GqiD067%!#VR-QMaAXCQ4xlwKSB{`J%EJb||tx+RyL!C)-E zwEp2|te8jtP!M-sX=3`j!B^|GH9)ZAy6PLwzxF2Mc=ijgopyGi1wHcy{lV8a9RF)W zQv0`u=lJjjT3Pn8odl=v(S$BH>S_*s!{`$mI!{s(V4UvBN}e$@AN&4f`fQsR0aZ3R zq7QRIv^ov2ctXsKKt!PG)!p$43sj6z=kC!%e+s{qWUhO8l@|EA=Z1q`MkW_K8mRS= z=><eV2Q+Hr|4$YSa3r3K?jrw)*6<%A&=LA?iJ4p->TtAFwuI}X;kJJ~cW8Llqd4_$ zZi)-%Fg^@@91jiNIv4-nftj}V&3Y!7F3U1nPYT9L>IA#7oJe?s!&{;)95BEgg>`#> zS`?F-R#&53IH((?2*Pe-x_u(LoMV3A=-(BlguZxAK5^$QRnrZ7U96*ce5ToPTE!|l z@4L>ZPyA$`yY%H|cUro<!B+PY3%L5;53WID&1Rb#X>!TN`WiFBiDH*7X}50eBx#6Y z&=l;fX{ZON_3Zh^P8zAQS~RkMAy@USW>5C9dqRe^x3-Z7nVOZgmm+<7Q^K?2QIY*E zyNiu$4sF|T%#4`8=0`KKq_Q#{9?xac9;B?h<3eJV{${wS-QIPK1No-fVC~&bX%QFR z^+52EzVhM6jH2KW2!)-1O^CB3=Bpx(KX@n}X$0UI3nH9==`iVftGO^rGFC$TIzox1 z+mfRueambcHhPaJ-E2;RMt1D$v~7#wn+oluz(9-r&rzm|#XetV<{R5>AR4VxLnhsN zpXk^ywXR~?sgLi{7bF!hc_5RD<b>lW=9N@UN(_;4fBX&UM~gm?R*-ZBuTZZ**7Di# zx+c&`p>}A`0gKmcS+y|B%C*F<(aNuC^VjEqR!ER}wpNTyN_*h*vN6q_Thv{g{`w)d zxr5{Ku)f2uKUo;JA`Pr#mGgZx*Kj{GNumWQBl~qd{2XqINS4GahN?t(IozV?>YkGX z;1uNxYy-6RhfKv|?8R>!hrf55a{kc@!@Es=3*KCL)=<&B(b&;!_DHJoEf*(8v3Z_R zURH8>p()kQjQ`Z!e70o?yeeaJUo(wJLX7vuHLob2?V8Wqy==hkdjkLMIMA6i$90z6 zMP&@bIR<qO4rKiu*L=!5dU%i%Nv46v04vv|aJz=B>XAoXXE}$3<c<rt0@Ke(>FAO! z37o+ge0G06GM<E_^*4R{%CsN5RSqxv?W*~VpGs&q``(@Q%tp8bz;vsipkP9C&t9_> z4|6NGTpfJ~v<$+e(;44&jf|)Roek7mvt;FvJ;Xf=a)qjWZYC;ERZ{9|oB<<M3*lrG zHQv=5-?G?zp}SWt)iG(FmB^fYQ_h(k0!4=9U%?J~?6E^-5EVdvJM|o`{pZJVq|vCj zs4<14S-$>b{h)#GI2q_)rcxW(E_%7r1|Kie__Ej+QNRiLoA<?}RU;xMwF<9ZzNgyZ zPJs2Td0X@9YC`?%pY|+dMxgTV;1@EmQzi@%mGG+TL{{SyJ2*m8#nihO0&nK=rtP0v z{q3r0rOKVm%;xascd1Q$IXn1)bmMG?cb2&iay0T?L@ZwLX~M?WCggoGiVA=*CR62Y zZEZ|Xhu66$eclIO4GSA!y~MBg(~<dPdXfrzZW<gJ6El1Y1A)C`cHX{Vdlhd-;}K4Q zLEny8^zgC81|r+CLfB9TIj`GP{*)KAql=Ct1w`%+)_BM`b&b2V^I1whxI*}m4vjMH znj?&3*sj^iXEsM}3>7SJgihC93+W8)A=e~L4AR^%Qft7xqs>>Cws300dsh?hSZ0V) z@rKhJ&&mJpX_ANA#keja*`tQL&7+F=>#o@a_hlE{OS*g0bn~~g{J9NYxxIH?`xu)B zgT9r_X$cwYxFF)E1M=s=G!#a!c(98u)YuHC1zSRk$@Oxxna7icDf}qwKi%TVN2}Hm z;J*yx4H!)#xp!UBi5<|N#Rh~xTt?L|&XTLpESjI_N$P~ny;L&bHPXKZf+=oHc-D)o z>=dpL;qNcos7lU=B5Z-1L)oE_bSQWCC$y~OW-t=YYFvaI4D~?l>$P<`GX_{~z2aaX zhjFJ*FU@C|p+guzKrADJQ|dczA0g7v*o2CvH>43xgYlkYVq$i{g$aaLOX`jnyk*3W zE=2O<TU$O$vH>7rwXvUfH;(HnE}3^~6gRG!xIuj05jaRY7~_N-kT%U7_q6mk2DR(g zlM+UV<UC|umsbys65Ubo$n_X362h0AG1S|xI~x<|;Id2g!S@gOYG<3YsMht<+ZNW9 zY787!p}sD+FYiP6pyF~*9VLdJRj*ssj<4aqOSd8@M7=c`A2)bjUSNfm^R;L=2FoxI z4R1p!#bRS|)8EdhJAbm3T1@G&L7l=BiW~H0@Pi)4;S&MXMP><75nFCO9?!vaBW*q3 zC6mn~&W;3fTVjqdyP&h$Iyl^7i*!^}Y*j)kDn4%hl{1iQcoR0(HPlHHVj62fJRPVN z#BY#|y93BYOp*_EX=etZ03<?u7-dDvN5k~>Ic(KnO^}cldDK7MG`P|1G4qamhee0w zUv{!<ucW%K#O+}&%(*Ne1)$?SF@9vsQn8SYxC26odP1reDpDBGtA%6vX^V@qvn{=- z{BC{Y=U@pT$QEN`L{2I8yC52p)r;E~RRZ&&GAXE3CRH?GXCsk-j9scwQLHkFNSuKA z80$u{Q>UBuy2NxcppxG;3r?G#?1V9{Xh`^~GN#Ut1X)fP&Wtq9fK0g(dz%KD&>Yy} z#8<NaW|G;QKb2jaoWxy5sFwgY-}LBVzxaLAonhxJJ73qY<_7cqcU<myZ5K76_jHP< zA#+c(rLq@*LUF;7CK(L{bC&{eJmdq6btJ%|*Yo7b3k+SA?Pz|0I71<&3sfjc2ZCQ# z_dr<XBv*cbkP9|G8b@_TELJ0-e{g0#nwz}_x6C2FiVqf9{KE@NJ9F(a!V#@a0AnNW zWI3?Q#kU&Z6B*KKVPHJCVj7tW|0i{>%rB5>+JL-5x0c_hXrJq9tvnQuJE0+x*__9c zYj)TaKfq4bRVD1q2-th#-!!*?P2rO8cg<;!Lw;egu1vysQxr|?E!$Ufbwf>Lk{Ec$ z8vu+Lc#|9(4B#0_fH@)FF!*2c9@L|>H<|R&TYXQ;U|z&Sxb?EyFx+AvzJ0PTD~BPl z99W?xRR>KpDE|#f6Rkj&r0V0)Iy~8p)9k_=zK|#nKyYMM?ZpDIJ)`Q34_fDXVe~A1 zN9G-{7X@?&@&qoYZ+jAG1Tbx4dPE#$z6q8#Vz*db?ME;KD;4%j%!p~yu?1N>L1N<l zc}mc|6#LY9(fHozUIYX!fA%a5AW?br_MsW&(x75rlgDVxy_O^XGXhmpmWZK3lY;t> zIeo=rW(BQ3W|S{{K4oU(1{Ar#`X(|L<H&r?y3sNKAaox8`VzQ`m$KXGz~zW;+_}Ui ztZ{BF@G?{zE3<iYW2j?8P|9lC+}y5u=}F+lYBGK-miJO8JYLl<-roJ|$4J%iUsmNk z-e<9xPwZ>Fz2#HQR<QS4Qm9idngM&ngNR0rM!3wJ6k^AhQPcc8c3+S{1OG`SVVU#u s412_reBNGH{=ol;|M9QC-qN8id|Jm}JdOWU;D5%`XB+%E&3fB^040bMeE<Le literal 31563 zcmbrmc{G*n`#yYc^N@MW5N%UQp%Nie+pI(?qzEZQgpv%KGB!vel_C@>Ljy^LGK40i zlm-$FRECrx@gA3+_qV?9`u*1X{_#F*eb#3^Pwl<$`?{|4Jdg7@j`K=fx7M7GM~a7{ zC_YOIhCM~GFe!@G&BcNLqSgAg7yq|1*wit2{cf+|ecSeIr`Bu>4%oFjc$c@k{NC+* zg1mS8FI%F$WT~3`&fwsHAYFBJzyI|Ymh9fMLw(_vz9@VNcYuXc5Jd@WBmbin80LFZ zR9J;2!`LA-^ZUDfj_Y?wa}4S2V&hDbW-|?Z5#7x(c2)WsJEwEeOS}5=&X;z+MF(#B zoGas($rcNIxuNKae_%JIqAD+c{Ul%0#I!}Kt!I4dp_;#UmZ(2&I{VW=@=*Lf{St;C z8;yZ~9DKfb>rr(4)261(B>&~&91$b@YmR*!x4e*$kln&sw&Mv22?Zy&u~?7~G;7cl z@#m`BGbmyFdD>DNDiMEfHTVDX;~4_Rg2yv5xQqpF%JSf&DGM^FPYMecavwW(%)-j5 zZSMl|`#Em^e}Cuy{V`enIF0Z#ic*E56#=w_1nTl8P8Sy{BErvn!?)RY%Q<nsOKxa{ z<-vpLs3*-lj%7RnAuqPN)C=Y_@un7I6MtC!{hd+`DK3Fiu47{zYhCI^XV<LF&%Q9c z*H-YlVSdGmkjr;vY5(4(^2dYNZwgU^B5d-@9p0~f;<<G0^wdw=va$6Jvx{eO%aoDu zrkHDVbkxnwjp^G_TzIb}r(0Q&Y>VHED~I0{A2(UOdU&MI`sVJgc~p0Qf6~d5qGh8> z;>XA)r1%E~rVRd0v)B{4l}@`_)c2iHzPrm{xnHMfadB}%Qj+~vZ7nUad+M5+vwQdM zQi;0yFBVT*7O~__#iC{N-0baz8;(kIYHMrPJ$zXE@}+oTVPVcY*LSmfobn?mgnep? zc}DBT)?3$4xJq2(?=ISWtNY8Bgf(YZ7}HbSK0Q7?KGqer&BH^%S+2i9iyS0}r4^Md ztQ-a36$On2OZW6yh0pvQv#_-_e{x3aTF~dU!Ts%)C;#{g&#s*6%F$mmEhSIkM7HSY zX#A~*HPNiY59Ok=?JrvSe6{}fAVPifmn{?F;21gS{j2}Qm$r+%aRPGumi}kg22&lg zr?F2S@40pBR!I#v3ad<I?|OY*Qq%p=T9qiueD<7P-#^q%d;9KP;Mk8XRL#WC&mnt? zT>W)s&)ldSo^)F+hmNjp&yRO^3S4e4ot_*h5l)+;$;>Vs3sqNF*ZuRE;lpTFPKsI; zHp;wpOZ)PME0^BYv@;cE<A>r9n0JJglfR~=MX-8mg!h=N-bQ+=A#*mGgebSKZ5Ma8 z<#88py_Zx{HOUherlh?_^>)@=5^M9$>+g@bcVJ9a?v~FBo?E-S;tn1>D4yFkXEwGR zE_1~&zrVRvd;dO7!R&~Un_KqC?GyU_V^wX#P9zp-78VvYLmz52_w-sQDl6w~xU#We z-G!L)+BTgZEXp`cPg1O2?A^FKa*1ozk!`hc^A#1@CMPE|^!6?BdYnOv;QQs8`7gSk z42^N#y~`{uopHT!on>PYi_4Z5oxghO4{ILyz02aF{j1K-2M-@|`n@UN;G0!H_g`TA zo%&1DzIC-pjye?osN~O|D`f|a1?AJMK79BP{pQy0Q~kXHlwyoLS0{gtj<t==q1;>r z78VvafB)j|0mvZ1$qqcPI=yVCMHPi7EZi&-3ZCCjOiXOfdS_oKs<JlIXz=~LkZBoL z&4Ag2)IRVa_AP=?@Ly1|Sz203YWvG{mirw>Y*5UqDg%S*aS!FFnSY6^UrcDe{~i6e zl_9*yH?MAbi~Q)SPOhGrzSB6SmiX_WhixcX;(te`(R1k(_I>B`OMLST!V{#le7f5U z*MI4%u5KPvYpUSQz|*!rsyD4_P}g@cc=u<1y_AuJM4G?c+_{N%xn>{g5=AH`jg`ag z=cgwH4u$r1IZImqov{9kvys=BdT(suyb_nf5su?csVX1w0S!&f`;)&0u0_p6Do0JU zG_h$cUd*>_$J0ZpsUn%@&RyI7C_UU&L|mMMlatfsUf2d!A?&!FcyTc^OK#l_ua^TG zH;xRL7O;*#Qm@WlpTCxii>qdQ_~Y%?Tq<}EPbf}Z#lFFLckkUB?tif;eC*x60=ry} zuwVUY1HPZ0o;%#z>&a(kiLiB=jatzO#p9d;^SZyZ=4Pz$lcH)mJEe9!&9pPGQ%ZlT z#B6G6dUa=?`U=0!Lr5FBfgc+}r^Yq+e(y~4WeNS#GG9?i$y>)B2d>%7Rqji><;7Dx zG0d*08ClneUt;$5_AS}QtfSxGY<fFx#eME93qL=<nU|NcjEv054jEb5z|T)*8$GY@ z`u?$}r*zlrrH{{Q+oo<Kc`m`--JK-A;g1g$l$6*>{H3O*;(R;lo}$dm&1)Y#U@<c@ zYxYe~Pq(nPUL77DUjF;AgnC1^@xgT^TUY&iMfa6KpXQv=@{Rj1N*8UsT64|g;kqJc zEx-6nH-i0nbd;2otVcMFCUjP<TJ`9c^{FNE<>e{!V=}Fd;iPt)H$5!A?d4S`6wzM; z9q}b4yVISF?2kS@N=Zpe@30;fO<rSTbF#yJ{d&zEPi3aYKPEdBi{SZx6l^M)$7Wpf zIV<WIlSwH@(OYi|)gXV+mn`8wefqS9#_@PLSy?_25s{ONHm!ZVL_<UH!i5VNOSfC4 zcO=W|iQ|cT&!9Z#xc9%fvWaXVRrBNtpSJ&7W^Qh7dakNd@DkdtSEcjk%$XDL=eJ*9 z&olhaa_=^_u&^1qmR+4U7p^_8f4Z*>OII^B@w2rm;=s`c8@$tmHF8IuWokfA9XA^X zN6uuFd9CJzNY>Lew6kZ<xOdk?lQhA?${N_;z7ScJ{rvg!RBvuH3I&dC^wFbh(hZs6 zY$-49qedp3dUQ&iq^Uc5zni$Y$UHr#m;e2^)$()J6wR&S$lRT8D>TiH$s}e)PI9NJ z*qSBJTSZY%pFY(I8!bpz0KCYFIc{|^!!XLg#l=NXSeRw==FKjs)<1sy=;SYAq;i?8 z&0m^o;_gnzaS;*}q=JHi{Nh)m=(rz>i*p|z`k)gvtxwg|*0K(Cl$cM9^p(jtHRY$y zX6U>>>5DhV{rvo__U&7l_wV1oD)W^{HRNl(y+(evY^gcl<l6oI?)|&_f3Cabzr`<p z{pEidXL--dqRko0y-$pM*|==kvhne8jfv+hv#B|6<I>Y}s;4Ixb>8$o7&7n@$(65@ zzp(wIs0Q+K;Tlg*MYE$)u_7vKDT-r`B-7ULufrodq1lpTvG~}$RY_h?&XBzEKhnU< zkBd3}WsRK8mB9<E{`}CRj8c`?lpYxKU+&Xx@yITP<=+<sZ>bE9PDv5EWOF(O;6Za@ zk=MTunksX#kSv#MFv&%A_x2u&7m$1K^5w=m(RRoF`y0X{A_wmu<g9IN6}sjB&VK8T zkxk+MP(s?dRR_dTtZS$L{+W;Od4KQ9_u~cBY^Joku-((s4G}<!@Ppb~yXwdYWF;{i zicbEcJVt)Ll!u3hkhpkUMuw!KqGC;59UEe@V0}Kn>c*=D#r_0HJvqPXK-`7Z@jW=p z6jRfq!R^K64I0aqJ>TQ$ur5cL2_%36v0%y8y9p|`=X*PgHx@a6X)hF+92-nV+BrOz z1#8>KP-S1@0tCzKe?gOwFmiRhxOw8wpO5%X)IOHTnZNty%w5Xpb+dd&YLDr?rdVEy zQm-dcIOHtH2iaICgm}i;v$gH*qPx4QOs%Y}UOrV4zUlp(18W_Vn!4VucHQB+ItD-i z9ZBHRr%#>y@xa4FsCMe=@@n5^I<8)2Zn#2i>M-eAtDO4b&tUb#Q?8MT&SgG{k_)`8 zQiK5o)=iB*x2*^rrn4Cvozo5J?&)DMH#dLv=1pxw1BY-T<Fx_OeM(A7#r~hlD2f5Y zJ*GH}M)Z{IEqC^kK)AfAWJ$A%vN$<zdCy6u)$Xp98%xT178yCA9KEU7<^D6nnIixX zIX0)4`tY1wZrR5cfRgE05io!0(xn=1HB`l&JGqvr%CVL4@?0c9et&y?FzEBs`*?Ly zX|C{8A1&Y|c&yFIexaN1*^u7B2;szVMGZy8hhN<a9g;}e`|<WRiA_xp4)G-}1@AK~ z6&%)5(#v-;-`_j%@$BxZ=(MyADXjY^M;DSX!`IJWu%MuEOys{W-tmm(oZjcIACKpv zN~xU!tkn!356Co(0;~m2KswD0pBU+RXepIHqI~?E-oB(t|7TC1@=LGSweH6Pl$0EL zQo_2zcU2L8H1>XTtgElTk1)omuTKzCxUM!pe|KpQOF{7(X&}^%k0Nl+tj@(0Ux_Mt zhZFIkuaBMD`TDvN3ImDl5Qe#Pok<H;S}E=D;qarb{tEkqx!wF0?;guSP~`6EYaXBe zGZ={7P>xbM>C9uSqOAO&qeCL!Hmd;{Bn90JFE4NVl}!Rz_y%CsZ98`s94p4J-ZZas zK9JMWaq3i9U{I>cfkE{@GwKUZ{vF|cnz`z5@s>L*l+VkniRQ`kE||pfICEtzH$AD# z97MvDs+ykk1<sVu-)yZY4B)|eWm8GqxpU_-wp?M?jusTMi%xLywRKoo@8x&2Q7)>k z;NF2iWNjIp0E(4kPRrIKd3HI+sH*S@VdNVIlH}E^S2cFF$hVic%6WNtg^vA@jfsf~ z80t3GkC+tw*l_e<b+zHX+4}UT=;hYXim5P~&-cQ#D~mMTjM7`1JQ5QnwY=tRt&Svx z2`lccZqTM<XHG?pf8hM~?vBK}%8*+1>M0flh1^|!r0DG*@ti^!WatFam2J<fZ5;Et zFN9;&yk5xnySKBCk80J-^nt6+Wjs_%o~6k6<mAZCm-c4uYvfNY*%DJ&xKMnt^GS~p zPD)u>+3~t3_Z&XHz;CZKHSeh4Xx`<O*ke0QO%%B8EVz3vot>Ti0=kgq-rdb77gk{P zQmgGP+83fZ*@@`NMWxzbe#P+`9lIZoT}yhs3U&BXp`)*@bj-nn1Zp@sJG;Lr_xtiZ zKe>E0eX)=h?W+Ano}8Q<p7s?62J^(l#ohPXBIx$3ws$^PG!{I`p2^84bx6ziWzCBh zVlT=>1;hk$*3!nlzmaHdZGDA`dj9*`bGorZLjj}xFRUN`z-d@+8|!@F%$`q==ic1e z%2KlRp8eLd%e{qD4TZI7nOG%)TI&x>cmSq_{+$d-{|s1j&9`HTO_qV|@87>OG`6vL zE_Et5#7Wg%ZRab{c%Ds_;Kx=z`MGoFW@!0}SML3u1d#3VG;^iYz}et~1$UU_DIY(! z(T|*RT=~}JqLtW5b=P?d7cK;PWjd%n(UVVP6PTwjxG0bQafur``r|G>-?#Ny9ZhGF z=#Ar-O+r}FDAenNX^S?E?2z0(zh9~*J6pc5OKksk2M34f4tAijuUBP!zI#t9l3;c3 zsY@j1OVy!Z7VoW&o73tzlc&d6L;c+R#~cMZ=j#CLIbH9I1B*A8yFJd(q^KP`c6{qB z<1O=f(PpLQFqIf7>S9^D5D(PG4opu#xWPoP2>vQ7V9BxJb=<irCCBpe6f;hr_Rd*^ zgkW#1nnr?hbv%FU`SbH0KYlDID0qCpcn)JwBt)Xr#KvaM#;cCe$QV7j$p$8m(ihXH z64&YmBn*;gLxQdGT&e&LIe1@mi)@h-eG+{k%2B97$<+J1`$b$MCzN`6dQM6ki|WNF z(CGo#BQ`cR()UUlgjc+Jhu}%uIXyi+KK1jNkc0#$@QA~ejccY4Ib43hw;*pbLzZ^r z*s+E~+#-U~(uXgtIolI27iomJNqe@b$XN`1RIH71Z45gvo?PB*j3lIu=*g?bdwA!} z)0Yek3?y00|6Qe$oXzkU=PtkKr{|(CtnQQZ^XuzK+#~P9g2LfN3N6s!f#2Wmqm5lx z?6R`&CrWB9V9SReKlstyqVJxKBGnX>b6yn{l_{jR;}(h}%MA}!&opy7{cGZWanj`V zT_Vp}%J&(yvkiTFeS;tq^!Qmj?tFcok7{{x7EG}pPf3YJT;%}X=!Eo(B61a72UXIV z58HlrXjvWT*YB`jFJ~;_3}wb()I5E<q-)PY<e-8rcUHK2c+`J8k4jpHet*;SJrtLr zpFeAHidrsOpPJFG44D6?vFi^Tjf*esmO@x^0eAL=3-g7Ag%Otk|CcUZnvF{bx{Ti5 zIeYi+#R^;+ah{p}yUpACblElr<G#OR%U%Z`{C4N-*B^kFL$TZEzmp#O_;JBkuU{w5 zCWE!s*4DL1v%}yU6x7t_@JcLs{D=L;6^U;R?0)7K;|jRDK}5_sZ4>wU#twzWix)p! zaqZf*41)+=MPcr+-`^I_?w9VO1q--Pa~go=DQf*Co2IB6d53x3wzE%d@?R{S$W~gO zKKm9S)GB@bQnYz4)seER4*X_x6mJn4?kfF>nlRMcBqFmaOq83Oo2-z&zJ489A&bvw z{QpP$<mT<2h$C~sHp^h5+R;_cvCOLK%_e$N<y=OJXt7IE81v-OFFR-e7#<_NO%)M; z%CA$Sf7pzf_wKEFS-geU*Vk9`&PLwz>;V4#H}NPHm6a@1<-S4w-Me=?sDC?IR9zje zJ@iM~1YA;6SJaqtSk@Qr><8F*g8ucxM!C?KYiMbie(x-6y&be8d}{ciZjgl*!4CE+ zSSdipT$KLt{wpQf`T0HlYUkNZVzbNHO7mtJG56-i#u{W~&B&=xgfZ>-vtxbBea-Tg z<oL4;kB-(M5O~`6;@Q!E#%GY|67zj_e$~Olhj|e@ao|m?_MdB#^FCAXvlPpB@Fw#` zLl(BRvC-n+@h;!{<85N1h)U19JNpO>#Jd#(yo#=0Nef4ATjBpU<NG8E`0)2zU7sd( zjRl9$6(2fuC>s_3@@%#jd0!QA7~kNI@Be?~D1g&5g_*NQ3hWWroH-6#Q!Nw|5)<zO z%JKv!gBiNqi6UcnO+r#K0slt-$Fhv$cmd<iIro~&QfX646$`1J=ph!!4R%$_0UQT3 z<#y*W=*Fj)Zf_~DQy@UkA;-ztxd)U_>9uRo$;kqpWxnX65~zf`ycNa=IRb)%9r%Mz zvArc587m|wm&_xkx<JnL&R#wg4X0uu@3SZapplN@<R0E{4fJ&G!{DHRo}S*fu4>o) zMIeZ5rVC!7<n4NKSxjoht_7bKqgo~1LwXG#u9uM157j`J2EA3ZbjVD&&Z7mgHd)TF z!CWrlq~EpykSDxya&lzd-0PDFG)K|6tQH9#A@7lYR~4UtfB+jO=W2RN_nY0-^=PPW z%8J{aPrAzyfWG3@_3gX>3&Th%x#)hHKVw%CP-s|=pNgXOo;BL#=XcHfxw4~^lh{Sy zquo+CTg{2ck~lZR@Ah@gU$sgWG5rwyT1%PlCck*IlO^<{)YG%t0##L2&AlM-_BDU@ ze}6X--)SPq28{3Vs&pG6^+>;UR(rQQ5W}lmyXCHAi3p{vtqlHpVq|$TPOb-No6?&% z+xIvy*mL)=gbu!61&Cr|YbyXeRd;;;>UF54eLs_WZ9#pa(F@qKhlQFWy<(1tvQ5qH zpw9v0L)%8Nsb8|#fi>b*>gjzPD5(a5lU26KsG;$-EmYmjvNE8XqDTQjLusGhtG;4| zIO*cC|A2reo8;uPSk5EGfGhynu^~AVS>Rw>Tbl+>(4pAag(XFf*I4j4o+IYE=6ymn zfQS#!W1-$Q{eFuVmQQVxPqApZvE$jUXK!A=zTorXG8Yezec1t=fur0!JTdX{oIvsi z@wuMSug`5JE%FQXS;i-S`4Vccv2nqhppFt(WM$-;l~q+%X{tEkcC#NZ6Jeua=k@+$ zY5<>l@OoZ&5L-M5h$KmA>CfYDk4kG(jj8&+z5_CE@-AKaG#Q4U_B(v^X#J~e9^@2K zU*6x-10iL1FDzuSxtT+-stOZeE8+0rM|=N1IjbEy(#wqu>wL@q+(+MKOP30Yi#NQp zM^LB{%m?5M=zS0s0VU_qKPENje3nt3mTHvJn>TM1mM<3t9CIqp(g`$i62Ii}@Yu?# z-=I7!EG-c_d=B49V)fg#YZs}LfQlcR(-w_?I=ee#$}TZyWK$5jVJxD??)Unho}Lti zx>M@+W(CDOeR?iAD&Iqp0v)fpv*Qs#QvCZm2aq+#n>;Dz((TRNAYwtn866g1d{bs_ z$~d|tFltB86@DOO;_DkK%4^OXGKiRzN6i86{qbRPJjj&m5n*Av;7j5`A$&mC7Hlk% z0%IWGtYVB;7uDPMg8{^T4YXKanQln`5ZeOPYYtmkckbMoU^qR_iq`~=R43`IFX->& zuUa%}*KZEC=5hki%5$;UtXXp{blBaWWj>u=6ecYZZ<!UDbS`R!kJ1U8(z#@t_4Mmj z)#0RIQI{g;vVh)3AyQoT4Ze@XLgO{}sQtCaaxu)z9)R6h=JnX<`K2|7+q97GQe2$w zO0M4V%q+oyXBB10k})x^Im$0XxojzukxdD+WUD{Mr*#12JJy+V(V|6bf>qA^ueO@G z9%_^lP-(ret?lFC;b>7NA9D-U$;!;B%F2>UHBxO|o=)|vlBB3)J@og_&fl%(7M$Ju zZmXC)gWAU}s6~t971>%kI?NInKKUu8{7CK(8&edtTE*~e=cO3t9!g9<DtyQoZAA=q zD2_#9$<7rTbiOtVmhtZUtS)!<uDSW=;>C;G5RwS!m>V~imG63e!uJhO<%J#3&hG?u z2H8MJQgVT_40d8lNkzp2Xt#O&WkI26Zpw_M@u$(P1g>A4=6OoGx1V_b561OlvY|T? zo(`&A`CdeM%~hux8i75Gsb3wg0RaIu2#A!EC-Y(p5)&+*le~$_ebu#kUTwU9Ie5`^ zExB()d!^IdKyoVMSJBiH#4n}oe*{VHBatJ39j=!h1;PY{q=`=7ar0)6wmi#(<m89X z^KHunR|*MvC&_a;-rl2xcsknM?dDLjRTgkJHaeQt-9vZ%sM553%J!dllwegrPm6Ai z!{74s^1?<N?|%Y%2pu@nM){HbokE<4=IVUlFSJeIw}rob`?g$a8Gd4|gM+BSpKr^^ zp`lu;qoQ`bzdJGW`Psk#>y?c(fBzBB!N2#8C*=k7S@}~Jgx7pFqd;43+qR9SprFvF z1t6Ap@#1^~1A~VhE>+=k!3#Uy@Z$4&n#m2mjn5P~{fwxva16oESTll1D?<hXz)jAm zMDY|CkJzhg`Fh>+-(D}Q;h?(HlWm<KTfzoE<^Rd%KI|afI-t4^NMO>2qP|?%|MOE$ z9q7aQ_@hVpKtl`8o5%0!>KZ=UYJOXLZwNcboa2MPk0u;1nFtE}&?uM2BV`G}Fv9fC zscY8)%Ne%Auj=X)A9f%WK4@zb=96Ag2NX+qy4mwuhkdlW;k5=8Xf=um;Y3m6uV<QP zEsBW9UawO2(8A$uK?skdAR7X!r#06+=iLn?Y-GaJ!QM&R;NaypHa198x7GYuLBImy zs&!z^1tla@ob^#_Wn<*s5QjKQ%u}aMjf7paO5;hN5=wLTv7`trDyg29Lc7KI{Pdh= zK(`TP#9*)i5gh|(aXEZ2j!*hQO$`N@uyHFd5*BE$>$2~^fBqa#+MY*cf5rxL(a)-m zp$YDkke23!N_a$Cn;)Gd7ayOgv-6qqT6){u92I8N%wO%dZ{G%>SBL!M<uoH9Boqzq z>+$JjoYv`Tc3-Cf+9h=Y<cZ=?Q)85=8^W`>d{=aSz6v$5(+#`;)&1PoupafR`}_B! zpPyxUK<5L*ssq!_m~cUO{3NLnXyJZWmt0(2oMZW}xyR%T`9U@U0OT!0pXquxG#Wrv zfe4;qzaHT22%75q8PeY#efF$03TnCEn}FZnoYE#wgTx|Hl%inXlb;p&ptmU%i!VSi z5&XaiJQU%nL2|Mh8XBUY%VGU$KRr2XhgOvyRg8sqBF*=z11og*{WwxuszFZHDRtKd zkcZcwP(pU^6=NLV1kuRrM@>(tG$hL<7p~7|p-5}J`IbM3F^kve=|UXug=ry|AHu;& zcyZ4eYxe;0Mx)U*w6#0->Ohm<h`>6t+&c!W#*uleq^@i%>KWTk7fY<<9fv4~zxEJ; z|8v*a#OEvz1ZBm6v0?(7Q*<pZ*5^1_p$WVe@n`Sdh(F?4;UkF+e^gX08M4cw{!VHE zW!-r3^$*%Op{qqjDaVh`wx5bCqM~YEihp@KS5ntuy7lG8$SqF@AQn<Et9ka!c7M^P zlC3t=2@Euzj+boa0@+<M@)Hpj4yAJn*#&+YR5x6*SWRu<*HFMjh}JASX$(+BWM7*M z4I0{dpMk>#%Y{L;xK<mWG7+Lu{|%Oc7s;T&wYqBd=^gXzYaT5cSb3W#$#;Ez^P9xZ zB2jc_hp;^+CMIqk9tWX^1k^AsJi!*Dri0(jSQ(_8lbai~>*u%E%WTfB;6r6a85Fqz z;3NDfmiM*!^m`R9BSIZnJb3V+rD(JE<1<>kP#B>tpi^4Wd26@2vm8TFrC<3tB)smH z3#%)_Czghdeu+jK{Sn2LVxm3mfoxY1{8f1AvSqDz_Xkysl={(gdKS`6-tL`9ICjkJ zhSw8x`n*V6SGU{|2Q7{!JLhdy!gaiJ`}60uK=MRNMYFfu`dl^oMpa%Dz;X{r6)m5m z-yjvO|JC19e!!LgPST=H#YYHDj>0)KJZy|p%@gHHSJki^psLh$bR_fFo_mmMo=mJ2 z>({SuFWw@J>=TE?s$1#6`;w-ZV{fUNHu#%Q;MsoJYzB*?6K($d*hSnQ2i09SONz(o zQK~GZ<wWH*>3=AP45i*>CCqRL_OYg{d62*yd=~Ddu{xz1)!)1dtuf=G{>(dDLAI+L zLyMW^)LPb-Qx-f?hIIS6&z{bw=VaclY)-xY&nloAe*Je>anAx{BROh4Esu49fV@+y z8Q-0x#05KYifIly5tGXaKu0|x!v1Z(rB;f<YAzM)WakSCrAWGLiKek8MG7~@^6JbJ zkF%q4AU-(4Q$cYb57`u$zcwe5hjH>;0k_+pJxdK{rh=gDTJK++PjI=BAREcOl}4`B z7M5tDq`cI&B)QHUm=dD8W(>R(+KS!j&1cctBc20)ep*A3-WWO6&E1{uC4tcPfPG*s z2Rn_bVk;G8dix*U@Opy{BUagmB^2q+LQ$AR^}KoW+~1#pbe$m21uZZvEDTL??BMj@ z(Dy~sll2Xs?p=9RZ~F7TaGD7^TW((7)kqx~Q&25TRoS1OXAh6)le2!+5dZ3xBSZ=| zk|VeJw%uJh`FHxx)cyGSbHtyaHq~iDw@(E90cN!Ml)5_U(X7OyqV`AJ`yVWjH4_c@ z1*aQcq^0@vEGz4m<2ucKVj^}aAV605zMS(c%h252JtyOq5?yt!Xvi-EeGhF=JX_*Z zYmOY5Gc{Bz@a668We5tQloCQ7Y4~Yp%L}p4X&Id$j-la{s5#u*M$GfJA=<K{rdOgJ zDFv3)+-=Rs`!jvz;Hi(3+VxtpMF+<ad%<5?Xux1l8YFSXvCF6b6xt?jsh>A*Bsuf9 z68K~7Xoq5#xw&RXACQDRGaV89?UfKZQb5gZ#U+939Zsvz?Kkl6cqD!&ujikE=6S1b z=zp9bg50hud_Uo@#(Pt3(O3%gt!`&4CD=qN>i+%vT>Sh`$F$(DDX@62w|V2&>Koe+ zzqsjcJn-hLqpUsxlf3b(8#@FbTeC|`OZ&w$80;jU<(lzn?lxzf)UH2MDUiQG%(p-3 zc|%Oh%UjQ*bwdf;fcA1v(in91I`DZgVWihyOt9cnH1Yp@Q;3q+)D(ih0LY(t`}Xbf z+WB<$LWimYD{gsjFx_(d`0*Z1*{ccK`uc;qq4i4dW#FYy+fpeS^yx7qR6Z1g*z4OL z`RJ+9?V}6M8i6>zq=DaOjfj}IxjZ{HWQgRfdk6MC&Ri)1y_f)18|CMF9T|D_)`tVA zg2P{073vxqSSU3$wRI5X9IiUreJf5VSpK|TD<nxbbY+o~%kAsqHk+b=nph>5dCft8 zzs<`luFSXN!Sm-LCj>;CNHJGp#`DWQK(P%(_uISt%G-)Sk2!J%GDx_hSFGu(Pl7|% zoSPL0hP%q<$fFkr<#ivb9F1DLcI|&@3Vn}|*oKr2s-doqw7Sro!jC1orax5_CO#${ zcVj@6q_(1l06uv;%?&Is+r{?9$Y+Ds0#??k`ulq`w0{98`knlT6Bz6A*fc;upn`Ej zIpi=FY(eH7{nE;cx_Rj0MWxYSzrel5gP<nB0*Y8Px@ojA-Dozk-<pOJWniBij{M*0 ztlcxPll`g!6glJvAS`N@6$Wk43O)fHDZ^dWp5;qlACYad(E(>@@1CdIxBl)Zd>VxQ zL){tzspC<fBzAdkktU8>((TNC%6YTIfK~{Vw0*e0pkP7x@7FsDpeNvrUwYw~U~vz9 z;O=*o?4U*<@$=&>mEO3qsAXVlRd^y)VbS5?VR9(Xtnj;RH6{h`odu_8t*5GOeQD?~ zFaerA?F$&DruSdHk{lcCQXd)%p|`D4Jr(`BI?528sPfuO$AaC$$TAPmWJ0y&m(j`g z0iQgRZ@uG@>ZKw>wQaczwtgQhX$?U54xs_bMZ~}&_5hsDA!%)YjR^(S4|qmaICF5e z+fFC{{WBI1bWrSCT}-~I(p)tyd8%*uaL%m9?HmUfo?Iv}<M=LGqwOfsU<@X|C>$NN zWpsi*8AmJHUSP+%9c;^}@0vAh;1WAndN-*;SSW3IeL-jlIvirud0Q1Bou<0s0V>@P z*dmCM4&Nh0QhtMLYxcZOm!)~`+I0kSMw+WFBQGe)ZrBG{`0D0c{MayaH#d1~O8#$y z#Dej4w*`Z-CByjLG`(ZcI$~pEn@d#aZ7ZFZY>k+n+7F9cZq&>)#oT%?jI?(8tp^K4 z?f0{lf)@bht_AnN`?nKfih{B-$5AP*S=Qv;#~ymu_p_THD5W%7zpAK6==3u)<A?-@ z2O}Fd3OvZ=`U6{*V)>3@`Ov4&fmC!jp4Un_bsHWB^&fo4oy$OFU*|<c>@Sp%KOkHX zdUwyof;$;m5W@Lob={_?CQwG;+0zXY77;2;ZssOYY6oKq;*S*w>d}~_j#1T@P$;(* zk$-g|3KwU_$<tM}T(<P7oNT{(`d4LI+832m;1#pc)Ku*0<3Ytp#u9lz_PFY8#W2<5 z?gf=xFX0+a15t}s${o_m&aSG6a)tLe5(F8;Kte+X)mXap0ob*q2%(+K*zClDgguMw ziyC@f!0M0{@p~>ThWr#hxaz+IYWKsIQEA(^MxN^a_U#BeueckusEVM^{CDr(1qmDv z$4w5TPL1Wu873wyTLIItn_MvzFbk>qDklt~?KTb8C5In-=8y&<2Puqv4sIRdG|<pk z(01zpU3FX1a+U&8E=b-441IEA%fQQ`VRXT{@CTTFxA!2<235Pv@!=1*9P6D8Q}Pd7 zv|kARX5BS+Q_2Wk23#zymp3eGos`cxvHB$2x)1+>IjExOsp3aRgB=Ri(fFj68v$Cm z?cS}vaTAHe1fDgj?7RGe)F;(a8qHZ&n=noTR0%wUjzm-tNaz7KlM_ai`u8QGEzLnP zcYl8Zeo@g+b;<O#ta8w2JsOS}@7kpXi7Ej8LmeF*(XZzj4kLwkiKlGOg=i7O6RD|$ z6`v!uoC_)g7~VR7LqG<*=z>fNO^KB4d3~W};%}>?<Z$S6-lry5d~6Tzc?ify3?5+J zSSXS>T`GgQ(LAmH8p&{&#7E=e<2`DawDAw|a!J98G8CfLV{-JX<Wf&Al+we&>fnRc ze@v7^EBWo!HE@L{_%z{WJ(^A^;UuWKw=f*e;EU$rH4btL#DSI}JtT6{N3iq|{kBAJ zB?dUdnJE#_koRG^BK%QkXs8<+*2{6=O!Nze>jV-RF?b{Q#^dwJ*}y_=yy=)&;GjCP zq?>TEh$1w6hZyu|82A9l2pD<w>J>3^0$bH1DoKt<VkJgCp_!rxAvPL%+e4>LNn~bb zGA8os>bBG{@~+P&;zy~~CMTIIQA_5n3OlrL{iOss1)(&noV4hb`+msa03U--X|-}{ z)9&-NqwzU<$S67h;UpklU%x0hzknTzhUy7==6FhadfeP)Ufmt8QL%V17${w?R=WzP z{17afV*?Jl^_qt{<hO2+(-mRAR8gh{VXQ$<{Q#MoIA0*=IN#hEjaXU8n~v1yI60}B z7RJrbkB#9ZoDU^w7^zPZXP}2dj=goeNsAKk2U(<|W0i}Vg4aYnFE5YyVP92-EF#(N zdrLwA+qT=GaD&yMbR*@kQNaT*`QT|nzZ{pIOV2qGhrmrpN<sA@JwA+V_0TkMoKl<v zcy#14S(ND^&|-{2Lv_#y(i-!ba#$#Qxv#bcU3J#9n5P8g1tgd(R5=OKK{D@#v7`d; zYZHLoub9RHOGj1JNV5Gxss^4=WY32{jzs4?F!sY+%Z9GHrGooVg2k3S3)udaoAHG^ zmCW0lsmJ**cl>x;NS>l?7OwYq4+>P=rVTE>QN>a7@x>zj1HWL94a-=zgC9LVJ1cAC zB4zrMo_(@(`ZddoBPrDazh5op7@A|Wn7X63_=Em|a;wiY5h1a(W?#Z*97Ewwn9D0< z*A(!<<mJz(nHxeiq0~M~r#f;m5MCZT7-#g8b-%yA35uA8m_Ru0U%ye~7SUO#0E*dh z;M5{kM}h1IoZkV(*7)6F-U<H7qOM*tC5<iAXc8CCpxgwRFDU-Lm!)ALN9!4kQG>E# zn&(k(x~d=eb=KsPYO|K8<b2jIzqTnr1ia4T;<8*?MyNH)k@I+h1slQ*akdlc3zE;e zA`|v0KDR;!OYWGomEuV9qD^~PxrDs=h^{7dWbQH!SeuE38rfQC{EJZK_cxr#`DGW< z#JWFoR#f_^tfSM3fmb;8&K*5M^}^>QqZ`bH{YY2Y|167j$5YC{U?h3|d^GPC1GA67 z?(OYOtf8RuX~fZ71JrH@Say}~*RSZc=a$RK!<S4_s)L1qoB;#uLMsjeG@k_}JFKT6 z7A+*_fL)P`WqSOh0I@fKIgbHd3`A4-y)+?#r^sS`4IF+k`T6<H&tPc@J#aumi48gg zDYg}XAG<$)=A^#7ERrT-0MIvqUJjwAR#ex$1M%P}3#aZf?D(S-3MBPlT7u>K7&s+K zNlCX44`K;ppPm2EQk34d2D=FoKzJEH0%D;1{z1Y{p;_+!_>mP#H^KJ~io($kLwXnf zZy23Vh|PGVH3@QJae|3&%D{-B6tniJD+tN|jS_~c;*MMjzRjpQ@^8T8s5hUP6{E5d zr$1tH)EIz<szKPJAKN}+&QOY5tIQ;k2fB^WS_oz#-<MbKBXiy;<K^dXAZ9VRjXt8( z2khGyk4y$<N|~URkk~#r(qh4mfy9afv)?!INJ%JV$@=|#-Oz0q+}WjiUr;Zu?Hk+* zXD2suzZtZF;qhS)VyL(mwtdx}zu>@Op*Xrn<vRL{>50#BgquZa6I6;s8wlQ*14vZ= z!0$Q##TmSqMP%37P5RhMvG}GxTp-`!@nXY3FlvDB_|V;*efJa0|A5^_%BkC5r)k4n z1d+t^&ci$V1|0;>Bv^^UEC!au9SETD@nZ%+IKFEMa;vZ2ayq@tZY8b#<@*${S&oj5 zHBfE$KLKxwg9yUf9AGJ}<NLR7*YEzu5)twRyt|gTx{%mz(qo`vP!#C_h+GK0bEh_3 zVOkJ&0U+;W1d2Ms!I$1Ez#Iw08x@xj!HMvCIi|dJX6lIrp5r5r)X9peIbCN%^XVR8 zPgfNKV%uEiqvR#S?zL-I=Hk}%`YSXwn})TB4|GRX1bU6quwOpnyOmVyvEYI5@jx`= zq*_Y1t=5m4mI1bH-Qx*%IydO_I*@HBQTb}msK)$Q_<#v<yX$*yPL9T|R~mWw`5u3M ze|Nf+kXR7RJ!cLBu&fnCxy#*9J0fWdrL6$Vzhc)bK@JWMqTG)-8yQic{!aLLs;^u* zm-O8j6Cp+pha%?<wXMQ-f!UIOcO!w!(+_2;Zn)fAVSFT^;O4rOG?$w@g`k5M?s<sx zZHjm0`Kt@CHAhBA5J%G#n3>p=P&^k{-(#4Y6ULM(4g6?!7tS$4MHooP%059cz1Rp7 zZR@SwuA%$)&xcJh;?HmLVKA0?C_xD~&HC6{-n@}U6@2t-0w=2HN=a4o&v}=BD4zfS zB3N|-d)SC!%(GDthhx!<nq#Ta#mGd9TbcU%h0#>hgOA+?+z+!AVd5a(UN2v*KyOn` z(~j;{(bt#3Vy}O64_(DI&nDr%#`k|<*e{qmur~jopxsv?XaztD%l+R<qR+zAMIsDd zbXD>8bPS7lA9PS9rJQmDwgx&U#vi22GW4u=!xw@`^Sy`uFR3jDdjd-a)tk?U7(0et zYUP9tHs$GIBruh%@EEwqdmtP)r>;s!Sg?cCvvYetJYXl9Fcd)z4UHN&8Z`Vmm;1$= zG90cFf9<)Gr%xvWqHnt&!w$)?VDqgdFeSEJ-);>eo8j;+kS8M{xGmXmIsscg7m%9} zC8({Z!5)bTq9C+c5;HwCdoCyyyWUlbVM5@=Ud%q}9>l20*F$LQwjmodvy1?KfDm4S zC?YSH@?&9}R1Kh34iv_f1}Z5QBjM*03bL_rvu0dy=L^<fnqziEl14R$MImYGm`c$^ z*)CW_B_xc{)2xVoNZNOF>81$mb$9mqk=M%m=`fERxs;4RL80wJ^X44`S=<eljAGgv zOt7J~WW@4_-ISITvg6L)!=i(nq4`msZm+xdn<UBU;J{!7`EaW%vKHtC;`L2gDIvSW z8eK4u-z24^=upT(tbkgGzIX55>_*yQ6YQ=XKY5Z3?LB3Lx<e3W@Zft{PzKJ&5C*;5 z=1StU{0m>gi~`H7-#Yj_UjPV(u0<w8oPR>}#xQ~eYAcxA26RL>Wrc<83Txm&YfA!z zaO-VML5Cc3?V4t~y6Z7GXuUzu<k&}JoxL3hxA-(O5n2g5)dH3TR)HM+mD!&kC2kj~ zvc2D(w~uHC)=+A4%egLTYBC*ohCBsO0FQI|=$=qOHG-?ysrcQ!0}>FxORrzIH$UY= zyrs|qVKejm6Ars^+w<qG)git%TzvJ+jEot>ZQI$eI<uqbtLxxdOh(tpLy#uJ8ky(L z;j<Ziw~?Q-A(M||lx6t$_s!StDCf8@z@P!hrhhf+3*_DEAQfLoq8~mm&q{X>mc%Xq zw<uvJ0>d@~(7hYw!(z=EQ^?EdpV38u6h%iE_srnW97rF;e^vh&e(IL1PRpiXeFH;s zKKx9_Bi=^u9^-%BFBpQP@pU-4ACb+8kENtU9j7*<ZvxLp+FS4+wP+|xJs$GIyp!Wj z2Ng^n5xtk7lvEOFrCh5cYhrng936kCi_VVwpsjB=gD(x1wKyo41bLvD>V0(yEj&?E zkRpi-WA^*x;lGj94X%MbnwYrAfDTAs;-`a^w)Cv%O<6ef8SPtY5URwW4jJU<u1)!C zW!o=r;GI8zK6y*$y&!#IpZ*9h&Vo(FGN?UV`}U2^Xcug~=ccW?QxC667+UZoJe7s7 z8XLLb>4x*u;!%Hl;X(AdH+?(!A^gP@6@8z{I%(AxrMi*L-6N<96jc}Bx1OAoP^|kR zyL_hai_5X_(|Ns_8oq$srN4hfu`9)dt~yOuP)Lh{ZPDOqmSHl17Hg(q)!V!|d-JWk z7=v=Wec~4e+}drxfubu$N+Hll@a!Y)i9?6j!hb%|#(yFV1eBE&jwa8gr2dtcmsbqE zzeMzANy)p~x4Q?`)dx>>d=fA+82SA9AwrKKCEqVviWY?oEWX771wtf&MkaVpE?mz6 zHa2whix2@8Xn(Td0+iJ9IZ1va?ofPuE!vWqsh<Lr8xU(ilXvHelI!X`X{xjXzo~P& zfxXEI7ONrIJ$drv8piHy&gqH~cM-rw;86E{H9t7k09qis#Df$i&Ry>s{F$cLW68R@ zx|+w2=TxK7{Zak6Z_{sf%H6#;QI16QQK7$TZqQQ3F$IA0(Qta%jv-7=lEFq8-SNs_ zUR_f_>&ptw88JcEva+<iuiX`X5Z-fE3QZaf2^EHr&(rkui1~EYbcQWOBh!2UZkmLt z3VC}z;U+n`FdYLbI+nT_Pggfy`-Vp2T3$MM0;_rZ_U$9{Dqu?)K)yV`=@JqXpN>sk zibktF4`a1r8H@!xKU4{fPfXB`4^C-mYd_mlqyg>J;p<ipjAVQOyu#p4-)Bn)sk@PX zrNAxJVGZ22ZM!ge6r-5)SC9C6`0*V&6d)@Pi&23-iw+sgQxwLOf~tr@ec6KrKe*-X zyn{!2Eknb?=&GtbKv$-Ex8AfZomr9eGfFSS{+=9ap+=fW4d&Y#cMom7r(UlG6pg<} z+y!vV(q{EfkO^;c-og3`s4tr)Vr2TaWU5#wjR&KyY>Vh7RTu8pn|{8uOwJ&j3rLc5 z<&=hZo1)t0@;Oj@P48%G#|R3E>E)bfvzJ5>4IS!^F0WJ(PVULL@jujykUA)%EdXic z3kVH}jYju!CPyWzW=1=vw_Kk=Pbs1GPE_MaD)_L#$+a4M^j#xAWVoXkK!cYv<x}6@ z$4;=XSHiv+JoL?}reo8zo$3p-y#0G1jmO5a6O%M_XK1;uchAn65}IbFw+T1?j7m(n zY0^&o0-~Pl10g5D?0ys-Bo1AQS6|5E(><LPfy|?8uF1%T;{`@$e9<A1?9&5m+3)md zpkK9%&xMTP>Cc=>uvp}<g!L!9oJVB!R#-ofm^+snXAM-mW=98i3={E)uHrtXYyp|p zUp#OWMXLG5>l-77ic3Xvw>n#e17#CJ%L9BMamis+s_^zx>l$ZEbo54G*f3#)K4tL` z*of`7K?5CYY-|j7Edg1^Euvh|xeBalrSAs8UQ5g^qEV4r!-QT+;3siaP{h6bM3E_{ z9GMmVpbjy3I4M}T5mb)=tk~2@$~{C3ri8>@!-q|aOHwQpF_W_zBB~u^=bT{Mye+1$ z%T&3fN+LcF3>+r>&;KJS?)$ax+n{=uYE;y@ytK6NE%N!1WO4fzc4sc%*+P-o%QD|n zj<?!%*V5J(=h;t$RSgYgcF@ERB@1+axbU*D70BfSQCHBG(<nGhYXME%e0>)+|NWWw zO7Z81>g4o%Oqp5fp{~HBmJ4d${avvmYVCe|MIEM(W%uc2M!T*$n>UhF35-idl);!1 z?(4c}z$R(an9MgTYlIW0e)b9l;5@1HFX>*grP6I`d{_th7Sl3yceU(>Pj>uxX_Yoo z5+NabE^6*+dVk9WRx;Cq_K(;F+H6KR8_kyoSx+;sKJSKPXa`*kLNXZTSoCmkOO?9a zXJKP!Pkha8H~g`mM?Y$b+>@V>(ZZ)?#{ce{_<8$9)t^3z?tzk@OB9xc_*@v^_>ZN7 z^(}Fv6FG@hVvU6JC-RW>=@2WiF&ZKL4&cM&F`1yZ_Z#!dl|>w|`OE6<m0z-C?c*N_ zNdnM{y!ZtKCd?!LPWg5@IVTyI?8U4H0kK5WfpKQTqywW?p&J3c*sg=B8idR<G&c4S zTM7N2N@Rc^WexuEb}oqMFL>wC(VKgPw?qf>$%{GpOY1|ltiSLx_tk<E@ffrv8awz` zR(ZqMwH+N2$e|J+m#te9dDgRn0|bXJu(m3io{vDW_eo;^+NOlVj~yXQ7V(1M@!62A zL*mvc+EA8$JkA!Ak5B7c*OOucO}_a?S@pyxX*32@LqkJPos;NozY`zGyh4%v0haM9 zJOa$=WJJcy$|`9jjD*GO9a$IPuQ<bVu_l8$(V3uOvKK!a{I!j69R<#1s)YFiBUqsQ z<3V#Fe|A~f)Re0-psw?U6gCam@i6ImADpm|sA!(PK`}5z=+94l7zAL&+8{>A<O*6o zyL}ey4Q$8TFDEE?e*OBDP~n$17A2vf675=Qqt9|Ip}^5@GYha-0_6D~G|^<bi<<DB z-}mU0Ihcd)PoLuO`bMg0#DhuXrT^wzL7o>VD5inRDO=Rb^%|xF!dC+Xvrt5?gI~(h zUug&CI1C_IE=aQq{PA|BjDDyfj-Dy(gQ%OJ&&f;)Ry-Fg0sgl6cPH#Q5~P15iU8It zNz6V4oygGzHC#^k5Rz>yo(l~1T#SreNRLsX)3>Pjm^$KxQ9BpqhZYqTX#%ywES>`w z<Nm60gcijFxY1lqZ{tf^y7V@I0x+$g%>4>K$3b~6nO_)|NQ3^|fH5uDQwhC<WoQMa zW2s(&R+9Abg!3fd;0-PeCn-aJ|5EhWc7^szWXu%mllt4E6&%S43GWs9MTemLwE!Op zYi(#==+>5AQK550&Tt;F*H9C7hd~dUKM~-y{;ZO(6-Pg>1z&WfdArqP44N)XRivlD zsKOET_m>35V@uI6PW>J0x~vrCW8e$=Jvg~pfeHJLxDpd|Y0@6KkPlq)-fi&%lSoAI zc;gG`uwtuE=R9@ufB0lr5j2y%HAoU157Vv8B`Fps^Cd(#;S8@oZx}_IA0p=fYk0Ko zUeIQ#fJSOW)Eg$RL-fBXO`h%%^BB5*V?W;Qe0g>5^xrX8n={KTGiRiQgnG&q=sCix z`?l0TgK=*#c2EUz#E&uAINUWrO`PC@<@(Fl#8UlRVIhHU;BHKu-;b*zR4dS>=aM0c z-@oIq{m@$vg37~X4jVK1U0iy7r}k{VJU^w;RwgiT;rObH_7m;bOG>749aPQoki51z zuc1(+%24oLlF%c07r*J|=XdnKd>0cPop5pGuU~x^w=%z3X7>y?r?GcYJ%j394sExW zX#^Kc<rXd82i@wceCsoowE$IUiHRN!_pjIHM~Z1yDJM=4i$0kjz>C9^(;6tkRfuyk zj^Rieg%%Ok=GaNyMZHe7x8K)3;Ts=%6H8AS{pqpBm`<0a`>>3oj{yu<0M>jHB$s|` zUGV0&9l}d#ir0vja_5`!#TYN;GK!o$idjmpV?m3WE#=izRx=!W5_H)?UhgGL7c4<g zAj}};Lm-vH<RT`69yB#^PtN>h?tE9Ng=4LRnT~pUD=Qx2(ItaJht;drO^<l&+I98z zjc~CYd<Q}s4aJr1?gY#q>lHYj%o7*ajIBsnEBH&T8;6k-zX2Pr8I+ItX;!W1>TwMK zJH!S~3W%PKL?JQ0K;uM<qPmw1;{Y;lzh@020xSW69g~I9kD3I9(sVG%2cH`e&YBPQ z|N8Z-6~O21-TmudUxL0wQU<Ek$&2a<R#IqkLHFL=^;#HHT}B{A$WWjfgo;}y44HkN z7=)BZ!9>1<8msU~3pp1?GrGRM%l;zpL&V34I;%`x`~ld3mJ(M(oAdf}kufnDCqN`K zSmQ7sFBNh!m6(TNWLgf`x}wN;`*wB;ilkHe04Q{G2L}TG#dvYC1QBx3G;b`~dK4B& zQl~rltwY>T&3;}4<}Z*QL<hu-aGw_598)gY+1YA-@G)rJqRv}9A@6op4?5rwCgywK znL={d(=@KTFC^_$SfSIhfsjRu(wXz=ZN@0Q=<$p|y;e<)Sd+QN+0jC3<i=Xq(kKc8 z#C6DdCvy)nD!<`P$XHpq|GUd~UtU@xjf+wo4%-z*Us*IXwKCw-obHbbGm^p&NPEJW zc!mlK4b4XQSA-0RLm46RAknjlp%Oky3;=O&+O#SB*NaU=;%&Ql*y`{9&KVE-`oFD+ z#`<1vm$O?7MF%*dyStl-g(gtKBrL45yw<)Xhb(Q5JMpm~_aTtYKo1g|i{#w7GzwiS zC#Fr2j6Wi;V+glk!xiy{W3pH54gb&da**H5(1+Dv?TmX@+ZKp|tvU!@fXws3rBBKs z#x3n5pOEpI9XWFB`9!k9CM}eE*wFCc*)sv+#Y00(+7ewS1+b5Poj31;A8Dx!@ug~z z!~-TqwnHsc*!+hm%n%Ao%gfE+ph7#CW%BD`s?nF#Gt+<634M*kN)#w!%Z5Gjr0G*K zXQEdorg*?`f|Z#hw#XecoN3kW|0Bn2Y^y$y{R!hXgyqAjC2nfdA6ZtnAOw*202cuk z;UJg==ki^%qC*0Y=|j~v7EAz?mX<ayec^?Xshc;KlS1~)X@lb}e_5)gtIIVv(7wqN zDQ<W5Uvaem#BJQ1z8G`vsI5F}M%gs*(vgvoq;6xpIUc&LOUHXl#S<o394GLP-?_A& zz(iN3t@E`V&*p)FsCoZ>9y5H+K)k{e9F9sxJDUb*E7-(BCq|U0-aB7S(k6ET$GjF^ zRqr7pl4{bGyg(bZvKGBG880C1oA!QqFlK8n3&at#yP?A@h$RBVAcrz+n+e81*@HD( z^_c@a-*JhF+^`0!__#WRZNW?#g1H`<n@H!FF3=dBttQ|~05Nbv$t%LHs`jZUjc|uR zdc=4E&kn`>cT>b6!|Xy_fHIdLT{7?nHs<Bam*kQJm(Zt+G3O|s<7H!KXNSX=-g&~1 zBC0HUVa<I%Trg7K1KSB{S@c(}qBWYc8tNx!6vzX!w8FGRE&zfWa0o73NMp4KuLLU@ zq$JE-dFKiKfWAfevm-Jqg$@6XiePXUR@X3!0-qH)`o*s7p^=j9>+9RWZ6%QL3Q(gp z$fGdT@e=xxp{Q%eYkXk;{ttLh9Xw3AF0(QzHru)!8Sl%$z@n(Y{lCA>MMfdEC?YX2 z@(hq%*TRoVX`p#d_q^af2tPd`hEO|m?`{MIL_ncKPhur^F9@ump`jWwZ3Ox2rr#U! z|7BGh9vKl{#+DO~)($QzcTfW4?h#yj0ba0aOE12`2i4~-7!x$;+y7SI?VsX~0nCnp zQ|VgeCf>EQQ))P<#3y%5W+e+n@-1c$VnF8aT)7leGf#v<*h))x<XpDrI%BbcxU8|f zLg*@yC`q49eA?Cgi3tVmnB;m3B|7>I2+$r7UZOV+<haxWd|@n<+-?98xf@LzVwiZ$ zki`Z6s&!9g>h0r)a?=1NkBq!*Zx&q>Z*=iE8s^A9-?d$C?;(}T9G-PTj0&Z^OsM40 z(1tsKjB8>G5;5saxv*OFD{-+PrzRm!Un$eT$U({y`vI<Q6oqTB!V^yxENjhA=7Sda z(orHuX2qWVC_I^jAqBWmqKR=p%cs2tXveYTbV9<HRXHk5f+I1VPHrl|%qF!?ITCUy z$zqs^scBtqf0%$;%wW~x3Af?yETHxqcX@?C&@-$l9PSTzTPSCcE!LbKO#@+tt7}LH zz%OT@ws9ZB2~pI8+R}i*p!umyZaa2x0$ttod13YD0xp*j(}N0$QS3l)iRd!SZ>Pif zio=wPOBrw-kRehaxsNBkQ@+g7#)biMne;al6OtVqD+h^{k8EI054np0XI>|KTm#rP zEjI$%hAB8PqN)&!-c9+$5qB>y4oqeeR~V4o=T6+8lM@cZ1;Bwc-~>|MYUJydJNtHH zy5sO%X}e+&MSwXpL;wyJG3{VZ7)FC~B9YKle~%`G9fXJeBM3BNRff%@J|S^|Ee24? zP?AR2=+3?`LEz+xAyr81ILt;PeUDnp{WcSoAGiJ7e|tNqwQ#)(v9Rf2Tn!1Y?no*Z zv<Q?NQwYICX#_tlPp6x_*@o}<nn{<40X16##Du8~8qi$RZq*cW`3G(&F-FIu0c#5e zqxsONh$(m-b)WyGhYaQpVdfLM1C8?c_a6r)cj`iJuwVyggxnwol8lT@frsAkV@iyT zhA!nH-l-KZ=33=*E@futKI{*9O6d1we3EE&$Q@_&9-+uM<fZ3!W2FKi4S*6<(9l@- zzBS75D%Loev`65U$t78@wj4gjoxS(srAs-a-y7~>#~cWW6=H^%jg@nke&1kXoPq_@ z05S&QlA4;DBWsm}(Us+3Juq5xQTLAeQt~tyGbWcY5c@0ULq6=5Ps6kkB_w3@tDkw8 zH@ieVG8@%F*W^2Qwh0i+)AOqiLsNsA%}dfFgKj5&d`WQz<&%zyojZFMqw0{BR_t%w z^fdF7#8B6y1I0`e8yYBKUQ|jo!B(eXII3z8De4t$fWHU3lF@JaRQ@s9#!|5Ie`FSj zfBK<A6e!TkT`gU50YR)hBTD}N=?ItK%OPTqwOFwfSmU=FU=riAwY4RqUZAsqLux^F zZRq+kKaHil+{t`;B<IV+XUUczGt)v%m$0pI5JeA6Y>~t&50sB9YPi78JVa$nDt|Qg zAU1|jdFI^tYtvMc@%l=VB1Uaz;5@FVsBnYronjIy39F4sEAQ6b#a7WfjVY7WeN75z zak$ZE-)sN}3q9Wi0x`KJi%dwN3m5wQ>BW^R)6VCYnBYrgI;c+veW`je48x9(hxUCs zhW-@dW^S0BaCzo}m+(PJ>=J4G^E8vD8zQVd4>9I`NP(HBNpK1rY8=3FfNwMk9bz4> znb>c6YQvS@DD^MSCbJ{lrw#%8s@^BG!gen&cf>SdlZkl~tR5KeE7cyGHcb@2Kcn?? z_Z17_O~sz8jPQYBe8Bl0Dmm_cc>tRy+i4pPEG{X%m`iE3p^A&)yBP7_A}2TT&~WBt z$KL~4X=2tjP$&wwWq+p%dOKkXCD#EEmK_<{u{v@UNuU!(qrYyHd<_ce8wN?_;Dd&~ z49L*2%ts84w-$g~Vg-cjsFQzSW{(vQ0>X-0nVBaJNI_~kbd6|2_th|6eXuF@?2l@C z4{mqkG5hCQBm-oSE^o^6BfR^fYnM8B;_?bOIaJnW8Sqg7K|#2n?jW2-SjV>k5*lJn zA8O-UZ+frKG>DMG=FFl+xO8VLG-H1^mwX}a24lxe+Cx+c8igZ^+q2Zsgk|?N0&^`e zw4n-H;K(If9!aG&pg51BF2P3S_$UPrVP~vC%QL~mbq21@z=SLhM%G8ay<Y!jKKaA> z0TKZQ6}*m1bakcFx85bUgN-=DZ3xPvq`m+@U?KLF&17N{my?jEBtt!jkLCf`VL$+g zh!{0*02P8UNJ0<EFI_4urs`ni;*yC8C%hruoyB4$j<*n=Jsw$zl_DcOXj=jMw*GrM zJ55@i8t^^IPhhi=9m!}k3CouyuF1eHJN5MORl>A=wDl{XaGBti2Qobb0xug3Oed;% zLIR_X?Ksu=Y~;oB=LZ>daxGjdHqs_jM+m!y`<2LFX;|28zXaF}N>D5Sm8e)WFkn2& z-!cLoRWInUI3|$;M`|PZBN_$L#0;x?Q$`F6RVZyvSr_8=fou#6kZXy^hf&ltXFsfj z4>uE-jS2i$<$lssHp&%*S7VR@We{*N={E<TKaKQBrj1%K&mv$r#YDdQ_v72i=O3Tb zlOV1ikQTbZU$SSf__N^6pwa{Ic`_J_@<*dEUR;l4<%Vl>W)FbHW*v5y7+IuOqW)vn z8bS~&xr+mb^P;p!jqm~-AA)ztB~aSC-@|3dp`p?3rj9Rd(xj<v1A>c(4|E$Im12^? zH{t__V4SXiWy$G@sWnk*#DWgL*yKvQ3<SjC3DL#<Ya0qOK5)5lk3bSe7PtT=8a*s# ztnuqBd$_WXVM6gBI$DYtQcG{$iU$x;6i)o_yOR`{x~g!(cSVfJ67mh*J%vlGXkf%< zORU}b45~l-VX_>7501Ffi`f0q82jpx3su|Z+fX5xBOwb;Mi2!BNmuj$b8h4kmAyOh z2zje3IM$h?hw%VYO<dv>x#<rK6DMx?A!d7&V5wtNKbPr9eo09(!iK={*|Tk-BtvT# zB3I#I&iI=DJKdZ9@0{O!5Jbx7|H$R0iMt(nkx;izyaMqLgy1&&&ZZ4QHSRkBYsNu< z96x@X*$Ecx)aki`LNv?&YFWv5K^$3&c&xn{s;nl!BQYaTEe-GI{mzQwXP8Y_r=*FM z`iOP+U%m)+Il-bMkf$O8tqlh;M1qMi0X<)SR{E)7*dg6PT+}Hzj~^z$>FG}HlO!tV ziPX5eVLNwr6wA&tjGTkn;mnmGJch2fS!y$-cw?qMwihxTF5f5LU4Z)~9OpP(cHl-! zD2dw(kf0A93k#c-j>%1txbxLG$QUTrF7dt=v!00!#DyJkfc4GKhL0vJc*V=x2?PW1 zX+s@O1QYak#`sr1`vE&+&^tmx_u|*r)TiL8s9PnB6F-0cyDN&E4hIX})6}K^gI5eQ z-)``_5|)o#3`#7K8unse2Oe49qh4dC>SNj>CjruKI^?8sceh=%CIc66$bH0}BXuZ9 z0odwY-vJsm{om^7tXxS3A2Am@yCIpGS7Qr!n_h?w{bcod`8e!dvsL7|5OU3_g{iRd zx^}TvKEPc<b>ODPJbiIL4A>Dr6_S3+#UdDZNlwBE0vnHcDP&O*GKB6+6DffDk5-ia z@4mB~D1(U;R(*FO6zILa|5s(_9+&go|M4$sl1ZW%wV1;?5oX9K9h^y&v#HRq%HfJ( zq8uVkPOA`0B_vZ$MWZBAa){Z4km6ElEk%;L^n1SB?%!Yc{rLUv{k2Exy1s|c=Y4p+ zU$58a<0=Ck9)#g8%nqr2r9I?wa=e)92%i_1qVVLEe?4Z<dB|@;Pw8^d=RX5MjrcTW z;ag!8Dl(gB+|aI$|Jk-Nc}l$Nb=3)m1VQz$m$#DeJK3?=0Q@S{3Wrhkb4L74lQbt8 z-nW^wD8{!%lt^*4oJ5KnJKV?CyYa0=l+12QE}VjC6$&$#)ji1zJ$m$Dgg-ku8FJTk zRXHi>;gLX|S<u+F_!FXXrYYWN)yW1MHgTsaDfQGW=S6G=N4Gn57tcrFx3*N{_vUBw z;vF-CF~s~ob*)j%<X6?b>uF>)Jpe4q9oCN+5zyWw)ta*l+bjy>V-&G>5V}&=QQNOl zCTt>}6CFQS#3Tu=U^@l3)a`JP<jpXhktZ$=chp!T_ZNcW$j2CNxZm$Y`wcxL&Fph( zGzf09>s6y|A8yT`JkpCg?{ae|MrPmH7|3J&h59md)KjBR+NS%FoSP^xa^lpZc_2Zj zC$HGi6s8pv=|Uzu77HH89lwhCZe7MdrqG3rCdL1aS@e`pSe8<9fVg7$2v{&K!N(Nt zS{H}Sl8qX7?UM^fFONPJrTZq*LX;20%Rh!MkB!f`m`LZ4iMOhNXX`W{q}lJdEmZeV z?}{>nBlulR6xU<sM#Syk-D6!l#QT}*;S;fOV>29g<Jaqq(_in}7gL95>i!H`%}qch zx~$*~I9#Wb#*H0oAPP{cITxqP5biZo*p|eew)8${^}kjhj;c6jzjW!zI|<(mjwbbX z+_qcycvOX#v-3{DE-vTi&sz1dK1Jwx(F~>S`MFWdk@|LT*QT!1{QCOtDAN|r^^EnY z7tQ4>;QfXn+l&j0Gq(-Y471YN&>M2YWvU638^N1ntUlYT#CJ9~9|L!7Ot^J2@PaN0 zBlrIIEF62fLF&jJIvYBJ*F+OpGU&Vd8Ck0ndsiImtg|68F;Ns!a;F5dQ#?GX__n}* zKdB!<*$*U*jwoL?xv<^KwG3wBPLdVW&q;YLFptSVPtec6(r~uNrPeJsbY{5Z3^#Q_ z)3~NiO^$a)bSmnzrAwXX_@YqYcfk!#pG{>s8k?AeWu2uTZDay3DY!pBJ9J0J#iPp7 zkM*nn;avXa9_l(haJ8Ellr6e<#Iun%`upc!8ajDA%>!qfm$9jZ5<vR&&QERy8DInE zcO&N=sW*IAw-i`KkSUmRi~8O)*w`XUm%jGjI0($j2R@mfGL<nBEzEacxLQQXRme>u z8FSd!X19gzn>37}qNyNqNXoX?sOY5ooe(%q+x0<1q9W)fn4UX(U1Y^<lYZ@b8oGKE z<WW$~g8_q^Yvd<~tZY6#YZA~47RmK0ZQVxC*wn@V^k~Z7n$YE2;w!w6d<dLHBwYH_ z#6Gb(bu&>l2tNgSoLFypczOnc?j9eE4H>VA7i$me6EG;|)cdg=djwd3BHn(7Sjv0u z+{sbNrpaB)EpDzE`>R7(O*QfB{hw!Y@%8{`Z#8tNQ)s&Z@fQ;z{o!HbNckrocYi)j z-QU8!kIS8!uVHz@uDT3-mR*rMBE7=fLhumW&35kFckH6k|GLlinw3v|H9zGY7v_C0 zBlerX)f308Z1-+$2x2kp#dbnFVDNv4@;2hrJ-TtdE4(0u;x>9v9r5$eCVXJy`mrm> zgK;6QtmwaF#Uy+N1KCOtw#xG>4BHMj?Dsd>bw+=cRLfB39*%7JuXwAkr37@=5%`s# z9J#*J-1P6Oc)50hlhZYT8$H}R|9qU%mOAc_rDt88Syh_)`q$70=BIp*NQnApRNLsu zUD$zu*J6IV6;h5_gCg#?g=+QT!nhUf{ZAiDhZ@8`Tm|yL;v<Np=)q<Im(c3m5Yk<k zv3HJ7vld{arIA%SF+*niqQ_vrJgRS3((GT?_2m4r#&)p#xNx(BKW^Xtve&b@Jex95 zei3q~LzG2`n}tq<@5wf)&vdpcn1-z)GyVCs&9X=7TTY3Oy*1J9+`^PkLvD|CaxxM% zk2q9dNpj{Sav8O+m^;0yf0E)miUE3*Bq{K{3FrMA(u}?!*t{G%MKr)=ijlAOS~uL_ z%waPvsC$EH(js$J&Nz>cmp&5L>?yzlnQ!TkNLX$G{Oty3b=}D#oBK_yjJI~KA22`+ zp&bu5ZM66P|2X{rd*^*pPQd7VQLdt^R6m!=D0p6N+F}H4QU)uvZ5+X=rp?->T@9jy zi^9a`8g|8{fvOieQX_*VFciaGBZg@45AJs4?kaKbz{ko|Y;HiV{&<upj($XI`_z{p z(hDD)jIKKM#Z&19All4$M749txh4(W9No^exIAHKiC>P`ngdrE=d(&>>>=B4`t%O? z9kj&9?*SnKNFAY6kkjW^3}GZP>v&F~wyOG-A!)%m+Fo0jrM-Ifk})RJn%dcJfQUPQ zHV`<^*f?py*S;^uOGAwN?nuJfS)=Bq;^S&6>}S!nY<1kd;DSIlW-;~TiH?x3FO%d! z3~fTHI~f`Vf{lrxts<tnGJ9eoi22bKq&#BR3|y(p()#_nR&*3WFLuaJ{cP#I&EIN# ze0(OInw#mDUs_hy{#6?EEAed=BMGbx=C7G!5ytK{#IkZQxHyzsnfAg4lg1bK39%4o zbR*#S`IHpHcz+)OA&{2uU<9@|A|i$-iwBDxAsKP#cEkc6@<y@bpyAtDUQKIw1%y<* z|4;M?aP!xEjaA2XR8-=NKJ&+o%dv!wsX@r`jM_p3)0pihl;a31C2*^e;R7;tW;4e8 z`hGEZfw;sXY!+S;DgZlBh|m_J0nhY49};9}IrSMWc?TO+HxgqjURp?I+!*0}@@oJ1 zzQH=}eNz^BOu<U-Rye-A|1+B3eu9AwU+Ni=Kq%Tgn%=4x*;Z}~kl0DnEr_9*L(s)$ z5U5A&aPby*4?;wjU88$5Vi0Rau^TUTOZ_2`9a_k5F`?{RUtfPZB*A*-+0pkrinCER z$SUJI<pag`Jip5>J^ajKYQ$w&kqUoEzWK-oo_y@Py}^fgx_+AKyr7T}!Rt>lS)$7M z@<z!|^pes;&@&5}LQ)nVn$*6Z=NplHV8lv@yI$EoQl5w0RR8|h;4H3ZxYa-3`ZGvl z+`grhm<q=htUT4xB=;7IZE$bFf?@m4AM;TW3a8ivy}Exw8d%ZS&kR264jp_waQ0k) zIs{0VlEgNnqD(zXS*rvP4(;_f&CWNWLPc~@^5nv~Bleb4VNr;OP)TX22Xh-hR{I2> z^78UNbFb<s>7;okIdRcu=C+7o*5m6&gp4RpVWLO((@bGRv6&BexjGTTuBddC0Ib!F z>S}7~@Rb0zjrtL?axM5F%@qbu-*xLXW#9h&0_Re=1{^$i5beDQ9PXGJx~6DwWDz-& zETLBJJ#h<8gTArxHu|FU-+t?xTPNIW$Rk;-#LAbqItjIv|IMP(#5p(0yDX_)Ctb0- zXMed9Z$x}6ekk;gji#Zt>-@r#+$)wC@FkOntpkn@UvrRw2ULJB!S8=0OI*gA9=d!6 zmDgRGUwh494O3Dj0P@u#f?RE~DFTE)%gYO2ya@HteNSqAkh*4o*xk@C;3_aF<@4iI zoTQ|i$vSS8HVn=i`*~tRG^0LVq)2>MUxT5KyV?#yRu{@NOnRgY;9pRV$JsZ`Dv5_x zEVCF~IBgdHhTMF;tHlVW0IKo&zEz+i;zBOxNC58ifibA0gU=V1mTu*Vdq9~Gv;k3h zKzr*s!ignZfCer|*dGWzY9ll8XUs_;f^LC<BGhLlknV6bS&F^h7B5-2P)8BpC4{4q zUKd2}4j!6wdfk*$i$)cm$sN|L^Ff1k{t?6QZeucjjWX+>DHjP$3a+IqR@{2m;I^!Q zAd;S$sV(Pg$Y7lvL;iG^-&naaxB86<yfV}jQigwOGU)7`h90iJ;^AI0l+~FtY1`u? z>kaSCLaKHB&7A%HeKJJC!SgC4u;V>5?B=ao%gP^pEn8ni7fi-fe&r0G3!!UK7|4qh zq5~-c!5YS@k)F#B!@D_|l%(CcA^JYBAGNQ2tsl`zRxuS#Au)s5t9&>#ehC9&_km#3 z>h+~b3m2LZ^I$sWpR>N>CDUDaGkkH!840#%i&)@UY+G^jXHwLLRk;P#inzLqC<eQv z5ge$nz2;})j!X}kAW3Luz4z{&t*LoLCoeJ-*84TO2j^t(8Dax1BWJRj5YUJwhJTvc zXPMZpGp48qW%(w#bmH)|k$MVNVc$H{Iu3fAvqEL#Gg4eTdH*tm7g92wt3b^nccqHZ za&s&2PHX|LcW~I+lbF$>BAT4iM9sa3g|k@r0>ig|>C#*oICyX~ML1Owe0ef9PCs)_ ziJYX6TXiGy#0j79hIy1Y3jt8WyHzrEDB;QYtzBCQDn~unobcvIdHii|WC(_uFFvf$ zz;ciWDiA06RSt51cIhGVKq_OEChaaIl1REmvO*0S-7q0;Iogl6O)}3eUVJZ|>R=;+ zMUdTHWka0~3|vnqK+Efay_>)#a;a?O=ICnKOILY}uf;a>{RaVU8QjQ<=A0O7VUr27 z03Qle%s*PW3_*1yK#ihs$(!Vq2-8?Nv>9OJqUpyp)l>#^^7%}VJSd7-Bx3dsD`t8W zR=c^8<_&Wm7<4!N0Cg3VU_G_C^b7;h%+zZ%%NUMHOU0_zY0=^`UZW~M4Lz+$DMa>3 zQI!QSyK{j*?qF9y+MH8Vp56s%Cc?>=Z)IpYkDEjpFdeNJ#qo^T+;Q#A3@#oRnc~^^ zGca(isMi&zVlLj=()EraP%N*jIe#SzUu-pY0Qp)T-2<Akl`)HF9ic@~YJ!S*vIu~0 zY@^Z=!krY}L=Xu{Y@wd6+XDQGf0<kS7en8TG|J|q*89t-nIi-M?rhS&CnwJMxZ@V4 z&pbmJu_)4X7WlnsB_^028CvqIU~5-FNMsm<Yo*12=oeQ($AIk9S#>hA6|E!NcQfW7 zpgF>+F?K_R%E-34V)nXWfki4wY;8JbTcAyL9MV7_4pXO4r-{FlY=y|Or|UG;)0C>x zuE>6^x>^YZhF(O;_J0jB(KWw}dVLe&V8OA{KPYcK&Ba;NsKl3YAVlTp1>&}J*|JH) z1do-%m+DPlD>}VXKc+LG{??;s?7ld64QHn)&uC_DSEXQpB>FrOMbn+WeVb(eR86<r zbZ5is8)ZXNu%|*$*-R0I3w!f~-)$yy$d+=BIZgSlIU&JR_E1eO34dzInr2nqx+d0H z+@E2zX?zct`@L=+TRfjpNGv+-7qv?w+GdWPf&1n5+==`8YLn7jhV`12Y{lq9Z}=P+ z>u%i~Yag@P#8eh)bmP@cE;vcWCo7x?ImN=?6|F9_*=W{oju+OIvRA;xn=#lDegs7x zB#7D3e`&Du5Vuy4i!Ao92PQ;*31sy~p-y$RyeN^m<d;<=Y~&4CC4FIHY%Kgtr7Gzx zR1=$bK1-IgR=`BYM$bL++m;rc?=}Z^SACVe<z9MlX#u_Lz`Q(y<?LDqZ^T+Q$;)Uo zKOR|!%ELSN9K4#WMFZfwDcLJ@OB~o0qZsl~;OsAZ)PF?zIr>xdBrn$F)_6TgwVmlQ zT?BJDXMi0as*kQ7cFZ+?wlzUD;`vZj$JCsVFKWFQQHGaZs0qQ3L3s^yuGX1u#}XQ2 zqlwHrNTp7(tsA^3>+#1IZ<p-dFN4Up^PG}e797;QwdjX|*1;=$QhZ`wv6+&+f)l-l zr}c1^3AYmUIH<A<GX`~Et<1Vv9VmXUNF7RzF9IHb+zH|H%gItQx%&afg=gk}kor6) zy?<Op1wM>1nqsG~+IE522~~?-av1;{axy%EZdAWHfdzqLwr7#PdNblil@JLe7JTV+ zARX#WX!Tt{@K5H=dp=}hvh(;|-0ruvm)}Lp(IJeDa{3lU@0>N%Hna874L#-rw@Ica zqhfsV;HAd&cLoGE&P{()r@f@*;J&+QX4^6eeNLSwtS53?<=avh0&*9~Q~`chEiO&3 zI<(5I>d^}iFE8n^m^9&$9P2zLtk)k+{A}-9;|Z8tOq9VZqN$y}zNx8O+#1WY9;!|z zw@bb1G_m9QRP7B7-6XV=s=H<6&Ei$IBlg$q>CrCy#EG6%LR$zavf?;F<UGSI=@Iy4 zKwuA$CAu!hv=)_wuCNwwsX;=$>ZoGX9ar*reN+9}3UY1<tEBk-#Ujm3AFc4wDE%hs zF_dw}s+Nve;B3-JI;ojJ-xA>btK7NQf?IX0H5}6atFM{`7`NYG`MsG@*xXq9qf^80 zU8~rU_TmVehf&!^N?l;~Gvg1x?2ctPbe0>Z$2^B=B?i%FOb&m$c(ERGbrS5L++BNW z+HR>gd%EX<L1YcdxeJ_wJnawNFW23B59ItXazGzBdZ!hH<2fa_wCwEcl$~Bx2MY3% zi`wt588FCje!r=eN$cn%VTW|d>d*I1OSpFfa%>ySGQ=;0;zZnOgtCPjP9SB%%anID znt@8YxaodQhiX@w3_aRmeQtTfck6Q7nnOs>5mO~z_$hXkF(m`gsdmIi?T-f^sHZib zP}yqc-8m-hY#)O<Y$YoGsG0~ng;7Eg>sVd|O3%m&FPxJot2?u&r2mvNWFWrxmJaXg zRcSyZdx&eJWc#U8r;-&#%;uPXEAIWu+e<#ouDm#?puNu;|J9eN*?o=2lw({b%p)NK z5s0+MCUX*0Xayp^pM%>yuiIK$cx6k=AssuxxGS?B9K0)6N+<kzWi+zxL+R1k6`pX1 zq|2r{58Afv=CZRN9V)ElG?pV{-9`oPsyJYlmiVIo<iyc`O)(E~INXxs=F~-3o-~#Y z`}WO9vwc9d+e`t|zf800>vOb&iCIbQsd(pKnol0DT`WUyvWWv@PSuB=w!sDi@shmp z=BX}Tr#SwQ^Kwd0P)c;caftlo99|?j4<%xl%U>xymrn~UTAM<aOYmXh9baE~{C3TV zeBHhw6@`UO92^{&2o?|0e2AvQ<0ym}(wt4C<?IgXoG_QgX4e-T+LS@)6*0qjzuDh> zQ^}1RH<%r{LxvZoy7VY{Kc|V&M$Y`>o%ct|*OqmD6FivHr?7muxh&QuvB4!+Yw)=b z`-5xFbUS!qLJ?w{&>zVUQl|<KLX9W~UO>j|Ua)H?&A4S->&!8_roW0Q|NQj_^!8a> z4INtRuWsG`=}LUw7-S1&IKi`vbcvD~(JArt%$@xicQUXYN}l3Sx?RPCP7$9ymwgLA zTHU%DmrbSW$zB_c`&#Uw8+}u>oyNb3dElqtp~K!Xon3RYmg9aEJiF+~z~eKM;#IL0 zKpR>8`A<4?r8Z$v=C#d_!9zPC!*8ZgOnq5!?A-?E`qN2tSH^kk(;(X;sRCJZPiV+f z{dROBZ2M9ff226Iw-oj^&twxGb;-Z(33hXR+Tg!{6YL(RM$RfS1{|WQj=@~Y=6<K4 zAOzc!TIC+F*%%b0)8VtvJRhdk56iTk5bU-pNQIw}S?k~@>C(ak)c<H}2~D?&B0M)K zCLT^%UHxhb2l;3#&x(pR@N^AroHAR#!FI#SMOLrakSp0`ks5%!oMPyj@BVsn+-l5M z>~p`oo)+Sc!HCI>j4th`bXq{%xx3?VIKGTc1+NXx@gUmGN_g{@2dC8sFa)^<pcI;? zZ70VfR??hWj#{7_vG-*r4!|iNy+D+x_2a8jNIAdENu9Ym<2qvP<MoAe490XbQ1^P< zdYnn>66nC<ZOucMDh>MikXdsLcrHwK3K?l?DV}q?ZYDfOb8`YP0d1V|_NHUZqA4cS z?*L`6m}p|IL%vq)X}DE)viQT1Wqjgmu_FS#hb1MBu%`|CPOHD<;$7k_ua>G?8Y@ok zGP_|`0GigDfhndGLaUfDw#W>y)}2}xaKL%a1-XxlYA!<^;cmGu#r&q;YKxx{ESWW) z<3q`XsC*NcqUFTaX4#G2Y`|BBAY4Kg_%1IY1Uia3Ayc45ppBSr9i=U!<)t_koLI>I zl!2RJP>MEOI&K@^Pq2p{F6{~PNb;kle-v-~%$>~Tw4lLExICzN86MeSm*tG7DkMK~ z3ntV$%$(U#k#nHltM|B_{$+;>)(wXN2y-4wBe_2~&corN6GS0W1aVb`?7)}_+SXWP z5%aSs{h!goQ@6$taOoB7AZhc!`anKv`sIfTd{z7s2A{C~9quS{nzTD>gO1S<OTeaX zR{0zPU;ttFAU6sjO@I(F8)s9A7@DsQb;~6mp&JdaYOmKvdU1k}oc<t!d=9v2DvCw? z(j?x{DG0oB3G_Dl=#WbsYX+lIcq9Xtm+0eywh2&6v;txrLT}9ITi`Z8BdLKN{w1_O zIXZ*#QV8FWpmZ@LmJ}mAPD;TbI$96(B{0GrVAt@Nn%L>fkr{wL#plNf!GT06elslp zUChuQSCd8DJPT`DbEuiw0;8(@KkZ@Qn6;*i3JeP~{M&P}#=iWvhGy;#BO;m4Q)XZ> zIWk41mY+z`PB}2m>4{Mo30XYpSrT1ws6Y|O0hqj@0Ei<%(1}Alp96m3u(bgzdCK@! zP|m{U9ix5|X+0nE98RqrJa{oVWPUD>1Ke8z()O|*Xr&8_i??uTf;I{Y%tDinS8V7Q z8#}^(yo0MP{$9_CBo9Zr>mEluBCw4R0|n+F8Hz<M)#Y8*s-Od)IFn1CQe0@qAJS>J zqdE|5<T4vkK!=N1N948X&V>*iM2RmaKhmHEQ6Y)RzKEhYqmrSq&VT{uzA*eGcLM!% zCA=*U-s$<Jg-y|(atf$8V2f=8J9BeV(u(K`OWx|*56cBal}3(>71Id9)D>_gF3y2J z7rhy$7k0~9gGHRUmP`rv{ozu-8zLfOv?=dFka!v>%nzZ*-+`_Q^K(#MY!a2t^fW&k z5uZS8Cm=?#f}wC{`?h6s<n4@z2<G_9WA!V>AKv1aePZU9a)b|#_5sk1nNGRHnY&Kj zfx`E5{I5l<=Q=3l+Kn46xxTF3-N*IDN4v6FtU;g`bc?aape{O~8c%t*Yxy*WeL{}w z)M>wQo}t0oAY39QnR{Y-$e@w_cVON(MX{Ygj-c+Y=hUppGd6EFvzBv!$DXQ4tJ9OB zpL_Bhs@f5Jjw)MbpbS`ze46(U^ZWbP0Cxo3SoQvO7v5=N`O?waa`qtZC4Jb)K3nVu zZ4&zu7!9BOJ^ueM4r%`PBOLi&`8cK)G7<VuM?}|}#<s`{Jy#uym!_hO8R6uZVDG-+ Fe*hy}SHS=P diff --git a/docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png index e37dff82f3f00b49153221afa6a879c5a279a039..82ec4a20d036cc6811bb7e17ec29833dde8761fc 100644 GIT binary patch literal 29159 zcmbrm2{e{%+ctb5O_Zdhh?1zxlzFO%5DhBxJVxeOqREtqibOP!WXe1bWlE-^%=3_B zo{98tr~7-J@A=>Vd;fR+>u)XVUhC#Muk$>P{n+<y+xBfg{m-k&Z`-<WD~Uwfc2+_5 zB8f!qO(K!?Zr+IhbK+~~ApRrjD5vgt$;Q;t#lYT#bk4xh*2>1w%EIt~vx&Wfg^jf! zk1&q_*8y`!M_UImUf%2f{sSHxdo$jnIzwK#$QD}#4F?j5#(?-Cdn+ApK_c0yos~VQ z<{CBL<)VFgo~3HKg;K54gSSyzbmvKjUY;i93$gaGPq^iSKKq=@cxf8;J0pp!PUlVT zu`D&FC$DY8pAK!d`*CL1p#4p+&WUrzT~scl#XmPBZhVnk45aCPoYP|=KwTyyi+_ba z!SPf(Xz_3HB54=?GcB;yn-%|b&ybzR@5^hBkOKVt{9N}^$`F6W`<iqFzwI~KMUo>v z9!UNIzx4~h@IQRGRGGo&q_#GbkAZ7G@inyZ=Sj8YPdKk`-nzB_^ApZ!9^!K4NB=)w z_W##C{hvM@#U?5mA$OnNEk98C==zh3_y-5m+`M_ys;9WCDbDy)LlY(O#Dk{%{QRqb z7JD+Uec$T+{ykrcZqcv4cLHzdiOYs?X{H-*d9d#UD;HPbrFfOLpgmmbU+!EsQTb<8 zPhL&Y?(^q}J|H0xMYV@#$BrGPS(YslI$>S^ECADchhdf5w{Jh?H`>Vl$Uj`y-Jiv4 zHRRX#M34OA1w)@nPai%kRpKp@wYB9tc<^9tl&D~r0}YdiOpKJL`M0;UUHMK%qs__I zNorWtnXP4fOTBk!%)1LkH*IHnVL$XaNv|Y2r{`wmjpZ4)d<oyd&!nY^g2{B79x2m? z=(^F=lKM=GCiNnBr+2eFdd0P+w<z-Fo3%9Z><_ziIqgxEoZn^E5IryR9ib2!8KKO+ zFxGb3(2!NqeNKOIqASy~rM}6;<ZMbLB@+uvLyMkQUEp59w|XT~^P?@py&vw>#z@E1 zKNpV|aT#fh+jUIMkKd@WcXYI7s;{gq*Y42D!uad!9ofvkx8v^FgwHA}er#yi8SzHt zxuWm({jO6VHt|O&GpBHk+Urp1J31aed-m*xjT_s8C@Cq^JQl4(A3eGhRp7beEap7s zmztV7-WqyXY1bNYtxGK_I!5Jww0C`c+6B&heI+;2Q{t7f-RmJ0MQ?<l*%6I&w&mI3 zoc)}a-cWDZvPC=B_FzRGHu~|g%K=yQJfB=i*0lTkXOV9A?x7z6h*-9qWrTo5bJAXH z@=(EKu~E~TbJ0B23HCaqLihP=0aKe=bvfAC--s|{A>w0hDz>GYL|{|TMG6glm1h|$ z_ouT?yoy`1r@<yI4Fqr5y47&9yXY~uR+vr6AEB0@c_q~pMhf*8GPl%DHvDey>5;Cm z)6>&KC{$s&$a2TCJC;V`W7Laph~C+_&0h5bu_v5Sk{)4I%pQRzHDP355-xFIThHCw zx?4i)Db-*;ed)|ljl<fiTWDygRaJeI=$(=h>572eN_N(`y@U&|&jc8ek&#^^!$}QM zbD#fJk#17+DV&#OvgEH+;+5pjkNRlr4M<5>Q<-rv#@e%LoVku23-H;-*gx=Sro*B$ zmmhJaU^n*V<!QAP?Y8}KNFfS5GYGN9_Xel4uXj{7sY*XTc6IP@-h?=(`uhV!;M6|l zA8X6-#W@gg_@TU!nt`OQNs+9THaF1~NwI1BRLS2z!LQ|m1x!BgGBh;Yylvb3V9o(N ztY~SB*IG4#?7Bx+l7^u3c>5E>xRAnQEtE3eUth~#D!J*&?Edp$x9h+*+~JvcmFJ|* zTekFJvnYt8aATp(O!mwf`^lokoqP9I6MM8gGbE`|j6?4F(14*fLeRIgROa{MWJ9#1 zht;G>&i4i@Tu8s~RL#{4QID+rfNDPKvL?AfV>>&$4V09$yLZclu*ym31mOzCWfk}L z>=iKHOhwN{df_zMTpKAIeC_)?la+2-T0X-KOyUbYX~!<>f8Mp3s%BvvYm)s>*11*_ z#*Ls(*Z)9%!_(7kwoceTFwjwRiHb&b?;6R_h`Z_#Yx<wf@#|STM8aluX*$!o>sgsE z4IPrnkGe>=p(j@_j?sr*HqD3|;!T#iJen4A$l;31`}K@H=;mXfFBCb_6i-v&JpKw9 zPo-e-M_q!}+C?cwLqo3Np<Q{_2hE90?NMG)(N;Mr{daY?^XAI9<49x0$jVZCL7}aR zrjI%|Q7uB_we#%naYVb`rsvkQo&R;YG+n$^a<Ow@Zfeu{v{w!d4x84?N422yMCaM_ z=XVI2*7hR*4EVC#v|Sd|%E_)>ILE8?O!<7)yP?9UQ|n36zG!gJSZb-4;+e2b{Vj@3 zZ`BfZTDGLbc`Q!wcx9)ink4K|*iW9E%AuMa6@F>GOev%|&kUZO$Qx#kcKfk+Cmr3E zZQD$+ZH_;`e7Jt%?!6EF6}Ge18JFLw2MuRBkxp#E!IQPmGAQ#&Gp*aV`sdFw(xmH5 zHHVHsGq0e5!H#{`BE~)X@0!%=jW&xW4mBvQ-<iZ`t9o5?cO-)4;TW%_sWO^8oI>rZ zZ};Eue5oTI9Hzg=iR?ahze-kCMuwDWL*u!kS-f$BaiK#cj!?bFWRaPNPS&-{tcMOs zGOyXwtuM~>X89adLFzlIQp<OHd-bC|CPa&ue=siYj;#8p26u?&d$zY_m~Ek=GWzo3 zq;`?}6P4#;v`kE6+3vIZ9uTLMHpjtu<#?H2tk>VQ^2*9kUOlPGV3vkYA%~`?FNj~c z@=r<YXe;1uuVM){`uZxIpZboN)35ioeW%DND0H-<_MCU5C$5v>qLqmRX27s9SaN~k z!i5W4y;KYZ0yEuvDvt5+1ig51;MJ>FR*Z^xOofG~bzyDAX=xS@C?tZa)%=fL?wvAd z&EgEeU(u*VxFk>#P@<D(e7uQ=oQ6Edh=IeZBmU^BXv}&ny2^eYZ^|a!D!tJwp(<U( zUv)huEc~BGuDa{<%x!f#XKLDF8`GOp_eerE-L)%|iwlkxZqH2D*I2&1ynG7VmTu9+ zPWnAJQZ_fzbX2!6*O#+q<r_HxR+#zi`elW#x69&GK9%$knjLPqy5vBYwSLg#0~v*b zyw;X|(~PR@N0W2h^O;g#t=Dk7`_IK)1RQ^!M?E1sqMgf!BYb{ombAWEb_xFxoboK5 z<y9<RV<Vx3(fQse3*{y%(Gt~VG}5)tPFRwVs)lZDVxF?S{AwLMIrB#v%$n9n74POe zHQF3@?^)AqmrKv|r$fQEzvnFeBMSNd<jiEa`mHaL@iA8NGlAYlRcqo`;sQ*BgEQ%^ zZZ3AYxb@xJLo#oDe^Bq{^5L8==g7!+PioDn2~=i$gq8eb*(D$8_hSQ$%+_vyP4|mA z9`IUQrDl<e8t=&ATpi+LFH<O@%vk04_m&>H80!~bg9xzW%-Bm#e$iK&^HFu<PS>9g zPdd)o95??`y3ieRNPhF?&9+L<`x(|3Q6XQ*Mn!s+jA3JWc~snKUtMIuGys?mkF8#! z;p?{_J6i5vTi;6Rtp8D`gqSPxSj;azQZ00j9A6dUcsc25w|L7f{!)?B7uUXvap@I5 zM9y`2!rt~`eaS|yb+rpr@(MLKc3IkO{z+v;@xr!GzAeXQE6Ucw`6vBH)(@OQ{tZuV zr6O;WkG^sn!ltg0tCvTK#vEN8`Ulj8zI@N8UdVtP%j~w%H7H=WONMc^r$q|I5z)x? zePcVVm18|L+t4$(*i*uO?A;Cym8J7({RI&d8(9DKAk|hvg4UfPm4SO_C%sB4#3u^} zM$*f-tm8JeCr_U;FyELuiD=tEU@<GLg0mCx{OdXLBTz{0pM{RLOba@>fL#W_Oau<< zuec9PXSeg8aIE{5aA_~U;RX@_g2{*5l%4LQ$;>H=QRmk4tYK5)Ro>5G+%@-ki$AGK z|B0OnmtQ|z<@XPBaM11Dd$uZQKiOODJOQuO=}m_fA5U2s^j}=B<I@*8RHFxxRQl@0 zCyi0zvIn+$x&3nh_h@Ea+l_QAZrPmlp{FMX2gD<v&F;{8r1RR3x1Tr@#8i!ZUWR&M zfmkb9`^`xsz4|_0iB7nYfdIU`X<mPC5~pvXtALC_>V^m%yK-qyPfyts|AO_T<LdwO zr@8ZF_qhlGlcoOqyg%Es662M2(ySAQ>N4uGQey$KFBxX4Z_Y)YQCDZ!&3R?c)};UJ zJ_;P+P<gpCXBuPA(gm?dTZ;?fOx~0J=Yy6hdL{KWyd}zb@U|?=J*h<t&*GIrna=2x z;^e!1()0Qw*Hi4-R_rNG3YPkP<PnGJg4Ko4fwx3TVhesMY{Pu()-7QB=Q|H8zBm8M zDz!2u7_jST|41X7ubmrJFDOc`<H%0Rt-D^x`tMv?I^qB<Bhmp>;5PSFo(xFBU~P3- zE%8cS+2yk;Cg4@J&2^|e%hOdX%P0?R`A(d;w?hXHepo1uiDCKv?((4{NBj^VU$U=j z0f!AIRTapB8J#?NQdUlGMDIKv*GLED6gAudB}UMF&U|(C_-CcY;m0IbfHu?mC<5)& z0a&CBhk+d+f@Z&*rukap^_Ph;g$P784uQxzF74ch#hxpM3{qCz)cpMZYRS)Hnv*}Y zv^4EjR0&HanF~cOug*3s19XDoR2d6g$g$P|=Xk4AAe7T-PX$a6cmCxm?LyZO1cbw{ zZ%3WST2HE}?ehBjM;KpgSP?M&^L6kHe)=QJ)`BmC0)|Afv$OlG{aMMd8#uF(`st_N z^RGpm$4V9x)ovZVbbzbY;e=v+*bvEes`q5H=Yq!C(g4l7tEu$>(W)R0N42t(x)w#; z=Z@%k&hM@X=N$$jM9%TvLdSkS!?Z5x@!(E^o`ya`J(+4X3bw;~tp2%gJKpZt`(fAc zYQcoy{XLu;6x-98NQ@`0$s*WXe~-lHEp~fcPYg7ZyLfS@QaF#v=f_92i*K}L%NwYy z=NHir*k;!s5oSwsO{!#@w_ATsGoli4n`u7YARNKPean|7QC$6E&CKSnC+}>W#`lIu zd##P_=^L){T<oeUTI{Ydi}4H_U-1VoJPdp?w6-$IAttu(QFS>LZ=sCub{k;u-_m!s zQ5YKP&zhRI)O%(rPCOhsq2WWlxBu~!%j?^eR03A5eDU3v2vSjS^N*O<pZPscj$Jk! zZ_oOtM5RrdP5IOD@Tk}yuA|*UOIv*lVRn5R_qk`s4W|1mGOgM|fsIM}lg`EM3s)Qa z(=JB{qgn#GOU%0>$ue0Y_P!-)JlZer+#2LG+1*9IM$*j54xCo9*<4(`;c}v8Jzbtg z7==DongBsfo`1vjZXOg96YE-xSB<Iv`So?i+}Pc_8+6@A4&T0W2L#FBM{UGNw7Bb( z{;zKdkNU3~8ykm3b3z|b#4iJxL_<9$@_db|f~JvlVm(s+6SOsN*l|`XtiYRBCm-Rj zoj<ua*%JfYgRl?(E+0FJl4ydHTN^DIIZzcm`;m(Ge8R=UVlER=zdDhB?)%emG`^Cf z>PSQQix;uu0moWPyreyU&!r{b<-f6H%dY(NVc!V@gRnT=Gl2)bi+Mldu-w9MHb$w6 zBxV;b8o9LOTD<n#(zN#J$v?m66TnV4(J&k1Fei}f8yd3W(7pyR+@7q81k}&tV=x7l zHG}wY0KP>4mF>~HaT0WLJYR~7`qXLe3{Tal_kqhzDhEO%w1u~@iHSXnjb#I<x?Y7t zMNqr5Dk_y#!zH@or+v2@2t>Tq%zDS6xEmY!4SU3J+<XU#AlDgY4R2oE5fr}Rnf2|y zNTtyune<=CHjlY91S)nON9cm@--2ckE3ycBwd?4`yTG8)4<GLMSVoZN*O8pSZ)f$) z&5!65I1eE<jpl!S-|j0NlJv=mYznk7p_wJKSVdV`*=`jV&@OO(fDaRCuRZ&E4aj+0 zflF$~FWgt&SJ3r{;}$z1R*;oeRr%tO%#39;y!lzN(=JZCg;M)JN$K>#(>`BHyu3PM zgY%t6XF6<3(vVK~30eJ&z70sU=JfN+!QTtxHDG@tw!PlhwQ|O+;*W*y&YOtYH&hco zyt+KgcKC2x&~s6Tjb2_}0lPWF`n6eDV$=k0T7G-`A>mSd=fy;zn(u{fv*uu!9og4k zBO`Y@wdh3ioGnyOq4LIqmNhg4<GI^{<mBYOzBedSJ2%W*ykz(;Yvb$Jufc46#EBK} zYm8HpfFv@9kVBTC+qv^2>Q>%l@rs1bGuJfjJtSZ~g<xh$20>G5kA*P<971R;kH{!C zlERux!9vKebepcEXjfzFh<pXwe+U$K6U&V+k9ux{Ir_CxGYXY{ycd>gnqe7}W7fdD zPuRK^4>>(QZ!*@J2FiIcS@T4TlsX$-+dc|cpaJR}hnk;%u>YpZc_6w7On!;EFY8;3 zK>P|8Pdat#6oarelYxPO<F9Z2r~=PXksVf->_90ikb}-3W2CQIA*P<QvNr1SxqaT% z!u?eHjq+X|95bws#(RA-Zmn#`x;wH=#scZNnhubPo(q^e{ayRhR_x1JQBgs=YnLpt zJRr)nqbPDMB`xg^=Iz3N%QMy(v<h3@4o-c%NA>u~g^vsF5Nf`q=!jSF)2h~7+4J6j zq3!o!9F}YUP)-){W}`#$V)5c4tgN>Igwq{IE_Q%uJv+L3Ht5k1PcIOGS@S`BU}vOo z{MbTKgn^85;80=G#2dSz&$|f<*OAT>b7N+Uq{l*Sd!20OftGc=Xb07#UUJeD%#~F{ z<SBsAu75IZDWWFbS&&wEy7kl-uyNA9s~oJXJSLO%*McTW!=j>U0j~(ete~tMi~3dj zTs%BzzqpugtY4&CEaeooXTzpVO;8b3C3K-~Dgd6h6_3<u9glc%?Q)G<&=M-T8w$!< zd>(lo)k(fj)qY#BHD$d20piJvi;-?VmT?<QR<cMopBHgUACi#2d-v{CtzZKx*tv}~ z%t_;&Y*)V$sHuAP<3}!~BDIvL8P_Mr7I?`XLDfT+6?HBP3_bgZo!|J=j(gJ8P7>_| zkLl<6FNAa<YjwP*1Ss?Od#GN7s)B>J>o?ogON1Dk+BZ7ddXd-1fX}Fsn!s=06R#eH ztkobf8~8D1(0yIzwGeTcU<#IA69I%H5l4wgw*f&ad+4igts`3Nbl^G=#B$IhLIE16 zd@z7KSXm{1dib!-dE$%m&LXakUP)qra%0-}@!oNZ#`31a{vwydD0+EIRzrt;b)ikw zv;a5do>Eq(Mi!|=GC6f`YnuI#BDCN5ZB%TkpY2IwJtZX@KLq$GMDy8nOQ14-1hA_` z0VT;mNtMuk;x}q+$#=(qL1T@GGK+8?omb$w|LYK=Xzc@2lhg_Gt1-!=v_)LkC!Gct zOU}0LQbZ*Yy|Kh6B_&n3_ItE&u2JdP&x5)LYslWu)YT+_#O6k{jV*nwH_eCu)|hIy zOf?XSOwcV71N4c%oOos8s3zN{FVYUE&Q}BcyuC^0J<PusvTkhK{GO@d(}z2o+y)*9 z^F`JXVioew(N+JJYI`buW#zz2TZu$}Y2pwoYxg07koEmIIFyzaHn4Ym5^O_<T{T+y z<*9oNeEQz(#{;ek?A3@00TS!7`uh?p=PHmyTfT4Nzt834F!CL=)nv7%N=iLxMiwPD z0w<pwvt4>0CoUo-6%FZ*-?mo<#aTyJ>&n7Ddb=De-VM*6?=5<^jf<5vsJ&41&(x-{ zD<7UR)VLj19DAi2BPA;@pK<e#`@(pKZ%mBkf{dS^H|?Q|%~#^KDo2Fgv;-YA9#y?% z!0=R=9o0h~CC{R#SQ3EP_R4i&?1U&0g9eR_f@UgxiK)5=`1wO{2xt0yS@Mssnq(<y zmeZwoQWip4E?$`szmlq3H$6~=2P0Hp9KFUY%d21?L+R4y6R|w{0FWkS8*A<c&pjt+ zCc8rRrO3lY>hG!p>g_<NJJWb5ef1VVI~yp>JjH(&w3?GN2u?y=kMl|()DXJ{$;B_I z5ZdCOu0X3{GkYJDdjG+__!u9rwX<FQ8+~v6C?zPi8<6$h<K_>Qyf&IJD{!+Of^HN3 z<Y51f_zg0gWqf`C0sWCS#dwOFy*T7}bj5I<bHnw|^M<26?7Mv0iPIAu-n^8bd6;7H ze6l82&&`5|8@KJtEzG`0#a;8o*z^PW&!(!OA-~p&1hT0!)z#Hj9oY{c>4ve~T)K6C z53eB*fO;CMPQGIwSlP>)e}1R9W?mw=lIs=sfzdSw(p&0XFRxv8ovq90^zu5mm7eQo z*l#Erc_B6Ay$>MWuFJk)Zlf)!!ypnQwN%~bIOGMtr`rgjp<i@waH^npgEwvF^$uTt zBdNtt>732`jEtJLCxBInhG;pHPtVR0N;~y_vB&bvZg+7eA^6A#GdoocfJKBWdR2!z z$cRjBu*IP@L^=O(h^(fiCH)(f`p51ENs!*IEIo><7;17fGBi}jtt4JeHAN-yHc8S? zGpzX8n)W)8g_o5zPrY5K*lGjwh9I(KZx;s9nY~+gO?6JZSqEp0nwxBAjJe4Ui3Tf? z_x5=GtwWeyx>pQQvJpi#-*My=G-o8A7*1wf%T2Z-K)5)6dm&ljow$`8`u96Fzp^)3 zY;n$b&$oHocfc=k5pEK*r29Ae->+W3CR6&!sh#_td+y%dyAP$kZkkD6L`NjIIhi}z zJZ#!&*jqL2Zpp3EUCM7*)e;Ovc{!%$zsu58?@!-N3T6^Dr1FH4P2zf)3MQ0k(-Vy{ z`sK|z#(Pq<ArhbVuXJlxwcPW)Q?15a_LM@Ke4=GZEX3-QJ&78U57H{}LH!T65v@VQ z2SNC*Z{c7K_02Qt=>QMm2a!E>`m{!&$ZF59lghNjgH*ceXI#7#myj1q1<G>VW`E$b z+X07sC+V1(qn}h2JP5l5cz3kOX*r48zP@$Q8Eu}q@s4l^zn4(vgP25bmv|ZALUy56 zlrm;k$wtrFLf6A;wd(sy_p>@v;Nnut)FPiYt2dDL2?+vS4U<G`T(w506ad2BbU(^I zRcr}Q%)RxBEFsTvnn~O_kl=L&WgAKEixYK(%68$E?3J3`A&$nPB+&5g2q!}2f4Go; zLqocOz^sH^93}1&1iUbdqa>hTN{XL5?yqo-M{lzxb|L8T=#0r<i9}$i#vB_x<mu1g zL2oZ7h7DBs`*o#DY_u))CPT^g1rKR!J27;GPy=RI%qBbvQ1KL>9Fqg3A<K0h--pDm zDW+V$ExpjD9+`~j$~@OVS|7B<8a4A#zfq1jg$nVrr$pK`V=oCSdQTcKp|jE-kIJdB zSXAV;l6CB|fFAh|e4itF2oSkKpFULpvjRJIx&DDQG@z1D49n8fC&v+?ZcBZ>qE5ed zt*xyoKRfP^5@7kt*KZS{TPlRL7EE40b&4D)?#CS(DbfqjMuLtL2_jpD_G~zzHkHSr zTrDHNlezv%h;J?SEch7X=WCKkX^UdzIM&Kv(-Nu7-khQn`FtnJ*mlzlqpqwr6NZgG z2E79Vry&k$pgkoY#PkwnB*o*cUn{p386Wa*Yilc)UaB1nD{IOgTCziDVmm*)R@_}< z)jc-R78&L+Tt8h-r@CX;u5v&PGD14s#wg^tq&u-ag|(Yii|jps(cQd$8yaYU0e+h| z)2J4i>RBZXxteA;@6Q6SCj>5}?#g$U6J5vBs*Y%7TZK&wP4ph?KbO9r6yyHus2R~1 zR52FPB3IL-5@Pf*=_(F6#GT=m(ecjt&u^#7p#3%A2M-JwzP-DA{~pyIQ_j^q!Q22> zMgrbdzlNIF=I$(5=1o+oHaif9_DqX1Et{fy1`O0pA`d}ear=+sR!r!%KkU^0t0S!` zn=VsRitWC@p(3(am`SuF2>s+o?cRVry!B}6d`3jX%ro|@+h&>V-A8+NT~KpVs|cu< z<mJXK{P=!VFAjx%th-3OqmQPOPFt?<IiyPk=I{AgCXKT0R6I}D+Y=J<gj)3W?U9br z_-OY4tljw{_jxO|6&9BbS7~j+he#rj2cvJyoHc7ER$*||GJ>Bf;q;67`N8T1RT6(> z9l_^u=B7(2Xl$Xxc}<$2zv*@;Kzp~g+@Q3^c5mR_SO|diNgz&9TwJvRM%ng5kE;^Z z?5x<wfy8o0Qwm&1;=&aa6$t<gDSCKM;Ow15zgTE1U{^A0tFx+R%^y*2D!s`lqTE$! zX>In<Z<G&`F=pdZ*I#yDdjF@-2F*U=h!RvJ*VdqTsh^6TN1KRv9nB2E+qfEc&Gm(} zv@~=}chb{Wf*U5NC0q=<?-vyGW41wh7<=f{&ajh~b~!jT)kx5^&Q!fbDr5_tPlk0D z2MI_z1Kleylc8s<HaqD}HC~?=zvU;Q2V6)HUho*Gb0>g2iN+Q9S5v-|WdJ?*vypj7 zmBwIHH~!4GjCJJDctggT>2zo?$znrH-u~;{`)Skawx>rl=iJUb*r$}Nx!k3B|MiE< zvSUW*P=vK-_8>eNq;E=qx_vMf01#HHdv+q>aw0v-W4!s-SG%~>A0qz}>JczQ1zORf z)03;7WqjuCnMV&nEf5iDJ=*hF@ZYHG-)x8{%h>;<t1Ef$-^q}sQeEXYLEia#ax(!| zn`9CnFbWHp)iY&VwHY!pSScE94WG9|f0RJ~(8wAyEiRco?j`iAes1WPa&^ywZO1i5 zd+)1FMk<H#yFz4Qyuy~_ph?Qicb#Shzcs=Sk#|7}?ME69Z{2l727*@ZP?(l^W9$~t zxL9mQ{f$f|BIyXDU4O!JMXGjrHr|`O)JCT6c*BE0wVWgc>*2ZNUK|4zv_5}Arpd73 zMqGyd!!?MG*P3J_NnOFck0z7CS3&p1b2vi^)zDY3ixkeeR}w85L!NE4<<>~kb|%)m zb;Wp(qM~9yP>TV;;A0-0XAo<r=3D;0R(x{1{nqJnD)<Thp9X~{OYF)Sef<M?Du>he z(726+9?If5M@z^a*eW8A5g)(*-`f#<T3{sbfpQ~!`3P(1tW|raKfb$F*JYwJ;)G>$ ztokQCaa%vvP4TMac3J<ez~ea4DOzWFB}tu-{W`T1{1obK|94tC2jtRP<dJi|B`z~; zv=I;OrU!B1>({T)CukWM%*E-EJJIh^wzn6Mk~+$Gq9I1Q0hBaC)PWsBV`@RoZrU{d z$S#(5+xLsfmfZZ)3JsV9&W@!LYZ@gMxjSq$-@747^mKLT;p&*bCF6cTxTjGu_TT_I ztu0LtjI?FMcPymrqCKl5yJ5oynU#evIjKXIGTuYEiCpUMy(=FuW;l&rA+j5aV>PO6 z)|&*s!D9MSV6Zovy}ZYr&?OHTK-*Rj-MbrTAX_J@$_|?T!{&JlSQsruC&tp8fJ`N3 zJ{`gZHhCi{@q4Y^(8@_g4bvhG*G80qw?}E**Fo$(JoZyVZ)IhrBM1cNL5r@(1LzNu z6?U|=E&`DT<$mA#Xx7Aq>|=){%%-QOUm913E-fwbnbtB0S+%~H;$S@rm{2@hFP`nC z^JWN72#bPHVvg!nWI$1`H3^tFvW?8@qlg9sDo|U{ILrWOr})sYsOamzxL(^k5G-8} z;PPWGUskp;-A#~5JZx>nhm4;;Y&N~jO2`4b+A#`tkzrCpuj}>4X?4|J$1K<{_i4@A zm7iZ;QhfOE0p0h8gvI248yD_Fa3rCTi7IFiu;l<~X8+1$iK^Xo92Hk|1I@p`dveKg zC+*qbqK-EvW*%9st?Gmm0KypB@#PH-G(ZwqqOkeM0EOrujb8NoEau&eFQGV(e0{yi zs4}qcxl4B+z!D)J*$q^ZK|TVo?Vp^CF%6f;qmMr$<6!NlUa*b~`#%0Xj9aVnb+FXu zJcnVT;|Pi2T$<u-KLt(uE-wB5!Obvhi!()^tffVN!Fgff8uoyC-*G>97VNPwkRV(0 z9O8j6S`S~{PJI-f3v_7oKi=Jv&@GOFk>6r67wzZAsh)F{iYDi75u^B-^!%cBWM#r4 zu(CM0sTb16aGh|Ea@Hx_kh{GJ6^f&Ef9?2p>at!mJIsLl)soah2zmnwePq?&@AUur zqk*LH@5=0Sz+e3jgu8pu7mc-QOHVnfE<1Ca=rIXb|5~_)u>AqZtiLqXNBWkk7lZbL zXpRW&^f7{JiK_i?G*#Nt5)PZe7vxjb@WZTbGTG$4Fh_krp-W#at~NG=l-73iq%50i zKv2*d5L_5ksJwx|zP>m~(Lj$+oWUm`Iie}#5!NFdm&1bxV%8TDm-^AbM@=Z9X95{M z5ORr5{&C#&E952cf7!xQ(8l2=c&_0KjbbOQK^w(V<@MfoYH3_s9(_&ffjZdgd_Er` z9)Zv%fV5o}YyWld)nsuSKx0K(+AycT*4@-cg_`XpUdQQaW}1H>4ljC$Wf=`pk%sq` zXlc#(_olR_a?|+VzfUxZ05sOyQTDRs=h7QJEsJ;#95AWrd7#X$q}1D27Pg{Oa`QQ& zlAN5Jq_k%W3vu$fm{Zh3k)L0u^54u|kY{A?9xl)Nmj^*T$jo&1$6OhUx+bL&d>&{C zuQ#K>v_Mfds8^oh#Lgv}<WVi*jH(=!G15Xisaj=`TV0cSJk52Q9GI#ctA`%D2w~KM z!$^ZyTeh4`V}d;8YO3x6i?zTs4w2c4R0<e<NN3GgYi;=go2GPwJ8>0!p5fl{-gzXr z<^sTO+6W-rc@VtA$3SE#906z?$Ji1w(J_~4eSM3+MXG(){>>YH^bZV_!3u{%dIu4l ztAn-ya??Pq7~m0mVpr~hbC0QGG|^oMpR!fjtgKPy+PlN#vzpOJ<1)%`Y_3ouovN%X z(~}`mE5toQbnM?{nql&%C{v7qx@_m?GeLL1eEHV*&QNY8I;71Fy!4^lteE$cW1gq( zY#dttm1qaSleOZ6hK?{q1<p)rW%lD6&$GEy-=L0`GiJ-Q44--GM6GXS)lSwJ&F?K7 z;HO@>x*T%XSGD@R9B&c5lBKY{>H00indl=j({dPHs&^`>ACLSiZaQ9k@0-yQ!KUja zRx7Hi1kp|bGdn9=uC{fL?#TjY3%ziS`-jyvxonay$+8t7psF4)nsuZc;Uon0>p5Lv zL!)xCjfOVD6PXqks?=%DJy)P9t{%HQX6fHi<ROgAcVl(BiX^ev=|B{}uG#1O)8G^r zfHeud87yj~7;=()^v&NR)RQl}&1mhi1I_y0m_BE)ci1dpU*34GQ2*A@XkXc0U+FTw zr(#aWfT)a!BmegNE#k<#^mv9tK8^QyF365VB66(ek&FNxBcl>rXWC94v?0e$B&dY5 z=Gc9fe!pQp^fUyfiN_ilrewA-UJ-^bw6!Bgoa&#sLfYF5GfRjBN0D{soXG3QESJe{ z0<l>%y+LW-TJVv@xZBV|;N<srVY3JTIp{nyv5W<FG47v>Ety74&nYMzICkvwLOwis zOrrL8U!L|gOwakdclewH;Nx9@`az9m>rUOGG4Y;m>t=Y%zMzpL=J>O;ugv#)Rm~I5 zVb>62uYPp%UPA<BYjC(CoM^y67{#D%-i0fK@YSrXt=PnWd-}v53bV#g`dP@oqH<^G zPLGY7N}`;pN=$En%CoVTXtyIHsl8LHPH++vz_PO?6227OgW_FDkwVw|7dmXrfVr2J zm!}C2CS=TbA88cTi%778?ugQjlm2wZxkH-jPholTs4w~ovB%%v|0Sw5fdo*(T5dpH zv?fHKJWbiVCY)J}E=_MQRx}PpUP+8D>Pqr4Xf>TBYVR&?&BxFPEmUo3tLc=_(B}-i z)yhdZt+YRuNZ<dr2EgOB{yo(OtgKuEzpdA_YJXq7bj9oo555n_me_{XRqe$|V-GDD zccZwqj7ZzcJ~i{wh9n6@a&voG{JCr>xsC-qAaMC3Gk!_clg15K0B1bEXsL<ouI$Jm zlG2+Z-ZVeIMC0X{Y>3Mag!Zt#tx(h?XR^+^v#Ttcrw2fnj=9@+po*psc>tR5nYw2b z8o6JVlO0B~v^5tGH@AC1dPn|Qp#McqUmZculbIQ1V%jZHQ`<9_uTf_zNFzDNGdo<e zk&W!T_wv$jpICFr%plI%&upQL5p`i<#d9AMtBu{7`^tW8r>AZC$_9z`$yiqwr0%}j zh`+i$5+?_{J$y6V=jHzvdq_`hInR2z#m*|pL_VZ3u5YYuZZdqYS&nvqpYgqBM6UMH zj4sI}Yih44Jz-1PyKyJ1hwJ0L(@T|mm0rj`TTEMc(#f!9T^u7h)@9Qg;^{9Z%M?fK zU8s`_%_oTs-JkgS`2%hCitZG$^1c-Ri%i~Io>X;J$ky?re-U^1LNp|#-KdJ8mT@@+ z@@?rOI%%8Cw>omL|KJd^de6-JI9bekEW0i+W8pA8ZS{8IEHNG89PN+GDv-D0Lf`9w z+1}E>-ov*xydbyj8kz5Cx;`36+(o()TqdE1jLmc;Mkkm(f2$Y2v7(jO)=3pux^r!! zqH$+ykn{K$qr5q<N5S=s3}W8|&G$Z4Hs8xY+v2bg#Qc{#YetffBtp51#7=gaFG4vx z?n)SMjs#<)u39^@hEBd52%uklZW1LQ4-e1xG`N85xn9rH$d-@h8p;R%W%pyM+`0CP zqJ(t%x|X_Tfku<%g91^%Cp_1e-5o3qob#{SSm&uN(b2ltxsw9#-#7a9_5zXLx86eC z<44`=k1&fSntSy(GyMin1tr}#qnRx^`*r77G~64i@9&oQ$h3DQ85r0OZk9ds@it{x z>h$zcy%;e{rLeahS7c{4#;cH{@V*9WV3Kg%;SKx=smIKt$4clCx<^nv*3Aj9S^>H_ zm~LSWtr4-*k$`m<PKpQ#Hv#ECb969f#9!s5lXRCbxGJ2Obz<U-aEXfLNB{ima%{r( z*Qk2Yu&sw)M6?&s$W^9YD{UmtwQ3W%)|^yL_yarh9G)+`(pKO5{3K(tFpyn6O4=(L zB{c@5BdX`-$^$rAJ}pFN6li?E&dKU_>k>sT+&JD~Q!$<kd*NrXh$a+7_@x8jUFW=- z;`n1gjxF5Z&)b)!ekrQ9xuT=cO^`7BAVXE6V@k9<&|nuSx<VULSe~F$3d=K=_70xc z)KwR#`0bVCeZDj-&ct>0oVEEe`9R>Ae#qm;<*jK(ZY$$CXl?&!*0M1H?md3J{hP^& z`+kGFRj!jkU6fu(Ira#`YloEt?KIEcy49i!L!U=f-@Ag`yMdJuAB8w*ihlc6FIHA( z`)i~md>dhZz+LQ(c7gE(az=x))>b(-o6lB1Qorxfi<K@|KpX0fZjn2otD%lu$TYtY z-waY1E_#9lmuUn<5yD{tZXf<bbPuoYV4GRYVVL_1Ch}_{g7<`If9#GZiC6u**b~mH z_v4YORNB12<D-{+5R;7sE;f7AaIG9s6m>M%)HBU#fyLucP&(a)pMOi%3WroqG${!K zOLMAT1NI!+$W9tFq2F4vGi)9?${Eo)u%HtKn(!c@!$W9~q~gEZslNs7Lo3Ga8j0&- z>}Dcx56K4&LR^9CEegOYUZjeTnlQznLdNni(()1l6xmQ~>s%FC7&jF^ziG6qA!~+d zks{nIsN4f#TGnUnbdmQZ`X1<fhPl<5{A+(LE31V=wQgxks@^cV7zYFdLQDRxMIzqL zzt=wrv}oRu%?%=V7=FO|n@LLMy!x~)@9W8Z=1%EzePQw2@WP~KWqF}P^$1kncOD{^ zGzQR+`a(%h32SDqie|D4eWLfKTMjY?y<VkdTpEVylwyQqurWs2P(VYIoQ4HnW4}*z zQN?@S`}v`i-*M@dP|8V_lj7>*-P}qhaT`#5P(f*mVtDc(7b~<w$i1k8-X=d%vrKB; zYiawMr4G<eD>f&8zmlB)F+%X(UrBe4scp7<^!M`E8@<4gft$zX`+lykxi*zv)petM zR4se{{6lV%X0Rw6#N`EheLnnMTa?!Ioaff(JU`ImlK;l7_{0}cu}A9gBRm=+sl!lG zvu$<t;(j{Xv?>rCx4{s3Vi00YQh<UXyzfN3%7>5l&Zk6~#6KLUYKoRM+)weeoB1O; zdCEXHFH!ST2;(qeFNcmKcZ;0Tb+Ky^KQEc2trcA?81#DnR+7jc^K3I~&_u_i#hm`V zpF*XC(3U~CR*2qSw0ohSs{+VPgVYKakOiSW6YK|W&U<}jF^0d?l<bvk*s34&(Qx(A zyiiLZRKN?!fYpANDD)vb64MalVlKU<w<I9z*54@f8zcuFJ4jgdNvD0N`#>;W!am@A z_&v9-)ku3$Nbr8IOUXJ^RP^emyI7=>z|2Ew<0m#4GHhI!J5p6u<p2{ZsK8)t)qMq? zsc)B5ry-pZm=vvwOGPa78(+Tsdwtw_RU~lVlWECh?pvO}b6=oyCvl{PY^p)}?;yya z>h7h5i7q8H=o^db`KWnx3yEjMOo4_cZgA&tL(DLA@!qT!UBa*d+=$Zpi#j!pXB)-T zufzpOv$BmpTluvxH{kjJy9?>}3H!NcQ{y>3Z$zS>KK)evh(oZb8(n0iQM37JbStd7 z3nS1&qot#xC`D3|)DH3UlMyQL*1It+2iF8isivElmw_D{{%Yg=7PR!XBKM+ZAh&6T zMMpvoW3(y!u-M}jjA;mfLSw9{MRWn6j@0VZX_Y9E4?x<L#6@18d5}J*9nx8AwU(-t zWhhrOb0npmmHX&Xe`vEaz;i^cNY3e2L>o8VG1YmzH#uia90X*{SmXk}F%WvD*ld`^ zK5^%yugO}%MiBff@x?YU{O?1rOVOztjdMf7Y^$Da6)}xpF~S2S457i4FysG8f@y^j z?RFsy=Zw|boGecc<KhoIcA2^?T|Hpv>XTz(1NF|3&0m8=Z`3DMf^l1E=Q|#)SyWOK z2yBeI)O>R`wK0x+7!cPpN{q@CVk=U0PehLLVRVsGOV*wtY$ATok2<vIxDbZ7xaCek zvwA)l5)cImnpwx-VH!eTXLY_sk0<#88wZNZ)YP|?AC;AwLGrR9lNxxAR}efv^zT8= zNrS;0&JQ_^++^HP)zgE~F0f&F?ovyZX@K9LoXR$D5K(=&+HasSt6g%lD<UV`KPZSD zYYHQ9M;e^ugn887-af#gz;VQo(aaULR6?>Q8nfUcIu|1qRE&hm{AevE9~%IB8vmM~ zPk#q}7^>aZbQ2}w;~1=YZr=kFa5~%k3?DHDU|9_(U6qK_UWSaeT1<7cV*H4x-=O?= z@7=qQsw){UIRMNG`&B5yv?J&VN(3x$JleT336WRtQbO(hi18>`nj!rKV@2Y{<k@)9 zXLkZgrzZS<wG1`X?MZ=D5^ghWyN{`nULaKv!X?ym$qJ#6dfV9<z7*!F_0p9^$%jOZ z*3(7IUg0}R0R>?9e~oD?!m9!^g4xPmknD4`Yov}*INy(_60$Zlbi&gOM_LF{x=F$I zkj~!nEi3gNqulT*kdu#1?o?4xF@@R#De=AAtZBzI7PO!&rp-#D*aU+tEw!4g$u+Rf z8!f*h62b{=2=0~GkR;urXQ{=%12!?cdJ`K7KS;OR@P48rLyT)-p6A_!rpN9h=MP-| zl2N>{xTre&3_is3SUOlZh}P}WObu_woK;BY8$-FO`WzQ+@MJDR^Mx~u6+S^Ojr8)q zb})il8J-?LJ6fhm$3Uru(~G>(zi=kzb_m!1VD%%5mX!Ty^kwfS5#Np}is7ldf&bko z(*%^SB_xcCOo<v9Z!T%F%GuvR*LPsBu|sRobRm!m4p3171EGB$Z`=2g5a?*6mP%jd zcZ%|(?{d*WgOvFh_yt+n6wYR=uKXv&0;5A|zc8jjxDqCc7Ikq(s@k(G$Mf}&jHhO2 zmA*b;!~js4&$j72i?T0>rH%!rtlzkiw;*2GCA=Y>r7e5jD5tIF96HFyUMbK#aRwV| z^6BBhxu0L}fQ=JAXE<#?912E>?2a5*^FN9n<+B=!1EokWgpqKhC6xv0-2o{n<}$vw zm*Tg_DTTAceTFnYjbGEjHP=@Jzrg>ygKY}!r}vPMiHSc%9Kq=!!uJ7-InU>Cg&M7o z%Ose6G5zsl`VOX%jIao#z<nnISiCwvP7h>_Gnbf|OqM^6q<9OyAM6l6(scl11TfJB z0mWn(Rc$X#*Yb06wsmzC!QfMcl@QuOVnpu`GT$dee;t0F_Z7izPzQ0x_2)}p3?FR9 z%Do28A_3R)p^^6S<40m}5D97kWylPRL-Y($W}>k11;0AztL<t{k6hkb8SE=)##Peo z#xXl;%`Dvt2#2UqhKJAZ{;2GD`_VhvNQ=#twPD6-E`|X`MCuz*_6fNnuiX5!&Q!>{ z^Aj$JaHasYX7ITM;LNR=`2t%*Aif0CY4TMpt;9mB(Jo%yD0n7B6JxFJ|5uHU398_T zb&=>>ouaM7^>JO>ECn>`+p@=x;{@%b<4}o(Dt>fgFLU&7O-tUX{))Z6rP~;}=%fVb z_9l#X$;xV1ByfAf;EBOQGRrqf$g^k#vY`W*t)lLpG%#Zw)p?LrBV70V{Re+0pH@Gf z@wD{wSn8YHLd{ePhopjng8oEjp3IMdMP>GyrJ^ri=EBX>RFw15f*3U@%txDpJWE(v z4W;8ZmLio5Oirp2Eurs;dt5s0e7joM+1PB6ew(0^nuW3TKN;I{El%-PNy%Y9jms%f zGLP7KXMaRKA`$z64JaJ<l&_qOYjHPw{&XjE^i9%p2@OkVhq<BPBPgYWIX2_lW%nBH z96skUb+fQT{aLu8S;NQntgshO{Tq<xy{jLspx>-Q^4Y%sGg>InrC*GTnLX&qP*Fac z=;1zNe0`i9+#czOMq<ia(1wFm!`SA$xMzEMVkM3p=DUc31;!@`2`S%YGWypIk3C`8 z!xHPC6NSEo?zEjE8TQilv#0E!bkN2u6coiUuCw>)xt|J}ZHG>)ID5k^xF^FJU&T7{ zWJseF;p${Q-E#b2QsyXg${UT+b*<p~cY&?fhHC!8Kr6Yr%H<UVm&mECYu_(&zr86q zI=EB8V^!BQgJ|#1rf2QQBrK6nzW?!-V>8-2NeaDdP*^|Gv@Jr~D`?zYMn>jMA5Ej4 zdcNXrxDi4+KPHTle#l)Td)&!2FXruA9aF;M?;m!M3ANS)8BZfWr!8sP19qAx<ofKp zNQZMWHf9}Gq8G4iK9HiFCks_(Y4DNi&#r=;_!>G|S{mE7E4B-;u^YdklD)66P+p~H z+ME>sRIsYCs{<ABM?;LNjEuJ#75+9Dgc<{@$Iulej4QoZ+l0#Hh&oe@HGc-$T1NAw zHOD3<Lup3{<5*|jBXlSTRhBcg@MB_XaX?Ir{NHgXVaz|i#gG%mJZN@I7z6q4;bRc{ z^h8p$8SSEZ)0=I?830?$Y+k#@%6dx3jI75B^QTN=j+^1&-0#wP5;PFC6h_ANflMZ* zz{!%=<&(Js+hK$l1ti_P1DYbt%^2B=A8&<fo``zIDg>^HaFWDW0vbR~aE;ZP3ZjD^ z2O~fs3NvXH-3#LI!g+fK_Fd80X;xhuaCnK|KVPE}6^7@g-Me>WBoy-8CUA5(_oy*H z^Zxq5A#{(C+JPy3$D;@!iX?20ZXwd<IPS+a8I22KO3Z!k=LcdqkD7WAYqj90fuwyJ zToe@b#CQ6O=uWaBJ3^`NNW<{Q`Fux<Y@415;tFw~oI9DAYQgt5sONj+Shbx3NR-fy zfS*B4Wt^PnVywbD>#k#$-;rzq>W=licap)_)1|-*J87%_fEr`G@fI-2=Wt%M6di~b zZ+y*882;;#Ome#2Igw$9;9|=*LQezSasjUA{!aKnOgwlE;0s`4Qby7pY0u(B{wKHv zZGp%MQnp2t4-D+luyepM$xDKhdvht#tw2d5TLN_vy}6p3>~vmsY!Sn}c32V8%y2^k zFseGn#D_jVnYA^+5@_L43nNXwTX!ELX4<i_XABKlap+z_&W#eWyNx;ANQ}=zdA2~0 z2q@4NBEw8(dATea%%Xtqgivt3O6ke5xMoM_w*>D2iX>Vr#0I-g_YXG2uy|uvE~Xow zB`_1HPY}#auoX&xo`Vu8M9)JxAw=E|dquGQX2V~&GZXeQIcd52Xh|gup<hpJcrIQA zQYPv=#&F`=H)1fKRW2a6|EtRGJ$vLavWg~Z-YeZ|E$T9TxDH|N4n%($;Ij_s@VXK= z%8Bv48ghNzm!u=n_AnN}>r;+SABpHZ!iRE}KN5~&HqNMaN-oxVxF`M+Y^HyBxa#f} zI#`KVhynHTqfz<W56IlOApv2AP(*-HB4T4>9jlIE$bNhyt35>-pB0@L;r7OcCO{{_ z8qBXRbT}3|8fGilfu}$iD2&AvbOiUvtvL*Sq9t0E2wgdJ-pP>e!+jGG9)+J^OoZ;y z2OH4R@86BEwLyULFDWSzWH}hh;ki5%F0nlL2)w}`3VJ#m>^E*4KLtmvc3A=$VXp;+ zB8Iil1bXgf86q))`iW(!71~9N6GNGn=>z4_{+u9fLXczRNIc@$6!hCKKsY!)4^U$< zueA>%IO~A}rA=}EXu}PEqc0`ezw}=8A#>wh1>tCkhCY5Q3!wv9Qz}_2XZ>jPACB5b zx^w4_26V1>axdiUSu^$u+4#_LyfBnA@7j06v&$hBJ3tJSl?pg#qwH#UK!#AD7s;Z+ z@X-fS;ml`FHg<Ng*=AOi-Lzn)Uy3E?#x0lk?a&vUc<12>Q47|!?eA;7W5iu}Fpl&D zohQHC=a*c_iP0|NH3c|9amo<_ndV<_<Ix}S>J>YtVs`It2J__3_AP9a1SPV~UH*Gx z^quVeJ*U;5&Q5%ftGNtHQh}o`3s{5_)VNat4UY>ba0Es&7HDh!Tl3R5%IQPLpOr=N z(P^HSq^CKUuWimVs%pI1V|<$!uI;NWxHF!2HP7AyElOeNI^rZkKLAIpYG@d-d=KMW zYh8l$J*KW?ndAfg*09&Jw3ar8?x8ahP1PwF>#Ql|lN$V%a3M<Ui43wJUVX3~Dd)4{ z5FAVO1K&ung=-fyNV?6$UVr34>mq9R(QP=+t6sXatTczTVWYFj{I7_|oLB3MeF^LI z(~gyw1b`y416)^7^i0vko!~iHs1ARXIPV{a8@D87$n;qsu}jM3*<Tx{htIc+n`{c~ zhD*2bDZ+)8k&!C4b56HhXMG~+{)2Opb`)!j6y^<~H;zs^?XO|spgX@)eZ#I-*S??0 zv>5nia}uYiLwg@72flaWi&3%gqKNc!+lk&bqVu&)PjL#){;}d>K9c~GA1T$31vY5@ zl@xGG_Z%hDocx~H!Xm?>e}r$U$K!;{PhC9u#NZssb$JG9i*@N&VyXxX<KEm$h`jsV zMILnf_PyJi)z6RK;E=|dKm}2MnCg`DGhDVXl<~nnMNhev8dHAEt`x32K21&)MO8%H z*Shbg&^?q%%EjS<wdxb}R6w-%_llt&(z39qV(IZtjkpYEj!<sG-2ZI;z2iTr!#MKO zw_?vt8)>3XU%YrTm@?JRFZ1;H<D(NauaLSjOgKsK&}YCuU;O(QXNUVucmh$UfYL|c zUvmKph6=oIl0!5o)#is%QS1=%U#woqP2z<mM575>E&sJ|M{Y>Aehcfq{f)4r2XR@$ z7UV{_vw+5l91aITc5^Yp)ebE>q8wj*r*<@c_tDTxAfCkh3rwFXpx&?{zAPKn6N_xK zvRGEKym;S0y;3NM$--h<4GJJs81i0hkbku?(dvo^n87Oq?qMm!GK4~y;*rZF@M-~E zlJ9!^arnP}jf6(!=VoDs#@8yCOu~L}v$)GdEb=^2=3pEXd(9KVGyu@S%E$K*yG}u% z%Nxj<+TXpR^8WcC*WX>Bhl{X!Q38yxf>YxSBVRD8!t6FkOPDNR{E5<t_v_F>f+d{K z*x)8uP%%+QX0NtHG0a0OM)$!QZtv!_n;a@p`Y`HnX=MjumMsnT62huVKwP|z<Y#ko zq~PdoyZeM>M;O(h?gJT}2Ksf-p31QQ(GMG7|9()7=HS4jHduj(+>U`FOkULq&9?AS z6CR!<jSS*V9#QimIez~p70Eh3Dd7=~5V2!TEu7g3hm2aX<`cZ2q)E&-o@10tU%|u9 zvW^_5i4j0U6K=I>7&P!UpAX=^Tr|HIIS93h!1u&?z_1kBB27n0?!m}hc0HbpX|Vm1 zFgCV~skWmU>9M|?8LFD2q$|n4A}AB!|2A_tODg<Iyj=r@k$CwFWQZ(@Z^o>8#W#q} z04^+;nQnVS`YB0-f`Y<k)B+<+0>Y&E<>fr5!$%$_(U%)x>0H)kCMCnNjt)MBQW%%P z?2kIgG8lrhqEZ-E+v=SceCXg$bXrh{LeVQA!8lb8j}0hI(CLP6JWPoB$Xsqx_fOo6 z0m?O(PX1#M&HAoj7e8|SiGGD09R)7DnEmVP_LwxmyD-|^O*IQ_%~zij7>ap|cb-4~ z%)n*`-midG$wN@_kcSUt;uN0{GlKwA4oeZHf*Ur(mh8xI+BkteV8CwVs6%EcmIwP^ z)MlTisBt!jEAb_|07U5r+RAqx|BU8B#;&!}G#wG*JrV&{lN(q=>00-c-+XcN4;jiw zC^_X;KD>N@08zf|?w!g7wx~hYv7{qzr+$!)O6;i5a|kE)T^&wdv{^ncIAl@z;+D_+ zA`g=|IO~0R=Mqa-0nHDbA3MdRJAa+~6jEuw8mA{!gqO91K=&cu$K-bE*8x+K;4OvH zedQZ>&`#5$<!;~Z;OWUN7?|9U8$uXQpOKK%MDLTo7~CSfkw2F-w~?8WxxDeuX#N3u zdeaC4;N;H(m*1IhVZd%hfLk<drn)2Cvr4Pi({W2q-6wZD%zcJDG=~4#JM*O>ydnzq zv1bp2a$rBFe?;2MAhm=<MJdNAp1^zs8PrXO4rlY?;x8%BM`O^X4%OM}9Xg6owZqE` zW`Tn!_+Sesa0Eb|B!PKxJ<^dpT}%=YIwSUm5FTK0Z^2Lzmc~5Ko?0JbGH=n`PWT!K znE>$0v7rm0_44IQNT`kY(&l8X;Y!7d%l-ZRB3|7Bd1b}|(<s{+m>U7rqNNr8?>&a^ zW-&J421_<^aF9`7gUSRl@ZZL?8jTuv{S4lYL`XCnw=?-UagJha+!hSC)sqGQ#L64P zA9{7BD={(8EukUV-^P^W|L_W=Ma-GJL{FWXfsf>kBS6gJK#`Wik(InqY<wCfLu4{$ z2*LtL0K~W^I217o>pMXQ3zG0sW%2-FK|1uPEvaiv8^gDumxx#V;C6A~Hd9k)?x(^# zg)m*s#=#LUf=R?^h&ac-OA^}gj95h;yr_gT)Rh>EQ&fDVkzp&fYAZZcBhI{rw?|Ae zGQlXwOxyQt{eeO;8~JbFqIAi)It-~OX}u6WkJcxH*#(3Z-`PZiG))h8Q(uaG(X)(v zMGTOhg=nsxW4)jK(!pNAY#Tk<#figPHoup4MgP6Iz{}&orT5H?q5|V$84k&a0hLFi z<h`IPm+nfJE{LHK*fom7R)vX9!ukpWxTD1b!!0KNhHbwT)H84*_kqX^02#|1@JXYn z+0GK?+010&*#R|nv};|B<r)^9)x@lD6nhJ0DNbkXR`=!>Soke;*C9E^=rI5T`Kh9c zqs6l$FF>$B@z0+>z(-PY0fFS%B&UmolM169{&WkHg2}V#|GL-E^C;#22Nz?U*k3es z=0;nB!DlcleLCS{T&G4#OSSdY1Oo=;NTCN<vbLbZ9678!)~x3bJhvRP^h{LepZ(n= z&MQug%ncsT;~_OCUZs(imd2}%OvLH`Q5P>|jln833u&G(MPTCTh+c7FWa7M3+sH9V zhac2k1ukaDw!~x)+KZ%mOHWI@Lc^%VSaHSk(Ux~tvZ97-*LU>Ue>d&gl>My+4^B*1 zkcc5nV(cHP<NG2PIKb-2onI#TWD(9E00g4!JVf5cyJl1|3`jPdRlNLLGtBbVx0Ch1 z-Dd_f7q`{dXAv3b9GtWyj77XbuZ9?IEu5}oBoZnZ0@)6X;lf6=sT3TN_*Ntc7HDDF zdXRy$Icgf3n6_C$oLe_BVfq!Xa<K2Xo-++X!H02g<-hElX4%E}h?o4F|0kQl>y{a& z=bS>!Sf3>dusL^Hz`v{OKejr1iHtUZ4q+joaXWAJM~C_LOHo%>w-BT4sAVL=L5V@k z3vbWARBO@T2$T7|qIvq|YqDCaY{%0vA_iMtCu?=|xQGAiwCB+I4O%s)G{S`@qQse` zQK0DQIr}xw!Hh8E6Ou7hf{+Q%<YXI*@sIp&=>y3&>#5PGMPy(-EM>U8BhFLlH^*j~ zXnub;wa~H0yz4j7I3ppy+F{|3wYRxg*^EYNj-cc;#*?0Ip)5@*5WO3rEG?ifBTW!e z;2Ws%<0d-kN-OgSA!DwqD&!%ZAKEXw_jsPgTLvJuwa<VfI!yP=85$Ys%qWky5P3tC zQpPR;o6`uOM9A*I9((!pNyH1Hu*oW3VGE;J%Cune7rC`~8(IYjY(0i=3E}A}xEZwT zH$Yq}o<Zxg&rkW=Q}JfAa^`20Ts)YjM5fq^K71^49bsG2vd*g*X*g?HsArm~2YBl$ zj&I+hr>6&5ssY5n4f73Cvkq&2uQgBK3{o*_yjcVsHGZ5*gzg!oRSequ=m?f$a-%c7 zDZuH2g}mmPwf0?2rH_~q7rsRG!ngxU5OMn$kfI>yJY>IcDTi6psxlVy5?$1QFKj!i z-v9_UzP)e=^+Fy!dg6V1u<<)DJM7<I75~Sb7+il%FH;=~k%yQ-m<CZN|B`EGL<~wG zLwC*G@Ti(Dn%U|&_Li{C%M!JHJ3t03osWQ3_IjH|yZHc*SDA==#Kt*UDkbW|B=|@s zY2Z&~;Dsq@EqM|7-W3KAt}s7;zW9b4F(Mgz_EG1)wbGrx&?jhiaPSOE1(wGK2~o<V z5odA<UAOD0D`-97y;!*`69tl2F2Nn9tvB@fXs~4=h^j*6H%hLE-?&{2mXdIF<bzAu zRvILL&56FxTnBlNe-q$bD&JDmD)V3ww^99+{6#D$riTDEBA}Sjc%vgr3`l5^YLBkJ z)=Kp%Mzupd-cqSI&Jl776hy5hM!dFwcpD6wkqd+j@lSofjn}3<J8>Tyv;)}+IZqwU z$EJAIcudhn^FS4ljgrwR4gC!Co@YO_74IT4gKVu<>=t$pyEk}ktG8~Ym5Rql*>#7# z6d^RI#;5=^5^qu~es+$yk>MjG_Yl9$4E(G^%=u3^IljL8uBBa4?0Z-#OkW`42<DMd z3mksWHc`dkw)%CqdNcn>IlqgRtglaa@|oS^*4CR`nsanScnrX!=eSmOR{X}51VfEl zjnR1kjofhdTdwE-tM^_I<!b7Qz^W%@=l0bNyI!j$EN9wG=1=Wmjuu$GWx|*<WjpKz zjh!`=pHha#&g@8g;d140Iax5N=+=E12mL+6_46jPro)m=$Hu2Dh(0C!@`yz{8vMDf z%MmfV{>rOG-n{Q`%%ze?dTTX`G>BgGY4}ET5^c{eVca?-6A0eB;PizGj6v?B*gy&? zRX?C7HZ1B}^s;exAKo7#+XzHXHt~PecIHt%=l>r6+GfsNjAgPW#`4R)N3>vOFqY7y zK`2Wlw5e25ksq>^ridveex@4PqqNG<Dx}h;1xY0Bl92A>mHD0fJNN#6=bqm^=l(Hg zoSCn_eLkP}`}Kakp4+F(9OjlMrAgMm>vhsmb?bd|&@AP`>8ju8U)b#DR%-FqeZlfQ z_=0-OKhgfwmr<jYF079BVVh^hHo1EAZ`54dbysV|djq4}WoZ}vT#I6+sPD;mxVqmA zw!|<?ZswC$x?4%Ykk%I5{XRZ}AJ`Ct!+9kgdRo3nJ4}C9(jmj^N{2p&ezdg2Xt+>} z<~>qUXy5cby%Ek^%{<qGZoGTjCIQ*ei*>NPb?ZrLkNqBdI&~hi2EkE_hReEj+lGDq zn^MA8>;JbB{?IuzVwx>l&{{L=5ivG{E2;`ZC%eAS9yV;4Y$sv2h!UxK=zsMG5ufcZ z>+<NU#ND@UeXFKcd3uDsn^xCh-TUV6%{iBFiY9nmn3nl}t@a^Y=dm2!H;;3zzM8db zr`L@x4>!EHU-I$YnfZq2<RPniw!6Fc?uNcIw0hj$)-FAKT;3<wlx{C~r<r6`Bk~#I zGp^Ik)f=DQ+MSpj*|DUPL;KN7<1Q6X;Ta7Z%G9$TRcT#PAFa15zU|)WX!{{<AF{O8 z#G3P4&RePW#f0NgTb{Nj)UlLd_kGhm&9&on|59vzE!Le6{5EH-TlAhOADiF5r{%8E zjjms*6LRf#kV78xpSQ>FohBR?2NaPeWf%%d4{KJRGSW^Tv-U{Zr1FdfpTA?n!}h=a zprXC~DAJcgqc1yltfjZLO;K@FJTe65n;pJ*tlRVlQt5#S1^11{di1g=da@$q#9Pnw z>hH5;X9X%L?0$)gnubwB##qHhVn!(k_iOuCU1XyUIjq-b%hz>(cFkx2_5uX>E;m{S zrH97sw!nVierI~$93aksGGtxlkg7PF_Ht5W;+YkdKgh(<l?ppIm>xTzW@=Ls-h^IY zruZnij1FSF2V=%#Td4nyHvf5cz@l~&_H7LHV|W}=s){g_wf_H<ZYv2`|1%mlD715G zMVH$OSke2peR*d4f71sj(osEfJ8z_7gT&q{ux97}`TvwY=SB{=ao=RciKt<VFy;iz zaMeivYL#5%&*A<{+7cPa+*Orz{BK0^`V3_=)MhOlPgbA~Q#wixou(q3i{1P8Uw|Qt zeQV4=R_7mTPOoo1zv4<8DBo5K9IeqVKTBQ^e`IKoR}LUM1BD4NveoYooTt|pbQ!1x zZ~W@FcfBA_k~hbD4GqfRNP3B9gmG4k6VT>4p1v_|gMB)9sA%R2T5^RjfM>`V)d|P( z#Bv#|VU75BE+IUg5GcwxS|@@cnjwRF%ygdcW!T$+Z+FZ{_*j2P92~~D7L##_TY3zd zS+X(rykCz#eO~Mei{BR?l;Jth_03ObXp@sU&x`eg9c|05M&p(AfRI)u!YHz3!Mu4} z?2<~7N-|!sl;$$tc_JN4nr2(Je<H!6I4)A(0pMuqM%W>9OCg6Vsm&WSXg00x={n4E zAsUXQi3h}`<6oMO^23ux>hW`XSe6+<0!h5X>t}HZFxY}<l)`+BWUDqe&OPihV;Q0y zb*$4D6ff5wc9}|xITv2Z13WDH>g-G9sV^owzqOE~NUbfF72?~2M-&yjY`Jcq%iC7} zi=5j+5N@87@2i0j8xBr^C3n0tLSeIbGJV}(x+pJeglq!ZugLX}KQJ_?+f5K8!IZ$V z?9Kd)(!4XuLGw6d!$5wjC&eG*A>yH~$iFi5#*G`1#sD^_A-t{5<rOF?lAgiWeYUOr zlkra#5IZ7%#<tpTp<Pdd>sF7-tDIKfyuuP05Dge}ew%`d;l5b^LX*v#hJF+&vAZ(z zTAj~g{S`n4?uUA*h1epP=X+psWp%5q^3c39h|+b}3tmtMI-3>t^XTe?)*ZOFidJ8- zBRC<&5UMR{(5!zsFqVuN9kk@aN}$F4Vl#>(hfcz4b4<67<>Aq;`Qf&aM6Ss9aehOC z?m>IV9KsAD+JCm{G2~`f9czrP=Tuq_Xm$bSYlF^KMUElR^b~|v8=W}K=9Xon#tF86 z_mR4%W`)hP_{R~Gn8&q$j+j5T9<%X1?=pi>r_&G#56><4qD^afetb<9E#~s$uY6y( zxLh=hO~&<>OaVPv{X+Hr^VFcgKcklD<-VT9#%-n%7m2F)xR?B4l<b|6f=7xPDKS_I ztJtck*gA$E8^7j0M)abkmycA0hHP8??@g>WWesf3ZV7~L95jZ^EJF=tbv8%2;6pR> z_CFf+in9N1)O#Bfzia4D2e4U-rx_x}3V>r68?k*TcN>I24@f{M1u3enedkl{Pi<TM zcLmL&zW>XN%WeWhEeFhAV)Gge&%(w&q0_wLgO>0Ug_<-#HCNR~WP?fjdkigF9pSj8 zEaSd}Cc<?s9dPLI;oKmVKDK2I|0Jq7#yvE2@S(o$>*C(OIyV;-*OO<WJo97i?o3c0 zWAJYGb$ydLF+G_H`txV##v7T85XKT<mwSi23Ll2m1|A)o$gIer&@vg_&^p-g{9h-T z9?ay?K10lOa?H$(t4$5je#ds%Xgkk5!9DE@<f(paoZ)6%D~azOw|+8H+x#$f|A7N_ zCe2PKsc+O@4~(D602EV5dRVV>Qfqo%YjKET#Psi7T;l53GRllEQ(;X#1Xw6JXBZoK z#U!og?Fn6;p=+JGIqzcNt2`ogo}9iDzdJt2i%a3K^#m->qt2b~0T@UMq0_1LHKnO9 z>_~vjvn#u5aBZWv#7~{UL^l@$oF@&t^XHE=uq^wtf7SY{ZCr~_y!?Geib-GhrxnZQ z$(d*~=yNRX|MA?dYuMUuh*Kt)Em0y>5>h3x#*^Z!#ePm(*J+Hhul1vAX{J*iRidCq z8kAm@(E`xd3>1msaX6T5qQz86#^_HRAWtY77fkWW3|R{t02R3xltI@{E)2>^*tt^( z`3F-3@`JRG%*)-*LrG>7T^g_|bg|05>K32|-SaCZ?z>okejb^`G#Ix~*mZ;VClx%G zAIYucY&0gcBJ!Dt+q|^u=Y`~Xsc0Uf<aQ9+leuxDbIs1y4})lb&U{W{FRAvYMbWD& zJmH5?rWUna2eBgyRnF){M5R%ftJ$2Q@6a@NmB1T*3y(CDJGF6g+$`#4T8+I_=hq3l z(3$)4^kb@`f%8g9suBOVLTQAq8%Y`7Se7KTLHPNr8@h^ntG+A^NAe@fi0D;7>bMbN z3}5sfrJwrr1FfKLnEqLvjE2TW`<FME+D#CrsafRz^5B84f<?4QPfst*uBoc<N635} z*b=67wq!|710~o#q}>CwUg)CtZFbZ@qPpm{#wAFe$kzMhM@b?Ex%X>{PZe_c;;1FV z0ECB(D~AYYPaQaTkU)cJ#SM6uMQ)-zI{nv(P8VI8>#dy2xxOS+!>LG5h@X`Tj+d>V z_biS}Nk8YC)`Qsys^kK9O)_4h<&>DEu*)>Uap~2qXk&;mD1Y-|F?k)LW<?oOICDGt z@Eo;_DrkD54&J=VIfRU^z<#ITZ8atLBxbOS=d!}<*0cqmY(6S)x#>Cf;9*hE<4bDm zt<fpW@6nwHRCRyIxZF4=yPH|-y?dBfK1MHGUhi-FH_<W>W+nipNQh~u=TKLcUYpU} z+?+|GtH@Z%B%02#Dfy(UGkxxG&@{iVytJt#&dI&?N!no=BICYuLoPU@r)+Oq9dwqn zNrG7!vCAs-5<7GJMXey8s7;wMvCGin28mmu^1jRpJ`}qk{U`YjaBVofb+j+yW-PX& zJy^t+YWI$B!u4Xe)Yr$4Lk#%-ZijAKPr}VswCb3?@d`r*a-;Q=Gr3N4nU)QgEp#B; z!47+LI0?~T57c^DVVjr^E|8c+@Dv%IV_1LdQ7;e!9IWwz-hzm!Z0-vl_ajeR5Qe8C zQ+dZRC#}{9{olLWzj`=WP3<VGRY?C!rYbHoi7cZ#PGMp5B-cm)s~96_SODZkB(1ot zJp3y;OWwYx0UKEz?@+Z8M{pt}UbIW|BoF}67)eqHwN3NQ2N?PxibvP2GtgxFS2RPc zhteZ*4WX~v-{5CUbTU0g8KMl)=%DbFXg>K1c#20D#cexie*W<@kgh~5u_z8igeoF^ zkeCfC4i#7+8~ZDppu397J#h^avW6<-xd<uWH4V8=f2_ov4n|`-F7P+I7lDrT$;#H3 zVIl~Lo)1@}7buSP`$038Tr!H6oTK#3X&2dOf^PxrZX;QYF!^mgNC=Ml70m<~sroNF z4(*NjaGjsquf&|qR0KLJjXOOihl2{(<}FSiC+?o))Nog9{qw949oN=wK}fX_HuEgu zQ#>PM`g>+4%{il2vVquFiZLB>PpED3S%29Xe+dWbdK4@cMNvJlSHLEcoL45vSS&AN z7N<k&z@)^)?x^l(u`*xBgs@e&)|3QNae}AqKzbtB0B}}5*}SlAe*@=*<EuycIT5yZ z;I=32P_FruyYB0%oUH!X<p;=i;qNgOCuNLp**Q8TDJc`@ppnz_UR3ABa71u|U(@Pt zfEe1dZ(jrLZGMxoclrfn%B<6skrwlb@HBiIfC!~$&z>Mr1)nBA-(>mdTDvg`j?7d8 zFV2lSfCp50MXY^x<rhYzR?!KG<g}thPIr-u!IVr!>Mq)DLYr40EE8;#7EPjGShF+k zRkGI>a>e^SJj%G3=MoggNN|h3ixL;Ut!{l`G+vu)_jJbm9Ht~LuZj7ttpD>|En{je zW~4gKF!HB+?)cPfCfA4h$vVEVHx9BRh6yuamcv`FpILac=rzeS@}3AatD;|1Q|Adc zG<$YOMk`bF+(ayFc~`p*$oE)zBh}9s98K1NNTW2J{@BcfEowcwEr})(rxF*GA8y(i zYsg^SP$=}p-eFex;Z9dBxBV|0Mqj^b`mk#NCJWFx<R#ZEyI3v$nKgx7;x2HjXsX+m zBhGzw-rn*zP?G>HCm&?eA=_%$k0P}rjCRs(8|y(Xwe1uLHx*>6%*(u$MGasA_~Itp zvKa_-so@$X6M3x%D4<0Ouj|-ozDtC5hL6+1lZ?$XQRjK-d0eV*Yz!vpPwQjtZ5p$t zj-GUXk3JQiG@U;w?4HK4R}xE57w@m53cUUuCy(31;(}!uPft_L+=3gYp2;ww;F5FG zeuDig{c6;Nh0dJFX1oa`^2!zi1I;ZQfDkpqv)8nKc-%VRhPuQsPL8`|9}uh!3T|2X zG1|B|RX|<ws>s$2)bK5h$Gqh&IWjO6nsIwP&%ADS!AAT`bt8bQC=*0peLQ!R?NGWP zn+|LsRt-htYdXipnSq<D!eWS%%ZmR*K0obYU$=b&w~x4I$5@Pt(1g#PUv+(4PM5pX zHs*+3`gu8|_ok+%ZjC)`jXX~kwSv5sC0nJicmMu(i2qEcf=*+K3^8vkj&e6#X`WPY zPF5|_bt=VELML9iUM;Xt9*=n<LapZMyh`6GA5puFC~kZLpP4F953kG+v8Tn%*H!Ck zG~D`)-Rt9hH@Dr^-*Q>S`7t?FhOrYgsV_v=UyxcT*HZW5R$y_$+w&c;s}yv3oB&KD zX%m)x_w3gcx7d6{7^1Z$Uwj>nL}X*8tYYj^lLn|+R}C4<j;H<AX^KO#B`U#HE8>3I zUfFE0+b8~y^hdYkr4DewN6M$GJJ=1<*g7IxvF%y6R3yxWKmRzFlk<S5rzzeJQMbt3 z0LUc~n*!rmyDSxJ--2$H$_6u!d#F0$e$f4GUJoIKUEW>(;q)l=3zEEQP`I<t*p&}Q zHr>_X7uD-@6MCPmvV_`Pl+`OR$6jG=($Yfo@ao8#zpNiG3!euhJ&y<&aa@oJlL|?d z%)N^Sji0>wZobBML$7x7;+A#!rM%;pX7~17i&RR+yOU$-!dlbG5bYkv2$jYTIhvB; z*?t8m5fI5Om^l9#N2bJ7o{dx)jUpbF8z9L4<;#~_Yy6g_uiJh;XyJxk;#t596s%b{ zY<$!e<pgr(teiGbHb&^4QlOYY-nI1T_%$c#2x&8WQEGSTIF_W1JfE;e{nNmMQU0Wv zMIp*rFhx24Wc8YN<MT};O9H1@7-Tqhs_b@7WSL@udS98W?VY_8Y2_+&&-ibCWZ}<Y z1laWG+F8p%d6dO^Fp+Saj>uCJS;sOE8^k({6;gnq__Fzde$ECvgC<wbxPGJNPDWC; z)c~WlOI|v<!x(2%43rLiU--dn)bJ~xc1*AL>gN5L7n%@aY}Vn@%7}yV8Rp+Em(Klk z<_iT{{O4Gc7Bp#9F0;KjV7EQIyTT-DOBIK%1iZr%qvVgRHdnbMF=es6lMG3_bzdjV zG~Iv4o0L9w#~5_IBIgn83bas<_i(H-e{k=cFT!uXk2X<rZs0q@?KNYKEdoNP#VS8G z%AL8CMXrj@{Bzisy@VP^_76{g|G~GQ|EVFOS16s!eGUPd7%+230HsBe4|o=nT8_7l zy<W7wvk`Y{aYF#a^<}hRPZA=M!f&Uf)aTv4m!1o-&>P+h&C5|M^J0mWrYS?&{v0)c zgEmcmwLn^2+FYCxRNyRaN1i}rk{@Ce%MX7z5f!K4?jJ2LbEj$^VLi6XSJ9cAb;H$p zm3D>wzEx&A%YWA$T)42P%!D@T`l9mb2>S|87V-f$PXS|+Ow)W@w3WI_WzF%%jHu81 z*3Z5r9C&Nc<((m=4AKfw#feV@7B;d;0%zq7L>r4^rqBxCbqLHIRL-cNliA3TV9K~T zLm_SNs;1E^RF_GXSL9zdQTdNoM9mpZcU`jMgC%mWJ9KSMPj-DNZ<{)JN>ZulzqZk| zNX9PLt<`XMa%9tmv9<&-jeL7xN|)W7ZDL7llN`tH-ZYnbxrugMK?I^16HjClb<t15 zKFo685jm^#Jm#nW2elWVb5>aJ;nim)$`LFF2C*>whr{<jEy97!l-KW*ZH~ikxwPIU zjyfxQT*)E^K)o_6xHGdt8SSVTKZweXz4QU_QgXB2@#YuNAxm05)q3dEslD9*xaD;t zp&dQx`y{6isfrwp+!B{h%`e19CU5hi-9T@i#>_f$`NaV|mElvT=1x5#2@o5~QoX;{ zzKTQ%OI{Eow+(F+i8SBAPZA|NimD+H88u2lx05Em@bzE$(b%E6y-5Y?hwL6!GX%}6 zlqVLR53?V0UostGU~aFEe8;4X55C1Doko8vZ;PNaE9FsAPRL42XBS_+HY|<JnH9Ln z^(xO<KE5yLsSaJ#Q?m4ikgjndqY5H`g8q~{BXjp@zgz``(<wl|ekZfZ5}Co_t)EiW z;__`(zuPFw2SYBRmdSUYv_EP$4cG||p8c%bEv(<_n96bcHoTa@NGNhzkR|~Zda9^M z7?-H{ctO-DmV4yI!q;Kg=1GbWRS>VZSwHmz)jybvl|fgC42J?H$3a9ENrf!3Nm0Sb zXBvHs$8mXc&M4rOJ1j!Hg@1yc<c=()%jLZP(f^t1AsT1IZt{vQZ*Ol2Uz2yk2(cjI zL43aaF_B28`M|L$s2w7F1I{csI^qx3j{zL#=w;_|jdozXaYquI4e`%+$<TK!*g1%m zl2kvcU)opCIzy?8-9_G5A+T+s?F1kdVMh~FQes`}_r-VqJ=6@o)9rcFxZyPhW1PIw z=9#Y<1+z1zul9B|kd?T%kId1PWMn@t+lVemvPn_(U11;-6t}`B@le}KV5Z#z@~drZ zjPP_l`o=Q<@-A3du}AEkX#?L5K;zH0&jv$gHmowr&RpfnB1KRi!~p6Xqg403q%W1~ zkB_Tuk00;}kjo;+bZzVFO!wkZL;1gmGfVox!EHY-j6<;w413Fyo9(#OuPSV912EW4 z-jp@Wf2!pj2%gsriwigP6b6IW29j~o_HPN2{{LlX8g?E}C<?3CAE3lb7Zvm8ES`Pw I`?Y`lPeD)3-~a#s literal 27155 zcmbrm2Rzs9+dlq5(U8)lL<tQul#x&=gjC8V*`mzs)exl;%1Ee?h6vfaL_$K@JA1D( z<9A%TpYQX0pWpxakKe!7%j@pWXT0CnbzbLr9LIT_@7t$O$*x(obrpp|StBnebCyD( z_M}j#+E&owUk+3@b>cr_cE{E2R4k3{94^`zQIsy)Sy@=xS(xeXus5=?HM6`T#3#Zh z$g{)L&d$nKoS*;lzh1y+X=B2_SEtJZAF|R)PQ#W$S$mQEP`#9jHKS0>JmqDMoO66K z)a;=BsB5u!td{EBxu%yH^0r0{GD@p$yFY8obe$?cDO7C{QePVvDYos>V^!mf>UB!I z2hxn=t=c~DY-v0C>DrE;R_-28MCCo>gghtQC0QkVn|j2j{#@HI+MStjQ6N;2O9p=m z&C*3PDUA4&$iG~g{C9U0We+<$dxzmh%5l8l$zAHFKKK)OhO!aAwH93EiQlrHIze@s zyuD-(<qm!;%KZQIW<~E3L-u|9Rt1)H_MRo*9CwsTV7tR`Q$caD_aTR2(M<C3jBif= z|NEN%+iMh&-LeDM9-zf^@15;#?d%m@Uye+4xAph;z7|wox*rZ2YMP7FRx4J|O{ssn zBj6@JJtGz$ANP${57*{zw~Y3Bp2zqo-8$AQlB`U<A(=@hm)_PXdG~~EMP$jt@r-DL zT_-{syEgw_rl~|*lP?i@E}@F+wqCjt91`+*ysLCbO?&g+rDba>{^ZMS+0)T+JuGb7 z-0z{}%V+AN<;gX&Po0U^cIG{LZQ>#KlqkjB-Mz4|ur4#s;>=pI9iQEVEw4ljnF~b! z9$fX5m5<N;`%CM3oA-JJ*MINhSNOYi!ykYA$o{^%X&dzjg`Z|2f6eRj6YP=;@yGg0 zS1oOqzuolA5oyY~mlUgYqg1(6^wTpr#fb)aXJSrn@~#PC{(BG1-*U2UGwzbyeIfTn zzP!X??ssO@n^ohcLtJOZ#WJ%M{{}`0<wik%WmG|7adG8aEs>)~8LD3>DaZyt;(WZE z2lr#cwsPgl^o-D5)pwbNOq(+gxXn%$JeZAppnc@<QbfH<cbVe*=)E@9yf&VLi;Mbn zv>fB?@1d|aAK6~gkyzvK`dRE(9VNT=K8qN2{{8KTOzV?k41AN=e=V)${6|B<{U%>o zTwPs7Z8}%PC<J%+M0$KxX<z%Dh6FTqeu{Z5ld$Q!H>z)`pXy}UmqkiN(y=<`CwkR; zIxoe#r%8m<t@CeG{rN9QO3n=m)x6aT-u;p7Z-`R_jJ|B_uX$VAlH=m^^PY&6bxGF# zy(8|%OS?5zd%<Jur=OH2tB6_E&12pS?L$LD`L73$Eyc%^J0*tt1=p85y0}!QSzfq) z;|6a<SC?*{i&(MAUUF?s*-n9yZnL8wR`ajhwCQ*tm(m{H?DVM8q(e&^WxXoPVWg<B zF^p3o2;ur=@lWew;?)7weQNQ8y;Xy!O7jsDauN7yjuwwax4S2H>FVn8Xe8Y<E`KbR z$>aSuXgZy^0#`;=%2h-h7PISppyDlcdMTJX&)|=vqa$VX>!Fd;dG2oF_Jbh<Q)4N# zODiiEVbk+<yRQ3;(c=T=ClnRwDe>{~62HEytnye~I<5W^Kidj!OixdbmW#An4c6^H zc%`YgH^7Et>Do(vt>3U=W`3f=tl|CM-O3Ts2pfxr_qX|UGDS1rv&b8erRPw~vg=#_ zXKu>m<E=FmyP<|hmU(}I&c>V+OHX>DuoNHxZ`N!(co#oy!^2+(>ytGyY+^oK5dXWm z84V_pZbd)dsD&Od-@I08{#{gQ$)<mP^=0g5@l0o+GLIkk`PYTm(@hJ4e`79yJ4BHy zcVX7N#qsAFeSQ6x=ih8rmiqHPs`SgFr6a^qoMk_>^#<)4{U5K-Q{J1`-am0~t61tw zZN)TCa@DepnOC`g4b+<8*PW%oet7f4s{=J-`7~>BZ`%J{xPj6WQUCo0MS6mvFEx_6 z6oUCaH8wU@8g)7MQz2jZv%GQs)sHKoU9(l@^yvp5A|L$?c$<f-xA2|Lb#<c5|M@e6 z;2Iqpv$i_#<hvBD9J1+76I?h&&)omaYG*rE+Gw{|%r0GaVY~fE%e}NT5#KHRT!MlD zLyc*NGNUDwmST~m$W&O+ecti0h~?K%A(PSZas8gJ;gh|UA1h<J%(gC_6$ZwwTTezx zy458b&`}P%{5JeOSig~5wXEP#m)p`cGkS-JU)^`eelY*z#~U*fJ;{z^`d(6p{>GVT zVd3o!8P31<vAIn$u01^YAga`Q-`}@oi8_w%!0F)Jzn{_bgV*Y0!;<x0QgnZle%MJx z#m<QeRKe;Ot}|W1WuZdFm7HRSo2fS=`-Wxww+O-MZYel_ducHWx!h;Ew)EE~RNPv# z&C0>S1Q#5qoe@AHC{s8#mX(yY#LT@aF9a@q-$NnC7*Ue{ajn!~+iQ-0KBHgry{S^J z>x^0J!d1x`L$(9v`{uhimXct=Wv;;6QL=v2>zGz7EZp<)lPbBjrrNBvWjjCrgmnq? z-)PZ_*U2gps*t$h=eIV})n=WC$BNZ7qe<^quBG$v@XCL3bZG;UEE-cUe^0m&`G{Lh zUobR8pnV0G$`^b2;uCi%QI+wWC-?pRwCawZKbeJ$>ECLlaY;x#!Qm4$uhC`SH5W6p zHE`w8V;6f)9ooF_)mc(7{T0RBW`)bcL`**1T!~Q9bsal?{rdIM(b2Ep2KaT~TVD&{ z<E7b!R1><MiRm*^*k0Xi(F-1b4pd0a@ld+H7!CenzPvm2Z|x5iwCR+K*UtE0o{)2* z>&qkF;xG~KJDbWkNS@tBXDSd9Y1z_r|I^9WcB|Eqmxs-fQ0)4@-|p=-rD$twhX@*- zOd6$Y>t9tJ8@AY}C+B`Yy14p9Xya{zt}hg(ZQDv+Msj4MPTU*)^~<Ow+sQ0-yP)BU z1Lie~zbyx9-rC2h;$%4pZoPMxO^Wrxg$rb7i~QIkht22bckH0-J16I!t$%#NT1sl$ zChpJxZc8fVP(d$jj-bbaE1!0H{>w8_doJWkY!|natNcBZw`kn|{gtqJO_@!cdZPYs z9yNS??aDQ4Vw58#2kR1PJR=UfXr|gL_(ihW4PA;Dav6K>RCZyayMLxs-bROV_x}Cv zVm6P?Fw5L$r^3a*zf$p5i9MC*IM%U|N`Q~={=0Vq2q%TUs+jHP5|M?6lk`8W(sl0R zWZ$)GY;;twCQf5=sBz0jkCA~E?>F4<EzGjEa=O?w^R_*Bux|bO^`pNR9A0N2V!YR~ zaqZu~U!$=)R@p#+>E*ds0Xe1E+mc7dWg*w{@}&Iz{e`bIh1{T9Ta|DjPti-NWQJUy zkIz$?+v__kB3o_Bs;dL*>eR0^rTfPX^;O5>``f}DS_*1ww|IDXq*}IK>A8cvW#dN* zT~&<2#BkO~l6fs3t8?#8*<g-?4+g096crV*d+MykpFVLOa-W?v8*a(*O-SIk?fDvW z+;=lQv*68LviIxW>LTi%@a*63kEqjPReO2*n$+Ta;Oz~&15iEbRJl?g4ir%~?>W8W z_%{XT!QtVE!Ra>%xhGq5-FO2fFa3D^Dbb)<J=awX`7%Yh0Ff#79W}eRHBV~WK?^-3 zR8)aYo*kcj52XquW@y*asHa+JU`^%a<leFP`uOBGH-|qz?#pF9(9sc~Uu21QWVp^G z>0H``C3}T5FZ*ZqSN*$-G{eKgGAB-mXL2M)X>XwbUEBlM@@Ckc#IkzTs^?dlGt<*K z7VTQxrgTpDv4#(-%gL<(21t>8MXu><bCv`1e*I<2hktEUi`RaJm(PzCvrUe)wpPa6 z-rki-QGcg*B=hP(QM&cd6$}i<Ks{w)B7VrpRNXE4DBINEGOwB=2tK|(|3=hdm_^9s zE61KaKB)Gh<0|WbsC_o<KK1RrnaYdBWS!!HguJi0ZgVeHV(;JJ-ryn>qWEg4COG+1 zVWGg4CQ<i4ztkioBpk;+ZQ=FE_#UT0*1Kb>;_OeUh3Q?`p)Taqa|ycPl~J<QxFGrq z+<@F{Y;1pX!MjhfNCBq`P3@YfOSskI3o9ztDl04JE-tu;U+p)HT&-|FkelY|;U#dI zvW)h_X#2X&zef^r^yhjSF8K-vmOQjrlc$kv+}_h8n_<&sVSA)x6G9p2*uQCyz`FTA zf!<P{qgE4R`O*UyY;l@}E$SYiimBgUvt9gLyYf<IvnV$eo|=!bui)*g^I`0^uUW^& zRuQt_z_j|s8tjWc4rPs4V&`A@c`)_g5VVfR;vbRR++4*F0jj`~LSO#e&+pBulRWfG zRQ`VUV8`)+RE1JR#^~?g#)!6!{JKxNN`r+QhPCfq4BlvwyWw;AA&2y<1Dltx+V~X5 zcyCSr287Ijpm-<Wp<@%><zdD6*cG0LdDPDrHeFvdq@Hz}sfI6S*|Mb=xTFX<<tviL ziq)%k9Xwbwo{e}yE-wO+*&X3{eKW6S=d>H`0g@b(N==ucLfrKG?mn=UzPmo(<g1~Z zd<ggs+3^UMVKKzqy)do##-a0rpZ8G-pjtakbQ2_LGtsRiXmj#Lfpnt&C&zZLExnCt zmiO1Fk$R-RMn2hdr_%~W&1alDcGxNuFx@*5#HVvIK{toTp|+!=<8!1`-lKg%**scQ z-%N$ahtgUF0gazWMeS#z|Ni>?YJ|k4?=K5(td*4WXFtB@+{<+$mMt9k^3q^_+qQfv z#EYoMf&}f2Ygowf4qtxq({U-`hk%eADPnP)DOV}n=dYVp3vO9BdhA$VV;Vm$&jgt^ z`&W&YYQp*PR35r~B+9YwFw1e2YE<G)<bE^l`i>%33c21#xF%ZE3%bwt?Iv||m!HIk z&XT}as&6$_PbRG-*fH_N;P5h1ubv*c;dA$HA+Sb$vWdB`O38bCI6zZ-Uth^K%j|+` zWvOtBM`L9t<>d5GuM{IC!*MmX)1&P?k);hJFgSLn+x9RZadqN^(LS~QSw!HmH>>mK zf=BXK9(FN4M_M)h_VkD<vw2&0cTsYMWGOziyeY#r*-aARo!$A_f0x8mlkLr$H}wRV zm=0Xp$mTx%tY}U?$c(?uZV9om4CP;=5x(5;5UDlUs{N>D+LfW+0FK~l7HfP0OK%Hv z4)80-jvb!GtS<C8d30)APS-xF8ntBD?glNf`QAmca~x9QnYWq;7+6I0#A;#`ws=V; zKE@4mDLo$j!krL3Gr#a>4s1$(Vg}10<UH9Mr>l#iR&o2@J)=S&MuN?6dU@@?_+c56 zy3@HJnl@kvpF4L>PRt;;0Q^nIMc@sEubzlnUzcTbdx%m&GdeqvkZfAXPN&A{^hl|g za>@NRc;+AI1pfsgk4{c@&W+^7Vq@>~DUAy#Q3F?5KSjRW?>MSQiEtg4@2-ecMrvTu ze$;t&2|F=-Pcc_Rev!c)I)XUy%+21Z3s_PWwtp%v7BVQJr(Mgc*#5!G+S>YJ`Qroi z4QCWMl9!J2VCQ6C^<@7K-yD}IoAFNh($p)fJoGL5O@Ig!Z=f7iK0QM7d$?I)tmBi@ zc**YVIsu~lmsm^PH{7%1uODg}*^c`u$QV4j*&$yZaW_t8p4c_mv~mmo(F~h!*<$IL z4U+)@zDxU8cmmAK?Auc+ocp35KOXr>&idd}8YJeI(_RnW^8V8D9OqCeXQxm<U+IG` zlmw8-)}f~1sd+HVBwI7G#}%l+T4`4ZgAU@+_+a37*m%O67P5iVyu;oWk{LZIqZ7$a z%mE*n6*aWqW{_}ae(&5-c-tcKW6<;G&jIpx?%A_q?b@}n!N}HPP4~8m5Hi>`M(V<u zs1r#Bsrtf?U1mRgIP1}(=XqySdl_>xgS~xy=5h<c(4=3=2nGI?^z_l1DVKnOt8C)r z1G(&1dg!rE1glQd*e2+izRU<0P2*{$W>~p0(Re%RTt*FYB9oX+w2?+|LhkIH;pVKy zocq38-Zq{Z=Udw3l3Rf7l`qfiBGEukO9T9-|EvE8!z<3HL!ZrJ#jMpSckkSR!t>=! z@D`-gpu$3#7fNA8QGSv*l~7t98T>4|r}$X-_WaDmq39Rg<>dzRRa>813P{JC?6&V{ zVp&hwwQHBz_m_-HkA<xZ$w^2WnrijvxhK-H12_wOaUBbbV%WjUESiCDxD_L>EQ~DP z$|um$jM_(Zdh<K3>wsTZx&{VHaa0KrM8JKhuCA`+r0V9>rKWy*)T^_YTlM>TfpPw! zWw`4As>H;^yW2$WKRf1qHc|gLMe@(23Z2>`aVe?ZhVzH6$t|T0nJVj!!j;ay2TR&N zde>(;@P~<5rhd^(Pwnhq#j7QrxvJ!qFv;~CYrqi_@)outxiYXx-i?jjjeJu!)~?KP zn6Mvz?T@DKTQ8Rn)Fw<+DeyB+SI}lEOU`gi^i;$?sX6U0Ax$!?jaR-jlcdDVsV$qh zADiT8y6AsIjW?~0-+uApMZz)hf8#~lEZaJsZ!-xH>0LC$VvkYN6tqfp9W-fg@BX=H zSEZr!Xy5rgn(y}<xb$5Q=sbI-J8YFlK&)W~oz>#PAC25zP{QPSO`Ki66HysG{3(w< zDsoNC+C#fC$uaJ+w=y-|vuWSC{7;{}j(V+1G%nxz<>B5N-rlk`%KHM28HrJT4>hXY z&^s<u*lj<oSU=&wPZ&x0P?Gq-)yn6`vuFF?K<2rHXr(%xa_L80xFW}v&#E4&C5B(d zrEpH}raM+<4h<vG@j~NKK$*^Pn>z^Lw*v@xV)yzIqogJp@LOZ^nw{9E*LP24mJfC| zfr@^6qsIUBiP#Na-_>lcqf{d~QxQe8w^csdP~t{g4;zDzSUh#Iov>&f9sOQRg__$F zCQ?y114$%sx3V#y1xn@XbFV%CSULg8z12#WV|XEDi|v!Oy?TKE7j^eLhXfK-My}MK zU#Y}SkA7Ucf9pN);~O{td*^Rgm)NY;Gc|pAeicFJ=fC2NIt53vEn60y%h2}TEL4l` z9nLi3^d45OGU0-hlIg}+Ljfb}6DQtkrX>AQ0=5?g|4#jS(Hy!9WRrCpH%7-$WMo)) zxDQ>Ek(HgO(X!lc)h505z$L@TqsML1Ek>3Fgfr(nUIQU5w7vgvMvN}Qc5xYOEULi8 z(jZ=z!_FH;L@r<F40t<29awU+K<}yjPy^v)xRf(brK!w+aA6ra5uix*LZHCF2z%7x zS(1&*&rWZckqxq>Vms{o5M0X(a)xdDhvhipUaK}eR|-2wJ_ZCxLfJK{Z}U449GjVO zBU*Z<e4t)RZ&l3dR`)sHh=>S=ctwsL>_c`fj??r^Pb2}~z{~}D0;k`bo@q!iFIG`L zd>jDTc4&td>brkg$rsVX=3=(p^!OTN2=4T(tciM)NYs)x?YTp8g{pDuDS@D~fFtTz z_PpCfu5{vKnCNLwKH7I2P|>mfRjg^<yJsnUCR<3YVcx<NGOO6!S1lx>BJu{hlM`Nm zRB8gmAu;}WS8Co%IX=T56vqT)CzOrS3aQ1iqI=siprion0P0mH8puXC_wNOeG4NH; zdW141SF|Mie);q`P`s6z+F}E>a)g9D7KcYF3MJsgv!h;n)Zab*#m^NWTgO9b;oiCP z8Y)BLm1gl&i~0{ZX;d%I#;n4zPy5xCd>n_I!|lW_M<>panJl%cE89)Js*l*egFrak zltIrX`2@(405;8@LiSVne96L1O--hi&zGad#^AQg!Vd)lK3WYoiMeQ_h+A0ne0~3s zEyQK{F`Pfa<hu%*@fY&GD)8sX=;`gyYk9Kc;6YXHGd_k7%4ra`dw|IeK#WlNsRb5) zsFIC&Z)Px13P!bEJKMC`ek$2*&VF{X@0hwe)69660R)A+EMn`oY#F+;FW}6<m7YBn zktX<ry+<zqIua#!`yu;Ia3bi#uBqwSK0bAj=4vhnKc1d;l$;+u#-$kISz`DcaE(VN zb0ZsDWL)IALU|2dO2i?Duj^-z9z9C2?d8@^f5$mFGdmj~<>79wgARd>&r*r_@KNOy z^)-)T%0bJBU*_6*?%^cG0{R1Lg-W_*78&~*o=K%{0}|qWHUa?YDK?4B<%7Uq+Dpfd zm0psJ{t{n_7VevuCpOr;jXKbnN;xbjXXOr~(jX(~vQ3#+eS0EY__TiA5zXSMpdM~W z;r3XV*{#RmwtV&GXuQg9u+E<V`_OCYM9&Vee)X0{{Z)Y2ypjFN5ET6VcD<)F?T7B< z<>j3p*l2C9!QhF!M_nf5ih^|#N7UNEp<>d>W>zGHgjBb>!R`nDC7YpEm{df>Shash z-K$=>(r94wy8^v@Q(;2Ragra!*mh2mwoK^H{>Q&!bpNli*`@`VIG$3fcaT?W;*Ozl zqm2!8oBM49wTVK~7gk6m+tJ#)hBIIbGWeDHq)(5pLO0%kt$#{1M6507qmX$G&+M-O z@116}w|&$f>DUv9mk|j_TaDBUUF~B4r@~9!oXjYN^(ECwER?xl1F}GEkgDz;7{F_H zTThQ(JQ>6zk8q_BnUIf<Pg7j6kRd7Cl5krjzw`s<<b@DKLnPhAQSYBww&o%+Q<|Ea zaUX*+L|!et!gnFJ%#(^nJ?+YN%BMS<c8QBWW<P#MG+mIf=XrQCUcpzBa6uWdsDW32 z=9fJ_;EQ8|BrUq6=oh}wH!xs6>?|DVu^>kAlNetC?X}BametzIi2du5I;*d8`}Xa5 zKwnDs?*{Y1#x&)2<-&rbA2g1Wz2syBcGYg9r#%Qv-&gyosA#<1n{6Bnq5~Q<0+3nM zs?F`fhRvH#KthF>$^gV@s;aP=(Vrd-HY6^{VaN?T$k#%$wi|A$hib5*p57Bs_olbE z$;>Yeoi<z4R%Gyp&{0X|g~WUu2e0qjvty4Fm_@FzR904M@Sc~E5$wgjWY3S6qGkv< zPnr@LQ=vKY>UQl=AC3}L3J(%G8bvj)Igf$q&EQC)>{~)gV7S`<y&b|8Ah2jTCEfYg zC8QdITJhsDPbyK!Q4-byT*PUm-9K`J_DrUonQSm8`=b94WxByULE}vjJ<80^oja*4 zEiFs;1Ko6&g&xz_XXoQ9@jh_k#0kNx{pSd424A{dtBDUi?&>N*v|l!l`TZz4{XgC$ z+x4CGlJaNSS6fbf|F2+GvU&Bd$w`ib2ZNB-PbV3c7!K`LdMq<F+??|EflsH|e@E#( z4YA6RFC0c%u#j|~*J$Vj-Dd3|h778B`WR})ul59T*sFR+*%>T7cyJmV{nY8x>mc`} zXt}Yo|K7~B;-mL5^}4*`eX2M6stanOup1f2P>n!yo<bOO{qw7q@VnHUmp+DC@mjPm zq_0u$y{=nj;Naj;ng}IF(0))|a=Pt?VexU9@qm2k(J5ut1<Hqy<;65KmtFqg>`de> z{_#Vte1bj}_x$b7^%;DWAYQGY&!6R>^zjq}b3&YsMLG{MC@3hndF$3VLht9$&}C+! zX<)+r_=c=nUUvP<1eH}7o}AIHN@UzsHWX(Dh_r0^at+ozgee5%d;8A6R-Zb`t}mpK zyIjJ$Lk6Y6s9aMp8rdQ}Bg4A)+fKlTWztvzfGnkhRMEOQ&Ou(XfuxCnhRDS9Prt?# zV@5`;x(p$siq;%Ot`%zzq?y+X&eaNSy{8C875W?@?F_6EL?aMjqEFB0%49sweu}rW zH4(B{n(=mK)&CT~LIf^SquO7DqO15slu=-ccJtn|%N!i0&Mj``)k=*!cKwZyPm0b` zi6JK@&;3swx+j!-TMw{f>*jWEX7v6%&fA8a9Y-@wBY87geB@PCmqiflD99gI^JnLK zv$=nMckv?u#Dvx1H1MmwRRF)=r}i~-s4D5wkA0*K0R3;TcDg{T$Kqiez8LIp{h>r3 z(YpnPCofOR%{HW2DkDAOv=BWLJz0|sAIPz}e|~GZFU*(_fCzMx<jQC6d*{vv2s=-o z?%*}s!7g^Ea)qvL_e!=`e>g;#%95{DJSk}i5Ws29UYH$l1>N+_sI@E!;M7!A;8^?F zW^Ef(KgaG+lNBpgw3Y0RG#qU!AeY>f>vrf^LLNFA5L|iyd173qhU0Xyf>8XE07K$} zIM_SRY3r0L<anZDarceAH`BPVRsbdTiIfK`TCHG$bt&e-A0p*g{kvH6Ip`Qz^s1ue zm(kGyUs_$g8Uyx6lt9D@>7=gO%=4Lu0C(?p;%-?a93S{D(x2QPsz?-N@ER?ytDPKR zDgICRmXsYq@4T{j8dJegpL_RGcp|TMec6Pz>roW5IJI{^qS~o6dZewCtXDvdUeje} zCX4Bj$mkG{bPA%c9TW@_nj8q<+cVdd&_9*CqIWM``k#EhN96@QHYP`@A758+iAtaZ z5;7rHSOZQTp1U}+)*uF}Htz96U0f?Uy#uOAXJRp{LgHnuQ_-k{U-gjmp}|IC^;7_F zNu;3Mvgic))cO3N(bFY;3?~f>3m&uuy3taiYeUw*_zwyMP?fQe=XSW`Bl-%PPM94K zD`nz7tI7+-XK10{=fw+70jL$O<mBKO{jQ>H-Cf3v6ZXlW)lC+uemnUh`S`{qiLau( z{2qVae8BEV*MPP*JufL(DE52yePfblEaA2vtSbP42Tb<u3Mnx>dG!-5V@V_FOa>G~ z{8&mD$Pf_prM`wAGI2VE@*`$O4~W1D5N75)HC&F6fMhTcb?((!hWL{*EJ~yu>hv8| z*c4KQ%kRNwi0*mN6zI;%A2noTmVU2<1WiLrkGok;kyx1iN#qa20x6y)dkMM1&K<O< z(<|2m)Dn(jOuc*~MxmVT-U(T?hvHkdY&rRG&oyu?Y@q=F#3~Q=0L35^s@lKW;bv&6 z{S#aGw4WkJX1K@QHm34jA+5KAo{iz+#YNUP>WnXx>7=BJe>^HQ6(&Mv>E<wGZcb6r z5Qtby*Kph7AniM(CLrD>)z~eJiDCbqP`9hp=3Tuxfxc}O<RfHH3NokK^ag+7db{;L zK};L{o4zi|$iIK`5E=P?*7j_2_J6!7#@QD`n^E>Yi?!lT_JB90U@f2B2Ae{YdPa1) zHr%}RxUpWv&Q2JL-wT8saL;x^*;yrg($do0R)eU^WA8g9`E<+Fvo>)LitO(kZT~2I z>AMf4kyV}!El%DD-*9|Y<3a~}z;5CXXo4KhY%XHU1(8TsH+B049398Lm|#)sj%EL$ zeMCrvLi=bOe$F1y^Y=Xy-QD%M%+34DYc?DM;c9i8+D`g$=mfpE+?bl0LFypkHB;^V zmvsA=3vQ;qynnwTV!te8|Htukw4-<~<URp3)9RsS@3141Syg)v9TnB-XGe<~w9NJ8 za0DPiL>m;}2Z#?K4fc_n?Cgu5YgDZWz{N+Ej!%I40DG9kE@NcO8lAWiuT%c)%BNKm zA$gOAcGU8hfY5vElXrt}QR^Xp5h4Xe+~@P*q)imywsK&)r1|WfQ8z9jlqhOyru9|x zt;{PunZu=R_<_`+p)yOliXzEB+;{%YNbcNy66y<v?CfTsi-RpWSqBEzF_s(%;JS6w zJ2qGPeSw~PuG2(WWY&c|cOK36D?C##H<ISwmtg+Lax}04N^K}0Fwg(>w&}UBk1aVn z1^SAL-kWvXhyIAu5OSYxrvvrKN8j$YukWo(zCOSDcKiCsZH_*$KYFQun0GgPx1?l5 zvPp#|>spin<cjg%jg~0fKIKe`4ezFM|2H{K>~5uL1e47_9YW@jnL}v1LYwR+Nmbm= z2Wojksyd^;hvCfTQLoio;?8;&r9zalA1QoLrk-TD5;4~e$c1M5UGjBx27VejpeNrA z#VDy})m)8RM40GVq}(MMdmwNI4NmqN6%rkDD5X|C-D)SrPijHbcQfyY($Z3AsBH4G zK{QW?S$ySmH~bfPBdk3Axi=(tu0G9@4IG0=sOjnH0>BnUcbRX5X{D-mu4jaVzW80^ zo!&LFh}JLjr6=NA78fpuy-%|==CY4ALM?eB>Gl|;ob>ix-Q1ELKG@Y|im1rg@GDWv zdvjg$>HIG=Hq#xQ4Ss2ecI11wPCb!>BAe{~oc`Ta%NIVKS2PWzWuCNr4L9?a`aIi` z3uR6p>U>Z!T7Jgh6p$wu7WPWTMz@sZONT+twxNPRVC~G@oGcPG1T@Qx7BMh~3GN_H zH%Gj=78b6{+jw*?Etm=$4{E*&H<=#&?yyH*{Gh%}O$9%@(=n*tup#g^ZZbNKwus|& zyLa-uGb5vUNpD0BvzY@p3II~!6ERzg<J?dh(Dp|lc_!1`<;|}*P4*it^maW;i4fQN zWqe1jC7_Ffc8DtyOK|tZ<%aj|&?M7G#DwQh%h+6x&T$J&wVva@LACFE=3+}O5HGmU zw`lndr=Exw(5#inhr$plXtWAvOnKsJO|FjlP>UjCNtlKGYEd-y(TJ<-pJZoO4Sb)* zA5DLaxw&!b)0PW{Sf9dApT;Jul{Y~5L=qlx(a~y(PcbG7szZz#M}JTK9;%F@14lIk zCda}&MlOhTQ6eupL0;yj>2^-0lg-`zTZ(y6%~#)h_quv3qre|1-{N%+p^2q{3lnrb zj5jFtmrqWKc;~pdY!PY*+sp10Nt_kK{s@SQh;F}E%VQv*RpDTvt5FWF!&5A@VIki> z%{;kr>D7eR(xO|?{WV+xCLWT3!D4<Q<7r4n@><r;&J@%qzq`uKqoKdx*nRgyq)*6_ zoJ5P}>^LCNhIQ*G(zwe|DGy2X>ng^@F1uwNe1(AP%v-9LDDw8iokUGdZDng~0McYp z_im?FJOAw~wRe1W5e1)PyVRe_4bPrEBgh9{sc+D$-$Y0W1ws>#J3^F~O|U?Hl$yG@ zTQNi$sMc}dZ5m7x?@E5zWvR*xmgs%yuxA01NSe1QyN|xC;T%iq#!Z{N(4IeX<jBFf z0)BR}QdC~`_oj?*)Dnuyl{|L)%C!W(-S5d2xL0ct2`~U^0FnJj=>~=j5wVN_Q)W!O z&9i8B2tb`EnjNQM_KO1J#}V6#Vq0{7dnxE7ffYVVNCkjY&4ina-mEk=#EstFekkbX z&1JBAl|i>0`}OOpof$j(!KeaW_Kxqm9)GR^2P;rWdo6dipZm@h{^7dkkcJcoP?=O* zkUaM2WD29R`4i~*^z;VC*rzbWSnmU|v}n$Jgfc>PI?Mht2?vN-ETEG<f5>t^Ibmfb z0QB+#5PGm7rPOA=Wz<CYL+a&scCTilDoq!`AT5ZiXj}qmp7{EzqE{CCvBiQuzy}y> zx`hd7`P+Y-CnHHd_JCl51|#f&CnMZv&2Ft_JB1Pj-;^-;(EIs{)NR+UwT*y|mRw+i z$ufQ&aNKQ#R;NbZ!Ynr$4nKo0xIY8xD0?Cn2)~yK`iX`^cTX@$vYge5lq5(Xb2|^a zlXp3_-nnz<?ic_C>>SMV(Dn7H`;4efcbx%U{eBTDIQ@2U;UBy_4*ylZmXR5!enXf4 z^5vcu=K)@1J(ZjJ&?m`ZIdPw*1gSXA4Sk-wQ+Cxk!0`m|54Pq-VxM2kJoeopWIud2 zbpvBbU))wo)br=0Y68r5p_>A#$K}#uDjWa{C|=DZpeOzM#=GM(Ngv=^L~ELG7NB4% z&@966H}a^D<|go}@fp;9Ha!HR#n;zVqm7n%U10IRfnG?PXJJh<tf&YT@`mI=+J*Po zq`rcKzIAe9v_1>32qClClT`{VhwRIel4VaEuw>Z8eRQ9LH`T=0$wy^FOZQ5fkNxdH z!xKe%?Dy|eDM}Ej(Tk^rXcMQAT=W>toZatjdrsj3m8GRASFT)91df5QA2XNfQajg} zu|aaN$#p&t#TOi<t^d-Gs^c;&MXwKC!!4gpxpW?&)2`K?sF&b1NVk7KGT_03d~_ZQ zL6#MPRN)d^rx3*R41L+Y+=Bw&BnGBhZnJIT{-9+UO4IJ)ryNELVPla6BKfD+R019g z^C4%oWOv?6eN|qB_4WZt@Laipi}Eec!vk)aBRJYsZaP)Ya0{H-#L5uqN?jUsq^}CZ zt?Nsu<=pEYWts7<Z)o#T-fYp_K?i%JakV3KZLm0gP&VUo2%;N^%~L(Yh7;&1M%Is& z*;&bX;14tXXm^-Z_uZqBKAo3Ux6{-;J4Q#8=81BV4EI0IuP<L|$u0rtkjHJIy%T+a zAJwQ1Txq8NO#+Vtv&7-UVz5`;`p9OQBgN>?p*lnbMPoPNfoNM1dJeR6_Khm<s!jU} z(UqG9eW_0}T8#!hu}%`_2{y%F;t~`A(izQLTnHnb2KY)T+Cue8vA-5%WXceLhb1IJ zAW7{yakmXoGmkn#eo7i3OA&sHa}@e)Oq{(V8)_o@xm_NMbHt;2E?zs7j?Gn|w5QtH zgPq-&R^^1rWy7P8bfD81LOuoojUbW@sDE~LHj$>o#jmnsOEe@saF)>(-$~9Y^f-DJ z(FdUOFB<G|aEUmFzUnxL%0!FH$Qcyn|7K75Y*Wr2AlBaA4!=||jvDo7lWoP=548(t zvK_~GBCG#pr5I<X@7Bm870@aCybC3ew8dg0obTZ0%jQ^<|4~j$viF!DqcbSFdvShx zj5t$JB1R#*BpQ`&UbThKBCtV*V~#k7V0}z?|050n*9{)~)MIg>URNyt$EFv`A{EpJ znF73}-%jmj|617ZLe;1VCPCac_*mi(lYrHfeygz0C*LixrrQ`93yA5dHeTn|xmWAa zK<WY(nEx}?%J5QKMsOeK{C}_y7SVi2opGra4@oac+G|ybpIV%$WIT4WkQseD0a)Jz zD(00k4?MQx5rQF9=!YZ+2h0dJ4G-NTf*|}I2t#Y@8Ifv`&dTvt5UwuLt4F@jbU(pC zmucH`KgJ{%#DWM7V5@>!ACFgc^w8;KJvN!UgkZEj0ofYDnI!EEDlj4iilTTBbLbjR zXtfXboMEr$Uk=X#B-IS-pR`yy#j(eLWJ2gvDROOybREBaW${ku4!|G&-0ADkm;kI< zU#q@dB^`Sz+`?IBi5R>H2GUQ2YK(Zdl)M(Eb;lre<Ax1^&t-S?Swk&-o$w0q?%K6$ zq%eVP2jAJmU4<UMR`9-nSmt~6BS*4qx>kYMwjqA0+HlS;qIJ&j3Sw|53MWKu&SS-K zZ~s%en>u&lXJ6lK2nlG?cZ0_frVLJ1|DZ-UvpUwqmA)C4QR1&5r2&?pwm@aci@o2T zEq!LasKxrv$jf4A*uA)*_CGk#wz063K`jf|4p_StnOB2bQ-;T-Z^1$K-jWw8e=biF zI;TjhYase|R!B2M&4P);38h)^@J)`a6x4cd<Rf&q;QouGPuVVNwF9DpO;CJ0k~8E9 zo?<k?UIJSrt=yp(@o*fP5hwh&b^A-1b}^PbnfzU~oN51uCO%%Y^padJ00+GJr!t90 z!z^K9^n(IQ`uTHa6{@~=hK(0K%4l{gX>Z_fqzMk~0y=kLw$ns!Z33I#@S1EG@+s($ zNkC(`xG+ES<giN+kS)6KalE}UNuRg6^yNa0d~ewrf%Lu~eFLaWz3(6>JUJg!Oj-C< zdx6xSz0|G#yJStBB41raY@m6*e%m(R;9#|&_`}fka2|@G>v?*6(*ZR`p>al;o}Cp0 zP@12gC)zdqa-DGF{Dit3gC3GA!1_a8t%C}+o6u#cR}g_|3D@`p>yI*a0+*?g=bm%y z5@I$Po*6iNe6YXcNTRzBvBJNF%30->17+jbE&7tGl-1D1&@>8_aI(46a|)Gh&>EX) z3Nv9){4VwqBYXa4<lw3-2TRlls_EV+KNeN=OXj(?T&QGaWJpb>>W;{nYH?Z1&(E*x z)b;2+zp23KbgPS(YU1R<{F9x{vMP1+*%!a`>So5C;v9u6KZc(`0D)s`4;K;{rzt(L z*}O70mz{mD5jSN)4J8;Z5c#WGsZE$K;mC*%O?xn_o0)7JezE;S3kdTzPXrG!&fM9! zSAR<LeM*>*Tz{PSO2gv&+nx5!-<CT$)u=F)GgUJ-oVBJd$3+BA86&7)u2anp2UmLZ z{dhjQn?2y*o8J^kka?_k2mE}v@6RCEDW`@6AOy<mT*|VX|5Yo$^`M0u9PsLB9R=-n z)`;5N{Cg9leAsy!y`hE_2#Dy$;*638$QVQ_$j>#IH=E%gY-+_)JGNb;<MF$J$OV;8 zy37tEmPukiFYzM@>KCpcbqM%_P+Lf`b|Wp}NV%8j82lwTU@R>O-2Jnw>(f`4&5aXd zV`qR{OWNZaXdDxuu5W-V4Be2wjaw=a5fR9pUXhV($`WHTcft?ZkfD(1jjXJvARzP1 zd8|cW6|Qoz)ID%Z2!t)q=YV8uQJ>@$={|d#h&gc0LV!WX>N!pWxez#1>yvI;V7Nto zI5yI~1B{IHo;UJohmiD!^*3>5y8r_POBh|g3;dby;AJDwLs9W=mm@-%SwN41T$`e{ zm-z){kN)Nj8$Kdim7vyBO-v1er@cB8bqgL(w84`~rNdz(u8G$vMRW5D93DK4#Rx%P zP>l5(Hx{CX60bCxvK#lE`^;)|TaGJEGv&+r1p-nf7p4uydn!iSUc<w{ZLygE&*`q< z3)6kEk!Vj*^jdg1%<SL6KMY>;0WC$iDkNunce&3EiIDe_AFW56>>A%OT4O)lB%gMr zna9NvO3AR{53T$YQU1U#6F7Z|mm3elHh{(!)P!#!f)P&L%nhlR!!9Kh5LYBjdV5sk zn4qYosLI#B(8}+KBOY>?y^DcA_MUluGpZD)k~`_r+NbbVid<=W*VOSAWf4WBxT1o; zUi{*#SDGpNC;%ZfJn}M0ZD<3;Aa1mCT|==yoB6acRRY`E9e^8Hh0sxrMY@ofC=-&H z?Tun~zYO$Iad?UajXs|P3hVt9p<Nq6DRZ6ei>2TayrJ9`V`2bp2#sHxN0W?7d-1Ok znde{QT6u8z<;i3Wcq;{knxtr;@NWf=mR_GiHws5XT6dpJ;VEQE8C3M!_wT<zZ6$8s zrCAmpC^r5`Ik+9I7*9;zXmm-i2V8AwX+iP4j{i@D<O&IEWa%bup$5{?Gd&o3w77<r z2BfK?Mofmo93cJ_&K2*WiFrs-z+z%*7L{oH)(5_goLr)qDDrQAIe}{vX}W-p?R44| zoy|PzdSb&}T`@S;9UM@1nbxn@FBg|7WPnxX*hotb?=jKeBdrxkv~46#3R@<7a(x2T zGwt@S*($7up4&!V&4)Ozg0|g?r1{6r(Dseu*jL3VtE*eM7u|}mExL}^&_W`@Jgdv# zei)n-{a6pP=sZT{^7%1EegF3Di%56#SfwT;oKK=sm^8w$P%xiP2l8_#WQQ8QBQnF* zqisj9lRn761ZgbJwJuuOCf6TqgFj`p`meNnqz1f}&gAP8`LQQZ%nzE^-5F5!{p=<& zR=7biT>KFf<ZszdwtwAmp64y8_|@Ne{`Jf|PFBj|m=hxw@&S=fs-XaA>*!FC-_)a> zOVGHqqoXJSrweB-iNyuD9W7^>!Vj+xdupXxz^x*HF)Gl?>y@U_;3E(Bs@wvOU*-u= z>x=%VHSDpl#G8X5h~|*=vE_oR|358@)GN*XWOq5}5ah&d0Th^Kv9qhc|0Fh5%&N^Z zrko^wFdrEJYB;~ry1R})QN=nT<YJI|w6pBjpv?+3=VxycKFB-i66Nmg+wzHC=Wu-C z;bfPPXtEi+y*uJ5Vh{c53XlQolVG{ez{$wejOUE~2ngpZjpP?g+2zTX*J+;M0%x0Q z-l`|EQ%DyTd?Dy3c1z*l33(a85xDGPA;lLK6`cjbK`=>!TaspqPp1{=iEHmOZ{l1> z%qO$Fft3Zz3vGnM+y(Nn#Rw-)#~`FM8FZrizQ5W49*=GTd%#ZBHJ-@E%W#@WYn^~z z%J;WgTRcG!t^oy5kD`xmgMPQd$WtG~CRl)d{QT&4fJZ>qxpu(3=JX3iW*B5)PV_>t z!O?U=F(LCqD9Bte1VVC!$K8NWH(L&B3W^WdT1&p30P)+K>*{hS`}+F+gpsDGwl*02 zFyAKIuq2>gX$yvB5-(AB&PJ~w&O7Wd5n~}!(TrBgvYmM@vl8PI1%R(`eGx7ctm|BY zH7-eBn4eI_5RS3NsXi#Unu<CwD3<9ZRa%nWKlA$B-qN4&_&~;zMrRR#=u>Me8(f@P z+6}M&r6ccA`_3&ES-Ikn!-r44r{h_KEg2T${35QG7<Och<Qmkyy9oc63O)<{bQ6tB z^8cjt3CwFIz97axmJc5m4yz`zD@HQ7yO%VQ?mLXPLHrjOq(RM~=N?I_hnC2$aS^$j zJs|bpn<{~9c^J#WoQBXpJRZdtaKXptym}6u0PGc>o)ox_^O5l}W+7)A7f_)0c`WN1 z#GtqW(65AtP}>UhhF40sHQWqq%-bNY8mE2)fn){<<F*Qu7qNOjyq;n@<rGP!aF9!9 z-uQmN_RNz{OVTfexCASz-d((qJFPFltNEVN7HSfasN#irV?|{3Jg8Ub4S2tK!%ad5 zCGcZ&bMxcibtRAZbQKdmt<C3V5wr0H9A3}Da@W`Qgem+hm(>qYz`2>x6vf1t*^k$c z4k+^mym`bOTqBfr?V_jC9+`0Qa`?w?Lv!@0dwAZXlvET@e<=O)vgpC=cQ27WJV>`y zpTUD~Bd>VS)2G`Vot(s;%b>ioBaDHbQg{}4*cWY<9D?#YM^}t8jcz%;vaSdeMeZ84 z>d-@?#y>)w!WSzFLmN3O-Dbzfmq|&jSV5UM$>&FDzGm|3bh`kzs<3h*0~z9diwkF9 z7F_0u^B0+(E@~d3$x%Cp1Zq}4E^bosq2?`Zcg2OzLA<4+4)HgSeMH<erWRDibo-r% z`bhxp+`IliFMtuMl1-G6Q{@l=W;Joluq$zbS(@b<Lqe|MIl@s4k4yX8w3d!i{=CB0 z)=0>!)k!f_d)j)d1XF~t{hix!mjHQ5aYGKj`p*N1gc)ls`3O%_9!d;BmmmfrjNj6n zh9dxa!Idlb*#?5&3zc{T4=6T-a_o@a32`+cso(Nh*|6$Uucw>|f}x6p@~5>p`=WS% z!ChQmz8Y=&@F)eEvNR{}waUy3`L1Ppo{YPmnyXDL14${-LJXj*7}B1KvXNwA!ygc= zo#XrnU~2*Zp61&tmEAZPve2wqwCnQ(Q-yA4=#&Q8b1TTm3>FT*DZkR18@}_{t#7H9 zwU9v#MoN9_o*<Txk~Z!+?FsJk4rQ2S)54BjhXM`b!*Sz$_RKzZe`c>jR3^hn5FhgM zFp)Zb9p*I{?;yvK;*AAUd3ky#I(|L9Rc9al{>q_Yz8i_jSBa(#vvf0X1=YPb+kjLT zvcPP04yRBYvLX}?3y5)9M8WBU#6wU;TmCf)CIr40oNvBR^_8Q&{goe$FjHBDh~<^V z22`Qn^js>Jq!yBgkz$t7(k1~^qJ-1vt#pUj84D4LOhqB9nCsRU|7TXI{m%3IqG0GW z#<`ddS}=j(k<3LzoyW~q0mf9v#@B^>-=Cn7PrRx>oGCYM+|WSnN4$^$mi4ocnq(kj z5H}p0Ts4_be2yLen^{U_UO*5*oBz7@hK07)aQU-ZbXE<*Xmb0L;S!QFkg5sxMYDq{ z#N{}Ot<zv)G4X{0{bJBn>*pViifQEG-sUV3cn6EoE++;Qm|WW*udY=Xn-Jpvm1G<1 z^Z_l@?#A4VQ56`3710@Ff%gfMQ#-}Q8>$-B5_As}#hCoSTxOQSe%$8Hw{NG>jZi`< zMXE3W!3ot}6pXdL`r|A=JNs9zQmRo1-Y5yAZ9rZKdITLN;DGst5^Ly^g@;D6kD*F~ zVn)N`a-Wan+v=plw4*@j#3P~q=}sl8$;n{8%l9&6Z0=%#FxRaF%L;{1K`ZOTx%H2c zZB+xiTJ@oTti<a5#Cj3&0)?eA$w+}1Wg#eH`sDtBfsKh-m`f4T%co#y;|SsdFo=c- z^8VWVrH4J`4gR2-eFmLUPr0-SmMl=cFm&)n5nolf4g{4&x%2TseW=$<(%-`DYIF}U zTM^xcaK!9-Emc#p$yzEZDl%aW%aW$?!ed1)d>~0w#Ta+Bcw1WURYPURhm=6T6iGd# zvw+1wXTdCflRqfh+QiwT-}D%iP}evG1Rg*bnDAOyc>^2WiFEx8gF6_iYHA2h6<;ph zV~x`j;r3etURP>QunaO(%6amk6)bk_>@`M#FxwL3gvvsO;2^@3Z8^XMqR9rJXAFm3 zYPM_YUW&LM4jlA$G3UuWLdIo!I47EUaDk3<fL?LnU=h#LCT`VtkfLD*z_|=s9&NjC zd44u87Z<IXnp!CXE07Hr`nd>5akv~985<KDU)qBjf}@mTL+ev7Z?nvu;Q|(Tn#0E* z@PVijP3ilg?xwK3(M)+w%0s7<{Qy<j+wU|XQ_x9~Ek0>k<{SsHKnmzVKM^?jJ1l&t zBVIKr>tfe4G9E#yB8Lv61jH$g2JccuRRMF1O|<@bf;bXD9QU4nN+CCf2mzuj`uiE@ zQ`4?*C|N@$MW?2E5I7Q0r{I!(2%!UW`?v>hj7G<v1}Mp$?pRICm(cBwk|vE#04m5w z)Ae%V!YeT?8>&dSy#(B8?=SuMQrU%HcJ9frq1VHu!z0g%ko^E0gxRu~dV6%)NE!jo zL`0P#qYkOR5+y{O0B^8cc1)G;6cntg<RrCWPsaG>QCzbqwg!l)2R(+nTMyhJ@C$|5 za0vDQ(w}zh33ni3aNeKa+d$BFW6B8_T<oQDVhjx8_mC!S7v`OiQ?Xbf#?yFe$h<6E zd&pJu@S?c_h7*d0Shni_zLywG*eSq-L6{*S<`@c*3Sg9h7c`t&k2MhO1M(1r{m|t= zh%hv^m9!5q=z_5%9OB}Vt5gC@^60YcoE+X_JQRXx;RyQl^m!MxERa;vpb{+x(iuAY zrcU<UxsCX14~_%fQ>O{{?73k@SJx%0DVi^PYwuD0GiDo<qeTS;ACp!@$t`J6g;1}d z+=@W~BC}v~lhrcx>7Zg1>Ak#JaC)P9+1Kz1z+&{l)-m<=_G3Q5CIVxq2KxE|UQ+qv zqUf`f*XqD}d-3C~%6Au!5jS@AR?f>Oc3CviDg-~4+Immpkn*{64~=Y!zdWL~A9{SF z#BN2ek=X`ThJ}Sy*Ko)dA7O0w8ujwC%W|TNA-liI?MBdL<6#mcgv&zw2M;FJDeLWU zbUN}z_1l}^83R@4K3Ia*LfZL^wxhh9z<yUzY3sHE!=Zr9qFeW~4;)d!BJzK^D*qB9 z2^zF$*XW6%LSM_ua(SJ6nEyhqB3LDS6$bDZJ>=1-is<?xMNLzlV~x(8pUI10KIk`? zwkI+HMDgRWA-+-7QMCTL^dyv3ha8L(+>zz8Ick>^&GYAf(>U)$<Y1T_&qKHgnh3wc zb6kaeylQ^a+(Fmryu&tAUrhO%vB<;)2*AY-Zz|@S2`6=RouU6>HKyM^K=8yH%t!OU ze+z12(3EZsb6he^7E9Q=A0+oW8P6R>gTWe+$)y}&;l#(zel*#02%h?fhdriFCInx> zmVSSu<_8U!AOJMCOA+`GGvMkmin~R+eT>$$8CRH@JTY@Cgc)63$M)s0rIM5Y&PS7v zQwO#Lfvp%iBzeXO96lCFwNU575w2p-?P4ERwxoS;XwGp7MhsXFH5@?5lJbmv`(C0o zTkKq%Y3`#Znrox}`eJxCqqqgok*Pyd{<gu!)N+aTU5B;Oj+O@P^&4qOAr@$IF5sVg z&@mG|=Jh~g8RlU8sHoa{g8PzS(HQ4ho^Q8@VjKxG%TSmue{-l%TZg_r@-vaQsAy;n zr?+0a)@^-js^wDrxs@-JR8XQAXZV-ir73&Dx$m)vA4$=qkpNU@Ge2#OR*<0Uny5;N z`=cGlVIAr&FZl7pIr0WZvUL5qizz3EfA*L#I4usvyiu=gQc#QjjLbMd<XFtAO5uSg z&*85N&Aj!-yg%&$u8|`{TV%Dm4ZoC>s7sARjb+LDiDKx1bvbjF5isO^#L`ip=OKmi zB7Q4+_z1FP;y%cJI?as=TiDqA5-e>2qUiGRY?2u$0ejEAeDHG>=IO1eA#Zw@paN|d zw|fds7>5T%z#LC}KR$KWxUNMX3zUu<!0|@Nd=$*sg?SDipA}!~tzwLo81OCfrw)7S zi;z5rSs5r-e!#aBBsPn!3pfG_-~KfxXk;;RkwiH}Tnj#Y3V{UAkFWk3DMfm{E7z_~ zo4Fbo6frY$(2fjlk;B{(w~_Mc1vro_W(H=^s*qPyjD`kA%te3^4VYexaw-Y^PAkf> zeH--tQ^1V%XsMU;Xr<nTAexMUfjgUdzo3{CBgOGM8xv<z{#%P%=E=RFYm8w6u=!$8 z7F4_nv&dvx5#zw7X#*FCMHgv>^xf-j0zRW_XN>90Y>3?H8TC^hDR;keC$`TcB>Y(v zO7o+SmF63gCN9K~y-@YqFuZJUC&qLS-nRe1w~gPz#iBSb;$mmOC6k6wLna4m$*H1g z8yWF~@9vnn%~h|y>hff_HMcr<3Yd9km{o5ix{R3FdLP456!n*C39Pu-7x1=`Y9Va; zEx*!;syoM<=07I;gxRJhY!!8I?e|}>N|&J5&eY;QcNtZDxipcmai$=m*jO2r%*-6A zjc-_qY}~24$}_+WiUzu_<7k)B2%tF&-xxYb%UQHF`n7eQ87auBktsCWJ(QC#nCWSC z-F}^4SeUaUlde!6FmsA(Yk1w5D)v~iXF0x|h$SevNi12IE5hJ{t=@V3Yrv+*%h>xG z(ye!)8yW>m1-@PV{!-L+p29`E00ktM`%idd=2H~Cr2BjzH!?|ZB%b|+F>2x%8-tOU zA^_S?ayZ7g+uOe2B^+5BDV)skVGxfh^yig$xWRu0H%VTy!2pTus!uiYPnDS}W=KZ( zt|l;%45Pn3&rJxV*J6CMg}3<oQ$RvKLwhLVAyFStIF@Xk^0Mn+saIl))|5pY4ntWU zM?bsc(og-fH5Xf68O+)Zea9l{b8FIaZBNyvEjrV_Ti{!^Z2U~PGNp*7Ab%A-?TzMC zc?I3#ynx^RKkP5#T2h};LKrX#ChNr(#h9HPaI{D4smQy5ND~o#f%h@`$3<<6QV;u= z49Jz}<@1qQ713Qv)Yf=PO4CHJ=Hw}uy>4cNrwlcPZp;j#`nBIX?|HudQ{I0po>h5Q zqrd9@eb;hnIxE9zF<Y)xOq5B=6>6nx$D|Vshxl~gFQg>fxNFto(mU>Mq-@XkZ|^DP zkEF^ip08P4TSp}k@>~bgOnLUIUo8j3vCL2Wsh`q_3eLMO%!q31<aoZ#mzB;ntk_0> z`c;6_!6e=TmRDGR1`BtO<qe+q#uW>lrPxtiZbjk{CD{*YmKt1WHRjP6-%a~*!`}GF zNyi4^M&)d)s|PCVbEh)zzr^59Av55xXU>O;e`20ELZJ_*v3Lw;xGr0k?@{XzuU?s} zkT5;tzpI}-LE<1;^WVC3@6K}CPM4mVCUhT!%NPlrf@9(d+7eS&ED}4I@2|4-20Wdt z@0}54*}%<6mkR5t;WLtRal1h-m+N859gkK(?5|SKu}7{e34g!o$&HLm6c-H-w{hLV z`uuIuJfD1nyS&-Dt8?94-h3wyuyXRI*5eDZ5%K4i=K1EoO(YlXAi?|G$WAggJWA9h zF$>K~9KR!?3_LmWE12kwEK<X;Ht+?m^33>-;i#Kl_oyFJIV_6x(@rj<Z#W4ICsP$o z30Vzm!5uYSC;AX-mSr{3^{QI0kfc9*?4@+G<IF-qW(A7<`eY^bN$P9)$!57H{__Rv zk&B+fV@1y-;7MZq`EdnS$2I}lV^ilE&AAVdMDB~LCWGq$mbZ}THgGBG!K_^N_&nn` z)l1~aisAYSqvZoJWVjQWs6J$n^vujlOYbgOKeQ}>K?}2)L108<&{hNtKd&p@58b3a zui4G??~7P?mxa?{{^2g}!VBqZyzR!?Dp*E5&wXSw^bo;SU<NmH`I8$m$B&btPY}Zb zq($<uEDY~j*=dl=DBeTqUPrtO#0U?e@S~o<=)&AcvOzHe8tnbORpk8pRr0p6kjKqr zj}>hLx>dnMGe*{AfS;DMT%XP}{ZwUCD6?cfr$S<a9zJ7LQrQ{9&aTyC_=f%u7KjQ@ zLGtgGSxc_W{0yaRr{<-t3D~Q;+gGU2`TT$Js6W)$|MTYr{@a_2$}5+5+QG8t2}c&4 z8kT}RKpUk9Pq7Gf2M-X(2oH@kxj}7&g^!#KOnP{bnc?hJT9va~f}{5Vg~DEGD0Y!t zdEH6X3F0NgpTJ5E9Gw3z4?DEUMwN$e<uIh~n`mFbO{b7Ngrd=lSwZx8FijMSeQQiS zE3aVO@)2qcIUT^^I97-8V3<^c7#pcjC$>O5nE@I?Irz}f>1WZqg+|&R>^$}o&s0hB zfc~FikfN`E!eo;T^lXJ0G!&T%AYJmj2~f~jXh<auUN>)!VlrzCk849zuO^*5Vx)sT zi<Gz1zo?l#)-Iwpz%2W5dhDkef*0(lJtkOJAE*KtukOeXg@C)cep6fjj+fcTogF4u zf=*~TSQtda&@K!Wd>Yh<uX{lKwA1u|W@#OCH7HmlT+s}md5Y))R`)_T0ZHsM76QnL z)Jx1puFA(mTd0U7D|v(qN}#RG%(!1!wx7Ieg6`$|Qz|U{LL(QowOOEhpidBt4(Xwn z`iY#))Ilt~|4!vZU8e<*<4PLflMIKz$o)wS%HQP)an8%3PHW+c_>3i`5Uh<GI!v0n zV50VKHO_u1n_IDtnYje_I0OA=P~%0DEUxk29=LSq4CNsUFUY}q&XW%?j9R+?U#6E6 zNvY786l$s+KvBte5B~P5yMPbi0FfY%=Yzp;95K@O+mhiy{ggF4)TEV+AZScHnGso* z?G1^d2v0FVBa#kJwt|bNKG!WPoO`p@S@NY^TwL{Qx&KE}*tL)}&Wgnf&(D`8rcd(7 zBKS!R2Lw5{BeJlp4)BPOXI>1cMn+>b+cDXffmOhq6d4;vG9VcWb3FQK&Yx0+X;0g{ zN1~6OE_gUu*W7&gM=cRs0sqNd=8^?)_E51uzSG&+xudU7p0u#A@>27oH0T<>yZXw8 zjoFP2Jg^kciJKvL&DG=6e!+>HX&{3t_|ZzE{jl)XFsYzHaS9}CNabDKLwBK*_f^qn z`rn`B^up8w-WV8Xl@JDSW)N_W=XIU+51kUg=m>-w&O?~%J%D$Dv`6zsjTpe73cD{E zzQVH=wTi-WU^szWwr2QhX&$bm_rE_Is!;_C{T40-j45$uEoa)ek@T%pCI^qp$_kl& z+W`v_@%Ex(rr4$gJjRwmOmqERZ&E_+$9D+?k2;s(hqFVxsAy0dAtHi2DA2~?KySB? z*BpKnIzr}zg24xOAKJWr{qvDe5;9a+YcQoMJQ@?kLKc^eQ7V1R$_2&fBCC;DrCf0P zwTTQ@k9U>?_23_cSJxE?POwTY&QCzT@+RY<zXvsl#{h4~D2GFv$&LqOQ<ZoS7W6xg zIlAtaSn|NTaTP1)&>3BbkYJE;7jc144msRMY{E+*18W=0%wC@Zp)xk<;MmoWKn*T^ zdwQ*;wzhV%*`ZY<;thNWW<af6fhC488RopgK<eHF;Mp%n9+9l!jbFTnW3C#WfXlD7 z(&9@hDk?T^+}P=Phw}j9aO8Z{i8a-jS{ghT)1-{kfxH!l9AV<z=`X8@?zjk)mI|s( zzHgZn@llmK&!KwB49YG&Qt|)vd9zI{I-YX68!=aib>;B`EH4F^j~pH#BYiIvL#ad3 zzBVKpc$Z%)gQ@7$nH=Y<Sr?|#THOs0)kNwA9=|BOb!>8y3`*MdRe2$`H>5<6jl{E^ zsxoX%BxgD|gz3J)#0%Q4{3;0=&}boNrb?W8odzb)gK<NRMqoDM{-(L95i;Z%>Rb*c z=Y%n!5Mi_Rh_lkFo@D}mtxa`V4hcr3f&w1yFtf=`{UY<Sz=*U@p#eYv{65}<zH$K? zTCYw&qefr4#7YUSxV^til$2p<VZw6(DPv<}r0T#yl!PzaI5&Ey?gq-+cG5p3kDC-= z8g3khO)gR8cpq`i!N1>)0TMkdj(yfJcEt%^Dl&IRAy2}Bx0LuO;X}b#SU$80V&=wp zY-#meY0$p&Uy_2aIHOPSuy4Y}$>|K{b?q86GBb_)vtdj%1TG>u8-gA6Y3x1}+OzNB z?%D&F%|mXr`*8loI7+^qazKsfqm_=T{EW{RJnv$Bz@kpz*bt)EaApJ-upfpn@}SY& zneJ`yQf<<Dv^#(cgCN^%ZEb&|VTTD=Jc1@lxY6s@EgKZaL#p;3@`tU+X>B~P2ucQv z%tw&&vGMT^xX(zn45ARLF%0H~aPfS>s1mXIK{;R}wtubv)!Mno^?dJf{FfYUbZp{5 zd8low;~GvXQrIzTBK@`y6)`2+iqb@-ayfIX9dwFR6hpTSsnPwSi>6GG=^`ym(uJb< z(T_UMcjs~bIR77i=lA&izTeOHeR<u!2VVA+j#LWae=M&5$V%5Dj&fVeTi^obiFIIc zQS$@)RE7NscQR;cIJOa+RL$T5YsWRgV}Nt|;d5F3iCY9tdtXtxh7OAH*>Y&T$Hh%I z_mJ}RPjLpKOP%cW@u)Pd7gpcbt>T($_#L|P*62mwT;N`d)JYS<0MeMe(SpW7SVZ%C zZh|Pf=PRLUMc=Zs|E(%3H`kB0!{YTQ>y+`g9AZfcV|Bl!?kkGaCqd(iyJ<p0F7ll@ zAGU-j30yl&TY8QRXim;3-vwwMEovA%6%rz6_m^d{Pb658diS_4?R5U)YhOqV99XUo zAxxruneJ+RJ%bVSBnMt05G@00ead3nD$XRJ#6yw8)SE{XBt2Dqwqn78L8AGEC`|xj zw)n}|O3XDPbpVsF+}3s&>Cc4IFwzz^2r}SPu5<j*Ln!9Wn>Xo>7>zJu##(rXB_*{4 zS8acIbp$Ge%kX~L@YRw-!VWK~um~HHbW@L*z3M|t^#e8o*+nUZM9rB%F)rcZ*R|f^ zlb*ItWv_#vGrjp4DzeD9ATGLC<?zD!s9$<xZu}dNlP)4l%mC%T)p}x8QGlF>p%<Lk z_>!BMK-I(YH}Q(eJ;t(UP$w72#SnQowA(~5MXEv~NSnYI&_u>TORHP50AEn2lmakv z@I?)bIH<GW63GUGG&P;_1I=;KljD4kW{R=0wW%;Pv2b+uZLS5{l#z^i8LdWf0z_~= zw_@Q&S5%6DDE!oQe)xBIccEWCg4*Q*=Q2@M$dSf!CK)yQYYxsH0s2)J4P;7z)vA7) zwkYRq1LsH`-_F&#pWPDg#t9WM4^`^H{rg2c4N^$Bek~wnDFy!UW3SX|!ARu_k>W+f zog0!$9&baZ>CT`EJXwQ&)vO_@C)(=*$7Rr#{1>Bz*rceS1QgLDy<1EV2vj$KcIowv z<zdwsJ9g-0j%)l6wTL%~s>>nQ_O<&js01jyYhLld5lQ?$qT(~$X;!SQ3=JNY$IGEk ztI&Hm%<aVV4To<+u+b^l$ivgtjGdKvvET7vSlB7B)I6Sk%JL!dAwK`vV=3d{b;d4X zRcHfMbz{aKbtw#yxE$-3b#9WC=}|ig1qhQ-v|XZCsM7MzNTsz#bfG_U<?!=1$7N5@ zH)^a|+HTQ>UqkpR8pV?Car|;o2!F(8v2tzj5!%_$<b6ue3lb9AhdZ+TyzWY?l!6cz z6>;7Q8At#{4E)M-ba@ZRZ_f5>!4Q7o;+mXqQZ{(3$1e`v>zQ8klu2|Wh_iLqWlfpb zM41Tb)x~e<X5Yivt&2C1MYQ5ZZ40)(sQ$_h9pnBNg~SffJVYps1gU~A{EoJ^I~NzP zh8_$d6tcumIt0IEC~-n<iD<xso9$`uy68DXtcbGYp=3g-!)h?a@vLaVB*lp&ZqedW zGitqpU9*cghZ+t~nlm!gjYAGnwRo;Dbyk=TnU(Y-*~T2<N6a^3Ie~*32?v&@iIN;e zehG*z;+=KrqOvWAB)md4ntzk;mVs;zQ5jQAl{F=TMRMccz06Kd(V6d%S0Ez0=)dkY z-B7XW;3dynW!0K!uy$()PlFS_)UzoV(NlTZN}7?(oV}{fL!?Y~4L-oK?c8T&crD^? zYQ~wg<^8JA{-Yg!=DOP7wAUQqF;z_3vT;IdU78^`X2`GknW`Wq)3gLcYsmCVlt*%N zHaB(V&R0IyR%M+%tDm*Cc`+wE=4qjjWVc#|=+fhYtB#pfni|4FnY6#DcWb=L^D7}X z@>x9Ck#kjOgng+`WDcBYHTE){V%VvKdB3$Yzi8b_s*Hl+?w7<%VRx4xJm?Ss6IEKu zAEh(0(SB*p3iAAv39oz6({IIxu7F|qc6kHyF2NDki}q^OqlOF9i{{*%7Tvom`eAd9 zLF}V*fAll+Xi<x-dv1K<dC<F^T@|cCeAqmq3Av6<`Cr#1WH_$22?z{KN3JhfuTVT; zEeC8)Z^*mH>0UPXGmpU3h~l;RfiJ38zqgqyFHo}}9>9^#A@v&<+SF>~H91UFMgBf# zS4_$a?tfH6o8Fq-8CLKhn|{x;<W6|d`O6EYcLb<s8MZ4Z0KElO^I<C)y>6IlFDNy@ zyWAl*W$2n+pd6BKa;<k0*GYbpO5g0nH{VOT{c`xW>;4kuqV=A^#lK$(O1Koi*5UJt zO<jxIzLX4c=yJPD*Yh*8N%xK0tjJT1ap&K@wyY0`@hC1S!F%%#L-qtSv%}(?LC5yK zzW$%XAMN<5C&Z~~N^5X-laWZd`Rb_c9?<M!hOWr=)W6IHbLz`QCg5nfF;EiH$1BRp z$`Fx`7SJU3Zdhg8oL#ELy@8N<a~oQ8$|o|oSmCKkL~Xx1Z^yr=lCt<CR2O`h5?$E2 zzRY%{S+z@77QsJ?6nmcwHFj6e#1|(gCu=ua6cF&9<BjJJq0=zR(s816>d*bAw!~5n zyr8tO{eu#)6AkF34E^q|u9FA=Mc5x69&T0@<6QNnbHwjQdj=^fL*J48==Z?Z)u+?@ z4bS+U=2*yg$v2V7S27e>$4CceSlzy(?{&+q#()V)i+Tea)#jtp%nxXcN*lSiD15zz zkIm-(;=-b7`7fSE6gxTuxh(44a2u3Ebn{OOgH#LcmwcyVJT>23FK)Jkgyn4Sw5QLR zsIy#i{ST0Mdtpn{JQT21`$r%;Z%>t`C#Y7_&hDlAqN2-_&!qYVW>!VUXEv)JY*G!M zes$5~%zbbL-3)0)E~Dn0Zdzt(TU|S4X6VlS?nk9Wzui7ipC>YW1G43HwhtIi&{N7b z9Q!09BjV>~-Lv?a-oIBcthNl9HeUfLKjJ>UIa{iCoZ~)h-1Nm=IuBbf3dAJFe+5C& zd$`~Dj(icg6@exQvrX$s?-}xTd-2EqM)}iK$MRpRnnGPRTQ#_O9Y4KFZ4qK)9IQOI zwlJQ(iXvdc-(%P~-}u$l^Jh|527bLOY*nOuXLBG~NJPo{f#<eEjmq7yS55EdYu?bw z1MxZW)iF)2;TSEn-{fYj^3TeO4z+%>y*`!=UgG~*>u+_OQ_~Sz*+h`C8iO2sQX9mY zil}@Dl5NVoA6MS2VThKN%jk_yZ=U`~^O%k35*3sr<+iOQQh;Ia6{h3tI+GY<q5`XP z^yAIkdUtCXTz#oC%hCzYnMq^-o!ULKJ>#p}LwS583I>n~BY*bhS#kWPYE{<$xv)=l zPSw2|P6WTy3x5_9Mjh8$$N!d^4W)6-53;?PX<Uf)N78=l&(CZ<`Wmn-vN7;H+s!}( zIg{DNyCHw-E)63!=w`anQgDS^`kB$CjM*$L(KL1+#=`i}z5qg>PE1WEjfo{hhkCDM z;*g+2Cg`Y^$iRmTu@O&3er*~f@%8XN<LAvY_^_m?Cj?&All!O;#!zJH*0jc7u&qwD zIxa0c+r!S;ks*dZ({Px&&3%aso{Y5gK<6m^ijIyZQsW3$3uy|?xU-q;@5<`EUWaU_ zjJGv=cVTHIHQ_4e8OfPtdoHQ1omG-*g#=O>9HyN0%u%AxkRiXT%SRDw=2;95bxy~O zmI9MLOavs4Z|tVcbphTxp!pEEQ8H6#N#cLsP;vMNz%{lI7Y|b&*Z2{JHtytswcU1O z3DGvNj;2GH(yc_!1?t5lf1e70tanhC&_-eza#>J@0Fcuz0dfK&O<tb-F%NClj%VZ1 zEE+Ogn(+b)24JTrj-vWtoSs3c6%KMa8Q(0M(u0_R$(WI_StsWRah-TpUhGR1fQjfn zyR{{;@#L<7vOxTYuSsp7p~+H7c}eE+O7w8nl!C~l*O<xP;{6Ky_p)(SkJXwfRl386 z4;OVW1g6VA)BOV*Z5-w95F#d>(WA?ly<$@8vs3e)52fMBa4LD+*x)2%kh#`|{bwL; zrN-NT1x{JGchjJkUQ~V_c`n^Dt}D#=Wty%@u9w7a(E`M;V-Vc)1(u`q)l2wX2^16H z7`T*=_92Ny+#ySjCJHz}FTd>@X<%S5c<^9(<pnV&j>W~r<Z4ZR&$>%N9eVif7}t~V zlo!1lqF8JQr4L^}CHvROP<n)6yCawtuRi;@=!bYzNFUIY6bZ)u%Ia)F1*5zIj;PDu z{bQKbwzg7Ei44b?TbeQ&w$8vDbc&BgC)^PH{4<*|pDV9Xw>7$Jr%mW|ni_DZ=<uC_ z<)BAS(n47~^sM}+^4T!5md?!U{Pzb87H26_V6~PxM!w?*-ud7-li>{Uo{8HB#t8Sk zW!(k_`SiZBLy~Y7o=xP>1<j<rXs5P5J}bZqzLP47<G3|pvp=HSjY#{E9wcwS;9V() z%`)%$`OCPB#6&0wB#HGh6b`(w;HSx?lft*$!+Zt*UVXFq;cV~>v1&*n5u2Y?&NyEO w7*qZ|aR1M%JpSMBB>Eq&;lD35dNV&Ra;ma@?B?CPph;o-sr|z93)b!VHw}<7?*IS* diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index 99df7747..d3ad51e1 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -650,7 +650,7 @@ "import os\n", "\n", "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", - "N_datasets = 25\n", + "N_datasets = 50\n", "\n", "\n", "# Estimators to compare\n", diff --git a/docs/examples/robust_paper/results/ate_causal_glm.json b/docs/examples/robust_paper/results/ate_causal_glm.json index c0ae9be5..077f220f 100644 --- a/docs/examples/robust_paper/results/ate_causal_glm.json +++ b/docs/examples/robust_paper/results/ate_causal_glm.json @@ -24,7 +24,32 @@ 0.1563234180212021, 0.15700078010559082, 0.19665764272212982, - 0.3422546982765198 + 0.3422546982765198, + 0.34216558933258057, + 0.3437070846557617, + 0.3731553256511688, + 0.41093921661376953, + 0.42433691024780273, + 0.3440423905849457, + 0.2885344624519348, + 0.2176325023174286, + 0.30118075013160706, + 0.21895116567611694, + 0.3363306522369385, + 0.2881864011287689, + 0.3109566271305084, + 0.5502058267593384, + 0.19899773597717285, + 0.37711605429649353, + 0.19911745190620422, + 0.24518877267837524, + 0.5873903036117554, + 0.3669925928115845, + 0.45322027802467346, + 0.1830274611711502, + 0.4631514549255371, + 0.3272465169429779, + 0.3190767765045166 ], "analytic_eif-one_step": [ 0.34353700280189514, @@ -51,7 +76,32 @@ 0.010259732604026794, 0.2545619308948517, 0.11344790458679199, - 0.30539238452911377 + 0.30539238452911377, + 0.24778860807418823, + 0.2743147909641266, + 0.1681550294160843, + 0.05331215262413025, + 0.24705490469932556, + 0.3112924098968506, + 0.17059417068958282, + 0.2445177435874939, + 0.20261558890342712, + 0.37346112728118896, + 0.17238087952136993, + 0.1756667196750641, + 0.28845158219337463, + 0.17961084842681885, + 0.15993772447109222, + 0.6067849397659302, + 0.17112401127815247, + 0.41263389587402344, + 0.2350786328315735, + 0.1982877105474472, + -0.012186408042907715, + 0.3428800702095032, + 0.2197500765323639, + 0.2696734666824341, + 0.08713576197624207 ], "analytic_eif-double_ml": [ 0.6361349821090698, @@ -78,7 +128,32 @@ 0.4951963275671005, 0.8120758682489395, 0.6665371954441071, - 0.781407356262207 + 0.781407356262207, + 0.9903062283992767, + 0.6714800298213959, + 0.7720862179994583, + 0.6427560746669769, + 0.8552378118038177, + 0.8470920622348785, + 0.6630908995866776, + 0.6561408638954163, + 0.6787867248058319, + 0.7898553758859634, + 0.6569745391607285, + 0.7045330703258514, + 0.7726262509822845, + 0.6773330569267273, + 0.7441492974758148, + 1.0908786058425903, + 0.5965169966220856, + 1.0021242052316666, + 0.7290205359458923, + 0.6613835543394089, + 0.5153348445892334, + 0.7640452533960342, + 0.7928963005542755, + 0.7241942882537842, + 0.4839417636394501 ], "monte_carlo_eif-tmle": [ 0.3155955672264099, @@ -105,7 +180,32 @@ 0.1875062733888626, 0.1575453281402588, 0.20332394540309906, - 0.3585251569747925 + 0.3585251569747925, + 0.339942067861557, + 0.3490593731403351, + 0.3534829616546631, + 0.4144774377346039, + 0.44729042053222656, + 0.35336554050445557, + 0.29882264137268066, + 0.21189936995506287, + 0.2798171639442444, + 0.24114775657653809, + 0.3190464675426483, + 0.2775978147983551, + 0.32670706510543823, + 0.5442466139793396, + 0.2352093756198883, + 0.36977940797805786, + 0.22053493559360504, + 0.2218434363603592, + 0.6146228909492493, + 0.38115185499191284, + 0.4502531886100769, + 0.2135109156370163, + 0.4553815424442291, + 0.3162468671798706, + 0.3212607502937317 ], "monte_carlo_eif-one_step": [ 0.35389941930770874, @@ -132,7 +232,32 @@ 0.01757611334323883, 0.22711563110351562, 0.15685753524303436, - 0.3101191520690918 + 0.3101191520690918, + 0.23816123604774475, + 0.2897639274597168, + 0.15883100032806396, + 0.07443976402282715, + 0.2450403869152069, + 0.30531036853790283, + 0.15060684084892273, + 0.2828177511692047, + 0.2171541452407837, + 0.3851669430732727, + 0.1901264786720276, + 0.17384007573127747, + 0.2987920641899109, + 0.16489866375923157, + 0.17583397030830383, + 0.5671582818031311, + 0.18640074133872986, + 0.38012346625328064, + 0.2844260036945343, + 0.19556564092636108, + -0.035295337438583374, + 0.3175429105758667, + 0.24713431298732758, + 0.24698291718959808, + 0.08802415430545807 ], "monte_carlo_eif-double_ml": [ 0.6464973986148834, @@ -159,7 +284,32 @@ 0.5025127083063126, 0.7846295684576035, 0.7099468261003494, - 0.7861341238021851 + 0.7861341238021851, + 0.9806788563728333, + 0.6869291663169861, + 0.762762188911438, + 0.6638836860656738, + 0.8532232940196991, + 0.8411100208759308, + 0.6431035697460175, + 0.6944408714771271, + 0.6933252811431885, + 0.8015611916780472, + 0.6747201383113861, + 0.7027064263820648, + 0.7829667329788208, + 0.66262087225914, + 0.7600455433130264, + 1.0512519478797913, + 0.611793726682663, + 0.9696137756109238, + 0.7783679068088531, + 0.6586614847183228, + 0.49222591519355774, + 0.7387080937623978, + 0.8202805370092392, + 0.7015037387609482, + 0.4848301559686661 ], "plug-in-mle-from-model": [ 0.29303088784217834, @@ -186,7 +336,32 @@ 0.1574830412864685, 0.14008547365665436, 0.22331777215003967, - 0.3969348669052124 + 0.3969348669052124, + 0.35803231596946716, + 0.34050214290618896, + 0.38944780826568604, + 0.4019252061843872, + 0.4624779224395752, + 0.3412792384624481, + 0.310230016708374, + 0.209136962890625, + 0.3130667209625244, + 0.20723508298397064, + 0.32483574748039246, + 0.2931257486343384, + 0.3167368173599243, + 0.5223131775856018, + 0.19896377623081207, + 0.37878167629241943, + 0.2266479730606079, + 0.2115226835012436, + 0.6411014795303345, + 0.3979807496070862, + 0.472644567489624, + 0.21288113296031952, + 0.48229551315307617, + 0.32302606105804443, + 0.3556915521621704 ], "plug-in-mle-from-test": [ 0.585628867149353, @@ -213,6 +388,31 @@ 0.6424196362495422, 0.6975994110107422, 0.7764070630073547, - 0.8729498386383057 + 0.8729498386383057, + 1.1005499362945557, + 0.7376673817634583, + 0.9933789968490601, + 0.9913691282272339, + 1.0706608295440674, + 0.8770788908004761, + 0.8027267456054688, + 0.6207600831985474, + 0.7892378568649292, + 0.6236293315887451, + 0.809429407119751, + 0.8219920992851257, + 0.8009114861488342, + 1.0200353860855103, + 0.7831753492355347, + 0.8628753423690796, + 0.652040958404541, + 0.8010129928588867, + 1.1350433826446533, + 0.8610765933990479, + 1.0001658201217651, + 0.6340463161468506, + 1.0554417371749878, + 0.7775468826293945, + 0.7524975538253784 ] } \ No newline at end of file From d5e833ccbe8b78efadabcf41581e63c764d03a58 Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Mon, 29 Jan 2024 09:14:03 -0500 Subject: [PATCH 17/26] ran for 100 --- .../causal_glm_performance_vs_estimator.png | Bin 73681 -> 69187 bytes .../figures/double_convergence_causal_glm.png | Bin 33425 -> 36115 bytes .../one_step_convergence_causal_glm.png | Bin 33991 -> 36061 bytes .../figures/tmle_convergence_causal_glm.png | Bin 29159 -> 30959 bytes .../notebooks/quality_vs_estimators.ipynb | 200 ++++----- .../robust_paper/results/ate_causal_glm.json | 416 +++++++++++++++++- 6 files changed, 505 insertions(+), 111 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png b/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png index a4c178847a8aafc056d5b42c11a585b974a9db3a..49e333e723edf48716064660c9dddd0ba6540277 100644 GIT binary patch literal 69187 zcmcG$byQbvw=Vo6L<vPnu|P%X1`$z0L<MP-4r%F@22oJy4r!2-?(XjHZV>72GndbM z-o4NM_Wu4jW1MFU$D{CzweEY~b6)ehuDRSK#9m#-xq*X1p)L!(ej$ZIp?^Z5(55b7 z!B054$L8RFTo!_I7ShJr7FKFzS}0L93ljrl3j<wsijP`m=DNm4Y>b?Ytn?HgEG$gS zpD-~Q{`&=t#%4N9G|KZ%@FCbHujS2AD10^K4_Y#Bk}e7jg%Wzf|JFL<Po$N-=;F<) zN=`c}g1h{6B5hZH%A|$H7Q_Y0bp7s1c$fapQQ=p*X!lDR1-<$SqS(GWcVE_hzb<rj zcqM9*oNDnQXR+I#n_G^&(c4^$6^mu-HXZFnoeqh@{*>6xd=ytNVWRV=RP#-u!(aaX z+nqEPh4atf16f_rsgYMW^GUQ^#fkaX%V`9tKmGGs%mlpuXD{{)5k?E7bPEl=PASw= zNd2!bWMW_nLcf0V=9}3DFE=l*{zB@6sG%Xbx3t8xLBc0b|6aFsvvmp)-~Yb3>TCa( z7he1NpGApzAD@`$9}$se%mQn5SIxk{Gd5PuZ{nX-*zmt6M1W7uGdG;8b3`IYag~Vm z<HwJWxwvAbQrtBi{w`1T8~3=AAUfGAxVRqoj$>d&>%UP7)z#N;9&Bo$A|fL_=6If? z5naOk9OB#8_ttA`acU~Y*cACTbt9uIRkMvgG_n~=ennpd3BJQ7l(|r|IFXQ$*ap9V z@4L`4`BAcdALoJu?52vk%cv}cH3!Y_O2hxOH81e~Zk{k63kyr0UO&#8H*c<x^LS=w zo5p6Wuda$l^Eg~*({97f^z!h~*j{MIBV)z<v^m>oetza4ldcf7c>MM2*Pd8`Zw8%( z`h(sHZ$f?tc8`vRAMdZPL~!c%P)^vcq#Fz>XRBX`jg7VV-RGuO`*ogr){~4aSuXot z?bpk-U0uGPKB4}suI6V52@A(*wFbX<^~!vz3XS&NubrCx2ZfzyOWpS;Y!+`1WUBfE z1-<jm!NKvCKwUthPkks`<KmSoS6se*%Qc?lDY0Bh<dhF)Qte0-4QI!ha=|8iEh@^; z6^2VfU)9^o)>@R3qU*hF@8GbxyW7qh6%c2!zov}5zQAylnUj->f`ZR{t#rL&cDnW( zy<8R_o3^20yDKg!ikX@D{bYp$-mS+H`tA2$d0!<Y4EUKgycws%q+0QGyu@<$8@c0@ z)A_M##oFDbXMcAMo!?Cj!ZFWiJS;1Vc_2eMRrZ2}KV|yhz`(!-mEM5?l2@-9pLXVE zWi@S=33`xpm)ltx7#gDGErkF00pS#}8pfcI_kMqET%M>(x%6Xqybz&S<ddLAs;2Q= z{M%0g9~q7?o6WseQdZu!$LC+W#)2F_s^;dcht`MRz1kh$yly(p-`qT`e8h`+@uK;3 z%>{*gJ;B9}NG_W#%DZ>(hHXzwOdK2@vS_zmfBNj1?m!w@Z<09Gg9n1<?MJ&S%iTXO z-nny!-C)SK`pZT0<(>rkePJ=NM7UIXr6LJ^`_++rYa1IDosL^1j7m+X$NQ#MR#fcl zfe;{U7E2QC`BrOV)2I8BNv5+6?D_+qoxJA}O-)UoE3dg${e0zv>>Upe&tcg5>};*m z`DtSSl{mWwHa7OHr`B1ogUq<OxfSw0RJ#+?x&OI?PWCr!8D?guM~$loNYc_{&2@_G zWGAJtw>}5G+f%G`92pFz3pvgIVfl^rT~1|zX!z&Wa<y74dV2Z~y@{72pIFum4Ta|G z59%qi8BaV7dt}%$kgiC^X>==9HdFg}Zx!`tw9rSlH<1O_^yBJCJ$&(N5fROqIyd<> zEdv9Q&ZuX@C6=iJMU|Dj&+PW*#;qpn2M0;1sHo7<(Q}Aq9nMa;1is?krJ-?yL%nsp zUTF@|#dEsT`}d9^q_VTKt7~iTt8RxsG8F#7sx?&URH^-^D^{SWs7RvRcDIH=nhF+m zcB=Y|pe?>L66E|?l-O8TTvDX91esZ`?X7<KxavYcO|2j$B{fuHNe3Gk3c;eI^DQKV z^z7`6^5H|hjP0#0^`R^^_|UFQRbIXRU-giL(1vYy;EKCbWtnJY(q6;STb``s9n4f6 zwCQv_Ju+SFxZM%SeUb0=>q`(JKaD3V<P;Rt)zqA0WAA!<d&}c&u8kE3Ku*=}j1q-w zO_ol&0@0EL2b%K11FhqTpD(>?kTdpiuF3D#WHjUg?H_D5TXP1PyiR9NIrInGOxq(Q zQr=oD_qdLiTIn~y8Vf>jZ<lKuMVD*0hds4F@V$tCXYN?#GwFSG<bZ!Y+*TWX#LVH! zd-iE0KUSRl(v`2AlP;f7SG5*}dARDAbd0Ed2MmR&uzrYO78);}jg*-kY^dG4cQ22~ zb8WoTe5va$@-6EV<!Cu_(P`=F9Ij@_+MAd#dyp{9!2XXFn}5?E%!rjqwVdc}Z})<@ zpIu+~NlK!E)wH&@mVnzLZLvE&vi$WnCC)7qBl%q_Nxoixi|NG9a<6Vz41e&Z*?i0V zV`KQM!kSJK7aHZgdvR5_)$Q%=t&dj=ZH{(m9zJ|{i_6S~sq(}>D=W)BJ!Dizmer@W zw$>x<au^>{H7UHhB(8Ic@ths%TxaIv;W0t|9&nv)R69M|G39Tr?~ytmTnG{ke}uNt z{fb<RtMKEhf*1M+b#?#wGXJz}!_h|#KIGy)cb<LdOTP96>+%fThDi7$hSB4_l|dga z^95$L8cfJ>4NZRc%^zg?NuKfWw1zN6XU<_MDvmyFXJcljHfb<}Y-MU<^8Wnn6oP7c zVd1NX$D0b`sxh9@xU8E*JBqFpuj)$au3kcgvR(A|uWH5RIiPSD>nE-_66U$XDfST; z7t>WpQ<L%=K6xSp(82MszTxT7?kp_waEU2fj=?bN(f)d8+YdG}R?SOrJM$Cec6<Y# zDz1a7PLXwWFC5QLtzh@8Pj?477R`Rw!RlW&nye7Du*lP<XJ+;#VNlrEn64e;vGto? zT1pP8@*^kW+=zN+H<Yh0o2{iu=f=*I<Bo&#H4DKi_wL_K$6_FSsA;+FeH*v(1l?+_ zYN4I^gT!VlwY4RjWjY!f8qXUX?r=nOBX2&lWe?{x?(oNp!`0Ez>4<vv=KXsL$kmXJ zeEU;nG0f03l=Jr>h?e_O2t7!d5(U0p85$b;&>=4;ca@k}5Kc}I?Yo|^!Jn`2NtjgH zLR1}_lcF7kg@th@9gi7|jEtcCYtd=EeM>YwJuTmG84nNVPfq*yZ{IH3?fv0TmmzKP zUw~AYrB=)Q=#h3yAkDDK{-0sCNK?<u^1ZNy$Az7a^DV@*Z<8sAs_wl+!sgSYW5635 zc7sdGC7RES)zH=}=%~KYD?TLR<Dbsa?R}_FVDLlm5F=TsnDsXIN6nRibS8^Zu`qVG z<^Zbg0Yzgqb@k`z3i+WN`Xtc~dt@Pu%5g#g51%aVu8xKnjuvzm8mp#nLR#3~pLFWY z)e-l)m-61o$U8ck98ke~h<J#T?hIw_3aPgFVPEBo1h}}kqO8JdQ>&}(fi%)ZX7f0^ zzx!MFpF~AP1u`g-kdl(NK-jHXLf+BoOO}*xz`yl4hO1*WH~Og!fwA$reJg#_ZCV=c zQfq8TWnUY-ZkjK&k##?O*;&`npy!AMI7N;`%fvUPDMgi>Q8k#j`r3U9Rr7tux9K=~ z97?5FMn?5?Kl#F4Tri(Mf1a$6Pphr1eMGXDpxv7&3Kjhm#>FdTQl>f@jo$SG1CJUF zru|=9TR(<0YA~E5xJdfB=Y{0CSLKt+WjK?nN+pzZP7fc->(^<;o6YZ(m{gvtJk7zz z6cmEU7%ec=I6FB6NHiBDb;c)_JPa}Dtbyhc0Ku|-w2~QOJD>U3I^S^=vUt?twz$n? zIrq@;ynVWqtNQHAV(l2d4Oa8B6Pt%(5wGFQBFSQxK`d4}U+>*Z&+C2(qe4RS-9KOb znJ5p>tUObgj0?nZ!Ikr6a=|_5=kt$I;3%#bJ=*Q6bm|!7b-Lp1ZC0=mL}wPs=j?cu z@xAj@|I4CJ@XdavyT8&-euO<YqJ26v#P{vWR(7S6r0_#Po1f!eKoIfMXfKM{OlCwz zEQjCh`MvpXzz7&WQ2pbo|Gu!<E2E`h>521w@+Io!2go}^P)IpW2weK`*!C??hX?x7 z9Oj)nTbr>0u1q0FKj41npBD&xqu`PkLr;-sGd2%;T!qJ>f&jM-g1dmkV*<?%vdY$n zSoBv4Nn@!$9q*H7h#-KT`VP`y1WFNI$TA!)>ogp{fp>X&fPu?Hj)M9U>{U@|>9*Y? z;sz@5D9_STZa{a`eaCm6SbT@`OJW@v6GKsPvh`*pPxlL)E#(pmwT=j`y1F_dYeNVz z^k@5k4o$4AIzMi2Z(~rfvlCfaS&2nGP1$`?x(bPtYhwQkJ`V!-AYdpOEyu>ku`gfl zS3=Hp<Vqy3(;awyI*BQht+CnhAAq`x@baeK<0D!MTQpqE1mDF@e%<onMA3=G(V;5+ z%<9VXn(8l8D~d&1*uvxu4Gn*WbH4!^(DzD;kI%ANS9xp0AzcY~zs_d6^Wp4Z7GJL~ z*>Bx)Z*9C4Fp>=<!ymj(JOFc*N<VtZ=W1WOdbNt&=|uA0?|3Xs%%xjSlA_}ma7m_? zmw#xLiTq$4?ij9c;O_43MgVs(gCZ-n!1l)3>2YtmBBwpuNpDtR;l;U0=as=seZ5I7 zO(^$=XSc1pbFKai`2ljtfs=1Q+$DSM?+*HEG|oj;^#9^d5I$7=smv{phIa{izL=@J z58a;u#cgd&3q(Xju+LJ7MO4+)pqra-KQC8R+u^%}kFRrbuvvDv5N^NS&KYJ3hktBr zY-4j%4RS)+{)F9y3R!+r3yZeFr+<r(sHmtGU<{Fn$JbEMh%j6j>gu`*2_;fL7<wdg zbMueu6YYSiGRZ<H-2g%K0v@iZso6TgEB2cl91L0RO+xMv@ABnsd!LYN)956Zgzvuc zZaO|Gaw1kJ2;6V<AyvBLTvc291&5e6%WmH&eKV5VYG!sep#I2wZH(*d<(n^|1R^+C z)#;Q&JesFJVt;S1p|jJ6MWX?hS14DzJ(S1c36u>N=zH!m_*w$ay1}Uqc;X-UL&(I$ z)Zl&ljrEm$Z>YB~pFjT`oD(NeY^EiXp(J#kxA#OZKMbHzPr@70-~Fkt;9b|b%qXqa zC;Hm}J?lV1$upk31D|5F@;%eK+k7Rw+-{!$s>_>s@)viONAmT9C!N`JI$~r}bs@mj zz9%DJ8Ta$&aH*BC-}uk?zXyvbF3?GL{K>gK_4#C`@|W-5Ketj+-=f4;*VxS^?n{O$ z^K*APIxw&fqGEZXymY{llu1Q%sVlZGO+KLV{4CsNd%+a?1;|7EdV0SWO5oq!dAiaC zM&m6-rn7S8wj85{#*M?nKXz9JF>*Y5VfkPA++nfacn{skaF!azn_&98kieZWE)tNE zCP_~K+JMB?Z!jJ!8pd8-U5)mM_v`?JmzNiS3jk|7xx5e83XLbV7utR(KvLWmU+9eP ztZ~InkxCG*h*FIHGg%o8p{x#&C-V^ct^3r}uiqSwLkj_&QGHd+(pwS@4GrjT>Yb(D zj_^V1`z-ZRiXejPK6#;D(7ENG)p?aF+02%VDJh~S(&~Pc8(Tfm?+V`09kvQO*6spW z+t}Wog>)Opq#6a)LCx4$99o9F48;n2uC=u_kuY}bPq1Tbc6$c4yT1kb_)vzuBBLrQ zDtI>^;rWi|q{^hvz`@>G8&@Q>($?4aNJw~4d3Ho6CMI@c@Ie(oAl->Fu2QRY1R2TC zdAYmSG&DSv?UaGoa6Fj4I8tbg&j&q+r9vt4`2d?NGyFyu2V65A^L#=s$WP#AjO5Rk zTBdGD6Bs2dcp#!wWa7-@aHLr};gPIT{sa&PQY_#m0>Z+6NrvWn%-dZncGwH!&<8M( zXg@xy^-sc|odK}&iB)S$<(!F)L9qpADqG_@8a)k>S;mWOsZ5n;N=iyYxjOjJBxpc( zkWX_$LBURXmuSocdss6v5(bc2mnat2Ltk0L&`{LOET_Xxwb;z{Ca006Bnbf_A@-Fk z16)avjEcR5w)gfHU@P0gIemP6uR>ieve|w(-x@Ml($U|~G0I_fJXW&NnP@fZp-z`1 z9xVpNFt33qHIWgZpxM*tn<XDtTOdTP13)`J-6w~NZf$G3porGCnmal=N+{_*Mus%p z!eqMd?tB=aw`%!J$uEK@C$Cjr@s4g|JAjza`AjlJ6Ibccoz{DeHE~lo$un4Y9-B~H zymG6nNRQ25869{RV2|`wR?GynIc3vjddqCg6GeXb&qw6v=XY=ceEBn!{RQgH2YPq1 zA1oSJGBPqiVtfU<BKt%4y`!CF0ca?!7bC46n@pj&KmO?o)6mvV+*aXgjyf#1^b1h` zK?rx;*{c*J{O-M#MWNAnDCBf#kkw#;EdZGDt<1}u_4V~V?|j76{QWvo9ib$)hcDWX zZ!a?I5rzI}{!vph)25i`yRXy1CoDYOLeoS}ODw1GTZhiGQ;eLP93X2eASP2&(`NO0 z3@-T@Pl86@ml6_J0X03z96aUG#Z}DJ7XAF?i|NOY4gLMZva+&3I2@iGuji)QEY8k; zftZDZ+ib@8_;Dx%FYf*O!yP%A%>+Cr8v+%ldx}_BZXumxvBUAls2o4s*<z0{Xnu$v z(<A_d?)&x51M+DORJ2H$skkY?n1OV%8FFjy2(FSK_hUNaQf53rf2?0qtF<!Wb#rSa zTVp=7vaP7tIX{0)_Mjzb=T0>`zhjDW>7$H{jFXe?&f#EjG9LTKUoPS=0J%aA|9^Pz z-skhvb*H`*>2D-0h5n>5b&krFj&N3gGei1;madUh)jpO6=cE>&*JO2s18R;%1{~*3 z0Hg=MQ!~H2xv2qi9U2}sg`5xhMgUrF8`wFN$;XciP;jm7>@?oLuYzndRBFWpJKVl& zC+)k~!5BW=*T+6+>RAj;{Fkb#D)h`*fa`$Ggp1AQlOYck430xPd0$xm3wv8zoAqk` zAbY0y@gHYrXZf-5OxC|adQChkp3ai&j91Su49B>FMs;xpolfQhl?Q-LDt~q(JS?oO zvuLNOJ{l;$I2U1C9S`S243wQN-(a6AoOINH5(!D>CLJB!laGIz;CfY;aNbc%C*ncM z&=^RQM><+JLaH3wT|Gx=CnxN;MWH0>LSyK3viVJHKjS&{i6&-dHBi>@c@91;c1BBQ zs|y0~X@u5_!&DoJoH~@2os~g+D8o0{KUBe5AMTABW8>ofeC~?F6j)5rxjH|eU|t6I zOs`zB<GSC$fb<tV@j{eTREtMCu^vX_B?vL(g&uhu0m|@4L4%l1W)7&0u)$)Ry}3C} z=qnoYbbGfLwp+gn(|YQK*1r%D!SlMw346lEl%~Q+t)Q-<!NL0_G&Gbza#>|V9q5ma z$k>-&7J|%dx!TpTs?Ch$=VxC$u|t!@njJokkIk85VKE!sIytyswtr`7!v)*@J9Gk~ zjV=*04Va&2D7m<PFe;ZY8;w1Nobt=q)T|v?g2T;5@?qfKoQVc&;*-xC1c4NlaF-aV z<j{*hwts<XzaME|_TIT*>!L)0`>jRG1-vwc2R=0U^5W5q7N!@_tKWRh&IW+ssRrr$ zh~$BIno_a956@9@9ko+ELgIXua*0D}OOtqTTUAw~-FW0uIN4!4(S0qDRhN+Jxfv_F z?!C$i>uUsrxXPM<js3fm#L3}Y=Jq|NPXSH}|K`nb^nNCLGKApcr<oDNc|Y55l?YQ! zEyqrll-voA_Z%-aHp_;sZ1Zax`^%S*YKI$1a4<jj2&gp>lW@S%h?7ndJ0kfxbnWjs zyh?~6%;E8ehz~_bINTq8J27!%t#oU%#x>_nk*UVv?%}rU-ITu|{>EPj=;A>-AJ;)l z%eNpUOGA_8SROGRe)FXd*8Tg%OB?kbF>l5G5dq3>T>`S1`oY}X*?2xK7M?GIO>qjY zL@fHw?+1#wol!v=So|LWA)`CT%pnBNXQ}66I8(p-w<<Im$E?f`4t0tzN2D`QTdFcC zq4J*`AMhh&eIQ<4r7mO--$?Z9w=XLewloTSopXF9D=UA9VPX}cfuW%>J&^vnmG*B( zBlx$Yq2MBe=Fo?Y?o&9Y!1K>aWAZ`^O>{y+5}8r<^fL&x1XqA`AVenf?rHJE!&>bd z%B3<B_+<We`<XSnD`t|*w!1xq{4Z`H1bLwS-+lz0Xy3T@>xl5g_M*wuC;O9zn>MP2 z>VjNnOq7@rk@GBSXhcLVAWXoK;cE?M5E2t(+}SNJFPG1H&!3TjQ0VPp9ASe%+v@<- zIxI2%@PQhj#jw$O`Myd4tF4`#4!3bWK~?x=I+?WhP*W5%5Lnh0%7S2RIH0}7<~rIP z5kkOARYWoHj+Iz?bn+Z~0xKc`MZ2uL{KpeZ+H2RYh3QiYsRLdA;^j+{5r<5r;^#o= z2nh+%<Z-CVya2600;h@bY<)v*n009ZD_?KkLHW&_4}(|s)?Aas|8*a5o~Nd!_@Frk zYV@aKp^<HX*5TH?!APFlUES(z!iORh931(4`iyc}DDm4r<PsATxjMFYcYzY+Y%S^u zP$mUX`}ZBA1)^*PHrv`rp9@T!e2yl7gDYc2reEqkNOPf-oSd8>?N}}0eB}4<m$2~f zh`?7Kt`<&a<>x=3q;!FnhXwfL0)yetZf@8~Vv*|5SKN}QNFYJC{M`rn;Km7LC(k?2 zO1cId$Pirz?oSNuPd7$}it9K0?X|OaOB2d6C?;ip)ydL};!&T<9nR*7163XwjlG7_ z3wccgYJlxKRDuYH9}qmaWr^wO48VHYMCquh`7PQ&ZI83tUmHr)(sy-nVFqD?=j=d( z!9ycKIOs$FFJj}$(?@{OpjI$kzj0$hzF{l}+6&-l51_V6CY;WuyiFkh<|YQ#@zyij zz$>?(Xxg0P;;O2uVqLyb=X+NGXI|$h=blm(dNrN5LgZR-|2y<V8HQo^5Y@oWMW^#1 zwlDm0XcrFeLt(}xW$gO(j_!%oS{syD;LE%J+b*RfnA;di!p>4R20lLijYkGekb8^c z?YW!z%*<F3WnsF;Rbdv8TzzY+2XKU0CezQ?CM$iQqXw}?Ua9K;OW&7Cj0atqMo5XF zYz!z(?L*LzWas5+9&Aj@XK=ExsKGCgorT3fV}?C*JX)rzI9#|H#;*Gu>2<h2(gPR? zvw;o(h&LQ@aq+UUGWh8qT;*okAa|gb0o*2VsFd4Ue*81U4>62r6#zLfVt{Rf;KTlu ztJBG<*>v^k)2AN>Gj2m->-TR2$q8bKO}*|C{CElIIp}Q%OMJq^Z^EL#hjyXZhE7!; zImBf%W5xGFY*{$kqr*8lXxsu_Tnst~lhOK9TgP9}zV&T{6CdRU;z{dCZrk$6!NG}Y zIQ}Dpp---^7Ys-8V&VG$DCWvEO9^XCl-aNwjs&JF7P3M`k;znP-&-9;8!9v=2eL^6 zXz)KH`5sX8d_zN<>f8u($67&v03e(5F2y<U>wl*rn8<uq>b3qKbp-TEeAnuK>j(mD z9|6F?4KuT_baN#^Zx9+5W(IUw`w&PM)QSc4Hy;^HHIh5cRXJm%$z`Ma2DO2ZGyD5@ z3v{}2a13E9K+*CAp}+tDSvhw{M+YA0N<hl8nJ<X!txt+WLFUlwtLo_?fUOd}H3vy( zc_95JKR-VnIZp(Dtz63$8Dx{uF)%<=>(3!!R4Ka-uRS?EML1rNbhH8BDzN53oJlCR zGTlc&5DuY`KgP<0y|kEDx{^E~y>Y9JB|nuHQZ1Ez84QKS(g({_#m9}+UrHA1zkM@5 zDqSBM`bF%-sasR9HjXWH9McXNVkFb`f<z~v{L0;xBnQJ{vtiZnYFo@Enz>=+(L_<_ z7h*y}YH+#{IH#@sH8S!xG;2Uf2ZV+u7k5C&gK{xFGxNOC@f6|obvmOiRCRQCuU8y< zL-<JoB!V;Z8WLx5277hK3wT+P`Qnwu9(79SU>;ko-EuhItA~XFh63;v!eh)30Q{o@ zk*6){SvWLtl1XB>6QQ4h@D*x_O^Q=zXf3-9-G1Y~4v1;;3nEcZxkgtyqj|-lF61}F z#l>ai=C*$Hc6LT9C@6UP4&B!I28Z6p;bOFOf-vzDizPm2GTGOGD?fD&2p}Y+5{rj# z=}VRMDK=juAR}uAFq2#E19u7<hdLa5pimJRYJ2({c@Ja^s1uk?BtrF;K!f@G_(A6A z=m?^(I~-@^^WlirfI`;>a_7N%<vII`osG>Vs11_o3LJ?1igaNuL3H_=$?~}mfhm5T zE~7)YnCkr<*-($A57!^zot)-3u8#*u8&)1);W@fgaY{l@?=DJTf1J>_EIpKkCKlzA zp`6>{W_J>LYFsJWHeMQ*y);^QxSyJNiPs@4F@Wl4OV`>@9r59;x5YVsa7b`sC^B#H z*oQ({D6v}4AVY%m+m+kugBi*Q2KF6yA<Hf(7|_J!gmXAG^&al94vr(TQSg%1J%NdT zOFEg^*KPi*oFgfPg1$vCoiub9={qqJw570Gw2ZnU-T1#?X*Kh8&>&(C`o)VE)3aH% zTCU!{9l3f-$R9e0Sm<oSvJu??>3`d|7dz3oe(pi@CGW5BPxre3@(`prVK$Z6=NBNC zp*0Cl=|np;Ab$V>90H>*U+MS^-idU{cz2#Y_OJ&re;QEnlYkG<qaifZC+$q2otk;i zPkwS}10w;68_1H{ia;!STm)+2-FfEE;1I&pdf9B!=2bzSoMP))R}h_+;YuU<e8Elq zfw{`xW_Kj%KamkvC5iK6S1$Jn9UsjN$zAcj&JW8k2TEXWigb3me8A0>0n@_})uLj@ zXCxgReXUntO#J*-HePXg(upbLtB|n&B#!f)rmKWOu5Hz)KIA%1h6_=Se>JzKadp-X zJKeA3JJJC|{Y3pXQ>9(21QL<O@x<D8XNez4{OljDBAh?;6~hySs+Eoa@xNgcJ_LUE z(M#`JKj>_{Jh%2Y<tyxMl!D$5KYg#SAD(1`g@q;08VsC58!)!7g@t<#c~nX)+=_}G z!?}njYwb|RLwGRYRiqO|n$0>!MsC1qJYloHD>IP`#aJ>$>VfEIap=yL`cp}t+3vib zs&a;Sl1oBQmPrMD`&-AKaZh2Tg=jB@^ahIF^`xx?zM#oJg|aAwJ;V_<%Tao<zNJ-s zeZud4mIaN)8Xki})$w|(Hb`!B6vg_3pn<v5pyWf)t(SZwZb_w*6cv3?buyiP9yM9w zYIC?{Ay;uLiG+cfNs+_{pErKEFJ+4Zmt<q3&W(OwYNYl7RjUt87h!Plu(7swapl0+ zQ_%UGo0?vO3qu45=*|88!4af0g9DypIjgNXoTIg~J{kRkO$Vugh=vXH;HNaQavW$w zOBybdUAtDjl3AI-6%t_fFYzp0OA;3kFCJ8T5LlTDeN;3bKY6k>*G!n64N>mr>stkH z4Vym-3?xGO#b57IzlMa!W~@p64^%<1<LMKiV8Tp+Jpd*7Qj_H+C+JY=^AGUw@Un7p zdgK_{D$hahom*%RSI|$4k7w3sfcUnKDP}b9=>#b{MLNlQKn`?5&_xvnI(oh$J<{i` z`4E$xWP??1>#MBA<f!6?mX*xbvmv8#O{e7;er6ppKlk7WJbwNy)0Y=aCV6b4hmZDm ze!t5|A`UiuWOzd+h*mc;b0U+x|9BTeXr?buH<^v-x2UMA2dNe(bCV38GX<x?cu5f4 zCICj)@bK_~M?km3HRn)%S*igl6MzN5_3KT*EX!xByCzDQeKfTJ`41t{0bKOP3S2ZW zFkoK<Kf!l*_j{C-ly4+|-+5*Wq1p;kpAhgYAxx@>Y*$oVJUurTnC>YlDGAOLonahq zyCKm1J&s4~>s`s%w6iRhr5=ig)xpQwY)qlSe`Tk8qhn<@0RY41N-*01skwQ3FL*Fj zP*78U2R(!5_zxR&t}fx>iUr#U8}nR1fQ&^Q6W%&j=@f0g*uer7yCa%6vstR(HEs|# z4$i$tk4OMtW+)ZcK>rSqdjXgM&)eL=z}!kd5b*d32tP}^{RZf(?SNk!Djbe;<UT^P z5En!ted8~Rz6uH3W_xb7KUFnCTi=+e^b_x~h~w$uBC|tD#8dy<YHC}%Yini^vCkRx zG?gnE>6`}!mfguTGf`2eo3AMI!&;O}rS9IO<NKLeQPWoKyu@u)w7EYSwrROCCwAX` zQy}{(4knX2prt&JhToKt@y)jcEe~bi2bkQ>l1ua+2nCQ0g9|5YJvb+<zW@Ye(I23v zNPS0rAFA(zyLX-8s`b18sFJ;U^~!edPc!Hjc^yB(!)F&4UxDxnRH8ZnF_0R+z!6%Y zo(*Nu*x1@?$+7%1#L{SEQxC`y=mmN+tJRSpg!n!<u;w{hx{rWsShW<{%sb!%VDeOJ z`|ja^3r3NLj~)ecPTB{+CE)_$3~G(ft=ZptgBgfp2E_9zIDLt4LKvYN2?IqZ7leAz zEq4H-kPqj3#d);=j3SO2P=vkU<9eXgpKv;N_$8Cx0-!BVso&vfM-5IsRPQegiUrJ8 zYf2*phN%x~0qQS<QzRpLH|T|}+jnp8U?8^QmzF*_cu4(rb93`UVfz9*zQf?{_*yh5 zK2h`mHsx}p)z%}~^x*jOQ}HNV|DAr>EjewObbm_j(j7oXZ+Q1l_x*Qj@wq0nR~2j& zJV=%`3XPgIUcU6-Ik7pio||v&343B0&$}`hqP{lSX*#~LG9aY?yU*`Yo&n8+gwtnV zU7A*ITyHv_C|BKicnRmZy?vPl@PRZuJSvlMWjh9AMUt>Ik%2%*8%yE@0lZLx5Byb8 zPb~v{H&c5wFK?qhAVz8cnXhe+c62u{1&O;K87s*_MmWi_&5saL(|Vx8Gj|~f=b@O( zaPIsd?+HqyF;Q`s5<5}!ORT{3Fx!Wo7afdi-2y!4CtPizXsQkuxa4kG)SnB!tkKWp zPnF5hl9>LzUWqaF8wgIRE~cBm_E$Yb`+rgL*fq3YzBy-;VfOv!j41&LAHNV4m<*80 z<#cBM6_3pSf#j6d;!htQ1aQc!qrKrq8guA%xPt3wE|)1(uA=js#>%v6DWe{W_y+K9 z)#^l7`#;#)jNoF{^|ya&!%F?blJH@Z5M{Y-!o`u%p(dQ4ISmu#PeKNvy%QoFHZON_ zI+8PxyKdDxkevSW4WBlir)To+-Y7~*H5bcL7>70}?JX_lMN(nI_s%g*kHJWTpgzdk zWs!TwC$y6Pp%v>sgi8l{Jzd5+fjEegSTeW#9>u>U^dGlY$g$lKOJsW|{1gj_y=QOU zwiyhgm)o_{WbUu&hWBjM&eU&Af7>#xbj&jSY5&yjqixxHZAxsO13npw)VF^5<swd` z9C}UeL)Bm8JvemcdS>dC6!N}rWtPAE#{Oo?<F(N1_iq}#DR~^+2mcJeUF&fzHrF!s z@|qv2Wz(S$5X9!c>+kD}IE>z<%GA@FYiIINSTBPm(G-}IOIx)*ye-H3lT1Zh7jy3I z_lj?AHh4*7$0;#C;hx9W<lKT^R}798YNe`SsRQN1a446JYN*p>xJD&Ja94LV>(}mb zfWDtsTU#=h2?c8K{DkmzA&OR&PViwC!r_P@?cZFGbkfui%W&K7W}|E2+EBLH7<tv{ zv6)8(shU>M+H;j6iGr7jh-Cgfi0-1d_tsDt$9o;Bm2jMEi=Bk#>vvu*EWB;ekWupD zoInM+HztWAKfHXSsXjVrOXc1b#A_-15b1HY+-L4N%zB+Po@u@GpW4yoV$nRYw)yh~ zf9}dXrZ|1s4})TR#YPipWCO(KRy)Z`nc|X{(4H3<VpJtaQ+aMLwG^6|)uagGqKpj3 z-QC>&w9ABXUBdm#M*VXqt}d7dD@=!vNt+3ZwVMe(jJy>)0J_1VH7?V5?a9}ZodAQN zw)cL0L1^|n$z{gl3KOZ)(#0XcCz68A%pLdpdlW2Ofl-N<VN-WWyQve%50{Sl02g8W zY}8g{is`}p(;-i6GY$BMg1zZXAxM~Ul~pe=!ti83G0V=XLm$X$21RXok{Cw^8>jt@ zGO`=bw->hL>JGP&5ODLlJg;iUpgJS_y9-T&j!u1ja&hwq8>%mzF1pnGRq4lkgkluN z`ny=4XZ&wJ5*dic5w?F?c9>`&zq9Eq&g0kkj`m=l^!ih|E6FR9s@~7qE_ZkPhK1E% zNRvAUY%NZ5<(UcU6VB?0ns4XA-b-!mqWFBG;!-=#=H`t~UNbwnPa<Cb_>|ERiqcmx z>Zwp>A*aQM=`=2*2!mgAsw&<LZ9^wxT@rUz`h>)mS0`>8O|;pRXKPds|5yy)`?>Q{ z);8uMW|Lh)Z8)Wn%bll}_!7_+*yya5(W}ZGj`@VOhM46wubLv(O9=v`YWgY`7>A^r z?yj^t*x<Gl7@93GHU-yj&F89UsNV~5)$O}fQycq+)Ov^dm?OZS;!&`RYA}YG>-B^} zhlPX6<Zw>6Vk>+MLc$0(8Iz8S^Whw|Lk*B;Uq>lDPJMSQUWbOG;!;@13B=(^l<4Ia zs0*`eYaO32;Nt97KyNg&;8F2*{_}tDfzZq-4#WqZ5O-$`TEv?1@n@s$ot^lfy1FF5 z<p5Mm{J_ee0w7NottSy}z(KfolO39uYDMF6L6F8bcXr}|);j@K3rv6W$A?=j&^?kp zv7q+4!4U^(2`O6uy#2ryUHp)d(HmeO(QgOnYnq`Q!nJJwd=FSxib8`C%5^pl&rZ_L zs01oM`?KP-A+2xu9MC1bOKovIJ=$}vdoezEbc8--(ihG8w7c${hU4c&JUGu^Z*!r4 zlHOjh={RfmjCI3ioR~n3jpu$#6z%QA#*LxiMA#YV<>_ZZr2jKsO8V~QiuGL+@GHOc zxg)Bnsp&ibN;#8p*(TP@moM9*d7}XaZOn(L=I9SHPB<PLsuN2WMPXmM)Kz>Lda_0v zFqd?3WrDE$AFc{v+{<X8?79TNfmQ*JV7W4o4fH0|i<+(ZR#=P=*=6F5-nVBF>nkWE zX{wb`(3H-sth6FHYYxxyEda#~Nl#ply8>TLc9tZ?qIr~)S4c3CvsZlgAyy!MQjG*^ z_0Rh=`wyd!tQ=e-Bj#RQRaAWJsI+z?F4fH^iK;KtM@^XE+3+v7bLkY#3nt?wI|R)5 z-sBgn<hw87et#22aMN~!LNqMC^&$PI&tqM2g1CSVJ8Qq;=QoH)Jaz^;tHbr38I4!X zhtI@!?sOb%&f0)W1Ccf_-+TlfbXV`&+*_l@mDh-f>Okkk<sbPx3zj=`HrbIk+F#Ps z(>rWl4%gu&B_^^0MggNhEZJz84I4CTbBIa>fq;N6FmZ1*+%(>RZrA*H&k%&}Z_gc6 zl+zRoy@5PJ2)kdhnck_XbO3v%b31uzCnuvpkiu}?F7+e3fB*j1E4N!BxXf`JR!{y$ z4(Fc<1}eR`nNd+PiV|4(Uj@EZ^|4zGmKIl!<iCH9a&*j6cU@aK+Qo=r3eOX+(xiOA z6Sa}&sk|sMTzgm#E+oA6b~mS;?<HE9x(&58Q_H>2i(M%=U;E$sl;V?)B98#W^V?(z z=kXt`6M!n+e0;=7aHjw|UVmh89i*v5;G*rpkn^;C!((A})eD?(ApBJK_6C4~mBr!6 z0yy7GmoFy>D0FWFzoe^__FtJ!dhq@Yx0FHOOF%$i4SIHNL@L<xKq!rOIzK}t_v-~S zDhmU@k_^}rP89-Q$I9(w*&4^kBM-M1dn+95fHN%u-aOb+b!Y5I8#whBf`Z+wwdX<L zsrUk}@UD2F0Qy1jPj3S+o(r)V*DH(#rm0-8LnBlUaE1s7?&&-LM7gs%N(uxq=%;ni zAgC@0%cQ);yl`QYTKc*ud^BPuf|&xuI|s~=862d4{7@G_gz`E#IGCEAcJ}eXhwFcl zE`zbWX6EGNq~dgH3Ey#@`8`Uf=d6*|0>Cx2D5jeL7%IXAJ&=BH%vXl~$3%HPA;(sW z8{r6xy^V6J{@{+$U2?}{wtAW0_Z6wRb4h*l%<%jxc`+{}IPviEINY!_V+D3XPBz8A z@mrTyYzmE7dsuVh-B(%pqP$dNRrM~E%{-2Dbf*UCilncxcQ!Y>rBDI>{$RA`g}!dE zWNoJ21EDy<60rp_i5S5dm5QpqU%L;30{u#y|E)3nF*kRv<Ebs*N7JLDqb-<50ExmA zVixGt_s#wfz*)v@GQ~GDGh@+WcetemSROHVTSOW6#3M?_r!>q%FxdkcviE1WHcrq3 z_U;i_c>2P&7o(k60cwJM{&kRuQ+Tn1kwcEtxg*#%0nuYq?B{~fnij&Y<D(cz5Ws-U z_)C?b;1H27^(Of|G%~6Fd?7+O)x~74=^7Z#rLpj?R{;kN-;PuIadoDZsj^M70FGlg zPzsRtZNOqEUu1F@aZ!K|?QN#YZ8&59j6>8nH@m?gMnq(!e4){8(12$U+zV;U{%ASr z1ni6_@p<ba8lcT0txbD4r!??_@YdpxSZ-;cC6HF>V$Uz8rJG0=V|t!0W_7N8MA1SR z8{75s+n+?R44tpopXNu9r3Lyn@1%3KRovlBb|!P{!x<J-icktI{%BaN#aso_0c!Y2 zHZ=Pk@kpMZ)?>3!Pt`qFbU9BVvos}BFl*JqA>behLo6}Ch~0h=^vnyK6-W()s7Gut zaBVuU(wJCS1TpcY82_^9fcp$Ov{&GEeQdW!4`VAsqoas(si(&uaXNsDAJM14nTFg5 z1nUD<R#uO9Qg>--ac(|n{e9tWhSCpm#{*|rUPLa3$pn6A3UVo>cYgPKz$f>DZI@nu zZm-yKdU-j;yj86DodkUxSz)?vd95YlrGj>iJ@aQ=oG&FdH+QMQA~nv=-+>1pa1v!R zRe*$N20j8P2ITrsV8c+W`h<pegZ(>@uCw#=_0W!jN?q8Xp5y@pMmD&6ZTHti!FdK1 z>OJU*;2VpA84Qf~y1H(V-Qf;_=TZOgp$S6X26h~LUfhg~jEMan5e>nl8W4K-&N0jc z%<m4WCIORtvhH;L6UfLDC|nAxh!qUjNgX;3Q~$~uRdjycnVJj3dOEt;hwZ_lVqopM zd+)gnDpjVv@+lTpmR^w3)cTCz!5VkylXvDvJzB(yhj><vN%lQCG-y=wbMx-R{4CT@ z-_D%lgr0`~z|o~?@FQhn)o*5VOwnsJu5^1`r7n=*-&?M_5v!spCr9)eEgg0Ot{AaP zLN3jQO`#v){RHTwm$rA^2zD~7pkM(6s64|_62zPJ*Nc=MwEDvjvXRMb!(|w1$!{S1 zPi7<+SJ(O9!*CLcO{UR7T)GeHBJ@l~6J<h&BE#okIvM`QbX8ed*|AAlkAlh^2@2f@ zFB%z9<rgnre26T}%@s(Hnh(8}UN=D_%r$qCTM8~`M5hIsKWtlgOa-<CC>Dg`4>LzN zM}@G(c-S%f{c)=>eehniz@;9}1&Fhg^^(DS1U5{GX+pe$JxZJ{Lnc*7M1&GNsl9p3 zQQEq9uKEUq!Rtc=s|yPYNdaDN^u*X7@9zb-=FAZbecqo42;ej@R6Dh0W}^!gnQw*s zk|T9^y5E(AA1{x`sQOj);M1AmbO7zuEa9l0lKhk5h^u5mZ$~aF9O9j4;b64l5NDa2 z&kpad4DnvBYF)8(JQkHhc@PKDmgDTd`Tk#gQoa@c@=1j+9oV%SWhkU4f-?|8=0Pi$ zzP^5>Ijs0Fc=E)fo_0&A5NWrCItK+2Bi0DW5cJS1f?ExNB1jRsa_eywV2r$l(D{wZ zyM>+U9uY*X5!9*`_E8JJ-qJEy<)x(1fW2@L0w1q4(P06QE(lh!DqmyfBOE`1kJ9h{ zE8tI}=?5Xudt&%8z{JO{0mdvqFDziwLykMhx@hvvX^<6QVkaIrBTP(8O-WSH<4VL* zO_w2A4a8rg4>^XWkf)HSXnj@Hl&i*3oaXDhR|7Rc?jOuJ4CgfrQ|cP7LJg$(5enga z;hgYg^Ou&I`su@-E&D^Uie~pbP;+vFozva0JuGx#D!7@+{pP`$-~F&)lNxmDCI*;s zig;!h3~3xngnvB&rkOwA0_qqLI`B0XDw!T_vG{<KDIY(+4s!?KpER|!1ao>Yh%h{( zVOH>Vn8X1WmOuR>$O4BzFtG~*%kbu4qZS{r<&c4s4KUjrWG0)I#|)l}jCznVLLSh7 z6dwYVnXVq5_8iP)fdu#&q`k$FNEM)VLBwL`r2-E34v&3PntbkeFR%KJ4zZPpFJCZ# z-ZdC4@Pb(>ZTU`+xC8_QyyjVF*T2H=_V)J1O0C58+pDVh7CItR^TisjH^j$Nf&iG3 zdO|2N3)=*?FGkr;`98!B(&V<-d6z+jQ59!uy|T7&vZc|3l$8D}GaD^&TR2texcBWZ zyyuu*d7t#(&SR(O52!m?;QFedj!YG9|1u*><siN1j($Kv@#)L4)*C+S$MFtAB$v$M zVXCL7q-1NmlXu19939ajL0{agJU{gT6$rfC&ENs!2jk0!?l{4WH4Qa2v?il|Aj=SQ zfrSOTs{Q5#&@d60GprMBy4>?1J+#0RDgv8U3lSpZ0>xJg>};{!KBHoRfm+@7Yke8Y z;Z_qi7kwE75o{0KHe$a(WOHbUo)YsjHkp6>_HAX1XkT9qARhnwhXIv<ix6i3*gNik zGXT-<!9*~<zTO$b|0NlGF`xr=?mmOuP>(oZp)kXLCm`4%2ot6tXOsJiTU<a5L@pQZ zPYbwZr7~+j=+?Z+c{^a@@Oy49n{w}2qvF3OQ2sU10t8Do7{Jyh%3FaUJ%A`-VPosf zc?;@Nh|}5OH!#X?!A)KzC-3}(hLJaS2@kIh80x`Jg}_UsS$w{P;F~5X_}zc^TTRjv zA<>}Y!_N^#n-g}fOsWaO%xa&@91b6(JhL^QcmH#yZ);5WmGIRj3&W7c;2UXz1=Fw# zU6QYazShJTe8CDG8uBF~u6H^kMag&L;kgiO)yqoMY;JB&18E)$MDLSM&JSHM$a3rj zFuMm_%xP(9OTYm`fp>_ddy6P-U`6u*;El|vL6L4k@+XL*NWa3u&K?iK_X$|Io{X2` zK)S7JZpH>%G0(viMij4;7+A8F2Q$f`?-{Hc0-4nVjIiA>6-Up&0K;2oAm`6Rlk<R{ z-V;3GWfBXYpBBnK+T|I|*H0p5l9fO#b^t40i-`^R>mqLUy%GI1g?t?N27iD5WT^xk z<MLe+_!BG^V*C-oxky9+y&p_3{~3f>7*?>~tS9B_u$%SbG_kPI1cDoxI07QBC79t2 z^y@Ht6Wnk*0nVPYvtwg~!iL)eDZRe0FA#PSq4nXFt*}c6z%}O&IKf>X0sy=OW;|%P z;bMeaNA^L^fg;unmQ=ITG(q<rf@`;LGZYsW3&9k?5lKQH6v6vw1QhjkbxVp7akJZ< zzz%~Y@TA?x-9J9s(LcVguO^7@{^q9t+XQz?=N;=YdTh$(F(rSg=WD_QuW7Eo#(2W( zM=9j*ANMnXccnjIpfBZqJt%3K_bzzerb|zeesvVPa!Y7W^i9vnq2R|-DWU&^3%-PT z1J)}ZQq&s&rZBf%-vGFWl9Q8j8JrPZD*LbrLdF$`+8Kikzy~7go7qgX(;Q^%;^^%u z{-bQK%kVvo=fuR?+BZ0J+nIRZx10a&L#w4_j4v5%xXKZr*kYMf(-|osm6~m4TK!yh zefoLabA?B7=3{E@p|3HoT~cxI^zs7r1&=O=?yagYOdHQY5ts=O=jkq_zJaiVnvK0l zCTkJ)V;Rb83%7U~t^OQrHr^wALnZL_Qz>_1x1W8Bl2_?qmdBV!?%J#X>Zuqm1yhKC z<UkD{4U@)o)7v*|FCpW6{JOC)JTNpeqOHiA&AtUFzb8@jX1Wy15i+&K|M3>rJv*lH z@w=Q3uf?OW$fI{Es%dBhnSOru^J~C4VA<AP3YNJjCaHwq8eFuIhv^&VelE?NTbLx0 zu7K*x^=CV0RHl^(xP;8nWJ9cpO4X-|ffK0+#0Kw{aqji9+PUTc56NGoh{uhBdu!&b z+ycAA!Y}vhXn||GqE<rMW1HE_Yh<zs^nMnOS^kmS)5kw`Q!1UE<Hst+H5Hx($F-n3 zzDp~}sJG%!p}xEK?0F-$JwB(u8Qpt~_S0Ynf1~%t95%Z#<NjqaIu^+Y%Eaqi(k(iu zBE#tRqx=++Q0d@TTJuld`h`D=6%%V1>%NxS66B#2wbdSuA<;Vb*sQ`g`6BWdfd?0m zj-lkcckpN$zwHGr<+0L>x1Tf#u7Oihxs)P215Hp=;_87=t<5jmFWsx*p?}7&@NL8s zU%k;X%kB}cG27rea?Z)!!lCEloXxV(j88Ul%1H5#%P*b+R)CN6{Pdz8M^VXhvEAYV z1Lx!Y*pG%orKIjLxi5{1?a9=$)n)4?Q9Jf|co-PT`(lL2a+1Ah@yUdIM4t5Y)ZqhB z9l33lH^`95r5Gn9P?ly?<<<CKt-oRbo5R_GK*5QYv~xv~L}03Z`YZIQTE-`XB_3Xv zlclA;zi$1&C|ie3K_)qaikxwUH^Uuc|Gi_Nb0!uO)BCz&a}p#vsTrnP?E2>v9vI{K z9t`Pmp(NRK{0Yc?vVV{jaOe?*i-_r(ei9ly7Z9Wc!T;+!#2dNv_n{0wBvYqdB8q?Y zqFetCR9Z`LNywue$UoXuD|Y^aWpwk;#gvp6n3$-k_xc{cs6C`~YATGBJ=4>e#T>+l zb5rZ;tH(?0KiXBlfoW$va2dw{fd@8T(xW5DUdRZP>|jHEeKtsi?JNI`c6Zs2bN(Hh z+BYcNXU%hOqyJf0XjQNcl{0eIK{idU8rVKGxvclj17m>Cw@36dS?mu?pf3N&Tx@Y8 zEmv)^^AEA3%UK^Um0di%cK_uRv~QsCqlWTy2@y+V1}QnYIGE%i%eCg?W#YskMd^qB z;M~2gg$3VYT`To_W!qPjiqQk8z<~W*u&(W?(-o_=rpEqX)xU>$>;=jG+!vUJQQ)2& z|JH^^(o`j&`vRR-IiBO+iI1AEbfN6K&wTaQTN)V_Or~q#KdY*)cHY=9fdmy|$U8Oj z{(oB?1-_z?*;y%RY23U8AXztdcG_5t^z_1JpMtmw{co<PqP)Bo_ab<2=v6BsKs?fI zfO#mG3&KF9q@*BECV-Xuicj7F<G;^G^B8#^kb!UrfL{QNQTYw<h=nj6!GW={2!OM! zrZX?#aRe%%>cQ472zc@&INu-9_bskI!sl&jfe8y{bM4L6;LE}v4P9|0vhR)A8Ms&9 zJe{xRJ*Q-Sas%~EvCw_|Vrt-*6dfAHcY?QKD5(BraztDbfFb%*>7^1|=0VpUB4+Mx z{pTSB^x<A!qTbt}Y+H1&adLKk>;RTx3Hn8_A%(F3`fFE${`mJ`CNjDU1@tnM%?AmO zafcQIz;R;&Q`X>l$!~yR?l-l0b-86-Fl3&gT-s3Ca(*WD8DV?cG)d2`PM;uF>ZeXT zj~^3dDslOCpW>RWsFmVlVfezfclM6myyE9)RvxPULI~3|_pwJdtt9oQ&jyHe4W4Gi zss!-Gd}~e!!LEy7tliw+)&O@bOoQ~K$+LmgDkC8eVOv3!2U?0B8Zxq{*32P+s9`(^ z=w4_%fN4qQsDL~P6j~^o&K2-d&Yhhe12;x^<3?D0f(<|nM323}X(R&sb^s#?Zl0d> zpRFFQKegTYj+kO$Tr713;EEx5B|8x=LIV`yoj=2bK%zlsDPLxt-GKh9)ge`%{=oxR zWK@A&ukSlT4}yP?-d*t}Y{+*&hQKreeU@c5FWmQ|BCkMy@JhZyJFw~b4M05pVDiX@ z!F!lpoCcbzb9*f0@AD6SNLF0XmO)jXK{buhm%Yv$b1>h6CYT~+i^AwC;dq+{&!Ztg z$zWlHCL|{Es<Nm_=hH5D&oi^Uj!QhW>G{=t$=!D-e#oHECD27l^=9;~=ym;VSf-mW zmL3E1*x+@**3g)xm#O;i2EJN3nD$pk5zjs{vj%ls9nck+I3Q~4==WJ4JxT=P9XuBA z^pzxSCmi;b;dq#uncbtHKrM=_QbhAQb%4_qSS)owL5PL`yk>|v?`dAux`3Jq4DJK@ zvC(v^!T+*wgp$MvX&rbc5w9&woiqVkjP%Sfq@|&K=NUPC5fC)~K)oXX2@X@O&+v}0 z>i@u8?evN(41y`37e<w#b4Vcb`2KwgXoW)~Uf^)J0Zfb8vwUk_BD{tHAYg7;dbl2U z1ZQe<HcL0aK;d|qiu&vG78`yRlfGmSD4wUvOfBksU#@a~akhC8r4<>eCAwLMeVrw4 zNK-oHZJ_bqjlx3XB=32lf(e6{dbHPXkc3}KxROdX+~{hVI_!1(i`55}kmHnFrp=~L zG7VQof}JJ~Uu~DPl+nJZ6I?1Q5ORGOal?|<3H`pJJ51cUgW3Zf88VVTKko{*(P*c$ z;)gH)Wnn~{nwhBq(+<KK0i<g9mxU1;3Umo%tOMrlVZ$sYa=~<kbb!F306``6&to1m zz-0c<V;*8ne`q64O&GIgh2GiA!xDal%sfGBCn6$ZF<}mL{`KDn^U<8fcOa0P_fF+Y zEOdjf=fh#sJT?H*J_%xcI3t8?B@cZMVCQT9I)GO#pd2N*Ii&$o2Itx=;zphI;qBNh z0eAHYG_YV<m4a@R=Wvb?@_z%2v*aq3T5qZ&hY&1(SDJVy!BL+m7TH1-Z68NWwG0Fx z7$DPaGNJ8i*Uv2fT3+suq1E~wEEdhp%|QC~#9x1-wM>XqSTac^5Uf88?jE1n*{rYN zTCR{(Dlze8qaIS#u8!t7>gnO&UFUnDP-QiYby-ue;1VmH>Wf3|_M&nJZc7I7oV&M- z#y>x`e}t`K3Woag>Qi8Vy6Tf~{VCxoK1hE2PhP~o1XO$e|BoAS=J{I_bMv3@unlmb z&Mq&z!B#>N=xY*8i3d^(@nEJOlz`GC2{zALi}3IrgnH5>V*>fidmH{5_~7TGBurm0 zL*ClDnn(~yLS>+#Q41vE_wV0hOcUca;V}U>U=g4(r5n^AOqCUXW4gVuK><@QKuGBa zihY6sHh6x506fB@z078teHKU*c<KUpiSixypmGWNQ+nTdCJc8q1V&aM6FYaewO>l! zf+tmMmp8Qj^=WxhZ#w;GtvuN!rB}rIf<;^@!Zfw1aS1;pi01*JoX}jr4bF#B&8Q4X zjJUWRbz8d0G%{2_1EYra)z<meV3;y|u3q8YycZGcyw`cYN$QhZKERi7`+>!Az-V#B zp5SZ$7a~%4J{shNLD%blS*D^6G~1|rwcpDo+)n$)U6_+b(|iM&DS~zkksG0XX8F)X zk%6deL3o;UQ=k`xGOM8@F9GeO*nhH#+KD_GIO0YQa!Ub2L&I(M&Z;U3ak5rt-5;7j za&-8g#Klne3_mN)IMyooNUN`T#zi}ei~uuO22Qs8`OL}2Ry#Tx4hn*Fp^-8$9T55{ zV#Q`(?v?Vh1UL6^A{sWUzw$|RKF5-Yd>|&#imKO*<rzAh_dDfwlw{Z6C(CG9E#PS; zbI3%<y-WM9l^|UA_3Mn0X>e85z|QhdQc2+p!4HFND<4r2ND~wjT7Fw}>qPr*d!xRX zaPh-p@0DmydtYDTs{%oHzZKrnE<OJUw|iD&8N#6W5**4f_VG*m54gNhH&WHL5%mS8 zM4Ev=5_|Ci6Aod?f|A&?T>ZY}YT(4cST-P+12;hiNgQ~9hUR7(()AbJ@IZ~G)zLz8 zhf@$oOiWEd)FKQL76jrBT3S4)e0@sh;Lw2{Dh2c^Q&Y1cJ`s_lkju*ZFtAFim#^?s zE|SX}i;ay9rbn$$9-p>;`wzP4O*dkVhOrLhIY~`91yKJ~9d?OfVin4|aBEzsY~WlZ zsl2-C0|Z`E!;`g0KgkePEq%z!p*3Z30`PpUHyF&_!(W1a-E{LFY7ZOxy20qfkWVbk zC?VQC9W%WF&#5kn-*ORiJqZK#w#NL;cUc+zxh1V6-srterq-7XX5;RN5qr1Y->5pR zP#*-tSv7IBuU`vQl~!_c7p}Fv6GGUx|Kq7264KOMG&Djw>Cb@MMD#IW(!qoEMTUz6 zY^Gq>zJ!e(3-iee+u(AohF>0TwK5$-v4K$=cuG-aeS15@t9d3`TH&PdZY1IuM@fMi zs)pR07s&KF^Ymg)|0+~`L=A;W`W$HGyn47n69tjw3)szo;hup{rB^EQg*j?<Lqjhh zQ=ujzBtqB$Qlhma!^odnS0QW_NLWRX-w-hXqF$c<6O2;;GlC4^!4pIHK<<b3@CM)( zhTBgpx^xc>$4fZ;DYJoAyp>*tdgXnqnRQ`pO$=}^8uHi<WSob@%dsD^O~jbY<J8qP z;Y9n39e2M1=A2%EQb#6`kLW^I!^<n{=B7eSdADq)(NS8`($mudp)43OnOIDj>P+g& zXZeGdCW`!?1fOpnFRPd5$6!(j5$yk5-)3I8iyC-1P&87kuC3Xymp-aHob#an*E@`= ziLpD}Y}{+SmH5Q5rl}1F#+!|Ga#rfDHNeA!f~5)}T);SrOq&8H^e_G{!Qjg=L{}@! z+=HP!bHEx(*&#4#%V0MR_P)8Wuz)=03Vf)LMqR-Ajyz>4HM62cvH^a$bgM8cYf4F> za;>Hcjya8Vq6wJTerr+VBgqnwB1(QaFF$`ie^6x>PE$|I^*!Kc>grs;Y`6tl$?V)* zU9#lQ>wdv}iBc)Z!&D}n&YvOAbL#I8f;WU&O*+WF@rI$F_aMgrlLL<$Tl`<7opn%_ z>$~q?O46c1K$H$?kS+;H>Fy9orIeCVM7lvrkZ$RaMoK^$0Y&L<6s3f7J?poBd+(XE z=bSTh{#Y|>tr_QieV_Ze@9X|v-|y!F^=%2b5yLE1@85I6uMI*@DA;;6Y8~{Uix=%U z8Upe+JSv1k0j(YJo<ElV08>Cye|MY=xRh8V4j)6xqe-czFfDYdL<{0LpVJIfwS!CK zXROR#{|--*v){q*;m^_ay)afic=V0*{22Q--EvW1NJynM*PY1HoRMmlH1W%SPoWUx zvU**zh_<ycn~|^kenGo|C*j#{V*ljX-@2K3dKDpdj)jjOr)SEj?G(CB??zsyuvZcY zi48OY<JrG$T>}HkvkObF=@n)oJ}7ylIe5BMx@2X!%f?Q8g6bx^NVRLY?HVr~uY<B9 z7tj4V717teZGYEJQRC(B1JaX?-}To)XA9cRu$y6<6q1)$SoT<e!xjXM!#j#0i~!d{ zj03Q$)Axf<vLLQ$nm+3mkeQjL%B#ep6A0$_<)Jan9Xt(m$lukWygVddT*~yht{M`y zsBbW%rJpOfQDc7~&UG!c;YUJ4HXW|c1ecmZeQL%^cZ5ZAv*ZISopU1xqXH{U+yup- zfIk27H7=Si@!Rae;f4<$JI{|`Nxyv+{pL0q{?pp0d1<*+NhAk+^h(qp1FwO37}g(> z>jn&%XKuWNKcqlkg9BtA<T<cuW`Ui%=kw=M85X~;_Ts~D9M_0n^4cstk7INRp8?z8 z=18`Z1HoL4ePyJb<7Fbow&S0b*NNu9(|W(K@J$kDRFDSE{DN%53k5s74=Z}%ZyNeT z-HgNHQhvJ!>ZW3IU*+q|NRFX+K=jsbkb;6-z5N5as*Y-W4RaX$-&p$V6gL&jJTmiE z%otJ?^^)~sSaV%gr_&v(w0`!%JtkSu`8JX<hTI)TJiihlh>zBwd+UEl#~BR?3;PIh z;oSrc(_9>+NcMM7)HI;ww?&ta>WH{e`S5Th)r;f~6LHK9zGznwT4UV^wS@~&(Y9&* zmNn=amQqu&ur+l#OuIRL3m38ZqS13epTv<+|B5wGn(;PAApFLkH($nM5@=nb&u6rB zVE!{*W@aqqY7$Ft5og&z<{mY*q(`9bf_oh!0Zn_mh>)D-zMBT$gN2MaGxfcfA>gBV zgh$<4v{YN7V%ycx5eVI$HA8}CT6TYQv|f&pPMPwWppA*K7i&K}?&+W_F^8hBZQwa4 zrwB1qv{@%)RVhAD>+;Id6Ag=b)oc|s*0se*b}{i?U3$;%S}JjWy3Mod?yZ5Xf=xZ$ z>@#b*Tn%$;3)#}jngZim0qcSOM}x_f$qNn)lYxzY&S+RRh|}$Xku!s!I8dXD7nFAs zPyd#ga<=+z-ue3}9-kO%Z%(SbF8E{c@?<Q%EssBS{D63_{L1&n1Cy(Gnb!IuGzH75 zxC(a<W(AbhDiu^!R#xItQ@ngxwd%iq&}de6SB|JmwD=URm^)e|^d>DuSxcIDxwOyn zp_N>yaF9HARH(9VKqeoeeW9v}Bnd8?cj1xqvo>d%;L5~~lC)u8wc@PJ+UyeZx$om4 z$Vx}IwK*PxZCH)M$8WfCKZ4Tbq?{%cRbXtzW)`#**J(9dF8vSBvM%F0qyC*b8(to+ zES+fsK?C6xJv|{2q+}uV4N86Rm@tFtf$U}|N;LGjjEmiV9X~OS2?=td``4mBvchck z`cyir_(W$89GA=)w=Lor)fhipk|wX5-&x#=jA>{PePn@NI)=v;ML{Q#K@)3u$&W5+ ztkB+BW2{SNZ{yl^GAS(!Vpgp;H<C3Q9k$Wkfo#Cn-Z3Set@kx$+QQr+z(C_Cvkn9$ z1J5*EB)&uB`!=`wU}O+@qd3t*4<F#F4-8i*#Gw|{T3de8=gL7IPL|E+O1%9jCjTqJ z<R8lxl2c(!I3|<}af=P`C{ZMitQtKPs%1tk2)MJrPM3rZgKj}{!IS)_TAbj4;O~mp zSm5;xK{O7fB&&<UX0e$MSekizJmbvj#BDFDD<46gF=NaM#fD3Z_u~f=?YHW>TXICg z1(wK#?G5xN7gK&+zJ5|8(XlBV6R(R482AP91mf%1GdPppbA%H_JgIO-Tk05o`0642 zwX+k)Q#81NP4)O6C06fZ_s^{PS3bQKqgI|}bXhcI<@kH2@xQwY6%<4pT(D5V19+MF z`7^v6tcpMWtf~8;+sutyPG3%Q14`+8bXa`x5-456Nomb<;;qly72Gva(oQV-#KFE) zr}e`d=zw_oN2*jjGyV9g{CT5KS|QUT1sQb|+Sh8d+@Zf!_LFK%4CWU0VeZR)U*p2V zM~Mt$W34uAEG#N|Q)3EvD$(_B9n;&@9PRl|BV}!;*72W$EcY(a#^<2l0P$uO<c&#B zU&x~%2l{sLQtMDY?gQ?-0zJKLuXD!f-!h2yC(ZohYy6ygoRS*zKN}l~r4!@enhe!X z@1HXeGUj)7-fpj~u3}66D`TA|>Z!5m{`A{nHOIZ_{dVk|<#42;z+G0v4=Upaz*s_; z#`GF!K)W{mkz~y_AW#~DUKs*mSjZTUOlvd1$pe`U5Gu<&-~NXT>M+>^r4rnem>6XJ zZV_Z%tCq}U_Rq_!v@Y>Ec5^krWnje%e6hS(s5w^UC)IeKp~?oq#d62g5&}l1duq`} z^dHCzXNqopYK(B^^6IT2dRF*-v_@~2lUej_x5Pxf`JM1PPimhIP741|)F@Vx?pNJ{ zdV*&DXnC*l1PufaXjjnS=43xXFmU<L?yL>mj*!L}lnr#IJQ!exOoGGo0IWQ`;5nkA zqCyHn5V=5~L0~-DGQSkBg6~nN?3de6<bUHyZO!h&y?}_CS|y*r8La){%5S*P{NdgR zl4lQGwvcR>-^Jg%5Vh@fY9M?84NEA+EFo%Tu!z)^Rh6a?gaoElJ7Rxa8_CAk95A8- zSK_FOk7$b4xQMN+J%|@0)2-2Ot27D;mERIHd*^3p!oJ937et}X6Tr)<6VB&gG5Fa- zf1O~)xpE4AAu8B)a6L)Uduz^%k_zAM)*!4<IL17W_TGIt0ssa`T5$a6uoqA1v`-9@ zSl+YjGl<8c6=pFQ6A`Q<e)UaIR5T49<j<AV$2?FOLfR9_c)&#mF)#vO==Fr6x~JHG zm>!ETt^ULGDEDDo9)#Ti=%j*h{U{<eIoWnar${RVq|mw5)nJhAz<|n8y~?hLd?omi zs={|62+VkVpZ3?`p*38nptno*%EL3y%C8S#a-X}xqGQn`BcCk27X5v)d6K|FmL|fq z>rSy#se)2}G<A*r&hqM|W@i7(lx+!m)0e3{<vP(?!r`X8H)f_XV~UlEht+p<xVM)H za1MQOvw!&(qq<W{w}t?*!91%T?)98__s}cKd9i2zm#tR@7^faX=pN9%1VFYy(5%4D z0-KyPyev?1qRbGg)id+|ooeObQOolfoB;sU0D<N&`zot$*#MXg)-%2Y8<y%+!42kv z+qyKAz6gjCIJrw@8gHl}-2dOfR<kYX*55&f;&zzg`nS#si?Zge;ViO73ksOQ|90GA z4<6I?Z*`oomgrbmaQ)#k!Eb=HObbj~#o;4H?I!a6ky+t6f_ARa>iZngUqMZ(bpdzs zKIlxNAKdN1l0v~{=C2x72$xRqL1iS~5QN|mpkzkDl}jf6_#rs6kZwFAV)}p1bU*_y za5Q4!yi^<CX*SV*nI*ZBfn3&sj?UvZ`*YFxPec|w-!T$c?QzMm=;$2D>S%~1pFOK5 z<Ye=g`*T4gbruA*Zzp3H=`m?Kxr&<w-~Ok4*wb~ob+FEbPL7TqvG~LF5W!i&%CI+n zw!a_?+mNDAk_)?j!^^1Fzi*VtIOjJvmHsc>E!*$DP$m1r<pC^37JGB;;D@LLe<w7a zrJ;`loI`pJ*NiSc4$~H8xK~xV2MgGa0tUFjW*^vb0bpE3I254CA+{Wlhmgc~sStPr zm=Mz!paHZBHDp&^C;vAf*t^4-6DtrMAhHFjf1Hu=?#c?nIg^|I3u-x(xHgd5LXvif zf%p?sMmgA8ML$98%MSszAp|kN%p3z{nU6ap`4}F&(9<n)iNC*y5G|soCm1foVC%Se zWIPGLF>pL&GxP&s8U%@VUWaG;t<ah8fvdp*e_bX}lBCw<xO24&X#b!&eI+)zMx;r2 z#QFJAtu_8k{jIRe>t7^Ni?q2v6$V!%kuhI%j;;_hb$3S_w1|h;@eYL0vkH<oqIa6( zTG1<XY(C?RoE0fcI<0fD?HZYv|Cx@q1w<tHo#6M)@;N@B5_wAC?d`q6{M}(x51?RW z6UG$3=4t!%6F=sZ;NDa{i+(Kk{N6uYFv4_%Y5<O56xi30iydqTh_eAG*J#sSwzjsP z;8qy=uL$?}j13Td_#XdA19@poSKdMo_@Dv;0u5fZ@M8T0h|#ckDX9(OV}Mo~X_(D> zljeeq&q;OvWqL#*u1EL?#71Z^RElrx?1KW2%Eyk5+uuAPeA@wr5qpP?@j~cQXs#ZT zaTsMndm52ym;Z)EMlymg1(r)WaGu~3u-hOahl7V_0nS#p0llqXSZ-TW>O_XR5H&&^ z<X~~HSs+A%@B9&(W$?K3K~YB`4jed{VN>CA@ppK>Gn$JJsI!OQVD#TQ|9pnUz=_xi zLA!vwE;sYaFdfLVigy)R9WTzyz4({rc34<sWZU6l=G@@oa%vIunlI=JcdFlS%6LIZ zS6A?IOjD<+C_hl=o>L(zE46zWJmI`UEyP0HCM_+y|LhqHJD4VdVfO}*syS?4TQd!s ztHGP#9<v=aifjlC56cPnq%g9tx3IPT1d1PDsM^Eg@R}O_PoF+~>oNW}xGWxtZtL)r z@u=r1Q*^lKD~*9104*XeumwZU3FMx6@cjTG&SX<qcXEp;3sg8&5NoTuAqgG?q_8ip zn;o;WrmII`7N~znX3F#Qq1u{cz+@46I|R+VhalM2RQowZ@Sbblt8H55SLOOiiQKx% zN7=<NbVn5*Lh3-zUQ|d(Ki>u6VuOtI88FLmx1R@guMY%m8_vag!<(DMK<E|q{;m8o zH>V!qG5(58V^5MoYY=}Mb6s$i^F@WAdLL<E+F8uLlBGwAwBc{S230*g_}Sg_QDm36 z@c^6axiEprcY>>tuA6fT8>d%To>G|b12A7c_$C8~eqDbhmk0UJXdB$2*GiEeC9Nf? zdxYPR_w9(|TZW-eUTDs=!J0wH5|p}*a`66)4-*sJBZ}3C;}YPyBzdQ>CYH#p;Z~Ka zX7Pkw3Sf1CzEzo4HQ1f91A%tf8axh`LI$tyf*-@o+PVwYoL;R11;SZ@rHSBfz-twZ ztV+wCE2Tov{t(_xh}(*#|A&h%+N)nN5CSW4)wWDAw=Ygan2QS`MFOH3ZlibIOz0Fe zY89CL2SLuwE1cM%^1BIOtT+Zk!z9@<fRPId3c{}B3ydxh<Ue+GG0fs+8r<m_CVl#S zV1^dRM7c?sv<;+hYxuQFiyqs$eD<SoVdVMZtg528pkN^P6aS8XVq)be{u3jK9ch&I z#3br<gA1NJZiu<pw0fbIUWy*+r-xQ%omDSw?%#iNBUQ7j4DX+7B3XCv8#skv+qDMw z{B*ZYLt`TXxK$y20;>0)$KRR61>WM7HLx+o>VE+lmXL}n94uboONHGXkwIV=&2aoI z0_YVmFab$NA<-2A24`U4rR=hXH`WKd;XdH<Mz9{x)^mZwQ?yHkbtoOFo55~)K>b2z z3bE%+DzLzsf=0v`P-{Wc<qx4s&e(^xeTs%yXjW>)R%n@3a+~t^G(e}`1K(7YQGR$( zaIn4a4tyB|0t7|2b%TjB#xA&XLE>0uJEo(HiHU(Q?UJ5Oe!)LWGOOOt(IHdrvR;LB z#^@3yL*#TX&L|-Tng=DZjGe{pyr)m~R#$F#S3O+Ee^|n593Ee)8(go&rWG>eNlKhU zMuFL|>NIXlZxz(l+lIr$mM`6k7&^foxeS(?L4jI3h+-m4RO_lF=ZnGTs^`yQWoPeX zDSl?v`qP_!JEybR7|3w{C)*z!{r?GvL#URsK0hymmv;%yW&i>QL4l{%_|y@xt0K;L z<fMX#z!E4>2)_W@ZFZHWt}hN|E>34JaG*^MDivb)0>^4FDQAH(B@IoPtM~btXW4GX z4G2V3GMm!c^t1jUz{jV9q8nsEGKJV95RB2LtwHAVM%rA!dbkJ8IixWSFV_)dpoYuM z@4<TscN<{U#=~z7X`>bPKi|M-3slWj_%iH<wc+5DLhdzI4Q0@VL%iUKj0rjeQl!Ia z(+9-S!nB5HUrNM|D(D;t<r{b`G8|^ZU^O~}WXx_DINC}yjkO(P{Hg3V0uRn5V?d*4 z!QGin;w3UcS51%o1FXnfK6r2&oUZnZ=FHLO)vH%?@&*=4nwTNx=*qy&;)uN(Cuz$~ zKM#)s`rn{OHxdNS?6c2PsE4ShOp=tanLRRl^D+Dk0p}q&^iua+FtH){CZwJQ7ps|* zxoUB3>Do5rvg65BY*W(yU(t5*krn#?AO1gf-Job*acC+wS5#JJf%!0;TI6>ShZ)dS z(-&dYS%RONv+0==5>Jm~RJjd1v{D}OFd#RE3J(tlb6*bpDL}s%vR!dO{M-P60m8-J z*|96EGQ<qS=m~pG5YP`0r5|COLiIZUI8}s7SiM9Jaekzq2!B+oL@x%KyvYyyl71*7 zD-Aj@T%HG3&~iXf-m}nJul>aSpG@r}cJ4F^1j_(teIJkSzh-A(_8Q9_#PVsrDac7G zI7doC*ctz4vYz;mhxf;K_txKk>`?R}nEM#Ke!EN`gJLl;%J+oj5758b^D{JbdnD4? zjaoS-Ez$IkBf{8z>$QC;ZQtm-N#K5hX7m0_wQ0&k0^>0FiqXNvss-VYp8ARoI0z7F z=q2&Ai#e!MaE6c<{Mwa)19?em00U~LMX6sHAPtXKzYi%*E^U!zOF*0&K*t4^iaa#u zfruTl0&6!#PYqHN?5klA4!`hgMh_5=&T+%1l}s0AFgbqVwwaN7<JV@AIEMn^yyB%N z*>VkrB@b29&e!Rg`ti)U$C`vN^GII$x^3`nIPmacoiN<ykj_2}nAxqPXZAz=Uim%l zUZNz_?=qD<jkeTyw>)zWa)iJ*C)a7Bzr}xI1?3l%I7aygx>WJut&qj8Z}8SF%Pg_- zY{u-$koxx5KIPrIGz7VEuU{_+4|my>(=ME=nH)9U%w{mUhoe&R>1RAo0nuWJ)>nW) z6Mz^?lWIcqX{OgG`%wa>7Hb0I(yeUtBSN7%6Cr54|8v!>wM9<`lQk3`9^fGU#rOh5 zrd*<~ll;ztCQLjt7ar#CA@LnUc%g?wEl(Z84;EN^$%(SDv4%fl&=b^CGD!?)$7OLo zQGmFD`dCYz+@~UtR5=;gOkNt4GHNS!!I@qu+;CrM9>-(=e&HZ|LF^5<0zn(zguGEd z9t0*eVf9KAqA|)|wxAd#uEuBi!R!zDW~5$WMw2D@eF}d|0yiHnzJa+*q<Cf_^tZ*) z6=D_B4j)Lx(sKlxu6!9Y)FbboZtl3uer+lIpD!<q<}jgHmS<F3BA)w+r1hM2_wr@y zetkK<eZR{$RvbsdQ>?n*^jdUuVixHJv)SUA9UcG7VO4C9-0fuQiOW)*S0A85rIn^D z<s3c$5!UfGnZ1q$5n`(Q_XkWL?}!Ym-z&<wPJ-qAySHa&r*a`Dnu_S2mI2mZQ9~3p zwNxPm_@P}~l(~7Ivdi4p_J}pQ;;s=ZmrSyjv@~v2CTGO3=S*9u!TM0{VLUh6xkXKb zY|I^TA(J2ZopJFgr<C5{?*#cQa1;iXxDet<=FucEBuo@9y9A-qMeH-e>OHP2ly+B2 zW3l%o^q($#PVM)$w-@5hdE@!Vc<+~%vye{WVta*U7sm@)wt44yxEy}66XN|gU1Vi& zL0qCz%D?ZoBImN*yc!Z@xTHtxk?_x1`mWxOO>4L;$4EMczIV!&)wO;hyZXnINR^bO z-Ajs8nKX;Ll>C88+%9pa!Cnq3OwkJ3`G`KbQd9Vj<@ZY0w*v9^<dozVmUh`fmtHHr z?z0V*R4anOa^^4K%$l346(^d=e!AQfDb>BfoQ!`0z3bzF<Xa80$<KZ<Z_Q@3^5gl_ zOEtJW5z=w8KICJ5ef0XZMbf=!RVyEQP`tHzNm~zemX)gE@jGKM<@f3ILknA0h&+Jy z1_;2+&#l6_?Pv7-T$TtWT7OyIy{oUUaX&I*Ss~@=TD(eXHrZ%FjM3HoPekMrEvVR) zs|kub_y34a1T-qRqs+0!nLw(H6{n{s(0(QOMr2LDXy%>U#eHR!PBrWMMe&Nt8sTqq z3ICP6j~6G#r?ukGPmo+03g$3gS{&G!-K(;R(a@|Y(ZcOF`b9Vq(%|W(RL`dSD;6hf zsVvQ#qcPwnEceHvDjtd!c3IsgtB=<8ti=3CKu-XTa?$~)$63%!Z94p7n5I#bk#AgQ z)*k3E$qhPqk+JrM(3dgh7oHIqqQI&H_ad;tS9Z-Me^P<lf5Oujnke8etAe%`3bL6m z8xQWjxtu(k9iD1q64#1}j{G`~ojo5Xir3$pqGJtzrOe7+US!{yt*&qOY4jtB4_aKD z|Ir*+ns7CK=)Ds69kgk5=E8|Cj$qnmpU{^t@ko~0f5zy=Uj?@n<d$inumItu96WAd zq(_We=H})NCOz#PXuOuZDxzj4B$B{qgON2)ILz$tBUVJ_!cz(l7*oRZlj3pezF1%V z_T;^_S98nq$Rd|Dqs__d>mem|;jAQ7v|}xps8r1(m#S1kv6Q2~8F}uruF?Lb4_ndr z<EHav1^#N<TRgG(lVv20ROld=Yinx0hntr&fr<%N_7g;s8yUF{s1~@|27_Y<@ezU+ z1GUStr`R$D^m<^`u6VtL@FVzxz&JIqF%7Og=$y(}<b6nl-!s=-{9@P2CyK}E$`#Jw zFcY;(<n3^a8TtFINr;?cT_v8Z;_037Vr+?X=DGk^mq5FT(IF-=zris+`-cG}ZQ&gB zV$A-1tLChXGk0S%mftaB$8pjt+-Bh5m^JhnaD6K;n-%$>v%QV+fh)eqd<XOP?pP;% zF=0`^V^O$RgrDNtPqlFcyTl=VB$0`zT#y1c1EW;<(5)~0Rrru0xq(p4!zo_C3>he# z5sOt$PR<guHyGyBiYEa7sAca1?P-8M-_af7yhhBx0I&;MW;`B7|9G=M@9EnIk}K;B zj?W%_H)vFzT^!4g8MiAb)@lb4BJg{;lW{}WeLL1PLG|*Q1F7w=F3y`d^n$-L84|WH z=3!eIrx2=~vi3}lp$~Zup?#TdPGPLMhcxe>L3DqyNINK&`wSASEf?qB$Y3Ne$-G^W zW02eDn`a5LVP`z@;+j>ycqLrsj>J)r-y13-PlLyRIz$TF@Edm{FV0V+psoSpXfxF$ z_uacl#2o=0!krHN8sMj(;|_i7EAVJP#}S}Qng65o1|75uK8l_}FMC;O8EkaY^;KAm zDSNoT)v9xxm{m71iqnYs$jh^{m${koPa)I6i&q(9N-ho#75xX5KY8NqN4nsE0{B2D z1g(**CAjIGX6oA^uUr`2gUMv5X-FgCG0=K(*1z}Q)#8`o%MdK^J=>S>Tgl$ehRI0K z*+xn)uwH?;>JFGZPCGoI4yJ;U5joYYJ0Ml8Ky&}&4{<+!65bYhiaIE`mh8nqT>=nI zZt(cH+w#Lv`T|Y))`O+Cf#iep=09fBJjeC-NhG6<7jE5x81;Nfk~{$Kx+8!L-FLq> zK}|mB;OLhv>&nQ9W(r&i-Hd8N$B&LgKgZFB^ha^Vl_Y*o>Aoa4en(e#O!sabc}nW4 zAsRJE>5$mM0=P5^;@f~C{aKS(Q^;TUe)dwP+;_@OhMt(%lt6F>&`?m?!lN-}YhTjM zoqx&*G5WIR7DUka#2KFrUM{fTfszJx6%3JQJ4m$r*yDC`_^!Gfm@E&P^D=)yo?dGG zmLQx6?E}gj1-jBgURoUI4i@OtA{`GTJ|(shWEdidoT_U25EqqH(!mJB+Lx{8f$~v! zHqD}=r8lTVaRLHZY4oc*SvDTiP>C=Sw~1zH6mnIT_l~9I?>|uB=kL4*3ClixAxjHe zZ5hIiZm=8c=5o4=E2t>#@$hhU%xLW#ScY?>MifTkxhHq5n*ak<s@DQHdsnGoEF)b4 z3#SF@&jSN{zkfdjcky_sVG>kC5Rgwf#qN_aZ<0q32z-KuuXq)%Bwe9<N6v$j8`rx| z{tSw&(`?o{*8J2a-pp`3+<3G4F*lcp;W|B8S!tFu_rr6tWSy&O^nt|qZ~*%oonU2_ z$lm&^N^+~ayV8P;D9&W<7{slVMX#DoX#3Su(983PKk!l2&>#escZxk=&t4Z77e55A z6PugRa~~f;k3D1_Og8k)k8ADlFoPnHatB*B74J&oi!DW3-S-`X=`NO$oUNF-C$jmf zUnj+Ia8Q{(VWdINz}dIN9#_t#*Zg7c%V!M5=8wj@sqc9B=udt-0(EIJj6FA~GhU|D zz!Um{Zzz*D)U#$r{)a2!!_S^|MNWM7li{YTXMM}ONewRBc(6Im0Wh?Sl4%;Ga4<0< zs^0Dou^#&<Dxp|w@A&rHH_6E4Jv{0Mw`KJ|p-Vl45i@FDy>HNr^qj_|0YM8n3Du0m z%L%q#Zxhj(6$s63nOLnj2=uO%l(dbg*G6)f5Ik<y_OM@#4Z>gk`%}hVxyUQp<0sYC zDC+EPf3z+RC0nVA^Fp;-x8@(s7ubZjATk|n4%Tp=I)fA!Vp#!6W`9E06wS=qqN|rv zuJYkL<%&vj@JpE?8Zmh5$Mvp;VQ6Y<0s*rP(rl05svJ2PNy*RJKLvp&@cVG0;38}T z)+OOpr>XJ)*dfS_%%lpX7@B3$D=jT79?jH)4=Sy_ugGIj6Z7kTjS@02*wD{1np?xU ze%+<p-i?oVF<8=fs!`VoO}8RzChZ=#foP*|diwJdvqul*E*5*Z`R>Trs{~(hp?%vd zCL>4s<3oehQtz)HJ^io#1}?7$)w^xo-}L+$YMdCW`d=5J*)k=C*!sOSbe5E*tCpgD z-c8lXofMGsUCQ`)F<fNLw$nY!z#$Y_^i-Q-#)b=-EeEpic-?DssYv4PSr1i+<Yl_; z?Y%2G3!vK6K~HEMPzX|dmLEx7vw0B&K$PC;)}W#@DX*e^s7O1MxyS0APxg<Qg|6() zhY%IG#yGR|im)c&J;Q#cQWsT8YHf<Ue08ZaV*Mf&k>xxnf5!*EyP7Ow!vUoNWZ1N4 z7jQAX{yF`n<o-PVxpERmcY{VezJE{UO>`N~L$ZtfdcX9rtZ4UCf%P5=Z$>hr2+G`> zyZ9wxw#hv7J)PG~?gvF+>2s@5M8~sE7J5^}O1Ok6=2}4OOZ53keAIJ^l~ncFC+lYn z5O#Q*TM{OzMhS59z)>kVtgP~l6_VSot9^=d5*NMGEm1e~!_vOUp!{<Rm^;QQ-SGL( zAKbE3s*huOrx4fCDu2bpqZgOUj(?`!_VvA}FE6*J6{qGu#&f=NPURGd*{>|VuvR$l z#fJ*c;w);hCe~;piOAUn9SvAYnbl8Lluw*-HBlL2ajqPw?x>{i&Dd9Z-;0UTS|-(< z%16@X=2;k4F@8~8jqK?BVZtGd^W^Bs{QC>^fzQijZg}d})(pDN-uQS?z4iB<#2REh zO42YF%yu$uLhx>adkAkjx3s>&GS<Q+<@U9B%1{|Jf~!|KL#@G{;JTmHl)-~lq&@bY zJz1|wZ&0C7_i8-r_2~w`dt-bb6@UDYWr|*)8M~(>IQ$eNBf>7IZ#cRD+%8x0qH&|} zR~0&l;Mz=HKv2$GHCA_DHn67fEJ#ir6S)iZ5{IBqft&=yo6ivVb)Ddlr)Ad2%AW7O ze0=ocYBewGFJ)<TX52E<=M0S!3<@G>GPf9RNoigTF)W63$mutlf6Qu>zXDR2aw_9Z z?6lfkhZL#~JUtKkJbF78X}B%JMFO^=tdHF?PH~AjiHlVK{#dYcBMR0>Fl$RgKMiT~ z)E5Q&y9A)_*PUw@pqBgAbrY3K^yzv|8|vHI0uJ=dGL3$BjcbW8Eslf`<#hH~&M(O% zLph<!oO}2+flc;V6x9>g7vj&C7UP)OKPmYVy+qgZV0QMyd!Kcx+deZz1%?cj&Dx`Q z50&W~V0zJ{$D4zG5u&OgLLpQmNCO784Ft6ZGfTTbO%GeBMGtU_cfeEXC!9c6jU6rZ z%kYUcNq+cEz;pH;hLyv*m(36jiq(BhtvXkjxPNw{IQosA+n-MX!tQO|a%D0^i}Y;% zQL1kNFJr=a(4+{xSSzCsFp%$2V8W+jLtX7$4coUvy^@38;VP`>g!1T83fcFGB_TwE zJQI-Q5XJ_m+prV80tz5vB1CE$1S)=hyn1uo&RzXA?X{e^FL#q}++F%LN*Jh!l`8P5 zZ?-pPeeqje|LLUZgPTeU_lG+#8cvRgOU+2w`a)<63)TF)53$U@e{fs=v-LeiPf#c( z<xOw4X8y0+L&Y)<F6<$N>!Hmr8a}dVo^pzSeN&RbZ%LL(c2kooAX7$d$=VD-*8?*V z@lV4jMk`1yQ%U%yfzVkAehIJ)tbK313#gv!HOZK=A47-^S{(GBxQxOQE>OS6ftG!Z z=E}u6+f5rS)W=AwTMPoEFiEdZ;E`IjbfK17UO1IVgD0=gne)hx2iRz6F8Wmdix)*l zIgW--Wrp(b)@lu`k&!JWGwna9HWATVoBS&R(3Db|mJdj>F<{5s(&8RP6%RQ%`85i% z;|@W|8iNQheL!;srYmAXZw`g{2BmXN%>}3gpZFt%Dkz`A-|Rt4>H&Z3!8t9u)GZAM zca(~<^4ixLHmF<~#KqqtDH3o*+@_<0KoR0|LoDTxR}&_+_HTr{MH)eS`+;+}0Fjqm zX(Ew0PnCVr7Nyg$WF)>;eU&Q{t>J~%2S!qX8vBpm8=w638ktqX{+yDV)Gg#o<x4(o zpo5B{l8VjI{b8XU|Aqo4yP7Fi3RAEVu?>XdwRUNlAU`%JhZ9Y|4-OVmAGaW(=%A-p z@rm$@zUeaEVhDiE#Ujo^UCAy2=3W4-fC-o+z$6ALc^k~C#YcL;@XSVyCR_ddU<B@~ z_wV07R8}WNLyv#>Bl%jXG8RLC#T`s)O3FScY7qJr^a&?0m%o7F2C@Vo@PCB2WFLlx zLY1#)Mc_|}a{%U)tv*cV(2h^XFz56ZtK#6(&u@m_bzU5<#fT^VI%{A=%Tm1M%e-<w zjI`Y5s;C#eeNI=&6KnPfk5hsB390607Kj?A-rhbnQJ`J|uSk3qyS$7M=BEuSFdCVP zzIzSe=5B*Rv1Idsk&%kwU2D5Xp)mp1K}uMNT`1?7D1GfXQy&9QP7@%$F#2~09JVk$ z6w)eohhmUE?1b*7p?=r`1!iDJR<8=eGDo;2FplpfAmq8BV+Z8Zz=(*;h+h~ITF~ln zg@_T1m5KseI&f1F9*69mluW+p3M?b;XEnW%F#BL@7J!hJK{@pLtLTn@cGJ^E6jYQu zYvnF)*-fz0+!A?~_@}VYMbU+OafOj7fH_UnM(UWaRLu&5EFA2M3$cqRW=kEpq0yyC z=#PvPYBC}GZ)o)#&%L-R_@C;4U0J^<qXHo^9t!Lh00X#$KxBxpm!hM$7Er*5L3l;r z{>}oBD3Xza)j{(29v)_wa*Q4d3Lzl?`2v<;9yDT@HjAqe3*(710q~3D<l!0yeV(jj z%SVsy!!5I-yd3d^gWtfEIUq1Fo8I65X)3Q+<--W03z!)(J)8%&6RM}WEuvOiUZhR7 zM5u|=-*?0(ZMp?N(#g-2)`hON*n+WjH(>Po@{zO0dzBkBn3&g|F5$TI)X!jkg`LmH zC~a^bOj`=~RcXaJXzYpXV+mvL-!2^9aWpqqw`$FXd&)8xZQu#S0c{xaDkg$ZjR`q` z5d>fnnq?Y7PpN-&#bzXOaoN%S#r{1hWCMMx|ImS9UL^9sAiCI9%<T0TAJZ!bc2ZE~ z!9}0vy#E1)+``6w@_RdM3_ozMIM?0wzQT3)6wDz1EY#QOgx8e*v6%gdpL%wi^SAZ2 zEB%075p~taYn$=`ItDivPnP1)+b*VpprABdD(cI@S4bMsaVl>JW}^#kR|%vYpL=&e z(ro?MRhcIR2g|=?$<x!*`RvHKd8QR=eYne4_r|wf6+c2HRnJYq-)zOh(a4Ajp@f2r z56t^WEq7LDQs<Wv1U9E%x81hKv~5J!VNu&omEQut6sipj573W3fPvqA%lm$>Z3Ik( zqo_p~rPDCI^6wJ^v^&G%;X^rikO;*9<b?ji8E{^KLtGLZFrm_XQnu2uqayxihe^KH z3X4k$^=`NW1J&-V3W24D%ZuSVvvZekpbFNuBlMFko=!G==veNrZ1VzXx@}nPw{(UW z{wr(a@<N@6)jFsBxg%%jrcHzyF{JAXN#!XfutFjS8s136@9CBv!Dx$mAp?IQOCdZO zqKs<~q!rOux>r(!5R%5FOGtM0vmV^i^r4T2AOxX?9f)g!S5FvB&!62PLEfbh!|V^W ze=Z0M$9--^FrITPUR(`bN~+$Q?5!9E9o?f@li5+!`j^%K4D0M1NPS)-yv_DX@yOUk znV@b|T+*&-U#_ALw>M>Nx!*<dEzRD3!0OQQm(Hs<I|?<`-l^OG?zSwPB+_W(0gAZ` zU^as}7P0is>-!8U*aV;x6n!J}oi5FCL&JEJyqs2%Rz&X;(2c<l2)hWIb?8OhUZCUB zp|G@aka5$fPDHO~xihl>IRYCHbPVht!{q`xu%_9{E0=V2Cyh+mt`Xmr%|g9`kS|QV zf<<`RVGl5DGO!eOMM<3N5oh5j@+zX=|Ng<nlwXX91SlSTJ+|5md^yi|hHHvv$}#7@ zB;LvC>@wFZrBtPOffW{}Q@YeVs`&nmy04Xq2^8~^sPMJnSD7SiIQTe{8pUDx^n*&T zVoOtEKwjk*;O!=F_^zDGlTglAH_rbzw_UK*T~%4xOQjV_d_@)m%sVFLI+XR*4^Gh^ z5k3BRKKuLU+z!SoKLMj1oY>z2@F<ZH4&Yb$rwg?Mi;)8vUJI8-etTR@P7&FIS(qFi z1Snr_-ASlilECgr7DOI6I+)CRATDgM!;)tM(>YIfm&NOn4~1mWv|4x-?D!`qOVHXJ z_sss*ABu`D{hcUw39WpqR`drG)88^%j@(c`=4c5wCW&)XB`+x=e}8>_<q|8#mh$#5 zCgoem<Y6FKLGiH&ABfCLfjot=>yNKrQ(&t^wssf=(*Z@vRHGxOeNtIhw@Kv9&wAHR zK!!o?7x3K|S61?BBFMSXVNBK+*4a51n3)4tT<`}5{ba<!#L8?VZ38^>&-bGrXfdFG zaJV04IvNCk&!8Z<j9$J@z{X4&Nt5vBD$S$26B9%_I(zH+2Z=HEleAoFzqQy7FLo|| zULAS1W9DEjJF%dA-V4^jvzD%|fQ*|>7rqr?Pxffp^>5$2DRCt>fLryBq08f4la4Dl z^GdRE@G50Loohqc-XC|PD#rrM53o{tAqFc0PE=$P1q7oKbkcr6%25SCZf_uO8Ff>l z8#qN$beBe~CCN~PnnVf;D($_)ui=nE5Wog3iMz^wyZ|wx*WkhcPdf!OhAhT-<q1fR zdPVsHVIY$zYM?V5Kyn%#$rQtGe4N6mKnVYC4u?KxlfZwHf+^G$jQ?xcuD+2f1I$10 zIFRAEaQR)*lKUx<f_E<Uai$X4RI}lE>eT5<s*K48ne-Ss+oNfl6QaRs{VQ#o-wmDc zbq9c;;hm+;Dx|)0=2_lSlME9pH1brbeZIWse44C?2a{`FzI>?$H!~{BMJR9@D4n%` zet7l#GC1u+r8;6i#T_xh1*aa||DEHV8W+AeiX07pZwzr_)uK|8lM%x%7vw-b>*?u{ zLm&u1;R4@~kpb}EYG62pT~=3(8x!5}Hk~zGcWHUVs@#i>el<LMTwXIFo;3aUbZtrD zHm7mN@Hp|GclW}y2ieC9Zn61$xzq{RVKOqV)2#WU_NbXpUuKvzNP|y)CF`4XBzQYC zN|pJY#F~TN^<BF$Hfk&`zwUd$cg_;B9|E>Nn4YV~$`Vf>4YAl^x_i5@$JwcaGx1AK z7Z)yO*I*F-de7(7%?FWu4+mfRT?BdloLh_<>)HJgG?s6T4~1Ff;jT-{P?bT3_?y)| zh9W)S`hNM)(eca0o0qpCAWY*62Q5nctepMI;U<yiI-$9Iq*}A7M04+No_C!erjw_w zzg^T;Bk=?)pBx>SUZs}89D9Lz^Z76JG9G`dz}s}${klWcl3;C2kifuzVdJN13)+s~ z4R%lm@O#i?2nPW~!(A?%`Kf-@aA90iR9M_yv1o4k%{BFD5+m4QQ9C!qOURN<80i&8 z=>y`q{e%o!N%%)XMpB-7RD7x+<|0(2^<ZJ)!79%<(%~3pQbd1UQSoDAilxMmY4=;r zi-CwIJRkfR6c#$pj#1;}T&RAdODSY5ig+BoD|f|)E3HrV|DIM(R9k$jMTv^~rfG>i zWKMFU=@wlwSmOWJoma8>RCnHd;9kdKhx$9|JB1++LMH3(qf1q2nvs>qqRoKeS$Pca z*?J$I@AW&s)K``FHVDa?ckRE#Z@yo<p6k+?DfM+RiY9ln@cD`4S^$QONxlEOWD0lr z2v(C@RUiBC20q^sr}@3D^j^a_+)4{2<va9>pe#avx{_W-W)D=|G+tAB)Y41ru2F`W z)^%7DgbCz9?*eW?^$H8Qg6oT4E-D~X5IQ7+aF2i)1CVRRhJs-td^y)C84f1se_)Zw z(nc#NzZ7}4WSQGu_O{;n-k=dbkK%&^wW&(kr#1q%=p^Z}Q~Ns)P~(&WB~}aqS|vDl zA4{u6n>d-4QB^y#>Hci+H5QO*IZQ+YR^t4ZJe8N!;zmUzZCf)*3M>kg%U9ce47AkC zu<q+6_mU9A|KSi2B9&Jho=uaK{O$KGNcNo%wN~+jemA{B-(jA8i)8fUGNXWaVrI=t z2g4%O%xb@eY8{u0_$bt8TK}H3*Gv>6Xku4+`HgTY{U+%aoGv&0ndaBLfLewm_d&%% zNLfKA8jVoa&3C`TM{(&~zugV82^r$+>G)e5ky~L@%c>n~*b^diO-e!L_?yo87yMLo zL(~g5JuJ5fcsE{+qL-Q96Kd9pn)$#G7JW;BCfbnrrXcOjKYNjL2l6f}k6%2H=&H|| z&~o^y^<%S;AuTYL0G_Mvk+a=2Aruc5J3p3>x4%uT=}&Vy!Azcts;aSZv(GcZcMm-2 zdrSQ)1Zo%+Iu-5AS(9Vmq~J(G{le|cO-@0q@JuU!_Q5};EETfX4h~-VcpJ}>uys37 z_bNmA&WZbUPmw&slXmnsd<uiIpL!#yD`P5Esb5c5_{UG_ZEb#;FrAD_Q~Tg()Nq?@ ziI;mMjNH0kdZOqWAv!cvqi7NZ1#COAiI{LieCo$p{86g5`m)LR`^60Wf3kLLO^VsI zUy9<g=u9gLtM99K(s!F>@*vKM^DSX0uue#oXot;gZ1Rt9aN>m;qbc9NKL@y8AjQJS z9)L_4JBQJFOdRh@cs_nq@C=`}m)jv(y|0zP&M#rEx@9_|<pfV#!C_>1BgG`bbK8Ji zpkzumnjS{yB}hbnZxi$Coo<U#O`8K^-MH83wndWCY=IUlxSd#Z%c!88kg|VA%J67o zN*4uAHrJUy;P~Nj=3cvPN_~nauO#1L`SG}XJviJ}KyKCo7t??1_Z`;$4CxpafAGyN zAdD|?r8k}KG@`(K%nNTr$H$Lo5P-CSz8Et1w4n1^yZ}8e#3U@mCNKI2^+<1CZG4J$ zf86^BV?S1Axy+blvfkM_?2U*PYk772&9yc(u)?z@7=2y^Ije>cTkoBt;o$im#(e&R zI4YtS&Mc1CZ67@<I5ao6HCHGyXMHzx&og`QMtlY7;RVmwbTh9z+xYU{_D7%UO}gK+ z--ePLTgU74VDnI?UwH)qc`h?R{lOGbAqE<J$eUJnYn$8JDnL~a*El-8<2INJ)0Zyd zLdw+}^yk-0gFB6E&CSbS-6^K)9+VZ%)NDvCynox#)`>Y(VR=aR$Vs7pZ*rqtV<0Et z90P`W>c`YNyLWw^AOX1l)%d5k&_7IWMHap@N-XwEKle(^v(o5pA~@`u$j%K*lk(;H zMQ^7-PmiZ~6{<|Q&&I-#Wpx-B3`7i^Hkh?42+3>!eIow9G~md9p)3jJo9LA2sS{h^ zDAK`fp_^=<@V(%38@#_J^XeP7rQUrJ!y0mT%MUa2x5#dMc-D~6Ncp=a-M-B=G^pJQ zVOqP>JM491sSZV3fpQKG(GQZ4G8hOSSkuw;&~wT1JwDMRH@pVn{E5Bd6H*Ws1whsm zby>mzoHk@^M>sy?A@*&QOZ(a@+nbCC9T`L}m|8%L6pi=0ok%WIuBi~@o+(Y1eZ{4P zd6JGd+$u~X^v}cw0CeVQZR$4C(%b}GvhQ17nd)>695pW943qNfc!Y52jAdr&8WE8{ zUhKGuIvU>_8E+!8THbn5MKH^g#`CZ;>`bQMh@CK=2EJ3%vt1hPA}u7Kol*f__iT;A zN6GKZieXk~0;_gkY&+w%r5jzB*VdTr@x7$gQGf@gxr*bzWE1ntJ-)w%k39sw%KReT zE`!3iK7(*_3fn(eI&RN<x+AzN%aJZOQ*KkH59IBB*n9J?c2Y^!`<cps-@ll%jFFWa z&)@If&mUcI6za1OVsYKNg%(FJvI}&+G+}o<|49>Xl}#8fcO>e5`8tk^i~^C0EB?ld zo~SYRoHFB@A;%vC&D2R|53eafw7=kMv2F!4IH;&bc@3WKen};#SVTqL3?A3w;^S^- z*$%U|eTH$f(L0<8_W_EFpKsZ{Zt{>_{Zk%rM97N>_=7TGP#Tm0gjnT94K6S)##A$L z4lIg*$%K_%=zI~F#^M7#A$aA(qzR;K7pHkWj?~1@4|SVEVo10BgM*)*Wze_J-+AZ8 z{PQjfreh}#RB}$Mw=^}idTKtt?Y4Wx)R(1>DKb>?@o~vIp371B4=Xm@BkW`Q$j0%t z+aLUzP)bBfl!Cdzp+>l^(pu8-Gz^3|p;5Pwe<4GzU;x@`eunRDNZbGc5ITs^l`hTG zl7az2roMm~EGz^+a27MNPs9^w>*_Yw-VyOo`{C_v9|iZ<&v9htIg0K3H7kPrk4?@x zNM2s2TY7F+^h0KE&tJc)tzXHvlm$C34<+8<lYI0VM@%8#6zs|uDXlB+7hgO6rrt2n zik0wlP;rcj7WBNxd5e6t;M?jbd6*IeNpZikeQX3y4tG&<N=hEPab^@iVO5~pC*;0C z3>+n}Xp$mJ`&BGyN_$_))uyIQo1tIjLP6<lj~A>PFQbNr#7ACOVaX^Ijkqca-q%GX z$XtU%6iyASxd0x$zWSrJ+mNih)Hc=jhGs|1<t;60I6grAYujIMg!Zq94e!2Au?|8x z)`UtDXe{7bUPN|QkhA@_Zo{0rci{O%HUWcAx6!3qrdv&aWQ_0LJ*xW=c*ii))n<Et z2?HNe&i9?DS&FN5y_40ft`NkBNpFTs>2W0}%{c#h<fT(?fz@QHxQM!o?f8Z2S<w$% zNa6p*{Ym&v50$|viUlK_r3&Bm3I_mf2|;IXit2(e2V?&D@!m<QF~juYT{hh>@2P|Z z8~@&}nTj&t$=qH_s{XE(!0O`NoI?+NM3cz}f$i&KT)s^{J{RIM&1~L(*{W?nE}Qxy z#)TY42J?daw_PU|n>D2=mh<PI#k9AI)F8y1*qu1z;qrp$9aA(t3>d~U@%%*?;ACb- z8!sU>9~+N(m8Q4HE1X5l1=DGEP}tXZUIXQ%-xL{U6R|*2R1RZNsyHQ`Y_gbA_%V2^ zeocZ|>Rz+g8zI4H!W&95udc}C1_ga5MXzN0d9nT7-6!tP@3#F1DIbQV4Hcd57=#kr z2;RGNk13qO)uyrW+0%T7Q<XQo4MYMD>j`d%8{rYN=ol&DYj3<2ZIx(I8AZ3e!{^Dm z`q%65P*i1fi7$R>iETC9PFvw=k}m4fi-57X2d;Y|FX}}aU0MDR_QXc5Qzu?~YV!w^ zi-{>1B2GXfmoze(nf1kWzL%A)eZgPv@+auUqm-0$$I;GOB68ZpPTUzw0kbazM^Rof zNmG3)&0ZVi6orO3J39=UXa<RA6lrM%nr>ad`E!4*Y`Xd2J+_%yT)_B2L9Q!bLRj$# ziEcJq`U`CdCl+kFB8)~Aw8x;cB1Gu5wKcz{(sr}CC;XgqtWLv&>9s#q)7^0AmlR%y zsHaPWCN<7nwBAIUO6QKYVH6l)sC6Nt#FLNOFu=QHHYcf9r<zXo_?&R2n9L@TeZJ#M zsoT+H>n~hYrk_y**mJc4b?a0L%M&#wy0zU7);-76Gn4b;%^$CKlx^{#*TcBfw;l_) zS=remeA_o$2S!^IW|)<>Nv>Y~#+;yf>(?6E+qWB{BDVsjwENKn=l;^Fq;3!z99IxD zuN&Fr9?|Z;yBD2hcazA7@2?+=@Q1JQFBJMzvreympMK_iQ|^U&p5OZy`n+S0<s|a9 z(=N3|>QSH(YiAhCq5O{Kv6UH#VAj%7VgB1Jpqtf`nO4)ucz&yrhdGK^>7<X}>+(Kf zh>at1UGZ;-UfWW2qteBFc5dR-iQ};&q>azTc!`p4m?0b)O?~<P1Kruj-}0ngAsF*; z^NW$N%geBDbz@dm6P!Vnzt%K12Pe=}E?t|W$aIxSh>B|u*$Q;P`yAxYS{Rp*5tL-l z@Mw&=)4j4EXAU}d&aSSAeF%o9y@3~89UM3Iur9vyeXd2b+S2>pQLZd&@HHX%;HQxI zGa6KJ>~0JAJ#)uPlD`t5@2zHh2%6u5so>Kstv;0GMMJ`E3(KZfCx1@#``SGky)3^B zY(;)6b(N8xN-`#FOG~4Iz=52kzYihFm1{)VuL9H3=UXo(7+$)r&YgK`Gjvs+@6Hry zFXht5X<X~%{)n>QUr_vPR<2U9*>vSeAF;<OwYy2vZ`UX4rgLH6YTs$^FEb<`Czl9U z;GweHd)IWb4=)!GZjMMY_;aXo_&Z=;hu@#D4H_1)X=x5ojid{TiKI_T4ZpEsicHly zZcJ5VJN}&w$r66E$?<(KGu8Ns^9V!r!Cl)};f*stQFelzCe8HSuUJAt8={$(@@ZnC zy(+4VhimG8(cSzLm~>(v>&P~o^6$uJ>ShjJ*EUJK-c~I-f$wqZxsG$YNT8A4ADlLb z`)vE$vPAR~RKJ#Ks=$XJE)OntJ+mAB$}o_!Q_P!dv9o)eTifzvvSXBRFxF$Uz*e(B z6B`fDAsrP_RyNK-`KokdtgS2Guct0%Vd!qwyT|ten_qC-+l^0YTr+rG)W?AfsAzFT zppARZ2a)MDKD~1+TpK|+;h#%$e_w;^%F6Eh#G8>cr7aQ-Pi~=3d7KtB%!f&y2e8#F z+?mJ2cfV}CC|5#PtZR(@h$5vl@IdAG%g#;JOHAFhUmpLp*EZuKFBxl-dl7R~;xU+) zJ|V<<YpaK&(H~SKZHRn*1gXap2>#rZ5pYl~9Ncv^gI(w_@lL#S{KWp^#?-qPW#)Qs z@bLtjJ-AUfG<vSnT#=Fz#@2ot_+o;a^TFjt_uVo>=6uychRi32m|V_7G<a22&b0&m zy0;Xj6~^DIXRPHsIuY1Va(w^QD_Cp&vb{Kg%9vpE6MrHWrYUD%?1;aVo5&t#o^-tp zj<BkRN7}V{w#1~Q@Yn}{v3&7c>Gfu8VqS}6-4)u?--VZP6WjCfztzR1rYXOzk~rl# z0VoNF`E*ngncm4(nNguVGY0n8!8#}H30ss8C4Wb;iP!)ZR_E^5+y_sd>P+Tyv^cf+ zD_<lg1q+LHc8HMjlr&IQCPmO@u~xxr<-OZ%6V*tvpm_Rwdhqnu2*XS3{<(;|b9?4% z(?o;7w33uLQB$tx@+gPT)J+$*+x&Uu`7G@XevM7kgLI7yj4RX{yyNg23X6F?aXI(l z*qM?NQ*wOEx@7cK=(YtNms#}stjm{X-)FGVFydwwjBdrEA}M&gX`ZolCa`bcO-mc+ zFr(J{P4R033rk-=jWg8N@eHS_Ny{dhHQ!n(0hO3Fk==Zm5_9%>{$QO$#U1sMv1Spb z)gdO6Rr(1Ui&2Xs@dIy^lbsupd(-9`mHNaxTgpQpt-|bPvOlr9Z-P^+#3!&`mNPXf zqv6kUB?g(+*k{Y9lT=Z#r+)YT{c`GXlY4ndZEegyPUzjQO`Fs^r5fy;=j>SZ?pi*S z^+&>OnNHtP_r;!u&#>wwZj4c991K3x##9fLNP5q5BY${z)fY~$xW|1qLqTF*!SpYU zr})tZhK7)t3!*1$*~o6SBm0;VYTc%@gZ{MPhUk(4^eZ=YdY*2(c^_5tX`gbk6ATi& z_XrkzM<=g8erViLDbdfFnDO3QtnK`-<LZsOMcrJeof^A&GD398rbv;_{?t6%0g)2n zf!<$_k2lrwF;Ii5c7e)y0kYcGb(nh)Tbl9OEJf_Opi@HziF`Dabh8JbrH@8#Bh6pP zHExpx7Hr_*(K5)U8-@~zys=)CuXD;=e&|xM7I+*yar=O^;ruGdn%^1*MUF5qv=1D) zB#qKil55Mu(Yxhk)`*Rdzrj(mIyQtc+-UxI<79k7D61&f24g~jkynDqpSmIz6<YL( z1J_JW?h<r_XraY*3zC%zag2ytYx5BNhRC$&rY6z1&wuMfo9<y0my9-y{-O*x*srL0 z`GgD1jx=JlPZ#B2Ig?}Da22kiINZEftn=ZewV#ud*|Eg)rPnPLnTnDIE~5)$ZSJ8D zsX27sx%+p8Nz8sfccP{%OhIo(w^c6=I`l@4w*=T;Azyb53|=t3Sg*nYi?ij(BBZ4a z87l+!D`dDtM~5^rzS;Zbts>$6D|6*sHMla^*jEZHC(EvB@}*F3@{)7GyFi&xRJqfv zOFgt<%04+kV*h@50oQDM?+Jk%T{1VfRae(Kk&#g9^CtqCp(IiEVydf;it<b@Yf~#= zTpG+xr{l=BJKM%lPc#0(4-#htMA~4?GKZ7jSn9+hKA5aa4AlFO0_ic{jsW(4Y&cEt zyxjh#Enh7z=JJ}Y=y}6INy?wn%FPRQRy8HTat8lYfoTrc{omaZe8dHm+5SCoQLdu0 zB;q%xtZ2{Qo(s!s@gJ7F&kGPU)<WBV{qul^p@;_ddxTL4UDBgd)6=d(sGa$2M_)oS z4f(r&$a)#3neT?*KCPcX{Yki<T2uAPPR$E_SII+qjuw#`#maHRkkCqPvo9l|BpV(_ z<&L(@Q(w?@f-s-V#{_-(>D{q2!W~=mx$+9vP&{@oUwJ_ay!Mm?mt0(3mD~-hro-(M zO5$#9DUQuG8Q?q^dVA6xXmEiYJy{@OM$4}3s9G?}zCCg9bAC?hxn+AT{dI>_SJVda zH=Hc}S{z-C#Lmu%wZ4MHY`4g~4Lj<o6f*DT-t6?7SZ1fk+rsknGRIX)=u;qjLhJY3 z*=iRqD?q%61s}sS9f5j6S65*thS?OCv9!mrb^V3!RLz*OkvckgLbk9fF{g@#^+a+0 zD@n9O&dIx^T2hbj`{%eMC4;Y!f1l}9QM~<kNudW@Co+-B7`+X?!k+Wb5uuc#{!eqA zr!Ri27M49gD;IIPi<O6-o=uqLGtGcj?XdF(9_p;`!r;)*YU{;c&XhcE$$Xl!EJ8NL zdX)AHS?r&yABQCFEd<al(K9Y=&9}$?H4d;tez0rAIfG#~M#8vPv7bG2ce1^PVdmya zSFXu~;?P+*?0)!pVC$Y8M(@j~MDSC1;p|^+ANn4Z)<JcJo@!Wx>Q*1J=0R+Vg0i9# zOe0Q>+X9FYI5sS_3mn5S$OIQ)1ysWLC2+vkuhQuE-|l;KXy*>|<|z58&i9fy0&*i8 zE^;rUj@&o8=CjMl37(9pT)C=n({bEO-d@UyT1e#E*B#>)cJ6{=#Q`FKncu2B;Qm$j zF71ItR8;ZRj|OYJ8^-CxZ}6s`Jj9SP*|-;3f<deuVN56q)CI(M4IY~|n8OY?d)C#^ zqAKy<Q<gr!=}3ba5nyg@IbOK|PX6uduL}Bp6W7?-ebq_|o^3XyPUU~0)Hq%{M&Bxq za@)e;x8ER4Ts+iR=Bf8kpA`%Zrzwp*K4x$o{W?F7qL-1=>2vpI(p0Tn8K*=!**fFz z4DP^tak!j%&~QANDwN_w9F3`_CH9o<Ekt8^k(M7!3sUy*5Nv5_A&-!Q<O42bbAZc& zP>r7o*X$7{%&d=X=rz#=Vd!6q^?IsIw>*3cRohi8O#k;}oiJ8hE>f%K%%=d=cpR4W zgT>@Zo=)Ra^6%e2DJc}jZ=(F7PQH7cKRU`5meY9d(cq>3ts{WmKWoFeb~WAG!{Z5r z+MMB^>7V8@?r||_`uZDZG4gJ>?Xjq*#YAYq{C0efeH+_$MFj>W7gcLh>ZMrYe0sIY zb%{QtcMPQ;6|fFByE2I$=X{u1+fbC$Bj3M%oqp>_1h^MJbnEL#?3`Xl`Caup;oHb| z<(rTnx+E6TbPcy{KYo$khDm8#R^2+@8~sg2#t?KILx2toZxBET{})eR8CF%-MSBiN zmvn=?NQi_=cY}zO(v5V3aOmzvL_#{G4k_I!-Q6A1Al=->ckgq5@Rxn|+H=hqW6U|f zydeRy6J)@<XM=$iV3xCC{um2hfDR>323}M>3`Pwt(yR4nZe^%M=}ssJdO2xwPo2L} zs`7dg`>-9UDZ5SI$9c(DDf`8{@(oy3M@MLM$P2Ga8_n$rzGF8ES)s03j!Emy2kgsr zqmjHlIoL#@C>ux{f*M9swLi1+4;`(<f73d{mJRu;WP_$JDhh46JHdDwE(c}^O!afh zHT*YQsMEj|EpUPgl)kMpc|^KdcomgCdn2L=!NWa@xAJT2zMtVM3$AKaw;7)q@||Qg zyCXkqs3HB-tg=EE_8=J>w{{Qkt9ROLRnv<pvz%7ZOJIWUxaVpOv%q!&47H12RD!Pr zXH3|C&CVW+LhbSGX{Oo4gffR*2iScFmgj)Mh!TKjd}@V|%Epx&2eXa;raCXJt_D-n zgO)u3vFDS$S(3X~IH@N4o!!!O#J_`wVqrqa{-Zs)>=rM}HH-iEJt;x240qIjqM~q# z%-ptmSatusxVZgS7#;Vk&<OOAmfA|M&>JM5Ezace_Ljh*QDrtP5Au-=DMXHmu1w0` z<=BeKl$-AmfXK$k2`p|KHQR{dKU@KSPvgwh7FdOa)4^mBpl0X>tpu~x*5quB#|Qvl zGIj;-A44`vDZ|!a>g*oQFCub#Q8d4XrCf3DO%$Kjs-*NS7LkhTrpuo!2Pu&orz=9~ zuM{^oLVNrBnX2%BRQ`g|+TS++;#6%!S?dDfg=6?SX(PNzy9K8HX#>0*D2jr<awhmv z4B10UX!h$iF3;@lZ_dGd3_{=zo)&I{ah>Od^xhEAR}Bp86!h>u?aKz;!s<XH$PX8Z zmS24l7+5V;%EvihrHuRgEdJbT+CP?I@}^~}35F)47cVbFk6)x+V9PAY%xnHH-cJ8D z3chW@`sX%E89p%!8zvhmZhF{+v%ehdyicyQ>@+jgrOqWLCS4Rr9F}O%lvr(epxWg| zXvqF;nuj@+^-c$U0K$#VHD}hTKa=WsKH1sqS&g|*<T<q#5F~b_YqC}ie|Fp$uK7qz zlq)M|aSvRg*FW^{kR`W5zBFOmTL<4S=Md+k5`uGx_tGOfBwI7GeRxq}Ssz|-A$qX$ zsT4zJx4@X{6SkBxA$JM{Y@i_li)d`B8rS06z$Ab-WRYN@-J%&uRz0}klsY;zJTt2} z=_#1O*rNMsX6Wpn>@QBf-2PO|NQ!#UP|yAR`h6aj?B=W3o$}^Z0|Opj{#bl!1(S~v zLTz!MCOR1{5nOC<-C<Euj-}I-PhO7_JQOg-PdkIatkS-_3eZYBQL2}YL73qYkpHsU zN~s%MZdaFn<c(%5aLyU5Ix~%O_Xlcw&qm(ec<=sbwkJTtd}dq49J}mQp>w#u>g%2V zVFsV+isN|vbvivKcN|#>C&`#c$kM8^B>?urQ|<Yov!LvZBA17po}3(4H;(C|fC2Jf zzkaphz*N#vzj5E05bY{|{N-Qz0dmiGQAQsaUt%^n_ATof9ZRthzAj~zO>q=C7swx- zln#2rQXnS8Miu7^s*tWK(%_0%9m|Vw@3PBw-3PvVVn{F^c1(Qo1&%O{M5l8HT1Kx? zyIJD{rP9#QP~zYgIhMFGv5d@a{^zE+Cik}YLeM%U{0N7GsM#VDk-f=rx?f?LS8fCS z?5wGnl<+9ip&!~PBr^s0kWj*3R)dos6?^0#lRo@f-oMOkZbP-aBxLXh)6;Q(<AO}& zPh>39K-LL}NpW8GU;W(AHSnuUaX9|*_{F{=<`x!qN-;xLKHbrSu(6#Zl^KOL|0ekP zMwAi@c@jSJ-wS3;OyqaNskGfEzFB_PUlW)j2|v9K2x`W9`goX!AvJ~ECURJ+>QbhG z`2#PZ1aZN}C7drMSWdo{6*2R;5oo%>sSJHn`F<N0?$RqA;EVV)drto2n?jriX*68b z#Y<>^*|uS!N($>vGvBXIhR}}S*lJf}=vNwa<(g`Ek30g>I@|kG7t6|0pt|cl0wYt) zR}J;=F)?i=dkgmo1_d)re}qTtkhCqBFsZQ@hot;SEnwRNQPh*L?+^r9sn&FFxb+yJ zPg@2+maiGWK2x2Bxe|>1nV1Q~?)M7~3CR8^d&C<mjW^SUkXkYw*RpYcK51HJP(Sy> zV+$cw__ZV2jF#+@IjXGQO+f1TS2s0xLxS@p+#)nb?KhBo>F?mNW4ImA2vf4UI{(K{ zZZTD9&67XH%(BWH>W1{3ssyjqc{xy;8i|`5<|>jWPm#_Z3}B2itYfBlO0}FQWNu1k zN(QEH&5@I+HBdwiBn^p9>CsoUu}|LVzRwt4eY}$V1}{sQnNG@LdWt2WGM1rNL7{tl zRn(}c@5b%?Q$Jo|(}*JHzSF-iy=(4#yR)Au@$vL;o#r>I!nyo@QSgcxAi>4#7+0Sc z)D`fMrX=#Yz?W7-zO?TJt?#UbCWLG5G3`Mljh#DaefGkfwTRnwKYe<^rjQy<mLrSW z+xrc#VCoSmAn>ii?ql4>CQ@3O#qW)UI*^6ANh^PubMD2P-?ATG(HHRZ>j{l!xNe2| zg)J*er%QWaw*rvKoRYDlk1(`wG%kEtZ{sv%*wy;p(kgPcZ+tOril3RLWFCvb=Nt|@ zc&d$^e320y5BHCQ*%%e8^3rXd%P?@G?oa$=u`S$t31!Z;H>^S~yRu7;vX~0sgH2>? zq<gnBr04fzAtM4Sv$yF)$Gbc%evYPZ<;L71b&uX!v6wc=O=7PiWFs^+_(E&QT6CNP z6WRaia6OIIJ}6@ssuda*YcioZmTjB$3{RVVQKt0?Y)cJ^R<rEu8$oE;tq>sXj@~1e zek-V4Xc9&FncS5bA$*Ei^#{yHf!W({p<SEohU=HJ__eZ<60CTdFuU!&l@#;ev7sHx zsOQsicp^%?>Px@E`<!_+l?k5J|M@h|OXXj|uGDMb)91`gj`6J8Z)9gjQPk<`@~AW_ zNwr+6D(8jc*u#Ay>`?c+Z68>3?Ts*!XFyG=0?*)(Td#Jlncanc;3ZKCl>zr5*g-PA zlBMv5a=#U-b>E_CJy%B8jtC%+*92?@pG5im2+|jID!QBwr?ZfTbae@gjJ`Ro1*{{! z-nwTw`fSGfY^9@(dY3Hx${j!`AaCcG*pdW}k%?UfA_*2lLF(#uJvQF!h=qjtsY-$p zb0~GTTv7*MXvTMUOxn`l%HG(qY945Nbd<(cRP3VrH6vwm%B_cout88U&}nBF)N9oL z2$RnkWt?kE0V8_?V6NoTkt;|A1RVQoCr~qzg1|hF&}`|+O+Kvms#b%_?gC6bb9~Ou zS_j88;vaNPHBb^W42xbX6usnSAoY25pSw*I%$O=FhoUiz^OE9)Ae&CNSwO89sP@B# zKdJL@OmO{J7kTx{?iFiXy-gmJCno!Q5SR5t8iD=K(d^0JGWa?jj3rv!u!s;w*E$$w zMii|NTahaiBJ3<IRXt%Ep!Gx7Wzo)}w&2SdsBWT8`5mL)hwRZl{cz@NcD^c~*Ni^i zi#c=2uH0rKe|(dCtep#LBXVYRl?MNWwU%Ss{gr?Ju~O%dnk=l%?B#3LsQqwaN~-CH z@LS$Qm*<K(dPb+xoqzW?K0bEc7cckoi7072eaPn0=1<b8d_K>R+76nqAxs7{N@u=$ zc&#l;9PqB6>~J?HBz+l3jkUki!9<ikgqV%ZqS^vUBnvy+=g8KR+Q)=5`Cq#3qy5!- z_}*s^GMCf3u+ej=kn-;!zFHL&eEV521k<)l@BnZx=X_APLtcH6yYpgu!HkEe%DR!y zqjJPA`j--cIp%Pdg&5lEy)!t93-dLJ1NIP9zwH3y*zup^Hu%@vwLAJY7WsUOR>Cc* z9-o_saM6^Up3c75rBF0gGyGsaFNPooua%=zvAi6NnlWQZt$)zHzuCQ-*1PFa`qK;c z9{u*z6JQ1}E*%YNuGtji;E)Q6cv&Q$C{C@lkQfhTnRR)TLJ&mkWf?G5+h=VO$IE(( zErj!+(%(KPZl)Np3evjOeZfXYni3&FmtRzBsTQXk(qf>2LV|C&w^~%Ob`A+=@PSIw z`V>e<fBF`;`AvN&H*GV)vjt*jJJW>i`cbJF6B*Hy>>R2;J}U=2zQk;=R$~4P^E_8F z*W~7^LVA$CT;#)bU_hB+4gZVrmdBn?ww+eUl+H6mY68%OGAQ$)uz=-sSD}|DN5#*V z{ZS75?hYNviAswvcc=4@zI799mU=3>L%337!TAe49nitVD;Tqo_g@hurI7(~Iv?nP zHMz)3=p9OY<O{I1bB9>^Z6|)KY=Zuy*ZgHX%txk^tul7zOx-K;hR|mUTD+sH_mIex z9Z>XaY33Lil00lv?erBltNbZ}(ndr3mRdX%j1A}kM3jtzV5afaC5|84X0W=KK98-_ z#0Urz*SnK9U7ud^kdSeddFp+k)K?0nkuE~?@a))2<lwx!GFj$6cyMVKvT&cwl@lR{ zl%to=INPT;L(!C^o__ypIwWS;sN)=zqH5Vl4l+y_gfNnYbalIjC%^fnV7}J7rT8#x zh&kH_)XjIX=!{{UyiDj7#x^t9@;ZE-5uWT2GsSG^`dmcCydl~x`NF`|t>zRotWOHK zNPA<W{691=M6i~(SJv7-*Ya=r6rwZ?=fgtY-Kc%=53|Urs}t2s0?Enmmp*N6boe66 zL@ag}XTM&s$5!Gq>|b1!-k+11$Q9qaH$2Xhq((ToGaGn|C(XKH*a%c8b-xc_<bQH# z#v0$20gdr;#J9YnjW^Q_@%^6Deu$#&)OSey_s!6gvuDD>I^w^}Dg!DezE;}3BSS;| zoTI*9g}Y(5W?({@f+SMr{Kh$O$vB}KH)_1pUU(tG24cn2(3Z4-p+x$WpU}%yu#Alr zAf?P=USfYj(@&K@C?fw(2W8HYPgP#?dJxziF*&f{b5`AS|MEq@4Pi#zYNYEEHvOnP z+REOZp0s#2OvF-%<P6FxMcg0|Wk-QSjDyTYi7Miz%ZGxGy-N}EJcj}JqD1+$fZ2*k z*V~KwfRzL5*U;F%)L<Hbi;IX^KI^J705_{GX31Z7oh8<AS1RpYkP1^Rd5k-y8q>c2 zPRZpb(Ir{F(u06WlIT1yaER8Bw?HkvZ730rJLRB(!pMaGbXb+v$p?)NraEGb1A}zu zGjAbLCg85g65GZ2CXZFz&C4Zb%-yI!I{VwK=8*yGv(Vz{{)VI{B9w;VRlx$4!Z?Sn z_U(TV{CS06)(6#_PRnB6*;<iX7hE-nz@GXf!FPYF@gbOpzfh`#PyOFGE=MdHx3_mS zwQ{ne(M?SRGd~LlmL;xass^5jnp)3rjpvlz`M<VONsJ7SJ!lDET^KBH+Mu<SWzhCj z%`G9Jc$vZ*1(+wgPoyb#mdZEEBeA>xy@!#jn#}@9MAY)D0`ucjcZ-|p90NxF|0d`O zWFY-L<$DPF{Y3X`j9qx&H$Cemv}(VmA#?qdyK_HpnOx7br#Tg?TPV#}ub<irYpCEy z(?F8|4O$?!=llmggUi>iD+8pklFumL!ih7ZH0XS)_=CQdnOQsk^N{)>*5Fjldx}1W z3tFnG;wv&lJiV))l{KP^JvKg?V$)F}WRxiAP#vAP{O<{XcAPjZKxs|-Z0Q&!6*3kB zE;g7bHxp!PQ!kV7JS!{1TnVFAIa+bR1PmeFotmUe>Fe+8thO{+7U~(C<fKrfAv{v9 zu_XSLSLh%sTl+&i_|4|u<&kxxP1Aq7dZn5z-q}awfGb!Y@eB*=9h{AviBN|SQRrqk zaESeg0<gl`t7g@rZLHZ*?8A>aQI?pHTl|SE&z}!X@}4*OU}<Nd7K(Z{a^lA_*NU5^ z22sR?`}?=V1mC`19b*Z0<?#zu$zOAwTlW3Jj<wl-km%e5LGB2-2|7bsgC9HVwP+N; z$oAw(@^tuN93+l~b$AV0kq?`<9AD;jg+=gM_f+pf3tw#NGk?B@?Y15+R;!MK61W=s z7<m`kW!iPxHic8Ptgs_2x%;4Ek1x)E@J%bRe<}Rp=oQPK$fr1`O+&M?nbR-+H|-Nz zhIEBuwXIsh`F5r(V_dp%?O-ojSiSZ3Al?{&%}%BMc}i{m#y&2Nw2k?hjwX`q!?iGb zIJDQyro_8|_ho*SWxO@(-IH$>i09S?ahZU?$2n;3e0A}^AAclN-bv)mZugbi8=_(= z7Xw~zl%Gr_kl=a7Loplm_G{heH*O!?fSsJ`Y&b(2oyILag!UbFuC=h9xgm{J=I#<d zNS{&PAk#{`dh!|Z;=wD!!jpCKqzSp^j+jhykTp^Ztj<BX1Un{S|2D1-Cs%ykR05ox zyz~`c6szPPmQ~i_vEdkXy}d@M!g^b`Ugig~i5&Q};!^L7TM3XkDV3vQ!o&8fr)r>f z%$p>xfvzCL{i%9NGvf8Uei!#ln?HLa{1%&q9SNkO{KM4<M)%8D70mToRUfPyiXqH2 z)-xI3Tj^vcE&;M)QKdre-8V<<vR7BkEL2Yds!M9jo0W8L5#?9W&aM=Q$OB0~i{EOU zV@rovK79set?(wZ-5(HoYQ=1CPOWOATUO3}2DBBda?Bb~g*D!lKRNP%A2FFx+?vld z$2lG<wuB+Iq>I0{9XKa?N^xtdRI+`@hpa}pNoXFPP{}n$Mn$c6qAVlm=woydgAU3F z+f%<%VV;REeYo>oM30KKRB*UbHN?WgKX-4N&AYC^te#!d-r!WHtMLbYG>L@?Cg$sc zQVX+HFYoatSPMOq?a9Oawx!-6EKWwlnPve|Nd>{>9sdtMgt%ZXi(gtiD&obOiW8;@ z3k&FPvG$K}29pXLWX<-b3$u-%y*a<KM}Nou_^%mU+Q!Bqu5#YGTO+btPS$QSN#nO= zy&@1mJ%;p$jPsJwJf!{a4TSCs$C1DN_$B6t1&18xWMREntS^v5Ttg<LmRc+mt$7nx z@exrAIhh5RuKo+%^F9CE7y`oc=u39bVaF(XYNU)@cRAg<9YXhj*G#_joXIOYYciR2 z-wNh)cY!<dmVJt&R<mFDr_Jw|mpB>ho%?9iPX9akh%a*U7@wRbX#JP4=*8LO4FXDh z`3px?FCtqjmyVlza?j<~;l<KBKY}=U*~fSYbChn8!%ML{!KV|-Fem&EDo`ak`6zp8 z)*pqc(jYhW7#F)1R|%L<xy!wtcVNdM0S~`_9>cGkf_`RC-$!%G6^40X8ll%ziY&{( zxz#SN5yo}=xq!d!`8mOEv|+qW5&2y0DTeWQ-6>HvHD<Uv=!m_KR^5C2D4LlfSeWYt z(~eTFs<cE|TG|z0$P+t5Ivf$D^9l`pPKt(t<5yZ*KLmK4r|e>9w@C-T9Pi3*K6vVA znfgqqL`sccnuVzzg--zD6VwH=jx#K&kQN&7G!jhY_i`$K4GiGciP$Ol$_R;Cvc->< z<H!~hBXmFD-DZ`8b@FLe<X+=3!6=osr9;sauUu1Y!-;s@-9CN$DKi&}uXcZ3!`&=r z;zhp->zKXV7V4@Ywf!3+X{<P&O@D4Vfueu2wDWY;CZDbvcE9H9CUg<gO~rmk3Lls3 z4-n&<niP#GsBZnQ@>2BHckI?X*C`2ksxNs+BcBnRvo_E1wN@0EUUQvjL4+vIXbfQO zZ+dKKl#D(WzJ&l-Am)2)7I4+tGEb3H;F5xwRr5{pXM{vD&u7_f&xCv_`0}a)`;fnV zHR-LcwjSq>`U2pAZtMA#DXq3)oG3U})0Fv2A?%r}^JxP?BF6S)2JwCJne+Q1fDf_l z3yH`f4h!*U{RP$3_m@6m-mhLA&zjopC*J7hJ$tj6k@QeZOx1Vzp7T2`nm%s@W<eSc zG+J1frmsyMneOsXMMw0jgLQ-NV+n0ffo$HDeW_*z87XmZPKs<4x|%W`r9c1LUM|s( z>ikKu|Nn?iX@5Y!n?NbmHCun~Rx;3vA4tBkKzrRMJD4<5U&Z&h{0MR+&vhtRJz5XT zEE}o5>`8(I1^Tv$n0&<Km64fBcXc11Pvoe2yU`|(^JzRgH@ky~Fa_v%VpyaR$_~%? zQnPW_K75Cu-xIPy7IPzQEIXL#^e-6l&%Jf(^F1-LQV2^?YMqL`><QkISdF(14ei!? z2__D%?5_LkDJ4Je=W7MJi7VqrHi!$K`-#*tH*1S54TNOMg9HL<(g|KXnv$1j86$fC zZoE_Z_wd9Hb7V=_zd&Z}K4RVWo|UE&l%kMOV*MT7BbLRLm3KR&xVp+$L5oLz0Zjk{ z@SbW0PIk!S!KoggiYY0I^O=F0ihv^Cu3wHn{(Ue@%5{D(rVh;MEG|K<oUI6?iGlAq zZ-|qpf8f6r@_FgwWY}BHoZ~gMBP?h_3SIb(2$F?Vy=Lq;_D8x=bkv4hzCR()+>lSK zE=?)P^YfL#V|GZ2(8FwbDtw!WOLdgBQKf;?-hTb=6P<6o5g;XW+Igx)iy!%IZKNG5 z>p$X&@SD$mvaaGRRH_VngSC+9uj%^j`xh-gi(Xk4meo(#lxvGH+ssTU`=_)ujWsO_ zM^%}LRx_e3?UCp_IVuKK>B*e*x}ypLolGh|Ai(CJDP;<DKBPjKiGw2<zH=lm_FH=k zHaL#ws^G_IQQF!QNeYSwD?!+Ss~wY<OIYM<s)a;beh5xBI_M&MK2r<iPNnjrVSPqs zr<qfy%h_Iet;!jO9AYydn4ZBN@86h4@!3v(ux^6AksE)Lu7;)$51xbkm+kGO>OnSR z7I5X_x!%Tw<#9++qz+XODrn{=!JKMtmBZKG8PCTFPU8s}m2pB1^_~LSI0V5-zR`gF zXYtf}B|w_R_+&|xe7~ajiXZ#KB0F<d@Q_T1zno|yj6we#x?QSv`#|FCCkwJ|1!lgE z6;9?~Sdb%#ye~&#J9m@e^9uRI&PPg$`z0Cq!E5-EPl`4m5*&?iym?E{d)S2eeR|Hr z@@%7O3hxnm4{F9DY%h092Oke)m`&MGtBSGF(Qhf3zkipRMAlo8-a^gd);hkKh&zTU zDyxFbx;m|tr`a#DlH}eGH!I}n|5AIQuC7+Ud&3tA(Zp+Y@)wu+@QM99-)7b%SdD&D zgEG@d`jK8GR73jVp_**+bW8O92F|JdL5hpI^HDxCHb^1Lw+LOaT1uJKKMA|NLt~Pg za@-;UMjP|o?a5?J7x_4E6251_{}B~eeC3cuP=B63^%xjf5D94?Vm?<DdLZ%hn`q}x zJwGUn8XbL3Q8d>s*s@~yu8Kl_dI0*u{Lzb&mLM)BFPuR<sW4`JBtsEkX%oEv(SUN_ zt#_Dh7W&Ir_V~idg=DK;>=QOM@(U&zE+3vBbkK-UC5MvN=ST4X6XCY@gCauwLw}kI zA-wpVmdtk;r)iV5FI7c>)Vh`(4vUgN1+_+IG}v9tKhHIhJDy?*e0kzmhgWZI$~@d@ zMesy|cP3>c?{ahDM`}eQO%xGy(1n`P?W2tQ(hc%4C<(`z7vwwmP16PvJ*X*|^7{?7 z(UQ_c|0ah@&#+~b9e`1ITFqocd3o*TpWd`=S2|IIsy<R0p_KVg6qy&+@9$&gDFEeH zeGmZ!CJhY}&iOIqrckH_*63H~I)e!LMI|gRYLXkJ6xPSs%I>p-HF<dX+oAhpA8uNg z=&JNLUjhbzvyosr=z!85{OkjS1t~*eE8u&2>B%S&iz8Z&Tlq&s6qG_kBu$jsiy5M0 zew5Z!-=h_c2Cn~`M;MtCFiVg|*zl71klAoDauB}*IfW-8-el5CpZ0%YrH>7cGL*1g z$XA2t9HbP@$LXrV;@7Em5LpB>a||^oUaQyrguV5N!tTprp)UVeh?Z9EFV9;Fs)YPI zsVX4EVn3ZkJ>hO>i5Yq!#(kp7K*7dIC>7~PFE???q6y|9baXHd4AFCMJ%fa@GC_ar z=%G}+^dV-=2Q3`cN(QNC2N*6dV<`mieYoca*MuKc1Je=1@I(L(St322B!^Z^8K%L$ zzP>9Ckk#-kVj$7Y&jp_&O09uwAPEUaD8G5y{Dyn82NyHx=Y}8O%fAJxa^=uJZM&17 zsh<s)aNgbAox(4|hG-_q?gVQ$#s(_zZ9ncxDt@aU8pC4+W;j3}`*ipg+I#kpXRd|9 zaC82=9nz@$F7T9_*}Bh^u)ca`auNBh%8DTg=L5Jb<7Bcif!(n_5Ls67N=R#5Yy5Ij zP8Lqmach*A=Y9ew{$1q0c^y8HEq#uzr2NIXx&hyZy}<MhS%ejDO`st0#WqD;e$>-T z#HC^ZeGhJW2rBHSyhZ{GRC&B*Fu8E5vb~KNZDnf^pk^C!uu^@l4gcBDUs%p|y9AAo zLLT>=Pgw&|q<XiOP%EkuG7F;S*l&4hkdHc|A4-TX2bGhnZ;RoHtg7!#iQZm}<Y4^i zE<B;;bB64^l?QGVu0RbSP@i8f)yw}>(~q<PXgSYQG8wD_HF=b$uC@tG&<kQ~Q6`>d zwJ=C1bFX228`d}c4t?9gadU&6fpqCh?GSG0?)#?){9g=xmxM@F*>3Rn*Vx$Jut+3- z-w+3Ds0;))o)yT17@L5?y<EhF$}(nZaJSIm(AzT%$!Lp-E@M6gGXcS1gMAIu-7zGe zfS&gNe<<mvv_699YYlL%v9ANFNm&itu<r7gKJnNu_(~rOM}MJdITsK)mLsY&AS0d~ zl@77&JvuU>oZj3_L-Y4OCn!mVE>&BPWCybpB^7I}?{7+qy78HJNA2L{<u8z3b7lc9 z*vxD|kRt)%GqeI{h<^D;sk2!Y87R4VKp(Z;J_$+Ww{ID&<4-R6+tG!C!Al1J<Ycl4 z0zLph#V_)B-)|l-wgOtuo>_y=aoEW{o<@9Pvrx6I%wYF!Sh%fs_GfF>RN%h`CteOI zCD=zZZm7>}u*tZo6g53_V(S1RJ1pvhGQ-nrX;es2HaPm;-%i%)()viEtwcE-Kv8i} z)YNpuaP<qDJ&s>Y6l8N@STAgkJpUh}G9*7FFnca@bB;(%JegV2bi;12$QPG@+{-6o z?{8!2Kn~QXPF;t;6vV5rop!6i2E2-)n9$uPW8jJ3SuT5{vOhho#;FT?GiXI$V1|Ar z5#?rjG&{a(5VDrU>3?&cnbBJP%53LTaFC+DDkbTYTm{5+7kBYe-QD^MQx#7USzXNr zvkg9jX;zMkMuYHjj1%lHBC$|NEpNl>`fkVSpN=A)5^rt_B|#9ADLRb&sw^k#a`vPX zSnJPpWQAd2?E@db4pRLg<Un}|ozVFQ^I5WxqwkOR&E?%#W!u#LvQEmmc4d*SR-CZx zQNdTDi`5bAp8)o-pj2trbj^17-<>&rGN#O^m5(=HL6RmqHv20LeJTUof?mRLWgvm; z1F0BnO~3kDd~C*B;?KSFzRoF`IU&D<7Ci^Is?K5Muq0?PE1U9Uq=ot_Rp2v~fvABa zb~P;bcM+m?3&6_ozsCZ}L|boXXFaxJu5af#*|Zx4)esehyug55q)mHgZ=jqhH?F;q z*wKgt=L@t9JIrh)n48OyU>S4D^lK=yrnlM82kRwmSROtf@OCjVV8DM04-^Qh?+w&a z)jPGwml0OlqHyB3??y8bpCOBx&{GJPaA9anu+GsjlZ*a2KY?9tR#(?R%Qm+04sE=- zGo}#G4Q!um=7WM7(xgZW_6q=UGkl$>)+4<yd_l&osuvgtmp7^~!FHXg;cAxr<b(%= z$e{-e@wG#}GM+ZjD)O2C_~+vx^oL6<dyr0;d;YpOWI%`~xx0fJW#!W$9r{Nkw;(z= zxVM4#bPOF8!|*Yx2IoSUP9&MA;<pa*S3Q0If!Y@`L<s5(^zL`)wO&o)REI7n_SNaj z*saGz9R-_8dtvkHxxQ*8%H$%NRemwFZ7tSX5mZzgIk|nnLKoe*_vJ#oQ32JtAH6(0 znio}mxCE;u(K*AGQ?>_hcYAxPaCPSZniZ_>y<tfYa=b3jIk1N7VZCpW5xzrI839ag z+^;`Ck|;QDgL5hQhj{9=cadK2oJ%|?Uy#ZD5{}!JEv@ojV$$jtYYb=mTc?IjMmU#$ zn?5Ddoxw0pz!iV<JHPxi;CwG?j6(UMD9mLcFjmOH!kdVJkNHZ7bCsiX1ooWqZs4Pp z8DoHkqk&}8gy6ou;V+=R0|5eQv_<BzaEX<%=K@6Y50lS0ek#W$tz^AswVF3cAdx(O zVi|b&dGT?{+k9PAvY2RI`$|fmnTImMQNYQ1Zf?#JDMXTFE={zl>JPSo$g&oh>0|L| z!m*1NR-1&3HBmxQF<Z@UdTw#<tULsOXGwVjY>0xBD;`6~atj4er$>bo6^P38yQXG6 zd-L`kUM%Gof=Tgj&d+PA8wq`O-18i6#>~mQrxN*z7i6s5iiSo*;UDls4D`sk#iWp* zp%ngS(c8PS3YDbeppY5o*hBuN@V5Z#1>a`onkIASB><P}VUt7)Smb^8213zS6c9vY zdRU6%;5^79vB>pDUcj0y2zjQ>gTMlS#FLZv3l(__h6Izr#$;F1`Fdy<X!MOXJlDA% zv$)Lr0N^*!1A=^(Z12T}y)kE&*Ie-n8%F4yrcpTsC;yc{lp+AvZaI~&WXO0~@&X7K z8?_rDBqK%;P*S&S9VBxH1I}yWXlHz+vcLkZ#v<-X>kWB_#cVS$IB1g#2$EkLzup{; z2v~}q9R1fqE5xq*cUwXQlx1bqauswwHB0Ep+=Zt?o{nhU@9y2{YdDdT$mQkhStJRj z$4b+a5`?|9Vc}4q^fpv`?%&p0SLpx|7~Ks0bRS_%a2zeHzpoB!w#&38Q|IyvAvbV* zr`g}?QNR8fnUhpv+D)hT)xc`(LHagDYJS+0O*yRkxs6|(1waw4&{pKQcktxbmYN_b zJ*wj^nVA;&A^G|qo|BAk^Q*iM58?dpl^(ZF%GR#XS&ejGjIe*hrJ?<B&K_Uo{aUR( zU+H+o|BX4`#H+CLu^8G7dWHn8h^!me%F}EY9`~}#HC?XM`9GGPx`-?NI(px~#br1+ zX2C%an^9P--;KOo5q<yOCy~U?sk*5SD6Au=My;wI6mz{_zv<^V4#I+C%a-a|u;}y7 zI6C1US-avWHn8bL=4eHY{HEow$`2FKtA8eZmobIt_UzfpbU7V_*m<Av$-Fm?c82dM z4^dXu52zhv!?m_Q)nLpb%{I1ddV2cfs{kmu9q?;rV}H1ArVC={FxT|2NlB-^G<AOk z{`VO8Ur^`;pT)CmUf^7`ClMEQrc>`<=@)npf$kMVHf{Bjdi|fsM4KD4hr<YE`!nxA z9lL(;V7qSN*g4y)vD_*V2k6+c=zXgvQlP~=mT$M}0n9@i;rKp?^`7Mi!U&<R3(wWH zs0AF}sx6-tn-v<OQpMQe-(gOvUai{!4mX@gR6dGH?W!F^v9N*#I?-oeoR#k-b|+3k z`VP-$*vX>{Sk~6S2D=B(m3S&Gz6z+*+J%~Lv3DnY3nb|<&vYNvIPQ}*K1V(vAr<(_ zD2ej&7s9eLb|HRDG>-l((_SHWZEg=P3Elm7r`(Iba(&0gCYpczHk?eq8VXOf13J3f zHOVoBw8l)5K$qt3Q^*~l1im|b2Ull`_dg{<Q1R~@4pCGo_AOH4;nVPG>Xdw573@cT zel(R@WI%#hAMoFY{&!f7Zk%@y4SjPAdQCc7<{QZ;*9JhZw3xbB*K}}T+>FD(kjSHy z`uM=V088LY=bcW?$YR<2P0p?B93cidGoJ{SZ%gd~_Uqm7XxilAykL_n`EmU1JEb@f zw)>WUvX>bK_my=;*GxWI&J*3;Q&L5}5?<DXxb5-AL3TOtS^C<)Lk?%4#hL~GdF)LU zn6K`Y@wmM;cQuDB5V{dBl`Kuegq@d*qr4x`I)V#NH~TY9)FwZ$I^Eyw)$7qP0}}Re z_kj%{Po^8j&r=EH5{?><7vO(+(mb^PqDrcOu*&G|@E2~_;sr+x8QObfrR4Q{#fFm| zP3<kkG0IM$L?oA#k3J9>X!p=)F{iDVQC_@vJa}kZ^cTn3?W(@RVqEILn2A|A&U~&$ z&EVj}0Y<)CY~8C@)1HrFkcH7hZ1Bn756Nr+!Gam#NZ2Y%km1<J#;SiTO&$0u0A0zt z8~m9a<-Cv?MF=4B<_&oxBa-6_Rol5NJkW<wqOMeqW=pzBwDpzani0ZcQI(<9WW!Am z58TGIp7rPU78bNz&~;{J!fqdrBPgcLV9H>+w0(;Vp|*YunOIQHJxfM-N(OmTT-1k; zB?zD1SFby|BTWz<+fi$8QPpTnoHvA~KS4KtBb=?2peUiCq1ncwWg-hvD^k?5>u+70 zO|2J#L0tsy>@m4(A&%|Os;KO`vA0LXi=AdF(Hi;tILa7>ohzICxqPkP56W$hAEb}f z*_d`!lKBU90mgBfUqGoBSX*C$@tN(&3sh9aK#xHaR5ah4>BAZeoTOY0{8mFrqzsm1 zSxLH)mX_U@_~eD<MN*N<p)~zNE1%}P?S&iGoz~Yj+64_R7wpslb+gj8>oG`HHJ2$* z*;7)A*NUN4^QEu9VYM4~GI_MON9%sV@d(gGbiDjOU)|^Fx3>DxV$2XW$bwJ)q!3}$ zTkmRnUD|3GU2rsAq|`0y?})~5e`bF2>GNCuI(t6pNsrib@3@eL29|LqA#@j4x-H8P znef&59$P5Mqy+&q4Z4Xp_e*B?ssrbZXT61+!)a6TKUDOrHw?j7sr>z$_7dk@)#vJS zC{+x!!v4`U-X9z04vMJ1=&IlPO{5b1PqovmSrg>ETvhc^y+M_d#dZzbg!H~aAGI^! zbxFxcT?K1q$R6h>!cOCzPjN8MTNaOF(E`w#ycw;<?ISSpFY_yg<p7Dw&#iW(nJNr` zXdMpT+}}X$baQ<rdF|fuXiPtN;{HP-W@kHbdaN<upHAeTUkVL+>9ckNZr(lvJ#rM4 zN_bA#5&<QP!KDr4g1%s9If`<UyeeP9rcRR5`6#GW#AO4Qb6FGJp49%%^-ck@>oRSi zIDSGN>I75Cgm|3C@g;ryhHIsT!kEpTC9p7eq6#?752~U>rA6+6K*PuT?+lF!%bS3~ zzcDg=kJg(v%Y%TZ9T6vg&5S;_e&F2=KmjcFHuaNoaa#;gAh+WaCBvEGlvF{rO0Zp# z1lHZg2s(X}{bU158rNUrv?Qs0&X>d4(QquuWG+#OXB?Ku9aMW^u?;A^XlTDE_>@bc z8Q=^cy#5szMp?=86#mWbWLo#g622=9@N6N~l7MtCzuQ9)uj2!6_(~r8Wj_At?TiZ_ zqJ&oEi71P8=J{4sfZeLK2V7Nc+qR&nsR{?cPb{B2(`DK2WCe@t-426FDc|ybS5A^e zxBEv@?n2am&h^trL!Jhg(BpwH(>InTfU25BF&%f7())_uSIw@A3RsToL?Io@qdMuq zNJ64^xXSLk)S$&R+W#QO7LzeQ9J#f1CiQ>i2mCK=eM<)`3)u$Jai(X?em|VKeHk-^ zY%l*322FMIbd?P0%H7LepS>R-7}=A{ce5&mMr7(gk}r{)gFz<(5M<ufKdOO-!N%GP zNv_nzKYwx4_bAiyjX>7G%_^iDIRFv3%HTVk$#jEG!;wI;gs-ZEkhoEt3#R0u;NXCI zkeiA5V;pYZP>}V!)gD5r62VP_;IhUr_0CzyqS)TN8jN?K&##t;aBHhJc_c-!htE6h zhd;`}#Y~nPh%8%GLZ#5-wK?Km?#)yAM`wtOcmFNW+wYP7zaL?<^^x;nzNx{D;aL^Y zbN!y@I&fA?lZq<L?)LX_kPr7Q9|rWx2m^KN3A&<G{$dcft4PhfJ9($>C_xwOW#biL z!#P)yDR`N}_jvZ%<;J{cT!1%m8pp>WKE@941RIC3X#a2kGnB?X)zbFo0v`=X#8}uO zow-{Ion5l<K;p7co9?YD-M>veL$s$QWkIn&Hk!SWmt$aeYSbwd_hYw`K<P(<maR&6 zXShl@4>vcDJwjGiNo_%NGL9!%fnPL^liPD{Q|^W!r(r<=Ei<95uOV<+w$IhlKvHym zBOH)f5(YA2fR1ZD%zFf%VOk+m(D?im;~~up*L0viV;3BS#MAdU{DXFUQhxp*=&jrI ziX7A8nIa0Pon?hq6Ybi}egOBg)atvk2Rz~Zt-+NYtS;yF<J-69Z;OPpcaH@Zb_^PV zw|3Fln)8Q*Z!yzc5H3tFGDo(>FFM0t?thQM%DTfe7YexLUx*IsLJX%~$qnMRcc@5y z^@jNPi1Tp@_+%O22W+(GtL<(NR@Sqd^~xnE8=Tb+RvZE1>C6DU!MEoHk7_%0^>ft= zL#dXxt#3``L(TQ&#uJ;J`Ah~swshT_k&cYftO_3cz||cNQ*J6AEn~@xyWoQ_@>m`j z0vH+67w*v7mf9%fg!;(ImsZAU(!X$!Jkh2XdJCmb9|X|f3$>K|R&jQ1Q@Q=+^8Ia~ z6K)D&BzXwv&9{<aXp%p;WyiR^g)qr;cV&Sb4Rk>y3pAb?2+hFx0tM!-m4XAeR-upA z(R`05vgg-%GNYB}b(4j!28Nu}nX`5aca&mnrSpk`scjz<&l(+tDDG(X4t1o}Q{SX! z<HrH!cdd&Qv-+T_x3|o|pKtmV^rMhtNc+B3#8Pug-=}Qh%cVkD#;isxihQD(&TbGk zE#?nSHmAhacU*Vw$j)Ea1=d~F-M-D8Bnb|>+|FJ(!eLN==lq6J@~`18_<(=(g*k9E zkdc8_D{ABL!Wpz+f7BER?QW+w6v3Z8p#K0olfCCxhf=TW?{pT@cEQ-70udK4F9W+y zPIkkB@L5^mNGbUU+S*ps%v(cEStY|oh!s8Dn~2By(zmt`wJ8b2jVEDz-sdXnjWezL zwAoOex3<mVhPaa*zzgO@Ia7k<)YWFwVXA-q-P-$JnNg5?je=-gGTi7|eY$4M<Cyk< zR11nw2tU5^ecQL^obO-3L*{MOx{)xH+w6TROljzYN}Xoo{}ULS^{RR-+3aPQG))gD zHrP|0NfShlHF;^T@Y0;YI5?M4uS&q;-(I5LTm2Q_G~Qm0Xanah+Ee*lSMRZYX*Cob zP(4acNx4M48tUbRvI$lP%Z?u)r=LE{%#{*}q&uq_i_m`@p(8M0yT2`ToeFq1Q;WOg zhC#+{a+Jqr_hJ<SX(GR%|067saJ;avf>`w|rnK3wfeP!3t&|=lYA8lYMCk!&6aMr! z=^;;$sS`601Buy{$t5>Mi11V4k_9-0AB%e4ZIek0!|A1Sj#aj{uTc;5iL0$d*^~d7 z&-q2(g8kf=iy-&i-#js28A^p->Fs~nlcRS|E|NA<UGEYt)%_Nm9sf#-)E04Nrcs!2 zASSIi>#k|?k;RR3TM}rf^!D%k-WGT1PYnn(>}r@xGnRm|Sjj2f%k8}1`&N#ULEcfD zIU#hh63wTm-t*1R3}acdiT5>j0Zjzy$cM}65h8kC=9zefBEupLP7jP&{!pO@_0-1; za>w+U@T)syp3S_9zX>_@UG#8zknEX6CCkWNChbQohL$$4fyR%gb^wfM<_U1ap-)-Y z){3ZBLiE~DTA*C=1b@1`Ly1XjZ~cR<{I8_1;{{m1%WAytGn}le_B`(0BC@4$6_t%Q z^#mURD`G23R-ul2xlZYpWXu{CFXyB^-^`u1=;g47l!b%n{`0LHGKBqNP~Xf!CHU(} zU+)w1PVzH91UNTicH=O<C@P(klVxwUZm)Y~1!l^ZN6QLcnxIX>qRb;5?mzh}lr<C8 z-NC6kl4Cegbfd>^=+;jfDloQ1<!ShOYdu;;X&Vuxwc?iK>wxY=kFezaR<T<!Jv?7a zYpL(4BGsSlRhhUfP_U9JvO>jG1FqypTJJM?`Q5$E1nY$4L6Dg7wMe&cFV94hL*H;5 z$@$7eLI`aYnBj@HM{3<UveyD~)<X6C=oZDZS$ZE6&`=eNS0T;UqsbXG>yVI#e<=5U zcq&WD_e(~Al=+I`0Akv@m0ef&p!89h1QI9^83nU(O@@QmO+VP2sJBqbr+)I(WU;Jp ze6CjcVB0Urd9Rg?JW*Pz#nZktR|7V^B?`Pq56qh=4>0{t(L0;4N5{EsB=cJWirzsz z4g)bUp3~8m*dLoFaeS2@fM3?@zWWfa$@0EOK0~&NwQ=sRN>c|OVFV}{_F2={ScGo= zZf;(=_!QOhe2S**SlRf~e%}NIX?cQT0HRK#Gqau(5>{l%!-4r_uWj|<d%}P=$g)TF zgJsczYH(#2oWEBA$E)8Di4TPV&J3i<54EQ_jXF)UUS9msEbbqj5X14nhs-%0<hJ;6 zWD(Euxm%iY+WGveeKI$qVm8cui0%gZ#{9d@Dk{n~(@R2!(ZU&T^5w!Qq}?-`4NWrc z>bM~f!%gu-tRar6>VIvq4Lshu(<2guQ}kt64qd}-pqo_T*6Rzfo3rH6Xb1DZPv{&3 zsJFa{2$2*70E}69ft<jeoat<9Ha+z3H(@BtuCT|PhTJ0=rrlm~eIPAG#)x)LC8EVy zojmfwiS+NeAB1+l)|+ovhWKfON~s!7CN*1e)2JPe1j*=QhuT7!EM3A^Kaz7{`2h0e z_rHB9Dvg_v2xN4SpuK85_5=2c1>4oW=K|5BqrFbsOA48|#u}K}j<3($y}L%Tx>Dy8 zl3Cfv0m;2X>e6s{!Gb09NFgSknNRys(%?<&<3pYlqG^hoAA&#1pdj|QCR0M&#!P3& zEGGA?etd!X1C71QLiv&}UCpHRowQ8IGLX)yVr{fSRo}wbcgv4D_gpkIZ@vT=t~1d) zbs&S9^NPc|t4n+k!fD6?XO$bjI?{VYYMmZTxW$+kbK)9EgaXt_p0njH%l4bv>K+EW zsaQ3Q58Xc;h@IMqvd~Qnvjz_1{%&@%@Y+;-wRueMYX<yeszO5vF@W)gwWz714hxqK zrMn`7ZJ#;`sn-1I0vd5aqcR_r9#<E-xMO27ogT2sfKsU)%9S#!uhV_@=_tX@b?p@l zOyZnzd%VA6-`LtESY6&5Ma-4+H|~vn-%~2>(=XYD?A76US-`g^HscS<v4P+0JIZ<Z z5YlT>5dCCw{R-cLS6{w;aOd$Lxw{8LM_61JYnJDRjvB{*+Y^5N7AL8#bQ#V0mKza_ z4l23MxAT!RRq3V1F~ym@yj*h6RAs4B>a|kkpNx$y$nUU-7DUDoJZxhl1`9>hTT3ym z+)2E>QxWl1k`fAuWsfvJP_kA1FdZ!5^k~|%1U<`cF*P#wFmv-`H>FK5G6_Lgcpg?z zP%V`Qv9XDYC<-{;n>HL8?ru;r^wJ#_JF*KvOdOy$p-Mq(eUdVDbRr4}S~rY<w2R7B zB2{gr3X<-L?4@&q8{@?;g<e;>B|A|Pm*=dW6;=qCqN>Z1&JGQh?`h@s=+}YQeA0Ah zpa`$!BPIs<D=JP+(@)LD+ma6muYK5M6A->lTk%|9Ba2KA_OwH{sFM1BZx~KEmiB@c z!!n(J8?kW7g{{HoM7dAXFg$0NUJw(52F2<rm(&R{@2`gP<;Gz*`c+>7HGOFU1S^Xd z<0mhRx&V;?^aWH}tegmiwBX<;UEScgts$uA^2YWn5RaPzFMGn+*dKp759Xoj9%I#L z#@mWb|KVK_B~$gU_XF4-gaeiGCY2^P3nr2YJS5qc-N*ak-=gupEyBzipJVBAogpFj z>&`;8rlcLg9qZ9*Jr-P9VcUyk+!!``KBM{@pcd2MRten|2a)i5?=vK%>Z5SBT=wM2 zhVQ+~G-3OxT6K7XMp-8FP6LmHxDZOd{D;CU3pcY~@n4%ctDx|ASWIs?K?ZcogGmBg zaI&pxF&a!eTayg7*FIf%TgIgpS&1UqEXp3u=yfG}(DePK;!J-}Jngm3lR6S~08nXN zB!WhQCmqU<vxAzEI(CA{qPXK;28$1a{TM=bBmaKuY@L0lgBWp=%IJ=c6}W@${uc&n zrsG59yBkc9LyQI-w&_C0JG0UD_+u75Y=^B@Id0xJeS71TukPIceWrL-i>4;d%EsZA zqCY+Eah)?4!X}eZmVm-1)S5vLw`#A6Sw88#PynqSJYfLoqi4m|_0o_#m6D2YWPOLH ztHe)qy@9V{DJh+hl`X^Th{|AUN<>L%(&RBfLg|m^1iSQW7#*?WtUuX2W_x@HDs6^K zL5bO#Z%}@s!{zvCFYo`o{k>J~!k=?u5sQ+)=k{GeSmlw04vpZV+^8K7NbZqWt|y1t z_ga}#Y4M_f1(vy9XuMYd`|>7~OC`k+7Mzlk$f)><sJzr`p0miaYNLuMrb7qxGA&Fw zEOYsMLg0TD+i9x*<bM?&c63MP{%dJ<ZnH>s*rJ!(a)y^8P_XqW{6<pEXmH-e{x9_- z?dS-CupvSzXLI@%)h$@({YxvPj2uYKorEg!xwCJ)VV7AA8ulK6pi#;lbTBXi42~sK ziq_*YmCujq7rz@vics-ZLGz>TdF7arfemY3xYJ`=F>e&;H`l;_xVP2mz;klE6jNE4 z9q)|2L}`8mBT2WC;Z!5Ja>o;&;U`~_)T<TAur#N>tc8*Os;$WhazHQTs!#XwI{kMV zM<Ze&9C=6f@2`&cmsTGQc89%vtyve7>?H#<eqbZ5ff&`eEeU$6{I9^m48yK`a}HtQ zs$94#OH)>5m_}2Q-<hL;zyHtQnJ5`^gu?WD35CB@7*v0=c~L_in|4}2e*jqOi$#`? zYprO#3W;a&r<LBvGjzo;Tj}l~_x`W2?S*y<x=ZaLxpvY6^ZeAyUCN~Vc4Q99)^6^? zPZEJ!VnGi=KT?150}gj4{H4!iYHpi2j_3z;Fb1HsDVKG)$44n!nN1%D=V~3tZ1Tgj zqUgDg;LW@(7##a1EiyTh1he{3FU#T;2l2@fr-d0efc+EPT9j|7z0OstI=Sej<S-CD z&SEIDDsi}N*{E(#KMP0;NM~++wCvEt78dlw>AL&#Ioc#{yNgl6)wTMzVu9L87_h<{ zL|gB`<b(Z|TD_yP0Lu3W>WjuEV!CNbtheWCb}*IkCPDZ1-AOprN*LitP3ck$!Z!-S z94_zL`ttuCoJ1z!C0c5KTLW}Hh9`El1^7RCHs5ZX6(hzD+4!28V#C4S*xjpsHoFU} zYZMCxWUxKO`}n!1SBe~5T1z1ECkM|3ZYDYMsny@oP{A0ub4XU$TM|qU@4ZV2Aqtu3 z8^PKi?xD~YO#P$4CXg%k{d_vQ=;#k;)waQ0*7^W@zyo``CJGSurqvQRaLS?{CaK@0 zJl<#JX4BG63K1$*rM%}gV3EIV%fLZB!DhNmU9b;^qa>!|G&jSWx=^TpIQ+Vpu64Jh z3Qr6?O>}<G!TG0~H_}U9@At)&*8m0g&qPkkH=8o#yh=hTsVU}qd4jL$OP(NGz#okO zwH@e+csRug{~(Uny4z<|p32W#5YuANp}!_EiBjwC43NBfr%AT1ext6guQqXTL}z;a ztSa)w<5ea#LX}_*c6NidXJ64Bp6C!9nA`TvVUNBdd4h+>#4=~=+0JuvEw47dkl4|l zgQJMmw$y%}g%d;hzm;8`ijYyu*}MIUfxX$}NCm;pf4mMo0^Yhuc9MiEdx!lEh)bPj zZ@$MTNIj<CR=jpfSTYFx`X8D;?0n+0#liAy#V(k>rCR*SdU?s?bKJm4-PH1C)ZG<r zQm;fRmEg6N6ZzgI4w%WM*v=}nbnzCTwTLT!4}({woYJ_h%#zW$NG9?#rd7h;tKlti zMl@E~F^X>ut!GalG0P@Nd=;e}sGI#$qBmUf>a+SgBh-GWh!-bKmm%|wKie0yn>vW% zl#^SzI0|PmeIo8Kk?#b2LQX;FF<qa(XrTmU2H45AvrxwlR*}27yYErTZ*p1^J&_U4 zz#I)TEbct7GMVx(amvffiyo1G{`~1OdyU1(@HVECdpUwvINj&3*O#6S3!`_SIcTf4 z9tiLQ5hc4M$2{Gffz*WrODamhHGt@W#b#fgQx1mulV6Q4ASOm7%T(V>=wl)zoG8hB zgG$c*h$X<)mgbKGwfv0Kb)=2W1U3#st+TD4`(>_FEIoI;a<~QEEB;W?7R)c+jexM) zS?k?xP#Z5aU<2e2piCi!_;R0n_5OL8Ute1!U6!_heWRb~yxn*NJo&cnJnlN$uPkJU z_co)2S$p+VzQ-qbQJx-w1Q@kdwn?}C-v*A==eEJ*jyeZ;qY@zrp#v=N`gC=CIOVE^ zdR}p46L(4I6eC)~b~C(oxK{Bm*8;+xeqaAOnAx{~y@D7YP0>4M&@uCP{TC?3N-j%Z zF!@4;AzVu>F;wR&v&CR!9rE2fAK~c70tcftsUB2)m_AqGJJzF?(y4Qe2$ClNW;af= zYZM|K1u+3rm>jR{0Fc9ReRO$N-@uTRYoU_P&%p0fh+bi0vY+z%N#yT?%t`kAUYP^v zJ~l631mlQDQ^D6GTUu^LKKpX<#sweDPnL&lo1Hfm7Itlf=8;U>V`wO8y`O0!uP~mV zKE^=Hytb|xb=i2Y?djndrm2h!_`!%|_NX`pH3WTtGP#}&f3y--`rGs8{2Oj7yeI?W z^*}mT4W|8D^`<8fTP>i<CWxgA)jMBYF`zU*kU5A#MRgiZW~l{7I8;^*7kFeI;NP9O znhfP0AP?MY-kZsHzH>W5-px133i7J`ROlG}mv0@n4zL*H^G`Eg^bB*qInOgWb6*vj zntVv3DXbpb%Z>)aejxI!tiP!EyAGX~jg=lBBpUxA@8l`$-C~5G_k+-7?98&hsmu0x zzL1N4@XuoS;Z_A@`VM7ME%rMEb;8Y!Rt4dLbC38WQyUxjQhG3o)gt^u(*l@P3dyAq z8t9w1MUl(CZm8H6suoN_2FipSZNiMVp^PZ(IRB@)xBiOid&7poRvH9EN<IiGAPv%> zA}C5pH;8m2-H3>YfPjDuC7^Ul=b&`g&@o7N4IRUC&v!lVAMmdCm)Essahy4G=A6Cn z-21+-tMl#5lkxk1VH<TZLs@ck6CZibfD2d;mQu8v0l;B%o38ckdq69Bpn{tARU$>@ z<I2QV1Ky+dqFDVO@P!U^#5la^rt9Y@B`3Oan~?5#d=SH5Nq$wNfwZbQw0zW*?6XLO z91{8&bp;h2b~oWFEQnrxgNi@37mxkf9JYsa?6*^^IPPD=?r{w^8Ta1><i%3#@)hC# z@KK$n>S*srZ6^l<OEEfb=dD2;I_Walg&wYhAPNyJUk~x8zKn8p50V$FBxQ>cT@~!R zoR%J_lFf@~*JL(AqFx9+wSQV(RwvUb&z`S~!LQ{pzQfk8P9e(is`Po?GV7qxJwUDt zJKd!yS{>HeOvBdue~?f5Vs}}OmSgvq#cf90v#+s3S;XR{wU)(4*sc$kJ}lA2-6wj1 zmvcd0#VStU{W3>L6|JBukt3s<ROqpPc7VEqoqgDIJVj1Tsm9fU*|p^tT^cd(Fe76^ z!hh<#??ArswDIWLl{1k~ou{vw%NROp#uuXV##=(U;|F)Hb-bxu=o-8iQpnvRR(J93 z+sv3&No-~v2?=mNqJ75IskZ-I9enF=!Y{twc%4`=xjW9JqqozPP(gv8p6_Gty`*}T z8@8WwzE>Fj$4AnAsUGrY%w+On9@g&PznK(JOYOHrh9<7le%g&b<2GzZ1ca)Ls{k5I z_0ak{lWKNfD0B&rV*Ul;m3Xhcz+%*Gq-xE4cY>25f_HxNx!l7IH?$q>d9CoA4K0Vg z#7a?0ozIe}osgscRzi?CVL`c^{ckpn^)l7N*IY!dQ`aGP^cf6dR4<Q{(32-U-H$)! zSRStt7N7DA{})1~>^m(5jjK@vRE1y+_PuwmUyh4A#=cJdd*nT_*-0l#0n2l+SCTcJ z(Z4nD5w~yg%5*>y?cW=C*DqR4`?lCYf_6u_d-3gg&ogHMBnG8IN`{{%n=`l7N>-cp zkOC^pX7+|3w01T^SD^Y`bOZKHZELc(dU>z3@F<iU1o+TM0~89NAT^pc?m%~%h{V^J zhl5+xne*hAFK;!|Lis;ioa!^dPXMH(e}z?19ewpuW}APwr25Gla*9Ex?dd;dJzwg& z;s+K(xNe=EY=TK|`4-#qZ+SpV#7pSjLRW)z<Rcd&J|?FNEy)rJP)CaYy#2+@l;nm< zd-DSkSyn96DMW{+XN9liY5y@x&Z0!mHfLGnmQTHAJbGlO&Z6(=2*|-VbjCL~>5i7~ zaavD)j>=&tV(^W9G{vi3P<@VN6L_leunVdP<9%!6!1-BL4c{&2js?@X<$)gFyKcVo zHhL{lvLNiwAIpCedahZ@yCjmi-|}WT7dP$37bD|yVx%O_YmNzz+S#W!h+zYLo^rFs zIERFBB<12ov(6uz8V3IcDtMsLYt0KIuO7zO(?l=2dlD!eH~?)@LCHd$4T%h8`uoFi z$WQVb=e`O*6YlV^D;I1vn&8CEaT*U0nB=ykYK7(FuRQ%YVq5I=V8W-`BXDz=1baGA zcBPPR)QZUPG+*y@Z#ux7l_Jr=JK)8Dt=Wl-vtAd%em4`*P97bu#fRBQcXjs;klO{E zJr+)8VlHYfLHRN1jA;cCS<SnA6cT4Q>kcnOMq0-P1vU8L_of6K-y|o0ZQ)96W|s34 z(#g;1-roP>%nVzIHH&2?fm`ozLv%?kcQoLEWb*901^emMf(OqPul~UL;X553AhY^1 zBZ!f~nVCKz$Dy(@5>KH1Nnp}yr|ThlYuq2L>HLP$SSNhGuRFXSx^S1Dr$Oe-9<b{) zn0yezh57`RxaK(f_;6#vw0g2fGKX@KHR?8g0^e1!j><4?GS9nyVC0KEW~6rp`pw1O zTw>Rq0*oXZF5-cVmuPf58xMQL)fln*-^Lwy=JdS?<`=T|4vt1gdL1+q)`i_<RBb&P z^G3XUgNze3?v@#a^G`J#pmAj2x#16l%HEbztR8lLB|9Ipt7`E0vE?@&A<fY4;S!AP zqSZ*z#U!^U(P|f>cSC8?$dQey{)iTafP<-vu*%Uh|0~@Q20~cEQxMViA*z^<iQ-=W zMp27>o^r!KWKiaJ*e$5^{5L7B|LIaax*>Tk;qB@k6P5`!oS8Om)P{HntSnQSma<$o zd}b=x@P%e;rvq2TN-L_q{=VXLVE;2Ew^xM-@<3m0qp8Vf?|Zu_erH&?*}C^Z1Pw)i zulfDBG~YL;m&RM?m--6rjRFg_9_GIRJUy|pH6HR}3J1QbK7d*rO4JYPGkiN#p*1nI zFIfAuWVGmMUVK-N2J<o!n@wIpKy5h4R$AtA?xUv2!k**BP^>R5usK$gcDr<Q@Fk^H z`F)NL6+)%*!#h>fj0KinLpd4b?X<Uo`QuO>O}^(jmc|zWJ!y<p$Ax4#D^0>TjL~4{ z+;FTdYVB-^c}CFYLVZ_HcrRcBLxk~9^@XhLvm-Wc%B}8nS2t*Ef<8#V8~t!Okj$Ma zGWWusZ>yg_R0m>~dlj9-$~+=26{e1r$#=C$XDcd{n5XFb2NBmEuVXN38sSs5g9w&; zC!CucJ(svefrl`PHNs(^V~XhZ^!e!~A<e9!dW<&4bvxCAk@(vEjOoCquy=~-Y&ZMv zE)^YQu2D^o{DQq4W%{O<E8x4&suLgqWxV^QQDhx={6kIY$I3-cP5b%#iKDqgmswe} z3x3|+^DQjw-G`3=Db7;d)}<`FgPgGdn>jm!8lF!`RC}}dF9m3BXVqfL%p;EPUSdn% zo*k@=qTn~jOBBt~>x2%bQlyFQaYW>Wg4!#92^-CPCBX95ZYwjiZB=!BBC}lHp6~cP zTV`#v8N{d^^5R6$N~VQc|A+17l*#AEs(PZvt~*=(LBXHHFWiBGy?}UPQ%q$Su0p*a z$hT`&kKroHs^udeZ3W95)kNat9$~fR09(YDFN6>8-j(iXZS57kc_QqpFu<yS;xFZw z9q{tvUr!6?f5^4jKE5%UW`SEuD*jDJWol}ge#P7`CN9o>^5EU_lCn*oAO6^;&6v3_ zAw%xErn5c<qk=sC%EaO;t}HxvbIzR6_DBiiiB_DI=g%llC^6HJ$eE{_)3%FBNX#|< z@C(qC5ew^iSkid@zLJMJYZ#%dtd%<Ck<7sHe(4z!dD_wTVqnU>W&hpkkcIZyF~XEz z|7?FCebm-UF!sUEzfZ>9Uq_|`%;J-G4$7|UGpQCm;u#igv1b&B8#hYQ_{=OZp8F^t zy8)S#T6tQVGvjcJtb2+vM;2siD*jngCr&3Bh@SH!^P1r1c=OqXF9Yhynt8t#ernqt zKvM|+lr#n_X}8{e<Z6i_?i%35<ez)X=q7%_Q@=^gS$}?(tnC$*Tp-~ixrGO-i-?&A z8~rS$?@z|<)QT1l(5`M3^E?;aYzNs~YW8mjYH1zv+kR=ei00eyuY%rgzyOF~G5ags z6a$#AL;hMdb^n|J)bk=L(*Q7Uz8Cpk@tr)O)?0Dz6K>}P+!p0DXdn`3-=B}&I-Gsv zgKTTF%xG$l=C-~C>2uZTp+jlWPakiAMMOl(jeSb4a&oHl&qtFESNSbc^Itw!s!ns+ zR6nbO70suvF-BF(1B0eTMaF7Peu6e~wJ|fZ;s4O*>Wg)CmN%#RUo`eWU8}^=+qPe= zM-~=zr~5i#SgfeF5!02lh;-xT-Ua`wTM?oEU4Yiu*<Ynx+d3#ab8{Rh(JhgP^W9UD zl*cJv)JOE2)*eCMcW<rsK$#<V$&pfXDMQQ1knyipCt=t~vI9xanzTcziMl?{dwhIo zEbvPuq3vh8s@Qprjr{c4K@PQ>sN#|Sh75Uq4N7(2=aeUdiu2MB7dPs#k3P-s3J#T( ze%Q{=q)wgO7UVeSeJ1>}TVbKPb@R}1IJVIw^Fi0Kz{)$tCGlLb%E(&kLE?anYKboD zhPqxp)bE!s12U4;f5q-^ln&l$Z&g~|mWz6BV)7?R97?_{^ESE@Efa`UUzZvBiwyG% zx#rIuPrZyqbz_C!8Sbq_I$y7LQEQvFDbM=Z_hdk>S>Vkj4g=d0iMx$4gf1IiUFbj7 z>RrnE-h=9ly-k-bQH6Qbxf+Vj@m_Oe<K)l$cKOUkPWohrh8$t><u)P1tDKX~7Kr&> zd~Xo5(#`Gd6%^n1h}ioL=s4Tf<z_VfUe_WVB043-lA%W4UPNt=-^s<1^&k5Bka`4- zJI!Im(Gt0@3H0JJbefJ7LXYjsn2{+$k9}2f#PI>wQ7RPFP5DjvqRVb&sIea|(3YC( zULB6YLjXKAkFUg4<k3ec<ONJd-;)Nf!m}4xT?-xVjfM1`2}g4+j<vh!Z#{@8tMBgi ztRZ0>|7CDr^%I$Lz6EvkX0Lk+Pb=g^AYht{VoJ6*81arikUw8i7T~bq@u|7(usTXH zRvJ*{thR3~m?O^|Q|fW~vgH!Ty^OiDz>nE`HO#WvrZvAJ5^{&|yOW5ZDZAx&#WHI& zVHhJxsg=ReyO*)q((m>xb^Ilj(mn-|<oxG1Y{;7>uc?xe{a8i`X)5JsLj$-^+1LIs zhiB${P+gCRpi7cStO%veiygEc{Jc;;DU+sS`)5#gswSs;(xLjuel>_g=r>+*IyRpc zWnQnB;_BdfQf)yy65~yMPy|>Sk;PF*rj3t=lGl&DHTp6A&>|!B6hV}t(V*{Etv+*F z?|&EH>889&^;NU3*9JUgDK;O8)%0L*r0zIlTSH&j`Igi}ne!fBl<dX~hLc!5<`VZK z;kbd9v5vdt=+-)We7qhR`QYpW*7F0aMRL7zgrHgg{l9-ML<YJ+b8BzGE4Qpf<!Ifk z{q!y2=BhLOs?%KMMo-apr%d{0JaJ~y52%RzUVdEr?5k@pdeUazm^X`@Ge+b=tktl> z;9AKw5{;K)F!SfbL4*&4-?)m;%ntTWtc@0$gseN>yQh}36()+>-J5^L$H$8z4i6+4 z%rtbXIOewGan6=gxJsx*GiAb0Bxv*c5#~UHUESw|NaFCv+u=6+i>eYL9wiSyyp#=h zeA`Ueou?aIdH=ezqXScg2G?z3nkThBXFor@5^xpV<1pAbjbz-%N?F=w@9nshjyphc zF|O5CoOZXdDU(h2=X|>IN!r+4yfvfcmn+PPt1jtwX?28lJPAIbC&Gv2nPPYGOEL(k zQ91Jnft4uN?VklQL{I^{zH2imrw54EGL%CF8g^7rz|hf?q?Wvu?4wONpN*IN{rmxb zXO+Ew$awiXm!=y~{ng8{`n~2%SZS80ZbU3;ttQfrTTv`!N7tRCHyKu>#g2rkXIzTd zU8u*q?U;Ev$%)20p81*K*<2jo*<?6h0Gv>6So^F#(Mi(YzUf+yj0cF|=Q)1rB3LSy zZ;5&21(Vf-nWwOltz1X-5cQid<O|^mHaRp{dGCf<PZ?2<)`yST!x44fYQKkn_^&@T zRS1*2OZ!M2eeZ5&<B+xb-VM)Pr2^4f&!-s*8fzZ}<LWh0KNsuq&C%HQUg)WwB&13G zyEH~wvZkuQrPto(j>)b5_*kB7*m6AP$HwIKmHt3&oADX-n!_KNg0Oq8<zp?3q(2Ou zZ;4Sc<Sf+JXvn)FzH;zG_Kd-)0G$|veF*rhZ9B6aN2|LA3G$*=wd;+&!KZ@zkEEj0 zj@(XKoH7UeiJe7A=O|U(&F;1b12RMV+t<waP;m3i>YhS<oww!Hr+wzJ(ebnsjv<yj z0@mNPRh$mF>BKViA2a`lq9w>Y>PZ!F9BL`58B@oD9*>HEt2|9DQ9j4~v0)4>7MuFN zIoa(T`g&t1x66xo-0xmSAZ%BLx&%eIO+K>)u+jlf`)kM7;8~ajnPUJrsXHTi(R@c4 zVrD?s{kHagjopW{o(~VUDWT3fZ<v}3{WUrnR}=C_;t%new{!$xc#<AZRDLu6VM9jx zBeHTRPc3yUHBj4}mM&MWyXGJXVOr<5-Ja~d%rgE;TIQpiUp&8mbEvTQpTr(XjpEM5 zo^x+ETiX?k?QApYlvbMjI>ljQKC4*|8?Rz=&CEXZVyfh*vDeTFHUfBx*KPiK_#<XH z-0T)jCa1gyi_nETjdJP<nt$3U*nuviYDN$c<$GaAKJTLT?CL_tuZfq9z*|_;WY4g& zO9}`<#EpzU&d#lqUwi3#o2%#0bXf|fx#ulR^v_V)TK<eNhK*5JC9M!%ebljVEfP;( zUiyQFLosTjy{*XG#3T!05FETh@y_wL4H0^t@XGj*Fe9VdwDTChb7Fdyh+fm8<m1UQ z1Ig$+E2#a{JM1CFzStMGayQZ^bb)i=L1&lRqOkDXub=kIGtV|O%ZdG#^R)l@jI!R0 zGM#&6%fA1X(9m;?dxpT8V0^QRAjw;u=(#zf;h;d3hP61jNtT)-it1_7gYcTaVmJ~7 zj9Aoh`-4^{0PBQ3eCJMiW|n(_Vk%Ptn`kI<hC21kMU`;(phdmCW81x|?N{Yyx5hnk zYklM$YRSn_w$pp7Mkg+NkBbjkBReXqb0%AyS29WBwY0t>vh`<qc~Pd365c3IQUi2k zfuew=RGzju7W=mHs5oUl%_~_fhF-M(;XgW+6<<Z`&sW<<H#+AyXR_WkH4M8Tr99Es zL{4OT8{N4i0(mqh(uJt)>?Q!|(MQ<RV`gn5tZ%Zmv9E2Aa`OsN=CI;en3@Y9$JK?L zj4%`@(ie&1{zS#*pH3?!;9soUsB;6fSG}+D`bG?+ff_y)^u2C)7vqT=yOw0U)WuZF z<(r^}0Kp)z?XdS}e2|v$5hKiQMfKSvlvmzX5=9`xPZ^+s5uh$~zi{HzP9pSpWcI;* zjc7Lf&eqmDVqyuQw2#KP@cH8h@6CMq(trNt&yS&9ew%FamKhV;&#Mz!>z7Jy3BF?S zhboSKLfFrJm&lho`)={~W?Fc@d%SLOT*harGp`A;Z!a&)f9kQ5*3~ywSSVXmRnXZH z5O`?Ko$0)6WW-iZFRD%?^oh0@U>2IYixg*^6;C`5t(VL!Tl7pXG?)^n6&V;K4h5J> zT<#~n7wr=%6zEpyUr*_8?FxJvgt87WDVv#lAuy(a8ZJjcPfT;{yT$w|GLse6Qrb(; zwW^}ET}jII_nHnvYmli8l7g?dxqlAyp55P@R3u;WN|GAZw5(3LS1(LVo$=o4d`pcm zNt_m$F5hwbF7-`EYsa+a4i8TGW4vB<ncwWPY)o%?ovAnbw8z;4K*GcZ)@ktmoBfYw ztHY0cSh9baEPYC>bb|3<)6LyoZ#XDkE*ek2#tkg+$qM~P(kIuLqyz11i&J5c`Qy9p z-o2~qTE_kX-dB!R4Iz_a!W}xyzCEh_->CvnfAs(6zz~3%50}!ym!@h(^{QRwfvdUE zTJU^*{a}5(1<;#cw1m;qz~&2PL2r~Thpn}vV>Vhtp9!WKXlXFBz*xI(L&7N0X#;T! z(62&Y_3ZSY0OBvIk|yq9IAVH$&0ZQQVrG^Nd#k4`TAQ8E(G$@G^t;ROCK(wS@h2r; zI5Uhvy#2#<3UhRwIPOgN(f@l1z$9-dG-|+I0)VZjdsCjm2fjx~M_+F7%plHvvsbOk zo>?TNv7I6EuFDVb+W<|DHoDHc6duL`sE>y|0GIO&SSdAXZJs=NPsPBH?+qC1W6J{> zrGN)*k&==!cD%PrPg`>FRUGd#T4BeZE?d)&ZT40Mwc##GDcRL^9jbG4Ir4z7U<?SL z3lfDKo_L<DmzM(XxTThsmWcGa<}dHCFgoGXQk!x0lcUAtQa-b8bL826{7{*V&U$ME z^Zz~Pv**^ItEB*yZG(Hj&#w*Od;J3g|GQE_-Ep?dLMP2SATAG!xNhXKu&^wXmKsOz z3yZj{eW$v8JNuz_$#YT9{oMT_9XktvfbIWXT<rX5Y#XQrO7A6m?cjz3V^x+Oy&;^v zQZO2cN3}d&UQh-=CaVX~A`mo$ghh@i_>s6V7{`~o`(suG7U##q$hF&PX(lEnxWQn- zzVq4})EJut3c)6u#jf}aBA|xli|<0DTOdZQhpvz?=*|e|eQw{OlTQ>;!#xmOsQw~} zIov?-<Sa@>D~UM-*j%h!r`*j-0qAUp?(7uPGX~`LCBQMUY62YggCH2wUp4n$$1E-Z z;afj&Y8#(PFf%_TxPHAaH8quvJSQoPQ8IrLxID&S_zI5BGpg!Lt+bbu)3ULMhbr|_ zUS49<oVq_=#&QmIz-Ya)w&nmDVT}oFdc5sS?ZH&_%uZ+LtLo-u^S-`5;F#$zGL~I6 z0j5OLbL^SMtME-UGPyfZRA;;o;CTSKyb;umj^=km^t|wGK2yST>;bZ%A{-VRs#~%$ zev8TC#d7MsSwUrikTgXYtz>ib^ugC@gzWFgr1g1>zQ0V6?UlR>C;IsKBnUaYZ*6_v zY05`H&Dl6U9{-VuRz$=)XuR^>y~!$PW1zeruF-S$?)um-DXVrSFs`+U$MzlwDDW(Y z^KEK5?%cui3!&zUsi6Deb9M}P)aYYxM@L>@{#yhjE*Ghiu&D3fp92=XgR^s~J{2jc zl)gR#u;Zb!)p8nb;vd=R&n%vuoeBG#Ry52EKGjM}1JbDI!%^TqEcQNe*{H!N1U2OZ z;ht&}c`gCWj48l|5k{=`2s9ChBXC++P`iH{@W`n_4`!jN>nEVzdwkg7h7&%LFm@aG zo;B&YP6qawH@39}+ab@W0K!`QiEC!{Mpce#c}{-8sM7IbW;W7PBc7~&VnVkYlx47B zZOz(`oQop{b9#E}TZ4wFNBH^SZB)z>pPXF~X>DtJu$@qkmgMqv9ryLXvQ@(!HvRhf zb8B}%aUe_O9*|v{58v8Tk68p_KpSZpnVm)oMIny5&p$YNlwDi|fi-~M-{0Ts{OrJK zxjztxQ4bR!#Gx<#ZC<#_X_-7YIJgFTyxN&48mpWpv%S;76_t>%R_MlFwQ`K`uFamY zaw!EU8>BDPcYAKC6`b_=VlQS=lRbL$XjK^K4_nYvX6ouxielTBfYikZ6lu7|#$;|` z!KEq0f8Nv8_2Bj7{?53X01OjHWMt%behYF$I*y-udQP=eG6;0Jy{l^;`1uw?A5CTM z_&$nd??6ZNng4WN9TLNI^5K<O41CPfDgW)Z{f5DFji=>OZU77dkT#PsF)_VL5F+y{ zNBhJZHGC`rt!8kaBWt;&`$uhfcx_c#&7e3({=!I}&Lc3U9)qhhv$Hs633%qAEWM5r zdqcx%HD6Z1?hj({_yp6r4Tff*!oj$@F)!oCk2{=tRnkUAOt6})$il-z=IZKdiOO_n zvWbd}l+%s*_RXnj*Ii8|X7L&djhGK;<wBl1Htm|Jsg5n)XJh*eOgmO@jJLtg@_Fno zvDLB`a`?)^TmaEcNuvow1^GM7elRM;^dgPo2NT9;Qy@3Myh27zz1aJm{Wpxip1RtR zK(*!1qDS|E0J6$?m7IpgqLzb~HxiEZ_ap;GtrvipZRYd{b9%g5pyrbI8dzf;TwFMd z?hC<Ly}iBc68M3Efkm#H`uy@2RrQ+0J@-kes1{nk(u3E*NeGGfE~$co!s-6F9aNhM z3u$|Q+ZVP3anI2xSO8yE=!0K_=@xyJBsTg-SyWvM8z$nkWZx7ts*<4qtZU-3AF|_N zK1!{I7v|ccSU5$!kDY=`#|LrB%o&0+P1g-0u2ZmX^~o}Vb9*c;eTmWc)FJr6EU>Xc zr2*b|NnZ*00QV!POyvO3d2$Mh7xMCh4?Iq`wzdp+m-=dsfJyz?vmziZ!4a0MVj@A= zZEZR6a4cow;)ytJS71@y+Nj=)e8|ghh=)h?^M&+d)u<?(quf2ET!V-012O3U63xTu z0FvBc7FJgEIC>M`jFO&+>S|G7{bAd_ymAqNL?V-r?<b?@H<!9aH|Li6(q6?2h8COk z?0^%aprB{~=l%a&aJ?MeJyH4Y2wc99=ROBcBpn<azR>W=^cXyL-9eo7WDi=EB?*|b zMS_`MKgVvKmuzj>gD~5U0mo?GUIf4Vz%f8gdxJh8Aj+2liPrGa%gNU=iB3$+tKIrb zpbf>)B@kCwNuKRS?NxpK`juQDZGRJ85XvB~O+Z96*c{GSKriC-Jm?dBK}k>FSEi@= z$F7)}5U!P*m9yXGH!~|%USCxjXy2z>pQ_cN<um;<l&>dh14BIq*4p{g$Ts+`u0&A{ zS`nwrp_HdrZT^0^J_Z~t&Yv_5V$aVbhS%8jR{!TcugJ6CKBt7}>jUZFdmzxl#aXI+ zfD-Wk<)GW+>YWdnhcORznnC+W>gio4{(O6&BBy)a|B?$hs#1B;n<`L;GoUC*B-Ih~ zuw%Cv09DtfYO7VtLqC1G-#1po4_2^s*uYmWJ-HGATz!#IQ7?B>^eXI`KqYw}KYnaI zR<it&u>t2m^-WF^36v9|-2cO&ECCfqVG@^K)i-K=H+-O+SjDMjItaGL=|qunOKWFm zBN~k!8!xYzo}Omsw3?{kDYYCtZuwqQQzJ*~bA5fRv|(^?kI`<p#vQX-bv_frsn-rO zizAh*TrV59J;c&20|6IKw%?kW@mURV0IB1XZvf~WNIHCzni^sr(Jc%n&0)5AZM;1E z*UMO5qraC{ZE%JTUPbIa`$?V~MvL8vaFrln7DcLtL+?yDugltqW5~H<vM<&>xKGIQ z7~DZ{csO!q2PTe!it1xXNXXNf3I-T2V|Z!EuLY7uC+Fhde4;x5>@7U;T~2_zkALwB z9=BOHU1D1HgqN3>Iq=OA(F&qOZ!$>>ut3Z_V`qUd)f*qqP-FymeWf1Afkw*hlEWgd zGkvHa!_7N(2p&Ml7b^x9gpf|?E1cF}=k4idS;=WV`rG6cf<HK4ueu)u4ou*m01}DT zNS2q}@m)jX@}N~P=j61s1ob%ub#ic*ae_9tf5@omlv%$>mhhIFI0viv6yC=Xk|--H zi(wCEffPFw>?OBp=Pmdji4@BMPzM^XjTAX8_4<Q2V<74}wRsJc72RMHS{j2XmWT3a zZN|%3AUcN`<+T~(@9*!=Ef<G-#?e{=ug@jJo+R-S>(PFZV=E)xIL#7sGH`VTdq=mp z4e@Y3AN*QnM26P2e|W#IC|%nw=ZKdC8W1@pB~m<QsJ{YJ4-xTJJ9`0u2IrW4BF2ED z<N=xzyp_Qm*?=oV&!0axgvS^vAdx&JRf^Zag;p?4Z9P31;BpU7P7Is}ItFur!TK@* z)%41W9Apf^AtAF{{~BrzXF@=5!2B?PXnjT^CFK$P|A62eI8ve^oaeEd64NfVG^s6s z@Y)`n{JVdDFDE1>T8tDjL72iUC<u6_#12cnOfg&r-4Ky&?d<&R>Izi=fPOd=2vz;c z%O8Q&xpd`<_9h=Ydq_r-$7dKXeA1gYrGbk@7)Z3iF`BeRvb;(ZiH^6ct4v7pW|DS~ zf;6j9&E)OdLenlFQL5cjU!8~1s5j@2jE!~G`(?nE7IjaN7lvVCV&ZT<*5Gip0I6AM z2zB^&3m^#f;OrnI){&hC1J;wPRh;``01~ZEIJzb2<CVo=2O%L&q(D$`Om%`bW73;b zwfJt3W@y!V2mo0X6GS}5eW}lKwM#!wPaE!FFh^jcUrr2L71TBd5>PMora<*#fmzdM zN)FwMuk@lWn#Pje$0g2AJivRaRcy**J0XPk<jE7LM5HqC+I|*%G6A9nDTU!wjXT(k zV7SO{hz@bu2nof}0rI>HgvfTzg8Iow#21kNe9NPqc?fo9Ha5D!^5({#oSY04n<)y~ zO?9q~7FV8QPdfG-a7;S-K1cJuHIrbFo|M~8wtJmlyh_Y5xQ>fUn%tvU<@4P-`_g17 zC@G(5Yu|=k&LnKn5<)^ZnA7du-AnZ_X~33OWYR$eF*MF+r^$r=o~qNY_4vXlnFP$N zxJh1}fAQi4oum()Qm`@Dro3Sq!YeZ23|*<uLPj9i4@Mto1Bt9wotH2J1H-FiiAfP& z>tIafV$v+!Y_4X}`vN2u0d%)(GD%?56QS$zLj_yMt{vnDFHxr#1^%mW$Cv`cbJza* z_%hsXrMRudD&Fzoq3-ZmY#FhkL(J`ukXv{`1~lQ&LeFnIG2(Fs^X{Wpbed>8S)~;v z8RD_GqSbS<zwX)8qZwHZ3CXGnK0ZFEa08;%p!+Sa*163);Dw`%H&LxP{O#8Po*AcK zg%$&>Kt(H)R-}6H@Pj=a&Vp(HXsHlb2C24XgAmZD+2!RI5Yd3Pa1LK8{BH=~dc5o* zE_Vh#zth9naNG-(qY(~c-dpWzXIr(L&K@~OtDK>b$j-^G&$nfH{dX}2jZCPnuBM=+ z?eM>NRmgRNj)#W_M;Hee`kI(Xr1XZOqf}GoCJBR>W-8TN2X}XOKW+9~jEn&D;r!AW z{p4B;^v+&$2%JfjWPlXYYRUyZxXZ#qMoMb9S?DC-yrNp=y4ejLV+5k=qN1YCWXWX6 z{%02!nsE^Y0auOELSIwP_SRO#!rqmD6x4z93hKd>+bj`~H)+i{f}{G|(D35<^E)CU zRj7m5hzNWs$c5V4+Roihc6Xg%XfpkvA?e9CB_*YLFt~USvoxbwnGxv`U%yHLQx;~i z2WfflYj&d{H@DBJ8)%@SoZJlv!qz4#qre4%Rqg;$xRDvjMfnUa4Yu?VTppFD6FY8) zOaxu*EwNBp87>G1Mr6=AE|m-BN1kre{#V@~l3Ay$BK_O_zZ)oYn3Y*=?S?S&xw-ig zddj8o6B)>70)$~Zt<2$&0tgTgyfrm-ge+f7T-;);<N-L?HzS2cgtUS{mFl*dSB}$8 zNd5Aau1|vjd#<3++|v^dmQvSw<<K&3CI1)bg`W-Ep(TXtjae!g*NBN(g@h_zF~`7E zWFiWNo_tyr^u>DNX_r~McR?w0K7L4OsL4ROyi2$|7wG+E+zJhtq>334z$MbVXu%to zUqOsA&tm9KmL!pa*(*St!V<?{?W**Wq65IT^;?In37~(=k=STq^zDSSuL*or+W02t zhYCTQWU51oVLeqt^Fx)E2Z#nuCi6r*_pPg&(=&AJYAK=b9vtN%kbps0U#L$FaR4OO zlGE*zl`)IW{bY@O2tk`0nUftk>UqEQ^DS=23);-21Tp-^ataFzv-k{kbP)IfANCCb zf;_142^s)lhGTcQ;%Kp1_+HiNsaGBL*a`@}u4WzwsX41^eri`p_k;TafxHU&Kt)A` zuJgg>JTTeO2s_?=x6o+><Dpw&pMsj?#CAiZY$bmO+h!at?lKs8f0);OXYREf3Y-zw zHA2Fl;CRGN){4C#%E37W$aQ02y(lUw>glUjgm86!>yfXq`W{)0J>`(Q!Q#d?SazH! z*w!7H0+V$xZ<8l8&g^%_@d-oV+#`k|1aE~)$>A3K=jPr5^VHH<DZ8laM$H?x3djWg z<n-;}%di;G1s(#oo3q;|i(^z>gGCF-E|h@$9hd=O*qOw|>HT0)Dp?d-q>PX`q^Z_8 zQPg~>TS4jv5zn`5D#lEUX>e?Yym8<8%!qOJj;P5VGS}A!kq^5#Q62PRZiT4w(r<kD z(bizeankAt))hjXkB}-r_-?v2{b%A`kGKYE!xID^RKo~-QG$f`v0cWi^(I)}0DZ=s z9iWSS&OAVf6koq42OUl^K>AF8E&)qCuX*nsFvZ~Ji#J9fFc-x!t|0*%23Bn578j$w zeUk<bcLA%RISBg9{{0Q)&?)<p2D^6KGtVFieGJQam9t@@a(ebU*HTCVSPA%XYo1Km qlv)Y@Tm4k-!WnOP>;K0Gr;?>5zZ|o?ZwTYTzZcIGr1PH`zW+aPPM`k( literal 73681 zcmce;1yq*%x-a^HqSA_hC?JTSASET8B9elLbf<K8qZo9Tq=1NkbeAaI-QAr^$9>+p z*1lu!d-l2ao-@Y1YYfMl)A<qaKc44T&-+R4#dBP&J6I?b3Rgm0R33#w_d=o2rmtOr zzv1W^pNIeBwtJ>*_sY`H&QZt4041YiXJuw-XJ)KR>0n@EYiw!3#>C0Q%0T(X&d$n~ zhnd;@|NH=xrHv6YoyLMEd=pG7aTQw>3SS5LgZ51@!x)8zLP>}UD>%ikCpan-4h?xt zr8~-r3O~!%ewLs5hLHYF<;&C_(SEnDF@}C(fju$xG4&Q-9t`VxIJ_llAXX#b7`*)K zzAhRmWi@l<6vb}_1_p7hR`(>M#Mo+Eqs8`Ln~B`7QxvlXu3bj=7NWd<?K{quV`AhV zkbjpexrQP4uYXSVyGJPW&nGVXJbEvO!ua>cNkq^H|Mj)sbpOBmumUZHT#jORNQjqe z&R{9+zkc>*qA09%)Pn~PdVgr}65PD`mr*j3l7;1luZXuu%Jr@Cf1V=pBYXzy-w)>( z{J;B)i~W018t&h}|Mt%xJ9qzk*RM%l)6>(#!NryOd*`23@WJv^%~{-;YpSoWUsfZ8 z_4_qEGBUHg{81!@HgXU9pS65MTd=ECXea|aA-VBEgz|a>xk+(cS{iktfIF5~c=)SK z_2;x+$l{~5G&gtCdhYM<9}_=^&rvQeE~5C{k0atZOr!jGm8rd9;e|0ogN_RG^12JD z(Y=$|Lj=dtiKgiP*{W|pua)Ks$#K(dbd~(4=cc;y&z3H{fA{Wkg~NtldOFQR*~I$c z;WE*#t&NS2Je}V$Ps~Scmmbj2?2K8~vRI6Bp$yw&gq@tYYEO5iQgTU&iN$g?t9Qqz zQlH}wW~-#W60@|l{Pp{{{asF@PBKmBg-E`xN}b<Ve*OBj+?RoW^MQ1;Qe$rIc^r@Z z>SLW>mtoDgZ5MF{vXqA6!lR<ZqM0;41_v(<=j-+7s2!AVNeOSAwS+&c+8;GtAI`r- zPoL#xKtxE9^3O&lk6TUuL|fn3`1JAPbaUu^9FoUEmIc{rr9l&wj;4e^RSFG5LqlI? z+x}3_N5{txR-gO&_3P2yrL{Hx++52aJEw%{CkI=F785-C8)F~J%ejp@<9pUmcH5Yp zPY+q>6|=hoRMgaL|E|79;o#tK+00){eI7i>vy}NNLtI?ES!uA!X|j6g`)hx+OINl# zIH$H7ZfOy&-0|_b|E~)YLF%wE`mEGq!s&4Po?5BJKo<Hp#jIjYH8r(cC-EgEC0txw zgvVUi*#06Er6vQ9uHcb<%*kQ&8r(57G$bJ<U2=XQD=Ujf&J!VphI8#&vir%tdYM&9 zh@)JdD}|s3A3UYe?tDvGSsCFm@>6wD7(Tzk!q8<C#ZA4F$--h{4#M`Pp467Nv^+~v zBNP!4DRcSzNTbpr-EyiXp4Ty`yIZcq12*jR^b|Jw&fU9r?N<9gVPF#{D?H+`byy#+ zOBQ}><>;6p^Z7{tId7Eb`O(9n;@-Z#oj-r{7g{4#=isRt+S>eSq@wa{79?Tgt!-`Z zGcW|)W;1;3aq84D?s~Af(@1LB4Hpx(y#6YMn~;Kn!p_u3f@#=SGA=6=1w|4S`PSUr z@1wmX=l%6qLk1ce-?^sX=<o6tMn)epG9HpXF{3Ruy+-{Go%T9mgzJHJKW$zf>iP3; zDmMj=uTfF`VT=_@_Kkc!+4k|qsIBQ>yQ{^Ei4>2sswIalO}BE#&GApZz8J7t8L~+N zu&#ynYl=DCZBdM=Pe0(TPgF(+3!cYxeC90lyzoE;k#YYzJ=%-qb7n<DLo*xB^X~6g zFziZ9SW|6mY{a?C7gOzeU=nz8a>C}ct*z;KX7A?iZf$2b-yX|W?zhv}(12W%-rnBf zGVA+mL%B=mXD3w$ldcV(_a(i;AIj?L>AkJ2bPWyK>Q1>d@cs4Na!+bp{>_Ikrq|b{ zQ^f)$C8ZPoEcd)cR$!ImGEz>k8e`eItGL&P(F)XP1@6|I;PKe|@H+mXPZE-=d-*65 z9^n@3VtadgUSZ+ywl-`}Pfz(*uQ>Lqs;Xih>!5dbb_S5J_<WW5Y_rl!BUEtL{b(T? z=Qis**pcq9GNfWbWGy*rrFNRO^&fAHS2_l_MKfQze&<f>LR<800XMFws3>J+W$ia_ zKE8eX^oK~*Rs6fuEG(f7{<r!H45Z>XEjxWMu;283#WglIcAi{{pnJ8wz1>_K&tv~+ z>cFTYj-x#2XuYs&H{Nrhl_-wgIBbi1?`eu$cdnKo9PYH+*w?OUPyh6OCSWAIRV@+z zfY0MJ=6Ud4q9awGrGF0flJy!f&TVuFiRl$g!BbR@YF*0H?M~jX-K7N#qb`xRSB|3; zDJg47&f;+~SSw{#>M1{bn%<o7>0)Gnf=12CDlb(>!evGI@I^c&C8bx&(+}wL1p;ul zO|cdnmb<+}Lar<3s5T_<I+i(Z-Q~2L6it_3srs6gMW3Tu{A+580v{j$^z6(UZi4k( zV^`$CV2)aUu9ntKt*hNd#;@X@n8$5dV-qtZ+pi6lH<}J+$8K5cH3sz92tGZ>q=XvO zuz7tBPIy*sjij9&J2Wa-8^2(I<BuQkDTr6xFsdE7_qeF2zVweKV<M+~URL!E4mnm= zqHt4?)t_(k=vPR%u8)@Z*0|41dCn|E&-^i_ohcwf7uLxy>c1HORp5GHftvI<*-$?z zv6%S&5+x%oZL<*_&H;t32`(cE`<v_LV<iUdF)ZU1_Wp3wmYgSU+!xE!Zy_-3h-;{E zKZ)UUw(*|lV0|>&Wdu*{7cs7FYO3~a4l18tLjE}1UnsQPE`MmDHkDOW%&xDi>tp04 zA289cUukgvetq8G$q~cnRJokMVmt&reSfn<{#-vkUi-o$uh`diiIO5ZK!~ziMnQoP zwnYDMd*<-u#FX*ASitn|LYs4ibzz~yhAMIrfB9nH=CmLq=dqivdwW%AGe9%;mX=#6 z+`_?JEm?hiYUY~5FNRtk2_$zoE!yE$e+mvB3c$d?m|0nAfm?&^vX=h-?c2AC=+9q< zO#Fluen3f?+|F9CAR4!%@(iQlsWQ~bDyL(@#g6cj+G8w}fnNTu1`Ln$+nO$z#Ed@H zbIAs61>uF)#w##LIVCMBI}>z%qOOxSTt3#_nyjXcl>90kEAsn2Zt=cM0#D?W#|fhn z>qn;e`1mrr6#`*lVP$9<r-#;0EGKcqF#_xdMhi<yzC!6a-TsaLXLSIZl$5kX02><{ zN&zM=Zo0)pg=!P-9rlkD9*5q@p@3?EpDy3Bl;jx%M=&TT$hj4&mejkTdX`I<-s)DG z70zvLcFz3%@G?`Op&;$(@DORG0fB+Byp9%$d%C*1c8x4hy<Zk;bi3AiobeQ!4iRH% z(;;n%l9`z4T^UzP$H}PaQ5QKk!F$}dQyy>e1tVrJwcRt}XiuPDDIBSOnkrT&D=1tt zCE%UhK3wg}sZn9ql75QTpQRK$S?yZrxHY*{p|R8+``akVy^%h*%IDK3Oz08@Jzt(x zU7Vks?sW+kSx#v#S^xfkZ!}fojzb}!ZdwI>$84%50ru6DP~x_&tu1t(!{fDFXcste zu8_JXD{w@ub$f4BNLNQEMM8`Yv#Popo{MQHS1ZGS<*qnCAK$l^saK$;^|!9wBcXd> z(;7in-WW{I`*nnAxjKp05zo?ci>i%*mITXQ(_zO(TtozYW3+f~w!#04jP&i0n3!+Q zNjC`y@)$Z$$-83eWc@kx2FFm>EAPH%vY3iu_3i7!_K?0^XmzhWp3eN_P<+7biFsH= zoz8FVAtoKnY}&N6G^mb}Z?E7!W@GCs<syoMQ&_0mfE7f}tC%_4340xslVe^p@@paR z<Hu|803(G)!JSuqzKa@rHqv)_RtU+3nGI9Yd0JRN5v&U`9#?L~y3*3F*qNAvN6{&N z9>ox9*KfZzeK1v5FUqQExQ0b@UBSi2$7i_Ik^-u;(Ri74q~y^hx)kJW59fOsv@CXf zcHU{Yl~uK?M6B@R!ofS4S}wDIJ;iZ~QQh5NRshY`w#*8x_2s?3eua>5`YESN1w-He z?uNDAMt3_lJ~t4)?9*BwNv{;c@A?Fq!Qz0D)<7v2%?o(S7<Oa%9~KW6BH`v*EqC8D zY?b})Bmb<-3&UC7K;vgq@Y7sPc|%Jlrx{Z%>CX=g@DDa|p9e(*1^CXd<<cy4_S~?T zY$spKP?UOYael6brE|KkcGgTs%k>L`;ktswJ8Wf2TGJA<(7{wOqMYrK4-`J3p$cq| z3!i!mNQgJfFV~zYI)3>ef@YIh^g1g|u?YeF->MLf(fmR+E&>znv7vNN=gcfs<U6#( zou;tRERw?`VLNtqW2%Ago&M)%H1ygw1$Iz>u3vk^jC9lj+rzvxMsDspY-YkUKFb=q z9M3Z%U0q4hqAwwdc>cGf#wa{uDshz^2>lrHhjPM2?jbBkj{62yvQP+l`SAL;mq(^j zU8$x@nOash1qw#bWXbrh9c)PjN<WZxIzJ7)UpIP3L}Z$~dgY%l^hUmrGCUFQDxUaQ z>hoJ$>R7%J7T9?%#gmf1veh2uXu0BI5j-_Bj$Js?tgY#<CqS4~;y+)t*F<rh6r00D zyw0k@V4N$Hx;>CoUL}SV=mmqj!)(0|#X~i$gY{55U4)b=2q68VnT(G%=j4Dn@kFVy z@yh(5JSivfg`4kuv{o7(g+_%?-&YL5&c0VRx0@OzpMAmb>1`YvBjx1e99+_IIEPkk zWoxVV3{&Zmp!MwKQp+i(eP^GyZ;zH!gO?|(c{evV|EvuK1Lg|}36Wd3b#-;UwN?Wa zA@K8OwM2g{!$u`#WfBVu3xJp}*6cOg;5hA8Z+^C{X$igmTsdF&S<TYFV4^geWp}bL zPnRR$g7NY3BGr2St!ldEY6^ar2+jr6@}qala6+1!7Yn>ENo~gG);yx|F&m*W&|7{Z zrls0EV1}hOHQ0UqJx-|0rIVAVNdh!XaY7;3z{T(=1lsG#7ERLSCA76E{RlQ+lH9%9 z*`N7pq{#SYASox)t~2_jOR)m(JkU+3;6^n~P9}clv@CejL)972{Tr}S>oeQ|HlxmE zGEYsJ2_kyM>6w{O(<;?s6X&}KP@J8cTLDG`bx}vU#1HHK^C117UTBBM$0m*6m2&aK zFD?P_@9nQ?>x63Oe=4|L<+?Rd$w|s-p%3NI`B8G@4TQy@4mO45Li+!vT26)W=9Y5y zXWl1@n5?A`3!p8xUHZK-W_47M*1oW8sYUr7FOu*lziZl8+Q{suQORVp(C^?^K)4F@ zh`62F^J8c#*zFx1Qh!`%dM@3gqVj>lx($40ZzUtiq%k2O!P?%wp{eNufZaXMiv!Pw z8bxbsD=XSwYhWY3JWqhqrb|V$y6&5y7#JCg3ALX-eG2r{F0l|$Ld@sSws89=L-f9d zo-2O9BmV+~gwO3Tulqpm`jNZ4yGoumFd>|udDMJDM5vu&rt2s&9_k!^{(F1IMWT<{ zBXrU3(f=;P8P0#I>gp<WFVMGPj5Ir&y83TxBmt2OW%sAbN?zzyiy@~f?eoz64-XIL z3(nPR++s0rKKKs`0XX|pP|)D|!Tx?@S68rc-`AO$8R3i<3Ci%}CDX(5@*cbVUB&qM z4)Z3vMLj?}=+-_TK3vt*)RayVNci~iBcH(}lu<f5I@MM`>1vn12vUdryDXG0pKsW4 z=lJj1b07!CV<n7$*B1f5$LYc8RIYL3=6Bt%2f9<~xV6Z$wYRA@b@|E_0%*-ZTm9_Z zF3!(v&+yf&oNh!)K7;j<6cJ5ua(2E!K_RQHt(~cnT7@VlsOfr4V~wyu!Pu9nX@9*h zg~TZDE)98Sqgtbx7z_=&3p0w`|Ga5wrluwBH(8mIrjW7@G!YOGP!&7}d`$*d!<+jT z>ko~}#>1UC<K8sPu~JK;kpcttYL`d=&ww^lVoS|NSb6N0e?qH1I-Cn0sd8omrrXrg z@`*f_A`CgqYin)ra3s7A3`|T+>*M9&K*2#(`3Y=9LQ=AqZmag<TncLcuHA<c%c;9? z$59{!yh}==U^8r=-k$lL4HyyDp}=ZJBuDT3p!Oo)?Z`&)hq}bs@n3(tspEP0PIfE3 z7&mW*Q;`d5|3pCrDUO)BbQQ0=*3%P5vWG~=%uIUdhX%L9y7GUljZjW!s{?%DeAiS7 zBl{c5fy)s$Wh6tPyN=gB{Xl%iSM4fgnu1a!>o?in4a+V)R<5w8Pb63dYz?i;DQv`l zdwrx(Ay+d2+M#I&uv2oVLvUg~hy~v1DYrEuX4aIJlM7L-ijI!H1!~04k&#%y!3c_H z(x`Zsl9KXZvwd)oSV~H&#B9VTHT8a>QP<a(sbWQDBVlkg2#AOx+o))6l9Kud{+RUR z`KFY6cgk%K7Y;_+iv%7g7nj~z&kOYy!l&<XQeI~%78&<F7~;lqnW_Lv)LlNGKHl0) z+_T;W8?zVh3Ce=HhQ=Phkq5g;|1~8gr4HA`(9k+ScDDSN3E%rnx`scHDU<hAzl%sU z*YKj^rs87ZsMC0Q_==d|mc9wqj|(5Gk0dzRmstCu;lFqu85%D5BI$iZgwMbY!sR4_ z&Pdu<swHNh3Jci)2pj%-he^igB)5cr0X2Dfv^Z-}x$mn?eo4t9tXEaYNJ9oZs;X^v zPL4!ZYDv%EwV@+82sSi)X<xq*5D<LXi!mAaPQ>+~V{GhmmQrp395a!pPXWX(0MY3G z{#wd3(cfPxafx=>eGPb%*3#jgF8wVAWudjfoZ?3PtglQ|_wVmbXk9-(*y@7Ej0K9g z)RolK-i`zM%+jNB=a=2CASm&`DSlQV_DmGfzs%0odei?D*co7K|3nj)M922;Eyq*A z(zXsCiF@KZT0ArJG*eW!WEI=T#!uuP-Fg?xYQ!C4;szxl1I}$YOmM(r8ee}-s3PF% zH{k#hA2(Dn9^y=%C7-!GUe0E_)Y(zoH9D%g!~y~cC=y;qU5UmFej`T!O!Es0bVrMf zvo$KCU^iLJh9C3s^E+=84{<rHlU$u&y%Za>u$T6_`(*H$ho6B5EJ2oB(<4GZ-OALf zI3z!T+LQyM1j39ohXx)k;f1c|X4)OIz1;vSH0;YZr&`8ko(7}EZ;$_`Yb|<t$yZ9N zt49g&I&JSQWes%arSzjS8ReMxij;ZNQ(^h`_dKJJk~noZK0LGnDi89KZ(*S=6a8KB z7J^&Mn*DdM@$eqpyC=NlHnA06k|z_#evg{^^23J@)z#IltgY(?2cvRpjvsZ!IYL2E zR#h#4N~wMV!q9Mm0k)V}Sc1Xd)q$$NgKBQ)M@s>B`GMJvS{-aoh#1_Du6K`q!eKhd z;2@76-#4R0bchb{_;H@{=F~5^c5Egf06rLcgb%<>uDIpy#X}?hh${u8Bn7POlHNas zGF?ApWfc*MW(VL57fVTcO_s>G)@I*K6403af_0h4crg1mlp?o_vjgYzZP|FPP-w@y zpr~CZAovBX@DRw8@njWuVqzjjMbNXaMCU-l>zkU`pnO0tK7#*@2@lW36>~7<nS>}+ zT`?uA16gd2o9e*C+B1?oTA{a;d0q&(?yu|grb)1y4h9Tqy5gc>eL55Q+cw5Z;pgB< zMKRoa{5Yhjs0h85v!Q$o5PX6Zte<zT*7fm;2}~@kNBhohZf=K1OG%k<R6*ZpvxWP7 zz-7N0F=%ss>YT%kQGJk{oUFdN*@|rQ_a~(m!lLg8rxpEZuAn5)0RCp)1w`8a;Kw^| z8&TDz1r`aZw~kZ3vI3zvVU({kjuuHX7pI2vwN2?co}$sEpr%iqZ(OCN<wbt%P7}qR z-C424L>xS2EYhy+mgTauvzGx~^kv9;{-9U*3Md6pe4hCeTW?MBgL>5g_xtGlWXl<p z9~O4@G!O&KMvEjeT$Ky~fpPKh%q}f80YZ~dS0}Sy?biVzNIIVD<xQg!07Q#jNnIar z+|Mg2N&|tT{WE89m*7P_)D!tihmECH_4t$hFu`_ixN28H`*7YY#+C%09Lr{ex3aQ= zajv$k3x}UUr9d=QXbF)Th95Rn>&s`nAP=Gt{Nbz-;tDF~5?qz`B*EHdRwb@}qK}7% zPQ_VSSv&#)np0mCFroJJd)<+HNKL)07T>FL=2Cp8--J2Wr$gz{!0KGnz-q9dVwT`( zp4E_iX1~d;TPrS=4n&H*=sjimcE|1~c3T(gY{s6cVkY*gs)J?nnToP8lp9$ZYB@nc zodo5EGRCLvvGO-m<V3^5!v}zAD#4LhLv;J6s;Y4nP!CqUyio4`^0esa=>3zEldDDR zS^+L!iv;I@!h9P&J<2Fz`Z%_nTbu0eN=K^OL&fE$?0QjPV6`GY*+fq$pGO|yM|ybf zo_XnG%00WSMAwUrv9NUf={DwjC%?;6?qzG@%EXCFP$ap9(G;9+tJh!V1G?e+_{kGP zs6HS<3Qv6@^aI?wyFX@mtIb5E=Q>t$vdJqsjO`|hbcPvvC2ES5cbMBdO)4jEeWu^y zQ*-VPX}y(9KjE{XFY`jtDd%1ewN)7{y{eJk@>TXjze46<@elN##l`KtuHOCQzhOpX z1%y8fjieOR;Rj3Hequ%fh|qaZmcf#Y+vYMT7VmLMzgoU*CA@Ko_FF}LLtX8K<k%6N zS6ft_>z2frUemAM^pL{Rw7|f$@r_}$Bo8dl3tY7lp-xspa$d<MF;VeYA{u42XL!y% z_N}Hv+FP2L8C$yx&PJvVTX<9MSD9;K?EP&<^4W~2s}=uX5MR68l}bH_CG}ar{kZ!% zlbfpVb*%-T?`=nW^2AnW$00dKRmAu1g{|OdH{uLeahL91onB{1?>;{*RFJYdu#mVX zEi;g4^yZq4;T-Tk2YM6xbFAcXbCZ5rE8DgU^}Y<#uAS|WFu^aWG@J$h5JLeVh6)<z zhfh_PjLuB^#U*pj*4YkMvre%S=m;Mhhoue2#!9SV-n2Pr45Me?8dEx5%#0nc!PU_T zt79?3-*-JIXsv9{cgLXN*YG2X_<Vmpn}`<NGatWC;JLhxwp~j5yEl8`vj5I`4@+~Q z#L-fsobAc`jj{COM<XkJ&wKkVrrv+%#AYtV(_c%<Fxq@@KPoEhFt_>&&#3?E{El9W z>Tp%L!oxhh=f-@`of-dOdvc;Ua1OpGN?E%kSDsI$OUd7#aQW-!a6Va`UNmadrQLje z!+mg2-gn`rki<*4@^6Nk#XL5r?r>=Dum4Ku>U^<!euJFx6J71Ie;HS;-gzjj?+<iz zw*O=jhDR6fR?1_}HA#6}H5iXAu3=Mnd>gZ&t@M_n#FI;JZACF<#uKoytuUg7i|E?W zosJ3=q*W1{kcbq>&G!d3f*nzewvO}4wReALMZ0BSn9=`LpPkii3CoVDy`>c~Xw)UC z_)GUXAvFz=nzBBf32R%FgywQu+OH(Xb!FpfOA6paDj2U`%_MpH(fmO6sTB>`CmH<% z%SwmQxkr)xKXA1N86?)|m?YEF#67l!6`zQm9f$44hQ8#Zzpt`6Wie5d)k*z8lE_#r zxbwTiD-s@u2)^l0pX57CM{pSkgn?)$&w?~2JRquw8Y>;TJ5`E*ej+P>`c;}P*7Wx6 zx;AF1v90Q>EG)exW*F@~O$B-LL>G?JUiwF--N|pvgNU#)-C4vJa1At$S|h3aA6@5* z218A?)ItVmfjIMw?g|P@Mz(VP7a`m5u&~3wLs}fUM(r_mPTWtvguL_hompIrTG~bU zm~-p$#zw-DHMG9Yg9`9HwCj8@RP_KQ>p>kr(B=2n^vC-fZ6K0<3dyRCMgwzW=Imgq zsjn|QicxJAT)Qggy^jF_{p~Na22P<`h(3E3Ic%S+Roe*;8JGn$4xN5}$brN!=4r!- ze}#6s$!mPpNUCk{+Ggl<`99s(?UtJ{Y|rn6&!O<iAK55fPbcuW&=~E^M71_HzD&hR z{26~!1#P@kmQ~pYzE66a_ob_OrKJxA1QLNnEx(R}9x=1Dl;O+|%>bP~*f}4RG2qnU zrbmkVeDmJ9xw)NlV$Bo2c2*bX?$E@W03@^P)t>E>GS{B6K6&yaTeCV2>aAgCd<%FC zNG;-&`o=2kAu1-u3f&%j4y5;T+bv(;TkMzvfNFSpxRYjZ<&IXFl{YZnkob6Y-60DA z3s5l^jmkG(5{c<U8@_w6^;ADiKh|{Mk?CnKP3DB}Crtg5Uko>-WpoC<XRl8?PI|vc z_&Is^%a^n;cAKtZ5g^}$nr}=$zs|y^<mPTG%+A_S6JF^Smek$srJ+|j8s%uQHGiZ0 zXAkSLbH3>iQ-%FnBM6=&z)FA!cYkJBC9~(r$;t6|e*vZ+nH^EMCmt~|v9EvyI6pXK zT-V`mQGlS4Ub$7Z7ZlZy4{EU8#%QFHmU|?Sdte`Tain%Gc>wW_kfhOa|4Zc8p3w(C z2YRph6@ZxVp%vh6KMrcsw*US`6#f8G&L5)ZUx|ox;G*ysJoJi*H3+;o$&I+qYDU7v z9eHZ+`qu~T5_RE3Mv~+mg{hj}8mj&Fh>2Q$mpAnKwHHiu-u7!I^zXSp)p{mfl$g)$ zI!;*&J`VX)FPd|9<;wIHb1}a~pO}klSU3%HxsY7%yLa!bfXaY{ED6#Ex8o)m@Tj;m zIP`}{M=a)}?0kHD%l(-*!6E6kS=t!Re=Gd<%FO2Gi_cHY5e9m(+^jAsD~p_AU~wja zmJ=03%*@PD3@X1`Bk3{m@tZn20`v0n)S0ILM>|pO%Vz8B_I%4tJTfk?;NW0GGvFEk zZCR~n>(%$b2Z;fZ2S6o-5gVyzFoj^^R?SI}tl(J$FqXa&b2SiaUx<kKESCLWm4dVK zANsx`!n(Rtw`?vGa+>siM7S$($j$`bCJ+};%L7@rfB|2b?oF41HOF&wbaeT%TnG2B zXntpZfAIr_hc>{>vdT(;WfY*tr`VGN$1@rF(eY$NM*bP7d&#TT8*xRQnYU$PP{DT% zXuKn?Ou5}Re$DGBsR+V-+K%ocuZr0Vp<Yh=a?gQBM8bk+_2;*==E0#HSFTND+p6sn zprD{)(&%+&eEQVNF}fsIlQFE!r+|uuWs8O;ecINxViJq3^cpbyo1hI<IBva25^zUw z1+0>)EtCu~5GsZ%9hoUSPg$Vs7<w9Vr^7vd?0N3?KsHfYNC*XGh?0XNa)bv<=ME_; z;(G3HPGoUxZf$jeM&o?;cWCGD--e!^P*BUJBj!>bGcg5$O%@sz)z}zFs#5F0f3P*R z#9wY%mdNeAOAQyitc^kyv3+1^^uh|CJ3jz8+R)!0;lKNoRUStt@D9gtx$Q&5SZN9( z*8``;D@`A?b>dsMZaHj@w*ab_5Es`5w}5)#l?!4Wa&dFBfZhBakGvHsBjQqFVq&se zjFSM5hr`FdxlhjP@c3UML@bvz)kKB89zayLg9*pWU>vQjWf?G@&U~N%lKyjQDhYo3 zoe`rX>+NY_#@ooA=Ib?Koz*Bl0B!Y~OEZ>s39}e(Nr}AHRPPr9sZ5RWcbM|BIwR;# zJ6{x{^RB0**RAyRI=sNxK6f`}EG6#@f-p#zN?}=r9o=dG4%ui)dQWqxG8cC!0Z~K# zr<=}T@eQ(shHkIrcBg&%qgsy^Wt*M7t7+31FpwR4Fm5(@U#L~H%hcjvvw=V*Q=!0d zOA|~57B;ruot=1aQ^1>)=oox82GSG=Wnd(?16)TKS~M$#>iYWnAxMC?S@o|0vQI&* zd@}AZkVaOj7U5%v;~CAYb%pxY%716~GDR<gekma>t-mwdfQTSKBnq|b(4ifre$wgl zHDhcm|DPDW%@o7ZU~%QQWfXXOd!qrK6n*~Ov=MRbkQe~yL{PYHK^$SAN=vQ8tPV~P z_#}PbUfu`Aq6IG1CqKVgIRX4)2M34s$?DG#y9kSmYXy1Ec`XM~FX*!rbI_^%)7C3~ z01pHI2+B=Qv8mG67YUN^-HoSe_@_KC_?2=s7u-r7{RIKA>TG|E-E24nXkQ@cdWF_= zVvM)Jk^UukaSCX;^;KtU&_|?d+R=GAZb=GS7}Ujaj2f0dF{ciE^!U1_bNbH=ZpRm_ z6<l0G{=}mv`<uR3@za<nxy$oC_#F>6pXum}HkI2NbD5D~`T9V?Jdfk>zxq6AdnqGH zcPtI_2@?}4fDH4YtRW2>gn;CF<N2J!nw!P17`mF={;vjg*yqQ*gM)){W;5WCBODl{ zg)*S0z{clb51h}ND=R7xH~^c^L3|&I4=n7dx-%5dXYlYGl31Yf%NQTWcMAODOn1)i z2?15z<F|koQO+8Yvn!$C9}rMKIjLz1TAm3|=_Tg?)f|uyyg}qKZoKov>>EeZ=xAJ7 z8JAG&w=?Lo+s!jF5;8J0)YR`l9{fx<@|~{#hsNhiv74B@yu6d17w*U(h%dDQHEn?# zi)g0M>GZPIKeCeouUZ10T>j%~7=6X7j2Ec5;sDTPYuiR_ViCy>!c<^ge$%XWN!*e! z5To_-`Tk4<{oOn9F&lJoyCVHj8^`FP_^|f#foCt0%r4Hu#O#;lQV4&pZ)#~+XllG~ z|46S?R^Y^f+AwTS?%AG+b8n1_vOC-44U&zk@2kC7T)#@u?bt?)BO|>$Amw6uS1@W3 z3nMQ+obkb<TikC?_6qq(=dB1Ps@1m3eMN(ITuwGhZ8yd$?6bts`WI><COl8}H^A1v zjRRh7iTN0-VLJ(Q>jp@R>{V^TRZ=d{#{{8V7n-1SB44@C?w#928%u={Ehy6$P$s&f zrZy%*ZltX(BqsKLP#L&0VyuFu%QMvkdc_AwevOuxXWedV)4)mouiDfdsNKju>+QOh zfK9$Sn3E;3ovBjz0AxlTAn=G(19I-F0_yc6B2Z^^SNk)|_YIV0ZO+XV9uZyPJ{Mg0 zfd1^I6tx06*llvfA@o$d+!9~eLQLXmBg15iiy~{+$II^hRWluwc5y$<Q}|fn1t>Q< z`amKwv~Ehv1Ji`#?^WC{EpT=gNl0$@ER0#Q<R}I%PitEkU6DOlrP-);c`g5hLNMyl z=(Se5s3>RVk()`0oSa6{@xR&`FTZ^G5;^Oj`OR%@<rX9A1h_6sD~%nUqs6ATxoxPi zZ!zhEt!W2&3=o!3<!mknnr=JK0!dj}S;KBE9LLY+l0iw2ftG8c(hpwf_MboV`KM`V z&&kNhoT5_Ox?@=M#3dwV5T%DhD^p<?aB*Lr&eJL^<<MkM#sQ{G&(FW}_s0gEX?S#W z7c5TewLwO}lk9WwSKtm^4OzW@4uW~P$<A#O5^V#6^nCS2w~NnEAwV4}?#xtpgs6hf zf0jcHH9OOiK=|*ea<<LM|MrbmT3Wg&JK;4hLVSoT#%n7bB-Y$kjU%zxjD9V4$n$cU zn$mSMv6U=3Q`6KXc}k9r5d9oh&MXKBmfhU9yjSUP!~Kk>ad5EcY&n(BbDY6su>LH; z5p{UbL?PiHobmLk1gFQo^eP@Y)eTWO8zl1s83D-JSlQbTs6xr}^Y#}0p?_TidZ9IF zgRnq|)&<SGn^3Zoo(UArIN;JClL>ZbDKVRwnX#XMwevABkcOR|UFqd2U@{$3)3)an z?xI#!kHN!d24%EiV1VfP^Cmt6NEgshQBe)a_m2Dq1F(F23LJPh=>6F$h3_H4XSFfP zahKnv9+0fl-vOm^_md|eq|sSn#z5~RprGgiG$an+x2~@4Kk@;PB3X1ESF@~s26iC` z-Ls(6zx(jP_Gs6Dm`S}Epdh<SE1+f6<*Qf2V`4t$<}yPGF`BG$MgS*#3ZPpjIPaNB zjPI6ydS5z)of)rojYecKKrr(K>zcJ58uY?nA5kIfPQ-&1Fz(Jy*a9x)XWk-drvnY+ zYG+$Tlsj6XcUNMh$#Y)br~4t_ehRgm+`9AV@0F+)ahZjr_UTnc%W7YhpyA|r_B#S( zpReGG2L>%RR@jq}{>Ek&Kba@7nYlt7K?vvaE1ZY&*$xx{l;;31Mk*aGYhu$;wu|j4 z;6GIDw$fMq>6MD-vPP6SwDKI05hPv(Mifrh4taN}r52oI5<aKL@R&oRMiv%9V7Gvy z#iG~v*m+kUX`Ij%_adzkF6ivMpz3n4nXqLIF51{<SQ2#`z*$LfPQ&Bl+aazg19d2t z)ABB(T1oxX)I~W<z=*YjgCTGx>IV;Y5m&I4K3Ch!tR2kG-M}nhYqxnF)_;N#3Y*vq z;Vh(&LevvUE>h>hZtecUX5OB3-O4u~djh}0Z%_fji{>N&f@WE@a|I$S+JMzmf2`{K zes4YBd;|WbK9S#58DI}{vzB%&5;@c?Firjb<%bZon=B0tje(t=onzv3NpO;ruk?HI zfOb7K8-X)XnKh8CXgNw<4JxtAL|MLSj(lVe!=p&mjZG#E&qFKYjfL0APmS6`zSbUp z-!IF}Wj{YZ8J#OFF@JS2T2+3`X=z!^%26ydR7OGa?Y!98_HR&U!obR>Ppb|7>9&>S z5^MQ-*mie>=|bb<vuixgDtNfLRX}NU0!cl6vnTZf&;9#Pr@lncQn+n<yTFk<CQg-J z87a(gIoe$~>MVEIh^qk5y1Kl)TyW$2$B2C+bx0MxzFN#vimm(u?TMLk&q+EFk*~-q zE{?n;v$%&wxssItq8rS<E86UPXeynqV52v|gNl=h|EMD%JTa0vw-Fdw$YyM9{cdwC z!&rgfE>n&#K!hF;oRN;gpj8tOxO731@Y^y1V=iC5Jnx5*y^16hKizz|s78?SNUr>R z3^G{Pu)`zh@88eNjg^dCI3IZZIG2HhO>8&w{RgS@@c<mB7uLPQ%7-VG#+620**jP9 zsfTjC(d;(jIPQX}fr)=t+r%g%^6v>B4i2t$Tf1aUxieeN#B0O=bGvdiT6{yxTgXzq z+{PC{YZdmUn(lvJyZgVIWq;G{1!Z<&)j>WIerYOpc7-e=&DYN&yl*Iw>#u3`eU-P= z1To?CM$lahS=qB@eG8^Y50!HHFidKU$4T!ZUS&{E6ieLPkY|1<E4k*dL8(T@dtp9> zCp6cS%F=q5L>YAl0RF&2+owv~4@S{lY=+WC{U&tYJX9i*-#5Ec<T41awc^e-2I@f{ zfelljd!zEX^_TS#CI`N1(VG-HZiRsx#83+EWt<E(i+{JOa^WECW>kG79ZSWru`#aV z;yy24)3;}H(Q5yqu!*N>s<J7(_8i^Ur}6%CVpVoMdiqr()#p#yCKj-s4XVY+s#pJ` zNU<P_6#1?xT88ZSL^^t9mZHQn7t5*6(rF7#tmKRgg;%PZOjoU3mtLUatE%$AOcI~v zAmJnNy6WvM$|tQm^DEn@o0|aVpQwm1qU^4QXrKjr@;=(Tr*<J;t^0=d$b5lV%()&- zOjLhDEcou|$paZ5FR$#&*CXX6mJ|6=>>exWRQ`&oGNjq@2`?W!-23x~Mc|Ck$0yBw z_g{h>Hqw`_(9v1#eJz4Zl-|~vfW1Js&C5?duPFOGcr~7?(pjz8XQ>EpVMogon2E1( zu}OdE8V)g<uSiNhx&U{DQr1@9Kk@No-G3_TbR#a^?-CdLZyLN=nv%|sF0Za(tCfh4 z!FMymj*dP#_=VwgDCfGaY1sGI<?L8T+f1FIXP)cl!=<i0ktULk!?#yMMs8{TqZeNL z=hrKkDtb*%=P&#uec?~)YPintv|^Ea#lGPNono3;(}z298A1J+mxW@(e!Z2Z*F7Vk zwbt91^-0L0H%Slc+?TJ&&wk~{g$yTrb3$Awj^;^pCRWxpdlvOnn$Dlz@-jl@#_o8W zR!&^w6*L1!ZA|-fO<|5|Db0^scDLsxA|xb~2Q#1I;s+Dl$hG)d+%F&>Cbc=oH2xDw z|Lpvuxl@|bF{(-W{z0i0IeAcOsOXwtJ^f%<-T4wvY+tq1G<qQTR>A5;u?6nHcZsnB zHW_cGvy)WX6ugI>vDK1uKjPC9^}dM762T^yIk~#dEiN_!p|Rh*_;>22!H<Z>Wdh>B zRsc42lOBSfg+u~3Cn_QH_X0El1Z2Yh8U#v9OC9q!K@gcJD=%Lt2kwJ*otXIj=g*`| ztd#^UxqGSrP9wMM!Jd{0nm@$jwVFodR_$Uz7Vh`&-)|uVTi|i#0zqXzH&5S%KP!C! zAdUd6?S(6iRBnJ}60)*G?GOi;9n$h->{+uIFAIfmc*HWdzd!9|#&353G3I>ccuUTk zwy`^Z9_hS715U-LfV--sCJWo8U5ATStYq~IS=g@M?ZQjz96ElV5<ZhEc6L(pIsKfD zGEyNQ0T2T~Kdf$eI0l3>HtSi@9P58VJKCT4o0|a+u-UIFfD0)K*V^AL@#jw=r0e@L z*EEBEkY8H51YC$gwdf;g4|(9VLuy4zZug7zV*BlcIYmXqqr0V`KJwW)-T2-j`<Z9B z)>9BV&YzaSa2t>$*T+hOfmQB;ny9C*U+;^33n45Zi0FbqkbW+Z!0#HxIqCEk97V1B zzrkulvWke?y_Q>hMH@Z?w6s#x|6MRCXAr2MyYRU_@V5wfEv>IZomqIua$|EMyyP=y z*J`EOT_T8M6plJkKa{HW4z2oSV-T}M=M{W$c*($ql{hF!p)0XKO_~r3!=81jFx32o z={zD3Q6nf9R4~3Me`!PTbUbx*bi83U<ddc><L5koVNub`NFZY&T5A!0Dd^-+xOjQ{ z0C;$u9~nX90-_(bnBr<~fGq5oU~J^*u92x})*jdW`xE=A0RcEsOd73e65;>SUxAX$ zLh95I*aL#J;b89dN-^iVpEztzR=06ZxxM;wkmV13uO~;1^MOp9DCE__R@NEL(?LKT zBzkW~$Mz@^T#$0Ju(IlcDhsY(T78#>0wSE8HZN$I^oK+h8qqX78tXGIhiu~*Zv5@5 zBzLeNI``?;$jPaIdtBtZdrcUvEfk#q<;n)q92TD?YH-P9DfUL|JC!LYzLa|*mzDnE zt~*1+;fx(5vY}&>@z{x*p8h*9bgWh5cEk$Vj%1J)ka-3`GX&)1IU_LA5(t0_STS<` zA+3=QItGZUu%drj1B??M>-aP_+ySQjuK@c`P*Xr$y?NtCXhu6+#``dq0EglJPz{*o z`Jfz#89Gcu0v7?m9%sir5QPW&);8Y~Uha8Ovq%DQx+rF?7hQ?`JG;BHKZ1O=yDBZ+ zZ<3IRo0%~{Y^*21e6;8jT<o<XH*Rk3oz<+|T|jM+7Qz?b`VAr@5;K6|i>9_VdL_X* zC<%|<4z0j}hF=OFfTsHodX*Hp>ttl@!2F6D;ncQ*q>-POrwuhETfIDNd)o?POV_Zm zFKL5;09mT(&CQ_TV0?(6iAJhq3Ohu@5X0WAKl3c4i6K*42J+RQJ(A1-A*lT8{W@6+ zq=+81Y@TCC_^*!NHZ%YG@Rdh<Jf@Zhow-qCck^J$iv-HbXi+>PvjMF2jr7^Bnb<m? z&h+HzL9?m5Pi<|(?o&-$Kek^LpQE~27N4y6Hu{#b%8JnnD72EvAl62q$AC)MlluI2 z%>D2)s5nRsf`gR@M-H^fMi91R*^LQq-@YB^{NK-OK<aup^fp!j+;W)1aNeEA)N2ZA zXlU?-Fh8UlOu#uN;dfz|kdWY606yqx3~V`Zvm3-;Jxfb`amkSX^+Z#6`+gi?cEA{K z;p{Cq2f&C5u=vyPoYHx?Bfnv6irxAxj1NtN7KuWV{t)O6hG0!T#0D2T6Qn>>rEvfA zvZkQ=cr^?BnHg|xU^Ji=t~s~oIUgickT?*`m%$VLCuwzlz8=c6a?zU`mbGV3uHunR zS07By0o$Fe+^Xs0xqzRB<Y^Ei1()<m{m@V}@aQ-DL*HJ$g|)O{2o||82!#F}nG}zu z?VMrUmyJFECT&m89gN3p_)nh(-<4m1nW?m)P^H{3;#(g+<(PjmxG-$$uD{&WZb3u3 zlz8un;+<O-(>9`*YVK`7>L`Awmw(Ccj)yV{l&242!p@*8{|31pC}B4!c|4Ar?GU^M zIN0lFzqh-q3kf7BP;9#O*C1Qc3MKV1q!HjkdCkv4;zA!b8Nvui0Pzloi75P=3XliT z4$8B`So8Dqhnw$_cBOzq<hIwr`DcA3941(%S5_pD<Q>f`WTXYemL8~r>i>!${r>%Q zDJ<HC{=S-MA+syniO<?B{Gvc*Of;Js8^0)K<+ml4M_p3!_CN-Y%$B=T;Coy)GBQH= zbYLJZ7<lN~5Qz!6!{G-kOfFZGADO8EQU%woS9#s82OI>4t;vq=l&8>?b>QY9Cm2YN z4SS!huCXyPc=Z1Ldl>YB@tJ0P!PB3lmQ{5?R)L9q)vEPC%BI<5RV++5bR22HFcBho zz@yj0$b)dP$-r=pdu0?3<alP_a5%RrM}4He4t=E(9Xe4j{`{YnkPWHt<1aIUsJ8-1 zzXc^s59iMaZB`6$nEZ@kQOTt+IKReazv1VdqLcYivC`G?m*)j}Y9)-X#h_oq$Qw_V z6K{Fib@Y;%gMq;>-3uab1n}@kcoqx}E>QZJ)+l}=QL#9FVsu1M21Rnb$~pWN>m^-M z4wE!QA5^6uW8+1%J&*+3BDX{0;v~hzKX&SYUfK9No@jXQ=l@MuY#dkoDn_-X2DFYf z2-@OIBa=iE6~%)wW&{1t^!3vQJwRC`#Cle9yT@idAv@yfxo0IzRoQ~v;9$7V^C7#e z8jPF$t*I`Ug=9ZLfD7nxXm7^LShNgXlp)&0<$lZ##>V1=<J5q@w9yi<7Csm-0%x%h z;%gmclIc|7Za`{OT1EzKnYwgWRMNx*@{~*%wQw#GSG*NR1<lSL=uWzFy5nR|u5{O2 zC%H^Ywb58G!EJ6b`mAilI_I6`brl5OOUkexjSqzV(D^P-{o0(4@aOJ3kreWWa&<6^ z0^@^=Fxq%W$=qQVt5+#&fW&Cc<$mQoY@2M!^e-T{v?uU#4hfi=nkF<kIXNNIjc}B{ zrdKs1N{h0J3Sy=W{?KTHwMAGrWTBABNHSiBfJ?FDuKMk)h39(}wN;IQNqrj+1shle zLCJmnO^*o6H;Q)@3>LT@)-%6+)o)L-E_EjSuCIRwmBFNO_hbx|EwFpqAD26MJOQi# zd%yt^u{aibdit=iFy$&I78un@R{6>ajY15{(?@Ux`U;I?5o1>hEn@Bz+}cc{A=){p zQebzX+0ZoV>+3`8b$gt}{iQ$}%*guxu{+!nG*-h4DlKNb*o5(-=4@F*H+ED=fm@+x z)5XqJ2K8%7xstiz_wPGLWq#sbIsEd7E_=1VuE0Wr(1gZ<I7|vrg~NV9f*BehVz|a9 zB-pC>2uTt&-`1ueBa;Rvuk)y)IaUFTx0|r}RyH=j8X7RbI5IOgKSEk;w)4{5+*=rR zh5TY?F*8WnppU$PykwTr-k_T04q!nxmp^dUjemo-gdJ(EN^qbt+Mlh$rX=qN5){mk zb@!9Bcfv!C<mp_32|~s^USw+R-TU_@=Py7<L{1mzJE0(1LE?H)0J5_nF(Q*2prmxz z3xFpDW3L=hZR;1LFw%=i-$<cOV56n|2nEq3;h`&zo~r5{m?SD{e)(wz4s1We!@owp zfT6}g4v%7&l4|*WWqO6KxR17ri|b9hdr6O@^^C=j!&*p_xP=y*rVk;w(#xbHPG@sk z?)&V(>hKM(D8=4e$EnZdf(UM45XC^CduDd_7qrCOzBBB{0KCC4Z$ZkAxcD_V9CIKP zraA8|Lg;{anFnMzuX)A5;9w&d!v*bT56m(GSZ`4Dz!wm>INb|YlvZ<q<a_6Mxh;}6 zPVzXW1K0uSn-hrSet-q)fi={0-QW-^AVh#Q#FpX2lY`4E0>>o9kMh=1&3!5=J0@$I z@%FKaiG*Y_*>`XTtDs4qPF<W)m=66woCjE|X|Nta?ER)u`2@f(?KTHQynlhTtWs`6 z1J?5?>|r~cO9+vX?H+EjJ$^h552FvA3GP=ke487@#Esy0)`9lu8y<f1?Bw8T+C|{- zurgTj5Rs6MW%bqaIP!&*ye&i&kcm)a7ArP3HYyz^DAP|NNH<>N{uyzR=jZi7+DoT- zE+=;#bWx0&p<YoKjEN*2IU6gYJN+A7r16vDS~Z(ljM!pge6pjZ`GG{|u_yk*VXeH~ zMQi`+a42u3W8dI8-$h&YH><fT5npCr%K2{Z?94P#cp|A$o&y*p`2Qp8r&s#_F6-Ai z=b8fJnW{TB4e2oTfuyU!x?zk(Cd0w6g@mEj_TJuoM#eydK!C=$4S$yHjuxLmn^pn( z3QD66G*x6G9*_?R!!cXd%+-Gg@bK`=fMU|U`U*o@;@|=!4!b`wV{h3M;O{oTY_~xi zg<9S?G<56Lt5+UJ3%5WiMm|ZxVM4eye~rcke)`ekXG>(_8v52^w3bIt!9Jw31K_xs zV!c=+x5uD@P~pbv>UY4xL#4E@eITsE0pcU{?_=VOH*QDis<<Y9>2tpg1d>XPeZXH+ z`|e}C;785;%`q<Ub(VrkWg`EpEFvu9gVg6H_V490Cr8r#kB1gJ24tDkY3+LmC<GM| z4bsJSFUkb_tun{A0S}LK4;A4j{}16pLq4Z%A><c32PU_hZ~!B8S#S&&(346bXt*$r z|KK{|%@OqF*)N|mQ{#(foS8N^sbfdbx}TGO^vPCE>C2$$$TNOrW%q7*ZAc>UQ{8Z@ z-yLq&eaTDys>OLfv?wqhjdGvj3RziYYAM271Z<IsK^XfcosWt_7xKd#hYT=;BYxC8 z>!Ad}d_hbRMNXD%t!LcQl<?|@94(gtok77!nwTM7-p{RCskQh`t)|Wd7TRR&?6UsW zfR}C^Cw%aQpuQkEX{fh**-6SoM^hK)&q1UpmTXuiMZ$ws-xMIceR00MSt;*1fh8i1 zbGmDGU1K;HcgJe>GP^Ole#1q}MgT>m&S{c#P7rx{wK=Kc4SA}Mr;6B@(S;=%iJ~0A z0Be&eUwCu3xkYFJ6Un(~-sKCyn!Q8%R-I~w`<ZOp&8Fbr7yZnIbsjE*s4zTwr4r{E z3~~QJnnw^)d=*5MpDvMuM~;#csPc`}VRzgVD*1AV%$3nLA)LoKR7PGVF5;qqndjQI z=JDm8SE?81yx#_5K0no`Rxd-nc(J|QCHOK+Vfcy3I|DRUOerijS64GO#;^_sret|z z=gfb!yr4{{T0eAQ;ZUNV$A42)IhMziY_OG3{i_TZ-R2?>AG5uED9e7JuNxNG_67eS zH$WXf&o_Pk3bFM+Z+&1v^rl@ZdatOc>$e`#$r05P9B$QKzst1Z&a7~8RbD=gyeY`I z<5kwc@oN;@rMS;(lAkE4@Q`;pJi0<OHzY?%i&gL>Y~)q6!^WS7Quji`mt6j!3h{C% z3lk!24(_TJ`*2Q0RE#%_S3I*3z5L$Lu)qKJS<6tip37Cz-?Xg~!M3m<*ZHo3H1~+v zMp02et!RI{JBh(p)Zyaz5cT!z9naj^E<wy=rDxj75o1(p7bf=WA5$`oW7~{Pj6Rsp zHKN$_%R~&B{Qi+ixe=N4g|6N`Dv{ABS5&ESDyKEpQ#U^SRr@gXYmaTHQSUP)%4}s` zUC1VFYTf=&Vvc6TQQKbaDu4P#NKOHm9D+)KIp5>wude^+4HbSw^jPeibabBpI=|j? z0dE%Ica8WEE;crh5XP>Bi}S6E-H4vCG2X5@NP)+1*>jo={{)bUf;lwb!F+JUQ-Lr- z);ScK5z$J`?ws>h^l2jD;%LD-q!xG%WaHQ|fyx8O$NdRp9R}Ch_S{-pTHs|9UdZ&e z^7~ewZ*Z*Ro9*RI1|xh{P_4;N#U!lOW9BE#`^8`F%l2MoJ5k@i7ox~qbdR4U^rKWX z>RXO1M)&WxV`HUrhSr~oGOiQDK&#8m=W=!2pwa;-5ET`*nr{x}J3))afWD;%7YK<M zdHvT{T98j(>swOZhS4jai5(mk1_qH!EMNi|A=1=XsVv*#@^UjM324pD&6F?=2CEba zUcv5Mu;8xGshvs?%abRIFw662V@%1EkB6tC+b<hlg8_Pi;r{vvyh!5*bpyk}HSFYM z8#so=CK796fj=9?Ja-Av84q%$3k|<9q%$9<9QLJN<>Pk5i<JKa4#pr8lL1pkz|+NG zl}me+0Yr3EH<jfWfGHzy!axQt5I-pkl5vY>0CXXp;%EO)IF5|WhVe<)kw*c%!o)e= zS)f=(31;$vD&-d!4+Px4bH^me-p(!!#xNMv%R*o)*eoacB5d02xEmoj2J%QqT<Z24 zZEZ?(zv&eu?E-WB^I%ai+{2{5^3T!J$LJopef;;WX7wL>S+o|-^Jh|M3bY?hdT)E} ztAEinPyc%MSzw|T1qnu7#ndZeSTwjDZ$Y`DWxaRro;r+I!O&GNxOA|h&)>ZHW>{NV z%IV?ZA^rIYF1#QBdA$W3tfGi0By04hn-ZiN7*4~5<F$aqA?$OYOTzRBfRyyXL&!aX zETr2KM)O~!nzn`BDhT8tb{goksgROz+pfEcWH^8EzH&<BbN&T7<95$6Rm*22AqW!B z5B;|J-Gc+8?ddv*Q_Vp`1(und@3m?(wR-2(qewRx3NA7qivqoi#q<1B-A~N{_QDr* zAb=|%3gv;4rNG_l_;<PIQlw-9h)czd_;>k~sr~a)Qtm;LL=XP>3Y1VG5!r?LK}O?= zoX-*RYQm!gXD7M+KkNJ1vpPPvTjFMff8_p}_NB&7;^sw$>;bcvNJ_UdnnnjRCPp4v zNK91BQrH)1y6y4#Kl4;wj99#moAtkb`M^FR={RUbwkl}R|6?2tAOi{#=<nc_1#IAf zLjE4a#!H!*neB=EyhFwCbqFQ^6@1`MbYvpvqvTpzT4dzp$XjJzym+CyH#<8^0<RMQ zfd;`AFwulSIxsP-b!+iy>fjwDAS={OOjHZ)EpU}jx8rX^&^-p?n84Kx!NaXO*{lR< zJ>P2Pmq-iZtAF|OWw^wg6w(UwaC6)sBf_&}F;)@?h<^v-wzk08;JfpC9IrgAIoagR zaR$*9nQKSM6uUt#cOD$vKU-6^a=R_a-wK`|zMaSFXDTZDNCL|%T7ok1S$=e7*Yf4o zq_$2<HFJT6=Q&sZ2LTD!PX`ANS@_5<>FJpmmgx6A#4{h;oo>D{&cI~5Ur>{NL)ksS zQGM9!!6mN8`(BIo9a|=05s7KGo7hau+b5>ArkX=qib>6B;t956Ll#}WbhrE-JPu2L z1J4LPgr1?{``Fmq5Rn2VzE^jZ;*IY7zuz8$0;B&F++y_#yH8*m=o%R{0jg>NwH58# zs|-AF+V#&*k9cyqLDP8x#TCTEo$YN(cJ>Gecpy<R(Bwgt>@JhGwShn#yq-y=$|=9G zp#uIBWHyC>>A(vy;Oh-q34s^c1~RLa1i-OaIywhw^?_2rD7=Jbx2lw0=|^|k^(jw~ zAW*<SQWdj~V5-$=AySEbCm8ni;e!Y7K^Tj2oOI6kxnyr;H4XDDbwGMSPXw&7#R`c= z@D379H~*6igSN8u8Kxc~v*`p`z~WhIcQ>~fZriVm)dCPNhKr17Qy5W<ygz>YNXy8$ zNk}N_THfkG121OsY5kOq8vK@`#f{aH$lvSI95Q|amRsJR_XqoknS>$(qh+|aVEuCY zr4jVu`dL;%^_TbJv3-8o?9NNT92|$>ISL}B^Yx!@f|>gmrg%_yTHLGIU>t8yAKUT2 zdoM}*f=C36@TNl%gngQunencz6@+`t-pODdK>_vk3n<a5JN94$q(kii_X8GLbxmA6 z3mQjFQ{Zj32TEe*9{;?PWO!_B$+dGGmhzIep5AXr8pH8S(+C><0*_72&kq9XCgI^Y zu;YIDaj0cr1}(w1+)2MV{XdmZa%#`8nXBN{T#Mi*!K*I(-(JOMWi3<{c^N{~bgkS) zlt>6!xaSIlqR*wq_+7+DYKf9Xc7A`5_tf&brPZz%mPHRiSb@vf*wvMu2H!c%{YHn$ z=x4u0S$`NFZqz@~AdFzaWV^XLZuBPE+}-7P=2gy+WMtSM9Ep^I$0d~9{ISasZ^HV* zxldbla0su&ki=;lnE3`~;x@i-M3K<?W&CL~;Qh}pO0icNoYxP8L1vKma6mm09V;<M z-bMjy+1e+0C5*@lN_0D<_>hl)yo}yrs;^L#)CGyT8Tof<jQ^48lKU30kep13Lf*(l zfAmaDF>SBHQDdmhcq5`KbZ?6`xC;m=jF`^z$iBE$1y>Ey(O_GABxY2-PDofEr~B@> zxqwt5#Ns85@Id)1;Q`^2^Og}8o^j5F8|Cs(GhT$uRR-ndwZDH*S4T^Pl^7R`HVv=H z8o`RadZn>(_=!2DS5||;`I%A|`fF<j8HPRe_JDSqj$&98l~`$Ib&?DIli<pH$4}yZ z(;nmQK2$zVBo`sDSPCXYuz+cQs1qOX(@;Nn_idgw05_0@vy+$fuQN0rYdEa$@hO68 zYikEt;fgb9)wDBm|KOxRbSM}pP${#z2i_d=Dkb1AsOE$odc;%scT)5JpzFQkvHah^ z@yp7}$jD6g$`-Q8-dy%b_8!?0p&~nbWrmPZHX$P;dt_6Y$tp!5#qT)Z@6Y%9c|3mi zegDTF>b$P=HIC!Cj%YaXdfjwx6GSkNxrzX%gs8W892vRv0J{R9q`9EU(gn~_#I}n$ zb`I7E07m<Oo&gM0w@8_Y&$6vdWD_8UJ|GBTD_XnpaGl}fo08B$=Q-l{5x5K|Q!9SU zwkY^YN5x%~<`)<B+k^ZVr0#~r#>P4z;tA;3CG3AeLk)gugz<x0+{KiFFgB;{KuW{S z{w4@F(nw4(>gyk6W)kS5BxH}ib%x(L;-kO4M9x7-D0J~6XwN%EA4_WH-w1Hl7?-8G z)6m@WeT{#jq2di28O5XZL%XkiOKgJDdcx1;3InU;!W+s(2DS!nm`GUJINf|9l$M!F zot!oxr4_7hfTkF29G!c*xLI+1qZj0m|G^vJ)^XW;`Y(KM9<JW~UlrFYaOc94XWtw^ zq=K${5L<-YnD>uYaN!FnF-+)%YxH02q8uu=886WPtwK}1T=Ya{6&L~qP{IKqvbMb) zQ&eP|Cj|7uwy!WPH8sVc{izgeZ1Lb@0q!*Qp%-kkXIKN<zd%b5JVPH;0&r-=fBq)s zJjn_u%>(cd1EaP9EDch!0BK|E?jE0(Mv3?ZVFhF#ciIo7Z~Q)$4LXcMpb>z4ya3=a zsx;{WQq0E`MM;38|6J1=HWZ>7g|o!x;IkHBM?T2sc>iM&NJTK{BDmb_AmJS#AuVjd zy*)L^L+Stbw;#Z>2fVbm=Ktx6n68C+J4X=|px9YeaSAYm)@DT%m7h7+e3mMpB>B96 zv7DM`S$uVI@@5LYn^%@?=$0w9e0Y>b^qT*nJFNH%1X%Pu^if*z({==sCO4Mt_Im2p z9eYapz6+c&HB`B}rXSbe?Oy9v)6_{#eOIrzJwJ2p@O%Rv85WZU-v&tma1e)IKRgB7 zr_Ai01Z3UGobC!k-W?^#01==ZV2f4Yn*cXD8xU>|g}ftV=>bON8h^C6m;ov+5YjJO zToD@{8To#2um&wZ;3tBaSy}MKlE|E_rv%&QK(5rN7f3((Zu7NV<c5eZV2_62s&k&N zEphk*@q%~lBNM1WsOajd0muu0*0Dx?uK=Wk3l*t$!0St(qN0MJW5DsE)U3^cJVHDq zARFxhjtrR5AHZn44orX|5bj<YKvAGvX@l#&4Nw%=H6ZMV8x#S<fE-7|h6J!H&2cii zyqpVy6INmW0}CVk>B#n<5S}+@zg<T@{Q(3JasJ2V%m<`VM@aCu-W6vBRWX#EULYb5 zes~*)O~J<I+2;^#Rn^W~dR97$sEpf@Rpwni*prjR3*5YBU(8<w&o^jn|C%XFD(-MN z|C@T$;KcM{@P0tRs*uor(4Wj8ojkcqwn^&Q^~ZPpeSay2XM3XWZ@Q@V-f1^BK%>!k z5Ud1>M)*FZ&rw$%^+z0Vu3Va7pP6c`)^gJ1bf$5prlWpeV20(U$>wSj$^8)5i{v^@ zrwFg>s@q!J%|f{<Bjhhg)=G@b8lN{_fAO(wPB7el0G5DhjSUWb+Z6_aR<IHK!BagZ zbE*kqF;@^%e3_}D1L5NYO=uav`l@i=)wE67Ta1ZGykXAHRt^^^JB#^SLvcxFjLsN` zgy!lvSRX}P%18>oS=A@VenEZQM%O~KvP!Kcxt62K=H<32fWT$$x1Rp?|5@&7e7opR z7ERVfrLu7R4_x%BYDnwvsk$oM&i6f~Qc?Hd)>SUl)m?lo8vjD%CNRTKX*HaDo$>?& zHlpEX0`#mOBoh#=Wl@4YlT$Cl$HU_^;IT3UZQxa7<5};LKWR8=I~}(ebt~#(<gn^p z3_W=40+S%KMrf#ZBko+kqC{ebQRI4{?=K#dNOFpxUOD6w4QY>K>b@-^Lj8<?nP{4& zpgXGjB9(SS6P{$Gl9p+LiFRXb<RcL|%LkEN7qgk-H@8CM(3tt`4)&zR!Y`FmwzYH% z7$w-UPiu}e{#wQif$H=Boj~+}2e=5p7T16+oowQ?*FHJ3hj_f9_obv)`LiZ0LvQ^k zPxsNz0SAAv-9}C{d6TDt6DKvD2BkaS$gtZvyKw~xUf2^EZu9ViLi>)H9Le^F$_~c( z_~DzWisY$+5Wguo5rA?ssbY#GP(aRfE@^xgES78o&u3ga$y6ddE(Hc$__2g*Ufs4X zCR5|7={154h1Ly`j}#x1CQ*b()Zh@OZ-`M?0Xdv~vp+5EL3%^^3kc7<bG&aI5?e_1 z2*9P5#19=mx4X8dH`K)kV*#=M8Fi^8KCSlW*bw2c*5utk8L7AQPX8X2T*=^9U~&mb z6D7{V7b6-u+vw|X{!OWz7z<VWDtawSCzeB+B^Q2qrvFT@pup>AUncFwX!5iY?~vJ4 zO|?QD?TGg@Wu=2<yu^$js`=`@{GNKnTcu#n3_^I6K_whljEp?ute>7VBHTw{8MXc> zl_`Y7%zs-sEA>%UyY%$O(5sb|#X@2dv>$c|cYgm=6Rz*=VT_mktE9jDL(xo`*4oP& z1^?AUOdOKcV%k7Iw{0tX^nQ;7B(J74=;u}2s9aOo9p2R~V!zH#k!BXaK^!huAd9F$ zEZz|m#K#+$m*n{DW=_=?J7|qhU#K?~KHe4PsySKNdXKsRk)+){tV6eR<}X)E73gwC z2{>bQ{*_f?Rt;q4;fuTIcOktmeX724;KN*N*%Y^%rhA#aX@$vVf?D*7skhNH2!Abj z;~o-66sM;5+)3yW4yOExJo3RKDp6lQ@;d8m(9i#s^k)6?h2*8u-RIAYOYa46NwO-v zn1249JcE3gRwG3F%KMjJj7h^I;hT$pU^Ldqpr@wCTFhouAkf)6Q)2Ry)fR2j{C8LM z$mXlF**gqs8rl^bjt0JaIr}Ef4{#oMBLTT-gr*qS?~MM`OC_gY==jU-zT<uVbpb~* z;HI0K!%2o@fG#!d>QqBT>iUl_N!P#cn!YQVZ<leJ?jS-DjZ~A;l;1YRMz22J-EI(T z>Yo}n7^1WoZ|-P&CDPE_J@??EU*u;AA%6#d12Kpl`W{AEcs))|*YKZfvcO2gxlU-q z0z)dufMCVPH#J$xTga24?Jzm@%Qf?JdMPP^By+=`+iX>9Yb$;6wU=I_e5ohN(iXyK z`<{q@eR-Z&W|-@1C;`VMCk4T_$?S@Cw}Q|VO(tEI{`=bF5YVyV@HHlvd^7zaysE#} z@N2fAJT|;LtmjZsi^&;Ybtv+xrKEzeqvw}2;W>uK{?ak<ZTNFijF*x~4e#2TO5Q85 zYVt5$+~6c0ugzw&c1C~S3nh-eAs1o`wbM{Gud~aiRQQa~{qyUpe4-o3>aW-Iblv1W zBD+lW+<r?~@OojRpF661?d^bWT7IQpcmel6NQFEfir4snzn{R$h5PtaV-Xbjr$-t) z30Jyiq@qEAh^2cPK~>9c|I#e<&)qFNGsQe=4X?0)CggQvprAm2A{ze*2`=lb+b`{_ zZ1V0q3$z-ye?1~iBv5@yoBhmyD=_((b+!iTc+lU~fRrwG^M9hV5D_2u0U&M7xgT70 z760$qS-P0fPVmRQuOSvH0Ca%qn~=ePEBF74&f3K}^4kYO!yEL}sJ<sxU;4R0X|WHA zBA~Qbd3Y+{9i>x^-5Xm!DlPzhH8DVy5Xf0wSD_AlHB=d3oz(J?1(wVT)L4Oe2SCYy z{fn$*sm_ul>gM=^!H4wcrD(P1b>2DH-iKuri<|4It;fQ4X^}Ld7a8&;A9M2(4J|a( zj9AXSwv;Z@!(wJ(g9PT$4=UO^WjpoZD(2<soIGK_S=rex(CO2u32JB{Kg#KszGM{? zz1}x47`=ad2i{J(gQeDck~ukRxhqd4mZIOhU3=7j!{o*~_YHTDpd(o@H@u!9*$hae zNKa*&5TrV3LcAihvJ=f-Ys~o^5Xu2HDbifjLevnXQMLpgup@OAFi_xZFSj_<llA!= zH|o{$>7FInt^bYu1p<8NAQLD)a)@C9@8fk_;1r>uBMbx01+)pB$!r?b(C`62gh<J7 zT8P?htgJdAsSI>O^)tty4<xAC1uQWK%!~)X$$%hnDw(3L6M;p4BuZlx>#+d3k<T;6 z)OsrBcslXr3(v!kudElo-mbbTAHiJVujyOwPbek*{!4@?YDyTNnMK5~quW3)c`_L} zIOKI6>XsX3qj`RP{*_7hG-FW2)pVx2Z|Z_0og;<QHMi;E7uMEp;p$bZUN^ta=|WTn zh;F*q`WH^me(&&ug99jTZaq^CW+w1w{BPbDG*X~*qXT+;3t~aQ-6jmi3lN&LU>Ebs z0M8~t<#X`o93jR17#veoVn_Zf1+4EI0Ygkb0F+elpFuz)D0C3>ADe%ENi;~E9{U>H z!uvOkI6@{LicN%<m616+0UZFs6p++ng8i-SD9=)9;K^o7C<#5Pgv_iyj9qIAcprbj zjhj5s`8PO?y~5nvcgQO0`$^fxs&CIW-7rk%P)n|up6o%|Q5$_*j61b_rb^tB7cN|A zUT8&+w3+e6=H@)Ygqya|Q0PJ@aKzAzZ2k_HzPrx|Z?$DK09+yLnwfGSWJC<dAPQxb zkT9$J1nG9Nva+BHiUh-1u7sZr*y<t26G^2`%Tg49^vw8H0pON_dW4v_aL9&&%8mp4 zE@P)3{*4N2gB0VIUmy~A0*(hE?{!ra_^N=&ECdk`Vv9ulV1Q44{WP|nwF{zND7C;Y zzyW|7SW?7h1M>qu{bq+142{}rI}*JCEWo~@tsaGSrmB%5fm>2s{K|I=4V{n@xEiU2 z?4pb+L~ngHzh!&r(j{<{Wr1`9VM~$h>{0~HM36_{of!mqMhNKlHrxM3C^sy1f;f#{ z_I?gzu}K}>_gUTERtGG39+XOjPk51d*)pkj1j{=w-<o@F8K(#1U4%T)vRmeoaXO!W z*gH!H-{=8CXmYKeIy+0R!nS`Y<x>!$%!%Rxp9+E1!DoUqjpS9-+cA8xJEkC@idIWt zk`m2<7MLfVw}m|9G5j<fKQf?e50I}3_s~J5i|BJv+G_<lamiOeF~*A{0K_nWt3r2{ z<*EXaWCJ*ut~*aYg2M_C<bahE5v0C*H$&?M4Gm%kgC^?-q~6p89yox|=Ls<O;8Q`d zQkBBu)5(*v21x>BV88O&)HZr)sRePL@Xxbzaj9+o{d4-Q18fP5Iss0=AcI^O*9x`; z;z|d!2a!1;rpkLqe1PUoNqi1HKm8|M6OyeV-M2&as7fb+L0TO|U;=^@AK2JA5D>7q z*eUe`iOewyLY6;6{wcKZ$QA?Qln+R8-c)+zAOWxl3|bio*8*&~iw&8Nkk4mLBxESy zQ%FCcl#zhH1Gv8d@{8H**+K<ec#!98L+}T8Kp3WX>2&l6&|#nsD7WTTnIYIa4J(b{ z=FQ?g?~fO>{m<VVqCJlIJC8oSUG#jIMI*Ju!R6N=;n)3cnCHmO))t`r%RGq`o!(@) zCRMaj*PXEFH@`_|I1B$2k=8H}(XE)iTXnSh1UK}{#_wCXt=>Va&d_-4q~ww1(#M+< ziD~f^+$`*bqdV~nX&$>wNxC6#+{N1akq|$GRNnxaL(o+$AckmwT9E6;FBb`YfMuT} z?t^lgD!UX*#qaPgkqLf{q!OyyfNh|eDS&|jaT!9|0}j>$in(&jTQ78+TwP;9jU)T_ zkQ`DB0o6D6$psb6BpB_%_}T*)yUzztP|S*o;%GcC|8LwHb^$ONfTYW2^!mX6RPQpa zzWGnk+BjuD?wwz|raV9JGqQy22_nS@DUi$}5G?Kd6S)RZOZw)CH*-fQNWl>++3uVm zvT&X-NTZM#bT}!I1PXYuRL5<w_GWr!M8Mht`131>imz%`3<J6W(Q<+#(>a|+Oa`<w zaK%Fx|Nb4u<hdt^^lt!oTqN{C3=+g!7<35~z}<$>7TjqBShr;HN}`aW@Zl?ga^c9r zLIVqambH^AkwZCkg57x*q&OUe=)0<y4l>R&KYxwht1acArcrCKy{S-W{gy1b@vc;z z6P8?GdqvswHW|q()faZh-Q2uK%WB=^lZIPT8uyinVrjE)K}85<T1urRyAT%{l;I<9 zK<fpu&HvoBkO~eunyHhuATU6_{`@Y_<$uL}`bBsf^@EUMoRU|)7nI%t`N0sMaGOtw z3uJPnCo4j@aHX!TynWEnasVj@pr_;kz#gLHK|F)l{u*4In^t_hyao>F|55f0d2xu} z{Xdm`c5hC7nEN~C=jQGJ3WS1#yi}wVK*Wdy^5MV)xP5%kk2BVT9HIt6G1mYT-+Mpq zqyNZ(z(;OtW77>+^D$(S68s5GOo7Kp$;;E$)QtOFSX_w|zut_kp~6C}5u8kg7TiG9 zYWO|fnTZ(9_4%fwGe)qbfk(1ws!K|Mg|+QqM&w(2FCcyT+xmca5o)2yEnMgwo~sfz z{>qbA)#hh)f4C*U&K<)4ZS7O(xs9FGqo?h3vn<u1ey?c1X2gEt7<`$E(3TLi`verw zkEq=ZzVU>d!Pe8`WgtqIlVYm&Mem2q)?`Wnm8&-ML{cCx18?+muW1Jke&af`s=m1! z*IG?7=!9O{f_)cocZaV_EiJzQ=-ZJ(uuvcgkbJdV*^fZrih!#D7{_?XGOLA|0kD`O z;ZlCt{)-kci8Y{0&H6L9Y7b>qe&rlh#r~lf`cnpML1BU+Cq;y}3N8)~$lc|E(mUb5 z$9!9K%Ed2FjU#MyXQTdkxxtIz^Q-qkX=pLO@S4XPF^Yg05DrrtV22=y8i*hzBa~s* zt(?Q}VLZs8^l+Evm4}CJ=W$9cyX=vtI>8Oex656mlsMK|GqR8U_XLK%MK8=4P0|-y z^kVq$+Vr)jK1VIx@HQQrG`K?co?K8&w6t357GR^9G*4How&Zv#%uKRP(zlcUstk8c zVkkr~aSc*vVa90s9c)w6P$#7)Df`CwV?KoA6OLsYxIO~=j9OY+09aD0s##vP0+j#( zeQeqRZWAKO`@cATH%t#k`}=W0ga>;**Y!52-5`8mf|Cz$$In$wA6KEoa|CJ*LTMra ztiH@m*TVX>wI0mY4&f<i_~-YJj!Hw%&mfW8l}z0G;~GQ^RB=tSTzfq~RQ*Lg9g#W* zg}~!vf59V3?uQ!UdT0O=Tmg8sM+<|osGS&`X^#=~1^q+CALv8@LBWBhN^NHd2bJF_ zz&r$XNo4!IlD)PVS=m5lws3=r3nW66a+~ym*IK?zak-xMM8hd?T<?Z|&enjzco?UO z&2?>iVXCA>!H1sbN|b7{kcq~#4RdQjA%X5L^fqy_@XA{`>w=^$wFmyzU;Zon%Lmy$ zkWWY%eLMI&4v^f4LlfAsL@?E?ZEcw{o~$=F0bLKCgfZBK5VXFDbRFPHf^QnJ`a??K zRY<V(%PWGy1rgUF!Lq>0!P*h|`l$oF5BDHEnhaDHYYnrOSe+0+3b%&$=`VL!;cubx z#=CfN%C6(`qt&GVMa4&>Y<zsxowNLSKxwt4@+a2XZ@9_?A16W`w+}caP=0~@buN%i z9{&YuD<u2}?oSS28UZ!(hEp8$@)*b*0@$pOaSCoIWsur;3}{W)c>yhpL=C~_0^>ce zN&@`~4D1*dc?qio(UC*&pEpFdBmDqC*FC`EK(c4v=)FUr4WQ%#mCHF0WgKHi%)l}t z7q=N;jMsgtZ+9fLw%&X5&DojBqVvI^JdQ4k<Z8Oq`_a(_iKlTEd~QoORO4e9nT4;+ znSMQaN+sg@AeL~c6&L(28Vt8B?A7WV_HTQ;{;tb<w0sTZXX;Sv%iEz@)ziqx703ZO zIb?qPFairs6Y>n&(t(?!Z@%gMVS$~aS8iz556SQQpyl}Ne9IY>F-VmUvRHdiv4d22 z0z5!Kcb-`omuZ5KoLbNZ8|jtcR)ahCDe|5Go<0Cx*Vf5N6-X}#M%I5*V+!O!_r$~{ zsFpv1Zw$!ue>h&{IIGp3=xpj%V7Wm>5Ud<W9S)Z^DC``-8;gWRN<j{=Tts55&nL^* zv<WQsh1I5YpIc`6IN<^XX|Onur>+AOq@;52Mo`Mi%E0-I!xKUtYb7Oa%iZrb@Y68x zy-~5TZ>Oiz;Pp8ri$VL61zLM3vsaMX3=X6<<l~aJ(B5Q64CAnPc`Vy9L3y!{c>UF8 zsrZqJ21sxa#L_E6)c`WXwITkFVd1Rae@P_>N6EPsDJ%*6p3VOR$18(e2BUmP)jHWh zoh;%t>kI4jOP~g<cb!duI!2}`sHNZ-EYmI@H{n3>)*F%0f!xtyl+CZ*q(5aFaW%Hq zLzj=2vAEugN$TmLjT1rVd~{pjB!G_U^H~4K54rcv8>P<I`8zI}H>oc^6_uCajT$6q zpxri+U6;&|DHE_6{uF9`m=oqB-vvGuCRoovL|Va_3y5xsk+l<J@0*WN&GI%u%zl+S zsV;jRn_j2$?lx$fGH272pvDCf_JC*KoRSr{7pG^Rggqc*1M(Jl;O|f~zlW?{@a9$n z0Tg<@rT~&uT7GT*iOaZ-<Q5)trrp`pp4sts!U)bgJqkTfN$n7IFUB0FJ@S+vVz3LX z<>l}SBfy!Hko?Zm&VGS2@cCO_R!;Hc$&}{b>)ZjhUqNA@2#-ZTVMf#ZyWqLK*6zq& zjo8?g{%tF!mZ*F-S%vb++d2mF1CaGQ3@0iSK@Z<3aUK1~w7hL_Hi(Jzj50^x9NFYS zU6-(0z7ZaVQq@eAsuSN4ub6)2UsW&dOjg|2v%mf)upo+HdFwqJqu0RlO5w79t-(#^ zAA^*C3G@e|)e~-(7$p@BGD5YJs9wFk+hRU5m$@(w5I>KW_dU`|%}N0=A(axNOKB7( zxLepIUEtGs1=6B<Q7y(x!0S6XaY6#yd8rr;(jpzd`yM(v`pPBk>-J!f^61$W5$=H8 z&mCvUOK%(;K+sAG8sM@t^<9fwHGEDdI3!Pa*!E|5xt<o&kH4T?8q~y&CMB>chk06o z={yakFCd=;{NszikE9?`p3vT)Ru^@R9M<(EzU5%2iDx)))9~FHc5&7{yUztfd_Zlr zx6suS{X0zwS557eXMTOv(BH*M8lKz4!VY1%SC%C2l5)!Z^7J1bX4OJ}Wh^LqQX-Q` zCcl6p4gBd+sBVczpl#to52pyA*MQgAl?e+7UhnFwx4OkW_yD@=uf0nwGKQ_++uK{D z2`E@gIFeGUTQ{;n{}}l1rzfxycgG0WFDS;Ic}cRjxcSl35%>M`uBDbl`b%AFvgg<Z z_w^PC{dU-^baJ_C+t}bByCYU2zQt{e&X;Cc>B0($HQ*W;`fZ%|`lTYPGPLYnDT?kp z^+|=78XX$tW<nqsaS~Bc#5}Pm7U9!28>E5ItDOwXp!-ihw&D^O7?qcSk*DiJ$9xjj z#UV9%t0LvFLi|E5UeqOA6Iy_KVf~|CU2kNS(W-rwCU*4F;)tJ}EwcXN-a>%)162cb z>MJrrZ2L!0YExdF$82`cYKaa>QB%WcdH){91f3?Q6b5*jbjVzL%ywPauvuPjaBitJ zV*|6p8^^}>AlLFT)Z8KQ8;Xe>8P#8m3)EF@%$RAmF9Mh>!e?NvZ?1W9$XJZt$JR&d z+Up$rtMH+a`lMQ&gRkL_bPqoEKk@F|qFY;xO&CI=>Gkp6JxMI+@0+jvpxfIEkv+fI zp8#25$@T)jKWo`|eC+82g8T1p$}^jv+iJXua_YQiYfn}`$>GC#`jf%R6}Gaeq>`B2 zH}x*{wWwdYy`e^ms@})SP$57iYa2_(09b<>DLVwx3=a|Gs0}$Sfj7$?F|mPlgM>tL zHR7w)xj8x61duQ0K9<Q89U>;IU;K-Z-^wiYwJt$~e|(lV1@7Hz^H^c2r%kFlnp1U( zX@qbG(O1z63JWQeqix)*Dx=G<c>>Iw01z*QJwf!=!Z~iOH+2e`LL|4s)m40KLjq(s zi_V*OHaHc^iu)$!@rkfus9_8z<X?UCSRq#;dDaN>WIp^o#c&dE(w*QpBi_Lfb-f@s zn63C+yim6y>i6c=e^yQ&o=pK~{_VZU3y}uGDV0;=A387hUCyM*DnwUAX-CgIzKbcJ z_AL0m1|BvJTPx;VTg%z{c-OZM1>ftC&Gbt&2OnP`^i93A4|`^IH<<XD#hEc7wc7=v zvkqhZzveWAtURD_Ds#*hNtu}+>cUfxJf6DyETu?oNcBCZR_(QhHXBdtCpF<UMsd$T z<_9RGy<*d)xE*ogmyMQ)p3c}Z5GYYTrx9})Xiz=~IgH}}x-GB$Kz@J;3M>Kwx|?TE zU?7e0S52xe;0_V_=ur;9zS#$#Eu^HRVt6VFw$X3?*uy9vu6}}o-Loqapn|IF>o~U@ zG<2TATbEf05Ymd^laZm$reL`%+e&zYmuNKqF_k6f?-O;@Nx)vmy6D}73#5T3F_s(U zJjIU1<SAqi<=Zf5B5BH|T|fo$N}zGP$?3$#-6sAX?~w(Y3zSJTver_wMb%JImAhL! zrPMG+0!Bbw2bK!|taqC$D_y`1Llo29nEIL;LD0EAhtVx&YZDU_P$E{VZXN<LJUc5k z+bl26bDy8{JnEcQzL&iflk;izAO;Q1{-Wok|F7LoWt=AUstae^*8~K^Q_DCR?92#g ziRp~m2#sG>-y*4@Zw}|>?doN_c8T>zHYeUKvK&I$$Ldwxy4Ga_XH_1S?H1E#?;(9) zoT~_YbPc&K`m;5B)4LUVMIJ;@qN=E?6F?=$C42&;`<**?It5Pk%`YK_X($XJvD_sd z_rirqK>e6=-*<Oo;^IEGh;0w~Dl>%Hb%1n;HZ0e|ZU^gC6Ud*Rl-h07)jiCUuIt;| zE9oA8*}*rBgN--ePG=Y-F%1DKmS5NP=mGUnPDpJW(qn$tP9ZiG`MkKdkBr~ZgD^(Z zD(Z0D%HF1gE7!);;|W~cS*gVtB?2++kf<ffCTgrDC$20Y!A4s&T+By)#mMWH$NL;x zkP#zAGXNQgj}RHe1hT0tL0jcK?+srJ@C~g&Dl=T05KC78MJJERD^v+O>)YS|(Q49@ zSVI?&m_$w(u92qVAeVY`aj|N{H@P1rm;3$9y6w^r)_SRYVt;lMnp&C~DqlEvU%U{u zlPSpN^SxZZ>%IO9xxf8a3k<fUC0{%1-|&6+2>WTeiOOx()j>+PLDPY}m`CW6^>?`} zcbk6Bum+oCnONI6btf5HPMCMO<C2gBo&V4XuS^7<x2L#AxBqp*OmlA56_QnkJF<Ud zD*hNjv)M(cbkX*BJw>M(kOZK{ktiHQKmt~N$m=sJJ3!zAr0GDWupMlU2}8y>1i)o| z3j}Gz7Gz2Thyqdzx-iH|Lx3ab8o+>Qne<hSk&_GIQ@vVzR9*x)x)X`GZkyBc3XumT zkq6n@)DBWQY_62+vq}d&imu}Q862#x9hT0^c3^A6Z1Kjzdy|*BHsGxLt)|$kFL-F4 zv*3M>iGc}f&Wox>>d`XZKU4w(Fox#9UtubO0Vzih5#(6X_jAMjmxAt7*F8SIMk4BB zDbA0UyRZ-_5mXN#4j}}L5P@c=%JhzDqyK|@0qg}8vphhLEg+2Wd2#?<1+qj-)Qm;} z@|b{#B<6mq!Ebt__8^W?lE^P`Vc~(RF?RQ?p(cEJ*_#}TnhaCSLseO<CGX(gx2|7% zQ1|a|SD5;oFV^CODZhPeYv*9(eRHXei!w4HI%yT<V^qjzyDmGD_D=M!WUS`$YVCRY z;UjAoJM|bLoQR|cE)LUxOdOtVPzfET{&jcqu(ZON8XYFaAwZ{cmFWRtL=9{cNoQTE zW6!nKa>Mq^9wI-+5=LUde2L{`|1LW_8*Jm%fzkluGROp%1^#`nBfBMK482)BGT<<U zuj$(WoKkl&8q7w3wn3u)khNM&pQndi+o3WKvRken;y8v>$c@2Qi%HGEXWB9tjmc?# z{yG5H+<dMUN7N&gKwiu(Q-uHQqclUVG$8>_yh=Q`DHDXBxXu>uy)P|kmXm(Qg_Dwn zM|SU2)cO1OOcV1Ry%#YWCT$e?&*jjKzzGY8UQh3%Ac*4`3}6=sOA1Ho4_K@P=nAO* zdfDpE8;T~ALi5fPg3YA1XtuKN-D9AkF~up~U?omdPuu(hK#NQ755T-)D<l}SeR~-% zvwoa_fdwGP0f15wnkqaHnL&#?Ce6nWpA1vp>k^-peDB6gRpp-dvp*PiqKC`9R3*x6 z`laeG9Y-E`e5n0AP!p<cX=yTBVO)E2od+*>?1`1BZc(HGi6CFm1)a$p`6_mGBVC+` z&$BBX-Y%<LT9`_KFaKsx)h=;2sB07oi3m-n)<vsDPXt!h)D%;&v$8t%eRYVfgKGyS z>)l~9Z<YuC4;+Nqvp`pY)<>x>Gp(3X21u~0kh(q!DHTY7;>>EHimo<t7Op05tzy*1 zjBI5!-?$NoUikXyYF~Ux-)rCSF`1$43|jKHd*CCu1Cs-jsTBF}+^ra=^YDrJPFyL- zTJ&O{)5qPxqKb7?8w2$Rr>@iL7B?-ZW?(Y`)?8dsf9u73UAQ0Ab98`_fis>FMnpow z@BI{4ehfgBK!%RY&fJG41;GZ8gAfL}4PDo+1@HkbF+gfY0NFaoy@!axkijL_Pa#Pe zM!Af)Nbz4YWqgN3Y*SLrW16&zrjlnEgB`_p=QDfD{qsNi-dN|qEBu+ij@%Aa$^uJf zh3j{T(Dev6jMOUbjRN{OWsKonJTg7vWPNVtfh~0K?an=v+~AqWbxz9>i4bcit1>-H z{j`OjKVD>M5LiJ~y+zZqw)dab6t15xaHB#b2GVW0j+_8H=`>q?1-7~^fJl%-ySlPc zSi_Kx1EWU~K`iiWK@KnROCAE%v~C8BK*f9qpvs`N_T>}vSmN9>zhodpu}xr*B=WzK zDJ4kvKjhUe;SdzmJCK18d@E<?a>wY}q*PWz>I+{WI=pQV<K-KIr=n`<9lYkOV>{`* ziS6AQK{qkaLVhPQP(7Yq?VU;yR#GQxpmmvk5QVq(bFL=&@(UHCmyu#0QB-ww!P9qG zA$u<F&l(BEvsn4?XszgyU#kngFW%$ZXD92J-&cR?;C`pxXR66biCtV8gkr<--W-V* zPnxySL1aJow)NjWyEESky8Il7@Bo8^>t9P`NECqXh^sa&>JO&8b5e5hP3W>9CyX;~ zA3F(~xh!+(O>P6&z0cO)K_;TXI?Zy!EI7`%WZ4`8JIu`+k!RO>8#y&nNn*6hNs&yc zJE|(~r;aB0X~GOzc^Osbo3x)vuj=gPO3sORRElMB(?0gN%FHDlJ)WsMwdDe<ZHApv z^7-)6oYYtdAmY&JTuLF+cfuJ5?=!et;pkOCfDRD9%|qlY$yJ$R!1j76%^Az~qf|WQ zds*)h-Y~5j7pCHqvnyWz5we1F{Kv<n)#zS=5LfhaJROG9_uo6z6m=gA-n{d$ioopd zu8ncn!9N)*?#3(iNB5_wu3X@wI6Y6H_-&TnfPxnE<txamT=!L-&sxAprZXwwvtUlS zS!e~|2Ef{g^%x-CJ`mBHHhJ8tuI0m&H&g_LDkWrY(+kL{H}T@Wv;1P&E=Mdl=zQnY z^VA<*R8-^b-&ev)D7#}bOzkvF{CPM$mI`K`{rz2XZzQ<P@=8w5@&wDfJ8oDx96z=V zX@{i0W1oP4LHs!1&gMSp5z02MMu6*T&C6b;tipBqB`mT@GAF&di07@*Ej8lok?h6x z#h3DFpHyYJ7mhB1ix-eIV_IfHY)S^idZ!6<AZ=C3`&7l{el_OF@^guc>pd^N!*bAH z*1Pl4R(D}<o>uI_)%;3_9`T^$ZLE2M<EdgIMxk)RxA`Pz>mVJe)T#`mJ|`7s5gv~J z;h%5BdXea2v{pQu2um7N<s;2NM$H(YfB)ddWg*3jQ7-2UyX3tL$<t;K#hb$ZJB=l! z`390_W?mhbk&+1UTG27x-2>6T?VTG^>*9vOG3RPa>hq53D4J*jHi@~$^0xGz#k_rE zjt*A?mK_x|^{ucb)zFuMAmaJ1hPIN<C|QD5Jg@F|dq{l}P~TWAf(a0a7NN7V6SHCH z5+1<_Oh4FpCGdAJX`tzogQ3NIn`!Ghp`5arS^7`UA9ff0d@plS^4)r{)?c#sRE)`7 zOE84d9Q946hK0uv{V{(qyN>a;9gYCAT2SXNXNy55Y<taLuS4f4rCs%1LADOxx3l^( zbjYjv+yqw2$`?AoeTXoO08}Ax7KKO*Hk(SYl%nJ1$3#TaFL_icozr<!8l|OtH;W(r zK21417W^#YI(cXARW13~ZXA=>6zf9Nj-#m`8vGbL#0YRKblhM}%PU4`qo4cbC{u0n zDY7bA0rVJ<fiAmRu2QL4C15}eOGHg|X#1-*h@JWjw*BZ*Xp1<uU{0V4yaDjRBl#n5 zK}MRHNsX|22%Qa>$}upWQ3*4*=5H}_lFn;%*1K28iVK}=UsL120lH@C)NpGH`|lrN z)JSlM;B^sR$(8j4#_}vB|DP75W=5!Km*%87jIdHFzAx~S!lBa?lWbGVczp{+y)MD| z=?53(C9H_>z7J39P9ID0t->hJ<RSwKIYj2FojV8y6~vtboCV07BS9U$4<Vlu?f1YG zvU%k`@L0igeuUT(1D2S|W71&?3M)@Ni`}uXxVXeQ0!Zi?!CwPDPTD8*rj3}5PE3@; z=?a2}^tev8Sfgk>x-PXnRMg2gS+uRqA%_8ue3en%+HYc%aszdVqq910%c+W{dR#i4 zi8i*Hy3XXGEDW*eEt(qYtOx%#Y#F4mMebez#UWx;7NBo%__2TuLl~?du<6-dgz*U? z>CbV4j8Xdfg?cPpZPh-G<rl2{4QObvfgeR_-43B-IS^`Gpr(<3OuT>kZF1+DF{7wx zQ-Qr|?B)Gsj2`=`<t%xH7ZqPgy|&2w+S`A!>jy&rk$(R7Kz4g!X2Wt<8hHAW<Bp3V z8b(4OI98~5{E`ox|IsBiY|&fMJ<oeqt;$)3USLDA5%_}0X=nz3W`j;p0tNTB6;R_R z4EF$PAI+B?1KrI*&H>ze!Z3!6EX|aWlgHi&7dctM^p7BqV0Z@E0Uh{&IJmh908<7v zi4%-f1G8RnfU}<psR1z`OhQXfxAJ^xtd}jCMw_(Mh97eJ8MuXUrqypBdwXWm*~QHt zqyj6ZK~T`puwHStMp7;nm*+H3Mtmi?@uhN8Y3bgTJd0*VicYB;^xm0bByZIGeAMu1 zc15WHok0ts4|z-n1mL>|G`u=+Z)2ku35X4&|LiL%ED1<DR8d@hIQ!6jz&Cjrnl2<_ z9OT%<VIVKCtqn<lJANPx0}Imir{e|&nLD$jY&?{%>B|QeXcEdlj)*|0U>GLY;<Xmp z*YgQrbnx~7qRk8hRX1cvfhgYET2DMChd@2^saQ!vZ0!9Fx$U3#4$JC4ls@?-)1a=T z>u@dcIXSyk<x9&!Z~B5Izx6}scJ46vF^$rs$%({z5j2l7@8&zUXp-qVYFk2KqyV^t zMnyg+7NsXwW$~^+KT+CSgsxC;z#avE15$zjBX19sAcPs<fEu%;q{K+a_up}T-UHkz zv(0k444gli(ExNKBUvF@aspfhNazv>%%M16Ni(+W0%>q)XsC9c)WCnz*5H)~gT=mt zH@o23kNv{>^w`0z@NwNt5e=bA>3i&4!dE0+#_%DJWNxuZu(7t=Z`Y*#9ll+|BbX*J zq{bW-fx~mGq>depPt49MBa>hBf_=U<6dv8y-bU%XFRkNA6$qW+MnI>be@p^iLL+2_ z_te;<FQ9;5|5N55&@>3J!eh==mSYQQT?m<o1||LkMAk)Kr0E87<DlIiV1H0zlz=P= zsX$=voe!8<CtFR!<#bYGa0Fn+Arz3cFjxW3g8-0hLx~C+dWaDkfDC73iW2+0OCk78 z5vwQYt1@8{I5Le|y?{|nP!RorGAx4dCwpJhr|u`R-C+E3<>%j92!)3Uovp;^^jo^2 z>E?FGXwROIfL@I=BdV@OJ7=!jaqish(KM@19PE?|A^5(XZ2}Ro-G5INEnX|;Qdy42 zYHm=roIWlu<SM5rzvxBxAS=oAO+S>r$?IdPG`5iW#_V|hmKlLjbH7L;B6iRtqLw;B zuV(b|1vmV<R!LM@h+=cd0O|uaA2M#+Y3>yh6`#d2@R`VL4tT*^QV!>5e}Rd458<wm z3=`4bE)4}X3~;ZQJ7ObE<xp3`IQ;PgD6VU<LogT*Vq66watz#*2hxr33yL#saFSF@ z7n1(Nu3RaU$7+uvy#i5Fs*BUb#Y*1Wyd*Ge`*$Bv-0+CAg!Rp=!P1nzcs88ziAxZ* z{d;;#ZNBzZB5gB_RW^S4!0>>Wb*W?QJt5U2%uKBLzphSJcr;@H=P#yRpcqCD$ibU! z7RV?JkYZ7p<JSwMcZUK%Cqls3xubu(zq1Pk^#Yj!`GK?w8cl}GN&%4?)ZAEMz(rnS zV1V%4+j#`b$Zh~iu)$Sh_UHHS)?lWV3i+K11mERt835pX6Y{0+89lmzf2a+Y8sJ-q z0|Q<>(yw#O;~1h~kUC<E;QjgKBYCRrpgdtS5gjX**-$#;tk_s}lU>u92#xRseaTj# z3_T749N(P_lK!e*VL=9sOCQ*9?z|)2uXh<+&u&$uFChN)DVbK2qlClS-A3z^vV>5) zw=UV`8zHvVtXwJsgLd(B)CGYlpRxO~Q=Vx~^eBGr1x*y>t=s=T+@hwoQ<CILQ-}2m z8P{FInv2=AkiP<{BS5Im#1YZoNCZx=&bFWUW6)03%@2>kuQlrBBW?N#=fQ4j4CW0m zOJ5+SH!0RE1#$_dJN!W>s_Swk+Ip?79(o6+5g1h^H$EnLd~(1RYjpa{)F&@TQe~>a zl{!0n@~~m{klB$hPbLBeq{yD9S^^6C%FlJKg<MEz*-Z0Z89wOuIHZ{{Y?GMqJByB0 z{v7SfQgG@PIjFwhYz+mCa48(U`+QakSPDSHsH$p=bDz1{TSpisU(d_TTg1#vN;0HV z`6b7D>$Sm}>0m;1f`1$^A{UlH=f4U^@ytRKh`T=CT_fVP{}I@n`cP2>23f;ujf;w6 z1W_IE>9PS|;RJsN{O&Qha5WSt<n7nC4V|yhRb&iZl_h3kLeogMq*`Y1S-ItiY`)IF ziXwVU(%+^`B&}MGWo~}FC#E{Y^Xzmpzrf(Lhna<PbE}3cIeC<NW&Gd_&BHldslTV5 z+jg&aUPx%qXiMdtQ8%S|D6**tzV7{kd4<EV!pIlMTKHs)qK2n+9<DALH_b51T?T|Q z6BDOJzrsnDy$4sk4Wvv$%q0>e><|n+BBaPjW)wo6K`zjwt8g`O<Oe@%;D;IY;O`2D zW6C90gR$&gs`}-9$p4y$*#_aCK@I6`P+i1CD)mG%ptvE=qH||f1LBN<r!L;x&6{$B zqUZRDENIFnE89%YQJPwH3!hC*<ZeDNpfJ;^_u#EcNh$fMPgrg+Vmp763$+ZI7P2(@ zh}^7O;Y%H9_$`0UgI%O(wt0r~mZ;`F&NF}k3Gw=YgjO7)w01T~IMG}HQue(9wH}<T zZ^7d^_wTrls!@|SaJQ3QxiSLq78q5h&A@#72s~nN%<xW0L#n|Si`B#RE>gT&{oVd^ zu&<v@eJ{BJMFzBt2U6N#y4hb*0BTM4(#30>)GlD^QeO<(#&C1wn9jfd(~w%qgm$PJ z_3>T(+4Rn*s}-;PhAcb(d|5J`G`m8-{84&<DCa}Kt;neg4hW46#pPGJClq!?N+v*? z|3|8EBaS<kmC1YSOzO}6rbZ5cj0*@0u_$Fdqr`Org)JaMnQ#BR{9NZU&kcihkm(8F z!qTZ<Jp}$*4JdSzMmHKz5ch(J0m{k8pn}_c{Sn?QL<(X6Yz{*t2M1$-z=}rZw86*F zdhqVx-8G+PxI8Kc;>a)K9Y);DZC|mu1REHUjlwxpui9TR?E;$Y{#yH?JV~+tw)}c( z?H5%wHK*FfkX)L8Yf8%MgM^)LLiSf<gD$Z!$-CIkw25bCsiiq<YB2%b_KS!*%&er_ zah4BEvoEMle%LLFQVU<$o(kW;LoH$8O}G>7@j=cbRrf7(wms47aI=wDpF;cFVyMPH zT&jQg?d&*#E=dd=C7a{T3Wu&sI<DFPu0xg*qMkGsQm|)|5n%u}NHboup--Vm0qEB5 ziNU1Tq8q#N8jPGw>Zih%ZiEG3!i0s(gczXWq=S!1$;ruG*2&Ky2P_N#d^n&s;aumd zrhf23PFHuPF^?yVkuoIRhf^XTJykZ0ZfCa0sQk{4AKamXSMB_;1OJ$q<;(EeH&8H_ zwX-^ZFhirXG#Q1;X&rt(O(F}*?cW@okkc6q3A7#?k<JOd%YeG)bFYogU_>-<=J||7 zzq_V;lzIZQD4P+C5tYBhm4vqsmM}s1L?b)E3LwKX5VceC4Ty1OazxS5K1k8evXtN^ z=<Y6F$BDqy&P(mHKk^)`b6gtWIT>$j6AgT6%wIuVP+sn<-_@1OrBCuMnz6z4?GV3$ zU(u-4#rDw@+4IUt0vM2U74`NhQUBcdjeM3D4BqrswL)3-arNAr3{-gP572>!p@0z+ zreg4rQV_I!$!l*fj!0tyi9@|J3tMQ}wCQCzQ;duA4Y6@k1}~bN<VPUf)1VQPWfsG2 zmKZ3jzC#mYWoR?h!e73@Y&y$BNuBNR$IxV<3r`NZ@)b*A(&F!{{7%0-?b&2R^3I=~ zvGDL!Y&@+g6in|!tEyYPP%VQ-`er#-7^J#!#_JQl5c1|<Jo<R;K*KY|=KI!^TFf~i zNd|YB?>6>}xQia1pNPYD=4v)tNxwC^?;m|KrQs>n+0jY`I6e39yKT8<p~7M#b!KKF zG@jq*2Lla6*1ukLnL@ldgg-0%11L(=)!bZo0ADvFxll`~i@c8s&PGC$Q50k+@IKG# z(tTa~Apdb`siT+DB?JF;K1Tc9+TcIb>!W5YDh_OX@Vyus;Gh4!9!+x9#>Q2n{p|0M zLE}x9Yo#ArHFh94Jmon<s*#39xx$CgbG4wdmKK`n4~wKHM}Z9ICmf<dE<Z~>?Qlq~ z?CO%xeXf*!psxp{TNn+-AS(~IYMRj0{ix<Cm=EiBa(|x;Z;+|?%mEn`1cxm$5%xmX z?KwEuc%~lf0lEt6xz!lvvf;96^fv*{1KKbcznk%G9AkTC1btz{G2J|?p^KL2YJ#59 zK%d)tWg+Xx2sJkA?b+E_>aSmuG2x%>>3@5cmA8*P-A}4&+x~4O_7HE-bUa{rDDqOJ zDfrE4Q*G-I5z$4kmobToq=SDr9@EL$COW(u1z%;Ykm-9_6UH(g$enLhwCLnc<DShl ztK!>#wsSXJE*6BPX9RLI0kd;;DrO`!%vJVS0U3OxFb(2~kk$hPLf3DJfrLjiS-hL| z`y!-aO<H7oTvn{P0mhgjH1|)IR0beIi(-AKP&t)3U$%2v*>mH(98PIM8bYpVtUnu{ zU+kvgy!c8qsrFF>orx|@lI&t%Ur7qKksF~EFmd61*bi)&N<VN@m^Pq>!X9BK(oJ7v zxTvOPN_73TuYAm3{R*!iKe}IAwhN_Hn3$NUe^2%1Z`Cig`g6BEikaiVyOmtWn`ZmT z@kCo*UW$KD`H9^Zz9{j9Oe__O6YbLO_dk@qTYjQJO-ub*e0F@C6fz>Uh-Q+v-<mTB z_EMxKmT9ue2wJ94x+jR&44OP!E2<H$eg85MQyw1_8AzZtFcJ_yX6=-DbR_3hw$R1) zAm*W)e_bQr`@0Rc{fr`pLg@`|Tr@+e>gB%CDGCIe!cY6fXoQ~++i{ELO1|BO9cn0T zhLV;l_EUURFB9-F(fO_90i!^oN~w;qIF5k0be6OjTZ)hTQU|D-QA&J0x09k`pRn+< z5c$>9UL!A;3kgz|Ncx_n$n{KDCH4j=|GGy<wGU|ci3Tk&G+~MfR5NlV=!rKW_jc_* zJ&{Yidj5x{YN+|_cD@WLDJnWT`6E~Vx!)G%N`FG~WxCji6!M0R;+UAQrEP9jMYB(B z_+XH&GPCE4sjFL`$dLT=p@Ss7s#U>?kwhoIFJz%!C`0WwwlvYI$rnL@`|dP)J@@%A zIwlPkQW%n=2y}*8#z<LOYG((Qc*yy6@<q*J!-qy;c3fF))<Qbu`Kw}cPpW_Z@XrDi z5Z~5z4PEzVAT=BF7|i4M|9n?LhMXZiv>eDQVdJneA9#IQTFQa+aG1gsHc6(SYl=sd zHe;WIva!aPC%;$65%%b7O05VT@s${Pu25Z#ROR>r2d(m7>Te_OY$l{GR6d^6g89c& zYy!HIdsn3&oGPQwryS+wixriu9XxM;_TG9prgMLEbO(KYD9GDyRls>Ss<9AaPtQ%h zJPmmL=54Fxx6ibmdaXEA)fUV9?Qajg_eyXFU^=H-`4mR*uU#5ou53UD2h-+Fh0kF6 zm$L<BdU`r58ykY`lQWw$V?St1)KUy`!zeX!416Zp7`5#6T+~F?oGOPP{AQ9pPHNTt zAYZJZP&8e6B^{N=4JWfL>EzZ918-o|=-bk!|Jt^--nS7N5)Nz$IK94$)tMxzGKi)R zFX#r8ZyDT0NO~~{)@%=*xDqy?m$iUN^3btEDr+~0Op)(10bOnfBQ{EyA`5C0)7cwh z!zzFK*>~ni?@y@Qv2+<2gt4*=E;ilgbpfANv<y8_{T<G)sUox&Y6lr`20y-|NaQky zOb0!$XN=kwoU4ivSGfzycBSn+IqB51N_HD5B%HPwoWFj1@}89!$E|Q+??(RSmXD{0 zhxez*#5dw=(#=X|vCn4LJ{IFwProqbd2VB?imI#aG3uq1zldL;jMZ^YJSG!cqw)83 zjF;(TqsF4@`)Ha_t+uD6U#^sSE3z3|n9-lIXf`;s;`-t;t}Ek->3h7OOlM7#a8N@* zq(gi|g9waESxHi1z_5Z8$}mpq57z^%j-U~U0R=LI9t)^YqfL{Vl<gi9UvAxffU`d$ z_Ue9nv-_7b_X$j9(I}X0T2l3MtiaV+Z+bx>aH9vmImA3oH(QKqQ6r<rZarJ1(EDqL zkMA9U-#PS#tF^_3n?^K8_B)^2uS7|O&HD0R;HQbRytMJQEvL<C`#nXNdqz_^buMcf zA3P!CQzB5<+1Xj2E!YH{uG4~U-BHTH*7jjXM`%lC7rXSM<GVFg`ncZ4PX)!=Er(GT z1TSH0e(1PGdy8>p*Yx_#3|r{NlY96C4@xvv_L_Qc$hCh=T`@C>r+vn(Wt7e}d*n2U zO=oOe>+y;GqX0%(+~CCKs~=11a#M|TG&j&2QR>x<`Hb}L^wHYUpYOsPn55kc>BY~4 zHIODM9B`rUjoPJapfE;(BZB)=3rt}4-hSg78c8X)JykQu_T(y6U}<cu$v3LbaosCr zm#RYFT#>Z7UGuEhMu<+K>9zVAiilhuHB<F4|Nevat{H>TzqQm=tH+8OH>mv*kA1(h zmAd|{K7M`tf?jXH-qBrUEL$4GB;bDT_hu%HrC=|gtlKn7$uY5}WO6W=4%E$Y_V~b# zYXTbyaZG(*Z<7i~Am5_f$|M7t%a)VJvZ=b4uZ69NsZfLJRm~-w0-an;qM*vLXj!R} z#?x6jy&eg-NC|y5EBfUa%KZh`XQv;;n&^&$Xmn7<`R49Rp)UsXxs}lphZQVmpEhm@ zw<t^#&zUd!NS{bJu8qE+%w|pFhBpTdh!(&EFbshR2{(m&47s8SWSXOG`2)xaMc`p? zZ*M1869dEKub;<cduJBB?XMBx7Zjcz9eceS2=_mGJP_9&9v@41zh%u_qbi<GsbYJz zclFoG+vfwKmmR2aGqbrHaY?fKFg{9zWc$A5*%vC)ao4*r^l>3i{wHoZz1D!^-S!0K zs~bT-{8036^j}OGSWxDCgC16D+!yOu!@%v+3wnDb=?Y3z?VKCoMiutpZTctw3i=}- z@Q}kC^1Hw09Y-!j8eIPJGcmPw)bVZP<rX{TNX}T?@mrb#tO3|q(tpJ;bW2o7>Mzqi zaT1Muvp1BXbgQ$H=Y6Upp>RFQvX#A&L+8Qjx?1lAqOjTzL~Vz|Xw-$-t8Nx6H(!f+ zI(nL&L>DTaM?O{GeCHLGY;YaEKP*hlOvwN~h{S`*S;*{eAZeA_mli<bVEM&Y0y*y? zfBZn}a#C?{_gf`nwy+4ZCB9o^B3o_kTJ58ciKD-M+MZv;NJ6CtrE$x9JW#4Kv#CIf z!Q3bq+??!5LNc)cV;lR7X5KAvW7=y4`AeMV{J{mQypmUw$1;NmghJ*T`zNQRl&D)I z8)Roz>F`2cE}KUB(oY${D8U|QuZhRDmDd>ii16CASL0L>7l?Y8X~aCU)33+`Q@cpy z^hiiwcIDs*Uifn)bgSg%=`Wk@s)rAotI!%x(<raIbDLRK7*YPbOv3QU@zi%^YB)>r z+oFoLgTt1mU%Nto;1#uD)#4aeN?U2kt&Vu7>zKExZrcV3$k~4DyDJ`eDTVg!S=3+O z>W`+qcx>hC!Wx9jp_WsF>g=KD6>68424z-*eLQkSf+O+Gei{_v9xJt!q)IUjfetMh zE|Cw$BL)4tx(eQxlf_um7qqv}ef>m2xii~8(f>H-(AOiiCa7?8b=2$nZ%&ks=>1w% zjgGW{2JY$A`2xr9UcUqx>LyKSQk-C(vD*2_ZvG9}^*ud3NFNSh?za;bwE&n+KW9{K zlQhH?Q{F!hxnZb?#xzCiPVNswx*G9J7*akLmX;XVaBki-!0$~&(MAR{NU)}#3;ANH z?9CObTYMJU+2>h1)8Ms43nteV-6P6EsiUp4stE6b2jdS+70VG$-ESs|SALt-Rw1pL z+%;Wc<4RrO;NUnaH<q{R_)*Ghci(!yt~KiMv|**2`}e)M3FaCTC^VjxAM_EG-M)Rj zb?+l@x@3)jLi9KxmDPK8eUI(g62g_(%a<B_IAScH^u2JaPvyPQfFiw>VaRRrnNZs_ zIXSKSTmSt{xpj=o#FkOYMS=-fQr>^@@VD>{d^xfFB-jQthkvLAfqB~U{)s(oqQ}oT z&rgbGcmCNy)UVBWuYdOVB-4EVLu$YSr)Za~#lJi68+k>wwJ%5aGGC|76eWTglW$+J zbH`&<-ZH;_)4?vRRg&7$;hRy_D}QfhY0>n%3bA*%0#^I*S0@#lPJ8^x4HKUGPP){$ zHSI}X&bJ&Mpsq2C>!^_ZDYac_e7M_;ZZwNyD{sy!!GT696b8*dhH1V+_y61l`Q0nA zi@Qn1&jb$pX|o>=59^-w-Vs;2gui^eg#Ajr$>VbL+h=)$xgpydI9Ee?$mEo2zGP~U zlFLJq=g8AuY*K}cS2Hu0KM`MPZgeKslk{JnXs%IO6M9^J?>VMbX@jlFTWf9=eF^co z8{^UilCEPf?cL|hXz#@yDLn~e$cdHgPmeXf+jh3bt-__omSJj!f_b?Kxw%)60cL<= zKnOOutZc{WnJOBi$uqXR*{a5V`PUx#;u39-qEa}YObjitW7i--2j49ovd7$T<<^Bm zu0W+k1sAnC`PU_U&o}riJ{i3tZC~~5L3#0vvy$XCNt9i$vTKpIVu|r5i4Y7ED$6^j zH^hl`lfRDf;9CU+Nr<@4^uWLldL8~Ws>o+OGBVEu^0SmwVrj!@<IDR`_N|@1GOJJA z74b|6DN%QLqCk1?SLbFvm_(j0atpAAF)_j7eC2eBl{LyQL&S_XdUGs=jHX=Ot(T2Q zCT_~DW__UamW-iz_T*m8!V`=;0zVi9%LP%ek!h-whk75yT4*;{I^0b*c$US60kTK1 zI)9|s;Z5WC*3QvL$!)Uk9XZp{7q68e6-=Ei5xe@X<o;IE>B-qW2(r+BR)v;#daIbY ze+Lhb^z`)TA&kGtYZ;1A^jLfjPq5Kc)PaG#3i-5$2C!-le0~<0Za8pq_8H1s>{&Rk z@xSSEZ?88T*=H?Rge!gyVtQai=A38Q3cDJ~>Lgo@h#jVb1NFJ%mouCjUaYx62`Z?; zncYv4e%q{#k+eLm0aTTbZ87rlKwC5?#-nX>93YtbDSFN<=)A1du0&w-m>%`f>2Kug zuGC8*uABZ?`57NNlMN`~zz0W=5^w~)0OZZXIBQc=Z&QZysL4r190yR*@p=!qOe<0q z7Z&R1F>VZBa;vI+<^M9SB7#iM(0BBf2$VXn8YADm`=ejT`)-_Q_X}rZWV-4368?>v z{wFC6bQL%4NWLvDd$!~TK3zz2dEe^q+3=Q&{P*<U<J=kIs<fHA!@9%{iyQ9iUwozr zw35}Y*9~KyEk7iIU~DESsT>Huq^vU1h#;VRRnu~6<p}3af{@X=VUTJd2NU)4huQN_ zJ!7suq!TO1*1j%#60jZC*Ij4BJ2?I=<g}3*_wIHoHLgkf>s|#wmE!)khL|tO_~O;p zc9whFFJWOt%feLFy~T3r;O1W2d(=TnV<fntsDfG_`<5Rng3d-QG+yLucN+af8C<s( z$*9b!JW4*T9>=fM?>7YB(oo~x8vcP_4~Y=J)`^*oDR6SNRf2=1K;6^4`Gj?YoYrNb zY5d}{<P}-s_*^ASlZ_<eb1#3c(&_BqZ-`Qw46^ZT;0b?UH>l*?#$=G?OEBDGGV^C{ zOnp<`{&Qsqt|9E@hMAY;bec3aB{hQ?e=z2p+`pg|hUqibP;6UYhbJ)+4nBL$x#Y+C zvV6_76OYgZ0rm?yxmq6*P^G^Vmpub;q-XBZ{bn1g9nH1=8#M8k_=OfWJ^@+#xS-&8 z!?heqj$iU|SQk&eN_sFzC_30M`+Io2=s9MT`S@KBl^~jMu}n;E!)fq&Sutnut>E4l zj2O};<c*QM8IAiMj{~NO;f2vdMFVCcq5k*k8v_$XHalC+W<))4>^hOMXJMWlFiMBC zf0cOe?bUa`JYuT;Kc22Ks;Vy9UO+%v8c9*gOLs{qAf3|9r5kAp0g+BYy1Tm@q@=sM z6r@9>dHa6vjrY%iKhC*l@4eRAbIvtaSlEKGQW{8o)Jy0X__zA{L|ENj2+3Ty_I?E! zffG6M+(mlDYTacD6nJA9^j7`Yh%y7_BTbD?<Q>$>>=_mpPiEE4bOLI0CmB5!$85*^ zej=rQzUIPuLq<aX<)E!Tq4t0a6wQxYS~Nvo-~5$wV~Wh=tLl{g9dC4J&i$r3GxO1N zepI*8>TB`A`tn5_p*@UPR!*pMs~>|!3bu>Vt{&R#*D-$|@bU34Pelod^nyuVRd#lp z=D#^Mvc^bHisD9fYDW4nA&r?&RI)14X0uP&7#YLhrl3F(HVG`f7Pr;s+FbUD0I2^9 zR7$^ETUC>vs|Q&cJ#;M<6~S7cC(@DA_n9lFAwiyCGti>}a~ungnre=?0Oi*RTui;@ zi3p_(nNuO&Vu<dq_!1AMRSB~hlY^IVncmbr^KCwIvD}`K+6f+{>n?r)B0F-=_Kzck zZ0BflR0e6}BxZzGFcb+u_`hQng^fVUh8qkEx@oV0`T*{l4={W)>(}Lbv`G`!=P+O+ zx4`Oh*JyEHqPy_jjZ({n+_S_v=7MtCLTBuN{X1V^au7&}T*H$5u9&NzbuQkqYQBh6 zD^$Zs_%kzMZjN7Pod7*0?7dju+h)in7+_>JQ07mr9;$>&If%2JTEeIiwQxX$9{~~G z(UD_J=pVU8xo(!@&S3a+&Op!#PkQY3@gp^$L57Fr;Iz*u*~7dnUS}L*J{>=XGXfQ^ zOZs^nS!**`v%aQ!)}<YTqwmo#P*&xWUfN~w89}ja->Q|oi-ShTBH`jbBQ}RVEt{|i zFZ2CIdxx<d@ty3>Rj%}oy{+Fl{i)YG@X^Yx>}D(uR!G{i&9?#~AkG-i>Ru@>zv&wJ zo?;b9FHIY%h1UtUY6arg^PjJxfXW305F6j^_0~a%tO65x!C>`Bg#u&H4EO7)7_=;3 zAz-M<+Fknv!W%D5n{ztfg$)={wfDWNS;yy)%|GZR8ewQJK7Y=(<l@teQ^H*7oNYR% zJ>z*#s*c>$?#bI6j)@_u?!I>T7>TKeVtcp5mUZ}KnPO!IyGV|+6dmlxOUJY9BE=7) z1Of3h13gS=>p%_<6al}$V6@KB(WCO;7IoUf_ZJ_4!3RH>9!M>1m<&i1^FOiNIfD3| zrArMTrG=10BUpAa?_~A0%Pe0LP*GK>HIeZNXv;l8!@%!tLV$AH1jkmeHeWbrV<92S zCIUx1Gs<+d&RdE#Rs-e%D0?{4vb)i%1^xwya|Lxjg3RPfA}vBV@DK$yDvd2`k4tuW zGI3yB2uf9+vljgT=7q-;^aY?&QxUa?36S+aA-|8{7~yUDyv{&MP;Ne(sYS0?qu(Ju zsOLDMiKCPk@*?ob$%#>w5}L2k&y4puT^(O5u-<H~W2)4An6Rq-+8a<rq0lrwSV73j z<>|NJZJzUDwwi|tqhV)~h=6dmAwD-|kO^QCIn+7PD;yqelsXvGpJHV~h7zcS^bSx_ zlJ6Z!v05Jl?(TU>+AC6*XtZ-#O&U~;X}SJB$LkCu?!6qRt|AW#;I^Xi$o-&_X(aa1 z=C{JseW2ZXA%)=2*GBsh{RO>b3Eo4>jz0%qYn{faX)`j{+>V&J=WNVd^{&N5stLjh z>RxfYj;VA{3YLxW1G@<97hvddn!)S|xMl$Gd;@<-dWBiUg^tF4sAGt7veH8(_(U|{ z-r9**G+yrSe%R{?EiI*@n0ZP8lT)@@ZHp!K13~@}+g0g~^tW&4J>8?zQin(Ic*0Ho zq!-bch7NG@sPnv8JNOj$3fcj`Wh$w0(c3pMm?Qo70;1j9k)Xr%B3=GSOG^treuyFT z9f$|;dYCkg&uFnh6tU5*BGJL7kUin!K2fqJJL8H_;f^<@R-h#p42uV1Z(pVMiQG^( zRSE?an;_>2giBprb-=caN!l98pv61F0Q(WvD%3jR@Fs0=LkW>EU=?#GY{kIg^#<OH zp)RTac8S-`UIDNyHwA1wCWXLvAnU-RV*SGbG>~Bc>4G;e;*yiYhl2wl7Y_h@EKN3u zI)A6*>qRU%(m(Cn9(~#ruN>4l8#ny<9<RjHZ35YT*Au+edX`u=!^Lz_wnE+8xO5?e z;N^CTHtH~qh3{z}ehv?a`&$Ud*F7@X|M-RGRk?+vo~DCr$n>=Bd{AL)`<XQKcsK?k zASiea#;_^p$q<6R40uBZ81_mE0S1>_;Lkh2X8=q@;Q(?XD7ex^vgJ<h4$#Zr{vFow z<)=md(cSxbDj(*#<-05MCT(%hy;RrZ#_}mv0FLmp27iC~kzAtb;y4B*d_v?VD(Y+Z zItxGB0CW>NnrGIDLgm?<RpwXcJsA)N2CkCj@Y&BhHguoR#w!#=I%}D9m3Ce%Q!Go5 z%KTFLz7xbLr6O%oA17)xTRH&VDG7*Fn6+y&-vyw9lMlqnXh2;COn}S*SHWdPlOPdu zR;VIjbAQ*_+gqA|jzw4l{499>I=@E=6T#llDl0NrD@|Zs!1Sjg<3{A={l@&;Hm&ZN zq@=~F?g4PVZ?Nx<q{zQ5D=j-6J1l2GvqtIwx0{PlN=Y(F(b|WBGuC)>O+u5gIw6`| zdc(Xb4x)f~P)O1wx-c7}qN1jMzOjV66#={FPM{{^1BRo3BHYMJ(El+rGn1N-AoEHL zjReEQAkd4BLCR(d2KyP@8m>Q)Su{(|&rk7ll!rU2cpA$_Gv58&jQvB4wZ>1uj^Re= z&w5*iK!h*Qc~4u`l5Tb5yein<c%GbvpdW6;`$8hcPwF))@}wR-;stZ3_E#Og@LcFt z&2mIPJU9sVt|Wis9=*G30W7H}EV#f_FqIlJd0=Ov05tvE4X~ojSCWR^C{^};Pfo5r zR&j6h@;aF}>kn9n`qw*ZA_Dui3jJ*4XX5zFDfZfL!ddhTZ=BgtDnEPkxyXFwaIb7! zE3nnENllZE3ZZ7<B2Xl}IdZz-Z*AUCgQVklqzl*Gju*I6h4oV>VV3enPgNCY5!Qz) zM@T-$@AUtMDm^IoV@82TRBA`3iq>~wVPQB!BxCLKZ3OU_A&X)RRe7-h=BaH45bGNP z8KI_DQXQ@eRQ~hB^LS?bTWBqWR3;=<+E!vI->k!HfS80qrgM2%Pt0YEa>CqhgVmlw zAUK9Cfwg;g;w8dz-OzW4l`XiFFDI4k&*4{@hC$KRQ@5O+HsO8&Ru>s=_sg4e3FxGn z_Ad+$uD@VykV%ax6eR0#Td5WI2Ivsae=?=N-@nul6qi;}t1>YF21IZEEjz)7w1K|q zYzA}y{ekJ~K>#zF07KzGf)7+Lkx(XpPaFc8|54yAAkA6I^FsPle`3-n#9#$fdc{z0 zvG!+9+fO0pQ`)4lKO9F^R%+jbmNEDAr!?;}|LpCpmCG>Fv{?Nz4pDPz@q1OT`MECW z>&27RrAtbP@ThS38r!PoXK@w9^TCl*x4)9sc{?_#?rWqiex^dA($I9GPGA<F2D)#u zW~|^-xPdIA;7Cu#-Ne)smy~o6^!8{0qW}Q%!e>JO7uP1fAJH;3rA*4vW1YjyzM+V+ z385h`5D=m%$z%uXtak~qeouV~myL?^1pbn<FefMfM3M>g`Aomw?%hdbZ1QJ+A#Z7p zPfzn_bf9j#zO!9^(}fIpqv?4u$i}JFPUxq5r%zNd*L9cqfmvG8taoy6IqOQ=`y8X8 zU1snfY~|>aHD-!t6iL1LVJWJS3Nh3F#+iMA5t1Po4$TLG7Wj~76OvRb+}B8;u_X_b zo^YEGP_-a|69_l-1(O3Pc`%D=1wCg~&3Z;gxR~dFR4vm$#Qo7*S%c+ro*nX%V}G4& zJv24r`~HMvFAu+#jau^4$-FQCN7>)lMK|!&)%hc}H%TBdYZ2V24!<^@Fc%>ffq*LN z%OATLW+H!QdjNAhj}`GP=Wd4Cn#y05Ik2oY7s5<pa@&IxAn-ma0Mf*JzAgoT9Xs&d zB9JfwQ6GmaP<3(V@S)6LZF6fF=4kyhP1ah!egAQ#$i=$qMTL>KS)BS4%QbzPLtNZ^ z_LNTv0Z3CehO#b5jG;fs$ktv+4rED+;#}%4VRYtR#twhBEwUhxp#G2$Qj%8kQL8b6 zn5URLj;3fo3%?VXNx=PbA0H&Blmr3LTC2?rbPoT$!AcS&d4bBO^Uq8)O}sTX5iYi@ zHB0~Fj;S$H(@>d_DJ@53i3T%eU{20Y34~(x0IeF}s(1JM%H=3LE&@wBfz|0Y`&!3M zJzZy@erTz3YH`Etci@+IkIna%yEEelbMff*6Gal!*G<^R_C8d;`rMufl^Z`$xVvL$ zW`vv(fu)FS98|3m-Wg>~K|yf}9RCFbZj8F4Wdt!4Aj$;p`^;742fbm@ajA0eG)Z3I zi7p_z2mQ_<cF{yc>a~s2M!;I>oGb5Y{HRo-{%~uXns#bAhwU3Yk}^Un3lLR&9!1D{ zli66+<}x~D)%LMmtAdd*t!y?Pqk#oZG7v~)X)u3-95q(^W9kJCPDsaiPzoS`;blDw z3)g;B23hZrC$Uy!Rj&VTMXevRUXCmylHeksp}|6`P_SOG&^#dsNYPT0n*KCM)G7FP z56Qj*t4XiaDhg@2N{r^zEw+JdJ~baz@wdz-;ho<7!0;#ySU1H(;o7pv+0wbV(4&du zD44zNSl(CJ)KGp2fUK&zmdk0ol6saGpX-ByZXK*&YASiH_92qU>f$14Tj<Pf9$vPQ z*c5>xCq2-lC$bouC-~fDBoJR+CH_6j)98-`C^LrPQvK!{qX6mU5#b8h0KY`~J7kHu z{ud0g$uIxI7@~3Ic$@(7n%W}!d~pBD!1XqUwfVDA{Ju0!|G?~k=7Z@>lNz9Vj2SaQ z5{WafMz2xj&}~D1{(O_w>+c`4HW1!m*1Hp}PIno)xxZL8w<M-si)tn^pcxxoUS>0J zb!JPPoDBgr`n3+y;qvl{-3+>KaGA~sUjNq-{Ga2dCsKbUNoE~L6zUaPmp1g0Z@9RJ z{^kbpR<4Rd;xqKHMUS=H5CAL&T{A&sD6+qHWbebwRG_Fj@PGasT@)4Noq`$NX%p%; z=K3w4yV7toTln9V7L7PIk4u6j0RP+m34Zz%XUr#(zs0XXe{nFhhH>1g*6Z@8IfG{X zZtaC3$~1+qdd^PI2>#dppeX=Jsgp8}_NDDuW$(~)ORGpLF$HAC#>N7#@Dn5vb?9%i zF_dm9Ym8wC4~=gG>D#-a54$xr(ld=toF&ENk8Z4Kf??r|jIztCZDf=_%bP|Q2WYW$ zAwvz;^y*I*n$^7Q*-nlZ<GO)M$K60|?t95C)h5R(T|q(mlsTSOlumGCG#V^_-ILeL z?$)WBtY)XqG#5SGy)uyo0!uZh@Z;08`nDruE%(>ry>V~n!9|3%-d*|d8STJkC@lge z4$kkQf^=wPLCheTgx@mlJOnVzT3QZAsq;9PU*|`%Ar}kZfBC+zhpdLh#*!d4+NLy? zO^QeNMMy-Pr117K8HyoZoekwXk-rf-wV4f{&yft7Z;tcGkt80f^AWhf2i(%;-mdf< zsb11Y3lvatHwrcq51q^Y`fD>@Gwo3FHh~nzqg<igCXVQ?Oy{d|t;(d=8JhiOI!ZNC zubnA>n-pa=i65c1o+wt7h?VH?HYUF4C=GA_K#`#(?(3*_Z~0ql>in0XL?9m}hudiK znccchwZDx!TJhW5M~PI3G#ea!4Us$KiF@7B^Uz4NRrpK2OtYLiX)gHvtHc$19&l@f z<bU+lVYQ`Yq?a8Tr;#SaSwU<Z8Grdg8t2FOjkzf@KfhT8f`uuq4DiT<gzB@(bV7_Q zM=Z!qE;oYmmW-pi0EXt>M)}1*OM^8mXrhMx?!IAn(9OxeN1V@NT9ttm{5G3KE?5-B zD<K{tRK>BN=3gyKtF-)#N$Q_rU!;FVJcG`wt>=tV2~D5N{6Q-w|C$g>4A9jll26XT zexJscj$!(fg)a&~<;`F*Xay#kp+cku@~~jy&~OZk-b+FLB=Rz(IgUT$l97)s9gB6n z9b?z$o0mr`#WcQwM4iU{;vqjG$Tlwa?$9#9T{VuBdvmtvgSg$A!r?|Y)s+Y8OB}Yx z9xpD7tLEFea7|%E-zWi}uR%He-FMKKfIUJX)K@swtS~D32G0~pYvDnIET1ZBFMl~m zo}=U_V*GTR+nqs~HZVJy9lAe3@;Z+w6u?i=dB*mB>8a4H<DG%go6AH?gS?Nv!ouUZ zAtb_!HS|E;JSMDj(puchMy9DuuoX&WoE&MipI5dl)%12Gl>1ZO4kON8YmC6=)aD*r z1=6w2ryyCh1i8Nu)<F-jPhR1RN(+S|_D7}I@miT&%067ZYCXr+ZGy=_6luATqd9#s zwhkL4=2$zyUB@*C9dpMpk_uI0t|enDBL%Yh#w)QDmAs-6MDs7NzI?3xxIM*^Q7z&q z0-5rZ>mOY_@=O1q5>*geP?cxDkYsvMx`2;G`p!NO54fV(ImDP4aUgu3&Y1|#=MyVY z=?*k&({-$tx)a?_4m|at-%QDz(WZH1y7%(Vk|&$MOo*e65hWzS>l{gu56C=DRs5x0 zMp}-5z#u>|N0n)3<f_1PY)5E)t6(9BAfh2sAHMn{pPu(Q28L|j*f@$9AOk)4JjN$0 zXdx*qb}F1q($#!`R{G=ULg?*oc!@b&i72%*xDNA9St4ddbf9iS1J~g=y4-i;7IH&B zil(uav;0Yz9qyZVXYHp@R=pe6KRq;IJOzZ6^0xJ`ut3U*&IX|7P8C|*AmbKX*)vis z#2>7%ig}+hMi2&7Q2hdVs0kXZYB%siNhKjWk%JS1*4NMP88IokXuOkJj1zR=)JNju zuW$w=q~bCR8+)9lIDI|`Q6|;uQ<m<=J4gJ^eb39xu~C+bb9z&{(}8kD$W`Nc|F8&q zMy}jYLff~{jD{NCv=SE-!_%VPz9$Y-0rJ)4;i~07!2`!iDt?@gg|%y0TeA;Mho7+^ zn3Fsb+s+U~Z#{<gfNJ+)vE3Uv>_jU#i)mwLC|j9>iy-@mq;F@nk^S=M$b_Dr%vzYJ zZ|=68j)gfA0Dwgn2H#G}`lc>?<OfDyd*Qu~(|tu2@nk(C_7VP#R~1%i%xP<Giv<iU zXr!^=>-;Si>HHV)blG%WX@(WCa}F+zTS!3^dw3C|nSH@nYAoj7#@8wz<jed3zb)Xg z{gi7+FT}Q+qRm}ho4Gwr!dlUUy2Wp6a`S4V69?jT_LIJ~<K7#RZg<ATqn#2IsiU$M zjUrY#vxt{LsR-Juqh^Tg^XrVP)gt9$m?Y_ZV+4Nu5I-}Fy+xft<YXI79cWV(6FP0u z>qIRdcyi*mtw$&h&erG8n``pQnc7D%^2Y~ovRbzw7&T|(4P9$7$!Rh*gCmVG?g2v~ z38yZnJBP0J#Kspf$xs2+?Vt0nbUn97(-qP$N|tbZ>weD-&J^W3iou_Y`S%g3PN2g% z20>|b1_Fh^;z;5F1?jiOzeUB(Y(Z%!1E7WLYFc}t&NM+ajPB?lI{L7~*)o`;qrR~1 zQg&U<D{g40xESufCtNi9yITb5#AAOhso4z*0mTCzy-oovLQh2_)9r;eW7UNs;Me9Y z4;xe6lWIPi|M&C%dyOL|DwrP;XkN3JK#Upv3W?rZC4mQ)HUFpRc*Y0_u7^-f|K4|) zXkgmX`rd22F7Mm34CBLfU&W>RPM<N92fIhNT$8+K+xR_ze5Ns_n`m`(O1HVo<^Pc& z14+}DtQSrJ2@|oJOcN>sFC2_J7z$-!+4I_!Pcw|}3}~uv|M_WYy%m|T?K@lo*%4|~ zYx5efBbP>*)NEJD!Ii~5-H7Mpz7L_VpqF!N921g`;SsqOd5_ZBAk@(-^GXCdlpR1% zy!n_Wa%|_F@YzKU&L2_!ROB8wQ~5y6i}s39cKYJ!Q?`BD0(t)geLSqFK*aHWJ04_E zsi5+h1Q!u1<vFsctHXp0pV?>8%F3O8I6K>4e$GW|mnI;gEjO%}v44xS78?`y?n6dC zW}%=jxFNHHsz^ZN*rsnyR3)|`mY(;6eKpW9c+3*a^!}4PqGw@N*!f8_a0h;|*6K{j z`i?27yGDnwMV(pOcD3nA$75GrrFaaB0g7S&rv=0-B}z|B{Q*534S+cs{z1|A%m^ey zRa9Ii<3)M#hsBr(e-c0Oqf+gI_wu#z9plyc?@kdd0cXpPL5y)P^orfO`WJ$9_8R!` zJp=QF<tG?D9<VxwY+g!3jp5JN_eaS#Tx$HdS)bJ6<K(!0VOeD`qdojk_qRv^mQO~= z_->}ytj%j-wkUSKcpC93Op8xPhtEfU_(IrocDgfl?p_`F`f%PJoZYFPCAdg`u-PaZ z>Z^HlmSb*e)wSPqks|~r`+8f4+2g}k83(?~VmY`|j$H?6>v^)kfhQijCISuN#mdU` z4ZX%+kEgba!7o>FakEV|kju-*%Tneu_>{dmu1>nY{C*C^D?siF$c3%bgH)T(pwNM& z6}!TAC$m6Vm?C6XWa9qv1P!7dRQBuJpD2i<+Os0BXtYcdtx#ApyH(+^%`ZH*z#&jh zoMvTP?iQE7e$oFZRJ(05CG%o`wuq=N^DQi8sb#F*Hu~GQ$5eD~>rLG}^qW$q>~lFn zeOI&R-=x2}3x29O{qY_Yy`x`ZF_;LNa_foOnJsn~q5&3ebZP{@5;&@J2(l1+-&;sS z<>4QBHUMLN3#mIW1U7DXNhI2h=MX{qiv&ZIs)@S)kr0E5iai~_2NN?6ljMcA<5(*0 zMs6PALENkbSALzJ)3GW+z%jNLswiWJx~@#?*M?YL{6+ani@O*PE>f&96d{bx*62@t z)DXteawU+YI0QkGim&q#>FEhZ&{<f>HVaBZ-2j;Ji${#z$w?p!fb|z?ZzkFxc)9CI z+;c@zd|-aU%BqZlB8NYCTn`O4II4D-b31-NvA+O-E6H-IE+7%=Rt`eA%A7*wTUyPF z4_7r4TDX$dx4#$Y_;;Juzb}H_SzM_G$WT=fI)~w*)AaeD$LrfLTacrvz7i+sOk_2? zJcUHok;Z;6?HyHMddnn4(`}OeouJWCqK*wv41sEIXDe9UBh72fGzA4>aH+=1IL*V$ zd`7&IKfd~l@=*Q|Zp$<K4p){o090ur7dL)%!t(C9H?JBOTK-eF{JggcqLVfQ1<FA6 z7ysVbS#_HfF7{hmw*w&Xbx_1Xc+o4b%gHugOHg(3Ou&VsExN|MgeVYbOFY$wWt$wb z485(&%9w=@7ZDt+rl9H>I#`|8I<SyN9D&ciX?1D6uy`3tva*OOhFYL$8{~k0<=9l4 z<ZmBu>*uvCo~5Ud(WK(xi<X;k*KOww2}hv(nZdvP$Jl3XHd7Mj^Pz2(U^JN<dF*_M zSr5P7R0tjYuh1X-1R_b7hCktGq{db5c`@cbJ)hIHuL&N?vx8teO>qoRW+w*eUvy!* z((z8a(O3JrWywXBeTdAykmqP<G{56t^OL+rM;to!=`Hl}VV^&{`@}w^leZ|~kMdS3 zm_)nTF0JT|oPK)~TBegYUin*X;o}?r-&<oJR>}nGwRMJ2nBo`!8@E$2-r-*RD~C^0 z(1G?8otpRE(_$tOrrc`DdhjEScOS6OzwFOR#5^Ohn3YvXLl^SsTzm|f6`AveV5gNr zeiC)U{w_PgQet2AAr_mQ7*+{+5G*zjmtd|AbWP#s1>!*T>@|!<R}kQL$M;;MMqN^Q zF&1(kCJ<Y}UCCHp6exq@)IiE->R&UsQ{x%FDO1DH)gAU25b`wKSC_)VN6Y+>M%TB@ z>1N7S#5Y|?ePj671C%;-Y@|E{A`1(ZRx@p5f=97WL@RQ&Ey{*lFtb!Wrd=!K`->?+ z#7nhFg-ihPk?A)%2<;H)hr2uqfU5P_VmLpN%5*l;1wf(?pygUE^}BT6L%(HVhlTDV zB4JXo)cf2a9JgC*m(RZd13hLUVYZ?vQ{V19)!PtfRR+$zYJMZkQ#DapGg>S3r_(X9 z$n6x;6kUOGt-3Y(SrxYvmCd^^nvV166bp0WbR_B2eP_V7&&*H?;&b&j99eas(g@aK zPbHTE8tCnySV*vywJ^dd>H48AVT_m9s3rJ?sxPsu`7m)Lak67_o?hUIshaRssuIB; z5MH7o&*=jMfJr(KtTG?wlAzew^hhB8lVk~lb~)Z<Ja;}|s2>=peAgzD=&~Q$5U)Uw zE^cnx)fchZE~iK5^YzXThD<H|k`c3cqgcCCWxyC98%}Rwa@K(grSy&4;T|Q+QZY@S z6s!*CUHd8%x9;=EIRfEvA~KVLEUBX*bPBOWZ&9nqH#%RdR#5T@gx&{WsA2U2HO2ai z`oL3P#<4OQVIfK~MzVL%umNb5rzu9}_ci!cP_2lln+&^uaWFZWj2fO5oIsFF%Ft&e zGMm}&tsPD!cW^@$5vkFiRdTG1_fLeVWWD`hJeGTa$b-bl$UcDjf=iX@x&Tj%*FpU2 ze7PP5aP-=rFhKY@Ywpx#5cR%f=dw2pD6fkMid6B`+)75TPk&!ueT&+|9Y^x*Eg&ud zSQ{ZLIKRK#Zn%2-{bOSj)6ue{Mu+P*ZEVET_u@$rMYEmo36Q<KI+vD86IePtF4zt$ zKG-`-j3ke@HeHP>S7q<UJ%C~HuDT0GZ+?gHjBS2fBJlE_V~KkiCj*r~`aN|Ln9ctQ zz-iSCwW|5xD{_c*Q_Z;^%H3BJ?fR#&BfT8$&5?dKBKrG-34%opXE@Rm%{J{eK407> z(|ZsOMUJ%%TNO(t&eWG0!b`r0AFkT&o#d*#03^PRH_xp4Y-)tyP*LIss~{Tb-uEEj zUV{iPNm(`j<eC4~`0~+OGB>~Ld(6uOw9Mz+)@H@+t(L&hsfcJW7&rUd^^J=|N#QfC zJywXm<u&?;7Wq7YmU{`hp}hDtPVKz6SKJH6%y&@U$5mPPZ(1<-)dLl15z{x2N%cd) zO;6C}3;s)LPB)CqtO7cge;$<bDUJt;e6yuVTC95Q3ybekcwimMuav_iX&)ZO>q_RS zPCcHUE^>kz^D29vSk6WS*B2d3DAa7?;7>|ge&Z}vr<oUHsk+VpL=e)0cmds}V4ugs zzgk6&IW>>^MfWF<cPcSbcz<R%_f0{=Eyf3O12RNkot*r>RdWD~CS_3?`lFa-?4BFE zk0M&?HGf3O^jUdOt*kI?HOQGDnL~-+g5(LRITu>!QjW?P_X6b<d6;m%qiT%m8~E-6 zGZluDk&oL@%}0Zcu}Fhy#>3x<QJ-1y@O*>~D`bDbudeADk*5iac@Bz@qm>dn^3JU& z|F)dSCqC>CtQ+z1^GgHQqXUB2O7%xlZXPuwDVmn0<BT4nu1K<`K_Kj$pPl%A+yW9I zrrCRp>?d-Bx96HWCWc#o2KUIgArm*|zXtkMQh2gyhIfW4o#=I2^0gUQSjNq^T^*Vb zI%j257TRa%*7#S;baFs-&n%F<M9fl%o3Hw1rc^Z+WMI42w%zHM#vBaNc}egn$L}a^ zGm;81GY?Duy@*W>ap$rduG-oxW1;ue^erjbTWgdhzS<#H>#nl8Y1r$ls$$YH$vFOO zjf&c;U5B{doQlP?(kpnl%6=qCBT;!<?3E#vmjVABO|DpGqXY|y*_|8Ro9;vLW^Fd^ zjhYW7?bb(6qUq$fpFER=?`E~3u3WRc>c>mRk75;mueLN>i16^wGt75U;?XjldCTs! zCyKP`p@JGHh7TdvP)QnT97XsxE;YcZXhj5WwBNqLOrMs`9x(gD#o_#6PbyWm{&%&t zrB<J?G0EK(8XjKP<?cJmPNW`(m9z^aB~{vJJ)C3WzlB9n(GOLc)zR#n9^*e&n-pr| zyYD6C<(iggY)Zw10tAKje4$p_#`BV^?t$CNM4NS1?wwc{=<-FT#p5EyBPWfGrz3h< zqz6uaJmwjlFT{Gb5U=k`Z2smLxkns9)g3F~fgj(P`}pW0Au2gK24lxtBgrE3F9=Mu z%XnHf&b**y3;@#oB8nIqAp&@)`Id&Y)DU*x3L<uk4+2ypus``LBztrz#A(#GP^fGs z`Th(q*yn39w|s-zE{ejaoEco|q584xootldu2cWf@gnF;|0S}<+Q3k&ODNt<3`Aw% z`U*)T=1|<RpxN7iAD1Y9)Lz8Wqa)a_0A&154Shei_koc0=L9tUk43b{)ec=<u&2Jr zWr7`%Ac_zkK;g=bbRPw;<>#cC8s)=2?O9}e9%GaQZK*r233D0H8OK6+M?s%NW4f2# zPB6`07sx#vQ9#XEFNssdJq)QA1y=$lV(@6SKs!Tob9OX~L+vpA&){SGYTg_IF%l>2 ztJK5fWKYlboc@qcySdUcp3zY_rEFhF$|C&<fYx-iX%OEpKQibaRQZABj030xhP$I7 zmj`IXES-l-u!#Bh5yKt}wOmqdX#-_b1qcrIli!zhmFRrq%`5=?|5dHTaGD`ZPk^5@ z35%Eq!dAM$CaPNSoX(ZsL-jj!cxbRM5Okg(J)_k?{&r*}H^mN0MuZd4iD-K2P&6Hb zmZ^HDq9P}e;d#O4^2lS0&cpN1OY>u*2vzcJ+S`n@h_)8Ns%GeNJ2Mfb<#;F95l5;m z^_pmOf;t3G8X6vt`w3!aj7kQ^O_h4<(EarN`nJY|=<CCX!*J2vE-!9=MIwD4`XT)f zW^UW>NwJEw?lI_sEiu5In>Q&9px09;SE8-9dK&~EpRmYB46K0UOZaKE9X#EFEkt#S z*8EPNOyeT`z*`xR5OBLw$skOTK@_Dh@jR-*9>-<>ofY)rm^B_*ZnTCY)xJOh_Uo$2 zDR-sOXd*Fha%*O`0%p(LJ}$Q`pbXz6UGPnULrlE8s&}Bnhfo^Zf*4HxsDTJTGWnGC z6}OH1e<Pm<wueoOONQ5P{Htb?XVYdafInRCX9qTM@!(leq}x!!Q_utwz0x_^wxF|g zt{ZKku<GQ314Pa6Q!GIb3h-8EfpGtm;Zz6|J@rkIQopk9SdQg~BuMcH8#WNl0NJwH z2%g>#vkSM~wZ1xS`g+!Rf%9R`_4OC(VF*`;4R`-;h0dP7z!%+nXg@mT2I-E0fk~iF z>d=9v=qCu*gcaw%vm`LCE<Sv`9eysQNc`$#f0}(iuR;&$_Tnj2v)|a%SgiL3%LK8o zK3`q}`RF@Ap(ZXKp5Yi}jlScrjU)(Y5t;nivYwk0O1Ke1-_!mc3G@KO8zYGcb*2D& zUs*+7>r$1y9gD4>l4-(->!3hZjeH*yssLZG#a1o3Dz4gel4hS2URJ)t>cq4+XfEVp zkizLoYTm8G;0VIr3?!uEN6i5+Eiubhr^=ureZE}Vw0<bhH1DH;`Y?oQ%6V95%=3EZ zuEFPVrPd(J)s~!5gN~jvHU;!(ypk&lCt31h0>AqpYI*FxH2+Fk;w8Rlk^7F`1C|_` zVHlBFhqDF4^eDw+Lc?vtXYYv}`l22->~S^>^S@RT2KFc&l(&S)r^|sJwr*3U&ui^$ z(TyLx!iRb+xky2|O&A(gg9Au$RKxMjx4_|~g!~%~VLCvTH)GSWm+C|ASKE4Lzu=1^ zB4x!@FhTS-N%|1<n`xlz1}!Mm)Mic7Hz$7~)5WS`q1c4So=1!o!DV%I3?KM?+V_OF zsvTNf2L$XVy(6$lF**SQf8{(^0F`_t9RM~*(Ur{-T})@OngmaLs?CkqT7JX=>*MaL zfkv}Hr1gp15OxGibdKA+yjviCz37&FpB(d+(-~?1LLgEyj1{tWd3Px?MkSzHs#-V| z62lfHWY#>^qQ+Q@jh(yJD*dC$sh6@u)%Buu2A`FlRWw|r&Gj~U5La3S9v`8?fqCsW zmxMX{IFtl&t|e;zP&ELgU5zU#5u}!=c6Cin#k7r0-t+M%B^#A!0F<SMz;qehf&u*v z7?i03*Nn$EYpay{9?}Q>y&(-nezT*IgH6W7Ij%=cmzsIi-JrC%ir+#Qe=U%IkeY2L z8lx%&;#p`=c4xB-tiwR0iksk(I~;*CN2g(ZdG?uT_+wb;mt70?W8fDT4+)GHY16;! z_Bwf6Ww80r7)B@A(R^Q7U(GUYtp7=(6c3-Asl8$w9>>s!*q%Qz)c^TLVVN(>Q9R}X zPP`FDZJX>)N;p6M!p>|kL2R_6ut^71lKE7A-)u)9mITuGT#jzf7krw|sKhnjhY5&M zbs?gHkFEiqQ=_7aXog#kfA}I~$KWOcKrQ2qw1=*4RUcy<GhD7<!S%!awrs?Hpejct z1ip&yjm@C#uRD%0+g`o^%mC@}K3qz<Kg*(bEZlj~?&A3b`QvslpPgS5y^0ZtE>i^( zPG$P6R^mjl&x6b|Gc(_I7j`O3cpQ+~2OpN}RhnVlo%lB%uJ1|b1!5sIwtKo&ff`i4 zqRwNavE%;Cl}CTR5XSX?Ka({=(=!2|zUNB`AvD;Gk2CBIQKUZ)Vef1Rs4BG_&#F{O z($itM`p@cKmZ;+52ZR=x+OD2{f=qv`@XskT{zkc!i{`v<21Vnvrv#p)m!|wAMU~n& zb-588i3hVKW~|M~&L2Q7Y8C;R8|QeIS@|ogeL_&yZG*RG4Y4oE&t`H&U*j@5i_+R^ z<VqTjaHKb%$y-~+88eNRcf}f$9Ah!ih#~Ggy9=lukqxVOI%Py09CBs8K$}N)dzm%# z(|ukkk7INkoy3t-d_|O|=owf%p;pr&an8ZaxB13I<d4K;7{UwG5LL^$@3INv3Rc#P z8j#n@n8yL&M!YUsWdL{;OxX?uprpG7&(LoZ7jsciIC2^#s6$}nvGC1_!U4)iyMwjI z@hTb9rF9hlq66I|ve&$t0L22DNPx-~QpD4&#DNnZWX|viv#|R>5Ju+<L*rAv7^{f0 zM3kD!e~I>8=BXIQ`nOjvw-6^NQsXa`6a^o@7Ov~IsMtdJRXs9)S61W2ii*}E($R*{ zDCJD7uhlU^z6HMg9pj7x@CAJ<YAX!y`dd!Y5v!i1lr?pJsB|+Np2DGL#;Ug?ybU~K zO<?JYAbWXRElPE;cR%62@IFg2OcHF^BLl6+imye=NT5_Yuu*1f)#k2RsowF+;DEjk zWo)Q`oT$bYP(oXAUx_EZ6JrFkUc(w1e0bU88G-!zLSRKqPYimiZ}ET#-ea^kZOiNK z(`TU@#-JSR67a(b;kjkQW|0O(UaiFJk3Dzo1DN1z_9R6s2VD}O@~kW<A~rS^qM~BS zm9O6EeH)rbrdcwP{Z{UcNAtiG4q<v7w_Ga2<>3MFFY0w8sUTvmEK~9u?h%J!4Gl?6 z3b_k<lC6b(X8Fv((0X?~^nb|MSu9%Sg#%DPEp_?JbNVvR-(##3u;O)k2x$W;uw<`% z5+q*SjSRKXwTyx60x}y=A@(*az#gMm)%|M5tS=nF-u~w>!9YeGx2Bd3nK_aq@N_JC zwrcl}hm;Gry=<6l^?cixOmy;!Tr`qa5e9*WQr9fs@@_4fI)95u0b=ZU>(!=RyS~h? z?A<qj_GhN%=T2TWTP*|y6&NJ2S>l!IUUzc#Y5?6~XJ<1~80{NR&qTJj2f#!BOhv*E zy3&FKBhO?yVZ2WeIC3FbHe`bU2|I-C(~`;Nq{-WlMcV*uy;!fe!DaNlx_-MqyQyiz z1Xt@X5q_2kM0L5oU<SM?VrPvOr}01g^Iv9K4(~iP{ruK9U#{|+kNwav2+2J`!0WX1 zET{s)69~#InWhbOBpK?_(KAYH*47vAIrzA(f`B-w7Rcg4t9<KJUFEGr(0sReBR`Dg zP&Q23vwzw*iOY$h!H4g%qX|g~JWx*Li>wQ}2HWPm->fK1I=qt&ACRdm4*<<BAm?vY z-!^=mw30W#ygc|+Sa7p*!n#W+q#vo@ryjovUW}CPEt8$o0m<Ekh^Lp6Mr{b2oJUOV zt;NRD7d!6etf_r~%WUOT6zED8B;beS#=NETo%FA%@;h2Fx^kGRI$Y|lPcbcC@24VO z=@3}^<!kx3iN}`7@zdU0l4d_FF=W>RUk!cSp&r6XkmiBSA%v}VLy0T!w4ir8PNPS& z^T6ig?UH@gJ4R3d(hvq_5juEZdDKU)4mN+=2blqWr10ROpS7Dx67YkwU|PfYt3rOD z*?)`ota~UI4|**AOagbvZa5WfD1rFqHBq{*#WK~bgC;xb)5yq^RZrMj-}`Cg)S4Df z_1W3wcK2`KW=U|%omXApDeCsc>x<Nog7NRTwB82^i5SX25HL{BH-cdIrRTrp`^)wG zF^I)^4UX`%e|`Tbh>WwM1tGn(19Jnj15-LWNa7jdl*+V2N}&RzECxIyDEe*9N;nz> z0U`=_2)<!u7>C7)=B0ZWtik_cvItKS_{;ftaaTGuc<s1`zzay^Oq{$ez=2$yu177) zbVvY>j<v!kX?NQqE+=*n9!Mk#rn*YCvQ_NFzwR}H_$-0p;KJ=mlJ!X0FOx&hvy{>4 zDompOC#KAYHr3z1NakO<(0D@yiI~Sh;uYR0Rip&f7QEf{X713wo`u`f^GfvYuy0hs zt7)EKg3Q;NjCXLMQzZ*7fCStLT-*wpf}XcIa@9l*Z`MUr6j3Crj)E%@w0Wh4T3BE1 z?B~*?`Nq*2hM@RA`%*EdTJ3POOi*8=Z&RS|?x8i|GoeqRCpO%=N%>R%<3Hbsc>YQq zbzqpll;!uq(U-oy$lRd>X|7?{gxuT8Nlo^=a{`DNd2$!HhQBSDs6H6xnEhI&%*$7# z3j&F%vuu3uVW(fV3X8A4z4qkLJ2b;VEp~(+flOpTcR*h1KxF*@8JQ(j@@3x2He!hq zK`2zDzICoTAWDSjHZro(%AfNgP2$YMr5mM2Uk54(zJem)E2RcxjHY_!<t!LO<=a~y zOk+4=fuIjs0pDfv>O@R>&`ZfkE((#4h<2+4go(^b5bcEYNZCngNVD)tD$uXfy~kw6 z043Z`UXZE9DFz09-78egXAN$LU%r056`t<uL-0-K_S`htHyLF>a6gkS)X^XyS*yb+ z2AZtkkG^n35EEQ(>7%?5JI8`P|H_MJ#Z_YWE!<{UUAt@ty00C0+R9#NeQvjcNJaHi zj}imYoq1KRCs7lU)En?MbGQ2A#c9@-Z!su)%tn~1?<y}Up=w_Ov3kK2X{DO{LJF-s zbf9iu3o6IPU+MAxgT>+Y-Pd9}ok#w_=O4u`l7Nxvv8a_Q!706>HVsPAg=gudaqWez zw3RDVgz6><sQHny(|14@4^)yqP7+?={mSs^aqS7dleGoKj(!^xUSEViDgs<b74iO6 z|6|<yz>o0sf4H<UNjvrAo8az+06ZN@=0s(`6$71x%mfS<$2e&MZ{MR}`b_$*H?lx< z!C1Sn-WPFppL7s>`0p@WR`$<8&QnslGQ36o{|D#4$5N*L7eX)B9~0BFSZ-ML<YdzI z8^r7?n06PCm?}S`AxDd}^`IWg?-e>|t_~xfYkMl%0zEu&JmYZt`koXE=tky6yhtur z@M`>6A6wA86(V~#qh%WO>pUi!w9Pr3pL;cKiM8)vQ-EqLf>tb{5bSzv_^wYb`alhZ z`Y?{v+h%jg9WFk5W_bCmIwn@S!yVn1Hc0zhJYC?6FE$8FYR$pwT#-~`B1f!(I+&b| z-Ps?)!f)YZp_?-@pmYP<py;|aN1|4dj9RDcJtRb#@wtzg6F?W!KL>&96~Gj6xbQ8G z99l%4q)G{YxIh_?kL<`|9c-zkL4t+K^MZl&V)<vO%ocV#xe)YhY%ng%N-gZ`3&J4q zFMXkJ%FneC9`Ep5Tjjz|e+>=}z<|D_Zwcv-mN3GT)BFBeqD?&8g$$b2<6HAGC0e2) zXRDpL&<(7hoJ=;$l0ENo26XH0*_fOJ=D`?K_*EkH%6($e&D>2x0lQR({t{L5SJYXA zSP+CSZ8kZcS-x$|c4@T%RT&zBSq5~l!DSsHG<2bkYyxSgwaml~(TPb<MZKeLKuKym z!G=|=5j1B&eB1BXL-RfNm5#Jxbl#iXAGZK3o!4Bb+}ot9*Cp7gd0$2%@hEYBy*I81 zUw>k-@zBk$9d}nLZvGDW+^xS=?&}7^+5Dm5mzTk(KEFA2a7h^csQCA?uo4!O>-El7 zhRXdO&F*y9P2Hm+(sUnGz0-;~T5DbPRFs1W<4y$^mTJ#O4}8HfkrypGu~IJ{7-9x2 z^yX{`NQr4BA=x*_ww_TK4cppNG1u@lAQwCReYA2;We}?~(5^_O{B>-Y10P?yg+|@5 zvUmdB+Iqf0ahV>!Q{|_U444h4{BvTF6lEvgO^dGCYpQxm^OI}dDYcc$BWYt(7kviA zVm{#|Y$jd%Q%4Z!;iYoEfoKS7ytDhtc6{{)UdriNJJaeVjMkK7NL1|3U@Jqn;4x{` z4++*byU~44#mlrZ)6pUEqY?B;lYLVT3oLuX_X*E#4TYH4VzTEYwN&pwHxel?(XF6c z=;3%4tObU=y<PeH59Oq@CFF|l7If>x?QFGnH-5~v*@&F~yj#`Bb3_wDY*G@T>P{I+ z={3Fg3PNX}GJH6^G`uH*N>4_Ask_w88uVG2|6NfjJSe*hd49>r*t=Y)0dfV4>l&r& zgp{VgkE6Mvr#^vrVDMa$dS0wdl3+iA>mDK@V`!Y|<<ZWBb`%HHfdF#<=b~FC=b=Bo zj{EKcpJu1giAl?OMNj*+4nkh`r@Fr-)8~bfHrh-dTotmKbzHWXHryK8hJYLuD#;LM z`o||mCa*t{{o0-^{_Fk(5>&NP>M4++O;FYSs_K{P?h$aQ{<%Zz1iBJI_nxKgy6KP- zL4ck{FVlwKUli>(t)8E@aY=YEU1>3&tlyp0wO(uUIJ)o^Roc|m5ASj#J(KN83+3+x z3=W)j%{47xcRkc>TFx}#f$ziqkupPG`y&!&<Ak)Nyp_hcK%#C@TDr?sjGUgHPbA9! zP(iRiEse<v!~@fbPfks@a-x(#*NWx^`^(suAFDrRTSt`t>GKHMo7(VFki>I2+Kt^p zdoXVKs98!w_&zFGh(y)?8NUp6g{?al7U{2x3^Dv;I=Q_!$PrAoFOot)M)>iZhw1TU zi;<DTS?(?d{L}9)FHpuIB2|If{KR2G*20`V^g<@@drFFC&vPIJp%DB*pK+r3Q}9id zZmGaji8|{+ne6PlF}0E(Jp()kQY46-IY&OgAs#lsCk-t<ro6}YwDts?PuC=;ELtH+ z=P7a2R8{hw8Dmn^f6o^YezX<)XaDaA)*6ot62Vr;A-~uB^vztc+QYIcG~kFF^{F_$ zBwRo+x+Z|rnd#v_9eg0tc8;CuM(L+em7l)Fela%&TEeX)Y2)Z-R*KK<Zru>vy|?yh z%xHdDU$?mLzZ7UY(#J{2MTNsb4reT<`3xsxA~2tMwlH1&iYeT*mi6P*G>S%d<;X|I z4`-1`!xZPWG|trMur+mSS&(XGLXX-H4mV<tTlo?%7$8yVH(_HULHl!R*m6)cI+sLo zy?@O>4cOJ9v&K!C;bepDSA&>T@0J`o@gISj=Q%XodV6Zz-*aH#=}Gct{$*AV01I4I z@Luzm>&n@Sm#72jVbWw(j;8V9AmLBug6j3!qARDUQ078I`=pTfIIvELROw$Dh%KXy zXbNus_5c)>HV%3@dpUe0NanjBX<qYhQ6F+pdMGuQ$7wo85UDRxY!<BY98mjvN295@ z`}!+jT0+R7FT(mCt8Wh3|3Y!PV_zShp3TCHkrG(Lnvf0`wP>CUq^723ilJ6@e0)W0 zueYsqf$K;Hc}~7js&5mJInf#PXgwTo(Kl~Vy{G>X?fkcxRPf?A={)J*f6_h)?|y>x zl~c&(v&1U<<&Z2IG6=LqPB2Qwrl*xVepk_=hQG<@TdbH^49dAZLnFJP;oS)$jNcx1 zh$P9A>6eG{-2N%t_!60U9P7#x0fYJSaP;!PTC^nSfl=YZ<bKI?2`bN_IHRH4KZ)V= zB-7H5%+djWU7{*ZUC(}jCX4pvTUyeHK&_~eY|z#Xl+LDSJPWl<ggBt)Mb0bert(?7 zZ<kN$iP1|>2NyVG6B-(a{R>OTyAMlK<rDy5J8E$<6(}MYl83}mENMBMQEhFo%#}Le zM16TBIUQO$u7BM`-|$yob-XXO(Tzg=3|}3Z4WPWF37u(_)Mj&N7O&VxH)nH&M}Eki z`-x<|{^Ea2KrND<`z4+wphvt=C2!rk<DRZwQP}cZoezTD6IB1~XK{K#z%UpLhnrm< z1K>$Kbp)Az6wb|sv%bUOmzuKOO9~renj_xnG^Q@y6`PY?P~g@FnLGV5;d%W!I=R@< zP^E~Ywo3`fy+wgGtUz5@LS2EV&HCT3JR(*EHnYDZc$4_5kt)zbc;M&K!4wCL0sVjX zEA7(esRU<X8zg;?dbvoKm#{`n)$fIm^q}&hCdIlq2vPUzSKOB#ka_DL=$B?d@Q+nX zWwXT(0p+tmDrT9WKF>Efil%r0m-U*l=}17}0%c9j?P%XhR~kOA;>q>V7((Kzu>cTk zaMmwq>K-z2K4?Q*z&q@?ozXl&4MQAL6pDLn|8)L>UJ`S*!eH&?27^ai)x+BN>%)+6 zt~j5)aRhR5CcHqC*G<+k6;FwWES(D%5i$^RIu{mdg&ok$x3;BL%+rxGC4Sk!vTJ}p z!`8N$G?W0i-8{_w^YPt97bbThq1U00Y;zBXi-c!oN*uqcSOO&OwTRgM8aN7D#scZF z#(y5_4ENW7bmnJpRR3ZZ!kF2JgImH%hv~vMy7aqQa-x=ymmdE6I4F{mPjQHMg3j9M zWh-Y{vFQ6(DOi`Jn%zt37%^6T3=Ru(C(Xbu1pt3v$Z3NLG=zZXjYs>811JaKmJ=}W za60htj)pZNpCWxv=Eu{&`x}4M9NZe_7ds%|mKu=Or?QZ=b#$BsreL@n2uz-z1s;i6 zFZ}PDx)rG^9vkOh(}raDfrg#86>m^GjqN(JX~Sq`*H4V%kYZ)hGCUu{0YVLQ>>QT+ zG+U=bXbG0*o@5^PQ(@mr2?$?(mth1);aaT%JHtoc;ZsBsXYXYIG1KBPX<W*ocU3N< z_f2P)fAn`64<!FDOhkbu2(PtXGl|sbE{}`$b14?9HX6SKhD+k_wS^l|mF&0KN_Ce{ zt0S^yB~Cr%lU1RC>@u{$EjfbcD8oqW1!5vq4|g6A-2>fV*)TYi)b}X(@L7>s+Km<- zgfEYf%ZT(pFTfJyLV{nM-hHZ^tUflQXVK%BdE8%A`}!)5VrYzso?DPmFd|V;_)Lj( z_isKvXnksJGujk)L1cOfXxHR69&BIgbde2M+RsRe%1<?ee$sggK%(!S^Op?rrsn23 zu$Mk{xG?NmM+V$7yc6@zWs}v#|GNwY_!K|dy~hipEPYp2FrOnnJP=_FjNhlIRPh&i z@GdqaY&{NE!|&Xp`7sgdIAYj&zm7R=#~~aXhJUT3UQ_7l*9BsA^JikS%r-pVq_%2# z-rggnqU<$u&j?yQXn}yYKwT(6e_{$yw!%ku>=4#6pODH%wz!<{GH|2??lm5WRcSHN zHU^xkhI;*9BfLqT#PTI#?s;PBYq!Eg8fs#ufmcxpZ)H8Cv=nyFr;ZWwk7v$OXO@!n zS1Vzn*hH$TuFoIohh0j}g~}EhoXYua)WxWhxX!vVk+(hrotz*B4f1xaK~6sR|N8{5 zK+Fm_O!l6s)R;_27de2#_*VQ$sV4O$m_mK@+xZZEBt?fQH~i}p&;WpBIFnTFTPT<o zM;rKhw%D{vi3Wqs*?@nM50R~U_QglZRdK}}KjoaBhcC!P;YS;w^Y!h_s(0li17^ty zGX83eKR`X;2$RU=4X*FM`nLX~l9mZ=ORbYDM6XnDK0>F^2HuV`kQyvOYLJ#Y0ghZO zdN0_<o$vavrXIec5n=7!jHWSo2&uRK4Ia42Gn40YmRg>x$oPqzTMb&U@vulItEVa( z>dn_P2VQQ8coO|MbG8PO4nVcWe9aTDaa(;4#J?2%&$4++VO?Ktqw1q`JwLJR^1tV$ zl#h(*J~8bMQHad1mNkvX)Or_GOyOR!<!Kef5Qhhj-tpw*`kX=ULz?%8H811E4kz`> zB#B?64-wAK%6#;i6N#wzmgcgvq=?vB)@-&oG@@ocV`@}DvuB>4H6YHGV*vz*mC5JP z_F9xGItNS}><dFv&GByE{A*qg=-tF=CTWQ*a9J~0d&J!({0R&UFb#N8fk0D0egEf? zxhruhKdbb=7x<lLc3p^#E*MtR&*ZI9KqFWxfBq7YjpI~a0hz|5gZ*3_iymki3XUYg zc;|d%179Q{-kJ>v!}Kx9`L?;<=?C-t?cQPD3njZd4WfO=$n!VdBQ!K3d9zS{Lt^b; zBVk7Du^<3<<8vZj4Fxgx&y2<i&0WIbNl}_06zd<e&yrLHWay;-M57sg%4c8`8<sNx z9da&e!uJH#HEL*#9>@BQ92^d3e|M6k|IWATaj7C+9K4=|#1958J#T0NLT>N5%a4bb z+`2Y;yB}Jj8f$!fs)QUwO&(ERz3-eZf=#NthMC&R2kjYrDMP|a#h&WFS8Tt|S1q-p zj}xt1u|Dtj6@gs2J~l83)(pF^4R>=sue5+55o6IX3(Ds`&5Hp9<4%%3j(z*<{5eu> zHOM=$-sTQy)7fzcMAiF3d5c$j;a-Pl1S3hZGYTPGCqsv|qU#yCjUz_H?ajOWS(wiV z>6AZI)psDaYi#Vtgz%e$Vl~UZ4=oVP^BY{ndw$_Z-JQ%_6>FvYx2;nOs^;#le{1?U z*u1U&b#+hp=S)a`GMnMtCA_~+e-AA%LDWpQtYb3^FRc6Zi!SnYXyQrMTzBSMBAnNP zv;l?7?<_45qHxI=Nqr*8asU0>Gd_)`;Z!CFc%-<dLPrNh*OMN|Kd+-1EZxu<@;4;^ z>h&sTcimSW<sYurY*~hObn{TT>XuXm)b_=JWY(VuXVbOMwzx&jeCH$%e(wdMY2lT> zMT<r<w9%lxzMCz>pTf7bl>k(@gw0B*Fe3&~Af~5l;;^z_UFv)^C{c$|KP|GE_jch0 zN?ZVX4Ga(D0{4?eHOquYn(l;X+a>hT#Q8>Cj|<->CuV#HcF<Vu{BZN(f~EaxUT^Dv zTKlSiDBGx83_!{x6c9{0q#G<ix<k5KS~@I{M!G?VmKeG_B}W>hq(QoyIeWhUKlkV2 zc%zQa@Xix^uU%`AOqa(UmC3Fbq$zencUtQ!(z&JC_OXg$%xn&{tHZU5opk2LN?AMk z(wa${Lz%7I|LbZ&P5iS<COx0_90v*`iCq>*3ek%R1_pjU+CA$R?=45Z29j}^l|ndJ zA?JjSW=0E~h+r{{sa@8CPK0Be!YlISEWilfmYT1dg0A}wvOWvt(@eLl3yYA-{!<R7 z-+ZnFCw<-)-u<lgSlvwzigCI3DsgZvaiJvkOCUkCd@J@l8zk!_U4%;i(L3o*=9$DQ z;ys4jMkh#Fht|nmb>Bv;9DSt@K>X&i@be^)3JsitX<Mu>V}5m0COr?z9|cKcb$yE+ zyc?bJ_Ig9`AO*M=7vL>)7CGK-Z*|(f&wN)-2zz+gApBPxhe^<|-~`2B<}igW*6&^m zsxD!jbLvnqMwY-kw2Rw=`}gZ-k*#rs?N&35LvzBPJbt-}Ob6QInSkK`dnz*+Lmx)k z=$Z|_p%m~K<$UZi%w%g{<f1;p{qAK)M<cWqa=HJ!WH1z3!g=l5hYT5Js&FHk-4N7c z%@i5qrB1B9{2>Zmvgzs+LH2T{ZeOBr-}Lx5<_u3%<?%HyUE&Y&j2z`>#rjsb_PB|s zlazd=k;wen7dh%%ubg!*4vn33#{7pRyF^0K%|$=DVkhj1I&Vonh48T}VcG0xSFTXR zJbcu|w0X49V75I=VQV?{=NFUZi~aS!ugsb)g+QNqUsx;uLLr)opLsZuJ2DGX=5tS( z$O=>cLBrTpV)Ubt;E}7rhSnQdZt*M=o`X#_ydGD=JTdXDFFgLxk+S|RO>ynBEcG)) z*j-#+mkvFcK=%6_eC0C*A$Kwa7_tUp2)7rKPd{NEX|wK*_nYz3&)4rux63Aekc?`n z!&gKg{0Q(a1^Y6F)Jn*asR-MzzTohvV9=K^DKhP*(s{SfP|jg9H?#a=njhU)Z)1jC z<3RLRoSJ0rYDBm)v-aLI%A{Pg{bMppxs5MG_pH1wNMO)gMSv+gvfqredtD?Zx{kRr z$}6y)R8Y)Aqh|X8xK*lpHpP(j?qP{<`Rh3)T46k7p(B`LtBchDREt^<mK6qE>TT+= zSy@$HR!3?sHdnv59A4kM+A@mnmfk+BwKG&{3Wqt};W*23<fSd6zY%adH_=$HE>{@h zzFvGg+0cnU<??C`q<^FR7=5Uy4Vp<~HhE))33GCd%7jEzX+HK}aVW6U+xHv7^hBw9 z+&w2fcr2tTeN-K0|GD7k*>$n|X`4-TMe>e(G;sFkSCOr`)5Ce7qRh7HJpZN-`|N0L zYj5D^m-La9iV7|1Ygv3Q&r-Y?-1dr!TF#*ewDzV``lSLf%Tp}_nOM4wGlM`f^yx=v z-QLe4Y32OVsJCafJWM2f{H0$o%4vlGw=VF=Cvb(RLsD9Aycl)cdnXNwZt2R#F`ZU- zOE2-b^Bjn8n9lEnLG90MdFY#roOKiUqe(Sq4zukC?C;|&zA|P%$1LL=IX}tH6pu&j zKOqIk@#*}m<$+sNQSNHBbA;C}jS~r@V!FF?O|9tp^aNfbJSd^7SI;izg{~oFqJlm( zrE*cHCyUoM35^zL^h{J5)cpJy5kZH`VG$Wiq9r+$PjvpZa%W+PFHz1?QdRNxc#3!O z=v+IHU@@eVyKx!y_;Ep7ruzM|_-%BExB{`pQQ6;V+Kbk{wy+*D9uBLqXK`1fUjc}a z(<r%FI!NV79L;pg`wbU4D4MZd$UP;CX2E^{CFznf1f%T`L*KKptXrIB-!;q>*{ULK z{TEDQ92gdfDVkdO>yV{hd0TSNjILEwG^9%vn1;q7lt+;DpQhS;%tyQy44epxloUtV zoS1lSP^$`XW=H;0&wGp(ytqNmdA7NXj)kxkYdxoohvqA|@@KG$hCGGJ!eD>}`C%aC z1lwg($YuW4&`XJ0ucXyV?`}(=r4M8cxb3qm%Evf%qUL*M`xkdFAQ>l_=uNL=hEw{V zy`MulTXKKruRa#@JD>JubwRI>&-NJJoz}qT6KHOye&svmk77LwH3}_T?jIQAv!)z5 z+)xn^%#I|7;wOh&AiFO0+R+Es_SP@i)|V#L4_o*tbMNz#dIl{=nsGHpGD)Y>rJMei z2C)K+y1;T@Do`Qh+?QS1J{UK6J~`hp%PPG(Y<rL!i(TU>FgiN1uMZl9Z6kb7pJLN; z4B`l~8RBG`qT1j#S$GNpdsVAluo&S|mF*>qVWxvFNm3`qb5fN$<VzSrSJBR~XI>%a zaO?RCU0<Z@{@1BVEieE16M$H7rDeV^h#9Oo+)bDIyQy1K|Ha41B;fM0Y43;H_cVdN zbua$;PeM(3;2;N?HC$c(=b>c!;Cx=hvBk6+I0JQdBmY9qZos_R^YZ>;I>E{z@<mTT z&T`o2Yv5Rz$oZ3DW%tE<VQ0rDP5tSI(icoGkOOw~<@uU<`pxP^uPKolDo?y8t2*Y9 zxv}qENX^I0VFtOeG|jSNv}#Lwz%=0EHM3>_aFXTdZ7&VywZ0Bpvtpacjw~wv42>hJ z&c3}Np)b8#>P>C-`0t;By2UO5+0Xk3Wl0&BFg8sP4%F%6us?n?Cm$p`#J?bQQKmc9 ziy_DIp?~WKX0%EoqJ_EzI+}6=NElzQf?AZpQ*08^^$7_#D<-EXh8bvE2z>oc4=O0b zzonR?tUKa5JiPz9V*$(IC55B0*4~B2WZN^B!Xz#N$EveK&%LnGi<2BWH=O`bigR&$ zxooak#w(M0ur>3Yj`H}-K^7!A=>OG(NnV3G7V_N7*rj>KXtmhYqhoHZrif#j@AUD( zfb$F8KRxfnT|6rIKT485ywgx*b!e?#sW>RPI?&F^{VzuMwOU+f@51%Hoa3W8ttyMI zLc>RcEw62z$v8fy_uD{^WlLte(oiV_t#$JA&%WDvZ6Zf^u2axdd8H!s#){4k->)+S zB$}E;|5JT_6OBt~s8@d`Nl+`2)+fb!$#y43mzV!NgXGlM?{MX7zzyY1bQ0+G{v_;2 z*Z0j&b(DUBdNtDn9TA-@QI&9!4;;oFcqL_@Gvoj&R9)GBrvGV_npeK$Dg%BH1ujYi zX<>$C_v1$kl~6!bDcAdCm!^t_&(+nV!sjK+_z{xzY%=Wy`hz%^8L+Vop1b8qmQtPJ zLHh*MxG&Un7m(+feGQTH2HuO(6>j^D$W->=HsxJ@THIt0#aBISbLJOQ?pTSs1t0!O zMEqhS?v6}*S=9HZz-ak)6|N%$)=^XFJsRNa#sb#aM@M6NFp@6@iSp31<7+v+d5Rob z9nzS`X4bkU9`Yge@m1Vc8svM+58JLpV=dVS9;>bn;JVA%7{1eO{!@?f1u&0`X>J2+ zePv7M>mk*JM0+UNop^A;L=-#IBeepKE05anfJ(kuS4tFOD)J2XiyHO4w2dr<k_2v| z;8HF%^m)uA(1Nqb<S#xT39c;_%JD@aJfjXEk=s9qq)M!?yNm_t9Vg-12$=1bgd2<j zz+FPRo%*=O7)16+^cM4DEa-50`j?zr*|Y!ai=CmI*H%-6&udMSAJXG}`qa?)f>X<B zjV}6Qgk(98%gm9d>24g~u0>=5YsFQS>uyK1NM(r<lPf@$*{IrJKJeY!JC=8Q2APhC z=bGu^k%8tzZNllisdCmN?~8kRwL*RFhee)i>QKZ{2~=W8O0-H+N4L0XoRmF*8~}^A z)6DXIDptj4Wx<C3`yd;7U(1^Ea}14au1SR=TLhlo+qAxhwqS$fTxe<2v6$o!BaFt% z^gbmLMYfkam@*9K1tuhz6>xqzY$EvSOG^?#FOVtkASP3Xci}4{=SMRIvsUsRWI!ZR zP)^1?u{yF;k2ID7CQNDNTi$5Ttpn~!DgjmH2B7B+%Xe7RTm2`g_2!ypidTF|g8gEK zGAGFzV}bD=>7SGGrw<>0wBD_@1Hx1+=%YLvT%t7*;tp)w$K`+oV^nHXKm=xuYjp-M zs_jLIQQFm{h98BZ(qD_&y9TRYDGU37jSK4VoXRIB0c;nFD^A<Pq8NG`DE*vKT(CkR z0;1%gGL=81)1nM&q+Rze*PQ0xrE_Mbr*C!ECUAI95}b&o!3?~v3!x774aMe4RUoEu zT}Nl4&h{g-oUnu<J=GyycIqcjY;K`S|LZCOwgS7{LKpuU>nu-$4nr;3LegKwAWHi$ ze~V6P3+WOV#8+dW0Y5CY=Qdh%Psd%xNO^b|u_l$HY9VgoXmMZ_nMmpJr?ffM&d_Po zx8T{)hJYP92N}S?rMPl1yjTv?F$2T(d4=6AOWGo=7X&i#SE2sY1S+#6H6rwQIvxro zSIPeoM7=}_Ug=2)OhffvcUor;-m%S538eBy2TPS&r0LZ6M$@Lu565x*gvLN&7AF=f z%F-~Fq3UmmQQfKDBAxk+)j@}r_9Fq%<Dqup=>t2k-4_!2t8aQKFh&{CakYTyc%7%P zcayc=Zya*&sN{cbk?rdQWEC|QBv>DjHJ=cE^*#Il{0q-yWoOHjtKQWVgmLLx{<e5+ z`SDXzfp0UA-j^J)sun)-IQlW_eW>jh*yUZH#|$a&6X|K!%p;`DLyf$<=yC_z?quc= zu+&FGSc&In%iANm%DK8J-J0d2C*2(X&78QT_jhy*`@fgVO$Bxy;O$%4mt%zviw@@y zoxVA}b!bXAtrkf=6sZNZR~34L6@9=OxsaYvx=Q4Ac1tWonK`kV4_q{`KYN#3yKukj zjAk~lyPY%PJk!M*SihvW5|!OGzaNayEVD_fgI3;9AhThu_B5{x457XeOc6*w@7phq zb8zs?G-MRLuQpoL>R}iAb|B+-C5PZXanQZT<Y&-o5fxDA?3{T@?frMC=xNoUvQy2` zqg%d&3gP%2fYP`gkNhr|)rGt)p@yK}bZFf0)Ubwm-0`UN1=Su8u%TL2miT-hdtp^y zqurX7j*D?LKwBTw9z<y8zGKu!r1hzVlJDhw**hrLnT1n~@2$)VW26D<c5TX)PoyU~ zqIS2Jw(a+3rE`~kJz%m0!}l~T`p)XLeP*AZ>Sn3s?(}qJvHAWt)8vn_cXwM2E|CYv zPH2@fw6rz-{(+b!`Vd^qp>2X{tJmb&d;50HbB>5*k)@nY!p9h9&I0rq?+!Q;0*mcv zso}BJi0N@>Ly(1Fz$+9rBHvvbCf}C1>nZMV%RG1$^TS|XQ2{oA$z=vxVfTjKmfnEh z_U)G`5zS%QN|!K*p2T8kc9d<jYRIuSkaaO-g)T~V?8GXUQ`IrAFY12@7j)VUvQ7_m zHGnVbgUD{s@=nic4)VK~XNA{&vEVB7m$2I#og{2j&mLXY(rUpEMb=sT`~pKasm;#Q z3JzO(#I?zvsl9&Pd&QXbgd{w8*q85oY0FEz@YA5UfXM8^KI;<2?#S<)hvK@toq;{A zqe=BI(U`=B%4`(X2gQIVw6u&MdaeNKqeIuz=Z`MjMQ^2?2lN-EBj)Fc$CWbE(m;QR z4oJ#woCz+Gu_#}d|9<}VPeyRK*J?C^E`Z(0j~=)(MJrv4D-h9xWyeh>trrgNKJ;|x zFBgWD1g0hol7XbNGDB6Rh_sBwdi0*TsGTG6p<J?6WkokcG250$M|1&Vj}N?$9-{f& zYIXQ7*2_CZh{);C0Ng1Pgthxt_y1jGin0))K6~3I-A5<=;$Df_xA6EVjlZByH%y04 zM)ORw9eXj384>82ml^nEOXeb3*TzlB<<(xMP1Z>KI-I`p`)Ek55UC#a?c14DMcr-) zoeEu)j->T&gihb?P#qFTg?>N&j{BJ6tu5uxJZ2^xCY*38K|0d@p%v*v9<v8k0ovNF z@r~)Daw7gLe<^sRBhIGX7|{qiD2rv-)QZY)%*Pb(F4{q@v~S5%26||9%=hu7d|4V* zExS4$7*}|ak6~Q<xOYXZoEj=f+*3OCZg@%PWodP-*!1Qo{A?p*0>zm;Z2}DThw;X( zncIb(;yM8c+oNLCFIkC32wE)`@~ylV0`BhOA|l5%ZL-Ny_qHIHAwJIc4lj^kZt0as zG4v+G&>H5t8LnIl#JhGA&9k;Hy1o1G)dQhPM|75g?aU{2mJNMllO`DK@)pU}!1g`| zp&38KV0*|5`<-UpIVuL_o6YyR$ir5hHd)&GuTx+D@#7ekhwkPm0W1kTP%*0cMw<yX zh_2K4H;NAlA)WC~?p^ahUxZ;_yV<d()b-a?6|0qR`QFyl9>0T*gjW%0E`8wJG+Hv! z<TY5SHzQ8pW7>!Tee>PL9zM`908x93uc^rZhIe9;n9FvT5}qDQ$;fck^}?~^<>Mn+ z*^W^~E;hJt>O(dYY20O-qfPN4N3GR1chB)?iP~k>-{6LJ5hiC8-(xMNY)=d&B@<{& z1S{0N#t{*tQJ1EpXE_oub}KIKcSi7=Zi_)W7abT$7CAlJMV}{jbQlbUO;+opsKQ+D zndnYP^~4(xZBPll&Ph#3Sg8vlgz`0Uc3JTqiJa6_v!3!YmO<okuYf_Vo|@XW_rJd{ zwzfaky0@n927p9+?H!u$s7?g%!8l$lkO3tU!Tb9X>=p4YQ5S(HcicFL1@i85P$zU0 zzd%jx*VwJL7ef6VE8c}?=J29^psCy^{Z;ga=p@xLBFqqrMD+OFS+ua|_Gd3&BBjVR zt77ec`fXS=+O<EKtovRbXTG?yFrWLjZO*M00SK~bb5LR;S~3%|-3wtNR0^@56y3$% zrC*@-Nix3go#mKz+&wLJFGo~V#^J=vrsUJPPPd1J1<cMo-0)s`%I5nFS-~s%wp3DK z%1tVWdC)@UiW-r~!9jpxUOb}2;_1|=b~S@!u}Q$}g36W_Bcmv^Xo$k(TF}sBapqsX z?{sBl0sRF(5Z#H#DfI>>8Hf@~TB?Kjh=Bfz!cnBcD+L4sKiPZt*DYh93;genGgdpP zSSJY_+9C^;S)Q8GFhcTKIrdW%aipoLki&@GM=%J-t1Y?#r<~4UQ&dyb>22P5NL41; zY5HWP3xEgbJU+1;pvJSb6dkJ^$c=ITeN{dt?1$A|er3$!81F9kNdnn0T>t`-^oW`x z#LA|LdTiyRm~I*52;<yHs9Nl&**%|#e(x9F@B@UWkLdb3`YgVh+HWHej%#Ko-g7fQ z=;qq!J3FHS>@R8%HHXP7b>v*c3&A|Z3zbX6=?baUw5D7Zbe#iojGc*mv)j&6j!KHe z#&cq#_e69QEQV_icWcmf2<4w?bVE6ek_(*%)5F7#y?ZMeOF==(k!*&<#^Ys|@6;j? zEs4?H(xUz$+}cG7k4621neXcK59M_JX{S>9&+JYhd~R1-sukAFD;|GKMJZ%#B>z<R zG)RAj{nXavyFJUnhAy3T#)PsM3x&D#-OosDO<s)^YX~*SmmLFk788!qAFkpsbiUj5 z3UvE+RUQ`LblO|D`{doYszk>--wLN5&P#V*voJ>K3>?b9!XV-h3dtJVkxj(J>$Doi zBT=_wrI#HM3xW^+Zgh;Ed;J?Bo7T8E7O(L~cP(@sNANbwO;!c$j2Z^?SC=Dz@|AyM zJ&$hM%hDpQyW<vWd$azLE7#Gv^MK(`HMK~3CE}-#X7e9v)zIxo+$8cJL6Qm5a<D0k z0V4YEa1fAlDfafJ=(20POII{KO<xZ<bhLp@3en|dKntAJyVu2B2%(WXjb%4>?pMb> zl6syc8ghwg1p?;>mHVqOZm&vm1?B14<F<l(?~2Pos0gpHCqFqOW7XLSBZin}B=Dj9 zxC23}$c|surL3WNHkS@7)nx6CIVmdKzi~9EttEdE1^J_kzD?KE9*TcB&LaFtE^~S6 z4{2nQgc#edeRAR8=k?omd0?XU#rGvNbeHMZq#!^%RNd<_Fw*O*r{XYa(e)}3Z=?I$ z#Jam7V^`(af%fKNmXW@Dyd%Z-fV9Ob3nhGqi*WORdaeZzq@lIi3o;5zOYdG0)c>ST zb5&sqBK@tcn^KR(MPID{G3cmZP}vbNmQ^MFDsmbmRq2Ds+24@KkrQsr@H6y$O3%+1 zufQl)IM;a{>tt}FIq||_%kH3ZbB={pE2A%0Ot5bmPk4QYK1!QG^O07^^L4@!x=w9^ zzYAR%`E;)eS#4}Y`P_#e#jskyYx}FgT<yKx38sP<LuwgqfdM3&!rT%JLbHl<k>AnS z^q)zI6s<&xRboHwbe^b5>Jo(OAnESiL+tn5JdNW(<h*bAaJIy4<+7>T^iyRw#eLUg z2B+M_*gt=soNK3i&E}8eW_wx&L+7-2;H47ZcC*qplR6-^p(cae7dZjZLto#i^{2lX zO_xw=q@32dX9M3q7M0oPKHYBeL5FCEZ(y!K9l?cc;c2(5K0d}oETF!LY@Sv=n`<C6 zEH7wYy50#KeNmuQNFb(N*FoA4ZqY;DNB-^GgSwSf$G<C}z5N^%f$)CNF@G50wlSF& z*!;fiIOw?}5np@C0<L|1T=bP6!p}FDS@ZR0Kjo@C0?qaU<7P}JP}bCgXhQ8zr7ANf z`O)|RkAP0H%9%{c#*EW|23cNy<UN|^f0V7Z*@%pa!{aawAZ_?^)h<_!wNMrD1AE6h zgz}?JJxQqu`ndh>!QPB`Y)J{9MxL)t-7TW8OQ_Gq#b(df#>@XIwNeSRP2t^n6YBrM z(nbynuRn&jW};c=9!I%l8pyJ_Eto+^BG&J8H!@5-Sw;1J)9nI6w%CgIj~Z_^VpaU9 z+;JK&#~fdXlhqm4b|n7Wa!~?3F<(T@Mb*l<=k8)ae_jIO=-5AskSVKx`9sV|K3Ca; zwFFLyzNPDmHwe0*Q5I#SmrdN!w?r3FE~{EMb{GuUuV`(L-LOkH#ruRVuBsdDf|D&j zJJ<Szn1W&;cc##AQ`nCcSrhTHsfoB&ELAJ#ZdQ=qN5oUj<5>c><I&&jyzl-@k`YP< z2CI|_9VQl%c%Krm(q-HGQ>|PwS}BS|9(bhldTe}uhTVF=p=sP7Eg@;(Hmi*qS>5lf zDtIp$@m3~?`O_s49J(>S#T@&G9TE?oLQX2+HnCA>?39O+^tzN3;{?{8!GSK`cO9gf zEczf}`$k)xL_`pW7m28XSP17i!)Cor$FKBRd&c$X{-qS*wi^p0JT=x@xBqRohBB^< ztyp|jKlt*MvGZ?Z23qj?b%S`DmrdRxPGjt%c6MyS+==m>4rduP=N7&@6Al@fYh$|` zuIa+G;(UiAx}Avc4;E5zh$5fnX)@{)kj|faLs!-NmEou9htsveDbyPS6H(MR&RkHe z!F(r_#xos0l6m(^BX5*@n+fKRtS%Vc;(*Gq#J6w+olX6_pByHpw32FBf*Jq3@bpwA z1B#Y<c!E2o!&vJ%%g$CD1K8~lE1^A%$gGQ;H#hWFTk`_Y&KLMf@@vAyN*Wh``j1`+ zTd(tq#ypO=T>Ph4T;Wd{*SYvlWr;{%33O<hz{3{`P>t=YuBM=-@K3pYzH!)z6v?1J zILHX#?TVf}N1GV9R1`)I{D;i1-$S${^Xf<IDlv@jxz!e(Ojx8(q4tAPsE53>^1B5& zcawD=6g$3^)KHDzKFA<QARb3qMbWAWW?$7MVDX#_mZIask(Y53V$!&}S<gBweueE0 zn~-+UDxdw;PoI<tHgycK3_~dO(e9gJ<Rm_Af8rM-XcTCj`9f84{(hy_8)4a|O!1`g z65@-@+Jhz-!=2%dGp{peESTV@5$||N!-WGhC|(<%O&YSWu&CK3UcXR2=fIF&F3@%1 ziQby~IPHD|diUDT&RcIkKndX<CKM*b<JX^y{V|>X>%u6R8|JZ2Zu&U!9-pE#6VCG3 z9h2@4RnCNc{Y&#V>&$Nb5IKMCv{B@AF<Cv}a^^DkQ#f2%9h&kg+n08>dqXS3?5|+i z(ArFy?cx|F1h-E1ohOHS8qQH*n%CymX{Kac3XcN`EqT}KLL)YSXuv|AxKMfMbh9~I zX*RaDR-GKbg<N)b@kM*czt%U6SLGw(C6_}*?9GQ5uo68y%@6lSTyU_%l9+!EF=^6J zS=!uiSZewe#(>Q#bW>EpjJNn9bShnz2U;EIa>0^suf6Vl<0K*4i&-V8AhEOKmXxs9 zSz&C@<-Z%(k#+cUZT=GUhv9MboxRJB&#WoO%bV4d_G|9&_{+)O&`-%H;pQgS@wi1J z%cRaiBg7JNXWAQ;K9mz2VfgdlpJ;7nauXzZcF%hi<02t9G%@E8M<tkqDXi(`lo~_R zR?Y7<Y$&Qbj&z8_yc@^vFKN;wjn}@iXL%)sMHog~rn@y*&`}e3L1p|=8jG2pUc$o< zl}K;#?W4Ib>xVq%nqwM|y)gAV%EqpD8nid-QTy*#f13tk*IHh`_YzEE<DZVW6mMVW zX_AE=;2W$rCMbz?eVJ{`H^W}XQaAc`{L(Z1-K&fDQ^XZbZbPaJ5^yzRi!zwjA=$Db zvBaOC%#IS7Sw+G$gk3lEgkS$crgQT>ZeH%jv90K>B>ZIBvI3uEEG<X;`H?{dCS$rV z?n{skvB1N@S@d3me9^V@1+qunHr!MWHwe=XeuXKoOlc3_hzxiF6s<m)q&v+Ysd>HT zDz;sS%E>VS(+?uw{-$!#k49UnC(IXw#}UUrcJV52y@Vd(FF^!sQ}qxMKtvG6g-rkx zT#M0R4+Bsvx-9YSggz!oi7lCHO+E;X)vcMWl}v0kTQv!Q17zIq5IX1!tnO^J8}5vZ zf}cNKb6dKvwaTOaPx)wD{EalHkoB_s_F+fki=>M5CQL;Yh@N*~<lPFAT5+Bx2A1!- zavvA-+MoMv*Ic|m<tSZMCU<+AvG+~+4S&*+G;{$fr2T0?-M}IG1oLA;*M_mBcE5aW z<}s8Vu$~?vG{n1ZbazwWrGqNR2iG8Qdm<=dM*FTBq98m|l)d$ZN51u_-r38n((-ns zgDK|dEI6$f&@-o?6#Z@IPi|obPzYpM9&iZ^xzvDkoZIl41TX<Q5-j_EO%<<e$8>w= z?P)sx9LD=Fn5#Td??lT%8*xIlzoJ-60KNF>+EGsI4{HeGut>`jDWTK;VC~kG8EzZj ziEWM#M^2DcfYuQ+hVf$Yy-H<dzQ!*cdIg0EAhm}X_uHlJJRSc2g3cS1SgXli@ieSK zOOEygs(H67tzbH#aDTOVwiR2GC@uUU>}c)Cf&GIWEVRy13^d^U&^tjQUl`WBgVPR$ zl^`3gWL2-cLHxH2xxeL))_MAR+)v9^Tv`KuXGf^JWh&7sDE%HDb~(H_-<pfOs>Q=< zOeeL{=~S|O$t<5}MqTz!Axc4GNG{{w_r;Qbi_k?gdul1>AxIX@JO?7&X<F4L9y-CQ zf$n+_0K(4prQrdQYg3(pXkZPo;!1RURHrWig}R#w{c11rF|z>k^f{1>_jMvk^3FBx z%-?w}6zZnz_^h_Pw>`ylFhGH_){Ap`iQ+>_AoZO;vVVKZY%T+=dXYJD&t16laU7QL zHPKsRaz<%Km9{S&SCWO9!|QACrhx$>Y_HVDrqX5ix3SSTr#asv|B%Ilbg3uFB60@m zcUKAvk9%c`5B1gxC$TE3GQNe+e$lb1(#;`Zl<$eX1P#$!G2if^-+w%=++QMiMy!(v z({})632h6<hk#*$_|etAH3D0hco%;U-w3#Pl8M|%<!I#y#8^oJg9r;3$9EPA+_)yH zqR?Ix6iVCwfbTY@*J9{eS8i{jH{<T6?)Dj*EeUhcC;wv$mK3>Cg9EGBi2WyWJ2%`o zwSMp~Wy$4=dluykw-lC9tDc|84+#^VXA@2E7clFxoBf?%4YYiY#iEikt5y1Q^yFB7 zbLL}S4rBbOqo&k?5>!X3ksBsmt<0EgW&|7Urdx7mDlIIu!bmO4C6X&!@3&i_yTC8) znrv(0EC}xPwj|RRXf60t(3%}sasVUC_k3~CXv*@1bq5;-{13O6@B1MGU1Ky=5~Q&< zO+ZRvR!lK`h4#@)#~~9VzqeJc96toc#^Q$WVrQum@jH`>gbF#KFOA=zU;K3||J~S2 zCwum>>};dVw7ZAJx9@axkL$XBrOd62obpqm&*O{w6YXIB{v&?nVFs4$(aFntuV;E6 z)ccyJrmX4%l&{@iboy&QYiyuo*fDHMwo;fJl3cQF^9p#hU2}U4mGk6>q!lDdVZ5=> zu)rbn2=6)!47ku*T1HAneOG+S2_luP0bg2`joS5THSGQ3wVHxIg`%2p+}Y)C;!Oy) z0R_~9Z4$~i_C=4Hxp9-YiyP*4`prs5D=YOkH$q@xHE?lr?^71oIdX2O<!Z+KbJlEn z&A<bX!$<u7&6$=+3uYXZM)YE7u7sR9-zguO1hSUpZ#j!QV@Q)Bop~U(R=YIk<+6vX zW3$VT1=yERlb;a!9wyc%ZA<K0V9XDiltALe1<`kBFKg_|2T+4b3=xunmeznJGM-e$ zQm2TAYBFp=sT{bqIzjDHGPP(|XD`F`5jn!>Y+YTCBkO6F0}8I&#uPj3hG*^>(hobH z@t#ou(*v)Y`{x{<9OvUZac>iTb!)sf8Ga<iCL`Qup`<{(?S7pi8i)+=Yuyuue~zql zpX9vpu@$=D=dlh9$5$n`62-`A2vjIF=i$J~xn@i7J=s(!{+KgW(ud&~M?GO59cAPS z98f7Sx}>$FBl-SR0Z@e=%l5lw$HcBksO=xv#gpHB!-XBCX%W@_W|2`&fnGSMDlyYD zD#vWk?axuB-9hg1QC993rN;t6<aW<Dl#I<epZpn*&|Ceb#%;q^;(m3QJ}hmj#-D{A z&yk;fcD5qPS-L}8aJb-MNOF;JRgHba$KJZ6m9_CP7baj50s{P-RMfIA9=s#r`F0%p zG>|dD5H`OQyNr)tUfdEB6WbE&fpRTJNyD1=g_|u|o=FD<1wJ`Z<?MSs%~kfi{P`cZ za_qm3^X5Q~nekcftHE(CL32mP%E#z;Oe)MDK>qE!!%{EW6Y)eM{E37j-`4k9L^{Kz z!PR|`>J+psW5m21t{fd1Nc#N0KLX=WN46T~#U~OAP_KOY->aSXpdI+X7o#{}Ea<;~ zWiW;2-2ZudFHFw;_pjNB^v-zS5p)<4juP_$U(j3F20@tAdQ_41!-o%p?y`OQ{`~<J zv^++$Xj?RzF+b+uh&Y^b4<%*R`2FJ*?qY9}JIVo-$kz;iGw)Bs1BtLjV7Vs^Qob+h z>glvG(edHZm23F;2;F)wtMRg)PhlE53OW>@fBky%>C-1(m#y#EkCfFEbW#cmo{5D} zN`QjS$_SDgB%BNM(*DtPt`-}2k-8mj$jHmzd@LR^4@x`m+1j8=w6{KfScf+4oHH$c zp|6v&b1<ewC&>r84V3Ayf}JzXU!y?DjPX9dqr4Y1M^z_N+_{rlVg|XxgK?Xvt{7HE z&=pd;eC0~ieE}zRZZnS4y#cu>5EY`&luc6PwwcPkij7TA!EHqcn%NdV`$1!U0R*NM zS+pv?dw{yGWVO?J<RC_1tt_#rPS_NctU}Ihm34N!l+qc+s1{idG{okk1wZT$*51PZ z32Lh2b`=7>6Uik0_mNj{udCBZ?h_yEgU-An$U|p(Y&GKk_daUxGX?Xhn#$W`WSK7q zqm@QSmd=iMCH3^a7uhdJVx)ySX;VB9B{tJK5>ir14=uVnfBllzab1Wyy-1-)rt}0T zR*Yl{dU2D9RZ3wAIB(=K>DEoSxDWkQk&I!{-f-LagiR2sQ|q=M01WQb3^uj<(Qge) z(%`E+efI3xw0EMe*RiPUP6sVPVzuLnV!Gu>egK4##G(oi@qkEOs0Y&Yty1KO>B-(o zrvpkTW^=R{C7k1NIO%K>szfB}`i@W)sgnn12ch-k1Dt}3vt7A#_rr|<*-Q>9_p|Nx zs3Z`Td!h=t#QiuPn`Cy-*O*(MpEpVeEhR}avn){q6)6{&ivD_U?{qjfhvy<nR3Zr^ z>03xS*HbH&T>BwxkKIKXL~5$}SW)`6I;7M60|Fk=&}1|m!)DHRvDP!$P82+M1S<at zK1crhFJ8Q8GsLw1fKDZLRhwKQ?+bWOuv9PbUd3}+ioJQ`_w-d+7NSNN#0Y2C%cohk zC_6ej{`c!(-M)Q077}PFUMJa4o0VnYw1Of`U!EpUiALV`=*~h{*WRBc_@WaN6BV{I zmq7$o6gg6C(25TdOW7KwKhvAbvM}$cdT&X}s^c-e3x}42_+p>lCZ)&6IL`?y8d3)Y zA7Wx+wqrrg3Ja{jFK5bz>1k@vES_5j<!KmZ0VeP|i;r*vxw)gEf5yPl(o%47X6A*7 z35~zMzw%FI`svD2i=oa#7iVYZrsig5Qe-;G!hZJKVw1Xd->+Ybz0qD`L3i~FU6`}! zkVFk2j@kPC#pf*T>az4#Dx&bE{QRzw^H_I->h-UK_3`c9UBmt~2~L|St>E9x2s)%( zim>Nsou`1D;PzFGJW@tRMiW%B42WwEUYj&XhFZkSBsJKkAOClk*hesG;~?>?ws{f} z9gXvC^GbU`N>tQ-3hMM<4sv#8rV$j#dkzWI)!p3O>PD_?oX1bBPN3b^C5vGvTqmpE z2f{-}Vf27tt&7{Tq7gORptVCO<Q5sD3v(XZ*?I6-AR1nOal)9KoO}j1yTocNd@0#I z1QeSBTP@9DS77)2f`b=QLxl}G!k-e}eFk0Gw0EefZ3<naigu31&c1gxn>y%6RgyN` zp_SblRFa$AuXCPq#l1<*;^gGi=81)i`_*08)WU*+wf>A9Gzj$@KVcsVm6sG27c*`> z)USAFW22kpuqlXSqVHTayh%m|<DZ5A^6qh%=ozal)|p=N@qWk7&VIC<8hUgz7uvPw zz91{=x(Om&|3;7~OJpt1b2xaT<u=qgYDklb3cF~$(WZc%6O38LNFphuie1_oGfnBM zcD9LT)$Ju<t<j77AXR{Q`Ujm||2_THg7>S#UwnMv;S+dl*ga1i4Gax~wX1!t_2uMH z)xtDSpZb9MPcmHRZTMZW*6n?9OCG6>jSVc&?-fUN&A{?kR#r?02L~W>Mas*=!oo6L zSW!`NS$oM?LP3Z6<|bP+**6d#CtJp0mcl>%^TpTq_N`m`($<7+A@{o)K3?fsi)}#E zI1361;a$7-%Q@F=|DSOcb^#p{wK9--PspugyDTb-WdGmL+{ykbX>es4Lug8ms7kVM z0zr;edi~VY)X~X_{ob;|Tp^g5+0|9|5i|^NC2!BQ-??|M113sTXxdqZkh;{Cma870 zeL|^(z%J^2v=Nty=bSm(o}&x6>=z%<9&$fbB7!EGa0~%!n3kTteYT4-G&XJp)wtmD z@^YWl(p<2{!4y0uU<GnDN;{9PY=PO{Jy}I+@>+D!ce-bq4`g81Rp?YX;)#feNPYPk z04EYe%z>?dZMqPg#}Y-Vc5+lZY7uf&aEVp&Z$JMK_E>zUhiBS$>+g%}RD!?Cr@a!8 zT}Q)tnrRssmkX@M*fFod(ud^aFcuaTwzRc{$HrzU?zN&C8$W{X61#CH@yl1QFhaWE z!MN&Z;6E7|8DaefLErH3@FUO&#h4dek8Ny_?%vy&)a=o1%!RMmoh+09+sgLt59gdG zyvf)p<E_3fg}b}^PnDeLP23hTcCZOt-q^(qo$jVBez)M5Y!AqZ*zNyQf#xakM~@!e z1ymeg|LHAQjC<TxcR_ef9~_2Oty`2$-LWqyT4L1Z;S*G%4pEC}#bl8L0+}9iP763V zop3^<01wdRX_l*I$fa24CZ*8@z}zvJaa+*60E8d^3n;YHZ#mTSo2DyhXvDMI&Av{D z1(y7J{eC1gzTDf)QOcC9(@><9O_ayEPMLKT2Z!MmbXLfw2<N?QiB<s@2%1&3tEa|b zkQhX*z2NT5t5E&JVvy|9NdZ_u2e(I&oXa8&ZufIO$K`MMw=`A3qJi)`U9H=`Vm$Y| zpFxy-ihLk&j(;l$02aqiF<<>@t?<Hjapg14fbek>rvHIcQ3MP51nU0-j%r|({6FZV z$cGW!z`U4dk70TJ@5TRjw56VN|HBiBr_&(e9s5lGG8U+aEy42lIHRjK>$<?}2cFf; zwTCVZsp(>Wx1OvLb;}I|3gHk+`oT)v8&uup^M?A4tHUr|XG*%l<{XinEbHOnQTN_b zEmys9z9VA$;K1Z?WAcV4kpupX8>|tl-=!2?VJ1-`{9*vSQ}8Jq4&&Jtf9QIp2vtGq zB!NjKahZUtEFmG$I5|04fn%!!n1#g_b7jGmtcV!e+uJi(9UXI83|`aJ97hWx0UF-e zJ9Ev`u8xM=ybBhb-EsN%S1)0yDiDnY?K3`eb8{xG3VC1$lwdHypw5-LeBFBB1;Dj+ za7cwu{_^EZ0mz^N&NCM@1uGXs!IR%$2=21y$E&Z)gV}d<J@-|9<R8L8-vKj8rg`Rk zvfWO-TiO;k04nGx@ZbcLn|-OcaNQf*+S(r33HpK2D|FtRs-`&ze-h<oPY4@!SPHIp z&rJxtzT^5Ik`Jwcz%{J0vN${YvL~Jk<B<RScoVKH3)BhWY3_=|t-NJOIDl}WC_WUM zqQD#?K#J^%M1M2zD8a`${%yGQ=<(yiq9TN5xwUehMpRoc1&~SF=>vi^?2ctCu^0*v z3nuR?d#9P~zQH#P`eiyup5#N=V}){SZ3F^=bp({NS(@c6AgX!ifrbjaQ3_z@ju0A} zk+b1-aBE;rclxDRF(?7fD=?6KRxh<6d2kz=g<~+wF;HTz1iFL00JhvwX@d0h{$LQJ zz%^jr0nqae2okygx;kFTOsN0?>+J~u+5d*~x=<o$!iGY_cKG@Eh5I89KujJsdj}ld zJ>AfJ?dlzXfuO1z_0=`*sYDSLB&cCo*rhMbhJKQOO~wMG9rOFQJh#;-bRyJ>V>~`# zpF!Q(4#>{T-M~6KJA)}?d^R?=waMy{qg{v}t{B)$6`S<%zW+xINL@KcmB`uouv*rw zG69Tkk;kz;D9&c<)bfuYb(gy1IHcn_{Yy%K0$-pFps*cuS;1<40mx!AEqt9?xEpLq zwi+^dyE0#^vWWvF_{!0db9i_dc7g<~Gb}H!=Lu(~T#5<kSOVCkq@|@@TU#?5&b<fn zsjtPw=hoJw0d;Tf?Q#C+#^J8f^77sVky4wp^Ycpk1$>OXgR>!>BAk>g<W4$}!z!U@ z7r_iSeNqLqhc$s0^t(6uqHWIe^guLcC_FCi_<6dnWNIK8>rOkhH&Cv&14thObOgQu zLIELA)guCg1VH$m)kg|;qiY0YESxklhV3E7Gfh5(_t?|qM>N5i0LU?ftDCD;89rI< zJOCjTmhkBs2ZSHppgbsJYx|5*E#GJ$Lq<7YD=s&e39vNE1GNu3cKiN)8F+oH&=41b z6us5}5=vg%hP$T=T`|FVfq;hKE`DqLjC(~sQ}#Y7i`FktP2LeQg-<NC95GqxPhXp= zO(0=ZEdX2y=UH4#49BMaoO=nC;vE~K9sYzP{J*b11g^T{*RO`Dsbq0#&q0@o$;l*; z?kzM$QGv~Gf!$Eb9Z3RVQIM>@K<zG~Qsm%p2cDh5H)9-h)z{a@KCm3jQurGWG0ziD z&PY(EjEIU-nRnS}>*)zb)}7#si;H8ye%<9TA%rLapz|(#Ne;aRthr+^FE3cDFMz!L z2QuX-LfczgZ&6WQ_iO3v>gsX<y<dG8N5m{1eE9sS=jPHxr9-Z6Jr#%<E_TNmTHWJr zIbKY#u?I(oIlge#j$!Gj7Sq2y!L%aNN%U*qtMZ00Cmgv*?MK=u<bGg#ykmT@IStw= z?*iAWeDba=&(7{VN9m68e{j8LaW`4OIRtzco5zvuPc@`<4H|Hz+jsSibTCA1^QZ<C z3RN);Qni;u*`7E<8&8`d6%`d`XJUU%)6>$nAK4^?g(+l>66Q6ze#M?JZ}@P@YP8@Z zY(5C5eOAq39(0fjH{!#X&+HFdTebiQKAuh1;|RI!HT3m`!8L(pSI&j{Snie40{vI8 zJ?(I5;sl&o05r6sP@$mW*bnE3-)T+#frQsxVPRqHkcGT^F6CFp{+vuvF9E=NEh=ia zGcN&mU+nekt7NP?^J`<p0F*AxC$!j$_>#7QIb6uWj#Go+;z1ie2v*<G)t!DRRQ^OF z`ae&I|EqeZU5d5VaoVB$-ME;R7A1Vk#p_ZkuuGXT3EWKDRgnOuZ<3I-78!S`ms#Bd zBuz-c9prz9mbCKywlyUs<%WB`SStOVgLajpf2hbsEJR2P0vm8u!H)U)`_I5u`gqkr zSco+{H|HN3ibm~5gok4`4Ks%LRHBnsKp=t3av1CHzkhBhqt^<=1|SLB+iNmbWbCYs zm&OG7CCuS@_{!IIb(k3gzQE0lmRgc2C@7qPmHI$;tf<)1-o85@A$OaaIuwayZT2Nf zeeoIR>eZ`%H#bA!cr|^&6P3gw+Wcyo;u#H~GLF$vBD`A~g8>$bEk{@kyL#a{K}WY{ zsS<3%Q$fK*03BOR1gwDkxd{l;($Z`fI!P$+-v>ziT`^q}yiCGWt$QjReu~Eq9#{n< zz{5`&E@{<y@NI8zS5#IOdK~XmJKdjtmtA$54Z_%Z|M2S)zI?gLqFwbGtaP?krG1zN z6Ikk?qGNMriT*2Ko$g`5@vF7j!M)r5K~z+<(EZT5xIH5p0+44eTY4EX395gEgT)Lf zuf#<}DE$?_nU`+i7Zh~0VPxbmzhl~mfGjB85}(2y@&j0-_1+?IB5GFO*PkC~LR^O@ z7D|=(XOl&u{~Y5npQMhCsvoS4U6If7zk3CT@L8{_c8OWoTSFx!^u;->`PxKf1R(9C zwKc0bPmG24MxRoHN>KhxDO>I}1M^FlE`5lttuY^<nX?mB7_D{Z0S8sH?JNOd%Aq>g zZY<bGz?E%t7_DMB70ZtNm;eJjSkR%du`!#&qBLZq@cmA{8kef5hf4~KVFP&U1FQ@0 z8XX;-cz>{&1fe%3kpu=B1C785khzrxy2lB*m$7|3VudtQwWUPlsq6Xv2!W7-Aplhn zZ0-lyS&Wc;9s0r@fNU>Z3XHm@kWdnY#*ju($x&UHbe`@xJi;L&x4i%=OZ2%j7(-Do z_^!tbF@eOJ&37@jo|Ne?<Y{US{#YIzA1f3Zh(f9YPH=l$8z#eMxBUCZW$niO)e+p+ zJAaBz<!Wkb)GKTsLf-`jY?JAFgu&f;W<UQ1yi>OPkISG1{^aS?OIzI>sAmQ(*8<P; z!=I1@W9|UEMxg;VmNsb6Dl-e;oCHB~RvK{cfOvM>C~O3k2_agg-_t1aiSvO9Or|W! ziPkH=J2yK_@-#}-i;VEVX)c1}8XX^3s`v7A+gok~JVG$Ik-%ww8)A&BxVR3-I}7ek zaq)r0#T;Ewih-yGoY?)>Vq%b>`WhN~BUEtb8fb-chJ=JTZcZhE5wbf!c|R94$^Gun zGeG(m$4k_hi`m)vh=t|4oSYnkYVI!)^jR|`G1SYg?}LW`D0oFpO^x4qqZ={;1lqQm z;ZNS15%WEN^*=aVE9^7Je~>%;V%qxtH7xjbZKs@S9$_YibqUMl>P;-Y|6hNg0m8XA U$uA1oFh4{>RQ7fLOTAD32L@W$Gynhq diff --git a/docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png index dfecd0b1c87982818784c27c240d377635b1bfc7..b0c73bb777f10174510565c6ec3ab5a90f3dac48 100644 GIT binary patch literal 36115 zcmb5W1yoh-_ceL|0YO3pkw$401Oe$#X(<InkPvC4yUQRH=@cZSL<OX~1d&5Zhje#K z$G5ir{xQCL$GGFZWALKF-e>P8)|zY1xt^d0%Cdy`RQM<qicnroN)?5|^hKdCCeGu) zzufK`nSpOYj?$WrYBna0F3;?ZQA*DoZ7ppaEzJ$CIvd+NnA=$M-W0gW!*SKj(b3jH zn2XEmzklJTjlC%syY8$H+yu{7PRjv>B7TN^VSJLvGDo3;edVR@s=FqxjJoJV&z}9< z9KcY&^CKtw(<gRo$+l=LS^xJR1Lr#OGU8_~Pb{AcQE7DFY^8~xy`$6~|4}{jwMS<8 z#Vg8EB(EI2eSQfA(M-??uIv@ch6c=w4|wSglwaPreb;Hs9k0kD1^=bj*|sba1;T%F zyQ(NQ1_lPzIyMv;2?+_iJ{jr_{5u&pK1v#X6Q2Ux7k<ou9gKMgejJV|gOP$<hLMJP zfZWLU;s595i}0z9jg9&|@%9xrkb4oOqPS@VY{nV~24WKgY~~yvBhN&0@BjCm|2~*v z5)o>8dfIKLOLnNn%cH{h>~NV&*m)(6u=)}_F?w{%5=C+4%FkDLq=N1{W);SvG(z78 z^9`Ne2f&M*cjH5eI<HW89qrEUEcPtMoJ<DLFAU_-8@EKE{gZ;=S7yB3cu*txhPdqs z0`ouVeH2Sq`g641=0CVl2s@hfyC}lLGZEXO7m17-eKD{sZypVqCSzQ@bSdJ|C_DfO zT5Zed?;u6QNEtFb%W4pGnWnk?_HnvWAHSf8(7a=*J>lO$j>vmOga2%0I6pB++^~FZ zaZ>)954WEamcuvGa@iyuEM%Sxl$uj7z97eDzHo|SNMpo%LMGLGjS~;+-wjlls7NU* zojF)kGrf49QkOc@1T{6TtUbw%T*s{Y^oh-SyyipC=tY=EOLAvnjvvkAql$5Xfgx8{ z95rM9)SK}Yn00;R_%i-*OBXd*;bh@`dN7~3<`08lROlekAylLzBPb*^IW_e?gjS^B zE1_)jcEX(y8li|ei^96PJD6C-8)`5ZwVySsK1#L@U1wl;(UYaJ{Y#P<L;jYmrR7)2 z-?l}b0+`5~V5=p3w%XX)X!NaLI>I4P-~4nB{(+`{t~*1)y!R7{pP!#jnQguOXnTA6 zGb<zPe{aYIBSyMHPY}j%p074(QKIMC0m^2q3LQwocpe{rYPi_K)y-`-TkQPb<;ygF z{wsBJM2*6ym4`w@PAbGIF*~|bVWPg3&a2;H^=TEFJmaQtJ(!I>_%rY{TO;R5=5ohP zoJe$(eO{3!EZT|XWd&DP!5?ofw=S;1Fa#17Ofg-IL^rm@^B&Zm9Z5`0PiO0t^Nf4# z(RzD(hb%hsx4@7f>v?T<WhOW|Hd%6+kAh%jv@&*o-_?D01wHW8M=74im}XeU`QP4& z@PGB{oQ_V!z06SY!$2ZhVQ#ZuR|#;I6O^_vb1;xinLo9=`a76bB*xbl6)eMu`tkOP z>BdC8oA5r?yT7|N-F9Q*?%|*1pEX`be<v<UXE~PY-_MR)jd(VHTzLww(h5Ijdz?%2 z<#Are<9d3tbI2Ax=i3^~wLKH5Qs8-Dj}mrSYa1zdC||ERvM({zy2JQ)_up9`tJbRV z6nc$+dFmyxxPJTl+Xp=R{}vqss_JO1lFxE5Z1s03ulKQQV&axfT$GPQJPel{6)cpd zsUL4S^Ac`bG_8$RZf<U(>-CI@|4yfBN}M9gTzf)5XejYZTw)XWn3%n-?N8&Q_)S^J zn`7iYt?fv;`_lDz^Jh>{P%2DV6o(d1R%P_+-#8`b_s!}zzo)0?{^`-WowM^?PU)Dz z_FTJq&Xe0&l`*SPI5O~xTh*oG-U*MNdPO%jN>x==A-`-sRA8*^a^vT}LD&+DL`xYM zFq|CineMHPV*l63%86elC6&xK_<?eTF*F}3yQN#<h~A#x7k(jw#7Cy5<!;+7j55I~ z#;q}pO-+~+D=Ufvxw@B+PZa)!vl;6DS!#9@br}3GPCdFU(f^)4$&`=kV55Q9yz3*j zYzR%NYQ}xbk+N_)G4~nA0ej}ZvmY+HdVH`=Kr0;OwA_zz@#4igfpJesSV8t>rsc8! zo<fCM%$;w!Kc}IuFS=9WED;`teP1EqKR-O0cu8F5xOgeq`;h(5QXj1389XXvVTFs% zPSn3_i@VjZRdGN1y5(4vR(&!p-{0-&_5y7*^(C*y;CO!%UQ9JhnU>CLrz>$yBPsoF z=(n%_zt)XnPo|PXiJDtnrY|xwkAGLW@xG%LFge(oCA+R3xHs;TeA}Ym7PiW)<G>cj z-_<FXd)w>KQO|AmvLXxV*w`3{PT56z`efu&vPh7GgD!sF_xUwE3Ys;UNmNv)KKbRp z8MBWrH0@N)R%J%Lc=2L?Ij?r(&!6^5y;-h*Z>BQ|+kt?JFR<wywZqzoP^%NRPe|6k z2OlZ1`n|Ea`K<m0wobKsG^cjSjAQK9?SBEr@DeF03@*OaaM5IA5Gh0x5^{2xTJID4 z3X6mH{m6Ec3xSob|10IL<!Hra-_w($?SuJbwU1IkG;3O?|9NbZ_3Ax3?NaLu&3uEl z4<fPH7s%Q6TUq`+9L+6w>6^M0jR`h2=##zi<epLX2mgkB$N*N4=yn?~#XA|)8*<JT z7`D6#F5JJ_Ih@1zf7VN~*DmE#&rNA%m+ZP8vTyK6M)BM8oxF|<4<J3^-nnxJ6)Lu? zNwjx=Yv;Var>7`H7q;J}HY&w!^)GQ3d$NdOaxz`k$5zH`C8#|hBu8vtdKdI6iNJWW z<~4j{?uv`!Ts=MB?&Px`iG#&AxwO<WpX}37VBE^4mZ?}$#FgDzh@r2eLqp1<II+-` zmK{^>v`mid2J@cGSFc`8Oiwqcsfhi^(Jn2>&w`jQ#5(ifyk5OtQS$F3jB8kC5~X7B zH^_SEZf95*x)n~#O%UA72ftmLoSY1%5xVrUJ}hJPjTK~2*OkFrhr6q-@Z>!ld)u87 zYUy$$dN2Ub`@f=Z<W=44`Y5ec>1@USM;%t*Jlyn}*~5#bA3K>U=_qOCe|=2hAjA}L zT(~p&{WT@834w_7N{U{!yH<b9AXjTYO0V4hHLq!h?ZK8Y#GH^XUv9uA%8+?S6?Icr zSj}mDF#jb4DkQ$qxlZ`0x2_Hq;y!))wAF?h=cONu()({7d)Am(SiF6>u&+iqy5gD# ziw$o*yT6%)M~XubtSu6PT1O>*NaP(&g4JB-hs9_13+0=slwd#8Oad{9N4HckNfN8B z+5-Y$r^NPen_9b-!F=`BM>$U}OGrq#?=0LsIsCI~ZCdAx!5{th&71rA#Z`*&Jm$*O zZvmv>0<cgGJ|1m<6%@E|{=0I(<os-GcFE1BHEkP{4O-Riw|$N`amB^OQTV6!w*t=j zK701ea|_K1ndyq$At$O1jSe#S@n&*vPR7xZFF!vYl_>1=rrcqk$7W3QNs(DSZ>Ojv z>~PwyXd?qdLw3l`1s1O_TwxIxADgrSoHWBSZ=L91R^I!{-=7CE7y~0CyZWb#N4u+q z=X@hPjIX1DVb^TUppSQd+d$ZV-uvlp0>2e(+YIN`Vf8N>oI?hkWo3dL$r7C%A4Kqo ziRU3|8~!e_g4j=A^t|s4yIST02*rv%Y=sQcZ_>~2Lh`k9a&pq7hNV^h@W)iiiRkTE z1wNKb&ceX}S03iON)M}BMb7nZd=PPo<1@bmVC1I}&*bWA`}fxuE+5a5P!gf8aOt%b znfH`A%o73R*jXM3^(UmxbW;gukp85RL)G{BQQG4?J$T>}>(Q1_iPPp7PVI%UY7ws5 z<AR3%`A44~_$kERWUh2x>}GC><ysFrgy?7gh5ym-2|#d0j_G|)zBmM~?ylXL&#CVb zz2<ga9W+vFN}@|TcZ5r-DKOhPGyOA^SHBLWKvlw;<oaS_LLWB%dnbB1n(GafzE3?? zZyz5YYgRgQs%0rF3G5d(>2v?e)fEvxTH*CRTx5OEZ`A_pKceeq#;-CvLl{is_z3=Z z#g&orNQizSUkQ;F**t`f*FV$0%05_`aPUX*EdHxt!S}Tf->+S}W(lY;f>{BuA4ARQ zp#`>&o1#Bf9Uy=ZVu|C6Dk>@gj_D@t?>!*{0_<T``y(YSZFjV5g*rYt2?_}@S?bNE z5pfRAR?E6XO$}qAOY_SR`AE)h)>jn{Z>AQ=&KeDfiHT};yWnArVVJLx;ZT%1>%3{~ zLgL2jD|-5!Nn!xLS(n9qjVNEDMMYy|-8?*aVd1?Ou(5U%gJ{{xw>4Jn;WqtFU>`!z z#_p~OtR&}IhBrY$^#J5HHf-Nqz7fNmd>8iqjf|2St{o$`)%g;Yy;Buxua>2}$Azw& z1|y@Rg{eOy`s_JS!9hV7&42DjBUKGLk9<wL**A<&^$VpRx9#xKt6N>o5c}i#t)7(p zQ7KT4VSb_9+V|x#U<PJoWn~&sS8mpdt57seWR$Dz3tz-c#AK8?+*|Kd;Hr+&)s5tc zVZ=z$qD%H7hnx4Pj<S1R+{Y^;^k={^@wt@%C4|oW{v4y*7Kc8P;Y7cPygoybW0d;c z-`Ly=r4)Aj^rF`DZ=EHF5o3^LD^^GQ#)YjT)WUm)^32q}$S2~qy(7LR02}+5F0=H; zp-YK09^D0MW&6`V7Q0gv-n072jb$RaF5Oqy=TuZOfXH~X!inQ)O`@-_Z{k{n5vdXe z#0;`K7X4pS?WUW)YL^l{e*8FBuljwDZiHnzl6k{DTb^Jph-|v-BQW>H%Se771mnSu zSA}FqvzB9&MS$z;b9!LvPhD*{E#tp_87|s+{jY*SckJg})W#^CwN4bZzvO$%uxX=1 zWPM_3X(^0fO~g*hJ{c|s#Md8V3s>o|L0Y)`_JwNVrpM$Awjd^5hUmWc>7L_qe|xIc z7`3gtyX&PDdPV)wI|0RvA>rA#eOV&e8_wVz0;ie(mOLvy_2Z)w_RE;Mse^+C`!1U< znhA5h+iMfVS9ydZQl#%fKCsi&o}!?n!#JLeeM&P#3}bKS`>p5M8MXnduTveWZh9QE zeS*UjPlVP#IreGhZv_(Du^+GSuyC4kXk~p}d^2A<gu`P;-+1h7Bmtw}h|w`Bh#Pc= z?c(nGkM=vh(e^}P91}&~|3=^d?8GUGVQdaatDzKm6wmlC9;{>2*R@#M{Fu5-+nto- zm!5{HY!?hSga;v7yc2!>5shjnpo_bEqb}pOp7*kqVU%Hx`gs&oQg9=o1gVa?Gym6h zusdxtT`;H#aERzc<FbVjFmKYC)K01*ig9O@#Yw1l_o9D<zmdf@0n^C{QY<}N-h_bo zGAn>JyenK|07F=Kg6wDWj{Md5Av{YyD%wp-xCegKPdbla973v%xdfX6mm|HH5Y79> zp)<>W{_Wu)$)1A0RakPA`Y?_Ddt>Q3_K;`X#@wt5AB3GEu5(j-(k{IT3oZ?cYBF}! z#$0#%Ba?`@I0{cMF9_>d0EvV~Y2mF=fVH&BY_DPA5Y(aB%|%(o#giZ~pFL)wCgs&` z#nSwdhR=IqdYxGRX4E9g_U!cJ7|_U9-O3oieg@nWwk|GNhCf4KnY;_2b3cD2wyO50 z6au?v_~GsZFerfSHpV>GH{r5*$5_;da}lvdf~czBP>3KY5g_w46ekn}IHIDW3nS&c zx|Plu(r?M#c6%=(a4;ePP|OC@M)rS}9zlR<Lf{XSBlZ>A5kCc)P<*`cimw6!3ZM5~ z2Vh?}+Zua|f*^`r4adjFr^0#lek6+$O`lH51El;fuLu4hp8FZ*Q?EalQN-f=SI2$M z!~ov;5bY^$F$CcFy6{rHLt-&Rn!;xdI0y`>*H3|@?6}zdI#;(6*?2oQTAm@LEmIa$ zTcJ?T+3#;WBf!Dps@}O2$*Q7UGN#f<r$vq$<GgmwZ`}Ln9YF4H<~=OdBV`zO082v} zvC<GoX8+GUAVrXKK6#L<BQ)2UtP(&(YhR%d@zc2VCgGJUSD=`deMfa0$%0RPPHqEZ zGt--`Hq=!Gi!{k$FJugXJx1tDPl}&QLp9}4W*R|n<c6XkA*p|V+X6{ga6`<9etdUV z*I)UDKfhY*L3Hkf8jcV!qCOPWF~!?r?mNwsdh*^8%oq~@?GOa_CMC~xbINhymmgr{ z4ImF5etMAl)eO1#r!f=reE^)u>(<xH2SsNoB{U(qE3HzsPLKm7>a-NayddJa&mBg# zbD8r=@1>n$>(M6{G+@NW3uDNJdKx12&nDC(8R&%_0|C0jLZU-81yeSNtXD;Ho<zT3 zRl}G-N3wF>9S>)e^-WLd93A}t)pt*Z!j<6?t1l@kut9>uB(I(0#J_KM)7r?92?<;@ zKTLSy_m{rpQAl`;0(gqz)Q%BynEM6DcV(@D>?+_4EKJP(qxD+9be}3~YwP`kgD&Kj z#TKS};kJNZ+;l(Q3eUIrnR)&cr5MMpuRk(ucg_`8GxKFE*VB&Xa7GN*{mFoM5to~n zZ)mjjdn?pOPNIH^PR&nE-Fr*Uxv{;C6n_f~W*WIV<_=<a@7|?ceE}1d3t)(2sA#?V zzla_RZ$0lj-M;rrtw+tqYs3J1lqN3Uh=}I7jcv(IA*ZM~1F&lvzFHs9{#_-tz*p%U zzP>cMtWq;qk0w?vGJBYqn5bE9Z=58?^Jps*iHX-A+5e4+TUBp<^u$+GV{*L44!>X< z>Rd{*oH?OVpbdyZDS=<8fF5~Qy?@jX6OS;@tZ<!J-Sew-_hUHF4a5?Oq`G7ncP<bj z93_I}+<Bs2ACV*9qzn!Gm+YMFSq3H81CsrGWdB%WDfk)R5$&~7jTQ6Q{gtIcAF!j# z#8|NNm!W}76&Mnd32;bjC<+qAqeW(;4!Mq+qI3O(M)Kp#@=m8-INqpS4CP<z@!oA` z`r<cl4%uJ&BKMg9lFO{#A4MP$DUd1)%D7p;{tzV~&Nc%irr<F;4=cEtxAx%wg(ITv zzObg+I%G;$T`6FiECw|d$B_f&+4dPdL>A*s;<x5K6-rGK!GTypqNtH?&#<_vHZbk% z?Bc%gQxMR=vIMZo18@*_HDvDDj95K;qaH1U9hDo4(zD$&{1vLGDRHf@i1RP3v^Q7@ zatyVt{9SA@z@}A@KKlnKmroGL3M?3e+EXo1Dj$S@T;QszgMhKQw>STms|KliV)iTm zfIoO}ktH$+R_aA6s-@;RK(4+!q&x}Sn{Az)gNcQm8UPP_ziLH7Eu@tTp+vDhc}AFs z5LJ6HM-60RJFH8Dzu-itwj&b=p^pMeE4U@S_kMkNJ0xh-BC8QjV8vx)HQkb#-AZ6f zRK)&}L6tMt5`DuHm^c1D&ImOcD%2^I1!Olgf*+>IWN58Hx!>5(@DehJP=Ol+^SY*2 zZT$DSO2;8`n-3SUBS91juUI|zAfPqS&sN_WVM7Q7FyGkP`WBK%mR7Fq<ahOK)z`}d zc_RPr;s2`b%$_Ix)bN+D-*vi2W@>$hO@wJ(&46mn$Et(^i0LyS+FA~N^MmCtGMfAn z89<Db%1RLcwFTKLf0hT>pA>y!Ot=EfRKz8oSSPLoYoRoB+}shQsmuzD83^a52x0Q` zXHS|e@$TBF!p{&|-rlF-D|5e4zkdHtxNzm>H=napzSh;@;<r$5POPqeNOhi?nnFlO z<i#LjnCz|$WyhQVBe~d>hLmgw`2+jZ^pg_6MG+_>DSOQxWG=bZ^lh7G`kIec+(v+O zkBWpba4k`s3Ep+=5!!-$sFl7iS70t;pL)lCVq1P|5lS!70kvf)WEGFSHOlAn05pst zt0Dx2z?kbx74d^tu!=I-Ty_>7Cb=)M!wzT$IyU=p9vxIZf{+2)9#leFbb-iZw6BMS z<2`sLSwMOho<B~|j={9!xU_r*2o2NTPY;D#A8*kF%wh0Ry%XJRB(?lqLMR|00D!&& zvR0Y>EdJTi_?aMVvD&j!5j;FR(TzGR4$W^UQ`6rLH!wyjoFJ+_;kM}KfJ=z#Oq0Eu zfVPQ~@bNAvu&Fs<@i{vYKv>r>M;>_yWyqF<^0+WtY!;X;bOP);S8NY0iP!;|1p0B{ zVd)_keT0!;87d5bc)s1rRm-*zDcYXO=nIhE^KdcCadkL)a8UD}q-4400S}CuP3=hx zOh-gm*c|{02UBmkR)|hfMD*f{_wHfBw8U7{)YQQJaMNxbJONJVJk{;zMs8Mse{?JP zD_zz*HcZJ#!mH|+%B!58c)JS0nvI^FSA<jvFyO@t3G_CNH$^I}E_%-m3?w5?H@!QC z(L-Vil;J?tnL~hv&!EhexPJW&kb}&>pFJwE)PmtIb6%y&P>5A_K7qB4UXuWr&wM|s z+4cyT9VU_3z1%9_*^$<aWZVt%b1LWkCFQw9oR%7^+_pbyc9qaJF_de?e2~!vRu8JT z=?!;7Lqj#oJb~Jf&`=fUYAE7@scyfKrT2>Bw;KLtKYJgB%rst@oFx313#vM(;$D3q znjU%Qpw6Keu91}U>A<eNH!C2`_uvknyo2$Fi3jtNmX#&%>fTU_P-`*?pbJpkUjPd- zNvD{4d+lX2qO5+G%*LB0J=<C6Vq#(Gu5xq0#Ka5?3PP8hz6q4;aB#M7>TKm2Du$ZX zoomVJ#Q)MAk`#hp)Su3o8g^VNVhd#UicK#U(PZLkwngp3+?Z6@y{xZ;G;TN`cKN#c z#8TfECMKqdsfgYaP(E~YbvHLQ<Q|?bW+l%Bu_jGGWem~EA7F`Ux&5r-o-WYDC3O{k z!4~2n@xM)3bjygySj`*rxPQ^J-B-OT*F9ACUo?)d`Zm^^8EnYuK2Ux`n5G<(vaI1^ z#Lx%O3xPbJrW)%a9i4K6KcSud{hBOe;1G~y_gK&EHIH$NEtJkBHsc+@@L0Jd128$m z`$QT>hFZhWz_Rk|KdaFaHg+RrVfe^+UW7KrK<XhA7upsz2uOuy2zwS522{)O2$_+o zgn-w!B(VelPFV73en6f5?CZPC#l^*<oY)LRy&OatH((+^Y34J)fVb~@zYUaI>nSrb z9C=*faN}5``?cYn8{O^Ulg)+x()KSTw^1gsum=FxH3EOiuJM&ZMRfBVq#^7GWyW}M zFJV7F`DNaio4PGo$=)pxE6kuIwyE000q9#=&iQxmC7<q36XOt2)CG~U)}e~9hEe=& zEANpZdjvvSn=%3_wqk*aA9I_h)$=)Vf+_%{7b?cgkcsWL$*c;O4c}p40Y8#R;c?Fb z#jMg=pj#*6vS!p8!wEQE0%4;+O5r^p$W?+pjbu8F+n_5(WTwf6^0;jo!R;17z)SWy z6=GJ1nPHw*fVFD)WAbv_h&U;hzY)7Z8#>+euv2}5)h($(7S;Jd<R>J>X27`EVV04B zZteQO!N!Za*(Xsv>^To<_Y@&`r`Ogx!mdev%{3(!B8y;fbG#gSy=^Bsn~Xbx3u0}R z-82rMkQtb0kjT1<%+;!@t07yk30RLz0L#8ITzo-JxW=j@Q7A_JW;sGUgjcNsQ!9FQ zyiGvO-xeYEogCCUBXrttb*r>+3A6wWm6z9cyMUC8jQ!ck-ZrpcYSr$4$c%|-1nUYr z#Svi&DIp~N3G9rnK;g#WygsG`;amvdFEDEc@JD`PQB4Ip&WNY!g<YfKkO_Yd4aKWS zob*25z<p5b?InJ)qXx`7jI4vvHjE)I2#9!Mlr7)AF2J|@A8&O{S^@QC(vc_+stR&r zK%X;^Y1;o3Dl-iINPUWD+L4$F!Wlx~wL_WImj-{4*It(f0Qaq=q~kB()(^<ZskEKM zL_)qMz<(5={s|zzQ!R%Ih_atDN!Tu&PYDRXh0VhsTf4WS2YId*xU!2yY;4k>qnj7x zK=LH9XJhKIN))tv0jzAkNjnwn>4TNRPR$z6@)r`}9xw@zWH?z>l8oj%KA6Xd*w37Y z?3?!Ji$;hg6c6^<Aio+8geb*m{AlyE0(>2xM3f5)4?;PRvAz;#$C0qAg=Lj<E}>Te zk%Z7oByG*M@`7p%smG+aZL%T2Z7$xF?cv9Bkd9scj+nyh4@3&Q=&e!HmEmQ8LlC@C z6Eic7FR(9w$c#%!#n)`>BfV8gkcUAdXq(cvoSU1AFssiR{3mB;rC~zZV39xuo7&%; zrV;m~03ix8U!0f+KkO?Re-SRg%P(_G=Z77da)CJprNabx;hpvIWJrTJz7SB1K7}%1 z%LBmP*x2A37l>DcF=FU@M+emDG^A~$`cy?hpzcbY&DAOY4s<Xcp8OL5sMGF3DJA`e z3~fs<jUNFpzEk|@E922M28IL8|3>KDOTu@yDJ!i$m&ljqLOqSBldy;-Q}4Zw<F}#$ z<N?%i>X-LY{}hynS->ZFyTmMJ4<Gn<4psxL3U#T;aFIE}`SyMO0r_eZq?)bMw=)8V zPV!Px1=7IlfG&H_IJ5<w>*4NNkiLEia~UTyp6L+5h=2MX!=*sVHUTYs`8dcZOtKSG z+++9a=$`s#b@an_Q@jtrvrK7bf5^8`h**HAbcvQ$X}reE;qwv0!3!YOL01?aD66jn z;e)o%GX@zEgUa3)l{;~fQmoqauGIUK8~z!QvBE3q7fI|k9@>^PMF6^l$rmXwhqd^O zQf#jMdBnnbeRf+yK^(*P!Pn|U>N53JrMl;Tp*8?u$^WT_xOr2rsu|*@R+Z~5S`p_N zKyd#SiR9OiiOum_Ho%4=7cMR67ch0nOgz@-)yKS#s=mf^GXjXHeYXSS<@rnKJXm{B z#R;|Vt=FD;Bh@u*E4QQ7QW&=kC`Lk7ps+J4+zD~x)z@!qAqm#v^>3yQkJ)QWdufMv zmUPXBRud=jxbzNI3iU+H4i6AuzamqeRk+^D-6zt;GW!C0^OI6*CAcfFFVCC>Y_1?m zqrCP4`oVCgmCc^l+zwadG0SGtlE4J%RdO~d)rkR$vSo^K`?XmF%DY*$?i2)S#xk0N z_@na)^lw08F3NncREdl_H?3TFLn%6jh2FO8J@WUz!>rG=fkMBKxT>{*d#1z{0BX;J zBL-}(kaNDU-xGx*4^t-))H3Mqsf|Bh?>O2RzrY*3L5o3*)qtW7-MWn%Hr*eh5X1FA zsylWSV*>a$*V*XDW!@(hqvnP{3hdpY-!>_8X})=zWrA3r{30gB<nxtBF+@6VOwBuu zq+82l6|%0Al5~+k9%z*tkjCc-u*Tfn-d6a7sJ@_#4dguqSpb{>3*)u52)Yla3G2UQ z!`_H$^)o!HamHS&Yg^?tSIZ$}{h6d#q9zC<dbbrP<99%?vgBJbbts5BlO=4l({A1r zJ(!6c#^7LxP>Bn5D~!SA-c{y^HRNh|81bSuf$3ytkwEl3MFjt=e}a&&QMTSa760L0 zZ~v@RX?(VTHB3FgckkClL`2j+tFu!HSO*6L1f;!hS4F5q0}%^WKaQc&SKf?A%t}X? zQMm3{msQ7V4>sG;m~{}{KunX8k$pm|#iw5fftS=!g^f8lkd{UmbMVV!E?Es$h$X>= zkq&Q++QpvlrL>jv=*;o(F`5_3UR&Nv$g*E2VhRwT>`4D5&z>3EZb9@fOB%33W!0}! z>-_=TI!N_@KZ-59uRSPG?k1Rez}<s*Lgwb0DxX%hl5QuV){EA^fB~eBakiz5*^01+ z#$(979UdqCls6jp0TWgM6Dl}3cm{H=K42J~a{C|lqfqNXrM?N_ToqWVuVzoc5L7p; zo|}_q`8ZeSSz{o{{^22y-INrHZv9x$hQ^(7G<)Pz!b8!LnS3(i+(y0nS3zj+$m~ZI zG0K=EXr_SVa|MP!iITi_wSfBaEB;J~g9YXHECj?%C}u+}g%!*QQv%8O`ei08#heTw z10rfMQjtNP2Lg!wac*M97!b!OjV${s*qjE(hnwi(>V*EIAi)s8Nix#Xw;(ZmOiRmt z^o87}`VTpj%3q(>CIJZBZlboag_KQ7O4<Onqd?H=YE^-4E`upbWtG$_v$+P}ID?|v z?+#br)pXCY2FpdjfS$)YfY}anZInoy1ZconPk&shR#qUp<uLsp{lxffjMD9Wtl48y zJEj0-41j_OkkxxqC2?Q8e95lPc5r<hBIPAAvc|YuhUW<hjX~`UrsQo3*S$y*?wHR1 zeedo(aojiW7k%tcNHdMHpRCdOiFh9iV&M|agi1t|91v0S2ZI9jrH}=jM|c}V--N5H zY?G}_8*a=e%gqTn;+I(r<R*%Hy@$$3wZd^xy;ZlyGalv+>UKgzj*`8d%*+Ri^&rUY z)%w^~uF-QHF6APRT7mt{)~gnN`0!yA+rt-mHHUAY4m9~>10!KBQCh9L`c|$<&=G$h z08}riTkR9XDWGn`9}}9J<z)FGwwTNDvG9O;rY^g0`KNXBDTi7UGNr`>def-^bOtaM zltiajPZ(zjC_YU+d<_bmQ5hkL{1jB)`rNkj9kdB8W91IGX<Z8l)4@atifOlwq3IX) z3t0~q5AMhOLGm9*YHBKiWz_z>4~@c7J?GbQz&fP~JG{E8N)XiF3Puk=Jd=Pf(K`Yc zz?-9+InV*Jkv_jpqC@!Dwg;&_+qswZzHi?e#zbY6K9mwVlcH%zO}K;9(y5XGu9L5? z>~FPlvFla2zTrW@Az(|0K3&;rtjJrcULAU0*qO9#ul5zMiiiVC`ShsblK%Il%&@lm z7HNTjg8q^Mep0fsJvrI}K#xj+S78Cle4r+>fHWN`vwI0s`OR{O2l*k;0b0e+ALUAr zYJw?=NuZF`G&yH}$@|yA%bA0HA`A(Pd?wn-enK;XIjtvFEf>)qG)d<V0U!|2izh<W zMBpEF!A*&U6se~W-pG2i0@*oWMhG9do<m#h+sEG18*wXv6aR@+@}U>Z&(^U2K-_7I z<N33__WO5iQWUp5nr_qo3^=71o?;A=<F|7~otHPqAm`Am14>QE`?zeD11Z&z6$}cR zjgz~e0OClDf7ZHVDHEd08#-q^(@S8H=5OK>N@p#eA5Zi_r0&+lwG=UpD@4%_TtCa_ zJIvo`FcG68P}~xTql(fPxinO_)v}|C88_yaseE#jK%DP6O<`MhC|skGb&_td=G}Q0 zT+GN&9x3SpQkdK&?GW|Wg?tPBqmVI=R*;kqX7{;7ix0D-UK{#i*L_ZJr$tFVA(RL8 zHA{jaLPXH=iY+7#pOZgVz)~>}+gGR3nGmKOKrGN?>GF{*qWcpsH3qPUZ``~Y0`a8K z`@|gpD<!`r1=NLwPn_ZZo*VVJi4s0Z!cI#Yz#Sv}zT)0pDW=<t%sTw~a_-|s1xJIo zd9_`3#&mDyWIqkTZ1qd<<&{cQ6m}4(6Y-7rX)F3dOM*V(p+&*U4shF^y9I;^2nR_f zLkMy;Xgm*t8XBqAgiDY3D|Xk%JK<S{Ja#z{1|3-2@8jbVTIEnVZH+i|Zi3*`oi0y0 z*B1ZRw*isF*1@3_Ar;K!xh4zn)GF}emB28ljQ{<sQqFsob~zi_yo280UjE=Pl;ye8 z{XHkQ2!8h{Wvaeq?OHCV;TCgm_b$>fxG7|2`WhM-xK0JJLSVvy0;|D@=ON@0DC6FN z`wMV#3LqUp5fMY4_%bLJz+m!he`6AOpc|4_W55O>aTRoKq-vq_-hY<lxg`&)DD2}3 z&@@fJnyA43&wf&T9RTT7U`s$3GM+Ph8<>s`?@7(>i|)^+3?sWEJ~ShL^&JVCZNlR8 z!s&aA20wgxt{dcefrXL0C)7JCg`vV9B$?LHz2_$Yy$=C^@95|d-5oSUzFa*#ve>TB z(|3XrjX=B`k8=^iZywBbAQ6e@SGG3<5J!fE`G$su&c4sf;{tmQwoKZsp=JYg1hWuO zU0`b2-`@vo=soZ;e1i9F5(ncBA&n62dkx(=HD1+(elNxyLX*|R9~&$UYBkAn-R`v( zzSO@+_xFD9ji$L@3%D1zc@(UEwBh+>-bpw>jxK7Fqz*zW;um@NRrA(d$A?xZ5v(Cc zu<4X>1LZ#Z!DZ|j0QR!-@=YMUHb8#X<V=o%-3^v6X-E&811&L}3Q!5aq$p^VKzIjn z;ua9lRD5Q2mFqRFKm$nyU1DCbek|3fF}J8?!RWB@)b~-j>nvX@*ft~9dY=P=R}K+F z8`YvU#zTWcX5Rf0P_3C%RJ}J(RoBCG;q<DxI<cG+XIl9R@sZC}Fcz86OEc{Ot+5Za zBmggjJ%nd1KBxhs!6q!lXFTyBFl3CbHZ?V|fY-Hk83xX1AWyIQzPdVDUhSzc6iEzM zulfSXU=N<1%HNks@*h3vDV!UqKdtxdolWCw9po(*wwUMmG7yPv!|K=IQmu7?f?&ky zYR{<Y+HgTkSC#TZsjV0&dvJ;A(t#`nDym`4!{g7=>%u}_AV`ak#{T+DcG~iELDkny zhJ_2og~>#nA5t%tj=5g|Mia=8=Pp2s?sBViYV3YWTsa)Yh~YH04A)DMVfcm@C)w>* zGqC2qK0a6&tWC*JG}auH&&shnToGgUBm6RdG->z<V}k`M?c>K!su}p;uq-&zb8&GI z6%!M3={SAe{lB@aDTBqgU%ptpad=G(900HioOvG-6F;eC5t()-6<6w#ka%GC6Ef9_ zTy0kq@4jK!%6`op=lNsbi*+>g{mka=<6`~?1ZjrvLeRNG@cH#)*ln(^{`@ZjXRZO8 zH9a-O<HREaQ!VJgvdVY9zc)u4WWXyxD_F(56M=FIMJu~%YZTYhBoO{thl)vAm4AJE z)<CeQE!9ZZq1>^U6c+x3Qd<R2{`Q047rtUn`7!+3T8y#0QQrQ%i?X&e<+bTsqdz6t z!~Xv&iD;9vG;8UM_qj4s_E`if7_2NTQ!9y^<GWB+a8tc=9-sdox(g=67zk2??z$%F zj{>@@)Mgwf#b&&Q<4I8<@J|j)z3e7!arHxmri&n<`X6yJM9{7XF{ekH-e{?aJ;@6T zPtP8^2tfS0?f8W~yNZ@YzTY%pL@6Mog=Z(Q6#SKWy2Nxbyoy<c4qE`Q=F&i3CjiEd z8FVBi7%V}T!ASuh93n#B>ApFE05)&|MI?!N6gbQ)!`4$kY?%c}xV*p4M6%DNc|nNm zUVEs8{bGN{8NLukE>q6>HkSYQo)1YZTQWwUtujke8B>&fmAv}puQIs3Z3ubMstoF2 z)pmyn^2I&|B!*nUfY;$pstWxL+@!aGGpZ$PH6N(>qvca0BVOENw57H^D}BJ4a!oz^ z@oPZXK?;F|*{dG1TW#!Cj+cPDipWN70NHsAFoUuNoc5{(PQtfcJ#1Ast|)ao=8?E2 zrqlSmMya^2Pwt)HAexw_rT?uyU(M(?7sU_KrtgfoF;4_CLAWo`Mg>WeytGbBz4iLv zlInWtfPZUoq}5_)VZi|}-1mkC-r_}R2onFnbXKp4|3VCGuLj>r1HsVmbgpEZ6K(H` z1V-a!x0Hj$p52ZsEV-8PMz(w5BLdq!cy43H)em-kC&5S$l=C)Ba()*aQdB_%dW~vF zMp?lz{;M;Oe1?nXMhY8+gvH-4>@V>BE;MH9jEUlh3KFFK|MGT(d)~;=@y@O`O>(}i zFL!J8xv<T5w|BgWhkH|!cRYBGX-9h+dNRXBwN-~fRtuX%0m<F6c;yla`$TukEN*)I zybWlC>|=R4RXn*0x<bxN6lA#Wn74VyYKiF&!n)dJESOZ%ebJGXV0PWYbhugTk!8o2 z#<*DhskG0d&F!>Jwq7g2`DFhzlv2V@)Di2ANp9%nHyRH%SXEKRg2WTBZ9x2pV?6rq zhlR_5_W|z0qiuhn<zSN}VajT1O<$wbJIB_#vwU<`MeK!k+`w%aXH|V*?0SxyhbIDz z;9xd%1JxoMBrbmuJ8%KifF`Grr>6w<6OT@4pqzEM-p$&gxb5Q;`<*NO-sLffo6FSH z^ht>&y4_yDDz{W~(qJUGHhxLw((F1Zdl?T63haW3Kqez*seAYCne}860#la)k(rE3 zPjvJMs@zVfOOWJuw6~6{4_3sgjqevIS0e)DO5`mJaRn#Lir6T+9L{ACv99h=2-!11 zV%I;&pXGo2u(ABSK)JaSBOaSivfSMmP_NtD-+`Ao686s)h<~J1RFH_YZJnJnAqn(; ze)JZ~podkib|WJrpybX0N(9rPAMm|)YdYNmvf#o8in|%|H0Wm84dQWuavEBm`nS@& z>7rNmiz@|e?yzKPDAg(J6DHmll!mRj51+C)R$V#zM=zwh4U$#cu+WVkGNj1c5Ar0Q zKh)DMv3xdG<%Wn=X<c#Z<<X)(b8`@k%s^v9m~gOdq%c;5F-M($BDJ<V<$uL(eQ)n; zjcJ|!P*u2@V&jd$8-1|s@G*8-PC!}+X-g2`8oHDKb>C$Z^$GhG<Al#g;u{`^{p(+j zy%-{b8=NspEe1lMe6l+^bV5`Cr~pBZB>>T%MOgSf<evvX2z-8=rwm>av%b%lLG*D} z2dY-f8tuoNjpGwm<ZScLDv+Msx;{RzR};YcjUFeIzAe#ZeqtudGjHq-;|)tFjLaJ! z92`l_=n+w4wB;dg<1oH0jm?ucQloF~m#)nH6wt=ZEed&nMN#NZ5N^tls^0C9^h3-o zzvFH2n(***`l=Vzmc!>F#=5K-RKKwkgUyBzY^FnG$9J~~ubR`QIv?-biN?Ac1oLh6 zhuS<WG*D{I9RcErz2$dhp=N?8;97+Gx76><9rF-)u9@)S1bq0EutuN?N&$q?d%G_q z`tcV&6{5dE?4;W|BfifohTknv?la8J6RGs8`A&OQLVNb+bNz3@c-S0vjetflj9S1N zaf#%E0~a8bv5?Tr*hBH{#jCP;G{xD(Z(oqJ?~{h}u|t28yeyb3U@|gByT{9R%?DJ} zJ~)p8$O8trEm?w|m6a8g4=FP<Ha$H({tq-ffF7=2$+JXHyKrIQe0Eju@+T&dk430^ zKJb&(-xz$Wye(qKynFw>@l&%um|jbJ@_$Z1J5B(4SPcrV63e0Q)HYRqZxy=lC+_s` z#dF)S91<{k%A(TGV@8i?Nv)*b2Afefw+cEl_GezAhmiR4A?4jaFS|MRz@(;{dJj8O zC0W@nbPCPy4$RJQZx(PKidzYdUpKT8%Ts4M7$R=n$BHD%@FQW5vy?Aa9nRr{7uL$u zkyP;q*8~|#f=eQIy`}>;)_Ec#W#{DLcXY<1tt=v1Ok&Y*ADg^dKmR;BHv?{DAb{<= z2+XJ|_v<8rjFXYw#hBm6`zAoGJOhsLjM*#%w;#)gxmqXR02_h=%98$(VyIiofaeyN zE5vxJaBN}YfRnHz+zS}f+m=d41yjxVpxvE3-Eg7X$5+^py%Cs0ej#j5CbTl61U+p% zCyW^DKx4?Pc@r3CEQm@3J{e>Ng+C`CcG({ktp2<l1is>uOt1J_Q}1qi#X9;rj)_YT z2q*{~{pSV5RN&e<sSbFb;ocIw|1f-o+2Bvoidn^6`<03XTd@7}lC)HT`aeEfk$3h! zte>-Y$2q8e%%D3~6Xq!<-P+(1*Hp&L{}B-C*$f+Dd*x;4u=-dMQy&9E*bj64qN)c{ zTh}2fzc`8Hw@Ht&H`Z_PJJYqiqpJ5h_l{4B+16oOrI*#ip)?*a*X-mD{Lb7e>A%5X zkSU7H?*H9`M82cx+jCG^^LmUp80)=X@m<Mn<<T9P%U@j8^Anj2Y{2iJjqEQuDqz7c zP0;Gcf6vjZV({2qpWIvvb(rs_rNE0iEVaEJgefW}%4z=O#mP&&CuFZMBn#TF_Fodp zdPEn|kE$6z@XGKZA*nQnHXkMg;Q+tfm3LQyy!~loS)JWTP*vm(?T-859_|sY_KTt- znaepDj`BHGL$Cgjo(G95wUe0v8)+~}uj5&7yB)fc5v_OtK^Wl%APTYQe!|!}C#srP z3$V6k=`^}8Wo_)?kD_6suhfBHOYMw^`q?HeBMVxs+yP3u@g{73rtxpADTU5d0pLIc zn9h!w4J^`Mvp5jIGAF{AobpaY#<pqoqGS7Z$5qFwR^y_d&6ek>YQ3xa_WqYG%1l^O z%JL&vT(_`2Fe`eMr{%2ptiVtPDwyrl;v>QF15wT7cf||#K39bM7vc_x|K#m#;f{SE z{?(tNDurL7i0z9Q2tYbRZyW*B52hZI%nJ+*&e)FTADZm;%$|e}i`m*KACe1M4fDOZ z!lk?|-o99oZc%Y!=lFzNd;6H3J|%GQI+(?vivm)RtpB(GMYqJ7oD>sh36A63;==@) z9`AQ4e^hb~oEq}V%E}%<y8;qjKHegMJ~Zgv%PB$9#^$nI2~JcCE*nEh3#hyZO}i^8 z#U#1%flkRGYK4D;_uG|{!Vdsyk!rs6_im=09hrl8-3pPYG}9SeA_(s2|GC5|YK7n% zSs6Zs{=#4?$ch|4nj)a_0OS<|r|L<teQOnv3S?obfWB6Q2a-3RN0zKwa3-bSxNket zN%^@bdYz=a@w4}rWi`?(@@HlSBl*__ozEd3NPv7GPn@H^&3gj_1MuxLi;5;1{7{wO z=3)?H>52<{{Oxf?Pa11e(zkPFb4rY;%s!U=?eXt_{A8akPRo~{ezoGb6<Ri{J|^a& zIvN<5&eDJ}0q;fI?FW^pP<ql=m{6uY1@d27@pQ%HU$wfMjva7nu<39*OOnpH;IY(+ zpz`AN63d@?S)~@kG=eU^L1z0iB)-$mn43b-bwfXMnI!y0d(vVTs3iGr+a{QmCoWV! z(brfvudf?ZDtS1xCP2zesq$cm*n@b}+m;_FsA`hM-~RIp1U+W(jH6%!qcMxXpVVCo z&P;moHo!Y>3#kDSV|#~rNnre|B02O|vbw&W6gcw;il+JYg!yJ~XoxlGWT!JPcj;-0 z`o<pR#HDa{cp_A|N$^En_s55Qkr9`1FJN9+f1J!Ez9>;06C*)?0ldX;(ccz+@(roJ z5tR%aEM*lHJeGspK)u9#9Dxcihvg#?0f9Odb-2#=F~gyiocZn_3ZA0NhJuIduZQ(1 zj3(~CzQMjE$CWiP3K{Ala_7P=BdR#~z*uN!<8@in=|~c51#7xg#{KAxy*(9;H7IJ7 zcePIecu@#g(}4Z9dF|%gz{^2RQcbz}CuE^~%lXb!&A0R^ZAoo$=rHJBIW#$Fyj1YW zbc}XU0ic?dxaM08WZ)0*AGm=Q^XX}=IG{^zFc2|aAm@;KSYm{Zh`RAO1VHt}3P*GC z;|)oKu%Y6&jKdPU67HcynDBht<ZV&9u)WK>^ocwgZcH<rGi!pTS3!0bKLJS<k(D~# zcPSV*njIw#7v+SN0u2HOYnAIfBOB0U`{{9B(&!$oxMv(_S<wE+F#z6TSWCn}xAYwW zbOm)t7}%K32R_n`xBk=CbX>7X^xHCg<)pHfa33=?g6#N_oZEPHeTI;Hk&svLZ3OLP zwBO+Wqb<c$itH>Hx_&UfEggZFX9Tt`^ZxMg4~+<eef`lJ&@r`hp?NF@dRD-k8}nQQ zG08$iNrfI-=Tdft5D=`-<>_;Qq}JIcVKVIb)Sb}*`hL%mUL^8V?vAes;~O3zV~aYh zcEJsuV;qRER-!vcb$Y^8rtmG(Tbik}wSavBxNi^@7ycS_BC&mZXPrDftNJ_;q<FAB zzxcZ}ui=Q1q2!iL_cy1=*YTVQoK$2szQ64SH1VW1$I*2BWddb<^JHKD)BoB@BXS}s z)!4N)!V>nsxQ(2#LhDgs@V6=eQB_#s!C)Z$If2hIY|P~Cw{P4yL^N`iQkLC$MYO&e zC4C>B*qV2+e_|YfM&A|(41J(1%H0242MbqO8Iuz@mGGgL0Q}Tn$4JS^o4}}$@L`lJ zymBy7Z`OLQoxDFnDj}F<%yU~QQ~AT2F0&Z`fu{Stlb$pO`ASxl-074$!XiS;QN$$Z z(k&g7W~sF<0Pw+T4*@7cu&+YQfxR&`6J;-R1nMvZd}wS<7}dQT?qfXDNC1nfH$qB4 zWF$B&?;Z*owAtvrc1VCboEMsQ_>T{SW3bfsllDXQrQE59%s)u%pch#_=>lqWWMpBe zkQH#Y0jSkFRj%Q%ptLWE$Vy8iRY<GjMpv3FyK4IRq@*OFA**oA_9Rl?wgj#hmFEhl zn%}5LN(DrnuP^jxU03o7^*%Y&8siNeIdD6;m9|X)K$!%Z(0X&&TFPV73S^Cy=HJNq z#m-~iK?p1!^KK?OpJO}3VGeC{i(o`YL1HvH+*v$_!*XKc0|XZgC^v<w%JNkPt=2?6 zW^m4qI{5;@3KkT3Ul$Z8e`nF5Si2ZC`aE&mWVh?s7J5omK0BFH0clquN{Olf`VU$# zkT#5T*L5%|p!Hn9@CGZ_<ne%1Bff)sG>Bak+)NiR+w*?jrkB0*0oA&ovw;3}mg&`r zQt<o2l;LQe7}b2B*hUGj!`@6vlCPE|f(NLahgC)IpQaW0K`;DqdbFOg`FMus!r3*5 zrZL$=(2K>)>_miR5=nL(jk?d0w--BIJA<CT#t;#?HFG{i?ry{|OCb2Nb*h8vhdXpV zy1$f-IQqHOp?$b|Sr^rbOJ`;?UnxE7ND?YPbwgUsaMWZ%rcsD>EzHU3|G`3lZ%I?x zMHZjJVQa=?pgmsTzLBYcfdz){b82=T>Y`k&LjSujaqMPWqSM*ObL+6ehViU~Hbo&F z7APuVBq(Moo^UZ_E8K7^I2bM~o-Qyx++JfG!FffH0<aitg|M+3pKDpLdIKZ}c+<Qj zws##`-9V^JH92{|K}O+8fK+BDosh`$S~t#5K*uCaw;#A}|7JOoJ81TQ42f68EWbpJ zkb=P7L0<g$|J09CZU61-B})qc&!wWX#vb4<H}HQSEH0=7AG$^Ld3yI7{n@@qf+hy! z&0UIX&EWPS2OY^9VKk(%`0gGRH@EewAZ8Vot+T!IUL)~eJ6Xi)$nAJVPiUtDVePfV z>HbSFETX`pE`YS9d7ro@yp(7jMrP%y*_Cf{H;pZ}j65PZezzw160HzF;R16*TBvvn zpq-#MZww$Z#_(h-F=w&fNDct9CRMkNp<LY^9ogQK4Q3H^f%qWxuOZT+T<y{j;G7e| z`HDawgrS30LacPLt+@9^NdaQi(8z?33EdWdFUBxA>}>14(=_%_PWwfF%UZP7W*5GR zw;-u6_$mr*C+|TDF=LM%YfdY6uRq*w{$lP34w49HIkwL)ieNRBZHWXsBHOYaf8QP< zA_M;5Su!Dd(!M^bO`bBH5fU?D1Kw1G3<F2PHy_%k&pR7PL?VEFVT6X{8T(x(6=$aH z=AjJj*snD<R=u2EadPIR-N9D3V%B%PV^{RHDQiRfJ)^cgI<|LuR7fvg)GxNymZf`Y zwSAq|!m;R3bY-*-I>W+@1PK-Irt)#CUd)I&oV9)=`kA}OC)2pMeOTQBDv+JsrS|r3 z32pChy=8d)cCfcb=!2sBGJQ@)<gHdt<elu1=aJ(nv8-d8*9$RB3w=14!5!vaN8!Zw zhAYz(qif<(_gi33c%`-bW%zrv&&<9zMk*y4b@@qWhXl^~^9e)C$)gx--&?nCvCFgp ztstANnz6C7)Ard!nu!!rqG#3gw4;DNl>bwEytUKwv!-JDiswpD&wTN8S}J}OYY!MX zrI!jdRGn^)@nnqN$J&rtpw?LDZ6*Bsd^ZAYFrg)X5{5DhNKx=TF?p;bBB8^42OxDn zXho0};ADu`7hki2%*!7w5So}VnD23UpL`HM&KLLUGgr(nHn}VSx&U`12$+H|AHkcJ z-k!YUxZa&W&*%hIf1e#}H6O!0Ta~ME;e9hy{dcuo3J5_pH8%bNgcd?4h7ZRroP#GQ zu1b!JgUZ7QPA5PdR$xK*yZ0JjsNe!a1V+i%gUVR98IB>crHA@>7jelj=&(;mCq$Sn z@zuU(PH2rp4!+Wahk(W=Z5gWS7u72jTv<H}5NkAuXfcjK7=yz@*ucnX2#N_T9XTot z9XJpHw7wYtX+R!~gWFvWXE4F>U@W}QNbOSwitzz9MG*6T<?ncEQ`O*!(bcgY>9!TI z=yUes??Ql{oBc&nM@Y>-0jFsoXF`A&_caE3D2b4SHCpLn3gHRy1tQA52%ytqp~ZsY z@WLmrEly&O%fI5C;;N%~guXei&jkltFEwZXo$z=`Srj7!!zU2qKi4N~A>BJ5iLojt z+JaflAPeB@j&AqB+?GpdBIP?7#DMv{{}WoxI2rSl@Zyc|#R+0n>yGBOOrmW*`!oNO z&Fw9<4ORwhq%j%rj-kbqdgL3o=F>z@2CdGjwe|ZemlIli_L~F1toZ`$vtU--f`))? zpR+xm#vecI*pDxP=_!BZS38J=re<nz=E^h}^}fPWf$tIN!84CRoPl7}Q*cRtAXVTm zwtU*=*SBDj2E868P|_jI=aBgktq^H$3!wLwgM4Rw(>i|X@sk8zOCb~G1ZaJ;oCcYH ze4HNa{0^OBKcjE-%U(tfkAS}f0LXw-U7V32NZa98AF;?hQ=FQfqP+c_46$s2Qp#uE z{R-&)t=h8_VrV&6pC|l1a4E*_hUDzju2sjcOd=AJpSBAuVMxaiw8<j72|R|2z1gG) z84fK%;EdS<EwB(;t>9CY0civF_}eK!JyenUu}It8#3>mux{=@1X@)K`qB?N0c0xN9 zG*W*Xb-LFbGMZQ66P?;lIot~Y*o{cy!ajHmo`IlGBj(<_L?r=MC17^<!6EZWt1y!> zq2rS91<fr+?N0QlperN9o^w~P-SQig^ia!l5~FjQ2`9T7o8yETWp|_D^*k4w87T?+ z8WAxHV{N1yq-W|uIGrI&C3y}ykU$wktcY+>#R3#N1>rA86fsg}WarXb{&fjbaX;vK zKys#b3MWOpY@Zj%0)BMVX;`?p3XDfUDhSPN0|n#-ww^W>GSq!<<1uW4bV23@$2KYS zZRT?411hV^Qsy=R4(SxLF;~02;Yws3>h=E4+^r=(PC6w#5}5*LL*NG*Z}M+}GlUuf z>P&a?e;I7h>Qs8ZgShX|cqI4vXC4M(nvyd7n9%`FAFJo%{Z-x!3{$Sq(Wz5lgcnw{ zJs`?sQezUDl9Ce4muv+T=K<1G1<txx>xA>Iv9&V`e(8l($!>F$6%xN`&*)Eja;Dc7 zM}QFQGtpq&l1c9?_^;H9hdAQU*C@xZk3pG-UPGvNi_gi-S?o5iVx-7<&#%lVe$PLr zw|*uJ-p!AYP!VAY4(|A7JAN-=smufS8%WCZP6up#i^dOX#7V*vf${zZ9)9`DWo7AQ zETHy)><#PWBiNCEChpuSPVAZ<XzOY1=MO=A#hzYpUV>u6o$ge2eRLjl9?5~boHK0~ z{H^Igj0*~#l=2P6jD9y)ntz`a8=a>j#pHs{SGo#jws{CiLVZ<oe$ioJb-+K~g$a)r zut}P&h>d+*FhR`YbNn8j4&kb72I+wz#P)$?@#>#Z5}{)fS`Zr$38Rc4G|tHX1c+(| z&RV4HLI#}4XUDvw9*)QFL0p>tl{NwX;gUACaggZWUf}}g<tn&qm<S==8_hI_gHvUC za<U$ANP@e;vSOfrdVp{C21A4Zh&9N;OHib#kZeG260w9tt@=7NR3b(*sHu=M5r(S* z2NyuR5<OZej9DB5XK}uMJ!WO?X<JWuHXd+PYvy29jSxY4I?qmz%)8U^;A|UZXEA6b zg|jfSJZd8&Nx|;yD3fpn9otQHw{>A*7$7gSk%&XrN7dodm#W=i3$*jP$cqE@a1Vhu z{}7#=+7B2I#Y|@m0RNkyr(?=9dVti&1H7>4pI)_W0W<Cg5Rky)4VHuFG6^00;LjUk z&LQEus+MK}woP$JLTM1Th}K=)O;I;C3&D3k{fmzq2^Y|>4#$BAer}<G14#-bpEH`_ z5NPz|#f@XB2Ior4$nZe#@0ib-*BW%jd|lqD1&3g_xAl=@>!{Zg*d{X^0UcOYqZQGA z?Wy;}J-PlJHqj$$GXQp60t#rZrval2q_RSTANWbGQ+OcjD8i`@5TrU<a<R}V7<N)Z zK+?JsDPbYT8<`LqG;A{<!9p0nkfTch-e(AVy{G$&8VC3HWRhUZAZHhW$_nTAOhT5o zwN=S!UHCit4qz{gi;I&3+8pGQR3P#l*T+7<aZpSBIm^u^Qd@~d{I*8!Mk|=2n+v2; zr=)mwNQ6dWpK7|CNu^cfzq1nJVKS{g3wxiWg1a>s79-di3gJwm80dcl15G^48F*L_ z_3;`vcNo%}0=r45Kt5atyew?-RFoowZLgaf<p7G;L7xk>^E%9lSmAD)Uqd=HziJiD z7Qx9^Xm2fN@(o}<&g0=dgZ2nmZM1*wS{GmqvciqQ>YXX*+@b;ARK+CGcraE?!?`CQ z*HHj@(N_S+^gIKP40ySHVHa+KBjwje=?e+JyIC%lCsb!^b`OtW$WtTGyIa&oKnd@G zne{a~>wRwM#kC5_tuH2D(AfO+9f-L57>o=NtwvfXGjgz$z(F9;7LAScLcs?^t6@tN zyCgVTK*_!WAfxC@2iOhXA{KR!=NxudwDjB;nc*V;+FG9idk|3S#>bQ4>?ovJxd3Jz z%vC?Z^$h2<1S?sA^ug;vO8YyK&KN8Q(7B2!ck6|Tx7ES6c?u6jV&scv8xfz@Fmg$5 z3Ig`wW@}+!2AOP{w@@3_(93$fKOO1`r>jBV#gkkCNR($M>pqSkwvP$}1q2Q}&bZqa z<S<#d6Hx3U2X+7)lF%AL+(<sBp8dJHZwno|iBrKf!)ww;fwa7;C0|D25AsL2*jzNT zagQpJ`GsowO7(v_`x2;}*LUr=Qpu1V8KMCVM72|)G{`oE28yUinkhq*G-xnoYSxU9 zN<?X-(nQisY0`x1O`2%b=(}#5bN=UB|8v&2zP;97dvCkm;rTt!{oMC;U)ObSZtGY3 zAz(RsA8TN9a{FayxcxH~hUGo>-c+!=1Q!2OP^j<~O%+t0Xx`dzFvI=|Y#&bdHtv8r zi;J84byJgab;kcC&mz-`Mc%JXtBT3*1?_-fP=q@xqxHUViEiBWw?+vbS!uV1Z$OPx zCLzM}-5#j6mEZRkd~s~h_+9AMiNO&q@ojt#h<2Cn7v4Q;)Vlzr&U;(0Es{UAlpwFX zygc|p{55CJ94O{Z>m^X?El?TzB8=zv5;T=!Z1bx4^}pp?d%(6XNySghg&!T5BmqI* zmoMLex|@QK(%{?8gFOwfviKJN<5_@jbVy9W&8wntW=rbg+u>`hax&fh<rJ>lPo}4g zp%4TgCK0RydLQ5h)5DhJYq|g20-GD&v$8@t3s9o^Xo&;bxbm_p7cz^B5N<%^xf854 zu|O_#y3hvR(bxO}IgC!y$1=!5z^*5a3z$qr2)R|WSfL3EY?^bxu&}UjMW$O<jb!%N z*VUJ1@#V+sB_Du#o*JU$AYr_<U1VN^soXmo^QtIvRDyDv%tV%oS1?I%28QpY?V`|@ z=H{^&;aNRD-4kwhH65Vu;MeMF2o!+zvgP_7?}_4I07W_)yc%)gs%pi=hwAFJ^$uuR zBa)PWHpT$#Czvxiykxa*yb&&!G<Rw7w=&^h<dZx1fbRGTMKTW05x@c@stFe59=^Ww z>yNcOJkLWAPH;1P{`z$%<!w!w>1KhKB%!1G(vbc)TGVRE2mdD+D<DV`RAA9Wxq<%v z&y5NKJ4Dk;O4SU1YZi<hMiNs8SBeD|B}Js;dLkv|!+nqX;kYHXI<~bI=SUW8eoX`t zoE0<6bZesWL)23xx&Pk+TQP4||Hx&V^9?DYSSEE;VY;;^b9@E+e)H}9h%Gkhz=>rD z>Q)aku8(fcbUgXU;n#O8ngv$7Q}@%tB&B^<BO;a{vo%Ga>W1?iS`;!a!`66otRmhV z&Dm#{cmSWWf$@;a)DIJ!rLCt<Nh1}XHf@^eQ6bTvYkb@G5tAt+)+@G}S9&DF(XQ}C zUJJInDi}Cql}9od5Oj{#)D|btNB=EmESrT5HSQN$?We0=Ix*6ZH!%IUQ>^PNdtKlc zM1nOCmC5^o)Sytb@-j}IJW1V6BcsYWZ8*t*1B-#M#pxxBC1myQyxN(PvU1Ly#<F); zd`>n#%L6b*>UY1vp>JUOwYIkheO0dMXK$8#+;{<+a=$B#M0}Gl6{lQ~ec_I$UhQ9v zY*cp?i8i0K5d#&*OL!cS3mk|$Wl3q(;qT{aw`de5fXMDFlgc5T>~-0+b(zTW=fz+q zQ?DJHkp#rJtl!3He5gu28tt}b^?z35ryiOCeFTUvsN!1ZKlzvVZV={F!BAuT;`i=* zxHn_ue#jX6(yzL_Igz7>;tpdsXw=*C-n^WPol0ghXrsZ*M;cLb#zHCU0qq&lZg>pa zi;|)~TH8!bjNY_16sh`yUjLUV*FAM>Yl#Y_fm!6}1vT{zv(C+ME9%zYZjf#1<P|&* zO^}jL!XAj-5(a74ai>Ipq#CPc;g4GyzuHj|*?|<r9QrVy-g9kiKF!A?=EwKE9vb{6 zQn#@#iNyrk85ki|B-K-P19@gl-539bDhbFvgusN3ii#qE9jZ#T@6Ams_(Kn0J7BWQ zD5y%i!F$OstF5IAM)XRbYjm7}f8>>G*P>8{BgOSagfVzr5?tZ(ow+q)%-jk&i1;)9 z4eUyA`hnPr+%9lw*mu|QpxA+z$Qj%^q=_ys-x0MK7mNjVAEaFUEOtf?ede5YM)M&F z^Ku@JOgY^eE`229sPXQ|P1gmL!;wDX@_UUNM`!kw^<>rtfZkj93+}Rf#FdFlE^BhQ z0itg&5Z&PkBz`zs2{)4w7MwhCX3Sqjfwkm{z;Zt~BX$DP9(8pm>11Tu-1;~i*1)^! z;AL`ZgbqbJ+qKxiY1qwhAnX-$OT|cKC+rQ3S3@p=$9O|RF8~DrgDe4&y4OFqi;XVD z)~z7dHx!!^u|k(s$2^1nqj6pG+A!MGA>{b-1E1yB-<gv&G--hEvH?P_-*2FLb?Mt+ z*(->nr3fkDMZ*0q+O!t-XFJr@XZid42U|ho8I4i|`mmS&WxE7J_E+A#A8Pz!n(OTD zj4u88mc0#1WzI7FPO>gyPoY<qKQm7aL{x3{E@x+F)>DW!tM*UzdFi6+23s{$I3h$z zDk0uRR;%Flb2l;ZF9vLlENKky+>gdkm&i87)m?XMM$Hc~xMeMPtfrwrA4sx<b69{> zQL+}H@Dem@Z|`m{Du%(DXq5%muBm|iHO~|j6=YM}_a0!K*rNSyzrdO0;y>6LVvJ=6 zoNnE^3DhC#_f-et1{&CZgngtDM;$;CPzJ1s{_-Gz2Qh^$5qSYxTvmJnyq;OfuX})| zw5Jvto@Wy|%kIF+!6rDYQNRqUfJSuGH?J-Cf-CV&KqmzCS`Zf@sU`vH@WIuI_}q|D z-$7!@uV6b3SOR=SVd;?-6<#qHKpHeCb0O@-n%QP3vY$)MI`bRXGxtfbf!Dzu@RpEu zdcpmqzv*1xvqPUw$HW-h4Rp+hZo23$%hFunJ7=J0BQ7xVB({%mnIZ-Yn8XK)k>PkF zATY=7>|`-2e^a)?gz6K7m~Svc*o_W^*ZZ)DjdTb%qK+@4oF1GeiG&1b`e1<~57yAy zWAB%)T)9$f+TIzAc7U$%A@D-J#bzY8*v7W_ok<EdkBn#uYwpB9EJk?_t*>X3^$F4| zRJZMbFJi%hRcMk#;-zk9xp<NP#Amoknu6uUuXuVEmI{X|X3s_>(d+pI8AQs4z?RMm zBsQC%iI1Btvv?NAEso{Ssju^5q`9?v|6x%W(X86{Q2OMQ(gTibb0llVl<)!zv4zQU z4}gl@*x*r*y=)t0+gWZjwxOqkPM;S_Czii>dAE#oWXPUrFe<+=mA<+5x0bZ$EtXTM z1cw<_Wf4}C7-QJ^@Tr)G&eQgF%L2@(V~<18+uKW->kVtY0F`UwM$1S4mI0)J1i12@ z73xL{1-V~@K|}<y!b>^X((J$Qn&}lN`EK621!P7VNr5r;20heqt0rG`xUYVkll$L1 zp`awGu=(<EnDA{-e%RduR&>Lfhf~Pa4;!(`_A%6|V2;m&4U3*J(<KZSt-z+b&cyC7 z4^hl*#zz?Z_HB3gtfC<l(vhzHTx1Qz$<0sU2-Wk7)potF6(YVSrQU>_%L#~CVCNG< zDp=TWDkkakcma&Rx7}CpU*aArTx5}Ix=)P1UtL#iL2=f<@f8*fWb4@?0zvbIg!<k* z+$wV%xPaWT_m{Er_5hlvv=zF;9Z-$v+&He2Ivee!MTyxuO@G`nQZ;lP4Pblqi9ds@ z4Ym-ZK}8diDo9Euu~sYhKG^+m1WvUz{)hZr-|%#_q302F=gv}+Ng;Q`8r=sp85ivM z%f}`3cgLw=mNEAXNX_1JP<<B2@63A;!5<GLc*6Z?`zRm*vR)C`(Ds0td;ttuJ+R;x zWuCT9n$5>o_7V*eB~qNDvEZ4R>2j$d0oosunz6$AwO<0U7(QcZ2{z|FaknTu9F>6k z^dEP~-B<TYFnOM(^nZgdcJ}130eE(|I}dgGEb@dqYLN!^=!aIAuM)u+7G`Z*2G;2? zLS=t@OGJlXE%0Cgl><Ch|K<2RJ&VcN1m(~@gb$c%M&cjheh6TrAulUfH7h{DLI+1^ z`plUxE#;VFjNP`El%0Nn<swmwxU(N!);Fl_D1ikL{S3t2_N(-eF(i}(Grr+eNa!V9 zK(WQc$Hz61&awN|VwZt$q9P*SY79=UF)QYUFED}8a2Se(!BU*2XqVc0T(1w3<QR?F zXB9V2d}9MoPGs4#yd;HJAYQ^p1)hT*@wiT<UHZQj08StEFSi~)yM%FzO>v;LTS#2* zp2#mofWRZ+CWN8h&)<GAiOCWR1OnU7uecbyKJJHB3fRGRRaK9gsvY5_PCtm+!f}$h z)&pIjBcq?%yR%c5TxB#FosAljyV>LiLx-K2LFI4vCSgCjF%uc5P=&zv)ej&4uZ0UO zxW;fLlaDBf??%ADeU54NU%q}KOYQGDk>cgAZUpNburXynyI>@5@G{p2mgs=u&{)U9 zv_#2@`#$!R`iOv(gYGHh1ET10MTtg67MbRwzCn79^l}cwHWwh!s2QKH8rF1%=fHYD za*}uuH8IpLe^H*tR{BEDhp8Z5{tW-YpE;#fnKOr_65|p!RBE72MH*@9594D&m{KD1 zq4vQZVam|y@0}r1J&Bq-n_C}vFZ`{YgRS-=|61Ytj8m(VH{RAjgly=wZZ$5u_VLK1 z;XJW>C$^%N%eRz?`EV&YFAROF?lD)gN793q8<!i)`8>+EJHcav>+oIFOFrEZZF_v0 z&wK*<*pr_c*5AnMHrl?BEO5UlJ&_e&=c6_<m2z&Q#}=LClyX{uZ%JP7*6gH;hrVM1 z&33pqD%{fwD69{BS+M{&51q&fDAJx~W)2{=Cwv={vGxa2RuD*D=3NPri$!VZCMYDy z>=FDt^>8^ZIFe3{<otXJeC9-RR_7bHor1<ExpPvm*CMdWcIa4xmQe@B3ss36=kt<u z>Rja-U(8yBc2;>zHi<7U^NQfL(eC&K>;qoQ2Y#>%Ym0ow6xSnk?EV0x2OtOGfjv=f z?@mJ_E$*B_P-Kz-144t~1hD2(dU|@h@_^=%rCa~9bejaJvugHSQ0eZ%UVYsVoglai z=;f08>8@ajBUn&;#)hPUTIi2pBSFU=*?7kTX41uS*0jh`{+2=8A9{*~`2XKw7D9Sg z`lj{oZ>KiB{(aq(G*WVjwg+#gDJ<pi#Mpf{v#BP-;SPIjtD4$$Bo`XTzO)#T;}4S1 zsuwPfuty5JI|hkGsFITm88N`E3v!Az;QBl=gN4yi7_Hps{;=?{>n2#H9-oq!!RQI` zzq1<10e<W$9OgiS2FGbBQBzYx472kF&uu|mKPR$_H8&FkmFsjD!>NTR-0vUcsY0Ls z1v>|d)|AZu1w6W%gjAFX$NFgC(7{%e{U8Zgmm1BGZtdpEAX_`QUdY2=28LQp-03EW zPTXZeVejt0$2T9<2<G%KM`{Mw*ChK+?fm?FH8nMqU<c-^HK1%aNU?l(4gu*M3|<)I zSc)h18X+|>2&%6RPfkH26vTTI43#%e>M}wVI@u2aV9e?-UX5M@n}_qz*QeC;U3=_? z&LJ|>96^iV)Wg}&pS^`#5-0f;5~c%b)_mBU$(gtRt^A5-MeNVvRQ`=_{O9xTOk7Za z5voG<gmZf9iPow{ua_>|Y~SANXnoJ)6oT^iwzo1OzxWxMTb$CuV565n3pt7oXHWJY z2uCy`A07x2h|&=JN$S-T(HF8SL--Jrq#k}+z(9+vM+#WbQ{UZ{tj{myB;Pqbz0fFi z<XFvZ{fMeVxDkp*Y|$*aURPJg+ci>}fbj)^hc-fm3q?x|I+?EUeo6r?9BvfhaKJJ% zaG#m+-=xFOUE5_2TTh(B{kg-6t${VlBjcyQyoIQSVlyR?*d&-W@Fqt6hu==m*RSE- zW9SJkLW+wA%(#ODod8T+C+`4kc6XkF&*;22;-?IIXPq!wY`$}>wqq8rmb0R8;VYVe zFxlTY{a<K4!B(I^;ShfQ^vORyJ}@v)^oQ&tfHw=#+6vBIV9Jz}2BB7Uc_LpZ+m-$E zc3juEVA(ZN@1l+j=5pQN73?1Pjs*n;7t!T3cyiIps|p{&bcrfss_*BNFPaTNKA>lS zL9oBTA<0Bsx7QCTt`q;+vet+@kzJa_+tXjQGo|ZoeHJ#L-iithmdxkR-z8gUk!?*1 ztbEUWUk(B>(PvaEEsef^E`UpLG)!!$Ee&84%l#4;Dme_7t#2v)q~-s!@m6npe%Z_& z!tUdqL8|<_r+wk53vJqJF#x-7(_7XhY+uw+O(XDK0Uk^boKW>k+yXO1Zpq1jfXT`h zICnM(5}|<n!tvhPxE23+@L1Xz6tjh@SaC(JVNLdw^;q$C$;u$NLmaa}5-b7XSPixp zJ8Q@%lpQYDp|^M$F=bgb6(7QK4zdq3j;np>_GdvN2D9L<XJl*1S!@SWoDsFOnu_TY z922(tJ+(cA#LsPH<0yU>bl3KKacA1y_tIDyvOAZN0Df4Ra#BuOsE%=g<!E<{w4Z%i z-~OhY&+nh<{A#rijrk<?nmrC)?<?W+yT7)19Aazj-QtWJvlKC^f}cf&Q<HJgqc`_# zTuW=`ry|T#0HxVdLF@^LOj1o5H@+(GoQ<yY=L1eB7ACfF6Yk)Al1E}<B>$|)@xhfL zN!|24FW#H~fNTLY1kz$$_Z)-$2u~sS=BJV00ZqO_RyLjg?7AZ?KDCyQ3{Q*D1FmW; zva%{(7<~ypY%>e4wN0>FirVIV4~Rn6SoNbtf@_RUtr7Wqpie&sIbW4VE4eHaD|+O< z<s3q!-r~OtT^Wd!tePx$>O>GvbEd{h`kkTTLydPK{ZdZBuK^MMuG{;XtR^d4akpab z236a)Rke}#SVt*=7%!+=2dh5N`|^yW6JZ)z9JK5C+rHjFRc(W=5J4-hXk0l*&{rl4 zBp#ckk6CW)EhsD`#j3Q)>sd=X`I*2U^=C866rE*-e_LpUaf_fH6FEMEQSgvJ8WuJV z*hX>Zf}G_9_W&D5$BN3+0z7JDIsYMrV|TzBuy<esLd|O6Wym+mfF=JxL7c2ag|dJ5 zU7lQi=H*m7pENk~;$Gf?tAzR{L91ikO(Upx1WQLdzf_Gmzju_ycnsw5m(e5x2bu;4 zkmVY>Pd9I+PHEz!<-C*nXm46A^$`tQR82`Y9jweXU5u?}1eNYoQ!@hgOz<LNu(9-k ziUPPxEFbl%VK6-qtjK7GXM!cz3f>*U5PU{Y$~%1J#f%JUs^F?2Evo5J7yL?q;z~v- z3&i*V8sp6+|6yNlrYGb!@qIfSFiIity#!q~h^2SnfUQ?^o4$ykK3gao{C7Lv+781u zjB;imI-DN(280j7DapYrT@)|>Ja8MsPPG80yZYsV;Pt}GNCAcw-#=I-Jay>OrPe2= z+tAPaH_IC~4(y_%I;q|-8*T%;q}~9CEzzhmxf1~7ZL!_x&0sQqX#&oB{yaSy9(K55 zFtXFn)dK*t35X)WwKP}&aELA#6w9?e#2KZ_kT;MCc93$w3ccaMZ}tafgaSpXL=uTu z^%CwhlC?AEue#BhLHnYwlJ^w%jQAy>J9xa*V!h(+-43s8s^Qo_;^NFL*y;{_!t#JE zdp2?Zwo9uxGSq{*gGdVQKiwE&>Y=#^hby)3Uw-)krImk{28)qOKdv*fu?P4KTUAzk zsdoSEf<>Jt5b4#VqSkthz5!j5P&>H5;qmUzi^O^#h~~*vl9G}tmw7QxDs0%aBI}Qp zJxiZ--(r^1D44N3bw-C+0@fs9W-j78KFI1{8x!K2((|C|?0lsA_zdig>r<^3BJ(Wm zl%h8kyvLB9gS}19Pe@1zGD7h~8FH}G01&VuHd72=(&;j`6%P_v6Ttq|kYbQV8MrBA zvhbayWvI2BChaTD=?=lRdc-<!VBV{Kz4xql(~ShbL-}O~Q$xx?;3s&ystEzbUrAlq z<2m{tv2=zc6X5VbgCuw-=tIREohH3-44zv~U*lqlO$<qCA^0V#op6fV@@knJYv?s_ zo}`h#VIc}}Ezl4TG<C{<ucS&8Lpp?B&Js;&H2)-V!U%gtWulD$cC!}^rOR!y;jHEC zWWx2@R|r02j^Jmtp&1edk=rjEhNmbNZ#0vf>fnI(7HB^@KT#iHLc1mD2KKBc;rQr^ zzJp`N@j-gV60vw+xWSfmO5qLESil~jugWT>#X)vES;+L|kZB?_lXV&Hw(ldc8RSp! z9adiAOmlexT-uAoKmL36xCB?=V?y#lc_@Buh`4mQP18w=k%ejvy?=910^cVbj>LM< zT9Z2e*e`*%4T=Wq-Z=U^y*D~yJm&lx3&nS#_a?B!<W_eE{gLG42z-|1&vik}g*1BQ zzK6WXKhqAb{;%X~>nJI$05I&quCnfq{`K-fczNnu{h>;`CUsFu6}o$395swlhze+E zf6kNpFYIT@TCy_5qbdhi!oNF%UT9ND$2P`lbdvFzb|9fG5RDuE&#dQ&c)DYsAzlh* zr|L=L@D0^Ot2R}P#_7z6S6axx>i!W<Yrx@#W%_UFG$<SE@6_Y4*I<#ln}LSQqHq^< z{&Ls4Cz;;#L;wiEkd#xa9TpN~uLVO>Q#^9@NEHFK#j_<0v*h6)M*buPlc<l$A*H_Q zI6dHl!WEBMriDUBkFD5&eQN=Oe8PnAHf(SI%yQ!IMQ<Gsfd+xa-0a<Q0?o|xFe<?M zz<O08HWRn_@_?gGw!bC=ZIQu^Nk7jL*s;#jbp60ACv|mo?h$;;Eh5j+0|hOnJ6nsz zXalYqE>$2Pw$EsRmlv7Y`<`3pc^^23EwZm!+w160^+cn-Um?GJnj!`}5?ZYP_vvD# z9su<X-A2tY?qDaHh+)bur!z(XU2-5~fDV6qwsNUJx05E=9&*ji%|%l!s5sUQud}Eh zh{uGgf6(lpsZvNeJ7|c*>)T8vgUD(SWppMY`Y}%6^Q$ZMjjcD2ROG~gl=l|i%&1@C z2elDU+^?T@xm}IqZl|xHGR=-bX@!zPX^Bg?B0$!GFHycRqXXS|IRY=KeM(0a_?WkH zsj{_`t~nvgDAGzV8BUgnoOZzu*T6?vk8s_R0{A)*h{RD)MvgWQyqc#K3R8^ZW58IF z;zATEZg&iVQ5OMD^O1}*-|w~mgqX;>*QWgGklxnq+apNw=nPC*10}v3J11uZln@&5 zt1b34?3jhGhz%S#>GC1*$oV(~ix_g=RZU2ad|c6qyJ_88>$<mS(#cdI3aX>ExK0^u z=ooT5R!y+Gq+Ib3=PM>m)*>989HA<5*F4sgkqW`L7^m|sS)7~W&S(WzPB@(&U}{ut zo}+B)j0{Bs%{JK=Lp265D2T(bJHpCdl<B1@txiUX_UjWLLyKwMP`Rl#!!j1MlyFjp zbpJho%WW;%u`8=x=<7m%f7lasu#+YtR}8@CaI)mPb*OZ0x05a|uC?HT6y5OiGkU&Y z(IU$kvuBqDXERf-yG{bm)A{G0b8+yph@mF^HJ4Dux`CTi>+7IFCy{`L;*p04vYuQw zdYp9Wd(Y;Vf7}1TQZ5{xM#lPR)Ta%ujTHd(?maLg9n6HPns7>kELsO_bM<Xe?@VPx zm199htV==vzPtN`PtuSc-nI(-NR2s4-#p@g0n{&M9Gg+6iTEJ4Y#9soX~|m1<Z+zO zzq3@|GX>ZoYoysFYm^yLUth*=QDL_MH6ZQ@z~!cn>+RUF<>2M~rS7RPF127Q+{cpA z7W*2;hb$J%otuwy&Zpvf=Cr+RVg0~OvR{Vc7@?-nQf}^lIgFfkRNP)S5Rda`iF&F% z6+Jenjk__xBr_u->U4MACIshE#EUA4bfmS|Jd%Zhe6u*h<&J6_-JVxB70;l-CwTHN z(Z)&6HgU8ms;TMs{P+GLQCN(kVzlTRsI^a9({aDu4dseXm>ZJ0GvEz>90M8x1^28k ztn=REvz2c2FvO4(03cfc{!kKuc0}2gCl-n>IWR*PoA)omn)>=Cev=BjuOCuZlf@-k z6Y`3Rt?*kUh8dhIZ0~i|m*2J^ms}}eirrJ7fh`QRSPMIoh~ll7TY|)TMW$|yIv>)J z@cB)}LDpw=8RYWqH+b#X>B{MMZlPNpcs7b5@w+U?&$pmbBuC@=nw-Y`+a31klVRg) zAP9|)btN3FeIh_X2dQ>R+7wd+4-I!bdBM}DwPf?M&wv)%$Z{54g!-D6o2C`XG7W%a z(T0tMdxc)}?^nfyfqVDvok8iZmtx7o!oouE7chPc19!Us97H^QsB3-D28G2;E)xH` z^pir4y^S!;WwOU9SG~{PCTd|h?X4yZ9d~^9tt0kCwQUcms##NGU0L5>FDZJmkT*?b zg$MAyHDC~woP3A#^C{Ppjqq(yLd~_j?EI7~ih-5ltjU0@!@w3TB!3++Od#er9k!R} zL_8%;+;#wT;(AG@x{iO>CUz*4ff|ZVlkBOFs*GmmE=ioCe6$Go));U3^Gi?{m4NvF z5`BE&d-nmxgKqHGym^7gC(CAPyTq`6w>WYlhDTMAChDM8g6%TS_5Ar4F^Wl0P%zjE z2MYQ8IRNpPD`ld0SjP3oDcHvjS@$`fP99QVK~f(b`cE9b-PO3@iDGO5cqkHx&>ari zqbDCrN{3$9#fulsP9D2>Fc;4e<JdqIF>&f?Yol(nJSY-rL+e-6kDvSs;LjX+`~;pa z^@$Oo$R)VD+xE%+yZ^9*YXYbaWzWjP*{<9}YVmsz#js>ZsROLnSOL1n74oX6GpF#+ zZC?SZp5#w9+}0OyM$PSh#b2(EKLL1Xx0z#H!KtgND{_)L^ps(OjjB|nJ{7>y!cZaA zVWrg`$dr-U?J=rR3&%mnj3HEN6M@|yfRqrQ-{%Qa9-9um*3b<OtB@su8X`kDEC_8? z;NMvHI`r239t?y*YM96H2^3(O4AgO|qU{92Yuo>9vXY}fY!P(T@an<c?rLLY5Afm& zqdZ9$A6IbQ0j3gv@Oi)=^A(lRws}b2`Q$8jYr0*qHmOKP02cyb?h~#uCi3iQoSZ0A zH(@l{pa0KFq<ZXEap}D4VagS8zs+3U^bf5^&5V}rE(B}aeqqxkF1Le1LKadJ1JG#) z{8*Gm8twMRAMgj9$ZXsGW`3`m=G4>_`em{XU-#0?LA)I*COji4g~pP`PtAQg3Std& z%iDyJyUM(PVRQ_q;F;X_NQ|nGMp`)~AS%W&H>^@~VrYns->yP>qmG1p-CE1Hx$mLM zt-_1i-YHZlylnxH+&v7?q}j2zcRa<HE>*2q<!zDbJamlizZ+~XO2G)2933Bzz@#u1 z9V8waFw3a$6zt%~V?w7^9S>&x=#L+v=nR@=0N>;ul<#O7ALwrGNI?kJK`yC*4J60T z&K?G@U%z=xrSmIYbj|Ty%4(E6I08Fc*61UR;t~IgjP_7m{{IedwZYKR%WW5c5o12b z_L44CpaTe8F)l4omb^qEGE+pLD;;Q^L|4>fs^1>#rzX8<LOc#4KAu&r6sC1)85=K0 zK_88Sha&tuuthT~_g_#R{`4K7Z*=DTqmR$VI2MHdItMxoaR%v6+EbtBAmuSZpR=gg z*~O({-v}7*SG>Hu4vk#6cySF*MKef~NQSv`(aSDf5$S52>WXeGfRR}A)@US{%Gt4x z?d@j!BlHO0gZKb;xV)&PO$N>~J0^P4TPm?})rXECI0v`3F750aasN4fIAi1m?p!jP z_W)#=h$m3u!p!jnCP@7Ki1B`OL&zYW;B*|flZCN<=P*}hdLl%UG^=nH$L8>MnvH7x zb<Y@izS8Z8MJYgwFX?d;x|}dY`8)CK_-O|A_CFwbqcQpyYy*#g2SPy{Jb^Xvd2~HH z>_9F24%gVirlzLeF;t){(dx;7hwiPOha)JY=n_C^)>qz+jgk-BsipO)7$izQzAzJ{ zegLlTR+RLV@~1!QIcE$`YbS>XlsV+Z@aeb~Fi%vaWSvO{F<!NQkDg~$75@fJCE=vV z{80|dRoh}a{T>7uyl!ARk>Qdam%-%8>$gyDKG@ha*#T!Pn1LpuC)kAF&`U~&$0jKk z-F|%H#2pcQ{A{TvAd+^he0kDXBt{6prbfW94{b>;_;yh<Vr-WdLTP~amL{5O>57&p zk#5k_CUzv;>iWK^FwKB8kYhS>ygeXWVA>wW@6l1tn#9FNfgdPN5gsiugan!|wVl;x zTgh&S-mAy`mbxlq!AV>ohQTA^>#zFyTI65g0tN9RPV9FCqDtJ(rcqjWhr!;a2$a8= zN=3Z0Fa<mFatD2F3P~ttV*rjHoyrdk$8b|$$7UxlaKc!nu~ew1I=-A>9+lKj$u*UU zR?J9;74=f9>{yyN1U<AYLKm<Q0mGcOvojdcxdS63oJJ6KhpNZyEtv$+K=$B>OWj}f zpU7C0^MkN@9RM8aUO{wl)KjhT0Evsjz=uGZEf(P{-U(A#Ezs@Agf8qM=K39uL7qi* z2mW=#1IN+xqb@u3bM}gfh!7VBQc*bfc)uLq4WK#$a|l4}K9FR3<z-h$mKMt$-ZLOq zL}NwBO$Q-PU(ya44e@4?tPq7ed<X%`ccI}K7IgWD(}q8%)&bgQ%RqwJI*KsSiWz#y z0>}{@q}pgi1F}RzjFiC=VRr^}3xeJU2z<K8<D!A#;j|1J8gFoY(VBBOB$anIu|Z?| zl6c^-Ke9fnv;fbJ^$waHjX>HugN5*Rv?8FF?pVXlFCb6`?4$sx3eD+2;v0+oGL3_y zADi9|Ys)(nwy_m!N_;c)kBDIpk1#*{Wqz1uOu?@RBmgE7vHRH1fR4W7e42jIYuksO z1${1FNqxDh2z#{Hke+Zdawht(Rm<>A>i<CH^|)pPZ-Xy<e6&43ob=GM7o&qb2diiX z2O2*8Oc#v)Yo_s)82N-3!zZ|lxtb&hL8&I!*8`al5lK))fO6D)EDR^a2I-z4Yz#p* zGj;q4)1k%z8pg3>G9VqeJ8K1)irWR9rm0P?KRqzV58Tmcy;{(2iLSbFa&R6IzR_F6 zv=ZbK+!T}0j3@@?72li*P!s*&x@R`Rq%Oga5Pce3pfcfbj%k#oM!4M5?pz53B>dZ5 z7*z(3%$qxxR*p!WbxCG3InJ}-*<laEJ9Iv*$DVe1{R4>|A_Rj-%IlhRkOR>?O1yr+ zp^Z-cB4p=|Jefx4VqE58{5ArZ!l{o8;<I21=^~IcQuO<<w)B!<M&^Nm0B?X#p998h zndqAm`2oXbi?M-L0^>jd58=~!v_-FgJOVpPlZNweTEQgWg&sQYpnHggVNWV*YZt10 z@UuthZrW$Yv|1KLQUT&GqvuLqv!MUbJgx7og$8a%lC~l*L;P(8{q7U0bYga*rA|Y6 zO9mbH?%n%v1YpZeY-%`A%1kPY%AzJ!gB~8i)m<JTCpcBuDG>NYqIy@%y4hJc^>}z& zkR|FoHo@ys!xDrswAt#?j?)-KGeAGY2S&sqD`IL9J{p08RwQPXT_$Y<G6rg|VzkL6 z7!Jm2-rBj&n<TC@d4)mfK5EH8$q#e5xy^}i07#A+gVe&8Kmv+>$t@g;`51Zuc<=`z zuC=pDp|4Q-M13=_=!kPqYAp;5_)&{8kiO!^SxdwXT%^Ay@u6Zu`gj1=`PE2HS}G8V zN0Ic_!EQ6*#jt*Ok1`n4m|1WQPiYMpHYm<sA)l|uR|9&PZ~3e1ML1SmMbJXB%lh&6 zsS*jyiY$rjGvo}dPq=s;xpNMHNU~POJ11gWlHmW^wV6P6*J)fdMD#(*VAJzqEsb>r z7*URZPP}sB31ccDKinPgk7zwD{i<A?DVmIo>-|8@QVVu)(E!gdtf%xt&TYocykp~E zD3vO}Mgp|FKWPMrPd-itNWA;PyP8X@bZHYJP-(2ouY_D<ms=`M?|0;J(a&;_&=N_k zsQ<J>cO4~^toJu4pH09M)Z3N<z&P2o>l(H*#bo3d1*S6fS({pW#q|s-1MXL+!#b6f zI5?u?@u=LPE2)gvlU>^R+<+#A3A%pUw6v4+puYPG%!48B1cTm6?qqXgzaeoB6;H>T zNgmv0%PD1$Rw}dhR?SZ#_lc)X4s5vh*OWO|h0k~q^3F1)P0D_6^Bbh3`ctl0uaZdF z@l?M(2)gYMWZ%P14z|L;FBtRid%#$ERnrE#>cIlr2C(8x*W>hn_Wgu^Qke^G=dSgW z80UP8{6X=lnAl}S&ErUFLGNIq74TH?qVEeJP_uLKS^T4W)S9ChyD!}TD4g0-SEjQT zsENkV6d)7*m|X3WYfRK)qZp<T9*4LN-o8I?ErK?e(SUtDj?xu0`LSPPfjsG!gxclw zpk0Iq6>HGZq|jYya55wqr=uU6l3Hpdb7C?@#l-Y$W}@h5I3_TB-7xHY4w7Wih~+sA z=n*rhg@pSdz06^=_S+M;-L$%Xrf7TKFDNNlUFQ5)`Q>{w<;!FqpRnr9A4IjiqG0WI zS4q>oyLbClCoCKAX-E4=bV*s7If67Jc*uKXl|fp(8Q+D`mXNhr7t(9##(E~3jMc^? zSzrW<@-H_pW?R{xfPzmNhX7v)4e@!Bqwj!p6Xm+{pxnu+yPug%bFRAhqi^r900Z6m z)NqE{+Q>+htwt^%%><7qqD}E|w7&N?Qw438jH2+8pw$+m1Hk2uf8DMRPv9%pubTo6 zBU5dVauJAyKz4hDG{xgS&!fSy0KtKlUB^77McDQ5A%(@nF2Q!QZvNi(XeAFv&M`D= z9Mx_){?w+SD7kA6&*Tfz-w=ezMy3@YX67OMp(wsoZHpi*A|o?bZLxABS|HW7Oqovh zK%|?WfSr&OD%eOKUd31h>L@5!uTqg@=5)EN54Zxq=g|<{cr^P1%L?#l3BW8GLqdGk zHvjFF>gLwXcl-_WO$Kg+rOxK|`M_m4b(eqMJd>&v8+&{E46}#2kFO%jfqW1LMl52d zMhVi_2iU<$8jBIMI=t66tJ&PpN3v%L!Z!l3x?Ns|!qT*hqkW<;JYr#~f@aJ8jPD{F zpSdh<DJv^m<}d{!aPY>6^UCy$-#6)0)--2g2n)K;m_zLn6(kvz*XdpCh%th|`V&#B z01#5eJxo^|mZI%xq2R8Owq@I;eVZHl9i-T=v8wvJ1Nnk>mWLdW0cRlUJUu$LaB4g+ z&@fwZVEv~Qx7}vJqh8~}zDoH}ptbpJ*cG(uA`o=)LnUR(d5+mTM{c76c#E15>BJe} zM?~O)pPxA<hC?$RhDY-*{p4XlD>PjZ75pnB>2KgTDOeZY8&!xMIsak8O?T==TS{i^ zHc;(*7VpsT9$k2y?kYSN7QOLQBSC@kH{;Bl)|7JfY(;7CXrm8B<<%gGxq<MNgxQhU zsUZ@g8qO^`{000!3?deXoYMdqtk<^h#7FS#NV|xc!j)MSO3}PamPA<ZZ8Ky>wehEN zlq?<y7aG``xE9L@8u@u_$Va1f@oN(He@HUh748sm7GDVCH~=AR#znw5kYO*5?-m2U zAG(x?v4TQ+PxQ|DC6;eq%w)>GoEQnveUgLqDV!^B+TOBD({51CO<nzZyU4Z7+9X`t zd0ESrrv|P@9TjRGsT~oZj2Vt*_t!i=)J?FDQY5hCU8#-G6v^9v@vC+X8j*3=(gXPh zh5T|kxf@X75sAgV&tcj?i-Vg=pn(i#=e{{a#W#S|S1@tWOEh8uKOPe$mo*(N#KIe} zCLkfwmrMqC8AIA;8r9cF@M&u9IGjBa^OyiV;H?DjSHGWwGVKV|B*3cfD~O&>N5M(G zPb8IL@;1gvha#b7cKjQR4~{#BbKHW39-_))3D|Z$zDG<2ByYr5jzk4%ja?`}B`0?r z-_V$4GL}8wTVj7TqA?yMhTzWJ_W|=7OppzOQ8?4lal|y!Uv?nXsQHDJq#wrR#fW-q z97A4nVyLAIm$c{owda`r#qUGi>rt5W#Xs4M;kz8<Is^ycIJfM~)RQfOz-#aj3IH`+ z_VzBTsk%^_vpYJich3wd*ldhQ_i;=?pTuZz{OT>BxWI#=JSj=)SH1>VNTbmi$3LXp z#n}w&jUDwGMY<iRkq$5o^Pk2T(ns>AAsH}rs<4zx0v^6=u@5r%m`vT0QgnO~53~_> z@H&MizRIt0gaRjXy{}zsMFD7mo(XCe9V{O@(?(7$6Pv$)UR6}S1Fj_2(6gezuS`mQ zUy~~*iH;-)P~0>)n9Ar97o0@8IfLhEB9AJt)(Z^MnZ<G5tfS{eoKLq6sHnGHdKF;B zJVT`2A<L0XXIsvK(un0?qXo2^WueYBd3}9{W8oM+D$NcFI4%+{YvCi>>E!%;oU!ft zA~1)kNl0d<MeDQ(M}XNHU7%=R@yVYu${2c{nOTJ<>-B9>qCEjGGTptm3B&JY$H33` zF^y$8KTHcR%_}khNux~=5%mZ#{>Ch)z`3G3sr44or+9Wu4vfxa$Y{lhMu6)D1z16n zRU)`?)CAz>l7pDo#@2SZloYqSyE~zLz+g5*;z5%EpTi;nSsW87T8!&xKWNsTaE2<G zlY%1QRgBPOM+|f10YQRPaW|Em0+9QB?(Nltr^G2+Tbe8sY?1^tzJOTi5*O7$QzN<! zk^p0*t$Xokfv4E?HY$*)0K`Gdns}rp-KXmIegg<p0zwLP>}i)F9Yls7jvENj?RliF zxJpZQ@AScK6_ZglW{7KqTJ)#WKdBK80BB7Z<!G5LdZ*$l`yoD}Ns2c;h;B~JyhoWg zWXDn|R^m`7YTlG5nO7WA6{PMY^Z>Xo3kT$E;B|Y)C*KJB(qsdqomyZGDu9!SSIbBA zL^XH7jZbYC3=%>W?8JbbS|IZyqy_sf5}or?{cStY6v4pY4wM;5PWsi5%L3OzXIcXR z#BAh4J|4?EBqMSq&>#8JAc*=@{NjOa`o~ARwG(j#KHrXjM-6%)!lxsvM!_1yFYF@6 zb;5oy910WW?f~-|?5+=l@aI6x3E4+jbo6d-5w`{p3}FM8{e2{C;O1SxA2!++zXnS} z!T!r%z#F0o6zAYDfA%*DAXIyo@M2GaNRbU4ZgH9|3{N#AL_~Ib)FpNeeo;8&p^)Fw z=D_D7Q7qyDwDcPZmI0HRJSxzDVggPP_X$YekAj96K}gD55v)Js(Si@W2L&C5xzkR6 z*txlq*t-$^EC1;|aJUZcKp3E`43SJ3%1iAg0RbVQNaNzH-FN?iC-6<+XKx?wTMfu9 zkZFHxTlNBst}mtrE7j`&t^le_fO7Z|dF3aJ`}>3tc9g>dx`UT^05BA|gruB+AKXTZ zR9xkee58&l0I%{vdIYqHA17Ge=JPbbS+q;3asnHcu9t`|D^M1&O=v<u!#Z~c-Bysf z>3|F{>*tb?i4vov)4f5L-~3E>^pc;kc@yK(icLqRum7ST&+?F~@!q*<-f_!C9^Cev zx%*nD@<)!9(-y!3?SyXP(Gu4duGey-1I<}Cd{Y{FKD(-HKUC|Uk#Cxn)^PlwREWHw z?^FH9V}f3uPuZsXsqNbb>Pohw(5Sj^p>W2NYxh>(lPJk9JmN8Jsp|5rdtN!e@aY;F zYh7*gy3c+?LD$gLGV_7HpG(Yd9%Nw?8E{pJk|+tS=y;db(=e>fcWHc5u%umP!L@<} z#lSY^BThB*brA)hPF-&3S#Z2zSeg4|jhu#hY*k&~$XVZf4+WNK47Jx>OBt#%tbZ|< z^07>3_+4k<&$cmG86v_A7DfP%2ZK>GU6tWsI_-b^u~F9VtFL(b(@0wUwPUO1mM8xh Go%=shqL0=9 literal 33425 zcmagG2RPUL`!@bD8cIrJ6e)d>vO=<=?#RffG)QJfiV)eeY0HRYRaV^%nUPs4eUhvS ziR{RhjI96p((m^?-{13pj^lA09d~zlf8Ouc`+8m1d7bBZy#f#GsI6JGaTSF^S)-w@ za+E@$_oGng+E*^ezwEC2){XzjIjibB>)Bg5yP93Jq-dKtJJ{Mg+uE3Kak0GUWMh9p zN^Fmqq{x=D&dv@_^5Wul|LX_D>@Ql23mf(L;v!5A>IO~}%33q>hwg!Lh7E<{zo4PA z|Cn3CP^+u{=bpvNu^)8b0z9vDs(-uw;9=qJh7YP1=QORl9;KV-ms%aDs(WUCuI2rw zqgDPftJTD^KfT)PAnwq?$he;+`8%KUM_r2w{|dLgPah5Z^!hkI^UXet-+p+kRQtLa zR{*}${X0xYC@T1(S_{*$v$C=Zn?_TD@EvvuO^Pc1eHE90A3wg9g{<F)Z%5D{WV%a! zkuHVtFutAYcZ7aBzJ0WO`~U0L%cZ%l@3*j^UN>t}AfLn<f0+LE^_J)MT|GT-hFYEv z@siIC(Efj3=zrcvR0dmlQ<MH?-#JH@;cszPExrpgH^huybniPx?z_-1Nt<%U+?;FA z1)j=qKHZ$5x)imsU%&KrS(AGhKTGe{`I_m^)JRRC(~o-F*<UrvQl{mU_qTS&GDHO8 ze!6|!CF#E9o#$M;cVb=H-1wn8cF*lv-rnHzc~^VyK#sBmt`ebGPHUsHczbvMmHO7n zD)BPL^6%f3i(ZjWpy`cwuOv$rU}2oZv$RNs$#QL4?S=cq4xUccr0vLk)W)K=Le2BW zt({BnAVRTbF+kae&gbgY8~T}rFA5}cX0?h3JX>~Uf78$_$3sPC%<zp>T4(9r?_Rq_ zT9G~ZjnXt<os!>dPZ6Vph)9Tn>u{3-Gx>13UN<RRiHrK=_xVB@8B>v7Hz%DNF^}+r z&+J<A9~<PnZ8O8Ysk5w9<h*UsJz~6k`HzYkHa9T|wa&osaO<(oH*)nSPjt`h_EQVr zto=Lh+Vkgo+S*PrY{6|LpAIQF+u&l*)86hE6~$KMGIZavDn_o}+NyhIDLhs^-68O( zDc8s<LD@IS>Bq;h(TcSUOjmU3>gt-mB#U+z37g_Bg=N1Ux4-cyc&DHit<?V2)%f5| zL;pUnidl#iH9Qz4s69G4**o4-^=05q&<6agt?E(;b{mATgnbxPV%wsz{;d1nlv(EZ zMuuYVSsC%tX?`UorEkUVY_6_Tig`@O|Ngv9+wT&CufD>cPyZ|MV)$q0`}&1LcE&3l zYLfQ4xx0@IJ^yYl8Rzz^de2N(xblM|Dc7}UX*z-x5|P949FN|;Q!g$qUb$-3tLEnB zwhppntFmXQ>nbE}?>e)5ael-%Ff=snRNlF{i3VNg!KOMZ?{0QGdOTf(*UP$>5n;o; zIYTpoQGxTbQ*4_y9r|ACJu_T1vi;=K)A_$+6aQ|yRDLlr7NLx*(KcTm8#p7HUtU>p zMpB?`j4EEcaVhLAz7;yHP*zs{Gtqy*%S(|Wa45#&-Oa7;@-qyfOZ%4UI6Z2%oSCcK zpJ4^1Dc{a8>GVMC`oG_stCC<YUHEez4<hT(PT8>InCb7IpJ#Ub_wQ01hQ97j@?CI0 z@mQa3-}4KtxA)j@zP0mYzP)MlzHLispqx!t@nz74o!gH+ShqO)eNn&2#jaFqVc*gU zucYvKPq2n=+GF+OV`A~#c$KVd_jhUGMITGD6CcVtb}}A{5y>jO(3~ycIW_#Ip`jt{ zNDuQ;f;oDZ>-x*NnO|cQ6Xbygzn6XgW9+l}chSk0(>q8a@-uvXVVB#eX_9*{H~+S6 zHn>LGq1b#^eL0<_4`*NLJO7(ADJe<hbiq0`HMKV1MQ<7VHZ45i5bvolJ#H?tAEuP1 z+ZozC21GwtNevGV<7aPvd1Bb2AW~Gfv`M=?ubY|5#B<9!Y{4Ch*3$USE(P-VLj*uV zVj`W}{O?Aaueri|Jtp4$`Z4|W@<Ea&sV0U06?jh!p9{*%Z=~Q3HhT{467XF-x|ASi z-X`t!933C;Y_TuvLE^A&diJtyYmeqqmZjDNhll@Y&{a-5{^$hHUXSR1k3Y(G>^kT% zHEcIhdr{yCNgEN0L-lEgn0e)+FAaR5tIg2mD_vahM540(@$RDd(i>J!lJS^0iw9Dx zyRSO)^HHXLsaIiSne=}>LoNx^(<L6autP2{U$e1EAwhBf(tC}};8F6r-QSR5Xi`*I znC1S<O2C)<E-MK+8XI$k>EvKDd7mn3YI>Q*dr7WEwi5cAtxk`8%{#BSIQQ$Zeipso zh1M5!#qLhC%WvJVS*4Hnb}_FyeosTLjUIw(|Gj^%O?OA>B5FiQT)CcgkNLZPjdx`1 zHnIt7l}j)yDk|#do@K?GoMhQq)A0AToLZ|mO1W+}I`^xFjh#KUyXK(u(y|u5zqXq5 z!Ku6*6zq6>QyDwCS?=FC)xVFm^V^F{!PrFP^Tmvd&z3yxu9^J1*IMx+P24;@vQ1yo zU-(w=600`Ed$crt>2(jjwUv=_AGg4hRbo{fU0sDFByQ3%EN@cC_0m31UioXWkIBk4 z?Juu1=i6<fFsxwdew0~y|JcK$E@2{zfA6~KO(mc2hkUyh2NxHswqkb?{b&1VCM<s= zt?5m!O+$gl?;Rs0Q=2dS{FLP?yDaGMVyF!^=iG=j_6VGwcB5!UZ-4pf+Ui?#&HhUj zn08`pY|QIVe@5=P&q3m4bCH>43t!u|Zg9Ip&w(JZb?UDdxX!}+{>7yMN?$|9THU?B zqZ3p9W&rhk{T!<`+VL`$tuHRIi=K+kEd3MIQx&WC^t7DkjT7xQOAl{5oD#0o8M>#9 za@Voz?HpEB`fP3a$0LE)mTvELue|H9R>!Mr+)`G|Ncl&{W2-D$R<{2hXk=ny>cEnb zMZd2ae6-Zd{oLn&RhzRda~>HYl(HBqBxIfY)`f1|O@G(@S1%%+-PP4~cDW!e+lIct zchLuFuVQkjMb5EPC1~j1lbJe|VZ8mVMSIq4lF7APNh)8(Q=_7{tHq1Ue|~<{oONae z!i1u^aB){W)z9DG-)mv!2T}*0`m`ve{N+nJYinyHtAlQCvZy$eguR|I_}E>(3tmXn zZw`r<sQl`!wqphEQ1>?v+k>iiGnke0;nD0>s6Q2TFLzb``JQdL`pSy6g3{8_|6E(; z*j`R|o_>4b$(Z%!zkbw6+0;|>?Z1baaeeytc(BXR*N&bZRV?kpBPq)TqiA211O=}j ziWST9oVsxN^5v#dZv~R06kG=H;vHxuDaP$M{%>m0BH&ALNs0UH&~x{`dp9EOoRGrX zyr+!9cohyN%3r!IZ4-IP?G&<%_%G$oa1%eitsPsxoPR3zlvKZwKW)ulr&^0#<76HA z8`8Ba{(A>SM3yPOve3UiAbFM+DWH`of2+uKq#kJl)nQ-P+fb<q<_ZZ^h)n=BtC3vO z^s~&1XL{q^JTvBFWpL*wjoq(d;U#<*<}BFCA3WHOkUE%YTrxT~W`_Lo*r+H5-#%RI zHs<_>i<>(zHkNb${{6BF3f6rCY)Bkp3u<#?27_1jck*=!u3v5(Gih?iPNV&yUIv%+ z*>%7ujO~R#lFsygh>PBSgx~NFTT9i1So`zuKJj?)_3PG^M+@soCd|>@))JI=?kkTq z@uAt5%^$=oi<2<>=gw53tmEZ{`B|soZ-vKNo*3j@=T{9H{WFlMhngg)b$egndV!TI zSH5$2jJPShCf62X;%5@sz4n>$rcDwYPQ`MDf2O9UtWm0>#f<K}3FiFO>iS)Hrs|C3 z-eq&h#m&yG#zSB8W^lzPW!kA$jZcjfCu$FnJg7L`enmJR(3Q{F*!YXh*(jPyj#YJ2 zk*ocSgWSjs7gNfe`~%ayN32a9l;#xuf6({$wbk{`hi}>ww)5muiF2Q}Be*a8`f+Ho zYGS3Si``ds8`1A2NXf6`?MpSX%-<Zf<K*Bt2)x5)gkRu^6*GE&Gz8Bv>Ab7+zIIER zB{s&yU~?4S$`-SzA8LHwsommha}2lkp-!PZ=H!L<^_^YCDVET83R@M}r22~w?krpH zR%CyRL|mJT@UHl35`sui*;dsX1$C1W(tc<Ro5iNIBOUG^`0~Wj!J&P$qoVnXZUnz- zB-Jx3D+@V8y5$dw5$yumA)nvesvWJhXr#L%(eblWv*_oL--la4a=r_S*fLfv`F0d$ z9@$!t-1u&9m%XcHqc+`acycsN6D4SOUEZUUnSyZ~$XT>QJiI({&EA4a!QK*Pn$e7> zT<B{0lo-@jRBGJ{GRU{(7B3#9-*e$xd2@3dDP3C+T&M0B5_C5#Ra#wO(~ypIS$XH| z#_KPKTZ>SeA0}=i;kOYxLu9Bd`kdJ3^+v9j9P?uQvZ8|ueJj+@x^2ftjVgC0cNgNl zy{fFdv1!kR+WV@jQRl8?W@bi~c?o_O@G~<rBXG+Pmu1_$`6I%SKp4Grt*!F%cQ)_! zXp+-J#(n(j=}0wqnUt-MmDK7I&g_}E>V!#!P*?8}t=J&<&Oo6Dhwsso%G-?nk3>(4 z$W*-;W0c{Sa{wz8hq~9+)`ndJ;Q4(NkTBbA%p5iF_Fhky7s302(Sf)C=+a_5H0&SV zeqx<(o*>g#pSGIv`QPJRO<89Uyz=q<G2gb-2>nIqmw=d2_uX}_Q~CMLUHf;?vC)aI zb&s3(9kxb5P4qXcKoz39`Gbv=ca0s@>m@4vH#}RM^a<h+eGl|@kza%+uv8?w4+zHu z6|S~rT-fXO2(>`uRBpiaHJiR})5?m{67ut1oOexE2n`5$H&H@zbH2r)H&^Kn`ByR7 zZQeA;RaI(~{-<Es;dHirpBdKUjkJoSnLoZn$)t~cdG9L2V&Z~HI&-r5$htQ=+0x4r zTCXJ#Bz6AJ=K(doaHTI%`&^`(UY@Thxqg24NzWUCF46srqf_5~pIh?a!_3db@3FV~ zR^a&9xa8}p9ag4Y{*!AoFJ8lKVPOsxMF6397a-$FqL!p<$D`pb{`FD8YpT_ClI74v z)*(_YX=*x2iZpa>P3Z3^HG8Lcq^v$Xvp*a;U?ESZ=(|v?P4D33ltXhOjqr<PpFe>S z+tlzR`}^zD;`VyV=qk;mmQ+NZ-rm5}zAaoavd5?#2*2XSTI+sp+#|hH?}rYgIEM9M zsz%RD={`R?`P}JPq@kR}Z@TMd7Jtf6-wnOi;3t>0>FMrOO*t6q1~kxA?4A>;tZ_W1 zsvN2BXJe)=I+XrT57|_M*t3q?l9i^<Y5sr;OY$fS3k%Zc<-2#cV7J?j)Kb^JbEYqF z?mvoJdSKr^%8j*rz3(EndCm01I``LK0}35S6V}z8x>pca%6D0qbHC2a{R;4>(7As- z0KpDFeE6Oyvn%I50s>nCKR-5nPSTdCWSoPe<2{rL#mS~KfFC<*k~XgY&&1!Nz=LkC zeQti<9fi2iv1|Fqj~~5$e?G~)S+S|jOk?wzD8|}X63i%u`g!LzY?85U2VB5I0>!FL z4Y#@vW>ud3@;L6viz-$EjH!DH0IN}}&R}Cwa?jRX2HHECp~I`7Cg>ia%~tODrm`}} z>4(mVOyg<<X2GQa4!i(V)!G!bwFG}}l(D_dYt#}=pi|a!ptRaDU*F4DuI!4_Djs{g zN$ZZZhF)o;O>+#TZKs%67-)zbm!VTKc3<(v87aHZecFPrqN1Ahq)9QiM?9qiq91#3 z*q_gLF5+XNLW=^G)^*V$_B#4sUU}!cC!U=8^x(*qk<!^P)CVWrcS4^r!ny`2I4^8; z_xUd&Xj|&jwKu(w6}SKKj;j1_l5rG`4=Ja9ev~jCIeO~j4bE6-AcyAG5@3@ga!<xy zW2!#$zfTT)ooOraY?0eR)6Ev3Q*<5Xp`)YIh%Aj1I8<NgWbHB0_p)NGvKEkbc8VwI z8iY~G_q4fPiIcLXm$>xvSd5s_xgQUx%Gn0=Zj*xs=j%Cnc$leFZvoBdXM)}!1wLOs zj^DH3k-K;mO=PzHcO}Z(5bg>v9%u1;W$%yDyY+tHK40%T(RXCa*8ZKw#j-#t_dyTj z+J86JNG7fE|Lhr%je0*O9zqxo@!sh^6Q@I&o#?N~I#cO)wX7I9DdkLM*hF956&7Ck zK6P1qxHGWGGVG%j?Gb95%>bQ&tKWP)qTv(UFDZeDWLWPqoe%sApl}pn!K_?aSt%sd z`h)A1KYn|VosUm<q^*SP86adyY>`P8ja7ak0)F|jZPnQt)aOf0S-$hsu#r{uoj0g* z!bk2i-js-nIT@~Xt5~=}uN<#AZz<EvwEb_y(%n~=^5slx^qhuH6?U0TgP`Htt=Ny5 z^XU3Rc;q4j0#<6qid7>Sn2+2{rBp>k?IXG4-_cQrgJC?h%ga_%&bH)>uI3QA>+$=i zOPJQGY+G&0nu>Pt8&_}K&_mNbIH-wuX}R51&6u>h+x)faztNBXL1}excF_7&3i_bo zxxI-_vQlVD$~J_VSXX6uB~7DC+}xyje9!3XKsK_~ZCmm~5r1bS<7B<2B{;>6UpF?! za+-KW2qk+-D!f4M%MxDPwlKYHnsxnV^O9b$As>dN#-5HO{wzU5Af%|6uyOafXAT|z ze9g6qYV8P04X&QLW8d_Qj*pM;%TuF6OGKV&^I5O4PF2zfDU1}S{OrY24E$fHsWIJ) zHRuALESlt<J5De@BKX#v##8eK#!(dS;>oY)QD|?V&i+DE)^K+}!@z9bxz8xfE5NTP zxQZF(+h$o#Az#Uy3ER`f*?wbx{W+VM^Jf^|0=64w&d$yf&=(_aa-rl)w&fwbg2bV* zAd9O=5hG=bNx+&CUegXrcuUkbWM5Tx)1pZGf{qeBV@ozR3Tr&m|G|LW!~*_GUYxrj zl;n1KpfQsa=x)<ges1dqMu*wSA=GTCi{0;zwd_UnTToEofabu^MI3oTigTM-`lD78 z-$hk|R#7RCmB=3a?lUVIpTb7l7=MAj@M6!Z&6_u~i=CGJ?1H@Zwz@hrMJ@d6-<x@O zR5J9JTbF<0=<p}k!cCsBNNS_tr9c71@&xKqciPI+>leE@J~b}6^E4k}lfBu08<xL? zu!$!*Pimz)XjZzL@_q{~IfSjyT;^LAsZ5iyy+iSEb90y(w`AF@c>7$@UeC#O>(^ht zapQ()5hsFz8$_g)OES|<DwDL?t78=^nbsa-Z#Ye!nZH>@5>~i`s^pt=Cet#Xc?sq# zd%k-`J$v@dq9sB8QqV~g?+pOLXb^0hk+s-OK|LCNv^A^qSGASP5vUvf_F)`I38dS@ z-lJ<e{X)y1T$T4bG%Mcoys&K7#cuV)o}KPtv}f%7IsPb&NuI-U3x5X0(Z9Vtuy*fj zq@n_!dHEWpSrH+fg!UTcMJf{Z3O=u!%T{x=_uY08xa1iW67ssLDj2m8RLu}?7}~nX zHa6MpqmSkGvTT-D|1LCpar~WzqM>K@P6tk%F!|~O<6`lGev~6ejuij?VhB1^&{0h= zDsPK3fo4kH9!U>^Q_&Q^R;rKxWDvCbyGdq^q+L<&V!f7-KY|CGp`^>8{{55`VUtot z<+)LYaODNJ^dzB3!zM+#+?6aWR;XS)3N9gl<)jm>zO$QTCA1CAb(Z;u!C^%H!lmQ7 zstanIb+e3<nb^?R0Y4@_&G8($H2>$4v0KNoxp_97S-0*8HA2XtWz160N14xOlqg%e zLQcKrPs-^-FD_aQa?{r=tG6-EkWMZH4s~`OxjoE<lJXA%WF-^Ry~FqZ(TbM|MJvU< zVnuxN-5utfr^><P7HXana-dvQxhf&xuxf3z_ZFiXE&C(Z!N>vaot^)nr8NL0gPX6a zo#!t!N@pu?H1XYAl^Cxjs5CcPA)>6NqB0&nA$We|f_tn6uTa_}yF03h{%Dc)o}Ajv z!^5*3MFJ2a)%;D+o%skaVE`udaxrJms2$WF)+1ET)FIt$t1-LU=LIp&0@D1_G>`q$ zi@W{MW*qu0r#wHwwoNU};?-3qQno2*w|AcL5ANPdO3CEVqZew}bxtvCvh{bCU%&dW z9k;N{u}Vej_Id&Tm^Is^Ueu^NGN^+F5HFcnPcBA5f76xMXxwm0nKqTnL|yZh5u4}N zT=?{_*PTN3pIz=v{Szd+LF1_8+v+!=+^yEB`t(-lxtKRf2Q*~pcD1*c7mbuwp<4@# zii)W`v=A%g`zA0jumd?F6&ypZT}zCdQ*Ub1Bm&~D>qr}zCQ0YM!#ZfguS5g|s9U); zPJOKXql6|UVN9W-*IFj0)^lroa`Ok9vId%<uT@SQ%1%89T%>w!Feao^A1VG77O5;O zk!{E50Jvn^`r=lUknVb3UhRiR(;gT;7fV!dO$P&K;4wKnQdaZbcd?p~Ob#9N_Qhk% z^)htQfan6h@+$izlzPvOcLs~sKRPK6K=2*_jA`Y{yDEX}`+puH+}WfV60K52;?0#U zU#3^Hu%x5Udj0mTB(#yzKR+KSI!gElh&C(Iq5J0t10|0XxOU%tZT^_uSHkyBa-j!# zf9#(P8#bsuI`L$Bvc;Y-8<gqsD)E_t%(CgRN<OwcZ(v<nm%-bY>~5HGp_yefF);x* zar5Z{X+E05)3!+89Q;%IA!&T<^GTB<NOoS{-g!wztEllhbO<;Ju)%<td<d_zV1r9b zOGCJJT?X6N)!)xlerNZ2E+sEnM@L8Q;cc?Aw+jkn2XZK&zMIgtgF*}ZngmTkQ2UO) z38MJtmnT&yN>WqH@NUBXcb*chyL6JX(OJkB83OgaUS5Ju!u<70LP87p9yM)t&Z<C5 zqI>r2c64?fIW<Ztz!Mr@dE}h~j-(u9WoLg4?r!Y&@9M*R=qAxYUdR6%X5JDvNxFTT z>q1+x2G}t+qcX3Vi}z>6jb5nWMM_TJg78#=07(y5<HLr&xT}-@xaNs_M!#M9?@WT~ z5eJZ^;+eoAIgYmPfAWj%<=bt3C{dMlbrD4VXzpHn*R0~Y7g9H9bzlprZ)(oY&X9|q zm4=0eYCD$ztr1v{s?5g8S&5&JgCc-9t#&BGR#HaCiRAIF{4E2Ri(S>-H4@BLA8x-y zB35y5kU0MDv5sKz(!=9I<Kpo?LRYU|9W7^Adl>!8v6kIuYyDPicJsq7u`UP)B}3Ji z5XlVY5WUmqfvxJ^pBBrt*FzfdePgpnfRgv0b==%>9pEOY_61%uk|e*MZ+yCp>>01c z`Kf4eleUi$f!&*Bc<BI&w+adhwhoQxHr1zqNI?6yt?tjuXU`<ix&{RYABvpZb}+;g zTVoCp_90Or79|`RD33Y>NHwD1{YP5+j<gwA9YTH*@^r(_6WeFl3c~>uz&oN}<8~Qo zOFUPELVOXrAOSxeN@&5tqoVZghw^~mlD_awR-<PU@Nk#qdv^3(S}5cNPCr-xGIEDE zbFfCtr5z02i2Yvw;K;Uxg<1Q9Azb%DH}lh;+0c^>pqFK?dN6!*wTbWiE!+6^c2i@I zegVx}JKjJyOz2P|RRZD=Ve@9EMyydjKEz2?zKEm#)%7Ut!^0m@{675oJ%9wmoLM}+ z3a{gy_v~ccB0pB~q@2gZgG)BySFSJsWtxJul0Nt89a%!-xTg%$z^Ou>^B_+_hH^yF zt`-$3tl20{Hol6gs^ia3!lXH3zJfNU10RGO7vE8ul9EDhhr|~cm>)@=VfPN)pl|Q! zc!}&XI;pdL%`@9($<pc0)g+*iS>4fPKLmt-F?*DEA8W)n8g!@GVTHN5IW7n*NJ}Lp z2iDW1BoirLkbeeHM$l|ft7>REr0ctwOu*Kw)#B|VBWue6rC%XC2_H=j0Ix&aBw4cB z4INf~$N{`kqx=i4@4>-?cwVY)$gZF%1PoS)@e40i757(!8I`tKkL348f_bxBI~|AU zsjIQEu_XuCXigIYK<wR2jhQCa=+v-Po|#oNm3TgnRF2Tfw%OXZnp4bTItgmbS4l?D z1sMQguhFY9tzDbpySOmPqDIxtv7v7QCS%Uq6Q>0cr731B`X41$@I{1QPFC^;T}pQs zdDkUOlj@5YxZSqqjbN1Ufi$QisiuU(zOsBxCukW)Mn=-Mg77yRd8RVH2Ee2Y3Qy38 zs+yWp&-)EuGEHjG<q`6S6aoU+A{0N`%KYlM!8iU<VFbDrA)fKpu`$ih$Y@g<<j*`u z#lc&;j&wNwas^*I2x_!pcHEco+())<-O9`@c~#UfpCa)$vF`+Qwrea<8Q)ekNbduc zLHPK;>?oK~dtczwP75LkL8fF|R&fwr0-)WXz~LTN%htSswcOeDXSWJ)J4xz)e?A?Y zIVqXE3NECVAj|K0gBqFHEI=0$7`T;6eZAQzI6z%23r*(x7}4PP_)Qcv*1OQL0T(xs zsP&aoTIK~D1V*<U;@Sg~G9MjRfNU2luH{nJA5$SX;~5xpnwj^3l)g0=*u5@3jN&rb zwCl|jbHx@JnHUfjW?bOo0|6nI&^4_`%%t=Kl64i>2fFQeLS*-75rcYs9=izE4T(6q zR3|aP4do?|L0=`6g5qe}S9|YtsaG6$mvNd<GARREf>8(~VF#dUop>41&r`g-yuy0v z%*e}c@D;Q4Y#Z3vDyGM}X0Y?<&VG8pid1+XwE?|fYCB|)rc4uMLnR&HCyG;X9C%}B z>gu;5kDonR!iy~YWvZ*I%b?6>F9q4P6T-}W?5oLDCsp408<hjqbs}vM{s-C6($y6h z<_a(jIwUpq>BW;S4woD)-=;|Qoy_DTOS<z!h-xs0BeEQtPs_@)s;^&Jq)t)3Gp}5; zrU!M|3i`q6GM`qD);Djip&9>gqI%#$jw%{HpjO6Z%Q^-JZATnHa<3r`q{!sU_ENrW zN6Izb{?uj6^y_Vy<D`Y!CQ*jJ<^_`y2)68ExJ;ZDxk)7V5F)o`57^!Vs-_A%6?0F; z4F?`vpvW>zGjy2lu<d+Rxcb=BW6dnALF(sh<(KhraJ)s+O?DiB4T)CpJe`As9}o+& zZlhbUL%XrMAz;?gT!pUMu%l^dtI~mVXA|<ZGTr%X=zF8(%h6pc!&uTKkzX!u!p0)Z z9PWf@?BGR?f}&L!0mICN?v`*JF`|Y<gFN##B#X#S=Gz|H4Hs}ZjxBC77Bc%(6S>xd zzSW<9DvvGVbVa(5KY-W<At52cp;Kpah&B#b_7=4mZ0b_PWwBzdfbcUKiglRB`BXC8 z6CINK!xmY6Dd_GaYb7|Q!>)E`u54$Rn3-A}u@mAi3_?l5!ySHp!3aAe{#?<;p7%%a zKmQy!U`P6^CO=E3%K3rWe<}_Tkp-9fFB3)jE5SZW0o^t~-$S!;5*@vr2SFZOl=ouQ zSb+z}HXtO{qaox)_&c7Ht~yfnuS-P3b@KA>uL1T4*8#VH4q$6L%f!4)Q>a)v!Xdjd zWLRqbX9o6&Zx!iGcTqMnbP_e4EX1kQt9aO>DF@dq^}Ytt7EYBVoiA5P7mPP=__Mb; zT<tWUnayHaLCnQ#p5uyWO`!t?fgMj^I;A4{Aw7QI3+Mh<;8op*@*1am<Lx8&epBG* zzf9W63W>h2d5@M7ortDm!&^~PIVMYEzn{M!vGMVRyL#LIahuTiO<l)I^TrnZ^XCuB zYuPjFn$2Lr_M=5T+w^QJ91`HcFCVzUk_C{3c3BgI6A)S6-zTSC_$o_3w_JtcnqQK{ zZ(qG9847apvh{T(XFe_y<~OkV^-t}~moJM)U#-4+?V6cnTx795*YaCCj(1|ar2EYO z$sd$Ae6TJzfSnpcUvsbH9+%_t$IC7+c<1aGWi0sY+?D&?CUiWo+f2rYVdY_H=k)aS z#EP_5X?ly8n3$}*d?)%VY&&lJBcSqoHd=Ck`u?uI_UXlV1s;vtf0ns%aV<~3C}=ml zj<zDZN2GmD1^E2vO_-^)w3yH9WWE$H55Kv3tHZJf%Pxpr_|T_xAYMUboG$(;Oal?c z1Aumlvx7Ou9_vTI;dIXFhPskf4lA)t5$p`h6b;f>q5CgE!6+t9PR?_UPqm?moJB6p z9-w!Ug0Db*|2l|q@GKMppMit!z+XQ-HEL-y3sje(jjwDcC05y2iI5w}NSb+eSYdZS zPvW9rVUaQML3_u|{{K&dN5_RCYb<RTB_t&y5dh;r(r4U5a&n}=;HLnQd@J#c1)=uO zKmP;(+x692+(%;dnryaa7dQSUW1=ck4|72GNxUD(OgBqBr|3z-Qu@=k4Is+udpTG% zOJO>%;ldw8dJPK;Q~do&kNk0SbEDdDZrW4@`OqNSf(f1JYYAo&hd+{ilhWFqE`u+o zh2zzbrYb`>IsJ6uMfACACw_jeB9F~N1N#S;1fZQi?iWyQH;}Y$qc`+$^XbtJ7$tsz zg{=j0_~4v;hV(NfAoQAFFm2ni#gB+V1e_9FF<8hSIcw>$wGHj?b()3w-1Jz#&I09B zzAbHMX7Ob+uE(bfC39MX1G>*cPIXM~*Fo;!>Q)ZW`6b_>)WD0p66ezk5pNn?#N$lt z?T?UYqvrzI_n|jl%cn$?dPwN5*GGmLGx@Lx(8raRE@=2ypFN=Nrhb0_p^IEjb6D>N zPPENu)=u7KFd#Ei+`1+yuQaxChg3j-orMRdgT6l3g$ozhcO2VZF~uQn%yaVONik!s zj#@`p1K;!5zZEZEdnNUe6Em~cZLUocF-n`gk0!8`y8dh`Fq0SvH5mMs#YVBWw+Et& zePVJ2?mFBh?0MO_f=|KmqLWB%JxuED|H;L{KNVFx;`>^;gE4$`UDMjf^yc^6Xq_Ks zTv*_6IF|oLjNe@iN>fgANXnUB&TR^Ea;$uO$^EmcD&r}^to`xa8c!1~e>Mf|X+LTD zz0AH~|0lL1?B^oKxHqxVl8=QNs8Bb7g#lA_O?3rFR?i2H4`aQ@T22ZZ2pH9rlo{QB zcxJ`j?WXkd^aoxZlQ;V*`(!~i)wI^2oS(m$TIW1icG~DP(J<jLiz>-Lr&jQyVEx9; zcLwG6u?gaPHYZDr?hhF1*l+DVx>En=fa|i23{RQF8}7(+L|hg)#AFJ+VM$LL*}zK6 zeB6*VCEys7SPXhhTZRcHQVXl8I8tn{3fIjGu6G&y*r&9uZu!mH+QV;|c^G2-tto1E z*L{J1uRqb8BVtbU5L5eDS0%}F$3+OgW3_sh^(CQ9<V+&N5B7DCbS@CK^TcB!!gryY zVAA-$VaqLp?g^b;!RiY!X^ax9Gv3ktt1Q4@c+x7}UNrkf6zBTLa`HyC_NCmvPtw?T zuU~5@fCX2bS9fSe?Brg08ba=Xb-hp}3y=`{LFfA^3rnGq5{SG}+Uur<S}Ng9le^fx zl0tX`{Gie!ou}_>KV=25pGGh|I{wJ2N>-8z)j}gmFw1A&gUFJ=<y<nh`q_4SY@5m# z7p9cq@Kh=B`0Y0E<T<ju8H<_-zxy@ORlBTRgzP;4<cUQDIpx98v{hiCGSE`)dz&7H zS5_5O<fn`j0siw{vHP$8CP&cE{cm!FI3K3kJj2n;?{XbJy#N*%H?W*!m5e@B>Yki8 z6Lwav<LNhO7M+kqOzTrMNUIH5Xlvc9jW%y^z&?(j6qZdgA#6fAO_CgdQqjHXkr3-+ zL=3=Tv5~?Qt~kCDjhrIzx_Z3}TH{X-^=^QcIa=az>22sHHGTct)m?a%6&wxE5ecJ< z4VfHqabatKL_wi{X@vK}0`5$W$TH8_k-oMF|IigETPcystQ!0cOs1eDy#GveAS?8O zaxhrM55Az$*fSIYKqNR@>6W)|-gNZ$+m!Ueev6NdOb|Kx!r4YAL3aP)!|OnhOwZ1C z4Gt3jTn>9?K(|T$ozgt5$`ei+#i!qXezUuJ_f?KvyLJ)a1u0wto&l}c*7dA~Cg~Y| zCm-vR)=(d*bzxy4_k3d*xRr*dMhcoSB5}R*a5I>LUllRPz6zfJy~NqtEykWhdB;Xp zuU?&^6(>oczsD1=neo@JUpxHj)dv1KU$W42COiv*3rrGMp?m<&dV!FS-f?_Klu2Gd zw`+bvY4v+&Td)%#M=U^jQ<(Xb63~fTP7grl)&XY;@^P%qbEFRX39$j@+cp!~)6v;E z71kX^P#8qHLx*+rvHq@C6%|Ckj1@PrLcesn__l<%IoER0Q@Q0eHQ~M0@fpqb|ASDO z$14Lu7(*Xc3$q!T%-4XW2BltdJ@0Q-4aSP`&*)pN+LiIUI3%GM?Z|iUDPA8RAL6Kc zdRoqBx?=?(`K^1I+j4d@BTC<m3*JQZWx)$TT3qy;gg2A*nAlESxcMKG(_HRCafDje zgYBCFRNp_GsUE?vv3_sW06G-G7hqPL39AJs%bIeqfx6+F_v#ZbMd`Hp^->*}ZU+nF zzd#j&tj**HSCyoFo8rGGp4_=-ZIO2UUI06@Ov|!w_YSNe-W!1Jra~tnP#jd?J!~fQ zw>ofzYzONNNi)?T#|rOTu+G`e17t)X+!X6LId4Si{WA^L3tS63CugjR-CADx*EhEw zc$~@0!}H)|rg2Q8NRax1b!KZWuHTE&Yc(EC%N@0O`Q^Of!}akCr8%c64y&wM9v|yZ z+EB32Dd^?$eWWSynpM(hfhhT>GD5CgzI^su0eZ&3sayhc07nI1xcr&0*|_IIf@oHt z`a)z~f6Ipg&ndBc_wE%G6)}+B?ZdE@lKdamt;$xmj5Qp87kxs^)KmDxV=j;>mYuHy zQ7Myw;3cg;@|>T!4VDQ`BZhs83-boWZo5e4gm$Hstdt0tUW+cy(}#(lpF*S%^kfat zVi7}h`kRY+%F5;=Kk?^=v_l%x9dqY?XtteynPJi)3f)^c(krg7aqo?@hRbU`Sf~6j zSrqdaEGmVm$PbXpu5_k%)1S$qDyU;|d+d{B`~m`6Md4c|(g*r#2lU27%jsRYxxC3Q z3n+u8)Nn$r7LRQ$DN-a!HHe6afGZMQ<kg&<oRSKV24ax+(3_1xPof_Mtoel?A#_iS zlwqHArrw>JrnwKj78&U|9%TXSJnrM$Wq~ALAB_|kt=U()d2u)AM<clF{9W7JZ!ob5 z>hH<5yu>K35Pp{}VvcyT)nzn~MfO;?^T@_HTGT(*(Be|udZguX(m&<{8?W<6`u5Ov zgJE{}PooB3I&)C`uH0=C>0gty=TWwO&qCT;eSMx?V`2lAeHhl>+__SomA06>Tw={B zhVA@uV5Rl?XEaYMc&+iEdViPX_53>Ge4kbBQ2k1(!sXaDs*aF<fciON4mCf6uHJ3d z!)fTo`Dbh^b-oIwMT+Eae3|n}f~U!C3va8!jv2NH70Ht24hwTL#}Kw~QGJRT12M8H zj(0zpLq24ZZg?k{IKNUZ9lenJ?K+ER;y-X}0BDqho$u`Fxn)l8*70gt5WDdGJD&WJ zKP-Ahj4C9i2XksjI};EZ`lj%)RK(tG_gsE@r{&)}kakLiT4d3}?f}~vrVy&0Rt5)H z1XDNdS-AB@gNHa!kQBhPcYdx5e)r?3?m~Uey#wS|1~%>+631Jh?i6xl=92J3F55Mb zV{rQCB__?(wpjmrks|!&dx(lLo*9ymwuz1Hz~RF;f0v+XCjI_K<+j_k0kzVzcRo2r zYGxyZFlGT~I*+|oRh+1Z{IdX2>-<zV;|u5NJ=d$%E^ImV{DL;_QO^5M?r#s^`G)zB z74Ma9c1)|N*s78>PKu4w-bB;*MP*@VT4V1$&Ea}~<S<ehJP~RTiW(L}nDg0KX)GJ@ zIf|k=@iLK7KQ(yj%Ga9;d<!f+0A3QBq%tfM>Wfmfy=z%%yg<q7Nw;p{s#ytXtEoEe zA1NkuH(u7!BEpsa1i<us1l-buGDBkJPX59h1fl0hX4`BliQF65wS67S@pH$_Doqr~ zx;r?i&m`+E{hU$E7s_S@7iJTa?^<Q{l5tSbj8FWtE`na9WRqBZ%g0wmrF%*SCVRtw z`8t4R?^zz*6@JZk^w0Usj9D#*#NMs^g$6Ktq76ZfxYa+Uo!;H}E+8OP&Bd-jzrbND z5tGX1CpYQnFfD4i#D5a480(kD(>wvWIXTc+6XS@%kK2|Ns%PEKW)D!bZ-bK2p1K}P z3<04V-#{H&S)L&0<OcX)w=pL`{ld+DMhDQ&gx{oAh-?z`;+Qpk6}_G5L*Mmaqav4G z4(<Cu>0JZ83lfk8$)hU2NmXS~BYvL>Rk!Nr_0_NjAs4$%cifP>^phQ)BWp0S#k0dj zU;0fzPQSJ2D{niddhj5kSM~7W!!I~}gVjGuRmMx@b#$i18h4WMmAha!m&z+Km!)iA z&B}zYic{?LGYtyubKQGwysB<{wJ;v=z${+)B)HO##W$#=?t3>ecM#=0`&%KP5o{F( zJ`iC&R8>`pr`%(5kZIripWlSHM$@@MQYrFwbA%cztLJ-X`Ui1mSZURfhd!|ID!v9n zWP)}Ikf4aRi}IygJjHrd%TH!=)`)}{ED$y<S)kWekyNYY(O*vnJ=lGODBY;Fg)rMj zdScb6!a`@{^rjcq2yokp^GC*?WmDgnyZkWKUL*3hDt#G!C7ZFwr3v$)&OG3@mmc9T zh|X$Fv4`Kv`NXC;<hl{|L+0n4tTel0%MXH$1yxl>DO=QC(4m+_+?<o64}aNX94l=2 z)b{?Yq({1gOTvB;{>Ug2#HA6`T}4damhYg5(~EAU@>4a;W9p=?<qRy{f8^g6Oy)%5 zhTUrK?uWa2OT$!fz_$^YJp#&pT;?2=Wulp+&3<j&hR3#U<bKW1FDC$04ZKPQE`ml} z;pBpdEBD#@?99D@rXVw}LE{nyv-eXU%Q&_7Io&<nnLj0D6(e;`w6e0b<|@-3&OHNh zua?%Kg=8OqLkynKNcPchEpWVoK3K|o&Uq!nf=;QCW?)>8mf4~B*%ANO`1srsL}dyx zc4Wq~<;&{<ZXg&20el=V=B8h3t+=bIFYc9ap14V|qR*4L?S_vrW0WIWsscwvgT@d3 zNFgwx1a=X*q_e9Fd6NYNGZm0!VsHJPX8LNP&gRJ^{}-JzMcX`;zqot)BnOZFKK)}{ zQZ64%--dDTSS89|7(+IF6tPC9>m;rsltkR_^BiD!7$uY5wO0KdvuWVHk#c2QV@jr! z(+{l;*Ek{+51=MB9n(n+odrLX0~V;w{Rb~m8oTF11TV(jN}X)q`yUN!uHa#in=pKt zOEtU8TtH(+NJf|-URy{Wft87cN7gST#XIWX5cX>y1Fs%aq3+FPLRx%vk+Ef-cGGg> zr^Nu@^yHHBjbe&>_i`(Fx!wPhGO4q$PxYQE)t)$y3}U`WZzt87yKTdHnfRIZD{Ghb ztNS<=`!xf=PbRSW#4+|$D&zdPk4VdAN0|$FXQX9--A4$<2fVQykmg_W?al|^v^z*H zE&jfKV>vo~JuqJu-=*TT9+ryp&)j&Du^-8{3Mxq<{1b3_wS)OS12X%z7EQ=t(n5IE zP)^edwse)0yGx6F93vTPD&E!m@<%E+d2GyuX8@cquei84q_JxJZv4(D<=_yG2oBb) zJy8)fE!&y#9vP;)Am(M<bzB1f4-ba=QwyRbHzfXtQ}{hJZsD-2t)<@a>jgCRP2d^1 zqqf=oDjF4Q!*BU9dII2=vv;72*$<wW>Mc^_=Wmg*UvDFm6!R5ABGaOTa?Vt;V7lOz z6kRa9CZzDmM8w(Dg31}4?h5bv@dFJM7r{^2H8Pd)%@?g!2eXKnqu9xxIjKSomZrbG z9Nd<LmIs#SiD9Kwjba6iPmq~bLb?2bM5vklMO-2H5UzeqlhSaP@$l<>b93{h`LOSc z3oG^^^%7GA0HRL)7s)4Hi!WZ~rr&}BcZp15#2>PIKM9TWnV>I>3*BgQb!29$o_5yr z^WV?$Q+@rm{!TXU?sM#rx~yLZutNS3dh??%#0c)SC?U3Th{KSkU!yG-K9##2A9V&z zuEy6j;J;A>1PX_ThKRI(k^fi(y+E^Y%h{F9?627NB$+msTS7P%gln<dy8nM&jucR| zS_I;Yoh~TfWM8}=99uuSvimN{-jm!ATk}fWfOufUvqVag<ITo<t9o&Xrqi6XEQYRw zNxU+K`w(oo{XY*_4+aHTB-<XMY}>Xi%JvQ!C9`}V{qpnY-3>LNK|$u2Ht@nMgS2ha zO>p%W=cXMwMD%Y`=|qfP<o;f)wvs}}A&kz~D|;YRWz|N*k*C_ymgkq})BF3;MK4o) zR~!ALC!{qu=+FSU|B##+!JBZ;7<>Odj%bJR%;NqOnPLOY^|rvVYvtOtHJ^T6xpE~i zIJg26WggXV00F=g_NUVu?I)Q=;WT#Jsy83hEfn+c>uh7wwd*%J5%b{AxP8(u?U-CX z%J3}oYWK+gcIAC}vtQF*UVJMyxA@d#bz;^|{>a@>gz%_@%>+Z+34nuXMy0x0NZ9Zx z!3<YLHpN*FnUYtpUTudx{C?Lxc%^hf&+fqh+T>;cLioBzfB)8Y{uHggKia71{G;f{ z^p*aZ{_F0p9A&JX`o389+(`cK`!a(m4*l8>w2!18MV>r@4XrYQ(UC@sSY3u@MFvEC zAaOHKvGVdJcH17V8fa>2vKVPbkA~`dAJv<CSeIHxdpG<wga}EuzU|nr*uI>3#jt^$ z85yuNyq;lOGT`a-c7&ODPW~CWM(pbu^Ex|c0NUsw6u_KYjuwkJ4C~m3N}w25!XD+| z;9!a(%RQX(pUtLc4PM4ssMhy)`|~fm^~AN&_=Pi*`t9ub`l&;199*lSXBK4)60<Kk zJEW#~sx6n<tgbc{c4rb)7e7AgM@@1D1m}Xn!nChX@t0$n#)*IJ1CYg`=dJ+}`?!%T zF7^Ugb6Oi47neF7RPtM_x{t-9);!P^1csouW8=ClajMVgd%WeD*p5?zV+X~xn}Y(n z#bjESoi1=NtM|-44MH?3VJ2?#|864dF|kI0Dfw?3sqw2ByV429Xt+iGenfC<9$Lrg zKYy~(<x-#y9V~KT8o~lLRHlSizpsMXe5l!(@fmvf1VwjAT5tHQ5u`$}KdEv@Zzlk( z6VgFYC*d9Pb~9WH(4Ss5Hgc+Sttoc;5tcU(t`4b3Jw~%8pj*hG)o2#oJ}I@tiHtdx z+fvpn*flnhO462yNY7?zy1}**1w3}~`1_qm&wHC?h!}Y5r14fIr6lB1s@{AEpK=m5 z;P?lr8VL+4!E>OEpIP#sp8xVVE5F2LXd_lLZlfJLEmJb5NCs04pv>xVr=SB4l8KfQ z&pk-Dk4wuSPjW>?w8^zN2ZV&ABGrSbJ$zq<>4nRXCTKWf?|N?ky%+NcwqJ7t(WL{c zA18KYFg`YqhW|r86cDx*jcl~+rrtzk8|c=#!yX<dX{i~>I|C(ZPm?qqqb%wm5>XF! z04;14gbvuBh;!nhQBl5V(MJx5c<Vv&5q%vo_hj2J`9*k<7)gtdxHX08GC{#L3=;x` z%K6sX+FyH*0anKkQrlK&QdYAVCI!dyfzrQ542*!_w<Rs#Q$2K(Zw08g#fVzdaqc)4 zP`TMN1b;*nHjA4(<tuSECfP<LJTWQT43`q*V=a8$zdtn7pR=A-_(8TMql9wZO49{v zInJqt$n7z_S(E>o#Kx(JJ)#*P4`8yfJxW)Jj+k=D5CzC0!{p>m$~$2yxD99xOX{-q zE(@pDZ>!1+fnk8AH6?eD$#lVLsdVmrlTs(LdrW6G@nfCv#}Cwt2HGD%y1fTVZqBh@ zOChE<qMD;kW4JhCEdaG8wx98taft`4^<)eH{TQ23RRD3&rO)!DFiQBgpdKdnUee5I zh4ZWdYS-inmEQG*j|Hv4-K6R!E5QwN1l=5lvkrhuds4%q$oXz0!^iq!d$)8~4Xp+q zWl5oi^Au#nJEIh#3~Hmw_$tHX;P~TzwM1G{t(mFmtJeTI6Jvc*+ap$k(KjErE}Zh3 zc88})Fh0TE!{cbW7B@Q0YVcc6zE{ygRni4Al*pnKV^LzZ-K~Ei7#ZLE^Ym#$nk_$; za}q7Zr%V$*_)OutL7i8AU|jOzxB8ptgZvGVdU$~W7D?K=^t1qkAen^ryd+LPw^&#( z#TRJxH}U=Zqx0)gD>bTA<U25+V@S;P1&(KWYm)7~Yg$`d@64sitdEPF@H+87?5oQ# zFV<QEhiprh4ZVJukN5Jmn-iYqqcC>?80JDt<c|65<Y*_Fvn{e;+rp^&DZKDWghNJ& zfC*^Cq9tSZ`nYeuw*IbpkLmtlsn&k!Otvd?E9z{Fg}zz8=z_G-I*&YJhaDizd}U%# zI4~%v6FLcO;V>viXwg0t7Br`Yp5OGIQ#`qIRyZ^}O(??q)f}U7b@4RP1!gnISRNUH zq)kTEUGlQ=J`vO3Jsb7DcJwg%q3ulUtTeN@wq;&0Ho;YTO@-+@j4Rtu7f8TEp*^Nl zAwlLR!0955V&dQ{QLb`V7qmZ{>gTYrD^R!L94&F=EAvTc-k-x1x-zfS<cY)X%}p~D zL!Vaqeep7O`j~OSP_^*e$hOux`#jHJvAh?le+(Xag#@S%NA+uOfk*Bhs6veF$k+Xb z0tpg7x_2H_E{uEp(4?9YQm&-5zK>0{uDFKd07zpHJttr&{w#H8l)O%-fA-qLX%-to zq?Q{AIz#pIf8OR1jCnzYy5ae5{q*Pq6$he1X_2|wgR;A-Q$-&sM60U2p3HV;|K__m zYXb1Fni?`#f)PhNATf3ez46o%WCJ2Duc)|!0%x7(8R4~c?<TPw?3v|(bwb+*Cl;;G zW<*P0KX_FVc2c68+&o-ZfvH}tsu)q5MltVr0};LVf9KXdsU2)Q*qeV8;<b5KN;|H1 zBQ};3?EbUQ+lF-X$|GAP^+M!7+kAE~-F<BGsn)yho5wP&8t#niT)0~I*JN5-nWFn5 z{e#n)EIZbS%VIEMPN4<f1e02B&o<TZk`Ey_(`27)WS(24Qjw+_yV7gdjLqkX&AWdX zeOG8!8EH%@6V%wJC;zWlNdryp_&InaZHrgKoA!}V;V5@b(Dn?KPEo(V*Xoa@YIGTy z?jDs|x8~UL&ALj3ANs;hSABi=S4P-Ywt0Sc#ng}VzmLOMYb%oj(+?b6XB(gL0^`tr zTh9kI8CnG$5z^c!&X7ZQNTcja`LBI5sWi(|YSnZhYq;Xg{d+&OCC6(HA*@3iDZm7Q zcobqK|Ck^bRqIb93Jy8k0cO1n@c#Kf#njHOx!cWH^?>K*;Pv2c%`$i<gt|>veqi{3 zBL?^m3-#3RFfa;_{X9ElL>_N_6N)a35#;$WJmWLd!<ppqQ~31h)3Oklh~X7TFoav` zIxJ4>`1tIro~n-LhJ@WT$T<Fp{+8bgKUV!HWp$rZyB{fB%<)*9Z=h)|sHhB9z?Crk z8RZvK>F*)eH09c`l`Z_)iK9h?2w=yykKTDQ45~>zcwb27iAH7t-Q2=<1qI3)U!gVQ z<6eVUs#-33#yO(ybU=}x<#M?~4}Xrhz2`ojK(DVpY}z2)o=bFM@GTPOzlgv?{-iN} z_xZN~lZVi-IqiscZeV}b&4IB?t=kg*7(<uxnR6qJuZ8WY&ytrCzJz^?*6Dfoa~|bS zflOeRKq)74Rk+0;pe?_2brIBtj*@BBDsz}3i7A=5UEtYGSb2zA1D_Tb5liqb3Z_&{ ziAhShnN91c=Bk`i?aeXE?qmP>z-&wedN9(X$TFvV&vIYi50`4+=SQ5Fx&QR&<XSLJ z=x;uHj+9j9oo~bowR#^d+{}739p?@V!uv<m4T3(79b*S}JcE}%G2E)~#<O0pOFWOh zroTBN{xfvi*QGFV+^M!Af(N8Qs6K{-K$T!72H(~fyLA=E7}5NsIdr_z7@kM&C-y8N z3=rq#adiWX?hp2$vXWo`y~MzDNe0ML7o_3QVxhFIj>s16gwMQ%U+aLNT$`O&6+k_~ zAt4R&Zw7jM4+3N3&eIW058DhMoe-q}blP%9swZ9g{qs2H%R<3;AnrS0MSk;gYftE` z&F4NTGjrQ=eV9<PbXHjR^yNKngE4LL3;;ZCgIG+#zK*?S(;clnur_VB4NV;mn$d-k zYw5z~28}BYKXMT@o}J#!b4Z~+`%;w!Tajm)n_@ywGgrb|W!?6|?(iFINCmLmw5MuB z^`YFSr)vVe(XiOoRSqiS(eS`YSiD;Y@6m_6pvDE?(AEv@`tPlWPFds~#3&X!kBlhe z{3ktKo5Tcl!KGn-yRXs83)5G&)~xLaOiosxGAQEsw@!$kzyDT0YdONaN{}x1LhDz- z8dh2`MsA2(7l4Z4?(u;W3H=uZn@y@C4J%#6tKwc^35uq)%rNtJ8+4}<Y$jjwG~+Cn zhK?L!lD0;koS&ciP7HuR!tL<P8`_ts&Tbv*f3c<Ka^J%zU3#HP(B?8lwCOfok4Rf| z-u#UH*a{XNG9I5G>qx(6&mJPu;mHfudkYv%W#ontA`si>Wi;*;-dT)a9lZ;KYXGb` zbfYle9|oc*ml>F@_e_6=r#~&{!Qo(>`cJL-PRtvpgG7!NHRMp=?tKixugW1|=Pyse z_cO*1(|>$G=IwGkBY4nLRRdeTMudIzUHHX!{@w(!F&n6zH>g~(SazZ2p8>GouTR*X zTCr}uPm`!gX#&uyBiM54z#PsM!BGRJk?P35!JKV)V@u?D9$5!JoF1XSN8$5>BMk5; zMCrZ%?h$jzi<bD~QH~2@p98Uf{l*P)_7DvJiQOfhy0XuTIcS;8744uA+7WgFki^jt zk>d-{ZpA#E%?r~7O%es8*vzjVPVnyjmlGaCm$u&uB0wIHsVL%g#MGxmd^H(gN1A;T z(;D2p3i_KRNFdbWjehtx5xI7pczpZbkG{u70AONhdiU6Uy$Txl%33Aj#=c!`bt?Dl zet=92#>i915YEhxWNRc3*gz|nuKCwpl1%3Ck<z8keR?3$_yN=6Spt}MB4gpe8|k>z zzE^8s;qG!k)t--+zw~ny^q?bZ(?I_Ue)m>TSWl`pB860Rz6ox|WG-Z}6XOWb!Z184 z#@dNW*>?&qZg5RhY3WZMPmtwFLPmCI#`shbNo;U^6K(#P@%hjHZh-4M>&!6(voP$s zeS?Du7N;6Nf8OAS3L)^qI8Ac{7Rk11SoLDG85iu<D|B^r3E73fLk7xo)yHqzz^kc` z(bJDfd#jM;9yj9Y{LQ$CarsCKGVb|vTH}uao{5o`%r4!(f4}crp%f_@81BMRC;xQ~ zSdl!DTDZXd2<av<k|Q=KWLtu!SnWq@G~}d#zPswi<A52XU<S!NctSz~jvQDE;JKGf zH_uvO%JFo0dXXPC%R8`UqzynVH3w8b_T<zpsL!JySVsF&!jGa4L$44-v$zi&kUwZl zZArx314s-422i@Hp%%mKb*-d=jA#?@EIbO47-0dHT#azEOk_c>nF6mt*o<xfa0CKQ zFZc+Dg(;RSI}xT`;^e^m1@urg_}2ouH3YZChjkaA!9>E(DJ=!@h}|Us?HC7dbaZuH z$;okYakkU@U(#f>Q0Io0*`8RF(TRyvWKGKJ%F3>G)5bHo<j?|Y%lh&`Vj#l{A!~rn z(Gq+Z^ClUQ=6nd^M64wv7Z_KPFnh&_&$u;Q8cpFi4fiRj{xH5l?MTz!j7n+WO*0Gm z4?c{r$x4fJPB7+0=3?636c;8=44h9h{%%`19AytwilPNvCH@DA2$|I-^arY5I`AyK zT%0Rbtbn}n@Q5-NagXf_CdMs0b_5~Zvk~KX^DwY&OWS`iiWc3`un(a@tP#X72NOps zdO{vd<Y0`g7J{~l9Xtm*Fm@2jJvJ1%f5_t(kxHP%-|WvkVq5ipJs#}GSrI~mfEFU7 zY_H+&=<R)rfl~lBqp+nZ)kNeN<1(K__@FNif0OfLXHyRT@QIF@g+&nOP9f$8c7x>w zw>CgMPZ|JKP%IA4&bh`Izcjc$8$F07g%4+qaZwkF;B5-3G3O(Y_>2ZQ`^6om=w{#~ zau5&lO*~p^3R6lnY`BG7juAKptoEsg0z5!^o~ba%Kea%S#Pn1LLIx(tdTa{fJVead zH81-+DI-{S>M_iVY<TVP)(IQtKmPp(dCsOjl?h%s8T1?X@uJB%7&-GtT9C%`SE8ZP z_;3^q6B~v@>w%}ksHcbp6bJ^{L<&F)l!(>5fWV&6qd51);uca01`>FqXs=;F(1EGd zcIND(zP>){>M_yG5#{SU@gA|gx&{WS&}vixQMtoVNKP<<(}5N<O`Rv*tmcE^|2*qs zm!Ww;SQv2tV{_`k7DW6Y$ZKPxqtEhb+9}ZpiG0(B<3o7Y=^%}oW9I1Cg^yA-?pwQi zFP_D~#WpOd^@rQLAP)J#wzCH3<bY0Tv<=p+Co)lde$@j&H*#hL5i<tzuoMEC3-ue* z7-75nG8}&bfKKuioVsvM=Eu?=9DBGP{$_YiKYED5#E(snBoz6;?%Jvi58!>ee;$n+ zTrPW=u32c|h>%U!wC#%~B*C^TFDBsT9r|jwf^k%Xr6I~LKH>;D_@Wq>CsYeGV(0uA zgNG(%N;*0^gRMo49-`Z~6OKbjxM2)w>mp{mRT`mMrb!t(1-g80!C@gmf!%u;{@b3M z-{QXs$DJU1aRY3i10_eBfUlv>^t^o4wzOK!F9Cr9ShrD#<113D;j1Q2N$G2OE)Ha2 z0ZRrcc+eHw2L@JC=lserk3(Tt&fE^`I5CVS;N%}5-;3b%aB^CdXqLuXS$SD<uAU!N z<8IGynBi>m=`4WjrETi3Vm*$es<T3dd4v9&46+1GHJy(>ih&b_<_YRef}XG?#{m$? z8An7aPt&{w+xOw1sp**+%YtMSDMI$4#yPsU^unP-^cD(irxJEwrHf10IC@qrTSf%2 zFg|4+6f$z4&GnD=pEb5Lix=|d|Ml@!SjF#zt;!!>>yC~N5(vn9bx48IBamx}74$ze z<sKM~Z(YS(Q3O^rMJ<qs)B5tq_u=OmDP{%)H;6sk&)%3gy)AX|{@XSXE@biqENh0_ zSZ9A}P4;Rob)39%P&jY3KFGd}pk)tSXQt`@5M+aY?)mYl=6{$>+bp=2lOb310=cxO z6A4^t<R}<nnI{Z{ib*dTLc+sPauyuhbnP%n&HYebG(##QH3{;=DcxW1L@@9OLMVmI z#Zyh5%@~y1G{3mCQ+aVt3g<T=>*EBD+X*cD$Fs;PXC(XFLD}l)?tYCosrRiT{s?9w znory$ry&XTsMEW_{X*s!vA3DltXa0;yXJ0yD01HzC>L^o7ZDtdie!l32$ExxMH|1@ zNc+M_{1>F^lCU9@fxR~gM~h)3*vq05TMyB(a&yx#ZSg+YcY(<dK3M!$4tXTk$pTxq zFsho3SORd+1!nC{WHDFfw|mU9-G2p_gee8KySnSEvf?0ZwfFX34-Q^!0_`HZOuzfJ z*qNn$W5v`?oX1d)V5I`AdwXYMgSc@5{DEY^2r*~(Ig#A~#gRCmkw|g!#x#y0B8D_9 zy;i+UA#d^D<Dc{t+*BdE<4gvw4I4<blKXl5dycuH9LWdlaV<HN$Ym%fBO~L>Y+yAx zB8}j2B*D&+k#L{_9122?kkK$aw5Dt}SoX$}G@`b94#P?NAj{*#8<i7JPiq`I7JVr< z6OK&9h3PKx2RQnR)&3w-sHKnq$bn0zPxH7Af9t@zzzg5qJo(>DVEcbCfhql_XcrEn zU0A@qfS*K*t%;Y>Uzf51#^U@C?bkn%GeNbFLj+-H@9L^RiO_p8!igg(>vBitnVCP{ zNERABefND4>WF{m&Ce^~pSX@}F^UAfo`b_G__oS=gTjjf0DT>>l#>7_$G(Vwqpt4! zNJjGqX8JK=!5?XYV_p&fX0e$CVTnSSk;3D``TPz|c-8+`*_nX#oWJY8qA`|XWGnlq ztZkB|ESWS^l&Op%Wo;p4E0RLSPL@KH$kH-Qi54?avK2{EjmT1#QrS{eqUF4vX6Bsp zKiB{FyUv;Gn(Lb3yL~>N_xpK2&;8u@{XqA`e&oFNi~UNk$d~+kj}75*!I{i}iY13Y zZenBt{+;Eo)KkzGnpW-93R4z81Pa%5-u!iI^FvE$!%88@`}0^`TRQZyA|pccxl?=o z9c!bu4;t|gyTgkQ(KdivZ(h5Wy9PSg`kM}xt9l*#M-Cn%j47LAj)scK_c6VgalvyC zxn?mRn{VCynx1HwWMd-bJNuKDAzSqhv0E3fBo^U&*htQG-a!tR$Dh!+YkIZFQp|4x ze!0o%AhI4H+^a=JVK_?+@cg(`zYJX;E|1^SlnvB=6!2)E;%!4tWRfj^35)>h?fZ=L zzj)CnDD1F(ukla|oIS&i7gLI~33oGBs8|WS__w4u3EvWvsQ#TeWF-6KI8z^eF<W2L z&u{^umS?;IvpOV{x5X!q4embO*amLVwr%yPZc`PsIiVh4zXsH%j{6?l6uniVpXU;& z$v$alkxBI;r-rp3kA6<nI+El!Z_m1eS=mZf=hO!bkoE-WPZBnJnFn=UXjB{CODQ&? z1c{(lc=Ir^t>3e2TR$M@!p3Ylt}-l&RHvl&bDhzh9;Iuzf&KR2q)&z>!Xi^j9nugj zKB*M1Khz7ny2T2&Hlt#5JD+ublI*>Yj)UDuFXB%}j<W-UPp8RCY~ScB4OGqpq5mGC zM`cY78rHAOW^_L$_e{g3<U#lGgZ|)JWeJc`jw6d3WoWqkUA>{n5tGCrKT~VA->+bC z-pHp$YpQXB0nD^kmmzL0Ie9V1+o{xaW!+!g^T=0K=!T0GEEOl8moTspn@V1L11*>2 zmZhm{RpZu}{dddQ%HM4h2xrQWsWQ6K&Xgnab-p&aL>jGOZ`aGV7=&p0=){&h{EJ#> z*o$)6Y{Q9`KgMk)M*-Xv?sLnpzOspTwv;?bt?uH%4Oi;kf55xs_7yXd_cpyX{pNT2 zJ2M5yy0RvQgH^nkMj2{q4Np;BKGY%GV(Dm7^^BhGN&#9Ik*iZn)DyuKK7IQ1w7fhx z+<}y2NNr%@Dt>LCVRuEudjDgC({L77wv6-cv^$B7hx9bRTxw<!Vu+#BLmkJHj+%af zgDB4ZyAR*N<#}Ls+S~X2_w@Y)NE#X(IQ9MCMr7jyHSSienOLiKx)suuz${9MzW+}< zKr`>6`#0B)5e|g+tso?5sF>(`QVc4p|I_56Sv)Rsr_)C4-hPJT7b~>=nx->1wIKXc z9BL2Ph4c%h+Tuvu<6O4xuhVe?=D<f_H1wO_O8jTLI(7P={<Drd5PS*A=~Svq@G8sf zo3mg$pw)=rqw*Ki<`XB%^Bp+KP2&zk`h75@f03DX5cd3dr1@o7Dzc^XUklo4TGe0E zug-q@4&58+=~WLG$L7S$8Y%R8Os+U_Ca&5tr1{6Uo;&Vf!ShT^5KaQmz<cf0ymv?t zMWc5326w8T;pD^PtCIY?&Kc2eBiWdq>+ON>OrKndNN8*9+ti}4@Zk|YD{#)jmU>~C z3oD(t7ygYa47^Uf^BcXXL)#k_(S6XQc<8yxVn7T0A?YI@HSa3*&Qu-mz_KMdcU!zy zw*TAm*gSCjtb1L^KmtM|^z{~<^-@#yNG|AKT^0CSi?Wx`(+4;hKf4<@;mxDp_oO~* zuv}=mFs}Bn7}W{=g!J!W!Wr>pF2)vU(ODOzg|9VUH#){$u<lj#-6gl8OJk2;`S#eV zSHgvLJ~L@f_6mpZ`B~@sSSps<1U=8HJ-Ez#k<o~;EEy#7!oFYOUFsC#Jz@b=e%xT* z=O!G?ozke);F8$<H4hG|T(-a2HSMr1mRT8czJLfx!v&y1>h@3V-kaZVm}fmN_{<Qw zl}Q_U{lFn<hx4jH5cQBDfbOhjnTy}3WC68Hp0!CE68>A*$NWCcHzBH}UbkCrl`lUY zqH-jicPmT|mJSpeT*dMtZTqYUy9=Mqmo~^{WO!RBcpUfdy3{HQsB|U;b2Zplj3K}Y z9#~Uz^FgmgdlFcT|Fqr6RgQn(t8qrHHM+%V%tS7fai&GWen;~m8Fp+%!nryn7S1c( z4HhcQ?NvQ__uqW3b07Vk!tc7A>(GD7_U9M9!u5tlxU%RpTv!C%EGj(REX=2SdKv(V zo**n^4S0Zh=vndpM*F71Cs$$V_AehWR(&&fB+#cM!^n^70N1YVlt;D+3$4jx(vfBa zx{rp^O}ewc>><u1mXvY1_kLdT*E%4%x0w<rdJhqvpcz#VN3mCbo}%Kq{<5;*v{|Ao zgXl4a%Jn&u`Uq%8?s@~%X7!6*BgI^n%57?H4W2<#T?;n4CWh^0sJ0|BVt6l~RB=&- zjTa2QEBPBNz11IcCdF%y%QQPKpRDbzqv-!{h%x@Vb_p~>!DM^f<nyhA#Us?r<IdP8 znb0}LXLzm{TypICY{b?OKd)>}Kbl`s;Qu4vkW0o5x_$em)aXjU3(~5`j~~D3=(>B` zj@|R`mpF)~_)*lOD%&Gr;u+W9w(6oMvDviBUP+acivIgR`#;WB+i^gOi3yi4>D`(J zK5G=tgyf#_u-OLxd`3=bn&}(In*4r>2Hw%`b)A1}G2O$f%;A&Qf>?TXX}|1Tq`rl# z4j-8PRtfyVYJa_A{(YJl+s}kSjR&j0d)|GP*a3*JfQk<O(D>bHhkHpWw9)yS8I=uf z-fbz|yr8LR_Vhb#h}!|be3!jTq3hq}u>*czruX5VW{8yF@HlC4KtrYM7z!2ZPOzW* zO}%#V{BZCV_qfh2*0iXuShVp!5rI9cO2qJ0#d}H1vwwL=Y?KNh@xNRoPMoJDX}+pH zA5&L3?9HOjdm=;m3am|-K6~qvT3j>VUq92aB${LvdOEjJdcvS|c!0)M+aaJ4T3CW& z%crSL4Z2YFrh00lzu{mlG5vMKaNkeMq*c|kS1)hZ+qO^JhFfR#ALgPA`efF4uvA5a zD*uiz(p~|d7iaQFp##OcWLJU1;);A7ir?EhO~+d(1I7z`gGSV~IlYgbF;4n;bPy`A ze#(=Nj%lp{&0~9~0dXj+a7mIpR@KOJpaND@SOa~tv|^LPSzNlGVg2xeL|kMsLL>nL z2)`H`YSg;8(RTRh9`|N;JG;B1`otNJRJUx%aJ-80Z*8Pch>LPq&FJP_wO^p=(J2ZZ zHdY#b=DU4Ie;xT#fB(^6abQ~3Z0aOupjM5d6r6Y39DT67AC^PoCnb=*$YbfYY~2F$ z;&$z}DdsErG+Dl~f42YHDaQ)2*~C0*UuYNm^D|6(UHAGudGSBSWk2<hVFU`h6e0(W zo}{2{n}kE|w{5gqoN4;v>~pNe9|x9CUOBk7@YIVJ_xB92sMpdu`3uqQClv{_-4zp} zklnY=DAMVxu&tH7!cvu6$$p8UlPyXWXWeOYUSnT|wYyklCjET$k#cC*jLd}2>fhD< zHr2%4xVH1dh^rw}BF85xvYoD*sQayQ)XvLQUDp4*%3%??I$g?R>Y^UX;eVpz_|dL^ zr6#Qxe{>Dhs2eu#DqUaDkchhIkNNes&EH|Iv~Us?t{D4%YTDJkYtVgZg5M8GG0ku! zzD|*q+ZfsM?j5ZX`_oC&CVM_XiZub0Kltyzl7sNRdEa2|!qD(v4W4og68@$sO_bZi zId|(+%ez5t2mX3-xWnbTy%8hi5Hwo>HJaD_ALcp8E&12uAj$^*VK8I2#cqH91P=|a zzu)!)1Fe+=|NOFFA@Ugt83oU>yI1arPv<s`uFc{Uk1Z9$)Pla#F`rH_6MnMi7H!@c z=%9Z%?4PL+!nFNA{mjeuI%0qb(TAXrN9cjlvTyz))-DDn0cEKaeWh~~l4eaDmr0*N zpI{VXu*if=4ldKA_7g)cM$~%r)OR!sOx?p4VJnQ=s(vR$zshR`XCA;-3vg4348))n zdby9a)x%y$rq78kR`$R?2!aN;QqLMaf58H8jt@9`E<4;Qp>9J6GN&LAi&&ft=C+@B zXk>V)N5Ut{SUf%^w>PcUov<R26Fi$<>p?k|7C@fgLM49=aMtnBIPmFokat&@OlhCz zKuBsoDby#THojel4kM8eED#(xot)mEf4ig7Os5t6_S=>3_7!GVa@cHd)LDWHJzB)$ z=kVShZF*!hZytkrM-Ggb1)K%x0H3?4`PDiY*Prd!rHj+nUp>P7eRor4>H*WtBX`aH zgnnojlBMpz6|1%x)n0(lR+&3lSB-pPI5r#sZigvCddMeKVnCuw-$+lq`6Y==C)e;8 z_3-hKv0*2FShnnJ4z`#l>B$&&J~EH8Gi^!HC3o@L$(a*MEqhgU%<5%<=Scu2avyf_ zx)R52-V=FnB)w|XbHgd3qVHp+y^H>Pu~~G|%!%V}_&`5b!;<PyWqDMXv&SayNxY%Y zx=&Zi?#G5zuQWB?B(4gYc@xve-H=PA!b+%K>Fb-!oRAI+0Hh677SCP=iS;DuiP&G; zSlZJenU8fu!L~hnMz&Lb7VnESn?LwciQ^wh)7KbJx~3;S8)}IBkAW%0_Ow(Ot{l2k zxm7E?mbUWzszYFgkS3~wm0LgJLNFG<Xg0H4@53g4-ylTfCuo;|i`&I=B~wbj-*+y% za`u1YvJSX<yOVy^Ov>yK@K5X-OxZE~J%_a|*k5S-(f2cMmaYcwWHHZQ>#iRxLUv4O zvQ91YNa0ph(vhx&c@X}Pd%P9^edeCNKoth$m0=~RxnB&G#6gmWwwHD7ynb%&vRgTY z8Wa8^xVDpq*6bl@|9BRUDSPpQv5Vz&Z-d|Z?DY*RrFX#b1elu;7_{E0I6k3%Z*%HO ze}MiKSykcX%*@Q*$4dvR)%Db-#mQ-Njgl6$qL_PV$0HK{9ZO^=;t0-3@uH?(aGbQO z=IgOPb8%vEOxG^7tdUz)?vZ>$hOgYy0o$gbsBr<z%8-oWXs>xChUE=C6)!QlrSG$D zC86ORFWYMw85!jmyZTkHbaQh%@tSN=SiM4z5Z_iYJ|UZg=D&AA<x6L^IxW=B*fEb_ zV8&C%H$?Dk$J9RduqWlX@jNR=zsk_=&Euqf{EnQ|^-XSjbagXsx_HA|<#w$uJ>n&~ z383ISwEgz<4gP*l4Th)e5fR3)Zn+#ont4xwmJGWtT^EpBdNen+Mc^07l83+y4P4s) zA7Lj}i8%JoZ?HTM!%x~?Wbk<~p+j4b>Xv!arM#@njQt_@mI`dtxvaB#5;#~9V_cN( zM98ld=Px3#Esh6o+Uri`*9wOJuhgP<T82(`D)x|)NSKC1Nv0!wU;*V|CXd&+hgM;I zzliLy5$juN)Ix&PnSRPfQ#{1&?L({Ws7twDY(h=zS*==a)yPwYkYS<!#QI0pMKspf z^1q&0Po6la`#HYxNcg&4yW0i1UwNCd22dAS<C*b#G2J!E^H_H)v<e>&KDmCtB!5-0 z`<78HVh{@XG?MJfP;JGfANh81#VmVBxC6XDaim|Mzm3wNo%bLP4j2$+y3_T)6r)XL zjZ87=e=4?X&z_+g0fT;JSD_8w$Y=tCoJ4kJ`PP<dUwSH@nUi{l=2PN2eKpnt+RvLv z5IQ-Cqu7}<XAC>Ko(T%bUe4O4zrI;|J}f`d_G>IFalTtI>Q!>=UryyXn)+=@yd)+p z`~faNxrqOD7>+SiM4s5UiYyLn&k>po2$@pr)RICmA?K6gSCm-r!Yf4OI>1w5^}K(2 zrKVnG#V(@8zif9b$kzSt=bJZ6zm_N_N<~vmxd`0FvVm%2ii+q0l=7QDB3C;)$K=f3 zfb5(nPmX|i2UBH<C)#Z9k}jvGoibr5kzf|9)vgPoBodQY+>Vxk_eD+#Hlc{4SUKyl zKdGpp$?Boy?k=GLQ{XV_OGJ1<=r@3Yw^&>>@@rXYhkt#U%h&Is*{cS>SD#*q3xHf^ z`;Miv`E^a0lbnQSJdwiCA1!lPd@_Ms$V|;gNv{*Pd+2{|c#`7d)%?+%zzchhS$&sx zTM<%dT)KK<wN5wL0N>q3_%`7vRQgTr{n(35@sEFB8YcPmNqX;F_UzfSoaUC#rr_5R z)rNLj0DO~PD}=OrQB*mL45#CuZw-_scm7{YxcdKRM9bs66#9wFstLa;I(92&+t6SR zyj)A$ajXo?*}F{jv$*hUr++kBS3inLC*yb5egP6ai^Vp!WY_4bz}^r<5>3e8fPToy z%KF;WCMQm5Oi6aa4s%&NhmOrW;7+v*V)PDAw&?Rim8HO^b<KW@)$iZmyc^$yBOUxW zeb1?L=k^t2{XV|$72!`Pi!A5`?SZN1*TRq=1X>Q<8$k#jQ&+qX#-z)y*>A*Q8UvzW zkV?GI9WPYG``!`U+5KTchD|%+Vf8j{)q4FUNaJoHwJGE-S~Jcn_=%EzVNSo-K0(HN z(Ci@NkMz7zh1ZW@{IaTrd9oqaq>b2YUxwUrNN=7JT(o<ugz9vBTkb0C&oCODAeJ@I z!$KA`%!Xbsb_P^JN<Y%9gA-zb=QSp$^U#S)fCGisUy$C8p2)!k=SmX9K8dyDnW#NG zdG(gCig)j<fZ_ApO3TV1y%Zu6frBq$)G_-HG%|XuNefSH?88J0P<*qaWNdWJgsv`f zZT&yJzVG<$w|kwH7LGVP^Y<l3vcB}j5Cbo6sg{6o3twg*tpC3CvCgY$iVD{pY$OEz z_WX<V(>&O>J^i&-26g-cibctcFsLAo{Fw-?==5SiH36_WrnMO@6(Jsm`Bt?iD|jC4 za`YMLVR}J<;f?vX%}Jk1VdvaDU)n^c?aHG^k6vasFkk2Ewr#PlQp;$}1yI;*s%~9N zq*oEy4UucEARjvRhr|tSOnUNdL>@f;<@7Ds;(_xok{v9E@^-j(4kxU*&x-LQRyC_h zUfm0~7OzE!tvs8if#0U02eu2;8WtlMI)7t-4}s)K)(DkWAo8C;poOn?A_r$I9F(88 z77K96r&!}+vp?r<;B{`f`0oILSune4|Fkpq!IE>(9BUy~<ubD8wcdvWG$Hd;Cx)E# zp?*}3D=pg31c)Ew9L+)ht=!zAv%++}!&Jr(dVj&IqWxt8$<TJ{X|i;LBaAsk@wA0W zw;(AYDNW~3gz!&(9rb{fB#vS&{b=lo3*`iB3p=5L&@}XZbt)?EW2lr3BsVHp1@WH? zPL@@*%1j+}s=-DL(@g2^V$;t38nNdJ#)zE8au-R;T$$`q7}fH}Hp$c+JOX9mU~yXQ za~GBt4vR9q3bonPYHvC7@u3w5bfK}yqJ_=JXY43&v&vn^C@i|hjA|RfyAX^^)-10B zpOqWLy*^sjyF-T#7zuRHh|c)%{=M0%7ne!y#eZoLq)C?X34Wo-L`W3|ru5l!kEp9y zh6yojZ1eaAP?S$}Z)Z|N1`c|PEE&IxEc^hn#Osq1XHj9YVPRpRbkU(G4axYRHRlCU zSoQ%04tC3J5k!!{*z8J!BkY)=LT+M$$DLT+fr%G$ahZR*1pF*|7(fx56Q<j>mgb7f z0%Vu3o{D?wc}wTiZeq^u7*<>05=OIoUPDPgL;AtY4N#@NV&CkJja?d<|BicnK*&3i z9b!cIoMxO%B%=ekZToif>OzDon7Msd^X^F|Q8Uo}1JD>z{+r(3#T{wk?0@zt_gY+1 zDShQzK|CWUqix2JT=ZW1(YkL*(F1Mc@vwM;U&UW)s9`)zMYar_K>$kSM`|q?nAi}a zI~Ay+rVs)tWTtD}ZgKsC4}zoNCW?`vb1(auo?M9RBL3ZFE$ra<uagjkF!Ou<e6>Un zTu>iyH}Tx!;ZYfaz;=;{x@Fx!8I?zfXd%W7QLm2@A9Cp7Hgn!SQ^*N+FX4b>KEkaa zj@9wBtW2uUPoxC0YyrJi#bx&*fsw`@rsAMU)~vzMP@CCzdV)R6!{r1@<6aG%8K=Ru zQBl7qv6X9Ko8;AgU%Z?^2Er%>nGBIBDfRaDR&3oPP`HU@Dbc>Vj`=h#rEenX3UG8d zT~<kg!9fc?x3yh^QD9EI%TyUrM&_~=ju@wj3_i;*ufTq$bA>Kn&HYQ$^Ve!R9N2}W z5lJgrx*-rn-Cj?Uu0+4NNAfKfbzd!ptw?g%t`VR7$(hNfJ=vscaPMTG5+~dO3hCR2 zNA7_zBu|NyquQ~eiDS<jSy(}`_b|#ZXI8U#<5O3y;8-=y8^tnxZroJYL4n%MiO{sY z>)ULY=}^CHl2$kgMoE);s<d-ShBCNko9Wq96=z5)>=2fXlsUR7EJsDN)(2cZ2`6N+ zb(6Uy{9x>JM4~FE7Qnqw1c<$6HI48fgg8>g{XWOG#%k;Bv@bra31Vdj8OevXT{Kku zq)HaA7^8Ek!HkLFAj;0Yf1q1E9{HD>1}vFb^6HgqLzB;aMdxat@8acbl-4ztHkHgZ zLO4J!s!Z&KrI0tg%Nji2Wds3uM>!5)!b@a*%$M;V1QGU<O3$9R!8~l&TDj(32CsF4 z8o{AC^L}3!c1SCv%!;`UDLT?D?5MJ?FNtUX70)@EPNo&Cw*3^MclnrU_fuv@#s~`^ zv4G01v&;#(WjDv2=fnCmHh-tfI3vJGi)p|A#@kWJ^zNHhUjD@)Wz9XK6V)6V{q5!y zv!aPhf=h0h7w)q$J;gSze9+`TJ2ysnwt5Puc<RT*kSYDim?!h_H*CrtYe?>Sj6wry z=^uYKPLTjW>{UF3KD@)&j~HQ);35ikacFxm_~8an-QTS&qiTv=!qx^oa}k7d+`ZDs z5na~EOqRuOO=jKOrDc6$Q+3nQ!sJB_P0{Og9j9}vL~f4O_1x?am6gvncblyslkpaf zdm(yFzE12U324UFYttxKK5#KORfam(MMzjeeJH<*u-_@AmhA3ZEMj4pggjFjr}h&N zgm{()#59-{GFf%gregI7!jbK8kc$~RYQqdY(v6gZY#a=PrML7&Moh3P!CeRgE0FO$ zR46~HBT5k-n{XHDiu8Y!Zu7+*Vw6;zSuTaMuIa>7uR0|*^~@ieOVOX-XG*R9;jH}X zUm$Im*QlwfrNn8hr-^QE@WZ-TNQsKe7!N{UGw7Ym1e4q)!Rw~JP5skiLwNd=Cu8rG z_ODfoEasZ?7gc}E*O`(@L9ow0KCRZox$e^^jW1&z3(on<E<0I}A%aK0m1|!#$OHk0 zujbW#InWcN03qwaHwMeBrZn0889lSp<qMl8TsK*%@I&K0kQW45A}UoiUYDFW?jbJ~ zdF1JxFSU)Q5qO*}9|Y!Mugn+hb4=;HcmCK^7;Q@BQ<JsS-AJ=7&W(GNlJ}Ux-Xea6 z2lRvJru8&^JktU~c%4f3?zhN(Mlm}Dfj}vJ9hr5~JWA{O;FuY?^Xoj!2X)-CNh6a} z9qF+Iz2o77b?+%AkIW%%g<Br=uF%zl^QGkWA!YB(K?iJ4(Ipa^Ax@JNjn?ce$44S> z$Dgh5ypO)@>5TL73FbqB-@tQ8ZE74%e9d<p1v1HjZcVT`Y?&67Ugfg3X`()T>V8dC z@L&o3U<?7AlXA`x;zz*%;PhK~I?Z3Zhe7V()o#K1lb3AV7_&;rZr#916DLM4emH8( zm@!!+IH7Nl>adsv1X(zE_;5NV&o;E`ZI)pEf+Zq;Am+PmJXWoW<h&w1)x4+tI%w0V zv0CeO7HN(_6V+E=Uq5ZvfVwLwd7r2z;xmR#JvwGl!}`f_E;}nW-@f2wVU$!$T?Eh3 z>ReVzkC@zH-9z(#^@YnLB6^>iH_r&hVyh$U)h{*fgoINDQ+vlLXO^v9b16GwE|Y_+ zbfKmR%}TPfufM*Wmp9BIFZF$s@#@^uUM_W$MkV%{Hq)u@StH$XeYl&4$HWuVfU7Fm zX7L<!BEx$*R#=qOisEm}A-ahJN1FTm-EElK(!hh;67~JYuNWK97Q;eBU)W~v$Xcak zH?A~c-H)>hv+o#p^SS8dYQW8#pnhu5x%yv3z)HibonuAqFHWALhE>dJ^tT;7tgi6- zYJs-8e`95ZFc;DDSW%o(b8NTB4X<9hBW8w5Q*mT^#N52@;{$vir}mK3OU!TCNs(*9 zoGb<j`1I?|2CYdM1+L#rU!k>lotEvDjP1L3%ZNFVmKUsX%QzSP^TP`(7oW~PFD^4% z=Hd760G6k8nRHZm)nqr$)Gy+A4_*b&%(>S$N$aopPM^_eZ}VHVS5>k3;^%vhtk<b= ze!ZxkMf}>lYwa1Q%v)T|1XGIvKQ}+~LpC9SFsGtdh7V12c!L-?-zW@#*6iF9&`&)2 z%;K0&=~6dR6W2=i2Vx8p5=vy!%R_1({!)@{WE_^E9ZbL~?kz{39Pz|iYCb;}K4T(7 zUnVRd_lenlpQdP_yAb|sDvCPHeNu3^OZ)b%i!Rk3j1g@veL{iB({ejN6LKQ<iyy+h zLFatNQhWXxGpidCBh<j1-hNIlqBtN8YNmIdZT#_@)~yv)Ci<i85m_Svw(Z_`*A0d< zkSH#FU#{mLPB%PxT=DkL`;YvM@@A!xUc~e3YD3ALVPPX%UVY-p&6#zx&%P<N)Csr< z-7C@GQb3k~H${iR@jIH$P+zEaYRdMKgDFx!6|DRfOaNR4m2^?()Jr}j(c1|rahT4M zv|Hc_xBF;+$GXjfBUbLQS;mCaq&-d}--}~m--glE9El227;!93KVj`p@v859TUCMZ zHVHm+T+gVIt#xb{70chbm15PW;z#x>ws)_6pwQr$cinEw3xI4ZkC>p%e<_lVJ7xrm zz9j1CnjL<Aex}CD@Y9q<1A3-O{Yn&-vh4MjpgtxPMBDF}4{x`T%&U^~c$QaC<jA3x zyP~27;KZab7lsx|T;Au|Zf;r}(s4(ZRRJBA-VD+5VPaPPk>6_%8r|9kUi9JCkXSK& z?|b~ri-sPD^uMIcE$G})RrS!XIxk3{REIfF2^pO)hHuE{RNOT}Mthw}Ec3QY{GuR( ztafzMwW3KHn^I$UBC8Y9`s7c~cM6h>`(72sI8k-UR4IA5&yoAYI33i#@MR>rb~2!- z&vPtn-v!O_T`~XK_F`>n(G_9hKZtw<BwOZSFki|2LIqevPkCT-d@GgVD9cy|it8sA z7M~@tk~aPbbJ^_p8T2IxQ=5q`fAc_HbSEIq8JyFSH!@G)7zwn*F_!e}4hfiq88{e! zTX8Skyc50_mk~sWpAoiQxYG7Su_W2v%2)fV4@JXAcrX25>(*-cN_W+<ze$cKxGXrK z_#8@br=qo{U=hDpk_5LAU4()#lm;<J8>0wy<)x(guoOes#312nqgS~_TC4z`*G@Z< zU;CQf!=S9}Wqr>735gyyAvgBN({F*E1AQGtibP_yMrk5ueXH?H-1j6Rw4h=$YN<-f zfaRFvi7=1MM(`d$i0iOMpTUSD#f(y|mB>sG7yEHi{9=k`K8+zi*B8g8I5#-;9nOHX zxd06^6EoT_aR;fnH4>S3g(;bAQ8W)HsR|GSjDC`Ln*6|pDEXK}k?|0HuPtt?J3HA7 z&WWviUc}^X;PaWL_j%{yUW+WV@9QY<R2d)e1vYj|0yerveP63m5o3v3oLC}FGN$XG z;NDEWD-*F~R`QVxS*Jw<0?C>oB||`}7;?F3%NCKD1AJb^Q`%{)(+m3Ka^MR!5)}K{ z+qXx`XRsE8(=RMRQq`8NT1lnOK|w}-jJD9nw~s~^tlWpP3Kx_8GP;I5x|52^Rl%5X z=#@$raOODI>xoYo$}}*fy~P8Q{0BQ+Fo4ReE<^$Z1TP>^;1g*LNDW$Xhlg-ve%;;# z$<iEFcK*&QS{J(O03y#&3Otnj{nDP~kky$--fjy65o;AjgR23zJ%h72Wu?*ZVMT4l z(ucBZ9xH~a(i`A9|9E=ZASr2OvV>@?U6n?;J<yZy<Jiom`48JePku5ftPJ8UZwwVO z203dmSUPn#%dLBS!4#l*@g-6l;U=cP<?CjXP>Iz8#(IY5>srWg2@dY9@;vj_-_UH* zSIao!!;h}ZC0J90(EBJfzu!5s*n#cAc!T#$DiC`KpRMXi{`r_9ckavDWoSvxg&OOs zD7N<pGHF}9#NhxrRV>r!CT>YKe&U0|lI;ja|6FHz#uw7g8>}a_;jYuphmF+o-WTcj zgK3*XfET>)2Y74vZYE{rM_9aD2EkPB1k2{gs}S=+4?a5R0_DQb68J<+6j<L(ihA$> zNI)b7kTBy?CIr||!DO3%l>M7$9-FpNB`ItEf2yB<*Z2OXPc4?u=nwT|pamqz|LxoB a`%Kxh-ZZJI>jwq?G5^8J^o+^eU;YD^Mqz#c diff --git a/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png index 8cf9c4d5558874b0604a75ec284a7d3e8c345bb0..7590960f05c13e2c4e2ca36f8701b81cec12a350 100644 GIT binary patch literal 36061 zcmb5W1yoh**FAg=DTp8qilC%OgGz%62ndK`(B0DAWe|cO2c#ra1WdZS@kk>`cS(2G zx3>P?|NF0ReB*PBJMO*0*=O(PS<hN?&NbH#xp!Co4DmT)6bf}lQ9)J>g~IViq0nQe z2;gt7bq-CzzeJtoG@KvUnK`=|I+~(X44v(*?VPPGjV`*HIyza}*$VLr^9pibv~YH| zcM{{{v-uytz-#Ad&c~@c?FSbjvRBY_LZQeEk$=#iq%$m0sIWVVvNs>PCoT@V>GVt= zG_Ccb2`?w$%D=RnxK0=l|5Efy!H~uD?{01$`MNuK-G!pL>sIlizq*Ge)kS~45r5dC z=0te;<-{#KJgF+_@L9avwg6nh@gr{u@5?D0^uip$p$C;&3xf24h8LOPk4Io=9^v2r z>Q_R)3l0ueU`$2bla-Zajku4x48MSBx{RVCCnv`mQ=wkM&!+^4QF8F}bvk_izZVF@ zxdA_pl(_x>`EfIhA)V~)+eUPOc|piWjVST`<DEBVRJ?tBN<22w|NC$Me_r_i*L`7U z7t!z>!6xy$?tPUP+ERiJR)W%1YI=J5Qys<rJ`_g$y9T;0NJim@UsHMcSSa7ci`5>J zKhgPk+;C?l?Juc23aq-u$Sz*(_V#Wdo(<y)`tb&BpsY-iDj(6<-cIJ{=V#WQoHSq3 z6n-JqYiGIpyLRmM(qJC^r!~W8_;L{)?{bfsc79Bindo(+=X>Q6SJVmoQ+&4RgUFc7 zM#??HE{Mli{z_kK=Br{AUuk@Gesg6s<;tTk*{(}k$=>UVdLGl%y!w@k&+muA<z%c! zlf7s71nT_93^qJSU6%%~-F|gW&e=J?Ze>ZK#Fot9;X^8=SYFeamiX>W-M0e;R!)bz z4m7+v3U9C5U=qt5r|%F`U#Ty4n6!6sG40DXv>q;fQx`<`>zk$!hYtZfHHLGxU3^>O z{8E2j(DHDZ)BdLAhYufiCqnrcJpJT<x(NBpM=-MS@{%$!F)aw_>+3&#`cwh_I4+M= zczGPIH94q6pUL;$bHw+9tL@|-thg<D%)u`h)IKNLJvcxv$mqR#({uCB_-iloH#)Sa zz@VT9<?ar|RF@4TXXfS<VSUxJ^i&*e?d|#5?nI9byN#wPMzKpAEau@65SVQQLtNIs zPq==~{2)P@v&vTR<_qGP4F-eFKb_@A+k^ED4QoSA?G9tr=yTVt|NQ4mMoL|{6juo~ zq{CmmQgM{Ha^<C6)lrOE+8qI>8Rc_VEvo&|xFRcKoQB95xSg3fqsI4I;#9u3gv*z@ zd35FBdb7KS#~R#9jzuT)a@mSh(2y|KbSWy^pynwSN3yxe*49=rysWfTEze9P@XYz! z-?a+pL>*b*TzO=)HQ&>JT@Sf14jP?pO&`sdvRND(yXVe6k7{ge%r$F0x6nj=<>6R; z2qg&}KkC7gyl_$F%+sjmXwJGfT<XE0p?Qp_$;g^oTVH*U3uPBAdiYuGVWy5KHs{Wp zE0zRsj#-6Io$?nlH`p-S9Vgrv%}_PJvMcVf&U$=&?7Y|)>9X)!GlG^|qXUiGCRM_V z`lgx3zPCO-Iac%Rw{bu~K=<bdgtW0AQ*4j+w?thRH892*cauIG4p;F&)cvTy_E0AX zLpZehnH%-*1TpzmH#G(M9Bki*Z3{ZnN$;qO`m?-z7d}HLY|9Wtdco*dx=Ks3<Xb79 zebh|<4JDgo+u>4&#Xf<`{dumoWXWb)eeavFy%)q?oo6zTyd<h%V#3-keIg<0wLJyr z;Hu3)^t*Q#A%bTY-<xW{VxW;wUm7Tw8!Y0RY>F`d)A2t4Ne?j!4x#V<90%%WEML3Z zs87>O>s!m-%nNUY?Pdn;s?wC>1=|=Z;W4Jp5|T02_x8q?IL%H>Ow@X<R5U>h+<rk! zohX^FEI@zzj*nXX<H*R!-Ti$EI=bMXprE`U7PvRk&B+Mqb%?3^mEIn3nBuIS;Ln!c z{hLnX4e0`}z1`_IPhn$berlfV5r|<^67w(#kK&5;M-r%@QF9E>?%p0b4UL)Pc9#<0 z^p`QRe>wInEo~Frg*^O1f0duKaZ|X<_Uqi}802%Ts;5aw)8VWg9v`frBu)-D;6WO& zsXw(6(2%DiC9I}@zUls|9x>{7lEf_ca<z_3n%`^NnwXY%5f+TI70KcX$@vB~IG#Jp zXZ%xAm_lj!8V3gl;Tnv8b33c*eD`O{p0DZ|u$A7Mo$^$d?^XB9{Jj33pYoGDOYu97 z&q($pj>0|<3L-q%S;@Hf{#F~KR^cy3q#9tiGIh&t?<@~n4i@q}$hcd;*dAKLWPt>v ztYZo6WJiUU+ual?l}hiudz4rI-4pMd*RQLe6H%`2?96V?{d)N2VF>aJy?S1){W4Bs z|9&Zxl7{AXij+@Zu>%XLU2=y($gE|o@?hC%{f8sLfBgyrLkn!SgPYrI*-E8x=Lb3B zV~S1^q`FNJ2O1g(#-l7c-l6l%+Z$omwF;hydYFhtm?33tD%owKo`}(FQ2^4{w<cJj z?_+$_f1i6Qc^j5;71nd(V5LgP<i}aly@4Bli{4NaGYgBy@!srQe;$Xh?NIgTXo_4Y zjb>~Bo6CPb>$Ey{^YHL6PTY+b{*>>Gcv+8>#}hKT!@`zvTqc5rkzMBWJ6t1FNw|9H z`t_Lg>E=B9@mmSktY#Jshadb6j31H>O-+Ol*RYu_iK0%IHFH1a1v$6>tJI<+ZAs#5 zYil*b!xz-m)gOP?dI6ETePH@}`|qBIQ$tww<rrHI<Xd8BY~@~3-CvC*`WH0}oFXD| z$&y~VJ_oL-52>jk5fM#2Jv~^KvYo$aMAk78;^9G-o@Alj=-o(WIgMibOHMN__g7~x z{)>vH;PCL7H|l!tVY_uo9Py>4rAZk?ZbUFj!XeU;`@2q4bg{g;Mz9~KFa}W!obj=x zrKPF+(^3Pt2-FY_jE$v*Etky9%*?kP9_X*b7F_-Nle<n((MsJ`+F&`pH6{D*TjKj^ zD*wHvNCVvV>e`y&+C)P+l%t_imu84np}z~JP<ZEmDKJn#>%TnuYVYRew%xH#vh?@F zSR)~|GE&i>Z%O&Th15FDKRm!wTUYm4J&Tc+Pp^J>I4P@g|K;(~!PnFv%D><!()#|F zhn!+A^Yu^}HOt)vu37zd-hRE#{qNJJcay|kl$D9^jQUBVV0rL6e5E-aP(U~an}k4| z+^_QW+25R#xpfQ2fb@{+P&DN)q;mN5<A=ij&Wb9mArxkU&dyGqGS}ArI*jbUG*QPO z;eHm@8H)O|(9qK>6+4WO+|u3GCfXPiSU*T2_zYtiXt>&=t}oy?rC?-i%&A`~ahjI* zqh_8N{LaT4LjQiJ$adHW?x4Q0v8&R@i<D8~(-<G&ufHe8MC<*n7aH(PgM~K6kXpYi z#nzc>qUgxUZz?F9isjRvfmB>vQZfz&8Zr;({SUH_B_BgH=ib!OVPNuJMK^@fn%4)D zqq?*7Q}(wO#9efZ&Ib|rLp7MlNbxlSh?FSez}&OJ=st!{5VpGuKlr3uR>%^Bx&aj* z$>?=~XW$z;tNi?6IZ`G{o)MDG6pi{SIWE4nt9%bDNXiroNzL{w1A}sVvSfdykNDok zYzE{_iQTbhZHzH}HJ8w10Knp}+nj^T<FuT=iPq84SzTSNhV5MF&2n1!&4WVfPr1iN zoV1@*gw+0JzN$k(UtiyFI%3rS1k4h}-NK7TJVPC)e|Dy;yfv9kmhixYP;zvAy3ev8 zu*Cv7Sg{{s?9XVGw0fq_dxc2mmN)^Kno-Hi56}is+K>l=XQm6h3Cjg%k+ZV0^5NIV zuU=AfvG?PirKFV6(4d2i%E8b78jeV|RWGmW%81Hj<EuT$J>TEd3S9w^lBN_Z2ms|t zZ>9>AK2hI85!hB`8U!{_P*B`T5_7ry=*z?OWG)DSw{r6G8~Y1cLO%P>C`dxcZjOwM z++~b6kqyMHhD)ulH8D9IY}?@A;Qaah{k+S3x8g>^Q~c9vIYx3<9)DAqZA%g$d1f<M zm}7#aD6}2EbFjTcFX?#|jYiX7p!wf`U*+D+dAJ9AhdsWvJpd4K%IsGjetJDV8v^;) z)xp65DdEz`ySV<sBks>&rGs(siA6k2F!$+saW?=$b(MQK`R+~AO6~tib$(VA4HtlO z-v~7U!uAYoN%=;bIQ<0*)cE)~6`!62mNqRTgF`?-K&Qg<8ny(`1{~LC!lT}lkSa@E zmwx{Kt}V3X4aISYUBA-XdExgfz;|mK?b1RH6SDYzJH;gJC|-^1>h}lR<rNisGjHws z^2}A!mmw;?o9IW!L>L<zM=?md%u~S`1gt7#(Q)y*?a&WENt(GPih(#<$h9ZnAn264 zN5bagtj>SU>dH`MvWfr6Cdhyi6Wmq$W>^;(Ut0OUz(O*5!RG*vwWFk-bbqSR_d9Bc zkUsu|@UQi6|EZFIes35UTzqkwwy~i>u1231vmh1_&G{gZOFa{Cg1ICOBV+j6x98y$ zs5oYZhAQQTjsASY<uqRV!kYdooQk`c|D~+f*Vn@_SzI(!r1^ol)d8r6TPr^pzWR-j zdD~ffdPT^ipS23Es7h{8K@>6j9Up40Sv>juO%ZDKx7JT+99-Pbki;-}XZ_&}o{z%T zbEJ41NxE+Sxeres!)8ng_5X8iZS9|58D!^$ZSlqc1DQ64(Nl3fZYjZy;vQe6udeXg z=}K2QpROEVTl0*t#B=Kk#05Z!Jg3>aBVH?#NG`=jaa=gEKn)EK&jB2P_@C@}FJnEB zAJU&^{tMQ&TH-w2g$rkH07}v<bLIK^`1{0Sf1dK&>#wS-Z+LFazxj77A-}WqRVzHV z5X7*&ybN_S3NjMcZgYcjVA_*B+(W45czAg5=pj&klEmH8;j>f#=HTA#r+$z>&eCfI zWOx}eC)eX|KQJGfk!(3s>|ouOjV2=_BOsx>QRRDt`lMGOmg2o0QqbMfm8P@@aV+At z%<$;zW7F<W_pe%XRL_{p-M&4su%HCn$;ik^dZL45N#Mj#>k?j_7go!-dx3^ePZ<b8 zq9ixgi1Z9<)MrOGhFA9|rK&^??75mUc|>@)8DwDu{E<NLyq5_Qn_s*89toLcm)Z7| zS%_-$!`-!Fs5f3Bm@^FLP*TUclkiR1HiOq+QgLGDJpiCxwHsmJ<Ksi{vApC6CsI#- zRG2|!1|lVB*~My3CYM<MUhyKFFvv|jtz%^GWJ4$(e%HFzyp2Wxq@%FihzZ<COS}-F zKb)_PsVG%3kB1W^aMwUIZJdbSd{xg<10EnRr3c@x<^TYw)McSD$GFMf!NCx6V!lOZ zZGRmFhl2%;eVOahDKawi<HJ46<RZ&o?6+iOfc?H=Vd8@t<_5V3{;xCJP$v9>_-)`P zdrL*5bke>Od+Vl(*F;JPMK-GrUtf|rMNB;LOI4bUn>)<+X#akxv*p6V{rmU7>6BoA zC*Fs<Cm>@~zIE$X?!l05mMYwqpc(MH-)y5$8-xHPOGrv4NnCNb9>gBtygU?NTr3jK zBpr@5!D(r(-5GXcf}Ni@+}o&cYRZtj7!^`=ay)on=BMKw960}p)~yNG5H6~mC=#iz z=h3jgp}BrNs13y^>R7*Tr=;UCUW;Gw7&#s65bTGrQNOi(FNishL5_^(Qg6euM8TS) z4LrS0;LcbV@CXTiK^i*VU*H2Uy1KrOb8nRcD{>X(Jrya>;2{p%nA17m^A!P}C63d? z0O-b7s!j?C{F&XFW2{2~3z#(-!==^}|NOB)1Owz$0c~8}*qE#izy*@@vtK5Pfm;cu zx;d6l$Yb3EGFX!gnWV|mcdxYxIY7Uyu;959Ij{*HKO<S>E&8%Aog9pwpxh^42?LZQ z{8vV%oJsjm`F<z9$d_d5lo+Yt<(M!eN&6)Ofgq=&`|+nUb*|@YY$~#y7<2EVtzHH( z=U_M{LUtn(J-yU#MV$%wB_t9d7r^@594>Y7faU(ImiBUMOVIRZ2h>)mD7$}BBTkW! zI8RLiQeTfIqGacHUp0WWp*jrFg+K>%7;4p<*;!zv9>cA93y=V(R{p20tz6S)LGv~S zh`vBWI&R4GkrA}83q9M5{W2%VN8hix3=a>-OZkYIHpjTE|CrL7>_FK=RsqbhwmoQ< z;W6?fk_FgA?_WT42JR$|-w+2FGz5QB>8vViDnlvZD#Qh_Z?@rZI>^;CHH1u4K$0B( z-m7C$b~ZoYt2*GXX!q@Y^SNIc5mEANJ7N$XGwmrUaM%6L%ipz&5M=uABX71AdJ()0 zbtV|jG9+gc$jnF%g+231l)=4WXJ-e1zX{2P5-Xq(Bue>cS<b^&eU1}zxneVrKR(eA z3b7Rgwe}f^fL~&>M4rpwRs-ltc>Wv@GO~=avhvGWh!B22hsx}fa09OYzWo&qkJi0x zgPVaQ^tFJmzg76iG1R>GLX`mqhw9Q_WXA-*JOi*{7M>f$tyBD(lH=|w96kp?>WGd5 zXnpxd6NpnozaT!DpfnKNfGjNHxp^6+569(qtm=aFF;7}aDHs{sAhC<WR=)hz9cXwD zj_L7E)ye($xA5hq1~`FQt!-_w@$>T!Pr<fDMh}&HL_ywLKiU~Z%7qmnPvp0W)w>td z?z}OAB{~G8gH#E><GmmN&`>@+R%-|?J3rv7N*`U_TNgd-gf-ep1Q6NvU0WCeJP>PQ z1i?a#Vrv%lCg9IPsG<Td6%*NDQQh8#{mD^BK4)>M*d}?PA-~PQ)e-l}GYFJc&tyQL zw8zE_sZY4V9fkxvq~Zf;VR_pI$NDnlamT-_DQMR~{OKW)R=z)@WQ(8r7Za)FivXO{ zp^B26zaG}trvc|q{@%UUPo=TfA%6mIZU!1;^Q(m8ZsJ95?v{V?nn=y{&Px3hk)o#r z=#E1-Z;pYYpaF;opO^|X%1Q&sKklBMVpAi`g+Eqrmq&4^3K}<tnKZR0iq-*8j-Pi4 zG_+qH5=DqNoGrn9cSs>kFC&7+>n^1w-48^wQ-;x@FNm9YiteVx>P<j0LDJ*y?ykwa zo}x)zSHTo1Jt?Z9qC(VZh6HeQE~8MqvQDLUJn)@9=Ln!VrkiW9xZwppUhDBy^(44Y zpS8~RmpnWVj2x(VbH3#-1amhuynuXeYFqB6om0zD&3?@AUn*-(JH|^dUCjY0e7>Mq zEi?hlssHsW3ZQnn=PRtq2hbV1zdZV@RZlUv&Hol#EEh*gG^EpZ$ZhscPLuGg*y^z^ zL(S4kukEf}=L+lir#BR9-)mN1d(vGElphYL*nEs=axMrejv+kSEksOn{pJ1X^wp3k zk?lp|4?Q+J+XN@*!YIKtsgcBwn1RJKtVuGFgpXEpjnQ<6k@v=Idj`P!@bWY$h}#we z-3*ss1g4jhbx<gz<@_{t0Pp}g$84B=3qo=O&dP&#H_-ACVoI@@Y&(t?sNEkd5hClZ zuweZ_w#)T1t<;^eV?|EoI~OL1T^rWS*Z19f!K+((mYP})_A5@<juBAV!{4vOLt`@8 z+?*8X;~>X7LSY5j3RAKDA6X*VbDq<4dl3uS2~pKq7=Z&!XrR$EM~>6Y9Foh^rW;bF zK*T1;$7=vs+`M=1-fsdwgSKs#DGv;Xi6_|@Y*U=1*L489)<eaS>mikvW-W0wkV?=_ zd$jx<0f;mcFJwM3J^dzLe)bf?-Z7{9HSbQtSHjP*`)h#RkW&YB&Z1<vyGiS_+P+dR z{9cI3Jl6&s+*!Yq112cVtgNi|lZ|H<tbp*CgX#pV2!N}{S_3V;kQs@TloTYj1OU$O zd7Ythu#}W0>@>Tq*dB|&sph<NDWF~IAPh3gI>>NZ5nf(i=Blga^b555aVIvKUkbC0 z=d{*iNc>zFrM%++ACH5$2bre7(B?b@LUyGs(J>^wQSXg2K<KYPa?0Hnm2jLo1=-l; z2gfD&!eM~1?(?5B+!wwVf!JycAhhj#7%r^79o8OI{G{g!>UIQUD~R;Ub6siQv!E`m zHZl2u!elX2EC5X9cQ1Sci+p%JKM63J*sut|e0VKN@3af6Kgi8gO-|+=MxMWJ{Z6mK z6M>VE{mt4Ev4E@JPPeN(xPeL(HRgS=QAajYrJC}1m4oxAJ<TfQV<gJs&65?k2S9L8 zR8Xik5$uFeGs%KAc#y749}e<LCz5<W4j+9mmJceD3w`y<6fhTH6LS9`M$ce2rI>qw zn3acn4Y`$indg4kqd+&11A!#kNF^1@w&uz3r~9c?S$w*sSAniJ|AC^*nx*vhKrWW| zUM{On<>9CUVZPj8j&abn-SZOe{E%)MhleHWb|@WUNC^F5bE&vB_#1iweUeo(Og;K^ z?^1&kNJ|eY01vXNtgX$&S4Crpc$z2({l;DwTJUJ)|5fw>x#3w@K3X=UAHfBt>Hv5G zv$zW+Rm5Qek3A{`H1u@$rKQ0lA|j&q8aYPvKIOnGP3K&y6hhee83bx)e4ZlU6q*<a zwEt}kX{_?8bjhr#2<qG8@8@c2YJg~Ph*tU@d0KhInzbgRxi1yk3>LdAJjQ%OWd66N z`CsKG3N7XUPu8}#M<8+!2-csR<EO4b{!4M6AOZ~FesZ)^;&X5v)tjknygXEbLO4SV zk2ZT5ub?0X4uCO4!4nzjhOpp}5JTuefYv*{veFJ5<yQqeD=Qu}N&taGu^Cg4lN0{5 zRu8?3qfo~m2NK1Z^Vg>UG65(IcbIH6EG=tH{vpX-2j$m!t`<IzC{<8?)8XFn@bMi0 z1w)kaRcxwZUgf}_#esrQ&IjrC^WB`wRVTh;u8V(Ot4`Sdzl#_Tv*<ydhaJ-tWF#kd z$WB*jRlM*S7F6{LdTP2kmY&}L1!$h&9mEBw_vckr+grjOvo^sQ0I|du<X3S$_i=ng zKLMunEvvw8G#QOcz$#h=^hc-I9$m)|z-AWEK`Vy!^`}pl5l#cS;<Ikq^#ZHjnccNX zKoI;rD0?Vj^Qzp;j(j=JReuJ0hL&o7U+n0j;dw#*30iCGKr&$EZw27s$AM%d6GYn6 zk}`D%_5_WHM?gcGpi|HSxJwaX5}<RwOw)f!!*d4|704xF=dQg(67o#vXEA^wker~j zX+-Sp@2|HCkG_UK->xAw1i3bd!|A$Zu9|}EfE00JmTBF3(fQGSCF1<b89Z7IFR3~8 z%0-|<fFKS<-|*n6>+!^>8VSn2_cDZH&VOFdc%=C5b3)Q-Xl~}pi%@>x?k)nfQ&lac z1FBhE99^bf#hnleHq5K9PP1(Yu~KS+MuFzVix;P#wtrRUAR`H-6=qV(Kl%dzE1FmL z97wu=We^1<IXT%KE;k-Uj+N@~{(SC9&zCpTHB8kI5*X@?LT`ri=ig540yZQfAu;*! z`jVy;FFXV@Jj8I>+Y?upqb#0*-8F-~25DxT#_$(+-WW6xBPA4&e?5T1Cl!wXN>Lpu zCcoxEk3myMVYJdGZ}RVP)_iBa*Vo$G>d7r8mH;Al1kgElQC?_@*3b9w+a`*xDzS>O zK36YwMs#36mf5z$Vo;qDg>I8`U}I3L2&O=^*0sw&|Mjpi-a?>KPtMHT2KM>AiS&Y) zEQn;F_|-KvHI?>GzM$0`tJoQ7j>&l$tU#lf_TUHkyGyH8;!IU*16D|v(tA&^g5}Os zGG?K2!s?$)A>zJz5t1(o$SjcRBvzt}(C0FRYbs{i^n9Ua^4+}zDm(VK0A$E#!#$k! zs2P+@z~y&T{7e?VDkVR5{L*7BYIZ@t1K<rB2txL~p@WHtJAk$~upBASysC!g${5@> z#6py=J|x{cG2HRPbu{GU@}*xpHZ%=RoyC<15Zx`4ezAcGj!;e3!Q9%;{!wvx5OX8< z7vxqDIj_TkG6l9yazQK_^m_Cd<Oe{hQ&Z&durY8MCpLpCt3=t!;x+ojvzp&E4DZrd zcH($$@}Kx{ctCTHpT56iC>sPg1`&IIs52&8skyq)Fau2y9{!4xvMg(mLb1<VEa+W* z1y)y;=kSznf-n=+J`We;<@ec%kXO1;W7^!J*~q+;Z5@xkN+P3@52;=o&TT-(K*40I zRqr2C)`gAe$2b2x#K`-fgukLQlv15yjDhHYt|K6N3s;Fdz{s*8Z)rpwLX^@=y7RyI z8LR4viB+G^b9ZBlU^S*Y%V7QYV`+*P0fzz^&eX5ETkxbOcl|z;+QGia=j7+lH^&JW zBj*oACb@Z5DTez!EDPw@g&a;F-%w#9H)I_@aTB3b6Gdu34n!(Br22Xj!8Op<osJJZ z5R&!o<}>%1c=Ox|!1<W--!vl2-@J{b^<I<9jV}W<*#WwfRYm`$XM{qZjpc`)Jsq2n zbE@twFR^J?;o)RWoei0~M@oi*HqH;AeIX$siiImHa0Y>nZ-6xA09kd!bAd-*8p_zR zT!{HWIFqp3YOxoSp2a6o(aFr|L8FG3NIw7s-Nwb6&;A_Nk*#j^p6JHj8H@V>sdYB! zFG$2lbmdz%N~uON)B>>r{?UwlU3fSk-Rs^Pt->>NJK|T=GnJvdfc^n_6=*EYUomA_ z&Mj7lUi|tnYuMY~EJ}KMB7a~oP=fG$_BoR{?kZOXwNr=;sMR*~^e}KI*d=Zj<YVMz zmj!@Zn71Z?E#N8?3kpb+qOEU*RlCfKpp1I&jt2o7A*thsqMP$qc-c9pgng4w!ABuP zLP|h5dC(zK4(;yWb;%8(8`VKfV(ebR7po<jEmYToe@HgIM~?xQ0(fr#DaSHd*naE= zq~4$KEprGD{87RB9mB=R?{69Bwa0jx)C@YoaG*L5Kcwcl;o^+7(D#+o`|j2p?#%rn zK4$H;|HD`fjY+i%PZ#2kztnSX1mHaUbT0rjebIH`7><03d=^Smg8V2ApV>**SLX!G zThD=f(@L1j?A6=Tvpq!~=@kZS_tpzyhq*4cw`~=!OXmRi|0TGrT%x7InXhX4uD|0r zi$jHCr{QYsuo>i^R@!7y?k{6@b@)SJ58e3^`*9pFWZc~-$0Z~^3zY}{?!q3>ChQf> z&hx#22HfZVgp2NeC0-;5Y)yD`b@$mxuG`App=|#6cen6OS6&W{cc8K%!P5oSKtU%W zBU4>Vgv7Ykc-{^HiKCSje^*-bQi1FluUxAK7`+aHVziULZ}su|^qOX+#I%q5&k^dJ zP^`T{)CjP4P#deEw+iJn17Pvt-fRja*;NpeoS<*%x;&%^O&3rO%mGCgk4%n+{U2TH z2@M2!Rd#RBYtBz3NhRfs{iM)&dX3)X+F3F@R2UQ4qpt&%Q;BNmIb!OTniL8V*>51N z;K<6!37ECefnb>NkxVfD+l%){M@R8jEh!-VIZdS|-GXM^)D!K(xLB`Wc^*!Ul<doR zHl3F)h1Dm{X?L42TkJbuso2CE?Ko}f$wlh?7*D!^7*0Ucq`PWy7ECA;5Ga=f1t|am zBl3Y(zC}P*R#qw#kQdEM;+~TPc=q|8E~PGu3LV%eR5`y9-t_)Xd*4xX1s5ihdk&xE zUh3^LprS`UDxxKiTzuWoUjTF+WS>CN3wOZ%f~W)FtC861en4NE65Pk$j5OJ>__^7_ zw$OM3^#8XpDFJZR8<5>lmKqPUCND^1h%|n5u;T<Y1qzvh&D-}_lL;UkZ7(lD1CK=` z*Q(6*8lhLniC;2yQHz$A77E+5uV1;4)|Xwy77bWpc<x?e{%mhSp#b**fiwl;+izpe z;StPPBf)qP$En~{#uX|M5hEpPZuS=duGOjX9ZtZbqok}GuL~*=)?q~iGW*}(G#5v! znEavMk1s8~di(Y*3lp#uO}6ig{r;#weF6{q3ut+D-r0>-g3T&-x>p}tWJQbLp3<~) ze9+qdI6YIL&;e*CP-VcT?$9s%Gg48;V#fp5;7p^J;^W&`&oT|<qwFt0IEiYC6oTD$ zaPUP|t8uRuy9;cYhbY82{xJ)E{rc?B7@n_Vs<Kmhm`6!c{-9wXNl~lFwiV8p`=0>9 zU6$+DEmvpXnTG@~K>(GOl{o>a7hR8mr2J-LvQ@~B(}u8!;k-btcXARa2Xc@P_7?Nn zQFMo0N{g=|G9KUIFkjbXHe!NaAso9&z>DxyV(W!)fk%aQsuiv0<8bp3zmw~HZx$0+ ztI{BiaqCwm&b3Phd0AM*NGQoJB2wO1bpUidR_8KO{`2YF>|ae`^g<SZ1#aHF3F5zr zsyc|NAu>@nz&9fYYCh7Z_dD961w5Jx{)(yg6zLwTQmq94P+<B_1{o26J#)<484=iv zg60So_;lWsCNoBq^gFIb>g`vo*N&~<i$QnwF6c;auB465O>SClG;u};Kr#pU59!At z9XB8zx1-oq1b4XKJSWGhktU(J)iV=W>^NXgT7TbV`l?=+Z8$U5`oY_S89HUhT<W== zTTf7kU<8ZuAd53jPFGI0UJ5?g5{fj-OKxc;Nuf%e6<x8e?+78eUB2fJ?MXrBKlhZm z)YEkzblijwuuf8;%rI~Qu-7Qs2j1}7NYT&`$OKkzEhyeSqH-rxKz%7G&USBS^gcX1 z{>tgT6G78VGMZ~TbCK_RCJQrWnxPbr<SaRPjr7T(3{YAWVA~Ann?zr}U)vc0ghoEs zd0!(3XMam6Pol*q?pc*@wK@@zyG~h*ugc9Rtj=tfag(wOHF;!<lLo37j7~J%8r2Z& zik50QU|-vu?+JyV5S@<%?ZV=7>lnH>;Dg)o5yd@93tJC|W*Vn^=2j{<!Ao}c&77D& z(uQuJ)t~xaG%^7UA5;Ycy4$(Zh?fqUFC+{iuR-7zH(G>?DSU1jlf#t>*sm@SZ&GF@ zKIVQnt2&h&`I){N#1urqgYSJ|NQZQ*0ejpB*A|o&2NxIU)^ah3IzET;oa41)>$=!? z6B_dna`;G9>7Ip?NJBrYAA3vvb?Ob_i+|dhPJ%?&SmI||ZAR-d#NbY2Kcnn{2PQ(p z7^LqFXyN>lc*E%fUO8B~hDx6U#20pQe87iv@_|mbwE*XyvgI`GV{fZgQTh-siW~RL zV}_6TGJXqg@|`&`+Srq)%6wUy3y0Tip3HKvF^9*DvyDN9$p&tV6kqU&sGT{cumZOW zB38l89&Y|p1?~NEDoEgwWt!V7!xdzFe{GB}^Rudigxt<-aF3!|YDOpH%ux?Wj!_g8 z2D5D^^M^HlnpHGkHaIY;CL$;>c(4H#za$d~4i1|Q7cz-e6wB?fYJL(}vNwqGpkOp- zI`(vLOW*H>Txg5p`L#90cB=uZ@^bgn8YIOyDX8=Q&a^f>c|o8{squj><uly8P({Jd zJmCy<4i^^}8_UN8^3J#6Ue`YzK=_EWA+3cWy(9`H!xKG<Kttc75RP~9WY9$#is4!N zHkL$RP8Psky;42cxBHBFt+LV|CE`SI-7b*H7vS=K{!{5l238=^?MdSD&@Z_RJ?Y_I z$V(5P$$hJ9Kjlqua33#Ae1Tg`AfNZnh0|Gj(*?bH&-_smv3%wS*@kfxMLtR@PY(7W zU|8jwS4j+@Za~uoyZY=j4YvaL2?DcVQN@jn4Vy?0vV=&k(-CYDkWOv&>LZP<nMpJ{ zt}Xdm`_L;ov|MPqcg6Nr$WR`1hhr|^e+n4Ebhic?co`tW&{x7^`U{Ufzx86w?<8*_ zcd%OfhSk_p!`0VcxqQ_#58a-A<&u?+ixOLYf@}Wh!*k?Hsc@y=pYA^ch?@<qn6fNM zU_pq?3|m6)<DFn+WJCuod*DS#2it3V@nO3zkV!;G01qBC#Z}_5%#JJW8!B-k0v>|$ zVt3KU)#h$ej3h`l{{1!UxQK)Q`$vk?Zf;K6!sI2h5C_(vs)N4-P{^Q*F02Nk0Rb2X znr^naX1NdPT*P~qe`BWAc(E@B>CA$n$=2_I*qm0%H%MgyNybl(S5CmbGmh{)Fl}iH z0#^+RdG&(x!D6K~^%(9V{!4Uoz?^^C^mB5SV=mUD5viruXE!{U9os>E===PD11eE7 zliy(th&?GrC!=Kz4GjoXftFqqD2=M1Qw#3zK9rM_%W<Al8}XRE0H%r7C{-yelwr+c zd&69(y1F`gL6cM9w)hBe5dpd2BzOZ4Bj&9GF28F&r8Yu-OM@~Wmae}L*x9tfVZg(1 zkFYQId@ZzPF3|!H1;pp-cn}j}9|TH-cqpKPDmZL&v$F>R9NTCSYR^VIwlv&7Kt{!; zNc*J|q+meOprEA<iin8Vip$F4gIWZci&b>zb4Ie!>R1g*R$gAnZCMA1rKP&A3^{p5 z_7-hVfmKPoh23aW+`_&M`bxU%tF#x1Jl(%lrjnySKemUi6yn%J3}zq=33%_?LtG-g zYrttN(8~u0(JRn4kgqkQgC_A`S-QwpSKfzI5oEFdi{Ca48o$`g{sJpnKtWCXB<v~) z9~m2%F&q7>p<mF4WV@(7{D+lJ62q;+F~>De7T7UXrtSnC9{}be&H|9@jOYl!mWGIN zNY@XV^LK&1fq(~<5Ppi(vt-XJJ%P94UBPd`TEk>;By#=w^#-dikH7x}5=D32nhyM! z*TJRPlDifXEK(2rB1W%-#JO>(d53xRJI~HlfZH!l_V>Shf2)xB<}>|`P$-Z{ZxQ(> z5JE&j)de#S;;Tc*86ZW(XawAoVXgQoHOQ3z(KUD)tRUI1ZEk-2C#~hUuNeWjNa>NG zCdU@z;ExsbwrEzn^%HF3-SWqY%kv3JbE}GeDyF1eG+r0Q{@mRozWu=-ZKPL>49<;E z5L54^-JzBr>9RHb89fg511!jqd?|as{>!uLMlB@i{O2tA-N2PS=-DzjsI7RBd0n6V zo2hi7>0@q=y~VqiVzE&mb-RzrjQLzde|lMxu~NsWtNr15Nn48ClbgCX{7br(?)ve` z*7E|tb9xF^84RDk6qvYz!P{xDtgQpS0v#PpraPZ@VOU4UJ$N)z$?aZ!KT}PgTjVA3 zz>wF!_0sB_`2KG0446)hs7=Bv$_j9BBi+HT#8F%71()m0?0_I?feR(pMX;O@(#tVe zwrV4d$Fr)AKLAdQFAe%{sRY?%D3SuuPhaqXqXnFnV60>|*%<m-EpLS*pq05g9jov7 zr{fZ!rr3T}Ioah$T#s+`W@4?zhUeZ|@kA$fdZ~ZG<Iw{9;7k?Qqo2EyJPipeba+<@ zD+y;Xw^!BQKNWpvHNsHpCVcXpUNTaGtqyHqVR0Ew@Dy0FTOTTk@W<NHLpg1i*dPHD zLwqT1_5WRRnQ55@rH+VyMK=+YSLpht0<=SPB~aS#gFi(%;->8KO-q-mY=%6lJB8{r zcrl$-$3LTApXJu=IgP49S9F!x|D?kAX7)(LW~V)MB8F_*0G60qsPP~%zK2eiGgL63 z^=cm#zErtsy?F5=2fP@F1`2=&4ZM&LlpE!1nk*bI!@>|FG8_>J=!Q!&kYmH3JC)~% zbf+rWI(OYSl=Yt^UE^kZbT_3?lwTZF%;ACjOMezMM{3G<+Re7Z^<^ECNLJ9!Ah$yD z%YZ}Z4V2BgKlcSB`ic4ZmqkUwAj+n}F`(j9!vnSjDoh)Qp2a|Q2}v2o4wL5B0u4Lf z-NX$fW3nC2<6xek((E~6t|)vGB5wbYDvoVH*dm&zdFVlgyJ;Uk%m7?|QSvoNx4S}y zwY}My;s=;yTfoKi1Ju}faMFRZV*;E44C1aNzlIA^s|N&OY{45TK?c<*@YlcYHv}7` z-eZ@lAIb*tmoH79<EPVbo=t1gE}S}GkuR3!=AJfP=>Fstq>wzD%{>Ou+X+Jnz=0c~ z5duCfvP+jPAw5WN@jZZ!HJo3h^W*_?h9&5BFLgy_WfR$jhWWaFF1K13SoRBB&@rG& zaWYWqEyI!{=dPZLHEuFk)EHdOTE4*SC?B|kdKnLf*(t%8cc(D~0Ws9COq(&WPZhJQ z!%WExJOfN8B>ml%&Px#Q;-vAnqpss@loYam{CLfm9P1=FOng_CRUO3Z%lcAt4t!bN z&#E?ay@a|R+Z?!lpd8L4$fgnbku*l8Ms}R+)waDBs*wDVzFN79<edWV>#lX4kNVmD zBa0)^X2e_%6>@N!8o@ZRgXQ2RSk%fckl3gv>MMCLEH29`SCUBy>{D0p*Vbfe#A5Su z!Taw6kW!JZS<0~G#x{{Ffq#to^3Z2BK4#1n@S$RaOy&7TWEE)?HDc_3X$k7))NW6O z_Iy1#-PmRE)fH4N#9s?$G_?C?d&Bgha?3%X<#goJ1YnbmOl<1p`K5-5&t!5dFi`5P zeKE&w#jTn9w~4VltdwKapiyW|uiu^$;YB>1Z~fu#2a&JMGcn~GsbA!5vtB81J58p# zC*_7jBVzkM+ILB~YKa)Ypm`SRvF(7RI53k^Vc)2{^~RGt@~Q=qhOA@wrrESI`^Gn& zrto-u(l%v>nK16GbO)6@vsT1O@fkWgpb4?1U`=e5XwMOsx%TVNPcMfV0lEcmZe$`$ z9?!4ENVso$O+6LoSB7-JBZK_?QBYWrzCVc0#l^)R6Qhl|__C;kUP3NHFf`al^8iE3 zhs(*XmwuaXcuS$w8_~VpmsSv+Fj8PS1oZ4FTm(^LKyLiDB=EgP|4#F0;qIZ6QwcrL z5~F9(0|Qhd0JvLqYYX5Vv}vZpM)YLYKOon`<i8Yp=20k6kk-AKSx{`H=n9rf#I_HI z^FFkRVPV!sODuW(G8!lhNH2Ub)hEx<jA0ANx9-P`Z<}C4f=$dG^RjU3N~_H)>E(X3 z=^Yt%V96057M>7yH33N(9A1e)(~%kqJwkdZZxQz9*(Y{JL>BES!6q6p%jkzuz<EPW z&Avn1**;2*Z{*u4=tZkZ#pv-=mz@>|6PJ|l+J^$y@x4&mx!<{LU;xHNv<1L%JRY;} z!nm!hkW0PB+>m88&uRIV)}2(>Sraa)+4S1caQ-T>I4;6H0YyzfqZ!JLN|s)QnpEeA z=~BCNZ*EqwNgTpIJ*+L_XU-OyUGUo^Z%K^b9HL-rL2LX<S2R1ccbNtdH`N?V7KN3? zV%Rh*y+vW}=N;%DpQXQAKG=|aknpNp8;c=6mSsg8@(OLyZ7m5heWi;DYRyH#r|6`m z<^=fo!Yhx(hSODy>nT+`vb2RQ2BMK3;t7em`iAvKo~3Cq=WLrHu+7}#I7^Nd2W<jB zhL%V0#HK%Q7Dd3%!HPGkuFv*oO0ls$?&h}&p2ZOlj-x3`e^Dof(Q~S@>r_aX3xDj^ zFV+?=7N=*y&BT=qIKDx(Dv}NM@a=+ZCzls?RR!NG-=%6#hA1R|0JENBRW~uWekopL zB&WWy(piE<TyK9lf38)5Y;<<Ivj&Co?Xc|cX>Zwx8k@P6HWn+gf)$wAS)LVWfx>kY z?DwCbI=#$V87>oU?L|ZL4<L5)k4dL6FucGp9rS&a^?u)!P2`grk=QV|dsRV9Z7^1S zb!X}YYUBp@J#weBeV4y`*^XErR5tFq@6L0%Z}n`p7tB^NGn)t!A!9a+aOt|Nx7LSe zv}IZA7J0IDKHwehPWqvHhWo2R+|gOM9R79zMsvf1$y$DynfKDeY-YtiTuTm~;spLC z?zX)6!vXU88Lwxl!NDi<2LUDgF0s77V})_v2(%xfz5PA-Ua3^I4QMa$iT_;{%s6Tl zK)J(|2)Ob%q@<=x1L%puDF|P|WR$mPk-mF?06Dog!7f^ypTnc?VMx1);L2QH(Tk+C zYkHnSak0GpZu;cdjc7dZ_Ebg-dh?j7buI8EV#=O2J^-)7%2F$EnQdGO>k4%<t-g}d z+Gx(Xl$`;qJ9y;ee=~)b_7nQb7(vdj>ThEmE*)L$FcIYN(=eN#nABV!@!FNxJnOZy zl!{19UQ5?135L|lnuc8t%gei#ITDlA(z>KMMEWESIGK%7N%+ty;%;r1a4&<DBeTrH z?D6`0g|}+T-I6MMBe&M>VSvT&aR~*vt`PgPSKksAzH;3XM<s!^N*(6atS$`Kw*8h2 zL+(o)C>GLq5<0R6n=LX7Gj{+3nx<BKqf3KP+q8NWAe$(Zy0tYoGgRZ_RllSz-u`p* z?95cSn}5!Y%G0zhc`ut|YM{|d=o!w9{U8hRjRV^wq#<=rw%%be=|bKnt?dW{yR!e> zukk6x)$PDbGW_ea9G9-s^p4kY`j=$t^LNxbrqt)X1Y3~ml4LVuV@F4{xtLIb0ayYN z<^!2h+>wEqdAs?~V&6+@Y>Yh%`&|?NSlWUwkEc_`o4yY1MafQRHq5j>XqR%Of-}e1 zn5o-Zqc?GpUx<jE8IxgJOc@;a8Oj-@W~ui_VR6^}O+Pe-&62fqZ!mx{PTZ@wq|{MR zD%shM<75?=iaXAm?_F%Hs6n$um;4j8Q(E5-NG}|5!^}}MJ~#6YZ}TD@Yx}B-rR!qm z$4GYri4F#*RdwpV`yUndp+~wlX=%=srD1q78x`Y^+Ni8+ecM_yJJUK-{kR5_nZFrI zND+o2G+ScW9&4L76<Q1Qo~mb9-NGvz_V`T3f95jIOcz%yZ<^Fsjmq-90$7Zu#IBaq zU<FdlIDNIM)ZTOa(=;Z#HIkm2?-1e*On%)zpICy!Y+<o4$>g<0i>YoQnz4GPT4EW` z4<+f9aB|n?@!AVoKm-@(q;8T&GF+}hkHHwiF0kqcK1Ybf4(2yhHW(AUsUjbysgLa~ zU+ln)4_1`ztwfuREVDkNsxIGLIK4>dIqaj?6#h?6k+kKU*D3g7lk)7zMu-CGn{n>3 z&F6uEYK7MQ-%2Zjr_2gz+?V@0XV`v2^>iq>fj&3F`MIl?{}0iWs<+w%=1OMfXIht$ z+=jw-nSwp&BC5f>%9O-5mHS;Fjz|rK#&8^OyHxWunKWL@i$78Ley^`uvKDAIZ0?^P z9aaMz6okwriAFpdNYSnGO#-ym1rr<q7!jv*k#^BtFOS}sZ(3e?5#Yj#<JQKZA;<C) z=1UKPxey$nlR%V<OH0>5CfoRZ-fpbaiKD*0dZ=WEl25Ni13l~SAAN9|!<LhS86z|O zlfD{y>fptB1)K+QW+qFD_c#?<_um5V3XsqD&}c=Bf&C(C<cjy%V>Hgx8FYPAylOiX z2N3x_C_o^@HvTzx&3<e9m@jn~lqG#>CxO{+%<Zn!0dHo<h@WrJW9BJ7jR4u>!H5N` zW(xiSkP7&)Z3qs}DqknoEp;~R`SJ)EFo0ik0!KVYm=-HpXoN7wV{WZY{iiQXL#|!3 z<j#SL91G@RWHtfNk0k?5jEme`8>|$pfFaCG(os`40DJJ5O>#q;#1K`iAnKVwPY2tk zTV#6ynNER;q4xmdH^D)IR5Q(y+p-MY1zLV6yYfhhca9DLg*NYHiJZpV<w2J*xVyXy zPS$u(wZRO^0@z<sJe976z9D3p4O*|$rIP>q^zQ#O#4)opR3cOaV_&WjV2L+r){t8- zr`lh~nTapq)m5uxzXZk$p_8@siR~$Dd6}PdhH8o#xFTR`12EQY@D8-j4o2M{Dld_d z;sKj9Z!f<8zrl?}P)#%<JUl%qpwR*2WiUJRe5<&q$PHr^!6e-V5FdmT?ZQ{;tnGKQ zey5zd8}z*6sARU!X?7C))i2WBR!%$LyPX4rKNwI}!O7bV#GnBjR}T`h%-?7i*|HHG zqZXRL5%XCOoTD|M3?sAv8Ai@?4r9KxFXS|*fkT3miQAyeZg#Y5ygo+8fVs~ND`N75 z?42Q9{C*TQNH%fY9eLi1Gcz$0*I<6_13(~X=f&###+$@z$mS|&RXG2$r=&aOT*q*D z4NZEE0G*u7ssHXnD>F0J#GT+jRgD3-q#)>~FJRx-r-l4HVGY1wd>>r!CbRm`Yez<6 z`9Yy!RjOyk$UXk%@9g;^gB{g}it~qTKD5WA`9!HW=0mG)S;Di5`YP`)7J0!__y0$+ z6;Xy>1tOXv!>llZ?hhmq40Tb~W_&O%1omG<n}RkrCpf%>0=Y3oA7ge6Q{DVoswLjZ zP0)5})jsMpBadWt)TKcD#{Z}7LED@g$ULXo$5SxT3|6vdekVt<VCNXwKNlDKGL(vQ zBES76YwsPdH5O{}YIJq3(2l^E$h{87r&trxsV?o>GpGE+FSL4*wWqXe?2wU1>eQN; z^q5I-fF*|pjCID~&j4EsKhQ#u1qg3Idt(oT#8anEy#qA?e1Ky8TQHq511_S1InDp2 zugM~PCUVFengU5xn?r6);8om)zPrbfSZu(JcekI+^66)4N<cZC`ibHf=R{HE)PecV zsC_P&Q0mIV;5D!zq8<g9-WZO)%P7q_;?@4Xj*1++<Ty-`9W0{gedyQxHBISlgl>79 z^XHyGHLgoH_zbKbQlWhOEpL2zEBNJc>tP@81muMX7!R(;tg6`xEzQk|A%i)od!z5) zb1viR;7UYzjHVE-UYjxCSNSr^!E7W<T}~O<l4G)!{-|3N1bDDjuhyRa%pR~<!*D1` zvdED%rn+?iSV$Sl5yqj=Y)eaT<#(&M%0w)G?<OuCVKjjr^ZgjRI}fW=0sZD$6YvWP z3ijtfLni9Aa=tGGImgQgVe>U!EtBKH+}ZZzl{wj(6m(0CPIG%#8HY4~qVrQ}em)jH z?erFX@<<X#cN85xJ@&j^c|cW_U+%?KWV8;*Ncr}F71ECEjG&49mTQFn@QVrWcvouB z-U|4Jw&qEOm&H0L|BzYZoArA=!$wgb0yB9EeRn{j2%&_{g~3pE5fKsW0_0(kH&>zk zxPdgRq5Fl{8FFtu@jARE)2fv1IzYIK3Pq`p85_Ko6y}c~+v<(tVFQm2(m+Ja{V+O* zi1px2mxW-DDnAC>RyLS0k)bV67r$Q+fueKADk3F`O_tJM@rLmj)jje)g0NbKQ(8M) zX-)hWMf%DQ;T;b26VMifm;-JRu~K;e?<`26YX5vcl>pJFVLtbpZrN|dbS<Cf7NLJ` z1m`x6!wvPZi!%ltKSvTa;YA20pbL9M!efq&86YMNaDsJ7WWh|J1hh&y3k@kF8S3xf zj0rvcEI_cDWK8~@#ze1Bmz?|-jCKgNz>o)YG_niwbl_k7{iRbZ%x#e&7Z<LtD(x(1 zTT)VS-8OAes(QAYwHW$F@`Uf+ku-UvvJ<p!t8vNf*Qc;1SSGhJ5NyC7uHcq_Pd4$J z<pU=ywYZ<A_?fHt$$I5+Dggm!$<a67ck>oI;J&0OI1>GG0+o^zB*MXbOJvj(q&qQk zx8oqcBJjJeL2ZnJIWa)sf883X;I!2)4rs0Er+6ndlgYS4&Hdv$nN;?YW^dNp9d!Cu zA~E&D6II!Iv~u8gtHSHSVPPME<34}>{2nOk$>5Mghzd-O7JF>4!;Csv)zS8OaI&OA z2K=U1k-#jcX0zt23LpC~y-P4RLXovhlUO80+D>l*Z1zX}`O%(7rKRkg4UUvf<mO5$ zRgHV=Yce_v)Jyb0`o@-%H$~I#+-y&tk*X~Wdn7B{HQj+aJ2r)l3ZoNv3bB$=+|<rb z0`rSMz$>)2z3uwXr!e$Djd-fOyd}?juJz0C1(Cp@I;VED{SSV3QjwP)exnu!Iw2HO zN~UZVA@9ULlkmcMh!EvEFEqGo^NbpZk<mYBRqTQ12$@`ip)$k}jR;ePHrg<+H}*$9 zR6&ayKWXJS3F)m#RHQvpSGO3E*vlEO6Fo~&dw<N=;O1wweD7&$I`QkqljZK@rEcwg z1;{H=g4$6t*AmdtUbno<OI);{Tss2wLwRiO*Y);7cxg-WG)!jq6~5w|ZFw($1C=&V z6vX6rwJ5APxb<xmVFENb?~$$m4i>%ViJ!Ezn3)3u-H6Bn{@Lmt>Ib1TBq`D<YDr@8 zxIe<8Bkp@Z!^%1e1P>T}LC>`4H@C!2$4LS%N$9*3JS9o5IEETLEtcV==j|=Q<g-Z* ztI7dlG`RlOtHAUumEQQOrU9RV!rCj-v?}{&8Bo4Z5I$h+pvFXSd||=r42q{|5ysQ% zZ+~X9_dXVN_e{S)H6X?k+vowh9n$6nT@t6G%*{^9W=LeOD>s(*s-=<Fs(Fdo{KGeH z;Lu=)64(5O%e?Ytr8hP_B=}icOf@+LPP;_Zrp6u3ikPQukYaa+3Kr4Irh6`s7m#-% zfGK7SR0r7iIo)FWc}1{0_icMrtP?xWeNcvUXPwUaS{Wd%Ny#RK$g)}diz@N2y%e&y zy58{=Y^)#?zyKKW$DpNyK$(Z%b^0p>(ryzmK|g;ypY=6zVqo{h7uJ|}!v9`@X!&-S zg4(Bef0WZ}^-Axqjf(OvZuGAlV|m2QqUW>q2r=gXI9y_SaOY0+z1pDQ;3$!NQe<cY z##>Blp5o6X4+1{s)hVuqs=E9G)?ccS-JaD9JAXl#l+o|7itRUCUH;Wo3PqB-F%VRy zfg3cL2qME0<WyAA`Btj88=avMQLxCxJi!W%fU6|9+K;1CKYS>5Ut{jg(nm&%95ceA zALi_?%0S<=T&K+F#a>+6JExuHnJP+rL-?Hxc*TkY6ke$Cb)FKM&5bQ#L@o5Sc2!9w zUNGhm2)&(E7&a=0Vui)2M5@h<<>DN)a$rr!VR#vQHZWKcS<X^{N2=Y|v1AMY(0OGf z3DJS(=kJ)9nEZalc?s>h+<h!f4xUhZlm_%j7=3pQp%Hk@hg}0J3ApaNDn?*R1i-O5 zOq6&8ef=u>wFq>o+S|8rc<cD}#f_eO5Q%ht(}zR`hId2g@*}Tzf%KRSuVFbSY@1kM z>{u_D3-PFsp6d_5<-6A|NgeVk804(NTSf%72osbM%Qj5VZNTSDHakTFbN$%{KRzVn zXQ411(F05dJ^5Ua0l`i&R|}j6JiqRZO%K0NJr5eZMhda1N6Lfy0J=$_xj-lJeX4Ez zBqu7`+Qp6I^=aC7=ItN*E9ds$y(`Dk2Eaokw)>0`NsO;@_bIKOd(E=ZGPbe1Sis#Y zL*J<*ys{2XuLfx=&Is5V;k>#|@yM|XDB%P(#PDnbyJ8JUg(*hF8hG_dcQBave+U>q z?T!uVSyz!|mA8i}O6XpA%%#PG^BouPy6e<;Iaapq#Z;kq5ucMIiR0q~@GsOeh}ibm zo@Qi}aGvY(3z$lp<kpJLK~W0oIKd2QZjel@Ke%W^4C#Ik7=ob8aq*%*bd<#A$wk9s z{2M+4A&&LPsi81{IUJpcmmf5j2CPe5c|6EQ;Vm!$qr*)rwmjxv%l`YWP;@jjjWDC3 zQSgM?ye&~cy4vIeys!mKHyxR};)BAmqbyXY8MLTqa87=RQYu5Uih=BSt+(3-JL=U} z_1PffrcV|NLS2^BAi3@&b1&dCP*K$bvDk$cQZZQiz=q(xw+=GyX&7F2U(7+KOSL$} zfP2O@r$tXE#M9?|d#x3rKX!ICF@r;%Pq)@YP+)(lh+f!FYVVfJlGp7gA?HO!MQF$) zAHsVLED#SWbQ>EXEc#2Fkg0MYGSGol1S9buAVM(ls44oKI}OE8n}_$<%`bGSo<4^P zr$;jFPUqtMBO}|<8DOmDaGvv>9dbOdB61;0MMI}+a$p$aa&yKQGS3Zmw_SvcR(J^` zjZG*ZNCN{Ty-rvXWON6bMY*1rnG1nA5(;x3`I^8>qGTH>SaW~s8gI3xFu{bk2efsF zh>1Ty;*RI5v@RK;U>Mbn>gaN)?{Wp>wIgd#On{;ayrKbmstB}3wthe=6P8}5)Hwvm z!S>2%L{*ivzV{kFpqHQFh9Mtq54Q79^PVTbBYfNL<SH;JEA-~Tzx2k<i=%rRhodD< zLpz=Fad5s<-u5$A?u^=b*?X%u!N3Go^dJ1hfZ!boJRkAtuY(_F6>{W*e2e>FZ!uM& z>4!HhA@~Y%@f7@EHZ?-(=a)+%eQm$v1<i|u$|%Dc&J!@9B5+c<v*-zck}1RB#i(*x zT2NL+L$V6{r+YQtKAtrKhqMgfFA1OOps(KDb%(bR{6zLD*Yq?HJ-WaEcoW8}bJsq= z5VFKtJtdsCYR^|-$Af7+WOfYk0zp~lJViB|=aSByC-fw~z(@Q&lp>tMAyFzctNm<| zc(d}fx<1fF`h{^Xw>2|uN`*z0t1x<K4Q{dyc()78LTJUhb3OcY173_k1Ox>-8kTz6 zCGaLVU|9^JtdU_kEzxPcZQm?^Ei!2{TmAk%>Cti%!T%jxX_Wdt)a<Iqd^e5zbEk+H zsIS~8vgPbWCyH%V)st%P9}YMrL)sDt+Z(j3Yv9#Rpd)prL387}wZ%pO1SB$KOm8R8 zHlbkI8a?grY6gQ^<?z-5@2wtnlmrPk!IyNGNVc1Gn2Mns<DYCgARjDs@x~lIpUc?- z8iaL)td$2Zau_JIS=e;E3|9<el1>KmHezQ78a+2!<(JS4ESaf%>$f%{o486UnHo5j zMBhmQ(~R@&7NWL9Q5mpI7W*EFWqG=<Dw?q3+m!GZc=~Q<7aS-|U<AOVmnz~+unK)& zr>P}DaDqrpu8!9s{a?U|YrnPa?!$YkKtYL`*n`w)yt_J1#clB`;vQD1D}mGL1NiU+ zz`P0UTVXIR?&0a19FxI|rp@c_S|Jx>o+B|A34WL5fr92sxEA30lvy?=|EHS7i#oBx zP)Rzt$QY!36TzI7PKV+JNDTAB5CHfvPkKR&2Fs?Sn<N$G0zD|0alHj^6hhp9D0GPV z#RmhwVq;oN_&D?!Rc-t}QpPrcU(N?m2sl(dl(%Cr#i8H;g&N(~ExC-m_5k)A94tAE z8(Uk`z?uNpT+%!!fwyG{7}ny!*TGvy5@ec~Cr&|&ClzFgL@}3_Jlcf_ke|xyiHZN2 zRVm2f{gETDzuA%vBjVu!Lt<UApT+zG4Nc}%Z{2#DDn<5nb=CeQEno7-?y&!U*G2yx zya%DE?Njg+E0{)=6Rw7Ua_<DKJOuMX4>oT9F}}LG)92Xy`9rqM*;q|9+I}X?BWsjQ zI_?Cfg+cNBA$hUx@2lHfrbHNKKyFcdDqX$rU~T<PD3H~t2nJj`itO~^6$)g~pVvj! z7K|iH2epfBdTr?DNkFVs+$;Er!~c^^G7|=e8kxa|W1j=nb7*J?O#K8PJQ?rTq`WZo zDD>X@*i=z|%!P)RXnANAZsOck(a_K+#s9GM2<9kY;OqbC>`cIV%-g>IFUB&m8xxug zT7;Akq2jicvKvz(A!W(h(1s#oEm1_YAcY}OiqJx%q?ENp*-9~`hzc#<&)Gfi{oK#{ zyw7_)$9o(z$IL-p|LcEUzu);g&+qyDe$RivV*XMUWSYpg=GXfo;~pEP>Hh;%9}84# zkCQ!3MH|2?u6eGp-9e9dkp1Gl;yw2-Vt<kTzIpS;zOMdNQBeZ$!h!`0L~pNVvf=Tu zoaq1XI75=9)In9%479^v(+fAA&!0avi~h58|G+zMKbGpO{iqfC=*-v$k(Oa5VyT3( zVX#R%-5kUj{U}LoK{ch1iRi#Sy4$vW8p|JVu09zc+-yKapB}^Z$UA_<ZU7a+<V$g9 z94o9&n03@`X_0GodiBMFd7iqDXAF02t>4O3#j4q%YuDbJZ4gH?Z=>HPIYAj?q2RDJ zj_56z6**S-q08jd#KUEE;^5BrF+?z2@uc`xx(yL;7nGL=ysI?7o;e_0Q9()IphEYv zu9}-sH2bW+_WXi*yL0YuS#fihM(H>uJw>5c*`c~GwpZ)K8fCzXyR%p0=OPqGb_!Q$ z$-`R;bFgjqA$Ewx6;M95fFrF%ZZ`YeTys?1YSpeMr}ZDMlx1-RimUC8d|D=pRa<P` zR;B%3ZFO^frSQ#bG!FZ_jLS6S6J`+)^s$)&wY<mG6?5m!JI$*=`FSaHCOP0U&u<%7 zK2N#>_N@lTzBE6{_}xIYZA!g*sUADFq3w_qwfe&LPRb_5=NeRZ+Qj1~GyD)0I2(#k zlAw_e3b511M`Os4hwh{S>f!bo{&cbOsN>hD_pmur*j!6_P|AbR9q!&&s{MhAZaW%! z{5y~&2B>|+T|s;p%=ge`X@K^8{eS<On_~B7os<zr_j^}BMPJySu#B<JOH_EA+4=5g z`8J?|cq8}+_uxyuwY|~Xy0?jXywhfQkDeGscusy0sS|IvyXCW%NlG8fUT1#Xk@4r5 zL$!?&xA*BjrBJB=jCjut_Zc{FkKmeQ%F5H=>?kk;$s&>|B%kA;iOp`et5~)F>_Y)Y z|B{mL-!$P|#CJWcyi+>7SYtOpU-`33mg?uKOpVeLef!UmXQaW^&qI#huWJ(sh<q1+ zr@zJPyw?{1g5o|xD_cqw8i~3#kqLF?8XoyG(lm8<&N&~Y^p}#&<{Mg_2XAlpdh?I- zEfXi(EL@ha1wA?I*kZdT&m?xYXft7YD8HcM5=eT-j#g!EKlGn%Pt&`0!`m(gHfLx$ z=@cy-YaJ*OYvifXk&~Ofc=^XK*4G@`{<8d`S>v^FA)^(SU8l=4(XbglN^$Wr(k_yP zi<{H{Z&AOrLhuR1@*55Vzc#o0Ug0d#?aV~TF-DJSmcg}=ytv6&=-2rh4@fTt3nMM# zdx5jpps}{T^)k^*7qO{=9<m(-7whWb0B~^yle(9par8sHvbH3v8-;y35MxOFiT!hK z9_aU-u?H=L?^}o>YWQ#MkKQuQ%w5~xCB%h;gF|ppgDa&6ab<Y*3%v;AZ7*MzY;AAT zS(I-E880nT&{Ph9b@gxhfp;CX0^WvJwm7c&V(yKYmij{;7W`hcQm0(0K-wNxUBpO_ z#nsZqrS?;0*z8Mt|ICrr%MSZq+XpI595+s!^BNm!=X=d+X4>YF(tfqn8EFwzMvB1C z%WKcY43L8$$l!N4ZXL0EYu0|$XykO%ofZv^Z{v1deY&G#iRJ>`mPE|1tCuK-FG^EE zwz-=G$<Me(mE;yo+Ni2yZ|n3K$6r?%yW859ywLmEiy(HN*dne>|6U9|sf|AwVe`r) z<+Mhy+~YR(BYKG+=vJa3U$VerLWJ3r&K>MR-7I=pxWMrLR8e>YFAO;n@Tl-^(RR{^ zfRH)cR!x|5<#hY=T%jZT3k}1I%`Me&JE=ANHP3$VDk!E@;+?Q*!|O`?)ewhGyI(fr z_WSB813g`r@+FNf%RE$O9yduL?-;Lk_`A7eor1*Nve{MKiV}g-I<#Y-3FloZpY8Wf zzIbKFS=*4azs&94er8WQa)}whVh#26!#92%0%LZ4@rv=m3*n?(tE;%0>qnlKQX1wt z1vJY)RJG@-&@UEC9dg!Y`9Iue+hcgO5|x9~3-Cl?Y@#aIMgcqV*&L0hNM=6VXruR@ z+^34$X8{LZ)HBmd9kyl$$PH_!qOZZi=C1Bq#=maFh(MzU>+1z|AEJzr;(|wtaDvw; zUQcY(lkRuOpMElGzR&UtN10niLMh4-`EK{ZCl*y!hSiKe^Xt>~A0M5$<IzUfL*>Y@ z?dAawyRs5vLdLcklXq%*vw#fysBZ^nhnS}7>jzm%K``CV<@+DDWd8)7P!^qB0^PX} zgjFHZ(#5>X9beS*p#)j^@@2g7k}qrTR1i=eAVoPp#=FJ(km!Hh^`9xew20G`-*eJ5 zkyQ`$JJ9(1(@Rg)>w_%&y$5VH5;#~I1trd@H*S0;twch#PsIaWy%jEkowY3Ou!oEP zw)VJk4#KYXI3p|(R44}WrML}J;&TFKR`lA@p=Zw=yr)iKPo;nxK7f?<1o5|~(CoWV znDXlTb(QQ8BTxnxp~B}R=<<10`=e&-+q7*P0jyCEBMwsR^mr$0MV+^)zV?b!DX|!o zEQ%STD1b=DA{SK}K!SC^SkC&T&y$Sl<KDWRzFQ+!e0RzJAg@`{hm%(jrDV75@|DAF zM!CFO_N?gO><&u|g1pE7;EDoN+u0S1oV2#H&str_q(jz~*U<N8xNjPp;q(q~A3EUl z16?n%s-Tn9Iz?D>m`y}kqy%;9V0W(2#ihsPKWDwLrjqNh{W~$s6RJ`@SqxGZEAJ$D z%H+G_gA5j1U0oj)uCBc>mdoD5&;;UVtCSrb`+Vkw=vbXUTBObevnvXd3g=d?x~_cr zx3|f8iPw;JO-cdJX}9;ZLZ1c^=S&PG!E$332Nu8)=+1?;s2%F6j86SsZReAG)c4X` zLcYxVzE8jOtlQr2N1^O9?oG!UheGZ|mw=a>p9JcEpFBlWDrlV549)gs9UA3kK#1M| zHxFI=Ko9k0tqH2*)*b~e<iSH5pN`wjoOq{<nNy9&Q<16}8SYaNqZw=_7f>ib(-KGz zhg#gRIBC^VHDiYj$A|>hA`*p=txunH9HY^O4ud`hRzN^mKxB8YQYb6$CC+nZWboRP z5068fBV`L>4+r>xLX%>1htla-gA?_{Ju@{X<MMcd>|H!6rAZ6+<D6q$TE3v^Dq5-M z<J?!=&JHup!J^X|RHK*6Bs(wyV%n1Y7)>yfQp{71Qxfve&5nz*r~k{rW?ICGVEg}D z6rAyDAMc`{!A}aUc(f(B2&4f4&^U>2R2Zsw6Ndb-T*0tGccSsYiPIEs=8|BFIw4hG zMcC}D9Je>ew*UQL?i^D(I^N!1Ln?o=b@;fq>W_}RPPgU}L9GpUC`yn$n|_O*$(2Mn z1+Ij@uotEQ)39l9Y;VpHoSx~g(7%AI&v~bOgWPlTGC3sgv>zIDE6miC&}2_@(fZ5h z0Vfsub)?U`U^m;Cnve#N9j$q_L-HEkplszSQ^^xu>)u_G>YuM3jwGJ0ekV{CkxYI4 z;J<rZ;pOXX-aVOqW<uL7^kQBWDdFdY7DFeq+34M1J5=#g5zx+$MgMB+*{}{k^?JGc zwSuC?`g-Sje4wv>t214=>bXN*8@<sHlg=;vJ$5_!3HpF4%@+kzEK;m9^;MGGK8&{w zF$z!xdRMyJDaHTwRi_~La;(fA_j-!;YdIO=n1f!5Z3V3xLtAXQ+__%KYLiR1K{u|b ziRbs*nTozfxrAZsgk?W`At9V|^kJcPH;|Ilns@moCagz~U(Ts07X4R|MFZi~4L$NH z{6irKyd;XlLNL!H!|wL@_;**mel;)MU0wgByxV@(z`(uzo23xjd@$b@0(%<P8yuyA zcKL_=NQDdC5dSWDub3CS$9}uh4?n!#I^6>HGq_@J9Lkha9z8k`RGGLvTKC82o}Phw zCi6CjWvo;B+XVb|h|4o6odo3-ID`sWYh*3L|KoPow+N2l>fwRGVkMoNmtDNaXUt(8 zg=0{%fww3=+TRlKLvnR%IQV|euz=S6X6c6k&Gk0q172_x?cBXvl>6>k@R;#w#;$?H zxJ#F<h&kgdc2YQV6qaC|m~T`(N|(ciQ(1CS;;N6IUwb?^t30(YU%4S3E*o7sEcxR4 z*DS;yWZnV)y(9Sgm8|Okwf@#I8dQ4ugC35mq*g!1;kjXrc1n*y3ne!_j?u6ARFzix z2i<j=$hmq4yaY$^+K{d86;53KmkoH_+k&e(ozv@h=5L<F^%sTte+0dUdKhZc$IeeG z%{fww@am5saO{05w|KsQegWUD4)iN5C~z84Tk-MF58}^BkIxmyHmmb?N@&O|zV&YE z1!bnQMumEzIRF132H0NtO()|5SeB+u*KFrkMX~mgx>$!`-KW4)GX-gbcr~6od{b=Y zW{c7LKUo0t{m1S3Cjr2Fx5fRbmw0_@!&fUThK)+qm+F~NkeS`9bO6<aM|u1bC1dwP z-?Hs<3Q-tqO%3&dBo3S(rye~$eA^bjUPo=d-oLpa{2Rre;<wo$$7bQ-;Yv#$M7E`a zAi&-$S_Gm)!Hst}KGC;>!nS-oYo#>r>OaiP?iU?9R#Mchm9n1gH8V)1Zg|P<z<j+^ z(|iHv;wy>{b|lr}QxE=bu&LfvB)pXU3UP*Johqkb?+MdcD)-T&706~}X?E#1>sC;z z%bw4!swVZxjaPOqK3`fv6qS9)s%iskL9lb_YYZx4x9?LKjI+1thnxSWvv<Inf617y z>{JCvFTsCe5ia$Z6M-UH#n`td|J*xvTR)a%P(Xi2g=RCScM;1|-K5DWwjboHY|NNd z&rO=l?v2dOWhw`%#Kavu*S0i{a!Y(qA?@7f(6wx5NvKqtJlJbt;Lgyi9G}-P{4*bM z3a6gw;4_@i5sGP929KR<u{MF&;YW}bNakY1EVgwiap~KI@X~V&RK-;fn@Mqjz_P6N zBM{qyNh?}R=kjZDI^KW6=}iI^>UB{)A>UJo8yZkt1g3S_-Q&pKACFNws&Q~aPGR!5 zMiA;J;-(7-5ro$r!DwpkVvg%^YX^SkWi+pA_3JyQFt7F^4Vg~qPNnL@27i#o6Y1S8 zT%oUxu~$JzpEb)3_4J%$CLC{YwSLDTg*_Qy*%mCrC8n_M(I99%P5YVO+7rI+YH+_c zU#lGcBzrjZ%bBhmadGNr@Bow*xX<H|a!PPt{{Bh-{}eUEY;$MM4KbHBs{!=yyyD+S zAy~ioS0Fe!@C8}XV{qbgQwlu9Z+cI-v6s=lW_49hI7YGI%fZJf3r7U>KlZpOTwL)q z_gD^#zhmhPu4{B&FWMMw=uI9VK~4@byac@{NR2*q<7%%qP?7ewnEwn;V6D<&%aXjA zZ!(W`qofi;Nazqi?-82w`6_cKDcMc2f1CL<?u^l63$^Bv_YUUSQnziu_6j=C77oT{ zB@LUnrvFu))*2^c!u3{75c3g&Hl`=hlI4Lr9XMUbm%f!acCeRGP}_}egI|&GNS?d( z_upTZl%$=BPTGY=+`9$(?%qZrJidXxN!Mzw;8{ef_?ja3CfUQIKW>ttx{^Jpt@Y%) zCJgJV$P#n=C0Ms(_t!#7<qcT)$^OddL!8-H9;wjx>pT0bDlVo4IO~h0DhBsy`C}3O zcGTu2>_%<3z#s@DES)Z%6kk;R$f1hMk8#U8I_tC<e()<Pw7^S%{C`wif!_;tWXBHW zl!7#l>&CCk)!m9a(z9A6)-;qwP03Bij4^bJdW*3#0ZJTDxv=<w;~TT}<jIpwCx34v zt}<7QrA5RG3b7ZHBC(n!9~Ju3-#_HzKYHzE%!ue4Nq>~QMLfju_#fe6CC`5B(iZwZ zq7v<ZVCe*fBij4<vLkn#$go%4_Og6TLFT_QyAJK!`#E`3uyU!aSU84HLv^@Dg13XB z5`2A-FcRRhi)}5|rXj`}@}$$3w0~hNE|JH;up~Tt!2#==`YYRvNBmxWkZ0otF<|_U zB{`?DDRA+GQ*nZS5Ix_NHK-W9yQ}Wsrwcat{C2Tz1S>n3qMoxojclO3Y5iUtX8V1X z*JnAs>uRmV6T7~r#u|?Lj4CS2zYdy>*|X!_Qarq_UcK5B^Vj@OC%h1`?>6ZI|CE+a zAHPvIAWBZaX7x&5h_Oc?55*z1l?0FA{(0+^F^+2|nJFnfm6rID(_PD8%EpYu5)v%& zaHqtvFEVTTjXrsI6y(}rm|eWYk54`TiqiEPzqgrb>`6XJA932z(VLyr@Zj6A<74q3 ziue3ou&HNgi?-dHyq!9A?|vI~`pjz%)P*WNO@dwE?_b>}ujkdQgk`OVj~b=sMG&zO zCZckOu3ZC*FW-dR+A4)kR?LE}{9NyBmz?js=&}F|_PUt|g;n7{pma{-PZ%v#d-F^? zCbTSYR<fHmtpl`7^AB;rfyyOz(If=9p56&z=j7fByp`;zV`YlQu4qUJn^EQCeYpqI z;rGvDR%7rNv%*I{%*guOBxRz!3fV#*ef_)c18@Q-%tTy<a6`=3yHe_)c<=xm2r1?` zHja|iRzOUbl5p=DLlUAh_wxDJZ4%=6h65F9*msFS9(R7G+#G;cd7qV2R))V-1SbII zLl1l93|498MUdKFAfNH)Y*{$=2KgI9GR}|F_wPkoHDq~iR(>j)|9zwS>g%)M+h2{I zsW@a#;375+O6_==FKIU6pW_0?Ir*OvcSUY@W7B+q|H-_dH=}XvpmtN=_-VRqev`WL z!Q_wk6Dt737@;$)$Am)-3;(AXcIkyu<CxDFb+`)ObHQ)fjyZKwLSTosgG^KEG$|0% z_Kol}uDnN&dr9Gyq4-2a$Yw+#-(%umYTa<EY2EsIdir+Uxb0J0h2(_RE&=D6MX8z* z#=dkcS`r&(T$uwQMqnHHb%-P7{BMtm%UX_AbHKA>0#YR({<IrOs6l;0gD;G;fp?aZ zyE)2~gHyEk41Ene5>ft7phM)#VJqrk{S6y7_z)b7^4-Wlhd|<4C{n&OmW@w)lg}wQ z3|^3uRoiETNB&oJYc*r{hw^8@1iRS|A0K~h6o>}`ar__(3GvTJ0}u5f2=<t`M{UiG zQ?Uo5D({hE`3RSQ{``RtMC$G&FsDD@OsNXRa*FD@nhUiaS^6rAFan)`VAcnS((ysx zkj&89H~3%;Avmw#_22udt)-=PWMXil(?(Aeg)3oJKUN*=;IjruwP?3ql0Fm(U-DR^ z1rL`}b&e)Y9UQ1Pe`SL!*yPg2z0WSM3IBk<Z+G5H4peKNtuJ=P8ee+8`PNu#8qTo> z_xZ|ir|-oqw&>HV*L8iBRE7uXGYct0qtbDueU1k~S!VErV<0Hc@#8qflAJnmc$a%O z-&0-r{P`h+#ghX49t4%U`HA)QB9hl!zf}d77hrOI2e-f5ffq)2Cq7?<PcS`RLn2q8 zqw;8vXW@{`!y$O3fj-4J@0hZf3(GUzsU50MLUT5qGX$k(Jmlzi$29F)if@o;`GLuD z7vaSP^L{O7OP6SAQ}ccyc0bEFx4_}de^r2I1g3f8_FP>Z7auz)$Z}Nudl~bfKXQBU zJMLN`G<EtjZ^*L^_uO|Yck>+KQjtYS+*htHmQ_`2Mg*{&!BB2eWGMk5IFQQhA7I^S z)v8LTpPbjn)OwUtt4mHo6W(;>_P4WYu1yrrd=MBQ&T{q02ai8C(m6Q@hNec>tLy@m zKZBrRoq9w^by18k!+TD_fgG>?@zbZ~_|p!!x!k1w{aATg)!xqj|Ljywq3k)Ejv5mO zt(P?DGqNYwKU3mH3?9`EymY?QSosi?Uh?VNX&px#G_Lfc<`7rTO&b_YMWQnvoF-=Z z<tYm)^E!y*k&O7@$na4&K1_gje89d-tjwVrm<0Ai5wwiQk-!rY_YU&QRY_m8=zq~G zIKVN-Y^Yd1V=7Si|8;QGig3l<cEtB!Tu*PBl7uVFlSU7~>CAWCZP$=@(iVNz-mJmc z{9Qpo<Fova>aWLl+bDCaR0$LuuB*>Y4a~UZF)MD_F9P|UUyv%0hjX>%Rbm4h{cj__ ze8Z&R6gCcxCZzp<wlbG!BH=-mH%!ygG2VCB)%=f*f*j*;J_8dm>_E+d$yEvAEU29L z4^R4q#kO3nC)5vw*E&gt9H1%nH^r>EELjQEGS<<{$WkiNjm}mk>z$orrJBuL!K0}< z?sLr)2Ky_NB?$SMU1c$M?%LYQgl=wr|BdW@-S;-n!=z(_CqGcck<z5v@-N-HW0rt} ze+kBq&5n}R)`QL<4)f!CX?wH?zQd~gySc}K*GzOU*gtr~@c+n|qs2L1D^BpZNjr)W z2J0lP#|lmhA}h<v*48$~GxG)<vfJ^4N;LknYTiPZlX}>&VHlenE-#Rp*l0a<zkTM7 z1YhII>9+Qjbxb|@u1s&Kc%9%{kUzrKk%fjG<G(BHkI-LpC_5Az5LKCjjdZJa?cy6> z#JgfZA@Akfv(8I$^7iiDy?f~A*i&;844~NFivQp+%;Q-YLnEeR9HNmfE~$MS70$KM zY5Aw9Vg%64nB$4BKSy?E^%%=#f6q(sAE5W$KRpQTV-2Xe#dckFo6@1>{VJX(hY2mF zbZ=}()6M79bq3wx?*T~{@cMxLiKk_1Y)<l)MVLyN@1eq*pRvjAwN`wR>EDiqXW2ks zTV{F0;dK*wCp_D>W<%RVKpMO*`(<1py!^HN%^nI0?IV2&r(M{|IeU&hNzbspm2jJ8 zg^H)Ar|9z2P^7e#|HqLuH&1VVQt-mWC_;VdZ3BIRAt%;3A-DGF+xHf86v96+iKGXD z>tWc%L9JLqnGeS1Y_w})&y5cmxzgSTqG%5sur}X2VIDAQdds`HwS6Uwl<t)4A^u)` zyGuAV%mmDb{lD4^H?ib>?C+dM2xolR&D}^$hGJB`LW-gZOds>5ZRFU_G;?t=$C1yd znJv=vojm;@Cae<c1gnI#5lu%8MUTN>xlzk;>l^9(**ZJ7R$D{k&r|c0lRg0FbQcC% z<dl%Zi;~lcWYgx(4gNqsq6elZ6IiOgoUL=po<Ose*#xvv^#bM{Jkl(MBB2PX`4Fri zR-s()0U~`NiB%n^v91yOc^_g<H)f^`f;YQN_QDj^H8rthAGffnj;i#FIQq5FQ|z&{ zeMi+j{y6j#<Hx$u$r=oVx`+f;l|gIrE4Ode9(o;jt&`%(NA(7_(*!V&rn4E|S?mRu zN?UE8;U0tsVdhLA>iNpK{f0fXXQQX(wl$7-0Q#_DY`(%k>ahRsfsMt^HHvX-4<jCi zbh@T2ot=*T^Pso3!<Od`ken=~pocrSjkVzfo1b9VK<;y>bEa+XLYz#8A<(K#n?5Ec z>Qkpq<xN=7x^R89Q#)qeDB-%{PIH1>2(F5bMc5-~zcm$w%kzfvO^<WF=KARtZ3HX( z423Qr*aq*vs}+gpZS32YJ&1+LEOuKa4WDMRl=r_f!pT>ftB69#5^rF$@&Rx0r%#Wh zt9$tTJ#g(wbwTjYa1JN{#4DaqoK9w;L+wfmCH(H!5R{6DV;0q4Iq}z`Nq~8Dz&Cu# zGN__LeL@Ogm{W87;3sq==IQJtQDsr&Ee)-uf?oV!$9)%kleH`lrHBU&PayVP0u4;N z;#>Qqjq4>-bYMDFAp;jOUS6~#i+NPltv;3^BdSAHIyn8p1R3}`OCBW9^oXfz1TQ~M z@Om3s`qy8}@-{|ebRf<ZGf%d|-NLfQ9VWI!Fp!yoq>YSU8ON_7G*u%-$MTc;bhns% zlS)q>?DEXK9G6I1=uPd@9zAv=>=CObpfFJ)@g8JV@!W3$*SSHO@SkUj-%RAxuxvtM zNOwgN;t!FyiE}RWM57gI{;~2L#xe^=YGxKL6q*QkTMkwNP<GnBh&u+&ex=rS@+iRq znV}B|uR03lmI97>Nj;V;u%1aL9|)pmk=6rmcYy6RRjHqQELtzj4QX7(mNyET4Iial zawUv@oD;<!JY&;apqE8t;7(8Ded!Gh617mxyCcPA<$^d8AkW%d+Czo6T{u9%(IYnm z?}=xG62z$O$o=W~KzsdNglF^ls%j*>CejIFk_k&`Kz{jGoICsrQ>W~MWu<rRy)q!w zVzA+h%k#4H$NH<8c__x;e@~vO{2s9Agj?w1nrH%nwMk@fU(98gRp$%!;crj{1Z9PP z{z;*yLf<RN4%y9|o^-#S@=daPT-1;eoZ<pPQG586UejCUH*n3~OT68~TO6FP*G*sv zh+@2f@4Xl`@cLB#hlirR0;!eRV8reKbfla+i?}LS-tU~&NewkN+P)7B*}0Jz)C)0; zrnF=GiEDpGqqJ0TS_KtGioVaRoH}grpv=C+bLjTDW3T%XcVA;kLbpwy)ES~};9m)( z=-~h$iUISk?7wyEmQ(kL6OQC!BIaTEp{9o}JB)i%dXKFovpT|uR6e4>#0;=)3T?r) z^`52h#5;R&-bJ{}tRki(C|=P2$Tv9WQ}zD6*h)*qPJ`TtnKjLpwzkrcF;DyZ{jv)J znz&g}Jp_aZvu%&Mu{^I`Cr>&auB`(?5|0tG{UR!O5UL%lEFs;K6bCyc2WlR(V>lY_ z$%j$99Oh%hbAqEZRs0(*^b+^m>sh_h*vyAmnVQIWOOI7djt~~B5Zh5ZPQ!p)N>O># zMC^C&VVmkdOjO`bIfi|D-#D4aLhaDj)HK=NB86kjku+c2W9XYPQD|wQ-y@jGlo<g| zos(WW!^dnX0C95T*Z#<i1mea}_@q%FL{>>0sX}Ey22GqBbR0l1?(!V3KHu%}h<f}n zuHXD;${_Pn;EA72OhV~>N2vZBS&Tk-F;PXlok+4|XpDk$d@Q6!0l)cn#q^*Yd5h$? zfnY2%X(Y-$EsMEJ9xqn^Ht|lqXqV$&DQYHGp-eJ_18|W?OutXT9x0*Rv#h>W8tSnJ zj*NDG@-p)$7hC}bl|K5fezo{3N+^jW&}fMN;XofU)Qo`JxkHD&<#o^3KBpaX@8VL} z`aw5%!egAA1^~^8Y6!io?xUPbY&D9V_5!M^8@oSl2sNvS>^6Ge5SF5q<DeNfE#e2P ziUV4^<8em-$FPlojGd6_0e}1&y@!;aXwuZ$nxDLhZQkeR@=JItLRu8JbmD(0X9~H% zRu(D%>LrcX{v_`DyhY_K(}$uzkbld{NWZRO&nhNhyhoTU<H*}~;+{_w7=@3Q%#SIl z?aPYi3oE25e=w9hl}xf-QyTaIv{1-%v{AB<FQ92{&O2eg!vS9UZp?t1X3*l-(4}%D zo{StA6A9qL8D#UKNBdftK;bPMszHm=;H%i;(<8RC2}}Tlm@lwrR>;V__+hWqYY9P& z0Go)AEkM_Ei<_U?Xp)!}?atF79aCnKu-|{5@afv<U6Zkz60ZTHnO%hcm%8nyFN@L9 z?Jqw}n+PIsCiUHC3c{$?eZ}_@QihD}(i}CaAav?aGD=lJwVo|~yDRIK@3J^a|1q5X zY`!V~4EOC{d4ZTGS)q38PQ#oYj|S?ny11mHbkaetaE08$$3P-X6`Zf(3VTgeoS9@F zumU6Llnd`r=#n(!O&)z)#rd<H<-Mk=*qklDtk$D<!V%@s`+l<uTsPCE1*<MBgLO#? zHmO=&4pqe5()nj9C$-~EI`c9jN+xDtmM*odMAMsP$=@11L=7eCtz^2!qU3_HChm?L z*5-k(Jd!^_D_V_)w!jyd?%(r-5afC25!~Yjh7y$*Bm|JiN;bjw@ur~(b;BO4*)Y+R zDmtd|>9bCnE*|`(k!Ry5vEt=j3vc7B)rBo&)Pvy!W636z5R#rrAHD(y$V3dP!0uAq zGbZIo$wwJN(vI<&GWLT2CygZ>EFD?Rf_W4Bg-&Yd^my;-oHARx*6(K=em%v^Oqv}^ z<=<90>8x_DRT+{3i)b>KB>ticIVa=>pfy+@Jji`cBK57VCp5?-!;Ahl1no?At2OfS zZ?&G^(&<l0@W5JpG3=c6z8$lIVgs`ZpKq%31=X;2+?>qBUZPAYfX&QnHBcTVRT&#D zd)QahnS2?A1TxVWf_|&0k0Qv_oX6>ndrkp<;H2^&+FDbSiE2mTA_!@hW>^*RJQmgu z#i&%wG!b=d8Ogf^xX$a&^BjK{9DU5U<CMpm(Sl$MpHai@9SN{dQ#s*JCRD!T5-qbb zCrZc4Ahn8<+5jzKO1*a}i_2{NMr>rhNM8T&lRIg2R77875UU=PuEip{%qN$%z|?@# z)kA?jn0_@GCs=T%lIFfF0PAlnEUf)6rmg4HR+v!aXoQ_%9t}}TvboW>uQxyBT}{YV zl=R%Rv|$+YjD99+ws;|V0?AKq9(=SEn*UK2A>B^@bQw25;#O!y82yji6&~J3x6I0t z0yhT6gKC}69cX!aWid|#q_U;iN8`ym7e@*W;bz`;CP;JKMfLnqXU5hhnE9|fg8TR+ zLy~iuDL}FKg#yg~;jj&m=mcY-OVh2Nr;LS$S2BUWk4lI0Du#Z-#{fhS+9X>)jNJV3 zx=QY67uf=v`zHM|CXK`<&E~vlLJQ{2vxeg%s?+s}=qzYwOr6pHKJX}vVMR&eMG{}L z5BVH4&Folk>wT@)nv!t5C0)+7-+T4oX5GwW+K#{dkr!s<oc49>S&~1U(4wNEMGcc1 z0AnZDxP$u4n7jUCO-&24b*<m5<RrpJIFN$-#Fa~TX4JhSw8FbJD)i7gx8R^_e44H7 z?`Mfepc<65kNOkJa#Y=SG^1x;n>Z=<*V7l8dr~M*j@fX~3SooNaF3d^>sn;Z7FOxN z<dfBMCR{2@>KeE>{bA#YPwJ5+<PW*8)Auj?QOoxG$xGda>jrLIaawbF9Kd4!pv=6X zhibRZVY*c7l*2pL45Pv+Gi;veIp0IrwUT0p*CR#4N7kKI>)e$Em!(tT2Gh;Sc80I7 zZ(P^kr>-dAJU9tzqPlJ0tILQgr;BTK`DL^?z#jy$6JNi`@>1lk=-#_4uFw~*3z{d~ zljKdtf{Nu%tRw!x?Xo)@qC3Fi1o|QKP3H^W9ow${3IM^}Vbp*bXG$HvytH5bVsX%d zHXO&CjXI$LpIcS~VBXiw4Zi#$DsgZhoi)zReTEF#FUgLm`8m<1gpSH!S?$=hOF3)s z?6<y}#)01kF4c4+g(nAZPs7UyE7f^p9wj1^TXS^&3A26n(a(XgU_ikl<ngU9d|EYh z-;?syljc9&={+zrtB8uLb=$VzWyR)+8P<nQfzF*)O+NoCD35r5gKqWa6^o(rqV#s) z$&;b7(BNA@mVb7BvPq%WXQ1m`g?s(y52r&tK{IEiV}emQt8zZ|AzdaoV3xb0Z5lly z4V(3MJBX=N52!KH1DA;a(BTB`mqX1lCp3c9u(a#oc#S0|eti$W<%U9MW%En+eH{W9 zN1~s<AGx^b&se?FZfqTrVuT!<Me%AqX>ukH1>WH@e+^%D{2<(a?V}!I(kHX@XlyUd zzNXPm?f#-KmHsnQ0<2&NLPfV+=e=p=Ue|8V8H<x&M$t73t3}d-$03$7Skh~jcj>33 zc-`c^8dH9YS!|eF6_>;e8k{$dbjcAN-`ToPGh}2Gbbt96@Cyej0jy1qkThK&TOLx| zsHNW0Zsvv0`yD<$SZ3@Pw0Y_~kD-Zqnw2vYeHTLb97m@u2-goGKrjKtQ1{yn&&~7P z*WDfP%{x9g?1urS1++WvKtX)F*Mly;8fL^|oJtn=hGlvskB--7j)kI%ifqcTr4Zcc zBt?S8i(-wV{R8aQ8H5MYMjtv^&{5gPg$we10&9&DUpr@(S$}zcGr!X+Jvevny9S?T z%8wnq>3TLE5a`n`PTRL`wfg2U6f}M2B1^SHAicRI`Hwm5PJYeYv$({WI)f5C5MXKl zhobb8?$J{9fq4pkk4|((a8$C*E8R}02y*V`?OR+jQZw*d^a;b6t6EnKp3!~c+F`iY zUAR}6pTZ&^Ea`J?OB<>srOU`dw5NtzR*%Xo_ljy*H26kL&s#BFg8g|OQWbCU_KwP2 z_4dO46v=oF*T&-<(v!*OxYO7iyd=pGSpY!7p|~!B;GUXxY|FX~<2c`nZVSFtR!Jw` zJ(~(R%5+0)Rr2oGe8#fUu>%Ik;knM7w<+-{hYe%4WrCPa%@6St>DxrL*3{9QWTiH8 zy$>u7t7c*F@_7@1uX<*7a)$;Bdi4@Qqs7n%c{gym?T#1Rr&&mv%1mxuOxuW`X44N_ zI?s7NGf*)y?(31jZ=neZmGSmIvMH#^!gynAm;lGDdNXzz9Schrtzu>sUTk3-IOTg6 z1jxy8v7;wVE_wI*)xyTyB#)wvd^rfAuSv*G2mUU;=u%ZeAP~$ivecNASQ32-7(&{C z5|ayTWb567UCMIrd>v~HS4rP-QRFA@YRYr^7#&hdQikXT7JKtk5hKRhXB-+x=@DsB zP;5^UeIa`m=9m$#Q^+p6fvnqQACA{BJRuTneBIT&Ftybyj971DwJCoNc=xWl(e!S{ zQ0547`^WR;I!oa!P*+Jm^3m&syD7p}mp>lFLk~M!5%K!R1Ajhu;WW%GuYRapO0aYP z^kxk3)Qq#k=w0M?S>9tMGueqzY`V=F`I$SdH!Y2+ztLx*=-1l?I=!GNt9bjPXS^zW ze+2@hmz?dE{=X!UPm?aS7|nSqo|BR3OTR4GM7gM8kt3KVle(0$*5z!fdpE3XIqg!) z=!bLsBMC=uD{L;_!m~xB^)kK_ichfsQtU_bPN!~ST2FE*H6W4=aXO4ntT|l0F0;K1 zLlR+f*&66vsD(*{E@o#x?tUl|T0r`hZ_AO4kkF=ookNucYd<0`H3O>g(uS9zJ%TK? zmUsPH7_q052bi}sJ)t-`5NrjLv$pHRsJ@Yv*HX!<l+JGU?W59t$POVJgDWwgBtXNE zUX}s#k0sZ@i0<sXiB1e7SaIiW81|{+V`^yQ0*XdvZN`Z!L>cO_jPHYNk}3zZ{;4bP zov9=Tc-$YTCpCgz*OBncF~ChkvbD`8y|NP07}nyeiQZ)w$Q!NdblZ0y()W?#X;r4L zaknb}VN2Q{-SLymxpT+~y~FX60AO;qu$far1u5@qQoyG9awOhGtZj|ny>A?jlVN*; zXl!rZUFtDtDfILRE@NpSFu$-X6`~`MpTs*ZIGAaalMW7?4C%R5+qQ9yqgNDOi7UKG zr5cqfCpyttsK;cPqSs(Bi4vtVFz|RLmMdUdI0#-}GjSd5C#DT;rs6VidaQr@px#v0 zvJ?hHQ7z1VBz2@b$y;D?UB+|CSQhBaGJ0XZ-u%!`nv+(4{j`)rmkLGFaKn~gcap;E zKC&#kO7ZJ+(AGVp!g2WhMSjwq8QBWoziKt^B<b_3HbSA1IfjEc9%Vuo7^4|6CSk*u znCi0p!a`b*Zs}j!Z0{qd5@|sdBdcSW)&!(3oZZXNgoro25GC>Yr5*JovG@z5vX_7T zb-N(J891=5)T2~?g0Dc7CSvZ~wTm|5Jz!mky4CizLgNx}hWf^l&*u4h6X2PFoY}JH zh7^{VoK3?12DK$wURzUct&*Ao^ug654r$J1o+_j4Zpf5Ede0;!F%Au&dD(M5H>_mA z#Ge~*;$V4iH$TGLNC*Ny!iyv!aIMPFes?>2Bb|e#4^MTJWkB-(0#vID6)$1x9(^jh zLU!}6+&K%v#gB&x!MJnx?vp@Qj~zS4RPkVd+baIh=Bg9&o<po`aW+AN3*~V1wse_G zvx$x!O1KP>ivu+p(O=>GlP(d4+D#C3k~uk(u(`=#@~^oKCZQ1ZHNpy+aSm{4LC00l zaVeHS^eqgVEsCb>{U8M)6$0#mRI(yQBcG(r1P^!{oD`1RD3!uGYFxud)3*j+NM^Eg zy!3OKXu1|GV3y;4gj{&5i|Ls_cHqXVX~Xk=#Jve%4JwBZ@4?{@eb;3`PHEp7o_`wz z@Qd0ao7og{DzO!ZEWN+Av7v4z2h7#V<px2j*eQ#{8DIplWjjvN3`d><4y^-nDw1om z65_D-O^*$fR>&W-=Va@wSqj2=Wm@v4jUucflaNvMS^=#YUMm*dgov4K=afA>ol2ib z8ZOj*8u$atzQ8rY_>RU!j+;(D{WO`PVsQPs0SN~)%%Vn|gwL--D$Jw$L9EW#>mHkw zD=k-HHVT7a+OWekl0Fij^{Kac7sa2IZW~Vqy&czFKkugVx^;0C8~1xawQ4RH1VEJK zn)%0eu#P&SX1pgpQ!JO$4m&dw7ceyjr2UADbe^+qg2o^!n%)Z$Mz0bf3H^}RSvD2s z*<G<YU|TR}_V-kS^P08deb;+7B*E7#z7m!?HSYcV)O{+S!2%d2TuNmBt!A1=%bB9? z!qxHPw|9>>t0)u-FMZFl(O-4uwEJ0+;-hG;IMmTgp%|>(L18yRp{&SiHCo}-O{tYa lzmGym;n4iQ|Dhm7$+x<DZ-?){$3+$9<EM-}_Yb=*{|mk~W{Us- literal 33991 zcmb5W2RN4h|2BRbB@&^~K*=mCNo6)DvPuKVOd&gaYZ(!_jmS(Q4SQ#XBrBs5Wrwmy zMn?bhs_*mrJ<tDn{>T4#KF9GnKA)rR`?{~|{eHb)uk&@D=j#eMqkd`=-F7+>iL^;s zNkNlDqVyq=D0(-n$DbT*`8tUI9C1+8b~tBq&B58|##NG<k%O(Jjf17R@h+#UH|))A ztPhAB5)l{LW#-^uYcC}#YV}_q5V5&oDk`Y=-5VF7wN=uwCy^M9h<_=b$)}o=NOvTZ z6^@;Ei5>su>~Vj1N9}a8@~vvlEG_?6C#x;fbUL4J%{`fRNik5ws^z)Ji^a$HTAz1V z1?W7<`^dWIS?QywV?jzhWSMe#_2#s9>gw|gt8T6}u1%{l&5pqe>GmO`lG}WZc5&mM zXkWeSG=Kk3P=z+p-``(}?+Nu81qB73u(Ol`_yD<1fMN?16H}c@IO!h#-bDN~NfCc9 zzKz%C?*)RGkK(UGRZsr^e*JBw7(FEw)qeUtc_;7{rYu@sA3K-DsofJ36EYr)V*mZ8 z|Cbm3zq&6wrEwDOU%_&(%uc6?I~Jc4&CZ&&Zx|atC;ePP@$W;J?9|s6a}ZcuT6KP@ zKpQ2sRYpcrG+pB4i3(yt_Pe+qOH~gIllR%D)lgenqRF2wA)v?SvwQdMAol&YI`b^r zGL23QHO1|?($$u#(UxbS^;|1MJx0>1<F&I5-O0VUh#<Gj*RQjt>}L~lcDgU<*-I-Y z(DvS;WjXA%ayULd-eYZPST#W|Zqp7)1@nd8N-DR73ALgd-?ekDZ4xb+qMR(5Whk)e zQNQ~PTfy^X?CZ9?;Ynp*in6jHr#kG~Ed%e>gF2H&8-IvU(9_ZN7xZ{t8GILd{?(-j zHhq_yWuD8~W-UnqR$4E$kIj#L*(U2I@l@sEwRaD8hl}Vv8mJ9PJw8Z0s%5%fj@4_0 z?3|pc%F3Io%M1LYp5(pXgei|W{@h|@WK>vGq!Uu~`t<>ePeS;7X3nx`>bG)l`No(- zwQB(p^7RizHCkMVdtA3g_<ENUZSUx4z5Jh*D*I+x8XrGDn##&b$?_24r>{AWdV71% ze6K(C>7~xA+Ss@_T3Xt^iEpn%JAeQFO_ulel3N}-5br*5MBnq*g_6?JbBB!)LCQ|| zw?}I&KT>Uxw-G;9M;>yL%=K8hf#nXy#SdA3&E#~xaU=VOr={>WULX8eC|SPZ_hAVM ziPq`F#9h~#<JBr}t&2Wj%Ha63{jo-Z++~%a2?OH7M)UJSnxdNz(&oy<i`QKHMdFh4 zQ2MjB`lCIK#d)&3rl#iZsSwUDgPnrAapf=BR|Cl-I-Jr{QYWRXZEQ5YzmM1^ZnELR z^Yca>nMQ?$g_+ksKHn0kg}Br7_oETgU0)M;N=`rN&Qjt1pCnE>4|Zo~=b?t^+jS<! z)&5K?zdjpaCp!5)Yta6%W-#n~bC-6*hFwBJ^fWZ~i)(9Zhoz**(#{h~RvTG`l<@0& zIPC50&NAWvE1mq_M={>XTd%&$*GTN@`z>-ygDl5SoDkG6IlPfgBw0C(PdP;TE>^ta zeZ;<j-rkDPJ*QsQVoB33zPgmWZWrmG#izl!A8nz*dWE((`!&49H{*B*RdsVdlDL_! z*JxlRF=iniJvBYOTS6jye!R<mZluK_dgYjuDk&~D)~u&Y_Q|P`x3#r?YB7@gohL54 z&5zMWxl;*WnbIqE=);vg7QQ)OF7t>^JD1u&I;!&8X<T!bmq<NQ%&e@}R+n?l+oI&X z<@ELSw+Niswe`vA?O{d=TH?}H8yOg`;m1~{Yq<s+qQm4oe}8zPB}&?R@#VXYtSi%t z&apjRT`@LgOTIa#&HX=rzJC|G2S1`-GP#wV-T(D#>F(}seDS~${b2lD^05$3+5UkL z`Jt~x_AhmE_}(jBaf=jTsR&OdG9b_F((+{akJhIfYlAtc3`ak``r%Y~NNO8@#8x6e zxU0mX4qC7xb?uaP3c9s!;|xA+`fQM~wQsK>J`g%+yF738dw#qg%S>VRC7T!LdW)bY zk0X`J`k#MODutBO*gO1f9hrAy7{A7~)!)Cii7!$8%c~^SD6yLenk<_hxo-E#Qu@4+ z0rPDO5dpaK>VDku%6ym2r&`C+Pv7yBIEYV;&kLUTm%jF5=e^fFzJC2W((;5xPEPLA zu=tIPo-py+7ep$jz`3d&%_wi$VN-I<&~O*7!E4i9lE!iI-nqXyKl)9)th=qf{gvtd znvSln*^gQVS&pL@Pe<&H=zRS2aKugG`RHxF7TS6I{*ije&u^f=|4n~Qp!tWyW7qv6 zuD4SWm1A4H5j*~l$bZ2*kC4;C<j)WR>Th!9=FOX*tS-y{9@%rf{aK}oXz86glhfbd zX(qT4rG@8&D{2CgP3p1R>#1pIs0?jvn(IZ&Uk*+h{H;AaRfkb6u=3bSKNf!Vso@0g z6elHf^506Nynx?c?D?~H3hQ~q^S4CJy{%W;Qq*#-x+L>XsDw$-5aq2kZ}f9U8=^<j z`s;?H2*Jf4sR;sS5|8dZ73q}rpN~}Drpd7zIJuL9Bl?Iv-@c14&&;sV{36PrQn94# zjEVKP;+lmXZ;gn(7c`|~9gfKo6_K32vbxZt8hg0qccDog+45WQg*pSuXa6p_4pAfP zE;(7cz=>>h{`vU;EqzzsysFa=|5jI}N6!DRpvklNto|*rb^j)cK>X^Xd~5@;X8$8< z_|%N?ltftJ(F*>pyZI*^hHXb~@c0^?$ud@q6w<jB6QhfLnfT9hy+oe9!oJ>fdA_Er zOSdlJ{(o*R{DoGA=1cAUBq_(A)p&M0XJ?J?&%{~(J@F&k$!=MF?^Q?Q_i<Y<Ya>>^ z#Q$4zNB8-$%Erd~ih-<5n>SY=DV&o|rdRtLjmeLurfkdBep_rmc)hwNkj>7{&S<zP z&f!NZZ{CUe5C679^KM3lXh!*(l(f?rDbeC{dPD4yFd5gG<Qpd2{u31!JeJ1`#yYZY z#~!io|M8<PNii^d<>}^sH}{-f+DWLu`ddHZB>8gDjWiDRlq609m?$&(F>(!!7xeIP zb5r{4JaQw`W!jk6JMHM-o8x)m@4sPfq5RL;axc$o4N;ZI1Devwsv7@Fy=A&Vxq60v zX-A>m9@2Pc-ay`GeU<Oe6#i|r(5F|rr|xe*Nc?SZa4>PpTlZZ=4Bc#J5N<p7H`qJA zqN*=04unuVyZCCm%0rQ-I7MkYBeegmxn;Ue&NY+9M-jVE_-1<l@#6J15Bhfq>gcO_ zs{>eMW*ZKW+SB!7fFy9r$i+96bN_9#@(qLvG6<2^@QC@GS~Cxw1c}5Fs?e2hwd3IR zc0+_dyKiOIm0C?{o&cJ^iG^$!!X<wVU_ip+Q_w!GbRtBsU#FVmWzR`V1o)Z6P`L~@ zajdMY6nZX8E=+u@YH49IH8p)7DXg-#vJ^I&PsDhG$mPPLSnrMk8!nRP?>VEN?P<8? zo`^kRi`nLsBLf3f#g3yLc@}Kg>cA|$G|i`Gt;w`jUHMOu)AH$$;RuqWA3kJTo*&<X z+|v**8`Bggo$91SKFU`m7L=@Sf-eFBad2^QRg^GqsAy^mCN_R>uo{_xmVqJI!#*tF z_pi}eq``AIzsbI#LI9VD*;>>9d^E-BFzz%>1ZYS@Zt0G5x2vD#)6-9#4wE-1pC);F zdM@<`iV}B-W0G&FgXp}2mE5}bd?G?KN|n!I`N4zD*#D--C;X=7=dXQE*Sq}MX;WBT zbxyNf{bvJjwU#7BtC=^Je=(zpll#KN_ZJyu1lHPv<B-M?+;KFCrJ~1c`TqVZZ>JWg zuK*dIdMKiYK)7@(-H3fXV!ijFftL~1;P8<n`;DtN$jHbrvuI|YA+m7v1s9jYS89W= zqT(Tq9`IPSeH*kbtkWo96OR<d%kyc>qNQ`pTpm*tyr;vJZtNOvP8ja8DZh%V#mcyf zf650Q^_Z?<BdR6fLMyN*P$Y*{*G(##?5p*WO10M-A1NX+4s3l$oZ`A54^L0m)nA{# zWL=>l>AiNk3Oq7c7sem4hecOuj{CvgjclC`-SID8?8~{<n4K<gukpVD>*Z3nh!FX; zDC3%Y(^%G%<`&9AJ0#2uWm9bQ5s*>B24%5ctG|&PjyA;}Sz243{L%8{RI<4qxr|qe zLN#4+$FH9s1Ai?6lzA<WAHpx6`1t%n+NHd`rp@tJvCwCkD?WTUgQz5q(YlSSs*wb& z6epXvrR;YYIYUDHRiOe2YNcP^dx|$da)Rzyf|TRE%uF#<!I!mPzkNI6HYY&Ttxo+y zTZ*tc1ytR0QmXg^hcvA679s5iM3~^Y1()g*$xk;spfajN3K`7~HC`@u2&kwyO2E^l zneVPYo<1Z{j!w3Ta(mwD>T2CXQ7#<gJWhKjCq5pYJFlH5Pwo}>?0d8B#!y4uDK5|I zwbf-o&EyTYX&6)N2Hq-r-A80@!x^B~EwFy-Iy*$#BB~#Iz_jVXA)D^@O{(OGMt9P} zWKVOTXxTe#!I2w-8vr_af&YM#ohG{*pPhX|+yeGoqy5+I+qd`ddi(scGcz+w9$*u> z+>5o9u<Uri<-HR6_Wq6ow!KHk&mp8JFPD48A>yK0g)ae+?j5z`_1>1C=1%&vG|aVr z({=?sGJ+}bgdfBCm)fC7mQ(0G)Z=C9o?m!z*n3R|N3XWNeqZHAaYPU+Z%dAhlY>K$ zg9qw9qO{5_1j)^=znazyVMP7#C6>ER=~IS*22yCD>ntCVBsDd4G7kAI%SWUpRBx>6 zrChU0JmZtO_B_ik<brOuK$M<YoUi?WFzXpPV-wxCh+K~H@gco=^TyUz%x`jXGUUmV zJw^7z!R?=#nnX_D(#f~{qFs>2f}eZGDeG3vpWyMPJ?-4Tlp873^66!CkeZ(lmhgU9 z7z=j$K6yn4XF}X-#U<OM?hVoii$}=+4u`ipIfWWD5m%e)tJ=dMjiv_&M{UXzq1XBv zkWCX+!(!%;$-B+-lRX)OqAW!2!|maWpj*3_r-zIwXiz4jm7%XTB^&$yfsW1Z_hG|G zL_!Ak&b;ycw1`1j1Ay{0nh<mX8jFE=H05FSIH?+Z(9X%}8fuYhoYcYhbAoj{v6QTP z&-?E<WL^DY{M&11grh;y1d%VEUwri*7_XAez@xIa^0qlZC<!g4$+u!hAJ=zFv(56R zIB6v_@Ap}L$-awdY2lN8NFZYx4eP~^k5AB~Xe0!OgfPC+El{s{#*@eEwy$tRHCp^G zqMb-r79p1%B&|BDr6)_O0K4mIxx6i3bn$PrTF1}L{o^CbqwAm5g(bit!HX}oulwyW z_eZeNAkc2F-^6~%dM68?YVS~EOlUkK9i1^wIsFdFAiAx4#SpMKAxSI5d98L|S=_(p zRB$rR2_o{&*(WETB7)v_<kDm!Q19HmYd1fpGwOBoF5Omcjd&R#M*H#3gKw&;t}wnc z>7OkBlYc=;IfVcN${I}x@)|_hLXY~lY`qT`{LR4^+L24&fK2zwyI?Q^U8Da($zSMD zf|Mt2_F)&NjLXsP64z)Tw>W8M_3=!eP1tB02oGI-byTu`^NFuT*e@C}QUA-iW=yE@ zr@uerd6oTRv+c>`-G=yOM)Y_}fNHM4K5A`~^C)!8p$;8Me&<|U<;Pfu4(#;T(?GuG zHZU@pA~89Pe%d50Eq!C~-OYjm=b#<?{blM>Uib)G_mnlFgLC;Fq2)3ZZTjq7Dgy~A zBN?seSZCh8Bi8#+n*|qt|86aDwRf8vAr96n;522AFC{cnA0D45nQfBB5!7b8rG1gw z;I-3zDjJ5~@$p7~raifP{D3eiU0q#U3TzB0DJkDYAK+KKw?%pU0S`y^(nVW*bPzTA ze1?8pv)ppfjqmSu5=1W-if!61u?s~OZP$7m8ynqjeLP5&8z8(*=`U6eX(ygtyL^Bs zZ5OAE98;8%IwUAofVB&qCpqJ!9Q%=u9;0;P%3Z%bLb(+emxdFFrpwnT)3)#UzpI@9 z%wY%QyauiUm6_;h*M5&?D8&hJ(;Br=+XC+3G|$xWCscP@qu)j~{R?$@I639L8kS~< z5q|98UyK1CA5HQr8@C<tK{<Z{plBDVg5{^V`aZl5-9&y#Db~Q3Y5Vr;YBK;THAr(V z^IuGH<kY{v<JpsG&S(fcjeFNG>0D1kGcY)4Qr`t+Zn6RGMD53q5r{~gkUXodi**Sd zL24{VwY0R-^-JG>uYY*O%6a1J5fS|o{}(S_2-}Dfox<Haccvy@PtMHLiw+DA*GBEX z0-#oxh!b75GE<Mpn!-vk;~%RSbo*kK9b&3{EUzx*Wq%(hQqU_R1!d^Vrl`l!RXo^z zB3E0Jd|z#t<TTzH0VHbMTS0Lt@46x?Sdr8Cc2Xd#Fwr=^)GY|cil0qU-B#>0e*N$L zCrhJcbf33Grzv~u_H81?AmDSYze(kt2%~Qpa;I2cUOwP5Wr$ABSeUfRQziE7%wx(P z??1A<Dv`P0UxnSBr({Lbn`>M{2NXkc>ATA|H8+<Gnh9vv#6ePnBrIvu-H@UdbJ%`} z9vvVyiMWMFl2-2l+GDLH(=HOQr%n*DlyBA5nrnWJxSO#t^3{hILzT$F6b%A@n<Qab zuazjG?i*H+a6%}ePSqyuILM+bBDosXl4Gh-wloyulE)$AA||YxpZjm6azTB3cy@M= z+{)Am;;KaDBez;gF}w!oMyeS2IFFS<dHfbxz=q?R!zyz5UE;CZE>pb}$UEQfvgs?~ z9I&W3P`a$GxOy%2`3cmD>J?X`x^Ee9^)<5n_45MuRta<xi@Q64D*90L>9<SxqbH*H z074KoR{=0s^N4s|19KdyxVZQRS|AS(kC(OBRIqv10D-29PXfRFyMjbBI23|kneJB{ z8y{a<nQcbqa4FW=Ah=D7Tt+1iMvPTd-_o-LYYxWDy#3kPpkbVkY}00Sw4}shDfteK zjWwWb3+fe11hR<^eKAc?8-Jic+L1w-4hV@R`jhoU(9st@iVEUBQ??yNEa`f;lN2AH z-IH0M5vlvF)cs+J>+B`2$)55AWM%~^K60mJF&!3RiRk_FOOkP5T3uXk25ywFqWe}H zU0z%m71FhGQNOUbI2$)MnuSJS?^2jVZ3?X+3QyYQqHJMNY%<wJU;jfI%i@xvoY(hz z;K9fhXk%tR>DshpT{(q>$}-Na9GY9rN85`WNkAYFj%VYSL0qs)m~AG&FN&Qh;3T-? z53B`XWdd|u4%2hcwKO_p|7BHQ^Bl(4{-?yTwm9G->bpf*SXtj!!t=KSNZNXkRg0*E z;KvGo6>)hj-V{8SLK>P8WfRpOM8<S+b=_~;wDWYhKxjL0(iLomo1V#fYlzcN{W@4+ z(aKZ#FvQ>Aurt@(14#RhudimV*;!U0?V~t#EL=HCbjq5Pi{Bp_0B0}@jVhs(7<x|N zx+tR{1Be`pr${xdyhR*KWRLv-6Op0@=|6|DL^6N**VM7@(yro|Q%G}5$OpDaM(ueD znwQXBfU3>Gg~!Imh&4fRN;&iR_@`mvx&#o3pi<E}?(hNKLqNcMo1Uge2QAESnO1|l z{{HXU?y=@sy-p>NB3KMAq^k7{JD)LZQr2RYrLhHtNh0`c^fS_$GtTG<88~J9>rDC) zLV0}NhUrv?k&o!B46A%M1%zBi(wDry{a66==5xl^{&BI>o0*vj<TUMWtnJ#Gtc=s3 zRF{w}9iUEH81M1}hQkd9&wTAJbqA|OO1$vm;@Nm-N5`kOeQ$^sCW|<V0db|Ws&UuX zmfL6_eV+~&oPJK@GBc?B^W%%!Lc4*HFFE{@LXY>6_u@z)W3jNYv1MOt-2S}?)k^zM zH;%Abv*`I}n+djjj7XaOLwywWk(DePw@}zO_hw%m8oq*741D3+Z{Ok&3;j4RVDd6J z<&_t(3}ONT{s2dc%U^*?DeTEw*hjN28T1lwXK^*lw^dY~c`8%tyO7);8<cywtm^KY zLbj-AYYR&Xh&>=cjzqWCfDVCzg5qp&PqAa5{cw|!c=R)1!rWmwuN8KAdHGk7%uJ!- zMm7nFnxgy~@yS4$i%UzPyeh13gE{KK{`tklsV!)1Rz4W8rk;+(TUJ)KxV(Hystokh z=vRM${8R1|!5q>X7#LE}<pz`zbu61W^nl4J$M4dOeS7_oQEu^QU4n@~z?!vnOVT=k zE03k`4`px*KBx!#ZMtQMe0mJsT~aI?JNr8z4Y6odiF?EE_W@b+ys0wklGM(wUO5=> z+JWazMn;As#T8U!fWb{5R@<Ln*jHn`Sy>R<i_TUFxvnGEoTyg)U|&)|GUAc&;Ot_C zBz1N5PxFr-Kep^Ci$iqUqgA@MMesP<c*_}XL=x513?&<zS?88w#{=GucKf+BhN=-1 zcL@Q*rhN62+e_>TnB2CM1t#TvswbI0W&gn$o*Vrf0?FZ&RCkKn4nEbW&E^s3!}!z? zC+mHUdO;n4-7${c6BS9letrMML~ubt!P=h9Ov)leCOX&d>8{2%n?<><x9&X?@98L& zlIpB`heXha4dVf>;pa&bR-NHswap8)#7lU1eGK(KWO=Q+Vckyx6__Fa<~}v(%(tqC zqCn86i&K3-OR|CmYWoS|lC0xWSd=Q7@9Ca!?T4ZNDZz(P#I}G%v?Egd*w`YG;`bX< zGcYK3@?PN4Q@TOPZs7fA^$@X%3IWW5cG59~Do1X}w>rSMz!zR;GC^D7vR}U@Rv1NW zo4l9Ilau$kWn^MopQ_}TwQxf)DY{d;i>&@so68v`p;d1N$Xu|uGp{<%l)ZP<B7O29 z2_5!#G@85cH4(jH+rf8CsEt38mE;%Dgf)K2F`b^9Qw`_}@K<spx4(H$tGa97h3D1b zg6H1F%Sy|-&$p*@k_DeCG4=utG@|HPIhBGrIT^%$3|)$ON9Jw>{QcRT2y?XJAdC_k z`=Kc8Fl&D&6CTr{AQVh3jzb2nSI0SxlKrslE}N2RU$T5%ZtHi|&$KLjwkwNvGP1Ho zZol@TMcEHdYoLbBAZR)ky%5WmE#bG$qRy3cbj4`RIsKgLdET>_*+3EWG-V91dIYFA zRXxtQK2mt6yw~MG83m=vBR=`eTeegqj@}ab05}FhQUe=G@C^IobKHJi2Uw5#d)Zqq zE>T)@tfHJtE;lKMGBg(2bvQMmna3$NgUA)XN{5m?fKGFZ@TG7x8v1n!@5)>CC|!QG z3lhz$y?qX+y|eQ$SolwL6B=|IHuQp_`3|_FS|_Hc!1Gu}rni;1qj)Q;sHAtP9aW@g z$RqYh0j4XoQRwDA6gyfFSOwyZc~`zLXnYAA(X0A?N>UFd<mi@`m%~I2<dE&I%c97u zDk=Tt2)x%;LOVlt913ROkUCT5u@ug5K|z7}J)zzNcr*~%>OjVPsZ*oOa0WO8_ca2v zpf4%<*Pg4!%S2O9tv5?gcy~^dheCp$N;hH$J=nJJ6^c)_II*F@lij5|fb&14>sf@k z2l^}R4G`HMbP|x=&fb0iJ+zQc_8rrBSH7hyEXsbk#^r32fX4I)U@?J!`0rUyo;>;0 zfjrB8@cNbn?={!t0o1vySUJx}pl`wsn5yF(XrqTh+=b+FmHI10zO*N8s_I3L4pQ&! zwa-FmIgm@y_vwUS4F*O=lmHf9)dERP{rYtk8YZDi4%5#g8K77EBE$(i#{qbYDvkhT zmvVUa3!Awvd?lcIw~iaBIZnDZXxjmB<}{34a@8i{(<p{S2MOIunNa)y43pCJif&xp zxpU`#@WlEGaE#~=6gx{^yZ!n}P;8-1R%oxGyXNgJ3-nL6{~z_h)s+IGrwVolGM&oz zcM7dEG5kK>YfJp7n|pY8c;1(kgYWKxFbYzq(6)~fL2h>|V{&j%0Z@>TX0N;r>O+^@ z5GVZ*R4(OEcLf@kK0G%T{Yk7rp{)^}_Io-c{NfzXWha872T%46q7v~%G~XfZw7a;o za(-ykqYc?6X~|CLUN}M~00`&PG;4l);MNFX6N`ih1Ne@7woK0@7Yo*B=IsufDSTE8 z*#A<#CGAn%wN8O`oG>AwCGQ58Jk+Kk{}gZ>+5K#phbsy~l^>Vavq24>o2tIAB8MrF zlKPQJu2%R^lq^rYCZV~#4@75=DsJ(5+ktChR-F=$B+TC+Ia@j|n=t(^Qe9pZN>8r! z*TQ3Y*FR1zETn?O0rPR;k7ytdnc#HBI~4%|w@H|tc{xs~Z9uDsoyMa6Hdx!Vk+h>B zB@#ItB9_{?twQU;nEB|QzewSsfaCA@$b%LaPe%&XmAWrjx&pY@9i8YY*T4#C=UYbq z>Of+|qg1a)O9r(#4t!nBi*bAwnZxhjZ&U-mq(MDIhhfrNakH~oulg-M4Z<T7hZxCg zTDyw_0x$9(gi14_-BOZb<K%2Yw`Ydz2%MP=#`)#WgZFm6(5lL}+RPc>x%-~U21zxN zw8Myx5j#*ArMLGVv#!e!WEtgGHiNxHH~aT?m20urXtLIUA>ko1c(ZJ6Xl;nA^}Ed@ zpEf;`T*t<?STLX9&l9hvMR7_&;X|qh7q;j&crYS&A?gv{eusKI!jPr2+!w5ox1Ql? z&?~-?d(>Fc#{3F>o$~+M8qCU-*%5mIq(OX};0ae_Y+CJqJE&?Ll2~;&`650wmFc?3 z9eo!qun$OGxz)KgfxJakzOXzo2ZT`cVLNX0Re40?2=Y19WoG8q;}ez5ljT=<Pgk_3 zO`rY*>aPJyt5ae~@!h~>_l#q_iFkB|`?PThS%oR!{Sa+p4YXYXVxR}uEhe^^pPwIe z&IvHugm4wC%eRpbJf?fUuZxqaY%A9^{*fgO+y*)z6}lV=7;XbNmh6dcE6$1g^`BmS zNDkO^i>=Osd{xxY2SnysAeh185`_8IzP_RO@{!*x^i{FcWy>PD7Buto5LO^mFOED3 zG5e565p&4qZFe{4sZ*zBk9<zAnJk;O;^#hVHn|hY=TV&W70&c^_RJ%r8>O9Z47}ZR zsldAN^<<ffnVEp=%;0VT0f8IKNs0r`J&XA)p#tQmG`8p-c1k(;(=c*wW@SC8`lNv` zEC4bB&#4XE+%w4vB2E4eUFNH;Ix=tSqh{lhgh(l2&J5UFZQ^!4?bMT#Lr1){(iM7M zm_<`{JPRcrEf#UPe%(5P-(ldAvy^4kn=#edoCLL-XC057-O1Sgk${a9zEpp9$aDH2 zhx?+ppK3VJtl)jCwDd^6dR#+YcUbu;p}PKX!Am4=GBrZf`}Yrilp5-zpx<mA9anAC zK_exXM6sG)E;5$04!p6~ZmvU-<bC+?QEhg@w*t<uqOMMG-oni#>Pn!9U7WZgub~3@ z9r!H+ucfP~NAHjbQMg~c5{Y@9OQ#K<W6IXPZ!laS)mT}Af}V;>jP%;ueJoh*QT5Xs z-<iuOj^1F<3p6pt-9W)qM{e&ZcEq=%f1(9eD$cXgeII#l$@$q*a01sVg|nec9r<S# zi}GztDe5s~*?1XOOIKy=)5~y?)c(H8`4U&xQWdt9l~tX|5v3kb_u9$+R_UVLl2_Wq z#UQKo0$l+?cFts@bg>I+tAC#!sPzC%Bw^J;Devw5{-J1daQT|%3_tng7YT~7t^!fQ z8?@t4Q{=;~AT%?Dnp}z-?%l1U=2hV<ihU23(FKN|B~V)X>2D+E?MFU5cE}qIQ@VDg zZFeuc1+5UOQ2}ZLSoqDp7VgIv_#jQ}k8i6JH`hiNvTxn{G<3laP$UN?CqaU`5-M{> z+3)ME{j8xTrfrJ5fd1~C9}txTn{Q5Vw8E6&C`5~;d?YW&PJeUmnFcD!1Y|t@P$X_} zG(c%42V7T{agC+x5jeHhF*44|l}PSQ%?4irGQuuHhs9}PFphE^`(h2-;cx+?u0lng zFW~mR!U`iS3Y1C9q}wRtzRdH`jTWTln>(~Y+YjDrZED(?pR?!l#}`qH3l9T?o!OW| zO(KNcp>8YDsSJ&GBb%Y8OU-Mt7$5x{;8Gm=?;}^i*pX0D36c&ehsMWaY33}H%bDS3 zoBFO~_C|^=p%S7g<K05`Sz%ERUR^FC!yRIarWGAc<$_L5>w#T9W~OdkH=ME2l9DPU zv|o*H?ruB+!&Qr3HfnhZD8*cxo^I9x6#}jEsHwai?*{fFd_3|iQ&c2^N0|QbIDE2Y z&qSD2-fMq<o<N?@6eW;J38s_4rod<*?F`vZhRfH6aG3)C&y9CI>?*M7;&x(U`ciaY zXppg?^cG>$q76rPhh66a`9pjay-$*dN0^fCI26k|Ze{zwjw7hDMSuj5D08l<q5dkP zD+f7;R1*(DgivNcsKHhyN_-u~32X&WFN1C=15;>2-P^9xZNA3<Js`Y*7a}4E0K%-h zq^Rf^-30MF{K_XPKssC<czb^fGz8J|6*8Zl!vfb~TOXq;UuadE^l>Kn)E<R*=%%v6 zga8YI)eZ@(?ar@V=<B)*?QRpL1N|ks$C8m9I=-<Pmjk4RH$&CuiBp=}jPBp>3*>P$ zFhg)@1Rq$UB1r4m;bwk(0h9*=7gs_xK^a$E>nbJE8Fbi8^i+`|dhd~`pV*jv(W|~O zo;tZsxmypKu>nXAP@9J6BSCnir>9TLWdOqvzyb*w@bfQsc9VzS1V^sq&)RayT&oI< zDz}Mr2L0{r+Mi-SKi8}1xI#l73Or^O<Nl$L#Bvmf7VwTaZ}<x6k-A3*_t7xyR3Zq2 zYVJ_lFDNw649eyHEcg5&xeUiS6Z%23%&h9lgzFEotC{Px@$Bb7wuqI0WR?JZ>Kot! z?`_?85_OivBOR3%-nzG<`4JY}+|1szUnhixgn|ea4t$(u;!z5(wIx%~M`4-norXko za-f0U!_ECJVxLx$O{}o4BuX-~XPLjh3@-(3^zGYJ2d*_3&J5Ny#2nIaFA6jf$LF!2 z%^zQAv3L#uONNRXlqn>DV5ZHvQp+&WU8;g-qHuv0Z2s~2)|4pL2jB%GMvnL(CvbzD zM*#3Heg%La981s7r;$4dj3PtOtOU`I-Tweu6`GW#r+Qt2(opyabtV8GyRT84%ud2B zr}{{O7P|OG(UJ%MzDfWUY<D~y9owtKrlzLgiJ(m5i1bg+R^ay;?<z=D3gzA@<MIFj zC*Ha0oxsY<`ZhJe>piq-flIm19NCW}tJ%PQ)c#T@3~-Cgufa)#1kV12c6|lr1QNvS z2;%Z6;pn}OLsy5%LLP2Xqi{m`w)|Qc3b7&w3?w-Gw?wx}=2aozLcVzhX~|{a{-KfX zQfWp`nckw|_yKfiCG(vYX&jIIlbMt|Hx?rLJ!TsZ5jhLZiZ~=@^d$oD&;hleDhhpl z?MzS&EUc`)M(l)of31NXwnOU|pLe01x$9T^x@4nITpS1V;wo4d(1`56F{re2CwZ=? z)Zau5Khe<-dk10iB78WV5DDBopbdrZy|6c}Ky`oTMr{Snh=**TGc`V`37>savRuoe z_^_&~stCdfP6iDj@-8keo`IYd9*iPM5yRud3e&_Dcs9VEk&z#ez3O5PiDH|RVEa4& z>`djwg42JK+sD5i)~Pw~w#bOre<o(^zQ5l}=tbZYzo2%yEi3S-ORgFuq0tE6r-iz; zg{dy~O;w|;cZ=uAL-h8{ixaP3QvaSCtp$Y#EKCEBTvcPE@Y5$-Lml{90*`{k**5-l zn`_5{;4$Ayl7WGg!`M4~))wtBlEUdV1s;7+cJmY8VgNk9h_kB49zKR_XXU1by7Z$n zk3B((u6@1N;==T)Blg+u9b%}e3Cy7ZO6wntP*35Y=URLYMtBM->%O6gh18E7Ybmlf zU0Gcnu;c>q2ubUi-fKxh??ivNKVPrZ?I6ggB=l>5+v|{bcxv`?lezD)-k;B;n4?ms zRJ<vFbDEKh48z)N>@k2+B?rnvsIi1ij}U?&X%OTUoPYx$1E;0_pxmyYXgf@NjRpkD zbQ-?|LFGM;e!q+}3LY4Tof;Y%a(`CrA*43>ZrYKQEA2A13n$G4iunp$5NK6fG7QSi zEVkbBPZs=ZW|hmk5JS7TZ6v6OKXcv6A&seT&W-joiVFP?1}{|jF>n|on_=IP0W%8< z2+0Yygmc9X=FsErBG;hR;m4UKS`VCs^i}B+(3^0#8Ua&)4VG?NUbk)?;dUZC7Nu^# zNN7XW!>?il<s!5bewTQdl=g!z?k(S1TE053TQa)&`qoVU<VW6G6gFb?O{5e3_Duv@ zR-Spw%l$*2tVQwp`Gr7qz7aUs2Ox?PE#Qx~RCw<ka$|yYXVhqDXtqmQDkaEyBKgCO z>HLo$;Z}2Sa&|UF7fKXO(*=XnA$S2zR+n+&q7K<`uA|#h@$uuq{h}8XcogsES~pI8 zdbQNknb$$J$>;E&f}pDtuoK>Gd|x49{WS^{s67m7&`WAzZ=RZ+9l-y7E%#0UQ`iT3 zBwGCHvkNa+5iu|G49$;&J^eqhC->(cVJRM?$3!u~@g$U4vi=Kyr5l9y4{r$JwItD- zbv!U#`@L_1<w}&a0QU?}jMm1+Fn&f*90YTlUnW8NpRLB7_6XgOhY#6MpTCD`$UO#= zhqQJ;r=NvMnXXD~5$wS>DF*?32Yy!{kRs8NqUH1=BDUX8&*f2olZ>rAd0$iJ8k4ej zt6tF}K}Rdk359TQa6FN%{qkiS+*y0I(i@Nubc!9)&`v3bfxD;!x}hLCL&A5u?_$Hs zV0+q7+%eUZcgl%ZiyOlQvnH5?xQ#>Ml!0aBKmLlBwKzX^4h}rPTCiR(F4`hXDb-%q z(}Vw5DO6igf%*BdQn$k$T?ZapXh||~sb{^p`^UYts;XpF7bR6=@7XuS!m<8J9?7q% z;beG!`0!!v;Tn>QyZbpDKBbLENs+?3Zy`&=PGQ$iP1q>{&LFtw;Ew|PXyVoTy{5u$ z;4u2*OkR~ti|(&7W6LI<@{Ejy4s|7+&i_?um2Tk|KGf9(V4DxP&6$DzA$mb1le~#t z{gac;2qKcpc;06M9bpLw4-bk~-@w2LxKT8xw}LKV=49x~uO#yhWk|lvaRvz;4?+qO z$=lVt8y}zJSA{!ny6&V_dX%4Mf+{6s@~8U6P6xd|W@h;FoFR{rQha-R4Nrz}HUhvM z0}Fwm4>_3jLOY8a9+R4Z9*rt&`Bju^!tIP>dH^K=NOS9?dm2Nc65r7Gbtn8zc83)x z(9T{i;h8TK)-Q<y`$m{<@TIEampZPJDRB>kHp+<TpU>v!mJI!mJDDf!cfkhx@T~=r z>(~Nf?VbOud3L)Fb6E8K9*%bpzB^fRTjg`QUBXN5W^Us>Kdd)sV@TkxUyS=&(y~YF z9jni9tAxbF?0|Wk@YNx3a$t_V+`oL}vSPeq^~qMfZS2711@@+cMm?$9)-V2k)K$~I zhmF(PYpwI3z1`5s`-FP~VUq~#C3Gp*AGz&O*V_V@oO?2X{`aAH#DaA}o00-eCqL+v z0?)6Sf>hxYr~M5aD}D8%g5dD5M1<@3kL>4Xp{~IR_#(72AV%aPxWB!EnhoG719aw} zpMi~!Ur_c&WYKLeub0%mmx2w7j#tWgMlHSBIM^7mZ|1$Rn5fXrKSS)7p1sTQ=eZ3J zV+RhHF9XMR=Mzk!xo=zdEI5c?Ga_WEv+jdyankH2?DW)9q&A<AQwGiYf7XUNz0_ik z7g!Hvh=>dgpC8mw&{WE9qG+eQ!le9gY@DTFyt658=dal(`)p@=PE`4@2{-RpuW(4* zIPF(~WpvTI*>m<ZA7}Zw^HUU2|GgA)GPx`3&U$1ReQ^>-k996Z;Ya8J`rDIUYtcqW z1<p`wlXCqvRDF%qg?c>MI`ht@v0;{h=sG+z70(Xc(MO$+eNeN<Xsj)jQT~s#=<ltD zB4@<bGDN)g1u807Dmdb!<L<^riaae=Vb?}2s_nb-aC``jW3I$iM~yP9oImYTf`2jz z90>VvUEwL~@MWGn0ekT+P7JiPm0ew3zYB5rh*MGE{Tt|Q%Pb|i@Y#QT#a3UxCBx*t zhQ{UNyvYLgH}nT+Q#BG&z|s>oB_P*JA#8ikkCYePZh18%d}dc4abM({MDk(RrLW~v z&<qss`nf%d&20s^4BAj2D8I0OEl<L;O|mi#FI)?(@74r18i?xM`jXSw{DS!HY#z*( zz#jlvL3M7HFN{`0qdED`E^a}=^vCV|TG^d@L$him%_R#@9z9BetZfSBl9X%KLWw$y zy34vsiy+ZB-TU4cUIb%tW}TNAAgPqgi|VT3?-{U;y7#|%MAIy~Bxh}HF6Ue4eLMYW z%64|@#^tVLy%Ki&*63;r!f*`olEc8j0Q~{`fot@H<I>3~V5dTs;*lLpp*%_sjD{%7 zmfQT`o08mqTJ1r{J@MO1c9&121_+O>WZ(pV+*JV()PYokCQ`js<zBus!MVWth~a^k z!ULN_=PFpMvI2~@Nsaf&|5V?;j*e$+JFUQ&<?lmBcH&3jChM<aly6t^fz>2{ld?%j zf?8;Xnpo+`tJ8ID3wd+BHO}Q<UFgd}phV&1Wkbf;x%0fJfr2<ED7?_c9Qvon-=~}8 zUF9g(rjzbLWrWGUwbb1i2EAtBYAdEv7|g>)Lh{iX=P8r+rzf(6<_aFXn9Y|QY!Jp5 zaLFVK%|C%~aQV!foGZ|~<|e*%<!78w;JFX1)B*05r|0WG2AzLNiktj~IrCJy8js1e zvg-1xdOZEQjVA*Rfu)41BX+%A0F8~n1&qb83MT=*)y-alr^N>i!XqgGa^2Ix)1MYa z_8xI(JsA=#b$)D^CZlZol6C;InB(3Pk~Y_1h9#B!{`q2Ms)}(C14nhcb<+mHydOh5 z1Ye|0{b2<K<4?~*j~E47O)F3Li<zp=HJ=*piBsu@gRO^-)dbY++|Q5o#WSDzjDOyb zmAasqpvZH<e5>tFuueP?I&L2>z7lWVACwl{Gtx4(G;F6YvY~@6H$PT4iTW77`U6=l zk*>E^FD}%hktQ4zJmWJn;t%7@JKJMdC}c{PPX;g#nLPJcIxIR&)lSh#`MRRmh39oa zf$)?3@#j~OU=zW-weqPjcDB9n7FKS1J^830_O(mP*D%(4nPXH@>)Y3@t*4_1Nw74m zk5k!Y^deGGLGu_IU^MaBkV6%E3Q=E>fq7eo1C`dZ>N0MR@;eKxQIhyZTw^Njh{vIu zT0W1DzAN`$pm-$n<=rRghskdvUC|f<Lw|rm{cH4d?V<;`MpsNE7?=8{aNSeNWp2C8 zsuucF-sfk9)UF+39NL?<<i=k4E9nomL~2^p%K#dm&fnb&_c*~*5*(?CNx=BAoGoc0 z`a69BD>fzWwI#Xi$ed^78*bu_?0#4HwfJh+@;(3RupuyVond!4bEllf|7u?mYk{oO zRm+q1hJDAM%f>y>VGZG_J$>2T-M;TfVacB|)?5Bc&p(I^bCc(*Qzr}pm_vQ7AqSyZ zGzQxX*IS^B+9N~C-_E1gtUo+edCzJhqAfNdsbOuv&rQBXSmn(%BxaXhCT%O;1pN&v zz*ETdv4`#66z7}@w~#ca)O-D?oNvK;=Os|9=21iji-~{KZ2GJH!#Fy~w6jmvLp>zs z-@x+C0;{DSuh3{Y4%xU0XOP?cp&w1I1>5)sD{XCd5SY9@9<!U!6QgYk#<T<QZd`kC zrw~(B2ZUEe3=WV*JVE;qy&h06F~5Y?A>4^9uQVl=@2_-P6Z)?C=P=z$$I)wRKl>^5 z^-C7I-Os0AjwZ-hh}>cO%G{TH{U8)%nglFa+(>_OfB5e=+cpEGt3|%*UE`4RBnhqm zy}SflKJD<;dI%!y^Rz#X&CZBVip`jW{$S>9kaet!2&xee66PjfQa#CCFz^g^pp;a< zgUo$EXUb(KZ_v@xSAYA)0cHd?=IG8oe-j3R2QTwx$y+^os8>deYs4s89*As(B`uZt z^uGE(SAK@FgsSp@Q}Q+9`4Qr;l)LtgPGl!Ljf~%srG4wdZ;lk)3RJUn->4g0x}1Jb zJWwembYyog!nso%gw>zRuitJ>q{;l%b0ULMp5&TvolTVXi>|g}v?31+kze3Q)qT60 z+c<A6!P|wSkZO=rM&&kLEmy`m{pxXQQUf=4s2LO3=Oa4TKMgYU2D|p1x|_O#C)YO~ zzvZuxBre8H<|EufzZY0vUCMH*-rz*F)$;6#X3g*;DR(XSSi*(Ua22vSMqWeDUu9np zE#V0|2hbUnZY94C^*nG~y3y7C;oRA4Orc5yx%`~aO*2Yn=)j@W^z<a4b}3l*_E9_( zsr>XR(<N(Zv?efM-{&Oa-Yy2DQ$^`K1M-_8CPtuguZP!kmu|i#hJ`|5v)OS@L1Bhf zje$`M*RPvvy~=0^?rzV?phOt@2$v36w6`5jAal_BG8{T|2&_>RNWF7nnoO>j@KK^P ziIy#L<FbjsnLEMaVeEmX?cUb?)~nYJ{2gf|qJ8fYwuu;7CH%LG(*rJUZZoi59zeq; zY1y&)={N9Q1kE+lk+lbhJQ*D!J%SXm{PcJ`cl1%2`7f(zUZdgBA;HPpUvZP_{ELg@ z<#AH+>7ShmGna4}L>*O;FTOrhR8&vsqd74nM^Ls&3IPfRhamT(b%x)4Cu}@46@>bl zW6B6L>fi2!euL=dfv8*DUScAX7=R&8z_b^~c1qR&KbMJ|9ox5`#v*KkRR9iy16&^0 zM+*d)bAH8OGVa}wtPjkE9x&(;D^^iaq14>?=wKkajv3stJp^3^y+4rbp&$1R4PBER znwV$;04B!j+VioC#5d2TPs1*)@J(02c(cl+-qG6Npr2o(F-t&#+WsC`cTHt&myxt1 zBv&#BetLTPWYBIe^<GE9h>K#s<Hr(ao<K8cmwU;=7t05kRDDVot=0c&RN(sP&!4AZ z8Gac8{-Yj68G6#OTa){^_xoQ>_!j4TY*X)xtM!XF=0>729>+;xf;3!>r6R%WcK}>V zn&|uq{tLYD9$9&e;a*I?902<{TqDlmySo-)2>LJWet$gS;)1TGidGCPu<O4cNm&q~ zXJ02l;c@fE(U^`DFSAsf#VuFcsHd9c|5PLI5%hHH=JBw-uzf)CB1%7&l(y^?Z1+h# zZjrU1ms{fPM?VR7y9{iysML{u?NBtY$YbU9Tx)`RN@i;7=9{jJJtvOF$+#Mq`8xeX z?mR2mcn9+)g1Y%a$ZJ7)dH`~QzyeT1Ri|X>*hEy19`$jQV~X;-i$1pw%u)C}5!Ns& z!DA;5mI2g%La+NhC879{2wlc@>UmPpP2P&;qv@%givPV#5S5vG6l>s7s@c+(x~h#t zx(##$VPlmY8Xs4Ks+=@821<kw#R%&`q3v$K8}+GG1s(>Cx$cM>nU!TB+btuM)03rb z>KsVoZ((GFG)UOL!)v%SUc+5P3_0bbib=uO1PLJ?@iZ_nPz7o}O4?ZjSeCk>^d=-_ z%3dhry=d@_06jhpBOdbTw5xs7mzQ<jC-**59V6vai9DF<me#di#kksjn9LV3O~cH@ zR7u0-QGrEBvLZ+hxy4>8g2V(N#q5{>iv3*P^$Wz>mo4^hB=#2ti-ORWK~+&T=&H%O z&H1C05o7722}C^FiJnn-i7T+fF(e`)GA5+`q0@bs;js30-joG(BCvU+V_0u7Ah2;H z2rC*y1q^B&Lzoh-9~dQwIa)9gcL^skW(G0Sq?mrWh!F4}AOFC87JiAl2xVeSgoHi} zrTScMb2^l7quIElaN##Y{;Y?`<4N3rDf@b2_#Ai+meJ0cPj$y>6HNppm4Lo3DS|U1 z=FnjO?8P^p0Gb9oYJ<x?t!noKAML=;pZ7ql5N-;BT*1l`GvlCR`}+HjL(?#a;WYe` z7YtMVMHh5c;cQ;&z0DYfVNMb@-WX1MkfOVbxZzwjAzQjrm~Qz=5l&y3eW9yOej-x; z_0{@Bz1Q0AG!JpYz_XGTfi>YSwwXJouD}zgNy(0xI`YI_zuSQ-4esNQR3FI8AC`I( zvF{Iu_jBhDV)Wf!%T}EQHa2<=ycmc$2k3^Li()9EJhJ-T4WZp|#fL|ArLq$jvFci{ z7Q?rg8&k2NS64%0W>xnZt-nl2=9$OdJ9^ra7{QA2w9p!21K`OcyrSY)-z!K(ZiZb@ zwPE}m-ol_f+kdGe%Y=4C+F^%*H%a#9j*8JQIXS&+p0fv^#GG18rfv}2!W7ybHlnx- zCaMDl6_vZ+?`KlpZ6`dofzfZrp?=Sz&4xT;KYD5}+zh#OtATbXPP(Oek4J07z9GrZ zBA|S=ww)D+F}c|KvXIruWASm(f@n&2acb=PVoJY1o-2}5!oAd)w!1xMlvmQPibsci z2xL3BQ-~Wfs006lJA~z*$V=D1>GODQWIVL6qPVcsDAwRzwkp|O*Ta{{Iy9I;%A|{G zDzD@6DfcePxNGE-kx}#+1G}v}F?>EyM2LBq5UnnVq;2Ki-WU(yne8Y#utuw6c{<!D z_J|$T3`HXaSK@e>72Osec(J|4Ek2Vu6A;iFaRFbW%?V83;YYCUE~!KNXyxi2+EBH= z?qd&^caYu0PLukR6uqJif$~Xlb-Kw)Z*0!wnvp@%g$@#nhEx6o*zqiL8MuoUxFgw{ z(bMAqFflXV3Jg>+wh<*4@$N37qbj2;ix<0DRI#JLhN_pjjrNb626_QSPCL(cISmw+ z?E)?P;aQtG?LeuWe)$MWbq(BOoIhOP2l$*}5HjllRRdn!D5xnW*7vv9$nSQ4qnIe2 zNmC(q*mh*IP4#efo<-xnKxs#DewY2<+OkO)^cN;UJgOq+(sclcl&}5XD7G+mAJ!3A zV!Pc&Uq#uNi4(^dCKSS($?QJ5<;jH#@->}{6ekAn2>DW5(Tb4@83!e!7meO+E*8?a z)bC~Ds(%<${`MkGrirrsOe(}OZu3WK^%WRgro{^jU}-mbp^_qn_)SsNB0e<Ix{gns zy7#8h6oc5Gp45bOtiL8h>SK>gPs(~^=WaCP-;6kJN}=sV!zO65boaqA0g7-4w{b_; z)EJx>ms+UBC=!O68clOd2d4V`0(2Ei9QRd=*8(fI`YAM11gXQFN@O-pPENv%3A0(p z%nUa(hx_tKm^dS&+2`P4=ZV2CF7IU6Dka>O$CD!-oHd8)LtlxsjfqMChb!<^0pt2% zbg8$m?-)v_mFo_sfEt7sAu7T#O6VRRl4-eQqIdD}2AuoP_Z>;d0q>E9RA9U+a$gW3 z__M02+XVM-Sc!=lN=sL1CfE0{O~Jvi9$thLgiFhz)2hs-;?cvb&vPNG`946N;gWUR zA}T5huvGzZ3kMoB=?4rvU~+<R0%YZ=<i(BjtJ#xyWl_L~(D|DX?Jm@UEV#X~gY0>B zW&$S7*74HpT#>CJ^^xX|8U9M^LoI0&6%j<hlt!4Jgl{|sR!k_&pwbheGdYZWpwQC# zJV?J=Ih=Gl$oP^+=o`Yl3ia%zyM^~uB@H2ZnV@|9X34j@joDCdst738b+%+#3J*<8 z-<7Y-Yy>LabNFzFTL}_$3DP0>7w)IW0}k=}FuxjkpAjxV@n@Oquym@nW+4nk;8RZm zo1&;*!_Yvk<rne!ut=esnAI*@l%52(+B*&#1;tY($`OpfU`BQcI^JQ7emgD~661t` z71+wVbtVKy0P9!87(Cz3O4BLSXOc3v7zQ_osN-yLNRZPOgz!QNf&c~ceE<aZVuaB> z8^*-bkWNjs>mo_(*RRjUL<{b^1~a{+S?k@AA1g6~lmv<h6Sg_7v)3?oqk<u;`*?c@ z`Z%>4cftiulX%HfwzhTw-rFKfqT~xKK8yVP^vXgw#{pmpq^@7xjf}$TU)$q_e!q;D zN1)XWT{R@U_6UOxh~+R%vishI6o@&gL?jNZ)-9ltOU+pY1*s?pCa{$eFJu8ZEfGPw zr$GL&-3`RxGhc?gQ1NoFoWy<-iV=V@;mLyui1a|-dyQI;#@&6Sb1QNul5@QlUYn3( zPKn{C6A*kYIQoTkvUxb99EkYjR=ij3W(|Nvv0>xJoU2OQ`4Lfne`9+>?7*Xn$9+KJ zA0T>J*!+e<E=Ip+t<+0nW+vO3h3yqMr(38!^f<GIpxl7;u!Cg84CanXT@rRcXv5I+ z`vc7~SLz=qSk_;>`yk`;{^mVF@V1H36Vf(;N6wttj0iM@4oAF&!ERv_lK;uK60=n% zx1OpPjcF4@{3eEyaa4<Np}_Y#N`FvG9nsHRzf!bMtG{3akEC5g-o_0Z3}GT9BsU+| z7c~$;&y~1LSNELT2)U7Z$0YSkr$vUKGC8QRyIaO#;~(KEC7~)vkFzCj%)Y{S4}UTT zu;1Gdu4Y1H$sJ_um%sB$)*GWQxeAOFlV5Ub`6wjJK2-31W>VhI$vO16D|M8__lc$M zQ`qcpLc~c;<wpTd!XTz{Ib*f=23_|x+_)JCISkWi_VXpmyV{4<6}un1^mMmjKueqS z&BbMK8~ieYE6#<#U>m}e>}{ylkDylZ8#H8XGI~KB<gb+SAX#yja-3Y$t(XV?9r2}} z?XEXCIsfq9y-QcQ!=~cni!6TK``fb|erO_*3&Wq-3>)$NkPwZTu?VYeZzkI7JpS+x zWP2o@qXlnIFREB@OC<!<hqCdPgVafC`J6)96}`anZEY-K8Ufm%Ap!bAGp>w!`aS7- zum1NTwx1>qfR7>z`I?MYNKbySYT60Nh`9p}bQ*#Qp7Yxn7#QplYyyRcEaT3jdKHI0 zr|kuSG$6D&t`ohmnoNWxM}5r0OO8JY%Nnqi4s5)5J;FUVebr?K{wh{`oO9O>84CD` zuK|F-&g+YQ30w)&-+l?`tjKFs3U0nW^rRTYYZRmiA2eiNpHVu$okUo0z@Y|qD!I?& z*Tu7bsPBT-nTp=@G9Ku?y@rkM(@@hoOD`ad?Gk23k!<!ys|smn`rx#^EqNrD?&+8C za?i7*M%)HTQtui8;PDy*A7mDY@9<Xd#84YZ5Tcz2P5XAOC;lG9;w<wvJ_2W`(IFFK z5MU@mOTX_`L3g&-Vja55v8Ul=&D<k0UrR=72_>nok2Y8rlj$Nc^CX<Pb?`-C{s2Qa z!hL;x=sW!gv&?i)<1l>3r+Ugfk2s8MMr%M!^tHwkxZ<e_n?-3oVub}Y&hhan2WH*e zBSozD(QNt}wQ(K=6FoE3Xz}eCW-j8PgTpMg+ad;1+cc((W+0<k{MZ;8^j3@>nll2` zaS_ujgx&&0n8+d1Dr*%bdkYi-q6%UUL^nLujlV2otRpcq_hA|^Z$Mu^!2K8&=rOEZ z*rycjEaTANqT*s^%!8mlzN{rAGP*4S6@<|r=t-gvQ@&^leW8f6Ff$)V$1y!U{TO+T z_VpRdWrkB)#M*qFW&5^rsXoiO=c8?=zki1$1-b{*aHDU`yr)UTV24sINZ|MPcN~JL zHB_P!PD;G@fiQ01(!@)zU@koUwMJ-}fsL@@2QZ&iso*hbAzmhP9a)0_QRwL2Kz|lI zlSnd*pEY4$4`3X0es~N)tlX`l&GX6yW5fty_@I!qYjFrqL#Ix|&bNwA5N}MX(aSv| z7<dxKg>zZvjJ;smG{EQ)jzu(@G>I6bBnjyihXd|WOkvQO82Gc8OjL4n8fw_~^K(Ws znq^DbixAekc)VF7J^fxoDZHOPVPMQgY+cHgHtI}}1b9`;9Vj6vp*x6|GNF<~o9{!_ z%f0@Qj**d(F#MxlqrX;Z_dI_5I6KBc5R56Peei!DYO===mJ%^q<|rRJ0+AZTJ>k2@ zKpF9_2eig1`bPtFB+%PMlfe=YM)4m%lo1B+AW)+VUe}8Z%VSXH@fDMO<$u;<Kvba0 zDwcb#qLuvbX$xZ7Fj~%2YKNqyf7U95O(@jQ?r8)zVtN=fE8Fk6A19F2ai4_W<kRHR z`OpuFa4nI|dAsy0G@7WWXJg9|1%1QAr~bLi&n4ww+(<{4gx5e|^ZjtLD8#P3r6<e+ za5yIA8WcMS5^o-ZHy7qg?0qElHx1p&x;FUoM2np#oABN+yy<`nMuUSG(|erv&h>d< zI)fqJ`ZcHlJq#5<l+v{|td5H~`ad9WkTcx>*FlW`vKp`zowJ8yC5THt9&aGPL5Uw+ zRv8+COapjH1PY90#2f<}v}7mk&>H8zp>_!vf{*Rrd&=0%pEZ8@5)E`k_^a@Pd-4)s zq#)_<_1PzR25*Lt!`qEOa1d(<@#ggSgCx_x+rqpF_WxtYjtS}J2cfqH(L@Cq#5mR+ z5RtiLhmx(WZMf^j$l5e13Ip+%Ae7gE=-Ye#87XQwSMFPz5SBO|XS2k9n1S#kppa4c z+qWnh|0G=lY&)G~>N++u64g5zP(6qvNxx0}4jfB;6BE{YgO3L_0HdYR2NB5>ZwVl5 zgs^FGV)8GB|0)m~ADkngxklrk|JOUltYf-W#4}(k{_slo(0?id-Uo289~RGTz6dcO z`77elQFu;FM&*5f`U+lzAnenr;GK`bTmDD-Ke&1;^3F!akX&u<{-WVUrx_Di!p{)` z5O504{=M8l;+VLZf_6f2By3P$$nqq3y3s#3!=8mf0eXzO6JDMm4rx}gh4n-4BUGv? znF~_iKmAG}Wm^9>^1<f6uPy>WB;J9rcRi*|jol^3#p}VG+}rX7F8sq#<G$ZN)ZGhT zzaeyXfEi{V5PHCc#U>%gT91f|j<gGEzACwV)DROJH@PtL4*Bl_F*dc0thVtOBY03e zF$OK>1sq9j`?lU{@_AELS+j0sWyAV0iu&5fjTKiC!7SKLCtV-L%#^Q58z=Zt8cJf6 z`#DcwvYxc_tpu)GAE}CG{PBA?o~I_}NQqZh_*hEOz~Q3nfaz_#;p77pY@&`KJEE4V z1Pz0NZI9gTUtL$|7BP8~@#f}TBz6h~ofBT;m*`G`E+UJ=(0mzc5aF_4Gq7}Aqpzwk z5x2vfh3+>w9kUIGuS^pI6tA6Le#@cp`BZT^@r=X$0_%^~+wtYyUvg>09=j*;rEJbs zGF}B8JU8|w3{&l4n-*DJX#8jN%)~L>Ikakzu}AS;2qJ>7b?(9^W-*f6IWy0_pKim3 zM3BUU>jqq5X0is?bMM8+OLhFxmBL)7Q&d|hG5A%NX<5c*dznDUC~{wl?D+uz5T`s( zPdrc+KxQId4@b<!!R$<yb$6OQ-OK-zL<s}F;JJ#bJ$c75=&S66tj`{XnfOff6L{MQ zUi(r+X~vMqBMLta?j19{s$+NS5;|$`oV?!-VV--Ne)c?RqN^YTTobT1A42bC;0fZD z9BnC^KohAnb*b<<E==eV5R14X<m9(de6EO$SlVRV<-`*G0Cw@fKsa7Zhv|G#zz(L0 zM~e}n2Jw*IKfqh{D@8eb5sN`M3;(RlGNRnjbIL@0Eqb!WW&gQJun;DiupWDQcxY#v z&|>BId3dTH&0R$Y08Rp=X9JogOGoKYCEVTO&leesfVyC&;ev`Fv?R2jBN{H(xeQ5k ztam29J=T-vy^Z^N%qvK|<<S_)n7FS`MIOFJ#5>dQo+85eX$}vNwYBx$CGI*tF|h|| ze~38`5@8X-J1go?)#v!N=PA70e!nO$ccpk2E;v-P>s;#f{X}_Hf|?4DhD%#)prgy4 zpTB_DKj58ZcZq*jmS($zltMf3=BL|)yHeWO`dU-$w&ky_Fzh!QT((G9S!q!2&%Sz^ z64P!~%COS`!=k*wM4yOaM0j)2!na0d1yl>**5H{YOj-B<(lFsn1;<tG)nC3$s_lX} z>UW1Aleo9}>uHMGVs;)g3=N?B5Yz>Vs@z#pu+|ZDF5Fw6x`&}fzl~ct_GW(JATg=U z|M!(UN?_m+R;`K0s0b8|!$!<ksXdbD`+rJ16R;lhw(n;bVU!`pax;}IQCYHQOj496 zGs-q?CR-?k78*mOLPd)$lxS#3%2tUMvb35)v<r8VvZRHc&&hqi?|VPb{l3TZ9PfS1 zaoopD|E{k8b^U(l?>xWf_xnBX>lNDr1W0oz$Z0pr^akceBJ4mNiA&m{m5RnMk`o^5 z9anbkhPr?Bh>RV_&Mm%{PA*|}#aVqzOV{X76AeTd%k$6nc6@x%hC)TodK|AF4XW&M za$5HC6bQO6sa=tZw3lsy=hei@#w|JrHYs+~t5M*Pc=A?3D}MD)(U^~9%vp6+<+kWw z;#c)X>{AA&oPXfDm(4zyuu@jR)3rTQc(S9qXVe<rj@02#k~|I(`$nOs5~ruPcj{iu zOqg`#kAe%GRBg>#LnPbWYvhWqkml?)GcJ6)2)7?HlC-;bJCkg^_51v3>0&7LupN!~ zgSQXqor_-MxNfF`MFCVQNsQ&hz~l|Q9@DV25VKuTsXiq6XaDXu!q_)j6vyoYUb(dj zL)6Xb9fJ&;Qra;oD#Jk3u{;+xrEzBA;85k{2U14Us>mR?n$kwFUA?S#k)KwS+l3if zx+V!Lz8$`(<E7P~3gW??CQnu0$|`TA?$&x)cX-0+(=3zR$Fup>FhmN>u-E$Ak)MaO zGO_Zqd4t+55R9t`7-Rd9gnuyAUsS$ZzHl7R)IcJSub%VTW0*2X9S&&BH|Zo=E%Qsk z>yrV_c;vAVIVCVWzBq(BsrLF*_xV+Z1(`)lI!`7$%rkQCmz;_SU%(xJ*b!q^k6fP3 zIc$ue%Icbn77KeR98LT?C)o7zUgI5{x_=uXE@P^>daE8W9lLZ{N|i2EGy%Zk<jilo z_8lkY9m+{t*MAZ1=o}HW=);&5``;yt%ZCo|+6s(JOc2tApqo-?(_RUoSsRfSJN3GF zNiW#-x6{VQL>zyxkVqGbDWCBe+^I^@?Wv&$J!=`WjdYt}`XOTRgOrp-S5I!Ly?txe z{yD}7?ESZ%d^oh^-OMwdj#ca?^1_0vm%smNHMRS(>yo2CzsK+}RWt49RzBiOy1u3e z*+-}I4q5$4(!5B?ssr`2{}7um%Vixc0~V7tvtY#NiX2j3h|<zSbg`72G{{v!-D)vx zIQ~*BOW^;ELPNhCVw-Vp3Ahh0e404xVb4a>^H+qq^eAat68e_{(c5>2A4oAJTxd#A z2Z-V{7&PKiXq9va*}^*Dk^{da)nE7T4NFRntr^4pU7WVmy6l*)b}iSr0e%ZSH9}w> zoQb%(m^qhx)gJw)O$y|8(s>>~RaVhz?XSkW?ZPJ<$iM}+q1}rx<NkxLv*_j99`8TZ zct4Cl@`-dbq}`^nFL@1$@UL;V<P&Nm0F!PgrO5IFoBPg_PmwjI?}IryZ;uI|D8s>1 zEd=idT7XdK`a^4?@Hp^zAg6#)>t$c4DG%?lOyQp6dgoY0D1YrwsWZ<P{~!b9AAP2( z&EC5D;nept4=iiC+4<wSMT(`R3ec@tNJ9YV!f{da!iVl|@RG8|BNHpA0zA9+OHON- zkYGM>5>L+<UAuBLh|rr=2Zov~O6=p=D*1<Ft&<N7%Z>^RATs%{pQ(7vz*e<A`S>W} z?aij<&2?>td4^8?(9$RLdQ#J$8^)nYGRW=5h0^GOiNmugn7qe&KczPA9+*G&@H6ED z0VDn|rpZ#4kZD?k8+H-uHHWZ;cJ4b~QETnsP2$9vSG4JO{ol%*j%=!4;JlO6XV6&j zZT<JvOYR8T%~Y%MxDpZ)5M`HBy$?cj_w9+<hd5|eIRYq<Zkl)31T<87{a5FvkBg2i z%YpsIU9K95zc2Z^*sc0vV=9{1o1I5y(=G)5?s2jjwCZ+{)^`Iu-gT#HkdL5;G&nT; zS3}}uB)oem%w!9aHn!B;ha1j0(Kf^+Cv4SMlMHS?;7_QmZ~kjfIyxn!CueUOB#gZ4 z#=hVboZVbpP5sB&fm<4$^%5t}KNnwvSFa}=Dppm?nDdu*Mp@k7x^====i(>gPnCOM zeXv5~zh^w>#^V^`zvo++$Hb#k2B(*sb1q1eP~3Wnh7ZpD5(fTA`hV%P-{eWwk90LZ zKQKv#LUL>JBy){rteKdo^_O~4xU7w+=yrZe*DhWB<`<qST&jWjQw6So!ZD@uP=|2{ zmKH%FW^dkjx>Vmg<8f~BsH}#S4~!-g-v_<4ygExER&3>d`)Vuq^%{f{*e9ceqChyg zRiozSwk%i8s+^tfzd!J`ax!4A3buko9q^eq*zx^?zC8DF(bWyZa$|_`(|h;Kyv>91 zx^ziT&>aP!*b*)pQ{O22$}8w~r2>Z$jp3a}oyUK*LjL0YHk%lP*X;SY1aBKv(PN@c zim<;O)4AJD$3p82<+t;{dwo_8Ix+aBJtU67;<v?B?N{Tyz24dR1eJns|H*wgV#O+r zdR=k=>(^b}-@WLrAZ!g0C*z)0%8vwn$G_}t?yQj~D`$P&8{F&i<*9j*+0lm@&iKp< zsDIdn?|5Wm(Q(oWK{Tn{t=wjDNEqNc&{6yUZTY4kp2<|stzh)fXc&hU76O3=g!h=m zQ<i=agp@q*)$mbZAa9e_!dW0M7O%!uo3L?v1dkebOrsU4vSfb<QM&u_w81cRw={xy zWEr8OPC1U~(|`5rmu?dpVzq&mejc&JBzr|6M^7EF22HYQ)H0Y!L<lL^TS))9zUv>g z33-iBWL;ecYcZr)9V~`s$l|vLFL71mp~=}lNLkBNX;znlKOdc4Oe+CAp^dfJnV+8X z8=1G|Mt#;`_ss`g6)b+tIbpoISD!w2Kxj;$rkwRz0&P>oqyh3F?*pVB*}^ye#l-ud z=r0czPA?~Cn$+|vR<t-kGsN?Q_DVm?-Sr{4x>3|Wa`u>(PcPN7>o(olui1GjmN#%G zI2cOd4^a8n*43SWcAhaHRuX1a5+<L2b@<*nBO-;Q^d!laq-o@s`_NRaefMJSg!M1> zZTkUNAtS?M)BhHg@t^G(W?ffO)fQQ7J2yACq{hDw6I9`q%A7&KR0gTj6s=dpUfQW) zTDA%o<8hg=N1)P(u}s}9`ptuS2YGqso#vz%wXV{n%8ouD;1OzeemB5mvgh(_PT89a z*CeLVgVMB!RFcK$c?VtNu3e_5J7{-Nr2Wr*qn7#7<p<iTE~WK8XzZ!B+J57{X4_@p z2k9_ADQ+(yHp3<M(BtE>soIAREE!$Get4c(UsO&E->*4$8xTV-Xz17TQdjf6LS!rY z0ww%({i}!mZ1pckyuba){>h_{f$T~K4H~fWIGSQ83F@B94FWekXe}nzG3~xsBLDx) zBlsVU4fy9YgmDor3d@h!s44(bMLMhTI0pHn3TYFVvA^W)eueZ>=oktJQpO$lURI^| zy{~Sx8XqSG+Po|aSjJ`*(xia{d_Z`k>M;%8P!0)ZCWfM#V%7ck2P@q3{I3XMdb}9M z0C+?i`t^~gDwvve#B#G~MH78!@8Ashw#Tj!)iZB5kw}jPdEAK-Kq?mQYKZFc#{v85 z9m!<DBX`5j$^+{|T4Ao8dr@6S|J0T(TcioOrZ}m`E^SAyF&3qe^dnzNF=e6s_@es2 zny0{pD7c1^D4hZmqL$Zd_r0mYkGh9haJkgmeok1r(B=Cq=B&OXa5I+IzbXB(93|c; zUbn;Hi5oMe{PlGR^T-!T-%x`iV^ixBW5+?5sC^Zi)rZTP-q6fD`Y~w@$<JtvE?00u z^r4Tbi*wsDe1iU>g=o+3HI_9c^&7V~S^N@xsdf8MnD5o${e%Pg7+PCf%V(4u*R4?X z%E22U9C$vRQ_n*4&c(q4YYNdcTAy7IcL)2@$oFPOMn;`x9CqPW@Qq9Qawl|0qMRs3 z?Qo#=x_0dv{mSThyCy*99~FY1Ss&W4KJUVwkm;>S0Lh&D_%n%wAwk9$=jjxhZG#5g z6e5o=-W;ba6P6qEo-jb>oK(dObhHRVqg0Z&C#=CpIGQ^QGrC1_=ZI0u_($a?E0yb# zIGDvt!s~r4uUG6dRY@95{VnqXwtqg{$IC`;{{>J><ZK0%x+E+IRe20!>3r-97#L}# zxVZ*%9Y1mzRs7ZR?Pxkkg=Mamzdie#we8paD}pLkHnK=pxDmZD)s0?w8$ewM3>@(0 zx)r0pA6U~bnf9PCi%^%%l$XTK(}^shi|y;|hoga-+u`M=(~2$;;k~~gV=r>PhsHPq z;XV!mra2*b+qm=gZQH(~-BL$aBho%(`D}gxiw$INAcpoccZO=HS0onxW7Y(%5Z6_7 zhh*QSmHCZS_w4O^{klH?<@=TSXY=hcGc&hu-@X>4TGO%fw?lipwB&DHMrJ&7`@u@J zio`Y|f+GnxxzRB%lG`PbgW{0U_>rT5e|F-H8!_+la>HrYixmK@e`m+w$L+wR1~I+f zal&+>a$^$Ri+`H#^zhZKHoo2D^1m&B4KTeBixmVdW~ICG_H0ksh=*52R#=!wY^7;E z{}+#M*Q}}Ov1i<t{R+Qoi-Ii4xK&koc6RorIFGA~fA!y2Kl^jw!+K#Q3#>$8u=LLK z(cYVj|DiAY(MzcmeObo-#P^Dui?IpzBj4c=oEh^m_Yc0+zjp@aWd+&RMyj(Awr$%c zz5#%b_NbYpStTNRIF>zp^pg6TPgIDBhVnJN!Rz7|-`_6@T2FqjLZ6#pJEf7vkSLiP z8rx;!wR`neqjhQy#m0`}=?j0E@O}aKX9aG&PmV_psr4upuU?oEVte)e?W~(g#o|Fs zGfa9F3RF$Bt5l@h3%OKlY3tcPD%TY~DlD|XipNW-ck8KqLJGt)KS$ewwhU2OJ85ZB z%Z5>x|5rvB@;-!BK+jryOJ^2JCOE-`{i&I9Gjuxptifzr@g;FuLIpuLMx0`3Dw}1j z&GY4PA?AlBJ}6fQcpPpAJK~~RQq3X!aCvh8mTdv%tD2+fXXucK_t-lzPQ9Y(K411= zv(<jBg+W|6!sx85^lLOY%t1L2(k)t)Yv1jda<Wq7S8<#{pFVXTK1A{^=ek-+KF{%? zqtr$rMSC2LL2xpojD2!98|%WjCF&3i1$w~_BFY(&O<9b4e330zNF^RJX|r>L-Qd}W zch&i5jM`Px^mC%?1`H3ok=ZW))aDJP`zZJyJ@!m^&wW<2P;Brg@ZUq}Gt^?s^&f5) zT?6EErm<smOWfV5sVxRQ*lzsnr`rKaGJPc+3%7J&C_f6nJhYTSF__O0!OGdYhHKZJ zw^6M$S0<S2NunZ2RXR(0>7@E6>q4(~wRSJQHW(N1NfyDs>G*!Uz6R^ku_Bo6OZ8>$ z5z|y(bL!WRQ5s1#g=7PMz{jebN23t4EQ9m$*J-QRNGEKn9w_PmrB?a;fwr&7;`Zs< zWi3gLPcG-2UJ&<(;5g+(x%7?29u7bwPcefsz5mAIC->rsSnq_B)k!nam#pFo4i&3^ zh7f%W+_gM>?-X5D<#Gta*#aI%XZ-{#(EP}CR%{=7ih9(1zpnrLgN$)bWd=hH4!aOB zV&}A0ZK;nYlQpC_d7`-im6OGgx(5gL)m$Qws8VL^o*TVj3_`9H+~a5O*p=s%+I)Dy z(YyB)tqEmxvHn{2=)%cb>{V2Wj@n4_!a8+7)sO8&mbQ#Hb>qvCMGNKRqxwvH)s{Ui z<W+PMk9nI)>Ja+hAH4LhiO$XkU8kzWH8eEjU=mza&L))BMEd3;GyPAgpq>hEe=QXQ zzj)R1D&FFL5ZSbvCV;vaa52GLdB<3Kpgawg1R>sWA<R(!6SeLb@JgLanWK#fxP9V$ zwlYo$TeoEjM9QHPKP5huO}H3)%x|I`p#Z3?ucotP@aWi-5x{v70BX3T>_O2vqcFXo zAXrdDplUopE^lp_0@#uY5H7v~f(~FQ(DtdGmUw<a#}cX&gsVEz2)g65Q2S!X*Ik$c z80>i0Hsi@Iz41;xhhywEV=U&?;zrAY*QAR0BW>VIALXh3r^GdYgUnVp)YcKO{c4Zh znyaPh1YK9$3dFeJp$BtMZk)GC`7-)|QY4%fIX%mEEHbk2+vG1%fPw;b1}jc;m%5r& zdFzZL=Z1>A2T3@@<pB;8-Q1emF0Loj5K<2ZXb$zMX4X!O{+H*fRc3`{k+;EL03Hv( zRG+oKfg%e-@L!Qji}Nb$amWw#W~M^Ol9nhmBXQLIW8M%zXvDgL5Wd2+Re0i9-39U3 zrr@Yic^^}--e?-se^N;n_z8Wy)QwxX<UF4rb9<o|G^3IiTW%5U;wz$B*wEPM%p>UG zJKQCxzl7IQ4<~2A;guOOWSVQ4=z;B80$k<%wqUZp><S&e1xq?h?8o{3REG|`(?SA- zJ-`zRURAKQD*6(}5a$~<3^qNuoiNQ4^R7ESm|rI+kXk|tH{_4#Bn%^JVyUonC}Xgl zyUUR)8bO2+=QkgYj|YfEgX;%q2D?TwUSU^-UB@m^<T$Lir8N3zw+^3K(}>@d#e`SO z#!sA>ckUPcNR!Z;B*2@o)5dq%xC5>w6^g9x4&rGV6CeBT^G~$X9@)+G;dBrybFj#+ z%ycRh(`urjziXJ>Bq|A>D|0gZsi$dK?0?XC!HIEhABH2J|BlP;{m>>0v{4-9STE(2 zrWJ&o-E{-SfEw=%Z(@F%l9w+-BVBLV)sZIAe)s{KJ#y%kAS%aLV7#y$Lh&F|5*xCa z{^?>-1|y>v^KaCcXBC&?zn+mMw^f$7*G^ZEu&<P|D~9^L3c7yz$>1e3qq1}N(+YNC z-H6pBSt#BRYkVDt%{x7nYbksr+3KjIWO7_neFVu3K=QVNC2m@G)^XaATepUeIe^B= z@0RDt>ql~6wM!-$%xk{>7X6L_$GD-dqx3&ux+rjT4pE+O`wYN=Xg~%r2Bu8V<5Hsu z#|B#a50-f*9EswD1$UCM8*cl@N^(NR0opXc!Qr{(xs9(L>c}==F<jnI)f1}tDfWSw zKJY6Bq34!b%@IdG(%n>$obhW*WMIN6xFisH4kMyAIN13^#h~9fge?a^BA<{Orw9B3 z?c)x&E4I!ghf)HdU_3;8*LdO4$O+j>K!5r`KCvD=6S264e6dHjtvd>9@gE^Hjo<jX zuM9A1u%rk%NqcCnM(M&O<!oQkM2im?a2Hq91x&QGlR$a*t`Cn)GHEDIxc6Y!Jptk2 zup2egT{rLycti%!S57RBh-EenMxOFwE-Kot7cz*%6zX%!xE0@G6x_iY%8)c>#U=;Z zC6QFdcwKRC&?PHA4j#l0N2R9;Tiupc`KrQrKhSq;2M1wcK!+d0MFv9;WkdtN&`QQy zCA_)WSts5NsXQZEj3XM58BXje0HAe$<t@%3MFe6ABCri43Xy5ZiHDm+8%l+_TRv+Q zNM9YG%ofeML`e{4Or%WF#7foSd+rhl*zm8S)Ld$nw$}#-T!BMgq3Gh`lE1_^#OpV$ zl!g3Y&-(@ID({t|&|6CVIcd`yPg<}6I6_JsVb8z(>r5~FT6TSS0<M@HWieQu8xEHk z#%NNPvPSuH!A6P01e8uoYp2~}eg?+}{_Vn)0rZW~UfhN^cb|7iV3h$E+$r&77Qmeq zCW;Bg-@~nfv(akP>nqR8Hq0i!lGV&P<+Hw-Yr|a(0?&2AA(a?z#($^H@SGe~M)k2* z{HXT~-JU|bkPZ^2g5LOXkW8oE7+9GV$C{DW35V={nCF4R=8vFmzD>zyi7^RDm@S}E zt=spxSBS_E=+f!lez)|a%Dr_J3LLY&YTBM52;^^e&e1YNQ>{ZXWJgSXf|DWnDU0S2 zqj(7PV=<@kcE;LPk4IXGOtOsDRLqE}<8fQyND&)EglhT&NA?rmL+kU5%Y$itn}-tK zZvqhYNZgft-`QlPrK#s;S@NdI^nQ*|tEE~mf%#?w7lFSS$TE<re4I%Q@AAwj_|1Ha z3q&DKoL*Iw5K0+6H^Qj9I8;$vZ35S{Yu*}8)Ih2mj9U@AA?nLYKx(&n_fku}NE;y& zib5QltKQ{fWZm;<NqMa4g-8)bfh_Pa)vDOjBjj>_mop>h=yj;qG8ix(ZZ<6XOulXg zFc{`2&uPy3du^imC@5Dup#`z#jU#@Ep!>R%=8V8gh@1S`6R|u88LYws+nr%w#>plF ze`;N~k_uaYa+@}5|D<Z(NI4-{EQxrw7bqX;Cy2cS&-E6Y9##AWg7mU1eN4q*M_99r zML#YsHTg{*2+R74uWY-{B445$xXJwy*Sb-nk<V=-F4b{1sJ+~9vO`eYkyn?z1|gsh zYopd&{Ls9p@@4;Jp3)j2E7c_t#X83a?F*UO`K}|)I3FPHtZX9QX*00`Qqo=F<<e%8 zmL$J6acgsJ2QjZB$ckH*;_mMEhB;^r+jwc!qiH<M=)~k=kNSR4;{~kCIXV%lt&zSR zfT#?lps-f;<f+vnL=snTk_%aHlwM^l44E@^`SNAUUdIh2*nu#K^E-)33Nk-DNrq4q zkxabhgfVnG3Z)b}f<o=*%|Z8=IeOay9AO9+-6$W#8p!|XM25DpC(G#<_-!aEZ24TT zpFGKZG%2ys<l!t52Wk}0hF)20H0)1t(4xuIGb(aEhj8>sO^$~i=L(#mjj!CB&q5#% z^nX7d{1ktqA0RCQxfFFC&4Y3H<mP@SAhl;9@1T{yO@k7D##xgmAPL+z9=fe6?Gp*r zo(;jjtZLDK&tyZiRG_-`gS=)Hb{Cg07+i5(H)uq<ln|><b5I}Nn#99x`^$Itj15;6 z#NCVa#T~xm%Icdh{%%-AD-^n&r)6>83rp8KglCoNw-oKiIY{zv!4k8jG3<FIvIwq6 zbQX@8W<t`VaH(a}PXA+#CCME#%EW0z<)_c`*MvmI&#m@~!i)B<HVRI$tz|`hgb5;^ zE!lC`n_f?p{Dql3o1xaw9=SMWK7D7Nnpa}tWB?LrJ<QF#gszX=(0|4Bt5>e*Fd8R$ zQt$_owVa}B>}Cal@qW;mRmphRiXj@XoK-7c+F0LxQv;!}Z+QdI+l9}{Zun}Qa4bhy z`6(iaY(q0kkJb3tMvoZfsk+niXgrCX5Fa7z_^vA)9#;&wh``I`aDn>;ucv{DV-%lZ zz)o7sxTe$ZgYyH4Y|(GqiD067%!#VR-QMaAXCQ4xlwKSB{`J%EJb||tx+RyL!C)-E zwEp2|te8jtP!M-sX=3`j!B^|GH9)ZAy6PLwzxF2Mc=ijgopyGi1wHcy{lV8a9RF)W zQv0`u=lJjjT3Pn8odl=v(S$BH>S_*s!{`$mI!{s(V4UvBN}e$@AN&4f`fQsR0aZ3R zq7QRIv^ov2ctXsKKt!PG)!p$43sj6z=kC!%e+s{qWUhO8l@|EA=Z1q`MkW_K8mRS= z=><eV2Q+Hr|4$YSa3r3K?jrw)*6<%A&=LA?iJ4p->TtAFwuI}X;kJJ~cW8Llqd4_$ zZi)-%Fg^@@91jiNIv4-nftj}V&3Y!7F3U1nPYT9L>IA#7oJe?s!&{;)95BEgg>`#> zS`?F-R#&53IH((?2*Pe-x_u(LoMV3A=-(BlguZxAK5^$QRnrZ7U96*ce5ToPTE!|l z@4L>ZPyA$`yY%H|cUro<!B+PY3%L5;53WID&1Rb#X>!TN`WiFBiDH*7X}50eBx#6Y z&=l;fX{ZON_3Zh^P8zAQS~RkMAy@USW>5C9dqRe^x3-Z7nVOZgmm+<7Q^K?2QIY*E zyNiu$4sF|T%#4`8=0`KKq_Q#{9?xac9;B?h<3eJV{${wS-QIPK1No-fVC~&bX%QFR z^+52EzVhM6jH2KW2!)-1O^CB3=Bpx(KX@n}X$0UI3nH9==`iVftGO^rGFC$TIzox1 z+mfRueambcHhPaJ-E2;RMt1D$v~7#wn+oluz(9-r&rzm|#XetV<{R5>AR4VxLnhsN zpXk^ywXR~?sgLi{7bF!hc_5RD<b>lW=9N@UN(_;4fBX&UM~gm?R*-ZBuTZZ**7Di# zx+c&`p>}A`0gKmcS+y|B%C*F<(aNuC^VjEqR!ER}wpNTyN_*h*vN6q_Thv{g{`w)d zxr5{Ku)f2uKUo;JA`Pr#mGgZx*Kj{GNumWQBl~qd{2XqINS4GahN?t(IozV?>YkGX z;1uNxYy-6RhfKv|?8R>!hrf55a{kc@!@Es=3*KCL)=<&B(b&;!_DHJoEf*(8v3Z_R zURH8>p()kQjQ`Z!e70o?yeeaJUo(wJLX7vuHLob2?V8Wqy==hkdjkLMIMA6i$90z6 zMP&@bIR<qO4rKiu*L=!5dU%i%Nv46v04vv|aJz=B>XAoXXE}$3<c<rt0@Ke(>FAO! z37o+ge0G06GM<E_^*4R{%CsN5RSqxv?W*~VpGs&q``(@Q%tp8bz;vsipkP9C&t9_> z4|6NGTpfJ~v<$+e(;44&jf|)Roek7mvt;FvJ;Xf=a)qjWZYC;ERZ{9|oB<<M3*lrG zHQv=5-?G?zp}SWt)iG(FmB^fYQ_h(k0!4=9U%?J~?6E^-5EVdvJM|o`{pZJVq|vCj zs4<14S-$>b{h)#GI2q_)rcxW(E_%7r1|Kie__Ej+QNRiLoA<?}RU;xMwF<9ZzNgyZ zPJs2Td0X@9YC`?%pY|+dMxgTV;1@EmQzi@%mGG+TL{{SyJ2*m8#nihO0&nK=rtP0v z{q3r0rOKVm%;xascd1Q$IXn1)bmMG?cb2&iay0T?L@ZwLX~M?WCggoGiVA=*CR62Y zZEZ|Xhu66$eclIO4GSA!y~MBg(~<dPdXfrzZW<gJ6El1Y1A)C`cHX{Vdlhd-;}K4Q zLEny8^zgC81|r+CLfB9TIj`GP{*)KAql=Ct1w`%+)_BM`b&b2V^I1whxI*}m4vjMH znj?&3*sj^iXEsM}3>7SJgihC93+W8)A=e~L4AR^%Qft7xqs>>Cws300dsh?hSZ0V) z@rKhJ&&mJpX_ANA#keja*`tQL&7+F=>#o@a_hlE{OS*g0bn~~g{J9NYxxIH?`xu)B zgT9r_X$cwYxFF)E1M=s=G!#a!c(98u)YuHC1zSRk$@Oxxna7icDf}qwKi%TVN2}Hm z;J*yx4H!)#xp!UBi5<|N#Rh~xTt?L|&XTLpESjI_N$P~ny;L&bHPXKZf+=oHc-D)o z>=dpL;qNcos7lU=B5Z-1L)oE_bSQWCC$y~OW-t=YYFvaI4D~?l>$P<`GX_{~z2aaX zhjFJ*FU@C|p+guzKrADJQ|dczA0g7v*o2CvH>43xgYlkYVq$i{g$aaLOX`jnyk*3W zE=2O<TU$O$vH>7rwXvUfH;(HnE}3^~6gRG!xIuj05jaRY7~_N-kT%U7_q6mk2DR(g zlM+UV<UC|umsbys65Ubo$n_X362h0AG1S|xI~x<|;Id2g!S@gOYG<3YsMht<+ZNW9 zY787!p}sD+FYiP6pyF~*9VLdJRj*ssj<4aqOSd8@M7=c`A2)bjUSNfm^R;L=2FoxI z4R1p!#bRS|)8EdhJAbm3T1@G&L7l=BiW~H0@Pi)4;S&MXMP><75nFCO9?!vaBW*q3 zC6mn~&W;3fTVjqdyP&h$Iyl^7i*!^}Y*j)kDn4%hl{1iQcoR0(HPlHHVj62fJRPVN z#BY#|y93BYOp*_EX=etZ03<?u7-dDvN5k~>Ic(KnO^}cldDK7MG`P|1G4qamhee0w zUv{!<ucW%K#O+}&%(*Ne1)$?SF@9vsQn8SYxC26odP1reDpDBGtA%6vX^V@qvn{=- z{BC{Y=U@pT$QEN`L{2I8yC52p)r;E~RRZ&&GAXE3CRH?GXCsk-j9scwQLHkFNSuKA z80$u{Q>UBuy2NxcppxG;3r?G#?1V9{Xh`^~GN#Ut1X)fP&Wtq9fK0g(dz%KD&>Yy} z#8<NaW|G;QKb2jaoWxy5sFwgY-}LBVzxaLAonhxJJ73qY<_7cqcU<myZ5K76_jHP< zA#+c(rLq@*LUF;7CK(L{bC&{eJmdq6btJ%|*Yo7b3k+SA?Pz|0I71<&3sfjc2ZCQ# z_dr<XBv*cbkP9|G8b@_TELJ0-e{g0#nwz}_x6C2FiVqf9{KE@NJ9F(a!V#@a0AnNW zWI3?Q#kU&Z6B*KKVPHJCVj7tW|0i{>%rB5>+JL-5x0c_hXrJq9tvnQuJE0+x*__9c zYj)TaKfq4bRVD1q2-th#-!!*?P2rO8cg<;!Lw;egu1vysQxr|?E!$Ufbwf>Lk{Ec$ z8vu+Lc#|9(4B#0_fH@)FF!*2c9@L|>H<|R&TYXQ;U|z&Sxb?EyFx+AvzJ0PTD~BPl z99W?xRR>KpDE|#f6Rkj&r0V0)Iy~8p)9k_=zK|#nKyYMM?ZpDIJ)`Q34_fDXVe~A1 zN9G-{7X@?&@&qoYZ+jAG1Tbx4dPE#$z6q8#Vz*db?ME;KD;4%j%!p~yu?1N>L1N<l zc}mc|6#LY9(fHozUIYX!fA%a5AW?br_MsW&(x75rlgDVxy_O^XGXhmpmWZK3lY;t> zIeo=rW(BQ3W|S{{K4oU(1{Ar#`X(|L<H&r?y3sNKAaox8`VzQ`m$KXGz~zW;+_}Ui ztZ{BF@G?{zE3<iYW2j?8P|9lC+}y5u=}F+lYBGK-miJO8JYLl<-roJ|$4J%iUsmNk z-e<9xPwZ>Fz2#HQR<QS4Qm9idngM&ngNR0rM!3wJ6k^AhQPcc8c3+S{1OG`SVVU#u s412_reBNGH{=ol;|M9QC-qN8id|Jm}JdOWU;D5%`XB+%E&3fB^040bMeE<Le diff --git a/docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png index 82ec4a20d036cc6811bb7e17ec29833dde8761fc..b40fc0b9f6421809cc6139032cb0ef76ac046aa5 100644 GIT binary patch literal 30959 zcmcG$byQVtyDz+GC8a?LT@um=N|y=(Qi>?u-Q8)BA_z!HsnkOwNQX#@l!$b<bV=8_ zrq6r!K4+Zs{qc=&e2(#q{cc@rt~KX<-`Dl4YlS{kmL<Z!jE_R0h~(v@RZ%F+02B&i z>H;qO&F$W?Iru-qE;1S}kL}G|+zp*fQA&m`4mS2KHkL+L-AtXFE$!|2xdplTIImi` zxHveA@bK9F*9*Aqoy>XIbm#ryA{QOxG@Vf>5<}!ajDIAvEm0^B9eL?HY97gJ<DMFC zO-I|dHc;&B>on`C3vmi)E3J3hi3`u8V--e>UTJc#C2}o{#$<6>X^Cq}tjDogy;8`j zRO6N@%*=WgSUFAn{A$zb+0CvCjSYmIgdHamZC{@`_tYhaCuT3Y>Mok{5jIIl!=Kt9 zl{<J>(eNiV6Z;|jMRUgu3<^?G(k;FRC>i7h;bc<q|B~{g|9|{vUg{vjHwg(LhBP?z zVIlBG;RZ%$OQu5n1tOyPpP35({kQ+WeEk25dvlEX^T#EQQ&aVa;oO6Z?~(8NiYABz zkN9BA|GeVG>nmSvxPOO~EUN#&K>m%w^wXzLCp$fo$zq;Y(q)@^)2F5QP-RwMKU-d6 z&?~;Fr||b0atvJB<>9i7z7$P2+5-u^1K#^jvJpw?(EZsUQHZB1WO{h70a*v-YL_Ll zNXavy)8qZclfxY*W*n^e(qwa*xDDf0io$6F=77I18>(_<{qW&K=eyekG40J$UyB^( zzeh@q+w=$$hP)2s(iXd7uKX5-JZYf@UP6h3DXful+m5d<@NzUuS*{4!yf^PTM=?9q zogFytxBb5H?^TI;6BSp7OM`#?QajpP>sTMJS^AYj-k&GxR+ql$`MjMsKfNzHrMX%9 z?!9}VVPWX2SKq&HLbeBqf`UR*Pfv-Rz_n}7C^?>PHeit$@Fk);#_FdGI-W<amaxaP zms}QN!S(SKr|}?G`@6~7Tjx9CX~n&685$a9WM^YNEYuMWBBV7^IBSUhrIciL=o-|N zsHc*m<q<9U5c3AycXSJ*|H+*s0ow!#Uy;101x7myUBao140e{gs-<QNvt?G4^;Ep? zCH_7mfgr=p$@8;#^Hk5Ej(DEOWmZoXIuq|@E2lWs35d}(Hge|qMlx1(9<KcO;PB)u z|KG=jGVe~F?v-UUZ3&}9xvUJlu5w;fEzr6hyfM8(Ks*<Qqx|neHHhzzHHA@f+<5d6 zd*jQwM4oz)8~Y*0ga2G5hE*j?v((IHvOWbqAD1z0CW(b|GEEWA6cb)B3FAUuQGH3B zU0)VajoTWUi75k>zD@Xd6=t3gHW^IRdcJ)5lGo(hrM=Z*WA7p%^;5p>h%{ArJ9eeR zG$x7ICW=Jj;A&`S=x58mAow0sE*cEke-CMG<3NG-#fujWdq2t?935@@pYQui-@mU4 zH~yTsYxWoJeYi?koqP<-)^c8vkwDT#Vq!CR?Pu%3!uL(=yt&A>k^5v$gKJir_zVlL z#CnjsNcl{P5~&1iS!Cqo7cX2uO|?+#&n+a`XURq~B<^kPKK;+zoR)qvGx(o4j+9x; zIyv#7>g(&reD}v6oSpq!VZvUgh`N(qzWhC3v-IF}fAVlTm=Rwj<F5SQV?yD%w6x^2 z`4#W8?P%N!JQCIP`$TXzvS|{&_#&V0$~z-fDML=waR!@2{Ldx$#yD3wIm3G-&y(4o z<We}81je%?i?5pTnoc;n%zAKUW22{y(Z6$hz9aTz{_el`@N>fd+*dhGqN_8JziKHf z311``edphQQawAEC7JNu*BxDfJ0Pc}&6(@D`0uSn8PMRS`5m$Ki0u%zx3}N2`W2#A z=SAtFArt+#NV5IuP8M!TkCgZh%YphmP;jL{tBif<eHbqh#w&QDAE;<)+q(oOJOAgd zsoQ6OU6Z9%W`#qq6_tsP)EWvca#m%Nu}VjppgT=LM4Gi8H?Lj0hC}}}N}Y)#fQ^l< zcKbWWXRH3Oy|qz(?>(EEMFugCH?T@SYdxL4_Sd`0ZAZmua^OmHvZqJ8Hwj-r&3XIJ zKR1~DxmB6S8`@)7OYJ8m7P?b9uGrUp4X5STC^b`x+aV((^O%d0t=ww8atNzhxzjD` zd9;)*?7X0eA<YF(G(yO2HDPTKDNBR;{-y)@ns1?^q0-s)!gMeb-o9;C*Y|Cybey&L znQ_s5!jm;p;^+neHFu}eIy`!|$6184ZIRh_<JIApZ--U8u9VnM)?<d~z)o~>nr_5l z@R-7|t3Nc0V3f2IWw?A<E8r2OiVQb~n8Osx($X^VK?4-K)rq>KKY#uxF0K!KdQqrb zGqbW{En03f%qQZu`umdy><lJ%?Fu_)CZ?Bx1k_IJW5NQqBWgdcQCh>5!XqQIRWl#F z<<$J?#>vb3=IvXG?lj4?rLmzQ4R-ayPV1t&R@;j4R@keuYHDwTuT#Np_wF>~x3Al! zq8D+w%EA&fHDv%F*BF3-1)I^R;2U!Q?1RK;$%Z|V+qZA`ZDnLINu2(9ocpu@+1(c7 z)wf{@JJg=SrJ|mbUb&(W&#mjRRyN3+)I~@q^hUSFtru?NIdQ=dhtoxa9M#McCa;0P zv$M0rh~uN9pOucr_g`ObQ4$<)Cg7=+a&X|~P%r$WQHvD9I(VeTo;zlkn3xHo?%eRC zGwufB<Iu;c-*ECpy?Fjy$bFp-p4`EqW@BVzWZUFq8mxo4NQG5@?wCUpQDOuR9$p?& zR8=o1CJRONWvlQTHe%b=ZnZuqZp)*n!Z(<0eZ94_V?0=>TW<X5(W6_|1B8reos!D0 zsJMhYHVv|jdwY9-<!knle?aO5uBiL^Tuyds3D*dxb~z`!NOfBs1$#rI+Qky-CI?%y z!=>gVD+2|_P}2zMMJS5Zuz2+A2^-*f!UeA~`W;;JKR*@h%Tl^7AP`qvT>Sim6gEih z11~QTMxRX#+tKoQI9@X`DiZDc;$3D#m84WW<vxN*JH84<s&iyg0iTT9erCv%Gx%)Y znef?Bx*tI&Utu?nvY4zFhi-xS-mL4L&)%>(RKuTn>OAP{*AM^nsa(XzXaBYdyQj7_ zl$^Elc)i9GUL&uh#0-_?c;kzt{(g8!z?8x7F<M-r5+4%caysg^1i~g>e36hw&YL%* ze*Ad&@#Dt}7cSiR?SPcl@F#gsw>FzdoR@#Got&N?PQ4)Um<eTt=IU8*E{zQxf6$C) zK$}|^e|q}x!iWMz`@cuu4rv<ZJ*mkeu3_*^Nhv8?Hm93*Vowsgc)eT(=12+|uJGS) z`Wo?XGvpr3RG@wk$JzeRqx7Lq#(09`E|2Rss!R#!#I4SKn*xS3Iy|FG(Sv{MJw9U- zFH&wTM?SNmpf1f!eSLwAhgY*o8{y{oxT0b#=Wl~ObXw@7U9VbXfDS)g<($1%$q^$N zu-vcfohtbF?<+jZ9cS+&M>UQ|Kf;h^b-ZTKwrBYVhE;!c_0+5u3;C}?TVWNatiP=& z*=_ziW$WuJvas<(UcE{;Yzl&oI6e?RNblooIC86_s7ML3peS3pESXjK-|H)fMnq)Y z3ns2T-5aF|`dVz*6aq+tiK%(5hnS?|vsabd<07rHUzn}5TG((9W->d?I>Lr$+qiZX zL|h#5z2K~Uh1!^-@d=F->-`DuqT?4`SCM|dO;w}u7v65&B_)kZ^`afk{grD<Yt&T` zwzpc^;{?l<>UU(h{3~a6LTYVuH&8hRf3COXc~AhdIl^59Ut4=Gx3p+R=+|Tdni~B* zT*@jhFONDnIN<f%p2Ji)V><ua{yj*|-TL5=*X|Bzf3}}YMfSshw!P=Ar;dz&dw~b3 ztb3nEIcHoVKcfL)7!*<t;`O}R?>!2uVMD;t7CK@r`ct?pgJ-xxd^hQ(OAXMgX!%U! z>i4!{8)g1BOX_yt`0%9$3vS)#&?8GNdeLxv?~_Qgq-F>sO;C@iV*J(=gO5+{_RK4u zojw&wwnL7rf4^Z=evM@Y){1SBQr5f9#2wa+TK*_9<_8pN2{UGYq5t3ewdz+!xnsGb zIBaVy329Y78DLRV{9UG*zz6YquZMm3i{r?cPdt}0otv92IBwg2khzL{@Ep{d+LP@L z#bbB6N`OI}dbJ5GO7EaQ&&dCK_2#qFV_G4Ht2OQ$xCX!ec~lDx=ivC58eU=fpI1Or z8>;gbeD>@a+<I`TxHpxHoT}8{uTNndWH@wLXn$oel9u1>5(9(s?${N~2RF0+ete6X zi3t-lr#Uzk<;E)FyKkc<AN?(L6bW6$j@LQ$Y!2-NMkV*Z$8v;U5sZKuj9?r+|5I0$ zKsK3|f7ctoD&zu<_~i(OI<NevTnhIVtw+mGb_ewdFW(BG7j;AN+6?h#DW@z~8Dz>m z!~uShap_e^z}YE3t3S%oF>aQWR6UYQqd4Hrn@eu1Lw5j8YL=K>O!M8N#lXNwCDOtd zuV4*;V=)7DF<IO@4oHW)q%vG)h&PEVR<h~aQR?nc!p*oHv!2whH`ngT$;rL<J>Ell zp!Y`IVEWdB-szv$rU0t@oovbaogJFPj%@8smxUc!b;K(v3ETLdg~g4ENk38ezP}`1 zz{0rM3`du3$441T+2qgHLTyU}5E%d!FA^%}%wo2to>7{@*$UD-O3ix^j5^a8NI)}u z`WUAp<JXt7qgTj(MfFEv_>}CMP}leXp~hyz@hEgW@lf7)_pGJiY6Ro{2b<v%X&ISE zB_{Gc*sL;I^<+||!<~z1{@tu;K6h1B$yj1Sp|z1-zWm??E>Y##(F&V-A)V>>x6|-( z$)fHNPzkq3?Y|%;Q1bkw>CaMMBELB?bW`J@VnYD6)ywAO07ieVx?6|h1fh}w$kmF@ z!~&uv9TVD%)eAdXKHLlTfI4;n#4Ja<f_s15ozeH`&j;v!#FMjt*B3f?zL?B^k4+YK z4$4wW+U^$Jcvz%YSNn2~3NZD{pdd0z%1rjmkPwfz!-QYI#^ErkpO;6V7;w5VFw_c9 zZe}Zs2Q0JBgp08TbR`M00-!)>2H?Tr>hMJwEv?JIILt>rTQV^*4ej0FBt*8Y+?|p_ zOo4EPhSG-Y^ZLHa8qFo9f@YRD*Q1lfiZB@@e1xnA@;|t5OdLKqYbX(#oABP44`%e6 z0yfe$Sci0$m`w+djk>yn!?MQclf;1doF^-?%>L2lo`oncf+v8DGu}hlv`4@*K4tE9 zGx<){Yy`K<?|>j*=AO^@0d8endZ46K#<grQT*3@+WFE>AGcRv(!1b7!_YH2C!zHE! z98dFa{L==Al|l=Fr*KavaLsaKcz%zzS-E2tv5S?G1jM$Q$=xQ;=i~K-9Hs)s_a)B` zj9Vk<lLr63QxuRYV9TJIjryc?*~o3U05zOm#d%?jT8}L>JYu&=Mn01Z0Ir_u+4Ddh zf3mkR*~3*a)(CtCMRVJVf`E$i#?(I<l};;kQUyE!#@Z7NX?y^aitTjrGqJLg)6jf_ z)q^5zekYivC4uT00-xUl`{eaGa6qls?kEGwRd7Al4hX4yi6HbSObRNh`#q`ROJi01 z2#S}Gz$uq_TpRKizLQit&Eo?MGC$ay253vg$jDd^P}s*l_v*~Q%#4AIp1$k-ofkv( zzM^;}40C-dT%*-&hvX`cQ#3(3!J^{SkOJI;{4;!uIoML1zuz>!`rBD&?o;#VnKXxx za_UrG3V;Sx>M(s5j_Bits8GSjNd0J50$6kp-mg*Va1B_KVgm28Z{OsY{Z*&7pJ0dq z5RVwGn*VbK2Vr~10`BH65A3>E)I6#ccDhttTFnI&6Iws6g`y1jwucI;9;M&sPW)W$ ze}3jO*_48iHX<TscJ{D<fPm-3veMG{RXdA4k3Sj7jIKa=_yM=2*d~pyRUZ^U)bwpI z9uS=)87b+VI<H-gdY`H_4ItfbPvk$uEMA+!!XvKaBZhB)UIFC>mZP-uovGytpW}@G zQeQS>eLW6cLJ0le_8d(D^!B4+)6;Vnf0ij`zr%U!=P$0IKL)%Y|CHW9)&N=pd^LjQ zEhtWh(W$B3@I)-fkEmv5h*AB|7z5;`<Y#8y`};FLQx&3=!}I`54|GWez6}1%-}mgn z0f+t2^Cm71{}09<O4oKnJG(lP7R$^T1N%nw3ru><fxDNWROF6S*!R@!OAvHoKSe#$ z?7sOQorEq~j{6oK^87K7Jxebd2m5s-Vs~Yqffuu5O!7E_u>v5b;p}~l+M#XT*gI@D zM)09dGf^M!Z<tRtJVPq-+Gs_o->H`(%U`j?cXv6iDj;3<UeHODR++)btqUaXDr4xq ze~=!8lU1zVOxQF9-I)2-t+(Sf?#M<i?UAq^z3|~4o&uS<y*_r#MXSM}zHBanyL>GE z0aRgf&)A|b8a#M_UsSt|59sY9bb`)Q@zM1v6^TP8qALQOe<*2bm%duaNHd9&l3t=C zOc8B*e<yAuFvs;f9FV2SFOn)^Ti8HX#yl5O5RC^ZRoz>=WonyOt;sBWPba)LJNebA z15Gjl(W(!_<}Pi{wZ{xs*fTmWcF#j2t^M;;0s8a{5QWTz_lv%q8GV1t29iOyTRsqv zR6=#=0i>jcIX=2c6q+1HplPiZ|Ar>N1Uwidf=ua9GK6RRD~9B|LM2nE9VXfUh!x9W zUT|Xi*71W}wleqmBk7VSb7T!brRtBCIRFOB#IPtKq%xS8-U!-*CRqdYya)#I2c>3R z|G;Lzkl{q5X%oBROxpEiI!#K{nj0H$6sO!;9w<;a+@81m@g4&XNtS6x++U?A)V+fW z6shHYuBN8?`nYLZSq<v25Rbd5<PG|Mhs-@BN^)}FcFORG>8^5dy@u{R`U&v8#NizI z&%QqX(-lq;k;^gd(rY7SJ11hKq>cq#+W6)&oIqIK-L{^YoxPXHXL^7z==JIqpac%T zz4RwnXI;%cgoI?z31iUlnS|Jq<+HZ5wLQ*JWd`_eDN02{BL{1{*prs+H2=MP7I+ml zHuff9wi4qu;@l^Bt>2=qFAaT4Z<_^lOCZako0=bjU<=ag_j&Ecg!H_Ym{E3>)41_t zqty?YxVevWv)fHPzud!%7gKJ&cBqAIL~}p&%Qm~Pu<+w;ydk~ptSmDT1}J8rzgYq| zLQn}5fw^|pG?R^qI#0iPkPlQ0b3=4%J)*AN3qo`#5C}AC+_*rV;r0!>P9T$|%vCvU zx1|iNr%?aPM69DwzFLl|CYkt(wZH$lVw-F@%~yaeHm@Wu-?nOoj@up0tY9&apJ<Lr zMH;1uuOMSHT5gM=se}X?A0Ho&+1G*|^RenBy1%3JtGFm0F-mQHhVFXXdceh$j+g)+ ze`c)8IU_T3deQu%q=W>6zrX+Yc%HN+9Y)49P7XhNxPM|siB&`gVB)uy$@lkHD2cy$ z@sG4jr;Pxh%Z<&Wl_Fz-Bm@osU7jl+wR7&lI*;uGGN8f%r1^@VU3gj=BNVI>`d49L z9|5;&p7rl-cku87f>UfGrW2Yf@uUaxEjSQ1R95y<t88o%bK%E_d$rIf61vQA327Pt zOOB<UXlhc25z<*47S5^QKS<>W0Ew9UV#6?HH3;5WpnQZgN~QuZOM0BGd=o?*7Ukp% z8oAE^jx+&J=hm&>MCeEdR|P!vM!`v+dunPF<SdE~@~iOS?z9|6j*gcYNuK0s9W5kw ztt&(zPZ$a(qOt&U;(CMg8jaIsGUq6BKmGpe?bF~^6TU4F!GwHuL6HL;W)YAd>(#3P zK*zRz+#|vGQEYe*zJuJsmvf&J&;$?Q<3aAB*<BelUmg1N*=~Gf+MJn%1!TJipp7&Y zprBkju1{PSWMc~jRF?`g<>k`?t@-mHbc<9ydBaKmZ=8-dHiifbZ1!DUyI@qnGv_Yk z0a^mWbfAGC9|>L1JV}y!rfYjQ8$@cTp0BS6A_UlFeJl~QnkseyDBP`MRoB5bP)rhd z4Gbw14u7tCQ8!S6*ptu`jvmSesD+=b`q@E|Y627sEc-3n;~-dvpQYw1b~S6|Mq7AF zh`6^8dVz?0_i61nxkWc@Vb5(Q63G)@fH=7CRnjDU&JGu`ad8*el%r;D^jtK!5Bp#S z;EOSwbRgvTaOR<!Al1x$(pM0q2(FSCn^-_~m2EREycQ4F7rC@dG;gmU+ZmDCa~@}J z?(Z)G<wgL7%-+UTQg=srObb&QTo6-yj@K$qKq3GDg9}%VWq<N}qD~a9>|S`qidq0+ zKx0kxlnuZN*6WY$W@ctIKnVoc)uj@GZi!PMZwQBSGz>^%(s!R;@?;Z>np>yAgzr(E zm#f#G-(SbwCzYUXj%``Qt3ubAVKC#z##^kvE~lv2?TjK}5R>bAciZ+nQo?zG5McKQ zm!+TU7j-gd9JDbL9szolJpDrsPsR)oHy~KlLx33Yd}r6&7GEJ?Y5!9pX3(lTcDp5I zF@Q3VOTrn%-vMFUK3q(z5!8q>YQ8S|?BMXQ_mi<~s@F0bgWrK6^otjUG$-&xQax$` zs;PR8lg>da`m-^a2D>wPZqCHW*cj-GqkjXs<>hGnQfq5#iPx?bva--gFgm`Bz=>CZ zO*DS$0Xt(ZdHlLNs6}F6H_(ZDy#@Zd2|#lPcCXnmv6}R%vjXM;NW;a@ML6a4tDNS4 z|I8#1_xdvhXFnYp()!=;ao4Q$l&Pm$)=s%lr>Zf@zP=rp$7@5HUq$+9Ew8A_FJE5X z8<GjT63-j}@>B)aQtA4-Aq_4xlrm;t==|9x?a_r^8E{9Wg+uFWa7PGA1?}Q`q!@x` zZwYEMMQ+EU7=)dg<NnZ#d0Z-H@<kpxbk;w=dtbn(>B^^tgix1OjBD#8&sV08*5Tmb z+_aw%<JPPF27=^bs)0<cbgDW=aA4pToHxw4j3QVum*rof4B}otVAmU!GABO;m3c0u ztY4w^yZ-%wr#!(VO+Aw43=H)yOK2d$6yUP_so<uhB8~s%j5+krqw5VNjTNww;W8JY zm}YN09y))>fk6@Vku%%V{ZM%DQG|duG2zR;?4tTYE!Qh7eOHxJ!ma`6ye=E@5QNYZ zoa<<T!^@ZgiHcDD{En7b;lFIL@$_O=kRN1X`FGZJZp!kAwnd7%P4v(85Z2!FIavuv z%zd&_;&XVH*&odv2V??>U}5+@X=$BriJtC-@6zL=%HAEcF?Q>?QqW-9ob2zS9>kp_ zl-qKM>^qRQ$I7~4vVFgW&54$IoxZbfWW-rSFFx2iJ++z}TJ;L=GKTrHk_JVC8wO5R zJO-qW;iFgX0F^Pf9gcS1Hl?|A#aftzPI54csZ;OvP4_3y?lcG(kUpH;i%4(!hMuW} zh65Ee6AB{;1KqJ(0D~oj-|-5^Xb3wM4%_6M2){ddlKVNyd66Bf>|C4=r-L{%Q9Jla z(Yi<SaLe3sAQ!sj4nPU$rUn8@y0q}ol>zA&WB5a3uP-h<_56HUuzTv{aH;d%E}B`P z{Q7NI@hJk6btXf4zx5wqw}kke1{<lOW~8pLhJ{e@(~D@3(Z{eI?<=wVqdV%9%D=eK z`YCKizW%hFmESz#+U?u1N%n;(CR5M)-Q8a~o_Rx>!5@p1<O&G1(dgta*>a8_p#Wzj zATY1$s<n~3t<ijERaJZ4gG$`W#PJ&SS#U?7l0qH9>4=L>H|y#&AIOcZ=x=+o^YHV( z@`H43ES&f#qZ?vl(Ne<E8p6(YZk=WdO^cO6|7hK=u`<Hn<otmpBoF8C5Xd0SO(Sd| z_nQtvPT1({hP~*H{e*;Cl0K1|3-_C^K)DE!yGhAFY-kw4_1s}$ES%2zFq+thL@JU& zrPOBF<Uu@l$`YZz{$QDxV;h<#YKBGs;#9rw@oz9=Qi}u=74xBcOJ0LzAx@UhPgZJl zvO6?7IiAOQ%W}iwCW)la%TRJ}(X#+Cr~mnG11l?bM@L7``Uw;JflqKqt%NIlgbBoy zhf6-3?0+%N(Igjfu?Q-%=Z|bQ(;XtCj`#IB)YauwrJLix$k(eAnbuO^LC?6Z_LPod zi=pVitnPoyb$la5<jJmu%|Ia6>9KZnUAVk~pE~s`3hF~;6v&R2>McNMWTXcUoz(*_ zHux74o(|wq#U!r|&-Hw-E@rNg2zZ>GOY%PCFKO-L>q&csi)%dGN;E}kaQV{1_dBcG z)x|Q+R}i%l8ekdaJJ;0l-t_o@aw)Ng7zY%UML#-Ao#&U0qGqIhp;v_Egj~2l9Mqj+ z_~UBF{*=Ms>T|;sb925}96c;MEH`7NGpU_bX?%XfU~G9KT5)$2^kQg3w3p>1iEOZ! zF(2lTg)`dI`cXzTvh`e?Y7Qm$1jSnC-~C%tewRO~3qkLrb~UdN)#&9*?8c_)jW0^I zhgpoV9NOKY-c%DFO|Jd-NgeCNdN5W8i_$n|fp$?lnsMBcU2LO=@(hv;87=KaDNy3& zR8;T`<{v1uY>d3@)3WVcNv%iENC1?S*#35%PQqs>_R}VO4!_Iqhw&4SrJF}9y*NGt zr*_!s5(H099%Kqc4?=J~kPo{&fv^y7kQTyUy}AyX{h*%rYvZ<vd5#bO8k>y-JPsRm z`(ywUi@ZZaJVe!P1LNMk`wr6j(qIuIz>Q4MXIICnV(=J!ew4?=@WWlOiESsN3N=1G z_g=3G2GHiTnBvX{vI2k{nY(wN-y2L5a(vFL5Qkj(XOX^S{pp@IE-~FV&^>o9M~8$s zl*zoR8y7&%t>``#7xRO+Q)&KZUjc{xRN&bH?)T`6->KqaW%bAnbgCg=_^P{;%P;#c zr>z<CF+<>zu*6){*VkX}&qGW+1hE1(Oox)M1ArXbhU4~}yxaO%J0ue9!Idn~t}ug6 z0=D3HX34i4|Ao{jKx)LHj|d65m!B7K?YQ8_YF7t8;R~?7z?Q%ea$TnI-e1oI6Lna^ zeD=PAcjB1z{anU0Im74;>>pNeqC1lWBjMsBEVxM}hl|;}j(&cAh@cJivA5dQTK(k| z6+o4Yf5Jk(Ra0uMjqFw275xvc8q{^CcV6gp{7q|CYFcKmqqDbzDj@n`Z1ju#VFs_G zCZ>fe_<rT~lidIVg}wgVKz#R^mRE1tpM*6uNELoL^U^A}X$Mi`fvI&*z+FX<yeUj0 z*0D>>x-K(#{l2E0Dkcw>8DeVWs28Q)+YgTnKHjLPT~sW4B#j@@LEbQPA9;`Ye`q}@ zou~ki`bs>ujNS>@zFRvSenM&8|NI%2;g(Tb-fOgK@n>o%PuFG&>%2>yry4Mj1qB?5 z@_??EDCWrr`=EQzXQj4r_2{(jn441*^Vzf1P+jS5S;__-gxseZ6;;EPSMn^tdAnut zBLJ{qu3Apx`PsfCc(b<qJ}Wws-dy^(230aFdi5I6=)eF`@N6T1b};(>Aw_&s@4eOb zn}=At$K-5wh-gT`tFo5$37o$Avm@)#6`<dxZfjarzy66@DT~<K(yrnw>J1HHMN9)W zgZ08f-72R*<NzqAh}?wZ^D+J0Z1#S~$Ja_pip0!u5$oI~4j;;W$=UEfUjQ{QpvqbB z_3z&nxFignpdX_kjgx&ZnB8RTYhRRS5WR?0e{NhHk8o&Y1yioo5vOV;B4(*JB!lZ? z)B5!5Xl1Woi!G8Ju;_gbP~y>tyEv)7dj#m~=FY1c3plnBo&0}x1HZnYfAx5@K9s@_ z0k-%F{NDTbr8S<;x~&twz}2kB!!uW&066OjzCW#)2QOq7#@t42L3Ol?<E)a@-yv*j zq&xPcQ?iiBm;w%+ENIUFo7UA7h`(O@;2<A1fo6=<%1X{-E&@(EO}15F;h7I&H@CJ7 zPY$+h#ww%y&vKGBJc9<L`%}Y1b3-WXJe5(Ce#f>8T}kHqr%)>#AsJFTp(ov(_cB<! zTrp3Re`iapjI*t&(MgZZhQ{oEgqU^Sl+wFfp>Q0wTd&wpEiNub|GlEgci~4*7^R{b zdu;DD=vKQJ!bx1Ou7d;xIRmk5gp>&rr<Tbf1G%wq>du2{p(*7EdIICw)>|kzgO+_+ zuh$687ikwLjFm%kk#)H|R*kQb6b`wawn4#T%FlZo&Kv=T4Vv#cOU*antiIti*9^9w zohd0oYq@Vbm>dX*jj2Zc`4#7ip7BC7H&twtf=*_Pre}T{(O(`Ku)d-SG^u$v*y|Y- z!Qwa|?K&mZ;SSJC1_AO>=&SIZ#-%u>zMR(|qc`~<G8TV%r`}Y7HbT-G85fG5ZgM`h zX29CRLex0-far2(XRWTJKCyeGTMQ!?VF!g=APK%{tf-iBBsryI!1D0+sI#+nS6+&U zbZrt6#Rwb&T?C2ipz6FZZehe3@El^@Hl~V7rfkRNWo8ak*Yga7s@hxc>kUpv%F1mJ zix3ANfVKD4yQEPk3i9&P!{%w3@b_ZJtEGymCj=zoZ+C86wQ`~(z71a5gu>bmBz)(j zzTL!VeXL4Z(tUfbeRZ@V5~4d!t3x*teFP57o&ELkH9da66OZWV=w!b*(Bnb6MnR7- z0ZM@aVa!3#E5|Zo@E_XJ5FvK`iw1Y^Vv4%0e%m=_@xG$Ja23cdVts%v&<iH>yIYox z(9%Gy{V<Nbxl;6H0mM*cNd@WVK9Ki7&@+aO06O*K%m<Vb$E*I~bi<;9*46``IVpOd z6O*<p0ZjBeJ*wg(4yG4!2>AT@*4?{>0wP3^hXaHAnz%SUxb5j9i`^;4z??wsOVW#L z=SQQ7ZzhRPu6q3b@qtsL7!@G8Uikv<b9*uEw$oVKpj9{B?~(qxtx&nGH;dQ~GtHre z;Z25HTU%<mYN650Mde}_2nm_sxg+W?Fe1f871-49J)j}^ssyv6Wi+H;nr@7ThpHE7 ztrrzTgeHQg{@^*HRxGA^6N4%)VC%Fx@;G~pIzEJgN2ZWS(aFgvYmTJ2PFYE*%jE?4 z2@B}V(98ym;cN6#lVY46n~(Exad$h7#VW-V=~Ug-(V_eL^(#v3a4yDWZ6r2`=*mcU zdyhmt&Z8EJsK%mi1(@7Va@FFbdyYmXCnw!j5jzn~A_ym3NsIuN(`-|&c2+XVGw9Vh zp?sw~EFxk7h+j4s8qgp~i#uO;C!BmOs|heMwV$__#>U3Gx7f{V@n+uJfp^%Bk`+C{ z47vdv`zodY&`yM0{T@EN1P}T;7Z;JFq{0C_F7P5B*ZX*mdO|(E2I3yb>890ZpV}DG zLB0WL{@VH1S8|_9O$mg%EBFaJl&>)QUAS|{vnZEK>vPC-Q*g|iH&TK4ltUn(R!n$V zmoI9$V=PThMz5MGDz6os?3z$MmJUk=x}mGf+c)$dea2PkzcUxm%<%&tw*#jq&Jsfo zOya|{;|+e@(E@G339l6{MA`6Z<3_hURu;7|`2*Um|61o*13;o}$TaZ#pZTERB?90{ z7+k?DZ>x^9ciSP$R_;1k9m`E%iOCG3rOKK%AhnNO#QyPZ{hX8G;eY2eJVBih26O-( zU^&-=P)N?`R5>9}1i}=YgLpVNk6=%$1akqx)pPsd@Hw{}bV}%!axN~nBqSt|r9$YF zz`Lj<ka>T`jV4RT<|SyCTPO(q{XhqZx52h#gB37^3N3ap^#TM>UZ*)ZNCx3b9q+9r z3OS;uFOOb|5-aYmc=f8I{&^g|Y@QWJ8fX!bcOtIK#bBR+qQBwd6R|qGVnNy0yCa=( zaFZ72$#)L)|KWCsHHi2V)2Uz<3>1`3zT%OPNQDej3Zy+$KN+?7v?Rp5zQRQy;Y3cG z14)bucBi}m&|kVlCJW7!7K`dO>htn!)blmI{_ah;-FQ-<6$=5)p@MeCIp6-`5D$_k zDCZCGzWwh*_crF*I|(G6;-o9=dbUpY(@@1u^AC>xEX|e+l^Pd?St{X|;cGP4%vWIw zInGFry2*InwD@t=u6ij;OdnW=-N2z67GC4GC}H*f!V#j+CCvUjwVTh!eZP;l>rs|I z<$}T;9f|D#RSu}h$oervh~^V@&Z_cf(nBaH2lRaQXd7JK6BRT5(^|7})&LTI>8jow z+w`=<T!?Am<VTsfjhi<goJlvo*8bYk(((v`jqc;n_PCrzM7p$|c(2|R^4=Ta2*x-+ z-XE+B7zV|^-`H_<_FJwY>6Qf2UbcB;%asX8f|(UcYP?VnFDGF55UF)NR=EQzDw0cS z?3W=Q6nI!J;Mtvk?Y4}4+d2#Nh_-tbmkuMf(S*-3wg4y?0vd;ICUMnm{u|(NmXypc z=xg{nH|&+x6ilL8Vj}vU;8M)=>dNYR7YAB30f<nPQA?EJn6+@V>&QYEE+Scj#m~fI z#tP8*y;Z+IUrOGSa<nQ%r7bBKf!UhDjztA?^X2MA#@ZiKX6NG>&D$a}QDcs6^qnc9 z@$cVXuKIiFA>`6`*|Pg9_quy5X#}(Dyqxv(Khb&w(2E*D{%LltVsfEsF}10!jW6UQ z@(I5zE@(*O)4LPT+6_nV){T`bzlfFM1fAOa<e=cQ-6>|!^z0v<d!$i~NZgQ3ZA>&g z>C|(8rDvd^kCU+B9cjH%DA|0-EALcw{W6=2nweqqDg5SzdXMtr<|W>pv!VlaQ};3L ze*Ht5HMTGPy&t5n9bXdnpMV!rjRmyXeTAQFTMd@4nmMlVuD8C9dthjXzP?5>Tz`O8 zOvEg{pb?OnmNt8?JRnhnR#ls`zA(fa?&BL8V)AyBms=yqu`RMkWbWN4Z(+N1MBQPh znMUO%nwNrxFdx@Ul+xdg%NWeTbTJYPm1LpbSVsUNV&TUF+HpnU_X=r)9hI^=?|qB- zvvB;UkDg9c;8IBIw*BnjGuh-bTE6bvzh*9xvgHGJ+FKdy`TOQN*OA`z#6=vf{bhP_ z5ohL45T@9UA1}@ywlRY?UAs53B6ay8p2mgGJv%)Z5v-+PkzMO?M%4u_pu#*Nh6(#~ zzb^H4>$<PiN||eY6Wjgu*ym5L?0J1P$pO#9oA#1YxWZkx0sLB6VO>WD1{Lv$Z3gTN z>W)sd3=T&75P=Gx`uMydJI(JMI4Rr1=A}8BI|PbZVcmERL~B@_=)e>JLTqXf4|3Dh zNj6?&W@g?3`E*!M&~7XNlr9K*G%2RpC2}TA;^K|q!P1KYjaL0fLw!etLqcNT8L@34 z)dtol3CN}~YaZYL1>`wjupa32(T!!F`c3%ertvS#`_lN5f8Us|bFsa>TVK5__jDhk zzEB;I2ru~OFIEa`A|T>t;%(<4L?>*N*i>N=HGkY5Q*0Dy>^N8UoDDrAkAN<GBJY|2 zp_!4~lzxzzJr1UWH6i&70BhF>zKmMeHRI3S*E;`s%&K6@2X4@TB1^~dyJJ?g%rih_ zs#CD!Ul<s?Iqloi>SU-V{m;cA!eHK?`vl=$kf+&%n9E|q$*}Pn{{_v(3nD+w0SfXu zf;0?&=7%QgyoKLsC;5|Zu|te%ZF0R5t2@<oT`Q)2+mvT3EHrc)%FZpzpD)2r3(QXS zV%OK!7)iRWkr!9)PagHLW&U0R`2UCn;<_@hH5(~uww$sL*3%^*_5jgnCH=&}mvY~o zmC3l!-t&gXWO8cQMqu{XUf|$hHqw|ckp_YT2p4m&HRD$+B#dFvq)JgMu#(4hUgigO zLQ{1MLs3!D1iV=~5tj=A5b(S#XlLU+X(JE=4l|JIL(mou!Cr&x_Dha?m!i;(Hsz~B z(>B}tU-2$cye<1$3Zgdn0g5Riv7qLJLf8e;>>ejuZMtOk($agP;fhIFb1Kqd4DrMm z{_sNhoDd|`>ajJW*v_cI9sn7N_egL8Y?sb7$;sGA>L|2HM_gySnjI{L&X=g`{DK|= zNz+O8-M!^&zG8br#wXC;I}^4@uLw5Iv=lq0&nKT0KV>g^EeN<t42|~RrzYLH6>o<# z2+73=I^jmRj~v~a^6{OI>FG=3HG)K!ZyA6?QRr2xZNVW#y01}7gG=PJ{!Vc6rQ>Yj z(=jyK;9!%hxs3$Z2t)Gw+e4DGvmV&H{r-~!xu!XExVR$a;yU+%tVevTGwH8B5}bYN z0`NUWAvnO;*tlvs=!%o#*<YIxNfs$8hI26TpczaAv~#Vm2x{rMFTXu5x26Rp^n`!X z1Iw(dgs|pam*trIB=(e~l!(Li;*gAt0VZbC`t{$BD)%Qk+wz+#s<_6!6{uR$s8@XK zoA?6Ns`3^Fr)CLL1Qmrs9A{!tzXw{?0gay8YMbs%%xjzQ>L@X8%}x_3zdTSq8(dfK zwb$J8ReWD+>8Cx){@nY|H3N}{-8sj9Hqwrl$7kE<YtqseLqjNVxl^z-vX$upm+*ka z;4B7hItFAWp}_|qLMS9)7h`i8Z=1U=gwKo1E;P^oQs-eEFu5swNc;X~G0g#EH<Lf@ z2EQ3tC)o4_Y73bk*}x*#d53b_m`DMKCe?dgxO>F%HzH^t{~M@m6&Q}XhaRMk{43wb z$G~~#)JZtTB=q*(Wmo&QzKT8dC7?`)=E5ot?i|A#dfH(7wNFpk*O+q#TG&P~Am~FD z&y6XssF<Pu#Sd{dN=!SP+UVVXfM^d~pb0iV5^NeSHbiWn_aLC>>r_QM%_kqL*JN9g zhJ1T+Mey!ocen2BHyzQ^;pmqII+pTuv$I9w#(2CJ4hRzP2pzdYbbyy?YT87)Qu3NG z6qd6DT44gBd#$jD!Ugvk4kWKxCk-*ZNHfIbG^?CUvz!T=-a~>LDZhadhvZS6+k8D; zQB`hWqw7j#Db=`1yr)gTti{d1a1B^VAwg4D5lw1w#18Lb)RvQ$g~&ac`icL(KbAus z3KnBXc=#MJw`rJhdCRT)4x-hN;+BJO4x464Mjqn|6@>0}V&n0z&P}j0A3x=A)L{3G zI<K!M>A2(|vxxQ0SZBDU%JkJvfeF;ZD-}!uEumymQF%@)q47!IzO8C!nkaFqW09Z` zJ0Mu&=}~o|b3l;2Omk9|3*Z9^ds_ANg}}ONbNOuw2J&ZjpG^h?e`4{E<q+M=hqm0g z_A2}(vjS&qeK?3}$tRk}m#eMzFF{Y|gx&<PJ$#L)XIdP_1IFFI?Nc=2x!v^;;?#~S zBYA&)wX3X@?#s)}5j+RHO)dvk2iNLvc#Kk|W)5xiRg!tugPr}Yn-0fK6~b#fdCQ<s z$$B#UZTFy?gA(a@x;tntvJj3W?dP``(ytjrt(uNqY_<ua<-dCE+Of0qJ8PKD=*#l` z1|wZEUPl5`L3BlU9lW3GaDR4puap*U*6rzCMuUyO2`mTrjfUOSV*GqU2!$?W!8XAQ z80OX~R_k#>Qf69vBgaVI7;<I_@ZqQ4CR8ka%{JNvCbfPew-kPw@tID>G1r(6A8X1+ zc1uLvBJ4PA4S!b5%e8kY%Bb}X9dLCTC_TK855O0pAg$0Y@8q@W=Oa`&GCs_G(&02V zamey`VW;RcS6B3SWiVgU7#jzNHsDdN8ry6WiR7&~Zl#`WAMZhP>byGVwb1ZRVYd}^ zlZ@GYXzBPmBX~b(VT)ho=8j0RtIm0U_fiz$DpBCb%541O-OgOACqh0nd~UuEKqH7% z*P8|{5br$<wjg;^F#RxoR1_Igegh6-$}MTD`MIYk#DgQAB>-$@7RcV<5Kzr|ts^cS zOhp_*sn(@^9x``FFX(QCO?w@^qL0|giSt4W4ZnH!XJ-HF_rs?E7i5A8TfXn>TWU`k zLvP;Q6|r|nNoqg;Q-C+f&_Jj-iwRT$u=O3cW@OYQ^rW|MRG4trh&$64gmG!dRTX>( z?YB$UeH`+j90|hCEO3;$i>WwA7S*L|pFmOo84UuK*%7_DHeM62EPN6(#!JYpQz-=j z5unVTzO^Bh58)fa^|}%qBWU?0Ak;pXC@~(nxfu;Z2d#U4cghVu?cH{1c}_+e^^pm- z01SFW#c^pn%`{&erIxa?vdS_Z^h*)*jE0ONv{R4mc2?cJ7a?*J^QJd$RV*jAP)(Ul zork<ik~%+&d)f0{WMgw=Io)-_u+@3IrMkzy>s?}`{^$)Y=?tOv!KV(umdk~GVA$$A zN6{rZx=wNxF~jp`EaYI2B1Ren6IhEP_h@!8DC`Z!XS#+byaup|3+uRv5>O|2a(H&w zREB(I-9LMJCVrHbo}O$g9G`@R2s$q5HN3uj8x89O!yMJiaX88s-+K^vx9ivuMU@gy zx1wG=D>$$ByO2Qqwp5p#*77jb^L%o3cvMnDT6()X^Ocl<{e(__v?Qs0^x%gdh#}P+ z{m<@t-DQhs%!eE#oVaJQt#c%tPXx*gz)Ke5w#Dj$JUwtq6%P*~m{H0CgSfD^E~HXZ z&wE946q)fOf|-KL4jjdoE_Cmmgj}dx6KFG1C4fxMp!3ot2jK2E39-P<2j2f18uIk? z^ewxwp(l}>LPQt}j}}5bZCRC*-$P`ne6jx@(w3aZg0tVg@c8<!RrqZEp#xjpeg+b~ zkojv3r@c2`?JDelCI&JMtC(0a2sAf=(s!qbE`Dzdl_N%@IbU&#xCh;N!8!Tjg>FTO zfVg-H$j8CRh|0<6)gOb@5@$jbvUt>R%B8sAk&LxuQZ|G`$5Mh3GRT=CN!tWPmXni{ z-;joc?>gppymoL<dF$K-lBAh8N-7Gp?FdBZD5<I2KwU$64GNNRX3+f0_gNJK1ScF$ z{3Imar26f;u8qXasr>zrCfkeepMc{A6NU!B%TC~-Wn^Rkc=|^*6CZ*anb6RF<*E0i zT$=;^k<BWfOLwea&lFxtcts!_4jTB%ni40Q_Yj3&k@hksx1LRoh{)`?`-l5P$U<#l zSHw?2Y6?M$hX4nM%Wbdld^th1d601V5!Ci)x&IXmI+ogvi?|1)XDGKz3X-lz$HdTr zH3?r~9$xmVXfpaDx!>&33L}fSxU=BzTFx;ce+FFQ&zvlfaYfJQaqHsP^gx>W5OgI( zcp3iU|HbyW5?Za}(H~0~_c#Dvh|HCMOOmdb$d`ISAw$DS-8&YUeuPqjb?MTkSTONv z@M0>*MU(OBu4;vly4If{&lZn8{R>aqqnQaA#1cd|YGXhKhPU`X1i=Xzgn9~61C)oM z&z9tnN6mf^%YFiPda@{a-o88We;WbwgO)MoI;eZgej*tI3%A(3he7=;0gcmY69mFW zQ^6HMJ>7><@wVf1Hw_w45KNOd;0g_W3~KIv1VbQLT-6QD)oq3Rzg9J+n_m!8KTuHM zA1Kndhgq%f78A8qy?{HA*^T?K#a~0#r}<#LW+VLV+qX7F<0YoAdLhe`4%w6{3G>F6 z7w<!Y>@_@RS|Ap6q-119h>6<a`Q>E$RqtoZ$Hq|kS|QVItoZI$y9MaqQ3(mZH+$Z` zd1C;MNdv^(9S$y&y8l-j@$K4v0>s!u>)reyG?M)SeU=zQWX6Ptl~pD67D$I?5T765 z4bR)}T2CD)Vt>LnDOe*!Uj-S>v5w4p4Im^VgS6$>Ccs1P1Fx>Jsma#2aTP(dC^-d% zj;-4N$FQNx#)NoZ6%@E|nw!syi+NE<mIVpq>-(R|fF!qbWE@6u!<)lrc80Spp1VwK z@@T(;^WdAEcE#98p3gZDJbID05M~F(xe>mEbBgu_hXq1dN(iIH4Gy&aJU>+F&U-F- zs<v|`EiKlbd2tHFTnG+j!ywz3bC38K#K4I4li=;hU>a;T0Iv<tupkGAJX%pI7^FlZ z^<G|HSp$3!Z7DuI0<jdfcrY>#x%{LJEetx#@w%B{Q8zE=#R-@wpsZJj*Q=2&GS$;l zUVEads!AhhM+ZJ80}A|&X&CForc2`&@)Zfc;(q(X&TbjgOQod7e;&n2-!>MbT_lEK zFLQwDK48TzL9{e=BvZ%#bQh6C2X`R<n4^|M#;l;1;!p9vr5ZI0hpSyVBE_~ZK%;=& zqYCFddzPGykWP16)KAQ14d4G1NAi4iNck#6s}b%7jx2exT4%Om;@zd6nHX)0gsH;D zp*thOzMkV|u(&!s@UWoaZ+ss$<@jHdjowP&nTCdhT;=17j@S1~gn|nW6@hy&`;1e> z(`g_P+olD@M05!|hd|pA!x+{dvFRYW10-H0%UQ%F;Ct-!>hM_F`Gof{&vNlPEm%!B z9N>(@pp7n>6lBAaMZWwmc}JU~iy*5*GzNy9O1<~(>JMfJm;+Rqgln7xXKNdhgEa5m zz55S58c35FgZc@&+Vu`$0M6G;W!(-A_OuU{Ie-+cPVP4|Ky<)JAIt%OWI~S@&ubyk zhVV#bU2+PFX4oCDXJOWw%+JS13`Pat^5{$SRe?(Vb#O2qW^9Hc`t#HyVN8QV-|-a% zh57M{X)-kpRh)495m0T5vC2=j=BY469R(ulT&2uxrDNRkM9Zrx$>W@CPWi<$@H3_N zoXKxfLvqh~yC3fdA|7R$kDc`00oc^q(-R4r<8XwGj7%@U4NiYYF5T+4`;-2XeV^dK zkC)GAWc<@Uq~Fu)@2^|4BMVuwqPjg?NShfqhj`}eldKFj4HSH}nOHSmVYS#ej1-$0 za-N+nT^BIY{pzzOcHcVl=ZGxCvBx@_Y|V9M$_DlLz4lfuA!R*^(1vgTGMB;pyAAVq zU~!s)!Ez<YkOm}Ueo#?aj9~=#K5)~Uwj;OFWnNJs6Mb}kaYv{JhB0wwH1_qDG_f3u z6Z$-}gCAvrNa^WqZ`llWqs~sf*gHH2{GRVg-xt~M40l}oMgF7r>?Oo4{p}_RhUj6k zK>@^P4{#_wVdK*ZJ263n#^S8I05%DZhL%QW!p#@ZgMMbKfIl+Q3?_*3!-o&=1o1?h zBYA9_#9(vX@JEHo#)D^DZHWK;_3I1mXWI=%2KVE3obp;-f1{q{iR^WTJ5!C9$wE#= zsK<l`r{eJ<+<=qqCEXwYrD?OPtl3KIwCG{|woVc^kCjT~Q;B(dYh!Y-0+ZmGL5+HX zVw|Vv&6~qK-5S-u&M-;xM#}j0zj)jvybPup>J`;|y%kI=Y`10F8Z`XOG6l)t1VVc{ z^*+KKVv?o9;O6iFOqqh*oXKRpZz1uWjG^`djL+h6a}KQS?K@BmuZf>NnY!(CoSydS za=h+n+}Y`LAhp4FisPO-w+d4CwMTGRx}7aCK@E|COso`$F>#=Defw-xi6V$Z%S2os zEylpcZCvV8*VlGCt4v8qVAH7`3c5%xBD6b}d_=6AN?4%X6hfMqrdi{*SMz$V{Q0n! z>*|zF2rLL5ka;1<#)0f{Gf@$#k6<BDHt|}3U<wM@M_@4rXWC?y^Ksf56?Q40QmEPp zkD$Dk)igj@`0<06ic<)72s0`4y~W>S!-t*h6;@+)`;DRGmuM|%t6zsV?{!+we||pP zD;_2z-F#a|3u6q>53n;}7QL{*Fcb_f@Iv)UA>fipoGD0>h#mb-N2X-pF%E;+!&Xj5 zPxU%fv;9|2lMqBK(|p~czP`9_yCK|n??<9lGJlQt%iu-ZNFxZ4bHzVJfxrR-iyv(X z*dX-*VpS)&XJ(>{;4_;*M+NgG5YBz|_7+4xZ+n(b9`hsy1Z2Y`aXm7=1Q~URr^hIo z3ys|SG2UPK7wUe>g}`}bFa$;C=H@uH<&dHP!xj)EwP~~aa_WZEy9jzwokNc(BaX#6 zTb>_b&CQXh4u=h>aF#G+6?d$`;c(k}#2?JrDtr2P{ej%+hG%gbm-i@1w>Uxf!o$Wk zp&clAl1GI?27x;_E0lMa`-OcDijha2Zz}0?#Qy_F?%ItTA)Y%6!(K)UE@{QZdEQ%k z?kV#JIgdx;)uo%Yp_3r4-f)>UH3UFD0M<eMD$u5G;vM?pFG<g*ctZs8(e?XeR9gar zEcVZy6+lMZ3>YL5n=ULhbFOkQn@9ydFUd`h2_c+Oc?E^prR=l?7*N1`4^A!0XRC!; zr_uo<03ztAU^oruzCcfO{sl&_qXq4l+Uh(o%59HBA==c~@XTQ`g&Fo7*QC$R>!x-H zWrn~Lgj_4~3lI>vkq2x8gTa{q10fXs4x-e~kaHnrVCaT<q8IHZAA!vk`qaur8IO+? zIGcBH)24`O>zP*&X#*w(CkGP&Bd-M~is``xEy-PixhD$XqX2)6!O}r42~y)jn3_Ye zC?-fjhCv7@fd9!B9{dsm)a%V5<H#!51u|i-))m%Q#LO$GH$X#?hjwJM_V(jPvXJmw zP6brZp-tLsy)n#_@h-#(`y*ps;9Mb7-w?I^BDY$QR~OJi4<WwHo{4pa0STYpUfi99 z)5xgnve9qfzKm3%{Ml!Y757x3%u4eqUS1$vPY8g$f#V14hhs1Z)8Sy>-QAnh=OAFG zv%Rsx-5@r#Y~{7`tw?mFJfTaSI?2Knax_~&W{?1;v35<0*~-Ai=FU#~-9YyM#&6)w z_S1{EhPV4|g_IH~<|ij6!C;I`q{3(e3asf*E=#JwMFc@nKw6z%R4>>@f|s@)>{Yn& zqgI|r9Kn<h7&szghbGoF`+P9SQ>M1USMC-X^3AWKY%D0ez?r=0)Yv*D>GwKIFFa#E zZY2~bOC7M1Q0n|qt1Ny`wW@ARupj$+MxyQoRPZK>sOC}C9xaP=Y8veeB%0*2FWe60 zPvVj{cJp^3YT}y{GYik8{t)y6vUc3CV&|tD{)mdawYxiyIM1N<f<IcbO^R-L4r_CE z$|WCb=z}4Kp@W^;$h*hunoCk&FZ_gnzce*IjmfTq>X_hh{-C+KEd_A&7639wyDL!5 zkS@|))#E_DWi>s`>+gpdDJc=1!L*_l;-*$qA41FTV?$ZJnir_9sZOXZIMS{{wm38< z-PS`6!c;p@UL+HhBI5e?Bvfb$pbx}f;+UAbVAdpfb8}F$!jAvbr>}`Y<^}zCE320W zzaM!m|H|vHJz;X}etHFl2Q8c|F*SgMXz!SlezJ$XKIXMj06J*`Am>sT`;O!Cjw#eV zElJU)X0S`FQ0L!48-P3}lExz`pIkO#VeeJgj$LN0UjTVLx!yS-vrs%f0e9t8dN0FN z3gIgxE7!p1UXG;u^~jNn#B1A2m<NaRe-{zFx#Rk#3IcIxnHU9*0A#WW1$hhhoSdA( z+A_H)nBkms+*{RbD8ckQJ2~`$xG6ES|5E@_p59n^TJ`4fA;O|2K3wLxuYqRWs&Zj{ zgWwz>CJ1%L#KN*1ETq44=g!+3j~dr+1DAE{pB=v|`b^G`nU!@1nCcD;TK0bN_lF<< znTev31Kl!TcQpD$IJ()A6{NqJj?9Y&Fi-(_to+Ph_>>t`IA{u>)730**}>V^Hikz= zNW&WdUJh0Oq$MJtVN5SBZZoV7tCF#z(Zh~QlXnZWM<)xeidtcFwZt0IbS4YOz?Mvf zOy2Y7&l4co1h`_yf^OiNF$~y+gUAfOQZYI03})O{GT92DOKf+MA1=8^BHj$s0(ere ztrNw)1)vJ)V#u-pa-2?d{D%;MAk_LkUjZyZ)Y2T#W-9TW+K{{;sg$b7q9PuEiyr{J z0&fijV@#{x4ipeIv1k+wS;qm2DKTj$gH8b^7D{G}xkly^QV70@lt%YQ9YZt1k(88# zVGS5sFai>bOj|&Xs%9Y=W;7kIfpA?l0Fge#UxEHh4@tQ4mTPd)EEsG>#(ki6R;_Gt z!=$*(W9gTf@#AIZuG;5a(-xxSvg>`Hm(q|45wMNIfM_y7$^zP0B82%Mate{!1DM7E z^9ZAu*$){`fuD|e85l^JX!78qL9emEJ%{4cQ-^}rZ;Xwc7>K*qBn%$r{5+SFr<I41 zW7p+h(;#Gw5Fx{l;_*Zn8!!b2`TkOvFaQ!{Bd-s>9n4{&p<&tAAGSV;B>!aH5tke- zpm3W<V8BUFnZv}nzDL+VXt7(9iqCcFXCU-782^M6)inqU!AC&XAOh1Xvmh<akOrod zomL7e5Jb3MIZJ%^?%i9HDq;%<j)TeO=ckO%zBWh(Fk<1>wmiq_0dq^ZHxcK0p+?3> zJ%6ZJ`H&1j26bQ>;TjuT`_nb+iCSR<-KZDosTJxBo8Ac%O80UdOpZT2{v<^`h5wL5 z1BAEF<;*(WoMMhGXi9Z17g&#t=tbdx0bNPt2OHp*rS3*Di2s9_M$UOKRR&yThur$v z54whrZ6fYG+}tZOL=Jw$1_Q^nVa%k-ch*McZT20_rb9WXaw@Gpz)E;9rkzYo!895Y zmxa!gcyQpM-pmxu1BVcwMn@?qKOcU~q02YnmltEx0oeh6lPEcF;(`%hZ0UF#WOims z6od-eTEjmjFRoVoHM-(9%u45d&DXD?(M|<$(;M~2+`y4$Aqfh<(!mE|E$DmAAQU6N znp5er6wNV9;rPI#G86OZzd!gStVgRLCpVR3&wz{j@MH7UtGko#J|$yqiGi#UsmE#O z8tiZWGYbh*+~3pqp;nSsJxyIiX)PYnOZp{)N(mVdd>APF4#)?QlVI^#AjgLcVi$(1 zHych*U!sbuz##(#g8-)0X8`WOFFtJkR1jbK)gJsfyW~q`9#yKzpVljyk?`vfLB3E~ zPIq8J%DeJ2$rPk2;TKTYfN9PGF)xq(5@h%gQj%x;U(Ox-lmx4m9T`3d2TU-7eh2eg zn=pM99T#^W9)Tu|0mJXkeA}u94TrKCqPbf2qE3b4Eacd@&#&`yqpv&Lxn4)3C)&A0 zpE0qpY=a;L6T4sG40Dnk3DVjO6<^yQb0(?t-p?{>2`lvb+`)$NhXL}=^DtO$3X?sk z|Iyl4Mpe1C-7Y{y#a0B7P(UmY1QC$5z`(?yCB*<Fl#oWn07Xy<B}~{V-Hl3zEG&>x z1U9HNNY|N{?)UxPGsYR;IJN&cHZ0b9*7Mx=74w>N-lV)apgeIGRoCAaqop_wtZ1|4 zJDR*ML@6})$_6aM(7x=M!t!7dQ&a=pxd)%o2gTSSjDtXOLq5$y%|*x|#NLH`zFZ}D zpkM@|vkQBPRAib7CfB!@2*{@P<ont3ZFGn;{(b)gbcR?f)<9p0mNEebJ`j^^L#HTm zr=&_HBku6@I4hf@{ml>rIlH)6$UE#%Q?rGU2+wBopvbg@_Oj!dDXW&*KAmsSS=;md zNey!<^O%Wnh+_LN?)r`>g#gCM^8o2!P*Q<gj?wtNNP+GptK6^nzPvBc4Okro9r?eY zTW(1h?#o;}Q?|P3pgifv6K4yvh{D5b>4un7uIAh`fJnBWWLOQt90n<IZd0yMAa#d5 z?0+?CyR9|+=)N#?2%Pnj><9Dtbl;83f?1^GGD=J)gp`@Hew6qulV7Wo(j)BFcwfm2 z0SqGGAmFPU^UbuQO{zy*(BvC{$`>MQ&AMi~I!o9L_4Iiwxjss~^s>i0^Yzuw)Xx9F zYjGya#<Xf$4-}B}%s}#Dm0|JrXiXW4o*R((GA8{@g`xiN=nG#|tZ89h?$35fJowhK zyQo4hO`=`G3Wa3Q=FaV<0WOP%ulSS*d=}W2Qc;j;S0{b;4fn!t<p;3)4$H1#xw}nV z@J+(H!C*69d1WR3&F3};A-DKdMa#H1Ha^nL__gq$NL_nOgK{;e?L3%1%oYN${pjSm zPpAj`OV=NWv#47HqUCD{Hh^{be;6A8Lk4~mhYBwr%1gp$+@;T0A2~b3MVa+dO-iQ# z;Ad_*o2Sv)dk22rYIuDyEOFKoh&v7;o6e4|UuPBkjAe>FYx+}-Sbark+;>3uZEkk{ zyMb1gpUx%Ivr;F`7ONspKHfI#;S!$e6ou>Fw!cbuG;U}#V_4=mt^tbp5D6=|BBASb zU2g8GnzESJHiZ+mq5HUT`8Q^ZvHER$ol*J3){J{oai2>$w_5B5j*l}+7cYpr{nuaV zTK>xG&3Ht07OSS+U!iRpD1l0N3Fnz<p&ZhlcDY$%zjjTFVceT^ap9LON-(XK|M-9z zJfrMib;D^jXyn)4xXWn8)j2_+TzbONc0zJ~&E)JktoIoU-Ag)W1>3byWwFDS@k~W7 zqf$s>K~wVw4|@-56&}uYx~-k5zP2CsN!N$n{_|%0p;VuJ_lejMc$ILA>DaO9v%XJj z3!BW(?>L!%jpzK&r)NU2iluItum%A#TpydY4QdO5sfo(x1h&xfSsWZfpx1)LL5lVH zs_W9Nqpp<7_$;pu`Rb~3;Gcav{PX21Fh<u7?Ztf(_7;?zrr_9Tg0x<QB`61JsCf3T zMbc+JK7@cExI!K@TEvk}Ac_i-ju=?B3CORaK!%X*IS>BwKV%twtWh#2M=0H)cMc5A zXg~+(w)NuItcscsy2=3dXTYROeb1gIn2s1h@kay_Y*4H3l`EHS&<$F}Lhbcw$S$GJ znoeVA#5WIgwd+~R*^i6tP9JLR2l{*^L_XCMgiCJ}n15enb{y*jxxsP&e(xQ97ei@b zX|#B`&*zm@LA1eTq!|CFtr$2<zNp2N$`sLh)bpEJ-_y$_|H9s^()tHX#!1GMkVK{b z?S_l(767jczO}@<O+|6+Z36b&SrA;^P7WQ91p^s{|4VH*^SmVzxa84M8mUv0T^$n| z`9|!R&0hb24Oeb1hYF$0KC!0P>FU)y@!b3mF?s?9!aYlRd)0!reCidJip;)5sk4OV zxPB~opS&R7wUCgIwIgiiu^Jj0o%;4Dj>+<>EI{l>ZSqSUW6@i9^EqrY<`g@b_8`)% z5PmABlj_QGcbW*x1&eO^!`I8ApZWYYgHbrHivsoV5zh2!asLypDd#e}3DSubUWO7b zqvxHSZ`AwhHS+$bwzs%a?)Ov-5;QJ-2M;_CYm67i$1Pc1SyWk$51p`mx~T2c-r%#$ zvi1w4g*xh2b{Q4!Kel8k#{cHnj)=B}V-|^t%^j$6vK;&WLTkJ_Pi%9a|9DG466qOm z#|SfKt&C(^y?^h@a=$M7#QuA-McSn%isG@=z<QS)#p(gme<K7c9kt12cA}slV*+gY zyo2~$-=Fvbn$Gi_&M|ZL>^`fc0yX_R;UWGhP>fb)JK2-}8#2W*iALjx>M=t}fj-(g zr9tX7x#uLc275XyUrYb{Gq9#NVYorGRdP`PgNcv`otJ^Agf@_wncLLZ2@IhGzBsn= z)j8o?YEKVgPhYh=Jzm5D3ja0~PbtlSM0o&N5|?-+CSOaZRHaImY_Q(eCW!p=eMIt# zb0#be142T|;SfY4D7Y>f0GLtuRk@u3)bciv!9{RB!XOM4afyu!6h%RsPWx|X?on44 z#@H0(VYtKTju<bIt2+A1Aog^ki=SU(+mH}^6(f-H&EZeCSBERHCc!FSU*puB693DE z`7I$nrq#??JBz)G+RO!1)M99<ok2YL4&x@NvJJcY29k3n@=QGMOMd|IEOnUaHt7ov z4A~F34u50Ezh36u;d=h3qw~e|erifagsNa)bZ8*$t#a4Wdmvm;ls7S?^CffhX(^xF z(;rVsu+JNx_6n7G@J8bqKtd9o+|1Tl@A-_s3ONqs?~Z3%I|q<nMLUc4$I|-(g;y)T z_F&^$b0S9p?cGLHWTgX>ziTunqsZtw{8^vsZ#~ZUG@nmt?J-jQKVR~FhI|zI6jKZW zXj-wsDd56i!?jZvL;2QWeA}zK2Nd3E65U2dMnTGMO60QP>Ke0tvksT{=d<da-+|L% zI(xLG_SLR$cflb;&x4^!NatrT<(Q!UcO|qiK_T0y;F^_Ui91#@|1Ha1XJ`kw5v@!y zj`&?_0jLX963QHLO@CKqfjv<JE3J_}I70T2SsaYlh}#1$;Hh_yuj~ysJePCAGU>tt zM6JQQj6GpMy8EGJlYkKnLp$lzYgEXmp#s_)PMlCTP?W0V+-oy1-4%A)!zeiIWeB>7 zeGh0tY~&_D4m@TxHF=yV=GK;<kL#pj)3ogOR>?n&78X7d3ou&%)g10ZL-qdS9{v5p zXFeU~;6`q(hq7rNIN)sn&>{e?GH7ZEyo0npsY=k5ILGG9*2_GD7J1=9H6(c5349&$ z_k%Gl<Vw$Bg6Up&M>$UTeDayCe{^8|6NSFVn-FlC*1!)<4;_9Mbb92r3ir!{Fr}eE z_d9N97crg8*PljQj@|FZi~VKxFtXi~J^Gpo&^Fk1RTFSc5JeUotQd0yltM6;yEkJl zPnvX92Xs8G1D~RTN77oy=)2zZa`#T};8{}*vfB&OK-pzpuK}K-_z(u0=P|0*-=pKs zx@zN%W~<39E~ZObH#_6cAKzQ_%BWnQ?e8?%GHa(#iP-hlusPjg-&KyG-g8HbAJOi^ zquV0)Pv^UW$Mw!ka*o;1@tzIXmw%k3cB-hTY`xknk+{tBQs+=*;)bwm*W-nc!AxU# zSo7pGh!*?+@Jbrx&vZU&L_5yz2#aWh&Dn?MP%Lx66d~TUOo!qPx)npoU|RcNrvB26 z!zyYzw1?rnwuooV)Z1$dFOBpw!x@^nu8Q{c)>q4nBpyhc9e_$l;{Cctqjf_wN>8xL z^tXj89|FpQwEF#h3o1UzX$|R8b9f+9;x(5;*)KSKFXt=PR`Yx1@A{+X-c6jxwwhJQ zLym!|c>r))A}D3HvI};5<DGZpOW{&gDvp&T$WLXo8@#z3l|A9?^6k?6gv>HUO@C#9 zMrr>$G8&P$+Wj$IcyA|?`D|yEv2menl{cn5JL<R3GGK{6RuU6ksrAyNTEt_pLYcIr zY@VJ8`ir@P8txRay;c#H@OYxOH_mjglI!iWvKhnq_n2f8-L{5Bl!1@7+4A*g`7#tj z<+~r6t}M20teaz&!Ztb0GPhNs+Ul2njB^C4B3U(jlvT=}fVz=ors}-LMma3^*@jr% zOk`i~h&XF=LZV4lt^4m%GZcB(Y?Ed_WytERvW?JQx}2lEY88e)wDro}d{b6rem!$m z>iaxC-ds1!DDMMJ&|+z3GzZhO;6-Wgo_JKU?S<)>Lr47Pl$KcgZZi*yAoaPDp{9~r zIR`?2)_!qDyX@j6rIgq3UtgnYT}|>V|9gM%Gk+~6v!O_3y^-<I!ao~xc(i>;GbL(C zwPl;m%Yd=kc&zWP&XV63+nmH@wV^(lEAGNkaTES^$s$*(MfMC{Y&RVgkqGN*aFT$X zTTEuxmOA<A@$0YJvEt6H4MAD@MsL;%P5+E{Po%_qpf8u)abNGAs30r9TYW;)(2FGx zKj!A{H!cr~b(or93F^4Vg;FrT{q(|JEVJidjgu&PY4Yt`z>8X!A(p4Cu`J3AWwuFo z0lqDBlH`*x01{+<!DuZF<y0|4<r}Jk{(9P+X^$se%w}1|T3oSE_wmxE_fryMUAqm9 zhU~@j^A>on8hJTqhry5jNWIOpv^IkOAqf17xjuFdd(QaKuGI!gw=X!x+0`J{_=Wt% zs=0%9y+vKlG-r=gj|(P&;~xL~?m2#kBJDcj@51F{3nLGFef;}9g_&5cPbVkG&MCpL ztgan=8L%ggV}P<$CDR;Fe%F_^ETWflKtm$TL=`SJ2#e-L`$X>id1vPj3#?Z92L%<w zb#oZ@((EdADHbpp+8WRw(kTG&QqbRT(W`AuWRi=Y5(61=5XQI;*fwhqJ>HShHb$W$ z?B*eS#T)EsOUc^Nu{GE%4F+V%XFeId_MCnmwkr?w(9ZiA?XhKWcwm?hgKti9<^+W& z8r)-Sojshdwiy5dJM!|_P0VtP-mB)&8iao%$Mya9D-kgT`5yy=$AACN{_V6^|F_NI zU8nP@7y=z-AOKG1MfAP4>m(vqfr+G?OX#9ky1E(XXSw5wms$zq8U=$fOoqc{<F5-M zdD>0>=FcJ{VV(M1!R#~rw#&bIe8+FU%WHw}1S!e<pL`o%k`IC!_z0Pcl*?c&)G6A7 z#{%3C42o?)Y_)P-v)P5jTth}WFg8C6dd!CVGUqDbN(&{nFMbmiGBO@bj#ImK&8$m3 zH>7g8;DJ>56jXY<fucyc>{L~~C{f!~6wI80t17{lz(YS|+MfgV)B1W>_T`mLhOqCE zAPF#JiOgXXbL1>#jCq+>MID$$q$i{aP$5PzKA<IXgNd;bhIl6!hq&h7avl8d$TOOF zjleC}$z@~%cB=BBN4!Pd=J`9doaOpQzQXLQ5cJ)fU?kS$0FqGP1u^k^n;Ryx&9MHH z)^`~=+KiI45M_rCe6E_|v^agig<hyX6@Eg`s0x!U$qCcVC_0)w*!hcXx={M~fC^-~ zWw2Ptmm4)fPF=I;E!;Z{!cB=9oKg+_t}4nhA9$NN30E~02Q$B(L19;jJ9?Lq;Vu>K zwr9_ccW^fr+Onng&dQ}{E1V}GYL_OM6cW6*cA^Rj^`FzMpQsrPEahQc>bvES3p5<j zKi|6I<&^+}1(|V$)?u0u2L8S15YdL5LJIt(!d(Dy5~GnN&3Qx;0h>mO#5jAhk!v+R zhw+b|5`2}{cMO;6;76EVf(Un}I;Qi7gtwTP(iB?Q%H*@h341C6g05P0i%)TBtPIpm z{G0*;2Y{xm0Y|x0)4%|d(6u2b7(b-rQ9gl2Fr}Jo$^iC}Gn&S?rRyu42ao;ZNlnTM zP$Y@rHh-tixa`=-e@HLqtNwmqYHDhW;q~e*p0f-60ok?z@#wS4FYv})GdC9ndAb#p zh-MhrH0O+U39VaKnzxY44F%s5Fx;dj<}-fFFq54cu+WL60FBgB&?PKy#;28*nSp)h zf-Ob%ibR^j+|b58s2~7~8RLF+xJ-fM+N0@Q5Q<NOmM>8JL<E{0^7lbN@&nPxA86u5 z(JxJf!AxZkT?ssdsz&l`m8f>A<y)Bb%DRrj5NTirLV&wNAi8kVkvvP6&hGB;PU*-4 z;jN%>6iUpAI1qlz*LSkPpUZKigN=<1<e8_|&4py8M`BYTJ!$eEjY{4zr_h%DOZjQ% zb|T-gVthy$WfG1Ffbjz-gNFXOQ<L&g3p~QKH*8i~kacVS(7?bmydCIkv)w@(zD^nr z+&MCEU{33p{!8Y$!;yb0KC#^X%T4U>agenn;ck6cLn9c&82r_sNm{`TsqhKls^m2w z#Rp#`eypPYT~P&oPr|40^J_P6nqtO6>$6o|5n2wqWPabwG^^HJHz;Oxpe@LoD`N%M z2H$*bUTjQkmV3)B(^h_nM*=#^(1W{y!x2O!2+qnj-R0)w+=-2saAXj?F$e4^w2_9$ z3YdF4%GNd&1T$ShiOQOq91FfRfncIF87X?p_E-No3<*fi1NBLq3k$gq!<MNw5AgxF zZR$Ub%pOin_P-8h=7J)_3&s71cz6K7XX(fFgV}0Z#kVi*?yo%?yG+q(a1+vv1vyoz z0#&#PH82m@HaJN@63cCFo`esWKVOL=7!M#wJv$ACMVhz2ah*oY`tK+?vn9s`nRvR| zXV{u~5W9%b13k5o=!u+qS(}z?y;vqZ5GBb#t-<lnYcaxBg*&DXC|b+aZt-q}`R<FU zsWgv8D!pkSa5aOtwh4WaA)@>jO3Y?Xv0E?AD1>wZR3V?tu8cAS$E^XeFcEw1E+{_A zJJEqJu-W{x0SKdI<V=S-z9n31IaU5_H#CKMdBIFK%(zq`eD%Tjk_W9EnZX@mEaVbL z=s>=B`3^zo2^gHJnwmjMZZZ<fybPC|=cJPwb||R*KCj^a`V<w~M7yld|N4F?!>;p; zlOk;R5U@Y##*SfHNsbf-H3}}EAzFIBJA}CN7?W0hw=RfNiy_x8Lk}f~a}q!ufS(33 z8s>&BY0bCf6?m91za_wfWJ4IZ#OQ=e<E`<%zI~J?<r4NzN_CY(UDD}1^f!pJAb3W0 zP2jJHq8b@PF0sCfgWO<5JqCo~I2z0#`#A8Xksi$eXQwn%c!9l|RZR5(_?Uvh&03jY zx_7C#zPVF3ra2~{IKw;s4sw<(uLf>aK(?nX*}VXvWPgQSM+9p8a}ujwYw!(c?=(o~ z_~YPwffrmYF)8iMLPB?3^SB=4?i)mWrq&OmUdN7f&_Y|TaOoRh0WwI@2H^hS;~leT z!h<<Bu{$hklMGQ{0Ou+&j(60?AoDMs)R^sU0c$l1e~huSvy(Djm)SosaN%bT<Oc?* z#$bQ%Lkv)!O{V}JY6sX3b=IkY)d2`H^5VcxtuLuVW0^plbQhD=F+M2tMuQfj@A>ap z_@6pgJzN4<WMv@XRe(50hVb>AVb}B^g{?0Ks}30&ZpS+T*LH+)Eni*+3|)a+t0Ias z?cu`}zKZ=Iv@{_DH=+xKZ@bi)k83jQyDj&synp{*Gu>tt@i&1tG`?mRXhHT^v0}w5 z<Bb>JV_s7MOPPARsnuZ)o;^KQfnSHRhrS45P66d$?c_V!=cZ1U=z-zS-VF7UVN0>! zyoRs1BBXCy^3S-V`Vtw@3KNw!H^yp{s5M)jfJZ}pH>LY}@KS>p?SS~VK`Z|)obNa^ z*DaNcKQ={lywsv2+|ME+`T#)K0Gy;BQDqH6U-0wMmbSJcIz$C+I63730fOExxzRH~ zln_!JC$STiwe2|j0ay4LFU^y%iSCNl&*oW^<rYCST%c{`aKb)b`RE!KJnouK;(FN| z!O19+n3LHClhoOWyzs*N_PlcTJ)C1<1xFY7Oa@7s&IU(2$;lEW3MmO0CtL<NQK<6Y zIBX+jTsVi+TZpU;gLB%$pNC8yx)%^oh$we}-2W;cU8*xG@dc~F8g`ZBM1xB`Quz&) z>4Lax4^X_{KsvNRoF@VcJoFPo7Q<!}^QKckuHj@Z_l0N`WUE8sNRUl6BEWLrY`#lV zlN+(6O!7q8mJwIL-@5h<7o0)+go(O1!2?(Xhyi3;B=vTBS=z2fDA-b}L6t2(-wik- z9uxb)u7<sEtiWo1jE$fGCnaF$`OlfB5x^K^sR$j&#_Im7NS*lVr=a%?SlLK^0f?M& zr$30mJyYGtNRUSLU{=x}z~18Uqit8zt2NyUDTjj205-=(+(j~E#YmxI<5DmNAt=D^ zlV*oys+=4J8>$7sP-yvOcIXJ{OmKmRQ1zs4cn>>jc?a~;#P|9~453v)%wQx`T78co zy0G+KmHXz+8-k)V;|y3Zjy<{cs*wH;h^b{jKm3MPmbK%!XjEP<@>6-QJ~Cw!YN2M} za3VN<2xEmsD520^5U-g|i9tPDqn`;2ozbCgCS;n#!^O63Lq;JfI2iLLFCHhu8XdX# zm84-dT9sgt%*bcYdWjVBQR6^7?hXhK2v)+tyTQ#1z`_eeoi01Rte~)P(emXSuW)uq zYo#>kC8(gUBIKBCacwDH2yt&7URwkkA;8*_c!zw@bdjS&Q4LywA8w08Zr&}AfgxE) zp$OfF9}r~b>N+?T9fCd+ErsL2r{gtn>_QPuG<-NRl0^3ds4CDrG=FA4lpn2?=miH6 zOW0{c9PuNi?e6;Hw=k|T=2p#_Qs+#9#~@#vvmF4Z>AW``7E>@a;HyfOnUe+f!=Afq zwj#lGd?Bw(!qRXUlN5@q6w8KEq)%YZY$(rR6Mh?UfNW_`I!z!iq<5y&k%|u{navn3 zG~#HEH~4ZW#~xq})mFefHr~S22p5f0Y^)<ny1-s<2BF~*2-#Fpk~joMh4>JU0K1(N zZL-d}>;lx3EtPH9HWp*aiDG+V1`wgF$A_(v+qasS2&4J}&4tW{dg1BX*xU>$>Kr7k z;=xgrs>Jjmqa<j5%<X$UKLP$^A`j9HMV>T9yGEY~_9kI%*y-dmfv4e;;~MkW0A)L) z=2*I(SfnBO>SIQuhRtMdYsK*ri;5WQghim`(Z`PG!ifS+EEpfrPR01v6Oy5m7rXJa zh%kjOB3<Noj!rFM5IGubqlzPjG`^BLS<W!Pb&SJ}Rx2hJvKT~xHgHK3`EcUU%V^eK zJS(|hi6KfZwa4CpR_ZJHHnwkIz_j@GN`n5my}tFZ-h_eeXV=bMI{qO>i51UAbEn2~ z)s<g@fQWRAR&GY|mJo|iPt@jPC(hM;kt#P4hN*}*q(uyr-W9M}H*$)FZ6%e5o4GcK zXS1Qy-;b-Zoft)}7n&pU3YO2dS+N(wo(bVLS-4Cpyp!p>CExWBe?@PFG8x|%gT5Bv z(4;-tpBiT!Ae5^N=0v<xHm(H47Xuc*Z=>UHn9z9?{7?aXhsh~D2hhQTy)>e?9|eag zwV~aYS8f}gBAsYukkW=SntX5Z*wC4~1aJmnRX(AaR18(#etv#-S8v58V>_cpPX~9j zv%-RdgQJ-ocff{1HW8`TcUk82*|d6n#UaF8T*OT%-QaZ?{<lkFjrjv{2gGvo?r-V? zrT53B&wZ)4R8AuYThuK6w0faV`7jumEyUZ&7NoKMZb(ZNdu6aDGhF%H@?-W%_J*!7 zD?zqmheU=6a7c0@y{DxoR>*~c<bA(+49Jfn4#P>>cWJ?&vgJJ<1Q3BD)B(%n?V_TU zYoXm<3xk_mcUq&jKc#>RJl&3Y7H6~2AFlFVR@2^Qw(l<QzN{$D6hPt<NM7d564`P- zKEBYitU<lv#n_Q@OVswmMC!V?HxbXUo0Rhl3;Xc#@!%<-b6|8-q?UGyxBEopipz=h z&73z+1g5`W0b|S&vggH(!ve3sZ}{x0hjZ5g>!oBMA1U@{L_B?pK5ho`SgYTo?^%N^ z<;A0pq2K|09a?047hVbT5l<hX<lzN-23i21C~vSQDzJ@6*m%!a%DA2NW<3voAWZaE z)J>lkqdn_%^b!#fku96doqL-KbeSkU=D{ZhVa684qWbpda*$UkgV4oH+67GDzRVW~ zp|TWX@J{HYD^y0-$laMk<uNv=d<TYBrCyH2`4XVK)ap4L<~89h6xYj0QVXl0<8bb+ zE1<d;CUgkJ=Cw)J!G5(yoNdeK-*0F*ktONsCD;`~%h5<OI3a3=oyjm)jEG(OKxU$d zPE~)y>(RhZB+tY+(srUWZFPboLO{aNv%Gq;l-5DhR70465-beNYj%Ee39&nfL&{Lk z=7CCAiu5v!*c=Skp6y~Mc}*umY?mvg=5$wl4fG7KzUU#eJ9j-!7YM*6SJcorrH0@v zQpGF_c5}Yg5=b{U!%n(~mIuyILTOvtn}Ifw#T`@_60c{rX{Gwxw(&+G*YU3?Dolo< zJ8IKQusA3`nYeu@xuP0pPTbkP&zeh2`?4W7xDQvb^4Q|Qv4JvTJDoz3V!n*1mALMw zcAd2;XDX99X7uHaj`@H1`4;_5+2)Rp>*?0h3qIuy-%6Ma)=oS*7oNPBt@`1+y^qq8 z@-4W1qKdgbmH(k&%+qNH^Dj|khE$UrqY;<jKYj}q)-9vrQ@F}60S<3N<z?z@sUU(m z1)FZCSb%_%Qoxj=Ze2<Q3Q6$faIS(ys<w<-1uymWD24E~<|%fArt}O^1m3b#IFIzV zb%td^+NNXMij@6%vPl81feerkitViPV7Wv!g?rxh)B7=(G50R|VwHBG03)YQeGN?! z67UaC5Nkz0bh#+3F5T7)`v&J5t;XtDB`23&+!<j!krm}uxKm*(pvJ3OV}aSI0o|L| zbHa9P?kjL<OOV$={Z7tKbs4h*t6{ndI<7Rd*!_Hw1LFPo;wC&7V{o`4P8<?vFqOul zEQUEnIo6Ylz0ThISWj(R&b=!|T|EW)7o}%FDK=P{C|~ZYU)P4d7u{{sqpSRas4+0a zqN!c&m(*h|@EyN>I$RT!_$)^$I#%s?j4q3n?8djdfk)yoPNVa4=vbveee@&9CaIlP zbl}g@V7b$dm=?{PHH)SA{m<f1j2E{9%i4Y5Kx#6djorXGoT~*o*xTMRy}G#(5fQcP zio-OMVEu+uboi?fv`FcO==9gYM?_q=AI<>Qb`?R|9p;;o_FcMU(pV9uxEFR_4C<-y z0ULI2LOFZQ*LOakG05ja$<qYjQV4ctFVHW-KydDsBM`cDa>$=OTX4gMiWL7|RCPw6 zKkrve1glb1H|;i_s4XfeRbT#nAf<q{xEUb>Ot3rt{;aQH3t5U%Km7R>^{L4Tt)x>6 z>Etkwb06bwlGPbe^D$AS(gp|`lG)WVFHdxa<*1<P!IR4`y);HQ7$%Jn*MP}wiqCgE zvpWH)C3Agr>?e3vd(9W<`X#0N1JlzsY;Qr}ZBLk;$YMY&=-5(tM1OsLuZmIRD-elT zC>Qntz@ZYdwrCOG#Dqu3@j1jYMKQ7NSk$Qpr7N0dq2E)8!dLwG;;1*O0x}lelDuW; z`s%QUcmt|Osgs2%9ktrX7-|sV0P^s)_`W$8M;esDnw4~~2>N;En-_C&sSz9rsUK$H z@fLw3yrk$~8XyMy`1)-Q4h~_)kaMggGjGazAkE;t7x3}jM2f~~I3yWGt9erXoFsAs z9yVclctGUH3A~ODCnTF`Sfk#SG%cl(GWAg?t^**tGdmik>4$xp=saRWRo6WDibIu# z$V*S2LMRxHfB*ObWk<<J{Z*^47`hq~VHqtLphv-#D_<LT8eq4AhfK`dzTu|%YRmts zOEfK8`l_#nvsKai87#eOI`^(3-49{~{`EwDqqpWdFM9=jI1kFMR`kSZsl9NwaL7#4 z&fmv*=$?e_I$}$0tI`b@So^m?hmI|*Rub+If;hX-I+(&4up6@Vw>qG9ZUDn%4M;D- zocl^V=k3h8{rgqV$#Wj}X(Q-QnL1sLnSj9PWTIZ2Idbs#iJEHvdL;mreykJQp2n@9 z9->BEvIRT!8`|wBXn?48gBPpAS}~fx;eg%Whe}>^ScY00|LK!gDgF!meNdHPlj-ks huKs`hx^;?0-9cV2QCv(1hYvG$@7%v5W}AWM{{U^^@`wNc literal 29159 zcmbrm2{e{%+ctb5O_Zdhh?1zxlzFO%5DhBxJVxeOqREtqibOP!WXe1bWlE-^%=3_B zo{98tr~7-J@A=>Vd;fR+>u)XVUhC#Muk$>P{n+<y+xBfg{m-k&Z`-<WD~Uwfc2+_5 zB8f!qO(K!?Zr+IhbK+~~ApRrjD5vgt$;Q;t#lYT#bk4xh*2>1w%EIt~vx&Wfg^jf! zk1&q_*8y`!M_UImUf%2f{sSHxdo$jnIzwK#$QD}#4F?j5#(?-Cdn+ApK_c0yos~VQ z<{CBL<)VFgo~3HKg;K54gSSyzbmvKjUY;i93$gaGPq^iSKKq=@cxf8;J0pp!PUlVT zu`D&FC$DY8pAK!d`*CL1p#4p+&WUrzT~scl#XmPBZhVnk45aCPoYP|=KwTyyi+_ba z!SPf(Xz_3HB54=?GcB;yn-%|b&ybzR@5^hBkOKVt{9N}^$`F6W`<iqFzwI~KMUo>v z9!UNIzx4~h@IQRGRGGo&q_#GbkAZ7G@inyZ=Sj8YPdKk`-nzB_^ApZ!9^!K4NB=)w z_W##C{hvM@#U?5mA$OnNEk98C==zh3_y-5m+`M_ys;9WCDbDy)LlY(O#Dk{%{QRqb z7JD+Uec$T+{ykrcZqcv4cLHzdiOYs?X{H-*d9d#UD;HPbrFfOLpgmmbU+!EsQTb<8 zPhL&Y?(^q}J|H0xMYV@#$BrGPS(YslI$>S^ECADchhdf5w{Jh?H`>Vl$Uj`y-Jiv4 zHRRX#M34OA1w)@nPai%kRpKp@wYB9tc<^9tl&D~r0}YdiOpKJL`M0;UUHMK%qs__I zNorWtnXP4fOTBk!%)1LkH*IHnVL$XaNv|Y2r{`wmjpZ4)d<oyd&!nY^g2{B79x2m? z=(^F=lKM=GCiNnBr+2eFdd0P+w<z-Fo3%9Z><_ziIqgxEoZn^E5IryR9ib2!8KKO+ zFxGb3(2!NqeNKOIqASy~rM}6;<ZMbLB@+uvLyMkQUEp59w|XT~^P?@py&vw>#z@E1 zKNpV|aT#fh+jUIMkKd@WcXYI7s;{gq*Y42D!uad!9ofvkx8v^FgwHA}er#yi8SzHt zxuWm({jO6VHt|O&GpBHk+Urp1J31aed-m*xjT_s8C@Cq^JQl4(A3eGhRp7beEap7s zmztV7-WqyXY1bNYtxGK_I!5Jww0C`c+6B&heI+;2Q{t7f-RmJ0MQ?<l*%6I&w&mI3 zoc)}a-cWDZvPC=B_FzRGHu~|g%K=yQJfB=i*0lTkXOV9A?x7z6h*-9qWrTo5bJAXH z@=(EKu~E~TbJ0B23HCaqLihP=0aKe=bvfAC--s|{A>w0hDz>GYL|{|TMG6glm1h|$ z_ouT?yoy`1r@<yI4Fqr5y47&9yXY~uR+vr6AEB0@c_q~pMhf*8GPl%DHvDey>5;Cm z)6>&KC{$s&$a2TCJC;V`W7Laph~C+_&0h5bu_v5Sk{)4I%pQRzHDP355-xFIThHCw zx?4i)Db-*;ed)|ljl<fiTWDygRaJeI=$(=h>572eN_N(`y@U&|&jc8ek&#^^!$}QM zbD#fJk#17+DV&#OvgEH+;+5pjkNRlr4M<5>Q<-rv#@e%LoVku23-H;-*gx=Sro*B$ zmmhJaU^n*V<!QAP?Y8}KNFfS5GYGN9_Xel4uXj{7sY*XTc6IP@-h?=(`uhV!;M6|l zA8X6-#W@gg_@TU!nt`OQNs+9THaF1~NwI1BRLS2z!LQ|m1x!BgGBh;Yylvb3V9o(N ztY~SB*IG4#?7Bx+l7^u3c>5E>xRAnQEtE3eUth~#D!J*&?Edp$x9h+*+~JvcmFJ|* zTekFJvnYt8aATp(O!mwf`^lokoqP9I6MM8gGbE`|j6?4F(14*fLeRIgROa{MWJ9#1 zht;G>&i4i@Tu8s~RL#{4QID+rfNDPKvL?AfV>>&$4V09$yLZclu*ym31mOzCWfk}L z>=iKHOhwN{df_zMTpKAIeC_)?la+2-T0X-KOyUbYX~!<>f8Mp3s%BvvYm)s>*11*_ z#*Ls(*Z)9%!_(7kwoceTFwjwRiHb&b?;6R_h`Z_#Yx<wf@#|STM8aluX*$!o>sgsE z4IPrnkGe>=p(j@_j?sr*HqD3|;!T#iJen4A$l;31`}K@H=;mXfFBCb_6i-v&JpKw9 zPo-e-M_q!}+C?cwLqo3Np<Q{_2hE90?NMG)(N;Mr{daY?^XAI9<49x0$jVZCL7}aR zrjI%|Q7uB_we#%naYVb`rsvkQo&R;YG+n$^a<Ow@Zfeu{v{w!d4x84?N422yMCaM_ z=XVI2*7hR*4EVC#v|Sd|%E_)>ILE8?O!<7)yP?9UQ|n36zG!gJSZb-4;+e2b{Vj@3 zZ`BfZTDGLbc`Q!wcx9)ink4K|*iW9E%AuMa6@F>GOev%|&kUZO$Qx#kcKfk+Cmr3E zZQD$+ZH_;`e7Jt%?!6EF6}Ge18JFLw2MuRBkxp#E!IQPmGAQ#&Gp*aV`sdFw(xmH5 zHHVHsGq0e5!H#{`BE~)X@0!%=jW&xW4mBvQ-<iZ`t9o5?cO-)4;TW%_sWO^8oI>rZ zZ};Eue5oTI9Hzg=iR?ahze-kCMuwDWL*u!kS-f$BaiK#cj!?bFWRaPNPS&-{tcMOs zGOyXwtuM~>X89adLFzlIQp<OHd-bC|CPa&ue=siYj;#8p26u?&d$zY_m~Ek=GWzo3 zq;`?}6P4#;v`kE6+3vIZ9uTLMHpjtu<#?H2tk>VQ^2*9kUOlPGV3vkYA%~`?FNj~c z@=r<YXe;1uuVM){`uZxIpZboN)35ioeW%DND0H-<_MCU5C$5v>qLqmRX27s9SaN~k z!i5W4y;KYZ0yEuvDvt5+1ig51;MJ>FR*Z^xOofG~bzyDAX=xS@C?tZa)%=fL?wvAd z&EgEeU(u*VxFk>#P@<D(e7uQ=oQ6Edh=IeZBmU^BXv}&ny2^eYZ^|a!D!tJwp(<U( zUv)huEc~BGuDa{<%x!f#XKLDF8`GOp_eerE-L)%|iwlkxZqH2D*I2&1ynG7VmTu9+ zPWnAJQZ_fzbX2!6*O#+q<r_HxR+#zi`elW#x69&GK9%$knjLPqy5vBYwSLg#0~v*b zyw;X|(~PR@N0W2h^O;g#t=Dk7`_IK)1RQ^!M?E1sqMgf!BYb{ombAWEb_xFxoboK5 z<y9<RV<Vx3(fQse3*{y%(Gt~VG}5)tPFRwVs)lZDVxF?S{AwLMIrB#v%$n9n74POe zHQF3@?^)AqmrKv|r$fQEzvnFeBMSNd<jiEa`mHaL@iA8NGlAYlRcqo`;sQ*BgEQ%^ zZZ3AYxb@xJLo#oDe^Bq{^5L8==g7!+PioDn2~=i$gq8eb*(D$8_hSQ$%+_vyP4|mA z9`IUQrDl<e8t=&ATpi+LFH<O@%vk04_m&>H80!~bg9xzW%-Bm#e$iK&^HFu<PS>9g zPdd)o95??`y3ieRNPhF?&9+L<`x(|3Q6XQ*Mn!s+jA3JWc~snKUtMIuGys?mkF8#! z;p?{_J6i5vTi;6Rtp8D`gqSPxSj;azQZ00j9A6dUcsc25w|L7f{!)?B7uUXvap@I5 zM9y`2!rt~`eaS|yb+rpr@(MLKc3IkO{z+v;@xr!GzAeXQE6Ucw`6vBH)(@OQ{tZuV zr6O;WkG^sn!ltg0tCvTK#vEN8`Ulj8zI@N8UdVtP%j~w%H7H=WONMc^r$q|I5z)x? zePcVVm18|L+t4$(*i*uO?A;Cym8J7({RI&d8(9DKAk|hvg4UfPm4SO_C%sB4#3u^} zM$*f-tm8JeCr_U;FyELuiD=tEU@<GLg0mCx{OdXLBTz{0pM{RLOba@>fL#W_Oau<< zuec9PXSeg8aIE{5aA_~U;RX@_g2{*5l%4LQ$;>H=QRmk4tYK5)Ro>5G+%@-ki$AGK z|B0OnmtQ|z<@XPBaM11Dd$uZQKiOODJOQuO=}m_fA5U2s^j}=B<I@*8RHFxxRQl@0 zCyi0zvIn+$x&3nh_h@Ea+l_QAZrPmlp{FMX2gD<v&F;{8r1RR3x1Tr@#8i!ZUWR&M zfmkb9`^`xsz4|_0iB7nYfdIU`X<mPC5~pvXtALC_>V^m%yK-qyPfyts|AO_T<LdwO zr@8ZF_qhlGlcoOqyg%Es662M2(ySAQ>N4uGQey$KFBxX4Z_Y)YQCDZ!&3R?c)};UJ zJ_;P+P<gpCXBuPA(gm?dTZ;?fOx~0J=Yy6hdL{KWyd}zb@U|?=J*h<t&*GIrna=2x z;^e!1()0Qw*Hi4-R_rNG3YPkP<PnGJg4Ko4fwx3TVhesMY{Pu()-7QB=Q|H8zBm8M zDz!2u7_jST|41X7ubmrJFDOc`<H%0Rt-D^x`tMv?I^qB<Bhmp>;5PSFo(xFBU~P3- zE%8cS+2yk;Cg4@J&2^|e%hOdX%P0?R`A(d;w?hXHepo1uiDCKv?((4{NBj^VU$U=j z0f!AIRTapB8J#?NQdUlGMDIKv*GLED6gAudB}UMF&U|(C_-CcY;m0IbfHu?mC<5)& z0a&CBhk+d+f@Z&*rukap^_Ph;g$P784uQxzF74ch#hxpM3{qCz)cpMZYRS)Hnv*}Y zv^4EjR0&HanF~cOug*3s19XDoR2d6g$g$P|=Xk4AAe7T-PX$a6cmCxm?LyZO1cbw{ zZ%3WST2HE}?ehBjM;KpgSP?M&^L6kHe)=QJ)`BmC0)|Afv$OlG{aMMd8#uF(`st_N z^RGpm$4V9x)ovZVbbzbY;e=v+*bvEes`q5H=Yq!C(g4l7tEu$>(W)R0N42t(x)w#; z=Z@%k&hM@X=N$$jM9%TvLdSkS!?Z5x@!(E^o`ya`J(+4X3bw;~tp2%gJKpZt`(fAc zYQcoy{XLu;6x-98NQ@`0$s*WXe~-lHEp~fcPYg7ZyLfS@QaF#v=f_92i*K}L%NwYy z=NHir*k;!s5oSwsO{!#@w_ATsGoli4n`u7YARNKPean|7QC$6E&CKSnC+}>W#`lIu zd##P_=^L){T<oeUTI{Ydi}4H_U-1VoJPdp?w6-$IAttu(QFS>LZ=sCub{k;u-_m!s zQ5YKP&zhRI)O%(rPCOhsq2WWlxBu~!%j?^eR03A5eDU3v2vSjS^N*O<pZPscj$Jk! zZ_oOtM5RrdP5IOD@Tk}yuA|*UOIv*lVRn5R_qk`s4W|1mGOgM|fsIM}lg`EM3s)Qa z(=JB{qgn#GOU%0>$ue0Y_P!-)JlZer+#2LG+1*9IM$*j54xCo9*<4(`;c}v8Jzbtg z7==DongBsfo`1vjZXOg96YE-xSB<Iv`So?i+}Pc_8+6@A4&T0W2L#FBM{UGNw7Bb( z{;zKdkNU3~8ykm3b3z|b#4iJxL_<9$@_db|f~JvlVm(s+6SOsN*l|`XtiYRBCm-Rj zoj<ua*%JfYgRl?(E+0FJl4ydHTN^DIIZzcm`;m(Ge8R=UVlER=zdDhB?)%emG`^Cf z>PSQQix;uu0moWPyreyU&!r{b<-f6H%dY(NVc!V@gRnT=Gl2)bi+Mldu-w9MHb$w6 zBxV;b8o9LOTD<n#(zN#J$v?m66TnV4(J&k1Fei}f8yd3W(7pyR+@7q81k}&tV=x7l zHG}wY0KP>4mF>~HaT0WLJYR~7`qXLe3{Tal_kqhzDhEO%w1u~@iHSXnjb#I<x?Y7t zMNqr5Dk_y#!zH@or+v2@2t>Tq%zDS6xEmY!4SU3J+<XU#AlDgY4R2oE5fr}Rnf2|y zNTtyune<=CHjlY91S)nON9cm@--2ckE3ycBwd?4`yTG8)4<GLMSVoZN*O8pSZ)f$) z&5!65I1eE<jpl!S-|j0NlJv=mYznk7p_wJKSVdV`*=`jV&@OO(fDaRCuRZ&E4aj+0 zflF$~FWgt&SJ3r{;}$z1R*;oeRr%tO%#39;y!lzN(=JZCg;M)JN$K>#(>`BHyu3PM zgY%t6XF6<3(vVK~30eJ&z70sU=JfN+!QTtxHDG@tw!PlhwQ|O+;*W*y&YOtYH&hco zyt+KgcKC2x&~s6Tjb2_}0lPWF`n6eDV$=k0T7G-`A>mSd=fy;zn(u{fv*uu!9og4k zBO`Y@wdh3ioGnyOq4LIqmNhg4<GI^{<mBYOzBedSJ2%W*ykz(;Yvb$Jufc46#EBK} zYm8HpfFv@9kVBTC+qv^2>Q>%l@rs1bGuJfjJtSZ~g<xh$20>G5kA*P<971R;kH{!C zlERux!9vKebepcEXjfzFh<pXwe+U$K6U&V+k9ux{Ir_CxGYXY{ycd>gnqe7}W7fdD zPuRK^4>>(QZ!*@J2FiIcS@T4TlsX$-+dc|cpaJR}hnk;%u>YpZc_6w7On!;EFY8;3 zK>P|8Pdat#6oarelYxPO<F9Z2r~=PXksVf->_90ikb}-3W2CQIA*P<QvNr1SxqaT% z!u?eHjq+X|95bws#(RA-Zmn#`x;wH=#scZNnhubPo(q^e{ayRhR_x1JQBgs=YnLpt zJRr)nqbPDMB`xg^=Iz3N%QMy(v<h3@4o-c%NA>u~g^vsF5Nf`q=!jSF)2h~7+4J6j zq3!o!9F}YUP)-){W}`#$V)5c4tgN>Igwq{IE_Q%uJv+L3Ht5k1PcIOGS@S`BU}vOo z{MbTKgn^85;80=G#2dSz&$|f<*OAT>b7N+Uq{l*Sd!20OftGc=Xb07#UUJeD%#~F{ z<SBsAu75IZDWWFbS&&wEy7kl-uyNA9s~oJXJSLO%*McTW!=j>U0j~(ete~tMi~3dj zTs%BzzqpugtY4&CEaeooXTzpVO;8b3C3K-~Dgd6h6_3<u9glc%?Q)G<&=M-T8w$!< zd>(lo)k(fj)qY#BHD$d20piJvi;-?VmT?<QR<cMopBHgUACi#2d-v{CtzZKx*tv}~ z%t_;&Y*)V$sHuAP<3}!~BDIvL8P_Mr7I?`XLDfT+6?HBP3_bgZo!|J=j(gJ8P7>_| zkLl<6FNAa<YjwP*1Ss?Od#GN7s)B>J>o?ogON1Dk+BZ7ddXd-1fX}Fsn!s=06R#eH ztkobf8~8D1(0yIzwGeTcU<#IA69I%H5l4wgw*f&ad+4igts`3Nbl^G=#B$IhLIE16 zd@z7KSXm{1dib!-dE$%m&LXakUP)qra%0-}@!oNZ#`31a{vwydD0+EIRzrt;b)ikw zv;a5do>Eq(Mi!|=GC6f`YnuI#BDCN5ZB%TkpY2IwJtZX@KLq$GMDy8nOQ14-1hA_` z0VT;mNtMuk;x}q+$#=(qL1T@GGK+8?omb$w|LYK=Xzc@2lhg_Gt1-!=v_)LkC!Gct zOU}0LQbZ*Yy|Kh6B_&n3_ItE&u2JdP&x5)LYslWu)YT+_#O6k{jV*nwH_eCu)|hIy zOf?XSOwcV71N4c%oOos8s3zN{FVYUE&Q}BcyuC^0J<PusvTkhK{GO@d(}z2o+y)*9 z^F`JXVioew(N+JJYI`buW#zz2TZu$}Y2pwoYxg07koEmIIFyzaHn4Ym5^O_<T{T+y z<*9oNeEQz(#{;ek?A3@00TS!7`uh?p=PHmyTfT4Nzt834F!CL=)nv7%N=iLxMiwPD z0w<pwvt4>0CoUo-6%FZ*-?mo<#aTyJ>&n7Ddb=De-VM*6?=5<^jf<5vsJ&41&(x-{ zD<7UR)VLj19DAi2BPA;@pK<e#`@(pKZ%mBkf{dS^H|?Q|%~#^KDo2Fgv;-YA9#y?% z!0=R=9o0h~CC{R#SQ3EP_R4i&?1U&0g9eR_f@UgxiK)5=`1wO{2xt0yS@Mssnq(<y zmeZwoQWip4E?$`szmlq3H$6~=2P0Hp9KFUY%d21?L+R4y6R|w{0FWkS8*A<c&pjt+ zCc8rRrO3lY>hG!p>g_<NJJWb5ef1VVI~yp>JjH(&w3?GN2u?y=kMl|()DXJ{$;B_I z5ZdCOu0X3{GkYJDdjG+__!u9rwX<FQ8+~v6C?zPi8<6$h<K_>Qyf&IJD{!+Of^HN3 z<Y51f_zg0gWqf`C0sWCS#dwOFy*T7}bj5I<bHnw|^M<26?7Mv0iPIAu-n^8bd6;7H ze6l82&&`5|8@KJtEzG`0#a;8o*z^PW&!(!OA-~p&1hT0!)z#Hj9oY{c>4ve~T)K6C z53eB*fO;CMPQGIwSlP>)e}1R9W?mw=lIs=sfzdSw(p&0XFRxv8ovq90^zu5mm7eQo z*l#Erc_B6Ay$>MWuFJk)Zlf)!!ypnQwN%~bIOGMtr`rgjp<i@waH^npgEwvF^$uTt zBdNtt>732`jEtJLCxBInhG;pHPtVR0N;~y_vB&bvZg+7eA^6A#GdoocfJKBWdR2!z z$cRjBu*IP@L^=O(h^(fiCH)(f`p51ENs!*IEIo><7;17fGBi}jtt4JeHAN-yHc8S? zGpzX8n)W)8g_o5zPrY5K*lGjwh9I(KZx;s9nY~+gO?6JZSqEp0nwxBAjJe4Ui3Tf? z_x5=GtwWeyx>pQQvJpi#-*My=G-o8A7*1wf%T2Z-K)5)6dm&ljow$`8`u96Fzp^)3 zY;n$b&$oHocfc=k5pEK*r29Ae->+W3CR6&!sh#_td+y%dyAP$kZkkD6L`NjIIhi}z zJZ#!&*jqL2Zpp3EUCM7*)e;Ovc{!%$zsu58?@!-N3T6^Dr1FH4P2zf)3MQ0k(-Vy{ z`sK|z#(Pq<ArhbVuXJlxwcPW)Q?15a_LM@Ke4=GZEX3-QJ&78U57H{}LH!T65v@VQ z2SNC*Z{c7K_02Qt=>QMm2a!E>`m{!&$ZF59lghNjgH*ceXI#7#myj1q1<G>VW`E$b z+X07sC+V1(qn}h2JP5l5cz3kOX*r48zP@$Q8Eu}q@s4l^zn4(vgP25bmv|ZALUy56 zlrm;k$wtrFLf6A;wd(sy_p>@v;Nnut)FPiYt2dDL2?+vS4U<G`T(w506ad2BbU(^I zRcr}Q%)RxBEFsTvnn~O_kl=L&WgAKEixYK(%68$E?3J3`A&$nPB+&5g2q!}2f4Go; zLqocOz^sH^93}1&1iUbdqa>hTN{XL5?yqo-M{lzxb|L8T=#0r<i9}$i#vB_x<mu1g zL2oZ7h7DBs`*o#DY_u))CPT^g1rKR!J27;GPy=RI%qBbvQ1KL>9Fqg3A<K0h--pDm zDW+V$ExpjD9+`~j$~@OVS|7B<8a4A#zfq1jg$nVrr$pK`V=oCSdQTcKp|jE-kIJdB zSXAV;l6CB|fFAh|e4itF2oSkKpFULpvjRJIx&DDQG@z1D49n8fC&v+?ZcBZ>qE5ed zt*xyoKRfP^5@7kt*KZS{TPlRL7EE40b&4D)?#CS(DbfqjMuLtL2_jpD_G~zzHkHSr zTrDHNlezv%h;J?SEch7X=WCKkX^UdzIM&Kv(-Nu7-khQn`FtnJ*mlzlqpqwr6NZgG z2E79Vry&k$pgkoY#PkwnB*o*cUn{p386Wa*Yilc)UaB1nD{IOgTCziDVmm*)R@_}< z)jc-R78&L+Tt8h-r@CX;u5v&PGD14s#wg^tq&u-ag|(Yii|jps(cQd$8yaYU0e+h| z)2J4i>RBZXxteA;@6Q6SCj>5}?#g$U6J5vBs*Y%7TZK&wP4ph?KbO9r6yyHus2R~1 zR52FPB3IL-5@Pf*=_(F6#GT=m(ecjt&u^#7p#3%A2M-JwzP-DA{~pyIQ_j^q!Q22> zMgrbdzlNIF=I$(5=1o+oHaif9_DqX1Et{fy1`O0pA`d}ear=+sR!r!%KkU^0t0S!` zn=VsRitWC@p(3(am`SuF2>s+o?cRVry!B}6d`3jX%ro|@+h&>V-A8+NT~KpVs|cu< z<mJXK{P=!VFAjx%th-3OqmQPOPFt?<IiyPk=I{AgCXKT0R6I}D+Y=J<gj)3W?U9br z_-OY4tljw{_jxO|6&9BbS7~j+he#rj2cvJyoHc7ER$*||GJ>Bf;q;67`N8T1RT6(> z9l_^u=B7(2Xl$Xxc}<$2zv*@;Kzp~g+@Q3^c5mR_SO|diNgz&9TwJvRM%ng5kE;^Z z?5x<wfy8o0Qwm&1;=&aa6$t<gDSCKM;Ow15zgTE1U{^A0tFx+R%^y*2D!s`lqTE$! zX>In<Z<G&`F=pdZ*I#yDdjF@-2F*U=h!RvJ*VdqTsh^6TN1KRv9nB2E+qfEc&Gm(} zv@~=}chb{Wf*U5NC0q=<?-vyGW41wh7<=f{&ajh~b~!jT)kx5^&Q!fbDr5_tPlk0D z2MI_z1Kleylc8s<HaqD}HC~?=zvU;Q2V6)HUho*Gb0>g2iN+Q9S5v-|WdJ?*vypj7 zmBwIHH~!4GjCJJDctggT>2zo?$znrH-u~;{`)Skawx>rl=iJUb*r$}Nx!k3B|MiE< zvSUW*P=vK-_8>eNq;E=qx_vMf01#HHdv+q>aw0v-W4!s-SG%~>A0qz}>JczQ1zORf z)03;7WqjuCnMV&nEf5iDJ=*hF@ZYHG-)x8{%h>;<t1Ef$-^q}sQeEXYLEia#ax(!| zn`9CnFbWHp)iY&VwHY!pSScE94WG9|f0RJ~(8wAyEiRco?j`iAes1WPa&^ywZO1i5 zd+)1FMk<H#yFz4Qyuy~_ph?Qicb#Shzcs=Sk#|7}?ME69Z{2l727*@ZP?(l^W9$~t zxL9mQ{f$f|BIyXDU4O!JMXGjrHr|`O)JCT6c*BE0wVWgc>*2ZNUK|4zv_5}Arpd73 zMqGyd!!?MG*P3J_NnOFck0z7CS3&p1b2vi^)zDY3ixkeeR}w85L!NE4<<>~kb|%)m zb;Wp(qM~9yP>TV;;A0-0XAo<r=3D;0R(x{1{nqJnD)<Thp9X~{OYF)Sef<M?Du>he z(726+9?If5M@z^a*eW8A5g)(*-`f#<T3{sbfpQ~!`3P(1tW|raKfb$F*JYwJ;)G>$ ztokQCaa%vvP4TMac3J<ez~ea4DOzWFB}tu-{W`T1{1obK|94tC2jtRP<dJi|B`z~; zv=I;OrU!B1>({T)CukWM%*E-EJJIh^wzn6Mk~+$Gq9I1Q0hBaC)PWsBV`@RoZrU{d z$S#(5+xLsfmfZZ)3JsV9&W@!LYZ@gMxjSq$-@747^mKLT;p&*bCF6cTxTjGu_TT_I ztu0LtjI?FMcPymrqCKl5yJ5oynU#evIjKXIGTuYEiCpUMy(=FuW;l&rA+j5aV>PO6 z)|&*s!D9MSV6Zovy}ZYr&?OHTK-*Rj-MbrTAX_J@$_|?T!{&JlSQsruC&tp8fJ`N3 zJ{`gZHhCi{@q4Y^(8@_g4bvhG*G80qw?}E**Fo$(JoZyVZ)IhrBM1cNL5r@(1LzNu z6?U|=E&`DT<$mA#Xx7Aq>|=){%%-QOUm913E-fwbnbtB0S+%~H;$S@rm{2@hFP`nC z^JWN72#bPHVvg!nWI$1`H3^tFvW?8@qlg9sDo|U{ILrWOr})sYsOamzxL(^k5G-8} z;PPWGUskp;-A#~5JZx>nhm4;;Y&N~jO2`4b+A#`tkzrCpuj}>4X?4|J$1K<{_i4@A zm7iZ;QhfOE0p0h8gvI248yD_Fa3rCTi7IFiu;l<~X8+1$iK^Xo92Hk|1I@p`dveKg zC+*qbqK-EvW*%9st?Gmm0KypB@#PH-G(ZwqqOkeM0EOrujb8NoEau&eFQGV(e0{yi zs4}qcxl4B+z!D)J*$q^ZK|TVo?Vp^CF%6f;qmMr$<6!NlUa*b~`#%0Xj9aVnb+FXu zJcnVT;|Pi2T$<u-KLt(uE-wB5!Obvhi!()^tffVN!Fgff8uoyC-*G>97VNPwkRV(0 z9O8j6S`S~{PJI-f3v_7oKi=Jv&@GOFk>6r67wzZAsh)F{iYDi75u^B-^!%cBWM#r4 zu(CM0sTb16aGh|Ea@Hx_kh{GJ6^f&Ef9?2p>at!mJIsLl)soah2zmnwePq?&@AUur zqk*LH@5=0Sz+e3jgu8pu7mc-QOHVnfE<1Ca=rIXb|5~_)u>AqZtiLqXNBWkk7lZbL zXpRW&^f7{JiK_i?G*#Nt5)PZe7vxjb@WZTbGTG$4Fh_krp-W#at~NG=l-73iq%50i zKv2*d5L_5ksJwx|zP>m~(Lj$+oWUm`Iie}#5!NFdm&1bxV%8TDm-^AbM@=Z9X95{M z5ORr5{&C#&E952cf7!xQ(8l2=c&_0KjbbOQK^w(V<@MfoYH3_s9(_&ffjZdgd_Er` z9)Zv%fV5o}YyWld)nsuSKx0K(+AycT*4@-cg_`XpUdQQaW}1H>4ljC$Wf=`pk%sq` zXlc#(_olR_a?|+VzfUxZ05sOyQTDRs=h7QJEsJ;#95AWrd7#X$q}1D27Pg{Oa`QQ& zlAN5Jq_k%W3vu$fm{Zh3k)L0u^54u|kY{A?9xl)Nmj^*T$jo&1$6OhUx+bL&d>&{C zuQ#K>v_Mfds8^oh#Lgv}<WVi*jH(=!G15Xisaj=`TV0cSJk52Q9GI#ctA`%D2w~KM z!$^ZyTeh4`V}d;8YO3x6i?zTs4w2c4R0<e<NN3GgYi;=go2GPwJ8>0!p5fl{-gzXr z<^sTO+6W-rc@VtA$3SE#906z?$Ji1w(J_~4eSM3+MXG(){>>YH^bZV_!3u{%dIu4l ztAn-ya??Pq7~m0mVpr~hbC0QGG|^oMpR!fjtgKPy+PlN#vzpOJ<1)%`Y_3ouovN%X z(~}`mE5toQbnM?{nql&%C{v7qx@_m?GeLL1eEHV*&QNY8I;71Fy!4^lteE$cW1gq( zY#dttm1qaSleOZ6hK?{q1<p)rW%lD6&$GEy-=L0`GiJ-Q44--GM6GXS)lSwJ&F?K7 z;HO@>x*T%XSGD@R9B&c5lBKY{>H00indl=j({dPHs&^`>ACLSiZaQ9k@0-yQ!KUja zRx7Hi1kp|bGdn9=uC{fL?#TjY3%ziS`-jyvxonay$+8t7psF4)nsuZc;Uon0>p5Lv zL!)xCjfOVD6PXqks?=%DJy)P9t{%HQX6fHi<ROgAcVl(BiX^ev=|B{}uG#1O)8G^r zfHeud87yj~7;=()^v&NR)RQl}&1mhi1I_y0m_BE)ci1dpU*34GQ2*A@XkXc0U+FTw zr(#aWfT)a!BmegNE#k<#^mv9tK8^QyF365VB66(ek&FNxBcl>rXWC94v?0e$B&dY5 z=Gc9fe!pQp^fUyfiN_ilrewA-UJ-^bw6!Bgoa&#sLfYF5GfRjBN0D{soXG3QESJe{ z0<l>%y+LW-TJVv@xZBV|;N<srVY3JTIp{nyv5W<FG47v>Ety74&nYMzICkvwLOwis zOrrL8U!L|gOwakdclewH;Nx9@`az9m>rUOGG4Y;m>t=Y%zMzpL=J>O;ugv#)Rm~I5 zVb>62uYPp%UPA<BYjC(CoM^y67{#D%-i0fK@YSrXt=PnWd-}v53bV#g`dP@oqH<^G zPLGY7N}`;pN=$En%CoVTXtyIHsl8LHPH++vz_PO?6227OgW_FDkwVw|7dmXrfVr2J zm!}C2CS=TbA88cTi%778?ugQjlm2wZxkH-jPholTs4w~ovB%%v|0Sw5fdo*(T5dpH zv?fHKJWbiVCY)J}E=_MQRx}PpUP+8D>Pqr4Xf>TBYVR&?&BxFPEmUo3tLc=_(B}-i z)yhdZt+YRuNZ<dr2EgOB{yo(OtgKuEzpdA_YJXq7bj9oo555n_me_{XRqe$|V-GDD zccZwqj7ZzcJ~i{wh9n6@a&voG{JCr>xsC-qAaMC3Gk!_clg15K0B1bEXsL<ouI$Jm zlG2+Z-ZVeIMC0X{Y>3Mag!Zt#tx(h?XR^+^v#Ttcrw2fnj=9@+po*psc>tR5nYw2b z8o6JVlO0B~v^5tGH@AC1dPn|Qp#McqUmZculbIQ1V%jZHQ`<9_uTf_zNFzDNGdo<e zk&W!T_wv$jpICFr%plI%&upQL5p`i<#d9AMtBu{7`^tW8r>AZC$_9z`$yiqwr0%}j zh`+i$5+?_{J$y6V=jHzvdq_`hInR2z#m*|pL_VZ3u5YYuZZdqYS&nvqpYgqBM6UMH zj4sI}Yih44Jz-1PyKyJ1hwJ0L(@T|mm0rj`TTEMc(#f!9T^u7h)@9Qg;^{9Z%M?fK zU8s`_%_oTs-JkgS`2%hCitZG$^1c-Ri%i~Io>X;J$ky?re-U^1LNp|#-KdJ8mT@@+ z@@?rOI%%8Cw>omL|KJd^de6-JI9bekEW0i+W8pA8ZS{8IEHNG89PN+GDv-D0Lf`9w z+1}E>-ov*xydbyj8kz5Cx;`36+(o()TqdE1jLmc;Mkkm(f2$Y2v7(jO)=3pux^r!! zqH$+ykn{K$qr5q<N5S=s3}W8|&G$Z4Hs8xY+v2bg#Qc{#YetffBtp51#7=gaFG4vx z?n)SMjs#<)u39^@hEBd52%uklZW1LQ4-e1xG`N85xn9rH$d-@h8p;R%W%pyM+`0CP zqJ(t%x|X_Tfku<%g91^%Cp_1e-5o3qob#{SSm&uN(b2ltxsw9#-#7a9_5zXLx86eC z<44`=k1&fSntSy(GyMin1tr}#qnRx^`*r77G~64i@9&oQ$h3DQ85r0OZk9ds@it{x z>h$zcy%;e{rLeahS7c{4#;cH{@V*9WV3Kg%;SKx=smIKt$4clCx<^nv*3Aj9S^>H_ zm~LSWtr4-*k$`m<PKpQ#Hv#ECb969f#9!s5lXRCbxGJ2Obz<U-aEXfLNB{ima%{r( z*Qk2Yu&sw)M6?&s$W^9YD{UmtwQ3W%)|^yL_yarh9G)+`(pKO5{3K(tFpyn6O4=(L zB{c@5BdX`-$^$rAJ}pFN6li?E&dKU_>k>sT+&JD~Q!$<kd*NrXh$a+7_@x8jUFW=- z;`n1gjxF5Z&)b)!ekrQ9xuT=cO^`7BAVXE6V@k9<&|nuSx<VULSe~F$3d=K=_70xc z)KwR#`0bVCeZDj-&ct>0oVEEe`9R>Ae#qm;<*jK(ZY$$CXl?&!*0M1H?md3J{hP^& z`+kGFRj!jkU6fu(Ira#`YloEt?KIEcy49i!L!U=f-@Ag`yMdJuAB8w*ihlc6FIHA( z`)i~md>dhZz+LQ(c7gE(az=x))>b(-o6lB1Qorxfi<K@|KpX0fZjn2otD%lu$TYtY z-waY1E_#9lmuUn<5yD{tZXf<bbPuoYV4GRYVVL_1Ch}_{g7<`If9#GZiC6u**b~mH z_v4YORNB12<D-{+5R;7sE;f7AaIG9s6m>M%)HBU#fyLucP&(a)pMOi%3WroqG${!K zOLMAT1NI!+$W9tFq2F4vGi)9?${Eo)u%HtKn(!c@!$W9~q~gEZslNs7Lo3Ga8j0&- z>}Dcx56K4&LR^9CEegOYUZjeTnlQznLdNni(()1l6xmQ~>s%FC7&jF^ziG6qA!~+d zks{nIsN4f#TGnUnbdmQZ`X1<fhPl<5{A+(LE31V=wQgxks@^cV7zYFdLQDRxMIzqL zzt=wrv}oRu%?%=V7=FO|n@LLMy!x~)@9W8Z=1%EzePQw2@WP~KWqF}P^$1kncOD{^ zGzQR+`a(%h32SDqie|D4eWLfKTMjY?y<VkdTpEVylwyQqurWs2P(VYIoQ4HnW4}*z zQN?@S`}v`i-*M@dP|8V_lj7>*-P}qhaT`#5P(f*mVtDc(7b~<w$i1k8-X=d%vrKB; zYiawMr4G<eD>f&8zmlB)F+%X(UrBe4scp7<^!M`E8@<4gft$zX`+lykxi*zv)petM zR4se{{6lV%X0Rw6#N`EheLnnMTa?!Ioaff(JU`ImlK;l7_{0}cu}A9gBRm=+sl!lG zvu$<t;(j{Xv?>rCx4{s3Vi00YQh<UXyzfN3%7>5l&Zk6~#6KLUYKoRM+)weeoB1O; zdCEXHFH!ST2;(qeFNcmKcZ;0Tb+Ky^KQEc2trcA?81#DnR+7jc^K3I~&_u_i#hm`V zpF*XC(3U~CR*2qSw0ohSs{+VPgVYKakOiSW6YK|W&U<}jF^0d?l<bvk*s34&(Qx(A zyiiLZRKN?!fYpANDD)vb64MalVlKU<w<I9z*54@f8zcuFJ4jgdNvD0N`#>;W!am@A z_&v9-)ku3$Nbr8IOUXJ^RP^emyI7=>z|2Ew<0m#4GHhI!J5p6u<p2{ZsK8)t)qMq? zsc)B5ry-pZm=vvwOGPa78(+Tsdwtw_RU~lVlWECh?pvO}b6=oyCvl{PY^p)}?;yya z>h7h5i7q8H=o^db`KWnx3yEjMOo4_cZgA&tL(DLA@!qT!UBa*d+=$Zpi#j!pXB)-T zufzpOv$BmpTluvxH{kjJy9?>}3H!NcQ{y>3Z$zS>KK)evh(oZb8(n0iQM37JbStd7 z3nS1&qot#xC`D3|)DH3UlMyQL*1It+2iF8isivElmw_D{{%Yg=7PR!XBKM+ZAh&6T zMMpvoW3(y!u-M}jjA;mfLSw9{MRWn6j@0VZX_Y9E4?x<L#6@18d5}J*9nx8AwU(-t zWhhrOb0npmmHX&Xe`vEaz;i^cNY3e2L>o8VG1YmzH#uia90X*{SmXk}F%WvD*ld`^ zK5^%yugO}%MiBff@x?YU{O?1rOVOztjdMf7Y^$Da6)}xpF~S2S457i4FysG8f@y^j z?RFsy=Zw|boGecc<KhoIcA2^?T|Hpv>XTz(1NF|3&0m8=Z`3DMf^l1E=Q|#)SyWOK z2yBeI)O>R`wK0x+7!cPpN{q@CVk=U0PehLLVRVsGOV*wtY$ATok2<vIxDbZ7xaCek zvwA)l5)cImnpwx-VH!eTXLY_sk0<#88wZNZ)YP|?AC;AwLGrR9lNxxAR}efv^zT8= zNrS;0&JQ_^++^HP)zgE~F0f&F?ovyZX@K9LoXR$D5K(=&+HasSt6g%lD<UV`KPZSD zYYHQ9M;e^ugn887-af#gz;VQo(aaULR6?>Q8nfUcIu|1qRE&hm{AevE9~%IB8vmM~ zPk#q}7^>aZbQ2}w;~1=YZr=kFa5~%k3?DHDU|9_(U6qK_UWSaeT1<7cV*H4x-=O?= z@7=qQsw){UIRMNG`&B5yv?J&VN(3x$JleT336WRtQbO(hi18>`nj!rKV@2Y{<k@)9 zXLkZgrzZS<wG1`X?MZ=D5^ghWyN{`nULaKv!X?ym$qJ#6dfV9<z7*!F_0p9^$%jOZ z*3(7IUg0}R0R>?9e~oD?!m9!^g4xPmknD4`Yov}*INy(_60$Zlbi&gOM_LF{x=F$I zkj~!nEi3gNqulT*kdu#1?o?4xF@@R#De=AAtZBzI7PO!&rp-#D*aU+tEw!4g$u+Rf z8!f*h62b{=2=0~GkR;urXQ{=%12!?cdJ`K7KS;OR@P48rLyT)-p6A_!rpN9h=MP-| zl2N>{xTre&3_is3SUOlZh}P}WObu_woK;BY8$-FO`WzQ+@MJDR^Mx~u6+S^Ojr8)q zb})il8J-?LJ6fhm$3Uru(~G>(zi=kzb_m!1VD%%5mX!Ty^kwfS5#Np}is7ldf&bko z(*%^SB_xcCOo<v9Z!T%F%GuvR*LPsBu|sRobRm!m4p3171EGB$Z`=2g5a?*6mP%jd zcZ%|(?{d*WgOvFh_yt+n6wYR=uKXv&0;5A|zc8jjxDqCc7Ikq(s@k(G$Mf}&jHhO2 zmA*b;!~js4&$j72i?T0>rH%!rtlzkiw;*2GCA=Y>r7e5jD5tIF96HFyUMbK#aRwV| z^6BBhxu0L}fQ=JAXE<#?912E>?2a5*^FN9n<+B=!1EokWgpqKhC6xv0-2o{n<}$vw zm*Tg_DTTAceTFnYjbGEjHP=@Jzrg>ygKY}!r}vPMiHSc%9Kq=!!uJ7-InU>Cg&M7o z%Ose6G5zsl`VOX%jIao#z<nnISiCwvP7h>_Gnbf|OqM^6q<9OyAM6l6(scl11TfJB z0mWn(Rc$X#*Yb06wsmzC!QfMcl@QuOVnpu`GT$dee;t0F_Z7izPzQ0x_2)}p3?FR9 z%Do28A_3R)p^^6S<40m}5D97kWylPRL-Y($W}>k11;0AztL<t{k6hkb8SE=)##Peo z#xXl;%`Dvt2#2UqhKJAZ{;2GD`_VhvNQ=#twPD6-E`|X`MCuz*_6fNnuiX5!&Q!>{ z^Aj$JaHasYX7ITM;LNR=`2t%*Aif0CY4TMpt;9mB(Jo%yD0n7B6JxFJ|5uHU398_T zb&=>>ouaM7^>JO>ECn>`+p@=x;{@%b<4}o(Dt>fgFLU&7O-tUX{))Z6rP~;}=%fVb z_9l#X$;xV1ByfAf;EBOQGRrqf$g^k#vY`W*t)lLpG%#Zw)p?LrBV70V{Re+0pH@Gf z@wD{wSn8YHLd{ePhopjng8oEjp3IMdMP>GyrJ^ri=EBX>RFw15f*3U@%txDpJWE(v z4W;8ZmLio5Oirp2Eurs;dt5s0e7joM+1PB6ew(0^nuW3TKN;I{El%-PNy%Y9jms%f zGLP7KXMaRKA`$z64JaJ<l&_qOYjHPw{&XjE^i9%p2@OkVhq<BPBPgYWIX2_lW%nBH z96skUb+fQT{aLu8S;NQntgshO{Tq<xy{jLspx>-Q^4Y%sGg>InrC*GTnLX&qP*Fac z=;1zNe0`i9+#czOMq<ia(1wFm!`SA$xMzEMVkM3p=DUc31;!@`2`S%YGWypIk3C`8 z!xHPC6NSEo?zEjE8TQilv#0E!bkN2u6coiUuCw>)xt|J}ZHG>)ID5k^xF^FJU&T7{ zWJseF;p${Q-E#b2QsyXg${UT+b*<p~cY&?fhHC!8Kr6Yr%H<UVm&mECYu_(&zr86q zI=EB8V^!BQgJ|#1rf2QQBrK6nzW?!-V>8-2NeaDdP*^|Gv@Jr~D`?zYMn>jMA5Ej4 zdcNXrxDi4+KPHTle#l)Td)&!2FXruA9aF;M?;m!M3ANS)8BZfWr!8sP19qAx<ofKp zNQZMWHf9}Gq8G4iK9HiFCks_(Y4DNi&#r=;_!>G|S{mE7E4B-;u^YdklD)66P+p~H z+ME>sRIsYCs{<ABM?;LNjEuJ#75+9Dgc<{@$Iulej4QoZ+l0#Hh&oe@HGc-$T1NAw zHOD3<Lup3{<5*|jBXlSTRhBcg@MB_XaX?Ir{NHgXVaz|i#gG%mJZN@I7z6q4;bRc{ z^h8p$8SSEZ)0=I?830?$Y+k#@%6dx3jI75B^QTN=j+^1&-0#wP5;PFC6h_ANflMZ* zz{!%=<&(Js+hK$l1ti_P1DYbt%^2B=A8&<fo``zIDg>^HaFWDW0vbR~aE;ZP3ZjD^ z2O~fs3NvXH-3#LI!g+fK_Fd80X;xhuaCnK|KVPE}6^7@g-Me>WBoy-8CUA5(_oy*H z^Zxq5A#{(C+JPy3$D;@!iX?20ZXwd<IPS+a8I22KO3Z!k=LcdqkD7WAYqj90fuwyJ zToe@b#CQ6O=uWaBJ3^`NNW<{Q`Fux<Y@415;tFw~oI9DAYQgt5sONj+Shbx3NR-fy zfS*B4Wt^PnVywbD>#k#$-;rzq>W=licap)_)1|-*J87%_fEr`G@fI-2=Wt%M6di~b zZ+y*882;;#Ome#2Igw$9;9|=*LQezSasjUA{!aKnOgwlE;0s`4Qby7pY0u(B{wKHv zZGp%MQnp2t4-D+luyepM$xDKhdvht#tw2d5TLN_vy}6p3>~vmsY!Sn}c32V8%y2^k zFseGn#D_jVnYA^+5@_L43nNXwTX!ELX4<i_XABKlap+z_&W#eWyNx;ANQ}=zdA2~0 z2q@4NBEw8(dATea%%Xtqgivt3O6ke5xMoM_w*>D2iX>Vr#0I-g_YXG2uy|uvE~Xow zB`_1HPY}#auoX&xo`Vu8M9)JxAw=E|dquGQX2V~&GZXeQIcd52Xh|gup<hpJcrIQA zQYPv=#&F`=H)1fKRW2a6|EtRGJ$vLavWg~Z-YeZ|E$T9TxDH|N4n%($;Ij_s@VXK= z%8Bv48ghNzm!u=n_AnN}>r;+SABpHZ!iRE}KN5~&HqNMaN-oxVxF`M+Y^HyBxa#f} zI#`KVhynHTqfz<W56IlOApv2AP(*-HB4T4>9jlIE$bNhyt35>-pB0@L;r7OcCO{{_ z8qBXRbT}3|8fGilfu}$iD2&AvbOiUvtvL*Sq9t0E2wgdJ-pP>e!+jGG9)+J^OoZ;y z2OH4R@86BEwLyULFDWSzWH}hh;ki5%F0nlL2)w}`3VJ#m>^E*4KLtmvc3A=$VXp;+ zB8Iil1bXgf86q))`iW(!71~9N6GNGn=>z4_{+u9fLXczRNIc@$6!hCKKsY!)4^U$< zueA>%IO~A}rA=}EXu}PEqc0`ezw}=8A#>wh1>tCkhCY5Q3!wv9Qz}_2XZ>jPACB5b zx^w4_26V1>axdiUSu^$u+4#_LyfBnA@7j06v&$hBJ3tJSl?pg#qwH#UK!#AD7s;Z+ z@X-fS;ml`FHg<Ng*=AOi-Lzn)Uy3E?#x0lk?a&vUc<12>Q47|!?eA;7W5iu}Fpl&D zohQHC=a*c_iP0|NH3c|9amo<_ndV<_<Ix}S>J>YtVs`It2J__3_AP9a1SPV~UH*Gx z^quVeJ*U;5&Q5%ftGNtHQh}o`3s{5_)VNat4UY>ba0Es&7HDh!Tl3R5%IQPLpOr=N z(P^HSq^CKUuWimVs%pI1V|<$!uI;NWxHF!2HP7AyElOeNI^rZkKLAIpYG@d-d=KMW zYh8l$J*KW?ndAfg*09&Jw3ar8?x8ahP1PwF>#Ql|lN$V%a3M<Ui43wJUVX3~Dd)4{ z5FAVO1K&ung=-fyNV?6$UVr34>mq9R(QP=+t6sXatTczTVWYFj{I7_|oLB3MeF^LI z(~gyw1b`y416)^7^i0vko!~iHs1ARXIPV{a8@D87$n;qsu}jM3*<Tx{htIc+n`{c~ zhD*2bDZ+)8k&!C4b56HhXMG~+{)2Opb`)!j6y^<~H;zs^?XO|spgX@)eZ#I-*S??0 zv>5nia}uYiLwg@72flaWi&3%gqKNc!+lk&bqVu&)PjL#){;}d>K9c~GA1T$31vY5@ zl@xGG_Z%hDocx~H!Xm?>e}r$U$K!;{PhC9u#NZssb$JG9i*@N&VyXxX<KEm$h`jsV zMILnf_PyJi)z6RK;E=|dKm}2MnCg`DGhDVXl<~nnMNhev8dHAEt`x32K21&)MO8%H z*Shbg&^?q%%EjS<wdxb}R6w-%_llt&(z39qV(IZtjkpYEj!<sG-2ZI;z2iTr!#MKO zw_?vt8)>3XU%YrTm@?JRFZ1;H<D(NauaLSjOgKsK&}YCuU;O(QXNUVucmh$UfYL|c zUvmKph6=oIl0!5o)#is%QS1=%U#woqP2z<mM575>E&sJ|M{Y>Aehcfq{f)4r2XR@$ z7UV{_vw+5l91aITc5^Yp)ebE>q8wj*r*<@c_tDTxAfCkh3rwFXpx&?{zAPKn6N_xK zvRGEKym;S0y;3NM$--h<4GJJs81i0hkbku?(dvo^n87Oq?qMm!GK4~y;*rZF@M-~E zlJ9!^arnP}jf6(!=VoDs#@8yCOu~L}v$)GdEb=^2=3pEXd(9KVGyu@S%E$K*yG}u% z%Nxj<+TXpR^8WcC*WX>Bhl{X!Q38yxf>YxSBVRD8!t6FkOPDNR{E5<t_v_F>f+d{K z*x)8uP%%+QX0NtHG0a0OM)$!QZtv!_n;a@p`Y`HnX=MjumMsnT62huVKwP|z<Y#ko zq~PdoyZeM>M;O(h?gJT}2Ksf-p31QQ(GMG7|9()7=HS4jHduj(+>U`FOkULq&9?AS z6CR!<jSS*V9#QimIez~p70Eh3Dd7=~5V2!TEu7g3hm2aX<`cZ2q)E&-o@10tU%|u9 zvW^_5i4j0U6K=I>7&P!UpAX=^Tr|HIIS93h!1u&?z_1kBB27n0?!m}hc0HbpX|Vm1 zFgCV~skWmU>9M|?8LFD2q$|n4A}AB!|2A_tODg<Iyj=r@k$CwFWQZ(@Z^o>8#W#q} z04^+;nQnVS`YB0-f`Y<k)B+<+0>Y&E<>fr5!$%$_(U%)x>0H)kCMCnNjt)MBQW%%P z?2kIgG8lrhqEZ-E+v=SceCXg$bXrh{LeVQA!8lb8j}0hI(CLP6JWPoB$Xsqx_fOo6 z0m?O(PX1#M&HAoj7e8|SiGGD09R)7DnEmVP_LwxmyD-|^O*IQ_%~zij7>ap|cb-4~ z%)n*`-midG$wN@_kcSUt;uN0{GlKwA4oeZHf*Ur(mh8xI+BkteV8CwVs6%EcmIwP^ z)MlTisBt!jEAb_|07U5r+RAqx|BU8B#;&!}G#wG*JrV&{lN(q=>00-c-+XcN4;jiw zC^_X;KD>N@08zf|?w!g7wx~hYv7{qzr+$!)O6;i5a|kE)T^&wdv{^ncIAl@z;+D_+ zA`g=|IO~0R=Mqa-0nHDbA3MdRJAa+~6jEuw8mA{!gqO91K=&cu$K-bE*8x+K;4OvH zedQZ>&`#5$<!;~Z;OWUN7?|9U8$uXQpOKK%MDLTo7~CSfkw2F-w~?8WxxDeuX#N3u zdeaC4;N;H(m*1IhVZd%hfLk<drn)2Cvr4Pi({W2q-6wZD%zcJDG=~4#JM*O>ydnzq zv1bp2a$rBFe?;2MAhm=<MJdNAp1^zs8PrXO4rlY?;x8%BM`O^X4%OM}9Xg6owZqE` zW`Tn!_+Sesa0Eb|B!PKxJ<^dpT}%=YIwSUm5FTK0Z^2Lzmc~5Ko?0JbGH=n`PWT!K znE>$0v7rm0_44IQNT`kY(&l8X;Y!7d%l-ZRB3|7Bd1b}|(<s{+m>U7rqNNr8?>&a^ zW-&J421_<^aF9`7gUSRl@ZZL?8jTuv{S4lYL`XCnw=?-UagJha+!hSC)sqGQ#L64P zA9{7BD={(8EukUV-^P^W|L_W=Ma-GJL{FWXfsf>kBS6gJK#`Wik(InqY<wCfLu4{$ z2*LtL0K~W^I217o>pMXQ3zG0sW%2-FK|1uPEvaiv8^gDumxx#V;C6A~Hd9k)?x(^# zg)m*s#=#LUf=R?^h&ac-OA^}gj95h;yr_gT)Rh>EQ&fDVkzp&fYAZZcBhI{rw?|Ae zGQlXwOxyQt{eeO;8~JbFqIAi)It-~OX}u6WkJcxH*#(3Z-`PZiG))h8Q(uaG(X)(v zMGTOhg=nsxW4)jK(!pNAY#Tk<#figPHoup4MgP6Iz{}&orT5H?q5|V$84k&a0hLFi z<h`IPm+nfJE{LHK*fom7R)vX9!ukpWxTD1b!!0KNhHbwT)H84*_kqX^02#|1@JXYn z+0GK?+010&*#R|nv};|B<r)^9)x@lD6nhJ0DNbkXR`=!>Soke;*C9E^=rI5T`Kh9c zqs6l$FF>$B@z0+>z(-PY0fFS%B&UmolM169{&WkHg2}V#|GL-E^C;#22Nz?U*k3es z=0;nB!DlcleLCS{T&G4#OSSdY1Oo=;NTCN<vbLbZ9678!)~x3bJhvRP^h{LepZ(n= z&MQug%ncsT;~_OCUZs(imd2}%OvLH`Q5P>|jln833u&G(MPTCTh+c7FWa7M3+sH9V zhac2k1ukaDw!~x)+KZ%mOHWI@Lc^%VSaHSk(Ux~tvZ97-*LU>Ue>d&gl>My+4^B*1 zkcc5nV(cHP<NG2PIKb-2onI#TWD(9E00g4!JVf5cyJl1|3`jPdRlNLLGtBbVx0Ch1 z-Dd_f7q`{dXAv3b9GtWyj77XbuZ9?IEu5}oBoZnZ0@)6X;lf6=sT3TN_*Ntc7HDDF zdXRy$Icgf3n6_C$oLe_BVfq!Xa<K2Xo-++X!H02g<-hElX4%E}h?o4F|0kQl>y{a& z=bS>!Sf3>dusL^Hz`v{OKejr1iHtUZ4q+joaXWAJM~C_LOHo%>w-BT4sAVL=L5V@k z3vbWARBO@T2$T7|qIvq|YqDCaY{%0vA_iMtCu?=|xQGAiwCB+I4O%s)G{S`@qQse` zQK0DQIr}xw!Hh8E6Ou7hf{+Q%<YXI*@sIp&=>y3&>#5PGMPy(-EM>U8BhFLlH^*j~ zXnub;wa~H0yz4j7I3ppy+F{|3wYRxg*^EYNj-cc;#*?0Ip)5@*5WO3rEG?ifBTW!e z;2Ws%<0d-kN-OgSA!DwqD&!%ZAKEXw_jsPgTLvJuwa<VfI!yP=85$Ys%qWky5P3tC zQpPR;o6`uOM9A*I9((!pNyH1Hu*oW3VGE;J%Cune7rC`~8(IYjY(0i=3E}A}xEZwT zH$Yq}o<Zxg&rkW=Q}JfAa^`20Ts)YjM5fq^K71^49bsG2vd*g*X*g?HsArm~2YBl$ zj&I+hr>6&5ssY5n4f73Cvkq&2uQgBK3{o*_yjcVsHGZ5*gzg!oRSequ=m?f$a-%c7 zDZuH2g}mmPwf0?2rH_~q7rsRG!ngxU5OMn$kfI>yJY>IcDTi6psxlVy5?$1QFKj!i z-v9_UzP)e=^+Fy!dg6V1u<<)DJM7<I75~Sb7+il%FH;=~k%yQ-m<CZN|B`EGL<~wG zLwC*G@Ti(Dn%U|&_Li{C%M!JHJ3t03osWQ3_IjH|yZHc*SDA==#Kt*UDkbW|B=|@s zY2Z&~;Dsq@EqM|7-W3KAt}s7;zW9b4F(Mgz_EG1)wbGrx&?jhiaPSOE1(wGK2~o<V z5odA<UAOD0D`-97y;!*`69tl2F2Nn9tvB@fXs~4=h^j*6H%hLE-?&{2mXdIF<bzAu zRvILL&56FxTnBlNe-q$bD&JDmD)V3ww^99+{6#D$riTDEBA}Sjc%vgr3`l5^YLBkJ z)=Kp%Mzupd-cqSI&Jl776hy5hM!dFwcpD6wkqd+j@lSofjn}3<J8>Tyv;)}+IZqwU z$EJAIcudhn^FS4ljgrwR4gC!Co@YO_74IT4gKVu<>=t$pyEk}ktG8~Ym5Rql*>#7# z6d^RI#;5=^5^qu~es+$yk>MjG_Yl9$4E(G^%=u3^IljL8uBBa4?0Z-#OkW`42<DMd z3mksWHc`dkw)%CqdNcn>IlqgRtglaa@|oS^*4CR`nsanScnrX!=eSmOR{X}51VfEl zjnR1kjofhdTdwE-tM^_I<!b7Qz^W%@=l0bNyI!j$EN9wG=1=Wmjuu$GWx|*<WjpKz zjh!`=pHha#&g@8g;d140Iax5N=+=E12mL+6_46jPro)m=$Hu2Dh(0C!@`yz{8vMDf z%MmfV{>rOG-n{Q`%%ze?dTTX`G>BgGY4}ET5^c{eVca?-6A0eB;PizGj6v?B*gy&? zRX?C7HZ1B}^s;exAKo7#+XzHXHt~PecIHt%=l>r6+GfsNjAgPW#`4R)N3>vOFqY7y zK`2Wlw5e25ksq>^ridveex@4PqqNG<Dx}h;1xY0Bl92A>mHD0fJNN#6=bqm^=l(Hg zoSCn_eLkP}`}Kakp4+F(9OjlMrAgMm>vhsmb?bd|&@AP`>8ju8U)b#DR%-FqeZlfQ z_=0-OKhgfwmr<jYF079BVVh^hHo1EAZ`54dbysV|djq4}WoZ}vT#I6+sPD;mxVqmA zw!|<?ZswC$x?4%Ykk%I5{XRZ}AJ`Ct!+9kgdRo3nJ4}C9(jmj^N{2p&ezdg2Xt+>} z<~>qUXy5cby%Ek^%{<qGZoGTjCIQ*ei*>NPb?ZrLkNqBdI&~hi2EkE_hReEj+lGDq zn^MA8>;JbB{?IuzVwx>l&{{L=5ivG{E2;`ZC%eAS9yV;4Y$sv2h!UxK=zsMG5ufcZ z>+<NU#ND@UeXFKcd3uDsn^xCh-TUV6%{iBFiY9nmn3nl}t@a^Y=dm2!H;;3zzM8db zr`L@x4>!EHU-I$YnfZq2<RPniw!6Fc?uNcIw0hj$)-FAKT;3<wlx{C~r<r6`Bk~#I zGp^Ik)f=DQ+MSpj*|DUPL;KN7<1Q6X;Ta7Z%G9$TRcT#PAFa15zU|)WX!{{<AF{O8 z#G3P4&RePW#f0NgTb{Nj)UlLd_kGhm&9&on|59vzE!Le6{5EH-TlAhOADiF5r{%8E zjjms*6LRf#kV78xpSQ>FohBR?2NaPeWf%%d4{KJRGSW^Tv-U{Zr1FdfpTA?n!}h=a zprXC~DAJcgqc1yltfjZLO;K@FJTe65n;pJ*tlRVlQt5#S1^11{di1g=da@$q#9Pnw z>hH5;X9X%L?0$)gnubwB##qHhVn!(k_iOuCU1XyUIjq-b%hz>(cFkx2_5uX>E;m{S zrH97sw!nVierI~$93aksGGtxlkg7PF_Ht5W;+YkdKgh(<l?ppIm>xTzW@=Ls-h^IY zruZnij1FSF2V=%#Td4nyHvf5cz@l~&_H7LHV|W}=s){g_wf_H<ZYv2`|1%mlD715G zMVH$OSke2peR*d4f71sj(osEfJ8z_7gT&q{ux97}`TvwY=SB{=ao=RciKt<VFy;iz zaMeivYL#5%&*A<{+7cPa+*Orz{BK0^`V3_=)MhOlPgbA~Q#wixou(q3i{1P8Uw|Qt zeQV4=R_7mTPOoo1zv4<8DBo5K9IeqVKTBQ^e`IKoR}LUM1BD4NveoYooTt|pbQ!1x zZ~W@FcfBA_k~hbD4GqfRNP3B9gmG4k6VT>4p1v_|gMB)9sA%R2T5^RjfM>`V)d|P( z#Bv#|VU75BE+IUg5GcwxS|@@cnjwRF%ygdcW!T$+Z+FZ{_*j2P92~~D7L##_TY3zd zS+X(rykCz#eO~Mei{BR?l;Jth_03ObXp@sU&x`eg9c|05M&p(AfRI)u!YHz3!Mu4} z?2<~7N-|!sl;$$tc_JN4nr2(Je<H!6I4)A(0pMuqM%W>9OCg6Vsm&WSXg00x={n4E zAsUXQi3h}`<6oMO^23ux>hW`XSe6+<0!h5X>t}HZFxY}<l)`+BWUDqe&OPihV;Q0y zb*$4D6ff5wc9}|xITv2Z13WDH>g-G9sV^owzqOE~NUbfF72?~2M-&yjY`Jcq%iC7} zi=5j+5N@87@2i0j8xBr^C3n0tLSeIbGJV}(x+pJeglq!ZugLX}KQJ_?+f5K8!IZ$V z?9Kd)(!4XuLGw6d!$5wjC&eG*A>yH~$iFi5#*G`1#sD^_A-t{5<rOF?lAgiWeYUOr zlkra#5IZ7%#<tpTp<Pdd>sF7-tDIKfyuuP05Dge}ew%`d;l5b^LX*v#hJF+&vAZ(z zTAj~g{S`n4?uUA*h1epP=X+psWp%5q^3c39h|+b}3tmtMI-3>t^XTe?)*ZOFidJ8- zBRC<&5UMR{(5!zsFqVuN9kk@aN}$F4Vl#>(hfcz4b4<67<>Aq;`Qf&aM6Ss9aehOC z?m>IV9KsAD+JCm{G2~`f9czrP=Tuq_Xm$bSYlF^KMUElR^b~|v8=W}K=9Xon#tF86 z_mR4%W`)hP_{R~Gn8&q$j+j5T9<%X1?=pi>r_&G#56><4qD^afetb<9E#~s$uY6y( zxLh=hO~&<>OaVPv{X+Hr^VFcgKcklD<-VT9#%-n%7m2F)xR?B4l<b|6f=7xPDKS_I ztJtck*gA$E8^7j0M)abkmycA0hHP8??@g>WWesf3ZV7~L95jZ^EJF=tbv8%2;6pR> z_CFf+in9N1)O#Bfzia4D2e4U-rx_x}3V>r68?k*TcN>I24@f{M1u3enedkl{Pi<TM zcLmL&zW>XN%WeWhEeFhAV)Gge&%(w&q0_wLgO>0Ug_<-#HCNR~WP?fjdkigF9pSj8 zEaSd}Cc<?s9dPLI;oKmVKDK2I|0Jq7#yvE2@S(o$>*C(OIyV;-*OO<WJo97i?o3c0 zWAJYGb$ydLF+G_H`txV##v7T85XKT<mwSi23Ll2m1|A)o$gIer&@vg_&^p-g{9h-T z9?ay?K10lOa?H$(t4$5je#ds%Xgkk5!9DE@<f(paoZ)6%D~azOw|+8H+x#$f|A7N_ zCe2PKsc+O@4~(D602EV5dRVV>Qfqo%YjKET#Psi7T;l53GRllEQ(;X#1Xw6JXBZoK z#U!og?Fn6;p=+JGIqzcNt2`ogo}9iDzdJt2i%a3K^#m->qt2b~0T@UMq0_1LHKnO9 z>_~vjvn#u5aBZWv#7~{UL^l@$oF@&t^XHE=uq^wtf7SY{ZCr~_y!?Geib-GhrxnZQ z$(d*~=yNRX|MA?dYuMUuh*Kt)Em0y>5>h3x#*^Z!#ePm(*J+Hhul1vAX{J*iRidCq z8kAm@(E`xd3>1msaX6T5qQz86#^_HRAWtY77fkWW3|R{t02R3xltI@{E)2>^*tt^( z`3F-3@`JRG%*)-*LrG>7T^g_|bg|05>K32|-SaCZ?z>okejb^`G#Ix~*mZ;VClx%G zAIYucY&0gcBJ!Dt+q|^u=Y`~Xsc0Uf<aQ9+leuxDbIs1y4})lb&U{W{FRAvYMbWD& zJmH5?rWUna2eBgyRnF){M5R%ftJ$2Q@6a@NmB1T*3y(CDJGF6g+$`#4T8+I_=hq3l z(3$)4^kb@`f%8g9suBOVLTQAq8%Y`7Se7KTLHPNr8@h^ntG+A^NAe@fi0D;7>bMbN z3}5sfrJwrr1FfKLnEqLvjE2TW`<FME+D#CrsafRz^5B84f<?4QPfst*uBoc<N635} z*b=67wq!|710~o#q}>CwUg)CtZFbZ@qPpm{#wAFe$kzMhM@b?Ex%X>{PZe_c;;1FV z0ECB(D~AYYPaQaTkU)cJ#SM6uMQ)-zI{nv(P8VI8>#dy2xxOS+!>LG5h@X`Tj+d>V z_biS}Nk8YC)`Qsys^kK9O)_4h<&>DEu*)>Uap~2qXk&;mD1Y-|F?k)LW<?oOICDGt z@Eo;_DrkD54&J=VIfRU^z<#ITZ8atLBxbOS=d!}<*0cqmY(6S)x#>Cf;9*hE<4bDm zt<fpW@6nwHRCRyIxZF4=yPH|-y?dBfK1MHGUhi-FH_<W>W+nipNQh~u=TKLcUYpU} z+?+|GtH@Z%B%02#Dfy(UGkxxG&@{iVytJt#&dI&?N!no=BICYuLoPU@r)+Oq9dwqn zNrG7!vCAs-5<7GJMXey8s7;wMvCGin28mmu^1jRpJ`}qk{U`YjaBVofb+j+yW-PX& zJy^t+YWI$B!u4Xe)Yr$4Lk#%-ZijAKPr}VswCb3?@d`r*a-;Q=Gr3N4nU)QgEp#B; z!47+LI0?~T57c^DVVjr^E|8c+@Dv%IV_1LdQ7;e!9IWwz-hzm!Z0-vl_ajeR5Qe8C zQ+dZRC#}{9{olLWzj`=WP3<VGRY?C!rYbHoi7cZ#PGMp5B-cm)s~96_SODZkB(1ot zJp3y;OWwYx0UKEz?@+Z8M{pt}UbIW|BoF}67)eqHwN3NQ2N?PxibvP2GtgxFS2RPc zhteZ*4WX~v-{5CUbTU0g8KMl)=%DbFXg>K1c#20D#cexie*W<@kgh~5u_z8igeoF^ zkeCfC4i#7+8~ZDppu397J#h^avW6<-xd<uWH4V8=f2_ov4n|`-F7P+I7lDrT$;#H3 zVIl~Lo)1@}7buSP`$038Tr!H6oTK#3X&2dOf^PxrZX;QYF!^mgNC=Ml70m<~sroNF z4(*NjaGjsquf&|qR0KLJjXOOihl2{(<}FSiC+?o))Nog9{qw949oN=wK}fX_HuEgu zQ#>PM`g>+4%{il2vVquFiZLB>PpED3S%29Xe+dWbdK4@cMNvJlSHLEcoL45vSS&AN z7N<k&z@)^)?x^l(u`*xBgs@e&)|3QNae}AqKzbtB0B}}5*}SlAe*@=*<EuycIT5yZ z;I=32P_FruyYB0%oUH!X<p;=i;qNgOCuNLp**Q8TDJc`@ppnz_UR3ABa71u|U(@Pt zfEe1dZ(jrLZGMxoclrfn%B<6skrwlb@HBiIfC!~$&z>Mr1)nBA-(>mdTDvg`j?7d8 zFV2lSfCp50MXY^x<rhYzR?!KG<g}thPIr-u!IVr!>Mq)DLYr40EE8;#7EPjGShF+k zRkGI>a>e^SJj%G3=MoggNN|h3ixL;Ut!{l`G+vu)_jJbm9Ht~LuZj7ttpD>|En{je zW~4gKF!HB+?)cPfCfA4h$vVEVHx9BRh6yuamcv`FpILac=rzeS@}3AatD;|1Q|Adc zG<$YOMk`bF+(ayFc~`p*$oE)zBh}9s98K1NNTW2J{@BcfEowcwEr})(rxF*GA8y(i zYsg^SP$=}p-eFex;Z9dBxBV|0Mqj^b`mk#NCJWFx<R#ZEyI3v$nKgx7;x2HjXsX+m zBhGzw-rn*zP?G>HCm&?eA=_%$k0P}rjCRs(8|y(Xwe1uLHx*>6%*(u$MGasA_~Itp zvKa_-so@$X6M3x%D4<0Ouj|-ozDtC5hL6+1lZ?$XQRjK-d0eV*Yz!vpPwQjtZ5p$t zj-GUXk3JQiG@U;w?4HK4R}xE57w@m53cUUuCy(31;(}!uPft_L+=3gYp2;ww;F5FG zeuDig{c6;Nh0dJFX1oa`^2!zi1I;ZQfDkpqv)8nKc-%VRhPuQsPL8`|9}uh!3T|2X zG1|B|RX|<ws>s$2)bK5h$Gqh&IWjO6nsIwP&%ADS!AAT`bt8bQC=*0peLQ!R?NGWP zn+|LsRt-htYdXipnSq<D!eWS%%ZmR*K0obYU$=b&w~x4I$5@Pt(1g#PUv+(4PM5pX zHs*+3`gu8|_ok+%ZjC)`jXX~kwSv5sC0nJicmMu(i2qEcf=*+K3^8vkj&e6#X`WPY zPF5|_bt=VELML9iUM;Xt9*=n<LapZMyh`6GA5puFC~kZLpP4F953kG+v8Tn%*H!Ck zG~D`)-Rt9hH@Dr^-*Q>S`7t?FhOrYgsV_v=UyxcT*HZW5R$y_$+w&c;s}yv3oB&KD zX%m)x_w3gcx7d6{7^1Z$Uwj>nL}X*8tYYj^lLn|+R}C4<j;H<AX^KO#B`U#HE8>3I zUfFE0+b8~y^hdYkr4DewN6M$GJJ=1<*g7IxvF%y6R3yxWKmRzFlk<S5rzzeJQMbt3 z0LUc~n*!rmyDSxJ--2$H$_6u!d#F0$e$f4GUJoIKUEW>(;q)l=3zEEQP`I<t*p&}Q zHr>_X7uD-@6MCPmvV_`Pl+`OR$6jG=($Yfo@ao8#zpNiG3!euhJ&y<&aa@oJlL|?d z%)N^Sji0>wZobBML$7x7;+A#!rM%;pX7~17i&RR+yOU$-!dlbG5bYkv2$jYTIhvB; z*?t8m5fI5Om^l9#N2bJ7o{dx)jUpbF8z9L4<;#~_Yy6g_uiJh;XyJxk;#t596s%b{ zY<$!e<pgr(teiGbHb&^4QlOYY-nI1T_%$c#2x&8WQEGSTIF_W1JfE;e{nNmMQU0Wv zMIp*rFhx24Wc8YN<MT};O9H1@7-Tqhs_b@7WSL@udS98W?VY_8Y2_+&&-ibCWZ}<Y z1laWG+F8p%d6dO^Fp+Saj>uCJS;sOE8^k({6;gnq__Fzde$ECvgC<wbxPGJNPDWC; z)c~WlOI|v<!x(2%43rLiU--dn)bJ~xc1*AL>gN5L7n%@aY}Vn@%7}yV8Rp+Em(Klk z<_iT{{O4Gc7Bp#9F0;KjV7EQIyTT-DOBIK%1iZr%qvVgRHdnbMF=es6lMG3_bzdjV zG~Iv4o0L9w#~5_IBIgn83bas<_i(H-e{k=cFT!uXk2X<rZs0q@?KNYKEdoNP#VS8G z%AL8CMXrj@{Bzisy@VP^_76{g|G~GQ|EVFOS16s!eGUPd7%+230HsBe4|o=nT8_7l zy<W7wvk`Y{aYF#a^<}hRPZA=M!f&Uf)aTv4m!1o-&>P+h&C5|M^J0mWrYS?&{v0)c zgEmcmwLn^2+FYCxRNyRaN1i}rk{@Ce%MX7z5f!K4?jJ2LbEj$^VLi6XSJ9cAb;H$p zm3D>wzEx&A%YWA$T)42P%!D@T`l9mb2>S|87V-f$PXS|+Ow)W@w3WI_WzF%%jHu81 z*3Z5r9C&Nc<((m=4AKfw#feV@7B;d;0%zq7L>r4^rqBxCbqLHIRL-cNliA3TV9K~T zLm_SNs;1E^RF_GXSL9zdQTdNoM9mpZcU`jMgC%mWJ9KSMPj-DNZ<{)JN>ZulzqZk| zNX9PLt<`XMa%9tmv9<&-jeL7xN|)W7ZDL7llN`tH-ZYnbxrugMK?I^16HjClb<t15 zKFo685jm^#Jm#nW2elWVb5>aJ;nim)$`LFF2C*>whr{<jEy97!l-KW*ZH~ikxwPIU zjyfxQT*)E^K)o_6xHGdt8SSVTKZweXz4QU_QgXB2@#YuNAxm05)q3dEslD9*xaD;t zp&dQx`y{6isfrwp+!B{h%`e19CU5hi-9T@i#>_f$`NaV|mElvT=1x5#2@o5~QoX;{ zzKTQ%OI{Eow+(F+i8SBAPZA|NimD+H88u2lx05Em@bzE$(b%E6y-5Y?hwL6!GX%}6 zlqVLR53?V0UostGU~aFEe8;4X55C1Doko8vZ;PNaE9FsAPRL42XBS_+HY|<JnH9Ln z^(xO<KE5yLsSaJ#Q?m4ikgjndqY5H`g8q~{BXjp@zgz``(<wl|ekZfZ5}Co_t)EiW z;__`(zuPFw2SYBRmdSUYv_EP$4cG||p8c%bEv(<_n96bcHoTa@NGNhzkR|~Zda9^M z7?-H{ctO-DmV4yI!q;Kg=1GbWRS>VZSwHmz)jybvl|fgC42J?H$3a9ENrf!3Nm0Sb zXBvHs$8mXc&M4rOJ1j!Hg@1yc<c=()%jLZP(f^t1AsT1IZt{vQZ*Ol2Uz2yk2(cjI zL43aaF_B28`M|L$s2w7F1I{csI^qx3j{zL#=w;_|jdozXaYquI4e`%+$<TK!*g1%m zl2kvcU)opCIzy?8-9_G5A+T+s?F1kdVMh~FQes`}_r-VqJ=6@o)9rcFxZyPhW1PIw z=9#Y<1+z1zul9B|kd?T%kId1PWMn@t+lVemvPn_(U11;-6t}`B@le}KV5Z#z@~drZ zjPP_l`o=Q<@-A3du}AEkX#?L5K;zH0&jv$gHmowr&RpfnB1KRi!~p6Xqg403q%W1~ zkB_Tuk00;}kjo;+bZzVFO!wkZL;1gmGfVox!EHY-j6<;w413Fyo9(#OuPSV912EW4 z-jp@Wf2!pj2%gsriwigP6b6IW29j~o_HPN2{{LlX8g?E}C<?3CAE3lb7Zvm8ES`Pw I`?Y`lPeD)3-~a#s diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index d3ad51e1..01c15ed9 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -292,7 +292,7 @@ "<text text-anchor=\"middle\" x=\"200.6\" y=\"-53.3\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n", "</g>\n", "<!-- intercept&#45;&gt;Y -->\n", - "<g id=\"edge6\" class=\"edge\">\n", + "<g id=\"edge5\" class=\"edge\">\n", "<title>intercept&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M69.66,-115.65C97.53,-103.38 140.19,-84.59 169.18,-71.83\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"170.71,-74.98 178.45,-67.75 167.89,-68.58 170.71,-74.98\"/>\n", @@ -304,7 +304,7 @@ "<text text-anchor=\"middle\" x=\"318.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights</text>\n", "</g>\n", "<!-- outcome_weights&#45;&gt;Y -->\n", - "<g id=\"edge2\" class=\"edge\">\n", + "<g id=\"edge6\" class=\"edge\">\n", "<title>outcome_weights&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M291.82,-112.12C273.12,-101.02 248.19,-86.23 229.12,-74.92\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"230.67,-71.77 220.28,-69.68 227.1,-77.79 230.67,-71.77\"/>\n", @@ -334,7 +334,7 @@ "<text text-anchor=\"middle\" x=\"482.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">treatment_weight</text>\n", "</g>\n", "<!-- treatment_weight&#45;&gt;Y -->\n", - "<g id=\"edge5\" class=\"edge\">\n", + "<g id=\"edge2\" class=\"edge\">\n", "<title>treatment_weight&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M433.15,-115.73C376.52,-101.67 285.18,-79 235.5,-66.66\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"236.11,-63.21 225.56,-64.2 234.42,-70 236.11,-63.21\"/>\n", @@ -372,7 +372,7 @@ "</svg>\n" ], "text/plain": [ - "<graphviz.graphs.Digraph at 0x1877d4c10>" + "<graphviz.graphs.Digraph at 0x184757010>" ] }, "execution_count": 4, @@ -630,18 +630,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Dataset 23\n", - "plug-in-mle-from-model 23\n", - "tmle analytic_eif 23\n", - "tmle monte_carlo_eif 23\n", - "one_step analytic_eif 23\n", - "one_step monte_carlo_eif 23\n", - "Dataset 24\n", - "plug-in-mle-from-model 24\n", - "tmle analytic_eif 24\n", - "tmle monte_carlo_eif 24\n", - "one_step analytic_eif 24\n", - "one_step monte_carlo_eif 24\n" + "Dataset 99\n", + "plug-in-mle-from-model 99\n", + "tmle analytic_eif 99\n", + "tmle monte_carlo_eif 99\n", + "one_step analytic_eif 99\n", + "one_step monte_carlo_eif 99\n" ] } ], @@ -650,7 +644,7 @@ "import os\n", "\n", "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", - "N_datasets = 50\n", + "N_datasets = 100\n", "\n", "\n", "# Estimators to compare\n", @@ -777,91 +771,91 @@ " <tbody>\n", " <tr>\n", " <th>count</th>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", - " <td>25.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", - " <td>0.32</td>\n", + " <td>0.33</td>\n", " <td>0.22</td>\n", - " <td>0.70</td>\n", - " <td>0.32</td>\n", - " <td>0.21</td>\n", - " <td>0.69</td>\n", - " <td>0.32</td>\n", - " <td>0.81</td>\n", + " <td>0.73</td>\n", + " <td>0.33</td>\n", + " <td>0.22</td>\n", + " <td>0.72</td>\n", + " <td>0.34</td>\n", + " <td>0.84</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.12</td>\n", " <td>0.11</td>\n", - " <td>0.14</td>\n", - " <td>0.12</td>\n", - " <td>0.10</td>\n", + " <td>0.11</td>\n", + " <td>0.13</td>\n", + " <td>0.11</td>\n", + " <td>0.11</td>\n", " <td>0.13</td>\n", + " <td>0.11</td>\n", " <td>0.13</td>\n", - " <td>0.14</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.14</td>\n", - " <td>0.01</td>\n", - " <td>0.48</td>\n", + " <td>-0.03</td>\n", + " <td>0.46</td>\n", " <td>0.13</td>\n", - " <td>0.02</td>\n", - " <td>0.50</td>\n", + " <td>-0.08</td>\n", + " <td>0.42</td>\n", " <td>0.11</td>\n", " <td>0.56</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", - " <td>0.20</td>\n", - " <td>0.14</td>\n", - " <td>0.57</td>\n", - " <td>0.21</td>\n", - " <td>0.14</td>\n", - " <td>0.56</td>\n", - " <td>0.22</td>\n", - " <td>0.71</td>\n", + " <td>0.24</td>\n", + " <td>0.15</td>\n", + " <td>0.65</td>\n", + " <td>0.27</td>\n", + " <td>0.16</td>\n", + " <td>0.65</td>\n", + " <td>0.26</td>\n", + " <td>0.76</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", - " <td>0.32</td>\n", - " <td>0.22</td>\n", - " <td>0.70</td>\n", " <td>0.33</td>\n", - " <td>0.19</td>\n", - " <td>0.71</td>\n", + " <td>0.23</td>\n", + " <td>0.73</td>\n", + " <td>0.33</td>\n", + " <td>0.22</td>\n", + " <td>0.73</td>\n", " <td>0.33</td>\n", " <td>0.83</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", - " <td>0.40</td>\n", + " <td>0.39</td>\n", " <td>0.29</td>\n", " <td>0.81</td>\n", - " <td>0.38</td>\n", - " <td>0.28</td>\n", - " <td>0.79</td>\n", + " <td>0.39</td>\n", + " <td>0.29</td>\n", + " <td>0.80</td>\n", " <td>0.40</td>\n", - " <td>0.90</td>\n", + " <td>0.92</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", + " <td>0.65</td>\n", + " <td>0.61</td>\n", + " <td>1.09</td>\n", + " <td>0.65</td>\n", " <td>0.57</td>\n", - " <td>0.44</td>\n", - " <td>0.93</td>\n", - " <td>0.57</td>\n", - " <td>0.41</td>\n", - " <td>0.92</td>\n", - " <td>0.60</td>\n", - " <td>1.04</td>\n", + " <td>1.05</td>\n", + " <td>0.68</td>\n", + " <td>1.15</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -869,44 +863,44 @@ ], "text/plain": [ " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", - "count 25.00 25.00 25.00 \n", - "mean 0.32 0.22 0.70 \n", - "std 0.12 0.11 0.14 \n", - "min 0.14 0.01 0.48 \n", - "25% 0.20 0.14 0.57 \n", - "50% 0.32 0.22 0.70 \n", - "75% 0.40 0.29 0.81 \n", - "max 0.57 0.44 0.93 \n", + "count 100.00 100.00 100.00 \n", + "mean 0.33 0.22 0.73 \n", + "std 0.11 0.11 0.13 \n", + "min 0.14 -0.03 0.46 \n", + "25% 0.24 0.15 0.65 \n", + "50% 0.33 0.23 0.73 \n", + "75% 0.39 0.29 0.81 \n", + "max 0.65 0.61 1.09 \n", "\n", " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", - "count 25.00 25.00 \n", - "mean 0.32 0.21 \n", - "std 0.12 0.10 \n", - "min 0.13 0.02 \n", - "25% 0.21 0.14 \n", - "50% 0.33 0.19 \n", - "75% 0.38 0.28 \n", - "max 0.57 0.41 \n", + "count 100.00 100.00 \n", + "mean 0.33 0.22 \n", + "std 0.11 0.11 \n", + "min 0.13 -0.08 \n", + "25% 0.27 0.16 \n", + "50% 0.33 0.22 \n", + "75% 0.39 0.29 \n", + "max 0.65 0.57 \n", "\n", " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", - "count 25.00 25.00 \n", - "mean 0.69 0.32 \n", - "std 0.13 0.13 \n", - "min 0.50 0.11 \n", - "25% 0.56 0.22 \n", - "50% 0.71 0.33 \n", - "75% 0.79 0.40 \n", - "max 0.92 0.60 \n", + "count 100.00 100.00 \n", + "mean 0.72 0.34 \n", + "std 0.13 0.11 \n", + "min 0.42 0.11 \n", + "25% 0.65 0.26 \n", + "50% 0.73 0.33 \n", + "75% 0.80 0.40 \n", + "max 1.05 0.68 \n", "\n", " plug-in-mle-from-test \n", - "count 25.00 \n", - "mean 0.81 \n", - "std 0.14 \n", + "count 100.00 \n", + "mean 0.84 \n", + "std 0.13 \n", "min 0.56 \n", - "25% 0.71 \n", + "25% 0.76 \n", "50% 0.83 \n", - "75% 0.90 \n", - "max 1.04 " + "75% 0.92 \n", + "max 1.15 " ] }, "execution_count": 12, @@ -961,7 +955,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1045,12 +1039,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1086,12 +1080,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1127,12 +1121,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] diff --git a/docs/examples/robust_paper/results/ate_causal_glm.json b/docs/examples/robust_paper/results/ate_causal_glm.json index 077f220f..992cff1b 100644 --- a/docs/examples/robust_paper/results/ate_causal_glm.json +++ b/docs/examples/robust_paper/results/ate_causal_glm.json @@ -49,7 +49,57 @@ 0.1830274611711502, 0.4631514549255371, 0.3272465169429779, - 0.3190767765045166 + 0.3190767765045166, + 0.4023485481739044, + 0.26572510600090027, + 0.35448575019836426, + 0.29157865047454834, + 0.43992626667022705, + 0.22037973999977112, + 0.3610759675502777, + 0.22566869854927063, + 0.23757776618003845, + 0.5053218603134155, + 0.4674651026725769, + 0.6472631096839905, + 0.5025660991668701, + 0.15235765278339386, + 0.19785749912261963, + 0.20855219662189484, + 0.36760589480400085, + 0.4717724025249481, + 0.3667448163032532, + 0.31002289056777954, + 0.38428932428359985, + 0.39412227272987366, + 0.4664827287197113, + 0.2637614607810974, + 0.39049017429351807, + 0.41675034165382385, + 0.30378878116607666, + 0.3408553898334503, + 0.3599216938018799, + 0.42854785919189453, + 0.36476433277130127, + 0.15343733131885529, + 0.3367450535297394, + 0.1642351746559143, + 0.30034878849983215, + 0.21686823666095734, + 0.2961113452911377, + 0.4661010801792145, + 0.2887129485607147, + 0.27136844396591187, + 0.3301190435886383, + 0.3257361054420471, + 0.22117461264133453, + 0.33639076352119446, + 0.2383575737476349, + 0.2682618200778961, + 0.3125735819339752, + 0.3407227396965027, + 0.4198980927467346, + 0.23820869624614716 ], "analytic_eif-one_step": [ 0.34353700280189514, @@ -101,7 +151,57 @@ 0.3428800702095032, 0.2197500765323639, 0.2696734666824341, - 0.08713576197624207 + 0.08713576197624207, + 0.22755663096904755, + 0.19966742396354675, + 0.26737338304519653, + 0.10679563879966736, + 0.32873862981796265, + 0.26920491456985474, + 0.3943629860877991, + 0.3750118613243103, + 0.31667327880859375, + 0.28761085867881775, + 0.30544939637184143, + 0.11848920583724976, + 0.11486601829528809, + 0.17888201773166656, + 0.21536558866500854, + 0.23146675527095795, + 0.3208797872066498, + 0.04642191529273987, + 0.04861673712730408, + 0.24245290458202362, + 0.1223706603050232, + 0.2452482432126999, + 0.1822974979877472, + 0.08586792647838593, + 0.2604559659957886, + 0.14748501777648926, + 0.28919723629951477, + 0.3047313988208771, + 0.38353487849235535, + 0.19043558835983276, + 0.2552395462989807, + 0.18827350437641144, + 0.13185745477676392, + 0.08581338077783585, + -0.03463560342788696, + 0.3274173140525818, + 0.29686519503593445, + 0.246217280626297, + 0.30773651599884033, + 0.27545028924942017, + 0.11580538749694824, + 0.1547469198703766, + 0.32753315567970276, + 0.3246723413467407, + 0.07220754027366638, + 0.25920385122299194, + 0.43208712339401245, + 0.19863197207450867, + 0.24285081028938293, + 0.14414356648921967 ], "analytic_eif-double_ml": [ 0.6361349821090698, @@ -153,7 +253,57 @@ 0.7640452533960342, 0.7928963005542755, 0.7241942882537842, - 0.4839417636394501 + 0.4839417636394501, + 0.7323970347642899, + 0.7487899661064148, + 0.7685292363166809, + 0.5243690013885498, + 0.8970370590686798, + 0.850658044219017, + 0.8084072172641754, + 0.7975745797157288, + 0.7716194540262222, + 0.8063054978847504, + 0.8272348940372467, + 0.5709719657897949, + 0.4908824563026428, + 0.6552877575159073, + 0.7610478848218918, + 0.6964351385831833, + 0.8935368061065674, + 0.5778515934944153, + 0.583708256483078, + 0.7840211540460587, + 0.6533748209476471, + 0.8121792823076248, + 0.688183456659317, + 0.7039596289396286, + 0.7151157259941101, + 0.7607858777046204, + 0.7027317583560944, + 0.7864372730255127, + 0.8293084502220154, + 0.904606431722641, + 0.7980606555938721, + 0.8397910445928574, + 0.6480499505996704, + 0.6134044006466866, + 0.45798832178115845, + 0.7531958073377609, + 0.7424699068069458, + 0.5738276839256287, + 0.8450541496276855, + 0.8704737722873688, + 0.6173290610313416, + 0.605359673500061, + 0.9669099748134613, + 0.802715003490448, + 0.6141620576381683, + 0.7771597802639008, + 0.9638209044933319, + 0.7258258759975433, + 0.7045946717262268, + 0.6474638730287552 ], "monte_carlo_eif-tmle": [ 0.3155955672264099, @@ -205,7 +355,57 @@ 0.2135109156370163, 0.4553815424442291, 0.3162468671798706, - 0.3212607502937317 + 0.3212607502937317, + 0.411346971988678, + 0.30455929040908813, + 0.3375064432621002, + 0.32896342873573303, + 0.45161035656929016, + 0.2122500240802765, + 0.3787088394165039, + 0.2121228724718094, + 0.2642918825149536, + 0.5285912156105042, + 0.4681228995323181, + 0.6546052098274231, + 0.46523159742355347, + 0.14528605341911316, + 0.17726370692253113, + 0.20050464570522308, + 0.3707367181777954, + 0.46002626419067383, + 0.3755575716495514, + 0.2895314693450928, + 0.38197779655456543, + 0.42401689291000366, + 0.42389243841171265, + 0.28902745246887207, + 0.3791349530220032, + 0.41283154487609863, + 0.3118334114551544, + 0.314208984375, + 0.34356480836868286, + 0.44577836990356445, + 0.33902984857559204, + 0.14284539222717285, + 0.3357377350330353, + 0.1560792773962021, + 0.3127923905849457, + 0.20744819939136505, + 0.31365346908569336, + 0.435160756111145, + 0.29514557123184204, + 0.2655172049999237, + 0.36706283688545227, + 0.30309945344924927, + 0.22880253195762634, + 0.34706076979637146, + 0.25253140926361084, + 0.27676716446876526, + 0.3239942789077759, + 0.3127383589744568, + 0.42953822016716003, + 0.2821480631828308 ], "monte_carlo_eif-one_step": [ 0.35389941930770874, @@ -257,7 +457,57 @@ 0.3175429105758667, 0.24713431298732758, 0.24698291718959808, - 0.08802415430545807 + 0.08802415430545807, + 0.23395343124866486, + 0.18231052160263062, + 0.28385794162750244, + 0.12243841588497162, + 0.327273964881897, + 0.25112035870552063, + 0.39117470383644104, + 0.3667139410972595, + 0.29749974608421326, + 0.23334291577339172, + 0.29140663146972656, + 0.05932044982910156, + 0.11683085560798645, + 0.16100558638572693, + 0.19129854440689087, + 0.20573341846466064, + 0.32739129662513733, + 0.07700455188751221, + 0.07302576303482056, + 0.24367770552635193, + 0.1342063844203949, + 0.2524726688861847, + 0.1825893223285675, + 0.09149438142776489, + 0.2756613492965698, + 0.16756439208984375, + 0.2940676510334015, + 0.2916421890258789, + 0.41005298495292664, + 0.1777900755405426, + 0.276382178068161, + 0.1930345892906189, + 0.1170177310705185, + 0.0761890709400177, + -0.0753144919872284, + 0.34296107292175293, + 0.29970791935920715, + 0.24088823795318604, + 0.3255411684513092, + 0.2487124800682068, + 0.15751716494560242, + 0.13011175394058228, + 0.327239990234375, + 0.3215688467025757, + 0.05291299521923065, + 0.2784276306629181, + 0.4723959267139435, + 0.19121208786964417, + 0.21731945872306824, + 0.1587335169315338 ], "monte_carlo_eif-double_ml": [ 0.6464973986148834, @@ -309,7 +559,57 @@ 0.7387080937623978, 0.8202805370092392, 0.7015037387609482, - 0.4848301559686661 + 0.4848301559686661, + 0.7387938350439072, + 0.7314330637454987, + 0.7850137948989868, + 0.5400117784738541, + 0.8955723941326141, + 0.8325734883546829, + 0.8052189350128174, + 0.789276659488678, + 0.7524459213018417, + 0.7520375549793243, + 0.8131921291351318, + 0.5118032097816467, + 0.4928472936153412, + 0.6374113261699677, + 0.7369808405637741, + 0.670701801776886, + 0.9000483155250549, + 0.6084342300891876, + 0.6081172823905945, + 0.785245954990387, + 0.6652105450630188, + 0.8194037079811096, + 0.6884752810001373, + 0.7095860838890076, + 0.7303211092948914, + 0.7808652520179749, + 0.7076021730899811, + 0.7733480632305145, + 0.8558265566825867, + 0.8919609189033508, + 0.8192032873630524, + 0.8445521295070648, + 0.633210226893425, + 0.6037800908088684, + 0.417309433221817, + 0.7687395662069321, + 0.7453126311302185, + 0.5684986412525177, + 0.8628588020801544, + 0.8437359631061554, + 0.6590408384799957, + 0.5807245075702667, + 0.9666168093681335, + 0.799611508846283, + 0.5948675125837326, + 0.7963835597038269, + 1.004129707813263, + 0.7184059917926788, + 0.6790633201599121, + 0.6620538234710693 ], "plug-in-mle-from-model": [ 0.29303088784217834, @@ -361,7 +661,57 @@ 0.21288113296031952, 0.48229551315307617, 0.32302606105804443, - 0.3556915521621704 + 0.3556915521621704, + 0.38511231541633606, + 0.3122364580631256, + 0.3662373423576355, + 0.33023765683174133, + 0.47563114762306213, + 0.22208483517169952, + 0.37216296792030334, + 0.20084387063980103, + 0.21159575879573822, + 0.5526059865951538, + 0.474670946598053, + 0.6805239319801331, + 0.5023016333580017, + 0.17324042320251465, + 0.17706646025180817, + 0.23558706045150757, + 0.39317330718040466, + 0.44017210602760315, + 0.3797096014022827, + 0.2932527959346771, + 0.41001781821250916, + 0.4348998963832855, + 0.4578908681869507, + 0.28075897693634033, + 0.43518924713134766, + 0.4283755421638489, + 0.311260461807251, + 0.3089047968387604, + 0.37665286660194397, + 0.43311020731925964, + 0.3805909752845764, + 0.15232667326927185, + 0.34888797998428345, + 0.16224858164787292, + 0.3169839382171631, + 0.16351719200611115, + 0.319029301404953, + 0.44582250714302063, + 0.282720685005188, + 0.2622433006763458, + 0.34118038415908813, + 0.3215940296649933, + 0.20642822980880737, + 0.34262216091156006, + 0.245849609375, + 0.28617408871650696, + 0.300485223531723, + 0.32751673460006714, + 0.44511309266090393, + 0.26790669560432434 ], "plug-in-mle-from-test": [ 0.585628867149353, @@ -413,6 +763,56 @@ 0.6340463161468506, 1.0554417371749878, 0.7775468826293945, - 0.7524975538253784 + 0.7524975538253784, + 0.8899527192115784, + 0.8613590002059937, + 0.8673931956291199, + 0.7478110194206238, + 1.0439295768737793, + 0.8035379648208618, + 0.7862071990966797, + 0.6234065890312195, + 0.6665419340133667, + 1.0713006258010864, + 0.9964564442634583, + 1.1330066919326782, + 0.8783180713653564, + 0.6496461629867554, + 0.7227487564086914, + 0.7005554437637329, + 0.9658303260803223, + 0.9716017842292786, + 0.9148011207580566, + 0.8348210453987122, + 0.9410219788551331, + 1.0018309354782104, + 0.9637768268585205, + 0.898850679397583, + 0.8898490071296692, + 1.04167640209198, + 0.7247949838638306, + 0.790610671043396, + 0.822426438331604, + 1.1472810506820679, + 0.9234120845794678, + 0.8038442134857178, + 0.8650804758071899, + 0.6898396015167236, + 0.8096078634262085, + 0.5892956852912903, + 0.7646340131759644, + 0.7734329104423523, + 0.8200383186340332, + 0.8572667837142944, + 0.8427040576934814, + 0.7722067832946777, + 0.8458050489425659, + 0.8206648230552673, + 0.787804126739502, + 0.8041300177574158, + 0.8322190046310425, + 0.8547106385231018, + 0.9068569540977478, + 0.7712270021438599 ] } \ No newline at end of file From 0eecd6b9ab54c178eec437ee89c06ffda94e9c63 Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Mon, 29 Jan 2024 16:06:52 -0500 Subject: [PATCH 18/26] progress on opt functional --- .../notebooks/optimization_functional.ipynb | 1195 +++++++++++++++++ 1 file changed, 1195 insertions(+) create mode 100644 docs/examples/robust_paper/notebooks/optimization_functional.ipynb diff --git a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb new file mode 100644 index 00000000..24a242ca --- /dev/null +++ b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb @@ -0,0 +1,1195 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Robust estimation with optimization functionals with Chirho" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we install the necessary Pytorch, Pyro, and ChiRho dependencies for this example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NOTE: Redirects are currently not supported in Windows or MacOs.\n" + ] + } + ], + "source": [ + "from typing import Callable, Optional, Tuple\n", + "\n", + "import functools\n", + "import torch\n", + "import math\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torchopt\n", + "\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "from pyro.infer import Predictive\n", + "import pyro.contrib.gp as gp\n", + "\n", + "from chirho.counterfactual.handlers import MultiWorldCounterfactual\n", + "from chirho.indexed.ops import IndexSet, gather\n", + "from chirho.interventional.handlers import do\n", + "from chirho.robust.internals.utils import ParamDict\n", + "from chirho.robust.handlers.estimators import one_step_corrected_estimator, tmle\n", + "from chirho.robust.ops import influence_fn\n", + "from chirho.robust.handlers.predictive import PredictiveModel, PredictiveFunctional\n", + "from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood\n", + "\n", + "\n", + "pyro.settings.set(module_local_params=True)\n", + "\n", + "sns.set_style(\"white\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Probabilistic Program\n", + "\n", + "### Model Description\n", + "In this example, we will focus on a cannonical model `CausalGLM` consisting of three types of variables: binary treatment (`A`), confounders (`X`), and response (`Y`). For simplicitly, we assume that the response is generated from a generalized linear model with link function $g$. The model is described by the following generative process:\n", + "\n", + "$$\n", + "\\begin{align*}\n", + "X &\\sim \\text{Normal}(0, I_p) \\\\\n", + "A &\\sim \\text{Bernoulli}(\\pi(X)) \\\\\n", + "\\mu &= \\beta_0 + \\beta_1^T X + \\tau A \\\\\n", + "Y &\\sim \\text{ExponentialFamily}(\\text{mean} = g^{-1}(\\mu))\n", + "\\end{align*}\n", + "$$\n", + "\n", + "where $p$ denotes the number of confounders, $\\pi(X)$ is the probability of treatment conditional on confounders $X$, $\\beta_0$ is the intercept, $\\beta_1$ is the confounder effect, and $\\tau$ is the treatment effect." + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "class CausalGLM(pyro.nn.PyroModule):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " treatment_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " super().__init__()\n", + " self.p = p\n", + " self.treatment_fn = treatment_fn\n", + " self.link_fn = link_fn\n", + " if prior_scale is None:\n", + " self.prior_scale = 1 / math.sqrt(self.p)\n", + " else:\n", + " self.prior_scale = prior_scale\n", + "\n", + " def sample_outcome_weights(self):\n", + " return pyro.sample(\n", + " \"outcome_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_intercept(self):\n", + " return pyro.sample(\"intercept\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_propensity_weights(self):\n", + " return pyro.sample(\n", + " \"propensity_weights\",\n", + " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", + " )\n", + "\n", + " def sample_treatment_weight(self):\n", + " return pyro.sample(\"treatment_weight\", dist.Normal(0.0, 1.0))\n", + "\n", + " def sample_covariate_loc_scale(self):\n", + " return torch.zeros(self.p), torch.ones(self.p)\n", + "\n", + " def forward(self):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1))\n", + " A = pyro.sample(\n", + " \"A\",\n", + " self.treatment_fn(\n", + " torch.einsum(\"...i,...i->...\", X, propensity_weights)\n", + " ),\n", + " )\n", + "\n", + " return pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"...i,...i->...\", X, outcome_weights) + A * tau + intercept\n", + " ),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will condition on both treatment and confounders to estimate the causal effect of treatment on the outcome. We will use the following causal probabilistic program to do so:" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "class ConditionedCausalGLM(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " X: torch.Tensor,\n", + " A: torch.Tensor,\n", + " Y: torch.Tensor,\n", + " treatment_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " prior_scale: Optional[float] = None,\n", + " ):\n", + " p = X.shape[1]\n", + " super().__init__(p, treatment_fn, link_fn, prior_scale)\n", + " self.X = X\n", + " self.A = A\n", + " self.Y = Y\n", + "\n", + " def forward(self):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " with pyro.plate(\"__train__\", size=self.X.shape[0], dim=-1):\n", + " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1), obs=self.X)\n", + " A = pyro.sample(\n", + " \"A\",\n", + " self.treatment_fn(\n", + " torch.einsum(\"ni,i->n\", self.X, propensity_weights)\n", + " ),\n", + " obs=self.A,\n", + " )\n", + " pyro.sample(\n", + " \"Y\",\n", + " self.link_fn(\n", + " torch.einsum(\"ni,i->n\", X, outcome_weights)\n", + " + A * tau\n", + " + intercept\n", + " ),\n", + " obs=self.Y,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", + "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", + " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", + "<!-- Generated by graphviz version 6.0.2 (20221011.1828)\n", + " -->\n", + "<!-- Pages: 1 -->\n", + "<svg width=\"563pt\" height=\"304pt\"\n", + " viewBox=\"0.00 0.00 563.39 304.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", + "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 300)\">\n", + "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-300 559.39,-300 559.39,4 -4,4\"/>\n", + "<g id=\"clust1\" class=\"cluster\">\n", + "<title>cluster___train__</title>\n", + "<polygon fill=\"none\" stroke=\"black\" points=\"93.6,-8 93.6,-155 235.6,-155 235.6,-8 93.6,-8\"/>\n", + "<text text-anchor=\"middle\" x=\"201.6\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">__train__</text>\n", + "</g>\n", + "<!-- intercept -->\n", + "<g id=\"node1\" class=\"node\">\n", + "<title>intercept</title>\n", + "<ellipse fill=\"white\" stroke=\"black\" cx=\"41.6\" cy=\"-129\" rx=\"41.69\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"41.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">intercept</text>\n", + "</g>\n", + "<!-- Y -->\n", + "<g id=\"node7\" class=\"node\">\n", + "<title>Y</title>\n", + "<ellipse fill=\"gray\" stroke=\"black\" cx=\"200.6\" cy=\"-57\" rx=\"27\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"200.6\" y=\"-53.3\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n", + "</g>\n", + "<!-- intercept&#45;&gt;Y -->\n", + "<g id=\"edge6\" class=\"edge\">\n", + "<title>intercept&#45;&gt;Y</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M69.66,-115.65C97.53,-103.38 140.19,-84.59 169.18,-71.83\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"170.71,-74.98 178.45,-67.75 167.89,-68.58 170.71,-74.98\"/>\n", + "</g>\n", + "<!-- outcome_weights -->\n", + "<g id=\"node2\" class=\"node\">\n", + "<title>outcome_weights</title>\n", + "<ellipse fill=\"white\" stroke=\"black\" cx=\"318.6\" cy=\"-129\" rx=\"73.39\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"318.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights</text>\n", + "</g>\n", + "<!-- outcome_weights&#45;&gt;Y -->\n", + "<g id=\"edge4\" class=\"edge\">\n", + "<title>outcome_weights&#45;&gt;Y</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M291.82,-112.12C273.12,-101.02 248.19,-86.23 229.12,-74.92\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"230.67,-71.77 220.28,-69.68 227.1,-77.79 230.67,-71.77\"/>\n", + "</g>\n", + "<!-- propensity_weights -->\n", + "<g id=\"node3\" class=\"node\">\n", + "<title>propensity_weights</title>\n", + "<ellipse fill=\"white\" stroke=\"black\" cx=\"128.6\" cy=\"-239.5\" rx=\"79.09\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"128.6\" y=\"-235.8\" font-family=\"Times,serif\" font-size=\"14.00\">propensity_weights</text>\n", + "</g>\n", + "<!-- A -->\n", + "<g id=\"node6\" class=\"node\">\n", + "<title>A</title>\n", + "<ellipse fill=\"gray\" stroke=\"black\" cx=\"128.6\" cy=\"-129\" rx=\"27\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"128.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">A</text>\n", + "</g>\n", + "<!-- propensity_weights&#45;&gt;A -->\n", + "<g id=\"edge1\" class=\"edge\">\n", + "<title>propensity_weights&#45;&gt;A</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M128.6,-221.07C128.6,-203.8 128.6,-177.12 128.6,-157.09\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"132.1,-157.03 128.6,-147.03 125.1,-157.03 132.1,-157.03\"/>\n", + "</g>\n", + "<!-- treatment_weight -->\n", + "<g id=\"node4\" class=\"node\">\n", + "<title>treatment_weight</title>\n", + "<ellipse fill=\"white\" stroke=\"black\" cx=\"482.6\" cy=\"-129\" rx=\"72.59\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"482.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">treatment_weight</text>\n", + "</g>\n", + "<!-- treatment_weight&#45;&gt;Y -->\n", + "<g id=\"edge5\" class=\"edge\">\n", + "<title>treatment_weight&#45;&gt;Y</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M433.15,-115.73C376.52,-101.67 285.18,-79 235.5,-66.66\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"236.11,-63.21 225.56,-64.2 234.42,-70 236.11,-63.21\"/>\n", + "</g>\n", + "<!-- X -->\n", + "<g id=\"node5\" class=\"node\">\n", + "<title>X</title>\n", + "<ellipse fill=\"gray\" stroke=\"black\" cx=\"200.6\" cy=\"-129\" rx=\"27\" ry=\"18\"/>\n", + "<text text-anchor=\"middle\" x=\"200.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n", + "</g>\n", + "<!-- X&#45;&gt;Y -->\n", + "<g id=\"edge2\" class=\"edge\">\n", + "<title>X&#45;&gt;Y</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M200.6,-110.7C200.6,-102.98 200.6,-93.71 200.6,-85.11\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"204.1,-85.1 200.6,-75.1 197.1,-85.1 204.1,-85.1\"/>\n", + "</g>\n", + "<!-- A&#45;&gt;Y -->\n", + "<g id=\"edge3\" class=\"edge\">\n", + "<title>A&#45;&gt;Y</title>\n", + "<path fill=\"none\" stroke=\"black\" d=\"M143.17,-113.83C153.35,-103.94 167.12,-90.55 178.63,-79.36\"/>\n", + "<polygon fill=\"black\" stroke=\"black\" points=\"181.07,-81.87 185.8,-72.38 176.19,-76.85 181.07,-81.87\"/>\n", + "</g>\n", + "<!-- distribution_description_node -->\n", + "<g id=\"node8\" class=\"node\">\n", + "<title>distribution_description_node</title>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-280.8\" font-family=\"Times,serif\" font-size=\"14.00\">intercept ~ Normal</text>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-265.8\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights ~ Normal</text>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-250.8\" font-family=\"Times,serif\" font-size=\"14.00\">propensity_weights ~ Normal</text>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-235.8\" font-family=\"Times,serif\" font-size=\"14.00\">treatment_weight ~ Normal</text>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-220.8\" font-family=\"Times,serif\" font-size=\"14.00\">X ~ Normal</text>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-205.8\" font-family=\"Times,serif\" font-size=\"14.00\">A ~ Normal</text>\n", + "<text text-anchor=\"start\" x=\"234.1\" y=\"-190.8\" font-family=\"Times,serif\" font-size=\"14.00\">Y ~ Normal</text>\n", + "</g>\n", + "</g>\n", + "</svg>\n" + ], + "text/plain": [ + "<graphviz.graphs.Digraph at 0x189ab6550>" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Visualize the model\n", + "pyro.render_model(\n", + " ConditionedCausalGLM(torch.zeros(1, 1), torch.zeros(1), torch.zeros(1)),\n", + " render_params=True, \n", + " render_distributions=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating data\n", + "\n", + "For evaluation, we generate `N_datasets` datasets, each with `N` samples. We compare vanilla estimates of the target functional with the double robust estimates of the target functional across the `N_sims` datasets. We use a similar data generating process as in Kennedy (2022)." + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [], + "source": [ + "class GroundTruthModel(CausalGLM):\n", + " def __init__(\n", + " self,\n", + " p: int,\n", + " alpha: int,\n", + " beta: int,\n", + " treatment_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", + " treatment_weight: float = 1.0,\n", + " ):\n", + " super().__init__(p, treatment_fn, link_fn)\n", + " self.alpha = alpha # sparsity of propensity weights\n", + " self.beta = beta # sparsity of outcome weights\n", + " self.treatment_weight = treatment_weight\n", + "\n", + " def sample_outcome_weights(self):\n", + " outcome_weights = 1 / math.sqrt(self.beta) * torch.ones(self.p)\n", + " outcome_weights[self.beta :] = 0.0\n", + " return outcome_weights\n", + "\n", + " def sample_propensity_weights(self):\n", + " propensity_weights = 1 / math.sqrt(self.alpha) * torch.ones(self.p)\n", + " propensity_weights[self.alpha :] = 0.0\n", + " return propensity_weights\n", + "\n", + " def sample_treatment_weight(self):\n", + " return torch.tensor(self.treatment_weight)\n", + "\n", + " def sample_intercept(self):\n", + " return torch.tensor(0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [], + "source": [ + "# Data configuration\n", + "p = 2\n", + "alpha = 50\n", + "beta = 50\n", + "N_train = 500\n", + "N_test = 500\n", + "\n", + "true_model = GroundTruthModel(p, alpha, beta)\n", + "\n", + "def generate_data(N_train, N_test):\n", + " # Generate data\n", + " D_train = Predictive(\n", + " true_model, num_samples=N_train, return_sites=[\"X\", \"A\", \"Y\"]\n", + " )()\n", + " D_test = Predictive(\n", + " true_model, num_samples=N_test, return_sites=[\"X\", \"A\", \"Y\"]\n", + " )()\n", + " return D_train, D_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fit parameters via maximum likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "def MLE(D_train, n_steps=2000):\n", + " # Fit model using maximum likelihood\n", + " conditioned_model = ConditionedCausalGLM(\n", + " X=D_train[\"X\"], A=D_train[\"A\"], Y=D_train[\"Y\"]\n", + " )\n", + " \n", + " guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model)\n", + " elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train)\n", + "\n", + " # initialize parameters\n", + " elbo()\n", + " adam = torch.optim.Adam(elbo.parameters(), lr=0.03)\n", + "\n", + " # Do gradient steps\n", + " for _ in range(n_steps):\n", + " adam.zero_grad()\n", + " loss = elbo()\n", + " loss.backward()\n", + " adam.step()\n", + "\n", + " theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in guide_train().items()\n", + " }\n", + " return theta_hat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Causal Query: Average treatment effect (ATE)\n", + "\n", + "The average treatment effect summarizes, on average, how much the treatment changes the response, $ATE = \\mathbb{E}[Y|do(A=1)] - \\mathbb{E}[Y|do(A=0)]$. The `do` notation indicates that the expectations are taken according to *intervened* versions of the model, with $A$ set to a particular value. Note from our [tutorial](tutorial_i.ipynb) that this is different from conditioning on $A$ in the original `causal_model`, which assumes $X$ and $T$ are dependent.\n", + "\n", + "\n", + "To implement this query in ChiRho, we define the `ATEFunctional` class which take in a `model` and `guide` and returns the average treatment effect by simulating from the posterior predictive distribution of the model and guide." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining the target functional" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [], + "source": [ + "class MaximizeATEFunctional(torch.nn.Module):\n", + " def __init__(self, model: Callable, *, \n", + " num_monte_carlo: int = 100,\n", + " optimizer = torchopt.adam,\n", + " learning_rate = 1.0,\n", + " n_grad_steps:int = 100,\n", + " ):\n", + " super().__init__()\n", + " self.model = model\n", + " self.num_monte_carlo = num_monte_carlo\n", + " self.optimizer = optimizer\n", + " self.learning_rate = learning_rate\n", + " self.n_grad_steps = n_grad_steps\n", + " \n", + " def dose_response(self, treatment_assignment: torch.Tensor, *args, **kwargs):\n", + " with pyro.plate(\"monte_carlo_functional\", size=self.num_monte_carlo, dim=-2):\n", + " with do(actions=dict(A=(treatment_assignment))):\n", + " Ys = self.model(*args, **kwargs)\n", + " return pyro.deterministic(\"dose_response\", Ys.mean(dim=-1, keepdim=True).squeeze())\n", + "\n", + " # Y0 = gather(Ys, IndexSet(A={1}), event_dim=0)\n", + " # Y1 = gather(Ys, IndexSet(A={2}), event_dim=0)\n", + " # ate = (Y1 - Y0).mean(dim=-2, keepdim=True).mean(dim=-1, keepdim=True).squeeze()\n", + " # return pyro.deterministic(\"ATE\", ate)\n", + " \n", + " def loss(self, treatment_assignment: torch.Tensor, *args, **kwargs):\n", + "\n", + " # Penalize squared treatment assignment magnitude, while maximizing dose response.\n", + "\n", + " return treatment_assignment**2 - self.dose_response(treatment_assignment, *args, **kwargs)\n", + "\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " \n", + " treatment_assignment = (torch.tensor(1.0, requires_grad=True),)\n", + "\n", + " optimizer = self.optimizer(self.learning_rate)\n", + " opt_state = optimizer.init(treatment_assignment)\n", + " \n", + " for _ in range(self.n_grad_steps):\n", + " loss = self.loss(treatment_assignment[0])\n", + "\n", + " grads = torch.autograd.grad(loss, treatment_assignment, create_graph=True)\n", + " updates, opt_state = optimizer.update(grads, opt_state, inplace=False)\n", + "\n", + " treatment_assignment = torchopt.apply_updates(treatment_assignment, updates, inplace=False)\n", + "\n", + " return pyro.deterministic(\"optimal_treatment_assignment\", treatment_assignment[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing automated doubly robust estimators via Monte Carlo\n", + "\n", + "While the doubly robust correction term is known in closed-form for the average treatment effect functional, our `one_step_correction` and `tmle` function in `ChiRho` works for a wide class of other functionals. We focus on the average treatment effect functional here so that we have a ground truth to compare `one_step_correction` against the plug in estimates." + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "# Helper class to create a trivial guide that returns the maximum likelihood estimate\n", + "class MLEGuide(torch.nn.Module):\n", + " def __init__(self, mle_est: ParamDict):\n", + " super().__init__()\n", + " self.names = list(mle_est.keys())\n", + " for name, value in mle_est.items():\n", + " setattr(self, name + \"_param\", torch.nn.Parameter(value))\n", + "\n", + " def forward(self, *args, **kwargs):\n", + " for name in self.names:\n", + " value = getattr(self, name + \"_param\")\n", + " pyro.sample(\n", + " name, pyro.distributions.Delta(value, event_dim=len(value.shape))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "element 0 of tensors does not require grad and does not have a grad_fn", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[199], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m eif_fn \u001b[38;5;241m=\u001b[39m influence_fn(functional, D_test, num_samples_outer\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, pointwise_influence\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 19\u001b[0m correction_estimator \u001b[38;5;241m=\u001b[39m eif_fn(model)\n\u001b[0;32m---> 20\u001b[0m correction \u001b[38;5;241m=\u001b[39m \u001b[43mcorrection_estimator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:145\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43min_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam_eif\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:434\u001b[0m, in \u001b[0;36mvmap.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _chunked_vmap(func, flat_in_dims, chunks_flat_args,\n\u001b[1;32m 431\u001b[0m args_spec, out_dims, randomness, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 433\u001b[0m \u001b[38;5;66;03m# If chunk_size is not specified.\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_flat_vmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_in_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_spec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:619\u001b[0m, in \u001b[0;36m_flat_vmap\u001b[0;34m(func, batch_size, flat_in_dims, flat_args, args_spec, out_dims, randomness, **kwargs)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 618\u001b[0m batched_inputs \u001b[38;5;241m=\u001b[39m _create_batched_inputs(flat_in_dims, flat_args, vmap_level, args_spec)\n\u001b[0;32m--> 619\u001b[0m batched_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbatched_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func)\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:146\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:916\u001b[0m, in \u001b[0;36mjvp\u001b[0;34m(func, primals, tangents, strict, has_aux)\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[38;5;129m@exposed_in\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.func\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjvp\u001b[39m(func: Callable, primals: Any, tangents: Any, \u001b[38;5;241m*\u001b[39m, strict: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, has_aux: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;124;03m Standing for the Jacobian-vector product, returns a tuple containing\u001b[39;00m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m the output of `func(*primals)` and the \"Jacobian of ``func`` evaluated at\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 913\u001b[0m \n\u001b[1;32m 914\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_jvp_with_argnums\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtangents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_aux\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:965\u001b[0m, in \u001b[0;36m_jvp_with_argnums\u001b[0;34m(func, primals, tangents, argnums, strict, has_aux)\u001b[0m\n\u001b[1;32m 963\u001b[0m primals \u001b[38;5;241m=\u001b[39m _wrap_all_tensors(primals, level)\n\u001b[1;32m 964\u001b[0m duals \u001b[38;5;241m=\u001b[39m _replace_args(primals, duals, argnums)\n\u001b[0;32m--> 965\u001b[0m result_duals \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mduals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_aux:\n\u001b[1;32m 967\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(result_duals, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_duals) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m):\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:147\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>.<locals>.<lambda>\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: torch\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mjvp(\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m p: \u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, (target_params,), (d,)\n\u001b[1;32m 148\u001b[0m )[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:1600\u001b[0m, in \u001b[0;36mfunctionalize.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1597\u001b[0m flattened_unwrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(kwargs)\n\u001b[1;32m 1598\u001b[0m flattened_wrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(func_kwargs)\n\u001b[0;32m-> 1600\u001b[0m func_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1601\u001b[0m outputs \u001b[38;5;241m=\u001b[39m _unwrap_all_tensors_from_functional(func_outputs, reapply_views\u001b[38;5;241m=\u001b[39mreapply_views)\n\u001b[1;32m 1602\u001b[0m flat_outputs, func_out_spec \u001b[38;5;241m=\u001b[39m tree_flatten(outputs)\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/internals/utils.py:114\u001b[0m, in \u001b[0;36mmake_functional_call.<locals>.mod_func\u001b[0;34m(params, *args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mfunctionalize\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmod_func\u001b[39m(params: ParamDict, \u001b[38;5;241m*\u001b[39margs: P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mvalidation_enabled(\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/functional_call.py:143\u001b[0m, in \u001b[0;36mfunctional_call\u001b[0;34m(module, parameter_and_buffer_dicts, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter_and_buffer_dicts to be a dict, or a list/tuple of dicts, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(parameter_and_buffer_dicts)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 141\u001b[0m )\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_functional_call\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_and_buffers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mtie_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtie_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/utils/stateless.py:262\u001b[0m, in \u001b[0;36m_functional_call\u001b[0;34m(module, parameters_and_buffers, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 258\u001b[0m args \u001b[38;5;241m=\u001b[39m (args,)\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _reparametrize_module(\n\u001b[1;32m 260\u001b[0m module, parameters_and_buffers, tie_weights\u001b[38;5;241m=\u001b[39mtie_weights, strict\u001b[38;5;241m=\u001b[39mstrict\n\u001b[1;32m 261\u001b[0m ):\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[194], line 43\u001b[0m, in \u001b[0;36mMaximizeATEFunctional.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_grad_steps):\n\u001b[1;32m 41\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(treatment_assignment[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m---> 43\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtreatment_assignment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m updates, opt_state \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mupdate(grads, opt_state, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 46\u001b[0m treatment_assignment \u001b[38;5;241m=\u001b[39m torchopt\u001b[38;5;241m.\u001b[39mapply_updates(treatment_assignment, updates, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/autograd/__init__.py:303\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(grad_outputs_)\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mRuntimeError\u001b[0m: element 0 of tensors does not require grad and does not have a grad_fn" + ] + } + ], + "source": [ + "functional = functools.partial(MaximizeATEFunctional, \n", + " num_monte_carlo=10000, \n", + " optimizer=torchopt.adam,\n", + " learning_rate=1.0,\n", + " n_grad_steps=1)\n", + "\n", + "D_train, D_test = generate_data(N_train, N_test)\n", + "\n", + "theta_hat = MLE(D_train, n_steps=2)\n", + "\n", + "theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", + "}\n", + "mle_guide = MLEGuide(theta_hat)\n", + "model = PredictiveModel(CausalGLM(p), mle_guide)\n", + "\n", + "eif_fn = influence_fn(functional, D_test, num_samples_outer=100, pointwise_influence=False)\n", + "\n", + "correction_estimator = eif_fn(model)\n", + "correction = correction_estimator()" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset 0\n", + "plug-in-mle-from-model 0\n", + "one_step monte_carlo_eif 0\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "element 0 of tensors does not require grad and does not have a grad_fn", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[160], line 50\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m influence_str, influence \u001b[38;5;129;01min\u001b[39;00m influences\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(estimator_str, influence_str, i)\n\u001b[0;32m---> 50\u001b[0m estimate \u001b[38;5;241m=\u001b[39m \u001b[43mestimator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunctional\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43mD_test\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples_outer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples_inner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minfluence_estimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfluence\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mestimator_kwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mestimator_str\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPredictiveModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mCausalGLM\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmle_guide\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m estimates[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minfluence_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mappend(estimate\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mitem())\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(RESULTS_PATH, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/handlers/estimators.py:257\u001b[0m, in \u001b[0;36mone_step_corrected_estimator.<locals>._corrected_functional.<locals>._estimator\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_estimator\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m S:\n\u001b[1;32m 256\u001b[0m plug_in_estimate \u001b[38;5;241m=\u001b[39m plug_in_estimator(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 257\u001b[0m correction \u001b[38;5;241m=\u001b[39m \u001b[43mcorrection_estimator\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m flat_plug_in_estimate, treespec \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39m_pytree\u001b[38;5;241m.\u001b[39mtree_flatten(\n\u001b[1;32m 260\u001b[0m plug_in_estimate\n\u001b[1;32m 261\u001b[0m )\n\u001b[1;32m 262\u001b[0m flat_correction, _ \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39m_pytree\u001b[38;5;241m.\u001b[39mtree_flatten(correction)\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:145\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43min_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam_eif\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:434\u001b[0m, in \u001b[0;36mvmap.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _chunked_vmap(func, flat_in_dims, chunks_flat_args,\n\u001b[1;32m 431\u001b[0m args_spec, out_dims, randomness, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 433\u001b[0m \u001b[38;5;66;03m# If chunk_size is not specified.\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_flat_vmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_in_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_spec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:619\u001b[0m, in \u001b[0;36m_flat_vmap\u001b[0;34m(func, batch_size, flat_in_dims, flat_args, args_spec, out_dims, randomness, **kwargs)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 618\u001b[0m batched_inputs \u001b[38;5;241m=\u001b[39m _create_batched_inputs(flat_in_dims, flat_args, vmap_level, args_spec)\n\u001b[0;32m--> 619\u001b[0m batched_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbatched_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func)\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:146\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:916\u001b[0m, in \u001b[0;36mjvp\u001b[0;34m(func, primals, tangents, strict, has_aux)\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[38;5;129m@exposed_in\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.func\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjvp\u001b[39m(func: Callable, primals: Any, tangents: Any, \u001b[38;5;241m*\u001b[39m, strict: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, has_aux: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;124;03m Standing for the Jacobian-vector product, returns a tuple containing\u001b[39;00m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m the output of `func(*primals)` and the \"Jacobian of ``func`` evaluated at\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 913\u001b[0m \n\u001b[1;32m 914\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_jvp_with_argnums\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtangents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_aux\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:965\u001b[0m, in \u001b[0;36m_jvp_with_argnums\u001b[0;34m(func, primals, tangents, argnums, strict, has_aux)\u001b[0m\n\u001b[1;32m 963\u001b[0m primals \u001b[38;5;241m=\u001b[39m _wrap_all_tensors(primals, level)\n\u001b[1;32m 964\u001b[0m duals \u001b[38;5;241m=\u001b[39m _replace_args(primals, duals, argnums)\n\u001b[0;32m--> 965\u001b[0m result_duals \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mduals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_aux:\n\u001b[1;32m 967\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(result_duals, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_duals) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m):\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:147\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>.<locals>.<lambda>\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: torch\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mjvp(\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m p: \u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, (target_params,), (d,)\n\u001b[1;32m 148\u001b[0m )[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:1600\u001b[0m, in \u001b[0;36mfunctionalize.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1597\u001b[0m flattened_unwrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(kwargs)\n\u001b[1;32m 1598\u001b[0m flattened_wrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(func_kwargs)\n\u001b[0;32m-> 1600\u001b[0m func_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1601\u001b[0m outputs \u001b[38;5;241m=\u001b[39m _unwrap_all_tensors_from_functional(func_outputs, reapply_views\u001b[38;5;241m=\u001b[39mreapply_views)\n\u001b[1;32m 1602\u001b[0m flat_outputs, func_out_spec \u001b[38;5;241m=\u001b[39m tree_flatten(outputs)\n", + "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/internals/utils.py:114\u001b[0m, in \u001b[0;36mmake_functional_call.<locals>.mod_func\u001b[0;34m(params, *args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mfunctionalize\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmod_func\u001b[39m(params: ParamDict, \u001b[38;5;241m*\u001b[39margs: P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mvalidation_enabled(\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/functional_call.py:143\u001b[0m, in \u001b[0;36mfunctional_call\u001b[0;34m(module, parameter_and_buffer_dicts, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter_and_buffer_dicts to be a dict, or a list/tuple of dicts, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(parameter_and_buffer_dicts)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 141\u001b[0m )\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_functional_call\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_and_buffers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mtie_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtie_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/utils/stateless.py:262\u001b[0m, in \u001b[0;36m_functional_call\u001b[0;34m(module, parameters_and_buffers, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 258\u001b[0m args \u001b[38;5;241m=\u001b[39m (args,)\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _reparametrize_module(\n\u001b[1;32m 260\u001b[0m module, parameters_and_buffers, tie_weights\u001b[38;5;241m=\u001b[39mtie_weights, strict\u001b[38;5;241m=\u001b[39mstrict\n\u001b[1;32m 261\u001b[0m ):\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[158], line 43\u001b[0m, in \u001b[0;36mMaximizeATEFunctional.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_grad_steps):\n\u001b[1;32m 41\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(treatment_assignment[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m---> 43\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtreatment_assignment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m updates, opt_state \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mupdate(grads, opt_state, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 46\u001b[0m treatment_assignment \u001b[38;5;241m=\u001b[39m torchopt\u001b[38;5;241m.\u001b[39mapply_updates(treatment_assignment, updates, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/autograd/__init__.py:303\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(grad_outputs_)\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mRuntimeError\u001b[0m: element 0 of tensors does not require grad and does not have a grad_fn" + ] + } + ], + "source": [ + "import json\n", + "import os\n", + "\n", + "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", + "N_datasets = 1\n", + "\n", + "\n", + "# Estimators to compare\n", + "estimators = {\"one_step\": one_step_corrected_estimator}\n", + "estimator_kwargs = {\n", + " \"one_step\": {}\n", + "}\n", + "\n", + "# Influence functions\n", + "influences = {\"monte_carlo_eif\": influence_fn}\n", + "\n", + "# Cache the results\n", + "RESULTS_PATH = \"../results/opt_causal_glm.json\"\n", + "\n", + "if os.path.exists(RESULTS_PATH):\n", + " with open(RESULTS_PATH, \"r\") as f:\n", + " estimates = json.load(f)\n", + " i_start = len(estimates[\"plug-in-mle-from-model\"]) \n", + "else:\n", + " estimates = {f\"{influence}-{estimator}\": [] for influence in influences.keys() for estimator in estimators.keys()}\n", + " estimates[\"plug-in-mle-from-model\"] = []\n", + " i_start = 0\n", + "\n", + "# ATE functional of interest\n", + "functional = functools.partial(MaximizeATEFunctional, num_monte_carlo=10000)\n", + "\n", + "for i in range(i_start, N_datasets):\n", + " pyro.set_rng_seed(i) # for reproducibility\n", + " print(\"Dataset\", i)\n", + " D_train, D_test = generate_data(N_train, N_test)\n", + " theta_hat = MLE(D_train, D_test)\n", + "\n", + " theta_hat = {\n", + " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", + " }\n", + " mle_guide = MLEGuide(theta_hat)\n", + " model = PredictiveModel(CausalGLM(p), mle_guide)\n", + " \n", + " print(\"plug-in-mle-from-model\", i)\n", + " estimates[\"plug-in-mle-from-model\"].append(functional(model)().detach().item())\n", + "\n", + " for estimator_str, estimator in estimators.items():\n", + " for influence_str, influence in influences.items():\n", + " print(estimator_str, influence_str, i)\n", + " estimate = estimator(\n", + " functional, \n", + " D_test,\n", + " num_samples_outer=max(10000, 100 * p), \n", + " num_samples_inner=1,\n", + " influence_estimator=influence,\n", + " **estimator_kwargs[estimator_str]\n", + " )(PredictiveModel(CausalGLM(p), mle_guide))()\n", + "\n", + " estimates[f\"{influence_str}-{estimator_str}\"].append(estimate.detach().item())\n", + "\n", + " with open(RESULTS_PATH, \"w\") as f:\n", + " json.dump(estimates, f, indent=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>analytic_eif-tmle</th>\n", + " <th>analytic_eif-one_step</th>\n", + " <th>analytic_eif-double_ml</th>\n", + " <th>monte_carlo_eif-tmle</th>\n", + " <th>monte_carlo_eif-one_step</th>\n", + " <th>monte_carlo_eif-double_ml</th>\n", + " <th>plug-in-mle-from-model</th>\n", + " <th>plug-in-mle-from-test</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " <td>100.00</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>0.33</td>\n", + " <td>0.22</td>\n", + " <td>0.73</td>\n", + " <td>0.33</td>\n", + " <td>0.22</td>\n", + " <td>0.72</td>\n", + " <td>0.34</td>\n", + " <td>0.84</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>0.11</td>\n", + " <td>0.11</td>\n", + " <td>0.13</td>\n", + " <td>0.11</td>\n", + " <td>0.11</td>\n", + " <td>0.13</td>\n", + " <td>0.11</td>\n", + " <td>0.13</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>0.14</td>\n", + " <td>-0.03</td>\n", + " <td>0.46</td>\n", + " <td>0.13</td>\n", + " <td>-0.08</td>\n", + " <td>0.42</td>\n", + " <td>0.11</td>\n", + " <td>0.56</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>0.24</td>\n", + " <td>0.15</td>\n", + " <td>0.65</td>\n", + " <td>0.27</td>\n", + " <td>0.16</td>\n", + " <td>0.65</td>\n", + " <td>0.26</td>\n", + " <td>0.76</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>0.33</td>\n", + " <td>0.23</td>\n", + " <td>0.73</td>\n", + " <td>0.33</td>\n", + " <td>0.22</td>\n", + " <td>0.73</td>\n", + " <td>0.33</td>\n", + " <td>0.83</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>0.39</td>\n", + " <td>0.29</td>\n", + " <td>0.81</td>\n", + " <td>0.39</td>\n", + " <td>0.29</td>\n", + " <td>0.80</td>\n", + " <td>0.40</td>\n", + " <td>0.92</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>0.65</td>\n", + " <td>0.61</td>\n", + " <td>1.09</td>\n", + " <td>0.65</td>\n", + " <td>0.57</td>\n", + " <td>1.05</td>\n", + " <td>0.68</td>\n", + " <td>1.15</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", + "count 100.00 100.00 100.00 \n", + "mean 0.33 0.22 0.73 \n", + "std 0.11 0.11 0.13 \n", + "min 0.14 -0.03 0.46 \n", + "25% 0.24 0.15 0.65 \n", + "50% 0.33 0.23 0.73 \n", + "75% 0.39 0.29 0.81 \n", + "max 0.65 0.61 1.09 \n", + "\n", + " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", + "count 100.00 100.00 \n", + "mean 0.33 0.22 \n", + "std 0.11 0.11 \n", + "min 0.13 -0.08 \n", + "25% 0.27 0.16 \n", + "50% 0.33 0.22 \n", + "75% 0.39 0.29 \n", + "max 0.65 0.57 \n", + "\n", + " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", + "count 100.00 100.00 \n", + "mean 0.72 0.34 \n", + "std 0.13 0.11 \n", + "min 0.42 0.11 \n", + "25% 0.65 0.26 \n", + "50% 0.73 0.33 \n", + "75% 0.80 0.40 \n", + "max 1.05 0.68 \n", + "\n", + " plug-in-mle-from-test \n", + "count 100.00 \n", + "mean 0.84 \n", + "std 0.13 \n", + "min 0.56 \n", + "25% 0.76 \n", + "50% 0.83 \n", + "75% 0.92 \n", + "max 1.15 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The true treatment effect is 0, so a mean estimate closer to zero is better\n", + "results = pd.DataFrame(estimates)\n", + "results.describe().round(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", + " if pd.api.types.is_categorical_dtype(vector):\n", + "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", + " with pd.option_context('mode.use_inf_as_na', True):\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the results\n", + "fig, ax = plt.subplots()\n", + "\n", + "# TMLE\n", + "sns.kdeplot(\n", + " estimates['analytic_eif-tmle'], \n", + " label=\"Analytic EIF (TMLE)\",\n", + " ax=ax,\n", + " color='blue',\n", + " linestyle='--'\n", + ")\n", + "\n", + "sns.kdeplot(\n", + " estimates['monte_carlo_eif-tmle'], \n", + " label=\"Monte Carlo EIF (TMLE)\",\n", + " ax=ax,\n", + " color='blue'\n", + ")\n", + "\n", + "# One-step\n", + "sns.kdeplot(\n", + " estimates['analytic_eif-one_step'], \n", + " label=\"Analytic EIF (One-Step)\",\n", + " ax=ax,\n", + " color='red',\n", + " linestyle='--'\n", + ")\n", + "\n", + "sns.kdeplot(\n", + " estimates['monte_carlo_eif-one_step'], \n", + " label=\"Monte Carlo EIF (One-Step)\",\n", + " ax=ax,\n", + " color='red'\n", + ")\n", + "\n", + "# DoubleML\n", + "sns.kdeplot(\n", + " estimates['analytic_eif-double_ml'], \n", + " label=\"Analytic EIF (DoubleML)\",\n", + " ax=ax,\n", + " color='green',\n", + " linestyle='--'\n", + ")\n", + "\n", + "sns.kdeplot(\n", + " estimates['monte_carlo_eif-double_ml'], \n", + " label=\"Monte Carlo EIF (DoubleML)\",\n", + " ax=ax,\n", + " color='green'\n", + ")\n", + "\n", + "# Plug-in MLE\n", + "sns.kdeplot(\n", + " estimates['plug-in-mle-from-model'], \n", + " label=\"Plug-in MLE\",\n", + " ax=ax,\n", + " color='brown'\n", + ")\n", + "\n", + "ax.axvline(0, color=\"black\", label=\"True ATE\", linestyle=\"solid\")\n", + "ax.set_yticks([])\n", + "sns.despine()\n", + "ax.set_xlabel(\"ATE Estimate\", fontsize=18)\n", + "ax.set_ylabel(\"Density\", fontsize=18)\n", + "\n", + "ax.legend(loc=\"upper right\", fontsize=11)\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/causal_glm_performance_vs_estimator.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Double ML\n", + "plt.scatter(\n", + " estimates['monte_carlo_eif-double_ml'],\n", + " estimates['analytic_eif-double_ml'],\n", + " color='green',\n", + ")\n", + "\n", + "# Plot y=x line for min and max values\n", + "min_val = min(\n", + " min(estimates['monte_carlo_eif-double_ml']),\n", + " min(estimates['analytic_eif-double_ml'])\n", + ")\n", + "max_val = max(\n", + " max(estimates['monte_carlo_eif-double_ml']),\n", + " max(estimates['analytic_eif-double_ml'])\n", + ")\n", + "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", + "plt.xlabel(\"Monte Carlo EIF (DoubleML)\", fontsize=18)\n", + "plt.ylabel(\"Analytic EIF (DoubleML)\", fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/double_convergence_causal_glm.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TMLE\n", + "plt.scatter(\n", + " estimates['monte_carlo_eif-tmle'],\n", + " estimates['analytic_eif-tmle'],\n", + " color='blue',\n", + ")\n", + "\n", + "# Plot y=x line for min and max values\n", + "min_val = min(\n", + " min(estimates['monte_carlo_eif-tmle']),\n", + " min(estimates['analytic_eif-tmle'])\n", + ")\n", + "max_val = max(\n", + " max(estimates['monte_carlo_eif-tmle']),\n", + " max(estimates['analytic_eif-tmle'])\n", + ")\n", + "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", + "plt.xlabel(\"Monte Carlo EIF (TMLE)\", fontsize=18)\n", + "plt.ylabel(\"Analytic EIF (TMLE)\", fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/tmle_convergence_causal_glm.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# One-step\n", + "plt.scatter(\n", + " estimates['monte_carlo_eif-one_step'],\n", + " estimates['analytic_eif-one_step'],\n", + " color='red',\n", + ")\n", + "\n", + "# Plot y=x line for min and max values\n", + "min_val = min(\n", + " min(estimates['monte_carlo_eif-one_step']),\n", + " min(estimates['analytic_eif-one_step'])\n", + ")\n", + "max_val = max(\n", + " max(estimates['monte_carlo_eif-one_step']),\n", + " max(estimates['analytic_eif-one_step'])\n", + ")\n", + "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", + "plt.xlabel(\"Monte Carlo EIF (One-Step)\", fontsize=18)\n", + "plt.ylabel(\"Analytic EIF (One-Step)\", fontsize=18)\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig('./figures/one_step_convergence_causal_glm.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "Kennedy, Edward. \"Towards optimal doubly robust estimation of heterogeneous causal effects\", 2022. https://arxiv.org/abs/2004.14497." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "basis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 079d4bd2df552d0be0052ecdd1de6726f1fc5be9 Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Mon, 29 Jan 2024 19:30:17 -0500 Subject: [PATCH 19/26] progress on alternative portfolio allocation model/functional --- .../notebooks/optimization_functional.ipynb | 583 ++++-------------- 1 file changed, 105 insertions(+), 478 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb index 24a242ca..8a4abbcb 100644 --- a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb +++ b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb @@ -24,17 +24,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 352, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "NOTE: Redirects are currently not supported in Windows or MacOs.\n" - ] - } - ], + "outputs": [], "source": [ "from typing import Callable, Optional, Tuple\n", "\n", @@ -61,357 +53,74 @@ "from chirho.robust.handlers.predictive import PredictiveModel, PredictiveFunctional\n", "from chirho.robust.internals.nmc import BatchedNMCLogMarginalLikelihood\n", "\n", + "from docs.examples.robust_paper.models import MultivariateNormalModel\n", + "\n", "\n", "pyro.settings.set(module_local_params=True)\n", "\n", "sns.set_style(\"white\")" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Causal Probabilistic Program\n", - "\n", - "### Model Description\n", - "In this example, we will focus on a cannonical model `CausalGLM` consisting of three types of variables: binary treatment (`A`), confounders (`X`), and response (`Y`). For simplicitly, we assume that the response is generated from a generalized linear model with link function $g$. The model is described by the following generative process:\n", - "\n", - "$$\n", - "\\begin{align*}\n", - "X &\\sim \\text{Normal}(0, I_p) \\\\\n", - "A &\\sim \\text{Bernoulli}(\\pi(X)) \\\\\n", - "\\mu &= \\beta_0 + \\beta_1^T X + \\tau A \\\\\n", - "Y &\\sim \\text{ExponentialFamily}(\\text{mean} = g^{-1}(\\mu))\n", - "\\end{align*}\n", - "$$\n", - "\n", - "where $p$ denotes the number of confounders, $\\pi(X)$ is the probability of treatment conditional on confounders $X$, $\\beta_0$ is the intercept, $\\beta_1$ is the confounder effect, and $\\tau$ is the treatment effect." - ] - }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 400, "metadata": {}, "outputs": [], "source": [ - "class CausalGLM(pyro.nn.PyroModule):\n", - " def __init__(\n", - " self,\n", - " p: int,\n", - " treatment_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", - " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", - " prior_scale: Optional[float] = None,\n", - " ):\n", - " super().__init__()\n", - " self.p = p\n", - " self.treatment_fn = treatment_fn\n", - " self.link_fn = link_fn\n", - " if prior_scale is None:\n", - " self.prior_scale = 1 / math.sqrt(self.p)\n", - " else:\n", - " self.prior_scale = prior_scale\n", - "\n", - " def sample_outcome_weights(self):\n", - " return pyro.sample(\n", - " \"outcome_weights\",\n", - " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", - " )\n", - "\n", - " def sample_intercept(self):\n", - " return pyro.sample(\"intercept\", dist.Normal(0.0, 1.0))\n", - "\n", - " def sample_propensity_weights(self):\n", - " return pyro.sample(\n", - " \"propensity_weights\",\n", - " dist.Normal(0.0, self.prior_scale).expand((self.p,)).to_event(1),\n", - " )\n", - "\n", - " def sample_treatment_weight(self):\n", - " return pyro.sample(\"treatment_weight\", dist.Normal(0.0, 1.0))\n", - "\n", - " def sample_covariate_loc_scale(self):\n", - " return torch.zeros(self.p), torch.ones(self.p)\n", - "\n", + "class ZeroCenteredModel(MultivariateNormalModel):\n", " def forward(self):\n", - " intercept = self.sample_intercept()\n", - " outcome_weights = self.sample_outcome_weights()\n", - " propensity_weights = self.sample_propensity_weights()\n", - " tau = self.sample_treatment_weight()\n", - " x_loc, x_scale = self.sample_covariate_loc_scale()\n", - " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1))\n", - " A = pyro.sample(\n", - " \"A\",\n", - " self.treatment_fn(\n", - " torch.einsum(\"...i,...i->...\", X, propensity_weights)\n", - " ),\n", - " )\n", + " scale_tril = self.sample_scale_tril()\n", + " pyro.sample(\"x\", dist.MultivariateNormal(loc=torch.zeros(p), scale_tril=scale_tril))\n", "\n", - " return pyro.sample(\n", - " \"Y\",\n", - " self.link_fn(\n", - " torch.einsum(\"...i,...i->...\", X, outcome_weights) + A * tau + intercept\n", - " ),\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will condition on both treatment and confounders to estimate the causal effect of treatment on the outcome. We will use the following causal probabilistic program to do so:" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [], - "source": [ - "class ConditionedCausalGLM(CausalGLM):\n", - " def __init__(\n", - " self,\n", - " X: torch.Tensor,\n", - " A: torch.Tensor,\n", - " Y: torch.Tensor,\n", - " treatment_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", - " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", - " prior_scale: Optional[float] = None,\n", - " ):\n", - " p = X.shape[1]\n", - " super().__init__(p, treatment_fn, link_fn, prior_scale)\n", - " self.X = X\n", - " self.A = A\n", - " self.Y = Y\n", + " return scale_tril\n", + " \n", + "class KnownCovModel(ZeroCenteredModel):\n", + " def __init__(self, p, scale_tril):\n", + " super().__init__(p)\n", + " self.scale_tril = scale_tril\n", "\n", + " def sample_scale_tril(self):\n", + " return self.scale_tril\n", + " \n", + "class ConditionedModel(ZeroCenteredModel):\n", + " def __init__(self, D_train):\n", + " self.D_train = D_train\n", + " self.N, p = D_train['x'].shape\n", + " super().__init__(p)\n", + " \n", " def forward(self):\n", - " intercept = self.sample_intercept()\n", - " outcome_weights = self.sample_outcome_weights()\n", - " propensity_weights = self.sample_propensity_weights()\n", - " tau = self.sample_treatment_weight()\n", - " x_loc, x_scale = self.sample_covariate_loc_scale()\n", - " with pyro.plate(\"__train__\", size=self.X.shape[0], dim=-1):\n", - " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1), obs=self.X)\n", - " A = pyro.sample(\n", - " \"A\",\n", - " self.treatment_fn(\n", - " torch.einsum(\"ni,i->n\", self.X, propensity_weights)\n", - " ),\n", - " obs=self.A,\n", - " )\n", - " pyro.sample(\n", - " \"Y\",\n", - " self.link_fn(\n", - " torch.einsum(\"ni,i->n\", X, outcome_weights)\n", - " + A * tau\n", - " + intercept\n", - " ),\n", - " obs=self.Y,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", - "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", - " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", - "<!-- Generated by graphviz version 6.0.2 (20221011.1828)\n", - " -->\n", - "<!-- Pages: 1 -->\n", - "<svg width=\"563pt\" height=\"304pt\"\n", - " viewBox=\"0.00 0.00 563.39 304.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", - "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 300)\">\n", - "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-300 559.39,-300 559.39,4 -4,4\"/>\n", - "<g id=\"clust1\" class=\"cluster\">\n", - "<title>cluster___train__</title>\n", - "<polygon fill=\"none\" stroke=\"black\" points=\"93.6,-8 93.6,-155 235.6,-155 235.6,-8 93.6,-8\"/>\n", - "<text text-anchor=\"middle\" x=\"201.6\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">__train__</text>\n", - "</g>\n", - "<!-- intercept -->\n", - "<g id=\"node1\" class=\"node\">\n", - "<title>intercept</title>\n", - "<ellipse fill=\"white\" stroke=\"black\" cx=\"41.6\" cy=\"-129\" rx=\"41.69\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"41.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">intercept</text>\n", - "</g>\n", - "<!-- Y -->\n", - "<g id=\"node7\" class=\"node\">\n", - "<title>Y</title>\n", - "<ellipse fill=\"gray\" stroke=\"black\" cx=\"200.6\" cy=\"-57\" rx=\"27\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"200.6\" y=\"-53.3\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n", - "</g>\n", - "<!-- intercept&#45;&gt;Y -->\n", - "<g id=\"edge6\" class=\"edge\">\n", - "<title>intercept&#45;&gt;Y</title>\n", - "<path fill=\"none\" stroke=\"black\" d=\"M69.66,-115.65C97.53,-103.38 140.19,-84.59 169.18,-71.83\"/>\n", - "<polygon fill=\"black\" stroke=\"black\" points=\"170.71,-74.98 178.45,-67.75 167.89,-68.58 170.71,-74.98\"/>\n", - "</g>\n", - "<!-- outcome_weights -->\n", - "<g id=\"node2\" class=\"node\">\n", - "<title>outcome_weights</title>\n", - "<ellipse fill=\"white\" stroke=\"black\" cx=\"318.6\" cy=\"-129\" rx=\"73.39\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"318.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights</text>\n", - "</g>\n", - "<!-- outcome_weights&#45;&gt;Y -->\n", - "<g id=\"edge4\" class=\"edge\">\n", - "<title>outcome_weights&#45;&gt;Y</title>\n", - "<path fill=\"none\" stroke=\"black\" d=\"M291.82,-112.12C273.12,-101.02 248.19,-86.23 229.12,-74.92\"/>\n", - "<polygon fill=\"black\" stroke=\"black\" points=\"230.67,-71.77 220.28,-69.68 227.1,-77.79 230.67,-71.77\"/>\n", - "</g>\n", - "<!-- propensity_weights -->\n", - "<g id=\"node3\" class=\"node\">\n", - "<title>propensity_weights</title>\n", - "<ellipse fill=\"white\" stroke=\"black\" cx=\"128.6\" cy=\"-239.5\" rx=\"79.09\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"128.6\" y=\"-235.8\" font-family=\"Times,serif\" font-size=\"14.00\">propensity_weights</text>\n", - "</g>\n", - "<!-- A -->\n", - "<g id=\"node6\" class=\"node\">\n", - "<title>A</title>\n", - "<ellipse fill=\"gray\" stroke=\"black\" cx=\"128.6\" cy=\"-129\" rx=\"27\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"128.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">A</text>\n", - "</g>\n", - "<!-- propensity_weights&#45;&gt;A -->\n", - "<g id=\"edge1\" class=\"edge\">\n", - "<title>propensity_weights&#45;&gt;A</title>\n", - "<path fill=\"none\" stroke=\"black\" d=\"M128.6,-221.07C128.6,-203.8 128.6,-177.12 128.6,-157.09\"/>\n", - "<polygon fill=\"black\" stroke=\"black\" points=\"132.1,-157.03 128.6,-147.03 125.1,-157.03 132.1,-157.03\"/>\n", - "</g>\n", - "<!-- treatment_weight -->\n", - "<g id=\"node4\" class=\"node\">\n", - "<title>treatment_weight</title>\n", - "<ellipse fill=\"white\" stroke=\"black\" cx=\"482.6\" cy=\"-129\" rx=\"72.59\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"482.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">treatment_weight</text>\n", - "</g>\n", - "<!-- treatment_weight&#45;&gt;Y -->\n", - "<g id=\"edge5\" class=\"edge\">\n", - "<title>treatment_weight&#45;&gt;Y</title>\n", - "<path fill=\"none\" stroke=\"black\" d=\"M433.15,-115.73C376.52,-101.67 285.18,-79 235.5,-66.66\"/>\n", - "<polygon fill=\"black\" stroke=\"black\" points=\"236.11,-63.21 225.56,-64.2 234.42,-70 236.11,-63.21\"/>\n", - "</g>\n", - "<!-- X -->\n", - "<g id=\"node5\" class=\"node\">\n", - "<title>X</title>\n", - "<ellipse fill=\"gray\" stroke=\"black\" cx=\"200.6\" cy=\"-129\" rx=\"27\" ry=\"18\"/>\n", - "<text text-anchor=\"middle\" x=\"200.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n", - "</g>\n", - "<!-- X&#45;&gt;Y -->\n", - "<g id=\"edge2\" class=\"edge\">\n", - "<title>X&#45;&gt;Y</title>\n", - "<path fill=\"none\" stroke=\"black\" d=\"M200.6,-110.7C200.6,-102.98 200.6,-93.71 200.6,-85.11\"/>\n", - "<polygon fill=\"black\" stroke=\"black\" points=\"204.1,-85.1 200.6,-75.1 197.1,-85.1 204.1,-85.1\"/>\n", - "</g>\n", - "<!-- A&#45;&gt;Y -->\n", - "<g id=\"edge3\" class=\"edge\">\n", - "<title>A&#45;&gt;Y</title>\n", - "<path fill=\"none\" stroke=\"black\" d=\"M143.17,-113.83C153.35,-103.94 167.12,-90.55 178.63,-79.36\"/>\n", - "<polygon fill=\"black\" stroke=\"black\" points=\"181.07,-81.87 185.8,-72.38 176.19,-76.85 181.07,-81.87\"/>\n", - "</g>\n", - "<!-- distribution_description_node -->\n", - "<g id=\"node8\" class=\"node\">\n", - "<title>distribution_description_node</title>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-280.8\" font-family=\"Times,serif\" font-size=\"14.00\">intercept ~ Normal</text>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-265.8\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights ~ Normal</text>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-250.8\" font-family=\"Times,serif\" font-size=\"14.00\">propensity_weights ~ Normal</text>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-235.8\" font-family=\"Times,serif\" font-size=\"14.00\">treatment_weight ~ Normal</text>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-220.8\" font-family=\"Times,serif\" font-size=\"14.00\">X ~ Normal</text>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-205.8\" font-family=\"Times,serif\" font-size=\"14.00\">A ~ Normal</text>\n", - "<text text-anchor=\"start\" x=\"234.1\" y=\"-190.8\" font-family=\"Times,serif\" font-size=\"14.00\">Y ~ Normal</text>\n", - "</g>\n", - "</g>\n", - "</svg>\n" - ], - "text/plain": [ - "<graphviz.graphs.Digraph at 0x189ab6550>" - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Visualize the model\n", - "pyro.render_model(\n", - " ConditionedCausalGLM(torch.zeros(1, 1), torch.zeros(1), torch.zeros(1)),\n", - " render_params=True, \n", - " render_distributions=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generating data\n", - "\n", - "For evaluation, we generate `N_datasets` datasets, each with `N` samples. We compare vanilla estimates of the target functional with the double robust estimates of the target functional across the `N_sims` datasets. We use a similar data generating process as in Kennedy (2022)." + " mu = self.sample_mean()\n", + " scale_tril = self.sample_scale_tril()\n", + " with pyro.condition(data=self.D_train):\n", + " with pyro.plate(self.N, dim=-2):\n", + " pyro.sample(\"x\", dist.MultivariateNormal(loc=mu, scale_tril=scale_tril))\n", + " " ] }, { "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "class GroundTruthModel(CausalGLM):\n", - " def __init__(\n", - " self,\n", - " p: int,\n", - " alpha: int,\n", - " beta: int,\n", - " treatment_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", - " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", - " treatment_weight: float = 1.0,\n", - " ):\n", - " super().__init__(p, treatment_fn, link_fn)\n", - " self.alpha = alpha # sparsity of propensity weights\n", - " self.beta = beta # sparsity of outcome weights\n", - " self.treatment_weight = treatment_weight\n", - "\n", - " def sample_outcome_weights(self):\n", - " outcome_weights = 1 / math.sqrt(self.beta) * torch.ones(self.p)\n", - " outcome_weights[self.beta :] = 0.0\n", - " return outcome_weights\n", - "\n", - " def sample_propensity_weights(self):\n", - " propensity_weights = 1 / math.sqrt(self.alpha) * torch.ones(self.p)\n", - " propensity_weights[self.alpha :] = 0.0\n", - " return propensity_weights\n", - "\n", - " def sample_treatment_weight(self):\n", - " return torch.tensor(self.treatment_weight)\n", - "\n", - " def sample_intercept(self):\n", - " return torch.tensor(0.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 153, + "execution_count": 401, "metadata": {}, "outputs": [], "source": [ "# Data configuration\n", - "p = 2\n", + "p = 3\n", "alpha = 50\n", "beta = 50\n", "N_train = 500\n", "N_test = 500\n", "\n", - "true_model = GroundTruthModel(p, alpha, beta)\n", + "true_scale_tril = pyro.sample(\"scale_tril\", dist.LKJCholesky(p))\n", + "\n", + "true_model = KnownCovModel(p, true_scale_tril)\n", "\n", "def generate_data(N_train, N_test):\n", " # Generate data\n", " D_train = Predictive(\n", - " true_model, num_samples=N_train, return_sites=[\"X\", \"A\", \"Y\"]\n", + " true_model, num_samples=N_train, return_sites=[\"x\"]\n", " )()\n", " D_test = Predictive(\n", - " true_model, num_samples=N_test, return_sites=[\"X\", \"A\", \"Y\"]\n", + " true_model, num_samples=N_test, return_sites=[\"x\"]\n", " )()\n", " return D_train, D_test" ] @@ -425,15 +134,13 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 402, "metadata": {}, "outputs": [], "source": [ "def MLE(D_train, n_steps=2000):\n", " # Fit model using maximum likelihood\n", - " conditioned_model = ConditionedCausalGLM(\n", - " X=D_train[\"X\"], A=D_train[\"A\"], Y=D_train[\"Y\"]\n", - " )\n", + " conditioned_model = ConditionedModel(D_train)\n", " \n", " guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model)\n", " elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train)\n", @@ -455,18 +162,6 @@ " return theta_hat" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Causal Query: Average treatment effect (ATE)\n", - "\n", - "The average treatment effect summarizes, on average, how much the treatment changes the response, $ATE = \\mathbb{E}[Y|do(A=1)] - \\mathbb{E}[Y|do(A=0)]$. The `do` notation indicates that the expectations are taken according to *intervened* versions of the model, with $A$ set to a particular value. Note from our [tutorial](tutorial_i.ipynb) that this is different from conditioning on $A$ in the original `causal_model`, which assumes $X$ and $T$ are dependent.\n", - "\n", - "\n", - "To implement this query in ChiRho, we define the `ATEFunctional` class which take in a `model` and `guide` and returns the average treatment effect by simulating from the posterior predictive distribution of the model and guide." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -476,72 +171,7 @@ }, { "cell_type": "code", - "execution_count": 194, - "metadata": {}, - "outputs": [], - "source": [ - "class MaximizeATEFunctional(torch.nn.Module):\n", - " def __init__(self, model: Callable, *, \n", - " num_monte_carlo: int = 100,\n", - " optimizer = torchopt.adam,\n", - " learning_rate = 1.0,\n", - " n_grad_steps:int = 100,\n", - " ):\n", - " super().__init__()\n", - " self.model = model\n", - " self.num_monte_carlo = num_monte_carlo\n", - " self.optimizer = optimizer\n", - " self.learning_rate = learning_rate\n", - " self.n_grad_steps = n_grad_steps\n", - " \n", - " def dose_response(self, treatment_assignment: torch.Tensor, *args, **kwargs):\n", - " with pyro.plate(\"monte_carlo_functional\", size=self.num_monte_carlo, dim=-2):\n", - " with do(actions=dict(A=(treatment_assignment))):\n", - " Ys = self.model(*args, **kwargs)\n", - " return pyro.deterministic(\"dose_response\", Ys.mean(dim=-1, keepdim=True).squeeze())\n", - "\n", - " # Y0 = gather(Ys, IndexSet(A={1}), event_dim=0)\n", - " # Y1 = gather(Ys, IndexSet(A={2}), event_dim=0)\n", - " # ate = (Y1 - Y0).mean(dim=-2, keepdim=True).mean(dim=-1, keepdim=True).squeeze()\n", - " # return pyro.deterministic(\"ATE\", ate)\n", - " \n", - " def loss(self, treatment_assignment: torch.Tensor, *args, **kwargs):\n", - "\n", - " # Penalize squared treatment assignment magnitude, while maximizing dose response.\n", - "\n", - " return treatment_assignment**2 - self.dose_response(treatment_assignment, *args, **kwargs)\n", - "\n", - "\n", - " def forward(self, *args, **kwargs):\n", - " \n", - " treatment_assignment = (torch.tensor(1.0, requires_grad=True),)\n", - "\n", - " optimizer = self.optimizer(self.learning_rate)\n", - " opt_state = optimizer.init(treatment_assignment)\n", - " \n", - " for _ in range(self.n_grad_steps):\n", - " loss = self.loss(treatment_assignment[0])\n", - "\n", - " grads = torch.autograd.grad(loss, treatment_assignment, create_graph=True)\n", - " updates, opt_state = optimizer.update(grads, opt_state, inplace=False)\n", - "\n", - " treatment_assignment = torchopt.apply_updates(treatment_assignment, updates, inplace=False)\n", - "\n", - " return pyro.deterministic(\"optimal_treatment_assignment\", treatment_assignment[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Computing automated doubly robust estimators via Monte Carlo\n", - "\n", - "While the doubly robust correction term is known in closed-form for the average treatment effect functional, our `one_step_correction` and `tmle` function in `ChiRho` works for a wide class of other functionals. We focus on the average treatment effect functional here so that we have a ground truth to compare `one_step_correction` against the plug in estimates." - ] - }, - { - "cell_type": "code", - "execution_count": 195, + "execution_count": 403, "metadata": {}, "outputs": [], "source": [ @@ -563,44 +193,26 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 418, "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "element 0 of tensors does not require grad and does not have a grad_fn", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[199], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m eif_fn \u001b[38;5;241m=\u001b[39m influence_fn(functional, D_test, num_samples_outer\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, pointwise_influence\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 19\u001b[0m correction_estimator \u001b[38;5;241m=\u001b[39m eif_fn(model)\n\u001b[0;32m---> 20\u001b[0m correction \u001b[38;5;241m=\u001b[39m \u001b[43mcorrection_estimator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:145\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43min_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam_eif\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:434\u001b[0m, in \u001b[0;36mvmap.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _chunked_vmap(func, flat_in_dims, chunks_flat_args,\n\u001b[1;32m 431\u001b[0m args_spec, out_dims, randomness, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 433\u001b[0m \u001b[38;5;66;03m# If chunk_size is not specified.\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_flat_vmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_in_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_spec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:619\u001b[0m, in \u001b[0;36m_flat_vmap\u001b[0;34m(func, batch_size, flat_in_dims, flat_args, args_spec, out_dims, randomness, **kwargs)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 618\u001b[0m batched_inputs \u001b[38;5;241m=\u001b[39m _create_batched_inputs(flat_in_dims, flat_args, vmap_level, args_spec)\n\u001b[0;32m--> 619\u001b[0m batched_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbatched_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func)\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:146\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:916\u001b[0m, in \u001b[0;36mjvp\u001b[0;34m(func, primals, tangents, strict, has_aux)\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[38;5;129m@exposed_in\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.func\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjvp\u001b[39m(func: Callable, primals: Any, tangents: Any, \u001b[38;5;241m*\u001b[39m, strict: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, has_aux: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;124;03m Standing for the Jacobian-vector product, returns a tuple containing\u001b[39;00m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m the output of `func(*primals)` and the \"Jacobian of ``func`` evaluated at\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 913\u001b[0m \n\u001b[1;32m 914\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_jvp_with_argnums\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtangents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_aux\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:965\u001b[0m, in \u001b[0;36m_jvp_with_argnums\u001b[0;34m(func, primals, tangents, argnums, strict, has_aux)\u001b[0m\n\u001b[1;32m 963\u001b[0m primals \u001b[38;5;241m=\u001b[39m _wrap_all_tensors(primals, level)\n\u001b[1;32m 964\u001b[0m duals \u001b[38;5;241m=\u001b[39m _replace_args(primals, duals, argnums)\n\u001b[0;32m--> 965\u001b[0m result_duals \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mduals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_aux:\n\u001b[1;32m 967\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(result_duals, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_duals) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m):\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:147\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>.<locals>.<lambda>\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: torch\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mjvp(\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m p: \u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, (target_params,), (d,)\n\u001b[1;32m 148\u001b[0m )[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:1600\u001b[0m, in \u001b[0;36mfunctionalize.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1597\u001b[0m flattened_unwrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(kwargs)\n\u001b[1;32m 1598\u001b[0m flattened_wrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(func_kwargs)\n\u001b[0;32m-> 1600\u001b[0m func_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1601\u001b[0m outputs \u001b[38;5;241m=\u001b[39m _unwrap_all_tensors_from_functional(func_outputs, reapply_views\u001b[38;5;241m=\u001b[39mreapply_views)\n\u001b[1;32m 1602\u001b[0m flat_outputs, func_out_spec \u001b[38;5;241m=\u001b[39m tree_flatten(outputs)\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/internals/utils.py:114\u001b[0m, in \u001b[0;36mmake_functional_call.<locals>.mod_func\u001b[0;34m(params, *args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mfunctionalize\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmod_func\u001b[39m(params: ParamDict, \u001b[38;5;241m*\u001b[39margs: P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mvalidation_enabled(\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/functional_call.py:143\u001b[0m, in \u001b[0;36mfunctional_call\u001b[0;34m(module, parameter_and_buffer_dicts, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter_and_buffer_dicts to be a dict, or a list/tuple of dicts, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(parameter_and_buffer_dicts)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 141\u001b[0m )\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_functional_call\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_and_buffers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mtie_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtie_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/utils/stateless.py:262\u001b[0m, in \u001b[0;36m_functional_call\u001b[0;34m(module, parameters_and_buffers, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 258\u001b[0m args \u001b[38;5;241m=\u001b[39m (args,)\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _reparametrize_module(\n\u001b[1;32m 260\u001b[0m module, parameters_and_buffers, tie_weights\u001b[38;5;241m=\u001b[39mtie_weights, strict\u001b[38;5;241m=\u001b[39mstrict\n\u001b[1;32m 261\u001b[0m ):\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "Cell \u001b[0;32mIn[194], line 43\u001b[0m, in \u001b[0;36mMaximizeATEFunctional.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_grad_steps):\n\u001b[1;32m 41\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(treatment_assignment[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m---> 43\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtreatment_assignment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m updates, opt_state \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mupdate(grads, opt_state, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 46\u001b[0m treatment_assignment \u001b[38;5;241m=\u001b[39m torchopt\u001b[38;5;241m.\u001b[39mapply_updates(treatment_assignment, updates, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/autograd/__init__.py:303\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(grad_outputs_)\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mRuntimeError\u001b[0m: element 0 of tensors does not require grad and does not have a grad_fn" - ] - } - ], + "outputs": [], "source": [ - "functional = functools.partial(MaximizeATEFunctional, \n", - " num_monte_carlo=10000, \n", - " optimizer=torchopt.adam,\n", - " learning_rate=1.0,\n", - " n_grad_steps=1)\n", + "class MarkowitzFunctional(torch.nn.Module):\n", + " def __init__(self, model):\n", + " super().__init__()\n", + " self.model = model\n", + " \n", + " def forward(self):\n", + " scale_tril = self.model()\n", + " cov = scale_tril.mm(scale_tril.T)\n", + " cov_inv = torch.inverse(cov)\n", + " one_vec = torch.ones(cov_inv.shape[0])\n", + " num = cov_inv.mv(one_vec)\n", + " den = one_vec.dot(num)\n", + " return num/den\n", + "\n", + " \n", + "model = ZeroCenteredModel(p)\n", "\n", "D_train, D_test = generate_data(N_train, N_test)\n", "\n", @@ -610,55 +222,70 @@ " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", "}\n", "mle_guide = MLEGuide(theta_hat)\n", - "model = PredictiveModel(CausalGLM(p), mle_guide)\n", - "\n", - "eif_fn = influence_fn(functional, D_test, num_samples_outer=100, pointwise_influence=False)\n", - "\n", + "model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 425, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.0290, 0.1965, -0.2255]], grad_fn=<DivBackward0>)" + ] + }, + "execution_count": 425, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eif_fn = influence_fn(MarkowitzFunctional, D_test, num_samples_outer=10000, pointwise_influence=False)\n", "correction_estimator = eif_fn(model)\n", - "correction = correction_estimator()" + "correction = correction_estimator()\n", + "correction" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing automated doubly robust estimators via Monte Carlo\n", + "\n", + "While the doubly robust correction term is known in closed-form for the average treatment effect functional, our `one_step_correction` and `tmle` function in `ChiRho` works for a wide class of other functionals. We focus on the average treatment effect functional here so that we have a ground truth to compare `one_step_correction` against the plug in estimates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 426, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Dataset 0\n", - "plug-in-mle-from-model 0\n", - "one_step monte_carlo_eif 0\n" + "Dataset 0\n" ] }, { - "ename": "RuntimeError", - "evalue": "element 0 of tensors does not require grad and does not have a grad_fn", + "ename": "TypeError", + "evalue": "'dict' object cannot be interpreted as an integer", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[160], line 50\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m influence_str, influence \u001b[38;5;129;01min\u001b[39;00m influences\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mprint\u001b[39m(estimator_str, influence_str, i)\n\u001b[0;32m---> 50\u001b[0m estimate \u001b[38;5;241m=\u001b[39m \u001b[43mestimator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunctional\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43mD_test\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples_outer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_samples_inner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minfluence_estimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minfluence\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mestimator_kwargs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mestimator_str\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPredictiveModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mCausalGLM\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmle_guide\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m estimates[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00minfluence_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m-\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mappend(estimate\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mitem())\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(RESULTS_PATH, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/handlers/estimators.py:257\u001b[0m, in \u001b[0;36mone_step_corrected_estimator.<locals>._corrected_functional.<locals>._estimator\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_estimator\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m S:\n\u001b[1;32m 256\u001b[0m plug_in_estimate \u001b[38;5;241m=\u001b[39m plug_in_estimator(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 257\u001b[0m correction \u001b[38;5;241m=\u001b[39m \u001b[43mcorrection_estimator\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m flat_plug_in_estimate, treespec \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39m_pytree\u001b[38;5;241m.\u001b[39mtree_flatten(\n\u001b[1;32m 260\u001b[0m plug_in_estimate\n\u001b[1;32m 261\u001b[0m )\n\u001b[1;32m 262\u001b[0m flat_correction, _ \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39m_pytree\u001b[38;5;241m.\u001b[39mtree_flatten(correction)\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:145\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43min_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdifferent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparam_eif\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:434\u001b[0m, in \u001b[0;36mvmap.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _chunked_vmap(func, flat_in_dims, chunks_flat_args,\n\u001b[1;32m 431\u001b[0m args_spec, out_dims, randomness, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 433\u001b[0m \u001b[38;5;66;03m# If chunk_size is not specified.\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_flat_vmap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 435\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_in_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_spec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_dims\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandomness\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:619\u001b[0m, in \u001b[0;36m_flat_vmap\u001b[0;34m(func, batch_size, flat_in_dims, flat_args, args_spec, out_dims, randomness, **kwargs)\u001b[0m\n\u001b[1;32m 617\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 618\u001b[0m batched_inputs \u001b[38;5;241m=\u001b[39m _create_batched_inputs(flat_in_dims, flat_args, vmap_level, args_spec)\n\u001b[0;32m--> 619\u001b[0m batched_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mbatched_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, func)\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:146\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>\u001b[0;34m(d)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjvp\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:916\u001b[0m, in \u001b[0;36mjvp\u001b[0;34m(func, primals, tangents, strict, has_aux)\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[38;5;129m@exposed_in\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch.func\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjvp\u001b[39m(func: Callable, primals: Any, tangents: Any, \u001b[38;5;241m*\u001b[39m, strict: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, has_aux: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;124;03m Standing for the Jacobian-vector product, returns a tuple containing\u001b[39;00m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m the output of `func(*primals)` and the \"Jacobian of ``func`` evaluated at\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 913\u001b[0m \n\u001b[1;32m 914\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_jvp_with_argnums\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprimals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtangents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_aux\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhas_aux\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:965\u001b[0m, in \u001b[0;36m_jvp_with_argnums\u001b[0;34m(func, primals, tangents, argnums, strict, has_aux)\u001b[0m\n\u001b[1;32m 963\u001b[0m primals \u001b[38;5;241m=\u001b[39m _wrap_all_tensors(primals, level)\n\u001b[1;32m 964\u001b[0m duals \u001b[38;5;241m=\u001b[39m _replace_args(primals, duals, argnums)\n\u001b[0;32m--> 965\u001b[0m result_duals \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mduals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_aux:\n\u001b[1;32m 967\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(result_duals, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result_duals) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m):\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/ops.py:147\u001b[0m, in \u001b[0;36minfluence_fn.<locals>._influence_functional.<locals>._fn.<locals>.<lambda>.<locals>.<lambda>\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124;03mEvaluates the efficient influence function for ``functional`` at each\u001b[39;00m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124;03mpoint in ``points``.\u001b[39;00m\n\u001b[1;32m 141\u001b[0m \n\u001b[1;32m 142\u001b[0m \u001b[38;5;124;03m:return: efficient influence function evaluated at each point in ``points`` or averaged\u001b[39;00m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 144\u001b[0m param_eif \u001b[38;5;241m=\u001b[39m linearized(\u001b[38;5;241m*\u001b[39mpoints, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mvmap(\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m d: torch\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mjvp(\n\u001b[0;32m--> 147\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m p: \u001b[43mfunc_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, (target_params,), (d,)\n\u001b[1;32m 148\u001b[0m )[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 149\u001b[0m in_dims\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 150\u001b[0m randomness\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdifferent\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 151\u001b[0m )(param_eif)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/vmap.py:39\u001b[0m, in \u001b[0;36mdoesnt_support_saved_tensors_hooks.<locals>.fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfn\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgraph\u001b[38;5;241m.\u001b[39mdisable_saved_tensors_hooks(message):\n\u001b[0;32m---> 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/eager_transforms.py:1600\u001b[0m, in \u001b[0;36mfunctionalize.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1597\u001b[0m flattened_unwrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(kwargs)\n\u001b[1;32m 1598\u001b[0m flattened_wrapped_kwargs, _ \u001b[38;5;241m=\u001b[39m tree_flatten(func_kwargs)\n\u001b[0;32m-> 1600\u001b[0m func_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfunc_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1601\u001b[0m outputs \u001b[38;5;241m=\u001b[39m _unwrap_all_tensors_from_functional(func_outputs, reapply_views\u001b[38;5;241m=\u001b[39mreapply_views)\n\u001b[1;32m 1602\u001b[0m flat_outputs, func_out_spec \u001b[38;5;241m=\u001b[39m tree_flatten(outputs)\n", - "File \u001b[0;32m~/Desktop/Research/chirho/chirho/robust/internals/utils.py:114\u001b[0m, in \u001b[0;36mmake_functional_call.<locals>.mod_func\u001b[0;34m(params, *args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\u001b[38;5;241m.\u001b[39mfunctionalize\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmod_func\u001b[39m(params: ParamDict, \u001b[38;5;241m*\u001b[39margs: P\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: P\u001b[38;5;241m.\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m T:\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pyro\u001b[38;5;241m.\u001b[39mvalidation_enabled(\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/_functorch/functional_call.py:143\u001b[0m, in \u001b[0;36mfunctional_call\u001b[0;34m(module, parameter_and_buffer_dicts, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 139\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected parameter_and_buffer_dicts to be a dict, or a list/tuple of dicts, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 140\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(parameter_and_buffer_dicts)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 141\u001b[0m )\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_functional_call\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters_and_buffers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[43m \u001b[49m\u001b[43mtie_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtie_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/utils/stateless.py:262\u001b[0m, in \u001b[0;36m_functional_call\u001b[0;34m(module, parameters_and_buffers, args, kwargs, tie_weights, strict)\u001b[0m\n\u001b[1;32m 258\u001b[0m args \u001b[38;5;241m=\u001b[39m (args,)\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _reparametrize_module(\n\u001b[1;32m 260\u001b[0m module, parameters_and_buffers, tie_weights\u001b[38;5;241m=\u001b[39mtie_weights, strict\u001b[38;5;241m=\u001b[39mstrict\n\u001b[1;32m 261\u001b[0m ):\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "Cell \u001b[0;32mIn[158], line 43\u001b[0m, in \u001b[0;36mMaximizeATEFunctional.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_grad_steps):\n\u001b[1;32m 41\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(treatment_assignment[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m---> 43\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtreatment_assignment\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m updates, opt_state \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39mupdate(grads, opt_state, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 46\u001b[0m treatment_assignment \u001b[38;5;241m=\u001b[39m torchopt\u001b[38;5;241m.\u001b[39mapply_updates(treatment_assignment, updates, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/torch/autograd/__init__.py:303\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(grad_outputs_)\n\u001b[1;32m 302\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 303\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 304\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mRuntimeError\u001b[0m: element 0 of tensors does not require grad and does not have a grad_fn" + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[426], line 36\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, i)\n\u001b[1;32m 35\u001b[0m D_train, D_test \u001b[38;5;241m=\u001b[39m generate_data(N_train, N_test)\n\u001b[0;32m---> 36\u001b[0m theta_hat \u001b[38;5;241m=\u001b[39m \u001b[43mMLE\u001b[49m\u001b[43m(\u001b[49m\u001b[43mD_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mD_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m theta_hat \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 39\u001b[0m k: v\u001b[38;5;241m.\u001b[39mclone()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mrequires_grad_(\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_hat\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 40\u001b[0m }\n\u001b[1;32m 41\u001b[0m mle_guide \u001b[38;5;241m=\u001b[39m MLEGuide(theta_hat)\n", + "Cell \u001b[0;32mIn[402], line 13\u001b[0m, in \u001b[0;36mMLE\u001b[0;34m(D_train, n_steps)\u001b[0m\n\u001b[1;32m 10\u001b[0m adam \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mAdam(elbo\u001b[38;5;241m.\u001b[39mparameters(), lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.03\u001b[39m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Do gradient steps\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_steps):\n\u001b[1;32m 14\u001b[0m adam\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 15\u001b[0m loss \u001b[38;5;241m=\u001b[39m elbo()\n", + "\u001b[0;31mTypeError\u001b[0m: 'dict' object cannot be interpreted as an integer" ] } ], @@ -680,7 +307,7 @@ "influences = {\"monte_carlo_eif\": influence_fn}\n", "\n", "# Cache the results\n", - "RESULTS_PATH = \"../results/opt_causal_glm.json\"\n", + "RESULTS_PATH = \"../results/opt_markowitz.json\"\n", "\n", "if os.path.exists(RESULTS_PATH):\n", " with open(RESULTS_PATH, \"r\") as f:\n", From 73398d189ac08385d2a6a2c976df6961ad5f1657 Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Mon, 29 Jan 2024 20:05:30 -0500 Subject: [PATCH 20/26] first pass at markowitz experiment --- .../notebooks/figures/markowitz_optimal.png | Bin 0 -> 32476 bytes .../notebooks/optimization_functional.ipynb | 486 +++++------------- .../robust_paper/results/opt_markowitz.json | 16 + 3 files changed, 145 insertions(+), 357 deletions(-) create mode 100644 docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png create mode 100644 docs/examples/robust_paper/results/opt_markowitz.json diff --git a/docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png b/docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png new file mode 100644 index 0000000000000000000000000000000000000000..2ccaebd5b9966a0187b73452b53ffd4d8a62aa72 GIT binary patch literal 32476 zcmaI8cRZH;|2}@%JFW<2Mk>il_RP*6g|f*=vS-8<;ffI1yC{kf*|KGiLXthN${uC& zd!F9!&+q&B=lkvP=)UjAHO}jGzFyC9Jdfiz?`Jw%Dpcg>$WbU1)lF4JJroKbgF@j= zkrKlz!e7Vc;9ue%N=6>~&bA)jmTopEO-m0KM`sVm`&R5;Hg4|sot=dEMER~=Ww-b6 zaB-L5=YRPBe}K=~&5objbUqL+Lgu1+#~p>DwnYBo<;mvWN1+04-c-DC%O`Dl+}q>U z{Le4blR2WXI`XT$TYQK8N;Ep*;Z!<wt=b<WtVlk-Vm;${ISW7hvL+F_C73RTLp729 zZr&4}fv%<1=$_|__=MM9O24csDl5Uf>l5Jip4g}rX%?VVgg-5IJ9kVmSolMCl}HW^ zf7r57mpM2%W~}K@O7H>y=lEI3+tIoxI(T<okQ@W=a>yv-=^$TkyNn8lcXt^7-(OyT zkFsYZzIpQ|W2mJkH*x{9u6%q8H@9n=sUlqB;v;|b6vsHk#m}bpg!b4D;Ht3L%1Fci zT(d*=|9$QMSs0!H13L#tVSIYJrmgMUAscM_ZE{M=mdVM<4}GuTp7c~1LmOi_Rm431 z#0|Z{s>&#-sOVRF+;<d7HD)!3+sS$1^0YpdOZ_xG{X#h#GslcfsO5{;Sb2GQe3VUR zoNL!6a<g>!BO@cdYlgKxuK8W)7=KbKDkApt=bykAt8FBVQsopC$?@7!>Whm7B6!ef zLl=MEng`NFu#2kaU*9*6X=)rd@A}`HD5aLGm5z;!oZsw|?fbjOVD2CD<A>3gNG9Aj zoN29(V2#&u=k~1QXnACA#-nhr<)2@(ZV(jKtVm2e-ZqVmjomo>kuKrcIlP!5{DA)B z$Bzt$Nn)-XD3Vjm>>?rwxAP6=ceYz)yq12rcHj={kN0H*cIOkB6MIrcqa{~sU%u5$ z{Z{E}{b+lo2bUSBrL6qrc6}XBnd8VcVoFB2mlrkde-xkp{OIq@fVOOp;BrcAY?Gn+ z<N0s6!;LQfv(k?~sk3dW-KT{h@JVZEkfXn9)G+QnnLfKwP;ix=&*ay}?~lPG)Ym+I z>0H##(D?gjsA$g>UT>$>mb%fGA$?ZT>uPHRgWdkW<>zOGLg%{@g?u;eH8nSz8to3< zsq^<OOjJ=+G}#E4{Suk&I{Sse#O#y(*N6$bm9eTcX}^RAMW4U>j!^K|Hbl!FwfDbL z@P)f0BPF%yNfCa|BAJjGc$9qe<;AMQ<KNqpfw(6mtWzJ`Slz!DJB>@21s;0-TN-Iz zEUG`%kaP285^SLNm}^*9#(tFKN|s7A>%lHA^ZJAD-8RvFTu45&%zknh3b*M>aGh&M zt0nR+*ZR+Q#y<@S5%<1X9d60E8}Qt^Eo!}6sLy5eVa57VX`cjtk<Kf8;RoMy{I*v# z(k0^eSL<ab`{K<71qBT~>+|yRc&eSfyhK(%Z?pEv9@lP`jFj35xi4tIZl!S^+$uCl zE`K=st;XAV!hcg!Ir5z6ZQ+HXt=|K?eSt^K(%WORwFP=_)N<au5p)>5to)qu#=Uzv z>N287yzJ~4JObjim73LRe-FW;>qJ*)I7{^ORAoIoc_+JDg3l6PxMaNT8lUM*b?$nW z<4DQD!DgR0-|_F<%wH3q_lY<$froz?2uM!l+#h%gdme=5RCd{zyNUY?;bt|{@{E#6 z<T-@tz-uK6<!Dw-0|R=9xh!{FFN%^`tY5tK90Nn!i}MQhW0isrN6Y7*UCNmH@zI*$ z!)_Z};00dZND@|`si4Ix1_kYj<^dg=qT_^^aVNWjoplrManHeg!|02eDK4-tC4t8Q zxW`u3)<~?dNP6YK)t1I<wu9O}k1dw}Yzm<ef4q17gK>qInFz$r&$0)Ji|vNV*K8QC z+tZZV^(`!olwx*|1+2eNu2e7box5%?H(LI1cPg0LZOnOnrgdw1G?AF<oXc|A@WzDS zs;SqN5!=#_4@X6!Sfx{DHzq#&33;!WG&MCbiaIf~`LE%TP_tfu)Pk)+3BXBJd#`E* z6H~#aNty@ja(UIT?ATTN_oj(&EseZHF5JaiQ4D#@+&A#SSoCObkzd4VEUE2Sb65{W z$30gfbj@i03sbk_XhQPCp91yH=BGs;mQ-KmCJn`VI4ZCB)RXOax9@gX!^(IKjudnD z?Afb#KVey=AI(*c!}gyQus{hr3{H(Y*UukrPuewyQjNLyN#C=xlXTub+Fy0=5guM^ zrj`}(-*I`XmEJj4<<71DmU8m(Hj9Op>nT>Lq->RFxBcY`{dam?w%tjMmook3_4H03 z?r&e>;^G>9B<eh&5r5@2p9IU#f;&QX(O)`VUJ`PhQOxjNIE!R^7+XL)?2+$mq^R2% z4sywM>BmR^wMG(%-QD+Q74h`EcNG`Ee;^|#$7hFQuRlIYg`#7&CG+8K83!k4uqN(t z=A+FX(cc5P(eC3@m9Dc!rM6Tk^+dk;uPSWw3C2#AknFWv-_*M^Wiz1&{)sJvkj1vN zC`x$#Y00_~yaQ)x^!btSlP6DZ<r_p|FeqYb*6yjsU~bdu>s^Wbxf;pWG%{t<1+BlV z^(#;dI!{QkKHk!m*<TWx2-sg{jz0dv<SYOs%C;{<bLjo;vr;|+>+9>ii|TQgZ$Rza z`8#Z0v~xUk25JDgV6&55>wtFY<4(GgfdM?RGHa5EmFfsu<8mXDbUIRgCyIg3e%{}U z%vYIT?ZSz08x$~l{u%6Wt${j(W@74sVkKARFl0Yc!gYMO8aPs7qaIb}1VL>x)$rt2 zp3aki!#&-61K#f+jKk{dWn06~5Oj0*uC2Mit||Zi_Qv;U7x&KK!#Sv>v!AyoI0XgQ zgLH3(4diOmo;{nKJ8baLN(m_z5?)KHRc`b4L+{UwS9{i22(?CvPN*L4{k=b!_mwC* zW_UO~6=^ejt{+_%rx?-F$Fp#jmA{rn9e>C{`Ak2SfxHT}q+wxs{B>Upf9o0TrL@Gq zE90q5&b2Y*XRaVS1_yYj!ifcy>Ayi%G3G{5G4Y7!437blqtBVeW1;+xh+8G5LMjV9 z_<j|-jHZbRGt_eH1`M9Z(vKLW2zopkR#q(|BgUhnqn4JIsnyj!6}HDM1k_S;(X7&W zP(6oDy;-8A|1s+4YH=NoZQsG{uhc|mNc-u1xGQYtJAWpep0~4NG9WcO^qjCmG3;K% z_g`9f2`=klm(yg@cS0!EDJXM#jGc8~yILDa9bQ*4EWG!-nR)<jmvjq5^n2!_2-8(# z;>6uoD+ZR9hAi@nL-{Hp<Y$bEE$|@8=W;Uxsx7vtE!v`3KHVR<*k%K#_gXcE!*Q}M z{onE^fer*Ra|O3vPP<a-lGAu~e7dB!S@HCFY3cOJ%1V<}{ZIC~r&uK7a)-aa)4wF* z_@e=u0Q5e;AEkEG(B5C)dZ#DuWcW_+O@xfHnYp*qLU;1Z#rZ^IWAR5@_UA+#lV(fQ z)1};@G%^pXsHj+%j__4+?sX>%p?Y;rB?hdPJB@e3NhS+ghi*P<(@U(Z6kF&`@7h_P zO&^9R>8h$Le)RWG?cSgK-cdf|^5^gLb2r*Kqs6>6GFMxk(K3s>Jv~0!uCF~l+VAaJ z_LBVl`!}<g3ubD{0(JM3{dBF2fWQm5fGN#d#2KEqfTarKy=DlRCj6)|U$mu_Lfj?S zjy5O!4*WNJF0{6l+8p?tlRl~pxg9<_;yBfMeo~NXtUfR>;P9^@?$+`yUwPq>03|*F zLHe<mhQh*pomWkI0<ZDLH+6nf7t<IHCAZJo5FyE;&a9WT(%R8x%t)gK%*ZI>$e0<h z$ITY7bN=z()+@j^W1hpN2YZV}-KnDdZI1z}xjimaA39ov&{ikvQs=(?e(=orux;sl zQq-war`|$r6x=CY2x6Hym2dEY<ML%J+?F_h&7hxM#c0*`gdfzPKB9_e&z`-qY!3BU zof!AMqo_#8&CPwVU4Q(>ygp#C(3D9)!4@K*aypdh*N=~!P+#iVQ4-&GBN+ub+Nzbq zuGw^o^{r8nwx9i2Qd+vcxmmMhL!_kNz!5SSRaD0z#Qp4b*#rIA@5bqgBxktgU*={K zTa3<i<fQG}a?L!0Zqi%rxtJvGe(}teTa^|UNV_%hkIgLVp&<d<bNl?*>FD#j96$ms zt*yFmHO^kS{a*2nT0Ay9e5P#Jtj4~t1{wt|D{C(la<@iLS65e!RFR&u<TAT+w5<MX zH_QTdZT7ZTwX>BYf7E!do_YAqt##3>0`RL@F}KayRO3~%+8-m<YyM<pWd7@|3|lMX zl6?cep~qN2%=T_Wn5rQW1I6@3TAGH3hey<6I0L_ysw%1M(GKS%1OZVr11oD(eSN+A z+{Qs)<V<UXeu0tT%<QbY)B>Ee&*~We*eP6*Wp{hKMOOmvAhgA{hgxZ3H#%auHn${9 zHSu(EXgbwyTDE<<j~ZmTMpz%na>1-nfy651dSga%$Ek?BG}t+%<)3BmZoNCbTDz%L zI8cxBo_s7iTH%a~H#9rX#}@_Bu|D@wTQ66O<!+fnd@jy4t)|+#?No1ubk}mlWG~<> z-;It-Bjpd701nIoDzqMWt115dQ+konM=PY=AaKnt^Kn8XlPH2g>W+{6R;qBU4#R~# zrrzU3Nt{a1%OoLzt+rRjYoRDzHK|++PhrR5JlO!M3{$sl<>z0w@5k^S-*p~?rT}1| z1v)|>6cjZmgV16ihr^7WKgXBYbZr1?oZA%-r99gdL_~qKG^9KP9;}maDnFkB48!p7 z&MXes_EKA>Lv+%=S)KoL3naa`%M^;opkS(zge?c!oX6sKDgjwhtxTC7C@}4vomTBH z&co{=kn_k*0QhG>A^=I1=eO!0<PZLhIxqb!i$LylaPXE>)qDc8C&0lFSnb?yS#|7N zj1u{)LkjvAu6x(fXRhefsV7ntR*X0f0+@>WVBFU0T>oaG{}o|nWl;(?ACe{=pm-7h z0h!@NCK1Q>r)0D~0ONj**NDOd>GAtp$fXRQnG*e1r=a*FIfVfK$NT@*0e=d4{<$^b zi(_%BSr$1sJiPxchwSAg?OxAE!><gB%;NY=Q=kcL1Rie(rir_Uy3KcfEi#u~soQtL zovVR>6aWmo_jggn$488cN#x#Kdra-YMi;Y$2NpKitbF01b_LM%!Xw|XQ!H);iCsl} zrqydr6#OrelasUG+GY5LF7%`d*>uuf)JjuTRwj<;F<j`CT%Ciz-JYG>R*;xqYF6uz z%DM}&g^h}O3r$H1{yVXDvPX1MLqmfS!GH>(l;_38lTUN0^;fyueR+O%HZ5TFXBp3A zz&@|&m<s`X3CT1<5)$OcCVXb8)Z#8T@>YyB0a8nwU3Z4tM_M>^4MhNSfMwO9zShxR z(kgy1%xBm9JIGe3Hjmo~U>eNNHjoF2%LaTik+Cmdre}`Am~A*tc0Ak|EMDz!hANaQ z8&0Qc;!8$)PxN@rOaC?Y50m~cJEi^1Jjbm*fBJMCW*`Lw;Z-?l<gnY=+PaN<4hsTY z#9{AR;(qan`)<rJGu=3P2$&zKwf={HhpWt1SBmay;ziJ1<5r1s@%Zb2y0ZPp^XusD z?!NwfC==?(;o&xsUMhAc0fxd*DyA<m*TU$}$_0QA=81J^KSl<@_*tswX^$6+EErp^ zJ839Zxh2+q;6$;SR>cmgr@63mxRvc~{Z_bjYfx$b(%$Q$`lA%U(WY)s`^P^K>*Tx; z-x+mA#Vrp0E~((v`^eF{IDE-<;Nq!+)xg1DlaoC|&#?S%^VRa&hA>B#J5L65jEad6 z+_}k#;RsQ+b-PA}i&D8V{7NBXMgM5*Y~DIfVlo1&I-QFG<iNZ;OTCnwtaz(H^<MlH z-)ij;_nN2Cg=U(e5yo;b?AjsNT`?--LBU;sWfbQ|%{1V1iU#k3V&Yq4;(5mJWWYpB zN+cH}--1S|n@=~*e8ct9N+0a6@)g}wRFsNF?mgzw5@YV{OoMIqoba&8*T-FH45-g} z!g*M%2^aEJj_E#GJZ#~ems7uf!JPQcwr7okoMV2?vUg>Cac>3)m~7xA=uWqNFCxjh zS88kMKZz+8P`mC(dO&+Yso)#H4VatK*$}`2=yS{$mtG)V(KYT`1EX|R8Nv=Ai1Y~o zN#UL#bCLCQQ%IFrV4w6h4ZI4S5`~P!z$qm9cLyG`r5}F<aGqay+dpjY<X4aPC@`lZ z&zTM#TBlq;Z`pJ1^!E?hdp)8x#n)nZM19sQ0ESayEE&@zJi`%yG3K}W88E!8wjG=b zw0M~Bce(nGdm{cr9LNn9n!1|=z>H~-Zd~K{ruSG@A~Q4di`U%7)*TK#(tB@mT--)Q zP*#Q4PzjDd=`hw0hO6ISjmf8{r&or|gONJ!>YdmW;Rn~;=2X9Z`?fQmXii=bm6gSA zWo5Otz1=r_GQR`g@G>JKV|F8ig5le*EA_U0@I%MhKI=2P?{E8`1m%lL8wzDFkymfj z&<1b2Z%ZpbQ;sa&-Hn0dfq1X1l#e-}AQiauAt)$_uad9g{y?C4LP|;})MUj7dR}`d zTd;}D+EVL;eNvm<N|~FJ`VIfLvH3gKgVp|51b~8oSeUOofbK8@X;!~Q!Qe`OM8Q<c zLJuk~&s?_e@c4*VKq?LJ1Hp?d-3EDJ@nEFCy6-YnxB+9_H7I{Dr`k`}9p0Ayf)F;V z^~b$P!9p4aG(5mm6K7<55%>x52DRp(VQsoOS;F%QjP0N29?L5z{D!4$;`+SJybg9; zVFXdBa2hYHB#t@wcJ5>;lYs;}pS05brL{C(UQGK@67sa{!Mm~SFdj8pinF1O{N58C z>+VcX3up!^1x?>-RLz)ce2Hf319}Ao-3XcY9ERSnBXjo9Uj5FF4>CeRhp->77DhRL z{!0&&V$DpO!)Q4#KySh<`4DoXdwDK?{|aD5eWD0xl;@0sLC>F4!rt!yKXZSgcK3D> zztr*ZFMkBBLnE(w-$pkHoSz<Png?NnJExCf{=I>>j+xh#7Eu&2`7G%2pC2kFI)NWS zx$7%<cwAQsr>pwYHd^h;BNxR|(%9GtUAtj-6^3k=U;XEC^|Jd*r6z3ofFs0@OmkGx z*Mx-ljTL~i5~K`<L;DIvK49<nC8z4&Z+RZE;3Sc?#KgoH9wyC5!pvA}V&L$;)Z6@{ zKXF6SMNO30=LZbGbG1xeWpAus6-kZc(|`LSJ;mV;)zPb0hG*onU%$?Sk)SR^Qc`k1 zQ=<3bO!4S4!{<W?j1+5La{ypJiY=RoD!Lw0S(&}9R*;wfHTaHJ_V_>uwh5NQ`<zYo zsARi_(eQxn*(wyOb+=rvrH0~BGH$c9{-h*-X#>^u8K_lF$<lseb(RG4g>E0>eNLtJ zD9afo4cSzhT3E27pp5{-iz?o(eY*f9;D@6K%(U8oc2MpAfo(vAkz=3*SAB+RRI|A? zpsinOtNHWO{g*|$nlT4D$nO@W9C=<gsF$((w=~_7z)Oyq5r!G<X^*Y-Sfwi^qW}i_ zm!RK2N8DckyIF9ywHuv0&(8^mCnYf=VFP?Exkt)@?C~yjsO4J6rA+AXdFm5hFa|T2 z&@(Zq!)BmVZ{C~+W~pkT!v5>)Uef`Ns;_oo*hI<c8I)12lj*;sHG*e2FZu|dU}R{8 z(tuof*gQ}Z@ZMP&e=%*@!%v$cMYS8`kVCexw~V1Tip)L(arM!CtC$_S^Z;_efSh2Y z_URZsb@tj^P!}?k?EPD&K$t_faYB5AU-~m#lzANVroZH@na76T|7p*?=L2GzE&$KH zTa_Lcr|9FeW3#%s$y?!K)kXgk3gfNXm1_TC7sta#0adf7<kjM``kd>1moMU*zf4H@ z0zfWL?+y9+^XCl`XI;My4BRPqJj-ib-o3s#TfJB~8_MK_z`sw$5-}82`w#9ye>3X_ znEWnX67TZe`~-E@QhUd%B7uS5qS!9|{iWNamgv<n8*ao6^ABwBw_0EKqt)Xh>cGH2 zgaCS_5H=X5?H_yYMug8tA-{hXeeLY*QfXN7GbS5Jc&}b5-Yz<obm=*kju=80_AU+( z+BcZ*pj9`U2OhS<e(>2Za?DU%$G<$&nznfCFx0frL@*UGoZt3tc~tzPuh1C@Z?6$p z!8>T@45mVtW*(e;R%`Vqk(@{5IljkZR-TLa_MBKC&EKHlDq3x=cr^1f+yyg#|KK6J zmY6<&g@YqQ0Z*qTB7NjD6Q=$BZPUF<U15hQ21eB{zXvX`iA{ZLqer6`xl!Rh@0PYa zVyRgPO=R(ZvC%1d^rWED#Joe_gYLl!WW3p*?upNgUXo8@VxGobp5AEl{uD1>^)R)I z%~(;f09nBsxR3V+p6aCwzkV2@w45eaHjs-yKrYVh{o&Z%<vA(?QVq@Rfh2q!C#w3< zVP>#3l|^R^XHKTXd;*T9DsE}XvF>Q^0n~|$=R}<}N^N^8|9&?{=JrwVT<<@F&ycqK zS6s0mN4Xys^(j>a6&y}?zScw>7C$!_#S-T*F~-}+8JpVOt$_@4z$CUSP)jSq1k=&m zYYSXb?fMrc06@n`g6-_@8=Jnz8X_e?{$Ih>-jxx8qz9<+HxLdTB-Czh`(8wH+&Z&u z(Kz6q?*UG|CB5oi3uW%Q^F*4lQ&k9{Z8x9_?-;$oZhxaI#xkf$xS;$D=#S2yf11d< zSdMdbB>XBN$E^z83uwju3g=1Y>AqK5=@Mf;vyn*Uh7ylZx3U3J4}X4o1#RX67uQD^ z!&d5#0}87a&iw(d1=pL-FGlPHtKV9k%mlRft<Y2wkvCv|cK>i6*sC$uHWp-(0zix; zFbu+D8+%IRLCUb#FWd=K<FD{Z6YNZeJ6tLr7NQJ}i=*qxkiB|!Dct+r>PTrA;O(yv zmJt(g0r7Mv2@=$dpK5@SY8Nz&o0^)flarZ^!6Z-)nCf;Hm|*bO1=`#PILZ4!o|Qcq zUI6GhQ@6h|3okFglsyaFqSW6(eL_M)0mmQ3%zb4rog-riP}+EAkN@6|Vw3Gn60~Ln zA`y5w?=C)nw6{KT3M&(MOH+(^QqTe5Ko0P?c$-t9+`#$W`DhhlBMbynD3owhT~!Gn z-%v0ieg5^3c{p9n6(0i>Z#rz3BtMgw3p)rEB-7K=D5-<BW{o6)C(EN1`ZZn;-)bbw z!^D7;H481?XsD#XsZnG+0PK!j8gd(7r^?L+nX#%pemBA8?tgu)lwqEV&F%MgNAr7l z9S5K|cnrN3Hx=XfwdIT$?{IUcjnpLma+kGe_4Aqi6~{T2F75a4b%pEG7OU29JRRVT zah||Z&cYyx$TE(=fB}14x@@B0-iDoyVv)3mosDXP8n+1a3=s7t&XY0_|NbZJ1oVV> zBxPQ3qLgnH{Q8djxVwusExp(_T!b6^w@i*P`vP=4=ut(f_=QFvIRNDWG7T*>tyy@K zI+SlnOG`WJzx3h-4bBq?y6nK?BRU>~=MaHk%O4s;LDq&gonAp!8T8e$W?T#EdKxDN z=T4}Db_S($CWINU7@JDKj+7l{^ZJZ|8n3Fus+PZ?3c8;gG=>av2l{b+I*iS2@k52) zviWB|@0GC*m_8XLJT3=R6+8j5j}H_)tHvNALSkb4;=?fs1C`?jKsg^_&|%)*hZswS z&p;ZPMs+<H%xi?DyFI`5rL}eayRq~7&TO=Pj%v(7y?qS+Thj&(vx_{!@q^haPlCuW zx;W)SW|h8YbWf4+grr!hoIO>y_lE~)$F{o1`#XQ~iz+vJCGP{53^QlP-?}&Ii5G>1 z;}O|QA?(yg|6MoMzynv*wQJW<l`!nzsSl6_Oeh1L)&|HH|E;eo9)NI$?;;dpdkp8Z zh=^Gv)<LYXSe^KM%WLx*CEEKdX%;r|CS*P)t6eoHJA3@dzZaE<mgANS2naw}Lxjcv z>TDCz!-|m@kt3j0&%I4`s{S?T1_1I$i4BEw?K+|W$MG0Or8?IoL#K#`=I;tRnJ&bw z%)y$%mnfDlU?G%YVP_%2sioE$h@yw{bU80-Bq5ZkcBaf@QVNP}$YH6&tpP$JqN@FR z`4(Qs(3-vh*H<|?7g9tXc7p!%42vzUW=1EfYiW4}IDw2YFX72hZ;$R`^?hpf_s`dd zdma_TaWH$8N_sRb;M#ZtkDdj(6pv==7n<C?d(Q9Q(kr$2E8^dqpJBb-wue6f15&GT zqwtu3lv_6_EAKL8&G<-wvZ7*bD#*HtH?K^g>`O<|1{!idodOwI2SGm9h=%YN{B?0& zmJTc+{Z3~=tl5x1B4_-BR#eO>2GM$*W90Ym9A<HRxtPsIu~n%MlE4vigapAg8IaAU z3~s=Z2JyRUz0Vg{M!lSw?NC$aDaOU~;FKa+6z5Kl9hMcqRh4?pj}2eg)rQjXJN;w6 zPT9LOqHod1<BJnpZ|bz3RBZ8NHt&imdT6N6X^-aD>zBl^FiSq6Jvw*^v94R-wT%Bb zph^mQ|Lg4Ro2sg)!WBw0^v4WK#+;m-7cXAO0Xzm~-pQ90WO#lz5gAqtAOZ4&s_gf- z-HGMaH6Y@MT|(v1>t<iSe(f!~?~U9(2ecP_8r?#ZC$be@_<k|~ErG<ie5d3BFf9g& zoE(PS0s{RHIRj52-M7W{l#Al-2GP+NgjIS2YafXA%d%36Ej5U+oG3sUJf{$bBIP$O zZ^)diqTOl0E78-ZIM@Y2))Ofmig&FzM(*FYD1~87<B4pb>PIWv#k0-*$*`YhS^yE$ z#gvbm)Z&|DFSo~@OS8j&0B3?VL<;ihsK0^J^o}T3mo(DXXmCKR0>3@ORWOFPM02O? z3MS=71lC6!x%FvMj6oDi+29>MVgvAzL|%)T!{bDf6xFvf8EyJ_0YV}H35m6*!Wd+B zq&XKJ6F*-73U)2b`&b5nB`>|+W&HBjWS@e2Y~2e9q&`G~X~9e#)<}_a@q$HPXVb!} zwtYuz!uJ^yl02Z=XUdQ}#8@Ai8zo=cAu{p{$`%O%Y5{RY@K2HPS}+fiD=W6_BR~XC z`v+=fiV{=fZOs_kI)ufx4<UzvA7wq&)Rj<HdHOQ2BqFl1*Ive|N(PMmaZ^;By8?IP z^CXT_1=UB2Il6J<Y3As&0%TDWH<3kMV3VPg^ZEUa-q7lsGR=RtXPUB^ecl8`$)o>e z#z8~(4-qVL2$m^jve)tQUT!V{%>p+k<|o<J|Gu0Ct5<z?dWVGQj!ERro-C#RWKNiS z!SQC?SJ|CQsJG1G^0sazCu@#J;ED@!F03U-)a+ssDN|OhD6tyn%>Vs1w}tiyY9{Ms zrO5U*bNNk?vfCpED~cSfOGYIz%^-!2VoNHrHDWk!_6!mHJOhFMZg=pAkkCy{Rg{!7 zRTd{IW4wmJ&miGHKY;9+1veR)1>+78@o6`w;Saf_{QAVlQx?DoU0{>4_Y~*ga3ex) z*r?NY@z^mS<L|t~=4Q6)d?)7Z6S%ubyF*2E40Eis2VlVOcn~g^;JBz4nDMp-Q9e$W ziXS1bF`T^i4vvtyCu;~<Y7KH20@@GpW?ce=?3)Fy7DAQTP=nhG_T8g%iBXGhu;rZX zKKo5O;}aA(bO+~uF45K?PWUwRv|0Jptos&dwDW6-;a70V?NWa9Yh%KgEFol7F%W`b zYBG=LkTQ<n+{|0JFvI-8;uEcrVv8U0%Xx$DOmb45hnWg-)`U8r!SGNUihRfI`jdZi zzs)i24nrv5{IEW>$j`?AnMRiW0QHiZg@Z?UXvRTN@d7`xD{<CTS-B()FRWTgqoRnX z09z+~2mQtuX}GVLHM10?{mB`7C27U@dS}%B=S#PKezZnCjlW{!%9Y6Q?j$Xg;Kmi+ zh~Zmw#L^qO`8p7Ba?F?_KXA+I?m5&eGXZ?YH|a=@BOeSVQc^<GoVM~jn>Y^utHzlP z09Hgk8XePxR9lOQ;v?$r^iDvoKj7pgeELtq|JunZ<}nl;69B)1zEgEA3%TNi+{vDI zk+P=^lPgQY?n!09PLOPdQn8=vH4yko3Kpq#<hx0tpD?a2ON0hIjA(QpaECLEwGM>+ zf<(1`qM?>XCXvA9g`a%&ExLgt{)*-1CMQThpJv-W@IhVRyMg>rh;O5K!zm>D68Whc z?k^M4qu1@BGO~8^3%r1aliyXzp^MWbh`?fTNOtI0a;aODjui0ChUcAmj>VqTT%Cz; zgnRx`NCkz-2sD%yg^({TMy5??1^~JL&u2WyvBB1a0>bB?bmv?+*`5ewdvwm5iQzNe z%E7wN=?x&?M{-KRSD(6J^S7$lfA8#-e627FAT}1;hNKvK==jQkUlM~BUbC;{lOf8x ziY?YiuD#fLc_}@Rnpd}}Nv+WAR+$3^?jL*U<SUR?z4iM<#CMH4h9U}e2{1W3K2Xeg zkwAAt;^T&19|dOON{)|fPy{_ubH4=CWNKsryP+4&Qd&)zh<$Iyg3*`szH0()ehz$S zV0jBg%Rv#zm?-Nyi6>EH>vD<-8|)!Y(a}gnzoAFoMYmQJ$qsLEWFVz2IOj|(T26&0 z33^MrhE{pyIW&4#6bWoSBV|-@cJ{G2t<fRmfK(Kc1MrcHLfjpHu9<wPLMxs`#L4TI zuQJIor$H7$>u;GwvIrZOt@44d@y$tHc1Jb@+eDAbE<@7nWPh;oqD1~qms}j=@z3UJ z8zUi-rj2YjT{*e|dVW)99Nx+022L*5B!W+h`#^9D39x7+v(M{AF@J=eh&YM4=p5uF z@=eky?vuNnNkI7&yhyWU*V9?Lb0>%E;$73wE~ViDvW9mok^+>?NSd}Z^5PYPQ;8B( zX&^yklaqf(<<fB90)n8ec=RI?p^<60y3+0ZP+oS-^MPE7dAbTLw!*DTI~68aa7>6M zT)iWI>lPh&BVIdC)`2KG%A$Q~<}b4(MnOHUxbpPy2KA|nC2D81R>wME<|mV_;4y+p zdOiW&0PO4Luk2_&7#jejx5jbn{~E2}2R+xaGmbk=%J&<KHqT5MGoC=fy_*p{uAg6* zIp86*FXE;{lT;8ke0!rV1kQtiqg}hV+L>C%M~BazKc53si%e$OrZry3NQ9_bKbF0` z<Jf0zGc(D_TUlK#Z224h<qN(GOuhfT7QdJSnA<=pnybQP%myCsvrPsbOM{wb0Yo?m zNBngBW@+GUff0`FeY&K$T+6UMA^0B2^`4YH0YA%Y;|eGLgY~uold4!mJnHY)IZaEO z4E8%1kmkTcW&yJm(d}4ln@b_y)u(s@dt;;)-;NINUA%O1>T-BM8mu-vA?Qw*OoVgT z0E?RBsQlpj+YvC6mwGMVMF?1!4SkOfx5+3dc-l7<ug)gnpW=P0-*CP)9DTRs!aFju zw4Wb?lfFcL;Jc~Vf~%57UF70g2l96vq{dRPxuGbVT~<!j{eGL8cRQaR+>3~agK%tM zw%ddOVgvqG*GQqGb&^z2jcLSMnyOQC<qwfWVQqN$&;ri72^I#7)S)tHVxg8(IGhT| z=f8%F*i5TE&R@RV?#l-FvD#}{4Qw1RW$*(B4rMCSf|2-Lhz}Ke(m+F40^fYx32-5V zBLor7?av@Lj?Niu7QY5^sX_jG1Il1xtuxqf@PH_GQ@PK<dTDm+K6q@NG>H>Yo>biX zW2aHt8BzdKN#UFk?GTJ<9DUoG+S)kq3B5A={AlH}X3MPD_hTXzm031sM_1vMX?r4f z2b|W@$jHv_zOhLR7VGS;jsgk$g7l*;cBtdwtHy8C&VZsmANpQVO5f7_n1n|6BL|SN z_j2F5c}>c^?v31n(kndnjd%fwMv!?0v?~r_aP$51E_X~f6l>`64^`W}LF&AKTh#_~ z=>!ExJu$|gB4P7yfaz${V-(K!{dVTgbvueAc`3+fO@ar&Z(-**q>233rfwX;<tX@M z0}jN5Y`ZIe9a1(=ftzt@tV;jzs3lii1*INeQh9p&1cdd)tOB#?;|lVxN>i0CB`US& z%hBP!?{o+QpI>0J09bx*2XxV_-;F}cWs+fVDGj5JkUyu<>!!P>Fu)@siX|dg_jVx) zqmB*@ms%YB=E1=MGG9SjQnJZN)!SfwJ^(PpG|3V%!$6J@h^J|9g5^`E$d?Yi?!h-g zG?s734;G5~18?xa<eAsYm_Pm-&%<h#qq7;0Zou^GyncUs7I>=*P9spcIIPZBIO6;M zdKCsKUnI$TH%F&KOZ(E{Aa~YUGzy=t$eayBn<{FIRCYKcwuUg)N9`=sJbJ$XzaJ>b zlZJ<L@Nl)xgpcJLjaRqyq}_atbe1<8EGQO9OzQRf@@rN4ly0ME{KX+$>V6$u?5`{4 zehr^Rfl{HwbVE_`YTPQyXM5ZmQPOLDH-T3T-z?$T{o+(Q@}t`GL4Z1XU83$A{+ILB zwwed{1RY|JQ|1Uruz5O-e`3K69jpEw^>g_j1)mTlXCGVDxOof1jvB2!$#E7t?iu?t z!~+J^e6@3%bgJAWrFy|D8Eex175|H&G72vo_|Ip&#$v#K##eimBKR$HE&(R~SW#}7 zZ!j&k{uv_ZM?f}3l?}t%8}M{~v}z?^@{$BI7N>+nb(T8KI%Ay(5#<6YdlJ)){}s@y zuEr>-M_ZTK0uKZc5ecX&0(S5~fb^3JWgd^_+_}zQU$d*gd^mnP6sxjXrpb<q8G4VV zp;tCgBhU3-er@kqFvRr?yTe?eftM-f8icTa$Vd-7xj26y_(0O_h{M9dUV{G@BqQJn z+*QtUu=W&>-jv?%dcjX5CmR?TE^3czSf^q_U~7wL6&LEnYApRIF8mA+!Vsen+5+bD z_3dpt&<PNC?HIT=?%CMD_021@Dbdjh2}#(P_>i8y=oji~siuta_CIJav$(?`Fr^c< z@#47v^N+A@U_g}c_=ReKqC}iTmCYpVK-d7H2=@8&d*H}jxvbEVE&eu8K`pL<KdT{e z_U`+Zs#Uymcy#EGp~m<Y{{B)R?Z5Tk-RSKyC@^}`W19`qNpBaJlDN(5YTfS|wIuG$ z5YvR6#=XV#t&FPtd`3%e2<X!f3edo}Z=~epZby<lJP}~*MfF1*F;{46Y7V*T%)Crv zl2c18uZ#_1U}n+0H}oE(jHjenFqMEZD!F$P?3Svwwii(kz89S5;tByzA8w03Nit-1 zb{5#v=aQW?>-N2<U%{Lhmk9|uXy-BbxCGP>aM*&Kv>Eun9;lp%nWA{a@ssl;>#@6Z zJ#-wT47K<6BIM@Doxj<$ph(&jAI2**cfQZU!BN)^8wWm+r%#^>fI}7`>VaehLDUXj zL?Ooj7t4Tq(E)7>G~1=9Yl>1SfD#AW2rOpaYX0c!p$96y(M26qaJTFVn8II!lM473 zT@ZX^j&`hp>>^6iZ@F{l&S0J{qVLcDfZ30SIaSWzlU(e*!|kBguiLfMIbFNx!8DIv z=XP3@1})jT?(pBJNfD2Eos|1Rw=1zc+T5Cv7z9=n&7a^@Ih12SK%C?E=LGdfYE3D^ zEol`p03yb^csVhNkM(3_LuA+QC*Qnz@6B8MI!Sy|osE<SDAk=!ZKS)e^E$1{q=F<1 zswXhxUj&{<Oca8vWra_v#ac`RL#U*Dn&zB?tMGU3p@Orhw(lRBGvepnsXoGC&6KZ} zt0!EY26Lq~G~dL}A3(1HU2^4$Qp@bl9)tyG06`p_9b)dGw8j+@Op4GP{HkoocYsBo zI|eP~2bhXNElvJ4J5|&mQ5`=6gZV68{{l`$W8f|7LfQj*eh!Wm9Kazm+KVsXkp)M` ztJKn4G;o4k7cR80XmicP18nh^M&ZX*FPuBYXQt7V8K{TL@GF3+q2*E~7wQO1sfd9D zG6BpZ(}3UB9eUj{XHdr@q$OS-kg1c729i1V?OVhs86q1KvG&(`suyQOk`$k&F|*Kv z@Gvv95YZqRs#{sTJ}JnmLl<c-K`yIhN}k$STdh9<D$a27OW!{*fh7#!4%>>454tFE zW)_D!Hvawn1D;rkRY+?*JO)Axy&Z_UOrVv8z(9gHf0dO(B{P&;cx>$My$IO2kcIlC zvtWYK7^{o{V2Qx~oHh3@hedeyAOnD1sXI;!LNgS6ue!Rrz^5)e@`L6E4zh9GaBQ2a zBWfLh<>f1#GY(P;J|iWH1|K?;$7{q(+_=sZ)$}4Ynp_wb;k&sYe(c0Z3=x@VLJRC1 z(xp}0M4s^7(aYS&aSv1r+j*@ntc9OJV2;y50%`|9QO&HS5;+lWum0N`-olpBxcj3C z%lf%)JKK|jlnT=T-y>nO_P2h|MunZ>rqk<V!ix)x10zCx7Q5alwgn9nX5FEDW7Aly z>Z|z#O|Y-H{=rMCK^z%n_IM3=AIRv@t~?dQ4Io}4I>U(s^7-H0yXB5#4WP6z3R)3@ zzUqb>i14|fiDz!ItCfxtTdP050}WAidZ8RuY1hZ<R5?wo9K{SIsX8_~8n;#T?-aP1 ztiZ&xwKyCNf<aY2Av$_aidc&i!*F@{6@f*0@J(s2?w?k$H-fM?V2O7FcS5oEsu>{e zW_Vgd6WIIoVFqr!r|)m)$7W>oW=G3(0&jn^X=>tbg#DxzPEBeFIX;JbDCWWIFaVB1 z1O`CWM!Z#sC=+<(p8*m$GY;SI{jF!vhho55^&&pL6%KX<DJM~u6)zthh-C?581xIR zRpb_7vKs4vx&%-g3i=v=(9_J!X^Rg*eK{-aKzHND4X8DpEC52%KD8)5FSrWT;Az^n zddxxd$5U(Vo$>!g(W=vPF!4+^;DIuH1MK|#zBM3A=mMgPy8j`1hqR6O#f#HiYZNZ( z3>S+SZhw|QpW(6UYoD5m<w6ob?=tkMpN=A67uoPI28BRJ=LJ%S#iFR#B?xwA97UL* zTN7AJYRdw71;+rtW(2bfxEc8+OT2g1EN5nBK(3GdpD(>4$0XkRy|;)bR_l27EUuaa zK;9zkmV}5mfNt_;0k_YZ0HYCqNT_B1@39oNm?pkfLHs-__Cz|<Y+{fwfd=F-lZL)w z(hD2ZpLHXS*Ej)A))f>fq5I!1K*`60b^@hCBMWSC1x6o(z-lI7(SY)fJ3r%^rkN#- z?{g=<+MDD$71ZHVPIp3B(db6t*1v$Qx&b_HAhktoRwHT<V02JakSRt{NvR4cR<lwh zMtEx_S3FmHzkIY7uXf{l*A`9t9&~jMzS;7Zau6e5ka+-$=CuD_a2E0uR1#V=`UY6b zedl6SlEAnNu48vYG7eT1PK>W%j+IjQa8a~nMJYT-fz*HQTVp@NIIdrhyAez*Xy4E7 zEfa|~6rfb##2Dr|WXfD{`q{E_r!-I4o<=0K=Om|z5=oG(IXc+XHu!M2b-FoB-183| z=+Eu#s<0kq5SZ?NFG#M7iRkN)@=GWfyd{?^xA9(Yd!@i{QxnwVn9BRKT<5MAS6@KK z$T!;H2?z<jga;WmN9;0P_iD?jpjy-XQ%kto@pyZ@?P0}uXlCZ9?JFfIwT<a!6rqX= z?MbtkIDh$CEV*e-$#xsth_qihf<7ZEFFb|^C0yY^*I?*$hq(smQ7Nf3@F9K6xk-x% zoZ!!eZrdh636GQ@K<w<bd!%40F1&oBWmfzVDm0$&p)1$Ko4c7Zm@1|pf<Yi{@6N`e z8&p8B0EugMbyXY2+~1S+vd9BoV|<*P!C*?=fYe=|jg~C|lo(3Q)(f-7R5;SP@u!Bk zeS3s~Ow!(XLa>x7o9Xa)|1Q)e>~I$#Q>doMg9!k#r@Hv-=Ky+j3m@P?JO2s~cp$dJ zx}6ycumbHuhq(h{Au>w%kYfznpnXNAIul#84V#;Ps;UM0#k7uK3S2}OAhXN;I`Sd1 zHd|=5A*R3zY<=>*4*24SpPHi6Y|PyqFr)8;Ql0djzW~}n8B&Ac100dfXC^B^-hn_W z1qu8h;rz_HUhhMEGJar~)oQg}JU(suPqCN>K6DiZa*lrzZw=-!<XMS^h6bKGXBazS zn8mGCu|4MqNp>fP_<cWEd+3FlVYtfv_lF?DbFDkU>;ukrFtwQcJp_-9>QJu?_Z(%- zVWz&^bt3=U;Y05f5Sy2s&Bx7D=aW90KQD>0wX=i$4;<R2CM9ifs=!qsC>8AQ|DID8 zj|>t@usNhl-nNG=d6}FB<0dRm-84yw9AmvF`bm-OpwO!7X;P>6iiToq_&oq|D<h={ z@z;Vl2k1~`7*{H2whmxjMg<106aDMfIGNM5`0f)v{<n1Jd%+#|q3tu=OLQpH+)gUd z;@`p4s^b1hV8_*|veV92hnY@-j|?N}y>btp9x3#>93y`duiq12UOvyajm42!GNE4f zI89CZk%`MhB~Xzm>TfYr&F#Zss+PGI`wMSp2L|HHD}dL5NyVT55zWv=FIUkGpw11Q z@x;<OIN&eeZT$N67PJ;<g(aR%gq@%6TYKIF(v@oxuh^u@tuRj^M@OI5emEbz_)7;1 zOi_hqJun1Z{#yJBnJ9dymhtIbg}^ibR0ZZO#+17jOzWAFL2e-SAte>-4FsfX>7aER z$Rmz;cuzsd@)QqIOK%Krw4H@$P5W6jfRME(WOS-{ZlHQX3qM=#LVE~D{;gL{WyAQz zu&$FcM$8;eZ|MbAZhN=Wp#Jq_YU}yeBCNg-37}~Z20V(aJpbr`(EohNu{_|YYG@wb zBRYAHT5dbv-HFa+dub$xRnitY$_#{->$#5qD}XCiI9oAW`G?T8A0oabC*P7f`4+AH zcIk*?Zy83iemC?4(x@u7l)~XwCa1_TJTF-urG?oUMD(R3ksjnOAU(6NOb8rGNFuc- z|1l07#&hK}0|xdtHa^zMRq4O%Yb5hP{ZV5h6dz;b=hS&V<DQDQ^5j5aSxON<*$*j1 zYhvP({C*tI6V_u}TA9++$Ip)W8B9V@yd1&b-rHVW$pO?YLJ%sS2y$lS1+DQrQSae7 zAbSl;0NTa>t;p-D$i~I_|Dwr48NE0N4t@v47iD}&t0g1-;mL|rU?(DD=}^47hY7}{ z;m_xmCO$J8eQH0sC4>oO63@kS#g|r|o;=OSs?{U8f|^?8uPZJG){^>U1u6LDT9L-i zt+2!v@U4Hei8$FtI%FF^+S$g!{E9-K!@KJ8ic3vrx_S{c$aq(A&KyYgu?t-1@tU35 zfDC^z)B1t0?a~mE-2V=@Xu(Ez#~2uJ5+sIRZW=fIIM{k)8*-Y8N$-=_Lm-cyr3!bQ ztT8m;%CpL6A{e7S+fs2AJnw+b$h%FzydtFY#a?~6&bW`Tb_3|#WI!|QPTe@gZ)}Sw zJ*)^tuL(hL7Rjpb75pg4x?K>y;3&Z>icP)1je7L_kLV<2L(F90rTMX5M>N`;-og`) zBP3p16YmmNW5-LEz3VwS7|W!@u%~h{95iQ$lp=zZQZ2)8y;p>RfZ4bvL0a}eZv4E$ z)prVtE&9OwRz8uIOhwoi`9Xqy0gFCXqf%m#hu37iRznZ>r@NE8CS?U^I^&*@pUJY- z#72gMQoZtZziiOGJa4!>5C+cJ60p_%8S~?Z7P{QW%`x*1*+_Qb^LU&*6vlF>^MZl~ ziSB=+Sb}#qd!pZ{Pmgt6tRg2BP}ys0ZbTP;`!=<7$NiRo&;Vt`{&w?fJ+9W-@#-gg zJYHk@(Q=)l3EAjrjx#l0Hp}@%2t^ik{FgycGo^BC9YmvNNH~Z^V|et5p}0llSIJ+} zZt|XN9DG;ozbiXXWf5BYCGwRnXP!Bm!yra2KFi-19{BdL?`p?ntMs6%?5LN1-~}F2 z1s;*rcs2f8UTB-^SXkz{g<+NMj@C+Nzj}3&p9g%f;lrkFE8_0hRB2;)e%t>fvC<|9 zfATOA7nqREerfDZwzY7#A0&PB?`dy(&+ni4WY#xs*yiQyG&28|gx*>FsBHIu$1vrU zkPNHLZp#5FnP6s3VBoKTHp&#kcK}6}GK9hoWsfyV(VV0%H;RGvQbrPG3WU5Zlzl~W z!JV~*)CK!Ag~L4o3(VQ-D+V6UR9tEee}_}qQ19Lq`v`WYQ*16I>wkfNuvuF6*NBe! zUNZ6fAkN`SWR)Vq^;(`Nc4l^@-sSvlziqEwWH=^vAmE%KW@SSydnL)RoV$)mbrxwh zcM%x#Ox)d$WSR!bk?qKh_MAg#W3)`rXTP4iW{=3;8;TB}4md3HeO%?XHutjF-7bUe zCj`2}By7-_ULB~SAZxl3*rB#sSQ_dNZMEj%Nj=9AE2lA^btU(oe>eMH-Ph0OIe<bE zrCfJVQsv|_bcIob(#necJl=$F+nBEZa@hi~7YjCQCD4gbFV=9Gf5<L#$RJ7)RjiaN z)zr~pe+9qC66u5Cln1|+qpqurz&{k?FO`mTPf2)PWtJk9J+O!({OMS1kKS4&iRVcz z;yOS5nwl1bOTkE{V9Vmb#-N~lbH$cmBm<IQWN<4a`=qjk>h27xm<*NhwD{+^wjBl! zv122IRE}O}e<)n*BqEkCGFzx_7_YHxA-#O(+n5bd_X~r6A8$97KdhSg3{U5v00Y&d zB0yDRhU4Q)fI$R0t>e+r(}b({9|{^*$mbg@JgS?!SK2nQ_jheN%+}%x<rTxwVKbrJ z;#|$(2Zc|zJa(3Z@=Ql5!|M+dvePwN3r)eO6jSNSd6VRnkwG;Udm9qW<m#?T7Aend z@_L@JH-D~q)rkKY&Use#j#J{?MGXv>diTYy5M7-YRfVoYS4!{yjTB88Xm-b?3{3Q- zB;yGRz3fk7R1<MNWlsm;{1UP#B0VyigdKw90lAnPVPQ=#^IvuCu$6JkP1{NwYgg|K zuZ{@Q0ZvxHE=rt8$|G+?-DO;&^3cm&biLB`)}KK#ICS*n9DyvAB6)(fn&4~y-Jrk? z^8mrVVbf3Tj=3AZa~CAUwT&X|LsEpNXQO+l1fnAOAmN*-^`}3!0nL>5vo3wW)z!f8 z3Wr|S$sKltqve`0!{(wYN2gDBf9HlQRS2y-&@e5cqqTPO8L8-7;x~Fj-q;qUyU=|W z^$#8etSW`!Sp_*$qbDRVfcDT|?vr^y+~~I=INUh&PG6vJRa0JJ`Uy#QMiBUN-{f6J z{c|1((tSfd=2zsK4lYncShb2m8Rf9!gZFOXQN>7Uk0woKfP>RNQYz15cfV2$s&ZMR zv&iU4X1%OkP_VdlX=BSXo%{q}39tki7(xXULC!udIZXrXB04Be43nsQ1bwk(@W?lt zj??a)h8tIlRncl?PE>LSwAx9$)|3Rc-2&cJ=6<{fdk>=MuYoz|37iQ9SEjKFJ>A)x zI`?w+Gwd7jOLHe-N!X#z`2LbN`x!+v`TbQwC=##bYuXGWpU75f2ON_=+ryJjR>aG9 zO1u9_z=aS=l;VskMG_fpmI}4|vibhMVk@Fs?`G<7Jae6%OQa%9m&zT_@2**vYR|-R z7e8Q-+Ile}Z`*UuJ>4c<Lg4=Cd-se?%dx6;$gaLH7R4}z`gV`JIR7I3VU!-P5jY2{ zkE;At?Vp`q7-?vD0#6!e@<2qp&UU9a_sKeo3{2FGkKNNRyHdN)cY*7UUMW^J06{G; zM)9*O;i&*yen|m>j=2sR=B1ywb=`TEfrbcrQy*}Rt#$I)9ye4>1YNV~eg}h)XdiRH zZ!WQbS_}_8x`PecIfI)zt$htkD3wsO7lF_5W1$y$^_g9_etjdtz|#UZWj(KKGo9ha z*Onpk`<H~H9L~do{*b4u^X|0NUf$W$&_PbNr>d4n!%Jk$o6Udc)!Cgd(P3e1UOm<k z`d}4OZ2Mw$&(7UteMWs!Ozg)J18_8J%M}a0UEJKZ0!0Q?E7cU`PP{Lwl@tpRloau! zv^2d2V|tA!EuM&gB<1t(g98|TdIT;zO*0ZbKmI#hJKi`s3D?187Q3AH_;at$&OCt3 zb5TB1rr0t0ZaWsM0*aPv3>fo1+SyDq(8$@p)7uSfs96arsuxM658R}pWPEYl78l`G zKp<6OMFfb?w?4b)pLTsiJWtOQiGbayPY*~UnNlUg^z}{6Kf(W=<m7^-xbJ}>i9BJM z^?^rhF1^f~9?=jZF&AC)f29bM@f${gb2+NuuBRvU%rAyY&wO!B=5r1rf9i0oc{+F* zzRHV)N-dm74786t-iJpX-hCUAZQmUTIXV(>f9op|Qkf3qwquPKiW$VhKx%`U@+#fA z20TWgFJl*ClSrNG?*DUK8m11|*Bof(usVyJUWYfz<I^M8odfq@BLhV7H_hT7OtNBv z)f25MInvJX5Nk^lxw<a+_M}TB*Hqi}1Vyp*lm)GRCb^_FcU+~YScn{61b9Xn+Eq~g zE8;slvw=tB70yQyu{?V3eccZf6hK6Z(;qFbT6uDK_{!VaJ$-7SN5JFq(~xAzXKa3y zoe9EBe*C8Ib#I`#X^^Ci^>BJ<2*kc&0M5`nWZ2yM;SIM|K&jjV@6Vt}HYKV21w9v2 zZXB;!ZuTihvP-kn?R851EYf1xjEiYe+?J}xk|LL}?CW&PAn<SH#IQZSm$8IrOGBqF z{vEHH7bT&noWl8J@q4lz74=#C>FMH!slJ`6BPIRyvIko)N6bLa2tkS;`)>bvprYWX z$#_g9R=v{%2|%Jc*pJ|E;*74$mFo~v0dDitv%w&-nI79<+n%Wb?H=7mZ5cu1wtM%I zBH#LL33$^`vz;<7udHW9uahCmn)-n{4SJ(Uy;Q&*-aGi-W0r-x)yDxoyEfZrF8k6O zyr)xI9K>s$S}U<3+MiPjPs*f#m9j-k;RDi$;am`J`THl?ay^*(y3$pSUBn@PZ&*v< zGjOqTUD-V@*LyWE>Je?U%aM~s*L5ey6&e|z{w1=Jnu-xR|K+=zj=vy}y{^d~ppuin z4{>vtA?64oQ1Ur9PouV1!<@#aw(i@HewSKqJ`<VUYQv1T2DI?Y^cc&Bf;Vm~@Kco9 zrRuj>x252y?QBz)?>W_?JL7{@*o0N6$|8&uHxMTc(S{U$QWEK~DM{F=<eD3SJB?Dj z@=|Iat38oNQVQno!E>Z`p5n)sb(@$Ejce#doSt6F!05f1`XLDA!~W-B+QooP%~G;5 zY`YQ?>lh)F*?S_5N7cSsOXUxB>ZA@1gy?wh3J`Ukfu?*|%Cu84S-`ts^=CM&Vp1e{ zLR+@+LxsF5)|YZzs86mneBo^C%S#G+u|q@Q*f5wJI&znuQ^r!W;PVOM+cVmBMcZ_B z|6`Lqb_|tUH|q7otT>UpG{?j1|17DIYU@OwEP(g3Oh^_-&l_|jxLddB2|-Vwot?4i zN30JD9@z3%`&rLM8H`zTkq>9+J5+YAr!x1)3ka}%5MAv_@kzPL?TvB87Z`(b4)?`N z?V^X~=QMB8jk!lxHT!QnF$$9dD2}5`y4K2!E-*r81l;AdQS?uP=LKcb#hni`c`MRu z0Y@D*5V@y+_Z&WB;ptWeZZ3rJK{1!OiLMR3*CR6X!7FzTlRd(aunHtHGfNO!e@S{k zlqw=Ddx`dRL7{;ndD6)Rkw+);-rwf0bvt~7;0B46CnWGN2}QYBM`8&)b%P2cuV%~Q z8F7Tx<~!3p-Mr8{J@NUxK>wKV!LRIvcbw9GDcJ+}?;99i<sgEizL2SvNr8zuK7~<< zYM>I8bGrs@FX~g?aHb)*pA(A(=s5r3Z7B#zQki0G`$SamWp_|1qN2k@y{UGr(uUQa ze6Nm|*f?-=>S3WF%?%`1ilIP8-p4f!-g1xQl3}&7?um(Skb6KLmv4}x$Hj5wRzt?g zLzgfamZ`-FsN@)axaR#<b~~*~W^L<tph)asA=U9Almj&PQ^?|tMgdA3U9mf1zd(1$ zPQF3%gTjCV-DR`dIH0kj@L4yu#v69gOcdEF%^A`F;m00}J`GD<B+2w?a=iW+mwxUH zJo<@T=R7=#3Fam6%jo3D6Kytq&F)EH#>0ccTScK?`+av271_iZaBz^NK$3L5!-RjQ z<I+Nm$M4^3Lxu}^7UR_mXwOSpMy4OIb1+D%%7AO(%sV4=eS@6)`|VbS9v;~}QVB13 ztj9G(Z`dR_(Ejp?*`pk21ahqy4wvIY*#CUP=Fi*mMvMoCqEi2o#IK0m!~X2R`hXWG z#@}THRcJu<y+AN^C1{txt(}H~ht?1Tl(!xc^q9m|;<~#cOeXGB{{NE2uk3tP=`Vg& zW$e>LSemXeTqqDI^JCe-kcYz;hWA+N-oJr@f-bX>3)HQPMPU^aC6(7)-;_+aUBT8U zDW@zyKicauq5e^nDgN-Fi+=(47;f&9t6KO_e0aMJWb9n?{kBt6&h^47Du0HAhK~^= zMslSdJbaI*3R2LPer^Hp7up|={pGyfNq8{y%|@qAyNrqmu^Z%}IF*w=yijc6M55vU ztL@CgsqWr?zatICJW-k`q$E@3AsUei4Tj9~oM~r@Yz>A6q+}i{mC95kWJ(l;jYl$1 zVVe@#GGzL_KYh>lI{%(?uIoJ4_59<?_Sx&R*1hg^ulxOaUnj42t6~qmDe1kare<jT zpumv01!~E-X^~I%nfFMqncA=W^1Tw#RAZZ6^HR5tjo-D{!gRLbsN2ss+y9gQ>kE%p zt|v=ZON2_Z<`%jPe$k8B?Q(Xn7L$7#|MA|w5Q{2yy3-?F5%P)35WXY7N8MUXXzLz@ z`mDy7reC7<N32>`Un|&~TmSI%HdO1pi5<V#Ut#8l^X>F0^W|mRjnh38IX`AoKT&I& z@))xtNs4Y`natPtd9gqS95W2cM`pVjcfLHr#3S*({c`0v1iKpM)`F;IpiF(gdC&*_ z(m#X?+#a=Ym3X`6-khEuq1i?v%KX00Z@C^%A(H1UKK--qGkA25{bt_0<rUr#uxeG5 zg4`FQ=h{r}A+)+WhrZuqbzQ%_lupGl<T=JIG3BZ0Jb8~HG`^W@_eft{-s?`&$J>k> zc5HKUKXX>+4ZB3jR@oa0WC+K)ijI28o=RLN<MiRa_UI*CLK9O!zpV{Wt5?2KV9+G8 zvZ4L;$_czbl;{R}DEIH(HTP8LPQM^EH?oJE1XRl1NQ<9>S$cIp9>qo5dIs?ap?^}t zl#P#_PCKlz?(@bysC8X4Gv68cVBZCkI4+{FBW#+@OA3);j-_N8Rb-dNr-gpa=19|u zGqZdBTEaI+3%6(Jca1ELl~}dBU`C$+HR{FP4J7__J(+sarj#>pO_WINVkVC6yMu1j zt?7Cfmv-BKSs_ZCV_`O-Y9B7yMDz_!9%($nxA9QG5jfd}H2JFrcqTMZF*ZzRi+iko zFCe0K7h9XVOi*Z9S(^yvn$MDFZgy|HjIC<<D#qT1UFjlR!soI2#cW!{=*3ML`*SI6 zx5XLU&|*Zo^)17}#?$gDYK$As7Rw_C+-{Ul#d1w)(D9*dky_V%CEvI0>kFD%WQ%Fj zALLrtd<tXFr9HIMCfixkX%Y_i6ik>_?vVD8{%lW3=}|4ZD#P|e<>|=;Gjh6qLTcdW z&y$_xhKE-RU38B9LffmL+=UDH2CuFZ6$&{}x09_Isx?pcqc(>mEZgH0qX=nJpi`El zH;Z->v(1<H)TD-|ET}+iKj`S``M2*}P%c7+xonZa!eX*Ng2s_drdEEmXdPd@SyU4( z+uxe^Mhiol`!<jST|q_L(n6+K+GK89Z@5zLL|NaXkXj1m_k%W#KTX<sMh1VI$rn39 z_~QA(`3|6;s^HInZ&TS@^0^P+jjln+_RQVt(CJ4k=p4MW%}$F&&cRZwsbD3*zGKrR z<&<h}42D9_hoH%70a}k4rAxVN+Dyy2jp0zEVIK1<i7Dy9_I%uEIp4IctEC6^_;K*_ z_eQ8bbhpY4G55%6Zoc`qkQ-8$(Dy8%cBASVc4<oN!1u~;nIx`sqx_xe8#(%0*Li?d zKxn43KCNY19b1-hvBUP~*Ltd~Hm5_wd*?1e{uoRT2DgEA)58|YzdN#$3bXdcjF_N& zo%Z%3BPP9xb)Mr5B<u$TuBWE!8<~Vp?9*qrtqm+BKRUwHTGF9x`m=oPE_8~H9wQ30 z8k)@0qc+JZI=Mo%$!z82h27JajJHGjv0Ckz&J)Xz3}WY%Z~onPM$d2HXh;oFxJ5x= zFGK7fZN7f|c1GsO%A;7et~FHIjuVUXdr%!u{%w@edffGA?6Rm~k*uuhmHD}MIcO1; zCb~OsRBOx)Y?PYT&0<}cDbH*9fI7%Xlb-&b?`Nhrp7QT#(oPTvKou;zY_wV2myUib zMxA2Q5{A86$|f(IvdUHEW211rR^k+c^6b$eDlH35Y|<-BZFV&K-g;FdqnN+N)}G&= z_e@fiXgy_fADnl)Z=rI&SM=;Oi1oRRuCKrM-G?tKPwwaB1PP25eAlrVH?+guJAzpy zTmIadlV>_KbsG7VY!nmn9nd~<yio%7_|>`Q?)P>Q|4`IKD`%ijrVRbmcn5k_Y{}jU zY$%`vv(+^nKjx3rWiI)2LakNz_Hol>ii)<k(vHo<3wh6fJhWlW28A@^G%fR7cT2kM z-&Wb}pHKV9<D9$Sl$BfLL-IkI%L=E!PJs%%qWzGGmkv($QQAu`4V!1Ned%}Lr7lxE zM~7E$aAy`hb1jyf_OWv1PkP?R>Zf_G9NXkRJUmRBa(69WC2AO36mpyZWX^?4+4cSz z+ojU&JbM6}|GYp%xN9rv`AUlMbN?7I(smuvwEvkiwD_Jlsk)?<v6iDv&nA0TMo(Ex z2}Az2?cb(H+I;kcD>z#>X~#Iv-b?5oYhjeH59D@#PLvYXQ<hKsd2#OBCIR23{A`6^ zEtn;2i<KK`Kl`pMWMrLsa%e9}v(GL+;hf6KLQ0{O+p<4dzn8mO{z`irEw!?6M1?a1 zq8)%FHVP;uT)({;vvxpt<X^0KiGbolt;zhJ+SCCd?KQtWqmDo?!z@=3Sj7iL*Di?- zwsqHpPG`+ogu8aGI|Kp3y21~UN2l1r9n3Qsrw_;`wC%lIvBtjni}Ah#_8U6=r3BG$ zoD|wYJkZQc$K>+D-x;FJ-Mer5!kGA>rZNxraED$Z%o2AUG9Dja>Rw0nxmkbL&iBRw z24yPy4a_}l_+9W`f?A@4CHxEKXT7_xqW5VuL@_{&<oHKlOh{2Sdh*vGdIyIZehuO} zQD0yuDf!AKz07Ovd39N<y55yna2$1fiI7kb#qf|zi%bKQGJ!(*zMi~wZeLq&j(n-Z zM$&GGjyGH6wmo-q+3HiK7FKL@U+9&WDC;)cWxq~&a@u5}+3MrWjF8Cs2h215nO#Y1 zq{m9mUoJSO<ORLHCbsM|y6NwpYY5-LWjT9n)d!oujlBaW?f2f<;gr1f{5$EzrTjLj z$vU~Z3DLh&B<7gGn!Zol>fW+1{Xu=IH>OORT=l06_Z5~kLslpH^fFvb`1k1vDv6#h zy2^5UEK5q8W3*%Q0ja(?yN6cltwk-PUl90J>z+&+<Wql)JcxJ9>X;uDA)nS(*`0BL z(Vj8XIQefl`MET*2X|sI!e<x|ole}Z)jL}g9XGUIJMOWUB%vyL>el*>`<<7Jk^CDc zyPv%*?9|zctzJs%`q^t9UaE<n4^S3f+-F8PNKy}2F+tScm{4b|Uu?nn#^qu__7?dK zE+yPriLc%%MiNM{Zr3XI;eS8=Xlu@hYipI+U%Q**3YxYLXr&14R5n(Be<B&OaV@mQ zw&v!9ni1DVmCY~pf_9%YpSC9l_jS0|(Hbvjf2={S=-pAeL-CSChs@V>JF(XAJD1uj z_%0Ow6L_yKzHjK-i1ifP18Nw<&n9WNKCTU<`h#ml^kRo@L6$Sab7j$8<76%#Nf}$o zMBYUs>g4QLHm&cc*rjP>(%0#1<MmaF?-91|U;Yt`Yrzg<a#-Pv&#(@?XQI@}^sUA9 zJkOg0DDwHFm95BD^h65S<}a@-acD+g8aZc;E4x=NJD}v#7kcdPOEfzMmzSnc-oD%J zo;K+=H)j>A)UwF?##c(()g}3eb-s~FQuT3!?%B3?&8!slh<GR0<yqCxo9^>XXmL2^ z<+yLx$J-lqQ-f>x%$W1P9K1|=H59_PKkFFtu?Sw<qS$RN5*<1Re@7L{DyVehwR=DY z(QVTtl)F~{2!phvb#U1~yE}iKhf?Zkyj+!SZsDN96Wn{Ju0<}G*!k|k{f@U6Ov1Tt z+}mAuE>vadYiH<NpVtaUB`?M|Ke%a(T(r3A7Psx~m>a9@J6k(FhrY{DhLqk|uP5Pn z>eWHk=;J>7X78Y)*J^WIiK6|H>67jCT}REAF4G6@Hl@{(t6j@>eBGp>9E$M@!7b8A z)VZ`;USpTmN%zvI8>@+}TXvgGM;^I@uF<ELr_D#2p(dz%sX>~iarNby3gx)r!5CEC zwlg_5yjOmCwvrKVe+aNLWBvQ_%w0tZJ%?^@@*F$I`)6E^gyBB)JmFdF(@Bx0XKaro zC7+}yk8D4)iSM5cB=M5fWh?B>=?P;vIs`5Jlu}=Fd_T7F<naoQzHp_TA~lIe=9m3q zWPD4b7>5|l=h=63E*qcqna9EHFRVz$ijd3M7`_QCTfP>;dw|&OR;ER{UlU(Vs6v85 z4jA4=pWrxXom{kqXLu%!s(IRTW<>;5>G|{H&k{)T;QAzXmGyjgtUSQUDdDnxac0~3 z^fhbQW2LrUU+Z-7p}~<=I?CFH@d~4Ij#2XykpdP!3?@ZlwOa%zi-)}~EB4vcyj;3H zU=(?aTe#`1D9@Ur(xq=$B#z@931N@<d@DO8e$5Z*<crlN*m>Fzbmot_f`W(Av}o_j z0!he-P5m1e)h--j#~yd6FnmYz@^RzGpEC0qHpn?`x}do4)Q1Z6Gn$W_CFfQ@I@*xC zhjpuUvV#~(kfe`fTmxl20Pu)>ze>F9{lQr-`}J*Gwp6AleJwdML}mKZdDiE_#g=f? zpNM|wCBxa>>=!%Y$1Rh6J}mC`{$>4Km!)*^U*kXg-*G$k9aK<>j-nr3PCuiK3jb9e zPYv8*`1Ve%l-)W}-CMD?b<B(NPu+geXVd<O=969lqshADwmIaL1iiHY$0oVp?W*kF zT{g1C`i$;GzW+M9i#@w!O^X?a9W{2$N-yh1yo{)@T~DE>JUX1JpcTEh^@-cd0D9eA zNU#TD&XAA*XPRE7d6%LO{q#CE#F*OBtK!AxZ{PppE{V^h;ySiJ3~99OhT<V!gMx-k zwq)_$&}3a7YA-wgv#klAH^~gNmtjvS$vH{-GhV6y(;Bor(|m^x`W>+qy2E>scTc|C zqvqxlyKsH{&7f#gy!6#V@-D&ELR!*t{;QcT=$&26F1weMtc4a}u1wvU97QZmqM?aG z3C98OX4wkD#?<)-B7pEfAfS`Az4BwZXG{6T{PObaNY;XGVBgwbA(8O#t%Ft8Y`S5y za=-VadAf*I{cI1T4sNOQ7W6y$9~&#|^BQM<|Lx{R^inFv*R8p-@S%rNVHFW~@fB{V zjd`Umzj+q4y9eRES5WB4$}&w-V`QWuJx1Z5)`Hi~58M^S!c)`rph`;}4U;oOxUlr3 zjyw0P(Cdk|3w-r{RO(lj#24q&=5LcVZxxEY^12u+d9)~K@TD+GvwUvej=ZDByC-gG z$&|8#R(o~WK3#;-d}w+k(D#=*4(A9QK^Gc<>H}ccoX7dk<WaPMxi%I^?l-cX(GWG` zof*H58_uHL875Aip5IYs{aAALENW170uk(dvVO)(Q)`v!r>-Xba94=?{Fyppre0yn z>HTu0;V1;ZD>dSYUkwj^7D-^keuR=&jG3%Ai?|6hF4V>t`@d&o=@f_BEv}CKFcP6! z+heHur+RU=f+OJ~yK+WZcegLS`jqBlji;JXF3n%C+}0k`&(?Z6mc7=zqV+YN+;q?W zT4j+d9kTS+Q`r9a4AM8TZd;=sfpetKDyBp3CHMDO4C|0f&@D+5dmF^kw=`SVV{kAO zclYDxo{@CWUgOMrV4rZ1+0t6R%1s;IKYDsn;f-?~Qfs-F2At2dWGJtHeq+nY0q*ai zp$vUp-Tgh8SvtzG0qr-(c!KBap6iM7sCF;Tns}E#JB<<4(~n6<^N*YDtP6vHz>CXS z>IoW$Lo?3bY(>L`c~`6?#Eqg^8pV34YN+4<1madFb2pJSyV&yWecwJNdG`;a%pV!C zDLFa!u$#OCh4t=T^Pf79bfMf&H#Xz2ZXI_9DU#&Kwp~Lfl7ExFVCl1NMve`}89eBi zwMCy3l1HDS@EsP-`^esIOMhr1nyKmuhbQNU>a<w-hmiO!3kt?MX`#+TSzuj%Ro_g8 zL*BKgdvsLHVdX<mV)$(Vi39gql_sEIojQE@_^pP?NOe-{TwjPFpQ5$<@$X<S47b!T z@+#y0BC)eAmo|%z{=TWIx*e6@>0G42QNcY4Wf%0)ZL<_Uf4Rnc(7a}o&&-9B+SApy zQN^`m2{&Y1V?V9E{~UH4%dK)kA_{T3;w3j8mAZUqMiEwE1Th|0EDqnF2>Q&L&=+QQ zc*nNftUpVN61gAekUVfEHMsDtpz!*5*&s8DOoS)a-Y7HOx=P>79ix8jHn)J+dlKd_ zA0hU+-I$7k8m;;kH{>V|o+r-6&dncAQNaO!p8kkzS4|DZ^-x6rkAIm078{FC`9lgi zaN6;u!oT_@?|ko7^;PIRy)}Q@`nTio*U$JX`v-^53=Nz0SMjwBrx=Lkc-U-JUX&n> zb^AlD?+x)1m!fP!;W?j1{#^Iz@%-y$W%D%)Y;v(>#bq<`hShT$m<|=bX-ex0ldH`W zs0c1!Ueb9Pr4Tkb+ha>VBkq)x<5J6miluP(t6jR6ekAyWwyt8_i^4I`YOfYwS5<4C zc>PXLI0MlvyYyODQQzl1j|?Q~ehcLK!$Hin+21_@K&Omqehe!f+mp|gXj0VsBJJVX z-8_;XI)9ug%xU}TJ7MZGeP$i|+k$G5d}K4N2qdkGiv_g#q2^g_<zQUSUhKYn8~5CR z{d^pa6v*S%=}fmg?lUGT@3TQjJ2+aj&PJuj-|FYyEW>8w(Nw8)mfcI!_bI5P&GuAy zw!}Ftb_aB9UzKrg?Xy#o35u$dfrU*iEJ<DY5i;4u46);K=;O0O%aQ`buMM;$?^p8+ zMBk}w_%=Jw6X<I^QoG5NvS^XTT|A)@dPmxuC4!et#_7n|H{hjc=7C(y%z>86UbAQ+ zaP=$kOGxu-b#`0&P5a(^I-!@Ugv$4*X0{>dfi)@q_cEfmG-aLCx)vt#-w8$<KX7i3 zax(Sj3>59J%C%~jMsYCZ--6d|)hpGU=>Af!>Z-424dGCRoj`2rb=*N^F>TJakt((i z-z6-36CA-DNd6#{&Je3A+M&YQl$J1d2raKEH7BD2{eE6un2?k6<zhhRLNx2PPskXc zu71{WXUmNkVJpuIg_{4RU2yX9y0{;=d~llY%0Oqv*B`(|`*vK!N>&-e1)1H)=@A8r z>EBu_DiiEHUtd_63Yi{W)e!Te`(pU;(&SMpQ|a<@LteVV7x1j~v&M^f{Jdn-VNB3P zruFjub7bhm{lDFPHYPY-$-`FRom{v1!q*ohY5(ane|wIW0=Y%TyPRZWlO-ATvTA)z zKHkD^yL_1(rK6g5FrLBOB0A22mu0#?!ZxBb(}--^^DeKAxtEsk$ohnn<E2zAWP;Q@ z#j@FwPVZN>sybV0rJi2%PLS!4LW%{C^LDv#j4`85p}u&0+4Mi4Qf<-~VyLW282d!| zvwo9!@S8W3Ec&cm{qCO)<*e5)wg;A`_;NA5{~#z*WBk4dY4C@9|H8KCZRI_D%XB_+ zL8*4i5vh*}3FtzcTY8bEfo?0*;SEcs*8J6KWz--%bULRfKJc{qWNvLo^z>z#Qj#D} zy<ieut$OxsZHkiWlC^B&vbT6mAj$q)ZKp%$<DhxFr<(Bx-ri++D5vD#>inVDHf(-~ zk21jOZ$9Ha`mK%?h4RCf-t2z5>B;qz(XW0L4r!?R-owpNd)SVXBhjLnVj|iId6wZ> z7b+&;n1Id&l;TDqvGn{|7y@NM`pJ!j4c#TLtZyppeC_kP&2F+sGg`XB{Js#X`M$;` zgI}0~n2xP#*k)}TujDD`D(gMq(r)eLq3YKAp+ij_3_IFgzYunvveFhA$5KPP*>S5b zBUg8~DNE90D=P>3H=|>^>9+1RzDRXr?g5$Nq!vMY|M+-dn=G*{=;V33F0wSXn+V^S z`Eiw7q}KDgi2jE^ziAoGWw;lxc%`)?G)%^*q4HCFxx~(g57WiIa*+-mR|{?<JIuPE ziD#v=q=Pk}BR^r+WY6Mat@u5ZuJbmD8pU6&?<wmY%MS%Tvb8ww;{83jlyU|%ZK7$X zvfFnL8&cx2fW7-jUj~6sT7+EpH&qF2hINPBUR$0oo44IJ`{P7ZOk$O&ikJ*HZYL7U z4a<0CodZRUYEyy=*RKn7Y+ha;r28^9E&6<h{qlu3&(+wo@t%sd1h<_++XcP9I+C&+ zFekBH(R$gIi;ps5<;~Ck(6of2vQ_5Vv(r~RVjf_Z2lm|!_YUYZt&gLBi;sdWgZ;08 z`#9{nSOn>#ee~BpC4uWEJpna6aMu6}j9QcG`1PS>OIDO)<2BICe7nRCf3G+oWr52M zR+kQ2UKP}G6*k<9zdW)K9JI7?jmUY41@2Fku}!r@6fW_s$VxI7Nqn~m%f0(`o}QZ* zq=eUNC0*ONr+45p%gU)!H>RhH51JXow%sXo)TPVqsOlN}CZqcLcL&>vwH!J2z3;cH zPA*=%_PjMuBoEYj%RDC|1CwQ8(w7H0b~c<~<P;6n_Eqyy%oF7@X&|ZLidMM$;VRD% z)?H5wBw0kb3pKrp3I{%OuJisOVORUTzb?Y6>esRXb|u+T`!k`pzRYv(xYB168A+?R ztx~riR;6&PpBmk*cdLBGmWRgPmO`QWxz*erw%=>gzL;4#)i>w;V4rP+h_2?Yi35Z` z#le6?l=1d9KV}+Ky7Um`v(?<fmYxp$(UUCe9-hs2IY0U>4EIIyxR?upNDHb>Xxu&g zmoA3`PY7kk*U~Yu^cwC$kAMK$PK}SZ8lKh!wWK|ZUnQy1-Rb-fpDXVL@8zo3R&I+0 z^HM63jf-wUckfb;SZIW{smtyAqO6(c>C~belHk+4t{37l$O^VV4Cdam?qQdUR%UWp z5Q2E3BOAxckih=h;{E$;S-l$@X|7y_OBZsU%&+GcizF^KbT$M8x$&q10u1sn13{4t zBYn{Ynhd;yf8<<+mMYg1GxQb5rBxdWpZ{<sd;M&cBs+YIlcH6PG%2i%&&hi-Q<G&D zwmo~cOm~0nd27gT+1F=OIKNCVaB^}o-EqT&?y5;3n;_lm9zB4r_G8K)n#FF@VYQRr z0fltI=o=C2B}hmrfp5Zp11YmEX3)@cMy3;5-2W4gpN69d?kK&ThC&RSO$Dn@cgkJz z&8BC7t|rjy(vDw4K`Koqn(qq&pdunHFQDH#;MRKx0-2;18*6JcNV*a{Hv;xXAnU>t zz29aHm|ecCSg*3MAJW!Tcm<q_mmYbX573(kNTpIx<UfJ7+Zlhc>Nk*aKn#gVJGb72 zz~L?+$nx1m|JHB&oO125Sv@-H;dY@8bsoXM0&JDlj+*G+F?R2opKHAwP;qdv>HW85 z*XDiI6IGDHK84PcfZJ_0^|^XUpI3OyG1%wE&5e7uf}bXbZhI?2-=+rDUl`Oi1D7EP za+wA}-v>mjJCK}jfzQFs##a_c2?AWO1<xmNxx%0v--5@IhR2SDGQj$3;H0zR7h!f( z47{IOjF`zC2>Z66NnaX-ER|GzH|p5#75ttJ@!_q-zPYd6`i>C(HlG&f>EZ<@9v(T3 zXi|r#Mz;hQi8cflQw?7-Y+%3|&{r$e#MR(Aat9N18?FoT-G`FAw?fvOLSNSZ<ts3+ zuQM?Q5tk#pO<Gz(<b3T*x9tay>i}Mko_TiDX6~<kV=yH#6p)quMi9ihmfQVJ4wvME zJ0J(<zXdc!A3(yq-uU$7=6>MP#a^}eK1Jw02AWz}oRpp7%0WBo_Ed~$VYe4vyc%%w zi78PS5<J!cki49TZx0rLLg@>Dq<A1#k=4sGcEC^HxOBBcBa}@41Kj3Ts7?t0b^n)` zfn4yb!+`HrS}9|zkN^>^!nmxFf^Ve;zFJ~mhfDsp7E`}WIvFjn$p;`ea~K-u*r8}$ z`RUWvh1m%?*8VRlu7VWi(JQC5v}A#4znEcG*6q6lYWE$ej<-2DIQ+cqtCG31%od*y zj%FADuRK82>60$yUc2m?o0D?~c>1ltGJ2STxX`l=;^h1yQ^QgR=2ph-=vk@=;UN4} z<i(xZ-v|>nFuQj3T?1(1)m1o=Y}m9Zx_@S@CEMGy2SS=5&qt3Q0c{`id#uGOzrz>! zI6!WHGerO|c42erz8-=Zn3I!Z56mb%he-ZlIl%JFj&R?AoGGbl4#?GbX$OzS*&Kir z2?-e1h4Iu-*nsxol5CLI{DH3rdPAwo3L2IBiHSd+=?`@<kCu4OfyH_=EbJ-xYA*h| z3{11#!1CQEFr9dtL%#{V&F-{>%x9$GRZ#h@IRq_XQp|(BXBc+2<oN=kSL1FJ2$7JI ztKW+;KmiCgFTwU2BOIJSw7^Guejc0pi`xTn6~Z9}agj5RS3_I_((~xG4%5++QwTuA zzxDrME}qkwa00Lal=7M>WjursymO$?E1c6^&(-P2vH><hKodgzcw?LC%|#<jLc+Uh z-@aS_f&RL-=eyRYXP0V1N$O`B%K|n@V7A7|ll_FC-8AOjJ%+pe8^2D?cB=TEJ$H`W z`OV$0>Y}*!(md3sm4H6-D0mDo%|@n^U$0OKCYOpk{T@U|do_+h#_gdz^E6rgSN6m1 z7D@sj$lxB^eYmlHK7j3B5#|G1MK82b)>sBqvu~9p7+K6KAz0XWcl(CG4appDb>wLb z;Jn3v$Y%vy5PL*2I7Ik+p!<Ma@)NU^p;r47Y&3>?$`ebM@_T!GSpvwPtE+cH<LkQw zHJ8+Z1CXrOy0<+(e%l8yzCtX;!g*Rk_TupqSz<FQ9P&M>HC;0v6E^nt_TjFUzVpSA zi^9)mfBOTpz)j$4D?>ToT<rL^JNu~Ed7X)II7311{0tPttbp|%oJ_yF*Q{QB7Z6#v zi)qtob*p@6adtika=x%Kn55H-*XuLAXZRNia{$tl$2?(rH3&BHcgU-v#Lal;><s0F zglN|~V)p6YLi`&n_yP7k_0b4SAl%_c!jh#*Io${WJP&}Ag!c<#`j6%^iNZqw^UC}F z5`+ME_3BtNOUx<(LBWA*8v<U>j^gV8*9}pehl-c!|5~!1glif07mK;cKGv<$)jPyb zQ1L~*AXdjd5C;Pp5-9yAV3mbXV%uHpiv{t`M7+_Ch-7o^z)!rSzk?UXUC37v&y_tY zTj=~RIPeoL5D^GQKDFjsmdQydAH@w^NezhMZ=Kt|Co){>EXBg3j@45#V1ED(;%M<Q z3=GWi90YZgS4n!j4zc9)p+kY>K8e@~ZO9zO=R^S;2wsFhjwg6k?3E|rw0Jkp{sI|c zyNZ7-f~@I(*C`2dTTpc+31~I!BdY4MPhlcmf#DKh+`N#zTR__1d3h|8aJ*6=(}tbC zoyI@NJeS4}J0sY>)%jk5R|HU>3g0^YxN2tjPO=k0y8Z1%Cva!}H9$8RLAtbp){r^| z7-kqah}a?oL_}%|z<UUTna)?d%@E0eMW56&scKY}K<wUX_!ePP{Z&c}q#0~Mn8Rvt z&1-ygbgdQirfRT<2#=VazI4*m&te(|2|PfhNML506I2{E008bVv1~#t=Yb+z&C&5C zkjd@{YpEFOPa-Hbfm_n#Gx$LNj_k#Dd85S)bU{2f%Fmj@PNKM=)rBpKI#k#>6jq>j zxHx-{cCs|6(AEUhakGDzt&PnE`iCor4qvHDc7>OeBjY&|Mr4PA$CJi9Cw+oXc>v>W zXY4ng_X+-QdXwVv5yaM{rx!id?v%X+IfWFlhY_2v!d5BoAFYjTL-p7-LA3m|1MQU< z>Uu<vkSS=@hkT0R%1}4lo|jG->k;3c%6lHxXEA^q)ETQ%C+-W*gL+{JBuc0K*XNPM zVE1D+d7J9vA8apKAch4dDILI{l)5*amfK?V*7@_4fs`Fp8llS-Ia0L`F2Te8lezr& zOB2BL;TC&;fB&n+T4v{U%yNg$k@oXo!9Gh%+lG}bOC(IG7jkVsJwKDGeA)b?Ezi&= zMZ%M@a*sXR-;D16&6M#}U^5dqVhLc95`cOVHb6UI8x*oA2o13y7jdipJj!vf5;L3x zU&4~d3!9(}C-CCh`6hXe+<>eXKdzeSER^EAGTq4CwMXv7ix+}ILTBN!Fl2)<u|!OY zLcBoQMq5zmbO7ELpI?C_welkxX9jX?sfi<t3s_{;1WF_{)Xh{6GE-u&D_=qqbr|4m zP@>WhV>TO`foRJjmTp{l09i(n^Jy#uUs2T%Kxa6)xGuGI=w_QPz2sf-C%k6}empXx zeq@}~*?#UP_%0G~P&#||Y^*FUy&(0wjml^BZd@;hg8%W)jvc2_S|pgL#Bb+!c_q+g zK~1$8Zg{l0(;KzX4AT<AaRNwX*MX6FLaz_v_gK!Km2qST^^#kMv?fXy#1Jz}dVgUi zMq}as!^2~+)6+PtkUW$FzKHSl60jOGS8+w2gdTVkFv)=SG{KjF1a_5+yKZ+TjHC$D zG9Xe6O>TwG?L`N6;ob^~M5hJhEb4HUSv7}giia!-dH@VS0Pe`Dk7t57h4(;^m@v98 zfW?I)8fo&grv>KKAd<{yARrJC1#0QP%4A$KV7p4n-v$pmXo$Bf`)mj#m<#{0np_*3 z`Vv2V19y?Cehy&S@ndjN%JHney;s*_lV=LsD)Sf$U9gPSN?e~uVx-<vdIe5moP+}j zyi7K;4I=aI19xhWFi(V9oydJ`>*5>pUCr=0IMt*j<sXDw4gL^e4@W{!2*=+U8qPk7 zs7{1QC|Id05O^t0A>@Eh(7m`QcUxms!&YKQs%R0A7oU519&)>xAD{7&ajFy*;tT-= zx?Z$_IRRuRl*>5FiV*f)8X6kr^S=hE5io`!(P+`wLS7*a1_~E)ePV*udp4bPAuwW? zk0+o*Cfr`YTq5|z1Zp1;X@r?7OjFSEKVm;)Mgl(o<+~jMj6c#+aJp*d4S>=egdOL1 zOl5EkbYQ-Qzh8xO0d^V|gaQlfRkeu<_W^skcKtfxXcNN0N5j9yO4b2C1=bXa5pju$ zwZxjkEFkU0mW2bA9io*#wh5%c97H6$G~V8Pd}V?EyCX0*k;Y&!N5g7{DR7%hLAkFl zjvS!s>^XRF>7`+rPbwV1h@rqAj8pbKu(HszLKsTn6xR=zn#%h62;kDX6&Sn`t|TwE zOWWCRg(4kUCvisj(nLQEvpi~&QtqXZv#`XgL)z#7Lk1Fo$Y<FsW{vHPU=HB$k!xPg z0Eom<01fakHsMv5!Jv}&pOe6JvBanmj(rGWuRR7e?Z%P#x`&H>s;rd2pG2O&mT;Mi zlfAeVr--_#Sa8=K;z&e{?0%DXQTR0+#VCZy9M}v#gjLgPEA@coj^mYKXx!j0jK}_o zlpatUHyG9^h>M#R%L6V=K+xeqB}bf3#jbQCPm@4Es_Az5HSig<u<y6{%CJU!-*o{- zBtGXy7~l9%0eOS)Tn)BKU^9H;Zn>W@JZw++^1x1mw2qyfh9zZ-$poa_&yrbFm*Oe4 zq465LXV_L&7P$8VaD1Eoi-5KPVDg?deD}%dOQv?YZG`C&F|!e76ig?cVmlAR!sG;w z3BGGd0u|wke11P92`m!Mee=NL5pbVhbhBOjF2otV(Bsq!0-~gPy{d%z>KIWY!V3VP zFjWw~9@HeuWnUrP<`VYMjYw%x0n~b0q7&d5*V1_*#Pl>+Gf>COVBwYia?q0Sabu;h z<2lI7$UbH^cUE!!1V%lP$osEh<||%bEVF@WWKYMNH*b`iX*}F2lnd|6x`1&fULs=o zy`-eDvNB3CuhB7PLhx&Bj?m-WS-@GXurx-vXTmsFO;eK@4q#y?86WPMS%noYMp(z< zHCiJ*!6E-99;)%Bt_ZT18p<*~h2@Ax09>8^T&tl@Y>72G#J;XzE}ssVH%Lj<I1{y& zQB;ww!0K!Y3lG~c!(FS5HBaT$xZq@xn7Vmj9>=&<<jmg2RCQr2w*UQby^Hr%d3}1X zp6s3h!fFs%YZTN4T{ag+k={N=o<htsu-mT^?zFIOK#q3U%}s*vFvKaZ?0e(K&KtqO zroS#H)I|?fwU_wGiI+_Oldhjlb@x4XY&B-C09=1jx)_|Ff~6SCAxles_{PPQEa7yF zT}~Ob#Lxkxte?S0ARV%{9ynfsVy2*=An`L;UU?i`|3u<-n{(%-rqjg#iGXgQO7Qyf zZ%2Z-hX4X%GPC(x|0ynfE@9n6?6Gg1FTkN|)9BY1M*(>QtV$UW48l(f7fbzg3j|^! zi~70G<XGn^XktBp3AstOw!`Ru%{E(koIl={yN3v4DC!WDu!{O@H`z@Znwpk~Ht?}M zljo?57^H@6X9Rwx!4?E<AMb$xeoUNM<pyU$JOwt+I&1{wBt`1@^LK;?2lHh8c{dlW zWaZDW0s+d36^6-$k2z0r3iAZfi}FH^^R`IYlHe-j_jz)yd`sXCWuLp4QtwJ+)9=8g z7c{9UY}%%75G#?huGUhirM-$0>L)YZ&^1W<5-pnGG}i%N*IdujrcM(u-6IjcXt1^` zRGEN@aRt&!D%@Q5`;GH^I6zYK9oCYPILfcNn-<0+*X@%^&WAwTzyDi%#-$22IrH4M zIs8hjgard|)YLHmY%1ZF$6#Eb)G|fsm*;%)P&d<%Tf$ph+~C!F-$Dx>A|S!MsAb;p z?-^50S$TWljT@_Rd{2y#bNY4%Sgw13-N5eFAm1g4{tm1Xl|RD*7-6ONKPUzU>Oseg zlYkMgbU?Nb-WewkPwd#w9tyxw1i^(cC&h4h;LM?%;^i)hK;pe7v}TLqCHpMVGh&00 z>b#%nYGLqA0Q%)9mK1ST^u+`wstP>tR=|F-2iSL*E#Q%!BOO2`>{R$d%Zqa%aLkpk z_^_HFcEVq29uClDA}{4s2@eXwi-L$fNDfZHDRi&lo4weZgd`;+VaSB=N(H+_JeC=} zdRg;e{BqG*Fw8I;;L%OgglMpr{p&aD)o~Gr#pLJD_oC>BGYl&U4!)al9CW%`qQ_Zy zfOsRYQQl8Nbs#Fu)Zbi-5vfEHSfWUR2-r}3_SPBriVo#k({osU_sC*x4|OQE#|3VY zB~~2FJ+XBe;S{qL@IsV45{P6A?#9LqMHBEVCW!Yi9erh2pL|;@?%heOPe2sWps!+u zJ&3Tf#DEfB-$2H_hhHoKIYdd3h{j0w6uf`nBzO-@8G5t6StO9>*v+aU`1S}11>;!$ z3qO>^!xN607%Ws1*1_6`0Hy)CI8@m8Hq@ZP*8XZRQDW6@{LV^LSpUD6sH3F~AN_yn ez+St;SitYHW^IFKGo0p0Cyr|!%Q|9y?SBBBu*C2H literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb index 8a4abbcb..3d6f6326 100644 --- a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb +++ b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 352, + "execution_count": 515, "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 400, + "execution_count": 516, "metadata": {}, "outputs": [], "source": [ @@ -89,27 +89,28 @@ " super().__init__(p)\n", " \n", " def forward(self):\n", - " mu = self.sample_mean()\n", " scale_tril = self.sample_scale_tril()\n", " with pyro.condition(data=self.D_train):\n", " with pyro.plate(self.N, dim=-2):\n", - " pyro.sample(\"x\", dist.MultivariateNormal(loc=mu, scale_tril=scale_tril))\n", + " pyro.sample(\"x\", dist.MultivariateNormal(loc=torch.zeros(self.p), scale_tril=scale_tril))\n", " " ] }, { "cell_type": "code", - "execution_count": 401, + "execution_count": 517, "metadata": {}, "outputs": [], "source": [ "# Data configuration\n", - "p = 3\n", + "p = 100\n", "alpha = 50\n", "beta = 50\n", "N_train = 500\n", "N_test = 500\n", "\n", + "# TODO: set this manually\n", + "pyro.set_rng_seed(0)\n", "true_scale_tril = pyro.sample(\"scale_tril\", dist.LKJCholesky(p))\n", "\n", "true_model = KnownCovModel(p, true_scale_tril)\n", @@ -134,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 402, + "execution_count": 518, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 403, + "execution_count": 519, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 418, + "execution_count": 520, "metadata": {}, "outputs": [], "source": [ @@ -202,8 +203,9 @@ " super().__init__()\n", " self.model = model\n", " \n", - " def forward(self):\n", - " scale_tril = self.model()\n", + " def forward(self, scale_tril=None):\n", + " if scale_tril is None:\n", + " scale_tril = self.model()\n", " cov = scale_tril.mm(scale_tril.T)\n", " cov_inv = torch.inverse(cov)\n", " one_vec = torch.ones(cov_inv.shape[0])\n", @@ -211,41 +213,29 @@ " den = one_vec.dot(num)\n", " return num/den\n", "\n", - " \n", - "model = ZeroCenteredModel(p)\n", + "optimal_weights = MarkowitzFunctional(None)(true_scale_tril)\n", "\n", - "D_train, D_test = generate_data(N_train, N_test)\n", + "# D_train, D_test = generate_data(N_train, N_test)\n", "\n", - "theta_hat = MLE(D_train, n_steps=2)\n", + "# theta_hat = MLE(D_train, n_steps=200)\n", "\n", - "theta_hat = {\n", - " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", - "}\n", - "mle_guide = MLEGuide(theta_hat)\n", - "model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n" + "# theta_hat = {\n", + "# k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", + "# }\n", + "# mle_guide = MLEGuide(theta_hat)\n", + "# model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n" ] }, { "cell_type": "code", - "execution_count": 425, + "execution_count": 521, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 0.0290, 0.1965, -0.2255]], grad_fn=<DivBackward0>)" - ] - }, - "execution_count": 425, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "eif_fn = influence_fn(MarkowitzFunctional, D_test, num_samples_outer=10000, pointwise_influence=False)\n", - "correction_estimator = eif_fn(model)\n", - "correction = correction_estimator()\n", - "correction" + "# eif_fn = influence_fn(MarkowitzFunctional, D_test, num_samples_outer=10000, pointwise_influence=False)\n", + "# correction_estimator = eif_fn(model)\n", + "# correction = correction_estimator()\n", + "# correction" ] }, { @@ -259,33 +249,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 522, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def objective(weights, scale_tril=true_scale_tril):\n", + " # This is the actual objective being optimized, under the true covariance.\n", + " cov = scale_tril.mm(scale_tril.T)\n", + " return weights.matmul(cov).matmul(weights).detach().item()\n", + "\n", + "oracle_objective = objective(optimal_weights)\n" + ] }, { "cell_type": "code", - "execution_count": 426, + "execution_count": 523, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Dataset 0\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'dict' object cannot be interpreted as an integer", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[426], line 36\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDataset\u001b[39m\u001b[38;5;124m\"\u001b[39m, i)\n\u001b[1;32m 35\u001b[0m D_train, D_test \u001b[38;5;241m=\u001b[39m generate_data(N_train, N_test)\n\u001b[0;32m---> 36\u001b[0m theta_hat \u001b[38;5;241m=\u001b[39m \u001b[43mMLE\u001b[49m\u001b[43m(\u001b[49m\u001b[43mD_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mD_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m theta_hat \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 39\u001b[0m k: v\u001b[38;5;241m.\u001b[39mclone()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mrequires_grad_(\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m theta_hat\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 40\u001b[0m }\n\u001b[1;32m 41\u001b[0m mle_guide \u001b[38;5;241m=\u001b[39m MLEGuide(theta_hat)\n", - "Cell \u001b[0;32mIn[402], line 13\u001b[0m, in \u001b[0;36mMLE\u001b[0;34m(D_train, n_steps)\u001b[0m\n\u001b[1;32m 10\u001b[0m adam \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mAdam(elbo\u001b[38;5;241m.\u001b[39mparameters(), lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.03\u001b[39m)\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Do gradient steps\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_steps):\n\u001b[1;32m 14\u001b[0m adam\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 15\u001b[0m loss \u001b[38;5;241m=\u001b[39m elbo()\n", - "\u001b[0;31mTypeError\u001b[0m: 'dict' object cannot be interpreted as an integer" + "Dataset 0\n", + "plug-in-mle-from-model 0\n", + "one_step monte_carlo_eif 0\n", + "Dataset 1\n", + "plug-in-mle-from-model 1\n", + "one_step monte_carlo_eif 1\n", + "Dataset 2\n", + "plug-in-mle-from-model 2\n", + "one_step monte_carlo_eif 2\n", + "Dataset 3\n", + "plug-in-mle-from-model 3\n", + "one_step monte_carlo_eif 3\n", + "Dataset 4\n", + "plug-in-mle-from-model 4\n", + "one_step monte_carlo_eif 4\n" ] } ], @@ -294,7 +293,7 @@ "import os\n", "\n", "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", - "N_datasets = 1\n", + "N_datasets = 5\n", "\n", "\n", "# Estimators to compare\n", @@ -319,22 +318,22 @@ " i_start = 0\n", "\n", "# ATE functional of interest\n", - "functional = functools.partial(MaximizeATEFunctional, num_monte_carlo=10000)\n", + "functional = MarkowitzFunctional\n", "\n", "for i in range(i_start, N_datasets):\n", " pyro.set_rng_seed(i) # for reproducibility\n", " print(\"Dataset\", i)\n", " D_train, D_test = generate_data(N_train, N_test)\n", - " theta_hat = MLE(D_train, D_test)\n", + " theta_hat = MLE(D_train)\n", "\n", " theta_hat = {\n", " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", " }\n", " mle_guide = MLEGuide(theta_hat)\n", - " model = PredictiveModel(CausalGLM(p), mle_guide)\n", + " model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n", " \n", " print(\"plug-in-mle-from-model\", i)\n", - " estimates[\"plug-in-mle-from-model\"].append(functional(model)().detach().item())\n", + " estimates[\"plug-in-mle-from-model\"].append(objective(functional(model)().detach()))\n", "\n", " for estimator_str, estimator in estimators.items():\n", " for influence_str, influence in influences.items():\n", @@ -346,9 +345,9 @@ " num_samples_inner=1,\n", " influence_estimator=influence,\n", " **estimator_kwargs[estimator_str]\n", - " )(PredictiveModel(CausalGLM(p), mle_guide))()\n", + " )(PredictiveModel(ZeroCenteredModel(p), mle_guide))().squeeze()\n", "\n", - " estimates[f\"{influence_str}-{estimator_str}\"].append(estimate.detach().item())\n", + " estimates[f\"{influence_str}-{estimator_str}\"].append(objective(estimate.detach()))\n", "\n", " with open(RESULTS_PATH, \"w\") as f:\n", " json.dump(estimates, f, indent=4)" @@ -356,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 528, "metadata": {}, "outputs": [ { @@ -380,152 +379,68 @@ " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", - " <th>analytic_eif-tmle</th>\n", - " <th>analytic_eif-one_step</th>\n", - " <th>analytic_eif-double_ml</th>\n", - " <th>monte_carlo_eif-tmle</th>\n", " <th>monte_carlo_eif-one_step</th>\n", - " <th>monte_carlo_eif-double_ml</th>\n", " <th>plug-in-mle-from-model</th>\n", - " <th>plug-in-mle-from-test</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", + " <td>5.0</td>\n", + " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", - " <td>0.33</td>\n", - " <td>0.22</td>\n", - " <td>0.73</td>\n", - " <td>0.33</td>\n", - " <td>0.22</td>\n", - " <td>0.72</td>\n", - " <td>0.34</td>\n", - " <td>0.84</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.11</td>\n", - " <td>0.11</td>\n", - " <td>0.13</td>\n", - " <td>0.11</td>\n", - " <td>0.11</td>\n", - " <td>0.13</td>\n", - " <td>0.11</td>\n", - " <td>0.13</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", - " <td>0.14</td>\n", - " <td>-0.03</td>\n", - " <td>0.46</td>\n", - " <td>0.13</td>\n", - " <td>-0.08</td>\n", - " <td>0.42</td>\n", - " <td>0.11</td>\n", - " <td>0.56</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", - " <td>0.24</td>\n", - " <td>0.15</td>\n", - " <td>0.65</td>\n", - " <td>0.27</td>\n", - " <td>0.16</td>\n", - " <td>0.65</td>\n", - " <td>0.26</td>\n", - " <td>0.76</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", - " <td>0.33</td>\n", - " <td>0.23</td>\n", - " <td>0.73</td>\n", - " <td>0.33</td>\n", - " <td>0.22</td>\n", - " <td>0.73</td>\n", - " <td>0.33</td>\n", - " <td>0.83</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", - " <td>0.39</td>\n", - " <td>0.29</td>\n", - " <td>0.81</td>\n", - " <td>0.39</td>\n", - " <td>0.29</td>\n", - " <td>0.80</td>\n", - " <td>0.40</td>\n", - " <td>0.92</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", - " <td>0.65</td>\n", - " <td>0.61</td>\n", - " <td>1.09</td>\n", - " <td>0.65</td>\n", - " <td>0.57</td>\n", - " <td>1.05</td>\n", - " <td>0.68</td>\n", - " <td>1.15</td>\n", + " <td>0.0</td>\n", + " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ - " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", - "count 100.00 100.00 100.00 \n", - "mean 0.33 0.22 0.73 \n", - "std 0.11 0.11 0.13 \n", - "min 0.14 -0.03 0.46 \n", - "25% 0.24 0.15 0.65 \n", - "50% 0.33 0.23 0.73 \n", - "75% 0.39 0.29 0.81 \n", - "max 0.65 0.61 1.09 \n", - "\n", - " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", - "count 100.00 100.00 \n", - "mean 0.33 0.22 \n", - "std 0.11 0.11 \n", - "min 0.13 -0.08 \n", - "25% 0.27 0.16 \n", - "50% 0.33 0.22 \n", - "75% 0.39 0.29 \n", - "max 0.65 0.57 \n", - "\n", - " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", - "count 100.00 100.00 \n", - "mean 0.72 0.34 \n", - "std 0.13 0.11 \n", - "min 0.42 0.11 \n", - "25% 0.65 0.26 \n", - "50% 0.73 0.33 \n", - "75% 0.80 0.40 \n", - "max 1.05 0.68 \n", - "\n", - " plug-in-mle-from-test \n", - "count 100.00 \n", - "mean 0.84 \n", - "std 0.13 \n", - "min 0.56 \n", - "25% 0.76 \n", - "50% 0.83 \n", - "75% 0.92 \n", - "max 1.15 " + " monte_carlo_eif-one_step plug-in-mle-from-model\n", + "count 5.0 5.0\n", + "mean 0.0 0.0\n", + "std 0.0 0.0\n", + "min 0.0 0.0\n", + "25% 0.0 0.0\n", + "50% 0.0 0.0\n", + "75% 0.0 0.0\n", + "max 0.0 0.0" ] }, - "execution_count": 12, + "execution_count": 528, "metadata": {}, "output_type": "execute_result" } @@ -538,33 +453,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 529, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " if pd.api.types.is_categorical_dtype(vector):\n", "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", @@ -577,7 +472,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -590,30 +485,30 @@ "# Visualize the results\n", "fig, ax = plt.subplots()\n", "\n", - "# TMLE\n", - "sns.kdeplot(\n", - " estimates['analytic_eif-tmle'], \n", - " label=\"Analytic EIF (TMLE)\",\n", - " ax=ax,\n", - " color='blue',\n", - " linestyle='--'\n", - ")\n", - "\n", - "sns.kdeplot(\n", - " estimates['monte_carlo_eif-tmle'], \n", - " label=\"Monte Carlo EIF (TMLE)\",\n", - " ax=ax,\n", - " color='blue'\n", - ")\n", - "\n", - "# One-step\n", - "sns.kdeplot(\n", - " estimates['analytic_eif-one_step'], \n", - " label=\"Analytic EIF (One-Step)\",\n", - " ax=ax,\n", - " color='red',\n", - " linestyle='--'\n", - ")\n", + "# # TMLE\n", + "# sns.kdeplot(\n", + "# estimates['analytic_eif-tmle'], \n", + "# label=\"Analytic EIF (TMLE)\",\n", + "# ax=ax,\n", + "# color='blue',\n", + "# linestyle='--'\n", + "# )\n", + "\n", + "# sns.kdeplot(\n", + "# estimates['monte_carlo_eif-tmle'], \n", + "# label=\"Monte Carlo EIF (TMLE)\",\n", + "# ax=ax,\n", + "# color='blue'\n", + "# )\n", + "\n", + "# # One-step\n", + "# sns.kdeplot(\n", + "# estimates['analytic_eif-one_step'], \n", + "# label=\"Analytic EIF (One-Step)\",\n", + "# ax=ax,\n", + "# color='red',\n", + "# linestyle='--'\n", + "# )\n", "\n", "sns.kdeplot(\n", " estimates['monte_carlo_eif-one_step'], \n", @@ -622,21 +517,21 @@ " color='red'\n", ")\n", "\n", - "# DoubleML\n", - "sns.kdeplot(\n", - " estimates['analytic_eif-double_ml'], \n", - " label=\"Analytic EIF (DoubleML)\",\n", - " ax=ax,\n", - " color='green',\n", - " linestyle='--'\n", - ")\n", - "\n", - "sns.kdeplot(\n", - " estimates['monte_carlo_eif-double_ml'], \n", - " label=\"Monte Carlo EIF (DoubleML)\",\n", - " ax=ax,\n", - " color='green'\n", - ")\n", + "# # DoubleML\n", + "# sns.kdeplot(\n", + "# estimates['analytic_eif-double_ml'], \n", + "# label=\"Analytic EIF (DoubleML)\",\n", + "# ax=ax,\n", + "# color='green',\n", + "# linestyle='--'\n", + "# )\n", + "\n", + "# sns.kdeplot(\n", + "# estimates['monte_carlo_eif-double_ml'], \n", + "# label=\"Monte Carlo EIF (DoubleML)\",\n", + "# ax=ax,\n", + "# color='green'\n", + "# )\n", "\n", "# Plug-in MLE\n", "sns.kdeplot(\n", @@ -646,7 +541,7 @@ " color='brown'\n", ")\n", "\n", - "ax.axvline(0, color=\"black\", label=\"True ATE\", linestyle=\"solid\")\n", + "ax.axvline(oracle_objective, color=\"black\", label=\"Oracle\", linestyle=\"solid\")\n", "ax.set_yticks([])\n", "sns.despine()\n", "ax.set_xlabel(\"ATE Estimate\", fontsize=18)\n", @@ -656,130 +551,7 @@ "\n", "plt.tight_layout()\n", "\n", - "plt.savefig('figures/causal_glm_performance_vs_estimator.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Double ML\n", - "plt.scatter(\n", - " estimates['monte_carlo_eif-double_ml'],\n", - " estimates['analytic_eif-double_ml'],\n", - " color='green',\n", - ")\n", - "\n", - "# Plot y=x line for min and max values\n", - "min_val = min(\n", - " min(estimates['monte_carlo_eif-double_ml']),\n", - " min(estimates['analytic_eif-double_ml'])\n", - ")\n", - "max_val = max(\n", - " max(estimates['monte_carlo_eif-double_ml']),\n", - " max(estimates['analytic_eif-double_ml'])\n", - ")\n", - "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", - "plt.xlabel(\"Monte Carlo EIF (DoubleML)\", fontsize=18)\n", - "plt.ylabel(\"Analytic EIF (DoubleML)\", fontsize=18)\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "plt.savefig('./figures/double_convergence_causal_glm.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TMLE\n", - "plt.scatter(\n", - " estimates['monte_carlo_eif-tmle'],\n", - " estimates['analytic_eif-tmle'],\n", - " color='blue',\n", - ")\n", - "\n", - "# Plot y=x line for min and max values\n", - "min_val = min(\n", - " min(estimates['monte_carlo_eif-tmle']),\n", - " min(estimates['analytic_eif-tmle'])\n", - ")\n", - "max_val = max(\n", - " max(estimates['monte_carlo_eif-tmle']),\n", - " max(estimates['analytic_eif-tmle'])\n", - ")\n", - "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", - "plt.xlabel(\"Monte Carlo EIF (TMLE)\", fontsize=18)\n", - "plt.ylabel(\"Analytic EIF (TMLE)\", fontsize=18)\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "plt.savefig('./figures/tmle_convergence_causal_glm.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# One-step\n", - "plt.scatter(\n", - " estimates['monte_carlo_eif-one_step'],\n", - " estimates['analytic_eif-one_step'],\n", - " color='red',\n", - ")\n", - "\n", - "# Plot y=x line for min and max values\n", - "min_val = min(\n", - " min(estimates['monte_carlo_eif-one_step']),\n", - " min(estimates['analytic_eif-one_step'])\n", - ")\n", - "max_val = max(\n", - " max(estimates['monte_carlo_eif-one_step']),\n", - " max(estimates['analytic_eif-one_step'])\n", - ")\n", - "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", - "plt.xlabel(\"Monte Carlo EIF (One-Step)\", fontsize=18)\n", - "plt.ylabel(\"Analytic EIF (One-Step)\", fontsize=18)\n", - "sns.despine()\n", - "plt.tight_layout()\n", - "plt.savefig('./figures/one_step_convergence_causal_glm.png')" + "plt.savefig('figures/markowitz_optimal.png')" ] }, { diff --git a/docs/examples/robust_paper/results/opt_markowitz.json b/docs/examples/robust_paper/results/opt_markowitz.json new file mode 100644 index 00000000..91f48c19 --- /dev/null +++ b/docs/examples/robust_paper/results/opt_markowitz.json @@ -0,0 +1,16 @@ +{ + "monte_carlo_eif-one_step": [ + 0.0033553107641637325, + 0.0001748679205775261, + 0.0005432892357930541, + 0.002301593543961644, + 0.0011094644432887435 + ], + "plug-in-mle-from-model": [ + 0.001417180523276329, + 0.00015472801169380546, + 0.0003509732778184116, + 0.0003669464203994721, + 0.0007656660163775086 + ] +} \ No newline at end of file From 4b01d346191c4d2ba4cfa0f58594a62c1d77a576 Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Mon, 29 Jan 2024 20:08:38 -0500 Subject: [PATCH 21/26] nits --- .../notebooks/figures/markowitz_optimal.png | Bin 32476 -> 34529 bytes .../notebooks/optimization_functional.ipynb | 6 +++--- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png b/docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png index 2ccaebd5b9966a0187b73452b53ffd4d8a62aa72..9faf112b4919b47a0ae16347291dcd4cc2c36a29 100644 GIT binary patch literal 34529 zcma%jcR1F4`1Wn2jL23Qb=%ow3l*73$lgNPdt}|n77-z{B9%>6Wbd64vUg?g&3k=4 zzvF%1|K9gGj^}xfxbN@xGp^5do!5DupTbmC<Sr6iAwZ!}7Zv1X)KDmF3<`xcc>xc8 zBKUoD7XBydBCGA9Zg1h@ZsKH)x^Lp*U}NuM^VIa3o4J$oQ+qoBZeebI&TCIxTpXOm zczA68_XoJ`oh*6S4d(pdA@~mRI?gB*i3##A)<>!Arzljgih|5-4Ud%NF?ap=?Zejf z!Kp&WNfhh5ca!6l&gPZHwDA|^bcB58%58tsmz4{BDSj)&dEYrMDeFVh{J4n26gKV0 zCD+rFid(D-8WhC4qHDgA^b2oCH^owL7N!OAqq}*Da7f`FcE0LdVrd!pC;T;;H2ffj z>>hqbXlSTB{(D?i_;Kd*huCarG+M2J4T}Q#f=L`I9DX0itB8_?-z(BEW8l|l=E(oY z*F7|_pU~hbC@9bbo46uRMALV^C###CeXLt%qu}IJJn%NOLdMC7Unu!va`%Vb%Fxh? zNV)$WuIBLn`LgvgF%*nZe|UKKv4w>$U2v#A3j+hgl3SJ1>RXskW+E?9AfsH=%f!Sd zFbFm2`uci}eErMG-Se@F956w47TR{BCr{Y?{r$NQCoho>@(~5b#ZgSp%wT;~%L-Ro zK~^P`rmd&Pjm3}hhDM<nEh#$&1CaqA!M}LXRl&WG#XmHZ0w;>k4<14#heik0ua_)+ zUav;x5KiiP7*0Wwf1;!Le=lo!f1aN3yLa#QrXzSZMo*VYddVm$oA=gd2)An@sJKiA z^9|0`sJOV?jFvhPNfmQ_r|^np>(BQ<_X(eb%*;$|qbj{}yK~RRrB3!3A3l7z;G_KJ zCO!t|Jn<!XOh-J2vyV?rYZRS0x5vckVeRQm`Gjw0sqJ{RClS3w;%hFw{%TL>gYCsu zY9sI4_wKcrOVuisy8KlOCc1nFUjO9x7j~b+ow-*JB-;m;-^9i?3>x_veB3?$ms3C^ z?q>7Wa8vCm83aWI9W1u*@wV1V#Y~j`NJ(+AcA@d@x9-t&5*{zTR=*{*zhYI-R!Qxw z=?OST=&|)(u5#zM^<`l@T3AZs!=2^o^_HugdS#LDIdTdLjnEjQH1p2abCusTKB-Yl z*dOh!-?Se;N?D%kh!<ZKbzbP1+xq)M-)DQE$F^5ulgN3#YiitUb#8wly>(<H`B9E0 zpWUd)d~bU1Lb~6|#_A6vzvE_~-Lck25~-y~ljYx~)d&A_mWE40w2XWxF}+f!DGYu` zF(Y<Wd3`HBBevz=vmY>M78u^o(aa-Q{+e(HIT{tIZp*)mV0EHsMeo2sQb)9MwQn06 zXE?upY{EzMzHU%nUfvjXXVj^aOJaZaRnM}CiHXd;d;K|DJRek2J8tccs!w`RYRJpC znWt)%*$gX*35$y-?~Z##XQ%uAuC$%+O6p4F3xq{jQ1rF>m8$?-_jj;>p`i9ejHhOA z+Ppc@=*NfqGaVcSvpwRgE_Q?@B&=LqFJSj#_{>^VKPV^N@w7imC@&Yj8%#7c=03r- z^{e?vjk-`CKPEo*&NI_@O1?>9R|SaVRmxTzlUB=F?!QqjwR!|Am2Q0q%b;9f_^szD zmVUV%-Q5u4EZBoDPk#i%vmK_J@E1<(#=i1B?Pt0dMXRtm-~IH*hl^&dQOq115fE_> z1CO+@cwPReT2fmpD-$u^i_GxcoOf9oijiaRiF^M1`DTlRWe;tCe}Bbl&0){5*86)G zYYzT#*i}s7p`4tY5=9)*x1McfJbZue%aiW`RV!a3jt_Umd&9!RO-CynU6y~xLm(Hs zuZ}zCdlfC%mYyS|a@d$v_+4(lIx6J3@mS3DUz}y9R{V`eG(t8s0+v0q5F?m<pF~L? zF^GHd`)_W(h2eUy)t|5I(fjg*6bFwe1I`l?M%Azh)XR!x!!|N<a^%$13D#b<zQ>8; z?%Z&eBN?R9NF?_#f#*q()9H_M9+k*vhWG8+_Std#HxN-!a}b`ALOVIJvY}IA;kaBn z*q^CDZuj*s%dcFWSBsLjymlUseRZ`Nt>8<a=x)fuRn1K7>}fOZwl3g%ZzgBq@~4gm z2hYLg2TM+4>^ozcz9PoB8|GuM(LCb#O9MGt`MA7{jDbOf)cx5H()C+yM=N5k-wjqR zw=<|YJ@Fd%JM|H=8LCTlA6Ia5E8DEto$yQN$gc<(F0mA_`pJd_-)N=tC$%gBsiQR< zV>d@)2Fa9)3K6?++nNoHjY+UV<Sfe9*xAEw2ja!V#>QG!Ckxwu(9F|aSSGd|_#9ty zZ)UdrRnpC;Wbfso=OGKs!AW|&I{vL1&T(bW{(Q1M4V-2M?=>tz>tEHm3$|IvU~7E5 zynTGI<du}nM#^lz*bHw}FMP2cjEamT5=!0n`nNr3l<jwV(&HF7(;96#@c9wy2#&DC zR^Q!Gmml}LQpFP%(!8RKeD{M|;d}-ZUA!t330wFbl4kJ5t1aglyz8V+j{>8kNuWY` zczFqU?>PjMFsQ)k)G4#M{OGfW$?q>uk>nz=R)^D-EF2v||19)@)Nx$-_%n{dZ6mK` z!If{jh88_33Xl{oOL~hG6&3XyQ*vl#LL45Q9IhrD`EU_I1`p?BWDS{nAW^@=uHL5d z;QTemR${7gU-8y*n<5c0b~1W>st;NPhPg(y<HL70VTB&ncza&C`Q+td1(IGqeq>AY zoWBg!@5x9R*J|}9v0j-?yWp=pm<|!0SX^NtFCH<iJj9fCl^d5^RqDoECyoWRrK;+s z3ExAmcn+=kzuD<5Xmr567gyFH*Euc?6*=oN)2XCMC?xTlZ)}fR{rp(JSWv41HRF?J z-W8b7d&}Om1?3MFCkKm9A@sW62#POU+4mXud<qpHEqar=oH&Z=dkI5{Wv}Gw`c)yD zYyABE``eIzp!!WtPHv{n$Y(?|NUe<iDln4DuRVFz(9lo;M}=Me1CjM${yF1@=lIA@ zje2i(|NQtc5Dw@@10fHu--)+ngn^;%mnZd5xa8p|g@%QhKo%#u%>O*!psLtm>JBO( zAm9QKQIlOcIT=~NnBBtrXvyiC!<F|B(j}h<1$}=nM?&wrX9BUSc#t$vf`XDKJZR{} z1v_26)vpxCp%t}Sdn&0#<huCF{P<vd;l=m(n|P|3G$W$6SBeO!Cg*1Eh6qn&nr_Z> z`c30cYvxY&o*qX_mRQcr;NkQ)%i0KOf2H8wc)OzJ@1KzRT*&HYDC`1K#e#|HI$+bh zRTdfQU`l`b-rmi(-TpW5-sNw<%|uOVPpUZ1C4fweiTznh>E&%Jzw-2kE1efa$?5xl z{0IvRYu`+9n(J_cFM8i9hoo6#(uju~(w(K@#<W{rL#5W#kfu6Z^*ucHJPTqb7E%)+ zZ_HI5o0@Sz&R2pY?`&Ju*C8MmP2XKAB_cwT-0fC>`8$+QYzLM(hp9ok#b0@lPGbK4 z$QIuf5B}QJ)Wqw)qR*m|@@I6g+}>zmuL1Jm+D0djc(lP+7j~$=bM@yLTzV2EN7iRr zMV)3ZAVCN#SmW%aqC))l?_XLG2b7eQ6beqv%;@UO%-#3*Ui2uebtdo>0QMo!`n^75 z<FR@R^#-7biKWG)!Tz{cSMB<}SW~lMen)gSy=PMcTl1Wa(Ap%^#P^F5Px`L;o*uhf zYAp}<2<H#*5_x-jrx=NUy}s#e2XC|;DVwWYNE5d-`rDtiDUl7u0y&_N2o@9#X$^h0 zA1TJ)Xl{#Pgi88gZ>^E!??kN>1oh0$i2VX2I7^7(%AWPz-QG<@>hR;*UU433MNXbf zwJgO1m;7#v&H=jRg$22pmKQpuRs*5})pVNq`e6kHJTI;YI@wkAStq=D)o9>8CVP5v z+$+k$#Ps~b{bZz6FO<O}APF{zuU0qK`Qv;ts+BZmNrJf3cOP>_>JXI0)K@66mi-r( z1y}|v?uFcV^jTzZZF{FD)zsY5a%OJM^}DUCtU(>R`Dau?EsFqqnA7&6`tE#k>WGh0 z!iQ$^2dGQ8Jo(uzeRaYr-Wz^<R^+y<YtfZ>TU&cbl<T&!3fbN&>hRB>Cy<IaBqVuG zQ#I5e*PEJ}0vIKjnwokZ5~5yaqhshf7r*(ddT}6!`u6SH%89&FDL#CBeBOu4<vp2a zhtu!$SmN|(ljzFLP>b$2MNmDJ-ef+a=^YukbN6m<QXD^IOe!wj*<3!4vb{ZznxhRR zz=H{^D_dV(7mF=>=@%9$=;?c6809=>UI@#psIZ8Lhz!;cP~MnaUF}T~b>3U4TJ5Pc zggoU|H8$+zx#RWzP7pyHk5Sst(2)M%Xt6orYU+OA$kY;F<mJnkZH9}3Aa&1+%>&4N z9vnQs=t(66V82;aQ;|Tt=9mf-%y@5dJ3Xi=$Mx?W`3S1n#}>tn@5pe``wwe<eV|x* z!sIrWPR;?+6d$Vb5$E}~MFw-u)vt_grT2=&yLhD!GK}|FYthO-IJ_c02RD&q4|Q|C zsFj{kOzaJ;E0S4+Y=6gDD~}9!B=Gb)_ugv#`AOY#qn&lQ!jbWCcXa_E*))KxOyA>! zQQyO5%&^)PWc4EJL2kFz@$RbCnofYS+@CHu-A;4AbLUPPiQKNW$<zK!e7zHa@d_vf zL{O}}Rw_dtNbWyROr$Y2GfNV*rV^g;765!|4mfeYrZtq5aSw{%{$wD@P>Cf8?3S=u zOXL&)mjx~zh@)n+=vyt2`C5?yw!FA1>M)6d1GxZ&DI1Q**3vLJ@5EKs2WgLc({4RB zH|H~Lx&SD!0p>DX>nBxvymJ*2nZ(J-Nu}<M39r>#R6K@Vusj|xQHA^W=RPFcbx^iK zBk%+OOHI1934#^*zMKvkwTpPhs+pK*td1Y291A7Ccve24((t=<>FC7k%9t0=c#yu< z{mjBG06&o76UV=KEmYP5AoSiDwnV_x*5Y6^9HZ~wyxi%1w!TNW1&JXU8?e(x%T@bA z`w7W3--D+JFd4D0?dB>Ug$j@@8$qR<CL!9pVh~c}bEsD7WDfYW>GXKw)bZa>_Ro)U zTHrWV!oH`NEscB3Tr~7vmqn^31mFE+p}8Px!%2V-3y`ig@^rV~@lnrux-Sj!^)5pO zY!@`l&-m)T+9S{_ao57aB0uq7H2r<ZqEI{uUa{Oq1HO7AXwC1jW{gKdpJJJ?x(}=S z3Td>$$XWSK8gK&w1ONWYyBd9KXXspS6<`W{eEg}Lf*RANP*NsVR`nuN+5E5nZXg4* z+&vT=)Nh0=)$2NFfJ7DoEqr}_m6P~`poqN)r{r+@abI}7_Vk!%aNE9ej>^#Q*p--0 zOa@Xvj7Ty6R}Eys5{Nd-`oHfi1SCGPX{)J`ap{#khB8i_r^!7X7W?W|>gqDIft~r! zPY>6on)37&Ud1pzby@x9Ne{p4PBHu@FX{1e2S5QJ&2LK;&Ci}beTuX_x}{bI4x@?k z4?DWLGNH&sUJ>jVey2Gq`cDx6h3KHn%hNXi_G(X0#3#Hr@24Fa91G-e8^@Tk%(anD zpX{H<yzx0q+ZExdO4S`SIA6Dt?t3G4NG9{~`ue)bY+H=?{#=4)$h0OuKmQ)&Yfr%3 zToJeNGvxWm4-Z$3X`bJ-SNUW$!0nOCh!v?<6!4QU*Wnu4sq$cV^}%qld9R*S5z_&H z%0E9p{Wzz0!Qj3bAh4vs2x=VFJiYP=dcCjrF5b9l*Hv^sS!Au1;V0eAw&w3)@(LP> zGX!G>P^yO^fX+obD&rN$>cUrJx@I3w_i{#S<S8Y1r>Y-!UHMpKd1E3XRDL}hz&MTQ z#_j}U8nw^&E_VF<9ADtv(9=a0b-2L5Pf&^b9C|-iBuB}QCN+G1B<3QLUAVj-_vujt zf2ozpV{^yH%>-J(tZ1|y^ojC()AA?|qv^Wo4$EKUw~hvlg6Lnt0~nA8$XMOMW;HXd zvtZl`=ivT3dc%?#L$7f~Lq>-C6`6F5DcX123KhV}xK2o1lj+P+Y88}Fz{4Iw;{0rN zyn#eTMrJhp>|-X!&ro;tO25uJf5XA~H1vgi%^uBe8V^W=l#+7J`)7_;G$eIih-J;x zq`4IlJAhu?FU$cJ%gD+)PS*K%)&$RZa%vY|>pgZK_hbd?;An3q8io7^K4Gx-=Jc38 z_4vtn^}c!Q4d#%4sBj8$xxqY~4AT;e1Xo|k%S{EkvPZv5e^#!y+=*14?Y{^e#Z+Sm zjxh}$0HEs49`R8K{UZP%`??(EywJX<K-n~KIG#9KTVJ0-T8KB+`R%30O=0A@5&~|! zYN^M!#F7&de!I<1Z*h~d;}xr^iICD=lx{O;!Mx?t!+u$4ao;3}*=ZOX2M3Pcb*R{V zeX4M7J*R@Gp~XPR4US?FXgJ`v^>*ddf5ZSTn0<dIo0*-x@Vmyi9{Y{KeDBim#QPEk z;2qdhbJh5P++xRzvK<bHNxd(frHHHf$w_DM<52EpAs(Js5_+b&wwOCns+p73-?~*e zjEp9ncjg9c4<>9seflJ^JL-&d$FS0CKh+<s|9KzH|7`2e=J;l<K^=5^W{^@NX+>k_ zyOZHiUu^+gr>XhtoY`uB7NMx9D5t^Kn}a*)ekVzgJdO6kA}TP*-cIh#ccV0HMnxvJ zPft2o*+Kz1;9M%utTS!oC4wd4^rHBcy8*o$lKfyL{*G2|nj6f7v8GJ|O)w2T{~Xk& z8K@wDrdDGMHlb8FH&bfydhOUi>(C6jC`sIX<y<fRg$o%<3EapI_1HoRFo(s0jJFwU zc8Q(s#o4mhkKjpH;$0+gNqtGhB~oKfxNiMRwd6s1!-e~tk7@8~FVc5?7a)J0d#S%# zbSzuBa#l(i0B9R<F2vHBnp9Bkfhb&qX2sywUhUA3HiE4s50?a>Whbdiwy$abU1|;C zK_~9^7@#r=P(Pn}2gR>^110DfxIGMkjGz&)AcE)Y9USmOQ=waK=QBJ**n2{sY75_x zI9h8=aJ|ni3e+S84l6DGWD-?%C*|*A=8<gW?;>@kvrc-?^V~MOg#{kBU8+m@d5M-* z8PF!r;WLCbYZJWttG72AYK%zj%8zW45Mnwf-(zUA<~{R()Y3Of;5K;E)zuZz(pTwh zNz`ZzU)6UVcyx5M**ogccrjs1qQ`A#_m7QvM_lESwPHIkjZKZ_Ss%o#cjI}Cv1$6> zokOv@FQaeqt;a6nhkF$b+qk6j2QB^7=rk=nkE^dq1{A0cU>ZV~)up8+zZnsMFh*9^ zh3Zy6sLz!x8_>XpLC>4wtZ!&`Y4gXx^6KgX=qD1T{3PMWk2_y;DW{06zgtLsVB#H} zIje@vHa$h=zEs!NUSE6X9QK_%1TI{s1e9#*IaJv@OH+S>FKSeE6=_aO+6Y}8_xINf znR1eplteKxGe7*S!H(KVd-PM{#tnkx?iK)ne}8@=NBs|E1Lg}$HBVPCp>-G#a+=39 zKAgM1P-y*i1!V~kdMtz^cf}}V<Hc2Ht6usj`{8%EsuK4~EV>JaZ4mwh*owlZKUkpA z{yWM*MRgCh*5hwhg2#F@HJnzH>83DRQKwA#s`i5F4MkXLXe_IN6s4BfLGX*Eo`wbm zbWGUSn3;osN|Na1(#lV9{`E!PaBJn#x<f0WC{}J?;dL*|-ua=Vq-J#5HBQb*YC{iy zsNEag!V`VP=1NHPlRuD{l9<m*Ff@hs84f2|Bk!M)a-QyFVfQk6s&ml(ASE2&p72+< z<!AL~a*;HSJ!L==#$(~&q1F&|A&QS{e6aQRrrl`8B$iT<xq!rzx!uf6CfLe$01zqn zkI>Bst-bjzH7-s@RkcsHH_7kEecFlQps3aAyD#W(5zyXkIM_aN8?oKf%9THDj=1GU zc3a`q>(?#N8-9G0Lj`b6Gi1gMdPyB<-JntG-rh(0;ryx<v6b3WU&8m~q6s;E9|mB* z>z()o6h<q_F;3h6C_4l@Wo1oDMij)K#r>=h!RFB7j#~`ZZXX;>Ko$eq)J?zBUB97a zw+|1}d*0p+(Fl2<$V3@HwQdaYah;VBXEo!RVo(q~=o=0;n_f1taXv3vjdsXJNOFIc zTqQ-MKI3-aA85n?IL&PKN=aqsM#w8<P)U=L%+sD~70{R6(MQ$Xd4G?|ZlY(sI~l*~ z-{(73`JwUu1`GVg`JB<rFQ6Pt47D3np)Ta>m8%x$)tKBx-Nyy;{sWw1kHS}ce5g3C zS?*kd7gw+xygOgt(d&E!g)anT1`;=`T{~3A&wPWAgIs#$+=h~pk5eQRHvDmdp4pO; z7NsGdiBC;a*{Bbco+?NEhEfm}i95NMB&Z=Plewn~>-AP;c`PF4Rp5gpepr3&KZEXZ z@uH-327d;rNJ*{Pu_)G6d)EC*t*bJ%ogFkNP^@M4JX_Wgp`oS7@Yu9ooH{#vL9yIG zGy}J@AIV{B56zs?(wd>vqiC<+;``3Ld#Uo}(vnTh{`^&h-Fzc#uL4AJ1+Zkmw0-Tg z%ywV=^#Z!^sx5l7rrPx<q$IBW&0Lt8EG2S{yesYJNn$I^&d2MPyS4ksY{VudwIA<J z^aA?#ob*5cXQ+q~R1T;faR`tE;y)0cWL7?sNBpRX5gEU<sE$s`c*%L*R@7I;%8zVN z#%7y#mK9N1^7m6jVt`E)@Hud?O5diB4h_u!_5@*YKsN}3x-rtgCM%Kf6(cL5+V77u z``FxMcbD~})uV*5Rz6ah8B`YA-0UnbBxyV%nwK2=&QJO>u%MLRQvaZQT}bFPuv}08 z<ROt({QI0Euc&AWY{n?`F8YqmlsAE3$${gQVm|qr+n@si3DFlo)If?Bv$(i;Zz81r zPTTUadw|FPK$YaFUc(0Ah}-i7Eqj{_gQjm2#mdyoyJLW}SKINFrLa}tOr+^tVW6S$ zXfOG>yEJ^))D+F7`$c4x52^idPG@IlQM}Ic$_Ul0rmkKzvP=X>1(5a{yw#+9WPd7z z!Q*&)@abSa-S*<3B^2&aPzpvJTj{DdIyh)SzyR)%*52M8Sns4o!|&g}BflGk#-JK% zC+)3gal+#sfdG93Y=7TO=xU&L6a=V8-!>qCp`+!(Ao-t_gOJDmxMPhe*rcz>Ozt(0 zk(~~ZelAEsNr+=gmGC6P0AG~4OVf&=ejJCzymGZ{6}p8oIHFD<N03rb;MSd<>`Eck zv&?=%1^6zcZ-K7I9C$a(aOn{UNKObPxFQGyy%-S6-*(3drJ+|q`sKgl-^76hAhG@Z zrG3|#+>!dgmV4S?Wp#o?I_Zs|!LxL=ft(pS8hPl!vv*dQ_qxvKdJsRbNp)g!hrU^I zl`o2^$a{YS1+;@Wls2O@J^OF*KzJ3qE^6+qPCVF33Yd?gYJ@@#B_TwAa0#+i+V1bK zt{kB4ya#4yVJRXk3<O1b1t=BgotQ9LRzDs7`q&s6;>z7~ayfoRR&K9_{l^6A5@3?; z3hv1(f>xKv$N+BVRLsAz-jJ9Fqy&(!$<x-)&u^jT`Ey+8^kw!p=8&EYUYzMT)8eu+ zI_w#_%lyS+VRp6;STIY%$zfXjkD=M{>S=#keB-CotQb_i>mu?>m6hLf8(s=iCBrs7 zcB&5`7AlW-M)rYes)Umow)Y^fGgaK3-})CPLJ?PQ(BJ@W(kCM)--nJJ`W!6d!z9Pt zr~`jshHM~`2R)5^wkZ*9Dultet@In9@ulCT99kS^Acxr3?6X8mY+T5K>FvFuVmEzr zj&s_iSWR8wMFdVb3l^u>hk@RrtrYo(G|(v`hoWr6ZU*RFi-{VarIGSz0C#Wd1JC~` zv(>v7NfWbB36wC4pkVy|=7RH|zU$D!w=hOaU`yY-hlc{1ih6OdU;)yGC2;hdI>pbU zB@aTm4ZhA^?t~(M0A_@cg;<w3`15{gtSaFRH+}H+=^Iv%KX?_+6Qd^eJ`nlV*K^mV z3t(vwW>8$`;o(6TOdye(0rYz8j=2Lz3>?wMkXf{2<3-_=a_dT<n-P8o$xBcWk$(M! z@OT?=j6FcDRm{EC`v_DTgYVvTAmwI$Jdk3(c{2`)@Cl#YROfH-==EXC^mPENKWhE_ z0NWyG@`1!UZcR;1{)uL2#q(~5?Pp>^Y@k!Xe2Xo*uK)*P?ka)~;uaMh-XB*{RO~2O z<aV37^ICH6+T2yacMD&ha!0cZUiVKj_-eelG5uj6r|59M@+i&eF9=Zx<pSJbhakO* zjKnLXpdy<R?sUX}@oNs>d(8!fVun+S5sgl}k3bxS8`HRb7A2xp{^WwhAO~wB1;r%% zGj7*#M_N+QCr41kn*)&|r2f;OE+gz3{P6|y$5^OI9WjikILREAq#RK_6ubzn#tF^G z`ZZW>jLD?>X9{0xZPwC4QC-w4Q?qtOC6405j;zg$24b`m)_=0`^z9z{Q>{4b>I1Eh z#=mH95~(Z?t3Te2KXT6XFqtd1N2__zN_9r1FE@O+(}$+d{Ah_17qP%@Eimkcddp^1 z>l-s}#N7jgJHTLXZwUua>$ULEZK6g5RRQ@21P7!??#gIn$WCHHR2U$DWPMkQNlANf zRY%pKeZ)#p$O!ewpPV#a=>5WRHhw5q*s7m(OVPFE7T8r%$=&w=s9m^tky$|C$3F*| zS^oXKy^7qqUg-Wq&pYPg=zo5;4WAXj`l)POZdpEJa58lr*7udPjKp&j8YTuYTxr#T z&$7OL3(Au?=i3zTb8Q?#pFV{g;p_cTbd2I$7j1;Fb2QjD4i6J}>|dwx5#`}?qNk^j zYJ5R%(a1PUamdGqE3CefrLYAnJk;YRl8_ttPI%e^@UIa^QQrOu8vOL|+2=W6k>WlX zT$OIlkd={0L8d+>kIJ*b;snr~0mOIH52UDGQn2g8*4|Mjocw!y*#0n8l$10YWW1)k z6^%4?7{&NC;tO?c9ZoGstjHlV<#lqB7Zo5J3P<-53nh1p60c>l1cZiqa3W*5CMcPf zlFMo1CXV&??dz6l+VdZgH<=-CLPf3n`9JzRIcK{}5cTE)8a=&_3>4+ghBcF+#tYAX zfIQzJ1g6r1>#%D_)L1BTg_N|C7E~$;jM?RijL(m_qHq-lKl*ofMs#H|^+(Z{f?a{M z649|Fq#+P7UQ8G_Bcp(xE}364pk(xB!c=gW7$k|LRp&aW<V^d|e)<7^+I>0rc1IE} zZ=oV(#CyK@M_gbQi9Y*9R=UV-w`XD~|CN61k4bgl>p%`kvUB9=W`Q<oRY5`X_e2dM zU74m5n3kh6Olzh8;QT2Ih8=tuNhvmtH_>RPPcXL%(bm1%Yr>aL&red*MjAn#Nf|Hi zoqi%CGpqLBSGkSdlTg!V2P#bod5Mx+Bb9WY2#n>y*;p!PXrxt<q5bzqQ7tUTaw_Tg z08-L*ZJ6|^wu%MIr}cHr;R`GmCQSD33NtH5=YMa2Aa*okUtc$-@z~#+^t1StS9RgD z+PSK92GOJ>CI*QokYCVF_{cbm;!0XhhpR^yTL<RnWdFVq#B#-n2=%zKdmuv^qaQow za+Dr;7H}-E$#z}C*nje&B~7aPd=A1*2YCXYI~Uf>BpY6yE~JVyS(wUI=Rq=FVoT~k zA)-cPKuop&eQp(-n~t7X=<4Ncl^EaY=L1t<&5=Y_!))a%QA(h@H0t)F9PEF1RHW-n z$P6_s)X0*uAK<Kr<6Zi~QWzS_igbe?VrQmNYh3Dh=^57|-iu1fvD0+p!dH-38P)ig zgnw;C?4<W??)!79rQlML2!{9<o@2va?is5^nWNDtS1gzy88V$41Kmn>A|fp)76vgH zO{>q%-(f_)$m*#_@nKfdPzMP-(MAruC_oUUHvvqyBb1k2$3tAPy6!5>RIZ@EnJH%! zFajHQku6kIl1E)DRAY`{tgPTai}Q!b%s)}cVbff(a%7^;J_~kajgmW5lyI)q;pn0| zt&pk7A-SLhdV}VXiHU!_+#TPsJ)TWSIu#dlM%@E04RJDE-F&|8XWXMvc6j*eRgyw~ zeV}Ft98PMaM}Kk7qi#<Mm7G9rZuJZICb_mu6Zu&fr1fK8u#6A*u53(7S`Nfu(+kfA z+)L*z*=djt#nB5{wM5o9jH5#rt|mJTDe3NiAA$<`p3vj}9K}m#`~ON8r4$lkwz+`@ z0qDznxjbSy7@RaRIKnNAD~4{VrUv&>0dbuIvL}w#$kXEzqDgAZJ9X(=nAyEFg<-N4 zJZG!Vdy^u$+rpJ5K_<&sMn(duGQxB0vKhVe@+d)M2H%k2P{+D{T{Fa?{U_9y>C4D^ zvOmy0*Hr%<JMjv`0}HiSWY^mE&vxyEehB-I-q$ZBb82I7dRNX4)<)(PjOCA52lz9R zW|B8{f8weN_{++SBF&Ys>Flh?DZ?LsgF1&_%Epv|Fl;?!*zyNq<UOb7HFBSrFeMb& z$l#u3&QxSbjBvJkPQCW`v}xMs2=n!*F_}Z!0DQQRt=a&Zpy@?sT0H964InDkIJAg~ z?#@6Yj>aLci+%Ib*!4otJDXH76w}J+Q%5E=I+hw)k4%v-sD@hnNhoap#njn@C=zga zwPDzydg>4%_te6bsMnIhuE7XpMuA|32Wl$oqUf@WV@c@H0;0ob8Q|=|#Z32O|DmQm zT76UZ`8=Ffq_wLE1J;=TRctQt`#urYfG0@kjUb`N65jb=RJ_2UAaxW+7C=T(KV{%L z#SJ9oI2xV(#8C+qc(yaP$TE;O=M(OADNp{~^WaxW`+{U_(PU&u4@Y%TK8o>ZNU+jp zGe;1^*-xi(9Wgh~F`}n6&ju66D3?q8w(ndHMI=lRIo&Mb&V<JA;Nzs_)3La`ZxeGZ zuBmCjF_0H`>-xt_VBqkW2^A*pW=s=|?0aaS5?LLHKTfOPc8h@x<rfeza3qW|WnTXC zFfxVKW_(;TMD1h8JsIQ89TxC=m<~5{p{GCTZ#;dKn%X_x9HCSE7#~R7Ec=NXA{t%{ za70zpZfN>mJ{*w@Q`rbcc6~P~Dd5kW+f&6AMr%?t1WE#AWtw!k!BGT$8<JaFmq5E@ zVqxL`uY;5Z=@n^XSXELSnr&qzUNd455nU`5iEYrzH@t;Xk&)A*4GRqwsbInad5<4t zC7#WNUP}<D5j_(gQfNCO1R5wMNZn0Hdj#s1vWiL+$DK>-Y*F~vPOe|4l2hC3kd?)9 zFg<jOU<9OoK7mBU3eY+t7=6l4n+El25~My;=p=v-$AH=51&Et{;CDc@h^C}?92vzc zrC@!Oe|V#qpBJDgA`2m!7nnqNpKU$C#>N(LTcUxMb^5n;!5qMLv#Ew4S<2YZ5r-%& z&R~4wwIr&#Oh66FDrLyF`=g&=NsNFJhS-Y;sW^?n8q-sJ2}FyTKi}Wy>XZbRS`YR^ zpNQaXK*16G5~Q6XUokl0Ce(#~`y05=gJa<^Pe;*#iel#~4<5|4PVzcH+`cFKmc9pn zS)q+4#F)_Z4A%>fI^e*A8Np^Y;dh#f7{y=%*Malhgnz5VBuL9uQ}j^vOL!_Tq8DUb zz97LQJP8w3dd)e7tjqdz^W(>lfz>xOG(@aBFz$X3^?+qhT>u^_zjFQxjInYtze>Bp z;VqdoJ1OAFQkrNi+>)z%ed&I&9}yUASI>-Z3|xSMva*ZYdN9~K0O13NAPtz3$HNYK zb*#3I4ky_sXgk)KS%u;sKm4bM&k>!K1v~v%RGNq*HuQbS+YrGoS;%(7$L`rPG2t;6 zazxKZzSzol(#s=4Q<a|H{JkRhJ%#dgPwMPr%Ng@>J=&O1`S$r(+%=F0GoYc3yYc8> zY;PV=ZD6Z=NBEA+@j<iC5rg!nD}unwzD#}7@z>{&&Fe#!k&I04R4hL)aFmEzV$|w; zwB9OlvNw}|;cgS7h&9UavBz`P&EgS+XZm>&k}fff))(Y1F!C`PwQ_40N|&By$~eEq zu(xnw93Z13C{R1U&Y<o*@G^*l#qtokWgvzQJbQu90NY)W^McC2;VZK4VQlSBvCT0R zuaWOmL}RmJ5W6I3IZf$Ln^C~5E=|;?TY|6X-NM!2+f{DM1XqGXTb;h3UMJ$NDU*6Y z!Els;@X+!nmxk4&_gdl`_t`aAD1(<wEiNiGhg0@?r6nX#gURjYlRgYso+xh;kw(XZ zJ4E%U3KPi4STwwxNhf~KN?*T-eiQQI3V}J51GJ=nN6HH}7oTna`(EK_Rs#m3hKg$W zB26`26ha(cCA|&dNjp-ERPWFreSZ8||MT?5br`JtiZAj-NIUe*6s>xshr$9CkEjlq zLM}iCnU9?n=<3eXd(=@TP_nzx=J~n#`9Yay=3s7~4%N@`ZVYbfO04ID0)T#f!vXtG zBG%|TS;C{9T+^3>xsws!WiGLY^>AYwkg>?d*$-X9%;=RpK||sTB1l1v18d{q>B(n5 zE@3tBd2ZB=y>`e}=qd;STu<20^x2tE)-OawHg&d!M80BGkaR%0AI%UZv>KYSgQhMe zCB?bya&XM5oZ&)594>R#g)f0)gM*dmwjwS)1a@T3-f$o^cA=Lw*!<0cUT})Tu(NYv zP?T2$r=gRiov2P)OS^?YfJ_;uc1~l`_VhS1HSBpCXj8HK5V%cP`Iu@gC^IVys21=; zIz1WqObIq+S-ODG5w@oWb;f0=08vny#jo;IjZKGw1)DyB8RO{a=nXLmf_FUdcULjs zGyoGWF&P;d>cWK!#y|;et&EL4G6}6HjTOSj%}|!X1?K;8R;Gi~AU9-29lF?1`LLN- z?nz*toI#W30~!lBf`!o&a3hNi5)l&+prJp6*dQe*p9$xc8GRG;>h8ZJsbEDl>_6t^ z>TeauQBih7#t5Egeg!24aYKSYh4|5c<U<XW+cQ}I$^}JkbGdw?_}RAAO3k5NadGh; zq#y&*;7~m!aW^7H4nn|VC)C=)RU)53(L~Ah9^gC#dwLW+5lnvMz`%i0BLkWYO1s*F z&zLIG?pN+*6qvd*K~>MyE^45JNeasC7C`Ur;Yq#kps(~uVpCiih(q)*2L)`Jt5=nP z5Bp^J?W^+$*y})z`C>mYUfJE+8uD)-N4zRXRxvhK1gq)obQ1UEkHei9x1$QAZpKM) zE1-V#_pfblyZWZa$2SABfqDYuNxqr7x_T*XT%3Fl{<U}xJz9nDn}oz4l$U>Fbg^V* zawnlE1H}QpQ6O>g>)>nh3JTZR*xH7V3FnokKoR?_@oD;ONd>St{UJa65|KuNTeb(% zB_uf!*MAiD-?piMbVN)QK*Ni<uS6azcNc(da2>>2D9rca-0PpMB9}k2anC&M`3m1} zS&lGl85lMQ82+`jQm<Gad;p`R%}^m$)mRU>PEgw4o(TfIWdWwkIFPx3zpO$Nk(SRt z!6IUt!k+|Ty-oCo9;9yBMi>hu=zUPV5Emg-K=3qgthX|#7Z~2ETSE7Abi4zu3o#v4 zvP6W&QA8<RV=g7Euvz{Mfv@DId!@C^rU)9)7_w&bQdVHYdk}x>LJcgs0I+M8ci7bW zX`sslf&@3NPT?8~v{z8Kr|VX0$!o1BuUoz#fp%ihNr7-u*8bmI*6ahtFVNNkhV+je z!M<?mSu-W7sld<{L5Iu{ojJW9K74RG*m_!AQo=_xQ{DO~Pj>*hWN@(+_tFHvIkf7~ zFfVFY4G>_Sms+>CcieD1R|MNNT|7~Sls#Zh!6tO@uJyN#P?_D>zt5^NqbdsNsOd|z zltK1M{Et(_n%DF6QqR2ua8s(^f36y0`egO=BpRGWM2!`HK*MWAf-#Sx7561s8tQj- z^@XRyWSBjH=4trKwtheM9xz=ouz8?skw~nepyIboOiWOglda1GaACfMh5ON^nUYrj zOX%J}&QphlRHS=RLjw{*yxEA81PFU@k$jz|LeqCw5*L!y`LPD%>byrdDA^`vgE~;C zq1R&DCl`&5tsxp6rkz2R#J&<O^DIGn$+!y;93V<`6q&W^J3fsuz5eKxbV~J-+l_P` z;*B*UKONc^P%|Nd3xU;ZYHi(wJ*%4DK-2SNJukpHGKwE5KR?;FF^61qP~(SjRfARc zqk1+mxCTuD00C&Im=;3Qvx_qZZjrO1m@&kUdYCY+LUv40;3M9H;B%tS|N8*s)(^ma zFO61$bw$p!e1u)Ej1xpjPkQ^3VeKzZVmR&q#D+BiXbGjDROHI;C5_S>=2nCAKUc<R zb&3M6+<K;sB(WkW@D5-K>^X&Eh$!u?xUhj*&#zCvPLa*Hdv|+WN-2J4S+vwkI3$5b z3!#8aVG)3|eWazYnW^m%b(*6;JS{(}1V-~=N!J@8rw4?Mym7P<p%pJ737s4tR!tY9 zoA3P7;lgYb^u6X<-`HlJTQgB<Zc;5%q06~TM#^3WwN6H+=Yb$<a~=rwEFoKzNCsa$ z8qGb|f9Bx^?|T&D3IpTNn=;!GY=@&g$Ck*;jeyJbJ-tZFGr1=>bH?+oqRyY}HBQSz z0u+WU?*;1Z*Twu9z%Rz_vCK^^ElwNCo*_`j`9RwMNYvI4L})-65US_Ijgw7P=d7{V ze~!1aK9hw>`)XvUJRbsnz!Na)xsZy`y<k{$Xr*&k1rPqh37A;btK4isZuI=~He{&C zEQF)slA{vPG$<>qE+yk9Y?N1o@g_6ALK~>x!i+zeVC35lye%Tp)E;dR0!1s6@9N(O zMsfj5;;|}sR=5!W!0Id^(}8XJ=X*J<7g!SMRt8nrSCH;X5!RiWQyT*p6?kfo_#vD< z=|RHs1T}bldmHhoAovPF9>Fp3LEW8%rzIzBv3{a0SoE;r(3PFVPZ~fidJzW<u+VC6 z?*tLjYk}n&YP)Pe%Yi)pR3d8Hs0eL1&i$!jcD>`*r@Mge+S=PDCu5n<4ijf36V{hu z8z{JQi$mbv2S|%lqY+1C)b}fO3zj9r7gmSKF+NhKXH-=)Qrgzm9KgrN^Sju6Z_UKi z)bs)giMuQff<3XAr12-u1?{d}teeVGY8O$z1-GjRqLs*`TW6yHcat{qZb-0z$t*kV z83CxyZWdk9@{S49Pq2RCz9b~b*Gm8~CH7UkY_1h{cqoNf0u!b&_~Lt*4yS&_%d7U) zF(8DeK-$MDeB4fk=s*WM%bifhK4}-B;kJk4*)Vi)b-6Iql;b(n8Fs*7XgRdAZGjDM z9Zng-vKM)7TEM-N=}~uR0kHwpU%!4GK@V|UdcTl_MYkYUr-<UYQLV)z6m?Z@G@LS% z{+1%f;(kOZ#g&9STryJ9dT`G-!Sx3Ooj@zmY*(%h)`2U+c3pual5N$Vn@@gzjLErk z$x$3)r?y|vO7*Ry8FZK6jkQLYwB~T7MO9o}Twa?YUc?C6F%s&5+sOM9+at_sceNMl z&d~8p^v|n8L<rph%}w*2x2zbjA><l<djpz>6D&MvFi^bX*wuqWLoXn=$RLqzMd&N0 zbp6O~SG<4jCO%VbgV(m{@?wEAKTEJ#ve3gK1@g1=Nkzbg1(uG-8?)`+kb6=ja%=)k zXcm~f-oAZ%ru)I&9S_gSTx#8>JcJL$0HZaIg+(PdbJ@Ey?)uUj>mhMV;wsA?4agsA z@&wF>ii(PePjoh~d>qZk2itrB024odEZBnjK0agwcL$K_P~P<QX<_%w!7K=lJ&Jo5 z(008Kad-=z%p(}rIZ2NXaprA|<p@8+6*A?wUr>^JlYvlhrJ%(EovR6ORA_j(*g^$z zbHZ-4jYR6G4$QaOHQu6-Z>M4D*&-2SMpoBJ4&@M7B^6M`mk#Hs(Rf6KVobo<YE4qr zfx&Ve<XXh+f#{)WK0Ct_ucS~$Mn+%=YzDQd2adLav@{Aa{&8sLvOR&=et<tYSLEsW zaJS_u*3f#l@B$Y%4;sC%HHqs0es;hlmX!%dR%2fUL1SVIHsQ7GNeKWgX(u)wWmpz^ z)vj)3p*75QMC7YUAX0fes-ceo)ZGlG1n3%9VtbdtPXIR_UR)KXg6^X{!O4P5UN!y( zmE5%FQ9xkoBF^gG{8i+{uX~?ULBVZ-3xtG?t*r~Oy%&jyGNA{dmE5dnQ%e!8!oVwE zx9QoRyS6iEq;OyUj2Q3LoW$k5bt@Ilfx*r`#4Ph8fy2y10Vk&ugL5(lbqQJCfp{7= zzaoK=ck|s%Jsac~7$nH-L(4mzk`r*H4B$U9sbBq346I<2uo0s4=f*s<)TI0kEAp6y zRS(K#=z<!hMGSiK&&kD)+N1@YG~Cxe{L?a!Me>e_YxtOer7m4mXlN@?H)^;V*dH7` zhL?ZV&Jd%@eD;}vY^IW9LQf|VWL<(posF2*&C@y5M%l4AB}((FqV6+zOLt5)E}d>` zSs8le4O|Zk^+>s+fO20MZ3oqra7GQ?97|T$Ftfb+VAi(0$Z=Gp%xC@Fl+Qs*$#@0W znY7=!U|kJ<m=PlHn2qiW%2~;{?QtI00WJ21u`@yMY-H>Xgp<&c8qzq1Qdr->HmHF0 z52F!zxvZXDyj{}!vHxx0*?Z$)bpSYWM2g6S3&KixZ`;+TrcU_CjY^$5xHRJ35VEyG z&My7k5{#@qCo4w;&|cC8bwTVO6)COKbOM|@v(t5vC9_2MG}BM`_%x!TL)G8b3smd> zIfTdKHHHLsx75o<$d6VcD$;+<G_|waX>joLBg`Y`S$%V>0rb+p9IT;(VM{7GV97)g zmMl@Ru0pdB@a@oUCl6C|%YQFHbUIT-ltB)%(|`N07{!zGjJ!>dd_<4vQW2l<G8P3z zeh^f6%`2;slOg?F(=kHZ@s*;!8a~O<Jd2M=qIawh$`C2#j2v#(!2P{#Bb4mSIoY#X z!-&8dMlw=Idtm?cm1Q#`?hW*$0x!VC!5l<4;d_?->=22B(A@IzOQ4K0xQ(?ye7;$+ z$c-v{kgBMu7~V`8_8;NPJ^SaR^l*QQn%2gamsuKILu`~(_kd+xdc)U<EXXKwZI@cn zE%G!+6YkAgINoedPbe4O4k0;z?VyvVy{D~E1Puf=qU;2L1qAmR*f@Zq6SV@n2C&4L zw4x1gr5I^Qo4AmfzMZ`r0w_(kePu!Jn@J;TNbD(@=<x7aQ2HWEs~Srtt@N|<FAF5X zsjcyEwAvMYXCsM1M&jg#rIlmh$iy7+K=o2g`50bZ8BgsOA>hb`&xY{xoK5Oi;fY8X zBVfQ;=nRk|{2#R`kEk<_j;J^_LgN_1kJ>SX^tVtNG`cdgQpk(t6}=_ysStzPXAgu= zR}p44NVeF8s|)E050w9V1;YNy_HbjFZT3gMp@i^LtUTCmS9!oX-^y?{McY5>3Qe*% zv6`NN7&lB%v01gML3U4OmI&G{?rHsVnILa5vJW?S?Y5NL=pdGvwf?NL5mF&T2-QW6 z1(w%J!JR==;Y`))S%BO^X!IB+W{lnYo)^N{4zmTdAo&UC$z(EZ!hPhJ>CZB*Sgg$T zVdM@QOqrP&lf;qZ<F_tP+4QJ7;&CEa8smsbSToI)$bRLTfGzbVEnh>d2<&VSvYu`2 zJ&4yjgJuAAlW3^qyk=g&{p7CJm9auUsT48S^-Uk|3`NQdgn@1Hq2U1^?!R@lW-}U} zn;Kl1NPjM9J#@>N<>Ja$Lb%|(;T0ZQ!GX;4<rRi#1W%1zJ$-p8r?Ju-AAhZrXMUzv zYO1S)#dBjvNbevZpe{x~E35wR4~raQiw{YhD-$Gs$MJ`wAK9M#!24Ym6h-&;;d{S= zhw@hX&KIo>eUqS5c61pNyQBMs?dRhEs72h9`q=r3l+aH^yswmDRjDJ9s{8q=&}O)H ztkRgpvpe~2iA7%p-_Jb4sVUWOVsSTIPcpd}ai*ucaGiSThl+5@Yy$^!Oa{4!YR0^k z(`@J&sPKFa5~J<LuJIPuJwSZ10Q(EQesP*woRwncH^`!iR9*NY$4jJLgxwMya#^73 zdG}i#fr=g7ihnmss&<@W`HYMNJ`oZM=pFR9ZLu9~Mth!8a&%=12vM;p^D>;A%zrz7 zG2rC5TV|7g0M>D=#G(jsoxZpn=zg8)H7IuS$nh1?Lyg_S>T36A@OwUJB&;_>dqp_- z`rWR~oO>4sTU3kCTPwDbC^dDH@90Ev>iKSgs>p?I0S15nz5)g!dLk_&+cJ5>fS0ei z*x^X4*>E0Gb7r{J`Uai2K%VpOs0*#YjId%vMR{$n+<b~HJpP2m&)3)CaC!bz*zS2N zgS*d0LIMUQaQWyD<51OLB;2CPP@!|?oD!Z$Jj{6%*h(LmD!%FS6t-UZz^(VqT<jVs zd;ZA!IIv(=L~uOTO-nf7GLlIrmGg8<N$;Dnd?RlxE<IhksahMM62XoAg>J_{4y}f^ zm`h~X>+5SfBlD5dIR!#`pcwExYeyXa(ntRWKI5~Yna|RYyi+S5=5V(WgVk5ck*i}C zmE|>ct|Wdrh`KAYBX!uuVp`0OmR90IiDfdEztB}q{R=qf?{vSJcMgk|S~U;&tjV0| z>C8*Cb9!L?y_p9+eGkqY1wV8SaRRa#WAE^cHL0q%sct?&30MY<5rc#A61iE~(Yf<_ zbW>TI>;5%9-H!fM?v06jm#$d#@cW*4mMk8uR9zPn>-}N9_3vC=jgc>V1L*sRi!kQS z>t|bRP;Q5o+_3N5&AVu^sQF~DhqgB%#pc(LUM;=kQJ->RI9vvqir4DmUb3$Z;>gEr zx$JGJe>9p<rl98OdDQcvMQ{-DlMJ=IV4@Q-*7<`*YeFScYm4n=qVSKuG36RoqCt3H zC2IKJ;JYBvPmCfol7~dEIr$<uspV>F<||s_IlA8s3>w+Hk@TioyLpvcnX`l@CE-4Q ze(CpD!3<4lQuZWd5sH5l6`|q?2uoNnjB*)rBqakpx9Fa22XrS-^@wxnMU=}A+c?a{ z^IE?iKIgsq&4R1G)OzZdUiWa*Oe;CGk%$hb_<?Ng={2=4v<Bf>pS5@nl23P%D{EF7 zf-FSiA4u}GwpTW4%W%ltYiMOiCO4)fH`rMXft!+rAq*`RB{3B~;&5dlH%db$Qy%IB z`(p*oFQ_Yc)d!!4H>erxU#sYRyK*m*ZA;MgmYRI1=#=?{WJ!&2p6(tqlj_`0Fcsh0 z2o){SmdSh$RamVpTmnnN=QpSS#_TH#w8ui~yhsJ!#qjx)G^T5)m8#xc_PuW5=LuYd zbthbUyjBBn51fBDER@0-iYh@8BNnFt1^#4x;Jin9l0dh@rF3som7NP09FD);N-7r~ zbs`){;A!o&TZl_%$hHwmyovvBKtM}Qr=;Z{8}zQA3(H^cP*!1C9^%Wmpo@azBxGyc zh*y%?8Ov;uP%xYP;BUW%)dW3eNRB~Jsf7klIo+=3O3i`Q-tn$J^J8enaUhQdvF&$> z@FPqTmwJn<@WHlL$%l#hK#PchaLT^2yu}r*$RGn(O4NC`WqvK{;V<}jBtnE1Jo*>L zs*=NYqNU!<xx$mMkSBeVSFH2ixxDq)Eb4f1^zv&SxS6BxUTd1KJD6`=;^^H;6-&Y( zw8*5Agk9xUk3*8k{8%%Ozg0)*2?|X1moICEaG})@eUl(Yi4{W=T-h}oO8PR@woL8# zfCDaE?d{&UjeSG&<{%y(G4y%0;#R+&ug`QRH9nBCrTQ^mGnVpLy-KM1kei8FhpZ%2 z{x-71GTch2i#YdQ@LLc2C%sfoBl7hP@=7QB{Pq1+kEsS{_u$|LC7z^hoC$C8&l-GH z;|!in`p$cm3zL7oqoZJ>jgi1bCs6qJwEM>}c4v*D#{DLg<uxV>`kuS45=$Sw5zJ&| znfFos+rKtsR%jVr+2b(w)nOX9dJp|Pr136ElES;t(DO*Rgc5wiRdy2^^%3^odz-X& zKjp&dq0yASUWy`gIAG7c=O%QXn(OOYy|1suw>sOA$-~v|?bqsRT}={bY`a>zGCQu= z3RxNM1mxVuzDiwAIj7HNP^Y8A>+(@2xu=t`=Ac6vubG)P4^vconpR7`<W^!h`EQ`m z7|UhRqQ=6==iJ4scTP_VT|Ozc{inLB$*avwx3zaPbdDs0@rS^-e#Ab+zJ@P?%5RsB z=(5_Hb%*B}QBjkU8y9z!{-oqskH?{zKwupwQIJr~HDJCQ*8kI|AcilYZRSWXAg_{s z`IHizzlLsJ^*kchuPeW{LX>dbpH{1niNSr8a}R7ba+>+o)e$29&e@Gwxsjj8O$_?F zT20>2V7&F@1vv$2IUMxNm*ATr!Sqi}CHTdZR1q$_CGs`ur{2-;u_$?1s?Lfc=F(9s zj2I}eSD_GfK#hAerZ71z(Nv#&)hThDl-91+9&1FKUPj_FvkpJw{m+O-YNNW+L8?2C zV~qyH-0>zS)BQ{-?m21JNJyNBG&LnI3c4>^^!z(nm;3X3fR<x#+{@v}qkfh7Kfeq` zth(1O_BnY}`ZEH_lf8+8^F(YOs!j?wO6^|Ux`qLRI@$a8gsTm1;#-TBQRJi5msh?~ zW8-uuHIYaWJ<53#^zHOWJud*Q#)Je^Y^H!&Rmaa<&%dld8C4%xP2n*mbOBh6cUwk@ zX!qGoZay9AQ{IB}a%%gFsQ<^*VnKJ-@s*prT9-xLx&(KrNs$X^Z(-}4@^QS|xiAbq zkMozXEYNJPog#5ubg=)Zeux|<t8mM!{IwQJdWiz}`}g@9jE`~}GH$PJ_F8yeCXKlR zST^$#nADyFjvDKYuIq^3^qb!L3xB)7{As!m6}@+#AeLf{C|Mnl4l%kEO4Y+QeV-}4 zH^PQ*h?oTEh_Ns>a^4)vjus8{_{la@+`PRG>I<@Wmqb`Fai1UJBx_0f<-X?hAF&f? z^&WF3kvOeh#E$bO%+&~Ux_AX+;^6VO|J^swzy0%GCd;~;>yv>1IA}#p^Y4y+j7j43 zUGx7MDw`>dtXP0j8dmjFa(%yV!yP{gCTf+wh3ScS2^E^Z5f-+*0M4dg&zA;!ISFbk zUQ`OX#NV(GO&Tt_F7V^4i0qk@oE<mna-I3cD-=M$TV4(AulkDKSN@hWp@@2YXGR`H za=J}DeEIS(yqQJolOkT{55uw&b}5x74i+DDROSyB-|7l_{JCB-pjr|t%fxUtAn$S% zy*S>jXLWDy&fD}2mvj%HoAMB)4!j?2iYhEHqnyTlt>$ox#fdk9g^r982W%RjaRH{D z9QGAw4c|XHShOIuB8}NZlEZ}*{K+82y3Fk4#io05cenrX`bk30yNT7o41C40_*DQp z)`6nwEib0>%DZHsUznOtuAi9D54PKS^6_D#{(Fy&$MTBBV!EV`0!R*J1FjmnAPgZC zxL}<Pv%hL3N3BQu7G!L8oub5j19Qd%9V1tv`r6kDvPMAOcKP?XW`T<Pxk+P}X2aXN z-328jd5{4|k(*rDioa_ah>!~vM;sb$hZ*JL0X((1JUk^|<Qjuzue{g)+Oa$`Gq-%d z|KvF3cS-9%hmC9-or+Br=ivR7v9;+4)pG94%(^&siwNTvSJs-yZ?H3}H6ZypL*4Sl zX~~@6EB76nO&sbjN8MY`C`??OD!t)vRQNVcfFjlry}IW#@cCS?1kOl#<Ah(M&u-FP zVq&@g;c?zpf-;+@HLNo;c~P1&;fcsd#efn=nOhG^x-T4TgZbRT9jLJS<6UZ72eoUD z3aZI4cV!Jo3V9)x%knCxyQUi9@7wV`u4`>g5j^U<JO7z+(9pU}B|A%pt|*iODQxFl zHk7fo=fR*8!{{*P`Z9$GY9ANZ=k0}upVKa%rvX_fslA;_PW{5sQCFC}faM!5e+F+< zo?h|{Helt1G+^TOU)%jvi;p8^Z$V<G?^7ZT3cM*tReXX2Hs)b3f<Cg9m^Zh!zCq0u z#}kueYiQHJ#Rm`s8vZ#DAR!d7)e%46wUwR5-+DexVt-X8_F#J|)&0vqpu>@4j3R1u zaKjSULtSj74Xt&`5}mi|p^f)iS3T<xJ=(@Baj0c9C?e?yy>{x}$pT+~iQ;h-gZI|M zlD@6UdMio__A{J{uriOcKA3Lqd~nfDtaMz`E@28O7w$?l>1m*dcHI$?eqX^VWoX!! zMd*D}DQf7@fa|RA6#(Udb;+Yble^AgUuRm8%^(14m^_w9%V~W6LmVCkhLW7!Vax7R z1|M95DqWgXieN=8eSaiz?s-7s%p=bJ6UTjVchoFjY@=7oVbI_b&4rP&`bLuEya2{~ zA5$VjLy+o%>bYD;N?K?Yuj4}9Mbbvf$Qh|j5COTX!}d3dUJ?QY5M(pu+TU!8oJl!` z+dn?c+f4OJ3UszZe+TUT?5s9jUg#`xKNTA!5<-0c#R6d|3a{N@;fXII{Orn)Zka#2 zq{X4$!fE-%E>)D?VN$weWUqYUf0cIT(NuSTyr<G6qzMsCND-xQgeJ<=T!zRH4rPiH zk+F%$tjyDsN+m;`gp4KgTnQO79c3mV!@2L>z4za{?z;E4*0a{{={a%E_w4<d_I|(K zRa0(7UeruGb>D_y-!c_6%VM6Ph$tN%w(kP=T{R70(pw|~G*_N$2-w6b_s(wHPahuy z6HY6c{wD5cr@SxabOs-<>XDNFuDQDCqfr$veO<`?EMc;`I;~h+UoSp#q>8V=tU^cN z(YklI^NLZ+r?T=Nqi@V0o-#n`nAzdAcv;oO@C?i7waNxA)vZ&7i?2~L1i0;lu-kW^ z1JHtM8DxCAM5?Om{eH`fo5HeGU+b*@^hiD2i#gBbI4I?@;d_R(x3cT>mai`sXLi`U zTkU&a5243C@}%3`9+tzAkxm|e#jf`o*(rCa2iSRfhf8b|J<!r+(HhZav*9pvK$8Bf zvqAf))&|$f^=MyYJ6<WD{=|Vtku6XeP8-jgV%fpJVd+sX9sa;9{o-dQ#xT?V{Yg2% z`n#!v+;A;b+H&P?9it5@BZ$*m6|JKvx~Bc7X>$qpb)dN`d-^?x=kH|q-Aq}Q57>d$ z3TL<H=H|VyfA%O-Cv9cdN^GaLj{}X-^j@01QN_CF+O@AFpYOT7Rpli4%W`fT|Ngmm zcgabu=PZY=oqt$-SnUi%lZ&o?Uq`LpYf1g{i><j?>dG241}1@59JqtGV%KahctigY zre`!k^{aqxYBkFaR&lkLj)Ti(_(k7YZxdE(|8-44J6Q_}vrq0r>ZL8)iel6RxQ)Nn zX=>2rj|5-FtwL3I|BY-DeYXk@mWPUfOLZG}9?d=$r37gD-uJk-^1Q{@<(@@X*~GK{ z4E>nusJ@_#_WG0Z1Veo1N|96FDsYxY-UzfXtJ<AQ=a|}t!Tlo;vV6~|-tYPJ)AR3; zEiPlued$v5T@lT3^UpqQo|!K8=LVbpjL3#xuxi<$^(vjb6G&0Yip34AQf*j9=O(Uw zIkA1XiS_wx=61+apwSUMuniY-eYtbmUaf~53;m9+_O*tc^tjHTE!X;k8n}Je?IO#_ zv~f2r@5?K3x_dLdc2Q6kO_Zf~>$ViXlj5iI4=+aS-;DVm&Y7`ar&lcZtWT^~9t}89 zWqmFwYORWavr6}3m6bl&ToY=VcP-QQn~S`&;fVLJan$}$AmORbq9G6+-dI4t)xa?* z|7chAK^k+;<lQ0>(V}yw4u^{LWgX@gBezYR%PVe2+uR;%sotYTr$$PwJ*0JUBknn~ zJ!A<xA7EpI!Ct5DuBzsb=aP;*+L=xhy*|QuXW3C)TCDf^cPHK1xqr*_knHkRcPX?g zt~hBNY8v(QbZVyxEUe#kktb2-nf>e^OlT<?=3GfrBbo9sUA5FZEjD*hbl+N51$oMM z@$K{FqEQm!_IwD~bB*3YQCy|onsjQmW4v7QY@_2d4IT9j9iN}BxY90g`b_`U3-84J zglrwzGcAlczhp54vF+a~#Fq)9k+y%w#@_j1Sv9lZ@5^lWpB_+8vi(yy8XI-lymS~| z^_;(HP0Gc{0rr9^!G}B9JeVBS(b4;tgodUBocy$n4Cb10`E0U2=#QE7pPne%&Q%J* zZvrk5sqn>S@s%nEWkF6vt`%9RZ??6a2+ZiWpgnVUe3$L38Y)8lDZ-t-8Bdkvc)+1} zrM`;=vGS4KzT^Gv)#Vji_sDNNy-4TtBcJ=5pI_W>zjyC_Z~~Hx#Ij#rxbyRi@vuII za&0-)y}iQ)LLniq@P5bzP&G9&rf68Ub(?XY=IBJ_L>-ilrb$WNP<}kM{JNv-P;o3d zOWBp6#b&n{v#ykBgm(A&`r~!axyAJ@to66rsIN$PXw}?IrT8=?|96CqmeMiaTkDdQ zIGd?I|F<dkclWu^7Zfc85{!8<5PvAPc&$i;nqhTRx~-7D+kW&=F$z~4u5Yzbi8ng? zMse(Ja~EU4^4s6!bBx>GLLIq$o{(IwKYXogc;_WMHXOH~<rM1cNxg(AD5v%%vznFp zO!tX_QwR6!EoRha%IxNr#ne?jb;a7wq0DNg$IMe*^{%Zk5}q6zv!tZP*5TCb{M9c@ zJ?}(0bsDHYy3M5|C3;s`83d2IWF9|W<ABNaVvua8m+$k)*FC+VvzP~QW%o^W%z{O0 zG+ACGtk`USY%=u{>-`2D>8Y>T(pF7I8*XGachYMUuAHMG9<9;D^H7MEiLA_}ROBhl zU(Y{zfN$;5lgE}S;Y3?~?V%n&982A*F2VIOHH!VGU)Q!v9zHxeoRe<B{q;Gk%by7P ze5v0pzg5YJYhT~R)kR%Sp@qy=D75;T5?oXJe!OBfCfw3u;ZE}OUB7}tzo<DlQlQ80 zr?11PL2bH<pSoLU>bqs#Gk=PU&OctJaOsP4bvd%1N}_rVs8v#;9z9>`Irv!`n4z=_ zBkh|!7pt?h88*M^i|D{`GLkvac(gKXwC2FYb2{qlggwvheUfyPxzHy$TX%YThQS`@ zQeIg<IlvlYS?DWwmrNAKlC?wQA}p+>LJlro8X@6<Wztp{sC2bP5Q2$cWy-$fQ!Loq zSM82JxFBV|bYc7fW!LB$h-80?Pn;Rkyb*u@=!<kI5GMA#p;NLff*XFQx?e~IKe6zW z58`$4gX*U}BBz!%I6S<J9I?*Uixrx(FVAiTa?MR|duw4=9!`00w~gg#QPEhzr?8`9 zd;)ES8<!E?9{u!hFOIs@Sju_RCaJ8kV{*8G-2ZRnvd-2vk8PyDc7a(bS$%<Z_tB5< z9X#KA^v>b$k~sTs7xsjxC>O?ie5{nblbK05mTqS<J*l0%ZLQr3JQvp!k&6<KMKV2R zt+OV-Rpx)KmtK*8pWu2s@$vGP2C;*`uX9T1Q61#$DY<z+%0-QP)iJ7jwXu#aIyd4G z6m=D={vI~Zx>I!OhfOykm>#I%_^0>wg57cbiwMikmu#nNil}sM$7lRb1sDAl^d4+T zr*ilSTya-)&!wE3t`fSvwHOmrF?aV_j-?!bGwUY!ZP)eN<8C!Bi~2%K{4nKGPSEX# zT|01eUZ}re_rJ^KPYx|wWYj80Ye=YgWZWV{r*;mcMHlG(lNpH_gh|{%B~>ZEN?>`Y z$Sv&;?R}8QtdEu{)+)4A8`FX=TD{@<VaJP6)WtkJiZ>&YmvQj2L`F*8+;@FwrOVXC zO_8d%`oC!U24+}RM;W|$(U%pQRv=UQd)?`|mlr%mJj;rkNj&LnkN3d6W3hefcYQ>o zI>Vrfy?CloJ$W}t^Gr4`k;goBYI?suHM>OoN8O81%AMNUw8HTH_Vy&KvFyyW*6SQl z8`GxWuw@CDn+*3o!XqjZO-XesT58eJK6gp_`i@ptX@P?_c*fz_kWTrV4kWnTpi@x< zP>mZ43y);~_UX64o%c@%cBpC;s^vdSIp^J3riyFo_b-tXq$lYOsT>+%g-x%iTtMbT z7bi|$eBg~F5NAE>*#Eg$ys_?iFvTbbaLb&z;Pty$mRmQhOgqwlGAv}voaU}hC5Od4 zr`p@W;7E7P&Q6`iB+kG7xZtg~`>?Oep%&=Mew;`X^(bK4>>f6_--TO9zZM>8{NP>2 zJ5_s-+>jCQ&#GFl9bpnUlGix*x9vu?HNT7wf4Hg=@<{Dl>*&b2O_a-81I%u&<s1@U z^-Rr8R~I-+pljcK;>E9q7j;io@GhP0tId=buzQ-Yd3xF;;y~lvrnk=0Z*8nok30-D z!LwmNEv8WWErmMH!($$hH-n$GLhN^oK+9Vn@E}koBv`e*zvu2I)_(QR&&7pt&2-#| z^;M2nqGm@7q)JzwR$R!U$WUayVVjSm{!yrWc7n>0I_+fftx9uYr`GlI6oGCg8Ld=P zY2}JfrdN)9Ste;~d(x_{0?$9WDEKjk<Eck}my7l(zg2lqCFpBgkRiV}m_|P2Yu9Yu zE@34>G8t%FKORv)-*oZK5>IrfIfH^{U1!2~9nU~0#;MY?;(FEK*Ud(smg^L>>r;Aq z)M%T=apL}nZ4Jo>3Pq9y5p7~VK2(m~Jet#TdN{iSB9S%P|7EOv_Mp<)TpX+jdFD;@ z1;+*(+wD91<kz?T+9Ed>oYPyXwF*T}R6X6PLi%Sr4;+~WB&(gcXOLHAn9y61{X5Ki zgXGeLBcUQovYtQC^)1-BNq+U}HAjf_WP_R5VQhWOAkzxSVTeNK{>H+#8_^PXwr)TZ zG(1$q_2XU3=3SM8M`nH|9oaSRx@8&GK|{`UyP3Qv)8pfRo_%v@WKFmHdmR@ID&JmN zJn)T-TMa$_{w#3NP!Y#RYYjDy@Sq$V^_*cY6O?Lf`#okilR`gxE_RanQjGQCB3}YB z<-|~glt*$KUu(WNE9I^rZm^1*`&y@GbK)M?tdga^!l;dA)ik8_<_%oy=l<T~L4X+D z8I?H@T)-4Lpcsor=E8!?%2(I@H=h65#J%dyCG<-Ua+fn|VylnTb17Mz<V7`W(X_g+ znl8WhAC=j~C(zrbt;)^=o1oczbdcUBS+UjLsD3NUONXJC<C150{AzcVX4wN0V_4YA z(1gvP_)SloqP;2m@94Fm3@NLK>}T1k+~{kDO>iXis;a{yI#dCXZa9~&dbF&jZ1bP0 z$HT_jmo7Y+3hPR~nsjo>g%tIY^3cJ?EJ?fmrh0LniT=bsq3lr6qj@(+8?^iCc({4U zV+ZD^dVIIbR202e>y?!D?2gOcVY`JVzKSi{d13353&~px3f7%6a6I5(^L~Zr(79(u z{Y94qFab2{Uie!X!)yP0N2_~|?x+FVr{a5fZW;FWosYB=ZrI8>R0s&sFS98hySSz% zU%LAmZu@+!w8`AQR4ndStJAhWo<uxyK$Oo%F2iz}W#h+zw4;Yu#C0!!<8bZcqb~_l z-2#EoH;Ub&)B?e{n-!W%FiYJae(lOF279@tdRJ%Uxa9t{yl)}ASR3q|B9{wUSl+oW zWaBol-uT$9Gl^A`HEDSp_DUNM>d+|JZ0xA=d@(q^?7!bKL4_TseslB2pSROKdZ(qC zZ7E(@IMDp_Csd}<fOP0?@Lp#)>BUh*&7_&m*az6QAaQl2VldxKH!fNAT#MuKWgPTm z^=^=Js{7UdBm&=yN~KiQ*5{k#Bf96WHp(<kZ&GUgRn|1|<rK@^so|97g03}BUtUPW znG{R9!{_Z-!I@P02T#^?Ykq~fbls%`IU`j+_L!8u)YDms5WOzeq|~TxGmY=b@9X_7 zjaDnH+VSJ$P&&0%JZj3#eeO0tiK4Gq=Z>^=%T#UrDF`W0P}-zjzv9H$q?7l3u81OB zEWo2vprjd}+WgVJ8D(63#K8`M((xiLs#}4!;XPgYX1S(cWh_mAaT=x0ai7nCU{KXg zD-{gC)clRB?wk;EhqAIuEK1fJ*=Xa7hUB7rw`<-0)C`<n<8dkkAqJ#8T$jakr<Erb zc~@`LMv<R*oTs@$kS8=UW^i!9Vcp<*yQd{w3M?}GuM6f&R`|0$oO{#UiIdG)N2dj6 z+Pl$sk0Z%ieba>zyvf_vadipC7oJs^pU<4p9yl%(X~-P$phhm1O-{?a(XIfY*N#ns zCwm=-UU42*_w1B+SU%Fm@5#|!f~&AjF@=X{DZkMqG;2mz3kd0?3Hk}Fu(Q)TzC#dG zn|1kXMLXMFs>gO7aEzcgZ4mdI>X2n#cG!js6jt$j**xFhS!zrR)lw5%*x2E5P{!7l zbACn?djY!KG3EVNaW>v3^i9#_2nGeCtjfv}8WwSK#twJ8N`7Avy;mw#Wq54z*9Vkn zjhP*s(J~(zwDmS!io<c~t|Z5ik|a&OtMM=DMb56Pxa+;vp~afzm0LFlxG)j{stQ_k zD$e-9NW+W=V>rn?)38v0Xyx2ne=RTOK-~KH+U)?E!J}S>TyrT$t;a4;cd^qh8OoaW z^rV}8T*C5;W0SA#&(F9AB77oocDN|b)%oO5dv$<qKJIjnUB8s`5o5N&&JzFXhDRj< z``3O*ikI27=Y9-?mMcY++68Ezp9jx0CKVjaoqLmA=*+0mI)39Yj=VcfHCHK_Rjl6c zl#VKNMUUS5eYoPZ+S6aiU3L7wqxgM-XfSUruI~6CO^v85k5#f>Fi6#13(RQDVR2!| z5wjJ_%liz&xVifxLSKpZe_8g<>XvBy+v_TUa|l}3!aASJn%=+2{IoY?c1jcn%x^+M zb!S>>ye|ht?tQ4N6yKg_vuSQRvH3*I!zCm6mJ99qLo&4Zhp32pKbumcb~%4du?&z< zQ@<T1LFN!<ZC4xL$$xZh9-C~8`JTsLFy8C4aWA@xCrcmzx+3i;j-Dw%-0)<TAI{~r z7yE?s{2swk%S<m1?jMxZc;S#JynC>HQ(x7dOf?^5lyOmvGJhtYE7dRW9yNY`p>j?b zlf1f==mzdSJqQZvlW8;C0-sNw64!o4JrRXk7T@f=!^z=xYdht$gH2Wt5!_?1{#c*c zzS1#grgYY!G14QqVBA-|>>&;Xdq19xyW40WT^fM3Iy-grsXA5m<2!5W*cq-}v2XtP z%R*fL^vIWIUq8RG?GkzHwQ38fbY<#l#?3`sp^ri?JE)%3&#+)M^x(kRx=G;Sneo1n zKS^=7)1@Y+{&p1yp{{w-btG=GTtdaXaktZ)-EuDPJtkG<g8MC_RAQ%w?|BO@I(YEG zo!sj#)LJTYZU(U>tLtadg{f1mUg2#gy=6PLZ{B`4G{<qQmR-_9At6f(gYLfQYonWS z=xE&%Rpbg#c+467oOhJ3$1SyZH2(e$^)NHT71YClf_=ONneBV^SorsOik+)^Tr<^X zUh{kMP}0<pNrQ$YD|N<yoMWSoK(ML}ZaZCfnze@7-i1+P);&FjZ$}ua2;GkZBL&s3 zZ{O(PFsq25-zyEcSEg#wA&9#m{EPp=9r_G!)E=~5$Y@N9&%Wwiy)@4y`pz9$>)Rjh zd?+pbr7EdMwclF_=;*75>mXQo{6ldHH_=t&j_AGX@imWUGphXLGdTT6@4>c^HBX&s z^dM&Nw$Y)?KKWq-1FF4Khui6=zRmK}^~d{ku8rx)6y5pD3*f6o^BZC4W&Q*D9)rwg zP9d)=U#jdjCu=iv@-D03fB>TsTFCJ|L7|c5A;H@yx!1)&>i8X_6q~T~!Yj^Qr^?IA zYo;7Qiv$xE+eoV!y#1PT^^`dGs4_j9*K%9MUULe`9NhKnMCH7*G5v69KwV~9vr%2b zt?UyDx7QSgm`>TyRc!wZwk~!Za?I-6NPlG8c_>hz<%SaC+1$h+eK2l6drrYYZMOvq z8kP+CR};wgoL<K1e=-`IaC4sJgwW0VOK-H@J0X;Q`Dva@^`b9dl8SuSXHPXeHx{{# z$Cz^WZ;T@p&kyKTnl#-6%$zp5>1Fn{cjV{kuIwz~-?Hy(6z?T+AOq(1;lbN%WbN3u z%DucXbk#wJ2L*5qm3JvWsNs0VEMEs(!oSrjdfzzYSsd)c^7?gO)>yoUm&$|95<?ws zjyc)8?ZS6c_cr~vxPK~A@5~~d7*n&z#ZEjDd-ZS;O`3iGfu<qcqM*{;m<z?v9>_}K zLgL)ybT=S}_b{9s0?_22cx&J>GMxeL>Q#J~Z7(dV5s}*);+U4No_%xj6{kjzp_o`7 zqo(#?G+Po=ukv#X|HX{G<{LAwm@M!MrtUUj7i0Zx>dh6c5}}>4mpMN>(z-$@VloyJ z%Fz5c#VvA8A*UaF{vfQOGxqh|lX$sXH%_mPE%8sZZ+WNX{YVn~*|ke6r7P{YkTC8q zK&`tkcVPF%R%g+FBsUTJ)I1mC@-_6sm+QCEYdSsdc}0~QJ=jq9_C>b-rRpg4l@IQ( zIdm#0@=DFuw%q2J;G8R=y5;kQw`hSJoL(p{MW%LNO0Y8t-1XHp`{YPV>37+Vp3cWA zEK827)c>fK731eNwlkf9-hKCW@Ki3Cl-e8pI8rFi|D`{F_IX>H(4Wiu9JPJ_Q*<$! zv8T0m`mRrR&ug}vG{n{_H@dTQ^?I|g1RvkMYmeHFJPvrTb8b=p?!9}fqBL@D9M$xj z85wS)c4fNdL@rqf4-cO?`uTkZ<=bOxJ?S6xmIq4i4|u&kV2a}trw}<`u;3dU>tAgn zdUez&N@+Tw)NRL3)`t&(qiJGe*O~g$0%*f>Phcd#raXqLvJz&BriwOVKX4(dO`g8Y za{eY%Q17hv9Auy98sgyQW{TX)xEh%p_Vu~(f?jIOMNY}!HHWmNSJ5NJanRLW7VN?F zn3fWdH(fAW$M5pP>;{He$NU%E_E|2K&<iWm)xxGAbho0)u-7BuhH?(ybV7G(8>l@f z6-<P1eZw)Q8LS$2T>4BOm>p^UQ_O?AmtCy}?`$rSD|C;(zx@xuG{LFkK@W{0BUf*- z7P*6i=PsPwMRRO<@q$N5R%*1vR7$++l{W91%Z}3V^UqHomhds(!I~TNN?i4%Old>x zNXL`ys+xDZOMiUwp)nO5ar`}TqoD8*otHNBz0XiSfGKNbk^g$Kkwa}6*PCnob^1b1 z2Ko74vAKV>xgY2qXv~y)e;YM%_)c8bZMw(atZ2F6_`(8z_bZ$IIEt!}wNkw&a>7FH z@{)z#N<H2eWo8xSd%P7WYd&r|P$fuj(wPVj3SwF|TlF<f+xI3|HMa(KcG5B*iJ7>| z91vt>WiCM8l<2J^#<3t-BxPP!6Ccm~BysTeo&W63CeqfF7L7TiT%<nMei0Iq`v5s; z%Vhr<SM~5`XQO4#k9@xOt1YJ+sDg(7e00dY3v9*}Bh8^WDd7~q_~}1k4ON|-u(u|q za_`+0k2j`?0xR*W!#$_rhp#^Dcj(+tj&bz2&$rIMctO3*%Wrv7#>anu_^Si6!9gs? z1Eb={N&_MTKX#5VZO`_P*nX>r8bV(^!)^9sw)X?4^FY3QNxA~L1GHgwm)MPPsVn2= zpLLG-!O8n~^miOLO30Rws?!|4eW$u$$M|2_eP3Pg;GTiH+K0+Up0vwNe(^Cenz$~_ zDtEm<=pv3QfmFe@zfu`nk@;ymvO?X36IObbIh&bB=h0TI=&O5mE~%=tJT$w&QDM#6 zAHT2A^Np|mDhQnLKc~?0<y293V9uNv>k>A$OIo*_0@Yap1t>=tHL)q2N0pUvn^%80 zT@HUr!w4rZuwc+i8-MylPK=ufAtBbf7I(mnIQS^<et<iH|98$m?^hH_Ie|(>%nMQw zr@?jF{0~%F3MCSRe_vkdO5dCsu6|=yX+LBLRRSUP6FM0voe(R@PnnsTj{Y4;`|{$P zXlhp1N<uLVh{<@Dx=tS|ZV<A!r(Dc<41I*_!VH6<=m@-ED}dwsfar$c{0#_V`m^|i z8dJk<S0LVp7VThZpwNxsoUc><>om{grGkv#1v&p?*wwtXZV7{;ng(QaxT2)k`^G<3 z1SAKIjM!Jk7RJtsj#BWa63HuV+M*zz_WJ!%|LnVTzOD@rn3@uZm`=vkM`QRU1T3}> zS{u4TV0C%<9w=@s2{5Y!VlBTFYejhR3HNgfDp<hU2%xw+i*K(Yi9ZTH$EFf!GA#66 zhBA%;NMOwCH3pjTMrfmCL7~3_wcD4h+2I_2d^q_jjSqQ1T^@s~+8-cFejov)Ad<8p zWLAbicub7d_5QjKPB0bmyOiU|2i|!|J&ZNB0eL00s;DX5_)a~{MoeHHQvyMmA0T1; zfYW78%xO4xsD4=AFz~nTC>I~hPNYCQH??ZdZO;1P#qS5T-G=4~aUb7b84fyhsz1oV zW^e^+>a9(Avg>7|BaocOAVKx(tqkXfHG~w{**3Y8-}F@eC`dk9b4a!xOjNMS`D)V4 ztE0?-#ou3R5)QK6KFe=aGtc&|DuN431hL@(x3(LS%F0O%VPWB7Fom;UUoM6OY8Ckk zN}w2uneFcC@+&J-H37u@-poVwS3NzZiQu`_jAR<~mVl-f!I1n5ZrF$ZZz<8zGd1`s zQgJDd;qn72R9QkJWsHG2xEDrBe(;Rp_p-Y(!Y>x__~xb1&`^J1D)>P|yD@B-<CsDq z^4laNsxO>MQ{Gh(uUjPL_xtyC;yF<Qjm<-r_c~|t-s$S<9s`uYU&v#wKAQ;&!d@b& z)>h0|bdD#T|Nesq-J?H>2-2{|8bIzI+((2!GM+XEJre;ULQS6?0NPd_%>%-um{|}a zESl1{;5~?nilXRs4gE9J5>IRUl5~ovi@p=n$;5JKXtBuu{XA3Xz8KeQZJN?=fpR7v zK1PJf7WG}ZX=Wg6J-?W-6JJMG&;MHZz%qdQe+wUy@t*4FD1+i}_f1D(J$ww#c-Qe2 zKs%G=bFWrCq2HhdieGy;`QW=8ZuMvbpgBFK`6>=24jnnNhy@a;8f#E3mXy(~41lyx zHDTR({&}aupChw*xw#e{`HGOE*SHZav3|BaIMM4XCd7V1X}$xegpdXQ`^(#*w1F7~ zEg-MXl21s;qkTU7Z+`G?v6qbv9Ubu}W956_*TPB$u0D@J6udY0YKiY~1{-K}KLCO^ z12ti{lgPNC%H(&V>_a$t17`$$xZ~LU&VY)Smz7<sUb|~ayVkPPXCd9=hF1?GA5LAK z`nIP_nG-iBo;-WjC;!bZx4Y@V(7_hSB8hOB5btUz$9LDx<TI}g4>-SW+$kY3lsgMm zxBaErBU8b*vE(NU2#+3u#E<6jZd#p;&CdA4Z&IrulhOtJ@#mb$3H{{f1}#{ffX6u@ z?D2t443sk=L4z|nS|we(?$4K<>rmE~VJ+~=U$=;x74(?1D|D6qJ2OLJa#5#-JZ5HQ zVkg2i0O|whwPv2_W@2t?dK%Pz{gw^8^~;WODSpC=0#}<~a$Zd>u4Za}Sxj1eaX?;^ zCEx1RK?C7B?(MQ?rBo*0$3fs$KqVwwK4~v_9c-wW2%@cTusi?>Kg7>``z%fyA7rBh zKE7$4mzSpoL^e^|g80eeC#V>8T+sP05hF@UP7PeUut-N4T$LiM%!yVW9-dO*nboi; zKnq>AFtmYl#R>{z8niw5AEfHH1PChrvWiMQ|6&cm(Te8GW)ojfu1lYvNhFX{;-VnQ z^eLx6WeU2tD99ckTUzG0HB?m#J35##+vi-<8j1}ngMwD$6+O9*|NHaE6HzmvX#zH| z2l_y51z}1e9yojjh;k8jGOUNFRFUT9W`YyPemMj0Ds9E$HTYKaN^1#5BzBevK#@~O zdZ^rBIHjSck%0G2UI{Oz?$&pp2ENovxmn}1*b`JjtgsE{JPF;?`2X@#@op(!zn&+W z?^Y(lKk-ho$r+N#w2t~?3OFdd#abxWc&i$d5S$ou>zm$QIR{jNw=u7$?(wVm?F=^w zE+Hk{kMc&A`YBvs`^3oimjhb_g(hN~JGPWqI}qQlG3iizB^@^hB*;?(@g?Ga8AAYN zavAUPZ=^#oUckT4!V}g;5dYWA!{Gw?zIP*SL`yu{Lp&SY=X#)E6G<pF0mtJQQjR|q zqOI9X2zZa3JsX0Y6a46r8KFNoLDuVsRK~kj^yEG;iu+zI1`NRx2;(NWZat=R8j;)s z3G6m7`=IK5Kpy@!h_D;CDJr&wFbo6~C#s~6#Vfh_0XxkLSBYp))!DltCEUNNND2;k z{&;L8&O^EOFLH((+$?X(rX`Ji){x`_>GWFIv@W7=J+3=-&yEz__!j#fa`Bj{?55dc zE49z$rUC&Zg?C0(82hxAG3Q4$Gt-^;q~ZDaZ=_;NmR<MxrO%eHYp|S!xe#xoKF$BN zO`E(49(YLu)D(o%s=~ZGfeRhcSZjL;5hxKJ8~@T5@!QZg?nB0wADH9wG9GNpY0MgW z*Ww2pG@skw-+RC=Nz-rC%XNBfSG~WH7cOCYh%WWj;SBtaZ7?vXEp2uPd17?->{&mo zOTOx;{nGM|gBGq3E7!OM2M1?gow9C9|Im*i0fC`(d-2;yL1Y^;a8Vj(zyLN_k(8Ll zT$uF-V&lNnr9roC_mX!QGy<R+H)Xz0M&_MroVnWh^9SkX=N9SgLqa}r>Xd9rz$QzQ zV~!z*_(4EipTe(Y<z@FQ<s7Nq)RzF!j^geA*_IKfK4N_(fkLN1AjpJcop7uyU`EKG zPJ^#Y_1EbxfX96oN>%-qR$-YNL7-j$PHk_kmu)YMl!D+-7S|cm89HD!$wya<1!F#c zU$3I9tP|AHB!mS4!l1g=`WZVc3npRkJ_&31PyY=#lQ0B1GyHyFck)qgvEbYk*ig2f zQ&n9K!Q<9_`@V+EmV!1=e_W_HdnEX{pfLoq093rcpJ*?M<*Sf1$wf@t2!ox)$t8!8 zMiiZU0A@ufF@?3GaEQwSbv1XTBI-0!t7&-$C&4D5a?QC@t5>gvCi`xK@%{V!NaWzc z!0eKst6}(7N;K^u!Wwc;qnnXZiJ28q=tJdx7Lj=(j$cTq7Dk4p<RyS|AS^OqUqE)V z0wWjIrZi;uM37CuxR!><!|>Fiq9lWzTxdwh5jaa?7jzOo8DLJG+Ydqrj;;bGbpmjc zIRW=etk<V7X(F&>0M1#Y0IWwTEW~iea_3+nITyty<I@Iy{z|NQe`DUN7)mIl!9C7Q zK`5baj2K|SRH82LZxzIAo~VURBRR-{<O6j*F(Le)z$MQ%n}bYtdqD&hvVeir3W1~B znsw`xp(D=v`79a;s^f7Cf|n-5S#0-fsG|`;RZLB{!^W-$?k%4h8+klY8xhYzx!Pzc z<3%u!O8{Pkn8A^sh^MwFodj0d^zK<1$3Y#UE+%Ge09Ft`9`f|?b9&$mQc9)228xWR zkx{&&R^}uetbEvj0i?_L6bmRik{i%cTUs)0889z^6Tn%-a_qpeO*dMDlapn4?yQ($ zf;Yox#7k-&+F(34jx|UJZvJGv(ur*QUa(sB7kP6f85enl&qduYE5KTH3WCNt8bvT6 zg}|ALpes=KA(`F*#e?9{k-BJZh4O^(42<_JLbQP;<Gq#Z5!giWiH&MvD8$bN3Fp#2 zFBAoWPMxe^J9ENE;?Z-1Jb9O?;a&tgbEu?qkrr#7RBusIibL5Sh%$fiYC#+}R6J_V zbCCr-+jwDaMl)K*A3uznk55fq-FKo1VW^A2xY54H13r?ZSAqO}FGvsyYYUN#==jP} zS@t(1htp<>t0wdvNbyCevS6za8WmN6c#6WF15S<IaO>N?V@Lnv4t{U)wPLYH0#FWc z*9&0O;nyLq_pXX!2ZyKjvPU{_fQiH__@r{O;4D>&3_cA@2l5Xw9)KI4Fx1)vl7<}M zIM{R;lSUX7{D(!tIFZY=L|7(dY`762KHaLOZW9slB1m8aKNEU063$lmCH#kNK#XWm zjM&vGPK}m}#@C@YWMl*g9jr+=E<zGp2G=1_pie(lU*FZ$MIJi%%cgjk#HECow|YWi zPw*LkhT9$>PeMOlv)&NC6Ijsj8KAJ7e}4KFh~%6Ca)od|MDb`yY>)U9oUY{?uH~NL zI;yPj5e=9h-U=V$cnO*m%kA5@%S}O$-oXp&K712bVEI;z9siKlfiy&#6r_aDXoFK; zUv1dQ$VW%V57WNAutV_2-rwIiPudak8sc(Fv-X(&;q8yV^{>Og7aG%olG6{KE2a1S z{PqExU<E@t#YSiRNz%VxH`3nLl<Q2Vx#;W1NdJKyBPA0(wtRPc&W#ZjlV}R<*}s<! zHr2z6R*5k?g+Rzp-hFo<eEP75WD=33j-5aMa172|8$p4oaf8+19)eE$_qGt;YqHyC z#0_$rfE3ufQO3%`b+SSGn3h%;4fn|^{o(cS?{NSpHPGq~hfUIo9Wyn3f+*XHO!@x$ z3pAXe3R4{D7Rje*2UJ9#n4$idYd51(<uecD+md$#SEtKpjQG8bc~P>JAhP^>5(arU z$=f51GGl5CHekmJ+~@zbf1{lrvWI)K`bbj8;t*}j2l(j=DDf}IXZ~k3`KKD17V=9| zk@ac$-k>9e{d*w4zLrmemk>+LXNiq0KL?Z_q#;Clh^3|8?+GlyroWHHj1l`01T%tO zL$h;zn4O*71kEm@F6Lre5bZ-4``b~QA!zl^y@v-TaSR2A0e-O6Ko&{z9)%w-Hr2Mx zn>~?<Nb}85f^&o*JjDPbK=FAql`j`P8o|4RI5tLd*{^(d4vt<JbOM7#M`3}e?^fQy zFO_rS&h6V6)0m@%%l<>2o_R4RuT){S8!&HR=nxMd!JGYC=H};PXJIE<h*g;0GE@~Q zjr-A#y}JU?>hoYWgKw3z8>w3P`1;1+&%0=}n8%Q~wz6Qy97REl=kOHvZ`rAhKnoHA z7rbh21qRj%yB@Ep1>u0K$>!c+c#Z*#&v6Km80DQJ!e(h@<=lO=crkPhXUt`42H>## z28$o8b`-RfBsP>^Y1ftFQDFA$dWX$7j-9q>6rb9z_mQ^zRP&G{{DeP({e?LQ!B~aQ zwW@r=1pm`fEJ9vSJ)7)e%7#HJyg@Tq6M0e`e-)5Oi;ljg*#KY~^Rro|<ss}Pqv#M> z>p**%2pb`umaOCpFR!Ut+6f#ZOI2zC{!<+7BqnW<D7b;INWin;@%!P5Fd0+sl{ipm zj1&+2<XJeGpxId*(ajAi{wWqvDBdfi@vUF4f~^drXkaoB(cx(ezT`W01C%&5#Jue7 z+kFcQv$iZRwbKGXt|6{#aB)M1XUG#nHuKgjJI3{7!&Jl^^Bb><n%kJp{u8vS_ewcC zG7+B$Qv<7lnC>uQOn8@KOyEg5DRpoGZvkZ{5>SnE=?taagkRR?yuWI2<&!-G6%E)q zQV$Cv;q~@z0ig)z2CtmDgc<x2Op?G;tYYiF_Ud~&x}k|w@sPMU&Il7W%u~pm05*2Z z60qHP{QgTIj37b{A(>)0n-L>%^WgmNwJaEyAO*9M`T_5O#R+}#xaLb%_#-R{N((Ee zYiE$cwbmu3+odOqisY9CW=8~4KCE3xtAbrq2P8exu_{JzI}G#ZR7T<rkq#*4EzPwB zm=iDt3r;H@#{NLlSb+%GlygIJ8K-~|MtN2(@BC5OETUFkc9+ZdJ`Q8$5tk~dRSYH; z>IwX^aR+Pe8EE&oxq@P12IoBBU|Zu7V`8YTzS7HBE_74p?NDBU+1=mNX-MKM=zzo0 z!opESk!}x=7}l7S!>PpL8d;T;ugDt*``dE<eRJSi;<_yvn<#u6(`R9rhO$#;r{@Id zSTh>(3vh*ahB&GjEB^@9N@!3}VGM;90lZOrdplaHXiNy+o%S_cwq%L^wSvR}*@GB^ zIH6#h!LP6vc4lQ|MRyf}9E)cZIx;}X7lo0<%wNGcd54`HjT?eJa3y$*#az^ud5LGr zD&SjKVPT<uxln}Kj~TgC=l|C19kuO}f;S_wi4Xg-*ivV6(Ko4kjS5-X-DinU^igpA zNk%IxD<c*juuh@`9yq{<m@p(<J+3HJy8q6dJG!WSgyTIB-D&tI^x?yvnE4#ggNT74 z;wuHE80wfE*JADoV`_uy2!y|**RDySA7Dr7#jyA`mYos6Ou+q1im_hL)ke_xfDoj= z{=KSC5?P-KIHs&&o#nZjVn0}!EKqjuy;P=E^P)qhraf%k&q3G>%1NFmAkHx;N5}x2 zo?OFSd#}c&FVZ{<J>YJ6&aun*8ob+V%;Ee7D<VqiV)>He^5i3lTIgHcE`{?6?A%=P zL$ph$MEC3o#+sWD%mj1o^5x4h5~;AX!yY_Px`64JFF<sA(8svr(4FBc4iEDRZ4J{i zx~Lz~K@*2yj7CU2#RTjmM$R!EE!&O<=zrcnF(Ie{KSW|OAtUpRQgdE;_djxfJ*<XV z^5_5PF`6>yFzN__Pb+KB%P)N-H0VpqZ9=Dq`>X@@ZQZW03|bj6xx7}^0EfICec$wK z&B}td)GVpn^TG&SqjSJ11yDc8wtK)M=@`&*Acuc^aE~F++Kn449^t?U6Crer<fUl! zW6!x}<I$q*+bCr@+A9oq71CQ^_XWW6!SHuGiYwT0ES7Tk^;IDd?}{U{&5=LPWr_-K z5EfEOqWq78b%{bPywBWR7|hy}P75s+dS6iwu|!CGb)K)c``v$r(esokay6iFs-QL# zaSnNotPc34KVrB6?xjIOH~cP6UA=mhHU6Gz`M<dB$GhitLc(Sge;+XBj<8&1U}Yta zAs83>zNg^q!1{79W`d*02v&gE)Chbjk3jWg0rsjPxsgru3d`H84Zhf}A!sWBBeeIg zCygU90LHNQTFJn4kS)B$we{_wKxrn{&G7a_5EOJBJq$`2Og4RJVZ+Ur-Uj&a&h+k> z97ps;Kt+2}MQd$=Wh{tG#5@|lP5ZTXb>J6~VS72O4w2J;54Fe|P7y>a>vRvCWq*G; zMH3!K)=qOAwgpfUUn^Sz+&ibIr_)VJIf=une%mCbNerY#wgil#mMmL#V2YA|9Q@WQ zZL53LdrW3A&?Gffc1Sh~a~i;Ea-|*%vS(<YP(GFG7i|Rju}}YLFWOnVn$62-Tgyo# zMnl9>2)7%@4|`s3wRhjR82$$ES0+B%a2wz3F*mjh<64X)<xS1ai4!E4pd_lpWczTO zG#RsKpb){gorsqd7PMKx_ofrpRu$R;gc(V`4y2pTFg=_9P?iAk6$aoW!*dQ<G#1l6 zN~5!F!MqId3OV37NT|3ND|90LBT&YueS`UJw}O(KTm)jL-Du}wa$<9}F}0d}-{u2- z9uDxqg$;!HxSiur0X+lr4$ORfh+Q`Zs3C-M2R0WRDw{$7^fEpTb4(nrTyNL?$ZSYD zC4`c_5|y7bj+9740MGCq{A-PJ3d*hQU)#OByhu?oihwN*XLodCClQZO@KQmHhMV8H zRDB9VeG4aQJR?k^NHsLnlKBNhqByYiT#I#iu_ce8M$-i>5sqRj<K6#?Av|o|vW4_< zs9f-Jb0D8lo-(2$)~#QEFFc$F9>|34jA;Shsxo3HX%~DRJRlZ=#P0`owP#<P6~=*2 z87!ArX?~rU)t)!4z3?RK3_C0te!`lGG+StElkmhzdxP@n98Mg_!~<aFq=_(>6<k~l zEb;_U713*?dobNmQ<eg#HjScfTruY@Ql=~Xh=}_c>G^O-KzEyU%+1S#)1R~ueo(5z z)lFowiUDOUCa&aY0j1JqBm{&^JAmEBznnVQ8})>h%5H$Sg98C${I?qWy*%9m0|QpC z*}1rCFU<WdAQb_Dx#8Sn`HB_B7_bW{xklhyBeNN5ZQPJRc$gtMX@h%VB9PyJCi|i_ zMkq&D!r_Ds6o~-<<*5d-B}3VK1f$>`Fa}4U#bH<wP<f~`1i_?c(5~(XK*S&rJ1iM| z_bxRT-=|VxzJ$+AN>}0wgH8p_cM-*M27#*&vEU=T`EZh;FHr($c<$d{$zPF5KjFw% zNNG?rXnt;P8q0%ZrfjSgY;IHR2<#N%wMfiVVLO;tc`;E}SyQI_?9xWatT9?VB?aIp zF)}AEt;t0={5<jm6QJ@?ld5mUS4#jJXVmlJ--U5dgF^)zm@SE@eCPw4P+ZqijzQ== zgi$FT=@tnI4iIUHTU>I{8vK}NyN$T*eEt1}ckH;2w2yv}Pu3=e{?!{mH=(E{|4v>% yK4&|PP4-VJLQY~oVhH#DIm7!u{q-v5;+INi^A4<azrce3si~Y)PCa6D`+oqRxRe3_ literal 32476 zcmaI8cRZH;|2}@%JFW<2Mk>il_RP*6g|f*=vS-8<;ffI1yC{kf*|KGiLXthN${uC& zd!F9!&+q&B=lkvP=)UjAHO}jGzFyC9Jdfiz?`Jw%Dpcg>$WbU1)lF4JJroKbgF@j= zkrKlz!e7Vc;9ue%N=6>~&bA)jmTopEO-m0KM`sVm`&R5;Hg4|sot=dEMER~=Ww-b6 zaB-L5=YRPBe}K=~&5objbUqL+Lgu1+#~p>DwnYBo<;mvWN1+04-c-DC%O`Dl+}q>U z{Le4blR2WXI`XT$TYQK8N;Ep*;Z!<wt=b<WtVlk-Vm;${ISW7hvL+F_C73RTLp729 zZr&4}fv%<1=$_|__=MM9O24csDl5Uf>l5Jip4g}rX%?VVgg-5IJ9kVmSolMCl}HW^ zf7r57mpM2%W~}K@O7H>y=lEI3+tIoxI(T<okQ@W=a>yv-=^$TkyNn8lcXt^7-(OyT zkFsYZzIpQ|W2mJkH*x{9u6%q8H@9n=sUlqB;v;|b6vsHk#m}bpg!b4D;Ht3L%1Fci zT(d*=|9$QMSs0!H13L#tVSIYJrmgMUAscM_ZE{M=mdVM<4}GuTp7c~1LmOi_Rm431 z#0|Z{s>&#-sOVRF+;<d7HD)!3+sS$1^0YpdOZ_xG{X#h#GslcfsO5{;Sb2GQe3VUR zoNL!6a<g>!BO@cdYlgKxuK8W)7=KbKDkApt=bykAt8FBVQsopC$?@7!>Whm7B6!ef zLl=MEng`NFu#2kaU*9*6X=)rd@A}`HD5aLGm5z;!oZsw|?fbjOVD2CD<A>3gNG9Aj zoN29(V2#&u=k~1QXnACA#-nhr<)2@(ZV(jKtVm2e-ZqVmjomo>kuKrcIlP!5{DA)B z$Bzt$Nn)-XD3Vjm>>?rwxAP6=ceYz)yq12rcHj={kN0H*cIOkB6MIrcqa{~sU%u5$ z{Z{E}{b+lo2bUSBrL6qrc6}XBnd8VcVoFB2mlrkde-xkp{OIq@fVOOp;BrcAY?Gn+ z<N0s6!;LQfv(k?~sk3dW-KT{h@JVZEkfXn9)G+QnnLfKwP;ix=&*ay}?~lPG)Ym+I z>0H##(D?gjsA$g>UT>$>mb%fGA$?ZT>uPHRgWdkW<>zOGLg%{@g?u;eH8nSz8to3< zsq^<OOjJ=+G}#E4{Suk&I{Sse#O#y(*N6$bm9eTcX}^RAMW4U>j!^K|Hbl!FwfDbL z@P)f0BPF%yNfCa|BAJjGc$9qe<;AMQ<KNqpfw(6mtWzJ`Slz!DJB>@21s;0-TN-Iz zEUG`%kaP285^SLNm}^*9#(tFKN|s7A>%lHA^ZJAD-8RvFTu45&%zknh3b*M>aGh&M zt0nR+*ZR+Q#y<@S5%<1X9d60E8}Qt^Eo!}6sLy5eVa57VX`cjtk<Kf8;RoMy{I*v# z(k0^eSL<ab`{K<71qBT~>+|yRc&eSfyhK(%Z?pEv9@lP`jFj35xi4tIZl!S^+$uCl zE`K=st;XAV!hcg!Ir5z6ZQ+HXt=|K?eSt^K(%WORwFP=_)N<au5p)>5to)qu#=Uzv z>N287yzJ~4JObjim73LRe-FW;>qJ*)I7{^ORAoIoc_+JDg3l6PxMaNT8lUM*b?$nW z<4DQD!DgR0-|_F<%wH3q_lY<$froz?2uM!l+#h%gdme=5RCd{zyNUY?;bt|{@{E#6 z<T-@tz-uK6<!Dw-0|R=9xh!{FFN%^`tY5tK90Nn!i}MQhW0isrN6Y7*UCNmH@zI*$ z!)_Z};00dZND@|`si4Ix1_kYj<^dg=qT_^^aVNWjoplrManHeg!|02eDK4-tC4t8Q zxW`u3)<~?dNP6YK)t1I<wu9O}k1dw}Yzm<ef4q17gK>qInFz$r&$0)Ji|vNV*K8QC z+tZZV^(`!olwx*|1+2eNu2e7box5%?H(LI1cPg0LZOnOnrgdw1G?AF<oXc|A@WzDS zs;SqN5!=#_4@X6!Sfx{DHzq#&33;!WG&MCbiaIf~`LE%TP_tfu)Pk)+3BXBJd#`E* z6H~#aNty@ja(UIT?ATTN_oj(&EseZHF5JaiQ4D#@+&A#SSoCObkzd4VEUE2Sb65{W z$30gfbj@i03sbk_XhQPCp91yH=BGs;mQ-KmCJn`VI4ZCB)RXOax9@gX!^(IKjudnD z?Afb#KVey=AI(*c!}gyQus{hr3{H(Y*UukrPuewyQjNLyN#C=xlXTub+Fy0=5guM^ zrj`}(-*I`XmEJj4<<71DmU8m(Hj9Op>nT>Lq->RFxBcY`{dam?w%tjMmook3_4H03 z?r&e>;^G>9B<eh&5r5@2p9IU#f;&QX(O)`VUJ`PhQOxjNIE!R^7+XL)?2+$mq^R2% z4sywM>BmR^wMG(%-QD+Q74h`EcNG`Ee;^|#$7hFQuRlIYg`#7&CG+8K83!k4uqN(t z=A+FX(cc5P(eC3@m9Dc!rM6Tk^+dk;uPSWw3C2#AknFWv-_*M^Wiz1&{)sJvkj1vN zC`x$#Y00_~yaQ)x^!btSlP6DZ<r_p|FeqYb*6yjsU~bdu>s^Wbxf;pWG%{t<1+BlV z^(#;dI!{QkKHk!m*<TWx2-sg{jz0dv<SYOs%C;{<bLjo;vr;|+>+9>ii|TQgZ$Rza z`8#Z0v~xUk25JDgV6&55>wtFY<4(GgfdM?RGHa5EmFfsu<8mXDbUIRgCyIg3e%{}U z%vYIT?ZSz08x$~l{u%6Wt${j(W@74sVkKARFl0Yc!gYMO8aPs7qaIb}1VL>x)$rt2 zp3aki!#&-61K#f+jKk{dWn06~5Oj0*uC2Mit||Zi_Qv;U7x&KK!#Sv>v!AyoI0XgQ zgLH3(4diOmo;{nKJ8baLN(m_z5?)KHRc`b4L+{UwS9{i22(?CvPN*L4{k=b!_mwC* zW_UO~6=^ejt{+_%rx?-F$Fp#jmA{rn9e>C{`Ak2SfxHT}q+wxs{B>Upf9o0TrL@Gq zE90q5&b2Y*XRaVS1_yYj!ifcy>Ayi%G3G{5G4Y7!437blqtBVeW1;+xh+8G5LMjV9 z_<j|-jHZbRGt_eH1`M9Z(vKLW2zopkR#q(|BgUhnqn4JIsnyj!6}HDM1k_S;(X7&W zP(6oDy;-8A|1s+4YH=NoZQsG{uhc|mNc-u1xGQYtJAWpep0~4NG9WcO^qjCmG3;K% z_g`9f2`=klm(yg@cS0!EDJXM#jGc8~yILDa9bQ*4EWG!-nR)<jmvjq5^n2!_2-8(# z;>6uoD+ZR9hAi@nL-{Hp<Y$bEE$|@8=W;Uxsx7vtE!v`3KHVR<*k%K#_gXcE!*Q}M z{onE^fer*Ra|O3vPP<a-lGAu~e7dB!S@HCFY3cOJ%1V<}{ZIC~r&uK7a)-aa)4wF* z_@e=u0Q5e;AEkEG(B5C)dZ#DuWcW_+O@xfHnYp*qLU;1Z#rZ^IWAR5@_UA+#lV(fQ z)1};@G%^pXsHj+%j__4+?sX>%p?Y;rB?hdPJB@e3NhS+ghi*P<(@U(Z6kF&`@7h_P zO&^9R>8h$Le)RWG?cSgK-cdf|^5^gLb2r*Kqs6>6GFMxk(K3s>Jv~0!uCF~l+VAaJ z_LBVl`!}<g3ubD{0(JM3{dBF2fWQm5fGN#d#2KEqfTarKy=DlRCj6)|U$mu_Lfj?S zjy5O!4*WNJF0{6l+8p?tlRl~pxg9<_;yBfMeo~NXtUfR>;P9^@?$+`yUwPq>03|*F zLHe<mhQh*pomWkI0<ZDLH+6nf7t<IHCAZJo5FyE;&a9WT(%R8x%t)gK%*ZI>$e0<h z$ITY7bN=z()+@j^W1hpN2YZV}-KnDdZI1z}xjimaA39ov&{ikvQs=(?e(=orux;sl zQq-war`|$r6x=CY2x6Hym2dEY<ML%J+?F_h&7hxM#c0*`gdfzPKB9_e&z`-qY!3BU zof!AMqo_#8&CPwVU4Q(>ygp#C(3D9)!4@K*aypdh*N=~!P+#iVQ4-&GBN+ub+Nzbq zuGw^o^{r8nwx9i2Qd+vcxmmMhL!_kNz!5SSRaD0z#Qp4b*#rIA@5bqgBxktgU*={K zTa3<i<fQG}a?L!0Zqi%rxtJvGe(}teTa^|UNV_%hkIgLVp&<d<bNl?*>FD#j96$ms zt*yFmHO^kS{a*2nT0Ay9e5P#Jtj4~t1{wt|D{C(la<@iLS65e!RFR&u<TAT+w5<MX zH_QTdZT7ZTwX>BYf7E!do_YAqt##3>0`RL@F}KayRO3~%+8-m<YyM<pWd7@|3|lMX zl6?cep~qN2%=T_Wn5rQW1I6@3TAGH3hey<6I0L_ysw%1M(GKS%1OZVr11oD(eSN+A z+{Qs)<V<UXeu0tT%<QbY)B>Ee&*~We*eP6*Wp{hKMOOmvAhgA{hgxZ3H#%auHn${9 zHSu(EXgbwyTDE<<j~ZmTMpz%na>1-nfy651dSga%$Ek?BG}t+%<)3BmZoNCbTDz%L zI8cxBo_s7iTH%a~H#9rX#}@_Bu|D@wTQ66O<!+fnd@jy4t)|+#?No1ubk}mlWG~<> z-;It-Bjpd701nIoDzqMWt115dQ+konM=PY=AaKnt^Kn8XlPH2g>W+{6R;qBU4#R~# zrrzU3Nt{a1%OoLzt+rRjYoRDzHK|++PhrR5JlO!M3{$sl<>z0w@5k^S-*p~?rT}1| z1v)|>6cjZmgV16ihr^7WKgXBYbZr1?oZA%-r99gdL_~qKG^9KP9;}maDnFkB48!p7 z&MXes_EKA>Lv+%=S)KoL3naa`%M^;opkS(zge?c!oX6sKDgjwhtxTC7C@}4vomTBH z&co{=kn_k*0QhG>A^=I1=eO!0<PZLhIxqb!i$LylaPXE>)qDc8C&0lFSnb?yS#|7N zj1u{)LkjvAu6x(fXRhefsV7ntR*X0f0+@>WVBFU0T>oaG{}o|nWl;(?ACe{=pm-7h z0h!@NCK1Q>r)0D~0ONj**NDOd>GAtp$fXRQnG*e1r=a*FIfVfK$NT@*0e=d4{<$^b zi(_%BSr$1sJiPxchwSAg?OxAE!><gB%;NY=Q=kcL1Rie(rir_Uy3KcfEi#u~soQtL zovVR>6aWmo_jggn$488cN#x#Kdra-YMi;Y$2NpKitbF01b_LM%!Xw|XQ!H);iCsl} zrqydr6#OrelasUG+GY5LF7%`d*>uuf)JjuTRwj<;F<j`CT%Ciz-JYG>R*;xqYF6uz z%DM}&g^h}O3r$H1{yVXDvPX1MLqmfS!GH>(l;_38lTUN0^;fyueR+O%HZ5TFXBp3A zz&@|&m<s`X3CT1<5)$OcCVXb8)Z#8T@>YyB0a8nwU3Z4tM_M>^4MhNSfMwO9zShxR z(kgy1%xBm9JIGe3Hjmo~U>eNNHjoF2%LaTik+Cmdre}`Am~A*tc0Ak|EMDz!hANaQ z8&0Qc;!8$)PxN@rOaC?Y50m~cJEi^1Jjbm*fBJMCW*`Lw;Z-?l<gnY=+PaN<4hsTY z#9{AR;(qan`)<rJGu=3P2$&zKwf={HhpWt1SBmay;ziJ1<5r1s@%Zb2y0ZPp^XusD z?!NwfC==?(;o&xsUMhAc0fxd*DyA<m*TU$}$_0QA=81J^KSl<@_*tswX^$6+EErp^ zJ839Zxh2+q;6$;SR>cmgr@63mxRvc~{Z_bjYfx$b(%$Q$`lA%U(WY)s`^P^K>*Tx; z-x+mA#Vrp0E~((v`^eF{IDE-<;Nq!+)xg1DlaoC|&#?S%^VRa&hA>B#J5L65jEad6 z+_}k#;RsQ+b-PA}i&D8V{7NBXMgM5*Y~DIfVlo1&I-QFG<iNZ;OTCnwtaz(H^<MlH z-)ij;_nN2Cg=U(e5yo;b?AjsNT`?--LBU;sWfbQ|%{1V1iU#k3V&Yq4;(5mJWWYpB zN+cH}--1S|n@=~*e8ct9N+0a6@)g}wRFsNF?mgzw5@YV{OoMIqoba&8*T-FH45-g} z!g*M%2^aEJj_E#GJZ#~ems7uf!JPQcwr7okoMV2?vUg>Cac>3)m~7xA=uWqNFCxjh zS88kMKZz+8P`mC(dO&+Yso)#H4VatK*$}`2=yS{$mtG)V(KYT`1EX|R8Nv=Ai1Y~o zN#UL#bCLCQQ%IFrV4w6h4ZI4S5`~P!z$qm9cLyG`r5}F<aGqay+dpjY<X4aPC@`lZ z&zTM#TBlq;Z`pJ1^!E?hdp)8x#n)nZM19sQ0ESayEE&@zJi`%yG3K}W88E!8wjG=b zw0M~Bce(nGdm{cr9LNn9n!1|=z>H~-Zd~K{ruSG@A~Q4di`U%7)*TK#(tB@mT--)Q zP*#Q4PzjDd=`hw0hO6ISjmf8{r&or|gONJ!>YdmW;Rn~;=2X9Z`?fQmXii=bm6gSA zWo5Otz1=r_GQR`g@G>JKV|F8ig5le*EA_U0@I%MhKI=2P?{E8`1m%lL8wzDFkymfj z&<1b2Z%ZpbQ;sa&-Hn0dfq1X1l#e-}AQiauAt)$_uad9g{y?C4LP|;})MUj7dR}`d zTd;}D+EVL;eNvm<N|~FJ`VIfLvH3gKgVp|51b~8oSeUOofbK8@X;!~Q!Qe`OM8Q<c zLJuk~&s?_e@c4*VKq?LJ1Hp?d-3EDJ@nEFCy6-YnxB+9_H7I{Dr`k`}9p0Ayf)F;V z^~b$P!9p4aG(5mm6K7<55%>x52DRp(VQsoOS;F%QjP0N29?L5z{D!4$;`+SJybg9; zVFXdBa2hYHB#t@wcJ5>;lYs;}pS05brL{C(UQGK@67sa{!Mm~SFdj8pinF1O{N58C z>+VcX3up!^1x?>-RLz)ce2Hf319}Ao-3XcY9ERSnBXjo9Uj5FF4>CeRhp->77DhRL z{!0&&V$DpO!)Q4#KySh<`4DoXdwDK?{|aD5eWD0xl;@0sLC>F4!rt!yKXZSgcK3D> zztr*ZFMkBBLnE(w-$pkHoSz<Png?NnJExCf{=I>>j+xh#7Eu&2`7G%2pC2kFI)NWS zx$7%<cwAQsr>pwYHd^h;BNxR|(%9GtUAtj-6^3k=U;XEC^|Jd*r6z3ofFs0@OmkGx z*Mx-ljTL~i5~K`<L;DIvK49<nC8z4&Z+RZE;3Sc?#KgoH9wyC5!pvA}V&L$;)Z6@{ zKXF6SMNO30=LZbGbG1xeWpAus6-kZc(|`LSJ;mV;)zPb0hG*onU%$?Sk)SR^Qc`k1 zQ=<3bO!4S4!{<W?j1+5La{ypJiY=RoD!Lw0S(&}9R*;wfHTaHJ_V_>uwh5NQ`<zYo zsARi_(eQxn*(wyOb+=rvrH0~BGH$c9{-h*-X#>^u8K_lF$<lseb(RG4g>E0>eNLtJ zD9afo4cSzhT3E27pp5{-iz?o(eY*f9;D@6K%(U8oc2MpAfo(vAkz=3*SAB+RRI|A? zpsinOtNHWO{g*|$nlT4D$nO@W9C=<gsF$((w=~_7z)Oyq5r!G<X^*Y-Sfwi^qW}i_ zm!RK2N8DckyIF9ywHuv0&(8^mCnYf=VFP?Exkt)@?C~yjsO4J6rA+AXdFm5hFa|T2 z&@(Zq!)BmVZ{C~+W~pkT!v5>)Uef`Ns;_oo*hI<c8I)12lj*;sHG*e2FZu|dU}R{8 z(tuof*gQ}Z@ZMP&e=%*@!%v$cMYS8`kVCexw~V1Tip)L(arM!CtC$_S^Z;_efSh2Y z_URZsb@tj^P!}?k?EPD&K$t_faYB5AU-~m#lzANVroZH@na76T|7p*?=L2GzE&$KH zTa_Lcr|9FeW3#%s$y?!K)kXgk3gfNXm1_TC7sta#0adf7<kjM``kd>1moMU*zf4H@ z0zfWL?+y9+^XCl`XI;My4BRPqJj-ib-o3s#TfJB~8_MK_z`sw$5-}82`w#9ye>3X_ znEWnX67TZe`~-E@QhUd%B7uS5qS!9|{iWNamgv<n8*ao6^ABwBw_0EKqt)Xh>cGH2 zgaCS_5H=X5?H_yYMug8tA-{hXeeLY*QfXN7GbS5Jc&}b5-Yz<obm=*kju=80_AU+( z+BcZ*pj9`U2OhS<e(>2Za?DU%$G<$&nznfCFx0frL@*UGoZt3tc~tzPuh1C@Z?6$p z!8>T@45mVtW*(e;R%`Vqk(@{5IljkZR-TLa_MBKC&EKHlDq3x=cr^1f+yyg#|KK6J zmY6<&g@YqQ0Z*qTB7NjD6Q=$BZPUF<U15hQ21eB{zXvX`iA{ZLqer6`xl!Rh@0PYa zVyRgPO=R(ZvC%1d^rWED#Joe_gYLl!WW3p*?upNgUXo8@VxGobp5AEl{uD1>^)R)I z%~(;f09nBsxR3V+p6aCwzkV2@w45eaHjs-yKrYVh{o&Z%<vA(?QVq@Rfh2q!C#w3< zVP>#3l|^R^XHKTXd;*T9DsE}XvF>Q^0n~|$=R}<}N^N^8|9&?{=JrwVT<<@F&ycqK zS6s0mN4Xys^(j>a6&y}?zScw>7C$!_#S-T*F~-}+8JpVOt$_@4z$CUSP)jSq1k=&m zYYSXb?fMrc06@n`g6-_@8=Jnz8X_e?{$Ih>-jxx8qz9<+HxLdTB-Czh`(8wH+&Z&u z(Kz6q?*UG|CB5oi3uW%Q^F*4lQ&k9{Z8x9_?-;$oZhxaI#xkf$xS;$D=#S2yf11d< zSdMdbB>XBN$E^z83uwju3g=1Y>AqK5=@Mf;vyn*Uh7ylZx3U3J4}X4o1#RX67uQD^ z!&d5#0}87a&iw(d1=pL-FGlPHtKV9k%mlRft<Y2wkvCv|cK>i6*sC$uHWp-(0zix; zFbu+D8+%IRLCUb#FWd=K<FD{Z6YNZeJ6tLr7NQJ}i=*qxkiB|!Dct+r>PTrA;O(yv zmJt(g0r7Mv2@=$dpK5@SY8Nz&o0^)flarZ^!6Z-)nCf;Hm|*bO1=`#PILZ4!o|Qcq zUI6GhQ@6h|3okFglsyaFqSW6(eL_M)0mmQ3%zb4rog-riP}+EAkN@6|Vw3Gn60~Ln zA`y5w?=C)nw6{KT3M&(MOH+(^QqTe5Ko0P?c$-t9+`#$W`DhhlBMbynD3owhT~!Gn z-%v0ieg5^3c{p9n6(0i>Z#rz3BtMgw3p)rEB-7K=D5-<BW{o6)C(EN1`ZZn;-)bbw z!^D7;H481?XsD#XsZnG+0PK!j8gd(7r^?L+nX#%pemBA8?tgu)lwqEV&F%MgNAr7l z9S5K|cnrN3Hx=XfwdIT$?{IUcjnpLma+kGe_4Aqi6~{T2F75a4b%pEG7OU29JRRVT zah||Z&cYyx$TE(=fB}14x@@B0-iDoyVv)3mosDXP8n+1a3=s7t&XY0_|NbZJ1oVV> zBxPQ3qLgnH{Q8djxVwusExp(_T!b6^w@i*P`vP=4=ut(f_=QFvIRNDWG7T*>tyy@K zI+SlnOG`WJzx3h-4bBq?y6nK?BRU>~=MaHk%O4s;LDq&gonAp!8T8e$W?T#EdKxDN z=T4}Db_S($CWINU7@JDKj+7l{^ZJZ|8n3Fus+PZ?3c8;gG=>av2l{b+I*iS2@k52) zviWB|@0GC*m_8XLJT3=R6+8j5j}H_)tHvNALSkb4;=?fs1C`?jKsg^_&|%)*hZswS z&p;ZPMs+<H%xi?DyFI`5rL}eayRq~7&TO=Pj%v(7y?qS+Thj&(vx_{!@q^haPlCuW zx;W)SW|h8YbWf4+grr!hoIO>y_lE~)$F{o1`#XQ~iz+vJCGP{53^QlP-?}&Ii5G>1 z;}O|QA?(yg|6MoMzynv*wQJW<l`!nzsSl6_Oeh1L)&|HH|E;eo9)NI$?;;dpdkp8Z zh=^Gv)<LYXSe^KM%WLx*CEEKdX%;r|CS*P)t6eoHJA3@dzZaE<mgANS2naw}Lxjcv z>TDCz!-|m@kt3j0&%I4`s{S?T1_1I$i4BEw?K+|W$MG0Or8?IoL#K#`=I;tRnJ&bw z%)y$%mnfDlU?G%YVP_%2sioE$h@yw{bU80-Bq5ZkcBaf@QVNP}$YH6&tpP$JqN@FR z`4(Qs(3-vh*H<|?7g9tXc7p!%42vzUW=1EfYiW4}IDw2YFX72hZ;$R`^?hpf_s`dd zdma_TaWH$8N_sRb;M#ZtkDdj(6pv==7n<C?d(Q9Q(kr$2E8^dqpJBb-wue6f15&GT zqwtu3lv_6_EAKL8&G<-wvZ7*bD#*HtH?K^g>`O<|1{!idodOwI2SGm9h=%YN{B?0& zmJTc+{Z3~=tl5x1B4_-BR#eO>2GM$*W90Ym9A<HRxtPsIu~n%MlE4vigapAg8IaAU z3~s=Z2JyRUz0Vg{M!lSw?NC$aDaOU~;FKa+6z5Kl9hMcqRh4?pj}2eg)rQjXJN;w6 zPT9LOqHod1<BJnpZ|bz3RBZ8NHt&imdT6N6X^-aD>zBl^FiSq6Jvw*^v94R-wT%Bb zph^mQ|Lg4Ro2sg)!WBw0^v4WK#+;m-7cXAO0Xzm~-pQ90WO#lz5gAqtAOZ4&s_gf- z-HGMaH6Y@MT|(v1>t<iSe(f!~?~U9(2ecP_8r?#ZC$be@_<k|~ErG<ie5d3BFf9g& zoE(PS0s{RHIRj52-M7W{l#Al-2GP+NgjIS2YafXA%d%36Ej5U+oG3sUJf{$bBIP$O zZ^)diqTOl0E78-ZIM@Y2))Ofmig&FzM(*FYD1~87<B4pb>PIWv#k0-*$*`YhS^yE$ z#gvbm)Z&|DFSo~@OS8j&0B3?VL<;ihsK0^J^o}T3mo(DXXmCKR0>3@ORWOFPM02O? z3MS=71lC6!x%FvMj6oDi+29>MVgvAzL|%)T!{bDf6xFvf8EyJ_0YV}H35m6*!Wd+B zq&XKJ6F*-73U)2b`&b5nB`>|+W&HBjWS@e2Y~2e9q&`G~X~9e#)<}_a@q$HPXVb!} zwtYuz!uJ^yl02Z=XUdQ}#8@Ai8zo=cAu{p{$`%O%Y5{RY@K2HPS}+fiD=W6_BR~XC z`v+=fiV{=fZOs_kI)ufx4<UzvA7wq&)Rj<HdHOQ2BqFl1*Ive|N(PMmaZ^;By8?IP z^CXT_1=UB2Il6J<Y3As&0%TDWH<3kMV3VPg^ZEUa-q7lsGR=RtXPUB^ecl8`$)o>e z#z8~(4-qVL2$m^jve)tQUT!V{%>p+k<|o<J|Gu0Ct5<z?dWVGQj!ERro-C#RWKNiS z!SQC?SJ|CQsJG1G^0sazCu@#J;ED@!F03U-)a+ssDN|OhD6tyn%>Vs1w}tiyY9{Ms zrO5U*bNNk?vfCpED~cSfOGYIz%^-!2VoNHrHDWk!_6!mHJOhFMZg=pAkkCy{Rg{!7 zRTd{IW4wmJ&miGHKY;9+1veR)1>+78@o6`w;Saf_{QAVlQx?DoU0{>4_Y~*ga3ex) z*r?NY@z^mS<L|t~=4Q6)d?)7Z6S%ubyF*2E40Eis2VlVOcn~g^;JBz4nDMp-Q9e$W ziXS1bF`T^i4vvtyCu;~<Y7KH20@@GpW?ce=?3)Fy7DAQTP=nhG_T8g%iBXGhu;rZX zKKo5O;}aA(bO+~uF45K?PWUwRv|0Jptos&dwDW6-;a70V?NWa9Yh%KgEFol7F%W`b zYBG=LkTQ<n+{|0JFvI-8;uEcrVv8U0%Xx$DOmb45hnWg-)`U8r!SGNUihRfI`jdZi zzs)i24nrv5{IEW>$j`?AnMRiW0QHiZg@Z?UXvRTN@d7`xD{<CTS-B()FRWTgqoRnX z09z+~2mQtuX}GVLHM10?{mB`7C27U@dS}%B=S#PKezZnCjlW{!%9Y6Q?j$Xg;Kmi+ zh~Zmw#L^qO`8p7Ba?F?_KXA+I?m5&eGXZ?YH|a=@BOeSVQc^<GoVM~jn>Y^utHzlP z09Hgk8XePxR9lOQ;v?$r^iDvoKj7pgeELtq|JunZ<}nl;69B)1zEgEA3%TNi+{vDI zk+P=^lPgQY?n!09PLOPdQn8=vH4yko3Kpq#<hx0tpD?a2ON0hIjA(QpaECLEwGM>+ zf<(1`qM?>XCXvA9g`a%&ExLgt{)*-1CMQThpJv-W@IhVRyMg>rh;O5K!zm>D68Whc z?k^M4qu1@BGO~8^3%r1aliyXzp^MWbh`?fTNOtI0a;aODjui0ChUcAmj>VqTT%Cz; zgnRx`NCkz-2sD%yg^({TMy5??1^~JL&u2WyvBB1a0>bB?bmv?+*`5ewdvwm5iQzNe z%E7wN=?x&?M{-KRSD(6J^S7$lfA8#-e627FAT}1;hNKvK==jQkUlM~BUbC;{lOf8x ziY?YiuD#fLc_}@Rnpd}}Nv+WAR+$3^?jL*U<SUR?z4iM<#CMH4h9U}e2{1W3K2Xeg zkwAAt;^T&19|dOON{)|fPy{_ubH4=CWNKsryP+4&Qd&)zh<$Iyg3*`szH0()ehz$S zV0jBg%Rv#zm?-Nyi6>EH>vD<-8|)!Y(a}gnzoAFoMYmQJ$qsLEWFVz2IOj|(T26&0 z33^MrhE{pyIW&4#6bWoSBV|-@cJ{G2t<fRmfK(Kc1MrcHLfjpHu9<wPLMxs`#L4TI zuQJIor$H7$>u;GwvIrZOt@44d@y$tHc1Jb@+eDAbE<@7nWPh;oqD1~qms}j=@z3UJ z8zUi-rj2YjT{*e|dVW)99Nx+022L*5B!W+h`#^9D39x7+v(M{AF@J=eh&YM4=p5uF z@=eky?vuNnNkI7&yhyWU*V9?Lb0>%E;$73wE~ViDvW9mok^+>?NSd}Z^5PYPQ;8B( zX&^yklaqf(<<fB90)n8ec=RI?p^<60y3+0ZP+oS-^MPE7dAbTLw!*DTI~68aa7>6M zT)iWI>lPh&BVIdC)`2KG%A$Q~<}b4(MnOHUxbpPy2KA|nC2D81R>wME<|mV_;4y+p zdOiW&0PO4Luk2_&7#jejx5jbn{~E2}2R+xaGmbk=%J&<KHqT5MGoC=fy_*p{uAg6* zIp86*FXE;{lT;8ke0!rV1kQtiqg}hV+L>C%M~BazKc53si%e$OrZry3NQ9_bKbF0` z<Jf0zGc(D_TUlK#Z224h<qN(GOuhfT7QdJSnA<=pnybQP%myCsvrPsbOM{wb0Yo?m zNBngBW@+GUff0`FeY&K$T+6UMA^0B2^`4YH0YA%Y;|eGLgY~uold4!mJnHY)IZaEO z4E8%1kmkTcW&yJm(d}4ln@b_y)u(s@dt;;)-;NINUA%O1>T-BM8mu-vA?Qw*OoVgT z0E?RBsQlpj+YvC6mwGMVMF?1!4SkOfx5+3dc-l7<ug)gnpW=P0-*CP)9DTRs!aFju zw4Wb?lfFcL;Jc~Vf~%57UF70g2l96vq{dRPxuGbVT~<!j{eGL8cRQaR+>3~agK%tM zw%ddOVgvqG*GQqGb&^z2jcLSMnyOQC<qwfWVQqN$&;ri72^I#7)S)tHVxg8(IGhT| z=f8%F*i5TE&R@RV?#l-FvD#}{4Qw1RW$*(B4rMCSf|2-Lhz}Ke(m+F40^fYx32-5V zBLor7?av@Lj?Niu7QY5^sX_jG1Il1xtuxqf@PH_GQ@PK<dTDm+K6q@NG>H>Yo>biX zW2aHt8BzdKN#UFk?GTJ<9DUoG+S)kq3B5A={AlH}X3MPD_hTXzm031sM_1vMX?r4f z2b|W@$jHv_zOhLR7VGS;jsgk$g7l*;cBtdwtHy8C&VZsmANpQVO5f7_n1n|6BL|SN z_j2F5c}>c^?v31n(kndnjd%fwMv!?0v?~r_aP$51E_X~f6l>`64^`W}LF&AKTh#_~ z=>!ExJu$|gB4P7yfaz${V-(K!{dVTgbvueAc`3+fO@ar&Z(-**q>233rfwX;<tX@M z0}jN5Y`ZIe9a1(=ftzt@tV;jzs3lii1*INeQh9p&1cdd)tOB#?;|lVxN>i0CB`US& z%hBP!?{o+QpI>0J09bx*2XxV_-;F}cWs+fVDGj5JkUyu<>!!P>Fu)@siX|dg_jVx) zqmB*@ms%YB=E1=MGG9SjQnJZN)!SfwJ^(PpG|3V%!$6J@h^J|9g5^`E$d?Yi?!h-g zG?s734;G5~18?xa<eAsYm_Pm-&%<h#qq7;0Zou^GyncUs7I>=*P9spcIIPZBIO6;M zdKCsKUnI$TH%F&KOZ(E{Aa~YUGzy=t$eayBn<{FIRCYKcwuUg)N9`=sJbJ$XzaJ>b zlZJ<L@Nl)xgpcJLjaRqyq}_atbe1<8EGQO9OzQRf@@rN4ly0ME{KX+$>V6$u?5`{4 zehr^Rfl{HwbVE_`YTPQyXM5ZmQPOLDH-T3T-z?$T{o+(Q@}t`GL4Z1XU83$A{+ILB zwwed{1RY|JQ|1Uruz5O-e`3K69jpEw^>g_j1)mTlXCGVDxOof1jvB2!$#E7t?iu?t z!~+J^e6@3%bgJAWrFy|D8Eex175|H&G72vo_|Ip&#$v#K##eimBKR$HE&(R~SW#}7 zZ!j&k{uv_ZM?f}3l?}t%8}M{~v}z?^@{$BI7N>+nb(T8KI%Ay(5#<6YdlJ)){}s@y zuEr>-M_ZTK0uKZc5ecX&0(S5~fb^3JWgd^_+_}zQU$d*gd^mnP6sxjXrpb<q8G4VV zp;tCgBhU3-er@kqFvRr?yTe?eftM-f8icTa$Vd-7xj26y_(0O_h{M9dUV{G@BqQJn z+*QtUu=W&>-jv?%dcjX5CmR?TE^3czSf^q_U~7wL6&LEnYApRIF8mA+!Vsen+5+bD z_3dpt&<PNC?HIT=?%CMD_021@Dbdjh2}#(P_>i8y=oji~siuta_CIJav$(?`Fr^c< z@#47v^N+A@U_g}c_=ReKqC}iTmCYpVK-d7H2=@8&d*H}jxvbEVE&eu8K`pL<KdT{e z_U`+Zs#Uymcy#EGp~m<Y{{B)R?Z5Tk-RSKyC@^}`W19`qNpBaJlDN(5YTfS|wIuG$ z5YvR6#=XV#t&FPtd`3%e2<X!f3edo}Z=~epZby<lJP}~*MfF1*F;{46Y7V*T%)Crv zl2c18uZ#_1U}n+0H}oE(jHjenFqMEZD!F$P?3Svwwii(kz89S5;tByzA8w03Nit-1 zb{5#v=aQW?>-N2<U%{Lhmk9|uXy-BbxCGP>aM*&Kv>Eun9;lp%nWA{a@ssl;>#@6Z zJ#-wT47K<6BIM@Doxj<$ph(&jAI2**cfQZU!BN)^8wWm+r%#^>fI}7`>VaehLDUXj zL?Ooj7t4Tq(E)7>G~1=9Yl>1SfD#AW2rOpaYX0c!p$96y(M26qaJTFVn8II!lM473 zT@ZX^j&`hp>>^6iZ@F{l&S0J{qVLcDfZ30SIaSWzlU(e*!|kBguiLfMIbFNx!8DIv z=XP3@1})jT?(pBJNfD2Eos|1Rw=1zc+T5Cv7z9=n&7a^@Ih12SK%C?E=LGdfYE3D^ zEol`p03yb^csVhNkM(3_LuA+QC*Qnz@6B8MI!Sy|osE<SDAk=!ZKS)e^E$1{q=F<1 zswXhxUj&{<Oca8vWra_v#ac`RL#U*Dn&zB?tMGU3p@Orhw(lRBGvepnsXoGC&6KZ} zt0!EY26Lq~G~dL}A3(1HU2^4$Qp@bl9)tyG06`p_9b)dGw8j+@Op4GP{HkoocYsBo zI|eP~2bhXNElvJ4J5|&mQ5`=6gZV68{{l`$W8f|7LfQj*eh!Wm9Kazm+KVsXkp)M` ztJKn4G;o4k7cR80XmicP18nh^M&ZX*FPuBYXQt7V8K{TL@GF3+q2*E~7wQO1sfd9D zG6BpZ(}3UB9eUj{XHdr@q$OS-kg1c729i1V?OVhs86q1KvG&(`suyQOk`$k&F|*Kv z@Gvv95YZqRs#{sTJ}JnmLl<c-K`yIhN}k$STdh9<D$a27OW!{*fh7#!4%>>454tFE zW)_D!Hvawn1D;rkRY+?*JO)Axy&Z_UOrVv8z(9gHf0dO(B{P&;cx>$My$IO2kcIlC zvtWYK7^{o{V2Qx~oHh3@hedeyAOnD1sXI;!LNgS6ue!Rrz^5)e@`L6E4zh9GaBQ2a zBWfLh<>f1#GY(P;J|iWH1|K?;$7{q(+_=sZ)$}4Ynp_wb;k&sYe(c0Z3=x@VLJRC1 z(xp}0M4s^7(aYS&aSv1r+j*@ntc9OJV2;y50%`|9QO&HS5;+lWum0N`-olpBxcj3C z%lf%)JKK|jlnT=T-y>nO_P2h|MunZ>rqk<V!ix)x10zCx7Q5alwgn9nX5FEDW7Aly z>Z|z#O|Y-H{=rMCK^z%n_IM3=AIRv@t~?dQ4Io}4I>U(s^7-H0yXB5#4WP6z3R)3@ zzUqb>i14|fiDz!ItCfxtTdP050}WAidZ8RuY1hZ<R5?wo9K{SIsX8_~8n;#T?-aP1 ztiZ&xwKyCNf<aY2Av$_aidc&i!*F@{6@f*0@J(s2?w?k$H-fM?V2O7FcS5oEsu>{e zW_Vgd6WIIoVFqr!r|)m)$7W>oW=G3(0&jn^X=>tbg#DxzPEBeFIX;JbDCWWIFaVB1 z1O`CWM!Z#sC=+<(p8*m$GY;SI{jF!vhho55^&&pL6%KX<DJM~u6)zthh-C?581xIR zRpb_7vKs4vx&%-g3i=v=(9_J!X^Rg*eK{-aKzHND4X8DpEC52%KD8)5FSrWT;Az^n zddxxd$5U(Vo$>!g(W=vPF!4+^;DIuH1MK|#zBM3A=mMgPy8j`1hqR6O#f#HiYZNZ( z3>S+SZhw|QpW(6UYoD5m<w6ob?=tkMpN=A67uoPI28BRJ=LJ%S#iFR#B?xwA97UL* zTN7AJYRdw71;+rtW(2bfxEc8+OT2g1EN5nBK(3GdpD(>4$0XkRy|;)bR_l27EUuaa zK;9zkmV}5mfNt_;0k_YZ0HYCqNT_B1@39oNm?pkfLHs-__Cz|<Y+{fwfd=F-lZL)w z(hD2ZpLHXS*Ej)A))f>fq5I!1K*`60b^@hCBMWSC1x6o(z-lI7(SY)fJ3r%^rkN#- z?{g=<+MDD$71ZHVPIp3B(db6t*1v$Qx&b_HAhktoRwHT<V02JakSRt{NvR4cR<lwh zMtEx_S3FmHzkIY7uXf{l*A`9t9&~jMzS;7Zau6e5ka+-$=CuD_a2E0uR1#V=`UY6b zedl6SlEAnNu48vYG7eT1PK>W%j+IjQa8a~nMJYT-fz*HQTVp@NIIdrhyAez*Xy4E7 zEfa|~6rfb##2Dr|WXfD{`q{E_r!-I4o<=0K=Om|z5=oG(IXc+XHu!M2b-FoB-183| z=+Eu#s<0kq5SZ?NFG#M7iRkN)@=GWfyd{?^xA9(Yd!@i{QxnwVn9BRKT<5MAS6@KK z$T!;H2?z<jga;WmN9;0P_iD?jpjy-XQ%kto@pyZ@?P0}uXlCZ9?JFfIwT<a!6rqX= z?MbtkIDh$CEV*e-$#xsth_qihf<7ZEFFb|^C0yY^*I?*$hq(smQ7Nf3@F9K6xk-x% zoZ!!eZrdh636GQ@K<w<bd!%40F1&oBWmfzVDm0$&p)1$Ko4c7Zm@1|pf<Yi{@6N`e z8&p8B0EugMbyXY2+~1S+vd9BoV|<*P!C*?=fYe=|jg~C|lo(3Q)(f-7R5;SP@u!Bk zeS3s~Ow!(XLa>x7o9Xa)|1Q)e>~I$#Q>doMg9!k#r@Hv-=Ky+j3m@P?JO2s~cp$dJ zx}6ycumbHuhq(h{Au>w%kYfznpnXNAIul#84V#;Ps;UM0#k7uK3S2}OAhXN;I`Sd1 zHd|=5A*R3zY<=>*4*24SpPHi6Y|PyqFr)8;Ql0djzW~}n8B&Ac100dfXC^B^-hn_W z1qu8h;rz_HUhhMEGJar~)oQg}JU(suPqCN>K6DiZa*lrzZw=-!<XMS^h6bKGXBazS zn8mGCu|4MqNp>fP_<cWEd+3FlVYtfv_lF?DbFDkU>;ukrFtwQcJp_-9>QJu?_Z(%- zVWz&^bt3=U;Y05f5Sy2s&Bx7D=aW90KQD>0wX=i$4;<R2CM9ifs=!qsC>8AQ|DID8 zj|>t@usNhl-nNG=d6}FB<0dRm-84yw9AmvF`bm-OpwO!7X;P>6iiToq_&oq|D<h={ z@z;Vl2k1~`7*{H2whmxjMg<106aDMfIGNM5`0f)v{<n1Jd%+#|q3tu=OLQpH+)gUd z;@`p4s^b1hV8_*|veV92hnY@-j|?N}y>btp9x3#>93y`duiq12UOvyajm42!GNE4f zI89CZk%`MhB~Xzm>TfYr&F#Zss+PGI`wMSp2L|HHD}dL5NyVT55zWv=FIUkGpw11Q z@x;<OIN&eeZT$N67PJ;<g(aR%gq@%6TYKIF(v@oxuh^u@tuRj^M@OI5emEbz_)7;1 zOi_hqJun1Z{#yJBnJ9dymhtIbg}^ibR0ZZO#+17jOzWAFL2e-SAte>-4FsfX>7aER z$Rmz;cuzsd@)QqIOK%Krw4H@$P5W6jfRME(WOS-{ZlHQX3qM=#LVE~D{;gL{WyAQz zu&$FcM$8;eZ|MbAZhN=Wp#Jq_YU}yeBCNg-37}~Z20V(aJpbr`(EohNu{_|YYG@wb zBRYAHT5dbv-HFa+dub$xRnitY$_#{->$#5qD}XCiI9oAW`G?T8A0oabC*P7f`4+AH zcIk*?Zy83iemC?4(x@u7l)~XwCa1_TJTF-urG?oUMD(R3ksjnOAU(6NOb8rGNFuc- z|1l07#&hK}0|xdtHa^zMRq4O%Yb5hP{ZV5h6dz;b=hS&V<DQDQ^5j5aSxON<*$*j1 zYhvP({C*tI6V_u}TA9++$Ip)W8B9V@yd1&b-rHVW$pO?YLJ%sS2y$lS1+DQrQSae7 zAbSl;0NTa>t;p-D$i~I_|Dwr48NE0N4t@v47iD}&t0g1-;mL|rU?(DD=}^47hY7}{ z;m_xmCO$J8eQH0sC4>oO63@kS#g|r|o;=OSs?{U8f|^?8uPZJG){^>U1u6LDT9L-i zt+2!v@U4Hei8$FtI%FF^+S$g!{E9-K!@KJ8ic3vrx_S{c$aq(A&KyYgu?t-1@tU35 zfDC^z)B1t0?a~mE-2V=@Xu(Ez#~2uJ5+sIRZW=fIIM{k)8*-Y8N$-=_Lm-cyr3!bQ ztT8m;%CpL6A{e7S+fs2AJnw+b$h%FzydtFY#a?~6&bW`Tb_3|#WI!|QPTe@gZ)}Sw zJ*)^tuL(hL7Rjpb75pg4x?K>y;3&Z>icP)1je7L_kLV<2L(F90rTMX5M>N`;-og`) zBP3p16YmmNW5-LEz3VwS7|W!@u%~h{95iQ$lp=zZQZ2)8y;p>RfZ4bvL0a}eZv4E$ z)prVtE&9OwRz8uIOhwoi`9Xqy0gFCXqf%m#hu37iRznZ>r@NE8CS?U^I^&*@pUJY- z#72gMQoZtZziiOGJa4!>5C+cJ60p_%8S~?Z7P{QW%`x*1*+_Qb^LU&*6vlF>^MZl~ ziSB=+Sb}#qd!pZ{Pmgt6tRg2BP}ys0ZbTP;`!=<7$NiRo&;Vt`{&w?fJ+9W-@#-gg zJYHk@(Q=)l3EAjrjx#l0Hp}@%2t^ik{FgycGo^BC9YmvNNH~Z^V|et5p}0llSIJ+} zZt|XN9DG;ozbiXXWf5BYCGwRnXP!Bm!yra2KFi-19{BdL?`p?ntMs6%?5LN1-~}F2 z1s;*rcs2f8UTB-^SXkz{g<+NMj@C+Nzj}3&p9g%f;lrkFE8_0hRB2;)e%t>fvC<|9 zfATOA7nqREerfDZwzY7#A0&PB?`dy(&+ni4WY#xs*yiQyG&28|gx*>FsBHIu$1vrU zkPNHLZp#5FnP6s3VBoKTHp&#kcK}6}GK9hoWsfyV(VV0%H;RGvQbrPG3WU5Zlzl~W z!JV~*)CK!Ag~L4o3(VQ-D+V6UR9tEee}_}qQ19Lq`v`WYQ*16I>wkfNuvuF6*NBe! zUNZ6fAkN`SWR)Vq^;(`Nc4l^@-sSvlziqEwWH=^vAmE%KW@SSydnL)RoV$)mbrxwh zcM%x#Ox)d$WSR!bk?qKh_MAg#W3)`rXTP4iW{=3;8;TB}4md3HeO%?XHutjF-7bUe zCj`2}By7-_ULB~SAZxl3*rB#sSQ_dNZMEj%Nj=9AE2lA^btU(oe>eMH-Ph0OIe<bE zrCfJVQsv|_bcIob(#necJl=$F+nBEZa@hi~7YjCQCD4gbFV=9Gf5<L#$RJ7)RjiaN z)zr~pe+9qC66u5Cln1|+qpqurz&{k?FO`mTPf2)PWtJk9J+O!({OMS1kKS4&iRVcz z;yOS5nwl1bOTkE{V9Vmb#-N~lbH$cmBm<IQWN<4a`=qjk>h27xm<*NhwD{+^wjBl! zv122IRE}O}e<)n*BqEkCGFzx_7_YHxA-#O(+n5bd_X~r6A8$97KdhSg3{U5v00Y&d zB0yDRhU4Q)fI$R0t>e+r(}b({9|{^*$mbg@JgS?!SK2nQ_jheN%+}%x<rTxwVKbrJ z;#|$(2Zc|zJa(3Z@=Ql5!|M+dvePwN3r)eO6jSNSd6VRnkwG;Udm9qW<m#?T7Aend z@_L@JH-D~q)rkKY&Use#j#J{?MGXv>diTYy5M7-YRfVoYS4!{yjTB88Xm-b?3{3Q- zB;yGRz3fk7R1<MNWlsm;{1UP#B0VyigdKw90lAnPVPQ=#^IvuCu$6JkP1{NwYgg|K zuZ{@Q0ZvxHE=rt8$|G+?-DO;&^3cm&biLB`)}KK#ICS*n9DyvAB6)(fn&4~y-Jrk? z^8mrVVbf3Tj=3AZa~CAUwT&X|LsEpNXQO+l1fnAOAmN*-^`}3!0nL>5vo3wW)z!f8 z3Wr|S$sKltqve`0!{(wYN2gDBf9HlQRS2y-&@e5cqqTPO8L8-7;x~Fj-q;qUyU=|W z^$#8etSW`!Sp_*$qbDRVfcDT|?vr^y+~~I=INUh&PG6vJRa0JJ`Uy#QMiBUN-{f6J z{c|1((tSfd=2zsK4lYncShb2m8Rf9!gZFOXQN>7Uk0woKfP>RNQYz15cfV2$s&ZMR zv&iU4X1%OkP_VdlX=BSXo%{q}39tki7(xXULC!udIZXrXB04Be43nsQ1bwk(@W?lt zj??a)h8tIlRncl?PE>LSwAx9$)|3Rc-2&cJ=6<{fdk>=MuYoz|37iQ9SEjKFJ>A)x zI`?w+Gwd7jOLHe-N!X#z`2LbN`x!+v`TbQwC=##bYuXGWpU75f2ON_=+ryJjR>aG9 zO1u9_z=aS=l;VskMG_fpmI}4|vibhMVk@Fs?`G<7Jae6%OQa%9m&zT_@2**vYR|-R z7e8Q-+Ile}Z`*UuJ>4c<Lg4=Cd-se?%dx6;$gaLH7R4}z`gV`JIR7I3VU!-P5jY2{ zkE;At?Vp`q7-?vD0#6!e@<2qp&UU9a_sKeo3{2FGkKNNRyHdN)cY*7UUMW^J06{G; zM)9*O;i&*yen|m>j=2sR=B1ywb=`TEfrbcrQy*}Rt#$I)9ye4>1YNV~eg}h)XdiRH zZ!WQbS_}_8x`PecIfI)zt$htkD3wsO7lF_5W1$y$^_g9_etjdtz|#UZWj(KKGo9ha z*Onpk`<H~H9L~do{*b4u^X|0NUf$W$&_PbNr>d4n!%Jk$o6Udc)!Cgd(P3e1UOm<k z`d}4OZ2Mw$&(7UteMWs!Ozg)J18_8J%M}a0UEJKZ0!0Q?E7cU`PP{Lwl@tpRloau! zv^2d2V|tA!EuM&gB<1t(g98|TdIT;zO*0ZbKmI#hJKi`s3D?187Q3AH_;at$&OCt3 zb5TB1rr0t0ZaWsM0*aPv3>fo1+SyDq(8$@p)7uSfs96arsuxM658R}pWPEYl78l`G zKp<6OMFfb?w?4b)pLTsiJWtOQiGbayPY*~UnNlUg^z}{6Kf(W=<m7^-xbJ}>i9BJM z^?^rhF1^f~9?=jZF&AC)f29bM@f${gb2+NuuBRvU%rAyY&wO!B=5r1rf9i0oc{+F* zzRHV)N-dm74786t-iJpX-hCUAZQmUTIXV(>f9op|Qkf3qwquPKiW$VhKx%`U@+#fA z20TWgFJl*ClSrNG?*DUK8m11|*Bof(usVyJUWYfz<I^M8odfq@BLhV7H_hT7OtNBv z)f25MInvJX5Nk^lxw<a+_M}TB*Hqi}1Vyp*lm)GRCb^_FcU+~YScn{61b9Xn+Eq~g zE8;slvw=tB70yQyu{?V3eccZf6hK6Z(;qFbT6uDK_{!VaJ$-7SN5JFq(~xAzXKa3y zoe9EBe*C8Ib#I`#X^^Ci^>BJ<2*kc&0M5`nWZ2yM;SIM|K&jjV@6Vt}HYKV21w9v2 zZXB;!ZuTihvP-kn?R851EYf1xjEiYe+?J}xk|LL}?CW&PAn<SH#IQZSm$8IrOGBqF z{vEHH7bT&noWl8J@q4lz74=#C>FMH!slJ`6BPIRyvIko)N6bLa2tkS;`)>bvprYWX z$#_g9R=v{%2|%Jc*pJ|E;*74$mFo~v0dDitv%w&-nI79<+n%Wb?H=7mZ5cu1wtM%I zBH#LL33$^`vz;<7udHW9uahCmn)-n{4SJ(Uy;Q&*-aGi-W0r-x)yDxoyEfZrF8k6O zyr)xI9K>s$S}U<3+MiPjPs*f#m9j-k;RDi$;am`J`THl?ay^*(y3$pSUBn@PZ&*v< zGjOqTUD-V@*LyWE>Je?U%aM~s*L5ey6&e|z{w1=Jnu-xR|K+=zj=vy}y{^d~ppuin z4{>vtA?64oQ1Ur9PouV1!<@#aw(i@HewSKqJ`<VUYQv1T2DI?Y^cc&Bf;Vm~@Kco9 zrRuj>x252y?QBz)?>W_?JL7{@*o0N6$|8&uHxMTc(S{U$QWEK~DM{F=<eD3SJB?Dj z@=|Iat38oNQVQno!E>Z`p5n)sb(@$Ejce#doSt6F!05f1`XLDA!~W-B+QooP%~G;5 zY`YQ?>lh)F*?S_5N7cSsOXUxB>ZA@1gy?wh3J`Ukfu?*|%Cu84S-`ts^=CM&Vp1e{ zLR+@+LxsF5)|YZzs86mneBo^C%S#G+u|q@Q*f5wJI&znuQ^r!W;PVOM+cVmBMcZ_B z|6`Lqb_|tUH|q7otT>UpG{?j1|17DIYU@OwEP(g3Oh^_-&l_|jxLddB2|-Vwot?4i zN30JD9@z3%`&rLM8H`zTkq>9+J5+YAr!x1)3ka}%5MAv_@kzPL?TvB87Z`(b4)?`N z?V^X~=QMB8jk!lxHT!QnF$$9dD2}5`y4K2!E-*r81l;AdQS?uP=LKcb#hni`c`MRu z0Y@D*5V@y+_Z&WB;ptWeZZ3rJK{1!OiLMR3*CR6X!7FzTlRd(aunHtHGfNO!e@S{k zlqw=Ddx`dRL7{;ndD6)Rkw+);-rwf0bvt~7;0B46CnWGN2}QYBM`8&)b%P2cuV%~Q z8F7Tx<~!3p-Mr8{J@NUxK>wKV!LRIvcbw9GDcJ+}?;99i<sgEizL2SvNr8zuK7~<< zYM>I8bGrs@FX~g?aHb)*pA(A(=s5r3Z7B#zQki0G`$SamWp_|1qN2k@y{UGr(uUQa ze6Nm|*f?-=>S3WF%?%`1ilIP8-p4f!-g1xQl3}&7?um(Skb6KLmv4}x$Hj5wRzt?g zLzgfamZ`-FsN@)axaR#<b~~*~W^L<tph)asA=U9Almj&PQ^?|tMgdA3U9mf1zd(1$ zPQF3%gTjCV-DR`dIH0kj@L4yu#v69gOcdEF%^A`F;m00}J`GD<B+2w?a=iW+mwxUH zJo<@T=R7=#3Fam6%jo3D6Kytq&F)EH#>0ccTScK?`+av271_iZaBz^NK$3L5!-RjQ z<I+Nm$M4^3Lxu}^7UR_mXwOSpMy4OIb1+D%%7AO(%sV4=eS@6)`|VbS9v;~}QVB13 ztj9G(Z`dR_(Ejp?*`pk21ahqy4wvIY*#CUP=Fi*mMvMoCqEi2o#IK0m!~X2R`hXWG z#@}THRcJu<y+AN^C1{txt(}H~ht?1Tl(!xc^q9m|;<~#cOeXGB{{NE2uk3tP=`Vg& zW$e>LSemXeTqqDI^JCe-kcYz;hWA+N-oJr@f-bX>3)HQPMPU^aC6(7)-;_+aUBT8U zDW@zyKicauq5e^nDgN-Fi+=(47;f&9t6KO_e0aMJWb9n?{kBt6&h^47Du0HAhK~^= zMslSdJbaI*3R2LPer^Hp7up|={pGyfNq8{y%|@qAyNrqmu^Z%}IF*w=yijc6M55vU ztL@CgsqWr?zatICJW-k`q$E@3AsUei4Tj9~oM~r@Yz>A6q+}i{mC95kWJ(l;jYl$1 zVVe@#GGzL_KYh>lI{%(?uIoJ4_59<?_Sx&R*1hg^ulxOaUnj42t6~qmDe1kare<jT zpumv01!~E-X^~I%nfFMqncA=W^1Tw#RAZZ6^HR5tjo-D{!gRLbsN2ss+y9gQ>kE%p zt|v=ZON2_Z<`%jPe$k8B?Q(Xn7L$7#|MA|w5Q{2yy3-?F5%P)35WXY7N8MUXXzLz@ z`mDy7reC7<N32>`Un|&~TmSI%HdO1pi5<V#Ut#8l^X>F0^W|mRjnh38IX`AoKT&I& z@))xtNs4Y`natPtd9gqS95W2cM`pVjcfLHr#3S*({c`0v1iKpM)`F;IpiF(gdC&*_ z(m#X?+#a=Ym3X`6-khEuq1i?v%KX00Z@C^%A(H1UKK--qGkA25{bt_0<rUr#uxeG5 zg4`FQ=h{r}A+)+WhrZuqbzQ%_lupGl<T=JIG3BZ0Jb8~HG`^W@_eft{-s?`&$J>k> zc5HKUKXX>+4ZB3jR@oa0WC+K)ijI28o=RLN<MiRa_UI*CLK9O!zpV{Wt5?2KV9+G8 zvZ4L;$_czbl;{R}DEIH(HTP8LPQM^EH?oJE1XRl1NQ<9>S$cIp9>qo5dIs?ap?^}t zl#P#_PCKlz?(@bysC8X4Gv68cVBZCkI4+{FBW#+@OA3);j-_N8Rb-dNr-gpa=19|u zGqZdBTEaI+3%6(Jca1ELl~}dBU`C$+HR{FP4J7__J(+sarj#>pO_WINVkVC6yMu1j zt?7Cfmv-BKSs_ZCV_`O-Y9B7yMDz_!9%($nxA9QG5jfd}H2JFrcqTMZF*ZzRi+iko zFCe0K7h9XVOi*Z9S(^yvn$MDFZgy|HjIC<<D#qT1UFjlR!soI2#cW!{=*3ML`*SI6 zx5XLU&|*Zo^)17}#?$gDYK$As7Rw_C+-{Ul#d1w)(D9*dky_V%CEvI0>kFD%WQ%Fj zALLrtd<tXFr9HIMCfixkX%Y_i6ik>_?vVD8{%lW3=}|4ZD#P|e<>|=;Gjh6qLTcdW z&y$_xhKE-RU38B9LffmL+=UDH2CuFZ6$&{}x09_Isx?pcqc(>mEZgH0qX=nJpi`El zH;Z->v(1<H)TD-|ET}+iKj`S``M2*}P%c7+xonZa!eX*Ng2s_drdEEmXdPd@SyU4( z+uxe^Mhiol`!<jST|q_L(n6+K+GK89Z@5zLL|NaXkXj1m_k%W#KTX<sMh1VI$rn39 z_~QA(`3|6;s^HInZ&TS@^0^P+jjln+_RQVt(CJ4k=p4MW%}$F&&cRZwsbD3*zGKrR z<&<h}42D9_hoH%70a}k4rAxVN+Dyy2jp0zEVIK1<i7Dy9_I%uEIp4IctEC6^_;K*_ z_eQ8bbhpY4G55%6Zoc`qkQ-8$(Dy8%cBASVc4<oN!1u~;nIx`sqx_xe8#(%0*Li?d zKxn43KCNY19b1-hvBUP~*Ltd~Hm5_wd*?1e{uoRT2DgEA)58|YzdN#$3bXdcjF_N& zo%Z%3BPP9xb)Mr5B<u$TuBWE!8<~Vp?9*qrtqm+BKRUwHTGF9x`m=oPE_8~H9wQ30 z8k)@0qc+JZI=Mo%$!z82h27JajJHGjv0Ckz&J)Xz3}WY%Z~onPM$d2HXh;oFxJ5x= zFGK7fZN7f|c1GsO%A;7et~FHIjuVUXdr%!u{%w@edffGA?6Rm~k*uuhmHD}MIcO1; zCb~OsRBOx)Y?PYT&0<}cDbH*9fI7%Xlb-&b?`Nhrp7QT#(oPTvKou;zY_wV2myUib zMxA2Q5{A86$|f(IvdUHEW211rR^k+c^6b$eDlH35Y|<-BZFV&K-g;FdqnN+N)}G&= z_e@fiXgy_fADnl)Z=rI&SM=;Oi1oRRuCKrM-G?tKPwwaB1PP25eAlrVH?+guJAzpy zTmIadlV>_KbsG7VY!nmn9nd~<yio%7_|>`Q?)P>Q|4`IKD`%ijrVRbmcn5k_Y{}jU zY$%`vv(+^nKjx3rWiI)2LakNz_Hol>ii)<k(vHo<3wh6fJhWlW28A@^G%fR7cT2kM z-&Wb}pHKV9<D9$Sl$BfLL-IkI%L=E!PJs%%qWzGGmkv($QQAu`4V!1Ned%}Lr7lxE zM~7E$aAy`hb1jyf_OWv1PkP?R>Zf_G9NXkRJUmRBa(69WC2AO36mpyZWX^?4+4cSz z+ojU&JbM6}|GYp%xN9rv`AUlMbN?7I(smuvwEvkiwD_Jlsk)?<v6iDv&nA0TMo(Ex z2}Az2?cb(H+I;kcD>z#>X~#Iv-b?5oYhjeH59D@#PLvYXQ<hKsd2#OBCIR23{A`6^ zEtn;2i<KK`Kl`pMWMrLsa%e9}v(GL+;hf6KLQ0{O+p<4dzn8mO{z`irEw!?6M1?a1 zq8)%FHVP;uT)({;vvxpt<X^0KiGbolt;zhJ+SCCd?KQtWqmDo?!z@=3Sj7iL*Di?- zwsqHpPG`+ogu8aGI|Kp3y21~UN2l1r9n3Qsrw_;`wC%lIvBtjni}Ah#_8U6=r3BG$ zoD|wYJkZQc$K>+D-x;FJ-Mer5!kGA>rZNxraED$Z%o2AUG9Dja>Rw0nxmkbL&iBRw z24yPy4a_}l_+9W`f?A@4CHxEKXT7_xqW5VuL@_{&<oHKlOh{2Sdh*vGdIyIZehuO} zQD0yuDf!AKz07Ovd39N<y55yna2$1fiI7kb#qf|zi%bKQGJ!(*zMi~wZeLq&j(n-Z zM$&GGjyGH6wmo-q+3HiK7FKL@U+9&WDC;)cWxq~&a@u5}+3MrWjF8Cs2h215nO#Y1 zq{m9mUoJSO<ORLHCbsM|y6NwpYY5-LWjT9n)d!oujlBaW?f2f<;gr1f{5$EzrTjLj z$vU~Z3DLh&B<7gGn!Zol>fW+1{Xu=IH>OORT=l06_Z5~kLslpH^fFvb`1k1vDv6#h zy2^5UEK5q8W3*%Q0ja(?yN6cltwk-PUl90J>z+&+<Wql)JcxJ9>X;uDA)nS(*`0BL z(Vj8XIQefl`MET*2X|sI!e<x|ole}Z)jL}g9XGUIJMOWUB%vyL>el*>`<<7Jk^CDc zyPv%*?9|zctzJs%`q^t9UaE<n4^S3f+-F8PNKy}2F+tScm{4b|Uu?nn#^qu__7?dK zE+yPriLc%%MiNM{Zr3XI;eS8=Xlu@hYipI+U%Q**3YxYLXr&14R5n(Be<B&OaV@mQ zw&v!9ni1DVmCY~pf_9%YpSC9l_jS0|(Hbvjf2={S=-pAeL-CSChs@V>JF(XAJD1uj z_%0Ow6L_yKzHjK-i1ifP18Nw<&n9WNKCTU<`h#ml^kRo@L6$Sab7j$8<76%#Nf}$o zMBYUs>g4QLHm&cc*rjP>(%0#1<MmaF?-91|U;Yt`Yrzg<a#-Pv&#(@?XQI@}^sUA9 zJkOg0DDwHFm95BD^h65S<}a@-acD+g8aZc;E4x=NJD}v#7kcdPOEfzMmzSnc-oD%J zo;K+=H)j>A)UwF?##c(()g}3eb-s~FQuT3!?%B3?&8!slh<GR0<yqCxo9^>XXmL2^ z<+yLx$J-lqQ-f>x%$W1P9K1|=H59_PKkFFtu?Sw<qS$RN5*<1Re@7L{DyVehwR=DY z(QVTtl)F~{2!phvb#U1~yE}iKhf?Zkyj+!SZsDN96Wn{Ju0<}G*!k|k{f@U6Ov1Tt z+}mAuE>vadYiH<NpVtaUB`?M|Ke%a(T(r3A7Psx~m>a9@J6k(FhrY{DhLqk|uP5Pn z>eWHk=;J>7X78Y)*J^WIiK6|H>67jCT}REAF4G6@Hl@{(t6j@>eBGp>9E$M@!7b8A z)VZ`;USpTmN%zvI8>@+}TXvgGM;^I@uF<ELr_D#2p(dz%sX>~iarNby3gx)r!5CEC zwlg_5yjOmCwvrKVe+aNLWBvQ_%w0tZJ%?^@@*F$I`)6E^gyBB)JmFdF(@Bx0XKaro zC7+}yk8D4)iSM5cB=M5fWh?B>=?P;vIs`5Jlu}=Fd_T7F<naoQzHp_TA~lIe=9m3q zWPD4b7>5|l=h=63E*qcqna9EHFRVz$ijd3M7`_QCTfP>;dw|&OR;ER{UlU(Vs6v85 z4jA4=pWrxXom{kqXLu%!s(IRTW<>;5>G|{H&k{)T;QAzXmGyjgtUSQUDdDnxac0~3 z^fhbQW2LrUU+Z-7p}~<=I?CFH@d~4Ij#2XykpdP!3?@ZlwOa%zi-)}~EB4vcyj;3H zU=(?aTe#`1D9@Ur(xq=$B#z@931N@<d@DO8e$5Z*<crlN*m>Fzbmot_f`W(Av}o_j z0!he-P5m1e)h--j#~yd6FnmYz@^RzGpEC0qHpn?`x}do4)Q1Z6Gn$W_CFfQ@I@*xC zhjpuUvV#~(kfe`fTmxl20Pu)>ze>F9{lQr-`}J*Gwp6AleJwdML}mKZdDiE_#g=f? zpNM|wCBxa>>=!%Y$1Rh6J}mC`{$>4Km!)*^U*kXg-*G$k9aK<>j-nr3PCuiK3jb9e zPYv8*`1Ve%l-)W}-CMD?b<B(NPu+geXVd<O=969lqshADwmIaL1iiHY$0oVp?W*kF zT{g1C`i$;GzW+M9i#@w!O^X?a9W{2$N-yh1yo{)@T~DE>JUX1JpcTEh^@-cd0D9eA zNU#TD&XAA*XPRE7d6%LO{q#CE#F*OBtK!AxZ{PppE{V^h;ySiJ3~99OhT<V!gMx-k zwq)_$&}3a7YA-wgv#klAH^~gNmtjvS$vH{-GhV6y(;Bor(|m^x`W>+qy2E>scTc|C zqvqxlyKsH{&7f#gy!6#V@-D&ELR!*t{;QcT=$&26F1weMtc4a}u1wvU97QZmqM?aG z3C98OX4wkD#?<)-B7pEfAfS`Az4BwZXG{6T{PObaNY;XGVBgwbA(8O#t%Ft8Y`S5y za=-VadAf*I{cI1T4sNOQ7W6y$9~&#|^BQM<|Lx{R^inFv*R8p-@S%rNVHFW~@fB{V zjd`Umzj+q4y9eRES5WB4$}&w-V`QWuJx1Z5)`Hi~58M^S!c)`rph`;}4U;oOxUlr3 zjyw0P(Cdk|3w-r{RO(lj#24q&=5LcVZxxEY^12u+d9)~K@TD+GvwUvej=ZDByC-gG z$&|8#R(o~WK3#;-d}w+k(D#=*4(A9QK^Gc<>H}ccoX7dk<WaPMxi%I^?l-cX(GWG` zof*H58_uHL875Aip5IYs{aAALENW170uk(dvVO)(Q)`v!r>-Xba94=?{Fyppre0yn z>HTu0;V1;ZD>dSYUkwj^7D-^keuR=&jG3%Ai?|6hF4V>t`@d&o=@f_BEv}CKFcP6! z+heHur+RU=f+OJ~yK+WZcegLS`jqBlji;JXF3n%C+}0k`&(?Z6mc7=zqV+YN+;q?W zT4j+d9kTS+Q`r9a4AM8TZd;=sfpetKDyBp3CHMDO4C|0f&@D+5dmF^kw=`SVV{kAO zclYDxo{@CWUgOMrV4rZ1+0t6R%1s;IKYDsn;f-?~Qfs-F2At2dWGJtHeq+nY0q*ai zp$vUp-Tgh8SvtzG0qr-(c!KBap6iM7sCF;Tns}E#JB<<4(~n6<^N*YDtP6vHz>CXS z>IoW$Lo?3bY(>L`c~`6?#Eqg^8pV34YN+4<1madFb2pJSyV&yWecwJNdG`;a%pV!C zDLFa!u$#OCh4t=T^Pf79bfMf&H#Xz2ZXI_9DU#&Kwp~Lfl7ExFVCl1NMve`}89eBi zwMCy3l1HDS@EsP-`^esIOMhr1nyKmuhbQNU>a<w-hmiO!3kt?MX`#+TSzuj%Ro_g8 zL*BKgdvsLHVdX<mV)$(Vi39gql_sEIojQE@_^pP?NOe-{TwjPFpQ5$<@$X<S47b!T z@+#y0BC)eAmo|%z{=TWIx*e6@>0G42QNcY4Wf%0)ZL<_Uf4Rnc(7a}o&&-9B+SApy zQN^`m2{&Y1V?V9E{~UH4%dK)kA_{T3;w3j8mAZUqMiEwE1Th|0EDqnF2>Q&L&=+QQ zc*nNftUpVN61gAekUVfEHMsDtpz!*5*&s8DOoS)a-Y7HOx=P>79ix8jHn)J+dlKd_ zA0hU+-I$7k8m;;kH{>V|o+r-6&dncAQNaO!p8kkzS4|DZ^-x6rkAIm078{FC`9lgi zaN6;u!oT_@?|ko7^;PIRy)}Q@`nTio*U$JX`v-^53=Nz0SMjwBrx=Lkc-U-JUX&n> zb^AlD?+x)1m!fP!;W?j1{#^Iz@%-y$W%D%)Y;v(>#bq<`hShT$m<|=bX-ex0ldH`W zs0c1!Ueb9Pr4Tkb+ha>VBkq)x<5J6miluP(t6jR6ekAyWwyt8_i^4I`YOfYwS5<4C zc>PXLI0MlvyYyODQQzl1j|?Q~ehcLK!$Hin+21_@K&Omqehe!f+mp|gXj0VsBJJVX z-8_;XI)9ug%xU}TJ7MZGeP$i|+k$G5d}K4N2qdkGiv_g#q2^g_<zQUSUhKYn8~5CR z{d^pa6v*S%=}fmg?lUGT@3TQjJ2+aj&PJuj-|FYyEW>8w(Nw8)mfcI!_bI5P&GuAy zw!}Ftb_aB9UzKrg?Xy#o35u$dfrU*iEJ<DY5i;4u46);K=;O0O%aQ`buMM;$?^p8+ zMBk}w_%=Jw6X<I^QoG5NvS^XTT|A)@dPmxuC4!et#_7n|H{hjc=7C(y%z>86UbAQ+ zaP=$kOGxu-b#`0&P5a(^I-!@Ugv$4*X0{>dfi)@q_cEfmG-aLCx)vt#-w8$<KX7i3 zax(Sj3>59J%C%~jMsYCZ--6d|)hpGU=>Af!>Z-424dGCRoj`2rb=*N^F>TJakt((i z-z6-36CA-DNd6#{&Je3A+M&YQl$J1d2raKEH7BD2{eE6un2?k6<zhhRLNx2PPskXc zu71{WXUmNkVJpuIg_{4RU2yX9y0{;=d~llY%0Oqv*B`(|`*vK!N>&-e1)1H)=@A8r z>EBu_DiiEHUtd_63Yi{W)e!Te`(pU;(&SMpQ|a<@LteVV7x1j~v&M^f{Jdn-VNB3P zruFjub7bhm{lDFPHYPY-$-`FRom{v1!q*ohY5(ane|wIW0=Y%TyPRZWlO-ATvTA)z zKHkD^yL_1(rK6g5FrLBOB0A22mu0#?!ZxBb(}--^^DeKAxtEsk$ohnn<E2zAWP;Q@ z#j@FwPVZN>sybV0rJi2%PLS!4LW%{C^LDv#j4`85p}u&0+4Mi4Qf<-~VyLW282d!| zvwo9!@S8W3Ec&cm{qCO)<*e5)wg;A`_;NA5{~#z*WBk4dY4C@9|H8KCZRI_D%XB_+ zL8*4i5vh*}3FtzcTY8bEfo?0*;SEcs*8J6KWz--%bULRfKJc{qWNvLo^z>z#Qj#D} zy<ieut$OxsZHkiWlC^B&vbT6mAj$q)ZKp%$<DhxFr<(Bx-ri++D5vD#>inVDHf(-~ zk21jOZ$9Ha`mK%?h4RCf-t2z5>B;qz(XW0L4r!?R-owpNd)SVXBhjLnVj|iId6wZ> z7b+&;n1Id&l;TDqvGn{|7y@NM`pJ!j4c#TLtZyppeC_kP&2F+sGg`XB{Js#X`M$;` zgI}0~n2xP#*k)}TujDD`D(gMq(r)eLq3YKAp+ij_3_IFgzYunvveFhA$5KPP*>S5b zBUg8~DNE90D=P>3H=|>^>9+1RzDRXr?g5$Nq!vMY|M+-dn=G*{=;V33F0wSXn+V^S z`Eiw7q}KDgi2jE^ziAoGWw;lxc%`)?G)%^*q4HCFxx~(g57WiIa*+-mR|{?<JIuPE ziD#v=q=Pk}BR^r+WY6Mat@u5ZuJbmD8pU6&?<wmY%MS%Tvb8ww;{83jlyU|%ZK7$X zvfFnL8&cx2fW7-jUj~6sT7+EpH&qF2hINPBUR$0oo44IJ`{P7ZOk$O&ikJ*HZYL7U z4a<0CodZRUYEyy=*RKn7Y+ha;r28^9E&6<h{qlu3&(+wo@t%sd1h<_++XcP9I+C&+ zFekBH(R$gIi;ps5<;~Ck(6of2vQ_5Vv(r~RVjf_Z2lm|!_YUYZt&gLBi;sdWgZ;08 z`#9{nSOn>#ee~BpC4uWEJpna6aMu6}j9QcG`1PS>OIDO)<2BICe7nRCf3G+oWr52M zR+kQ2UKP}G6*k<9zdW)K9JI7?jmUY41@2Fku}!r@6fW_s$VxI7Nqn~m%f0(`o}QZ* zq=eUNC0*ONr+45p%gU)!H>RhH51JXow%sXo)TPVqsOlN}CZqcLcL&>vwH!J2z3;cH zPA*=%_PjMuBoEYj%RDC|1CwQ8(w7H0b~c<~<P;6n_Eqyy%oF7@X&|ZLidMM$;VRD% z)?H5wBw0kb3pKrp3I{%OuJisOVORUTzb?Y6>esRXb|u+T`!k`pzRYv(xYB168A+?R ztx~riR;6&PpBmk*cdLBGmWRgPmO`QWxz*erw%=>gzL;4#)i>w;V4rP+h_2?Yi35Z` z#le6?l=1d9KV}+Ky7Um`v(?<fmYxp$(UUCe9-hs2IY0U>4EIIyxR?upNDHb>Xxu&g zmoA3`PY7kk*U~Yu^cwC$kAMK$PK}SZ8lKh!wWK|ZUnQy1-Rb-fpDXVL@8zo3R&I+0 z^HM63jf-wUckfb;SZIW{smtyAqO6(c>C~belHk+4t{37l$O^VV4Cdam?qQdUR%UWp z5Q2E3BOAxckih=h;{E$;S-l$@X|7y_OBZsU%&+GcizF^KbT$M8x$&q10u1sn13{4t zBYn{Ynhd;yf8<<+mMYg1GxQb5rBxdWpZ{<sd;M&cBs+YIlcH6PG%2i%&&hi-Q<G&D zwmo~cOm~0nd27gT+1F=OIKNCVaB^}o-EqT&?y5;3n;_lm9zB4r_G8K)n#FF@VYQRr z0fltI=o=C2B}hmrfp5Zp11YmEX3)@cMy3;5-2W4gpN69d?kK&ThC&RSO$Dn@cgkJz z&8BC7t|rjy(vDw4K`Koqn(qq&pdunHFQDH#;MRKx0-2;18*6JcNV*a{Hv;xXAnU>t zz29aHm|ecCSg*3MAJW!Tcm<q_mmYbX573(kNTpIx<UfJ7+Zlhc>Nk*aKn#gVJGb72 zz~L?+$nx1m|JHB&oO125Sv@-H;dY@8bsoXM0&JDlj+*G+F?R2opKHAwP;qdv>HW85 z*XDiI6IGDHK84PcfZJ_0^|^XUpI3OyG1%wE&5e7uf}bXbZhI?2-=+rDUl`Oi1D7EP za+wA}-v>mjJCK}jfzQFs##a_c2?AWO1<xmNxx%0v--5@IhR2SDGQj$3;H0zR7h!f( z47{IOjF`zC2>Z66NnaX-ER|GzH|p5#75ttJ@!_q-zPYd6`i>C(HlG&f>EZ<@9v(T3 zXi|r#Mz;hQi8cflQw?7-Y+%3|&{r$e#MR(Aat9N18?FoT-G`FAw?fvOLSNSZ<ts3+ zuQM?Q5tk#pO<Gz(<b3T*x9tay>i}Mko_TiDX6~<kV=yH#6p)quMi9ihmfQVJ4wvME zJ0J(<zXdc!A3(yq-uU$7=6>MP#a^}eK1Jw02AWz}oRpp7%0WBo_Ed~$VYe4vyc%%w zi78PS5<J!cki49TZx0rLLg@>Dq<A1#k=4sGcEC^HxOBBcBa}@41Kj3Ts7?t0b^n)` zfn4yb!+`HrS}9|zkN^>^!nmxFf^Ve;zFJ~mhfDsp7E`}WIvFjn$p;`ea~K-u*r8}$ z`RUWvh1m%?*8VRlu7VWi(JQC5v}A#4znEcG*6q6lYWE$ej<-2DIQ+cqtCG31%od*y zj%FADuRK82>60$yUc2m?o0D?~c>1ltGJ2STxX`l=;^h1yQ^QgR=2ph-=vk@=;UN4} z<i(xZ-v|>nFuQj3T?1(1)m1o=Y}m9Zx_@S@CEMGy2SS=5&qt3Q0c{`id#uGOzrz>! zI6!WHGerO|c42erz8-=Zn3I!Z56mb%he-ZlIl%JFj&R?AoGGbl4#?GbX$OzS*&Kir z2?-e1h4Iu-*nsxol5CLI{DH3rdPAwo3L2IBiHSd+=?`@<kCu4OfyH_=EbJ-xYA*h| z3{11#!1CQEFr9dtL%#{V&F-{>%x9$GRZ#h@IRq_XQp|(BXBc+2<oN=kSL1FJ2$7JI ztKW+;KmiCgFTwU2BOIJSw7^Guejc0pi`xTn6~Z9}agj5RS3_I_((~xG4%5++QwTuA zzxDrME}qkwa00Lal=7M>WjursymO$?E1c6^&(-P2vH><hKodgzcw?LC%|#<jLc+Uh z-@aS_f&RL-=eyRYXP0V1N$O`B%K|n@V7A7|ll_FC-8AOjJ%+pe8^2D?cB=TEJ$H`W z`OV$0>Y}*!(md3sm4H6-D0mDo%|@n^U$0OKCYOpk{T@U|do_+h#_gdz^E6rgSN6m1 z7D@sj$lxB^eYmlHK7j3B5#|G1MK82b)>sBqvu~9p7+K6KAz0XWcl(CG4appDb>wLb z;Jn3v$Y%vy5PL*2I7Ik+p!<Ma@)NU^p;r47Y&3>?$`ebM@_T!GSpvwPtE+cH<LkQw zHJ8+Z1CXrOy0<+(e%l8yzCtX;!g*Rk_TupqSz<FQ9P&M>HC;0v6E^nt_TjFUzVpSA zi^9)mfBOTpz)j$4D?>ToT<rL^JNu~Ed7X)II7311{0tPttbp|%oJ_yF*Q{QB7Z6#v zi)qtob*p@6adtika=x%Kn55H-*XuLAXZRNia{$tl$2?(rH3&BHcgU-v#Lal;><s0F zglN|~V)p6YLi`&n_yP7k_0b4SAl%_c!jh#*Io${WJP&}Ag!c<#`j6%^iNZqw^UC}F z5`+ME_3BtNOUx<(LBWA*8v<U>j^gV8*9}pehl-c!|5~!1glif07mK;cKGv<$)jPyb zQ1L~*AXdjd5C;Pp5-9yAV3mbXV%uHpiv{t`M7+_Ch-7o^z)!rSzk?UXUC37v&y_tY zTj=~RIPeoL5D^GQKDFjsmdQydAH@w^NezhMZ=Kt|Co){>EXBg3j@45#V1ED(;%M<Q z3=GWi90YZgS4n!j4zc9)p+kY>K8e@~ZO9zO=R^S;2wsFhjwg6k?3E|rw0Jkp{sI|c zyNZ7-f~@I(*C`2dTTpc+31~I!BdY4MPhlcmf#DKh+`N#zTR__1d3h|8aJ*6=(}tbC zoyI@NJeS4}J0sY>)%jk5R|HU>3g0^YxN2tjPO=k0y8Z1%Cva!}H9$8RLAtbp){r^| z7-kqah}a?oL_}%|z<UUTna)?d%@E0eMW56&scKY}K<wUX_!ePP{Z&c}q#0~Mn8Rvt z&1-ygbgdQirfRT<2#=VazI4*m&te(|2|PfhNML506I2{E008bVv1~#t=Yb+z&C&5C zkjd@{YpEFOPa-Hbfm_n#Gx$LNj_k#Dd85S)bU{2f%Fmj@PNKM=)rBpKI#k#>6jq>j zxHx-{cCs|6(AEUhakGDzt&PnE`iCor4qvHDc7>OeBjY&|Mr4PA$CJi9Cw+oXc>v>W zXY4ng_X+-QdXwVv5yaM{rx!id?v%X+IfWFlhY_2v!d5BoAFYjTL-p7-LA3m|1MQU< z>Uu<vkSS=@hkT0R%1}4lo|jG->k;3c%6lHxXEA^q)ETQ%C+-W*gL+{JBuc0K*XNPM zVE1D+d7J9vA8apKAch4dDILI{l)5*amfK?V*7@_4fs`Fp8llS-Ia0L`F2Te8lezr& zOB2BL;TC&;fB&n+T4v{U%yNg$k@oXo!9Gh%+lG}bOC(IG7jkVsJwKDGeA)b?Ezi&= zMZ%M@a*sXR-;D16&6M#}U^5dqVhLc95`cOVHb6UI8x*oA2o13y7jdipJj!vf5;L3x zU&4~d3!9(}C-CCh`6hXe+<>eXKdzeSER^EAGTq4CwMXv7ix+}ILTBN!Fl2)<u|!OY zLcBoQMq5zmbO7ELpI?C_welkxX9jX?sfi<t3s_{;1WF_{)Xh{6GE-u&D_=qqbr|4m zP@>WhV>TO`foRJjmTp{l09i(n^Jy#uUs2T%Kxa6)xGuGI=w_QPz2sf-C%k6}empXx zeq@}~*?#UP_%0G~P&#||Y^*FUy&(0wjml^BZd@;hg8%W)jvc2_S|pgL#Bb+!c_q+g zK~1$8Zg{l0(;KzX4AT<AaRNwX*MX6FLaz_v_gK!Km2qST^^#kMv?fXy#1Jz}dVgUi zMq}as!^2~+)6+PtkUW$FzKHSl60jOGS8+w2gdTVkFv)=SG{KjF1a_5+yKZ+TjHC$D zG9Xe6O>TwG?L`N6;ob^~M5hJhEb4HUSv7}giia!-dH@VS0Pe`Dk7t57h4(;^m@v98 zfW?I)8fo&grv>KKAd<{yARrJC1#0QP%4A$KV7p4n-v$pmXo$Bf`)mj#m<#{0np_*3 z`Vv2V19y?Cehy&S@ndjN%JHney;s*_lV=LsD)Sf$U9gPSN?e~uVx-<vdIe5moP+}j zyi7K;4I=aI19xhWFi(V9oydJ`>*5>pUCr=0IMt*j<sXDw4gL^e4@W{!2*=+U8qPk7 zs7{1QC|Id05O^t0A>@Eh(7m`QcUxms!&YKQs%R0A7oU519&)>xAD{7&ajFy*;tT-= zx?Z$_IRRuRl*>5FiV*f)8X6kr^S=hE5io`!(P+`wLS7*a1_~E)ePV*udp4bPAuwW? zk0+o*Cfr`YTq5|z1Zp1;X@r?7OjFSEKVm;)Mgl(o<+~jMj6c#+aJp*d4S>=egdOL1 zOl5EkbYQ-Qzh8xO0d^V|gaQlfRkeu<_W^skcKtfxXcNN0N5j9yO4b2C1=bXa5pju$ zwZxjkEFkU0mW2bA9io*#wh5%c97H6$G~V8Pd}V?EyCX0*k;Y&!N5g7{DR7%hLAkFl zjvS!s>^XRF>7`+rPbwV1h@rqAj8pbKu(HszLKsTn6xR=zn#%h62;kDX6&Sn`t|TwE zOWWCRg(4kUCvisj(nLQEvpi~&QtqXZv#`XgL)z#7Lk1Fo$Y<FsW{vHPU=HB$k!xPg z0Eom<01fakHsMv5!Jv}&pOe6JvBanmj(rGWuRR7e?Z%P#x`&H>s;rd2pG2O&mT;Mi zlfAeVr--_#Sa8=K;z&e{?0%DXQTR0+#VCZy9M}v#gjLgPEA@coj^mYKXx!j0jK}_o zlpatUHyG9^h>M#R%L6V=K+xeqB}bf3#jbQCPm@4Es_Az5HSig<u<y6{%CJU!-*o{- zBtGXy7~l9%0eOS)Tn)BKU^9H;Zn>W@JZw++^1x1mw2qyfh9zZ-$poa_&yrbFm*Oe4 zq465LXV_L&7P$8VaD1Eoi-5KPVDg?deD}%dOQv?YZG`C&F|!e76ig?cVmlAR!sG;w z3BGGd0u|wke11P92`m!Mee=NL5pbVhbhBOjF2otV(Bsq!0-~gPy{d%z>KIWY!V3VP zFjWw~9@HeuWnUrP<`VYMjYw%x0n~b0q7&d5*V1_*#Pl>+Gf>COVBwYia?q0Sabu;h z<2lI7$UbH^cUE!!1V%lP$osEh<||%bEVF@WWKYMNH*b`iX*}F2lnd|6x`1&fULs=o zy`-eDvNB3CuhB7PLhx&Bj?m-WS-@GXurx-vXTmsFO;eK@4q#y?86WPMS%noYMp(z< zHCiJ*!6E-99;)%Bt_ZT18p<*~h2@Ax09>8^T&tl@Y>72G#J;XzE}ssVH%Lj<I1{y& zQB;ww!0K!Y3lG~c!(FS5HBaT$xZq@xn7Vmj9>=&<<jmg2RCQr2w*UQby^Hr%d3}1X zp6s3h!fFs%YZTN4T{ag+k={N=o<htsu-mT^?zFIOK#q3U%}s*vFvKaZ?0e(K&KtqO zroS#H)I|?fwU_wGiI+_Oldhjlb@x4XY&B-C09=1jx)_|Ff~6SCAxles_{PPQEa7yF zT}~Ob#Lxkxte?S0ARV%{9ynfsVy2*=An`L;UU?i`|3u<-n{(%-rqjg#iGXgQO7Qyf zZ%2Z-hX4X%GPC(x|0ynfE@9n6?6Gg1FTkN|)9BY1M*(>QtV$UW48l(f7fbzg3j|^! zi~70G<XGn^XktBp3AstOw!`Ru%{E(koIl={yN3v4DC!WDu!{O@H`z@Znwpk~Ht?}M zljo?57^H@6X9Rwx!4?E<AMb$xeoUNM<pyU$JOwt+I&1{wBt`1@^LK;?2lHh8c{dlW zWaZDW0s+d36^6-$k2z0r3iAZfi}FH^^R`IYlHe-j_jz)yd`sXCWuLp4QtwJ+)9=8g z7c{9UY}%%75G#?huGUhirM-$0>L)YZ&^1W<5-pnGG}i%N*IdujrcM(u-6IjcXt1^` zRGEN@aRt&!D%@Q5`;GH^I6zYK9oCYPILfcNn-<0+*X@%^&WAwTzyDi%#-$22IrH4M zIs8hjgard|)YLHmY%1ZF$6#Eb)G|fsm*;%)P&d<%Tf$ph+~C!F-$Dx>A|S!MsAb;p z?-^50S$TWljT@_Rd{2y#bNY4%Sgw13-N5eFAm1g4{tm1Xl|RD*7-6ONKPUzU>Oseg zlYkMgbU?Nb-WewkPwd#w9tyxw1i^(cC&h4h;LM?%;^i)hK;pe7v}TLqCHpMVGh&00 z>b#%nYGLqA0Q%)9mK1ST^u+`wstP>tR=|F-2iSL*E#Q%!BOO2`>{R$d%Zqa%aLkpk z_^_HFcEVq29uClDA}{4s2@eXwi-L$fNDfZHDRi&lo4weZgd`;+VaSB=N(H+_JeC=} zdRg;e{BqG*Fw8I;;L%OgglMpr{p&aD)o~Gr#pLJD_oC>BGYl&U4!)al9CW%`qQ_Zy zfOsRYQQl8Nbs#Fu)Zbi-5vfEHSfWUR2-r}3_SPBriVo#k({osU_sC*x4|OQE#|3VY zB~~2FJ+XBe;S{qL@IsV45{P6A?#9LqMHBEVCW!Yi9erh2pL|;@?%heOPe2sWps!+u zJ&3Tf#DEfB-$2H_hhHoKIYdd3h{j0w6uf`nBzO-@8G5t6StO9>*v+aU`1S}11>;!$ z3qO>^!xN607%Ws1*1_6`0Hy)CI8@m8Hq@ZP*8XZRQDW6@{LV^LSpUD6sH3F~AN_yn ez+St;SitYHW^IFKGo0p0Cyr|!%Q|9y?SBBBu*C2H diff --git a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb index 3d6f6326..c3dd10fd 100644 --- a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb +++ b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb @@ -453,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 529, + "execution_count": 530, "metadata": {}, "outputs": [ { @@ -472,7 +472,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -544,7 +544,7 @@ "ax.axvline(oracle_objective, color=\"black\", label=\"Oracle\", linestyle=\"solid\")\n", "ax.set_yticks([])\n", "sns.despine()\n", - "ax.set_xlabel(\"ATE Estimate\", fontsize=18)\n", + "ax.set_xlabel(\"objective estimate\", fontsize=18)\n", "ax.set_ylabel(\"Density\", fontsize=18)\n", "\n", "ax.legend(loc=\"upper right\", fontsize=11)\n", From 2b1f3f47fc7b62f56d8402a0d6dec352a61d779f Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Tue, 30 Jan 2024 00:46:28 -0500 Subject: [PATCH 22/26] got markowitx working --- .../notebooks/figures/markowitz_optimal.png | Bin 34529 -> 30205 bytes .../notebooks/optimization_functional.ipynb | 381 ++- .../robust_paper/results/opt_markowitz.json | 2710 ++++++++++++++++- 3 files changed, 2975 insertions(+), 116 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png b/docs/examples/robust_paper/notebooks/figures/markowitz_optimal.png index 9faf112b4919b47a0ae16347291dcd4cc2c36a29..5c52a1e0a65768a7a1a312e11f5272225925084a 100644 GIT binary patch literal 30205 zcma&OWmHyc)Gqu00ty16NDB%|h=O#72nv!4(k0!Uf`myUAkrX$fYROFEl77G-Cf^Y z?Dsw6`*p@~499l!tY_Ud<C@pJZhYirpW)$<<DgI|JV^<$mnamvCkll&c^M0S!r47K z3;*G@7gw=YurjoFdTncfl6h@!ZEj_6ZlZhJ(ZJTu#LAMLnVXsI!EIxEduuyB78Z;D z^8mAztq}{O=9~w-<chU~svQbN@EZAxmMWZLf<if%N{T&JbdFgabFwFTMcuf$o*W|j z?_R$A2hSc+tC#4P3B|8nTJ(PBndOUzdGia6uJ~g<357Q|zTOuvCBjU4pZ|wHsp#!F z=?(9E5l8lxwQXLbE?Gk(Bagb5RBN%WSq-KIYJ|w2lqp?DG#dC5Qb>dn7ZVeU@kjse z4Szykporje8#WwI<YU>VX!7uBeG3E17e3`7!xTZjPM?TkfKR9O|L<R>dx9yF*7saR zg;Xi6Z{;PtNQ^E0>P=K#UEMF09Ga=ADZ_ANl#tg3sTVJ#`IYkj^y0>j6?B+Y;q=Um zPe1@?*;PUpv9-0e;?mOLU3O$nlpp#`=SEBHG|C)1VBou9xrQCj$ID%ER313;Rv}A$ zFLBkQ#BQN+WMsth+}hjwR3$?Rrc!J@g^oHqJ99jwM3#zujhZ^1=*)Ju`L!c2O$d&b zmKI8**xCzTJ@S)Z<-LfQ7^TRxpvU<5IGr*LP2raVqam&R?fbA>Jl7nKeR;54&sCNF ze_I-oc?lc)1}SMXf$)j-z`*hD@-rQs+q6&pvlkaKRr5a4J_}4b62Qi%oUGU%Pjp-z z{`ye;+tUE@%?|E~YsAFgmh7;xu~U{x7rNr*baW;Ksc+EBUb@Y}@wK{Iq|kCKYGuJy z5dRjR{Y2O5P#!&#a;9|QdbqZG!rS9L#dMY6@854nahf&4;3Cq}?X=nxb!t`ag7Exz z(T_M^nUn*K;@^I=nQ6SY>=@wfJ=sL<k-xYg8_D*u<~{bvP7Hw*vf{gAHcn1Phg);y zJ3Ue@drb`uIl2uv(N00JLT{ti#>&_omK3R6S8|>Sb@|?8a$bmaa-NSgI#?@PZw{u# z#wMVe-R=@Ls{4c|9m{u<nwpwI>CBkRe9(H}w6d~t|Lue~A?;-xoKiiHLi3@u!?=4= z->kRiWyrbABl6d0f`kn>ryK0o$79hjuqNl{8w)K)kXNe|nv;dn$>bhq{cQ}OSnN*3 zwyM}wdWpyHw5I>>PXZ;sqkqDa&*+a#dhX7{-jzA7n(qy*4i_X!hSHf&RK`9JyFav2 zPVfY=_nz3LzO+2|(>I;5{LjE@LienOaxtx}){|a@6zUeJDZCBzBVpO9!KVI`EJJa$ zw;G*arBn0nE5nO-U%p&RmW%I9%c_i8>Uy4`QDQ6DbDXQ+vL(MB5ENu|urcMf)BQ~H znD~K8b-BxS1fPRx?uJ6K^=s3<-{N_O9i&|5q!`%vZb{?{rv2%v0*AAq<tJNhlE>)i z=-$4*x|7vjJBxp@g@uKqPsR^exVgFc?Eh7BPq^uA_cwffNs=8&Lpycil3i!TOh{OL zaC}c-w7iM->{O}HTy<-%ttr=_EoAk4cR>5fl`AEQm>XTf=Sg}^frxGVewEgfB1fI6 zRS~uG#@hN3Sk^2&NYB9F4Ii;NZ|F=<PhZBv`{{mqgtA{5Wc}s0`>#9Eeswqu%nIh; zz`tHz*WgF8uDU(l;OFD(`{P*<l~cJ=s{EZ~=}6JGZx7KVBqVfJ26H$}d%b&pN+~3W zVrsd}-IR?Jh}?+-3$8p~<A<$(mZ4U_<9>2*YvJu5hiHoi-<uj$?y=;YrgvAP`5f*S zm<?2#wS`wz3CHp|=+?Z$94&Xb@yPwwd}plGLT4<adf|ft(>~qV=HQm`BEt^yIFB=z z)^87`z~ab++zv3xLLL(1A})4vxZSeHZPxD(CN{ac+5v_~%4>T!MJ}H1(W5Va{>Y=| zJEEUSNL)5(`*!c{-4Bck$zQ&FL5(gkYZqJJ=@Qt!3LpG@m8Jwsq8(b`upEsLK0gT^ zD|1TOQ2q;%_3umRtJ1MJVNCC{Se#v-jF-!&UbA1tsXF=bh=Hb1uRt_It#N|eP%zGI zEBIUs_sSJXVw9e)u1bOFO_?YTNyky}ro}%A7_g~Edn-e3$E!sv!v%ie(2)lR2T3!P zr+e!6l{4Gn6s6b4D<Xy7@=1MrP%|+R&ucf2dP-sx&aBDfaVB6fQh4w7ZBK}IY=-Tm zwY9azW2KxB$DK0v*Q?HxC4y=8SMW0wf4%IX3{XgsHJGmVaoecDexO|$1E0suGzD!B z8O7go?UEBdt#_Ju+XRlxs8PH@t63DOl%XabK+d_bv4OgM|Gw|(@xB6t1J}xwH@i#q z_4V0CU2#coVe1oJw&x}H@d8O%-@$ayHjdY;{-!Cjs1=wtgxr(*H&G=l70x^dQLoI( z0?u_BtQA7#-(L#!PXj4is69@+%F6h`6zV!U0+=;R|NN99*_!WY`-DfavA=HwzT$Rv zxNx%WaXy2@JLN1be`?`4mZ}rBp+n*074}l6_0D&gxD0Cf44S3(Q@y_wB(oOUBSXOq zw@0k11b4F19x|Yk!>Bzz=r(*;pdYLpJBYXW@kc)55n7xJ7CGlTy{7gw1u;q~tT<vi zFovqLBc@*;4QCny^t<9>AyNtq*+2lI0m}&zK8w(8@O_b?##woGWVW|97QLcX1S{>% z(R+qN{6NBw_#uO8?p=sY^?iL|D?@o0Pf1dOf(Y0R+izQqmm?v9h)KyG=Em-JV9l&u z>Gmv5|Htw`*37~}_zyEC#nfxaj%roBneB>?2aow%Y%{};r690BYWI~<;S=oLgiYXA zt^Kh}n3!X`Hd)ru3_0REdre>{Ze24@tHa_x*YAb6SGu3_DW!#_tLBYw3;LiPwT3ZC z9wQ0Du1oM4IM(8L1;4wy`^sp^4Nq3R#y`JbF=@GNisRh8F9t4pFdHfdVORB~-`JR9 zs=U|X_QKzERrWwC!B+4j2)Y=%OMQBMzh9xiSi!#n($bhpZ0BlW$uAP0b|*d~h~h92 zg<(v3Qwbqj6aP(-qvzw}TRa&nb(o1Xic3(*(Y;E};cY&Y%j&wfqR&ahq7?)Cz8uFn z9J+><U-aS9zrWZ*=c5O_a#a8NaMkiXM@wqzF#0pt)z0apJ;Rt1>`e}i%p|b)eY_>G z40EcFb~ijddRJRVj)R{VFZTRoRLQ>G7S56aA@JmAIm;>k42_b{-W#@nZbUg(Uy{wJ zvmH|3FNG9*6xg1Wqx5`x<T`K5TIJhg9=C&yTNqS75)*mf?EKT52z{u|)fvMZ`ojzt zAK!2wQxkDlB=5k>ZDyMZw`N;HSBgD7Jze8ye-6Cm#wB4cUp~VgQ|E=y3OgTeK5?oP zS7N{TGIsznfp3Fu$-4JH3T{i(-H*1#=T1dd6GD#b;~juKLbc?|t=1v<K2Xj5kfYZ` zSFyK}*F`s`eK4pO#H!!?B}s1}OS=m)h0Asacj!~;i8UA?hTkbLtLiMus_K->#6wgR zy}P^n!>3Qvi;Gu8;FLnOoSd9e>=%2SE}ei$GNJgAye}IM;9o!g{`TOG+YX_A>(`Uw z#(;n(3M*30n&tk-`)k&__uV!gM}<G6!LQf#vVXL+^WvT(MiBs!3HRfNztZ*`_e&)w zObe#h*5Y>Tbh-7Pk4Jy`A_CzV9RB=PmO*FCVdnZ39Gq!z+9{Z0ORdxo$kWry%Wcb9 zRh?kg2WQ9YCgbJ2eQC-mb_<;+dm~nm5LsHrLl>*gwUWN?or9T=vZ##9#R<4jEmaWv z-BWeSPf4LSX#M(nq|n02e?vCkVOdE)KmhUlp3395PLTY{w_2Gvtj13Q^=#)_#lb8u zL-ZY9SYDPA6T77Camx8nE&n@2L{CCCzne@?|0c`)QqH2H;4&|!MbV8f3_z05-{+*; z+TWK`s~hNe=u+<+6a-Lc&g;0xx72g>-W@bPM~>R~&)CxO`0ueN$j&U4%X!kZ@O(U` zb`~+-WBrsYp`lrP0oetCD1b7E9;j>te!Kk$>&nSK)-iwpz<Z7ZGwbtJ@1OgUuylgM zZ0_&VLmc<?L;<9zO^J7Z)5W_kDk{3Mx%s+1g4L<qap&I)!}f?^BAiBlvO2S@ibhW~ z3?$gkwS_MnY|apHPWKWISXe<OsMM2x4(UN9&rqg^@_kVm0N+r0xn$KmL&h~(7Oirg zrodZLM{$5Vc%0TK<2}xJ0k7+KL~#z8@!HPbR!Ek9pj93b$!;`mBLpB$LsK(UjoIqF zBAE6Wey&khC-_WAtQ};ftJFdQLoSw8pIPbXK2^BxZ`I$ByURZhhdm4UN_@b?499bi z&G}J#w9$vEn@s-Ss=r#>c%flTPSBYg?Xs7?7Z;0>|Ml|c<;#~R0aulp?Xy%%u~gK9 zUy^YcUjvL`u-u<fe!NyDaCR`Gn588s9v@n~wghHW?6OS<$z~4HIPy6qkF_W4>qfu2 zRSu*j{r&tgW{4|0>l5*de^dSzS!pBw)e=gtnr}>qq>i7@!+wCxd3L|dx0!)7#9Isb zy$;Y;9ROP@)9bUn8v6*~Ig(g$F9-u{mFu)Vu73<C@~`*TjjYO}`;Z=Hz)xfN9DG0G zl59=;v4pT_3kf-Uk7s3SmgTw~+9KHvTzIhDh5hZ(&J;{_KPI=aq2VLs*>Kq2o+=Ly zn3jyOF+(($#dN`dRvqMbz_=KV>j97w>;Q=YF#9)LzywCBceJw@Bj6Ge#-z-qQ;lK+ zCrJNk3$Qa?v+Nrb7k^-pG_<rP;P1J7keH-~at##RG76KJbzD`7H<Xyh|8-l(A3s|A zWu;Afb#D=)!j*#dbG%zmvVpI+{-ry2?)Xy)MuFuWWoCs#y>R6Q-6Kd*5HLSwWwGFq zb4<oMjl1Y58~=cy-TgxxSLpo2PBw<8<{9<ddWc7dOKDlgf0OYtGc%Et0Y)-j;L6>d z*CiwcE)dCMLnG+AHw&vn5*zVDwT&JrmV0A*`-cnh?ADVaYWc=qkSFI_!)8L|gkD#B zqQ&sqeR4b863m|O)o_BOv{qVWIab=T7A}>6g-5=z*+hMVf+9LSnLy~k^Zu*fk1U2c z=wxFgq@}OIc_c$wly5b0mZ@2}-&1U(S9v&3(HMBEErM0AIf|31rltll8z@O|S;|E+ zHA*7b3~)5d9PbGT#K5>OtK8qh^0t%K_EX~C_zvY6{mnN~0QfHpezmc)qd%N);<h(r zGz%~TP|{{2h1Fet4>&{w<QsLyBtUf_9nJMn)ocQei^q0WqUXb3lMda2rmLSFuM-nL zg^V)<rh#F^#NFX@qw#iN_9N$02dH+Q35WOL+~Omp^*%j)y&$VMOiCDh4&}l0V2&OU z<ICB~{Q#e&pM!%cfSJX>1w++(DK)3dLjc>!a9x|Nf*ojvEYJvW&VFEE_0&jBv${~f zfINZa!Bx{<&#cPlV}}ojN%@JXJSaQkJ{DTsiEUrKOGC4D8>;MbNWju@0z)e=d%_|j zNFm(RL|avwsNvyw7J}bA(7L*|_Q<__Ge%8FsIlUMkWjWEo<DimksUAyNx@*>p#ZmC zJLCWPyB{smn+{~Yogfx<RUpaE7%ex+@BR3Re1H8O{&aR&j$Xj8*K9_gPLJ<+xR1nX zi?MMyp!x9-JD?d6#k=W#sG<?~KRh$fFjG#|8oL}t$3^Dn?Jd!mf-=TUk$W;-+e`b5 zoPA;>%FnMG=ij`c{S_8f-Q%unfO}KE!!z-3vAVk(IUW{ua9P?Jv{sK;QH)rXqY<!b ze+8;Tf(Em?7YFa7&5Zvl8pcZ1#+xel#;=c>0>_Rt;0?i_zoR15;@=wP*Y{=O?Jf<S zZ@anqU%TAMX0xv<uMSMdtR)djVWyW+uax8wme*j}E=zYC%aa^kh_fd~tB;X)V81{~ z2Ut~cs+}+OdX+l-U9%e-3n=)6R(XKVtogjGi<mGcZ*o!x9Rnkj3id1sYILyHI0Vh} zTh@CMI%Xh|?-CZ)b#n5f<^_rJz?)1;qr1CEeE`)I)VT!JP~%*=dbNC=_x#w*{+O15 z;a{-X&4q*lPwH1<m!xBi&8zmchCSTVlpdLrp6rsi9scaT`H*J%cB%cMD3~!STP*`f zlmG-bC~LWV{3#WlY@DH?4h6a{K+t{7bMAYiV`Hk7ZoG1WyNbD|0KAb&1=)9wcm!B3 zO#djn<?`s1*+o(K?D9b7<L)0ijYprDnV%N1S3{&ZeKS08n?{!&_><YzFh6jJV16gd z=U*9W=;Pf)6%~mP1|yFTa(8nW6A1*3Uwm=^Dj`YX0eETI%TmdC2VhsYhu7+yv-A{l zPoQGo{m#c|8!uEcisnyUS2-o-w66xOL>l?`J+mBrBns3IhMgTdUk1E$&3V;wEctwl zgp5q`{Ot6XTEYFB_Z27W2!v-LicP&I5Gr0UIxE8E;o*t!xk0}L2=N9Hkr>2IG6CmD zHa0f<85Iz0?%utd4K=a;vB&AIavT`-v39ZdEe4d3@9y&JeM`OCg>WslEPN6Y=kvFE zt_RlMzHC{<gp%RuN=!C{gvo;D1;hUUJCpHP--dmF%48w9a}!Wc9^@P(A{lnZv~)ys zAI^nqQymE)zzV{}{6f4(GGrU+c%j&P1Q9V4ZEb#~4$HZK-;&<K!RbOJ%)ymNA{i66 zhUP}(li{&C@}$swzP~3K&uu4|mByu&$a(jb-7;aYeVu7RZth!G=F{D96V6#2oF6s$ zCMs06-w<fY280O;@{6~R&jlQ(7CH%DJLh*;A_ClORQusdZ|VySPnODqcd`6VLOUf0 zj{(R5jRBA~l8=ySnDwtzu0wU3AQvwLCE?`MR1M_H)R#Y7X(zOukF&a0`5af8)u9BC zSRh(RKpkXv#=iG>X1Y0SHs6ol=soE)?xj^Rx!o1Os5`n-;Ojm0`n~d-O-QCa2~V)A zNqfI=nkfNPKWSbVDYBv#$tBE<i;Dy1jB9a$PA1A4Fndyb6TR&AE;Rz;_w3*Yz#G2R z!E;#Zt%1b@)w48^)dfcW99RnKB|b|hWL769Csc<q(*j?KsQQD1Mco7x)3Az88(#lb z*7EYgn2PuC=X^)KQ6*1kgHP=D5$kU<AvGDq!Avc!AR^H`{T5LX5s}|Q{6a!!O1I9( z(_+7TvF@5_3ZMvI(W(@1*~&H>$m9<!8xizoTwLn>%6J3v%>(s9fBHD*cYu@F99IUh zE7!HmH_h&LN}+AMmWeeLa9Yy0yj8Tl>18?Q|4Zj-pdyMW_%5*@CbyMUURyRlAb1p% z_Zz#rn3DkaB(t!wuyk`&4ggE1YL;=KSXo(D#>&E>V#N3&?BQNEKz99lEmY!iP!6Vo zSN&Gw6o2^y##$LRxO%p+;n|<jkx5swHWt7<IpO~P^f*D0F^}mwp(Hy1$|l{MN4oWw z5dtbs&{ZW{=W)+6CN43!!-vQ3u~;Ep1GH{{l*H?_rV-^BDhy;6u&f9&t@grLcU=K) z!M&*xA{CTI7(gsuA_3gF?3xFx3WurMtmRJG%xmdLT>)+j)3eE-bDYbMmeh)`Ri2;q zi`)bfS{_CeI-I?MWNgS}VsN&LV`bdy^qpT(NZAMgaT{D#uRzTOvM>}G&rJoTaltZr zv$W&Eg<h_sEjK|eyc_uT$!GjIfEd_1JxP*Iw=SVSf*H4dWeh!f*YH|3y^uUH9t*ZL z6-aA-#}x_FX*qTqn=isuIcIBK!gYCF$*T(~^;T7|>d9ruJ?||q5!;Zzc<}<M{%FK~ zzc4D$<+VvVL8+(;bSuOUslD}QiBAKJhw~Y;etv%ZmZm6YKi;GRBjP2K66q9+GP9l1 znS{@z4uk6T+FHx8G+^7Eot-yY=;QwY97HPY&6%b{D1(6K@t>c%k&=Xpc<JNy+d^G$ zDQu@%*X#VWYmkbwRV4>9QMSqhe+urvH07+GQU|jrZmUi}^DRk|p=%NS;`L<zsH!31 zJxW$aGal$1RijFJg8gr7g!FQ*3;PNG)Gu%EJf>H_dj;1cZ`g2|qDlMAwcQaIB{hEW zD`co|G(Mg>-_*cdF2>mMA;RP{Goq5~M@t&*{D_}&o4wiJYHQq1SGC?wE-65gYH1?s z;C6R+wsTHStnWS79>tERYox~cYMoIAj+`_hpV<D?<I~tu@15nP!-Gu9<Qc|?+fVv_ z8!Csg5w~S1RbRQ$o*^#QBQ~8>Rm5UIM?<DO9dF<&^DhG)FLF9B)&H)Y8F;Iv1Fz8X zW7RoV898ANyRxz}R3{}iGoOZjdVAxI7TeG+b`=O36K$N<*H`}#Z*+XRcqa3eK8RIy z_Z{X&9l=JQvZ3-_8b|#cmF8G}ZYUKR{{FoIod6EaX=yQY2$$EXs5%2ExPf^p8j07# ztUgw)AkWl%4;X&l{(dYU8QMllyw2dGL~(gBN~LcPRK5NEXMxSx*PvAr>k;;A5Cy)f zCFGvpn9viSNq6_NA!qkB-fV>%>6#xuhy+qvuf^m@Ya~AX-&w#o^iFG|^^J{Dnt|Q| zG~niC4ySZ4QRO6QyZy6Wi#B5uUoX5h;ML<~83h!P&Go%<D+w{mCs^ctUmEvTycANZ z+uBt0xD%KD)HrKpSF6yffN8|Up-)!aEj;VXvieZ%|J(ExgVv}@r{$SjUK#>W>_yNv zw60@JvUf$OKQmQ*@TamON6;ebf6nqd%9)#lrS{vyM#oUP`F>qoAGD34iOF^{J2Ut) z;a^77ct_9A#@k=E<`P8E^3o(LeuyW$ahQp^PG3k252XBr7X<d4x|a241S|~Yb!0q& zDM_cG4z!h+9%x&d^S?xy5A_sNThBbzv(H2NPY*Gx&vRA2+nb~+VBc4U;~i;<r#9_l zE9}F20R5<5BGkI!%u|&zVhzfb@RV0fGpb|NV3x8SHP-`2mBA))7rs5Tp8hb@2XFJ1 zBtpgdR0;QHT9|i5>RlMHE;*)%QDXPSm*mj?3<;c<+gQyMT3D|{{P&$J@Ew^r0r{fL zoD3zT83wNR&%gJ7hmU=+JwLgG!|zlZOlzo23uBu|qx~V9YsIXoX)Jt-0SJ}n77P)_ z!?=^KI^TZ>c|sQLk1D4XzNKZh840C;F?a%a(kB?`RUqY`?_N0>1zymji}6MO&TiIS zi=5slAMDg!8AcReUb0L;ftS;YXDEF~=2Q4-k2gHl7E58;&rwLi%u#@}^CbCkJUcU8 z>}FJv_C~!41B$P#9(8cMZhe^h-(g0{qwF2UeML%auTc~Dkwj}k`^P&84O66~4=44% z?RbnPZ;4rblj*j==%^YDRKG@q!fgG@TKX>Wht8qxg~br^gYD@ti#S{?6c1B`^)6QR zJ*nGV{r`O>lL*xyUs1}+x>I15?U?c3mfm7S`PTH^jC1WARN~Htai1ulzN$xYvd57; zeD~p!f)uQP@c0G%Y-!+gd*oC**<fMEiYYP$St8UNv7DUqk%^xA#Yy8!p~;6xQR0<b zC;T!%&iIr8_1yN(oyj$PYyxruc##bcj%P?rJauoHk@@*a-o^60H5pK?u~A%{Fd|zK zK0K9!3rknfMUCcPVb<a(Ld-RX7x_`mJNX23m5jz7YdEB7c^H4~zr)W){&_w8qk0ZH z4Q&SmDKRng=V<b4nHszdip`fj(v%`EHg)g|`uEY;h98zX@J{TXD2DxSgLvi6tatB5 zsDpKQ4?-t)R6UmO%RO9;Vw8GO)PLWCQyq;iE6d@aOr-$Nt@nZ%p90rrrpg2F`J4dn z87O8gE~z;?wLZxHZ)5S0XAC-U9G1yPOfkU_Y%b$?Zt3`pRhJSVc5nwie=m5PzFBTV zt5K@|Uj#Qr9zjEsuPkRJnPjPo_|FnT(7z}7mm+3J`3e3hJ)LXa7EZB@9J9nHSOnt@ zqDz>#qGFfQwJiR#UmV!xi<teNG3Xn^|J(YYS#Rp<i694~FM<5PVfkG=bqqiDNa26G zBS3>GBFRfYaQO=Be?A@Zni17nC%J;vpNs4`**Em>0x@5%S=+s@aGm=1;=efc>^q8q zfL@&|RdeJ&6O#Cp1V3T1d3Ei=5|OdCHm<K@XjD-#Yja&3rwdGIYXgVmQ?5Zk3CiQJ z;liS3@4#@0FAy`lSXU4oD~eei>&1)tei)sGKpah;O6DWZ7h&NKpX-+T{&R5xa1E+} zh98m%us>`Jm?AFrMHAQ&Uv&Q4QC%45-%|faFx?f^!JN8_f$n|;d+Bz0BRbRASg3>v zXV~&+w4GUGg^5cbrp<NF`m~v_wkzh&d?Q}^l`F-DtzyZPyc<j!5tKirX4F%!5&O+e z2~U<R=yX!jqVTEAk8zR0Hd+B?FOoAg6?3qwEc-yS_2Vu6?8XMc4M8e*_t{@BKZ@SD zGn=JJ!ELqv^{tA;<>J!X36DDAbCK>JztXk9Vpn9){&>}ombaWqhVpRO(dBSk-d)|_ z+owOC37S#Qz8$BUr)y(#hnX#;8JDEk@!vIN0?OMkMcE9shF`DJtarD5{85m5vGg;% zS*`rwRG7onF;$bIKb;lp{mzoXgdf;oup-P61ziU{;KLS_wF%$u-bYn_#CfrDxWEXF zfJoe%rR&Etdb-7LCQ%7#%67VoFKeGX;Vm>KY$AS0qaDj9u-VEq`&)i$oZfi2_p+xS zF~kqBClqm7pDAaV_g5TT+(c-<KvNDwTU#4|7s(H1Z9}_>%9yciG*s0OamM}mXrjyb zjni8jdTvmKg*&cJPxk(bQ7gH=T=ln%$oP-v_o4|pJCW*GD?Z2ee@6}uKo<EcAw|1n z5fO?#TdK?{uTq(XrYpXwq3(TdR+Vf^;4K@QJej(+jvMrY+$Pu0+v9^c@1T)zb3f0w zk{cS(D%+-ls^@hVRRkB_a8Xgq0^;m%jmaafF7^I?b<$tL9=y;sqnytWu+ToW$*gHk ze1>Afz9hdJDtB2m8+EQ$AmQydQ$5>q(vwcMz-1-tZaw|`b`(Cu{JUccsCLgY)#5L^ z6>kWsZ^=~hIT*v~sZzVPTgHd)<}{^pJ3erD8q_#45x?;_MfJ3m$$WJnXmG=N=6n6C z$KM{l|7Tp*hZr~rFd}MB@~rQu3msifSMn_|e!RL~SvS^1FC)b7u{zA5n(sYrF^sad z9~bymXx=hcb^gis<{fRJ%ed@VA7Diir9V*-ULP`5>-ybKt4)6%__4g43zah;o{PS| zCM2YI_K$W+F3#U+y!=C~KufFR>Y%UzPQOtcEB?z1rz=fHMLgCRDhm%-FMrP|6!)Ve z>cwJW5S7IoK1GW{37Z@=?`uBcNy1)qu~g}w7MRtS7L#tH8OG#@!~A8Rp*_OzE`1X= zG2s?^@Xz=p-Yw)lkVU2kQnSJLa0@CAKee77&rGk${+sZ~D_3E))F{2g?--#O2Xh~N zkFLWPevP^-dA!1h$CY?O^keGb;eyHds;e**c+(kb-8xH(<y*5;pUU{#{D|(@x0B{c z-2j_*l}AZ0?D<o+l*mLQPOw)rF+H_l<S6MBmS?U?-W4UDiH_;*;4rt}5k16iN<?NH zB?-oql7NZ`j2+7Ms+eg+Lr%I`?Z=0fn`80HnHrbmVxRkXgeQ({pYEHVO=3*z=}Kox zgO6Im5y>`yk5Zo=W5}O0kT8xW`Lx{p>2DS%pm_hqyZc6+IyN>_2O;;;-RExp$kJpl z<AAXa!(nl|3*p+=ZPq9&F>$Z&bn+X9Z(GN?1~z4CCTe|qP<(u3Ytw}Pt;nP<;0AsB z#bB_m@J`Fo=I#<Zv{XLhKe5Ye$xi3YxZzH1#N>hGALT0DkaP6qvMB!ET>}S@tbmd> z(ygl-I<gwD@+q`{Gs|tcDfmveVbX{GwoqI;Ho;b}KiMpU`nQPX0oA_+OyJ|unhCJ| z3dmQ_fL`{_GumX7_q45e6RFvmD5XK++)Yz%JD+rxBsrU<90bSOUv)_L?95^ci6{B6 zAAzGhHt*j5O-7SntRwhq9-v)zP(%^&Q|b;MIX4SS9Hmf<?n5;l8HvfDv(q8=H~Gmv zup_Kqup?UwB-&rDk$m>ZP=m-gvSRx(#nxbB|8q3=>s2+B#^`A3Rtvqr=2Ds`Ytz(T z&3j4jAnqJ_`1V2~97D`lzRzE?ejOW!pQ7vIhT)-an3;>z&=$JnhAnbb81@?}6puEZ zWUKH9*wIk{pAS<H#ev$@MhM4qQAAXOlK(Rv#&_pvE`3I&_E}%^Bm=1#mm2f~IVNq` zyI;F~%EiTHcBTzD{pCmZS-GvE<da~dwt+Xq4(yEY+NFKA(H!!TP4G>GX5eb?FN~o) z5MTfqb&G`rBo=S<nx^Mx>ONYGsPyH~29vNv$*02C?OvcbRNbVnx;6!3ezy;zayINR zwR~JSyN2HpMK-cex*MHh`ifjOQZzY+w*+E5({Rzihi*7qN5gQG^NS1~Mo0TK4Vuo_ zIwm<P-y%~3N8ZBlK|BE~s=IebcfXkbe7@Pcvv>e~Zf9c#rjeqB*2=dgD#*m`a->k{ z&_)A=gb`HhP|pm{&{B$OC=$6g1z~@;FS0Ct^Cn6o_*QQgfog7biCwnIUqOQHhbmt_ z{Y?qYP-Og(rSbaJLTAiK?mawQWFM&<uXt{5ZX(?cXv>5GYZ!9p$){|Ob9Yxa#ZQj< zos={z8r2Z?esl?2Ir8pJP1%@nY?Qm~3-&S@$&yRQNypuP<&8^VWU;>%qfse<=ZnB3 zW_YVve>$QfxOC}~_3AKZvP^U%sFvim=Q|>~EXd)5f~F=z2AkmE6)E#m*WFhdf&$W+ z&4Jg;ycRkujJU07oV2x<0?D3S#Hs*;I{tNQ=lOOH22jD(yQri)YWco~7IAyLa6GUb zHm64p4~TIsEiHS~l!JiTiyJO5o0$&Dr6m79Z2)W#N9{crS;Lo(tnKU)Zb}hBi_^@# zMB?O>CzTtY%FrB!TU)EsMx3g)>tzL2YIOYN4rL@Ku4j*+>&s*#xd+646=}$pIXe<~ z`sjknq~Z-H2oZFFr4xJhOb=>ZP-j7Z4-sk*xmKSz1ztzp(A7IEvC)IGPHgo=6WFW8 zH4NAHuo$jxkK9-*+kn{f#;?!RpeyyoiJ&Q9d-bB^>Ce>D(@UmZ&k?Ms6@^(nBL4I# zn_2$@MBD?dr_eoRX#9k0z43wm%;@f07$xYTR{K1G&~1f4N`WHVpL+Ob2U#U{Qy)pb zOAzwBA)@+mZ~c!ynK?DvrF116=xFSA3E8bM+kq4E+ex(%;NsyKKrx*TTrj9lLL&r0 z1JeRTH84-u&|6J{-V4%-cXoA6F>o=d1-{P%I8F4CBB#I&>}%K2wY02PNKX#w6;r=V z7kxASi`8X5G<b4$4E7N7Z+YO?>6laT@xfpY0@#}j_I<wB)z(G`x^f}vvDbB<E>t-q zMFgP98ZNTxs{M$o3hE%>z-P+VD}j)vG#||N0;3!(<4}kh$@ek)j6XF$C%y^P8Z-Us z!9n$3If{|}2%;l*baL+GwZWWX7gsy?Hew}*HR;~DHZEqwWOxUE;_?Uy#W^hZ=NNa> z^k-^HK?_SM?IsBc$#3N>SvW}JKi|=)oE95LvR`9lm2csw=IXzOS+r=`ePEvK%POxe z>4?5!J`v~p_9#MAb~uU^W4Q3+bi>qS&F3X7S$l(Zt$zD;*_|+^1LFH)8wZ;Q#0)y0 zb&*pH*DUok1AP|=uR=<lalx^{4O~EO(-O_S{&qq=-}nVW5wE<6`jxpd(FNn=6qmlt zRiFp#<th{9nL<w~bk*Y=&!AOchcQ=p^AcW)jGoLu=0)akTutS+q#Q!WSd~X=KfPQj zk`3tfZ|>|QnfwLa7~4mn>U#2wy0AY9d;a@pj0lE+v9t>6k#>M=i~@uB09lAmSr1)U z*toZOFphnpTTY?)5uV3MaQeOH*BlnFr-7cRc5jvIKC+dYLqBnyfq&sJ>8YO|%O@+r z<ymUINsNUZdX1t@f!**6Wb8aCwx)cOKRL9QuxigvgL?5|7y#w=SL~6KfMy3EpPKet zaj!`o3SuUx0wY#>(49A@#g#xs_d^dF(jBhr6-Gg9Jz*a6UG#UjzOBd!OqEX&09^OC zoP3Wxl~wi9;XWA5QUJBw*_&MnZ=ctv(N?xQXl)%num`c|li-Vy;k6##%IPXKpuSsq z@w7A;fXS2x0L1R(fR?_9_O7mCl574x2?{L_73h!@2O$n1r&q@5QI6}ZgmlC>toCl5 z#P01cAgsv)ujb#=F17b5*^gWU3d?rBoeZB^XywcH>DXHPdesg0`Kwf_x$+Em>sM^s z2a@sH<E<`LCp@|HQ3`F`&o7$d6*hi7#LT@wil&LgXEz*=I9>@Kirsh$ICfP0H--+h z$4OarFOe{7NDLe0sPMRK-38UwCdPzSf!Z&SDw(!P4B(%S^t=L~Z`1a*W!ALEi%#aY zogt6QbuOz<QJe~Ip93>i!aY$>@8MpEN7=j???E@cqH5}K{19d~_}v(Bz~Ck3NwA7d z(0op>ugC8|gQ~c!jDBQ);@R2BAtMMEkQnB1epXog&3<v-=LUlM9}rWS^|h|p_F%KH z827gi7Cz?QSfw%>3gJt9I`uy(vb$37b&A}`4m)5hHoh`{bY_rfK|^i!LIvx>KFu&0 ztL;|f)e^etu_d__K&I!&yV#UfMgGvM#IA1f@1)@fBV!g7PxWw|ppSq@oHVbk{KD~i z%jV^6e5g-&Xv(glA5NT~H9;P+3Q{m)*kom;RL{R$UGnX%l+P!;JJ0Zot<n0^a}F2t z!X8K_lCd|FlS)7YNM9O_{L`0aamEWdm@Ie<T?f2<#b)X1+|F=;n0AFPpv76&ElKb; z?a{FW11Y_c#<6Oj^6lA#seF@zf#2e8ibPdan^@S%vVE6dLkf8FYt2cD5={g2mjZ%< zp{uT>fjrk{|D7D7h)nvgl2AC=@O|L2&{<K5;3jw)z&4Ma&+0@?z2tT-ID$<u-yFlc zLdoU7{47u&kU57a&=E08zt_<~jC7ZonHf>%Kok0|N&MU$C1)U?S4N7~y_yV?Tv4>0 zv3f7+KB?Te?mR!By@`AIQCZRsy$}`7&3EiZI#Z&0A;$bUY8jLfY#)WIPOhgb*((1Q z%b2Qb`T8(=Oh^IDxguAR#9CCe_SSK_l9?~HldH$meF(Ym#%9f5A+-|~#igRt6~T@W z(SW^W=quUIAgmoJ$^)2O;&_m)DXa`H5zK94&~a^Hc<9;x;S#a%DaY#+*%zyS<rqzy zzGYms8sSQPiCJC|fObbn$nm@x)|tKLaZbC^%Q#wI@$0aGWHw8EU@aazKSXQL;F@<{ zS++$qA+wtsLvMD%&|H#L*!{;jy4MKNgwJE>s3Yfcnw(7=ui8Q<Q$y_1^4Y%do=j9y z4KRPx96bW4r{dx{Z+1-~Ng?sI=V<0{eK#g9is5AEZ!zxyo6|3lt@*K#kFXmi4K*3I zehYXr)3`Z^%i`JBs;&LD;alZfpP)e5JaVwwcHJVB^Vx|D)>YDKP)>#H-4cBB6_F+k zXt@RqXnVv$Gua(_l&)bqzEJzgW@uay>7MV9$)tO4{I}6rtMDqbwrpOI!|uj++w(KU zl@Ss@Kinqwv&dJme1z_M=23mOX=rq@$}UwyTNV)m8g@iAKrbOOze>BxUD6Ee_}~Co zh{vEf07+b`VCy4<6z~r6rLhx1@wR^BEQ|4<f+~RFvaa5$5b10cU>>j4;TKLTDgq}b z#pOl>ZE6M6CxsR!YM-H!RLhk@#em`hwC*YL38MLDPG1+G4U438=mdk_>HX?R(ccVp zZpb|m0xr48c$@d6VC&l12{>;C{$1{Eapqdwf9DZE&JiaVo$lo&rd?9I6s|pAoo`ag z0u&f!h$i|Zw4nl{qbWe|oU~Sz$ONzqm-NQda29PUPuR3ukk>(ot_$H@qy8gq%=x|= zj_|gBOP~HDixKi=G^+`G#)um|RBpJ$v%jc4?tYiNk-oXPa<anC<*|VV05Q22Xkc9y zfRrh+u|KS*YKa)+Z<|a2q6FW7?kTNFJSbZ+afl?Ah&%tuo*XfORoZ@Ik((F&*v?F^ z*Aj*s=RQ0b$JUU~Hd<bu%4$w}bKHa4FO8`w{K0F3<Gl|bKSe2pLf*(2x`&w4l+6g6 zI&`PakN4w#gXZNfKNlNY&+F3XK`dHVMS_juIZHyW{<brkaXGTr3%eGo@__g`bhS1< zTyZw+>g>b7RsozBGn8GE9|cBb-olIq?Xb}p5ch+k1O<9~pd2m<2?<G#Rq6N>2|+6u zE==q((8_4?yG5j7<4;b{KTT*10Cb~g+H=Wv_R+Ub>IJPUrp*dJaqig7e@T=k)3qGc z0mQvR#_4<MMjiB+ydMtULxB&3f!=Wg8r`;&)o7mxr~?~x6J%rg;9>{5_2HJmxa+zg z-1&fPQYzRgp#V+oL(_pqb~7bAzm`-10ZW-Hp|V(5E(;w}?->;s`g8B8WUL&Xx%NA* z%l3vb8HUq=gr#}qIttYP&jDJ1`u;vN){WrAVT7InGi6kv)dY;^Fj#0o0oto%kR)F$ zY`B8Lvv%oBCbqu28keMYcRBf6NORxc(BGg^D7f>)y5p;lT{KrtzJV0;+Ng=62?Xk( zdS<k2<L)2QQ5-%X!-A&wV`#FH3cle0DaR#$=IP>M9z@pXPsSdCN8FOy3aZR75Dvdn zJ9_AFU+F^=_2*QE2*HsR(-6ktbg}@$_UbGNLSUSX`lMKLJht>@Zh~Er1vZ%0R%wBt z5p=5zjMtJC;kFGVEq&<09~>PG|KejEBheOFAD7RgORDoC0dCNIbs|1gDjZ$@`(upX zc;p{G$H%vX0qjWfX9U><v=n^ujG$j5A>mo~c`B!=J@X$vuyu9phS_$5kFHNoZ$LSw zHsSVU@8!>SW*HE=c&!u?TmNWomSEN>t_Bqp^d#E_sc9rnPN#mQ_*>jqxKvIJi5&`B z6P~wxLWkQo9G8fgh70F5R)&_Y6GNM+%krToKvg}^sewjsvPK+|hE&ZGfhn3j4Z9IJ z?K|9?Aiu6|SUXpC(PtgV$}6$bmOU>UmJyM-{0i%8wPw{bak0CxxouUyK>&~f4Lf)Z zDy{FR`kAQcJHgg3{wpO5x21>yX#f{l7InM@Rr4_IQ~9&iB7my4Krtd@R#=5=$Hj^= zM^|imK-vbkf8dtQ?epV_b3`~E$!3t-^vox5Yl}?{)#UGGGxOd3Z5~r83xvrakPDXF zSJattUBBmWCER0Q6Dcqv`YZu)vI2N724RGlxcDY$&4byEWTRu1HLGO}2bdYOsYb`9 zdKsHTn^t^Z-aQ)TEZkdu_Rp#c?Ms!G;BP#t^eeTnwy|s}5^H_^yHGXfnH=1pSIneT z%)rUXfo>)AMs<Og-I^_$c$s6RtuZ>rTVaSNrJnz%9*Xu5OK`0Dw(ua3YmChFPMd4J z?Q^CQ9*-cVdk^vFQlVKBf_ZswdwSa~sPvVvyEX;XMR{dHD~>W$6I}-cj3pphisrT| zY+7uH%(t|-igma^t%5i-l^WQ9v49c&$9NYkGdF><%F}P0W2J;Hn?Htrf<@iPlR~)x zhvBl4)ASEI^Jz*MG<0;0RMfPc)gu%jk1cHSXD#~nY<6wt3Dk6nOOrLlZ(M9Fw++7) z8rSfiVoO3hP&fSX?e+3)d~OICr$@UGte}|-<sL3b(VSRnbzjTiBp>~`eT}}riRCLZ z2n!qYA02K*YIf6>x$iJgjJteZ{!H+`q4tJm*~|HY0c)GQHcvO8jmjXqxD9f;!(EW$ z{dJE?8yrvBl@|H{2QO^=4?)6QWl@Sge!jX3*{>-h1t~l=9zX6qn8tNGIh8?(v6zpL zIN&}3)sY}X5RXY#__q0x{l7nGIK&)EST+uvWj_@8OWb(Xk9TRg9$XE2pqk3%{0Q^i z`wnJM7{_>yeS4ROjVVG;d=v9<yDp3=+nH5h+kfchkl5fa1#`GGmGtTfSWwCd|N7+8 z6x=fexX|3Qj-EzS)b{lXpA%PMQy^ynk^0tu<cI=PTPS?l_|De7)e@JjqzjLx%`-#! z`}n*Dxl8%cQW}T(;1{q$H0!z6n;;Xd1GUu3;pVh)sRMhQ$7vtw)#=;7Vx;~l<92u& zPzzBtb7ib8mqcrFc5Ch;cH*~MqHRF><**q3DkpUK0g6$ZrC!EN?JE3gh?bxLK?Bva z&MT~#5gF<0q?dP>$Zm)RQCm53a)npP-V&YfpjG~eQ~&8>&eUqrkdXf6&8Vkv#OCeH zXd8$`7my>RusfxPhlkdi#d~ne;OovCm^&l59;m%`miqiar!kax)kaM44Qba*<74~$ zl7c}m;F5dKr?9C!=5C_!Pj^B~KZuLf&qJ7}my2tMd8MisF(Y?NPWLC|3Yo1%L{M;h zD;dNZ^$iW1Pq5S$UP<?-lMykXNSLeBRSlK(NSG}xzj`b`1pX$pT*|ot=Yr(LejSb~ zhKp{aqobs3`j<gR;w0DzcNx4eu%|)M)Sscg`r;E0^ydE!{T)2pQ*Y*ZKS-@sNgvNq z`r-XMf99#8aoyvT_}@~1mtS^3QiWT4a8D`}D1u|Ea6wl`Un0g@@eIHF1c_$KdWsvJ zvDqe}Lw$p<UWRY9v0zyLYob;iLTVJ*NR3G7cjyD609HCRHI?WVi0VB*&kKZp4rZs% zH#0K#IL;_3NJ0a02uUjl^G_>9j2veV9A|H9*u`;@eFgu9-2hnfMyG8YiQI2?7|_tr z_86W}4uQzOYkT2+X#S_UEY0XIF5Atjl>#~3R=LpkC?vS`n;8lT7h{-%&wG9>e9Gnu zVsalJpD7T})q_4FeJ*SEb=X5Q=_@y=2`1heIdYmbHciB1h0-hhmLh>;;<vF^F-}Fy zRN|>__|Hj@*C1*b2;Fl&^l_7mDe;C-qa#vgxOifHuz?tH9iTr%2MGMm9r(-0m-X#k z=-sLl-3M0|7L4-Ij}9X>Uqta$Sl)_0-nlb><N&BLbrbM<+N-veTmb$U*&}MzP@n&V zzwqkoehs!w%aGhacgXr?#~AeRu|MqYqn@)}xguZ6u`)rR{T9=z%ED3Hg!!bMjH~>V zD|~+i&F^FG`7yPe{qm;<@iYNn@8*i3hbVM0V7XlP%n=+vRrmP{E^Zwt@<99}4}x@H z^FB{J$sdM(>n%FzM^7<H9$bDFME<v)C-NYI)bZ-A*5IK$v}Wxh=DyGP4RtMQVi5A@ zR>)BnGSET3b`ui~t+D<OC@3vPqBMg|0-05+3%xKTy?ttm<nJKmv8vS9M`aJ7JN4O| z3b{cxSoxM;y~uJ7?s0&66VI>V9SC(GcOZQ+#ovmHnPasCKTr%#oX(HjPFOEt)&$<# zxLj34*#Id}bs~*q%BXX4X->RPPAI`Tl+G~x9$Z!M??BfvH-CWK4?8*7gd2H+CRKf= z6|^gJow3m%gY1U8kQ!9eTKAw$p4JwgAb-c(+tZ-!`wQAUCbn15@Hcl`7wkOcmvq!Q z-n&$B%*-r1D9B7h<75<yA_bbWD^75z&_cZ@p9PpskblFa0%~17y%NyyCnO|*uaZ%l z=g22M#e3>c>Qz+a<SYCPHfJfLNaw&B<RJZ-oMq&H@6|Wu8O=%nNr5dA_xsha-Q9r; zdiyn^O&Yw5s)QJ0K3Yr&njp0zOBnzih&pzr7=+A-_8(N;D$t7ulQ`O4W`}FPL~jG3 z@vdOZK=cr@ZMF?<Z|ZAD@x|I-6YhH3*woiZ2)(Ro-u064hCB!oG;~x2q@SzUbu$O9 z9dcQYdV?k&E=nXob>xKSfuf?Kg3b2<&XCn*OTWCl{9v_cVldZ0%Czm8k@9!ARB3EZ zaLd&4;i5#%(XPu<6ZK|Ge0;V-ipqH5!<2yxcI1dzRexhl!aXj3fX1MwgcmY4tf#&F ziGw6bm^j==NGZHZh)B5LmdKTrap=b{KJCAL59tV&FukH-7Km|xiy@UhH`Ys@2LUlr zsfb+-LU_2|@ff&Z>Z1zCXiX5<#*UiQ%oD_}I8R~fxi(+3Gm|u8Gw717*qm`mf5nuo zcDTHpH8fhpvf15z5l`qO98l%Xn>3&StUo1uO8UmtRpGk`S|^lV!)#(#sojkQx*{=- z_afD$_-2--9?!X-rtVCAR?*PpJ=^X=SdL~tk_!OVqXK-Ay}kW!EPef!kdMvH&As35 z=rX7iM{#4P-NeFUD~qxR^`ZO^3-zS{iqD_psl%Pt4G;ZTY<DLgBf&bD<LeTNl$tsy zEp3^$Y67kde)#YK-eD^RDsB*@HS*QielG9(-EG|e;KY~s-t;K1lH<SXp#X}D^xWgE z<WJJ1P$|G+maJ5b-K>#%O-?3W7A{fwAgzKRD#}>yu8W8Q5({6w=jNC(yb2kf`%}<j z+-(d<+{o+F4G1zat8!mC+EWj>Moeg9ZCS{JsCdD|M`){*>*wFS^H-|=h#T;T$0%^< z=1=uZFb--yq6u`&1J@m6FyLo{i+>d<1W1($2=dyX$;dCU`}{W44&{0o6VGylF*7*S zu82{!PIyqf7fn|hth{w!iKFa?{rbOmm?x|{sQzLeggsd$q}|xqzObAe)@XY)^^AT+ zL9vw|_9meA8<dTqdb*aTvOV8R!HxwT3ug0)&fl`J95VhOlYZ`iI|=t<EM7_gu}1Ce zuZEgSINc_=gav)8bWL1`r3lRzpR8fW`|X#X_WqPQnC+4S<G7%8z)*97!LIT_hY^%y zBu6Sp4i^jXDE#N9XT54gpQw%%FukUi3t@bQk90})ny9%E<IX_~Kqqe6?|wEphZ)U9 z_FnX^xEn6fcc8KKnO^y(wuP$#LR}~{XZ`PCOc5Kn`%!}^_o>}bIXUzFp<d2&?UL_X zLR%Dm`m!qPAYGlD+KZzKgZ>2X&Kh2cZB4=xxS%l9r_ZofS=oDw{nI-<iVz@wKNU^P z0||B!?tX!!q_d`r0PE_bLhP%a6x{9UbaghnJ4y4(+W$kpfF^kUkDPF%<~MK9<iT91 zG|NPNu3K-?T)k!L$VnCmC3@KLK7F>{GgKh~S#70csC4FYF)^x-=-+t=oSY6$?DUS8 zv!<E}?Uq|SfeX3$W`EvCSsQaQE2~l$dMZ&}3SxKP!*66*=*ef~Fulae`X?<MrY!+! z-`1-9F;JzKbh`E@wg(2ZnJ&TooZC2_(;5$m5BAg(IXA+!by}|HF|}sL{p%0FS+63f z22sc0=3)wvWf${!DT*c^Bd2q_eD_<<SQ?3#c%+)_Q>9$JXTNvW35an&yC3;jU|`=z zUW|el#|RxYM^JD4dvUPDl$X}>2N+e~rFeH=xT-{JH=k|s=k`YtsO4ngr3&-y&>L#p z`*O#6E2(5x-&#D1>*sJv{JVx)NOVJ7&e@>Dm*7i=El!i6aNd&i_W$5C!C4k{HLE$V z>!l#;rOv1ZXtr`bf}|b$5;njfr`nrvavI%kz8X>8e4V;!GW^x_$tTZ-+9}n%K_TNW z7jFb>>&(#LMG%<#DyMfKR}mGq{7%UNVDF8LYpLIm2}0J;pA=~hg&W|WHSaqz8}ATV z^!uB>$0EC9JumI(wikKvDoBqZ4=LQvD{@YUjIzzbOLlp@e{@7EIQ+Sd!k3Bf%!G>@ zB{Yz7VNCQtKR#}H`@Lt@^f!_$(1oWCXM)fMx!nakf^M1Ff-nIcexF8>Kcz`N*4pT= z5;Tic&_!I|8FxjkoUQRgOHUVh2pN*WO6y>rLa)rjgU7yk^bMuNFSyaZJ#2<rIXSVz z?~1cP9_$2IFza}WUo`P)qw!?8HkOQ`Ir~r7oykr3x6Vkl7v=GxQ7GuNZM2Ya^HhM! z-PrwzN1?N0oX2EKdSO2m;y#Y6)usk*pQ&waLBH1qk3WaU&u?DS@yEhCn8Y|(ptf7K zxeZVIQoD(CFT^)ReW`8)a<9TNcF4fzLdh6hw%A>#LdX2zDs+icbtrO+i{%ZfQRs2* z<YpR_%dWfAqu(AP9yP59znEjKu>mt#7E@Swu$=7ERn{_^&(JG;zAGpEQ{?pMZhig6 zG4SK}I;t3GLJX0|e_Bte@1yW3FGq7lss$$6*f^!khiiuvy>XbPp;i4`!LLj}#eu|v z6mVBtVT%#bGDp~vz168kJXk0L1l3;HV1!Lu37|+{91f#s3W`P?whn}fE??HGxUoJ_ z@?&LHSvqp!H+76emnJ?7&Cr)b6hpghEYKA3zgQ^;rbsY$O9*DNT$O|NWP7Bc@~kz2 zE}?bhF7A5`Bo%4eE+3nv!rP&|s|U@RZxURQ-kAE_=m@uDr$2!8e20d>HG$XYLWf&6 z+x1ua?N^_-?=D|#%@<hf%cYs11k+c=?zhy6K1Gni?0Lprwy(n%B5mhqhZCNpFOJ6o zYkP4{pkbt8oLj?ZHx%hl&vsxcKh7Mw$(z)UhIUCV!t>d;2O8rQ(G$S<g5cod3k>iD zf<y_z2(FGnDpt{bjA6<C>4sk*3$h#^kC`Y9!?UWA5j9W0SnO3;?C0Hf_CF~>IUp6v z_NapCr&1cUBK-`AfwRzeFhARPa(#LRNtcL@&{x3M3nM{G@G`+?;)nkCX6om+6n}|k zSXI_c&o}yLyT3mE{us3X$Xr@BV6{{qqoZwvzgJatqYq|KFhmTBpYb`QZVnmg<XA(~ z{_JRp35h0(;QKC$E?d<rLz7FUA8KACwr4iK#{BRBu`$%%`345i73@W~34^af+_}qX z+V`$Yn7hz*_g#TmM|xv|AaH;XRk3vTohEFokL(ejXemZ^dpV=XpY#39^mk~NwD9Ek zg~al`C^eEpAz)k>mpDQr7_O3mYlo;2<4Q-M!tU41wf)J54uFZ{EBrL+uDDB%E0?Q5 z<HS?qR?>3Q$6H?jwzv@T7A}eCc=^Wu1l)fIO{ZqqlU$VtivPx<|5KpoD6Egis8)?5 zDT$2;e#eY@OZ>!Tuei8uW5S(fBS&%kHUKsy9v(QHmeP=D5f7>jyoH!a$ZOc=!Iyo% zA$jEp>R)b;GO!fHZ*S-eTYB<2B+JoaI`N0fNFuiK-Wn(PMDSIRCD!-z*?aDfvlsF^ zarNU7mstb1-_JeqrOeh~y0%XBZwAFc5G9gLgYD(uX{t~7oZtNYeBT+xe@uMV0ef9y z&?*)iOxvPW%F#WLnR67Te3{>&6`6exC(PbeB8IY56tmcw5F9>JgSG7b=ef0IsfrxE zmaRcOF|G1utE_U-`oryu1z3V#4EklUJWxf)#H~$u@+UpAn})%0DiZEm-k_TVf#Fm_ z!i8!nQ|n?0_L4BEI3N-Sv>_MwXMGOW>e+eZI15E!%?<kLFnR*+v%}8_=ak4xCN8!H zQ9#$~r&L|J%j7&3yHV{k>PXGtC)*wI@sn3>Y_xw1NITxk$uyFK=z&=7T}?=?lDMEl zh@vw5Cr`@W2oYD~+Yj+y5W3eQ+3jo=WZgYx@TPlDnQ21Az``=@*WAo(|HTQuUei!$ zv#}qN3j48^`r?AN`b+2Taq3V2`SJu*$af#&k)pCHag><y&9JLZ4dG-T0{ejgts;*n zlcfV%3YUop;fU^9s6N;@-T(6gv%a2`_I<&JNX@xSMW0ecytCSIn4zu;XKF)8i0q*5 zeQ?eE`6V2v1#4=q5o6uCgQ$I9@#c@-WO`{e$}{TbR-A1?VdlDXn@eC6IVNfNT}`@J za&ju!ll@}da)OvforqDk-6TSq{D|i3^|2!`<eAqPXk=mD^$*<Lv+WrL_DCs(@dL=c z=$H%BV&+;2ArP&(yK9V={EeXBfanUIENKTOZ`c}3p_KlJ&j0M#N^eYOEZ$I=xAkkn z8FUj44s8~*st{5!IGh=QNfJ@u&#dafG}&&v`W-kF+@j0<*C@=uqaGgLkdp^en{BSS zrr<rcD(ID_mfn#N(jz&M>MJ9LAIXzs>Bgg)SpFw&<;a4~y}USLxbWiL4vSr;FER1k zPaX-MM_ddd4~NPEw*u%sJKDXEcYjAE=zoabU<qo<KM(`{7w<c)G+z+D5WqmmF5Uyb zyu+VSS3|@9=ghdf`kg<WNCUN|_m{rM^pziQs}X2-8`vsj;W<oj3_HoMm?C((2dRWk z`nPO!xg&E8692!_&O4s#_5c4ObUKyZls!r!*_9}(^sWez1|d5Q8kB63SsBMj%1Sit z5Hc$>A%v2dnZ3#UKCaIB=l9>|cKiL&?RGkOd%s@Sd|uD%@wh*ULM_?gQTEt%#Kff) zn??R?2Up3jkE@O{=~gjw8Z(|7+Kr*Z@L%k?I6VN#W)+D&i@v`4)5&aT)?W>lDeqHF zN^iv|X3@re^Vff_F2B!N7781Nga=U{z0=J4wV3y8IZEY*UR=eoF;~WUF)eLreo*pK zFji|KJl*NCDAm*M%5~eL{zdxua2C>;yK;6>C_l)cA65Lly3Mij8zh3^`|4X9BX5_O zb|QRuUdm?F@pGga7k6_>YHUx*oQ8Zbg0$%<LdyY>{>CbHN=q!AsKLcsr;o>u)oTig z@z^;uiJ!@^Eb|8?g-omlyz(yE`}9Zr@5~Hu6|ZI&&a+QLw|TWZ*!f2thO@%AiV)Fj z_1?P&o5+r=@*1m~`KIJ#q_nfb?pc0kc3o;1dL5}8W?EuD@FJ!h19qq?B60A0$Wn-q zSp&B}fAB%9C*}fIuS&YSqb!@6K5@8V#$%EumlC<}ujoB@|C9XHNE8?&#$o2;xk4^x zm=HS&#HkwWgdbwtYQ<UZL<218$KN-6^ng%Qxun2_+z43J@}J3{VkVlobB}$?5248_ zd|O)mYl~_W7;9^>6A+<0K4zM>@mn&_bZJ0ISNr>|m3mpdbtA3hI2;VwH$TS%AI8to z>ucocPMj#6Y|gaM>De=JCgphCqut{odZFc$eP<hD>3l3ce{pf=4<`Glxj8Sdq@B2@ z=K*tpJ(p-XV;@_PaeJ|J-P*kuiDD2p@D?}VqdZIvCr{==n&4)1w4|FNP6!knyIZnr z(QMwiDH569OhWznv#EF==W)}ptSgoNU1j8e-5rhY;Xe3W9U0p_7il^>zhp~UwOQeP zK4SC1)!=~UV=7Vqxz0(FE7?$nK7{L#`TyLJmuFndzh;AXs^qrS%i})i(g)~L$}T(c zoSkO`kXwKRInC^wB;=~(c1VB_<Bt9uUaz#W&UT>4pX*ENR$Qjyn@U7<#0NAZRVu2Z zR>{iR?j%CN-inZ3RzZFnF7lzSE;_Za1H;Wn);JFcx3Ywn;%4L3Sew&VUMX6n)3e{$ zNvP?+?Jll+Mw*)zZ6_;eWMuXz+i|+1SHtXf`DkIpxd-*a8@biX^Bf=;cgM8cTd*Lm zk29J0uDYQ*aP81~vagH7ii+KVHb}Mb$NTW`xw#D>ve1O&Qdg8aO73BgL_vAxwxi^I zBExB)_-<3n6H2u<GrJcqsA1f&Gd;m(Kgk$7b*;Z6gzVm5eEU93G$!raWtS`%z)Cqu zo(vekKR@nQ@ifMTHB3S#cjt@HEInzG6TjxqS$y>H_|cVRk|8s_+O`KX&YBSTs<)gJ z440|Vm6P51Nmp*}q0#5$Pr?p3;9`FjUTM_VZE2Csxhagho{qZYbEFxLW0NXAYwyTu z$*8m<UtD}(vbQwPJ-Am?TwHz`#1pb&($cR{a(VoKXMd{QqCLz777vB^j~~G29@*Oy zVc&<pH*)<grvHKD!h14oK+Y;}$yx2`X^fOc6G##x%@2J4W+Sb^9f{URx3=@aB1@+E zhUN@+IP{V~U2-t5R`-`Ec+&jul@8==VTvpAe{xuOH)HZ{<yGsStGkc1zQ631e9mO< zI+{@rM-7~@Sax05@cmer!2H1J#_6GofY?Ksev!W4aBTKXx3P9V5!cab=O5GV_%|{^ zw6WnIa4#DDSjwYlW*8&MWxq&sMrUc7nV^m!e*pM}#gDMR{_c~!{$%_{q3hW4!diDh z8-zioF^XStIV_)kzH7jpPSmB79(mO#`{)iAlcPsuR0%Dg+d6>N7NH&ak}h;{&+Atc zO-X9yi@%AJVy;}}t?Lve!g01S(w*Ow1#e^u1W{?A3~gt))R$KwQYuvSF1QD$1=XJz zq!^Mc*Z5$H=#}rmFb_mv>Y882XKX4(QrNe7wze4KF8*Ek%N=Ni%i_t&^0k>F>z^Ml z5i(MQ`QhyMSh@v4^)L9QiE=!gKn|Oc=kH1XyK!vdo-d;BB<NHnfBMz4(<mBw`W;`h zrI7qtp6}%%iJ9(_`($8nz;@)@B5a*+tfZt{$a}fT`->L7%Mr$quZt6EuQzb}T0U2g z{E_#_2=dcEYcd{Dl02fF?ErgcO*`N6G8b|<OAF+?&Wf@UZ$Q`g^a{PFt7~<VQ{Ot- z-CD=>9PKAif=BFb`xNF?6|q%v<MF6FkEKoO1=AClx2UC=?s>c~gn77m)%EKGr~d31 zSdzrlsk>HV&gT@~rumk->h&4v5qhgHG^`SPb9b&XP*8aFHA^D%Xh1Q!lN$L^_9jeh z-^^qUd97b$1j_4HM~?ltHa1nik>jtbh>yj0Ep)c8?9@u(-8$J9LPA~sZQom7FuAQ` zKj3^b19y{u7Bha~t}ZjbwIeMYKfC;e%`$Epyz7h-VnSd^E((iGJ0WyRyx1NsxxNk8 zh1dXtLT}jlsD?bz<m>8lUFvxD@PG$X9X*?l&Zns%)mAC9;CG%63&v<)G$9qehoX9c zG()GZ=-96pl9xBZYZEG5YT&Mu9DeT6qKHwF-kxZ=hur5YnVq^A1|iTX#A<y|B-gjR zkbJIos(%%hzuwQOS9xGN_hG_XwpdB7P)<G<@MmsAfNJ^f*7k|I?I9v8K~tE%RNiy& z5@kqwt>x%U(UUeCIiSi$?(@)g(jZF6NJvz#j5Aj1z3u*Uo)F|0#1S~?tMEBacx88< zhB9$}+GNo_Jt6qVCfE5(oK=s#jB20$3E@H8S*{34yG#@4TaO4X=xb#JTVt}<nWQay zS>M@uL@P+k3h<~#%I^PH{IUp*l$G=BIoJTmZljK<od0cS4?P0%f3}t$2&#?qZ!DVZ zt4da1?!ncszsbixd0?`bS#gLoXLdogM>-eQNiC{XnW=k$i~j@V#YMl(R0t!k{D3uK z`8?EJwQt4NyUw|*@4I2MG-eW(d9~`7?I2l~>d0XKW*8zkR>r6GMj%pgy{00r^Dox* z-xNR$z9)LOPh?Lei_@gwpyP}=GHpnS{rTK&^0}kNPo!kyFP-!a@J>D#ds66&!i8Vn zP{5E&#(P<p=!&983ok&giOiTR9FmkYK1`|9qHx(}BsVlXK0PoQHB1)?am#VE*ZLh7 z_;RoL8gX7;&tyZ5xnK1pz`JFUX7R*<81?^ilovHMH4WKNyG<6C<?`nEBNZ&yv2!Ri zU!;WI>?*y>;u)i;(KgTkc&Ww1ZTz?5Sr1TDxiT$38o3?(=#3#CCC9ajgyi_n-}I|H zjN-gM#Q3t10h<vbCnKuQfZW!(4<}zJe}AK=fQ*_yc0HA5tfYa8yBe9qwO1dHyuIJH z!-L6>I5<r0$XXVlmx(J&B(#2+c(3<(eY$hB5dB3h?!;$(9Tpdje^SGh_-uMT&*l2v zjbmJ2x61|w0UVh5doA0zXNIH%{|UJ3?z;3unf_be_n}WjYlX&*XL6Y2R4ZSaV(I>L z{_|$tbiusk{p3(>%g?_7<~nK}(|vmEB1;SYmrN7Su<v6Af~NO#tu}u9UYrH^e`yAA zBSK8I*^V2T*w`<xH-AmCT*pm(Wr)#&rV<X)b+r|(qVsDw<hs(DEdwui=g-6G2`rQf zI5ZCnxJ=G?FE4c#tuhndm$13i-vbKh$(pVFABNT*rtTup*9vyX5jl1vJO5-0vN3IL z?fv{nnS`j^_mwA$RE2J|GxOUk$T7!WH<5MYZL_C47Ao1h$?yK|T;+ey(bQeMIy(Jd z|JuOoSsMp)6m%zs4)nBi{w_g57COQZWsn{y?de?Anu}}rvFWDLMfa6OIt3@?1Gm$j zmiJWt`Kb{^4l1R6J@nV|>B|B*X6abPt^AlrnrgD`#{6YEzHCiT*hEp4!D|&;Lr1={ z%;>|!C2}uR;+h{buhfTr&SazZaIO7&8;2mqS4a59A5d0RrF+7>@3?LnZL~wF?s@a+ z%Vx^Il)XCQ0{uoz0aYM97*J{I?!De$+B=YEx3W=N-t`ggbsV%M_c#XE2l6I4(H|~; zrJLC3m!56$|A>u<H{G0yTe6E3KR%{}GY6ILq_tPAS5|&<MIR1b+(Kj>$2A5P5K5nI z@4C;Cf7QSE7~|H~JlbiJ!Hw`>&y}_NZamhB^Ck3@d%iS7t$*X3d!vujLxnjfn>|F+ z$MZl}sn*-X+DuXYV0SVSTdFU6xhxnjI|Dc3P`QPDcBA6-rahO<Gnjinoj)E0LmOFq zY?R6Z-b)k~P4txR`04AbpK?}IEA1&|&0wP!r?lUzi%4d`+)D`xy%*r^&*d+fZN)Lv z7PV+ABeQK|<0~`y%Z;9*XFtC>P3=}hV^et>zNeSUdlW7)3^sBMQrfFaO2Yl-p&e;_ zKhO|seDLJ4&6J!djCS9oNLiEB@7vvEq64{eCc_th`)>)Kd#SZbUZK9=48<B=TYml- zn_fQ(gN)VfstA)Nao<2qS3e&g3hgx}FZ#9NCsTZ7VWWo2iJ5o9k1Q?|$#LuPpHXF- zcNnjRFx>yB*V>NZsF~?|z8^`uO2Lhhc+}ZxOH2Mj^%-RyogF6`UZ0YpHF>a8>vd?F z5w)8_S~gy&vy*668CK@#-d>evh**4>B5~^V&4dT3$2NB@V-#<~>OoN*RX%jh>%G#l zB(16aTvS<4O-$Z{RPF897l#KoSCl87$-Y&U?K9i5FWXW2cUQBtnc|mn72kXBrn$K+ z6UCy1S=Mn`w3G$~ke)K%Gd^B=d3%=62L&fTH|MJt(u%qQ+t+%YPEh_;SL*D=U%gY1 zA%NG5j?u4g_l2tp$7A+J$34}a<?vttJnZ{(LpjWEiuGzd`HjA$1KGBNQIpKH_6{5& zr`vhg{=K<wzVxRqp6DiK-JUyb!!-bp^|`#Y3lWP*n=+djDxDc-;^T{ZR#RDNZ~n>L zuiwsE$}r)yNbAZ`WId?GC!*voc)tIkzxG;7_GGp}j-Qrp?49wKx)fU4O>4I=-S?N) z8y{hv0tIY#!PzC&TotGUcH0;UqY0nFLdp;~IT-k+Lp6t7To&_4Gd+^kyb%fuNe9B& z*WPE9){~{yHTP87G34P58}o9Pmg&2c$Yy;|LQ?W~dSPMx3L~y2sKt*CALck&Gt}~z z+%5JRrx|uJk(Hi4#`Be&vy?FLE9x32-(^r(4{M9;WewoTb^4O){JU**ay(~u2R#F4 zlG9_QNpiTO3gq-({FXa=W=l)j$2E6rYweU2jS9NmQ*T}}8@sza{$S;ep1(fJ2EKx6 zm30nB16p#WnaESo=P&+_Dv%yML54}WM@cE{4@U+&+TGe66|H}^jSA~-#A2ssdesfv zMoVjb@-0>UGa?4r|HyaqC|a$~+jew|=uq3}^vqu6RyPmB@t;T3Vu+(1H)QX)Wc(UG z{5aGZtx&q=vy-*0*9R|ucR@R+O*@SJUWQHyE4GD4E*AgQWj)ay^<(yL2XXPzS))8z z`5E1bCl8ehM|Nv$BlE>n|L)1_+=^D~_UA199y2jxZe2*z^^2HVTJpanyPO*(BLSMs zJ{$Jg?XMRkrhoqQQw>NlkQxEu@ckMGJ11akJ1(6PW!?6fO~O^AfBRK7;itD_HpGvr zf+!%+g<tjRyiLy?PU+x6i%d?IVcvZ<2@U--WfdW%`QCR~CKY`nBBa)Ace=H0qoCr2 zN{hrhKL?#=0UpZF^E(hs@rcI^q*7RK4N`cc7yF7`pj%sOuahj$4%lj^VpCPs#aBz$ z2Cu{_XT4QW7`{>U=jgy6d`pxyE@IShkX1;4T}=JOaSjjq>Zsj%QOq$cSv4^-M<mNi zILeH&|Cw)=m-qV;JXann{JSPP`p9hN{mPFD`bnj>&!#AR*9>%SW~5y{eDmhS@XAQ~ zGtskt8>8fAlfs%-6q)sn&z-xn@ICnXu2VGXLrEKQ+w<umi%9!Dul!28w9A!$+cjJL z<=NlO(s2!gy;b^RrgxRv0XltIpDo*Eon;$VpYUV!;pEI-hGTQ{g6%WoIhj$}uS<p# z^aZY7s0f~D5aHi8d_!U4i?g$Hs=V@`t@k^|G0;lCzB*Ypv<p&VYMs|n9=4!ZT4TUR z;S;g%&VY!X|EEgiQ+kN8A&b%s8HN`4f9A|@;Gl_%i~IE>OX9)K(@!D2f!Ov}blJ#{ ztUxwHdw1Z62!e1KEG_dC37}HpIMV`;!4Oz5%wyBh37!D4!a!==(ZF87zEX86HdLKy z_kp1ej2OU&37>ku?LaYva&<qMx$h+v1kTObLqehkHn*yl*2ZfEzXieCI(A>}`i_~o zh07qyg2vXIVD4j4IU1+^>=^zg=;V1J{<M)*)xd4Yq%OJuMU>>WvXo^DIZh+M*@NK1 zjLM43%eUwO#W@H7yM=;Hx3BG&CkGpW@6!b=KMDB!ca}}!mVh-T!G4uG(q7o!T;Sql z3DRoayv;6oeHXsFtP+M+Vy9n+P7|G50-osYydoj&!MSxh3Gkn{5LqZSqCfSZda(rG zLuF9y(-*607e7wvPkK-(4Z^NtqToE2nhA|hBOH3dNfJUI)2G{Y0F%vv8mnIKDfMo~ zyVX9X38P);ScpoF=>ofYDgSz>a@{s$P?98sTd!x+u=Da>6+8Ld&lWT-)dc{XoGce+ zhDT*@fsfnR(a{m#P6NeqBiPM)eN_<`GWC)gXWN`4&X&spXCy5t`NeUfTUE};t1Go< zmcqqlIoy;IlL^yLBg_oPR*g}<O)S{M9+(KSuEu^vNJ{kzoE--7>xP^+2Yd``F9Ux# zQRGab%6?b8d?R3E&A-=j-zR;|=Q<ZVUBYH`ipF{M+eJl)bS@?VeAT=u`k<@<+Ze=r zzW46~legoN5jyC5rX?Gmv?8Dt{{dOGx}I_gGGrJRfFP#CH-U^4dNU6sa?tS2AS0hC z+{kEBISOI+9S=q(=)s1}`$9s~duD5M?XErEd*$)Xyb@Co$7}$!Uoi6Z!269Ct}F%O z(f<hCA#yu}p<LIi%<U;w$aGt2dCr6U0SEkr>QX={UJZkBhjzTm<A!L8tid0>4Mqh9 z$+k;IXx+O30liS0*>Oh@s_X(3Oz**Zr9oMP4=zRz8Sx~YeSKw6MGBvN6W=;`>lE4m z76B@u+#5#hzOVtl0l?sEUD-N}t$w=*%)@5&@<bd1e%n`<H%t{_?MLA|#6XdP7Iz!8 zO}A27Dc>g+WT6}k3JQV%)}3&!A*B!Ck+Yb!RH@+>r#W)uh~PpMt-umD3romTGnT;n z_wKzL9h@-6mkUe}O}h}NIaE>Oa1MnaEy$%aoINz|LTJ{Xk$xk5YtX)NTpH5KF?)k1 z!w8=QMOG`}jF=qY>v+&bjBNsm=T&f&y!>?s1ou>s=c49od_K}2(A2Dvb2SrKUTk0S zY90Jm+%+=&?%~6SNtL&B|9ygSmqpOOi2n3z^0^2YiR(9<mJ@e!=7SiL@U$VfihTKU z2mE~|KA(6(uns_o3M91^*#DO8cX%SjR-Dx%WrIM8(1t(_u=z?b7*%iH1o|#9t!s1q zwGC`Elid{`VQjp6|GrwY9o&L=M$ed-cAm0jyT_i;r*4<t);?&Pc|AT7J+G>u@5ToO zA8f4RrxMQigC)C`jm>94q$C+{3t+eRWM7R?e($eessL!`I8MLjNhwPEHn0F>Ha({6 z3p6L#-=rgG?%cUEhu0f++q&UHn42QNhu{UPI#$)!htSfm6<FBaWyL*mZ4k><XK!!V z(2*7%xm#MWN60N(11`_)w)|TNMpMaN=^r^agn(XEb98~bK(#DJ!6^*BOu>9{CM<V{ zv}-Ur0o?`emJ#Y;fsZ~+Wv#6WRmmSuV7?OODTvqV`xQk+{Q#6dnW!t3YvdD`+Av#- zl`KsB+^7GXvzRyx#+@I$umHdKK5c#z%NQ6PK+76KhNN0{luEt+v-q!jtYVJ^n}T`w z8PH5kOgzODUdJwZ8^-eV%MCjL{i6~HVV4^dC~PzY%nx6Qq<Q&9!tniP>ml&CfT>%3 zd+(KIcr^HlwiGj;Xw;!;eui2O&(9ehG5hbOjuyHufxrdgv!X%1;{%9?1)U1z!?Bv6 zK8KX`Pb#FpWzc*&jONeo0Gb^(uD!$ayEw5Z<FNqV-|{O+NRzjyKYDh0TKd0#L8cS9 z#$Dj&p=`$|e43!^;L&^r`-z3&)11%YPNi+w91}qTd>bZV)TW2QK8V5{|4iu-c6(KQ z2H6n+3Qq*%Hw_M@Iw0=b%=^H1i_XL^X#^Kj-v~S1Ao0%*?Bez&%OAW>1-+}Bsc+{L z;npw~JMpym9N$V5qsB<h88=0EYm%_0X5xjK^gwpE4CGZv#5_`~RI^?~HaM*3j~T}( z-v<^zn|*^qBVWm;WeI$CB1Y)zX)G+49E5+tl(LkSe~N7Bk#{}<+KN5Be2JU_{`b9| zgnIuLpecUu<?YSP&K^FcV(@V4AmFv(Q_-QKLX9a#h4Ec(;9@fJuAmr)K!V8jKvZLR z_HrgkKT9ybZ^o;e=L9NQxVyXK!Gs$6IN?vlUotM$_Xf3rW+E*Pg!RCFYVk5b?@;SV zoBc+10hyLrg%Oy)G{*PEJ$)MNRX{+@Am(k#N2cZ>w!QfNZ<vJ7vjtW4LCv;+jSHCT zNa%<k5kMPBBe7WDxY30n4GfJ3ii@;PW@A?!pKW)0F_(vC+=gj@g)QA>;TEXXOhEb_ zL)ir0xi+{>BVl}gHkYnV7|N=v8mp?(Rvt&lJM2nJO9L|d$JhPd8+a}c^xZH2c!Dj} zm*v%&Bu?YiZx>Y8X`=Fg{k>|rYi;s00PAXC^IH|S`HQHbp&{xCh#p?GCyvw(z{Uo} z)@M?DhXY(GUzSTP*-|vvFN-c`woPV(eok!Vpjh+TSL316uKd_bGw}6Q$P@>Rr~Es2 zequO{9!)}j$_C-pg0N&)$0*f+<~{28nGmuumq7Q#VZPV7V;}5$fen|R!3src8laX< z=g3I)^kCD;J?zE2BhqUGtUD@Tf+CegFvG|KjELxD?yW0|i(`kB4;?x=#ZeZ>r#bJ3 z6|a4K9w2EdJUMu^T+44)0ftA1+GUhp{qfdcrp?|1yU&StH!ofO`tl$V6r;w<xxwuA zE&pH@^Fi!Gn9Tq?9>S;qzP1al1F~fSL5W%D*feRFW8+n$xZsEVSeyKo#>5J)YkBX1 zw+!ZaIA{5||Jg%BQsDu(R-s`z?0G<TZ<Un|Cr_MadYF_>AaroRF-PZr^P3uB5Fdwf z*$Z#IYIcO-_|URO#g+h1xK70l))ei=z2I#Kvq2$U^?8_~AO<Sj@s=AkX$YsF4ko~J z;CsmyNTOLRwTffhgj$WJqybc*GoVT4O7rR>Y$~LLz?&x033x}Kr@KLlRBv8^k*<y` zt`bJav1@NX7FvX70YKItT{Ve+d_KpCy+RYc8ZL8<7dGNp>RsmI;$l)I#}Kk#)PixW znKeYgBQ(?`r2RBn@hTMfJ*qKLFi17tNa~?>%}$ULrfv{~DA;I#!_yFbay+;e1aAT7 zkanIu&U6(Yu{p5swpa%htY&RWmhU&PynI7chZ^&BWX8~fs4CR=3K3pM4wmI&%s3i2 z0xs1p7Y~zV9Y_!T&?_g=3cYD%yj_@_=Mi)fv+?K1eSkCaA)^0)DXM)l8a7uN1a{$8 zD*)Kay3F6KtI>aJr8+Z@6_@6vVg7!Y7jIL-FscI4`bN&<^zf-OVt8FZP~(h>2|z{c zM*W#h8nZUUb?CZq1xwLIj5YB8jS(s(8Wl!^I2wu)F&p?4ooi-&P)1>3;1iLL=h$q= z4q<_#z$S5na5yGW0%S1WEEn}hh}F+^r2Yk|^Pw`YS@b3L5L{AE5KBOk9z@WoOYd+j z3dfV|Mo9@q!;t^lh*Zl$7}txKn*=HqhH~~}TR@hG#kdlFY_mNrLIPEKU`G-hP61xU z)zy`z;=}vaMbOWMAJdKDY-3+8k}qRtXLlrkTQ!LPaPSO0J^eU>Cc2h%r^P}b9$`mC zaUPZCm~ZnsIX35S%e_0>lz5izfCs~R8nAy&fSaaqp8xft;zI_60<dTFqEri=U(P)5 zo~%wZJN(8B=<%L<O(l)BkFdKDczbWeHN~-Ja$rJ86ft!Jp$c)pGZJ2`Z$N-+$3AHL zWN8jy0HS`3%B=z{k1?nl6Qty}yeUeJYK4SIafPmESiC#u$}pKeE_eh!`)F|Qg)}}p zI~lJcpBuPr2+_WYiOH;dAQbmtINNu`A>tJ-C}k8r_ipN3-)ps@x_pO8i+j}s;EYl_ zh?sUd&5mf`efhto6FSP@a1IJ7D)LHxXbTh5ziw?Ei@|@pR>SdE-F6&kUm6uIpHoV} zpwG_E7O>@O*Zet4aH<9dBa4;=Kym{@&_20@o$y#=QpApNL#2d<PlyCY^bb8ny#|{3 z+0hzIB4CF#9L2C=z|}Ga7XI^o1AbGv0EL5LzAdLw3j)h68P*`>3tiIgNA!p49FM;4 zvQ;9Um;L)W?d)cA16W;0a{9s%tb>Ojs+Zx5Fr;-_ekPo8>>#Gujix0S6=_w*2e!hE z|20YfAy%t&laCB!-7_%YMpU^SVJV3!%Xppq5T>QujT(}e74MA=dJ8%H{G(xNXJutI zq~I1s1HYZBib@BJ-l~oJ?Q&t22;fuNZ~z{>QAa<}_z&OP)QO>N=dxsYV=YAgLRVhg z^qd6(11=kdZ#X!C42?Q#5vcfZ64CJhPM`J068yq1aE~FhY11Z8oSzV6bOFCiYYgvr zBv?+Esi*<6P;^6}dWnsTOAQr6M*81eK{}!?4KdG~N?ZBY3z@D8iN&nB#b*iu<)^?x zf=vPE7uCm3Qsat6Xnb0MzrWuV3n0|vmYm7xz~aVq#DOj8(LWgNT6n)L54yIey?D{5 zVC1BWpSBoj$pI8(HP#YLcipV3JeZl!P^u>5$`4XI;oHz8Rp9(r+8sYVF<emd$P}$j zfXz>yJn<P=Rsir=`{4x~!mMp=^9QYAStI0hgtKo@Ln8N<h{a85%f@0v-6&ymKQ%Qq zeqn{#!wA1897G`!>*J3q-GQs41Je@|w7_LWtL%086ql_q)tWM>fUx~^%%mjt+Y%C( zz=;DFvnh`9R?QiA(ZX-c$~Nq`8~KdN6eC=^hl}5m2aR8r<Fv<8gaSNT$Q3Xa$fQk6 zp!3NAq8(X?<UrU(gv3h13}1Mvib+UF5S9#nHJ(I+bL>REqb<$rQ?Ks89Z!yvV5Fm7 zj5a_!=5$)jS%G{?t~DbUFwQY>P_IiC?L3<(gverUVS%niYDxWRe}8}cRZ}OFM(HcQ z%c9ESm7AM;@|E^>l7Vqu96w6_pe@0DR$p7qAG{Zt%g9q0{}m4<_|TG_2!^^b7jP61 zYGt&Oz!#wC2>Wd$N%np(uU`3t_tsWx!uftgZr0#Pm@KC~=<qmA{_W<r60b$7xnL?_ zOdG>lOk>w#b{0z;K;i24_HzpQG~{bxenaJ#POPl6N#xLUoAPyZZ$k~%Qf>KrGS8St zx8*NQVv+xRQd8kDemEPT7amfv<``$J^TLkH6gV+3jPcd7)`nT3>dSdpzv8zvJw1G% zkryU^uA+;!da1_m<H8;%W20ueU-AL}gcViw@{(HU+>M`0ZYw;$28V|=&_6W9SO~U` z8mljoS%=ZytKxdzBHe&z!$A<eI0iTrDeOT&N{0H_YY@daW&E8VSUmEh*kS8?c2Di6 z)V~nNJAil;Lf?eRN)YlmJCDuGJjW4lZ;C2)L}M1ATN-NBk%N-Gy(~~cvG_tJXHC;t zQey8o%B}F)JEJXywd2>|BxQ!|52^O2nsnGW@ly~xGha9MmOk>MwP}}xaXA_)es2Gz zM;XN!(+w|Acwp9S=W)uS<Y8v^AOZLpCLn|+6Ue-15l;{z6+|*LW*f@39`cq?<%Q>y zR0=`cl^|3HbT3BuOd>lN3^<zY4&8bB9_HUhE-t5<1sT3Zd~G#sH0%K8V<&xr#W%z1 zdoYr5-fTwBK_p*75`Fj@5=5<E--B`@@b~Y+{6t}zt0DU_i^)LKMdCzQ>j@3qWw6GM zxyPB+AYR$iEWJI+?JlWvg)p0Nw((<XZ&=@8#%|Kgi?mlVaXf_+p%VkHlPbH=aiAf9 z++IO@ogbb1<e<kzP%L*}sP4vkW{NJfcXZT7dMetXmjjkFmv-^lAJQO9;;biyVr+Pi zQiRLpAwo=~UJUtzBMkl3^#@dwNQi+dhosMKaEyp|TF2)!B*lu-ftXAPZSf_MNG1iN zWCE%qmZK;R6}1tsXnXj^N0vBdapCw?Y_h%j&>B)~t^I}}m11r1juBT3@15s5eMov` zmRl=WcC!Z7tj){R$WW4}_kba;8+>->rRgR+QLZc;P|%vI+0{PW^}T@tgZK#~nVw^5 zB=hVxG6;mekBV%edziD(?c{U6<_nJDS{xID!nlcr<s%Bb5p%H6@yHHr0_K%r{`fz2 zh|M=8&GGXGkoy-rYDGUUKJY(R7ybV#P1?I6Yr8dzo#hrMO0m+Y9zIS@RyMx#e=9)- AKmY&$ literal 34529 zcma%jcR1F4`1Wn2jL23Qb=%ow3l*73$lgNPdt}|n77-z{B9%>6Wbd64vUg?g&3k=4 zzvF%1|K9gGj^}xfxbN@xGp^5do!5DupTbmC<Sr6iAwZ!}7Zv1X)KDmF3<`xcc>xc8 zBKUoD7XBydBCGA9Zg1h@ZsKH)x^Lp*U}NuM^VIa3o4J$oQ+qoBZeebI&TCIxTpXOm zczA68_XoJ`oh*6S4d(pdA@~mRI?gB*i3##A)<>!Arzljgih|5-4Ud%NF?ap=?Zejf z!Kp&WNfhh5ca!6l&gPZHwDA|^bcB58%58tsmz4{BDSj)&dEYrMDeFVh{J4n26gKV0 zCD+rFid(D-8WhC4qHDgA^b2oCH^owL7N!OAqq}*Da7f`FcE0LdVrd!pC;T;;H2ffj z>>hqbXlSTB{(D?i_;Kd*huCarG+M2J4T}Q#f=L`I9DX0itB8_?-z(BEW8l|l=E(oY z*F7|_pU~hbC@9bbo46uRMALV^C###CeXLt%qu}IJJn%NOLdMC7Unu!va`%Vb%Fxh? zNV)$WuIBLn`LgvgF%*nZe|UKKv4w>$U2v#A3j+hgl3SJ1>RXskW+E?9AfsH=%f!Sd zFbFm2`uci}eErMG-Se@F956w47TR{BCr{Y?{r$NQCoho>@(~5b#ZgSp%wT;~%L-Ro zK~^P`rmd&Pjm3}hhDM<nEh#$&1CaqA!M}LXRl&WG#XmHZ0w;>k4<14#heik0ua_)+ zUav;x5KiiP7*0Wwf1;!Le=lo!f1aN3yLa#QrXzSZMo*VYddVm$oA=gd2)An@sJKiA z^9|0`sJOV?jFvhPNfmQ_r|^np>(BQ<_X(eb%*;$|qbj{}yK~RRrB3!3A3l7z;G_KJ zCO!t|Jn<!XOh-J2vyV?rYZRS0x5vckVeRQm`Gjw0sqJ{RClS3w;%hFw{%TL>gYCsu zY9sI4_wKcrOVuisy8KlOCc1nFUjO9x7j~b+ow-*JB-;m;-^9i?3>x_veB3?$ms3C^ z?q>7Wa8vCm83aWI9W1u*@wV1V#Y~j`NJ(+AcA@d@x9-t&5*{zTR=*{*zhYI-R!Qxw z=?OST=&|)(u5#zM^<`l@T3AZs!=2^o^_HugdS#LDIdTdLjnEjQH1p2abCusTKB-Yl z*dOh!-?Se;N?D%kh!<ZKbzbP1+xq)M-)DQE$F^5ulgN3#YiitUb#8wly>(<H`B9E0 zpWUd)d~bU1Lb~6|#_A6vzvE_~-Lck25~-y~ljYx~)d&A_mWE40w2XWxF}+f!DGYu` zF(Y<Wd3`HBBevz=vmY>M78u^o(aa-Q{+e(HIT{tIZp*)mV0EHsMeo2sQb)9MwQn06 zXE?upY{EzMzHU%nUfvjXXVj^aOJaZaRnM}CiHXd;d;K|DJRek2J8tccs!w`RYRJpC znWt)%*$gX*35$y-?~Z##XQ%uAuC$%+O6p4F3xq{jQ1rF>m8$?-_jj;>p`i9ejHhOA z+Ppc@=*NfqGaVcSvpwRgE_Q?@B&=LqFJSj#_{>^VKPV^N@w7imC@&Yj8%#7c=03r- z^{e?vjk-`CKPEo*&NI_@O1?>9R|SaVRmxTzlUB=F?!QqjwR!|Am2Q0q%b;9f_^szD zmVUV%-Q5u4EZBoDPk#i%vmK_J@E1<(#=i1B?Pt0dMXRtm-~IH*hl^&dQOq115fE_> z1CO+@cwPReT2fmpD-$u^i_GxcoOf9oijiaRiF^M1`DTlRWe;tCe}Bbl&0){5*86)G zYYzT#*i}s7p`4tY5=9)*x1McfJbZue%aiW`RV!a3jt_Umd&9!RO-CynU6y~xLm(Hs zuZ}zCdlfC%mYyS|a@d$v_+4(lIx6J3@mS3DUz}y9R{V`eG(t8s0+v0q5F?m<pF~L? zF^GHd`)_W(h2eUy)t|5I(fjg*6bFwe1I`l?M%Azh)XR!x!!|N<a^%$13D#b<zQ>8; z?%Z&eBN?R9NF?_#f#*q()9H_M9+k*vhWG8+_Std#HxN-!a}b`ALOVIJvY}IA;kaBn z*q^CDZuj*s%dcFWSBsLjymlUseRZ`Nt>8<a=x)fuRn1K7>}fOZwl3g%ZzgBq@~4gm z2hYLg2TM+4>^ozcz9PoB8|GuM(LCb#O9MGt`MA7{jDbOf)cx5H()C+yM=N5k-wjqR zw=<|YJ@Fd%JM|H=8LCTlA6Ia5E8DEto$yQN$gc<(F0mA_`pJd_-)N=tC$%gBsiQR< zV>d@)2Fa9)3K6?++nNoHjY+UV<Sfe9*xAEw2ja!V#>QG!Ckxwu(9F|aSSGd|_#9ty zZ)UdrRnpC;Wbfso=OGKs!AW|&I{vL1&T(bW{(Q1M4V-2M?=>tz>tEHm3$|IvU~7E5 zynTGI<du}nM#^lz*bHw}FMP2cjEamT5=!0n`nNr3l<jwV(&HF7(;96#@c9wy2#&DC zR^Q!Gmml}LQpFP%(!8RKeD{M|;d}-ZUA!t330wFbl4kJ5t1aglyz8V+j{>8kNuWY` zczFqU?>PjMFsQ)k)G4#M{OGfW$?q>uk>nz=R)^D-EF2v||19)@)Nx$-_%n{dZ6mK` z!If{jh88_33Xl{oOL~hG6&3XyQ*vl#LL45Q9IhrD`EU_I1`p?BWDS{nAW^@=uHL5d z;QTemR${7gU-8y*n<5c0b~1W>st;NPhPg(y<HL70VTB&ncza&C`Q+td1(IGqeq>AY zoWBg!@5x9R*J|}9v0j-?yWp=pm<|!0SX^NtFCH<iJj9fCl^d5^RqDoECyoWRrK;+s z3ExAmcn+=kzuD<5Xmr567gyFH*Euc?6*=oN)2XCMC?xTlZ)}fR{rp(JSWv41HRF?J z-W8b7d&}Om1?3MFCkKm9A@sW62#POU+4mXud<qpHEqar=oH&Z=dkI5{Wv}Gw`c)yD zYyABE``eIzp!!WtPHv{n$Y(?|NUe<iDln4DuRVFz(9lo;M}=Me1CjM${yF1@=lIA@ zje2i(|NQtc5Dw@@10fHu--)+ngn^;%mnZd5xa8p|g@%QhKo%#u%>O*!psLtm>JBO( zAm9QKQIlOcIT=~NnBBtrXvyiC!<F|B(j}h<1$}=nM?&wrX9BUSc#t$vf`XDKJZR{} z1v_26)vpxCp%t}Sdn&0#<huCF{P<vd;l=m(n|P|3G$W$6SBeO!Cg*1Eh6qn&nr_Z> z`c30cYvxY&o*qX_mRQcr;NkQ)%i0KOf2H8wc)OzJ@1KzRT*&HYDC`1K#e#|HI$+bh zRTdfQU`l`b-rmi(-TpW5-sNw<%|uOVPpUZ1C4fweiTznh>E&%Jzw-2kE1efa$?5xl z{0IvRYu`+9n(J_cFM8i9hoo6#(uju~(w(K@#<W{rL#5W#kfu6Z^*ucHJPTqb7E%)+ zZ_HI5o0@Sz&R2pY?`&Ju*C8MmP2XKAB_cwT-0fC>`8$+QYzLM(hp9ok#b0@lPGbK4 z$QIuf5B}QJ)Wqw)qR*m|@@I6g+}>zmuL1Jm+D0djc(lP+7j~$=bM@yLTzV2EN7iRr zMV)3ZAVCN#SmW%aqC))l?_XLG2b7eQ6beqv%;@UO%-#3*Ui2uebtdo>0QMo!`n^75 z<FR@R^#-7biKWG)!Tz{cSMB<}SW~lMen)gSy=PMcTl1Wa(Ap%^#P^F5Px`L;o*uhf zYAp}<2<H#*5_x-jrx=NUy}s#e2XC|;DVwWYNE5d-`rDtiDUl7u0y&_N2o@9#X$^h0 zA1TJ)Xl{#Pgi88gZ>^E!??kN>1oh0$i2VX2I7^7(%AWPz-QG<@>hR;*UU433MNXbf zwJgO1m;7#v&H=jRg$22pmKQpuRs*5})pVNq`e6kHJTI;YI@wkAStq=D)o9>8CVP5v z+$+k$#Ps~b{bZz6FO<O}APF{zuU0qK`Qv;ts+BZmNrJf3cOP>_>JXI0)K@66mi-r( z1y}|v?uFcV^jTzZZF{FD)zsY5a%OJM^}DUCtU(>R`Dau?EsFqqnA7&6`tE#k>WGh0 z!iQ$^2dGQ8Jo(uzeRaYr-Wz^<R^+y<YtfZ>TU&cbl<T&!3fbN&>hRB>Cy<IaBqVuG zQ#I5e*PEJ}0vIKjnwokZ5~5yaqhshf7r*(ddT}6!`u6SH%89&FDL#CBeBOu4<vp2a zhtu!$SmN|(ljzFLP>b$2MNmDJ-ef+a=^YukbN6m<QXD^IOe!wj*<3!4vb{ZznxhRR zz=H{^D_dV(7mF=>=@%9$=;?c6809=>UI@#psIZ8Lhz!;cP~MnaUF}T~b>3U4TJ5Pc zggoU|H8$+zx#RWzP7pyHk5Sst(2)M%Xt6orYU+OA$kY;F<mJnkZH9}3Aa&1+%>&4N z9vnQs=t(66V82;aQ;|Tt=9mf-%y@5dJ3Xi=$Mx?W`3S1n#}>tn@5pe``wwe<eV|x* z!sIrWPR;?+6d$Vb5$E}~MFw-u)vt_grT2=&yLhD!GK}|FYthO-IJ_c02RD&q4|Q|C zsFj{kOzaJ;E0S4+Y=6gDD~}9!B=Gb)_ugv#`AOY#qn&lQ!jbWCcXa_E*))KxOyA>! zQQyO5%&^)PWc4EJL2kFz@$RbCnofYS+@CHu-A;4AbLUPPiQKNW$<zK!e7zHa@d_vf zL{O}}Rw_dtNbWyROr$Y2GfNV*rV^g;765!|4mfeYrZtq5aSw{%{$wD@P>Cf8?3S=u zOXL&)mjx~zh@)n+=vyt2`C5?yw!FA1>M)6d1GxZ&DI1Q**3vLJ@5EKs2WgLc({4RB zH|H~Lx&SD!0p>DX>nBxvymJ*2nZ(J-Nu}<M39r>#R6K@Vusj|xQHA^W=RPFcbx^iK zBk%+OOHI1934#^*zMKvkwTpPhs+pK*td1Y291A7Ccve24((t=<>FC7k%9t0=c#yu< z{mjBG06&o76UV=KEmYP5AoSiDwnV_x*5Y6^9HZ~wyxi%1w!TNW1&JXU8?e(x%T@bA z`w7W3--D+JFd4D0?dB>Ug$j@@8$qR<CL!9pVh~c}bEsD7WDfYW>GXKw)bZa>_Ro)U zTHrWV!oH`NEscB3Tr~7vmqn^31mFE+p}8Px!%2V-3y`ig@^rV~@lnrux-Sj!^)5pO zY!@`l&-m)T+9S{_ao57aB0uq7H2r<ZqEI{uUa{Oq1HO7AXwC1jW{gKdpJJJ?x(}=S z3Td>$$XWSK8gK&w1ONWYyBd9KXXspS6<`W{eEg}Lf*RANP*NsVR`nuN+5E5nZXg4* z+&vT=)Nh0=)$2NFfJ7DoEqr}_m6P~`poqN)r{r+@abI}7_Vk!%aNE9ej>^#Q*p--0 zOa@Xvj7Ty6R}Eys5{Nd-`oHfi1SCGPX{)J`ap{#khB8i_r^!7X7W?W|>gqDIft~r! zPY>6on)37&Ud1pzby@x9Ne{p4PBHu@FX{1e2S5QJ&2LK;&Ci}beTuX_x}{bI4x@?k z4?DWLGNH&sUJ>jVey2Gq`cDx6h3KHn%hNXi_G(X0#3#Hr@24Fa91G-e8^@Tk%(anD zpX{H<yzx0q+ZExdO4S`SIA6Dt?t3G4NG9{~`ue)bY+H=?{#=4)$h0OuKmQ)&Yfr%3 zToJeNGvxWm4-Z$3X`bJ-SNUW$!0nOCh!v?<6!4QU*Wnu4sq$cV^}%qld9R*S5z_&H z%0E9p{Wzz0!Qj3bAh4vs2x=VFJiYP=dcCjrF5b9l*Hv^sS!Au1;V0eAw&w3)@(LP> zGX!G>P^yO^fX+obD&rN$>cUrJx@I3w_i{#S<S8Y1r>Y-!UHMpKd1E3XRDL}hz&MTQ z#_j}U8nw^&E_VF<9ADtv(9=a0b-2L5Pf&^b9C|-iBuB}QCN+G1B<3QLUAVj-_vujt zf2ozpV{^yH%>-J(tZ1|y^ojC()AA?|qv^Wo4$EKUw~hvlg6Lnt0~nA8$XMOMW;HXd zvtZl`=ivT3dc%?#L$7f~Lq>-C6`6F5DcX123KhV}xK2o1lj+P+Y88}Fz{4Iw;{0rN zyn#eTMrJhp>|-X!&ro;tO25uJf5XA~H1vgi%^uBe8V^W=l#+7J`)7_;G$eIih-J;x zq`4IlJAhu?FU$cJ%gD+)PS*K%)&$RZa%vY|>pgZK_hbd?;An3q8io7^K4Gx-=Jc38 z_4vtn^}c!Q4d#%4sBj8$xxqY~4AT;e1Xo|k%S{EkvPZv5e^#!y+=*14?Y{^e#Z+Sm zjxh}$0HEs49`R8K{UZP%`??(EywJX<K-n~KIG#9KTVJ0-T8KB+`R%30O=0A@5&~|! zYN^M!#F7&de!I<1Z*h~d;}xr^iICD=lx{O;!Mx?t!+u$4ao;3}*=ZOX2M3Pcb*R{V zeX4M7J*R@Gp~XPR4US?FXgJ`v^>*ddf5ZSTn0<dIo0*-x@Vmyi9{Y{KeDBim#QPEk z;2qdhbJh5P++xRzvK<bHNxd(frHHHf$w_DM<52EpAs(Js5_+b&wwOCns+p73-?~*e zjEp9ncjg9c4<>9seflJ^JL-&d$FS0CKh+<s|9KzH|7`2e=J;l<K^=5^W{^@NX+>k_ zyOZHiUu^+gr>XhtoY`uB7NMx9D5t^Kn}a*)ekVzgJdO6kA}TP*-cIh#ccV0HMnxvJ zPft2o*+Kz1;9M%utTS!oC4wd4^rHBcy8*o$lKfyL{*G2|nj6f7v8GJ|O)w2T{~Xk& z8K@wDrdDGMHlb8FH&bfydhOUi>(C6jC`sIX<y<fRg$o%<3EapI_1HoRFo(s0jJFwU zc8Q(s#o4mhkKjpH;$0+gNqtGhB~oKfxNiMRwd6s1!-e~tk7@8~FVc5?7a)J0d#S%# zbSzuBa#l(i0B9R<F2vHBnp9Bkfhb&qX2sywUhUA3HiE4s50?a>Whbdiwy$abU1|;C zK_~9^7@#r=P(Pn}2gR>^110DfxIGMkjGz&)AcE)Y9USmOQ=waK=QBJ**n2{sY75_x zI9h8=aJ|ni3e+S84l6DGWD-?%C*|*A=8<gW?;>@kvrc-?^V~MOg#{kBU8+m@d5M-* z8PF!r;WLCbYZJWttG72AYK%zj%8zW45Mnwf-(zUA<~{R()Y3Of;5K;E)zuZz(pTwh zNz`ZzU)6UVcyx5M**ogccrjs1qQ`A#_m7QvM_lESwPHIkjZKZ_Ss%o#cjI}Cv1$6> zokOv@FQaeqt;a6nhkF$b+qk6j2QB^7=rk=nkE^dq1{A0cU>ZV~)up8+zZnsMFh*9^ zh3Zy6sLz!x8_>XpLC>4wtZ!&`Y4gXx^6KgX=qD1T{3PMWk2_y;DW{06zgtLsVB#H} zIje@vHa$h=zEs!NUSE6X9QK_%1TI{s1e9#*IaJv@OH+S>FKSeE6=_aO+6Y}8_xINf znR1eplteKxGe7*S!H(KVd-PM{#tnkx?iK)ne}8@=NBs|E1Lg}$HBVPCp>-G#a+=39 zKAgM1P-y*i1!V~kdMtz^cf}}V<Hc2Ht6usj`{8%EsuK4~EV>JaZ4mwh*owlZKUkpA z{yWM*MRgCh*5hwhg2#F@HJnzH>83DRQKwA#s`i5F4MkXLXe_IN6s4BfLGX*Eo`wbm zbWGUSn3;osN|Na1(#lV9{`E!PaBJn#x<f0WC{}J?;dL*|-ua=Vq-J#5HBQb*YC{iy zsNEag!V`VP=1NHPlRuD{l9<m*Ff@hs84f2|Bk!M)a-QyFVfQk6s&ml(ASE2&p72+< z<!AL~a*;HSJ!L==#$(~&q1F&|A&QS{e6aQRrrl`8B$iT<xq!rzx!uf6CfLe$01zqn zkI>Bst-bjzH7-s@RkcsHH_7kEecFlQps3aAyD#W(5zyXkIM_aN8?oKf%9THDj=1GU zc3a`q>(?#N8-9G0Lj`b6Gi1gMdPyB<-JntG-rh(0;ryx<v6b3WU&8m~q6s;E9|mB* z>z()o6h<q_F;3h6C_4l@Wo1oDMij)K#r>=h!RFB7j#~`ZZXX;>Ko$eq)J?zBUB97a zw+|1}d*0p+(Fl2<$V3@HwQdaYah;VBXEo!RVo(q~=o=0;n_f1taXv3vjdsXJNOFIc zTqQ-MKI3-aA85n?IL&PKN=aqsM#w8<P)U=L%+sD~70{R6(MQ$Xd4G?|ZlY(sI~l*~ z-{(73`JwUu1`GVg`JB<rFQ6Pt47D3np)Ta>m8%x$)tKBx-Nyy;{sWw1kHS}ce5g3C zS?*kd7gw+xygOgt(d&E!g)anT1`;=`T{~3A&wPWAgIs#$+=h~pk5eQRHvDmdp4pO; z7NsGdiBC;a*{Bbco+?NEhEfm}i95NMB&Z=Plewn~>-AP;c`PF4Rp5gpepr3&KZEXZ z@uH-327d;rNJ*{Pu_)G6d)EC*t*bJ%ogFkNP^@M4JX_Wgp`oS7@Yu9ooH{#vL9yIG zGy}J@AIV{B56zs?(wd>vqiC<+;``3Ld#Uo}(vnTh{`^&h-Fzc#uL4AJ1+Zkmw0-Tg z%ywV=^#Z!^sx5l7rrPx<q$IBW&0Lt8EG2S{yesYJNn$I^&d2MPyS4ksY{VudwIA<J z^aA?#ob*5cXQ+q~R1T;faR`tE;y)0cWL7?sNBpRX5gEU<sE$s`c*%L*R@7I;%8zVN z#%7y#mK9N1^7m6jVt`E)@Hud?O5diB4h_u!_5@*YKsN}3x-rtgCM%Kf6(cL5+V77u z``FxMcbD~})uV*5Rz6ah8B`YA-0UnbBxyV%nwK2=&QJO>u%MLRQvaZQT}bFPuv}08 z<ROt({QI0Euc&AWY{n?`F8YqmlsAE3$${gQVm|qr+n@si3DFlo)If?Bv$(i;Zz81r zPTTUadw|FPK$YaFUc(0Ah}-i7Eqj{_gQjm2#mdyoyJLW}SKINFrLa}tOr+^tVW6S$ zXfOG>yEJ^))D+F7`$c4x52^idPG@IlQM}Ic$_Ul0rmkKzvP=X>1(5a{yw#+9WPd7z z!Q*&)@abSa-S*<3B^2&aPzpvJTj{DdIyh)SzyR)%*52M8Sns4o!|&g}BflGk#-JK% zC+)3gal+#sfdG93Y=7TO=xU&L6a=V8-!>qCp`+!(Ao-t_gOJDmxMPhe*rcz>Ozt(0 zk(~~ZelAEsNr+=gmGC6P0AG~4OVf&=ejJCzymGZ{6}p8oIHFD<N03rb;MSd<>`Eck zv&?=%1^6zcZ-K7I9C$a(aOn{UNKObPxFQGyy%-S6-*(3drJ+|q`sKgl-^76hAhG@Z zrG3|#+>!dgmV4S?Wp#o?I_Zs|!LxL=ft(pS8hPl!vv*dQ_qxvKdJsRbNp)g!hrU^I zl`o2^$a{YS1+;@Wls2O@J^OF*KzJ3qE^6+qPCVF33Yd?gYJ@@#B_TwAa0#+i+V1bK zt{kB4ya#4yVJRXk3<O1b1t=BgotQ9LRzDs7`q&s6;>z7~ayfoRR&K9_{l^6A5@3?; z3hv1(f>xKv$N+BVRLsAz-jJ9Fqy&(!$<x-)&u^jT`Ey+8^kw!p=8&EYUYzMT)8eu+ zI_w#_%lyS+VRp6;STIY%$zfXjkD=M{>S=#keB-CotQb_i>mu?>m6hLf8(s=iCBrs7 zcB&5`7AlW-M)rYes)Umow)Y^fGgaK3-})CPLJ?PQ(BJ@W(kCM)--nJJ`W!6d!z9Pt zr~`jshHM~`2R)5^wkZ*9Dultet@In9@ulCT99kS^Acxr3?6X8mY+T5K>FvFuVmEzr zj&s_iSWR8wMFdVb3l^u>hk@RrtrYo(G|(v`hoWr6ZU*RFi-{VarIGSz0C#Wd1JC~` zv(>v7NfWbB36wC4pkVy|=7RH|zU$D!w=hOaU`yY-hlc{1ih6OdU;)yGC2;hdI>pbU zB@aTm4ZhA^?t~(M0A_@cg;<w3`15{gtSaFRH+}H+=^Iv%KX?_+6Qd^eJ`nlV*K^mV z3t(vwW>8$`;o(6TOdye(0rYz8j=2Lz3>?wMkXf{2<3-_=a_dT<n-P8o$xBcWk$(M! z@OT?=j6FcDRm{EC`v_DTgYVvTAmwI$Jdk3(c{2`)@Cl#YROfH-==EXC^mPENKWhE_ z0NWyG@`1!UZcR;1{)uL2#q(~5?Pp>^Y@k!Xe2Xo*uK)*P?ka)~;uaMh-XB*{RO~2O z<aV37^ICH6+T2yacMD&ha!0cZUiVKj_-eelG5uj6r|59M@+i&eF9=Zx<pSJbhakO* zjKnLXpdy<R?sUX}@oNs>d(8!fVun+S5sgl}k3bxS8`HRb7A2xp{^WwhAO~wB1;r%% zGj7*#M_N+QCr41kn*)&|r2f;OE+gz3{P6|y$5^OI9WjikILREAq#RK_6ubzn#tF^G z`ZZW>jLD?>X9{0xZPwC4QC-w4Q?qtOC6405j;zg$24b`m)_=0`^z9z{Q>{4b>I1Eh z#=mH95~(Z?t3Te2KXT6XFqtd1N2__zN_9r1FE@O+(}$+d{Ah_17qP%@Eimkcddp^1 z>l-s}#N7jgJHTLXZwUua>$ULEZK6g5RRQ@21P7!??#gIn$WCHHR2U$DWPMkQNlANf zRY%pKeZ)#p$O!ewpPV#a=>5WRHhw5q*s7m(OVPFE7T8r%$=&w=s9m^tky$|C$3F*| zS^oXKy^7qqUg-Wq&pYPg=zo5;4WAXj`l)POZdpEJa58lr*7udPjKp&j8YTuYTxr#T z&$7OL3(Au?=i3zTb8Q?#pFV{g;p_cTbd2I$7j1;Fb2QjD4i6J}>|dwx5#`}?qNk^j zYJ5R%(a1PUamdGqE3CefrLYAnJk;YRl8_ttPI%e^@UIa^QQrOu8vOL|+2=W6k>WlX zT$OIlkd={0L8d+>kIJ*b;snr~0mOIH52UDGQn2g8*4|Mjocw!y*#0n8l$10YWW1)k z6^%4?7{&NC;tO?c9ZoGstjHlV<#lqB7Zo5J3P<-53nh1p60c>l1cZiqa3W*5CMcPf zlFMo1CXV&??dz6l+VdZgH<=-CLPf3n`9JzRIcK{}5cTE)8a=&_3>4+ghBcF+#tYAX zfIQzJ1g6r1>#%D_)L1BTg_N|C7E~$;jM?RijL(m_qHq-lKl*ofMs#H|^+(Z{f?a{M z649|Fq#+P7UQ8G_Bcp(xE}364pk(xB!c=gW7$k|LRp&aW<V^d|e)<7^+I>0rc1IE} zZ=oV(#CyK@M_gbQi9Y*9R=UV-w`XD~|CN61k4bgl>p%`kvUB9=W`Q<oRY5`X_e2dM zU74m5n3kh6Olzh8;QT2Ih8=tuNhvmtH_>RPPcXL%(bm1%Yr>aL&red*MjAn#Nf|Hi zoqi%CGpqLBSGkSdlTg!V2P#bod5Mx+Bb9WY2#n>y*;p!PXrxt<q5bzqQ7tUTaw_Tg z08-L*ZJ6|^wu%MIr}cHr;R`GmCQSD33NtH5=YMa2Aa*okUtc$-@z~#+^t1StS9RgD z+PSK92GOJ>CI*QokYCVF_{cbm;!0XhhpR^yTL<RnWdFVq#B#-n2=%zKdmuv^qaQow za+Dr;7H}-E$#z}C*nje&B~7aPd=A1*2YCXYI~Uf>BpY6yE~JVyS(wUI=Rq=FVoT~k zA)-cPKuop&eQp(-n~t7X=<4Ncl^EaY=L1t<&5=Y_!))a%QA(h@H0t)F9PEF1RHW-n z$P6_s)X0*uAK<Kr<6Zi~QWzS_igbe?VrQmNYh3Dh=^57|-iu1fvD0+p!dH-38P)ig zgnw;C?4<W??)!79rQlML2!{9<o@2va?is5^nWNDtS1gzy88V$41Kmn>A|fp)76vgH zO{>q%-(f_)$m*#_@nKfdPzMP-(MAruC_oUUHvvqyBb1k2$3tAPy6!5>RIZ@EnJH%! zFajHQku6kIl1E)DRAY`{tgPTai}Q!b%s)}cVbff(a%7^;J_~kajgmW5lyI)q;pn0| zt&pk7A-SLhdV}VXiHU!_+#TPsJ)TWSIu#dlM%@E04RJDE-F&|8XWXMvc6j*eRgyw~ zeV}Ft98PMaM}Kk7qi#<Mm7G9rZuJZICb_mu6Zu&fr1fK8u#6A*u53(7S`Nfu(+kfA z+)L*z*=djt#nB5{wM5o9jH5#rt|mJTDe3NiAA$<`p3vj}9K}m#`~ON8r4$lkwz+`@ z0qDznxjbSy7@RaRIKnNAD~4{VrUv&>0dbuIvL}w#$kXEzqDgAZJ9X(=nAyEFg<-N4 zJZG!Vdy^u$+rpJ5K_<&sMn(duGQxB0vKhVe@+d)M2H%k2P{+D{T{Fa?{U_9y>C4D^ zvOmy0*Hr%<JMjv`0}HiSWY^mE&vxyEehB-I-q$ZBb82I7dRNX4)<)(PjOCA52lz9R zW|B8{f8weN_{++SBF&Ys>Flh?DZ?LsgF1&_%Epv|Fl;?!*zyNq<UOb7HFBSrFeMb& z$l#u3&QxSbjBvJkPQCW`v}xMs2=n!*F_}Z!0DQQRt=a&Zpy@?sT0H964InDkIJAg~ z?#@6Yj>aLci+%Ib*!4otJDXH76w}J+Q%5E=I+hw)k4%v-sD@hnNhoap#njn@C=zga zwPDzydg>4%_te6bsMnIhuE7XpMuA|32Wl$oqUf@WV@c@H0;0ob8Q|=|#Z32O|DmQm zT76UZ`8=Ffq_wLE1J;=TRctQt`#urYfG0@kjUb`N65jb=RJ_2UAaxW+7C=T(KV{%L z#SJ9oI2xV(#8C+qc(yaP$TE;O=M(OADNp{~^WaxW`+{U_(PU&u4@Y%TK8o>ZNU+jp zGe;1^*-xi(9Wgh~F`}n6&ju66D3?q8w(ndHMI=lRIo&Mb&V<JA;Nzs_)3La`ZxeGZ zuBmCjF_0H`>-xt_VBqkW2^A*pW=s=|?0aaS5?LLHKTfOPc8h@x<rfeza3qW|WnTXC zFfxVKW_(;TMD1h8JsIQ89TxC=m<~5{p{GCTZ#;dKn%X_x9HCSE7#~R7Ec=NXA{t%{ za70zpZfN>mJ{*w@Q`rbcc6~P~Dd5kW+f&6AMr%?t1WE#AWtw!k!BGT$8<JaFmq5E@ zVqxL`uY;5Z=@n^XSXELSnr&qzUNd455nU`5iEYrzH@t;Xk&)A*4GRqwsbInad5<4t zC7#WNUP}<D5j_(gQfNCO1R5wMNZn0Hdj#s1vWiL+$DK>-Y*F~vPOe|4l2hC3kd?)9 zFg<jOU<9OoK7mBU3eY+t7=6l4n+El25~My;=p=v-$AH=51&Et{;CDc@h^C}?92vzc zrC@!Oe|V#qpBJDgA`2m!7nnqNpKU$C#>N(LTcUxMb^5n;!5qMLv#Ew4S<2YZ5r-%& z&R~4wwIr&#Oh66FDrLyF`=g&=NsNFJhS-Y;sW^?n8q-sJ2}FyTKi}Wy>XZbRS`YR^ zpNQaXK*16G5~Q6XUokl0Ce(#~`y05=gJa<^Pe;*#iel#~4<5|4PVzcH+`cFKmc9pn zS)q+4#F)_Z4A%>fI^e*A8Np^Y;dh#f7{y=%*Malhgnz5VBuL9uQ}j^vOL!_Tq8DUb zz97LQJP8w3dd)e7tjqdz^W(>lfz>xOG(@aBFz$X3^?+qhT>u^_zjFQxjInYtze>Bp z;VqdoJ1OAFQkrNi+>)z%ed&I&9}yUASI>-Z3|xSMva*ZYdN9~K0O13NAPtz3$HNYK zb*#3I4ky_sXgk)KS%u;sKm4bM&k>!K1v~v%RGNq*HuQbS+YrGoS;%(7$L`rPG2t;6 zazxKZzSzol(#s=4Q<a|H{JkRhJ%#dgPwMPr%Ng@>J=&O1`S$r(+%=F0GoYc3yYc8> zY;PV=ZD6Z=NBEA+@j<iC5rg!nD}unwzD#}7@z>{&&Fe#!k&I04R4hL)aFmEzV$|w; zwB9OlvNw}|;cgS7h&9UavBz`P&EgS+XZm>&k}fff))(Y1F!C`PwQ_40N|&By$~eEq zu(xnw93Z13C{R1U&Y<o*@G^*l#qtokWgvzQJbQu90NY)W^McC2;VZK4VQlSBvCT0R zuaWOmL}RmJ5W6I3IZf$Ln^C~5E=|;?TY|6X-NM!2+f{DM1XqGXTb;h3UMJ$NDU*6Y z!Els;@X+!nmxk4&_gdl`_t`aAD1(<wEiNiGhg0@?r6nX#gURjYlRgYso+xh;kw(XZ zJ4E%U3KPi4STwwxNhf~KN?*T-eiQQI3V}J51GJ=nN6HH}7oTna`(EK_Rs#m3hKg$W zB26`26ha(cCA|&dNjp-ERPWFreSZ8||MT?5br`JtiZAj-NIUe*6s>xshr$9CkEjlq zLM}iCnU9?n=<3eXd(=@TP_nzx=J~n#`9Yay=3s7~4%N@`ZVYbfO04ID0)T#f!vXtG zBG%|TS;C{9T+^3>xsws!WiGLY^>AYwkg>?d*$-X9%;=RpK||sTB1l1v18d{q>B(n5 zE@3tBd2ZB=y>`e}=qd;STu<20^x2tE)-OawHg&d!M80BGkaR%0AI%UZv>KYSgQhMe zCB?bya&XM5oZ&)594>R#g)f0)gM*dmwjwS)1a@T3-f$o^cA=Lw*!<0cUT})Tu(NYv zP?T2$r=gRiov2P)OS^?YfJ_;uc1~l`_VhS1HSBpCXj8HK5V%cP`Iu@gC^IVys21=; zIz1WqObIq+S-ODG5w@oWb;f0=08vny#jo;IjZKGw1)DyB8RO{a=nXLmf_FUdcULjs zGyoGWF&P;d>cWK!#y|;et&EL4G6}6HjTOSj%}|!X1?K;8R;Gi~AU9-29lF?1`LLN- z?nz*toI#W30~!lBf`!o&a3hNi5)l&+prJp6*dQe*p9$xc8GRG;>h8ZJsbEDl>_6t^ z>TeauQBih7#t5Egeg!24aYKSYh4|5c<U<XW+cQ}I$^}JkbGdw?_}RAAO3k5NadGh; zq#y&*;7~m!aW^7H4nn|VC)C=)RU)53(L~Ah9^gC#dwLW+5lnvMz`%i0BLkWYO1s*F z&zLIG?pN+*6qvd*K~>MyE^45JNeasC7C`Ur;Yq#kps(~uVpCiih(q)*2L)`Jt5=nP z5Bp^J?W^+$*y})z`C>mYUfJE+8uD)-N4zRXRxvhK1gq)obQ1UEkHei9x1$QAZpKM) zE1-V#_pfblyZWZa$2SABfqDYuNxqr7x_T*XT%3Fl{<U}xJz9nDn}oz4l$U>Fbg^V* zawnlE1H}QpQ6O>g>)>nh3JTZR*xH7V3FnokKoR?_@oD;ONd>St{UJa65|KuNTeb(% zB_uf!*MAiD-?piMbVN)QK*Ni<uS6azcNc(da2>>2D9rca-0PpMB9}k2anC&M`3m1} zS&lGl85lMQ82+`jQm<Gad;p`R%}^m$)mRU>PEgw4o(TfIWdWwkIFPx3zpO$Nk(SRt z!6IUt!k+|Ty-oCo9;9yBMi>hu=zUPV5Emg-K=3qgthX|#7Z~2ETSE7Abi4zu3o#v4 zvP6W&QA8<RV=g7Euvz{Mfv@DId!@C^rU)9)7_w&bQdVHYdk}x>LJcgs0I+M8ci7bW zX`sslf&@3NPT?8~v{z8Kr|VX0$!o1BuUoz#fp%ihNr7-u*8bmI*6ahtFVNNkhV+je z!M<?mSu-W7sld<{L5Iu{ojJW9K74RG*m_!AQo=_xQ{DO~Pj>*hWN@(+_tFHvIkf7~ zFfVFY4G>_Sms+>CcieD1R|MNNT|7~Sls#Zh!6tO@uJyN#P?_D>zt5^NqbdsNsOd|z zltK1M{Et(_n%DF6QqR2ua8s(^f36y0`egO=BpRGWM2!`HK*MWAf-#Sx7561s8tQj- z^@XRyWSBjH=4trKwtheM9xz=ouz8?skw~nepyIboOiWOglda1GaACfMh5ON^nUYrj zOX%J}&QphlRHS=RLjw{*yxEA81PFU@k$jz|LeqCw5*L!y`LPD%>byrdDA^`vgE~;C zq1R&DCl`&5tsxp6rkz2R#J&<O^DIGn$+!y;93V<`6q&W^J3fsuz5eKxbV~J-+l_P` z;*B*UKONc^P%|Nd3xU;ZYHi(wJ*%4DK-2SNJukpHGKwE5KR?;FF^61qP~(SjRfARc zqk1+mxCTuD00C&Im=;3Qvx_qZZjrO1m@&kUdYCY+LUv40;3M9H;B%tS|N8*s)(^ma zFO61$bw$p!e1u)Ej1xpjPkQ^3VeKzZVmR&q#D+BiXbGjDROHI;C5_S>=2nCAKUc<R zb&3M6+<K;sB(WkW@D5-K>^X&Eh$!u?xUhj*&#zCvPLa*Hdv|+WN-2J4S+vwkI3$5b z3!#8aVG)3|eWazYnW^m%b(*6;JS{(}1V-~=N!J@8rw4?Mym7P<p%pJ737s4tR!tY9 zoA3P7;lgYb^u6X<-`HlJTQgB<Zc;5%q06~TM#^3WwN6H+=Yb$<a~=rwEFoKzNCsa$ z8qGb|f9Bx^?|T&D3IpTNn=;!GY=@&g$Ck*;jeyJbJ-tZFGr1=>bH?+oqRyY}HBQSz z0u+WU?*;1Z*Twu9z%Rz_vCK^^ElwNCo*_`j`9RwMNYvI4L})-65US_Ijgw7P=d7{V ze~!1aK9hw>`)XvUJRbsnz!Na)xsZy`y<k{$Xr*&k1rPqh37A;btK4isZuI=~He{&C zEQF)slA{vPG$<>qE+yk9Y?N1o@g_6ALK~>x!i+zeVC35lye%Tp)E;dR0!1s6@9N(O zMsfj5;;|}sR=5!W!0Id^(}8XJ=X*J<7g!SMRt8nrSCH;X5!RiWQyT*p6?kfo_#vD< z=|RHs1T}bldmHhoAovPF9>Fp3LEW8%rzIzBv3{a0SoE;r(3PFVPZ~fidJzW<u+VC6 z?*tLjYk}n&YP)Pe%Yi)pR3d8Hs0eL1&i$!jcD>`*r@Mge+S=PDCu5n<4ijf36V{hu z8z{JQi$mbv2S|%lqY+1C)b}fO3zj9r7gmSKF+NhKXH-=)Qrgzm9KgrN^Sju6Z_UKi z)bs)giMuQff<3XAr12-u1?{d}teeVGY8O$z1-GjRqLs*`TW6yHcat{qZb-0z$t*kV z83CxyZWdk9@{S49Pq2RCz9b~b*Gm8~CH7UkY_1h{cqoNf0u!b&_~Lt*4yS&_%d7U) zF(8DeK-$MDeB4fk=s*WM%bifhK4}-B;kJk4*)Vi)b-6Iql;b(n8Fs*7XgRdAZGjDM z9Zng-vKM)7TEM-N=}~uR0kHwpU%!4GK@V|UdcTl_MYkYUr-<UYQLV)z6m?Z@G@LS% z{+1%f;(kOZ#g&9STryJ9dT`G-!Sx3Ooj@zmY*(%h)`2U+c3pual5N$Vn@@gzjLErk z$x$3)r?y|vO7*Ry8FZK6jkQLYwB~T7MO9o}Twa?YUc?C6F%s&5+sOM9+at_sceNMl z&d~8p^v|n8L<rph%}w*2x2zbjA><l<djpz>6D&MvFi^bX*wuqWLoXn=$RLqzMd&N0 zbp6O~SG<4jCO%VbgV(m{@?wEAKTEJ#ve3gK1@g1=Nkzbg1(uG-8?)`+kb6=ja%=)k zXcm~f-oAZ%ru)I&9S_gSTx#8>JcJL$0HZaIg+(PdbJ@Ey?)uUj>mhMV;wsA?4agsA z@&wF>ii(PePjoh~d>qZk2itrB024odEZBnjK0agwcL$K_P~P<QX<_%w!7K=lJ&Jo5 z(008Kad-=z%p(}rIZ2NXaprA|<p@8+6*A?wUr>^JlYvlhrJ%(EovR6ORA_j(*g^$z zbHZ-4jYR6G4$QaOHQu6-Z>M4D*&-2SMpoBJ4&@M7B^6M`mk#Hs(Rf6KVobo<YE4qr zfx&Ve<XXh+f#{)WK0Ct_ucS~$Mn+%=YzDQd2adLav@{Aa{&8sLvOR&=et<tYSLEsW zaJS_u*3f#l@B$Y%4;sC%HHqs0es;hlmX!%dR%2fUL1SVIHsQ7GNeKWgX(u)wWmpz^ z)vj)3p*75QMC7YUAX0fes-ceo)ZGlG1n3%9VtbdtPXIR_UR)KXg6^X{!O4P5UN!y( zmE5%FQ9xkoBF^gG{8i+{uX~?ULBVZ-3xtG?t*r~Oy%&jyGNA{dmE5dnQ%e!8!oVwE zx9QoRyS6iEq;OyUj2Q3LoW$k5bt@Ilfx*r`#4Ph8fy2y10Vk&ugL5(lbqQJCfp{7= zzaoK=ck|s%Jsac~7$nH-L(4mzk`r*H4B$U9sbBq346I<2uo0s4=f*s<)TI0kEAp6y zRS(K#=z<!hMGSiK&&kD)+N1@YG~Cxe{L?a!Me>e_YxtOer7m4mXlN@?H)^;V*dH7` zhL?ZV&Jd%@eD;}vY^IW9LQf|VWL<(posF2*&C@y5M%l4AB}((FqV6+zOLt5)E}d>` zSs8le4O|Zk^+>s+fO20MZ3oqra7GQ?97|T$Ftfb+VAi(0$Z=Gp%xC@Fl+Qs*$#@0W znY7=!U|kJ<m=PlHn2qiW%2~;{?QtI00WJ21u`@yMY-H>Xgp<&c8qzq1Qdr->HmHF0 z52F!zxvZXDyj{}!vHxx0*?Z$)bpSYWM2g6S3&KixZ`;+TrcU_CjY^$5xHRJ35VEyG z&My7k5{#@qCo4w;&|cC8bwTVO6)COKbOM|@v(t5vC9_2MG}BM`_%x!TL)G8b3smd> zIfTdKHHHLsx75o<$d6VcD$;+<G_|waX>joLBg`Y`S$%V>0rb+p9IT;(VM{7GV97)g zmMl@Ru0pdB@a@oUCl6C|%YQFHbUIT-ltB)%(|`N07{!zGjJ!>dd_<4vQW2l<G8P3z zeh^f6%`2;slOg?F(=kHZ@s*;!8a~O<Jd2M=qIawh$`C2#j2v#(!2P{#Bb4mSIoY#X z!-&8dMlw=Idtm?cm1Q#`?hW*$0x!VC!5l<4;d_?->=22B(A@IzOQ4K0xQ(?ye7;$+ z$c-v{kgBMu7~V`8_8;NPJ^SaR^l*QQn%2gamsuKILu`~(_kd+xdc)U<EXXKwZI@cn zE%G!+6YkAgINoedPbe4O4k0;z?VyvVy{D~E1Puf=qU;2L1qAmR*f@Zq6SV@n2C&4L zw4x1gr5I^Qo4AmfzMZ`r0w_(kePu!Jn@J;TNbD(@=<x7aQ2HWEs~Srtt@N|<FAF5X zsjcyEwAvMYXCsM1M&jg#rIlmh$iy7+K=o2g`50bZ8BgsOA>hb`&xY{xoK5Oi;fY8X zBVfQ;=nRk|{2#R`kEk<_j;J^_LgN_1kJ>SX^tVtNG`cdgQpk(t6}=_ysStzPXAgu= zR}p44NVeF8s|)E050w9V1;YNy_HbjFZT3gMp@i^LtUTCmS9!oX-^y?{McY5>3Qe*% zv6`NN7&lB%v01gML3U4OmI&G{?rHsVnILa5vJW?S?Y5NL=pdGvwf?NL5mF&T2-QW6 z1(w%J!JR==;Y`))S%BO^X!IB+W{lnYo)^N{4zmTdAo&UC$z(EZ!hPhJ>CZB*Sgg$T zVdM@QOqrP&lf;qZ<F_tP+4QJ7;&CEa8smsbSToI)$bRLTfGzbVEnh>d2<&VSvYu`2 zJ&4yjgJuAAlW3^qyk=g&{p7CJm9auUsT48S^-Uk|3`NQdgn@1Hq2U1^?!R@lW-}U} zn;Kl1NPjM9J#@>N<>Ja$Lb%|(;T0ZQ!GX;4<rRi#1W%1zJ$-p8r?Ju-AAhZrXMUzv zYO1S)#dBjvNbevZpe{x~E35wR4~raQiw{YhD-$Gs$MJ`wAK9M#!24Ym6h-&;;d{S= zhw@hX&KIo>eUqS5c61pNyQBMs?dRhEs72h9`q=r3l+aH^yswmDRjDJ9s{8q=&}O)H ztkRgpvpe~2iA7%p-_Jb4sVUWOVsSTIPcpd}ai*ucaGiSThl+5@Yy$^!Oa{4!YR0^k z(`@J&sPKFa5~J<LuJIPuJwSZ10Q(EQesP*woRwncH^`!iR9*NY$4jJLgxwMya#^73 zdG}i#fr=g7ihnmss&<@W`HYMNJ`oZM=pFR9ZLu9~Mth!8a&%=12vM;p^D>;A%zrz7 zG2rC5TV|7g0M>D=#G(jsoxZpn=zg8)H7IuS$nh1?Lyg_S>T36A@OwUJB&;_>dqp_- z`rWR~oO>4sTU3kCTPwDbC^dDH@90Ev>iKSgs>p?I0S15nz5)g!dLk_&+cJ5>fS0ei z*x^X4*>E0Gb7r{J`Uai2K%VpOs0*#YjId%vMR{$n+<b~HJpP2m&)3)CaC!bz*zS2N zgS*d0LIMUQaQWyD<51OLB;2CPP@!|?oD!Z$Jj{6%*h(LmD!%FS6t-UZz^(VqT<jVs zd;ZA!IIv(=L~uOTO-nf7GLlIrmGg8<N$;Dnd?RlxE<IhksahMM62XoAg>J_{4y}f^ zm`h~X>+5SfBlD5dIR!#`pcwExYeyXa(ntRWKI5~Yna|RYyi+S5=5V(WgVk5ck*i}C zmE|>ct|Wdrh`KAYBX!uuVp`0OmR90IiDfdEztB}q{R=qf?{vSJcMgk|S~U;&tjV0| z>C8*Cb9!L?y_p9+eGkqY1wV8SaRRa#WAE^cHL0q%sct?&30MY<5rc#A61iE~(Yf<_ zbW>TI>;5%9-H!fM?v06jm#$d#@cW*4mMk8uR9zPn>-}N9_3vC=jgc>V1L*sRi!kQS z>t|bRP;Q5o+_3N5&AVu^sQF~DhqgB%#pc(LUM;=kQJ->RI9vvqir4DmUb3$Z;>gEr zx$JGJe>9p<rl98OdDQcvMQ{-DlMJ=IV4@Q-*7<`*YeFScYm4n=qVSKuG36RoqCt3H zC2IKJ;JYBvPmCfol7~dEIr$<uspV>F<||s_IlA8s3>w+Hk@TioyLpvcnX`l@CE-4Q ze(CpD!3<4lQuZWd5sH5l6`|q?2uoNnjB*)rBqakpx9Fa22XrS-^@wxnMU=}A+c?a{ z^IE?iKIgsq&4R1G)OzZdUiWa*Oe;CGk%$hb_<?Ng={2=4v<Bf>pS5@nl23P%D{EF7 zf-FSiA4u}GwpTW4%W%ltYiMOiCO4)fH`rMXft!+rAq*`RB{3B~;&5dlH%db$Qy%IB z`(p*oFQ_Yc)d!!4H>erxU#sYRyK*m*ZA;MgmYRI1=#=?{WJ!&2p6(tqlj_`0Fcsh0 z2o){SmdSh$RamVpTmnnN=QpSS#_TH#w8ui~yhsJ!#qjx)G^T5)m8#xc_PuW5=LuYd zbthbUyjBBn51fBDER@0-iYh@8BNnFt1^#4x;Jin9l0dh@rF3som7NP09FD);N-7r~ zbs`){;A!o&TZl_%$hHwmyovvBKtM}Qr=;Z{8}zQA3(H^cP*!1C9^%Wmpo@azBxGyc zh*y%?8Ov;uP%xYP;BUW%)dW3eNRB~Jsf7klIo+=3O3i`Q-tn$J^J8enaUhQdvF&$> z@FPqTmwJn<@WHlL$%l#hK#PchaLT^2yu}r*$RGn(O4NC`WqvK{;V<}jBtnE1Jo*>L zs*=NYqNU!<xx$mMkSBeVSFH2ixxDq)Eb4f1^zv&SxS6BxUTd1KJD6`=;^^H;6-&Y( zw8*5Agk9xUk3*8k{8%%Ozg0)*2?|X1moICEaG})@eUl(Yi4{W=T-h}oO8PR@woL8# zfCDaE?d{&UjeSG&<{%y(G4y%0;#R+&ug`QRH9nBCrTQ^mGnVpLy-KM1kei8FhpZ%2 z{x-71GTch2i#YdQ@LLc2C%sfoBl7hP@=7QB{Pq1+kEsS{_u$|LC7z^hoC$C8&l-GH z;|!in`p$cm3zL7oqoZJ>jgi1bCs6qJwEM>}c4v*D#{DLg<uxV>`kuS45=$Sw5zJ&| znfFos+rKtsR%jVr+2b(w)nOX9dJp|Pr136ElES;t(DO*Rgc5wiRdy2^^%3^odz-X& zKjp&dq0yASUWy`gIAG7c=O%QXn(OOYy|1suw>sOA$-~v|?bqsRT}={bY`a>zGCQu= z3RxNM1mxVuzDiwAIj7HNP^Y8A>+(@2xu=t`=Ac6vubG)P4^vconpR7`<W^!h`EQ`m z7|UhRqQ=6==iJ4scTP_VT|Ozc{inLB$*avwx3zaPbdDs0@rS^-e#Ab+zJ@P?%5RsB z=(5_Hb%*B}QBjkU8y9z!{-oqskH?{zKwupwQIJr~HDJCQ*8kI|AcilYZRSWXAg_{s z`IHizzlLsJ^*kchuPeW{LX>dbpH{1niNSr8a}R7ba+>+o)e$29&e@Gwxsjj8O$_?F zT20>2V7&F@1vv$2IUMxNm*ATr!Sqi}CHTdZR1q$_CGs`ur{2-;u_$?1s?Lfc=F(9s zj2I}eSD_GfK#hAerZ71z(Nv#&)hThDl-91+9&1FKUPj_FvkpJw{m+O-YNNW+L8?2C zV~qyH-0>zS)BQ{-?m21JNJyNBG&LnI3c4>^^!z(nm;3X3fR<x#+{@v}qkfh7Kfeq` zth(1O_BnY}`ZEH_lf8+8^F(YOs!j?wO6^|Ux`qLRI@$a8gsTm1;#-TBQRJi5msh?~ zW8-uuHIYaWJ<53#^zHOWJud*Q#)Je^Y^H!&Rmaa<&%dld8C4%xP2n*mbOBh6cUwk@ zX!qGoZay9AQ{IB}a%%gFsQ<^*VnKJ-@s*prT9-xLx&(KrNs$X^Z(-}4@^QS|xiAbq zkMozXEYNJPog#5ubg=)Zeux|<t8mM!{IwQJdWiz}`}g@9jE`~}GH$PJ_F8yeCXKlR zST^$#nADyFjvDKYuIq^3^qb!L3xB)7{As!m6}@+#AeLf{C|Mnl4l%kEO4Y+QeV-}4 zH^PQ*h?oTEh_Ns>a^4)vjus8{_{la@+`PRG>I<@Wmqb`Fai1UJBx_0f<-X?hAF&f? z^&WF3kvOeh#E$bO%+&~Ux_AX+;^6VO|J^swzy0%GCd;~;>yv>1IA}#p^Y4y+j7j43 zUGx7MDw`>dtXP0j8dmjFa(%yV!yP{gCTf+wh3ScS2^E^Z5f-+*0M4dg&zA;!ISFbk zUQ`OX#NV(GO&Tt_F7V^4i0qk@oE<mna-I3cD-=M$TV4(AulkDKSN@hWp@@2YXGR`H za=J}DeEIS(yqQJolOkT{55uw&b}5x74i+DDROSyB-|7l_{JCB-pjr|t%fxUtAn$S% zy*S>jXLWDy&fD}2mvj%HoAMB)4!j?2iYhEHqnyTlt>$ox#fdk9g^r982W%RjaRH{D z9QGAw4c|XHShOIuB8}NZlEZ}*{K+82y3Fk4#io05cenrX`bk30yNT7o41C40_*DQp z)`6nwEib0>%DZHsUznOtuAi9D54PKS^6_D#{(Fy&$MTBBV!EV`0!R*J1FjmnAPgZC zxL}<Pv%hL3N3BQu7G!L8oub5j19Qd%9V1tv`r6kDvPMAOcKP?XW`T<Pxk+P}X2aXN z-328jd5{4|k(*rDioa_ah>!~vM;sb$hZ*JL0X((1JUk^|<Qjuzue{g)+Oa$`Gq-%d z|KvF3cS-9%hmC9-or+Br=ivR7v9;+4)pG94%(^&siwNTvSJs-yZ?H3}H6ZypL*4Sl zX~~@6EB76nO&sbjN8MY`C`??OD!t)vRQNVcfFjlry}IW#@cCS?1kOl#<Ah(M&u-FP zVq&@g;c?zpf-;+@HLNo;c~P1&;fcsd#efn=nOhG^x-T4TgZbRT9jLJS<6UZ72eoUD z3aZI4cV!Jo3V9)x%knCxyQUi9@7wV`u4`>g5j^U<JO7z+(9pU}B|A%pt|*iODQxFl zHk7fo=fR*8!{{*P`Z9$GY9ANZ=k0}upVKa%rvX_fslA;_PW{5sQCFC}faM!5e+F+< zo?h|{Helt1G+^TOU)%jvi;p8^Z$V<G?^7ZT3cM*tReXX2Hs)b3f<Cg9m^Zh!zCq0u z#}kueYiQHJ#Rm`s8vZ#DAR!d7)e%46wUwR5-+DexVt-X8_F#J|)&0vqpu>@4j3R1u zaKjSULtSj74Xt&`5}mi|p^f)iS3T<xJ=(@Baj0c9C?e?yy>{x}$pT+~iQ;h-gZI|M zlD@6UdMio__A{J{uriOcKA3Lqd~nfDtaMz`E@28O7w$?l>1m*dcHI$?eqX^VWoX!! zMd*D}DQf7@fa|RA6#(Udb;+Yble^AgUuRm8%^(14m^_w9%V~W6LmVCkhLW7!Vax7R z1|M95DqWgXieN=8eSaiz?s-7s%p=bJ6UTjVchoFjY@=7oVbI_b&4rP&`bLuEya2{~ zA5$VjLy+o%>bYD;N?K?Yuj4}9Mbbvf$Qh|j5COTX!}d3dUJ?QY5M(pu+TU!8oJl!` z+dn?c+f4OJ3UszZe+TUT?5s9jUg#`xKNTA!5<-0c#R6d|3a{N@;fXII{Orn)Zka#2 zq{X4$!fE-%E>)D?VN$weWUqYUf0cIT(NuSTyr<G6qzMsCND-xQgeJ<=T!zRH4rPiH zk+F%$tjyDsN+m;`gp4KgTnQO79c3mV!@2L>z4za{?z;E4*0a{{={a%E_w4<d_I|(K zRa0(7UeruGb>D_y-!c_6%VM6Ph$tN%w(kP=T{R70(pw|~G*_N$2-w6b_s(wHPahuy z6HY6c{wD5cr@SxabOs-<>XDNFuDQDCqfr$veO<`?EMc;`I;~h+UoSp#q>8V=tU^cN z(YklI^NLZ+r?T=Nqi@V0o-#n`nAzdAcv;oO@C?i7waNxA)vZ&7i?2~L1i0;lu-kW^ z1JHtM8DxCAM5?Om{eH`fo5HeGU+b*@^hiD2i#gBbI4I?@;d_R(x3cT>mai`sXLi`U zTkU&a5243C@}%3`9+tzAkxm|e#jf`o*(rCa2iSRfhf8b|J<!r+(HhZav*9pvK$8Bf zvqAf))&|$f^=MyYJ6<WD{=|Vtku6XeP8-jgV%fpJVd+sX9sa;9{o-dQ#xT?V{Yg2% z`n#!v+;A;b+H&P?9it5@BZ$*m6|JKvx~Bc7X>$qpb)dN`d-^?x=kH|q-Aq}Q57>d$ z3TL<H=H|VyfA%O-Cv9cdN^GaLj{}X-^j@01QN_CF+O@AFpYOT7Rpli4%W`fT|Ngmm zcgabu=PZY=oqt$-SnUi%lZ&o?Uq`LpYf1g{i><j?>dG241}1@59JqtGV%KahctigY zre`!k^{aqxYBkFaR&lkLj)Ti(_(k7YZxdE(|8-44J6Q_}vrq0r>ZL8)iel6RxQ)Nn zX=>2rj|5-FtwL3I|BY-DeYXk@mWPUfOLZG}9?d=$r37gD-uJk-^1Q{@<(@@X*~GK{ z4E>nusJ@_#_WG0Z1Veo1N|96FDsYxY-UzfXtJ<AQ=a|}t!Tlo;vV6~|-tYPJ)AR3; zEiPlued$v5T@lT3^UpqQo|!K8=LVbpjL3#xuxi<$^(vjb6G&0Yip34AQf*j9=O(Uw zIkA1XiS_wx=61+apwSUMuniY-eYtbmUaf~53;m9+_O*tc^tjHTE!X;k8n}Je?IO#_ zv~f2r@5?K3x_dLdc2Q6kO_Zf~>$ViXlj5iI4=+aS-;DVm&Y7`ar&lcZtWT^~9t}89 zWqmFwYORWavr6}3m6bl&ToY=VcP-QQn~S`&;fVLJan$}$AmORbq9G6+-dI4t)xa?* z|7chAK^k+;<lQ0>(V}yw4u^{LWgX@gBezYR%PVe2+uR;%sotYTr$$PwJ*0JUBknn~ zJ!A<xA7EpI!Ct5DuBzsb=aP;*+L=xhy*|QuXW3C)TCDf^cPHK1xqr*_knHkRcPX?g zt~hBNY8v(QbZVyxEUe#kktb2-nf>e^OlT<?=3GfrBbo9sUA5FZEjD*hbl+N51$oMM z@$K{FqEQm!_IwD~bB*3YQCy|onsjQmW4v7QY@_2d4IT9j9iN}BxY90g`b_`U3-84J zglrwzGcAlczhp54vF+a~#Fq)9k+y%w#@_j1Sv9lZ@5^lWpB_+8vi(yy8XI-lymS~| z^_;(HP0Gc{0rr9^!G}B9JeVBS(b4;tgodUBocy$n4Cb10`E0U2=#QE7pPne%&Q%J* zZvrk5sqn>S@s%nEWkF6vt`%9RZ??6a2+ZiWpgnVUe3$L38Y)8lDZ-t-8Bdkvc)+1} zrM`;=vGS4KzT^Gv)#Vji_sDNNy-4TtBcJ=5pI_W>zjyC_Z~~Hx#Ij#rxbyRi@vuII za&0-)y}iQ)LLniq@P5bzP&G9&rf68Ub(?XY=IBJ_L>-ilrb$WNP<}kM{JNv-P;o3d zOWBp6#b&n{v#ykBgm(A&`r~!axyAJ@to66rsIN$PXw}?IrT8=?|96CqmeMiaTkDdQ zIGd?I|F<dkclWu^7Zfc85{!8<5PvAPc&$i;nqhTRx~-7D+kW&=F$z~4u5Yzbi8ng? zMse(Ja~EU4^4s6!bBx>GLLIq$o{(IwKYXogc;_WMHXOH~<rM1cNxg(AD5v%%vznFp zO!tX_QwR6!EoRha%IxNr#ne?jb;a7wq0DNg$IMe*^{%Zk5}q6zv!tZP*5TCb{M9c@ zJ?}(0bsDHYy3M5|C3;s`83d2IWF9|W<ABNaVvua8m+$k)*FC+VvzP~QW%o^W%z{O0 zG+ACGtk`USY%=u{>-`2D>8Y>T(pF7I8*XGachYMUuAHMG9<9;D^H7MEiLA_}ROBhl zU(Y{zfN$;5lgE}S;Y3?~?V%n&982A*F2VIOHH!VGU)Q!v9zHxeoRe<B{q;Gk%by7P ze5v0pzg5YJYhT~R)kR%Sp@qy=D75;T5?oXJe!OBfCfw3u;ZE}OUB7}tzo<DlQlQ80 zr?11PL2bH<pSoLU>bqs#Gk=PU&OctJaOsP4bvd%1N}_rVs8v#;9z9>`Irv!`n4z=_ zBkh|!7pt?h88*M^i|D{`GLkvac(gKXwC2FYb2{qlggwvheUfyPxzHy$TX%YThQS`@ zQeIg<IlvlYS?DWwmrNAKlC?wQA}p+>LJlro8X@6<Wztp{sC2bP5Q2$cWy-$fQ!Loq zSM82JxFBV|bYc7fW!LB$h-80?Pn;Rkyb*u@=!<kI5GMA#p;NLff*XFQx?e~IKe6zW z58`$4gX*U}BBz!%I6S<J9I?*Uixrx(FVAiTa?MR|duw4=9!`00w~gg#QPEhzr?8`9 zd;)ES8<!E?9{u!hFOIs@Sju_RCaJ8kV{*8G-2ZRnvd-2vk8PyDc7a(bS$%<Z_tB5< z9X#KA^v>b$k~sTs7xsjxC>O?ie5{nblbK05mTqS<J*l0%ZLQr3JQvp!k&6<KMKV2R zt+OV-Rpx)KmtK*8pWu2s@$vGP2C;*`uX9T1Q61#$DY<z+%0-QP)iJ7jwXu#aIyd4G z6m=D={vI~Zx>I!OhfOykm>#I%_^0>wg57cbiwMikmu#nNil}sM$7lRb1sDAl^d4+T zr*ilSTya-)&!wE3t`fSvwHOmrF?aV_j-?!bGwUY!ZP)eN<8C!Bi~2%K{4nKGPSEX# zT|01eUZ}re_rJ^KPYx|wWYj80Ye=YgWZWV{r*;mcMHlG(lNpH_gh|{%B~>ZEN?>`Y z$Sv&;?R}8QtdEu{)+)4A8`FX=TD{@<VaJP6)WtkJiZ>&YmvQj2L`F*8+;@FwrOVXC zO_8d%`oC!U24+}RM;W|$(U%pQRv=UQd)?`|mlr%mJj;rkNj&LnkN3d6W3hefcYQ>o zI>Vrfy?CloJ$W}t^Gr4`k;goBYI?suHM>OoN8O81%AMNUw8HTH_Vy&KvFyyW*6SQl z8`GxWuw@CDn+*3o!XqjZO-XesT58eJK6gp_`i@ptX@P?_c*fz_kWTrV4kWnTpi@x< zP>mZ43y);~_UX64o%c@%cBpC;s^vdSIp^J3riyFo_b-tXq$lYOsT>+%g-x%iTtMbT z7bi|$eBg~F5NAE>*#Eg$ys_?iFvTbbaLb&z;Pty$mRmQhOgqwlGAv}voaU}hC5Od4 zr`p@W;7E7P&Q6`iB+kG7xZtg~`>?Oep%&=Mew;`X^(bK4>>f6_--TO9zZM>8{NP>2 zJ5_s-+>jCQ&#GFl9bpnUlGix*x9vu?HNT7wf4Hg=@<{Dl>*&b2O_a-81I%u&<s1@U z^-Rr8R~I-+pljcK;>E9q7j;io@GhP0tId=buzQ-Yd3xF;;y~lvrnk=0Z*8nok30-D z!LwmNEv8WWErmMH!($$hH-n$GLhN^oK+9Vn@E}koBv`e*zvu2I)_(QR&&7pt&2-#| z^;M2nqGm@7q)JzwR$R!U$WUayVVjSm{!yrWc7n>0I_+fftx9uYr`GlI6oGCg8Ld=P zY2}JfrdN)9Ste;~d(x_{0?$9WDEKjk<Eck}my7l(zg2lqCFpBgkRiV}m_|P2Yu9Yu zE@34>G8t%FKORv)-*oZK5>IrfIfH^{U1!2~9nU~0#;MY?;(FEK*Ud(smg^L>>r;Aq z)M%T=apL}nZ4Jo>3Pq9y5p7~VK2(m~Jet#TdN{iSB9S%P|7EOv_Mp<)TpX+jdFD;@ z1;+*(+wD91<kz?T+9Ed>oYPyXwF*T}R6X6PLi%Sr4;+~WB&(gcXOLHAn9y61{X5Ki zgXGeLBcUQovYtQC^)1-BNq+U}HAjf_WP_R5VQhWOAkzxSVTeNK{>H+#8_^PXwr)TZ zG(1$q_2XU3=3SM8M`nH|9oaSRx@8&GK|{`UyP3Qv)8pfRo_%v@WKFmHdmR@ID&JmN zJn)T-TMa$_{w#3NP!Y#RYYjDy@Sq$V^_*cY6O?Lf`#okilR`gxE_RanQjGQCB3}YB z<-|~glt*$KUu(WNE9I^rZm^1*`&y@GbK)M?tdga^!l;dA)ik8_<_%oy=l<T~L4X+D z8I?H@T)-4Lpcsor=E8!?%2(I@H=h65#J%dyCG<-Ua+fn|VylnTb17Mz<V7`W(X_g+ znl8WhAC=j~C(zrbt;)^=o1oczbdcUBS+UjLsD3NUONXJC<C150{AzcVX4wN0V_4YA z(1gvP_)SloqP;2m@94Fm3@NLK>}T1k+~{kDO>iXis;a{yI#dCXZa9~&dbF&jZ1bP0 z$HT_jmo7Y+3hPR~nsjo>g%tIY^3cJ?EJ?fmrh0LniT=bsq3lr6qj@(+8?^iCc({4U zV+ZD^dVIIbR202e>y?!D?2gOcVY`JVzKSi{d13353&~px3f7%6a6I5(^L~Zr(79(u z{Y94qFab2{Uie!X!)yP0N2_~|?x+FVr{a5fZW;FWosYB=ZrI8>R0s&sFS98hySSz% zU%LAmZu@+!w8`AQR4ndStJAhWo<uxyK$Oo%F2iz}W#h+zw4;Yu#C0!!<8bZcqb~_l z-2#EoH;Ub&)B?e{n-!W%FiYJae(lOF279@tdRJ%Uxa9t{yl)}ASR3q|B9{wUSl+oW zWaBol-uT$9Gl^A`HEDSp_DUNM>d+|JZ0xA=d@(q^?7!bKL4_TseslB2pSROKdZ(qC zZ7E(@IMDp_Csd}<fOP0?@Lp#)>BUh*&7_&m*az6QAaQl2VldxKH!fNAT#MuKWgPTm z^=^=Js{7UdBm&=yN~KiQ*5{k#Bf96WHp(<kZ&GUgRn|1|<rK@^so|97g03}BUtUPW znG{R9!{_Z-!I@P02T#^?Ykq~fbls%`IU`j+_L!8u)YDms5WOzeq|~TxGmY=b@9X_7 zjaDnH+VSJ$P&&0%JZj3#eeO0tiK4Gq=Z>^=%T#UrDF`W0P}-zjzv9H$q?7l3u81OB zEWo2vprjd}+WgVJ8D(63#K8`M((xiLs#}4!;XPgYX1S(cWh_mAaT=x0ai7nCU{KXg zD-{gC)clRB?wk;EhqAIuEK1fJ*=Xa7hUB7rw`<-0)C`<n<8dkkAqJ#8T$jakr<Erb zc~@`LMv<R*oTs@$kS8=UW^i!9Vcp<*yQd{w3M?}GuM6f&R`|0$oO{#UiIdG)N2dj6 z+Pl$sk0Z%ieba>zyvf_vadipC7oJs^pU<4p9yl%(X~-P$phhm1O-{?a(XIfY*N#ns zCwm=-UU42*_w1B+SU%Fm@5#|!f~&AjF@=X{DZkMqG;2mz3kd0?3Hk}Fu(Q)TzC#dG zn|1kXMLXMFs>gO7aEzcgZ4mdI>X2n#cG!js6jt$j**xFhS!zrR)lw5%*x2E5P{!7l zbACn?djY!KG3EVNaW>v3^i9#_2nGeCtjfv}8WwSK#twJ8N`7Avy;mw#Wq54z*9Vkn zjhP*s(J~(zwDmS!io<c~t|Z5ik|a&OtMM=DMb56Pxa+;vp~afzm0LFlxG)j{stQ_k zD$e-9NW+W=V>rn?)38v0Xyx2ne=RTOK-~KH+U)?E!J}S>TyrT$t;a4;cd^qh8OoaW z^rV}8T*C5;W0SA#&(F9AB77oocDN|b)%oO5dv$<qKJIjnUB8s`5o5N&&JzFXhDRj< z``3O*ikI27=Y9-?mMcY++68Ezp9jx0CKVjaoqLmA=*+0mI)39Yj=VcfHCHK_Rjl6c zl#VKNMUUS5eYoPZ+S6aiU3L7wqxgM-XfSUruI~6CO^v85k5#f>Fi6#13(RQDVR2!| z5wjJ_%liz&xVifxLSKpZe_8g<>XvBy+v_TUa|l}3!aASJn%=+2{IoY?c1jcn%x^+M zb!S>>ye|ht?tQ4N6yKg_vuSQRvH3*I!zCm6mJ99qLo&4Zhp32pKbumcb~%4du?&z< zQ@<T1LFN!<ZC4xL$$xZh9-C~8`JTsLFy8C4aWA@xCrcmzx+3i;j-Dw%-0)<TAI{~r z7yE?s{2swk%S<m1?jMxZc;S#JynC>HQ(x7dOf?^5lyOmvGJhtYE7dRW9yNY`p>j?b zlf1f==mzdSJqQZvlW8;C0-sNw64!o4JrRXk7T@f=!^z=xYdht$gH2Wt5!_?1{#c*c zzS1#grgYY!G14QqVBA-|>>&;Xdq19xyW40WT^fM3Iy-grsXA5m<2!5W*cq-}v2XtP z%R*fL^vIWIUq8RG?GkzHwQ38fbY<#l#?3`sp^ri?JE)%3&#+)M^x(kRx=G;Sneo1n zKS^=7)1@Y+{&p1yp{{w-btG=GTtdaXaktZ)-EuDPJtkG<g8MC_RAQ%w?|BO@I(YEG zo!sj#)LJTYZU(U>tLtadg{f1mUg2#gy=6PLZ{B`4G{<qQmR-_9At6f(gYLfQYonWS z=xE&%Rpbg#c+467oOhJ3$1SyZH2(e$^)NHT71YClf_=ONneBV^SorsOik+)^Tr<^X zUh{kMP}0<pNrQ$YD|N<yoMWSoK(ML}ZaZCfnze@7-i1+P);&FjZ$}ua2;GkZBL&s3 zZ{O(PFsq25-zyEcSEg#wA&9#m{EPp=9r_G!)E=~5$Y@N9&%Wwiy)@4y`pz9$>)Rjh zd?+pbr7EdMwclF_=;*75>mXQo{6ldHH_=t&j_AGX@imWUGphXLGdTT6@4>c^HBX&s z^dM&Nw$Y)?KKWq-1FF4Khui6=zRmK}^~d{ku8rx)6y5pD3*f6o^BZC4W&Q*D9)rwg zP9d)=U#jdjCu=iv@-D03fB>TsTFCJ|L7|c5A;H@yx!1)&>i8X_6q~T~!Yj^Qr^?IA zYo;7Qiv$xE+eoV!y#1PT^^`dGs4_j9*K%9MUULe`9NhKnMCH7*G5v69KwV~9vr%2b zt?UyDx7QSgm`>TyRc!wZwk~!Za?I-6NPlG8c_>hz<%SaC+1$h+eK2l6drrYYZMOvq z8kP+CR};wgoL<K1e=-`IaC4sJgwW0VOK-H@J0X;Q`Dva@^`b9dl8SuSXHPXeHx{{# z$Cz^WZ;T@p&kyKTnl#-6%$zp5>1Fn{cjV{kuIwz~-?Hy(6z?T+AOq(1;lbN%WbN3u z%DucXbk#wJ2L*5qm3JvWsNs0VEMEs(!oSrjdfzzYSsd)c^7?gO)>yoUm&$|95<?ws zjyc)8?ZS6c_cr~vxPK~A@5~~d7*n&z#ZEjDd-ZS;O`3iGfu<qcqM*{;m<z?v9>_}K zLgL)ybT=S}_b{9s0?_22cx&J>GMxeL>Q#J~Z7(dV5s}*);+U4No_%xj6{kjzp_o`7 zqo(#?G+Po=ukv#X|HX{G<{LAwm@M!MrtUUj7i0Zx>dh6c5}}>4mpMN>(z-$@VloyJ z%Fz5c#VvA8A*UaF{vfQOGxqh|lX$sXH%_mPE%8sZZ+WNX{YVn~*|ke6r7P{YkTC8q zK&`tkcVPF%R%g+FBsUTJ)I1mC@-_6sm+QCEYdSsdc}0~QJ=jq9_C>b-rRpg4l@IQ( zIdm#0@=DFuw%q2J;G8R=y5;kQw`hSJoL(p{MW%LNO0Y8t-1XHp`{YPV>37+Vp3cWA zEK827)c>fK731eNwlkf9-hKCW@Ki3Cl-e8pI8rFi|D`{F_IX>H(4Wiu9JPJ_Q*<$! zv8T0m`mRrR&ug}vG{n{_H@dTQ^?I|g1RvkMYmeHFJPvrTb8b=p?!9}fqBL@D9M$xj z85wS)c4fNdL@rqf4-cO?`uTkZ<=bOxJ?S6xmIq4i4|u&kV2a}trw}<`u;3dU>tAgn zdUez&N@+Tw)NRL3)`t&(qiJGe*O~g$0%*f>Phcd#raXqLvJz&BriwOVKX4(dO`g8Y za{eY%Q17hv9Auy98sgyQW{TX)xEh%p_Vu~(f?jIOMNY}!HHWmNSJ5NJanRLW7VN?F zn3fWdH(fAW$M5pP>;{He$NU%E_E|2K&<iWm)xxGAbho0)u-7BuhH?(ybV7G(8>l@f z6-<P1eZw)Q8LS$2T>4BOm>p^UQ_O?AmtCy}?`$rSD|C;(zx@xuG{LFkK@W{0BUf*- z7P*6i=PsPwMRRO<@q$N5R%*1vR7$++l{W91%Z}3V^UqHomhds(!I~TNN?i4%Old>x zNXL`ys+xDZOMiUwp)nO5ar`}TqoD8*otHNBz0XiSfGKNbk^g$Kkwa}6*PCnob^1b1 z2Ko74vAKV>xgY2qXv~y)e;YM%_)c8bZMw(atZ2F6_`(8z_bZ$IIEt!}wNkw&a>7FH z@{)z#N<H2eWo8xSd%P7WYd&r|P$fuj(wPVj3SwF|TlF<f+xI3|HMa(KcG5B*iJ7>| z91vt>WiCM8l<2J^#<3t-BxPP!6Ccm~BysTeo&W63CeqfF7L7TiT%<nMei0Iq`v5s; z%Vhr<SM~5`XQO4#k9@xOt1YJ+sDg(7e00dY3v9*}Bh8^WDd7~q_~}1k4ON|-u(u|q za_`+0k2j`?0xR*W!#$_rhp#^Dcj(+tj&bz2&$rIMctO3*%Wrv7#>anu_^Si6!9gs? z1Eb={N&_MTKX#5VZO`_P*nX>r8bV(^!)^9sw)X?4^FY3QNxA~L1GHgwm)MPPsVn2= zpLLG-!O8n~^miOLO30Rws?!|4eW$u$$M|2_eP3Pg;GTiH+K0+Up0vwNe(^Cenz$~_ zDtEm<=pv3QfmFe@zfu`nk@;ymvO?X36IObbIh&bB=h0TI=&O5mE~%=tJT$w&QDM#6 zAHT2A^Np|mDhQnLKc~?0<y293V9uNv>k>A$OIo*_0@Yap1t>=tHL)q2N0pUvn^%80 zT@HUr!w4rZuwc+i8-MylPK=ufAtBbf7I(mnIQS^<et<iH|98$m?^hH_Ie|(>%nMQw zr@?jF{0~%F3MCSRe_vkdO5dCsu6|=yX+LBLRRSUP6FM0voe(R@PnnsTj{Y4;`|{$P zXlhp1N<uLVh{<@Dx=tS|ZV<A!r(Dc<41I*_!VH6<=m@-ED}dwsfar$c{0#_V`m^|i z8dJk<S0LVp7VThZpwNxsoUc><>om{grGkv#1v&p?*wwtXZV7{;ng(QaxT2)k`^G<3 z1SAKIjM!Jk7RJtsj#BWa63HuV+M*zz_WJ!%|LnVTzOD@rn3@uZm`=vkM`QRU1T3}> zS{u4TV0C%<9w=@s2{5Y!VlBTFYejhR3HNgfDp<hU2%xw+i*K(Yi9ZTH$EFf!GA#66 zhBA%;NMOwCH3pjTMrfmCL7~3_wcD4h+2I_2d^q_jjSqQ1T^@s~+8-cFejov)Ad<8p zWLAbicub7d_5QjKPB0bmyOiU|2i|!|J&ZNB0eL00s;DX5_)a~{MoeHHQvyMmA0T1; zfYW78%xO4xsD4=AFz~nTC>I~hPNYCQH??ZdZO;1P#qS5T-G=4~aUb7b84fyhsz1oV zW^e^+>a9(Avg>7|BaocOAVKx(tqkXfHG~w{**3Y8-}F@eC`dk9b4a!xOjNMS`D)V4 ztE0?-#ou3R5)QK6KFe=aGtc&|DuN431hL@(x3(LS%F0O%VPWB7Fom;UUoM6OY8Ckk zN}w2uneFcC@+&J-H37u@-poVwS3NzZiQu`_jAR<~mVl-f!I1n5ZrF$ZZz<8zGd1`s zQgJDd;qn72R9QkJWsHG2xEDrBe(;Rp_p-Y(!Y>x__~xb1&`^J1D)>P|yD@B-<CsDq z^4laNsxO>MQ{Gh(uUjPL_xtyC;yF<Qjm<-r_c~|t-s$S<9s`uYU&v#wKAQ;&!d@b& z)>h0|bdD#T|Nesq-J?H>2-2{|8bIzI+((2!GM+XEJre;ULQS6?0NPd_%>%-um{|}a zESl1{;5~?nilXRs4gE9J5>IRUl5~ovi@p=n$;5JKXtBuu{XA3Xz8KeQZJN?=fpR7v zK1PJf7WG}ZX=Wg6J-?W-6JJMG&;MHZz%qdQe+wUy@t*4FD1+i}_f1D(J$ww#c-Qe2 zKs%G=bFWrCq2HhdieGy;`QW=8ZuMvbpgBFK`6>=24jnnNhy@a;8f#E3mXy(~41lyx zHDTR({&}aupChw*xw#e{`HGOE*SHZav3|BaIMM4XCd7V1X}$xegpdXQ`^(#*w1F7~ zEg-MXl21s;qkTU7Z+`G?v6qbv9Ubu}W956_*TPB$u0D@J6udY0YKiY~1{-K}KLCO^ z12ti{lgPNC%H(&V>_a$t17`$$xZ~LU&VY)Smz7<sUb|~ayVkPPXCd9=hF1?GA5LAK z`nIP_nG-iBo;-WjC;!bZx4Y@V(7_hSB8hOB5btUz$9LDx<TI}g4>-SW+$kY3lsgMm zxBaErBU8b*vE(NU2#+3u#E<6jZd#p;&CdA4Z&IrulhOtJ@#mb$3H{{f1}#{ffX6u@ z?D2t443sk=L4z|nS|we(?$4K<>rmE~VJ+~=U$=;x74(?1D|D6qJ2OLJa#5#-JZ5HQ zVkg2i0O|whwPv2_W@2t?dK%Pz{gw^8^~;WODSpC=0#}<~a$Zd>u4Za}Sxj1eaX?;^ zCEx1RK?C7B?(MQ?rBo*0$3fs$KqVwwK4~v_9c-wW2%@cTusi?>Kg7>``z%fyA7rBh zKE7$4mzSpoL^e^|g80eeC#V>8T+sP05hF@UP7PeUut-N4T$LiM%!yVW9-dO*nboi; zKnq>AFtmYl#R>{z8niw5AEfHH1PChrvWiMQ|6&cm(Te8GW)ojfu1lYvNhFX{;-VnQ z^eLx6WeU2tD99ckTUzG0HB?m#J35##+vi-<8j1}ngMwD$6+O9*|NHaE6HzmvX#zH| z2l_y51z}1e9yojjh;k8jGOUNFRFUT9W`YyPemMj0Ds9E$HTYKaN^1#5BzBevK#@~O zdZ^rBIHjSck%0G2UI{Oz?$&pp2ENovxmn}1*b`JjtgsE{JPF;?`2X@#@op(!zn&+W z?^Y(lKk-ho$r+N#w2t~?3OFdd#abxWc&i$d5S$ou>zm$QIR{jNw=u7$?(wVm?F=^w zE+Hk{kMc&A`YBvs`^3oimjhb_g(hN~JGPWqI}qQlG3iizB^@^hB*;?(@g?Ga8AAYN zavAUPZ=^#oUckT4!V}g;5dYWA!{Gw?zIP*SL`yu{Lp&SY=X#)E6G<pF0mtJQQjR|q zqOI9X2zZa3JsX0Y6a46r8KFNoLDuVsRK~kj^yEG;iu+zI1`NRx2;(NWZat=R8j;)s z3G6m7`=IK5Kpy@!h_D;CDJr&wFbo6~C#s~6#Vfh_0XxkLSBYp))!DltCEUNNND2;k z{&;L8&O^EOFLH((+$?X(rX`Ji){x`_>GWFIv@W7=J+3=-&yEz__!j#fa`Bj{?55dc zE49z$rUC&Zg?C0(82hxAG3Q4$Gt-^;q~ZDaZ=_;NmR<MxrO%eHYp|S!xe#xoKF$BN zO`E(49(YLu)D(o%s=~ZGfeRhcSZjL;5hxKJ8~@T5@!QZg?nB0wADH9wG9GNpY0MgW z*Ww2pG@skw-+RC=Nz-rC%XNBfSG~WH7cOCYh%WWj;SBtaZ7?vXEp2uPd17?->{&mo zOTOx;{nGM|gBGq3E7!OM2M1?gow9C9|Im*i0fC`(d-2;yL1Y^;a8Vj(zyLN_k(8Ll zT$uF-V&lNnr9roC_mX!QGy<R+H)Xz0M&_MroVnWh^9SkX=N9SgLqa}r>Xd9rz$QzQ zV~!z*_(4EipTe(Y<z@FQ<s7Nq)RzF!j^geA*_IKfK4N_(fkLN1AjpJcop7uyU`EKG zPJ^#Y_1EbxfX96oN>%-qR$-YNL7-j$PHk_kmu)YMl!D+-7S|cm89HD!$wya<1!F#c zU$3I9tP|AHB!mS4!l1g=`WZVc3npRkJ_&31PyY=#lQ0B1GyHyFck)qgvEbYk*ig2f zQ&n9K!Q<9_`@V+EmV!1=e_W_HdnEX{pfLoq093rcpJ*?M<*Sf1$wf@t2!ox)$t8!8 zMiiZU0A@ufF@?3GaEQwSbv1XTBI-0!t7&-$C&4D5a?QC@t5>gvCi`xK@%{V!NaWzc z!0eKst6}(7N;K^u!Wwc;qnnXZiJ28q=tJdx7Lj=(j$cTq7Dk4p<RyS|AS^OqUqE)V z0wWjIrZi;uM37CuxR!><!|>Fiq9lWzTxdwh5jaa?7jzOo8DLJG+Ydqrj;;bGbpmjc zIRW=etk<V7X(F&>0M1#Y0IWwTEW~iea_3+nITyty<I@Iy{z|NQe`DUN7)mIl!9C7Q zK`5baj2K|SRH82LZxzIAo~VURBRR-{<O6j*F(Le)z$MQ%n}bYtdqD&hvVeir3W1~B znsw`xp(D=v`79a;s^f7Cf|n-5S#0-fsG|`;RZLB{!^W-$?k%4h8+klY8xhYzx!Pzc z<3%u!O8{Pkn8A^sh^MwFodj0d^zK<1$3Y#UE+%Ge09Ft`9`f|?b9&$mQc9)228xWR zkx{&&R^}uetbEvj0i?_L6bmRik{i%cTUs)0889z^6Tn%-a_qpeO*dMDlapn4?yQ($ zf;Yox#7k-&+F(34jx|UJZvJGv(ur*QUa(sB7kP6f85enl&qduYE5KTH3WCNt8bvT6 zg}|ALpes=KA(`F*#e?9{k-BJZh4O^(42<_JLbQP;<Gq#Z5!giWiH&MvD8$bN3Fp#2 zFBAoWPMxe^J9ENE;?Z-1Jb9O?;a&tgbEu?qkrr#7RBusIibL5Sh%$fiYC#+}R6J_V zbCCr-+jwDaMl)K*A3uznk55fq-FKo1VW^A2xY54H13r?ZSAqO}FGvsyYYUN#==jP} zS@t(1htp<>t0wdvNbyCevS6za8WmN6c#6WF15S<IaO>N?V@Lnv4t{U)wPLYH0#FWc z*9&0O;nyLq_pXX!2ZyKjvPU{_fQiH__@r{O;4D>&3_cA@2l5Xw9)KI4Fx1)vl7<}M zIM{R;lSUX7{D(!tIFZY=L|7(dY`762KHaLOZW9slB1m8aKNEU063$lmCH#kNK#XWm zjM&vGPK}m}#@C@YWMl*g9jr+=E<zGp2G=1_pie(lU*FZ$MIJi%%cgjk#HECow|YWi zPw*LkhT9$>PeMOlv)&NC6Ijsj8KAJ7e}4KFh~%6Ca)od|MDb`yY>)U9oUY{?uH~NL zI;yPj5e=9h-U=V$cnO*m%kA5@%S}O$-oXp&K712bVEI;z9siKlfiy&#6r_aDXoFK; zUv1dQ$VW%V57WNAutV_2-rwIiPudak8sc(Fv-X(&;q8yV^{>Og7aG%olG6{KE2a1S z{PqExU<E@t#YSiRNz%VxH`3nLl<Q2Vx#;W1NdJKyBPA0(wtRPc&W#ZjlV}R<*}s<! zHr2z6R*5k?g+Rzp-hFo<eEP75WD=33j-5aMa172|8$p4oaf8+19)eE$_qGt;YqHyC z#0_$rfE3ufQO3%`b+SSGn3h%;4fn|^{o(cS?{NSpHPGq~hfUIo9Wyn3f+*XHO!@x$ z3pAXe3R4{D7Rje*2UJ9#n4$idYd51(<uecD+md$#SEtKpjQG8bc~P>JAhP^>5(arU z$=f51GGl5CHekmJ+~@zbf1{lrvWI)K`bbj8;t*}j2l(j=DDf}IXZ~k3`KKD17V=9| zk@ac$-k>9e{d*w4zLrmemk>+LXNiq0KL?Z_q#;Clh^3|8?+GlyroWHHj1l`01T%tO zL$h;zn4O*71kEm@F6Lre5bZ-4``b~QA!zl^y@v-TaSR2A0e-O6Ko&{z9)%w-Hr2Mx zn>~?<Nb}85f^&o*JjDPbK=FAql`j`P8o|4RI5tLd*{^(d4vt<JbOM7#M`3}e?^fQy zFO_rS&h6V6)0m@%%l<>2o_R4RuT){S8!&HR=nxMd!JGYC=H};PXJIE<h*g;0GE@~Q zjr-A#y}JU?>hoYWgKw3z8>w3P`1;1+&%0=}n8%Q~wz6Qy97REl=kOHvZ`rAhKnoHA z7rbh21qRj%yB@Ep1>u0K$>!c+c#Z*#&v6Km80DQJ!e(h@<=lO=crkPhXUt`42H>## z28$o8b`-RfBsP>^Y1ftFQDFA$dWX$7j-9q>6rb9z_mQ^zRP&G{{DeP({e?LQ!B~aQ zwW@r=1pm`fEJ9vSJ)7)e%7#HJyg@Tq6M0e`e-)5Oi;ljg*#KY~^Rro|<ss}Pqv#M> z>p**%2pb`umaOCpFR!Ut+6f#ZOI2zC{!<+7BqnW<D7b;INWin;@%!P5Fd0+sl{ipm zj1&+2<XJeGpxId*(ajAi{wWqvDBdfi@vUF4f~^drXkaoB(cx(ezT`W01C%&5#Jue7 z+kFcQv$iZRwbKGXt|6{#aB)M1XUG#nHuKgjJI3{7!&Jl^^Bb><n%kJp{u8vS_ewcC zG7+B$Qv<7lnC>uQOn8@KOyEg5DRpoGZvkZ{5>SnE=?taagkRR?yuWI2<&!-G6%E)q zQV$Cv;q~@z0ig)z2CtmDgc<x2Op?G;tYYiF_Ud~&x}k|w@sPMU&Il7W%u~pm05*2Z z60qHP{QgTIj37b{A(>)0n-L>%^WgmNwJaEyAO*9M`T_5O#R+}#xaLb%_#-R{N((Ee zYiE$cwbmu3+odOqisY9CW=8~4KCE3xtAbrq2P8exu_{JzI}G#ZR7T<rkq#*4EzPwB zm=iDt3r;H@#{NLlSb+%GlygIJ8K-~|MtN2(@BC5OETUFkc9+ZdJ`Q8$5tk~dRSYH; z>IwX^aR+Pe8EE&oxq@P12IoBBU|Zu7V`8YTzS7HBE_74p?NDBU+1=mNX-MKM=zzo0 z!opESk!}x=7}l7S!>PpL8d;T;ugDt*``dE<eRJSi;<_yvn<#u6(`R9rhO$#;r{@Id zSTh>(3vh*ahB&GjEB^@9N@!3}VGM;90lZOrdplaHXiNy+o%S_cwq%L^wSvR}*@GB^ zIH6#h!LP6vc4lQ|MRyf}9E)cZIx;}X7lo0<%wNGcd54`HjT?eJa3y$*#az^ud5LGr zD&SjKVPT<uxln}Kj~TgC=l|C19kuO}f;S_wi4Xg-*ivV6(Ko4kjS5-X-DinU^igpA zNk%IxD<c*juuh@`9yq{<m@p(<J+3HJy8q6dJG!WSgyTIB-D&tI^x?yvnE4#ggNT74 z;wuHE80wfE*JADoV`_uy2!y|**RDySA7Dr7#jyA`mYos6Ou+q1im_hL)ke_xfDoj= z{=KSC5?P-KIHs&&o#nZjVn0}!EKqjuy;P=E^P)qhraf%k&q3G>%1NFmAkHx;N5}x2 zo?OFSd#}c&FVZ{<J>YJ6&aun*8ob+V%;Ee7D<VqiV)>He^5i3lTIgHcE`{?6?A%=P zL$ph$MEC3o#+sWD%mj1o^5x4h5~;AX!yY_Px`64JFF<sA(8svr(4FBc4iEDRZ4J{i zx~Lz~K@*2yj7CU2#RTjmM$R!EE!&O<=zrcnF(Ie{KSW|OAtUpRQgdE;_djxfJ*<XV z^5_5PF`6>yFzN__Pb+KB%P)N-H0VpqZ9=Dq`>X@@ZQZW03|bj6xx7}^0EfICec$wK z&B}td)GVpn^TG&SqjSJ11yDc8wtK)M=@`&*Acuc^aE~F++Kn449^t?U6Crer<fUl! zW6!x}<I$q*+bCr@+A9oq71CQ^_XWW6!SHuGiYwT0ES7Tk^;IDd?}{U{&5=LPWr_-K z5EfEOqWq78b%{bPywBWR7|hy}P75s+dS6iwu|!CGb)K)c``v$r(esokay6iFs-QL# zaSnNotPc34KVrB6?xjIOH~cP6UA=mhHU6Gz`M<dB$GhitLc(Sge;+XBj<8&1U}Yta zAs83>zNg^q!1{79W`d*02v&gE)Chbjk3jWg0rsjPxsgru3d`H84Zhf}A!sWBBeeIg zCygU90LHNQTFJn4kS)B$we{_wKxrn{&G7a_5EOJBJq$`2Og4RJVZ+Ur-Uj&a&h+k> z97ps;Kt+2}MQd$=Wh{tG#5@|lP5ZTXb>J6~VS72O4w2J;54Fe|P7y>a>vRvCWq*G; zMH3!K)=qOAwgpfUUn^Sz+&ibIr_)VJIf=une%mCbNerY#wgil#mMmL#V2YA|9Q@WQ zZL53LdrW3A&?Gffc1Sh~a~i;Ea-|*%vS(<YP(GFG7i|Rju}}YLFWOnVn$62-Tgyo# zMnl9>2)7%@4|`s3wRhjR82$$ES0+B%a2wz3F*mjh<64X)<xS1ai4!E4pd_lpWczTO zG#RsKpb){gorsqd7PMKx_ofrpRu$R;gc(V`4y2pTFg=_9P?iAk6$aoW!*dQ<G#1l6 zN~5!F!MqId3OV37NT|3ND|90LBT&YueS`UJw}O(KTm)jL-Du}wa$<9}F}0d}-{u2- z9uDxqg$;!HxSiur0X+lr4$ORfh+Q`Zs3C-M2R0WRDw{$7^fEpTb4(nrTyNL?$ZSYD zC4`c_5|y7bj+9740MGCq{A-PJ3d*hQU)#OByhu?oihwN*XLodCClQZO@KQmHhMV8H zRDB9VeG4aQJR?k^NHsLnlKBNhqByYiT#I#iu_ce8M$-i>5sqRj<K6#?Av|o|vW4_< zs9f-Jb0D8lo-(2$)~#QEFFc$F9>|34jA;Shsxo3HX%~DRJRlZ=#P0`owP#<P6~=*2 z87!ArX?~rU)t)!4z3?RK3_C0te!`lGG+StElkmhzdxP@n98Mg_!~<aFq=_(>6<k~l zEb;_U713*?dobNmQ<eg#HjScfTruY@Ql=~Xh=}_c>G^O-KzEyU%+1S#)1R~ueo(5z z)lFowiUDOUCa&aY0j1JqBm{&^JAmEBznnVQ8})>h%5H$Sg98C${I?qWy*%9m0|QpC z*}1rCFU<WdAQb_Dx#8Sn`HB_B7_bW{xklhyBeNN5ZQPJRc$gtMX@h%VB9PyJCi|i_ zMkq&D!r_Ds6o~-<<*5d-B}3VK1f$>`Fa}4U#bH<wP<f~`1i_?c(5~(XK*S&rJ1iM| z_bxRT-=|VxzJ$+AN>}0wgH8p_cM-*M27#*&vEU=T`EZh;FHr($c<$d{$zPF5KjFw% zNNG?rXnt;P8q0%ZrfjSgY;IHR2<#N%wMfiVVLO;tc`;E}SyQI_?9xWatT9?VB?aIp zF)}AEt;t0={5<jm6QJ@?ld5mUS4#jJXVmlJ--U5dgF^)zm@SE@eCPw4P+ZqijzQ== zgi$FT=@tnI4iIUHTU>I{8vK}NyN$T*eEt1}ckH;2w2yv}Pu3=e{?!{mH=(E{|4v>% yK4&|PP4-VJLQY~oVhH#DIm7!u{q-v5;+INi^A4<azrce3si~Y)PCa6D`+oqRxRe3_ diff --git a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb index c3dd10fd..d0d74ddb 100644 --- a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb +++ b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb @@ -24,9 +24,17 @@ }, { "cell_type": "code", - "execution_count": 515, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NOTE: Redirects are currently not supported in Windows or MacOs.\n" + ] + } + ], "source": [ "from typing import Callable, Optional, Tuple\n", "\n", @@ -63,14 +71,14 @@ }, { "cell_type": "code", - "execution_count": 516, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class ZeroCenteredModel(MultivariateNormalModel):\n", " def forward(self):\n", " scale_tril = self.sample_scale_tril()\n", - " pyro.sample(\"x\", dist.MultivariateNormal(loc=torch.zeros(p), scale_tril=scale_tril))\n", + " pyro.sample(\"x\", dist.MultivariateNormal(loc=torch.zeros(self.p), scale_tril=scale_tril))\n", "\n", " return scale_tril\n", " \n", @@ -83,13 +91,15 @@ " return self.scale_tril\n", " \n", "class ConditionedModel(ZeroCenteredModel):\n", - " def __init__(self, D_train):\n", + " def __init__(self, D_train, include_prior=True):\n", " self.D_train = D_train\n", " self.N, p = D_train['x'].shape\n", + " self.include_prior = include_prior\n", " super().__init__(p)\n", " \n", " def forward(self):\n", - " scale_tril = self.sample_scale_tril()\n", + " with pyro.poutine.mask(mask=self.include_prior):\n", + " scale_tril = self.sample_scale_tril()\n", " with pyro.condition(data=self.D_train):\n", " with pyro.plate(self.N, dim=-2):\n", " pyro.sample(\"x\", dist.MultivariateNormal(loc=torch.zeros(self.p), scale_tril=scale_tril))\n", @@ -98,12 +108,12 @@ }, { "cell_type": "code", - "execution_count": 517, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Data configuration\n", - "p = 100\n", + "p = 25\n", "alpha = 50\n", "beta = 50\n", "N_train = 500\n", @@ -112,6 +122,7 @@ "# TODO: set this manually\n", "pyro.set_rng_seed(0)\n", "true_scale_tril = pyro.sample(\"scale_tril\", dist.LKJCholesky(p))\n", + "# true_scale_tril = torch.eye(p)\n", "\n", "true_model = KnownCovModel(p, true_scale_tril)\n", "\n", @@ -135,13 +146,13 @@ }, { "cell_type": "code", - "execution_count": 518, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "def MLE(D_train, n_steps=2000):\n", + "def MLE(D_train, n_steps=1000, include_prior=True):\n", " # Fit model using maximum likelihood\n", - " conditioned_model = ConditionedModel(D_train)\n", + " conditioned_model = ConditionedModel(D_train, include_prior=include_prior)\n", " \n", " guide_train = pyro.infer.autoguide.AutoDelta(conditioned_model)\n", " elbo = pyro.infer.Trace_ELBO()(conditioned_model, guide_train)\n", @@ -172,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 519, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 520, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -213,26 +224,49 @@ " den = one_vec.dot(num)\n", " return num/den\n", "\n", - "optimal_weights = MarkowitzFunctional(None)(true_scale_tril)\n", - "\n", - "# D_train, D_test = generate_data(N_train, N_test)\n", - "\n", - "# theta_hat = MLE(D_train, n_steps=200)\n", - "\n", - "# theta_hat = {\n", - "# k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", - "# }\n", - "# mle_guide = MLEGuide(theta_hat)\n", - "# model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n" + "optimal_weights = MarkowitzFunctional(None)(true_scale_tril)\n" ] }, { "cell_type": "code", - "execution_count": 521, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([ 0.0072, 0.0949, 0.0397, 0.2320, 0.0390, -0.0013, -0.0164, 0.2619,\n", + " -0.0973, 0.1266, 0.1220, -0.0946, 0.2408, -0.0671, -0.0729, -0.0603,\n", + " 0.0012, 0.0739, -0.1326, -0.0098, -0.0712, 0.1233, 0.0662, 0.1291,\n", + " 0.0655])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimal_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "# eif_fn = influence_fn(MarkowitzFunctional, D_test, num_samples_outer=10000, pointwise_influence=False)\n", + "# D_train, D_test = generate_data(N_train, N_test)\n", + "\n", + "# theta_hat = MLE(D_train)\n", + "\n", + "# theta_hat = {\n", + "# k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", + "# }\n", + "# mle_guide = MLEGuide(theta_hat)\n", + "# model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n", + "\n", + "# eif_fn = influence_fn(MarkowitzFunctional, D_test, num_samples_outer=100000, pointwise_influence=False)\n", "# correction_estimator = eif_fn(model)\n", "# correction = correction_estimator()\n", "# correction" @@ -249,51 +283,15 @@ }, { "cell_type": "code", - "execution_count": 522, + "execution_count": 9, "metadata": {}, "outputs": [], - "source": [ - "def objective(weights, scale_tril=true_scale_tril):\n", - " # This is the actual objective being optimized, under the true covariance.\n", - " cov = scale_tril.mm(scale_tril.T)\n", - " return weights.matmul(cov).matmul(weights).detach().item()\n", - "\n", - "oracle_objective = objective(optimal_weights)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 523, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset 0\n", - "plug-in-mle-from-model 0\n", - "one_step monte_carlo_eif 0\n", - "Dataset 1\n", - "plug-in-mle-from-model 1\n", - "one_step monte_carlo_eif 1\n", - "Dataset 2\n", - "plug-in-mle-from-model 2\n", - "one_step monte_carlo_eif 2\n", - "Dataset 3\n", - "plug-in-mle-from-model 3\n", - "one_step monte_carlo_eif 3\n", - "Dataset 4\n", - "plug-in-mle-from-model 4\n", - "one_step monte_carlo_eif 4\n" - ] - } - ], "source": [ "import json\n", "import os\n", "\n", "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", - "N_datasets = 5\n", + "N_datasets = 20\n", "\n", "\n", "# Estimators to compare\n", @@ -317,14 +315,14 @@ " estimates[\"plug-in-mle-from-model\"] = []\n", " i_start = 0\n", "\n", - "# ATE functional of interest\n", + "# optimization functional of interest\n", "functional = MarkowitzFunctional\n", "\n", "for i in range(i_start, N_datasets):\n", " pyro.set_rng_seed(i) # for reproducibility\n", " print(\"Dataset\", i)\n", " D_train, D_test = generate_data(N_train, N_test)\n", - " theta_hat = MLE(D_train)\n", + " theta_hat = MLE(D_train, include_prior=False)\n", "\n", " theta_hat = {\n", " k: v.clone().detach().requires_grad_(True) for k, v in theta_hat.items()\n", @@ -333,7 +331,9 @@ " model = PredictiveModel(ZeroCenteredModel(p), mle_guide)\n", " \n", " print(\"plug-in-mle-from-model\", i)\n", - " estimates[\"plug-in-mle-from-model\"].append(objective(functional(model)().detach()))\n", + " plug_in_estimate = functional(model)().detach()\n", + " plug_in_estimate_list = [e.item() for e in plug_in_estimate]\n", + " estimates[\"plug-in-mle-from-model\"].append(plug_in_estimate_list)\n", "\n", " for estimator_str, estimator in estimators.items():\n", " for influence_str, influence in influences.items():\n", @@ -341,13 +341,16 @@ " estimate = estimator(\n", " functional, \n", " D_test,\n", - " num_samples_outer=max(10000, 100 * p), \n", + " num_samples_outer=max(100000, 100 * p), \n", " num_samples_inner=1,\n", " influence_estimator=influence,\n", " **estimator_kwargs[estimator_str]\n", - " )(PredictiveModel(ZeroCenteredModel(p), mle_guide))().squeeze()\n", + " )(PredictiveModel(ZeroCenteredModel(p), mle_guide))().squeeze().detach()\n", + "\n", + " # There must be a more concise way...\n", + " estimate_list = [e.item() for e in estimate]\n", "\n", - " estimates[f\"{influence_str}-{estimator_str}\"].append(objective(estimate.detach()))\n", + " estimates[f\"{influence_str}-{estimator_str}\"].append(estimate_list)\n", "\n", " with open(RESULTS_PATH, \"w\") as f:\n", " json.dump(estimates, f, indent=4)" @@ -355,7 +358,172 @@ }, { "cell_type": "code", - "execution_count": 528, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'monte_carlo_eif-one_step': [1.0000000279396772,\n", + " 1.0000000838190317,\n", + " 0.9999999697320163,\n", + " 0.999999935971573,\n", + " 1.0000000768341124,\n", + " 0.9999999012798071,\n", + " 0.9999999338760972,\n", + " 0.9999999273568392,\n", + " 0.9999999846331775,\n", + " 1.0000001098960638,\n", + " 1.000000067986548,\n", + " 0.9999999878928065,\n", + " 1.0000000353902578,\n", + " 1.0000000009313226,\n", + " 0.9999999874271452,\n", + " 0.9999999916180968,\n", + " 1.0000000563450158,\n", + " 1.0000000125728548,\n", + " 1.0000000027939677,\n", + " 1.0000000055879354,\n", + " 0.9999999664723873,\n", + " 1.0000000540167093,\n", + " 1.0000000484287739,\n", + " 1.000000051688403,\n", + " 0.9999999720603228,\n", + " 0.9999999473802745,\n", + " 0.9999999722931534,\n", + " 1.000000017695129,\n", + " 0.9999999543651938,\n", + " 0.999999986961484,\n", + " 1.0000000651925802,\n", + " 0.9999999916180968,\n", + " 1.0000000479631126,\n", + " 1.0000000037252903,\n", + " 0.9999999834690243,\n", + " 1.000000077765435,\n", + " 0.9999999436549842,\n", + " 1.0000000318977982,\n", + " 1.0000000079162419,\n", + " 1.0000000013969839,\n", + " 1.0000000884756446,\n", + " 0.9999999990686774,\n", + " 1.0000001057051122,\n", + " 0.9999999965075403,\n", + " 0.9999999511055648,\n", + " 0.9999999515712261,\n", + " 1.0000000726431608,\n", + " 0.9999998931307346,\n", + " 1.0000000135041773,\n", + " 0.9999999506399035],\n", + " 'plug-in-mle-from-model': [1.0000000407453626,\n", + " 0.9999999039791874,\n", + " 1.0000000288709998,\n", + " 0.9999999525025487,\n", + " 1.0000000833533704,\n", + " 0.9999999827705324,\n", + " 0.9999999953433871,\n", + " 0.9999999031424522,\n", + " 0.9999999804422259,\n", + " 1.0000000689178705,\n", + " 1.0000000819563866,\n", + " 1.0000000392901711,\n", + " 1.000000013038516,\n", + " 1.0000000747386366,\n", + " 0.9999998938583303,\n", + " 1.0000000484287739,\n", + " 0.9999999694991857,\n", + " 0.9999999967694748,\n", + " 0.9999999317806214,\n", + " 0.9999999972060323,\n", + " 0.9999999972060323,\n", + " 0.9999999718274921,\n", + " 0.9999999182764441,\n", + " 1.000000049592927,\n", + " 0.9999999506399035,\n", + " 1.0000000125728548,\n", + " 1.0000000051222742,\n", + " 1.0000000149011612,\n", + " 0.9999999571591616,\n", + " 1.0000000093132257,\n", + " 1.0000000167638063,\n", + " 1.0000000190339051,\n", + " 1.0000001061707735,\n", + " 1.0000000256113708,\n", + " 0.9999998938292265,\n", + " 1.0000000060535967,\n", + " 0.9999999958090484,\n", + " 0.9999999930150807,\n", + " 0.9999999487772584,\n", + " 0.9999999948777258,\n", + " 0.99999997287523,\n", + " 0.9999999683350325,\n", + " 0.9999999650754035,\n", + " 0.9999999587889761,\n", + " 0.9999999352730811,\n", + " 0.9999999292194843,\n", + " 1.0000000656582415,\n", + " 0.9999999932479113,\n", + " 0.9999999972060323,\n", + " 0.999999969266355]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{k:[sum(v) for v in val] for k, val in estimates.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def mse(weights, optimal_weights=optimal_weights):\n", + " return ((weights - optimal_weights)**2).mean()\n", + "\n", + "def mae(weights, optimal_weights=optimal_weights):\n", + " return torch.abs(weights-optimal_weights).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'monte_carlo_eif-one_step': tensor([0.0285, 0.0030, 0.0079, 0.0012, 0.0119, 0.0152, 0.0215, 0.0037, 0.0534,\n", + " 0.0072, 0.0168, 0.0017, 0.0009, 0.0005, 0.0015, 0.0198, 0.0012, 0.0169,\n", + " 0.0026, 0.0083, 0.0084, 0.0013, 0.0027, 0.0040, 0.0035, 0.0013, 0.0080,\n", + " 0.0018, 0.0013, 0.0134, 0.0061, 0.0009, 0.0060, 0.0063, 0.0015, 0.0098,\n", + " 0.0012, 0.0019, 0.0071, 0.0062, 0.0027, 0.0019, 0.0036, 0.0007, 0.0009,\n", + " 0.0129, 0.0037, 0.0015, 0.0009, 0.0143]),\n", + " 'plug-in-mle-from-model': tensor([0.0261, 0.0094, 0.0162, 0.0041, 0.0204, 0.0104, 0.0324, 0.0126, 0.0288,\n", + " 0.0157, 0.0216, 0.0092, 0.0041, 0.0043, 0.0086, 0.0311, 0.0022, 0.0197,\n", + " 0.0085, 0.0213, 0.0211, 0.0059, 0.0046, 0.0032, 0.0105, 0.0035, 0.0160,\n", + " 0.0046, 0.0107, 0.0237, 0.0155, 0.0070, 0.0150, 0.0203, 0.0022, 0.0146,\n", + " 0.0050, 0.0025, 0.0153, 0.0170, 0.0092, 0.0062, 0.0103, 0.0018, 0.0021,\n", + " 0.0234, 0.0119, 0.0020, 0.0029, 0.0284])}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "estimates_mse = {k: torch.tensor([mse(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_mae = {k: torch.tensor([mae(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_mae" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -386,43 +554,43 @@ " <tbody>\n", " <tr>\n", " <th>count</th>\n", - " <td>5.0</td>\n", - " <td>5.0</td>\n", + " <td>50.00000</td>\n", + " <td>50.00000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.00718</td>\n", + " <td>0.01246</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.00923</td>\n", + " <td>0.00865</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.00048</td>\n", + " <td>0.00180</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.00147</td>\n", + " <td>0.00457</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.00365</td>\n", + " <td>0.01049</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.00836</td>\n", + " <td>0.01899</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", - " <td>0.0</td>\n", - " <td>0.0</td>\n", + " <td>0.05339</td>\n", + " <td>0.03243</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -430,30 +598,30 @@ ], "text/plain": [ " monte_carlo_eif-one_step plug-in-mle-from-model\n", - "count 5.0 5.0\n", - "mean 0.0 0.0\n", - "std 0.0 0.0\n", - "min 0.0 0.0\n", - "25% 0.0 0.0\n", - "50% 0.0 0.0\n", - "75% 0.0 0.0\n", - "max 0.0 0.0" + "count 50.00000 50.00000\n", + "mean 0.00718 0.01246\n", + "std 0.00923 0.00865\n", + "min 0.00048 0.00180\n", + "25% 0.00147 0.00457\n", + "50% 0.00365 0.01049\n", + "75% 0.00836 0.01899\n", + "max 0.05339 0.03243" ] }, - "execution_count": 528, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The true treatment effect is 0, so a mean estimate closer to zero is better\n", - "results = pd.DataFrame(estimates)\n", - "results.describe().round(2)" + "results = pd.DataFrame(estimates_mae)\n", + "results.describe().round(5)" ] }, { "cell_type": "code", - "execution_count": 530, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -472,7 +640,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -511,10 +679,11 @@ "# )\n", "\n", "sns.kdeplot(\n", - " estimates['monte_carlo_eif-one_step'], \n", + " estimates_mae['monte_carlo_eif-one_step'], \n", " label=\"Monte Carlo EIF (One-Step)\",\n", " ax=ax,\n", - " color='red'\n", + " color='red',\n", + " cut=0\n", ")\n", "\n", "# # DoubleML\n", @@ -535,16 +704,16 @@ "\n", "# Plug-in MLE\n", "sns.kdeplot(\n", - " estimates['plug-in-mle-from-model'], \n", + " estimates_mae['plug-in-mle-from-model'], \n", " label=\"Plug-in MLE\",\n", " ax=ax,\n", - " color='brown'\n", + " color='brown',\n", + " cut=0\n", ")\n", "\n", - "ax.axvline(oracle_objective, color=\"black\", label=\"Oracle\", linestyle=\"solid\")\n", "ax.set_yticks([])\n", "sns.despine()\n", - "ax.set_xlabel(\"objective estimate\", fontsize=18)\n", + "ax.set_xlabel(\"MAE in weights\", fontsize=18)\n", "ax.set_ylabel(\"Density\", fontsize=18)\n", "\n", "ax.legend(loc=\"upper right\", fontsize=11)\n", diff --git a/docs/examples/robust_paper/results/opt_markowitz.json b/docs/examples/robust_paper/results/opt_markowitz.json index 91f48c19..a2fc1da5 100644 --- a/docs/examples/robust_paper/results/opt_markowitz.json +++ b/docs/examples/robust_paper/results/opt_markowitz.json @@ -1,16 +1,2706 @@ { "monte_carlo_eif-one_step": [ - 0.0033553107641637325, - 0.0001748679205775261, - 0.0005432892357930541, - 0.002301593543961644, - 0.0011094644432887435 + [ + 0.023934517055749893, + 0.10391602665185928, + 0.030650828033685684, + 0.20428776741027832, + 0.09961473941802979, + 0.0828445702791214, + -0.03396529704332352, + 0.22358159720897675, + -0.08423313498497009, + 0.09937536716461182, + 0.04893643781542778, + -0.060195524245500565, + 0.21584920585155487, + -0.05662914365530014, + -0.03566783666610718, + -0.05134405195713043, + 0.021067213267087936, + 0.10323268920183182, + -0.13589322566986084, + 0.022852422669529915, + -0.08326634019613266, + 0.10152234882116318, + 0.027032099664211273, + 0.08156543225049973, + 0.05093131959438324 + ], + [ + 0.012502651661634445, + 0.09521563351154327, + 0.03785618022084236, + 0.22812537848949432, + 0.04504485800862312, + 0.0007587112486362457, + -0.017028439790010452, + 0.2623814344406128, + -0.09301409125328064, + 0.12414009124040604, + 0.11770625412464142, + -0.08926991373300552, + 0.23846431076526642, + -0.068234883248806, + -0.07055478543043137, + -0.0566805899143219, + 0.005033699795603752, + 0.06990224123001099, + -0.13647663593292236, + -0.006652794778347015, + -0.07479742914438248, + 0.12306735664606094, + 0.06291110813617706, + 0.12362442910671234, + 0.06597530841827393 + ], + [ + 0.010236149653792381, + 0.09706955403089523, + 0.04039406403899193, + 0.22339847683906555, + 0.056239694356918335, + 0.018663570284843445, + -0.02231811359524727, + 0.24955910444259644, + -0.09165553748607635, + 0.11526307463645935, + 0.1028037965297699, + -0.08611875772476196, + 0.23502478003501892, + -0.06234493479132652, + -0.06405985355377197, + -0.05581279844045639, + 0.00724783493205905, + 0.08133698999881744, + -0.13364359736442566, + 0.00018800236284732819, + -0.07372213900089264, + 0.12056759744882584, + 0.05590231716632843, + 0.11396871507167816, + 0.06181197986006737 + ], + [ + 0.007298532407730818, + 0.09441237896680832, + 0.04007191210985184, + 0.23042479157447815, + 0.038326069712638855, + -0.004947074688971043, + -0.015485145151615143, + 0.263778954744339, + -0.09680283069610596, + 0.12836259603500366, + 0.12423913180828094, + -0.09300480782985687, + 0.24021700024604797, + -0.06833546608686447, + -0.0717611014842987, + -0.06141665205359459, + 0.0009848333429545164, + 0.07088877260684967, + -0.13249287009239197, + -0.010305631905794144, + -0.07111578434705734, + 0.12210114300251007, + 0.06721726804971695, + 0.13122792541980743, + 0.06611599028110504 + ], + [ + 0.021678635850548744, + 0.11079815775156021, + 0.03452928364276886, + 0.2135300636291504, + 0.06585895270109177, + 0.024536699056625366, + -0.02983958087861538, + 0.2577701210975647, + -0.09637752920389175, + 0.1275022327899933, + 0.09668156504631042, + -0.07667440176010132, + 0.233979269862175, + -0.06856420636177063, + -0.05299955978989601, + -0.06300165504217148, + 0.00759439030662179, + 0.07559166103601456, + -0.14386272430419922, + 0.007969856262207031, + -0.07759088277816772, + 0.107962965965271, + 0.04586704447865486, + 0.11213669180870056, + 0.06492302566766739 + ], + [ + 0.018096990883350372, + 0.08949816226959229, + 0.051117122173309326, + 0.21241357922554016, + 0.06506651639938354, + 0.036908894777297974, + -0.025557488203048706, + 0.2365119308233261, + -0.08485127985477448, + 0.10150469839572906, + 0.09164004027843475, + -0.08919734507799149, + 0.22899901866912842, + -0.05920884758234024, + -0.05642300844192505, + -0.044081613421440125, + 0.015481491573154926, + 0.0892670527100563, + -0.12614654004573822, + -0.0011294474825263023, + -0.07898702472448349, + 0.12393060326576233, + 0.043125953525304794, + 0.10571420192718506, + 0.05630623921751976 + ], + [ + 0.014897256158292294, + 0.10926703363656998, + 0.03940379619598389, + 0.23527416586875916, + 0.06697485595941544, + 0.05426144227385521, + -0.02820504829287529, + 0.23645645380020142, + -0.08130344748497009, + 0.0922422930598259, + 0.08437874913215637, + -0.07604247331619263, + 0.2409544438123703, + -0.047831300646066666, + -0.06795413792133331, + -0.05184769630432129, + 0.030483610928058624, + 0.09380677342414856, + -0.16704517602920532, + 0.02559460885822773, + -0.09009218215942383, + 0.13193285465240479, + 0.019399572163820267, + 0.07859279960393906, + 0.056400686502456665 + ], + [ + 0.009978750720620155, + 0.09323801100254059, + 0.04244571924209595, + 0.230803444981575, + 0.0433427095413208, + 0.006634548306465149, + -0.01564936339855194, + 0.2537459135055542, + -0.09054732322692871, + 0.11741746217012405, + 0.11652836203575134, + -0.09247290343046188, + 0.2374374121427536, + -0.06475486606359482, + -0.07093308866024017, + -0.05413798615336418, + 0.0058693718165159225, + 0.07576984167098999, + -0.13230156898498535, + -0.009571695700287819, + -0.07298541814088821, + 0.12445204704999924, + 0.059866763651371, + 0.12260042130947113, + 0.06322336196899414 + ], + [ + 0.04761169105768204, + 0.08857715874910355, + 0.05756258964538574, + 0.19104395806789398, + 0.13948071002960205, + 0.16823413968086243, + -0.03166867420077324, + 0.1774766594171524, + -0.0529337003827095, + 0.06914474070072174, + 0.014799803495407104, + -0.0902218371629715, + 0.22184213995933533, + -0.09058848023414612, + -0.007650204002857208, + -0.001164466142654419, + 0.06742551922798157, + 0.13248400390148163, + -0.14450077712535858, + 0.0030344086699187756, + -0.12330087274312973, + 0.15176492929458618, + -0.05090641975402832, + 0.05104805529117584, + 0.011404909193515778 + ], + [ + 0.012635942548513412, + 0.10043657571077347, + 0.03870992362499237, + 0.23599380254745483, + 0.04663138464093208, + 0.012617727741599083, + -0.021365921944379807, + 0.25729113817214966, + -0.09118969738483429, + 0.1138194128870964, + 0.11117957532405853, + -0.08939296007156372, + 0.2425679862499237, + -0.06083076819777489, + -0.0743970274925232, + -0.055623967200517654, + 0.010239520110189915, + 0.07633360475301743, + -0.14755085110664368, + 0.003314177505671978, + -0.07726237922906876, + 0.12838630378246307, + 0.05152657628059387, + 0.11070942133665085, + 0.06522060930728912 + ], + [ + 0.024318290874361992, + 0.1029079481959343, + 0.03419695794582367, + 0.2176571935415268, + 0.07402241230010986, + 0.04170048609375954, + -0.0301106758415699, + 0.24907124042510986, + -0.08658751100301743, + 0.11102960258722305, + 0.08639150112867355, + -0.0772794559597969, + 0.234671488404274, + -0.06508374959230423, + -0.054060570895671844, + -0.051320210099220276, + 0.019145730882883072, + 0.08289317041635513, + -0.14633993804454803, + 0.011894061230123043, + -0.08683513104915619, + 0.11749687045812607, + 0.03695676103234291, + 0.09418753534555435, + 0.059076059609651566 + ], + [ + 0.007820388302206993, + 0.09374067187309265, + 0.04168950021266937, + 0.2326081395149231, + 0.03986261785030365, + 0.0022376030683517456, + -0.015517337247729301, + 0.25859373807907104, + -0.0938112735748291, + 0.12366423010826111, + 0.11986199021339417, + -0.09307393431663513, + 0.2393742948770523, + -0.06643065065145493, + -0.07249382883310318, + -0.058567117899656296, + 0.0026491908356547356, + 0.07623175531625748, + -0.13181787729263306, + -0.010138653218746185, + -0.07245369255542755, + 0.12364424765110016, + 0.06190395727753639, + 0.12657888233661652, + 0.06384314596652985 + ], + [ + 0.0063347285613417625, + 0.09513811767101288, + 0.04003283008933067, + 0.23203431069850922, + 0.038947489112615585, + -0.0004764515906572342, + -0.016172096133232117, + 0.25946566462516785, + -0.09788113087415695, + 0.12373822182416916, + 0.12089374661445618, + -0.09407573193311691, + 0.2402348667383194, + -0.06427770107984543, + -0.0728355422616005, + -0.060897767543792725, + 0.001239735633134842, + 0.07568393647670746, + -0.13213467597961426, + -0.008348437957465649, + -0.0697246864438057, + 0.12386325001716614, + 0.06580543518066406, + 0.12790082395076752, + 0.06551109999418259 + ], + [ + 0.007561175152659416, + 0.09475784003734589, + 0.03954215347766876, + 0.23114162683486938, + 0.040273092687129974, + -0.0010053794831037521, + -0.016357000917196274, + 0.26149672269821167, + -0.09620064496994019, + 0.12593014538288116, + 0.12102128565311432, + -0.09422239661216736, + 0.24030639231204987, + -0.06683987379074097, + -0.07247436046600342, + -0.05937030911445618, + 0.0010505970567464828, + 0.07424727082252502, + -0.13312898576259613, + -0.00950655434280634, + -0.07193635404109955, + 0.12369774281978607, + 0.06589265167713165, + 0.12881341576576233, + 0.06530974805355072 + ], + [ + 0.006102749612182379, + 0.09392181038856506, + 0.03925464302301407, + 0.22921203076839447, + 0.04107716307044029, + -0.00044219568371772766, + -0.017350146546959877, + 0.2636159062385559, + -0.10051006823778152, + 0.13127416372299194, + 0.12154733389616013, + -0.09541664272546768, + 0.24087300896644592, + -0.06999107450246811, + -0.07143118232488632, + -0.061622731387615204, + -0.0001563606783747673, + 0.0749051421880722, + -0.13050222396850586, + -0.011356392875313759, + -0.07042494416236877, + 0.12199512124061584, + 0.06803370267152786, + 0.13136087357997894, + 0.06603030115365982 + ], + [ + 0.02534121833741665, + 0.10589192807674408, + 0.031725309789180756, + 0.2145257592201233, + 0.07749392092227936, + 0.04634568840265274, + -0.028646372258663177, + 0.2423926442861557, + -0.0791814774274826, + 0.11555325984954834, + 0.0853009819984436, + -0.05765724554657936, + 0.2247491031885147, + -0.06530754268169403, + -0.04187784343957901, + -0.05623432993888855, + 0.02223975770175457, + 0.07253460586071014, + -0.1481981873512268, + 0.008972021751105785, + -0.08278486877679825, + 0.09948734194040298, + 0.03375548496842384, + 0.09491662681102753, + 0.058662205934524536 + ], + [ + 0.008189614862203598, + 0.0940166637301445, + 0.03892594203352928, + 0.22884146869182587, + 0.04161462560296059, + -0.002694191876798868, + -0.01641220785677433, + 0.2633184492588043, + -0.09787638485431671, + 0.12823067605495453, + 0.12080862373113632, + -0.09393535554409027, + 0.23934949934482574, + -0.0690569058060646, + -0.07218047231435776, + -0.058718759566545486, + 1.147482544183731e-05, + 0.07235768437385559, + -0.13136158883571625, + -0.01015026867389679, + -0.071063332259655, + 0.12331968545913696, + 0.06842158734798431, + 0.12983429431915283, + 0.06620923429727554 + ], + [ + 0.013655225746333599, + 0.10489026457071304, + 0.042001038789749146, + 0.23811402916908264, + 0.0649639368057251, + 0.05166982114315033, + -0.025143705308437347, + 0.24318578839302063, + -0.08729298412799835, + 0.10458840429782867, + 0.09462489187717438, + -0.08297739923000336, + 0.24110010266304016, + -0.060081660747528076, + -0.05926299840211868, + -0.05582968890666962, + 0.02815956622362137, + 0.0886867344379425, + -0.1514403522014618, + 0.006431119982153177, + -0.08847449719905853, + 0.12551642954349518, + 0.020971238613128662, + 0.09016742557287216, + 0.05177728086709976 + ], + [ + 0.009903005324304104, + 0.09619412571191788, + 0.04003133252263069, + 0.2351914793252945, + 0.04044502228498459, + 0.001990698277950287, + -0.01653888449072838, + 0.25910401344299316, + -0.09420982748270035, + 0.1193353533744812, + 0.11918065696954727, + -0.09193327277898788, + 0.24063420295715332, + -0.06329669058322906, + -0.07256574183702469, + -0.05964917689561844, + 0.005100538022816181, + 0.07436913251876831, + -0.1377996951341629, + -0.004902851767838001, + -0.07246173173189163, + 0.12357313930988312, + 0.061431270092725754, + 0.1229628399014473, + 0.0639110654592514 + ], + [ + 0.012090042233467102, + 0.09909966588020325, + 0.04000702127814293, + 0.23837317526340485, + 0.04840490221977234, + 0.018290508538484573, + -0.020857520401477814, + 0.2532919645309448, + -0.09154525399208069, + 0.1090753972530365, + 0.1074850857257843, + -0.09366843849420547, + 0.24357149004936218, + -0.05829538404941559, + -0.07461004704236984, + -0.055252015590667725, + 0.010377619415521622, + 0.0828692764043808, + -0.14464066922664642, + 0.0034373346716165543, + -0.07666672021150589, + 0.12840139865875244, + 0.050308987498283386, + 0.1087193414568901, + 0.06173284351825714 + ], + [ + 0.007889281958341599, + 0.1084492951631546, + 0.023524709045886993, + 0.23066243529319763, + 0.045864999294281006, + 0.014089595526456833, + -0.019862279295921326, + 0.2684328258037567, + -0.09628941118717194, + 0.14167895913124084, + 0.11733396351337433, + -0.07770466804504395, + 0.24083226919174194, + -0.07444775104522705, + -0.06289298832416534, + -0.0676550641655922, + 0.008699526078999043, + 0.06624114513397217, + -0.15140970051288605, + -0.000893683172762394, + -0.07515809684991837, + 0.11165918409824371, + 0.05448899418115616, + 0.11888100951910019, + 0.06758541613817215 + ], + [ + 0.005966433323919773, + 0.09526877105236053, + 0.038645338267087936, + 0.23236659169197083, + 0.03852282091975212, + -0.0026236847043037415, + -0.015849003568291664, + 0.2638472318649292, + -0.09909731149673462, + 0.13014695048332214, + 0.12367697060108185, + -0.09428907185792923, + 0.24052849411964417, + -0.06874601542949677, + -0.07206269353628159, + -0.06238774210214615, + -0.0006746174767613411, + 0.07238075137138367, + -0.13246768712997437, + -0.010521300137043, + -0.07068128883838654, + 0.12160728871822357, + 0.06791671365499496, + 0.1320120394229889, + 0.06651407480239868 + ], + [ + 0.008254551328718662, + 0.09350193291902542, + 0.04036496952176094, + 0.2271432876586914, + 0.04079800471663475, + -0.00810635183006525, + -0.015417719259858131, + 0.2662808299064636, + -0.10001960396766663, + 0.13138078153133392, + 0.12319518625736237, + -0.09320032596588135, + 0.23829907178878784, + -0.07010611891746521, + -0.07158311456441879, + -0.06136981025338173, + -0.0028776628896594048, + 0.06980860233306885, + -0.13050061464309692, + -0.01114844810217619, + -0.07108499109745026, + 0.12129444628953934, + 0.0723397433757782, + 0.13471777737140656, + 0.06803562492132187 + ], + [ + 0.011057076044380665, + 0.09034748375415802, + 0.043091967701911926, + 0.22407276928424835, + 0.04739409685134888, + 0.006746720056980848, + -0.017474744468927383, + 0.25325146317481995, + -0.09446538984775543, + 0.11957645416259766, + 0.11334793269634247, + -0.09537286311388016, + 0.23646871745586395, + -0.06592965871095657, + -0.06687437742948532, + -0.05516640469431877, + 0.0024597635492682457, + 0.07728829979896545, + -0.1286628395318985, + -0.009095937013626099, + -0.07172451913356781, + 0.12482696026563644, + 0.06403525918722153, + 0.12632180750370026, + 0.0644800141453743 + ], + [ + 0.00641236174851656, + 0.09550297260284424, + 0.040032029151916504, + 0.23195162415504456, + 0.04292680695652962, + 0.012307579629123211, + -0.018571674823760986, + 0.2536364197731018, + -0.09573128074407578, + 0.1228683665394783, + 0.11361898481845856, + -0.09496911615133286, + 0.2400011569261551, + -0.06350355595350266, + -0.07203974574804306, + -0.05853495001792908, + 0.004124634899199009, + 0.08365938067436218, + -0.13342060148715973, + -0.00675103347748518, + -0.07232445478439331, + 0.1246064081788063, + 0.05960570275783539, + 0.12211650609970093, + 0.062475450336933136 + ], + [ + 0.008172435685992241, + 0.09633325785398483, + 0.037667058408260345, + 0.23481282591819763, + 0.03870054706931114, + -0.0003484319895505905, + -0.01595776341855526, + 0.2642143666744232, + -0.09707459807395935, + 0.12754850089550018, + 0.12254014611244202, + -0.0944717675447464, + 0.2423664629459381, + -0.06829585880041122, + -0.07438356429338455, + -0.05969806760549545, + 0.002534611616283655, + 0.07238231599330902, + -0.13732880353927612, + -0.0088485823944211, + -0.07356685400009155, + 0.12413923442363739, + 0.06482681632041931, + 0.12774434685707092, + 0.06599131226539612 + ], + [ + 0.013332139700651169, + 0.0980292558670044, + 0.04146825894713402, + 0.22309745848178864, + 0.05491705983877182, + 0.010760316625237465, + -0.02173374779522419, + 0.2536962628364563, + -0.08803310245275497, + 0.11404439806938171, + 0.10965797305107117, + -0.0795711949467659, + 0.2348511964082718, + -0.06286618858575821, + -0.06381288915872574, + -0.05769168585538864, + 0.010134458541870117, + 0.07365784794092178, + -0.13904497027397156, + 0.0020107387099415064, + -0.07701532542705536, + 0.12053219974040985, + 0.05407930538058281, + 0.11201081424951553, + 0.0634893923997879 + ], + [ + 0.008902546018362045, + 0.0931653380393982, + 0.04038050025701523, + 0.2288747876882553, + 0.03995969146490097, + -0.00455824751406908, + -0.014882839284837246, + 0.26275256276130676, + -0.09683482348918915, + 0.12766960263252258, + 0.12277264147996902, + -0.0936017706990242, + 0.23738381266593933, + -0.06826993823051453, + -0.07098398357629776, + -0.05888396501541138, + 0.0003056926652789116, + 0.07075553387403488, + -0.1290484517812729, + -0.012701310217380524, + -0.07014504820108414, + 0.12162313610315323, + 0.06829500198364258, + 0.13201230764389038, + 0.06505724042654037 + ], + [ + 0.008546768687665462, + 0.09682165831327438, + 0.039112258702516556, + 0.23139046132564545, + 0.04051965847611427, + -0.0003757793456315994, + -0.017023183405399323, + 0.26313549280166626, + -0.0972883328795433, + 0.12643426656723022, + 0.1200847327709198, + -0.09377896785736084, + 0.24124394357204437, + -0.06635942310094833, + -0.07379212230443954, + -0.0599169060587883, + 0.0026417002081871033, + 0.07220453023910522, + -0.13643521070480347, + -0.006297340616583824, + -0.07246729731559753, + 0.12395960837602615, + 0.0643758773803711, + 0.12639419734477997, + 0.06686936318874359 + ], + [ + 0.012483764439821243, + 0.10014830529689789, + 0.03836658596992493, + 0.23125438392162323, + 0.05747928097844124, + 0.03750649094581604, + -0.024293415248394012, + 0.24015097320079803, + -0.0863446444272995, + 0.10244463384151459, + 0.09444975852966309, + -0.08694438636302948, + 0.2360498011112213, + -0.05400779843330383, + -0.06824710965156555, + -0.050927940756082535, + 0.015168417245149612, + 0.0937185063958168, + -0.14165763556957245, + 0.007904672995209694, + -0.07916979491710663, + 0.12562990188598633, + 0.04128171503543854, + 0.09955310821533203, + 0.05800241231918335 + ], + [ + 0.010672241449356079, + 0.10042688995599747, + 0.03532062843441963, + 0.23285438120365143, + 0.048513539135456085, + 0.014243748039007187, + -0.021508844569325447, + 0.25831860303878784, + -0.09530001133680344, + 0.12223297357559204, + 0.1084645614027977, + -0.08793517202138901, + 0.24288392066955566, + -0.06360001862049103, + -0.07199156284332275, + -0.057295605540275574, + 0.008369902148842812, + 0.07599424570798874, + -0.1467665433883667, + 0.0035940539091825485, + -0.07688283920288086, + 0.12395236641168594, + 0.05484294146299362, + 0.11450596153736115, + 0.0660897046327591 + ], + [ + 0.006610799115151167, + 0.09570211172103882, + 0.04074234887957573, + 0.23333221673965454, + 0.03782333806157112, + -0.0009430162608623505, + -0.017345264554023743, + 0.26057329773902893, + -0.09702428430318832, + 0.12369001656770706, + 0.12212461978197098, + -0.09546104073524475, + 0.24091655015945435, + -0.06473837047815323, + -0.07370495796203613, + -0.06071233004331589, + 0.0015191049315035343, + 0.07558870315551758, + -0.13219985365867615, + -0.008669300936162472, + -0.07061145454645157, + 0.12361899018287659, + 0.06565509736537933, + 0.12839002907276154, + 0.06512264162302017 + ], + [ + 0.007719922345131636, + 0.09969597309827805, + 0.04198383912444115, + 0.2304300218820572, + 0.046428777277469635, + 0.01377137377858162, + -0.020777281373739243, + 0.25042155385017395, + -0.09181752055883408, + 0.11320140212774277, + 0.11262507736682892, + -0.09181247651576996, + 0.23791757225990295, + -0.059734903275966644, + -0.0714104101061821, + -0.057532258331775665, + 0.00938576553016901, + 0.0835447832942009, + -0.13516952097415924, + -0.002222158946096897, + -0.07331611216068268, + 0.12352420389652252, + 0.055758893489837646, + 0.11583076417446136, + 0.06155276671051979 + ], + [ + 0.012448607943952084, + 0.09630089998245239, + 0.040464580059051514, + 0.22998683154582977, + 0.045879341661930084, + 0.009401664137840271, + -0.01888025924563408, + 0.2536448538303375, + -0.08877117931842804, + 0.11193642020225525, + 0.11156237125396729, + -0.09018561244010925, + 0.2401447594165802, + -0.0586053840816021, + -0.0729600265622139, + -0.05325120687484741, + 0.009138250723481178, + 0.07714705914258957, + -0.14055578410625458, + 0.002635459415614605, + -0.07551636546850204, + 0.1255001276731491, + 0.056311048567295074, + 0.11276242882013321, + 0.06346111744642258 + ], + [ + 0.007828177884221077, + 0.09693843871355057, + 0.038289062678813934, + 0.23165085911750793, + 0.03860461339354515, + -0.004619552753865719, + -0.017554400488734245, + 0.26471850275993347, + -0.09725013375282288, + 0.12723979353904724, + 0.12373974174261093, + -0.09230276197195053, + 0.24162299931049347, + -0.06638731062412262, + -0.07465654611587524, + -0.06063244491815567, + 0.0017184701282531023, + 0.07065289467573166, + -0.13642802834510803, + -0.0069651417434215546, + -0.07157815247774124, + 0.12327484041452408, + 0.06656605750322342, + 0.12717849016189575, + 0.06835151463747025 + ], + [ + 0.0041258931159973145, + 0.10111686587333679, + 0.041688840836286545, + 0.2436641901731491, + 0.04277666658163071, + 0.026303987950086594, + -0.020180722698569298, + 0.24617597460746765, + -0.0931878387928009, + 0.11017077416181564, + 0.1102420911192894, + -0.0972258672118187, + 0.2480282336473465, + -0.056106291711330414, + -0.07745900750160217, + -0.059080351144075394, + 0.01212766207754612, + 0.09219573438167572, + -0.14305871725082397, + 0.0005274522118270397, + -0.07640539109706879, + 0.13114559650421143, + 0.045731328427791595, + 0.10732011497020721, + 0.05936285853385925 + ], + [ + 0.007126253563910723, + 0.09389611333608627, + 0.04052164405584335, + 0.22889778017997742, + 0.040657855570316315, + -0.000960296019911766, + -0.0161849707365036, + 0.2610943019390106, + -0.09821522235870361, + 0.12825186550617218, + 0.12105259299278259, + -0.09572925418615341, + 0.23937980830669403, + -0.06735660880804062, + -0.07079886645078659, + -0.059987444430589676, + -0.00032823067158460617, + 0.07434052228927612, + -0.12911199033260345, + -0.011382129043340683, + -0.0708538293838501, + 0.12178989499807358, + 0.06773652136325836, + 0.13164536654949188, + 0.0645182654261589 + ], + [ + 0.007346010766923428, + 0.09658447653055191, + 0.03878764063119888, + 0.23160697519779205, + 0.03681807219982147, + -0.007632646709680557, + -0.017841476947069168, + 0.2670601010322571, + -0.09942599385976791, + 0.12921178340911865, + 0.12539906799793243, + -0.0944356620311737, + 0.24203366041183472, + -0.06746602803468704, + -0.07505284994840622, + -0.061426255851984024, + 0.000142175005748868, + 0.07044414430856705, + -0.1348210722208023, + -0.008785884827375412, + -0.07080017030239105, + 0.12324642390012741, + 0.06979065388441086, + 0.13038647174835205, + 0.06883041560649872 + ], + [ + 0.006104824133217335, + 0.09965795278549194, + 0.03911568224430084, + 0.23594702780246735, + 0.04621906578540802, + 0.020642610266804695, + -0.02097337506711483, + 0.2481960654258728, + -0.0956089049577713, + 0.11203493177890778, + 0.10691890120506287, + -0.09274102002382278, + 0.23984394967556, + -0.056670382618904114, + -0.06938507407903671, + -0.05942192301154137, + 0.007782148662954569, + 0.0890478789806366, + -0.13698697090148926, + 0.0012189233675599098, + -0.07242319732904434, + 0.12297601252794266, + 0.05421818792819977, + 0.11307692527770996, + 0.06120976805686951 + ], + [ + 0.010002466849982738, + 0.09845338016748428, + 0.04283113777637482, + 0.2347639948129654, + 0.0453454852104187, + 0.01278800331056118, + -0.018659045919775963, + 0.25234976410865784, + -0.09211025387048721, + 0.11107879132032394, + 0.11260388791561127, + -0.092500239610672, + 0.24059753119945526, + -0.058504343032836914, + -0.07299439609050751, + -0.05541451647877693, + 0.007680961862206459, + 0.08081593364477158, + -0.1390521377325058, + -0.002103577833622694, + -0.07607915997505188, + 0.12690967321395874, + 0.054532263427972794, + 0.11393450200557709, + 0.06272989511489868 + ], + [ + 0.007998604327440262, + 0.09523564577102661, + 0.039373647421598434, + 0.22722962498664856, + 0.03729867935180664, + -0.011738328263163567, + -0.014956716448068619, + 0.26542186737060547, + -0.09697823226451874, + 0.12940792739391327, + 0.12733878195285797, + -0.09140793979167938, + 0.23892873525619507, + -0.06776710599660873, + -0.07151072472333908, + -0.0613940954208374, + -0.0008923709392547607, + 0.06731651723384857, + -0.130777508020401, + -0.010649292729794979, + -0.07030016928911209, + 0.12062085419893265, + 0.06969790905714035, + 0.133805513381958, + 0.06869826465845108 + ], + [ + 0.009190356358885765, + 0.09641670435667038, + 0.03927445411682129, + 0.2311922013759613, + 0.04320488125085831, + 0.003770173527300358, + -0.0183156356215477, + 0.26157456636428833, + -0.09755147993564606, + 0.12583567202091217, + 0.11715894192457199, + -0.09382915496826172, + 0.24196912348270416, + -0.06635552644729614, + -0.07203560322523117, + -0.05913115665316582, + 0.003139580599963665, + 0.0743534117937088, + -0.13620638847351074, + -0.00651737954467535, + -0.07431814819574356, + 0.123636394739151, + 0.06294003874063492, + 0.12484483420848846, + 0.06575913727283478 + ], + [ + 0.012941383756697178, + 0.09480088204145432, + 0.04162988066673279, + 0.22862480580806732, + 0.04627488926053047, + 0.0037027383223176003, + -0.01782807894051075, + 0.26007285714149475, + -0.09309985488653183, + 0.11929299682378769, + 0.1163625717163086, + -0.09078936278820038, + 0.2381262332201004, + -0.06547591835260391, + -0.07078029215335846, + -0.05609112232923508, + 0.005956794135272503, + 0.07274916768074036, + -0.13721078634262085, + -0.006076411809772253, + -0.07508620619773865, + 0.12409797310829163, + 0.059934843331575394, + 0.12216239422559738, + 0.065707728266716 + ], + [ + 0.0076819853857159615, + 0.09505812078714371, + 0.03911247476935387, + 0.23105557262897491, + 0.039936672896146774, + -0.002010549884289503, + -0.016739429906010628, + 0.26174136996269226, + -0.09647051244974136, + 0.1263190358877182, + 0.12070579081773758, + -0.09259586781263351, + 0.2397128939628601, + -0.06658002734184265, + -0.07312659919261932, + -0.05985947325825691, + 0.0005833066534250975, + 0.0730193704366684, + -0.13302107155323029, + -0.00803760439157486, + -0.0711124986410141, + 0.12265264242887497, + 0.06702753156423569, + 0.12844176590442657, + 0.06650509685277939 + ], + [ + 0.007803740445524454, + 0.09611137211322784, + 0.03988335654139519, + 0.23100964725017548, + 0.0384150929749012, + -0.004413796588778496, + -0.01671358197927475, + 0.26301252841949463, + -0.09643732011318207, + 0.12667535245418549, + 0.12332363426685333, + -0.0927276611328125, + 0.24011638760566711, + -0.0664740800857544, + -0.07351554930210114, + -0.06054219976067543, + 0.0018564499914646149, + 0.07175689935684204, + -0.13394278287887573, + -0.008683168329298496, + -0.07154976576566696, + 0.12243223935365677, + 0.06653573364019394, + 0.12925148010253906, + 0.06681594252586365 + ], + [ + 0.017906850203871727, + 0.10576031357049942, + 0.035308562219142914, + 0.22848553955554962, + 0.06076333299279213, + 0.02661137655377388, + -0.02554590255022049, + 0.2513920068740845, + -0.08846081793308258, + 0.10994626581668854, + 0.09714669734239578, + -0.07817395776510239, + 0.2385827898979187, + -0.057811543345451355, + -0.06649363040924072, + -0.05340861529111862, + 0.013935573399066925, + 0.07664170116186142, + -0.15640555322170258, + 0.015285572037100792, + -0.08153805881738663, + 0.12226606905460358, + 0.041390061378479004, + 0.09904066473245621, + 0.06737465411424637 + ], + [ + 0.0075489371083676815, + 0.09674138575792313, + 0.04089305177330971, + 0.23485839366912842, + 0.04248146712779999, + 0.008113322779536247, + -0.015093985013663769, + 0.2554556131362915, + -0.09388802945613861, + 0.11923404037952423, + 0.11811036616563797, + -0.0931176096200943, + 0.23941880464553833, + -0.06334326416254044, + -0.07160738855600357, + -0.05845127999782562, + 0.007632527034729719, + 0.07696965336799622, + -0.13833022117614746, + -0.008012132719159126, + -0.07474919408559799, + 0.1259380728006363, + 0.0573120154440403, + 0.12293829768896103, + 0.06294722855091095 + ], + [ + 0.008459274657070637, + 0.09524877369403839, + 0.03858782723546028, + 0.22838981449604034, + 0.04292551800608635, + 0.0016848740633577108, + -0.01814219355583191, + 0.2610012888908386, + -0.09667479246854782, + 0.12624233961105347, + 0.11797045916318893, + -0.09199938923120499, + 0.23880772292613983, + -0.06722323596477509, + -0.07093406468629837, + -0.05941309407353401, + 0.002345507498830557, + 0.07393968850374222, + -0.13245172798633575, + -0.007279702462255955, + -0.07126683741807938, + 0.12211195379495621, + 0.06544676423072815, + 0.12637601792812347, + 0.06584710627794266 + ], + [ + 0.007317517884075642, + 0.09467779844999313, + 0.038308363407850266, + 0.22996187210083008, + 0.04079073667526245, + -0.0015246253460645676, + -0.016889911144971848, + 0.26296284794807434, + -0.09769292175769806, + 0.12918376922607422, + 0.11979920417070389, + -0.09306421130895615, + 0.24035239219665527, + -0.06808505952358246, + -0.07305863499641418, + -0.060226380825042725, + -3.372738137841225e-05, + 0.07289116829633713, + -0.13245530426502228, + -0.008917631581425667, + -0.07078266143798828, + 0.12266561388969421, + 0.0681220293045044, + 0.12897810339927673, + 0.0667196661233902 + ], + [ + 0.015245865099132061, + 0.10094515979290009, + 0.04417067766189575, + 0.23503555357456207, + 0.053309470415115356, + 0.029412511736154556, + -0.021813832223415375, + 0.24310822784900665, + -0.08321934193372726, + 0.09387160837650299, + 0.10157477855682373, + -0.08785809576511383, + 0.23944664001464844, + -0.04803742468357086, + -0.07624570280313492, + -0.048748429864645004, + 0.01883903332054615, + 0.08553953468799591, + -0.14834387600421906, + 0.011906376108527184, + -0.08114904910326004, + 0.13280296325683594, + 0.03676782548427582, + 0.09325306862592697, + 0.06018640846014023 + ] ], "plug-in-mle-from-model": [ - 0.001417180523276329, - 0.00015472801169380546, - 0.0003509732778184116, - 0.0003669464203994721, - 0.0007656660163775086 + [ + 0.02504074014723301, + 0.11208155751228333, + 0.04500477388501167, + 0.22421085834503174, + 0.07029399275779724, + 0.05685338005423546, + -0.03214738145470619, + 0.22596144676208496, + -0.06473396718502045, + 0.08683893084526062, + 0.09379305690526962, + -0.05932977423071861, + 0.22646310925483704, + -0.05335167422890663, + -0.040553588420152664, + -0.051937535405159, + 0.04780198633670807, + 0.07714685052633286, + -0.16326633095741272, + 0.014078385196626186, + -0.08709699660539627, + 0.10887578874826431, + 0.003299052594229579, + 0.08129291236400604, + 0.053380466997623444 + ], + [ + 0.007244753185659647, + 0.10109920799732208, + 0.043792691081762314, + 0.2427339404821396, + 0.04078985005617142, + 0.023706931620836258, + -0.022087084129452705, + 0.2428499311208725, + -0.08911622315645218, + 0.10524488240480423, + 0.11195557564496994, + -0.09180672466754913, + 0.2441134750843048, + -0.052781783044338226, + -0.07432284206151962, + -0.05917660892009735, + 0.013062574900686741, + 0.0900549590587616, + -0.14169052243232727, + 9.109768870985135e-05, + -0.07310055941343307, + 0.125862717628479, + 0.044194385409355164, + 0.106509268283844, + 0.060776010155677795 + ], + [ + 0.0024599963799118996, + 0.10762026160955429, + 0.03185362368822098, + 0.2458258420228958, + 0.05362491309642792, + 0.05684632062911987, + -0.029366198927164078, + 0.23028774559497833, + -0.09487929940223694, + 0.10049157589673996, + 0.08685491234064102, + -0.09422905743122101, + 0.24277500808238983, + -0.04835144057869911, + -0.07058725506067276, + -0.05996747687458992, + 0.014710195362567902, + 0.11447134613990784, + -0.13794924318790436, + 0.012800930067896843, + -0.0729358047246933, + 0.12246926873922348, + 0.03883059695363045, + 0.09368283301591873, + 0.05266043543815613 + ], + [ + 0.007506850175559521, + 0.09576302766799927, + 0.03818385675549507, + 0.2359219193458557, + 0.0432635061442852, + 0.012301010079681873, + -0.01558290608227253, + 0.2557198405265808, + -0.09502410143613815, + 0.12094639986753464, + 0.11167358607053757, + -0.09319347143173218, + 0.2425849884748459, + -0.0635962039232254, + -0.07351148873567581, + -0.059006500989198685, + 0.0034021707251667976, + 0.08066947013139725, + -0.13623455166816711, + -0.004350820556282997, + -0.07393238693475723, + 0.1257583200931549, + 0.058679718524217606, + 0.12062034010887146, + 0.06143737956881523 + ], + [ + 0.023687105625867844, + 0.09291765093803406, + 0.030056258663535118, + 0.2026967704296112, + 0.08718931674957275, + 0.052477773278951645, + -0.026270097121596336, + 0.24463409185409546, + -0.07736366242170334, + 0.13136884570121765, + 0.08716133236885071, + -0.053832296282052994, + 0.22145874798297882, + -0.07923295348882675, + -0.030200088396668434, + -0.05382630601525307, + 0.014926251024007797, + 0.06203952431678772, + -0.1362985372543335, + -0.001363255549222231, + -0.08737115561962128, + 0.0890607163310051, + 0.034429244697093964, + 0.10769951343536377, + 0.06395529210567474 + ], + [ + 0.007209716830402613, + 0.09809830039739609, + 0.027083933353424072, + 0.22296792268753052, + 0.050251103937625885, + -0.018981074914336205, + -0.0191304050385952, + 0.27593356370925903, + -0.10548021644353867, + 0.14558672904968262, + 0.1163817048072815, + -0.08035112917423248, + 0.2313404381275177, + -0.07692170143127441, + -0.059411995112895966, + -0.07133190333843231, + -0.010104774497449398, + 0.06112731620669365, + -0.1282939314842224, + -0.009314659982919693, + -0.0637008473277092, + 0.10337880998849869, + 0.08602390438318253, + 0.14429859817028046, + 0.07334057986736298 + ], + [ + 0.014460591599345207, + 0.1147109642624855, + 0.03275183588266373, + 0.23013466596603394, + 0.08562345057725906, + 0.10181892663240433, + -0.03926638886332512, + 0.21012087166309357, + -0.07945065945386887, + 0.08166471123695374, + 0.04983557388186455, + -0.07043946534395218, + 0.23405279219150543, + -0.040355268865823746, + -0.051936447620391846, + -0.051485031843185425, + 0.0365547277033329, + 0.12029023468494415, + -0.15602949261665344, + 0.03859652206301689, + -0.08437366038560867, + 0.11656869202852249, + 0.003861003555357456, + 0.0578363761305809, + 0.04445447027683258 + ], + [ + 0.01680327020585537, + 0.10506454855203629, + 0.03721469268202782, + 0.22718632221221924, + 0.06133616343140602, + 0.028730124235153198, + -0.025723641738295555, + 0.25191134214401245, + -0.08910637348890305, + 0.11236639320850372, + 0.09779765456914902, + -0.08140432834625244, + 0.238631933927536, + -0.06108216941356659, + -0.06496279686689377, + -0.054276544600725174, + 0.01658918522298336, + 0.07979036122560501, + -0.15438580513000488, + 0.012069741263985634, + -0.08419262617826462, + 0.12371719628572464, + 0.040367741137742996, + 0.10110946744680405, + 0.0644480511546135 + ], + [ + -0.00494646281003952, + 0.1039045974612236, + 0.029971592128276825, + 0.19273176789283752, + 0.019544774666428566, + -0.0702853575348854, + -0.024613335728645325, + 0.2883526384830475, + -0.12280962616205215, + 0.16456373035907745, + 0.14753246307373047, + -0.07008574157953262, + 0.21835778653621674, + -0.06769543886184692, + -0.05755145475268364, + -0.0896102711558342, + -0.03533397987484932, + 0.038879431784152985, + -0.08871965110301971, + -0.004388852976262569, + -0.034325771033763885, + 0.0712752565741539, + 0.1285107433795929, + 0.17252038419246674, + 0.09422075748443604 + ], + [ + 0.0121358847245574, + 0.10533779114484787, + 0.037964895367622375, + 0.24242617189884186, + 0.053263917565345764, + 0.04137387126684189, + -0.02655487321317196, + 0.24172118306159973, + -0.08549529314041138, + 0.09833618253469467, + 0.09843994677066803, + -0.08963484317064285, + 0.24510197341442108, + -0.052334267646074295, + -0.07429076731204987, + -0.052865419536828995, + 0.02170743979513645, + 0.09375189989805222, + -0.15429292619228363, + 0.011436223052442074, + -0.08090028911828995, + 0.13214783370494843, + 0.03382067382335663, + 0.08854100853204727, + 0.05886185169219971 + ], + [ + 0.013457363471388817, + 0.10394460707902908, + 0.04660739004611969, + 0.23767617344856262, + 0.05659379065036774, + 0.05237022042274475, + -0.02447238564491272, + 0.2171010971069336, + -0.07169211655855179, + 0.07943998277187347, + 0.09674621373414993, + -0.07260105013847351, + 0.23225101828575134, + -0.039919737726449966, + -0.058839038014411926, + -0.050175510346889496, + 0.03385259583592415, + 0.089564748108387, + -0.15333291888237, + 0.01334398053586483, + -0.07516029477119446, + 0.11835228651762009, + 0.013527683913707733, + 0.08515550196170807, + 0.05620848014950752 + ], + [ + 0.016244111582636833, + 0.0978177934885025, + 0.044188063591718674, + 0.23741234838962555, + 0.044758424162864685, + 0.01621367409825325, + -0.02183314599096775, + 0.2512064576148987, + -0.08728190511465073, + 0.10573741793632507, + 0.11179471760988235, + -0.09633554518222809, + 0.2431887686252594, + -0.0563637912273407, + -0.07561194896697998, + -0.05301317572593689, + 0.01390962116420269, + 0.08194244652986526, + -0.1413017064332962, + -0.0007866209489293396, + -0.07495678961277008, + 0.13351322710514069, + 0.044871535152196884, + 0.10659576952457428, + 0.058090291917324066 + ], + [ + 0.007366342470049858, + 0.09598720073699951, + 0.0407366082072258, + 0.23485153913497925, + 0.04304610192775726, + 0.013316074386239052, + -0.01799600012600422, + 0.2544057369232178, + -0.09443028271198273, + 0.12043716758489609, + 0.11440311372280121, + -0.09591865539550781, + 0.24131536483764648, + -0.0643668845295906, + -0.07210036367177963, + -0.0583217553794384, + 0.006158857140690088, + 0.08316715061664581, + -0.13461890816688538, + -0.007589059416204691, + -0.07463955134153366, + 0.12589047849178314, + 0.05722374841570854, + 0.12129784375429153, + 0.060378145426511765 + ], + [ + 0.0049713728949427605, + 0.09772723913192749, + 0.03978579118847847, + 0.2356628030538559, + 0.042374830693006516, + 0.01215779222548008, + -0.018757624551653862, + 0.25378891825675964, + -0.09659226983785629, + 0.11831757426261902, + 0.11334341764450073, + -0.09277421981096268, + 0.2418847531080246, + -0.060864612460136414, + -0.07224464416503906, + -0.06148652732372284, + 0.004844283685088158, + 0.08399823307991028, + -0.13449853658676147, + -0.0032070099841803312, + -0.07174735516309738, + 0.12321696430444717, + 0.05894231051206589, + 0.11977966874837875, + 0.06137692183256149 + ], + [ + 0.008243325166404247, + 0.09842584282159805, + 0.04191793501377106, + 0.23778554797172546, + 0.04548691585659981, + 0.02269810065627098, + -0.01960376463830471, + 0.24673821032047272, + -0.0913882926106453, + 0.10857600718736649, + 0.10688602179288864, + -0.09490268677473068, + 0.24238242208957672, + -0.056810930371284485, + -0.07310296595096588, + -0.055613212287425995, + 0.01159198023378849, + 0.08972065150737762, + -0.13881579041481018, + 0.00011234640260227025, + -0.0758453905582428, + 0.12906914949417114, + 0.04884544014930725, + 0.11014682799577713, + 0.05745620280504227 + ], + [ + 0.028853273019194603, + 0.09632012993097305, + 0.050376374274492264, + 0.19287686049938202, + 0.09054568409919739, + 0.06868252158164978, + -0.03663275018334389, + 0.20270377397537231, + -0.06742607802152634, + 0.0839870423078537, + 0.0675101950764656, + -0.05894673988223076, + 0.21284180879592896, + -0.05177157372236252, + -0.023607438430190086, + -0.04237346351146698, + 0.0313548669219017, + 0.08900952339172363, + -0.11672275513410568, + 0.009228064678609371, + -0.06614221632480621, + 0.08804277330636978, + 0.014889740385115147, + 0.0837869793176651, + 0.052613452076911926 + ], + [ + 0.007165177259594202, + 0.09552574157714844, + 0.04128532111644745, + 0.23319952189922333, + 0.04060375317931175, + 0.004829184152185917, + -0.017625708132982254, + 0.256346195936203, + -0.09568636119365692, + 0.1217629685997963, + 0.11848314851522446, + -0.0945698544383049, + 0.2409045249223709, + -0.0642932578921318, + -0.07192564755678177, + -0.059860508888959885, + 0.0038766765501350164, + 0.07922583073377609, + -0.13200309872627258, + -0.008075611665844917, + -0.0718337669968605, + 0.12349196523427963, + 0.06138193607330322, + 0.12523651123046875, + 0.06255532801151276 + ], + [ + 0.013663049787282944, + 0.0896734818816185, + 0.028995927423238754, + 0.1750616431236267, + 0.07454797625541687, + -0.00015515656559728086, + -0.023450186476111412, + 0.25298011302948, + -0.09371822327375412, + 0.13801263272762299, + 0.0881008431315422, + -0.049779076129198074, + 0.20126357674598694, + -0.07230399549007416, + -0.03318431228399277, + -0.06661950796842575, + -0.013055726885795593, + 0.062482353299856186, + -0.10233037173748016, + 0.0010978920618072152, + -0.05569494888186455, + 0.07712023705244064, + 0.09406734257936478, + 0.14028283953666687, + 0.07294159382581711 + ], + [ + 0.007203482091426849, + 0.10129950940608978, + 0.039982035756111145, + 0.23694902658462524, + 0.04465002939105034, + 0.022919295355677605, + -0.02517087012529373, + 0.2456897497177124, + -0.09241221845149994, + 0.10869739204645157, + 0.10813245177268982, + -0.09005871415138245, + 0.23983801901340485, + -0.05548616126179695, + -0.0680551677942276, + -0.0612766370177269, + 0.012404014356434345, + 0.08969696611166, + -0.13792237639427185, + 0.0027189028915017843, + -0.07212885469198227, + 0.12122439593076706, + 0.049895964562892914, + 0.11174862086772919, + 0.0594610758125782 + ], + [ + 0.015142648480832577, + 0.10422768443822861, + 0.04747527092695236, + 0.24206401407718658, + 0.06025819480419159, + 0.047708068042993546, + -0.021447639912366867, + 0.23014020919799805, + -0.07848216593265533, + 0.0799720361828804, + 0.08766385167837143, + -0.08673354238271713, + 0.2412276715040207, + -0.043633200228214264, + -0.07271557301282883, + -0.047062892466783524, + 0.02761814370751381, + 0.101305291056633, + -0.15779165923595428, + 0.018767476081848145, + -0.09160935878753662, + 0.1384173333644867, + 0.025102289393544197, + 0.08094600588083267, + 0.05143984034657478 + ], + [ + 0.028154758736491203, + 0.09565690904855728, + 0.03274957835674286, + 0.1944720596075058, + 0.09666881710290909, + 0.051757652312517166, + -0.03518573194742203, + 0.24168984591960907, + -0.09000307321548462, + 0.12017964571714401, + 0.06792990118265152, + -0.060626767575740814, + 0.2175217717885971, + -0.07149937748908997, + -0.02781124971807003, + -0.05420571565628052, + 0.015406716614961624, + 0.0798531025648117, + -0.1347212940454483, + 0.009286892600357533, + -0.08278432488441467, + 0.09549899399280548, + 0.039724431931972504, + 0.10764644294977188, + 0.06264001131057739 + ], + [ + 0.008391676470637321, + 0.0973747968673706, + 0.040743328630924225, + 0.237803116440773, + 0.04148852452635765, + 0.013926422223448753, + -0.019263433292508125, + 0.25254443287849426, + -0.09206318855285645, + 0.11479611694812775, + 0.11387477070093155, + -0.09368766844272614, + 0.24319212138652802, + -0.06031264364719391, + -0.07428044080734253, + -0.057601600885391235, + 0.008736682124435902, + 0.08265452086925507, + -0.13998517394065857, + -0.0024266887921839952, + -0.07396848499774933, + 0.12738429009914398, + 0.05389825999736786, + 0.11491165310144424, + 0.0618685819208622 + ], + [ + 0.006940770894289017, + 0.09954819083213806, + 0.039705246686935425, + 0.2397753745317459, + 0.03637868911027908, + 0.00782571267336607, + -0.02053917571902275, + 0.2577018737792969, + -0.09289030730724335, + 0.1182500571012497, + 0.1212732121348381, + -0.09443826973438263, + 0.2449788898229599, + -0.060970794409513474, + -0.07778196781873703, + -0.060099635273218155, + 0.00769026018679142, + 0.07956616580486298, + -0.13944220542907715, + -0.0033730685245245695, + -0.07223784923553467, + 0.1251903623342514, + 0.0561370849609375, + 0.11703293770551682, + 0.0637783631682396 + ], + [ + 0.010809740982949734, + 0.08579500019550323, + 0.04467885568737984, + 0.23668628931045532, + 0.03241227939724922, + 0.007512611802667379, + -0.014459202997386456, + 0.25426289439201355, + -0.09365314990282059, + 0.11980917304754257, + 0.12097729742527008, + -0.09833607822656631, + 0.2425336092710495, + -0.06421089917421341, + -0.07231904566287994, + -0.059612978249788284, + 0.002355606062337756, + 0.07719527184963226, + -0.13177677989006042, + -0.009947814978659153, + -0.07172841578722, + 0.125144362449646, + 0.06410019844770432, + 0.1278923749923706, + 0.0638788491487503 + ], + [ + 0.016090253368020058, + 0.09943967312574387, + 0.04487735778093338, + 0.2356692999601364, + 0.0489569753408432, + 0.01849088817834854, + -0.02146572433412075, + 0.24955835938453674, + -0.08736789226531982, + 0.10283786803483963, + 0.1084720566868782, + -0.09185444563627243, + 0.24174976348876953, + -0.056078240275382996, + -0.07153630256652832, + -0.05393924191594124, + 0.017149461433291435, + 0.0823848769068718, + -0.14791782200336456, + 0.005168371833860874, + -0.08274086564779282, + 0.1315002292394638, + 0.045013345777988434, + 0.10639984905719757, + 0.059141855686903 + ], + [ + 0.006169868167489767, + 0.09535106271505356, + 0.041323691606521606, + 0.23520100116729736, + 0.04109320789575577, + 0.009908515959978104, + -0.017406366765499115, + 0.2538709044456482, + -0.09628500789403915, + 0.11959456652402878, + 0.11522892117500305, + -0.09686131030321121, + 0.24163077771663666, + -0.0626436173915863, + -0.07307436317205429, + -0.05952596664428711, + 0.004121786914765835, + 0.08346229791641235, + -0.1316821277141571, + -0.007019544951617718, + -0.07161504030227661, + 0.1257069855928421, + 0.06002800911664963, + 0.12318962812423706, + 0.06023213267326355 + ], + [ + 0.022201642394065857, + 0.10250672698020935, + 0.05509708821773529, + 0.23794390261173248, + 0.04126475751399994, + 0.0115294074639678, + -0.011485804803669453, + 0.23643307387828827, + -0.0674016997218132, + 0.07847489416599274, + 0.11564873903989792, + -0.0773441344499588, + 0.22995322942733765, + -0.041648995131254196, + -0.07181819528341293, + -0.04582315310835838, + 0.027515564113855362, + 0.06658536940813065, + -0.15187978744506836, + 0.0056944782845675945, + -0.07759997248649597, + 0.13006313145160675, + 0.025563061237335205, + 0.09934639185667038, + 0.05918028950691223 + ], + [ + 0.00927142333239317, + 0.09491042047739029, + 0.041480958461761475, + 0.23263737559318542, + 0.04519198089838028, + 0.011838705278933048, + -0.017354633659124374, + 0.25323811173439026, + -0.09232158213853836, + 0.11782632023096085, + 0.11195020377635956, + -0.09382028132677078, + 0.23995842039585114, + -0.06345298141241074, + -0.07126962393522263, + -0.05598864331841469, + 0.005853781010955572, + 0.0820014551281929, + -0.13455606997013092, + -0.005973884370177984, + -0.07581815868616104, + 0.1255192756652832, + 0.05786307528614998, + 0.1209358349442482, + 0.06007853150367737 + ], + [ + 0.013185114599764347, + 0.10104549676179886, + 0.04050648584961891, + 0.23008616268634796, + 0.05514008551836014, + 0.024909064173698425, + -0.02249046601355076, + 0.24767619371414185, + -0.08714932203292847, + 0.10788436979055405, + 0.10305730253458023, + -0.08394972234964371, + 0.2364301234483719, + -0.058491967618465424, + -0.06559315323829651, + -0.05567542836070061, + 0.01419301237910986, + 0.08302506804466248, + -0.1439053863286972, + 0.005728192627429962, + -0.07961107790470123, + 0.12200894951820374, + 0.04537620022892952, + 0.10679357498884201, + 0.05982108414173126 + ], + [ + 0.023377297446131706, + 0.10460313409566879, + 0.043298643082380295, + 0.2239047884941101, + 0.07540032267570496, + 0.057078514248132706, + -0.03137959539890289, + 0.2287401556968689, + -0.07376666367053986, + 0.08620694279670715, + 0.07402116060256958, + -0.07657094299793243, + 0.23149941861629486, + -0.04998142272233963, + -0.059627994894981384, + -0.0468168780207634, + 0.02899366430938244, + 0.09978954493999481, + -0.1490432769060135, + 0.021525178104639053, + -0.09080084413290024, + 0.12457701563835144, + 0.02673882059752941, + 0.07885883003473282, + 0.04937419667840004 + ], + [ + 0.019687512889504433, + 0.10798162966966629, + 0.03777463361620903, + 0.23290704190731049, + 0.059821512550115585, + 0.032761916518211365, + -0.028241654857993126, + 0.24704350531101227, + -0.08324071019887924, + 0.09956468641757965, + 0.09815169870853424, + -0.08022316545248032, + 0.23933802545070648, + -0.05404357239603996, + -0.0668841227889061, + -0.05337892472743988, + 0.02144339308142662, + 0.08303909748792648, + -0.15855589509010315, + 0.01717006228864193, + -0.08386573940515518, + 0.12461994588375092, + 0.03309758007526398, + 0.09138600528240204, + 0.0626455545425415 + ], + [ + 0.00813294854015112, + 0.09869798272848129, + 0.03916938230395317, + 0.23841124773025513, + 0.045117076486349106, + 0.02053007297217846, + -0.019248390570282936, + 0.2504102289676666, + -0.09294331818819046, + 0.11405126005411148, + 0.10832563787698746, + -0.09385287016630173, + 0.242410808801651, + -0.0595259927213192, + -0.07331434637308121, + -0.058016687631607056, + 0.008240411058068275, + 0.08759854733943939, + -0.13813886046409607, + -0.0008130488567985594, + -0.0751105472445488, + 0.12613731622695923, + 0.05174552649259567, + 0.11305583268404007, + 0.05892980098724365 + ], + [ + 0.008107729256153107, + 0.10549106448888779, + 0.03808220848441124, + 0.22676551342010498, + 0.06390149891376495, + 0.048141960054636, + -0.030376985669136047, + 0.23556110262870789, + -0.09152338653802872, + 0.10808241367340088, + 0.08341524004936218, + -0.08164401352405548, + 0.23393172025680542, + -0.05396418273448944, + -0.06140049919486046, + -0.05954551324248314, + 0.010931244120001793, + 0.10232813656330109, + -0.13050982356071472, + 0.007861025631427765, + -0.07186201214790344, + 0.11350780725479126, + 0.04264340177178383, + 0.09792771935462952, + 0.05414673686027527 + ], + [ + 0.005319252144545317, + 0.10500136762857437, + 0.030668899416923523, + 0.2395523488521576, + 0.06927448511123657, + 0.07929322868585587, + -0.03300244361162186, + 0.22780966758728027, + -0.09471254050731659, + 0.10341034084558487, + 0.0679752379655838, + -0.09459913522005081, + 0.24350903928279877, + -0.05525190010666847, + -0.06335707753896713, + -0.055353742092847824, + 0.018912212923169136, + 0.1204070970416069, + -0.1426607072353363, + 0.018097996711730957, + -0.07917780429124832, + 0.12469176948070526, + 0.029479315504431725, + 0.08488896489143372, + 0.049824152141809464 + ], + [ + 0.0069051869213581085, + 0.09439900517463684, + 0.04019998759031296, + 0.23327870666980743, + 0.04120791330933571, + 0.006096913479268551, + -0.016819819808006287, + 0.2567335367202759, + -0.09620301425457001, + 0.12237152457237244, + 0.11752248555421829, + -0.09571658819913864, + 0.24046634137630463, + -0.06517498195171356, + -0.07135576754808426, + -0.059177521616220474, + 0.0031766165047883987, + 0.0791357010602951, + -0.13150028884410858, + -0.008791557513177395, + -0.07155582308769226, + 0.12426675856113434, + 0.06252951174974442, + 0.12584315240383148, + 0.06216191500425339 + ], + [ + 0.01365884579718113, + 0.10599631816148758, + 0.04866413772106171, + 0.2518925070762634, + 0.03341028094291687, + 0.01630610041320324, + -0.019486617296934128, + 0.24552130699157715, + -0.07774272561073303, + 0.09055454283952713, + 0.12208399176597595, + -0.09083825349807739, + 0.24840804934501648, + -0.045645251870155334, + -0.08133143931627274, + -0.05273433029651642, + 0.024765441194176674, + 0.0791550725698471, + -0.15983325242996216, + 0.005109733436256647, + -0.07949142158031464, + 0.13771763443946838, + 0.028707748278975487, + 0.09756610542535782, + 0.05758548155426979 + ], + [ + 0.007433218415826559, + 0.09519690275192261, + 0.042245298624038696, + 0.23662085831165314, + 0.04203767701983452, + 0.01359939482063055, + -0.017286894842982292, + 0.2522564232349396, + -0.09317891299724579, + 0.11621575057506561, + 0.11483917385339737, + -0.0965438187122345, + 0.2418103665113449, + -0.06209199130535126, + -0.07282894849777222, + -0.05706195905804634, + 0.008103624917566776, + 0.08326420187950134, + -0.13559316098690033, + -0.006977047771215439, + -0.07452673465013504, + 0.12749986350536346, + 0.05531363561749458, + 0.11921233683824539, + 0.06044073775410652 + ], + [ + 0.006716946139931679, + 0.09473974257707596, + 0.04029200226068497, + 0.23366519808769226, + 0.04185435548424721, + 0.00863090343773365, + -0.016815435141324997, + 0.2567133605480194, + -0.09557849168777466, + 0.12329709529876709, + 0.11586020141839981, + -0.09510437399148941, + 0.24027343094348907, + -0.06561315059661865, + -0.0719337984919548, + -0.059463270008563995, + 0.0034126299433410168, + 0.08034070581197739, + -0.1318710744380951, + -0.008740792982280254, + -0.0724448636174202, + 0.12414001673460007, + 0.061260320246219635, + 0.1253022998571396, + 0.06106603518128395 + ], + [ + 0.008236869238317013, + 0.10531985014677048, + 0.03207466006278992, + 0.2376924604177475, + 0.05935736000537872, + 0.0517737939953804, + -0.03063468262553215, + 0.23385754227638245, + -0.09232860058546066, + 0.1044866070151329, + 0.08605038374662399, + -0.089670330286026, + 0.2408277988433838, + -0.05367361754179001, + -0.0641653835773468, + -0.058406658470630646, + 0.012189309112727642, + 0.10688704997301102, + -0.14101837575435638, + 0.013549103401601315, + -0.07512913644313812, + 0.12133827805519104, + 0.04014192894101143, + 0.0972670465707779, + 0.053976692259311676 + ], + [ + 0.019949035719037056, + 0.09536083787679672, + 0.0469941645860672, + 0.2183641791343689, + 0.07012058049440384, + 0.042765822261571884, + -0.027695901691913605, + 0.23698781430721283, + -0.08218743652105331, + 0.1003064215183258, + 0.08551587909460068, + -0.08570747822523117, + 0.2352144867181778, + -0.059891197830438614, + -0.06135593727231026, + -0.044875361025333405, + 0.019895579665899277, + 0.09157855063676834, + -0.13741067051887512, + 0.007180408108979464, + -0.08505664765834808, + 0.128103107213974, + 0.037349339574575424, + 0.09363491088151932, + 0.05485950782895088 + ], + [ + 0.009191621094942093, + 0.10088298469781876, + 0.03861447796225548, + 0.23986105620861053, + 0.048122771084308624, + 0.02703854627907276, + -0.02077867090702057, + 0.2499948889017105, + -0.09244474023580551, + 0.11166045814752579, + 0.10528471320867538, + -0.09334976971149445, + 0.2422584593296051, + -0.05943862721323967, + -0.07201159000396729, + -0.057053323835134506, + 0.012986892834305763, + 0.08865771442651749, + -0.1449781060218811, + 0.0018293847097083926, + -0.0778854489326477, + 0.12803703546524048, + 0.047135841101408005, + 0.1083567664027214, + 0.05802663788199425 + ], + [ + 0.01335360947996378, + 0.09982048720121384, + 0.036072149872779846, + 0.224652960896492, + 0.05331714078783989, + 0.013235097751021385, + -0.023446256294846535, + 0.26093700528144836, + -0.09593672305345535, + 0.12425745278596878, + 0.10834414511919022, + -0.08978825807571411, + 0.23922881484031677, + -0.06736837327480316, + -0.06705943495035172, + -0.0562887005507946, + 0.008668433874845505, + 0.07711346447467804, + -0.14185665547847748, + 0.0012954259291291237, + -0.07794482260942459, + 0.1225513219833374, + 0.05566534027457237, + 0.1165541335940361, + 0.06462220847606659 + ], + [ + 0.014937380328774452, + 0.10225769132375717, + 0.03854452818632126, + 0.22553230822086334, + 0.05744883045554161, + 0.019246429204940796, + -0.023629041388630867, + 0.25225353240966797, + -0.08924926072359085, + 0.11227662861347198, + 0.1033308282494545, + -0.0792725682258606, + 0.23615293204784393, + -0.059585269540548325, + -0.06336825340986252, + -0.0565621554851532, + 0.012333432212471962, + 0.07652176916599274, + -0.14627355337142944, + 0.007538881618529558, + -0.07902007550001144, + 0.11958126723766327, + 0.047442179173231125, + 0.10731709003448486, + 0.06424443423748016 + ], + [ + 0.006370713468641043, + 0.09390486776828766, + 0.04096357896924019, + 0.2319260984659195, + 0.04070279374718666, + 0.004439717624336481, + -0.01595384255051613, + 0.25754645466804504, + -0.09671463072299957, + 0.12378624826669693, + 0.1184261217713356, + -0.09516096860170364, + 0.2394886016845703, + -0.06597548723220825, + -0.07113078236579895, + -0.05963036045432091, + 0.002247217809781432, + 0.07873374968767166, + -0.13007880747318268, + -0.010151155292987823, + -0.07096466422080994, + 0.12399595975875854, + 0.06405667960643768, + 0.12728656828403473, + 0.0618852861225605 + ], + [ + 0.006017026025801897, + 0.09636031836271286, + 0.04014946147799492, + 0.2350415289402008, + 0.03976934030652046, + 0.0053491778671741486, + -0.01596856117248535, + 0.2580593228340149, + -0.09637302160263062, + 0.12395194172859192, + 0.11886228621006012, + -0.09626492857933044, + 0.24167461693286896, + -0.0646955594420433, + -0.0735900029540062, + -0.059567779302597046, + 0.0024835970252752304, + 0.07951386272907257, + -0.13324518501758575, + -0.009371761232614517, + -0.07275794446468353, + 0.12480863928794861, + 0.0620453916490078, + 0.12593255937099457, + 0.06181560829281807 + ], + [ + 0.01945938728749752, + 0.11442819237709045, + 0.03556836396455765, + 0.23843950033187866, + 0.06861675530672073, + 0.055666372179985046, + -0.0321202427148819, + 0.2394302636384964, + -0.08015631139278412, + 0.09087998420000076, + 0.07973586767911911, + -0.0768948644399643, + 0.24322664737701416, + -0.04851270839571953, + -0.0712808147072792, + -0.04997728765010834, + 0.030034879222512245, + 0.09559392929077148, + -0.17107748985290527, + 0.031292229890823364, + -0.09295233339071274, + 0.1312979906797409, + 0.019615069031715393, + 0.07134952396154404, + 0.058337025344371796 + ], + [ + 0.013783090747892857, + 0.09696712344884872, + 0.043316494673490524, + 0.23851437866687775, + 0.04838531091809273, + 0.027041302993893623, + -0.0219745896756649, + 0.24226421117782593, + -0.08471459895372391, + 0.09866517037153244, + 0.1047210693359375, + -0.09481433779001236, + 0.2425696849822998, + -0.053894128650426865, + -0.07156006991863251, + -0.05120214819908142, + 0.015321014449000359, + 0.09152235090732574, + -0.14155720174312592, + 0.004323539789766073, + -0.08083559572696686, + 0.13183878362178802, + 0.04353334382176399, + 0.10205719619989395, + 0.055728670209646225 + ], + [ + 0.005994858685880899, + 0.09487453103065491, + 0.04062533751130104, + 0.23542821407318115, + 0.03876492381095886, + 0.0041758278384804726, + -0.016504650935530663, + 0.25775548815727234, + -0.09646791964769363, + 0.12193044275045395, + 0.11933811753988266, + -0.09744102507829666, + 0.2414218783378601, + -0.06483142077922821, + -0.073597751557827, + -0.05949563533067703, + 0.002732417779043317, + 0.07981766015291214, + -0.13177508115768433, + -0.00915740430355072, + -0.07138513028621674, + 0.12512697279453278, + 0.0635598823428154, + 0.1267414093017578, + 0.06236805021762848 + ], + [ + 0.007926496677100658, + 0.09517136961221695, + 0.04121360927820206, + 0.23354770243167877, + 0.04149217531085014, + 0.00656852126121521, + -0.017139777541160583, + 0.25573042035102844, + -0.0946161076426506, + 0.12002603709697723, + 0.11685160547494888, + -0.0957757979631424, + 0.24011193215847015, + -0.06331344693899155, + -0.07196789234876633, + -0.05795640870928764, + 0.004703433718532324, + 0.07934736460447311, + -0.133049875497818, + -0.0078008281998336315, + -0.07263065874576569, + 0.12504954636096954, + 0.06086507439613342, + 0.12422352284193039, + 0.06142197921872139 + ], + [ + 0.011108269914984703, + 0.11416453123092651, + 0.041530828922986984, + 0.23646694421768188, + 0.07328485697507858, + 0.07959862053394318, + -0.03905893489718437, + 0.21185165643692017, + -0.0768301859498024, + 0.07246819883584976, + 0.06475184857845306, + -0.08046355098485947, + 0.23789283633232117, + -0.035306207835674286, + -0.062330905348062515, + -0.04963645339012146, + 0.03228869289159775, + 0.1219068169593811, + -0.15246571600437164, + 0.030902594327926636, + -0.08531811088323593, + 0.12627369165420532, + 0.015592117793858051, + 0.06546328961849213, + 0.04586423933506012 + ] ] } \ No newline at end of file From a06df478c5f578847eefa91ab25fd8eb6cc8eb2c Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Fri, 2 Feb 2024 00:06:25 -0300 Subject: [PATCH 23/26] update results --- .../notebooks/optimization_functional.ipynb | 150 ++++++++++++------ 1 file changed, 101 insertions(+), 49 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb index d0d74ddb..35f048f4 100644 --- a/docs/examples/robust_paper/notebooks/optimization_functional.ipynb +++ b/docs/examples/robust_paper/notebooks/optimization_functional.ipynb @@ -477,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -485,45 +485,90 @@ " return ((weights - optimal_weights)**2).mean()\n", "\n", "def mae(weights, optimal_weights=optimal_weights):\n", - " return torch.abs(weights-optimal_weights).mean()" + " return torch.abs(weights-optimal_weights).mean()\n", + "\n", + "def relative_root_mse(weights, optimal_weights=optimal_weights):\n", + " return torch.sqrt(((weights - optimal_weights)**2).mean())/torch.sqrt((optimal_weights**2).mean())\n", + "\n", + "def relative_mae(weights, optimal_weights=optimal_weights):\n", + " return torch.abs(weights-optimal_weights).mean()/torch.abs(optimal_weights).mean()\n", + "\n", + "def expected_volatility(weights, scale_tril=true_scale_tril):\n", + " return torch.sqrt(weights @ scale_tril @ scale_tril.T @ weights)\n", + "\n", + "def relative_expected_volatility(weights, scale_tril=true_scale_tril):\n", + " return expected_volatility(weights, scale_tril)/expected_volatility(optimal_weights, scale_tril)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "estimates_mse = {k: torch.tensor([mse(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_mae = {k: torch.tensor([mae(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_relative_mae = {k: torch.tensor([relative_mae(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_relative_root_mse = {k: torch.tensor([relative_root_mse(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_expected_volatility = {k: torch.tensor([expected_volatility(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", + "estimates_relative_expected_volatility = {k: torch.tensor([relative_expected_volatility(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 53, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'monte_carlo_eif-one_step': tensor([0.0285, 0.0030, 0.0079, 0.0012, 0.0119, 0.0152, 0.0215, 0.0037, 0.0534,\n", - " 0.0072, 0.0168, 0.0017, 0.0009, 0.0005, 0.0015, 0.0198, 0.0012, 0.0169,\n", - " 0.0026, 0.0083, 0.0084, 0.0013, 0.0027, 0.0040, 0.0035, 0.0013, 0.0080,\n", - " 0.0018, 0.0013, 0.0134, 0.0061, 0.0009, 0.0060, 0.0063, 0.0015, 0.0098,\n", - " 0.0012, 0.0019, 0.0071, 0.0062, 0.0027, 0.0019, 0.0036, 0.0007, 0.0009,\n", - " 0.0129, 0.0037, 0.0015, 0.0009, 0.0143]),\n", - " 'plug-in-mle-from-model': tensor([0.0261, 0.0094, 0.0162, 0.0041, 0.0204, 0.0104, 0.0324, 0.0126, 0.0288,\n", - " 0.0157, 0.0216, 0.0092, 0.0041, 0.0043, 0.0086, 0.0311, 0.0022, 0.0197,\n", - " 0.0085, 0.0213, 0.0211, 0.0059, 0.0046, 0.0032, 0.0105, 0.0035, 0.0160,\n", - " 0.0046, 0.0107, 0.0237, 0.0155, 0.0070, 0.0150, 0.0203, 0.0022, 0.0146,\n", - " 0.0050, 0.0025, 0.0153, 0.0170, 0.0092, 0.0062, 0.0103, 0.0018, 0.0021,\n", - " 0.0234, 0.0119, 0.0020, 0.0029, 0.0284])}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "EV monte_carlo_eif-one_step 1.86\n", + "plug-in-mle-from-model 2.60\n", + "dtype: float32 \n", + "\n", + "RMSE monte_carlo_eif-one_step 0.08\n", + "plug-in-mle-from-model 0.14\n", + "dtype: float32\n" + ] } ], "source": [ - "estimates_mse = {k: torch.tensor([mse(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", - "estimates_mae = {k: torch.tensor([mae(torch.tensor(v)) for v in vals]) for (k, vals) in estimates.items()}\n", - "estimates_mae" + "# The true treatment effect is 0, so a mean estimate closer to zero is better\n", + "results_1 = pd.DataFrame(estimates_relative_expected_volatility)\n", + "results_2 = pd.DataFrame(estimates_relative_root_mse)\n", + "\n", + "print(\"EV\", results_1.mean().round(2), \"\\n\")\n", + "print(\"RMSE\", results_2.mean().round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EV monte_carlo_eif-one_step 0.35\n", + "plug-in-mle-from-model 0.35\n", + "dtype: float32 \n", + "\n", + "RMSE monte_carlo_eif-one_step 0.02\n", + "plug-in-mle-from-model 0.02\n", + "dtype: float32\n" + ] + } + ], + "source": [ + "print(\"EV\", (results_1.std()/(N_datasets**0.5)).round(2), \"\\n\")\n", + "print(\"RMSE\", (results_2.std()/(N_datasets**0.5)).round(2))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -559,38 +604,38 @@ " </tr>\n", " <tr>\n", " <th>mean</th>\n", - " <td>0.00718</td>\n", - " <td>0.01246</td>\n", + " <td>0.07710</td>\n", + " <td>0.13518</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.00923</td>\n", - " <td>0.00865</td>\n", + " <td>0.09951</td>\n", + " <td>0.09249</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", - " <td>0.00048</td>\n", - " <td>0.00180</td>\n", + " <td>0.00504</td>\n", + " <td>0.02069</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", - " <td>0.00147</td>\n", - " <td>0.00457</td>\n", + " <td>0.01625</td>\n", + " <td>0.04987</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", - " <td>0.00365</td>\n", - " <td>0.01049</td>\n", + " <td>0.03924</td>\n", + " <td>0.10729</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", - " <td>0.00836</td>\n", - " <td>0.01899</td>\n", + " <td>0.08816</td>\n", + " <td>0.21390</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", - " <td>0.05339</td>\n", - " <td>0.03243</td>\n", + " <td>0.58151</td>\n", + " <td>0.35913</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", @@ -599,26 +644,33 @@ "text/plain": [ " monte_carlo_eif-one_step plug-in-mle-from-model\n", "count 50.00000 50.00000\n", - "mean 0.00718 0.01246\n", - "std 0.00923 0.00865\n", - "min 0.00048 0.00180\n", - "25% 0.00147 0.00457\n", - "50% 0.00365 0.01049\n", - "75% 0.00836 0.01899\n", - "max 0.05339 0.03243" + "mean 0.07710 0.13518\n", + "std 0.09951 0.09249\n", + "min 0.00504 0.02069\n", + "25% 0.01625 0.04987\n", + "50% 0.03924 0.10729\n", + "75% 0.08816 0.21390\n", + "max 0.58151 0.35913" ] }, - "execution_count": 13, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The true treatment effect is 0, so a mean estimate closer to zero is better\n", - "results = pd.DataFrame(estimates_mae)\n", + "results = pd.DataFrame(estimates_relative_root_mse)\n", "results.describe().round(5)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 14, From a46cc00116b504ea21eec6de463d8b90d09fa96c Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Fri, 2 Feb 2024 01:22:12 -0300 Subject: [PATCH 24/26] updated figs --- .../notebooks/quality_vs_estimators.ipynb | 87 +++++++++---------- 1 file changed, 43 insertions(+), 44 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index 01c15ed9..e7654a5c 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -292,7 +292,7 @@ "<text text-anchor=\"middle\" x=\"200.6\" y=\"-53.3\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n", "</g>\n", "<!-- intercept&#45;&gt;Y -->\n", - "<g id=\"edge5\" class=\"edge\">\n", + "<g id=\"edge6\" class=\"edge\">\n", "<title>intercept&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M69.66,-115.65C97.53,-103.38 140.19,-84.59 169.18,-71.83\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"170.71,-74.98 178.45,-67.75 167.89,-68.58 170.71,-74.98\"/>\n", @@ -304,7 +304,7 @@ "<text text-anchor=\"middle\" x=\"318.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights</text>\n", "</g>\n", "<!-- outcome_weights&#45;&gt;Y -->\n", - "<g id=\"edge6\" class=\"edge\">\n", + "<g id=\"edge5\" class=\"edge\">\n", "<title>outcome_weights&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M291.82,-112.12C273.12,-101.02 248.19,-86.23 229.12,-74.92\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"230.67,-71.77 220.28,-69.68 227.1,-77.79 230.67,-71.77\"/>\n", @@ -334,7 +334,7 @@ "<text text-anchor=\"middle\" x=\"482.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">treatment_weight</text>\n", "</g>\n", "<!-- treatment_weight&#45;&gt;Y -->\n", - "<g id=\"edge2\" class=\"edge\">\n", + "<g id=\"edge3\" class=\"edge\">\n", "<title>treatment_weight&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M433.15,-115.73C376.52,-101.67 285.18,-79 235.5,-66.66\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"236.11,-63.21 225.56,-64.2 234.42,-70 236.11,-63.21\"/>\n", @@ -346,13 +346,13 @@ "<text text-anchor=\"middle\" x=\"200.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n", "</g>\n", "<!-- X&#45;&gt;Y -->\n", - "<g id=\"edge4\" class=\"edge\">\n", + "<g id=\"edge2\" class=\"edge\">\n", "<title>X&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M200.6,-110.7C200.6,-102.98 200.6,-93.71 200.6,-85.11\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"204.1,-85.1 200.6,-75.1 197.1,-85.1 204.1,-85.1\"/>\n", "</g>\n", "<!-- A&#45;&gt;Y -->\n", - "<g id=\"edge3\" class=\"edge\">\n", + "<g id=\"edge4\" class=\"edge\">\n", "<title>A&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M143.17,-113.83C153.35,-103.94 167.12,-90.55 178.63,-79.36\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"181.07,-81.87 185.8,-72.38 176.19,-76.85 181.07,-81.87\"/>\n", @@ -372,7 +372,7 @@ "</svg>\n" ], "text/plain": [ - "<graphviz.graphs.Digraph at 0x184757010>" + "<graphviz.graphs.Digraph at 0x1866a0410>" ] }, "execution_count": 4, @@ -625,20 +625,7 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset 99\n", - "plug-in-mle-from-model 99\n", - "tmle analytic_eif 99\n", - "tmle monte_carlo_eif 99\n", - "one_step analytic_eif 99\n", - "one_step monte_carlo_eif 99\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import os\n", @@ -916,7 +903,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -955,9 +942,9 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAJOCAYAAAAqFJGJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1hUZ/bA8e/QOyhFQBHFgmIDsfceNZtEY4pJjImmuellN+WXarLZTV8Ts5tN0RTTi7HE2HtF7L03EBAEQell7u+Pl0sRUJCBOwPn8zw89zJ3ysEYnDPve84xaZqmIYQQQgghhBC1YGd0AEIIIYQQQgjbJ4mFEEIIIYQQotYksRBCCCGEEELUmiQWQgghhBBCiFqTxEIIIYQQQghRa5JYCCGEEEIIIWpNEgshhBBCCCFErUliIYQQQgghhKg1q00sNE0jMzMTmd8nhBBCCCGE9bPaxCIrK4vo6GiysrKMDkXUs6ysLEwmEyaTSf77CyGEEELYCKtNLIQQQgghhBC2QxILIYQQQgghRK1JYiGEEEIIIYSoNUkshBBCCCGEELUmiYUQQgghhBCi1iSxEEIIIYQQQtSag9EBCCGEEEKI2tM0jaKiIgoLC40ORdgIR0dH7O3tLfZ8klgIIYQQQtgwTdNIT08nJSWFoqIio8MRNsbHx4fAwEBMJlOtn0sSCyGEEEIIG5aUlER6ejpeXl54eXnh4OBgkTeJomHTNI3s7GySk5MBCAoKqvVzSmIhhBBCCGGjioqKyMjIwN/fHz8/P6PDETbG1dUVgOTkZAICAmq9LUqKt4UQQgghbFRBQQGapuHu7m50KMJGubm5AervUm1JYiGEEEIIYeNk65O4Vpb8uyOJhRBCCCGEEKLWJLEQQgghhBCGe/755wkPD7/il36fadOmVfocixYtIjw8nLvvvrvktvDwcObOnVvl686cOfOKr7lkyRKL/6wNlRRvCyGEEEIIw7344os888wzJd8PGDCA//u//2Ps2LElt73//vs4OjqyceNGMjMz8fDwKPccf/755zVt7QkMDOTXX3+t9Jq3t3eNn6+xksRCCCGEEEIYztPTE09Pzwq3+fv7l7utc+fOHD9+nFWrVnHjjTeW3J6Zmcn69euJjo6u8Wvb29tXeB1Rc5JYCCGEEEI0QJoG2dnGvLabG9RVPbmjoyPDhw9nyZIl5RKLFStWEB4eTkhICGfPnq2bFxdXJImFEEIIIUQDo2kwYABs2mTM6/fvD+vX111yMWbMGB577LFy26H+/PNPrr/+eg4dOlQ3LyquShILIYQQQogGqCF3oO3Xrx+urq6sXr2aG264gYyMDDZv3swbb7xxTYlFQkICUVFRFW5v0qQJq1atskTIjYIkFkIIIYQQDYzJpFYMGuJWKCjdDrV06VJuuOEGli1bRmRkJM2aNbum5wsICGDOnDkVbrezkwaqNSGJhRBCCCFEA2QyQUMeyD127FgefvhhsrKyWLx4cbnuUTXl4OBAaGioBaNrnCQNE0IIIYQQNqdPnz64ubkxb948tm3bxnXXXWd0SI2erFgIIRqd8+dh7VoIC4MuXcBBfhMKIYTNcXBwYOTIkXzwwQf07NmTpk2bVnnfI0eOsG7dunK3+fj40LVrVwCKiopISUmp9LGurq4V5mWIysk/p0KIRsNshv/9D156CS5cULcFBMDcuaqDiRBCCNsyduxYfv75Z66//vor3u/LL7/kyy+/LHdbr169SuoqkpKSGDBgQKWPveuuu3jllVcsE3ADZ9I0TTM6iMpkZmYSHR3N9u3bJUtsZLKyskr+m2dmZuLekDeIinr12mswfbo6b9UK0tLg4kXw9ITly6F3byOjE0KImsvNzeXkyZO0bt0aFxcXo8MRNsiSf4ekxkII0Shs2ABvvKHO334bjh6FxEQYMgQuXYIxYyApydAQhRBCCJsmiYUQosG7dAkmTVJboe6+G559VtVVuLnBwoUQGam2Rr32mtGRCiGEELZLEgshRIP3v//B6dNq+9PHH5e/5uEBH32kzr/4Ag4erPfwhBBCiAZBEgshRIOWlwczZqjzV14BL6+K9xk4EG66CYqK4Pnn6zU8IYQQosGQxEII0aB9/z0kJEBwMNx5Z9X3e/ttNUxqwQI4frz+4hNCCCEaCkkshBANlqbBu++q8yefBGfnqu8bHg6jRqnz2bPrPDQhhBCiwZHEQgjRYG3Zomom3N3hwQevfv/771fHr76CwsI6DU0IIYRocCSxEEI0WD/+qI7jxoG399Xvf+ON4Oentk4tWVKnoQkhhBANjiQWQogGqagIfv5Znd9xR/Ue4+QE99yjzmU7lBBCCFEzklgIIRqkdevUwLsmTWDkyOo/7u671XHJEsjOrpvYhBBCVK2oqIjvv/+eW265haioKHr06MHEiRP59ddf0TStXmJYsGABt912G5GRkURFRTFhwgR+1JfBi124cIFffvmlXuKxFQ5GByCEEHXhhx/UccIEtRJRXV27QmiomnuxYoXaHiWEEKJ+FBQU8Mgjj7Bnzx4effRRBgwYQFFREevXr+ett95i1apVzJw5E3t7+zqL4ddff+XNN9/kxRdfJDo6Gk3T2LhxI//4xz84f/48jz76KADvvPMO8fHx3HrrrXUWi62RxEII0eCYzTBvnjqfOLFmjzWZVDIxc6ZqPSuJhRBC1J9PP/2Ubdu28euvvxIWFlZye5s2bejVqxe33XYbs2bN4sHqdOS4Rt9//z0TJkzglltuKbktLCyMc+fO8c0335QkFvW1emJLZCuUEKLB2bEDUlLA0xMGDar542+6SR0XLlS1GkIIIeqe2Wxmzpw53HzzzeWSCl1ERAQ33XQTc+bMIS4ujvDwcJYuXcqtt95K586dGTZsGD/99FO5x/z222+MGTOGrl27MmbMGL7++mvMZvMV47Czs2Pnzp1kZGSUu/3BBx8sef7nn3+e33//na1btxIeHg6oROPzzz9n+PDhdOvWjZtuuokFCxaUPD4mJobw8HCWLVvGiBEjiIyM5N577+V4AxqeJCsWQogGR+/oNGIEODrW/PGDBqkuUsnJsHUr9O1r2fiEEKJeaJpxxWJubmoJuAZOnjxJeno63bt3r/I+ffv25ddffy1JDv71r3/x8ssv0759e7788ktee+01+vXrR0hICD/99BMffPABr7zyCl27duXAgQO88cYbnDt3jmeffbbK17j//vt56qmnGDRoEL1796ZHjx706dOHLl264OXlBcCLL75Ibm4uSUlJzJw5E4B///vf/PHHH7zyyiuEhYURGxvLa6+9xqVLl7jrrrtKnv+tt97i1VdfJTAwkHfffZfJkyezZMkSPD09a/TnZY0ksRBCNDhLl6rjdddd2+MdHWHsWFWnsWCBJBZCCBukaTBgAGzaZMzr9+8P69fXKLnQVwiaNGlS5X30a2lpaQDce++9DB8+HICnnnqK7777jt27dxMSEsJ///tf/vrXv3L99dcDEBISQmZmJtOnT+eJJ57AuYqpqaNHjyYwMJBvvvmGjRs3snbtWgBatWrFP//5T6Kjo/H09MTFxQVHR0f8/f3Jzs7mq6++4oMPPmDIkCEAtGzZkrNnzzJr1qxyicVzzz3H4MGDAXjvvfcYMmQIixYtYmJN9+5aIUkshBANSkYGbN6szq81sQAYM0YlFitXWiYuIYSodzVcMTCanjRcunSpyvvoyUfTpk0BVXuh0z/xLygoIC0tjaSkJD744AM+/PDDkvuYzWby8vKIj4/n66+/ZuHChSXXHnroIaZNmwZAZGQkkZGRmM1mDh06xNq1a/n222954IEHWL58Ob6+vuXiOnbsGHl5eTzzzDPY2ZVWGhQWFpKfn09ubm7Jbb179y459/HxoXXr1hw5cqSaf0rWTRILIUSDsnKlqosID4dWra79eYYOVcft21WyUp0Be0IIYTVMJrViYENboVq2bIm/vz+xsbGMGjWq0vts3boVf39/TMXP7VRJ2z9N00q2Sr3wwgv069evwn2CgoJ44oknuO+++0pu8/b2JikpiU8//ZSHHnqIwMBA7OzsiIiIICIighEjRvCXv/yF2NhYRo8eXeE1AWbMmFFpfUjZOB0cyr/9LioqKpeM2LKG8VMIIUQxfRvUZb/za6xFC2jfXnWYWreu9nEJIUS9M5nA3d2Yr2tYLbG3t+fee+/l119/rbSg+ejRo8ybN49JkyZd9Y24r68vTZs2JS4ujtDQ0JKv/fv3M2PGjJL7lL3m4+ODk5MTv/zyS7mia51eX+Hn51f8x1v6M4aFheHg4EBCQkK551y7di2zZs0qF+/evXtLztPS0jh9+jSdOnWq/h+UFZPEQgjRoKxZo44jRtT+ufRVi1Wrav9cQgghrm7q1KkMGjSIu+66i++++47Tp09z+vRpvvvuOyZNmkSfPn144IEHrvo8JpOJBx54gDlz5vDtt99y5swZli9fzmuvvYaLi0ulKx2gtljdf//9fPjhh/z73//m4MGDxMXFsXr1ah599NGSYm4ANzc3kpOTiYuLw9PTk4kTJ/Lhhx8yf/584uLi+PXXX3n33XcJCAgo9xrTp08nNjaWQ4cO8cwzz+Dv719hBcRWyVYoIUSDkZQER46oD8oGDKj98w0bBp9+CqtX1/65hBBCXJ2dnR0ffvgh8+bN46effuLf//43mqbRrl07/va3v3HLLbeUWym4kqlTp+Ls7MycOXN466238PPz47bbbuPxxx+/4uOefPJJWrVqxc8//8x3331Hbm4uwcHBjBkzhoceeqjkfuPGjWP58uX85S9/YdmyZbzwwgs0adKEDz/8kOTkZIKCgnj88ce5//77yz3/7bffzrPPPkt6ejp9+vThm2++wdXVteZ/WFbIpFnpdI/MzEyio6PZvn07Hh4eRocj6lFWVlbJf/PMzEzc3d0NjkjYil9/hVtvVdOzd++u/fMlJ0OzZuo8JQWKV7+FEMJq5ObmcvLkSVq3bo2Li4vR4YgriImJYfLkyaxcuZIWLVoYHU4JS/4dkq1QQogGY/16dRw40DLPFxAAXbqoc32LlRBCCCEqJ4mFEKLBsHRiAaWTu41qBS+EEELYCkkshBANQkYG7Nqlzi2ZWOjD8SSxEEIIURu9e/fm8OHDVrUNytIksRBCNAibNqlBs2FhEBxsuefV25/v2AFl5hsJIYQQ4jKSWAghGoQNG9TRkqsVoIbsNWsGBQUquRBCCCFE5SSxEEI0CFu3qqO+dclSTCbZDiWEEEJUhyQWQgibZzZDbKw679XL8s+vb4favNnyzy2EEEI0FJJYCCFs3pEjqnjb1RU6d7b885ddsbDOyT9CCCGE8SSxEELYPH0bVPfu4Oho+eePjgYHBzXZ+/Rpyz+/EEII0RBIYiGEsHl6YnGt26DmzIEbb4QtWyq/7uoKkZHqXN9yJYQQQojyJLEQQti8miYWubmQnV36fVERLFyotjzdf7+q2bhcjx7quG1b7WIVQghRtWHDhhEeHl7y1blzZ4YMGcKrr75KWlqaRV8rPDycuXPnVnl95syZDBs2rNrPN3fuXMLDw+nduzeFhYUVrp87d46OHTsSHh5ectvdd9/N888/X7PArZgkFkIIm5aXVzoYr3fvq99f0+CBB+DJJ0tvGzYM7r0X7Oxg1iz4v/+r+DhJLIQQon5MnTqVDRs2sGHDBhYvXszLL79MTEwMkyZN4tKlS0aHd1VZWVlsqWQJfMmSJWgNvFBPEgshhE3btUvNmPDzUzMnrubzz+Hbb2H2bDh7Vt3WsiV8+SV8/bX6/u234Ztvyj8uOlodt2+XAm4hhKhLbm5u+Pv74+/vT0hICMOHD2f27NkkJibyxRdfGB3eVfXt25clS5ZUuH3x4sX00D+laqAksRBC2LTt29WxZ081c+JKDh+Gxx9X52++Cc2bl78+aRK89JI6f+opuHCh9FqnTuDsrLpPHT9umdiFEEJUT3BwMCNHjmTRokUApKenM336dAYPHkzXrl2ZOHEiMTExJfevbBtTZbedOHGCiRMn0rlzZ8aMGcPixYurjOHSpUu8/PLL9OnTh+joaCZPnszevXsr3G/MmDEsX7683HaohIQEDhw4wIgRI67p57cVklgIIWyaPg27e/er3/fFF9XWqVGj4O9/r/w+r74KERGQlqZWLnSOjqUF3LIdSghhCzRNIys/y5Cvutjy0759e+Li4rh06RJTp05l27ZtvPvuu8ydO5f27dtz3333sWfPnho959dff824ceNYuHAh1113HU899RT79u2rcD9N03jggQeIi4vj008/5eeffyYyMpI77riDAwcOlLvviBEjyMrKKpfo/Pnnn/Tv3x8vL69r++FthIPRAQghRG1UN7HYuhV++03VUXzwgTpWxsEB3nsPFixQqxZlRUdDTIxKLCZOrH3sQghRVzRNY8CXA9gUt8mQ1+8f0p/1U9ZjutpScg3ob8pXr17N/v37WbhwIe3btwdg+vTp7N27l1mzZvHhhx9W+znvvPNOJhb/Qn/yySfZsmULX331Fe+99165+23ZsoVdu3axZcsWfHx8AHj66afZsWMH33zzDW+99Va5OAcMGMCSJUvo378/oBKLqVOnkp+ff80/vy2QFQshhM3Kzwf9g6WoqCvf94UX1HHyZLWt6UrGjIFPPoFmzcrfrm+N1bdfCSGENTNhuTf11kAv3D5z5gyenp4lSQWAyWSiR48eHDlypEbPGa0X0BXr1q0bR48erXC//fv3o2kaQ4cOJSoqquRr586dHK9kf+zo0aNZsWIFRUVFnDlzhpMnT9aow5StkhULIYTN2r9fFW77+Fy5cPvUKdiwQW1neu21a3+9somF2Vz1qocQQhjNZDKxfsp6sguyr37nOuDm6GbR1QpQb+5btWqFk5NTpdc1TcPBoeq3tpW1gLW77Bd5UVFRpc9vNpvx8PCotD1tZfcfMWIEL7/8Mlu3bmX37t0MGTIENze3KmNrKCSxEELYrJ071bF79ysXbrdqBXFxsHkzhIZW//k3boR33oGxY+Ghh6BjR1XAfekSnDwJbdrUKnwhhKhTJpMJdyd3o8OwiKSkJFauXMkDDzxAeHg4ly5d4siRIyWrFpqmsX37dtq2bQuAo6MjWVlZ5Z7j9OnTFZ53//795Qqqd+zYQYcOHSrcr3379mRmZlJQUFDyGgAvvfQSHTp0YNKkSeXu7+HhwcCBA1myZAm7du3iscceu/Yf3oZIYiGEsFl6fcXVtkEBBATATTfV/PkXLIBjx+DBB1X9RadO6vbduyWxEEKIupCdnU1KSgoAubm5HD58mBkzZtCiRQumTJmCi4sLHTt25JlnnuHll1/G19eXb7/9liNHjvDqq68CEBkZSXp6OrNmzeK6665jw4YNrFu3rqQ+QvfVV1/RsmVLunXrxo8//siRI0d4//33K8Q0cOBAOnbsyFNPPcWLL75IUFAQ33//PXPnzmXWrFmV/hxjxozhtddew2QyMWjQoCp/3nPnzrFu3boKt1/pMdZKEgshhM2qTuH2xYtwrU04Jk9WtRkHDqjViwEDoFs39bp79sDNN1/b8wohhKja7NmzmT17NqBWHoKCghg7dixTp07F3d295D5vv/02jz76KPn5+XTu3JmvvvqKyOL2fX369OGxxx5j9uzZfPTRRwwaNIjHH3+cby4bUvTwww8zZ84cXn75Zdq2bctnn31G69atK8Rkb2/P7Nmzeffdd3nyySfJycmhTZs2fPzxx/Tt27fSn2PYsGG89NJLjBkzpsrtWwCbNm1i06aKRfaHDx+u1p+XNTFpVjoCMDMzk+joaLZv346Hh4fR4Yh6lJWVVfLfPDMzs+SXiBBlFRWphCE7Gw4ehEpWrtE0tX0pMFANxAsLq/nr3HuvGpz36KMwcybMmKG6RY0bB7//XssfQgghaik3N5eTJ0/SunVrXFxcjA5H2CBL/h2S0kMhhE06elQlFW5u0K5d5ffZsUMNxYuJAX//a3ud225Tx19/VclMt27q+xq2ShdCCCEaPEkshBA2afdudezaFeztK7/PDz+o4w03gKfntb3OiBGq61RSkuos1bWruv3ECbXNSgghhBCKJBZCCJukrxjob/QvZzbDTz+p8zvuuPbXcXJS254AfvkFfH2heXP1fSXDWYUQQohGSxILIYRNKrtiUZmNGyE+XtVhjBlTu9e6/Xbo1QuKawJLXlOPQQghhBDSFUoIYaP0FQu95uFy+mrFzTdDbesZR49WX7pu3WDxYqmzEEIIIcqSFQshhM1JS1MD7wC6dKl4XdNg/nx1PmGC5V9fX7GQxEIIIYQoJSsWQgibs3evOrZqBd7eFa8XFcEbb8Cff8Lw4ZZ73bQ02LatfGcosxns5CMaIYQQQhILIYTtuVp9hYODmj9x772We82UFAgKUolEYiI4O0NmJpw6dW3zMYQQQoiGRj5nE0LYnKvVV9QFf3+IiFDbrFatgk6d1O1SwC2EEEIoklgIIWzOlVrNJiTABx/AsWOWf129gHvJEqmzEEIIIS4niYUQwqYUFZXOj6gssfjjD3jmGZg82fKvrbetXbZMWs4KIURdGDZsGOHh4SVfHTp0oHv37kyaNInY2NiS+8ycObNO44iJiSE8PJz4+Pg6fZ2GRmoshBA25eRJyMlRLWTbtKl4felSdRw7tnrPZy4spCg3F0cPj6vet29fVVuRlARNm6rbZMVCCCEsa+rUqUydOhUATdNIT0/ngw8+4P7772fx4sX1EkNUVBQbNmygqf7LXlSLJBZCNFaJifDpp7BhA1y4oKqSe/eGoUPhpptqP/yhjuzfr44dO4K9fflrhYWwcqU6HzXqys+Tl57Oke+/5+iPP5Kbmoqrvz/+PXoQ/dxzuPr7V/oYFxeVXKxZA6mp6rbjx+HSJfD0vPafSQghRCk3Nzf8y/weDggIYPr06QwaNIjly5fXSwxOTk7lYhDVI4mFEI1NXp7aK/Tpp+qdeFm7dqnbg4PhhRfgwQfBycmQMKuiJxZ68XRZsbGQkaFWE6Kjq36OrMREVkyeTFZCQsltOSkpnFm8mOStW+n/3ns069Wr0scOGaISi23b1B9TQoLamtW377X/TEIIURc0TaMoJ8eQ17Z3dcVkMlns+Rwc1FtWp8v+TZo7dy4vvPAChw8frvK2nJwc3nrrLZYsWUJBQQFjxowhNzcXR0dH3nrrrUpfLyYmhsmTJ7Ny5UpatGjBsGHDuOuuu9i1axcbNmzAycmJG264geeff74kNiGJhRCNS0qKGkW9YYP6vn9/uOceaN5cJRzr18Mvv0B8PDz2GHzxBXz7LXTubGzcZVwpsdC3QY0YUXE1Q5eTksKq++4jKyEBj5AQuj72GEH9+5N+9Cjb3nyTjKNHWfXAA4z4+mv8IyMrPP7WW6FlS7Ww89e/qsRizx5JLIQQ1kXTNJZPmsT5XbsMeX3/qChGzJljkeTi3Llz/POf/8TNzY3Bgwfz+eef1+jxzz33HAcOHODf//43fn5+fPzxxyxbtoxx48bV6Hk+/PBD/va3v/Hss8+ydetWXnzxRTp37lzj52nIpHhbiMYiLU0lEhs2qKlyf/6pzh94QBUkjB9f2k7pv/8FPz9Vmdyjh1rFsBJXSiyWLVPHqrZBmYuKWPfEE1w6fRr34GCGf/klra6/HmcfH5r17Ml1P/xA86FD0QoL2fjMM+ReuFDhOSIiYMoUNZxPb3crBdxCCGtkyRWD+vTpp58SFRVFVFQUXbp0YdCgQRw9epQZM2YQHBxco+eKi4tj6dKlvPrqq/Tr14/27dvz7rvv4ufnV+O4BgwYwOTJkwkJCWHChAl06NCBHTt21Ph5GjJZsRCiMTCbYdIkOHpUfdy+ZIkqUqiMs7P6KH78eLj/fli0CKZNgwMH4P331fQ5gxQWwsGD6vzyxCIrC3buVOdVJRYnfvuN1N27cXB3Z9gXX+AeFFTuuoOrK/3efpslt93GpVOn2PzCCwz55JMq/3Hu0kUd9S5VQghhLUwmEyPmzLHJrVATJ07k7rvvBsDOzg4fHx88r7GQ7cCBA4AqxtY5OzvTtUxbweuvv56EMltjq1oRaXNZxxBPT08KCgquKa6GShILIRqDN96AxYtV9fH8+VUnFWUFBsLChfDWW/B//wcffaTGTP/8s0o+DHD8OOTng5ubWjEoy90dzp9XtQ8hIRUfm3vhArtmzACg62OP4RkaWulrOLq7M+CDD1h2xx0krl9P3LJltLzuunL3OXMG5s5V9e+gVlE0DWz0w0EhRANlMplwcHMzOowa8/b2JrSK39HVUVRUVHJuX7wv1mw2V3n/zz77jMIyNYfNmjVjdyVL0ZfXd4DaciZKyVYoIRq67dth+nR1/umnUEndQJVMJlXE/csvKplYsADGjVP9Xq/Fli0wYwbMmwfX8CmPvg0qIgLsKvnt5e4OgwdX/tg9H35IfkYGPu3b0/6OO674Ok3Cw+lY3Opw94cfYr4s1kOH4Kmn4KefVBxpaZCcXOMfRwghRC05OjoCkJmZWXLbqVOnSs7Dw8MxmUzsKlNrkp+fz379HxSgefPmhIaGlny5WGlXRFsgiYUQDZmmwZNPquMdd1z71LhbblFbolxd1TaqG2+E7OyrPy4rq/z3ixerd+Tjx6u9TPPmqdiq6Ur1FVeSnZzMid9/B6DHiy9iV43tXB2nTMHF15dLp09z7Ndfy13r3VvlXKdPg/6hWpl/o4QQQtSTyMhITCYTM2fOJD4+nsWLF/N78e97gJCQEMaMGcMbb7zB5s2bOXbsGC+++CJJSUk2W4NizSSxEKIh+/VXVaDt6grvvFO75xo+XCUG7u6wYoUq+C7zCVEFixdD27YqedB16gS33w7+/qreY/x41fq2mslFVYnF+fPQsyc8+6wqJ7nc0R9+wFxYiH9UFAE9elTrtRzd3en8178CsPe//6WwzCqNt3dpoyy9zXnxNl4hhBD1KCQkhOnTp7N8+XLGjBnDTz/9xLPPPlvuPm+88QbR0dE89thj3H777bi7uxMVFVWy2iEsSLNSly5d0tq3b69dunTJ6FBEPcvMzNQADdAyMzONDsd25eRoWqtWmgaa9tprlnvejRs1zctLPe+AAZpW2X+j2bM1zWRS9xkyRNPM5vLXMzI07fnn1XXQtFdeqdZLd+qk7r5oUfnbf/lF3d6lS8XHFGRlab/06aN9FxGhnVm2rJo/pFKUn6/NGzlS+y4iQjv600/lrj34oHrN3r3Vcdq0Gj21EEJYRE5OjnbgwAEtJyfH6FCsUm5urrZ8+fIK7ydHjRqlffzxxwZFZV0s+XdIViyEaKjmzFHF1s2bw9/+Zrnn7ddPrVj4+KjVkHHjIDe39Pr//gdTp6qU4b771MrF5cvNXl7wr3/BzJnq+9dfh6++uuLLFhTAkSPq/PIVi9Wr1XHo0IqPOzF/PvkXL+IREkLzYcOq+1MCYOfoSPs77wTg8HfflSvS69dPHdPS1FG2QgkhhPVxcnJi+vTpvPrqqxw/fpxTp07x3nvvkZCQwOjRo40Or8GRxEKIhshshn//W50/84zavmRJPXuW3xZ1++3qnf8vv6hWtQBPPAGff646UVXl0UfhzTfVfSdMuOJLHj2qXsLDQ3XMLWvVKnW8PLHQNI2jP/4IQPikSdhVNTXvCtrcfDMOrq5kHDvGuS1bSm7XEwu9RlDvDCWEEMJ6mEwmPvvsMy5cuMDtt9/O+PHj2blzJ7Nnz67QPlbUnrSbFaIhWrpUDXzw8lKrBnWhTx/VjnbMGNUt6oYbYO1ade3RR1ViU53CuP/7v2q9XNn6irJPm5KiujQBDBpU/jHphw6RcewYdk5OtL7xxmq9zuWcvLxoPW4cR3/4gUNz5hBYPGK7bVs1Q/DChfKdoZo1u6aXEUIIUUc6duzI7NmzjQ6jUZAVCyEaovffV8cHHlDJRV0ZOhR++00NzVu6VG2JGjNGtZS9lm4bZnPpEsBlqirc3rRJHSMioGnT8tdO/vEHAM2HDMGpFn8O4ZMmAZCwdi1ZxUOUTCZYswYuXoSwsPIxCiGEEI2RJBZCNDR798LKlWBvD489Vvevd/318N13pYMl2rSpfMjE1SQmwpAhMHIklBlupKsqsdiwQR0HDCh/u7moiNOLFgHQ+oYbah5PGV6tWhHQsycAp4qTFT0WNzeV1IB0hhJCCNG4SWIhREOjL/eOG1c6ZKGu3XYbfPGFOv/449KBfDXh6amyh2PH1Fjry+zbp46XJxYuLhAUBP37l789OTaWnJQUnLy8CLo867gGrW+6CYCTCxZUmLSqxyQrFkIIIRozSSyEaEgKCuD779X5lCl1/3r796vtVhcvqtf78EN1+/Tp8NFHNXsuD4/SFZZ//atcJXRenirehoqJxRtvwNmzcNdd5W8/tXAhAC1Hj8beyalmsVSi5ciR2Lu4cPHkSVL37gXUwspjj6kJ3CCJhRBCiMZNEgshGpJly1QFcUAAjBpVt6+Vl6fezX/xheo8BfD44+qdPqhOTz/8ULPnfPRRtbdo505Yvrzk5iNH1Jt4b2/VPfdyJpPa+aUzFxYSX9yDNnTs2JrFUAVHDw9CRowA1KoFqNdctAhOnFD3kc5QQgghGjNJLIRoSL75Rh3vvBPqeqLoSy/B7t2qNZKeTAC8+GLpysM996hkp7r8/NQKCJSbFF5VR6iLFyt/I39+1y7yMzJw8vbGPyqq+q9/FXpnqdN//klRfj6gOu+CikvvDCWEEEI0RpJYCNFQpKfD/PnqfPLkun2tVatKO0/Nng2BgaXXTCbVFWriRLU16+abYevW6j/3U0+p51i5sqRDVFWF23fcodq7Fi8glDi7Zg0AwYMHY+dgua7azfr0wcXXl/yMDM4V/0x6YuHmpo5SwC2EENdO0zTmzp3L3XffTZ8+fejcuTMjR47kzTffJCUlxejwqjRz5kyGVTGE9fnnnyc8PPyKX9cqOzub7777rtxr3X333df8fLUliYUQDcVvv6ntSZ07Q2Rk3b1OWppKXDQNHnpIza+4nJ0dfP216vCUlQVjx8Lhw9V7/tBQ0H85F9eLVJZYmM2q1WxKCgQHl38KfRtUiyFDavCDXZ2dvT0thg8HIK54q5aeWOiNrKTOQgghro3ZbOaRRx7hrbfeYujQocyZM4dly5bx0ksvsXfvXiZMmEBqaqrRYdbYiy++yIYNG0q+AP7v//6vwm3XYvbs2cyaNctSodaaJBZCNBR6J6WJE69thkR1aBpMm6aqpdu3L121qIyTk0p2evSA1FTVlvb8+eq9zksvqZ/nb38DKu8IdeCAWqRxc4Nu3Upvv3jyJJdOncLOwYGgy1tFWUDIyJEAxK9ahbmoiOholUfl5qrrklgIIcS1+eqrr1i7di1ffvklU6dOpV27dgQHBzN48GC++uorHB0drepNdHV5enri7+9f8lXVbdfi8i6FRpPEQoiG4NIlWLFCnY8fX3evk5iopms7OKjZFe7uV76/p6eqbm7dGo4fVy1w9XfgVzJkiPo5nJzIzVUPBbUYo9u4UR379ClfTqKvVgT06oWjh0e1f7TqatazJ07e3uSlpZGyfTseHlB2FVu2QgkhRM1pmsa3337LjTfeSKfL970CLi4ufPPNNzz55JMAxMfHEx4ezqeffkr//v0ZPnw4mZmZpKenM336dAYPHkzXrl2ZOHEiMTExJc9T2Zaly28LDw/n119/5d5776Vr164MGDCAjz/+uNxjfvrpJ0aOHEnXrl2ZNm0aGRkZtfr5586dy8iRI/nHP/5BdHQ0Dz/8MDExMYSHhxMfH19yv7K3zZw5k48//pizZ8+Wu19BQQFvv/02ffr0ITIykocffpjz1f1gr5YksRCiIViyBPLzoV076Nix7l4nOFgN4PvhB7USUR0BASq58PZW2cBDD9WoddKhQ2rbU9Omqp5Cp68cX74okbBuHaCmbdcFO0dHWhT/A6Rvh+revTQ26QwlhLAWmqaRlZVlyFdNP0mPj4/n7Nmz9OvXr8r7NG/eHKfL2of//vvvfP3118yYMQNXV1emTp3Ktm3bePfdd5k7dy7t27fnvvvuY8+ePTWK5+2332b8+PEsWrSISZMmMXPmTGJjYwH4448/eP3117n33nuZP38+3bt3L1fncK3OnDlDcnIy8+bN46mnnrrq/adOncrUqVMJDAxkw4YNBAUFAbBz504uXrzI999/z6effsquXbt4p0xDlLokiYUQDcG8eeo4blzdbYPSBQTALbfU7DEdO6ptUfb2qnPVzJlXf0xuLrz2GqE3dcOJvAodofQVi7Kz7wqysji/cycAwRYYilcVfTtU3IoVaGYzX36pWs6aTGrXl3SGEkIYTdM0BgwYgIeHhyFfAwcOrFFyoX+i3rRp03K3T5s2jaioqJKv66+/vtz1O++8k7Zt29KlSxc2bNjA/v37ef/99+nVqxdt27Zl+vTptGvXrsZbqMaNG8dNN91ESEgI06ZNw8vLix07dgAwZ84cxo4dy1133UXr1q158MEHGTp0aI2evyoPP/wwISEhtGvX7qr3dXd3x83NDXt7e/z9/bEv7rvu7+/PG2+8QVhYGL1792bs2LHs0/cU1zFJLISwdfn5akUAVGJRF95/H378sXbPMXw4vPuuOn/6abWl6kqcnODzz2lyZg9DWV2uvuLsWTh5UtU29OlTenvy9u2YCwtxb94cj5YtaxfvFQT27YuDmxs5yclcOHgQR0dV6xEWpq7LdighhDUw1fUHTRbUpEkTgApbiqZPn868efOYN28eEyZMICcnp9z10NDQkvMjR47g6elJ+/btS24zmUz06NGDI0eO1CieNm3alPve09OTgoKCktfp0qVLuetRFmpt3qpVq1o/R8uWLbGzK32L7+3tTW51tiFbgOX6MAohjLF2LWRkqL04vXtb/vlXr4Znn1X7kVq0KL9EUFNPPgnbt6v6jDvuUHMwqipas7ODm26CTz5hHPMo7DS65JLJpEJKTgYvr9KHJG3aBKg3/nX5D6q9kxNB/foRt2IFZ9eupWlx1tOpk6oH2b8fLPThlRBCXBOTycT69evJzs425PXd3Nxq9Hs4JCQEf39/YmJiGFtmsGmzMntgvb29KzzOxcWl5LyqFRJN03C4QuvxwsLCCrddvuXq8uc3m83lrjlaaHZU2Z+nMkV6C8IrsC87MbaeyYqFELZOH+Jwww3lx09bwrlzatie2QxTptQuqQCVEXz2mdoalZionvNKS+XFKzA3MZ9OHUt/iQcHw9tvw5dflr970ubNgEos6lpwcQ2HPjPjnntKh4XLioUQwhqYTCbc3d0N+arphzv29vZMnjyZefPmcejQoUrvk5iYeMXnCA8P59KlS+VWJzRNY/v27bRt2xZQCUBWVla5x50+fbpGsXbs2LFkW5Ru7969NXqO6tCTlczMzJLbThXPd9JZ26qUJBZC2Dp9snWZT3gsoqgIJk2CpCT1UfxlHTGumZub2lbl7Ky2cH30UZV3ze41hAy8CCKJyLyYKu8HkJOSQsaxY2AyEVh2f1QdCR44EIC0/fvJSUkhPR30FXppOSuEEDV3//33M3ToUO68807+97//cejQIeLj41m1ahVTp07lt99+o88Vfr8PGDCAjh078swzz7B161aOHz/O66+/zpEjR7jnnnsAiIyMJD09nVmzZhEfH8+PP/7IuuKmH9X14IMPsnz5cr744gtOnTrFnDlzWLp0aa1+9sq0b98eNzc3PvvsM86cOcP69ev58rJP1Nzc3MjIyODkyZMlW7WMJImFELbs1Ck4ckStVFQx8fOa/etfqoWtmxv8/HPpaGlL6Nq1dAbG88/D0aOV3u3gcScWoQr1mqydB0B2NixdChcvlr+vvlrRNCICZx8fy8VaBVc/P3yL99gmrFtH2e210hlKCCFqzs7OjhkzZvDmm2+ybds2pk6dyujRo3n99ddp2rQp3377Lf/85z+rfLy9vT2zZ88mIiKCRx99lAkTJnD06FG++uorIosHx/bp04fHHnuM2bNnc/3117Nx40Yef/zxGsU5ZMgQ3n//fX777TduuOEGli1bxtSpU2vzo1fKw8ODd999lwMHDjB27Fg+/PBDnnvuuXL3GTVqFP7+/tx4440csILlcpNmbZM1imVmZhIdHc327dvxqINe9MJ6ZWVllfw3z8zMxP1qsxIas88+U+1b+/cv7b9qCWvXqkTFbIavvlL7fCxN09Rk7pUrYfBgWLVK1VWU8c038Mc9P/Mzt6tWuocPs3KViREjoFUrVcCt2/zCC5xcsICI++8nshpt+ixh73//y97//IcWw4dzYfhH5Wrnz51TDbSEEKIu5ebmcvLkSVq3bn3V/flCVMaSf4dkxUIIW6Zvgxo1yrLPu2OHSiruvbdukgpQ9Raff65WQtauVUnSZfbvhyWMJs2jpWr/lJtbkj9dXkZxrri/eLO6KGCvgj4rI2nTJrp2yi93TbZDCSGEaGwksRDCVhUWqk/7Aa67zrLP/dRT8McflqurqErr1qAvaz//PKSklLu8fz9cwouf3j6lli9cXUsSi7J15Jlnz5KdmIjJwQH/4uXu+tCkQwdcfH0pzMnBLW03xd0SS2IXQgghGhNJLISwVbGxkJ4OPj7Vn4J9JWYzlO2Ucf31UB/b0B59FKKiVMvcl18ud0mf59Ops+p6UVgIW7ao28pO3E7etg1Q9RUOlqwFuQqTnR3NigsJkzZvKldnYQVbXYUQQoh6JYmFELZK3wY1YkTt28xqmhpaN3Cg6gJVn+zt4cMP1fnnn6vZFkBmJugdADt1Asxmjv66m5zMQry8oHPn0qfQE4sASyRYNRRUvCcrafNmBg1SnXRBViyEEEI0PpJYCGGrVq1Sx5Eja/9c77+v3tzv3AnFQ+bq1cCBcOutatXkySdB00o+8Q8MBN+mGnTqRMc7IunBNvr1K59LlSQWPXvWe+iB/foBqu3s80+k8+236nbpDCWEEKKxkcRCCFuUk1O6J6i2I56/+AL+/nd1/v77cPPNtXu+a/Xuu2q2xZo1sHJlySf+nTqhCr2Lp1uPZHm5bVDZyclknjmDyc4O/7J7keqJW7NmeIWFoZnNnIuJoUMHFW5qqpoMLoQQ9cFKm3wKG2DJvzuSWAhhi2JiID8fgoKgeJroNfnuO3jwQXX+t7+p7VBGCQ0tjeXVV9m/T/2iK84nSlZm/tp2OePHlz5MX63w6dABJ0/P+oq2nKDiVYukzZtxc1OtcEHqLIQQdc/R0RGTyVRhmrQQ1ZWdnQ2UTvquDYdaP4MQov6tXauOgwerj8evxdy5qpWspsHDD8M771guvmv1/POqzmLTJpzzlwOjKiQWwac2E9zyEqCSiJJtUNHR9R9vscB+/Tj87bckbt7M+PGl8zX276/9gpIQQlyJvb093t7epKSkkJeXh5eXFw4ODpiu9d8G0WhomkZ2djbJycn4+PhgX9t6TSSxEMI2rVmjjsVzFGosPx+efRaKitSsipkzrz1BsaTgYJg2DWbM4OY9r/JPRtK5uCMUYWGqPe3Jk6oOpLjF7vmdOwFjE4uAHj2wc3AgKz6e0B5ngJaArFgIIepHYGAgrq6uJCcnc/HiRaPDETbGx8eHwMBAizyXJBZC2Jq8vNL6isGDr+05nJxgxQo1p+LttytMvDbUc8+hffop0TlbGMQ6IiLUz/j119Cn2SDCT56E9evhuuvIv3iR9KNHAfCrx/kVl3N0d8cvMpLkbdvo6rkZPbGQzlBCiPpgMpnw8fHB29uboqIiCgsLjQ5J2AhHR0eLrFToJLEQwtZs3Qq5udCsGYSH1+yxOTng6qrOW7WC996zeHi1FhjIuesmEzjvU15w+Tc+PiqxeP996Ll3ILP4GtatA+D8nj2gaXiEhODq729k1AT27Uvytm00zdgE3A6UdoayhsUgIUTDZzKZcHBwwMFB3t4JY1jRx5RCiGrRt0HVtL5i3z5o0wbmzauLqCxqffcnARiVuwCOHSM9XYW/kuFkPvkivPoqULoNys+AblCX09vO5h+JwQ71aWFqaoVh4kIIIUSDJYmFELam+NP6Gm2DOn4cRo2CxES1SmE2101sFrIxtQOLGIsdGnz0EZs3q0/+Hdu2wuPf/4DhwwFI2bULAH8Dt0HpmnbqhKOXF4WZl+gfVroHSrZDCSGEaCwksRDClhQWltZXDBxYvcckJKiOSomJ0KULLFhgXTUVldi3D/7NU+qb2bPZvjIdoNz8CnNhIanFU7qNmF9xOTt7ewJ79wagf3DpkEFJLIQQQjQW1v3uQghR3r59kJkJXl4QEXH1+2dnww03qE5KbdvCsmXQtGndx1lL+ran7DadISsL9/nfAzBgAOrnX7iQ9HfeoTAnB0cPD7zatDE24GL6dqgI183oiyjSGUoIIURjIYmFELZkU/En4X36wNW6OGgaTJkCO3aAn59KKizUTq4upaTAuXMAJuwfvB+AwcdnAcUrFseOwY03kvLxxwD4deuGnQU7WtRGYJ8+ADie280zj6mBQ7JiIYQQorGQxEIIW6InFsWfjF/R11/Dzz+Do6Mahte6dd3GZiH6G/GwMHC+bxJmRye6azsY4r2TDh1Q27m8vDhfvJ3LyDazl/MICcEtMBBzYSGh9ruA0s5QQgghREMniYUQtmTjRnUsW2xQlUmT4OWX4b//rX49hhXQE4vOnQFfX+xuHg/AT9fNUk2w7O2hb19Si9vm+nXrZkyglTCZTDTr1QsAx8RYTCbpDCWEEKLxkMRCCFuRkACnTqnC6+I3r1fk4ACvvw7331/noVnSvn3q2KlT8Q333QdAwNJv1RwOIDcykkwnJwB8O3eu7xCvKKD4v83677aWrFTIdighhBCNgSQWQtiKzZvVsXgrUJXmzYP8/HoJqS7oiUVJvjB8OISGQkYGzJ8PQGpAAACemoaTt7cBUVZNX7EI0vbhbMoCJLEQQgjROEhiIYStqE59xZIlMH489OwJeXn1E5cFaVr5xOLwYeg/0I71Le9SN/74IwCpRUUA+GZkQFqaEaFWyaN5c9yDg7HTCmnvtguQzlBCCCEaB0kshLAVV0sssrPh4YfV+bBh4OxcP3FZUEICpKerMorwcFi7Vv3Ys7MnqjssXgwZGaQeOwaAX04ObN1qXMBVCOjZE4CO7rGArFgIIYRoHCSxEMIW5OWptrEAfftWfp833lDzKlq0ULUVNkhfrWjfXuVF69er70PGdFZzO/Lz0X7/ndS9ewHwnTNHTRS3Mvp2qAg3lfRIZyghhBCNgSQWQtiCPXtU3YSvr+rDerlTp+CDD9T5zJng6Vmv4VmK/sm+Xri9bp06Dhxkgolq1eLSt9+Sn5GBnZMTPiNHWuUUcX3FIsxV1VlIZyghhBCNgfX9iyyEqCgmRh179UL1XL3MK6+oxGPYMLjppvqNzYLK1lecOaO+irvLwu23A5C6fTsATTp2xL64M5S18WjeHPfmzbE3FRHuplaaZDuUEEKIhk4SCyFsgV5HUFmb2T174Ntv1flbb1WeeNiIsomFvg2qe3fw8EDtj+rendTi2hG/Ll3gk0/USsbx48YEfAX6dqiBoVJnIYQQonGQxEIIW6CvWPTuXfGak5OqM7j1VtUNykaZzeWH4+nboAYNKnOnCRNIdXMDwLdrV/juO/jpp9LBgVZE3w4V2UQlFtIZSgghREPnYHQAQoiruHABjhxR55UlDh06qDazubn1G5eFnTqlGls5O0ObNuDuDn5+5YeGF40Zw4U5cwDwbdtW/Xls3Ajbt8PkycYEXoVmxf+tXNP342KXxf797gZHJETVLuZdZP3p9cScjeH4heO4Objh7eLNkFZDGBk2EmcH2+syJ4Sof5JYCGHtYtUn3rRpo95pV8XFpX7iqSP6akWHDmpo+AcfwPvvq5UM3QV7e8x2djgXFuKxfz9ER6sL27bVf8BX4R4cjEdICJlxcYS77WDfvoFomk3vVBMN0On008zYMoMvdn5BZn5mhevvb34fL2cvHop+iFcHv4q7kyTIQoiqyVYoIaxdVfUVp07Bs8/C2bP1HlJdqDBxG/Um3N6+9PuSNrM5OZgWLixNLHbtguKhedakZJ6F21bS0qQzlLAeheZC/rX+X7T/uD0zYmaQmZ9JWJMw7ul2D++MeIc3h73JtOhpBHsGczHvIu9uepfOn3Rm5YmVRocuhLBismIhhLWrqr7igw9Ua9l9++DPP+s/Lgsrm1ikp4O3d8VP91P37AFUYsEff8Cnn6rK7sxMOHSotE+tlWjWqxcn5s4lwr10nkVAgMFBiUbv5IWT3PrLrWxPVB3WhrQawvP9n2dUm1GYLvuf7j/af1h4eCGPLX6MU+mnuO7b6/hm/Dfc2eVOI0IXQlg5WbEQwpppWuUrFhcuwKxZ6vzpp+s/rjpQNrEYPlzN+bu8Jvu8nlg4OMD58+rPJipKXbTC7VABPXoA0NrlAK52mdIZShhu45mN9PqiF9sTt9PEpQlfj/uaVZNXcV3b6yokFQB2Jjtu6nATBx45wF1d7qJIK2LS3El8seMLA6IXQlg7SSyEsGbx8ZCcrPYDRUaW3v7tt6rSuUsX9S7cxuXnw8GD6rxVK7WzKSEBQkNL75OXnk7mmTMA+A4erG784w+1HcreXj3AyrgHBUHTEOxMZtq77ZDOUMJQcw/OZdg3wziffZ7uQd3Z89c9TO42udKE4nIeTh58M/4b/trjr2hoPLDwARYdWVQPUQshbIlshRLCmu1Qw9WIiABXV3WuafDZZ+r8oYcaRDXwwYNQUAA+PiqXMpvVgPEWLUrvo9dXeIaG4nzDDarN7NKlsHIl/POfpX8+Vsavey/Or4gjwm0r+/YNuvoDhKgDcw/O5fZfb6fQXMj4DuOZM34O7k7uHEg5wOqTqzlx4QQX8y5iMpno4NeBrs26Mjh0MI72jiXPYWey4z9j/4NZM/Pp9k+Z9Pskdjy4g9ZNWhv4kwkhrIkkFkJYs+Ip0yVFygCbN6t9Q66ucNddxsRlYbt3q2PXrrB6tTofdNl78JLC7a5dYeRIdeOuXSojadq0fgK9Bm2G9+L8it/o6L6VxXuRzlCi3i04vKAkqbiry118Pe5r7O1UV4QbfriBExdOVPq4Fl4tOPzoYdwc3UpuM5lMfDTmI3af282W+C1M+HkCG6duxNXROhN7IUT9ksRCCGtWWWKhr1bcfrv6iL8B0BOLbt3UAgRU3OFVklh06aIqoKOiYOdOWL4cJk2qx2hrJqi36gzV2uUgeRcvkZLiKQXcot5sid9SklTc1uk2BocOxs5Uugv6+nbXc/D8QSKbReLj4kNeUR4Hzx9k3el1RAVGlUsqdE72Tvxy6y9EfRrFzqSdvLn+Tf4x7B/1+WMJIayUJBZCWCtNqzyx8PUFT0948EFj4qoDemLRti18/LE6Hzas9LqmaaUdobp0UTded51KLJYtg9RU+P57eOIJuNO6utW4NWuGyTcUu9TTdHDbzv79QySxEPXieNpxbvjhBnILc+kV3IvNcZv5ef/P+Lj4cGunWwH4aMxHlT42vyif1OzUku8TLiUwY8sM/jX8X9jb2dPCqwWf/eUzbv75Zt7Z+A53d72bcL/wevm5hBDWS4q3hbBWCQlw7hzY2amP8nXvvw+JidCnj3GxWZCmlSYWubnq+44dITi49D6ZcXHkpadj5+hIkw4d1I2jRqnjsmVqpsfWraWtea1M2NDieRbusdIZStSLi3kXuf776zmffZ5m7s3YmrCVuItxtPRuiYeTx1Uf72TvRJBnUMn3D/3xEO9uepcp86dQaC4EYFyHcVzf7noKzAU88ucjaJpWZz+PEMI2SGIhhLXSC7c7dgS3y7YjuLs3mI36iYmqc6ydHQwdCk89BVOmlL+PvlrRpGNH7J2c1I39+qk/h3PnoEkTdZueoViZZsWD8iLctkpiIeqcpmlMmT+Fw6mHcbJ34lzWOQAe6fkIBx85yJh2Y2r8nHd3vRt7kz1z9szh7t/vxqyZS+otXBxcWHlyJT/v/9nSP4oQwsZIYiGEtbp8G9T58xAbqz7Sb0D0XCA8HHr2VHP//v738vcpV1+hc3aGIUOK75Ba+mRW+Ofj31PNIAl1OcTBXRcNjkY0dO9uepe5B+cCakuTt7M3v932Gx+P/bjSmonquK3Tbfx626842jny474f+ef6fwIQ1iSMFwa8AMDLq18uWc0QQjROklgIYa0uTyy+/VYNyZs40biY6kDZwu2qnC/bEaosfTvUgQPg4KBGdsfFWT7IWnJvFsC5wlbYmczkH9tujbmPaCA2ntnICyvVG30TJlr5tGLzfZu5uePNtX7ucR3G8elfPgXgldWvsPTYUgCe7vs0fm5+HE07yvd7v6/16wghbJckFkJYq8sTi++L/8EeONCYeOqInlj4+cGKFZCTU/56UX4+F4qn5/mVXbEAtXcKYNMm0GsvrHQ71AVPtR0qzD6W4jl/QlhURm4Gd829C7NmZlLXSfww4Qe23LeFjv4dLfYaU6Km8GD3B9HQuHPuncRfjMfDyYO/91PLjG+se0NWLYRoxCSxEMIaJSaqLzs7NXH72DG1DcrODm691ejoLErPA/bvV+Mp3nij/PX0I0cw5+fj5O2NR8uW5S926qS6ZGVnQ/Pm5Z/Qyji2U9uhOrptpXgBRgiL0TSNO367g9MZpwlrEsZ/xv6H2zvfTjOPZhZ/rY/GfER0UDSeTp4kXFIT7x/u+TB+bn4cSzvGd3u+s/hrCiFsgyQWQlgjfbWiQwdVoDxX7Zdm2DBoZvk3CkbJyYHDh9W5XtRcYX5FmTazpssL1u3sYPDg0vNWrUAv7rYyLfqpFYtQl0Psi80wOBrR0MzcOpPFxxYD8P6o9/Fy9qqz13J2cOb3239n/8P76dVcJcweTh482+9ZAN7a+JZ0iBKikZLEQghrpHeE6t5dHefNU8fx4w0Jp67s3w9ms2rqlJwMLi7Qv3/5+1RZX6HTt0MVFsLJk/Dss3UY8bXr2s+fhLzW2Jk0kmK3Gx2OaED2J+/nqaVPARDgHsDg0MF1/poh3iG4O7mXu21aj2l4Only6PwhVpxYUecxCCGsjyQWQlijsvUVSUmwZYv6/sYbjYupDui7lvz91bF/f5VclKWvWPhVlVjonaE2boT8fMsHaSEREXAgW326az651eBoREORV5jH4K8GY9bMONo5EnNfDE1cm9Tb65s1M5/EfsK7G9/F09mTKZGqV/RHWysfvCeEaNgksRDCGpVNLBYtUi1Ue/SAFi2MjcvC9MSiqEgdL98GlZeezqVTp4DLWs2WFRGhKr+zs0vb8RZaX/Goqyucd1fboQLzY8nLMzggYfM0TWPsd2NJzVHtln+65SdaNWlVrzEsP76ch/98mNfXvU5KVgqP9HoEgEVHFnE87Xi9xiKEMJ4kFkJYm3Pn4OxZNQAvKgomT4ZVq+DNN42OzOL0xCJB1X9WrK8o3gblGRqKs49P5U9Sts7ihRdUkvHFF5YP1gKe+qAHAC2dD7MvNt3YYITNe3/z+6w6tQqAu7rcxfiO9b9VclSbUXQP6k5mfiZvb3yb9r7tGdN2DBoa/4n9T73HI4QwliQWQlgbfbUiPBw8PMDRUdUR6DMbGghNK00scnJUcye9s64u9Wr1FTq9zuLMGUhLs9rOUENv8OeCfRh2Jo0DS3cYHY6wYflF+byxTrVQC3APYNaNswyJw2Qy8Y+h/wDgP7H/IeFSAo/1egyA2Ttnk1OQc6WHCyEaGEkshLA2l8+vaKBOn4aMDJU3bd8Os2eDvX35+5wvThCqrK/QDRigjufOqaOVJhYAeYFqO1TqjhiDIxG2LPZsLBfz1BT3n2/5GWcHZ8NiGd12NP1C+pFbmMtbG97iurbXEeodSkZeBvMPzzcsLiFE/ZPEQghrU7Yj1GuvwRNPlPZibUD09/4dO6of9fK6dE3Tqr9i0bkzeHlBbq76fs8e1W7KyuTlwRmTKuB2OBtrcDTCVhWZi3jkT1XLcF/UfQxuVfddoK7EZDIxfch0AGbtnEVGbgaTu00G4KtdXxkYmRCivkliIYS10VcsundXtQIffaRqLhoYPbHo1q3y65fOnCE/IwM7Jyd82re/8pPZ20OfPurcwQGysuDECcsFayGOjvD9BrVi4ZN/hLz0dGMDEjbn+73f88DCB9h9bjc+Lj68NeIto0MCYHjr4XQJ6EJ2QTZf7PiCe7rdA8DyE8s5e7Hh/f4SQlROEgshrElKCsTFqXMXF5VQuLjAwIHGxlUHtm1Tx/37Ydmyitf1NrNNIyKwr87QO30AhqenOlrhdig7O2jZ0Zf43DbYmTSOrdpmdEjChpxKP8VDCx/iy11fAvDq4Ffxc/MzOCrFZDLxdN+n+Uv7v9A3pC9tmrZhYMuBmDUzc/bMMTo8IUQ9kcRCCGuya5c6tm2r5jKAmtPg6mpURHVGTyx27Cgd01GWXl9x1W1QOr3OQp9lUZyYWJuoKDiYrVYtjq6U7VCiesyamSnzp5BZkAlA+6btebjnwwZHVd69kfey8I6FDGg5oOR7gK93fy2TuIVoJCSxEMKa6IlFZCQsWaLOR482Kpo6k5AAiYml3//lLxXvc9XBeJfr3VtticrKUklG69YWiNTyunSBA1mqzuLiHhmUJ6rn8+2fs+bUmpLvP7juA5zsq7GSZ6BbI27F1cGVQ+cPsT1Rps0L0RhIYiGENdG370REwLp16vy664yLp45sK7MDKCREfYpfVmFuLhcOHwZqsGLh7q4SMoCHH4Z77611nHWhSxc4lK3mWTikHSH3wgWDIxLWLuFSAs+ueLbk+1FtRjG23VgDI7qyY2nHeGnVS8RfjOcv7dWnBr/s/8XgqIQQ9UESCyGsiZ5YmExqS09oqJpn0cDEltkBdOON6sct68LBg2iFhbj4+uIeHFz9J9brLPRtZFaoSxe4WORLXG5bAFK2SZ2FuLJH/3y0pLWsncmOD0Z9gOny/2msyN+W/Y0317/JrJ2zuDXiVgB+OfCLbIcSohGQxEIIa5GbCwcPqnNfX2jZUq1WWPEbiGt1eWJxOX0blG/XrjV7A1U2sbhwQQ3KsDJ+fuDvDwey1XaopK1SZyGqtiNxB78f+r3k+2nR0+gU0MnAiK7uvqj7APhm9zeMDBuJm6MbJ9NPynYoIRoBSSyEsBYHDkBRETRtCo8+CqdOwYcfGh2VxWkaxBTPhnN3V7XplztfPL+i2vUVur591XH3bvXn+M031x5oHVq9Go7lqwLu+A1SZyGq1j2oO68Nfg0Ab2dvpg+dbmxA1TCm3RgCPQJJyU5h9anVXN/uekC2QwnRGEhiIYS10Au3u3VTqxQmk2o128CcPg3p6erHGzMGKuskW3bFokZatICgIJW9gFW2nAXo1AlMrVRikXPmKLlpaQZHJKxVkbmIH/f/CMAzfZ+xmvayV+Jg58DkrmpA3uxds2U7lBCNiCQWQliLsoXbRUXGxlKH9JKCqCj49tuK13POnyfr7FkwmfDt3LlmT24yqe5Qun37rj3QOtaxexPO5KrBf8lSZyEuk3gpkdPpp/l+7/ccOn+Ipq5NeaLPE0aHVW1ToqYA8OfRP4kOjsbVwVW2QwnRCEhiIYS10BOLgwfVJvwvvjA2njqiv4fu0QOcnSte11crvNu0wdHDo+YvUDax2L8fzOZriLJupaXBkSNwMEutWiTHSp2FKO+FlS/Q4eMO/G353wD4e7+/4+XsZXBU1dfBrwM9g3ti1swsPrq4pIvV/EPzDY5MCFGXJLEQwhpoWulWqBMnVOGxn/VvebgWq1erY48elV8/f63boHRlE4vMTDhz5tqepw65ucGGDXCgeFBeUmUTAkWjtTtpN9/s/obcolySs5Lxd/Pn0V6PGh1WjU3sPBFvZ2+yCrK4MVx1aVhwZIHBUQkh6pKD0QEIIVBvfjMywNFRFW2bTDB4sNFRWVxRUemKhUMVv31SduwAwP/y4RbV1aOH+vPT93Lv2wetWl3bc9URFxdo1w4OHu2JWTNx8cQJspOTcQsIMDo0YQWeX/k8Ghpujm5kF2TzXP/n8HC6htU7gz0U/RCP9HwEZwdnzmefx85kx55zezidfppQn1CjwxNC1AFZsRDCGuirFUFB6hgZCU2aGBVNnVmwoHRn0vjxFa8X5eeTWtwRyr9792t7EU9PVR2ts9I6i27dIMvsw6ncCADO6a2yRKO28sRKlhxbgp3JjuyCbAI9Avlrz78aHdY1cXdyx9lB7Xf0c/OjX0g/AP448oeRYQkh6pAkFkJYA72+Qi86GDbMuFjqkF420rQp+PhUvJ62fz/m/HycmzbFM7QWn2jq26GioiA6+tqfpw7pO732Z6lYz8l2qEZP0zReXPUiAG6ObgD834D/Kzm3VZqmcSDlADe2l+1QQjR0klgIYQ30xCI9XR0b6DaoNWvUuT5u4nIl26C6d6/dZGE9sfDxgZEjr/156lCXLuq4P6sPoOospBVn47b42GJizsbgaOdIZn4mLbxa8ED0A0aHVSu5hbl0+E8HOv23E92D1CrkmlNruJR3yeDIhBB1QRILIayBvhUqJUUd9QnSDciqVZCdrc4r2wYFFqiv0OmJRWysVXaFgtLE4nB2dwo1R7KTkrhkhYXmov6cuHACZ3vnkhWK5/o/h4uDbc+ycXFwIdgzGFBF6e2atiO/KJ9lx5cZHJkQoi5IYiGE0S5eVJ2gAJ5+Gu69V+0VamDmzCk979On4nXNbCZl506gFvUVuogIVSGdmQm//666bFmZ0FA1eTxfc+VIdiQg26Eau0d7PcpHYz4iIy8DX1dfpkZNNTokixgXPg5QW6BuaH8DAH8clToLIRoiSSyEMFpxe1WaN4f334cvvzQ2njqQnw/z5qlzV1fo0KHifS6eOEF+Rgb2Li407dixdi/o4KCqowFuuaW0x60VsbODhAQICSmts5C2s42bpml8vuNzAB7p+YjN11bobupwEwDrz6ynf4hajV12fJls/ROiAZLEQgij6fUVkZGGhlGXnJzgpZfUeZ8+YG9f8T76Nii/rl2xc3Ss/YuWLdq20s5QXl6qvlyvs0jeuhXNSrduibqzI3EHW89uZe3ptWxL2IaLg4tNzq2oSiufVnRt1hWzZiYtNw1XB1cSLiWwL9k6/78UQlw7SSyEMJqeWLi5qZHMDdTBg+pYVeF2sqW2QenKPo+VJhagEosTOZ0ptHMjLz2dC4cPGx2SqGfPLn+W3l/05pE/HwFgSuQU/N39DY7Ksm4KV6sWi48tZkirIQAsPb7UwIiEEHVBEgshjKYXbv/yS/k3ww2Evtth82Z17Nev8vuV7QhlEWVXLIpnY1ib+Hg126MIR05pagq31Fk0LrFnY1l5ciX2JnsOpBzAhImn+z5tdFgWpycWS48tZXjYcACWHFtiZEhCiDogiYUQRiosLP+mtwF2g3rpJRgyBPQP4isr3M4+d46s+HhMdnb46bURtdWpk9qDBXDkCOTlWeZ5LahJEyheqGHrOamzaIze3vg2oLYLAUyImEDbpm0NjKhudA/qznP9n+P3239ndJvRgKq5yMrPMjgyIYQlSWIhhJGOHoXcXFXJCw0uscjPh1mzYO1a9X14OPj6Vryf3g3KJzwcRw8Py7y4o2PpFDqzuTSzsSLu7tC+vTrfc6m4zmL7dory8w2MStSXY2nHmHtwLgCnM04D8Pd+fzcypDpjMpl4a8RbjGwzkgj/CEK9Q8kvymft6bVGhyaEsCBJLIQwkl5foQ+Dq2qfkI365Rc4dw70XKE6g/Esqux2qP37LfvcFqKP7IjPa4fm2pSinBxSrXTrlrCsj2I+QkOjtU9rCs2FDAodRK/mvYwOq86ZTCaua3MdINuhhGhoJLEQwkh6YlFUpIq3O3c2Nh4LmzlTHf2L61Drrb5Cpz9fu3ZWW7+i7/zSsOO8h2yHaizSc9OZvXM2AMlZyUDDXa0oa+2ptfx92d/p1kz9xV9+YrnBEQkhLEkSCyGMpBduA/TooeYvNBCxsRATo3YknTunbqtsxaIgM5P04m1KtZ64fTl9xeL8+dI9R1ambJfhvZlqO1TSpk3GBCPqzdHUozRxbUKQRxBZBVm0adKGse3GGh1WnfvH+n/w3ub3SM9Nx4SJQ+cPkXApweiwhBAWIomFEEbSVywAevc2Lo468PHH6jh8OGRnq0LliIiK9zu/ezea2Yx7ixa4NWtm2SA6d1aZzYULcPq0ZZ/bQsomFkuPqMwrde9e8i9dMiYgUS96Nu/J8ceO08SlCQDTekzDztTw/0nWC7fXnVlH9yC1irjq5CojQxJCWFDD/y0mhLVKTobERFVf8d//wsSJRkdkMcnJ8OOP6lxfKOjfv7RGvdx9t20D6mAbFICzc+n2sg8/VBmOlQkMVDu17O0h/lJznIJaoRUVcW7rVqNDE3VsR9IODpw/gLO9M1MipxgdTr0Y3VYlFmtPr2Vw6GBAEgshGhJJLIQwir5a0bYt/PWvVlsDcC28veGzz2DqVDhzRt02cGDl902KiQEgsFcdFa3q26FmzIADB+rmNWrBZFLdcAer91hkNVOFKEkbNxoYlahL60+vp9BcyCfbPgFgYueJ+LpV0i6tAYrwjyDEK4TcwtyS1ZqVJ1ei6QNvhBA2TRILIYyiJxaWmttgRZyd4Z574PPPYf16dVtliUVBZiZpxVOxm9XVVrCynaGseAJ3TzUfj8N5KrFIlDqLBulY2jEGfTWIlv9uyU/7fgLg4Z4PGxxV/TGZTCWrFmcvncXRzpEzGWc4ceGEwZEJISxBEgshjKIXbufkWOWMBUs4dAhSU8HVtfz7e13y9u1oRUV4hITgHhxcN0GUXQmy4jauemKx8ngvTA4OZMbFcUlf7hENxqfbPgXAy9mLvKI8ooOi6Rnc0+Co6pfeanbt6bX0aaEaFqw8udLIkIQQFiKJhRBG0VcsFi2C+fONjcVCNA3Gj4ePPlLlDPpqRZ8+pUOwyzpXvA2qzlYrQA3J04s7tm+vu9ephfh4ePppdb59rztNu0YCkLR5s3FBCYvLLczly11fAqrdLKjVCpM+x6aRGNp6KCZMpOakMqDlAEDqLIRoKCSxEMIIublw8GDp9w2kI9SKFTBvHrzwgkos1q1Tt1dVX1EviYWLC4SFqXMr3QoVGAhJSeq8qAjMLWU7VEP064FfSc1Jxc/Vj3NZ5/Bx8WFi54bTtKG6mro25fCjh0l6Jqmkxe6qk6ukzkKIBkASCyGMcOCAegcJqnq3sn1CNujtt9Xx/vvB1xfWrFHfDxpU8b556elcOHQIgGZ1Vbit0xOX1FTIyKjb17oGDg5qYUV30qQSi3NbtmAuLDQoKmFp+kC8pm5NAZgSOQU3RzcjQzJMO992mEwmejXvhYuDCynZKRxObZhbQoVoTCSxEMIIZedXdOkCHh7GxWIhsbGwcqV6k/z006q+IiFBFXJXNnFbb6fq3bYtrn5+dRtc2cl8VtgZCsrPs9h8KgInb28KMjNJ3bPHsJiE5ZxKP8XqU6sBNRwP1OyKxs7RzpE+zVWdxbrT6wyORghRW5JYCGGEshO3G8g2KH214s47ITRUJRmg5le4ula8vz5dulmfPnUfnF7A7eMD4eF1/3rXoGxiEbvNnsDiZEi2QzUMi44sAqC1T2s0NIa1HkZ7X+ucBl8fNE3jvvn3EfxBMBH+anLm+jPrDY5KCFFbklgIYYQGNnH7yBGYO1edP/usOuqJxYgRFe+vaRoJGzYAEDxgQN0H2K2bKuBOT1f1LVaobGJx+DB4R0mdRUPySK9H2P7gdnIL1d+/qZFTDY7IWCaTiZPpJ0nKTCqZOC4rFkLYPkkshKhvmtbgVizefVf9WDfcAJ06QWEhrFa7Phg+vOL9L544QXZiInZOTgT06FH3Abq5la5UlE3qrEjXrqrcRpfkolYs0vbuJd8K60JEzV3Mu0hiZiJezl6M7zje6HAMNyJMfepwOuM09iZ7zmSc4XT6aYOjEkLUhiQWQtS3M2dUAbGjIyxfDh07Gh1Rrd1/v2oz+9xz6vsdO9SP6O1deV16YvFU6YAePXCobJ9UXejcWR0//rh+Xq+GPD3Vn2Hr1ur7HceD8QoLQzObS6aTC9tUUFQAUNJq9vZOtzfaou2yhrdWnzpsOLOB6CD1i0K2Qwlh2ySxEKK+6Z+Yd+yo9gnZ2xsbjwX07q22QvXvr77Xt0ENHVr5j5dYvA0qSH9AfYhQ+7j580+1JcoK/fYbTCuu542NhcDiqnc9ERO2JzM/kxb/bsGdv93Jrwd+BVQ3KAHRwdG4O7pzIfcCHfw7ALD+tCQWQtgySSyEqG/6Nqiym+obmBUr1LGybVCFubkkb9sGQFB91Ffoyra03b+//l63hvQwt24tTbwSN2yQHv826rcDv5GclczKkyvJLsgm3De8ZNp0Y+dg50D/lurvuIu9CwDrzkidhRC2TBILIeqbvmJx8iScPWtsLLX073/DY4/B6TLbonNyQP+AvbLC7eTYWIry8nALDMS7TZv6CRRUAbfOSussQHUfNpkgLg5MoT2xd3YmOymJjGPHjA5NXIOvdn8FgKuD2vJ3b+S9jW7S9pUMaqmG3JzLOgfAofOHSM5KNjIkIUQtSGIhRH3buVMd169XnYpsVG4uvPWWKllYX2b3wqZNkJcHwcGVd3Y9u3YtoFYr6vUNVnCwmsIN5QO2ItnZ0KKFKoQH2LHXlYCePQFIsNKYRdVOXDjBmlNrAFWgbGey4+6udxsblJUZ0moIPYJ70DO4J50DVB3UhjMbDI5KCHGtbPddjRC26OJFtVIBEBgIQUHGxlML33wDycnQsiXcfnvp7WW3QV2eN2iaxtnidlEthg6tp0iLmUzQqpU6t9IVCzc39eep27oVggcOBCBREgub883ubwA1uwJgVJtRNPdqbmRIVqd/y/7EPhDLi4NeZGBL9Xdd6iyEsF2SWAhRn/buLT0v/iTaFhUVwXvvqfOnn1YNrnRXml9x4eBBspOSsHd1rZ/BeJfT61pOW29Ly7JdtDZvhqDixCJlxw4KMjMNikrUlKZpJYlFRp5qFyxF21emJxZSZyGE7ZLEQoj6VHZ+hT4N2gYtWABHj0KTJnDffaW3X7gA27er88oKt+OLVyuC+vXDQd+WVJ8Gqf3cZGerYK1Q2cQiJgbcW4Ti0bIl5sJCkrZsMS4wUSNbz27lZPpJXOxdSMtJo4lLE24Mv9HosKxWVn4WzTyaAbAraRcX8y4aHJEQ4lpIYiFEfSq7BaeyAQ82QNPgnXfU+cMPg4dH6bU1a8BsVrUVzSvZ8XF21SrAgG1QOn2VxMtLDY6wQvpfC5MJLl2CAwdKt0NJnYXtCPEO4R9D/0GbpqpBwR2d78DFwYBk2gbExMfg87YP9y24j9Y+rTFrZjbFycR5IWyRJBZC1Ce9cBtsNrHYuBG2bAFnZ9URqqw//lDHUaMqPi4rIYELhw5hsrMjePDgug+0MhER4OCgal0SE42J4SqiotRRL+DevLlMnYW0nbUZwZ7BPNnnSU6lnwLg7m5StF2VTgGd0DSNU+mnSgflSZ2FEDZJEgsh6ktREezbp859fW22cLttWzVh+/HHoVmz0tuLimDhQnV+000VHxdXXHzhFxmJS9Om9RBpJZydoYMaxGWtBdze3tCuXen3W7ZAQE9pO2uLFh5ZSFZBFmFNwujdvLfR4VgtDycPooNVQuHt4g1InYUQtkoSCyHqy9GjqkermxscOVKxZZKNCAxUbWb17VC6mBhISVFvjPVShrLOLFkCQMvrrquHKK9Af9f+8cfGxnEFjz4K99yjzjdvBgcXFwKKJ+clrJM3XNbu8+2f8+O+H/l2z7eA2gYlsyuuTJ9ncSnvEqBqVHILc40MSQhxDSSxEKK+6IXbXbuCUZ/Y16H589Vx7NjyXaJAbYM6v2sXmEyEVLZPqj7pxR/F8zSs0eOPl3bdOnQI0tKkzsJWFJoLeXHVi9zx2x0sPb4UgDu73GlwVNZvUKhKLHYl7aKZezPyi/LZenarwVEJIWpKEgsh6ou+9absBGgbcukS3HYbLFlSuv+/LD2xqGwb1JllywAIiI7GLSCgDqOsBr1dVW6uesdupfz8ShdXYmIgeMAAAFJ27pS2s1Zszak1pGSn4O7oTqG5kK7NuhLhH2F0WFZvQMsBmDBxJO0IPYJ7AEgBtxA2SBILIeqLXri9YQNkZRkbyzWYMwd++QWeeqritcOH1ZejI4wZU/H66cWLAWg5enQdR1kNZedn6L1xrdCJE6VlOJs3g2eoajurSdtZq/bTvp8A8HZWtQJ3dpbViupo4tqELs26AODr5gvA5vjNRoYkhLgGklgIUV/0N7Fnz6o6CxuiafCf/6jzhx+uWB6yYIE6Dh2qOrmWlRkXR9q+fZjs7AgZObLug72awEBwclLn+jQ/KzR6NOjlFJuL318FFxevSJ2Fdcovyue3g78BkJipuo5N7DzRyJBsyhO9n+DD0R9yU7ha9twct1m6oAlhYySxEKI+JCfD+fPqvEcPmyvcXrtWzVNwd4fJkyte17dB3VjJ/K+TxT1om/XujaufXx1GWQP6UsBW693DffmgvKKi0u1QCdJ21iqtOLGCC7kX8HTyREOjf0h/Qn1CjQ7LZkyNmsrjvR/n+nbX42TvREp2CscvHDc6LCFEDUhiIUR9KNvatLfttZ3UVysmTVJdn8pKToZNxVuhL08sNLOZk8VZR+vKii+M0rGjOh45YmwcV9Czpzra25cOytPbzuacO0fG0aPGBigq+HHfjwC4OroCUrR9rZwdnOke1B1QqxZCCNtR68QiJSXFEnEI0bCVTSy6dzcujmuQkAC//67OH3mk4vU//lBbpbp3h5CQ8tdSduwgMy4OB3d3QvSiaWug11kkJxsbxxXo+ae9vTpWaDsr3aGsiqZpJGUmAZCclYy9yZ5bI241OCrbczztOF/u/JK2TdoCUmchhK2pdWIxdOhQpk2bxrJlyygoKLBETEI0PDt2lJ7b2MTtzz5T23AGDoQuXSpe1+srKluQOFGckYSOHo2DNdWVjB2rjm5ulbe4sgLdu6sh4fn56vuSOgtpO2uVTCYTy+5exlN9VHeDkW1G4u/ub3BUtuef6//J1AVTySpQDS4ksRDCttQ6sSgqKmLNmjU88cQTDBw4kDfffJODBw9aIjYhGg59L7+nJ7RsaWwsNRQWpjrkVrZakZ0NxZ1kKyQWBVlZnFmq+viHjRtXt0HWVLduqoVVRgacPm10NJVydS3fmfjyxELazlofTdP48+ifgHSDulZ9Q/oClKz+7Dm3h8x8+XsuhK2odWKxevVqnnzySUJDQ0lPT2fOnDncfPPNjB8/njlz5nDhwgVLxCmE7crNhVOn1PmAATZXuD15sprtd9ttFa+tWAE5ORAaqub+lXV68WIKc3LwaNkSv6ioeom12pycIKJ4tkDZbWpWpmw5zuHDkJoKni1b4hkaqtrObpZPc61BobmQi3kX2Zu8l8Oph3G2d2Zch3FGh2WT+oX0A2D3ud2EeIVg1szEno01OCohRHXVOrEIDAxk2rRpLFmyhJ9++onbb78dLy8vDh48yD//+U8GDRrE448/zpo1azCbzZaIWQjbcuCA2kvUtCksWmR0NNessnyobDeostc1TePoDz8A0O622zBZYzIVGKiOn31mbBxXMGUK/PwztGmjvpftUNZp9cnV+L/rz+TfVcu00W1H4+nsaXBUtqmDXwd8XHzILsimg18HQAblCWFLLNoVqlu3bkyfPp3169czY8YMhgwZgp2dHcuWLeOvf/0rgwYN4r333uP4cWkfJxqRshO3rfENdhXS0mDWLNWRqDJFRbBwoTq/fBvU+Z07uXDoEPbOzoSNH1+3gV4rvb1VrPV+GtqjB9x6KxSPr2DjRnUsmWexfr20nbUCcw/OJb8onzMZZwCY0HGCwRHZLjuTHX1aqOYKnk4qOZM6CyFsR520m3VycmL06NF88sknbNmyheeffx5XV1dSU1OZNWsWf/nLX7jrrrtYsWJFXby8ENZl1y51jIw0Mooa+/FHuP9+GDWq8usxMZCSot6f6298dUeKVytCr78eZx+fug30WhXPhCAtzdg4qqF/f3XUE4uAHj2wd3UlJzmZ9MOHjQtMYNbMzDs8D4ALuRdwsHPgL+3/YmxQNq5fC7UdKrNA1VZsid8iCbQQNqLO5ljEx8fzv//9j7vuuou3336b7OxsNE0jPDwcX19ftm/fzmOPPcYDDzxAdnZ2XYUhhPH0d4P6viEbMWeOOlZWWwGlP87YsaoOWpeTkkJccUV3+zvuqMMIa0nvDFVUBGfOGBvLFezcCXv3qvPYWNUlyt7ZmcDiAgzZDmWsLfFbSMpMwtneGYDhrYfTxLWJwVHZNr2A+/D5w7g4uJCak8rRNJnbIoQtsGhikZmZyc8//8xdd93FyJEj+fDDDzlw4ABeXl5MmjSJefPmMW/ePNauXctHH32Er68vGzZs4M0337RkGEJYD00DvUuar6+xsdTAiROwZQvY2cHEiZXfR08sLt8GdeT77zEXFuIXGUlTvUDaGoWFqR8QYPFiY2O5gnnz4MMPwdlZ9QHQOxeXbIdat8644ARzD84FwN3JHZBtUJbQp0UfFt6xkG0PbiM6SLXnlkF5QtgGh9o+QVFREWvXrmX+/PmsWbOG/Px8NE3Dzs6Ovn37MmHCBEaMGIGTk1PJY+zt7Rk1ahRubm7cf//9rFixQpIL0TCdOaN6sgL07WtsLDXwoxogzLBhEBRU8frhw+rL0RHGjCm9vSAriyPFD+547711H2htmEzQpIlqtbRhAzz0kNERVUrvDOXgAHl5agGsT5/SAu7zu3aRn5GB0+Uj0UWd0zStJLFIy0nDzmQn3aAswMPJo2Q7Wd8WfdkYt5HN8Zu5J/IegyMTQlxNrROLAQMGkJ6eXrL/MSQkhPHjx3PzzTcTqHddqUJYWBgAhYWFtQ1DCOtUtpVpjx7GxVEDmgbffafO76yiFb8+FG/oUPDyKr39+G+/UXDxIp6tWtF82LC6DdQSWrVSiYW+18gKFQ/aJkvNC2PjRnjmGXAPDsa7bVsyjh0jcdMmQstmeKJe7D63m5PpJ3Gwc6DQXMig0EEyFM/C+ob0hc3SGUoIW1HrxOLChQu4uLgwatQoJkyYQO+yjdevIi8vj9tuu43OnTvXNgwhrJNeuA02U7y9d6/qkOvsDDffXPl9KtsGZS4o4NDXXwNqtcLO3r6OI7WAyEjYvh0SE42OpEp+fqrdrN5Mb+NGlfyZTGrVIuPYMRLWrZPEwgAtvFrw0eiPeGfTO8RfjJdtUBZ09uJZPtn2CQkXEwDYl7yPi3kX8XL2usojhRBGqnWNxfTp09mwYQPvvPNOjZIKgNatW/P6669zW1XVoULYuk3Fn7LZ20OHDsbGUk16yGPHlnZkLSs5ufQ+N9xQevvJhQvJTkrCxdeX1jfeWPeBWsLkyUZHUC36r1Z7e/XnrycZJXUWGzagyZygeufn5seEiAnEX4wHYHwHK22tbINyC3N5c/2bfLfvO0K9Q9HQ2Hp2q9FhCSGuotaJRUpKCsuKO8BczSeffMLf//732r6kELZD3woVFla+dZIVmzYNzp6Ft96q/Poff6hPzLt3h5AQdVtRfj77/vc/ADpOnYq9s3M9RVtLPXqoj/6Tk+HcOaOjqZKeWHgWz1zTG435R0Xh4O5OXloaafv3GxNcIzfv0DxAFRw392pubDANSFiTMALcA8gvyqe9b3tACriFsAW1Tiw+/vhjfvvtt2rdd9myZTK7QjQeFy9CUpI6n2BbWySCg6F9+8qv6fUVZbdBnZw3j6yzZ3Hx86Pd7bfXfYCW4uYGbduqcyuus+ij5oVRVKSOemJh5+hIUD/V8/+sdIeqV78e+JXPtn/GD/vUzBbZBmVZJpOJvi1Uwwt3R9VxSwblCWH9alRjcfbsWTZvrvg/9vnz5/n111+rfJymaSQkJHD06FHc3NxqHqUQtkh/o9q8OfzrX8bGUk2Fhar7UFWys0FfoNQTi6L8fPZ99hkAnR54AAdX1zqO0sL0ZYDZs2HECGNjqYJeCnLihJrErScWoLZDxS1fTsK6dXR95BHDYmxs3t/8Plvit2BnUp/PSWJhef1C+jH/8Hwy8jIA2Hp2K5qmYTKZDI5MCFGVGiUWvr6+zJw5k+Tk5JLbTCYTZ86c4eWXX77q4zVNo68NtdwUolb0bVA2UrQN6pPxpk1h5kwID694fcUKyMmB0FDo2lXdduznn8lOTMQ1IIC2t95avwFbgv5hhz4gwgo5OamtZy1aqO8PHFADw5s2haDiCeJp+/aRc/48rn5+BkbaOCRlJhETHwOoydtRgVG0btLa4KgaHn3F4kDKAZzsnUjNSeX4heO0bdrW4MiEEFWp0VYoFxcX/va3vxEUFFTypWkajo6O5W67/Kt58+a0a9eOcePG8eqrr9bVzyKEddm2TR1tpOvZ8ePqU/FVq1Qnosro3aBuvFGVJhRkZpbUVnR5+GHbqa0oq3t3dTx71tg4qiEgANq1U+f64rFbQABNOnYEILHsUoaoM38c+QMNDW9n1d1AVivqRo/gHjjYOXAu6xwR/mrYphRwC2Hdatxu9oYbbuCGMq1gOnToQJcuXfhOb3wvhFD0d35z5lRdCW1F9NqJwYMrHxJeVAQLF6pzfRvUgVmzyLtwAa+wMMLG22hHnGHD4KOPIDMTCgqstsg+Ph5ee02tGIHaDnX99eo8eOBALhw8SML69YRdPgpdWNyCw+p/lsz8TAAmREhiURdcHV2JCoziaNpRwnzC2JW0i5j4GO7sUsWAHSGE4WpdvP3oo49yc1XN7oVorIqKSnuCdupkbCzVpCcNZVvIlhUTAykp4OMDgwZBdnIyh775BoBuTz6J3ZWKM6xZ2bqKmBjj4rgKNzeYNUslGFCxzgLUioVZBo7WqeyCbJafWA5AkVZEhH8EHfxso5W0LVp05yJSn00tSd5izlrv/6NCCAslFhNsrOONEHXu6FH16TdA//7GxlIN6emwfr06ryqx0LdBjR2rPtTfPWMGRbm5+EVG0sIWpmxXxd1dTQMEWL7c2FiuoGlTiIgo/X7rVsjPV+e+Xbvi5O1NwcWLnC877V1Y3IoTK8gtzMXVQTUpuLmDfLBWl/zd/bEz2dGruRpBvzNpJ/lF+QZHJYSoSo0+YtQ7QnXv3h3n4n+IK+sSdTVSwC0avLJv7vQ9/FZsyRLVEapjRzXluTJl6ytS9+3jZPEN3Z97zva7tAQGwunTVr1iASpHPXAAXFwgNxd27lQzLuzs7QkaMIDTixaRsH49AdHRRofaYB1MOYgJU8mbW9kGVT/aNGmDr6svqTmp7E7aTc/mPY0OSQhRiRolFlOmTMHOzo5FixbRunXrkttq8qbCZDJx4MCBmkUphK3RC7fBJrpCXW0b1OHD6svREUaP1tj66NsAtLrxRvz09lC2rF07lVjExRkdyRUNGACffw6uriqx2LixdHhe8KBBKrFYt47IJ580NM6G7LkBzxHgHsDUBVMJaxJGt2bdjA6pwXt88eMsPLKQdk3bkXo2lZizMZJYCGGlarwVymw2V7hN07Rqf1X2eCEanJKWPW6lPUKt2KhRcN11MG5c5df1wu6hQyFjyzJSduzA3sWFyCeeqLcY69Rzz6mjXhltpYo7y5Kh2vqXq7MI6t8fTCbSDx8m24qniDcEy06oYS4TOk6w/dU6GxB/MZ5T6afwdFYzZ6TOQgjrVaMVi0OHDlXrNiEavX371LFjR9WX1crdc4/6qoq+Deqm6/PY+f77AETcdx9ugYH1EF09iIpSx5Mn4dKl0qF5VqZ1a7VrSx/ovnEjaJr6K+bSpAm+XbuSuns3CevX0/aWW4wNtgHKL8rHrJn548gfgLSZrS99WvTh90O/k12QDUjLWSGsWa2Lt4UQl0lJUR8pm0zw0ENGR1NrycmwaZM6jyqYQ9bZs7g2a0bHe+81NC6L8vWF4GB1rieFVshkUqsW7durCennzqlp3LrggQMBSNAr8YVFRX8WTfdPu5OZn0kLrxayHaee9GnRB4DjF1SnvSOpR7iQc8HIkIQQVajTxCI3N5dVq1axYsUK0tPT6/KlhLAee/aoY5s28MADxsZSDb//fuXSgj/+UJ+K949KJe6nzwCIfOopHPSJ1Q2Fvb06/vyzsXFcxQ8/qHqXHj3U92W3QzUvbjubtGkTRfnSOceSjqYeZV/yPg6dV6v0N3e4GTuTfDZXH6KDorE32ZOUmUSodyggqxZCWCuL/FY8d+4cr732Gp999lnJbcePH2fUqFE88sgjPPbYYwwbNow///zTEi8nhHXTO0LZQFFzaipMmAAtW5Zur7mcXl9xZ8gnFGZl0bRTJ1rpk9kaEr3lbNnCeyukjwvRuxiXTSyadOyIi68vhdnZpOzYUf/BNWALj6gOB/Z2KgG9uaO0ma0v7k7udAtURfIhXiGAJBZCWKtaJxZpaWncdttt/PTTT+zcubPk9ldeeYXk5GQA3N3dyc7O5tlnn+W4PjRMiIZKb1nasqWxcVTDihVqNaJzZ7V3/3LZ2bBsGQQ6ncLn5C8ARP3tb5jsGuAntfqQiGPHjI2jmvRuUGUTC5OdnWyHqiP6tO1CcyEB7gEMaDnA4Igalz7N1XYoBzuVWUsBtxDWqdbvDr7++mvOnTtHy5Ytuf322wE4ffo027dvx97enh9++IFt27bx4IMPUlhYyFdffVXblxTCum0t/iRN7+FqxZYsUcfRoyu/vmKFapQ0pdW/oaiQ4MGDadarV/0FWJ/0+TopKSrbsmJ//3tpsf3+/XChzHZzfQp3wrp1BkTWMKVmp7L+TGmiNi58XMnKhagf/Vv2Jyowiq7N1EpwzNkYNCv//1SIxqjWicW6detwcHBg1qxZDBkyBIA1a9YAapBeZHEP/8ceewwvLy+2bNlS25cUwnrl58OZM+rcyoeUaZpKHEC1m63M/PnQznUnnR1WYLKzI/Lpp+svwPo2YoQ6FhVZ/TwLV1eV8OnNq8rOKQ3s2xeTvT0XT5wgMz7emAAbmD+P/olZM5d8Wi5D8erfnV3uZMdDO3h75Ns42jlyPvs8J9NPGh2WEOIytU4s4uLiaNWqFS3K9OrftGkTJpOJfv36ldzm6OhIixYtSrZHCdEgHT4M+qyWAda9VeLoUYiPByen0v36ZRUVwcKFGnc2ew+AsPHj8Wnbtp6jrEedO5eeF384Yq2KFyVK/qqV3Q7l5OWFf3H7XNkOZRkLjpRug2ri0oShrYYaHFHj5eLgQmRgJCB1FkJYo1onFrm5uTg5OZV8X1hYSGxsLAC9LtsykZOTI8OERMOmF25D6WwEK7VqlTr266fm+F1u61YIzV1Je7dd2Lu40OXRR+s3wPrm4lK6BLB2rbGxXEXfvqqIOytLfV82sQAIKk5qEy+/IK7JzR1uJqxJGAA3ht+Io72jwRE1XrmFuXQJ6AJATLzUWQhhbWqdWAQEBHD27FkKCgoAiI2NJTs7G3d395JtUKA6R8XFxREUFFTblxTCepXd6mflXaFWrlTHYcMqv/7HgkJuD/g3AB3uuQe3gIB6isxA+orMSeveYuHuDmU/t9m6FYp/BQPFU7iBczEx0nbWAm7vfDv5RerPUYbiGefz7Z/j9S8vDp4/CEgBtxDWqNaJRe/evbl48SLvvfcehw4dYsaMGZhMJgYPHox9cV/41NRU/v73v1NUVERfvUBSiIZITyz8/cHLy9hYruKTT9TIhokTK79+6o8/CHY+hebahIipU+s3OKO88YY6pqQYG0c1DB6sjk5Oqt6iTFM+mnTogHPTphRmZ3O+7CqauCaxZ2OJvxiPh5MHI9uMNDqcRquVTysKzAXEXVQ1UDsSd1BQVHCVRwkh6lOtE4sHHngAFxcXvvnmG8aPH8/u3buxt7fngeLBYNu2bWPw4MHExsbi6enJ1MbyBkU0TocPq2PZ/fpWys8Pbr0V2rWreO30yUKic/8HQPg9U3D08Kjn6AyirzIdOgR5ecbGchXFvTJK5vpd3nY2qLjGTbZDXTtN0/hv7H+ZtXMWANe3ux4XBxeDo2q8ejXvhQkT8Rfj8XHxIa8ojz3n9hgdlhCijFonFmFhYcyePZsuXbrg5ORE+/bt+eSTT+jQoQOgtkoVFhbSrl07fvjhh3JF3kI0KElJkJkJJpPqB2rDlv/7DwKd4sgxNaHb1CqWNBqiFi3AxwcKC+HgQaOjuaJ+/WDMmNJC7gp1FsXboSSxuHY7k3byyJ+P8MWOLwDZBmU0bxdvOvp3BKC1T2tAtkMJYW0cLPEkUVFR/Pzzz5Vea9GiBfPmzStJNIRosPYUf3LWvr16x2fFXnsN7OzULITQ0PLXzEVFFK79FIC8qHtxdHev/wCNommqZTDAunVQpk7M2nh4wJ9/qjCXLlWJhaapvBYgsHjF4sKBA+SmpuLi62tgtLZp/qH5AGhouDi4MKaddf9/3Rj0ad6HAykHcHNUHSdizsbwcM+HDY5KCKGr8/G5dnZ2klSIxkHfy96tm7FxXIXZDDNnwquvqkWWyx37YwVehWe4VOjNgCfvqP8AjWRnp4oWAGykVWvPnuDoqP5blq05d/Xzo0nx797EsoMuRLXpbWYBRrcdjYdTI9kSaMX6tFATuC/lXwKk5awQ1saiiUVOTg7JyckkJiaSkJBQ5ZcQDdKGDeqotyy1UocOQVqaGrLWvXv5a5qmseO/aj95rHYnXbo3otUKXWu1xYJ9+4yNo5rS06FVK3Uu26Es53T6aXYl7Sr5XrZBWQc9sTiedhyAQ+cPkZ6bbmBEQoiyLLIVau3atcyYMYNDhw5d9b4mk4kDBw5Y4mWFsC7bt6vjtm3GxnEV+gfxffqoT7rLSo6NxRy/n3yzM17D7qRRjp2JilItlvQJ6lYsKwtCQtQwQ1CJxd13l14P6t+fA7NmkbRxI5rZjMmuzhepG4yFRxaWnDvaOfKX9n8xMBqhi/CP4I7OdxAdFM3HsR9zKv0UsWdjpVuXEFai1v/KbNu2jYcffphDhw6hadpVv8z6qFghGpK8PEhMVOe9exsby1XoCysDB1a8dnD2lwCsSx/H6Jub1mNUVkQf7JGdDampxsZyFe7u5VedLl+Y8IuKwt7VldzUVNL1jmWiWhYcLt0GNTxsOD4uPsYFI0rY29nz/YTveabfMyWrF7IdSgjrUesViy+++IKioiLCw8N59NFHCQsLw8VF2vGJRubAAVW8AJW/Y7ci+opF8XDmEhdPnSJh/TrMmok1OffwyeD6j80qlE0Md+2C4cMNC6U6hgyB2Fh1vn+/2hrl46O+t3dyolmvXiSsXUvixo006djRoChtS25hLlviS4ddyjYo69S7eW9+3PejdIYSworUesVi586dODs7M2vWLEaOHEmbNm1o3rz5Fb+EaHB27So9j4oyLIyriYuD06fV7IM+fcpfO/rDDwDsyhxE5LBQnJ0NCNAahIWVDodYtcrYWKpBn2fh4KC6Ql1epy11FjXn4uDCpqmbALAz2XFT+E0GRyTK0jSNY2nHyC3MBVRnKE3TDI5KCAEWSCxycnJo06YNfn5+lohHCNukLwPY20N4uLGxXMGxY6pNaWRk+RrzwuxsTsxXrTWXp93B9dcbE59VsLMD/ZP9o0eNjaUaBgxQIRcWqu+rKuBO2bGDgqyseo7Odi09vhSAwaGD8Xf3NzgaUdb57PO0m9mOF1a+gIOdA8lZyZzOOG10WEIILJBYBAcHk2rl+5CFqHP6XpSQEPXRsZUaOhQuXIAFC8rffuqPPyi4dImk/Jbszepv7WM46t4rr6jjaet/s+LlBdHRpd9fnlh4hobi3rw55sJCkvW/p+Kqfjv4GyDboKyRv7s/bZq0AUoH5UmdhRDWodaJxejRo0lOTmaz9EkXjZWmwXHV+tCaB6rpHBwgOLj0e03TOPLjjwCsSLudTp3tyl1vlLp0Ucd9+0prZ6yYXm8OEBMDBQWl35tMJoKKh+UlbdmCuLKVJ1YS8Z8INserf9PGdxxvcESiMnrhtl5UHxMvdRZCWINaJxYPPfQQbdu25dlnn2XFihXk61NrhWgsEhIgJ0dtg3r1VaOjqVJVW5DT9u8n/fBhikzOrE0fz3XX1W9cVqltW3BxUZ2hTpwwOpqruusumDMHvL3VX8WyJT8AzYoLaiSxuLr5h+dz8PxBAPq26EuwZ2PPsq2TnliUrbMQQhiv1ns2XnrpJQIDAzl69CiPPfYY9vb2eHt743h5g/xiJpOJ1atX1/ZlhbAe+sTt8HCrXrFYtAieegruuANef7309hNz5wKwJ28E2WZvSSxADYjIy1PnMTEq0bBiXbqorx9+gD//VNuhevYsvd6suNNVxtGj5Jw/j6vUxFVK07RybWZlG5T10hOL0+lqu+KOxB0UFBXgaF/5ew8hRP2o9YrFokWLWF9cuKppGoWFhaSmppKUlFTllxANyp496titm7FxXMWGDap4OyGh9LaivDxOLV4MwOLE8bi4VGxD2yh5e6sVC4A1awwNpSaK67Qr1Fm4NGlCkw4dADgXI5/sVmXPuT3lioBv7nizgdGIK+narCsuDi5czL+Ip5MnOYU57EveZ3RYQjR6tV6x+Ne//mWJOISwXXpLUitvd1jZ/Iq4lSspuHiRIvcgDmT1ZtR14OpqTHxWp2VLOHy4dKK6ldNbCYNKLDSNcpPTm/Xpw4VDhzgXE0OrRt32q2plVyu6B3WndZPWBkYjrsTJ3onooGg2xm2kpXdL9qfsJ+ZsDFFB1tvuW4jGoNaJxfjxUtgmGjl9K1R8vLFxXEFuLmzbps7Lzu878fvvAOy1G4eGnWyDKqtrV5VY2ECNBaik4rPP1HliIpw6Ba3LvC8O7NOHQ199RdLmzWiahqls1iEAVV+hu6XjLQZGIqrjhQEvkF+Uz/oz60sSi2k9phkdlhCNWq23QgnRqOXkQEqKOi/uvGONdu6E/HwICFDz3wCyk5NJKu7m9sN+NQBMEosyBhePHs/IUDUXVq5XL3B3L/3+8u1QAdHR2Dk4kJWQQGZcXP0GZwPiL8azPbF0derWTrcaGI2ojuvbX8/4juMZ2mooIC1nhbAGFkss8vPz+fHHH3nooYcYOXIkfYq7kKSlpfHCCy9w7NgxS72UENZj//7SLVCDBhkbyxVsLf73tnfv0u0xZ5YuBU3DoVUk8ZkhNG9eOhdOUH7P2P79xsVRTU5O5f8KXp5YOLi54VtcB3ROukNVkFeYR+/mqsg9MjCStk2tu2BflOrVvBcAB1MOcjHvosHRCNG4WSSxOHnyJDfeeCPTp09n7dq1xMXFkZGRAUBCQgK///47EyZMYMWKFZZ4OSGsR9mBY927GxfHVZRNLHSn//wTgBOuYwG1WiG7Y8ooLnYGYN064+KogbLzLC5PLEBthwJIkgLuCto0bYO3izcAt0bIaoWtWH96PZ9u/5QgjyA0NGLPyhBIIYxU68Ti0qVL3HfffZw6dYqgoCCmTJlCy5YtS657enoSFhZGXl4eTz75JEeOHKntSwphPfSOQa6uEBhoaChXEham2pEWv68kMz6e1D17MNnZ8fvBUYBsg6rA2RmiigtBDx82NpZqGj689HzfPkhPL39dTyzObdmCZgOD/+pTanYqK0+sBCSxsCUfbf2IV9e8ir+bPyDzLIQwWq0Ti6+++oqEhASGDBnC4sWLee655/Ar0yM9NDSUhQsXMmLECAoLC/nyyy9r+5JCWI+dO9WxTRur/rj/jTdUV1z9jefp4haz3l17sfWAPyZT+Telotjjj6ujPlndynXrBk2bqnNNg8t3PPl26YKDmxt56emk20iyVB/2J+9n5taZFGlFdGvWjXa+7YwOSVRTn+YqWTajEmVJLIQwVq0Ti2XLluHg4MCbb76Js7Nzpfext7fn9ddfx8nJiRhZghcNhaapHp8APXoYG0sN6dugUpqpbVA9e4Kvr5ERWamuXdVxzx6rbycMYGcHQ4eqIfBQcTuUnaMjAcWT82Q7VKm3N77N9LXTAbglQrpB2RJ9UF7CJTWgJyY+Bs0G/l8VoqGqdWIRHx9Pu3bt8L3Ku5KmTZvSunVrUvQOOkLYurg41cfVwQH+8Q+jo6nSuXNQUFD6/aXTp0k/cgSTgwNrE9UyxYgRBgVn7Tp0UO/WU1PBRoZ7fvwxzJihzjdtqng9sLjQRu8I1tgVFBWUm18h26BsS/eg7jjYOZCWk4aDnQPnss6VG3IohKhftU4sTCYTubm51bqv2WzGycmpti8phHXQ51d07AjNmxsbyxVMmQJeXvDzz+r7uJVqH3lAj54sXecDlC/6FWWkpoJei2Ajg/ICA2HIEHUeEwOFheWvNyuus0jevp2i/Pz6Dc4KrT29low81WykS0AXwv3CDY5I1ISroyuRgZEAtPRS9Z0x8bIaJ4RRap1YhIaGEhcXd9WViMTERI4fP05oaGhtX1II66AnFsUtPK2RpqmOULm50KqVui2uuDubc7cRJCSoNqVWPILDWC1agKOjOl+92thYaiAiAnx81PgN/a+pzqddO1x8fSnKySF1715D4rMmcw/OLTmX1QrbpNdZeDh5AFJnIYSRap1Y6EXZr7/+epX7GvPz83nxxRfRNI1h8tGoaCiKC6C5dMnYOK7g5En1obuTk8p/spOTSS1+p7k/X/2/2LevamolKmEyQXCwOrehmoTvvy/d/nZ5nYXJzo6AXqrv/7mtjXugmFkz89vB30q+l6F4tkmvs8g3qxW4LfEyp0UIo9Q6sbjnnnsIDg5mxYoV3HrrrcyePZvU1FQA1q5dyxdffMENN9zApk2b8PPz4+6776510EJYhYMH1bGoyNg4rkB/LxwZqbqnnl21CgDfbt1YFRsAyDaoq4qIUMejR42NowaKikqHhVc2z6JZcbOB5NjG3fN/S/wWkrOSAejk34kOfh2u8ghhjca2G8vuabuZe5tafdqRuIP8ItnmJ4QRHGr7BB4eHnz++ef89a9/Zd++fewvM6F22rRpAGiahr+/P5988gne3t61fUkhjJeVBRcuqHN9Q7sV0j+QLv6AuqS+osXw4ax+Qd02dKgBgdmS/v3V6lRKiloG0LdGWbGyyeKGDWpLXNluyHpnqPO7d1OUn499I619W3h4Ycn5bZ1uMzASURtNXJvQxLUJmqbRxKUJF3IvsOfcHnoE21a3PiEaAotM3m7Tpg3z58/nhRdeoEePHnh7e2Nvb4+HhwddunThiSeeYNGiRXTu3NkSLyeE8cruTR882Lg4rqJsYlGQmVmy9SU3ZDgpKWoLVNlp3KIS+rt0TQMbGfAZEqJGqwAkJMCZM+Wve4WFqTqL3NxGXWfxRO8nsDep3rySWNg+k8lE7xbqF5pshxLCGLVesdC5urpyzz33cM8991jqKYWwXmvXlp5bacJcUAA7dqjz3r0hcdMmtMJCPEND2XioFQADB6r6C3EFZYvz162DTp2Mi6UGRo4sneu3cSOU7ZthMpkI6NGDM0uXkrxtGwHR0cYEabD5h+dTpBURFRgl26Bs3I7EHXwU8xEZuarDV8zZGB7lUYOjEqLxqVVikZ+fz7Zt24iJiSExMZH09HRMJhNeXl60adOG6OhoevTogcmKJxILcU3WrVNHPz9wcTE2lirk5cErr6jZbm3bwtZvVMzBgwYxu7jBkWyDqgY3N5WZxcTYzARuUAst//ufOt+4Ee68s/z1ksQiNhYeeqj+A7QC3+/7HoA7Ot9hcCSittJz0/l699cEuKnaMWk5K4QxrimxKCgo4JtvvuHzzz8nIyOj5HZN0yokEQEBATz44IPccccd2NlZZOeVEMbbt08dw623572HB7xQXEehmc0kFCdDgQMHs+ZtdbsUblfT5MkqsTh82OhIqq1s0rhhQ8XremeolJ07G2WdxdCvhrLutPp/YmLniQZHI2qrZ3BPTJhIzlbF+EfTjpKanYqv25WH9wohLKvG7/QzMzO57777eO+990hPT0fTNNzd3Wnfvj3du3enc+fOhIaGYm9vj6ZpnDt3jn/84x888MADZGdn18XPIET90jQ1zhpUr1YbcOHgQXJTU3FwcyPBPpr0dPD0hO7djY7MRnTpoo579hgbRw34+cFwNVidAwcgJ6f8de82bXBu0oSi3FzSyjTdaAwOnT/EmtNrAOjboi8h3iHGBiRqzdPZk84BaltqkEcQAFvPNu52ykIYocaJxRNPPMHWrVuxs7PjzjvvZP78+Wzbto358+fz/fff88svv7BkyRJ27NjBt99+y0033YTJZGLTpk08++yzdfEzCFG/Tp1S+4ycnOD1142OpkrLl6tQNQ3OFteEBPbty9oN6pPpwYPBwWJVVg1c+/bqeOYMpKcbGkpNLF8OAQFq+vauXeWv6XUW0PjazpYdije522QDIxGWpM+zaOraFJACbiGMUKPEYvXq1WzcuBEPDw++/vprXnnlFcKr2Ari5OREjx49ePvtt/nqq69wdXVl5cqVbNki/6MLG6ePMo6IsNrJcnl5MHYstG4NcXGUbIMKHjSI4lEWUl9RE2UzMBv6HWYylXb9qmy+n9529lwjSyy+36tqK+xMdtwScYvB0QhL0ROL3MJcQCZwC2GEGiUWCxYswGQylbSVra5evXrxzDPPoGkaCxcuvPoDhLBm+ke/ZbsFWZl9+9Sn1L6+EOCZTmpxTUhAn4GsX6/uI/UVNeDrW1qkv3SpsbHUkP6rWv/vXlaz4sQiZedOzPqo7gbudPpp9qeorV/DWg3Dz83P4IiEpfQP6Q9A3MU4QG2FMmtmI0MSotGpUWJx4MABnJycuPHGG2v8QuPHj8fe3p49NrRHWYhKzS3eRpGSYmwcV6C3me3eHZK3xoCm4d2mDYcSmpGZCT4+0LWroSHanpDiffhbbWvf9oIF6lhZYuHdti1O3t4U5eSQ2kjqLH7e/3PJ+b2R9xoXiLC49r7tCXAPoF3TdjjbO3Mh9wJHU48aHZYQjUqNEouUlBRCQkJwvIbJs25ubrRo0YLExMQaP1YIq3LqlDr6We8nnWUTi8RNmwBVX6G/uRwwAKRJWw3pBdzHjhkbRw3pK1MpKXD+fPlrJju7ku1QjaXO4stdXwLgaOfITR1uMjgaYUkmk4lTT5xi38P7iA5Ws1lkO5QQ9atGby3y8vLw8PC45hfz9vYm5/LWJELYkkuX1BeoCWRWSk8soqI0kvTEon//ksRi4ECDArNl+oT18+dVRbyN+MtfSs8rW2xpTAXcheZCnO2dARjbbiweTtf+75mwTq6Oqu6tT3NVbyHzLISoXzVKLIqKirC3t7/mF3NwcMBslv2OwoZt3156PmiQcXFcQUFBaX15p6AzZCUkYOfggH/3HiXzDKw0dOs2dqw6ms1w5IixsdRAnz6g/9r+44+K1xtTnYUJE+eyVKvoqVFTDY5G1CV9xWLLWdtptiBEQyCbIYSoCb1w18GhdM+9lTl0SHWF8vQEx3i1WuEXFcWxM26kpqpGVjK/4hq0aVP6Dv3PP42NpQacnKBdO3W+Zk3F6z7t2+Pk5UVhdjZpBw/Wa2z1bfWp1SRmJuLj4sN1ba4zOhxRBzRN47pvr2PqfJU47jm3h5wC2SkhRH2RxEKImti8WR2bN1e9PK1QSAj8+CO8/Tac26wSi6B+/Uq2QfXpo95sihoymUr3kF28aGwsNTRihDoeO1ZxF5fJzg7/4kwzRd9D1wDFZcTx1oa3AJjYaSLODs4GRyTqgslkIrsgm7yiPLydvSk0F7IjseH+vRbC2tR4PNalS5eIvca9uJf0velC2KpDh9SxUydj47gCHx+4/XYwFxXxW3/1/2qzPn1Y/466LvUVtTB2rPrY/8ABoyOpkTvvhI8/Vtvkjh4tnfen84+O5uyaNaTs2EHHe+81JMa69p/Y/7Dy5EoApkRNMTgaUZcGhAxgw5kNeLt4k5GXwZb4LfRv2d/osIRoFGqcWBw9epTJk2VSqWiEzGa4cEGd6x8BW7H0I0couHQJB3d3mkZEUDwjTxKL2tB79O7da2wcNdSrl1pkO3sWYmMrSSzKrFhomobJSlfjrpWmaXy16ysAgj2D6Rnc09iARJ3q37I/bITsgmxAOkMJUZ9qvBVK07RafQlhs06cgPx8cHaGxx4zOppKmc3w3nuwfDkkxajVCv/u3Yk760BcnCoN6dvX4CBtmV6scOgQZGcbG0sN2NvD+PHqfNu2itebRkRg7+xM3oULXDx5sn6Dqwd7zu0pKdqe1mNag0ucRHn9QvoBcD5b9VfeEi8F3ELUlxqtWKxcubKu4hDC+umtljp3Vu/QrdCxY/D3v6sC7cVT1TvIZj16lNRXdO8O7u4GBmjr9IJ9TVPZ2022Mwehd2+1HaqylrP2Tk74dulC8rZtpOzYgXdYWP0HWIf+s/U/gOoKdX/U/QZHI+paU9emdPTryMHzBzFhIu5iHImXEgnyDDI6NCEavBq9O2revHldxSGE9dtS/KlXt27GxnEFeu1t1y5mUnaoxCKgZ0/W/1fdLtugasnRUbXbunQJli2zqcSieFwFMTGq9tzLq/x1/+7dSxKLtrfcUv8B1hFN0/j5gJq2HRUYJW8uG4kBLQdw8PxBfN18OZ99npizMYzrMM7osIRo8KQrlBDVNX++OsbHGxvHFezcqY4Dwo+Rn5GBg6srTSMiZDCeJYWGqmNle4qsWLt2qrFVURF8/33F6w21M9SW+C1k5GUA8HTfpw2ORtSXUW1GcUP7G+gc0BmQ7VBC1BdJLISoLj2hiIgwNo4r0N8TdvZU9RV+UVGkZTiijycYMMCgwBqSyEh1PH7c0DBqyt4eAgPV+bx5Fa/7RUaCyURmXBw5KSn1GVqd+k+s2gblZO/ELRENZyVGXNktEbew4I4F3NXlLkAKuIWoL5JYCFEdGRmQUzxkafRoY2OpgqaVJha+WSqxCOhROm27Uyfw9TUouIZkyBB1vHCh4lAIK6dvhyo7QF7n5OmJT3G7qIa0alFoLgTg5o43y+yKRqhPiz4AxJ6NpchcZHA0QjR8klgIUR2rV5eeW+nH/mfOQFoaODpq5B9T7xwDevSQNrOWptdVmM2lmZyN0DtDnT8P6ekVrwdERwOQbGM/V1Uu5Fxg3qF5APyt79+MDUYYwt3RHXdHd7IKstiXvM/ocIRo8CSxEKI6lixRRw8Pq22rtGuXOg7seJy8C2nYu7jg26ULGzeq2600H7I9fn6q5TDAokXGxlJDY8eWni9eXPF6Q6uz+G7vd+QV5dEloAvdg7obHY6oZ5/EfkLYR2G4OroCsCluk8ERCdHwSWIhRHXo0+ZbtTI0jCu57jpVT/zMLaqo2K9bNwrMTiUF3f36GRhcQzN8uDo6OhobRw01a6ZyY4Bffql43T8qCoD0Q4coyMqqx8gsLy07jaeWPgXApK6TZHZFIxQdrFbgMvMzAf6/vbsOb+r8Ajj+TepGoQIULS7F3Z0BGz42BmPOGHMf0998Y8CE+dhgTGEMZ4zh7lDcrUCFGlKXNPf3x9skLV4qN0nP53l4ktyk6Wmo5Nz3vOewKVISCyGKmyQWQtyMkyfVpaVI3Q55ekLLllDmUm6b2Vat2LkTsrPVG0o7zokcj2WfhQOe2W/cWF3uv0pViHfFivhUroxmNpNgWQJzUOM3jMdkNmHAwCPNH9E7HKGD5hWb4+XqRYYpA4DNZzfrHJEQzk8SCyFuJCfHNmV5wAB9Y7kBTdOIy11dKd+6NZtyT9B16KBajYoikntm37oc5ED691eXjRpd/X5nKYf6ec/PgDprHegtXQtKIzcXNzpW6wio4YgnLpwgNiVW56iEcG6SWAhxI8eOQVaWGmdtpwPRUlJg7Fj44ePTZCQkYHR3J6hJEzbnnqBr317f+JyOJbE4ccK2ucVBWDbxb71G983yTpBYbIncQkJaAgDvdn1X52iEnrpV7waAn4cfAJsjZdVCiOIkiYUQN7J3r7ps3FgNA7BD+/fDDz/A0im58yuaNMHo7iGJRXEJDAR3d3V9zhx9Yymgli3Vt3F09NVnPVpWLBL27sWcnV3C0RWNN1a+AUAZjzL0q9NP52iEnrqGdgUgKycLkA3cQhQ3SSyEuJHly9WlpTjdDu3Zoy5bBtv2V0REwLlz4Oqq3kyKIla5srq0jDV3EN7ekDuugs6drxzFUaZmTdz9/cnJyOC8ZbKiAzGZTaw7o3os3xN2j2zaLuVaV2qdb5+FJBZCFC9JLIS4kUWL1OX58/rGcR2WxKKKlju/onVr62pFixaqiksUMcsEbgd88926tbqMiLD1JbAwGI1qCjeQYPnGciCTNk2yDsX7qOdHOkcj9Obh6sH4XuP5su+XAOyI3kGmKVPnqIRwXpJYCHEjCapW29oJyA7t2QNlXeNwT4/BYDQS2LixlEEVN0vL2fh4MJn0jaWA8rYeXrXqyvuDmjYFcMjOUCtPrQSgbeW2smlbAPBM22d4qs1TBHkHkZmTya5zjtd0QQhHIYmFENdz/LjqCgUweLCuoVyL2ay2gdTx2g2Af506uPn4WDtCSWJRTCztlTTN4TZwW1YsAFasuPL+YMuKhYN9XWcunWHVKZUpTR88Xd9ghF0xGAx0qKoyaimHEqL4SGIhxPXMm6cuXV2hWjV9Y7mGiAjVFaq+rypbCWrWjNRUW3mUJBbFpFo19X0BsGCBvrEUUOPGtr3ny5er5DSvgEaNMBiNpJ07R9q5cyUf4C2asnMKZs1M99Du1A+qr3c4wo5sPruZ9Ox0QBILIYqTJBZCXM86tQmUChX0jeM6Dh9Wl40DdgOqjGXHDrXQUrkyVK2qX2xOzWCwbeA+dkzfWArIzc22ReTChSuH5bn5+FA2d4e3o+yzyMjO4PMtnwMwtuVYnaMR9ubN1W+y/KRqxLHx7Ea0y7sWCCGKhCQWQlyP5R1Xgwb6xnEdt98OCbFZVHE5AKgVi7xlUNIUpxgNHaouAwL0jeMWtG1ru37VfRa5mUe8g5RDfbD+A9Ky1SDLLtW76ByNsDeWeRYGDJxLOcfpS6f1DUgIJyWJhRDXEx2tLjt21DeOG9BiDqGZsvEoVw6/atVk43ZJsWxWcMAJ3JbQy5SBihWvvD/IgfZZaJrG19u+BqBFSAsq+l3lCxKlWrfQbgC4GNUsIimHEqJ4SGIhxLWYTLYm/wMH6hvLDVje/KluPgZrYpG3+48oBpYJ3Hv3OlxnKEtikZ0Nw4Zdeb+lM9SFgwfJybTv9px/H/ybS5mXABjfc7zO0Qh71KZyGzxdPa2tiCWxEKJ4SGIhxLUcOaLedfn62grS7Uxysup6uuIX28bt48dVh1x3d9v7XlFM6tRRGxbS0uC99/SOpkDq1lWrFenpcPDglff7Vq2KZ2AgZpOJ81d7gB15c9WbAAR7B9OrZi+doxH2yMPVg/ZVbEu4myM36xiNEM5LEgshrsWyabVxYzDa54/K3r2qPt4Yk5tYNG1qXa1o2RI8PHQMrjRwcbFt7F+7Vt9YCshotE1kX7PmKoPyDAaHmGcRHh3OsfNq8/xz7Z6TSdvimizlUAB7zu0hJStFv2CEcFL2+W5JCHswa5a6rG+/bSv37oUA1xjKGs9hcHEhsFEj2V9R0po0UZcHDugbxy2wlEM9+yy88MKV9ztCYjFuxTgAXI2uPN3maZ2jEfbMkli4Gl3J0XLYHrVd34CEcEKSWAhxLctVa0J8ffWN4zr27IE63mq1omzdurh6e1s7Qsn+ihLSo4e6TExUA0UcSN5BeevWXTnPIii3li5+9267bM959tJZVkWollaD6g7Cz8NP54iEPWtbuS1bR2/lzgZ3AqrtrBCiaEliIcTVXLqk6uYB+vXTN5bryDtxO6hpU5KTbR1yZcWihHTvbru+3bHOgOZNLC5cUN9PeQWEhWFwdSUjIYFUS4c0O/Ll1i+tA/F+v/N3vcMRds7D1YM2ldvQqVonANafWa9zREI4H0kshLiavI39O3XSL47r0DSVRNTxsm3c3rZNnXWuVg0qVdI5wNIiLMw2LOTff/WNpYCqVYPgYNvtNWvy3+/q6Um5evUA+yuHSspMYkr4FABebP8inq6eOkckHEXnap0B1RnK0iVKCFE0JLEQ4mosbxB9fMDPPssrzpyBjJRMQr1Ux57gPIPxpAyqBHl42LK41av1jaWADAZo08Z2+/LEAmzlUPY2gfvrbV+TlJlEvcB69Ktjv6uKwr5czLjI19u+xsXgQkpWCnvO2df3tRCOThILIa5m2zZ1GRqqaxjXEx0NTSscwNVgwjMwEJ8qVWTjtl4s7ZU8He+s+Q33WdjhBu7UrFTGb1DzKlKyUjAgnaDEzfF19+Xvg3+To+UAUg4lRFGTxEKIqzlxQl22aqVvHNfRvj388qGtzaymGdiyxXafKEGWfTh2vNH/WiyJhcFw9X0WwbkzXC4cOYIpPb1kg7uG73d8T3JWMgBPtX5KWsyKm+ZqdKVHjR7W25JYCFG0JLEQ4nLx8ZCaqq7fdpu+sdxA4p7dgEosjh5Vbww9PSH3JLMoKZYEdMcO27R2B5F3xeKrr6BKlfz3e4eE4FW+PJrJxHk7aKmblp3Gh+s/BMDDxYOxrcfqHJFwNLfVsv1eX396vV12PBPCUUliIcTl/PzUNGWw+80KibktoAKbNLFWb7VsqaZuixLUuLH6nklMVPVEDiQ4GKpXV/lQWBgEBeW/P++gvPhdu3SIML8fd/7IhYwLAIxpMYaynmX1DUg4nN41e1uvx6fFcyTxiI7RCOFcJLEQ4nJHj0J2NpQpo95x2aHsbGhRL560c+fAYCAgLMyaWOTdjCtKiIcH1Kihrt9/v76x3ALLqoXle+hyQbnlUHpv4M4wZVhXKwwYeKHDVab6CXEDtQJqUbNcTevt9aelHEqIoiKJhRCXs7x5atLE1kbUzhw7BkSr1YoyNWvi5uPD1q3qPkksdNK2rbo8cwaSkvSNpYAsicXKlfDll3D8eP778yYWepaNTA2fSnxaPABDGwwltGyobrEIx3ZbzTzlULLPQogiI4mFEHlpGkycqK43aKBvLNexfz/U9Motg2rUiIwMWz5keX8rSljnzrbrlizPQVgSi/Xr4dlnYdGi/PcHNGyI0c2NzPPnSTlzpuQDBDJNmYzfqDpBGTDwSsdXdIlDOIfetXpT2a8yIImFEEVJEgsh8jpzBvbtU9fteAf0vn1QK09isWePKo8KCrLrDrnOLW8HMctAEQfRsqVanMvIULcvn2fh4u5OQFgYoF851PTd04lMiqSSXyWOPX2MNpVlaU7cuiH1h3DoyUMYDUYiLkYQmRSpd0hCOAVJLITIK2+Red52OXZm/z6Nmp4qsQho1Cjf/go7rd5yfmFh4Oqqrq9cqW8sBVSmDNSvb7u9bh3k5OR/jJ7zLNKz0/lg/QcAvNLhFWoF1CrxGIRzMRgM+Hn40byiGgAp+yyEKBqSWAiRl+VUrcEAjRrpGsr1nN0fhZ/rRXBxpVz9+rK/wh64u0O9eur6jh1XTpqzc5Y82s0NLl68cp6FtTOUDonFN9u/ITIpkhDfEB5r9ViJf37hvDpUVZ3/pBxKiKIhiYUQeW3YoC4rVABvb31juYbUVHCLV6sVfrXq4eLubl2xkP0VOrPss0hPh8OH9Y2lgCyJRdmy6vLycqig5urM7qVjx8i2zHkpARczLlo7QZ1LOcfO6J0l9rmFczsUf4gpO6cAsmIhRFGRxEIIC7MZjuT2M7fj/RUJCdCxptoHUrF5I86fz+0ShV1Xb5UOlv+A2rUhIEDfWArIEnpamrq8PLHwLl8e75AQNLOZRMs+pBIwYeMELmZcBKBWuVq0rSLZsygadQLr4OaiZhbtj9/P+fTzOkckhOOTxEIIi6NHITNTXc/b4cfOVK8OfZvk7q9o3JgdO9TxWrUgMFDHwIRtA3dcHJQvr28sBdS0qdoiYlmM2Lz5yiHi1n0WJbSBOzo5mi+2fGG9/WaXN3E1upbI5xbOz9XoSq+avay3N5zZoGM0QjgHSSyEsLCc9gfILfuwR+acHM4fOACojlCW/RVSBmUHGjYET081x+LyYRB2ztNTjW4BePNNOHXqykYAJZ1YvLf2PdJN6QDUKFuDkY1HlsjnFaVHvnkWUg4lRKFJYiGERa9eYMz9kcgdCGaPLh4/hSk9HVcvL8rUrCkTt+2Jq6stKf38c7UL2oFYyqEyM8HH58r7LYPyEvfuLfZBeUcSjvBT+E/W2693ft1atiJEUbmtli2xWBOxRr9AhHASklgIYXHggNpnERQEISF6R3NND/VV9e1eNRpiMLpIYmFvLOVQ33/vcPMsLInF9u1Xv79c/fpqUN6FCyQX86C8N1e/SY6met5W86/G/U3vL9bPJ0qnWgG1qFm2JgDh58JJzkzWOSIhHJskFkJYWNpoNm1qt8Mg4uMhIEPtrwhu1ogzZ1Q5v6urXS+ylC4tW9qur3es0gpLYrFzJ7z4InTvnn+eRd5BeYnFWA61PWo7sw/OxoABP3c/Xuv0Gu4u7sX2+UTpdmfDOwEwa2bZZyFEIUliIQTAnj3wxhvquh13hNq/H2rmTtyu2Ny2v6JpU/Dy0jEwYZN3AvfatfrFcQsaNlRdlpOT1YLLmjXXnmdRXIPyNE3j1ZWvAnBf0/s4+/xZHmr2ULF8LiEARjQaQYuQFgCsjlitczRCODZJLIQA2LJFnfoHuz71v39PFtU8VEvcwMsmbgs7Ub++Lcvbvt3Wv9UBuLpCC/X+itq11eUV8yyKeQP38pPLWXVqFe4u7rzX7T38Pf3xcPUols8lBEDzkOa80O4FAFadWqVzNEI4NkkshADVW9PCjlcsIrYexc2YjcnNH9+qVSWxsEcuLrZyKJMJ67KSg7CUQ/n6qsvLF10sicXFo0cxFXHSZNbMvLpCrVbcXvt2qvlXK9LnF+JautfoDsCuc7usc1OEEAUniYUQYKuFd3VVZ5ztVPJRVQblWjWMnBwDO3OHEEurWTuTd1LhunX6xXELLKFfuqQu161TPQ0svCtWxLtiRTUob//+Iv3csw7MYte5XQDMPzKfladWFunzC3Et5TzLEeIbglkzs+60Y/3MCmFPJLEQ4vx5OHlSXa9XD9ztc5OopoFbgnojV75ZYw4cUFU2fn4qbGFH8i4hOdg+C0ticfy4ajl74cKV+ywCcwdeFGU5VFZOFm+uetN6u03lNvSs0bPInl+I68nKyeJcyjkA5h6aq3M0QjguSSyEsNQTQf6Nt3YmNRWaBKnEonZn2/6K1q1t4zeEnbAsIbm6wq+/6htLAdWqBeXKqVkWlqrAa5VDFWVi8VP4T5y4cMJ6e3zP8RjstDubcD7+nv6EBauOZ8tOLNM5GiEcl7wdEWLLFtt1O95f4WlMo0yWeuNVoXlj2V9hz0JDIThY7bGIjNQ7mgIxGGz5dfnyaqRL3pazYBuUl7BnT5EMykvJSuHdte9ab/et3dda8y5ESRkeNhyAmJQYEtISdI5GCMckiYUQnp5qwy3YdWJx/tAhNLMZrwoV8AoOtiYWsr/CDhkMtv8YB9u8DbZyqLJlISoKXngh//0BDRpgdHUl8/x5Us6eLfTn+3zz58Slxllvj+85vtDPKURBjWwy0np98dHFOkYihOOSxEKIJ56wnZK148QiYpOauB3YqBEpKWqmBciKhd2yJBY//QSvvaZvLAWUd1De1aqRXDw8KNewIQAJl2/AKKD41HgmbppovT2qySiaVrTfn0PhvGqWq0mAVwAAf+z7Q+dohHBMklgIYXljVKUKBAbqG8t1LPxBZRKXvBsRHq469VSuDJUq6RyYuDpLYrF/P3zzzZX1RHbMkqweOKD29miarUuURVENyvto/UckZyVTo2wNmpRvwvvd3y/U8wlRGF2qdQFga5TjrTQKYQ8ksRClW0oKWN4Y2fFqRXY2BGaqxKJKm0ZSBuUI8racTU5W090dRKVK6p/ZDJMnQ8WK8NBlw6+LYgP36Yun+XbHtwB83/97do/dTWjZ0Ft+PiEK69GWjwKQlJlETHKMztEI4XgksRCl2wsvwIsvqut2nFgcCr9IBXdVy96ge5i1bF/KoOxY2bL5+wA76DyL2Fg1lP7yeRbWQXlHjmBKT7+lz/G/Nf8jKyeLHjV60Ltmb+kCJXTXp1Yfa3eoNRFr9A1GCAckiYUo3bZsgawsdT230409OrhCrVZcNFTDs5y/dIRyFHmXlBx0nkVsLHh7Q2KiKo2y8A4Jwat8ebScHM7nveMmHYg7wG97fgOgaYWmZOVkFUXYQhSKi9GFvrX7ArA6YrXO0QjheCSxEKVXcjLs22e7bccrFud2q8QiM7AR587BmTNqU23LljoHJq4vb2Kxfn3+U/52Lu8G7o4d1fW8uZHBYCDIMijvFvZZvLn6TTRUq9o/9/2JyWwqTLhCFJnuoarV8YqTK8gxO87eKCHsgSQWovTKOxjP21tNBrNT2afVGWGf2o3Yvl0da9gQypTRMShxY3kTi8REOHRIv1gKyDLL4vhx28rYmjX5H2OdZ1HAzlBbI7cy//B86+23u76Nj7vPrQUqRBHrXL0zBgycunhKpnALUUCSWIjSK+9gvMaNbbMs7JBvikosqrVrJPsrHEmTJuDhoa77+MCpU/rGUwABAbZc29Isbe1a1SHKIm9nqIIMynt91evW67UDajO6xehCxytEUSnjUYZAb/VN//2O73WORgjHIomFKL3yJhZ2vL8iLS4ef0MsGkaa3lZf9lc4Ejc3aNFCXf/mG+jfX994CshSDpWcDF5ekJAABw/a7i/XsCEGV1cyEhNJjYq6qedccXIFq06tst7+qMdHuLm4FWXYQhRa75q9AdgUuUnKoYQoAEksROmkafkTCzveX3HhoFqtKFu7JhWq+lhLoSSxcBCWcqgdO/SN4xZYEovwcHj0URg3Ti28WLh6elKufn3g5trOaprGayttwwJbVWrFsIbDijRmIYrCmJZjAMgwZbA2wrEaLwihJ0ksROmUmaneKVnKVOw4sUjM7bgTEBbG8eNw8SJ4eqrqLeEALImFpYbNAQflbd+u5lmMHw+hofkfY91ncROJxdxDc9kRbUuwPun1ibSYFXapU7VOuBnVStp3O77TORohHIckFqJ08vSE559XCQbY9bv0M1tsiYXlvWmLFqrKRjgAS2IRHg7Vq8OXX+obTwE0b662HkVHQ2Tk1R9j7Qx1g8Qix5zDm6vfBGBMizG83fVtetToUaTxClFUXI2utK6sluyWHF8i5VBC3CRJLETpZXkjVLs2+PnpG8s1aJpmbTW76VQj2V/hiEJDIThYrVScOQMrV+od0U3z8VH7zwE2b1Z7LZYsUYmGhWXF4sLhw5gyMq75XDP3z+RwwmECvAKY0HsC73R7p/gCF6II3Nv4XgBSs1PZcGaDztEI4RgksRCl05o16p0S2HUZVHpsLN7mRHI0F+p3rWdNLPJ2MRV2zmC4clBedrZ+8RRQ+/bqcvNmGDIEbr8dFiyw3e9TqRKeQUFoJtM1B+XlmHN4f937ALzY/kX8Pf2LO2whCu32OrcDYDQYqVymss7RCOEYJLEQpc/Fi9CjB/zvf+q2HScWEZvVG7XIzNrUbeiJZQ6ZrFg4GEti4e4OKSkOtZE7b2LRpYu6nneehcFgsLadTbzGPItZB2ZxJPEIAGcunSlQa1oh9BJaNpS6gXUxa2b2xe678QcIISSxEKXQpk2qK5S7u7ptx4nF8fWqDCretREnTkBWlpopUKOGzoGJgrG8O3d1VZcOVA7VoYO63LnT9mVcb57F5fKuVgB4unrKhm3hMPrU6gPA0hNLdY5ECMcgiYUofTbk1spaylHsOLE4n9tqlpCwfPsr5H2Zg2nTBoxGSEtTt1etuv7j7UiNGlC+vPpxcXVVfQ9iY+HIEdtj8naGunw1YvbB2RxKUBPHfd18eaPzGyUVuhCFZkksZh+czTfbvtE5GiHsnyQWovRZv15dapoaL1ytmr7xXIOmaRhibB2hZH+FA/Pzs+2CBrVqlp6uXzwFYDDYVirCw6FdO3U9bzlUQO6gvPT4eNJiYqzHzZqZ99a9Z739cseXCfYJLoGohSga3UK74Wp0JTE9kbdWv0VWTpbeIQlh1ySxEKVLRgbWd+ig+rba6en/1Oho3EwXMWmu1O5QVzpCOTpLTVGVKmqGSmqqvvEUQN59Ft26qetr88wMc/Xyoly9ekD+trNzDs7hYLwa1R3gFcDz7Z4viXCFKDI+7j50qtYJgAsZF/jv+H86RySEfZPEQpQuO3eqjQpeXup28+b6xnMd5/er/RVZZevRoJG7tfTEMg1ZOBhLYlG5Mnz1FQQF6RtPAVxrA/f19lmYNTPvrn3Xev+bnd/Ez8M+2zoLcT19a/W1Xv9lzy86RiKE/ZPEQpQuljIoS2LRooV+sdxAYm5i0aRPGHFx6litWg71flTkZUkswsMdpgzKolUrtb8iOlrlRd98c+X+80BLYpHbGWreoXkciFelfJX8KvF468dLNGYhikqf2n2s1xceXsj59PM6RiOEfZPEQpQuI0bA99/bylDsOLGwzATIu79CyqAcWGgoVKyodkFv2QLr1qnWsw7A2xty92cTHg5PPAENG+avIgzOTSwuHDyIKSOD8RvHA/BCuxeYe/dcPF09SzhqIYpGkwpNqOBTAQCTZuKv/X/pHJEQ9ksSC1G6VK8OnTpBZib4+qqp23ZIM5uJ36tq012rSmLhFAwG26rFyJHQtSusXq1vTAWQtxzqanyqVMEzMBCzycSqVX+yI3oHnq6ejOs0jrZVpOOAcFxGg5Hbat1mvS3lUEJcmyQWovTZtUtdNmumWoDaoeSzZzGnJ5Nldmfmytps3aqOS2Lh4CyJhWfu2XsHmmeRN7FIS4MpU2D0aNs+C4PBQGBu56t/l/wIwANNH6C8T3k9whWiSPWtrfZZGDDg7+lPerZjlTMKUVLs812VEMVh+XK1adbyZs4BNm6fyahPhRA3YmNVjbsdhyxuhiWxOJ9bo71ihX6xFJAlsdi1S/U/ePZZmDoVDh2yPcaygVs7GgnApYxLJR2mEMWiX+1+uBhc0ND47o7v8HLz0jskIeySJBai9Pj5Z3jmGVv5iR3vr4jfq/ZXnMwIIyNDHWvSxLbnXDioFi3UxPekJHX7wAGIitI3pptUvbraImIywf79qqIQ8s/6swzKq5OovlFHNB5RwlEKUTzKeZWjc/XOACw6skjnaISwX5JYiNLDMnE7Pl5d2nFiEbVDJRbnDGGcPKmOSRmUE/DwUC2WQI20BrWS5gDybhHZvBl69lTX8yYWGdXKkWPQCEh3o7VHfQbUHVDygQpRTAbWHQjAwqMLiUqK4tSFUzpHJIT9kcRClA5nzsDZs2pPRVqaeoPXoIHeUV2VOSeH1JNq47Z79UYycdvZWN6dlyunLpct0y+WArKUQ23cCD16qOurV0NOjro+aedkzvirJbZXK4zCYKfDJ4W4FQPrqcRiTcQaqnxeJd+cFiGEIomFKB0s8yssZ4kbNwY3N/3iuY7kiAgMWWlkmL0IaVyDnTvVcVmxcBKWxOLiRXW5fDmYzbqFUxCdVSUIGzao3gdlyqgvY/dutZ9iSvgUjgeqTa3V41z1ClOIYlEroBYNgxti1tTP6+yDs0nJcoyW0UKUFEksROlgKYMKCFCXdlwGZZlfcTqjPoFBrqSmgp8f1Kunc2CiaFhO+586BW+/Df/9l38ghB1r0ULNtEhMhGPHVMdcUOVQX2z9gqycLGtikZg7KE8IZ2Iph/J19yU1O5WZ+2fqHJEQ9kUSC1E6WFYssrPVpR23V0rMTSxqdgzD21sda90aXFx0DEoUnYoVoWZN1ae1Qwdo2dJhEgs3N9uCy9q1ap+F0QinI7P5dvu3AFyq7geoBDknM1OvUIUoFpZyKJPZBMAPO3/QMxwh7I4kFsL5XbyoTq8CRKo2mHa9YpHbarbL8EZERKhjsr/CyVjenW/apG8ct6BLF3W5bh08+KDqnNtlzDziUuOo4FOB2U+tVIPysrOtq29COIs2ldtQ3qc8GaYMXI2u7Ijewc7onXqHJYTdkMRCOL+yZdW7nzlzICFBnfpv3FjvqK7KbDJx4fBhAALCwqyD8SSxcDKWxGLDBpgxAx5+GFIco1Y7b2JRpgz4+8PX274G4LGWj1E3qC7BuYl73E55wyWci4vRhf51+gNQs1xNQFYthMhLEgtROvj42DZrN2hgtwMhkk6eJCcjA83Nm8NxoVhO+Epi4WQ6dlSXW7fC66+rGStr1+ob001q00aN4oiJgRMnYMvZLaw/sx5XoyuPtXoMwJpYxFum3AvhRCzlUMmZyQAsOrqI7JxsPUMSwm5IYiFKD8ubHDsug7Lsrzh0MYzPPjeiaVCtmirLF06kUSO1kpaSYvt+dJC2s15etg5l69bBo3OfB8CQUZZKfpWA/ImF5iAdr4S4Wb1q9sLT1ZOYlBg+6vkRR586ipuLfXYZFKKkSWIhnFtkpBpI9uqrEB6ujtnxxm3L/opTGWFomjomqxVOyGi0ja72U5udWbpUv3gKyNINaumGOA5cVPV6pvAHSEtTx8vVr4+rlxfZSUlcOnFCpyiFKB4+7j70qtkLAE3T8PPw0zkiIeyHJBbCua1eDTt3qn6YlsTCjlcsLJtdT6aHkZCgjkli4aQsQyEs+36OHIHTp/WN6SZZ9lkszn4RDQ3MLmgr3rM2XzO6uhLYtCkA8ZafOyGciGWq/MIjCwGVYKRnp+sZkhB2QRIL4dxWrVKXbduqydugJnvZoZysLM4fPgJAREYYhw6p45JYOCnLu/MtW2y1RQ5SDtW+PRhcTaSGzgKgWsYdYPLOF761HEoSC+GE+tdVG7i3Rm1l1oFZNP+hOU/++6TOUQmhP0kshPPSNFtiYdmkUKeOamVjhy6dOIGWnUVqjh/eVapx7pw6kW3HCyyiMFq0UBsWEhNt5XkOklj4+UGlOyeCaxYALzf6CsgffnlJLIQTq+RXifZV1LDLLZFb2BO7h5n7Z3Ix46K+gQmhM0kshPM6dQrOnAFXV8jIUMdat9Y3puuw7q9ID6N8eTUwrUkTrEPyhJNxd7dN4fbxUZdxcfrFU0Dn630OQGBWU+7pVw2DAfbvh+hodX9gkyYYXFxIjY4mNSZGx0iFKB7DGg4DIDwmnEblG5FuSufXPb/qHJUQ+pLEQjiv1avVZdu2sGePum4pObFD1v0VGWEYc38ypQzKyVnKoSIjVameg7Sc3R+3n3RjPGjgvf4zgoLUAHGA5cvVpZuPD+Xq1wek7axwTpbEYt3pddzb+F5AzXQxa9IJTZReklgI52Upg+reHbZtU9fteMUiMXfF4oFxYWTntkSXxMLJWTZwr1sHlSvrG0sBTN89XV052ZOz63sQFwfDhsGQIVCliu1x1n0WMihPOKFq/tVoU7kNGhqerp74e/hz7PwxlhxbondoQuhGEgvhvIKCoEIFCAuD2Fi1YcFOW83mZGZy8dgxADrc2UgG45UW7dqpUr2oKIiIUMcyM3UN6UYyTZn8sucXAKrHPgvAmjUwbhzMnQs9e9oeKxu4hbMb1kCtWiw6uojRLUYDMHnrZD1DEkJXklgI5zV5shoP7OKibjdpYrcTty8cOYJmMuFRtiwnz1ciLQ38/aFePb0jE8XK21vNWQH17vz226FcOTh3TtewruedNe+QkJZAZb/KDGrYD7CVP10uODeRv3jsGFlJSSUVohAlxlIOtSZiDfc0ugejwcjyk8s5FH9I58iE0IckFsK5GQywY4e6bsdlUJb9FXGujfj5Z7Vxu00brHsthBOz7LPYuBHi4yE9Hf77T9+YriE9O51JmycB0Ld2X/r0dgVUYqFp6t/x47aRMV7BwfhWqwaaRoJln5MQTqRGuRq0qtQKs2Zme9R23u/+Pv+O/Jd6QXJWSJRO8rZFOKeoKKyjqy37K+x543bu/orVhxpaBzBLGVQpYdlnsX499FMrACyxzxrtL7d+iclsAuD1Tq/TtSu4uam5fsePw88/q47OL79s+xhpOyuc3T1h9wAwY/8MXu/8Ov3q9MNokLdXonSS73zhfMxmNQSvYkU4fFhN3ga7XrFI2LsXgOPpTbBUjEhiUUp07KhW1o4ezT8oz2TSN67LaJrG51tUi9m6gXWpGVATHx8VPqhVC0uOtGEDpKaq67LPQji74Y2GY8DA+jPrOXvprPW4Zjm5JUQpIomFcD67d0NCAqSlqY2wycmqlr1hQ70ju6qs5GSSTp0C4ER6Y2Jj1XFJLEqJcuWgcWN1PTUVAgLg4kU1kduObDizgdhU9c35eqfXrcd791aXy5dD7doQGgpZWarRFUBwbh/ahL17ybHzjelC3IoqZarQpboqaZy5fybJmcm8teotWk5pSXZOts7RCVGyJLEQzsdSRtKzp0oyQDXZd3XVLaTrOX/gAGga8VmVSMoJQtOgRg0IDtY7MlFi8u6z6NNHXbezcqg3Vr0BgJerFyMbj7QetyQWq1ZBTg7cdpu6bZnC7Ve9Ol7BwZizsmSfhXBaIxqNAFQ5lLuLOz+G/8iuc7uYc2iOzpEJUbIksRDOx7LxtW9fx5hfkVsGdSK9CeXKqWOyWlHK5J1nYdln8e+/+sVzmXMp59hwZgOgyj7cXNys97VooRZZkpLUj5slsbCEbzAYKJ/78xdr+XkUwskMazgMV6Mru87tIuJiBI+3ehyAL7Z8ISVRolSRxEI4l4sXYfNmdT1vYmHHG7cT9+0D4Hh6Yzw81DFJLEoZS2Kxd6/6z+/VC+6/39aAQGdLji1BQ8XyZuc3893n4mKbXbF8uVrBcHVVW0ZyR7NQIffnTxIL4awCvQPpU0utNv6+93fGthqLh4sHW6O2WpNyIUoDSSyEc1mxQtVj1K8PISFgKb2w08RC0zTrxu0T6Y1JTlbHJbEoZUJC1NASTYODB9U79OefV5u67cCpi2oPULvK7agVUOuK+/PusyhTBrp2Vbf/+UddWhKLxD17MKWnF3u8Qujhvib3AfDr3l8J9gnmgaYPAPDJxk/0DEuIEiWJhXAuecug9uyB7GwIDFQ7Su1QWkwMGQkJGFxcmPZvQ1JTVftOOx0QLopTjx7qcvVqfeO4jFkzM333dACebffsVR9jSSy2bIFLl+DVV2HBAhgzRh33rVYN74oVMZtMJFj2PQnhZAbWG4i/hz9nLp1hbcRaXurwEgYMLD62mP1x+/UOT4gSIYmFcC4PP6ya6A8blr8Myk7O/F7OUgZVtm5douLUVPCmTcHTU8+ohC4sicWqVeoyJgamT9e97ey0XdM4m3QWfw9/BtcffNXHhIaqjlA5OWqAeK9eMHAg+Pio+2WfhSgNvNy8uDvsbgB+2fMLdQLrcGfDOwGYsHGCnqEJUWIksRDOpUMHmDBBNdffvl0ds+eN27mJRWCTJtbuolIGVUp166Yu9+9XSUXTpvDQQ7Bpk24h7Y3dy6OLHgXUpm1P12tnvJZN28uXX/1+2WchSgNL+dPsg7NJyUphXMdxPNriUd7s8uYNPlII5yCJhXBeDrRx+8/VjVmwQB1r317HgIR+goJUMgGqO5Sl7eyiRbqF9PW2r63XH2728HUfaymHsrSZPXkS3nwT3nlH3bbus9i/n2zL9DwhnEyHqh2oHVCb1OxU5h6aS6tKrZgyYAp1A+vqHZoQJUISC+E8vvlG7bHIyFCF3ocPq+N2umJhNplIPHAAgNmbGhMRoY536KBfTEJn3bury1WrYMAAdd2yA7qEpWWn8dve3wCoWqYqbSpfP0Hv0UN1gzp2TP07cwY+/FD9WObkgG+VKvhUqoRmMhG/a1dJfAlClDiDwcD9Te4HVBnh5aT1rHB2klgI55CSAi+8oGYAnDkDO3eq49WrQ/ny+sZ2DZeOHycnPZ0sgw/RWTUxm6FCBbvdZy5KQt4N3H36qHfqhw/D8eMlHsqsA7PIMGUA8ETrJzDcYJ9SmTK2OX+LF6tqRH9/SEiwLR5a9lnESTmUcGIPNnsQo8HI2tNrOZp4FID9cfu5Z/Y9fLzhY52jE6J4SWIhnMOaNZCVpUZW16mDdcOCA5RBRWQ2Qsv9UezQwW73mYuS0KULGI3qlH9Sku2dug7lUJO3TgbAgMHaRvNG+vdXl//8o7qb9e1ruw2yz0KUDlX9q9K3tvrm/yn8J0DtV/rrwF98vuVz0rOl5bJwXpJYCOewZIm67NdPvTO3bHi147oiS2JxKKmJ9Zjsryjl/P2hVSt1ffVqWzlUCScW+2L3sfvcbgC6hnalcpnKN/VxlsRi7VqVF1luL16sLi2JxfmDB8lOSSnKkIWwK2NaqF7L03dPJysni7vD7ia0bCgJaQlM3TVV5+iEKD6SWAjHp2m2xKJvXzCbbdO37TixsA7GS2uCi4s6ZsfhipKSt+2sJbHYtAlKcMPzH3v/sF5/rOVjN/1xdepA3bqqQ+6yZerH0WhUI2XOngWfSpXwrVoVLSeHuPDw4ghdCLtwR907CPENIT4tnoVHFuJqdOXlDi8DamBepilT5wiFKB6SWAjHd/w4nDoF7u5q8+vRo3D+vBoG0ayZ3tFdVXZqKpdy6+ZPZDQmJ0eVjrRsqXNgQn+WDdyrV0PNmjB7NkRG2oZClICeNXsC4Ovuy6B6gwr0sZZVikWLVKMryyrc/Pnq0rJqIfsshDNzNbryULOHAJiycwoADzd/mBDfECKTIvllzy96hidEsZHEQjg+S5lI587g62srg2rdWiUbduj8gQOgaWR7VSTVEAxAixYyGE+gdj27uakmBCdPwp13qnfoJcjSDerexvfi5eZVoI/N28zKZIKhQyEgQG2BAtsG7nNbtxZZvELYo9EtRmPAwPKTyzmScARPV09e6fgKAB9v+JjsnGydIxSi6EliIRyf5cznoNwzq46wvyK3DKpm5yY8quaPyf4Kofj4QLt26rplCncJupB+gdkHZwOqu01BdeoEgYFq0XDdOhg7Fs6dgxdfVPdXzP3aLhw6RMb580UVthB2p0a5GtxR9w4Avt3+LQBjWo6hvE95Ii5GMGP/DD3DE6JYSGIhHN+MGbB3L4wYoW47QGIRv3s3AEFNmzrCdhBR0vLuswD44Qfo2hU2bizWT3sg7gA1v6xJuimdeoH1aFu54GPgXV1tOf68eeDtrRZgLLyCgylbty5oGud0nCouREl4qvVTAEzfM53kzGS83bx5v/v7TOw9kaENhuocnRBFTxIL4fgMBmjcWJWLnD8Phw6p43b6Tl3TNBJyEwufes3JXbyQFQthk3eehabB+vXq9L9lPHsx+Sn8Jy5mXATUasWNZldcy5Ah6nLePNVLAdSXYfnRDOnUCYDoYk6UhNBb71q9qRtYl6TMJH7f+zugVi1e6vASvu6+OkcnRNGTxEI4tsunmFrmV9StW+J16TcrOSKCzAsX0Fw86D6iATk5ULUqVKmid2TCbrRtqzbcxMbCwYMlMoU7KyeL6bunA2p2xagmo275uXr1UtudoqJgxw7VerZGDWjUCOLioFJuYnFu40Y0S+YhhBMyGow82fpJAL7e/vUVk7c1TcOsyc+AcB6SWAjHlZGhJmvfey9cuqSObdigLu349L+lDOqCVyNOnlaby+04XKEHDw/bcLxly2xTuA8dghMniuVTLjqyiIuZFwHoVbMXVcrceqbr6Qm3366uz52rpnIHBanVi/nzIah5c1y9vMhITOTCkSOFD14IO/ZA0wfwdfflYPxBlp1YZj2+6Mgimv/QnHmH5ukYnRBFSxIL4bhWr1bN8desAT8/dWz9enVpeVNmhxJ27QLgcEpz6zE7rdoSeurTR13+9x+ULau6nkGxDcvLO7TL0iazMIYNU5d//aUWFi23Z88GF3d3KrRV+zdiLCcDhHBS/p7+jG4+GoBPN39qPb49ejt7Yvfw7tp3ZdVCOA1JLITjmjtXXQ4cqKZwpafbOkTZcWIRn5tYbD7bzHpMVizEFfr2VZfr1qnv7WIsh4pKiuK/4/8BanbF4PqDC/2cd9yhGlxFRMD27bbEYtUqSEiw7bOIkX0WohR4tt2zGA1Glp9czp5zewB4rt1zlPEow764fcw9NFfnCIUoGpJYCMdkMtkmblnesWzdqprlh4RArVq6hXY9mRcvknTyJABH09SKhR3P8RN6atBAbbzJyIC1a22Jxdq1ttK/IvLrnl/RULXfIxuNLPDsiqvx9lY5P8DMmVC7tvo+z8lRe9At+yzid+0iOyWl0J9PCHsWWjaUuxreBdhWLQK8Ani+3fMAvL3mbXLMObrFJ0RRkcRCOKb169Vpz8BA1YYT1JldUKsVt9jNprhZukHllK1JSk5ZANq0sds5fkJPBoNt1WLpUvXOvHlz6NdPdT8rQl2qd8HV6Arc2uyKaxk+XF3OmqX2V1jOAfz9N/hWrYpf9epoJpMMyxOlwksdXgJgxv4ZRCZFAmrVoqxnWQ7GH2TWgVl6hidEkZDEQjimOXPU5aBBalMr2BILS6JhhyxlUOdcm1mPWUrnhbhC3n0WoFosLVyoWiwVoWPnj2Eym6gbWJd2VdoV2fP27Qv+/qo71IYNcJc6YcuKFarhVUjHjoCUQ4nSoVWlVnQL7YbJbGLSpkkAlPUsy4vt1fTId9a+g8ls0jNEIQpNEgvheMxm2/6KO+9Ul1lZtsF4DrC/wlCtuXVomCQW4pp69QIXFzh8GE6fVnuJioGlzewDTR+45dkVV+PhYZtp8ccfqgv0+++rvgvly+fZZ7FhwxVtOIVwRm90fgOAKTunEJsSC8CzbZ8lwCuAo4lH+edo8bWUFqIkSGIhHE9mJjz+OHTsCD17qmPh4WqDa2Cgqk23QzlZWZzfvx+AgU80JztbvU+UjdvimsqWhXa5KwhLl9qOnzoFkZGFfvrkzGRGzhnJ2tNrMWDgvib3Ffo5L3f//eryr7/UdpE331TJtMEAFVq3xujmRmpUFMkREUX+uYWwNz1r9KRt5bakm9L5fMvnAPh5+DG572QW3rOQQfUG6RyhEIUjiYVwPF5e8NZbqrbCw0Mds5RBde5cbGd1C+vC4cPkZGbiUbYsO06FAmoza5kyuoYl7J2lHMqSWLzyCtSsCV9+WeinnnVgFjP2zwDU7Iqq/lUL/ZyX69pVDYC8dOnKTrmu3t6Ub9UKkHIoUToYDAbe7PImAN9s/4bz6Wq/1KgmoxhQb0CRrhgKoQf7fAcmREGtXq0u7bkMKjwcAP9GzVi/Xv3xkDIocUOWDdwrVkB2NuS+EWf+/CsnzxdQ3tkVRblpOy+jEe7LXQj59Vd1efgwPPkkvPOObZ9FtGUGjRBO7o46d9C0QlNSslL4bPNnV9yflJlEVk6WDpEJUXiSWAjHsnevajGTlmY7lpVlW7GwlEbZIctgvE1nmvPDD+qYJBbihlq0UCV+SUmqpXK/fqqN2LFjcPDgLT/t4YTDbI7cDICfu1+RzK64FktisWSJ2rR94gR8+636V769+iGI3baN7NTUYotBCHthMBh4u+vbAEzeOpn41Hjrfd/v+J4ak2vwU/hPeoUnRKFIYiEcy7ffqh6Wzz9vO7Z1q0o0goOhUSP9YrsOTdPyDMZrbj3RnLt3VYhrc3GB225T1//7T02Z79VL3bbMcrkFlg3bAPc0ugdvN+9bj/EG6teHtm3VDItff1XVXRUqQHw8bDhSC9+qVTFnZUk5lCg1BtcfTMuQlqRkpTB+w3jrcbNm5nz6ed5b+x4pWTLfRTgeSSyE48jOhtmz1XVL30qAlSvVZY8edru/IjUykozERAyurqw+HAaoMvkKFXQOTDiGy/dZWFotzZt3S09nMpv4effP1tvFVQaV16OPqssff1S50r33qtu//mqgSo8eAERZShqFcHIGg4EPenwAwLc7viUqKQqA0S1GU7NcTWJTY5m8ZbKeIQpxS+zzXZgQV7NiBSQmqnfj3bvbjlsSCzsug7KsVnhUDyMzxxOAbt10DEg4FsuKxc6d6jT/wIEqid65E86eLfDT/Xf8P+JS4wCoHVCb9lWKvzXZ8OHg66squNauhQceUMcXLQK/lrmJxdq1mE3Sx1+UDn1q9aFTtU5kmDJ4f937ALi7uPNBd5VwTNg0gcS0RD1DFKLAJLEQjuPPP9XlXXepU54AKSmwZYu6bseJRez27QBc9GthPWbH+8yFvQkJgaZN1WbtZcvUEIjcTc+3Ug6Vlp2Gh4vqqPZg0wdLpBONr69tlWLKFGjSRHVFy86GJfua4e7vT9alS9YkXAhnZzAY+KjHRwD8GP4jB+PVnqnhjYbTrGIzkjKT+Gj9R3qGKESBSWIhHENysm0o3qhRtuPr14PJBKGhqrbITsVu2wbA3qQ21mOycVsUSL9+6vKf3AFar7+uSgMfeqjAT9UipAWZOZkYMPBAsweKMMjrs5RDzZmjFl4eeUTd/n6KK5W6dgUgctWqEotHCL11rt6ZwfUHY9bMvLL8FQCMBiMf9/wYUC1pz1w6o2eIQhSIJBbCMcydqzZo160LbWxvzh2hDCo1OprUyEgMLi4s2t0SUE1+atTQOTDhWAYMUJdLlqjT/H37qsnzvr4FfirLxu3bat1GlTJVijDI62vZElq3Vo3cfvxRdYuqVQuGDoWKnVU5VOTKlTKFW5Qqn/T6BFejK4uPLWblSfU3rU+tPnQL7Ua2Odt6TAhHIImFcAy5MyB44AE1stdi2TJ1aceJhWW1IiAsjBr1fADb5GEhblrbthAUpCbNbdhwS0+haRr/HPnHmlg83PzhIgzw5jz9tLr87jvw9lZ7Lj74AKp174SLlxepUVGcP3CgxOMSQi91A+sytuVYAF5c9iI55hwMBgNf9/uafY/v46HmBV+VFEIvklgIxzB5snoHYqmlAIiMhH371CZWy+ZWO2RJLCq0aWNNJizdQoW4aS4ucMcd6rplhHVUlJoy99JLN/UUW6O2MmDmAKKSowjwCmBQvUHFE+t13H232iISGam2h1h+Jly9vKicu/HojKX7lRClxNvd3qasZ1n2xO7hh51q0FFY+TAaBjfUOTIhCkYSC+E4atdWsyoslixRl23aqNoiO6Rpmm3FonkbLG36c8vJhSgYSznUokVqI3dCArz7LnzzDdzEcLlpu6ZZr9/b+F48XD2KK9Jr8vCAMWPU9S+/VJdms9o6cspTtdU9s2yZlEOJUiXIO8jaDeqNVW/kG5oHaqDlgThZyRP2TxILYd80DeLirn6fJbGwbGq1Q6mRkaTFxGB0dWVBeHPS09XZ2rAwvSMTDum229TU7ePH4cgR1VopNBQyMmxlgdeQmpXKn/v+tN7WowzK4vHHwc1NVXRt3QqLF6uc6dUfO+Pi4UlqZCQXDh3SLT4h9PBYq8doWqEpFzMu8vrK163Hf9n9C2HfhvHkv09Kwi3sniQWwr6tXw+VK8PDl70JyspScy3ArhML62pF48a89JqabNyiheyvELfIz882AGXhQvWNdJPD8mYfnE1qtlrVaF6xOc0qNiu+OG+gUiVb69mJE9WPcPXqEJ3gTWY1KYcSpZOr0ZWvb/8agKm7prLhjNpL1aNGD9yMbqw9vZalJ+TnQtg3SSyEfZs+XbWTdXfPf3zTJtWCNjhYtZqxU5bEwqVmG7Ky1LFBJV/WLpzJwIHq0jK/YuhQdblwIWRmXvPDpu6aar2u52qFhWVbyNy5cOoUPPOMur3gqCqHOr1kiZydFaVOp2qdeLjZw2hojF44mgxTBlX9q/JUm6cAGLdiHDnmHJ2jFOLaJLEQ9s3y5mn06PzH//1XXfbtqzZv2yFN06yD8U6YbC1ye/fWKyLhFAYPVpebN0N0NHTooJYALl2yreJd5mjiUdafWQ+oyb4jG48soWCvLSxM7UXXNLVq8cgjakHmn8Ndwd2b1KgoEmRYniiFJt02iYq+FTmSeIT31r4HwGudXqOsZ1n2xu7llz2/6ByhENdmn+/IhLDIylLjeS9flbAkFnZcBpV85gzpsbEY3dxYFN4MgDJl7HqOn3AElStDu3bq+oIFKrG+8051e9asq37If8f/s14fUn8IAV4BxR3lTXn1VXX5889w/jw8+SRkaV4cNKvs+5RlGKAQpUg5r3J8e/u3AEzYOIFdMbsI9A7krS5vAWpzd0pWip4hCnFNklgI+zd6dP5NCceOwYED4OqqVizsVNzWrQAENW3K1nBPAFq1kv0VoghYyp8s0+jvvhv8/dW/qxjTcgxlPMoA9lEGZdGpk1rBM5ng/ffhuefA0xPmn1Ddr84sWUKOpYZQiFJkSIMhDGs4jBwth4cXPkx2TjZPtn6SWuVqcS7lHBM3TtQ7RCGuShILYd+8vW27PC0s5VHdukG5ciUd0U2z7K8o27QN8bmdAy1VLEIUimXD9po16lR/hw4QG2vr33qZ2Qdnk5SZRDX/avSsYV/DJN9TlR78+iskJanzCG612mD0DyYrKYmYWxwGKISj+7rf15TzLMfuc7v5dPOneLh68EmvT/D38Ke8T3m9wxPiqiSxEPbtnnugbNn8xyzdbyxvruxQ3vkVZ422/RV2HLJwJLVrQ+PG6lT/P/+ociiPq8+kyDBl8P2O7wEY02IMLkaXkoz0htq1g9tvh5wclWRMmADhu12oO/h2AE4tWKBzhELoo4JvBb7o+wUA76x5h8MJhxnaYCinnj3Fk22e1Dc4Ia5BEgth3x5/PP/tmBjYskVdt+P2SkknT5KRmIiLhwepZZsCULEiVKmic2DCeVjKoWbPth3TNNi1y9odKjYllsBPAtl4diOuRle7KoPKy7Jq8ccfEBGhygVr5P58R61ZQ0Zion7BCaGj+5rcR59afcjMyWTknJFk5WRRzst+V+qFkMRC2LcGDfLfXrhQvXlq00ZtYrVTsZb9Fc2asWO3apUrZVCiSN11l7r87z+4eFFd79ZNDUrJ7Q71+97fSTOlATCo3iBC/EJKPs6b0LKl+vnQNHjnHXXMvUo9sgIbYTaZOLVokZ7hCaEbg8HA1IFTCfQKZNe5Xbyx6g1ArYovPrqYMYvGSFtmYVcksRD2xWxW7y6uxbK/ws5rimI2bgSgYvv2rFqljvXooWNAwvmEhal/2dm2n4umanWMWbPQNI0fw3+0Pnxsq7ElH2MBvPuuupw1C3bvho8/hl/3qW5Xx2fPkTdPotSqXKYyUweqOTSfbv6UpceXEpsay7C/h/Fj+I/MPTRX5wiFsJHEQtiXX36Brl2vfl9ioq1Pvx2f/s/JyrLur7gQ0Il9+9Rxy8BkIYrM8OHq8q+/1KVlFWPBAraeWs+RxCMA1CxXkx417DuzbdLE9uU8+6zqEHXYeDsZZi+ST52UmRaiVBtUfxBPtHoCgAfmP4DRYOSl9mrK5AvLXiAtO03P8ISwksRC2A9Ng88+g/Dwq98/e7barNq8OdSvX7KxFUDC7t2Y0tLwDAzk1//qAeDjo4aEC1GkLO/Ely+HhATo2BFCQuDSJaYt+cj6sMdbPY7RYP+/7j/5BLy8YN06WLkSXn3bly2X1Kyag3/+rXN0Quhr0m2TCAsOIzY1locWPMSrnV6lapmqnLl0RtrPCrth/39pROmxfDns369azF7Nn3+qy5H6Tw2+HmsZVIcOrFylfsQaNdIzIuG06tZViXZOjpppYTTCsGEku8PvcWp1z83oxoPNHtQ3zptUvbptaN7LL8P998MJf7UKc/a/JbKJW5RqXm5ezLhzBh4uHvx77F9+DP+RSbdNAmD8xvFEXIzQN0AhkMRC2JPPPlOXDzxw5X1nz6rTmGA7S2unbIlFRyIi1DFLAx8hipzl52HGDHV511383gTSjTkA3B12N0HeQToFV3Avv6wSjLNn1a+Elz5rwvH0xhi1bLb+MPvGTyCEE2tcoTGf3vYpAC8vf5nKfpXpFtqNDFMGLy17SefohJDEQtiL3bth6VJ1xvWJJ668f+ZMddmlC1StWqKhFUR6QgIXDh0C4EB6B8xmdXz0aB2DEs7tnnvU5dq1cOYMdOxIz7QKuKm8wu43bV/Oy8t2jmHCBFX1eK7KKACO/zUTc3a2jtEJob8nWj/BXQ3vwmQ2cffsu3m7y9u4GFyYc2gOO6N36h2eKOUksRD2Yfx4dTl8ONSoceX9DlIGdW7TJgDKNWjAr7MDATXfLyBAx6CEc6teXXUG0DQ1CMJoZOkzt5PtAmHBYXSs2lHvCAtsyBDo2VON43j+eXjy69tIdwnCyxTHmeXL9Q5PCF1ZWtA2CGpAdHI076x9h3e7vcvCexbSIqSF3uGJUk4SC6G/EydUj0mwFVjntWuXWtFwc4Nhw0o0tIKKWrsWgEpdurB+vTrWqpWOAYnS4b771OVvv5GTY2JypiobfKL1ExgMBh0DuzUGA0yeDK6usGABHDziTpuxquTr8PTp0npWlHp+Hn7MHT4XP3c/1p5ey4WMCwyoN8Ahf96Fc5HEQuivRg2YN08lFU2aXHn/j7m9+IcMgcDAko2tAHKysojZsAGAMi26ERurjtv5lhDhDIYNA09PzkccotXkME5cOEE5z3I80PQq+5UcRFgYvP66uv7EE1Cu9whcvLw4f+AA2//apG9wQtiB+kH1mT54OqDmW/x9QHVOi02JJSY5RsfIRGkmiYXQn9EIgwapiViXS01V5R0AY8aUbFwFFB8eTnZKCh4BAWyLUm2gQkNtJfBCFJsyZWDIEH5pCruTjwIwpt5IfF59y+5X+a7njTegcWPVSfeFN8pRO/drWfjqjyxdqnNwQtiBoQ2G8kqHVwB4aMFDfL3ta+p/U5/HFz+uc2SitJLEQujrRhsx582DpCSoWRO6dy+ZmG6RpQyqcpcu/Pef+tEaOhR8ffWMSpQW2qhRfNFWXTcajDwd9hB88QXMmQMnT+oa261yd4fp08HFBf7+G04EPYjZ4EpDn+18MDacpCS9IxRCfx/2/JCeNXqSmp3K+A3jSc5MZsGRBcw/PF/v0EQpJImF0M+pU1Ctmtq4bWmfdLmff1aXjz6qVjbsWHRuYhHSpSv//aeO3X67jgGJUmVNPXfOlFPXh/m2oXKdlmoHNNiaHzigFi1sJVFPvV6RircNAqCT+TteeEHHwISwE65GV/4a9he1ytUiKjmKEL8QAJ769ymSM5N1jk6UNvb9Tk04tw8+gHPnYNWqaycNW7eqHZwPPliioRVU0qlTJJ8+jdHVlTifjpw7p8KuV0/vyERp8dm2ydbrr6w1qSujVJtWfvtNdY1yUG++qUqi4uPhp4OPgtGVxr6b2Dxjq3V8hxClWaB3IItGLKKMRxkikyLxc/cjKjmKt1a/pXdoopSRxELo49gx+OUXdf3dd6//2LvugooViz+mQohcuRKA8m3aMGOuD6CGIdvxXnPhRM6lnGPx0cUAtIyGlgt3wPHjqhbPywuOHoUdO3SO8ta5u8Ovv4KHB/y1tCopddU07uHlv2DMGI1jx3QOUAg70CC4AX8N+wujwUhyllqp+GrbV+yIdtyffeF4JLEQ+njtNfXO+/bboX376z/2xRdLJqZCOLtiBQBVe/Vi4UJ1rE4d9Z5OiOI2ectkNNSKxFspuX3sf/oJ/Pxg8GB1+/ff9QmuiDRrplrQArz6z1gMHl7U9t5LfcNKhg8Hk0nX8ISwC31r97VO5jZgwKyZGbNoDNk5MlhSlAxJLETJ27RJbSg1GuGTT67/2M6doWXLkonrFqXGxJC4bx8YDPg068GJE+r4gAH6xiVKj4sZFwGo6FuR/ne+pg5Om6YmzFnKoWbMuHGzBDs3ZoyakXkhK4jll+4H4IEqk3jz1UxcXXUOTgg78WzbZ3mk+SPWkw31AuuRo+XoHJUoLSSxECVL0+Cll9T1hx+GRo2ufEzeVi/PPFMycRVC5KpVAAQ3a8aSDcHW4yNG6BWRKE1MZhPLT6pp1G90fgOXAYOgcmW1IWHWLOjdGxo2VAlGerrO0RaOwQA//AD168Ofpx4h1aUCAYaz1EmcpndoQtgNg8HAt3d8S88aqnnDmtNriEuN0zkqUVpIYiFK1q5dakO2tze8997VH/PNN7brffqUTFyFcHa5elNXtXdvpk9Xx7y9oXlz/WISpcesA7M4ceEEgV6BPNTsITWh/okn1J2W8dX798Nnn6l5Fw7O11e1njV6+jDt9MsAHPzxR1IiI4mIgJ079Y1PCHvg7uLOnLvn0Kh8I86lnOP2P27nfPp5TGapGRTFSxILUbJatIB9+2DqVAgJufL+8+fhyy9tt+28xWxGYiLxue9kAjv2YssWdbxbN7sPXTiBdafX8fzS5wF4vt3z+LirxgGMGaN2Ou/cCZs3q1P9TqRRI1XptSWpLwdS25CTmcnKl96ldWuN/v0hMlLvCIXQn7+nP/+O/JdKfpU4EH+AmpNr8s7qd/QOSzg5eesjSl7DhtceRz1hAiQ7Tt/tsytWoJnNlGvYkA37KmMyqRPEw4frHZkoDZ5f+jxxqXG4u7jzVJunbHcEBcG996rrlkTdbFatnRctKvlAi8E998CHHxr4OeYtsswepO7bRN8Kf3PuHAwaBGlpekcohP6q+ldl8cjFeLp6cinzEh9t+IgdUdIlShQfSSxEybh4EQ4evP5jzp3Lv1rhAE7/+y8A1fv1Y948deypp2z7ZYUoLofiDxEeEw7A6Baj8ff0z/8Ay/6k2bPh9Gm1ebtnT9VlzYFnWuT12mvQb1RNZsWpr7Wf20Tqlo8kPBzuu081nhOitGtWsRnzhs/DgAENjdv/vJ1MU6beYQknJYmFKBnvvqsmXH300bUf88oranNp69YlF1chpMbEEJdbBlWpVz/rieAhQ6QMShS/F5apsdMuBhfe7XaVWTBNm6pEIicHJk1Sp/F9fdUMmdwp8Y7OYIDvv4fsZvdxOLUlZKXxv8Yv4e2exdy5KrdykhxKiELJ24Y2Pi2eAX9K20JRPOTtjyh+u3erlQiz+dqtY9etU9OBDQb1JsgBnPnvP9A0glu2ZMPeEC5eVNtGOnbUOzLh7BLSElh6fCkAwxsNJ8g76OoPfP11dfnTT5Caqnq1gno37iTc3ODvOS6s8f+YlJwyGGL2MXngBAwG+PZb+PBDvSMUwj483/557mqohksuP7Wc/63+n84RCWckiYUoXmYzPP64urzrrqt3ecrOhiefVNcffdTu51ZYROSWQYXefjt//qmOJSZCVJSOQYlS4Zklz6ChYTQY+arvV9d+YPfu0KYNZGSoDlGPP66Oz5mjSg+dRNmyMHdlZZa4jQfA++AMJtw3H4DFiyErS7/YhLAnfw37i/pB9QF4f937zD00V+eIhLORxEIUr6lTYcsWVYLx+edXf8yECaodZmDg9Uul7EjSqVNcOHgQg6sr5bv2Yf58ddzfH6pW1TU04eSSM5OZdWAWAIPrDSbAO+DaDzYYbKsW33wDoaHQoYMaU/3TT8UfbAkKCoIpq7qygbEAVNj+Nl8+t4kVK8DdXefghLATBoOBHY/uoIyHaj390IKH2B+3X+eohDORxEIUn/h4GDdOXX//fTW063Lbt8M776jrn32mkgsHcDI3kwjp0IHVW8tZ544NG+Z0nT2FnZm6ayo5Wg5Gg5EpA6bc+AMGDFD9WZOS4NNPbasWU6aoBMOJBAfD/1Y8yQGtHy4GEz7/Pce2hQes9x8/rmNwQtgJH3cfwseE07ZyW5Iyk+jzex9OXzytd1jCSUhiIYrPK6/AhQvQrJlqlXS5lBTVEtNkUmVS991X4iHeCrPJxKmFCwGoOWQIM2bY7hs0SKegRKmQmpXKhI0TAPio50cEet9EIm402oZRfv45dOmiTu/7+DjlwIeKIUaeXvYRZ4xt8DSmcvzd0fw9eS/vvac6XS9erHeEQuivVkAt/r33XxoGNyQ6OZpev/UiPjVe77CEE5DEQhSfNm2gXDn47js13CEvTVP7KY4dUysZ33/vMKf6YzZtIj0uDo9y5fBr0c1aBuXjowbjCVFcPt38KTEpMdQsV5Pn2j538x84eDC0aqU2cH/+OWzbpto/h4YWU6T6qljFnbErviLeswU+LkkkfTeaHXO2kp0NQ4fC0qV6RyiE/gK8Avh35L/4e/hz/Pxxev7ak+RMx5kjJeyTJBai+Dz+OJw5A+3aXXnf++/DzJkq4fjzTwi4Tp24nTk5V212C+3fn7kL3K0bQ4cMUcOOhSgOkUmRfLDuAwA+7vkxHq4F+GYzGGz7l779Vq1iOEgif6vKVvDlsTXfc6lsa7xcUhmWPYYhoX+TlaVWFv/5R+8IhdBfBd8KVPVXGwP3xe1jwIwBMuNCFIokFqLo5W3B4ut75f1//glvv62uf/edKs1wEBnnzxO1ejWgyqB+/dV2n0zbFsVpxOwRZJuzcTO6MajeLdTc9eqlukRlZakyRVDjqVeuLNpA7Yinnw+jV3xPRq3bcTWYGOb9Do9XewuyUhkyBP7+W+8IhdCXp6sny0YtI9g7GIC1p9cyYs4ITGbn2n8lSo4kFqJorV8PdevC8uVXv3/WLLj/fnX9uedg9OgSC60onJw7F7PJREBYGBfd67FunTrx+9xz0Lu33tEJZ7X+9Ho2nN0AwGMtHyvYaoWFwaDKoIxG9XM4d65qYda3L0RHF3HE9sPNy5OHFkyg3OCnMWsGOvnO5cNad1HbfQfDh8P06XpHKIS+QvxC+G/Uf7gbVfu0eYfn8cD8B8gxy+h6UXCSWIiik5oKDz0Ep0+Tb0ezxcyZakBXTg48+KDqUONAzDk5HPvrLwDqjBhhXa3o2VO9X5MyKFEccsw53Dv3XkCdXfyk9ye3/mRNm8KYMer6u+9CgwaqecJX15mF4QQMBgP9PhxLu69+Js2lIiHup3kr9AEeC3mNM/udZ56HELeqRUgL/rzzT+vtP/f9yaOLHsWsmXWMSjgiSSxE0Xn1VThxQp0FzTuzQtNUffeIESqpuP9+1UPf6FjfftFr15IaHY27vz9VbuvH1Knq+IMP6hqWcHJfbfuKs0lnAXiz85t4u3kX7gnff19NlNu7VyUWoPZdXLxYuOd1ALV7tua+dXPJDLsbs2agc9mFVF/cj2+HjOf8KeddtRHiZtzZ8E7e7/6+9fbPu3/mycVPommajlEJR+NY7+yE/Vq9Gr7+Wl2fOlVNigPVO//ee+GNN9TtZ5+FadPAxUWfOAvhaO4qTK2hQ1m+2pOzZ9XgraAgnQMTTuvspbO8uuJVAPw9/Hmh/QuFf9KgIPj4Y3X9zz9V6WJSkhqgVwp4lPXnoVlv03zyn8R5tMTNmEXZo7/x7x19mNTmSU6v3oBmlrO0onR6o/Mb3N/0fh5p/ggGDHy/83ueX/q8JBfipkliIQrvwgXbafvHHrNtNtiyBZo3V2VRLi7qrOgXXzhkUnHp5EnObdoEBgN17rmHKblzybKypHWlKB6apvHIwkfIzFEdWj7r8xlebl5F8+RjxqimCWlptrHUn3+uyhlLibDeTXhmxy9wzw8czW6H0WCmUuoaNj71GLN63M7uL74gcf9+eUMlShWDwcD0QdP5aeBPTB2oluUnb53MqytelZ8FcVMksRCFo2nw8MOqrWytWjBxIly6BE8+CR06wMmTUL06rFtnm/jrgA79/DMAVbp35yJV8g3ZeuABnYISTm3G/hksP7kcF4MLLUNa8kDTIvxGMxpVOaKnJ+zfr1YxEhOxZsylhNFoYORbnXgpfCqr6y7iv8RRpOX4khN/loM//sjS4cNZeNtt7Bw/nnNbtpCTt+OdEE7KkNuK+qHmDzGx90QAJmyawLtr39UzLOEgJLEQhZOVBV5e4OYGf/wBv/6qSiu+/VYlHfffD7t3qyTDQaXFxhKRO2m7wSOP8O23YKmUaNpU/ROiKEUlRfH0kqcBeKfbO2x/dDsuxiJe6atTBz78UF2/dEldnjxZtJ/DQfj6wo/zanLPtNd4JXIVX0VOZGvSbWSavUiNjubIb7+x6pFHmNOxI+uefZYTc+aQFhend9hCFLuD8Qet199d+y7vrHlHVi7EdRk0O/0OSUlJoWXLluzcuRPfq81CEPYjLQ0++QR+/932xqRePZVc9OhR4KdLTU21/p+npKTg4+NTlNEW2K5Jkzj0888Et2xJx+9/pWpVOH9e3ff556rVrBBFxayZ6fN7H1acXEGLkBZsfmQz7i7uxfTJzHD77aqer1YttaHbu5Cbwx1cXBz06aPOh7gb0mniu5GWZdbQLmgd7tmJ+R5brkEDKnXpQqUuXQhs3BijA5Z5CnE9FzMu0uf3PmyL2mY9Nq7jOD7u+bF1ZUOIvCSxELcmM1PVY3//PUyerP4aA5QvD++8o+ZTuLnd0lPbU2KRdekS83v3xpSaStfvvmPRwS7Wii5PT9X+v1w53cITTujzzZ/zwrIXMGBg6sCpPNT8oeL9hHFxatnt3DkYNUqtOpbyNwyZmapy8+efVZ6VlgYGzNQtc5BHuq6jgfs6kg7vV6uyuTzKliWkUycqdelCSMeOeJQtq98XIEQRupRxif4z+rPhzAbrsWfaPMMXfb+Q5EJcwVXvAIQDOntWndI7dkz1wAeoVg1efBEeeQR0XmEoSoemT8eUmkrZunWp2LEzX4yx3XfvvZJUiKK1I3oHr65UXaA0NA4lHCr+T1q+vOoO1bu3WnWsVAlat4Zhw4r/c9spDw/V3K55c/Wr7tw5ePNNI+vXN+KVRY3w9X2Cl59IZFjzDZzfvo6YjRvJvHiRiH/+IeKffzAYjQQ1bUqVnj0J7d8fr+Bgvb8kIW6Zv6c/S0ct5e6/72bxMbXB8MttX5KZk8m3d3yL0SBV9cJGVizEzTt0CCZMgN9+U/MoQJVPvPsu3H33La9QXM5eViwyEhNZ2KcPpvR0unz1FTuSejBkiFqpqFIF/voLWrTQJTThhBLTEmk5pSWnL50GoKJPRY4+fRQ/D7+SCeCbb+Cpp9R1V1d14iA0tGQ+twPQNNWTYulSW8VnQAC89ho8/lg2qUf2ELV2LdHr13Pp2DHrxxlcXKjUuTM1hwyhUpcuuLgXU1mbEMUsOyebsf+MZdruadZjo5qMYtrAabi5FM3ff+H4JLEQN7Zpk9pDkbuB2erRR+GHH4q8bMJeEoud48dz5LffCGjUiNtmzKR1awPh4fD66/DBB6W+WkQUIbNmpv+f/VlyfIn12Mw7ZzK80fCSDeTxx1V5I0DnzqqbmwBg505o21adU+nXTyUXR46o+ypXhvfeU123jUZIjY4mau1aIv75h4Tdu63P4RkURN2RI6kzfLiUSgmHpGkan2/5HE3TGLdiHDlaDn1q9WH23bPxdZf3akISC3E9a9eq/RJr1qjbBoM6k5mdrXqs/vxzsby7tofEIjU6mkV33IE5K4vuU6aw61JH7rhDVXlFRMhQPFG0Xl3xKp9s/AQDBjQ0BtUbxLzh80q+ftlkgr59YeVKdfvLL+Hpp0s2BjuVnQ1vvqkWbQFatlQLtd98o7ptW45NngwdO9o+7tLJk5ycN49TCxeSkZAAgIunJzUHDaLeAw9Qpnr1Ev5KhCga/x77l7v+vou07DTqBtZlw0MbCPaRsr/SThILcaU1a1R5kyWhcHODe+6BjRvVabp27dSkbU/PYvn09pBYbHjhBc4sXUqFNm3oPnUaHToY2LpV1VsvWKBqsIUoCj/v+pmHFz5sve3v4c/BJw9Sya+SPgFlZqpWtGfPqhMHCxbAgAH6xGKH/vlHnVc5f161qf30UzW4/P331SXAiBFqkbdqVdvH5WRlcWbZMg5Pn86FQ2rvjMFopMbAgTQaOxbfvA8WwkFMDZ/K6EWjASjrUZaNj2ykYXBDnaMSepIdN8ImPBx69YLu3VVS4e4OTzwBJ05AerpKKipXhrlziy2psAex27ZxZulSDEYjLV59lQULVFJhMKj66j/+0DtC4SxWnlzJmH9UR4AGQQ0ANWFbt6QCVNa8YYM6oaBpMHiw6hQlAOjfH3btgk6dICUFHntM3T52TFWHGgwwY4bquP3uu5CRoT7Oxd2dGv370/fvv+k5fTqVunZFM5s5OX8+i/r3Z+v//kdqdLS+X5wQBTSy8UhGNR4FwMXMizT5rgmfb/5c56iEniSxEGod/7771Dr+ypW2hOL4cbXOX7UqjBsHtWvDvHkQEqJ3xMXGbDKx8+OPAah999341qzHq6pJD5oGFSvCyJE6BiicxraobQyaOQiT2cTwsOHsf3w/i0cu5qFmxdxe9mZUq2abwm02q1P0zz6r6oEE1aqpcy/jx6v8q0MH1VxryhS1F6NzZ3Uu5p13VCdfy+IvqKnGFVq3ptu339Jn5kxCOndGM5k4MWcOi+64gz2TJ5OdmqrTVyZEwXi5efHb0N/4fcjvuBndyNFyeGHZC7T9qS3RyZIol0ZSClWapaaqybuffabKH0D1UP3wQ7ha3a/JpPZYFHtY+pVCHfjxR/Z88QXuZcowYMkSps0oyxNPqLOQmgZff606wwhRGAfiDtBlehfOp5+nZ42eLB65GA9XO6uv0zQYMgQuXlT7rQDat4dfflGlUgKAo0fVy2HZDrNvnzoXs3SpGp557pw6/vDDMHGi6iR1ufjdu9k7eTKx29QQMs+gIJo++yw1Bg2SoXvCYcQkx9Dp506cvKDapvm6+RL3chxebl46RyZKkqxYlEaaBvPnQ8OG8PHHKqno2hW2b1d97C1JxQ8/qH0VFiWQVOjp0okT7PvmGwBavPoqyaayvPWWuk/ToEYNVeogRGHsjd1L91+6cz79PDXK1sDPw4+snCy9w7qSwaBWKNesUb8vypSBzZvVKfjPPpPVi1x169qSitRUGDgQGjVSjR4OHYKxY9V906ZBgwaqTOry03nBzZrRY9o0unz1Fb7VqpGRkMDWt95i6fDhxG7fXrJfkBC3KMQvhKNPHbWuuqZkpzBy7khSslLQNI3sHPmdURpIYlHanDql/vINGaJKoKpXV28eVq+GVq1sj/v9d/UXsXdvVTzs5MwmE1veegtzdjYhnTtTY+BAxo2DxETVPhJUO0lpQS8KIzwmnO6/dCc+LZ56gfWITIpk/uH5/LHPTjfuWN4xDxoE27apFYv0dDUMs1EjlXCYzbqGaE/OnlXnX6Ki1H73J55QvzfWr1dJRVycKqW8/XY4fTr/xxoMBqr06MEdCxbQ/OWXcfPz48KhQ6x88EHWP/ec7L8QDsHF6MK0QdOYPng67i7uzD88n9Y/tuab7d9Q/5v6/LrnV0kwnJwkFqVFZiZ89BGEham2Jm5uarLTgQNqc2betpYLFqiG7ABjxqi9FU5u3zffkLhnD26+vrR5+202bjQwLXcGkNmscq4RI/SNUTi2lSdX0m16N86nn6dZhWZczLhItjmboQ2G8ljLx/QO7/oiIuCuu9Q7508/VRsKjh5VJygaN1an42VfAPXrw9698Mor6oTEjBlqYfjsWdUbw3Jy4r//1K/ir7++Mi9zcXenwYMPMuDff6kzfDgGo5Gzy5fzz4AB7P/hB3IsZatC2LEHmj7AmgfWUNmvMocTDvPsf89y8sJJHpj/ALW+rMWkTZO4lHFJ7zBFMZA9FqXBypVqY4BlmlO3bvDtt+oU2uXmzFGtZU0mGDVK1VMbSzb/LOk9FjGbNrF6zBjQNDpOmkRg5340a6aaYQ0ZAvHx8Pnn+Rd0hCiI3/f+zsMLHibbnE3Hqh2JT43n6PmjhAWHsfmRzSU3XftWJSer5g7HjqkhDfPmwRdfqHfGlh6rvr4wbJha3ejVS90uxXbsUPsq9u1Tt2+7TS3wnD4No0fbqkw7dICffrr6r2OAC0eOsPOjj4jbsQMA36pVafnaa1Tu2rX4vwghCik+NZ7759/Pf8f/A8DDxYPMHJUc+7r7Mrr5aJ5s8yS1A5z/BGZpIYmFM4uJUSULM2ao2+XLq9rokSOvPtjujz9U95ecHPWYX37RZV9FSSYWKVFRLL3nHjLPn6f23XfT5u23GTMGfvxRbcDcuxf8/WXKtrg1JrOJ11e+zsRNEwEY1nAY0cnRbDq7icp+ldn8yGaq+jvI/ILDh9UMm0uXbAMyk5LUXqwfflDtqC3c3KBZMzWqul49tUGpZk0IDQWv0rORMytLzbP48ENV/jR3rjpuNqsB5+PGqZa17u5q+N64cVcvt9Q0jdOLF7Nr0iTS4+MBqNytGy3GjcOvWrUS/IqEKDizZmbixom8seoNcrQcyvuUx8vVi9OXVD1g1+pdWfPgGn2DFEVGEgtnZDLBd9+pv1RJSepd8RNPwAcfQNmyV/+YpUvVxF1QZVA//QQ6dSMpqcQiKzmZ5ffey6UTJyjXoAG9f/+dBYs9GTZMvWQrV6qRHkLcitiUWEbNG8WKkysAeLXjqxxNPMrcw3Px9/Bnw8MbaFS+kc5RFtCyZeodck4OvPACTJpka5m2cSPMmgX//quW+67F2xv8/NSKhq+vSjTc3dU/N7erX3d3V7NzypdX7a4rVVL/atRwiGmVx4+r8KtUUbdjY1VpVFgYPP64eslAVZVNnQqtW1/9ebJTU9n/3Xcc/u03NJMJo7s7DR9+mIajR+NaihI24Zg2nNnAiDkjiEyKxICBgfUGkpqdykPNHmJkY9XHPT41ng/Xf8joFqMd7/ejACSxcD5bt6q/VLt2qdutWqkk40Z1PCaT2mtRt656s1DC5U95lURikZOVxdonn+Tcpk14lS9PnxkzOJVYkbZtVam4waDysnffldUKUXBLjy/lgfkPEJsai7ebN9MHTeeusLsIjwlnyF9DmHHnDDpU7aB3mLdm6lRVywNqn9aHH+b/IdE0tSdj61ZVD3TihGoacfKkKqkqSq6uamNDkybqX9u2alXlZgd4Wv78lfAP+X33qf4YAweqFY3wcDUmJCFB/ep97jm1H+Nav/ounTzJzo8+4tzmzQD4VKpEi3HjqNKzJwb5hSXs2KWMS7y47EWm7poKQM1yNZk2cBpdQ1Vp36RNk3h5+csAtK3cllFNRnF32N2U9ymvW8yiYCSxcBbnz6s/8j/+qP5Yli2rWsk++ui1Vx4uXFB/uSxr79nZ6iyhzoo7scjJymLD888TtWYNLl5e9P71V7QKDWnXTp1ZdHGxnZD99NMi/dTCySVlJvHqilf5bsd3ADQq34g/hvxBk4pNrI/JysnC3cXB24t98w089RTUqqVOYvjdxB4RTVO/cy5eVPU/KSkq0cjIUDVD2dnq0vLv8tvp6WooREwMREdDZKR6jst5eqp9ID16QJcu6li7drayzkmTYMIE9bHp6eqYm5tqpxscrPafWZYqL1xQdUuBgYV+ySzMZlXy9Pnn6veMq6s6F2RZVP4jt0FYjRrq13nPntd6OTUiV6xg5yefkBYTA0DFDh1o9frrlKlRo8jiFaI4LD2+lNGLRhOZFAnAfU3uY3yv8Zw4f4Ivtn7BwiMLMZlNALgYXOhVsxf3Nr6XYQ2HyVwMOyeJhaMzmVSt8+uvq9NdoOqfJ0xQZQPXsmMHDB+u/vhOmWJXp+WLM7HIycxk40svEblqFS4eHnT99lt8wtrRs6ca4+Hqql7Sbt1U1Ycd5FnCAWiaxsIjC3l6ydOcTToLwJOtn+ShZg8xat4opg6c6rgrFNfyww/q94dew/I0TbVb2rdPbYbavVsN8ouNvfKx996r2md36KDe0b/00rWfd/duNasD1GNfeEG11u3eXf3r2vXqU+4K6PBh1T1q0SJ1299frZLWqQNPP62+NFDb3SZMgMqVr/48pvR0Dvz4I4emTcOcnY3R1ZU6I0YQ9thjeJYrV+g4hSguSZlJvLzsZaaETwHAx82H1zu/zgvtX+BSxiVm7p/JH/v+YHu0muXi4eJB7Eux+Hv66xm2uAFJLByVpqmehS+/rFrGgirY/fZb21m6qzGb4csv1V+07Gw1x2L7dnWmzk4UV2KRefEi6556ivhduzC6u9P1668p06wjAwfCqlW2lQo7fEmEHTsQd4AXlr3AshPLALW0/9OAn4hKjuKxfx4jLTuNdlXasenhTc5dpvL99+oNefv2Jf+516xRtUPr1qkf4msJCVEDJrp3V2VT3t7qd2l2ttqUHh+vkg/Lvo1XX1W1SnkZjWpFZMgQ1fbJv3BvclatUj02du9WtydMUDnQa6+pX+eapsJ8/XX1uGtVeSWfPs3OTz4hOndKuquPDw0eeoj699+PWzF31hOiMLZHbefZ/55lc6Qq7QstG8oH3T/gnkb34GJ04VjiMWbsn8GljEt82sdWRtD3975ULVOVOxveSY8aPRx/JdhJSGLhiMLD1Vr6CrUplIAAeOst1VL2eqfYT5xQfwjXrVO3hwxR9dJ2dlarOBKLC4cPs/7550k5cwY3Pz+6fPklbrXb0L+/GiZsNKqcKzhYvTz16xf6UwondyDuAO+ve59ZB2ahoeHu4s6L7V/kqdZP8dbqt5i2Ww1C6VWzFzPunEGQd5DOERejbdvUG3JQJVJvv118v1cyMlQiUb26rUfrihVqmCeofWJ9+6o3/82aqRMvc+eqpYFLefrm16unVjLuvVd1rLqW+Hi1ErJ6tfp36JA67uqqJt5Zvs7U1GtviriBnBz47TdVpbVxoy1XWbIE3n9f/Y4C1VTr3XdVyNeqcI3ZtIndn3/OhYMHAfAMDKTBww9T+667JMEQdkvTNGbsn8G4FeOs5VF1AurweufXubfxvbi55H9vczTxKPW+rme9XdazLAPqDmBog6H0qdVHyqV0JImFI9m8WW2UXLxY3XZ3h2eeUaeyrvdHPCMDJk5UA/IyMtQfv4kT1WkxOzyDWpSJhaZpHJ81i53jx2POysI7JIRu33/POVNthgxR5Qg+Puo9gb+/er/SrFnRfB3COV2eUAAMbTCUCb0mcCjhEGP/GUtUchQGDPyv6/94q8tbuBj16bBWYi5dUic2LBsEAgNVCdGYMRBUBAnV8eNqhfa//9Sb+7Q0tcP588/V/VlZasXk9tuvPdAzK0t1v/vjDzUENCPDdl/79urd+t1333ip8swZNZAiMlItL1h07Kh+kdx9txomeAslYppm+5WsaSqsmBi1CL1qldpaAurEx3vvwZ13Xr3PhmY2c2bpUvZMnkxKbk2Vu78/9UaNou7IkXhcqzugEDpLzUpl8tbJfLb5MxLTEwG1gjGu4zhGNRmFr7t6b5BjzmFNxBpmH5zNvMPziE21lUD6uPnwUc+PeKbtM7p8DaWdJBb2TtNU39OPP1Z/WUD9JRkxQp3KuplNeklJ6uzcuXOqJvqnn27u43RSVIlF0unTbHv7beK2q/rMSl270u7Dj5j3X1kee0ztG61cWb1XWb5czfRq3LjIvgzhRDJNmcw9NJcp4VNYE7HGenxog6H8r8v/aFqxKVsjt9JuajsAagfUZurAqXSpfp2yRGe0bJlKKCzlmZ6eqvXRV19df8/X1WRmquYTGzfmn5EBqtXsmDFqZeRWJCWpIX9//KF+v1rGX7u6Qp8+KskYOPDmVyASE1WZVXa27Vjz5irBuOuuayc713H6tGo7mzu2An9/ddJjzx61/x1U9esLL6h9GFcrkTJnZ3Nq4UIO/PQTKWfOqC/R25saAwdSZ/hwytatW+C4hCgJKVkpfLf9OyZtnkRcahwAfu5+jGoyirGtxtKkgq0hRo45h82Rm5lzcA5zD8/lzKUzzL5rNnc2vBOAQ/GHWH5yOf1q96N2QG3nLkm1A5JY2KuEBDWgbsoUOHpUHXN1hfvvV3W/1zsblpWlJmgPG2YrjZo3Tx2/+267XKXIq7CJRcb58+z/4QeOz5yJ2WTCxcuLps88g0+3UTz7nJF589Tj2rZVJy4rVCjqr0A4A03TCI8JZ8b+GUzfPd169sxoMDK4/mD+1+V/hPiF5GuDOHDGQOoH1eedbu/g7eatV+j6Mplg5ky1mhAerlZTY2Ntv4tefVX9fitXTtXzmEyq+1JsrCrr/PVX23NVrapWBtzcoFMnVeLUt686A1BUv8diYlS8f/wBO3fajvv4qHLRUaNUa6YbDQtNTFQrGbNmqWQl716P559Xw0kLKD1dlUh9+qntzwCo80KxsWrhBlTO9uST8MgjV9/kbc7J4eyyZRyYMoWLeZ4ouHlzag8fTtWePXH1LqXfr8KupWWn8VP4T3y97WuOnT9mPd6uSjvubXwvQxsMpZJfJetxTdPYGbOThsENrb+D313zLu+sfQdQe+D61e5H39p96R7aHR93KQ8sapJY2JO0NLVU//ffKjHIylLHfX1VQvHKK6qu+FoiIlSHqJ9+Umvmf/yhTmU5mFtNLJJPn+bI779zYu5ccnLLHEI6d6bOU2/y/cwqfPaZrbskqJLspUvtPs8SJchkNrHhzAbmHZrH/CPzOXPpjPW+yn6VGd1iNKOajGJn9E5+2PkDW6O2EvFsBME+qnzGrJkxGvSbAWNXNE11nzt5UnWgs6hXL/+75LxCQmz1PqDepPv5qaTiZlraFtbhw+r35p9/5l8lKV9enai5/Xa18ftGb8ITEtTJnFmzVOnWr7/afhdHRKhEpl8/lSDdxMwgs1ltEfnuO7UopGkqxKgomDxZ5V6gfpf16qUaAw4efOWCi6ZpxG7dyrGZM4lctQotN/lx8fSkcteuVOvTh0pdusiwPWF3NE1jdcRqvt/xPfMOz7O2ogXoWLUjwxoOY0DdAdQsV/OKFYmZ+2fyY/iPrD+9nmyzbVXR3cWdNpXb8Pddf1PRt2KJfS3OThILvUVHq7Nb8+apmpy873xbtIDHHlNlT1f7o6ppakP2v/+qZGTjRtvAp5AQVf87alTJfB1FqCCJRdq5c0StW8fpf/+1ljwBBISF4TfweWZubc+0abYzexY1aqg/0n36FMuXIBxEVk4WO6N3svb0WtadXseGMxtIzrINcfN286Zv7b7c2eBO3Ixu/HPsH+Yfnk9SZhKgVi9+H/I7IxqP0OtLcDy//24blpeTo1YtypVTezFq1lTvjPWmabBli0oy/vrL1sobVMeobt3UL49OnVR90vWaZsTHq3f4lmRk/HjV8glUwtKrlzrL0amTmgtygzMdEREqqXjlFbWIkp2t9lr8+2/+RRIvL7XQcscd6l/VqvmfJy0ujpNz53Jy/nzrPgxQSUb5Vq2o2L49Fdu3p2zdulI6IuzKuZRz/LH3D+YcmmPtJGVRzb8aPWr0oEdoD7qGdqVqmarW79+UrBRWnVrFf8f/Y8nxJURcjMDP3Y8L4y5Y98G9teotYlNjaVWpFa0rtaZR+UZXbBwX1yeJRUnKyFBnxHbuhPXr1b/La4dDQ9Xy+8iRV07LzslRJQOWzZDnz6vref8Le/ZUU3GHDLG1THQw10ssMs6f5/zBg8Tt2EH02rX5lvUxGPBs2Ikj5R5kxqa27Nt/5R/DgAD1N/3JJ9UfXlE6aJpGYnoihxMOs/vcbvac28Oe2D3si9tHhikj32MDvAK4o84dDGs4jN41e7PhzAb6/N7HulEboEqZKjzc7GFGtxhNVf+ql3864Uyys9UmrEWLVJum06fz3+/tDW3aqK5YzZqpVYjata9dOjVvnlpVXrPmyjMeQUGwYYNa1QGVfPn43HBVo1MndV7peurUUSF26KA2hderp/p/aJrGhYMHOf3ff5xZupTUqKh8H+cREEBg48YENmpEQFgYAQ0b4hkUJMmGsAuRSZHMOzSPOYfmsOnspnwrEgDlfcrTIqQFLSq2oEVICxoEN6BWuVq4u7hz4sIJTpw/QZ/atjOM9b6ux9FE2/sKN6Mb9YLqERYcRsuQlrzc8eUS+9oclSQWRSknR3VHiY5WnUMs/44ehf374dgx2yZBC6NR9X7v3x+GDlXXo6LUH6+zZ9XHHz6shkAdOKD+aG3davv49u3VX4fBg9VS/eWnpRyMpmlcjI0lICQEgN3Tp2NOSCA5IoLzBw+Sdu5c/sdj4IJ3U3YmdeOfiP4kZIVY73Nzg4YN1WbHsDDVBXPUKFVZJpyHyWwiPjWe2NRYYlNi811GXIzgxIUTnLxw0rrKcDlfd1+q+VejjHsZ0rLTOHXxFE+0foLxvcYDkJiWSPDEYOoH1adXzV7cHXY3F6eFRAAAGrpJREFUHap2kJKn0kjT1O/jf/9VJU6bNqmTPZfz8FC/fGrVUsujoaHqX0iISh6CgtRKzebNqrZp9Wrb/o6kJNtJoYcfVmVTDRqozKB6dfWvWjWoUgWaNAGjEbNZbWdZtkyVd27caFu9cHdXudHlf+mNRhVO7drq92P9+hAaqhGsHcUtcjNJe7cQt2MHOXlX0XO5+friFxpKmdBQfKtVwzs4GM/gYLxy/3kGBmK80Z4UIYpYalYqG89uZNWpVaw6tYrwmHBytCvn2hgwUM2/GnUC61CzbE0q+VWicpnKVPKrxOH4wxy/cJxDCYfYFbOLS5m2FtXNKjZj12O7rLc7TO1Auimd0LKhhPqHUtW/KsHewQR5B1GlTBUaVyid3WAksbieU6dg9mzVQjAjQ5UpZWSof2lpqjXHhQtq5eDCBZVU3Ojl9PBQy/6BgarNh6enuj17tu0xYWGQ24P8CoGBamndcrbIbL6pGl09ZaemcnL+fDISE8nJzCQnI8N6aUpPJ+vSJTIvXSLz4kWyLl0iPTubRw4fBmBq/fp4Xvb1RWeGcjK9EXtTO7EnpRMpOflb7bZurSbX3nGHenliYtTgXDnBZp9WnVrFrphdZOZkkpWTdcU/y/GUrBSSM5NJzkrOd5mSlZJvNeF6qvlXo0mFJtQOqM0XW7645uP61+3PohGLrLfjU+Ot+yiEsDKbVaKxcaM64bNvnzqJdPlKxNV4e9uSjLJl1RJqTo56t+/trf7NnGkbwX05o1FlDJbfj088oRKdgABMHj6cz/AiNsmbHA9v6jf3YvXtE9m0xcgnn+RvXnUtHh7g4ZJFqNdBankfoIbHfqq57SfIcArDjX7eDAY8/P1x8/PDzccHN19fXC2Xnp4Y3d1xcXe3Xrq4u1OhXTsCGzW6cWBC3KT07HT2xu4lPCac8Jhwdp3bxdHEo/nKXa/HxeBCGY8yeLp64mJ0wcfNh1oBtfB09cTTxZO/Dvx11cQFoGFwQw48ccB6u/kPzUnKTMLX3df6z8fNBx93H+oG1OWtrm9ZH/vppk9JyUrB3cUddxd3BtcfTK2AWoV7MUqQ3SYWycnJtGrVirVr1+qXWIwcaZsZURDlyqmzSVWqqBWE0FC19H152ZNFQIBKYizuuUetTlieo0YNdfYrLEzVIF9rMpKdOjF3LuHjx9/04zPMZp46pro/jA25n4um6sSbqnAmoy5nM+qSoeX/fnBzUy9z48ZqwadfP/VyCfuXnJlM1S+qUthfQwaDgUCvQBLSEq75mP51+vPHnWrOgqZphH4RSqB3oPVMVVW/qtQPrk9YcBi1AmrJFFdxa8xmteJ86JC6jIiwrV7HxakTUSbTDZ/mCn37qr8lZ8+qf1lZ+Vev+/dX5bVXYzTChQuYzWrbSESEWkA/cUItsF+8qML28VH5TcK1f4xwNWQS7BZFRfcIKrqfIcgtGn/XBFrUS8Q9K4GM8+evP/38GrwqVqT/woUF/jghCkLTNOJT461lUJFJkUSnRBOTEkNMcgznUs5xMePiFSVVBeVmdCPu5TjrynbVz6qSlHX1VfNWIa1Y+cBK6+0G3zQgOtnWxGLmnTPpV6dfoeIpKj4+Pjcsg7TbxOLcuXN07dpV7zCEEEIIIYQo9W6mishuEwuz2UxcXNxNZUdCCCGEEEKI4uPQKxZCCCGEEEIIx2Hfu36FEEIIIYQQDkESCyGEEEIIIUShSWIhhBBCCCGEKDRJLIQQQgghhBCFJomFEEIIIYQQotAksRBCCCGEEEIUmiQWV6FpGpMmTaJdu3a0adOGCRMmYDabr/n43bt3c88999C8eXP69OnD33//XYLROq7MzExef/11WrVqRadOnZg2bdo1H3vw4EHuuusumjZtyp133sn+/ftLMFLnUZDXfM2aNQwaNIjmzZszYMAAVq5cec3HimsryGtuERkZSfPmzdmad7KyuGkFec2PHDnCiBEjaNKkCQMGDGDLli0lGKnzKMhrvnz5cvr160fz5s0ZMWIEBw4cKMFInU9WVhb9+/e/7u8L+RtatG7mNS+1f0M1cYWpU6dqXbt21bZv365t3rxZ69Spk/bTTz9d9bFxcXFaq1attE8//VQ7deqU9s8//2iNGzfWVq9eXbJBO6D33ntPGzBggLZ//35t2bJlWvPmzbUlS5Zc8bjU1FStY8eO2vjx47Xjx49r77//vtahQwctNTVVh6gd282+5ocOHdLCwsK0X375RYuIiNB+//13LSwsTDt06JAOUTu2m33N83rkkUe0unXralu2bCmhKJ3Lzb7mSUlJWocOHbQ333xTi4iI0CZPnqy1bNlSS0hI0CFqx3azr/nRo0e1xo0ba/PmzdNOnz6tvfvuu1rHjh21tLQ0HaJ2fBkZGdqTTz553d8X8je0aN3Ma16a/4ZKYnEVXbt21ebMmWO9PX/+fK179+5Xfeyff/6p9e3bN9+xt956S3vhhReKNUZHl5qaqjVu3DjfD+U333yjjRo16orH/v3331qPHj00s9msaZqmmc1mrXfv3vn+j8SNFeQ1nzhxovbII4/kO/bwww9rn332WbHH6UwK8ppbLFiwQLvnnnsksbhFBXnNf/nlF61Xr16ayWSyHhs6dKi2Zs2aEonVWRTkNf/555+1IUOGWG8nJydrdevW1fbu3VsisTqTY8eOaQMHDtQGDBhw3d8X8je06Nzsa16a/4ZKKdRlYmNjiYmJoXXr1tZjLVu2JCoqiri4uCse37lzZz7++OMrjqekpBRrnI7u8OHDmEwmmjdvbj3WsmVL9uzZc0XZ2Z49e2jZsqV1jLzBYKBFixbs3r27JEN2eAV5zYcMGcJLL710xXMkJycXe5zOpCCvOcCFCxeYOHEi7733XkmG6VQK8ppv27aNnj174uLiYj02Z84cunbtWmLxOoOCvOZly5bl+PHj7Ny5E7PZzNy5c/H19aVatWolHbbD27ZtG23btuWvv/667uPkb2jRudnXvDT/DXXVOwB7Ex8fD0D58uWtx4KCggA4d+5cvuMAVapUoUqVKtbbiYmJLF68mKeffroEonVc8fHxlCtXDnd3d+uxoKAgMjMzuXjxIgEBAfkeW7t27XwfHxgYyLFjx0osXmdQkNe8Vq1a+T722LFjbN68mXvuuafE4nUGBXnNAcaPH8+QIUOoU6dOSYfqNArymp89e5YmTZrw1ltvsWrVKipXrsy4ceNo2bKlHqE7rIK85rfffjurVq1i5MiRuLi4YDQa+eGHH/D399cjdIc2cuTIm3qc/A0tOjf7mpfmv6GlcsUiIyOD06dPX/VfWloaQL5fkJbrWVlZN3zep59+mqCgIIYPH158X4ATSE9Pz/caw7Vf52s99kb/HyK/grzmeZ0/f56nn36aFi1a0LNnz2KN0dkU5DXftGkTO3fu5Iknniix+JxRQV7ztLQ0pkyZQnBwMD/++COtW7fmkUceISYmpsTidQYFec0vXLhAfHw8//vf/5g1axaDBg3itddeIzExscTiLW3kb6i+Stvf0FK5YrFnzx7uv//+q9738ssvA+qXoYeHh/U6gJeX1zWfMzU1lSeeeIKIiAj+/PPP6z5WgIeHxxW/1Cy3PT09b+qxlz9OXF9BXnOLhIQEHnroITRN48svv8RoLJXnIm7Zzb7mGRkZ/O9//+Ptt9+W7+tCKsj3uYuLCw0aNOCZZ54BoGHDhmzcuJEFCxYwduzYkgnYCRTkNZ80aRJ169bl3nvvBeD999+nX79+zJkzhzFjxpRMwKWM/A3VT2n8G1oqE4u2bdty5MiRq94XGxvLxIkTiY+Pt5Y4WcqjgoODr/oxKSkpjB49mjNnzvDLL78QGhpaLHE7kwoVKnDhwgVMJhOururbMD4+Hk9PT8qUKXPFYxMSEvIdS0hIuKIsTVxfQV5zUD8LlgT8119/vaJsR9zYzb7me/fu5ezZs9Y3uBaPPvoogwcPlj0XBVCQ7/Pg4GBq1qyZ71hoaKisWBRQQV7zAwcOcN9991lvG41G6tevT3R0dInGXJrI31B9lNa/oc6fOhVQhQoVqFSpEjt37rQe27lzJ5UqVbrqD6HZbOapp54iMjKS3377TWqjb1KDBg1wdXXNt3ls586dNG7c+IqMvmnTpuzatQtN0wA1ZyQ8PJymTZuWZMgOryCveVpaGqNHj8ZoNPL7779ToUKFEo7WOdzsa96kSROWLVvG/Pnzrf8APvjgA5599tkSjtqxFeT7vFmzZlecZDp58iSVK1cuiVCdRkFe8/Lly3PixIl8x06dOpVvr6IoWvI3tOSV5r+hklhcxYgRI5g0aRJbt25l69atfPrpp/lKp86fP09qaioAs2fPZuvWrXzwwQeUKVOG+Ph44uPjuXjxok7ROwYvLy8GDx7MO++8w969e1mxYgXTpk2zvs7x8fFkZGQA0LdvX5KSkvjwww85fvw4H374Ienp6fTr10/PL8HhFOQ1/+GHHzhz5gyffPKJ9b74+PhS0dGiKN3sa+7p6Un16tXz/QN1oiMwMFDPL8HhFOT7/J577uHIkSN89dVXnD59msmTJ3P27FkGDRqk55fgcArymt99993MmjWL+fPnc/r0aSZNmkR0dDRDhgzR80twOvI3tOTJ39Bceva6tVcmk0n76KOPtFatWmlt27bVJk6caO3/rGma1r17d+3LL7/UNE31Ja5bt+4V/67Xp14oaWlp2iuvvKI1a9ZM69Spk/bzzz9b76tbt26+Htt79uzRBg8erDVu3FgbNmyYduDAAR0idnw3+5r36dPnqt/X48aN0ylyx1WQ7/O8ZI7FrSvIa75jxw5tyJAhWqNGjbRBgwZp27Zt0yFix1eQ13zWrFla3759tWbNmmkjRozQ9u/fr0PEzuXy3xfyN7T4Xe81L81/Qw2alrs2JoQQQgghhBC3SEqhhBBCCCGEEIUmiYUQQgghhBCi0CSxEEIIIYQQQhSaJBZCCCGEEEKIQpPEQgghhBBCCFFoklgIIYQQQgghCk0SCyGEEEIIIUShSWIhhBBCCCGEKDRXvQMQQghn8s8///Diiy8CMHz4cN57771897/66qvMmzevwM/71FNP8fTTTwNQr169An2sn58fO3bsuKnHbt26lfvvv79Az9+zZ0++/fbbAn3M1aSnp5OYmEiVKlWsx+bOnctrr71GhQoVWLduXaE/R3E6duwYderU0TsMIYTQjSQWQghRhObMmWO9vmjRIl555RV8fX2tx0JDQ2nRosUVH3f06FFSUlIIDAykevXqV9wfEhJyxbHQ0FACAgJuGJOPj8/Nhp9Po0aNcHd3v+HjateufUvPn9eiRYuYOHEiTz/9NHfddVehn68knTp1ig8++IC0tDRmzJihdzhCCKEbSSyEEKKIREdHs2XLFsqWLUtoaCi7d+9m0aJFjBgxwvqYsWPHMnbs2Cs+9r777mPbtm106dKF8ePH39Tne+yxxxg6dGiRxX+5yZMn51s9KE6ff/45sbGxVxzv3bs3TZs2xc3NrUTiuBX//PMPGzZsuGrCKIQQpYnssRBCiCIyd+5czGYzzZs3p0ePHgD89ddfOkfl2Pz8/KhVqxbVqlXTOxQhhBA3IImFEEIUAU3TrHsnunTpQr9+/QA4dOgQu3fv1jEyIYQQomRIYiGEEEVgy5YtREZGYjQa6dmzJ9WqVaNJkyYApbLu/p9//uGhhx6iTZs2NGrUiPbt2/PII4+wcOFCzGaz9XFfffUV9erVIyoqCoA333yTevXq8dVXXwFqFahevXp06dIl3/Pfd9991KtXj02bNrF3714ef/xx2rZtS7NmzRgyZAjz588HVML3999/M3ToUJo1a0bLli15+OGHr5nsJSUlMWXKFO69917atm1LWFgYrVq1YujQoXz11VdcunTJ+tjIyEjq1avH119/DUB4eDj16tWzrlZZ5OTkMG/ePO6//37r69GjRw/eeustIiIiCvMyCyGEXZE9FkIIUQQsm7bbtGlDhQoVAOjfvz979+5lyZIlvP766/j7++sZYon5+OOPmT59OgCVK1ematWqxMXFsWHDBuu/CRMmAGpTeosWLdi/fz9ZWVlUr16dwMDAq25Wv5r//vuPOXPm4O7uTmhoKNHR0Rw8eJBx48aRlpZGeHg4ixYtIjAwkBo1anDs2DE2btzI9u3b+fvvv6lfv771uSIiInjwwQeJiYnB1dWVatWqUblyZaKiojhw4AAHDhxg8eLFzJkzBx8fHzw8PGjRogUxMTHExMTg6+tL3bp1CQ4Otj5namoqTz31FJs2bQKgQoUKVKlShYiICGbNmsXChQuZOHEit912WxG9+kIIoR9ZsRBCiEJKTk5m+fLlAAwcONB6vH///ri4uJCZmXlLLWYd0YkTJ5g+fToeHh78+uuvrFq1ijlz5rB+/Xo++eQTjEYjCxYssK4YDBs2jBkzZljfjD/66KPMmDGDYcOG3dTn++uvv+jZsyfr1q1j3rx5rFu3jk6dOgHw/vvvs2zZMiZMmMCmTZuYN28eS5cuJSQkhKysLH755Zd8z/XWW28RExNDs2bNWL16NUuWLGHu3Lls2bLFGvupU6esqyHBwcHMmDGDO++8E4C6desyY8YMvvzyy3zPuWnTJurUqcPff//NunXrmDt3Lps3b2bs2LFkZGTw0ksvcfTo0cK87EIIYRdkxUIIIQpp8eLFZGRk4OnpSZ8+fazHAwMD6dChA+vXr2fmzJk8+OCDRfp5X3vtNV577bUbPu7XX3+lbdu2BX7+nj173tTjjhw5csX1GjVqXPE5Bw8ezN69e7l06RJZWVkFjudqypYty/jx4/H29gbAw8ODhx56iA0bNmA2mxk9ejSDBg2yPr5y5coMGzaMr776igMHDliPJyQkcOzYMUAlJOXLl7feZzAYGDx4MPPmzWPLli35vt7rOXz4MIsXL8bLy4upU6daV7IscT7//POcPn2aJUuW8O233/LFF18U5qUQQgjdSWIhhBCFZCmD6t69e76ZFaBWMNavX8+pU6fYvHkz7du3L7LPe7NzLPz8/G7p+W92jkVelhkchw8f5pNPPmH48OGEhoZa7//f//53S7FcS9u2ba1JhUXlypWt17t27XrFx1iShpSUFOuxoKAgtmzZYk0QL5eTk2P9v83IyLip2CyrWHnL4y43aNAglixZwrp168jJycHFxeWmnlsIIeyRJBZCCFEIx44dY+/evUD+MiiL3r174+3tTVpaGjNnzizSxMIe51iEhYUxYMAAFi1axLRp05g2bRqVK1emffv2dOrUic6dO1+RfBVGxYoVrziWd+bF1RIvV9dr/+nz9PQkKiqKffv2cebMGc6ePcuJEyc4dOgQaWlpAPk2n1+PZQVk//79+WaZ5JWZmQmovRixsbFUqlTppp5bCCHskSQWQghRCHknbT/++OPXfezKlStJSEggKCiouMPS1cSJE2nXrh1///03e/bsISoqitmzZzN79mw8PDy4++67eeWVVwq8GnI1Xl5e173fYDDc9HOdPHmSt99+m23btuU77uvrS6tWrYiLi+Pw4cM3/XzJyckAJCYmkpiYeMPHJyUlSWIhhHBoklgIIcQtys7OZuHChQCUKVPmmm9yNU0jLi6O7OxsZs+efdXJ287EYDAwbNgwhg0bxvnz59m6dSvbtm1j7dq1REVF8dtvvwGqtay9SExMZNSoUSQmJlKpUiXuvvtuGjZsSM2aNalSpQoGg4EXX3yxQImF5fvh4YcfZty4ccUVuhBC2A1JLIQQ4hatXbvWeiZ62rRpNG7c+JqP7d+/P8eOHWPWrFmMGTMGo9E5m/KlpKQQERGBt7c3NWvWJCAggH79+tGvXz80TePdd99lxowZLFiwwK4Sizlz5pCYmEjZsmWZM2fOVUuoYmNjC/ScNWrUAGwlUVdz4cIFTp48SUhICCEhIQVaYRFCCHvjnH/ZhBCiBMyePRuAevXqXTepAKw19lFRUaxbt67YY9PLl19+yZ133sknn3xyxX0Gg8G6xyQnJ+eK+0Ct7ughMjISgEqVKl01qTh+/Li1Re7Nxt69e3cANm/ezIkTJ676eT/99FNGjhzJfffdp9vXLoQQRUUSCyGEuAXx8fGsX78e4KY2UA8aNMjavWjmzJnFGpueBg4ciMFgYM2aNfz0009kZ2db74uOjub7778HruzWZHltLBO4S1rNmjUB1c1q6dKl1uOaprFu3TpGjx5t/VrS09PzfayPjw8AcXFxmEwm6/FWrVrRqVMnTCYTjz76KOHh4db7srKy+Pbbb/n7778BNb/DWVexhBClh5RCCSHELZg/fz4mkwk3N7erdoO6nK+vLwMGDOCvv/5i3bp1xMTE3PR06Wv54YcfrG9Mb2Ts2LFXbb16Pc8+++xNb7D+8ssvCQ4OplGjRjz33HN8/vnnTJw4kR9++IEqVaqQnp7O2bNnMZlMVKtWjVdffTXfxzds2JCjR4/y008/sXbtWm677TaeeOKJAsVbGMOGDePPP//k9OnTPPPMM1SuXJly5coRExNDYmIibm5utGnThm3btl1REtWgQQNAJUW33XYb5cuXZ8aMGRgMBiZNmsRjjz3Gnj17GDFiBFWqVMHf35+zZ8+SlJQEwIMPPsg999xTYl+rEEIUF0kshBDiFsydOxeAHj163NQsCVDlUH/99Rc5OTn89ddfPPfcc4WKISIigoiIiJt67M10Jbrc/v37b/qxlrapoJKY2rVrM2vWLA4cOMDRo0fx9PSkQYMG9O7dm/vuu++K2RPjxo0jPT2dTZs2cerUqWuWDhUXX19fZs+ezY8//sjq1auJjIwkISGBihUr0q1bNx544AG8vb3p1asXhw8fJjo62trBqV27drzyyiv88ccfxMXFkZWVRUJCAsHBwZQrV44//viDuXPn8s8//3DkyBHOnTtHmTJl6Nq1K8OHD7/pQYRCCGHvDJoUdQohhBBCCCEKSQo6hRBCCCGEEIUmiYUQQgghhBCi0CSxEEIIIYQQQhSaJBZCCCGEEEKIQpPEQgghhBBCCFFoklgIIYQQQgghCk0SCyGEEEIIIUShSWIhhBBCCCGEKDRJLIQQQgghhBCFJomFEEIIIYQQotAksRBCCCGEEEIUmiQWQgghhBBCiEKTxEIIIYQQQghRaJJYCCGEEEIIIQrt/3MwQSa56kNSAAAAAElFTkSuQmCC", "text/plain": [ - "<Figure size 640x480 with 1 Axes>" + "<Figure size 800x600 with 1 Axes>" ] }, "metadata": {}, @@ -965,13 +952,15 @@ } ], "source": [ + "# use tex\n", + "plt.rcParams['text.usetex'] = True\n", + "\n", "# Visualize the results\n", - "fig, ax = plt.subplots()\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", "# TMLE\n", "sns.kdeplot(\n", " estimates['analytic_eif-tmle'], \n", - " label=\"Analytic EIF (TMLE)\",\n", " ax=ax,\n", " color='blue',\n", " linestyle='--'\n", @@ -979,7 +968,7 @@ "\n", "sns.kdeplot(\n", " estimates['monte_carlo_eif-tmle'], \n", - " label=\"Monte Carlo EIF (TMLE)\",\n", + " label=\"TMLE\",\n", " ax=ax,\n", " color='blue'\n", ")\n", @@ -987,7 +976,6 @@ "# One-step\n", "sns.kdeplot(\n", " estimates['analytic_eif-one_step'], \n", - " label=\"Analytic EIF (One-Step)\",\n", " ax=ax,\n", " color='red',\n", " linestyle='--'\n", @@ -995,7 +983,7 @@ "\n", "sns.kdeplot(\n", " estimates['monte_carlo_eif-one_step'], \n", - " label=\"Monte Carlo EIF (One-Step)\",\n", + " label=\"One-Step\",\n", " ax=ax,\n", " color='red'\n", ")\n", @@ -1003,7 +991,6 @@ "# DoubleML\n", "sns.kdeplot(\n", " estimates['analytic_eif-double_ml'], \n", - " label=\"Analytic EIF (DoubleML)\",\n", " ax=ax,\n", " color='green',\n", " linestyle='--'\n", @@ -1011,7 +998,7 @@ "\n", "sns.kdeplot(\n", " estimates['monte_carlo_eif-double_ml'], \n", - " label=\"Monte Carlo EIF (DoubleML)\",\n", + " label=\"DoubleML\",\n", " ax=ax,\n", " color='green'\n", ")\n", @@ -1019,12 +1006,12 @@ "# Plug-in MLE\n", "sns.kdeplot(\n", " estimates['plug-in-mle-from-model'], \n", - " label=\"Plug-in MLE\",\n", + " label=\"Plug-in\",\n", " ax=ax,\n", " color='brown'\n", ")\n", "\n", - "ax.axvline(0, color=\"black\", label=\"True ATE\", linestyle=\"solid\")\n", + "ax.axvline(0, color=\"black\", label=\"Ground Truth\", linestyle=\"solid\")\n", "ax.set_yticks([])\n", "sns.despine()\n", "ax.set_xlabel(\"ATE Estimate\", fontsize=18)\n", @@ -1039,12 +1026,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1054,6 +1041,8 @@ } ], "source": [ + "# fig, ax = plt.subplots(figsize=(6, 3))\n", + "\n", "# Double ML\n", "plt.scatter(\n", " estimates['monte_carlo_eif-double_ml'],\n", @@ -1071,21 +1060,23 @@ " max(estimates['analytic_eif-double_ml'])\n", ")\n", "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", - "plt.xlabel(\"Monte Carlo EIF (DoubleML)\", fontsize=18)\n", - "plt.ylabel(\"Analytic EIF (DoubleML)\", fontsize=18)\n", + "plt.xlabel(\"MC-EIF\", fontsize=40)\n", + "plt.ylabel(\"EIF\", fontsize=40)\n", "sns.despine()\n", + "plt.xticks([round(1.1*max_val,1)], fontsize=30)\n", + "plt.yticks([round(0.9*min_val, 1), round(1.1*max_val,1)], fontsize=30)\n", "plt.tight_layout()\n", "plt.savefig('./figures/double_convergence_causal_glm.png')" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1095,6 +1086,8 @@ } ], "source": [ + "# fig, ax = plt.subplots(figsize=(6, 3))\n", + "\n", "# TMLE\n", "plt.scatter(\n", " estimates['monte_carlo_eif-tmle'],\n", @@ -1112,21 +1105,23 @@ " max(estimates['analytic_eif-tmle'])\n", ")\n", "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", - "plt.xlabel(\"Monte Carlo EIF (TMLE)\", fontsize=18)\n", - "plt.ylabel(\"Analytic EIF (TMLE)\", fontsize=18)\n", + "plt.xlabel(\"MC-EIF\", fontsize=40)\n", + "plt.ylabel(\"EIF\", fontsize=40)\n", "sns.despine()\n", + "plt.xticks([round(1.1*max_val,1)], fontsize=30)\n", + "plt.yticks([round(0.9*min_val, 1), round(1.1*max_val,1)], fontsize=30)\n", "plt.tight_layout()\n", "plt.savefig('./figures/tmle_convergence_causal_glm.png')" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1136,6 +1131,8 @@ } ], "source": [ + "# fig, ax = plt.subplots(figsize=(6, 3))\n", + "\n", "# One-step\n", "plt.scatter(\n", " estimates['monte_carlo_eif-one_step'],\n", @@ -1153,9 +1150,11 @@ " max(estimates['analytic_eif-one_step'])\n", ")\n", "plt.plot([min_val, max_val], [min_val, max_val], color='black', linestyle='--')\n", - "plt.xlabel(\"Monte Carlo EIF (One-Step)\", fontsize=18)\n", - "plt.ylabel(\"Analytic EIF (One-Step)\", fontsize=18)\n", + "plt.xlabel(\"MC-EIF\", fontsize=40)\n", + "plt.ylabel(\"EIF\", fontsize=40)\n", "sns.despine()\n", + "plt.xticks([round(1.1*max_val,1)], fontsize=30)\n", + "plt.yticks([round(0.9*min_val, 1), round(1.1*max_val,1)], fontsize=30)\n", "plt.tight_layout()\n", "plt.savefig('./figures/one_step_convergence_causal_glm.png')" ] From 9763659196ee83191a186b8a36b063d540dd86f6 Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Tue, 22 Oct 2024 16:15:28 -0400 Subject: [PATCH 25/26] reran experiments with IHDP --- chirho/robust/handlers/estimators.py | 2 +- .../causal_glm_performance_vs_estimator.png | Bin 69187 -> 74386 bytes ...performance_vs_estimator_just_doubleml.png | Bin 0 -> 50617 bytes .../figures/double_convergence_causal_glm.png | Bin 36115 -> 18753 bytes .../one_step_convergence_causal_glm.png | Bin 36061 -> 17233 bytes .../figures/tmle_convergence_causal_glm.png | Bin 30959 -> 16644 bytes .../notebooks/quality_vs_estimators.ipynb | 479 ++++++----- .../results/_ate_causal_glm_tmle.json | 614 ++++++++++++++ .../results/ate_causal_glm_tmle.json | 754 ++++++++++++++++++ setup.py | 2 +- 10 files changed, 1653 insertions(+), 198 deletions(-) create mode 100644 docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator_just_doubleml.png create mode 100644 docs/examples/robust_paper/results/_ate_causal_glm_tmle.json create mode 100644 docs/examples/robust_paper/results/ate_causal_glm_tmle.json diff --git a/chirho/robust/handlers/estimators.py b/chirho/robust/handlers/estimators.py index 8c382e44..8eaed460 100644 --- a/chirho/robust/handlers/estimators.py +++ b/chirho/robust/handlers/estimators.py @@ -37,7 +37,7 @@ def loss(epsilon): ) epsilon_solve = scipy.optimize.minimize( - loss, np.zeros(L, dtype=D.dtype), constraints=positive_density_constraint + loss, np.zeros(L, dtype=np.float64), constraints=positive_density_constraint ) if not epsilon_solve.success: diff --git a/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png b/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator.png index 49e333e723edf48716064660c9dddd0ba6540277..a5240fe81abf02014f82307540ef5ed4e483b7ee 100644 GIT binary patch literal 74386 zcmb@ubyQYe_cnS{BHfLYNJ~md3aF$gNOyO4he(IgjR*)xgLFztBi)_S-Eh|CeShEi z#`)un@s05@2IKL$@4eSvYvwhtIX5Bi-@U~^B}Ik7U>H)85(+RF0u>Afulo2A_$1lc z-xmCb-|@AoqoR$mql=!s5ll|c(bm$&(b7zx%Gt=?!OX^*i<O_1lZDFE(b3l7B^#U7 z|GI$H#@>XD`W?a)SOmpZQq2Jd!_tF(;qpXs&0ugan3Tk;kFKdZb55RdYh+!AgZw5> zEI&n1U}WXg3j}2NqlOYQ@Hq1(PR>#JQ!!C}r^<Ka9UkW3(I?v$Y!*p(Ahi6{!H;(4 z)vWCyG+3Ka$73fjQM<q0B&S?378(rwNaC;)xxi?k9|dOyI2`EbD@_)n#KTW$I4<_X zM~s}(q7R>B|M|anQS48|cJoSHJiJe1=Fj)u|7U%ev=Tfn8JW!Xcu`11#KXtgkJ~#t z^*NK+lVq>v*l6tI7?e;RHXj^o{eQNt8vOs9b(vhbAZ~Q@qk@7$r~p3WGfge6GF6s$ zUV#tC{aO${X8w$mGrGcLFgiI|eRfHbab<sBeyt~tBT4pMZS#Mx>PzA>2@Vg>)~vOU zWmdr?A|lFdOo}ZC0ynd=Mjjm4!hU|tUyj}QkHl(2vw!@+B_MeH{ynyzpI^t5$XL$w z^mJH2P>^Do{_9-yE(M%AaI8N{wEX-eKtI*hE_OjQIk5=|M7lQ`_K%)XL0VFw#;A?< zw?Aus@}K{wQ80}4eb8jc$xg)}E-tS96x%ib`J9VR<awfqk1#bgbvT94%f{!lv;q7M z>#J9@@*=k<<%60vwwZ0s6Xg=GHy^Z5(|FK|CV1G6w^dT_xYc?*C^Go%)u-W^d7pbP zARn~~V+;-sj*E*6!`d!?BsA0)*?IPBJ$=dC%v}cF-rko@8HVrSp_OQ!MVFMYH8nL& zP5b-%m;d6nnvN9@!aO`*@ZoaY96C8mOe9t=QhyCJe0*ye))BqSzfAleJ@z~<AXr`w z-I&tWUM!q8R#8!LKi}(b{ft&YOC^)Q`V1UlaZ&eO+RL@nAekn{2XkdaC{*4YMj9`E zp1$F-hhCt}&xS(6XG^ux8PUs$Zt>e637tYP(d+t*e<M?Y>c7tkE^srR2kR_tZFP&> z-?V+977q>$)t{?#YB-y=K#~@Hmmw_I8~?nb5*ZQk0PI|VW|dRs!RjPQbU8_cuvJ#D zS^f|QR+|2$2}CDpUG4}o`}-X!j!B7W=$1*j@Xs%^k@Cuv{$z=&(*Niu7KaW`vmY%R zi}Xb>GKH(}<fQ7|)piL>h0$6<SQr|&#kixcqkb;_JID`;QJ=hYZ0YR{t995YcRMz| zI^WlsrIx}RMF18MCmy8Rg}M0`p(lZD&s0Jpo1wO*W~kamNBCk9VY<pn=Cb#On$LC- z#;j7@H@prk_jtV@PEt;8ZD*>2UweCNE6J)6r&|CRkiC*xBk5iZ!tAW>B)jXrP@Zy8 zO%^WB*YBLBLsZ_EYebK*C_JZ*o9^#C!0$DS)$VkqOvyE$!#cWFon!am^oOiBhey3^ z0(6U<e+(<ti#nc2Xm_-?57jwYR9Q}aFI1Em?&vfQHxqWjRR0CLcHGAxBYm9!%%3nF zU(C|d@@oGYm_tL!`tNNT%;+y)TE0Ggo2OOJLqS0Sd5GA+9d9-%HV)&M@C~+lrGF{= zijyu?Djysfuh{9oQX5Ki^2dMwc97))4<H6|mdvj-!4f=1C+CAT-^Ty7=6p3%RqD#| zF%Oo)+lD8PuagSwGx`-!WUREAneBe#msE7|pjNMQBaz$>PZNb^CNnsZ#Kgm1NPw`{ z?FSs4F|O%;o`LWDEKpvg|F&UoZmFL)Li-DoQYfo1bWM`+Z``j6^we<aKJ$~5A{6GM zH8+=oLK`=UK#jCEkiv2XqC~`8`4ExY+ORhEr^ATS@M6GzS0kY1oxJboCWVh4*VL`= zCjWX-bE=%Dii1hvsBH;$YVQW@HG4EkU?TDK-(LN(cf=Gr4$hav#|vH?zlOjAR#6`g zZBW+YG~t(_b5~3zFh2P%30_JnGEy3Re<TJSmDJAeOS1aGHZm5EaVsiTklQgiQ9^Jq zpNhFT6PIz{Vj!9IH>2KoV6uTg{G1l!EQQJzU)$Mj<^`?mw&>J<nZ5$PKqBnP$7M3m zXAdkCvH9)_T%ni@eDJHnN>)_(Sby`?Pu+?7y4Exo(rd*Zd`wI@ICei)R}J@?@4baD zSMiGL_g;Em@A7)zU79>XCvKUsY3~1oB`j&KC!E&v3B;Gf9_A9;5joyN@C}E@nI&|C zw$H7T@byl`$??3~#*_6eykh3z7Y#HDOsYlfUN2Mm5v;9|ng{!N$+rDjv`{%X7FSkk zh+j*zA2xvv^4QGx6{=O_DdxU&_W-_aHc|Y9cg_(nhE8rd-DA&A9UYur_u^n_*0v2} z%gX8BC^ZX9c)nup(U3S69KM>3tXVDv&=G<2UVxBC_^QDqd)DJdIWmLFW5VZ)Mb$?< zf(24|d5T>OZ*+eB+0qLOmZZ+k$>Fe?ei4X47XR$B)MB=V<NkW#K9R?Yl>S{R6$oIj z!wBtGB6x>?nJK!T{_7YNxldh7o~kh6vRxES=Cv7U@k6*=3Zj&hk*T*NH?BysnyJDe zAc)Jk1v{2%zONV*?zL&Y?d*&oLqS1-3lCTY(#mn!pVxHg=f2tZx$hkoFV|~(Bqk=N z+ThCJb#;;no;YjQ^@RMTW1gr;$*`CZ6_B57hP5R!<_kjVo1@;sE|(qO&u9_HKUD^A zh3W7FPh&sG4>vU)FB9M}el1j9A=0%wIO6n1*=Pgy0i`^p0_EEDkurm~#{J20=@Jd6 z;~EVo!!j>ShkW;&@6%Ouyy8G~#UUr}1Lx~Qru1y-WowoNVF(-)tEBOaR8LP&@b+Y+ zR(NHvet*5Jiy}*-+WMExLOPGt^iZZaax%BYn_BxdnCsGKB1uWfMUZPfueNewo)=3& zH<ue34Pe`&`HDeuDLjY&3QBOFJn7H-kTtx2e>P)t4D9HAt-aB_*SVH;{Voq7t7dRX ziA|HYcFt$W<qn637jrYhZ-axwokY{!QHvYLH%Z+7F{;+$*0}^(&9t1IQ@uT(11|hp zLYlmQpw7{+(jsi*GEJcEr_!ag*p3h;92{J2x`BxEQY#W{uVw`kq$!LRgD9cSldX}d zG6Uq_zkhokb`Z__+~0!iL+lH(mcG6|v52=|l;F|hy}dntVS^T5xR1q}Nx<C7jr&QJ z3svDni_|M!kNbJe=IXdX{&aP9UHtb?8sG&xJ3HAV&d*rF7rqoi?y&b+Z!my(JF=x> z%4w+vLA;x*cR^lkJfF{c8%5EVA(8>qw0>!#lnkK9S6rJJ9zHkR*|`YXHp@VfJKtBI z!dK?%US$Y(PAs*tTa(`$h#z*c&?wNXRUm(_nTiS9+}zY0ETd;!>rFropT-vOz!I~! z=Okj)d{D!|Z)DG#!>+9jVCr8J6aRF_&_f1$u@WUR95v^$-#BY>J^B+E-7nL>{=a^` zINP1+3MF8^IP9V%5pZUaiD$_a`;4l#d<85;QdV{~gg(8+av;xLQc8-NkufA7AmF6x zEU9kw_VzX(WCCPN@;@u``6nOX0kW9Cxm42;IhK8Mq^DAn&T;cogE#Tz81YZ1TCB){ z4Bj8hmFjwZ#9dwBG|P#yZ?Gg7N&S_8v;Yv9{AGrBdv)4-m>NOGi;IsR(CutfO>WFo zre3rq{tGx4|4XNTvAd~EN<R<&JdLuQ&~nMCzP!A&Tkj)*g2`Uvxi*tZarl(w54)Al z7uK_h!d_RlyOk5=W+QYUo_*O(f>C;%e26?{PFiua>%NK?<mTp<8Md#ib#LavsWnHZ zK$lA){k$TV+`?S4>Z!dkaIhTKd3Sr>_N>xhtx#2umSVqY`@$6%9ZL*KLu1%R0j!aY zFzmCyUJVo;*Hja|4i;an52W=LRnNP3@a_;Wef;Kmd6dd$=MVhlhtKYOV@j0p6~k?6 zU)Rm;t(}9z%7l(jzn#05$L(8P-EX^HDThMi`HIK@ssezMwwBJUT&O#oEv2?RS81Vg zb@v&Kh|hV4ST6ZR_chkf*awko$1#=KWR%_a^LD;3oNYM4ePNCM^{Z|HT;GSm^v;D= zuWPCoqnS&foIr>m;R>{#tK%>qW%wbVF;HgE1+6xcE59+WVUq(eVE7Y(v(R$@qQO7n z%|?D40-LeFxv-08QEM$IX$lPrdK)Q3uZzB0cfD7C2`uXJpbcxP-h~as&czI$yQRz^ z3TZZJ<b$Il8bQHSJo<Nw+uOZ=zCL}p_V#>%-)<SP!nl8V&SfD-zavyFox}Ua6@--b zCa-JP<q-PEsHi>TS@zt2u~|SI5WXi@PzayyCU@_zu(jiGe`Q^ZBzys*a6`0DmY}6- zNo;bN;ZbdxRiy-}RGOk<s;`UEr*F2_;Sxk3XluZBMoV-u=rcT{$*gOrWd_^U!b!Lu zxwyFekW1+Vfmu#hmkP=_SRywEMkO96f8XXS6%aPx9H9ZJ&{I?U{ZuO81c4tI*Zj#b zl<*)?R8DC30zm1uSG)1}@#94xBsy?f$S}b(9RRd9sl5-zdFl!RVh9MvK(MlDFA3!3 z2XF36rBPxk@rx2cRV5Ap_EfnMny|W%Y_{yhGMej|T5x0_zr!Mcot|>$KmVfHFZz}V z|EU;U_ngO*Czk4&EPFE;8Y@^<R<_l=Z=kP#aCXMJ9xNf<6cB(INzN|@(BI*~0mGi{ zDkqQSWQ$exTxYpaZ=QE8|7uub#XW}E<M`lM)$Z<ti<LJ=ABsrPpCngrv&4t1e>q<m zY@9DQkvF(LU-%rQ1VNKG5HwLG<0bvPp<8&YSGIaPonjN1%)K<rpS3|tuT@W~ct!CN zk^UXNur@$iwVVL2D(K%Hg_Ut9EuPIByA*}Vbe`13&~JD~W>EgJw%W0oQYZz<Ujp?f zfcrZO&CL=R%}l5eglaD@K^m*!xVYZ9znZb(jZW~n;~-?{>pou~YowjfQW;_Zx6z6~ zU@9aw@$uUz((CG3g;I~vLbJk1Om-vbfsGet@Chv1%m9_W%SHs|kgemxt;~pAv)|;u z&?Ss+(SvW`%mVhws0}?(BKhUmq;fI(S})O!@Pw90;icVjK-rbsJK<Y=_w&GKGN^at z5PT$q4n_0%Gsz4x1;ebDpdjZsX15hjVUb2ezN<<x%WMS@)oRbf#vG1lcV^A-zYy`j z!4dQHO%9}?nVC&RxoqlMPL#0SJY0G7aAk#MQkm6ACkN7UqsONbw$ztMDLh6Z^G%Iq zb2S_rh~TUe<ZmJI^XYgQSz1gu_uUE#>JCvEh~(<2%BA=x5NS3%1{KqK5}*ZK8{JQ7 zXlZG03xLO-Z2tYEqU{C(1uNf)8mKM%x~&RX54LhMh6_|m1Q%=uQu#pyA8PXQ0L69r z6D%~eEKnnWkFu*HD1?$tGQ4<v{1U_-WK^cEckOJ@Ol9G-+6g2$kQzQ!xudE1%}T>S zr>zvoIG)3xv()|6T)9NMU&rURLx!!f9U!Y305?J+BU$$jiWzu#;!pW+?9^&(smEpR zsp4=RR-URr#>EZ4O|iD0^+Lu(baA=Z7ny7nxVF1yFT@}tKbHl15@cgz19<_21R6fR z#Q8=KF1I5?F>&!rfJj0G8kql0mOTZ>>>IxWLG&+hX#L>43kdJxt|jP%7j+}1z2x5m zX?m!-q|8P~PpAU(N%)rg-0Z{Zc9Fodhe1uYKA3?eYG_Ei(iN5KalZG$W<C`p=W^;1 zdTD-)jGp}6gQd1`a{g$rJ`@O`l2FqiI5KiQCqer=@a$jKbICK-^$}Y2&PD<@i!FXE zYUQ-O?1dnH0D6MrYEq^5xxs_dk2W$otJJv<P8@d{k<r<!!U&B-cnlw>ir5sQfKsBe zT)n8Bu7j{QAuEWC8vr#BJ+T37O5`w@Y;fb+Z@Ol6Sntbx_36>9(}dQ}Y%Q10`3=bH zHG6g2P;TE`XcmbQIwJ;UpiR|rp)#{USL9lI2%hKd=>z~{Y*4mOv1v*Y@;Ec(ua?gc z2BeL#R4^fns_4c*8ly_F=I^FUg@K$J1Z<o#nRkz_gWhV@@!j3sUX#smTTs26-Xth< z_-iAMQ`@6fq+I8@@VSzHoUr#;0NKTOEUf^LKbW-{xBb^pxxP17e+jl^I`j<=1_Wy| zQLH7K{30Yu<c@FfykRX~gP7l*76zrl?THeTo><0)^LY=o0gYyFp_ytM695A)W>5(! zeQxZktmk?Fj&!@;t*&UF%8?-gU{Mn67w#z>370Xtl#~?G-JJ~ADHIsW41&G}`2Kym zWP1AVRocrh(eU>-G1qSS`H?<V!IDzCtsFLYGVmU7SBEQIho`4<^1?7#Iy|fCUrP|C zuQLJhxT2wHJ=ucK^+31k*m-|GV$3|pWH9}g<rL?w#Y~kIfanY}mk5v0b+?9d09^#- zMi6(SOGqk<d9M28C8WbElZ0a{yd+m`Z?D@DbV%Du12OyEcY-Jv<5U)>EtV<<Q;$7y zzAgj0Ox0<FX*qf&bnQ~RSQ~Tu7WNb+4<sM!TKs(=U`^JM#<Y>q8e@m?!F5GNMTZ6- zb#+o<VPXDvGbtmNpl(<K7N`$cl%2`4PXKi;uC2vcSgJs*fr5UahKZ$)JBs<J?En`i zJyn9e!zY6--EHL!K@ExQnj~4ehPj3?fp9SaKoo$|(EzLgLI7lLehA3J9<PA;XjEBd zYB##WYU=6$-<PsR(G~fm*LeXTSrbO|g*qo@3^JZRXYEgqFoHuusQLN%Z%JTmO&2ua z<5d7B`t6&dqOc&ZSZe)jxzLmbKpDYJN;B#M<ZVm$b=<e^^It=6bSF8b2*M=MaoRQ} zx0o89+?&)xZ!1?##rp(Ga4k(ua-aYtDv*kAug{PFzNP%o=<c-FaQseCP|$QV4@X|` z5Rt9%1Q%efMUeehfhrr0dYEC5x&QeiNhg;qaesHK*67X+*cRE6h0+vGBUEnl(Zyto zV!idgByv7mU%(%UXvl)S=73Nrcs6O!0I0T)`3j$biLsjukb^~HSI#+r`)hlhDgm1T zwQBF_H3k}*0pJ{`rrBKN%pgiuI$$I>Vjj7IWV+H}-oeu=Vo8fxnYfV(OYn<bp|_X8 z>ysKjK38n}iy;{aiO|W_?wBM&H%{FXL+Dw+u{n(Uh#MLkM+TGl>}arrPOyP_zX0qU zqvDUD@2}BdFC8~qYV1}L!A%*SM<`$yD<A+E{~OI$OFwNvz+wl5t+xBtvwvd+UAzmP z;s6d*bQqSYLN`G119G^-!^6=cjf6qr>+r}Y0_zuEC;!I6h;%%^0>!Vtv_#hhHx+87 zczyQ;^4{3mP$Eq%%rQmj1T2txwRL~|bB%+;L&iN@iFQBZK|oBf|Cpbv!Aa(Eny;95 zMbP&A>pEZ3Y%DZhmQ_Z26;5J%+&>WZ>WGeqhv)X(OIB9)Dg^_lTT4s~{%s_Art|JJ zD5CIP_!Ln2uNS^RR1L0P2dV)XCDyA~176Be6rIkO_BAI@i9uelnn@)CHX*5&1Bn{( z`d1Ac-241!hm5hRqQ4{dd*qts+8^EF%f0Fy3;-+6SO7)cG8x%`MvsVF<)YMkLEh?c zGw)p?HfO78(d#qY?vDomEi6NvWA>!gLVDSS&Ih@puWrQ3#?LiKxv6X#m0s8ZA}E&o z;R+*dmn^xI?{3EDd!0PFFCpUHjso(w9UjX+lWadT<<DMz0X#vVk5T*=*v24{91B?9 zC<H2LozEz~e8Juz`ZiNrFP{6Z^Dd{YRQL`kdnw5zl#UQ^`sETJ-{Ti#)0UPLolNMw zzP9vu@+SvvK)DFj%<N#l`88P-boOuH?4e`nLT_-F_pvI?33e;$9XYBj#tp5i+5Brf zs=q>1bMOk}Wco&w-eW%r5@las?^Id+*7u8{U%EI9KXX3~&v!S_FRuy%3obzkQ@_%! zqD-rzQxcJ!l@+t;8Z+G${!O^=gpT|mQ$r&cAw}OT+VVeCKn-4#20o)v@C3xjYf$xH zsk8-Rn1N@2a)&vE$4XjcV?<uW$()>w#}cAaV%}YfE1f;;rP4aBtWrHwngDshb5GNB zmbfz#l*_(4-hK(%oSp7-ZuGQtKiDJ_XV_VQNNGAO`Vu(`wHuSi3REiW*QAXHQbRyE zaar)<&6Uq!jV4yFvg`(Cp1*t`^3f;;5;0GHK8fNYkEJPkkbPo;w*PiXkU<v}A2Edw z`Mfuo``L@~G)6_;7Fb<%I6N@&!USeOPcxD<G&Dd6gMlF!)WE~vULkZwQOX1J1Z6N| zH1X{0Ecmu{c?diuLB<Z5ipFTd58+Fq=}xu9MhjW<<qwWg)WxmbrL}~JLuK8*qIyFV zbh1ai30!>Tl*NVmU6{PzvwlxZ@D(|j0P1jaq|47PBp9EoU`*QF)3Z!)W_c^5`&EZG zD{H5QDlcy}%Pz?AOSK#62F+gX9pux?D=SdygV0lVcQ>F-zW}5U*na3k2hVv3YwcFe zs8(5$072!dlxT;Oa)<8DR965{2KG+OY4{j;x~02iw7_iuuyV_p_%fT44u)Tp-o8ZI z>}LyZ$m#A(*Zf}Bqz?OMdl55d5uWd@1y@*`eg|TZpuDZN?o0^&GuCQeSq_3af3=rK znV?9Xk<68Bit;8cEnwq7oWTP?t<GhCpnAbOrDQw9ZJp$7XR0lT(pwT#R~cSsBw<8s z>vh}3y%S!-cNYffo<~yY-dAQk*0X=m*cwDfe#jkrWt2;bmx2xMOvjZiG}IzkyRJSK zJo!>~--^ahPwD(zqm_37cF@vMz1jNH(g|3I3fbFJQT&{e=DTDNlZMNUWFSha+IkKo zZz@pd;{Xb*f1>eSD&Oz#uftr|;#4+A^YJi9Uo3*!^BFrk`+}X**6?Qtx&iVh5R391 zu;nT9jOh=oG7tf02@GtK%@IC28MS)Bx-V^}k~y5xXU)R82F|+q$uX7)`)2`H*Hdk( zECCUI63i#?A|mwof5L+CIbymcGR#J#=dcSQvex~?6xL_Vb$54nG2PX>K9Vlv0n5sp z9hjR-TTi-sj6osDCu8SfEyUuqJ>GvbJu#86y1EMAp)~&BHWi?<-(8^WiDRA=QozGQ zStv)ExIE4;H7B5>q|9m&Ecxu8;VE{M5tiq1$Br@X88zl^bLCF!G7HCL+|nNMhA|{q z(#(W2$@cJwX^;J=V{kAEh>7sxYIJn;#LEN6ZQ4jV>2^KE29P^aK3e$-&zyHAzwS23 zY|FP0uhXggfWl2jN0==rj_ya~FPnw$cGRlgrT)oZ>y;|=O5RY(#l(1o{!P0`ef5&7 z%6^T#`9e_ej6JHWc_35B<$Xzr0F0WtlM(Pmpbui`;N*lvMMw9Dl9Cb@391cmL0VeG znC^B!L2Vf`pU6l{>!0t<Iqpp2_?m;TVl`y+#T*1@)ydrIB4rGC_#+pqn_34JGOj=a zpWD~co`UHV6sp6+!AA$WBS&kNNqihS8DeUCWHL1x-2VJh?ez;e)x740mb*gSpin9Y z%^-O2wA>8u9*7j%E@{5ltzH<8LPkc0*j;UpUGDKBjV?e6b^*g|0qsL+BQIGqDBpyB z7J@4~=Ui11lR)$VQHThOva`V6=*sH=Zen-7{}dT>NPkfjhIw=G)T8No57nioz3tOl zT6j{$zd?7r=xE>Yv7z%l?s!ZP>Z6~7dIn(73d3&PM}R$pDA<vN@Jug2R+$275nwul z_Fx=W504R0y`RkE*$D&A07E$}BqS49iRD!JvqlX$3F+J*3jaY7-ZPio9@UF|dw|(` z_nR|@lnM^eQNIsji7Z}sk{#?;4>bzh9AjO3-+V2qa+?)syk@O@gwdlYC?Y47eVtaB zFU@ds#XM_^fP&I_L~Uk<O^O=FOmtEo=N}poA$RXZ3Hra)UP44T-Ae!8to=+{U!SHx zxd^I*)aGT*_7^fT#e<F(KVcCXI6eykghM_uBs#B0fH3<vF*w7Wt5QtsIL6e+%6f8h zrr|j^7vUt*qUt(i_<d>f?+a3{N4`aP^wH#GDY+iiTRQ`W;oHMaJU=#04}7O9BUXMQ zfQWqy#Wg_GmNXop24Ox~-u#;dRS&7cUdeC>kMLbwji4?uqrEcoq_J4Gv7D_-^AUHx zl$2!}ACB*rzgdSRI+Vhf?Zm8O`FM_7(cad_b}I+@A3g;*Y#a&V7ibGzjGeQ>`eYJX zmfA!+*;&T?00Qu)iwUu5x(o~se*7k!`1k3A&R~_*48T?PRc1~dZV=M=D&WwWh=g|3 z1b^j6!WAs+j2?wbVCuf$h5ZCW$5W5I!SMI1uoSZA8J@Vljfy|8Q_O_VXA@=L=Zjkr z{v9+;e1YN%=oJ;6&u6>GgrHv*YZ*bxt$(!IZ8_fnH5ovwz!5YLHD>kV7$H3JX9{hI z+6E1quB-ECUquJbwm&Os`QHk{f*10K51{Y@+|F+Dzee{D`C0<Q(b1E4f8V-=7HA1v z1bT!M#n6k}b7sBy&0Hc{pzK!n8DIv@hi;mE65f{v{0z!8k&M+%Q&02JZv##fBuYzX zsAu%)Fo!c8BOnRoswDZy`%{&4_gB-EpWM*b{GSbN{!lBUVOxj<E3JNdPyi?tc6iO~ zFCB`MnUSrS33!P4abu>?eeN*;-0#heB8}Uwgo_~kkniPDXl8bEzFndcUSOpm`8#eZ ze}W97!Wixi|DT&nY$BHq4Y2AT0w^NVm}GqT>En}iacw_fHS=cG4?$H>Fnk`%*Ct%; zC&;h{>9f72w!P0`(0bW)blPXLOiJI}oi5_Y!UGq)KaGBT>F8@wqC^d~@}EAeXjEz3 z@YUzqs%E%O9@(;yvhopDXa68pYioErY%$%V_@coaR98y1oFO<k<T}4LLpQ1|kne|L zTnAx~v4X}5MsiVvT@OMN0G#^o9-YouG;4JGUOMF{4Xq5Ot+X$9z(y(ghfQzpJS3$! ze&p1xCo>BV?ktNnrdY#sMkn=}O$>lub<?oZo3=tVpk2WKNJ4m!@%o^ENr{U4^k{=Z z=U4ErSo|JdE9uM5h={$I{bpXw_003O;&}{SU1z7Yn}IY5O+E5BFS^cryDB}^3KPX< zYf=a>X7i~QDT7+44fx8|P$mLs%H-HAG&LM{lBEiHRMoWgj)9mcU83D+FL?GpLkcDS zp<!VLS0`I)>D=xooA3*RBoAg@PEVC9-`4s$L1be7`h&#~RhHhdF`q3=P-v->?D$3& zSbg4fqX4X{_+V(fC}KWoYspKJc>_5!^B1oTJXj(azY=sEC_E2d&3j*4xu5NHgT^M* z3xlf1dgonz(C|wY@oB1Q>#HWxcJ+ghjSrmQ{mt>zG%Dl}vgJiaN(vBgr7@Bhr80;+ zOl{8)lDUX~4nsWOwRmxNkyX~X_wwMhf5O26lNi==_!$i_!L{id8z${>;beMp*RY}0 z><<FpA=#M9fzAy9v+@C;r$M`s_}@f{{Mb*Wt@%a)#UHZ2$Hpq#o2Ldsz{td_SFg5o zYAXra*X}yw@F5?R+?~|3l%pHAV79lnpW-Z4&mU^wpI0gnv*(<&;InSzUKlJIZxkSu zm)8dbx!Ag!uftGrTG`yk-;(M~*4*xL18Jb5@f1-9oM?-A<q_aaI2||NC$F-b)CUD2 zgTCk*sCbbUL8S=F^8*leMrs{QKr>5DRu+)_U&ARy(gD{p(%|OsCX&1xz`^1Dn34DS zGBcpPm<STR%d8!8e+q9d7|!rKn=(-=hM4iKnQAuh;ReW4Pnnp0zF)Xc<#*Tw9|AD- zvPh$P-4B^U7oZ{VNLwK8lVi?mFF<8O3#7huV(N7RP*gUR?<?k$mrz+s;kpPnkS3s# zcY)fLGnvjpV-($poNcaT>oR!Tuu`?8!hLnhMH<h_%{2MRzh(YuG#0D2_Q&n{v=uZW zd(R~?CQW0c_9`n-$?7UQ`UJwq_mf>5tUYELMXOmZ)B;VVBrvd3oJh!hbad2e$hmfT z6GJa=|8JBL>IZG}16}owgRWxpVH=hRHYH_LZZ19Vyz5i2$p+A+h-K4B2Q55%&@KXG zB|7Nfrf;~?0R$7q*7V1sq_Okn@)&9rm@PC3fhfBMlCfcH019Moj{inOp2@@?fSoDi zNXH2-h|qUMQPKmdA548fvk(x=>Tw4Mx?tcbV2}k2+k_ph^&*s7&G7siFX{!o20o87 z7KoMGZ#c#Tzwa`anHiv((NJGMJjrV5i1{0Rs)+mrUDh7W%t67|rzL_eHmv5D#E{MM zSJ_fOPj{u65D5U~BT~J88IN_i*&qdlRox36Cwd?mh@S$gE!3*7QcYg~rUB-5;x=g8 z?@$_V<$le1ozErYAZGz61$vGmx2MmmXT^kOXcVEY62MuYNk|Dg!Z2S5i&#vs0-B>8 zP$;Pz7bT$4ad+N)pVP%|HcSgRIB7tBC4n9}V0-jqt?Xt!_G*KmGGh=9O;YHf1pyHm z8Nu+f$zgeSHv-H<5r#eMWiS5rjS7NP0R4i_z24BbR~={juos9j5~1(c7upi8hqhMB zjZ{a585XipgvScRJq7zxOvrydXHmsgs&nq>=nbnGdOtAL<-hjL&EaVmzYoQCfiqsx z<5G}@C8ZKFD1w&>v9V!atQbZv4p)){Tv#1828=-TXz%JW;nxOzrjuI)WK7V4N=f(h zJ>)VS`GEm-D^&#lcdUJ~@d6A~^d5pLVtxCr7W99VGxq@xgweMiUVRHDrvSB%f7OJP z15FxmCZP?HarfLd#PlX0V9$0jz?=M~$-1WHn^e8e^3@a+%$qMDdJAaB7?}jz(eJM# zH)`w2gkH>4{Vp~~*z60+ey;jw__eaEvEr*YzMv2k0B{r#(bE0cg`f(7x}c?c2%mN; zsNun+lJvs)@o}$n8tA%Rd0Qh*DHjkBCCn~7%FWI;v~X~6=sgs6HZwC*i>!TV+GoAK zPfwq(k~~6Ds>*^H((wVBY<b-|k1$-z<+vSx%nu+?fX(b<Vj)6UU$T*<P0jB-ZMFcA z+F-WsYdyW`CijcxY6>a`2QB38p<R!5Kw}^E)D@x;IFq10Q+&zIS(7RYA=I%4J<gQv z_dkY)hI;3pf<hlOq*)rDfSJAL8dcd?Blb>C&j8`m>jY7TDuob^0o<zEG@yIpN<3CS z3+0D|gh<&4fZ8bDL0X(G0m2KM*XLw2WY)ei?&TwCGfio+7p{U#AE~Hl5pnUj0(JPm zJ<5{KeV52W_#N{J&H8A*=Agw`&!qM28j4mOZ0OS(xu9ub5CaX(fD35AjZ~P(Ti31; zHeLN=hWgtO+`ry$hA3me%Krd(wN`rpu*sy{=Iwa$g1^DE2EXGbHXfZUVDJ%U!5GZ{ zMj*C>yOa1G=$K0msK;_Z(~kmb0s=0g>u=Uu$ScB9*&V9^f9LAvcG%CmU<+7b;D{aP zXGrc=fL{QUYs;Vqx+UH&P5R*bJKLs)q@gKto5|d;k9(si02PH1i95ddSfHG@q~dJt z?Tt7csI65@)Sm8ZIfpt)$g$HYP4hHj8gNc_!l2FhmeQ-8%%<V@dVdOZB#4j(F$)TH zfHVIJN^-7=?dODOAWlNPaKV!yadb-I)KAD*oJKwP>XUbbV9w7Ee8T}cDqw;P#4QU~ zFt~9{U=l?k<a&R54!Q(9;(_RThszxxi2Yz+aF6u#^mGN1g$6`G6j%nSYCXv$z1#j> zv-d6FNc1;{GQs#-*AfSxf44fRGIJTw@qfz|BudJRWz4q{N7T<T8XBpo;qI=`EVto} zk0&sAvCCZ8o-DYBO@lG7-Ric{e9%Mv5;Y~wYBK1uP<Gpfl|NlXCe(Dp+2x>bkg3W4 zbkD&-SwT<LrL{jUFP5-px{~gtv!f(~J<|VQSF}V`gen@(<ep3c3*e6sK+~=wELJbr z3)A1t^3zzx{fiTD;v}T}h1FcQ^eCu9VNBT<w@MFzv9=F>c{i<0;hIiSz}6*KU!53( z?;F8isD}v+so{t8*-Vr14qT>kZBHOz50z4<`}vi+b4$Dt)1}fhrv7<zRG>py(i0~o zCrjI>e2|U@c+CELcVDY;J6>fbXg-n=5i$u{&StX`9gz)(ol{Dsmo7Y{(A1f>$8$bg z)K@XxvGZv5wJ))+WmPq*qlK+hT$BND{eN#QIgeGusRK#Ua_(hIysv5zNn42}^WaMb zr5XI@vut{LkUgwsB-tdIg>{hzg*42iHvqttCVzPF&PZlT|Cf>P#ech^@O;i#z)+__ z{Li`ir9U_ZN}}|IH!5XT8nVV*9N=q@E62!<$q7b5cYM|WM?@l`hNtiBBNjdcbv*^; zHMFU(G<rG+Aou?<JDjRW;YE0#>E^()w+A+Kb;7~a`lPRaBH>6b1%6c>d3ishOuM`4 zR(q(b=urRE99m@AG{^M2BBNgFtqs)v<kM0oot(HqL+QRuwbg)ED08yN`W@0%vcohr zPJl8~N<HCFUWlxp;&HwpMaJj&$4n1|)X;(gX1W;0nSb*%ZUjU`$cBXtd#3!Jn2a>b zs`T$fr9=N79|BS-1Fy*l4Ms5Y%deEZ^jsR9V&_*4Q?<=tx=r(1Umykm%(8TfRSsaL zQOBq98C6?&QYo%Vp}|V%tcn8>4&yt*FeDKXk%4p}8Bha*E<jsH$Fr^lJDMcWyIEdd zevF0HwXi@LhCuJzEejnbtr6C!Ewnksb$vKFk{crAgc{SKKkCz$Zbfc7HB4NwM(g)n zL*LoynJ`QEra>1&g1DV@d{XH=2ksY($=|bccD@NC`gk~u;eZQT+1iqFb>$;s(`MPT z&5k`hKhFiDzVGUx!DqXd*S{)j<(kQwoS0#tyjuFVi-AT+jQj}0-dvNFf&#J1^wq@= zxd5w4+bP7=7P#QEg2?U?CgBgcW!CfXj!x#DG&D$1$Ui^t0&LO$3}Mqg3}L5@AM51; z(41<$AZ(D5oXmmG%;f>+-oaxPjz`GGgUQDa2bYBjqSh!S2hGf3C_$#X6BuoB-g)t% zL<-q+d?542?-VkV=mA5{rR8ORnRgXF!T5LtnPI^ryR&AN2`t@iLcdS0qQ;BBj9hTA zuP>~WAVfgXXa;pdkBmoGP6o^e^?lxqXYIUnYMqGb-2xQ5Jqf_#?OS)(gOlTVa0%n0 zbg3eP9c@8ykIl!J3@()J?=ZD)1$haQc{(~qV%q;mez#AWW^#bXWp9xboz5v*IboEu zV+3=jI?y`#3JPFgX3}Pq;rh@)z!|ovQbMtJcXu$M)9FV;?2QCUt(;cw37GBpIG#g( zA4;&y^*HT{n35QO)4Ighc94PWxuL<FcTPLRTjFuayo27e>Ar~C4iOx;dsSAQes{NF z46WgnZ+AKl63U6b?@u8d(y=XXlPpN~wx7Pn%>ZTO7}N~livzsLA2*y<7L-5bMsEzl z2;ZE#;GmQ{*{zNvC=`(*B9Xj4sds)=JKq_)neJgPtOLB-{y{xGO^+UnetM)9m6MZg zyR_f~7q0hSCV>?B>V)z8Cc0mLih;cy^<E!|K9~Y>o;B>*M4D=PQd%;eF4GF+NmHu` z?Z47Q9`q?tBu9UL%5Lvk`M2^(XLp(eMRK9(H4)X~^4e=R4L@mDRw*z^*6Doh9Go}v z$Ty%;qvfZVBOtkMpkecOPOHIXPk1qV5(eFOQmwq_QTvniuJ%Rv?BHi-zsV%fMB@5e zHj<h+<Rg@g<yROW2gKWm(OA2-zttCVtjQC@2V*5nP^`->m;Wu`>3U{xb`^!%pJH<2 z?YM`ot^FEbE0B52Z_-25dB->TbZ#=_Loh|7CN!HMaU>4OP)-4tT*!Q9q9o(?!c7B9 zQa6mSgXgAaAl+Bu$x8`+L&K^S3{u}9<o3?it&!4+UgAK43TP%bSkn99x%~F+iT)G) z{i?Hq6~J;>r#nrMc&q`5VMWfc4DToq<qPW>%STuhMDOiDO6K7{1CyFV=AB=jA6NCi zCd`6#DGKc)!V7giJG-!B6Sm>93*S<0EH`_94GN(EU*B-0`_NZktbQ_I50%Oou}VbQ z7}8RC2UY#V)&esxlu*SyVyKO<I!sc!gCZG!6{X$Sq7U>~UZAVN_*eMFjzSV1_PO#K z>N*oKT5@HG*^tx*)iF+P_l<Cqc4J}Jz|6$G={R~k3kPYK*+rbzH>;lo$Mw!%<CBwj zUDw4<rm3xWT6nm}jWODA!OROYNDRv_s>l(1d=AbAOsmz4G$$L$e2<*|wbVIv`su$D z1xNlAR?W+JcqlQIyWi73{;IV*rt??BSkPo(nAL)|<0~+37Hbc=li3cJ?pSeJrI4^- zDxlcd)mRV%f&lq6`V+}%o6sp(E%7;F*4syX8RAcZOKjc*=gNzZ`BhP1gFVpDxcz{F z4Gkj-m>oRZt#~^%<sowOZ@j~(12wBmo{~K&l)W_-X}XSoKr0i|uq#qV2hyDOgXZ|W z1r6*Ou7{opfZ@8!A97r%Lt$<gacPa1wAD$a#oeP@9QUqV!|bn283KZcJ_LZr<)lCX z6!p6O{pr8oMb*Ya#`|#j*yQL{Q>B|@1&_0O)4b>k!-BA491r!YR+3Cwlh0DH?)3_a zRoI~CrawHll%6Vr^wnAq$*vmekbyx&=XyUiLtgOHzNEL8M;D052qZ$>NmF=V8a*<N z<t8JGUP0Xq8aIfGNNr8`og^fZCn6%1)|WsuCL+4QB4;NB^L5jU#Vzw_(If1I|HdLt zaTOI>+CGKnyN&^jPZLcK$(eEI34E@+iB<JE(*?JwuhAG&R$0I^s;zCm)%U^HoJJ@9 zv~)iK8!JKE3|W-oqyVpFR?%?IgwJSEie0HjHBI<V4?I^<uyv)%u!+9BC>Udu6fL|y zdnG1@^BP1o1$W4%f-2OP`bZ3#Uwta<8r~M!QQYG(Q_%k>vU4LY#$I3hm6$}T?{H-* za>k?;^7DP=IERldvB}9oGr+RGT0)O*)2h)$m-jDJJWwxEX5E=W5WO6F4>pU@3}0GP z`=QCsfNJAZlyfjZsXkKbKdua6Exy!7*pqOzQox#tONEBEt}gp8j2FEzK#>()Jlghq zVUAc_j7@+<=?UB2D>qU*WBd$I8m$T>n<X#;0mch<bSCSGV9n~4rIWce&-G3zoe4O{ z{4{&|F)W|Z(ms1t`>rZyo*UGs604yP8{M5rD9hl73n_g@wRU?GF7`DLqk<Jz!G99% z@6)q=`e(wzGlzX`rKaXGqV}r1K(-R)3h*>Em?o4Vt4nZey(YhAwngA~(6Hl&mQ-Nc zN<SA#@B_G?JrmmN+d=tL=fr-eqT%u<iOcrR^Dh!eKnkc2BCfanJ=t{UK7RICV?wee zQ~ZrM$~#dobwIEKDRV|I1I6Op+Vxb3GBVlR=mlhX#Shz^vJidg+-EAo=aYU>%Fdqj zxD-o?={L$_L-e+ueC)Y&-&8>}mUlJ1KlM~0i^~KSN6y5E@-CJAY<slZw3_JKwQ`=X zg*nq`2^JVc5f5;4sZA>FNeCk44!@O-)8&;5Cj#P>kYI)0wBZ{)T=^3Jh!8Cx2%Hmb z2tiXTA`o}}8ty&NAOMVqdU}ABOWCby#LU*@zaZ)lcm`}0TA)31r>#Umk(!e;Yfk(A z13=(enfSC+fE9|ZkXvyANH>O25&meuxf5LnvhSx*JbI6eX}DvehR`mI5D#3rNEztz zhuo)H2Y?ew2Y)F^5+rjY*uN9q5u)PYD2)otPU?Gu<gJ=0p8g!l$Pap{^r|nbRQ)DR zU?9TJ6Q8)!1@|~ODrmZrns3q$%W7gkmE@pm225Xq`Sz8&k_LrMGdt$V@e2@}tb&B# zP_Tj|1o@V<2Tk8U`Cmd8pCY10M}@E0<&|B%yM?*ByDolBM%Ai+d6r2{y|`yR3tFaG zOoBA<bh5s0BB);0wNzKpQxQ4uce-~E{Zy}PnIMXeSt@C!a`i=p%8KHL%s*OY&{<{N zA6_LnQ&Z^s>qS<k!$zwRc#|N)+@=0}o(nfJj#=;4ozDr?`<5V?H^^myyF36Msx>MK zn58~^XrIhIFXu?%`Ukui!GVk5JK&}@p+N*Y;sS1i^@1;7!hvyN1vlU7Ca@y;w8*gi z7CIMg_9hV`i(~$jYtrdPhz2@Bp!Td@NI$*uEU&4>8Op=4y?puQiGc49bu?fEtI`if z2Z-)M1o$BUQ0g0z+ePRe!NY&kYx5TZuU~M+t56D&dU~?@G<8Im`COEKAS?IwYNGT2 z8uq{f)c(OJRu#2sy^94M7|i^t35Cy~GI?929MoB_>M|QS8JQ9|6iG?;K%FGCgMy9f zg~iWA;kQvDJZEx08g%YE$x7|A{&GbRs{QPl*%oD04EDZY_iXqkoKL8dWwIW<qz<e{ zAHpG+2B0djo(mrcNyMX<42kNp<XlRL{aAVxl_n5Df#qwrvIweQpQtEdPIv8hX`F#Z zYgs`wjo4oM+QVQ<lH6jrebZD*Ho$Q9@pBV0j~@>X#Q$JZG5m04GL+tNsg^gb-8Q#x zJq2eSW6Fx(W(b2hd?d7i38M8rI9^`WUw<|~bZ6ODTC6PZ%9(o#emJwHAd?>~JY_}J z<b24x|Ba58le(5s6#b?$Y@$(-Jo7izW--kvfq|srML$3KC2{@k$XSHrF-MdavDy(i zBW~|l+v1FwlKo8gM&GVhPZg$ts}#%%ES?{7G`*dIZzkH;Y>mjY9DgN+1qF{>MqWCF zPZbif1)>t5Qm#4-k~knzieQ4dz>VIX4ZT-lzLi9AAV>#mMndM2IP~5?qeT0SMtcs@ zCeqSY5=V!Xo}1h6AWG8f=6mZxnnYx`B$Qr5f1fgp-_!L(3W{h?iXqSg4G;54DBx-~ znC|de7+b~|=m!hA;ll0%0&d29JTIsl?!7x3nj#(NoJwt}Ef+A0i{0w7{dZGB1#Nvt z+uK>Xc2I)HP~XZqNje>~o2x{U+vyGgb*dmk!2r7}(nyxYh%(xc(t9l~HQYm;)bU?z zbxInao@~YSYD_0QS)?)NtNS~(36xs+5+<dCy!UVx(x^DsXGZ+W`OzP~!ygPvqs7y8 zwtj{tT4<uvpwm9SMpuQMZj;TNz@G#l8lcX$v^31^<mZM{1QcsU%%hq}Oa94AZ$<|d zdG5<EKKIWIY7&iGA;|P_-1cHEl53}b#4{{Nn{Gw-s*R-HcT=i`2Ql(T)swp3PlU|- zNM>`R%EGS+R&y{7ubsm?UP0h6SP~O*CGnjp1IxySmYJE!!C{2xge$B9f`^yZxahV| z6b9Z`&><qdl12Bt-X)N)p77JOW|~*4Sez5F5-(*X`3Vj|1F8Jg_wV7m0Vtj0$?<Pu zi;!stxpbdvgSRgI>IE8(LStz9+YSBSQgEC#-;oXvvwGBdJst<8l2mBqfPEjx5F%KL z<Ge4lcV=8VJRl4QjXt$g?{HILSnW3&fH$O`2~)_ZlQwyKt8J|Jhm4`JH5%@STu^;1 z*=x|{BIAI+xs4zxAY^OW$)|vWb5$vL)bd#-P-U#Z<m3*vBcK0iY-}vgfZwi_<O7IF zvFc#??4d$sP`po8Pxwp*>0Cae)8CuD?5QND!;?+7q(vMQ-flN#TUtW`V;DO=r(^#) z{7dv+eZTeoh}(~v!3E|8?F;o2dYNFunixQol9_2DSZ6p=XG%jOXhV3o-we7M=JL!G z6ZuE-dKsk8=S(c`FIGZ@2;H=Bgc|(+K{?GH0$`}mkMSF<tvh*!wSChQUDpt^Yqn`g zc>*Ye!ui(w>v!_c#7>Tra&ODcrZ^4mgM*&KeE@t?uq5HbF*tqx=u{V%MU;+7BGS@} zr|{J3SXPSd&$BzTqzdo0Gn6d0VMXLOfX-KWZk)|5u`J#4Kq~d!Y%NO{=eh4qN_0Mi z_$7v+&X(9rdB<d3o!I5k7yKu3ma4?CJ5Z<On(PskXrlBcwEZ$0&^lg`R{sUEO0jEl zK{c;;l~{tzwkW7U;@RLP4U`@>_U=I%Qxh;0^cVJzJ#K?k>^b*TNWD+jQ#zgdi1QRW zLO!gEOZOOIZ73rzuOVTO5?{Baqj5M7r1vv~1C9v?xSN9O+eqRW00OMi(S}@hIe#>9 zr!3{?rji>`qzahywJPOe($a6Zt84!gt=c&rxbQP2jt4?{3EgU1^;dZ-<L>QrokX#! z@7+>Rr}Kqvp4LnV&idEpW)AY5b`;fL3=<=q@*NhH>J{V_6MmKp?>fIT{KqZd0r$X{ zR={m<C%!)8UBH>Cg2P_i!>lx4^VCx%2Jgu2h}_jE8uW1y5fSqbPgZxuBiw(%2IB>^ zZ{FZ~{QDShaF+4A!OixMoZ=y59n#QxJs`V|_o9K<NI>6NVtRb|z-IczdFv5d6MK0= z<P$K$KoiP?^(KO|s`(z%&W=A_-_D^;q&@Gj<`=|m(9#+vqd+r4G&EZe;+>p;<tuin zVhCQfqbMjUlxF8F%~V?K5eZ*VHC!<@9Eg2wPcpHZ5$jLJru4bgQ>`8K`&jt6sj2fW z8SNj)c{sJPAo?)I_5rBIBD$H77!3MLvuuT`INEMVkFcEW11=3Y!OQ{KHF#%s+bjvB zIwKu4qe~JEEI=#25d-8K<oemR@oc={&8$)yUMqM@q|F2k-%?sIqdR$b__nx0FRuF3 z7u0bnzeYzvaOKjwCDX=%hY#-{#G-IiuK`QsJWML$YXl*inRW8CRx#;`>VEq`Y@cs6 z^G%*ItMhyIzfqJ&=)NZD9REJrzb|&S(i9Yu3W?hIE_7X979|)a&;E5<^r4zbdN5&r zuj$F@peL`Ve-7VTiN}^-9?7Uyp&Jjd_-~^A(f4b<^Eb&rve_q>=g?pwLBrKnKpxJK z>GU=<f<`Wa4Szrm*=PPa__0B=nWAK9BpnYm9PB<_s6q#uAdaJP_x7CVL+n6|HnP1G zyaI5%Dw>uLSYQ(Tf9~D6aRc4<vze;Tf9C4p4y508Hma2D5Xq*Q%UaE3OLg+)M6+-X zOD5V&Q4D*CvNB5Wreu0~7>Lx>xkC|w6JkM-oHyThdS|`CuC*kA-6>m~#q}2{8(a{y zc%82`2}?@Z-{0D97qJ_>i458mqWXG$9^)=m6bz7qI0(Lm_ue(xs=%kSSs<){WUO&O z&Q2&O&n8;(k<r7y%s%ID%;FkS)>|U2FP}MVzm;i~QgyY>kNsTQ+m}}d=_=vGb`tN4 zPvkgwWy!!u?D}T}(34RjWG(C<SHZz`dhDDK3;_n(yS(w-V3gtDnECr-G_w}i)34KY z1Y+V9Pk54KNk$H}InkM^S|(XFtbb2`^E(uKD61gj{jJqtYDwvZKdhh_5*~rn((G}* zLyz^u9RE%1kLBY~f{0~8cH@qoSD!Wii5Ay(PX5Ri)hvBLsyoo_en#3zLdFxU3UYKT zW@Z;>`x&>Z8rFWN+gzP=afF{9k<45kI|!!Egl;<hGYl7-v3Z~;f}w27SlWjox%&Mz z>>^1v5dk5Lg9*8Uo=PTuwT2-Rl-YS8DOH`q{5bHGYwbI|-Kqbhl!rajKKp&R(s@_w zfL-Rs1-m0)G8D2~d2_LfXD~27oaba=z@Y5->jRoS%3zCV!_SX!1#xIqX2H8ZmAI;y z+qfPI5rt03MB%#yGZjz&VY{6$z~C^*jD-$I<d?p_1u!#|mG3QQ%2I;P;;Pi<5OtTL zeE~w*J%e(389`=-fQe@aNM3r7(}{Aj-D#B+$Nkwfh203a4Df*(w8EI=+Mp9(&k=LH z(c_KF8%*ft4d&de1}U^+y@vM?TM%paa5s+?XO*?}B2Ha~Y8a9)?0T96#$`gft@FYx znj>XYiNWjjNuL_Y&<~7BZx#t9Ob%xtfM7`yh~-0Vzti)%Ji<0hq3%JmE%?Z|lHDB> zv75+|W3YDId$~n#pw|lCE_BZGjG@!z#PPU#fkMQZU46cjR2IKv$2Q~2@*oMLR7e@v zz?{Z@zFO^!fL+X}EFera&M=6&*!gMLJP&;Q#Ig%hwFdj{hHHkJS4XF3tQf<v1i|cY z>*Ss^WY{$D&{=#3QaP$HNJZN-#bJn}B^h?D;O(s$YVmNjZ0Y(YuZZ2~XS$Wf0-C}J z0RF?5UqR2s!GYj8l5VA#Y<Ss1u^j2w4<BF`Ch5<Z=sGuV8TvamlAQL?)6=m@%}@3; zzl*UtQ5C3=%`{`qn2Tak4%i8S6~2~3gx#u!YT_Zqw4wjC$L_P?4kXyUb|dFI(Ej7| zB7KC;@Ppw=UmHT56W1A_n4N3HDvW(O5POsJgW4x1CMo2Lq<<!0W&iLm>XQ5)Z@o13 zxhE$_59v+#V$Nd#=Lt$=cm4NYz)N|Rn-hbRtlFu&??oja(-9HjvT0JWzIf3nWT1wR z_ciVXI$#i>GH!Lo=bmJy&WYN7?j`I7v<NKAh?&!8<ZGr+whSy)iUU?xS%N@!<bs!- zkh~8th~tPLfPt*}5YA~AcAmh3u*BQ_5MYRt9wKz|fmf$`vo-pm10f*E*Ll5k4yZ7x zcrsuHOa$dm6<-k&xDi4zNB!{*i~Dh469GV`X&=&roqpO4DdX8GailD5eo(E$|Eb*X z%m;`tMJ&l$du(FUof32v2rmQ!m2_7t=7w8Eq1FdGIv`Meb1*R|3ir83a&f6S?9>k* z3hAr8pZ;|YQb`4}g}S=CN7kE8XA93Ch_pfZo}hs3fd>bOwmIu)s;@7h8W<yh-Ggd# zK8jkL>iBzrN*37im|tvxB63(RJ!4s!rl37-W21w~!CByaUeK6jU><`369-T#Cp7=G zDkF<dOGkR`ggXvf++F*el1ty0s7DTFp7leEgHQe$u@swXz|9W;PRLEe<?Z^*FV*w@ zdTmrVSTr{g1S8y#CVH5l4Y4KB*OaGnh2|rePw<R)K1Sn0r6`WC6tP##^e@wD5}e6A zs%4Qh)xo(9E`Y*Ywj~t(FU!qh%1-G5Hxngc@aolpNn8;KybI`6dTJ^~025kaqJU6; zEB1o|`;Vk()w4VZzx7lVn0{u*VMGNlDb9EkRN*e|Vfy-{@O1vFM?eOR1AHCZ*E!%t z%X;Go4vPLW2j6h9lVGoFNjd4(<@Gn}q@eV*^A!ImzW4A4WfY8|5z3<uo^4haUY)Mj zGi3X|ff&Dg(cVP%B_3^=kLP_Y>+$gPe3S4cLf)1gw2rf%(c?KCQ0dC18l|v=d*wl# zUk}fN48Yvo%wisQc9JZ3sb@K@Etyz_JBHDKcwFRO)BcadC7qmap%WGL*)W!+KvyIQ zN;nj1R$>uXEisu9!~Tr@KFbug-Z+C{zt2Ja@k4HDx4L(Hn($0dH6b6NTM#c^2##L{ zo5LEebFk;G4RH62;;GTTCp%LyU=Am$BaA4<vs>^I5Q#b2;LnI)|54M&Gk_hqIDHoK z&^^@8GWk}xIfA^`fVWpmKa+WaXt*(OQuPR9$Aj*!;sH^{YQ19=lb4e-x;$DdR4oG& z*CNoLb}7|sGyO=|85pM70jR7ht4Y~_AY>TanfzhNS)~Z8QnRpZdg@B1=uE82hak+a z_ynI*GIZRr9=B9o1pd;6QV|YgOzJSg5g7eB*vyJjs4$kJdHNJ;?1Lew74X8J$@DKX ziOB-h*V!MZs`@9Z3=qDa!Jqs;WSvz|S8KS&Nhv95=~P;}L%LBwQW`<JJC*M4mhO_4 z?k*|m?(RO{f6tjY7iVVg8!uq7))#L)&+pBV?3ls|Kxb}km%#^Z01xvSxYe{WJSuyb z>RFwj@>&o><i56(fl&ge17KMc1kSbpq`trz{9dh`2$&7GfLI_Ga25C;PrDd&Tho9n z&cu(5LP0O{w5V*s?Tz-=571u0!8T7<s|Q-8RXCx4RpzRx4N>Fd4qM1fEH$(~VO$=e zOc#Hn6Z>!b#(h-Cgg<~7n;vh^N;PXkfZ-<ySi|%<Q#F9T4q|hHc!q$Nc_Yfxj2M%) zDlEl}-f<~>ecx_iK)rOPn(J{8;6SqE2>~-fN~+)PeSd;a+CnSwhe1Qo-%}W2#i<bV zgY3@y{`@sZdwU2Z!Q*Z#u~fG$16W6a&`ohPQ)mo;4;lcchk(Nj8_1Iwi=)J-G9!q7 zyd`=!B7h0?(!gE#mB8(WX}do}+V@rOO=c1zA*T@!Hu&T8uzwZg<>uDcTcHLPiLx?b z;2{(!;F$J<qphb*Y8INu^_)uA^9m<NBHUoTJGi^2N557RfHok=0w9d`hlm=S4%I;i zHiVTObHI1})Ayl#5F)|8<@i};*wI6Kp%LeisDa*HVWs`m(*x9k-my+OG|Q(5BbO8X z%9zN4_<Cp!(=vRzySq;T6ypOh%imug4FO@)FwpKpz8u8u0#O<rFE)Z@_)}5v&JP)f z4&sAdcG*BR38bO8)2n?;I#+K91ZV0Mt;kq3*w%VIW$w;~&1PQ`j6u0(71BT)RYj=Q zgJ!ot#6>w&hLjZJJ}aaKIQN9$JU&01Ko&%9E)`I91IP%lYO#9U+5k1CZ)&R6JqMr; zcs<>(ZEf`cH^T@Bo8$e0;>dPJ3BI9~$rB>gJhcHIhFL*1;4c_9TM>iecK(X_4@OOl zCSz2Sdh3D?#`;gqPQ9Ap62$!{;PMvB9k!K$0Y3*1to*8f&y%kZ5bS|*l+9x91Ar=c z%$pzlErJ!6$!D3OP<lioeZ^Mv_hwE!sfd+CP|)SoD_m#wo{G<uh_BgMe=}-<eEU-L zV;yf4q<{Jea3C}pskdVOYYVb=(;|T@77p-(E)QlS*SZ3yfc2kWHx+;?<hho_fEgqm zGbIRnJVOAZ)9cMrxJD|>Xxficn?m)fBuaW?8Z3t+<hG|*^r}YE!gTEB0Rt8^*PGJo zV;Mbc?}J{UhGpjcx1meG$pcYGcK9M}Y;44yn48a*kN8y!=6}tF)BH2{UO4jiwD+xA z(XY*}<7AW7Xvr+?W`>b+LBZSU3Pn)K1&cm?wbj#fvRg`cau$Zi5&2in&^9j}DALpL z<RLx*sSAV7A8y4R0AWhxzxt~@iYO{ZwA&7C2!9ExN7cMnRto>>xk4rc1Zs$7bfd=W zNTfD;CEQERq4ek8G{7Q+kV%1k6w^VA)DJdJFZg*E4#xm0P<tIHMQd|Tb*bkn0b_SD z(nIul&t~tZpL`1b5ednES@AdJ>_EMAUdIq1WBX<<<a|s7q;7*(yd~3vds78G!2hD7 zqvMY%Dwf8u?LqNnDWbCLD?;dipLLEz53$lfwK3}BdFW?YRy!lNniho3&f_H@l%A~4 z<I9mCKw4_WLERf2e3J}xB&tpUM@F){xK4p5#w&N8z?~dpCJ&$G*0}!{rBG5z4^hDU zDp#}7tcR!yEM8oH1z|ds!uA3nB?hiOx6}cnb)d_GaA(y1ESuF%vM5jF2gUJ6M~|~Q zQbsTLV%I>|nj6-shj10f7*ErI_?@z*Kh6jA$TmJ_!yxU9+3Syy?AKZhL<)6+{}OQO zNTS~S#$PM1)P8KZkTgC@?^z4#a&0Yl&D;K}^`hBZM<UDkqB)B438-6IMBb5sMaF-( z84F?3>{$ZeSntS4c>9ptgehn0+4=b|h%^c?ZvJ~y-yfXtyLH9p%!mee42_#vyIM*5 z2jhDUNNKS2Jjk%-e!;&>WZYQ1KH5{hs566Sv+&=Y@qGAzBRft|m$uyL7mt-x@2%sO zS`hk<*q3jz)PU_R0!Qyk7b#v1N|9+1Q7CV(H{P?M`K~Hw=JN1pW%}Xkg@Fb0Wn(E{ za{ie$mZsT#ov;<)-iuQ~ONKY^k0O6fz)JPx2yP(LnNkAaCU@>W63<O4(?<EiGXf7P zIW18iS$~@vVmpBv%<G~-cCisqzFTTdmk>59j?N^qHAj@zTk?idHY=_YbHn7rvJbHJ z4mX@3G3+lz!kXpy6Oo#_Je+m!d+K!2x-Kx<lWsWS9hy{ah%AfFymj-`cJc7MLZIS} zadPPzNP}~A0%AK>Zm{wJ;==%_6h|9sgRp(sOtMf@CsD?SN}ehCfW4AKShalE?mftV z7dXMmRncNR9DuW@77Mq?@{YQ*%251m`Lx-${fHv==RYhp*TbC_tZYqp->>-_0(mI6 zZ46@xFXB9);N0L@i|^higz0o<AY9w%9z58vG*_;!!lZalrv>8ji^(a4#kNs`gPJid z!3Rj&776NcqYYN=ZlOBLU!pClfJURI!fI(9t;lGQ|22K=xqOPAlP&q$qpN{px=d!s ztXZ9n&#T^0ic;8M`H*j$htZ3IH%=69D86enQfr;e5B@zUnEX(wpMAXaz7SJa=YiUt zWUi}HqD!hK|LiV*tZ}=<T;0`TE8ZFe>uN0`=pEH#K0t^}zqqti^Lr~0M36G&l8I;( zgI%{T3vC}^hs(>sMn;-mQoX1&5KrL62yNzrmQS(q>O&g^>e?FPY3Q5=Zy|DalSCE^ z^Ht%3Ko^E{{Fl@_At%41_UXgHTapde>Z#ZUO`?aD-(LRr!>y*ALukx~!+%(aBDm!u zX$4&JFWCMpA2>1?%Nu~gn<@@3bmPS+A4}>81UU4s*1VuTS<aQYh&@>j95w}&*+D^A z>duF`66@r34+Npx@%7#w%=5L@=;h^loL&AXpRz^nvqQ=>ZNW2ka#`nJWo0)siy9d8 zYLc0{-|!U3^2VaR<NB~!K%N%PCmW@PVJ|V9EgR0H;Awoq+cNL?!jz$Y3AbYT5K%w- zXJmVP<dOaH9ZZW|W+4bjf#LnmJux96p=`vgcKeRL;6OarDCotFPFMoE=}pukXfc2c zUoC$VHp^h44$9AZISjx=e)<J|4TGcdO0}Pb(eU)(@GQ96vRKs6RJ9t|cIV_1xT~b! z(TT7Q^cosoV>@%YGTvLu`~95Hu`p)A9J2%HUjs_JBvGC`3|aBYsX=ifw6yBR3Vq0Q z!eb?a@%9#IX7g|4XnsW!Qv#D)3I~e8fhsrQ9LA{6UDkz}a003JXf0bgH78F(My(s` z$+qil!yWgLYa_vg1Dre^=h}T{#ZDW_%f!)x)g$LRm)q=~_9i$NrOs@fBHM!EjAav6 zNo)hSLB0s$It_+fuJ4}~KgXjbULH2Z(@P4A=G0SKOwpupas5+tBQXErBH{m0%&)6U z9sHrb`REkj_qICTpYJew<I0}v&;B-0<_upFi_{bdQV20#z5F4!QbfBTE;blLp(qR} zlea+bs?p>?3tR%?PcE1Pkwv|y^zJ8tx=T&!7P0bDOp}v(w>F5#Kc@<6h>GX(QT1MT z?N6iVcoDo>-tCeuCV0o~LQ~pea>IIAklkI-%~rWAyuHMa&aB29D40X`DDbGkZAQ+; z(HOk>+sNwaNVRqDQrtEWv?0X1O_8M3)$uD@@99=NF67-7Kt*+Twt^Jku^`mP9AHyr zP_Jy)AP<t#2OJao9T=OAR+y%Jq4HsifE>wiipwu0)kHECu=BrzaT)a<UGUjxaxerO z>o)=_0we-iZSIA^R^9#70$XF1;vX0q*mbO{e#~?XSzsilFNjd+OfFH1oE}QUpMm}H zvyBXl9pHdLNO=&n9q?@>EI$Jv1{MhknooT_7l3y`t^vSy=qJzzk92$AS^bTOb5<GJ z&1<WgMCwzCD%4kgk0if~9q2+ebhPHTNd1kq5qr>(qvJHJ1Dx#=Pv=*68{0xMVq<-I z!!&H9*Dp~fyh5<;lqjTYzK*2W_A0<=ZlNG8>~*__(An7uSZJ>(CBmRq=c)`r*a{!o z(+Q}b-9XO^yuZLBB&|OfM+*rlkh_dJ|7BCQnnn}A@5%_D(<H)mQ<6co-Csc?u2lQ8 zndq&F1QoA0Bv*pt_U6$QePPssvY$SvP2%axuYzWdWVt`n4~#my_b^b33|>E(xjZ!> zZzYJ0hZ<U=(GGx6nIL2ikR(7=dfeHyt5;S6Jr`6MAy)*D6jpgXxtaKpQ}|=Ip1l`L zKwQ_>?l7Vtqsna%r6X>d%ddaB(XG64nEO@^>v|%9&yOF`Kp>=cL+P(5EVyt>z~(g4 zZczQ<AYbDzqwcv{85ek(nTy&>s?82~^x_ZNVw-f^AiD&RlE1}YLV=wd*gUnHae;xh zKp}l{0r;GY0D<DZ60o4i1@94$+cJ|#jSFVI<4Uv%@hW1t!V1KYkzw4eC#W6C;(?T7 z&ZM(?)(1}f8DU@m-s^CGqr}zYYy;}g!i{#*^r7miI}V5Y8`knA?o#ze&*P)v1)+)t zh~nuTSR%mB;R6Y+58~pm0gwt#7&wut(+Gibx#G)|c=tf5>g^%h?LPgY>Py+08hpGK zvDCo~lo$!WRBTf;lm(A+IQ}17a{pY<K$Odd3)>w67IQdSQ`)fatUo3B_%+A-L&HHl zlmku50`5DdvZAmgj-OZWrbU?m`2~dDN&qnnka6PGN3nl^Jqr^LFA(r$?@#GJ7!UFS zXx5iv6<Y~>3MCBl+0Jay)g$@N`B1PTG<`mJ16)zL_F9a-InW+M-b|?~%tqb%_A5NY z;YSmeQNB9v7ysSsrpT#@0;D30PG`|J`dZS%Bza?+7i(70x39^`sehz;wG=gof>(iH z57K~EPj@DrKm7m|*9U_0TaXh2x{JffGI3}RO#9u%j_ZC=0l<~!0m96GYqV34w!2ba z1$CvIB<d#+Q0LriEW6hbSI_=PTdCvm#Gf(MU64~RxzI&8f4V+Nu5m^yH&!!#o%y*G z1(o+_=)8Ysz>C;Iv1KEMcXmS;DCt3-f)%83fbo$897llfBf#f$*ltku0sJErz(&de z`^r23#sRz!(5Lr8_N`Y>Z|87wuh&>cLlRa{Eo#B<igiZ+#72xIPB!KO&v25c$8vbP z-A;0n=L3BxorAl#WyQbkzGn6GFN~xLSv~uvcyUZEyis}+!a@OL_SgkfWDwg5FhBu+ z$ZIYxuD9&p+Y@!Yq(v79lK?9qCMHG%fNJglTB#blBvgt8a$^`|S!qvBmhQEXntF9K z#M7${cf>1UV$Q2_*S{mfbibJ_mS%@S816lh%v5$&6jqv2YP3AI?563PY%zAuIK0gD z{mDc?cc)@-+~2rYL8@_!s9I8c3j;n(ppETJIAF32Ft<I%Y*c~G!sdLuc<Bd1-5}ru ztaDzLJ`_p?lB`F<;^!P;2%E#&Pa$3JYX8z02++@v&}g>1k^X&|??o4K$oXt1+CXU5 zeMHGL6OwonZ_W))Z+LUX(w^Co#G+Zn9)mjWi)LEfEBe2RE19kKyq0ElwDIuQpi$mI zD?p~^Q8-_^DVm!rEz4$XRjh$!)t0{Ln96OP`m<gl(71=_E3M-QM{lVCpul=~B#2(? zU`z#%EIJYXZfq1ZO?s~g_rfFuT%Dxh{DbyG8L+GAttX$lDgrh(n15ubKf=kPG14A? z07)qZBoFWF11Gh@_wVmP1V4HO+(XIpud!$#6(oGA#vHyWtSA}Acpq%3rKV4LAysvK z0jL^iKwnomk_!F7)zxlhs?eFc_4d5$zZ3G=mAPu5%AsUF++A~J!*5kK<MW7Qw6VNl zcPZwh^I99krXYsgZz@^|fR7yzpGB*u8p)UiYw}!;No0)(5Vn3u26W$;Vb%4o+R`;Z zbfo$*S#G?g`SV<x9rfjIv{&IT%a5HXFV906mMjb!d!o)S0V*LvgFWF7KIM(ePX9(t z83cdhqvN5s5}z{~$8HG9@{%~F&E={CiI;I~a>z2y+0Ci*+!M-1EOe&L`)!N^#d4Ef zMNtj!8@Iv65>9Ec+~2k`=>6cba9#a@FjsF)s#<atq6@4*kllE0cA$E@fet!fI@W`Q zFF_aOp(XXsEU(lCx2nmq6>H5veq{)XXw#8|u9u(ZQjG~ynuKm@g(=o4T@22!p#J=O z`tnxgd(=r3xKSR)j!Y(Jr1{U)W;%}x68Ob8nBPwAUP$<P;EJ?&Lp?5E=iDWZG=95g zB#PwCIN9L|X%6MHw@MNHvQm5~P@kx^vnZ4D#ajrhQo@2u9}i$4fl#*Jyy+B07Z3wE z>}h37G9Y&CraQ0hWUlQM=&y^-HL!Tze$itCY&dMtC$LIq!5*6Cc>1)ZkbZlzlUbC~ zfVSI{&=9crS|w~wQVb3b&ZuoE)Vr;}vB91l&1`ssm~>3-J><!I%L(LGIMPKi11A?q z4{q^mAI+gLl*s+gGq!*qG*?r(G07!5G#GClr!~~O>t*ZWV`hPF{rWEQ*I_1y*T=i# zI!QxC>stPel-rY659N=xtLQYPN$n*9I*c(UK)zteO$|L?XGsWnp^!_oy<OOZUqp;( zs}uhAh|BP0I~BUpYWTfy#j*~PR5lPO;?RN9Q$kVs;d(Ey*x|6>Tj(Y1s^5(PcaF%i z6n-+w(_#_v>A|FBI$=ud*6tz^%0qD1{wrhoW)I{lvzoUr70h74N^I^)zn)&0;%Q(j z-wu0_+vWLzB=9b`EkZV8!{x7Sgc#A9O}9P_KU?*$odkitrr3kRBi&TVe?px}35KVR zHzN;Y`9A+t&4;)|Ti31|>)G^s;CNL}q+Z;fDm*||5QnI6*H0%Xn9<~TKzD~hi)uXV zlS}n|qVpE0F5UFJ-9WRo<V}<aXeRXFt)*ILDWo$5($!i=f>O-(u0Ai9P?umBxq2;6 z=k~M)-jXaxnc9CMR2qC!srh_aI88Z5?$-lQd$(o`G$^nKs7CjdduiVH{Pf4_9jPrV zka|L+&cW8ccvpMC>r$a8gav`5<&2mF5np7~Q^$)fsZC50?aTKaq^~X(CwD@OuFe<A z8^hhTR?Hvyx5L)O{vs6!DFbC=7brA=l#Ivw`45{FBSi$KYjqVdTko9JyhvCH{QxdX zuqg>>Q>l4WX)}^|VgnvcRu;pE)z;wWR#Rmtw;4@I{P>35H$MLCB8ettuy<YyHx~T0 zn7UlL%SR8#-$+3tO$Ya>{{7R{Re1T%h>o@0W7P%&eyxvI%F@qzt+jnOu!`gwt&sH< z1J15$b*HjKV1K>Iad976`1_DcGUX&k|M%$v5)n)k%g~SZWtxB^h$@eU=jlJFABYtJ zv{V?_Jb-o1zV}GH`*WSe4*<U%LA7e^`Mwax^0=>JMmb#WO1Mo4uiPk#0JWby3q$j4 zZw@1Te!Vnj-j#!u<GOZ&2jx=RSE6!O7PHX;ZZ{X>0XFuRVxAmtLU|(*DkyN`>@*2V zg8hmXxiU2!3y#IEWrQ16=kBj5Uo0fVM8vYO(*B&<oaxPk*PPy*nk)@C^4Dw<{P&WP z$3XcCl#^uvf)GNxgd`l&()TJdxnlR%T84Zc4WA33fDD?FpZ#%A-{~Y^Owd;}u$o;B znBJi3l@C~=d+Vqup^GqU_@4gOtG0=&Ti{;}&F*A7p>Mf72$<ZAA1^rlsktUrnpE1w zUyQ!pyd@fQd{r|r{POlW((=a2a8%yDp>^$QuWu$@T~f@$PdP)2FN;&?>kTXvspV~u zhPyG(77vKMxk2l|8xVhszBaNVppzIaHrN6$<KyF_&B#`)3UFZpY~4oKCow86Rr6kl ziW?)%ekNt~Aai+YGPH}$SfEk!cx2&!v~jZsrQ6l@J?X(76ozD5pYnp#_SkX1C5t&I zrXP}bdP@jUe`Zev78$HGtbm~9&TVLxziyWWym-WO<n;yemGicM7y3!-u~CCM8_%4u z%?*n~jet?25D*1Pi@swid?pJNvj5)yDdYH({iM`0kfkj$8`QVpQ!>;6N+UqoO&Aah z$AD)PY9rE6Mh1CjEOQ6Imm9r;r7+o&0fZH7y`^SCOOw$kw{t4Y<7245`=xN*`gWk^ zy}q5$`d)w9&#|GF-Ax!sklAE2$yqp}muubGayz0JX7h9NY-ay7H+VlkQRPyNS6B%- zEw|Oo#YCq0^#x$<;`ApcvAxb<Nh(O>m$x&k?#jH7IN{n=ts$I{o>)8`LKXk_0K_PF z8*wC{5$85UP>+LY0s#Dm-cvLbOH0yhslf_o1rIO9cv|Xe>A*TOZARt88PDgmc{vyl zBzCenA0^N~YNJ&doY*<3-ok_LjPNAX9=G=vDs(`km;(p=L$@H7>z|Dc@j4Po9gd<o zKd=Zk_NWyv_IedvosXJJ1zS^+xrTECbWsBZ`g#sKK_$TS%n`-O|7874Jn~|zR+sni z8L}W~%(tm+WE=h;*G#6O^26yVlu%pg5p&z{k1;_*`*=Ew$h|?Wz_v~U+6;^e-r(TX z6Lch`N@r$vizKi2=c^xazd92eJEPAys(1OF6e}~`@o)A1rpoW-JT`O1TS0wd+F8h_ zs@}V#Y5KdXB(@^eF|y2-+Pu9au3~_V(?Wl~5VW$3y|`CB3!seoFRhWm!5s?^=RHRt z63SkQe1f{Y>eX$`K0}Q_(g8<XzLBR24Tbnd?QE9Wm)rf4(xYTxH5lShK3|hMKGsV; z+n>}|_qy6&Bfu8ZG=P!yo*TX1q+uvUVBx4+GrsPxp1e6eUE)8VrOeIGdt2{l%ZxLC z(0<z6HScz!s;Of2(ZzyW*ZS_?+SsmEJ@c587p_n9&eBM95G9&7@Lpp_C}Tx|)&ziZ z4L~;kN~`Cd-ou7FNU|ZAX3)D3G7;1fvwMSwFqKcR;FdgdTd7%touy`#caI)8l!w-B z#QM3&P_kVe-6*k$W$cP2!qJvk^2)CFQ-fvp!tR+Uy5RgRl6ARTP2n%6LSAS`b<Z8M zjFWSIUDW6OK|Jhszl`4f-Os_F7x^EI3;w2oB1;DkBQ%fQCL(b5euFf>Kwk#J&&0uj zZwEE;JAnDY2Y7tYjB->(q=<<H+_6x+@Gp(-SA*Y$Jd$!D&K~6Pk~&jOIcS|8V6#Cp zr}|PwO*Y~&bLKwf;-vEG1P<@fi6C>^n!h$$N7>z$>6pl}q;8~|&ee3|XnTaPWu<*; zTFMEqPGaljoz#D=n%REqJteqxzpYvO`%342hdOXk?SHQ&2M2cNB}Y8K0tZ+Dx<%KW z^uLACpDlI|4rD<{1YvFi>cbW=B17UiyK_Kx25HX+lY5*>Q!G3{C|Q%9E>dmtV1aXv zKbV_H3US^UwFsnJc!QCdBw%r1;qk$K<*jaA1^rmt+6(JyAG8>a*kqmrUcz!++<AuD zn0Mw*qQIE;#ql~QS-oT88703d-O>#`3BY$`!<v;dI?L`sYpej@IYaqP6krd6wv#C! zfOJ3zuWV!dqI!CI_h-N|d=7eI;{}SO03>@0K6*N4K+xCy74LAx745I95q#AoL+p?H zpFG$sn%AXtbjI6+Ha|%n0SAO`i(68q64xC?yn){1J)#KZOWqjzQ;<|pmf=;zBga1T zU}GYEz-oGdvdugGulz~gHiDqiM@>gQNPa`S?81Fh-$?2+(H6cf7nbh<yqHL!*8`d{ z_<))XX}~R7cJVNM1JuJ0`Y={jRu*%W9UwpK&pT`(6%7>G8q|oiT<mv6M~6b|;p(Yl z_s7m|zwSkUN?{Y^uj@p(QCUp@0g6AQgulJQN=jN^V*RR}oLp3$*Nyg><hfL!Dxr1i zp4VdQ^5&sMu_j~$F%s3}(mF@FCZA`s74(N}z}2mg!7mMx!=g{wo!`djbzAvs&1bhk z*%k6ZS!HE@?oEY1xu1-<F;K&U>piW;(LGJdG9Xb6vrkP`OuS=CQ8ZI9D&cD6G^PCm zlxD_tL0222t+cN~XvMq!e{tgVOcd>GS=<y%Z_>>jwlf!AvVQ_wTKclsNl^<AsdMZ2 z&6UH|<_@7(@6dE;USQ(+Xo@otMj7~|0m^SMKz#+^K{7^xzP@W@WCW5t8(Udbn&bVs z$vE5Qsu6Gxl2xGTn2uK|@rQfq?ZGnCSlvU?^?)8qAb1h580|~nxvEhPc9!m8o)C*A zG=m*~$wC>KwI~R&j$aRH=>J`~_|x0!C6KIwtS)_CBGc59NGWL<6w))%9<hD!Uc90m z-&HHZfYP~m9|+P$QaAt>yc@@z6Tp2TE_}$S6;SK2D=Pl77W}RnHxt(4U$=(VN#BHD zz~90W7>oj2`=KOM^ZDw_?RvE~d9gw9h*<|{y~u%Ei~^+Q`QH<ErX~YlN;L9}8QKha zI<|hgHcd@6XnrNOTjA4M92k+erkPF|9I3H(n!{iQ>TMyA>tC;*BVo}*Er=Wd5*md6 z3EE$7-!hO@#E6(__s;vcEZ0jdho^ruB>Xn}0>={nt;HSakX~MqvGc}_aeBV;F;A6% zDi!z>e~~f%_jhV2p8i;dLGW*NNJjF*7NMbWegv(#0F;1A%-en`2opz$6K`Y)oeW%^ zfLSBliD0Jzgh@KM*Q*`&l&t=oNzf=}4BuV!sxL#>G@#4NV$_c@1MWz`)B{rloWKh* z;IhEKJV^Z3uZT;?Wh^7CInm;d*{g~>FXW(61rrnNYBItHxyJt(*b6G$V)?wqTRfGi z{xs$G+S)+9r3rt7@f8UZ$^5?<rHUvi-H$vsc8?xiV+~2#Nwp@pv32rvf1f}Ot_OA( zm?{K7Vk>XJyoffC*fyWE4gqEeG+=8#KxhFxfFcjzI1nMg?cE&@<!el6fH~bB3(S)p zu(evcb%}94p&8^f)J`%Vy2KP&r<(l=i$f<Y_d2@-_jDT9Q2cZ}>C<TAJDcp{vu*bZ zYYwQlHGv%FA&mbF;!_e*T0s;t$54UtOMaU3&%^;L6bYz{Jd7TJ_!&67UefB`xB*$i z9pLvsi0lx`2cUL<=nfDIzJuNwWTL~x&RBt|a#<b2x=N-u7_5Q%)9tF7bfqrbzPwyr zo~ARlSh-$Yd{A3^NG^`w*#7=QX)yf?1vxdeH@f#|Z+f2B-TiM;R%9Pa!QOjm`8jQ4 zER4|km{uY86N}@Ai&hczbU>|$y)0i!ZP?_tO7=tvrVaK-1!>56M^7@>&E@4LAk^%D z|DCrDL5w*u@d0EbLRMc#cP!?j@}6_5Xxi!CKHXzB*%Iic)<Iz;V+4avVvTi*e677P znwLN@T&9uIkNiX9K}>GXe@T*ArL4(v9`#3jkNl4=^(W0qa``Yzz}3Qbbzj8(Qq2RZ zz#Dhp#xA^GZitD%zTfO!Ly}TbdXvg!Z44p%1O&VxAP62X`UPfVL2$4Ddj~r2sW2~V z7||YJVuKVoAhN7i(#GaNGXAO*_Ka~a3o}}Lqi!Kd-SHg~p-{)dbn4uudMm*>>IfZ2 z*W0)NNj);8ol)DFg;etxCsEc?4Op&a^~E1}nPUi^qp-3ht51$6kPxy)JA?7GrTAWT z>*2+7Y&==?PVWifvScCh`RbnUgC^LH;azW{z+9Sqh4evyl$2FbQE_XYHXBK>P$^Z9 zhLC3f#+u+FZ|X-#kYR3Khe_|S4FBb)>mui)0?WDfJ2?R|dBYI8_G(7<H6mSW4%PgZ ztzO+A>5#R28ZKx(*3=?^WC}DM+m4GwCtquZrY5V`wkJf&ha=H_<Q`*mj{pJ3q_;6V z3zZQfdYxy~7?<bhk-fFa&J$nJuJ*(aOwdSDAUZ!5qM-?BK5hyH@VQ7p8V1#p<>h6_ zgcZR4m;lpb%w|f1dki|j1T|dTUcee01-1U*&`^6ZL~yiy8`4`5JiYQ?4EP8T0!H88 z(;Bnc773=wl}e6fh!YxXaM+HBfvjPdXfgSR&a9C;rS>!@Ry!>z>Cduvr&|WesYvwi zA{{-#-th{4oC=ZOQ*SZMKDn`4aW>VgqSdd`Uu3yKlOLg{P=b?FMR=@hIVf*d2P49O za0HW5;)6jqBKX49>4|Z1-GJS62gp(oV0Wcv+2!$4>eylCq|Qfxm;x{;V}OSS+aH_L zBDzEi5PH&(K7R%(DV|I=Z@~x2NMqS}y2GHoPQJc28h)amKSS6jS$7;RP0hSiV&i^e z<36t`zSw)&Y$6)%Gb%)ce{<Ry1p+nuBkva$vOQ8xO<qg_q;Gq(y8kz7s-*?oq?oDL zp%u*)q8Vq_w{5?l>KKt|kv#ssP?GGO28kZiQ&8~GJD~SNP~#vD0YGAaX!2SG*!NME zJ184_d%)@Cgb=Dg@UjXfhyjYCdYhN0eiufxtg9OsfV7?AAzTOJpr%GmD!!IXJr)&{ z0MkDIJJjYdiACFvrhIu>#bN>XgWE?pxI-!q98N2Sa~oNrnt?RxJJ9AQDPYQ=JAFz~ zwHSPO3Zu9zNE952VB<q326pew@^Pg8Kdu*`V7@nXwEYEi5g>Cdz>9^@mLO6CWP`3( zqu*qN>C~9ET?TzNKKCWJn)5=HmHggLfLbVy1Xy^!`V>p?{;Ag1%hF0L;jT(17RNr7 z@>Ex1Uz69{2J1aAD?87eH*D-i@1={R!_ov|8jMiUaWQQZfabyd`mh7Y96-x$`{5s0 zbtIs*1iml|VL;GaPdDglfLQ=h4$1L3WvT}C7UZMT@$4nZqGu!hc^67V-#1I6)vF9x zG;S@st&<+mKcGEcsnyvZIg+jE=)m>iXAUM#UkTey)k>(*xv3dR&Y@Y|ZoP_8jzw`G z1vzwXc5LCgvcKof_1Wz&Fwm$dC}<9}p?Tb|8Nk3DpgG)r&;+d5VzmlT&ixKP2WE4T zvi=keCA4khu$VIfx*g}dON|pl6?w2K^S^rLU-(0XAbP1j-sFYK*u918@ap*BQfUil z$mD=7bl1vZwyM^`s~U@BzDO>YTV+L@i)+PlS|W4g^Y77hDAT3Ut!nf1&GFqu1`J*d z0uU7(-J2$TIJ-)y06mBZ-y9rFOdDaCn4~22!k;!kO$cbakoN?oUM8^)otW;1*RS+a z#axGD%(iH+h}o?!L7hiqdr_JS{r*HFqu#CU8xa5*o6Y!ji%}vqie3GHh2~ZpjZYz> z<bCUOL|0uY>(BXR0i?xkdYsA0)t<=KWnLxiH~ZQJT#C+KZ~x1w27q1!Cm*~gY+h(- zdHEiLD(qV~5XeqWP9j4dg+bd)uH|3@7J%ID?71vf(>$R&8&ui9LvfgK3w2kUb^L%& z3l&0WpY?4URfipbpd6Bggcck-p2B<16drEJCw~)+e<)3RZS^5(?A2Q~y4KShl*h-D zG5FS5zYMJJ%C)GNNSKSfJE}@kf48V8)}=JzKW&;BoIC*q;Cr>>>H-?;IpN$u1iez9 z<v)$FhV&cHf@YUi>}7LtYvF+=0^D{~{C7qX(uTra0WQD*ZpJy)VS<zeN5yqh-;&wK z%m1o<X1ol(H8Qm)9JG1->PxujBg*vFQ-ULqKZ}V0`M2E_w+H_7<J6q@ZuU@SYE*lp zFG5v{Tz3i`X0lSWh=b7}PFj;oh;<!)NrpE#1LTrt9-y+!)m(10fii!g762(%U!g)3 zu9=6mXx0uko+TKcTze+EQtq%+S+B(exNRMr-DQnIyWC|aYIrM6^@Ij-4}POlxnSbu zO#uf5&aEb<tq%aHnhD?E?1W5dSgwp}aB)FhcN3V)Q&DmAwO5;Nx3ML+ICBOU1v{V} z%T@eqIrofQU2d7bIcZDs$f=A1h$t&C^{%5s0kI*jCW-Mi5<$!}FD*y#?WPO3qR%}m zAVo^?K-{ybHX+-gL#;Wl6{o>Q-BuIu4$6ngk@|s9tr4J{iEQaHX$t|nI!AKtxVggP zxHsHy0eRe6ybdM|eXXY|LXvo=+|@a+g7(ZTQmy93YmS<xQ|P7rc<HnoO9KrajW}e- z{RH!5U&cHW$0BTRXfXbRSzJw<?<6CI;7|Rw*IfYR>~d)U3RHJZ%SNncTdT|(&UVC- z6p^Rxj|jK6bCC)ms$#i^HvJe3CDWRP%;4QHY3p5ERF0%L@Dv@)<l$wV1Y05qS3G)u zU<SA_UG#!?r0KqVQ_W5%i;9BoGq=_^f0Z$C6Sks??7kH*Y%dc}8FA`u(%HEd<S5o` zUE-<|X?4sA_2TZHgl7hlvIYFtB=($j@LDW_0|93x@)B${n7AO>O->=cySP1)+t~O+ zi$^5ZOrMpYrXytI(REePYvslL{y?p%Eyn!>LHKGPK!mCS0RGk_6^jSZb$w07)-x-S zu!<OT%YUWQn<`ZywJqP$gY(lEh>w7JtxtAIcksrO_m%Tz{BZ@CSCrTYie3(-%h63+ z2fXA4z*UevE<gqf$oBn-K#n*V4`$}r?0#Av74pugEPM~8&|C*A7sreauP&=%hnYz< z*5xgb_S^(A*vq1+ok2Vu@0P0cSWoCb0LQ(N5F=t{2NUjDXS0iu#=TY;RH5^9aJ3WK zp$i51)yb{)Za<DMJZ5+HG;`=#q*)eOcM@PK8i0>C1ztAy@?jPB_EdC(o|)4MP^4a} zSgKK=>ZB98KQ3!N;es!h+tzAvBnbjL0!T)p74zK$3?<pGljh_7qK@2d-Nr++T(Trk zcCuEapa}iJco|=nc<6=Q0Jp}UH1H{?El`M1(Q-3>tAd+X4oG_eH$}?SX%i8^_zrbV z_q9{wILx`NOJ3By{}sM`Us<M|h?kb6j5OBZ`XzdWc3C#XWsMi;C!8Y@m6qoDxif(g zsg9bAQ6N!|BHVO}2<K}0<Vet(?azcwj8bgF#tk3xU*FGN6|Yv%?q(WR_UrF<f{<-N z&kdM3q)e}S@Ou9?Kiz-q3UF=OTucF4^^pd5H+zb@S4>Q&j~5CNHLr`-)O>?m-WjkE zaJ;Q>8Z$fzOb0FIyd^@%1EqPRPHGMuA$x8|{+DlkXd6OpUz6vJjQ6jU8>`O3hY5Mm z)nq$*iI&H2UmaVya;cX7r~AU9ASNsXQg^^g&HWt`?tjwCc1`Mad%p8eqs)(3@U>5) znSHz1MlS~TW*Wv21*k#>hrW}l&7W-5bibcIN^BzpE-hGuYGXWOwgmI?Y)!e>P)8nk zv1zaEcX(-ztJ^^qu50z^PlJ<+y)Q_Lb{hrGB-{0MBofW2`V2`B^8WXmQv``AD6_4W z_~lzr0QSKzIsD})@{YhdOsmd8$>W=xg%ZO=@C0a{oNaA0|KK0?h>r!Bq#cF1R$``> zx^J>SnRVc@4BHLb2N6iI1+S~k?k2YQxnDXw6vqxWw!0!{Uy=klPR3rI4|5U4#mW_c z?pKDg1P8#7!O=_pVduzuD-I0BIS|@DI)=k1rd4vOVDgTa$mCjozPrK7e52TGc7*@Q zTKL)8i1lNQ@q8@0PXc4nTP)wu0WtU?+I`n!y(3L9wf6KZk;BArjMb%AuXXMb9{doF zmD&1=<qW*0R;YH*i<6DNJ<4bipTsuz^S~Qtmyz>>h7GHwJpRKrmYA+x_qt@jFEJ1y z747YmZZozU2iYChqpsk;Q_O;2id8@bnB~(#nkXan;aO{;)*?7`TE<apX0B)v28<1S zI6=8ds(#<k7o_+KU-}Uu%WN_<YdtwLsK+zas%4Buie5Z>AkNj8A|lQQqkN?yiw>|l zPFfHoBY$S<W-y~HKu|Zegcw+ce|U~l`;bzoOaRyrqB@k(M7|oFr33}@x-Sc?l{zGW zJzLxHYFSFuw>(p)8NK14WTLq<3O(LoO>V5TyWC<ENmp`g=7=}u=!*8=ZGWCKbm7=i z(i0EacOb!oNG+#OGkM8nFr!N%-(W;f4#z}JR|3#`%`|%V^nUX4Vq;B?FP)b0N%}~L zA-zp~Ljii1t<*{2{A)aBAQ}1wI_lt4s6+&JEDfOuC0?*!FZW*``MaxVT)S1wHWm%Y zQx#CN9q#BK3e^kg&T<uw`+%|)dON86>yOO~j3O$FZ5IE6tovln5%of*O#hmJgIW8I zC1Rg=TVH5O?;_omHOvHpvw<`I+HUpLa>w`?paz@DHzRyCO{#L6y5a#wM^wWBzwmeK zsaI?m7O738hK=x#Ia}|Mhe1NR<*f`+YJni*bqY(6z3tAqW8}lX#lChoSZO(VqrYjN zAw4fzP4EH$HadEeiU|!EbKPz@my@=;=3Or;Q{-Hl8!9bAKfi#<FBu23)Ci=d4?a23 zl0_iYJkno)M!53wsp9u6QH@GL2HJ>;<$P2q4$IItb&U0K)ZWw+`KbFyv}B5Eie995 ze6>&JQ`v3^9!nnJLkt)hoWIa0chtZDh2Tzjyfrti6686S-Gbt1rV2#G;Qp3dQjy<b z)B&4eyxCunvgODtpW=>Y2Z=)cF8UV$l6_4z(~;GCU*a2|iJb7G%>e>S%LSNEwzEw9 zbgJD8Z{)+{zF5o~#KB8<s9cu!H#N_kZCnYhAFpSe9MC_gY!7wxUJhB8d&PpnHxcnK zuq6LGexqIYmk>yYb)OJ0aB*MxSRXLW$$SLUBch=b1Y%#flKpdV&u^mJpj{dth9dT! zDx0rVZ5;i}wAh-sQrMx<(d9mW{<}X8&iXExBp$2?Qs>2up%fXx_{o=tJdG*!+m%ia zZEv8qh4d71LDQ%2#QFRCgdiy<U@wfrrB`ild-<7&7e)|UAsY)wH=Ud_`(kB;P=<bE zPd9x0gRCYK+2Rn@3RngK?D*?*sR*w+U!F}47u-L7)y@W^YRps%p@d~uwp36;z=14y z-(N)@CvG~!Zgqb}^GboV|7PKwtM6>bSMc39eN?(W9|yLq7!?E#BNM7<sq7I!f(i|N z*Tnvqs0$XrZIYaYpa$LtiSxo|%9qJf(?3m*Ulers2Z}-qZVjgJp#CghIQCt8&4m*p zFt@#q6v~(|E`g=>-p4MFH(Rw*$by!jzOP>qG0NTOdM^v~dEqdG&^BY4H@P=9TjJs; zAs#IKw_MrT0E@#jwSXQyxjM&^#5X%f%Eh#j7+9_WD-39?a}5*UYgz*1JyNR7Uu~#` z6<l$ZC0822Kw`q834brg2cDfDOHCcOXFxD0ky?h<{o9r8s)EgU(d?+ohu4ve=2h$c zAWwE2HI|#g>UKU?Y)nFDWoBU2&EYb?ZVBX<Cjwh%E<3m5U=nk^8gtGovP==h47e<S zJ>65TBN5ndza0!p%EiynxPJ{*&(RisQcu`rzr1@DVDBbmXE;VuZ<Tl54h_YAp&>dH z5Dn$=XgdS{m-^+9P0JBPt)SK9Yy6smfreph)*SM&k0BrXRXT4+hJc1z&uGrBFG6sY zCW}T{lG~z`T-bz8bDg0m0}4%fM*rUK)Zgn0dC;C6FdYC_exemeGtbLfC~iml4+-K= zQiv|@qF;%CS4o|ov%4owq%jtSfOB{k+Sbw0OG=Foh+VcL(qxx>nuvhp=f(Ir5_EF; zwg3C0A+R|m&7={D&vt_T|6&rdfc{SM+hvBs-g;&Kpr2uMpNn-m%h-`2F^S=Q`JPWz zj8pXOHlcdMcp-eXdKN%JdTvPUqqBvqm}@_#Gq<tt&wN?g!2hB11JaC#iiE1moF7dX zXLN1ly!ceg=iL^&`Pb;31Re09?JCAeNgLO8rtx58)+=h;ziD#g(EIDnpkweFi#0V{ zgDp@@-I%}S(iBpN(I|Nb1X9!^DZS%S*}qF>%D=<7Y+@SnS-$<+098lpqS2ON%%v;) z7I)wE$Y@}gQXET>U=sBFA*YV_$}^X?*(S67i^up*+zrE6L?yAupEbjG0Ia3d1h_Y} z1DsZVYhBqo0#bwC<mP5dZ}b7_4vkp7s`GorXE6Soh+ucxa=PlvWAy_bSitDNJ$;g! zO<+D`_xb%o`DLzVcn`{~x5s?Da}(cn>(PMm*?<m24(MY~>AT-uo*Z`<X8ZN?PMc;9 zWeQdm_A0QH-kdvVgQfpxc6OJ?{o5Tq_%i!pr$DHK1H6LN^E;V@3xb}=aV%*Ib>Y}0 zI5z-2kBUG116HE>smp#K6*pe%)MCekVAhIA==c%SCpBfUn<Ku0wuwxR#^qMu5DY?Q z4bVGUMg=F)TZ2+OrRPghs_r~iTM94-8U+_yJLp@_HQNHx9fKb{!o_u0OHL0lM3x1P zRFbc!WUAl%DgN_l(-uKc6ujQ29WYB2f3J^gWWEx)w=Be15dK^qlV<DHDf3wJbS)Xv z3NzyjS-E+M0O6iT<e4~W{|J|8o(vdV60QBW_?v^R#iG<rvpYtrY6R2}!8OxjQfvPv zp>-i%_eAT`Ib&Z=g#A<nR~3iD0nQlEsd{)G%{HWHYuMO8*W8RU{RHzTWtbo@W*UG4 zDoZ#mv$Q+*UyG=f!DPvN!Z5FQg<0K6WO0*ZVrHRcd73f<UY!4wZ8O{T5F^0^0thk` z1x*jz?u;eWW||yb?wjms^vIwdyzbKbNMN0v)$7qz%Ng!JRTx9!LqW%Tj8vkbRRUig zT-U>pS@zc!{r1{n-a_avS06qjeI7+q217G}_gd*qUcPZDjavJ1<gNWq5F*<QW8q?} zz`+c`(MIpbE(4d7uWws!Z$ceX7K6*OwKP1wvkXNrbd9X5^0$yW-7F!b^AVQhn9zwK z6fTb82W)vc(Eivm0Fp}X$AE-voA+Fygf&)a;-JmN4ICsN*<N8LW4QJlWE|WR0inh_ zcA7ZQ&zZObrP#Pnkkk9u-9`BH*id31CMB{%C|g|^C7B?}Scbq97RHs==E#o=gj<)c zzEYvajPc#^OL#<5vj+?y%fivs6BGpC+%OVLDhfR+GE>vb#ulCeEb}!P{)Zi=?Y)9< z0{29QAM&Xo%$p_AAZ@&`PF947%y^mu^@Y%z*F$zdFT=yJ?5v}6Csoj7qbOV)vlt8o z1asMPss4R;Hv|Ou)jf?YEx&*He~RA_%8GSG?7{CIE$~sam#kogW|HwRCjYp4Wk>!D z!`JDwx^vujp`FQmJkH8)ZS{I6B*+IA0dPyY1=Vu!jAp}DioT!#LWqLrLD@RWg&EEv z-teQ=_67*Eg(P1Frb2dF%x(-EE+*8g?8ZoMKz&gOE*iWehX3BttZv}b!&+4zvO^;W zV>sLOl@PyxhBGVoQ<5N3TebKL<N6q^_qu`8TG_Jo*}71#O|nTdzbt$Oat~3uybQ9( zi$FsIe#LA#e3r)zd9H@MdZZP__NH`(=gcmHYfI3KEQ2}d;&+r1ny@rH>t*a-ie;3$ zwyiwunfrP)ttr2g1gFk9<kTs8%kD$>1=+zAINqqlBsXFbB=k}__YS<F*e&<7Zp)Ph ziU#=W`lTLwYYF=%yJUHQP|>V)+9mgivWG~UhL%>MUP3PH>>uZvaK%A!<-3{PZC+Mu zlJK`3e~ZQ&Y;ED)t`2l7guE9Y5w;eGw)P&$PRMAklM&l*aW;@5QJsxl0K^`ErlMo0 zSTIM^XfG{AW=o;73@jKDj+Zp5yT9Mu1Oiin=R?WA#MCyUlg1Wc4LX;4kaRJOXDKxn z8AEbu!nNQ|Q+9Cy(~bx#UhWXU0B$|QpDeG<S1K+l^Ygwn$|;}o1`Zg(gw0PbYhN&h zqBFK0-Wh5}WPgcQ6{D6on_OYW5f1{-0mjBe#~6(gA);~S7t{GoU9YLJuuwVg4wh?6 zxI>#<FtULX|Hbtf?CMoU#RW*gf<ebx`V$bpZeJTjFd=X)!C5Z6edvqm7>IRo5x!IK z3{t$J;dz8xt7CC~c|xdPN~lW6=I;BQKIP)JHwDql93n5oAGiVuRwRPQ>-KuBwr>x~ zN8;Goz%&B66=OUu!dZUji*y{kj`{VAyBPqBzx)^p9+9e=ZZw1L9AZnBV`y^3WD6Y2 z^<H}R_|xIyd+U^0UzNlNz7Cok&F$kw*leZ;O#AA=A9X?we4iI%!q8>^fX?5hy+Tz} z1)=<ttcGH-m_U{1yPNwx;E{c^x~l|8R66_fDXR>&Cj2AK{2ef9*&u-eHUfI*`BrbL z%mWM>ybUnB0(j0XHPz}us{sA1(I49x>_2M8EL!-$4TGhsKn6tR<s40{4bQ9VX!Op2 zeseI>`;4b6tb*-}&=nHe1<l>+f)pGb4eXrS&tuD;fm|A%&_pjZdphGH1mEIY5)WH$ zB(C3l!hpUbr&GrM^brFd8UY5my51tH#ppy{!#`pA<BeSG3TeHsjI34)r|{U0>pj)T z!}hh`B<D`cO@^}CRo&wn0$cAxLQP$zv1k1iwmoftN!Gh`HD?+n(&WRtsZ@o&na%{? z41_$<CJc(9C^)g8*`7}S{kkvo@6}J^1J49)pnN>Y$v@RIy>v5)&zh~JJ5Lg9Xbpa6 zufBaW=={|M)o{B>A<M)7ttU}KaCY#ZtCi_pZY3(Je`9604Oj0xxK8l-jiCefO`RpI zUMHO6KkK$1nVHE3BQRerwC5)6QRYi)Y%e=L2Urls93Dz^Silo#o3KM=K3x#3O{^so z6k~`a`BRaJ{6YKmOGWw%7!9xEiVsNf=!PZaTPSF~p9)B-N~-Wb2p?(TGXL{1WP{~I z&6>2151N!@F`Iq=S^`#k&>`FSIb|D-`I~$S)uQz{l+QIW`=R#?ywAeHj6VBY(+L<l zwSdYGw|npG;Zhvq1~o!Bv}d!XziW7Su?P{#di^tXNT1kr9*hL<PKe}z1uF+z1o7g( zmjwXys~fPc=fbIKy2lrn!vw#4eXC{;2C`@Ed&=Mb_@QTAQSKIsC%#Wfa5eTGswrV_ zFhYxO&fS@LhS%JfJzal$SBcg{;k}mXQlz}_LVaM)56d7{bu60;8m~mA?1}c<Il&O2 zm)C1{RRW?M%>U9f7>%>1b}MV8T7Rab=V?E_)D}tRuM=;kYmg(8`)NqQP;?&vmVhIl z4Vk)T>-mS}syy`zc!V4<7DKe)Z)%2S?VJ1Q;BI5m%#m~x=l+AJ#Lu-7XL~}$$%}=W zIqwg@&J9oGeSEs#2XS<RQ}`RKX65p@h}e(C=j}&xBfIIm0Hb#M&~?7Gjl@BJ*KptW z^>LS?s~FvV&sAXXDof!9ub+4zi`7YkT!;c9`LO64400U=*~DUGi9L0%Z|rZ++`L{S zTigy83L@eeOn>69h(wB#T#V>XQ+T*d?LE3aD_RLP%aa81hm|}8v>;jhQ&~D*F3T61 zDb?3-;6j5h%pl8HsK+9H8=`=q>s922?{xUR)uskHTtbv!){iL)8WZ|9;Eu+1S&`9O z#5W)hFVSxBLB%+L)(<0D=D&v-9WA~IY<{3e$8>q%%+}HQ9xibn;HOur%vH`se2~|m z5Yu}?tSF^|IDPS@Kx4w#!*kahDxWhWB&RkfIY$=w?p5-B9L%ouk#@`^tdsEFvRep7 z<{<w<N7;BT*Y^2`&hAvI*+|VA8JLN{fj5(T@UMIq^&T%m&9+2jCBzdNzs=giIPe{K z9trR~!vlVfFB=KETS!P9o!>VlMtS+&^CJY>BsJa_kY;R;U6^gWfIr1x@`p5>_^UI8 zWoMU)N%J{}57eWhpK6PnPIeH?XM9wLpc3?6hTlr52T%`F{=*1&uH-^ECe}O5sFKcL zYg{v8s7*L*tO;y!zBab=6G4pTp@PQjOsli>P2teL+IK+5(e>iu#ve@hHl~qyYgpwU z-EIH%=&Nh}W%Fg(I3AxYA8wj9RQn-r&4k(VxNi7JO3{LVI~apK?TE39SBVq0>i*0R zU2oNs+3HmGkJs*|oag6=+h9Y2zO>|*<-0&bq00O7P@$XS`K7zUeMzXW1wMroEM)R! z8S7nL4f5qTvI*_MST>I?O#w(ZS3NhK@MQWgeHj8seFro0v!~X1BJ(I!<qN#Hbz1%X z>69$^s1EcC;cou>$$Y=n@aoZx#z;7tub&_IAKfr#SYDkYAVAx=AN{+X!ejU(UgLGw zL)2j`pDyccY(dQNz+!VD%p}zE*vW&nuh_mknW2oXSao*aJB;=jk(@OR95zXzZEbXI zD5pC236K2z4v1kAUw>;JUb?-m#k%(9=J{Bpv=?^8_{K0u`tt5tYhm8oS_o|Ce6Dn> z{6e{0RmRF1-*<|u)oz9d*FD0$+gbr@$@EapoYoSgTHemxh*!V;QNEyyM)S0qKBCHp zvQsnghK-i+VG$9e?Yb~Md<`qklMz*%<02n;q7XqmqltwRL$udu(c~a2{R9oBqRf6e z+j7nngkKGj75MoV-WtR(vn|oi#&4S}-+OXiuG#h*Qe0x5eRCEnc_IL>e1!0;n}*iA z()x#;%-g^tgsWc~yS5?>=UBwpemmDkHe1MLTA~xbFfg)^tAdBKr}owFj%AR!aoW39 zd|BLk|JaNc)Cy7uTB4Od=$P!m-)$q3FEhpXAnG&8{?$Ca9QE3zq;PRK#D}_rh0PX* zcG9ND_vSAb@bkgad8cmU1U<ye*w;1YOCV`tvolY8&a-W3v6RaN-)5`%`&0cwU60V; zTKe58gNKcBD3`}eER|xv>%fycmn3MPMtgZptdF^51=<R#&KmL;zeet=I}(|R#*Aab z&#qLcODoVk#Qg**YDBR`f?iW11z~L4Yy|v!$e#Lmvb^_pe;^pE+b_`2#9a2eU#d`x ztM#Q1fmAq=i6-cV5yLP@fA=~YpJlSbK<J!r%j93QDb(ZmBLYz=(J)bV@Ndf!D|Av- zWkTU()_ucFU6U|Pl*gyX)A~wK=ptw;<2bHUY;eOdxKNLlHp40GDHm++--voQupwH4 zO5LXHMFJ<VI;-4fZ07*>n=NF&5v>~wY1a8IHcoN;^_|P(yn{l;&@LcHKrc4z3M?q8 z?brJDoP3w&>5yPCGoLi!@QTtooJxfB<3?xro@!lcV;5WcFY#;#)A0lHx)X##ji^<8 zJbueBCJvEKN9|w|c}p|5b*V6lQ}y@g#-F%QTGm$bzexetJwO_j2Kw}9%^E4>HLJ3b z2%^3w<m;h19)OtSgN<HOKyYx#`o}{D$&R`6;sWBgZ*cR^Q;=~ciTAYDOm~%O4pX7p z>%-f&F1w~ITB`_9%HS_FM?^2`EMtOG;pdw;p0mr%u5jqBE}>SJ)A_`R`4P7UqZ!v} z`AmD%Pu4&uY<>cHb?8ZV(&`(Z#351i?%54pi{t<i^%=^|MxeL%2W9Y%{`aBEv`RSc z)A<U_lioqbzhnD_=TMga_z}g$oLY{Hppt05cajx0&lcbl1Vy(~B=;iow6U`3sdRxQ z)11I*??6Dt1U=uXJ>m!TZKybs{^N;W|L3rzQ=8-EKS=KsJNrmsKY6^sG}!lj{cK`7 z62dN8G69KXRJC<*UK;#oep<ivhHWq1sByu49{visQU@ScD&e8m;hR*LM!O=?Z7?MD zDzYiXdn=^)GIbhW4DNKyvR3XWk+bb|ql&cg;t<U$!b+}GXkvKcThzZ=@Oivn#0dHB zuVBY**uOIJT&UL~=Y*pUC+8%XkE9}O4JN<aW+>Omc9SbCFy0HWApU9MWAa!vn#ZS1 z?hOAk=jYvCx8gQ3IGf}k7Y3`c6rx-@^36$s`+E=v>A2s(d>Ws{^T~34Yxn$z_jonx zc)!)!|L}iUx(bFU+orpSfHaD<fPjF~-Hm{>beDj@A|WAN($XMENJw{gcXxMpcgJ__ z^M1cz@4afyoO5P|W2S~<pD=GxN6hkWUWT6q$CEVSDd4{t*7p_{cQ`k?mUPM-QRPS| z&zI6<F0G=Ha(j?CDM_FJgTrbt>e4%wTWR9c^1cRpCz$a=>VbP<?9G@H-Gu8E;dQ<^ z%-d)ZqMEi5<O~G7vB2aP!7tcMV!=VT1ScyZZnDT989T-4%x;R$&Gnv3L0EWtz1wSV zrVO&r$`&lRnBwJ3O4Hmk8+f=;+tffb6g8R?RjX6Q@c5+J=$fnHT-_33Vp#xJUEyqh zuLKHT1)koxCtCrLM1M;zTRZML7m-|4!<1fq(TO`$HS~>}^>5Np%KaP1s{Xp)-|0n# zK0?mq23ZY%gAa25?}Hw1r57#_5)KbNuMG3_aNa8+S{S`sALy$^?N|%r{1#idvgF9= zYIVF-Bgov@c(eG!R_ng=tfpYI(T!;6DbL=#xXRz<YI!{8+JAzDPW^|hg}6tt3+IY} zfM+i^<WV!147vlYc(d1o9bG}E9v(MhqT!o}dZr{@(GOjH40J?tf`{V1vkaUJ>+*2@ z=TeI$QZEUf1oARI*B2$kePeTX6eFj!1-Hx`9H!le{K!_hMC?*ie;m^Yf)NF#PvPJ> zG1v3p^<Lr<cODbB!QpEdzBAVDi{);W(KkUln9aE}pKKSL&xz7Zx;jc^QO7Ct6fWAD zoUQQG{2tB82nxD!!xVGXWUmgP6nD!lzyYJr6uUNI^x=W_V(X#zsH}2N^OSod$fMq6 z=J0p7!m}r94|jfgH(?(vD+Wm+pKdzy$?Oa!&_bb(yK4=bIsgStxv!{D8*W)QLY&Vn zH?*!gJDOEv;>g5(tNVdsT5I;qedFrpc6n+ZwORr$>zV7sF|DotAE#OewJPwb)EGa0 zAX7RGX=Q)Bo{VR2|0rf)03@iaP<qYRE~h}TH~!^(+(oLgvpwCbNp5J$Mu=Of_vtS3 z0Gq#XY!_co&0Zq;=18VBHN&VBYjxlqc=+RtH>B+6%1mngf0+x!HW`SPQ_De$xr@ml zs;bbAV2+?iI~N2eX6GwYNG5xnxV179YuLbZece4LEBUUoanqSsZ-`e~;Mo&i(lYd1 z8++L1+pfQz@A5}-xr87WnEg0VjQU~9#`HZ^Qy_aRM|OvqIqqJhw6cpVtY*ap-cxtO zMWMasg@A!Tzaaloe*`jFdtOhfm4Z*VQI%DmJF2Ed>&BLvQ9x*@N0~a4o<KnGb570} z5?aLOqNdh+y4%KfQ#Ka6xzFIf=fpKpiRNM$bWf6PqdW7&>|VFxY*ZzMpxbSy92QA! z(<>B_yry|R^fNzzF72zNC4(M@(8($^a!~(MxsCLJ)@%cqJ`OS8>vu-bSbVIz`|x0E z=A3^dY$wAO!z0}}L5$QK>|ugMLTtJD!WWDD0Co;pwd=EE%Gk_OHGgM`&Y#kn<Kfvj z<)P;quoK#B=FdI>4asOIY0+~@#=J1!pl^U)9ZIh;ZAk_)zm;w8G_c{USsQO`{Y^+3 zoO9+j`cz7P^?I*yq!rbTp@;Y)G;R&8d0fz>Q0?Rw1nQ0Aflf&kCB)-4tpf?h@}CfC z*EA8)2?qrQNcC{?^g7&M_p643h1IaLjEJKEk-Z!spn(42oG52blb<<()f;LE@$8~5 z+gK2jH-yhVH#paxC2*?x2>gtw69<g&;&bf-owYw-WX;%opUt4eGV`!P7qusrF3zOK zvD2;e?CZ>mXbUgm;gQSU0^Ku=G%tp|hfq*=pfH%|k3t0M+bU7ZuNAQf&FSz_)jJN@ zw23%|d%+0$w<9|rcPR!4Qn5tR;{Z%F!gX<Zug#?+3eGPjT@!Q~E0h#Te<RHgqTPVv z@fcUgc+y!~QfvS+?|Q9Uoz4lOuB|%3Esb_onvI<H078m>;-s5xyWys$+NSp_(3>;q z0*0CFf$ghGE9%xIQi(3*{?zEef_EH-h;+K&cC$NdZ&mgs00;^U4m28$5ylTjL{uj6 zOh_Zr71pJzDQ=$!0qbKt2v{G*&#pLUK8J<A2`qAtrsW|dr(mwFIhksLK1qBEn6>?@ zGQsdU!G2-#`}w27W9=~>AzQx{FDa@8?ol^>RoQ*ANAno#<I2^NTGbhcYI}{02AYDx zyl#`gh?3QkT&Bs_rADYNPxh+@iBKb<<!8z&`8O6@h9+iRWJ99&-w!dr(S5tFzufZ7 z4ZxXV*8y<xIqVgV2o}%Q#Gz64+?yKXpI@NYg1jHklm!#Q{3!`v5oTU`m{vd&Bvn_5 zHv8C<%}<ggK-OGp@d84s@({3llbb+WJ|z)B4t??)i8ZZEb#ds9K|h=y!O~GUz&Qay z)mt~L1vD=gE%^e*NwBQR#Xr}n6|yU*DLh`66mN>8>*KHEU1GaM^aPl88T<y)k*jA} z2rJ1W$+y~)p8l6$?RF($YscgC>TuDQ*C9o}#SR?@=e5rj=s@|}Y>DIx(aXaS$!#w6 zEDSE*ti8T^g<x?C(MpV!#HA&Jjtm@6O>B>`BPypir%V++eH_e2%%rX<mRmz%OTm^_ z$;Eg^J8`P&yu8?#*UY+im;WZH_Ng^X$m5tRG}TK#+RJ#{<KLb~$nFqquMv`-fmCl> zr;$^YhZX=*d`*k@5Z2Ja=Gb-RXb?QyVV9Po<Kl69SXiY+%KxtJhe5zEa;=k9BL~}{ zr6=Ys=-4%w-35ywYjkzh!534LX`I_1qRU>BxRc26=SPMDCLv;gDj|--HN<WQ)QRMV zNz6Y<gAUyj2c7hj>y{Z(AD2L<%unxADxee6rzpI<Dn(O+7?=BA-bk#wI7Uvmny8@- z9>LrNo6ANA&zrj@1UofN29S@i-_$VnErZB`2#Xx=!7vKvidw6mo8I61icqS|{Wp&d z0s_hS^b`iKYr2xhYVB*A`ia)um)-o1@G+Xx5u>vIX0eD?VN$YCMQ(~Ej5U07V3oLz zFDd=`vLxZe<DS{wP@={Lx=BZch~a<v(cHjmXr}C1r}kr>Dl{<Wj~a>=pVS{e!AH+T z-U&s<2!nh()sw3baSHAOo6#mk51|W$mm&e2Ua$-NThxEN^tFkJ3JN>1NPYb=w=E(Y z;rv{=f?B-S88`M<!TI@d)u{YteYWAJqeqZ{E3kUVc8(Lb_4d)u*qUH+;BwrL-r}(l zhSapVKA@50PwME@rpKdn{z^c&vDaWgDOc9mzi4`F?L_2pQ0<D;@x#e-aR3E@@IP@S zYYtr!sXW*X1_QZWHTRU*OsAb~Y1P~c<9XT0d_G0|_k9)S6Bb*8ySuhL?E~)XIU3k? zd`_#_;l^PJaA1zEGj^QHqV1F2J@w_%Tv%cTu{19|B^OJq{13Nh&3G4$DcRBru%)y4 zA@|;m-Oc&FO{dl~2$jzf;x4A*-+K3SFS?y3ane^jS?HUct?ZuYa+S_zg~)63^y2Oa z)V;Sz8kO~kXN@L8R9;HIAk%e#-Qt&DzUa3h+Bca`*`WhMrv@*<v%1PWYw}pH=BGlM z!A6w0x>g?5l%9LsV#*R4=Yu7SWf9c<%J7Wz!=_^JiA&cR+xdqKo0!!zp5z4X)~Wey z1${H*yUPyh(#p!*o43tB=`(tC8hFGTk`icD!O)HMp0r%au1S6%^<VvSb&B5p%M<;* zh$4tc-RZ5$sVdofmH=cI>h?eDIzPS>EDA4v4F$}Tt{e<2{~USI^i)WL$!__#@ZUXy z4*B#Lc47fNm{S$vfp^Vhg_E=b8sTc>T}iy$A_tf~0)vv5sk&D`)P>mOkzm5$v5KRK zQj>x)@^dy8nu75!0!{{Z#!vGs`3G}k66Vn%^O|+Iq95NYWwqAY=h9jILcvv5vO$D* zJ&%y;^zAWY*W4_Rhi^``4JsbjR4mfxPemW;=VtEe9|7>7@4vUExY+aZz@X)seFZo1 zq2(f)kQbF~ad}l(6SRR|<lqSaRdVuCyu*Zc5%U(GTkPp+HwxxXd{~z7lHy3kH4O0( zy`Ok7!Jzed1>XAk&UIXEB;`K?(P++Z)#{`Y4i12itU0?{Lc@m~HRdHn(g;4jkdw3C z48A46B1rw=HG598gi0a)Sas%cQuekf@g}4>K!{ZWuv0y-m^-B|qx>51ZSW<1MZuld z&y6ZPl*%<@nc+7FSWSM%t=+Gk`AGP8c@&PA#=3lAz)R9@p6QG`N^rC4++Jj=vCYd+ zhZuGWRo^;<B;en;dBuEb7}pIalK%7M(;l6865nwMG`{QlWO>c-SGL$IK%j(zLo7l1 zhLVXTe3V-n`!zv3fRvmLrU1y(f!Bnog4JXkXEO+IjDI|hLIS|h_Q{E8K6dlg5F*eL zrrZ}@A_v=JaZcBLinco_(}O*uvz5>h!3pm8?J*qomHcxUOI<x%7th+xIb@QYGip|4 z)i$LEpR4;bApvUY&Qr>-I#c@cbQx~(?rLoZIf|Nkw(ik})a`jRO-q>xH0Mt@)=lCm zYvZ9K1UFgljUXz^UHiAxFN#|1LV!E^1LKZnOO2WAX<U;ZX-^#<ABj9gEM`+GiUSxE zokvR<BBu43JnQP9ZO4_PMCa$)OV~};f^_yV)w4Mt4c7}Je8l|~>x3*2RY^=Y-!BQt z$ssG5EO}t!F@7;a7F98{aKG({SzS<JjNX%uu|6P_iGN;Zl2tlW<BM>AUsn6nMVfnF z)Qyz}4f*0eceB6U;0<tT#1t7LrZ;KnW-p<wKa~%bWF+8|lvwb`Ep<Lu$c~Zi?<mz{ zzJa8eYP0%HZ>S}-C8Un!_-j_V*YzE_S3J>%&I|rbi>An{%kP|RjW6J=DqWdHv?C;L zs<=Tf5O<Gmh?Gkl_t(XQM-nl5=Z)lDQ4!ybN;5*+$ZQ}s6=c+xsX2FlXGPOhq@xu* z70)DW+)FMxtvSmA?iu*!>RRfo`Lj%ruf)l|%$41qAh^k`7gb_qf_WxTq63i5ZhAX# zd~3$X_BM~EF{0H}ZjhXBBK72F>zLI{yy9SNy4|`Il;LGT_w+r$Q|!;cHyANMTPoy9 z3Cm^wbz0Mx0QP#R-YHiQ%$Yjwp{}ldwRE?(MOj!~Wr5mU(1bhE*x<~^&?Ofw_@pdd zU#ghH!TRf0U}~^A0YEWaciVK}JhD4{0z}+1*a^(2cKB*Bm%E7q))~9);9?K%J?@mm z`uK+z7@KjEzGuqhbZFncr6{DGBR)7g<KKr15Z&sxcZbu6ZmU)jLG(!P35SH_QNGf4 ztD!ft*#5QxvvDRqiKlt#dH6Oxg0d8Q(*+)Dv$U8h{^`0uau<h?Tx-=_#(Y6ReiOFL zl|b@>BwxX_eMaM*+Dh63sdD(`ENsCzZW=NlM_9;+{WbEUL{ilGj~@Tym7WNxKznab z;IjecsxyF^UC=^CG;%N0=ruhPb!r>O&G$g7kTvX0(T;hXxCt9vh+2}a!6x&>j+13x z&9qiVB}2Yrj%vXx9t{k_j~Uc;*?M{v$JgdA3a6ZwXyOz#hxdWr-n+3$n<U;y7($6W zxf!+HSc8>=zXvtq@eR=$4H_Zesw?4!Ao8pd;NS&<7tbf>FE5hAQEqUGN}e6MJ?*8e z=E~(kNpXn+W~ZgllBg1oy`oqpl`H@*P6Y#&%n7(+TE&@rgkUe2@X^s`IG#5M%lk(X zo9dM|)QGy)$^6ZZcl#(N+!ZQg<_ht=Is2#Hgb8shc4Ix_@ObuaeKzOE@!L1NnU7;q zwykKa4Lf$_g!CY|WyNc;199j~?yDEi0)PX*U~AXAP`A9A%3+*tsJVJGuyxW`^bDT4 z^YDH_47JwY`*cGkdaO#t>l?!Kj0rWR&2NZ)&kg098jt5L(s$Pw!>vAcJnAJP^rXQ7 zDUS{X_%Vh!jg7^7Hexm^1;2!gvNF!}jQS8~%O5ZBxfKuCYkr`ER*?Z}Z#_o~Gj`Mz zF02D-+v=A0pNXW3^b^EjiM%5RX>I1|VO{RW&)Pd9YIY`T;uSadi4NVW`LWK{LnLg> z%-U(M8!r!c67`)dbewGh?bY<=SM~w6-KtaZ@a{pUR$|g>>BpcqFD_M-PM@t5!+zPq z*OVk<NGCY-EA{*iiu=*U09}^^P9`Cyv!u+}duZY+yeCRU0U?5{5qgXhqbbe6S0g0Q z<@#J4v2^|Zm90Oe57r5c%=}G9Po`F==3YCZzx-og{PKDR69%}@>3foh5z&7paE@Vb z?{#tZ|G~)P8Me1j16hVzs^QvYa2w-%7)lK-Q)=f6R<T&I>FMSrkGAa*NU$-FG=s|K z2siqS^lu?j+|GuJJY9?ZFhRe++$)px>~rKSJ}y5T1oBVGARvzwvJmIQR~l;;1MhAU zZ1aAGg-Pb#@$oqS9`?s9v9n1P(k9SLd%0$d+}-BC+n}XQuvi7lffpu!tXVee{Nlp< zCwszi(k17dY&23M1_40s&o!qqIsA@Y?#9h*#q*iXR64aBo>CIy`v<t{5?AgKgB3IR zpTuvJk$!lZjVaEIe6Ih6d_k>mVg`c`TpGM9D@QFDVZ@sl04zaZk41=TLCR;ciXIMP z%Z@?!ceAeU7c_*7jIAXr`EG6?8`9QrtEuY9dUEZ3bh-OpY@@FfCc9qT2j|&hsy}^c z@IP*P_jSJE=BKh)2AyMmyAWSjAv8@C5*{Bn<<}99kNu47e^n{1*5W3gNrNyoQ`UB| zS8Q)C{*m6355uXN=bXmn?!b7M*BkPoht|i@4VD}_^E;-*S35ZgkE9my$$(+Jj$Ist zGSHg|IKtKCT<0LW{p()6&5E+^ee%tuzo^Zna((ar<Y}iG0!6A5kajz=SqZf5bOoPO z@G2cNg(NMl%iD#dkPzU)mu|5*KlmA5C_+^YpWFjN<(!15soCpFzjVwkh@G*2eqUEB z#aw@D4ensw(f(%d812_ksTX9hWafdxO0~*<H{JZnzbjYeBw?tm%R&I@Tx+5<FtNOS zPK$psXN=Y(ZmM_WPU6ONP68rh#^sBV!P8E9hIwT&zxdsA)-ef28hm+8(^M_X9KWdL zvz8PzJw5aCt8qucK0`HM1DxPJB!npMfmo+5_i!tWgi>&Mxn;PFT3y{}F{r!s8~wKr zacS?~mc11TnDPc^Dt&6nBX7=5Mj8kl)fHdSA(VHQvRqiNer|RfKn6FUl6GvbKRny$ zrDX3-*lKQo|45X&qnL@RH~vIEQo?xY=A~CnYOK@80^%F1*n0?c$}gT>B`92PYBTek z>EdBJ`i!{NT<zt|89(r@ro5dHa9b9dFlaCkRWE-bA{s@i?flc|z4wSvfmlWLrVF|! zP%}F@KLGLBHUfaNbH_^=dq>3W;gyW6#^9|=i;n^>ehmo;yFA2O&2V<N?+;hyK9fvf zrYTVmn~uIbGdB`>K>oH}>D57uo91*VmI#D~CAlZWX3&z*Xzj*)c}#u+bo>x%yj&>h ziSYibis`g(+EL39q=LK~MXi$7vO6)+13VBwLGgOVWch~AE#7fB*<F#~;^r*wTB;Pc zK%7XAdr;9_5->4KL-&@O**;hriLiC-x;)5D7M>|Ro-fTQXCe&I)~kK(`KrMs&COCn zs+*_c$q=c)bWTomqu1*35i@P%lbuYfu9sW}uYr{@=~E534cC_|;5MXbV-nVKvWfm) zZQ`;2+3MRegsGZelA-+c2(hzH9NKKU$YnLCfF4h%S+72yZz`hE!J*H6b#c_FBuFuq zDePEH$aYw-QPzLeTIxxr;vc^BL2v}|!uIMcCwVR5HDLR>|GOWt`Zp6%YhB3a$IY#i zF{z2X)L;l{;9}~f$2{Xgq*bF=nF**;P{X{kXL{JSBcwNLj|!)jB++v=_|R1!x}v|) z-2au8m76ExO`;q-E7J9e#QVttBuylfQ+594r(+Isil;N5X9)WH>eb)jf$xz<<t-O} zXPCcK^z!w0e0HhCIvYyRZ!@rn%DkkccqI=}4=zQluWi|1lH(rGVg0bcuyb<KwYzqc zt77(=^G+dMi8}x9-&Yv(wZ3If#Hq#U>*3-%A?Fpv7n`a;&K?>;l9)LxdGKBHdg&Fs zm`1qlrtu^V7~j5qtPW|kH`z4X&W_EY4L^QAr3gZf{=|oM#>$x3CdyH61JUGk18UVh zkdSNIR%x-z!2^c6ahK_e$Bcty`d7{CHqYG#mb>e3!BpEi?|iU|L_p#-n&$2n%Zfy! z>Y9;-v{2X8=8XjRek4mi)o?Lr{4_5uz80VO4KlK%UiRjXfi1#@$*b0Y2r?Bvrj%1p zyuq8x<0?LMHz^<x-RKQG#gR#!uwY;?83oinwPAivRg=#zHEvs83%p87Wb`OiMQw0y z+2|Y23WJJpnMG>0zBrz<0-Dpm6BA=VQBaL1U_IUB_-An)+Rq2SL?$a`fxXOWM13)E z%abW^a!)&S_~qga|LSzJDUX^kH~JFNXKFS0P(8%&R1T0sQ8IN~obShXI}}r@?yZ=? zABDJoJE2hJ$&(3HOz-R>_0FJtBY?J5G^@n(gRwh$$-ON+{+H_DV6j~e&4jse#-*3K zhF6mys3Yw}OzyZ~SV~|iG=3jdh6X8<@8_@*5(%J_21}+ZCKIf=<i6ZY`{!i{3fj8d zK1rnF!!0EbE-}8}-GUqB@4&<>P;Ro+zpLi#okX(dV%_(Ii%^DRq*G-tNreY<%#8S% zYT}wp?yZid9NKSeW@<VL&C`8iEdL?*e4(K)V#y)hJ>~IXVkj&xFZT$Q$u={d;3Yw; zqDhZ08J_x#F(qWLsBlYo8J<;w(x>|uc$HUG-Ut@fay#sv_gckTf;vbUM=lVZ-#Ua4 z28Jd+7?4(eJQmUlzu@eH-<kdTMKuaTW)(E!01>e(lO&<r_~}boC-5AU?sQUtgh7z) zsRd2pS9t(!4`|8nc*kQs^T-dee7a%v8@Ce;Q_72x5;^6YGGeJ5tL?@Idq=ZgC?tC2 z*N$&^y)n+Xi6QwS0s&yi_Pl^R_le2^&9F#uW(5TR)1)qUqOy??g_cL@A=*Ez`pO{a zK&v~TvPx~lxmBbw`2gDU%Vh9H>v9!xZWGj)zk>jgHY3<Pt{?q#x`r>xaDH6;eF8s` z)Wv~x$|Azv@ZCJ9`?v6p{s8Zfk&_cWS2f{x5fBjkLW?iUo|(Px`fg?kIVh%q6I@10 za2d^v*>X!$k9#*9jw}|Wrq{Z_<hs?xN{duO`DFYLE>^UVEZaQ8MBX;r1L4K@l(sSJ zt>LJ4^a<y$2V2WfYwpik1~~?0pj;npy|IhFmD8wp{zUkk_m5xe9ueN!@~~)oEMDj} zBL?pe&!{M<?sU^rLfy&^dJ5-uoQn+u<Z<PZuBm#yA9lD2!QuXtm!}2_am>vD(KH~> z+1_q#zaj#Wx;P3OoaGrg(HR?x3iJ0dKe)cArD?j$q;G<yDQ+b$>YhfNzmYyZXZrs1 z5kkfJH#n$3I<uX6v`7+rVvvGPrs5=^aF}>$oA2YDV29yEZaJp<zn?T6c@wR>6HxEK zZY;26KwiLmx{;@LkBqDr36S}3ad4{cbIp`g{;-9R>@Ug|XHl!l$-Tal|7yS{(#*ns z_r2~DD+zyD%f!WiraR8Ru#~Y~IX1mnz7E<vB@DS103iVtz?!}rF7xYi>`(D1CR7#^ zjB-<p4#Z>wh_+6#=eAl7QKFznYf>(=;rJs~1Q|%5Kk53{L$pt7S^=;ccoTs|P74c} z<x&z?wrL~amU<wi7%erb&(2ZvxZkoVUnwAeiU^wn#+BWL%S^f^7Qzs;MV8p8Md}`4 zHPA$WQo0<XFA<&e2?1fiH-q88#mvXa{s6@n#Vag~#>k#4g?}+ZECB7r^6YAd8ELUF z{CgFt1y1}F7YE1N@pNX?4v;#ZTy%DM2<%<cv5HgpbVFgNhh9Wvxd+?+rb?%dP4Ho5 zrS18AwU=)JJ*D%7^UC#YGO3bx6lB`o!n>|+cK<H(`eZtcuA%yMqIlPAq4|eZ?RPV_ zf=dtF;6a$|&>BlspZ!ycd{$QcDLdd|EBSNbPXw1Ve|T%Ro>%1e4`UoD;_z+bbSRUP zG&k4RhS=hCNmnhrq|4APl;+BWx#|Otx&O`(3hWG_A|V~0`_QVrbvR_>pD<;!F@AJ~ z{JPrqvcKF94E6UH?T$ZyJdepnaXO2Cx87g6H_0G=q+o6X)<+B&7NYpCink0Z=jX|d z9hRQKBmE3Qov+QX@wIS>UsJ54yDera1Ul&@+}$ODi%USTGQnV_MJA>uMBmT=dx>}< zAcC8EGnSj2?4v3f?0#j82<?oTxyKL1v0f($hE0w}DjqdlwVpIR`E>&2WCHH4vfKid zT0WC=y8k<%;1{OsyY$(ZibsX~)JT)|YsAwc0P7Dq2C5|Bz?HxHeqixzaBEHPIBN1% z&HL9H@Dk#fuPqr+<uHrbAA^2-u5hKdvL%x~uuS9q;ZX67X9pfG@aKHn3;>V@MjfZC zD!<;oD7rtdZXgxoap;_!xbR(*#uFd?>j8;%N+X`D6xMXEjT<`+>O&4jO9xEb{pjW& zyxVv_tFw@SH)lM41-|rp?DsUlOh{mSZCzaArIe*C9X4OF8=fYGjy5u)9eaV|g6fB% zQl(Y|nl>SaNoP<+l~t5)%odviiS}no_uCk4^)H9v;EOzAUj*ddT*I^V9y9mcr)XM` z2)z!GlZb<Z*X26gLH2V)vVQwJ{F~()<UE;{pScR>3Hpj{lJ~QlYLgCHVHLjirCU4b z&YcG;8-fy(Zvqq2w7&YjXZc*Hjdiu;y#C4BcC1+XiQAOT@o?Jf$QRX&s8|F66rkgC zVr0t8JG!2Ijq0F#wO!65uB=w3YAe^@qR$4~zxFv85v#a&)YLL`6#Z400nAW?_-a;l z{uA=T3uV-JX7RbND3rSjj_rIuN0YpMaMc+J$i05b#3H~1$jI*jQf%$B2EU*J)U>_e z!C)*gDgt(?2M>M^-2-C1nkF`1-XG7F_k#({j#xqe1J?)k@xsLGpyvp245lxO9DEn2 zHD+OH*^jV;O$><_CowJqON%c~;sXLcC&rTvY&f_vYA>&}c$?bs^tzpopmw1nUY}u7 zSQui5>oq9b7Xafv&UZg8TVr%$HHUXh<V-4C{u2IY6L1ylP>}{?qpF@HuaUrh)qH%r z!~8=n&DOOpETt_&yUI#%PKO?J>PDZ$lK8x=nq;52$R(Y<YpYqlFMVW{Aop>cgAu^c z{RPy2|8f;(d5_X}a`3hW;8U#cF1c`M&Rb8sky?7r$bD=47f1bcZaaT}NK^e+U&tU( z)9on>KMlODXG8t+6Sg2n3j%&Wguf_E<+H<Rp`T+#wGCUVC0i~0Z4|yP3qb~l;>Bc) zjAjvo10_G*ul2_xkdW*IE8naANZ3bnHo<~yCI8*p9n1R~`su|92aNX}cWk^_mgZ1@ zni^v@alAijGNq$?&-rdgZpq^wj#yzWxqtB|)Ber`t*joE(vAP!*mo5PU<Ni-O(#g6 zyk+ay0`RG`oMH@eT{|*0tv+pJ7u$-KtK#j4j(z#dYW8`?-BlNdgQZAAJ;<w_W)aUX z{VW$=30Tc4t9%7r#$iVZL__36ainYke!%@Iz}`wqW@h9By|L>1-Bs<0ojTL4L9X?o zr`D5ZA@wE_o|2cI-KxYnIz-OrpVZn3#I1(M40SLM5$7|kFI)3>OHmS4|E!)h@}3d{ zi|Fq@l~4hkTO*P5R&8;?s*0^y_Ub(giH_;gdbQ0Vc`e)4p9udtY-MS(<kcF4IjsC1 z?qpw=1VoK7P#?v(<9Q}lIjoY#bc#oUZ<B(FB9Vq&<At;PGymKllm!n?PS*JN?NJIc zD2<UY<xc<N(7tuJ)}i?hm!>M6MmqQX{QYZsXzH}=xBJDD{l+pP;IDi<JmF-hWcVP^ z1&C{IY8CG?Gug}x8iSWsg)#L1v}=5ZIIUFsFcbTJlkvQJ=Pvmr7oG68b(6j@sEfDv zxOe=}mjDL~O^E1-X<o>8RI$8o$lA4Fj#tG<!6xT2)^D-1IteJ^Y0U1IY3fsj)6^pq z^QOunb3Db!5ZQ;@a0>sORgRL0KWb+WO-t~{CjNps;k@<<%2dR$A3;9njIcM7>6j3V zrce(gvTME{7oiOey)*2Z^eUa%n^9Gh`7KXS!b?0hi+F2~HpJ^y)Zz-?wJw7_Rcvtv z$o$`V)6dy(DTLUa7BYAuFO&cU9q_xlF0Yq&$u(*>i;u_V`O?|e2yiL#-hJN;rqP(y zNQ==Uwm%3+PDWlGcq{fd2$rm+nTKU+%~*aAgsa}QjTRhwLT*0K9MhXk`CQNMeE!+V z#q>QeGLZDCRM3Do1~DySY{@-nB)nG_yLRhqKQ4oEgX80s;Khv(a&GWnrkthON5728 z$QN$UiHa=P9JoXRs=~ob8XG%e8m#wKv0ftVU0o79ojQd3<R60sWV@o0qQTi%GuBKM zJl%tCj-ffSnVGlT*q5>}g|I|f0*d1(T1)fGuB;Rc#ZtIxdJ-U$$iBbXQlFk#0S;LQ zP7A5K<}A6K{D-q4GA)j3J7m=&4vl${4{>Gh)^>CK{sB*lj0+|n(EngTroKOh9{SS7 z(J?pqRR7BIo>8w8<fAM{P08!rWMn|g*Q!iqC#;4l#ofIIdbkpTB_Y^-t;ecW!6CCh ztP?I=k8a0|9(qqi1>$|<ojLNF?Q;ghZ=)&ZtKmcq^BCqfdU2Lex8}nHUqw%5{@m!x z6Zc_YK;EmTl2eW@@sUK&6F@UhWT$SBw*eM?TYm~4$3B=HuFv%X$8H-!tCqQI=dC;B zo9zB91?1q<@5-_aFQCyr=Os#gM5<zT?Y%VrBIfqK+(zvDeyZT}(7C3S9bzR637$!t zyvNLDgE611<a{TDwst;pxi5K8ao?>UQ{J7o<a`2z&_m7j*^n90`H}}VitzoBM>s9s zd?UKYqgib;0;Fc&cBJB)8szp{c(1+4i!s=NTA!~&+8XpyMLn3!*!=)RgSXd<{P~HA z9~PQCO1J3MA(uYGZ+A%{9HW7g1uFjvraFK-=H|;2-p6f<fM0=9W42XJ-_~IMCG}~h zoR#!o<j?h&tMM^JXiO4dl&p!d*u=>i+%f2^X~Jol?%uq0g~iR4&Homnl$mZ8qnIu? z#X<vjf_84?-xd0L5LYB%9QKy3x3iPxt6@gYbe-_IIdc7Rzd`V{5%tsGSq@)!`oqu? zh&`-Xhl=X<E(uqxTBc?S`qTw7{d4stc)<Jsd!tj6&8T!wBybxW06RA%I9uT7y&U7E z;7bkf1R@X<4xet#Ti2YR6RARHoA~I6avo>0vzPeK5n4_+a)amnNpnZ*7Z+*r@Tevl zja*K-*5UMl(6osz{^CRxhKbktmLB+UEa|kwtHUraZ%!5RwN6!1R~M<#`-(dCvW^10 zz5OaJ^!SyPeuV~R-qjw$Qgh#Voa>s4A+ucFiv3lV!fS1^I@uqclf(&L5x+QFvXXPP z04Qj@J>*UH2pycnO)fL3A-XDyiOhcgUKI_XD`Urk0)xM^bOTpQd26ldRP3?On`N64 z{+<dLFX{v906GkU{tCl<BanZXMGhATfc0*w7(^ML`B`GLO5~QWf7@t=zA#(Gg9@hi zz^t6zezqo2JqC>Gh`5umF1In#nX(6=KjXIM<?Rcq!{qGF99|L=t*u}brFt^(0Rw$- zodSQtX6tGc+`F+Lg9Cph+sjNoV16}5u=wKA(=MBHeuU|C25P|Rw7$X)oOd1?;69N( z!z$g3Hci1NqQXA0cd`Jw;+!Fsi}?u!09qkCYo;0LnDz$yJ)<G5CX#2*QcMcn)cu|d z6>8^S<aSKyCD0t7b2}cr{m*>%y{qH(kOH6V(9_NO5Z?=DA@{6%v?2jM3X#`V__Va9 z)1McX8i`5xrMOgg8?L%_Q=dn`!Tn8Orm%<aR2}&;KzMuxPn6(e7IW0-9g82;!61}2 zax5vUqIA=r#_D+O^gjS51cE@M4b6`nVQqrsKQG*_!-1|2`MeV)KWrNHRPsy9oxQY~ z$3Dx}#Aah6gQ~0NwLR+OM&GU8sYb!jwl<FiWz944XR*#7qvsOmtywB#ih6#BGSyW> z$6*0^1g<<LMmd>}fQ=C7*Zpg2T}mYRn(Sb{AvuM?$(M`i{|+DtNwH;O&)U!~pkq8M z1_?dM=KNy02?0F(@9V%Y8taEz=-2R{L1&vVBa2!EbiF`}bLPx)-c!`SSqfXWgSTYC zx6Y1PrJV{m)MMvOSy<yUPNC<vU*9Bpc~lE#*v4c6>lgu6qN7rUMMAja{cYZ?1PQf? zFUsqq5qOX|Q>TRmINs<J%C^#V-`_wpM_b7L@!pQ-K)q|32&5A!;c3tz%kIr?XyQ8p zj6R=8Vf!oB6A87rk-pZe>s$ci6tdZ%<I<ewjM~|jf<^9F3#@{!-I7rs&|e@kYqS@A zTdN59xQ)Gdonbu2ot>*u7B+;auNh^gZb~mhkmT6L&DC6N@VS=lIU-eI0K(@JkEb@E zUWeaZ)v$QD_rhQ9=MaW~I=;t;-IA5I3A|dzmz5T*4?*3ljpnz+z~?&e{39r_>PI{* zv=hN#^&@Sxd3T6-nr4+Hn<5zdY@?bTzF(uDP;3-f`y~9M;-e8oq9g7%VU`JJ)`7_c zt)2Qz)(X4{D!$tZ=MORhOmWt3TotI}nQlFoRH2{i(aUFjeyRvQ##USSN2gJ!d~xyC zzFWh5X1U;eqmO|xTvbE}Dx5B2gQQ#gyQ`LZ=>h_c%X&If9L9r>ub$kKD@S{PN=gdw z&`JHaX~j;xG+sr^1tkdyPdixs5lsz+@9W)vlIqN&X!2Fw7A1+8a6*b>3eQ>3q$rQx zTHQV^kb2(w?lz4!h7dJ!s@ut_KoS=1oa0U8KThhiwHE3?5hlj6u(a41biKX~bWatN zAvPpM>ACrmWuU^Xb>@8imnK2Y#{vCKGvIR1^@dA-_fPmnNIv%~U|0v$Fw5Qpr2oMp zA{nrDNe;&;G+R#F!_sq+MCZ<f1IiPrfJX8bZFFTMzN}grKTr$7OglMg!NkKbCmJow z%6TSVhcAlOz<J&Y6s%q+8XzW~s}Y*CHX;Lp%2X|t*dCRt2}#_@f)Jh0DUykq*-(77 zl9({WFBh9i-@pWvG@PCI5=TZ03ch|HeqF-n^y){wyhp{;5V;dVDNc&}zgx(?{R!hU zif1swVpSH#oea(4*Kya2A`S0E@*iCKp+8@!@dgR_Z&`!1+Yq8+t7J|3QQ0z8Hv*oK z^bE=x+XT8(wmU$dzB%U$@Q#N6pJvZ9VEE>WXYkg1ZzRP48kIwUmEo9R7fPT1)eISv zq}jt3&4rVb#^aIzg7<ZZ>iY3v9NqPZWcy<G5j^Cil{$6Htq5b!8CD;F)d_Nr$gj75 z*V;b<Tz$%5t6z5Me>Trp&KN*S6BVguvm$|P_+fPP_UvS|bq=_fn-hDtSytRbiFNvG z?{~wu$f!vFqBR#;xXao$VSaYe?>tmU+*r&zC)u!xW;F|5Js8W0iboC%RZXUzsDuI4 z^<34E*nk&ut~Zq8q9{0jZZEva#lZZL3#SP3;flOk(0w)24en<ox3nVgW<lHq?|8`+ ziiB~{!5xtR`@H&B0DW;0>TMoH)sytYeuSJHw7jQN4p?2NZLsSuF7{DHFx(>%K3hkA z<g)E8K|UBLSDORW!M8gmpbrCobe6oYKoQ&EpcXIpwi6bBLs3L>9N&MhTKuNQ9M4HR zoSV&^X{na%;vy`FiP!T88JvvC%98u;(&qOEGoH6fa|-9rj%aIRxJq90*q9xo5CphR z6_^!VfwO{D0w5}ESQ_}Z+Cij1Gv}~Z@`cNN>|V)6K8@S$2Tkd|KNvO66}zs7hZqy2 z*}G~(yY|a{Mf4@?O34BM)5RodZ7co5wAC7L%U}kdYj~AA6Sgwp4`g3qCBOHJUx4Yy zQb%XPLeYZv4a(~j#cu}wsDcmYr*KG^s#Ux{niuMJA9;_2KsOubjV@AI?CJnDZYyI@ zSb^4DuXOg}7n;-x0myM;0T^w+xwEpri#*)I+w3Q!VtF-1qtd-p{r#6{=LPSzbvWFC z-v#6~@fc;JNqkMUiGzt*RtA@PgC_bi&L36llOC~B4HHA{h3{0Ma`gkN?)8UDNW8pU z4ssS#*F~=yoL4k!SZ<GBONrt!Vu0#0y00xieY)1Z3ga@!l%ytps&gusU>VPjeV<93 z^!2~+3nMxq_8E4~kZcWX%__Om-&+Y7jb}GoA7T>tZ4u=X6^Seb=^S2@@c;ZaGajwM z3Y@%Apeb|I;dk<jh?4~Qu1R8PjJh{FBuYlaz9=~sM0y_R4|jQ8nN$i*LTM9<YYA4v z8X6gV+u>xpKSol0Mm1#<L^7nH#OsVrvE+6qe`gV(O3V&GIBunE!4^pm0c+%$>&WiR z2J8lrK2M*<?TTfIY<@E)uZDjwBL&X8v%zSHHqBz7=W)4Of>hCVtf{lrwrr3weXIu$ zPGY9s>eN`%D#143UWh25!AuH1<g^;4&l|6wQOQ7PX}Mh0-R}QH)$tmyMWVjl?}<uB z`Zk3zKd<Z^QJwMozo_<?JCK(YB`S8oh64h?c929%SU*uP)@le$Fv<zf-qEL-lt*~} z?l(R$&wIiL8Wnek^O>ZtPNZx%%SLj7d?CONYavzcwDBE0LN}YkoDdl(6Eet7m3-fq zzUAa(KY1ZJ^b_)Xe}VV$amQ>d^03*$44Jf@qw{XB$fv9BhYC@#tJ}(upe%NIz*Nd- zcqm@BtdYz+)~7_E3AWCYA(Jg)fj8s(6$9vj;U&pAA!R-${JG87Uv#GQGqo!w?vSgK z2t&RWJC&7glz4I~#Z?&7L4al&f75|0DADrRDEa!x3$si~ZNBgE?1h)XN$vhABB~OR z3xw6V&_v#q{TaNWF@dSDt&s?d`MFy59dOe9mORv=Nmtd?vm4(+1n<L#X+OsLh^H9< z{sz)7z^rxq^97lrEO;wo7*v8rfy8@itr?H;a~74T>V7vZthri*zDZMB?bbuzptM+t zf{8r5k?8IV6QN_#s*4@{+(mSk^PAIH(y+W=B1#D|9U=)eMPl_j?hiQ5m&(}K{4Tj& zjgLGVTZwTz(ZyNvdVJxGq-cccpzpp_Go(8L{&3#wx76(~HglDH9N#j$PVo3#wQJwE z8VLqqksE{MDp92m`(-tD|F@ij_&Jx|i$WcE7nkih$~SG!6;y5}mr3PL7ZdVDM(u+y zVdWMp`@V5wP`TR9cpGaJV1^p+R`Zt2h%9<d^%2@$G0g3%UXV0us!UdEyA|mXD!nuM z5dk3w(N~hb<Ch^D)i?2~Dt`b!9;V&2TkY5g(ODDn2v*;VzSbj^62txA(|roK7>&6K zbL*Q@P0_`FZK(cZqjpSk^ZOD<i_3P7M7S@>92LV^j;6?cE6J$r=l8h4QA7og)u^2G z_fk1-C#g_S8QYY6L+lBR1X=2EGN81c?uGqcuIuvtjE_cm595xRhTG`7a#dA69@p5p zy(wz9(M(xDMXo-Qw)y7``kM?kH@aoIynN;<m%*bjUh^Izn0!l+{F#(h;+>D<0(ekM zE30S1z790caF6^0b`b1%j8MYpFJr=<)bg)XozMuLz4XZ4P|MpEe)5XDTT=y*L$$Fk zGAATUF-t$uF65%)kuiX|bGX*goUob`b`$f0q<K^eeFCv3bi-v6B9jq%RLf0WUj~$o zWnr%#JgiE3d0Ai3^d}&VjO>;G!^*?e!~+4V%smb4pvg(Ix8-tf-|TwYN#KdpgA!A6 z^B`%V+Qw?m)%$#n_K)I`d7A8}oMw@^E4cKEM^4aj&t9=-C9`Gdo(U?>3Yoz@sd5!0 z8_rCbhp~1c-+S*~gE9<ZZ$C5H^p#kqL<SvYg#EFs2@7oz7Bj`up${`vjD;$>DWD|z z+6NgQ-LIY6?g*pC*1L+6Pf(f#`n%Wq@djxyne#%r=TsBNN^IwE2Hf>S{}Hf^{6}#Y zu}TVkoV0&%F6}FFWz=rAz56k2>S)p*t4z;5x@X}2Ehr>R;2Cf?S}4E!%`os28;4~X z;hn|Ug)ASs?3buRTA=X<FYw}e0+Q`BnZsrS#>r)0H0r9&-9#StS3s!A)so|8=c=<e z-5{cBX(c7`bcAg5VJGWbpo{wF-j$h9^XXPww{}K>!EQTySWsLeU`T@im<3#>g>e+G zMCu1&r*LW{ZMV{F>?1Izic^?0-eGu>6Z5Vr*4pLz&D9eeE~z0n)Y?=0d--W7$<#is z%w)B74b@I(F8Z`72FRS=c41X_gO-;I3;xlAzdnRy#=aFHdli&$QSF2+72~7ZFuui@ zfbbp26urqF7|p&KWiM)7I8Ts8{jxu<w<B}8^R1jzclzY1wKcd<1?s&sRZK?)eq^|2 z+U`CWJkm9Z^bE+~d$<B+MF9sVFS~5#IPMvA+gm3&RAiCX?$4uYAC5(f(cOO;4C!77 z&GY7?;gS%`6`?l-aC}sv^(e5rxPt^Rw=+3cIk$0??|z;yt4nw*MfPfMR7|BiWRLW^ z0}rfBA8v&SA9n}%qw)u{;3(OAyLUtzy1MR;mR9kC#In<_ob*OdxEIC7wkM4H_T0~k ztE{LyfB%4&tj%W22AnR@u;bX5oV>+pvfb9kO?u~-(Vrp=6wFL$?AeyejZUH*eG|&P z8@r|`T2^*1u|-L6z&(QDRq#gckP6@v420_tb_$g%?niB4W-$&he?vi!FgP%HWjRGv zqQl7@cfF4>$|FfU?ce5<p5e}Rz*0w$_MKJMS#=Q7-jw&0t=igukzSd2)K^laPc6jK zxXz%x8`z?LnRsT^h=T3*gKf7G3vomdqlO4fU)>mx&beBYzOc6aqV>&gC)Fl?SWo#P z-y`Te<5Ps4L_(@g)eq*abd=R043AZ4a&7wuZqQHSL?@(cibx(7_Cpl{eqUdEwxqnm zOvI?Pl2`u__Zpb%vw7V4Ab&Oo)@CmDkdbJWJx|sQx-cfvndG9Vpj7Yp-nidTdh1F~ zURtbKjhNZNis~Jv>dg<Wf%Ab3c4-XRM51YiQxO~;+XdfTo_|ooV|bo2N}a54U>KFV z;_CXU(j^!l?iN+e+QrV#l<qwf&)Ofo8^4=T4<!$XpBj-|-xvBZ{>}L)TPLh@&u4N7 z*PzM54vBogZ$L<ZDh+0Xlhs^qvSzgp%69fvQ=#`@vh(mBH;3gL=I`h);2IxP>1&+b zIX<UJ#v;V=;<dKQ-10mJjX%VTPN9T*fET#8z5D-n5>R=iNw!<dt6~*jX&er({TT=2 zyao9FzQLgd+2o!w=GEO176GzK9)PCSCXw+E(}oj%91mlh*b#(!2T#9%+v-DN85_vM zo4UO8Vo5<AJ;HKA=aRm;S;8T+@29}%==j+CHOz~fKYBkyWLNpuB}Yuzl<!WZ0V#)E z?G)AbJyGb1mWpZx^tym9{3&9*ci4$x3XB^t`I4j35G62u56A2m`#Ootr&|hk0ESA9 zcPmAatQ8kp9*T4~2A4eGKT}2<I|yIg2WWKwG0=-jTt4&w>bk^nWME-2+8xDKu*0@k zbT81y%g8BN`d<w+P~*NF{yJw^ntT2dC8E>|e&S%Z0_%19S5ZYR_*M=Rx!ev*{F+*8 zvPQh*x%)SSxiMd&84{W1K!swmd+y=Wv1q9@OGjRxadJ?Jy$bRXk}&4=!MW9c;t3BR z)Qsqz8=!S)!&#Uqy@yGO0>r`MC>55ngTHW;_~&mgj*tJvf7+SFssFQ2CNWNNZ~br4 z!GdKKryf=g3v)1YTW#2d*<a1*Y$pB%m3m)Uht2E(NDUazRuJ8Iv9gjY#n2qf98Ryb zo1|ClWIGQE=uMfdUGE*wS5|gCD=Kc|u8u#&>cWAZC=>mSE}Y-nL=0Hg_CmTkK*tjx zf@1x@j9CN-tdEnWR-}CH`HPO_rf{B9N&I~LZiI;y?9l=@?_}FLECimmDJ}&3Gje0L zn|z&Dbz6IXM^L{a#vb@34SV9Lh#f%o3tVqzK=>I5;^5qw7O4-0DfF-YP;>F-A#Q$8 zT3U?wox9d$m_Ums>~LfwP}pDS>)UGt`P%;pzkLuxAar5rwF{g`Y)pu+tgFW2@b>4< z0|FD6&iel!XqQuaFal3U535n1#}tHo&h_xvUzk_q9|s*H_G6*xk1PH{`fqRKzCBOr z9Lgz5xuAh*7Dwm}J68^ZQ6WyZD`u*?x`Bx3pxq7UOf`^hml|_#0hMcgk?Xvl7WOJM zt6ZmzMvDXLen$`CRLfspiJ;gOdU_0}4%@xMR%3n!=0rya0N=99Rs5lEfTK>IU50j{ z4P?Kz?0*kE9_*o~c=w#++vh98KY9z=jeo<DFpKT&Gy3n2vdpRpO70q5u2hdK)a!7` z$y4miY$^(&t<BiH`%bZD5}9zrjUQd2g3Z_n*Ag%;bB4R#_0<3q$9&B{3oUy=hMD3r zX|Bfw2dGDP-+F;g!mAS57xJzr2!IQOa=OeXg&j3YPlqQ__(MOTAPWw+rUTH{$3qV) z+im)uVwBroi9lfA_c70RrcA};l#o5(_-gCa3TkNN`}BY*?Q*A#X@5rbO*x9#^|8iT za7|E4rD?U{>eZhIwtt|?{`T9x_e%;`B5XcWNJZcP<m>&WDpHUL>v7)BzB5EQp%J)w z?tY8h8R~Wt8-^*97=MZn4$$)~a<LazCoAK>KR|O<0Sm~k!)f{M{`jZnTE<w*;R2NE zW2|-u9}a<n+jEF$6*?s!&wmz{et^Uy?5Ag1QiP$%e&THzPG<oa$SpN?X20yV!!!T6 zUklR{SWU^XD?V*a%~fl2i=b%+H{2Nwqk0<5`F|HY764)q6R1Xw3MR-2TOacnpIDJM zca!?J?<>Pnh*gv-3_l@HEy2>jXfp7V*Fjgn5BM!AGG#>#4O)O+3(5kBxOj5C$WFfb z*YVozU9JLtI+HE_C&<dGk{2=$N$mrFNg9ZTVXYQWAqR9vCh0T{7F3wS0eFPZHaq_G zp3fak`U^F$c$jKncbZucT(wsEhxq%ONDmtxVAJ^v(&3b`ZS_erg3!rODuOFu-~WC$ zV{yu}Zmi8e^NvPW4_H+B*T1_x*P{Y;Zok78glujTtN?HO*@A~ufbU<w>fdU(seA>M z5}zJ!P2N*c+lr;b{k1ZgY2Fr?f1_U#EFgQn4(i#aR~RO6K8z`4ePP3YKb?wDlcyp* z_StqbyVJV@DxAo3d_=}iPs;G@?N)600fl72aQ5fNDkXNYSalEb3Y2k{p6dU)^Zc`d zTo>**aH(_08flY_wwd~f1*N#VIi9~?IWZd>m+HOPQz0Gs{rhGa-bOk96ClC6sAZbB z_w#F$@fiMIw@XE*IN37ckDq6DV=eUk$Py_;f!sUZOB^CCa@v0UpPkhReJm?4FWG!@ zxyFrGz+^C`tmNORNpgl+?DDctr+1n}UIB@J*SC%kiRUxbkqsUaV_g{2S~Fz@Qyk8J zjs|>gGwW=)433_|3aFEcc-&}==0};yMn%%RI)YY?SK14Bdt*WhxR8S1#8uZgDVF2n zjwf-TgJLjwxukV*ZJc_p?;*%!2DnBmBFCpvN77i5!=`NcM~x%};z2E@DQ&&w^lmsJ z_52_XS%KQO<$i+V;<vRHT0l8K7Mc<E9##H<r{D7I1)2{iz;bh`wnwG;drj<_tqb2w zv*zqE-mT|tocPD!Uj<W-qX}u^#?V>r`0#%Xb=If69No}J3oY^JrS7lSdvdu~YHW|E z^ge{Gsb^yywNv43nG8`iw~12XgM!w9c<3bj_-$dW{lxhu=Thu(bifvl?T5~-3PGgh z`>3=3%Fkh^KzQ+cK$YG+<+y4=b*TaTihT0w=M+c=656LRCItm3JY9CLnkGG6+^VGX zk7wbw^KX^J(VJ@|wyN*XOMAN52}AC!ww{>2q&wU!QfZA+`sCQ{ruS+Iy2W3AS5vz_ zl?JNw_3ysZXRr5WajI@Fm_Lf09K|xXt80L6s@6M^vsZA?S<1rp$y71xyUHEi;7>+? zdVFn718ELOq&UXBg6(`jKe$4Ubz)*2dQ|AU!(Mwqc<{%A+aufg1!HXEryIMiW=VXx zAps*VIxy)!2EBZ8h!-mPLWR!%j>gAwuU6sWDtEuWVl3UTsHW5pkOeT~p@VD_D~Lm} zKciJzZStN71>XF^Q$rx5l9O?w16`s*J}4CrCM9|jaaNaqL1?FMx?c_QIRs><a9>Qy zR9m_vl@sx*m}W=}af3sOu&A=kYd6kwSp83c%zxi%v4A3<{G~t2pOsqv%kW;0Ru#hy zA-mKV)5yrN{Wq6jc|ino^WpaG9iog$f37f+Z@b+DHMZ}7cQm3;0FV82f@!82AW>k# zjY$vAVfDM0yOHGp<xXsl#n6!xJylg?my*o^C0aVAFMGRE7t`shv#DZy7So^Y1?t_1 zHU+ED-do;GoNiceNY$?5_0T5d(UGb>;d{DWU4Ea~%7rCIMpTkQ?Q<5u^H;A`xekaB zQ+m2=7RxvBY#b=}dyFcHtZEDa1^dWaRjE$rFSK<=>~>8@i~s0p4}wa?;gppkIgb@E zMX$7^>FLS^+|exk%Ac;PycP+^zTGmGme3EXit!x2AV0zl#T1lr66FyBbo~3#5(;r| zFGD>K+4Jj@-DdFW+v0(?!N-Sn&|j>U1)y@SOmi^RxFiOst=lDqNI;jf^?&En{SE}D zDm582F0PcS(p9@}7Eh56w=3(1Ntp(zsjt2Yc7_6n7?(99)k7g+h(LdKU<+1LYBYx> z6g_XTy*O-S2u6x_jJH7(?}9T~i_6McXQoAre4ZW{Zwv+RQ#jI-x#0z${??x41{3+a zHM;sZJAL160scG`S2vZ6dM>5RY<@B#4lpHg#|4gD1MEET$47y^`je!adbT_GpY0UZ ztiy^i5o0ocWfe~kYje%JAwkR4u(bU8O#b*>*+tL~4Ng!He{vFg;c(iGz+0y3hVTDK z`|fb8->`i}2^o=;85v3T-m*$U_9i5I@9e$F&L(@y-pU>!<3X~C?3KNK*Ykak<9EFO zzy9foXMH~ReP8oD&kK}$YO81N1Y>4qx{9@!ziVJ92V)FA>fR#)sZlB!q^3`A*8{37 zrP7oOXa~$UPBOlMA#<~{^pQqu^CDYoqr-pgr+>GkHVS^sM<wCPZ>B&o>=T)rbjKjb z*3*&f+xnDz%Yq1{d}$nqb~DD)in0PIY8{#b*HfI99gYHJd^S`M98DS*DIQ_Wc@%9& zlAzbrtc#iq{k&z&s&B3jxAH)4XtgZ{t8S{kqUpfaev10Rx7IHRRh5IGEzk0C^#d~i z+%(;l37Qz5|6Z76X<fdK9Zg&s$Dqh`q~B3cqKT2lOE8#rD|Gd>mRu5AeEjZ^@VeOh z&qp~^iZoV)C{V5K`ikED0|~&)g0jr4ED_VYI*(Lj(}tBcz=u&Yy3FH}1Gm!no`7@4 z_!nIA;?i=a&jf>k`7%XGlwxm)k9ODF6GyBuf!W+Sz-l4*T8H*Al~)x1%l~v9Lh3)) z=qKPME^L%3KRH-6sW!!}mqp(g{|>VJBt@I^-|JKge1x8ddCH}>y)#KX@oV!ZNQw$0 zj$`4cV(aie#sPx?LamJP)sK1DCXw|?HUNMuZvFdZ<UCdy4lp`3KAyLRo0A;4_wGfp zoU`iPoSe+(7TJ+x>L%0;^T!0<87;PE9)k7jcX5sTW_lwwbepL#50@_XhkW#~gJiP3 zIVD^Fzco`=$ycuyyBU<;^4`qHy5-?PADM$#%fFm)Qsd)Ya9YC$<d@=kmrC(l!yOUC z$v~8ta(f7)F(q4oZ#Ams$NWuJo@bxWJjc6@Ig)_uCoTzzMw7ec^yAHMH<*_DZi(c` z34I?&a1OLzT=9dZ%q`4s=R55K$>qkGQBgl`i?Hj9FVc{dJyoFSt!f{=6Nw{ttR}^~ z<{qspca4c`$8=6nSzOBbIcwi%Dn1ImKKdQno%_anBuClmlU}<5cUp5Y38%jFfsS@n zP`6By$j&T_Q3T)jU%Z(VJsrWjo9Yd^zOQtasKQT+;|qT{UG=)$wqSL)Q>E<Kc!7_% zcXYbNExX$5tCRQSWoO9!jrgO1ac^&oxH8%|Z{3B3&hHX;ZswAze*6BykcM{mbbIJg ztCfI-Y7OTpp>Xqyp+HUHj-V(H!t>#L>#4K!J9~VO$oO5j?P&e{X@ou)Wc3+t*n`Nx z*{g6LMvb=|HJjUCcde=H`IJ45X9XSFAoi45YiX&V6eRdz%;mdoiMXL=Z+TCenTZqW zgTfvoeGnSDofYC)Q%cHD<La@`92ntTTh{{s|Jvbx9p?i{Vo2C@y<Y5x8a};w5#?+4 zR240a@6$1+^5^+US#Mfc-L`}4Q#{?sjYy1Itd5$cx*t$g9{#<ty$#|MqEFgZl}nO7 zJ-JJU2@_8(r=J;gFDhl<S)im$d8$ipp!S*go3Rqf@RI4p)lqWnpMiJKG;H44%CR3D zL2)ZTMa6L;XR}iR*c9MNnv~n1I$>|F|JL#6<BEi*Q7f$5bAjrWV^D4!JI9rlR%2Nw zW(?<7^7Oj*WQ1(vw0rgR-2pmESP_2&7tK_*>yK5zJ8s`PE(vZpD6=NGGsQ$Oi+`xB zdh-{X$Ke?oT9fl2prh=|T3ZW0r6x&9VDa;t4t@3>AHm<re=1<R;z!1hpDRzyr6gS$ zCK>lRJ9i_~*$_tsM~nPpqLXy1G&oE$6^D7Kv>|D5dZI|{9w1+~+7V47zGh!n*U**l z>rD{`!PxD8x3M!Uey`$mlweuSk%v5xQ7s0>sJ#{<3fj^oA=Qf;lDc_jP1i^)_7qzX zWQOba^V`4mp~~>J;H`|5rEA<f+<fGg-jSUa7pD<oXn5;)O~Gr&f(3;J4rKn}Rq3+7 z`(sK&`TNIx@l+N&S{~l-1&S<IX~8<-uOd{k4533c1~Mp+<E&q)Cv3D#43Nd+wTUB5 zeYobq%HFFyF*6PYvsr<vYsR8P%$E`0Dhy4d4OZS%V>Bz$)wz%0#y^**%c=P#!UkPY zB;=U=XUvyD37Od))cK(U&x(yBrecTj=jN?=z>^;9{QV+NaRB}NSoJr1v_dH<Xxlaj z>-#;^k0S<NS!c-$)VsG)14AFstpBR?Q)qJ8&!?A%^3%d#s(mv~6q}~BW-dp{O%YJ0 zbhys%Pc_=#VFLKXY+O*SC}8T15*Sf46v}koGpI5R82mlN*x?j95h<a&UM4s>x0?LD z<a7rY2u5RLulT0HU;1|wmDg6S?vQ&fnQx?&?p^f*?oiILfT13;x+Dc&=|p=s${!`# z{NUi8s<E8fNrwA;7#NGRm?&kA-<AoHEXo-lD?pav?pS}(;teC9qfKEk{-hB>_#po5 zP@77dn8@-?nQb%5F4l3@;1|B>e~l(cKG+&`wVjt9{reaZP9X<ulKLXmyycTUXs}+K zS}*t%Ht{+<GoQU{HsRCEm2%^Sa6ydFsklsJwpc+*4v*jo$3|mGo>plU-D_q84&p~m zhAc0lUX1<FjR6%?<TuuNDMdrXmzp$SZ|4g^dRC-D0<)UYkn9YFCkTanM+Hp@B4%3B zF;kFS0Dz+VIX+ij=-sSg<CDsz^szx*60h#e$b#nlnoW#*i`aC?@2ag`bqBDCU+t|k zR%+*36c9zwPbgdkR2Qj$+6<G~1$w-P+}NM-G(lZnS>Iti&pE-y#@=M?o6mU~y~t`E z-<4FZKoK&@%_IS@EG7_lVa5rO{a)d`&F+s+9E0*~xw-oKvF~#6TkrqM%=J61-N#Uo zJAU#E6G}#g`yz-H*{w6Cy*|(}+5B7S8&c13)08&*YVnM;*>eo3G^GO-+{N&tfB#IF zIxX*%;6Dgx4wX*oW+7Hys`w_7uOd@Y{E$y+OR(fEUlI<t{)BrZc}d@H(@kiY%qCvr zfKH%$&T3v4;IH<i%W}CApGlW>Xv*dJ3c7;)8-j;}{&0g%O9>bQ&K{ymhOhr^%XqI0 zu>L(HeZ&#e!{-1rHV5kyYf#XF;XSI%4h(FuG1UaF=QOda*dcen?CFN9d)U+`J|Pfo z0%k!62}9dZWLCO+!0@|of{(r0a-@qa-<wo<G*7KUi4BXWkRjJ>qL7<Axq<QJ!F-G9 zO}1UcGbsrO8tWRKfWpMPu~K>QPVwdu9%ceE0-1Dd_dB6DSyBtMVa2>7%erDg6EfS= z7}#RnVczJt(Lz@LjE!eBlxtm&wUxhc(|~0qhM9<O%OC4eQj!iN6SWtsuM-7GW^rXM z|0rNfvVV|^R}j<AgnmBz8_DXu@4)mLnFDYgErh!3QYYy>5N*0bS_o<YKDHWk;cYMZ zD!!*iPzZ`1U}}fByWw-CMUr6wl=7GmMOWtMQj31!+554l)!Y0fHpJL>Cpo-4DK4a; zNPaQ1WCcrVLQY2mG?#_3c151wJe74z`UM|pD#G;IGgKJw;HW5njh<S~L;!~G7FJt{ zrm5mbd;2~EO<q0)t0QxJE-nE_>v_>qt=(By=*Ja?RNEK4mh?E6nIU?_1gU#Ot7pna zdl`G~;6s!a!3)MMm6~?$9}`IZ5e4bloFMLzQ}s$`x6OxqCGib9I%-vx*n|wZCU>*s z8Ee649uj_LdC{CO`4x3#&>!Pgf=^&zL*HqLphU~mHDM7`SsRFw1Z<N$UD8;I>vBoF zbUJ1~40;{AQkyCVNH*GQV$r5jd%lw;-0~U~;l@xRI(MB2(sDyA9-jQmB?`}!2AQ=% z8o+JT<nC0b@Yd&zz6iuICC)MI_ZtkN@VK)&l)TItoM2b&)CnI1d7S}t8jLwLYFy3p zK;oHa(`9mc0j>lYVRY43nh;DF%o&a~d8ThhMX|WpCUvV<-QluO_{|s3D6Q6PXo201 z{xb7Y5|Iv-8f*JE2Pd8>=e|}rMchoSwU?<~{v>%WVX*=jE~3zaL5d9&b9^c_Y)rZ^ zyR+B8p!{q>^;<VZ`@s9cDj4m;ppuV;eXvUR?}3a|rb@}vgYW1lDLo0e*MYd1c1G35 z28-gx!=i|DiwPWZLALJ<_yvbN%QhGL3_I_vh?z`!KF=6H6vl1!PBbV1u&Yh-ZiA^y z%h^&Wu>(=QqAy;ohePgULcBUv?PvH~F|SbYak}S~7ELP18o3^iv4v)ejgqd~lr4d0 zyyLY8o(Gtd9FbAcfKWp2Rgi$~Ylt_r+^GL=73hV|-&FtYNmOL)el6SRh;fPbGF+jO zwm@F5<Ietg(aSK&_Dr8!!92ETDrG8)EJZ2>&wKi@rfOB)Ma6SZijR!`6-_c<+4&A5 zmE+!;c+h|*h<;a(A09Jkt~wha*@fTr+F8UUqRKqnpNWg-ODEbizE$Lx{j_dLPK+s& zl+XPf@~pvC*Q1PCfsSxq4}r;FIlSYhmWY##ee}Bk1qR{Gm=&->wSldML#cv*-TVro zgqzPt$@B26LhA(PFHU32Ub>wX6w>Rlh#YTQZ)^1}nK|&S|M=`}kSXe?u4bM60#zSS z;OLb`fhC#<zOvE0OlczJ3f1>KBv>TC0Q^z>pmOoJ1%xA!SdfpN<5@dhEg;R9%=?<M z!xNdF+yOzyO{nmHxry2<-y-1PadKlIn4pl$GjVLEh=mwI5=p}SSXb!p49TpgE*XiS zMoIQO$Q1hCa60z;@Yp1Kbp51c%j&stEs6{u2@$Q~?*WT9d@LAVy1y@8n@o)Sw-zS0 zt19&(q;v0&sLeQbH_A-AsCm>br|D|VH(Y=iH<r#%gyrg*Cgky>x2&bjn4|E0ma#md zd2C!orU-^z+uPym*@6{|;(S?AG{j^(^+(+5XE)uGekP`@jwXEgxZwXJe4hCQA=hS0 z;x<~j=k7bv=4P9xZ@q2{Ux)dp!eGy+t{l}Ex}s-E@$sgooe&`xWuV^vR%P;SG!H|j zD_2&TL{wodcTPrf^P%@_2KpF9XP7aXbNlh_X0Ct0)r~Clh&4X-#uGh-@IdL61&>?^ z#9$xQ>njQaCW;d^Uc^P4ON1&M2U7fm6n3U5QM&TOi-Q%@@hV4)$?_MMJ|Jg&sXx3S zbJiffpY*__FDH@-_yr`Jj9b6P3<v;}NDHFKz36mHS26scnUVxSnqA;C@pz<5E|Iu( zyJE)|_YO2_HC0uK2a*E^d~TVNDHAj5q?F4$K!EPZ&8JZkuU0M+xBh<r{T&SNLS@8M zm{J3%vxX;deAw=-cl){%%F!LK0r7*q)=U(zsn#D9_ix5mvTvGU?B%i%>+R@NCsi1{ z<gue&JE$=9P36xN$EfqvrJ)oOqL18BDI-VF%A(Ehf03AOv3{Z4PRuPUlLyll$Nd<! zG2PFyOIONRf}x>~6uHksPe+0*NgrAJacYmxugpt=M5!+MN%YN1%-y}C$i3YI7N<JV z_v#QWPpAM8d6R@A$Y`QeFXjn)I1yu;P5*<#mn~3nLHrD>HVAxHkG0!+U2uGBc=%^l zU|~qsXeM#U<xw)<+WC$Fdvxp!Zj;;+P52(pQ7*{X2$dX~5W^fp5R?2^veW4>x3O&Z z1IZs`&&~_Nv*HVv<RlE&)JIh0JQIkD)v5=kK~FkGscxW3iGq_m5`z~TuKmS@O;?Eg z*Y9?xxq|4KRQ@8%TpM=Et>3L~MoldAP*)2krCHo*oNC->S*ARSKU%IBl7L9{Kz0DR zD>*KU=%tyT({MZ+`yr1NRSlg-9cjHtipo4);fpe@?|iO~#LQZ+xLnWu=)>+k8EXtv znyi+#;G5XUG?J_CTmm@eQG{Xtm@8VCBqHkPySIu|*d)28o31yGjqB<t)4bE}JJAoO zwYXYWJGoaAUE)Kb<$CLc;<rGJo`GIF_$ZKO?q1e?8C2^Z9zGQ<UtKtbHIg6I(Df<S z=aw>lG<#kWqsjPPm_&VQ1|V}E5N&$LLCmYw#4}`WuH-%^o0?&wSl!hRhp>{vIO8Zj z_>ECcB~m0vG)v5d`0V~mhL^9GpL*ZMcHP;|nVY-pIS?cNSh~~OP_zLRAXcXy(WXNV zNdsc0C&}usUfoeHR9g6v$j$?kI{8VXUI4^)asDO6y|Z1)ntHoY=kGTwK)4{60j%-4 zkZej&YK?O}8UL88ch9Rh#ft+u^EU53-bK65gbZU5_L`awkE-5%zu$eddyoOq)*j~m zmms@)I89DopnlnOhUFPd5qbGvJN-7cS!3y+8Md7dJrnm99SDetiLcLvsHxFhUC%iL z=O$EX?yn9NVXyN$LY-ME-E}*qkL2PKG5cwzQCY7j0a|eS*Yn?z+$Iuq^dz0e07Yw- zSmv^427(@UK@_Q48<Lj9s0N48f5hTvoe%ZZ99#$$aby;9(+hdt6;x+KLW?GG%Z7Pg z=b|2j4UzD$m^K+>tvXi3bfI2BC5Pg&XjAI@+u}_s<m5s>wr86$+)wZVeQuf5Kwu+O zW2M>~sDjmcomRNKG$gD%aNYB&y!@@qK)UX&d+BayX!#8(8B>{gRHs2eRY2^kfb^F< zu)lE*g=6)4yQdh;PB+tsP)=HY9Y?#}iMq{e*LHRJwkZBgey)=46vX#bRvQ`$-)GA| zao%74v$RoMoG)yI1Au;NDr&!G!e@AkE4jq@>-KM(`G6OXAKe{^$e8nFCahpflr72v zvY{5)$GAVK35o+H#K6UeRz&^b9wSmDamdtN@<pBx#H}{-vp0Z`@G4b!eV(n|pNnCN zWowtiac0V2&iEM@CPeeEKZtageTSazR-|y<?x-AaIzU0_^~|rzuQrY<$!+~{sB&^~ zPk*GGj(pYEAM^NGphzIBlNHc#qBh^^0`eUBHGgSdfAhz}U>sH~UmYX|{qApo6zwrg zXTUDwI&_?!k~)vu3g!}L2T;jMby@<Yp}W7cc8=3+YCY2Gf3{=!v8q-&jkzDu1V3h< zVQtS~Sz0oC%KEhmyPhA@?H*Zd+O6iU^+Zk9TBTdJRT>o*tQ9J+AMiOS8HVY1>Z~0G z{p0`RjtMY5P$R$)$rg&9X16mi&=Zqi{qm5-8tA!K3s=knwO)!hW_!<Z;trIMX6p6I zb19pIMNkI&aMTZ&MG*C6X$&$N;t}!?z{MrInei%57Kt6OSNTd({4!ES^)42n4}<DF zjg<%xEG=gqJ3bX*tUp`Cs&<;$Z>{|6dR)j!gc69GSI0rc!}jo{S@+2hwxpO_vx_3K z@A{x$1FsY}5A=?o?=%UB^_^kv@v$r4++VDFMsaeskxeHkD3D=epqx|-0zURd_Ly6a zZSHQ9Gi4N%$2&`qKgc9J|IVb!q*f`ZO?bM5Tis!|{ECyz%qWj3|L&vcbwLZNC7`oN zTy`D?|9naPvFUA%=yJ_acypZK?DyF{>AEXv-vFOqm<nu0Qm<EnE_>9Y@p)eZ$;~t1 zvINlI$jfIab3f<dbz!^L(gGck0yYl;i8*)fJ!KCHibM1>=v+tpUqe)aS+daneTaEs zF93TXK{wiE3GY4{AwyfUD~tKVAIZlfYD6jO--ymz=igq7#r{xd8z|#slyy$cIIUO# zNy}jXxp6tpJtZs?kB^nd=qcA*ck+#i>AN9xGT%u$zpaf}TpDMr_Iy7|o3AVE?@S@6 zKLi7o*SEqIqo_FG%FUCxl=<g5i4ktbyYDy&?dH8cDl~s&F;hu8C&n1Xio{t3*tg@% zY^QMT^+L!{+=;;)_j%fosi)-I_jr(e{@L)=%^F?5GZDB%ur6k;v>!LP_(2(zMY8Io zZ9-O`%EGQd0$7Cd+@e#0o&%^`@4-S#OUn6`>o#;_yx?n$CGrxiMw@sVcrK#7N&He1 z>{Yn6vw2teGW*Dj+Es6_`FOspbqtCX3EzfUdq<&KljRioVSG>hzp@yx_-I{9=|RcM zZ@gcVfRyl!uPFB9uxsF(jnL-|S@@qo3Rac*@Elc1pVjO^(}C>b0kjSXXAHWP4%%p> zl?1~QF!uKUyDOmVigOF|r}N4i(w1wT%a$9~Hu9qtl35MdxT>xBXUBn7t^Q3eGW*05 zXF!{^+x*TN;X;<BSRkupqbRkw-e0u#^{Vsw4lxJMZ$8C55i%6ZlbQL!O8z};(-?g- zJ!$!bFMAkLT0RPFFQou_6=hhvSOPnxM=9X1v4Be1%3_m?I68XPuqi#FRhTdU_vNwM z#NOwJ$}e)=&#Bq(H0xNfU9XTJMDF6pYhaTXb49h-r=Jxn7t)rg6pfo93SyQ`hT77H z4cu|B>MwCoVu;@?_TSe1FiGKudW`eaQ9={koyqcS(10NK(mR^#35oGId5k;xy6n(X zo+7Z}6`5;!hTfAdywJ33jgYf$zkPUGWy12|N587ft-02rmKVNv4)@z=jbw>0ulNT+ zdD&#DKLH_i3#)Lzl;1Ib#Xlqdv&T`QMsA#7W3NsT3nOU9A-7H^9Z~%s&(OvjPJ4HE zso7}py|8Dveiqo%J+`U%^1>Gm6-brAvQo-ZK9st$P?K#;QR>n5#;kvq|9hGc5>!d1 z*TLMN6Go-@@ZU$+Hhnp|o-||qSSYJ(hWT7JRJ;sI&U+ZTZke;Uph_!zO?th%^_#^6 zzgQFf-Voy`qviw9v{#bJ01<;ERJ7ZL4!>-gC9akhV?yoKM{nVJ;%+|QQX8?sfN4Wg z?M1T5G85d$y9jeQI2cC4T3s)~VOmdsyC6s*KT7oK*n^%?hf1kZy61_WsN4t-E9>s* zs%bphm`<fc=)9q42-4OFJG0#2w$fkw`^q=nlUB7e#FV)Q+QZB}drO6)UBLv;)_6Ps zGSDmKAVbEhFz`8OBJI2Jyi^YXlJ)#PB6_)8kuzmNOTGQ1c?k(7%m18Dl*V0#5CHA7 z?=af~AaY+<x5+t-k>p1<-eJ=!q^<zemlU9PW4c`SBCbifyq4{9C;;z~1Yim>!)~Ld z@jF_tCFtI1al%DOCgpu9Z2nN9(f&p97)NKW%+Ie`4Xx&n0@9j)RbfEgu36qW4TLMi zr}E^Up<-ey%b+mn2252i%)e01Iu-_z)x6<oNoWT?omcXL9i>Pwm!sqnRdo(xFK$gs z20&=AkghVcn5dpq%Pu8Oz_Y6S@k(SdMWCQm>f+R>4c4|6ku9D!*^=IRRk-i-*Vtjf zU`g_+j=(8vmX8k)kNHC(qoFUAHMxVh!k=2@pKz8`m?$nDY5{`ttW_?J`l7<lUWr5{ z05{O>lx@X7^o<^Xx=HsR!ln~I6|5(+#)U_c2x5sn(skis-0Q1vMvcPKj(5=+0;x%8 z);ak4R+;3&-8l0>u^k(e^BWZYp3mWzF#@nEAP5Kh&-Ti|wANRj41OmRWY2j76<y(r zk=*mm==uuJ?G%kLGW@o<+-n1!ufNQvvitP9hS!wnFAEeiHL5w#Kv70w4ia%E0;54{ zO-*(_+N874jhGo)fa5dL<Y^CbxMhxo0y@-V$N9qrKdRS@ox6((T&?di-C61JsTlfx zpi$GqWPkv_oz^lYpZg5AudlGQ8}jF*Wo66F6Zx2V&vvaN+|KB6ammrb146FPDGY^9 z9`7Z$m$LwLRGaz+=SJpe0TpQmQ>^_?sn4f@b*mD{k_0XUcJKaMuDP~AnkL8)k^jJu z5P)(?TLw;Rx!Fe5)2&v_7`Rp#5L!P&>i6tX0IWUUPrQi>3TL#0Zr4Ickkw#UxOiZG z5l4JPMC|rldbIo5yTl*I1N~To#f{RwHr2i4d!L&*a%uPh<h#Ph2h@w7)eLtZ?(Q4n z_MYuab874BWj2Ig<}Gi7B3!|u3XR9gg8B)2#jp4M<?GZ)D`qCf46zQzw~6qU4}kUG zM;8rR{mgy?16S4m`05grmWT5#OtK^k)FUlt=6mirm@mJo=BBRa5(Q%_>v_&tI}QT| zfmAzou|o1Z-BU1uxyV6VI)RpH)ySkES8H*stPDR21_K~sm7B<bB57T28`LUtc%C?i zl;xRlFKe}raV1sdS0%3J{^Byc!a+ZRcD%X$i1h1!>blv<LR2@)1YPSupI1Q0Lu-Ho zCBh$5{)>|p0G)P47j3RvOuyUgW=x@y+aA*g=F8DrT6<%Tt>S%8GXJ+-Ld!_Z9k6<w zpZBZHA1dz+t?#@G>mM;d+Kkj#hM61>wOu(zlV#+(iC(8l%P|d`IMBgtt#HsC!TQad zt$M2S=*q$0dGxdE?G9rlgssuwoBS&DLXGhR-9ocR;5x|}bal39n019t_bf})+t;jG zJ2NI?O4L}_2NHc1Gev{tLc)|JctF3*;<pmkF{PN&&L>2XG9k`e_WJgK0s~(55AiBZ zS6}qM)Pd4<s2o$5r<Sv=H*~D6r_XtiguKraKw1*}vRXe?uTb_@@MGMD#KvNYp+RF0 zFw25e^<i9Cv$c1l=XLmM1le9C8H&pWBJ2Im>~e`Bdbr8}`N>(K@EF8-k(#ymkrPQ4 z=3h`}m$T%e4D89byS{lBW?MFt=$6GZb#&wpZ0UNEc^dbJGa@aE04n0+2GlS*hDtmK z%hm6IB+yWv$4aHA&=c@2utw&aEv#b42{3$#-Rb6j)5e6}k-2gRH<joX`Xe@R8d|88 zjOMM2_WoPj-Rmyw(NS<4OkeHkiSj!?c?b4-z^WxvgiSCaC_+Wm1mGvil}*ivc@l2g zl&oQqdfR7rVlEf&Tg8BE_3ng!>n`Te>;1ZLe{8TJ832$#dM;>E+LXs1t5YpOuB<R% z%z+Oh;nIHBF4Y^LXU@*<bpr(Q>Ganp5>vI^nIbpBBzas_w!ThR+!{=m6Ns11JF)_G z1;$6eD!vzgB+2Kl66n-ue*e-)!(uWc8n;>J(nhE14ls6w8vA>+pZo$X5fr497>EW} zJs%&j*F6_b^ApgUU-8~EbJQfV8qg)X4k?QT(URefSnytl$}xpcB=bM4WSo5-Oi3(v zYSIz>EG&Gwy#@s|QgUAWxk=p!h*cAuy27NIs2O<aWWP<<7rK?TUWaxE;s$~sU*jrv zz%n~!)E_iSG~1(Lb?9;GuWqGkd3;RV{cEis#>$+M+|Gp#fci|RoN!oMi2zAq*u=0m zodw9@;vqhQg7vlT{Krn4k9Uu@PP!S*Eqdpw=q30Zshtkj9}e68#RCXjGIx<X*$~59 zkv}&;Bzm!Pe%S>hD~^+4C^o~O+fR6zw$eQDHQ&FF%Cu&C`ajpyK<CAs`-tWlEhcbA zLCPdH_>k^{8ip8b5Q^2S`O2B}dwBPNz?=jc*FykJFl-IPqjx4F_yk=_Wr<?5iLq7a zK3P{+`F%CeyEw@P@iK7<8<Saq(yKl6hvAc}{VxxtVtO<i9oJh=QuzzbC;H;nJ*LzD zng3F%RAyjspv=_~tllZn5qv7Y_($fAcsxG-qqfV8bAUO;0Qa|Ibuf*(|5{_CxInQS z$MtMGs#Tk62F$7yrt3c15E=jObPL%vgX$v1goehVN~;jaV9@Gbo`C>`&EA)fWv#Y` zi`nh7<jDRlegvs}KY<A0N0*asS$o080=WnZ?yWP!ta}i;I$)yyozW~w44NrkBU_f) ztM5d9V5R1dT=UY3-1Dhxn?GxWAlN_X?TU`#Ot-`Te7pSfe!XtRE3G^Cc4VYPaLD~A zJe4%Lug+DgoozV9Mw(nkhCFT8?{{~FKY3m=t-F9jmVdF9H@W<3X)`}PeZ9C&zcX}0 zD}8mnFd9k-%Bw1;uFuj4VB!tbP>_X$YoU{bZ$00{yRnRz-+-Mk(Qe`##gTiGAjV|6 zT=W9zhZj{Ol2${M036YQwRf}vC{uySW_}NK6fem<7^X#BS8FMA1p@1~wu@KphuoH@ z`+m7C8Lt&u&Pxl`9QL!5u!BRk8iCm9#_?*1&O63Bg<ttH&hn>-b}GAaRte0A{QKl& zgBc|)oVah=vA%A>GX~QA?sUiB*Ia-)eLtz3LPsfgVtbwNT%Z=uci0Ol@&x_VYs5u* z>?9!-_N*V;vJ1Br;1N8p3JQ${%S;qOP5qgw$DYe@W~LU5ptLMiyCL}m=@cor!WL&5 zvnnfIYvSET-TalsRbW0_EQGFn<LA3qC(~0JobSVncE6e>G}tONHze`iQ%@T<UIvd` z<6>?wgK$Xka85E7v#(e+<J5p=K#pV!aTWb=KtvAT2c2>xC-Bt+BKsX3w~@VzH|Snw zfBNp5&LjbV;vEznudbeDs<n{fP{{HO^<hp`$rE5(Oy%d?zCKTHz(X(W-3hw)Gv^Ds zir8)1u@dNmrpA`;s|?k9XmcJmSbJ}j60dkD1k!Zb`AHTvoEhCtUr24*`MhFA(@r-Q zh%80{xpXmE!hvZfF>&C7!eqU5fQJR;tfZC3P5jkg?1){rLDGB;<$<sc?J$$^Hx3%i z3d^0Vrzyfi;D!GDLKJyT>8c*J<I<00q5?^_3Vnaxd+NN#*=A9;)a5`lk*%;$W%`YD zXNdJ4)#Q|DDxWE4qn=o`o*X{@4L@=J^6PC6Xd)Rkb~|pP&&VB3--x@t`!Y}%MY&)D z#*`@t>N+5Iln|XsVIo56W_0JnaLJoiGa+((H!WwwgmZ^YZAujUZw@5MLsf9oc9oVx z$%0C^USZ2Aj6tGoIKMY^wpm$hFPsE{XeQ<y+taL_FDg@7l+XIO8J%C&qL5<%vxbi6 z2%J!4BR@a<%pSPQ+`U)pO6@sT{$9@8kAdTlU7Qg_EcW!HH0@kJXWShVT<BK!ekaaB zs}nV&P*d5%{OZXeCZhjS8NIm5ZZ7R(8>j@4@46l<-D@Faz>h%W-++R9iAHd`K~V6* z(b=Ka*{KngoBNIOq4ac6A9-7+&0qDPj3v~R1*mk-7^cc!#GT!WQ-cvh>#59|xz8*+ zIfcUvjPjCLpr(FQ#xSb^CDkG!)n7Jd=)VPP>`ty0#4le#|8k5`EkSZSyvHCt@P#IN zCxxAzeW-4YGTg$#2IbD-sbNxPvCqNOkJY_^SwcrAx4-+75^8%X`5)uD+I*%FMT+I> z>_C0<k%=G`mn=*?iDN))x~r+RsSz?1W@nkH*4ax{k4?5{Uq)?>NDO?UE^u{1)?Ch# zg()zTjk)Ik$}30jPxFq)d6&-U>k<)9C72JvY#rm}*{5Rh8c>9sbT{H|O}mO?<gV$k znkrgXi36iPQYy~>E1yoBuXzol$MbNf?CdUanprPUGsDPBLR1BH%U@HF50-G6)ioI3 zyZ1F5V+;K4R1TH||9@I<2p<sNH5B!{$m}ERvawa@eHBi;ZaO-gRfrK%#{6$1?`Xsu zvjy}Il#2Ak5Q&${AB;LQX<$+XQVny)fS=!T>l^<h|F8|X85knsC+Xi(-h0_dJI9(^ zI#v4!@7_0<&I)NOZ3}C;^ZBcr`|+49p6P*sxCgttdTD6_Qg5aaOd@8660=j)^goLq zof30(KoPyI9ntv}n`G>zh)A27M2N^xnrRyqk6PS-cI9nyVOgF$OKoM`Rwt`5ic(Iq zF^U|8oG}a-PbK`p{XDnv>^0P5T#uRF{f^*`6>v=-y1rFm_{8?Cc1-^s2+e1C=3G6h zSndTWz^gqMU{v&0g!Vh%-+y|g{!Bm~1%>iJO8kYA8jL4;1k=eFL#tyu#O@Hd{Km(7 z8)ujIMt<ko*O!32_NzBwe_StpZV`}A1o|(0{m7|$ed60QS<y(0g|*LZ{_3mcOpkUY z?Ud?PkjGWi>*wR8%7T)MFh(FcDuPToVEOOow~8khryV5fq@-gM-gPvZ%7yutv{e0o z69T2UE^tVbFpv}C%WvF2xQBURGAHB&PpJ5OzB+UxhYr|I{&|P^ql_HBkSh5S`WE@0 z8b!ZE0@Z5SYEx4L|5L5kurc*me<)p^?B2gI`m_{2LR}e}2<_RXIz*XV%h@4vJjnw` zM`za}LuJjHZ`(7$+;3L;^Jp0x9F>XM7d^%qlzp&>#@tm^mH+Hr!swH*MkmBzzwp&t z&z`em)$}|?XL}yHCOPZB+c%EVwTGfI82(nTw_0H6){*ZCB^8V+i9C7?fqZ*f;4c?5 zI+8N0Fe{Ba^ZFa2fT($>;{FiEr}TX)V-{CA;)0_eCh1V}VT8KcpD{ub703e^!N+sZ zyczouBt+!{lYwl^29m`6MT@nydgk))okAGuG?1i5t$e^ua~l=N>NjDEN!m4uzF|(^ zagAxtYnMMiFgC^uwkyzaTP8z+yr;MS-a+{Jn@bxhGYRHB6&Sy&<?_-0^7yFode#GH zmcOSm$cQ;b6=pAhjA@~8u#iA-)H}P#fhvYyI9?#-QAc8(u*lR&AKU!tzKe@5Z0Fk{ zn3!$XeS|QRMS&uLnSfk%i7URV?IwgnHbuG=fF}oj?bAqd6CUTc1Nl31xa8!)-HdNh zrfQU3`9#GKY{g=MN+vFJbPj_lyw>ZnN!ZZ6DYn$|vG_e!)mxy5;alI(1_M4EZkgac zAPX?^qRY`XD63jKj7`-}Dat&?PgR}9)mVQ$lDYJdcw)zDq=*tG$E5rd>ir+3;FK)f z2z){ULeFWKj~(BsF;Xk4t3hNHUhN;uHH<~6v&%*Ic*ylat$AmzPX5xvBMl<%P?&;4 z8zmj@ClZ+qgJI$uAuA!fA8TD&!sud#Xsdors5R$;o-mR#+Tt?v?k)Q`z+C2boVKZj zwimyA5Llm3B~g3+Txs8S-s|-%n8-sbGgiMbQS?Ux)Cm%!oMz=~!6J?X+0yV!hp={7 zudUHQ1ts#jZo%nEuU%?!Dx{x}`Dlru>^^R<E(UowD9L|cMvY0dx5h_-@>od2{T!&M zLYwLZR8*TZSF~-X63c(IvOJO!(NP5Rp8rimlw9EP?jYGsL4i9>?#U>)kwhQ3QGhG4 z{8Tqh3sD$V`I@s$>zU0#DH(9E*G8>Z?h3e_q^}w)JH&&dn@etVQkIUm*7-42CqK}( zw2IDcnSYl?Rts_et>>#GF6D$xK1Vkb2L@1kL8hX>1BWc!cHo$J!mVuK(R^_DcnDrH zy>TR2pfCCwq9@(gKf{}D?6^8lJv{CHrLm7`RvHfU4nW!nDSix-yHZ@;A{1xy@EMCj zVn81b26|>8vjyfUQhsSgCz<=A)HFrZkB1svRS{ci&3tQ;tI+Re`|=X^sUlmFxwp~o zsT1?;`O&?jizzh`BLlHPOhRjV&3N<9kb+1miSkz#tEyS@hzGM@#oS4Gqn!;N=ie=` zU-Rqu@?I*AmGJo;P!qWDfBNzz=dHO6@3ZNC>C04l1#vP0^}fnoBjk3H+KE9Xy$6v) zcIUsg-VY7wC8N@r(ZLLlCp^!1dM&pn0=4tr4@RT>-~*v`Rh1~X3Q%&)k2Js4vlE-{ z7IE6M8Y}nVEgKB?Nc;h5Ai=#Ce(Abxfo04KUys|<g=jkZL-ClI|7B+OMu5Vw@Pk|Z z1Pcl{y8Zjpp-%_MnE5TI-xB7h{ocPPmnH9ht)-=OYx)N27#nbK5tlrFN-TfY_qeS8 zX%utmZmhdKCt=qw7H2)SHul93`ilEd3rLKNAaj{+;+h?oKk+|bAX*8u`poRHuzn=p z^8_}UuWXk8{JVy-o$pEK{LyX}ylwL%%%4=1ztW6x@;eS_WUl>^mbq@tCVd}98St$~ z1e7IQ9_)odm<+xn2ox%y;P&|?W#lgAep~w->h&)5RYlhN=LAq_1awILib{CEHa2}! zmR~V1VcXpb{hRv&R#u3P-JMp1O2gm3L_@*9>(T@pqeAM-6zI=wFf~&;d6!A2t~sJm zMY6sN=?alfPnSE{wKG}iK*|i9yiQ>H6;~Q{gS&iAMy7kwOhi_~RZ1aLoZBOp^6D?! zxh@x1A(p{x(=)S>2o;jM&CLix)-3swwUW0lQm?ma3zZ(`qfvW$RFm^;A7w?3G3LM4 z*3(<ZP4e~qk=>Nz^TfCQWSI(pYV$ykMi$2!b{v{~niSkhVZ5y9FU+Xll-!_R2V{+B zKxCkGz3QYSq6ZcS5D)6<Rw@t%3^1rHdXy`#p}Jp+E7_b!gLfqRRm=%iluh#b7UR|* zcTwV3H=`vpBgq%4otUAD2V${%6s$JKSzpFh<>mj1i3LYo{tOJjC&L%DS9>VgEcA!@ zKEs=hko2z9m-3x;1X_0^z4V<W9z3C@J(o@=I&Kt_WsV;Wx|}5F`x9Nu62IR)$Q1qe z?<*>*CzCoE!c3<Qbt}OiI$MX;7`0Ho$#KqS@cU_s<P-YwsNGvni+uY9I^5q~W4Vu1 z*0EpAg}bXe+Nf8sx|G?sZ(Xt9!CejQDsOmx-{p3WduEkfTS<|DC1;6l(E|@eA8WUD zSl&X5-$py)<-gEcsNSQAqv4Sfrfg2gg#h8MQ!7M%I)?{lC@6s9BFNH^UMKnCt;8V+ zKCM0qH^u+hiRrvA&gtE3D{+YbCge7%rHa#2OQ$r^8g6>?ofqVxnmnd4EhqbJ2M!G< zejOvT<f-A<4}b(Q-gO(OUk_l~>JzG;XD1o%`)FG+bdzUi3V+8LtK{YBJ@35@etoo= zF;Rr6&C0CPdwbp&SLr6W$im#snIiJqaug$@xS+{j1z7L<6U2h?*H2ak3h#xMlzc4B z$vK0jbnWGtce3WXK4F<Zu<9(&W|jlyu(xkbx3hnXIe&2sZHx}~pX@ET6Gfm_N8R<1 zKvzMSo=n=Y<Jb5jfGa_NZhq51S!VFHnSY!=At-A<uX+GOHy`~Qd<@eq3t&W^6Y6%w z-kBsIjA4dSk!C6<Oho~e^(ST#)MW-bC^)X<E4!ENe>wQ<HMB2EVDo`Q^itZ)Wt{HP z#(a2^SxA4JsHo1Ru*yfW%M}vxD<|)5hMyXm1o=+i>Bjs;nz6orB=Y&kgx0m(K{tgx z_mtc1&}En;X7)`!qUFR2rg0XEDmT?7we3vGYu~qZ49E=-v&Vi*(xw_0SVD7szN!28 zejWCU&fIs3MGxv4?Y`_9a+#!Az0F6z_LG^EOBd`!=vZ7iBI*}&Z8|&qaT8dqgCAdU zS+P9a7N(`f|7rTjb;|pzj>{NDjDBZ^Jgb9d1vTX@tS;RFjgKEF=QdN5&OBAeazr$- zG*gOGp}8G8jZ@!iQ6>a^F-pKbA_u$#*+o$kU>n4;YPUA|;pGC}A%yD)-xB~BRzEo_ z??wrGE)vQ}X05YUIF)Hwy6&V+32*O|9qkK_tS-pEmA+&8=$iCpiEw?1V6WckXVM3e z)?eL3ar$5KtX<fY-|5gH@eAi7h3e52cMY1g#@O$sL;;tB!pkS1x0ruDdmAmE2#+w% z`N5x4jgF!7rO1x-?o=a|{D<k0?9}4!C>Q4d{`#CURZYOf7s4%R)4h1#>G@UL61ZY8 zly2>Adl-k`>3+PN>6*bkKn~*eq4K>Gfk_J!{xW(pPH41j211u+<){r0{tN^QMe?gg zlDofO4E-^;d3hTJB*HVCZtop;w#JYh&*<+g=F9ivI_b>xI=yU_29<p$?OyeZ!^z+J zR@bKN!d7E<dzQK*P4H?}l<rXa;lVg=uCj(_YvHd&0F*A5F692l9ERHb_XlEi{oCUI z^LX!rw1;;xJRA9!&q$T1{~iUsL5^bo?=jUV)Gv`Q0}ek2XxaSt7L;sAv84b1)K|27 z|Nbps9?#|3{^{c|pZw&HMU;C&%FN9COtth!FaaGKrRWWlbD^0!8{afRSA|T`TT|6$ z-Fr*j?5rcjjEr-6GmTE8g~|-@^fyCkg7vn4UeiiGTf9py@cFAjH?8Z%oac3p#qS!W zVznrb%d@@t@4j!sWILqR&Z|5wKWfyNOa1AKTbrt8Fd9th?f5I665AqY!=zOQn&UZp z`}=)<`5m4)|J?ap_NmGE>@)N`L+m%k!#5_%jP&hSXfR&UvOK&CANJ75$cW2w8hUgw zr-u`|zbXv-rPH}hMyT7`+Suq{N5$_bT3fTAqT^(%ebqPrRpI|(Ac@uO#QNgm;s>6~ zcy#<uOkf~-y2lwkg~thHGMB{{`?V3blZ8OPz`$h6{bIA<H5Q}-&Md!{a4Ce|PnPT1 zZ%-#WY>Y1~F1G#56pP|gPGrd08O;<6{LE&EDHe$HXQ%Z#S2mRoc_ZlL(%$RGQswL@ zru_YD+yVl<cW;Z)iql*nU*e07`;fyJZy<@&U^G`I_PKgAya_%bVO+3$QX~W(-zFv? zZk1+-Ynh&zG5MKsquLblaV;)uAekEuRZ=iab#P0EYY$3IC5xez`SLbXGz<x&^2f9} z-8W<xPpi)Ub8i3duY3q0gJgFE8NFKh<1ESWw(jmg&05PuxH$U9kG&qTe|X#CcKUuu zz0r{omc(HGoA-TYofMPN+=Z>Jf!-Lp{}!BcE-##fd*rLZ%c)u`xlB?2hrG7byml*Z z5XpLa#0959o$7VglsGsziFk1*Wn*sX@r;_1{+NUhh1__yx3|M6gvm=(`cwIx*jN<~ zqR^-bmiyvDe^nYS&Cf&Tw+y(f6h4QLbwm-J%T=Y((8<;LP9mG(YucAd)j4n#`^!>E z940=pd7+->muH6@#y=mkiw#DP?7R1+3G&whlndVB;oaL@=96B&zNojb9&m^)tB?Kz zg5l3h)Xoq>>7x`ZBBr-+3r~hDEG+0XYo4zTr4RYtxs|;Q;<xA&EbqQ~5s~BK;0*Ts z?66ZfaKBjxd3k>VvmAnrQST#MVJavLt_&o1ds>>A^~0HX3V^Z?e#VZ`UhgZq!bwJp zRFP-rP3Qy0Xo1znot7-cJXyGt0x?+xHVS7#g3h}rL_|civMH}p`5gX^mn0YabNRxq zFVU>c1-#t9bwntfMOK5ZyDL8u#cIrd{lK#rx7(a#l}#5KfW4;sF)cC@7kSYBsYQ_T zI$xaFNCwd9=Nz3Y)tIZp9$69=w*QNFygiczoBR=Jrq`HfI*9b>f_w@EjBw?(UGB+- zgEr=oPA=f=6&{ZLL^3SHack<|#sstUz@YTa=wA5bcB|IQu4=Q1&s-K$SluA?rwi#` z%%Z1O)K|C)e}DhI^PSeE_4PiVTUaNau1*xB$`02y7tLt<;&^CccS&A}P>cGYiBoZX z@%p)l=JN?Z?)H<{;bhs4RU+oz1@lUG$)3ySioCyXCe}lM_q0=k-a8<OEQDVEF?R4u zfuH!Z5w!hD;r;&0uhid4-|tYJHhuWqo94P*KR44pxa~9~xH|2+(=2fLZ&XP|rQz{Z z>4wIo{n4g6e989xKmEUIEJ)<#<*BKujY}gim~@(YVE3!Fxbdc?rKy$xay78Au~8<m zvC%c5o@gqLbS+QX{q#=)JfFq=%wAMfH05EYm%(LhTAHkw7^>aM4_10UlciU}i1G1? z4+F&8&Br=$U;J8Ig>7tXv{#N?X&%#(z)>KelclDl^oF&Za{C54O5T6^6DsvPF$0C} zWFKgkq@zqvPb1F)><;~opnDe=tHN|op9YcfJJvm2EM3#Mtb%tBz$WRMuD8R$#x{hL z=<MPW6&1DcC8#li8x*^L{(MXBJnwBb4ss^O6HbSRhwubm27y!Ih<}R7i;Mcmz4^@> zRlV9Yjvy<B>*#1Sk=;lQPIXz+KIVha&g{S0;ZJtxxVgD$Ws=0r&7V_JQWk5~N3X1W z5IXz&gn-JSdAk%RgCW1Pbmi}GmcYesyW}s}cP7Th00ckyxAmLI>*B)gwY0Q)m2uR- zDb|DM?_j?q5iGtxIwJ5YD`OT5xcik?`03MEPae}+XQyLgVg`kWbCNrO;cnb+2n`9r zR5aKQaom}ead6<wdKr0}1wRZ#I`$(yFWPfU)6*#s2n3g*MJRTV9q)+T&xQ&uEiF>8 z-PF9i>ca7f9kU*M&byC|hSF$gX>Wg1lVW0Hi#|DVUR+v|iD)vjS-xD}*ysmG6W$Fb zN3CR>=|E5vBM76fuTKq()A{jE_s|fomqB;LaIKYYGPAT3AiF5scD)Dc;iwuS3YFb< zT<6$1S4w+kJ#OB?!di-v6Z%Te@34L!79zLvw=#UI%k$&B{QREW;<~!YQo{GkXHCbm zt{hxk#E}!l>ZsqOnVZK-xAWz*%vavR)$IK;O8;uTAi9b^)d??FR#yIzE}Z`Dn+R+o zad4Y5p_N6IwduKU65Bqr8iaM{$)(eBam9nFWiy{-vGdt#BXtpmGiS5dfw5%c?k<>< zlY@qa#v}Zb#0wvfAPf@ZPoMB~JWdVq@$sKBGt;rM4xi8<jUkv5EIB#3o2aP2Q+HNZ z^Mc<amxSDPlhxz;d@d$7wj=Bj`(%qdKfI&3ghc&``t${y1~BR6_G=2FVq%X3KXRCu zm?$YJ?f%V*Y(W0KSfyxSsfM}8+uNIilXDNDsyp21WCfNdQP7nO%mx<;x8?iAj^L)l zaZUJ^c;w{UAwL`MzdSQJTt{$lb04f1l__Xynr$ln+1e_3-_g6dMMptFk(}f7Tg8-= z<sFLq**{js+0wqK<59Tvy>J%lSJPbUuCLCeWM#YB+kFGcKla>;N|v(|c{9JV(sg#Y z0kHxVEp34A#UfEhR~MVrtN;oX71hZS#r5CCr=3wA6&fYfbaa(gb3#;4p8Tq{qJZzn z;Soi&Y6Ew}W%(ZXsV+EopLy+QUq+AuQEX0GbVolfE>2ur{9!FMT*jx^SaC_o+rz`d zaA>a=N@{0E1UH!+O@ynfs&*FwDHOD|2XH-hQQ>35Jd?St93Aqtr~d5F)w62P%7MdO z4CkJG*B{ULCS54?%NMHXn3%nzBS)L;mGUa%BK3R7AJ$+1rz3wlI9~tX9?WQyc?I<h zSWc8`H=QhoqI7h0?DjE~O_J(1*wd@4tK-x*!gfsaymCSD@%1I+gFz=c7neJpwME<A z6rSdr99CuE@+%=k3mjZ~rL9fI$;k=#;Gtn+Vj>vH`PJ2zva*8?y6uUWSXlK3gZ$cL z0YN?35*R^2K{1B!-#_$(fUMGaPv6bWEvzCV<8fEmqtWfA7&<xIs|y!cub1!My=z>$ zy1Lrx^YQWd`@43B(*VeqdCd#I?_gth&dgjU9R}c#os~?rDmglG1qTNUTwfeq>;?)q zZ6Ezm($e}-s9cm^QbI#R(}`FJ3YvZ!>lGBlBQGIw%f-b79DX2tXawkY!;2$@uj=IK zgoM&gPEH(GySln;Pj>Za-+caNR@VGs;xNtqNF6*S!emc(ySDYRWWd;uy+pe)el$<6 zmLVO&Ias_eFC)owJN7I#r>a9CVsreg77}FdkOYgpC=Ry-HZ}Xlf5u=?(L_v53q!Nc zEH@BEL{8H-=)SkHZ5^IcQSCE6y>aiJO>}(x0|U!$KMVqJti&k5EaHW?cF=nd+om)N z4VGSNe}Dfe149-BXK&I35?}b<wmF`0LbdO8xzXsjwOEjsraxV0;};OX=5gVi=5c1? zxIHapXIClDOiv#?KmV?;zyHR`A7)P}2zaHWq(nCI72pv8?VEBFVX!Ii3|l+9-l3sY zI8#pq1X9Y&S;*c0$=6uT;Z)HEz)}B^#2E^|+^%0GUl~gG?9ASU186>77gcY!icd~1 z3n?7fPQ&qHb!r-#J&3kR9QynE?%?383~<k(&G$q-K|@C;$<nn*N=oYK>(ifYa_QP& zh4>X23rb1#9ya};qT&`9`0sYq^sX*-g|{OOa!=2z{euGt2635NFWA5#I2s1!o*tHY z4)E_Zp?V=q4Iw5~QdYLvoqw@3w^7!LQzctU!T#Yc1hrW$Eoop*VPj1!WS>V|tjT%8 zIY)JJa)RBJpPxTpIV5Dz8%;AdJ`Ul}y^6}pwTUtcSjpV-@=vXoTUKuSy>!_*IW`m8 zPLC|&Vq@`$h+Y~R(m-}&^fTjyT8RqWR{Ez;Gcq!~;CC@mQ7^!3@9pls`|+6-A`k_2 z^<ap~+5@oLCnhG04_ZM30|HHIZf+tJVPWB$H*Y?rr`LldVspAarlUg~QiCa}vX-Ep zaET|6si<Uk;nwYfS$TvE#2@|vKT}s<Z?m`9X;uAi+X*6>dpk`BIOOEy$lN7T=qxBK z>@E%|&o_va*Tzdo_#HP{Bp-jt%7O)FZK+~bEBA_xy$}E2YS1w?eFn#lmX0p4xcIrP ztu35Tly?wUxb1#NZ@N0$SYBC)j*IJ@ZFb!|KF)*t2<~tqx5ThNUS3^YN=@zI`O%gE zT<rS~AEFZyj5>6*w9LawPtMMwqodn4e^uUi$YuVJo}Ru~vvx3dV0v0>8(_L4BO^V% zy_D?iF^~)0qo5dqywZ6sCxORhfBCR!6W0HEIP$y#OrWvedf_I7Wp3b)xcklzH@YDm z9r8v+pKNeofaogq<BXpl8u+zRbt^ERkdX0DUqPO?Rp<mh#?cd(T%c}-6VgX14-X+- z&r41SiEz3{qlDbgcBjnBAbY{~g4|9{@bEr5E_rr+eWFpCOF(}9)2tSRDGeWAQg${q zgeYPA>+7aq%)G}tay+lEx>!Ry+rWSJ_VrOQGY_qt>I^{A`>WZNTSrHy>GJ4zSC5)z zitzcbAuzAZldx$aCtU>-Uze63D!v?$jXcU2Q<kksJT_kj(kOoNQQ6LayAcu^$_v3c z<oi2S8&i$AugY|qTa?d_woz>hY6RazPzVpgl7h9PeEPJ92NJS<E5iObxMq^xmn209 z(~C<ehSl)rG1l{VUNy-({<f%%f<>W@PB1eyWji}+^T%@R9OruGxMty!0FjnHEZp+) z^3Sm`zvSd(ZHv$#H&vt^T6+)C#PaGYYE7+fHDp+rv$L~sHYy;cm6Z{(>~I%>82Gc0 zJKwKgzszTw5+T_%sMdHS>>&VdN?`XJ>Q;2shRyj_Z7oBi{m`cf{HpDlvJ0EPYim*v zjb4L?JcYpm^o)!-`T0RZpaC;ArGb(x^-pz_>x(VqGRe*Fm>l*NB{CdJd)7wt9<jXh z`rltJVcYF$+HOd@dRzBupQx~?sD7*FcM4g&G(>#1bMD7c@$p@G=^n4)v)0zuvh(tm z78bsz@Y<<0Ie%@>VjzQPpkdu6v{T<nGfy@ZiUx$FLT;0}(~$E*RYE~oxf2$1%W@~# zs^vyTZf*crDzkB6r|lGcynFR)|J@*QY;0^etOQ<~mi2!^bOSUsH1Z1zzhq{5|NHk3 z$4Rs`QSJEyIAOsN5i3PyEsIN$!YjKA?VEG0>HGV(v#vXW84kTOBoZ<**urPSk@*D$ zdf&Wmz>WgZ6G@i$J$PUf%GI7Dy)zEYV~cAFu(d<cG}0-A4d6&+z>&V9n`-rJ-RnPs zi2e~|ZG;<=l9CE)YCSa;zfbleJ>S$?a<8`eLH?STosDK?WfgGuk?35Ddm=a%C?#a& z<oN4euB&EeXP1M$CV6(Fx2Ferv2ZXxMMVuSlxRYj>6kz8-4`A8+LJ-$<zSj%e}Q7& z?(uQ=+}vE|J(7D-J3h$=qq5>15+kFdH^9WVnC{<rXomvEu(!9@<|6=-ENA#W45bag zuKi6`srI~!^mA@*u48wGLsbtUMBFG3S>A~R_I9^k9WooNy>m54=dqze3B)1$0#><w z<7$Pwbs?B8?J*bExZ#ktct;}AjoU}F8x2~+aY1RM^?4~F@u-N?Vx|F6@U?yfqE)zS z7vL3WczF{80&Xv<fy-|_pV{#`DpIU&=Ca`bl5ElQiBe=^UEB3|o3jrV4ouMkoO;$Y zX>IM)fqFsLBeaad!jP^m$t<_c$*&M_{hXZafkc+ab~(tV7;GT;k(l`S+g011wv`bf z!LKHAHOs}t`FfAGew&z^XM@WoEA?>Tg{U=GE?pK(EH5uFxPhtW-^X$0ljYw}{@<9Y zbw7UmD68jLZEFAbrT^P`8vB4DRa09F?A1MO6^}~*o_wjIrnV{ZFdHx-tXREz@s=%9 zfDU5gl{x|(?#VqTZ<xda95@2@d1eCp8pfrsLh3$Eo^K)Y{%Z#?#qrnwV2%eKL<sCh z9?IHU^m^@fU=8F6Ol!cqm=b`^17LFBad&OR;dXxZsL9KLk$P!=eSJ_+5HRf4L~d?N zpI<x8-~R6uU|VlaLjyzA_jkUTnVQef&1F{5)9X7fSAAy7b1kJLHC15UG*`vgSh&G+ zvf9n9;_O@QwN0Hh%PTloc;(8Kdp;cEzAozQ>B$Lfg_T|1nl8HRG_WUo=H0H`(<hy> zHyM|_2mq#4H%CWCpihomzC8Ki;dbNHQzA*p$+zF$-I#Jx=+&!NC(fQ-x?{(WuI)OO z-M}gon4fcQZRxE2{Vgyo%njH<+<w2#n&0+|fIskHOkgPm>W|9U*UbU;B9o7<`u%Qq z|Iu#o&%kk_;LuQ4V9yBH+XB{2pb+^46r4PB=E-%jyDbVIvHbt{egDp<)1oVY^&YT* z-B^9t9hknAyu0%ZM8U1^$uFJ-ff<Iqz+wOd1~236e}?&HeEQ)Z{<Sjzfv2mV%Q~lo FCIA(1MCt$l literal 69187 zcmcG$byQbvw=Vo6L<vPnu|P%X1`$z0L<MP-4r%F@22oJy4r!2-?(XjHZV>72GndbM z-o4NM_Wu4jW1MFU$D{CzweEY~b6)ehuDRSK#9m#-xq*X1p)L!(ej$ZIp?^Z5(55b7 z!B054$L8RFTo!_I7ShJr7FKFzS}0L93ljrl3j<wsijP`m=DNm4Y>b?Ytn?HgEG$gS zpD-~Q{`&=t#%4N9G|KZ%@FCbHujS2AD10^K4_Y#Bk}e7jg%Wzf|JFL<Po$N-=;F<) zN=`c}g1h{6B5hZH%A|$H7Q_Y0bp7s1c$fapQQ=p*X!lDR1-<$SqS(GWcVE_hzb<rj zcqM9*oNDnQXR+I#n_G^&(c4^$6^mu-HXZFnoeqh@{*>6xd=ytNVWRV=RP#-u!(aaX z+nqEPh4atf16f_rsgYMW^GUQ^#fkaX%V`9tKmGGs%mlpuXD{{)5k?E7bPEl=PASw= zNd2!bWMW_nLcf0V=9}3DFE=l*{zB@6sG%Xbx3t8xLBc0b|6aFsvvmp)-~Yb3>TCa( z7he1NpGApzAD@`$9}$se%mQn5SIxk{Gd5PuZ{nX-*zmt6M1W7uGdG;8b3`IYag~Vm z<HwJWxwvAbQrtBi{w`1T8~3=AAUfGAxVRqoj$>d&>%UP7)z#N;9&Bo$A|fL_=6If? z5naOk9OB#8_ttA`acU~Y*cACTbt9uIRkMvgG_n~=ennpd3BJQ7l(|r|IFXQ$*ap9V z@4L`4`BAcdALoJu?52vk%cv}cH3!Y_O2hxOH81e~Zk{k63kyr0UO&#8H*c<x^LS=w zo5p6Wuda$l^Eg~*({97f^z!h~*j{MIBV)z<v^m>oetza4ldcf7c>MM2*Pd8`Zw8%( z`h(sHZ$f?tc8`vRAMdZPL~!c%P)^vcq#Fz>XRBX`jg7VV-RGuO`*ogr){~4aSuXot z?bpk-U0uGPKB4}suI6V52@A(*wFbX<^~!vz3XS&NubrCx2ZfzyOWpS;Y!+`1WUBfE z1-<jm!NKvCKwUthPkks`<KmSoS6se*%Qc?lDY0Bh<dhF)Qte0-4QI!ha=|8iEh@^; z6^2VfU)9^o)>@R3qU*hF@8GbxyW7qh6%c2!zov}5zQAylnUj->f`ZR{t#rL&cDnW( zy<8R_o3^20yDKg!ikX@D{bYp$-mS+H`tA2$d0!<Y4EUKgycws%q+0QGyu@<$8@c0@ z)A_M##oFDbXMcAMo!?Cj!ZFWiJS;1Vc_2eMRrZ2}KV|yhz`(!-mEM5?l2@-9pLXVE zWi@S=33`xpm)ltx7#gDGErkF00pS#}8pfcI_kMqET%M>(x%6Xqybz&S<ddLAs;2Q= z{M%0g9~q7?o6WseQdZu!$LC+W#)2F_s^;dcht`MRz1kh$yly(p-`qT`e8h`+@uK;3 z%>{*gJ;B9}NG_W#%DZ>(hHXzwOdK2@vS_zmfBNj1?m!w@Z<09Gg9n1<?MJ&S%iTXO z-nny!-C)SK`pZT0<(>rkePJ=NM7UIXr6LJ^`_++rYa1IDosL^1j7m+X$NQ#MR#fcl zfe;{U7E2QC`BrOV)2I8BNv5+6?D_+qoxJA}O-)UoE3dg${e0zv>>Upe&tcg5>};*m z`DtSSl{mWwHa7OHr`B1ogUq<OxfSw0RJ#+?x&OI?PWCr!8D?guM~$loNYc_{&2@_G zWGAJtw>}5G+f%G`92pFz3pvgIVfl^rT~1|zX!z&Wa<y74dV2Z~y@{72pIFum4Ta|G z59%qi8BaV7dt}%$kgiC^X>==9HdFg}Zx!`tw9rSlH<1O_^yBJCJ$&(N5fROqIyd<> zEdv9Q&ZuX@C6=iJMU|Dj&+PW*#;qpn2M0;1sHo7<(Q}Aq9nMa;1is?krJ-?yL%nsp zUTF@|#dEsT`}d9^q_VTKt7~iTt8RxsG8F#7sx?&URH^-^D^{SWs7RvRcDIH=nhF+m zcB=Y|pe?>L66E|?l-O8TTvDX91esZ`?X7<KxavYcO|2j$B{fuHNe3Gk3c;eI^DQKV z^z7`6^5H|hjP0#0^`R^^_|UFQRbIXRU-giL(1vYy;EKCbWtnJY(q6;STb``s9n4f6 zwCQv_Ju+SFxZM%SeUb0=>q`(JKaD3V<P;Rt)zqA0WAA!<d&}c&u8kE3Ku*=}j1q-w zO_ol&0@0EL2b%K11FhqTpD(>?kTdpiuF3D#WHjUg?H_D5TXP1PyiR9NIrInGOxq(Q zQr=oD_qdLiTIn~y8Vf>jZ<lKuMVD*0hds4F@V$tCXYN?#GwFSG<bZ!Y+*TWX#LVH! zd-iE0KUSRl(v`2AlP;f7SG5*}dARDAbd0Ed2MmR&uzrYO78);}jg*-kY^dG4cQ22~ zb8WoTe5va$@-6EV<!Cu_(P`=F9Ij@_+MAd#dyp{9!2XXFn}5?E%!rjqwVdc}Z})<@ zpIu+~NlK!E)wH&@mVnzLZLvE&vi$WnCC)7qBl%q_Nxoixi|NG9a<6Vz41e&Z*?i0V zV`KQM!kSJK7aHZgdvR5_)$Q%=t&dj=ZH{(m9zJ|{i_6S~sq(}>D=W)BJ!Dizmer@W zw$>x<au^>{H7UHhB(8Ic@ths%TxaIv;W0t|9&nv)R69M|G39Tr?~ytmTnG{ke}uNt z{fb<RtMKEhf*1M+b#?#wGXJz}!_h|#KIGy)cb<LdOTP96>+%fThDi7$hSB4_l|dga z^95$L8cfJ>4NZRc%^zg?NuKfWw1zN6XU<_MDvmyFXJcljHfb<}Y-MU<^8Wnn6oP7c zVd1NX$D0b`sxh9@xU8E*JBqFpuj)$au3kcgvR(A|uWH5RIiPSD>nE-_66U$XDfST; z7t>WpQ<L%=K6xSp(82MszTxT7?kp_waEU2fj=?bN(f)d8+YdG}R?SOrJM$Cec6<Y# zDz1a7PLXwWFC5QLtzh@8Pj?477R`Rw!RlW&nye7Du*lP<XJ+;#VNlrEn64e;vGto? zT1pP8@*^kW+=zN+H<Yh0o2{iu=f=*I<Bo&#H4DKi_wL_K$6_FSsA;+FeH*v(1l?+_ zYN4I^gT!VlwY4RjWjY!f8qXUX?r=nOBX2&lWe?{x?(oNp!`0Ez>4<vv=KXsL$kmXJ zeEU;nG0f03l=Jr>h?e_O2t7!d5(U0p85$b;&>=4;ca@k}5Kc}I?Yo|^!Jn`2NtjgH zLR1}_lcF7kg@th@9gi7|jEtcCYtd=EeM>YwJuTmG84nNVPfq*yZ{IH3?fv0TmmzKP zUw~AYrB=)Q=#h3yAkDDK{-0sCNK?<u^1ZNy$Az7a^DV@*Z<8sAs_wl+!sgSYW5635 zc7sdGC7RES)zH=}=%~KYD?TLR<Dbsa?R}_FVDLlm5F=TsnDsXIN6nRibS8^Zu`qVG z<^Zbg0Yzgqb@k`z3i+WN`Xtc~dt@Pu%5g#g51%aVu8xKnjuvzm8mp#nLR#3~pLFWY z)e-l)m-61o$U8ck98ke~h<J#T?hIw_3aPgFVPEBo1h}}kqO8JdQ>&}(fi%)ZX7f0^ zzx!MFpF~AP1u`g-kdl(NK-jHXLf+BoOO}*xz`yl4hO1*WH~Og!fwA$reJg#_ZCV=c zQfq8TWnUY-ZkjK&k##?O*;&`npy!AMI7N;`%fvUPDMgi>Q8k#j`r3U9Rr7tux9K=~ z97?5FMn?5?Kl#F4Tri(Mf1a$6Pphr1eMGXDpxv7&3Kjhm#>FdTQl>f@jo$SG1CJUF zru|=9TR(<0YA~E5xJdfB=Y{0CSLKt+WjK?nN+pzZP7fc->(^<;o6YZ(m{gvtJk7zz z6cmEU7%ec=I6FB6NHiBDb;c)_JPa}Dtbyhc0Ku|-w2~QOJD>U3I^S^=vUt?twz$n? zIrq@;ynVWqtNQHAV(l2d4Oa8B6Pt%(5wGFQBFSQxK`d4}U+>*Z&+C2(qe4RS-9KOb znJ5p>tUObgj0?nZ!Ikr6a=|_5=kt$I;3%#bJ=*Q6bm|!7b-Lp1ZC0=mL}wPs=j?cu z@xAj@|I4CJ@XdavyT8&-euO<YqJ26v#P{vWR(7S6r0_#Po1f!eKoIfMXfKM{OlCwz zEQjCh`MvpXzz7&WQ2pbo|Gu!<E2E`h>521w@+Io!2go}^P)IpW2weK`*!C??hX?x7 z9Oj)nTbr>0u1q0FKj41npBD&xqu`PkLr;-sGd2%;T!qJ>f&jM-g1dmkV*<?%vdY$n zSoBv4Nn@!$9q*H7h#-KT`VP`y1WFNI$TA!)>ogp{fp>X&fPu?Hj)M9U>{U@|>9*Y? z;sz@5D9_STZa{a`eaCm6SbT@`OJW@v6GKsPvh`*pPxlL)E#(pmwT=j`y1F_dYeNVz z^k@5k4o$4AIzMi2Z(~rfvlCfaS&2nGP1$`?x(bPtYhwQkJ`V!-AYdpOEyu>ku`gfl zS3=Hp<Vqy3(;awyI*BQht+CnhAAq`x@baeK<0D!MTQpqE1mDF@e%<onMA3=G(V;5+ z%<9VXn(8l8D~d&1*uvxu4Gn*WbH4!^(DzD;kI%ANS9xp0AzcY~zs_d6^Wp4Z7GJL~ z*>Bx)Z*9C4Fp>=<!ymj(JOFc*N<VtZ=W1WOdbNt&=|uA0?|3Xs%%xjSlA_}ma7m_? zmw#xLiTq$4?ij9c;O_43MgVs(gCZ-n!1l)3>2YtmBBwpuNpDtR;l;U0=as=seZ5I7 zO(^$=XSc1pbFKai`2ljtfs=1Q+$DSM?+*HEG|oj;^#9^d5I$7=smv{phIa{izL=@J z58a;u#cgd&3q(Xju+LJ7MO4+)pqra-KQC8R+u^%}kFRrbuvvDv5N^NS&KYJ3hktBr zY-4j%4RS)+{)F9y3R!+r3yZeFr+<r(sHmtGU<{Fn$JbEMh%j6j>gu`*2_;fL7<wdg zbMueu6YYSiGRZ<H-2g%K0v@iZso6TgEB2cl91L0RO+xMv@ABnsd!LYN)956Zgzvuc zZaO|Gaw1kJ2;6V<AyvBLTvc291&5e6%WmH&eKV5VYG!sep#I2wZH(*d<(n^|1R^+C z)#;Q&JesFJVt;S1p|jJ6MWX?hS14DzJ(S1c36u>N=zH!m_*w$ay1}Uqc;X-UL&(I$ z)Zl&ljrEm$Z>YB~pFjT`oD(NeY^EiXp(J#kxA#OZKMbHzPr@70-~Fkt;9b|b%qXqa zC;Hm}J?lV1$upk31D|5F@;%eK+k7Rw+-{!$s>_>s@)viONAmT9C!N`JI$~r}bs@mj zz9%DJ8Ta$&aH*BC-}uk?zXyvbF3?GL{K>gK_4#C`@|W-5Ketj+-=f4;*VxS^?n{O$ z^K*APIxw&fqGEZXymY{llu1Q%sVlZGO+KLV{4CsNd%+a?1;|7EdV0SWO5oq!dAiaC zM&m6-rn7S8wj85{#*M?nKXz9JF>*Y5VfkPA++nfacn{skaF!azn_&98kieZWE)tNE zCP_~K+JMB?Z!jJ!8pd8-U5)mM_v`?JmzNiS3jk|7xx5e83XLbV7utR(KvLWmU+9eP ztZ~InkxCG*h*FIHGg%o8p{x#&C-V^ct^3r}uiqSwLkj_&QGHd+(pwS@4GrjT>Yb(D zj_^V1`z-ZRiXejPK6#;D(7ENG)p?aF+02%VDJh~S(&~Pc8(Tfm?+V`09kvQO*6spW z+t}Wog>)Opq#6a)LCx4$99o9F48;n2uC=u_kuY}bPq1Tbc6$c4yT1kb_)vzuBBLrQ zDtI>^;rWi|q{^hvz`@>G8&@Q>($?4aNJw~4d3Ho6CMI@c@Ie(oAl->Fu2QRY1R2TC zdAYmSG&DSv?UaGoa6Fj4I8tbg&j&q+r9vt4`2d?NGyFyu2V65A^L#=s$WP#AjO5Rk zTBdGD6Bs2dcp#!wWa7-@aHLr};gPIT{sa&PQY_#m0>Z+6NrvWn%-dZncGwH!&<8M( zXg@xy^-sc|odK}&iB)S$<(!F)L9qpADqG_@8a)k>S;mWOsZ5n;N=iyYxjOjJBxpc( zkWX_$LBURXmuSocdss6v5(bc2mnat2Ltk0L&`{LOET_Xxwb;z{Ca006Bnbf_A@-Fk z16)avjEcR5w)gfHU@P0gIemP6uR>ieve|w(-x@Ml($U|~G0I_fJXW&NnP@fZp-z`1 z9xVpNFt33qHIWgZpxM*tn<XDtTOdTP13)`J-6w~NZf$G3porGCnmal=N+{_*Mus%p z!eqMd?tB=aw`%!J$uEK@C$Cjr@s4g|JAjza`AjlJ6Ibccoz{DeHE~lo$un4Y9-B~H zymG6nNRQ25869{RV2|`wR?GynIc3vjddqCg6GeXb&qw6v=XY=ceEBn!{RQgH2YPq1 zA1oSJGBPqiVtfU<BKt%4y`!CF0ca?!7bC46n@pj&KmO?o)6mvV+*aXgjyf#1^b1h` zK?rx;*{c*J{O-M#MWNAnDCBf#kkw#;EdZGDt<1}u_4V~V?|j76{QWvo9ib$)hcDWX zZ!a?I5rzI}{!vph)25i`yRXy1CoDYOLeoS}ODw1GTZhiGQ;eLP93X2eASP2&(`NO0 z3@-T@Pl86@ml6_J0X03z96aUG#Z}DJ7XAF?i|NOY4gLMZva+&3I2@iGuji)QEY8k; zftZDZ+ib@8_;Dx%FYf*O!yP%A%>+Cr8v+%ldx}_BZXumxvBUAls2o4s*<z0{Xnu$v z(<A_d?)&x51M+DORJ2H$skkY?n1OV%8FFjy2(FSK_hUNaQf53rf2?0qtF<!Wb#rSa zTVp=7vaP7tIX{0)_Mjzb=T0>`zhjDW>7$H{jFXe?&f#EjG9LTKUoPS=0J%aA|9^Pz z-skhvb*H`*>2D-0h5n>5b&krFj&N3gGei1;madUh)jpO6=cE>&*JO2s18R;%1{~*3 z0Hg=MQ!~H2xv2qi9U2}sg`5xhMgUrF8`wFN$;XciP;jm7>@?oLuYzndRBFWpJKVl& zC+)k~!5BW=*T+6+>RAj;{Fkb#D)h`*fa`$Ggp1AQlOYck430xPd0$xm3wv8zoAqk` zAbY0y@gHYrXZf-5OxC|adQChkp3ai&j91Su49B>FMs;xpolfQhl?Q-LDt~q(JS?oO zvuLNOJ{l;$I2U1C9S`S243wQN-(a6AoOINH5(!D>CLJB!laGIz;CfY;aNbc%C*ncM z&=^RQM><+JLaH3wT|Gx=CnxN;MWH0>LSyK3viVJHKjS&{i6&-dHBi>@c@91;c1BBQ zs|y0~X@u5_!&DoJoH~@2os~g+D8o0{KUBe5AMTABW8>ofeC~?F6j)5rxjH|eU|t6I zOs`zB<GSC$fb<tV@j{eTREtMCu^vX_B?vL(g&uhu0m|@4L4%l1W)7&0u)$)Ry}3C} z=qnoYbbGfLwp+gn(|YQK*1r%D!SlMw346lEl%~Q+t)Q-<!NL0_G&Gbza#>|V9q5ma z$k>-&7J|%dx!TpTs?Ch$=VxC$u|t!@njJokkIk85VKE!sIytyswtr`7!v)*@J9Gk~ zjV=*04Va&2D7m<PFe;ZY8;w1Nobt=q)T|v?g2T;5@?qfKoQVc&;*-xC1c4NlaF-aV z<j{*hwts<XzaME|_TIT*>!L)0`>jRG1-vwc2R=0U^5W5q7N!@_tKWRh&IW+ssRrr$ zh~$BIno_a956@9@9ko+ELgIXua*0D}OOtqTTUAw~-FW0uIN4!4(S0qDRhN+Jxfv_F z?!C$i>uUsrxXPM<js3fm#L3}Y=Jq|NPXSH}|K`nb^nNCLGKApcr<oDNc|Y55l?YQ! zEyqrll-voA_Z%-aHp_;sZ1Zax`^%S*YKI$1a4<jj2&gp>lW@S%h?7ndJ0kfxbnWjs zyh?~6%;E8ehz~_bINTq8J27!%t#oU%#x>_nk*UVv?%}rU-ITu|{>EPj=;A>-AJ;)l z%eNpUOGA_8SROGRe)FXd*8Tg%OB?kbF>l5G5dq3>T>`S1`oY}X*?2xK7M?GIO>qjY zL@fHw?+1#wol!v=So|LWA)`CT%pnBNXQ}66I8(p-w<<Im$E?f`4t0tzN2D`QTdFcC zq4J*`AMhh&eIQ<4r7mO--$?Z9w=XLewloTSopXF9D=UA9VPX}cfuW%>J&^vnmG*B( zBlx$Yq2MBe=Fo?Y?o&9Y!1K>aWAZ`^O>{y+5}8r<^fL&x1XqA`AVenf?rHJE!&>bd z%B3<B_+<We`<XSnD`t|*w!1xq{4Z`H1bLwS-+lz0Xy3T@>xl5g_M*wuC;O9zn>MP2 z>VjNnOq7@rk@GBSXhcLVAWXoK;cE?M5E2t(+}SNJFPG1H&!3TjQ0VPp9ASe%+v@<- zIxI2%@PQhj#jw$O`Myd4tF4`#4!3bWK~?x=I+?WhP*W5%5Lnh0%7S2RIH0}7<~rIP z5kkOARYWoHj+Iz?bn+Z~0xKc`MZ2uL{KpeZ+H2RYh3QiYsRLdA;^j+{5r<5r;^#o= z2nh+%<Z-CVya2600;h@bY<)v*n009ZD_?KkLHW&_4}(|s)?Aas|8*a5o~Nd!_@Frk zYV@aKp^<HX*5TH?!APFlUES(z!iORh931(4`iyc}DDm4r<PsATxjMFYcYzY+Y%S^u zP$mUX`}ZBA1)^*PHrv`rp9@T!e2yl7gDYc2reEqkNOPf-oSd8>?N}}0eB}4<m$2~f zh`?7Kt`<&a<>x=3q;!FnhXwfL0)yetZf@8~Vv*|5SKN}QNFYJC{M`rn;Km7LC(k?2 zO1cId$Pirz?oSNuPd7$}it9K0?X|OaOB2d6C?;ip)ydL};!&T<9nR*7163XwjlG7_ z3wccgYJlxKRDuYH9}qmaWr^wO48VHYMCquh`7PQ&ZI83tUmHr)(sy-nVFqD?=j=d( z!9ycKIOs$FFJj}$(?@{OpjI$kzj0$hzF{l}+6&-l51_V6CY;WuyiFkh<|YQ#@zyij zz$>?(Xxg0P;;O2uVqLyb=X+NGXI|$h=blm(dNrN5LgZR-|2y<V8HQo^5Y@oWMW^#1 zwlDm0XcrFeLt(}xW$gO(j_!%oS{syD;LE%J+b*RfnA;di!p>4R20lLijYkGekb8^c z?YW!z%*<F3WnsF;Rbdv8TzzY+2XKU0CezQ?CM$iQqXw}?Ua9K;OW&7Cj0atqMo5XF zYz!z(?L*LzWas5+9&Aj@XK=ExsKGCgorT3fV}?C*JX)rzI9#|H#;*Gu>2<h2(gPR? zvw;o(h&LQ@aq+UUGWh8qT;*okAa|gb0o*2VsFd4Ue*81U4>62r6#zLfVt{Rf;KTlu ztJBG<*>v^k)2AN>Gj2m->-TR2$q8bKO}*|C{CElIIp}Q%OMJq^Z^EL#hjyXZhE7!; zImBf%W5xGFY*{$kqr*8lXxsu_Tnst~lhOK9TgP9}zV&T{6CdRU;z{dCZrk$6!NG}Y zIQ}Dpp---^7Ys-8V&VG$DCWvEO9^XCl-aNwjs&JF7P3M`k;znP-&-9;8!9v=2eL^6 zXz)KH`5sX8d_zN<>f8u($67&v03e(5F2y<U>wl*rn8<uq>b3qKbp-TEeAnuK>j(mD z9|6F?4KuT_baN#^Zx9+5W(IUw`w&PM)QSc4Hy;^HHIh5cRXJm%$z`Ma2DO2ZGyD5@ z3v{}2a13E9K+*CAp}+tDSvhw{M+YA0N<hl8nJ<X!txt+WLFUlwtLo_?fUOd}H3vy( zc_95JKR-VnIZp(Dtz63$8Dx{uF)%<=>(3!!R4Ka-uRS?EML1rNbhH8BDzN53oJlCR zGTlc&5DuY`KgP<0y|kEDx{^E~y>Y9JB|nuHQZ1Ez84QKS(g({_#m9}+UrHA1zkM@5 zDqSBM`bF%-sasR9HjXWH9McXNVkFb`f<z~v{L0;xBnQJ{vtiZnYFo@Enz>=+(L_<_ z7h*y}YH+#{IH#@sH8S!xG;2Uf2ZV+u7k5C&gK{xFGxNOC@f6|obvmOiRCRQCuU8y< zL-<JoB!V;Z8WLx5277hK3wT+P`Qnwu9(79SU>;ko-EuhItA~XFh63;v!eh)30Q{o@ zk*6){SvWLtl1XB>6QQ4h@D*x_O^Q=zXf3-9-G1Y~4v1;;3nEcZxkgtyqj|-lF61}F z#l>ai=C*$Hc6LT9C@6UP4&B!I28Z6p;bOFOf-vzDizPm2GTGOGD?fD&2p}Y+5{rj# z=}VRMDK=juAR}uAFq2#E19u7<hdLa5pimJRYJ2({c@Ja^s1uk?BtrF;K!f@G_(A6A z=m?^(I~-@^^WlirfI`;>a_7N%<vII`osG>Vs11_o3LJ?1igaNuL3H_=$?~}mfhm5T zE~7)YnCkr<*-($A57!^zot)-3u8#*u8&)1);W@fgaY{l@?=DJTf1J>_EIpKkCKlzA zp`6>{W_J>LYFsJWHeMQ*y);^QxSyJNiPs@4F@Wl4OV`>@9r59;x5YVsa7b`sC^B#H z*oQ({D6v}4AVY%m+m+kugBi*Q2KF6yA<Hf(7|_J!gmXAG^&al94vr(TQSg%1J%NdT zOFEg^*KPi*oFgfPg1$vCoiub9={qqJw570Gw2ZnU-T1#?X*Kh8&>&(C`o)VE)3aH% zTCU!{9l3f-$R9e0Sm<oSvJu??>3`d|7dz3oe(pi@CGW5BPxre3@(`prVK$Z6=NBNC zp*0Cl=|np;Ab$V>90H>*U+MS^-idU{cz2#Y_OJ&re;QEnlYkG<qaifZC+$q2otk;i zPkwS}10w;68_1H{ia;!STm)+2-FfEE;1I&pdf9B!=2bzSoMP))R}h_+;YuU<e8Elq zfw{`xW_Kj%KamkvC5iK6S1$Jn9UsjN$zAcj&JW8k2TEXWigb3me8A0>0n@_})uLj@ zXCxgReXUntO#J*-HePXg(upbLtB|n&B#!f)rmKWOu5Hz)KIA%1h6_=Se>JzKadp-X zJKeA3JJJC|{Y3pXQ>9(21QL<O@x<D8XNez4{OljDBAh?;6~hySs+Eoa@xNgcJ_LUE z(M#`JKj>_{Jh%2Y<tyxMl!D$5KYg#SAD(1`g@q;08VsC58!)!7g@t<#c~nX)+=_}G z!?}njYwb|RLwGRYRiqO|n$0>!MsC1qJYloHD>IP`#aJ>$>VfEIap=yL`cp}t+3vib zs&a;Sl1oBQmPrMD`&-AKaZh2Tg=jB@^ahIF^`xx?zM#oJg|aAwJ;V_<%Tao<zNJ-s zeZud4mIaN)8Xki})$w|(Hb`!B6vg_3pn<v5pyWf)t(SZwZb_w*6cv3?buyiP9yM9w zYIC?{Ay;uLiG+cfNs+_{pErKEFJ+4Zmt<q3&W(OwYNYl7RjUt87h!Plu(7swapl0+ zQ_%UGo0?vO3qu45=*|88!4af0g9DypIjgNXoTIg~J{kRkO$Vugh=vXH;HNaQavW$w zOBybdUAtDjl3AI-6%t_fFYzp0OA;3kFCJ8T5LlTDeN;3bKY6k>*G!n64N>mr>stkH z4Vym-3?xGO#b57IzlMa!W~@p64^%<1<LMKiV8Tp+Jpd*7Qj_H+C+JY=^AGUw@Un7p zdgK_{D$hahom*%RSI|$4k7w3sfcUnKDP}b9=>#b{MLNlQKn`?5&_xvnI(oh$J<{i` z`4E$xWP??1>#MBA<f!6?mX*xbvmv8#O{e7;er6ppKlk7WJbwNy)0Y=aCV6b4hmZDm ze!t5|A`UiuWOzd+h*mc;b0U+x|9BTeXr?buH<^v-x2UMA2dNe(bCV38GX<x?cu5f4 zCICj)@bK_~M?km3HRn)%S*igl6MzN5_3KT*EX!xByCzDQeKfTJ`41t{0bKOP3S2ZW zFkoK<Kf!l*_j{C-ly4+|-+5*Wq1p;kpAhgYAxx@>Y*$oVJUurTnC>YlDGAOLonahq zyCKm1J&s4~>s`s%w6iRhr5=ig)xpQwY)qlSe`Tk8qhn<@0RY41N-*01skwQ3FL*Fj zP*78U2R(!5_zxR&t}fx>iUr#U8}nR1fQ&^Q6W%&j=@f0g*uer7yCa%6vstR(HEs|# z4$i$tk4OMtW+)ZcK>rSqdjXgM&)eL=z}!kd5b*d32tP}^{RZf(?SNk!Djbe;<UT^P z5En!ted8~Rz6uH3W_xb7KUFnCTi=+e^b_x~h~w$uBC|tD#8dy<YHC}%Yini^vCkRx zG?gnE>6`}!mfguTGf`2eo3AMI!&;O}rS9IO<NKLeQPWoKyu@u)w7EYSwrROCCwAX` zQy}{(4knX2prt&JhToKt@y)jcEe~bi2bkQ>l1ua+2nCQ0g9|5YJvb+<zW@Ye(I23v zNPS0rAFA(zyLX-8s`b18sFJ;U^~!edPc!Hjc^yB(!)F&4UxDxnRH8ZnF_0R+z!6%Y zo(*Nu*x1@?$+7%1#L{SEQxC`y=mmN+tJRSpg!n!<u;w{hx{rWsShW<{%sb!%VDeOJ z`|ja^3r3NLj~)ecPTB{+CE)_$3~G(ft=ZptgBgfp2E_9zIDLt4LKvYN2?IqZ7leAz zEq4H-kPqj3#d);=j3SO2P=vkU<9eXgpKv;N_$8Cx0-!BVso&vfM-5IsRPQegiUrJ8 zYf2*phN%x~0qQS<QzRpLH|T|}+jnp8U?8^QmzF*_cu4(rb93`UVfz9*zQf?{_*yh5 zK2h`mHsx}p)z%}~^x*jOQ}HNV|DAr>EjewObbm_j(j7oXZ+Q1l_x*Qj@wq0nR~2j& zJV=%`3XPgIUcU6-Ik7pio||v&343B0&$}`hqP{lSX*#~LG9aY?yU*`Yo&n8+gwtnV zU7A*ITyHv_C|BKicnRmZy?vPl@PRZuJSvlMWjh9AMUt>Ik%2%*8%yE@0lZLx5Byb8 zPb~v{H&c5wFK?qhAVz8cnXhe+c62u{1&O;K87s*_MmWi_&5saL(|Vx8Gj|~f=b@O( zaPIsd?+HqyF;Q`s5<5}!ORT{3Fx!Wo7afdi-2y!4CtPizXsQkuxa4kG)SnB!tkKWp zPnF5hl9>LzUWqaF8wgIRE~cBm_E$Yb`+rgL*fq3YzBy-;VfOv!j41&LAHNV4m<*80 z<#cBM6_3pSf#j6d;!htQ1aQc!qrKrq8guA%xPt3wE|)1(uA=js#>%v6DWe{W_y+K9 z)#^l7`#;#)jNoF{^|ya&!%F?blJH@Z5M{Y-!o`u%p(dQ4ISmu#PeKNvy%QoFHZON_ zI+8PxyKdDxkevSW4WBlir)To+-Y7~*H5bcL7>70}?JX_lMN(nI_s%g*kHJWTpgzdk zWs!TwC$y6Pp%v>sgi8l{Jzd5+fjEegSTeW#9>u>U^dGlY$g$lKOJsW|{1gj_y=QOU zwiyhgm)o_{WbUu&hWBjM&eU&Af7>#xbj&jSY5&yjqixxHZAxsO13npw)VF^5<swd` z9C}UeL)Bm8JvemcdS>dC6!N}rWtPAE#{Oo?<F(N1_iq}#DR~^+2mcJeUF&fzHrF!s z@|qv2Wz(S$5X9!c>+kD}IE>z<%GA@FYiIINSTBPm(G-}IOIx)*ye-H3lT1Zh7jy3I z_lj?AHh4*7$0;#C;hx9W<lKT^R}798YNe`SsRQN1a446JYN*p>xJD&Ja94LV>(}mb zfWDtsTU#=h2?c8K{DkmzA&OR&PViwC!r_P@?cZFGbkfui%W&K7W}|E2+EBLH7<tv{ zv6)8(shU>M+H;j6iGr7jh-Cgfi0-1d_tsDt$9o;Bm2jMEi=Bk#>vvu*EWB;ekWupD zoInM+HztWAKfHXSsXjVrOXc1b#A_-15b1HY+-L4N%zB+Po@u@GpW4yoV$nRYw)yh~ zf9}dXrZ|1s4})TR#YPipWCO(KRy)Z`nc|X{(4H3<VpJtaQ+aMLwG^6|)uagGqKpj3 z-QC>&w9ABXUBdm#M*VXqt}d7dD@=!vNt+3ZwVMe(jJy>)0J_1VH7?V5?a9}ZodAQN zw)cL0L1^|n$z{gl3KOZ)(#0XcCz68A%pLdpdlW2Ofl-N<VN-WWyQve%50{Sl02g8W zY}8g{is`}p(;-i6GY$BMg1zZXAxM~Ul~pe=!ti83G0V=XLm$X$21RXok{Cw^8>jt@ zGO`=bw->hL>JGP&5ODLlJg;iUpgJS_y9-T&j!u1ja&hwq8>%mzF1pnGRq4lkgkluN z`ny=4XZ&wJ5*dic5w?F?c9>`&zq9Eq&g0kkj`m=l^!ih|E6FR9s@~7qE_ZkPhK1E% zNRvAUY%NZ5<(UcU6VB?0ns4XA-b-!mqWFBG;!-=#=H`t~UNbwnPa<Cb_>|ERiqcmx z>Zwp>A*aQM=`=2*2!mgAsw&<LZ9^wxT@rUz`h>)mS0`>8O|;pRXKPds|5yy)`?>Q{ z);8uMW|Lh)Z8)Wn%bll}_!7_+*yya5(W}ZGj`@VOhM46wubLv(O9=v`YWgY`7>A^r z?yj^t*x<Gl7@93GHU-yj&F89UsNV~5)$O}fQycq+)Ov^dm?OZS;!&`RYA}YG>-B^} zhlPX6<Zw>6Vk>+MLc$0(8Iz8S^Whw|Lk*B;Uq>lDPJMSQUWbOG;!;@13B=(^l<4Ia zs0*`eYaO32;Nt97KyNg&;8F2*{_}tDfzZq-4#WqZ5O-$`TEv?1@n@s$ot^lfy1FF5 z<p5Mm{J_ee0w7NottSy}z(KfolO39uYDMF6L6F8bcXr}|);j@K3rv6W$A?=j&^?kp zv7q+4!4U^(2`O6uy#2ryUHp)d(HmeO(QgOnYnq`Q!nJJwd=FSxib8`C%5^pl&rZ_L zs01oM`?KP-A+2xu9MC1bOKovIJ=$}vdoezEbc8--(ihG8w7c${hU4c&JUGu^Z*!r4 zlHOjh={RfmjCI3ioR~n3jpu$#6z%QA#*LxiMA#YV<>_ZZr2jKsO8V~QiuGL+@GHOc zxg)Bnsp&ibN;#8p*(TP@moM9*d7}XaZOn(L=I9SHPB<PLsuN2WMPXmM)Kz>Lda_0v zFqd?3WrDE$AFc{v+{<X8?79TNfmQ*JV7W4o4fH0|i<+(ZR#=P=*=6F5-nVBF>nkWE zX{wb`(3H-sth6FHYYxxyEda#~Nl#ply8>TLc9tZ?qIr~)S4c3CvsZlgAyy!MQjG*^ z_0Rh=`wyd!tQ=e-Bj#RQRaAWJsI+z?F4fH^iK;KtM@^XE+3+v7bLkY#3nt?wI|R)5 z-sBgn<hw87et#22aMN~!LNqMC^&$PI&tqM2g1CSVJ8Qq;=QoH)Jaz^;tHbr38I4!X zhtI@!?sOb%&f0)W1Ccf_-+TlfbXV`&+*_l@mDh-f>Okkk<sbPx3zj=`HrbIk+F#Ps z(>rWl4%gu&B_^^0MggNhEZJz84I4CTbBIa>fq;N6FmZ1*+%(>RZrA*H&k%&}Z_gc6 zl+zRoy@5PJ2)kdhnck_XbO3v%b31uzCnuvpkiu}?F7+e3fB*j1E4N!BxXf`JR!{y$ z4(Fc<1}eR`nNd+PiV|4(Uj@EZ^|4zGmKIl!<iCH9a&*j6cU@aK+Qo=r3eOX+(xiOA z6Sa}&sk|sMTzgm#E+oA6b~mS;?<HE9x(&58Q_H>2i(M%=U;E$sl;V?)B98#W^V?(z z=kXt`6M!n+e0;=7aHjw|UVmh89i*v5;G*rpkn^;C!((A})eD?(ApBJK_6C4~mBr!6 z0yy7GmoFy>D0FWFzoe^__FtJ!dhq@Yx0FHOOF%$i4SIHNL@L<xKq!rOIzK}t_v-~S zDhmU@k_^}rP89-Q$I9(w*&4^kBM-M1dn+95fHN%u-aOb+b!Y5I8#whBf`Z+wwdX<L zsrUk}@UD2F0Qy1jPj3S+o(r)V*DH(#rm0-8LnBlUaE1s7?&&-LM7gs%N(uxq=%;ni zAgC@0%cQ);yl`QYTKc*ud^BPuf|&xuI|s~=862d4{7@G_gz`E#IGCEAcJ}eXhwFcl zE`zbWX6EGNq~dgH3Ey#@`8`Uf=d6*|0>Cx2D5jeL7%IXAJ&=BH%vXl~$3%HPA;(sW z8{r6xy^V6J{@{+$U2?}{wtAW0_Z6wRb4h*l%<%jxc`+{}IPviEINY!_V+D3XPBz8A z@mrTyYzmE7dsuVh-B(%pqP$dNRrM~E%{-2Dbf*UCilncxcQ!Y>rBDI>{$RA`g}!dE zWNoJ21EDy<60rp_i5S5dm5QpqU%L;30{u#y|E)3nF*kRv<Ebs*N7JLDqb-<50ExmA zVixGt_s#wfz*)v@GQ~GDGh@+WcetemSROHVTSOW6#3M?_r!>q%FxdkcviE1WHcrq3 z_U;i_c>2P&7o(k60cwJM{&kRuQ+Tn1kwcEtxg*#%0nuYq?B{~fnij&Y<D(cz5Ws-U z_)C?b;1H27^(Of|G%~6Fd?7+O)x~74=^7Z#rLpj?R{;kN-;PuIadoDZsj^M70FGlg zPzsRtZNOqEUu1F@aZ!K|?QN#YZ8&59j6>8nH@m?gMnq(!e4){8(12$U+zV;U{%ASr z1ni6_@p<ba8lcT0txbD4r!??_@YdpxSZ-;cC6HF>V$Uz8rJG0=V|t!0W_7N8MA1SR z8{75s+n+?R44tpopXNu9r3Lyn@1%3KRovlBb|!P{!x<J-icktI{%BaN#aso_0c!Y2 zHZ=Pk@kpMZ)?>3!Pt`qFbU9BVvos}BFl*JqA>behLo6}Ch~0h=^vnyK6-W()s7Gut zaBVuU(wJCS1TpcY82_^9fcp$Ov{&GEeQdW!4`VAsqoas(si(&uaXNsDAJM14nTFg5 z1nUD<R#uO9Qg>--ac(|n{e9tWhSCpm#{*|rUPLa3$pn6A3UVo>cYgPKz$f>DZI@nu zZm-yKdU-j;yj86DodkUxSz)?vd95YlrGj>iJ@aQ=oG&FdH+QMQA~nv=-+>1pa1v!R zRe*$N20j8P2ITrsV8c+W`h<pegZ(>@uCw#=_0W!jN?q8Xp5y@pMmD&6ZTHti!FdK1 z>OJU*;2VpA84Qf~y1H(V-Qf;_=TZOgp$S6X26h~LUfhg~jEMan5e>nl8W4K-&N0jc z%<m4WCIORtvhH;L6UfLDC|nAxh!qUjNgX;3Q~$~uRdjycnVJj3dOEt;hwZ_lVqopM zd+)gnDpjVv@+lTpmR^w3)cTCz!5VkylXvDvJzB(yhj><vN%lQCG-y=wbMx-R{4CT@ z-_D%lgr0`~z|o~?@FQhn)o*5VOwnsJu5^1`r7n=*-&?M_5v!spCr9)eEgg0Ot{AaP zLN3jQO`#v){RHTwm$rA^2zD~7pkM(6s64|_62zPJ*Nc=MwEDvjvXRMb!(|w1$!{S1 zPi7<+SJ(O9!*CLcO{UR7T)GeHBJ@l~6J<h&BE#okIvM`QbX8ed*|AAlkAlh^2@2f@ zFB%z9<rgnre26T}%@s(Hnh(8}UN=D_%r$qCTM8~`M5hIsKWtlgOa-<CC>Dg`4>LzN zM}@G(c-S%f{c)=>eehniz@;9}1&Fhg^^(DS1U5{GX+pe$JxZJ{Lnc*7M1&GNsl9p3 zQQEq9uKEUq!Rtc=s|yPYNdaDN^u*X7@9zb-=FAZbecqo42;ej@R6Dh0W}^!gnQw*s zk|T9^y5E(AA1{x`sQOj);M1AmbO7zuEa9l0lKhk5h^u5mZ$~aF9O9j4;b64l5NDa2 z&kpad4DnvBYF)8(JQkHhc@PKDmgDTd`Tk#gQoa@c@=1j+9oV%SWhkU4f-?|8=0Pi$ zzP^5>Ijs0Fc=E)fo_0&A5NWrCItK+2Bi0DW5cJS1f?ExNB1jRsa_eywV2r$l(D{wZ zyM>+U9uY*X5!9*`_E8JJ-qJEy<)x(1fW2@L0w1q4(P06QE(lh!DqmyfBOE`1kJ9h{ zE8tI}=?5Xudt&%8z{JO{0mdvqFDziwLykMhx@hvvX^<6QVkaIrBTP(8O-WSH<4VL* zO_w2A4a8rg4>^XWkf)HSXnj@Hl&i*3oaXDhR|7Rc?jOuJ4CgfrQ|cP7LJg$(5enga z;hgYg^Ou&I`su@-E&D^Uie~pbP;+vFozva0JuGx#D!7@+{pP`$-~F&)lNxmDCI*;s zig;!h3~3xngnvB&rkOwA0_qqLI`B0XDw!T_vG{<KDIY(+4s!?KpER|!1ao>Yh%h{( zVOH>Vn8X1WmOuR>$O4BzFtG~*%kbu4qZS{r<&c4s4KUjrWG0)I#|)l}jCznVLLSh7 z6dwYVnXVq5_8iP)fdu#&q`k$FNEM)VLBwL`r2-E34v&3PntbkeFR%KJ4zZPpFJCZ# z-ZdC4@Pb(>ZTU`+xC8_QyyjVF*T2H=_V)J1O0C58+pDVh7CItR^TisjH^j$Nf&iG3 zdO|2N3)=*?FGkr;`98!B(&V<-d6z+jQ59!uy|T7&vZc|3l$8D}GaD^&TR2texcBWZ zyyuu*d7t#(&SR(O52!m?;QFedj!YG9|1u*><siN1j($Kv@#)L4)*C+S$MFtAB$v$M zVXCL7q-1NmlXu19939ajL0{agJU{gT6$rfC&ENs!2jk0!?l{4WH4Qa2v?il|Aj=SQ zfrSOTs{Q5#&@d60GprMBy4>?1J+#0RDgv8U3lSpZ0>xJg>};{!KBHoRfm+@7Yke8Y z;Z_qi7kwE75o{0KHe$a(WOHbUo)YsjHkp6>_HAX1XkT9qARhnwhXIv<ix6i3*gNik zGXT-<!9*~<zTO$b|0NlGF`xr=?mmOuP>(oZp)kXLCm`4%2ot6tXOsJiTU<a5L@pQZ zPYbwZr7~+j=+?Z+c{^a@@Oy49n{w}2qvF3OQ2sU10t8Do7{Jyh%3FaUJ%A`-VPosf zc?;@Nh|}5OH!#X?!A)KzC-3}(hLJaS2@kIh80x`Jg}_UsS$w{P;F~5X_}zc^TTRjv zA<>}Y!_N^#n-g}fOsWaO%xa&@91b6(JhL^QcmH#yZ);5WmGIRj3&W7c;2UXz1=Fw# zU6QYazShJTe8CDG8uBF~u6H^kMag&L;kgiO)yqoMY;JB&18E)$MDLSM&JSHM$a3rj zFuMm_%xP(9OTYm`fp>_ddy6P-U`6u*;El|vL6L4k@+XL*NWa3u&K?iK_X$|Io{X2` zK)S7JZpH>%G0(viMij4;7+A8F2Q$f`?-{Hc0-4nVjIiA>6-Up&0K;2oAm`6Rlk<R{ z-V;3GWfBXYpBBnK+T|I|*H0p5l9fO#b^t40i-`^R>mqLUy%GI1g?t?N27iD5WT^xk z<MLe+_!BG^V*C-oxky9+y&p_3{~3f>7*?>~tS9B_u$%SbG_kPI1cDoxI07QBC79t2 z^y@Ht6Wnk*0nVPYvtwg~!iL)eDZRe0FA#PSq4nXFt*}c6z%}O&IKf>X0sy=OW;|%P z;bMeaNA^L^fg;unmQ=ITG(q<rf@`;LGZYsW3&9k?5lKQH6v6vw1QhjkbxVp7akJZ< zzz%~Y@TA?x-9J9s(LcVguO^7@{^q9t+XQz?=N;=YdTh$(F(rSg=WD_QuW7Eo#(2W( zM=9j*ANMnXccnjIpfBZqJt%3K_bzzerb|zeesvVPa!Y7W^i9vnq2R|-DWU&^3%-PT z1J)}ZQq&s&rZBf%-vGFWl9Q8j8JrPZD*LbrLdF$`+8Kikzy~7go7qgX(;Q^%;^^%u z{-bQK%kVvo=fuR?+BZ0J+nIRZx10a&L#w4_j4v5%xXKZr*kYMf(-|osm6~m4TK!yh zefoLabA?B7=3{E@p|3HoT~cxI^zs7r1&=O=?yagYOdHQY5ts=O=jkq_zJaiVnvK0l zCTkJ)V;Rb83%7U~t^OQrHr^wALnZL_Qz>_1x1W8Bl2_?qmdBV!?%J#X>Zuqm1yhKC z<UkD{4U@)o)7v*|FCpW6{JOC)JTNpeqOHiA&AtUFzb8@jX1Wy15i+&K|M3>rJv*lH z@w=Q3uf?OW$fI{Es%dBhnSOru^J~C4VA<AP3YNJjCaHwq8eFuIhv^&VelE?NTbLx0 zu7K*x^=CV0RHl^(xP;8nWJ9cpO4X-|ffK0+#0Kw{aqji9+PUTc56NGoh{uhBdu!&b z+ycAA!Y}vhXn||GqE<rMW1HE_Yh<zs^nMnOS^kmS)5kw`Q!1UE<Hst+H5Hx($F-n3 zzDp~}sJG%!p}xEK?0F-$JwB(u8Qpt~_S0Ynf1~%t95%Z#<NjqaIu^+Y%Eaqi(k(iu zBE#tRqx=++Q0d@TTJuld`h`D=6%%V1>%NxS66B#2wbdSuA<;Vb*sQ`g`6BWdfd?0m zj-lkcckpN$zwHGr<+0L>x1Tf#u7Oihxs)P215Hp=;_87=t<5jmFWsx*p?}7&@NL8s zU%k;X%kB}cG27rea?Z)!!lCEloXxV(j88Ul%1H5#%P*b+R)CN6{Pdz8M^VXhvEAYV z1Lx!Y*pG%orKIjLxi5{1?a9=$)n)4?Q9Jf|co-PT`(lL2a+1Ah@yUdIM4t5Y)ZqhB z9l33lH^`95r5Gn9P?ly?<<<CKt-oRbo5R_GK*5QYv~xv~L}03Z`YZIQTE-`XB_3Xv zlclA;zi$1&C|ie3K_)qaikxwUH^Uuc|Gi_Nb0!uO)BCz&a}p#vsTrnP?E2>v9vI{K z9t`Pmp(NRK{0Yc?vVV{jaOe?*i-_r(ei9ly7Z9Wc!T;+!#2dNv_n{0wBvYqdB8q?Y zqFetCR9Z`LNywue$UoXuD|Y^aWpwk;#gvp6n3$-k_xc{cs6C`~YATGBJ=4>e#T>+l zb5rZ;tH(?0KiXBlfoW$va2dw{fd@8T(xW5DUdRZP>|jHEeKtsi?JNI`c6Zs2bN(Hh z+BYcNXU%hOqyJf0XjQNcl{0eIK{idU8rVKGxvclj17m>Cw@36dS?mu?pf3N&Tx@Y8 zEmv)^^AEA3%UK^Um0di%cK_uRv~QsCqlWTy2@y+V1}QnYIGE%i%eCg?W#YskMd^qB z;M~2gg$3VYT`To_W!qPjiqQk8z<~W*u&(W?(-o_=rpEqX)xU>$>;=jG+!vUJQQ)2& z|JH^^(o`j&`vRR-IiBO+iI1AEbfN6K&wTaQTN)V_Or~q#KdY*)cHY=9fdmy|$U8Oj z{(oB?1-_z?*;y%RY23U8AXztdcG_5t^z_1JpMtmw{co<PqP)Bo_ab<2=v6BsKs?fI zfO#mG3&KF9q@*BECV-Xuicj7F<G;^G^B8#^kb!UrfL{QNQTYw<h=nj6!GW={2!OM! zrZX?#aRe%%>cQ472zc@&INu-9_bskI!sl&jfe8y{bM4L6;LE}v4P9|0vhR)A8Ms&9 zJe{xRJ*Q-Sas%~EvCw_|Vrt-*6dfAHcY?QKD5(BraztDbfFb%*>7^1|=0VpUB4+Mx z{pTSB^x<A!qTbt}Y+H1&adLKk>;RTx3Hn8_A%(F3`fFE${`mJ`CNjDU1@tnM%?AmO zafcQIz;R;&Q`X>l$!~yR?l-l0b-86-Fl3&gT-s3Ca(*WD8DV?cG)d2`PM;uF>ZeXT zj~^3dDslOCpW>RWsFmVlVfezfclM6myyE9)RvxPULI~3|_pwJdtt9oQ&jyHe4W4Gi zss!-Gd}~e!!LEy7tliw+)&O@bOoQ~K$+LmgDkC8eVOv3!2U?0B8Zxq{*32P+s9`(^ z=w4_%fN4qQsDL~P6j~^o&K2-d&Yhhe12;x^<3?D0f(<|nM323}X(R&sb^s#?Zl0d> zpRFFQKegTYj+kO$Tr713;EEx5B|8x=LIV`yoj=2bK%zlsDPLxt-GKh9)ge`%{=oxR zWK@A&ukSlT4}yP?-d*t}Y{+*&hQKreeU@c5FWmQ|BCkMy@JhZyJFw~b4M05pVDiX@ z!F!lpoCcbzb9*f0@AD6SNLF0XmO)jXK{buhm%Yv$b1>h6CYT~+i^AwC;dq+{&!Ztg z$zWlHCL|{Es<Nm_=hH5D&oi^Uj!QhW>G{=t$=!D-e#oHECD27l^=9;~=ym;VSf-mW zmL3E1*x+@**3g)xm#O;i2EJN3nD$pk5zjs{vj%ls9nck+I3Q~4==WJ4JxT=P9XuBA z^pzxSCmi;b;dq#uncbtHKrM=_QbhAQb%4_qSS)owL5PL`yk>|v?`dAux`3Jq4DJK@ zvC(v^!T+*wgp$MvX&rbc5w9&woiqVkjP%Sfq@|&K=NUPC5fC)~K)oXX2@X@O&+v}0 z>i@u8?evN(41y`37e<w#b4Vcb`2KwgXoW)~Uf^)J0Zfb8vwUk_BD{tHAYg7;dbl2U z1ZQe<HcL0aK;d|qiu&vG78`yRlfGmSD4wUvOfBksU#@a~akhC8r4<>eCAwLMeVrw4 zNK-oHZJ_bqjlx3XB=32lf(e6{dbHPXkc3}KxROdX+~{hVI_!1(i`55}kmHnFrp=~L zG7VQof}JJ~Uu~DPl+nJZ6I?1Q5ORGOal?|<3H`pJJ51cUgW3Zf88VVTKko{*(P*c$ z;)gH)Wnn~{nwhBq(+<KK0i<g9mxU1;3Umo%tOMrlVZ$sYa=~<kbb!F306``6&to1m zz-0c<V;*8ne`q64O&GIgh2GiA!xDal%sfGBCn6$ZF<}mL{`KDn^U<8fcOa0P_fF+Y zEOdjf=fh#sJT?H*J_%xcI3t8?B@cZMVCQT9I)GO#pd2N*Ii&$o2Itx=;zphI;qBNh z0eAHYG_YV<m4a@R=Wvb?@_z%2v*aq3T5qZ&hY&1(SDJVy!BL+m7TH1-Z68NWwG0Fx z7$DPaGNJ8i*Uv2fT3+suq1E~wEEdhp%|QC~#9x1-wM>XqSTac^5Uf88?jE1n*{rYN zTCR{(Dlze8qaIS#u8!t7>gnO&UFUnDP-QiYby-ue;1VmH>Wf3|_M&nJZc7I7oV&M- z#y>x`e}t`K3Woag>Qi8Vy6Tf~{VCxoK1hE2PhP~o1XO$e|BoAS=J{I_bMv3@unlmb z&Mq&z!B#>N=xY*8i3d^(@nEJOlz`GC2{zALi}3IrgnH5>V*>fidmH{5_~7TGBurm0 zL*ClDnn(~yLS>+#Q41vE_wV0hOcUca;V}U>U=g4(r5n^AOqCUXW4gVuK><@QKuGBa zihY6sHh6x506fB@z078teHKU*c<KUpiSixypmGWNQ+nTdCJc8q1V&aM6FYaewO>l! zf+tmMmp8Qj^=WxhZ#w;GtvuN!rB}rIf<;^@!Zfw1aS1;pi01*JoX}jr4bF#B&8Q4X zjJUWRbz8d0G%{2_1EYra)z<meV3;y|u3q8YycZGcyw`cYN$QhZKERi7`+>!Az-V#B zp5SZ$7a~%4J{shNLD%blS*D^6G~1|rwcpDo+)n$)U6_+b(|iM&DS~zkksG0XX8F)X zk%6deL3o;UQ=k`xGOM8@F9GeO*nhH#+KD_GIO0YQa!Ub2L&I(M&Z;U3ak5rt-5;7j za&-8g#Klne3_mN)IMyooNUN`T#zi}ei~uuO22Qs8`OL}2Ry#Tx4hn*Fp^-8$9T55{ zV#Q`(?v?Vh1UL6^A{sWUzw$|RKF5-Yd>|&#imKO*<rzAh_dDfwlw{Z6C(CG9E#PS; zbI3%<y-WM9l^|UA_3Mn0X>e85z|QhdQc2+p!4HFND<4r2ND~wjT7Fw}>qPr*d!xRX zaPh-p@0DmydtYDTs{%oHzZKrnE<OJUw|iD&8N#6W5**4f_VG*m54gNhH&WHL5%mS8 zM4Ev=5_|Ci6Aod?f|A&?T>ZY}YT(4cST-P+12;hiNgQ~9hUR7(()AbJ@IZ~G)zLz8 zhf@$oOiWEd)FKQL76jrBT3S4)e0@sh;Lw2{Dh2c^Q&Y1cJ`s_lkju*ZFtAFim#^?s zE|SX}i;ay9rbn$$9-p>;`wzP4O*dkVhOrLhIY~`91yKJ~9d?OfVin4|aBEzsY~WlZ zsl2-C0|Z`E!;`g0KgkePEq%z!p*3Z30`PpUHyF&_!(W1a-E{LFY7ZOxy20qfkWVbk zC?VQC9W%WF&#5kn-*ORiJqZK#w#NL;cUc+zxh1V6-srterq-7XX5;RN5qr1Y->5pR zP#*-tSv7IBuU`vQl~!_c7p}Fv6GGUx|Kq7264KOMG&Djw>Cb@MMD#IW(!qoEMTUz6 zY^Gq>zJ!e(3-iee+u(AohF>0TwK5$-v4K$=cuG-aeS15@t9d3`TH&PdZY1IuM@fMi zs)pR07s&KF^Ymg)|0+~`L=A;W`W$HGyn47n69tjw3)szo;hup{rB^EQg*j?<Lqjhh zQ=ujzBtqB$Qlhma!^odnS0QW_NLWRX-w-hXqF$c<6O2;;GlC4^!4pIHK<<b3@CM)( zhTBgpx^xc>$4fZ;DYJoAyp>*tdgXnqnRQ`pO$=}^8uHi<WSob@%dsD^O~jbY<J8qP z;Y9n39e2M1=A2%EQb#6`kLW^I!^<n{=B7eSdADq)(NS8`($mudp)43OnOIDj>P+g& zXZeGdCW`!?1fOpnFRPd5$6!(j5$yk5-)3I8iyC-1P&87kuC3Xymp-aHob#an*E@`= ziLpD}Y}{+SmH5Q5rl}1F#+!|Ga#rfDHNeA!f~5)}T);SrOq&8H^e_G{!Qjg=L{}@! z+=HP!bHEx(*&#4#%V0MR_P)8Wuz)=03Vf)LMqR-Ajyz>4HM62cvH^a$bgM8cYf4F> za;>Hcjya8Vq6wJTerr+VBgqnwB1(QaFF$`ie^6x>PE$|I^*!Kc>grs;Y`6tl$?V)* zU9#lQ>wdv}iBc)Z!&D}n&YvOAbL#I8f;WU&O*+WF@rI$F_aMgrlLL<$Tl`<7opn%_ z>$~q?O46c1K$H$?kS+;H>Fy9orIeCVM7lvrkZ$RaMoK^$0Y&L<6s3f7J?poBd+(XE z=bSTh{#Y|>tr_QieV_Ze@9X|v-|y!F^=%2b5yLE1@85I6uMI*@DA;;6Y8~{Uix=%U z8Upe+JSv1k0j(YJo<ElV08>Cye|MY=xRh8V4j)6xqe-czFfDYdL<{0LpVJIfwS!CK zXROR#{|--*v){q*;m^_ay)afic=V0*{22Q--EvW1NJynM*PY1HoRMmlH1W%SPoWUx zvU**zh_<ycn~|^kenGo|C*j#{V*ljX-@2K3dKDpdj)jjOr)SEj?G(CB??zsyuvZcY zi48OY<JrG$T>}HkvkObF=@n)oJ}7ylIe5BMx@2X!%f?Q8g6bx^NVRLY?HVr~uY<B9 z7tj4V717teZGYEJQRC(B1JaX?-}To)XA9cRu$y6<6q1)$SoT<e!xjXM!#j#0i~!d{ zj03Q$)Axf<vLLQ$nm+3mkeQjL%B#ep6A0$_<)Jan9Xt(m$lukWygVddT*~yht{M`y zsBbW%rJpOfQDc7~&UG!c;YUJ4HXW|c1ecmZeQL%^cZ5ZAv*ZISopU1xqXH{U+yup- zfIk27H7=Si@!Rae;f4<$JI{|`Nxyv+{pL0q{?pp0d1<*+NhAk+^h(qp1FwO37}g(> z>jn&%XKuWNKcqlkg9BtA<T<cuW`Ui%=kw=M85X~;_Ts~D9M_0n^4cstk7INRp8?z8 z=18`Z1HoL4ePyJb<7Fbow&S0b*NNu9(|W(K@J$kDRFDSE{DN%53k5s74=Z}%ZyNeT z-HgNHQhvJ!>ZW3IU*+q|NRFX+K=jsbkb;6-z5N5as*Y-W4RaX$-&p$V6gL&jJTmiE z%otJ?^^)~sSaV%gr_&v(w0`!%JtkSu`8JX<hTI)TJiihlh>zBwd+UEl#~BR?3;PIh z;oSrc(_9>+NcMM7)HI;ww?&ta>WH{e`S5Th)r;f~6LHK9zGznwT4UV^wS@~&(Y9&* zmNn=amQqu&ur+l#OuIRL3m38ZqS13epTv<+|B5wGn(;PAApFLkH($nM5@=nb&u6rB zVE!{*W@aqqY7$Ft5og&z<{mY*q(`9bf_oh!0Zn_mh>)D-zMBT$gN2MaGxfcfA>gBV zgh$<4v{YN7V%ycx5eVI$HA8}CT6TYQv|f&pPMPwWppA*K7i&K}?&+W_F^8hBZQwa4 zrwB1qv{@%)RVhAD>+;Id6Ag=b)oc|s*0se*b}{i?U3$;%S}JjWy3Mod?yZ5Xf=xZ$ z>@#b*Tn%$;3)#}jngZim0qcSOM}x_f$qNn)lYxzY&S+RRh|}$Xku!s!I8dXD7nFAs zPyd#ga<=+z-ue3}9-kO%Z%(SbF8E{c@?<Q%EssBS{D63_{L1&n1Cy(Gnb!IuGzH75 zxC(a<W(AbhDiu^!R#xItQ@ngxwd%iq&}de6SB|JmwD=URm^)e|^d>DuSxcIDxwOyn zp_N>yaF9HARH(9VKqeoeeW9v}Bnd8?cj1xqvo>d%;L5~~lC)u8wc@PJ+UyeZx$om4 z$Vx}IwK*PxZCH)M$8WfCKZ4Tbq?{%cRbXtzW)`#**J(9dF8vSBvM%F0qyC*b8(to+ zES+fsK?C6xJv|{2q+}uV4N86Rm@tFtf$U}|N;LGjjEmiV9X~OS2?=td``4mBvchck z`cyir_(W$89GA=)w=Lor)fhipk|wX5-&x#=jA>{PePn@NI)=v;ML{Q#K@)3u$&W5+ ztkB+BW2{SNZ{yl^GAS(!Vpgp;H<C3Q9k$Wkfo#Cn-Z3Set@kx$+QQr+z(C_Cvkn9$ z1J5*EB)&uB`!=`wU}O+@qd3t*4<F#F4-8i*#Gw|{T3de8=gL7IPL|E+O1%9jCjTqJ z<R8lxl2c(!I3|<}af=P`C{ZMitQtKPs%1tk2)MJrPM3rZgKj}{!IS)_TAbj4;O~mp zSm5;xK{O7fB&&<UX0e$MSekizJmbvj#BDFDD<46gF=NaM#fD3Z_u~f=?YHW>TXICg z1(wK#?G5xN7gK&+zJ5|8(XlBV6R(R482AP91mf%1GdPppbA%H_JgIO-Tk05o`0642 zwX+k)Q#81NP4)O6C06fZ_s^{PS3bQKqgI|}bXhcI<@kH2@xQwY6%<4pT(D5V19+MF z`7^v6tcpMWtf~8;+sutyPG3%Q14`+8bXa`x5-456Nomb<;;qly72Gva(oQV-#KFE) zr}e`d=zw_oN2*jjGyV9g{CT5KS|QUT1sQb|+Sh8d+@Zf!_LFK%4CWU0VeZR)U*p2V zM~Mt$W34uAEG#N|Q)3EvD$(_B9n;&@9PRl|BV}!;*72W$EcY(a#^<2l0P$uO<c&#B zU&x~%2l{sLQtMDY?gQ?-0zJKLuXD!f-!h2yC(ZohYy6ygoRS*zKN}l~r4!@enhe!X z@1HXeGUj)7-fpj~u3}66D`TA|>Z!5m{`A{nHOIZ_{dVk|<#42;z+G0v4=Upaz*s_; z#`GF!K)W{mkz~y_AW#~DUKs*mSjZTUOlvd1$pe`U5Gu<&-~NXT>M+>^r4rnem>6XJ zZV_Z%tCq}U_Rq_!v@Y>Ec5^krWnje%e6hS(s5w^UC)IeKp~?oq#d62g5&}l1duq`} z^dHCzXNqopYK(B^^6IT2dRF*-v_@~2lUej_x5Pxf`JM1PPimhIP741|)F@Vx?pNJ{ zdV*&DXnC*l1PufaXjjnS=43xXFmU<L?yL>mj*!L}lnr#IJQ!exOoGGo0IWQ`;5nkA zqCyHn5V=5~L0~-DGQSkBg6~nN?3de6<bUHyZO!h&y?}_CS|y*r8La){%5S*P{NdgR zl4lQGwvcR>-^Jg%5Vh@fY9M?84NEA+EFo%Tu!z)^Rh6a?gaoElJ7Rxa8_CAk95A8- zSK_FOk7$b4xQMN+J%|@0)2-2Ot27D;mERIHd*^3p!oJ937et}X6Tr)<6VB&gG5Fa- zf1O~)xpE4AAu8B)a6L)Uduz^%k_zAM)*!4<IL17W_TGIt0ssa`T5$a6uoqA1v`-9@ zSl+YjGl<8c6=pFQ6A`Q<e)UaIR5T49<j<AV$2?FOLfR9_c)&#mF)#vO==Fr6x~JHG zm>!ETt^ULGDEDDo9)#Ti=%j*h{U{<eIoWnar${RVq|mw5)nJhAz<|n8y~?hLd?omi zs={|62+VkVpZ3?`p*38nptno*%EL3y%C8S#a-X}xqGQn`BcCk27X5v)d6K|FmL|fq z>rSy#se)2}G<A*r&hqM|W@i7(lx+!m)0e3{<vP(?!r`X8H)f_XV~UlEht+p<xVM)H za1MQOvw!&(qq<W{w}t?*!91%T?)98__s}cKd9i2zm#tR@7^faX=pN9%1VFYy(5%4D z0-KyPyev?1qRbGg)id+|ooeObQOolfoB;sU0D<N&`zot$*#MXg)-%2Y8<y%+!42kv z+qyKAz6gjCIJrw@8gHl}-2dOfR<kYX*55&f;&zzg`nS#si?Zge;ViO73ksOQ|90GA z4<6I?Z*`oomgrbmaQ)#k!Eb=HObbj~#o;4H?I!a6ky+t6f_ARa>iZngUqMZ(bpdzs zKIlxNAKdN1l0v~{=C2x72$xRqL1iS~5QN|mpkzkDl}jf6_#rs6kZwFAV)}p1bU*_y za5Q4!yi^<CX*SV*nI*ZBfn3&sj?UvZ`*YFxPec|w-!T$c?QzMm=;$2D>S%~1pFOK5 z<Ye=g`*T4gbruA*Zzp3H=`m?Kxr&<w-~Ok4*wb~ob+FEbPL7TqvG~LF5W!i&%CI+n zw!a_?+mNDAk_)?j!^^1Fzi*VtIOjJvmHsc>E!*$DP$m1r<pC^37JGB;;D@LLe<w7a zrJ;`loI`pJ*NiSc4$~H8xK~xV2MgGa0tUFjW*^vb0bpE3I254CA+{Wlhmgc~sStPr zm=Mz!paHZBHDp&^C;vAf*t^4-6DtrMAhHFjf1Hu=?#c?nIg^|I3u-x(xHgd5LXvif zf%p?sMmgA8ML$98%MSszAp|kN%p3z{nU6ap`4}F&(9<n)iNC*y5G|soCm1foVC%Se zWIPGLF>pL&GxP&s8U%@VUWaG;t<ah8fvdp*e_bX}lBCw<xO24&X#b!&eI+)zMx;r2 z#QFJAtu_8k{jIRe>t7^Ni?q2v6$V!%kuhI%j;;_hb$3S_w1|h;@eYL0vkH<oqIa6( zTG1<XY(C?RoE0fcI<0fD?HZYv|Cx@q1w<tHo#6M)@;N@B5_wAC?d`q6{M}(x51?RW z6UG$3=4t!%6F=sZ;NDa{i+(Kk{N6uYFv4_%Y5<O56xi30iydqTh_eAG*J#sSwzjsP z;8qy=uL$?}j13Td_#XdA19@poSKdMo_@Dv;0u5fZ@M8T0h|#ckDX9(OV}Mo~X_(D> zljeeq&q;OvWqL#*u1EL?#71Z^RElrx?1KW2%Eyk5+uuAPeA@wr5qpP?@j~cQXs#ZT zaTsMndm52ym;Z)EMlymg1(r)WaGu~3u-hOahl7V_0nS#p0llqXSZ-TW>O_XR5H&&^ z<X~~HSs+A%@B9&(W$?K3K~YB`4jed{VN>CA@ppK>Gn$JJsI!OQVD#TQ|9pnUz=_xi zLA!vwE;sYaFdfLVigy)R9WTzyz4({rc34<sWZU6l=G@@oa%vIunlI=JcdFlS%6LIZ zS6A?IOjD<+C_hl=o>L(zE46zWJmI`UEyP0HCM_+y|LhqHJD4VdVfO}*syS?4TQd!s ztHGP#9<v=aifjlC56cPnq%g9tx3IPT1d1PDsM^Eg@R}O_PoF+~>oNW}xGWxtZtL)r z@u=r1Q*^lKD~*9104*XeumwZU3FMx6@cjTG&SX<qcXEp;3sg8&5NoTuAqgG?q_8ip zn;o;WrmII`7N~znX3F#Qq1u{cz+@46I|R+VhalM2RQowZ@Sbblt8H55SLOOiiQKx% zN7=<NbVn5*Lh3-zUQ|d(Ki>u6VuOtI88FLmx1R@guMY%m8_vag!<(DMK<E|q{;m8o zH>V!qG5(58V^5MoYY=}Mb6s$i^F@WAdLL<E+F8uLlBGwAwBc{S230*g_}Sg_QDm36 z@c^6axiEprcY>>tuA6fT8>d%To>G|b12A7c_$C8~eqDbhmk0UJXdB$2*GiEeC9Nf? zdxYPR_w9(|TZW-eUTDs=!J0wH5|p}*a`66)4-*sJBZ}3C;}YPyBzdQ>CYH#p;Z~Ka zX7Pkw3Sf1CzEzo4HQ1f91A%tf8axh`LI$tyf*-@o+PVwYoL;R11;SZ@rHSBfz-twZ ztV+wCE2Tov{t(_xh}(*#|A&h%+N)nN5CSW4)wWDAw=Ygan2QS`MFOH3ZlibIOz0Fe zY89CL2SLuwE1cM%^1BIOtT+Zk!z9@<fRPId3c{}B3ydxh<Ue+GG0fs+8r<m_CVl#S zV1^dRM7c?sv<;+hYxuQFiyqs$eD<SoVdVMZtg528pkN^P6aS8XVq)be{u3jK9ch&I z#3br<gA1NJZiu<pw0fbIUWy*+r-xQ%omDSw?%#iNBUQ7j4DX+7B3XCv8#skv+qDMw z{B*ZYLt`TXxK$y20;>0)$KRR61>WM7HLx+o>VE+lmXL}n94uboONHGXkwIV=&2aoI z0_YVmFab$NA<-2A24`U4rR=hXH`WKd;XdH<Mz9{x)^mZwQ?yHkbtoOFo55~)K>b2z z3bE%+DzLzsf=0v`P-{Wc<qx4s&e(^xeTs%yXjW>)R%n@3a+~t^G(e}`1K(7YQGR$( zaIn4a4tyB|0t7|2b%TjB#xA&XLE>0uJEo(HiHU(Q?UJ5Oe!)LWGOOOt(IHdrvR;LB z#^@3yL*#TX&L|-Tng=DZjGe{pyr)m~R#$F#S3O+Ee^|n593Ee)8(go&rWG>eNlKhU zMuFL|>NIXlZxz(l+lIr$mM`6k7&^foxeS(?L4jI3h+-m4RO_lF=ZnGTs^`yQWoPeX zDSl?v`qP_!JEybR7|3w{C)*z!{r?GvL#URsK0hymmv;%yW&i>QL4l{%_|y@xt0K;L z<fMX#z!E4>2)_W@ZFZHWt}hN|E>34JaG*^MDivb)0>^4FDQAH(B@IoPtM~btXW4GX z4G2V3GMm!c^t1jUz{jV9q8nsEGKJV95RB2LtwHAVM%rA!dbkJ8IixWSFV_)dpoYuM z@4<TscN<{U#=~z7X`>bPKi|M-3slWj_%iH<wc+5DLhdzI4Q0@VL%iUKj0rjeQl!Ia z(+9-S!nB5HUrNM|D(D;t<r{b`G8|^ZU^O~}WXx_DINC}yjkO(P{Hg3V0uRn5V?d*4 z!QGin;w3UcS51%o1FXnfK6r2&oUZnZ=FHLO)vH%?@&*=4nwTNx=*qy&;)uN(Cuz$~ zKM#)s`rn{OHxdNS?6c2PsE4ShOp=tanLRRl^D+Dk0p}q&^iua+FtH){CZwJQ7ps|* zxoUB3>Do5rvg65BY*W(yU(t5*krn#?AO1gf-Job*acC+wS5#JJf%!0;TI6>ShZ)dS z(-&dYS%RONv+0==5>Jm~RJjd1v{D}OFd#RE3J(tlb6*bpDL}s%vR!dO{M-P60m8-J z*|96EGQ<qS=m~pG5YP`0r5|COLiIZUI8}s7SiM9Jaekzq2!B+oL@x%KyvYyyl71*7 zD-Aj@T%HG3&~iXf-m}nJul>aSpG@r}cJ4F^1j_(teIJkSzh-A(_8Q9_#PVsrDac7G zI7doC*ctz4vYz;mhxf;K_txKk>`?R}nEM#Ke!EN`gJLl;%J+oj5758b^D{JbdnD4? zjaoS-Ez$IkBf{8z>$QC;ZQtm-N#K5hX7m0_wQ0&k0^>0FiqXNvss-VYp8ARoI0z7F z=q2&Ai#e!MaE6c<{Mwa)19?em00U~LMX6sHAPtXKzYi%*E^U!zOF*0&K*t4^iaa#u zfruTl0&6!#PYqHN?5klA4!`hgMh_5=&T+%1l}s0AFgbqVwwaN7<JV@AIEMn^yyB%N z*>VkrB@b29&e!Rg`ti)U$C`vN^GII$x^3`nIPmacoiN<ykj_2}nAxqPXZAz=Uim%l zUZNz_?=qD<jkeTyw>)zWa)iJ*C)a7Bzr}xI1?3l%I7aygx>WJut&qj8Z}8SF%Pg_- zY{u-$koxx5KIPrIGz7VEuU{_+4|my>(=ME=nH)9U%w{mUhoe&R>1RAo0nuWJ)>nW) z6Mz^?lWIcqX{OgG`%wa>7Hb0I(yeUtBSN7%6Cr54|8v!>wM9<`lQk3`9^fGU#rOh5 zrd*<~ll;ztCQLjt7ar#CA@LnUc%g?wEl(Z84;EN^$%(SDv4%fl&=b^CGD!?)$7OLo zQGmFD`dCYz+@~UtR5=;gOkNt4GHNS!!I@qu+;CrM9>-(=e&HZ|LF^5<0zn(zguGEd z9t0*eVf9KAqA|)|wxAd#uEuBi!R!zDW~5$WMw2D@eF}d|0yiHnzJa+*q<Cf_^tZ*) z6=D_B4j)Lx(sKlxu6!9Y)FbboZtl3uer+lIpD!<q<}jgHmS<F3BA)w+r1hM2_wr@y zetkK<eZR{$RvbsdQ>?n*^jdUuVixHJv)SUA9UcG7VO4C9-0fuQiOW)*S0A85rIn^D z<s3c$5!UfGnZ1q$5n`(Q_XkWL?}!Ym-z&<wPJ-qAySHa&r*a`Dnu_S2mI2mZQ9~3p zwNxPm_@P}~l(~7Ivdi4p_J}pQ;;s=ZmrSyjv@~v2CTGO3=S*9u!TM0{VLUh6xkXKb zY|I^TA(J2ZopJFgr<C5{?*#cQa1;iXxDet<=FucEBuo@9y9A-qMeH-e>OHP2ly+B2 zW3l%o^q($#PVM)$w-@5hdE@!Vc<+~%vye{WVta*U7sm@)wt44yxEy}66XN|gU1Vi& zL0qCz%D?ZoBImN*yc!Z@xTHtxk?_x1`mWxOO>4L;$4EMczIV!&)wO;hyZXnINR^bO z-Ajs8nKX;Ll>C88+%9pa!Cnq3OwkJ3`G`KbQd9Vj<@ZY0w*v9^<dozVmUh`fmtHHr z?z0V*R4anOa^^4K%$l346(^d=e!AQfDb>BfoQ!`0z3bzF<Xa80$<KZ<Z_Q@3^5gl_ zOEtJW5z=w8KICJ5ef0XZMbf=!RVyEQP`tHzNm~zemX)gE@jGKM<@f3ILknA0h&+Jy z1_;2+&#l6_?Pv7-T$TtWT7OyIy{oUUaX&I*Ss~@=TD(eXHrZ%FjM3HoPekMrEvVR) zs|kub_y34a1T-qRqs+0!nLw(H6{n{s(0(QOMr2LDXy%>U#eHR!PBrWMMe&Nt8sTqq z3ICP6j~6G#r?ukGPmo+03g$3gS{&G!-K(;R(a@|Y(ZcOF`b9Vq(%|W(RL`dSD;6hf zsVvQ#qcPwnEceHvDjtd!c3IsgtB=<8ti=3CKu-XTa?$~)$63%!Z94p7n5I#bk#AgQ z)*k3E$qhPqk+JrM(3dgh7oHIqqQI&H_ad;tS9Z-Me^P<lf5Oujnke8etAe%`3bL6m z8xQWjxtu(k9iD1q64#1}j{G`~ojo5Xir3$pqGJtzrOe7+US!{yt*&qOY4jtB4_aKD z|Ir*+ns7CK=)Ds69kgk5=E8|Cj$qnmpU{^t@ko~0f5zy=Uj?@n<d$inumItu96WAd zq(_We=H})NCOz#PXuOuZDxzj4B$B{qgON2)ILz$tBUVJ_!cz(l7*oRZlj3pezF1%V z_T;^_S98nq$Rd|Dqs__d>mem|;jAQ7v|}xps8r1(m#S1kv6Q2~8F}uruF?Lb4_ndr z<EHav1^#N<TRgG(lVv20ROld=Yinx0hntr&fr<%N_7g;s8yUF{s1~@|27_Y<@ezU+ z1GUStr`R$D^m<^`u6VtL@FVzxz&JIqF%7Og=$y(}<b6nl-!s=-{9@P2CyK}E$`#Jw zFcY;(<n3^a8TtFINr;?cT_v8Z;_037Vr+?X=DGk^mq5FT(IF-=zris+`-cG}ZQ&gB zV$A-1tLChXGk0S%mftaB$8pjt+-Bh5m^JhnaD6K;n-%$>v%QV+fh)eqd<XOP?pP;% zF=0`^V^O$RgrDNtPqlFcyTl=VB$0`zT#y1c1EW;<(5)~0Rrru0xq(p4!zo_C3>he# z5sOt$PR<guHyGyBiYEa7sAca1?P-8M-_af7yhhBx0I&;MW;`B7|9G=M@9EnIk}K;B zj?W%_H)vFzT^!4g8MiAb)@lb4BJg{;lW{}WeLL1PLG|*Q1F7w=F3y`d^n$-L84|WH z=3!eIrx2=~vi3}lp$~Zup?#TdPGPLMhcxe>L3DqyNINK&`wSASEf?qB$Y3Ne$-G^W zW02eDn`a5LVP`z@;+j>ycqLrsj>J)r-y13-PlLyRIz$TF@Edm{FV0V+psoSpXfxF$ z_uacl#2o=0!krHN8sMj(;|_i7EAVJP#}S}Qng65o1|75uK8l_}FMC;O8EkaY^;KAm zDSNoT)v9xxm{m71iqnYs$jh^{m${koPa)I6i&q(9N-ho#75xX5KY8NqN4nsE0{B2D z1g(**CAjIGX6oA^uUr`2gUMv5X-FgCG0=K(*1z}Q)#8`o%MdK^J=>S>Tgl$ehRI0K z*+xn)uwH?;>JFGZPCGoI4yJ;U5joYYJ0Ml8Ky&}&4{<+!65bYhiaIE`mh8nqT>=nI zZt(cH+w#Lv`T|Y))`O+Cf#iep=09fBJjeC-NhG6<7jE5x81;Nfk~{$Kx+8!L-FLq> zK}|mB;OLhv>&nQ9W(r&i-Hd8N$B&LgKgZFB^ha^Vl_Y*o>Aoa4en(e#O!sabc}nW4 zAsRJE>5$mM0=P5^;@f~C{aKS(Q^;TUe)dwP+;_@OhMt(%lt6F>&`?m?!lN-}YhTjM zoqx&*G5WIR7DUka#2KFrUM{fTfszJx6%3JQJ4m$r*yDC`_^!Gfm@E&P^D=)yo?dGG zmLQx6?E}gj1-jBgURoUI4i@OtA{`GTJ|(shWEdidoT_U25EqqH(!mJB+Lx{8f$~v! zHqD}=r8lTVaRLHZY4oc*SvDTiP>C=Sw~1zH6mnIT_l~9I?>|uB=kL4*3ClixAxjHe zZ5hIiZm=8c=5o4=E2t>#@$hhU%xLW#ScY?>MifTkxhHq5n*ak<s@DQHdsnGoEF)b4 z3#SF@&jSN{zkfdjcky_sVG>kC5Rgwf#qN_aZ<0q32z-KuuXq)%Bwe9<N6v$j8`rx| z{tSw&(`?o{*8J2a-pp`3+<3G4F*lcp;W|B8S!tFu_rr6tWSy&O^nt|qZ~*%oonU2_ z$lm&^N^+~ayV8P;D9&W<7{slVMX#DoX#3Su(983PKk!l2&>#escZxk=&t4Z77e55A z6PugRa~~f;k3D1_Og8k)k8ADlFoPnHatB*B74J&oi!DW3-S-`X=`NO$oUNF-C$jmf zUnj+Ia8Q{(VWdINz}dIN9#_t#*Zg7c%V!M5=8wj@sqc9B=udt-0(EIJj6FA~GhU|D zz!Um{Zzz*D)U#$r{)a2!!_S^|MNWM7li{YTXMM}ONewRBc(6Im0Wh?Sl4%;Ga4<0< zs^0Dou^#&<Dxp|w@A&rHH_6E4Jv{0Mw`KJ|p-Vl45i@FDy>HNr^qj_|0YM8n3Du0m z%L%q#Zxhj(6$s63nOLnj2=uO%l(dbg*G6)f5Ik<y_OM@#4Z>gk`%}hVxyUQp<0sYC zDC+EPf3z+RC0nVA^Fp;-x8@(s7ubZjATk|n4%Tp=I)fA!Vp#!6W`9E06wS=qqN|rv zuJYkL<%&vj@JpE?8Zmh5$Mvp;VQ6Y<0s*rP(rl05svJ2PNy*RJKLvp&@cVG0;38}T z)+OOpr>XJ)*dfS_%%lpX7@B3$D=jT79?jH)4=Sy_ugGIj6Z7kTjS@02*wD{1np?xU ze%+<p-i?oVF<8=fs!`VoO}8RzChZ=#foP*|diwJdvqul*E*5*Z`R>Trs{~(hp?%vd zCL>4s<3oehQtz)HJ^io#1}?7$)w^xo-}L+$YMdCW`d=5J*)k=C*!sOSbe5E*tCpgD z-c8lXofMGsUCQ`)F<fNLw$nY!z#$Y_^i-Q-#)b=-EeEpic-?DssYv4PSr1i+<Yl_; z?Y%2G3!vK6K~HEMPzX|dmLEx7vw0B&K$PC;)}W#@DX*e^s7O1MxyS0APxg<Qg|6() zhY%IG#yGR|im)c&J;Q#cQWsT8YHf<Ue08ZaV*Mf&k>xxnf5!*EyP7Ow!vUoNWZ1N4 z7jQAX{yF`n<o-PVxpERmcY{VezJE{UO>`N~L$ZtfdcX9rtZ4UCf%P5=Z$>hr2+G`> zyZ9wxw#hv7J)PG~?gvF+>2s@5M8~sE7J5^}O1Ok6=2}4OOZ53keAIJ^l~ncFC+lYn z5O#Q*TM{OzMhS59z)>kVtgP~l6_VSot9^=d5*NMGEm1e~!_vOUp!{<Rm^;QQ-SGL( zAKbE3s*huOrx4fCDu2bpqZgOUj(?`!_VvA}FE6*J6{qGu#&f=NPURGd*{>|VuvR$l z#fJ*c;w);hCe~;piOAUn9SvAYnbl8Lluw*-HBlL2ajqPw?x>{i&Dd9Z-;0UTS|-(< z%16@X=2;k4F@8~8jqK?BVZtGd^W^Bs{QC>^fzQijZg}d})(pDN-uQS?z4iB<#2REh zO42YF%yu$uLhx>adkAkjx3s>&GS<Q+<@U9B%1{|Jf~!|KL#@G{;JTmHl)-~lq&@bY zJz1|wZ&0C7_i8-r_2~w`dt-bb6@UDYWr|*)8M~(>IQ$eNBf>7IZ#cRD+%8x0qH&|} zR~0&l;Mz=HKv2$GHCA_DHn67fEJ#ir6S)iZ5{IBqft&=yo6ivVb)Ddlr)Ad2%AW7O ze0=ocYBewGFJ)<TX52E<=M0S!3<@G>GPf9RNoigTF)W63$mutlf6Qu>zXDR2aw_9Z z?6lfkhZL#~JUtKkJbF78X}B%JMFO^=tdHF?PH~AjiHlVK{#dYcBMR0>Fl$RgKMiT~ z)E5Q&y9A)_*PUw@pqBgAbrY3K^yzv|8|vHI0uJ=dGL3$BjcbW8Eslf`<#hH~&M(O% zLph<!oO}2+flc;V6x9>g7vj&C7UP)OKPmYVy+qgZV0QMyd!Kcx+deZz1%?cj&Dx`Q z50&W~V0zJ{$D4zG5u&OgLLpQmNCO784Ft6ZGfTTbO%GeBMGtU_cfeEXC!9c6jU6rZ z%kYUcNq+cEz;pH;hLyv*m(36jiq(BhtvXkjxPNw{IQosA+n-MX!tQO|a%D0^i}Y;% zQL1kNFJr=a(4+{xSSzCsFp%$2V8W+jLtX7$4coUvy^@38;VP`>g!1T83fcFGB_TwE zJQI-Q5XJ_m+prV80tz5vB1CE$1S)=hyn1uo&RzXA?X{e^FL#q}++F%LN*Jh!l`8P5 zZ?-pPeeqje|LLUZgPTeU_lG+#8cvRgOU+2w`a)<63)TF)53$U@e{fs=v-LeiPf#c( z<xOw4X8y0+L&Y)<F6<$N>!Hmr8a}dVo^pzSeN&RbZ%LL(c2kooAX7$d$=VD-*8?*V z@lV4jMk`1yQ%U%yfzVkAehIJ)tbK313#gv!HOZK=A47-^S{(GBxQxOQE>OS6ftG!Z z=E}u6+f5rS)W=AwTMPoEFiEdZ;E`IjbfK17UO1IVgD0=gne)hx2iRz6F8Wmdix)*l zIgW--Wrp(b)@lu`k&!JWGwna9HWATVoBS&R(3Db|mJdj>F<{5s(&8RP6%RQ%`85i% z;|@W|8iNQheL!;srYmAXZw`g{2BmXN%>}3gpZFt%Dkz`A-|Rt4>H&Z3!8t9u)GZAM zca(~<^4ixLHmF<~#KqqtDH3o*+@_<0KoR0|LoDTxR}&_+_HTr{MH)eS`+;+}0Fjqm zX(Ew0PnCVr7Nyg$WF)>;eU&Q{t>J~%2S!qX8vBpm8=w638ktqX{+yDV)Gg#o<x4(o zpo5B{l8VjI{b8XU|Aqo4yP7Fi3RAEVu?>XdwRUNlAU`%JhZ9Y|4-OVmAGaW(=%A-p z@rm$@zUeaEVhDiE#Ujo^UCAy2=3W4-fC-o+z$6ALc^k~C#YcL;@XSVyCR_ddU<B@~ z_wV07R8}WNLyv#>Bl%jXG8RLC#T`s)O3FScY7qJr^a&?0m%o7F2C@Vo@PCB2WFLlx zLY1#)Mc_|}a{%U)tv*cV(2h^XFz56ZtK#6(&u@m_bzU5<#fT^VI%{A=%Tm1M%e-<w zjI`Y5s;C#eeNI=&6KnPfk5hsB390607Kj?A-rhbnQJ`J|uSk3qyS$7M=BEuSFdCVP zzIzSe=5B*Rv1Idsk&%kwU2D5Xp)mp1K}uMNT`1?7D1GfXQy&9QP7@%$F#2~09JVk$ z6w)eohhmUE?1b*7p?=r`1!iDJR<8=eGDo;2FplpfAmq8BV+Z8Zz=(*;h+h~ITF~ln zg@_T1m5KseI&f1F9*69mluW+p3M?b;XEnW%F#BL@7J!hJK{@pLtLTn@cGJ^E6jYQu zYvnF)*-fz0+!A?~_@}VYMbU+OafOj7fH_UnM(UWaRLu&5EFA2M3$cqRW=kEpq0yyC z=#PvPYBC}GZ)o)#&%L-R_@C;4U0J^<qXHo^9t!Lh00X#$KxBxpm!hM$7Er*5L3l;r z{>}oBD3Xza)j{(29v)_wa*Q4d3Lzl?`2v<;9yDT@HjAqe3*(710q~3D<l!0yeV(jj z%SVsy!!5I-yd3d^gWtfEIUq1Fo8I65X)3Q+<--W03z!)(J)8%&6RM}WEuvOiUZhR7 zM5u|=-*?0(ZMp?N(#g-2)`hON*n+WjH(>Po@{zO0dzBkBn3&g|F5$TI)X!jkg`LmH zC~a^bOj`=~RcXaJXzYpXV+mvL-!2^9aWpqqw`$FXd&)8xZQu#S0c{xaDkg$ZjR`q` z5d>fnnq?Y7PpN-&#bzXOaoN%S#r{1hWCMMx|ImS9UL^9sAiCI9%<T0TAJZ!bc2ZE~ z!9}0vy#E1)+``6w@_RdM3_ozMIM?0wzQT3)6wDz1EY#QOgx8e*v6%gdpL%wi^SAZ2 zEB%075p~taYn$=`ItDivPnP1)+b*VpprABdD(cI@S4bMsaVl>JW}^#kR|%vYpL=&e z(ro?MRhcIR2g|=?$<x!*`RvHKd8QR=eYne4_r|wf6+c2HRnJYq-)zOh(a4Ajp@f2r z56t^WEq7LDQs<Wv1U9E%x81hKv~5J!VNu&omEQut6sipj573W3fPvqA%lm$>Z3Ik( zqo_p~rPDCI^6wJ^v^&G%;X^rikO;*9<b?ji8E{^KLtGLZFrm_XQnu2uqayxihe^KH z3X4k$^=`NW1J&-V3W24D%ZuSVvvZekpbFNuBlMFko=!G==veNrZ1VzXx@}nPw{(UW z{wr(a@<N@6)jFsBxg%%jrcHzyF{JAXN#!XfutFjS8s136@9CBv!Dx$mAp?IQOCdZO zqKs<~q!rOux>r(!5R%5FOGtM0vmV^i^r4T2AOxX?9f)g!S5FvB&!62PLEfbh!|V^W ze=Z0M$9--^FrITPUR(`bN~+$Q?5!9E9o?f@li5+!`j^%K4D0M1NPS)-yv_DX@yOUk znV@b|T+*&-U#_ALw>M>Nx!*<dEzRD3!0OQQm(Hs<I|?<`-l^OG?zSwPB+_W(0gAZ` zU^as}7P0is>-!8U*aV;x6n!J}oi5FCL&JEJyqs2%Rz&X;(2c<l2)hWIb?8OhUZCUB zp|G@aka5$fPDHO~xihl>IRYCHbPVht!{q`xu%_9{E0=V2Cyh+mt`Xmr%|g9`kS|QV zf<<`RVGl5DGO!eOMM<3N5oh5j@+zX=|Ng<nlwXX91SlSTJ+|5md^yi|hHHvv$}#7@ zB;LvC>@wFZrBtPOffW{}Q@YeVs`&nmy04Xq2^8~^sPMJnSD7SiIQTe{8pUDx^n*&T zVoOtEKwjk*;O!=F_^zDGlTglAH_rbzw_UK*T~%4xOQjV_d_@)m%sVFLI+XR*4^Gh^ z5k3BRKKuLU+z!SoKLMj1oY>z2@F<ZH4&Yb$rwg?Mi;)8vUJI8-etTR@P7&FIS(qFi z1Snr_-ASlilECgr7DOI6I+)CRATDgM!;)tM(>YIfm&NOn4~1mWv|4x-?D!`qOVHXJ z_sss*ABu`D{hcUw39WpqR`drG)88^%j@(c`=4c5wCW&)XB`+x=e}8>_<q|8#mh$#5 zCgoem<Y6FKLGiH&ABfCLfjot=>yNKrQ(&t^wssf=(*Z@vRHGxOeNtIhw@Kv9&wAHR zK!!o?7x3K|S61?BBFMSXVNBK+*4a51n3)4tT<`}5{ba<!#L8?VZ38^>&-bGrXfdFG zaJV04IvNCk&!8Z<j9$J@z{X4&Nt5vBD$S$26B9%_I(zH+2Z=HEleAoFzqQy7FLo|| zULAS1W9DEjJF%dA-V4^jvzD%|fQ*|>7rqr?Pxffp^>5$2DRCt>fLryBq08f4la4Dl z^GdRE@G50Loohqc-XC|PD#rrM53o{tAqFc0PE=$P1q7oKbkcr6%25SCZf_uO8Ff>l z8#qN$beBe~CCN~PnnVf;D($_)ui=nE5Wog3iMz^wyZ|wx*WkhcPdf!OhAhT-<q1fR zdPVsHVIY$zYM?V5Kyn%#$rQtGe4N6mKnVYC4u?KxlfZwHf+^G$jQ?xcuD+2f1I$10 zIFRAEaQR)*lKUx<f_E<Uai$X4RI}lE>eT5<s*K48ne-Ss+oNfl6QaRs{VQ#o-wmDc zbq9c;;hm+;Dx|)0=2_lSlME9pH1brbeZIWse44C?2a{`FzI>?$H!~{BMJR9@D4n%` zet7l#GC1u+r8;6i#T_xh1*aa||DEHV8W+AeiX07pZwzr_)uK|8lM%x%7vw-b>*?u{ zLm&u1;R4@~kpb}EYG62pT~=3(8x!5}Hk~zGcWHUVs@#i>el<LMTwXIFo;3aUbZtrD zHm7mN@Hp|GclW}y2ieC9Zn61$xzq{RVKOqV)2#WU_NbXpUuKvzNP|y)CF`4XBzQYC zN|pJY#F~TN^<BF$Hfk&`zwUd$cg_;B9|E>Nn4YV~$`Vf>4YAl^x_i5@$JwcaGx1AK z7Z)yO*I*F-de7(7%?FWu4+mfRT?BdloLh_<>)HJgG?s6T4~1Ff;jT-{P?bT3_?y)| zh9W)S`hNM)(eca0o0qpCAWY*62Q5nctepMI;U<yiI-$9Iq*}A7M04+No_C!erjw_w zzg^T;Bk=?)pBx>SUZs}89D9Lz^Z76JG9G`dz}s}${klWcl3;C2kifuzVdJN13)+s~ z4R%lm@O#i?2nPW~!(A?%`Kf-@aA90iR9M_yv1o4k%{BFD5+m4QQ9C!qOURN<80i&8 z=>y`q{e%o!N%%)XMpB-7RD7x+<|0(2^<ZJ)!79%<(%~3pQbd1UQSoDAilxMmY4=;r zi-CwIJRkfR6c#$pj#1;}T&RAdODSY5ig+BoD|f|)E3HrV|DIM(R9k$jMTv^~rfG>i zWKMFU=@wlwSmOWJoma8>RCnHd;9kdKhx$9|JB1++LMH3(qf1q2nvs>qqRoKeS$Pca z*?J$I@AW&s)K``FHVDa?ckRE#Z@yo<p6k+?DfM+RiY9ln@cD`4S^$QONxlEOWD0lr z2v(C@RUiBC20q^sr}@3D^j^a_+)4{2<va9>pe#avx{_W-W)D=|G+tAB)Y41ru2F`W z)^%7DgbCz9?*eW?^$H8Qg6oT4E-D~X5IQ7+aF2i)1CVRRhJs-td^y)C84f1se_)Zw z(nc#NzZ7}4WSQGu_O{;n-k=dbkK%&^wW&(kr#1q%=p^Z}Q~Ns)P~(&WB~}aqS|vDl zA4{u6n>d-4QB^y#>Hci+H5QO*IZQ+YR^t4ZJe8N!;zmUzZCf)*3M>kg%U9ce47AkC zu<q+6_mU9A|KSi2B9&Jho=uaK{O$KGNcNo%wN~+jemA{B-(jA8i)8fUGNXWaVrI=t z2g4%O%xb@eY8{u0_$bt8TK}H3*Gv>6Xku4+`HgTY{U+%aoGv&0ndaBLfLewm_d&%% zNLfKA8jVoa&3C`TM{(&~zugV82^r$+>G)e5ky~L@%c>n~*b^diO-e!L_?yo87yMLo zL(~g5JuJ5fcsE{+qL-Q96Kd9pn)$#G7JW;BCfbnrrXcOjKYNjL2l6f}k6%2H=&H|| z&~o^y^<%S;AuTYL0G_Mvk+a=2Aruc5J3p3>x4%uT=}&Vy!Azcts;aSZv(GcZcMm-2 zdrSQ)1Zo%+Iu-5AS(9Vmq~J(G{le|cO-@0q@JuU!_Q5};EETfX4h~-VcpJ}>uys37 z_bNmA&WZbUPmw&slXmnsd<uiIpL!#yD`P5Esb5c5_{UG_ZEb#;FrAD_Q~Tg()Nq?@ ziI;mMjNH0kdZOqWAv!cvqi7NZ1#COAiI{LieCo$p{86g5`m)LR`^60Wf3kLLO^VsI zUy9<g=u9gLtM99K(s!F>@*vKM^DSX0uue#oXot;gZ1Rt9aN>m;qbc9NKL@y8AjQJS z9)L_4JBQJFOdRh@cs_nq@C=`}m)jv(y|0zP&M#rEx@9_|<pfV#!C_>1BgG`bbK8Ji zpkzumnjS{yB}hbnZxi$Coo<U#O`8K^-MH83wndWCY=IUlxSd#Z%c!88kg|VA%J67o zN*4uAHrJUy;P~Nj=3cvPN_~nauO#1L`SG}XJviJ}KyKCo7t??1_Z`;$4CxpafAGyN zAdD|?r8k}KG@`(K%nNTr$H$Lo5P-CSz8Et1w4n1^yZ}8e#3U@mCNKI2^+<1CZG4J$ zf86^BV?S1Axy+blvfkM_?2U*PYk772&9yc(u)?z@7=2y^Ije>cTkoBt;o$im#(e&R zI4YtS&Mc1CZ67@<I5ao6HCHGyXMHzx&og`QMtlY7;RVmwbTh9z+xYU{_D7%UO}gK+ z--ePLTgU74VDnI?UwH)qc`h?R{lOGbAqE<J$eUJnYn$8JDnL~a*El-8<2INJ)0Zyd zLdw+}^yk-0gFB6E&CSbS-6^K)9+VZ%)NDvCynox#)`>Y(VR=aR$Vs7pZ*rqtV<0Et z90P`W>c`YNyLWw^AOX1l)%d5k&_7IWMHap@N-XwEKle(^v(o5pA~@`u$j%K*lk(;H zMQ^7-PmiZ~6{<|Q&&I-#Wpx-B3`7i^Hkh?42+3>!eIow9G~md9p)3jJo9LA2sS{h^ zDAK`fp_^=<@V(%38@#_J^XeP7rQUrJ!y0mT%MUa2x5#dMc-D~6Ncp=a-M-B=G^pJQ zVOqP>JM491sSZV3fpQKG(GQZ4G8hOSSkuw;&~wT1JwDMRH@pVn{E5Bd6H*Ws1whsm zby>mzoHk@^M>sy?A@*&QOZ(a@+nbCC9T`L}m|8%L6pi=0ok%WIuBi~@o+(Y1eZ{4P zd6JGd+$u~X^v}cw0CeVQZR$4C(%b}GvhQ17nd)>695pW943qNfc!Y52jAdr&8WE8{ zUhKGuIvU>_8E+!8THbn5MKH^g#`CZ;>`bQMh@CK=2EJ3%vt1hPA}u7Kol*f__iT;A zN6GKZieXk~0;_gkY&+w%r5jzB*VdTr@x7$gQGf@gxr*bzWE1ntJ-)w%k39sw%KReT zE`!3iK7(*_3fn(eI&RN<x+AzN%aJZOQ*KkH59IBB*n9J?c2Y^!`<cps-@ll%jFFWa z&)@If&mUcI6za1OVsYKNg%(FJvI}&+G+}o<|49>Xl}#8fcO>e5`8tk^i~^C0EB?ld zo~SYRoHFB@A;%vC&D2R|53eafw7=kMv2F!4IH;&bc@3WKen};#SVTqL3?A3w;^S^- z*$%U|eTH$f(L0<8_W_EFpKsZ{Zt{>_{Zk%rM97N>_=7TGP#Tm0gjnT94K6S)##A$L z4lIg*$%K_%=zI~F#^M7#A$aA(qzR;K7pHkWj?~1@4|SVEVo10BgM*)*Wze_J-+AZ8 z{PQjfreh}#RB}$Mw=^}idTKtt?Y4Wx)R(1>DKb>?@o~vIp371B4=Xm@BkW`Q$j0%t z+aLUzP)bBfl!Cdzp+>l^(pu8-Gz^3|p;5Pwe<4GzU;x@`eunRDNZbGc5ITs^l`hTG zl7az2roMm~EGz^+a27MNPs9^w>*_Yw-VyOo`{C_v9|iZ<&v9htIg0K3H7kPrk4?@x zNM2s2TY7F+^h0KE&tJc)tzXHvlm$C34<+8<lYI0VM@%8#6zs|uDXlB+7hgO6rrt2n zik0wlP;rcj7WBNxd5e6t;M?jbd6*IeNpZikeQX3y4tG&<N=hEPab^@iVO5~pC*;0C z3>+n}Xp$mJ`&BGyN_$_))uyIQo1tIjLP6<lj~A>PFQbNr#7ACOVaX^Ijkqca-q%GX z$XtU%6iyASxd0x$zWSrJ+mNih)Hc=jhGs|1<t;60I6grAYujIMg!Zq94e!2Au?|8x z)`UtDXe{7bUPN|QkhA@_Zo{0rci{O%HUWcAx6!3qrdv&aWQ_0LJ*xW=c*ii))n<Et z2?HNe&i9?DS&FN5y_40ft`NkBNpFTs>2W0}%{c#h<fT(?fz@QHxQM!o?f8Z2S<w$% zNa6p*{Ym&v50$|viUlK_r3&Bm3I_mf2|;IXit2(e2V?&D@!m<QF~juYT{hh>@2P|Z z8~@&}nTj&t$=qH_s{XE(!0O`NoI?+NM3cz}f$i&KT)s^{J{RIM&1~L(*{W?nE}Qxy z#)TY42J?daw_PU|n>D2=mh<PI#k9AI)F8y1*qu1z;qrp$9aA(t3>d~U@%%*?;ACb- z8!sU>9~+N(m8Q4HE1X5l1=DGEP}tXZUIXQ%-xL{U6R|*2R1RZNsyHQ`Y_gbA_%V2^ zeocZ|>Rz+g8zI4H!W&95udc}C1_ga5MXzN0d9nT7-6!tP@3#F1DIbQV4Hcd57=#kr z2;RGNk13qO)uyrW+0%T7Q<XQo4MYMD>j`d%8{rYN=ol&DYj3<2ZIx(I8AZ3e!{^Dm z`q%65P*i1fi7$R>iETC9PFvw=k}m4fi-57X2d;Y|FX}}aU0MDR_QXc5Qzu?~YV!w^ zi-{>1B2GXfmoze(nf1kWzL%A)eZgPv@+auUqm-0$$I;GOB68ZpPTUzw0kbazM^Rof zNmG3)&0ZVi6orO3J39=UXa<RA6lrM%nr>ad`E!4*Y`Xd2J+_%yT)_B2L9Q!bLRj$# ziEcJq`U`CdCl+kFB8)~Aw8x;cB1Gu5wKcz{(sr}CC;XgqtWLv&>9s#q)7^0AmlR%y zsHaPWCN<7nwBAIUO6QKYVH6l)sC6Nt#FLNOFu=QHHYcf9r<zXo_?&R2n9L@TeZJ#M zsoT+H>n~hYrk_y**mJc4b?a0L%M&#wy0zU7);-76Gn4b;%^$CKlx^{#*TcBfw;l_) zS=remeA_o$2S!^IW|)<>Nv>Y~#+;yf>(?6E+qWB{BDVsjwENKn=l;^Fq;3!z99IxD zuN&Fr9?|Z;yBD2hcazA7@2?+=@Q1JQFBJMzvreympMK_iQ|^U&p5OZy`n+S0<s|a9 z(=N3|>QSH(YiAhCq5O{Kv6UH#VAj%7VgB1Jpqtf`nO4)ucz&yrhdGK^>7<X}>+(Kf zh>at1UGZ;-UfWW2qteBFc5dR-iQ};&q>azTc!`p4m?0b)O?~<P1Kruj-}0ngAsF*; z^NW$N%geBDbz@dm6P!Vnzt%K12Pe=}E?t|W$aIxSh>B|u*$Q;P`yAxYS{Rp*5tL-l z@Mw&=)4j4EXAU}d&aSSAeF%o9y@3~89UM3Iur9vyeXd2b+S2>pQLZd&@HHX%;HQxI zGa6KJ>~0JAJ#)uPlD`t5@2zHh2%6u5so>Kstv;0GMMJ`E3(KZfCx1@#``SGky)3^B zY(;)6b(N8xN-`#FOG~4Iz=52kzYihFm1{)VuL9H3=UXo(7+$)r&YgK`Gjvs+@6Hry zFXht5X<X~%{)n>QUr_vPR<2U9*>vSeAF;<OwYy2vZ`UX4rgLH6YTs$^FEb<`Czl9U z;GweHd)IWb4=)!GZjMMY_;aXo_&Z=;hu@#D4H_1)X=x5ojid{TiKI_T4ZpEsicHly zZcJ5VJN}&w$r66E$?<(KGu8Ns^9V!r!Cl)};f*stQFelzCe8HSuUJAt8={$(@@ZnC zy(+4VhimG8(cSzLm~>(v>&P~o^6$uJ>ShjJ*EUJK-c~I-f$wqZxsG$YNT8A4ADlLb z`)vE$vPAR~RKJ#Ks=$XJE)OntJ+mAB$}o_!Q_P!dv9o)eTifzvvSXBRFxF$Uz*e(B z6B`fDAsrP_RyNK-`KokdtgS2Guct0%Vd!qwyT|ten_qC-+l^0YTr+rG)W?AfsAzFT zppARZ2a)MDKD~1+TpK|+;h#%$e_w;^%F6Eh#G8>cr7aQ-Pi~=3d7KtB%!f&y2e8#F z+?mJ2cfV}CC|5#PtZR(@h$5vl@IdAG%g#;JOHAFhUmpLp*EZuKFBxl-dl7R~;xU+) zJ|V<<YpaK&(H~SKZHRn*1gXap2>#rZ5pYl~9Ncv^gI(w_@lL#S{KWp^#?-qPW#)Qs z@bLtjJ-AUfG<vSnT#=Fz#@2ot_+o;a^TFjt_uVo>=6uychRi32m|V_7G<a22&b0&m zy0;Xj6~^DIXRPHsIuY1Va(w^QD_Cp&vb{Kg%9vpE6MrHWrYUD%?1;aVo5&t#o^-tp zj<BkRN7}V{w#1~Q@Yn}{v3&7c>Gfu8VqS}6-4)u?--VZP6WjCfztzR1rYXOzk~rl# z0VoNF`E*ngncm4(nNguVGY0n8!8#}H30ss8C4Wb;iP!)ZR_E^5+y_sd>P+Tyv^cf+ zD_<lg1q+LHc8HMjlr&IQCPmO@u~xxr<-OZ%6V*tvpm_Rwdhqnu2*XS3{<(;|b9?4% z(?o;7w33uLQB$tx@+gPT)J+$*+x&Uu`7G@XevM7kgLI7yj4RX{yyNg23X6F?aXI(l z*qM?NQ*wOEx@7cK=(YtNms#}stjm{X-)FGVFydwwjBdrEA}M&gX`ZolCa`bcO-mc+ zFr(J{P4R033rk-=jWg8N@eHS_Ny{dhHQ!n(0hO3Fk==Zm5_9%>{$QO$#U1sMv1Spb z)gdO6Rr(1Ui&2Xs@dIy^lbsupd(-9`mHNaxTgpQpt-|bPvOlr9Z-P^+#3!&`mNPXf zqv6kUB?g(+*k{Y9lT=Z#r+)YT{c`GXlY4ndZEegyPUzjQO`Fs^r5fy;=j>SZ?pi*S z^+&>OnNHtP_r;!u&#>wwZj4c991K3x##9fLNP5q5BY${z)fY~$xW|1qLqTF*!SpYU zr})tZhK7)t3!*1$*~o6SBm0;VYTc%@gZ{MPhUk(4^eZ=YdY*2(c^_5tX`gbk6ATi& z_XrkzM<=g8erViLDbdfFnDO3QtnK`-<LZsOMcrJeof^A&GD398rbv;_{?t6%0g)2n zf!<$_k2lrwF;Ii5c7e)y0kYcGb(nh)Tbl9OEJf_Opi@HziF`Dabh8JbrH@8#Bh6pP zHExpx7Hr_*(K5)U8-@~zys=)CuXD;=e&|xM7I+*yar=O^;ruGdn%^1*MUF5qv=1D) zB#qKil55Mu(Yxhk)`*Rdzrj(mIyQtc+-UxI<79k7D61&f24g~jkynDqpSmIz6<YL( z1J_JW?h<r_XraY*3zC%zag2ytYx5BNhRC$&rY6z1&wuMfo9<y0my9-y{-O*x*srL0 z`GgD1jx=JlPZ#B2Ig?}Da22kiINZEftn=ZewV#ud*|Eg)rPnPLnTnDIE~5)$ZSJ8D zsX27sx%+p8Nz8sfccP{%OhIo(w^c6=I`l@4w*=T;Azyb53|=t3Sg*nYi?ij(BBZ4a z87l+!D`dDtM~5^rzS;Zbts>$6D|6*sHMla^*jEZHC(EvB@}*F3@{)7GyFi&xRJqfv zOFgt<%04+kV*h@50oQDM?+Jk%T{1VfRae(Kk&#g9^CtqCp(IiEVydf;it<b@Yf~#= zTpG+xr{l=BJKM%lPc#0(4-#htMA~4?GKZ7jSn9+hKA5aa4AlFO0_ic{jsW(4Y&cEt zyxjh#Enh7z=JJ}Y=y}6INy?wn%FPRQRy8HTat8lYfoTrc{omaZe8dHm+5SCoQLdu0 zB;q%xtZ2{Qo(s!s@gJ7F&kGPU)<WBV{qul^p@;_ddxTL4UDBgd)6=d(sGa$2M_)oS z4f(r&$a)#3neT?*KCPcX{Yki<T2uAPPR$E_SII+qjuw#`#maHRkkCqPvo9l|BpV(_ z<&L(@Q(w?@f-s-V#{_-(>D{q2!W~=mx$+9vP&{@oUwJ_ay!Mm?mt0(3mD~-hro-(M zO5$#9DUQuG8Q?q^dVA6xXmEiYJy{@OM$4}3s9G?}zCCg9bAC?hxn+AT{dI>_SJVda zH=Hc}S{z-C#Lmu%wZ4MHY`4g~4Lj<o6f*DT-t6?7SZ1fk+rsknGRIX)=u;qjLhJY3 z*=iRqD?q%61s}sS9f5j6S65*thS?OCv9!mrb^V3!RLz*OkvckgLbk9fF{g@#^+a+0 zD@n9O&dIx^T2hbj`{%eMC4;Y!f1l}9QM~<kNudW@Co+-B7`+X?!k+Wb5uuc#{!eqA zr!Ri27M49gD;IIPi<O6-o=uqLGtGcj?XdF(9_p;`!r;)*YU{;c&XhcE$$Xl!EJ8NL zdX)AHS?r&yABQCFEd<al(K9Y=&9}$?H4d;tez0rAIfG#~M#8vPv7bG2ce1^PVdmya zSFXu~;?P+*?0)!pVC$Y8M(@j~MDSC1;p|^+ANn4Z)<JcJo@!Wx>Q*1J=0R+Vg0i9# zOe0Q>+X9FYI5sS_3mn5S$OIQ)1ysWLC2+vkuhQuE-|l;KXy*>|<|z58&i9fy0&*i8 zE^;rUj@&o8=CjMl37(9pT)C=n({bEO-d@UyT1e#E*B#>)cJ6{=#Q`FKncu2B;Qm$j zF71ItR8;ZRj|OYJ8^-CxZ}6s`Jj9SP*|-;3f<deuVN56q)CI(M4IY~|n8OY?d)C#^ zqAKy<Q<gr!=}3ba5nyg@IbOK|PX6uduL}Bp6W7?-ebq_|o^3XyPUU~0)Hq%{M&Bxq za@)e;x8ER4Ts+iR=Bf8kpA`%Zrzwp*K4x$o{W?F7qL-1=>2vpI(p0Tn8K*=!**fFz z4DP^tak!j%&~QANDwN_w9F3`_CH9o<Ekt8^k(M7!3sUy*5Nv5_A&-!Q<O42bbAZc& zP>r7o*X$7{%&d=X=rz#=Vd!6q^?IsIw>*3cRohi8O#k;}oiJ8hE>f%K%%=d=cpR4W zgT>@Zo=)Ra^6%e2DJc}jZ=(F7PQH7cKRU`5meY9d(cq>3ts{WmKWoFeb~WAG!{Z5r z+MMB^>7V8@?r||_`uZDZG4gJ>?Xjq*#YAYq{C0efeH+_$MFj>W7gcLh>ZMrYe0sIY zb%{QtcMPQ;6|fFByE2I$=X{u1+fbC$Bj3M%oqp>_1h^MJbnEL#?3`Xl`Caup;oHb| z<(rTnx+E6TbPcy{KYo$khDm8#R^2+@8~sg2#t?KILx2toZxBET{})eR8CF%-MSBiN zmvn=?NQi_=cY}zO(v5V3aOmzvL_#{G4k_I!-Q6A1Al=->ckgq5@Rxn|+H=hqW6U|f zydeRy6J)@<XM=$iV3xCC{um2hfDR>323}M>3`Pwt(yR4nZe^%M=}ssJdO2xwPo2L} zs`7dg`>-9UDZ5SI$9c(DDf`8{@(oy3M@MLM$P2Ga8_n$rzGF8ES)s03j!Emy2kgsr zqmjHlIoL#@C>ux{f*M9swLi1+4;`(<f73d{mJRu;WP_$JDhh46JHdDwE(c}^O!afh zHT*YQsMEj|EpUPgl)kMpc|^KdcomgCdn2L=!NWa@xAJT2zMtVM3$AKaw;7)q@||Qg zyCXkqs3HB-tg=EE_8=J>w{{Qkt9ROLRnv<pvz%7ZOJIWUxaVpOv%q!&47H12RD!Pr zXH3|C&CVW+LhbSGX{Oo4gffR*2iScFmgj)Mh!TKjd}@V|%Epx&2eXa;raCXJt_D-n zgO)u3vFDS$S(3X~IH@N4o!!!O#J_`wVqrqa{-Zs)>=rM}HH-iEJt;x240qIjqM~q# z%-ptmSatusxVZgS7#;Vk&<OOAmfA|M&>JM5Ezace_Ljh*QDrtP5Au-=DMXHmu1w0` z<=BeKl$-AmfXK$k2`p|KHQR{dKU@KSPvgwh7FdOa)4^mBpl0X>tpu~x*5quB#|Qvl zGIj;-A44`vDZ|!a>g*oQFCub#Q8d4XrCf3DO%$Kjs-*NS7LkhTrpuo!2Pu&orz=9~ zuM{^oLVNrBnX2%BRQ`g|+TS++;#6%!S?dDfg=6?SX(PNzy9K8HX#>0*D2jr<awhmv z4B10UX!h$iF3;@lZ_dGd3_{=zo)&I{ah>Od^xhEAR}Bp86!h>u?aKz;!s<XH$PX8Z zmS24l7+5V;%EvihrHuRgEdJbT+CP?I@}^~}35F)47cVbFk6)x+V9PAY%xnHH-cJ8D z3chW@`sX%E89p%!8zvhmZhF{+v%ehdyicyQ>@+jgrOqWLCS4Rr9F}O%lvr(epxWg| zXvqF;nuj@+^-c$U0K$#VHD}hTKa=WsKH1sqS&g|*<T<q#5F~b_YqC}ie|Fp$uK7qz zlq)M|aSvRg*FW^{kR`W5zBFOmTL<4S=Md+k5`uGx_tGOfBwI7GeRxq}Ssz|-A$qX$ zsT4zJx4@X{6SkBxA$JM{Y@i_li)d`B8rS06z$Ab-WRYN@-J%&uRz0}klsY;zJTt2} z=_#1O*rNMsX6Wpn>@QBf-2PO|NQ!#UP|yAR`h6aj?B=W3o$}^Z0|Opj{#bl!1(S~v zLTz!MCOR1{5nOC<-C<Euj-}I-PhO7_JQOg-PdkIatkS-_3eZYBQL2}YL73qYkpHsU zN~s%MZdaFn<c(%5aLyU5Ix~%O_Xlcw&qm(ec<=sbwkJTtd}dq49J}mQp>w#u>g%2V zVFsV+isN|vbvivKcN|#>C&`#c$kM8^B>?urQ|<Yov!LvZBA17po}3(4H;(C|fC2Jf zzkaphz*N#vzj5E05bY{|{N-Qz0dmiGQAQsaUt%^n_ATof9ZRthzAj~zO>q=C7swx- zln#2rQXnS8Miu7^s*tWK(%_0%9m|Vw@3PBw-3PvVVn{F^c1(Qo1&%O{M5l8HT1Kx? zyIJD{rP9#QP~zYgIhMFGv5d@a{^zE+Cik}YLeM%U{0N7GsM#VDk-f=rx?f?LS8fCS z?5wGnl<+9ip&!~PBr^s0kWj*3R)dos6?^0#lRo@f-oMOkZbP-aBxLXh)6;Q(<AO}& zPh>39K-LL}NpW8GU;W(AHSnuUaX9|*_{F{=<`x!qN-;xLKHbrSu(6#Zl^KOL|0ekP zMwAi@c@jSJ-wS3;OyqaNskGfEzFB_PUlW)j2|v9K2x`W9`goX!AvJ~ECURJ+>QbhG z`2#PZ1aZN}C7drMSWdo{6*2R;5oo%>sSJHn`F<N0?$RqA;EVV)drto2n?jriX*68b z#Y<>^*|uS!N($>vGvBXIhR}}S*lJf}=vNwa<(g`Ek30g>I@|kG7t6|0pt|cl0wYt) zR}J;=F)?i=dkgmo1_d)re}qTtkhCqBFsZQ@hot;SEnwRNQPh*L?+^r9sn&FFxb+yJ zPg@2+maiGWK2x2Bxe|>1nV1Q~?)M7~3CR8^d&C<mjW^SUkXkYw*RpYcK51HJP(Sy> zV+$cw__ZV2jF#+@IjXGQO+f1TS2s0xLxS@p+#)nb?KhBo>F?mNW4ImA2vf4UI{(K{ zZZTD9&67XH%(BWH>W1{3ssyjqc{xy;8i|`5<|>jWPm#_Z3}B2itYfBlO0}FQWNu1k zN(QEH&5@I+HBdwiBn^p9>CsoUu}|LVzRwt4eY}$V1}{sQnNG@LdWt2WGM1rNL7{tl zRn(}c@5b%?Q$Jo|(}*JHzSF-iy=(4#yR)Au@$vL;o#r>I!nyo@QSgcxAi>4#7+0Sc z)D`fMrX=#Yz?W7-zO?TJt?#UbCWLG5G3`Mljh#DaefGkfwTRnwKYe<^rjQy<mLrSW z+xrc#VCoSmAn>ii?ql4>CQ@3O#qW)UI*^6ANh^PubMD2P-?ATG(HHRZ>j{l!xNe2| zg)J*er%QWaw*rvKoRYDlk1(`wG%kEtZ{sv%*wy;p(kgPcZ+tOril3RLWFCvb=Nt|@ zc&d$^e320y5BHCQ*%%e8^3rXd%P?@G?oa$=u`S$t31!Z;H>^S~yRu7;vX~0sgH2>? zq<gnBr04fzAtM4Sv$yF)$Gbc%evYPZ<;L71b&uX!v6wc=O=7PiWFs^+_(E&QT6CNP z6WRaia6OIIJ}6@ssuda*YcioZmTjB$3{RVVQKt0?Y)cJ^R<rEu8$oE;tq>sXj@~1e zek-V4Xc9&FncS5bA$*Ei^#{yHf!W({p<SEohU=HJ__eZ<60CTdFuU!&l@#;ev7sHx zsOQsicp^%?>Px@E`<!_+l?k5J|M@h|OXXj|uGDMb)91`gj`6J8Z)9gjQPk<`@~AW_ zNwr+6D(8jc*u#Ay>`?c+Z68>3?Ts*!XFyG=0?*)(Td#Jlncanc;3ZKCl>zr5*g-PA zlBMv5a=#U-b>E_CJy%B8jtC%+*92?@pG5im2+|jID!QBwr?ZfTbae@gjJ`Ro1*{{! z-nwTw`fSGfY^9@(dY3Hx${j!`AaCcG*pdW}k%?UfA_*2lLF(#uJvQF!h=qjtsY-$p zb0~GTTv7*MXvTMUOxn`l%HG(qY945Nbd<(cRP3VrH6vwm%B_cout88U&}nBF)N9oL z2$RnkWt?kE0V8_?V6NoTkt;|A1RVQoCr~qzg1|hF&}`|+O+Kvms#b%_?gC6bb9~Ou zS_j88;vaNPHBb^W42xbX6usnSAoY25pSw*I%$O=FhoUiz^OE9)Ae&CNSwO89sP@B# zKdJL@OmO{J7kTx{?iFiXy-gmJCno!Q5SR5t8iD=K(d^0JGWa?jj3rv!u!s;w*E$$w zMii|NTahaiBJ3<IRXt%Ep!Gx7Wzo)}w&2SdsBWT8`5mL)hwRZl{cz@NcD^c~*Ni^i zi#c=2uH0rKe|(dCtep#LBXVYRl?MNWwU%Ss{gr?Ju~O%dnk=l%?B#3LsQqwaN~-CH z@LS$Qm*<K(dPb+xoqzW?K0bEc7cckoi7072eaPn0=1<b8d_K>R+76nqAxs7{N@u=$ zc&#l;9PqB6>~J?HBz+l3jkUki!9<ikgqV%ZqS^vUBnvy+=g8KR+Q)=5`Cq#3qy5!- z_}*s^GMCf3u+ej=kn-;!zFHL&eEV521k<)l@BnZx=X_APLtcH6yYpgu!HkEe%DR!y zqjJPA`j--cIp%Pdg&5lEy)!t93-dLJ1NIP9zwH3y*zup^Hu%@vwLAJY7WsUOR>Cc* z9-o_saM6^Up3c75rBF0gGyGsaFNPooua%=zvAi6NnlWQZt$)zHzuCQ-*1PFa`qK;c z9{u*z6JQ1}E*%YNuGtji;E)Q6cv&Q$C{C@lkQfhTnRR)TLJ&mkWf?G5+h=VO$IE(( zErj!+(%(KPZl)Np3evjOeZfXYni3&FmtRzBsTQXk(qf>2LV|C&w^~%Ob`A+=@PSIw z`V>e<fBF`;`AvN&H*GV)vjt*jJJW>i`cbJF6B*Hy>>R2;J}U=2zQk;=R$~4P^E_8F z*W~7^LVA$CT;#)bU_hB+4gZVrmdBn?ww+eUl+H6mY68%OGAQ$)uz=-sSD}|DN5#*V z{ZS75?hYNviAswvcc=4@zI799mU=3>L%337!TAe49nitVD;Tqo_g@hurI7(~Iv?nP zHMz)3=p9OY<O{I1bB9>^Z6|)KY=Zuy*ZgHX%txk^tul7zOx-K;hR|mUTD+sH_mIex z9Z>XaY33Lil00lv?erBltNbZ}(ndr3mRdX%j1A}kM3jtzV5afaC5|84X0W=KK98-_ z#0Urz*SnK9U7ud^kdSeddFp+k)K?0nkuE~?@a))2<lwx!GFj$6cyMVKvT&cwl@lR{ zl%to=INPT;L(!C^o__ypIwWS;sN)=zqH5Vl4l+y_gfNnYbalIjC%^fnV7}J7rT8#x zh&kH_)XjIX=!{{UyiDj7#x^t9@;ZE-5uWT2GsSG^`dmcCydl~x`NF`|t>zRotWOHK zNPA<W{691=M6i~(SJv7-*Ya=r6rwZ?=fgtY-Kc%=53|Urs}t2s0?Enmmp*N6boe66 zL@ag}XTM&s$5!Gq>|b1!-k+11$Q9qaH$2Xhq((ToGaGn|C(XKH*a%c8b-xc_<bQH# z#v0$20gdr;#J9YnjW^Q_@%^6Deu$#&)OSey_s!6gvuDD>I^w^}Dg!DezE;}3BSS;| zoTI*9g}Y(5W?({@f+SMr{Kh$O$vB}KH)_1pUU(tG24cn2(3Z4-p+x$WpU}%yu#Alr zAf?P=USfYj(@&K@C?fw(2W8HYPgP#?dJxziF*&f{b5`AS|MEq@4Pi#zYNYEEHvOnP z+REOZp0s#2OvF-%<P6FxMcg0|Wk-QSjDyTYi7Miz%ZGxGy-N}EJcj}JqD1+$fZ2*k z*V~KwfRzL5*U;F%)L<Hbi;IX^KI^J705_{GX31Z7oh8<AS1RpYkP1^Rd5k-y8q>c2 zPRZpb(Ir{F(u06WlIT1yaER8Bw?HkvZ730rJLRB(!pMaGbXb+v$p?)NraEGb1A}zu zGjAbLCg85g65GZ2CXZFz&C4Zb%-yI!I{VwK=8*yGv(Vz{{)VI{B9w;VRlx$4!Z?Sn z_U(TV{CS06)(6#_PRnB6*;<iX7hE-nz@GXf!FPYF@gbOpzfh`#PyOFGE=MdHx3_mS zwQ{ne(M?SRGd~LlmL;xass^5jnp)3rjpvlz`M<VONsJ7SJ!lDET^KBH+Mu<SWzhCj z%`G9Jc$vZ*1(+wgPoyb#mdZEEBeA>xy@!#jn#}@9MAY)D0`ucjcZ-|p90NxF|0d`O zWFY-L<$DPF{Y3X`j9qx&H$Cemv}(VmA#?qdyK_HpnOx7br#Tg?TPV#}ub<irYpCEy z(?F8|4O$?!=llmggUi>iD+8pklFumL!ih7ZH0XS)_=CQdnOQsk^N{)>*5Fjldx}1W z3tFnG;wv&lJiV))l{KP^JvKg?V$)F}WRxiAP#vAP{O<{XcAPjZKxs|-Z0Q&!6*3kB zE;g7bHxp!PQ!kV7JS!{1TnVFAIa+bR1PmeFotmUe>Fe+8thO{+7U~(C<fKrfAv{v9 zu_XSLSLh%sTl+&i_|4|u<&kxxP1Aq7dZn5z-q}awfGb!Y@eB*=9h{AviBN|SQRrqk zaESeg0<gl`t7g@rZLHZ*?8A>aQI?pHTl|SE&z}!X@}4*OU}<Nd7K(Z{a^lA_*NU5^ z22sR?`}?=V1mC`19b*Z0<?#zu$zOAwTlW3Jj<wl-km%e5LGB2-2|7bsgC9HVwP+N; z$oAw(@^tuN93+l~b$AV0kq?`<9AD;jg+=gM_f+pf3tw#NGk?B@?Y15+R;!MK61W=s z7<m`kW!iPxHic8Ptgs_2x%;4Ek1x)E@J%bRe<}Rp=oQPK$fr1`O+&M?nbR-+H|-Nz zhIEBuwXIsh`F5r(V_dp%?O-ojSiSZ3Al?{&%}%BMc}i{m#y&2Nw2k?hjwX`q!?iGb zIJDQyro_8|_ho*SWxO@(-IH$>i09S?ahZU?$2n;3e0A}^AAclN-bv)mZugbi8=_(= z7Xw~zl%Gr_kl=a7Loplm_G{heH*O!?fSsJ`Y&b(2oyILag!UbFuC=h9xgm{J=I#<d zNS{&PAk#{`dh!|Z;=wD!!jpCKqzSp^j+jhykTp^Ztj<BX1Un{S|2D1-Cs%ykR05ox zyz~`c6szPPmQ~i_vEdkXy}d@M!g^b`Ugig~i5&Q};!^L7TM3XkDV3vQ!o&8fr)r>f z%$p>xfvzCL{i%9NGvf8Uei!#ln?HLa{1%&q9SNkO{KM4<M)%8D70mToRUfPyiXqH2 z)-xI3Tj^vcE&;M)QKdre-8V<<vR7BkEL2Yds!M9jo0W8L5#?9W&aM=Q$OB0~i{EOU zV@rovK79set?(wZ-5(HoYQ=1CPOWOATUO3}2DBBda?Bb~g*D!lKRNP%A2FFx+?vld z$2lG<wuB+Iq>I0{9XKa?N^xtdRI+`@hpa}pNoXFPP{}n$Mn$c6qAVlm=woydgAU3F z+f%<%VV;REeYo>oM30KKRB*UbHN?WgKX-4N&AYC^te#!d-r!WHtMLbYG>L@?Cg$sc zQVX+HFYoatSPMOq?a9Oawx!-6EKWwlnPve|Nd>{>9sdtMgt%ZXi(gtiD&obOiW8;@ z3k&FPvG$K}29pXLWX<-b3$u-%y*a<KM}Nou_^%mU+Q!Bqu5#YGTO+btPS$QSN#nO= zy&@1mJ%;p$jPsJwJf!{a4TSCs$C1DN_$B6t1&18xWMREntS^v5Ttg<LmRc+mt$7nx z@exrAIhh5RuKo+%^F9CE7y`oc=u39bVaF(XYNU)@cRAg<9YXhj*G#_joXIOYYciR2 z-wNh)cY!<dmVJt&R<mFDr_Jw|mpB>ho%?9iPX9akh%a*U7@wRbX#JP4=*8LO4FXDh z`3px?FCtqjmyVlza?j<~;l<KBKY}=U*~fSYbChn8!%ML{!KV|-Fem&EDo`ak`6zp8 z)*pqc(jYhW7#F)1R|%L<xy!wtcVNdM0S~`_9>cGkf_`RC-$!%G6^40X8ll%ziY&{( zxz#SN5yo}=xq!d!`8mOEv|+qW5&2y0DTeWQ-6>HvHD<Uv=!m_KR^5C2D4LlfSeWYt z(~eTFs<cE|TG|z0$P+t5Ivf$D^9l`pPKt(t<5yZ*KLmK4r|e>9w@C-T9Pi3*K6vVA znfgqqL`sccnuVzzg--zD6VwH=jx#K&kQN&7G!jhY_i`$K4GiGciP$Ol$_R;Cvc->< z<H!~hBXmFD-DZ`8b@FLe<X+=3!6=osr9;sauUu1Y!-;s@-9CN$DKi&}uXcZ3!`&=r z;zhp->zKXV7V4@Ywf!3+X{<P&O@D4Vfueu2wDWY;CZDbvcE9H9CUg<gO~rmk3Lls3 z4-n&<niP#GsBZnQ@>2BHckI?X*C`2ksxNs+BcBnRvo_E1wN@0EUUQvjL4+vIXbfQO zZ+dKKl#D(WzJ&l-Am)2)7I4+tGEb3H;F5xwRr5{pXM{vD&u7_f&xCv_`0}a)`;fnV zHR-LcwjSq>`U2pAZtMA#DXq3)oG3U})0Fv2A?%r}^JxP?BF6S)2JwCJne+Q1fDf_l z3yH`f4h!*U{RP$3_m@6m-mhLA&zjopC*J7hJ$tj6k@QeZOx1Vzp7T2`nm%s@W<eSc zG+J1frmsyMneOsXMMw0jgLQ-NV+n0ffo$HDeW_*z87XmZPKs<4x|%W`r9c1LUM|s( z>ikKu|Nn?iX@5Y!n?NbmHCun~Rx;3vA4tBkKzrRMJD4<5U&Z&h{0MR+&vhtRJz5XT zEE}o5>`8(I1^Tv$n0&<Km64fBcXc11Pvoe2yU`|(^JzRgH@ky~Fa_v%VpyaR$_~%? zQnPW_K75Cu-xIPy7IPzQEIXL#^e-6l&%Jf(^F1-LQV2^?YMqL`><QkISdF(14ei!? z2__D%?5_LkDJ4Je=W7MJi7VqrHi!$K`-#*tH*1S54TNOMg9HL<(g|KXnv$1j86$fC zZoE_Z_wd9Hb7V=_zd&Z}K4RVWo|UE&l%kMOV*MT7BbLRLm3KR&xVp+$L5oLz0Zjk{ z@SbW0PIk!S!KoggiYY0I^O=F0ihv^Cu3wHn{(Ue@%5{D(rVh;MEG|K<oUI6?iGlAq zZ-|qpf8f6r@_FgwWY}BHoZ~gMBP?h_3SIb(2$F?Vy=Lq;_D8x=bkv4hzCR()+>lSK zE=?)P^YfL#V|GZ2(8FwbDtw!WOLdgBQKf;?-hTb=6P<6o5g;XW+Igx)iy!%IZKNG5 z>p$X&@SD$mvaaGRRH_VngSC+9uj%^j`xh-gi(Xk4meo(#lxvGH+ssTU`=_)ujWsO_ zM^%}LRx_e3?UCp_IVuKK>B*e*x}ypLolGh|Ai(CJDP;<DKBPjKiGw2<zH=lm_FH=k zHaL#ws^G_IQQF!QNeYSwD?!+Ss~wY<OIYM<s)a;beh5xBI_M&MK2r<iPNnjrVSPqs zr<qfy%h_Iet;!jO9AYydn4ZBN@86h4@!3v(ux^6AksE)Lu7;)$51xbkm+kGO>OnSR z7I5X_x!%Tw<#9++qz+XODrn{=!JKMtmBZKG8PCTFPU8s}m2pB1^_~LSI0V5-zR`gF zXYtf}B|w_R_+&|xe7~ajiXZ#KB0F<d@Q_T1zno|yj6we#x?QSv`#|FCCkwJ|1!lgE z6;9?~Sdb%#ye~&#J9m@e^9uRI&PPg$`z0Cq!E5-EPl`4m5*&?iym?E{d)S2eeR|Hr z@@%7O3hxnm4{F9DY%h092Oke)m`&MGtBSGF(Qhf3zkipRMAlo8-a^gd);hkKh&zTU zDyxFbx;m|tr`a#DlH}eGH!I}n|5AIQuC7+Ud&3tA(Zp+Y@)wu+@QM99-)7b%SdD&D zgEG@d`jK8GR73jVp_**+bW8O92F|JdL5hpI^HDxCHb^1Lw+LOaT1uJKKMA|NLt~Pg za@-;UMjP|o?a5?J7x_4E6251_{}B~eeC3cuP=B63^%xjf5D94?Vm?<DdLZ%hn`q}x zJwGUn8XbL3Q8d>s*s@~yu8Kl_dI0*u{Lzb&mLM)BFPuR<sW4`JBtsEkX%oEv(SUN_ zt#_Dh7W&Ir_V~idg=DK;>=QOM@(U&zE+3vBbkK-UC5MvN=ST4X6XCY@gCauwLw}kI zA-wpVmdtk;r)iV5FI7c>)Vh`(4vUgN1+_+IG}v9tKhHIhJDy?*e0kzmhgWZI$~@d@ zMesy|cP3>c?{ahDM`}eQO%xGy(1n`P?W2tQ(hc%4C<(`z7vwwmP16PvJ*X*|^7{?7 z(UQ_c|0ah@&#+~b9e`1ITFqocd3o*TpWd`=S2|IIsy<R0p_KVg6qy&+@9$&gDFEeH zeGmZ!CJhY}&iOIqrckH_*63H~I)e!LMI|gRYLXkJ6xPSs%I>p-HF<dX+oAhpA8uNg z=&JNLUjhbzvyosr=z!85{OkjS1t~*eE8u&2>B%S&iz8Z&Tlq&s6qG_kBu$jsiy5M0 zew5Z!-=h_c2Cn~`M;MtCFiVg|*zl71klAoDauB}*IfW-8-el5CpZ0%YrH>7cGL*1g z$XA2t9HbP@$LXrV;@7Em5LpB>a||^oUaQyrguV5N!tTprp)UVeh?Z9EFV9;Fs)YPI zsVX4EVn3ZkJ>hO>i5Yq!#(kp7K*7dIC>7~PFE???q6y|9baXHd4AFCMJ%fa@GC_ar z=%G}+^dV-=2Q3`cN(QNC2N*6dV<`mieYoca*MuKc1Je=1@I(L(St322B!^Z^8K%L$ zzP>9Ckk#-kVj$7Y&jp_&O09uwAPEUaD8G5y{Dyn82NyHx=Y}8O%fAJxa^=uJZM&17 zsh<s)aNgbAox(4|hG-_q?gVQ$#s(_zZ9ncxDt@aU8pC4+W;j3}`*ipg+I#kpXRd|9 zaC82=9nz@$F7T9_*}Bh^u)ca`auNBh%8DTg=L5Jb<7Bcif!(n_5Ls67N=R#5Yy5Ij zP8Lqmach*A=Y9ew{$1q0c^y8HEq#uzr2NIXx&hyZy}<MhS%ejDO`st0#WqD;e$>-T z#HC^ZeGhJW2rBHSyhZ{GRC&B*Fu8E5vb~KNZDnf^pk^C!uu^@l4gcBDUs%p|y9AAo zLLT>=Pgw&|q<XiOP%EkuG7F;S*l&4hkdHc|A4-TX2bGhnZ;RoHtg7!#iQZm}<Y4^i zE<B;;bB64^l?QGVu0RbSP@i8f)yw}>(~q<PXgSYQG8wD_HF=b$uC@tG&<kQ~Q6`>d zwJ=C1bFX228`d}c4t?9gadU&6fpqCh?GSG0?)#?){9g=xmxM@F*>3Rn*Vx$Jut+3- z-w+3Ds0;))o)yT17@L5?y<EhF$}(nZaJSIm(AzT%$!Lp-E@M6gGXcS1gMAIu-7zGe zfS&gNe<<mvv_699YYlL%v9ANFNm&itu<r7gKJnNu_(~rOM}MJdITsK)mLsY&AS0d~ zl@77&JvuU>oZj3_L-Y4OCn!mVE>&BPWCybpB^7I}?{7+qy78HJNA2L{<u8z3b7lc9 z*vxD|kRt)%GqeI{h<^D;sk2!Y87R4VKp(Z;J_$+Ww{ID&<4-R6+tG!C!Al1J<Ycl4 z0zLph#V_)B-)|l-wgOtuo>_y=aoEW{o<@9Pvrx6I%wYF!Sh%fs_GfF>RN%h`CteOI zCD=zZZm7>}u*tZo6g53_V(S1RJ1pvhGQ-nrX;es2HaPm;-%i%)()viEtwcE-Kv8i} z)YNpuaP<qDJ&s>Y6l8N@STAgkJpUh}G9*7FFnca@bB;(%JegV2bi;12$QPG@+{-6o z?{8!2Kn~QXPF;t;6vV5rop!6i2E2-)n9$uPW8jJ3SuT5{vOhho#;FT?GiXI$V1|Ar z5#?rjG&{a(5VDrU>3?&cnbBJP%53LTaFC+DDkbTYTm{5+7kBYe-QD^MQx#7USzXNr zvkg9jX;zMkMuYHjj1%lHBC$|NEpNl>`fkVSpN=A)5^rt_B|#9ADLRb&sw^k#a`vPX zSnJPpWQAd2?E@db4pRLg<Un}|ozVFQ^I5WxqwkOR&E?%#W!u#LvQEmmc4d*SR-CZx zQNdTDi`5bAp8)o-pj2trbj^17-<>&rGN#O^m5(=HL6RmqHv20LeJTUof?mRLWgvm; z1F0BnO~3kDd~C*B;?KSFzRoF`IU&D<7Ci^Is?K5Muq0?PE1U9Uq=ot_Rp2v~fvABa zb~P;bcM+m?3&6_ozsCZ}L|boXXFaxJu5af#*|Zx4)esehyug55q)mHgZ=jqhH?F;q z*wKgt=L@t9JIrh)n48OyU>S4D^lK=yrnlM82kRwmSROtf@OCjVV8DM04-^Qh?+w&a z)jPGwml0OlqHyB3??y8bpCOBx&{GJPaA9anu+GsjlZ*a2KY?9tR#(?R%Qm+04sE=- zGo}#G4Q!um=7WM7(xgZW_6q=UGkl$>)+4<yd_l&osuvgtmp7^~!FHXg;cAxr<b(%= z$e{-e@wG#}GM+ZjD)O2C_~+vx^oL6<dyr0;d;YpOWI%`~xx0fJW#!W$9r{Nkw;(z= zxVM4#bPOF8!|*Yx2IoSUP9&MA;<pa*S3Q0If!Y@`L<s5(^zL`)wO&o)REI7n_SNaj z*saGz9R-_8dtvkHxxQ*8%H$%NRemwFZ7tSX5mZzgIk|nnLKoe*_vJ#oQ32JtAH6(0 znio}mxCE;u(K*AGQ?>_hcYAxPaCPSZniZ_>y<tfYa=b3jIk1N7VZCpW5xzrI839ag z+^;`Ck|;QDgL5hQhj{9=cadK2oJ%|?Uy#ZD5{}!JEv@ojV$$jtYYb=mTc?IjMmU#$ zn?5Ddoxw0pz!iV<JHPxi;CwG?j6(UMD9mLcFjmOH!kdVJkNHZ7bCsiX1ooWqZs4Pp z8DoHkqk&}8gy6ou;V+=R0|5eQv_<BzaEX<%=K@6Y50lS0ek#W$tz^AswVF3cAdx(O zVi|b&dGT?{+k9PAvY2RI`$|fmnTImMQNYQ1Zf?#JDMXTFE={zl>JPSo$g&oh>0|L| z!m*1NR-1&3HBmxQF<Z@UdTw#<tULsOXGwVjY>0xBD;`6~atj4er$>bo6^P38yQXG6 zd-L`kUM%Gof=Tgj&d+PA8wq`O-18i6#>~mQrxN*z7i6s5iiSo*;UDls4D`sk#iWp* zp%ngS(c8PS3YDbeppY5o*hBuN@V5Z#1>a`onkIASB><P}VUt7)Smb^8213zS6c9vY zdRU6%;5^79vB>pDUcj0y2zjQ>gTMlS#FLZv3l(__h6Izr#$;F1`Fdy<X!MOXJlDA% zv$)Lr0N^*!1A=^(Z12T}y)kE&*Ie-n8%F4yrcpTsC;yc{lp+AvZaI~&WXO0~@&X7K z8?_rDBqK%;P*S&S9VBxH1I}yWXlHz+vcLkZ#v<-X>kWB_#cVS$IB1g#2$EkLzup{; z2v~}q9R1fqE5xq*cUwXQlx1bqauswwHB0Ep+=Zt?o{nhU@9y2{YdDdT$mQkhStJRj z$4b+a5`?|9Vc}4q^fpv`?%&p0SLpx|7~Ks0bRS_%a2zeHzpoB!w#&38Q|IyvAvbV* zr`g}?QNR8fnUhpv+D)hT)xc`(LHagDYJS+0O*yRkxs6|(1waw4&{pKQcktxbmYN_b zJ*wj^nVA;&A^G|qo|BAk^Q*iM58?dpl^(ZF%GR#XS&ejGjIe*hrJ?<B&K_Uo{aUR( zU+H+o|BX4`#H+CLu^8G7dWHn8h^!me%F}EY9`~}#HC?XM`9GGPx`-?NI(px~#br1+ zX2C%an^9P--;KOo5q<yOCy~U?sk*5SD6Au=My;wI6mz{_zv<^V4#I+C%a-a|u;}y7 zI6C1US-avWHn8bL=4eHY{HEow$`2FKtA8eZmobIt_UzfpbU7V_*m<Av$-Fm?c82dM z4^dXu52zhv!?m_Q)nLpb%{I1ddV2cfs{kmu9q?;rV}H1ArVC={FxT|2NlB-^G<AOk z{`VO8Ur^`;pT)CmUf^7`ClMEQrc>`<=@)npf$kMVHf{Bjdi|fsM4KD4hr<YE`!nxA z9lL(;V7qSN*g4y)vD_*V2k6+c=zXgvQlP~=mT$M}0n9@i;rKp?^`7Mi!U&<R3(wWH zs0AF}sx6-tn-v<OQpMQe-(gOvUai{!4mX@gR6dGH?W!F^v9N*#I?-oeoR#k-b|+3k z`VP-$*vX>{Sk~6S2D=B(m3S&Gz6z+*+J%~Lv3DnY3nb|<&vYNvIPQ}*K1V(vAr<(_ zD2ej&7s9eLb|HRDG>-l((_SHWZEg=P3Elm7r`(Iba(&0gCYpczHk?eq8VXOf13J3f zHOVoBw8l)5K$qt3Q^*~l1im|b2Ull`_dg{<Q1R~@4pCGo_AOH4;nVPG>Xdw573@cT zel(R@WI%#hAMoFY{&!f7Zk%@y4SjPAdQCc7<{QZ;*9JhZw3xbB*K}}T+>FD(kjSHy z`uM=V088LY=bcW?$YR<2P0p?B93cidGoJ{SZ%gd~_Uqm7XxilAykL_n`EmU1JEb@f zw)>WUvX>bK_my=;*GxWI&J*3;Q&L5}5?<DXxb5-AL3TOtS^C<)Lk?%4#hL~GdF)LU zn6K`Y@wmM;cQuDB5V{dBl`Kuegq@d*qr4x`I)V#NH~TY9)FwZ$I^Eyw)$7qP0}}Re z_kj%{Po^8j&r=EH5{?><7vO(+(mb^PqDrcOu*&G|@E2~_;sr+x8QObfrR4Q{#fFm| zP3<kkG0IM$L?oA#k3J9>X!p=)F{iDVQC_@vJa}kZ^cTn3?W(@RVqEILn2A|A&U~&$ z&EVj}0Y<)CY~8C@)1HrFkcH7hZ1Bn756Nr+!Gam#NZ2Y%km1<J#;SiTO&$0u0A0zt z8~m9a<-Cv?MF=4B<_&oxBa-6_Rol5NJkW<wqOMeqW=pzBwDpzani0ZcQI(<9WW!Am z58TGIp7rPU78bNz&~;{J!fqdrBPgcLV9H>+w0(;Vp|*YunOIQHJxfM-N(OmTT-1k; zB?zD1SFby|BTWz<+fi$8QPpTnoHvA~KS4KtBb=?2peUiCq1ncwWg-hvD^k?5>u+70 zO|2J#L0tsy>@m4(A&%|Os;KO`vA0LXi=AdF(Hi;tILa7>ohzICxqPkP56W$hAEb}f z*_d`!lKBU90mgBfUqGoBSX*C$@tN(&3sh9aK#xHaR5ah4>BAZeoTOY0{8mFrqzsm1 zSxLH)mX_U@_~eD<MN*N<p)~zNE1%}P?S&iGoz~Yj+64_R7wpslb+gj8>oG`HHJ2$* z*;7)A*NUN4^QEu9VYM4~GI_MON9%sV@d(gGbiDjOU)|^Fx3>DxV$2XW$bwJ)q!3}$ zTkmRnUD|3GU2rsAq|`0y?})~5e`bF2>GNCuI(t6pNsrib@3@eL29|LqA#@j4x-H8P znef&59$P5Mqy+&q4Z4Xp_e*B?ssrbZXT61+!)a6TKUDOrHw?j7sr>z$_7dk@)#vJS zC{+x!!v4`U-X9z04vMJ1=&IlPO{5b1PqovmSrg>ETvhc^y+M_d#dZzbg!H~aAGI^! zbxFxcT?K1q$R6h>!cOCzPjN8MTNaOF(E`w#ycw;<?ISSpFY_yg<p7Dw&#iW(nJNr` zXdMpT+}}X$baQ<rdF|fuXiPtN;{HP-W@kHbdaN<upHAeTUkVL+>9ckNZr(lvJ#rM4 zN_bA#5&<QP!KDr4g1%s9If`<UyeeP9rcRR5`6#GW#AO4Qb6FGJp49%%^-ck@>oRSi zIDSGN>I75Cgm|3C@g;ryhHIsT!kEpTC9p7eq6#?752~U>rA6+6K*PuT?+lF!%bS3~ zzcDg=kJg(v%Y%TZ9T6vg&5S;_e&F2=KmjcFHuaNoaa#;gAh+WaCBvEGlvF{rO0Zp# z1lHZg2s(X}{bU158rNUrv?Qs0&X>d4(QquuWG+#OXB?Ku9aMW^u?;A^XlTDE_>@bc z8Q=^cy#5szMp?=86#mWbWLo#g622=9@N6N~l7MtCzuQ9)uj2!6_(~r8Wj_At?TiZ_ zqJ&oEi71P8=J{4sfZeLK2V7Nc+qR&nsR{?cPb{B2(`DK2WCe@t-426FDc|ybS5A^e zxBEv@?n2am&h^trL!Jhg(BpwH(>InTfU25BF&%f7())_uSIw@A3RsToL?Io@qdMuq zNJ64^xXSLk)S$&R+W#QO7LzeQ9J#f1CiQ>i2mCK=eM<)`3)u$Jai(X?em|VKeHk-^ zY%l*322FMIbd?P0%H7LepS>R-7}=A{ce5&mMr7(gk}r{)gFz<(5M<ufKdOO-!N%GP zNv_nzKYwx4_bAiyjX>7G%_^iDIRFv3%HTVk$#jEG!;wI;gs-ZEkhoEt3#R0u;NXCI zkeiA5V;pYZP>}V!)gD5r62VP_;IhUr_0CzyqS)TN8jN?K&##t;aBHhJc_c-!htE6h zhd;`}#Y~nPh%8%GLZ#5-wK?Km?#)yAM`wtOcmFNW+wYP7zaL?<^^x;nzNx{D;aL^Y zbN!y@I&fA?lZq<L?)LX_kPr7Q9|rWx2m^KN3A&<G{$dcft4PhfJ9($>C_xwOW#biL z!#P)yDR`N}_jvZ%<;J{cT!1%m8pp>WKE@941RIC3X#a2kGnB?X)zbFo0v`=X#8}uO zow-{Ion5l<K;p7co9?YD-M>veL$s$QWkIn&Hk!SWmt$aeYSbwd_hYw`K<P(<maR&6 zXShl@4>vcDJwjGiNo_%NGL9!%fnPL^liPD{Q|^W!r(r<=Ei<95uOV<+w$IhlKvHym zBOH)f5(YA2fR1ZD%zFf%VOk+m(D?im;~~up*L0viV;3BS#MAdU{DXFUQhxp*=&jrI ziX7A8nIa0Pon?hq6Ybi}egOBg)atvk2Rz~Zt-+NYtS;yF<J-69Z;OPpcaH@Zb_^PV zw|3Fln)8Q*Z!yzc5H3tFGDo(>FFM0t?thQM%DTfe7YexLUx*IsLJX%~$qnMRcc@5y z^@jNPi1Tp@_+%O22W+(GtL<(NR@Sqd^~xnE8=Tb+RvZE1>C6DU!MEoHk7_%0^>ft= zL#dXxt#3``L(TQ&#uJ;J`Ah~swshT_k&cYftO_3cz||cNQ*J6AEn~@xyWoQ_@>m`j z0vH+67w*v7mf9%fg!;(ImsZAU(!X$!Jkh2XdJCmb9|X|f3$>K|R&jQ1Q@Q=+^8Ia~ z6K)D&BzXwv&9{<aXp%p;WyiR^g)qr;cV&Sb4Rk>y3pAb?2+hFx0tM!-m4XAeR-upA z(R`05vgg-%GNYB}b(4j!28Nu}nX`5aca&mnrSpk`scjz<&l(+tDDG(X4t1o}Q{SX! z<HrH!cdd&Qv-+T_x3|o|pKtmV^rMhtNc+B3#8Pug-=}Qh%cVkD#;isxihQD(&TbGk zE#?nSHmAhacU*Vw$j)Ea1=d~F-M-D8Bnb|>+|FJ(!eLN==lq6J@~`18_<(=(g*k9E zkdc8_D{ABL!Wpz+f7BER?QW+w6v3Z8p#K0olfCCxhf=TW?{pT@cEQ-70udK4F9W+y zPIkkB@L5^mNGbUU+S*ps%v(cEStY|oh!s8Dn~2By(zmt`wJ8b2jVEDz-sdXnjWezL zwAoOex3<mVhPaa*zzgO@Ia7k<)YWFwVXA-q-P-$JnNg5?je=-gGTi7|eY$4M<Cyk< zR11nw2tU5^ecQL^obO-3L*{MOx{)xH+w6TROljzYN}Xoo{}ULS^{RR-+3aPQG))gD zHrP|0NfShlHF;^T@Y0;YI5?M4uS&q;-(I5LTm2Q_G~Qm0Xanah+Ee*lSMRZYX*Cob zP(4acNx4M48tUbRvI$lP%Z?u)r=LE{%#{*}q&uq_i_m`@p(8M0yT2`ToeFq1Q;WOg zhC#+{a+Jqr_hJ<SX(GR%|067saJ;avf>`w|rnK3wfeP!3t&|=lYA8lYMCk!&6aMr! z=^;;$sS`601Buy{$t5>Mi11V4k_9-0AB%e4ZIek0!|A1Sj#aj{uTc;5iL0$d*^~d7 z&-q2(g8kf=iy-&i-#js28A^p->Fs~nlcRS|E|NA<UGEYt)%_Nm9sf#-)E04Nrcs!2 zASSIi>#k|?k;RR3TM}rf^!D%k-WGT1PYnn(>}r@xGnRm|Sjj2f%k8}1`&N#ULEcfD zIU#hh63wTm-t*1R3}acdiT5>j0Zjzy$cM}65h8kC=9zefBEupLP7jP&{!pO@_0-1; za>w+U@T)syp3S_9zX>_@UG#8zknEX6CCkWNChbQohL$$4fyR%gb^wfM<_U1ap-)-Y z){3ZBLiE~DTA*C=1b@1`Ly1XjZ~cR<{I8_1;{{m1%WAytGn}le_B`(0BC@4$6_t%Q z^#mURD`G23R-ul2xlZYpWXu{CFXyB^-^`u1=;g47l!b%n{`0LHGKBqNP~Xf!CHU(} zU+)w1PVzH91UNTicH=O<C@P(klVxwUZm)Y~1!l^ZN6QLcnxIX>qRb;5?mzh}lr<C8 z-NC6kl4Cegbfd>^=+;jfDloQ1<!ShOYdu;;X&Vuxwc?iK>wxY=kFezaR<T<!Jv?7a zYpL(4BGsSlRhhUfP_U9JvO>jG1FqypTJJM?`Q5$E1nY$4L6Dg7wMe&cFV94hL*H;5 z$@$7eLI`aYnBj@HM{3<UveyD~)<X6C=oZDZS$ZE6&`=eNS0T;UqsbXG>yVI#e<=5U zcq&WD_e(~Al=+I`0Akv@m0ef&p!89h1QI9^83nU(O@@QmO+VP2sJBqbr+)I(WU;Jp ze6CjcVB0Urd9Rg?JW*Pz#nZktR|7V^B?`Pq56qh=4>0{t(L0;4N5{EsB=cJWirzsz z4g)bUp3~8m*dLoFaeS2@fM3?@zWWfa$@0EOK0~&NwQ=sRN>c|OVFV}{_F2={ScGo= zZf;(=_!QOhe2S**SlRf~e%}NIX?cQT0HRK#Gqau(5>{l%!-4r_uWj|<d%}P=$g)TF zgJsczYH(#2oWEBA$E)8Di4TPV&J3i<54EQ_jXF)UUS9msEbbqj5X14nhs-%0<hJ;6 zWD(Euxm%iY+WGveeKI$qVm8cui0%gZ#{9d@Dk{n~(@R2!(ZU&T^5w!Qq}?-`4NWrc z>bM~f!%gu-tRar6>VIvq4Lshu(<2guQ}kt64qd}-pqo_T*6Rzfo3rH6Xb1DZPv{&3 zsJFa{2$2*70E}69ft<jeoat<9Ha+z3H(@BtuCT|PhTJ0=rrlm~eIPAG#)x)LC8EVy zojmfwiS+NeAB1+l)|+ovhWKfON~s!7CN*1e)2JPe1j*=QhuT7!EM3A^Kaz7{`2h0e z_rHB9Dvg_v2xN4SpuK85_5=2c1>4oW=K|5BqrFbsOA48|#u}K}j<3($y}L%Tx>Dy8 zl3Cfv0m;2X>e6s{!Gb09NFgSknNRys(%?<&<3pYlqG^hoAA&#1pdj|QCR0M&#!P3& zEGGA?etd!X1C71QLiv&}UCpHRowQ8IGLX)yVr{fSRo}wbcgv4D_gpkIZ@vT=t~1d) zbs&S9^NPc|t4n+k!fD6?XO$bjI?{VYYMmZTxW$+kbK)9EgaXt_p0njH%l4bv>K+EW zsaQ3Q58Xc;h@IMqvd~Qnvjz_1{%&@%@Y+;-wRueMYX<yeszO5vF@W)gwWz714hxqK zrMn`7ZJ#;`sn-1I0vd5aqcR_r9#<E-xMO27ogT2sfKsU)%9S#!uhV_@=_tX@b?p@l zOyZnzd%VA6-`LtESY6&5Ma-4+H|~vn-%~2>(=XYD?A76US-`g^HscS<v4P+0JIZ<Z z5YlT>5dCCw{R-cLS6{w;aOd$Lxw{8LM_61JYnJDRjvB{*+Y^5N7AL8#bQ#V0mKza_ z4l23MxAT!RRq3V1F~ym@yj*h6RAs4B>a|kkpNx$y$nUU-7DUDoJZxhl1`9>hTT3ym z+)2E>QxWl1k`fAuWsfvJP_kA1FdZ!5^k~|%1U<`cF*P#wFmv-`H>FK5G6_Lgcpg?z zP%V`Qv9XDYC<-{;n>HL8?ru;r^wJ#_JF*KvOdOy$p-Mq(eUdVDbRr4}S~rY<w2R7B zB2{gr3X<-L?4@&q8{@?;g<e;>B|A|Pm*=dW6;=qCqN>Z1&JGQh?`h@s=+}YQeA0Ah zpa`$!BPIs<D=JP+(@)LD+ma6muYK5M6A->lTk%|9Ba2KA_OwH{sFM1BZx~KEmiB@c z!!n(J8?kW7g{{HoM7dAXFg$0NUJw(52F2<rm(&R{@2`gP<;Gz*`c+>7HGOFU1S^Xd z<0mhRx&V;?^aWH}tegmiwBX<;UEScgts$uA^2YWn5RaPzFMGn+*dKp759Xoj9%I#L z#@mWb|KVK_B~$gU_XF4-gaeiGCY2^P3nr2YJS5qc-N*ak-=gupEyBzipJVBAogpFj z>&`;8rlcLg9qZ9*Jr-P9VcUyk+!!``KBM{@pcd2MRten|2a)i5?=vK%>Z5SBT=wM2 zhVQ+~G-3OxT6K7XMp-8FP6LmHxDZOd{D;CU3pcY~@n4%ctDx|ASWIs?K?ZcogGmBg zaI&pxF&a!eTayg7*FIf%TgIgpS&1UqEXp3u=yfG}(DePK;!J-}Jngm3lR6S~08nXN zB!WhQCmqU<vxAzEI(CA{qPXK;28$1a{TM=bBmaKuY@L0lgBWp=%IJ=c6}W@${uc&n zrsG59yBkc9LyQI-w&_C0JG0UD_+u75Y=^B@Id0xJeS71TukPIceWrL-i>4;d%EsZA zqCY+Eah)?4!X}eZmVm-1)S5vLw`#A6Sw88#PynqSJYfLoqi4m|_0o_#m6D2YWPOLH ztHe)qy@9V{DJh+hl`X^Th{|AUN<>L%(&RBfLg|m^1iSQW7#*?WtUuX2W_x@HDs6^K zL5bO#Z%}@s!{zvCFYo`o{k>J~!k=?u5sQ+)=k{GeSmlw04vpZV+^8K7NbZqWt|y1t z_ga}#Y4M_f1(vy9XuMYd`|>7~OC`k+7Mzlk$f)><sJzr`p0miaYNLuMrb7qxGA&Fw zEOYsMLg0TD+i9x*<bM?&c63MP{%dJ<ZnH>s*rJ!(a)y^8P_XqW{6<pEXmH-e{x9_- z?dS-CupvSzXLI@%)h$@({YxvPj2uYKorEg!xwCJ)VV7AA8ulK6pi#;lbTBXi42~sK ziq_*YmCujq7rz@vics-ZLGz>TdF7arfemY3xYJ`=F>e&;H`l;_xVP2mz;klE6jNE4 z9q)|2L}`8mBT2WC;Z!5Ja>o;&;U`~_)T<TAur#N>tc8*Os;$WhazHQTs!#XwI{kMV zM<Ze&9C=6f@2`&cmsTGQc89%vtyve7>?H#<eqbZ5ff&`eEeU$6{I9^m48yK`a}HtQ zs$94#OH)>5m_}2Q-<hL;zyHtQnJ5`^gu?WD35CB@7*v0=c~L_in|4}2e*jqOi$#`? zYprO#3W;a&r<LBvGjzo;Tj}l~_x`W2?S*y<x=ZaLxpvY6^ZeAyUCN~Vc4Q99)^6^? zPZEJ!VnGi=KT?150}gj4{H4!iYHpi2j_3z;Fb1HsDVKG)$44n!nN1%D=V~3tZ1Tgj zqUgDg;LW@(7##a1EiyTh1he{3FU#T;2l2@fr-d0efc+EPT9j|7z0OstI=Sej<S-CD z&SEIDDsi}N*{E(#KMP0;NM~++wCvEt78dlw>AL&#Ioc#{yNgl6)wTMzVu9L87_h<{ zL|gB`<b(Z|TD_yP0Lu3W>WjuEV!CNbtheWCb}*IkCPDZ1-AOprN*LitP3ck$!Z!-S z94_zL`ttuCoJ1z!C0c5KTLW}Hh9`El1^7RCHs5ZX6(hzD+4!28V#C4S*xjpsHoFU} zYZMCxWUxKO`}n!1SBe~5T1z1ECkM|3ZYDYMsny@oP{A0ub4XU$TM|qU@4ZV2Aqtu3 z8^PKi?xD~YO#P$4CXg%k{d_vQ=;#k;)waQ0*7^W@zyo``CJGSurqvQRaLS?{CaK@0 zJl<#JX4BG63K1$*rM%}gV3EIV%fLZB!DhNmU9b;^qa>!|G&jSWx=^TpIQ+Vpu64Jh z3Qr6?O>}<G!TG0~H_}U9@At)&*8m0g&qPkkH=8o#yh=hTsVU}qd4jL$OP(NGz#okO zwH@e+csRug{~(Uny4z<|p32W#5YuANp}!_EiBjwC43NBfr%AT1ext6guQqXTL}z;a ztSa)w<5ea#LX}_*c6NidXJ64Bp6C!9nA`TvVUNBdd4h+>#4=~=+0JuvEw47dkl4|l zgQJMmw$y%}g%d;hzm;8`ijYyu*}MIUfxX$}NCm;pf4mMo0^Yhuc9MiEdx!lEh)bPj zZ@$MTNIj<CR=jpfSTYFx`X8D;?0n+0#liAy#V(k>rCR*SdU?s?bKJm4-PH1C)ZG<r zQm;fRmEg6N6ZzgI4w%WM*v=}nbnzCTwTLT!4}({woYJ_h%#zW$NG9?#rd7h;tKlti zMl@E~F^X>ut!GalG0P@Nd=;e}sGI#$qBmUf>a+SgBh-GWh!-bKmm%|wKie0yn>vW% zl#^SzI0|PmeIo8Kk?#b2LQX;FF<qa(XrTmU2H45AvrxwlR*}27yYErTZ*p1^J&_U4 zz#I)TEbct7GMVx(amvffiyo1G{`~1OdyU1(@HVECdpUwvINj&3*O#6S3!`_SIcTf4 z9tiLQ5hc4M$2{Gffz*WrODamhHGt@W#b#fgQx1mulV6Q4ASOm7%T(V>=wl)zoG8hB zgG$c*h$X<)mgbKGwfv0Kb)=2W1U3#st+TD4`(>_FEIoI;a<~QEEB;W?7R)c+jexM) zS?k?xP#Z5aU<2e2piCi!_;R0n_5OL8Ute1!U6!_heWRb~yxn*NJo&cnJnlN$uPkJU z_co)2S$p+VzQ-qbQJx-w1Q@kdwn?}C-v*A==eEJ*jyeZ;qY@zrp#v=N`gC=CIOVE^ zdR}p46L(4I6eC)~b~C(oxK{Bm*8;+xeqaAOnAx{~y@D7YP0>4M&@uCP{TC?3N-j%Z zF!@4;AzVu>F;wR&v&CR!9rE2fAK~c70tcftsUB2)m_AqGJJzF?(y4Qe2$ClNW;af= zYZM|K1u+3rm>jR{0Fc9ReRO$N-@uTRYoU_P&%p0fh+bi0vY+z%N#yT?%t`kAUYP^v zJ~l631mlQDQ^D6GTUu^LKKpX<#sweDPnL&lo1Hfm7Itlf=8;U>V`wO8y`O0!uP~mV zKE^=Hytb|xb=i2Y?djndrm2h!_`!%|_NX`pH3WTtGP#}&f3y--`rGs8{2Oj7yeI?W z^*}mT4W|8D^`<8fTP>i<CWxgA)jMBYF`zU*kU5A#MRgiZW~l{7I8;^*7kFeI;NP9O znhfP0AP?MY-kZsHzH>W5-px133i7J`ROlG}mv0@n4zL*H^G`Eg^bB*qInOgWb6*vj zntVv3DXbpb%Z>)aejxI!tiP!EyAGX~jg=lBBpUxA@8l`$-C~5G_k+-7?98&hsmu0x zzL1N4@XuoS;Z_A@`VM7ME%rMEb;8Y!Rt4dLbC38WQyUxjQhG3o)gt^u(*l@P3dyAq z8t9w1MUl(CZm8H6suoN_2FipSZNiMVp^PZ(IRB@)xBiOid&7poRvH9EN<IiGAPv%> zA}C5pH;8m2-H3>YfPjDuC7^Ul=b&`g&@o7N4IRUC&v!lVAMmdCm)Essahy4G=A6Cn z-21+-tMl#5lkxk1VH<TZLs@ck6CZibfD2d;mQu8v0l;B%o38ckdq69Bpn{tARU$>@ z<I2QV1Ky+dqFDVO@P!U^#5la^rt9Y@B`3Oan~?5#d=SH5Nq$wNfwZbQw0zW*?6XLO z91{8&bp;h2b~oWFEQnrxgNi@37mxkf9JYsa?6*^^IPPD=?r{w^8Ta1><i%3#@)hC# z@KK$n>S*srZ6^l<OEEfb=dD2;I_Walg&wYhAPNyJUk~x8zKn8p50V$FBxQ>cT@~!R zoR%J_lFf@~*JL(AqFx9+wSQV(RwvUb&z`S~!LQ{pzQfk8P9e(is`Po?GV7qxJwUDt zJKd!yS{>HeOvBdue~?f5Vs}}OmSgvq#cf90v#+s3S;XR{wU)(4*sc$kJ}lA2-6wj1 zmvcd0#VStU{W3>L6|JBukt3s<ROqpPc7VEqoqgDIJVj1Tsm9fU*|p^tT^cd(Fe76^ z!hh<#??ArswDIWLl{1k~ou{vw%NROp#uuXV##=(U;|F)Hb-bxu=o-8iQpnvRR(J93 z+sv3&No-~v2?=mNqJ75IskZ-I9enF=!Y{twc%4`=xjW9JqqozPP(gv8p6_Gty`*}T z8@8WwzE>Fj$4AnAsUGrY%w+On9@g&PznK(JOYOHrh9<7le%g&b<2GzZ1ca)Ls{k5I z_0ak{lWKNfD0B&rV*Ul;m3Xhcz+%*Gq-xE4cY>25f_HxNx!l7IH?$q>d9CoA4K0Vg z#7a?0ozIe}osgscRzi?CVL`c^{ckpn^)l7N*IY!dQ`aGP^cf6dR4<Q{(32-U-H$)! zSRStt7N7DA{})1~>^m(5jjK@vRE1y+_PuwmUyh4A#=cJdd*nT_*-0l#0n2l+SCTcJ z(Z4nD5w~yg%5*>y?cW=C*DqR4`?lCYf_6u_d-3gg&ogHMBnG8IN`{{%n=`l7N>-cp zkOC^pX7+|3w01T^SD^Y`bOZKHZELc(dU>z3@F<iU1o+TM0~89NAT^pc?m%~%h{V^J zhl5+xne*hAFK;!|Lis;ioa!^dPXMH(e}z?19ewpuW}APwr25Gla*9Ex?dd;dJzwg& z;s+K(xNe=EY=TK|`4-#qZ+SpV#7pSjLRW)z<Rcd&J|?FNEy)rJP)CaYy#2+@l;nm< zd-DSkSyn96DMW{+XN9liY5y@x&Z0!mHfLGnmQTHAJbGlO&Z6(=2*|-VbjCL~>5i7~ zaavD)j>=&tV(^W9G{vi3P<@VN6L_leunVdP<9%!6!1-BL4c{&2js?@X<$)gFyKcVo zHhL{lvLNiwAIpCedahZ@yCjmi-|}WT7dP$37bD|yVx%O_YmNzz+S#W!h+zYLo^rFs zIERFBB<12ov(6uz8V3IcDtMsLYt0KIuO7zO(?l=2dlD!eH~?)@LCHd$4T%h8`uoFi z$WQVb=e`O*6YlV^D;I1vn&8CEaT*U0nB=ykYK7(FuRQ%YVq5I=V8W-`BXDz=1baGA zcBPPR)QZUPG+*y@Z#ux7l_Jr=JK)8Dt=Wl-vtAd%em4`*P97bu#fRBQcXjs;klO{E zJr+)8VlHYfLHRN1jA;cCS<SnA6cT4Q>kcnOMq0-P1vU8L_of6K-y|o0ZQ)96W|s34 z(#g;1-roP>%nVzIHH&2?fm`ozLv%?kcQoLEWb*901^emMf(OqPul~UL;X553AhY^1 zBZ!f~nVCKz$Dy(@5>KH1Nnp}yr|ThlYuq2L>HLP$SSNhGuRFXSx^S1Dr$Oe-9<b{) zn0yezh57`RxaK(f_;6#vw0g2fGKX@KHR?8g0^e1!j><4?GS9nyVC0KEW~6rp`pw1O zTw>Rq0*oXZF5-cVmuPf58xMQL)fln*-^Lwy=JdS?<`=T|4vt1gdL1+q)`i_<RBb&P z^G3XUgNze3?v@#a^G`J#pmAj2x#16l%HEbztR8lLB|9Ipt7`E0vE?@&A<fY4;S!AP zqSZ*z#U!^U(P|f>cSC8?$dQey{)iTafP<-vu*%Uh|0~@Q20~cEQxMViA*z^<iQ-=W zMp27>o^r!KWKiaJ*e$5^{5L7B|LIaax*>Tk;qB@k6P5`!oS8Om)P{HntSnQSma<$o zd}b=x@P%e;rvq2TN-L_q{=VXLVE;2Ew^xM-@<3m0qp8Vf?|Zu_erH&?*}C^Z1Pw)i zulfDBG~YL;m&RM?m--6rjRFg_9_GIRJUy|pH6HR}3J1QbK7d*rO4JYPGkiN#p*1nI zFIfAuWVGmMUVK-N2J<o!n@wIpKy5h4R$AtA?xUv2!k**BP^>R5usK$gcDr<Q@Fk^H z`F)NL6+)%*!#h>fj0KinLpd4b?X<Uo`QuO>O}^(jmc|zWJ!y<p$Ax4#D^0>TjL~4{ z+;FTdYVB-^c}CFYLVZ_HcrRcBLxk~9^@XhLvm-Wc%B}8nS2t*Ef<8#V8~t!Okj$Ma zGWWusZ>yg_R0m>~dlj9-$~+=26{e1r$#=C$XDcd{n5XFb2NBmEuVXN38sSs5g9w&; zC!CucJ(svefrl`PHNs(^V~XhZ^!e!~A<e9!dW<&4bvxCAk@(vEjOoCquy=~-Y&ZMv zE)^YQu2D^o{DQq4W%{O<E8x4&suLgqWxV^QQDhx={6kIY$I3-cP5b%#iKDqgmswe} z3x3|+^DQjw-G`3=Db7;d)}<`FgPgGdn>jm!8lF!`RC}}dF9m3BXVqfL%p;EPUSdn% zo*k@=qTn~jOBBt~>x2%bQlyFQaYW>Wg4!#92^-CPCBX95ZYwjiZB=!BBC}lHp6~cP zTV`#v8N{d^^5R6$N~VQc|A+17l*#AEs(PZvt~*=(LBXHHFWiBGy?}UPQ%q$Su0p*a z$hT`&kKroHs^udeZ3W95)kNat9$~fR09(YDFN6>8-j(iXZS57kc_QqpFu<yS;xFZw z9q{tvUr!6?f5^4jKE5%UW`SEuD*jDJWol}ge#P7`CN9o>^5EU_lCn*oAO6^;&6v3_ zAw%xErn5c<qk=sC%EaO;t}HxvbIzR6_DBiiiB_DI=g%llC^6HJ$eE{_)3%FBNX#|< z@C(qC5ew^iSkid@zLJMJYZ#%dtd%<Ck<7sHe(4z!dD_wTVqnU>W&hpkkcIZyF~XEz z|7?FCebm-UF!sUEzfZ>9Uq_|`%;J-G4$7|UGpQCm;u#igv1b&B8#hYQ_{=OZp8F^t zy8)S#T6tQVGvjcJtb2+vM;2siD*jngCr&3Bh@SH!^P1r1c=OqXF9Yhynt8t#ernqt zKvM|+lr#n_X}8{e<Z6i_?i%35<ez)X=q7%_Q@=^gS$}?(tnC$*Tp-~ixrGO-i-?&A z8~rS$?@z|<)QT1l(5`M3^E?;aYzNs~YW8mjYH1zv+kR=ei00eyuY%rgzyOF~G5ags z6a$#AL;hMdb^n|J)bk=L(*Q7Uz8Cpk@tr)O)?0Dz6K>}P+!p0DXdn`3-=B}&I-Gsv zgKTTF%xG$l=C-~C>2uZTp+jlWPakiAMMOl(jeSb4a&oHl&qtFESNSbc^Itw!s!ns+ zR6nbO70suvF-BF(1B0eTMaF7Peu6e~wJ|fZ;s4O*>Wg)CmN%#RUo`eWU8}^=+qPe= zM-~=zr~5i#SgfeF5!02lh;-xT-Ua`wTM?oEU4Yiu*<Ynx+d3#ab8{Rh(JhgP^W9UD zl*cJv)JOE2)*eCMcW<rsK$#<V$&pfXDMQQ1knyipCt=t~vI9xanzTcziMl?{dwhIo zEbvPuq3vh8s@Qprjr{c4K@PQ>sN#|Sh75Uq4N7(2=aeUdiu2MB7dPs#k3P-s3J#T( ze%Q{=q)wgO7UVeSeJ1>}TVbKPb@R}1IJVIw^Fi0Kz{)$tCGlLb%E(&kLE?anYKboD zhPqxp)bE!s12U4;f5q-^ln&l$Z&g~|mWz6BV)7?R97?_{^ESE@Efa`UUzZvBiwyG% zx#rIuPrZyqbz_C!8Sbq_I$y7LQEQvFDbM=Z_hdk>S>Vkj4g=d0iMx$4gf1IiUFbj7 z>RrnE-h=9ly-k-bQH6Qbxf+Vj@m_Oe<K)l$cKOUkPWohrh8$t><u)P1tDKX~7Kr&> zd~Xo5(#`Gd6%^n1h}ioL=s4Tf<z_VfUe_WVB043-lA%W4UPNt=-^s<1^&k5Bka`4- zJI!Im(Gt0@3H0JJbefJ7LXYjsn2{+$k9}2f#PI>wQ7RPFP5DjvqRVb&sIea|(3YC( zULB6YLjXKAkFUg4<k3ec<ONJd-;)Nf!m}4xT?-xVjfM1`2}g4+j<vh!Z#{@8tMBgi ztRZ0>|7CDr^%I$Lz6EvkX0Lk+Pb=g^AYht{VoJ6*81arikUw8i7T~bq@u|7(usTXH zRvJ*{thR3~m?O^|Q|fW~vgH!Ty^OiDz>nE`HO#WvrZvAJ5^{&|yOW5ZDZAx&#WHI& zVHhJxsg=ReyO*)q((m>xb^Ilj(mn-|<oxG1Y{;7>uc?xe{a8i`X)5JsLj$-^+1LIs zhiB${P+gCRpi7cStO%veiygEc{Jc;;DU+sS`)5#gswSs;(xLjuel>_g=r>+*IyRpc zWnQnB;_BdfQf)yy65~yMPy|>Sk;PF*rj3t=lGl&DHTp6A&>|!B6hV}t(V*{Etv+*F z?|&EH>889&^;NU3*9JUgDK;O8)%0L*r0zIlTSH&j`Igi}ne!fBl<dX~hLc!5<`VZK z;kbd9v5vdt=+-)We7qhR`QYpW*7F0aMRL7zgrHgg{l9-ML<YJ+b8BzGE4Qpf<!Ifk z{q!y2=BhLOs?%KMMo-apr%d{0JaJ~y52%RzUVdEr?5k@pdeUazm^X`@Ge+b=tktl> z;9AKw5{;K)F!SfbL4*&4-?)m;%ntTWtc@0$gseN>yQh}36()+>-J5^L$H$8z4i6+4 z%rtbXIOewGan6=gxJsx*GiAb0Bxv*c5#~UHUESw|NaFCv+u=6+i>eYL9wiSyyp#=h zeA`Ueou?aIdH=ezqXScg2G?z3nkThBXFor@5^xpV<1pAbjbz-%N?F=w@9nshjyphc zF|O5CoOZXdDU(h2=X|>IN!r+4yfvfcmn+PPt1jtwX?28lJPAIbC&Gv2nPPYGOEL(k zQ91Jnft4uN?VklQL{I^{zH2imrw54EGL%CF8g^7rz|hf?q?Wvu?4wONpN*IN{rmxb zXO+Ew$awiXm!=y~{ng8{`n~2%SZS80ZbU3;ttQfrTTv`!N7tRCHyKu>#g2rkXIzTd zU8u*q?U;Ev$%)20p81*K*<2jo*<?6h0Gv>6So^F#(Mi(YzUf+yj0cF|=Q)1rB3LSy zZ;5&21(Vf-nWwOltz1X-5cQid<O|^mHaRp{dGCf<PZ?2<)`yST!x44fYQKkn_^&@T zRS1*2OZ!M2eeZ5&<B+xb-VM)Pr2^4f&!-s*8fzZ}<LWh0KNsuq&C%HQUg)WwB&13G zyEH~wvZkuQrPto(j>)b5_*kB7*m6AP$HwIKmHt3&oADX-n!_KNg0Oq8<zp?3q(2Ou zZ;4Sc<Sf+JXvn)FzH;zG_Kd-)0G$|veF*rhZ9B6aN2|LA3G$*=wd;+&!KZ@zkEEj0 zj@(XKoH7UeiJe7A=O|U(&F;1b12RMV+t<waP;m3i>YhS<oww!Hr+wzJ(ebnsjv<yj z0@mNPRh$mF>BKViA2a`lq9w>Y>PZ!F9BL`58B@oD9*>HEt2|9DQ9j4~v0)4>7MuFN zIoa(T`g&t1x66xo-0xmSAZ%BLx&%eIO+K>)u+jlf`)kM7;8~ajnPUJrsXHTi(R@c4 zVrD?s{kHagjopW{o(~VUDWT3fZ<v}3{WUrnR}=C_;t%new{!$xc#<AZRDLu6VM9jx zBeHTRPc3yUHBj4}mM&MWyXGJXVOr<5-Ja~d%rgE;TIQpiUp&8mbEvTQpTr(XjpEM5 zo^x+ETiX?k?QApYlvbMjI>ljQKC4*|8?Rz=&CEXZVyfh*vDeTFHUfBx*KPiK_#<XH z-0T)jCa1gyi_nETjdJP<nt$3U*nuviYDN$c<$GaAKJTLT?CL_tuZfq9z*|_;WY4g& zO9}`<#EpzU&d#lqUwi3#o2%#0bXf|fx#ulR^v_V)TK<eNhK*5JC9M!%ebljVEfP;( zUiyQFLosTjy{*XG#3T!05FETh@y_wL4H0^t@XGj*Fe9VdwDTChb7Fdyh+fm8<m1UQ z1Ig$+E2#a{JM1CFzStMGayQZ^bb)i=L1&lRqOkDXub=kIGtV|O%ZdG#^R)l@jI!R0 zGM#&6%fA1X(9m;?dxpT8V0^QRAjw;u=(#zf;h;d3hP61jNtT)-it1_7gYcTaVmJ~7 zj9Aoh`-4^{0PBQ3eCJMiW|n(_Vk%Ptn`kI<hC21kMU`;(phdmCW81x|?N{Yyx5hnk zYklM$YRSn_w$pp7Mkg+NkBbjkBReXqb0%AyS29WBwY0t>vh`<qc~Pd365c3IQUi2k zfuew=RGzju7W=mHs5oUl%_~_fhF-M(;XgW+6<<Z`&sW<<H#+AyXR_WkH4M8Tr99Es zL{4OT8{N4i0(mqh(uJt)>?Q!|(MQ<RV`gn5tZ%Zmv9E2Aa`OsN=CI;en3@Y9$JK?L zj4%`@(ie&1{zS#*pH3?!;9soUsB;6fSG}+D`bG?+ff_y)^u2C)7vqT=yOw0U)WuZF z<(r^}0Kp)z?XdS}e2|v$5hKiQMfKSvlvmzX5=9`xPZ^+s5uh$~zi{HzP9pSpWcI;* zjc7Lf&eqmDVqyuQw2#KP@cH8h@6CMq(trNt&yS&9ew%FamKhV;&#Mz!>z7Jy3BF?S zhboSKLfFrJm&lho`)={~W?Fc@d%SLOT*harGp`A;Z!a&)f9kQ5*3~ywSSVXmRnXZH z5O`?Ko$0)6WW-iZFRD%?^oh0@U>2IYixg*^6;C`5t(VL!Tl7pXG?)^n6&V;K4h5J> zT<#~n7wr=%6zEpyUr*_8?FxJvgt87WDVv#lAuy(a8ZJjcPfT;{yT$w|GLse6Qrb(; zwW^}ET}jII_nHnvYmli8l7g?dxqlAyp55P@R3u;WN|GAZw5(3LS1(LVo$=o4d`pcm zNt_m$F5hwbF7-`EYsa+a4i8TGW4vB<ncwWPY)o%?ovAnbw8z;4K*GcZ)@ktmoBfYw ztHY0cSh9baEPYC>bb|3<)6LyoZ#XDkE*ek2#tkg+$qM~P(kIuLqyz11i&J5c`Qy9p z-o2~qTE_kX-dB!R4Iz_a!W}xyzCEh_->CvnfAs(6zz~3%50}!ym!@h(^{QRwfvdUE zTJU^*{a}5(1<;#cw1m;qz~&2PL2r~Thpn}vV>Vhtp9!WKXlXFBz*xI(L&7N0X#;T! z(62&Y_3ZSY0OBvIk|yq9IAVH$&0ZQQVrG^Nd#k4`TAQ8E(G$@G^t;ROCK(wS@h2r; zI5Uhvy#2#<3UhRwIPOgN(f@l1z$9-dG-|+I0)VZjdsCjm2fjx~M_+F7%plHvvsbOk zo>?TNv7I6EuFDVb+W<|DHoDHc6duL`sE>y|0GIO&SSdAXZJs=NPsPBH?+qC1W6J{> zrGN)*k&==!cD%PrPg`>FRUGd#T4BeZE?d)&ZT40Mwc##GDcRL^9jbG4Ir4z7U<?SL z3lfDKo_L<DmzM(XxTThsmWcGa<}dHCFgoGXQk!x0lcUAtQa-b8bL826{7{*V&U$ME z^Zz~Pv**^ItEB*yZG(Hj&#w*Od;J3g|GQE_-Ep?dLMP2SATAG!xNhXKu&^wXmKsOz z3yZj{eW$v8JNuz_$#YT9{oMT_9XktvfbIWXT<rX5Y#XQrO7A6m?cjz3V^x+Oy&;^v zQZO2cN3}d&UQh-=CaVX~A`mo$ghh@i_>s6V7{`~o`(suG7U##q$hF&PX(lEnxWQn- zzVq4})EJut3c)6u#jf}aBA|xli|<0DTOdZQhpvz?=*|e|eQw{OlTQ>;!#xmOsQw~} zIov?-<Sa@>D~UM-*j%h!r`*j-0qAUp?(7uPGX~`LCBQMUY62YggCH2wUp4n$$1E-Z z;afj&Y8#(PFf%_TxPHAaH8quvJSQoPQ8IrLxID&S_zI5BGpg!Lt+bbu)3ULMhbr|_ zUS49<oVq_=#&QmIz-Ya)w&nmDVT}oFdc5sS?ZH&_%uZ+LtLo-u^S-`5;F#$zGL~I6 z0j5OLbL^SMtME-UGPyfZRA;;o;CTSKyb;umj^=km^t|wGK2yST>;bZ%A{-VRs#~%$ zev8TC#d7MsSwUrikTgXYtz>ib^ugC@gzWFgr1g1>zQ0V6?UlR>C;IsKBnUaYZ*6_v zY05`H&Dl6U9{-VuRz$=)XuR^>y~!$PW1zeruF-S$?)um-DXVrSFs`+U$MzlwDDW(Y z^KEK5?%cui3!&zUsi6Deb9M}P)aYYxM@L>@{#yhjE*Ghiu&D3fp92=XgR^s~J{2jc zl)gR#u;Zb!)p8nb;vd=R&n%vuoeBG#Ry52EKGjM}1JbDI!%^TqEcQNe*{H!N1U2OZ z;ht&}c`gCWj48l|5k{=`2s9ChBXC++P`iH{@W`n_4`!jN>nEVzdwkg7h7&%LFm@aG zo;B&YP6qawH@39}+ab@W0K!`QiEC!{Mpce#c}{-8sM7IbW;W7PBc7~&VnVkYlx47B zZOz(`oQop{b9#E}TZ4wFNBH^SZB)z>pPXF~X>DtJu$@qkmgMqv9ryLXvQ@(!HvRhf zb8B}%aUe_O9*|v{58v8Tk68p_KpSZpnVm)oMIny5&p$YNlwDi|fi-~M-{0Ts{OrJK zxjztxQ4bR!#Gx<#ZC<#_X_-7YIJgFTyxN&48mpWpv%S;76_t>%R_MlFwQ`K`uFamY zaw!EU8>BDPcYAKC6`b_=VlQS=lRbL$XjK^K4_nYvX6ouxielTBfYikZ6lu7|#$;|` z!KEq0f8Nv8_2Bj7{?53X01OjHWMt%behYF$I*y-udQP=eG6;0Jy{l^;`1uw?A5CTM z_&$nd??6ZNng4WN9TLNI^5K<O41CPfDgW)Z{f5DFji=>OZU77dkT#PsF)_VL5F+y{ zNBhJZHGC`rt!8kaBWt;&`$uhfcx_c#&7e3({=!I}&Lc3U9)qhhv$Hs633%qAEWM5r zdqcx%HD6Z1?hj({_yp6r4Tff*!oj$@F)!oCk2{=tRnkUAOt6})$il-z=IZKdiOO_n zvWbd}l+%s*_RXnj*Ii8|X7L&djhGK;<wBl1Htm|Jsg5n)XJh*eOgmO@jJLtg@_Fno zvDLB`a`?)^TmaEcNuvow1^GM7elRM;^dgPo2NT9;Qy@3Myh27zz1aJm{Wpxip1RtR zK(*!1qDS|E0J6$?m7IpgqLzb~HxiEZ_ap;GtrvipZRYd{b9%g5pyrbI8dzf;TwFMd z?hC<Ly}iBc68M3Efkm#H`uy@2RrQ+0J@-kes1{nk(u3E*NeGGfE~$co!s-6F9aNhM z3u$|Q+ZVP3anI2xSO8yE=!0K_=@xyJBsTg-SyWvM8z$nkWZx7ts*<4qtZU-3AF|_N zK1!{I7v|ccSU5$!kDY=`#|LrB%o&0+P1g-0u2ZmX^~o}Vb9*c;eTmWc)FJr6EU>Xc zr2*b|NnZ*00QV!POyvO3d2$Mh7xMCh4?Iq`wzdp+m-=dsfJyz?vmziZ!4a0MVj@A= zZEZR6a4cow;)ytJS71@y+Nj=)e8|ghh=)h?^M&+d)u<?(quf2ET!V-012O3U63xTu z0FvBc7FJgEIC>M`jFO&+>S|G7{bAd_ymAqNL?V-r?<b?@H<!9aH|Li6(q6?2h8COk z?0^%aprB{~=l%a&aJ?MeJyH4Y2wc99=ROBcBpn<azR>W=^cXyL-9eo7WDi=EB?*|b zMS_`MKgVvKmuzj>gD~5U0mo?GUIf4Vz%f8gdxJh8Aj+2liPrGa%gNU=iB3$+tKIrb zpbf>)B@kCwNuKRS?NxpK`juQDZGRJ85XvB~O+Z96*c{GSKriC-Jm?dBK}k>FSEi@= z$F7)}5U!P*m9yXGH!~|%USCxjXy2z>pQ_cN<um;<l&>dh14BIq*4p{g$Ts+`u0&A{ zS`nwrp_HdrZT^0^J_Z~t&Yv_5V$aVbhS%8jR{!TcugJ6CKBt7}>jUZFdmzxl#aXI+ zfD-Wk<)GW+>YWdnhcORznnC+W>gio4{(O6&BBy)a|B?$hs#1B;n<`L;GoUC*B-Ih~ zuw%Cv09DtfYO7VtLqC1G-#1po4_2^s*uYmWJ-HGATz!#IQ7?B>^eXI`KqYw}KYnaI zR<it&u>t2m^-WF^36v9|-2cO&ECCfqVG@^K)i-K=H+-O+SjDMjItaGL=|qunOKWFm zBN~k!8!xYzo}Omsw3?{kDYYCtZuwqQQzJ*~bA5fRv|(^?kI`<p#vQX-bv_frsn-rO zizAh*TrV59J;c&20|6IKw%?kW@mURV0IB1XZvf~WNIHCzni^sr(Jc%n&0)5AZM;1E z*UMO5qraC{ZE%JTUPbIa`$?V~MvL8vaFrln7DcLtL+?yDugltqW5~H<vM<&>xKGIQ z7~DZ{csO!q2PTe!it1xXNXXNf3I-T2V|Z!EuLY7uC+Fhde4;x5>@7U;T~2_zkALwB z9=BOHU1D1HgqN3>Iq=OA(F&qOZ!$>>ut3Z_V`qUd)f*qqP-FymeWf1Afkw*hlEWgd zGkvHa!_7N(2p&Ml7b^x9gpf|?E1cF}=k4idS;=WV`rG6cf<HK4ueu)u4ou*m01}DT zNS2q}@m)jX@}N~P=j61s1ob%ub#ic*ae_9tf5@omlv%$>mhhIFI0viv6yC=Xk|--H zi(wCEffPFw>?OBp=Pmdji4@BMPzM^XjTAX8_4<Q2V<74}wRsJc72RMHS{j2XmWT3a zZN|%3AUcN`<+T~(@9*!=Ef<G-#?e{=ug@jJo+R-S>(PFZV=E)xIL#7sGH`VTdq=mp z4e@Y3AN*QnM26P2e|W#IC|%nw=ZKdC8W1@pB~m<QsJ{YJ4-xTJJ9`0u2IrW4BF2ED z<N=xzyp_Qm*?=oV&!0axgvS^vAdx&JRf^Zag;p?4Z9P31;BpU7P7Is}ItFur!TK@* z)%41W9Apf^AtAF{{~BrzXF@=5!2B?PXnjT^CFK$P|A62eI8ve^oaeEd64NfVG^s6s z@Y)`n{JVdDFDE1>T8tDjL72iUC<u6_#12cnOfg&r-4Ky&?d<&R>Izi=fPOd=2vz;c z%O8Q&xpd`<_9h=Ydq_r-$7dKXeA1gYrGbk@7)Z3iF`BeRvb;(ZiH^6ct4v7pW|DS~ zf;6j9&E)OdLenlFQL5cjU!8~1s5j@2jE!~G`(?nE7IjaN7lvVCV&ZT<*5Gip0I6AM z2zB^&3m^#f;OrnI){&hC1J;wPRh;``01~ZEIJzb2<CVo=2O%L&q(D$`Om%`bW73;b zwfJt3W@y!V2mo0X6GS}5eW}lKwM#!wPaE!FFh^jcUrr2L71TBd5>PMora<*#fmzdM zN)FwMuk@lWn#Pje$0g2AJivRaRcy**J0XPk<jE7LM5HqC+I|*%G6A9nDTU!wjXT(k zV7SO{hz@bu2nof}0rI>HgvfTzg8Iow#21kNe9NPqc?fo9Ha5D!^5({#oSY04n<)y~ zO?9q~7FV8QPdfG-a7;S-K1cJuHIrbFo|M~8wtJmlyh_Y5xQ>fUn%tvU<@4P-`_g17 zC@G(5Yu|=k&LnKn5<)^ZnA7du-AnZ_X~33OWYR$eF*MF+r^$r=o~qNY_4vXlnFP$N zxJh1}fAQi4oum()Qm`@Dro3Sq!YeZ23|*<uLPj9i4@Mto1Bt9wotH2J1H-FiiAfP& z>tIafV$v+!Y_4X}`vN2u0d%)(GD%?56QS$zLj_yMt{vnDFHxr#1^%mW$Cv`cbJza* z_%hsXrMRudD&Fzoq3-ZmY#FhkL(J`ukXv{`1~lQ&LeFnIG2(Fs^X{Wpbed>8S)~;v z8RD_GqSbS<zwX)8qZwHZ3CXGnK0ZFEa08;%p!+Sa*163);Dw`%H&LxP{O#8Po*AcK zg%$&>Kt(H)R-}6H@Pj=a&Vp(HXsHlb2C24XgAmZD+2!RI5Yd3Pa1LK8{BH=~dc5o* zE_Vh#zth9naNG-(qY(~c-dpWzXIr(L&K@~OtDK>b$j-^G&$nfH{dX}2jZCPnuBM=+ z?eM>NRmgRNj)#W_M;Hee`kI(Xr1XZOqf}GoCJBR>W-8TN2X}XOKW+9~jEn&D;r!AW z{p4B;^v+&$2%JfjWPlXYYRUyZxXZ#qMoMb9S?DC-yrNp=y4ejLV+5k=qN1YCWXWX6 z{%02!nsE^Y0auOELSIwP_SRO#!rqmD6x4z93hKd>+bj`~H)+i{f}{G|(D35<^E)CU zRj7m5hzNWs$c5V4+Roihc6Xg%XfpkvA?e9CB_*YLFt~USvoxbwnGxv`U%yHLQx;~i z2WfflYj&d{H@DBJ8)%@SoZJlv!qz4#qre4%Rqg;$xRDvjMfnUa4Yu?VTppFD6FY8) zOaxu*EwNBp87>G1Mr6=AE|m-BN1kre{#V@~l3Ay$BK_O_zZ)oYn3Y*=?S?S&xw-ig zddj8o6B)>70)$~Zt<2$&0tgTgyfrm-ge+f7T-;);<N-L?HzS2cgtUS{mFl*dSB}$8 zNd5Aau1|vjd#<3++|v^dmQvSw<<K&3CI1)bg`W-Ep(TXtjae!g*NBN(g@h_zF~`7E zWFiWNo_tyr^u>DNX_r~McR?w0K7L4OsL4ROyi2$|7wG+E+zJhtq>334z$MbVXu%to zUqOsA&tm9KmL!pa*(*St!V<?{?W**Wq65IT^;?In37~(=k=STq^zDSSuL*or+W02t zhYCTQWU51oVLeqt^Fx)E2Z#nuCi6r*_pPg&(=&AJYAK=b9vtN%kbps0U#L$FaR4OO zlGE*zl`)IW{bY@O2tk`0nUftk>UqEQ^DS=23);-21Tp-^ataFzv-k{kbP)IfANCCb zf;_142^s)lhGTcQ;%Kp1_+HiNsaGBL*a`@}u4WzwsX41^eri`p_k;TafxHU&Kt)A` zuJgg>JTTeO2s_?=x6o+><Dpw&pMsj?#CAiZY$bmO+h!at?lKs8f0);OXYREf3Y-zw zHA2Fl;CRGN){4C#%E37W$aQ02y(lUw>glUjgm86!>yfXq`W{)0J>`(Q!Q#d?SazH! z*w!7H0+V$xZ<8l8&g^%_@d-oV+#`k|1aE~)$>A3K=jPr5^VHH<DZ8laM$H?x3djWg z<n-;}%di;G1s(#oo3q;|i(^z>gGCF-E|h@$9hd=O*qOw|>HT0)Dp?d-q>PX`q^Z_8 zQPg~>TS4jv5zn`5D#lEUX>e?Yym8<8%!qOJj;P5VGS}A!kq^5#Q62PRZiT4w(r<kD z(bizeankAt))hjXkB}-r_-?v2{b%A`kGKYE!xID^RKo~-QG$f`v0cWi^(I)}0DZ=s z9iWSS&OAVf6koq42OUl^K>AF8E&)qCuX*nsFvZ~Ji#J9fFc-x!t|0*%23Bn578j$w zeUk<bcLA%RISBg9{{0Q)&?)<p2D^6KGtVFieGJQam9t@@a(ebU*HTCVSPA%XYo1Km qlv)Y@Tm4k-!WnOP>;K0Gr;?>5zZ|o?ZwTYTzZcIGr1PH`zW+aPPM`k( diff --git a/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator_just_doubleml.png b/docs/examples/robust_paper/notebooks/figures/causal_glm_performance_vs_estimator_just_doubleml.png new file mode 100644 index 0000000000000000000000000000000000000000..e622a649b835dc1ced614b82642b55c8a52d497f GIT binary patch literal 50617 zcmb@ubySsY&^NjP0Z{}IBve{Jq(M4;L_ty!2`K>q>E3jRij;J>qzKZ|rF4scbjMa{ zkghW~zTf+ub^boA<$Bh$_Z`<Y*TiprGqe34Dal;AaP0yLg}U@mR$2vx!l6T<ur<!( z!6)%(9~=0Ou)}=~hbPv?4o>=ZMkqyn2OA4(2MbdJI<%3Uy{WYoAEz)UFFV~!2L~H_ z5iTyv|Gj|I+RlWFUI}Le9ztLvt7(ry5$hxWus%v=o1#$7%nzmSsyZhwPdGV844$@Z z>_#}Osazntl`A!JedNiba}=xo9qKl-N35Kx<60yv7Z@t=@8L$hXCHA}yTtK!bz**3 z<{_CT^~02mTD_scrL^{lt#P_&ce@_UjYg~TF_N=CRvhvY96#hww)q3L^x0oW(1pwY zzT~?tb@oX{*GJUZCt<wl|G!-{_Y~J}!iq&)oVt3#3QhL!jaUV@rFeOHUyhY=P*G7C z%aD+S@rH(lz0#+o^!<=>%)qelM*S)s1M)XJq3Zo_<#CU(&OTu<dVqWOiC-ku|6HVE z{J$4%@Q9vWU-53jDmyjR`{>BMB;7A&eS5pv>`T=)&A+{NP*qPT+1c^>`}>oTk;PKW z)F?<xpBw!2w2_;zJgDGo_Zf-8cJt@k+uPr~eM>@4?i&&kGB&9ox36D5*51{1lb#;S zJ2sZGd|c*iHL}7gj~+?M$zAB{>r>6w9$6NeHNwWh!IG7gO_htfZKWBU$?O2T53{?; z#YGHnx3MW4dE6wAi;Ii()+=WG?*BX`gGpUQW%Z=`|9b)(vbAoz%n}ll`-6F!e6Bmo z?e@Lmts8T#I<5tKd(O;?@eV&RG9fgIYm>EsVv`vUBm6=_vhTekG8xC)|J9d%$7(zl zuEQ9XPr9-xi)~16__)#k6W?qR9x+YVx3?EHD{ScNPftV{9|S%B{UdYSu9G7pGc%5Q ztp*Nj?{KYN+3hGq@}$A4JR!8r{huV83}FW()YOU&4tI)*inN_)-UeM43~qc!ytR)> zNq~Dr9RD=YdL7x0&LPiU8U4aXAGZJRyi_lvtCNDp37^zy@0)sU{~7!}R?gL*rHDf( zEF2fcDDxv-hWJ_O%deLN#~XfR$|nt_nC=h6(TsgDrw|;s!Re0Yp9`1goY|f7k~H@% zExq#~i1Kc0FtsV@)oZ*<2FC~6**Z?`OflT{i(OBm^{GW1{1g)e`m&W%t{k7#CiP$; z>X?ICMvLmy|LT}M4pgl4iW%l)uP}VoiPkV1E~I~wt;EE^@&4;;{D_#C)&%RihnaFw zvE2IDr-xIgdy5IyFZ<H&hqWGUc5=BK{LXgS9#oGLalB0|dBhFUxMTivIdTaef6teY zo{r92S@JkCH1u+oQc}lz!O1qbN-Ba`F>z?;cmF$+6p!dk`54$H1u9E9MJa|;=lsQs z_U$Qe%lin=Ocz)Gk!RVWV~tCSs1xUPL96+d@~K6ii=3&R#|J?-gv0SJUE@YC4;#F9 z=~ooAZM-D-sN712#`PEdvoB`NdmD4g)vIgur^X<)n%zmaQj3xBiV7hbmw6K8`!@un zN}VHL%!fcuiYXFFSU7lOr-C|4FYyrHkb%uj_!`%VosN&IKk^WNAFuMSJAn+&L7m6p z`Ae5F^&5SAFocb%I@M@)?TSJT!!paUoyaw<G7Fa$yv_I51r5r_eh=mjv4&t47sDeW z>_@lZZ05Fy^e~_#nCY*tmnUlzCTrbNiUczl9-Y~_%3Vs|yM=oDLi8SUE$;_%p5RML z*7HeNmyd012p?)q@L9e$FG{Qu`k0zJ9W*y)A@JXS|MgT@uEpiA^=A_!Dm+qbl&4c2 zoh<GqqI|u;uk@TevXz<+P^?&SH-Q)(G}Xq&255MZ<(L3w3sf<RO<kql(*x!1;nCjK zCS()@HkT~0+VcLo^1df{hdVF7PnRxT6R?nVbro@OaT&<h8Qb3;F-hTZ+ga|<Cng~H zpjB>lw9!g?baJ?M)ph?WBEA~!yH=b8Y--cjul%RFwaA$?NkKxgWn^p|9vgdJ3XK+k zdlqVUC)43*#%)`v&z(E>y~5Uz#^di}M4?qx$@Dz7ua1>j&Q)TPaZ;b=s@-H^;X^NL zaMtY^kaKFc>3JRBmzHi~-MuF1DfY!|FwLkl+I@Gz$zrVRefgB<VX4AIm1DZ?T#MMz zdZXC!-~OhiruA;YDQ?pr86`VO+IH<sVy8!2An)DPiQa=lao4}MypFerOhZzZ_$Lu> z>zp}Wdgo{Dsf1gWBBLM^uJ=cBj+Y`1xA;#>nAKs2Llw4jl!8__9tlN|kdodzJvpWl zbLRf~^=ps0uGg{SYW=CFoxQ#3&pfKR){t!20m@-{I1JQ=$7)<?((BZt%4w|!hgi(! zS>ql^qO-}Ll#0jT-1gFtNmne-z-O)9QU$f#DA>XV*xcs#d&FT;QTMgAZ_v80V<m|= zip;I0JFQKMZFTV>yD`xbz$<j#Fd*lW@W?LytW{3_ySMW?C7MgbheupI3AD4=Zb1$d zrYo3{fw^d5foCn*O{n+<0SfE!<N2&i{myRO)MNYJzUiu^uAp0-;bCDjM=mMJ$*b-q z^b963NlD$S6V*9d<(ybpSjMd+T=T2-_x^x4DF8*++}P~Ap;>$`r9rC7YaL6yC#mx) zpUIEugC#7D{msss`I?4SkHGH;GlKh_eOl(VOD*boL_+ww2fRzSZf&8)=W}ZL3e_Zx zezm_CEcW-=-S_m1mGY^`;ig_BeAoD<ui>O_r-vl}T+*k+qt<ivU#}1<zH&uldojVd z34&~xJz_+<hD+*tVF~ea8nv!X#Mc~J%-vtV{{8o03AKE9?4;tf#@XoyA=M5EicM^= z@1liyE2i$%y6b_1u0PG$_^lM)!+ORnEGYT1@)!aDK9C>~p`g%lR(eyZ$8+G%&|1xq zU_FKgwR5s_J;y}ILJmx<3t6O??p-A!Ef%G7sr$9N$@V*qA5{9YRM+}&s=HfSlqDUV zq+vU(42TRih<XxatjuoT9sES%)_4EDr%p-r8s77@zrTALFCdz&jVy3(<z#^Z^+y}6 z8D=bbBQ0K#B~AaHg^SDb;g+&fGbwD^0NFGR$KjqW5r_79sR?;5j-Te28dFynVsK8$ zcmPa@yY8+ky1UnG<vvb-P-@!mt7TRBcA+yyw17(7wN%TVU8gD%q9oA`q3NH8s$|G( z(^rm<otF3ar3Fm?b3Q(3_Vif6!r^8ozoK5iY&j(%coW3!bPcrqA_eD*nQw28z^fs~ z_vXzT8%&au-LLN-c-y~MIhuo|$|@)*#Elo!JVDlJ*=t>o^=P-aQ||9U>g}f=M~fIy z)n3PgMwQ@iEzbRKRDJqM^#fQ6;^CK<EsT2-HJPc7j*k3-gIk^j88z6UkyRRIu1?7R zsB)xKWBYQr%e5ioSWtoOEna^TFk$8NRE&g#$3G;5_>l(X(bzv|Ac-;5)YKH)9k+M* z_5GEA<>>m~e&wxS=^>j<q{@lgo~JrKgVj#fEy2{Ch5C&zC#v`%dQNA?=rTx3PCcz} zwo^phLf&ipMbx7&_wcdV)HyeVifY~FNXeuRPL6MW)?54J3@X@aRUVNNXW9{yD}fRL zzrt0sj(zv;-M;`yh6`vV+=T+kIc6uErWUH#>KGx?n)YX1bxVZP>8tZ_^W0ycpaJMm zckqYJQ&iZT3$tj5Mv;V-2$w#`Lc#jD28AT<-fePN={TX{6T0Ii5v5gXb04;g7-F1D zC&vw~l9tFrnQ|+|6n|14!gP&-L)a%3xGL2C{{H;p;uQ*tR=2fU82}%F{69~Q5BIlv zCHIfEdVh~s@}aEj_L+0lKHvT+go4;+^gZ=1s;Q-g*LGG~?enwdD0N-U<)Yiy5FHNN z<mHvsuDo)~0}J~Rz?Plf*sovrw@i5~G;&%=Nj4rddu*){SXIp6S=DZ<q1eeUg8IMl z^^Ja+2ap8ot>OaF$;nCS-Vpv196Umy%*x}EUJ21T4O?5=Y<Jpg*W`3{b^Yz0-a!tV zk$V5o8XUu$rj{#e)##ko+{w?^De+hYteLldJ@)ye=6F|*gk%j|g9o!x1|3^x<e?T0 zj=U=B?YnnRs>x1?0P`M2%)B72>^2r%+BR$n63&cCHyiu};c};R>150c`(tmRAA-?j zM3&PQ)QP|?O7mMQfr8yQSmOGl=k{5?<toVrv#<U%DcK7&Vfn`PHZC2wRzNqXo#l}^ zcL@mznnrGo65~q~$|r~62??DLMSl<EkfQj_25yagzZx3<{eIKj@XC(Yfn=4V{YVAB zk^mb`qU4m3AvhY$%fU}qef7Cr|GorcYyjNSGv6MO)Z5z90{%T_eKwG*_(l5_fIDCN zl_k3&q}SNF#FwiehQ(skO2iv-<LjR_<*1z%9fACGl<?;6)VzsFwS_hb$p#@HGsK$W z_)N~8b-X*ZwKo@%V%HtWRVNE^mP){aa!Aih9O750)x@Z<8^fx}aP7;YXAt!*$I6Tr zI-+_^`AJy41m073#yd^YXK4w#dTjc4wtN{|QK(%1uQ-?{6T^~H;eftB&dA8vxW6R< zvYDQK9zp8wTnAQ+UYk^em`rf~Jhy1D4CEO4Uwv>PByL3RhEwRd^Tg<R3WE<x@&7IM zuKJY76i|d%PoGgI%J27&=FGWkzIWe8n0pd)Y5)3BbYHq@3v`*sZeBLoZB-b(I^GVD z$9d^<xsb!J%Yd`^M0MYudE@{N$&{wST&$0y`AMCv1*0Eu)|JB;85pj<)29rg;`1eC zQL=%s&u23&rB<NZ4KZD5Oig`Uruw<RpUlI_s;HwNLic<32&{*{FU*O$%7vF(G9ES& zU^6i4coKtPwE^j&X)JCneh0wn9Qh{V9r1&5tM?1@o-6re8kM{=ERqXy8g=6fbAf(- zZIw9?N<wCDNqs7{Tq<)@t(?Y|>MTUUM=BCNd?9P><H4ErJt2PgiRI$uZX~UX6h~~0 zN5yG{0n<c~hlQnT_M^#i*Y@wLjOF3GXqE7>RoD+h_L+liEIRsR7f7ygd^C<u@otl4 zP!g!19b|{c$i|&13Zs7Wg@)SBf*YSc5yN|%&iuXM&HQJ$73EkH9|8`LyjbCbJz{b% zn3%&|_PBkU5eR2huji1fCZ#|1&`R!t=gf>%eYT$;S;~Z!$>w~!Lb6!g%2;_sa<Y<w z0-?Cu&I<?u(lRm$)Ehw$LTI`{k3sW#Onb#Qnn>Z|{ltPP2oL_N32|}l@MY!xVnWic zi1RufoCkZK^J*&9Ws;5a50{2;E|XKdv$iQ5Tcfyq^}g)qXQ&%m<vlCAq$FXpB_kH& zPE#H=JEP`*!Ik-wvhW(V5GK1UQvEE@>m5|rQw*oDV%`MjS7I~sABLjgiL$uks38*q zz`)5befnti;p{!Z*ct3deAW8G;4vi;{8{tfw$(((avcMM${hf2CMQSxfFg<SSdI?t zJ0mIE_YXMWv7Rpv7m1zh%`s=O*M0x~=)ZgSl-;_nmI$M(YX=M~5{wZU-n1M>MOtF1 zR63fYt_<a|mbeA@$@UGepb`JhS%0+oB!Kvxw>OGeA-3TjvG}=$S2*~P#bUt1Av5Ac za?6bD?1;p~uC_47-+k%$9M4LpeXm;~$!d=+$QDU{m8lNvLz-J#x8N?@!<F*>wEGvU z&?_DIwC*+?=5?nhZU;Ln@(^_@4p*xmK77bJxaRdE<Kf?xv6vWLSAW2Uc5Ah}#SXt! zX6NRFZKl1qs=Ooxzz<36P2&*Jh&gvS0`jjpIaop<EQA4DxZDnQud={)2~pDD%K{0k zxPIxxH%%u*EO2o(@_zDGm;3oqK^_<v7ne0Idh6EtM(zOjW7wV$9CMPWlf)7~xJ99r zqV@=ut%Vr9a6q@upVJNLx^W;c1C3Dqr0P#V%Di5GdK>~#$(Nkt8JTeJQbBzWU$0mr zIIcE;GtS^Mkn;fb!vOG~drP4^os*rMW{>IVN)(O&{fLQ;jb#Y&Vm{niVWg*j#rh;G z6u>Od2)!=HkcI)V@#5g`?;}tXcp-uJV6HkT1W3Ut_j?8g3`kaMU9*7){~qLQMnwXF z#G^A>=P{p|Ww5==lID5LTWzhOeWQ+^em*lhqS!+;?EST<k=pyx9r3`%m=EQ%>(&fM z9y<O0@fP<YIa%d-TlfFgoJm(~5MRgp`f;tRW0qpV;?YS~f>qi{yO;c8Z%Qx3jeWpW zrp}v3+2H4kJ&#=xRPyn0dO+f72V22>09(F|F{LP)9U^s_$Kj4bqEHwFa?$Jc?}(^7 z;`z;oi;d*NS|t-8&c_T7Yj{8LoHDs`<A%bcN0-%dw<8y{fwQUEn+?=pC;u~;D+Ae! z;-MSrnGX{KO+())$w&ncx!x3SbwRTk)1Uclz1C7fdFk8Uvt3fL9K5_&rb{yw7-fQA zbVRZ$#PPn(EvSpn%gcL`T%c23wlxIdgz?Uu0Zn(QnUuTL=#}9zORb{Krsn3PO^=r^ zZ^7yI9NszYCG=d1=yaFQRZ0@cq)7*@(qKQWSs^ASW)T&w9+-=cL=4Em_)e;*s?}fs zV<1IDh4H0JRuvz**0>l{)Ce1&Ljch&Imad-5dG`d%V<s=IoL*0i2CPegZoSCd=?*Q zE?=HL+8()9yn+^$kTCJOo|uy2HEQVT>1lGK^{a77(q>{opv0rY-8GEDg$oyYM12Pv zqbYO2NB75RdJ7#$-NAuK0TkW2n6GM>{O4buQQdiYnbvcUUU1Tx@qJIB!p{0kBjyI9 zghVn>F<?iFiMT~$D|y<LKPnYEhlXN;f`XptmP49mU}QO7!2_o?yEA6(BvE!UUvHT| zf6MGge}7WQ%18+&(!54LT3)_Tx=9tcvNCbFhKf5I<!{!9P5Aon@nQSUx7vBosS?q( znh4!mS6iSvHriQI3XM9+5wMUbY)=VzT=MjYk6IKhwzLFdmje#L<!_&iW|;+*sMG2^ zSjKZWnsM})C92eHFkpWE1)@}7k$bDvYkOc<0a5ngw-K}Hl{`%ZH_i>H?*xFM%JF1x z1^{wE=sd<<lshY9tv<LHL4CNCDw%KH@{5clgYdqh&9G>SAgCObGV9jpn0I>4Li8*N z%N^l*FK_8PE@7Wq;IcC?B!4w1TAy@VgPf78#Gv^CFmVd?p2y(nC_bv@TtO25f||_^ zO+9OnH=tI<_&YN|EnxoS78rvK2hmC<tFf3gh@zL<0Hz};IO2<elM-8Rz-jInDKSx= z^gOimJlQwltX}3d?uz{iW-h;_C7BLTZPI;95um>`lCSfdkpXEZxey=7S>*>R1pe3# zzMJ19%zr*yJ5w4S0mp+dYi#QIH*el-S{7oM#fHQ+%&1pV`HNE0uj748)(efz`G{r6 z)8Z%GxHwj;dm{<$jDh0qN!k7V`?qe1gLIR^$jAu7_Eb48C6{|i%`1!_JIV6VF&Nx# z-DKk{Z}>crM}=zT2W4ZJJxgs=Ta{PCtc<Z{e~CXFEu6^{%|h5Pb0iV%AHHxwR-%&F z?ZsS6)`DM9NbC9rJ!a3Ax3?r6K%`$}Fyf*VN0Y_LUKr_LBD^_(i_Z}iIXIQ#7cs`i zT&X{XB%%B7kgK}=BRUHht_d_5Y|=k`%yKAIN*WE1a6?c^ztFYsY;nV<GnX?TIFPWK z4D>TIEv(jyB4GSZFx6$gL&+P7qWYGjZzhcSgx~wgw(lPt7*MvDX1fKFJvBCFUhi#R z%3qv|etciL2Wa`z65IlOGP)UCTMZoSHhE<h$6lImOM`r(;eN9I8lTm^H8ws}Pym+a zU11@&ySqC#VdL`4*_nR<1cVR7{i{L6&h)3CU#tLi=~;xS8qJu}?&6fpJ>S^)pfcBT zGherR<I;6R1J%LOuDyEo3T_OIk2gc#*x-d-tLCb8Th1Z&{Y1!E^4r{O;lW`-V%DP< z5zNTF{v28#-S!uN+#q4)Y2*e;(20p9`uJdPZf$i3-_-p!G&E$%M@xi=fUJkQP%IT| z;;SuXTDs5`k_|NE1#@$+LqaAu{V|V7tNdcWxuEpjYuX>4%>249TxS%B4uK`uk^-7D z2$)|P!xBq7ZKN0DBnk{6cFD;RA<+RirU;9djqB{~K!6AeLolD*@CPFU@defT1w62H z6UW&F@*|a`)!n{cFK%K*u_aJr$fA@u8&zmw#eQy}Bw@T5ZO_5N53MWmYrk-8@3aH< z^fQN34daceab%m;Jf<ZYB|simd-gc{9&hLGL>ffF*iE*N(%{;PXiyJUdsdnXoaooq zvghfGioA{Q0x~?~>?3pX3|rfj<ggp*YCJtk<`To~siMz-qqY)c$dRnA%Q7!NKifNp z7oWJ;m56`E3?WpT7S7&fm*~Cv5aV^Eo>4Y?736Up<e`#PlZFzmJjZ6)OS4&0te^yd zR;BK2BT>s=Fa08V)z^1U`o4boNTLhEjqsm6sc6h1V|f%;ssf*a&R74J7*g61L<%NG z%yZLsFBBIS?(GYgS8c2Q+p754O%ErFaJ&nv9E&tQGiM76@~44lDiC8}<zofJtt@^z zH+2J7cC*ZKcymkfY$2X!3yCPUXF-wZKJpgoVP$P=r-!AM%z>yPuY)>dbUF7E`so*4 zCZQ#6u-$%zh!CkYv67bgV5?AzK5uT0OU!yXfR9+XD{_OviyJ~xz!s@_dHI<Cyd$a< z_L5B(HZX+1c%yF+g4GI=Ti3i79<}q}6dPScS5M5}fx8!8pFMVR01Q*NUcz@!4&b<^ zOh`Pn3{WWA2peSd%@)tE{I3^E<M@~$_X)l?=~!9Z^#|~E;=ix2n@FKBN*e82<?)Gd zFd6R1$5tk6x9A*qtr|poQ2XV6zL&7zU>F$k(;zV!=R9t}vZ$;C%RQg}xN%vic{UNN z?+QDtH|FeKjL<1#o)R@-pwscNznG?X#_GtuyGp_BrttWh6sKC!%OnN!>{5RQ`Gg;C z;Hu^LKxEkQ!N#b4gIB27AqKUQh$}l$#mvGg(a4=xLdS9@D;2Y|uT+!-Qr-H=YfiJk z6@iS9L$)$1AFFjGAeWL(f8Y%=At6yeTS}D2GNp^DDJA*E4`0j^sI#RRsQKu|%AkUE zcAEccYg)p_k|6e`KNu6c_;7;2z{Wz2wueg0Y&?D7<AK6Kn4XHK9qrgR$K_-6u=dWK zyZkJhDAh;<zRb)-MADJq)Nmk(dG!WXC@n2_|Llf8?bL<eY8>#{@hzL#;Ha-5%m1v> z(Mi}=f2|KxH)9^O&~FTZijIG8O;3o^t@jej{#l(Mn_rAigfnV7bH3%a?DZEuYE#u0 za!2$>Dm@?kwD<O{KG!WeBaG6S);V(^Nuj`k(f(C$eM&QFVApMt1Dh29*KDolxp4Ud zBC0SIM4hbW#(jo5of_=?R2i!v6U;&UdpRqYiMP;^8$tAhl_2<UKR+iVLA28E>}0e& z^-Mit|9YW7kG9c(9<t8j5NbZrV7Ny8?3%DLYucS7cXuD3^Lft(mxVZINH0shOcL=v za{_X5h%B1xWAvze_3f?=+0w{<1c^y!NF#gK5<)lJ<^r1z#vvH=<AmeqMTEq0!^Hbd zOaz_-ek-A0G$h}Ck%$4=Uc_9<v-{z_I(<tD$lEMB!J#uVm_#a&6^Rp8T;;g3j}!>& z^!10gwjNGY_WwJ-v#+%m!x-mHjP&>RE?>KDj=mAYOW5c_H$!k{pJWNh<Ar5#T)|ao zxO*rn&AF1C36hlu5v1M1%DKO%Kh$dA#F?^T{o0(Fi923GXJ#4}j>wf06N^Q_m5nP4 z9O-k)7c5L+QL!)4H$W8&5UbJ7ui^T-NkDkB6m2P0f*4aXqB+{P1)1J1XWUFZ;{594 z6|MhU>OVNBSwyEfqwMVP^HR+Q*DnzX2){oRy54sN3RKt;5oLB9ZnEKH)N1098XhY- zvPzYOZCIrp`&YS%FM&Se+7bP5NY2D(U$HK@<Fmg@jim(sqG$mlO00{p<XdMefBHE3 zIhF4R90JfuIVnVz3gC>W@QJY(ya>oYBC#mrCURVz87mWk6UL2#cOtzzqPm0U(4zrR z{m9t+_vKM8)&w_UM1MsJ`r8#)lmzl--khr$SQGSVo*vwU#S!6cBIvGvr-BrGD-`aV zN;$0)Two|w<B;+|#B%>kEX<iciCCM~Jw_U578M|kTnylX5j<_`EIY^w{Y~XefBV28 zlf<+MIr#Y`-e9k*J2&)EADzfcnscV|24|9-(eS(q7E4Y`3kF2pdL?flNfe46ac4HR zg&080$CsdmUk7vNu3zy1mS|w2x*JL(K+PjtlUFU}aHb|~+{-4Ge*X&=i8)&&!x!vT z?*&lC@Nx?tVbD@P%$Yplb*dy6LuJp*UgZVsAY-67gq8(B3gPQQ5y!6JCyO`U|2mW1 z{1LoBHV1J*x$3Pf+~4Q#7;!-$1!09yGm5gdp9zZwaY2#K{`4eCHIHF+eiamo4IS1% zg_m3ZvsM%2y_wM}udaWmW}p_;&q2;?KJnk9qo2Q3|4w!J|1LfY6i=?Z64zQgvHfA! z9J|y2VtRH;$U;>&`S{v}9L|57mM>Hyr(+d$GU)6?ARTNDG55^yDkm<~2?@zL=?C$N zEn<ksNN)sjaD@zryCKyihS{RC*9ti`d}=76Mfo<*T1XG^LU;%(Ia_78T``KFpaD<^ zNig<5ahf3Flw*3TwJ!2o8u3(TdYPHVCw21SvomOAk213VSa?gR$06#xI@W*EuklbX zm~Mu8%)U7bEMgHzC@g}n3o#gkgMXwDG-9iOxqd@I)`C6tOc_cLWnfxdKK-ag(a5c! zG)Lt?_u+qtxUehs%id4Jl%-A!WY1he;@JavWN@kuBu{3B19smaaV3lo5dzbqhEFCe zZU+1idbuGw#oF!~SYa>Hvmn=GRT5U^CYZ|Me9DnwtO$OwaA7gL%p_#Q7W|$Vg2-9J zg6_S$)z;H8yCg5$9dF#aLFwDP*~`m*hCmXLf@7p#`YB_n5;ePo1atNVyGZCGIM8K8 zvi=#Lu#WQ>&U}HL8ggdRrMPP&_XVs78~ha$3ddHMvtcnCh?~fezJp_jCb@LoK!g4N z?UF(emlV)#RW9l#Rf_ZTw>vgY65qX6L907wCvAcVsyP}7o;f)XJoO#_#8S61J{74X zT-wW0WM=u_=MUdFrv1uq4(tagqRFR$Qr1Vq2(Do`MdELB)=9`#-~dv+Z=*sGr-6N! z_ZxHkypjNmf%Al~^WL00+woB#B9t;!{?wEhYa4DPJG61%GXCekh`@`eSd3!jzP!zE ziv1BGD(9yVK@`nrWrV^SQ<*d{=@Q7{^q;-Mw#)O}(_CNR3ENG@5)4_`;Q{|=L1`x; zX@T%=@2<vo?Gm=%&f-Th;)=-R1Ing3c<X_>GathH2hI>4HlVo(*E;q<GT`Rs3&yHw zeK2z}L<gF~{|Mno^U>W`c?fs=wXq^g3R^IG5o-JEGHPafDIu{%G_oAK$*TOxb2e^K zUvR}dv`81yt_{TVpx_oshHvBCx@kY!KRfdg-JoL;9x*=|=iTDncU&r9pRA94Dx+4k z`b*@8eSgu2C2D}EKry}J{Y$k>su+QqNUJYZ`d>5@l{gp}CJ~oSrr8lp?QJX>(8wj0 zI33U5E@b0Ps#W&rv!P50V^aU8#@xLZ%`EQq8*VE0D!r}~laaN{iX)vVqNnpKOxC2- zWZhpB^B2ODLN2R6JiZIw07*SF+Q7r$t9p~EXEH0c!4v1M3%Xih6>}NUD))GXTmST$ zbI-Gq*5M4PPec!n^?iEAmWG|aUbGi#-D!gHPqI)N@0!I4jH@!b?H}jwUOLMtp}9{W zC{P_{QYCp0cB61a4f4!J<as}fDl|N2AcNr0q-%O_vD;WiSn<_oZ$a|fuNS>)rO=)N zcz8pC)ydVPllON=CyFRdtgZ$Ty#HYSIiJu}^i7TYR&0qIQF?Un>@Icv*7iZczUSO7 z;Tmru@+xm-DXP<a08|k<1Uk|IU5~w`@4S3&cV!KH(^N&@{LO5+HreU$0<M`HVdb!- z6h(N0CXWN|l<qNy_|ji7^NkI?IyF7cd@r7U>*SsQdN#`M&r$nj?z+0`yc`l%U-jHh z?Y7oHHEaA4RSr*_e*xua)cGMQy5?D-i#H*Gv+Mt%p!DSB#z}MOLB>@+r|z3=f__~2 zdp<7Z-RX)72`cZ$%3QTPtA7hJ>j?V)`7k)*=`LDZx7@sRQ4flU)wgOvgZL0*xEV}c z=wo28eu;ob_`MT>U(n54%y{0UG=+pqyDRnfGjA>mCwj&*_bF;fybvxgucB_`)<%cC zdXHcHds1=y=ca%K`nqzLr_ZY?;+w*vBSyPmJ8Kz;u1j|o$gzAfeS!)Xu;AuzCBE)8 zy}8E8A}X1HbqTL<xyR^^ztwEC+gtoMTg_MXxEFu$>}ejyRyp3*X?0wX`h!(It_&hL z%Sx`Scs~_++?{|ET56SKN>!(MdP(re0S9DeWr1dlC`eH#)GO3lGo{44H%g@6H+^4~ z_;XK7(|ZS+u{cHlg}LVs{s%#*+yvWqNPAX_qBA{qeX=TPk5RBnu)B_4CbZ;>^v7YD zHQ(kaIV{he3~qi+K}vhI_hjJMfqEz^6F)k71R6t{?2(*pt9WcBBN53|@<7c>S)q^t zi10<S#UdA5E>XekC)#|5sp#HI-B#0c@{$4z1Dm5HUo?fB>8Z^erlofXI8Xi<sL?#r zToeX1LhAAJH3*z}sD#%@m!~UUGUv=RHT@j3{8+QcXIXwk=Ct52A~`{+K%|z^t9g&X zx?qVY>h*0t^Y83cKmW+dDP8bj*)duP`;xT)j;k2Kls%nAN4IYZkf)%iZ>y^qP2h(| zq%`tNYB%-;rI&sj^=ThcZkcMWS6KDnr1zv(s5cd%Hb>7ROG~t&Pay4{qJ|#64qwne zVXhVLg=<DS-eQ{=Ms~^4#5Ql)tV!;ub?<fFO6$jO4&H6Hc6)uVb9D<Z4zHMBk{3!u z4=+PHqk;!stj)y~pMA(iAkBK%`*Ui;=iLL>i6O*zQFT>+*#1Zl*e)H*F^JJ6{uWfu zzMF{$enkG%`XC}c!VC>z-WJhOJKa&NPX2F8LjY+)kGd29uA|31%9tz-e_7wdqU`fH zcYOV?(^#!suH7S}lZpl6Pv_M2yTkakw;K89ug=F&10OLi3w|vzV!~?Z3Ofa)=&E)% zWPfEDX&@>D=O5=1_cwnm0plu$i9W1Qscmyrs5ZU!weD-zIDdr9iF?zsEP#;*$Vnw4 z6cLv=bz|$h@1tJ;w{DM5;BPLb3GN>@OugBE$8!>}k!4Y?b!FvK`P@=&9p3co1={tI zj$Q?&n+y!ANU-c_=Q$)%s}@j`l#J+imXx7w;}ImIGY`pIsC0SDN3*u;mNLAq5i`}8 zl>gEJmuttB#QQL7s`Z=4Ma8pcAfcqEedP4Rf;#8p{TEo_#fJF%adFVbRzMdrmNot$ znIoigj?3gx=UvY0)$HOEE2jCDb{sBcV)-XCW<;4Zyugk5Rm38v%L1qS?A6oUE0oml zYg4`F5_Ft)nh)itOji27s9wlxij{ADPU+j(!YS{ozYcc#J<aUm72YcqmVYdbWdN8{ zAUF&&Zy}|QHG9QZ|EPqzATU~gMR{aR+jZM`N-*b|${^ERJku~&Y)byIKldqj^s=4- zlFQg4*^r$YI^lg}k|-9(S34#d#;V){DVn3XyS$sczn<l?fxnqxGK~LxR~4tOV)kN* zAq2<khyj+!N!ob(EL*U9dcJ9LRlirlV*mFQv`OgwDe@fKxgJrj(9$~F*oq_3Vi1`^ zyQI!v7obl;Xz%Gox8Us$+(FaRj^27z$i;f_{<>}#YSbEvs#`zGF@3^K$V*HM*?Lja z-26OdeVUdSdtJ6w1|nr#E`w_jg=;PF{-IO8kDlAF5p04^cp^HH2gO^Q(xZ++_j~`H z4++GP6TG;2@?&?z(STy`UM^o+>v{F&ty|#qF$EOMIt^NZQ@lKYf-8KqE`&CzxTmo& zB+oVd{EU)rX-!DXIAUFKp(CIos41OhLh1fKz5o`=*}2%B9Ct{_M*R5>68<BRTM@b* ziamT#o7~*o9Jjql!iv+*axwJ$RXR@f%1D+pNy#-A2+^jb(3_(*`KRM0%~HS~TU+^p zeSbzmTU$5O%i&R>@F?IS7rPT$UlU8nmRn6Y?=QrREPp#*z29^U82;f{IWbhBMTN>| zCknB&?rt1h6JWNO?~h8sUhlcT7Ma_!WluHXb%*~y^Fj{O>mf(`eh5Czcr(6P@N>M< z-sRVacOP}C1)wyKl#_Dvs{)`lfHZz6B?`?1j|KQ$WU`(jLW=_DNP79I5jxS1rRFY< zyU*+P-Zr|07X^xsFQxF`NSJ78c!2Z!akfm3UB*HNkJGNUgx<EWa<^wdtBHhX$Ch6H zemD<XE4SN_ML2JSoMXX{Ph=YY1UM|upK}`zQf67i8JN*PB%nf?xhGs+LxU!->5rRG zXeCxrQQ;=Up{1pD-Tu@3R9)vjH18E@1gPO>W@q<9l}RmM`=P9Cv&I^`LG+5`QNv~! zqdyehbPZc#sk61?MICOvqu@wed7@0>wbgy!wL8rt@Ylh8^*rAw#a+uZebq*TDJR2k z4?IWzl9r%*XV-ANBCI=|B!*H|aLO(nX49GNJmwQ%wmR3)kgBL~{ov<Pr_n}CUyd<L zc}hj{|7n<mHfQce6C%v}X6wsWDVwX@em)j2t<vHHt1#cMIK<=7D$9UwMWiXQD~|77 z&Xeq7=-Wfe6!5?P0Rgj6;CnIM@Tw_k{OSYgF385nrH*M=$tJQc!Ri@oxBox~8FX%D zFN)zdO#gf>uj&r<<ou%(IrNRG8TULTY3gh0Rtn%9OY=rIBbT@=omm|j{vc;Q7GiC( zFcXz0Rueu%Ui;vG`hLd(Lr-0cb_2Uc#RH$dQ|Fl<qCY~<SsNt-<15#*g6sD>C50B6 zD$@1pJzp3ZZ6M7fP>_j;jBFY%GNghkq~&PoOt9!`s&a~i(N%fro^O5LG$S9w+8=fl z{1UK4Ax%Qn6P}ZTeSGMi>4I9X8S8D#2u&~fZyMY(Y-a1YF3^~wXL*#qUp-dV3|EsY z314!1*KHrD2o|qu>YYAI8A_~|NJPhKRvT|%dL-kDjV^%S7G<(GQ@X8xqJO{R=`~L^ zNoJx%*(GHyK955O=Def^%g<K!haLb1_{ZzcElnY1GVA)|7U=pIu5sZD))F)u@H1=; zwp||Pfj(ZS)COJS4@gZ-eWF{yq`_d+XjQT?hf5Kf64mUT^Ms&4Z)#a~LB&&zToS$P zCoY-NY0^cP@!)-lu6fDW%TrA^HEP{vMrzubmB1;9wLLzkp7~Sb6WXQVd_!pds7T+Y z=Il(4kC0RHB<lOxhhiTu4?WS_#q=qKY-+@`p5MGA(tG~CeHN1jQ2~ehdlnPPhZSBz z*ViUw-xrpg?gM1T*c|?3g^Ci=i7@WI)s0l|p=Yefw4eFgw{H~-(OR**Mi-uBDd<DH zLe1&Pq4fRxMbP)4>$z{_BtsfjB0E)|a+Jr8IOCr=hG4y&5UAx8<58g%W`n{7Nu@4V z02MJbalK)wlCbSD@3;Fwx!HM{^WYD|Pih?*X{mF#=%Hjfnnl`0b_o=7ev<4nt82Zz zUdfBJf+j!nH92di;*Rt%r*l5nC_=*{zI~taRq;*9_V<%zt+2d4DQv&EKK+^)Y4%2X zcED8q^(h@z$K#wPUEV=ye7%dW_xrE;O`_bvj{6ikP38r)&<|tslYRGej)}o!QOKJc ziaqQSMrA4`{{>i0ToZD_GAJ3N6!yA_B6f1$ixfOL{qlSb`<dsD@LqK<qK4y@HIvpt z>hY09C5kMGiPjj;uH7Mb<?h1xWs#!5I?_YC!^p9RJxb^?aSwJ0ciUzPK(>kUMG$wk zDl<{PN58Ov+ott8*=G?J79PmcjKHUH_J(ah$GOJA?pjDn$`qBJR)MZK3JOHMdo_L) zDMWPaqCFC!_GEMsg38ACzTtg4YILrcYc#2~>gwS6dsNI)9pUtZt%ls*I-&E|NxJ_9 zyHkM<^2TqDiUo#qwbv`_kB=!S-79zGpLvbOFW|>iuhc1~C2$v;=-ci<n6u;FP90li z0$zS58hajEhW8E+?I8>x4HUfgiwdi?ySksA=AtKu7U$<}CLD)&-1lspq)EdVY?i$< zan4Wl^57siM7X^@Tim*2%z37O0_8mIgYQQeB(|i{{pn64MC8vLRz}C-OTRU<vqc$F zp2T`lrXPkF17II`|FJW9UYuQWz7G3bDVe-<(^y3&!7+?Um^64}aaxn9lUB)&#Qw-8 zj(bPkkLl#f`H`PAV%t%xhfhccJf5rj9zDP7j@Uvat5z2G`r)fL2XBipAH)H|IP??= zFj=M9F0{}Taah`+d66)IFCzA6Rt#hI-)Xs`f5OXSQ**nEc2r)?&MN^-46|Ryd2yhh zybUZmz+t78)~m}fM0_!rl0gN#l9lxZjRc{TbmMFaAAK`M+xLs>HfzY(3R*W-Pw0?m z*+)P7DlJ6szbD0lOCkAA5<hQN`OA>eqDZhv#D+KSnj}XqldT>tC<PnQ*zecIOAF4t zO@8a8MtrhC_wEN#<q-{_ifdM^Qb>yol$tRNI-(HZ`4a=3LrKRjD1Rl&*QKuK-c9*U zXR?M9SLSgoHzbc4Y)eK&+^)kO9?@s$PQ17p*O|)ty2fCnFI4XxH+ow`#B(z2b8A_Z z`;m;Oq}<`3m51#Q^@rCQwI%8Ri9E<DFBgXP8Tn{-;gt<=UK`NyleFy!(E!F?il7ms zzvu8W$<{}#+GXJjp&8ptP^yBaK;nj_KwbRlWb|(057|0NBS|F5>>(BPzZAIe&fIf7 zRx`xjr4F-&Bo0X|=pQSAR$Ud2c%MIgu^ZWwAg7h6Ke8VUHto#lj)(;}0YI(82x(|D zkE}vL#4>=^R;aWJ(ee?#hBfdQH2H1SO<I-rLu~uea+!qUF^=oV#m`Rzy+7W93`(fZ zma)%rw9xs~fP7WUFJ&;~oruJIjQ}g3e_&meA1pvcFpbmRD&by|pGcMwS1^*-x?)r~ z_Ybasm%U%W<+gz&GarocV<yt?50WD}e+)%U*Kz+^w$sF<^f~DY3wJ;ewJKa+FPuL= zeR{HX>gVro>p8N5hB*gim*sjr=silk`x;N%Wg!ab2+rSwT>E0;cS6}XKEk=uEvZS3 zzj&zfvWh53%dr52J#P@i8Md>y$lqwjySC$^`@~&Hl_39H;$8b+H^$=TmtPzxEH~DO zZ)v-RAoN4yN8cMtl3nX@qtHg$)L$|Grl^y%Itc!C?R%s@c>i*;Mi^!ozOeaHgz*}5 z_96|OC<bMkE4Pgps}!&A^v2)+_US35e?lCj+IV;&jwdD&!6q1q&L6Mka{w+$TJJ|Z zoB5)kplvsU1&#WhDcTzoSGz{U--%{Te*Jk^K5HVrwg0I3>NIJCNsPyXkPUIl5;H<U z{(65JN7srM79bw+@;e`<yiSj`9ix6u1UM%EoeR9>EHJ5O3}w!v8y<A6Y6XkkZ{L>C zs^6*d+3Ps%k`|xZazWUiDXXLRa4-t(zi%VCDI{X$Uuxd}7F2Z7+?e@Zi*{NybE9}P ze3Tp&v+!f%K9s(+d2u%NU~s8k)b}chpuEJ$DM?=;y>Bx|igKH#w1C+ge(uom0o6tJ z1CsyronxNF5WhWm$Gh#D!_4GRi%LPY=n0=hwt@%HQ46Y>#5j7699#rMgK4hUosYVx zXWL!@>8wa5r_>G6=U+_w@lGQ|%M(r2^R^yC*<c@NOEW1hH*!6_+vP96_GQZFcnyS% zc8~rjGL~6fiEE8#G<Pm+e63s5|J1Z=l0SjIo=AW3GoODhnfjlNZ#dufUpB}P9(mGP zv%9`tO3+pAG~>_-RRx5UK7iXs?KJ+X*+74^)D#9chW0gVPd?lsd<|XBQ33&ek>Q5; zdFQviVi`(}o&-aiQhF2JffH5F<b196;oO%Sonj9{tuNmjBjOiL8v`i!po{wJ>iopt z!z$W9d@g1Yp{jv7-H~5%`KET4BrOm8MZIbxm`=5PLYg@uPGQT^oSKIEB_m31T~_53 z`%4N16k)|Nc~qC5a1c%qO9f`wxDKv14@zox7j+qy`kNx|onJV~jmr0H#uCe8mNX*K z;I-)=)?7@Cf{jduA$2eAfD_CUD)rfosap%@L5_~FTJT1So67%f28LFD7$MBiZkJPd zVb_}sjPVesPCy9Jd5$L|`y)-YI`3lYf}83az8$oRY>iI#`d<h#7}UwFLAU14j=EZ~ zCAB&(I{&oAyF=M96yu>Nt#R(rp0$*Xjg5Z`b+sA0(}^p~D~VSY6;3$Y3`>>VUx4m> zSEX?O9KuIpUsfy64<II0&=7rfZ$xH9{j<#j=Y_l!EK0A1y$es=QBCT>nDXmhjNe&8 zO~<k~n(|n*c~=1`|9+ZHp}DU)empke+-G79)pVse3OZ=cL2$TciE&tBqV~^nim(zP z&tl?SnB8Dz$#Dke%$mdVqIX-_Tl^~misu(U5E0v&Hp+Jg83!m8CG8!2`evUYp}FBN z-vURj%yk?Is%pF%-~Q$csWxvuEIeiGLTI{7$R;#W@j>?##t`YoN(<zitvg&b94^%V zyE>8ZyFa^{&Bu@I-VfhPP|oI4n370ZX)PxuxqMe6fgE*5l)NRkR#!kc&IM0^rj4yu z&(?HJ{`E&Zv8g#16iw2`tybQUN(;oQMas$OT0>|tRZIDENMMG3)t=TAj~&e`2s=qc z#LUMR0kJX>S}>~^ef`K93A(*w7i;UMaGuL1&=X$U6MFCn^-4Ij(mo`ovTH|rh-D8n zpObnEm|}!h0RG6?p(UM@S_|0277pAtXxwh^OP9%cnoEv!kfYpoSCMvc=;^6D-qBLa z)A;)R!@c(2UX!D}ja5H-hWn9mp;BrF3_!<a0RR33!mr8B>WIcAwp~3NNvPNvj5?vF zfBnd0&gP5Jr_8qaE%9gVF{F}FW(s<_T!x&~uU~xy;l|dQqfMc4UX};Y+CH?et#54T zLsRPC<q=YEm#sytxA^2`W`h^Jp>Yj{SzEx>tsvk)8rXyiA*g>SDm^I*dM0wg8)&~I z>-=2DTkKN7s_WSE^~uRwcbpVz(+4iiQ=6fuCwF$SZFWnm+dDe#kVy+x29Pmy4Q4~@ zBH+UQ{o&c>KpvQ8N)i>N-P+#HgsHY3BTWVaA{i`|oQI?l5vuI|RQ~Q?R@qSKq~ZH( z1hw#}v_H^ZV7nj(^E}26{@#Kx>p6MEXU*wZGGJJ1H^YXoqrr2c&MtSGSH8eB%WEJ2 zppPErU=pV=7)*)#z8#9QV%q0Pes@R5?VmZnp|u1kxEqFQ!Sva5_^-dEyeiPyyY{N? zmGm78KQHNXKtA0}Vkg3#tomq^T?i>gd!9~-9KRZZ@3^QgAsE5L(B~u(`V0f$UALcp zvRR+LyQ$=9R}Syu<>MnDB0_W4`+Q3(-dUScR!|5MHGuIb@L0i6;NgwY3hjK?DS56T zI4N+Bb8$zl9Ot<PyQ=9F%_G{j`s0G_(~9&#>fvPptG(ZNJIrewgU&eDFU@c6b+!Z5 zF=XweUoxg02_ChlnFCiA`dDQZ6&F62Pa1Sab0E{3Fsm0rBVPFIGwLQ2)5fr2$YYp3 zh9ej!xhEY!$437Oit2>9QEhw9C3C8jl2%7yjlbVvoiD^&-|!Amj=O|rZ&bnBByQy1 zD?EKy`rcGtMeU0EvCY$*13|*Z>DTfSbO6upA5v`Wf9lnVRfBaP+8s)N@BI1m&%eDP zAiN<I3S5K%bS3h>IFDje%S(mfV|Kl|L}>noCdOq%xy}WjgxK(>#=x-ks+jZT=PwGv z2Hxknb?`%0FqjKyjJ4lAnf<$9XiC9h+{cDn0xVX>+hKzkuRh@>-_}i)f*6#u`{LY< zhmx}au~3sC0*DC>x~(vnfItRlUA<#5910W3u@6|vck@&-FTflG%xHQ;YP%C@>`20@ z)r052QGc5+aF@%tyN@3Q{Qb8^YFdevcxZbffFe|MLT3@bd51viyIktW3CKF?*Nzf; zijsOyeYPr2eGX04YpGd<S;hXg-peGuh46-R`LAGLvm2VBZCWs9Kl9onS)U~0`uoMQ zJQRH%Rk<5ohcET+8a3shHCdh7RDV9$!Y68xLTJbqDt5@Re;|Ef`(uBjk)bAsT8p-b zFFNJR+~NBvd#%o~ij1#JoY(cFrIi+<sY16V2Miu>(9dEmqpOvqxD4GQc6m*=ZlWZr zm-$2n#N6~}Zi!rp=3tAkm^5oYVfq{QJzMo1Sy`}Y_dRMF?CE6F-O#XvnKTCmhD|*y zE~(<;Vwgprh92D;A`ZXu^cYljfEKnDNR<JAtPm6{ik&d=V6CdwRQJoiaDgNE&7TD< zT=%T~dT#<ntHnI^ZF!=ka;dyp&QW%zO_wp}s%~H{tDB4G<W~MgR+zc48M~6VzlGU? zZq_28bhdQODe)4NJTI^@7`57-$T(}?kif!X&SpJDrwn&DIg(;k<cug{Y9%rrT>8(z z@F67R>7CJ!OVqiXWd4c(@rW=OyIfUUE56o9tb#HLYo7+7EBAp@M$Y3MmC55n9V;ta zJ_MLsJ_mBPm#{71Dq96u2TcA#lh|>Mo<PEmE(Oo)t)md*ZUrS3o^i3?#;BvuCxm9g zA7u-G4#V|syhu^S5mUYdz*U{o9oHNG#M#?XW+=)z59<=Xjb(&6Yas<Uus~$P7qqWF zfwQrL1}033m(tz4STH_34w!KuS3LyfQFVLPHjyP2nm|^fskK|eapX}I*~j807GjbS zvH3b9ClgLLbW7WkHfknM9Zw_|*OGewuJl*#w3=)3yukf%@`34^xoZ96u3_QVSKq0v z&ukLAq&DJkDzP!B6jd7Gy@1#S2`)1CiwrQp>?<-cfQj*o<fxv==Wy<XpIN{J=RmYD z-&lB?%tf~U<tbMM3K}YZAMQq!l{>I_#h5^H9i#5~@cs8|7V@Jl;{B_vwvw_6F9{2_ zT`qZNW5l{oB9HTfZZoLNKp1K7=wRUF4DN_xt2T5dv3-TqjD>Bn8(U>qQmaQG{TYtb z5E^sP${h>XZm)sDHRLfU8`;jno*bxZ)KxEv0+-lTqW~aPUhL=hvU|Tl#(&2he2*o% z-m(H01)dEy^-xaEuJt>^EJN@}h~L3>(ODbnQAd04%m+zY?1qNvoP6c0Zb2YItWv_< z^H4tOm~uMl?j1S~s#k75)zu=rn4){Y3LzuQDybuq<B2wfigbpLy*$>B0zF9x^cux4 z{6&2!NGOEzw<J5HA)Us<do(<)Em&+-)M`UUClkYwPRG)kx!;ivRa||_w_b~oL)Q4z zTzTK0CkMriBO%nTa(&7`qH2s08A(ptxQh3<<B-lkfnEv%9uTmUptWggLt+~OSthie ztuC5xFK8_CpiT<<PorK2K?fl?n=CrEz^13U+EfMF^4$*?vK|WBVhWt1or91Z=~!-C zI2IjhvM4t94NUfGj28N<>tdYAN9f%hE4MZQX!Nf!cw<|9E1wHS<D6EtVTLx0ukb3F zjfKvPT4t11*&SiU{T(RWv~~2R84X4|6in1g8$zs57|LIc7mK@hkwWG36BdeW_LQHr zI=xWeY%nhG%T?mCvUYSUYCZq=(tHF~%6Ah3S_zFEtn$)|w-?Q{rw<BYJ$_ooQZSp* z0<G@e-+r&b&uWB+hYQ)ww`tZ(XDnx|j^6itfG6?l)tjLLYoOCi^*SVc6=_?38Kq+( znM9A#U8WBbp|eXWxq<erMQ+&>oh@(PGhM@cbEU8|57a6$H2GC<PrfDt3SSpM+vj0o z$+5K%w4(KA9v0mv7khRIt9_nFGw%}x!)?u5ur5Q%{(ZMK`=Iln2)e0ILw2Mtsf$tO z{3o^Ut2~{xEY;%eN3~mVmFCPtPz~)|)qYT@XH(Sba+p?*5D?}e`3nH^=88B&D(OGN z&!S96JJI0a6FHo{Gk8J$@iWf_@&r;rKF<|r%&yay+CWsw59*dbv?O;k7rP%mtB!6A zBqwp&9?VqG<k29!NQ$qNF_`^%II_ZDzn<7|{OVkQxRQg-9qVn6z_0a$i<K_Dwu%wv za-|=jp{l}^AllDa0J{4QB*zV##z_DOpxoId#`{lbVuG}e=Xu$a@KwZ~-utxK{dDR% z54(HO;zb}CHPUS1q(q@4f0Ioz&@FpPYxXtKed*gG<>Yjgm6gcYF`}3V8yZY4ifwbW zXPfN4`4FZgXL`m0+o<NVvcjv(DNrya%MF@Rax`rRFso$ia4B9_X8ZdePO7m!d^?z{ zgyB+7s*<!{Ps=HK(O%K9p_B%VhVplO^(ozXr%8|UJdcXzi0h{IY6G7H_^A?Fu}Q(m zLpShlJusOSC+Q^tfRKd=ekTB!7#^3+7x{GuW`F}1^3OBa;em9ahZ=gAp$+)R#Y}1D z?rZY&_12}=)HDtw!%^`9&CtV9Q+g*{>BK2A%`NMYFR$8!8aB<oLAen%=TbG+j}kk= zG-kZWuDF#1X9x7(O&A?MJzn!#9w~VTzsj;&F-HXRtPAcEckV<&eIzU*q7f#_nqVpg zK}bDx569N3x|elI;=Y)Zle2^PMFyAWe{g7VsIl=RiQ+u-eR@SLFskEjRMHf!qU5tQ zTVE$CrPp$ogu*@FU>3zO$ZYdfUjT+MIt6ng^vDmgAVaDUUURhfQ)mwW94$fykO@_Q zc?@Q&nL@7u5{$&JMPJMj2Z1CHtJYqUSaokNVe%7?ZEV;ZMg9)yane>Z>>D2oHr3L$ zmM_(F?3f>W9POu`;-i9e1>*T-epU#<j`tU%v0ngrzqhxy4ud0eYinLD=6Pyt$Z$2F zBA8+ypyrXDmxM_)B07ikU(+u~pIXZp>oP;R%jW4^uC`~$pNXnh`Zfkz#=MTcD*l#A z;|dRS@$PwD?<mWd?fs@l=0q~YUrPk_tJ?lmM=KF%A;MsQWrdME1YmvK%Ht}4`4q>c zpG-VFeP=II*<GgR;=gJxg@T5wc<StNn`Pe@<zrM|9LP|+U+RhVatk`wu3o;nz)nNl zlYrUWU<<&wO2|N~$-`bnOciYbqo`!{uwjjTpXi&tw}tZ!SL!rA$GMY)rP;}28BpSl zjNCuew<;M~-`cX5Atc!+Zr#k4Q%o?}a6C?!3otv1-}sv}98;9rQhd;6hV70`Ig0)` zwyF+*0!8!@#mYk)NIa#zeF?e{N_KudqdDH{GYEOin1{#c7JTmcUf#@2Bj!qxEI--G z`DpA6^vdY+RQ+iU-}QIIw8og8B;=<)&bAp2+{vitkP$+@0^MGM?w&n1TEnq{nHs+f zc2h)#!0Dcw{x#(l5fe63b{Ofz|ILis_W4X49qs2mzZ8LE)ny)E9qypTr|}VyQB4sg zDGrX{#_ifUU}muL2nLgcmhLKoH6t}KNGISI3y@50lNK^Hwa;CbRDy%__>uv6`4a^g z8_qsJ(fnHX)>@+?r;ve8zp@&!V0PW*pKei3$A5MWonvNDM{6;sMJz7LMY?McmC~_j zvDQaE_3lCGKR@^ugUV_}CaQ9!Z0w_Mtw@P+_w1;7fzfz{?M<euN{YQHk}1ga6T;Rt z2U0w#v@=4c*PWU!hk3Pkw$Zs9qNOhV$`6NRr}LsNUaCvfBAAlv(2C;Lr|t<?y+-bj zwsy!kgxp%QF4vDQYU-<2Zi1kMWQW^2=8g-&YECGd-cq-=^53Dd@S_zQwyml3^z`mK zBPQeU^F|iKg|A?wSR|zyMm1l3Gwz15B#DzfLl}>l04qLWf`*vFF@k*Eq=sKFOXeak z&Z&hMLg4v?r*1SD_SD<0Y*IWTUfk{fq3SE6vfA3NZ$L#tLJ*`C1O!ABq@)xPkd#(B zB&18a6i~WD8bvy#8ziJbq`N`7OX|J0=lOnoKb|qpc*Z$5Ywx|*74w>NuEKW>m+-oi zf^(Bm{sXEEhpfRaxwV~SkJ1(6t{~VMOj#QM@w!=LjeA&HH{=aO;C9lo1TSikK7#R# z<4zmn+o3$FnVA_#!QdK#G#~haN4LPKC<T)LhTkpPi&@~<rjFo8w?f?#c{v}2fPP^` z;lBO>dQUvj^sO&o_P2EnhYkqjh8DpQ<8}VQ;~&%qshK8|-a?xA$Cx0r5wql9EtlN& z^@(6Kk3qA#ii<m6I%Ylx|Fc;#6Zv-47WKu%wdUO9Abo?8;m*f*VmLj@WOhFu<U|$5 z_!b=fm9J6bK9Gs~`NMJ7$Gk61YC~<3{>>fB<Fe=?&bKVto2t@)B0qOq*I3_}g;{u^ zppHOKx9gzeE8u~~w=Y~4H#ffzy&NK_r4)4e45~QX##{I91%Oe|XsMYb2<bqIG*Cto zS_C}H3z(vyH_@BFwZd_{>}V=fn@|>AEuEot*S@$mMT(>U&q#PpUz(NnhPtZ2j(E&u zRka_k&!t<ud@-x;m5JacRNtdooI4BirhpZ0zeyse-EF_%itKd_^G!?;5sBVsc>){( z#B2!=8!{#)rtX$?yOX!gqLIu>H^F=c(H)Zbk14<RZGG|f<RCOWKA^cLxK%U|2*&qe zd{v%3p#dWeOVYY8W}mJv(kQ2qW!bO$&CESp$<qKTw`4}?BQ{PzQKqzZ1#*SVx(Yoz zkbbjzXtRd4u2yA2Q+?vd7{MWsK}Qj0*C3`j5GiNE>@Sef<EHz?9CYNgyu65+7|gk^ zQE+_&loL#!0!B+r^h#7g{YWX`6oJT!Z$JAO5)-rR2l)@rHyfMc8w=;#4@#KsQ4*R@ z6K$eXkWsUJk2=%<`hza#7H&E^if@PTewn|@Q{?-~MRY|5rQQEq?zDP&Nw?-a*ME|> zp3$Sic@b#{?L~+Qw~g;JJ)w5ldK$&5_OV%a2K2MKEB%={ufBhPvBM5{{cOzz3++I) zT3|fDjL5C?%4e}bHM%|9jHtHrd~VS$g_P@FMk$S!ar62fQovaV6f|mrD&N7mZ49M8 zwD%ZF0O__l{u{0v>IxfLni`{9^3<E^N1J`qT33qZj)G|GN@3hiy?S~R72NF*kyCxI z><uhU89?YoFv0oy-2W;T)=URm-8?uZ(F+Qm2Mql^KJLxa;6prueg@OXg5<f#uxD|z z?ww`04<CJwI@XiOQZs3p*@?p4B1iohmNowG;a$FQ$8L>>DE`r9xjuGYndh?VC4S@X ztnQJ&oih(5ZF2`Ap;Cu&Rkv=99R$FB-0U`>dsdemv2xSHdSqZdh_MK6qosM7nj)#X zC0elH-@8!qBQuC%$eZ`rgQ@9VFWhkbz5fj60$*wXA#Hs6jVH&E!vNT#oHkpsN`hvw z??VgBtdLkvEQ?<#=cj0xOFi7IVvh3=WvjC5Qe<lcUF=2-o1M%Gv>sUOIbwLIkk+<C zgSFvvmv%N%Lc_y!v=~cBNC;8B>(<!;%K^g6V6K|V=uKK{5~Ldv4g8Pfc-Y?EvpC?L zkG^I3=@VI%(>?8twNKC1yh+gu+z0Q4herq@NoIF2=8=!_%eA4&Uu#zomYuR2Nu%L4 z`)7}V->A5Pe&^xCP>?^nxca+)Fa_;1r^(<v)GMihU}-e7cJXT`TmIN|!H`mdPsH@q zJ)mCww(c`htRRejRNk~^)O4_pQ$OR4192az9l3z9Qc5`{{|yXwrqA3q^|ZI3QGe}b zSrOOg>*MfAo)kRXc)cQK6$ZApEDH<!lNo34cb^CA-V2RT&|lE!H57u9svE(`unmRo zeLPn46>DlCx=tYp+KW%5_dx|#vA<uLzs&cYZRphUT8!iKu^->xNtqzw1{K`nL<yi$ zw-3mZzVu&MlMoYYmqeY;YXKd`luPZc9uThIB=J0L4{Dfjww1SQJF8q*!Aqw}Q+8m5 zDfDnsWfJobrFe^bT?RL=e0TkeDzI3T0&#>E>0nhz)@`Mc&*h=OUsIVMsu^B=4uC=D z7)S7w{prR}FCZNMI1SzqX)>c+a`$R!CVZJ0*7mk~2pL*$nAsdphv7R|EIXsdht36| z3hBId!1kR~<5K;X<6S_!9>S|rWc@~tFl(V1KjNG_*4-z4?xeBqGm2ZKeG>&AEWKWN zx*PB=YGD0~^F~<~UeKumF*VmPArYvHg)3Xk@Vn(@XEg?5kDn_R835Z`)Xjha_%Q77 z(guoTWHs0JX;FtVb<yv{{7Ze?6($mC*>8ni!=m_HVE2KK&d<kzpGE~YEGp{f&H}wj zo}{Ft&GSoyyq+Qn7jn$69bdnh*MG^I!H&vjbE5JXQ=8my=~RHJ2ItA1&X|hUclSjp z<AV(|yKmO3zIn0*+EeJ~!`qlc=MJnaA9HT?3+=_{`{D-7XPV1y4|lfQd}x1(`%%^V zCsXz868C6lU#rV|m`>Fuz`(IW_b#DTX!V}0jOipU)M%Nq$}SB)6?6`8j`uPqJcaB| z-!~U*xhGuotQi?9W?N{Lf*=}mv8xdT)v1@Nh}w@||JQo$r{|7D6$j=Ix;6`}Z}M)g zZq!Y?iLz02)ke4itnCLZ4!J9DpNsGZOGasrtRKj``ueQW=V=ClrsfV;6v&J^gXZ1= z``ppBcNHF=V1=~Z#9tEw`{d~?Gw2mJI+?*k=!qgRC;})oN<qW3pt_LUernl;A5dY7 zYhPAXC7zG9g*Bb3$%eKy+At*EE@f>9>f&If6|xYJVOP<90{UVKnnn=~{qFWoFmJ3? zL?z+7W#Td4FLl>0Py60aJjpC+HcmCzoLoEF7CY#Pe<2lp-$0Cc=y9{%R|V5_-I~Qk z67JoDu(H?AwybPy4l(xk-CCrVK~$lVi4#K3X<YvAZxo?vTo(VXKX0)0bCw(B0PRzC z)w~8wZh&%{u3r+xkj_^&T<bcrJr_**nd6fkov1cbGuN`bZA($mZvR@kLvD`=OpW9B zAN%Xf1lC$uZmW>){wT!133bMb=pbuVGHlH!;0Y7)?094Z7>xKvSH9g5)ZBgu6oP*L z9I?&EHOJQL(J20beWvj61g!y_`}J)10sxU-y7#$!3Ze9-vj{B!RuymmcKf*%X>YnF z&5o%fztF{TAr{2Or@+FEcm{?idZ531@HjR^wFJJ761z2rgPz>XT8qyH-OU=)M-ZVZ z2UbL*RTJyJwSklbyvk6+`OmOAIw@SI>U&mN01I;1+&t`dK7$z%+I03it?vSFtNz6& zL@>7StFGq)>Y4dRllr~q&+iyA5|NU%(C@R&{G$oJ8Df-{QML_I=soO-^+tr<(#^l} zocJbdjx<ozN&7sK2W-4c`|BpfhEU-_-K%-J+FXdl_J{x0|M((h{=Q$84;B8Jc0)au zOW`aLa72xia&JIo&cUY|+HAZ00rbQmSPd(8DjBh24safwXxx|1X9OblJ0Xo07*Q@& zpC2doU~B=Qf5geugzOF^FkIkow#R_nYbmaqd3_8KS`1+4KljgKy$QY6%ktew?yftp z&(0Ds2{*vIEWA&V5{R%P`boVHvTkF_<3BLkxn+7#)bpeSIO84UnrW{I2s15_HFE&i z^5qs^9H`r{f_FQ2nfc{ZWyrJfFF4d57%beBd6Sgvmikw{Z=ReeJW?uG@;;jOMF1Iy z@PXNHh0v<!N`$V%kUgzQ8nTFO_bOG<tts=PDAtzJh^L_JL$qd0hC!!i+^B)a?{>0e z*Sol!MvO)S-ZnP6^Gg98%!194!ed6`qh5)_lyWHo?En@4JLh^78#kYf_it#M5hk~i zeePF-S6w!mV+?86qFc1`kVkm=?-9hJh`H*NSBF8>FiFSBIPluCICnB?+vKOs-E{(> zw%P#NBrT)D4oqf4j=!}+=a9{sZREzE1Mf!hn@Q5<Ha}`A?;;AyjET<@B264X-enTA zR#4ytrbaUQHy%+os;=#vxhAn@n|E{Bj*dS%p6G0Tstc}bTb9T>Xw!dnV$fM16%Arq zzc%x-T|nc?H(FM^odm^KKM$(I=z49~5tv5pJYRgoM}UefA4I89OIg(=`f8#QLb-t* zWqwFl!fgDRLupG9GS)rIJn_Zy)e#@N&5+KL0ACykr_aqL{A}JPM&v>xNOo}Yz-@ew zE-B||K4*y6;A!pDmO$Kf?npUE{0RA<NsO{1Kg;+arFBy4uOMTg?5}cby(?VgM?Gs| zLyT9qupaPgEWdJb@mU`u{l$K-sNXlEan%(#GQDKMiSMc=q6Sp00X4uje7a~t0vvm8 z<nXlix^pIX9Mc;z?siXYq6|Nu8O==;(cppo9H#cg+Q>f<Z<;Ropw3O#)qQ%vtKt|Z zq%?oTL^lcy&GDk#hL1d*o`Ospc?B|WC<UcJ`3p%#>D{?lsHs3{ButJGrV+4z9iZ}i zGP5R@Y?0iytWF1JN$A<i^lYqwfZYSoAY6Ep1mRlKV$i;JbfBkO3g(}C$C_1YdGMjS z;;4&VhKW|*4%r(-P*X{C#m<nD7+8~UB9s|Bfdc;*L(;mlmA)WaP{}h8LP61|isWle z)<$;ezyfj?r{#21b>)^4$-0{D&s8W|@ak1oYiG4rez12i(#l8u`{nLkq5ccGdoW*w z3D@e{+099Phl-g0U>zR&`^T?eGo)vh=b1UNdaB}~Yd3$nVOX7}*A)Z^Z34d<HRyJj zI{;GB2ko0UuXfp(k_T}k9DkRha|(#usJWp4L!`0wR@5^Z`@)cJQ<JgSg_S4g3EBd@ zv;AlY9>s?zH9a5rNVc?FFngT3<`QTyr^9a~g~TVQi?+c>1L0k$HKLgYVPzvZ!IK35 z6N2WpH8r-Fxn(P3o`z$FLL1M5BXi(p%Oq4oa{-l6$;m#3cF*22tbG_*AAsK<#}DmU z{7N!ViVcm5`<c!n$eQv7mJJePao*`+H*TQo<eX1BTWY=@PAW{ok9NOW&&p~()4{1# z$gOWtk#J_2VY8fyqKp+v8jm0j@4H&R*wm$du&#f{;7;OBxz0`g!Q+RhNwvS!yvMu- zO+^2BA}n~Mpn4CgmJS<ENn4p(zc{aN?Bz^^N0U@vikKR~{{;BDuhB4pnkMy$K{r&6 z-iBpk6=v;br={!M)JJpg`x5KEj9fz+fzUlVDRu5%FDT<SK(5%-F|xBG{;HPpC7QLM zTCkb^c&rtw+G!@+rF4bkAayOp$!OnN;AG~dVLb|UaC#8e_M+(lt|0Z+L*>SHdx~1U z_9&)w{!PRExzM0s)YjrGcLHcbXcyjtPJu1DSO){f%tvA(1Y|T8c9g+fx*J#FYa;r! zC?&PWhC-sYG;(={LX^fy_ntt6W1QR43GWjhTQN(J%-x8w!-tHVv~!wZwsD8{)(5i} zW1lPav&aaai;N3{G$fj@I%A9fJ<JxhU+vZHjpRJ8_M_oG^LhWHqejbMudw&dn>#Pr z3aLHS@VWRjW3C4Iera!e#(bDXHbKWfw98AbWZnd?13^;L`AJu3+FjQ-F{byQ=j)Y? zbs=fY+Nq3oc7bv%OL=p!YyM6m=q3BUh+>)>yAhQ%m~8+3I)rv}#O4l=jl=sQsaP9# z?x6{y1ys3EUb#XvD5DK5*mQm&AQv8~oxOIlWWqUIzT-^DL<8r=;d@S#NRY+&)h95R z0DY&aS$3Iz+eOsYa%$4f;B1+Aw@lhwTLn-`rHu5pE9Uy&Z-D#`J`{H>#vneTK}BTU zZR=9G({E;<{ZVlN8LuPNPW$6n5Y<D(+t&R=O5#SV3i1f_loXJ&9|Mjdz@2b-?uPFX z?NsMApv%dvNVbcEpY9Y)4R#*<Z5^bwoqr@nch?7|5)Z~x5~!Q&r_0Rhkct5dYAkh0 zc;%#g;5(VTpt7{n5+vkcw^8)%4QZn5_DX|FTWd$M>;8c{p}Tk6a_5ZtM@}<xyEr?Q zol8K;9(Vt7cJ#_c#yJmEA%D65;7{{6ii$MFK{ni$2gDAKO%r$eR76ciNt<PQJ0^H{ zR*P*1>b#NY!r)*P7u<Rl(%KI?HyIcoi3L&%Y+buXzuQW9T{-U&U(l%-B+aD!zo!vt zyC4_AnD*z?3Q3GJSAO)L9mQ;FP_BlgrYiY)XsDv`X#W}y^;<wnb7Wu10*4v@or@3Y zV%-z-r}qI~pKqvyaN6?-Y&djJ4lWntcJx72QyH+@0DR1?fy(^&;KRUGNnrDRTl~hZ zvwn$)2>4uy$W~t%K4VTZQMothH_-Bu_$}{SevU&jRO0&iJ!qF7x^+3AG&jzyy^-VZ z499;h?xE*@0)5Zs0%XRD=3u(02fzB5#=i3dAl<5JJAOVYkQruE@r+L<Y@GCZDS!qb ztp0}bP{Wd@r_=Exx-%LEIc~CzsbSGvOrFHa0n!F(8Z%=qb#uad>3Bv(10sfJK?aoi z^VOl<Efyw@6kpuFnDUk`6t6oy|4*sN76EEirm-@;SNEE1mqFtbTm*#Be?<uQ-`XDl z3dVmTs0IEBL;5Z*aC8#Fi*8k=twfl~&Th{2oSaQ!BCw39)ZKAozJ8(p3i{2Hj!ABo z#Q%IHwuL{*nAfRQ=u4i`(RN;&d!HD0NC})P+w6frmbv^l06L>!6755xr1m+!+or$f zt0idx<g~DNYoFWR&QBqFOO+<Ar|)Z!sdRnC{K`4b2B!9C>Dx8_+P+R`e(6EW-h{&| zE3`Q|0Vi}BYDF8H-8f{*b!@Yg1YIk>d$thUp3{#tBWn6;O##pm)@!~@Hn7Ft+i2dL zwb(c8-r$uQ<;U{)D0gjHrH;mKZ8S_pq#E(OVN(c+>j<s8xYB=N#f6;s0VS<ohO|JN z1HIwPjTtvIJLcacrcbuL4w+E>xL^gn!NT|MV1y3#7PLpZR$?4~%LnF)>GORY=%HfJ zWB|KGaH6>y5XA6Rjt&%q5ykPv3bhwbXG7g)4oPbV-(F<$tL3_#0gxG1ZAJlprd;qP zTXqGKmWC+Y>1(a4cfY&spUBn8yUJV3%Zy~Hh&25>?d+cxdoZ!1wUqX9-(0Yk-HI;N z8?`=@9kO|Jb{80U2)V(7u}bhoYP|#l<DL}A_xt}0;JoChWGqybna~|GFVlN?Z+xn@ zfK&w8c-$b!oz=%<eTB-nn5z?1Jtsu8BIk+6myWM&&lKV#=Q37@GeJ^$zLT<};6N(w zBP(0a{3B}yvERWD_J+RjKc16AA(J|wE0{`*Hvw}(S_WzbeyH55#oN0R5F?4^p=e_& zzF4XjJ(xUfH=H(H*@eg?Qp=hI^j+L~909Fczqo&OI#9EvNs^*}{o2awHeS&f>ktKf z{E_K^Au<pGcJy^>f^IWb3k)RLe~y<C`R0HG*ieij9HHeL@Dv6!K8UcCh~$W9gwaSd zmDj$w(sPKkrrURo=ux0~dvcU#XVB6%ct`Y8KR|g*D5QO+5be$Y_zDr8xTp!r_BfJz zfbRHmeSfY3Z<t$CCsZoUpbBjAUrSPQ_-h3~RBU{c%{{*IfF5PnEA^OXLenotZ;P^0 zU9LBA)#Nj~Ruliah9&>f^mMi3{2j))*yr7AC4%`n+FQGeNq`ygc4c#=D|E@skL@6E zhyfVpGO;6DhIa1PZ6r^8eA+cx13(-hw&~?@zIDyD;fj*xT%>qfS`v^2arA`oI|OGp z5ZfaU-QGs{v*2P;)0ZAcCDxt%I?|w<tcKA*=slPrcYi=~yQy7V%r<cLq>x0)^S}@f z1>^++5OP_Q_Y^QX;)`(v0b=|U8cA)?K>f_8!mo#a{&iocd7g*NH9AViF(*_&Y}RmJ z56(~LD5t+koTem*R6x<d0Rr8kIbR=QL<-FYg2#0KY~q&16E2Rkv}Hw{%%3pwX)-9Y zf{OajD+TGl%}Ps!9R){6mM(Lm30SjVjK%sFC+!Z=w;-)x?Oe0&_6vB;`Lojq(TF3b z9l^C`+apo^Yke*Aj}qyDsCB=jvH;W`ZW9MAP?tcn`;ae61=}a!+hQBK{nED=4C$do zj#*<0{JjfUZq)~3sE?&o*!BIa3cMhE{rAhr*Tex2Gg9b7z)a1nA6!Hz`&dWnykMaA zc&Hl00v~(G@#;gkp2_fXs`{l*Dyg6$oTj@7rWF|f0%jQb2ab^$$qlKWK{=5EaT$B< zvdeeTu0OQ1$aN16XfUsfRdss~8@wugqAQ_pvdsiX3qu=9sC&ExUbm)hC9ZGhv|-h# zft%b_Ki}KjoCC*)GW^lNYXL3=XNtd%3K%oY&VbYMKd<)hs&36<9YVSjz3)V>h_Dy0 zzuYl9=I1?mfudeMy87_TLpbZ^B-xQp7?|h<yGu#ZGIUFYCr#!~t|R{U=PDl*^gf|Q zryUNj{zS%S_?b%jKgSS<@@F_C+8tf=;8X(wUT(giOuTd|0~pf(D-0F!vN}n#>$FXN zD}&bGA*Vx+f$<*A>=nFAjR;Ni+vXHhdc6KHIUWfqc#qU<)%xt7s_~|xTA2p6gxjVN z0{F+@yR+71{TXW)M8?>Q-+qG>dBfC(5j4I0+?%{8s0*KdcK_Ul)$wV1Wgv49T$A}2 zWx)j}UOL15Zm_*0r3a*KMDK=nq`%4rz4jY}M=(D_;?@srYa8C9AuW$6E$IpN$W3my z;DLf2VEuunth#Q`6+uA(rr$8PFB>83h%C>}zk=O!>H7`h&+($&i?s)bgutu-IRw&5 zY3NoFbET}lfpu#m>mCNCIHGR^Oa}Uot{}Zwm|+2(2iN0b_0%2dv%huTF(mpcS)<u7 z)iTxTvmgWKc01VT*6JTQJT}!g$g9z=)Bb4BG>*2haGA}<Fpdufdwb|{*kXj+Qvd$O zd5kwB#n9RWMWejch5dff-|gLZLN;((9P-3w!m<HGN95zI*`@P87rA5O3r@fPJ(hzb zgc?pYuno4eud2$VRE*JJv;&(fUtFIz7VVk_n~7$2@Pat9$~TQf_>ub<PQcxz1iX=X z1CK|CjCz1gB$aG&3*p!SAc4|c-6bb^Ce%Lu<2&wZeYN8oDn4i#dE;R1$lgK;nVtK_ z#ZBSXLxSrJpa)IxUlp~cXH=TyA=Ukg=-G<5E6vo3=04NXaeD%g0QaT<{{#9fNzlWW z^K9r6g(=|@ULz+MD5MAPC_C90C_Q*To!l=E20=f<^6xg-Yg1#efoWhHG;cDXk8gUm z9wSI~H~y>uacg_n(r1&H285F%hvSy+7KF_MEMSyFP0#8LH^T?^CYI|ZDAS8=u{AC# z%cV@fyvtLYS)m9uqUwuP9#BPky`j}wF1}cwR>Y>#A-OsL7q2@3=Ly{WAtj?1M1K;! z1$y8%)>isF`S{&!V95GX@eOfRL>+LB#(gf(&3M;FfArBi)N0xzwzozEN=OWf8`KQE z=7ESw$P#i=-@^||KDFbzU1BT;l{idjE8<Tn&ks8;3iOtsW=BvLROb{&5)jjMICHAU zPxQkTlyQM3*k3h^jVg7&sDU!b1sZRW@e?bgnFHoY4sYSdfln%NAd#O(44e5w^p2Gh z&d*rpucwe+IE8k#a4B$>Gh1lwaiL(QGgE|JMnU!4;Oe!n<e$mD3un4dy!+RF1~h&` z0>X6_rl6((ktiBfnKIQ06cT0rtzE}DEIdI{!}xI1!aBCF4CFbQ8b{11nuCiFiT9Bn z4If8SF{7`~Q#2I`Byw`-dJ}2pQzLvkxZOa;tZlUyTJ^a>k}~v=$fV2lL#uTRd_rTL zuYe{A#xS!w;12&DWRpHe4q&|4eeuEi4l84z8pFhgF7vr4k}}J8;SFZmyZ?VQGx(Vk zvVw5R`P~0vsc9y{t5K(3KfU|%RcDJ=lO^rRC52L{hujf9t#wET^x#6%O{w?N$z>a@ zlM|m{za=2`S_+;wudodRP!4Qc>eZdkzE~`Iu{F*Jpadjz%%v`HHj77`Ny>9{W^RW) zyzSlm&+~;5`J7LYu^7~F$-4LJ0CGY8_UguKF^4BSC@8Y3w}x;~{Gi*1z1A7<O6T?2 zX*?zoG@H8ugB1lYr$P$QJV3Jgo!c=#CR9WOF=uiJadkgMv*YW{RU=ZbsRV6YCp4sQ z<_&o24vYTsx}UZA$?}9ac|fgI0ZdARrtt9DyoxKho^+B?A(bi?1_G~#R5dD+e_oy+ zwSEheD!VmAEm)-I>w`jeMtkDhJ`X?66FF$BMrra>*FTr$2vwd(EP<ztqy7&TRKOp> z4*)Z9(6+z0&1w9y#@QZacYc(DyjfsD*Z5EA9fvIN_pOtmYG3VM=BmOAC;o?FZWClW zA6i4c28y*Rsg@}iZtVi*|Ek>LHcSZ#*GOI3Z8q6llbSk3W@$n1IzYuhAK`9dX)QUK zAao(+rW}R*2?W>I*tb(3IAU%%YaD?*R8h4Qi&#HEQIb9^ppOkGsXfCj=JxRi`fo;l z=p(ry*|KB8DRe)+2LavyZ$NVZsoIgMt90Albid`nK*ak-T3R3OmWZJe^MeX9goQ!_ ztA2Puuj?8e?FT4Hg0)%hY7YV3-hBFrO=Co8ar`6mzsC%at;b<*2;;R5rk*+&mI8a> zE%`(QMcjmuyu>k^|A54)G&fqo{%StVwHX-0b{&lm6kMui)RMRO6F~E2gW)UgjRYzT z%GcGO!P^L$o{ZjXz^2-pJl*+QA*Udm;b2S`mv-Mx#4O<O&>2+?^R*n3>=#T6QY+Ue zePp$7K|5vx?%PFkt&T^kE=T|Z>B`!vY3<ln!bm7EJCU{!%}yrv(vt*9w&s;3`^4;; zsh&8uMd*D&Hg7d80%={w@w~)4KQuJ@Y1PVlyrfu@4Y&r%9k#Qe4Ik_eEq)B$t=#-@ z0@$ZzV!(NGEE{5VaU%g<Pq=;(&yOFxHXaSKQ7j)o)bd9mA79qaHEg4#Drsl@eLsdv z@hKfjb7lWG69?TaFL|Q+$9KSS_<gtu9#HX;k<-kG#|#P=7hwSd63&o*J@j9l*#^vv zk5HDjGD!QF_P4I_=zn!GdUsyEAS-%6?l@}0cD8?=_cAZi9s1ASXTP05{M-R9Zn0fP zx8obDJsVu(v8E2Tb0WsT;PV#X)5;kHEBnugxt}i>!}7VGlV#lOTH*`dv^^iyeFZK= z@jIs*L{$k@dq`mIa&ss@QnR>>9Zj&P|0aIP_@M$QX4I+OD|SzOR~f=oLJW0jf!hkl z^7Egj#V$ZvJ~sE{kJAX2&;G(DcOU-zr4CPEl_E)nq*@qCI1r@&&b2FJm$}<~qc0Wb z%e$43=%}9?T5b;^g0x^zQKPV<b*Q>{+G+RzVGs3DRD=^LHE$#sT(c$oUpp*HRzWtt zw<|qAvcnVVI$Ior583TzogM#6hmB7Tf{nUX2OmsN7Ae-a5(?MboCcPgq2V?J{1X+R z#?d`5LMtx^2+G43FffK|i7?vTcaIz01v?Ms#xe<25%JMpfMhmh5e;dxhRD)zH^aAx zunhZ!$fHY`;Xs1?H!cl|_td$GyZRvDgsq&$W2*E_R9N&wTc*f2S}3J-!!T?XL!x6P zUX~aut`CD7ra6b>?i)e1{TQ2$3CIF6Rf|(aG(Gzcc>zQcZ;!5E64?(Zp-hi=Bmu>f zdzAe<0QkhA79C5g??y#mi?kz9NjOhz&#HzaJ&DONztJXiNgJp8GAM*JQL~bG9P%8# zQ<<pe6(IY<9YS7naok7;qX*(?FvWP^!12^Jw#Fxd6M7>C@rj~O5C}bVBE}X}Ak%@1 z%1@X}Afh(^8ZCgpW6!qSz@JVbTT2MV6|A83J{@NtN8kCk9cqv<$8caqiGdMzCgQe+ z1*uj5xle@=9NWVC%HckU@u_n+WACBaxa`^=Jx+^6lMqG)A63lZp$^WD8J98f!J)G| z)lS4L&)6k$*Eu2JKCM2u^of&!U$5>^1QY5Jn8e#OmXF*B%+m(tImpn2=5}Mx?h4UT zaHpv*br0iU53}oh7_pk?r&QZPcL7!tkUXTN=xA>Q0#wajYX8@EM_vId?{(4Eon>nV z4z_U5%nKLv4U>I=)e`t2tyo-YT;c=n1!yRtVa-&_H80NijqU-VD9t>^*cx`TIPB(# z+f@_Yr51IUlYi_F>Ah;7y7+LxJ$GTW{Q@V=EY7Zb_0fskIegwwAP(JaZPz`qpMuEQ z{o?^H^~zBQJQ)_?+nJ{|Cr|^wSRJ$i$|Sf3bmvNTglm<Zx3{nef_87a=)i@?=pf}3 z#2!Bx4WE{}?p`|{Q5~FXGdFdk3q=b>b&m6fV`O6f3p3ux^A)epr#F<aReT#yaB<^V zf8goP<g$BglrFYRF?5(SL@g!I8f}8p6W~yKQluXy+b){x&&E*h^bRz`!)G0A1}&jB z5WS}N1@po4DVU0nXfVrO?4SQpD0`UeDbI=91(nAY?s08!9W5L&8yDQS1Nn9eu)-l6 zEZAsXY)oy28Fr)qmTsG=bpBVHnH{tOf#y}$JXq&i^jPk~sg3UlDShVfb(P4Y`x3Y` z0bu?uU@i6$R3Fqp(@llTH4Iv;GJ7S(IP{iqfs}BxxMHz&xGl6=g3OYa+hx%lzIG7l z-;is6Ij*EKLrN_(I$49<*MtOjWQ_R1)pNwk1=Ih4jU9Yl6p^gm4yH>U;8%ir;%QyF zzwy!Q8o%JG6>75fj~R@EVBku_df@{q0#!w`r*(hN+G)Q=JWNWyMShL^Qa#)&)oflA z6EEIP5B#XKUTHMM^$q+0L;b)cPMnMf7`AUH`F*rr-Gny}7UuVr03g3GITN~X;y?zX zwx#$XgGGII<K?@_mIsXw0$`lWVC1mdb@cHg;uLUV*ahp1_Z%n)i-^2bFB|7ZE5JR0 zM50M#vWcN4OWjEVoN%zn{T%<Z<b#VRA{RV$ndn(!z56ql8ky)KwgDy5`K&2kx>vw$ zZ`Pug%;Q>;%hySCe3-a2R^ejaO274qhU;7#b}-W>?BMc|?XEc9N>eg0IBpELXl?hL zV6@XKx+p4MRL8_a2fwIoz|2t&IlFpL;E%`3c9OcU<{n+T918x{|G}a0ja{tf4E5<- zjiL6@)h1xq_`hF{3NNB5xnKKv`twaV_oDI!*EN2=`?A?o3&R8AWzH#!Q-o@od0pLm zOl@Lg(qonoVz-e)pEQD*Yk*SMQ<eB&-Bf(MzI%=jv3gpFfgs1-&hHcf=nDo<8N<FE z#3rQSpV@0fs6{?nt&UoH;I?2kV}j!EpSg|hiZMZ@z%P<AX5L7CvvoU=g16f83&aIV z>dl?<-J#8Ujx9}h{(Hq%DP~h{{!lspDK+yo9t0Z3{fAZ)B8W!~=s#*}S4`lYcb~*A zkEItqw4wvN7bx}Pfh+R0mlU%^JXiBD9^uR`1EZt2Qq?4R>tYu@uG^B3@DM4=8DLhk zc69EXeA?-@_F}RT1oW8NZ+b!@pelTWv8vF<TYG#E18?{-QfF;b*=uj_&JD+MQUci; zGBX~4tY}*PY;P;xg7CZrY)j>3U{6?uYbd0L!4+)4%>`mG%%(DL3}fGX<VXPjqHWN+ zOS^TT&1<L|;RrMUWdTrYYYXvhaWMJruUjNX4Z3f=hc`W(dW}sVGT)P#wm&er0ynLu zYe4QUI_5tgR732$8JTN<eA)HE73b<Uj?Qeo@AJv#_J=1{&K+aESgbVCNAfPxA90Z@ zk!Dv0RxWQ_0tkrV@gk1Vigp!o5dFrFU<X&A1E2%1K9|M@BvZoS_C@kMOp8VLDV~Fg z4%h@$rmTJj0R@nk^fah#Y=pPpJXOm2HT{zU?G>MKgpAmswwdneFJKx#DTCNSfkm@8 zFSzS9U4vA7Ew6?IY~qlu2E*{2T9cC9zTU46fbOG^aIm71wV7u$OCLM#$O!j(!Nm_T zXB>uL=iS}#02kmE)RArp(p|11Z}z+1>R@Pr3ol&Ex{G_sBI<MKpVN}M;0s4tKKo%; zUG6EfBfNUY_CiHdw9wE;<cbc1O_lh9^~%W_QTzkhq_x=f0DywHeGE)6<tL$KvidHa z<a*`FM8|!)3{_5GgLO|~o~Io+mpC5hNBd9koWe*3#9)@twLYI@m29sQEt?90P?A|? z_Z^ThaDO%IO_9N0y4T8KlL(-gPXsr_GxfWUEqHjHF&H18&i(N03Q}N(W@-H4K3?HY z(QsICYAXi~3X0(X4FM7l#!CK~JfB(bQ{iU`7Zy1ef9L?rfm3CO;3R0TV2HNN3&U8# z&>f^jz%e6NJ4)j_Xu!eBS1A@Xlign%Hyi<5h5_NMhgnk@x-|!9shi;mNnMvY?<WI$ zYy<1ACd>I^9F~tC?OZ*Yophop0?2@c+^6OO?UwGM-9#9j_(ael%f53q{S*q;E5zo! zD_vlK)&Le@=-QpBdKtv8GF$VdLBZ6xK!Xl$2+c9Pp?*IY@A{<KPh2KW3`$H&FteuR z4SzEA1XLB5>Vf2j^d1X-5E)2E*xmTS?L6z<iU?5G3|g4J15eaE$q8<-Kt>@QvurGb zt1UT{&p@WNEw`=S`v;plmDob%V$=i+FhNkZnrXS}Bg_7$)`Mh!kiLev07sA_tG>m@ z<O?kpF0nc1N>^gcF78-ZDL76&-qbkN{H_@Xj)HuWlW5rXwS_nT64DtO?C+*D7wDi& zPuDmKW&hOk$@T+p`*Dli&!7Y>6Gl+=i?n0a@DW(O&((5$4uxi9EN6=GCp=hpB)Jaf zR@q+;Fo6`gB1*DaLNp{jZ~0fp*lTbacv@9VUm{4~-LHv&9)IU|-^kH&I;8BP^7IJ- z6yUiZ(c?;GNJ4)j<Od1I<z>^812t4E9@*@^0DiEolh*|PhQX6Yv}Pxb_qp|sThquq zvS(!}$U#2nRgSXi#nc5_!P%Gv*qrsui&!%ZJ*xN!1?eyhCP1BH2wrte;9Kew@%sR7 z)dQkLfzy7*NLAU0l|-jO8LvAP5xK(JknALT3?i#1BWMdLot*Gu(`QVxG0b|m5dcj} z$z~k_WO_n-{CIHfYCXKRUg$R>Vxe}CZ|&(e=<sTI*DHN~C<I3L=WHcAza&&$fBQ9` zkVgO6hloE!QhTMo0Oup&Ea_~!gM4?1+n(G5+=H`ozLvpU1ZDWaQiBuMTeMSDOcw<| zYhSB&eVd>PuG@t#2z0y*j${YPDE{Y~PpJ|AgH@tIgo7sW=qJol5NHw%5uk4GY{p8h z218q)%4jXTdbo=12i#2@FMgg4Jh!Hg&FEY0A5@SV6ei&=263#Bo!yZ~aPwc_W;up` zetw&;#3Go#7A>}tGhbGot#R9oG{S9JYXOWqgHmzCd->Z}qlcz9VH^sx`6U=#zz5`T zsP33Utw~HaLt%m#4O;*v|IO>*Y#Wmd6K|S^?8W#1U{mQLrHp1v$Bw|<f-##seYwQ< zmRB(6D6;QCT>j`1sI9DoGw*)T)Agy_S5CGhX+j7?w8#0tRDaAJeN)h+9D?${2{98I za$#nV5azA(D77st+(*1}+aqgSA5R~9#ebkgY}dfTg0+|DA6GLjc1i@8q<H~%Z%Bpn zZ8p`()5Be9x+jNI&_<j$LV^KvF|;F_mL#wcfI3N}IugF;bz}enZ8tbsOEpqhK$7we zP!k9`c3vU^fGa)PP4(-#XBqWaJFvk~4%NGJARM_7pk<V?YuT_*<0dX+U0Csc{yz-^ zjAfB(4i2OP3v?;Xpa2@#YGXt-O}TKuO=*y*Vub9^vFZUL{%Zba;(FjVfEeHPmO%Ob zRy8$~W$wl08&E-kN?#mq1YH%pJCb&FRuI6uUnzF@sKw0~#A|vR@qPAN{-x!Pl;hxD z;7jue`f2(D@C$vgT=|0k*UbCtHRswZPZ@Gp$3GnT-j^+Bk5$x$VL+)Y@ww~hb|e0s z&GEPJ#@SkW0zgj)cqmd8Z6kh}elyV3!QD#lJzB-Xi6Y~nLp0?l$Zl5sI#hRZSOx9s zAI&Ph1YN!go&?1=N_@7-21$a@&b->~=7TMAQJyoS-{jR45)xY9SCj-JOdp5a$g8jf zVO`i@ZX+aNsC78_WWJ{^eA}MSVSLdh!!*G2|4QaAHy`KX<GPE++0_of+q;55asZTa zfCW)ogJET$Fl>X0bJh&bH?gPQ0o?{f#(*F$Ug>QCzQfm=YJ23-Zq9Ere;ykhi+x*4 z>jTQ%-+|2~5Q1M^^$ko7C1ta^w+W&l)D>?Em4MG2hzaz6zt-Ek(D4H7>HPEAq^|&J zMCN<0V0G+abPawz$3!C2HV@1v5RV9G)9<!zyrzBLyO&pWHAAs&?WXk=@B;b`)MHed zX@an>=+}z(B2C$lRGDbu0+gEUJlYInrSLxDU;sElnXX+lR-U)~PF?+C_Ifa<`92TQ znt+os5TtT(6#U}b!Qp-JaAN6H5G>%E9_?r$A}wt9+Ug>an#Z*Aw#ZkyWz|I<bbiMD zF&JNZvl{L?XhxK;NRouIuiAOLik_Mgl<Q?Ab(ib)5@^87Yc9)&)=&{{T#c`@bGdpe z5iw8h$7ADn+4Iz=2?<ZqgNl2b1o`dy?VsszYb=6Fw!QUZyiNC>(SVf8ude@uKI>By z|HUCiogvH>z_oP-aO|qPUs^}VqTJ78MmgUxGnSe@d>D8dr*pG=A%59$8R=}{-bbG@ zC^D_7Ur=yaI{ZM#^>1b|6iQ4uxz{$*hP3Q8>zl0GJX}?Q{{kl{_-SGIt3>$R5B$`> z@01LI-o`*IG#nAUKdpU=IxSovMf}14`%wxZ)^YRF0}nK>L2yltopt^)go9*L*()yc zS@a5hWwM5#6cauZMqt$YQg`JMjt2xa!_CdQII8C|*dYAfu(QVqiO8j<B4k1?G+naP zT~+?CZ0Y;*49XT-r4QL#3z1sCYei4w1Lr=UA*l>;m%0@rzWu23%nqeocg)~OMz;8o zFkOsKz%Rc!+WUm0aBZ3$xut^?d3rn`-~nDL1+W$v0VF20*jJk7Aperl1ua1P{QcXv zqXSvwDFZRD(E=+QFvO*cINe%sovO$@mPS<{4f#__2^b-t3E2YOv_NM&==zNIq^N3x z9S#8EWoJOcJ=eaOcSnG`rhg8qc5@3yK9~ijQp<k3lLI%bk_l3Juxh!z0Jb6EAD_U2 z487iB%x%v1wJc1)MEC&%3@sYwF~A*O@2}nxh8v+E9)^Nh)<%+>5R`7;7SagUDAcpI z!3VIVc)hOxkZ?fK{pI~4qyiHKGAC>JN>5#sYyW1E{}vDdQmzq2ABl$rw@U|rGw>G) zWg%nC<I00E3|w&RaoFVs^b23m76gI!SMXnV#g*{(Da3CJ$?1P1SE^{giu;m%d>Y4? zSPv0l=D9x)kA2=q==?WQ=bEce-q$?AB=T$Jg^HNh6YAHG69y?D8T^my^E@gl{3l?q zSL?U#H~QvNYGrLB<_kbvvk79H#cD7fN`poQRRH@7QaTrJC~7Zz3ZXWjIUH`<w{><r z0_4tt8MebksN2>pxSjZhhk^YRvc(+DCv7OD_{5<p;2+s<lMg<GIR3xt820nr2QS*Z zUfWiJ4_Qida03xnFa=a4Uo7obz?uLR5)<9V$YN_<;}Z|<V(7*2-tiJ#S%M5Y5fMAQ z?_8Mjfa4N={iidbd;3q{f9k{IFn>2gQI%dPsI=ljn>P>6TyZTlnAjId(n6N?^CIY$ zlKyH?n__uHUpU27f8a;X93yl!J8UsxK?No|Z|YABgaG+RKxmUVbx8mZBm^d_+%Mdn z?WswXYA&o!IdQA*#(DT*)86;-D`$(SimWh+B{NoDj5_Z7ZQ5I$CpkK>6D7GQ-J#w) z&Tcbh^`oEQ`AZS5*Oy14V~)?Bb_YIc)?GyHIto(nKI!J&6|mqz-;LQkcB_+Y9h&d^ zE+dwB;82Ns9b2w+WpBk>t;)`5XSZT&IbEWK(8GK5!(oyWjYpNEeMN-Biy0ANpB-^* zg4^e}OuNX7y}ipz%>zwV2PIk@Y0=(cTumTo-&u;jm@5U5&MUXt@`OY5$&(h05$^|= zA|;-$9!@^fH<wxa;n_qZlxKSfEx<$oPs~Kj_Rm#WES1MW1Yaq`&_#Tv78n?9^j4LV zXZPL44NY|Xi*k2`%$I}=FP<qsC2=!%+cW=Fg&T&YbMs(w@)7w%bdQkZ-CkniyYb?~ zs>3^WEoEr0EUnR06O{igR}_}(X1`x|@jN^!xG0Ine0_|CD!rd!Goe#usa7~;AmWAd z>&vfQ+!k$6lg<)OLL$dQrVW>J5)#^g6XY`SICbu-ws0JlVo{c;k^J4=pq}u3RWAa1 zaWvXX%{4THa8GhGn*npX{{~%IP{G<7JHy!{->EP+mT)eWM6DO#z<Dj6{K2rHWlS-X z*X`HCBQqCv+k6{eSd>F-mVz35y*x`W!pCH;OgA>t6mnbV!~a9D;7fc<_$B|rv+(Fo zuw+iYck+*AGUrJbILz(0b9LubgHxUdl3bK`Re$9d<U1onZC<JNAC)=_9wTnJ$$xUt z+&q=^bzJ@aJ<S|jN;IkU2R9^3=urx8iL^Dhgs%!PJ@IPmkd|R6L_cu5Cs^`%tKV`w ztNda9Nj3XhoAu_BJ8$S;rmIqEnx9)Ei+-4)$gEm-A;`?ZVZJ%hkua^DN*BW~Mj^&f z8guoDi0Y*iiwzZ?P=c@3A?PBXs|ze@pN1#N?vt=GOKr9U7E`!bpAw;>_=_%KntXB} zHwZ2D@n$4N-;!yu-`iBjmXqW8d-Q9%nG}uZBHz8uW6xS!dFl1vDSC^u)2O`<S8t<W zxxUD9t#=KQXZPIx*gj@Be-sLj?&PpP-JHf2^`eU68Ta)_YH$BsocvOk5~)$*hT&KU z{Da-y<5iB~CMHA7R4wzoulW8rKg~;3pPTOT#AduM9DynCaW$Nz)TyNQFh#T_NCFxt zN#E#UcN-yPW0u;LGo=_}Y1M?NW5z{{E%)^cd^Mag_wh6WiGm#Eb$5*4aOWVOpyrH- zE-d&}W-jXUR^O%N%Y?$PHKbI`t=U7>N>RT|XPdBlPtbmS6~*bUb4$G8s{E_I;%Q7V z88L~-E9;vF$_G+L9A99`!c2*-C$poKoU!BT!X)<<9|yJB6xdBC1Z|=$S<XnTVyuug z-<8HD&?%JIbF=-EY#YxwtJRm(%PJcELJii*E4GA!EqO3_p#M9MKte84vG{zearVY< ze9BuJ!w=!BRUam~(UdWp6C2}<5Ldof<`KT?{Q_1iFC%}QurBz_Nbs$^_n)&P)@|;* zI~q*8c4Z&U79@6VtmUpbW}m6)Pu3DhSZ*DFe)bmX_U@uwM0C0(ZJ&Xed2Uj!#iXy} zx204T3h}F7?!q^FfemAl;>(xIk4{v4lzKQ-ucg=rq&2sbm{t^~c8;FuZMO8|DRjKy zV!hq&p9bje+@h$m6t2#cONuRJIQMUhJJRa0AwcN%SJV0?uJ+CL&TTK@Zzw_rMn<M( zcMdJ3kjdcuZr5gSFP!1mqua$TefonNQ7md#CGl7^OUfta*-cb@cIv-M+)#yVOgv>B zLq?U0tk&G1yV|_D=GKY%+FX%B5>q@Zm>4+|SZXzu<%cQylH;Q#3Xi+>nhk&Mo_{$+ zd-cl|&D_o$UaDxe&c^qOC)>mL%Vz|UatWF>8NJ`>Mn9l;_rx)BJYdSvN^x`GbHsSC z^oP<K+lE-?j@Qc%#J3v0&EH{AE|1AiJLUK8{2(<tWZtuytI1YmSLSt=e>j(G$A0ik zQHD9vZ;-~tBgXqO7NZP-^R&nBP^nmnRNcAhK-4_R0{e3Mi+@|-DQp3G;b=CtjWf8B zTzJoIEpWIas(>{^$yk}c4xK}3<h0een0%3%b>r;ws9RR23q5m*F*Ws302#)U)(@A7 z7h0<}B#f1^Z`4#fp>3@<Tllv5$iWG27)IXjoTlv~?ou<1`pw+3cGpcYbDjG{?#!A` z9VAYejs~_^ib}b)Mmh%~vUybb+K2IVuDi4ZrAn-i&W~3-`{Q>*qUE8Z5bimeO(gGQ zTimh7Qphf&3y07U(9sd~rOzm1pn@YWSCxrs>HEEY5-C#-!lV^Jm;Qwmc9X+kJ)tza zkV$+U^3L4cw*oc{D3zf+dOCLb8IiQEk-^tO>xaw3eQR~!wIH}()q{NT*{e8PrJ@Ed zKynLb`N?;_Xs{@?$`d&C^WtMdszyvn)p9!nocVD0P3!S0+aEP1Dz?v`$Dhm-k%i<H zcngrszL~pJA2v;oolYoxGaNqF9reGbHYk3l!=K-+^t}7{RUu-HH>jU28FRdgDbdJa zdUFsuf|6Q&5NNDC(4J#<oB4w>hvC=KQikPm-mi8WaB^QL#AS{g?g;iXb2`%<7M}by zPo=}U9Uy{DAh%4PASBzcJ(tnh^;R(Rw5NP$v+v@kg*OeOdD7k8(z@C$&d|4^<;qr( zu;Q?_@rk!wZv3b^x4%q|_M6P{t;HpI1F@M@Y3L8O(|yM~>fiw0_Id~*hnX`$rlpy_ zS>t3M6F)8M)29Ys>UF1;cLp>u(h#J%f15|V9fM5wSE<L)K2cgJJ4=7G7C07jaw(QZ z!y%`8wr(25;>$b?7IsS`{h7+2#>mPXjk^?}4iLWip7_>Rd4D0NhzNgDIW@ID+i{-M zIsP!^^o5dy{7{t$vHZU^H<!5!rN%DS`VH^sGtR8fcdfE^m<tfzW+vyr!5Qt2!C<~2 z$7g5B$n$DhoFUgw{{};G461(NTgN@2GIiv0K7&o7s5<%;44yQwpEO2mHVh6ZjXVkd zzH=%OG$kl=!=E{44Q`)I$CBxkc(c9Sj5^I8t1Eb^7V&yaSW#pkjywH_KO4HGT{orQ z&JMB6o6*T@4x|<>5CPNI;)Esa{E+1O@y=zRjAC<`M8Jim`X$DX9QT#rS$?kvj7bSS zpz^pZKr+2N{W@*k-Fl6v5WCPwA^RHJBgZdNV<ca{8b8#>=E}=^Gf~?W8OahEq_=Yr zH0+Ok<8mx|qQ*teZ!0f?E6DFhV6$^nRGX5ax)`Jz+J8KSr%3KwzFFo>317fAtM7$7 zi|}h-DgQUm#kU(waGdYmJH&Z_t#gmW&aT*PQvlU4m~&fW@QPeSSrPZ9tqZ0{h!+7# zl4=g}vA-Z6JC%h+CCvTs{y=rL(F5Cz-&WL2=?^ebEG0LxPjY`sDU}LE(NO;Npfw}x zaTF38a?<E1hK*V|D&X{I=n5~NGuPu>W%vwcPlGS_+hkzY2VihqmBv*<f&c*ofk+tx z+eb4~u9#0cmirzS-JP1F%6g@&(<AA8m5f4oZbiRJw5_7t#<a2!>jpE)-m8~sW;k+W z|0@(0OH040SBuXN=1PdMzlErV@gDPKcE8a()hiZcIqu=2SLGa$XGqWzI>9xQBjm(D z&)8QyPWZhYoc$nMk=aGVwWIos4ATHb@P|~~rfd<}sFBYB=6owy;h+)ke{s%T(bk5| z$Zn=7qiO1<!;z<?UXkdO;435@p|t9q@dkN<b|_SI@u@{pmHVv!+9F>V>OI~5;FQ+Z zjtAl=a4xNn26gTYM0KvS58YZK_*!Um^jnhW_^<rum=~?5$4DIWbh+}~YD=_J(ZI!d zTP-R<BMq{U2pNPOMRL`^k2pA_=t$}EJzoprk^8!8JLlx&1=s5RT)o}MF8xWj_h}xP zCw?lViQ{(c)OZWdXri|x4APYQV23IibCw;h(=FDcA$d7(zp^mdc{?`+inNBFP$3pb zN^;7`WBd%vReMqAM%7_FV8r#s)G&-$(mPgIbMm0j?Z*!lwWu@V9~ZnQGz|)kR=Kn7 zn6mCK7uVra@E#vca~R;FmKM9|g#<K4o_sJ|5yJ4mqw?gsHfy}2<V}QiyB$)~SNT@y z7c;YA^iPhHz!A8&!t0LLw9$HNteZ)@%V74mwL6QH2^jU&=DP)DrEGl%-Wi>-fi$w5 zY-}(GUr^4mPIe4j?EIt1AH`aBH#%`@|BfJs=8U4SkGli}J0|s`Jq2tI4y&F0fqZjy z>VzQPu4<xP)T<Fg`i+6{j#J7_8kZ#J<3l5*q8s@~b@IsCV={AdjGz|$!p<=_xG~X0 z{TOn8_~DB(S)-*%Jdg5Dp33}H8CCPxhD<Ml3om$vhANxCk9wUClG!;@*4IWpXAVe+ zIc4O0xQAYb`E}qemML{BPOW|&?eX!^uFLK!H_GLFZApK!iq?xM18h?Q#RAc%*W!7t zS)v*`yE;d<XYq_j2QK-w{My%?id*|&>K%@C`~1G+J6h)Aukn=K-AuI;89%mWc#fmi z6-)J**jdBY8cbJ4hxA8m^o3IVp-LFvu=?}s;n$I?j|bKj7ncYc6d%Ps@-+t2r|7V+ zN#nwITe`XflSEIwN-8)}hO^zj44=_JtRa?R@H*my&wgVxwMU4Nh2hE=Ip@}iEA_=r z7F7JFT<6?&)#Z)1=Gdc>v}DAEuR{6gGPqjAZlQHmLD$?&x++-cN~B*#0-rCRwFsQ= zeE6?&Xo{(CFz56uQ&zn5(wy37A68rxzS<ZHoA^~5>3xD)HvyE{Qs<9`ZkZVo{KJ;Z z#aUy@>F^)wy9If5UYZoz6GZJ+_B3{N=XUno_R6%{K1|<k4MoMraQXwW)SJlnsi0^1 z`Qp2vI%`Vk=VV$E)5a>zzs-NUOC^1E`x5dv@^UhGKWN;pKBm&dz?5gX^<-HbUn!X> zxy4@KXr_z?*C{cxPHI8(TtIcmxQilp^1+`9uLty9jIuu`$Kv3Rbay7QvRFrBOv+3a zTUj*D$bHH*S4+UbA~mY36qsR}kq{#ijtGOTAY55sL>N<Mt~0l&oTtK{^xgf+r>}Sq zp>(<uSEApuFJOwxs`=2Nd6(s7Tx8GFBgh+zgM+r}<phwVX7Okie<D%qdR?}OR`g2g z09v`#;T5Xd<@!)Hv50H*UUzRWJrVIHa{faQ8prJ^@%)qHE;Iwu@H8dsD_l{Dud(Y# z(M+rgt(bRhK(W8|b7Ph|m}x!jSC(=%yU-_sv~H*=PA8pkJVLy2DoBxn6z%0e7IW?W zW}@JXmsZ@H_iJibn@J68LlT*9xAP)@Ei62uDFz>GTMhS&jEgPuEx&%Migk&U6C#nF z<1IN;{L8gs60$2bC6oY1V});UY0jL6VxCg?su902vANmU*S4;O{=H=*9Gu9YH#@Mw zk9D^2ad)Xc2F-p*d8yW_CREoP!um|SM^c3=nP8R;8UkiqGo9Ov>C$vU0#sr&G_lcr zuk$w+ru3%M-oLr=rgtQT1IOv!gH}g8F)D)rZzYsV-zJ4AtLZgs>f4txIy;SoET~Ba zvO-D7C6Izp?7BzlXxP5zAV=N+&ly*VL|19{*p}9E&rC)mb{diJ>A#n{y;^r6z2lAZ zYIuZjYs4^vl{GSG50xv^(Yj8DC3~%<CW|lbqiKV%ncU)1MYMbHL%tT&1Ngzm&UW{A zZLZ#y$dWH&Ff;5zpKi}mx|1l74X-wtOJR3lS8H3d-Wg?NOAc*JMWma*`D*1uw?hZ^ zU5*<vOkPKH$V(<T+wJknR|A*S<K4`anJ@5?`<88w6c2w$m^GK`jNx)oZ6GfycTde> z*twt}{azS@*LMg0L(2WXhhpgi);sHEZ6M8m9EMV4zHzZ~&Zc~*5$LRBFH>zTEJQc$ z7jomsWx4S%x=t*+k2zlPDq8YyrQ}f6*RNj}8xQ!wz4vSF%dz-h*HYDMJAd@1^v*BM zLHs`NOJky1{%&qKo|fmT;aN;YOwt73>gAW&KYvpA7q%mwcGHPCh9o59UbrSjLXKos zB9>9)zjjnR)7a({&{QH#QJJe57*kR4`+L)-HIB{q?*(YnFZ*vp0%6B`jfE<<v1l=@ z5lm5-kdY82ZkT_8?{jykaBhy5>+SW}Qx{_xyiG+gA>q`7X{eR8bHLp^1Fj6@Nc>*a zg8m?-50^>M>!n9UE-M;%SeTng{;se&Jy?i_Z|-hccZ6zXX{Wo*f@UFR0o#L@$o`|p z#g$*XRf+NCuEIYegg<k^yRV2$O6(5BB5IYCZvLY4>@lmVS4`z;t`%W8;j^I<jpfQx zmKP&0uJ8pzT%A7%1*~&Mt1*dJW9jCay0DK}(c#Y5_4wJOA1v9*g!X$G4Zcl{cMRxt z&CI3eC&W>s{l=aZF}#@O)YF+4g3s$?QCwOhQeQ+3IEflQLugu%*RkUg`x(qgM~hBD zO8VISn4V-|deC1;vPW|1m?m*aW{?V6xq^pfj;G=0X|h4C4L>qG0ck39`{i_RV8Mc* zmLRRE$lih+mj<tX*?x-tA0{x{qq*(w?Qfy}?b$lsJn^FC{c}olrSU(S`Fzl!#0T)U z734N%96AaJ^6a4vDde+Iy}lZLOAp?LLPT^+uP@OHT)dyZCljenhGu^yzyJN6Iz;Vr z2gj69<MvofrjCl|w2J4p<pa2u(UTkhH@wk5zrLH-KSn=8M-E|o+z!u``QS&FFR$U_ z-eG-2+`=IJr^1|Sk?W%~o$#pufCbm;%(Q>LCu`FGiS{dBM@Y5b=m+dkdZ+xciZVU% z@Y^t63i&IuAL(~bB$eT^&s`Plq9SBd_!{=;!gnsSsQu!!yC~7(<RO~<f3<yOSXI&U zE}(#-bW4eJcc&;IpdgKeNJxh?(kh)Q-5}i{E!~I+0uqwaAl-T3&iVcR-|qcz?}zL2 z_;?ieK6|e<^UganYpr)afP<?XG3i?PLUVCq%><G}zTSEQUP6nCujnv!jsLD$ft3pO zzV&C0>hnZJ`$$aM(`?ky+u|87D&a}6yW^Y18%=SyFAHKVu-tyg#)`LVlW3m>el(03 zJf9n@oUg^EY<3}wP|lK?9q)>_JNh<M@={3D)46-7Tea;_MuS?-FW52b@2o^*IE_kQ zS6IT-<nZe*EPcIdHPNvf&Z1O{u2CicRXT+n$i(cMyDLv`{eIZ~`q!E}Z53_&Zq~}X zMQsciI#{ZIH2u+T-e;lre^k~ST2qcUS7EZ}xxtY`7a|YTv2|b=r~1WSUHe(j>cRXx z?)vs9m7AEGUGCgc=N2JDC_XW(?wE~&_F$=V@yI8wYF$+*rD?Fnsu>325y$mYF!u!% z*9k{P(x`MtQ0QnB$&JUd{dhfyNi_Jln$8Q8RpXL-4f6vrjc975gJ(BBB}F(C2=xpn zSi6GomSe1ow5n41g8rV&E&|GF936HR`TFLVgtWY}w>4ZiD39Uh7sYOwK1FAKY1Gu9 z?e<Tj<dL!}PRs0BEcov&g<G`rv^SMWr3Smk0^r$(3eDJ%Ppdwj>n`G9WXH%==I>6l zj*1_uRPU8&63zH2J}ES!h|M65s|NyCj)}x@b&R$Cle$)=pD%eQMB-9jw*NNFHO}Q- zT*a3fsq|6#kg62CU-;$FZ`y7BV6gtH6|or!iSxxuvg4mHHtKC)p3N<n5)ZXM+)}}Y z6UD1s>CaM42`ULiC|wdVis0x4**5}bRw2}%FMyfzJK8-ajAvw^hOp23HW;r35=3UX zyb=6*OG4T^BpDp+otYpV;dMk@BVff$wS@|UmJFU~E4{G2(Xif9Aq9q>GswPNa_lG; z#Qlu81ZBvwIwe6gHUV)_;pm*bdVZvFz}>$@)c&UZbH{{(Ehgp{oaY^-7#o-+<2yx1 zFE|Lk66`u2w*P3mqjyK|>zzu4Ud6v!=Ny0t)RVb@D^T41)tPg<nX~!y+xakj!cL`7 zG<xtf5-Pa(=!uCb7pEp)PBu|9Tu-*06Qo-7m8*+J))T(jv$8$X`nuRfg|QQID-t_* zYSIo$Jo|;$MP4%}*7ZF)3B{pWekerCD~_*zGY^;Qx7C<E^C;DmkJYV?iDbB9OjXXi zrt;#truJdGtc&&8$n0?~8&4fYXc5Ly%0Xdqs?3&2fGKvuv%-|rOzKd?DmDpY06R2{ zAb#OX=lG~L7NKeZMEdY;<u}}IJ1r`3G=BbXE8WYv>mm2UHOI3DdRe2~uH!mm`uaCo z<C|Jcp$3j2X6UXcoI2+GKtL#VwK3hKCrk{|Z?a7<nHV?X3KUdiF43B=YC8K(RudQ3 zqu3xUA}nmU;xW0xPXz|3-yQ6&-d4h)6BkI?jd_EXjJdyjV2IY$6^eL&9U-8a*<KyW zHd<VmsZ}tv#!qj?<j8eB`)JU}$m}6styxv!3-Ru3%@QqsC)>c-`dHOwIoi7lPzvKe zTVL0|JZY)NC%WlpkSgk&AcPwq$3d)o{N_yd?4}*AsnP@qq;SRSwSlS}a^#y{Jmfs$ zM{{|>1Hrk^N(3pXT@qO1h!?x!@B-CT@8J1u`rpI}r&MtD634-&7)G>QcR>cfuG20J zFmjgURs6H$-2%&RCMZHvxY^?+lAN`cys)qG_^>?4#l*=M2`@=r?@eDrQUe(;uqHag z=<8My_I~!=qIvl|(bP~H7cteM!o$*#P<eR#H;_ms!}bEpb+!E+*w5;@_WW|rr$K+# z5xr~24ttva<6>=;iIG`bH!TJE5?}AD)vzW~rFi>>H;dcY_k;!hE+s_QX?${13drw8 zza_o0HAP7#@_Bm6GzC_}UI@*-qV;Wg8mXK|$0PQsx9W*qHZdvvmQ`(*N{1wh{x_!( z5;i^Y!+NuX`T4(XO_!e8E4h;8b0clh$}XZqqm^tDjUHq+5A|vLfL*u0f)Rk)<eR;@ zYevMm3(96wlO><ER(%>dBb=b9Y%PlaiR7U`k=0FX>@PvuxF1rvfGKd!xzax1K;x3< ziVPQ(p}G*o5KG4+R*>bAM&W%xC&Fz-1QS?SWup+k{3GWUKEo36GJX<KA(!%l9XUsi z<$IY5nLs)#@>d_^x+KUs%kk@#+8CX`TTd_wB-y%QJL*=fa^f+wK0o>a!os}GMsce) zeP<Tfc3%p-_0<FqluDV7YUfaZsYm24b`S05#B)PQ!{lC-C<k3l#9xhm79WpwCMY{# zyO!#$*RCBIa6K4)u<N|L1T5=!b(wYf!EMzcjX($s9PHsAOuC;ZB!;Mu<T@MFtJ(Ds zMCFXE*QGj8P~4%=FRDW;7c%C$A&8wJt-d?4nq1Jo_PpVS_r2px`;KOQ2TL{19zCoP zI*6!zq9t(?9YMDZD&9938KLAu!E<*Z?6mpHqyEYSKmxea2-jge6xZMsT&O=|h?zqJ zYS8QY?q1>^kkB`#hxp=D;@GZ|w5(tH)cm9!@EuQ&#=onm^fW)b@eSnLN|NV&dV>3k zo~u7G)uoRo;Bt_>5?@^y#3!JUaWxX85kEqOauj8-sI-NeMab%CRnd39?i*wi?)OzZ z#W`+F1{ecDy4C@Lge=@4f@tbUiP8P)Be1apaeE*eDNg<2c47}tSGMVnP9<7{J!2N* z7Nk5YsgREC4f#Y5glccDosOoYhPFYr$Fl0sSY>Xe+W2K%_9;0f+&1E#^NR|!APj6k zG*;9Itw-)w!Y+lQnT48~C|5hH*Cy+UIk*zD?fvh!kG>r^9>lLt<#Pc*kn@yqyFkEi z;b0St+W;BK2-_*<{{EY-?E{g4TuqO&KG9XUI%^2eFH%KGD*ru}{=)aALPL+>(u(^^ z0D<i>V9(|AgQ8&$rbxO+l<JRw7pbzKGKNGR9<k<YHffZv3k$;g-4xSdq3v)-KL(}z zKNVR3V+<M@EPovrUMu&ctUNiyhwNv?ZgvJUvQk5D_?wwE7*OJGSAw52No*gr5(rqN zI|+wKNC?~xnV}Q(@y$N{<<(qXVGt&^e0ZyMtDmD-<tPoY1y+K$yWkxa&c{JG;39%| zXQlN{YGAIR>b!oDS4r=F8|4NC?$X7>ITLFm>vAV7)gjGpw40%M3{uA{$@GSGetrFS zuWRRWhk8Fb`ZjYjrz*>Xlk{XoRWcdl9#L%&Itv8v{@x|%`I9gOc_>@p1<vYO;NXvb z-7o#%C5k{;(|Swv>g#POpsIGhB_QgWt@`SIH5%IU#6*^a@<ZjW6ywd(3VnrUwAKU? z6P@!-HShRp+RoXq<z-tgC;oG0luEHdGw6aP6tH%mJiV2pF@|<iIy!yukBC$7WW^1X zsMYj5D9jKqJfl>73k|$ddPAds%>|GJyr2FujTz}s;W9s5r}Vp6SN)y;7hb5ZT{1ia zDTAI!{CXEQK%6yCG3dzd-Q)H<e-!w<+O8|9LLqDPm(5$g(p>$VZ?-O4$1f~@9Z8_L z>QA*Lq?$bW@vLKDIP&`Vn(I;0U7j_OfP8jVCsed9uB-Nz>oh}B4w<~6=c6OheqCV> zhkL4`N!+Tdi-QMUiCm{5`{tjWu1*58AiU3PxR?VO-VB-e05r@%{M)at%CN(a!AwAj z-KEcuHl`<<Q}r6$qSNDY=eEb1C9nf=x}e4IJ1!23(Ti7h@&RB>+`ol>d3&nYTRGM2 z|LS`)4zlu=bN7z9><{L`uF*3ze`?u~HNL|b;Qgx+JY2Ym-s`PEOPi7;nVKTz#$8#S z@uxH7v~eHHY-I(?9E_V#WgIhnIS36O-09pS`81p~=e<oC(kM>nl5x%4XwoPK^k9_G z9r?(sbtb#3Q#woS(zRMW?K0&iAH*&-uYVym@dgzRGz$iHQyV>Z4dl)u7H0h1B3x%V zjP%@{c9NldpDEJ?&AG&J@3iJy&?t>8AiDqj(o^{K18%UG7a3F)e_9H;mG*yT=nL2= z7{85m#~5O)Pt(DMA+=EXo^%cN6b8|?MC02N#+Q*Vd7Bkt?xP3a9<kz|7I6tpmb!WZ zivXY=fy&J%)YGIy+K7RZZ6+-p;EAbRH1c(E5@&@v`A3%+DJoE}2VQEzD`tTRT~2!a zSCd>L!j2oaP$Wmab!O}Zs5W}<3dvkj<!bmz@7<}%r}3pXZ9~JV{Q5jGc|@4|BXkvi zOyH^7O*eW*XO!Qsc{$Vfr>>IWc+@Gsr&ExSeA<*72;#Zn@4Q@tXRr)H_u67%_;I&% zk}!+(({KnL9Q-kBO`aiFXXcKCghv;Y_XOIl|4NHr92VfIuU)->&Y0BY@eQRx_ED~8 zjjk_P_BGzo<fwda%3Zl9>gh%voSjbec)6{MIj@xq)|$!Xk@w$CV7Vi4dk;537uC_w zRR#@cEOh!%o=;QEcr83`#K10mV~-Wm#~~__WUF0^53U={!=$u%P<66&+%#q3GD$F% zYLnt#xUK%wQYwHm%^*BMD~43fFCqA__sJLE^ALF6@87)jT4si4sRt#!ckc<~<zY## z?M2<;)T}P<TXKy~9QMH)wJoC9OfmXy)Gx)8PUCa&@cPbiju{tE%Y|1xrdBZTt3t3s z2l*%hUSG)<JUl2GMcTN?JZNo34+~#)yK^SY<f3}Z279Ux(orQMY9KokdViwhlI5Bi zY5cZ2YWOntP*4WCQcdJfaVG0)vkbOep{J#}poAy)0R0IAM9kjcaQfq4w075*eCy+7 z8z%)NB@R22#DlX<1>|_oGcjW3;*+BLj;*KmoDQ-gJSk9084Z^qAvZTp4hi3*`H&T1 z<lzunCA-<&oP3>vz{SkM`B!^l_bd5NsyT%-2R`l7hyv34&&BDCmR5fHa~dl9c!;97 zy8J^H>;Xtn*f23=4{q;q7l;&GQl9ZO(79GZ;3hvKXE<)qb}|1rc>i2*Q%QOTMN;C7 zg@y|8q@^Y8k;LSvowokRy4oxtqP&q`2|l4^69CA6zT7B-LiOY6Pju&Q{Tc51@)63Z zyKr8>NvQ$bQd1`|PQZ{yzeevv(2&a-;fB(1GtFVs6<LY0^~VcE7f~t*&wDdDJEydV znA@f^sI5<5%U#PM-d-1+SZWq&CZ=oOQfET%y(Thy?cm*kQ3@w0UCk_v7XM`DD7fh7 z<0lgg|Hq-*D{ZLCNo<8BLhkf=X56jvdc}v6=_O~yoT6<&ZM2XXE8p<Ja7aDrfiy*Y zk3nvt9v=$lO#NeJBb=YpNk--@WteOaSfp9GA%+0B(PLmbt@e|D9!Sk%W|D82>mUAn zZ)-}u!PD*E;hHM#90QcdkafrO2fh#3MT$EmrG%tjTJB-lSNs0BzsmWG=#A(KzgUOc zS4eDywr79sIMq0;RL7O^G#|MqUhChw@fmIDp{$6=^kijF@U!GP&!a*gPfZmSE4K}~ zExVcg?Nx0fk|5$-WDN#Kmdg10Cjwf(>2>{en2~$sCmDeI`vPak#cz^4lww5-bLj7; zZk%21pX5IuYOP1*u&QbW2ExesY`0+hLsVGQT}Q6fY?K0NK!_*U*bK|V3~F44jLZAW z1RKsMIwGPAv-N!21`TRikAkp9MjV7rIO7Sqoc0D}VLemAh)bkT`W%)NjAu8@jo69n zq5bdlwr3cNTT2GH<X`W6y{&L--nrirXHK*qyy82{rQt*KoSHB8S<N>EahG1~nCGzR z+@kx4wp2Egu0d}+!uaK6TQ)jA4@BZO!#(3U>uPMn!dj#>twul3;;VA5JBtDf6@Gg` z53S+9rv2Z8N<|>k?#TOw%VAwZ4_yaH>Fbi+PTnm-pZU+u>kTHqKV;uN^<O@1q-@IH z(MT5^{Rb6LipvdxC`NSH@bY~SS_*;T6hcqn^b6Q{4UoZ`m336~X)dp(qODq!&B}RA zI>L49jm7VLnZ}K3J})MyjrcPLII`>!)bd?%bHB7z80%+;wmpi?sqGD@WF7AQQ)OHf z#S#d3oGVKZN7)^_#C9H~9bNWi$WoDV!+4|J2|F#zu){}u5fScugk@x^<`<)Eq(!oa zT;i~Mf3GJ>Jt6UhaSK!#IIpgRg{%XcYK4xEHca%g<N{7TX`iaVri(mdWe34+cG7MA zzcTIUpT9;^dlW--9uOSRNz_x#U}+uh|NOcset!LLNKg|dk|IM-Wi6_fyRx;T?Slu- zQT~oj`m%T2#))j6yn5GHETDVCAB<nA`H_99$q#NXX5Z)I?Nw5s<zfh!o|7``>4_$$ zP;%9$1VZHdM~>Ldtx(855|&~?kGW^4N510W5~P+;OFG&qM8us^Kmjj0CiSpIUYQ`| zMF<chhJ~NE5DhpT9|9|RoJ~TG#Wy7&`wL1>rh#s5P@$G)I<pEy57=)7`4)+9zXAg! zj*zRL%l9{t8SiwdWpj8~P*fL=eevEpzWMuh2Gnwrz6r(}%E&N3!TQLQ$HIJn{qpTX z6b;kzxS>LW2eHA>hn9nWTbulq<p6`N$%(Q2y_TS4iZ~n`9j&6k*Q15JQ*qCc2@x&Z zJLsF73e0P_tF_m~IYvM#?il7sbEXfA#9&5&o`F*_n5{TMKd~0k($zMJifN50XG76e z=H??C5DIM(C2@Emo&8M$gp&%eeQ0ySD-~G1AQn>+E7T?SjTy&|0<elU3l5`Oi8o~K z`zfoZ6P%lY^2|pPR?F}Q1X$d``b9$YJSJY=NK0#vjxYLsF*|>K)D`U+os*s(<dqH< zzyA8tQ1G|ro=c^x2{N+>7qMuw{32ST2}o*R@U_09Gvij>{9MP~m=<a_9}PY}GkeIG zc$Rn`6)-9Db?!&^SUbt-oJ688gNI_3kJY40{20=Npgc1SOR<s~1bo*-sh~KCTmaX( z`4|d1isInIN7n<rgZImrciydDBi|;ej-SOxgX?Nyx<tffb3?q%P3@MEg__@`lWcu` zwn4qjep!p-$)o_U-SmAvR;;Z@p`U{)@^!20$SP`kh6iw}s|BpsiU??amK1YWR(1Bb zqv>gD1A%DkZ*OlkOyqU}0+JA(uwYCtkg|4YVfT9a0fhdwa0@t0tZ!2v)}cWM(LbUd zTaZMMvhnS1X00D8L|VR*4u0t5@CflS!N~NtKZTZE4`6(3JuzGYr88KYgDkH6OK(^i z7kE4qA!C~>SE3ewYLQd3@YB^k?$3C3HkPKJshQa@99<eoEm5e0>Ii-Eg(%81dE0zj z+X`1#P9I?rBY>uoBDR|C?TOLikdg*5t>a^i21B>Uzizq8Z(jXCxaH2Yb+=>qRWx*e zUGK|n5f?O8yIJZA=g@LtzwA@Nmzp=;c&|iEis%8mDNrxLf%Hgf?~7N_BP~AL>nb&x zGOYrKDu>RYNqksR2z}RVI0>ds+oh)|2}FeFt~pK~-BG=IZEmh!q{V@<u(+6m!a_IM zE(19lWTUdm#t%e7LY8LhAGxX#oUyPFnVPA^#2(d7;htVPS-Gw~4-5%#cl?9?r_;PU zzK)NMhwW2#3jOxVECZd?2WUL^!fexVeK@>y4o7^Q_}(3oU;E`SUFy2y0Fsl1%pTo> zk|!Rk^R-`Cauj1(eK(FY0$8QDVWktuuf#^u`s2ymqV@lN(cs3QnUD6kX!LVT%wcU2 z$A5qD_z!moHQxVx?qcjX4EWD~$CHEf|G%HwEg-{2-U3gCECNb~|M_$)3wpEv{ba-s zOBDZmgK${MN*59txdJLwU2}8koCe=u2i3nGNhU!-5?^26ffNxFe}A;JP0@g$AU;qP zPZF|Mo@@37O-PuMkz@LC%j+uyQc4-A*akl@)vq`1%wA}&FzMOfZrHh6zEaOq9?Dap z0Rc-g5l1+gpGWQ~)3)cd3l<jE0>~pEzm@c*`#We>(d81JNNkJFxUCN}c)=mP5fK71 ztb68B4a(+&X*s^vSEpxZJkD#+OAQ*#I-UDIqjlb@(&Yd>?5?4qPg_&9eC7j`aa@K2 z@N??3MvyuPPD!Dp%OO&6Oa&QBa_D@`&dzR5R)?iYc=B2f)2peg6F;%jO<NL`ef*f) zc9MVAeGwZZq~1kE;o3LuVSqFZ7QK*x=kezJ+EA8ny_BS6S_mN%uf?zXHFh(h(b0s2 zj8CQG3V2I<4D#SHctEI(@o7RU=rle7g-S9pSN4#Q5V@x;)NZNUZ0zhyA8;k5i$?F! z#-3Kf6`<aPl!HJDDuIR-L`QtMT}DPmYcNYJ%q4zycJ|-J#SVEIZ~weJCJ=7T1&ygF zuT=}M5>s<?3Z5MI&l^?V7p^w8wr6WuQM<dlulAR|ySuyN-Md#P?KrYeKfb>79b_#$ z`Chf6{2MFz0U9p~%F2wqylU^3a#<uKQdOi7F(6lkSmx2IvB|U?$puyQKn(1A+`d;{ z3sA7|JTAtMPO2H!&qIzc+yw|+P*4!2dhXLqu#+4Q9`-&t%hD0=BJHy7fq{Wr1)b@! zE27@lh!cMM*%XCn#`*R2@18qNL5YdvLiV%##%jw?P>pYvmX?Bc6Oovh7!?0N2nUkV zNRX_OJUnf1+xcoS^Z;};te3KLw-ic8;7s{-*lS=_t=q!E%p43gWeUEp3Lc*~mj3>g zHZ`S(%Pp#P;p)KDR4UME(!5`9sayAk7IAUXGc|R=6;<!NPEev(JLYR=V)CJ^jEj`( zCBKXeYEFZ4S<~^ovc~<s8&iCIeAwjAE+1N!5=v`m5N(W?agEyQe%XYVNtN)7d)ef% zE>&AmF^`mmvYVbD!zX4!5B>z-76GCsx4~1=-mWADQCGfdL&LzKeDVPUJ3K{s`lN1y zh>-9DSPG5LDQ&8#i}Bj8Oh>LNG_VNRyYMWY)4+W_C*ESRiX|Nu;~9GGp&uU~V`U3S z<f){uT%H{~P2isY)Wg8UwD|9zu5of@C7;{&G>@tHUc>N9CPPC*aS4f|bQ&?&a1iPo zYWDWL!9?`@d?Op00Rq+}_RVLEFeh?~OxtW*E}<bIZIhEp-=mpEDoo_%A}KA^nht=g z{2?{;YD3CNdK|GHtPc9Vx&|Q?&@Xwr>8o#~1I`EP7R9>NSfr$+%~$8vAa>nRRMxC0 zBl8~3^4a)e>Bvlj8yJ<@Sn&&+*~Y{x#08(z%2V<plbfJX+Mgzo48lQ1wzh~~6DKF9 zimEF1il4*7!=~E-TEo%$c~alJ;k-cefo%J^=F{`@d)%3Dex)+JO-_0F$IM4T_gGTv zb`(n0m6dVANV!0lPtXf-G!F8+(I7sY+`n}KZvbELb8WJk4Q>M``b98YwXQ5L^H`6u zn+>KR9{b*U{KxaR3)=IdQBV=%2935Pe#>^ToyHH~Rj?WNIdg@j$Fz%XqdO1i+L(3* z<MZ22#()o#QBY94fB&9H(MI&@ApQDRm8E)`$G?IeYU|gpk6NqQn3-F<yStz7b?yLy z%#acjcY!1vj2%w8*M11<NXUhvXX@wZ=oep2Z#%6H$mXb#|L0wN(tcYB%zSg9<L1VL z8AQrF0g8d}Ew7B)av~d5D-k&Uo4o~DkHfXhxs|Q0XGTV}(9stb5kY-(w*T7L*f>G_ ztz*b)%hmbDT{3RzZhtZz%Q8a0u&_HXU%q5W+gV>{k+?6n3*HZkmNzjl{z(b(@ntzI zc6avlPzY4AOjerXg2)gc_KmeW;ocX=dmy76Cp`A^XGBa)48hd4is1db8w#a_M1$-? z9)}j7uhRi)w97wv=~%X0L>(4xy6w!q2AxKf^Teg|!}Si3sU;J#OZgO?9LIF_*3rQs z6a3|Tkb{H6!Ocx5aU35e>7k7cdo<&-1whie`g&A=C;(9P5K^U|$wI3jCe5-p4HxZw zXG;H7pw5(KCMcWJq>RF$TlIIk-ie`RrtxjGr1v=)M_B>V^Z)(p1HCLoo3PMO*3rAX z3+ZPNY?xVDhkcvf_l!|yeJ-{zBaaRa*mwe2g@oeE%efh!C7GUPFm8h_si_f|bceTf zbbKglxk>`TV=|7s0`QQ3e=3<c^lE<lrX?gi0fn9-t&%UGBHex4x;<@awLmtE#6Ku# zLyC);`933KASfSENLq+M4GJHGknX}%f*!1d*+7Z{D3*qia16H)g0lG2(L&9Nnwsv8 zj>UJY4fXZkrFrQ9whtG=xb_hzbCouKcmYy-kRjvg)4Kp!+NVYNmtauXRH89%bKdb_ z@(VjVgCKVl92I2_(m7bZ#IflO4W1!`J1sg#ySvg^Lm%Vf0-Ks7;^N}&Qi~^m+XreE zX$@6dYd2r+D_M<~J_9+cm|#0KxGe`PN-EL%(Q<Xf<>=^mxS{c9a#E4U_!SJctE($R z?rf8kx=D#{HE6PP>%LS!qL)(1l8Z0~uRyLSfjITm<H)MGxEM}=3`6@Kq0;R~_D;8C z1rVXVyE_m@Ywzk}hmee{7kmK~B$WT=rTeG}BO}$)_Vxf+k%-qRxA*zsJm~QNswiGZ zHgcyh)hjo7o~(4>N|}O$IM}q`9a7G6*DYO^HR8CBAEl+G{jTEtVZf2Y1ujVjO3WZk z<#o0k?}*#be&wDDQ3Ml*@;(DYfO)!ioSz>G!sKU?fTFyBrSH{Sg5{C)vgq`58l`y7 z*Wh_b)kq2<J0Ln%5ITvKtteSj*#+4COS`xUpi}*SPoDluqx^?p>FNJme-Bpu|L=^f zKNjb{KVIz2_Dn;fpzS@BjzDY^V0;Y6Eui=YA~A@NsZ@ST0wiRkMBEeb__C^1q8kf% z9RF_uaBWRHiK_&zJpi62G2}$e2uA(;w+75@$s9yJK`=Wwrjep2lIo&ol{-5)_|`ps ziwx(xwEoQ6{~?1n?qFdJUwe+18az#xq=LAM5L8i7(XFvLYdt76Y*E(IB7sl=+(Ep& z1qcDI&B5Ve=lAdH6^Vr!1!|M03;>EmN=iR^s8_eP5Ve!nSq5+z1O)0?uY+eV-RA>* zr_*?3Wo3~L{1G%L1F<P$BI#h)Q7Dpt8cBnI@J&q2#lL_30Cgb%o5GXarIFP4?f)-$ zOy3y91`Rk+wM41`vg?#*XsmiJMhKDdTRcx(zODpuzQ)J){<Iu$R#azaCuYqF=)Hra z6gz0PeO}y|Xz~<dQciKVFk<n?%T?}1@>^8fsT%AOt+19BDVX#duRz2OE3a^(P^&}{ zaeh>wS6e!)3Nv+y{U0{`4pLOaL?oDN(rTI~jvo*jM*v}TSf%NY*Y_Xpe3xEWI01B} z`uqFWfRI4&&jG=C6h;xwS0~#uKqJ_yY^UPDVTUsxVZQV}(sbRP4$&$arKtt81=X6z z01PNJUc0C;!N#E6EbUOH2F~SvzMiL4MMFmR1pXrI@{cH<%h2rdbg#;KJd%V%m;23M zX`jnI`HVX5X;6CxW&!$QBuH=MG3ma1t?IrrD*?9B6^u`30or}d&COMIGfAL1Q~}=# zzYcpKN)6|$(jkKw%A5Iiba<5P9mF@4tf6vXkH7>9u6>+=$$-%JM~J+LX!}Y$nHW!A z49FMmE?xaS01I1KS;3G_m-MdlI^*>`*<NlsneCXJefd3Djt{6}GXf5En4XzIMn44w zg%n{2nd{TT5tVlfZAXBp^q@`p{ymzwxcD1aSD&?vi=eZ!)%4p~Snb2Z1Ym}cfZe`x z2QZm@X=w@hgdj>809*$&lj$BjcpuHA;<!C6Qe*Q+2EzN1EzD*ZME~fIAN}g<MPM4b zW@b`BxDRrV_E<LU2?%cwA3g-LMQ3AU0~1$}kwLqC`}V}2KSSk4kM#BR4{goN&G{e$ zP)-#^_XEE{YM6o^8Ir#M&qnf3Ah()wbKc-ThS`|Nak$UN7Y*=y;}=LRBaR^yWoQ+B z9~`{aQ{T~gEMzzRcgnt{tFJE<9tT$G(gI2e`&<eGj&=6;hf|5Uu6f=Myi48=esRb( z3Hoa;f;JPaATzJ%(hQ_#2u@@KaP2<cni6r{Btu0-Me(}a>$wEYQ!g-&6j7H@AmEku zbCRILt^k7E*5jodLt{a3CIP%0GD-rTZq78sgU#bpQWg(V6BFm`7&tgM03>5WR3P(% zZ~ki3fzf=r5Jq5|1~&wnU;g?B3G!=Uf`z@$`C)YFxVWNf=X{cZ0AEMjH-H`J4c`E0 z!zec&qy~|~@|qgZYEB!ncXM^kg`9^{$|nUPF)xr6UWuLlM7~53rxlYZFe9q1$!gDq zW8m{I3l~AilIGN{$>WGxc-0T}2GWYi$;pX`h{PSzf*jtwdDZqFePVJF?bYkoU2J8I z?T}{uvZ`)8p0qyPnKSflY-kXB6nNJNoJPOiQMN?_fF3X|qwbA^i_7SadVvNqXut>& zQc~(KY<S1O@KnG9+t}Egt|Z$ZN|>BDUCmu1XoiM{kfT)Ad>)#k6n_<M56N?k(tiK| zb-D#n2-h);UHUChe_HH`A_PjeK9aZoEAvreYHihqpGJx9k$tt{q>J>`+SNTqfH0H! z_JF$-f_K4)5m&%N5hd)tIe?7k3yYF&Zf=McF}Ll@R;O)JQebfBwxdPbOv1uChEJ{% z<+6qhry3g@)m}Huu^snJ?Ozo`hKqbS7}d-n3!tkxR(iK+S+S7u<V!fDs7Usgv9zUq zas=3?r>6%2e4@_5B$4F)vI7;8OM+bb4@iD>D$TI1$BNs)D{!bp@$m3k)m0>pq(2?2 z{LWWhd}r&c&$ncRo>YHkcr8)|JB&d3e9q@00>rlo2?;x3Hj$|SuznN<j3KE$1a<7M zU%v>f+b&god?cTXYa!M3bgQjm3vFrr^O=#+C7AcPCw8zWisri41eaR;iJ~GFys3~g zyDV5{FxYe@*cp@-kN^ml@p@ydMCZi|WGbJll77|M#D;5PVsg-nV2v&OZHr`jkn%Yl zZ~h^e+S=T-nE56En4;g{TJn4AwV4^($D|}hE31DdQ7vveX%Jn46=B5-IO?N-fPl^E zdO|<YLq;rz89}=Nm>&G2&CIu>OtwsIM%Xn1@|4`By?Bs;2s^FZg}TBr5Yo=BuDuzz zIn*xo?Kskcf`UfyyX@RtH7E`R;!r69ZQb49{{o-{dr<)1j~}D=BCc@(uZ#e)0z)|= z%q1bioN6Gl=H+Yd3ETrX;U-tq)<_;MK0ZDYSfC<eY^Ei`#Ms~0w*X)R=Dx7J900lD zpu`C!@;59r1Lr9RCxs$}YAM#ojEwtGqX74~&&<4*S!>6@$SA3r45)bH%8hNTW4g&R zK3T|Kt<vlnpu*W>2|;)Se2sw?SD9OxA0gvK`<1VnB~L=*=W|yYh91}fpSxg=*IpMX zk|ze-A(bpw$x&PHX|>=_!aRQHVE>80*u(@iU$bcJQCL)O?-RuDww#}{KqOI=(<Og` zWl(#}4oY5xBZWR&Tcx|2U1|Nf>Hlk>^h``nAj9pahJABquBFANCKvwyJ^tj+$dZ9# z&H@m^;+IWH?d>v%=F?MmtzsQiabdAh#LiCy@leE1(A8y5YqEyg()kt)1USJS2=uPE z#7$366AYH9<zS(J6%Y{<uh-T>d;^LX8XD>jxnTX9`S(7C0Evd6m<Lr1)X;IMg5oA) znOh@_wXbf&&YH8m9vT)_*7o-Hh0IDrL?V<4AnADxe1H-%XMcZxwX$P`wpOWrfiLQX zFo2-vT1-{c{+OP9tVhNvq{r_dwE~cS-50~c4*UbcZ-wKsB9d5xiY!#L;Hgw=?F?qU zPi6=P?T52(kCz#m!!%KXCMqN~JVx#QOnnzBcgcDEp$_m4#C;i6(!v4LA|oT|>FL3s zLjwZP0lJrg|KLuCE-x<LAR<)(xNILCjfJc<L#;pu@>66{3Q7jXr@Ql@(oEs$=?TeT zo!bsIN|t=o3Mg;$PmZ(t<8{MkF@UvCa6{BzU<PU>U8r5PMz$cr86npJ2AshAx=*U_ z&Bhpq-OM-5LK7vvJ0s}<fq`-76Bi@jWgIFNx3+$+{7kNQT*iV|>6x6o2Xd28s8V?K z>hBF}xBdj457X275ItozG=3Y-el=x=<N@3)7MW0W1mO}=P$&Ti2w45j0L~qLPXG(x z3PMC11X@UA#s_sqp;!UVKLEy|ps4sIBf~Eyh6qCQ8)s)zU}rW{H4#u(Af%>-a*tcS zR>?j1!wgc+oR_<*0E!P{mF>8R|A(fBOzXp2Bmctve<t7mPvy}6TSou?<)&9Ou<vrU U{cQ;i3j9)#ReoG5^XmQo0-cUFW&i*H literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/double_convergence_causal_glm.png index b0c73bb777f10174510565c6ec3ab5a90f3dac48..e2a14146c418bbbb8b047d0e8ecdc36a5f868830 100644 GIT binary patch literal 18753 zcmeHvWmJ`G*Y2}O1p&7TDiR_kNC_f>l!=R$Zcs{E>1J7=BBF~DkPzuwN;e290@B?r z-Q8zC?){$c`_7;5oOhf*XN>b>569Lg?&pp<uX)YuS}&CqWvD3VDG&sq!rr;5f*?dV z1i=g)B7?sXZtfd{KSUjGX*sIenK`=Lvo}Q)?m0fRwsW+$xPR8!)ZW3u&Q^#|luwZ7 z>;p%~hYn)={5Jpd0zNx?bAA>@;%<1z;fHs$9T0@(9{L|9St`i_LGEN?Z(dV#{V><> zp{M4wU%j*;@Z<?jlbg7{?fNBsS|R?s2CX~}ox1xiwpNLeiHYsMc^!HM>x2XBdoyyK z+Ap#G63jmGNx;q`AvF0`#FI|8TSsaAw)QRxUTi>!e^nfrSnKl(yfWy}u;J<1)>FJ9 zF}ToalB<(Jgdn(kBx=qW7+10ui8TE2lm&smJ#_?g6hSJC4&mUh$ZjAc2qJKb=qz0R z^(X>YAMr&FA&8N{e{;)w%Mg8O_27tz)UQv;FYxm#;e&&N<CBxm6T~lExFDCNk)dL5 zpViXR;<!A9B_$)9o=ziZ6g$s3cIvyNc<rnkxLrn&MJ0~3cFT;}bE$aBlY9?`DjxUb z+r^aD>tY^mEswizjfXi+x24=C6i+2aUUK*?ukSIJ%S3=dZZiAgB&_#$x2T63&n&O3 z2pj)6Qvd$qr*Dg#SlvA9=8DI}YvW;t?zwyjGQ@lc$CTr><IJm9&@|KNHkaF(<GE$; zm)F&$#jEvMI#R^0;|=50;P>y7J?0B#%(Aud!9hWvz8F_e_u7@OxqHEE<zFP3WifAz zRzKL^b6Om_mgEr4Ks4~h*l)gU$Ch@#_p)W%d5QHdW5IBF#ZI5Kc1?OAv)A?x4(!;w zckga5R?*NNJ2vvp9VYo;m@3w<WOKxyuPP=9USGkoOLa;>`Z%Ye<LQ026uB_I2uZJ^ zy(<zD`f{N>OYTwdc9*{p38N}sDxEy*a5k>9XQf33J+n2VgTlj8_{%mcy&34~Q@K<V zgIx#VyPfvdI(q}dc(l(M>=GeHdL$M0wY9Y^Z7HgA)ijcuu{}8!qao%t10~%o1#2^% zO84&Fv+eIC7)tFlR=;6vg-4GrZNor)&LW|$*^QEEpJW1M@e!A8QV$>JaGle(u&|)6 zX1wa`xHHg^p?j6VZm_JMRT$Rf;0D6urzEwf#_YNHn61Y8^33y-2Fl9HZ#$!1w($H3 z;aV!?-d=vAZEb2w!LaLgk77Q*R*V);IW1(?6)5wDVQMwBzLmn6j_W7`!|Zw~2J^-z zjfPor_FV)zZ!Ab?^XgP<NO(9GwrGEQM%e7vDS}kCMurY`b&^ty^UlB%9ZH6)&cFWW zV4j9t(B$Ug!s1VyI+fg=ZSLVTnJ;Z(5?{46-(TEcwz14II6G?{*oKaU2J<=i4TEUh zM6^ePw>Ye3?t|vI>(O4L0U}}y1cTC^IS;A*O=G{&m5D}=cn;WAK8J7#M!7n|Zp$W7 zq7Jw5ZE?3K1DGU=Dx(K;EL8p0cFXqGbu+ZH6Tj`?gMy;W_6$l~zo<3l>15P4G??v4 zBS<PjWW>dzU!)d!*#>WNX4`A{`AHb6)<<FS-Nh>EYSkpg_8$Rf{7bw$GxfJ;3=gK; z40?(j?CnpSIg|F`!v~dqas=_D!89q5Q!_Mu5OtWXHvaMYuA`&l^Uh{i(6!0tDsRvA zS#62crW+ArPN`*Ezt5POn_mzS(M(B6iIDKfn=hPTiV|~@9UdMoTJN!%+fe@|;txAc zL_KYmX!r5=`^~CK5(#~MdRP@W(H{oBsmH@aT8;gDx4qtLH@C*l#&({C<?HxbdzxnF z1wu(2DJ9(lI3xJ@2w^J?ty1*pERr=**_CZ2N?4PUl3J|Kb_+(<hiE8iX}zc8)rl`& zDC@QGDzE$g?c4c_7w^JgR5Ua~OC`>otL!y}aK-%nOvSO;?rfQ*rKR=JN25PdELsyo zwB9MKO}DS*m+vXSxoE8oxd3O*aO&qfRxy|P57QkPX|OlbU~5kI7w1c)Gf8>(c^}Yf z>fO~&AVzSqw-KJNMNTse&O`5o%->Yy<L1xr)<?6kv$Ka-p2;7%DI+tJk`&FoRAgNJ zMwExltKX^9ZEOoZ=0JeN`sh`cdB-cZ10`<k>M%g=GnghC?F<bVdF^my8UDnq-ns5| z()q40pG0Jd5yZQ~#MIO<&_6J+GmTJYDYf4Amhk=lUD#8OyPJ>3$Hy%un?6Q}I+(rO zfVD}}&9_~z=qmH_;MFh6QEO~yXwa~>PS3L&%xr3E5^Se|6(_bs_Fi;bYs~fJ&JD6< z8kQT-`6qna+20vF5G&y=fh}C3!3x3Ei$q$gHBFNr4t5}O=hcN0)!8*PIL;8tZKZ5G z(!<?1=I3(Jv%bKJY~xl}9dF6VT;1ia9s2r|(bvelJ|ZDda{K30k^f14%kC^wdQtoM zy?~G1)*ZjXh14_XgUT_8fFK?hlVVt27VSRGr4V^p0q;0mNviPS%3VVFevz2tWbE6w zM<H6s!ydQS+EBtLDnur(&-H5FhJ;z+cNE!cg114G=c<pos=2kbg^!2iU@=(c#Ydo_ zrbge?UhZ@7isv{4{qw%Ky*60mT-T-hPB3dft)4u6D*SLr8jhLoA{>TUEwV(4EAFew z=uSu9I?)iN>grk$amgZ1YIm51;_%_6pF&z>WC$+xDa#P|P-<GH3cTw=@ew%DoTrEY z5&S#J@ZZRv|Nj=)cmGF-<KLJu6r=#kx%FjCH`rd_mP`Y~-KU?)URj;sbifs6)e-G7 z0TvnxI;MJD*n7@3!91b2H?H#2rSe8ksu!n~Uxr}z16hm^Tq9Oqp)Hp7DMMupo2iNE zgQi#4WaSmo<7;uLj{z(#a)o;O{~$2(r==@SQ1o!5N1c@`ONf&rPCWv+sv;?#LH?2Z zispND4Ms7qJJ|yt5+LKL8JSAAe-*(YNR0j1uXiK-S6D1EE=kxF$#Vp4ugxT(XXK8J zwE8uK1lbHGyGNxL^ZCxUd<z|ROT|bzmMLbnAVS2>;Bh-fof!QH)e-vdT-@Tar?pSN z8N@`e?{RY(rZ#8!mdWiZHh0QsYJ}qqN6$zjIHil^?}gzDd$0M%f4I)MbBCUT*UN=# znC3h=I@$@xl-^tY&W=F?Z*%rH)_prKw|S@z8Z%1Aen5}M=IfFY@$c2uR=<CR1FXQa zty#3lR2^K=dzX8z`!LDkQS=#{>ALxfqdx*#qa{4puwTA>c@-F_nWPxST__kkeecjI zM0sj|Z^36Kuh$m6<iuZpeFWH{3ZajE7h`Z8zF4j3+EcQr+6#oXXNQmH*bPbsoVs`& zgW0`6u}J5O!yvS^mbsSSU(s%Fjs~uE=-Sno2&UNf7i~`^#hjq0*XbU|_*VEBU=Z$K zfb80Gtu(0^#nk-icw79Ky&p))Y!Y)*qm$+o9v+S&wVqt7B>wVU6=BP^CN$!tX%cGr zs07&1R~Z<8(OUg+#_j<i(b`|Xg4w8;#I^WKSMiyJzawNbsRj7ZPd4V+qDx?qZ+<5v z({?ufv2D>bM}3TxAw0im*!RpTd`U8VUWh)2kt+aeoD5qchvjb1lB|+a*mXAbAavJ_ zy!<OQOjXHetIkj@gZ`ig<B1srz+TIHQTQ%#7`58=v?G^63!SdVZ4KXj=SjO)v!}6} z#GZ%XL(bM*_KfsAt6k^JtGfKyM0=Unw%?`M{w__Uo0iVm-m+3R{JLD0tcEr__IQWl z!(u69pxWmR2JvPttC!8szx7<k!aHV*{-{vw&mBn6pLTBdCj}Ws=LkQ;PP3<kumxq* zaHmn5GOE;mJ>g{2*RESmiX>FPZW1W?)^?Lh=i*P6U2NWP@3jkLTo;9#$Grf}s{vqG zT?;dmGsB9=(O*c(ogF58W;K7~TI7hB-h?$K&K}df1jlYi+2$TQ)gaw$Yj2^9`P5%n z33he<ZEdPbde=A;gh?8`R8I8$$m(yx=gO*lL!Z6e-}RB^QYVie|FlqkU|{IIoe-B9 zfUjw{lEQL?w+WPdgtOpQ<oVBQ9IUi1>qdw^N<^~}Hi*LlVD7qVb91xc-v<F}Pw|+v zdS&}pkZ8qhVI?M*5@z&Bde=Q||9A!bNWxf14L*Lj%5Ub?h0KRVj?%vn_9Iwj6W90~ z9%`<^$9&mNTzy9({apw!zyysHuFeDy*5%6@z4><LRnD;#5PRBOy^SGmT=6D<&oO0; zE$^W=Ha4bZX0|(+Clu^cERY=QJpV?-2S;=N^eIH(^OHXlS;H2@OA;NlnrZDr^=^N; z&G3X2yMdWQ&9w#wBeRr~6*K}QM-VcRvu3z~`GdSn>f)Xb<r^wAT$lLN4s;Ksc;A;* zWGs?b_$-pa*Zi`~<z`{IrljbuLikah+T)XKbEd2`W>`0tn-w9#T>~f~2M1OaN;WpG z{Sa3`Lh8165pI$j6;KMoE_PpSmT6B^IK$4#nRYvX-u-vD`RcGAvxc(;!hOgN!Bj{O zcx~wKjr037*97I2dXQ7m$BR1ro{X@{Oc|?1)Y#yVoVrsVA?yU@V6Jty3ZZ2ARguer zK{q=lndAtDSeiEU-Mf#uR-HY8JL_|40Ntkn8nW(U$ZjJfIPS^atrdWjp>dx+2|G?I zGopZQKG0|PQ>wZ#a!5cB9<Rotk*3b<z4;N!q!cKJ&U13g@apENX=rGehLNFI0~7nI ztJsCG4bLy0FOq{w#<D5)aVU>=9Mzf2$=cawA^J=R^T?NrI4J(a7$W64AkulHdLYsl z6+c}0m}k?=4bb`V<Hvv}@QL!_ai2ebhB$$vS=K^!XR{kk1jE_7ea}*OlOe8#H*Y?> z{rc3$y`7C2s6;!QI`uW-TNm#!F%v-z5Iuhkc^D2DN$1g{N89^53v)IO6AkUt?P-U7 z5ZSRM3nPx8lwT~1Wjpf@(^k!Kx1!HeBB{?ts{^fSgSm7H9)3*MNJEDJC7m={;Aqvf zV-`<J=2BMx@;!Z<40rvXi1hK-jh^LwI$k%^mx?ixyx5|R0gtsxN`5u_=g96mB~odm zV&Pv2aN$mvBz`q5qZI4Up|ktrzSDePp~?*LP&tqVLr>AG9(+HmZ5r~XmTvz#+uB=i z+nww;9ilolv_y;M>BNT+$=~wprzNr`|I*zIil@dGVcpeK(oHA?EGl~rXN~D{f00G@ zUqi~2thCIX?spkf&z>|BTmNx!Y9!&f88%;*qRcTWT`bt?4i~v9cZv$K{|3F-2<o5d z=1+dUM^49$`UT3-VasWK8dD|GxZ^J)>NId%xts0E_Y%?Sz@|5Edv~Q#{nKqK34vjZ z#q0PNBMVXarqM9^3vu#@I!b?V*L=L;=Lo2tOHvZR^DSa>>NZG3tG}fN)YSHpk1dG2 zw8LP6snAQYYHGpvjg8y0Oy#`RyUk_q+-VZ-)MItPX|L@3;$QMEBPCJRfS7)sSg!0= z4LLs7x>Jyy`Hu4puiow3zD(Ym_oBqzRPZ+WpV;L>CxKB=4`h;H$YeY!Bz;^=@nKph z#KhntZ{)`hF&v^SyG|OB-7;r$cld6^pm`bDw<B*?cc__A<Wd^z-V<7&&?vQ0VsCG6 zayfFX(Pv*3f8wvZtA5^lpLVPUe!H=Ew4P1Bw&i^TIpPO1moW5M+JOZi0e#N>w-BlB zv@BpA#l+|01#LP3b7N^FR^RG20v<|AUObORC<&!3FXB_QObG?IAB}JEVg$~@!O}}y z>@5Zaa-#FR;%_7y_Emu4)dAB}*|pq3^6r~eej(wJTr&iXspD8FgQBx)D*P5B*~e*n z1v@gtD9T6~;N9|qqT-!wy#`Z%hgrRb!HdWzprfQ&zEdr9P-V_Ed5fl{i-lY5lqp4; zQu<)<V%VZvg6bj`BT2{ClHN-dGWNh+C|ABE2@|fL5o(kiJAxztHp0ohq90Ibl<lmb zCH9XAe#bvmGF$AFTpX|0o%b-!&Wc;yD&J}})3#F>0_C>MhRc@5F{Jhha;V}n1<jhl z-4%*}9oG+a!~H~*<=T_mH+|y++10}?>EW>|FXF#u^7GfF>jPF)`}h~~3b-<(JDI-j za(E{eIhMyPVv#N#9dCIpO5^SL^pBqJUAJ-aU+~i|Mz|w2iHF{?%9!NR?HRVxw)?T^ zd#$yP&l5|xE4L{Vt~UIR%AIT7$&GI`J+mGX-+_0RUCT^2k0hx~*GD9Ojbc8ZO^0ma zv@lTmYa=Wvur6GlV{V|dxNi?x^!Z*t=2T~g9|(0EX<1UoZ_8SmTotzywTt=d7u;P| zn`VbdEd6dc?uAG{4t~m&K)>#EjHCBVg%MVQEjOqtTDJRS7JCgBB4~9I$G>EWTnd*@ zC>l`jx?saRzk0Z$Kvn`X`Xqi1FLp<;ZawMuQEcw&&TGSy%OU*Oh^9wRU=GbY_>h>9 zrBa*%N6?<8+dQuoGos9p&uOH^zIS#?xSP&#KxeW~o%`}G?fSs{#vvcX^Nq|PvYbbS zBkTxIbmIBNIC``A$oUvM@oGudjTh>Qe2^g<CQ2L@i;bS9KuRj{cFo2yk{YX|g<18p zdpda{xOO(D!qp4OvyHreu2z7n-@SW>Uj0g~O|o?U{3D{D<aibSJqFgP%OP9!hWFBU z$SXQOAH9T>-|d47eq}msaqt<I`%v|(t0cw`;3I;|4LbIShtk9)50fAzAB~oN*9{K+ z2fpX|638s%v%7azP9@IzxHyF#mqkt=wa>=Euy;FU=EO3?%syt(6i&V*q2PNY<-c(M zPf3V0Yx2uNEw`{|*uKm4aP@9@Yd`1X`94Bi8nN2)r8>~UtTR0I%xuAd?%x37^E@*W z^X;er5~JCY@1ddXA7x`j^L8h_MW@@>jh#!jWe#7-NC-Uivgu_uryBm};+=5fU+&u- zNy5Ummf__JB)C_QB!?XMVkY0c3mNY4x>cX&Ww+uXdthFqCP2i%oswj|vte+V`aT{@ zSo2aiE)}I^J!ySvplP^Q(g7EqV1R5=!9kB1v5b>(Rd&7TvYUBLi#RO%nGnB;EV(JA z2P1p`xfI`(J*THL(*fRg4*&$g5YVW?_9clQ#|^=z!u*8%YQe5<c{-)`t-gDz0=?@u z8x9Lc4{A*%wI=N0Bf84IpXs)ZU**KE@$Ye0zPfgy?Gu&;+k$(Kkv)YUvhTR)u}!q- z!ja~ex8QoPw;|CS(7C%5=#xHsY*)wGIS+zxF0i=IeSI~YMU$UE7}9mUU>j9-?!8IN zXcTkjhX4l?3G3Xskw<r0B=A%2LFrkk1`j1nf14G3xTA9P>_*_=!m0*Ae1@6`M_BGU zQ;`(yUYoQr&yEn4)#2^#?sjYnK9|B4!3^n?SbRxm)=8KPqbFuJ?e(=&c%HZ(VZz=1 zmTzz!ll)3tl4RC@2eV@@K>E6&UBoBh{yVzTFF*61_ZG_8WSB&b-K}$e5TefMpkxV4 z?LHbQa;k;Y$d!o(S4NlTdGRgLXRhhUausbBi-m+30^UYD%u3JDUB7?E(P$Ur>xenU z6eNwPy&9o&;S0&js$ZOANr`%;KTrmX0{<$_bOfhq=#`sPL}Y}p^UlwzM8^|MbgKz_ z33(5&qvQ74VMNb2#!OYn5mx~O_@Mz+eT)Duj?TuphMP-0&&xxpj&5B?mLk+<Chu5f z42<`S<pOtDUD2-vp%?&jrzkuy#K|WBffu+8Kf)da&FPl6X4&+1D1G-WtE5;T3yBA$ zFIwvK>t}5f)WyN?BPI>}5M|Ous<SxRzaULO`l|g|`*4rZDAPpta!paYr3dkYP{-_e zKKv8C4GTOrH?^}Rqq8q{k$$)+f30-d+r|Z}GP)I@FPUT%{{FBPXR?o@kMC2=DIyq; z8U>_Q3BsKASmiG<-YKi@N<#;%F}suMyJ4lQ=Y^^hN*3*9>N+J3Uu+HCi#a$I_?A3w zKsxywN7Qaa5+PDwp<1DxuW?ecoKGyw(&Bx?sVJA~WMNarvXZk9;Z+;(v%NC?IW;B| zwhXqVrZ4EbWcB^r$%FuYuC<$~x=ta}#W#^fV>s>xs;)ogXoU%H+3I`6mC}6l=3MWE z#y7Hg@%Qfs_O1;V(AeDgaxt$}X+mo5l7nVeJ)5NC_FE>`1I+k^@}FWIyE#8H-23<W z{z7&cFh_Bk1-Vt#x3H1yUR+a8GA!njtel45QTEuo`rjCG$aOaC{Gl&ww^DMyDu!)b zcZJqKLeIP*hRd0M=!wx`L_MmSXb5VPs2*-Bn~L}EPx%=K)x`G~yJ*Vt;{GaxCre_j z*i8+@-ryaJO#*VJQwue@W;{dpT1G-Gv32qeX^9O~S7(A^B)QfMDiCE_Hy*@!VW9us znS$CQ`ql?zYE1dgn|I!>?j^)n$wDodJKNeq;U_h<k)ShhdiBjY%iC8<PFw5br|3GZ z+4ktN_69f>QlEwGBS0TWjR_NRRfrbv9_}&`oZ6a*;a4>zy%#KSNbU8Lg+08tBDb4g zAQ|3SZu<NbT}VNq;kEa><s18r_eSnOT-<M-VHS-1)o@&v;$q}4_xL?lid4Gz{pLNO z8F0gHmyl2q#|*u~mXCqUuqd=iT?>2ZcxNm{kmFoJUZhEHWrp`uw1eBvI@qI48@|(- zlf^lf3#sQ8BD*Z&o}_kn1m5E}LKd5y5H~CGP3r#w-a^?2FFvF7T$m8tWem+xR*8!b zWTWlVQAnWn$vjbVdiVEFk%%@6{4b7b5jV3SZ6!zV>-_8PUv_DTaopcH%`s3pTt7QR ztXJCYPZ8MC*ZZ40&g_|Bmf)q~pu-J=JL7kB_$_u4My0VnZ(r^3d`_pJdsMcveRa)q zV<Ok6mngwBF~y=kas8elf@6(2f<w3s?+UrKX155l-Fq<;$%D_sZtJz6p@wZOSgp<~ zK{ktbgh7q>4gku<`KL%M#bOQcRv~p6;U$^z*ln%6MVF9xa(1d<-Qh;IAP%Ppw$_A$ z5IX-W#JC-R_8)?CTr!{fKld(ERFT8F+;P`x5uTT7<xZO~*;o9h*sUt8E1$dYi!F%W zSH$EmbL2cJkRw+fSt<32V-2y}w_6@yS@80Bvap51gU;!L522P@!V{W1pM~Hh(p!Ha zm@8b)SK7HP0^^m~qKew`Zm4iJN_4iX@E*n-<{3GHFahn8TJVH4LN0r8i6B~DWT3ZI zVBMR)=Q(vnuf-;dY9~|`TU9`OpX~DsA;c9}kL1c%IzJMUy__iLPvZr?JtBIc-jamB zY`ERclj0vA$?j$pLK`G+OEsE>-ar;4jpI&DCXJw6Z&#34a4bst*<H?oodB>|&mQou zO+SIchpOs~rAiVum04vlMdIEaqN)N!`LQeUA+>AYc}G>6E-zQTOI0roExJ5!>9g`% zU}xj4_3^9I`bA&-;_2`aA!X4o-X7Xpom4_MYPW>B&}&L01gagiYx6v#`0&V-Xn&7X z+lTrA<rXE0KNNE7_a#^@qT-{ncxMD>Td0K96QO&_*%00IV>Mv?bBzEWq6XZOGHGG% z>pJ%E!tjW9;x|^xBM8prIYIjurYaMHK4xXCOgxt2l2tv9R1_b=Nz<xL>tFN^7VI>e z&JK#{e#6SXR=V0*n|c@-5+;$3rT7o1A6ydEyMaH(x2LD7!jH`u?>O-VQN=B1Y<)K{ z#$zFV&$eL@$!FRX(zGVj0#-l|;nkMQikk7$b$M@BKH+<Q&PkmfU|P(REI)8in7s0J z+|79!K?Z@@NlnIFBn}o}yQb-<zvuBuJ+Fq*pPe>+iQ#;&hHWW(`}uyMXswNBK+;T& z5Ae^FvMc~`Sv%~h=6XV|1OXQ0(5d8+Svw@~jt*<@G9P|5D!xljDr`yV$qj8kl*iRS z<6@dbjV!XlC%xj<I@A+k9MU1z<2`jVHvUR?*~$v8GTTFYgcR8YYV;3M2+$Q$?BLay za==dTMHO7j!!qTHZ}6CH5aA900)G&9%Tij!oO;ea7zdD9c3TvC+FUTcKkIq<1@?Zv zlt(pQ&q#fz<OfdYN1y!b*w<MDWbp<hbB(n2{mzX+pYlH_{fUP{;xRKRBJa4fU4`3u znL~Z4rD-{n{<pU4w<B!?{jhmWE_d?m_WC|7JLzWkt{O~x*y``-40dk*ph0%uY7QcF z)D9ut<U@`Bl;eIG{lE*()|I?AylODT*_sf5S7FNyYny0!uBpGRPH}O~IOl#7MSs*o zmoHX_e94{*7N10RgR6<p+VvxiUzElQcwPXfZ8h}jah|FE`kn2YEh%!Gf!JEj@RM%b zU4;C2()etClU{R#7}@ogf!k)_wm+oYX57=R!&qjy?X8wa1@XOXxxunJJB8=hp3I~` zHKnsuMUkIr?qdiIe8Ay8+pRI;?X(CMigpHHFGf#(uDf|6US?Q*wtgP2WY5N3mvWyv z1|w$=Gkzf)ra}smOhbfl9iS5B!*-pS@8%9l6-+#4H1j<hxi=F(4?!1g{g@g0cspxS z^3C&p`Br{xUK*1o@8d7xeN+2}`OmRkw+KuWm7hFs%YScugA_TCK}fh?!1|35*XZO~ zh2v@10Ml`;Z$lw#8B5k8i#>RkbFD?`Ip1Q_{f2)Inc^I&$6*;?8L^z~a(-k}%9#km zE&WKYuz*6PaKc`A$?mw5{XTYTHsQzREZ)gk(pyv_=!I$uB+?aiuXJkJSufmps&T~L zJ@WG2VC!1t>Pg3?&0D%v?J5<FnA*T!8#)2s6@DARha|fLd0vuSkf8UcKVZa6?QLFt zb$iF~z?RZSn0+m5^{q+3YQr(PKl~+2kmw(Q2JR$naj?HN%VN{Z8?pm-I~<EkYk|Ga zOOtF<cd<6?q{-iIg;P>y(#>`_$^S->Q8+k@pDTCP=Ow=sPf}(;Hq_zdrPAVFxnZWO zWzE-15OhD7-^dj;&i-u|PWaU)?@`=s68e36=UWie|4>#3)-WJO((rXp^%T}0PQ9=$ zc4f$6wB3F9$(?#idwzoayfmD>cAI+J*0zEa>L3v@8K(}2R_NP-UQ^<-%<_B6fQ^ys z;e(1(Iz^*VrziTez9I8LQJ?VG1;aC~B0U=`rJl=V@P)`R%+bXc$2R6d;?=NeY*EFI zeJU+BEtT&mQSxS7d)Wk^qBgiwSh){D0S}L-Id75W$JSd}KVhO<x8R&|Z^k#G(>$6j zWoh>Z```k{`UEKw{0uqdimS`FO=90Wm$H^HnwBu^;G5>MG4LwYxL3xdeL$U^=1lR+ zJvR(8e-0sH8d~h&Rcg77<-#ws>+`0R&9%LF%sM+bTqtZk%Mt2`L4wI<;Ig9tYTw;^ zm$$HM5M&bvc<nAeJ@cfVvsG7@!fYcYa-M1cuKH}ZAldFiC=<MF)<s{**3U6TR%2qn z=iHzgB%JtNp;bsfX1Jaisjz{-^#MqnwgkD8d(cS7Ypa-JAK{-^Pgs$)x5Y{E*}Gqz zNU5Qhh*&+li$P?cvlzu5N$(ge?@a?ifezIJ_FHoFtSNr8!%FxpeiJK)ZL=iFzZ*cc z`+Ei!Ujq4&*mTGTTsGZVx1bTKrlNvpo2vXy*>g1a+O`zdNq<ntrBzSA!1e3LdIqD8 zUC9-8pc~&}nGsypS%k<|44SA+o{QI<X1ne}<Dwmu7wlNdJYMB8uJSGqUJZXbYu%wX zNg!2#Ryr>z-tw{Zp6d;X=B>lfOemZs!2fW7aHjUP9rE~`)-tcG==-=E;eI(&r3yO7 z%5R!Ce0awuu~<Qja=L6>yh8OE`>%>@p{!gBoL~5CXZKaIG~4|b+HZh@eDYiO@mGgl z0_V=Qvk4uM=g6Ugx9=m8m#nHUc*zvy=(J#UJ+d|GbH)PLi$E$g+j%*ow=F}`zqp~j ztn_;>F`~=_6m!@=Z9HqM)u<5O-RF0T<hG}jq_|kctA@8_dZf-}r)=gw0z4O(<MYi* z81ABnb$XjFq`kfJMCzdIsthGIUk(_}t2HN#TAt+|H>t_*S~v{J{$&&gUGKs-#pASU zyvbcwptw3uA}tL;H<aHn(^uil^lD|R%6!6c4y*|FKU16x^FCiX{oUhMjw{*`)pKM= zPwqZ5m|ctO^npHqfxjJc&-JSFY}2Pe$<*LrxsRE7@v2#VlA3yRBTM%QC)YM?eLA1m zPzHyFCnh>YPQP0ZT>MRg3^^Ra38V*cDp_u?PHlIV?<ef;da4ud>Q7va%AXtiOKZ1u z?Z)(lE$OV*^!`}OYryJUzZFb_NQeK0+)X&a({Jx4P~3;|snBMb=`{2SVWT=Nc=Mx+ zujOzfWxu;(47*n_VX$)_#kR0<w8P+?ClBG01!v{*O`wJPioy)Y#k=L+dpS0}mrIvx zFVvO@suF7+aoo{<4rR<g6bnzo5oD2)!)jPT;qkfuXqsp~c<{h9Y&70zif(7NUydSA z%R^$`$*XWtPGaSkkR`|#*ruRIM?p;;%2g5nW97_MlIpBi6#gxb50h)fpGJmS-;*9G zH?PTm0R1&ZI3Ve68rtQbEm!A0<vLEOC`4SU@#f><k#A2`SB32r_)H2bD;1PEp%T4v z#}GkWq5I?AXo}00?O~k~hEUc)sgY|rDBCE$^15{|(`+@g`t{gpX#cK0h=HpihIuz~ zRYbkbx!Gdh9qMp6=#sMgw3HIcLo05G9I_f(ir_Qkp+^wI>&UY~2a1O&_!hQs_I@6V z^mvVy_F>K95~<9)AJ9ZUNa_A0?Ry*sS)?VA-Yn(2tKTL~<xTT23V6HK7xB^flp++E z0gs;Z56A3!)%<U4#zd%Vy-^3_p@8w&%yeWF71q^L=+LpHeYd21{1`uvS8KdX*~Oh^ zzhd9+@JOjY$ThHtcRCTBKF=hyiNv(9ThOXuiy7Z6e<9)<3WFdaI=kBfjgZv|sRM11 ze#f9Z+y2y`)11Od^$fQlY3gZ;6lAhZnapGT)}J`-P+T{<ZyER7`U9(f`%-afUFVL2 zAC^P-h+Q(I;DoZ12=0|M^31TNuCBGO&;eDc&1B>c;Mw|p4)##h9|zU@c;Zo-$(0?c z&R*hy4)YD~MEZ8ig4raa8k7*2gy?mPk5yHNw5kbMWMf`vqc+~>Jh~YG0<~0#Q9aq_ zfo!O>5bZWYk8iH?J@K#Uo`N(sF4*gW4JIeLW1t0JD;7`MOXq6MSn*HJ>z76l$pGX~ z`J?d=%~bEb?N={eG*R-G#F8_)N@Fm$%D*|g9J{S!rc*fWUbs<Ll=VC0`@F;v1jo;U zRGN>EjXeM<5ZWK3Wn$uk>iYm%+o`HaZ!Do}F|s$WSouIF-y(cksOEC<W;1o*F%aB% zKet1M9iW3V;5NZ1Y&W3Sny7$k?X09+K%LlA74tWc;R|zqCu=ZBFv|MDI>*elCWiBz zAx9R?VRr;WP*1k%%%m4{Om1myZ7+Ca>@?N#eX0ohu_))(Pv>;O-Xg4WW=EfCo`Spp z&N;h*CVxTnznYed8IXtg!_zgawvr^{YU}>9&PX|M!w1@Q5__wy?yycFF)@UpAtR{d zwF9O03qW(jm<c5ms)+btHb?g?x9+JnEIlY776h6<LF7-Wq5rPmDOFUiLY+AIGo1C$ zs69xe3%>Va9<EL{qd|!sOD|~h!oYos9h&Yxr^KNC4ww+(v+uC|>f*Ljz2I`_0jMf; zF%A7QAZ0R_C%TpZTSwK%D3;CQ$LrJ3u@Bqd-zZ1*Qnv<z>;)3K*mWsFow1_t?;BFz zw%21yyfSY<j^LzNk+{l)q@<Y|&X~5Xm5H?hj|D8A{?db3LfLleH;;b(p66b!800)X z<|yrN*qISmT{5FR=0&3<JTy3hupaBPcUOMb_s>`LC+K(i!8VS&NO{V@JXdG!D>-wr zX<aC)tZRstNaNEldeEC^19fD}f?RR%*v*EQS)r7ObchV{Op{U6{w8cqR6n}5(kPV% z@&=tOlb0$gDs7qi#VQ+cj%UVu4fvLdurZ4EIwwJ*+jSHfR?oF;ZvnL;?THidKv&9o zY|OWRzE@=;SXe%}JU-o-Za5-Ldi)xK;DpLB&orMud-k9P&t5d0nud0_+~Souo*;;_ zB#E@EzMkF(nH}SEpzR&6Ix4XHrr2Zmhw!rXA_)?kdKRf%C@8Mxzv_x0LogBE7XM3^ zyA{YGq~b3ROcn3I0A)dX7gQR!7UN@wcIiDfh!ACWNUzBMEot$;A@uow_7?fvTD}TH z{Sw!^F`mmWp>>-Sb=8GiNJtgBlAzA*q!Q_iH7IqT0+6T)dtY<r%A=8IXsg|(CkHEa zu$v7%-HeI~X*fi8L<U?xZEq|PKoaYgO9m@xC=V5?PWP?N$c3^UMJ36tcyEwY-zWIL zdes7w%63p026n!M(+zjtK#v2TUV-u~knHR2LXF~|baE|2w%oeAyI%zaw1Xar7L+x= zy%9uu-1tyIFdmP;WZm`o7$kJ`ctNAOwIxALLg$9JcNvq<?jz9i<)d<JtB&+$eV6`( zr{py5In*$!7!b~W21$>?dx2{w`3-XNo#uMf(V&U4`*VGTsvsO`@!II;%<ksFW@+)b zgGe>UqLm}C{i4slrxyg;^-%~%0gP8Q&}YN($#I&|C<RHdWp7@_UIe5A%U|y=ig~Q3 zR|6w~?7w6gQaJ^25*6g`EOmV`UYq<BSXAP?x4lMx)j6Gq&Yx`x)af0ZF`jX;v9b2; zZ$m>9K_F1abiJywG6%$lAWpraZv{G_u^=T&5T35*L3eL@shXL8&EYhtqGG3qsA410 z=ld;orl=;JgGqtzojzz&vzeJCJ++{*HJ)h<ZG39Gvq=h(zXf-(c+gP}9_+i^x_Ps! zS0i0(xa0Fh0FFh6aJJmLbP9Or91kB`IDO?|Y}F$-zsJ0E{vGMs#!|MRSb#+~oR#?c z^=r}So2#>^`niKfYF9Z^4B~(fD^m1F;SzJ>#to-d1(6Wf#@tSWnTMVrnEd<GK73%< zd9*R#Zz>orTS9=QIa3B4-c6AXjAQF7+6#_D6__~?+`_`IW0}5-f}mE$ssN_<&-(Ni zYyK-F7A=4%<DYwpVk3!&h}z<@$Av9gzRh_6i9GkvvtGC}xcChyKy#saaL?Bc%g4N2 zx*Kp7RF)v)DxH)%*b1zA1pRRC65kts)E50kPV1u}BUTaVdwAn{Cy2^D$uUio4KY%L zq!@1<E-tS1Jm<L{E<V1@>oc3cD6ci$py3h`=^VK1JgY@YvY5|^AgsoRx`<&>mzS3v zL8_k+AOB#gC874`&(AS|uV1TqdX{v~6u2(MWo2iFfPF#Os^h_UZAfihU3LsJ>=F<e zjI5zEX~&AVSHv9b{?W>h7lOPDO+2Zfb=9=GVn6nx$a!vhSucQI_!215L7!HcCJejn zKpI)avC6zDu%@A+QkR#1&3!*Rq{@%xq+2EkOwhYwXu8>9`dph50D8lw;6SAoK^at( zD;~*jbfLgLODDq;CgMx66$u%cC8SGKq1L(oRjbrJmqQ^!ZB`f>pYBr$VITw7o!*Dp zOVzb2%}pQ2Aj;!gt<EW^;vY~}D6j4bM#c=tl&B$N&}$=Vz<bBWrt9<9;T~uiSV47Q zkTMF6f!dmyW{@;|f-u4ed)3f;D}jzrFB#5J4<YUDC*S>*n1f^p3JKFvQc{MXCEiFo zD8eAI=eAnQSr6k3a!hKDN$kvKQ`^{;Ecx0Nj)zjD4Fg1{7qR_t*|zV2o&W3CZPm;^ z>7Z`W)PfbNaQV#i;v^NbWS_;z_cL}ScRvb0d;WY16x`}?V&-g4pFU0PK>>Txm<4g~ zW(^dNqZhX93d{!)bhde;#sex?v$o{BpBQ?|c9-iYGGL!<QWM37L2ED-G|215{@>%! zuc(|U*!msG_{L{{Yl7mBZG;RI_u4#kQBpo-Y8!fGWJ84x6Sev>7K7(H(li@K|1985 z#IfozF}WkOAk$<5nb5UJaap_KG#y;EG=A@weqAVUD`=M6Q&c%&TWf%d$ac_Ud(|Sa z4VHhG8Ph}sDy9yookF6cGeJ5%4f)<>X8?4Qb6sDmycv!ii%ZKaN|kr|aMeW-wB%}L zW{GST5R7aGi=kff8wH7UXbKv?4<HsK>lf#NpvgjNAGD1Oq3T=>=n?(>>-5eNZ+;&d zuLRqh3mwe;M#t;IP8K6hn6&oP=aeVKa@HWk%m4Wz8N{)*>EjK?HwWwa<)S24HQ@{; zfI6!y5IXow?voz{JCDLn0ew?XU~6mZ8mJY)O9F<19ES<Toky^VM@B|!^*~^)e%I16 z)wW=m43y#*#Kd%_rlt%$KwuS0$8V7S7wc^ll3KQ>hM<B<RB#S<ket?^QAuBp=0w82 zC!=9X2Zh$xfetWBl!6JSXufFXL#Ee4>Bc3i4j#W6dJrcO;QN2KxwyL*xvx$RH|2py zEX>IhRs#i$@Q~8E$K*^+n@gjpr{>|@%j2AvJl3tfHi!Mz^I!|}8kBUg+yUo{<4Hcf zCfMG&kZU}2fdGk%ixb~ZhiO6uS<Vrx4+|r0i?BlG4Q*(ET%w1(^l-9?1+-xDP(Ebm zgX#kAH9$3F5c8c@C-0(37xuqPK|TO6cd#6oUXF~~=2bv~tq+DE=Tv>nfaE*flHgB& z**Y1lA~O)SS|F=4`o170B3cE(+6_2JyzL8TK!HA=5N6O4X;(JY^m__<4{11D$lMZq zPqu@q*=7p$ks^(w#V`Is<fg(5y_-=TtjC6Jex^vcJpIM{&zL-BFRG_$)Q<}%P~q^a zpddpfxwib|sssaxy;0@^Wf0u<6)_>mCvX!K`_eFH0uA8jtVCY1x#?QEJ)K5v1Ik?V zQRrepLQP0W-~x@{LdnV-uN;E=+O$s2gLg7A3x{x~4?)w<YgncS3R>>v9URQE(@ci~ z1PJH%(j&6kM6o6wtXm5{2fC2Dx$SK#LBMPU>81!^2Py+SwnKtc+(9w&{@%_EoE^mp z@zaRQY}ZGS_~|}3?u4_v+GQLFDqd0j0|)~aFqgIU^&xDxZrz%yqLFgk?n?tYYdjbd zvwpEN7vLi|H@8$c8nC9b*s^mUq<jp}2)Dj4NN7z`x*#B+3?ozr;7-fPIJeBqgCJ*7 z`T%uM)j32T!F{?Nfcn$+Tq|%G2)-I$HcDXYMF^U{+H-A*$GXFAl2AdFWyq-(U#c^g z?teRk&083Q<C+fpqpQ(_8!<C8O9jo|dcM!cj~`_f6*aW9v|etk&vZ6QUL1!b)OOO) zbNJoKt31RI$&PSB3<g6_F0<6;&{NCS!~o6cT(=c7zmJ1ZaTe0<Q)Kc!ob9^kwNfv_ zl=2d^TgZ`#Zu6M-=1(&0-Okk!0{5PDMmueFWI*BfU#(ZrTSIUkJwf}tJ@k}@4GRV~ z{*uM(;Vx75)#EdJA!b^I@Cxc`u>3@y|MTS6qhmEe)K*hqcxsvAjZ6X)iYP;v8UBGZ z&RFfL=jLY_OQ1#(r0P9b(FBEr*s;I@l|u3xT{AZ*JJLJVn$%yGAKO9nT;$Vv8X`2E zKl_*0%BFPSKU=N76h~t3G~6fyF!9f4rItW;oGhG(&Snmz7nbGK$w_`|KIl3cKwS;8 zYp`)ajoCF^kQ~4)>yJ)D>LmzhH9?#+kWujQU>SM<DUo)47ox=U_6#AP*=J`iUk47% z%MB2xUaj9Qh1~x42jIAEAoGLaEr_kRX#p+iD=I1|GKQBK#S;~yQUlJ2(7Ejaeq4om zv^}L?)#*4)rF4iB0HFGCp=xh&NbC^kG+h}XI;%pWovj8BVEYXkiFg2c({Oa}f-VJY zekhKBubzRru^Im51QS!H6`{1lJ0_`2JN@@N14s;>um2Y_m3Us5t@f{I30;_uw){bF zD*`{9zQczP^AW%sRAk^bPK&w|cQzKn*xJ*zTUm2aB`>0e{kObS|GHZg1#bX>_FVf4 z9$CWFuxps8mbyFb&Dr_1LS>m_KQ0SeHBB)9I1N!(lfWgF3MXm0H=h7*Asd=B2!Hv5 zKHq6IUE`^Pau?^n!MCYP!xIg>*3<*n!-Ip>Qf>OghXH06jfd*aRzsBNFb<T^nD<7J zGO~F0U-qd&nQf5Xz!xE~z;HA~JzF*LPom`5ddn(clds<4{Zmx_bnEI;y-^1BP^tS` z%hG6dNLW}B$Tuw^+_4XYMMY%*GV3Nb2Gua|doA{ztV`%TRiyng%LWQ21M)7m1p<=k z7cJQm(2MU8s(LHI=vi$njGi2%BwdqWupSf0ES2dp;Fi=ilW*M}2m8I>yDwc^Xl=f~ z*LdxZF}C(BcrrmYPF?;=&~?!Wz(!Btf9H{kn9x`N?*z;df7<`KNy;OK2|ZlWq^HDP zKPyIwB>-CC1_*{n9T9WA`HkaiPy~Q?ED<V#g(aOo{-xxz=!P2k?%xiRbdGQsOjKwR zG^?j4Jd=89Y6N59{ti)PtzKks5>^Q{%vDxZwF7kD8>aD5zH{e=W{d*z#}<tGAO3d@ z!T+TMPt-9z36&QINU&jDTESZp2Ubhe-v)Vwf`&#D+;S&CzfVR*ujU+%D5LW5z$uV8 z3JMCo5<h5`VNL<G%;aMWaASY7T8hqXADkP?xt8NQqqdFF5<11s8Bl<(js{Al#_L~V zJ$J4RFc9j@fKt}DFnW%QeQ$3m=jYEm88Gc#RwzaGNronAJw6`IDYPDT_wUDXC`IdK z()XhdPP7$4yUY`N4n*~_pu#NfxUlC9m8QXzCjPQ@SM1Jwu@We`JE34vf{7=fg)OJz zho)5Zv`e0w5BncgGxiib`qXJjf@^`$nJh3sfn)aB{ZhRn?mYYcb|6y*fTIV19LJ~s zK!T?<lF492m;#8#lzJ4YeFJh0+TVbY)pT?sz-v4GMG{SqV95(y<HQ<zE<NW^{E$35 zJA00h0$C57Kba`5zjR3r4M-3j(Fl~D&J(tgZcwTNF#{sn8b6elt~qAVn<6@kqgaD- z)oQlu3)4(vYgIYKnTU(VzI+BHlaBM?f@_Jr_OuN=qr$d*UE_Noqz9{ZOO|O}j%7PH z0A{}xsolk=D2Q+w^wI>kC{qR_(sv6XQT`~oHFi9ZR64DFy7}ICqvVb}aG4I<KlmX9 zLs9w}%mN93`%m`z)tIONG2LF|WCh4yPf2{~8Mwn4GDRN_;ZQJBy7c1=#i8Ur{zqnD zJj4)_saaA*k2QQd4u;1=6?Gu|XMD3U*E@0IMJ?3%HexWZdsnVpDfs2J2PPo*k(Znw zDh<KMfZBm9Ah@ynme-+118`KU!^B55K;?4{EY(~NrO*x{0UcL+zMWyxWhI540?*BS z;1NxytHD(SHXp^~m#$yGz6uWtu{s?l<}^JCWndEg!>HptK*d(be|3E#NH8rb-$L07 z78Vw28z6A7g*&-gJlm_SN{l`81*2~S*Vg8X7c2oyD$X@Z9h3l~x|=9TSqv=^VVj;y zN$U{yrho<Kb~!+lb<i=<GBBh=HP`;J{&-ad>P&~VovQ|qHX2N2`vW0~JRzn0&|_D+ zrqS=cGdG_4<@43~f*XJqT`Ua??GwNZFR{*mJS2y)br#Z-@hyx6K)G~<VPm>o?%K6$ z3G?Pq(4gU6bq5X0zkBp9hReGzekG6UffzWyJjaT-9!GEPcb~~fg?8f&T{j{l1A}OQ zu@g-i(ZhpvxfZRrFJ8Q8I!(Be-|rlWMp>vGEKyhRM)^So>S}=Y$J%B#5_<*EPsL2C zCLt58pZ`!N0Dz|^Vth1$I22_22LW6$a45B9=%OKk+er%D&+w+G9tqJ^T`dSkP(hy0 z?w$?V$5iN-U`Ui%#6{0+E)Ne%dTj@DsoC@wsRPb`Xc_45e?e7$9@?dgq%e`R=lJ*( zVcMu|Oiz<Z`s|lLv|#9|727R_k|rq+4v&Xpni#Tp>(904&)<>oSa*DI;S?EPz}T1x zkosMLXMiK_>gp0~AG74h;tch|Aci0^P`;<<rK{PKj|-zYfO>ik9RH@N=0NKz+5i>6 zt!ju2DZM8limh~<@6!hM*8Y9P7w0)0iRGWHw*5X}#`aqJeiBB-xE~Tf=)F^)k^xk( z>3kzIO0PrHZr!y!B)9q@SO;3ypPC8-ts${J`H5+Ya#0xl`g=sg#85@^`+>)1qsa64 zge(*`fI#M|0nKy(Td4fooo{ek0pIHK3gmo{;nb3sj}RM5?#@`6J+e4K9YhL?VUQ__ zsbGezrj`u;xKv0j)Hd@x<PK0)0$p+?r{8TNYCJVTM|gr@1R+A}KQ4#jZVGIB4i6m6 zPr1TILTM9b85x-{kHS#kd6Mz~5r?EHA&cblL>T2^)M^7+wfdtCoXqVy!+n)831+X= zcR*<k?qgNJn%jEwEmUB^#fCmA+Cjo4?1F+SGo1v&tRNDD&h{{B+Jfwj_91igX-^_T zLPCFVbKbJ|0q37zSAb%Up@$km%DlJ4M{yx-V76wy$|7pF06ru?#9%O}2mD4a_&ZQ$ zE3DH8kTUh)Z*&vGk28#CegXze(FQ8n@%fX8CnM&lbi%_mGD3291uU`q$Xv|WHm@$^ zA!yggY?C8~Xw>5ZF|reYpes3r6gbpf4f}t#8s5bhhUfAJN~1Yt4)AKiKy>CMG=F$3 zlxm^`D_oWc-Z=Of1w{D-L}l4~g5hmRO3Z$F@T&+(_lAHufCNA2?TP;G0BLoWWlQ|a zbLY-|=m`o6l4vQvP*YRWQ{<F3GCC@3-6a4X=xb2O*l$V@;qosYCXr5<g4_gMkpt13 z4E<safL?jbhGm=CoMs1f9mOtoyxLjcKF+l&NwvTDi&cD^0SjD{;;<l?nt+H^lg5qu z1PZ+)#)$xel1>1heL!Z}wxwSlFvs8aSAg<d1<Ou%ATcp<RL4;s0u*ysbAhOJ=_zzb zl8Isclc%T|#m3saw<491#mndij#{2_g%w$Uy9y1MEAl@lp*V(sG1pA5lW&^=iq)ib z_{{*<RZ;vZ1lj5o-YZwSEPPD<xqIC5y3AMIiaO5cu9oWy<7f%ewhp{saqg|C;$z2; z=K}Maw2q>=(T{Y9ENBRoQ{cICIf;XSTpgig_ZuDr@+cXI9D|gP5M02>o}jt|Q;-2C zKDhWGC+8Bl(&|zq*w}7@9Y(ouhy3pp?if~1+6TYz0rs#Ffn5)1R&rmu)OMi}>Th@8 zlW0$!nrw;=2}wW`I|O*sc@GEzMF;ykAt52-j<zG;UZPFO@EtN-7K#WQ!)WGoGBipd zJyF|4NAGzQqTO}K$u5=$Yn_JZa-wj*u&^*;+5z}TdLVwmbT;gm2}4IcTXlN;NHBR_ z1(NItHIp*9^%y%4o{}})2M>~MAsY}nmnw*9&a<*sSy)5>=tb?P(8SO4rDjY8DyDV( zvOV60WeU=e9wd)Yl%QRXfO4vU;aKJo2s|B76P9-P_)#;Wj5lG8Ce|9Hp`O=cB)wLl z&MFq~%r;k2htlNw^m{0i!KMs1k2%hNdwCpfxIzU08?~*(%>jrpnf6q5G)p>dF5a7i z)7Nz?3LKAX1fM-Qh{*%HHi-0>KMx1*z5fy1K*Zk6s~VG*U@p}*@ED>$BOod&%15{g zy^gw%{c$8DBo8(hhf&v^Wn1!aiajP)ev|AdPU*#)HyQvpqIzc9Qa(ZdP0dvTvI{_r z85ETz)vzSA=h^52fg=xz7QDraa7GZxs`kjTUfW^?7-WMHT2)-~+IB?4GU{rC-$~&l zB_}U-vI8p82{yMCEQU^*XCeB#DgE~`u_xp^DrbOCc!jn_p{X&AQaq?y0%&!#X9!pT z1-S57^cO(@f~h&D{0(ry?~Zyl|NGz8`7coVfA8mb{x98fKqH*FV`?=iMu0K_!QN85 KnR(sl$^QUTVN^N* literal 36115 zcmb5W1yoh-_ceL|0YO3pkw$401Oe$#X(<InkPvC4yUQRH=@cZSL<OX~1d&5Zhje#K z$G5ir{xQCL$GGFZWALKF-e>P8)|zY1xt^d0%Cdy`RQM<qicnroN)?5|^hKdCCeGu) zzufK`nSpOYj?$WrYBna0F3;?ZQA*DoZ7ppaEzJ$CIvd+NnA=$M-W0gW!*SKj(b3jH zn2XEmzklJTjlC%syY8$H+yu{7PRjv>B7TN^VSJLvGDo3;edVR@s=FqxjJoJV&z}9< z9KcY&^CKtw(<gRo$+l=LS^xJR1Lr#OGU8_~Pb{AcQE7DFY^8~xy`$6~|4}{jwMS<8 z#Vg8EB(EI2eSQfA(M-??uIv@ch6c=w4|wSglwaPreb;Hs9k0kD1^=bj*|sba1;T%F zyQ(NQ1_lPzIyMv;2?+_iJ{jr_{5u&pK1v#X6Q2Ux7k<ou9gKMgejJV|gOP$<hLMJP zfZWLU;s595i}0z9jg9&|@%9xrkb4oOqPS@VY{nV~24WKgY~~yvBhN&0@BjCm|2~*v z5)o>8dfIKLOLnNn%cH{h>~NV&*m)(6u=)}_F?w{%5=C+4%FkDLq=N1{W);SvG(z78 z^9`Ne2f&M*cjH5eI<HW89qrEUEcPtMoJ<DLFAU_-8@EKE{gZ;=S7yB3cu*txhPdqs z0`ouVeH2Sq`g641=0CVl2s@hfyC}lLGZEXO7m17-eKD{sZypVqCSzQ@bSdJ|C_DfO zT5Zed?;u6QNEtFb%W4pGnWnk?_HnvWAHSf8(7a=*J>lO$j>vmOga2%0I6pB++^~FZ zaZ>)954WEamcuvGa@iyuEM%Sxl$uj7z97eDzHo|SNMpo%LMGLGjS~;+-wjlls7NU* zojF)kGrf49QkOc@1T{6TtUbw%T*s{Y^oh-SyyipC=tY=EOLAvnjvvkAql$5Xfgx8{ z95rM9)SK}Yn00;R_%i-*OBXd*;bh@`dN7~3<`08lROlekAylLzBPb*^IW_e?gjS^B zE1_)jcEX(y8li|ei^96PJD6C-8)`5ZwVySsK1#L@U1wl;(UYaJ{Y#P<L;jYmrR7)2 z-?l}b0+`5~V5=p3w%XX)X!NaLI>I4P-~4nB{(+`{t~*1)y!R7{pP!#jnQguOXnTA6 zGb<zPe{aYIBSyMHPY}j%p074(QKIMC0m^2q3LQwocpe{rYPi_K)y-`-TkQPb<;ygF z{wsBJM2*6ym4`w@PAbGIF*~|bVWPg3&a2;H^=TEFJmaQtJ(!I>_%rY{TO;R5=5ohP zoJe$(eO{3!EZT|XWd&DP!5?ofw=S;1Fa#17Ofg-IL^rm@^B&Zm9Z5`0PiO0t^Nf4# z(RzD(hb%hsx4@7f>v?T<WhOW|Hd%6+kAh%jv@&*o-_?D01wHW8M=74im}XeU`QP4& z@PGB{oQ_V!z06SY!$2ZhVQ#ZuR|#;I6O^_vb1;xinLo9=`a76bB*xbl6)eMu`tkOP z>BdC8oA5r?yT7|N-F9Q*?%|*1pEX`be<v<UXE~PY-_MR)jd(VHTzLww(h5Ijdz?%2 z<#Are<9d3tbI2Ax=i3^~wLKH5Qs8-Dj}mrSYa1zdC||ERvM({zy2JQ)_up9`tJbRV z6nc$+dFmyxxPJTl+Xp=R{}vqss_JO1lFxE5Z1s03ulKQQV&axfT$GPQJPel{6)cpd zsUL4S^Ac`bG_8$RZf<U(>-CI@|4yfBN}M9gTzf)5XejYZTw)XWn3%n-?N8&Q_)S^J zn`7iYt?fv;`_lDz^Jh>{P%2DV6o(d1R%P_+-#8`b_s!}zzo)0?{^`-WowM^?PU)Dz z_FTJq&Xe0&l`*SPI5O~xTh*oG-U*MNdPO%jN>x==A-`-sRA8*^a^vT}LD&+DL`xYM zFq|CineMHPV*l63%86elC6&xK_<?eTF*F}3yQN#<h~A#x7k(jw#7Cy5<!;+7j55I~ z#;q}pO-+~+D=Ufvxw@B+PZa)!vl;6DS!#9@br}3GPCdFU(f^)4$&`=kV55Q9yz3*j zYzR%NYQ}xbk+N_)G4~nA0ej}ZvmY+HdVH`=Kr0;OwA_zz@#4igfpJesSV8t>rsc8! zo<fCM%$;w!Kc}IuFS=9WED;`teP1EqKR-O0cu8F5xOgeq`;h(5QXj1389XXvVTFs% zPSn3_i@VjZRdGN1y5(4vR(&!p-{0-&_5y7*^(C*y;CO!%UQ9JhnU>CLrz>$yBPsoF z=(n%_zt)XnPo|PXiJDtnrY|xwkAGLW@xG%LFge(oCA+R3xHs;TeA}Ym7PiW)<G>cj z-_<FXd)w>KQO|AmvLXxV*w`3{PT56z`efu&vPh7GgD!sF_xUwE3Ys;UNmNv)KKbRp z8MBWrH0@N)R%J%Lc=2L?Ij?r(&!6^5y;-h*Z>BQ|+kt?JFR<wywZqzoP^%NRPe|6k z2OlZ1`n|Ea`K<m0wobKsG^cjSjAQK9?SBEr@DeF03@*OaaM5IA5Gh0x5^{2xTJID4 z3X6mH{m6Ec3xSob|10IL<!Hra-_w($?SuJbwU1IkG;3O?|9NbZ_3Ax3?NaLu&3uEl z4<fPH7s%Q6TUq`+9L+6w>6^M0jR`h2=##zi<epLX2mgkB$N*N4=yn?~#XA|)8*<JT z7`D6#F5JJ_Ih@1zf7VN~*DmE#&rNA%m+ZP8vTyK6M)BM8oxF|<4<J3^-nnxJ6)Lu? zNwjx=Yv;Var>7`H7q;J}HY&w!^)GQ3d$NdOaxz`k$5zH`C8#|hBu8vtdKdI6iNJWW z<~4j{?uv`!Ts=MB?&Px`iG#&AxwO<WpX}37VBE^4mZ?}$#FgDzh@r2eLqp1<II+-` zmK{^>v`mid2J@cGSFc`8Oiwqcsfhi^(Jn2>&w`jQ#5(ifyk5OtQS$F3jB8kC5~X7B zH^_SEZf95*x)n~#O%UA72ftmLoSY1%5xVrUJ}hJPjTK~2*OkFrhr6q-@Z>!ld)u87 zYUy$$dN2Ub`@f=Z<W=44`Y5ec>1@USM;%t*Jlyn}*~5#bA3K>U=_qOCe|=2hAjA}L zT(~p&{WT@834w_7N{U{!yH<b9AXjTYO0V4hHLq!h?ZK8Y#GH^XUv9uA%8+?S6?Icr zSj}mDF#jb4DkQ$qxlZ`0x2_Hq;y!))wAF?h=cONu()({7d)Am(SiF6>u&+iqy5gD# ziw$o*yT6%)M~XubtSu6PT1O>*NaP(&g4JB-hs9_13+0=slwd#8Oad{9N4HckNfN8B z+5-Y$r^NPen_9b-!F=`BM>$U}OGrq#?=0LsIsCI~ZCdAx!5{th&71rA#Z`*&Jm$*O zZvmv>0<cgGJ|1m<6%@E|{=0I(<os-GcFE1BHEkP{4O-Riw|$N`amB^OQTV6!w*t=j zK701ea|_K1ndyq$At$O1jSe#S@n&*vPR7xZFF!vYl_>1=rrcqk$7W3QNs(DSZ>Ojv z>~PwyXd?qdLw3l`1s1O_TwxIxADgrSoHWBSZ=L91R^I!{-=7CE7y~0CyZWb#N4u+q z=X@hPjIX1DVb^TUppSQd+d$ZV-uvlp0>2e(+YIN`Vf8N>oI?hkWo3dL$r7C%A4Kqo ziRU3|8~!e_g4j=A^t|s4yIST02*rv%Y=sQcZ_>~2Lh`k9a&pq7hNV^h@W)iiiRkTE z1wNKb&ceX}S03iON)M}BMb7nZd=PPo<1@bmVC1I}&*bWA`}fxuE+5a5P!gf8aOt%b znfH`A%o73R*jXM3^(UmxbW;gukp85RL)G{BQQG4?J$T>}>(Q1_iPPp7PVI%UY7ws5 z<AR3%`A44~_$kERWUh2x>}GC><ysFrgy?7gh5ym-2|#d0j_G|)zBmM~?ylXL&#CVb zz2<ga9W+vFN}@|TcZ5r-DKOhPGyOA^SHBLWKvlw;<oaS_LLWB%dnbB1n(GafzE3?? zZyz5YYgRgQs%0rF3G5d(>2v?e)fEvxTH*CRTx5OEZ`A_pKceeq#;-CvLl{is_z3=Z z#g&orNQizSUkQ;F**t`f*FV$0%05_`aPUX*EdHxt!S}Tf->+S}W(lY;f>{BuA4ARQ zp#`>&o1#Bf9Uy=ZVu|C6Dk>@gj_D@t?>!*{0_<T``y(YSZFjV5g*rYt2?_}@S?bNE z5pfRAR?E6XO$}qAOY_SR`AE)h)>jn{Z>AQ=&KeDfiHT};yWnArVVJLx;ZT%1>%3{~ zLgL2jD|-5!Nn!xLS(n9qjVNEDMMYy|-8?*aVd1?Ou(5U%gJ{{xw>4Jn;WqtFU>`!z z#_p~OtR&}IhBrY$^#J5HHf-Nqz7fNmd>8iqjf|2St{o$`)%g;Yy;Buxua>2}$Azw& z1|y@Rg{eOy`s_JS!9hV7&42DjBUKGLk9<wL**A<&^$VpRx9#xKt6N>o5c}i#t)7(p zQ7KT4VSb_9+V|x#U<PJoWn~&sS8mpdt57seWR$Dz3tz-c#AK8?+*|Kd;Hr+&)s5tc zVZ=z$qD%H7hnx4Pj<S1R+{Y^;^k={^@wt@%C4|oW{v4y*7Kc8P;Y7cPygoybW0d;c z-`Ly=r4)Aj^rF`DZ=EHF5o3^LD^^GQ#)YjT)WUm)^32q}$S2~qy(7LR02}+5F0=H; zp-YK09^D0MW&6`V7Q0gv-n072jb$RaF5Oqy=TuZOfXH~X!inQ)O`@-_Z{k{n5vdXe z#0;`K7X4pS?WUW)YL^l{e*8FBuljwDZiHnzl6k{DTb^Jph-|v-BQW>H%Se771mnSu zSA}FqvzB9&MS$z;b9!LvPhD*{E#tp_87|s+{jY*SckJg})W#^CwN4bZzvO$%uxX=1 zWPM_3X(^0fO~g*hJ{c|s#Md8V3s>o|L0Y)`_JwNVrpM$Awjd^5hUmWc>7L_qe|xIc z7`3gtyX&PDdPV)wI|0RvA>rA#eOV&e8_wVz0;ie(mOLvy_2Z)w_RE;Mse^+C`!1U< znhA5h+iMfVS9ydZQl#%fKCsi&o}!?n!#JLeeM&P#3}bKS`>p5M8MXnduTveWZh9QE zeS*UjPlVP#IreGhZv_(Du^+GSuyC4kXk~p}d^2A<gu`P;-+1h7Bmtw}h|w`Bh#Pc= z?c(nGkM=vh(e^}P91}&~|3=^d?8GUGVQdaatDzKm6wmlC9;{>2*R@#M{Fu5-+nto- zm!5{HY!?hSga;v7yc2!>5shjnpo_bEqb}pOp7*kqVU%Hx`gs&oQg9=o1gVa?Gym6h zusdxtT`;H#aERzc<FbVjFmKYC)K01*ig9O@#Yw1l_o9D<zmdf@0n^C{QY<}N-h_bo zGAn>JyenK|07F=Kg6wDWj{Md5Av{YyD%wp-xCegKPdbla973v%xdfX6mm|HH5Y79> zp)<>W{_Wu)$)1A0RakPA`Y?_Ddt>Q3_K;`X#@wt5AB3GEu5(j-(k{IT3oZ?cYBF}! z#$0#%Ba?`@I0{cMF9_>d0EvV~Y2mF=fVH&BY_DPA5Y(aB%|%(o#giZ~pFL)wCgs&` z#nSwdhR=IqdYxGRX4E9g_U!cJ7|_U9-O3oieg@nWwk|GNhCf4KnY;_2b3cD2wyO50 z6au?v_~GsZFerfSHpV>GH{r5*$5_;da}lvdf~czBP>3KY5g_w46ekn}IHIDW3nS&c zx|Plu(r?M#c6%=(a4;ePP|OC@M)rS}9zlR<Lf{XSBlZ>A5kCc)P<*`cimw6!3ZM5~ z2Vh?}+Zua|f*^`r4adjFr^0#lek6+$O`lH51El;fuLu4hp8FZ*Q?EalQN-f=SI2$M z!~ov;5bY^$F$CcFy6{rHLt-&Rn!;xdI0y`>*H3|@?6}zdI#;(6*?2oQTAm@LEmIa$ zTcJ?T+3#;WBf!Dps@}O2$*Q7UGN#f<r$vq$<GgmwZ`}Ln9YF4H<~=OdBV`zO082v} zvC<GoX8+GUAVrXKK6#L<BQ)2UtP(&(YhR%d@zc2VCgGJUSD=`deMfa0$%0RPPHqEZ zGt--`Hq=!Gi!{k$FJugXJx1tDPl}&QLp9}4W*R|n<c6XkA*p|V+X6{ga6`<9etdUV z*I)UDKfhY*L3Hkf8jcV!qCOPWF~!?r?mNwsdh*^8%oq~@?GOa_CMC~xbINhymmgr{ z4ImF5etMAl)eO1#r!f=reE^)u>(<xH2SsNoB{U(qE3HzsPLKm7>a-NayddJa&mBg# zbD8r=@1>n$>(M6{G+@NW3uDNJdKx12&nDC(8R&%_0|C0jLZU-81yeSNtXD;Ho<zT3 zRl}G-N3wF>9S>)e^-WLd93A}t)pt*Z!j<6?t1l@kut9>uB(I(0#J_KM)7r?92?<;@ zKTLSy_m{rpQAl`;0(gqz)Q%BynEM6DcV(@D>?+_4EKJP(qxD+9be}3~YwP`kgD&Kj z#TKS};kJNZ+;l(Q3eUIrnR)&cr5MMpuRk(ucg_`8GxKFE*VB&Xa7GN*{mFoM5to~n zZ)mjjdn?pOPNIH^PR&nE-Fr*Uxv{;C6n_f~W*WIV<_=<a@7|?ceE}1d3t)(2sA#?V zzla_RZ$0lj-M;rrtw+tqYs3J1lqN3Uh=}I7jcv(IA*ZM~1F&lvzFHs9{#_-tz*p%U zzP>cMtWq;qk0w?vGJBYqn5bE9Z=58?^Jps*iHX-A+5e4+TUBp<^u$+GV{*L44!>X< z>Rd{*oH?OVpbdyZDS=<8fF5~Qy?@jX6OS;@tZ<!J-Sew-_hUHF4a5?Oq`G7ncP<bj z93_I}+<Bs2ACV*9qzn!Gm+YMFSq3H81CsrGWdB%WDfk)R5$&~7jTQ6Q{gtIcAF!j# z#8|NNm!W}76&Mnd32;bjC<+qAqeW(;4!Mq+qI3O(M)Kp#@=m8-INqpS4CP<z@!oA` z`r<cl4%uJ&BKMg9lFO{#A4MP$DUd1)%D7p;{tzV~&Nc%irr<F;4=cEtxAx%wg(ITv zzObg+I%G;$T`6FiECw|d$B_f&+4dPdL>A*s;<x5K6-rGK!GTypqNtH?&#<_vHZbk% z?Bc%gQxMR=vIMZo18@*_HDvDDj95K;qaH1U9hDo4(zD$&{1vLGDRHf@i1RP3v^Q7@ zatyVt{9SA@z@}A@KKlnKmroGL3M?3e+EXo1Dj$S@T;QszgMhKQw>STms|KliV)iTm zfIoO}ktH$+R_aA6s-@;RK(4+!q&x}Sn{Az)gNcQm8UPP_ziLH7Eu@tTp+vDhc}AFs z5LJ6HM-60RJFH8Dzu-itwj&b=p^pMeE4U@S_kMkNJ0xh-BC8QjV8vx)HQkb#-AZ6f zRK)&}L6tMt5`DuHm^c1D&ImOcD%2^I1!Olgf*+>IWN58Hx!>5(@DehJP=Ol+^SY*2 zZT$DSO2;8`n-3SUBS91juUI|zAfPqS&sN_WVM7Q7FyGkP`WBK%mR7Fq<ahOK)z`}d zc_RPr;s2`b%$_Ix)bN+D-*vi2W@>$hO@wJ(&46mn$Et(^i0LyS+FA~N^MmCtGMfAn z89<Db%1RLcwFTKLf0hT>pA>y!Ot=EfRKz8oSSPLoYoRoB+}shQsmuzD83^a52x0Q` zXHS|e@$TBF!p{&|-rlF-D|5e4zkdHtxNzm>H=napzSh;@;<r$5POPqeNOhi?nnFlO z<i#LjnCz|$WyhQVBe~d>hLmgw`2+jZ^pg_6MG+_>DSOQxWG=bZ^lh7G`kIec+(v+O zkBWpba4k`s3Ep+=5!!-$sFl7iS70t;pL)lCVq1P|5lS!70kvf)WEGFSHOlAn05pst zt0Dx2z?kbx74d^tu!=I-Ty_>7Cb=)M!wzT$IyU=p9vxIZf{+2)9#leFbb-iZw6BMS z<2`sLSwMOho<B~|j={9!xU_r*2o2NTPY;D#A8*kF%wh0Ry%XJRB(?lqLMR|00D!&& zvR0Y>EdJTi_?aMVvD&j!5j;FR(TzGR4$W^UQ`6rLH!wyjoFJ+_;kM}KfJ=z#Oq0Eu zfVPQ~@bNAvu&Fs<@i{vYKv>r>M;>_yWyqF<^0+WtY!;X;bOP);S8NY0iP!;|1p0B{ zVd)_keT0!;87d5bc)s1rRm-*zDcYXO=nIhE^KdcCadkL)a8UD}q-4400S}CuP3=hx zOh-gm*c|{02UBmkR)|hfMD*f{_wHfBw8U7{)YQQJaMNxbJONJVJk{;zMs8Mse{?JP zD_zz*HcZJ#!mH|+%B!58c)JS0nvI^FSA<jvFyO@t3G_CNH$^I}E_%-m3?w5?H@!QC z(L-Vil;J?tnL~hv&!EhexPJW&kb}&>pFJwE)PmtIb6%y&P>5A_K7qB4UXuWr&wM|s z+4cyT9VU_3z1%9_*^$<aWZVt%b1LWkCFQw9oR%7^+_pbyc9qaJF_de?e2~!vRu8JT z=?!;7Lqj#oJb~Jf&`=fUYAE7@scyfKrT2>Bw;KLtKYJgB%rst@oFx313#vM(;$D3q znjU%Qpw6Keu91}U>A<eNH!C2`_uvknyo2$Fi3jtNmX#&%>fTU_P-`*?pbJpkUjPd- zNvD{4d+lX2qO5+G%*LB0J=<C6Vq#(Gu5xq0#Ka5?3PP8hz6q4;aB#M7>TKm2Du$ZX zoomVJ#Q)MAk`#hp)Su3o8g^VNVhd#UicK#U(PZLkwngp3+?Z6@y{xZ;G;TN`cKN#c z#8TfECMKqdsfgYaP(E~YbvHLQ<Q|?bW+l%Bu_jGGWem~EA7F`Ux&5r-o-WYDC3O{k z!4~2n@xM)3bjygySj`*rxPQ^J-B-OT*F9ACUo?)d`Zm^^8EnYuK2Ux`n5G<(vaI1^ z#Lx%O3xPbJrW)%a9i4K6KcSud{hBOe;1G~y_gK&EHIH$NEtJkBHsc+@@L0Jd128$m z`$QT>hFZhWz_Rk|KdaFaHg+RrVfe^+UW7KrK<XhA7upsz2uOuy2zwS522{)O2$_+o zgn-w!B(VelPFV73en6f5?CZPC#l^*<oY)LRy&OatH((+^Y34J)fVb~@zYUaI>nSrb z9C=*faN}5``?cYn8{O^Ulg)+x()KSTw^1gsum=FxH3EOiuJM&ZMRfBVq#^7GWyW}M zFJV7F`DNaio4PGo$=)pxE6kuIwyE000q9#=&iQxmC7<q36XOt2)CG~U)}e~9hEe=& zEANpZdjvvSn=%3_wqk*aA9I_h)$=)Vf+_%{7b?cgkcsWL$*c;O4c}p40Y8#R;c?Fb z#jMg=pj#*6vS!p8!wEQE0%4;+O5r^p$W?+pjbu8F+n_5(WTwf6^0;jo!R;17z)SWy z6=GJ1nPHw*fVFD)WAbv_h&U;hzY)7Z8#>+euv2}5)h($(7S;Jd<R>J>X27`EVV04B zZteQO!N!Za*(Xsv>^To<_Y@&`r`Ogx!mdev%{3(!B8y;fbG#gSy=^Bsn~Xbx3u0}R z-82rMkQtb0kjT1<%+;!@t07yk30RLz0L#8ITzo-JxW=j@Q7A_JW;sGUgjcNsQ!9FQ zyiGvO-xeYEogCCUBXrttb*r>+3A6wWm6z9cyMUC8jQ!ck-ZrpcYSr$4$c%|-1nUYr z#Svi&DIp~N3G9rnK;g#WygsG`;amvdFEDEc@JD`PQB4Ip&WNY!g<YfKkO_Yd4aKWS zob*25z<p5b?InJ)qXx`7jI4vvHjE)I2#9!Mlr7)AF2J|@A8&O{S^@QC(vc_+stR&r zK%X;^Y1;o3Dl-iINPUWD+L4$F!Wlx~wL_WImj-{4*It(f0Qaq=q~kB()(^<ZskEKM zL_)qMz<(5={s|zzQ!R%Ih_atDN!Tu&PYDRXh0VhsTf4WS2YId*xU!2yY;4k>qnj7x zK=LH9XJhKIN))tv0jzAkNjnwn>4TNRPR$z6@)r`}9xw@zWH?z>l8oj%KA6Xd*w37Y z?3?!Ji$;hg6c6^<Aio+8geb*m{AlyE0(>2xM3f5)4?;PRvAz;#$C0qAg=Lj<E}>Te zk%Z7oByG*M@`7p%smG+aZL%T2Z7$xF?cv9Bkd9scj+nyh4@3&Q=&e!HmEmQ8LlC@C z6Eic7FR(9w$c#%!#n)`>BfV8gkcUAdXq(cvoSU1AFssiR{3mB;rC~zZV39xuo7&%; zrV;m~03ix8U!0f+KkO?Re-SRg%P(_G=Z77da)CJprNabx;hpvIWJrTJz7SB1K7}%1 z%LBmP*x2A37l>DcF=FU@M+emDG^A~$`cy?hpzcbY&DAOY4s<Xcp8OL5sMGF3DJA`e z3~fs<jUNFpzEk|@E922M28IL8|3>KDOTu@yDJ!i$m&ljqLOqSBldy;-Q}4Zw<F}#$ z<N?%i>X-LY{}hynS->ZFyTmMJ4<Gn<4psxL3U#T;aFIE}`SyMO0r_eZq?)bMw=)8V zPV!Px1=7IlfG&H_IJ5<w>*4NNkiLEia~UTyp6L+5h=2MX!=*sVHUTYs`8dcZOtKSG z+++9a=$`s#b@an_Q@jtrvrK7bf5^8`h**HAbcvQ$X}reE;qwv0!3!YOL01?aD66jn z;e)o%GX@zEgUa3)l{;~fQmoqauGIUK8~z!QvBE3q7fI|k9@>^PMF6^l$rmXwhqd^O zQf#jMdBnnbeRf+yK^(*P!Pn|U>N53JrMl;Tp*8?u$^WT_xOr2rsu|*@R+Z~5S`p_N zKyd#SiR9OiiOum_Ho%4=7cMR67ch0nOgz@-)yKS#s=mf^GXjXHeYXSS<@rnKJXm{B z#R;|Vt=FD;Bh@u*E4QQ7QW&=kC`Lk7ps+J4+zD~x)z@!qAqm#v^>3yQkJ)QWdufMv zmUPXBRud=jxbzNI3iU+H4i6AuzamqeRk+^D-6zt;GW!C0^OI6*CAcfFFVCC>Y_1?m zqrCP4`oVCgmCc^l+zwadG0SGtlE4J%RdO~d)rkR$vSo^K`?XmF%DY*$?i2)S#xk0N z_@na)^lw08F3NncREdl_H?3TFLn%6jh2FO8J@WUz!>rG=fkMBKxT>{*d#1z{0BX;J zBL-}(kaNDU-xGx*4^t-))H3Mqsf|Bh?>O2RzrY*3L5o3*)qtW7-MWn%Hr*eh5X1FA zsylWSV*>a$*V*XDW!@(hqvnP{3hdpY-!>_8X})=zWrA3r{30gB<nxtBF+@6VOwBuu zq+82l6|%0Al5~+k9%z*tkjCc-u*Tfn-d6a7sJ@_#4dguqSpb{>3*)u52)Yla3G2UQ z!`_H$^)o!HamHS&Yg^?tSIZ$}{h6d#q9zC<dbbrP<99%?vgBJbbts5BlO=4l({A1r zJ(!6c#^7LxP>Bn5D~!SA-c{y^HRNh|81bSuf$3ytkwEl3MFjt=e}a&&QMTSa760L0 zZ~v@RX?(VTHB3FgckkClL`2j+tFu!HSO*6L1f;!hS4F5q0}%^WKaQc&SKf?A%t}X? zQMm3{msQ7V4>sG;m~{}{KunX8k$pm|#iw5fftS=!g^f8lkd{UmbMVV!E?Es$h$X>= zkq&Q++QpvlrL>jv=*;o(F`5_3UR&Nv$g*E2VhRwT>`4D5&z>3EZb9@fOB%33W!0}! z>-_=TI!N_@KZ-59uRSPG?k1Rez}<s*Lgwb0DxX%hl5QuV){EA^fB~eBakiz5*^01+ z#$(979UdqCls6jp0TWgM6Dl}3cm{H=K42J~a{C|lqfqNXrM?N_ToqWVuVzoc5L7p; zo|}_q`8ZeSSz{o{{^22y-INrHZv9x$hQ^(7G<)Pz!b8!LnS3(i+(y0nS3zj+$m~ZI zG0K=EXr_SVa|MP!iITi_wSfBaEB;J~g9YXHECj?%C}u+}g%!*QQv%8O`ei08#heTw z10rfMQjtNP2Lg!wac*M97!b!OjV${s*qjE(hnwi(>V*EIAi)s8Nix#Xw;(ZmOiRmt z^o87}`VTpj%3q(>CIJZBZlboag_KQ7O4<Onqd?H=YE^-4E`upbWtG$_v$+P}ID?|v z?+#br)pXCY2FpdjfS$)YfY}anZInoy1ZconPk&shR#qUp<uLsp{lxffjMD9Wtl48y zJEj0-41j_OkkxxqC2?Q8e95lPc5r<hBIPAAvc|YuhUW<hjX~`UrsQo3*S$y*?wHR1 zeedo(aojiW7k%tcNHdMHpRCdOiFh9iV&M|agi1t|91v0S2ZI9jrH}=jM|c}V--N5H zY?G}_8*a=e%gqTn;+I(r<R*%Hy@$$3wZd^xy;ZlyGalv+>UKgzj*`8d%*+Ri^&rUY z)%w^~uF-QHF6APRT7mt{)~gnN`0!yA+rt-mHHUAY4m9~>10!KBQCh9L`c|$<&=G$h z08}riTkR9XDWGn`9}}9J<z)FGwwTNDvG9O;rY^g0`KNXBDTi7UGNr`>def-^bOtaM zltiajPZ(zjC_YU+d<_bmQ5hkL{1jB)`rNkj9kdB8W91IGX<Z8l)4@atifOlwq3IX) z3t0~q5AMhOLGm9*YHBKiWz_z>4~@c7J?GbQz&fP~JG{E8N)XiF3Puk=Jd=Pf(K`Yc zz?-9+InV*Jkv_jpqC@!Dwg;&_+qswZzHi?e#zbY6K9mwVlcH%zO}K;9(y5XGu9L5? z>~FPlvFla2zTrW@Az(|0K3&;rtjJrcULAU0*qO9#ul5zMiiiVC`ShsblK%Il%&@lm z7HNTjg8q^Mep0fsJvrI}K#xj+S78Cle4r+>fHWN`vwI0s`OR{O2l*k;0b0e+ALUAr zYJw?=NuZF`G&yH}$@|yA%bA0HA`A(Pd?wn-enK;XIjtvFEf>)qG)d<V0U!|2izh<W zMBpEF!A*&U6se~W-pG2i0@*oWMhG9do<m#h+sEG18*wXv6aR@+@}U>Z&(^U2K-_7I z<N33__WO5iQWUp5nr_qo3^=71o?;A=<F|7~otHPqAm`Am14>QE`?zeD11Z&z6$}cR zjgz~e0OClDf7ZHVDHEd08#-q^(@S8H=5OK>N@p#eA5Zi_r0&+lwG=UpD@4%_TtCa_ zJIvo`FcG68P}~xTql(fPxinO_)v}|C88_yaseE#jK%DP6O<`MhC|skGb&_td=G}Q0 zT+GN&9x3SpQkdK&?GW|Wg?tPBqmVI=R*;kqX7{;7ix0D-UK{#i*L_ZJr$tFVA(RL8 zHA{jaLPXH=iY+7#pOZgVz)~>}+gGR3nGmKOKrGN?>GF{*qWcpsH3qPUZ``~Y0`a8K z`@|gpD<!`r1=NLwPn_ZZo*VVJi4s0Z!cI#Yz#Sv}zT)0pDW=<t%sTw~a_-|s1xJIo zd9_`3#&mDyWIqkTZ1qd<<&{cQ6m}4(6Y-7rX)F3dOM*V(p+&*U4shF^y9I;^2nR_f zLkMy;Xgm*t8XBqAgiDY3D|Xk%JK<S{Ja#z{1|3-2@8jbVTIEnVZH+i|Zi3*`oi0y0 z*B1ZRw*isF*1@3_Ar;K!xh4zn)GF}emB28ljQ{<sQqFsob~zi_yo280UjE=Pl;ye8 z{XHkQ2!8h{Wvaeq?OHCV;TCgm_b$>fxG7|2`WhM-xK0JJLSVvy0;|D@=ON@0DC6FN z`wMV#3LqUp5fMY4_%bLJz+m!he`6AOpc|4_W55O>aTRoKq-vq_-hY<lxg`&)DD2}3 z&@@fJnyA43&wf&T9RTT7U`s$3GM+Ph8<>s`?@7(>i|)^+3?sWEJ~ShL^&JVCZNlR8 z!s&aA20wgxt{dcefrXL0C)7JCg`vV9B$?LHz2_$Yy$=C^@95|d-5oSUzFa*#ve>TB z(|3XrjX=B`k8=^iZywBbAQ6e@SGG3<5J!fE`G$su&c4sf;{tmQwoKZsp=JYg1hWuO zU0`b2-`@vo=soZ;e1i9F5(ncBA&n62dkx(=HD1+(elNxyLX*|R9~&$UYBkAn-R`v( zzSO@+_xFD9ji$L@3%D1zc@(UEwBh+>-bpw>jxK7Fqz*zW;um@NRrA(d$A?xZ5v(Cc zu<4X>1LZ#Z!DZ|j0QR!-@=YMUHb8#X<V=o%-3^v6X-E&811&L}3Q!5aq$p^VKzIjn z;ua9lRD5Q2mFqRFKm$nyU1DCbek|3fF}J8?!RWB@)b~-j>nvX@*ft~9dY=P=R}K+F z8`YvU#zTWcX5Rf0P_3C%RJ}J(RoBCG;q<DxI<cG+XIl9R@sZC}Fcz86OEc{Ot+5Za zBmggjJ%nd1KBxhs!6q!lXFTyBFl3CbHZ?V|fY-Hk83xX1AWyIQzPdVDUhSzc6iEzM zulfSXU=N<1%HNks@*h3vDV!UqKdtxdolWCw9po(*wwUMmG7yPv!|K=IQmu7?f?&ky zYR{<Y+HgTkSC#TZsjV0&dvJ;A(t#`nDym`4!{g7=>%u}_AV`ak#{T+DcG~iELDkny zhJ_2og~>#nA5t%tj=5g|Mia=8=Pp2s?sBViYV3YWTsa)Yh~YH04A)DMVfcm@C)w>* zGqC2qK0a6&tWC*JG}auH&&shnToGgUBm6RdG->z<V}k`M?c>K!su}p;uq-&zb8&GI z6%!M3={SAe{lB@aDTBqgU%ptpad=G(900HioOvG-6F;eC5t()-6<6w#ka%GC6Ef9_ zTy0kq@4jK!%6`op=lNsbi*+>g{mka=<6`~?1ZjrvLeRNG@cH#)*ln(^{`@ZjXRZO8 zH9a-O<HREaQ!VJgvdVY9zc)u4WWXyxD_F(56M=FIMJu~%YZTYhBoO{thl)vAm4AJE z)<CeQE!9ZZq1>^U6c+x3Qd<R2{`Q047rtUn`7!+3T8y#0QQrQ%i?X&e<+bTsqdz6t z!~Xv&iD;9vG;8UM_qj4s_E`if7_2NTQ!9y^<GWB+a8tc=9-sdox(g=67zk2??z$%F zj{>@@)Mgwf#b&&Q<4I8<@J|j)z3e7!arHxmri&n<`X6yJM9{7XF{ekH-e{?aJ;@6T zPtP8^2tfS0?f8W~yNZ@YzTY%pL@6Mog=Z(Q6#SKWy2Nxbyoy<c4qE`Q=F&i3CjiEd z8FVBi7%V}T!ASuh93n#B>ApFE05)&|MI?!N6gbQ)!`4$kY?%c}xV*p4M6%DNc|nNm zUVEs8{bGN{8NLukE>q6>HkSYQo)1YZTQWwUtujke8B>&fmAv}puQIs3Z3ubMstoF2 z)pmyn^2I&|B!*nUfY;$pstWxL+@!aGGpZ$PH6N(>qvca0BVOENw57H^D}BJ4a!oz^ z@oPZXK?;F|*{dG1TW#!Cj+cPDipWN70NHsAFoUuNoc5{(PQtfcJ#1Ast|)ao=8?E2 zrqlSmMya^2Pwt)HAexw_rT?uyU(M(?7sU_KrtgfoF;4_CLAWo`Mg>WeytGbBz4iLv zlInWtfPZUoq}5_)VZi|}-1mkC-r_}R2onFnbXKp4|3VCGuLj>r1HsVmbgpEZ6K(H` z1V-a!x0Hj$p52ZsEV-8PMz(w5BLdq!cy43H)em-kC&5S$l=C)Ba()*aQdB_%dW~vF zMp?lz{;M;Oe1?nXMhY8+gvH-4>@V>BE;MH9jEUlh3KFFK|MGT(d)~;=@y@O`O>(}i zFL!J8xv<T5w|BgWhkH|!cRYBGX-9h+dNRXBwN-~fRtuX%0m<F6c;yla`$TukEN*)I zybWlC>|=R4RXn*0x<bxN6lA#Wn74VyYKiF&!n)dJESOZ%ebJGXV0PWYbhugTk!8o2 z#<*DhskG0d&F!>Jwq7g2`DFhzlv2V@)Di2ANp9%nHyRH%SXEKRg2WTBZ9x2pV?6rq zhlR_5_W|z0qiuhn<zSN}VajT1O<$wbJIB_#vwU<`MeK!k+`w%aXH|V*?0SxyhbIDz z;9xd%1JxoMBrbmuJ8%KifF`Grr>6w<6OT@4pqzEM-p$&gxb5Q;`<*NO-sLffo6FSH z^ht>&y4_yDDz{W~(qJUGHhxLw((F1Zdl?T63haW3Kqez*seAYCne}860#la)k(rE3 zPjvJMs@zVfOOWJuw6~6{4_3sgjqevIS0e)DO5`mJaRn#Lir6T+9L{ACv99h=2-!11 zV%I;&pXGo2u(ABSK)JaSBOaSivfSMmP_NtD-+`Ao686s)h<~J1RFH_YZJnJnAqn(; ze)JZ~podkib|WJrpybX0N(9rPAMm|)YdYNmvf#o8in|%|H0Wm84dQWuavEBm`nS@& z>7rNmiz@|e?yzKPDAg(J6DHmll!mRj51+C)R$V#zM=zwh4U$#cu+WVkGNj1c5Ar0Q zKh)DMv3xdG<%Wn=X<c#Z<<X)(b8`@k%s^v9m~gOdq%c;5F-M($BDJ<V<$uL(eQ)n; zjcJ|!P*u2@V&jd$8-1|s@G*8-PC!}+X-g2`8oHDKb>C$Z^$GhG<Al#g;u{`^{p(+j zy%-{b8=NspEe1lMe6l+^bV5`Cr~pBZB>>T%MOgSf<evvX2z-8=rwm>av%b%lLG*D} z2dY-f8tuoNjpGwm<ZScLDv+Msx;{RzR};YcjUFeIzAe#ZeqtudGjHq-;|)tFjLaJ! z92`l_=n+w4wB;dg<1oH0jm?ucQloF~m#)nH6wt=ZEed&nMN#NZ5N^tls^0C9^h3-o zzvFH2n(***`l=Vzmc!>F#=5K-RKKwkgUyBzY^FnG$9J~~ubR`QIv?-biN?Ac1oLh6 zhuS<WG*D{I9RcErz2$dhp=N?8;97+Gx76><9rF-)u9@)S1bq0EutuN?N&$q?d%G_q z`tcV&6{5dE?4;W|BfifohTknv?la8J6RGs8`A&OQLVNb+bNz3@c-S0vjetflj9S1N zaf#%E0~a8bv5?Tr*hBH{#jCP;G{xD(Z(oqJ?~{h}u|t28yeyb3U@|gByT{9R%?DJ} zJ~)p8$O8trEm?w|m6a8g4=FP<Ha$H({tq-ffF7=2$+JXHyKrIQe0Eju@+T&dk430^ zKJb&(-xz$Wye(qKynFw>@l&%um|jbJ@_$Z1J5B(4SPcrV63e0Q)HYRqZxy=lC+_s` z#dF)S91<{k%A(TGV@8i?Nv)*b2Afefw+cEl_GezAhmiR4A?4jaFS|MRz@(;{dJj8O zC0W@nbPCPy4$RJQZx(PKidzYdUpKT8%Ts4M7$R=n$BHD%@FQW5vy?Aa9nRr{7uL$u zkyP;q*8~|#f=eQIy`}>;)_Ec#W#{DLcXY<1tt=v1Ok&Y*ADg^dKmR;BHv?{DAb{<= z2+XJ|_v<8rjFXYw#hBm6`zAoGJOhsLjM*#%w;#)gxmqXR02_h=%98$(VyIiofaeyN zE5vxJaBN}YfRnHz+zS}f+m=d41yjxVpxvE3-Eg7X$5+^py%Cs0ej#j5CbTl61U+p% zCyW^DKx4?Pc@r3CEQm@3J{e>Ng+C`CcG({ktp2<l1is>uOt1J_Q}1qi#X9;rj)_YT z2q*{~{pSV5RN&e<sSbFb;ocIw|1f-o+2Bvoidn^6`<03XTd@7}lC)HT`aeEfk$3h! zte>-Y$2q8e%%D3~6Xq!<-P+(1*Hp&L{}B-C*$f+Dd*x;4u=-dMQy&9E*bj64qN)c{ zTh}2fzc`8Hw@Ht&H`Z_PJJYqiqpJ5h_l{4B+16oOrI*#ip)?*a*X-mD{Lb7e>A%5X zkSU7H?*H9`M82cx+jCG^^LmUp80)=X@m<Mn<<T9P%U@j8^Anj2Y{2iJjqEQuDqz7c zP0;Gcf6vjZV({2qpWIvvb(rs_rNE0iEVaEJgefW}%4z=O#mP&&CuFZMBn#TF_Fodp zdPEn|kE$6z@XGKZA*nQnHXkMg;Q+tfm3LQyy!~loS)JWTP*vm(?T-859_|sY_KTt- znaepDj`BHGL$Cgjo(G95wUe0v8)+~}uj5&7yB)fc5v_OtK^Wl%APTYQe!|!}C#srP z3$V6k=`^}8Wo_)?kD_6suhfBHOYMw^`q?HeBMVxs+yP3u@g{73rtxpADTU5d0pLIc zn9h!w4J^`Mvp5jIGAF{AobpaY#<pqoqGS7Z$5qFwR^y_d&6ek>YQ3xa_WqYG%1l^O z%JL&vT(_`2Fe`eMr{%2ptiVtPDwyrl;v>QF15wT7cf||#K39bM7vc_x|K#m#;f{SE z{?(tNDurL7i0z9Q2tYbRZyW*B52hZI%nJ+*&e)FTADZm;%$|e}i`m*KACe1M4fDOZ z!lk?|-o99oZc%Y!=lFzNd;6H3J|%GQI+(?vivm)RtpB(GMYqJ7oD>sh36A63;==@) z9`AQ4e^hb~oEq}V%E}%<y8;qjKHegMJ~Zgv%PB$9#^$nI2~JcCE*nEh3#hyZO}i^8 z#U#1%flkRGYK4D;_uG|{!Vdsyk!rs6_im=09hrl8-3pPYG}9SeA_(s2|GC5|YK7n% zSs6Zs{=#4?$ch|4nj)a_0OS<|r|L<teQOnv3S?obfWB6Q2a-3RN0zKwa3-bSxNket zN%^@bdYz=a@w4}rWi`?(@@HlSBl*__ozEd3NPv7GPn@H^&3gj_1MuxLi;5;1{7{wO z=3)?H>52<{{Oxf?Pa11e(zkPFb4rY;%s!U=?eXt_{A8akPRo~{ezoGb6<Ri{J|^a& zIvN<5&eDJ}0q;fI?FW^pP<ql=m{6uY1@d27@pQ%HU$wfMjva7nu<39*OOnpH;IY(+ zpz`AN63d@?S)~@kG=eU^L1z0iB)-$mn43b-bwfXMnI!y0d(vVTs3iGr+a{QmCoWV! z(brfvudf?ZDtS1xCP2zesq$cm*n@b}+m;_FsA`hM-~RIp1U+W(jH6%!qcMxXpVVCo z&P;moHo!Y>3#kDSV|#~rNnre|B02O|vbw&W6gcw;il+JYg!yJ~XoxlGWT!JPcj;-0 z`o<pR#HDa{cp_A|N$^En_s55Qkr9`1FJN9+f1J!Ez9>;06C*)?0ldX;(ccz+@(roJ z5tR%aEM*lHJeGspK)u9#9Dxcihvg#?0f9Odb-2#=F~gyiocZn_3ZA0NhJuIduZQ(1 zj3(~CzQMjE$CWiP3K{Ala_7P=BdR#~z*uN!<8@in=|~c51#7xg#{KAxy*(9;H7IJ7 zcePIecu@#g(}4Z9dF|%gz{^2RQcbz}CuE^~%lXb!&A0R^ZAoo$=rHJBIW#$Fyj1YW zbc}XU0ic?dxaM08WZ)0*AGm=Q^XX}=IG{^zFc2|aAm@;KSYm{Zh`RAO1VHt}3P*GC z;|)oKu%Y6&jKdPU67HcynDBht<ZV&9u)WK>^ocwgZcH<rGi!pTS3!0bKLJS<k(D~# zcPSV*njIw#7v+SN0u2HOYnAIfBOB0U`{{9B(&!$oxMv(_S<wE+F#z6TSWCn}xAYwW zbOm)t7}%K32R_n`xBk=CbX>7X^xHCg<)pHfa33=?g6#N_oZEPHeTI;Hk&svLZ3OLP zwBO+Wqb<c$itH>Hx_&UfEggZFX9Tt`^ZxMg4~+<eef`lJ&@r`hp?NF@dRD-k8}nQQ zG08$iNrfI-=Tdft5D=`-<>_;Qq}JIcVKVIb)Sb}*`hL%mUL^8V?vAes;~O3zV~aYh zcEJsuV;qRER-!vcb$Y^8rtmG(Tbik}wSavBxNi^@7ycS_BC&mZXPrDftNJ_;q<FAB zzxcZ}ui=Q1q2!iL_cy1=*YTVQoK$2szQ64SH1VW1$I*2BWddb<^JHKD)BoB@BXS}s z)!4N)!V>nsxQ(2#LhDgs@V6=eQB_#s!C)Z$If2hIY|P~Cw{P4yL^N`iQkLC$MYO&e zC4C>B*qV2+e_|YfM&A|(41J(1%H0242MbqO8Iuz@mGGgL0Q}Tn$4JS^o4}}$@L`lJ zymBy7Z`OLQoxDFnDj}F<%yU~QQ~AT2F0&Z`fu{Stlb$pO`ASxl-074$!XiS;QN$$Z z(k&g7W~sF<0Pw+T4*@7cu&+YQfxR&`6J;-R1nMvZd}wS<7}dQT?qfXDNC1nfH$qB4 zWF$B&?;Z*owAtvrc1VCboEMsQ_>T{SW3bfsllDXQrQE59%s)u%pch#_=>lqWWMpBe zkQH#Y0jSkFRj%Q%ptLWE$Vy8iRY<GjMpv3FyK4IRq@*OFA**oA_9Rl?wgj#hmFEhl zn%}5LN(DrnuP^jxU03o7^*%Y&8siNeIdD6;m9|X)K$!%Z(0X&&TFPV73S^Cy=HJNq z#m-~iK?p1!^KK?OpJO}3VGeC{i(o`YL1HvH+*v$_!*XKc0|XZgC^v<w%JNkPt=2?6 zW^m4qI{5;@3KkT3Ul$Z8e`nF5Si2ZC`aE&mWVh?s7J5omK0BFH0clquN{Olf`VU$# zkT#5T*L5%|p!Hn9@CGZ_<ne%1Bff)sG>Bak+)NiR+w*?jrkB0*0oA&ovw;3}mg&`r zQt<o2l;LQe7}b2B*hUGj!`@6vlCPE|f(NLahgC)IpQaW0K`;DqdbFOg`FMus!r3*5 zrZL$=(2K>)>_miR5=nL(jk?d0w--BIJA<CT#t;#?HFG{i?ry{|OCb2Nb*h8vhdXpV zy1$f-IQqHOp?$b|Sr^rbOJ`;?UnxE7ND?YPbwgUsaMWZ%rcsD>EzHU3|G`3lZ%I?x zMHZjJVQa=?pgmsTzLBYcfdz){b82=T>Y`k&LjSujaqMPWqSM*ObL+6ehViU~Hbo&F z7APuVBq(Moo^UZ_E8K7^I2bM~o-Qyx++JfG!FffH0<aitg|M+3pKDpLdIKZ}c+<Qj zws##`-9V^JH92{|K}O+8fK+BDosh`$S~t#5K*uCaw;#A}|7JOoJ81TQ42f68EWbpJ zkb=P7L0<g$|J09CZU61-B})qc&!wWX#vb4<H}HQSEH0=7AG$^Ld3yI7{n@@qf+hy! z&0UIX&EWPS2OY^9VKk(%`0gGRH@EewAZ8Vot+T!IUL)~eJ6Xi)$nAJVPiUtDVePfV z>HbSFETX`pE`YS9d7ro@yp(7jMrP%y*_Cf{H;pZ}j65PZezzw160HzF;R16*TBvvn zpq-#MZww$Z#_(h-F=w&fNDct9CRMkNp<LY^9ogQK4Q3H^f%qWxuOZT+T<y{j;G7e| z`HDawgrS30LacPLt+@9^NdaQi(8z?33EdWdFUBxA>}>14(=_%_PWwfF%UZP7W*5GR zw;-u6_$mr*C+|TDF=LM%YfdY6uRq*w{$lP34w49HIkwL)ieNRBZHWXsBHOYaf8QP< zA_M;5Su!Dd(!M^bO`bBH5fU?D1Kw1G3<F2PHy_%k&pR7PL?VEFVT6X{8T(x(6=$aH z=AjJj*snD<R=u2EadPIR-N9D3V%B%PV^{RHDQiRfJ)^cgI<|LuR7fvg)GxNymZf`Y zwSAq|!m;R3bY-*-I>W+@1PK-Irt)#CUd)I&oV9)=`kA}OC)2pMeOTQBDv+JsrS|r3 z32pChy=8d)cCfcb=!2sBGJQ@)<gHdt<elu1=aJ(nv8-d8*9$RB3w=14!5!vaN8!Zw zhAYz(qif<(_gi33c%`-bW%zrv&&<9zMk*y4b@@qWhXl^~^9e)C$)gx--&?nCvCFgp ztstANnz6C7)Ard!nu!!rqG#3gw4;DNl>bwEytUKwv!-JDiswpD&wTN8S}J}OYY!MX zrI!jdRGn^)@nnqN$J&rtpw?LDZ6*Bsd^ZAYFrg)X5{5DhNKx=TF?p;bBB8^42OxDn zXho0};ADu`7hki2%*!7w5So}VnD23UpL`HM&KLLUGgr(nHn}VSx&U`12$+H|AHkcJ z-k!YUxZa&W&*%hIf1e#}H6O!0Ta~ME;e9hy{dcuo3J5_pH8%bNgcd?4h7ZRroP#GQ zu1b!JgUZ7QPA5PdR$xK*yZ0JjsNe!a1V+i%gUVR98IB>crHA@>7jelj=&(;mCq$Sn z@zuU(PH2rp4!+Wahk(W=Z5gWS7u72jTv<H}5NkAuXfcjK7=yz@*ucnX2#N_T9XTot z9XJpHw7wYtX+R!~gWFvWXE4F>U@W}QNbOSwitzz9MG*6T<?ncEQ`O*!(bcgY>9!TI z=yUes??Ql{oBc&nM@Y>-0jFsoXF`A&_caE3D2b4SHCpLn3gHRy1tQA52%ytqp~ZsY z@WLmrEly&O%fI5C;;N%~guXei&jkltFEwZXo$z=`Srj7!!zU2qKi4N~A>BJ5iLojt z+JaflAPeB@j&AqB+?GpdBIP?7#DMv{{}WoxI2rSl@Zyc|#R+0n>yGBOOrmW*`!oNO z&Fw9<4ORwhq%j%rj-kbqdgL3o=F>z@2CdGjwe|ZemlIli_L~F1toZ`$vtU--f`))? zpR+xm#vecI*pDxP=_!BZS38J=re<nz=E^h}^}fPWf$tIN!84CRoPl7}Q*cRtAXVTm zwtU*=*SBDj2E868P|_jI=aBgktq^H$3!wLwgM4Rw(>i|X@sk8zOCb~G1ZaJ;oCcYH ze4HNa{0^OBKcjE-%U(tfkAS}f0LXw-U7V32NZa98AF;?hQ=FQfqP+c_46$s2Qp#uE z{R-&)t=h8_VrV&6pC|l1a4E*_hUDzju2sjcOd=AJpSBAuVMxaiw8<j72|R|2z1gG) z84fK%;EdS<EwB(;t>9CY0civF_}eK!JyenUu}It8#3>mux{=@1X@)K`qB?N0c0xN9 zG*W*Xb-LFbGMZQ66P?;lIot~Y*o{cy!ajHmo`IlGBj(<_L?r=MC17^<!6EZWt1y!> zq2rS91<fr+?N0QlperN9o^w~P-SQig^ia!l5~FjQ2`9T7o8yETWp|_D^*k4w87T?+ z8WAxHV{N1yq-W|uIGrI&C3y}ykU$wktcY+>#R3#N1>rA86fsg}WarXb{&fjbaX;vK zKys#b3MWOpY@Zj%0)BMVX;`?p3XDfUDhSPN0|n#-ww^W>GSq!<<1uW4bV23@$2KYS zZRT?411hV^Qsy=R4(SxLF;~02;Yws3>h=E4+^r=(PC6w#5}5*LL*NG*Z}M+}GlUuf z>P&a?e;I7h>Qs8ZgShX|cqI4vXC4M(nvyd7n9%`FAFJo%{Z-x!3{$Sq(Wz5lgcnw{ zJs`?sQezUDl9Ce4muv+T=K<1G1<txx>xA>Iv9&V`e(8l($!>F$6%xN`&*)Eja;Dc7 zM}QFQGtpq&l1c9?_^;H9hdAQU*C@xZk3pG-UPGvNi_gi-S?o5iVx-7<&#%lVe$PLr zw|*uJ-p!AYP!VAY4(|A7JAN-=smufS8%WCZP6up#i^dOX#7V*vf${zZ9)9`DWo7AQ zETHy)><#PWBiNCEChpuSPVAZ<XzOY1=MO=A#hzYpUV>u6o$ge2eRLjl9?5~boHK0~ z{H^Igj0*~#l=2P6jD9y)ntz`a8=a>j#pHs{SGo#jws{CiLVZ<oe$ioJb-+K~g$a)r zut}P&h>d+*FhR`YbNn8j4&kb72I+wz#P)$?@#>#Z5}{)fS`Zr$38Rc4G|tHX1c+(| z&RV4HLI#}4XUDvw9*)QFL0p>tl{NwX;gUACaggZWUf}}g<tn&qm<S==8_hI_gHvUC za<U$ANP@e;vSOfrdVp{C21A4Zh&9N;OHib#kZeG260w9tt@=7NR3b(*sHu=M5r(S* z2NyuR5<OZej9DB5XK}uMJ!WO?X<JWuHXd+PYvy29jSxY4I?qmz%)8U^;A|UZXEA6b zg|jfSJZd8&Nx|;yD3fpn9otQHw{>A*7$7gSk%&XrN7dodm#W=i3$*jP$cqE@a1Vhu z{}7#=+7B2I#Y|@m0RNkyr(?=9dVti&1H7>4pI)_W0W<Cg5Rky)4VHuFG6^00;LjUk z&LQEus+MK}woP$JLTM1Th}K=)O;I;C3&D3k{fmzq2^Y|>4#$BAer}<G14#-bpEH`_ z5NPz|#f@XB2Ior4$nZe#@0ib-*BW%jd|lqD1&3g_xAl=@>!{Zg*d{X^0UcOYqZQGA z?Wy;}J-PlJHqj$$GXQp60t#rZrval2q_RSTANWbGQ+OcjD8i`@5TrU<a<R}V7<N)Z zK+?JsDPbYT8<`LqG;A{<!9p0nkfTch-e(AVy{G$&8VC3HWRhUZAZHhW$_nTAOhT5o zwN=S!UHCit4qz{gi;I&3+8pGQR3P#l*T+7<aZpSBIm^u^Qd@~d{I*8!Mk|=2n+v2; zr=)mwNQ6dWpK7|CNu^cfzq1nJVKS{g3wxiWg1a>s79-di3gJwm80dcl15G^48F*L_ z_3;`vcNo%}0=r45Kt5atyew?-RFoowZLgaf<p7G;L7xk>^E%9lSmAD)Uqd=HziJiD z7Qx9^Xm2fN@(o}<&g0=dgZ2nmZM1*wS{GmqvciqQ>YXX*+@b;ARK+CGcraE?!?`CQ z*HHj@(N_S+^gIKP40ySHVHa+KBjwje=?e+JyIC%lCsb!^b`OtW$WtTGyIa&oKnd@G zne{a~>wRwM#kC5_tuH2D(AfO+9f-L57>o=NtwvfXGjgz$z(F9;7LAScLcs?^t6@tN zyCgVTK*_!WAfxC@2iOhXA{KR!=NxudwDjB;nc*V;+FG9idk|3S#>bQ4>?ovJxd3Jz z%vC?Z^$h2<1S?sA^ug;vO8YyK&KN8Q(7B2!ck6|Tx7ES6c?u6jV&scv8xfz@Fmg$5 z3Ig`wW@}+!2AOP{w@@3_(93$fKOO1`r>jBV#gkkCNR($M>pqSkwvP$}1q2Q}&bZqa z<S<#d6Hx3U2X+7)lF%AL+(<sBp8dJHZwno|iBrKf!)ww;fwa7;C0|D25AsL2*jzNT zagQpJ`GsowO7(v_`x2;}*LUr=Qpu1V8KMCVM72|)G{`oE28yUinkhq*G-xnoYSxU9 zN<?X-(nQisY0`x1O`2%b=(}#5bN=UB|8v&2zP;97dvCkm;rTt!{oMC;U)ObSZtGY3 zAz(RsA8TN9a{FayxcxH~hUGo>-c+!=1Q!2OP^j<~O%+t0Xx`dzFvI=|Y#&bdHtv8r zi;J84byJgab;kcC&mz-`Mc%JXtBT3*1?_-fP=q@xqxHUViEiBWw?+vbS!uV1Z$OPx zCLzM}-5#j6mEZRkd~s~h_+9AMiNO&q@ojt#h<2Cn7v4Q;)Vlzr&U;(0Es{UAlpwFX zygc|p{55CJ94O{Z>m^X?El?TzB8=zv5;T=!Z1bx4^}pp?d%(6XNySghg&!T5BmqI* zmoMLex|@QK(%{?8gFOwfviKJN<5_@jbVy9W&8wntW=rbg+u>`hax&fh<rJ>lPo}4g zp%4TgCK0RydLQ5h)5DhJYq|g20-GD&v$8@t3s9o^Xo&;bxbm_p7cz^B5N<%^xf854 zu|O_#y3hvR(bxO}IgC!y$1=!5z^*5a3z$qr2)R|WSfL3EY?^bxu&}UjMW$O<jb!%N z*VUJ1@#V+sB_Du#o*JU$AYr_<U1VN^soXmo^QtIvRDyDv%tV%oS1?I%28QpY?V`|@ z=H{^&;aNRD-4kwhH65Vu;MeMF2o!+zvgP_7?}_4I07W_)yc%)gs%pi=hwAFJ^$uuR zBa)PWHpT$#Czvxiykxa*yb&&!G<Rw7w=&^h<dZx1fbRGTMKTW05x@c@stFe59=^Ww z>yNcOJkLWAPH;1P{`z$%<!w!w>1KhKB%!1G(vbc)TGVRE2mdD+D<DV`RAA9Wxq<%v z&y5NKJ4Dk;O4SU1YZi<hMiNs8SBeD|B}Js;dLkv|!+nqX;kYHXI<~bI=SUW8eoX`t zoE0<6bZesWL)23xx&Pk+TQP4||Hx&V^9?DYSSEE;VY;;^b9@E+e)H}9h%Gkhz=>rD z>Q)aku8(fcbUgXU;n#O8ngv$7Q}@%tB&B^<BO;a{vo%Ga>W1?iS`;!a!`66otRmhV z&Dm#{cmSWWf$@;a)DIJ!rLCt<Nh1}XHf@^eQ6bTvYkb@G5tAt+)+@G}S9&DF(XQ}C zUJJInDi}Cql}9od5Oj{#)D|btNB=EmESrT5HSQN$?We0=Ix*6ZH!%IUQ>^PNdtKlc zM1nOCmC5^o)Sytb@-j}IJW1V6BcsYWZ8*t*1B-#M#pxxBC1myQyxN(PvU1Ly#<F); zd`>n#%L6b*>UY1vp>JUOwYIkheO0dMXK$8#+;{<+a=$B#M0}Gl6{lQ~ec_I$UhQ9v zY*cp?i8i0K5d#&*OL!cS3mk|$Wl3q(;qT{aw`de5fXMDFlgc5T>~-0+b(zTW=fz+q zQ?DJHkp#rJtl!3He5gu28tt}b^?z35ryiOCeFTUvsN!1ZKlzvVZV={F!BAuT;`i=* zxHn_ue#jX6(yzL_Igz7>;tpdsXw=*C-n^WPol0ghXrsZ*M;cLb#zHCU0qq&lZg>pa zi;|)~TH8!bjNY_16sh`yUjLUV*FAM>Yl#Y_fm!6}1vT{zv(C+ME9%zYZjf#1<P|&* zO^}jL!XAj-5(a74ai>Ipq#CPc;g4GyzuHj|*?|<r9QrVy-g9kiKF!A?=EwKE9vb{6 zQn#@#iNyrk85ki|B-K-P19@gl-539bDhbFvgusN3ii#qE9jZ#T@6Ams_(Kn0J7BWQ zD5y%i!F$OstF5IAM)XRbYjm7}f8>>G*P>8{BgOSagfVzr5?tZ(ow+q)%-jk&i1;)9 z4eUyA`hnPr+%9lw*mu|QpxA+z$Qj%^q=_ys-x0MK7mNjVAEaFUEOtf?ede5YM)M&F z^Ku@JOgY^eE`229sPXQ|P1gmL!;wDX@_UUNM`!kw^<>rtfZkj93+}Rf#FdFlE^BhQ z0itg&5Z&PkBz`zs2{)4w7MwhCX3Sqjfwkm{z;Zt~BX$DP9(8pm>11Tu-1;~i*1)^! z;AL`ZgbqbJ+qKxiY1qwhAnX-$OT|cKC+rQ3S3@p=$9O|RF8~DrgDe4&y4OFqi;XVD z)~z7dHx!!^u|k(s$2^1nqj6pG+A!MGA>{b-1E1yB-<gv&G--hEvH?P_-*2FLb?Mt+ z*(->nr3fkDMZ*0q+O!t-XFJr@XZid42U|ho8I4i|`mmS&WxE7J_E+A#A8Pz!n(OTD zj4u88mc0#1WzI7FPO>gyPoY<qKQm7aL{x3{E@x+F)>DW!tM*UzdFi6+23s{$I3h$z zDk0uRR;%Flb2l;ZF9vLlENKky+>gdkm&i87)m?XMM$Hc~xMeMPtfrwrA4sx<b69{> zQL+}H@Dem@Z|`m{Du%(DXq5%muBm|iHO~|j6=YM}_a0!K*rNSyzrdO0;y>6LVvJ=6 zoNnE^3DhC#_f-et1{&CZgngtDM;$;CPzJ1s{_-Gz2Qh^$5qSYxTvmJnyq;OfuX})| zw5Jvto@Wy|%kIF+!6rDYQNRqUfJSuGH?J-Cf-CV&KqmzCS`Zf@sU`vH@WIuI_}q|D z-$7!@uV6b3SOR=SVd;?-6<#qHKpHeCb0O@-n%QP3vY$)MI`bRXGxtfbf!Dzu@RpEu zdcpmqzv*1xvqPUw$HW-h4Rp+hZo23$%hFunJ7=J0BQ7xVB({%mnIZ-Yn8XK)k>PkF zATY=7>|`-2e^a)?gz6K7m~Svc*o_W^*ZZ)DjdTb%qK+@4oF1GeiG&1b`e1<~57yAy zWAB%)T)9$f+TIzAc7U$%A@D-J#bzY8*v7W_ok<EdkBn#uYwpB9EJk?_t*>X3^$F4| zRJZMbFJi%hRcMk#;-zk9xp<NP#Amoknu6uUuXuVEmI{X|X3s_>(d+pI8AQs4z?RMm zBsQC%iI1Btvv?NAEso{Ssju^5q`9?v|6x%W(X86{Q2OMQ(gTibb0llVl<)!zv4zQU z4}gl@*x*r*y=)t0+gWZjwxOqkPM;S_Czii>dAE#oWXPUrFe<+=mA<+5x0bZ$EtXTM z1cw<_Wf4}C7-QJ^@Tr)G&eQgF%L2@(V~<18+uKW->kVtY0F`UwM$1S4mI0)J1i12@ z73xL{1-V~@K|}<y!b>^X((J$Qn&}lN`EK621!P7VNr5r;20heqt0rG`xUYVkll$L1 zp`awGu=(<EnDA{-e%RduR&>Lfhf~Pa4;!(`_A%6|V2;m&4U3*J(<KZSt-z+b&cyC7 z4^hl*#zz?Z_HB3gtfC<l(vhzHTx1Qz$<0sU2-Wk7)potF6(YVSrQU>_%L#~CVCNG< zDp=TWDkkakcma&Rx7}CpU*aArTx5}Ix=)P1UtL#iL2=f<@f8*fWb4@?0zvbIg!<k* z+$wV%xPaWT_m{Er_5hlvv=zF;9Z-$v+&He2Ivee!MTyxuO@G`nQZ;lP4Pblqi9ds@ z4Ym-ZK}8diDo9Euu~sYhKG^+m1WvUz{)hZr-|%#_q302F=gv}+Ng;Q`8r=sp85ivM z%f}`3cgLw=mNEAXNX_1JP<<B2@63A;!5<GLc*6Z?`zRm*vR)C`(Ds0td;ttuJ+R;x zWuCT9n$5>o_7V*eB~qNDvEZ4R>2j$d0oosunz6$AwO<0U7(QcZ2{z|FaknTu9F>6k z^dEP~-B<TYFnOM(^nZgdcJ}130eE(|I}dgGEb@dqYLN!^=!aIAuM)u+7G`Z*2G;2? zLS=t@OGJlXE%0Cgl><Ch|K<2RJ&VcN1m(~@gb$c%M&cjheh6TrAulUfH7h{DLI+1^ z`plUxE#;VFjNP`El%0Nn<swmwxU(N!);Fl_D1ikL{S3t2_N(-eF(i}(Grr+eNa!V9 zK(WQc$Hz61&awN|VwZt$q9P*SY79=UF)QYUFED}8a2Se(!BU*2XqVc0T(1w3<QR?F zXB9V2d}9MoPGs4#yd;HJAYQ^p1)hT*@wiT<UHZQj08StEFSi~)yM%FzO>v;LTS#2* zp2#mofWRZ+CWN8h&)<GAiOCWR1OnU7uecbyKJJHB3fRGRRaK9gsvY5_PCtm+!f}$h z)&pIjBcq?%yR%c5TxB#FosAljyV>LiLx-K2LFI4vCSgCjF%uc5P=&zv)ej&4uZ0UO zxW;fLlaDBf??%ADeU54NU%q}KOYQGDk>cgAZUpNburXynyI>@5@G{p2mgs=u&{)U9 zv_#2@`#$!R`iOv(gYGHh1ET10MTtg67MbRwzCn79^l}cwHWwh!s2QKH8rF1%=fHYD za*}uuH8IpLe^H*tR{BEDhp8Z5{tW-YpE;#fnKOr_65|p!RBE72MH*@9594D&m{KD1 zq4vQZVam|y@0}r1J&Bq-n_C}vFZ`{YgRS-=|61Ytj8m(VH{RAjgly=wZZ$5u_VLK1 z;XJW>C$^%N%eRz?`EV&YFAROF?lD)gN793q8<!i)`8>+EJHcav>+oIFOFrEZZF_v0 z&wK*<*pr_c*5AnMHrl?BEO5UlJ&_e&=c6_<m2z&Q#}=LClyX{uZ%JP7*6gH;hrVM1 z&33pqD%{fwD69{BS+M{&51q&fDAJx~W)2{=Cwv={vGxa2RuD*D=3NPri$!VZCMYDy z>=FDt^>8^ZIFe3{<otXJeC9-RR_7bHor1<ExpPvm*CMdWcIa4xmQe@B3ss36=kt<u z>Rja-U(8yBc2;>zHi<7U^NQfL(eC&K>;qoQ2Y#>%Ym0ow6xSnk?EV0x2OtOGfjv=f z?@mJ_E$*B_P-Kz-144t~1hD2(dU|@h@_^=%rCa~9bejaJvugHSQ0eZ%UVYsVoglai z=;f08>8@ajBUn&;#)hPUTIi2pBSFU=*?7kTX41uS*0jh`{+2=8A9{*~`2XKw7D9Sg z`lj{oZ>KiB{(aq(G*WVjwg+#gDJ<pi#Mpf{v#BP-;SPIjtD4$$Bo`XTzO)#T;}4S1 zsuwPfuty5JI|hkGsFITm88N`E3v!Az;QBl=gN4yi7_Hps{;=?{>n2#H9-oq!!RQI` zzq1<10e<W$9OgiS2FGbBQBzYx472kF&uu|mKPR$_H8&FkmFsjD!>NTR-0vUcsY0Ls z1v>|d)|AZu1w6W%gjAFX$NFgC(7{%e{U8Zgmm1BGZtdpEAX_`QUdY2=28LQp-03EW zPTXZeVejt0$2T9<2<G%KM`{Mw*ChK+?fm?FH8nMqU<c-^HK1%aNU?l(4gu*M3|<)I zSc)h18X+|>2&%6RPfkH26vTTI43#%e>M}wVI@u2aV9e?-UX5M@n}_qz*QeC;U3=_? z&LJ|>96^iV)Wg}&pS^`#5-0f;5~c%b)_mBU$(gtRt^A5-MeNVvRQ`=_{O9xTOk7Za z5voG<gmZf9iPow{ua_>|Y~SANXnoJ)6oT^iwzo1OzxWxMTb$CuV565n3pt7oXHWJY z2uCy`A07x2h|&=JN$S-T(HF8SL--Jrq#k}+z(9+vM+#WbQ{UZ{tj{myB;Pqbz0fFi z<XFvZ{fMeVxDkp*Y|$*aURPJg+ci>}fbj)^hc-fm3q?x|I+?EUeo6r?9BvfhaKJJ% zaG#m+-=xFOUE5_2TTh(B{kg-6t${VlBjcyQyoIQSVlyR?*d&-W@Fqt6hu==m*RSE- zW9SJkLW+wA%(#ODod8T+C+`4kc6XkF&*;22;-?IIXPq!wY`$}>wqq8rmb0R8;VYVe zFxlTY{a<K4!B(I^;ShfQ^vORyJ}@v)^oQ&tfHw=#+6vBIV9Jz}2BB7Uc_LpZ+m-$E zc3juEVA(ZN@1l+j=5pQN73?1Pjs*n;7t!T3cyiIps|p{&bcrfss_*BNFPaTNKA>lS zL9oBTA<0Bsx7QCTt`q;+vet+@kzJa_+tXjQGo|ZoeHJ#L-iithmdxkR-z8gUk!?*1 ztbEUWUk(B>(PvaEEsef^E`UpLG)!!$Ee&84%l#4;Dme_7t#2v)q~-s!@m6npe%Z_& z!tUdqL8|<_r+wk53vJqJF#x-7(_7XhY+uw+O(XDK0Uk^boKW>k+yXO1Zpq1jfXT`h zICnM(5}|<n!tvhPxE23+@L1Xz6tjh@SaC(JVNLdw^;q$C$;u$NLmaa}5-b7XSPixp zJ8Q@%lpQYDp|^M$F=bgb6(7QK4zdq3j;np>_GdvN2D9L<XJl*1S!@SWoDsFOnu_TY z922(tJ+(cA#LsPH<0yU>bl3KKacA1y_tIDyvOAZN0Df4Ra#BuOsE%=g<!E<{w4Z%i z-~OhY&+nh<{A#rijrk<?nmrC)?<?W+yT7)19Aazj-QtWJvlKC^f}cf&Q<HJgqc`_# zTuW=`ry|T#0HxVdLF@^LOj1o5H@+(GoQ<yY=L1eB7ACfF6Yk)Al1E}<B>$|)@xhfL zN!|24FW#H~fNTLY1kz$$_Z)-$2u~sS=BJV00ZqO_RyLjg?7AZ?KDCyQ3{Q*D1FmW; zva%{(7<~ypY%>e4wN0>FirVIV4~Rn6SoNbtf@_RUtr7Wqpie&sIbW4VE4eHaD|+O< z<s3q!-r~OtT^Wd!tePx$>O>GvbEd{h`kkTTLydPK{ZdZBuK^MMuG{;XtR^d4akpab z236a)Rke}#SVt*=7%!+=2dh5N`|^yW6JZ)z9JK5C+rHjFRc(W=5J4-hXk0l*&{rl4 zBp#ckk6CW)EhsD`#j3Q)>sd=X`I*2U^=C866rE*-e_LpUaf_fH6FEMEQSgvJ8WuJV z*hX>Zf}G_9_W&D5$BN3+0z7JDIsYMrV|TzBuy<esLd|O6Wym+mfF=JxL7c2ag|dJ5 zU7lQi=H*m7pENk~;$Gf?tAzR{L91ikO(Upx1WQLdzf_Gmzju_ycnsw5m(e5x2bu;4 zkmVY>Pd9I+PHEz!<-C*nXm46A^$`tQR82`Y9jweXU5u?}1eNYoQ!@hgOz<LNu(9-k ziUPPxEFbl%VK6-qtjK7GXM!cz3f>*U5PU{Y$~%1J#f%JUs^F?2Evo5J7yL?q;z~v- z3&i*V8sp6+|6yNlrYGb!@qIfSFiIity#!q~h^2SnfUQ?^o4$ykK3gao{C7Lv+781u zjB;imI-DN(280j7DapYrT@)|>Ja8MsPPG80yZYsV;Pt}GNCAcw-#=I-Jay>OrPe2= z+tAPaH_IC~4(y_%I;q|-8*T%;q}~9CEzzhmxf1~7ZL!_x&0sQqX#&oB{yaSy9(K55 zFtXFn)dK*t35X)WwKP}&aELA#6w9?e#2KZ_kT;MCc93$w3ccaMZ}tafgaSpXL=uTu z^%CwhlC?AEue#BhLHnYwlJ^w%jQAy>J9xa*V!h(+-43s8s^Qo_;^NFL*y;{_!t#JE zdp2?Zwo9uxGSq{*gGdVQKiwE&>Y=#^hby)3Uw-)krImk{28)qOKdv*fu?P4KTUAzk zsdoSEf<>Jt5b4#VqSkthz5!j5P&>H5;qmUzi^O^#h~~*vl9G}tmw7QxDs0%aBI}Qp zJxiZ--(r^1D44N3bw-C+0@fs9W-j78KFI1{8x!K2((|C|?0lsA_zdig>r<^3BJ(Wm zl%h8kyvLB9gS}19Pe@1zGD7h~8FH}G01&VuHd72=(&;j`6%P_v6Ttq|kYbQV8MrBA zvhbayWvI2BChaTD=?=lRdc-<!VBV{Kz4xql(~ShbL-}O~Q$xx?;3s&ystEzbUrAlq z<2m{tv2=zc6X5VbgCuw-=tIREohH3-44zv~U*lqlO$<qCA^0V#op6fV@@knJYv?s_ zo}`h#VIc}}Ezl4TG<C{<ucS&8Lpp?B&Js;&H2)-V!U%gtWulD$cC!}^rOR!y;jHEC zWWx2@R|r02j^Jmtp&1edk=rjEhNmbNZ#0vf>fnI(7HB^@KT#iHLc1mD2KKBc;rQr^ zzJp`N@j-gV60vw+xWSfmO5qLESil~jugWT>#X)vES;+L|kZB?_lXV&Hw(ldc8RSp! z9adiAOmlexT-uAoKmL36xCB?=V?y#lc_@Buh`4mQP18w=k%ejvy?=910^cVbj>LM< zT9Z2e*e`*%4T=Wq-Z=U^y*D~yJm&lx3&nS#_a?B!<W_eE{gLG42z-|1&vik}g*1BQ zzK6WXKhqAb{;%X~>nJI$05I&quCnfq{`K-fczNnu{h>;`CUsFu6}o$395swlhze+E zf6kNpFYIT@TCy_5qbdhi!oNF%UT9ND$2P`lbdvFzb|9fG5RDuE&#dQ&c)DYsAzlh* zr|L=L@D0^Ot2R}P#_7z6S6axx>i!W<Yrx@#W%_UFG$<SE@6_Y4*I<#ln}LSQqHq^< z{&Ls4Cz;;#L;wiEkd#xa9TpN~uLVO>Q#^9@NEHFK#j_<0v*h6)M*buPlc<l$A*H_Q zI6dHl!WEBMriDUBkFD5&eQN=Oe8PnAHf(SI%yQ!IMQ<Gsfd+xa-0a<Q0?o|xFe<?M zz<O08HWRn_@_?gGw!bC=ZIQu^Nk7jL*s;#jbp60ACv|mo?h$;;Eh5j+0|hOnJ6nsz zXalYqE>$2Pw$EsRmlv7Y`<`3pc^^23EwZm!+w160^+cn-Um?GJnj!`}5?ZYP_vvD# z9su<X-A2tY?qDaHh+)bur!z(XU2-5~fDV6qwsNUJx05E=9&*ji%|%l!s5sUQud}Eh zh{uGgf6(lpsZvNeJ7|c*>)T8vgUD(SWppMY`Y}%6^Q$ZMjjcD2ROG~gl=l|i%&1@C z2elDU+^?T@xm}IqZl|xHGR=-bX@!zPX^Bg?B0$!GFHycRqXXS|IRY=KeM(0a_?WkH zsj{_`t~nvgDAGzV8BUgnoOZzu*T6?vk8s_R0{A)*h{RD)MvgWQyqc#K3R8^ZW58IF z;zATEZg&iVQ5OMD^O1}*-|w~mgqX;>*QWgGklxnq+apNw=nPC*10}v3J11uZln@&5 zt1b34?3jhGhz%S#>GC1*$oV(~ix_g=RZU2ad|c6qyJ_88>$<mS(#cdI3aX>ExK0^u z=ooT5R!y+Gq+Ib3=PM>m)*>989HA<5*F4sgkqW`L7^m|sS)7~W&S(WzPB@(&U}{ut zo}+B)j0{Bs%{JK=Lp265D2T(bJHpCdl<B1@txiUX_UjWLLyKwMP`Rl#!!j1MlyFjp zbpJho%WW;%u`8=x=<7m%f7lasu#+YtR}8@CaI)mPb*OZ0x05a|uC?HT6y5OiGkU&Y z(IU$kvuBqDXERf-yG{bm)A{G0b8+yph@mF^HJ4Dux`CTi>+7IFCy{`L;*p04vYuQw zdYp9Wd(Y;Vf7}1TQZ5{xM#lPR)Ta%ujTHd(?maLg9n6HPns7>kELsO_bM<Xe?@VPx zm199htV==vzPtN`PtuSc-nI(-NR2s4-#p@g0n{&M9Gg+6iTEJ4Y#9soX~|m1<Z+zO zzq3@|GX>ZoYoysFYm^yLUth*=QDL_MH6ZQ@z~!cn>+RUF<>2M~rS7RPF127Q+{cpA z7W*2;hb$J%otuwy&Zpvf=Cr+RVg0~OvR{Vc7@?-nQf}^lIgFfkRNP)S5Rda`iF&F% z6+Jenjk__xBr_u->U4MACIshE#EUA4bfmS|Jd%Zhe6u*h<&J6_-JVxB70;l-CwTHN z(Z)&6HgU8ms;TMs{P+GLQCN(kVzlTRsI^a9({aDu4dseXm>ZJ0GvEz>90M8x1^28k ztn=REvz2c2FvO4(03cfc{!kKuc0}2gCl-n>IWR*PoA)omn)>=Cev=BjuOCuZlf@-k z6Y`3Rt?*kUh8dhIZ0~i|m*2J^ms}}eirrJ7fh`QRSPMIoh~ll7TY|)TMW$|yIv>)J z@cB)}LDpw=8RYWqH+b#X>B{MMZlPNpcs7b5@w+U?&$pmbBuC@=nw-Y`+a31klVRg) zAP9|)btN3FeIh_X2dQ>R+7wd+4-I!bdBM}DwPf?M&wv)%$Z{54g!-D6o2C`XG7W%a z(T0tMdxc)}?^nfyfqVDvok8iZmtx7o!oouE7chPc19!Us97H^QsB3-D28G2;E)xH` z^pir4y^S!;WwOU9SG~{PCTd|h?X4yZ9d~^9tt0kCwQUcms##NGU0L5>FDZJmkT*?b zg$MAyHDC~woP3A#^C{Ppjqq(yLd~_j?EI7~ih-5ltjU0@!@w3TB!3++Od#er9k!R} zL_8%;+;#wT;(AG@x{iO>CUz*4ff|ZVlkBOFs*GmmE=ioCe6$Go));U3^Gi?{m4NvF z5`BE&d-nmxgKqHGym^7gC(CAPyTq`6w>WYlhDTMAChDM8g6%TS_5Ar4F^Wl0P%zjE z2MYQ8IRNpPD`ld0SjP3oDcHvjS@$`fP99QVK~f(b`cE9b-PO3@iDGO5cqkHx&>ari zqbDCrN{3$9#fulsP9D2>Fc;4e<JdqIF>&f?Yol(nJSY-rL+e-6kDvSs;LjX+`~;pa z^@$Oo$R)VD+xE%+yZ^9*YXYbaWzWjP*{<9}YVmsz#js>ZsROLnSOL1n74oX6GpF#+ zZC?SZp5#w9+}0OyM$PSh#b2(EKLL1Xx0z#H!KtgND{_)L^ps(OjjB|nJ{7>y!cZaA zVWrg`$dr-U?J=rR3&%mnj3HEN6M@|yfRqrQ-{%Qa9-9um*3b<OtB@su8X`kDEC_8? z;NMvHI`r239t?y*YM96H2^3(O4AgO|qU{92Yuo>9vXY}fY!P(T@an<c?rLLY5Afm& zqdZ9$A6IbQ0j3gv@Oi)=^A(lRws}b2`Q$8jYr0*qHmOKP02cyb?h~#uCi3iQoSZ0A zH(@l{pa0KFq<ZXEap}D4VagS8zs+3U^bf5^&5V}rE(B}aeqqxkF1Le1LKadJ1JG#) z{8*Gm8twMRAMgj9$ZXsGW`3`m=G4>_`em{XU-#0?LA)I*COji4g~pP`PtAQg3Std& z%iDyJyUM(PVRQ_q;F;X_NQ|nGMp`)~AS%W&H>^@~VrYns->yP>qmG1p-CE1Hx$mLM zt-_1i-YHZlylnxH+&v7?q}j2zcRa<HE>*2q<!zDbJamlizZ+~XO2G)2933Bzz@#u1 z9V8waFw3a$6zt%~V?w7^9S>&x=#L+v=nR@=0N>;ul<#O7ALwrGNI?kJK`yC*4J60T z&K?G@U%z=xrSmIYbj|Ty%4(E6I08Fc*61UR;t~IgjP_7m{{IedwZYKR%WW5c5o12b z_L44CpaTe8F)l4omb^qEGE+pLD;;Q^L|4>fs^1>#rzX8<LOc#4KAu&r6sC1)85=K0 zK_88Sha&tuuthT~_g_#R{`4K7Z*=DTqmR$VI2MHdItMxoaR%v6+EbtBAmuSZpR=gg z*~O({-v}7*SG>Hu4vk#6cySF*MKef~NQSv`(aSDf5$S52>WXeGfRR}A)@US{%Gt4x z?d@j!BlHO0gZKb;xV)&PO$N>~J0^P4TPm?})rXECI0v`3F750aasN4fIAi1m?p!jP z_W)#=h$m3u!p!jnCP@7Ki1B`OL&zYW;B*|flZCN<=P*}hdLl%UG^=nH$L8>MnvH7x zb<Y@izS8Z8MJYgwFX?d;x|}dY`8)CK_-O|A_CFwbqcQpyYy*#g2SPy{Jb^Xvd2~HH z>_9F24%gVirlzLeF;t){(dx;7hwiPOha)JY=n_C^)>qz+jgk-BsipO)7$izQzAzJ{ zegLlTR+RLV@~1!QIcE$`YbS>XlsV+Z@aeb~Fi%vaWSvO{F<!NQkDg~$75@fJCE=vV z{80|dRoh}a{T>7uyl!ARk>Qdam%-%8>$gyDKG@ha*#T!Pn1LpuC)kAF&`U~&$0jKk z-F|%H#2pcQ{A{TvAd+^he0kDXBt{6prbfW94{b>;_;yh<Vr-WdLTP~amL{5O>57&p zk#5k_CUzv;>iWK^FwKB8kYhS>ygeXWVA>wW@6l1tn#9FNfgdPN5gsiugan!|wVl;x zTgh&S-mAy`mbxlq!AV>ohQTA^>#zFyTI65g0tN9RPV9FCqDtJ(rcqjWhr!;a2$a8= zN=3Z0Fa<mFatD2F3P~ttV*rjHoyrdk$8b|$$7UxlaKc!nu~ew1I=-A>9+lKj$u*UU zR?J9;74=f9>{yyN1U<AYLKm<Q0mGcOvojdcxdS63oJJ6KhpNZyEtv$+K=$B>OWj}f zpU7C0^MkN@9RM8aUO{wl)KjhT0Evsjz=uGZEf(P{-U(A#Ezs@Agf8qM=K39uL7qi* z2mW=#1IN+xqb@u3bM}gfh!7VBQc*bfc)uLq4WK#$a|l4}K9FR3<z-h$mKMt$-ZLOq zL}NwBO$Q-PU(ya44e@4?tPq7ed<X%`ccI}K7IgWD(}q8%)&bgQ%RqwJI*KsSiWz#y z0>}{@q}pgi1F}RzjFiC=VRr^}3xeJU2z<K8<D!A#;j|1J8gFoY(VBBOB$anIu|Z?| zl6c^-Ke9fnv;fbJ^$waHjX>HugN5*Rv?8FF?pVXlFCb6`?4$sx3eD+2;v0+oGL3_y zADi9|Ys)(nwy_m!N_;c)kBDIpk1#*{Wqz1uOu?@RBmgE7vHRH1fR4W7e42jIYuksO z1${1FNqxDh2z#{Hke+Zdawht(Rm<>A>i<CH^|)pPZ-Xy<e6&43ob=GM7o&qb2diiX z2O2*8Oc#v)Yo_s)82N-3!zZ|lxtb&hL8&I!*8`al5lK))fO6D)EDR^a2I-z4Yz#p* zGj;q4)1k%z8pg3>G9VqeJ8K1)irWR9rm0P?KRqzV58Tmcy;{(2iLSbFa&R6IzR_F6 zv=ZbK+!T}0j3@@?72li*P!s*&x@R`Rq%Oga5Pce3pfcfbj%k#oM!4M5?pz53B>dZ5 z7*z(3%$qxxR*p!WbxCG3InJ}-*<laEJ9Iv*$DVe1{R4>|A_Rj-%IlhRkOR>?O1yr+ zp^Z-cB4p=|Jefx4VqE58{5ArZ!l{o8;<I21=^~IcQuO<<w)B!<M&^Nm0B?X#p998h zndqAm`2oXbi?M-L0^>jd58=~!v_-FgJOVpPlZNweTEQgWg&sQYpnHggVNWV*YZt10 z@UuthZrW$Yv|1KLQUT&GqvuLqv!MUbJgx7og$8a%lC~l*L;P(8{q7U0bYga*rA|Y6 zO9mbH?%n%v1YpZeY-%`A%1kPY%AzJ!gB~8i)m<JTCpcBuDG>NYqIy@%y4hJc^>}z& zkR|FoHo@ys!xDrswAt#?j?)-KGeAGY2S&sqD`IL9J{p08RwQPXT_$Y<G6rg|VzkL6 z7!Jm2-rBj&n<TC@d4)mfK5EH8$q#e5xy^}i07#A+gVe&8Kmv+>$t@g;`51Zuc<=`z zuC=pDp|4Q-M13=_=!kPqYAp;5_)&{8kiO!^SxdwXT%^Ay@u6Zu`gj1=`PE2HS}G8V zN0Ic_!EQ6*#jt*Ok1`n4m|1WQPiYMpHYm<sA)l|uR|9&PZ~3e1ML1SmMbJXB%lh&6 zsS*jyiY$rjGvo}dPq=s;xpNMHNU~POJ11gWlHmW^wV6P6*J)fdMD#(*VAJzqEsb>r z7*URZPP}sB31ccDKinPgk7zwD{i<A?DVmIo>-|8@QVVu)(E!gdtf%xt&TYocykp~E zD3vO}Mgp|FKWPMrPd-itNWA;PyP8X@bZHYJP-(2ouY_D<ms=`M?|0;J(a&;_&=N_k zsQ<J>cO4~^toJu4pH09M)Z3N<z&P2o>l(H*#bo3d1*S6fS({pW#q|s-1MXL+!#b6f zI5?u?@u=LPE2)gvlU>^R+<+#A3A%pUw6v4+puYPG%!48B1cTm6?qqXgzaeoB6;H>T zNgmv0%PD1$Rw}dhR?SZ#_lc)X4s5vh*OWO|h0k~q^3F1)P0D_6^Bbh3`ctl0uaZdF z@l?M(2)gYMWZ%P14z|L;FBtRid%#$ERnrE#>cIlr2C(8x*W>hn_Wgu^Qke^G=dSgW z80UP8{6X=lnAl}S&ErUFLGNIq74TH?qVEeJP_uLKS^T4W)S9ChyD!}TD4g0-SEjQT zsENkV6d)7*m|X3WYfRK)qZp<T9*4LN-o8I?ErK?e(SUtDj?xu0`LSPPfjsG!gxclw zpk0Iq6>HGZq|jYya55wqr=uU6l3Hpdb7C?@#l-Y$W}@h5I3_TB-7xHY4w7Wih~+sA z=n*rhg@pSdz06^=_S+M;-L$%Xrf7TKFDNNlUFQ5)`Q>{w<;!FqpRnr9A4IjiqG0WI zS4q>oyLbClCoCKAX-E4=bV*s7If67Jc*uKXl|fp(8Q+D`mXNhr7t(9##(E~3jMc^? zSzrW<@-H_pW?R{xfPzmNhX7v)4e@!Bqwj!p6Xm+{pxnu+yPug%bFRAhqi^r900Z6m z)NqE{+Q>+htwt^%%><7qqD}E|w7&N?Qw438jH2+8pw$+m1Hk2uf8DMRPv9%pubTo6 zBU5dVauJAyKz4hDG{xgS&!fSy0KtKlUB^77McDQ5A%(@nF2Q!QZvNi(XeAFv&M`D= z9Mx_){?w+SD7kA6&*Tfz-w=ezMy3@YX67OMp(wsoZHpi*A|o?bZLxABS|HW7Oqovh zK%|?WfSr&OD%eOKUd31h>L@5!uTqg@=5)EN54Zxq=g|<{cr^P1%L?#l3BW8GLqdGk zHvjFF>gLwXcl-_WO$Kg+rOxK|`M_m4b(eqMJd>&v8+&{E46}#2kFO%jfqW1LMl52d zMhVi_2iU<$8jBIMI=t66tJ&PpN3v%L!Z!l3x?Ns|!qT*hqkW<;JYr#~f@aJ8jPD{F zpSdh<DJv^m<}d{!aPY>6^UCy$-#6)0)--2g2n)K;m_zLn6(kvz*XdpCh%th|`V&#B z01#5eJxo^|mZI%xq2R8Owq@I;eVZHl9i-T=v8wvJ1Nnk>mWLdW0cRlUJUu$LaB4g+ z&@fwZVEv~Qx7}vJqh8~}zDoH}ptbpJ*cG(uA`o=)LnUR(d5+mTM{c76c#E15>BJe} zM?~O)pPxA<hC?$RhDY-*{p4XlD>PjZ75pnB>2KgTDOeZY8&!xMIsak8O?T==TS{i^ zHc;(*7VpsT9$k2y?kYSN7QOLQBSC@kH{;Bl)|7JfY(;7CXrm8B<<%gGxq<MNgxQhU zsUZ@g8qO^`{000!3?deXoYMdqtk<^h#7FS#NV|xc!j)MSO3}PamPA<ZZ8Ky>wehEN zlq?<y7aG``xE9L@8u@u_$Va1f@oN(He@HUh748sm7GDVCH~=AR#znw5kYO*5?-m2U zAG(x?v4TQ+PxQ|DC6;eq%w)>GoEQnveUgLqDV!^B+TOBD({51CO<nzZyU4Z7+9X`t zd0ESrrv|P@9TjRGsT~oZj2Vt*_t!i=)J?FDQY5hCU8#-G6v^9v@vC+X8j*3=(gXPh zh5T|kxf@X75sAgV&tcj?i-Vg=pn(i#=e{{a#W#S|S1@tWOEh8uKOPe$mo*(N#KIe} zCLkfwmrMqC8AIA;8r9cF@M&u9IGjBa^OyiV;H?DjSHGWwGVKV|B*3cfD~O&>N5M(G zPb8IL@;1gvha#b7cKjQR4~{#BbKHW39-_))3D|Z$zDG<2ByYr5jzk4%ja?`}B`0?r z-_V$4GL}8wTVj7TqA?yMhTzWJ_W|=7OppzOQ8?4lal|y!Uv?nXsQHDJq#wrR#fW-q z97A4nVyLAIm$c{owda`r#qUGi>rt5W#Xs4M;kz8<Is^ycIJfM~)RQfOz-#aj3IH`+ z_VzBTsk%^_vpYJich3wd*ldhQ_i;=?pTuZz{OT>BxWI#=JSj=)SH1>VNTbmi$3LXp z#n}w&jUDwGMY<iRkq$5o^Pk2T(ns>AAsH}rs<4zx0v^6=u@5r%m`vT0QgnO~53~_> z@H&MizRIt0gaRjXy{}zsMFD7mo(XCe9V{O@(?(7$6Pv$)UR6}S1Fj_2(6gezuS`mQ zUy~~*iH;-)P~0>)n9Ar97o0@8IfLhEB9AJt)(Z^MnZ<G5tfS{eoKLq6sHnGHdKF;B zJVT`2A<L0XXIsvK(un0?qXo2^WueYBd3}9{W8oM+D$NcFI4%+{YvCi>>E!%;oU!ft zA~1)kNl0d<MeDQ(M}XNHU7%=R@yVYu${2c{nOTJ<>-B9>qCEjGGTptm3B&JY$H33` zF^y$8KTHcR%_}khNux~=5%mZ#{>Ch)z`3G3sr44or+9Wu4vfxa$Y{lhMu6)D1z16n zRU)`?)CAz>l7pDo#@2SZloYqSyE~zLz+g5*;z5%EpTi;nSsW87T8!&xKWNsTaE2<G zlY%1QRgBPOM+|f10YQRPaW|Em0+9QB?(Nltr^G2+Tbe8sY?1^tzJOTi5*O7$QzN<! zk^p0*t$Xokfv4E?HY$*)0K`Gdns}rp-KXmIegg<p0zwLP>}i)F9Yls7jvENj?RliF zxJpZQ@AScK6_ZglW{7KqTJ)#WKdBK80BB7Z<!G5LdZ*$l`yoD}Ns2c;h;B~JyhoWg zWXDn|R^m`7YTlG5nO7WA6{PMY^Z>Xo3kT$E;B|Y)C*KJB(qsdqomyZGDu9!SSIbBA zL^XH7jZbYC3=%>W?8JbbS|IZyqy_sf5}or?{cStY6v4pY4wM;5PWsi5%L3OzXIcXR z#BAh4J|4?EBqMSq&>#8JAc*=@{NjOa`o~ARwG(j#KHrXjM-6%)!lxsvM!_1yFYF@6 zb;5oy910WW?f~-|?5+=l@aI6x3E4+jbo6d-5w`{p3}FM8{e2{C;O1SxA2!++zXnS} z!T!r%z#F0o6zAYDfA%*DAXIyo@M2GaNRbU4ZgH9|3{N#AL_~Ib)FpNeeo;8&p^)Fw z=D_D7Q7qyDwDcPZmI0HRJSxzDVggPP_X$YekAj96K}gD55v)Js(Si@W2L&C5xzkR6 z*txlq*t-$^EC1;|aJUZcKp3E`43SJ3%1iAg0RbVQNaNzH-FN?iC-6<+XKx?wTMfu9 zkZFHxTlNBst}mtrE7j`&t^le_fO7Z|dF3aJ`}>3tc9g>dx`UT^05BA|gruB+AKXTZ zR9xkee58&l0I%{vdIYqHA17Ge=JPbbS+q;3asnHcu9t`|D^M1&O=v<u!#Z~c-Bysf z>3|F{>*tb?i4vov)4f5L-~3E>^pc;kc@yK(icLqRum7ST&+?F~@!q*<-f_!C9^Cev zx%*nD@<)!9(-y!3?SyXP(Gu4duGey-1I<}Cd{Y{FKD(-HKUC|Uk#Cxn)^PlwREWHw z?^FH9V}f3uPuZsXsqNbb>Pohw(5Sj^p>W2NYxh>(lPJk9JmN8Jsp|5rdtN!e@aY;F zYh7*gy3c+?LD$gLGV_7HpG(Yd9%Nw?8E{pJk|+tS=y;db(=e>fcWHc5u%umP!L@<} z#lSY^BThB*brA)hPF-&3S#Z2zSeg4|jhu#hY*k&~$XVZf4+WNK47Jx>OBt#%tbZ|< z^07>3_+4k<&$cmG86v_A7DfP%2ZK>GU6tWsI_-b^u~F9VtFL(b(@0wUwPUO1mM8xh Go%=shqL0=9 diff --git a/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/one_step_convergence_causal_glm.png index 7590960f05c13e2c4e2ca36f8701b81cec12a350..458289e98d9a1722d87b782e725a096c603b5b85 100644 GIT binary patch literal 17233 zcmeHvbySt>7v^_BL_xp+OhTm*5T%hYP!33^q@vQ@&A~ziL=PZHgY==1P6JSoI&`B* zr!<F}{oUWhntx{IH#2M2%vy8Ta(VCNi}!oq9nXIDvw5Yee20eW3>AVPG+6mt>Ig!H zLlDg9p@Z;$gxd%Iz+d9da=OkMcBam54;&vON)Mdvt?itxElgNlA3Hi(*x6p?7w5ln ziPhZM+1^P)K)~idU%+qYXeMx8nY<4ka@bz}o)d!5JwX3p(q+;t5adQR_SSVx_n7%1 z4`)reFZI7l4r7iVKXmvV`>PAzGq12Va`KRo&EPJcVLW;C<fGHqF>3FW7#ZHl$*Nwz z)u6_~^EUlE*Nvkz7hk5cUL?#t5S%-D|1NQQF(g`O+L?1&*)1(}aKwo=AZNyFR=efM zrn(EE(WJ1?nhZg37Mh2gDPdWv1&46(7spNH0D@dTO~wj8`*9S(AjsVp$RPx|f8~HI z{5<qL0(V6o!5l@9+DHGrTNucP{FluKifv~43e&D!y~>XL@#Dwq;NZ1^73Elo6b4>x z^R7&-{u;lUH%yWofptky!d97&o8Hg#7ZLi4tR_n)$q`&Z9lBQkg<Br@ckdDxhAONk zTVmHG{3vSY=B(M-*vuz>evoimFi7kvw(e8EYWh?4?%kl)ZCH6DV=B(adVQ{s_E)QM zL1Ce==c>6<lu*X^B~EP3&T^pV+Dr;LCGBRF9fBk{Uqnc4Dl)stZX-S=x8j5ye&1qe zXYXrDOiVl@Wd8O&pMI)Rw1~xge^JymkAj)*Y^$1y&A(%wzh80AX>`DD#2ms&JO27Y zA+^7=(%90nzFnxDYm^-#P5N&1=G7}Tb#?p;I+mQ<!DqPH*lxAAx6ce$RUAKYqATm( z)$O7R*Ln3DH=c}a!&WN@kR^oNjg=_6m8w$+tG4ME@+wmZ=ZTW>tDfm1OfHko2wR%h z2c4#(qAKX|S^mMfx4?lQTy?O3JlA=>?R358R1D5dd?lOn_qe&a`wPuNs;-&0u?k!F z=Cygv^yKCi+YIzK!8?iVAMfmT-+|*V&!-8BXo-^|2tKUuZGu&&-?(w(6FjZvnq6PP z<Mq~GzkYd6B?b(Z=RRzB(_igVk(87~fBJMrN16)3b-v$s`XVf0=n&4x^KaeB`HvPf zXN21F9()z@+w-ou)?`}9&CXtDw9uIm`sK@)!SWKFTqF3F2xAh(zs{*@X|2b2cBNG& zf{lWL!lE&ZJ5w_QKN44B*2)|!?bB~mJ)Ii8vwjJd>U#_coc{Le#2Fdi%BsN{KR;o+ z;lbS2L0HpG@x>V<FjF@{3WK|2^}!uKLOI#7!Dobtqo1Gd?)}`|-jEDiS@>Jxv0}=n zUwrqhh;<umK{y!J)RcJ<*pFekv)cCh{O+&}yh-FJ5_q1Q8?{<|^62ROD$)$WH<r{K z?lx{~Zk~SQ=FRZ;?^6$-;ON~B?eQn4bRMn6_^kJtl`oXf3c1W^*45Q*RmG#m^^mIf z=AAd^+M5JwtOe-KiRFDuHCd_NKY8X%=2J?#nPIO*m(zR8+gp?IT?8Ego4$g&5GioP z#~4f-xk$9q$J@aL@l^w0DzL5T-{SoCc09Hxv$8Hp?QM=p`0kXlWw^|CdyWN*ezF~s zfZOE=+Sx>EfyxBEBFnx-agXKXIQQR&x259p`m7aHR3e&9SN15uMKE&6YHgBSAbK0N zzH>2*TZ8`GIRlf{*2cyHKWQJ*r}g;(PV5;$lV{SKqvS3#o!EEp-l0eH`}gn83O74+ z7qj(>3PvJeCt$a|cIH+lTM1eOZQAqn2ok1rKvr~qphN}Cq$GlpjybD7_$+1KofiSj zp7P`ek8mw*gBe&Y43@oq^QO4RXKOryHtiLhpwoGz{Ig}z4D3ixUEoP}Y>$zbO=4n` z@p6dBi|5ZRrrHwahEIb1H86YqG-_#Sah<KY4o4<^3>jK#kzAaLF!p`Nrj@CQ&8;S7 zK6&y)p^{}wWw&%|BAS7jd7-;1R>H-R%Wkl=-E0>k+GA==-l2gKyPg<vr@8U8I8O!G z=tJ_xVC3f-<L+KJ_T3t%I(#@kqJv2E?iPR-73b02Kc<~ykn!{9T`v#P>NM>lS@}os z4chTPe;(D<*Lw~|L`I$pa0ElSvk?#wFp!qt8Y4dTcLjsUZq**Tf7-E2OGi8`gh_Iu zeYfnF%JQ{qWC(8gDWdyA$ZptxF|f{PxX3DEU?Wb#rLo#CG1(+o7QMiqd-TG!tv{ij z;P_JIW-T$|QIc*sRR)9HBRdx^Tv&~$*^!GDvB9%t>z63QkJZ*@x&kZRc1dfr<BWWI z?n5l7Webw&)(`q@46V0*`0xRZEczR}EAjizTy6`4eQEz}2_+^ksBALMOS8~a$!m*c ze=}g$8UN&xL8+?D{!U@lAOsOy_9MuT{&$c|6zUopU8N2W1B@v$^olZXhcMd+=x1xM zEshumGV-b4zWw5B(dpBtA;7;+RgSgHrKY9r{{HTw&;CtFG*_OwAx#N6h832J!&U3M zhp%3}Dr`YyYmOFG5gm5_XxWpKJ=PGy3lYR3WvSF*tP=u@tn*nguj2=5uULM6dp=V; zI||~nDa)3kqGHEnyx+ny2|}jDTyK8)V)eE{K{R+Fy$Mz6k=cQg-1h>;IsAs@>2L*q zZia4tl5NedD@6Nad$1U`AOFfX2P)kln(uCog-Cd=rla|aM=O(ypI;fwUH#UrXFc<2 zX=$jTlcv++V_Mi?BOX(0)Q;bR1Bc{wa>^F{G935)e>-jdPh{Hvf6Kp%P5w{rSjMR- zTFC2y1T6*ZyEo_F&2+x&FAjh5glvcxF}eVV%0TgM*bCZlbq#^8x_X5|GsVAu{bx@i zxq=i&5c~I$Tx(OmPVKQxx1IkeEhu<-mu&O|EFX7~&~<97urH>jDf_I*&r(|154Vs5 zh?oM_fqAQ`$uyLfSty5GmE#zoxHoUkaW5Z_PHwH)yusqV<iEd7UOTCKfDEzMOjAxY z-oHbX$L@a+yeVwi*=FcAlmZANOuh%%Ja`d@Aa`sA&JcX~7z1YZ70UIAd{=JVmzRHe zkoH_UYw>eT<2e9}wQ8zw^Sc7s?p=~#n?8nZX6Fl~XS0x<Y$5G#jI1^C=B7g;@}J?x znV_E=Q`exV_+DO6P~ze=`RT>owL^#7?Q&~!wv7F<%Uxy<mWCp)1KnWRv@P*6xpviC zx82~5q4JD-3a?Eow{vVQJ8oxF9vNjjnsAO4p2voTAf{JppU9yclm6+G(C$XfzE(%7 zQp)<gaF)-utQ#GU4&6!)v%<dBADr=1Glbmkk^Mb4CMi!%9Rr8wL?V%FNEq?G0sD7C zQCV4gwmaKp#Am~zC&v&HQ}-+^4zd`*u)@`Uff<o`Ky?baJ2P13G}HbuKsjDIJ1_6D z)XLARo)8Nly)aMZV3w}JwnTmhR6u|7Bqw0or6v1d78xV2^||)m{D&@mk0U6`y%2>% z8nwuMvO6KnT>w$K60Vc;8CMS)$yoPZuG%BbK9l(eWvjl4mGaUSs9buIQ$E8}1xc*2 zv60vq&PzVThhXaIFC$?${r0xfCYqzuB8)t-4IwNMY(=(1dT8?NFHE7n?+FIO74Um( zY;DnRUmxF@u6D6%xskVgW7zBQ54UOo#K{D{sSy+1%t7d~6rekGSr(Fg`+!|d_K5Gc z(%-*-pUEJu=tIW3bB%KW6<Bp=-CHlOSgh8sa9h;g-`my7zYr>goC9-92<k7kiC(DM zFe<X>xDERlYuXZnCQ2;6&O}XKULNOlDB%-Wg6ut$A|98&CQ`$RPjkv9&iKIoWvV7V zVfLC6MAO&j&!7Dl&O;RSz#v@Q+JrFt1t#$eT^SdPY<MXs%KZFHlBC^t9B9N`6!x1k z%F`4Ws;)hL9Yw=56Twf;6@LnI0Ed4Y$<;;RbeUq6+D!QCRpoiRs$yefDsfghR{ce? zKD&<q_jIdFm-VLo#sCJvGDbI>Qaf|1l2ZOlaHnTvWSm++9xE!lYwXq7I_Zq_-@8!P zAUX6tJUj(9P-S~#p-Yj)n>8g>0q+~`oXw^<>}v8brOHd{tlEM5dIw}npCgCpL%S@4 zR>z--a`JzHmuNsi09i7dNzyg_#&aqXpQOIhYtL@c5qaz7BegVlG2}Rzlw|sIdu$Ii zmqxFTNnl~a)gZBVR(ZM2_vYVJQ&aQIcC#~_ZYg|o?k_e16XplUv&1QSvv1GXPb-4o zuru){<?hl~Iw~3(NX=uP07}#N%n53_roEGJshr_KR_-%gMm9sE6E9U7Num1<6<H?^ zLoQ@HDAtu@IIvk@qyI2!v+exk#-S9&j_)=Dq`Wbt@#<{^QycI2RloXSFJx)_qtN71 zF*(=y%#9I0W2p8z&e~NvaPMI2EBp)1&c?5>OupBT6Aq+cMFuAdkJX;P`9ds=k1-JH zwlTs4Cc(Oq#!kvpo-wec&)QRo=!~7lpHY~JIx)OZy}j^c;8lpIHZ>j7%%<`K>#SpY z23Z#l;+||?LUQzq8hzVJn@z{s9FXq}EV)6?4m7g}$qU3uXFnA0>Y=10)IU4C5Kyw| zo}n+gC=z-Q+4{vK>l%*1DMB$A{`PHx!&tq=;&cZ-nPAyP{B!+j=fgSOdpw?-J@%?c z^{bGjs8;fXIumZ!LvsKcH1rp1_NxWTDG>IHBMx?bHvLm65k|z}Dle@HR~yu|0b-5) zG(s?1nc5h;-aLPcN62FxlO!SMbdl85nKM`|Hm#uGuGMOvqB2V@8YT%%01q_I0vMcD zEi!uI)vNyYstV8K)R}`=yYX%oiR7Hz%A|IssH1yG*ctR_iHYQtLJ4-phI|$oL03KL zCVW3^EBwrRP`?WPPloztD!^<j@NLW9^mmLgG6IC;8Z1eRXyqaB({1o9azHP`lkzA+ z#xp|pCaGB018Uc>w{Kao<@bp6*b@3~X|pvlE_SHuS^G}fi`f(m*3@+7C2FR<Dd_*y zUKe!ac|d?79xB+B{k@GE&*><u)skli7|N_|diZNxSdeSikpuUYJ??C~&f_`m+Dn^> z3TvH_F%rSXW6u}@$g&wRTB#3$1lV5WGGo3HBGU*EctOf(#~mIeRzxma3$Y_IgwS41 zmMnjU+ai|j@#E<~nOafRmf>F<t4lhwxS3=YhMo8r9}3Es*v1TFSQ8+J)fyZ>5$xm< zc*swmZHRqZ`}8k?TSRlLB%9Y}=pWzBP~9VB37z*39F^S)_fu<762+1q6jNE7Sb3LB z$==6Cf%P;XQsZYxnZ}n6VvgR2Z5$6JSPWZsB^R0*8rg=<1&T>1X2z{-E8@>=mD{<@ zY?jL*ONTJ<yim#JXusrExlKs{i`3*`JpR$MS}v?4yWDoUF=JqUL8*<tamf*c8AuZ@ z<1Fome{x@14By34vnk<M?<fzI;QJ@N*A+KIlkXMkB4n(i@G%MASS+`*|DEFG5O(2k zA>G@X1Oe{r^-t*HOIMULOqCkSdvz^-952RURCz9w9ZK-#e&jEgnQV*qZQ(ZNc<6A) z4;$_%;Q8m+^!h+>6=uX&U&8tOigeI9i@PhH_L+x}Fg7w)+;YlW3dW+xl`ALtr|)6E zhTADdq;pNjy75yK2|he`bCAVPzRK%t$IqNUJ`Dm~@|GjRlrl$=O=k3?3%tvA#}@IX z*r@Xv1wl1S(s=rhWW#^Gwrv{JBy`U6bIH&2b!-_8|0P#_u_=r&i#y&bzF_o?|CI<^ zhOJ@z{3u1jC%uAOQPPHY9$#9}W#v{>zlpu+#BXsPN=xF8(1?Y=z^hN9B^M{xrq1&j z&X5N0yA2>Xq_EffT@D-61-GCyI-hm#k{omJLamn0HJh&tkX|8tc@Dk#@Lz{*?Nu6C zY@wb@sy3gJq{EG??1tYn_6W(`_^8P$*DjuiVJ*=cE6a9Qnk9Xa#-lZF5Yh#G5?4GK zSA(@kw5ilun31%@4Nk|IJryDW-(OaKo=?eh6tl?Oh+coW=3p)HGMPN?fWo`VkERiZ zrkyD%9#T(C^L4{2ZDW<?EiYIUBzrkeKceG|`G%?e<IRjz9k#BHT%b@@;4``;_4k?c zPU-C8#f!lkGxb4&Pwt=B$bw?ODMCO8%6Cmp&Ywyi%h;in-iMyI<aX*9nVPZZkh`sE zXRi-f7prej%W^LbI;rOxQ3S~R&<zvxt<)sEckrq5R1csfKBYTT(?j57=a|`Etm(-v z8P2TPowth;vcT`ITanhg#`5#!_6h63qV76$xalqH1DD0c`*f5rTq^G`y-QVc+9#dh zG%(uNx6HilwwTlE)Ci@tnx<xF3iXpG$$f=}@tBy|J~KnRUt|c!SRr!i#ECINDiU^5 zn;<wp`}`@sUeVLIUeK0-O{zOLzFs9Vp|@AJA#iLgxb}lz;QJ}_>1NSj`$Ne6i{^+g zn$;yIv`!SVS4FId8xfoEQLEDr=PD)@yem7i&+30dWY0rTx;FM>&+U0klm1(3yt4f3 zCT<x4i;CoYysdMIVX-*|cVo->*dd%FzhHiC-D!T0ydj0u<Sl#-e)VPYV9BPX0_ldw zm-v8rfS(X%N;w7F-2?a+D_ro-Q*YH4c@7lY=r3v7&_*Y-<8$5#$rF!Zz1YfP#ENx; z9uD-@Qy`T94poH+UAnrzEM07Dham2{c#P-#<hviz+I2;FZi?F?EvUV^?@Fq-K$D+? z?b$-pztSv-`zz#7f;l&L%SAtrK5OFVr}Zvda94B1<G|#{cnJlPn!46$UY`wlPab~d z@OYnYk3W}1y3IMIhY2hcNbQBA$b!9_;GOF|<5@qOBgNFhhuv>Vc&NK6l<TPnWNHbo zzv9#+JXUOwW6!PpqF{t9ortHX1s=0gM`^20YGd%k6hHDfq9Tx=s&pYlX0I(!pgJA$ z&U8<wrWWZJB?XIkl5x^UhQY+Av-ca&`_IDtP(W8}#*Zr^2P)rIM2@v3K2hm=`TEK* z=9AvM|H52fVU*y*7p6ZW6M^ed#8+=FE7nck9Xl8yKZ!xMw!EoIh2m=Tqn>iBa{Q4p zZ8mXWB2$TZE8&7SdAK&YIvo{%b;j-5<j%-h+tTYAsdw9LN`|z*#8M)ECRY&M{x8}V zN0Mc0JqkWJ<ve+FuW9m@WoAyzzVCb!x2R>Oyg<!PLAl$a(afGNf)tt@J9J;<)vK2C z{36|_el=Bm>DH2;>sHD%GSsWmi@JJ8!mTA^iwmp7wtFw}2}kAM7tHfLDxW{!T_=;Z z_+5zTme?8^3sck@JBm8B$Xto5_~5i4!W-{>f#wuzthm#3)S!)la*TbcOI0!!KOC+p znYO@$6)q@b=uduiYm*$O-e88Y2ZE*Ak_sn7U-Y4{&cu3RXK$N0882xZDzIr2A*83S zuDtaa>*Ofi@W)6qs1S!i_8RCfs}9qz`o>p8i?dwQHD3u}Q+g}-JA&__=vDx`)Z~mw z%Nzwx9ZV;TlcH)w)G76oM`2HMNQ96Uao@3V=uyy)VeI~mz&dw+Gx(AWsQVfNpA0W5 zDusKlul~5$^8JusiG0+BX;Yn(4yFP~7{LF8Yg}`?)h3Pj)#~I~wxXXIXD^QJ3|Fu* zlqM>o!0Pu{V9C%Cq;~NTPS!)UbK8eG4@=U#-8;aM5t<w>J|d?`iY7!SeGImkh>TP` z-#RpJiXiN7V2N5DcY2C#*RY`@3Z6#w-TZiZ?&;j}PrYB+1LdMbTLnAID&WDatlo?W z9gFmw=E?Lb3;@{~)neUC{WBl;Wyw;W5ZZ+Wtlu5m*^AYn4Hq&X7UDBcx;|8WySm!Z za#p^gaOiG{?OjT8SsWn0Vb=j0+J<WsSDYPo&Ndc3x!qH_#B%yqZc_3dz8>pnMShZ? z1?i>yZ@uFAHdv;*dh+^w)vOqjpzAF`F=w-cR<}3%O!Cc_|NN-4<P{A%eo!rE(>%XH z5WCH87_YIwIp$My9)E^0?Q`Wt&js7PpB(MT(aX{_xZxK$$;oU1B9(`6vSok9O+uWp zx!5R<=_{ubbMeXdv4w#H=6lDa+(i9ML(P#xkz^yYv~0KMab%^Emfar%ZVKKc8enN2 zSx?09XS_)MSUopDOd&=I6O}|yLxoLYP5&@o-oe58I1Pf)<RMQ;N!YhXOeYu8`$|R> zu4pfE$XoJEW3d)!RLf7M!SioC9+_wfmvDZ2M(8cGdu@8tzS#$*Bus)PS2*NqOn)(- zx3dyHQ!DfhQ|Ke>$>oG(DZC}tQ-i3Dt@_g-vlBpjn)+s(M}6<f%elR}fGTk6I3L8n zfQ{>WuY8%;2AKd>(n~vH+`Ag9t^#E}O(^}u2JH+{TY$xfZGM=~XJ3lbACcvTwo&7g z%4lh0*AMikxnr+Q+y78Lo#vMHcD3RWoqv3owF*rc_08)2xNIZf;h^$MF+3J6&GKGI zjd%mAecEi*moG!*l}p}H>oqbY<v8Xj7a!myhWRMr+hsmp55s`NGg#Pm(<5qP3)u{R zW>hE=A7OhYj2`80I)60}$?u{@!W6*9h2-ys9X#?@Ii^%|gQ%$foQj`(Q0_FZms5#@ z#l>kYEbUps-54%D-O;{k|E^4|S*vHiml;5{(WmIbG+|->?;~}WlHSZ&Z{LXynl{8z zz01eDg$Et0saWym43vBQ8o#%rF*BgkUt_HIDSgWK+4Iwc0H>2S)<c%hR8{_22sj(- z+br$rj5MmMZyXjMf+(0570db4d<%j!q+%|-Z{}c<;rH---BQ>J45Xpnr+m|3FHD}P z(wIk?$;05K25kXE)$$nPFROqRTyN&B)x)+bl2nvE3X<8y;|Zh!7Bw|uq~IHuGA~+Y zi9aN%FM}4tBYtLtlL*&C@LpDiJcacd{OzVwTcJVRBZ>IiBm#?F&HTB3$BlDy?O#0v zvBMU_l^<UKKj|MTi<5QCS6CN6eO6Q;RIo}?w!YdoSN`?V+RE7PpsU{0Q8~dOqRI=S zx9AWq9jK_uxF^3CSI+!!U(BsYj$jvFU%T4yCOk^m$*){Q+GwVGo_M?S?h2Xghdamt zvO3GIEocHMs$a!!OlWRIbm|e;_B?GH-o&Z&9i$L!rbfmANgu)iH^6sJT>S(cOSc-9 zhRr2h6RV!Lb84?Qnhs$lV^vK8JjN(sl$HOm0oY(ed@?V)lKSD@2QgC78T!!1IJ!i* z)=-hHqSO^S;+GpN>85~Bx_Dvr%XG&X)`x#ubCZ&S!mBU!BgjkS(56cD*btH3CXAkA z`os1@N5{$p_fEf+lw@YM7{}eqjJ}tbDUt7=MK0q6#@}qxu;dI4RqzV7_EpESHNX3$ zPuvWV(cYZtB3k8c<qEX7*E0%?HJkwEn07~kOw6(~UA_IF>V@McQW9^v2nlNmD0u3t zGhj>P%P&ph&wJ#uNNCRVwEFFS5D(=xMsSMKbhzS}4l?*Y>D|>1laWQGvt=84jw-y! z4xgZ!S%q7V5cVp2Y->g}d#f2|Mh5ZQtG!u`z2;~P^8HuOG1+Ls%1!dNgQF^bMx_ZQ z%wBf|6eZ#`dh}4hN||)C%xtw_h#E;i;5|X*pRH;63^OO8DUceZA<5Cy+bPcKcYHan zy~)cD!I5nWAy@P$uR@Qg|C2?iUa>VdJ4fbT-h(ro8O6zW7)Ddp^7C(Rjt$8GAoE@y z$@E&RDwztzU9lZ1cR3^On5?d@zS^`pZTWYu5Z|2Y;6K;-^MiV}UQ9bRVjo>spT{)# zHZCqlJ6jJJb6<5`UEPEV4aY2PZg#l=5!=qGR_Ts3s~!bBk4{dOM`EJqpHA+5DM^m{ zNTQUpyyIGf(1u}6mDgb(1PRGICZziI+byPZ*K|+3VeXP)d)(A3w$lEFN%+C2Y~A|I z_{e0<Uin|I5a!#xO=rnGdgzc*3aGFYOp>yODhS!hyW(0K-*rdZo!2a<`>g^;Tvgu+ z7U=eF96lT|Jf>gpt@rBX#aHJmXDN{-GL2gMz<a4U0i()vp3k2P3_Mc8qB6zt?;*iv zC@sh_25J_jI?qK;(w&mhqoU>a-mj?(3|6bDbWBZh`EzKIgh49n53#ax^YSV#R&B&_ z+>K1Qx4TU+YcU_V4?$AQdfKEA(iixDjIqidP0{`9pFa!o=ZvaVC9r1rcx}X!!+wiV zpGq|??EB<At#0VlEPUqjeH<-|bQZ;{SL=(s>2G&;NUMUr8;=&7o_rbk_Nv~5Hq{w} zu;Y+J9eRbP!M2ruZ?8?Y-;{KnQ*DkC7PjiX#1}0+RCTc2B~*G(_x0=LoQsTst1Xhl zMHmD}8A<knJNT5~johmBD7FmCu1u8JT>E*o6{T_Ow_Wq|DeP)4oYp8X_I_}ncC5kB zc#J<<Ny5F&y&=TH2$4Mu<#wub1Cv|2N!{x+*WB}57l*U4p&X}wsa*LoG}rfB^nQ`T zRa3E?pKIOkE>>GnBe)w!5w(SsloW`Xam3CHjd_<?Nw;obSA?Or4=RDCt<+6-i*GNF z&!q%)RYB_qDfai*Z~QHC8RP&RR=HKF(;aENQc{M{sFtuF#X&3Vb#<SmFxfqaV$Z+y zoSs0ypIStcR#)ulDuaTKG{?&r3si3=y%|4H3iPfK;7|tv;H*{@cy_QcQ@}T>9gq#P zef26w<#VLefJYNOLCb2yTf@iLTg}Vqi~iRaG;7OE0t8@g3)~9~n-XtIPkA#l215GN z%J|(Yg1(RphrA^63bu<KyST4U1-W)j@x1|+viS9USIbOVg-kw+8@HY@drh-bY|Kq> z75a#iBF<hL@g3-ucyU4sqS|fb*7T#IGZ+1GjMR$yf>edABYo2i^+#Hw(<Y^NR15l? z!=Z-gSIG372y))vRj+#;7m_D;XL~f@`#Zm|5J+kNdPW#teTtD+(pn@~+FkXP=*XqO zWJ$JN_Q?uKCxONj^j+D}W*2P)1?y&tlRl=14qx^)3!_6+6^<cl5)65q!{@Ol*sFMd z@Qg(kSL3O!1%51zzEomk6*TBk-Z}yv?vNS-+jV`8{3VflB+h-3Eg0XaN+dh|Ytatx z%XC?G|1G_~rdOP8!CZ@JTMoV3pQ8xw_$~OhFiEnALfjV)i^SwWye&TL!t_e1m|;g% zh1a~H>xW67sqW@NCtpZIzT}8%BP@sKF|@C}M=^bl!(#-FX%}FY^mwO%4=Ai!DJTqn z&TiE$fB10yqPEqqjpSr`PUX@Oi}hu4B<vWRB@fir@xG*tC{eqdg^pB<>8q{1X<CaB z&LwP$27@Vl_ld+oD=}%KOfAKkw(Fit*T*(I*MjCBQvLq%>2KbHxp7K_>#FX7C+Zj3 z+2t<YdD9843O2MIUT!~ML=5AmSd5)q*eaWzuFtJBnCFa);51utot@Y#LXZQe%rIa1 z_O4O8H@)XW(I<2u`LWPYZeQATm+DD;nkj0ox+%f~)id60>g}u)d7yf)cx9Q*$)e+8 zBHH~WA=Ac)?1#_vm&qDU3#Z@RVQya@Z(^XQPxdbv$r*u6x11!G30eR-l`clNqJ<P% zgMN<GQk&K|rA_#HZut6A=%CJic}|UI1F{4f^#rrlxPA$%_Y>I$8Rc6eRZX#(N_U;B z-d>a*j;s$3y5yTi<vAiCZKPM;xn?<<&NU`EybF&KybB*v9Z9CFqSEv8!&OwAf(l;F zNO`WN2n0si7U>4zP9Fd5u4J7hNOqD%fSpq@R)T?GN1BNKBKo?8CeEt+)cE>*uyN21 z25E%UKKNS+`T6r_=MxHQA?W$;{%v5vC(|-XaEXiGdmrPo_sKL^aA-DTa696XAft&p zN&5bSMQHh`Dnmo3=0{6Q_Z=3WWN7_bE{%RBjfKc`LJI{|1wqJ&_!9qT3(HjI_;eVG z^~JKqxpA8c*0P>FKbC*QOH0w9{h0NqWNrgh5!#Tk03AYqxeH+ds!gr*w~PCpan@yc z<KgG$$`;8dWbk}EjayRHy}eILpomL2_mJw8Eq4q3QcJ94SE|xknzJHzzXs3=>`2>{ zIK4~u+g0=&^;eu9ACXk$ahCyc!rf=V3BN=^jiPR8nF;cm*VSTfP2tQ^K^c9&pVd}= ze0lwP&Pf;g;x=+<X5Obd<vXsu*`(FBY6E-equ=J`1JUKAn+5nLLjM{f3%m5NnATXd zT@d$>UA17NU5$-*yv%`uw{UxiAVm<dp=;G<VQRE3v1J=+iK@E+J;-aa$gRF(EOv8i z4d{OYr*W%Kz3n&r>!`>u=6|E~3ux5;n>(`qmW=KHrElRmSWf;}OI!QBc9w2pIIqr3 zkFj4DxclQGJuT32pwI#OmW6wyo*Eu8vEJNWG0^w`^3<^D&AW~YU?7X4a;TRt57mA9 zHud7H?LYls4yCBgA`p%w%;oi3i|8jvs&xZK#I{Op#H@qFW6C8zKi_k$OUGq*t!KNE zPMe^$GSS=tKJEPXcin~DMR+AVBKiCG?<P_LkfcFFK}5xNd3kvzfH)8Z%2b?GSWHY7 zB>a=7_t?0Fg|&RP*SkQP<lP?t>6+%8m})LSI{jZNcvN+>x#?a)q5<ug=xszQ&uLQG zWpUUL1Sad{(3+e@17uN;5dkz?^FtLniV^(ilO7f6v16}azm9f_0pN~>`UWve>A??+ zB6tCEQnGlfqVg|j<1m~%mj$jo3+>M?$;C=k*3*}Z_DhQ%{Wu<S=P#5&NWu;);yJpv zz1Rbcl!*Y{=_|KXq_>irtU%h94Lz>joA&`07n#&i0d@L=AicA*(h32Aa&A05or@1t zG$1E*@z{m45w;(d-6hTDu9suT51M~_d5l>q0yLMhTc7FkqCt3cGGM6(A{^P*c(x&+ zK4yk0N~*j!mmaWla(WGgfL^gD@tRHlC-moXw{On?*{kmEURt&Al}@HJ-fwT(#TY?k z`)(o59Vd;wf2lHGzAyW3!+CLdD6tY`U%4cE(7AvV$Zg#bq&m(k&7vngi~%He6`H98 zxQbZ!j%xk$06BO7G??e(zkd0$>bJk)CpYXg9<C!1M#p?@N{hvHuJ_kv#~z?jOBSm@ z0C%ntbaX43pp{CPGhvYxMTLs}C3f+}b|X2ftInYEWAJE&Mv_eY`mdd>70b2+|2NE1 znZPLWa&Xkod#%)Xxv$UNi(iLR4r2ltnO3^$^|jd^9XOw%YBT82(~k4zlzt&d4>~sS zk)Y+zhqVo8Gw|A2aN#=XCIbOTb^RE!YyRuYbJ}qbn3A$=RCIpl=;nV(oWs5a-QhSj zrj2fIXGNuIXSqoMi_Px=>4OV%RpF%KY)@`atIWQ&030T2tW@&o7K|Q4d}=L=c&=X6 zprk#gs-W;11Xam518DndLSDUU2kjUcv4r|0zxmGQUs@L_uXWA3*E=e72=3lHGVjs+ z2IdaZ+N_~(5A<cxej$p&e;Hu`mY{z0_4Noi9oTu^w{1|QDbSH6(47-^{Px&KS2r?B z#3obrTrnt<R=-dSn2$Gx)2<0rtpc+{(9T+Cd-(S-g4k;pS!&(8^j_BOE8jVaN%rXG zR5&il7`hl^d;7eDG-r}OKc$TLzAuPMVL{&Nv)X=#b`5s3!>LtDCrcy*b{~|STS;E9 z;fjx^OEt+7B0#mf26mJVt<u*oU$*rXnn6pSV|Uhgp9mr+rkweXv=ET=O>XQ3LH!P` z3%B$|VoD&&4O20^F4=H%bu9*UMPp};E$BnBkEd(i9gvkFL05Y1cc6eoC!cPfY{{@E z!8pzh4qIxiQ$zVq%aB6@ljnR<kM1Uf1~v!!7~{3+jyteYr*!?i`~JppF3=Gl^4s54 z{*|j=VhhRyWE6KyRz?v(NwesPwG1Oa7nl55QCm&WVztKo1zD*Tzyk@8LEhYjugOyT zcsu;VFLSVvE2l84g`nOjGH?H4RIHbcijiCUjq^>OGy85n2v{=9*A;U7BOlBx6}1F9 z>6ps`Q|Ad?nIOH?$}#Ah_gY@WL`t5S%4S!5Z$4P+0K(tm9{9;UC@0jQ1@FCl1YWXt znQZC>gtO^i0W8`LZ<x9&J)A;R*3i&<Q=EsVaaNk0gToTEYP7!uE?l^=zu6#j$PfHR z6|DZ&ty|{s<qAqlXjHG>nmAjs*bI0H^~CYEHdVAwPuSa;l-X~G9{)L!$Q=EWoa4uj zI}f?^cyx)trtcrZ*}jCc>(Vi{5!i=-MS?#=<r|?Y((PqVlXK(xc2!#8JX$=i#&1|; za?Ij=wC~*mX$Lshjl+izXByY|<`00oOnm0K|Bn~bMvEDHjEC!0y61oS`jxuU3_$G+ zbjbM)N?CvC#^k&IH8J#>FREK;07*-cWfzZE88nU2%Rw~ydWZu--hy^8HBQP4^&~tR z_MtmeBee)+pC0Ty5Ckcp)SU&HOo8sD%zK5YKt*JLJdiX`&rg;Rouy6iT%XGTT}rc2 z4`^bQVkNWyd^wl9P7_l+AtcQ~l3@0nIv>m=*_|=p*{SI=*V~>5tP_)R&+htw-RqDL zw{)-B>=Nezn-YduGjO*TkYmskBI&m9sb`B9F*P&eJ%9fCRkIf7NOtEG5T&Cn4-*p; z?|w?M1WH=wYw3`xW}%~!*K_X@qoLpKS~;kLwaOeHqoN{+P8xc8QD3V{hFqg3T4D(a z39M<ZLo~RHw}aZyC>$C;4x-;K(A#<w89_NX^vR+F%I`(7&aI2+%dJ+Y+7%QO9Jk{@ zGRXrg0X=J3D+yRB=9hti9gwRM9(;Xa0q?=Hfupnd?WoD@ZGLR>y2Ka=K_>0j*MN#6 zehUSC)})1s#n(RIBKGsZx)4#p&V|+}F^8m^o76NkS|imyT}4(pAXVmuyo_pf(B(q% zQ@k>~?}sV`!opDPbNuR4IacQK->-RB0is*|j1+vnM_?5b(=8eCt$5FGm<e{%J^Qg2 z6-pKYuiAAG-st=v_3*R<nOjXD^j&Me!_wCzC@2V@MqK*!wF_iO&!0Wxl913Fs&LaW zF+nE)Olw?$;!}{9U%F=;Nw!V|a9;Gdi81z?hdRi-APT8kTW94|EJ(ne2S7rFBpf}# zahL0|?v^8!n53jWh_t%Ew%D+cciZvoiZR{C{uF`s1suY#Krb7g{DS5zX-?~MDy|AG z7KA|=-!=qjm07RCHCt*2O*5iH&Y|5ohO-f7DapybkXKRJbsLNgp!o+BE=8uzXR!5L zqfkFo4Xfy)nl|Kwgx7i&sPZ2Q$dIN!LT&d~A2BGl{;=x=$r!c9k_E8nk7)V>0Dvay z)#(ltTH=1YgJj-ibxI9eZw-~oe@`>ty8R8hg2;UyW53-Z>HQrith%b|+tj-ses^&j z%0Yw<0Y#dPz=(5)lCRhM3h107wL;1<Vu^pp8U~ucv2*~N?UH_ipbf<EISVxOsZh?~ zEP~F8Ds!sDg|UGMP##q2>)Tg17DpCU(SWiGw?u<J{t5~cf%4D-L2uH>k6i_i6<wx@ za`31P#rS{TM32X#9)0OYZ|(*}oVD3byPQEc8uo`{lMt<d;Ia7f?C|>D9~aQdGl|*D z#`~;)Ko1jjVh0BY>G=~Nh(i(vB_02~H1geg?By|9>(4I25WTTm0}o)j=B^5otK6og zh!IH8Sw>Z!77%{mHgqH<%<5lac_PF$w$qEF|BGt*YFW?&BIEu4wLsq6H8n@FprBwb zUImnY5Z1By+d-!i>7+MhK~%2(=ut8Vg_BE5#nu*v^kHop(9ECPK))Q?P13XfTBtsN zqYf~D`Ef|f1jw$Kxp%)31DJ^*zoOcyhS3yM)eT!b?*iKMl&=;ShEb&>&hf{;D~fkR zr+8$`(BSevE{f;$L2Vl6Xa>uSx@z{OYNt~}CFA<jgP3VWO-*Q?g>p~9EQ7)M^XJzc zA<(IizDa|fB@Esxb4&ruFh34;gk&Z4)EfX^5@DgCp$uow&N>)F06o_TBQ?xJEX>Td z@%7S96G08FZqJW_A0RU2Gns^QjS!|+ruGJxI6at|sZm8Hvh)$GugN687oAyg+#c3Q zQ|{Xk2-D0uCa)7o27zY01(I0?XS_G@qZi1^KEm<$H`T>1H>2s)Z>z}|qALfM2!xIE z#Lv@Oxkgpry?Szu7gcTBlb-kNzC-amvQz=FabQmzauR@0x7ljHy_I;{aj5RMlVwmx zMPw@en{s&l`0Pg<Xegk+2?hQcQQNsC0xJNtuBrBq^N#^F?Cow_wI|6ne*bP?Dsu7S z%|g@We^H&Fb42;`R#J;34&K9_VAYe;U*VSP<M#zRBQUdsMa!vR(P30sJ_}<<fL0y! zxUYdu4~BZ$%zXZ-XT#n?*?}^&fEJ^C{s{2#DFOU1YntT+dxXx(4Cv`rEM)%KF|O-q zc?Bd=SBm0Etqk?LabZ;aHxG0YG&th}WUvKL=tZ~cL*kcx2bnzqO);oUHA5p!Gev;` z5=I7m8-(Mf0jI<0syV)b4)uR-YE1J8451}tiTmwUx-JZ6K#W3}-0A7*OoOrlz__Wv znWbBFq-Mg&CsZ6pD8M?GJ`C7aC<V|m@hokRH$}{55^|QeoJs&#crBEr)h_yN&x>kh zO$`~lp=GU(u}=!jWx)8BqFij!X(p!ZXxnluK=e);(ZSKKLZ6iMeIIW=4vyOdom~Fa zsQ(BWGNgfqg4*`^aCBs3I>1Jct~+kP;3$M#Tb6%yjhgE)2sOC`jeXX_jr$F&y~~XI z!;_1wdNS=ss$oVfdf0Q8Zyt(YbmR=|x&xAADooX+!}+2p44u7NqK8iHtG_x36d?jW z!xOO2iQs4%0J}(l7b`7iK~~xGF+@7^*u~pZz~D*GoJZ(cAWACy1WfeFYr_^&Sgo0j z?QrG2jEoFvD;0e8?z+Adw3A@eM_?%m$_i9Ab?n$NDr)ME=hP>ie}6k{Gf=Dq6rhVo zE5MP&Dz6R8y`3%4I<H&AV`@=(Ir|Jacqf>YcXwQFnqS1Fdr6Q$czJo7rYpb-QR164 zFtV2lp{lHM#Iz6%!;rMw;1fYYJhQZGSF?+Lt2r<L=y-<8HZ8IM=f<VRtDUuU5Bbz* zEgh?ERAd=m|AuLH_02y;%8^qEpqS2d9B+JH{2*Vq6M!;IcNzGQR@)0(r3nBMBn=Af zMx<$2qz$MUFR4DK^7lD08Lnfb=I!k*yFIG7aOoPR7My4&3t}qmIP?%CD3X5nmpXg| zc9hOboQ*BH*9NtMR_X05jm<#7#n8n{gNdKa?~qa;V`a+)GERd)+?8Yt*P)e4;E*x0 z1lmBHG32_sj24|hk!6*AR~SLkod+igK7F;`!v8ju8i44ea*G`@)IPpI4<69X*XatF zV7UonINYuSh^hhXt8Z7ekJs}TFC;p%p%k5U&DJJV?~$wm%Hc2>3E430WChNyycWu- zq74D!b(bmZQ5O-waO@14a3K4&&aPu>HPB}<96z1}HJf<nmdP1+C=(<cem|=^$IP4q zn7(Vw6_E0<_li;)39gyW_1h)r`1~Y*?O76&UupP&0vSp{pY?NyJtchINLkdub=SZ3 zHT%1E0gljBfv~_HGJr<(H|NBEYsvuUV6xGSitT`N?aYwo<7*zDx`=E-M#GJO(JMnd za7ka`Kuk?dEzuIyW%%y?L{E|&HPFPLbmti+0DJ4e=nM+yAr{U;wCM%}oesm5q@8<+ z>ZxLX%%~f%R@&g!t92)hduP#b0A8BzyGvp&DS>&!fVvYvxm$Lm1~<$bl-gg15kC9O zjqy%9BS<T#PXR0bCj$1zFbHhzShWF7j2~$OlHVom^!~gDPp@3L5;Fr3PCv`H?CBC^ zf)J!tu4CX0S^(2-8akx<1D&k`)QV&$gBxiB=rfm0u#%-|H5Wwn3@FFKUP99zf}k?3 zOIb&4oGx_NU`oMGpeIA)4lo5g+VKY2x7O>5p@-j(UVchvvD-s}DiT(Kp1>tW#e7mB z=$F#U!M`eXZ7{O;z5t$MpGb|iRR<{%6QjL1Flp#u>0tRj_&W)9jcMk!<Ss8ZO*z&v zlK}6|AK(o{g}4xrwkn5YV+FmZ+RmqqQ3hJUFcdl&=0jvu?MDTo-wuEf+G(<d)&+Wh z_TtAU6&xK4_>C&f1$qcNDm?~HO8MKN`%|J=<wb5XSr1SWsU}r^IM^byJ8StkpL6-e z4+A)m^-Lv%iy8o)LMNJqARLQzmK8GnghU&L$NyG!1M0-1E8;an?8uWJjg8Cy`Ep=D z7+kjJOC&qXuF_OGjw?JiHSKQ-J}bKJS@9n5Tw3}5?xLSj(P5Z%43tH}N?|T*3TS-S z^lk9LRbX|hYQ3OavY@)|x~hWm9TT69{(;F0qZ0l12Q2en)O`E)%^bGPf8n6&47%T? z-P!sC)8;62EV3#m4{=}qgUvRs8BS~&*w|;|77@|j-dJ=?m-ODu>zOA#5#_z-2F%sh zID{(%GNbBm9?kRw$%WFdiMD`!P{OKdwg&Z2w-vol=7~_K=5DLy8cC&<LyE{9puiD9 z0>#Q|5IoB8>5SO^YOcWTeYT#D)?i|xFlC9|MpX4+SqE+b3Ur1ZgLr2d70$xK0u4!3 z28lN)BJ_XIH=s#K6GrdELqgJ4zCn0hYr9GJPdfrG1CvE3U%61tv8?VT#vzZ%SXvi& zfm77MJ=mqN*jRx3_Frj~Kj)XC<GnD;nnnV4X*1bJ2c|SWLUC*kbAmHK9O{+WW<!LP z*a<>L?JkjFj$Q`Jvt4F?OC_KjhD8*f9;7LVSh3ri3+$@#KzSH605Z*>5ejOb*0k1l zP;O4p^P|mhlmtm1-CT5FbcDGB7z#LW7YIa6kkkc~1DNQQVFB$g2)zm-o<4mVyMA<4 zyTo=VW=6!OKN(7wE_hZl<Opq;3U$tGl{rp@?A=BV9KD>A>JQgh(qSP_*z3M^078=Y z!i6tsKIX%fWdLnvH1{W>?MSQQ_)qq~ZlsmJeS0)*<sGJW1k97HvFpj3LABbQz#HC0 zB+(iG*OFa6@B~{XwaJ`s-)=$u@;PJgGbPKaM>+*A39#`2O$fKK>#O=`oMV=nxxA|n z@CIcJ1bn7oUOA_lWSK@fY2=R5;~V9>kb`C+;W%W{!B_=Y>VnHQGRh62YFv{HRVfS> z`)vLSpzWxE<e>q~opGEUP)Hujk0>Z9B|GC`j0a`h0M7;vtpkIw2HQIef=cBPG+)0C z3cB>4@&qQr&$0)aeN`L7PU$OaGb1~o-c2$Cu~x<&AVBmm(b#TS>E8ZPBovQF=R#nZ z<mYq+K<*hRC3sx@Ftul`$;nwqp{hpnwD=q*a$~9lrMs+pS{8TR|Gc~7j0TQu3&8Bc zUhCHX5PLAPm0Ph;-ZU=gh|hnwDmvo*5uI`Yrp?7e2~v`Q@?pv^0EUDtJGp9#M#^2R ze3m{_`dYEb{)PPvqjDSeKqrwVmn(n_$bekVP7Hxc836ngJlc)40J#h;Do~^jrix+{ zC^=QpNCD`aN!nW%LKT;@#v~9y7Jy2q=;$^kLomp56g@DMg973{vf49atOHyJIzj^^ ztT~Wp5`Z3?X5r%}94$*&8F(~R<E4FsOuijL;jPXP1<nM<Kai9gFc*%_S*4+4^iyC- z{D1eHp@xMPfF|4!!x-R>{tf~Z%0vo21RhQSgd;fhuU+yZC^~-4Kz{Y!F$RVdz!%eK zf8g2S6P?ig12Ur2#m>MW2Iv-Pm?fPC#HJ4JurqKES;E!5cZ;V$WD_CZqJ{4+<S7Z4 znJ{#`4ftX%At6oZS870Ig`U*Z)Re3n144yP^TF?8K&$irmEXefPwDdS{o;my=az|m Z%tGe__djdU)`x$va>}=IZrp$J9{>$CT@L^N literal 36061 zcmb5W1yoh**FAg=DTp8qilC%OgGz%62ndK`(B0DAWe|cO2c#ra1WdZS@kk>`cS(2G zx3>P?|NF0ReB*PBJMO*0*=O(PS<hN?&NbH#xp!Co4DmT)6bf}lQ9)J>g~IViq0nQe z2;gt7bq-CzzeJtoG@KvUnK`=|I+~(X44v(*?VPPGjV`*HIyza}*$VLr^9pibv~YH| zcM{{{v-uytz-#Ad&c~@c?FSbjvRBY_LZQeEk$=#iq%$m0sIWVVvNs>PCoT@V>GVt= zG_Ccb2`?w$%D=RnxK0=l|5Efy!H~uD?{01$`MNuK-G!pL>sIlizq*Ge)kS~45r5dC z=0te;<-{#KJgF+_@L9avwg6nh@gr{u@5?D0^uip$p$C;&3xf24h8LOPk4Io=9^v2r z>Q_R)3l0ueU`$2bla-Zajku4x48MSBx{RVCCnv`mQ=wkM&!+^4QF8F}bvk_izZVF@ zxdA_pl(_x>`EfIhA)V~)+eUPOc|piWjVST`<DEBVRJ?tBN<22w|NC$Me_r_i*L`7U z7t!z>!6xy$?tPUP+ERiJR)W%1YI=J5Qys<rJ`_g$y9T;0NJim@UsHMcSSa7ci`5>J zKhgPk+;C?l?Juc23aq-u$Sz*(_V#Wdo(<y)`tb&BpsY-iDj(6<-cIJ{=V#WQoHSq3 z6n-JqYiGIpyLRmM(qJC^r!~W8_;L{)?{bfsc79Bindo(+=X>Q6SJVmoQ+&4RgUFc7 zM#??HE{Mli{z_kK=Br{AUuk@Gesg6s<;tTk*{(}k$=>UVdLGl%y!w@k&+muA<z%c! zlf7s71nT_93^qJSU6%%~-F|gW&e=J?Ze>ZK#Fot9;X^8=SYFeamiX>W-M0e;R!)bz z4m7+v3U9C5U=qt5r|%F`U#Ty4n6!6sG40DXv>q;fQx`<`>zk$!hYtZfHHLGxU3^>O z{8E2j(DHDZ)BdLAhYufiCqnrcJpJT<x(NBpM=-MS@{%$!F)aw_>+3&#`cwh_I4+M= zczGPIH94q6pUL;$bHw+9tL@|-thg<D%)u`h)IKNLJvcxv$mqR#({uCB_-iloH#)Sa zz@VT9<?ar|RF@4TXXfS<VSUxJ^i&*e?d|#5?nI9byN#wPMzKpAEau@65SVQQLtNIs zPq==~{2)P@v&vTR<_qGP4F-eFKb_@A+k^ED4QoSA?G9tr=yTVt|NQ4mMoL|{6juo~ zq{CmmQgM{Ha^<C6)lrOE+8qI>8Rc_VEvo&|xFRcKoQB95xSg3fqsI4I;#9u3gv*z@ zd35FBdb7KS#~R#9jzuT)a@mSh(2y|KbSWy^pynwSN3yxe*49=rysWfTEze9P@XYz! z-?a+pL>*b*TzO=)HQ&>JT@Sf14jP?pO&`sdvRND(yXVe6k7{ge%r$F0x6nj=<>6R; z2qg&}KkC7gyl_$F%+sjmXwJGfT<XE0p?Qp_$;g^oTVH*U3uPBAdiYuGVWy5KHs{Wp zE0zRsj#-6Io$?nlH`p-S9Vgrv%}_PJvMcVf&U$=&?7Y|)>9X)!GlG^|qXUiGCRM_V z`lgx3zPCO-Iac%Rw{bu~K=<bdgtW0AQ*4j+w?thRH892*cauIG4p;F&)cvTy_E0AX zLpZehnH%-*1TpzmH#G(M9Bki*Z3{ZnN$;qO`m?-z7d}HLY|9Wtdco*dx=Ks3<Xb79 zebh|<4JDgo+u>4&#Xf<`{dumoWXWb)eeavFy%)q?oo6zTyd<h%V#3-keIg<0wLJyr z;Hu3)^t*Q#A%bTY-<xW{VxW;wUm7Tw8!Y0RY>F`d)A2t4Ne?j!4x#V<90%%WEML3Z zs87>O>s!m-%nNUY?Pdn;s?wC>1=|=Z;W4Jp5|T02_x8q?IL%H>Ow@X<R5U>h+<rk! zohX^FEI@zzj*nXX<H*R!-Ti$EI=bMXprE`U7PvRk&B+Mqb%?3^mEIn3nBuIS;Ln!c z{hLnX4e0`}z1`_IPhn$berlfV5r|<^67w(#kK&5;M-r%@QF9E>?%p0b4UL)Pc9#<0 z^p`QRe>wInEo~Frg*^O1f0duKaZ|X<_Uqi}802%Ts;5aw)8VWg9v`frBu)-D;6WO& zsXw(6(2%DiC9I}@zUls|9x>{7lEf_ca<z_3n%`^NnwXY%5f+TI70KcX$@vB~IG#Jp zXZ%xAm_lj!8V3gl;Tnv8b33c*eD`O{p0DZ|u$A7Mo$^$d?^XB9{Jj33pYoGDOYu97 z&q($pj>0|<3L-q%S;@Hf{#F~KR^cy3q#9tiGIh&t?<@~n4i@q}$hcd;*dAKLWPt>v ztYZo6WJiUU+ual?l}hiudz4rI-4pMd*RQLe6H%`2?96V?{d)N2VF>aJy?S1){W4Bs z|9&Zxl7{AXij+@Zu>%XLU2=y($gE|o@?hC%{f8sLfBgyrLkn!SgPYrI*-E8x=Lb3B zV~S1^q`FNJ2O1g(#-l7c-l6l%+Z$omwF;hydYFhtm?33tD%owKo`}(FQ2^4{w<cJj z?_+$_f1i6Qc^j5;71nd(V5LgP<i}aly@4Bli{4NaGYgBy@!srQe;$Xh?NIgTXo_4Y zjb>~Bo6CPb>$Ey{^YHL6PTY+b{*>>Gcv+8>#}hKT!@`zvTqc5rkzMBWJ6t1FNw|9H z`t_Lg>E=B9@mmSktY#Jshadb6j31H>O-+Ol*RYu_iK0%IHFH1a1v$6>tJI<+ZAs#5 zYil*b!xz-m)gOP?dI6ETePH@}`|qBIQ$tww<rrHI<Xd8BY~@~3-CvC*`WH0}oFXD| z$&y~VJ_oL-52>jk5fM#2Jv~^KvYo$aMAk78;^9G-o@Alj=-o(WIgMibOHMN__g7~x z{)>vH;PCL7H|l!tVY_uo9Py>4rAZk?ZbUFj!XeU;`@2q4bg{g;Mz9~KFa}W!obj=x zrKPF+(^3Pt2-FY_jE$v*Etky9%*?kP9_X*b7F_-Nle<n((MsJ`+F&`pH6{D*TjKj^ zD*wHvNCVvV>e`y&+C)P+l%t_imu84np}z~JP<ZEmDKJn#>%TnuYVYRew%xH#vh?@F zSR)~|GE&i>Z%O&Th15FDKRm!wTUYm4J&Tc+Pp^J>I4P@g|K;(~!PnFv%D><!()#|F zhn!+A^Yu^}HOt)vu37zd-hRE#{qNJJcay|kl$D9^jQUBVV0rL6e5E-aP(U~an}k4| z+^_QW+25R#xpfQ2fb@{+P&DN)q;mN5<A=ij&Wb9mArxkU&dyGqGS}ArI*jbUG*QPO z;eHm@8H)O|(9qK>6+4WO+|u3GCfXPiSU*T2_zYtiXt>&=t}oy?rC?-i%&A`~ahjI* zqh_8N{LaT4LjQiJ$adHW?x4Q0v8&R@i<D8~(-<G&ufHe8MC<*n7aH(PgM~K6kXpYi z#nzc>qUgxUZz?F9isjRvfmB>vQZfz&8Zr;({SUH_B_BgH=ib!OVPNuJMK^@fn%4)D zqq?*7Q}(wO#9efZ&Ib|rLp7MlNbxlSh?FSez}&OJ=st!{5VpGuKlr3uR>%^Bx&aj* z$>?=~XW$z;tNi?6IZ`G{o)MDG6pi{SIWE4nt9%bDNXiroNzL{w1A}sVvSfdykNDok zYzE{_iQTbhZHzH}HJ8w10Knp}+nj^T<FuT=iPq84SzTSNhV5MF&2n1!&4WVfPr1iN zoV1@*gw+0JzN$k(UtiyFI%3rS1k4h}-NK7TJVPC)e|Dy;yfv9kmhixYP;zvAy3ev8 zu*Cv7Sg{{s?9XVGw0fq_dxc2mmN)^Kno-Hi56}is+K>l=XQm6h3Cjg%k+ZV0^5NIV zuU=AfvG?PirKFV6(4d2i%E8b78jeV|RWGmW%81Hj<EuT$J>TEd3S9w^lBN_Z2ms|t zZ>9>AK2hI85!hB`8U!{_P*B`T5_7ry=*z?OWG)DSw{r6G8~Y1cLO%P>C`dxcZjOwM z++~b6kqyMHhD)ulH8D9IY}?@A;Qaah{k+S3x8g>^Q~c9vIYx3<9)DAqZA%g$d1f<M zm}7#aD6}2EbFjTcFX?#|jYiX7p!wf`U*+D+dAJ9AhdsWvJpd4K%IsGjetJDV8v^;) z)xp65DdEz`ySV<sBks>&rGs(siA6k2F!$+saW?=$b(MQK`R+~AO6~tib$(VA4HtlO z-v~7U!uAYoN%=;bIQ<0*)cE)~6`!62mNqRTgF`?-K&Qg<8ny(`1{~LC!lT}lkSa@E zmwx{Kt}V3X4aISYUBA-XdExgfz;|mK?b1RH6SDYzJH;gJC|-^1>h}lR<rNisGjHws z^2}A!mmw;?o9IW!L>L<zM=?md%u~S`1gt7#(Q)y*?a&WENt(GPih(#<$h9ZnAn264 zN5bagtj>SU>dH`MvWfr6Cdhyi6Wmq$W>^;(Ut0OUz(O*5!RG*vwWFk-bbqSR_d9Bc zkUsu|@UQi6|EZFIes35UTzqkwwy~i>u1231vmh1_&G{gZOFa{Cg1ICOBV+j6x98y$ zs5oYZhAQQTjsASY<uqRV!kYdooQk`c|D~+f*Vn@_SzI(!r1^ol)d8r6TPr^pzWR-j zdD~ffdPT^ipS23Es7h{8K@>6j9Up40Sv>juO%ZDKx7JT+99-Pbki;-}XZ_&}o{z%T zbEJ41NxE+Sxeres!)8ng_5X8iZS9|58D!^$ZSlqc1DQ64(Nl3fZYjZy;vQe6udeXg z=}K2QpROEVTl0*t#B=Kk#05Z!Jg3>aBVH?#NG`=jaa=gEKn)EK&jB2P_@C@}FJnEB zAJU&^{tMQ&TH-w2g$rkH07}v<bLIK^`1{0Sf1dK&>#wS-Z+LFazxj77A-}WqRVzHV z5X7*&ybN_S3NjMcZgYcjVA_*B+(W45czAg5=pj&klEmH8;j>f#=HTA#r+$z>&eCfI zWOx}eC)eX|KQJGfk!(3s>|ouOjV2=_BOsx>QRRDt`lMGOmg2o0QqbMfm8P@@aV+At z%<$;zW7F<W_pe%XRL_{p-M&4su%HCn$;ik^dZL45N#Mj#>k?j_7go!-dx3^ePZ<b8 zq9ixgi1Z9<)MrOGhFA9|rK&^??75mUc|>@)8DwDu{E<NLyq5_Qn_s*89toLcm)Z7| zS%_-$!`-!Fs5f3Bm@^FLP*TUclkiR1HiOq+QgLGDJpiCxwHsmJ<Ksi{vApC6CsI#- zRG2|!1|lVB*~My3CYM<MUhyKFFvv|jtz%^GWJ4$(e%HFzyp2Wxq@%FihzZ<COS}-F zKb)_PsVG%3kB1W^aMwUIZJdbSd{xg<10EnRr3c@x<^TYw)McSD$GFMf!NCx6V!lOZ zZGRmFhl2%;eVOahDKawi<HJ46<RZ&o?6+iOfc?H=Vd8@t<_5V3{;xCJP$v9>_-)`P zdrL*5bke>Od+Vl(*F;JPMK-GrUtf|rMNB;LOI4bUn>)<+X#akxv*p6V{rmU7>6BoA zC*Fs<Cm>@~zIE$X?!l05mMYwqpc(MH-)y5$8-xHPOGrv4NnCNb9>gBtygU?NTr3jK zBpr@5!D(r(-5GXcf}Ni@+}o&cYRZtj7!^`=ay)on=BMKw960}p)~yNG5H6~mC=#iz z=h3jgp}BrNs13y^>R7*Tr=;UCUW;Gw7&#s65bTGrQNOi(FNishL5_^(Qg6euM8TS) z4LrS0;LcbV@CXTiK^i*VU*H2Uy1KrOb8nRcD{>X(Jrya>;2{p%nA17m^A!P}C63d? z0O-b7s!j?C{F&XFW2{2~3z#(-!==^}|NOB)1Owz$0c~8}*qE#izy*@@vtK5Pfm;cu zx;d6l$Yb3EGFX!gnWV|mcdxYxIY7Uyu;959Ij{*HKO<S>E&8%Aog9pwpxh^42?LZQ z{8vV%oJsjm`F<z9$d_d5lo+Yt<(M!eN&6)Ofgq=&`|+nUb*|@YY$~#y7<2EVtzHH( z=U_M{LUtn(J-yU#MV$%wB_t9d7r^@594>Y7faU(ImiBUMOVIRZ2h>)mD7$}BBTkW! zI8RLiQeTfIqGacHUp0WWp*jrFg+K>%7;4p<*;!zv9>cA93y=V(R{p20tz6S)LGv~S zh`vBWI&R4GkrA}83q9M5{W2%VN8hix3=a>-OZkYIHpjTE|CrL7>_FK=RsqbhwmoQ< z;W6?fk_FgA?_WT42JR$|-w+2FGz5QB>8vViDnlvZD#Qh_Z?@rZI>^;CHH1u4K$0B( z-m7C$b~ZoYt2*GXX!q@Y^SNIc5mEANJ7N$XGwmrUaM%6L%ipz&5M=uABX71AdJ()0 zbtV|jG9+gc$jnF%g+231l)=4WXJ-e1zX{2P5-Xq(Bue>cS<b^&eU1}zxneVrKR(eA z3b7Rgwe}f^fL~&>M4rpwRs-ltc>Wv@GO~=avhvGWh!B22hsx}fa09OYzWo&qkJi0x zgPVaQ^tFJmzg76iG1R>GLX`mqhw9Q_WXA-*JOi*{7M>f$tyBD(lH=|w96kp?>WGd5 zXnpxd6NpnozaT!DpfnKNfGjNHxp^6+569(qtm=aFF;7}aDHs{sAhC<WR=)hz9cXwD zj_L7E)ye($xA5hq1~`FQt!-_w@$>T!Pr<fDMh}&HL_ywLKiU~Z%7qmnPvp0W)w>td z?z}OAB{~G8gH#E><GmmN&`>@+R%-|?J3rv7N*`U_TNgd-gf-ep1Q6NvU0WCeJP>PQ z1i?a#Vrv%lCg9IPsG<Td6%*NDQQh8#{mD^BK4)>M*d}?PA-~PQ)e-l}GYFJc&tyQL zw8zE_sZY4V9fkxvq~Zf;VR_pI$NDnlamT-_DQMR~{OKW)R=z)@WQ(8r7Za)FivXO{ zp^B26zaG}trvc|q{@%UUPo=TfA%6mIZU!1;^Q(m8ZsJ95?v{V?nn=y{&Px3hk)o#r z=#E1-Z;pYYpaF;opO^|X%1Q&sKklBMVpAi`g+Eqrmq&4^3K}<tnKZR0iq-*8j-Pi4 zG_+qH5=DqNoGrn9cSs>kFC&7+>n^1w-48^wQ-;x@FNm9YiteVx>P<j0LDJ*y?ykwa zo}x)zSHTo1Jt?Z9qC(VZh6HeQE~8MqvQDLUJn)@9=Ln!VrkiW9xZwppUhDBy^(44Y zpS8~RmpnWVj2x(VbH3#-1amhuynuXeYFqB6om0zD&3?@AUn*-(JH|^dUCjY0e7>Mq zEi?hlssHsW3ZQnn=PRtq2hbV1zdZV@RZlUv&Hol#EEh*gG^EpZ$ZhscPLuGg*y^z^ zL(S4kukEf}=L+lir#BR9-)mN1d(vGElphYL*nEs=axMrejv+kSEksOn{pJ1X^wp3k zk?lp|4?Q+J+XN@*!YIKtsgcBwn1RJKtVuGFgpXEpjnQ<6k@v=Idj`P!@bWY$h}#we z-3*ss1g4jhbx<gz<@_{t0Pp}g$84B=3qo=O&dP&#H_-ACVoI@@Y&(t?sNEkd5hClZ zuweZ_w#)T1t<;^eV?|EoI~OL1T^rWS*Z19f!K+((mYP})_A5@<juBAV!{4vOLt`@8 z+?*8X;~>X7LSY5j3RAKDA6X*VbDq<4dl3uS2~pKq7=Z&!XrR$EM~>6Y9Foh^rW;bF zK*T1;$7=vs+`M=1-fsdwgSKs#DGv;Xi6_|@Y*U=1*L489)<eaS>mikvW-W0wkV?=_ zd$jx<0f;mcFJwM3J^dzLe)bf?-Z7{9HSbQtSHjP*`)h#RkW&YB&Z1<vyGiS_+P+dR z{9cI3Jl6&s+*!Yq112cVtgNi|lZ|H<tbp*CgX#pV2!N}{S_3V;kQs@TloTYj1OU$O zd7Ythu#}W0>@>Tq*dB|&sph<NDWF~IAPh3gI>>NZ5nf(i=Blga^b555aVIvKUkbC0 z=d{*iNc>zFrM%++ACH5$2bre7(B?b@LUyGs(J>^wQSXg2K<KYPa?0Hnm2jLo1=-l; z2gfD&!eM~1?(?5B+!wwVf!JycAhhj#7%r^79o8OI{G{g!>UIQUD~R;Ub6siQv!E`m zHZl2u!elX2EC5X9cQ1Sci+p%JKM63J*sut|e0VKN@3af6Kgi8gO-|+=MxMWJ{Z6mK z6M>VE{mt4Ev4E@JPPeN(xPeL(HRgS=QAajYrJC}1m4oxAJ<TfQV<gJs&65?k2S9L8 zR8Xik5$uFeGs%KAc#y749}e<LCz5<W4j+9mmJceD3w`y<6fhTH6LS9`M$ce2rI>qw zn3acn4Y`$indg4kqd+&11A!#kNF^1@w&uz3r~9c?S$w*sSAniJ|AC^*nx*vhKrWW| zUM{On<>9CUVZPj8j&abn-SZOe{E%)MhleHWb|@WUNC^F5bE&vB_#1iweUeo(Og;K^ z?^1&kNJ|eY01vXNtgX$&S4Crpc$z2({l;DwTJUJ)|5fw>x#3w@K3X=UAHfBt>Hv5G zv$zW+Rm5Qek3A{`H1u@$rKQ0lA|j&q8aYPvKIOnGP3K&y6hhee83bx)e4ZlU6q*<a zwEt}kX{_?8bjhr#2<qG8@8@c2YJg~Ph*tU@d0KhInzbgRxi1yk3>LdAJjQ%OWd66N z`CsKG3N7XUPu8}#M<8+!2-csR<EO4b{!4M6AOZ~FesZ)^;&X5v)tjknygXEbLO4SV zk2ZT5ub?0X4uCO4!4nzjhOpp}5JTuefYv*{veFJ5<yQqeD=Qu}N&taGu^Cg4lN0{5 zRu8?3qfo~m2NK1Z^Vg>UG65(IcbIH6EG=tH{vpX-2j$m!t`<IzC{<8?)8XFn@bMi0 z1w)kaRcxwZUgf}_#esrQ&IjrC^WB`wRVTh;u8V(Ot4`Sdzl#_Tv*<ydhaJ-tWF#kd z$WB*jRlM*S7F6{LdTP2kmY&}L1!$h&9mEBw_vckr+grjOvo^sQ0I|du<X3S$_i=ng zKLMunEvvw8G#QOcz$#h=^hc-I9$m)|z-AWEK`Vy!^`}pl5l#cS;<Ikq^#ZHjnccNX zKoI;rD0?Vj^Qzp;j(j=JReuJ0hL&o7U+n0j;dw#*30iCGKr&$EZw27s$AM%d6GYn6 zk}`D%_5_WHM?gcGpi|HSxJwaX5}<RwOw)f!!*d4|704xF=dQg(67o#vXEA^wker~j zX+-Sp@2|HCkG_UK->xAw1i3bd!|A$Zu9|}EfE00JmTBF3(fQGSCF1<b89Z7IFR3~8 z%0-|<fFKS<-|*n6>+!^>8VSn2_cDZH&VOFdc%=C5b3)Q-Xl~}pi%@>x?k)nfQ&lac z1FBhE99^bf#hnleHq5K9PP1(Yu~KS+MuFzVix;P#wtrRUAR`H-6=qV(Kl%dzE1FmL z97wu=We^1<IXT%KE;k-Uj+N@~{(SC9&zCpTHB8kI5*X@?LT`ri=ig540yZQfAu;*! z`jVy;FFXV@Jj8I>+Y?upqb#0*-8F-~25DxT#_$(+-WW6xBPA4&e?5T1Cl!wXN>Lpu zCcoxEk3myMVYJdGZ}RVP)_iBa*Vo$G>d7r8mH;Al1kgElQC?_@*3b9w+a`*xDzS>O zK36YwMs#36mf5z$Vo;qDg>I8`U}I3L2&O=^*0sw&|Mjpi-a?>KPtMHT2KM>AiS&Y) zEQn;F_|-KvHI?>GzM$0`tJoQ7j>&l$tU#lf_TUHkyGyH8;!IU*16D|v(tA&^g5}Os zGG?K2!s?$)A>zJz5t1(o$SjcRBvzt}(C0FRYbs{i^n9Ua^4+}zDm(VK0A$E#!#$k! zs2P+@z~y&T{7e?VDkVR5{L*7BYIZ@t1K<rB2txL~p@WHtJAk$~upBASysC!g${5@> z#6py=J|x{cG2HRPbu{GU@}*xpHZ%=RoyC<15Zx`4ezAcGj!;e3!Q9%;{!wvx5OX8< z7vxqDIj_TkG6l9yazQK_^m_Cd<Oe{hQ&Z&durY8MCpLpCt3=t!;x+ojvzp&E4DZrd zcH($$@}Kx{ctCTHpT56iC>sPg1`&IIs52&8skyq)Fau2y9{!4xvMg(mLb1<VEa+W* z1y)y;=kSznf-n=+J`We;<@ec%kXO1;W7^!J*~q+;Z5@xkN+P3@52;=o&TT-(K*40I zRqr2C)`gAe$2b2x#K`-fgukLQlv15yjDhHYt|K6N3s;Fdz{s*8Z)rpwLX^@=y7RyI z8LR4viB+G^b9ZBlU^S*Y%V7QYV`+*P0fzz^&eX5ETkxbOcl|z;+QGia=j7+lH^&JW zBj*oACb@Z5DTez!EDPw@g&a;F-%w#9H)I_@aTB3b6Gdu34n!(Br22Xj!8Op<osJJZ z5R&!o<}>%1c=Ox|!1<W--!vl2-@J{b^<I<9jV}W<*#WwfRYm`$XM{qZjpc`)Jsq2n zbE@twFR^J?;o)RWoei0~M@oi*HqH;AeIX$siiImHa0Y>nZ-6xA09kd!bAd-*8p_zR zT!{HWIFqp3YOxoSp2a6o(aFr|L8FG3NIw7s-Nwb6&;A_Nk*#j^p6JHj8H@V>sdYB! zFG$2lbmdz%N~uON)B>>r{?UwlU3fSk-Rs^Pt->>NJK|T=GnJvdfc^n_6=*EYUomA_ z&Mj7lUi|tnYuMY~EJ}KMB7a~oP=fG$_BoR{?kZOXwNr=;sMR*~^e}KI*d=Zj<YVMz zmj!@Zn71Z?E#N8?3kpb+qOEU*RlCfKpp1I&jt2o7A*thsqMP$qc-c9pgng4w!ABuP zLP|h5dC(zK4(;yWb;%8(8`VKfV(ebR7po<jEmYToe@HgIM~?xQ0(fr#DaSHd*naE= zq~4$KEprGD{87RB9mB=R?{69Bwa0jx)C@YoaG*L5Kcwcl;o^+7(D#+o`|j2p?#%rn zK4$H;|HD`fjY+i%PZ#2kztnSX1mHaUbT0rjebIH`7><03d=^Smg8V2ApV>**SLX!G zThD=f(@L1j?A6=Tvpq!~=@kZS_tpzyhq*4cw`~=!OXmRi|0TGrT%x7InXhX4uD|0r zi$jHCr{QYsuo>i^R@!7y?k{6@b@)SJ58e3^`*9pFWZc~-$0Z~^3zY}{?!q3>ChQf> z&hx#22HfZVgp2NeC0-;5Y)yD`b@$mxuG`App=|#6cen6OS6&W{cc8K%!P5oSKtU%W zBU4>Vgv7Ykc-{^HiKCSje^*-bQi1FluUxAK7`+aHVziULZ}su|^qOX+#I%q5&k^dJ zP^`T{)CjP4P#deEw+iJn17Pvt-fRja*;NpeoS<*%x;&%^O&3rO%mGCgk4%n+{U2TH z2@M2!Rd#RBYtBz3NhRfs{iM)&dX3)X+F3F@R2UQ4qpt&%Q;BNmIb!OTniL8V*>51N z;K<6!37ECefnb>NkxVfD+l%){M@R8jEh!-VIZdS|-GXM^)D!K(xLB`Wc^*!Ul<doR zHl3F)h1Dm{X?L42TkJbuso2CE?Ko}f$wlh?7*D!^7*0Ucq`PWy7ECA;5Ga=f1t|am zBl3Y(zC}P*R#qw#kQdEM;+~TPc=q|8E~PGu3LV%eR5`y9-t_)Xd*4xX1s5ihdk&xE zUh3^LprS`UDxxKiTzuWoUjTF+WS>CN3wOZ%f~W)FtC861en4NE65Pk$j5OJ>__^7_ zw$OM3^#8XpDFJZR8<5>lmKqPUCND^1h%|n5u;T<Y1qzvh&D-}_lL;UkZ7(lD1CK=` z*Q(6*8lhLniC;2yQHz$A77E+5uV1;4)|Xwy77bWpc<x?e{%mhSp#b**fiwl;+izpe z;StPPBf)qP$En~{#uX|M5hEpPZuS=duGOjX9ZtZbqok}GuL~*=)?q~iGW*}(G#5v! znEavMk1s8~di(Y*3lp#uO}6ig{r;#weF6{q3ut+D-r0>-g3T&-x>p}tWJQbLp3<~) ze9+qdI6YIL&;e*CP-VcT?$9s%Gg48;V#fp5;7p^J;^W&`&oT|<qwFt0IEiYC6oTD$ zaPUP|t8uRuy9;cYhbY82{xJ)E{rc?B7@n_Vs<Kmhm`6!c{-9wXNl~lFwiV8p`=0>9 zU6$+DEmvpXnTG@~K>(GOl{o>a7hR8mr2J-LvQ@~B(}u8!;k-btcXARa2Xc@P_7?Nn zQFMo0N{g=|G9KUIFkjbXHe!NaAso9&z>DxyV(W!)fk%aQsuiv0<8bp3zmw~HZx$0+ ztI{BiaqCwm&b3Phd0AM*NGQoJB2wO1bpUidR_8KO{`2YF>|ae`^g<SZ1#aHF3F5zr zsyc|NAu>@nz&9fYYCh7Z_dD961w5Jx{)(yg6zLwTQmq94P+<B_1{o26J#)<484=iv zg60So_;lWsCNoBq^gFIb>g`vo*N&~<i$QnwF6c;auB465O>SClG;u};Kr#pU59!At z9XB8zx1-oq1b4XKJSWGhktU(J)iV=W>^NXgT7TbV`l?=+Z8$U5`oY_S89HUhT<W== zTTf7kU<8ZuAd53jPFGI0UJ5?g5{fj-OKxc;Nuf%e6<x8e?+78eUB2fJ?MXrBKlhZm z)YEkzblijwuuf8;%rI~Qu-7Qs2j1}7NYT&`$OKkzEhyeSqH-rxKz%7G&USBS^gcX1 z{>tgT6G78VGMZ~TbCK_RCJQrWnxPbr<SaRPjr7T(3{YAWVA~Ann?zr}U)vc0ghoEs zd0!(3XMam6Pol*q?pc*@wK@@zyG~h*ugc9Rtj=tfag(wOHF;!<lLo37j7~J%8r2Z& zik50QU|-vu?+JyV5S@<%?ZV=7>lnH>;Dg)o5yd@93tJC|W*Vn^=2j{<!Ao}c&77D& z(uQuJ)t~xaG%^7UA5;Ycy4$(Zh?fqUFC+{iuR-7zH(G>?DSU1jlf#t>*sm@SZ&GF@ zKIVQnt2&h&`I){N#1urqgYSJ|NQZQ*0ejpB*A|o&2NxIU)^ah3IzET;oa41)>$=!? z6B_dna`;G9>7Ip?NJBrYAA3vvb?Ob_i+|dhPJ%?&SmI||ZAR-d#NbY2Kcnn{2PQ(p z7^LqFXyN>lc*E%fUO8B~hDx6U#20pQe87iv@_|mbwE*XyvgI`GV{fZgQTh-siW~RL zV}_6TGJXqg@|`&`+Srq)%6wUy3y0Tip3HKvF^9*DvyDN9$p&tV6kqU&sGT{cumZOW zB38l89&Y|p1?~NEDoEgwWt!V7!xdzFe{GB}^Rudigxt<-aF3!|YDOpH%ux?Wj!_g8 z2D5D^^M^HlnpHGkHaIY;CL$;>c(4H#za$d~4i1|Q7cz-e6wB?fYJL(}vNwqGpkOp- zI`(vLOW*H>Txg5p`L#90cB=uZ@^bgn8YIOyDX8=Q&a^f>c|o8{squj><uly8P({Jd zJmCy<4i^^}8_UN8^3J#6Ue`YzK=_EWA+3cWy(9`H!xKG<Kttc75RP~9WY9$#is4!N zHkL$RP8Psky;42cxBHBFt+LV|CE`SI-7b*H7vS=K{!{5l238=^?MdSD&@Z_RJ?Y_I z$V(5P$$hJ9Kjlqua33#Ae1Tg`AfNZnh0|Gj(*?bH&-_smv3%wS*@kfxMLtR@PY(7W zU|8jwS4j+@Za~uoyZY=j4YvaL2?DcVQN@jn4Vy?0vV=&k(-CYDkWOv&>LZP<nMpJ{ zt}Xdm`_L;ov|MPqcg6Nr$WR`1hhr|^e+n4Ebhic?co`tW&{x7^`U{Ufzx86w?<8*_ zcd%OfhSk_p!`0VcxqQ_#58a-A<&u?+ixOLYf@}Wh!*k?Hsc@y=pYA^ch?@<qn6fNM zU_pq?3|m6)<DFn+WJCuod*DS#2it3V@nO3zkV!;G01qBC#Z}_5%#JJW8!B-k0v>|$ zVt3KU)#h$ej3h`l{{1!UxQK)Q`$vk?Zf;K6!sI2h5C_(vs)N4-P{^Q*F02Nk0Rb2X znr^naX1NdPT*P~qe`BWAc(E@B>CA$n$=2_I*qm0%H%MgyNybl(S5CmbGmh{)Fl}iH z0#^+RdG&(x!D6K~^%(9V{!4Uoz?^^C^mB5SV=mUD5viruXE!{U9os>E===PD11eE7 zliy(th&?GrC!=Kz4GjoXftFqqD2=M1Qw#3zK9rM_%W<Al8}XRE0H%r7C{-yelwr+c zd&69(y1F`gL6cM9w)hBe5dpd2BzOZ4Bj&9GF28F&r8Yu-OM@~Wmae}L*x9tfVZg(1 zkFYQId@ZzPF3|!H1;pp-cn}j}9|TH-cqpKPDmZL&v$F>R9NTCSYR^VIwlv&7Kt{!; zNc*J|q+meOprEA<iin8Vip$F4gIWZci&b>zb4Ie!>R1g*R$gAnZCMA1rKP&A3^{p5 z_7-hVfmKPoh23aW+`_&M`bxU%tF#x1Jl(%lrjnySKemUi6yn%J3}zq=33%_?LtG-g zYrttN(8~u0(JRn4kgqkQgC_A`S-QwpSKfzI5oEFdi{Ca48o$`g{sJpnKtWCXB<v~) z9~m2%F&q7>p<mF4WV@(7{D+lJ62q;+F~>De7T7UXrtSnC9{}be&H|9@jOYl!mWGIN zNY@XV^LK&1fq(~<5Ppi(vt-XJJ%P94UBPd`TEk>;By#=w^#-dikH7x}5=D32nhyM! z*TJRPlDifXEK(2rB1W%-#JO>(d53xRJI~HlfZH!l_V>Shf2)xB<}>|`P$-Z{ZxQ(> z5JE&j)de#S;;Tc*86ZW(XawAoVXgQoHOQ3z(KUD)tRUI1ZEk-2C#~hUuNeWjNa>NG zCdU@z;ExsbwrEzn^%HF3-SWqY%kv3JbE}GeDyF1eG+r0Q{@mRozWu=-ZKPL>49<;E z5L54^-JzBr>9RHb89fg511!jqd?|as{>!uLMlB@i{O2tA-N2PS=-DzjsI7RBd0n6V zo2hi7>0@q=y~VqiVzE&mb-RzrjQLzde|lMxu~NsWtNr15Nn48ClbgCX{7br(?)ve` z*7E|tb9xF^84RDk6qvYz!P{xDtgQpS0v#PpraPZ@VOU4UJ$N)z$?aZ!KT}PgTjVA3 zz>wF!_0sB_`2KG0446)hs7=Bv$_j9BBi+HT#8F%71()m0?0_I?feR(pMX;O@(#tVe zwrV4d$Fr)AKLAdQFAe%{sRY?%D3SuuPhaqXqXnFnV60>|*%<m-EpLS*pq05g9jov7 zr{fZ!rr3T}Ioah$T#s+`W@4?zhUeZ|@kA$fdZ~ZG<Iw{9;7k?Qqo2EyJPipeba+<@ zD+y;Xw^!BQKNWpvHNsHpCVcXpUNTaGtqyHqVR0Ew@Dy0FTOTTk@W<NHLpg1i*dPHD zLwqT1_5WRRnQ55@rH+VyMK=+YSLpht0<=SPB~aS#gFi(%;->8KO-q-mY=%6lJB8{r zcrl$-$3LTApXJu=IgP49S9F!x|D?kAX7)(LW~V)MB8F_*0G60qsPP~%zK2eiGgL63 z^=cm#zErtsy?F5=2fP@F1`2=&4ZM&LlpE!1nk*bI!@>|FG8_>J=!Q!&kYmH3JC)~% zbf+rWI(OYSl=Yt^UE^kZbT_3?lwTZF%;ACjOMezMM{3G<+Re7Z^<^ECNLJ9!Ah$yD z%YZ}Z4V2BgKlcSB`ic4ZmqkUwAj+n}F`(j9!vnSjDoh)Qp2a|Q2}v2o4wL5B0u4Lf z-NX$fW3nC2<6xek((E~6t|)vGB5wbYDvoVH*dm&zdFVlgyJ;Uk%m7?|QSvoNx4S}y zwY}My;s=;yTfoKi1Ju}faMFRZV*;E44C1aNzlIA^s|N&OY{45TK?c<*@YlcYHv}7` z-eZ@lAIb*tmoH79<EPVbo=t1gE}S}GkuR3!=AJfP=>Fstq>wzD%{>Ou+X+Jnz=0c~ z5duCfvP+jPAw5WN@jZZ!HJo3h^W*_?h9&5BFLgy_WfR$jhWWaFF1K13SoRBB&@rG& zaWYWqEyI!{=dPZLHEuFk)EHdOTE4*SC?B|kdKnLf*(t%8cc(D~0Ws9COq(&WPZhJQ z!%WExJOfN8B>ml%&Px#Q;-vAnqpss@loYam{CLfm9P1=FOng_CRUO3Z%lcAt4t!bN z&#E?ay@a|R+Z?!lpd8L4$fgnbku*l8Ms}R+)waDBs*wDVzFN79<edWV>#lX4kNVmD zBa0)^X2e_%6>@N!8o@ZRgXQ2RSk%fckl3gv>MMCLEH29`SCUBy>{D0p*Vbfe#A5Su z!Taw6kW!JZS<0~G#x{{Ffq#to^3Z2BK4#1n@S$RaOy&7TWEE)?HDc_3X$k7))NW6O z_Iy1#-PmRE)fH4N#9s?$G_?C?d&Bgha?3%X<#goJ1YnbmOl<1p`K5-5&t!5dFi`5P zeKE&w#jTn9w~4VltdwKapiyW|uiu^$;YB>1Z~fu#2a&JMGcn~GsbA!5vtB81J58p# zC*_7jBVzkM+ILB~YKa)Ypm`SRvF(7RI53k^Vc)2{^~RGt@~Q=qhOA@wrrESI`^Gn& zrto-u(l%v>nK16GbO)6@vsT1O@fkWgpb4?1U`=e5XwMOsx%TVNPcMfV0lEcmZe$`$ z9?!4ENVso$O+6LoSB7-JBZK_?QBYWrzCVc0#l^)R6Qhl|__C;kUP3NHFf`al^8iE3 zhs(*XmwuaXcuS$w8_~VpmsSv+Fj8PS1oZ4FTm(^LKyLiDB=EgP|4#F0;qIZ6QwcrL z5~F9(0|Qhd0JvLqYYX5Vv}vZpM)YLYKOon`<i8Yp=20k6kk-AKSx{`H=n9rf#I_HI z^FFkRVPV!sODuW(G8!lhNH2Ub)hEx<jA0ANx9-P`Z<}C4f=$dG^RjU3N~_H)>E(X3 z=^Yt%V96057M>7yH33N(9A1e)(~%kqJwkdZZxQz9*(Y{JL>BES!6q6p%jkzuz<EPW z&Avn1**;2*Z{*u4=tZkZ#pv-=mz@>|6PJ|l+J^$y@x4&mx!<{LU;xHNv<1L%JRY;} z!nm!hkW0PB+>m88&uRIV)}2(>Sraa)+4S1caQ-T>I4;6H0YyzfqZ!JLN|s)QnpEeA z=~BCNZ*EqwNgTpIJ*+L_XU-OyUGUo^Z%K^b9HL-rL2LX<S2R1ccbNtdH`N?V7KN3? zV%Rh*y+vW}=N;%DpQXQAKG=|aknpNp8;c=6mSsg8@(OLyZ7m5heWi;DYRyH#r|6`m z<^=fo!Yhx(hSODy>nT+`vb2RQ2BMK3;t7em`iAvKo~3Cq=WLrHu+7}#I7^Nd2W<jB zhL%V0#HK%Q7Dd3%!HPGkuFv*oO0ls$?&h}&p2ZOlj-x3`e^Dof(Q~S@>r_aX3xDj^ zFV+?=7N=*y&BT=qIKDx(Dv}NM@a=+ZCzls?RR!NG-=%6#hA1R|0JENBRW~uWekopL zB&WWy(piE<TyK9lf38)5Y;<<Ivj&Co?Xc|cX>Zwx8k@P6HWn+gf)$wAS)LVWfx>kY z?DwCbI=#$V87>oU?L|ZL4<L5)k4dL6FucGp9rS&a^?u)!P2`grk=QV|dsRV9Z7^1S zb!X}YYUBp@J#weBeV4y`*^XErR5tFq@6L0%Z}n`p7tB^NGn)t!A!9a+aOt|Nx7LSe zv}IZA7J0IDKHwehPWqvHhWo2R+|gOM9R79zMsvf1$y$DynfKDeY-YtiTuTm~;spLC z?zX)6!vXU88Lwxl!NDi<2LUDgF0s77V})_v2(%xfz5PA-Ua3^I4QMa$iT_;{%s6Tl zK)J(|2)Ob%q@<=x1L%puDF|P|WR$mPk-mF?06Dog!7f^ypTnc?VMx1);L2QH(Tk+C zYkHnSak0GpZu;cdjc7dZ_Ebg-dh?j7buI8EV#=O2J^-)7%2F$EnQdGO>k4%<t-g}d z+Gx(Xl$`;qJ9y;ee=~)b_7nQb7(vdj>ThEmE*)L$FcIYN(=eN#nABV!@!FNxJnOZy zl!{19UQ5?135L|lnuc8t%gei#ITDlA(z>KMMEWESIGK%7N%+ty;%;r1a4&<DBeTrH z?D6`0g|}+T-I6MMBe&M>VSvT&aR~*vt`PgPSKksAzH;3XM<s!^N*(6atS$`Kw*8h2 zL+(o)C>GLq5<0R6n=LX7Gj{+3nx<BKqf3KP+q8NWAe$(Zy0tYoGgRZ_RllSz-u`p* z?95cSn}5!Y%G0zhc`ut|YM{|d=o!w9{U8hRjRV^wq#<=rw%%be=|bKnt?dW{yR!e> zukk6x)$PDbGW_ea9G9-s^p4kY`j=$t^LNxbrqt)X1Y3~ml4LVuV@F4{xtLIb0ayYN z<^!2h+>wEqdAs?~V&6+@Y>Yh%`&|?NSlWUwkEc_`o4yY1MafQRHq5j>XqR%Of-}e1 zn5o-Zqc?GpUx<jE8IxgJOc@;a8Oj-@W~ui_VR6^}O+Pe-&62fqZ!mx{PTZ@wq|{MR zD%shM<75?=iaXAm?_F%Hs6n$um;4j8Q(E5-NG}|5!^}}MJ~#6YZ}TD@Yx}B-rR!qm z$4GYri4F#*RdwpV`yUndp+~wlX=%=srD1q78x`Y^+Ni8+ecM_yJJUK-{kR5_nZFrI zND+o2G+ScW9&4L76<Q1Qo~mb9-NGvz_V`T3f95jIOcz%yZ<^Fsjmq-90$7Zu#IBaq zU<FdlIDNIM)ZTOa(=;Z#HIkm2?-1e*On%)zpICy!Y+<o4$>g<0i>YoQnz4GPT4EW` z4<+f9aB|n?@!AVoKm-@(q;8T&GF+}hkHHwiF0kqcK1Ybf4(2yhHW(AUsUjbysgLa~ zU+ln)4_1`ztwfuREVDkNsxIGLIK4>dIqaj?6#h?6k+kKU*D3g7lk)7zMu-CGn{n>3 z&F6uEYK7MQ-%2Zjr_2gz+?V@0XV`v2^>iq>fj&3F`MIl?{}0iWs<+w%=1OMfXIht$ z+=jw-nSwp&BC5f>%9O-5mHS;Fjz|rK#&8^OyHxWunKWL@i$78Ley^`uvKDAIZ0?^P z9aaMz6okwriAFpdNYSnGO#-ym1rr<q7!jv*k#^BtFOS}sZ(3e?5#Yj#<JQKZA;<C) z=1UKPxey$nlR%V<OH0>5CfoRZ-fpbaiKD*0dZ=WEl25Ni13l~SAAN9|!<LhS86z|O zlfD{y>fptB1)K+QW+qFD_c#?<_um5V3XsqD&}c=Bf&C(C<cjy%V>Hgx8FYPAylOiX z2N3x_C_o^@HvTzx&3<e9m@jn~lqG#>CxO{+%<Zn!0dHo<h@WrJW9BJ7jR4u>!H5N` zW(xiSkP7&)Z3qs}DqknoEp;~R`SJ)EFo0ik0!KVYm=-HpXoN7wV{WZY{iiQXL#|!3 z<j#SL91G@RWHtfNk0k?5jEme`8>|$pfFaCG(os`40DJJ5O>#q;#1K`iAnKVwPY2tk zTV#6ynNER;q4xmdH^D)IR5Q(y+p-MY1zLV6yYfhhca9DLg*NYHiJZpV<w2J*xVyXy zPS$u(wZRO^0@z<sJe976z9D3p4O*|$rIP>q^zQ#O#4)opR3cOaV_&WjV2L+r){t8- zr`lh~nTapq)m5uxzXZk$p_8@siR~$Dd6}PdhH8o#xFTR`12EQY@D8-j4o2M{Dld_d z;sKj9Z!f<8zrl?}P)#%<JUl%qpwR*2WiUJRe5<&q$PHr^!6e-V5FdmT?ZQ{;tnGKQ zey5zd8}z*6sARU!X?7C))i2WBR!%$LyPX4rKNwI}!O7bV#GnBjR}T`h%-?7i*|HHG zqZXRL5%XCOoTD|M3?sAv8Ai@?4r9KxFXS|*fkT3miQAyeZg#Y5ygo+8fVs~ND`N75 z?42Q9{C*TQNH%fY9eLi1Gcz$0*I<6_13(~X=f&###+$@z$mS|&RXG2$r=&aOT*q*D z4NZEE0G*u7ssHXnD>F0J#GT+jRgD3-q#)>~FJRx-r-l4HVGY1wd>>r!CbRm`Yez<6 z`9Yy!RjOyk$UXk%@9g;^gB{g}it~qTKD5WA`9!HW=0mG)S;Di5`YP`)7J0!__y0$+ z6;Xy>1tOXv!>llZ?hhmq40Tb~W_&O%1omG<n}RkrCpf%>0=Y3oA7ge6Q{DVoswLjZ zP0)5})jsMpBadWt)TKcD#{Z}7LED@g$ULXo$5SxT3|6vdekVt<VCNXwKNlDKGL(vQ zBES76YwsPdH5O{}YIJq3(2l^E$h{87r&trxsV?o>GpGE+FSL4*wWqXe?2wU1>eQN; z^q5I-fF*|pjCID~&j4EsKhQ#u1qg3Idt(oT#8anEy#qA?e1Ky8TQHq511_S1InDp2 zugM~PCUVFengU5xn?r6);8om)zPrbfSZu(JcekI+^66)4N<cZC`ibHf=R{HE)PecV zsC_P&Q0mIV;5D!zq8<g9-WZO)%P7q_;?@4Xj*1++<Ty-`9W0{gedyQxHBISlgl>79 z^XHyGHLgoH_zbKbQlWhOEpL2zEBNJc>tP@81muMX7!R(;tg6`xEzQk|A%i)od!z5) zb1viR;7UYzjHVE-UYjxCSNSr^!E7W<T}~O<l4G)!{-|3N1bDDjuhyRa%pR~<!*D1` zvdED%rn+?iSV$Sl5yqj=Y)eaT<#(&M%0w)G?<OuCVKjjr^ZgjRI}fW=0sZD$6YvWP z3ijtfLni9Aa=tGGImgQgVe>U!EtBKH+}ZZzl{wj(6m(0CPIG%#8HY4~qVrQ}em)jH z?erFX@<<X#cN85xJ@&j^c|cW_U+%?KWV8;*Ncr}F71ECEjG&49mTQFn@QVrWcvouB z-U|4Jw&qEOm&H0L|BzYZoArA=!$wgb0yB9EeRn{j2%&_{g~3pE5fKsW0_0(kH&>zk zxPdgRq5Fl{8FFtu@jARE)2fv1IzYIK3Pq`p85_Ko6y}c~+v<(tVFQm2(m+Ja{V+O* zi1px2mxW-DDnAC>RyLS0k)bV67r$Q+fueKADk3F`O_tJM@rLmj)jje)g0NbKQ(8M) zX-)hWMf%DQ;T;b26VMifm;-JRu~K;e?<`26YX5vcl>pJFVLtbpZrN|dbS<Cf7NLJ` z1m`x6!wvPZi!%ltKSvTa;YA20pbL9M!efq&86YMNaDsJ7WWh|J1hh&y3k@kF8S3xf zj0rvcEI_cDWK8~@#ze1Bmz?|-jCKgNz>o)YG_niwbl_k7{iRbZ%x#e&7Z<LtD(x(1 zTT)VS-8OAes(QAYwHW$F@`Uf+ku-UvvJ<p!t8vNf*Qc;1SSGhJ5NyC7uHcq_Pd4$J z<pU=ywYZ<A_?fHt$$I5+Dggm!$<a67ck>oI;J&0OI1>GG0+o^zB*MXbOJvj(q&qQk zx8oqcBJjJeL2ZnJIWa)sf883X;I!2)4rs0Er+6ndlgYS4&Hdv$nN;?YW^dNp9d!Cu zA~E&D6II!Iv~u8gtHSHSVPPME<34}>{2nOk$>5Mghzd-O7JF>4!;Csv)zS8OaI&OA z2K=U1k-#jcX0zt23LpC~y-P4RLXovhlUO80+D>l*Z1zX}`O%(7rKRkg4UUvf<mO5$ zRgHV=Yce_v)Jyb0`o@-%H$~I#+-y&tk*X~Wdn7B{HQj+aJ2r)l3ZoNv3bB$=+|<rb z0`rSMz$>)2z3uwXr!e$Djd-fOyd}?juJz0C1(Cp@I;VED{SSV3QjwP)exnu!Iw2HO zN~UZVA@9ULlkmcMh!EvEFEqGo^NbpZk<mYBRqTQ12$@`ip)$k}jR;ePHrg<+H}*$9 zR6&ayKWXJS3F)m#RHQvpSGO3E*vlEO6Fo~&dw<N=;O1wweD7&$I`QkqljZK@rEcwg z1;{H=g4$6t*AmdtUbno<OI);{Tss2wLwRiO*Y);7cxg-WG)!jq6~5w|ZFw($1C=&V z6vX6rwJ5APxb<xmVFENb?~$$m4i>%ViJ!Ezn3)3u-H6Bn{@Lmt>Ib1TBq`D<YDr@8 zxIe<8Bkp@Z!^%1e1P>T}LC>`4H@C!2$4LS%N$9*3JS9o5IEETLEtcV==j|=Q<g-Z* ztI7dlG`RlOtHAUumEQQOrU9RV!rCj-v?}{&8Bo4Z5I$h+pvFXSd||=r42q{|5ysQ% zZ+~X9_dXVN_e{S)H6X?k+vowh9n$6nT@t6G%*{^9W=LeOD>s(*s-=<Fs(Fdo{KGeH z;Lu=)64(5O%e?Ytr8hP_B=}icOf@+LPP;_Zrp6u3ikPQukYaa+3Kr4Irh6`s7m#-% zfGK7SR0r7iIo)FWc}1{0_icMrtP?xWeNcvUXPwUaS{Wd%Ny#RK$g)}diz@N2y%e&y zy58{=Y^)#?zyKKW$DpNyK$(Z%b^0p>(ryzmK|g;ypY=6zVqo{h7uJ|}!v9`@X!&-S zg4(Bef0WZ}^-Axqjf(OvZuGAlV|m2QqUW>q2r=gXI9y_SaOY0+z1pDQ;3$!NQe<cY z##>Blp5o6X4+1{s)hVuqs=E9G)?ccS-JaD9JAXl#l+o|7itRUCUH;Wo3PqB-F%VRy zfg3cL2qME0<WyAA`Btj88=avMQLxCxJi!W%fU6|9+K;1CKYS>5Ut{jg(nm&%95ceA zALi_?%0S<=T&K+F#a>+6JExuHnJP+rL-?Hxc*TkY6ke$Cb)FKM&5bQ#L@o5Sc2!9w zUNGhm2)&(E7&a=0Vui)2M5@h<<>DN)a$rr!VR#vQHZWKcS<X^{N2=Y|v1AMY(0OGf z3DJS(=kJ)9nEZalc?s>h+<h!f4xUhZlm_%j7=3pQp%Hk@hg}0J3ApaNDn?*R1i-O5 zOq6&8ef=u>wFq>o+S|8rc<cD}#f_eO5Q%ht(}zR`hId2g@*}Tzf%KRSuVFbSY@1kM z>{u_D3-PFsp6d_5<-6A|NgeVk804(NTSf%72osbM%Qj5VZNTSDHakTFbN$%{KRzVn zXQ411(F05dJ^5Ua0l`i&R|}j6JiqRZO%K0NJr5eZMhda1N6Lfy0J=$_xj-lJeX4Ez zBqu7`+Qp6I^=aC7=ItN*E9ds$y(`Dk2Eaokw)>0`NsO;@_bIKOd(E=ZGPbe1Sis#Y zL*J<*ys{2XuLfx=&Is5V;k>#|@yM|XDB%P(#PDnbyJ8JUg(*hF8hG_dcQBave+U>q z?T!uVSyz!|mA8i}O6XpA%%#PG^BouPy6e<;Iaapq#Z;kq5ucMIiR0q~@GsOeh}ibm zo@Qi}aGvY(3z$lp<kpJLK~W0oIKd2QZjel@Ke%W^4C#Ik7=ob8aq*%*bd<#A$wk9s z{2M+4A&&LPsi81{IUJpcmmf5j2CPe5c|6EQ;Vm!$qr*)rwmjxv%l`YWP;@jjjWDC3 zQSgM?ye&~cy4vIeys!mKHyxR};)BAmqbyXY8MLTqa87=RQYu5Uih=BSt+(3-JL=U} z_1PffrcV|NLS2^BAi3@&b1&dCP*K$bvDk$cQZZQiz=q(xw+=GyX&7F2U(7+KOSL$} zfP2O@r$tXE#M9?|d#x3rKX!ICF@r;%Pq)@YP+)(lh+f!FYVVfJlGp7gA?HO!MQF$) zAHsVLED#SWbQ>EXEc#2Fkg0MYGSGol1S9buAVM(ls44oKI}OE8n}_$<%`bGSo<4^P zr$;jFPUqtMBO}|<8DOmDaGvv>9dbOdB61;0MMI}+a$p$aa&yKQGS3Zmw_SvcR(J^` zjZG*ZNCN{Ty-rvXWON6bMY*1rnG1nA5(;x3`I^8>qGTH>SaW~s8gI3xFu{bk2efsF zh>1Ty;*RI5v@RK;U>Mbn>gaN)?{Wp>wIgd#On{;ayrKbmstB}3wthe=6P8}5)Hwvm z!S>2%L{*ivzV{kFpqHQFh9Mtq54Q79^PVTbBYfNL<SH;JEA-~Tzx2k<i=%rRhodD< zLpz=Fad5s<-u5$A?u^=b*?X%u!N3Go^dJ1hfZ!boJRkAtuY(_F6>{W*e2e>FZ!uM& z>4!HhA@~Y%@f7@EHZ?-(=a)+%eQm$v1<i|u$|%Dc&J!@9B5+c<v*-zck}1RB#i(*x zT2NL+L$V6{r+YQtKAtrKhqMgfFA1OOps(KDb%(bR{6zLD*Yq?HJ-WaEcoW8}bJsq= z5VFKtJtdsCYR^|-$Af7+WOfYk0zp~lJViB|=aSByC-fw~z(@Q&lp>tMAyFzctNm<| zc(d}fx<1fF`h{^Xw>2|uN`*z0t1x<K4Q{dyc()78LTJUhb3OcY173_k1Ox>-8kTz6 zCGaLVU|9^JtdU_kEzxPcZQm?^Ei!2{TmAk%>Cti%!T%jxX_Wdt)a<Iqd^e5zbEk+H zsIS~8vgPbWCyH%V)st%P9}YMrL)sDt+Z(j3Yv9#Rpd)prL387}wZ%pO1SB$KOm8R8 zHlbkI8a?grY6gQ^<?z-5@2wtnlmrPk!IyNGNVc1Gn2Mns<DYCgARjDs@x~lIpUc?- z8iaL)td$2Zau_JIS=e;E3|9<el1>KmHezQ78a+2!<(JS4ESaf%>$f%{o486UnHo5j zMBhmQ(~R@&7NWL9Q5mpI7W*EFWqG=<Dw?q3+m!GZc=~Q<7aS-|U<AOVmnz~+unK)& zr>P}DaDqrpu8!9s{a?U|YrnPa?!$YkKtYL`*n`w)yt_J1#clB`;vQD1D}mGL1NiU+ zz`P0UTVXIR?&0a19FxI|rp@c_S|Jx>o+B|A34WL5fr92sxEA30lvy?=|EHS7i#oBx zP)Rzt$QY!36TzI7PKV+JNDTAB5CHfvPkKR&2Fs?Sn<N$G0zD|0alHj^6hhp9D0GPV z#RmhwVq;oN_&D?!Rc-t}QpPrcU(N?m2sl(dl(%Cr#i8H;g&N(~ExC-m_5k)A94tAE z8(Uk`z?uNpT+%!!fwyG{7}ny!*TGvy5@ec~Cr&|&ClzFgL@}3_Jlcf_ke|xyiHZN2 zRVm2f{gETDzuA%vBjVu!Lt<UApT+zG4Nc}%Z{2#DDn<5nb=CeQEno7-?y&!U*G2yx zya%DE?Njg+E0{)=6Rw7Ua_<DKJOuMX4>oT9F}}LG)92Xy`9rqM*;q|9+I}X?BWsjQ zI_?Cfg+cNBA$hUx@2lHfrbHNKKyFcdDqX$rU~T<PD3H~t2nJj`itO~^6$)g~pVvj! z7K|iH2epfBdTr?DNkFVs+$;Er!~c^^G7|=e8kxa|W1j=nb7*J?O#K8PJQ?rTq`WZo zDD>X@*i=z|%!P)RXnANAZsOck(a_K+#s9GM2<9kY;OqbC>`cIV%-g>IFUB&m8xxug zT7;Akq2jicvKvz(A!W(h(1s#oEm1_YAcY}OiqJx%q?ENp*-9~`hzc#<&)Gfi{oK#{ zyw7_)$9o(z$IL-p|LcEUzu);g&+qyDe$RivV*XMUWSYpg=GXfo;~pEP>Hh;%9}84# zkCQ!3MH|2?u6eGp-9e9dkp1Gl;yw2-Vt<kTzIpS;zOMdNQBeZ$!h!`0L~pNVvf=Tu zoaq1XI75=9)In9%479^v(+fAA&!0avi~h58|G+zMKbGpO{iqfC=*-v$k(Oa5VyT3( zVX#R%-5kUj{U}LoK{ch1iRi#Sy4$vW8p|JVu09zc+-yKapB}^Z$UA_<ZU7a+<V$g9 z94o9&n03@`X_0GodiBMFd7iqDXAF02t>4O3#j4q%YuDbJZ4gH?Z=>HPIYAj?q2RDJ zj_56z6**S-q08jd#KUEE;^5BrF+?z2@uc`xx(yL;7nGL=ysI?7o;e_0Q9()IphEYv zu9}-sH2bW+_WXi*yL0YuS#fihM(H>uJw>5c*`c~GwpZ)K8fCzXyR%p0=OPqGb_!Q$ z$-`R;bFgjqA$Ewx6;M95fFrF%ZZ`YeTys?1YSpeMr}ZDMlx1-RimUC8d|D=pRa<P` zR;B%3ZFO^frSQ#bG!FZ_jLS6S6J`+)^s$)&wY<mG6?5m!JI$*=`FSaHCOP0U&u<%7 zK2N#>_N@lTzBE6{_}xIYZA!g*sUADFq3w_qwfe&LPRb_5=NeRZ+Qj1~GyD)0I2(#k zlAw_e3b511M`Os4hwh{S>f!bo{&cbOsN>hD_pmur*j!6_P|AbR9q!&&s{MhAZaW%! z{5y~&2B>|+T|s;p%=ge`X@K^8{eS<On_~B7os<zr_j^}BMPJySu#B<JOH_EA+4=5g z`8J?|cq8}+_uxyuwY|~Xy0?jXywhfQkDeGscusy0sS|IvyXCW%NlG8fUT1#Xk@4r5 zL$!?&xA*BjrBJB=jCjut_Zc{FkKmeQ%F5H=>?kk;$s&>|B%kA;iOp`et5~)F>_Y)Y z|B{mL-!$P|#CJWcyi+>7SYtOpU-`33mg?uKOpVeLef!UmXQaW^&qI#huWJ(sh<q1+ zr@zJPyw?{1g5o|xD_cqw8i~3#kqLF?8XoyG(lm8<&N&~Y^p}#&<{Mg_2XAlpdh?I- zEfXi(EL@ha1wA?I*kZdT&m?xYXft7YD8HcM5=eT-j#g!EKlGn%Pt&`0!`m(gHfLx$ z=@cy-YaJ*OYvifXk&~Ofc=^XK*4G@`{<8d`S>v^FA)^(SU8l=4(XbglN^$Wr(k_yP zi<{H{Z&AOrLhuR1@*55Vzc#o0Ug0d#?aV~TF-DJSmcg}=ytv6&=-2rh4@fTt3nMM# zdx5jpps}{T^)k^*7qO{=9<m(-7whWb0B~^yle(9par8sHvbH3v8-;y35MxOFiT!hK z9_aU-u?H=L?^}o>YWQ#MkKQuQ%w5~xCB%h;gF|ppgDa&6ab<Y*3%v;AZ7*MzY;AAT zS(I-E880nT&{Ph9b@gxhfp;CX0^WvJwm7c&V(yKYmij{;7W`hcQm0(0K-wNxUBpO_ z#nsZqrS?;0*z8Mt|ICrr%MSZq+XpI595+s!^BNm!=X=d+X4>YF(tfqn8EFwzMvB1C z%WKcY43L8$$l!N4ZXL0EYu0|$XykO%ofZv^Z{v1deY&G#iRJ>`mPE|1tCuK-FG^EE zwz-=G$<Me(mE;yo+Ni2yZ|n3K$6r?%yW859ywLmEiy(HN*dne>|6U9|sf|AwVe`r) z<+Mhy+~YR(BYKG+=vJa3U$VerLWJ3r&K>MR-7I=pxWMrLR8e>YFAO;n@Tl-^(RR{^ zfRH)cR!x|5<#hY=T%jZT3k}1I%`Me&JE=ANHP3$VDk!E@;+?Q*!|O`?)ewhGyI(fr z_WSB813g`r@+FNf%RE$O9yduL?-;Lk_`A7eor1*Nve{MKiV}g-I<#Y-3FloZpY8Wf zzIbKFS=*4azs&94er8WQa)}whVh#26!#92%0%LZ4@rv=m3*n?(tE;%0>qnlKQX1wt z1vJY)RJG@-&@UEC9dg!Y`9Iue+hcgO5|x9~3-Cl?Y@#aIMgcqV*&L0hNM=6VXruR@ z+^34$X8{LZ)HBmd9kyl$$PH_!qOZZi=C1Bq#=maFh(MzU>+1z|AEJzr;(|wtaDvw; zUQcY(lkRuOpMElGzR&UtN10niLMh4-`EK{ZCl*y!hSiKe^Xt>~A0M5$<IzUfL*>Y@ z?dAawyRs5vLdLcklXq%*vw#fysBZ^nhnS}7>jzm%K``CV<@+DDWd8)7P!^qB0^PX} zgjFHZ(#5>X9beS*p#)j^@@2g7k}qrTR1i=eAVoPp#=FJ(km!Hh^`9xew20G`-*eJ5 zkyQ`$JJ9(1(@Rg)>w_%&y$5VH5;#~I1trd@H*S0;twch#PsIaWy%jEkowY3Ou!oEP zw)VJk4#KYXI3p|(R44}WrML}J;&TFKR`lA@p=Zw=yr)iKPo;nxK7f?<1o5|~(CoWV znDXlTb(QQ8BTxnxp~B}R=<<10`=e&-+q7*P0jyCEBMwsR^mr$0MV+^)zV?b!DX|!o zEQ%STD1b=DA{SK}K!SC^SkC&T&y$Sl<KDWRzFQ+!e0RzJAg@`{hm%(jrDV75@|DAF zM!CFO_N?gO><&u|g1pE7;EDoN+u0S1oV2#H&str_q(jz~*U<N8xNjPp;q(q~A3EUl z16?n%s-Tn9Iz?D>m`y}kqy%;9V0W(2#ihsPKWDwLrjqNh{W~$s6RJ`@SqxGZEAJ$D z%H+G_gA5j1U0oj)uCBc>mdoD5&;;UVtCSrb`+Vkw=vbXUTBObevnvXd3g=d?x~_cr zx3|f8iPw;JO-cdJX}9;ZLZ1c^=S&PG!E$332Nu8)=+1?;s2%F6j86SsZReAG)c4X` zLcYxVzE8jOtlQr2N1^O9?oG!UheGZ|mw=a>p9JcEpFBlWDrlV549)gs9UA3kK#1M| zHxFI=Ko9k0tqH2*)*b~e<iSH5pN`wjoOq{<nNy9&Q<16}8SYaNqZw=_7f>ib(-KGz zhg#gRIBC^VHDiYj$A|>hA`*p=txunH9HY^O4ud`hRzN^mKxB8YQYb6$CC+nZWboRP z5068fBV`L>4+r>xLX%>1htla-gA?_{Ju@{X<MMcd>|H!6rAZ6+<D6q$TE3v^Dq5-M z<J?!=&JHup!J^X|RHK*6Bs(wyV%n1Y7)>yfQp{71Qxfve&5nz*r~k{rW?ICGVEg}D z6rAyDAMc`{!A}aUc(f(B2&4f4&^U>2R2Zsw6Ndb-T*0tGccSsYiPIEs=8|BFIw4hG zMcC}D9Je>ew*UQL?i^D(I^N!1Ln?o=b@;fq>W_}RPPgU}L9GpUC`yn$n|_O*$(2Mn z1+Ij@uotEQ)39l9Y;VpHoSx~g(7%AI&v~bOgWPlTGC3sgv>zIDE6miC&}2_@(fZ5h z0Vfsub)?U`U^m;Cnve#N9j$q_L-HEkplszSQ^^xu>)u_G>YuM3jwGJ0ekV{CkxYI4 z;J<rZ;pOXX-aVOqW<uL7^kQBWDdFdY7DFeq+34M1J5=#g5zx+$MgMB+*{}{k^?JGc zwSuC?`g-Sje4wv>t214=>bXN*8@<sHlg=;vJ$5_!3HpF4%@+kzEK;m9^;MGGK8&{w zF$z!xdRMyJDaHTwRi_~La;(fA_j-!;YdIO=n1f!5Z3V3xLtAXQ+__%KYLiR1K{u|b ziRbs*nTozfxrAZsgk?W`At9V|^kJcPH;|Ilns@moCagz~U(Ts07X4R|MFZi~4L$NH z{6irKyd;XlLNL!H!|wL@_;**mel;)MU0wgByxV@(z`(uzo23xjd@$b@0(%<P8yuyA zcKL_=NQDdC5dSWDub3CS$9}uh4?n!#I^6>HGq_@J9Lkha9z8k`RGGLvTKC82o}Phw zCi6CjWvo;B+XVb|h|4o6odo3-ID`sWYh*3L|KoPow+N2l>fwRGVkMoNmtDNaXUt(8 zg=0{%fww3=+TRlKLvnR%IQV|euz=S6X6c6k&Gk0q172_x?cBXvl>6>k@R;#w#;$?H zxJ#F<h&kgdc2YQV6qaC|m~T`(N|(ciQ(1CS;;N6IUwb?^t30(YU%4S3E*o7sEcxR4 z*DS;yWZnV)y(9Sgm8|Okwf@#I8dQ4ugC35mq*g!1;kjXrc1n*y3ne!_j?u6ARFzix z2i<j=$hmq4yaY$^+K{d86;53KmkoH_+k&e(ozv@h=5L<F^%sTte+0dUdKhZc$IeeG z%{fww@am5saO{05w|KsQegWUD4)iN5C~z84Tk-MF58}^BkIxmyHmmb?N@&O|zV&YE z1!bnQMumEzIRF132H0NtO()|5SeB+u*KFrkMX~mgx>$!`-KW4)GX-gbcr~6od{b=Y zW{c7LKUo0t{m1S3Cjr2Fx5fRbmw0_@!&fUThK)+qm+F~NkeS`9bO6<aM|u1bC1dwP z-?Hs<3Q-tqO%3&dBo3S(rye~$eA^bjUPo=d-oLpa{2Rre;<wo$$7bQ-;Yv#$M7E`a zAi&-$S_Gm)!Hst}KGC;>!nS-oYo#>r>OaiP?iU?9R#Mchm9n1gH8V)1Zg|P<z<j+^ z(|iHv;wy>{b|lr}QxE=bu&LfvB)pXU3UP*Johqkb?+MdcD)-T&706~}X?E#1>sC;z z%bw4!swVZxjaPOqK3`fv6qS9)s%iskL9lb_YYZx4x9?LKjI+1thnxSWvv<Inf617y z>{JCvFTsCe5ia$Z6M-UH#n`td|J*xvTR)a%P(Xi2g=RCScM;1|-K5DWwjboHY|NNd z&rO=l?v2dOWhw`%#Kavu*S0i{a!Y(qA?@7f(6wx5NvKqtJlJbt;Lgyi9G}-P{4*bM z3a6gw;4_@i5sGP929KR<u{MF&;YW}bNakY1EVgwiap~KI@X~V&RK-;fn@Mqjz_P6N zBM{qyNh?}R=kjZDI^KW6=}iI^>UB{)A>UJo8yZkt1g3S_-Q&pKACFNws&Q~aPGR!5 zMiA;J;-(7-5ro$r!DwpkVvg%^YX^SkWi+pA_3JyQFt7F^4Vg~qPNnL@27i#o6Y1S8 zT%oUxu~$JzpEb)3_4J%$CLC{YwSLDTg*_Qy*%mCrC8n_M(I99%P5YVO+7rI+YH+_c zU#lGcBzrjZ%bBhmadGNr@Bow*xX<H|a!PPt{{Bh-{}eUEY;$MM4KbHBs{!=yyyD+S zAy~ioS0Fe!@C8}XV{qbgQwlu9Z+cI-v6s=lW_49hI7YGI%fZJf3r7U>KlZpOTwL)q z_gD^#zhmhPu4{B&FWMMw=uI9VK~4@byac@{NR2*q<7%%qP?7ewnEwn;V6D<&%aXjA zZ!(W`qofi;Nazqi?-82w`6_cKDcMc2f1CL<?u^l63$^Bv_YUUSQnziu_6j=C77oT{ zB@LUnrvFu))*2^c!u3{75c3g&Hl`=hlI4Lr9XMUbm%f!acCeRGP}_}egI|&GNS?d( z_upTZl%$=BPTGY=+`9$(?%qZrJidXxN!Mzw;8{ef_?ja3CfUQIKW>ttx{^Jpt@Y%) zCJgJV$P#n=C0Ms(_t!#7<qcT)$^OddL!8-H9;wjx>pT0bDlVo4IO~h0DhBsy`C}3O zcGTu2>_%<3z#s@DES)Z%6kk;R$f1hMk8#U8I_tC<e()<Pw7^S%{C`wif!_;tWXBHW zl!7#l>&CCk)!m9a(z9A6)-;qwP03Bij4^bJdW*3#0ZJTDxv=<w;~TT}<jIpwCx34v zt}<7QrA5RG3b7ZHBC(n!9~Ju3-#_HzKYHzE%!ue4Nq>~QMLfju_#fe6CC`5B(iZwZ zq7v<ZVCe*fBij4<vLkn#$go%4_Og6TLFT_QyAJK!`#E`3uyU!aSU84HLv^@Dg13XB z5`2A-FcRRhi)}5|rXj`}@}$$3w0~hNE|JH;up~Tt!2#==`YYRvNBmxWkZ0otF<|_U zB{`?DDRA+GQ*nZS5Ix_NHK-W9yQ}Wsrwcat{C2Tz1S>n3qMoxojclO3Y5iUtX8V1X z*JnAs>uRmV6T7~r#u|?Lj4CS2zYdy>*|X!_Qarq_UcK5B^Vj@OC%h1`?>6ZI|CE+a zAHPvIAWBZaX7x&5h_Oc?55*z1l?0FA{(0+^F^+2|nJFnfm6rID(_PD8%EpYu5)v%& zaHqtvFEVTTjXrsI6y(}rm|eWYk54`TiqiEPzqgrb>`6XJA932z(VLyr@Zj6A<74q3 ziue3ou&HNgi?-dHyq!9A?|vI~`pjz%)P*WNO@dwE?_b>}ujkdQgk`OVj~b=sMG&zO zCZckOu3ZC*FW-dR+A4)kR?LE}{9NyBmz?js=&}F|_PUt|g;n7{pma{-PZ%v#d-F^? zCbTSYR<fHmtpl`7^AB;rfyyOz(If=9p56&z=j7fByp`;zV`YlQu4qUJn^EQCeYpqI z;rGvDR%7rNv%*I{%*guOBxRz!3fV#*ef_)c18@Q-%tTy<a6`=3yHe_)c<=xm2r1?` zHja|iRzOUbl5p=DLlUAh_wxDJZ4%=6h65F9*msFS9(R7G+#G;cd7qV2R))V-1SbII zLl1l93|498MUdKFAfNH)Y*{$=2KgI9GR}|F_wPkoHDq~iR(>j)|9zwS>g%)M+h2{I zsW@a#;375+O6_==FKIU6pW_0?Ir*OvcSUY@W7B+q|H-_dH=}XvpmtN=_-VRqev`WL z!Q_wk6Dt737@;$)$Am)-3;(AXcIkyu<CxDFb+`)ObHQ)fjyZKwLSTosgG^KEG$|0% z_Kol}uDnN&dr9Gyq4-2a$Yw+#-(%umYTa<EY2EsIdir+Uxb0J0h2(_RE&=D6MX8z* z#=dkcS`r&(T$uwQMqnHHb%-P7{BMtm%UX_AbHKA>0#YR({<IrOs6l;0gD;G;fp?aZ zyE)2~gHyEk41Ene5>ft7phM)#VJqrk{S6y7_z)b7^4-Wlhd|<4C{n&OmW@w)lg}wQ z3|^3uRoiETNB&oJYc*r{hw^8@1iRS|A0K~h6o>}`ar__(3GvTJ0}u5f2=<t`M{UiG zQ?Uo5D({hE`3RSQ{``RtMC$G&FsDD@OsNXRa*FD@nhUiaS^6rAFan)`VAcnS((ysx zkj&89H~3%;Avmw#_22udt)-=PWMXil(?(Aeg)3oJKUN*=;IjruwP?3ql0Fm(U-DR^ z1rL`}b&e)Y9UQ1Pe`SL!*yPg2z0WSM3IBk<Z+G5H4peKNtuJ=P8ee+8`PNu#8qTo> z_xZ|ir|-oqw&>HV*L8iBRE7uXGYct0qtbDueU1k~S!VErV<0Hc@#8qflAJnmc$a%O z-&0-r{P`h+#ghX49t4%U`HA)QB9hl!zf}d77hrOI2e-f5ffq)2Cq7?<PcS`RLn2q8 zqw;8vXW@{`!y$O3fj-4J@0hZf3(GUzsU50MLUT5qGX$k(Jmlzi$29F)if@o;`GLuD z7vaSP^L{O7OP6SAQ}ccyc0bEFx4_}de^r2I1g3f8_FP>Z7auz)$Z}Nudl~bfKXQBU zJMLN`G<EtjZ^*L^_uO|Yck>+KQjtYS+*htHmQ_`2Mg*{&!BB2eWGMk5IFQQhA7I^S z)v8LTpPbjn)OwUtt4mHo6W(;>_P4WYu1yrrd=MBQ&T{q02ai8C(m6Q@hNec>tLy@m zKZBrRoq9w^by18k!+TD_fgG>?@zbZ~_|p!!x!k1w{aATg)!xqj|Ljywq3k)Ejv5mO zt(P?DGqNYwKU3mH3?9`EymY?QSosi?Uh?VNX&px#G_Lfc<`7rTO&b_YMWQnvoF-=Z z<tYm)^E!y*k&O7@$na4&K1_gje89d-tjwVrm<0Ai5wwiQk-!rY_YU&QRY_m8=zq~G zIKVN-Y^Yd1V=7Si|8;QGig3l<cEtB!Tu*PBl7uVFlSU7~>CAWCZP$=@(iVNz-mJmc z{9Qpo<Fova>aWLl+bDCaR0$LuuB*>Y4a~UZF)MD_F9P|UUyv%0hjX>%Rbm4h{cj__ ze8Z&R6gCcxCZzp<wlbG!BH=-mH%!ygG2VCB)%=f*f*j*;J_8dm>_E+d$yEvAEU29L z4^R4q#kO3nC)5vw*E&gt9H1%nH^r>EELjQEGS<<{$WkiNjm}mk>z$orrJBuL!K0}< z?sLr)2Ky_NB?$SMU1c$M?%LYQgl=wr|BdW@-S;-n!=z(_CqGcck<z5v@-N-HW0rt} ze+kBq&5n}R)`QL<4)f!CX?wH?zQd~gySc}K*GzOU*gtr~@c+n|qs2L1D^BpZNjr)W z2J0lP#|lmhA}h<v*48$~GxG)<vfJ^4N;LknYTiPZlX}>&VHlenE-#Rp*l0a<zkTM7 z1YhII>9+Qjbxb|@u1s&Kc%9%{kUzrKk%fjG<G(BHkI-LpC_5Az5LKCjjdZJa?cy6> z#JgfZA@Akfv(8I$^7iiDy?f~A*i&;844~NFivQp+%;Q-YLnEeR9HNmfE~$MS70$KM zY5Aw9Vg%64nB$4BKSy?E^%%=#f6q(sAE5W$KRpQTV-2Xe#dckFo6@1>{VJX(hY2mF zbZ=}()6M79bq3wx?*T~{@cMxLiKk_1Y)<l)MVLyN@1eq*pRvjAwN`wR>EDiqXW2ks zTV{F0;dK*wCp_D>W<%RVKpMO*`(<1py!^HN%^nI0?IV2&r(M{|IeU&hNzbspm2jJ8 zg^H)Ar|9z2P^7e#|HqLuH&1VVQt-mWC_;VdZ3BIRAt%;3A-DGF+xHf86v96+iKGXD z>tWc%L9JLqnGeS1Y_w})&y5cmxzgSTqG%5sur}X2VIDAQdds`HwS6Uwl<t)4A^u)` zyGuAV%mmDb{lD4^H?ib>?C+dM2xolR&D}^$hGJB`LW-gZOds>5ZRFU_G;?t=$C1yd znJv=vojm;@Cae<c1gnI#5lu%8MUTN>xlzk;>l^9(**ZJ7R$D{k&r|c0lRg0FbQcC% z<dl%Zi;~lcWYgx(4gNqsq6elZ6IiOgoUL=po<Ose*#xvv^#bM{Jkl(MBB2PX`4Fri zR-s()0U~`NiB%n^v91yOc^_g<H)f^`f;YQN_QDj^H8rthAGffnj;i#FIQq5FQ|z&{ zeMi+j{y6j#<Hx$u$r=oVx`+f;l|gIrE4Ode9(o;jt&`%(NA(7_(*!V&rn4E|S?mRu zN?UE8;U0tsVdhLA>iNpK{f0fXXQQX(wl$7-0Q#_DY`(%k>ahRsfsMt^HHvX-4<jCi zbh@T2ot=*T^Pso3!<Od`ken=~pocrSjkVzfo1b9VK<;y>bEa+XLYz#8A<(K#n?5Ec z>Qkpq<xN=7x^R89Q#)qeDB-%{PIH1>2(F5bMc5-~zcm$w%kzfvO^<WF=KARtZ3HX( z423Qr*aq*vs}+gpZS32YJ&1+LEOuKa4WDMRl=r_f!pT>ftB69#5^rF$@&Rx0r%#Wh zt9$tTJ#g(wbwTjYa1JN{#4DaqoK9w;L+wfmCH(H!5R{6DV;0q4Iq}z`Nq~8Dz&Cu# zGN__LeL@Ogm{W87;3sq==IQJtQDsr&Ee)-uf?oV!$9)%kleH`lrHBU&PayVP0u4;N z;#>Qqjq4>-bYMDFAp;jOUS6~#i+NPltv;3^BdSAHIyn8p1R3}`OCBW9^oXfz1TQ~M z@Om3s`qy8}@-{|ebRf<ZGf%d|-NLfQ9VWI!Fp!yoq>YSU8ON_7G*u%-$MTc;bhns% zlS)q>?DEXK9G6I1=uPd@9zAv=>=CObpfFJ)@g8JV@!W3$*SSHO@SkUj-%RAxuxvtM zNOwgN;t!FyiE}RWM57gI{;~2L#xe^=YGxKL6q*QkTMkwNP<GnBh&u+&ex=rS@+iRq znV}B|uR03lmI97>Nj;V;u%1aL9|)pmk=6rmcYy6RRjHqQELtzj4QX7(mNyET4Iial zawUv@oD;<!JY&;apqE8t;7(8Ded!Gh617mxyCcPA<$^d8AkW%d+Czo6T{u9%(IYnm z?}=xG62z$O$o=W~KzsdNglF^ls%j*>CejIFk_k&`Kz{jGoICsrQ>W~MWu<rRy)q!w zVzA+h%k#4H$NH<8c__x;e@~vO{2s9Agj?w1nrH%nwMk@fU(98gRp$%!;crj{1Z9PP z{z;*yLf<RN4%y9|o^-#S@=daPT-1;eoZ<pPQG586UejCUH*n3~OT68~TO6FP*G*sv zh+@2f@4Xl`@cLB#hlirR0;!eRV8reKbfla+i?}LS-tU~&NewkN+P)7B*}0Jz)C)0; zrnF=GiEDpGqqJ0TS_KtGioVaRoH}grpv=C+bLjTDW3T%XcVA;kLbpwy)ES~};9m)( z=-~h$iUISk?7wyEmQ(kL6OQC!BIaTEp{9o}JB)i%dXKFovpT|uR6e4>#0;=)3T?r) z^`52h#5;R&-bJ{}tRki(C|=P2$Tv9WQ}zD6*h)*qPJ`TtnKjLpwzkrcF;DyZ{jv)J znz&g}Jp_aZvu%&Mu{^I`Cr>&auB`(?5|0tG{UR!O5UL%lEFs;K6bCyc2WlR(V>lY_ z$%j$99Oh%hbAqEZRs0(*^b+^m>sh_h*vyAmnVQIWOOI7djt~~B5Zh5ZPQ!p)N>O># zMC^C&VVmkdOjO`bIfi|D-#D4aLhaDj)HK=NB86kjku+c2W9XYPQD|wQ-y@jGlo<g| zos(WW!^dnX0C95T*Z#<i1mea}_@q%FL{>>0sX}Ey22GqBbR0l1?(!V3KHu%}h<f}n zuHXD;${_Pn;EA72OhV~>N2vZBS&Tk-F;PXlok+4|XpDk$d@Q6!0l)cn#q^*Yd5h$? zfnY2%X(Y-$EsMEJ9xqn^Ht|lqXqV$&DQYHGp-eJ_18|W?OutXT9x0*Rv#h>W8tSnJ zj*NDG@-p)$7hC}bl|K5fezo{3N+^jW&}fMN;XofU)Qo`JxkHD&<#o^3KBpaX@8VL} z`aw5%!egAA1^~^8Y6!io?xUPbY&D9V_5!M^8@oSl2sNvS>^6Ge5SF5q<DeNfE#e2P ziUV4^<8em-$FPlojGd6_0e}1&y@!;aXwuZ$nxDLhZQkeR@=JItLRu8JbmD(0X9~H% zRu(D%>LrcX{v_`DyhY_K(}$uzkbld{NWZRO&nhNhyhoTU<H*}~;+{_w7=@3Q%#SIl z?aPYi3oE25e=w9hl}xf-QyTaIv{1-%v{AB<FQ92{&O2eg!vS9UZp?t1X3*l-(4}%D zo{StA6A9qL8D#UKNBdftK;bPMszHm=;H%i;(<8RC2}}Tlm@lwrR>;V__+hWqYY9P& z0Go)AEkM_Ei<_U?Xp)!}?atF79aCnKu-|{5@afv<U6Zkz60ZTHnO%hcm%8nyFN@L9 z?Jqw}n+PIsCiUHC3c{$?eZ}_@QihD}(i}CaAav?aGD=lJwVo|~yDRIK@3J^a|1q5X zY`!V~4EOC{d4ZTGS)q38PQ#oYj|S?ny11mHbkaetaE08$$3P-X6`Zf(3VTgeoS9@F zumU6Llnd`r=#n(!O&)z)#rd<H<-Mk=*qklDtk$D<!V%@s`+l<uTsPCE1*<MBgLO#? zHmO=&4pqe5()nj9C$-~EI`c9jN+xDtmM*odMAMsP$=@11L=7eCtz^2!qU3_HChm?L z*5-k(Jd!^_D_V_)w!jyd?%(r-5afC25!~Yjh7y$*Bm|JiN;bjw@ur~(b;BO4*)Y+R zDmtd|>9bCnE*|`(k!Ry5vEt=j3vc7B)rBo&)Pvy!W636z5R#rrAHD(y$V3dP!0uAq zGbZIo$wwJN(vI<&GWLT2CygZ>EFD?Rf_W4Bg-&Yd^my;-oHARx*6(K=em%v^Oqv}^ z<=<90>8x_DRT+{3i)b>KB>ticIVa=>pfy+@Jji`cBK57VCp5?-!;Ahl1no?At2OfS zZ?&G^(&<l0@W5JpG3=c6z8$lIVgs`ZpKq%31=X;2+?>qBUZPAYfX&QnHBcTVRT&#D zd)QahnS2?A1TxVWf_|&0k0Qv_oX6>ndrkp<;H2^&+FDbSiE2mTA_!@hW>^*RJQmgu z#i&%wG!b=d8Ogf^xX$a&^BjK{9DU5U<CMpm(Sl$MpHai@9SN{dQ#s*JCRD!T5-qbb zCrZc4Ahn8<+5jzKO1*a}i_2{NMr>rhNM8T&lRIg2R77875UU=PuEip{%qN$%z|?@# z)kA?jn0_@GCs=T%lIFfF0PAlnEUf)6rmg4HR+v!aXoQ_%9t}}TvboW>uQxyBT}{YV zl=R%Rv|$+YjD99+ws;|V0?AKq9(=SEn*UK2A>B^@bQw25;#O!y82yji6&~J3x6I0t z0yhT6gKC}69cX!aWid|#q_U;iN8`ym7e@*W;bz`;CP;JKMfLnqXU5hhnE9|fg8TR+ zLy~iuDL}FKg#yg~;jj&m=mcY-OVh2Nr;LS$S2BUWk4lI0Du#Z-#{fhS+9X>)jNJV3 zx=QY67uf=v`zHM|CXK`<&E~vlLJQ{2vxeg%s?+s}=qzYwOr6pHKJX}vVMR&eMG{}L z5BVH4&Folk>wT@)nv!t5C0)+7-+T4oX5GwW+K#{dkr!s<oc49>S&~1U(4wNEMGcc1 z0AnZDxP$u4n7jUCO-&24b*<m5<RrpJIFN$-#Fa~TX4JhSw8FbJD)i7gx8R^_e44H7 z?`Mfepc<65kNOkJa#Y=SG^1x;n>Z=<*V7l8dr~M*j@fX~3SooNaF3d^>sn;Z7FOxN z<dfBMCR{2@>KeE>{bA#YPwJ5+<PW*8)Auj?QOoxG$xGda>jrLIaawbF9Kd4!pv=6X zhibRZVY*c7l*2pL45Pv+Gi;veIp0IrwUT0p*CR#4N7kKI>)e$Em!(tT2Gh;Sc80I7 zZ(P^kr>-dAJU9tzqPlJ0tILQgr;BTK`DL^?z#jy$6JNi`@>1lk=-#_4uFw~*3z{d~ zljKdtf{Nu%tRw!x?Xo)@qC3Fi1o|QKP3H^W9ow${3IM^}Vbp*bXG$HvytH5bVsX%d zHXO&CjXI$LpIcS~VBXiw4Zi#$DsgZhoi)zReTEF#FUgLm`8m<1gpSH!S?$=hOF3)s z?6<y}#)01kF4c4+g(nAZPs7UyE7f^p9wj1^TXS^&3A26n(a(XgU_ikl<ngU9d|EYh z-;?syljc9&={+zrtB8uLb=$VzWyR)+8P<nQfzF*)O+NoCD35r5gKqWa6^o(rqV#s) z$&;b7(BNA@mVb7BvPq%WXQ1m`g?s(y52r&tK{IEiV}emQt8zZ|AzdaoV3xb0Z5lly z4V(3MJBX=N52!KH1DA;a(BTB`mqX1lCp3c9u(a#oc#S0|eti$W<%U9MW%En+eH{W9 zN1~s<AGx^b&se?FZfqTrVuT!<Me%AqX>ukH1>WH@e+^%D{2<(a?V}!I(kHX@XlyUd zzNXPm?f#-KmHsnQ0<2&NLPfV+=e=p=Ue|8V8H<x&M$t73t3}d-$03$7Skh~jcj>33 zc-`c^8dH9YS!|eF6_>;e8k{$dbjcAN-`ToPGh}2Gbbt96@Cyej0jy1qkThK&TOLx| zsHNW0Zsvv0`yD<$SZ3@Pw0Y_~kD-Zqnw2vYeHTLb97m@u2-goGKrjKtQ1{yn&&~7P z*WDfP%{x9g?1urS1++WvKtX)F*Mly;8fL^|oJtn=hGlvskB--7j)kI%ifqcTr4Zcc zBt?S8i(-wV{R8aQ8H5MYMjtv^&{5gPg$we10&9&DUpr@(S$}zcGr!X+Jvevny9S?T z%8wnq>3TLE5a`n`PTRL`wfg2U6f}M2B1^SHAicRI`Hwm5PJYeYv$({WI)f5C5MXKl zhobb8?$J{9fq4pkk4|((a8$C*E8R}02y*V`?OR+jQZw*d^a;b6t6EnKp3!~c+F`iY zUAR}6pTZ&^Ea`J?OB<>srOU`dw5NtzR*%Xo_ljy*H26kL&s#BFg8g|OQWbCU_KwP2 z_4dO46v=oF*T&-<(v!*OxYO7iyd=pGSpY!7p|~!B;GUXxY|FX~<2c`nZVSFtR!Jw` zJ(~(R%5+0)Rr2oGe8#fUu>%Ik;knM7w<+-{hYe%4WrCPa%@6St>DxrL*3{9QWTiH8 zy$>u7t7c*F@_7@1uX<*7a)$;Bdi4@Qqs7n%c{gym?T#1Rr&&mv%1mxuOxuW`X44N_ zI?s7NGf*)y?(31jZ=neZmGSmIvMH#^!gynAm;lGDdNXzz9Schrtzu>sUTk3-IOTg6 z1jxy8v7;wVE_wI*)xyTyB#)wvd^rfAuSv*G2mUU;=u%ZeAP~$ivecNASQ32-7(&{C z5|ayTWb567UCMIrd>v~HS4rP-QRFA@YRYr^7#&hdQikXT7JKtk5hKRhXB-+x=@DsB zP;5^UeIa`m=9m$#Q^+p6fvnqQACA{BJRuTneBIT&Ftybyj971DwJCoNc=xWl(e!S{ zQ0547`^WR;I!oa!P*+Jm^3m&syD7p}mp>lFLk~M!5%K!R1Ajhu;WW%GuYRapO0aYP z^kxk3)Qq#k=w0M?S>9tMGueqzY`V=F`I$SdH!Y2+ztLx*=-1l?I=!GNt9bjPXS^zW ze+2@hmz?dE{=X!UPm?aS7|nSqo|BR3OTR4GM7gM8kt3KVle(0$*5z!fdpE3XIqg!) z=!bLsBMC=uD{L;_!m~xB^)kK_ichfsQtU_bPN!~ST2FE*H6W4=aXO4ntT|l0F0;K1 zLlR+f*&66vsD(*{E@o#x?tUl|T0r`hZ_AO4kkF=ookNucYd<0`H3O>g(uS9zJ%TK? zmUsPH7_q052bi}sJ)t-`5NrjLv$pHRsJ@Yv*HX!<l+JGU?W59t$POVJgDWwgBtXNE zUX}s#k0sZ@i0<sXiB1e7SaIiW81|{+V`^yQ0*XdvZN`Z!L>cO_jPHYNk}3zZ{;4bP zov9=Tc-$YTCpCgz*OBncF~ChkvbD`8y|NP07}nyeiQZ)w$Q!NdblZ0y()W?#X;r4L zaknb}VN2Q{-SLymxpT+~y~FX60AO;qu$far1u5@qQoyG9awOhGtZj|ny>A?jlVN*; zXl!rZUFtDtDfILRE@NpSFu$-X6`~`MpTs*ZIGAaalMW7?4C%R5+qQ9yqgNDOi7UKG zr5cqfCpyttsK;cPqSs(Bi4vtVFz|RLmMdUdI0#-}GjSd5C#DT;rs6VidaQr@px#v0 zvJ?hHQ7z1VBz2@b$y;D?UB+|CSQhBaGJ0XZ-u%!`nv+(4{j`)rmkLGFaKn~gcap;E zKC&#kO7ZJ+(AGVp!g2WhMSjwq8QBWoziKt^B<b_3HbSA1IfjEc9%Vuo7^4|6CSk*u znCi0p!a`b*Zs}j!Z0{qd5@|sdBdcSW)&!(3oZZXNgoro25GC>Yr5*JovG@z5vX_7T zb-N(J891=5)T2~?g0Dc7CSvZ~wTm|5Jz!mky4CizLgNx}hWf^l&*u4h6X2PFoY}JH zh7^{VoK3?12DK$wURzUct&*Ao^ug654r$J1o+_j4Zpf5Ede0;!F%Au&dD(M5H>_mA z#Ge~*;$V4iH$TGLNC*Ny!iyv!aIMPFes?>2Bb|e#4^MTJWkB-(0#vID6)$1x9(^jh zLU!}6+&K%v#gB&x!MJnx?vp@Qj~zS4RPkVd+baIh=Bg9&o<po`aW+AN3*~V1wse_G zvx$x!O1KP>ivu+p(O=>GlP(d4+D#C3k~uk(u(`=#@~^oKCZQ1ZHNpy+aSm{4LC00l zaVeHS^eqgVEsCb>{U8M)6$0#mRI(yQBcG(r1P^!{oD`1RD3!uGYFxud)3*j+NM^Eg zy!3OKXu1|GV3y;4gj{&5i|Ls_cHqXVX~Xk=#Jve%4JwBZ@4?{@eb;3`PHEp7o_`wz z@Qd0ao7og{DzO!ZEWN+Av7v4z2h7#V<px2j*eQ#{8DIplWjjvN3`d><4y^-nDw1om z65_D-O^*$fR>&W-=Va@wSqj2=Wm@v4jUucflaNvMS^=#YUMm*dgov4K=afA>ol2ib z8ZOj*8u$atzQ8rY_>RU!j+;(D{WO`PVsQPs0SN~)%%Vn|gwL--D$Jw$L9EW#>mHkw zD=k-HHVT7a+OWekl0Fij^{Kac7sa2IZW~Vqy&czFKkugVx^;0C8~1xawQ4RH1VEJK zn)%0eu#P&SX1pgpQ!JO$4m&dw7ceyjr2UADbe^+qg2o^!n%)Z$Mz0bf3H^}RSvD2s z*<G<YU|TR}_V-kS^P08deb;+7B*E7#z7m!?HSYcV)O{+S!2%d2TuNmBt!A1=%bB9? z!qxHPw|9>>t0)u-FMZFl(O-4uwEJ0+;-hG;IMmTgp%|>(L18yRp{&SiHCo}-O{tYa lzmGym;n4iQ|Dhm7$+x<DZ-?){$3+$9<EM-}_Yb=*{|mk~W{Us- diff --git a/docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png b/docs/examples/robust_paper/notebooks/figures/tmle_convergence_causal_glm.png index b40fc0b9f6421809cc6139032cb0ef76ac046aa5..fd709e406afc2be613039645b42980939603b27b 100644 GIT binary patch literal 16644 zcmeHuWmJ`I)aC;yDu`I1fPewgprnF?B5^>vTe?BI;TVVl28Wc8I&_J29uxru0VxSd z5#i8r=$d`}W@gQrKi`_~$E-DL&5!rB9-i3uj%#20+Amd=<)|p=DG&sqLdoA(M-Wmp zf{=_KCx^cg>Kgt7zub10(RJ5wwsQ9}b+ts4Ox+(jIJ-O8nqBs^bak_Jb`s#d&CAby z*~Z=dk(($VpX2|$fY;g8n(vA-*&y8H#3Ok<Hw2+JCH_Z}EtzGDAW|-<`*$_H6BkB& z{5AXb4_7u>o+0EXPvqRYXDMX5FQZYKLPB!)E>EkO47=Gj1$$T}`k4%tR_5sW;>r~D zu&9qmFKE(|vA=kcW)}US=mg1?D^w@sKU)L_8S-23M_O3%M=sRAe{+nwif!|~pLc1u zS7+6<8@F&m`*nDsI9VwfYWNkI3PX+~$V2`kQt*e!D+nq4+ewns2-0A291VYUT^c!p zAUEhpFCz&0+i8RZK^{E+PkuxrJ?)q(BP$!5eFK`LwXH3Zg_V_+gOjs$dZ}`Ev8K*< z<8OlVPqC%i$(|LsYwlAzDdx8J_P%E)&dpDBns_daF7zLeAX4ix2%)&jZQ5>Wv@S46 z#ntuW*cd!Nmn{YzVAJxN(RXvit8L0C*cx%JoFM1XFU>X_rG$T%utEa<3|G0u-*QP? zU0Z9L3h>*QFFusGem&E0^aNaRj5Os<?bh!wQD1^hRV^0}kJ7t$?>6j--?)5)<j1jc zhru*f#Ta=EDJiM@TD!>ZdVmb>@EXxLDyNY)b}lZ(NH&#n6UnXdr=hQ2t<Q;31|908 z%Tkp&40abSks(MQCCP{I_W>JvNw-|?VR~zQ++Hz=bSy1#UqTXu9dqM_?05E)5F};i zG7|Ht+GCE;CC7}ykdl!Jx&8g_dcr)I>~b3p4nZU8@rt^yZ*`_pf)y@sCN-qDKEMx$ z6JcX(F)cwoco5!^ELqekvHO5Q*x^T~<Y7L6;C}SjF|Mmu?=gzHs}>p8^%k4U;oeXo zh$ht$DL$nnQ7t+;It<pns7^aw_7!LB=Hg(99C$@wQetAh(@6DTi~3~|5gqH!_kEw9 z9xHRltB-h3zKXwT{+wRK$&hyvydnC6VuP`6nO!sngJD74y?fVpBKSh<f>>>uTtk6z z-4MrK)oQ!Qm<^o%&O`|Fo{%7dsLI?Rn@KG#6>N{Y*%&Bp_T_jPJZyM$Y4+s!^7139 zOSe?T{Wdf$EU+y2uMyYuE1Z&OdGxV~iI-5_?*yy`UH`}z7*?sPsi{o~TfyzB;C3Vl z?U~P>9Z3@R)5i2xd)U9abo--Wi>e^vIopfM(<%BHchmelBV)nax2J=jJ<C=~5cZuf zZYlL$w=1*h`WzT!WtDkdIdNvi&rvwxV@U~POfooK-+hG8@VhB&eZAjAkv}HTx+5_s zB_+j`Q99%lV{et4wUFbGW^r+Gykt=Q=g*%*>u_r6vict$KE5X{z2G<&xLZ)}G&0-e z=jQ5q{=x<8WF;_|78uM))PL)PReQW8j!DQaDlI*|E#T5*%bU%uce1bOZ3jM>#*6!v z*Da*Kkx<W4PV&F~KWs*x*O&Y99NX;P_8JE3xiDnAwLC#{hqH6otyBC||51T=;3fXY z(N5mG3l$4B*7iU1b1oQr-+lb}G4ZPV_wNsgypZpPJ4D6C>O8&%E=&`Q=!TBec-fEE z`fSV<o44fpto^0Ab#*B?Bt*>6+R94%yA-6S;NJ`l>iH(PQPWI^pZR*H#z?Ev{Wcd* z^`lTI8ujNHGpCF@PLm@IiO11e@_~W%`~m_VI|SQ|tSl~3(dsXZ4Kg&m7mXk&`to!- znH!0Ib4js5>ubJV3DGA<Jo=63McuRa1<!g}SXi(N3H1dso;!Cftm*d~Q&I%2@S3hc zJ)BNJ*1S1<W#%2cBEGEOxWr`F?puY;;5#0Ja-x4ZiPu=Q(}&D4IM)_`pM>O~bxr_Y z$KStJdIC?yAk5CgQ_wr@>gr0^_~rboh=(?8v2N2C7k&ofA@E28Ckr?CcT;JUlT+dQ zz#Ut?f}du|#rg&DcYHoJt}Zcp4X0jLir;d}hDbeo4bdj$;NS>-{n~R&D{*DAB~QCR ze`Aghx2OK*&gu*0_jmB_&USXW&#9RUAtJwz($dmqev4$=-5@5~-1GG$`DSTpX@uWl ze0IZ?F0GxNu`G^5r6_S<f-0OiF-I8HdaLbjF7=l?{&HXWEwfv)KHJyx<S6Bxq1z|n z7R;m%Y21G~R^a<|O!}~>$f)ROR#f$)i6_EN!`YBus{Q}Q+ZT4t50>=79ab-KB1rZ# zUDCodU&2i48_5IRr^l$ZlSDm2>-hNj)xoY^EzCjr;8W3)$HDV0EsJTynMk#ijg7$= zcs9m6-m2jJYOm;p&YwAx3ci~IAtd1==jmDg`}gls^OjQ(R$EvV2*;c781g4io`B0| zRaRDpGC@tvCe<Z{Li&GJJN^sh=Kuf5|C>h6QR{+Asp+VlSL#y|6?0w4fxW3k5%QKe zC&EfZBDFDb?BYAwQ5e@M9!`aLF`e086~CL0rdV*3kRi30IQI3WrHcnF9726IDi8AG zTu9g%0lAFd%<GHWE*VS=(*7arl<2~YZVdszvOMs&y+a_VTL-fk6Ll>?N!0UuCAHKP z(RsYNc?y>MuAqWq+)J?3tnD0sC6MT&36KPt><2!Kd)c$BM?`UbyJ{PU4KW-(mcnmJ z4v#6`@;0w1>P4w%V;3=jZ%d`R`#$Pon_s=!MKmP<PN9GOEHuhH#_X?3LL%Ir#HGi* z&Jw_9TZcVlLw(c@_cQSc_C-rs%D@Xm&BmGpszI@Kha^6ie7Kht!()(HACN)Ah0V^E zr)$GK&t^|g5=Bc*HHn}RwAud7Tv5ERWBjP^9M9DBG#5L&JcP4a=X-Hhx+_I0%*v!B zx%2>M8c-0+#(~?GP<5sC^z<;;`heZp;0wm8VYED#7O74~IaOtllAma>C%$kb8jbMi zeY_7BhK7apB;Iy|dN&=)M2j#JkAnsEtHN35SGl8BmNMf}a`IVX8cLN85%gYuc=X8f zw_B=7%g%yG+;CUAoRH0r%l~kLz_ja`>8&kqDmwmW%f~dOzayk*6tQ9pSa<Zo69~VY zao@~}XTH2VM<dRSOrhbZuA7(Xmpz)er}U0rhE3&t6pIJGTLy4Z7nEd^Pw8Z;OaM(E z@}Il@-~-=!xlPwSzB&SChk%FLq23ya<Wfq^BgnZAgAN;Gl6%XI)w8*Igw-~_IWTh9 zh}W3X7TJU6bW%w6^L*{}TWh0rc>NNq>e+tDtcp?JUG-0YX?OZ@ue1F&A5sXDU5E2Q z+g-n@MlZJUL|x<X3SJNO4#TbRz+d6rJ089fn*3~b+eO3I4rJe7us%exWwi@Sb4+me zGcwB~2+zWoFL2@4?oD*Qjptlnay}x9{JlrK-|vRUjn#(-yqF!7=w-YeYDr;7SzRP+ zmr{cAl1PUxKlwu@IFRh?n8-OU5w8mD5GGS$hz)-i)f6;iU4m+PO*ckKdV?BHSW|6q zaL^Yp>VlYm=leSK?z1RAmVG7l7rz2BGPcd1A)H*qt8Q*@Z|he+N-eD0&<B{L-WJOj z%K`z18etXMs8pHML#*z?i*Z`?WGd}6G9RSi4%Ukl6csh#&sr5uMksav+VeE5KI+|_ zW)buv!tam%dqZ|>2wvOBQj3=>Dqg}5=YLhGY!17<srFpRWe(iBODtcCe0A7Ae=Oj) zikR~kZK6CPhAAH(-K<>=%|3QKF1&LVdCep~G%;jWYy)_tewGQz4gPW;-zAq371idL z(KlH#=s+)M^R|9}!vLJl?#qiazJK3YpQojD$@x%H{EUIwnNVyNye9U!@l{Y}mRS8} zv`#kJ@wkX4?JfTl{ueJ_7BjP2lnh!F>qaFe&W9DMov^UB=Hlg57W14}m4C6z979&g zeX}dOMu*@$^5k75#GFJxIAc}I>nVJ0-s(b@hr_^!yCqib-9+R9SY7?8#V6F|;KtNH z!_D8BA1X^wqmPc9LGmt;ULKFnOPS)-)GJVry#D%e!HqB(3d%^kzK<M2>*lCQR`HbV zY?N}z)5Rkwt92v_tKN>BqT~PK@`yHU)Ne`L<RrD_edI`@&&R|X+sNKBdt31X_DRBP zs@TE=#UY-sjOXILRp(gJ)3DMZaU*1^wF(}z%Nq;7lJ2n}nzBvppJ^@?X}i-F{t@-F z#bnCsvwyqJq1!!)xt=>-gm;<b<)glE;ee&(b4SJSZ+tQZTJHnpMdXe}8NEPI;ZK!N zG2?<QQ9mx$vM3je1f}i(jHjZnvbwlm^3l|ektglvJ;_zif-iZ0jnu0oZR($~=i|`n zW@dGacEQM<i`}R7x1KpgAx6f20+R5ZKkeLxcV9^)IEG=TDM$L$yFKY9ip*c`(>U;R z<O~P|Cy`V>g-~Z&wW3`d|MXP<qXio4jf!Lqa6Z&~N?uGs$#sx0{W-M|Yt{)59_Rh{ z;G}E)aLUd|PmjR*mvfUe=Xw;lwDVP^q|nt~LGq@i5gUFg$uax_Mw^qIy)@_jUWdEa zig1RWy@3pC<am;hN$FJ@L>A=-od5or7>$BD33t1D*4*a`oWGg9yPi-mzV(W!wKKT@ zi}h6AoSv>>Qqheg{50rDEp#4ht9imPf3iUX;<E3Fh+KYEkDP8?m$1)A^GeReuVrl= zDBr*8TlTEB<=9`WKH8(Xo{raMs?J`N&$)n%nx42Q6|JKhS!8pXEBjk)+)My79bI>+ zZMT-r?aiKwBKNHigzafr)u@eD_Nl8Voz~+4hi?gySmRZ}Jyf$d#nN?h#N;wUn%!NS z^KBZ%e6+hKBXB=a`r*SA0`4$LQ?~gyoj?E=<-Yq1teoc0CR)24l4T*%%V^H_gn0k= z8)~`}H?|?u;<u-h`%~`nu+*_~n8@C~FqW3djF?O^NFiGBe9Fzs7S9(3ZsObc97TOx zbD%fMz9#qDaOWE{OC2}fYEz6$=2KODFM({_9@2Prx4#Sj^DF^}pxs}Gw&M+tMP2{A zAOhS`zp(Rk+poB3$DT{Rj1NTi;Gk0!)|$RuJQ`3r*CBRnHk7OIM@gqd9&~&*U*CT6 zUGGD7cWc!I|ETp)qa}-voJ>*R%DJ9gQ)2V@ad*SxZbiPT9ov}j^j}vkitiLc!X2(O z5OSY60}acsB~Erq0nhy6-AbuXJa~NUFM5%fzuzP11uG33i!|75=d{5V?96a%TE=7b z-H|HvskzHeV`v1w(!yxnWk}<Duq<u+Ge51)IEqk+&A|jlYw8I9BJ-V!esq+)^~xTN zwmbO;Pe*gZGiNeF#E3WJ`*$<5v@_$OF)qdWuQntPx{g0?q6w1-s-G#DV-dfCpH}Se z=e*+|GwLspKfpTpMD&SF3)F!9?at<TFqP$wIVrkxO9;u#$oQtMZd%|vD~+KSveN|Q zp((N0y9{K(ExUzLzoaEM?n76p?u)2oqv}FFJRRy<nY*o$$I{~VMF(MX?MK76Cht?n zc#vq46Vs4syH!b!L4}jCq)CiK=to`dp)z}&ce^C2+m!~#mLud8PQ4`0!>(hVu)K;9 zlu?gk!S|cANjz^)(EEE-_4bwr`Hu?%UiC9%QtMMrR5*<+!!qNyrE1M|br|;o>`{yp zrnh^d1EbKil}397iDJW%ONE83pEe8@SJ{1(rGoT@cx71(SX>$uzT0hz5@Rv)!dpu- zF1lx3Ea9^|Ln`I7>cc1%)?{|56=PkuK!&c0(mwWMumU5hNPijs7Mo`|zv}VViH^>s z_v#V!>YpCOreu(Fgb(R6xXUP&Q+nF_LAMFZ$Yt9wVnIYfK@^TvsWb?$!rpw8b`0J6 z)oe{oL4n6`yYy^mczFKI6-`?xjzlQ)no;ch*|X|hjU<VcoGY$_GN^WoXHqC%sqT}g zro+1B)wb@qo9}~#b<Ays2aX3N&XA<=ujnE7*JY9WT5l8V&l`5MV!|2gNH7$ce-5tL zzQf+e{0?16A5XwZhtwt%yJ^|Pzmr6wse)5TG#L^^E<D3gVVGhGrPwDq6(OwRX>E^x zyyJ_pCnMunCrw#h+rEApS-y~`1?`{y$1hvf6Xs(qwo|R*m;;ua8f+5gePr>Zj%y;7 z;Om!^ly3f`BhCj%O0GHiX{l({;hRow(+VPEiG?A|2MO8*@qCkhI4Qc40F}H-I^3Q= z$2IAA2_w5f%#{8=w4usDa}eV+CV_uvZJ1p0t7r^oSsadOkMYS_=SA@;gmSDi(uP5M zJQ}og4AG(?IW3i;vHE(nEnI3~91<EIBs7DOKx(;Ck{d)QQL$|vV&5g9l7u<fo5pl3 z>>JB2)JvLFFLL0|(UtAhGMpq{$w0g^ia_<gBH_B>I^9KG!!5;VL78qp%sm#5XhMym z<lMpt!aMOo7_pixLAE{}W8TW;!V&2~F5lx)(b50F{SHIz!M@_?ZG#VPUXA4DRb?*b zIR+y5?{s*Gj9u>P5}novLB(dTfN9>ceUy6O;DKuFvk<rY49MDw-j$iSxHyFXPiN@< zWvEjX(odKA{42~&L2q=_C;oi7Z@cw~Lwsc2jrDLLlFv2*i^9A%v8zGJUYTaNvm+42 za>r2A2cvWu`Q9|`fpG22n{<$o``fSm#fX@nAC*_rHB0L)N}gNRSvLH33bi8%1@2|I z#vS3d*a;Q>W*zr=M0I3)kD;Mem{J}&(%L*ibv7p_R>U2fsc=oscdfh!LvOef;D%B` z<+9o4WA#VfXJ~~VBR3e}f9w%83@l7OUj}}t4Tqb2>yY==?)FEG$?pWk4jnEkSu>fR zJC2lqi#qgXM-)|)s=oZv!Z5a#kr`9#V4wJDF6R!)S>9Jxy*Z<Ns`?VbM@bp!>XtF@ z;5<61|7kPKft2K-ummL<Si@y49fUKYK`Omh@$jI2zVCXv=U60W!_dy2C;jc(0&lWz z`GcKVF54l&&e8KsPTv)1N8Hty%UZdLK9C$iZ*TdZJC2^sQemECFR%6p=$n+HlPh0{ zIDZB2ffAeI+|%Qh#m5;qMO;@ACN*-Qup&qt8t!c3GF~yfVyx%VQHY3my(udbZCFo^ z<O<Z298taM^~Q4TdpkxO<@-SW36@K-wq}`o#K4)ZCM=9It}6k$z|oNv>wsPQD?j_Q z;t(mVsS;6i_GmVljdBwWdgHqfUD@tXTikkNxbva9Wt10(@%?9nW`>Ru_riO&lg+ZK z7P8ccf*kV9;5hoPhCazm_mrakRrBXdap50%Ygqc+=kPC%PmTzMRgExDpCtp7bmU}1 zMmL32>^v9hjp9%szPdP^pKtLnTEOOQsfYc_C*B)Bws@f<ybTBU$gE5Og?cZ3j)^z; zIa7>#QMmJ7xgte`pmej<o5!xxMTt*7Y_@W#_ZK#ETbQL;o0A|>->n7EEpM2;?DDdQ z&;|Nsu9fBgk-3Azk7j=l<xTgTWob{eb-;f8%D%r%s*!oI=2?IKV1d|@A#WI~RGlBS z#)I!;f_@u)OmA{N^lDCu-S?a)MK5h}Bc5|V+b1qohMqL?|9L#Bqfa&zjc`bgMH=%e z?C8~awb-p$;ei?J@6FNZ!vzJEIgc4D##D}JJWO%hiVUY>rwtQyN-U0yUD`uZ=KXi5 z9_l^Sxp(hLV_B0~7Z8xsS|&$Lqi!6(#pUExWIf^h3d_clVbc^EpkMskD>M}Iw_`tH zs6u3MprUhs)G!!FDxBaxA9vH>n^zE`>M22mwxluw8qIYkotxw|+Fm>F=GyfBp~#M2 z^9d$<KBJJffstLYkSYCdbsA!%WEoxQuPmp#u^B!>4-u&|(XJP6lz5^Ky>T_0Sgb9E z_AC3!Sr&*S4$M7PBjdt#mWRB0W>NkwY>2SKOw$JvM48hPDb?kECe;u^;k>cD*fU(h z-!aQGK-aC(P04S!d;8<yFm+r{mLeJX#mTTxeqsO09Ua{Gi;698=jKxgd#*aU6jE<m zrXXPDU7y#lXN1~R+|h5&VM+?Pd8f?5c2D5OkBjDq2eJcPmq;l$VrV2zokCQFdr6Mu z=0H!An-{*ce1|${Og=QWv_$n+7(8<O`X1`FVl}w)*eO|A(b01>GAAw~%R*MjW4o`r z0{(Bl-l{H032f6uF&pg!I9B+6&<#ILYysc+0nn@{QkxCFHi|Uhj-#c7%*zzaTYTvc zIuznL%zZXRP_!uOueX{S1CF+p9c2)$)$5*mA%ZMR#-JbDS^c<dE)cwXz>|}Kowj&} zGhhi|vrWZfRMt+guZqY|OzFqClccPgk)O_%z5d{B4DUK2r-xsmw)Cx_Vt2B|cO<4T z<&pa*4n~<+d%f<X=+>9Wam{m8Ro#iV7l+?*<HNDav4zYrD*<C<7(3rR<1iWhD)o4w zFjgZZn-h4!+?U&HiW180ad-xvoFr^2#+9$^7|K_E^qJ(hl{jJG;iZrxxi86%h3<Yk ziYl?>UOAA)XvwNgb0^m)n-3u=vky;P%)TBk#@K2aS*p-|1a%aDL%UgdtWY9IvZa(n zst@{E&eSLAztlA{G_t}=T(4bI9F<!6dJP~Ttugt5LUV*8rdL%K6?+A9XYBEri@JNO z{QrIsC?U>w|5{L^<SYgn%iaOEvXXKuCENJ{O5g3NaYe5&7CaN(*xswy^S?JPa??U( z4No7U7miX+j#;eh-=h0>8#%a*{3mXciPM`CA*{*c5tys%b8NvOibRmgAk?azrOY>* zS2X%FTTN`enc>VweIAUq!bkQSs?aLy*Hsf-;9BRpDZ?!1CW`5Y^fZ>wBmsR&^#Oyx ziO$oj&t}K(`D`jLS6({E#~fr#wy@bZQJ7zljl8~uAFg5(3aWP>N6WM)FtjxqR8(I; zVtJ#hy}X>V0P<@B9!^Q5@lr0zV;#ddMiDBp%zQ>78s1xB^df~tcD*-31uoDsJ3ss0 z+l%R(=E<>Q7TQNrd{a)IRDGSVZT`2j#LYLXHMZA8U4(98Nr8^ddmhGAl8%GFch0|Q zXlS!0=!Xt}uJA`5^1_oYIgZY^KQK`v!4$?6CwN6+12FI3S~wiA=P)r(uK}|J9_cz| zRQ$&fRn!)@V(*o+KnK+{x@QFX|49Q01Z_h0@ZP<988c+r+;_NQ93lzDST@V=tLtw| zwJ|d4PZe>zYgi`cu~iau)%G1^h&NyuT7Ckc3h#Y?y&ZP%pXvl@wv;DLEn=~cIC7ng za<mJW-Di8x@eBLPcQ3I}u@^fhymH<`kj0fb5j2d$(oO1T(klbexf7MHZil%&S(7Y| znyRWFoa#I<qyBEmSFbW*ZthO&dsh~hQX*2e{!9oDtzyRZ(XFR_oQ^{*JbGq(C6(8@ z4F_wh{tQamR`D9@vctUUG1)P@v3hZRT;TJ<y7KqRNt{(n1o!kH1W}mmVSQ*draD5S z)8Mmcxas*LaLcqS#vj$fDq~a~sN3zSlq?~uBDpWOYqaRZwBi(b0%>sYr$v^krkPAU zj%q42(L7RC5*jj!R^#v={#75)EaDsM7^C*}wqkb>YhNwFzF5@DbG~Jy{n~p+ei9@u zx~aL8arkXgQbA?qt%ZSN=_^;R(1<6xT9+v(#h*86EP8lsF;zNN#I!P=NAS{HLR7e$ zFKbR=csr8|?=HvQLAUY>`_3f<DO5d9|47>QXriMqexSH*Y@Fsht*AsKM><xKi*ABX zs#_kl%i1)!>rxgOt8oWG>Vf1K9{(odUHHaNF{1Zgw*$%t<Et>lk>0SjH1G9_L1(Yx zS>kUJWSItt%;DvlvFN}z7XDACeammCcdujAEuS^~GLkUSr@NpG1LQ4IlGE%mpDO%W zIs7SFK3ZambIjwh6&8sDrV)gC?2W3O{@29EPC<%_N~ui31=AdmFZJe}ix=OuMJ*Al z1}nZ#(X0Bs>>maRM9K&H;gpMz%DzUhhDd(fy6(L4du^y>+6N|o>SCVUZ_gZ%%hT)3 z`fWX@MpQ@RNRAlFKZueIf2*9#yFUsBv;F;L1xCv=88N3Y#xd?1#hnk5dCMHWiTESv zP)JLmkE{nPJ~;jo#?N%6hrW2xoueVZN~MX4oP*M%BDoNmN{WorkX}}OF?W_%hB3w> zy4MgRXxHoRG51rKia{dx7<0bHteAc|;gb{z!cGRTiGrhQ+${$C5@QCPUDH(|C+-ms zoo8pyp6$h=@DnP(D<Pvc!2mnBl6T8rtvi9A;x_Iw{t4D_&|&bDnAhawD=J1Am05Px z(-%jeMM2P#XOPGD60N6yDuhP6Sp4EZ^(*YiQ_=euMsrDD@?3N^WPpYQ7>VFp9lr~r zT+CYd_$hATIPump_lGljHN)CI@?7dCN3tmfj*y-|d-`F|a24(P5+BM7Wyf>vh;l@e zx(NnQnR<JC)8;1^M+Dva8aXac9A+5@tZNZRy$K@DKSOEm`VzY06;Ru1>Tg-hX_umP zh7+15iC<$Wi#+4dEhx19;~<=%ov)jk_1;$jMzk0f9;0eam~_<(hF$o=&4i>3T|v@@ z6dwrM?b7frouy%Y#Z+g+96*SJIV~s34)F8)paY+gpu>GkToW0hX>vqL)4ot;{Z>&_ zREVI<Z!?rS%*+H$KOYg_{u5dKJCuvlA`yY^m-KRO!EcrIY*zd;mK^1zV^N8Bq;ZD< z7F`K2`zmKAdulhgn!}_G<gNmaa4rn%gQ@SSay#t(^z@k|f`$S13%j;keb-RH&;?>j zHM%nt+U>OdNFc~}5JYGyf@Y!1zGxrSUBLf&EE*GEQyW|2LxNaIBlpWN@81^^2e*#D z%7?0fT6~OYmLKA?&2OwrY^OwseOhj`b#j3A+TZq`rmzbzYW1>R98R_@uH_8Nc-$E9 z?<xVfN)S>~iFM~t5t~vx7HIkc6Pz*FCZhTEs|g2V1PT2x;Lst%k4}U6I1SA97{xr< zSy-@XA=gkt#pWSQL3=JnE=@E$gdP>e+KS3Zn3>$T=1G#?ic(VQ`|~Ywy(nm3Q8+=V z%&xB+<OF>{p_(O6Xu?F4*ZjmqEm>vd*YAD)(i!<Jl=&{zZ9>u}P7@YJYAU`gA48B! zH_2XbP7sG9Fxv3g+j2p5D~XI?Z@c}SdCuUJJsv@VsIG${Ia6iAXmgEf%3&yyJJl8o zO<Fq`8L7RG+|MX=9+P+lgJd5c9}ISU{9(ZAH#Xo!Vs|!|_!RFlUO-@E6oa0)FuGVR z?)LYeYNKvr$R30%8t(3;vTvApP%!k}Sj#3sZX6@M?9Y2s&6l9Y!8+w?pcy6T<+S3l zxw^VafRT(n3?W*-etpYAWV=VIJ-9;7kU<6HCTYw%doJT(pRqBSB_or5;pNNM3t$xH zpuOUua>qP5<^W9)W!Uv(zJft}I;42b0)q-(+$Z?qvqz-1!u#yL?^SW~82_7RnV1#< zdA_kYlK0}p@w$~}hMkobW)Bz(ZwJ(Zcn2DIi4vcZweKdT|G+jBL0eJyEEbru*lf%X z`p)KLl?ELKzPfnxo{-(LB?tlz?jz4Su^G{xEk39qB0VS+u;cD$@8FPc1>y(a_1SGV zZ!pr?O#N{IDhArkNPm<f3#+_nCgp{2H~#|dJkr~5FCb{wmXnwLuS<*$EX?VqIfr2- z73jI~ainBHMNMI`Gi`i0mOVJGyT{SX?}+VYnC5BH3xI?P1=!Q5PTQ`u5txYVvi~`W zNFBY3e9qe0h}O=3d-WPCUIp+x707FL%Ums=ke|voN}VJ_5P!1ck~6<_W_#p@YPUHT z#)9N|^q*A+oCP`4vLxx{y{k7=tEXu3F$VXzeA!`ue>?^a`hfpby!8L4$^X};$?xcC z#L>C4Ahyx3@htdRWKxtIxPyX7_^4lC_ao&oF*caQeY16nj43E6R_AVk#AF>Xoz3pX z!cd)G1^h8OAtCR_4^<gi*<8@)RIhc2(VROs+3$jYtgO)tMn^Q_FLM8a%%K;=W$u5z zMx0#=3k~h2<f~1Q+?$}5V1C+X*9YQ})T5NNGa#|a7#&NOquyU|4${!ojni3b>*&xn zG)(lIA4mliQPB<pDi$)u1||gsh3=HQPZg+wFi{vx+7;>NHY>lIBp9VZNg^A5F;&MT zps!$=4K>$aq%&3@FhVZ^H36Ukp<%Snudm2Na_aBjp<hl_Q?yi!qM8dsWg4ogZ%;9b zVxzfq?BGsd0SvaGL8=bEXm+4DU8VV^Z8r;)#qL1HOPBI@mvAO^ew*4bl{$a^JQj3B zb76e-KY-yc(8y9ATmoH54%Ebn6>cU0G|bom?o$tYjRg?pSo8hqa&L&zD8tbj(?;@F zFnwbpWe~E%mf82`5(P^1jhkaZMF3QXiW(am*E__PvbFNGd6zoBe*N*{%r!{$l7qj9 zDi^l*-k;Mq&w?f}TSAxAvxi!8Z~XLusOP+{!&W2~<XyHuQtocd!P{|haO8ZS^ai~i zW1)#JKEn(WVQ_FT9tOHPkS7-$mvD#myoQyke~XDH_Y#@O1l5q3AvLo^ZLjst`rP!R zeYiH(_T+R&QV-MvTiokculk@CXwJG((F^`SlaO8783b+IyJkpexxAz)37(6?wub@o z@-LifSDMw6ZNVgs>rgaX1)sy*;nw%zrk0kP{du_U(xU(nRq?+%3YIeJC2{ZhXxZkz zwQRaDT4w+RGR4W0-0SZ^+0~w9{`K28HK;8fzrUQA`tygKpTB#-IxNsHE;@Rur<jDO zWrSCUF}jfb&j;=CLT<b7jA}e@8=APa3xQoh6qirT?wH|IsYl_o|2$f76o-<jx_$=P zpGoo>b*Wq&t*hzW*>GoDgc8MAD1PE2q%Pkp$j+x=$_s+#%_kj4YX?U?!5V&q_V;`M z0F=gp4(s-JfRNKPG&BUt2)^PDX$rM&f%eqY6cJ($)%ujs@)_s%_G<2SUb}Wp$a%Du zg6<d0_5~e>1VhS6$jGy0sSeVQAr1NRBofR0xC1R1^Sf=A6dKjo4VBtLFy6iWM&aY2 zL;MZXCylSiAmpHaqz$)#u{o*XVc3j}DhKKlC)9w=(L57UFyJ68zE9|ZHm6Q{ckJ-M zizouiJ76(i1O4SA&n6n3uV%%Xgd4-3)gD<w)hEgtL)IRLL7W5Zz9s~)%ypYj7G!0a zD#?Y1d()Dq78;*Z^*|KY&aQ!2V75CWjPLm|M5;_P`n4h}5~Bu6hx+Zm3^H<ZT!Mm{ zpkW}^#r372Lj!|y2bW5(<?&DPq8_<pEo;3RD)>kh2^X6sz!_GZ?+eh#l3mdB^z>Z6 zNl=0M;SxE5Ue$~~5WyfbazxK03OPf{Aw^`nO?L$Pm}qOqG{0t?bN@5!STT|bXyn5R z6eRiI?6+|Acf3D5r(*c~qx|6~Qe5raK(TrCQvGh?XOm=!faB2SHIN9P17o1)N^q+F z7RYS~BfR?EMX|mdjTY;rt%Kbq_;z+n3yWK&lFJQb3i9&V{5@UZ1O6a*w1x%^^rmGd z1_j!Tw_U$j?-v=>Xj9)=MoVv%=yDTFyC9^A&&*)s&($d*`PzBB#<kgic`A}Q^vOOv z6#kG;gvvP@S)7uRW23Z?N<dth>$Nm0;RA3hJ1~yKy>X&3WW9lm8UzrF(5lw8WcgZH zS&15kgoMQUJb3x?Wjw!SsPq;k6;+?);kF9#VeDr}eo&>Rrb5C_P*+zcYS$cxE4vn~ z*H%|c-KLdyJpe(wulMQ1+TJk<SdY$BPMTGHSWOg8ezbU5h&$NfUk8P1t|Vy+1IQ@* zGL;y1*AMo0%RJ_^E8ZKGJG|?7FK&-p9Ih&{`<ahl*HuoIC;$cC9F&fYa{Iz6S;ZJ` zHAsfFi-6|hj@6(?N*xCIL#}p!V6AU;stqIzBWB6l;0$??s%JnOj0tb20}+Hyjpsrw zQ%km5I?P{_e-%N#E&}|R=Q7!Bya}#KlrB(Rydm`uJ7TdcGBPsTdyJ4tD|9+X{ny%k zr<1(=`A=CPBfXH+L-ZP;3fX>rdu6D?Ik~4!J5Q@EhR5xA5Jv6753Mal6iN*cWh0+X z;P%vo5GW6w;+vNys1Y=O>;Eo)9%<rzC_>abz(*714<Ik(KoL>vV)Y#F{seW9D(HyM z!}*<KQ=XYC!u>459n3EK!*@o#eXE*zvlb>P%8rncKj;LE08<-(+s*1-GZ`Y~anozj z5CC@n(h^Epd1F^>_%1?_nrwarn%Ju#B2)&8U|0qngabf}v;*9*k%W1%uAsxf4e^Zu zbI65+oe{vUCV|@-{hi?T{ZJbxTro||6rITEqed$IQX93Z+6s2kl#s%zzx2dr>~Om? z2&TeI9IUKOQvskCo*gR7lYM;&|G|B}0CYg0r_TGn4~f66BS|a^q^hD(T5DO5qz8hS z&z;lYU2+dbW`aR8Z3mX6=eKxq>e8*0!-Gv6U~N?hdvF;V8k!8Sf-%%E&pcdqHhak7 zy~?w+wA!=c5OM;1^;>BmGtuW=0!@osHq`?p|8u;EbK&3i1RcT+cyh@9D)|O7oXv6Y zW0Ug|*Ur9)47n6Y2N0y^TWf1Kv@{F-)Dl}MX6EMmI~||L4*Rmzq(O{InA=qVVh%wv z*XBz)XCXsj!ux8$jkfobg2^6&jHb=~#(ljrP%P0o`34or#4d{^{rj))?*!Ud!)Z>T zX{z?PR;Vrm`icM%H#ly4;QkM3>YRYxjldvhXp|bQBIDvNCP@YvCkJf26LuI#Ne(*D z!{hN3r%q|#zyD0seI~`Y&Ns)r<#iw(HBrMK*9@N$9T9N_RSgZzzohE3^j_RgbBizn zjtz7|yF?6WRaKR!-$wo^raRdgim|iJ49>lvyY&y81=TVb?>xv{Gr;vxvUQ`NEKVJ+ zZ{+elGCbRtm-IQAYs`P`75T}FDIoXxlr>2d#vz)1wzjrJ!T7!}h;{6szE{<a_0cs< zOia+<dX(b3F`x7Gt30YZ;+kBk(}*bO_T_KizKscQybL~(0|1B<Rt4Dp`@QVJ*G`-` zL3}}$9C%^++pZQ>uTyQ1`QDsJ?IPoXo94~zqN4hc87ZLrKu{0{%-`5T17_Ei7QBD( z{rdx@#9Lj~lSBm@+RhQM)edxOtaOSv@H}AjRN-zAw6l;~h|S}`fNp!Tq=~7iY18Of zw%Row-49Zb**40Zar@dZSzrI={ZG}}_Y=}2RC(^*bx0ndGB;YByeJu1+tLX{jXbeb zfed%Sk$B3Y9<!Q%S8k9&W0K4IpQ*Y3=>d_xZh&%*W8wxSR!VitpD7{i^2gPDu3x?S zx&H2bjB?_w)Y{rw{!K-q$MtnAj?_R+zq7jz)uyl3XN}@g#OCM5M#e$`0fDw*wy}So zZoR*=eoDX+vZU)4?GHu}fO}OcD=4h$(b1}$=e^AHjjfFG1b{^(Fe*8@2<(<55ugv= z1-&n?%dtml!IZN7;Ok&nuC=&-Hds#AE3qQFTlHLFjiCFC>aH!z;#Y`CP(kmDUcW9& z8<z3i97YJ>6JkWUF@VlHzE&Xf@?9wFpYN~NU**yFw1N~#f8!zAptDe~q<LZo{EvIh zo5czu1=-`2kQhx}CO}1viiy#lbpuE7B?{g9X~SeTNBxRant@A%hA;iYhZ`YOtl-MN zJNPWiGX1cCX1g-G)j!`(wc)&mT|(joZGT7JB!(YS`xX%U%#2NlNfRBMm|b74CGG(9 zf$Lwdy_takSKiFb%(MeUxC4<q?+C6y)Pr|6cYyQ~tV0W_=7Nd88l+4Oz$YxW5Zkj| z=^=2;SjZ>Dnlyw9!hr%iGwcH2P;!Az(R*5M-SMuDiSB><Gp(P9r~x_zt;p+2>VPF! z@SlSzhbmptLB~C_`i@x7fRoV%2o@kp`>$(lMZIAXCjg$Y2NaVF5Wjd6S{}RaaW{9i zCg1cG8i^&pX*A=~H>M}PVQg${7O|5$M$Oa<<y{jr_%iqJXTxC;DF%}<C8Ei0(EXSD z;Y?ZL?oe(#EN4I=(UE(7Nev|7M1~0J5zqxWf%`jP)o6NQ2i>m5BWN=)`_=<uX^M~6 zpYN31{&SAFfHA$Kq^$fC1er$jok36#A(NKl7Ge9R0OE?Bg98O^o`!9fjV!!7d@|8T zAjPR>dhIIM9&T^x!>$M<LDk>lpvs>G*sS5Fu8si#l7heR&6{eFG%V@UYk&d;&-~%) zb3v5}oL1Ed7?#P{K;k>#2wW(DhFx#3NcX}3yY66TZm7n~IcGuzqnsp~0ku;VXvCky zEdZcnb{xZzaSo7FxlR@#TZ7ig+qIpr1Y`$2C46TbK+gpJ^)et<#9=AO_xDRS_r>Ac z%&$%(wJm3Fpj}IVgkrq)NUA+SWK%C$%xh6oK_RqrClU*#=^kkFE5UqWb!>oKZ{EB~ zK}DqnjSI0FLMebnDlRUrwkZMPN(<yF0&GulLMF&ECnv5WkwoPY7l5Ft+t3TDH$E@| zH6*MH5uHzTe;y`G+J^`GL<TSi6?vUExnj^^hlqn<ziFnCS~9B7&SPsEmKf52p0nRx zoyrAwRcSeeEM7%O*>&vu3)l6K(m@9aBkhB|wN4?+*7I4;d!Y0e^xt|!<d8mn`oy|n zTC7V%@Wk;k(PUdIlOE#@Bq4sMm?iRCnEW14(sF}JuPN~58r&n#d90pBl-$JCdh!GU z-1WG1S6A1met?@E6!%AW7HgK=SO1`PJ)kSDTeOGnnI584;~(IJI1vK8q26es1}$zE zgc=bmW+g+lmLUT5{>h{0kHAVV=Run-R<>=|Bil7K23?GA-<x);9tiFvVH>E4a+DW% zxi#owp|hML_Mng&lL9y11no&5Lbp=8$bi&>?sr_LV%Rlowl`<;M>%Xs@Ne3KE*UCT zNV(WzWn*4_44+A1l9-ohnIwo_q0W`*1kif+Y3bOuMsX-Fz)Be?ZcX3q9z#@!0iXrn z)0d%mAw<}Oo6H&dHps0TbD$^9g{FdO6Ex34K?nX>8@xu<M8?CC-rSU!xXjGV5{Go4 z1n=$nC)XWA0InSw_bQBe+*a)~39YzC<su{>A}n^1B(e?q`tcgO=5NIRt`8U%Lr<{$ z=~&WG(EhqkY{?sBTm__Sf&K6nMAr6{qvZ)SAlrXknt@(SP#829(CQb|&K9u{x3O$& zvI}b#Ma2m}q<!ZKDhLY|W3!O!*S2er6qcyRB;#Ii`b01Xh1k*gSB+N@5kvro;4?f1 z3ieCFD$jz0i3Ngb(+#4*ajbqMXCzh;7YyBw928S6Xnss~jv-z*$&RNOZn=s)g42S^ zH<G7X7O?9H9EoUIOG}HN5U<zi14NS?UViGTgoH8R01ulrE4nT42Vi1cZPq-uHzDP@ z+Cf*cj#u+QRO!IvQDJ}?XMY+vd)RR4l5c#0sECS;G{-p&d`N`_mRkDAyri%7?7O5$ zG%OBCRk^G?CS{eNWnzQ;)@NB^5vk9c*rfMvfKQC()gvjKrXY54?E!db<TDk0is2TK zu9X9sPQi31#43dH=hXIJQzj@P;i_Q(D4=e!h!c7^RnSay?iOSVJlD|BNP5jo_z7*f zkk$8#$%Lbt!XTz(7a_u8I7sPVfX@s&zEokNqOfbzoxh3_1Z~rxnD~mnPm{x;Tz8bu zAi39wc}Oe9ay+dKSLjLWm-Z-*`}CzthM5v1DV*T{>^ebvlgwJUz<>~8uhvhAptpx{ zil<Et&`E8AG4w)-xF=QKD`xL@j-Ycva-U*Igt2hs?lx}LblwmSz<!sQ$jQmYRv9Wn z^OiUi2xXUl{RE;#0*et*H4tC+`0k8Tj<9gn1$o49ULe_j^<|@np7kM14!}@lhP%+T z8T<{*!-NjyPw+)VL=1f}m6o9<%O-$)zjcI4WH^PKihhQG@7XBFkUo@^eGZ@b_lNe+ zr9&3j7FB?{;$kBfxaa+DcRl#yyv0a~^&4tA(iBlMP}5dX9zXsA>L@hYew$8=68-~I z`*S}F+_r@IgoU+XP0*4_4$5od5cH$1!+eOQCpZty=f0E1E;WzCcn^2XHdI{2+hP(E zxf9y|Mjnu)e1wFTecHvvWoKk5aQeqzP6P@<5iuRj_mhy2@G5@uP6wc<><Fuo>s#KU zDh`lzg0|aC2t)Qo_?o494dDL9Mvo#*05!8u13;-53#^egfVI^g{$=n)fK|@8#UH6p zH69WEF?VOqO!U^<_FPXUEVY7fmV(Pec~E&uPSs5`&Y5enxAy2r8pdFRmhzdfIAU-J ziwT#8h)xICd8jCiHfW=1aqP2G&4O?paObBS!2&%WOdI`om{360C{L^crCII0l155; z>G1Ft!);^>*5eS)2SDwE+Vl@Gt?BPB^>lX=Cf`U76+M0OB+>48j22K8aNv6@azNpz zNCdoL30P^m0K8tDhF{k^Rb*KN+>c_?j?TNk&}idrDcmH4icVz2JrX*x0MX|lNza2c zqPzh9yNUN>!4sJ+yF3`r^lX1c#+`{L(c1U?^%K2@w1j*9WxEvsp=EEE5jlf`YqP`A zadFuTzbeGafZ5uYpe{pHVV4C>(FYYKD?SjCTtD=PmtunK`*Je?`tDkugvE_K;Hf5i z58#FEHy1}N7Y`dAlldNOPbUq{_2#4>?&1!)+z$Vvf>Y?Y(>?+s(f4qFlQAZ!#vPvq z<Swpu4E9i}rr#5(SZP1NWq>*}A;0^MY!a(3P$JN%w&AUb)fzTG|3{Tjoe1S!7;+Hi zF|7QN+TLFSS@h-0F5s8&y@9X~3GAdi*r9)WZCZTRtuI$=w!bJD@Wm9N1m1m)FK9dL zU)2I>1)8ECwpfL1yT7E8Kb%M(1I0rxq-xw7^O+y13Q9^IoJx@Z?SQwN1xl>~JX2!C zXF7@ZO+;k)y>12(r(Vd1&;j$tZbD-<G=+I2BmW<{XKV*I@tmO)I(*zZb%PijK=fxw z=7l1o-i=e8PIc?rurwUob_=#0S;Csq09kk9tzjv#8=470=qvy}vICx9pX=9wZ9tHm z@2h}dtshdA0{84Cm<x)G`D;K{UY}+rLlgj#t8#$9)(jvsMI*REU2a;XctI3#X%?s> zkYgH-dd>E7#46S_oq#9?`9G-nRkESatf%Kiyn~$)5={!g1a_mfRT_Sfj68ajM51@S zeK!{i;1!4%SUExZjt8ue+J3{Z0y^iPpq9!2!H}wLWjuavup|rk!?>nUSS%(kJe&Bh z1QVx>9`gfgM1o*$u=y3kTxZZh39+>$l8eM8?xlw$4P>+i6QYy|`YbF}%@LVX0P-D) zx6=XXf+Hw<%=W6)`L3U5Vk-PuU;s<jX=x!8NE0Lu;?_K|TmCQY%m3dQIjtRnIHt!L Ts2xXmPXr~Se82GC!zcd@ev!*4 literal 30959 zcmcG$byQVtyDz+GC8a?LT@um=N|y=(Qi>?u-Q8)BA_z!HsnkOwNQX#@l!$b<bV=8_ zrq6r!K4+Zs{qc=&e2(#q{cc@rt~KX<-`Dl4YlS{kmL<Z!jE_R0h~(v@RZ%F+02B&i z>H;qO&F$W?Iru-qE;1S}kL}G|+zp*fQA&m`4mS2KHkL+L-AtXFE$!|2xdplTIImi` zxHveA@bK9F*9*Aqoy>XIbm#ryA{QOxG@Vf>5<}!ajDIAvEm0^B9eL?HY97gJ<DMFC zO-I|dHc;&B>on`C3vmi)E3J3hi3`u8V--e>UTJc#C2}o{#$<6>X^Cq}tjDogy;8`j zRO6N@%*=WgSUFAn{A$zb+0CvCjSYmIgdHamZC{@`_tYhaCuT3Y>Mok{5jIIl!=Kt9 zl{<J>(eNiV6Z;|jMRUgu3<^?G(k;FRC>i7h;bc<q|B~{g|9|{vUg{vjHwg(LhBP?z zVIlBG;RZ%$OQu5n1tOyPpP35({kQ+WeEk25dvlEX^T#EQQ&aVa;oO6Z?~(8NiYABz zkN9BA|GeVG>nmSvxPOO~EUN#&K>m%w^wXzLCp$fo$zq;Y(q)@^)2F5QP-RwMKU-d6 z&?~;Fr||b0atvJB<>9i7z7$P2+5-u^1K#^jvJpw?(EZsUQHZB1WO{h70a*v-YL_Ll zNXavy)8qZclfxY*W*n^e(qwa*xDDf0io$6F=77I18>(_<{qW&K=eyekG40J$UyB^( zzeh@q+w=$$hP)2s(iXd7uKX5-JZYf@UP6h3DXful+m5d<@NzUuS*{4!yf^PTM=?9q zogFytxBb5H?^TI;6BSp7OM`#?QajpP>sTMJS^AYj-k&GxR+ql$`MjMsKfNzHrMX%9 z?!9}VVPWX2SKq&HLbeBqf`UR*Pfv-Rz_n}7C^?>PHeit$@Fk);#_FdGI-W<amaxaP zms}QN!S(SKr|}?G`@6~7Tjx9CX~n&685$a9WM^YNEYuMWBBV7^IBSUhrIciL=o-|N zsHc*m<q<9U5c3AycXSJ*|H+*s0ow!#Uy;101x7myUBao140e{gs-<QNvt?G4^;Ep? zCH_7mfgr=p$@8;#^Hk5Ej(DEOWmZoXIuq|@E2lWs35d}(Hge|qMlx1(9<KcO;PB)u z|KG=jGVe~F?v-UUZ3&}9xvUJlu5w;fEzr6hyfM8(Ks*<Qqx|neHHhzzHHA@f+<5d6 zd*jQwM4oz)8~Y*0ga2G5hE*j?v((IHvOWbqAD1z0CW(b|GEEWA6cb)B3FAUuQGH3B zU0)VajoTWUi75k>zD@Xd6=t3gHW^IRdcJ)5lGo(hrM=Z*WA7p%^;5p>h%{ArJ9eeR zG$x7ICW=Jj;A&`S=x58mAow0sE*cEke-CMG<3NG-#fujWdq2t?935@@pYQui-@mU4 zH~yTsYxWoJeYi?koqP<-)^c8vkwDT#Vq!CR?Pu%3!uL(=yt&A>k^5v$gKJir_zVlL z#CnjsNcl{P5~&1iS!Cqo7cX2uO|?+#&n+a`XURq~B<^kPKK;+zoR)qvGx(o4j+9x; zIyv#7>g(&reD}v6oSpq!VZvUgh`N(qzWhC3v-IF}fAVlTm=Rwj<F5SQV?yD%w6x^2 z`4#W8?P%N!JQCIP`$TXzvS|{&_#&V0$~z-fDML=waR!@2{Ldx$#yD3wIm3G-&y(4o z<We}81je%?i?5pTnoc;n%zAKUW22{y(Z6$hz9aTz{_el`@N>fd+*dhGqN_8JziKHf z311``edphQQawAEC7JNu*BxDfJ0Pc}&6(@D`0uSn8PMRS`5m$Ki0u%zx3}N2`W2#A z=SAtFArt+#NV5IuP8M!TkCgZh%YphmP;jL{tBif<eHbqh#w&QDAE;<)+q(oOJOAgd zsoQ6OU6Z9%W`#qq6_tsP)EWvca#m%Nu}VjppgT=LM4Gi8H?Lj0hC}}}N}Y)#fQ^l< zcKbWWXRH3Oy|qz(?>(EEMFugCH?T@SYdxL4_Sd`0ZAZmua^OmHvZqJ8Hwj-r&3XIJ zKR1~DxmB6S8`@)7OYJ8m7P?b9uGrUp4X5STC^b`x+aV((^O%d0t=ww8atNzhxzjD` zd9;)*?7X0eA<YF(G(yO2HDPTKDNBR;{-y)@ns1?^q0-s)!gMeb-o9;C*Y|Cybey&L znQ_s5!jm;p;^+neHFu}eIy`!|$6184ZIRh_<JIApZ--U8u9VnM)?<d~z)o~>nr_5l z@R-7|t3Nc0V3f2IWw?A<E8r2OiVQb~n8Osx($X^VK?4-K)rq>KKY#uxF0K!KdQqrb zGqbW{En03f%qQZu`umdy><lJ%?Fu_)CZ?Bx1k_IJW5NQqBWgdcQCh>5!XqQIRWl#F z<<$J?#>vb3=IvXG?lj4?rLmzQ4R-ayPV1t&R@;j4R@keuYHDwTuT#Np_wF>~x3Al! zq8D+w%EA&fHDv%F*BF3-1)I^R;2U!Q?1RK;$%Z|V+qZA`ZDnLINu2(9ocpu@+1(c7 z)wf{@JJg=SrJ|mbUb&(W&#mjRRyN3+)I~@q^hUSFtru?NIdQ=dhtoxa9M#McCa;0P zv$M0rh~uN9pOucr_g`ObQ4$<)Cg7=+a&X|~P%r$WQHvD9I(VeTo;zlkn3xHo?%eRC zGwufB<Iu;c-*ECpy?Fjy$bFp-p4`EqW@BVzWZUFq8mxo4NQG5@?wCUpQDOuR9$p?& zR8=o1CJRONWvlQTHe%b=ZnZuqZp)*n!Z(<0eZ94_V?0=>TW<X5(W6_|1B8reos!D0 zsJMhYHVv|jdwY9-<!knle?aO5uBiL^Tuyds3D*dxb~z`!NOfBs1$#rI+Qky-CI?%y z!=>gVD+2|_P}2zMMJS5Zuz2+A2^-*f!UeA~`W;;JKR*@h%Tl^7AP`qvT>Sim6gEih z11~QTMxRX#+tKoQI9@X`DiZDc;$3D#m84WW<vxN*JH84<s&iyg0iTT9erCv%Gx%)Y znef?Bx*tI&Utu?nvY4zFhi-xS-mL4L&)%>(RKuTn>OAP{*AM^nsa(XzXaBYdyQj7_ zl$^Elc)i9GUL&uh#0-_?c;kzt{(g8!z?8x7F<M-r5+4%caysg^1i~g>e36hw&YL%* ze*Ad&@#Dt}7cSiR?SPcl@F#gsw>FzdoR@#Got&N?PQ4)Um<eTt=IU8*E{zQxf6$C) zK$}|^e|q}x!iWMz`@cuu4rv<ZJ*mkeu3_*^Nhv8?Hm93*Vowsgc)eT(=12+|uJGS) z`Wo?XGvpr3RG@wk$JzeRqx7Lq#(09`E|2Rss!R#!#I4SKn*xS3Iy|FG(Sv{MJw9U- zFH&wTM?SNmpf1f!eSLwAhgY*o8{y{oxT0b#=Wl~ObXw@7U9VbXfDS)g<($1%$q^$N zu-vcfohtbF?<+jZ9cS+&M>UQ|Kf;h^b-ZTKwrBYVhE;!c_0+5u3;C}?TVWNatiP=& z*=_ziW$WuJvas<(UcE{;Yzl&oI6e?RNblooIC86_s7ML3peS3pESXjK-|H)fMnq)Y z3ns2T-5aF|`dVz*6aq+tiK%(5hnS?|vsabd<07rHUzn}5TG((9W->d?I>Lr$+qiZX zL|h#5z2K~Uh1!^-@d=F->-`DuqT?4`SCM|dO;w}u7v65&B_)kZ^`afk{grD<Yt&T` zwzpc^;{?l<>UU(h{3~a6LTYVuH&8hRf3COXc~AhdIl^59Ut4=Gx3p+R=+|Tdni~B* zT*@jhFONDnIN<f%p2Ji)V><ua{yj*|-TL5=*X|Bzf3}}YMfSshw!P=Ar;dz&dw~b3 ztb3nEIcHoVKcfL)7!*<t;`O}R?>!2uVMD;t7CK@r`ct?pgJ-xxd^hQ(OAXMgX!%U! z>i4!{8)g1BOX_yt`0%9$3vS)#&?8GNdeLxv?~_Qgq-F>sO;C@iV*J(=gO5+{_RK4u zojw&wwnL7rf4^Z=evM@Y){1SBQr5f9#2wa+TK*_9<_8pN2{UGYq5t3ewdz+!xnsGb zIBaVy329Y78DLRV{9UG*zz6YquZMm3i{r?cPdt}0otv92IBwg2khzL{@Ep{d+LP@L z#bbB6N`OI}dbJ5GO7EaQ&&dCK_2#qFV_G4Ht2OQ$xCX!ec~lDx=ivC58eU=fpI1Or z8>;gbeD>@a+<I`TxHpxHoT}8{uTNndWH@wLXn$oel9u1>5(9(s?${N~2RF0+ete6X zi3t-lr#Uzk<;E)FyKkc<AN?(L6bW6$j@LQ$Y!2-NMkV*Z$8v;U5sZKuj9?r+|5I0$ zKsK3|f7ctoD&zu<_~i(OI<NevTnhIVtw+mGb_ewdFW(BG7j;AN+6?h#DW@z~8Dz>m z!~uShap_e^z}YE3t3S%oF>aQWR6UYQqd4Hrn@eu1Lw5j8YL=K>O!M8N#lXNwCDOtd zuV4*;V=)7DF<IO@4oHW)q%vG)h&PEVR<h~aQR?nc!p*oHv!2whH`ngT$;rL<J>Ell zp!Y`IVEWdB-szv$rU0t@oovbaogJFPj%@8smxUc!b;K(v3ETLdg~g4ENk38ezP}`1 zz{0rM3`du3$441T+2qgHLTyU}5E%d!FA^%}%wo2to>7{@*$UD-O3ix^j5^a8NI)}u z`WUAp<JXt7qgTj(MfFEv_>}CMP}leXp~hyz@hEgW@lf7)_pGJiY6Ro{2b<v%X&ISE zB_{Gc*sL;I^<+||!<~z1{@tu;K6h1B$yj1Sp|z1-zWm??E>Y##(F&V-A)V>>x6|-( z$)fHNPzkq3?Y|%;Q1bkw>CaMMBELB?bW`J@VnYD6)ywAO07ieVx?6|h1fh}w$kmF@ z!~&uv9TVD%)eAdXKHLlTfI4;n#4Ja<f_s15ozeH`&j;v!#FMjt*B3f?zL?B^k4+YK z4$4wW+U^$Jcvz%YSNn2~3NZD{pdd0z%1rjmkPwfz!-QYI#^ErkpO;6V7;w5VFw_c9 zZe}Zs2Q0JBgp08TbR`M00-!)>2H?Tr>hMJwEv?JIILt>rTQV^*4ej0FBt*8Y+?|p_ zOo4EPhSG-Y^ZLHa8qFo9f@YRD*Q1lfiZB@@e1xnA@;|t5OdLKqYbX(#oABP44`%e6 z0yfe$Sci0$m`w+djk>yn!?MQclf;1doF^-?%>L2lo`oncf+v8DGu}hlv`4@*K4tE9 zGx<){Yy`K<?|>j*=AO^@0d8endZ46K#<grQT*3@+WFE>AGcRv(!1b7!_YH2C!zHE! z98dFa{L==Al|l=Fr*KavaLsaKcz%zzS-E2tv5S?G1jM$Q$=xQ;=i~K-9Hs)s_a)B` zj9Vk<lLr63QxuRYV9TJIjryc?*~o3U05zOm#d%?jT8}L>JYu&=Mn01Z0Ir_u+4Ddh zf3mkR*~3*a)(CtCMRVJVf`E$i#?(I<l};;kQUyE!#@Z7NX?y^aitTjrGqJLg)6jf_ z)q^5zekYivC4uT00-xUl`{eaGa6qls?kEGwRd7Al4hX4yi6HbSObRNh`#q`ROJi01 z2#S}Gz$uq_TpRKizLQit&Eo?MGC$ay253vg$jDd^P}s*l_v*~Q%#4AIp1$k-ofkv( zzM^;}40C-dT%*-&hvX`cQ#3(3!J^{SkOJI;{4;!uIoML1zuz>!`rBD&?o;#VnKXxx za_UrG3V;Sx>M(s5j_Bits8GSjNd0J50$6kp-mg*Va1B_KVgm28Z{OsY{Z*&7pJ0dq z5RVwGn*VbK2Vr~10`BH65A3>E)I6#ccDhttTFnI&6Iws6g`y1jwucI;9;M&sPW)W$ ze}3jO*_48iHX<TscJ{D<fPm-3veMG{RXdA4k3Sj7jIKa=_yM=2*d~pyRUZ^U)bwpI z9uS=)87b+VI<H-gdY`H_4ItfbPvk$uEMA+!!XvKaBZhB)UIFC>mZP-uovGytpW}@G zQeQS>eLW6cLJ0le_8d(D^!B4+)6;Vnf0ij`zr%U!=P$0IKL)%Y|CHW9)&N=pd^LjQ zEhtWh(W$B3@I)-fkEmv5h*AB|7z5;`<Y#8y`};FLQx&3=!}I`54|GWez6}1%-}mgn z0f+t2^Cm71{}09<O4oKnJG(lP7R$^T1N%nw3ru><fxDNWROF6S*!R@!OAvHoKSe#$ z?7sOQorEq~j{6oK^87K7Jxebd2m5s-Vs~Yqffuu5O!7E_u>v5b;p}~l+M#XT*gI@D zM)09dGf^M!Z<tRtJVPq-+Gs_o->H`(%U`j?cXv6iDj;3<UeHODR++)btqUaXDr4xq ze~=!8lU1zVOxQF9-I)2-t+(Sf?#M<i?UAq^z3|~4o&uS<y*_r#MXSM}zHBanyL>GE z0aRgf&)A|b8a#M_UsSt|59sY9bb`)Q@zM1v6^TP8qALQOe<*2bm%duaNHd9&l3t=C zOc8B*e<yAuFvs;f9FV2SFOn)^Ti8HX#yl5O5RC^ZRoz>=WonyOt;sBWPba)LJNebA z15Gjl(W(!_<}Pi{wZ{xs*fTmWcF#j2t^M;;0s8a{5QWTz_lv%q8GV1t29iOyTRsqv zR6=#=0i>jcIX=2c6q+1HplPiZ|Ar>N1Uwidf=ua9GK6RRD~9B|LM2nE9VXfUh!x9W zUT|Xi*71W}wleqmBk7VSb7T!brRtBCIRFOB#IPtKq%xS8-U!-*CRqdYya)#I2c>3R z|G;Lzkl{q5X%oBROxpEiI!#K{nj0H$6sO!;9w<;a+@81m@g4&XNtS6x++U?A)V+fW z6shHYuBN8?`nYLZSq<v25Rbd5<PG|Mhs-@BN^)}FcFORG>8^5dy@u{R`U&v8#NizI z&%QqX(-lq;k;^gd(rY7SJ11hKq>cq#+W6)&oIqIK-L{^YoxPXHXL^7z==JIqpac%T zz4RwnXI;%cgoI?z31iUlnS|Jq<+HZ5wLQ*JWd`_eDN02{BL{1{*prs+H2=MP7I+ml zHuff9wi4qu;@l^Bt>2=qFAaT4Z<_^lOCZako0=bjU<=ag_j&Ecg!H_Ym{E3>)41_t zqty?YxVevWv)fHPzud!%7gKJ&cBqAIL~}p&%Qm~Pu<+w;ydk~ptSmDT1}J8rzgYq| zLQn}5fw^|pG?R^qI#0iPkPlQ0b3=4%J)*AN3qo`#5C}AC+_*rV;r0!>P9T$|%vCvU zx1|iNr%?aPM69DwzFLl|CYkt(wZH$lVw-F@%~yaeHm@Wu-?nOoj@up0tY9&apJ<Lr zMH;1uuOMSHT5gM=se}X?A0Ho&+1G*|^RenBy1%3JtGFm0F-mQHhVFXXdceh$j+g)+ ze`c)8IU_T3deQu%q=W>6zrX+Yc%HN+9Y)49P7XhNxPM|siB&`gVB)uy$@lkHD2cy$ z@sG4jr;Pxh%Z<&Wl_Fz-Bm@osU7jl+wR7&lI*;uGGN8f%r1^@VU3gj=BNVI>`d49L z9|5;&p7rl-cku87f>UfGrW2Yf@uUaxEjSQ1R95y<t88o%bK%E_d$rIf61vQA327Pt zOOB<UXlhc25z<*47S5^QKS<>W0Ew9UV#6?HH3;5WpnQZgN~QuZOM0BGd=o?*7Ukp% z8oAE^jx+&J=hm&>MCeEdR|P!vM!`v+dunPF<SdE~@~iOS?z9|6j*gcYNuK0s9W5kw ztt&(zPZ$a(qOt&U;(CMg8jaIsGUq6BKmGpe?bF~^6TU4F!GwHuL6HL;W)YAd>(#3P zK*zRz+#|vGQEYe*zJuJsmvf&J&;$?Q<3aAB*<BelUmg1N*=~Gf+MJn%1!TJipp7&Y zprBkju1{PSWMc~jRF?`g<>k`?t@-mHbc<9ydBaKmZ=8-dHiifbZ1!DUyI@qnGv_Yk z0a^mWbfAGC9|>L1JV}y!rfYjQ8$@cTp0BS6A_UlFeJl~QnkseyDBP`MRoB5bP)rhd z4Gbw14u7tCQ8!S6*ptu`jvmSesD+=b`q@E|Y627sEc-3n;~-dvpQYw1b~S6|Mq7AF zh`6^8dVz?0_i61nxkWc@Vb5(Q63G)@fH=7CRnjDU&JGu`ad8*el%r;D^jtK!5Bp#S z;EOSwbRgvTaOR<!Al1x$(pM0q2(FSCn^-_~m2EREycQ4F7rC@dG;gmU+ZmDCa~@}J z?(Z)G<wgL7%-+UTQg=srObb&QTo6-yj@K$qKq3GDg9}%VWq<N}qD~a9>|S`qidq0+ zKx0kxlnuZN*6WY$W@ctIKnVoc)uj@GZi!PMZwQBSGz>^%(s!R;@?;Z>np>yAgzr(E zm#f#G-(SbwCzYUXj%``Qt3ubAVKC#z##^kvE~lv2?TjK}5R>bAciZ+nQo?zG5McKQ zm!+TU7j-gd9JDbL9szolJpDrsPsR)oHy~KlLx33Yd}r6&7GEJ?Y5!9pX3(lTcDp5I zF@Q3VOTrn%-vMFUK3q(z5!8q>YQ8S|?BMXQ_mi<~s@F0bgWrK6^otjUG$-&xQax$` zs;PR8lg>da`m-^a2D>wPZqCHW*cj-GqkjXs<>hGnQfq5#iPx?bva--gFgm`Bz=>CZ zO*DS$0Xt(ZdHlLNs6}F6H_(ZDy#@Zd2|#lPcCXnmv6}R%vjXM;NW;a@ML6a4tDNS4 z|I8#1_xdvhXFnYp()!=;ao4Q$l&Pm$)=s%lr>Zf@zP=rp$7@5HUq$+9Ew8A_FJE5X z8<GjT63-j}@>B)aQtA4-Aq_4xlrm;t==|9x?a_r^8E{9Wg+uFWa7PGA1?}Q`q!@x` zZwYEMMQ+EU7=)dg<NnZ#d0Z-H@<kpxbk;w=dtbn(>B^^tgix1OjBD#8&sV08*5Tmb z+_aw%<JPPF27=^bs)0<cbgDW=aA4pToHxw4j3QVum*rof4B}otVAmU!GABO;m3c0u ztY4w^yZ-%wr#!(VO+Aw43=H)yOK2d$6yUP_so<uhB8~s%j5+krqw5VNjTNww;W8JY zm}YN09y))>fk6@Vku%%V{ZM%DQG|duG2zR;?4tTYE!Qh7eOHxJ!ma`6ye=E@5QNYZ zoa<<T!^@ZgiHcDD{En7b;lFIL@$_O=kRN1X`FGZJZp!kAwnd7%P4v(85Z2!FIavuv z%zd&_;&XVH*&odv2V??>U}5+@X=$BriJtC-@6zL=%HAEcF?Q>?QqW-9ob2zS9>kp_ zl-qKM>^qRQ$I7~4vVFgW&54$IoxZbfWW-rSFFx2iJ++z}TJ;L=GKTrHk_JVC8wO5R zJO-qW;iFgX0F^Pf9gcS1Hl?|A#aftzPI54csZ;OvP4_3y?lcG(kUpH;i%4(!hMuW} zh65Ee6AB{;1KqJ(0D~oj-|-5^Xb3wM4%_6M2){ddlKVNyd66Bf>|C4=r-L{%Q9Jla z(Yi<SaLe3sAQ!sj4nPU$rUn8@y0q}ol>zA&WB5a3uP-h<_56HUuzTv{aH;d%E}B`P z{Q7NI@hJk6btXf4zx5wqw}kke1{<lOW~8pLhJ{e@(~D@3(Z{eI?<=wVqdV%9%D=eK z`YCKizW%hFmESz#+U?u1N%n;(CR5M)-Q8a~o_Rx>!5@p1<O&G1(dgta*>a8_p#Wzj zATY1$s<n~3t<ijERaJZ4gG$`W#PJ&SS#U?7l0qH9>4=L>H|y#&AIOcZ=x=+o^YHV( z@`H43ES&f#qZ?vl(Ne<E8p6(YZk=WdO^cO6|7hK=u`<Hn<otmpBoF8C5Xd0SO(Sd| z_nQtvPT1({hP~*H{e*;Cl0K1|3-_C^K)DE!yGhAFY-kw4_1s}$ES%2zFq+thL@JU& zrPOBF<Uu@l$`YZz{$QDxV;h<#YKBGs;#9rw@oz9=Qi}u=74xBcOJ0LzAx@UhPgZJl zvO6?7IiAOQ%W}iwCW)la%TRJ}(X#+Cr~mnG11l?bM@L7``Uw;JflqKqt%NIlgbBoy zhf6-3?0+%N(Igjfu?Q-%=Z|bQ(;XtCj`#IB)YauwrJLix$k(eAnbuO^LC?6Z_LPod zi=pVitnPoyb$la5<jJmu%|Ia6>9KZnUAVk~pE~s`3hF~;6v&R2>McNMWTXcUoz(*_ zHux74o(|wq#U!r|&-Hw-E@rNg2zZ>GOY%PCFKO-L>q&csi)%dGN;E}kaQV{1_dBcG z)x|Q+R}i%l8ekdaJJ;0l-t_o@aw)Ng7zY%UML#-Ao#&U0qGqIhp;v_Egj~2l9Mqj+ z_~UBF{*=Ms>T|;sb925}96c;MEH`7NGpU_bX?%XfU~G9KT5)$2^kQg3w3p>1iEOZ! zF(2lTg)`dI`cXzTvh`e?Y7Qm$1jSnC-~C%tewRO~3qkLrb~UdN)#&9*?8c_)jW0^I zhgpoV9NOKY-c%DFO|Jd-NgeCNdN5W8i_$n|fp$?lnsMBcU2LO=@(hv;87=KaDNy3& zR8;T`<{v1uY>d3@)3WVcNv%iENC1?S*#35%PQqs>_R}VO4!_Iqhw&4SrJF}9y*NGt zr*_!s5(H099%Kqc4?=J~kPo{&fv^y7kQTyUy}AyX{h*%rYvZ<vd5#bO8k>y-JPsRm z`(ywUi@ZZaJVe!P1LNMk`wr6j(qIuIz>Q4MXIICnV(=J!ew4?=@WWlOiESsN3N=1G z_g=3G2GHiTnBvX{vI2k{nY(wN-y2L5a(vFL5Qkj(XOX^S{pp@IE-~FV&^>o9M~8$s zl*zoR8y7&%t>``#7xRO+Q)&KZUjc{xRN&bH?)T`6->KqaW%bAnbgCg=_^P{;%P;#c zr>z<CF+<>zu*6){*VkX}&qGW+1hE1(Oox)M1ArXbhU4~}yxaO%J0ue9!Idn~t}ug6 z0=D3HX34i4|Ao{jKx)LHj|d65m!B7K?YQ8_YF7t8;R~?7z?Q%ea$TnI-e1oI6Lna^ zeD=PAcjB1z{anU0Im74;>>pNeqC1lWBjMsBEVxM}hl|;}j(&cAh@cJivA5dQTK(k| z6+o4Yf5Jk(Ra0uMjqFw275xvc8q{^CcV6gp{7q|CYFcKmqqDbzDj@n`Z1ju#VFs_G zCZ>fe_<rT~lidIVg}wgVKz#R^mRE1tpM*6uNELoL^U^A}X$Mi`fvI&*z+FX<yeUj0 z*0D>>x-K(#{l2E0Dkcw>8DeVWs28Q)+YgTnKHjLPT~sW4B#j@@LEbQPA9;`Ye`q}@ zou~ki`bs>ujNS>@zFRvSenM&8|NI%2;g(Tb-fOgK@n>o%PuFG&>%2>yry4Mj1qB?5 z@_??EDCWrr`=EQzXQj4r_2{(jn441*^Vzf1P+jS5S;__-gxseZ6;;EPSMn^tdAnut zBLJ{qu3Apx`PsfCc(b<qJ}Wws-dy^(230aFdi5I6=)eF`@N6T1b};(>Aw_&s@4eOb zn}=At$K-5wh-gT`tFo5$37o$Avm@)#6`<dxZfjarzy66@DT~<K(yrnw>J1HHMN9)W zgZ08f-72R*<NzqAh}?wZ^D+J0Z1#S~$Ja_pip0!u5$oI~4j;;W$=UEfUjQ{QpvqbB z_3z&nxFignpdX_kjgx&ZnB8RTYhRRS5WR?0e{NhHk8o&Y1yioo5vOV;B4(*JB!lZ? z)B5!5Xl1Woi!G8Ju;_gbP~y>tyEv)7dj#m~=FY1c3plnBo&0}x1HZnYfAx5@K9s@_ z0k-%F{NDTbr8S<;x~&twz}2kB!!uW&066OjzCW#)2QOq7#@t42L3Ol?<E)a@-yv*j zq&xPcQ?iiBm;w%+ENIUFo7UA7h`(O@;2<A1fo6=<%1X{-E&@(EO}15F;h7I&H@CJ7 zPY$+h#ww%y&vKGBJc9<L`%}Y1b3-WXJe5(Ce#f>8T}kHqr%)>#AsJFTp(ov(_cB<! zTrp3Re`iapjI*t&(MgZZhQ{oEgqU^Sl+wFfp>Q0wTd&wpEiNub|GlEgci~4*7^R{b zdu;DD=vKQJ!bx1Ou7d;xIRmk5gp>&rr<Tbf1G%wq>du2{p(*7EdIICw)>|kzgO+_+ zuh$687ikwLjFm%kk#)H|R*kQb6b`wawn4#T%FlZo&Kv=T4Vv#cOU*antiIti*9^9w zohd0oYq@Vbm>dX*jj2Zc`4#7ip7BC7H&twtf=*_Pre}T{(O(`Ku)d-SG^u$v*y|Y- z!Qwa|?K&mZ;SSJC1_AO>=&SIZ#-%u>zMR(|qc`~<G8TV%r`}Y7HbT-G85fG5ZgM`h zX29CRLex0-far2(XRWTJKCyeGTMQ!?VF!g=APK%{tf-iBBsryI!1D0+sI#+nS6+&U zbZrt6#Rwb&T?C2ipz6FZZehe3@El^@Hl~V7rfkRNWo8ak*Yga7s@hxc>kUpv%F1mJ zix3ANfVKD4yQEPk3i9&P!{%w3@b_ZJtEGymCj=zoZ+C86wQ`~(z71a5gu>bmBz)(j zzTL!VeXL4Z(tUfbeRZ@V5~4d!t3x*teFP57o&ELkH9da66OZWV=w!b*(Bnb6MnR7- z0ZM@aVa!3#E5|Zo@E_XJ5FvK`iw1Y^Vv4%0e%m=_@xG$Ja23cdVts%v&<iH>yIYox z(9%Gy{V<Nbxl;6H0mM*cNd@WVK9Ki7&@+aO06O*K%m<Vb$E*I~bi<;9*46``IVpOd z6O*<p0ZjBeJ*wg(4yG4!2>AT@*4?{>0wP3^hXaHAnz%SUxb5j9i`^;4z??wsOVW#L z=SQQ7ZzhRPu6q3b@qtsL7!@G8Uikv<b9*uEw$oVKpj9{B?~(qxtx&nGH;dQ~GtHre z;Z25HTU%<mYN650Mde}_2nm_sxg+W?Fe1f871-49J)j}^ssyv6Wi+H;nr@7ThpHE7 ztrrzTgeHQg{@^*HRxGA^6N4%)VC%Fx@;G~pIzEJgN2ZWS(aFgvYmTJ2PFYE*%jE?4 z2@B}V(98ym;cN6#lVY46n~(Exad$h7#VW-V=~Ug-(V_eL^(#v3a4yDWZ6r2`=*mcU zdyhmt&Z8EJsK%mi1(@7Va@FFbdyYmXCnw!j5jzn~A_ym3NsIuN(`-|&c2+XVGw9Vh zp?sw~EFxk7h+j4s8qgp~i#uO;C!BmOs|heMwV$__#>U3Gx7f{V@n+uJfp^%Bk`+C{ z47vdv`zodY&`yM0{T@EN1P}T;7Z;JFq{0C_F7P5B*ZX*mdO|(E2I3yb>890ZpV}DG zLB0WL{@VH1S8|_9O$mg%EBFaJl&>)QUAS|{vnZEK>vPC-Q*g|iH&TK4ltUn(R!n$V zmoI9$V=PThMz5MGDz6os?3z$MmJUk=x}mGf+c)$dea2PkzcUxm%<%&tw*#jq&Jsfo zOya|{;|+e@(E@G339l6{MA`6Z<3_hURu;7|`2*Um|61o*13;o}$TaZ#pZTERB?90{ z7+k?DZ>x^9ciSP$R_;1k9m`E%iOCG3rOKK%AhnNO#QyPZ{hX8G;eY2eJVBih26O-( zU^&-=P)N?`R5>9}1i}=YgLpVNk6=%$1akqx)pPsd@Hw{}bV}%!axN~nBqSt|r9$YF zz`Lj<ka>T`jV4RT<|SyCTPO(q{XhqZx52h#gB37^3N3ap^#TM>UZ*)ZNCx3b9q+9r z3OS;uFOOb|5-aYmc=f8I{&^g|Y@QWJ8fX!bcOtIK#bBR+qQBwd6R|qGVnNy0yCa=( zaFZ72$#)L)|KWCsHHi2V)2Uz<3>1`3zT%OPNQDej3Zy+$KN+?7v?Rp5zQRQy;Y3cG z14)bucBi}m&|kVlCJW7!7K`dO>htn!)blmI{_ah;-FQ-<6$=5)p@MeCIp6-`5D$_k zDCZCGzWwh*_crF*I|(G6;-o9=dbUpY(@@1u^AC>xEX|e+l^Pd?St{X|;cGP4%vWIw zInGFry2*InwD@t=u6ij;OdnW=-N2z67GC4GC}H*f!V#j+CCvUjwVTh!eZP;l>rs|I z<$}T;9f|D#RSu}h$oervh~^V@&Z_cf(nBaH2lRaQXd7JK6BRT5(^|7})&LTI>8jow z+w`=<T!?Am<VTsfjhi<goJlvo*8bYk(((v`jqc;n_PCrzM7p$|c(2|R^4=Ta2*x-+ z-XE+B7zV|^-`H_<_FJwY>6Qf2UbcB;%asX8f|(UcYP?VnFDGF55UF)NR=EQzDw0cS z?3W=Q6nI!J;Mtvk?Y4}4+d2#Nh_-tbmkuMf(S*-3wg4y?0vd;ICUMnm{u|(NmXypc z=xg{nH|&+x6ilL8Vj}vU;8M)=>dNYR7YAB30f<nPQA?EJn6+@V>&QYEE+Scj#m~fI z#tP8*y;Z+IUrOGSa<nQ%r7bBKf!UhDjztA?^X2MA#@ZiKX6NG>&D$a}QDcs6^qnc9 z@$cVXuKIiFA>`6`*|Pg9_quy5X#}(Dyqxv(Khb&w(2E*D{%LltVsfEsF}10!jW6UQ z@(I5zE@(*O)4LPT+6_nV){T`bzlfFM1fAOa<e=cQ-6>|!^z0v<d!$i~NZgQ3ZA>&g z>C|(8rDvd^kCU+B9cjH%DA|0-EALcw{W6=2nweqqDg5SzdXMtr<|W>pv!VlaQ};3L ze*Ht5HMTGPy&t5n9bXdnpMV!rjRmyXeTAQFTMd@4nmMlVuD8C9dthjXzP?5>Tz`O8 zOvEg{pb?OnmNt8?JRnhnR#ls`zA(fa?&BL8V)AyBms=yqu`RMkWbWN4Z(+N1MBQPh znMUO%nwNrxFdx@Ul+xdg%NWeTbTJYPm1LpbSVsUNV&TUF+HpnU_X=r)9hI^=?|qB- zvvB;UkDg9c;8IBIw*BnjGuh-bTE6bvzh*9xvgHGJ+FKdy`TOQN*OA`z#6=vf{bhP_ z5ohL45T@9UA1}@ywlRY?UAs53B6ay8p2mgGJv%)Z5v-+PkzMO?M%4u_pu#*Nh6(#~ zzb^H4>$<PiN||eY6Wjgu*ym5L?0J1P$pO#9oA#1YxWZkx0sLB6VO>WD1{Lv$Z3gTN z>W)sd3=T&75P=Gx`uMydJI(JMI4Rr1=A}8BI|PbZVcmERL~B@_=)e>JLTqXf4|3Dh zNj6?&W@g?3`E*!M&~7XNlr9K*G%2RpC2}TA;^K|q!P1KYjaL0fLw!etLqcNT8L@34 z)dtol3CN}~YaZYL1>`wjupa32(T!!F`c3%ertvS#`_lN5f8Us|bFsa>TVK5__jDhk zzEB;I2ru~OFIEa`A|T>t;%(<4L?>*N*i>N=HGkY5Q*0Dy>^N8UoDDrAkAN<GBJY|2 zp_!4~lzxzzJr1UWH6i&70BhF>zKmMeHRI3S*E;`s%&K6@2X4@TB1^~dyJJ?g%rih_ zs#CD!Ul<s?Iqloi>SU-V{m;cA!eHK?`vl=$kf+&%n9E|q$*}Pn{{_v(3nD+w0SfXu zf;0?&=7%QgyoKLsC;5|Zu|te%ZF0R5t2@<oT`Q)2+mvT3EHrc)%FZpzpD)2r3(QXS zV%OK!7)iRWkr!9)PagHLW&U0R`2UCn;<_@hH5(~uww$sL*3%^*_5jgnCH=&}mvY~o zmC3l!-t&gXWO8cQMqu{XUf|$hHqw|ckp_YT2p4m&HRD$+B#dFvq)JgMu#(4hUgigO zLQ{1MLs3!D1iV=~5tj=A5b(S#XlLU+X(JE=4l|JIL(mou!Cr&x_Dha?m!i;(Hsz~B z(>B}tU-2$cye<1$3Zgdn0g5Riv7qLJLf8e;>>ejuZMtOk($agP;fhIFb1Kqd4DrMm z{_sNhoDd|`>ajJW*v_cI9sn7N_egL8Y?sb7$;sGA>L|2HM_gySnjI{L&X=g`{DK|= zNz+O8-M!^&zG8br#wXC;I}^4@uLw5Iv=lq0&nKT0KV>g^EeN<t42|~RrzYLH6>o<# z2+73=I^jmRj~v~a^6{OI>FG=3HG)K!ZyA6?QRr2xZNVW#y01}7gG=PJ{!Vc6rQ>Yj z(=jyK;9!%hxs3$Z2t)Gw+e4DGvmV&H{r-~!xu!XExVR$a;yU+%tVevTGwH8B5}bYN z0`NUWAvnO;*tlvs=!%o#*<YIxNfs$8hI26TpczaAv~#Vm2x{rMFTXu5x26Rp^n`!X z1Iw(dgs|pam*trIB=(e~l!(Li;*gAt0VZbC`t{$BD)%Qk+wz+#s<_6!6{uR$s8@XK zoA?6Ns`3^Fr)CLL1Qmrs9A{!tzXw{?0gay8YMbs%%xjzQ>L@X8%}x_3zdTSq8(dfK zwb$J8ReWD+>8Cx){@nY|H3N}{-8sj9Hqwrl$7kE<YtqseLqjNVxl^z-vX$upm+*ka z;4B7hItFAWp}_|qLMS9)7h`i8Z=1U=gwKo1E;P^oQs-eEFu5swNc;X~G0g#EH<Lf@ z2EQ3tC)o4_Y73bk*}x*#d53b_m`DMKCe?dgxO>F%HzH^t{~M@m6&Q}XhaRMk{43wb z$G~~#)JZtTB=q*(Wmo&QzKT8dC7?`)=E5ot?i|A#dfH(7wNFpk*O+q#TG&P~Am~FD z&y6XssF<Pu#Sd{dN=!SP+UVVXfM^d~pb0iV5^NeSHbiWn_aLC>>r_QM%_kqL*JN9g zhJ1T+Mey!ocen2BHyzQ^;pmqII+pTuv$I9w#(2CJ4hRzP2pzdYbbyy?YT87)Qu3NG z6qd6DT44gBd#$jD!Ugvk4kWKxCk-*ZNHfIbG^?CUvz!T=-a~>LDZhadhvZS6+k8D; zQB`hWqw7j#Db=`1yr)gTti{d1a1B^VAwg4D5lw1w#18Lb)RvQ$g~&ac`icL(KbAus z3KnBXc=#MJw`rJhdCRT)4x-hN;+BJO4x464Mjqn|6@>0}V&n0z&P}j0A3x=A)L{3G zI<K!M>A2(|vxxQ0SZBDU%JkJvfeF;ZD-}!uEumymQF%@)q47!IzO8C!nkaFqW09Z` zJ0Mu&=}~o|b3l;2Omk9|3*Z9^ds_ANg}}ONbNOuw2J&ZjpG^h?e`4{E<q+M=hqm0g z_A2}(vjS&qeK?3}$tRk}m#eMzFF{Y|gx&<PJ$#L)XIdP_1IFFI?Nc=2x!v^;;?#~S zBYA&)wX3X@?#s)}5j+RHO)dvk2iNLvc#Kk|W)5xiRg!tugPr}Yn-0fK6~b#fdCQ<s z$$B#UZTFy?gA(a@x;tntvJj3W?dP``(ytjrt(uNqY_<ua<-dCE+Of0qJ8PKD=*#l` z1|wZEUPl5`L3BlU9lW3GaDR4puap*U*6rzCMuUyO2`mTrjfUOSV*GqU2!$?W!8XAQ z80OX~R_k#>Qf69vBgaVI7;<I_@ZqQ4CR8ka%{JNvCbfPew-kPw@tID>G1r(6A8X1+ zc1uLvBJ4PA4S!b5%e8kY%Bb}X9dLCTC_TK855O0pAg$0Y@8q@W=Oa`&GCs_G(&02V zamey`VW;RcS6B3SWiVgU7#jzNHsDdN8ry6WiR7&~Zl#`WAMZhP>byGVwb1ZRVYd}^ zlZ@GYXzBPmBX~b(VT)ho=8j0RtIm0U_fiz$DpBCb%541O-OgOACqh0nd~UuEKqH7% z*P8|{5br$<wjg;^F#RxoR1_Igegh6-$}MTD`MIYk#DgQAB>-$@7RcV<5Kzr|ts^cS zOhp_*sn(@^9x``FFX(QCO?w@^qL0|giSt4W4ZnH!XJ-HF_rs?E7i5A8TfXn>TWU`k zLvP;Q6|r|nNoqg;Q-C+f&_Jj-iwRT$u=O3cW@OYQ^rW|MRG4trh&$64gmG!dRTX>( z?YB$UeH`+j90|hCEO3;$i>WwA7S*L|pFmOo84UuK*%7_DHeM62EPN6(#!JYpQz-=j z5unVTzO^Bh58)fa^|}%qBWU?0Ak;pXC@~(nxfu;Z2d#U4cghVu?cH{1c}_+e^^pm- z01SFW#c^pn%`{&erIxa?vdS_Z^h*)*jE0ONv{R4mc2?cJ7a?*J^QJd$RV*jAP)(Ul zork<ik~%+&d)f0{WMgw=Io)-_u+@3IrMkzy>s?}`{^$)Y=?tOv!KV(umdk~GVA$$A zN6{rZx=wNxF~jp`EaYI2B1Ren6IhEP_h@!8DC`Z!XS#+byaup|3+uRv5>O|2a(H&w zREB(I-9LMJCVrHbo}O$g9G`@R2s$q5HN3uj8x89O!yMJiaX88s-+K^vx9ivuMU@gy zx1wG=D>$$ByO2Qqwp5p#*77jb^L%o3cvMnDT6()X^Ocl<{e(__v?Qs0^x%gdh#}P+ z{m<@t-DQhs%!eE#oVaJQt#c%tPXx*gz)Ke5w#Dj$JUwtq6%P*~m{H0CgSfD^E~HXZ z&wE946q)fOf|-KL4jjdoE_Cmmgj}dx6KFG1C4fxMp!3ot2jK2E39-P<2j2f18uIk? z^ewxwp(l}>LPQt}j}}5bZCRC*-$P`ne6jx@(w3aZg0tVg@c8<!RrqZEp#xjpeg+b~ zkojv3r@c2`?JDelCI&JMtC(0a2sAf=(s!qbE`Dzdl_N%@IbU&#xCh;N!8!Tjg>FTO zfVg-H$j8CRh|0<6)gOb@5@$jbvUt>R%B8sAk&LxuQZ|G`$5Mh3GRT=CN!tWPmXni{ z-;joc?>gppymoL<dF$K-lBAh8N-7Gp?FdBZD5<I2KwU$64GNNRX3+f0_gNJK1ScF$ z{3Imar26f;u8qXasr>zrCfkeepMc{A6NU!B%TC~-Wn^Rkc=|^*6CZ*anb6RF<*E0i zT$=;^k<BWfOLwea&lFxtcts!_4jTB%ni40Q_Yj3&k@hksx1LRoh{)`?`-l5P$U<#l zSHw?2Y6?M$hX4nM%Wbdld^th1d601V5!Ci)x&IXmI+ogvi?|1)XDGKz3X-lz$HdTr zH3?r~9$xmVXfpaDx!>&33L}fSxU=BzTFx;ce+FFQ&zvlfaYfJQaqHsP^gx>W5OgI( zcp3iU|HbyW5?Za}(H~0~_c#Dvh|HCMOOmdb$d`ISAw$DS-8&YUeuPqjb?MTkSTONv z@M0>*MU(OBu4;vly4If{&lZn8{R>aqqnQaA#1cd|YGXhKhPU`X1i=Xzgn9~61C)oM z&z9tnN6mf^%YFiPda@{a-o88We;WbwgO)MoI;eZgej*tI3%A(3he7=;0gcmY69mFW zQ^6HMJ>7><@wVf1Hw_w45KNOd;0g_W3~KIv1VbQLT-6QD)oq3Rzg9J+n_m!8KTuHM zA1Kndhgq%f78A8qy?{HA*^T?K#a~0#r}<#LW+VLV+qX7F<0YoAdLhe`4%w6{3G>F6 z7w<!Y>@_@RS|Ap6q-119h>6<a`Q>E$RqtoZ$Hq|kS|QVItoZI$y9MaqQ3(mZH+$Z` zd1C;MNdv^(9S$y&y8l-j@$K4v0>s!u>)reyG?M)SeU=zQWX6Ptl~pD67D$I?5T765 z4bR)}T2CD)Vt>LnDOe*!Uj-S>v5w4p4Im^VgS6$>Ccs1P1Fx>Jsma#2aTP(dC^-d% zj;-4N$FQNx#)NoZ6%@E|nw!syi+NE<mIVpq>-(R|fF!qbWE@6u!<)lrc80Spp1VwK z@@T(;^WdAEcE#98p3gZDJbID05M~F(xe>mEbBgu_hXq1dN(iIH4Gy&aJU>+F&U-F- zs<v|`EiKlbd2tHFTnG+j!ywz3bC38K#K4I4li=;hU>a;T0Iv<tupkGAJX%pI7^FlZ z^<G|HSp$3!Z7DuI0<jdfcrY>#x%{LJEetx#@w%B{Q8zE=#R-@wpsZJj*Q=2&GS$;l zUVEads!AhhM+ZJ80}A|&X&CForc2`&@)Zfc;(q(X&TbjgOQod7e;&n2-!>MbT_lEK zFLQwDK48TzL9{e=BvZ%#bQh6C2X`R<n4^|M#;l;1;!p9vr5ZI0hpSyVBE_~ZK%;=& zqYCFddzPGykWP16)KAQ14d4G1NAi4iNck#6s}b%7jx2exT4%Om;@zd6nHX)0gsH;D zp*thOzMkV|u(&!s@UWoaZ+ss$<@jHdjowP&nTCdhT;=17j@S1~gn|nW6@hy&`;1e> z(`g_P+olD@M05!|hd|pA!x+{dvFRYW10-H0%UQ%F;Ct-!>hM_F`Gof{&vNlPEm%!B z9N>(@pp7n>6lBAaMZWwmc}JU~iy*5*GzNy9O1<~(>JMfJm;+Rqgln7xXKNdhgEa5m zz55S58c35FgZc@&+Vu`$0M6G;W!(-A_OuU{Ie-+cPVP4|Ky<)JAIt%OWI~S@&ubyk zhVV#bU2+PFX4oCDXJOWw%+JS13`Pat^5{$SRe?(Vb#O2qW^9Hc`t#HyVN8QV-|-a% zh57M{X)-kpRh)495m0T5vC2=j=BY469R(ulT&2uxrDNRkM9Zrx$>W@CPWi<$@H3_N zoXKxfLvqh~yC3fdA|7R$kDc`00oc^q(-R4r<8XwGj7%@U4NiYYF5T+4`;-2XeV^dK zkC)GAWc<@Uq~Fu)@2^|4BMVuwqPjg?NShfqhj`}eldKFj4HSH}nOHSmVYS#ej1-$0 za-N+nT^BIY{pzzOcHcVl=ZGxCvBx@_Y|V9M$_DlLz4lfuA!R*^(1vgTGMB;pyAAVq zU~!s)!Ez<YkOm}Ueo#?aj9~=#K5)~Uwj;OFWnNJs6Mb}kaYv{JhB0wwH1_qDG_f3u z6Z$-}gCAvrNa^WqZ`llWqs~sf*gHH2{GRVg-xt~M40l}oMgF7r>?Oo4{p}_RhUj6k zK>@^P4{#_wVdK*ZJ263n#^S8I05%DZhL%QW!p#@ZgMMbKfIl+Q3?_*3!-o&=1o1?h zBYA9_#9(vX@JEHo#)D^DZHWK;_3I1mXWI=%2KVE3obp;-f1{q{iR^WTJ5!C9$wE#= zsK<l`r{eJ<+<=qqCEXwYrD?OPtl3KIwCG{|woVc^kCjT~Q;B(dYh!Y-0+ZmGL5+HX zVw|Vv&6~qK-5S-u&M-;xM#}j0zj)jvybPup>J`;|y%kI=Y`10F8Z`XOG6l)t1VVc{ z^*+KKVv?o9;O6iFOqqh*oXKRpZz1uWjG^`djL+h6a}KQS?K@BmuZf>NnY!(CoSydS za=h+n+}Y`LAhp4FisPO-w+d4CwMTGRx}7aCK@E|COso`$F>#=Defw-xi6V$Z%S2os zEylpcZCvV8*VlGCt4v8qVAH7`3c5%xBD6b}d_=6AN?4%X6hfMqrdi{*SMz$V{Q0n! z>*|zF2rLL5ka;1<#)0f{Gf@$#k6<BDHt|}3U<wM@M_@4rXWC?y^Ksf56?Q40QmEPp zkD$Dk)igj@`0<06ic<)72s0`4y~W>S!-t*h6;@+)`;DRGmuM|%t6zsV?{!+we||pP zD;_2z-F#a|3u6q>53n;}7QL{*Fcb_f@Iv)UA>fipoGD0>h#mb-N2X-pF%E;+!&Xj5 zPxU%fv;9|2lMqBK(|p~czP`9_yCK|n??<9lGJlQt%iu-ZNFxZ4bHzVJfxrR-iyv(X z*dX-*VpS)&XJ(>{;4_;*M+NgG5YBz|_7+4xZ+n(b9`hsy1Z2Y`aXm7=1Q~URr^hIo z3ys|SG2UPK7wUe>g}`}bFa$;C=H@uH<&dHP!xj)EwP~~aa_WZEy9jzwokNc(BaX#6 zTb>_b&CQXh4u=h>aF#G+6?d$`;c(k}#2?JrDtr2P{ej%+hG%gbm-i@1w>Uxf!o$Wk zp&clAl1GI?27x;_E0lMa`-OcDijha2Zz}0?#Qy_F?%ItTA)Y%6!(K)UE@{QZdEQ%k z?kV#JIgdx;)uo%Yp_3r4-f)>UH3UFD0M<eMD$u5G;vM?pFG<g*ctZs8(e?XeR9gar zEcVZy6+lMZ3>YL5n=ULhbFOkQn@9ydFUd`h2_c+Oc?E^prR=l?7*N1`4^A!0XRC!; zr_uo<03ztAU^oruzCcfO{sl&_qXq4l+Uh(o%59HBA==c~@XTQ`g&Fo7*QC$R>!x-H zWrn~Lgj_4~3lI>vkq2x8gTa{q10fXs4x-e~kaHnrVCaT<q8IHZAA!vk`qaur8IO+? zIGcBH)24`O>zP*&X#*w(CkGP&Bd-M~is``xEy-PixhD$XqX2)6!O}r42~y)jn3_Ye zC?-fjhCv7@fd9!B9{dsm)a%V5<H#!51u|i-))m%Q#LO$GH$X#?hjwJM_V(jPvXJmw zP6brZp-tLsy)n#_@h-#(`y*ps;9Mb7-w?I^BDY$QR~OJi4<WwHo{4pa0STYpUfi99 z)5xgnve9qfzKm3%{Ml!Y757x3%u4eqUS1$vPY8g$f#V14hhs1Z)8Sy>-QAnh=OAFG zv%Rsx-5@r#Y~{7`tw?mFJfTaSI?2Knax_~&W{?1;v35<0*~-Ai=FU#~-9YyM#&6)w z_S1{EhPV4|g_IH~<|ij6!C;I`q{3(e3asf*E=#JwMFc@nKw6z%R4>>@f|s@)>{Yn& zqgI|r9Kn<h7&szghbGoF`+P9SQ>M1USMC-X^3AWKY%D0ez?r=0)Yv*D>GwKIFFa#E zZY2~bOC7M1Q0n|qt1Ny`wW@ARupj$+MxyQoRPZK>sOC}C9xaP=Y8veeB%0*2FWe60 zPvVj{cJp^3YT}y{GYik8{t)y6vUc3CV&|tD{)mdawYxiyIM1N<f<IcbO^R-L4r_CE z$|WCb=z}4Kp@W^;$h*hunoCk&FZ_gnzce*IjmfTq>X_hh{-C+KEd_A&7639wyDL!5 zkS@|))#E_DWi>s`>+gpdDJc=1!L*_l;-*$qA41FTV?$ZJnir_9sZOXZIMS{{wm38< z-PS`6!c;p@UL+HhBI5e?Bvfb$pbx}f;+UAbVAdpfb8}F$!jAvbr>}`Y<^}zCE320W zzaM!m|H|vHJz;X}etHFl2Q8c|F*SgMXz!SlezJ$XKIXMj06J*`Am>sT`;O!Cjw#eV zElJU)X0S`FQ0L!48-P3}lExz`pIkO#VeeJgj$LN0UjTVLx!yS-vrs%f0e9t8dN0FN z3gIgxE7!p1UXG;u^~jNn#B1A2m<NaRe-{zFx#Rk#3IcIxnHU9*0A#WW1$hhhoSdA( z+A_H)nBkms+*{RbD8ckQJ2~`$xG6ES|5E@_p59n^TJ`4fA;O|2K3wLxuYqRWs&Zj{ zgWwz>CJ1%L#KN*1ETq44=g!+3j~dr+1DAE{pB=v|`b^G`nU!@1nCcD;TK0bN_lF<< znTev31Kl!TcQpD$IJ()A6{NqJj?9Y&Fi-(_to+Ph_>>t`IA{u>)730**}>V^Hikz= zNW&WdUJh0Oq$MJtVN5SBZZoV7tCF#z(Zh~QlXnZWM<)xeidtcFwZt0IbS4YOz?Mvf zOy2Y7&l4co1h`_yf^OiNF$~y+gUAfOQZYI03})O{GT92DOKf+MA1=8^BHj$s0(ere ztrNw)1)vJ)V#u-pa-2?d{D%;MAk_LkUjZyZ)Y2T#W-9TW+K{{;sg$b7q9PuEiyr{J z0&fijV@#{x4ipeIv1k+wS;qm2DKTj$gH8b^7D{G}xkly^QV70@lt%YQ9YZt1k(88# zVGS5sFai>bOj|&Xs%9Y=W;7kIfpA?l0Fge#UxEHh4@tQ4mTPd)EEsG>#(ki6R;_Gt z!=$*(W9gTf@#AIZuG;5a(-xxSvg>`Hm(q|45wMNIfM_y7$^zP0B82%Mate{!1DM7E z^9ZAu*$){`fuD|e85l^JX!78qL9emEJ%{4cQ-^}rZ;Xwc7>K*qBn%$r{5+SFr<I41 zW7p+h(;#Gw5Fx{l;_*Zn8!!b2`TkOvFaQ!{Bd-s>9n4{&p<&tAAGSV;B>!aH5tke- zpm3W<V8BUFnZv}nzDL+VXt7(9iqCcFXCU-782^M6)inqU!AC&XAOh1Xvmh<akOrod zomL7e5Jb3MIZJ%^?%i9HDq;%<j)TeO=ckO%zBWh(Fk<1>wmiq_0dq^ZHxcK0p+?3> zJ%6ZJ`H&1j26bQ>;TjuT`_nb+iCSR<-KZDosTJxBo8Ac%O80UdOpZT2{v<^`h5wL5 z1BAEF<;*(WoMMhGXi9Z17g&#t=tbdx0bNPt2OHp*rS3*Di2s9_M$UOKRR&yThur$v z54whrZ6fYG+}tZOL=Jw$1_Q^nVa%k-ch*McZT20_rb9WXaw@Gpz)E;9rkzYo!895Y zmxa!gcyQpM-pmxu1BVcwMn@?qKOcU~q02YnmltEx0oeh6lPEcF;(`%hZ0UF#WOims z6od-eTEjmjFRoVoHM-(9%u45d&DXD?(M|<$(;M~2+`y4$Aqfh<(!mE|E$DmAAQU6N znp5er6wNV9;rPI#G86OZzd!gStVgRLCpVR3&wz{j@MH7UtGko#J|$yqiGi#UsmE#O z8tiZWGYbh*+~3pqp;nSsJxyIiX)PYnOZp{)N(mVdd>APF4#)?QlVI^#AjgLcVi$(1 zHych*U!sbuz##(#g8-)0X8`WOFFtJkR1jbK)gJsfyW~q`9#yKzpVljyk?`vfLB3E~ zPIq8J%DeJ2$rPk2;TKTYfN9PGF)xq(5@h%gQj%x;U(Ox-lmx4m9T`3d2TU-7eh2eg zn=pM99T#^W9)Tu|0mJXkeA}u94TrKCqPbf2qE3b4Eacd@&#&`yqpv&Lxn4)3C)&A0 zpE0qpY=a;L6T4sG40Dnk3DVjO6<^yQb0(?t-p?{>2`lvb+`)$NhXL}=^DtO$3X?sk z|Iyl4Mpe1C-7Y{y#a0B7P(UmY1QC$5z`(?yCB*<Fl#oWn07Xy<B}~{V-Hl3zEG&>x z1U9HNNY|N{?)UxPGsYR;IJN&cHZ0b9*7Mx=74w>N-lV)apgeIGRoCAaqop_wtZ1|4 zJDR*ML@6})$_6aM(7x=M!t!7dQ&a=pxd)%o2gTSSjDtXOLq5$y%|*x|#NLH`zFZ}D zpkM@|vkQBPRAib7CfB!@2*{@P<ont3ZFGn;{(b)gbcR?f)<9p0mNEebJ`j^^L#HTm zr=&_HBku6@I4hf@{ml>rIlH)6$UE#%Q?rGU2+wBopvbg@_Oj!dDXW&*KAmsSS=;md zNey!<^O%Wnh+_LN?)r`>g#gCM^8o2!P*Q<gj?wtNNP+GptK6^nzPvBc4Okro9r?eY zTW(1h?#o;}Q?|P3pgifv6K4yvh{D5b>4un7uIAh`fJnBWWLOQt90n<IZd0yMAa#d5 z?0+?CyR9|+=)N#?2%Pnj><9Dtbl;83f?1^GGD=J)gp`@Hew6qulV7Wo(j)BFcwfm2 z0SqGGAmFPU^UbuQO{zy*(BvC{$`>MQ&AMi~I!o9L_4Iiwxjss~^s>i0^Yzuw)Xx9F zYjGya#<Xf$4-}B}%s}#Dm0|JrXiXW4o*R((GA8{@g`xiN=nG#|tZ89h?$35fJowhK zyQo4hO`=`G3Wa3Q=FaV<0WOP%ulSS*d=}W2Qc;j;S0{b;4fn!t<p;3)4$H1#xw}nV z@J+(H!C*69d1WR3&F3};A-DKdMa#H1Ha^nL__gq$NL_nOgK{;e?L3%1%oYN${pjSm zPpAj`OV=NWv#47HqUCD{Hh^{be;6A8Lk4~mhYBwr%1gp$+@;T0A2~b3MVa+dO-iQ# z;Ad_*o2Sv)dk22rYIuDyEOFKoh&v7;o6e4|UuPBkjAe>FYx+}-Sbark+;>3uZEkk{ zyMb1gpUx%Ivr;F`7ONspKHfI#;S!$e6ou>Fw!cbuG;U}#V_4=mt^tbp5D6=|BBASb zU2g8GnzESJHiZ+mq5HUT`8Q^ZvHER$ol*J3){J{oai2>$w_5B5j*l}+7cYpr{nuaV zTK>xG&3Ht07OSS+U!iRpD1l0N3Fnz<p&ZhlcDY$%zjjTFVceT^ap9LON-(XK|M-9z zJfrMib;D^jXyn)4xXWn8)j2_+TzbONc0zJ~&E)JktoIoU-Ag)W1>3byWwFDS@k~W7 zqf$s>K~wVw4|@-56&}uYx~-k5zP2CsN!N$n{_|%0p;VuJ_lejMc$ILA>DaO9v%XJj z3!BW(?>L!%jpzK&r)NU2iluItum%A#TpydY4QdO5sfo(x1h&xfSsWZfpx1)LL5lVH zs_W9Nqpp<7_$;pu`Rb~3;Gcav{PX21Fh<u7?Ztf(_7;?zrr_9Tg0x<QB`61JsCf3T zMbc+JK7@cExI!K@TEvk}Ac_i-ju=?B3CORaK!%X*IS>BwKV%twtWh#2M=0H)cMc5A zXg~+(w)NuItcscsy2=3dXTYROeb1gIn2s1h@kay_Y*4H3l`EHS&<$F}Lhbcw$S$GJ znoeVA#5WIgwd+~R*^i6tP9JLR2l{*^L_XCMgiCJ}n15enb{y*jxxsP&e(xQ97ei@b zX|#B`&*zm@LA1eTq!|CFtr$2<zNp2N$`sLh)bpEJ-_y$_|H9s^()tHX#!1GMkVK{b z?S_l(767jczO}@<O+|6+Z36b&SrA;^P7WQ91p^s{|4VH*^SmVzxa84M8mUv0T^$n| z`9|!R&0hb24Oeb1hYF$0KC!0P>FU)y@!b3mF?s?9!aYlRd)0!reCidJip;)5sk4OV zxPB~opS&R7wUCgIwIgiiu^Jj0o%;4Dj>+<>EI{l>ZSqSUW6@i9^EqrY<`g@b_8`)% z5PmABlj_QGcbW*x1&eO^!`I8ApZWYYgHbrHivsoV5zh2!asLypDd#e}3DSubUWO7b zqvxHSZ`AwhHS+$bwzs%a?)Ov-5;QJ-2M;_CYm67i$1Pc1SyWk$51p`mx~T2c-r%#$ zvi1w4g*xh2b{Q4!Kel8k#{cHnj)=B}V-|^t%^j$6vK;&WLTkJ_Pi%9a|9DG466qOm z#|SfKt&C(^y?^h@a=$M7#QuA-McSn%isG@=z<QS)#p(gme<K7c9kt12cA}slV*+gY zyo2~$-=Fvbn$Gi_&M|ZL>^`fc0yX_R;UWGhP>fb)JK2-}8#2W*iALjx>M=t}fj-(g zr9tX7x#uLc275XyUrYb{Gq9#NVYorGRdP`PgNcv`otJ^Agf@_wncLLZ2@IhGzBsn= z)j8o?YEKVgPhYh=Jzm5D3ja0~PbtlSM0o&N5|?-+CSOaZRHaImY_Q(eCW!p=eMIt# zb0#be142T|;SfY4D7Y>f0GLtuRk@u3)bciv!9{RB!XOM4afyu!6h%RsPWx|X?on44 z#@H0(VYtKTju<bIt2+A1Aog^ki=SU(+mH}^6(f-H&EZeCSBERHCc!FSU*puB693DE z`7I$nrq#??JBz)G+RO!1)M99<ok2YL4&x@NvJJcY29k3n@=QGMOMd|IEOnUaHt7ov z4A~F34u50Ezh36u;d=h3qw~e|erifagsNa)bZ8*$t#a4Wdmvm;ls7S?^CffhX(^xF z(;rVsu+JNx_6n7G@J8bqKtd9o+|1Tl@A-_s3ONqs?~Z3%I|q<nMLUc4$I|-(g;y)T z_F&^$b0S9p?cGLHWTgX>ziTunqsZtw{8^vsZ#~ZUG@nmt?J-jQKVR~FhI|zI6jKZW zXj-wsDd56i!?jZvL;2QWeA}zK2Nd3E65U2dMnTGMO60QP>Ke0tvksT{=d<da-+|L% zI(xLG_SLR$cflb;&x4^!NatrT<(Q!UcO|qiK_T0y;F^_Ui91#@|1Ha1XJ`kw5v@!y zj`&?_0jLX963QHLO@CKqfjv<JE3J_}I70T2SsaYlh}#1$;Hh_yuj~ysJePCAGU>tt zM6JQQj6GpMy8EGJlYkKnLp$lzYgEXmp#s_)PMlCTP?W0V+-oy1-4%A)!zeiIWeB>7 zeGh0tY~&_D4m@TxHF=yV=GK;<kL#pj)3ogOR>?n&78X7d3ou&%)g10ZL-qdS9{v5p zXFeU~;6`q(hq7rNIN)sn&>{e?GH7ZEyo0npsY=k5ILGG9*2_GD7J1=9H6(c5349&$ z_k%Gl<Vw$Bg6Up&M>$UTeDayCe{^8|6NSFVn-FlC*1!)<4;_9Mbb92r3ir!{Fr}eE z_d9N97crg8*PljQj@|FZi~VKxFtXi~J^Gpo&^Fk1RTFSc5JeUotQd0yltM6;yEkJl zPnvX92Xs8G1D~RTN77oy=)2zZa`#T};8{}*vfB&OK-pzpuK}K-_z(u0=P|0*-=pKs zx@zN%W~<39E~ZObH#_6cAKzQ_%BWnQ?e8?%GHa(#iP-hlusPjg-&KyG-g8HbAJOi^ zquV0)Pv^UW$Mw!ka*o;1@tzIXmw%k3cB-hTY`xknk+{tBQs+=*;)bwm*W-nc!AxU# zSo7pGh!*?+@Jbrx&vZU&L_5yz2#aWh&Dn?MP%Lx66d~TUOo!qPx)npoU|RcNrvB26 z!zyYzw1?rnwuooV)Z1$dFOBpw!x@^nu8Q{c)>q4nBpyhc9e_$l;{Cctqjf_wN>8xL z^tXj89|FpQwEF#h3o1UzX$|R8b9f+9;x(5;*)KSKFXt=PR`Yx1@A{+X-c6jxwwhJQ zLym!|c>r))A}D3HvI};5<DGZpOW{&gDvp&T$WLXo8@#z3l|A9?^6k?6gv>HUO@C#9 zMrr>$G8&P$+Wj$IcyA|?`D|yEv2menl{cn5JL<R3GGK{6RuU6ksrAyNTEt_pLYcIr zY@VJ8`ir@P8txRay;c#H@OYxOH_mjglI!iWvKhnq_n2f8-L{5Bl!1@7+4A*g`7#tj z<+~r6t}M20teaz&!Ztb0GPhNs+Ul2njB^C4B3U(jlvT=}fVz=ors}-LMma3^*@jr% zOk`i~h&XF=LZV4lt^4m%GZcB(Y?Ed_WytERvW?JQx}2lEY88e)wDro}d{b6rem!$m z>iaxC-ds1!DDMMJ&|+z3GzZhO;6-Wgo_JKU?S<)>Lr47Pl$KcgZZi*yAoaPDp{9~r zIR`?2)_!qDyX@j6rIgq3UtgnYT}|>V|9gM%Gk+~6v!O_3y^-<I!ao~xc(i>;GbL(C zwPl;m%Yd=kc&zWP&XV63+nmH@wV^(lEAGNkaTES^$s$*(MfMC{Y&RVgkqGN*aFT$X zTTEuxmOA<A@$0YJvEt6H4MAD@MsL;%P5+E{Po%_qpf8u)abNGAs30r9TYW;)(2FGx zKj!A{H!cr~b(or93F^4Vg;FrT{q(|JEVJidjgu&PY4Yt`z>8X!A(p4Cu`J3AWwuFo z0lqDBlH`*x01{+<!DuZF<y0|4<r}Jk{(9P+X^$se%w}1|T3oSE_wmxE_fryMUAqm9 zhU~@j^A>on8hJTqhry5jNWIOpv^IkOAqf17xjuFdd(QaKuGI!gw=X!x+0`J{_=Wt% zs=0%9y+vKlG-r=gj|(P&;~xL~?m2#kBJDcj@51F{3nLGFef;}9g_&5cPbVkG&MCpL ztgan=8L%ggV}P<$CDR;Fe%F_^ETWflKtm$TL=`SJ2#e-L`$X>id1vPj3#?Z92L%<w zb#oZ@((EdADHbpp+8WRw(kTG&QqbRT(W`AuWRi=Y5(61=5XQI;*fwhqJ>HShHb$W$ z?B*eS#T)EsOUc^Nu{GE%4F+V%XFeId_MCnmwkr?w(9ZiA?XhKWcwm?hgKti9<^+W& z8r)-Sojshdwiy5dJM!|_P0VtP-mB)&8iao%$Mya9D-kgT`5yy=$AACN{_V6^|F_NI zU8nP@7y=z-AOKG1MfAP4>m(vqfr+G?OX#9ky1E(XXSw5wms$zq8U=$fOoqc{<F5-M zdD>0>=FcJ{VV(M1!R#~rw#&bIe8+FU%WHw}1S!e<pL`o%k`IC!_z0Pcl*?c&)G6A7 z#{%3C42o?)Y_)P-v)P5jTth}WFg8C6dd!CVGUqDbN(&{nFMbmiGBO@bj#ImK&8$m3 zH>7g8;DJ>56jXY<fucyc>{L~~C{f!~6wI80t17{lz(YS|+MfgV)B1W>_T`mLhOqCE zAPF#JiOgXXbL1>#jCq+>MID$$q$i{aP$5PzKA<IXgNd;bhIl6!hq&h7avl8d$TOOF zjleC}$z@~%cB=BBN4!Pd=J`9doaOpQzQXLQ5cJ)fU?kS$0FqGP1u^k^n;Ryx&9MHH z)^`~=+KiI45M_rCe6E_|v^agig<hyX6@Eg`s0x!U$qCcVC_0)w*!hcXx={M~fC^-~ zWw2Ptmm4)fPF=I;E!;Z{!cB=9oKg+_t}4nhA9$NN30E~02Q$B(L19;jJ9?Lq;Vu>K zwr9_ccW^fr+Onng&dQ}{E1V}GYL_OM6cW6*cA^Rj^`FzMpQsrPEahQc>bvES3p5<j zKi|6I<&^+}1(|V$)?u0u2L8S15YdL5LJIt(!d(Dy5~GnN&3Qx;0h>mO#5jAhk!v+R zhw+b|5`2}{cMO;6;76EVf(Un}I;Qi7gtwTP(iB?Q%H*@h341C6g05P0i%)TBtPIpm z{G0*;2Y{xm0Y|x0)4%|d(6u2b7(b-rQ9gl2Fr}Jo$^iC}Gn&S?rRyu42ao;ZNlnTM zP$Y@rHh-tixa`=-e@HLqtNwmqYHDhW;q~e*p0f-60ok?z@#wS4FYv})GdC9ndAb#p zh-MhrH0O+U39VaKnzxY44F%s5Fx;dj<}-fFFq54cu+WL60FBgB&?PKy#;28*nSp)h zf-Ob%ibR^j+|b58s2~7~8RLF+xJ-fM+N0@Q5Q<NOmM>8JL<E{0^7lbN@&nPxA86u5 z(JxJf!AxZkT?ssdsz&l`m8f>A<y)Bb%DRrj5NTirLV&wNAi8kVkvvP6&hGB;PU*-4 z;jN%>6iUpAI1qlz*LSkPpUZKigN=<1<e8_|&4py8M`BYTJ!$eEjY{4zr_h%DOZjQ% zb|T-gVthy$WfG1Ffbjz-gNFXOQ<L&g3p~QKH*8i~kacVS(7?bmydCIkv)w@(zD^nr z+&MCEU{33p{!8Y$!;yb0KC#^X%T4U>agenn;ck6cLn9c&82r_sNm{`TsqhKls^m2w z#Rp#`eypPYT~P&oPr|40^J_P6nqtO6>$6o|5n2wqWPabwG^^HJHz;Oxpe@LoD`N%M z2H$*bUTjQkmV3)B(^h_nM*=#^(1W{y!x2O!2+qnj-R0)w+=-2saAXj?F$e4^w2_9$ z3YdF4%GNd&1T$ShiOQOq91FfRfncIF87X?p_E-No3<*fi1NBLq3k$gq!<MNw5AgxF zZR$Ub%pOin_P-8h=7J)_3&s71cz6K7XX(fFgV}0Z#kVi*?yo%?yG+q(a1+vv1vyoz z0#&#PH82m@HaJN@63cCFo`esWKVOL=7!M#wJv$ACMVhz2ah*oY`tK+?vn9s`nRvR| zXV{u~5W9%b13k5o=!u+qS(}z?y;vqZ5GBb#t-<lnYcaxBg*&DXC|b+aZt-q}`R<FU zsWgv8D!pkSa5aOtwh4WaA)@>jO3Y?Xv0E?AD1>wZR3V?tu8cAS$E^XeFcEw1E+{_A zJJEqJu-W{x0SKdI<V=S-z9n31IaU5_H#CKMdBIFK%(zq`eD%Tjk_W9EnZX@mEaVbL z=s>=B`3^zo2^gHJnwmjMZZZ<fybPC|=cJPwb||R*KCj^a`V<w~M7yld|N4F?!>;p; zlOk;R5U@Y##*SfHNsbf-H3}}EAzFIBJA}CN7?W0hw=RfNiy_x8Lk}f~a}q!ufS(33 z8s>&BY0bCf6?m91za_wfWJ4IZ#OQ=e<E`<%zI~J?<r4NzN_CY(UDD}1^f!pJAb3W0 zP2jJHq8b@PF0sCfgWO<5JqCo~I2z0#`#A8Xksi$eXQwn%c!9l|RZR5(_?Uvh&03jY zx_7C#zPVF3ra2~{IKw;s4sw<(uLf>aK(?nX*}VXvWPgQSM+9p8a}ujwYw!(c?=(o~ z_~YPwffrmYF)8iMLPB?3^SB=4?i)mWrq&OmUdN7f&_Y|TaOoRh0WwI@2H^hS;~leT z!h<<Bu{$hklMGQ{0Ou+&j(60?AoDMs)R^sU0c$l1e~huSvy(Djm)SosaN%bT<Oc?* z#$bQ%Lkv)!O{V}JY6sX3b=IkY)d2`H^5VcxtuLuVW0^plbQhD=F+M2tMuQfj@A>ap z_@6pgJzN4<WMv@XRe(50hVb>AVb}B^g{?0Ks}30&ZpS+T*LH+)Eni*+3|)a+t0Ias z?cu`}zKZ=Iv@{_DH=+xKZ@bi)k83jQyDj&synp{*Gu>tt@i&1tG`?mRXhHT^v0}w5 z<Bb>JV_s7MOPPARsnuZ)o;^KQfnSHRhrS45P66d$?c_V!=cZ1U=z-zS-VF7UVN0>! zyoRs1BBXCy^3S-V`Vtw@3KNw!H^yp{s5M)jfJZ}pH>LY}@KS>p?SS~VK`Z|)obNa^ z*DaNcKQ={lywsv2+|ME+`T#)K0Gy;BQDqH6U-0wMmbSJcIz$C+I63730fOExxzRH~ zln_!JC$STiwe2|j0ay4LFU^y%iSCNl&*oW^<rYCST%c{`aKb)b`RE!KJnouK;(FN| z!O19+n3LHClhoOWyzs*N_PlcTJ)C1<1xFY7Oa@7s&IU(2$;lEW3MmO0CtL<NQK<6Y zIBX+jTsVi+TZpU;gLB%$pNC8yx)%^oh$we}-2W;cU8*xG@dc~F8g`ZBM1xB`Quz&) z>4Lax4^X_{KsvNRoF@VcJoFPo7Q<!}^QKckuHj@Z_l0N`WUE8sNRUl6BEWLrY`#lV zlN+(6O!7q8mJwIL-@5h<7o0)+go(O1!2?(Xhyi3;B=vTBS=z2fDA-b}L6t2(-wik- z9uxb)u7<sEtiWo1jE$fGCnaF$`OlfB5x^K^sR$j&#_Im7NS*lVr=a%?SlLK^0f?M& zr$30mJyYGtNRUSLU{=x}z~18Uqit8zt2NyUDTjj205-=(+(j~E#YmxI<5DmNAt=D^ zlV*oys+=4J8>$7sP-yvOcIXJ{OmKmRQ1zs4cn>>jc?a~;#P|9~453v)%wQx`T78co zy0G+KmHXz+8-k)V;|y3Zjy<{cs*wH;h^b{jKm3MPmbK%!XjEP<@>6-QJ~Cw!YN2M} za3VN<2xEmsD520^5U-g|i9tPDqn`;2ozbCgCS;n#!^O63Lq;JfI2iLLFCHhu8XdX# zm84-dT9sgt%*bcYdWjVBQR6^7?hXhK2v)+tyTQ#1z`_eeoi01Rte~)P(emXSuW)uq zYo#>kC8(gUBIKBCacwDH2yt&7URwkkA;8*_c!zw@bdjS&Q4LywA8w08Zr&}AfgxE) zp$OfF9}r~b>N+?T9fCd+ErsL2r{gtn>_QPuG<-NRl0^3ds4CDrG=FA4lpn2?=miH6 zOW0{c9PuNi?e6;Hw=k|T=2p#_Qs+#9#~@#vvmF4Z>AW``7E>@a;HyfOnUe+f!=Afq zwj#lGd?Bw(!qRXUlN5@q6w8KEq)%YZY$(rR6Mh?UfNW_`I!z!iq<5y&k%|u{navn3 zG~#HEH~4ZW#~xq})mFefHr~S22p5f0Y^)<ny1-s<2BF~*2-#Fpk~joMh4>JU0K1(N zZL-d}>;lx3EtPH9HWp*aiDG+V1`wgF$A_(v+qasS2&4J}&4tW{dg1BX*xU>$>Kr7k z;=xgrs>Jjmqa<j5%<X$UKLP$^A`j9HMV>T9yGEY~_9kI%*y-dmfv4e;;~MkW0A)L) z=2*I(SfnBO>SIQuhRtMdYsK*ri;5WQghim`(Z`PG!ifS+EEpfrPR01v6Oy5m7rXJa zh%kjOB3<Noj!rFM5IGubqlzPjG`^BLS<W!Pb&SJ}Rx2hJvKT~xHgHK3`EcUU%V^eK zJS(|hi6KfZwa4CpR_ZJHHnwkIz_j@GN`n5my}tFZ-h_eeXV=bMI{qO>i51UAbEn2~ z)s<g@fQWRAR&GY|mJo|iPt@jPC(hM;kt#P4hN*}*q(uyr-W9M}H*$)FZ6%e5o4GcK zXS1Qy-;b-Zoft)}7n&pU3YO2dS+N(wo(bVLS-4Cpyp!p>CExWBe?@PFG8x|%gT5Bv z(4;-tpBiT!Ae5^N=0v<xHm(H47Xuc*Z=>UHn9z9?{7?aXhsh~D2hhQTy)>e?9|eag zwV~aYS8f}gBAsYukkW=SntX5Z*wC4~1aJmnRX(AaR18(#etv#-S8v58V>_cpPX~9j zv%-RdgQJ-ocff{1HW8`TcUk82*|d6n#UaF8T*OT%-QaZ?{<lkFjrjv{2gGvo?r-V? zrT53B&wZ)4R8AuYThuK6w0faV`7jumEyUZ&7NoKMZb(ZNdu6aDGhF%H@?-W%_J*!7 zD?zqmheU=6a7c0@y{DxoR>*~c<bA(+49Jfn4#P>>cWJ?&vgJJ<1Q3BD)B(%n?V_TU zYoXm<3xk_mcUq&jKc#>RJl&3Y7H6~2AFlFVR@2^Qw(l<QzN{$D6hPt<NM7d564`P- zKEBYitU<lv#n_Q@OVswmMC!V?HxbXUo0Rhl3;Xc#@!%<-b6|8-q?UGyxBEopipz=h z&73z+1g5`W0b|S&vggH(!ve3sZ}{x0hjZ5g>!oBMA1U@{L_B?pK5ho`SgYTo?^%N^ z<;A0pq2K|09a?047hVbT5l<hX<lzN-23i21C~vSQDzJ@6*m%!a%DA2NW<3voAWZaE z)J>lkqdn_%^b!#fku96doqL-KbeSkU=D{ZhVa684qWbpda*$UkgV4oH+67GDzRVW~ zp|TWX@J{HYD^y0-$laMk<uNv=d<TYBrCyH2`4XVK)ap4L<~89h6xYj0QVXl0<8bb+ zE1<d;CUgkJ=Cw)J!G5(yoNdeK-*0F*ktONsCD;`~%h5<OI3a3=oyjm)jEG(OKxU$d zPE~)y>(RhZB+tY+(srUWZFPboLO{aNv%Gq;l-5DhR70465-beNYj%Ee39&nfL&{Lk z=7CCAiu5v!*c=Skp6y~Mc}*umY?mvg=5$wl4fG7KzUU#eJ9j-!7YM*6SJcorrH0@v zQpGF_c5}Yg5=b{U!%n(~mIuyILTOvtn}Ifw#T`@_60c{rX{Gwxw(&+G*YU3?Dolo< zJ8IKQusA3`nYeu@xuP0pPTbkP&zeh2`?4W7xDQvb^4Q|Qv4JvTJDoz3V!n*1mALMw zcAd2;XDX99X7uHaj`@H1`4;_5+2)Rp>*?0h3qIuy-%6Ma)=oS*7oNPBt@`1+y^qq8 z@-4W1qKdgbmH(k&%+qNH^Dj|khE$UrqY;<jKYj}q)-9vrQ@F}60S<3N<z?z@sUU(m z1)FZCSb%_%Qoxj=Ze2<Q3Q6$faIS(ys<w<-1uymWD24E~<|%fArt}O^1m3b#IFIzV zb%td^+NNXMij@6%vPl81feerkitViPV7Wv!g?rxh)B7=(G50R|VwHBG03)YQeGN?! z67UaC5Nkz0bh#+3F5T7)`v&J5t;XtDB`23&+!<j!krm}uxKm*(pvJ3OV}aSI0o|L| zbHa9P?kjL<OOV$={Z7tKbs4h*t6{ndI<7Rd*!_Hw1LFPo;wC&7V{o`4P8<?vFqOul zEQUEnIo6Ylz0ThISWj(R&b=!|T|EW)7o}%FDK=P{C|~ZYU)P4d7u{{sqpSRas4+0a zqN!c&m(*h|@EyN>I$RT!_$)^$I#%s?j4q3n?8djdfk)yoPNVa4=vbveee@&9CaIlP zbl}g@V7b$dm=?{PHH)SA{m<f1j2E{9%i4Y5Kx#6djorXGoT~*o*xTMRy}G#(5fQcP zio-OMVEu+uboi?fv`FcO==9gYM?_q=AI<>Qb`?R|9p;;o_FcMU(pV9uxEFR_4C<-y z0ULI2LOFZQ*LOakG05ja$<qYjQV4ctFVHW-KydDsBM`cDa>$=OTX4gMiWL7|RCPw6 zKkrve1glb1H|;i_s4XfeRbT#nAf<q{xEUb>Ot3rt{;aQH3t5U%Km7R>^{L4Tt)x>6 z>Etkwb06bwlGPbe^D$AS(gp|`lG)WVFHdxa<*1<P!IR4`y);HQ7$%Jn*MP}wiqCgE zvpWH)C3Agr>?e3vd(9W<`X#0N1JlzsY;Qr}ZBLk;$YMY&=-5(tM1OsLuZmIRD-elT zC>Qntz@ZYdwrCOG#Dqu3@j1jYMKQ7NSk$Qpr7N0dq2E)8!dLwG;;1*O0x}lelDuW; z`s%QUcmt|Osgs2%9ktrX7-|sV0P^s)_`W$8M;esDnw4~~2>N;En-_C&sSz9rsUK$H z@fLw3yrk$~8XyMy`1)-Q4h~_)kaMggGjGazAkE;t7x3}jM2f~~I3yWGt9erXoFsAs z9yVclctGUH3A~ODCnTF`Sfk#SG%cl(GWAg?t^**tGdmik>4$xp=saRWRo6WDibIu# z$V*S2LMRxHfB*ObWk<<J{Z*^47`hq~VHqtLphv-#D_<LT8eq4AhfK`dzTu|%YRmts zOEfK8`l_#nvsKai87#eOI`^(3-49{~{`EwDqqpWdFM9=jI1kFMR`kSZsl9NwaL7#4 z&fmv*=$?e_I$}$0tI`b@So^m?hmI|*Rub+If;hX-I+(&4up6@Vw>qG9ZUDn%4M;D- zocl^V=k3h8{rgqV$#Wj}X(Q-QnL1sLnSj9PWTIZ2Idbs#iJEHvdL;mreykJQp2n@9 z9->BEvIRT!8`|wBXn?48gBPpAS}~fx;eg%Whe}>^ScY00|LK!gDgF!meNdHPlj-ks huKs`hx^;?0-9cV2QCv(1hYvG$@7%v5W}AWM{{U^^@`wNc diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index e7654a5c..9c0dd3db 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -59,7 +59,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "NOTE: Redirects are currently not supported in Windows or MacOs.\n" + "/Users/sam-basis/opt/anaconda3/envs/chirho-robust/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "[2024-10-22 11:27:09,284] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" ] } ], @@ -211,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -223,19 +225,23 @@ " Y: torch.Tensor,\n", " link_fn: Callable[..., dist.Distribution] = lambda mu: dist.Normal(mu, 1.0),\n", " prior_scale: Optional[float] = None,\n", + " include_prior: bool = False,\n", " ):\n", " p = X.shape[1]\n", " super().__init__(p, link_fn, prior_scale)\n", " self.X = X\n", " self.A = A\n", " self.Y = Y\n", + " self.include_prior = include_prior \n", "\n", " def forward(self):\n", - " intercept = self.sample_intercept()\n", - " outcome_weights = self.sample_outcome_weights()\n", - " propensity_weights = self.sample_propensity_weights()\n", - " tau = self.sample_treatment_weight()\n", - " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + " with pyro.poutine.mask(mask=not self.include_prior):\n", + " intercept = self.sample_intercept()\n", + " outcome_weights = self.sample_outcome_weights()\n", + " propensity_weights = self.sample_propensity_weights()\n", + " tau = self.sample_treatment_weight()\n", + " x_loc, x_scale = self.sample_covariate_loc_scale()\n", + "\n", " with pyro.plate(\"__train__\", size=self.X.shape[0], dim=-1):\n", " X = pyro.sample(\"X\", dist.Normal(x_loc, x_scale).to_event(1), obs=self.X)\n", " A = pyro.sample(\n", @@ -258,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -304,7 +310,7 @@ "<text text-anchor=\"middle\" x=\"318.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights</text>\n", "</g>\n", "<!-- outcome_weights&#45;&gt;Y -->\n", - "<g id=\"edge5\" class=\"edge\">\n", + "<g id=\"edge2\" class=\"edge\">\n", "<title>outcome_weights&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M291.82,-112.12C273.12,-101.02 248.19,-86.23 229.12,-74.92\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"230.67,-71.77 220.28,-69.68 227.1,-77.79 230.67,-71.77\"/>\n", @@ -346,7 +352,7 @@ "<text text-anchor=\"middle\" x=\"200.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n", "</g>\n", "<!-- X&#45;&gt;Y -->\n", - "<g id=\"edge2\" class=\"edge\">\n", + "<g id=\"edge5\" class=\"edge\">\n", "<title>X&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M200.6,-110.7C200.6,-102.98 200.6,-93.71 200.6,-85.11\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"204.1,-85.1 200.6,-75.1 197.1,-85.1 204.1,-85.1\"/>\n", @@ -372,10 +378,10 @@ "</svg>\n" ], "text/plain": [ - "<graphviz.graphs.Digraph at 0x1866a0410>" + "<graphviz.graphs.Digraph at 0x186497e50>" ] }, - "execution_count": 4, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -400,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -437,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -461,6 +467,54 @@ " return D_train, D_test" ] }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "## Real Data\n", + "\n", + "import pandas as pd\n", + "\n", + "data = pd.read_csv(\"https://raw.githubusercontent.com/AMLab-Amsterdam/CEVAE/master/datasets/IHDP/csv/ihdp_npci_1.csv\", header = None)\n", + "col = [\"treatment\", \"y_factual\", \"y_cfactual\", \"mu0\", \"mu1\" ,]\n", + "for i in range(1,26):\n", + " col.append(\"x\"+str(i))\n", + "data.columns = col\n", + "data = data.astype({\"treatment\":'bool'}, copy=False)\n", + "\n", + "X_IHDP = torch.tensor(data.iloc[:,5:11].values, dtype=torch.float32)\n", + "A_IHDP = torch.tensor(data.treatment.values, dtype=torch.float32)\n", + "Y_IHDP = torch.tensor(data.y_factual.values, dtype=torch.float32)\n", + "\n", + "# Taken from DoWhy tutorial\n", + "ground_truth = 4.021121012430829\n", + "\n", + "# Split data\n", + "N_data_IHDP = data.shape[0]\n", + "p = X_IHDP.shape[1]\n", + "\n", + "def generate_IHDP_data():\n", + " id_perm = torch.randperm(N_data_IHDP)\n", + " train_idx = id_perm[: N_data_IHDP // 2]\n", + " test_idx = id_perm[N_data_IHDP // 2 :]\n", + "\n", + " D_train_IHDP = {\n", + " \"X\": X_IHDP[train_idx],\n", + " \"A\": A_IHDP[train_idx],\n", + " \"Y\": Y_IHDP[train_idx],\n", + " }\n", + "\n", + " D_test_IHDP = {\n", + " \"X\": X_IHDP[test_idx],\n", + " \"A\": A_IHDP[test_idx],\n", + " \"Y\": Y_IHDP[test_idx],\n", + " }\n", + "\n", + " return D_train_IHDP, D_test_IHDP" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -470,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -490,8 +544,9 @@ " # Do gradient steps\n", " for _ in range(2000):\n", " adam.zero_grad()\n", - " loss = elbo()\n", - " loss.backward()\n", + " with pyro.poutine.block(hide_fn=lambda msg: msg[\"type\"] == \"sample\"):\n", + " loss = elbo()\n", + " loss.backward()\n", " adam.step()\n", "\n", " theta_hat = {\n", @@ -521,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -553,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -601,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -623,9 +678,19 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset 10\n", + "plug-in-mle-from-model 10\n", + "tmle analytic_eif 10\n" + ] + } + ], "source": [ "import json\n", "import os\n", @@ -635,7 +700,9 @@ "\n", "\n", "# Estimators to compare\n", - "estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", + "# estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", + "estimatros = {\"tmle\": tmle}\n", + "# estimators = {\"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", "estimator_kwargs = {\n", " \"tmle\": {\n", " \"learning_rate\": 5e-5,\n", @@ -651,7 +718,8 @@ "influences = {\"analytic_eif\": ate_causal_glm_analytic_influence, \"monte_carlo_eif\": influence_fn}\n", "\n", "# Cache the results\n", - "RESULTS_PATH = \"../results/ate_causal_glm.json\"\n", + "# RESULTS_PATH = \"../results/ate_causal_glm.json\"\n", + "RESULTS_PATH = \"../results/ate_causal_glm_tmle.json\"\n", "\n", "if os.path.exists(RESULTS_PATH):\n", " with open(RESULTS_PATH, \"r\") as f:\n", @@ -669,7 +737,8 @@ "for i in range(i_start, N_datasets):\n", " pyro.set_rng_seed(i) # for reproducibility\n", " print(\"Dataset\", i)\n", - " D_train, D_test = generate_data(N_train, N_test)\n", + " # D_train, D_test = generate_data(N_train, N_test)\n", + " D_train, D_test = generate_IHDP_data()\n", " theta_hat = MLE(D_train, D_test)\n", "\n", " theta_hat = {\n", @@ -700,7 +769,7 @@ " estimate = estimator(\n", " functional, \n", " D_test,\n", - " num_samples_outer=max(10000, 100 * p), \n", + " num_samples_outer=max(10000, 1000 * p), \n", " num_samples_inner=1,\n", " influence_estimator=influence,\n", " **estimator_kwargs[estimator_str]\n", @@ -721,7 +790,86 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'analytic_eif-one_step': [3.7440202236175537,\n", + " 3.9927549362182617,\n", + " 3.9080309867858887,\n", + " 3.932170867919922,\n", + " 3.8274712562561035,\n", + " 3.861823320388794,\n", + " 3.6158628463745117,\n", + " 3.759370803833008,\n", + " 3.776322841644287,\n", + " 4.073896408081055],\n", + " 'analytic_eif-double_ml': [3.951414108276367,\n", + " 4.1413047313690186,\n", + " 3.925032615661621,\n", + " 4.17576003074646,\n", + " 3.9561233520507812,\n", + " 4.15458345413208,\n", + " 3.740159273147583,\n", + " 3.954193353652954,\n", + " 4.029200553894043,\n", + " 4.348143577575684],\n", + " 'monte_carlo_eif-one_step': [3.7492637634277344,\n", + " 3.9226505756378174,\n", + " 3.920912027359009,\n", + " 3.916611433029175,\n", + " 3.7910571098327637,\n", + " 3.840451717376709,\n", + " 3.6561429500579834,\n", + " 3.7997665405273438,\n", + " 3.7907848358154297,\n", + " 4.033873081207275],\n", + " 'monte_carlo_eif-double_ml': [3.956657648086548,\n", + " 4.071200370788574,\n", + " 3.937913656234741,\n", + " 4.160200595855713,\n", + " 3.9197092056274414,\n", + " 4.133211851119995,\n", + " 3.7804393768310547,\n", + " 3.99458909034729,\n", + " 4.0436625480651855,\n", + " 4.308120250701904],\n", + " 'plug-in-mle-from-model': [3.8888871669769287,\n", + " 3.4957659244537354,\n", + " 3.793285846710205,\n", + " 3.6561882495880127,\n", + " 3.6290812492370605,\n", + " 3.792243719100952,\n", + " 3.797788619995117,\n", + " 3.8203046321868896,\n", + " 3.810659408569336,\n", + " 3.7938194274902344],\n", + " 'plug-in-mle-from-test': [4.096281051635742,\n", + " 3.644315719604492,\n", + " 3.8102874755859375,\n", + " 3.899777412414551,\n", + " 3.7577333450317383,\n", + " 4.085003852844238,\n", + " 3.9220850467681885,\n", + " 4.015127182006836,\n", + " 4.063537120819092,\n", + " 4.068066596984863]}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "estimates" + ] + }, + { + "cell_type": "code", + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -745,10 +893,8 @@ " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", - " <th>analytic_eif-tmle</th>\n", " <th>analytic_eif-one_step</th>\n", " <th>analytic_eif-double_ml</th>\n", - " <th>monte_carlo_eif-tmle</th>\n", " <th>monte_carlo_eif-one_step</th>\n", " <th>monte_carlo_eif-double_ml</th>\n", " <th>plug-in-mle-from-model</th>\n", @@ -758,139 +904,113 @@ " <tbody>\n", " <tr>\n", " <th>count</th>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", - " <td>100.00</td>\n", + " <td>10.00</td>\n", + " <td>10.00</td>\n", + " <td>10.00</td>\n", + " <td>10.00</td>\n", + " <td>10.00</td>\n", + " <td>10.00</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", - " <td>0.33</td>\n", - " <td>0.22</td>\n", - " <td>0.73</td>\n", - " <td>0.33</td>\n", - " <td>0.22</td>\n", - " <td>0.72</td>\n", - " <td>0.34</td>\n", - " <td>0.84</td>\n", + " <td>3.85</td>\n", + " <td>4.04</td>\n", + " <td>3.84</td>\n", + " <td>4.03</td>\n", + " <td>3.75</td>\n", + " <td>3.94</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.11</td>\n", - " <td>0.11</td>\n", - " <td>0.13</td>\n", - " <td>0.11</td>\n", - " <td>0.11</td>\n", " <td>0.13</td>\n", + " <td>0.17</td>\n", " <td>0.11</td>\n", - " <td>0.13</td>\n", + " <td>0.15</td>\n", + " <td>0.12</td>\n", + " <td>0.16</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", - " <td>0.14</td>\n", - " <td>-0.03</td>\n", - " <td>0.46</td>\n", - " <td>0.13</td>\n", - " <td>-0.08</td>\n", - " <td>0.42</td>\n", - " <td>0.11</td>\n", - " <td>0.56</td>\n", + " <td>3.62</td>\n", + " <td>3.74</td>\n", + " <td>3.66</td>\n", + " <td>3.78</td>\n", + " <td>3.50</td>\n", + " <td>3.64</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", - " <td>0.24</td>\n", - " <td>0.15</td>\n", - " <td>0.65</td>\n", - " <td>0.27</td>\n", - " <td>0.16</td>\n", - " <td>0.65</td>\n", - " <td>0.26</td>\n", - " <td>0.76</td>\n", + " <td>3.76</td>\n", + " <td>3.95</td>\n", + " <td>3.79</td>\n", + " <td>3.94</td>\n", + " <td>3.69</td>\n", + " <td>3.83</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", - " <td>0.33</td>\n", - " <td>0.23</td>\n", - " <td>0.73</td>\n", - " <td>0.33</td>\n", - " <td>0.22</td>\n", - " <td>0.73</td>\n", - " <td>0.33</td>\n", - " <td>0.83</td>\n", + " <td>3.84</td>\n", + " <td>3.99</td>\n", + " <td>3.82</td>\n", + " <td>4.02</td>\n", + " <td>3.79</td>\n", + " <td>3.97</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", - " <td>0.39</td>\n", - " <td>0.29</td>\n", - " <td>0.81</td>\n", - " <td>0.39</td>\n", - " <td>0.29</td>\n", - " <td>0.80</td>\n", - " <td>0.40</td>\n", - " <td>0.92</td>\n", + " <td>3.93</td>\n", + " <td>4.15</td>\n", + " <td>3.92</td>\n", + " <td>4.12</td>\n", + " <td>3.81</td>\n", + " <td>4.07</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", - " <td>0.65</td>\n", - " <td>0.61</td>\n", - " <td>1.09</td>\n", - " <td>0.65</td>\n", - " <td>0.57</td>\n", - " <td>1.05</td>\n", - " <td>0.68</td>\n", - " <td>1.15</td>\n", + " <td>4.07</td>\n", + " <td>4.35</td>\n", + " <td>4.03</td>\n", + " <td>4.31</td>\n", + " <td>3.89</td>\n", + " <td>4.10</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ - " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", - "count 100.00 100.00 100.00 \n", - "mean 0.33 0.22 0.73 \n", - "std 0.11 0.11 0.13 \n", - "min 0.14 -0.03 0.46 \n", - "25% 0.24 0.15 0.65 \n", - "50% 0.33 0.23 0.73 \n", - "75% 0.39 0.29 0.81 \n", - "max 0.65 0.61 1.09 \n", + " analytic_eif-one_step analytic_eif-double_ml \\\n", + "count 10.00 10.00 \n", + "mean 3.85 4.04 \n", + "std 0.13 0.17 \n", + "min 3.62 3.74 \n", + "25% 3.76 3.95 \n", + "50% 3.84 3.99 \n", + "75% 3.93 4.15 \n", + "max 4.07 4.35 \n", "\n", - " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", - "count 100.00 100.00 \n", - "mean 0.33 0.22 \n", - "std 0.11 0.11 \n", - "min 0.13 -0.08 \n", - "25% 0.27 0.16 \n", - "50% 0.33 0.22 \n", - "75% 0.39 0.29 \n", - "max 0.65 0.57 \n", + " monte_carlo_eif-one_step monte_carlo_eif-double_ml \\\n", + "count 10.00 10.00 \n", + "mean 3.84 4.03 \n", + "std 0.11 0.15 \n", + "min 3.66 3.78 \n", + "25% 3.79 3.94 \n", + "50% 3.82 4.02 \n", + "75% 3.92 4.12 \n", + "max 4.03 4.31 \n", "\n", - " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", - "count 100.00 100.00 \n", - "mean 0.72 0.34 \n", - "std 0.13 0.11 \n", - "min 0.42 0.11 \n", - "25% 0.65 0.26 \n", - "50% 0.73 0.33 \n", - "75% 0.80 0.40 \n", - "max 1.05 0.68 \n", - "\n", - " plug-in-mle-from-test \n", - "count 100.00 \n", - "mean 0.84 \n", - "std 0.13 \n", - "min 0.56 \n", - "25% 0.76 \n", - "50% 0.83 \n", - "75% 0.92 \n", - "max 1.15 " + " plug-in-mle-from-model plug-in-mle-from-test \n", + "count 10.00 10.00 \n", + "mean 3.75 3.94 \n", + "std 0.12 0.16 \n", + "min 3.50 3.64 \n", + "25% 3.69 3.83 \n", + "50% 3.79 3.97 \n", + "75% 3.81 4.07 \n", + "max 3.89 4.10 " ] }, - "execution_count": 12, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -903,46 +1023,12 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 76, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", - " if pd.api.types.is_categorical_dtype(vector):\n", - "/Users/sam-basis/opt/anaconda3/envs/chirho-dynamic/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n", - " with pd.option_context('mode.use_inf_as_na', True):\n" - ] - }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 800x600 with 1 Axes>" ] @@ -953,25 +1039,25 @@ ], "source": [ "# use tex\n", - "plt.rcParams['text.usetex'] = True\n", + "plt.rcParams['text.usetex'] = False\n", "\n", "# Visualize the results\n", "fig, ax = plt.subplots(figsize=(8, 6))\n", "\n", - "# TMLE\n", - "sns.kdeplot(\n", - " estimates['analytic_eif-tmle'], \n", - " ax=ax,\n", - " color='blue',\n", - " linestyle='--'\n", - ")\n", - "\n", - "sns.kdeplot(\n", - " estimates['monte_carlo_eif-tmle'], \n", - " label=\"TMLE\",\n", - " ax=ax,\n", - " color='blue'\n", - ")\n", + "# # TMLE\n", + "# sns.kdeplot(\n", + "# estimates['analytic_eif-tmle'], \n", + "# ax=ax,\n", + "# color='blue',\n", + "# linestyle='--'\n", + "# )\n", + "\n", + "# sns.kdeplot(\n", + "# estimates['monte_carlo_eif-tmle'], \n", + "# label=\"TMLE\",\n", + "# ax=ax,\n", + "# color='blue'\n", + "# )\n", "\n", "# One-step\n", "sns.kdeplot(\n", @@ -1011,7 +1097,7 @@ " color='brown'\n", ")\n", "\n", - "ax.axvline(0, color=\"black\", label=\"Ground Truth\", linestyle=\"solid\")\n", + "ax.axvline(ground_truth, color=\"black\", label=\"Ground Truth\", linestyle=\"solid\")\n", "ax.set_yticks([])\n", "sns.despine()\n", "ax.set_xlabel(\"ATE Estimate\", fontsize=18)\n", @@ -1026,12 +1112,12 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 77, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1071,18 +1157,19 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 78, "metadata": {}, "outputs": [ { - "data": { - "image/png": "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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "KeyError", + "evalue": "'monte_carlo_eif-tmle'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# fig, ax = plt.subplots(figsize=(6, 3))\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# TMLE\u001b[39;00m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[0;32m----> 5\u001b[0m \u001b[43mestimates\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmonte_carlo_eif-tmle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m,\n\u001b[1;32m 6\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-tmle\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 7\u001b[0m color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 8\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Plot y=x line for min and max values\u001b[39;00m\n\u001b[1;32m 11\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mmin\u001b[39m(estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonte_carlo_eif-tmle\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28mmin\u001b[39m(estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-tmle\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 14\u001b[0m )\n", + "\u001b[0;31mKeyError\u001b[0m: 'monte_carlo_eif-tmle'" + ] } ], "source": [ @@ -1116,12 +1203,12 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1176,7 +1263,7 @@ ], "metadata": { "kernelspec": { - "display_name": "basis", + "display_name": "chirho-robust", "language": "python", "name": "python3" }, @@ -1190,7 +1277,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.10" }, "orig_nbformat": 4 }, diff --git a/docs/examples/robust_paper/results/_ate_causal_glm_tmle.json b/docs/examples/robust_paper/results/_ate_causal_glm_tmle.json new file mode 100644 index 00000000..39876026 --- /dev/null +++ b/docs/examples/robust_paper/results/_ate_causal_glm_tmle.json @@ -0,0 +1,614 @@ +{ + "analytic_eif-one_step": [ + 3.811537504196167, + 4.137718200683594, + 3.9080848693847656, + 3.996136426925659, + 4.015166282653809, + 3.8464725017547607, + 3.8703866004943848, + 3.7797505855560303, + 3.925191640853882, + 3.9323484897613525, + 3.8160758018493652, + 3.8378331661224365, + 3.9371795654296875, + 3.6806788444519043, + 3.9664194583892822, + 3.871370315551758, + 3.7771315574645996, + 3.738832712173462, + 3.842334747314453, + 3.99088716506958, + 3.936116933822632, + 3.7041239738464355, + 3.85697603225708, + 3.9458742141723633, + 3.9586732387542725, + 3.829660654067993, + 4.046951770782471, + 3.976670503616333, + 3.9545791149139404, + 3.9438703060150146, + 3.8190581798553467, + 4.001430988311768, + 3.880505084991455, + 3.8688385486602783, + 3.6270105838775635, + 3.8348121643066406, + 3.9116954803466797, + 4.1246867179870605, + 3.67423677444458, + 3.8382620811462402, + 3.844770669937134, + 3.8843958377838135, + 3.867672920227051, + 3.840637683868408, + 4.0203142166137695, + 3.8423256874084473, + 3.8996334075927734, + 3.9039106369018555, + 3.952975034713745, + 3.854522228240967, + 3.9894351959228516, + 3.8697874546051025, + 3.8418798446655273, + 3.9072277545928955, + 3.883012056350708, + 3.8110501766204834, + 4.116669178009033, + 4.078433036804199, + 3.9935598373413086, + 3.8877406120300293, + 4.028015613555908, + 4.002680778503418, + 3.812573194503784, + 3.88881254196167, + 3.9829585552215576, + 3.55172061920166, + 3.9023597240448, + 3.640474796295166, + 3.792064666748047, + 4.10162878036499, + 4.004901885986328, + 3.7332096099853516, + 3.822956085205078, + 3.915196418762207, + 3.7444167137145996, + 3.863767623901367, + 3.9317142963409424, + 3.8431315422058105, + 3.873445987701416, + 3.8526813983917236, + 3.910060405731201, + 3.886991262435913, + 3.9494197368621826, + 3.972322940826416, + 3.896486759185791, + 3.786491870880127, + 3.983001708984375, + 4.142300605773926, + 3.9770729541778564, + 3.8937225341796875, + 3.9332878589630127, + 3.8069112300872803, + 3.8995518684387207, + 3.879728078842163, + 3.8723621368408203, + 4.013252258300781, + 3.8342928886413574, + 3.765767812728882, + 3.8580093383789062, + 3.9173758029937744 + ], + "analytic_eif-double_ml": [ + 3.978489398956299, + 4.273711681365967, + 3.945242166519165, + 4.215897560119629, + 4.1247899532318115, + 4.200973272323608, + 3.984631299972534, + 3.975872755050659, + 4.109742879867554, + 4.111649036407471, + 3.965789318084717, + 4.022806167602539, + 4.112804174423218, + 3.8000097274780273, + 4.04800820350647, + 4.09093451499939, + 3.9628517627716064, + 3.9008960723876953, + 3.9747138023376465, + 4.112841606140137, + 3.902204990386963, + 3.8832387924194336, + 4.0018391609191895, + 3.8117172718048096, + 4.01831316947937, + 3.92330265045166, + 4.125944137573242, + 4.01739764213562, + 3.909925699234009, + 4.023115396499634, + 3.9718217849731445, + 4.139090538024902, + 4.030466079711914, + 3.933239459991455, + 3.831829309463501, + 4.1501147747039795, + 4.033329725265503, + 4.154767990112305, + 3.9462532997131348, + 3.967388391494751, + 4.103285551071167, + 3.9965388774871826, + 3.839362859725952, + 4.0559186935424805, + 4.164015531539917, + 3.9645676612854004, + 3.8882102966308594, + 4.068742275238037, + 4.073702096939087, + 3.9949193000793457, + 4.16347861289978, + 4.027357339859009, + 3.983051300048828, + 3.863316297531128, + 4.163138151168823, + 3.927809238433838, + 4.096214532852173, + 4.081921815872192, + 4.278963327407837, + 4.01162314414978, + 4.231368064880371, + 4.066718339920044, + 3.911529541015625, + 3.919628858566284, + 4.0176801681518555, + 3.6871604919433594, + 4.018279790878296, + 3.7944469451904297, + 3.9608778953552246, + 4.2301599979400635, + 3.993351697921753, + 3.9016988277435303, + 3.9409358501434326, + 4.085007429122925, + 3.9873580932617188, + 3.925424814224243, + 3.957277297973633, + 4.097668647766113, + 3.9452850818634033, + 3.9332616329193115, + 4.092364311218262, + 4.085864543914795, + 4.062970876693726, + 4.099432945251465, + 3.9875595569610596, + 3.8747456073760986, + 4.0005879402160645, + 4.159684896469116, + 4.0105812549591064, + 4.003722429275513, + 4.094360113143921, + 3.8591268062591553, + 4.016741037368774, + 4.140474319458008, + 4.074481248855591, + 4.119319438934326, + 3.9506094455718994, + 3.848675012588501, + 4.026105880737305, + 3.945129156112671 + ], + "monte_carlo_eif-one_step": [ + 3.886310577392578, + 3.986647605895996, + 3.8481321334838867, + 3.9144153594970703, + 3.8723249435424805, + 3.8127896785736084, + 3.872513771057129, + 3.849851131439209, + 3.8553168773651123, + 3.904918909072876, + 3.868342399597168, + 3.86655330657959, + 3.868569850921631, + 3.7888805866241455, + 3.889655590057373, + 3.8841545581817627, + 3.8395981788635254, + 3.824368715286255, + 3.846395969390869, + 3.929871082305908, + 3.8691813945770264, + 3.78287935256958, + 3.853332042694092, + 3.851668357849121, + 3.8774375915527344, + 3.879287004470825, + 3.9004580974578857, + 3.9386515617370605, + 3.881422281265259, + 3.8720571994781494, + 3.865736246109009, + 3.887582540512085, + 3.8671181201934814, + 3.8569905757904053, + 3.7561917304992676, + 3.865675687789917, + 3.8776657581329346, + 3.927882194519043, + 3.7859368324279785, + 3.79994797706604, + 3.8702893257141113, + 3.882352590560913, + 3.836264133453369, + 3.892845869064331, + 3.942333459854126, + 3.838653326034546, + 3.832059144973755, + 3.909579277038574, + 3.8872432708740234, + 3.8600049018859863, + 3.9237449169158936, + 3.869767904281616, + 3.8327977657318115, + 3.8425047397613525, + 3.8652536869049072, + 3.829322576522827, + 3.9115071296691895, + 3.930755853652954, + 3.9934678077697754, + 3.898998498916626, + 3.949323892593384, + 3.914181709289551, + 3.790423631668091, + 3.8530025482177734, + 3.9069368839263916, + 3.7546629905700684, + 3.8736417293548584, + 3.759167194366455, + 3.815664768218994, + 3.986612558364868, + 3.902860641479492, + 3.8390989303588867, + 3.815469980239868, + 3.865018129348755, + 3.8334691524505615, + 3.857327938079834, + 3.8774352073669434, + 3.8610446453094482, + 3.8633246421813965, + 3.8143150806427, + 3.869490146636963, + 3.903139114379883, + 3.9180212020874023, + 3.9165332317352295, + 3.854038715362549, + 3.8217856884002686, + 3.8451597690582275, + 3.9427990913391113, + 3.888766050338745, + 3.9021971225738525, + 3.8714537620544434, + 3.8794827461242676, + 3.8535990715026855, + 3.8814127445220947, + 3.8804593086242676, + 3.8903703689575195, + 3.8486297130584717, + 3.82654070854187, + 3.8604018688201904, + 3.899303674697876 + ], + "monte_carlo_eif-double_ml": [ + 4.05326247215271, + 4.122641086578369, + 3.885289430618286, + 4.13417649269104, + 3.9819486141204834, + 4.167290449142456, + 3.9867584705352783, + 4.045973300933838, + 4.039868116378784, + 4.084219455718994, + 4.0180559158325195, + 4.051526308059692, + 4.044194459915161, + 3.9082114696502686, + 3.9712443351745605, + 4.1037187576293945, + 4.025318384170532, + 3.9864320755004883, + 3.9787750244140625, + 4.051825523376465, + 3.8352694511413574, + 3.961994171142578, + 3.998195171356201, + 3.7175114154815674, + 3.937077522277832, + 3.972929000854492, + 3.9794504642486572, + 3.9793787002563477, + 3.836768865585327, + 3.9513022899627686, + 4.018499851226807, + 4.02524209022522, + 4.01707911491394, + 3.921391487121582, + 3.961010456085205, + 4.180978298187256, + 3.999300003051758, + 3.957963466644287, + 4.057953357696533, + 3.929074287414551, + 4.1288042068481445, + 3.9944956302642822, + 3.8079540729522705, + 4.108126878738403, + 4.086034774780273, + 3.960895299911499, + 3.820636034011841, + 4.074410915374756, + 4.007970333099365, + 4.000401973724365, + 4.097788333892822, + 4.0273377895355225, + 3.9739692211151123, + 3.798593282699585, + 4.1453797817230225, + 3.9460816383361816, + 3.891052484512329, + 3.9342446327209473, + 4.278871297836304, + 4.022881031036377, + 4.152676343917847, + 3.9782192707061768, + 3.8893799781799316, + 3.8838188648223877, + 3.9416584968566895, + 3.8901028633117676, + 3.9895617961883545, + 3.9131393432617188, + 3.984477996826172, + 4.115143775939941, + 3.891310453414917, + 4.007588148117065, + 3.9334497451782227, + 4.034829139709473, + 4.076410531997681, + 3.91898512840271, + 3.902998208999634, + 4.115581750869751, + 3.935163736343384, + 3.894895315170288, + 4.051794052124023, + 4.102012395858765, + 4.031572341918945, + 4.043643236160278, + 3.9451115131378174, + 3.9100394248962402, + 3.862746000289917, + 3.9601833820343018, + 3.922274351119995, + 4.012197017669678, + 4.032526016235352, + 3.9316983222961426, + 3.9707882404327393, + 4.1421589851379395, + 4.082578420639038, + 3.9964375495910645, + 3.9649462699890137, + 3.9094479084014893, + 4.028498411178589, + 3.9270570278167725 + ], + "plug-in-mle-from-model": [ + 3.853281259536743, + 3.462830066680908, + 3.7969393730163574, + 3.7084543704986572, + 3.6862874031066895, + 3.845517158508301, + 3.809166431427002, + 3.883957862854004, + 3.784637928009033, + 3.825434446334839, + 3.9305834770202637, + 3.9361464977264404, + 3.8050332069396973, + 4.055565357208252, + 3.8237879276275635, + 3.8487918376922607, + 3.971686601638794, + 3.8512980937957764, + 3.959775447845459, + 3.757720708847046, + 3.814213752746582, + 4.024913311004639, + 3.8687472343444824, + 3.8433423042297363, + 3.7881031036376953, + 3.932218313217163, + 3.752290964126587, + 3.8353116512298584, + 3.8782718181610107, + 3.8184187412261963, + 3.8876593112945557, + 3.8097643852233887, + 3.8414793014526367, + 3.8463058471679688, + 4.129661560058594, + 3.907897710800171, + 3.8415136337280273, + 3.5930867195129395, + 4.039006233215332, + 3.981438398361206, + 3.940135955810547, + 3.783015251159668, + 3.9398882389068604, + 3.8438515663146973, + 3.693995237350464, + 3.896663188934326, + 3.8311421871185303, + 3.8683505058288574, + 3.8611035346984863, + 3.901801586151123, + 3.781221389770508, + 3.7833847999572754, + 3.830843687057495, + 3.8904082775115967, + 3.821166515350342, + 3.9290249347686768, + 3.679781198501587, + 3.6168174743652344, + 3.72082257270813, + 3.9061543941497803, + 3.702427387237549, + 3.7753067016601562, + 3.919128179550171, + 3.8068714141845703, + 3.745384693145752, + 4.092693328857422, + 3.7852890491485596, + 4.142066478729248, + 4.015684604644775, + 3.5732979774475098, + 3.7762093544006348, + 3.993074655532837, + 3.9076988697052, + 3.7488656044006348, + 3.9558663368225098, + 3.9195876121520996, + 3.892676591873169, + 3.839564800262451, + 3.779057025909424, + 3.8865957260131836, + 3.771259069442749, + 3.8071014881134033, + 3.73764967918396, + 3.7247836589813232, + 3.8654887676239014, + 3.99128794670105, + 3.765838384628296, + 3.6490554809570312, + 3.816884756088257, + 3.813601493835449, + 3.7765276432037354, + 4.01057243347168, + 3.8193862438201904, + 3.79966139793396, + 3.7983086109161377, + 3.7344155311584473, + 3.9302961826324463, + 3.9614148139953613, + 4.007858753204346, + 3.852307081222534 + ], + "plug-in-mle-from-test": [ + 4.020233154296875, + 3.5988235473632812, + 3.834096670150757, + 3.928215503692627, + 3.7959110736846924, + 4.200017929077148, + 3.9234111309051514, + 4.080080032348633, + 3.969189167022705, + 4.004734992980957, + 4.080296993255615, + 4.121119499206543, + 3.9806578159332275, + 4.174896240234375, + 3.905376672744751, + 4.068356037139893, + 4.157406806945801, + 4.01336145401001, + 4.092154502868652, + 3.8796751499176025, + 3.780301809310913, + 4.204028129577637, + 4.013610363006592, + 3.7091853618621826, + 3.847743034362793, + 4.02586030960083, + 3.8312833309173584, + 3.8760387897491455, + 3.833618402481079, + 3.8976638317108154, + 4.0404229164123535, + 3.9474239349365234, + 3.9914402961730957, + 3.9107067584991455, + 4.334480285644531, + 4.22320032119751, + 3.9631478786468506, + 3.6231679916381836, + 4.311022758483887, + 4.110564708709717, + 4.19865083694458, + 3.895158290863037, + 3.9115781784057617, + 4.0591325759887695, + 3.8376965522766113, + 4.018905162811279, + 3.819719076156616, + 4.033182144165039, + 3.981830596923828, + 4.042198657989502, + 3.9552648067474365, + 3.9409546852111816, + 3.972015142440796, + 3.846496820449829, + 4.101292610168457, + 4.045783996582031, + 3.6593265533447266, + 3.6203062534332275, + 4.006226062774658, + 4.030036926269531, + 3.9057798385620117, + 3.8393442630767822, + 4.018084526062012, + 3.8376877307891846, + 3.78010630607605, + 4.228133201599121, + 3.9012091159820557, + 4.296038627624512, + 4.184497833251953, + 3.701829195022583, + 3.7646591663360596, + 4.161563873291016, + 4.025678634643555, + 3.9186766147613525, + 4.198807716369629, + 3.9812448024749756, + 3.9182395935058594, + 4.094101905822754, + 3.850896120071411, + 3.9671759605407715, + 3.9535629749298096, + 4.005974769592285, + 3.851200819015503, + 3.851893663406372, + 3.95656156539917, + 4.0795416831970215, + 3.7834246158599854, + 3.6664397716522217, + 3.850393056869507, + 3.9236013889312744, + 3.9375998973846436, + 4.062788009643555, + 3.936575412750244, + 4.060407638549805, + 4.000427722930908, + 3.840482711791992, + 4.046612739562988, + 4.0443220138549805, + 4.175955295562744, + 3.8800604343414307 + ] +} \ No newline at end of file diff --git a/docs/examples/robust_paper/results/ate_causal_glm_tmle.json b/docs/examples/robust_paper/results/ate_causal_glm_tmle.json new file mode 100644 index 00000000..c7ad0a5a --- /dev/null +++ b/docs/examples/robust_paper/results/ate_causal_glm_tmle.json @@ -0,0 +1,754 @@ +{ + "analytic_eif-tmle": [ + 3.839876651763916, + 3.5331175327301025, + 3.824369430541992, + 3.714146375656128, + 3.6636526584625244, + 3.807866334915161, + 3.7564496994018555, + 3.8284895420074463, + 3.783723831176758, + 3.826263189315796, + 3.9330756664276123, + 3.898710250854492, + 3.7766313552856445, + 3.926036834716797, + 3.774528980255127, + 3.792569637298584, + 3.9387059211730957, + 3.869884490966797, + 3.813490629196167, + 3.7400593757629395, + 3.7688889503479004, + 3.9319093227386475, + 3.815572738647461, + 3.813718795776367, + 3.817241907119751, + 3.8288776874542236, + 3.7090296745300293, + 3.775740623474121, + 3.83687424659729, + 3.7990338802337646, + 3.846245288848877, + 3.7503819465637207, + 3.8264317512512207, + 3.775503158569336, + 4.079380989074707, + 3.835303544998169, + 3.7560348510742188, + 3.669255495071411, + 3.9737491607666016, + 3.903839111328125, + 3.856783866882324, + 3.8265018463134766, + 3.959981918334961, + 3.7979114055633545, + 3.642767906188965, + 3.8436360359191895, + 3.7808656692504883, + 3.812312602996826, + 3.8355937004089355, + 3.8291714191436768, + 3.74246883392334, + 3.751783609390259, + 3.8517889976501465, + 3.828932046890259, + 3.8153371810913086, + 3.8854141235351562, + 3.723742961883545, + 3.635148048400879, + 3.7152202129364014, + 3.821760892868042, + 3.687731981277466, + 3.762479782104492, + 3.928602457046509, + 3.8235721588134766, + 3.671370029449463, + 4.005984306335449, + 3.800032138824463, + 4.003036022186279, + 3.968428611755371, + 3.5957930088043213, + 3.753859281539917, + 3.8993799686431885, + 3.9322030544281006, + 3.762279748916626, + 3.896578073501587, + 3.880298137664795, + 3.7625906467437744, + 3.8666727542877197, + 3.7621495723724365, + 3.835761785507202, + 3.7722573280334473, + 3.7797532081604004, + 3.73939847946167, + 3.648416757583618, + 3.783208131790161, + 3.939659833908081, + 3.6872305870056152, + 3.707148790359497, + 3.7717959880828857, + 3.742614507675171, + 3.743137836456299, + 3.966012954711914 + ], + "analytic_eif-one_step": [ + 3.721874952316284, + 3.975658416748047, + 3.9216268062591553, + 3.9260659217834473, + 3.825951099395752, + 3.8630897998809814, + 3.6129796504974365, + 3.7774622440338135, + 3.764096260070801, + 4.0642242431640625, + 3.9206931591033936, + 3.708268880844116, + 3.8033692836761475, + 3.61521053314209, + 3.8424928188323975, + 3.8446383476257324, + 3.742774486541748, + 3.9857912063598633, + 3.8339502811431885, + 3.8535807132720947, + 3.7041821479797363, + 3.7423958778381348, + 3.8546664714813232, + 3.681549310684204, + 3.790097236633301, + 3.7919178009033203, + 3.7309834957122803, + 3.7336056232452393, + 3.772038221359253, + 3.646641731262207, + 3.83508563041687, + 3.8592569828033447, + 3.9166219234466553, + 3.641303777694702, + 3.8352620601654053, + 3.756117105484009, + 3.7782461643218994, + 3.762101173400879, + 3.813594341278076, + 3.800577163696289, + 3.8008339405059814, + 3.8193061351776123, + 3.6668291091918945, + 3.7877986431121826, + 3.989220142364502, + 3.7240705490112305, + 3.663825511932373, + 3.8209128379821777, + 3.992730140686035, + 3.7217791080474854, + 4.0137248039245605, + 3.6028823852539062, + 3.8582751750946045, + 3.822843551635742, + 4.065920352935791, + 3.9217162132263184, + 3.7659502029418945, + 3.926132917404175, + 4.1629414558410645, + 3.82395339012146, + 3.9751172065734863, + 3.8031115531921387, + 3.7366745471954346, + 3.842092990875244, + 3.693372964859009, + 3.668882131576538, + 3.8136448860168457, + 3.769430160522461, + 3.5914463996887207, + 3.9516713619232178, + 3.984814167022705, + 3.6749980449676514, + 3.8262548446655273, + 3.8703784942626953, + 3.7231106758117676, + 3.82120943069458, + 3.629378318786621, + 3.9481682777404785, + 3.6560351848602295, + 3.737612009048462, + 3.9032130241394043, + 3.976604461669922, + 3.8223748207092285, + 3.812610387802124, + 3.7458157539367676, + 3.6460907459259033, + 3.630096912384033, + 3.9932749271392822, + 3.7783010005950928, + 3.9974801540374756, + 3.632181167602539, + 3.7163143157958984 + ], + "analytic_eif-double_ml": [ + 3.9292688369750977, + 4.124208211898804, + 3.9386284351348877, + 4.169655084609985, + 3.9546031951904297, + 4.155849933624268, + 3.737276077270508, + 3.9722847938537598, + 4.016973972320557, + 4.338471412658691, + 4.156325578689575, + 3.8916049003601074, + 4.047196865081787, + 3.736837863922119, + 3.9895451068878174, + 4.085540771484375, + 4.034946441650391, + 4.130852699279785, + 3.9660797119140625, + 4.001047611236572, + 3.739079236984253, + 3.926154375076294, + 4.017667770385742, + 3.6437203884124756, + 3.9009134769439697, + 3.930450201034546, + 3.887011766433716, + 3.9119513034820557, + 3.831204891204834, + 3.699113130569458, + 3.9883298873901367, + 3.9441864490509033, + 4.158538579940796, + 3.7595293521881104, + 4.035249471664429, + 4.083334684371948, + 3.890502452850342, + 3.874842882156372, + 4.13043212890625, + 3.984645128250122, + 4.146570205688477, + 4.018994092941284, + 3.668405771255493, + 4.021650791168213, + 4.106330394744873, + 3.899179458618164, + 3.698827028274536, + 4.068745851516724, + 4.138624668121338, + 3.879589796066284, + 4.225254058837891, + 3.7937934398651123, + 4.028165578842163, + 3.864980936050415, + 4.3509838581085205, + 4.065845727920532, + 3.8496155738830566, + 4.086026191711426, + 4.440109014511108, + 3.988475799560547, + 4.182727813720703, + 3.913755416870117, + 3.9225683212280273, + 3.8545446395874023, + 3.767693042755127, + 3.817678689956665, + 3.9067561626434326, + 3.9560017585754395, + 3.8408493995666504, + 4.150688171386719, + 3.995961904525757, + 3.8178625106811523, + 4.012617588043213, + 4.028895616531372, + 4.006508111953735, + 3.946417808532715, + 3.704528331756592, + 4.180272817611694, + 3.8766772747039795, + 3.861259937286377, + 4.109742164611816, + 4.206973552703857, + 3.9652535915374756, + 3.9604859352111816, + 3.881007194519043, + 3.7359368801116943, + 3.7266061305999756, + 4.065619945526123, + 3.8301949501037598, + 4.122846841812134, + 3.790860176086426, + 3.8152713775634766 + ], + "monte_carlo_eif-tmle": [ + 3.8681862354278564, + 3.516026258468628, + 3.8115952014923096, + 3.6937031745910645, + 3.6634702682495117, + 3.8138515949249268, + 3.7369844913482666, + 3.806765556335449, + 3.7736198902130127, + 3.8267769813537598, + 3.938678026199341, + 3.865253448486328, + 3.759643316268921, + 3.9090445041656494, + 3.8068277835845947, + 3.8100836277008057, + 3.9625961780548096, + 3.8734874725341797, + 3.818157911300659, + 3.7291526794433594, + 3.7733206748962402, + 3.8664772510528564, + 3.823476552963257, + 3.8070225715637207, + 3.7958805561065674, + 3.8245294094085693, + 3.7105350494384766, + 3.751678228378296, + 3.7783825397491455, + 3.8153445720672607, + 3.8622500896453857, + 3.7652499675750732, + 3.830846071243286, + 3.7687015533447266, + 4.074945449829102, + 3.881787061691284, + 3.769089937210083, + 3.6389219760894775, + 3.9601340293884277, + 3.9025206565856934, + 3.882110834121704, + 3.8108179569244385, + 3.93050479888916, + 3.775953531265259, + 3.6587061882019043, + 3.838216781616211, + 3.7784183025360107, + 3.807610273361206, + 3.823448419570923, + 3.8080248832702637, + 3.747066020965576, + 3.733949899673462, + 3.8482425212860107, + 3.8545985221862793, + 3.7852272987365723, + 3.8924379348754883, + 3.7472195625305176, + 3.6252167224884033, + 3.697585105895996, + 3.8045034408569336, + 3.7097625732421875, + 3.7676098346710205, + 3.904839515686035, + 3.819328546524048, + 3.6732118129730225, + 4.026763439178467, + 3.7722132205963135, + 4.014700412750244, + 3.9996402263641357, + 3.59531831741333, + 3.7544074058532715, + 3.9382550716400146, + 3.9497456550598145, + 3.7660555839538574, + 3.885580062866211, + 3.8767647743225098, + 3.7534098625183105, + 3.873401165008545, + 3.7678563594818115, + 3.8537254333496094, + 3.7630035877227783, + 3.765357732772827, + 3.778564929962158, + 3.630566358566284, + 3.8319356441497803, + 3.9698851108551025, + 3.6790788173675537, + 3.6781976222991943, + 3.7864577770233154, + 3.728658676147461, + 3.7527639865875244, + 3.974327325820923 + ], + "monte_carlo_eif-one_step": [ + 3.7633237838745117, + 3.974067211151123, + 3.920567035675049, + 3.896141767501831, + 3.792893171310425, + 3.8287408351898193, + 3.6751503944396973, + 3.7884209156036377, + 3.7959036827087402, + 4.041287422180176, + 3.907686710357666, + 3.7248659133911133, + 3.8704638481140137, + 3.6651365756988525, + 3.818131446838379, + 3.8078079223632812, + 3.7972726821899414, + 3.9758620262145996, + 3.843653917312622, + 3.8471624851226807, + 3.735469341278076, + 3.7776272296905518, + 3.8467626571655273, + 3.7519569396972656, + 3.8544809818267822, + 3.8233768939971924, + 3.7427616119384766, + 3.743400812149048, + 3.8080549240112305, + 3.7174954414367676, + 3.8533682823181152, + 3.8481802940368652, + 3.899125576019287, + 3.6569623947143555, + 3.8878183364868164, + 3.7652621269226074, + 3.766897678375244, + 3.8023297786712646, + 3.8299384117126465, + 3.7953238487243652, + 3.826266050338745, + 3.829505205154419, + 3.709527015686035, + 3.782870054244995, + 3.997131824493408, + 3.743642568588257, + 3.7085444927215576, + 3.8262922763824463, + 4.030390739440918, + 3.7333991527557373, + 4.026367664337158, + 3.626141309738159, + 3.8590316772460938, + 3.8490657806396484, + 4.079745769500732, + 3.893465042114258, + 3.7839794158935547, + 3.901862859725952, + 4.142735481262207, + 3.859386682510376, + 3.9587414264678955, + 3.8247289657592773, + 3.7813611030578613, + 3.8250248432159424, + 3.7045888900756836, + 3.7228901386260986, + 3.840918779373169, + 3.810554265975952, + 3.6413075923919678, + 3.9567956924438477, + 4.008106231689453, + 3.7361464500427246, + 3.8361880779266357, + 3.8709475994110107, + 3.7831151485443115, + 3.8360226154327393, + 3.712047576904297, + 3.95611310005188, + 3.706984281539917, + 3.767472267150879, + 3.8629326820373535, + 4.012974262237549, + 3.8426246643066406, + 3.8142032623291016, + 3.7750062942504883, + 3.6359353065490723, + 3.70906138420105, + 3.9873244762420654, + 3.7685766220092773, + 4.022496223449707, + 3.6586055755615234, + 3.7576844692230225 + ], + "monte_carlo_eif-double_ml": [ + 3.970717668533325, + 4.12261700630188, + 3.9375686645507812, + 4.139730930328369, + 3.9215452671051025, + 4.1215009689331055, + 3.7994468212127686, + 3.983243465423584, + 4.048781394958496, + 4.315534591674805, + 4.143319129943848, + 3.9082019329071045, + 4.114291429519653, + 3.786763906478882, + 3.965183734893799, + 4.048710346221924, + 4.089444637298584, + 4.1209235191345215, + 3.975783348083496, + 3.994629383087158, + 3.7703664302825928, + 3.961385726928711, + 4.009763956069946, + 3.714128017425537, + 3.965297222137451, + 3.961909294128418, + 3.898789882659912, + 3.9217464923858643, + 3.8672215938568115, + 3.7699668407440186, + 4.006612539291382, + 3.933109760284424, + 4.141042232513428, + 3.7751879692077637, + 4.08780574798584, + 4.092479705810547, + 3.8791539669036865, + 3.915071487426758, + 4.14677619934082, + 3.9793918132781982, + 4.17200231552124, + 4.029193162918091, + 3.711103677749634, + 4.016722202301025, + 4.114242076873779, + 3.9187514781951904, + 3.7435460090637207, + 4.074125289916992, + 4.176285266876221, + 3.891209840774536, + 4.237896919250488, + 3.8170523643493652, + 4.028922080993652, + 3.8912031650543213, + 4.364809274673462, + 4.037594556808472, + 3.867644786834717, + 4.061756134033203, + 4.419903039932251, + 4.023909091949463, + 4.166352033615112, + 3.935372829437256, + 3.967254877090454, + 3.8374764919281006, + 3.7789089679718018, + 3.8716866970062256, + 3.934030055999756, + 3.9971258640289307, + 3.8907105922698975, + 4.155812501907349, + 4.019253969192505, + 3.8790109157562256, + 4.022550821304321, + 4.0294647216796875, + 4.066512584686279, + 3.961230993270874, + 3.7871975898742676, + 4.188217639923096, + 3.927626371383667, + 3.891120195388794, + 4.069461822509766, + 4.243343353271484, + 3.9855034351348877, + 3.962078809738159, + 3.9101977348327637, + 3.7257814407348633, + 3.805570602416992, + 4.059669494628906, + 3.8204705715179443, + 4.147862911224365, + 3.81728458404541, + 3.8566415309906006 + ], + "plug-in-mle-from-model": [ + 3.8888871669769287, + 3.4957659244537354, + 3.793285846710205, + 3.6561882495880127, + 3.6290812492370605, + 3.792243719100952, + 3.797788619995117, + 3.8203046321868896, + 3.810659408569336, + 3.7938194274902344, + 3.924154758453369, + 3.9132726192474365, + 3.765573740005493, + 3.925321102142334, + 3.782444953918457, + 3.7914586067199707, + 3.9850382804870605, + 3.852663278579712, + 3.7967936992645264, + 3.675572633743286, + 3.7768311500549316, + 3.9280741214752197, + 3.791006326675415, + 3.836116313934326, + 3.770336627960205, + 3.8537957668304443, + 3.6922507286071777, + 3.7645535469055176, + 3.834435224533081, + 3.8319828510284424, + 3.8764665126800537, + 3.766538619995117, + 3.8184218406677246, + 3.7937653064727783, + 4.087764739990234, + 3.8645777702331543, + 3.770695209503174, + 3.6018083095550537, + 3.986194133758545, + 3.8974063396453857, + 3.89853835105896, + 3.7841391563415527, + 3.9648890495300293, + 3.7817022800445557, + 3.6197214126586914, + 3.8577919006347656, + 3.7943789958953857, + 3.798537492752075, + 3.7980518341064453, + 3.831646203994751, + 3.715691089630127, + 3.7825160026550293, + 3.874286651611328, + 3.8564281463623047, + 3.79241681098938, + 3.825185537338257, + 3.7039670944213867, + 3.5995562076568604, + 3.723207712173462, + 3.7994840145111084, + 3.6833653450012207, + 3.7401461601257324, + 3.9400813579559326, + 3.7679452896118164, + 3.6722140312194824, + 4.046419620513916, + 3.748612880706787, + 4.036281108856201, + 4.015773773193359, + 3.579007387161255, + 3.714299201965332, + 3.944594144821167, + 3.933716297149658, + 3.753580093383789, + 3.917356252670288, + 3.868974208831787, + 3.774965286254883, + 3.8547894954681396, + 3.737250804901123, + 3.867110252380371, + 3.7647109031677246, + 3.707094192504883, + 3.722935199737549, + 3.6000351905822754, + 3.8150687217712402, + 3.97530460357666, + 3.672628879547119, + 3.677774667739868, + 3.806727647781372, + 3.712677240371704, + 3.7888519763946533, + 4.022754669189453 + ], + "plug-in-mle-from-test": [ + 4.096281051635742, + 3.644315719604492, + 3.8102874755859375, + 3.899777412414551, + 3.7577333450317383, + 4.085003852844238, + 3.9220850467681885, + 4.015127182006836, + 4.063537120819092, + 4.068066596984863, + 4.159787178039551, + 4.096608638763428, + 4.009401321411133, + 4.046948432922363, + 3.929497241973877, + 4.032361030578613, + 4.277210235595703, + 3.997724771499634, + 3.9289231300354004, + 3.8230395317077637, + 3.8117282390594482, + 4.111832618713379, + 3.954007625579834, + 3.7982873916625977, + 3.881152868270874, + 3.99232816696167, + 3.8482789993286133, + 3.942899227142334, + 3.893601894378662, + 3.8844542503356934, + 4.02971076965332, + 3.851468086242676, + 4.060338497161865, + 3.9119908809661865, + 4.287752151489258, + 4.191795349121094, + 3.882951498031616, + 3.714550018310547, + 4.303031921386719, + 4.081474304199219, + 4.244274616241455, + 3.9838271141052246, + 3.966465711593628, + 4.015554428100586, + 3.7368316650390625, + 4.032900810241699, + 3.829380512237549, + 4.046370506286621, + 3.943946361541748, + 3.98945689201355, + 3.927220344543457, + 3.9734270572662354, + 4.044177055358887, + 3.8985655307769775, + 4.077480316162109, + 3.9693150520324707, + 3.787632465362549, + 3.7594494819641113, + 4.000375270843506, + 3.9640064239501953, + 3.8909759521484375, + 3.850790023803711, + 4.125975131988525, + 3.7803969383239746, + 3.7465341091156006, + 4.195216178894043, + 3.841724157333374, + 4.22285270690918, + 4.265176773071289, + 3.778024196624756, + 3.725446939468384, + 4.087458610534668, + 4.120079040527344, + 3.912097215652466, + 4.200753688812256, + 3.994182586669922, + 3.8501152992248535, + 4.0868940353393555, + 3.957892894744873, + 3.990758180618286, + 3.9712400436401367, + 3.9374632835388184, + 3.865813970565796, + 3.747910737991333, + 3.9502601623535156, + 4.065150737762451, + 3.7691380977630615, + 3.750119686126709, + 3.858621597290039, + 3.8380439281463623, + 3.94753098487854, + 4.121711730957031 + ] +} \ No newline at end of file diff --git a/setup.py b/setup.py index 0b425f72..da1536f9 100644 --- a/setup.py +++ b/setup.py @@ -42,7 +42,7 @@ install_requires=[ # if you add any additional libraries, please also # add them to `docs/source/requirements.txt` - "pyro-ppl>=1.8.5", + "pyro-ppl==1.8.6", ], extras_require={ "dynamical": DYNAMICAL_REQUIRE, From ff63c18fce153662d9cad72fedde2b62f63f215d Mon Sep 17 00:00:00 2001 From: Sam Witty <samawitty@gmail.com> Date: Tue, 22 Oct 2024 16:18:36 -0400 Subject: [PATCH 26/26] rename --- .../notebooks/quality_vs_estimators.ipynb | 355 +++++++----------- ...glm_tmle.json => ate_causal_glm_ihdp.json} | 0 2 files changed, 146 insertions(+), 209 deletions(-) rename docs/examples/robust_paper/results/{ate_causal_glm_tmle.json => ate_causal_glm_ihdp.json} (100%) diff --git a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb index 9c0dd3db..69077287 100644 --- a/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb +++ b/docs/examples/robust_paper/notebooks/quality_vs_estimators.ipynb @@ -61,7 +61,7 @@ "text": [ "/Users/sam-basis/opt/anaconda3/envs/chirho-robust/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n", - "[2024-10-22 11:27:09,284] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" + "[2024-10-22 16:13:27,226] torch.distributed.elastic.multiprocessing.redirects: [WARNING] NOTE: Redirects are currently not supported in Windows or MacOs.\n" ] } ], @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -298,7 +298,7 @@ "<text text-anchor=\"middle\" x=\"200.6\" y=\"-53.3\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n", "</g>\n", "<!-- intercept&#45;&gt;Y -->\n", - "<g id=\"edge6\" class=\"edge\">\n", + "<g id=\"edge2\" class=\"edge\">\n", "<title>intercept&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M69.66,-115.65C97.53,-103.38 140.19,-84.59 169.18,-71.83\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"170.71,-74.98 178.45,-67.75 167.89,-68.58 170.71,-74.98\"/>\n", @@ -310,7 +310,7 @@ "<text text-anchor=\"middle\" x=\"318.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">outcome_weights</text>\n", "</g>\n", "<!-- outcome_weights&#45;&gt;Y -->\n", - "<g id=\"edge2\" class=\"edge\">\n", + "<g id=\"edge4\" class=\"edge\">\n", "<title>outcome_weights&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M291.82,-112.12C273.12,-101.02 248.19,-86.23 229.12,-74.92\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"230.67,-71.77 220.28,-69.68 227.1,-77.79 230.67,-71.77\"/>\n", @@ -352,13 +352,13 @@ "<text text-anchor=\"middle\" x=\"200.6\" y=\"-125.3\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n", "</g>\n", "<!-- X&#45;&gt;Y -->\n", - "<g id=\"edge5\" class=\"edge\">\n", + "<g id=\"edge6\" class=\"edge\">\n", "<title>X&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M200.6,-110.7C200.6,-102.98 200.6,-93.71 200.6,-85.11\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"204.1,-85.1 200.6,-75.1 197.1,-85.1 204.1,-85.1\"/>\n", "</g>\n", "<!-- A&#45;&gt;Y -->\n", - "<g id=\"edge4\" class=\"edge\">\n", + "<g id=\"edge5\" class=\"edge\">\n", "<title>A&#45;&gt;Y</title>\n", "<path fill=\"none\" stroke=\"black\" d=\"M143.17,-113.83C153.35,-103.94 167.12,-90.55 178.63,-79.36\"/>\n", "<polygon fill=\"black\" stroke=\"black\" points=\"181.07,-81.87 185.8,-72.38 176.19,-76.85 181.07,-81.87\"/>\n", @@ -378,10 +378,10 @@ "</svg>\n" ], "text/plain": [ - "<graphviz.graphs.Digraph at 0x186497e50>" + "<graphviz.graphs.Digraph at 0x10a39f090>" ] }, - "execution_count": 57, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -406,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -443,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -469,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -524,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -608,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -656,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -678,30 +678,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset 10\n", - "plug-in-mle-from-model 10\n", - "tmle analytic_eif 10\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import os\n", "\n", "# Compute doubly robust ATE estimates using both the automated and closed form expressions\n", - "N_datasets = 100\n", + "N_datasets = 90\n", "\n", "\n", "# Estimators to compare\n", - "# estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", - "estimatros = {\"tmle\": tmle}\n", + "estimators = {\"tmle\": tmle, \"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", "# estimators = {\"one_step\": one_step_corrected_estimator, \"double_ml\": None} # We'll do DoubleML in the loop\n", "estimator_kwargs = {\n", " \"tmle\": {\n", @@ -719,7 +708,7 @@ "\n", "# Cache the results\n", "# RESULTS_PATH = \"../results/ate_causal_glm.json\"\n", - "RESULTS_PATH = \"../results/ate_causal_glm_tmle.json\"\n", + "RESULTS_PATH = \"../results/ate_causal_glm_ihdp.json\"\n", "\n", "if os.path.exists(RESULTS_PATH):\n", " with open(RESULTS_PATH, \"r\") as f:\n", @@ -790,86 +779,7 @@ }, { "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'analytic_eif-one_step': [3.7440202236175537,\n", - " 3.9927549362182617,\n", - " 3.9080309867858887,\n", - " 3.932170867919922,\n", - " 3.8274712562561035,\n", - " 3.861823320388794,\n", - " 3.6158628463745117,\n", - " 3.759370803833008,\n", - " 3.776322841644287,\n", - " 4.073896408081055],\n", - " 'analytic_eif-double_ml': [3.951414108276367,\n", - " 4.1413047313690186,\n", - " 3.925032615661621,\n", - " 4.17576003074646,\n", - " 3.9561233520507812,\n", - " 4.15458345413208,\n", - " 3.740159273147583,\n", - " 3.954193353652954,\n", - " 4.029200553894043,\n", - " 4.348143577575684],\n", - " 'monte_carlo_eif-one_step': [3.7492637634277344,\n", - " 3.9226505756378174,\n", - " 3.920912027359009,\n", - " 3.916611433029175,\n", - " 3.7910571098327637,\n", - " 3.840451717376709,\n", - " 3.6561429500579834,\n", - " 3.7997665405273438,\n", - " 3.7907848358154297,\n", - " 4.033873081207275],\n", - " 'monte_carlo_eif-double_ml': [3.956657648086548,\n", - " 4.071200370788574,\n", - " 3.937913656234741,\n", - " 4.160200595855713,\n", - " 3.9197092056274414,\n", - " 4.133211851119995,\n", - " 3.7804393768310547,\n", - " 3.99458909034729,\n", - " 4.0436625480651855,\n", - " 4.308120250701904],\n", - " 'plug-in-mle-from-model': [3.8888871669769287,\n", - " 3.4957659244537354,\n", - " 3.793285846710205,\n", - " 3.6561882495880127,\n", - " 3.6290812492370605,\n", - " 3.792243719100952,\n", - " 3.797788619995117,\n", - " 3.8203046321868896,\n", - " 3.810659408569336,\n", - " 3.7938194274902344],\n", - " 'plug-in-mle-from-test': [4.096281051635742,\n", - " 3.644315719604492,\n", - " 3.8102874755859375,\n", - " 3.899777412414551,\n", - " 3.7577333450317383,\n", - " 4.085003852844238,\n", - " 3.9220850467681885,\n", - " 4.015127182006836,\n", - " 4.063537120819092,\n", - " 4.068066596984863]}" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "estimates" - ] - }, - { - "cell_type": "code", - "execution_count": 75, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -893,8 +803,10 @@ " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", + " <th>analytic_eif-tmle</th>\n", " <th>analytic_eif-one_step</th>\n", " <th>analytic_eif-double_ml</th>\n", + " <th>monte_carlo_eif-tmle</th>\n", " <th>monte_carlo_eif-one_step</th>\n", " <th>monte_carlo_eif-double_ml</th>\n", " <th>plug-in-mle-from-model</th>\n", @@ -904,113 +816,139 @@ " <tbody>\n", " <tr>\n", " <th>count</th>\n", - " <td>10.00</td>\n", - " <td>10.00</td>\n", - " <td>10.00</td>\n", - " <td>10.00</td>\n", - " <td>10.00</td>\n", - " <td>10.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", + " <td>92.00</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", - " <td>3.85</td>\n", - " <td>4.04</td>\n", - " <td>3.84</td>\n", - " <td>4.03</td>\n", - " <td>3.75</td>\n", - " <td>3.94</td>\n", + " <td>3.81</td>\n", + " <td>3.81</td>\n", + " <td>3.97</td>\n", + " <td>3.81</td>\n", + " <td>3.83</td>\n", + " <td>3.98</td>\n", + " <td>3.81</td>\n", + " <td>3.96</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", - " <td>0.13</td>\n", - " <td>0.17</td>\n", - " <td>0.11</td>\n", - " <td>0.15</td>\n", + " <td>0.09</td>\n", " <td>0.12</td>\n", " <td>0.16</td>\n", + " <td>0.10</td>\n", + " <td>0.11</td>\n", + " <td>0.15</td>\n", + " <td>0.11</td>\n", + " <td>0.15</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", - " <td>3.62</td>\n", - " <td>3.74</td>\n", - " <td>3.66</td>\n", - " <td>3.78</td>\n", + " <td>3.53</td>\n", + " <td>3.59</td>\n", + " <td>3.64</td>\n", + " <td>3.52</td>\n", + " <td>3.63</td>\n", + " <td>3.71</td>\n", " <td>3.50</td>\n", " <td>3.64</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3.76</td>\n", - " <td>3.95</td>\n", - " <td>3.79</td>\n", - " <td>3.94</td>\n", - " <td>3.69</td>\n", - " <td>3.83</td>\n", + " <td>3.72</td>\n", + " <td>3.86</td>\n", + " <td>3.75</td>\n", + " <td>3.76</td>\n", + " <td>3.89</td>\n", + " <td>3.75</td>\n", + " <td>3.85</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", - " <td>3.84</td>\n", - " <td>3.99</td>\n", + " <td>3.81</td>\n", + " <td>3.80</td>\n", + " <td>3.96</td>\n", + " <td>3.81</td>\n", " <td>3.82</td>\n", - " <td>4.02</td>\n", - " <td>3.79</td>\n", " <td>3.97</td>\n", + " <td>3.79</td>\n", + " <td>3.96</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", - " <td>3.93</td>\n", - " <td>4.15</td>\n", - " <td>3.92</td>\n", - " <td>4.12</td>\n", - " <td>3.81</td>\n", + " <td>3.85</td>\n", + " <td>3.86</td>\n", " <td>4.07</td>\n", + " <td>3.87</td>\n", + " <td>3.87</td>\n", + " <td>4.08</td>\n", + " <td>3.87</td>\n", + " <td>4.06</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", + " <td>4.08</td>\n", + " <td>4.16</td>\n", + " <td>4.44</td>\n", " <td>4.07</td>\n", - " <td>4.35</td>\n", - " <td>4.03</td>\n", - " <td>4.31</td>\n", - " <td>3.89</td>\n", - " <td>4.10</td>\n", + " <td>4.14</td>\n", + " <td>4.42</td>\n", + " <td>4.09</td>\n", + " <td>4.30</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ - " analytic_eif-one_step analytic_eif-double_ml \\\n", - "count 10.00 10.00 \n", - "mean 3.85 4.04 \n", - "std 0.13 0.17 \n", - "min 3.62 3.74 \n", - "25% 3.76 3.95 \n", - "50% 3.84 3.99 \n", - "75% 3.93 4.15 \n", - "max 4.07 4.35 \n", + " analytic_eif-tmle analytic_eif-one_step analytic_eif-double_ml \\\n", + "count 92.00 92.00 92.00 \n", + "mean 3.81 3.81 3.97 \n", + "std 0.09 0.12 0.16 \n", + "min 3.53 3.59 3.64 \n", + "25% 3.76 3.72 3.86 \n", + "50% 3.81 3.80 3.96 \n", + "75% 3.85 3.86 4.07 \n", + "max 4.08 4.16 4.44 \n", + "\n", + " monte_carlo_eif-tmle monte_carlo_eif-one_step \\\n", + "count 92.00 92.00 \n", + "mean 3.81 3.83 \n", + "std 0.10 0.11 \n", + "min 3.52 3.63 \n", + "25% 3.75 3.76 \n", + "50% 3.81 3.82 \n", + "75% 3.87 3.87 \n", + "max 4.07 4.14 \n", "\n", - " monte_carlo_eif-one_step monte_carlo_eif-double_ml \\\n", - "count 10.00 10.00 \n", - "mean 3.84 4.03 \n", - "std 0.11 0.15 \n", - "min 3.66 3.78 \n", - "25% 3.79 3.94 \n", - "50% 3.82 4.02 \n", - "75% 3.92 4.12 \n", - "max 4.03 4.31 \n", + " monte_carlo_eif-double_ml plug-in-mle-from-model \\\n", + "count 92.00 92.00 \n", + "mean 3.98 3.81 \n", + "std 0.15 0.11 \n", + "min 3.71 3.50 \n", + "25% 3.89 3.75 \n", + "50% 3.97 3.79 \n", + "75% 4.08 3.87 \n", + "max 4.42 4.09 \n", "\n", - " plug-in-mle-from-model plug-in-mle-from-test \n", - "count 10.00 10.00 \n", - "mean 3.75 3.94 \n", - "std 0.12 0.16 \n", - "min 3.50 3.64 \n", - "25% 3.69 3.83 \n", - "50% 3.79 3.97 \n", - "75% 3.81 4.07 \n", - "max 3.89 4.10 " + " plug-in-mle-from-test \n", + "count 92.00 \n", + "mean 3.96 \n", + "std 0.15 \n", + "min 3.64 \n", + "25% 3.85 \n", + "50% 3.96 \n", + "75% 4.06 \n", + "max 4.30 " ] }, - "execution_count": 75, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1023,12 +961,12 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAJOCAYAAAAqFJGJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADEhklEQVR4nOzdZ3RU1duG8evMpPcQAgQILUDoHelVQAQb9orKX7BifRUbIvbesIGCCPZCUUGlI016770GEgLpPTPvhzCjSEsyk5xJcv/WYi0yc84+dxRCnuz97G3Y7XY7IiIiIiIiLrCYHUBERERERMo+FRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyjy0s7HY7aWlp6Pw+ERERERHP57GFRXp6Om3btiU9Pd3sKCIiUgrS09MxDAPDMPS1X0SkDPLYwkJERERERMoOFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyFRYiIiIiIuIyL7MDiIiIiIjr7HY7+fn55OXlmR1Fyghvb2+sVqvbxlNhISIiIlKG2e12kpKSSEhIID8/3+w4UsaEhYVRrVo1DMNweSwVFiIiIiJl2NGjR0lKSiIkJISQkBC8vLzc8k2ilG92u52MjAzi4+MBiIqKcnlMFRYiIiIiZVR+fj7JyclERkZSuXJls+NIGePv7w9AfHw8VapUcXlZlJq3RURERMqo3Nxc7HY7gYGBZkeRMiogIAAo+LPkKhUWIiIiImWclj5Jcbnzz44KCxERERERcZkKCxERERHxCL179yY2Ntb5q1mzZvTs2ZNRo0Zx4sQJtz4rNjaWKVOmnPP9MWPG0Lt370KPN2XKFGJjY+nQocNZt/w9duwYjRs3JjY21vnabbfdxpNPPlm04B5MhYWIiIiIeIwhQ4awePFiFi9ezO+//87IkSNZvnw5t956K6mpqWbHu6D09HT+/vvvM17/448/sNvtJiQqPSosRERERMRjBAQEEBkZSWRkJNHR0Vx88cVMmDCBuLg4Pv/8c7PjXVCnTp34448/znj9999/p127diYkKj0qLERERETKIbvdTnpOuim/3P2T+erVq9O3b19mzJgBQFJSEqNHj6ZHjx60aNGCG2+8keXLlzuvP9syprO9tmfPHm688UaaNWvGpZdeyu+//37ODKmpqYwcOZKOHTvStm1bBg8ezMaNG8+47tJLL2X27NmnLYc6cuQIW7ZsoU+fPsX6/MsKnWMhIiIiUs7Y7Xa6ftGVpQeXmvL8LtFdWHTnIrfuONSwYUOmT59OamoqQ4YMITc3lzfffJNKlSoxadIk/ve///HNN9/QokWLQo/55Zdf8swzz/Dqq68yffp0HnnkEaKjo2nWrNlp19ntdoYOHYqfnx9jx44lKCiI6dOnc9NNN/HDDz/QpEkT57V9+vThueeeY/ny5XTp0gWAmTNn0qVLF0JCQtzzH8NDacZCREREpBwyKF9b0Dq+KZ8/fz6bN2/m7bff5qKLLqJ+/fqMHj2aBg0aMH78+CKNefPNN3PjjTdSt25dHn74YVq1asXEiRPPuO7vv/9m3bp1vPfee7Rs2ZKYmBgeffRRWrVqxaRJk87I2bVr19OWQ82cOZOBAwcW/ZMuYzRjISIiIlLOGIbBojsXkZGbYcrzA7wD3H62hqNx+8CBAwQHB9OwYUPne4Zh0K5dOxYvXlykMdu2bXvaxy1btjxr4/XmzZux2+306tXrtNdzcnLIzs4+4/r+/fvz+uuv8/zzz3P48GH27t1L7969z9p7UZ6osBAREREphwzDINCn/JzIvXnzZurUqYOPj89Z37fb7Xh5nftb27NtAWuxnL54Jz8//6zj22w2goKCzro97dmu79OnDyNHjmTFihWsX7+enj17Ok+4Ls+0FEpEREREPNrRo0eZO3cul19+ObGxsaSmprJjxw7n+3a7ndWrV1O/fn0AvL29SU9PP22M/fv3nzHu5s2bT/t4zZo1NGjQ4IzrGjZsSFpaGrm5udSuXdv567PPPmPu3LlnXB8UFES3bt34448/+P333yvEMijQjIWIlCH5OTnEr1qFd1AQEc2bu32aXUREzJeRkUFCQgIAWVlZbN++nffee4+aNWty55134ufnR+PGjXnssccYOXIkERERfPXVV+zYsYNRo0YB0KpVK5KSkhg/fjyXXHIJixcv5q+//iIsLOy0Z02cOJFatWrRsmVLvvvuO3bs2MHbb799RqZu3brRuHFjHnnkEZ555hmioqL45ptvmDJlyjn7Oi699FKef/55DMOge/fu5/x8jx07xl9//XXG6+e7x1OpsBARj5eTmsqGDz5g34wZ5CQnAxDRogVNhw2j5n/Wu4qISNk2YcIEJkyYABTMPERFRTFgwACGDBlCYGCg85rXX3+dBx54gJycHJo1a8bEiRNp1aoVAB07dmT48OFMmDCBDz74gO7du/Pggw+e0Wh93333MXnyZEaOHEn9+vUZN24cdevWPSOT1WplwoQJvPnmmzz88MNkZmYSExPDhx9+SKdOnc76efTu3Ztnn32WSy+99JzLtwCWLl3K0qVn7t61ffv2Qv338iSG3UOPAExLS6Nt27asXr2aoKAgs+OIiElsubksuPdeji5bBoBfRAQ5qanYcnIAaD9qFA2uv97MiOIm6enpzq/3aWlpzm8gROTcsrKy2Lt3L3Xr1sXPz8/sOFIGufPPkHosRMRj2e12Vr38MkeXLcPL35/uH37IVfPnc+Xs2TS44QYAVr/8Mgnr1pkbVERERFRYiIjn2vndd+z68UcwDDq/+SY1e/XCYrXiX7ky7UaOJLpfP2x5eSx+5BEyT63HFREREXOosBARj+ToqwBo/dhjZ/RSGIZBx5deIjQmhsz4eNa8+aYZMUVEROQUFRYi4pG2f/UVOSkphNSrR+zgwWe9xjswkE6vvw7Agd9/J2Xv3tKMKCIiIv+iwkJEPE5OairbTu3c0eyee7BYree8tlLjxtTo1Qu7zcamceNKK6KIiIj8hwoLEfE42ydPJvfUbEWt/v0veH3ze+8FYP9vv5FylgOQREREpOSpsBARj5Kfnc32yZMBaH7ffeedrXCo1LQp1Xv0wG6zsXns2JKOKCIiImehwkJEPMrhBQvISUkhoFo1ovv1K/R9ze65B4D9M2c6D9ETERGR0qPCQkQ8yt5ffgGgzuWXF2q2wiGieXPCGjbElpvLgT//LKl4IiIicg4qLETEY2SdOMGRxYsBqHvZZUW61zAM6l5xBfBPcSIiImVL7969iY2Ndf5q1KgRbdq04dZbb2XlypXOa8aMGVOiOZYvX05sbCyHDh0q0eeUNyosRMRj7P/9d+x5eYQ3aUJo/fpFvr/2wIEYFgsJa9eSeuBACSQUEZGSNmTIEBYvXszixYv566+/+O677wgKCuKuu+7iyJEjpZKhdevWLF68mKioqFJ5XnmhwkJEPMa+334DoO7llxfr/oAqVajasSMAe3/91W25RESk9AQEBBAZGUlkZCRVqlShYcOGjB49mqysLGbPnl0qGXx8fIiMjMRahCW5osJCRDxEyv79JG7YgGG1UnvAgGKPU/fKKwHY9+uv2O12d8UTERETeXl5AQXf8P/blClTiI2NPe9rmZmZjBo1ig4dOtCmTRueeeYZHnvsMZ588slzPu+/S6F69+7N+PHjGT58OK1bt6ZDhw689NJL5OXluetTLBdUWIiIRzg8bx4AVTt0wL9y5WKPE927N14BAaQdPEjixo3uiiciUubY7XbyMjJM+eXOH+wcO3aMF154gYCAAHr06FHk+0eMGMGSJUt49913+e6770hNTWXGjBlFHuf999+nffv2/PLLLzzxxBN89dVX/HZqpl0KeJkdQEQE4MiiRQBU797dpXG8AgKI6tqVg7NmcWThQiq3aOGOeCIiZYrdbmf2rbdyfN06U54f2bo1fSZPxjCMIt87duxYJkyYAEBeXh45OTnExMTw3nvvUb169SKNdfDgQf78808+//xzOnfuDMCbb77JmjVripyra9euDB48GIDo6GgmT57MmjVruOqqq4o8VnmlwkJETJebnk7C6tWA64UFQI0ePTg4axaHFy6kxfDhLo8nIlIWFeebek9w4403cttttwFgsVgICwsjODi4WGNt2bIFKGjGdvD19aXFv37oNHDgwNOawj/77LOzjhUTE3Pax8HBweTm5hYrV3mlwkJETHf077+x5eURFB1NSO3aLo9XvVs3MAxObt1KxrFjBFSt6oaUIiJlh2EY9Jk8mfzMTFOeb/X3L3ZhExoaSm0X/i3Iz8//J8ep5mubzXbO68eNG3dar0TVqlVZv379Gdf9t78DUC/ff6iwEBHTxblpGZSDX0QEEc2bk7hhA0f++ov6113nlnFFRMoSwzDwCggwO0aJ8vb2BiAtLY2goCAA9u3b53w/NjYWwzBYt24d3U/9G5OTk8PmzZvp1KkTADVq1Cjd0OWYmrdFxFR2u/2f/opu3dw2bo1TDX6HFy5025giIuJZWrVqhWEYjBkzhkOHDvH7778zdepU5/vR0dFceumlvPjiiyxbtoxdu3bxzDPPcPTo0TK7VMyTqbAQEVMl79pFxtGjWH19qdK+vdvGdRQWR//+m7ysLLeNKyIiniM6OprRo0cze/ZsLr30Ur7//nueeOKJ06558cUXadu2LcOHD+eGG24gMDCQ1q1bO2c7xH0Mu4cuDktLS6Nt27asXr3aObUlIuXPlvHjWffOO0R160avTz9127h2u51pF19M5rFj9Pz0U7fOhkjJSE9Pd369T0tLIzAw0OREIp4vKyuLvXv3UrduXfz8/MyO43Gys7NZtGgRHTt2PO37yUsuuYQrrriC+++/38R0nsGdf4Y0YyEipjq2YgUAUV26uHVcwzCocWo9bdzixW4dW0REygYfHx9Gjx7NqFGj2L17N/v27eOtt97iyJEj9O/f3+x45Y4KCxExjS0vj4S1awGo2q6d28ev2qED8E/xIiIiFYthGIwbN46TJ09yww03MGjQINauXcuECRPO2D5WXKddoUTENEnbt5OXno53cDChDRu6fXxHz0bSjh1knTyJX3i4258hIiKerXHjxs4D96RkacZCREwTf+pQvMjWrbGc2mvcnfwrVyb01E+k4leudPv4IiIi8g8VFiJiGkdhUaVt2xJ7RpWLLgK0HEpERKSkqbAQEVPY7XYSHDMWJdBf4VD1VGGhGQsREZGSpcJCREyRsmcP2SdPYvXzo1KTJiX2HEefRfKuXWQlJpbYc0RERCo6FRYiYgrHMqjKLVpg9fEpsef4hYcTdqox/JhmLUREREqMCgsRMUX8qlVAyS6DcqiqPgsREZESp8JCREyRsGYNULKN2w5V1GchIiJS4nSOhYiUusyEBDLi4jAsFiKaNy/x50W2agUU9HXkJCfjExpa4s8UEZGis9vtTJ06lalTp7Jz507S0tKIioqiZ8+eDBs2jMjISLMjntWYMWOYOnUq8+bNO+O9J598kqlTp573/u3btxfruRkZGUydOpVbbrnF+azDhw8zefLkYo3nKs1YiEipS9y4EYCQmBi8AwNL/Hl+EREE1aoFwPENG0r8eSIiUnQ2m43777+f1157jV69ejF58mRmzZrFs88+y8aNG7nmmmtILIObcDzzzDMsXrzY+Qvg6aefPuO14pgwYQLjx493V1SXacZCREqdo7AojdkKh8hWrUg7cIDj69ZRvVu3UnuuiIgUzsSJE1m4cCE//PADTZs2db5evXp1OnTowMCBAxk/fjxPPPGEiSmLLjg4mODg4DNec8fsi91ud3kMd9KMhYiUusRNm4DSLSwqt24NQMK6daX2TBERKRy73c5XX33FFVdccVpR4eDn58ekSZN4+OGHATh06BCxsbGMHTuWLl26cPHFF5OWlkZSUhKjR4+mR48etGjRghtvvJHly5c7xxkzZgy9e/c+bez/vhYbG8tPP/3EHXfcQYsWLejatSsffvjhafd8//339O3blxYtWnDPPfeQnJzs0uc/ZcoU+vbty0svvUTbtm257777WL58ObGxsRw6dMh53b9fGzNmDB9++CGHDx8+7brc3Fxef/11OnbsSKtWrbjvvvs4fvy4S/kKSzMWIlKq7DabKYWFo88iccMGbPn5WKzWUnu2iIgZ7HY7GRkZpjw7ICAAwzAKff2hQ4c4fPgwnTt3Puc1NWrUOOO1qVOn8uWXX5KZmYm/vz/XXXcdubm5vPnmm1SqVIlJkybxv//9j2+++YYWLVoUOs/rr7/Os88+y4svvsiMGTN499136dChA+3bt+e3337jhRde4Omnn6Zz587Mnj2bd999l6ioqEKPfzYHDhwgPj6eadOmkZWVxYkTJ857/ZAhQ8jIyGDmzJn89NNPVKpUCYC1a9cSExPDN998Q0JCAo888ghvvPEGb7zxhkv5CkOFhYiUqtQDB8hNScHq60tY/fql9tyQmBi8g4LITUsjeedOwhs1KrVni4iUNrvdTteuXVm6dKkpz+/SpQuLFi0qdHHh+Im645tjh3vuuee0GYfq1aszY8YM58c333wz9U/9W7Jw4UI2b97Mr7/+SsNT5xeNHj2ajRs3Mn78eN5///1C57/qqqu48sornRnGjx/PmjVraN++PZMnT2bAgAHOhulhw4axbt06tm3bVujxz+W+++4jOjoa4LTP+2wCAwMJCAjAarWetqwqMjKSF198EYvFQr169RgwYECp/TnQUigRKVWO/orwxo2xeHuX2nMtVisRp35adVzLoUSkAijKjIHZwsPDAc5YUjR69GimTZvGtGnTuOaaa8jMzDzt/dq1azt/v2PHDoKDg51FBRT8N2jXrh07duwoUp6YmJjTPg4ODiY3N9f5nOb/mXFvfWq5ravq1Knj8hi1atXCYvnnW/zQ0FCysrJcHrcwNGMhIqXKjMZth8qtWnF06VIS1q2jwY03lvrzRURKi2EYLFq0qMwshYqOjiYyMpLly5czYMAA5+tVq1Z1/j70LFuF+/n5OX9/rkZmu92Ol9e5v+XNy8s74zUfH5+zjuNgs9lOe8/bTT8o+/fnczb5+fkXHMNq4lJfFRYiUqrMLCwcfRaasRCRisAwDAJLYUtvd7BarQwePJiPPvqIm266iUZnWa4aFxd33jFiY2NJTU1lx44dzlkLu93O6tWrnculvL29SU9PP+2+/fv3Fylr48aNWbNmDXfccYfztY2n/m1zJ0exkpaW5nxt3759p13jabNSWgolIqUmPyeHk1u3AuYUFhEtWoBhkHbwIFllcC90EZHy7K677qJXr17cfPPNfPrpp2zbto1Dhw4xb948hgwZws8//0zHjh3PeX/Xrl1p3Lgxjz32GCtWrGD37t288MIL7Nixg9tvvx2AVq1akZSUxPjx4zl06BDfffcdf/31V5FyDhs2jNmzZ/P555+zb98+Jk+ezJ9//unS5342DRs2JCAggHHjxnHgwAEWLVrEF198cdo1AQEBJCcns3fvXudSLTOpsBCRUpO8cye23Fx8QkMJOtWcVpp8goMJqVsX+GfLWxER8QwWi4X33nuPl19+mVWrVjFkyBD69+/PCy+8QKVKlfjqq6945ZVXznm/1WplwoQJNGnShAceeIBrrrmGnTt3MnHiRFqdmrHu2LEjw4cPZ8KECQwcOJAlS5bw4IMPFilnz549efvtt/n555+5/PLLmTVrFkOGDHHlUz+roKAg3nzzTbZs2cKAAQN4//33GTFixGnX9OvXj8jISK644gq2bNni9gxFZdg97WSNU9LS0mjbti2rV68mKCjI7Dgi4ga7fvyRFc8/T7VOnej9+eemZFj21FPs/eUXmt13Hy3uv9+UDHJ26enpzq/3aWlpZWYJh4iZsrKy2Lt3L3Xr1r3g+nyRs3HnnyHNWIhIqXEsgzJzq9dKzZoBcEIzFiIiIm6lwkJESs2JU3t8m1lYOHo7EjdtOucOIiIiIlJ0KixEpFTY8vNJOrWPeHjjxqblCI+NxfDyIvvECTIusMOIiIiIFJ4KCxEpFan79pGfmYnV359gNxwAVFxWX1/CTm1DmFgC2wOKiIhUVCosRKRUnDy1DCqsYUMsJh7eAxBxqs9CO0OJiIi4jwoLESkVjsbtSib2VzhEqIFbRMoZ9YxJcbnzz44KCxEpFY4ZCzP7KxycO0Nt2YLdZjM5jYhI8Xl7e2MYxhmnSYsUVkZGBvDPSd+u8HJ5BBGRC7Db7f9sNesBhUVoTAxWPz9y09JI2beP0Hr1zI4kIlIsVquV0NBQEhISyM7OJiQkBC8vLwzDMDuaeDi73U5GRgbx8fGEhYVhdcMyZRUWIlLiMo4eJTspCcNqJaxBA7PjYPHyolLjxiSsXcuJTZtUWIhImVatWjX8/f2Jj48nJSXF7DhSxoSFhVGtWjW3jKXCQkRKnGO2IjQmBquvr8lpCoQ3aULC2rWc3LaNuldcYXYcEZFiMwyDsLAwQkNDyc/PJy8vz+xIUkZ4e3u7ZabCQYWFiJQ4545QHtC47eA4pM9R9IiIlHWGYeDl5YWXl769E3OoeVtESpzzYLzYWJOT/MPR63Fi2zbtpiIiIuIGKixEpMQl7dwJ4BH9FQ6hMTFYvLzITUnRCdwiIiJuoMJCREpUXmYmaQcOADhPvPYEVh8fQmJiADih5VAiIiIuU2EhIiUqefdu7DYbvuHh+FWubHac01Q6tRxKfRYiIiKuU2EhIiXKuQyqYUOP21fd0WfhaC4XERGR4lNhISIlKmn7dsCz+isctDOUiIiI+6iwEJES5Zyx8KAdoRwchYXjAD8REREpPhUWIlKikj1wRygH76AggqKjAc1aiIiIuEqFhYiUmMzjx8lKTATDILR+fbPjnJVzOZT6LERERFyiwkJESoxjtiK4Vi28/P1NTnN2zoPyNGMhIiLiEhUWIlJiHCdue9L5Ff8Vri1nRURE3EKFhYiUmDJRWJxaCpW6bx95mZkmpxERESm7VFiISIlJ8uDGbQf/yEj8IiKw22zOvCIiIlJ0KixEpETYbTaS9+wBINSDCwvDMHSehYiIiBuosBCREpF++DD5mZlYfHycW7p6Ku0MJSIi4joVFiJSIpJ27QIgpG5dLFaryWnOTw3cIiIirlNhISIlIvlUYeGp51f8m6OwSNqxA1tenslpREREyiYVFiJSIpJ37wYgrAwUFo5zNvKzs0ndt8/sOCIiImWSCgsRKRFlacbCsFgIi40F4IT6LERERIpFhYWIuJ0tP58Ux45QMTEmpykc9VmIiIi4RoWFiLhd+qFD5GdnY/X1JbBmTbPjFIq2nBUREXGNCgsRcTtHf0VIvXoevyOUQyXHjMW2bdjtdpPTiIiIlD0qLETE7cpSf4VDaP36GFYrOcnJZB47ZnYcERGRMkeFhYi4neMMi7LSXwFg9fUlpE4dAE7u2GFuGBERkTJIhYWIuF1ZnLEAnDtDJW3fbnISERGRskeFhYi4lS0/n5S9e4GycYbFv4U1bAiosBARESkOFRYi4lZphw5hy8nB6udHYI0aZscpEueMhZZCiYiIFJkKCxFxK8f5FSF16mBYytaXmPBThUXKvn3kZ2ebnEZERKRsKVv/6ouIx3MWFvXqmZyk6PyrVMEnNBR7fr5zy1wREREpHBUWIuJWzsKibl2TkxSdYRjOWYuT6rMQEREpEhUWIuJWyacat8vSVrP/pgZuERGR4lFhISJuY7fby/RSKICwRo0AFRYiIiJFpcJCRNwm6/hxclNTMSwWgmvXNjtOsYQ7Zix27MBut5ucRkREpOxQYSEibuOYrQisWROrj4/JaYonJCYGw2IhOymJzIQEs+OIiIiUGSosRMRtkk8VFqFldBkUgJefH8F16gBaDiUiIlIUKixExG3K8o5Q/+Y8KE+FhYiISKGpsBARt0k5tSNUSBndEcpBW86KiIgUnQoLEXGb5PIyY/GvBm4REREpHBUWIuIWuWlpZB47BpTtHgv4Z8YiZe9e8nNyTE4jIiJSNqiwEBG3cCyD8qtcGZ+QEJPTuMa/alV8QkKw5+eTvHu32XFERETKBBUWIuIW5WUZFIBhGGrgFhERKSIVFiLiFo4Zi7K+DMohTA3cIiIiRaLCQkTcwrnVbBnfEcohXA3cIiIiRaLCQkTcorycYeHw76VQdrvd5DQiIiKeT4WFiLjMlptL6sGDQPlZChVavz6GxUL2yZNkHT9udhwRERGPp8JCRFyWeuAA9rw8vAIC8K9a1ew4buHl50dwnTqA+ixEREQKQ4WFiLjs38ugDMMwOY37OA/KU2EhIiJyQSosRMRljh2hykvjtoPjoDw1cIuIiFyYCgsRcZnjDIvQctK47eCYsdBSKBERkQtTYSEiLnMuhSonjdsOjsIiZe9e8nNyTE4jIiLi2VRYiIhL7HZ7uS0sAqKi8A4JwZ6XR8ru3WbHERER8WgqLETEJRlHj5KXmYnh5UVwdLTZcdzKMAznQXkn1WchIiJyXiosRMQljsbt4Fq1sHh7m5zG/cLUwC0iIlIoKixExCXl7cTt/9KWsyIiIoWjwkJEXJJcTvsrHLTlrIiISOGosBARlzhmLELLaWERWr8+GAZZiYlkJiSYHUdERMRjqbAQEZeU96VQXv7+BNeuDWjWQkRE5HxUWIhIseUkJ5OVmAiU36VQ8M9yKB2UJyIicm4qLESk2JJP7QgVUK0a3oGBJqcpOc4Gbs1YiIiInJMKCxEptvK+DMrBueWsZixERETOSYWFiBRbeT1x+78cS6FS9uwhPyfH5DQiIiKeSYWFiBRbcjnfEcohICoK7+BgbHl5zgMBRURE5HQqLESk2CrKUijDMHRQnoiIyAWosBCRYsnPzib98GGg/C+FAjVwi4iIXIgKCxEpltT9+7HbbHgHB+NXubLZcUqctpwVERE5PxUWIlIsyf9q3DYMw+Q0JU87Q4mIiJyfCgsRKZaUCtK47RBWvz4YBlmJiWQeP252HBEREY+jwkJEiqWiNG47eAUEEFyrFqA+CxERkbNRYSEixZJcQc6w+DfncigVFiIiImdQYSEiRWa32Ujdtw+oOEuh4J+dodTALSIiciYVFiJSZOlHjpCfnY3F25vAGjXMjlNqwtXALSIick4qLESkyBzLoILr1MHi5WVymtLjWAqVsns3ttxck9OIiIh4FhUWIlJkFa1x2yGwenW8g4Kw5eWRsnev2XFEREQ8igoLESmyirbVrINhGP/0WaiBW0RE5DQqLESkyFIq4I5QDo7CImnbNpOTiIiIeBYVFiJSZI5lQBWxsAjXlrMiIiJnpcJCRIok68QJspOSwDAIqVPH7DilztHArS1nRURETqfCQkSKxDFbEVi9Ol7+/ianKX2h9euDYZB1/DhZiYlmxxEREfEYKixEpEhSdu8GKt6OUA7egYEERUcDkLRzp8lpREREPIcKCxEpkuQK3LjtEO7YGUoN3CIiIk4qLESkSBxLoUIr6IwF/NNnoQZuERGRf6iwEJEicW41GxNjchLzOAsLNXCLiIg4qbAQkULLy8gg/cgRoIIvhTpVWCTv3o0tN9fkNCIiIp5BhYWIFFrK/v0A+IaF4RcebnIa8wRWr45XYCC23FxS9u0zO46IiIhHUGEhIoXm3BGqAs9WABgWi7OBW8uhRERECqiwEJFCSz5VWIRW4P4KhzBHYaEGbhEREUCFhYgUQYq2mnXSCdwiIiKnU2EhIoWmMyz+oS1nRURETqfCQkQKxZabS+qBAwCEqrAgrEEDADLj48k6edLkNCIiIuZTYSEihZJ64AD2vDy8AgIIiIoyO47pvAMDCYqOBtTALSIiAiosRKSQnP0VdetiGIbJaTyDDsoTERH5hwoLESkUx45QFfnE7f8KV5+FiIiIkwoLESkUR+O2+iv+4dhy9qQKCxERERUWIlI4jqVQOsPiH47CInnXLmx5eSanERERMZcKCxG5ILvNRsrevYC2mv23oJo18QoIwJaTQ+q+fWbHERERMZUKCxG5oPQjR8jPysLi7U1QzZpmx/EYhsXyz3IoNXCLiEgFp8JCRC7I0V8RXKcOFi8vk9N4Fh2UJyIiUkCFhYhcUIoat88pXDMWIiIigAoLESkEbTV7buGNGwNwcssW7Ha7yWlERETMo8JCRC5IMxbnFtawIYbFQlZiIpkJCWbHERERMY0KCxE5L7vd7uyx0I5QZ/Ly93fO5JzYvNnkNCIiIuZRYSEi55V1/Di5KSkYFgshdeqYHccjVTq1HOrEli0mJxERETGPCgsROS9Hf0VgzZpYfX1NTuOZKjVtChT0WYiIiFRUKixE5LzUX3Fh4ZqxEBERUWEhIufnmLEI1Y5Q5xTeqBEYBpnx8WrgFhGRCkuFhYicV4oaty/IOzCQkLp1ATixdavJaURERMyhwkJEzks7QhVOpSZNAPVZiIhIxaXCQkTOKSc5mazjxwEthboQ9VmIiEhFp8JCRM7JMVsRUK0a3oGBJqfxbI6doVRYiIhIRaXCQkTOSf0VhRfeqBEAGXFxZJ08aXIaERGR0qfCQkTOKVlbzRaaT3AwwbVrA+qzEBGRikmFhYick2Or2RD1VxRK+KkGbi2HEhGRikiFhYicU4rjDAvNWBRKJRUWIiJSgamwEJGzyk1PJ/3IEQBC69c3OU3ZUN4KC7vdTr4t3+wYIiJSRniZHUBEPJNjGZR/ZCS+YWHmhikjKp3acjb90CGyk5LKzH+3jNwMft3+K0sPLmXN0TVsOLaBtJw0bHYbALVDa9OociOaVWnGZQ0vo1utblgtVpNTi4iIp1FhISJnlbxrF6DZiqLwCQ0lsGZN0g8d4uS2bVTr2NHsSOe1Jm4Nn676lO83f09Kdso5r9ufvJ/9yfv5c/efvL3sbSIDIrmuyXU82ulRYiqp/0ZERAqosBCRs0rauRNQYVFUlZo0If3QIU5s3uyxhcWB5AM8Nfcpvtn4jfO1umF1uSL2CtpVb0eraq2I8I/Ay+JFni2P3Sd3szVhK0sPLWX6tukkZCTw8aqP+XT1p9zU7Cae7f4sjSo3MvEzEhERT6DCQkTOSjMWxVOpSRMOzprFia1bzY5yhnxbPm8seYMX/nqBrLwsDAxuaHYDd7e9m+61u2Mxzt52FxUcRddaXRnadii5l+Uyb+883l/+Pr/v+p2vN37N95u/Z0SXETzb/Vn8vPxK+bMSERFPoeZtETkrR2ER1qCByUnKFmcD9+bNJic5XXx6PJd+fSlPz3uarLwsutfuzqphq/j2mm/pWafnOYuK//K2enNJ/UuYectMVg1dxcAGA8mz5fHyopdp8UkL/tr/Vwl/JiIi4qlUWIjIGbKTksiMjwcgVGdYFInjLIu0AwfISU01OU2BpQeX0urTVszeM5sA7wC+uPILFty+gDZRbVwat231tvx2829MuX4K1YOrs/PETnp92YvXF7+O3W53U3oRESkrVFiIyBkcO0IFREXhHRRkcpqyxS88nICoKABOesByqN93/s7Fky4mLi2OJpFNWDl0JXe0ugPDMNz2jEGNB7Hlvi3c3vJ2bHYbT859kkHfDyIpK8ltzxAREc+nwkJEzqBlUK6JaNoUgMRNm0zN8cPmH7jiuyvIystiYIOBrLhrBU0im5TIs0L9Qvniyi8Yd9k4fKw+TN8+na4TunIo5VCJPE9ERDyPCgsROYN2hHJNRPPmACRu2GBahm82fsONP91Ini2Pm5rdxNQbphLoE1iizzQMg6Fth7JkyBKqB1dnc8JmOo/vzJaE8nFgoIiInJ8KCxE5g3aEck1EixYAJG7caMrzZ++eze3TbseOnWFthjF50GS8rd6l9vx21duxdMhSYiNiOZhykK4TurL80PJSe76IiJhDhYWInMZut5N8asZCS6GKp1LTphgWCxlHj5Jxqgm+tKyJW8PVP1xNni2PG5vdyCeXfWLKKdm1w2qzeMhiOtTowMmsk1zy1SWsPrK61HOIiEjpUWEhIqfJSkwkOykJDIOQevXMjlMmeQcGOmd7SnPW4kDyAQZ8PYC0nDR61enFxCsnFnob2ZJQOaAycwbPoWutriRnJ9N3cl/WH11vWh4RESlZKixE5DSOZVBB0dF4+emws+Iq7T6L7Lxsrv3hWo6lH6NF1RZMvWEqvl6+pfLs8wnyCWLmzTPpWLMjJ7NOcvGki9maYP5uWSIi4n4qLETkNNoRyj1Ku7B49M9HWXlkJeF+4Uy/cTqhfqGl8tzCCPYN5o9b/qB99fYkZiZy6deXEpcaZ3YsERFxMxUWInIa7QjlHs4G7k2bsOXnl+izvt7wNR+v+hiAr67+ijphdUr0ecUR6hfKzFtm0qBSA/Yn72fANwNIyU4xO5aIiLiRCgsROY12hHKP0Pr18fL3Jy8jg5S9e0vsOTsSdzDst2EAjOw+kgENBpTYs1xVOaAyf9z6B1UCq7Du6Dqu/eFacvNzzY4lIiJuosJCRJzsdruWQrmJxWqlkuOgvBJaDpVny+P2abeTkZtB77q9GdVjVIk8x53qhddjxs0zCPQOZPae2Tz656NmRxIRETdRYSEiThlHj5Kblobh5UVw7dpmxynzSrrP4s0lb/L3ob8J8Q1h4pUTTdlWtjjaVW/H11d/DcCHKz/ks9WfmZxIRETcQYWFiDg5ZitCatfG6uNjcpqyz9FncbwECov1R9czakHBDMUH/T8gOjTa7c8oSVc2upIXe70IwP0z72fxgcUmJxIREVepsBARJ2d/hZZBuUXlU4VF8q5d5GVkuG3c3Pxcbp92O7m2XK6IvYLBLQe7bezS9Ey3Z7iuyXXk2nK55odrOJJyxOxIIiLiAhUWIuKkHaHcy79qVfwjI7Hn53Niq/vObvhg+QesP7aeSv6VGHfZOAzDcNvYpckwDL648gtaVG1BfHo8t0+/3exIIiLiAhUWIuLkbNxWYeEWhmH8s+2sm5ZDHUw+6FwC9WbfN6kaVNUt45ol0CeQn677iRDfEJYdXGZ2HBERcYEKCxEBwG6zkbx7N6ClUO7kaOB2V5/FQ388RHpuOl1rdeWOVne4ZUyzNYhowBdXfmF2DBERcZEKCxEBIO3QIfKzsrD4+BAUXbYagT2Zo88iceNGl8f6bcdvTN02FS+LF58M/ASLUX6+hF/d+GqGXzTc+fGh5EMmphERkeIoP/8qiYhLnI3b9ephsZaNbUvLgkpNm4JhkBEXR2ZCQrHHyc7L5qE/HgLg0Y6P0qxKM3dF9Bgv9HrB+fuhvw0l31ayJ5aLiIh7qbAQEQCSduwAtAzK3byDggiNiQFcm7X4YPkH7Dm5h+rB1RnZY6S74nkUb6u38/eL9i/iraVvmZhGRESKyuXCIsGFn8CJiOc4uX07AOGxsSYnKX9cPc8iPj2elxa9BMArvV8hyCfIbdk82bPzn2X1kdVmxxARkUJyubDo1asX99xzD7NmzSI3N9cdmUTEBEmnCoswFRZuV9nFE7ifX/A8KdkptIlqw20tb3NnNI91ZeyV5NnyuHnKzaTnpJsdR0RECsHlwiI/P58FCxbw0EMP0a1bN15++WW2unG/dhEpeXkZGaQeOABoxqIkOLec3bQJW37R+gY2x29m7OqxALzT751y1bB9PmMGjKFGcA12JO7g0T8fNTuOiIgUgsv/Qs2fP5+HH36Y2rVrk5SUxOTJk7n66qsZNGgQkydP5uTJk+7IKSIlKGnnTrDb8atcGb+ICLPjlDuhDRrgFRhIXno6yad6WQprxJwR2Ow2BjUaRI86PVzKkZqdyvy98/l8zec8Pfdpnpj9xGnvf7nuS8atHsfcPXNJzEh06VmuquRfiUmDJmFgMG7NOKZtm2ZqHhERuTDDbrfb3TXY+vXrmTJlCn/88QfJyckYhoGXlxe9evXi6quvpnv37lgshatl0tLSaNu2LatXryYoqGKsJxYxy84ffmDl6NFEdelCr3HjzI5TLs0fNoy4JUto+/TTxN5yS6HuWXJgCV2/6IrVsLLl/i00jGhY5Odujt/MT1t+Ys7eOfx96G/ybHnO9xpUasCO4f8UOs0+bsbmhM0AGBi0iWpD33p9uTz2cjrV7FTiJ3ynp6c7v96npaURGBjIiNkjeGPpG0T4R7Dh3g1UD65eohlERKT4vNw5WMuWLWnZsiXPPPMM8+bNY/r06SxdupRZs2Yxe/ZsIiIiuOqqqxg0aBAxp3ZJERHzndy2DVB/RUmKbNOGuCVLSFi7tlCFhd1u5+l5TwMwpPWQYhUVdrudq76/il0ndjlfqxNWh8aVG1MvvB4tq7Y87frLGl5GnbA67Ejcwc4TO1kdt5rVcat5bclrdInuwqI7F5V4cfFfL/Z+kTl757Ambg1Dfx3Kbzf9VuoZRESkcNxaWDj4+PjQv39/+vfvT2ZmJt9//z0ffPABiYmJjB8/nvHjx9OmTRvuvPNO+vTpUxIRRKQIHI3b4Y0amZyk/Ips0waAhNWrsdvtF/zmeNbuWfy1/y98rb481+O5Qj0j35bP1G1TGdRoEFaLFcMwuL/9/czfN5+BDQbSt15f6obXPef9r/V5zfn7uNQ45uyZw5+7/2Tatmm0q97utMwp2SmE+IYUKpcrfKw+TB40mdZjWzNz50wmrZ/E7a1uL/HniohI0bl1KdS/HTp0iN9++41Zs2axdetWHI9p1KgRx48f5/jx4xiGQdeuXXn//fcJCAg47X4thRIpHXabjR8vuoi8zEwGTp9OaP36Zkcql/IyM/mxY0fseXlcMWsWQTVqnPNam91G+8/asyZuDY92fJS3L3n7guPP3j2b/5v9f2w4toEJV0zgztZ3ui17SnYK2XnZRAZGArD4wGIGfD2Axzo9xuNdHifAO+ACIxTO2ZZCOby2+DWemvsUob6hbL5vMzVCzv3fT0REzOHW7UXS0tL44YcfuOWWW+jbty/vv/8+W7ZsISQkhFtvvZVp06Yxbdo0Fi5cyAcffEBERASLFy/m5ZdfdmcMESmCtIMHycvMxOLjQ3CdOmbHKbe8/P2p1KQJAAlr1pz32ilbp7Ambg1BPkE82fXJ816bmp3K0F+G0u+rfmw4toEwvzDy7e49sTrEN8RZVABMXj+Z1JxUnl/4PM0/ac6cPXPc+ryz+b/O/8dFNS4iOTuZYb8No4R+JiYiIi5wy3az8+bN46GHHqJLly6MGjWK1atXYxgGnTt35p133mHRokU8++yzNDq1zMJqtdKvXz9ee+017HY7c+aU/D9KInJ2joPxwho0wOJVIqsj5ZTI1q2B8xcWNruN0QtHA/BIx0dO+4b+vxbtX0TLT1vy+drPAXig/QPsGr6Lu9rc5cbUZ/rksk/4/trvqRlSkz0n99B3cl/umHYHSVlJJfZML4sXX1z5BT5WH+eSKBER8SwuFxZdu3bl/vvv588//yQ7O5uaNWvy4IMPMm/ePMaPH8+AAQPw8fE567316tUDIC8v76zvi0jJ08F4pcfZZ3GewmLq1qlsit9EiG8Ij3R85JzXjVs9jp5f9mRv0l5qhdZi/u3zGTNgDBEBJb9dsMWwcH3T69l832YeaP8ABgZfrv+S1mNbs/LwyhJ7bpPIJozuWVB0PfTHQxxOOVxizxIRkaJzubA4efIkvr6+XHHFFXz55ZfMnj2b++67j2rVql3w3uzsbK6//npGjBjhagwRKSbHjlA6GK/kOWYsknftIjsp6Yz3bXYbL/z1AgAPXvQg4f7h5xyrWZVmeFm8uK3FbWy8dyM96/QsicjnFeIbwpgBY1gyZAl1w+qyL2kfK4+UXGEBWhIlIuLJXG7e/v777xk4cKDbG6zVvC1SOqb16UNGXBx9vvySKu3amR2n3Pt14EBS9+2jx0cfUaNnz9Pem7ZtGoO+H0SwTzD7Ht5HJf9K5x1rS8IWGldu7BHbryZlJfHlui95sMODxc5zvubtf9uSsIXWY1uTk5/DxCsnapcoEREP4fKMRUJCArNmzSrUtZ988gmPP/64q48UETfJSU4mIy4OgLCGRT8nQYrOuRxq7drTXrfb7bywsGC2YvhFw88oKuLT4+n9ZW82HtvofK1JZBOPKCoAwvzCeKjjQ848yVnJ3PXLXRzPOO72ZzWJbMILPQv+Wz30x0McST3i9meIiEjRuVxYfPjhh/z888+FunbWrFlq1BbxICd3FJy6HFi9Oj4hJX8mgZy7z2LGzhmsPbqWIJ8gHu306GnvHUk9Qo+JPZi/bz6Dpw0uE8t/hv46lPFrx9Px845sP77d7eM/1vkx2ldvT3J2MsN/H+728UVEpOiKtAXM4cOHWbZs2RmvHz9+nJ9++umc99ntdo4cOcLOnTvPOK9CRMyjxu3S5ygsEjduJD87G6uvLwCvL3kdgHvb3XtaA/bB5IP0+rIXu0/uJjokmh+u/cFjZinO5/mez7PyyEp2n9xNty+6Meu2WbSq1spt43tZvPj8is9pO64tU7ZOYcrWKVzd+Gq3jS8iIkVXpMIiIiKCMWPGEB8f73zNMAwOHDjAyJEjL3i/3W6nU6dORU8pIiVCjdulL7hWLfwiIshKTOTE5s1EtmnD0oNLWXxgMT5WHx7u+LDz2sSMRPp91Y/dJ3dTN6wu826fR52wOqZlL4omkU1YftdyLv36UtbEraHXl73445Y/6FCzg9ue0aJqC0Z0GcHLi17mgZkP0Ltub8L8wtw2voiIFE2RlkL5+fnxf//3f0RFRTl/2e12vL29T3vtv79q1KhBgwYNuOqqqxg1alRJfS4iUkQnNWNR6gzDOOM8izeWvAHAbS1uo3pwdQDSc9IZ+M1Ath3fRs2Qmiy4Y0GZKSocqgRWYd7geXSO7kxSVhJ9Jvdhwb4Fbn3Gs92fpWFEQ+LS4hgxWzsMioiYqcinYV1++eVcfvnlzo8bNWpE8+bN+frrr90aTERKli0vj+RduwAIP3V4pZSOyDZtODhnDvFr1mAkbGX69ukYGDze+Z/NLZ6b/xzLDy+nkn8l/rz1T2qF1jIxcfGF+oXy561/cuV3VzJv7zzunH4n2x/Yjo/17OcbFZWflx+fXf4ZPSb2YNyacdzc/GZ61OnhlrFFRKRoXG7efuCBB7j6aq1rFSlrUvbtw5aTg1dAAEE1a5odp0Jx9FkcX7uWNxcXzFZc2ehKYiv/M3P0fM/nuSL2Cn676TeaRDYxJae7BPkEMePmGdze8nZm3jzTbUWFQ/fa3bm77d1AQdN4Zm6mW8cXEZHCKfKMxX898MAD7sghIqUs6VR/RVjDhhgWl3/GIEUQ3qgRVn9/clJSmLfkRwiGEV1OX8YT7BvM9Bunm5TQ/fy8/Jh41cTTXsvJz3FbkfF6n9f5Zfsv7Dyxkxf/epFXLn7FLeOKiEjhFamwcOwI1aZNG3xP7WRytl2iLkQN3CLmc/RXqHG79Fm8vancogXHli+n/jEfajdrS8eaHVmwbwHrjq7joQ4PlYmdn1wxd89chv46lNm3zSamUozL44X6hfLxwI8Z9P0g3ljyBjc0vYGW1Vq6IamIiBRWkQqLO++8E4vFwowZM6hbt67ztaL8A2gYBlu2bClaShFxO8eOUGHqrzBFcMumHFu+nEYJAQzoMoL9Sfu57sfrOJ5xnADvAIa1HWZ2xBJjt9sZOX8ke5P20v/r/iwdspTIwEiXx72q0VVc2+RaftryE3f9ehfL/rcML4vLE/MiIlJIRV7/YLPZznjNbrcX+tfZ7heR0pekGQtTLQ4+CECLxFB61e7FNT9cw/GM47SJasNtLW4zOV3JMgyDKTdMoU5YHXad2MVl315Gek66W8Yec+kYwvzCWHVkFR8s/8AtY4qISOEYdg89wjUtLY22bduyevVqgoKCzI4jUq5kJiQwtWdPDIuF65Yvx0sHV5aqrLwsYt6uw0tfh+Obb2Hj4515bf/nRPhHsObuNWV2B6ii2n58O50ndOZE5gkGNhjI15d/TVhIGFDwb0BgYGCxxh2/Zjx3/XoX/l7+bLpvE/XC67kxtYiInIs6NkUqoBObNwMQUreuigoTTFo/iSNZxzhQrWAGd/WcnwGYeNXEClNUAMRWjuW3m37D38ufGTtn8OScJ90y7pDWQ+hVpxeZeZnc89s9eOjPz0REyp0SLSyysrKYN28ec+bMISkpqSQfJSJFcOJUn1Olpk1NTlLx2Ow23lr6FgDhbQsOymsaH8jDHR7msoaXmRnNFJ2iO/H11QXnIH266lO3jGkYBmMvG4uflx+z98xm0vpJbhlXRETOzy2FxbFjx3j++ecZN26c87Xdu3fTr18/7r//foYPH07v3r2ZOXOmOx4nIi5K3LQJUGFhhpk7Z7LzxE7C/MIIadMCgGbHQ3i118smJzPPoMaDeKX3K27tLWkQ0YDnezwPwGOzHuN4xnG3jS0iImfncmFx4sQJrr/+er7//nvWrl3rfP25554jPj4egMDAQDIyMnjiiSfYvXu3q48UERed1IyFad77+z0AhrYZyuBBT2AE+OGfDRm795may2xPdn2Sjwd+7NYxH+30KC2qtiAxM5HHZj3m1rFFRORMLhcWX375JceOHaNWrVrccMMNAOzfv5/Vq1djtVr59ttvWbVqFcOGDSMvL4+JEye6+kgRcUFGfDyZCQkYFgvh2mq2VG08tpG5e+diNaw8cNEDWLy8iGrfAYBjy5ebnM5chmGctnX5+3+/T2p2qktjelu9GXfZOAwMJq2fxNw9c12NKSIi5+FyYfHXX3/h5eXF+PHj6dmzJwALFiwACg7Sa9WqFQDDhw8nJCSEv//+29VHiogLnI3bMTF4+fubnKZieX/5+wDUDqvt/Ka5WoeCwuJoBS8s/uuZec9w5/Q7XW687lCzA/e1vw+Ae2bcQ2ZupjviiYjIWbhcWBw8eJA6depQs2ZN52tLly7FMAw6d+7sfM3b25uaNWs6l0eJiDkchUWlJk1MTlKxJKQn8NWGrwDYc3IPvSf1JjM3k6qnCouE1aux5eaaGdGjeFm8+Hnrz7y59E2Xx3rl4leoHlydXSd28fKiitvLIiJS0lwuLLKysvDx8XF+nJeXx8qVKwG46KKLTrs2MzOzSKd0i4j7aUcoc4xbPY7s/GwMCr4Gvnbxa/h7+xPWsCG+YWHkZWSQeKroE3irX8HOWU/NfYo5e+a4NFaIbwhjLh0DwOtLXmdzvP47i4iUBJcLiypVqnD48GFyT/2kbeXKlWRkZBAYGOhcBgUFO0cdPHiQqKgoVx8pIsVkt9s5oR2hSl1Ofg4frfwIADt2Lom5hDta3QGAYbFQpX17QH0W//a/1v/jzlZ3YrPbuPGnG9mftN+l8QY1GsQVsVeQZ8vj7t/uxma3uSmpiIg4uFxYdOjQgZSUFN566y22bdvGe++9h2EY9OjRA6vVCkBiYiKPP/44+fn5dOrUyeXQIlI8mfHxZCUmYlithMfGmh2nwvhx84/EpcUB4Gf145OBn5w2e+tYDqXC4h+GYfDRgI9oG9WWxMxErv7hapf6IwzD4MNLPyTIJ4glB5fw+ZrP3ZhWRETADYXF0KFD8fPzY9KkSQwaNIj169djtVoZOnQoAKtWraJHjx6sXLmS4OBghgwZ4nJoESkeR39FqBq3S43dbnceiAcwutdo6obXPe0aZ5/F2rXkZ2eXaj5P5u/tz8/X/0yEfwRbE7ay9ujaC990HtGh0bzU6yUAnpj9BEfTjrojpoiInOJyYVGvXj0mTJhA8+bN8fHxoWHDhnzyySc0OrWNZZUqVcjLy6NBgwZ8++23pzV5i0jpcjZuaxlUqVl6cCnrjq0DoElkEx7p+MgZ14TUrYt/ZCS2nByOr1tXugE9XO2w2vx8/c/8fdffdI7ufOEbLuCBix6gXfV2JGcn8/AfD7seUEREnLzcMUjr1q354YcfzvpezZo1mTZtmrPQEBHzJGpHqFL33vL3AOhQowPv938fb6v3GdcYhkHVDh3Y99tvHF2+3DmDIQV61OnhtrGsFivjLhtHu8/a8f3m77m95e1c2uBSt40vIlKRuTxjccEHWCwqKkQ8gN1u14nbpWx/0n6mbJ0CwGeXf0aHmucuGBzFRPyKFaWSraxadnAZfSb1cenwvNZRrXm4w8MA3DvjXtJz0t2UTkSkYnNrYZGZmUl8fDxxcXEcOXLknL9EpPRlHD3qbNwOU+N2qRg5fyQ2u43edXvTvGrz817rKCyOb9xIbrq+0T2b3Pxcbp16K3P3zuXu3+526fC80b1GUyu0FvuT9/P8gufdF1JEpAJzy1KohQsX8t5777Ft27YLXmsYBltO/dRUREqP4/yK0JgYvPz8TE5T/u09uZfJGyYDcHOzmy94fVCNGgRFR5N28CDHVqygZq9eJR2xzPG2ejN50GS6f9Gdbzd9S686vRjadmixxgryCeLjAR9z2beX8e7f73JLi1toVa2VewOLiFQwLs9YrFq1ivvuu49t27Zht9sv+Mtm097hImZwnl/RrJnJSSqGW6bcAoCv1ZfBLQcX6p6oLl0AiFu8uMRylXWdozvzysWvAPDgHw+y4diGYo81sOFArmtyHfn2fIb+OpR8W767YoqIVEguz1h8/vnn5OfnExsbywMPPEC9evXw009DRTyO88RtNW6XuBWHV7Ds0DIA7m1/71kbts8mqmtXdn73HXFLlpRkvDLv/zr/Hwv3L2Tmzplc9+N1rBq6imDf4GKN9X7/95m1exarjqzio5Uf8WCHB92cVkSk4nB5xmLt2rX4+voyfvx4+vbtS0xMDDVq1DjvLxEpXXa7XVvNlhK73c6d0+8EwNvizQs9Xyj0vVUvugiLlxdpBw+Sut+1k6bLM4th4curvqRGcA12JO7g3hn3FrvfIio4itf6vAbAM/Oe4WDyQXdGFRGpUFwuLDIzM4mJiaFy5cruyCMiJSAjLo7skycxvLx04nYJm7J1ClsSCmaHbm91e5F+ku4dGEhkmzYAHNFyqPOqHFCZ7679DqthJSM3g5z8nGKPNaztMDpHdyYtJ43hvw93Y0oRkYrF5cKievXqJCYmuiOLiJQQx2xFWP36WH19TU5TfmXnZfPwnw8DYGDwdNenizxGVNeuAFoOVQhda3Xl77v+5ufrf8bXq/h/ri2GhbGXjcXL4sX07dOZtm2a+0KKiFQgLhcW/fv3Jz4+nmXLlrkjj4iUgBM6v6JUJGUlYWAABY3BdcPrFnkMR2FxbMUK8rOz3ZqvPGpXvR2GUfDf3G63k5ufW6xxmlVpxhOdnwDggZkPkJKd4raMIiIVhcuFxd133039+vV54oknmDNnDjk5xZ+OFpGSkbhxI6DCoqR5W705nnEcgP/r9H/FGiOsYUP8IyPJz8wkYc0ad8Yr11KyU7h5ys3c9etdxR7j2e7PUr9SfQ6nHubZec+6MZ2ISMXg8q5Qzz77LNWqVWPnzp0MHz4cq9VKaGgo3t5n3wXFMAzmz5/v6mNFpJBs+fkc31CwJWflli1NTlO+fbb6MzLzMmlZtSXda3cv1hiGYRDVtSt7pk7lyOLFVOvUyc0py6eNxzbyw+YfsNlt9KrTizta3VHkMfy9/fl04Kf0mdyHD1d8yK0tbuWiGhe5P6yISDnl8ozFjBkzWLRoEVAwDZ2Xl0diYiJHjx495y8RKT0pe/aQl56Ol78/ofXrmx2nXDqQfIDhM4czZsUYAB7q8JBzeU5xqM+i6LrU6uLcgeu+GfexOX5zsca5uN7F3NbiNuzYGfbrsGIvrRIRqYhcnrF49dVX3ZFDRErI8XXrAIho3hyLl8t/5eUsRi8YzYR1EwCIDIjkpuY3uTRetY4dMSwWknfuJOPoUQKqVXNHzHLvqW5PsXD/Qmbvmc31P13PirtWEOgTWORx3u73NjN2zmD9sfW89/d7PN7l8RJIKyJS/rj8XcagQYPckUNESohjGVSElkGViK0JW5m4fqLz47vb3o2fl2uHhPqGhVGpeXMS168nbskSYq65xsWUFYPFsDB50GRajW3FloQtDP99OBOunFDkcSIDI3mr71sM+WUIoxaM4tom1xarEV9EpKJxeSmUiHg2x4yF+itKxsj5I7HZbQB4Wby4t/29bhm3+qnlUDrPomiqBlXlm6u/wWJY+GLdF0xeP7lY49zR6g561ulJZl4m9828r9gH8ImIVCRuKyxycnL47rvvuPvuu+nbty8dO3YE4MSJEzz11FPs2rXLXY8SkULKSUkhZc8eACq3aGFymvJn5eGV/Lz1Z+fH1ze9nurB1d0ytqPP4uiyZdjy8twyZkXRq24vnuv+HFFBUUSHRhdrDMMw+HTgp/hYffhj1x98v/l7N6cUESl/3FJY7N27lyuuuILRo0ezcOFCDh48SHJyMgBHjhxh6tSpXHPNNcyZM8cdjxORQnIsgwqKjsYvIsLkNOXP0/MKDsCzGAVfSh/q8JDbxq7UtCk+oaHkpqaSeOr/oxTes92fZcO9G+hZp2exx4itHMsz3Z4B4JE/HyE5K9lN6UREyieXC4vU1FT+97//sW/fPqKiorjzzjupVauW8/3g4GDq1atHdnY2Dz/8MDt27HD1kSJSSInaZrbEzN0zlzl75mDBgs1uo1PNTm7dmtRitRLVuTOg5VDFYbVYqRxQ2fnxsbRjxRpnRJcRNKjUgKNpRxm1YJS74omIlEsuFxYTJ07kyJEj9OzZk99//50RI0ZQufI/X8xr167Nr7/+Sp8+fcjLy+OLL75w9ZEiUkjH168HVFiUhBZVW/BA+wfw8y5o1HbnbIWDYznUkb/+cvvYFck3G78h5oMYvt34bZHv9fXy5cMBHwIwZsUY1h1d5+Z0IiLlh8uFxaxZs/Dy8uLll1/G19f3rNdYrVZeeOEFfHx8WL58uauPFJFCsNtsOhivBEUGRtKuejsycjOoEVyDqxtf7fZnVO/WDQyDk1u3kqEzgIpta8JW0nPTGfbbMHYkFn3WvF9MP65vej02u437ZtznbNYXEZHTuVxYHDp0iAYNGhBxgfXblSpVom7duiQkJLj6SBEphJR9+8hNScHq50dYw4Zmxyk3HLsD2e123l/+PgD3t78fb6u325/lFxHhLAoPL1jg9vErilE9R9G9dnfSctK4/sfrycrLKvIY7/R7hyCfIJYdWsYXazXzLiJyNi4XFoZhkJVVuC/SNpsNHx8fVx8pIoXgWAZVqWlTLN7u/6a3opq4biJ9J/dl/JrxrD26Fj8vP4a1HVZiz6vZqxcAh1RYFJuXxYtvrv6GygGVWX9sPY/9+ViRx6gRUoPRPUcDMGLOCBIzEt0dU0SkzHO5sKhduzYHDx684ExEXFwcu3fvpnbt2q4+UkQKIdHRX6FtZt0mJz+H0QtHM2fPHD5Y8QEAtza/lYiAkttxq0bPngAc+/tvctPTS+w55V2NkBpMHlRwpsXHqz7mpy0/FXmM4RcNp3mV5iRmJvLU3KfcHVFEpMxzubBwNGW/8MIL5zxAKCcnh2eeeQa73U7v3r1dfaSIFIKzcbtVK3ODlCMT101kf/J+IgMi2ZywGYAHOzxYos8MiYkhKDoaW24uR5cuLdFnlXf96/dnRJcRAPzvl/+xL2lfke73tnrz8cCPAfhszWf8fehvd0cUESnTXC4sbr/9dqpXr86cOXO47rrrmDBhAomJBVPECxcu5PPPP+fyyy9n6dKlVK5cmdtuu83l0CJyfrnp6STt3AmocdtdcvJzeHnRywA0r9Icm91G77q9aV61eYk+1zAMapxaDqU+C9e92OtFutbqyj1t76FGcI0i39+1VlfubHUnAPfOuJc8mw4vFBFx8HJ1gKCgID777DPuvfdeNm3axObNm53v3XPPPUBBk2NkZCSffPIJoaGhrj5SRC4gceNGsNsJrF4d/8hIs+OUCxPWTuBA8gGqBlZlzdE1QMlsMXs2NXv2ZPukSRxeuBBbfj4Wq7VUnlseeVu9mTd4nkvN9q/3eZ1p26ax7ug6xq0ex33t73NjQhGRssstJ2/HxMQwffp0nnrqKdq1a0doaChWq5WgoCCaN2/OQw89xIwZM2jWrJk7HiciF+BYBhWh2Qq3yM7L5pVFrwDQvXZ3krKSiAmPYWCDgaXy/Mg2bfAOCSH75Eln74wU37+Lipz8HNYfLdp/08jASF7s9SIAI+eP5ETmCbfmExEpq1yesXDw9/fn9ttv5/bbb3fXkCJSTDoYz72+3vg1B1MOEhUUxYZjBWeDDL9oOFZL6cwcWLy9qd6tG/tnzODwggVEtmlTKs8t7+LT47ni2yvYnridtXevpU5YnULfe3e7uxm7eiwb4zfy3PznnIfoiYhUZC4VFjk5OaxatYrly5cTFxdHUlIShmEQEhJCTEwMbdu2pV27dhiG4a68InIBdrv9nx2hVFi4xa0tbiU3P5edJ3by9rK3CfYJ5s7Wd5Zqhho9e7J/xgwOzZ9Pq0cfLdVnl1fhfuEAJGUlccNPN7DozkX4WAu3JbqXxYv3+79P70m9+WTVJ9zd9u4S77cREfF0xSoscnNzmTRpEp999hnJycnO1+12+xlFRJUqVRg2bBg33XQTFotbVl6JyHmkHjhAdlISFh8fwhs1MjtOueBj9eHudnfT+8uCXe2GtR1GiG9IqWao3rUrhpcXKXv2kLp/P8Hauttl3lZvvr/2e1qPbc2Kwyt4YvYTvNf/vULf36tuL65pfA0/b/2Zh/54iLmD5+oHaSJSoRX5O/20tDT+97//8dZbb5GUlITdbicwMJCGDRvSpk0bmjVrRu3atbFardjtdo4dO8ZLL73E0KFDycjIKInPQUT+JWFNQWNxpSZNsOpASpfk5uc6d/1ZE7eG+fvm42XxKrWm7X/zCQmhSrt2gHaHcqfaYbWZNGgSAO8vf5+ft/xcpPvf6vcWfl5+zN83nylbp5RERBGRMqPIhcVDDz3EihUrsFgs3HzzzUyfPp1Vq1Yxffp0vvnmG3788Uf++OMP1qxZw1dffcWVV16JYRgsXbqUJ554oiQ+BxH5l4RVqwCo0r69yUnKvnGrx9How0ZM2TqFt5e9DcD1Ta8nOjTalDzOU7jnzzfl+eXVZQ0v44nOBf8+DfllCLtO7Cr0vXXC6vB458cBeGzWY2TmZpZIRhGRsqBIhcX8+fNZsmQJQUFBfPnllzz33HPExsae9VofHx/atWvH66+/zsSJE/H392fu3Ln8/bcOFBIpSfGrVwNQpW1bk5OUbVl5Wbyy+BV2n9zN1oStfL/pewAe6/SYaZkcp3AnrFlDzr+WoYrrXur9El1rdSUlO4W7frmrSPeO6DKCmiE12Z+8n7eWvlVCCUVEPF+RCotffvkFwzCc28oW1kUXXcRjjz2G3W7n119/LXJIESmcjGPHSDt4EMNiIbJ1a7PjlGmfr/mcI6lHqBlSk+MZx8m359OrTi/aRJm3I1NQzZqENmiAPT+fwwsXmpajPPK2evPdNd/Rp14fPrv8syLdG+gTyBt93gDg1cWvcjD5YElEFBHxeEUqLLZs2YKPjw9XXHFFkR80aNAgrFYrGzZsKPK9IlI4jtmKsEaN8A4KMjlN2ZWVl8Wri18F4JEOjzB+7XjA3NkKh+iLLwbg4Jw5Jicpf2qE1GD2bbNpENGgyPfe2OxGutbqSmZeJiPmjCiBdCIinq9IhUVCQgLR0dF4exf9xNKAgABq1qxJXFxcke8VkcJx9ldoGZRLPlv9GUdSjxAdEk2uLZfUnFQaVW7EpQ0uNTsa0X37AhC3eDG56ekmpynf5u6Zy7bj2wp1rWEYfND/AwwMvt30LSsPryzhdCIinqdIhUV2djZBLvwUNDQ0lMxMNbaJlBRnf0URlirK6TJzM52zFSO6jOCjlR8BBbMVFsP8LbPDYmMJio4mPzubuMWLzY5Tbn294Wv6Tu7LtT9cS0Zu4XY0bB3Vmltb3ArAE3OewG63l2REERGPU6R/JfPz87Fai3/SrJeXFzabrdj3i8i5ZZ08SfKugt1sdDJz8f2641fi0uKoFVqLEN8QDqYcpEpgFec3jGYzDIPoPn0AODB7tslpyq+L611M1aCqbE7YzP0z7y/0fS/2ehFfqy8L9i3g912/l2BCERHPY/6P30TELRznV4TGxOBXqZLJacqu65tez/zb5/PhpR/y3vL3ALi//f34efmZG+xfovv1A+DIwoXkZ2ebnKZ8qhZUjW+v+RaLYWHiuol8sfaLQt1XO6w2wy8aDsCIOSPIt+WXZEwREY+iwkKknIg/1V8RqWVQLutZpyfBvsGsiVuDn5cf97W/z+xIp4lo1oyAatXIy8ggbskSs+OUWz3r9OTFXi8CcN/M+9hwrHCbjzzd7WnC/cLZFL+JSesnlWREERGP4lXUG1JTU1m5snhNaampqcW6T0QuLEHnV7gkMzeT9Nx0KgdUBuD1Ja8DcEfLO5yveQrDYqFmnz7s+OorDs6eTc3evc2OVG492fVJFh1YxB+7/uC6H69j1dBVBPsGn/eecP9wnu72NI/PfpyR80dyY7Mb8ff2L6XEIiLmKXJhsXPnTgYPHlwSWUSkmHLT0ji5dSugwqK4Pl31KSPnj+Sl3i/Ro3YP/tj1BxbDwv91/j+zo51Vrb592fHVVxxasABbbi6WYuzWJxdmMSxMHjSZ1mNbsyNxB19t+Ip72997wfseuOgBxqwYw4HkA7y//H2e7PpkKaQVETFXkZdC2e12l36JiPslrF2L3WYjKDqagGrVzI5T5mTkZvD6ktdJz00n2CeY15a8BhT0W8RUijE53dlVbt0av4gIclNSOLZihdlxyrXKAZX54dofGHfZOO5pd0+h7vHz8uOlXi8B8Nri10jMSCzJiCIiHqFIMxZz584tqRwi4gJH47ZmK4rnk5WfcCz9GHXD6tIpuhPDfhsGwJNdPPenzBarlZq9e7Prxx85OHs2UV26mB2pXOsU3YlO0Z2KdM8tLW7h7WVvs/7Yel5e9DLvXPJOCaUTEfEMRSosatSoUVI5RMQFzsZtFRZFlp6TzhtL3wDg2e7P8u6yd7HZbQxoMICW1VqanO78ovv2ZdePP3Jo3jzajRyJxYXtwKXwTmSe4Om5T/Nan9cI8ws753UWw8Ibfd/gkq8u4cMVHzL8ouHUDa9bekFFREqZdoUSKePysrJI3LgR0IxFcXyy6hPi0+OpF16P3nV68+X6LwF4uuvTJie7sKoXXYRPSAhZiYnOWSspedf+cC1jV4/lzul3XnCJb7+YfvSp14dcWy7PL3y+dAKKiJhEhYVIGZe4cSO23Fz8IyMJqlXL7DhlSnpOOm8sKZitGNl9JGNWjCHXlku3Wt3oUsvzlxZZvL2p0asXAAd1WF6peaPvG/hYfZi2bRrv/f3eBa9/pfcrAHy14St2Ju4s4XQiIuZRYSFSxv37/ArDMExOU7YsObiEpKwkYsJjuLT+pYxdPRaAp7o+ZXKywnOcwn1wzhzsNpvJaSqGdtXb8e4l7wLwxJwnWHZw2Xmvb1+jPQMbDMRmt/HiXy+WRkQREVOosBAp43R+RfH1i+nHrgd3MWnQJD5d9Snpuem0qtaK/vX7mx2t0KK6dME7KIjMY8dIWLvW7DgVxr3t7uWGpjeQZ8vj+p+u53jG8fNe/3zP5wH4euPXbD++vRQSioiUPhUWImVYfna285vJKu3bm5ymbKoVWosWVVvw/vL3gYKdoMrSzI/V19d5QN7+3383OU3FYRgGn13+GQ0jGnIo5RCDpw7GZj/3jFG76u24vOHlmrUQkXJNhYVIGXZ83Trys7Lwq1yZ0BjPPG/BE6Vmp7I27p+f7o9bPY6TWSepX6k+1za51sRkxVPr0ksBODhrFrb8fJPTVBzBvsH8eN2P+Hn5sSVhC0dSj5z3esesxbebvmXb8W2lkFBEpHSpsBApw47+/TcA1Tp1KlM/ZTfbmBVjaDOuDY/88QjZedm8vextAEZ0GYHVUva2bK3WsSM+oaFkJSYSv3Kl2XEqlBZVWzD9xumsuXsNNUNqnvfaNlFtuCL2Cs1aiEi5pcJCpAw7uqygabRax44mJyk7UrJTnIVE2+ptmbhuIkdSj1A9uDq3tbjN5HTFY/XxcTZxH/jjD5PTVDz9YvpRyb9Soa59vsfzAHy78Vu2JmwtwVQiIqVPhYVIGZWTksKJzZsBFRZFMWb5GE5kniA2IpZrGl/Dy4teBgp6K3y9fE1OV3y1Ty2HOjBrFrbcXJPTVEx2u53PVn/G9T9ef87zLVpHteaqRldhx84Lf71QyglFREqWCguRMurYypXYbTZC6tYloFo1s+OUCclZyc7Ziud6PMek9ZM4mHKQqKAohrYdanI611Rp3x6/iAhykpOdS+SkdO1P3s/w34fz45Yf+XTVp+e8zjFr8f2m79mSsKWU0omIlDwVFiJllGMZVFXNVhTaB8s/4GTWSRpXbsygRoP+ma3o+iR+Xn4mp3ONxcuL6L59AS2HMkudsDq83ud1AB6b9dg5t5VtWa0lVze+umDWYqFmLUSk/FBhIVJGHftX47ZcWFJWEu/8/Q5wltmKNmV7tsKh9oABABycO5f8nByT01RMwzsMp2+9vmTmZXLLlFvIzT/7srRRPUYB8MPmH9RrISLlhgoLkTIoPS6OlL17MSwWqur8ikLZfnw7fl5+NIlswpWxV/LK4leAgtkKf29/k9O5R2Tr1vhXrUpuaipxixebHadCshgWvrjyC8L9wlkdt5rRC0ef9boWVVs4ey3eXPpmKacUESkZKixEyiDHbEWlZs3wCQkxOU3Z0KFmB/Y8uIcp109h8obJHEg+UK5mKwAMi4Va/foBsF/LoUxTI6QGYy8bC8Cri19lyYElZ71uRJcRAHy14SsOpxwutXwiIiVFhYVIGeQ8v0L9FUXi7+1P3fC6zt6KEV1GlJvZCgfHcqjD8+eTl5lpcpqK67qm1zG45WAAVh1ZddZrOtbsSPfa3cm15TpPfhcRKctUWIiUMXa7XYVFERzPOM43G7/BZrcBMHHdRA4kH6BaUDWGtR1mcjr3i2jenMAaNcjLyODIX3+ZHadC+6D/Byy6cxEPdXzonNc80fkJAD5d9SlJWUmllExEpGSosBApY5J37ybr+HGsfn5UbtXK7Dge79VFr3LLlFsYPHUwOfk5vLLoVG9Fl/LTW/FvhmFQu39/APbNnGlymoot1C+UztGdz3vNgAYDaFalGak5qYxdNbaUkomIlAwVFiJljGOb2cg2bbD6lt0D3UrDweSDfLTyIwBubXErX677kv3J+8vtbIVDncsuA+DIwoXkJCebnEYAtiZs5fJvLychPeG01w3D4PHOjwPw3vL3yM7LNiOeiIhbqLAQKWMchYW2mb2wFxa+QHZ+Nt1rd6dn7Z68tOgloPzOVjiENWxIWMOG2HJzOTB7ttlxKjy73c7t027ntx2/8eAfD57x/o3NbqRmSE2Oph1l8obJJiQUEXEPFRYiZYgtN5f4lSsBFRYXsv34diasmwDAqxe/ymdrPuNA8gGqB1cv17MVDnUGDgRg32+/mZxEDMPgk4GfYDWsfLfpO6Ztm3ba+z5WHx7t+CgAby5909kPJCJS1qiwEClDEjdtIi8jA9+wMMJjY82O49FGzh+JzW7jsoaX0bJqS+dsxcjuI8v1bIVD7VOFRfzKlaTHxZmcRtpWb+tc8nTvjHs5kXnitPfvanMXYX5h7EjcwfRt082IKCLiMhUWImVI3JKC/fCrduiAYdFf33NZE7eGH7f8iIHBy71fZsyKMcSnx1MvvB5DWg8xO16pCIyKosqpwxP3q4nbI4zqOYpGlRtxNO0oj/z5yGnvBfsGc3/7+wF4fcnr2O12MyKKiLhE35mIlCGO7UOrd+tmchLPZrfb6VCjAzc3v5laobV4fcnrAIzuORofq4/J6UqPlkN5Fj8vPyZcMQEDg0nrJzFnz5zT3h9+0XB8rb4sP7ycxQd0crqIlD0qLETKiMyEBE5s3gxAVNeuJqfxbG2rt2XZ/5Yx9rKxvLX0LZKykmga2ZSbmt1kdrRSVatfPyze3iTt2EHSjh1mxxGgU3Qn58zE2NWnby9bNagqd7S6A4A3lr5R2tFERFymwkKkjIhbXPATzEpNm+IfGWlyGs9nGAZpOWm89/d7ALzU+yWsFqu5oUqZT2go1Xv0ADRr4Ulevvhlxlw6hm+v+faM9x7r9BgGBr/t+I2diTtNSCciUnwqLETKiCOLFgFaBnU+f+z6g+fmP0dyVsHZDa8ufpX03HTaV2/PlbFXmpzOHM7lUDNmYLdptyFPEOIbwgMXPYCXxeuM9xpENGBAgwEAzjNYRETKChUWImWALTeXuKVLAZw/gZbT5dvyeWL2E7z414u8vextDiQf4JNVnwDwysWvYBiGyQnNUaNHD7yDg8k4epT41avNjiP/kZ2XzedrPj+tWXv4RcMB+GLdF6TlpJkVTUSkyFRYiJQBx9evJzc1Fd/wcCo1bWp2HI80cd1ENsZvJMwvjIc7PswLC18gJz+HnnV6cnHdi82OZxqrry/RffsCWg7laWx2G10mdGHor0P5asNXztf7xvSlYURDUrJTmLR+kokJRUSKRoWFSBng2A0qqmtXLNaK1SdQGKnZqTw7/1kAnuv+HMczjjNx3UQAXu79coWdrXCoe9llAByYNYv8nByT04iDxbBwXZPrAHh89uOkZKc4X3fMWoxZMUZbz4pImaHCQqQMOKxtZs/rjSVvcDTtKDHhMdx/0f08O+9Z8u35DGwwkM7Rnc2OZ7oq7dsTUK0auSkpHJ4/3+w48i8Pd3yYhhENOZZ+jNELRjtfv73l7QT7BLPt+LYztqUVEfFUKixEPFz6kSMk79yJYbEQ1aWL2XE8zsHkg7y97G0A3uj7BuuOrnMejvfKxa+YnM4zGBYLdU7NWuyZrlOdPYmvly8f9P8AgPeXv8/m+IItpYN9g51bz36w4gOz4omIFIkKCxEPd+TUNrOVW7bENyzM3DAeaNSCUWTmZdKtVjeuir2KEXNGADC45WBaVG1hcjrPUffKgl2x4hYvJvP4cZPTyL9dUv8Sroy9knx7Pg/+8aBz6dMDFz0AwIwdM9h9YreZEUVECkWFhYiHc5623b27yUk80/M9n+eW5rfwdr+3+XP3nyzYtwBfqy8v9HrB7GgeJbRePSJatMCen68mbg/07iXv4mv1Zd7eeUzZOgWAhhEN6V+/P3bs2npWRMoEFRYiHiw/J4ejf/8NqL/iXGqF1uKrq7+iTVQb52zF8IuGUyu0lsnJPE+9U7MWe7UcyuPUDa/L450f57KGl9GsSjPn644m7glrJ2jrWRHxeCosRDxY/KpV5Gdm4h8ZSVijRmbH8Sip2amnffz1xq+d280+1e0pk1J5tlr9+2Px9iZpxw5Obt1qdhz5j+d7Ps+vN/1KbOVY52v96/enfqX6JGcnn7YlrYiIJ1JhIeLBjixcCBQsg6roW6b+W05+Dm3HteWGn27gWNoxsvKyGDl/JABPdX2KSv6VTE7omXzDwqjRqxegJm5PZLWcvpW03W7HYlh4oH1Br4W2nhURT6fCQsSDHVm0CFB/xX99vPJjdp7YycJ9CwnwDuCjFR9xIPkANUNqOpeOyNk5lkPtmzEDW26uyWnkbI6mHWXYr8N4bNZjANzR6g6CfILYkrCFuXvnmpxOROTcVFiIeKjk3btJ3b8fi7c31Tp2NDuOx0jMSOSFhQWN2S/1fok8Wx4vL3oZgBd6voC/t7+Z8TxeVJcu+EVEkH3ihHPHMfEsm+I38dmazxizYgzbj28n1C+U21veDsCnqz41OZ2IyLmpsBDxUIfmFvxksmrHjngHBZmcxnM8PfdpTmadpHmV5tzZ6k5eX/I6J7NO0jSyKYNbDjY7nsezeHtTe+BAAPZOm2ZuGDmrPvX6MLDBQPJseTwz7xkA7m57NwDTt0/naNpRM+OJiJyTCgsRD3VwTsFpu9EXX2xyEs+x4vAKPlvzGQAfDviQI6lHeH/5+wC81ue1M9aoy9nVu+oqAA4vWEB2UpKpWeTsXuvzGgYGP2/9mZWHV9K8anM61exEni2PL9Z+YXY8EZGzUmEh4oHSjxzhxObNYBjU7N3b7DgeId+Wz30z7sOOndta3Eb32t15fsHzZOVl0a1WNwY2GGh2xDIjPDaW8EaNsOXlsX/mTLPjyFk0q9LMOQP35NwnsdvtzlmLz9Z8hs1uMzOeiMhZqbAQ8UCH5s0DILJNG/wiIkxO4xn2Je3jWPoxQnxDeKPvG2yO38zE9RMBeKPvG9o1q4gcJ3Hv0XIojzW652h8rD7M2zuPOXvmcF3T6wj1DWVv0l5m755tdjwRkTOosBDxQFoGdaaYSjFsvX8rM2+eSbWgajw19ylsdhtXN76ajjXV3F5UdS6/HIuXFyc2b9aZFh6qdlht7mt3HwCvLn6VAO8A5yzGuDXjzIwmInJWKixEPEzWyZMkrF4NQM0+fUxO41mCfILoUqsLi/Yv4tcdv2I1rLzS+xWzY5VJfuHh1DxVuO6eMsXkNHIuT3d7mie7PMmP1/0IwLC2wwCYvm06calxZkYTETmDCgsRD3N4/nzsNhvhjRsTVKOG2XFMt+zgMsavGe9cU2632xkxZwQAQ9sMPe2UYimaeldfDcC+334jPzvb5DRyNpGBkbza51UiAgqWRDar0ozO0Z3Jt+fzxTo1cYuIZ1FhIeJhHMugamoZFHm2PO6ZcQ93/XoXry1+DYBp26ax7NAyArwDeK7HcyYnLNuqdepEQFQUOSkpzj934rnsdjvHM46riVtEPJYKCxEPkpueztFlywCI1jIoPl75MRuObSDcL5xhbYeRZ8vjqblPAfBox0eJCo4yOWHZZrFaqTdoEAC7f/7Z5DRyPntP7qXXl73oMqELgxoNIswvjH1J+5i1e5bZ0UREnFRYiHiQI4sWYcvJIbh2bULr1zc7jqniUuMYOX8kAK9e/CqVAyrzxdov2J64nQj/CB7v8rjJCcuHmEGDwDA4tnw5aQcPmh1HzqFyQGU2xW9iR+IOpm6byuAWp5q4V6uJW0Q8hwoLEQ9y6F/LoCr69qmP/PkIKdkptK/enrva3EVGbgajFowCYGT3kYT4hpicsHwIrF6dap06AbB76lST08i5BPsG80SXJwB4YeELDGk9BIBftv/CkdQjZkYTEXFSYSHiIfJzcjj811+AlkFN3zad7zd/j9Ww8snAT7BarHyw/APi0uKoE1aHe9rdY3bEciXmmmuAgjMtbPn5JqeRc7m//f1EBkSy++RuVsetpkt0l4Imbp3ELSIeQoWFiIc4tnw5eenp+EdGEtG8udlxTJOTn8Pw34cD8H+d/4+21duSmJHobN5+qddL+Hr5mhmx3KnZuze+YWFkHjtG3JIlZseRcwj0CWREl4Id0V5e9DJ3tbkLKGjizrepIBQR86mwEPEQB2cXnKRbs3dvDEvF/avpY/Xh5+t/5urGVzOqR8HSp1cXv0pydjItq7bkpuY3mZyw/LH6+FDn8ssB2KMmbo92T7t7qBxQmT0n92C32wn3C2d/8n7m7p1rdjQRERUWIp4gPyeHA6cKi1r9+5ucxnzta7Tn5+t/xt/bn/1J+xmzYgwAr/V5DYuhL1slwbEc6tCCBWQeP25yGjmXQJ9AHu34KAA/bf2JW5rfAsCEtRPMjCUiAqiwEPEIcYsXk5uSgn+VKkS2bWt2HFOk56SzNWHrGa+PWjCKnPwcetXpxSUxl5iQrGIIa9CAiJYtseflsXfaNLPjyHncf9H9TB40mek3Tnc2cU/dNpUTmSdMTiYiFZ0KCxEPsG/mTKBgtsJitZqcxhzPzHuGVmNb8cnKT5yvbTy2kUnrJwHwep/XK/xOWSWtwXXXAbDzxx+x23TwmqcK8Q3h1ha34mXxonVUa1pVa0VOfg5fb/ja7GgiUsGpsBAxWW56OofnzwegzsCBJqcxx7KDy/hg+Qfk5OcQUynG+fpTc5/Cjp3rmlxH+xrtTUxYMdS69FK8Q0JIP3RITdxlRE5+DoMaFRxyOGGdlkOJiLlUWIiY7PD8+eRnZRFUqxaVmjY1O06py87L5n+//A87du5odQf9YvoBsHDfQmbsnIHVsPJy75dNTlkxePn5Ue/KKwHY+f33JqeRC1lyYAkxH8Tw247f8LH6sO7oOtbGrTU7lohUYCosREzmWAZVZ8CACrnU58W/XmTr8a1UDazK2/3eBsButzNiTsG2mkPbDKVBRAMzI1Yo9a+/HoAjCxeSHhdncho5n5hKMSSkJ7DyyEq61eoGqIlbRMylwkLERNlJSc4lJ7UHDDA5TelbenApry5+FYCPBnxEJf9KQEEj6vLDywnwDuC5Hs+ZGbHCCa1Xj6oXXYTdZmO3tp71aNWCqnFbi9sAyMzLBODrjV+TlZdlZiwRqcBUWIiY6OCsWdjz8giLjSU0JubCN5Qjqdmp3DrlVmx2G7e2uJVrmhRsd5pny+OpuU8B8GjHR4kKjjIzZoVU/4YbANj900/YcnNNTiPn81jnx4CCIj0qKIqTWSeZtm2auaFEpMJSYSFion8vg6poAn0Cub/9/cRGxPLhpR86X/9i7RfsSNxBhH8Ej3d53MSEFVfN3r3xi4ggMyGBwwsWmB1HzqNR5UZc3rDgcMOooIIiXMuhRMQsKixETJJx7Bjxq1YBFXMZlMWw8Fjnx9hw7wZC/UIByMrL4oW/XgDgmW7PEOIbYmbECsvq4+M8ME9N3J7v8c4FBfim+E0AzNkzh/1J+82MJCIVlAoLEZMc+OMPsNuJbN2awOrVzY5Tao6mHSU9J935sY/Vx/n7T1d9yqGUQ9QMqcm97e81I56cEnPttWAYHF22jJT9+ibVk3Wt1ZWLalxEji2HRpUbYcfOl+u/NDuWiFRAKixETLJvxgygYs1W5Nvyue7H62g7ri0bj2087b20nDReWfQKACO7j8TPy8+MiHJKUI0aVO/eHYDdP/5ocho5H8MweO+S91g5dCXPdnsWgC/WfYHNrkMORaR0qbAQMUHK/v2c2LwZw2ql1iWXmB2n1Ly2+DUWH1jMkdQjBPoEnvbeB8s/ICEjgZjwGO5sdadJCeXfGpzaenb3zz+Tl5lpcho5n07RnWhXvR1XN76aUN9Q9iXtY/7e+WbHEpEKRoWFiAn2TpsGQLXOnfGLiDA3TClZcXgFoxaMAgq2lq0XXs/5XlJWEm8ufROA53s+j7fV25SMcrqobt0IrFmTnJQU9v32m9lxpBD8vf25qtFVAIxfO97cMCJS4aiwEClltvx89pwqLGIGDTI3TClJzU7l5p9vJt+ez43NbuTWFree9v5bS98iKSuJppFNuanZTSallP+yWK3E3nwzANu//hq73W5yIrmQp+c+zbebvgUKzoNJykoyN5CIVCgqLERK2dGlS8mMj8c3LIwavXqZHadUPPTHQ+w+uZtaobX4ZOAnp50wHp8ez3t/vwfAi71exGqxmpRSzqbeoEF4+fuTvHMnx5YvNzuOXIC3xZuc/Bz8vfzJysviu03fmR1JRCoQFRYipWz3lCkA1LnsMqw+Phe4uuz7ecvPfLHuCwwMJg+aTJhf2Gnvv7b4NdJz02lXvZ1zCYd4Dp+QEOpedRUAO77+2twwckH3X3Q/fl5+zpO4v1j3hcmJRKQiUWEhUoqyTp7k8Lx5QMFPgiuCTtGd6FOvD091fYrutbuf9t6hlEN8vPJjAF7q9dJpMxniORqeWg51aP580g4eNDmNnE+VwCrc3vJ2AAwMVhxewZaELSanEpGKQoWFSCnaP2MGtrw8wps0IbxRI7PjlIrqwdX589Y/Gd1r9BnvvfTXS2TnZ9OtVjf6xfQzIZ0URmi9ekR16QJ2Ozu+/dbsOHIBj3V6DAMDOwU9MV+s1ayFiJQOFRYipcRut7P755+BitG0vfvEbufvLYYFL4vXae/vPbnXuWvNy71f1myFh4u97TagYClfbnr6Ba4WMzWIaMAVsVc4P568YTK5+bkmJhKRikKFhUgpObl1K0k7dmDx8Sn3h+KtOrKKxh81Ztivw875Dc0ri14hz5ZH33p96Va7WyknlKKK6tKF4Dp1yE1NZe8vv5gdRy7gwQ4PAgXLoY6lH+OPXX+YnEhEKgIVFiKlxNG0HX3xxfiGhZkbpgSl56Rz8883k2vL5WTWyTNmKqBgtmLi+olAwbkV4vkMi8XZa7Hjm2+w23SqsyfrVacXH/T/gGFthwEwYd0EkxOJSEWgwkKkFORnZ7Nvxgyg/DdtP/LnI+w8sZMawTUYe9nYsy5xcsxW9IvpR+foziaklOKod9VVeAUGkrJnD3FLlpgdR87DMAyGdxjOAxc9AMBvO34jPj3e5FQiUt6psBApBYfmzSM3JYWAatWo2rGj2XFKzNStU/lszWfOrWUr+Vc645p/z1aM6jGqlBOKK7wDA4m5+moAtn35pclppDCaVWlGu+rtyLPl8fUGbRcsIiVLhYVIKXA0bde76ios1vJ5ANyR1CPc9etdADze+XF61T374X+arSjbGg0ejGG1cnTZMk5s3Wp2HLmATfGbSMlOAQrOtNDp6SJSklRYiJSwlH37OLpsGRhGuV0GZbfbuX3a7ZzIPEGbqDa82PvFs16n2YqyL7B6dWr17w/A1glat+/pgn2C2Zm4E4CN8RtZE7fG5EQiUp6psBApYTu//x6A6t27E1SzpslpSoZhGDzQ/gFqhdbim6u/wcd69hPFNVtRPjS5804ADvz5J2mHD5ucRs6ndlhtrm58tfNjncQtIiVJhYVICcrLzGTPtGkANLzpJnPDlLArG13JruG7iK0ce9b3NVtRfoQ3bky1zp2x5+ezbdIks+PIBTi2ngX4euPXZOVlmZhGRMozFRYiJWj/zJnkpqQQWLNmwcnF5UxGbgaHU/75ibW31fuc12q2onxpMmQIUNA/lJ2UZG4YOa9utbrRokoLAJKykpi+bbrJiUSkvFJhIVJC7HY7O779FoAGN9yAYSl/f93+b9b/0eLTFszYMeO812m2ovyp2rEj4Y0akZ+Zyc7vvjM7jpyHYRg83PFh58c600JESkr5+05HxEMkbtjAya1bsfj4OLfoLE9+3f4rn6z6hBOZJ87ZU+Gg2YryxzAMGp+atdjxzTfkZWl5jSe7qflNhPuFAzB792wOpRwyOZGIlEcqLERKyI5TP8Wtfeml5e6k7bjUOIb8UvBN5WOdHqNvTN9zXns45TBfri848+C57s+VSj4pHbUuuYTA6tXJSkxk7y+/mB1HzsPPy4/X+7xObEQsduxMWq/eGBFxPxUWIiUg68QJDvz+OwANbrzR5DTuZbPbuGP6HRzPOE7Lqi15uffL573+3b/fJdeWS/fa3elSq/z1mVRkFi8vGt1+OwDbJk7Elp9vciI5n6Fth/Jk1ycBnWkhIiVDhYVICdgzZQq23FwqNW1K5RYtzI7jVmOWj2HW7ln4efnxzTXf4Ovle85rT2SeYOzqsQA82eXJ0ooopSjm6qvxCQ0ldf9+Dvz5p9lx5AKubXItgd6B7DqxiyUHl5gdR0TKGRUWIm5my89n5w8/ANCgnG0xuyl+E0/MeQKAt/u9TZPIJue9/uOVH5OWk0aLqi3oX79/aUSUUuYVEECjwYMB2Dx2LHabzeREcj5+Xn40jWwKwKerPjU5jYiUNyosRNzsyF9/kX74MD4hIdS+9FKz47hVvfB6/K/1/7i84eXc2+7e816bkZvB+8vfBwpmKwzDKI2IYoKGt9yCd3Awybt2cXDOHLPjyHlYDSuJmYkA/LTlJ9Jy0kxOJCLliQoLETfbfurAsJhrrsHLz8/kNO4V4B3AxwM/5ufrf75goTBh7QSOZxynblhdrmt6XSklFDP4BAcTe8stAGz69FOt3fdghmEwossIALLzs/lh8w8mJxKR8kSFhYgbndi6lWMrVmBYrTQ89Y1WebD35F5s9n+WuJzvIDyA3Pxc3lr6FgCPd34cL4tXieYT88XedhteAQEkbd/O4fnzzY4j53FLi1vw8yr4oce7y941OY2IlCcqLETcaNup2Ypal1xCYFSUyWncIzEjkS4TutB3cl/i0+MLdc/3m79nf/J+qgRW4Y5Wd5RsQPEIvmFhNLz5ZkCzFp4uwDuAwS0K+mI2JWxi94ndJicSkfJChYWIm2QcO8b+mTMBnFtwlnV2u517ZtxDXFocR1KPEOQTdMF7bHYbry1+DYCHOzyMv7d/SccUD9Ho9tux+vtzYvNm4hYvNjuOnMcTXZ5w/v6dZe+YmEREyhMVFiJusuPbb7Hn5RHZti0RzZqZHcctJm+YzE9bfsLL4sXXV39NgHfABe+ZuXMmmxM2E+wTzL3tz9/gLeWLX6VKNLj+ekCzFp4uplIMrau1Bgr+nufbdAaJiLhOhYWIG+RlZLDr+++B8jNbsS9pHw/MfACA0T1H0yaqTaHuc8xW3NvuXsL8wkoqnnioxnfeicXHh+Pr1nFs+XKz48h5vNDrBbwt3qTmpDJv7zyz44hIOaDCQsQN9kyfTk5KCkHR0dTo2dPsOC7Lt+UzeOpgUnNS6Rzd+bRlE+ez+MBilhxcgo/Vh4c7PlyyIcUj+UdGUv/aawHY+OGHmrXwYJc1vIxhbYcBBSdxi4i4SoWFiIvsNpuzaTv2ttuwWK0mJ3Ld28veZtGBRQT5BDF50ORC7+rkmK24o+UdRAWXj+Z1KbomQ4di9fUlYe1a9Vp4uDtb3QnAlK1TOJl50uQ0IlLWqbAQcdHhBQtIO3AA75AQ6l11ldlx3KJvvb40rtyY9/u/T73weoW6Z8OxDczYOQOLYeHxLo+XcELxZAFVqjhPnV///vs6jduDtYlqQ8OIhmTnZ/PSopfMjiMiZZwKCxEXOWYrGlx3Hd6BgSancY/WUa1Ze/da508zC+ONJW8AcG2Ta6lfqX5JRZMyosldd+EVEMDJrVs5OHu22XHkHAzDIMI/AoAv1mo5lIi4RoWFiAuOb9hA/MqVGF5ezj38y7KE9ATn7329fC94urbD3pN7+W7TdwDOU32lYvMLD6fR4IKzEjZ8+CG2fO065KlevfhVAE5mnWTunrkmpxGRskyFhYgLtnz2GQB1L7+cgGrVTE7jmr8P/U3t92rz8l8vF7nh9u1lb5Nvz6dfTL9C7x4l5V+jO+7AJySElD172Pfbb2bHkXPoUaeHc9bi2XnPmpxGRMoyFRYixZS0axeH5s0Dw6DxkCFmx3FJVl4Wd06/k8y8TLYlbiv0TAVAfHo849eOB+DJLk+WVEQpg3yCg51/NzZ+/DH5OTkmJ5JzuavNXQAsP7yctOw0k9OISFmlwkKkmLaML/hmOvriiwmtV7gGZ0/13Pzn2HZ8G9WCqvF+//eLdO8Hyz8gKy+Li2pcRM86PUsmoJRZsbfcgl9EBOmHDrFn6lSz48g5jOoxCothwY6d5xY8Z3YcESmjVFiIFEPa4cPsnzEDKNhasyxbdnAZby19C4Bxl42jkn+lQt+bkp3CRys/AgpmK4oy0yEVg1dAAE3vvhsoOI07LyvL5ERyNv7e/nSq2QmAiesmmhtGRMosFRYixbBt4kTs+flU69SJiGbNzI5TbJm5mdwx/Q7s2BnccjCXx15epPvHrR5HUlYSsRGxXNnoyhJKKWVd/euuIyAqisz4eHZ89ZXZceQcXrn4FQCSspI4mHzQ5DQiUhapsBApoqzERHb//DNQsKVmWfbsvGfZkbiD6sHVee+S94p0b3ZeNu8sewco2AnKYujLiZyd1ceHFsOHA7D5s8/IOqmD2DxR99rd6RzdGTt2Jq2fZHYcESmD9J2ASBFt/+or8rOzqdSsGVU7dDA7jktqhNTA1+rLuMvGEe4fXqR7J2+YTFxaHDWCa3BLi1tKKKGUF3Uuu4yw2Fhy09LYPHas2XHkHO5tdy8A49aMI9+mLYJFpGhUWIgUQW5aGju+/RaApkOHlvmegkc7Pcreh/YysOHAIt2Xb8t3Hoj3WKfH8LH6lEQ8KUcsViut/+//ANj57bekHjhgciI5m2ubXEsl/0ocSD7AqAWjzI4jImWMCguRItj53XfkpqYSUq8eNXv3NjtOsdnsNufvo4Kjinz/1G1T2XliJ+F+4QxtW7ab16X0RHXuTLXOnbHl5bH+gw/MjiNn4eflR8eaHQF4Z9k7mrUQkSJRYSFSSHlZWWybVLDuuMn//odhKZt/fVYfWU2rT1ux9ODSYt1vt9t5bfFrAAy/aDhBPkHujCflXOvHHgPD4MDvv3N8wwaz48hZvNK7oIk7My9TO0SJSJGUze+MREywd/p0shITCahWjToDi7Z0yFPk5udy1693sTF+Ix+u+LBYY8zdO5fVcavx9/JneIfhbk4o5V14o0bUveIKANa9806RT3mXkteyWkuiQ6IBeGXRKyanEZGyRIWFSCHY8vLYMmECAI3vvBOLt7fJiYrn7WVvs+7oOir5V+K9/u8VawzHbMXQNkOpHFDZjemkomgxfDhWX1/iV67k8IIFZseRs3iiyxMA7Enaw6b4TSanEZGyQoWFSCEc+OMP0g8dwjc8nJhrrjE7TrHsTNzJ8wueB+DdS96lSmCVIo+x8vBK5u6di5fFi0c7PermhFJRBEZFEXvbbUDBrIUtN9fkRPJfw9oOc27KMGLOCJPTiEhZocJC5ALsNhubP/8cgNhbb8XL39/kREVns9sY+utQsvOz6VuvL7e1uK1Y47y+5HUAbm5+M7XDarszolQwTe66C99KlUjZs4ed339vdhz5Dx+rD4MaDQLgz11/kpqdanIiESkLVFiIXMChefNI3rkTr8BAGt50k9lximXC2gks3L+QAO8Axl42tljb5G4/vp0pW6cA8ETnJ9wdUSoYn+Bg56F5Gz/6iOykJHMDyRle7PUiAPn2fJYcXGJyGhEpC1RYiJyH3W5n06efAhB7yy34hIaanKh4/tz9JwAv9XqJuuF1izXGm0vfxI6dK2KvoGmVpu6MJxVUzDXXENawITkpKWz4sHibCUjJaRDRgB61ewCwcN9Ck9OISFmgwkLkPI789Rcnt27Fy9+f2MGDzY5TbD9c+wNTrp/Cgx0eLNb9h1MOM2l9wVa7T3Z50p3RpAKzWK20fbLgz9OuH34gafdukxPJfzm+ZkxYN4Gc/ByT04iIp1NhIXIO/56taHDjjfiFh5ucqPgMw2BQ40FYLdZi3f/u3++Sa8ule+3udIru5OZ0UpFV7dCBmn36YM/PZ90775gdR/7j8oaXExUURXx6PM/MfcbsOCLi4VRYiJzD0WXLSNywAauvL41uv93sOEWWmp3KU3OeIjkr2aVxTmSeYOzqsYBmK6RktPm//8Pi7c2x5cvNjiL/4W315tYWtwLw1rK32JKwxeREIuLJVFiInINjtiLmuuvwj4w0OU3RPTf/OV5b8hqXfXuZS+N8vPJj0nLSaFG1Bf3r93dTOpF/BEVH06gMLzUs7x7s8CAGBRs+jJw30uQ0IuLJVFiInMWxlStJWL0ai7c3TYYMMTtOka2NW8sHKz4A4NluzxZ7nIzcDN5f/j5QMFtRnN2kRAqj6d1341epktkx5CxqhtSkZ52eAEzbPo2E9ARzA4mIx1JhIXIWjtmKeldfTUDVqianKRqb3ca9M+7FZrdxfdPruaT+JcUea8LaCRzPOE7dsLpc1/Q6N6YUOZ13YCDN77/f+XFWYqKJaeS/RvccDRR8fXlnmXphROTsVFiI/EfCunUc+/tvDC8vmt51l9lxiuyz1Z+x/PBygn2CefeSd4s9Tm5+Lm8tfQuAxzs/jpfFy10RRc6q7uWXO3/vKO7FM3St1ZXaoQWHYn644kOy87JNTiQinkiFhch/OL6hqXvFFQRWr25ymqKJT4/nybkFDdYv9X6J6sHFz//95u/Zn7yfKoFVuKPVHW5KKHJuhuWff5L2TJ/OyW3bTEwj/2YYhnNZZVpuGl9v/NrkRCLiiVRYiPxL4qZNxC1ahGGxlMnZiufmP0dSVhJtotpwX/v7ij2OzW7jtcWvAfBwh4fx9/Z3V0SRQrHbbKx+7TXsdrvZUeSUW1rcQoB3AAC/bPvF5DQi4olUWIj8y+axBduq1h44kODatU1OU3Qv9HqBwS0H8+nAT11aujRz50w2J2wm2CeYe9vf68aEIoVj9fUlfuVK9v/+u9lR5BR/b3/uaXcPAIlZ6oERkTOpsBA55eT27RyaNw8Mg6bDhpkdp1iqBFbhy6u+pH2N9i6N45ituLfdvYT5hbkhmUjRNLrjDgDWvvkmuenp5oYRp8c6PYa3xZvFBxaz4vAKs+OIiIdRYSFyimO2otYllxBar57JaYpm94ndbhtr8YHFLDm4BB+rDw93fNht44oURePBgwmKjiYzPt75d1PMVz24Ojc3vxmAF/96kcMph01OJCKeRIWFCJC8ezcHZs0CoNndd5ucpmj2nNxDs0+acdV3V5GWk+byeI7Zijta3kFUcJTL44kUh9XXlzYjRgCw7csvSdm71+RE4vBop0cB+G3Hbzw550mT04iIJ1FhIQJsHjcO7HZqXnwxYQ0bmh2n0Ox2Ow/MfICsvCxSc1IJ9A50abwNxzYwY+cMLIaFx7s87qaUIsVTo2dPqnfvji0vj9WvvqpGbg/RomoL2ka1BeDbTd9yIvOEyYlExFOosJAKL2X/fvbPnAmUvdmKKVun8Puu3/Gx+vDxgI9dPhn7jSVvAHBtk2upX6m+OyKKFJthGLR58kks3t7ELVnC4fnzzY4kp7zY60UA8u35OjBPRJxUWEiFt+Wzz7DbbFTv3p1KTZuaHafQUrNTeeiPhwAY0WUEsZVjXRpv78m9fLfpO+d4Ip4gpHZtGt95JwCrX3uNvKwskxMJQP/6/akZUhOAd/9+l4zcDJMTiYgnUGEhFVra4cPs/fVXAJrdc4/JaYpm1IJRHE49TL3wejzV9SmXx3tz6Zvk2/PpF9OPNlFt3JBQxD2aDh1KQLVqpB8+zNYJE8yOIxTMJr3Q8wUAMnIz+HSVTkoXERUWUsFt+fxz7Hl5VOvUicotW5odp9DWHV3HB8s/AOCjAR+5fIBdXGocE9YWfMP2dNenXc4n4k5eAQG0eeIJoODvbNqhQyYnEoBbW9xKJf9KALz818vk5ueanEhEzKbCQiqsjKNH2TN1KlD2ZivSctKoFVqL65pcR//6/V0e792/3yU7P5vO0Z3pXru7GxKKuFd0v35U7dCB/Oxs1rzxhtlxBPC2evNMt2cAOJl1kvXH1pucSETMpsJCKqwtEyZgy82lSrt2VGnXzuw4RdK1Vlc23beJTwZ+4vJYJzJP8MmqgnGe7vr0/7d353FR1fsfx1/DsIusAioqiKAiCuK+lbuVuWuaGWpW5pb509Js0bSuS9rV8lZq5k3LXNHcMnPfccHUEEVQQDZZBdmXmfn9wXWKNMWYYYD5PB+PeSDnnPmeNxyB+cz3fL/fcg8AF0IfFAoFbd57D4WpKXGHDpFw4oShIwlgQpsJOFg6oEFDeGq4oeMIIQxMCgthlPJSUri5bRtQ9Xor7rM2s8bJ2qnc7fzn3H/ILszG39Wfvt59dZBMCP2w8/KiyahRAIQsXIiqsNDAiYS1mTVvd3obgEWnFqHWqA2cSAhhSFJYCKN07b//RVVQgJO/P64dOhg6TplN3TeVr85/hUqt0kl72YXZfH72cwDee0p6K0Tl12LSJCydnMiKiSF8/XpDxxHApLaTsLWwJTQ5lAUnFsh6I0IYMSkshNHJS0khYvNmoORFSlV5MX00+igrzq1gys9T+D35d520uerCKtLz0vF29Gaoz1CdtCmEPpnZ2BDwdsk75KErV5J7546BEwl7S3umtpsKwIdHPmRf5D4DJxJCGIoUFsLohK1diyo/Hyd/f+p07mzoOGVSUFzAhD0lt2xNaDOBlrVblrvN/OJ8PjvzGQDvdnkXpYmy3G0KURE8+vfHOSCA4rw8Li5daug4ApjecTrmSnMApv0yTXothDBSUlgIo5KXkkJkFeytWHp6KeFp4bjWcGVBzwU6aXPdpXUkZidS37Y+L/u9rJM2hagICoWCNu+/j8LEhNv79nEnONjQkYyeg5UDk9tOBiAiPYIDtw4YOJEQwhCksBBGJezbb1EVFFCrZcsq01txM/0mn5z4BIBlzyzD3tK+3G0Wq4tZfGoxAO90ekf7TqMQVYWDjw/eL74IwIV//UsGclcCc7rO0f4ueeuXt6TXQggjJIWFMBp5KSlEbtkCVJ3eCo1Gw+SfJ5NfnE8vz1682PxFnbS7KXQTURlROFs782qrV3XSphAVze/NN7F0cuLerVtcX7fO0HGMnr2lvbbX4nrqdQ7dOmTgREKIiiaFhTAaYWvWaHsranfqZOg4ZXI56TIHbh3AQmnBV32/0kkxpNaoWXhyIQD/1+H/sDazLnebQhiCua3tHwO5V60iJyHBwInEnK5zsFBaADDp50nSayGEkZHCQhiFvJQUIrduBaDF5MlVorcCoGXtllx4/QIr+63E28lbJ20GhQURlhKGnYUdk9pO0kmbQhiKR//+uLRpgyovj5DFiw0dx+jZW9rzTqd3AIjOiCY+K97AiYQQFUkKC2EUSvVWdOxo6DhPJKBOAGNbjtVJW2qNmnnH5gElvRV2lnY6aVcIQ1EoFLT58MOSFbkPHiT++HFDRzJ67z/9Pq41XClSF7E5dLOh4wghKpAUFqLay01OJuJ/Yyv8pkypEr0Vvyf9ztXkqzpvd1vYNq6mXMXOwo63Oryl8/aFMAR7Ly+aBgYCELJgAcX5+QZOZNwsTS21s9ctOLmAjPwMwwYSQlQYKSxEtRf27beoCwtxDgioEqtsq9Qqxu4cS8tVLdl6davO2v1zb8X0jtN1MruUEJVF84kTsXJ1JTs2lrBvvzV0HKM32n80zZybkZ6XzovbXqRYXWzoSEKICiCFhajWcpOT/5gJqor0Vnx1/isuJl7ExtyGp92f1lm7W69uJSwlrGSV3PZTddauEJWBWY0atJ41Cyi59TErJsbAiYybqYkpC3qU9Frsv7mfz05/ZuBEQoiKIIWFqNbC1qwp6a1o1QrX9u0NHeexErISeP/w+wAs6rkIVxtXnbSrUquYf3w+UDK2QnorRHVUv08fanfqhLqwkAsLFsiMRAY2oMkAvBy9AJh3bB65RbkGTiSE0DcpLES1lZuUVOVmgpr2yzSyCrPoUK8Dr7d+XWftbg37o7firfYytkJUTwqFgjYffICJmRmJJ08Sd/CgoSMZNYVCwaahmwDIK85jys9TDJxICKFvUliIaiv0669Leitat64SvRX7IvaxNWwrSoWSlc+vxEShmx9PlVrF/GMlvRXTO0yXmaBEtWbr7o7PuHEAhCxaRHGuvEtuSK3rtqaPZx8Avrv0HbGZsQZOJITQJyksRLV0LyaGm9u3A+D/1luVvrciryiPyT+XrFg7rcM0/Gv766ztLVe3cC31Gg6WDjK2QhgF3/HjqVGvHrl37hC6cqWh4xi9H4f+iKnCFA0aRmwbYeg4Qgg9ksJCVEtXVqxAo1JRt2tXXFq3NnScxzJTmjG57WR8avnwUbePdNbun8dWTO8ovRXCOJhaWtJm9mwArq1bR2ZkpIETGTcnayft9NZn4s5w4OYBAycSQuiLFBai2km/do3b+/YB4D+1arxDb2piyoxOM/h94u/YmNvorN3NVzdzPfW69FYIo+PWrRtu3bujKS7m/CefyEBuA1vcazGOVo4ALAteZuA0Qgh9kcJCVDuXP/8cAPe+fXFo2tTAaR6tWF1MXlGe9nOliVJnbRepiph7dC4AMzrOwNbCVmdtC1EVtJ49G6WlJcnnzxO1a5eh4xg1pYmS/S/vR6lQsi9yH3tu7DF0JCGEHkhhIaqV5AsXSDxxAoWpKX5TKv8MJP8+82/8V/pz8vZJnbf9zcVviEyPxKWGi/RWCKNk4+ZGi4kTAfjt00/Jv3vXwImMW5u6bZjecToAk3+ezL2CewZOJITQNSksRLWh0Wi4tKyki73R0KHUdHc3cKJHu5F2g7lH5xKRHsHN9Js6bTu7MFu7yvbcrnOpaVFTp+0LUVU0HTMG+8aNKcjI4LclSwwdx+jN7TqX+rb1uZ15m6fWPmXoOEIIHZPCQlQbCceOkXrpEkpLS5pPmGDoOI+k1qh5bddr5Bfn06dRH0b7j9Zp+5+d/ozknGS8HL14vZXu1sMQoqoxMTOj3bx5oFAQtXMnd4KDDR3JqNUwr8GktpMAuJJ8ha/Pf23gREIIXZLCQlQL6uJiLi1fDkDjl17C2sXFsIEeY9WFVZy4fYIaZjVY1W+VTqfDTcpOYumZpQD8q8e/MFOa6axtIaqiWn5+eL/4IgDn5s2jOD/fwImM27td3sXLoWRF7mn7p5Gak2rgREIIXZHCQlQLN4OCyIyIwNzWlmavvmroOI90O/M2Mw/OBGBRr0V42HvotP1Pjn9CdmE2beu25YVmL+i0bSGqqpbTpmHl4kL27dtcXbXK0HGM3r5R+1CgoFBVyLMbnjV0HCGEjkhhIaq8wqwsrqxYAUCLKVOwsLc3bKBH0Gg0TNgzgezCbDrX76y9JUBXItMjWRlSsiDY4l6LK/3CgEJUFDMbG9q89x4AYWvXkiFrWxiUl5MX73R6B4CQxBCWnl5q4ERCCF2QwkJUeVdXraLg7l1sPT3xHj7c0HEeKbcoFwALpQVrBqzBRKHbH8EPDn9AsbqY57yeo3vD7jptW4iqrl6vXtq1Lc7NnYtGrTZ0JKO2qNciGto3BGDWwVmEJoUaOJEQoryksBBVWlZMDOHffw9Aq5kzMTGr3OMJapjXYO9LewkZH0LTWrpdY+N8/Hk2X92MAgWLei3SadtCVAcKhYI277+PqbU1qZcuEbltm6EjGTWFQsHBwIMoFUrUGjVDtw7VvvkihKiapLAQVdpvn32GuriYOl26UPepyjt14Z9X/VUoFPi6+Oq8/VkHZwEQ6B+In6ufTtsXorqoUacOflNL1nW59O9/k5eSYuBExs3T0ZMtL2zBpYYLN9JuMPnnybJKuhBVmBQWosq6ExxM3KFDKJRKWs2caeg4j/TV+a8Y89MYMvMz9dL+vsh9HIk+grnSnPnd5uvlHEJUF41feglHX1+KsrIIWbjQ0HGM3hCfIWwaugkThQnfXfqOFedWGDqSEOIfksJCVElqlYqLixcD4P3ii9g1amTgRH/veup13j7wNusvr2fL1S06b79QVcj0/SWr2U5tNxV3+8q9MKAQhmaiVNJu3jwUSiW39+8n9uBBQ0cyet0bdmdRz5JbOKf9Mo1fIn8xcCIhxD8hhYWokm5u20bGjRuY29rSYpJuZ1bSpSJVES9vf1m7EN6rrXQ/Fe5/zv2H8LRwXGq48MHTH+i8fSGqI0cfH3zGjQPg/Pz5FGRkGDaQ4JlGzwCgQcOQzUO4lnLNwImEEE9KCgtR5eSlpnJp2TKg8k8vO//YfEISQ3CwdGDtgLU6nwUqOSeZecfmAbCgxwLsLO102r4Q1VmLiROx9fQkPy1NbomqBPxq+zGrc8lYsbziPHp/35uErAQDpxJCPAkpLESVc/HTTynKysLR11e7mm5ldCb2DAtOLgBgVb9VuNm66fwc7x96n3sF92hdpzWvBLyi8/aFqM6UFhZ0+Ne/UJiYEL1nD3GHDxs6ktH7pMcntHdrD0B8Vjw91/ckPS/dwKmEEGUlhYWoUu6cOUPM3r0oTExoN3cuJkqloSM9VHZhNoE7AlFr1AT6BfKCr+5XwL6QcIFvf/sWgM+f/VznvSFCGINafn40HTMGgHPz5sktUQZmamLKzhd3UsemDlAyRq3vhr7kFOYYOJkQoizklYioMopzczk3r+S2H++RI3H01e2UrboUmR5JTlEODewasOI53c9wolKrmLBnAho0jGoxis4NOuv8HEIYixZTpmDbsCH5qanaSSGE4bjauPLzqJ+xNLUE4Gz8Wfpt7Ed2YbaBkwkhHkcKC1FlXP78c7JjY7GuXRv//81DX1m1rN2S3yf+zs4Xd+pl3MPXF74mJDEEOws7Puvzmc7bF8KYmFpa0v7jj0GhIGrXLuKPHjV0JKPXsnZLNgzZgAIFFkoLjkYfpc/3ffQ2ZbcQQjeksBBVQsrFi4Rv2ABAu3nzMLOxMXCih/vzwk61rGvRsnZLnZ8jMSuR9w+/D8CCngtwtXHV+TmEMDbOAQE0HT0agLNz5pCflmbgRGKIzxBuvHmDE6+cwMHSgTNxZ+ixvgepuamGjiaE+BtSWIhKrzg3l+APPwSNBs/Bg6nbpYuhIz1UoaqQXt/34sfff9Treab/Op17BfdoU7cNb7R+Q6/nEsKY+E2dip2XF/lpaZydO1dWgK4EvBy9aOvWliNjjlDLuhYXEy/SYU0HwlPDDR1NCPEQUliISu/ikiVkRUdj5epaqVfYnnVgFoejDjPl5yl6m8Vk7429bAotWaF25fMrUZpUzsHrQlRFppaWdFq8GBMzM+KPHOHmtm2GjiT+p4FdA2pZ1wLg5t2bdPy2I0ejjxo2lBDiAVJYiEot7vBhIrdsAYWCjgsWYG5ra+hID7X92naWn10OwLpB63C0ctT5OTLzM3ljT0kPxbT202hdt7XOzyGEsXNo2hT/t94CIGTxYu7FxBg4kQCoaVETX+eSCTsUKLibf5c+3/fhi7NfSM+SEJWIFBai0spNTubshx8C4DN2LLU7dDBwooe7lnKNMT+VTFc5vcN0+jfpr5fzzDwwk/iseBo5NOLjHh/r5RxCCGg6Zgyu7dqhysvj9MyZqIuKDB3J6JmamPLj0B8Z4TsCDSWFRJG6iLd+eYthW4eRkZ9h2IBCCEAKC1FJqYuKODVjBgUZGTj4+OBXSWeBuldwj8GbB5NdmE1X964s6rVIL+c5EnWE1RdXA/DtgG+xNrPWy3mEEKAwMaHDggWY2dqSHhpK6MqVho4kAHOlORuGbGBC6wnabUqFku3XthOwKoAjUUcMmE4IAVJYiErqt3//m5SLFzGzsaHLZ5+hNDc3dKQHqDVqRu8YTXhaOG413dg8bDNmSjOdn+dewT3G7RoHwITWE+jq0VXn5xBClFajTh3azZkDwNXVq0k6d87AiQSA0kTJV89/xXtd3gNApVFhZ2FHdEY0Pdb3YOKeiWQVZBk4pRDGSwoLUenE/PIL4evXA9Bx4UJqursbONHDaTQafGr5YKG0YPuI7Xqb9vWtX94iOiMadzt3FveWxbuEqCjuzz1Hw4ED0ajVnHr7bfJSUgwdSQAKhYJ/9fwXa/qvwa2mGydfOamdIW9lyEp8vvThhys/oNaoDZxUCOOj0FTSUU/Z2dm0bt2akJAQbCrpmgVC91KvXOHQ2LGoCgrwGTeOgBkzDB3psW5n3qaBXQO9tB0UFsSwrcNQoODY2GM85f6UXs4jRGWQk5Oj/X2fnZ1NjRo1DJyoZLrr/SNHkhkZiUvbtvRYswYTU1NDxxL/k1eUh5WZFQCHow7z8vaXScxOBKC9W3s+6/MZnRt0NmREIYyK9FiISiMnIYHjU6agKiigbteu+E+bZuhID3U99Tp5RXnaz/VVVCRkJTB+z3gAZnWeJUWFEAZgam1Nl2XLMLW2Jvn8eX7/8ktDRxJ/cr+oAMgvzicxOxEvBy+szaw5G3+WLv/tQu/ve3Py9kkDphTCeEhhISqFgowMjk6aRH5aGvaNG9N5yRJMlJVvjYbYzFi6r+tO93XdSc5J1tt5VGoVo3eMJj0vnYDaAczrPk9v5xJCPJqdpyft5pX8DF5dvZr4Y8cMnEg8TFJ2EpamlkTejQSgbd22mJqYcvDWQZ7671N0WduFLVe3UKSSWb6E0BcpLITBFeXkcHTiRDIjIrBydqbrl19iVglugfirjPwMnv/xee5k3yG3KBcrU6vHP+kf+uT4JxyKOoS1mTUbhmzAXFn5Bq8LYUw8+vbFe+RIAM68+y45CQkGTiT+6pWAV7j0xiU61utIblEu5xPO41rDlZ4Ne2JqYsqp2FOM2DaChp835IPDH3Aj7YahIwtR7cgYC2FQxfn5HJs8maTgYMzt7Oi1bh323t6GjvWAvKI8nvnhGU7cPkFtm9oEvxqMu71+BpUfunWI3t/3RoOG9YPWE+gfqJfzCFHZVMYxFn+mKizkQGAg6aGhODRrRu/16zG10t8bDOKfUWvUbPx9I7MOziI+Kx6AAY0H0LJ2S1aGrCzV29zerT3Dmg1jcNPBNHJsZKjIQlQbUlgIgynKyeH4lCkknTuHqbU1Pb79llp+foaO9QCVWsULW19gx/Ud2FrYcnzscfxr++vlXIlZibRc1ZLknGReDXiVNQPW6OU8QlRGlb2wAMiOj2f/iBEU3L1L/d696fLvf6Mwkc7/yiinMIelp5eyLHgZawasYVizYRQUF7Dh9w1s/H0jR6KPoNKotMf7OvvSo2EPejTsQVf3rjhYORgwvRBVkxQWwiAK793j6MSJpF66hKm1Nd1WrsSldWtDx3qARqNhwp4JrL64GnOlOftf3k83j256OVdBcQHd1nUjOC4YP1c/gl8NLjUwUYjqrioUFgDJISEcfvVV1EVF+I4fj/9bbxk6kniEzPxMbMxtUJqUjNtbeGIhHx75kJ6ePXGzceNWxi1O3j5ZqshQoKBVnVZ09+hOp/qdaF23NfVt66NQKAz1ZQhRJciceaLCZcfHc2ziRDJv3sTM1pbuq1ZVyp4KKJmZaWf4ThQo+HHIj3orKjQaDRP2TiA4Lhh7S3u2vbBNigohKimX1q1pN28ewe+9x9XVq6np4YHnwIGGjiX+hp2lXanPw1LDUGlU/HrzV6BkRe+enj2pX7M+ucW5/HbnN66nXickMYSQxBA4U/I8Z2tnWtdtTes6rWlVpxXNXZrj6eCJqYm8lBLiPumxEBUq9coVjk+ZQn5aGlYuLnT7+mscmjY1dKxHup56nYuJF3mpxUt6O8eyM8uY/ut0TBQm/DLqF3o36q23cwlRWVWVHov7Ln/+OVdXr8bEzIwe335bKXtdxcNdS7nGD1d+IOhaEOFp4drtDpYOpLyTQlJOEkeijhB0LYiI9AiupVwr1aNxn7nSnCZOTWjm3KzUw8vRSybdEEZJCgtRITQaDZGbNxOyaBHqoiLsmzSh21dfYV27tqGjPUCj0XDr7q0KG8i358YeBm4aiFqjZvkzy3mrg9xWIYxTVSssNGo1J6dPJ/bAASwcHOi9YQO27vqZ1EHoT1hKGDuv7+Rg1EFq29Rmw5AN2n11P6tLYnYing6eeNp7Ym1mTU5RDonZiURnRJNblPvQNk1NTPF29KaZczN8nX3xdfGlmXMzGjs1loJDVGtSWAi9K8zK4vzHHxOzdy8A9Xr2pOPChZVySlmNRsP0/dNZFbKKnS/u1HvPQXBcMD3W9SCvOI9XA17lm/7fyD28wmhVtcICoDgvj4NjxpB+9SrWderQe/16atSta+hY4h/SaDTa38H3Cu7hv9Kf6Izohx7bs2FP1gxYQ1hKGGEpYey4voO03DTi7sWRU5Tz0OcoFUq8nbxLig3nkmLD18VXCg5RbUhhIfTqTnAwwe+/T+6dOyiUSlr+3//RdOzYSvniWaVWMX73eNZeWgvAl32/ZFLbSXo7X3hqOJ3XdiYtL43nvJ5j54s7MVOa6e18QlR2VbGwAMhLTeXgmDFkRUdT092dXuvWYeXsbOhYQkfSctMISQzhQsIFwlLCCE8LJzw1nEC/QL58vmQl9nsF97Bb9MdYDgUKXGq4YGthi1KhRKVRkZSTxL2Cew89h5mJGS1cW9C6TskYjtZ1W9PCpQUWphYV8jUKoStSWAi9KMjI4NK//83NoCAAbOrXp+OCBTi3amXgZA9XUFxA4I5AtoZtxURhwtoBaxnTcozezhebGctT/32KmMwY2tZty5ExR6hhXjVeRAmhL1W1sADISUzk4OjR5CQkYN+4MT3/+18s7O0NHUvoiUajoUBVgKWpJVAyVfjknydz8+5NItMjH7hF6o3Wb/D1818TnxXPhYQLjN4xGmsza9QaNfcK7lGgKnjgHGYmZvjX9qdz/c4ljwadqVtTesNE5SaFhdAptUrFrR07uLx8OQV37wLgNXw4AW+/XSlvfYKSd6MGbx7MidsnMDMxY9OwTQzxGaK388Xdi6Pbd924efcm3o7enBp3Cuca8u6mEFW5sADIun2bg6NHk5eSgmPz5vT89lvM5O+X0dFoNCTlJBGZHsnN9JJCo3299vRr3A+Ay3cu03JVy4c+1wQT3O3duVdwj7S8tAf2e9h70Ll+Z7p7dKeXZy+9LdQqxD8lhYXQCY1GQ+LJk1z697/JuHEDADsvL9rOmVOpZ0pJyUmh89rORKRHYGthS9DwIHp59tLb+RKyEuj2XTci0iPwdPDk6Jij1Lerr7fzCVGVVPXCAiAzMpKDY8ZQkJGBc6tWdPv6aykuRCn5xfmEJodqi47Iu5FcT71OaHIo2YXZLOm9hBkdZxCTGcPWq1uZeXAmJpigRv1AW40cGtGzYU96efaiR8MeOFk7GeArEuIPUliIctFoNCQcP07o11+T9vvvAJjb2tJ8wgQav/QSJmaVe8yAWqNm1PZRnI49zc8v/Yyvi6/ezhWdEU2f7/sQkR6Bh70Hx8Yeo4FdA72dT4iqpjoUFgDpYWEcGjeOoqwsHJs3p/uqVXJblHgsjUbD7czbWJlZ4VLDBYB9EfsYtHkQharCxz7fRGFCx3od6de4H/0b96eZc7NKOZ5RVG9SWIh/RKNWE3fkCKErV3I3LAwApaUl3i++iO/rr1fqP6IajYb84nztAnT5xflk5mfiauOqt3OGJofyzA/PkJCVgLudO0fHHsXD3kNv5xOiKqouhQWUFBdHXn+dgowM7Ly96b5qFdau+vsdI6qvIlURkemRXE66zIWEC5xPOE9IQgg5RTl88NQHZBdmczDqIKHJoaWeV8emDoOaDmJw08E87f60DAQXFUIKC/FEinJyiNq5kxs//si9qCgATK2s8B45Ep+xY7F0qtzdsJn5mbyy8xU0aNg+fHuFvJtz6vYp+m3sR0Z+Br7Ovux/eT9utm56P68QVU11Kiyg5Laow6+9Rl5KCta1a9Nt5Ursvb0NHUtUAyq1ivC0cOrb1qemRU0A/u+X/2P52eUPPd7MxIyeDXvyst/L9G/SH1sL2wpMK4yJFBaiTLLj4rjx44/c3L6doqwsAMxsbGj80ks0GT0aSwcHAyd8vBMxJwjcEUhMZgzmSnOCXw0moE6AXs/53aXveGPPGxSqCulUvxO7R+7G0cpRr+cUoqqqboUFQHZ8PEffeIN7UVGY2djQZdky6nTqZOhYohoqVBVyJekKZ2LPcDTmKEeijnA3/+4Dx5krzWlTpw2+Lr5MaTcFP1c/A6QV1ZUUFuJvqYuKiD92jMitW0k8dQr+91+lprs7jUeNwnPQoEo709OfFaoKmXtkLotPLUaDBk8HTzYO3Ug7t3Z6O2exupiZB2ayLHgZAIObDuaHIT9gbWatt3MKUdVVx8ICSqbfPv7mm6RcvIjCxISAt9+myejRcv+70CuNRkNMZgwnYk6wN2IvjRwaEXQtiPC08FLHWZla0bpua0b6juT5xs/LTFOiXKSwEA/ISUggcts2bm3fTl5KinZ77U6daBIYSN0uXVCYmBgwYdmdjj3N+N3juZpyFYBxLcex/Nnl2q5jfYi7F8fL21/mWMwxAOZ2ncucrnMwUVSN75kQhlJdCwsAVUEB5+bNI2rnTgDcn3+ednPnVok3Z0T1odFouJpylen7p3Py9knyivMeOMbZ2pnLEy5Tp2YdAyQUVZ0UFgIAVWEhCcePE7ltG4knT2p7JyydnPAcNIhGw4ZRs0HVmsFIpVbR4usWXEu9Ri3rWqzqt0qv61MA7Ly+k3G7xpGel46NuQ3/HfhfhjUbptdzClFdVOfCAkpe1IX/8AO/LVmCRqWipocHXZYuxcHHx9DRhJG6kHCBL85+wS+Rv5CS+8cbiaYmpvRs2JNhzYaxN2Iv9pb2dHPvRjePbtKjIR5JCgsjl3HjBjd37CB6zx4K0tO12107dMB7+HDcundHaW5uwIRPplhdjFqjxlxZkvlY9DHWXV7Hkt5L9Dq/d3peOm//+jb/vfRfAFrXac3GoRvxdpKBmkKUVXUvLO5LDgnh9MyZ5N65g4mZGS0mT8bnlVcwMTU1dDRhxK6nXmfDlQ1sDdv6wO1Sf+Zh70E3j250c+9G94bdZdp0UYoUFkaoICODmJ9/5tZPP5F+9ap2u2WtWjQcMACvYcOo6V613pHQaDTsCt/Fu4feZYz/GN7t8m6FnXdr2Fbe3PcmyTnJKFAwveN0FvRcoC1uhBBlYyyFBZT8Hg7+4APijxwBwMnPj/bz58usUaJSuJF2g6CwILaGbeW3O7/97XGDmw5m+4jtQMnfw1t3b+Hp4Cnjh4yYFBZGQq1Scef0aW799BNxhw6hLioCwMTUFLdu3fAcPJg6XbpUuXfM1Bo1O6/vZP7x+Vy6cwkATwdPwqeEY2qi36/lYuJFZvw6g6PRRwHwqeXDmgFr6FRfZnwR4p8wpsICSl6IRf30EyGLFlGUnY3C1JSmgYE0nzhRxl6ISuPW3VsEhQWx7do2zsWfK7WvoX1DJredzNBmQ1Fr1DT6ohF1bOrwtPvT2kcz52YyxtCISGFRjWk0GtJDQ4nZt4+YX34hLylJu8++SRM8Bw/Go1+/KjFV7F/lFeXx4+8/svzscu2iQDXMavBmuzeZ1WUW9pb2ejt3ZHokHx//mO8vf48GDZamlszqPIvZXWbLAkRClIOxFRb35SQmErJoEXEHDwIlvcd+kyfjOWRIlXuzR1RvMRkxbL+2naBrQZyKPVVqn4edB7fv3UatUZfa7mjlyFMNnmJah2l08+hWgWmFIUhhUQ1lREYS8/PPxOzbR/bt29rt5nZ2ePTrh+fgwThW8cGCf14IqKZ5Taa2n8q0DtOoZV1Lb+cMSwlj4cmF/Pj7j9pfnC+1eImFPRfKPaZC6ICxFhb3xR87RsjChWTHxgJg27AhvuPH4963rxQYotKJvxfPjus72Ba2jRO3T5QqKKzNrLGzsCM9L50CVQEAO0bsYFDTQQCcjz/PzvCdtHdrT/t67XGp4WKIL0HogRQW1YBGoyEzMpK4w4e5/csvZNy4od2ntLTErXt3PPr2pU6XLlVqIPZ9ablp7Li+A39Xf9q6tQXgWso1nv/xeSa3ncy4gHE4WOmn16VYXcyeG3v4z7n/cCjqkHZ7X+++zO06V69rYQhhbIy9sICSGfoit2wh9OuvKcjIAMCmfn28X3wRz0GDsLC3N2g+IR4mLTeNXyJ/YU/EHvZF7COzILPUfreabgzxGUL/xv3p0qALn576lI+OfaTd39C+Ie3rtS8pNNza07puaxmnWEVJYVFFqYuKSPntN+IOHyb+6FHtO1xQMm6idufOePTti1v37lXyXt3YzFj2Ruwl6FoQR6KOoNKoGNViFD8M+UF7jFqj1st9mxqNhstJl/n+8vf8GPojd7LvAGCiMGFgk4G8/9T7tK7bWufnFcLYSWHxh6LsbG5s3Mj1desouFuyerKJuTkNnnkG7xEjqNWypQyQFZVSkaqIU7Gn2HNjDwduHeBK0pVS+y2UFng5emGiMCEjP4PYe7EPtHFlwhVauLYA4PKdy+QW5dLCtQU25vJ6sLKTwqKK0Gg0ZNy4QVJwMHfOniX5/HmKc3O1+03MzandoQP1evakfq9eVfJdLY1Gw8wDM/k58mfCUsJK7fN39eeVlq/wVoe39HLuQlUhZ2LPsCt8FzvDd3Lz7k3tPicrJ15r9RoT20yU+buF0CMpLB5UnJtL9N69RGzezN1r17Tba3p4UK9HD+r16EEtf/8qs2ipMD53su9wOOowB24d4MDNA8RnxT9wjLudO7Wsa1GgKiA9L53wyeHYWJT8LgjcEcgPV35AgQJvJ29a1m6Jr7MvzZyb4VPLBx9nHxkcXolIYVFJFWVnkxYaSurly6RduULqlSul1pkAsHBwwK1rV9y6d6d2x45Vpmfibt5driRd4dKdS6TnpTOv+zztPv+V/lxJuoKJwoT2bu0Z2GQgQ5sNxcvRS6cZcgpzCEkM4UzsGY5EH+HE7RPkFv1RqFkoLejfpD+BfoE86/WsdMkKUQGksPh79yfjiNi8mZh9+1Dl52v3WTo54datGy7t2uEcEECNunWlN0NUShqNhoj0CE7dPsXJ2yc5FXvqoWtmmChM8KnlQ6s6rYhMj+R66nXu5t994DilQknOeznaiVM2hW4iqyALTwdPGjk2or5tfZQmSr1/XeIPUlgYmKqggOzYWDJv3iQjIoLMiAgyIiPJionRrn59n9LKCpfWrandsSO127fHvkmTKvEu1a7wXQTHBROaHMrlpMvczvzTgHKlOdmzszFTmgGw8feNmJqY0suzl07GTRSpiohMjyQsJYyrKVe1H6+lXEOlUZU61tnamee8n2NA4wE84/WMdLkKUcGksCibouxsEk6eJO7QIRJOnKAoK6vUfisXF5wDAnBq0QI7Ly/sGjXCunbtKvH3QhiflJwUTsee5lz8OS7eucjFxIsk5yQ/9FhTE1OcrJywMLWgUFWIuYk5K/quwMPeA3c7d5754RnOxp/VHm9mYoa7vTsN7RvSyKERX/f7WrsvJiOGGuY1cLRylB4PHZLCQk/UxcUUZWdTkJlJfmoq+amp5P3vY05CAtlxceTEx5OXkvK3bdRwc6OWnx9O/v7U8vPDwcfHYIOvC4oLMFeaa98F+z3pd26k3SA5J/mPR24ySdlJxN2LI+LNCO27BCODRrIpdFOp9tzt3PGv7Y+/qz/vdHqHmhY1nyhPXlEe6XnppOWllXzMTSM+K564e3HaR+y9WOLuxVGsLn5oG3Vr1qVDvQ50qd+Fnp49ae7SXH65CGFAUlg8OVVhIckXLpBw4gSpv/1G+rVraIof/J1namWFracnNvXqYV279h8PV1fM7e0xt7XFvGZNmX1KGJxGoyExO5GLiRf5LfE3wlLDuJ56nfDUcPKK8x75XHOlOaYmpmg0GgpUBaVmqnKp4cJ3A7/D3tIee0t7Rm0fxW93fkOBglrWtUo93O3cWfbsMu1zT90+RaGqkJoWNalpXhMbcxtqWpR8lNcNpUlh8agM8fHE/vorxXl5qIuKUBUWoiooQF1YiKqoCHVhIerCQorz8ynKzi55ZGVRmJ2NKu/R//n/zNTaGjsvL+y9vf/46O2NVa3yT50aFBbEtdRrFKoKtY8iVVHJv9WFrOm/RlsALD65mINRB8kpzCGrMIusgiztxyJ1ETnv5WBtZg38cc/j34mZFqOdgnX95fWciz9HY6fGtKzdEj9Xv4euM5Gck8zS00tJykkityi31COnMEf778yCTPKL8x94/t+xMbehmXOzkketko9+rn7Ut6v/BN9JIYS+SWFRfsV5eaRfvUrKb79x9/p1Mm/eJCsqCvVDio2HMatZs6TIsLPD3MYGpaUlSguLUh9NLSwwMTfHxMwMaxcXPPr1w8TMTM9fmTB2ao2a2MzYkiIjLZyYjBiiM6OJyYghJjOG1NxUnZ3LQmlB5wadsTK1wsrMikO3Dj30ViwAOws7ZnScgamJKaYmpgSFBZFVlEUt61pYKC2wMLXAQmmBudIcB0sHvnz+S+1z1/62lqi7UShNlCgVylIfLZQWvNn+TZ19TRWl0hYWWVlZtGnThmPHjhmssDg1cyYJR4+Wqw2ltTWWTk4lD0dHLJ2csHZxwdrNDZs6dbB2c8PCzk5v98OOChrFnog9f7v/zow7WJlZATB+93g2X938t8dGTInAxaZkrunlwcv5OeJnnK2dca7hrP1Yy7oW9W3r4+fq98SLxa0JWcOMAzPKfLxSocTBygEHSwccrByoY1OHujXr4lbTDTdbt5KP//u33G8sROWXk5ND3bp1AUhISJDCQkfURUVkx8dzLyqK3KQkcpOSyLtzh5zkZApSUynMyqI4O/sft9/l88+p07GjDhML8eRyCnNIyEoodSdFUnYSybnJ3M27S2ZBJvcK7pFZkElmfiYZBRlU9EtgRytHot6K0n7+/I/Pc/L2yYcea6G0IPmdh98SZig1atR47OupSltY3Llzh65duxo6hhBCCCGEEEavLHcRVdrCQq1Wk5ycXKbqSAghhBBCCKE/VbrHQgghhBBCCFF1yFB2IYQQQgghRLlJYSGEEEIIIYQoNykshBBCCCGEEOUmhYUQQgghhBCi3KSwEEIIIYQQQpSbFBZCCCGEEEKIcpPCQg9iYmJ49dVXCQgIoFu3bqxZs+axz4mLiyMgIICzZ89WQEIBT3adwsPDGTlyJH5+fvTv35/g4OAKTGrcnuQ6HThwgOeee46AgABGjhzJ1atXKzCpuG/8+PG8++67f7v/9OnT9OvXD39/f0aPHk1sbGwFphP3Pe46BQUF8eyzzxIQEMALL7xASEhIBaYT9z3uOt13+fJlfHx8iIuLq4BU4q8ed53OnTvHwIED8ff3Z/jw4Vy/fr0C01UcKSx0TK1WM378eBwcHNixYwfz5s3j66+/Zvfu3Y983kcffURubm4FpRRPcp2ysrIYN24cXl5e7N69m969ezNlyhTS0tIMkNy4PMl1ioiIYMaMGbzxxhvs3LkTHx8f3njjDfLy8gyQ3Hjt3buXY8eO/e3+hIQEJk+ezJAhQ9i2bRuOjo5MmjQJWVKpYj3uOh0/fpz58+czadIkfvrpJzp37sz48eNJSkqqwJTicdfpvqKiIj744APUanUFpBJ/9bjrFBsby+uvv07v3r3ZuXMnTZo0YdKkSRQWFlZgyoohhYWOpaam4uPjw0cffYSHhwddu3alY8eOj3ynZ9euXeTk5FRgSvEk12nHjh1YW1vz0Ucf4e7uztSpU3F3dyc0NNQAyY3Lk1ynU6dO4eXlxaBBg2jQoAHTp08nJSWFyMhIAyQ3ThkZGXz66ae0aNHib4/ZunUrzZs3Z9y4cXh7e7Nw4ULi4+M5d+5cBSY1bmW5Tjt27GDQoEEMGDAAd3d3pk2bRq1atcr0IlfoRlmu031r1qzBxsamAlKJvyrLdfrhhx/w8/NjypQpeHh48N5772FiYsKtW7cqMGnFkMJCx1xcXFi+fDk2NjZoNBpCQkI4f/487dq1e+jxd+/eZcmSJcyfP7+Ckxq3J7lO586do2fPniiVSu22oKAgunbtWpGRjdKTXCd7e3siIyMJCQlBrVazfft2bGxsaNCggQGSG6fFixczcOBAvLy8/vaYy5cv06ZNG+3nVlZW+Pr6cunSpQpIKKBs1+m1117jlVdeeWB7VlaWPqOJPynLdQKIiopiw4YNZbpdSuheWa7TuXPn6NOnj/ZzKysrDh48SNOmTSsiYoWSwkKPevTowUsvvURAQADPPPPMQ49ZtGgRgwcPxtvbu4LTifsed51iY2NxdHTkww8/pHPnzgwfPlzuNTaAx12nvn370q1bN1566SWaN2/Op59+yhdffIGdnZ0B0hqfM2fOcOHCBSZNmvTI41JSUnBxcSm1zcnJiTt37ugznvifsl4nX19fPDw8tJ8fP36c6OhoOnTooOeEAsp+nTQaDXPmzOHNN9/EycmpgtKJ+8p6nWJjY7G0tGTq1Kl06tSJ0aNHV9vedCks9OiLL75g5cqVXLt2jYULFz6w//Tp04SEhDz2P6TQr8ddp9zcXFavXo2zszPffPMNbdu25dVXXyUxMdEAaY3X467T3bt3SUlJYc6cOWzZsoWBAwcye/ZsGQtTAQoKCpg7dy5z5szB0tLykcfm5eVhbm5eapu5uXm1vNe4snmS6/Rnt2/fZvbs2fTv3x9fX189JhTwZNdp27ZtFBUVMXz48ApKJ+57kuuUm5vL0qVLadu2Ld988w116tRh7Nix1fI2eCks9KhFixZ0796d2bNns2nTplJ/OPPz85kzZw5z5859ol/wQvcedZ0AlEolPj4+TJ06lWbNmvHOO+/g4eHBzp07DZTYOD3uOi1dupTGjRszatQomjdvzscff4yVlRVBQUEGSmw8/vOf/9C8eXOeeuqpxx5rYWHxwLUrLCzEyspKX/HE/zzJdbovKiqK0aNHU79+fT755BM9phP3lfU6paSksGzZMubPn49CoaigdOK+J/l5UiqV9OjRg8DAQHx9ffn4449Rq9UcPny4ApJWLFNDB6huUlNTuXTpEr169dJu8/LyoqioiOzsbBwdHQG4cuUKsbGxTJ06tdTzX3/9dQYNGiRjLvSsrNcJwNnZGU9Pz1LP9/DwkB6LCvAk1+nq1asEBgZqPzcxMaFp06YkJCRUaGZjtHfvXlJTUwkICADQFg779+/nt99+K3Wsq6srqamppbbdH6Qv9OtJrhOUzLQ2duxY6tevz5o1a+RNsApS1ut08uRJ7t69y4gRIwC0M6v169ePCRMmMGHChApOblye5OfJ2dmZhg0baj83NzfHzc2tWr6OkMJCx+Li4pgyZQrHjh3D1dUVgNDQUBwdHUu9CPLz8+PXX38t9dw+ffrwySef0Llz5wrNbIzKep0AWrZsyfnz50ttu3XrFv369auwvMbqSa6Ti4sLN2/eLLUtKiqqTDOqiPL5/vvvKS4u1n6+dOlSAN5+++0HjvX39y81RikvL4+wsDCmTJmi/6BG7kmuU3JyMuPGjcPd3Z1vvvmGGjVqVFhOY1fW69S7d29atWql/TwpKYnAwEBWr15N48aNKyasEXuSn6eWLVsSHh6u/bywsJDY2Fjq1aun/6AVTAoLHWvRogW+vr689957zJ49m/j4eJYsWaJ95yAlJYWaNWtiaWmJu7v7A893dXWVAVgV4Emu04svvsgPP/zAihUrGDBgAD/99BOxsbEMHDjQwF9F9fck12n48OG8++67NG/enICAALZu3UpCQgKDBw828FdR/bm5uZX6/P6LUHd3d1QqFenp6djZ2WFubs7QoUP59ttvWb16Nd27d+fLL7+kXr16tG/f3hDRjcqTXKfFixejVqv517/+RW5urnadJWtrayky9Kys18nGxqbUFLP3Zy6sW7cu9vb2FZbXWD3Jz9OYMWMYNWoUrVu3plOnTqxZswYLCwu6detmgOT6JWMsdEypVPLVV19hZWXFiBEjeP/99wkMDGT06NEAdOnShZ9//tnAKcWTXCc3NzfWrFnDkSNH6NevH0eOHGH16tXad9CF/jzJderbty8ffvghq1atYtCgQVy8eJF169ZJoW5giYmJdOnSRXtrQL169VixYgVBQUEMGzaMjIwMvvzyS7lH3MD+fJ00Gg0HDx4kNTWVZ599li5dumgfa9euNXRUo/bXnydROf31Ovn7+7N8+XLWr19P//79uXnzJmvWrMHa2trASXVPoZHlToUQQgghhBDlJD0WQgghhBBCiHKTwkIIIYQQQghRblJYCCGEEEIIIcpNCgshhBBCCCFEuUlhIYQQQgghhCg3KSyEEEIIIYQQ5SaFhRBCCCGEEKLcpLAQQgghhBBClJupoQMIIUR1smfPHmbMmAHAiBEjmD9/fqn97777Ljt27HjidqdMmcKbb74JQJMmTZ7ouTVr1uTChQtlOvbs2bPalc3LqmfPnnz11VdP9JyHycvLIy0tjXr16mm3bd++ndmzZ+Pq6srx48fLfQ59ioiIwNvb29AxhBDCYKSwEEIIHQoKCtL+e/fu3cycORMbGxvtNg8PD1q1avXA827cuEF2djZOTk64u7s/sL9OnToPbPPw8MDR0fGxmWrUqFHW+KU0b94cc3Pzxx7n5eX1j9r/s927d7NkyRLefPNNXnjhhXK3V5GioqL45JNPyM3NZePGjYaOI4QQBiOFhRBC6EhCQgLBwcHY29vj4eHBpUuX2L17NyNHjtQeM2HCBCZMmPDAcwMDAzl37hxPP/00ixYtKtP53njjDYYMGaKz/H/1+eefl+o90Kdly5aRlJT0wPbevXvj7++PmZlZheT4J/bs2cPJkycfWjAKIYQxkTEWQgihI9u3b0etVhMQEECPHj0A2Lx5s4FTVW01a9akUaNGNGjQwNBRhBBCPIYUFkIIoQMajUY7duLpp5/mueeeA+DatWtcunTJgMmEEEKIiiGFhRBC6EBwcDBxcXGYmJjQs2dPGjRogJ+fH4BR3ne/Z88eXnnlFdq1a0fz5s3p2LEjr776Krt27UKtVmuPW7FiBU2aNCE+Ph6ADz74gCZNmrBixQqgpBeoSZMmPP3006XaDwwMpEmTJpw+fZorV64wceJE2rdvT8uWLRk8eDA//fQTUFLwbd26lSFDhtCyZUtat27NuHHj/rbYu3fvHqtXr2bUqFG0b98eX19f2rRpw5AhQ1ixYgWZmZnaY+Pi4mjSpAn/+c9/ALh48SJNmjTR9lbdp1Kp2LFjB6NHj9Z+P3r06MGHH35IdHR0eb7NQghRqcgYCyGE0IH7g7bbtWuHq6srAP369ePKlSvs27eP9957Dzs7O0NGrDALFy7ku+++A8DNzY369euTnJzMyZMntY9PP/0UKBmU3qpVK0JDQyksLMTd3R0nJ6eHDlZ/mF9++YWgoCDMzc3x8PAgISGBsLAwZs2aRW5uLhcvXmT37t04OTnRsGFDIiIiOHXqFOfPn2fr1q00bdpU21Z0dDRjx44lMTERU1NTGjRogJubG/Hx8Vy9epWrV6+yd+9egoKCqFGjBhYWFrRq1YrExEQSExOxsbGhcePGODs7a9vMyclhypQpnD59GgBXV1fq1atHdHQ0W7ZsYdeuXSxZsoQ+ffro6LsvhBCGIz0WQghRTllZWRw4cACAAQMGaLf369cPpVJJQUHBP5pitiq6efMm3333HRYWFqxfv57Dhw8TFBTEiRMnWLx4MSYmJuzcuVPbYzBs2DA2btyofTH++uuvs3HjRoYNG1am823evJmePXty/PhxduzYwfHjx+nSpQsAH3/8Mb/++iuffvopp0+fZseOHezfv586depQWFjIunXrSrX14YcfkpiYSMuWLTly5Aj79u1j+/btBAcHa7NHRUVpe0OcnZ3ZuHEjQ4cOBaBx48Zs3LiRL774olSbp0+fxtvbm61bt3L8+HG2b9/OmTNnmDBhAvn5+bz99tvcuHGjPN92IYSoFKTHQgghymnv3r3k5+djaWnJM888o93u5OREp06dOHHiBJs2bWLs2LE6Pe/s2bOZPXv2Y49bv3497du3f+L2e/bsWabjwsPDH/h3w4YNHzjnoEGDuHLlCpmZmRQWFj5xnoext7dn0aJFWFtbA2BhYcErr7zCyZMnUavVvPbaawwcOFB7vJubG8OGDWPFihVcvXpVuz01NZWIiAigpCBxcXHR7lMoFAwaNIgdO3YQHBxc6ut9lOvXr7N3716srKz49ttvtT1Z93P+3//9HzExMezbt4+vvvqK5cuXl+dbIYQQBieFhRBClNP926C6d+9eas0KKOnBOHHiBFFRUZw5c4aOHTvq7LxlXceiZs2a/6j9sq5j8Wf31+C4fv06ixcvZsSIEXh4eGj3z5kz5x9l+Tvt27fXFhX3ubm5af/dtWvXB55zv2jIzs7WbqtVqxbBwcHaAvGvVCqV9trm5+eXKdv9Xqw/3x73VwMHDmTfvn0cP34clUqFUqksU9tCCFEZSWEhhBDlEBERwZUrV4DSt0Hd17t3b6ytrcnNzWXTpk06LSwq4zoWvr6+9O/fn927d7N27VrWrl2Lm5sbHTt2pEuXLjz11FMPFF/lUbt27Qe2/XnNi4cVXqamf/+nz9LSkvj4eH7//Xdu375NbGwsN2/e5Nq1a+Tm5gKUGnz+KPd7QEJDQ0utZfJnBQUFQMlYjKSkJOrWrVumtoUQojKSwkIIIcrhzyttT5w48ZHHHjp0iNTUVGrVqqXvWAa1ZMkSOnTowNatW7l8+TLx8fFs27aNbdu2YWFhwfDhw5k5c+YT94Y8jJWV1SP3KxSKMrd169Yt5s6dy7lz50ptt7GxoU2bNiQnJ3P9+vUyt5eVlQVAWloaaWlpjz3+3r17UlgIIao0KSyEEOIfKioqYteuXQDY2tr+7YtcjUZDcnIyRUVFbNu27aErb1cnCoWCYcOGMWzYMNLT0zl79iznzp3j2LFjxMfH8/333wMlU8tWFmlpabz88sukpaVRt25dhg8fTrNmzfD09KRevXooFApmzJjxRIXF/f8P48aNY9asWfqKLoQQlYYUFkII8Q8dO3ZM+0702rVradGixd8e269fPyIiItiyZQvjx4/HxKR6TsqXnZ1NdHQ01tbWeHp64ujoyHPPPcdzzz2HRqNh3rx5bNy4kZ07d1aqwiIoKIi0tDTs7e0JCgp66C1USUlJT9Rmw4YNgT9uiXqYu3fvcuvWLerUqUOdOnWeqIdFCCEqm+r5l00IISrAtm3bAGjSpMkjiwpAe499fHw8x48f13s2Q/niiy8YOnQoixcvfmCfQqHQjjFRqVQP7IOS3h1DiIuLA6Bu3boPLSoiIyO1U+SWNXv37t0BOHPmDDdv3nzoeT/77DNeeuklAgMDDfa1CyGErkhhIYQQ/0BKSgonTpwAKNMA6oEDB2pnL9q0aZNesxnSgAEDUCgUHD16lDVr1lBUVKTdl5CQwMqVK4EHZ2u6/725vwJ3RfP09ARKZrPav3+/drtGo+H48eO89tpr2q8lLy+v1HNr1KgBQHJyMsXFxdrtbdq0oUuXLhQXF/P6669z8eJF7b7CwkK++uortm7dCpSs31Fde7GEEMZDboUSQoh/4KeffqK4uBgzM7OHzgb1VzY2NvTv35/Nmzdz/PhxEhMTy7y69N9ZtWqV9oXp40yYMOGhU68+yltvvVXmAdZffPEFzs7ONG/enGnTprFs2TKWLFnCqlWrqFevHnl5ecTGxlJcXEyDBg149913Sz2/WbNm3LhxgzVr1nDs2DH69OnDpEmTnihveQwbNowff/yRmJgYpk6dipubGw4ODiQmJpKWloaZmRnt2rXj3LlzD9wS5ePjA5QURX369MHFxYWNGzeiUChYunQpb7zxBpcvX2bkyJHUq1cPOzs7YmNjuXfvHgBjx47lxRdfrLCvVQgh9EUKCyGE+Ae2b98OQI8ePcq0lgSU3A61efNmVCoVmzdvZtq0aeXKEB0dTXR0dJmOLcusRH8VGhpa5mPvT5sKJUWMl5cXW7Zs4erVq9y4cQNLS0t8fHzo3bs3gYGBD6w9MWvWLPLy8jh9+jRRUVF/e+uQvtjY2LBt2za++eYbjhw5QlxcHKmpqdSuXZtu3boxZswYrK2t6dWrF9evXychIUE7g1OHDh2YOXMmGzZsIDk5mcLCQlJTU3F2dsbBwYENGzawfft29uzZQ3h4OHfu3MHW1pauXbsyYsSIMi9EKIQQlZ1CIzd1CiGEEEIIIcpJbugUQgghhBBClJsUFkIIIYQQQohyk8JCCCGEEEIIUW5SWAghhBBCCCHKTQoLIYQQQgghRLlJYSGEEEIIIYQoNykshBBCCCGEEOUmhYUQQgghhBCi3KSwEEIIIYQQQpSbFBZCCCGEEEKIcpPCQgghhBBCCFFuUlgIIYQQQgghyk0KCyGEEEIIIUS5SWEhhBBCCCGEKLf/B3pfMdPSeJblAAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 800x600 with 1 Axes>" ] @@ -1059,20 +997,20 @@ "# color='blue'\n", "# )\n", "\n", - "# One-step\n", - "sns.kdeplot(\n", - " estimates['analytic_eif-one_step'], \n", - " ax=ax,\n", - " color='red',\n", - " linestyle='--'\n", - ")\n", + "# # One-step\n", + "# sns.kdeplot(\n", + "# estimates['analytic_eif-one_step'], \n", + "# ax=ax,\n", + "# color='red',\n", + "# linestyle='--'\n", + "# )\n", "\n", - "sns.kdeplot(\n", - " estimates['monte_carlo_eif-one_step'], \n", - " label=\"One-Step\",\n", - " ax=ax,\n", - " color='red'\n", - ")\n", + "# sns.kdeplot(\n", + "# estimates['monte_carlo_eif-one_step'], \n", + "# label=\"One-Step\",\n", + "# ax=ax,\n", + "# color='red'\n", + "# )\n", "\n", "# DoubleML\n", "sns.kdeplot(\n", @@ -1107,17 +1045,17 @@ "\n", "plt.tight_layout()\n", "\n", - "plt.savefig('figures/causal_glm_performance_vs_estimator.png')" + "plt.savefig('figures/causal_glm_performance_vs_estimator_just_doubleml.png')" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1157,19 +1095,18 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 22, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'monte_carlo_eif-tmle'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# fig, ax = plt.subplots(figsize=(6, 3))\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# TMLE\u001b[39;00m\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(\n\u001b[0;32m----> 5\u001b[0m \u001b[43mestimates\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmonte_carlo_eif-tmle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m,\n\u001b[1;32m 6\u001b[0m estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-tmle\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 7\u001b[0m color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mblue\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 8\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m# Plot y=x line for min and max values\u001b[39;00m\n\u001b[1;32m 11\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mmin\u001b[39m(estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmonte_carlo_eif-tmle\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28mmin\u001b[39m(estimates[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124manalytic_eif-tmle\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 14\u001b[0m )\n", - "\u001b[0;31mKeyError\u001b[0m: 'monte_carlo_eif-tmle'" - ] + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -1203,12 +1140,12 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] diff --git a/docs/examples/robust_paper/results/ate_causal_glm_tmle.json b/docs/examples/robust_paper/results/ate_causal_glm_ihdp.json similarity index 100% rename from docs/examples/robust_paper/results/ate_causal_glm_tmle.json rename to docs/examples/robust_paper/results/ate_causal_glm_ihdp.json

)FZVg&cIS_HbJ}lJ3SV(WFp$$9Cn{ zq*%`V!r>2Jhg97|6*oDp?yrmnd3BVqIpWoEr%8nNGlm}sctFmwJ(~Mj$}3E4?AcMf z8pIIORDO-c8NoxHPRt$9Fq~^@Z>Z7ziCW4kPhYZkZblZr!D9iup$nh<1eFR43#&hWyv!bBdU{$;wmhX6nBdkgWu8gQ4&{!|^Tt9Y5b3DwD$|VN8g?=jw-i`EAIZ5up{}L0${2?% z*=V9_bCg#3u1Kw>gZhEMG%B9Q!Qjw(YHn`2^yiCQ$|e}@=r4eZ+3dVC`2_r2dwlvc z3Vo#-v8G?nRXW8N5xWMYQ(V*`ph4?$=S|~_Lx_>(df72IZ;|y|{T1gKGIG7#Z#`)Da{5h)m95rxgda+v#FI+_^pE9VeG!TOhGA?s}|V*^hRO>}Q+%o!wpuS`U#hd+(B>rqUzDrDSCThuyNYEO=&!;n%a>7qI=C+uQX(ac0};1bfbK_e*B!ZRGJF$#^W-ir-%RP1)2X4z)X!q7)AtgeVS^$$9^MdsWnfI{<_7oqT0CyAUOwK- zH>?VUo&`ZK#S@(1J!yJFZND`mi`ZttYK9~beve&?2aa!{!7_^3x*<{en%yv;i(+l0 z@l5Cpj|(FEWj1lR#OWr6{tpY|E@Lw6&+L3gzr5PhvT<_{V~I9bW<+jLB_BWP5B>1* z&G0Yo8v^t>Z3-FMdI2P*UzF8MA_$o_Gg=Sal3O5S5hgG2o3&BwF83>iP2&Cg+Boef0cV5?TKSAEQS80WvyQFMh;-SC+b#D0e127)uGS+za6!(vDi>BuEOF`w zq~H`-agbgNalyqHdf=X)pO?LuMsJQ_e8ZOF^=D?oH_a=4_X*E@9%mQ%-biEBkTbec zde2ldAsV+FEsjk%SUq@__d*=cRF zg>g4RUvIl(DV3e#;NqbaSD|d)WtkfwHgP`VR9z?S?yy$*AaQ<(ak9VWpP9)fe)N~T z@U=x0yJkizfF3X%G$G}-sPG4OfY8l0%For>-~O0g+j$)nBt&Qoo*b~K`2lE3{r;T{ zSs$EIA>e()j=KnU-}u`$p+t-#PficF^h&I0Zwd$?oz1VOH*+7A5`lfUxj#o&mNrt- z|2+R@(^BZo#h2KH@fPHIa6KCVH(K3(1{xihT0`|>FrOG$IZ1KZ;Jd##dD56Zn^=C5 z${(qgh>4I1G1!N2w3E-V9dmVO*I6fL-K)#@`z9~WU1enWTn>sz zv$XWZ?2Dhal0mvk2)kvCZ!9}16|9H!2U>s1zTS@7`jNx_T;2&Qgylpb+t zfw!Y8~JQ6a|$Zw{2{$sA-#!Q z{-9reu0dWi#iXj~vR5Ya#Q;=vY2aA3Zm6cNBg;F~?98RIv1LwFaIi)A*Wd4i z9N8HwA`M!4uXadKYM6gff6t<0|M9V8Fo|}K)36Aok|;)p>L&ej)vC>Ue2cNNLYjsg zfe@Kjh(8HdkKp48-wSWS=Y7APdO^T_dU4=M)nzqAB}FV6tRC@^6OMeV4x9NhAYr&( z!I69iB$u83AloB6B$G5cEu4)J(o~?a}0d`=bqT{wF067=OEAAljBM=A1)AfO@ zri7zXToT#~NRWqK{ns&F2D>D@K`BW|wDa?W8q=mw%Hr~bjqwUv+Z z4i~wBqllD_E)-hEo)%e#HyO1Kv_u#+w$6|A>UKn7xmee(U$5)xBEIXo{=;Lt4Xj;H z05^hd0kIzPLB3BEbtkK%C_Y(OqDyPM6 zwEW6#JVW<6N`BRa0+333psh|6amMrU@!1n&xIzNny`ep(j{X`&H*>-W!(Yu3Mhvgq zFUHrttG)02^QXJeRQ{Kl#^dufO3dRpo<2@|Ta~q*kQnQi7q$ldJx z$M2$|0>OtmJ5}f3&-k01oO}yd0n>}~_|Z`C(S!qbuRNT2)1P0p-Np$rTnm`#x}3z? zD1V2{`OHYBT|L*%WMl-#bW`rbBH%<{LS-~w>`v23SuqBPi5%U{n>YC&1z1m3GeS^1 zi)4u6AQNI5V~c^3BMNpH zZskPvtKBXZA@WV+yUP!SSXv3ACju7M2bpQ19#&^oS%oc z=KSAu;`?XaRAzq8+lm7Pnn|qr5QhBbzZp=qK+~`;|D{9)ynby000G=Wd{-C9yxNwQ%x0_CHJnk3 zA6n)X7EB>xkR56c%knI=of+cT;o)!L=nyz+jeSqtUtUAKskSXxmN)GIyeGqDBf5taThEIQ_xh- z#nJ)Yv3J&z%QUKBN3UPz_pj=Ao3_WnVz0IHnS4*}y|!D~6hxfKfiu9f``N0um!zpR z7|=&Mhd1*JXq0Nf7~zS#l{r0w_;`4m?Y!DKZ>^mhSfX1$u$h9*zRYof2!S#PcCkqO zXV*c^+px58-bIp(zV{qnpcGn^qW0{qhDmSla93+_(76pg*KPrv!bhNg7?mem9td$? z?NUtequZuw|1>XKRh_I?AT%)oBEZ0uRo?oNTXd&GKtWl#5uD*$z$wg*{d5EqhfYeD zKqWrdx@fMre@r$2(fGN~y`kU|bSv=U4OMyGL0Am{VgNh@ZAK`@%blg~-9x!0%L6SV zezGcn&8fc^Dv;|FDH1bx=bxGR9}SItE|XBpx` zM{BPQ1m#uo@7$Ogw7>_*O&+gTY^3VsZ2h_#d6~7jknOyxvND@~(rd3@_nB#+HGYpa zMW3@pV|c%AUT|2LjJ-WS9^U!!QI-AdC9ua&Z)_NG0*$%nw*GTr6J|DMAU^#v*(lRp zoJT$p+{=F!x@S8vy1eEOiMAf>c4(;f7~VUN($%@(`pd#4us=I?_S)@{cNU51$V79@ zonLq7(3Xk~x&5-(yo*WDq8o)ct`J9kg{z&@$^aoU)D*!W{3fRkPTW+*Dm9hGuRYI7iAVeQk+v-p1y z@^i;!ZT~kRpW2zB(cXTUY+f(F;J1!}G>lU;Q5#`)?YO}J4Zp)*g`D44wfrv;7C0?w zplYd&yj}vt*7N3Ze}Oq=TrSzy@Kp=f`1%OranfB^@ac9h4s9pn-Mjht3#IPq%TVrMKnf-)& zrT0zT5%EkS`V4?fwq!SS==Az?DAGTDDsW!WL1{bB{_m}(EHk-11LYkezNY(6Cs=it z^uGg!g<+300!2xZIlZP`TiL2!>Bq(S zy*|$7y`^?WfSy3u8gD36D&Wd5RY4k-ztaD#6W-E8uB?R;za%YZu0 z0gQ|}WUt9M&Ue)D)||dV93<_$H8LRigg5is;ZF;HOg9CF!{AF;9Au67w%SJYg3?{N&``xwzc}G43``*00yLSB6AVg-#8=^})IvjLzd0po zJyu3a&PwC^#56=#&-`mq1Ygs3)R$;~oR`6z1)w**Sooc^TvKY%gN}Luw{3Y)R7~uF zo`nx30ZWOx_F&`g@+VVjyL8}4-=dgz>4o~ZL3M-e31bqYLlvA1Ipg=B^7+RDgGiJR zB=7~we0p9a5-ls21TpySjbcxF9~gX$j{XFdAISo_BJu{e>?RlxRu(k{wdIw5r_Rob z@%9wXJrjs(f4AS;JS0*mQnmu<@%>@kXu2;eJC6XU!l0laY=mw`b+7CpVi3$45d-OAA7Beyh+1)vEKATe5eWXz1((5Ct=I=drLH)r&MlswEDq9bD&F2w)w^GcE=JvDqk8GF)H~03Mreb|i zLnWjfv@4Ay+k6l@q)~9W(H#Q6 z8l4Zcv$TKca+z7KCjwR>y(0}^hAc^y(1J5|wu;V`+AACeT~`6GJN#ro=BK7+ZKks_ zlcj*igTb5#-?J4Xog`_nm`oI0)wFgWA{~Kir$7zik~1-o&dQ zR7^}vKz5Icjky|dU)zAZmpSzyLr07;Vrc@MP+!a>-&i>Du!YTdZ~|i}$%8Rp%$kjun{ugzV-e0C+8>)|Wz zM__ICr$#w{I?~En14wjJJ48DeR;CBL z7gqpDV5|3&qyy{tDJ)043R(_S2esn?fhD1vo}odL701^+um)IySEpO|W()Ri?3Kk+ zP%u+2PTBsATJx7L7mMG&M#iNFPe%Y+R08^*_E1MSknZ=~vAt57zb3Q-%a{+U9rcS;g zVe0dfR8BV-f*>Iw@dGlELEPHwl^QrsGKbTZ7t)b6nP0bvvTaa(#r&F$jcpeCYlQfu zx2t4va&zH_A#noQo0$6Fs4k#K{U#D*ZgI^qcX@yxiDeA%ZO1fe3_jfmktikJk8o;V z_a~;mW6OY=s$Ek4dP@ZKltFN&uX5yBn-fR3O_}}0IE0}>WCRi13y3mxB_i+q1_hpS zydW;Bz+q1DgMG4bQtWT@udpH(;8z4ElTIY7(XEWozVbwz0~K?3Y&i#;w&eCX;Mg+P zjpV7>sz={l^ZVxwGHe1GuYnDOr~MB(c~Y|UpR8;f!mF3|Tvi~(!LY8yh!6n)b@{Hz zoAgS~D)8g;Zgx44I$+4I)XD8MLZL-RAxSi;-0h?Rbl*#;Lmv3{(CSRl_BcWg^g2djdwrTjld<=%yqmf>-vDg_;pd>o$4Y4^J;RyT z>kmJB)ur~IQ*8u=sokNYnWmhWXo(h0b|-nCCR&q5%tH|MWaEB27DW^-wv8AA*xPXD zGnY}HUvh^}#GZoLx_ilKu|J0z5&P#imy@(OngqH;F>32?OQAWd^ttl(xjzmdF(HQ8 zA(0Pjj(J>PuX{l%bvlPRW`yuXZT`Nyfx~3vt)QTA@9EQP>ytG`8kl zJ?+glF!@-(ltP6(wE>1brNHO?L!a9%`Cl$8fCn`2^C~HDw-K}(e92xhl9i|XW;K%s z=2DiJ_IK|z!xCIWyUQ<J=Dd>ifAc4Z;DVyh0))v}`y(WLFI$ysnytSKAJWFFw!7 zCqODFay_95khHbU>mhnG!F}@SO8B>EJ+poL&7>L2c5!2=pRfAZz_T{5ml9oXWyEb* zNdpfu5Eq}YWb&ga5vhj6fGDn8ZJhb{cwz*Yd7=Iff5sLRo_B3Up@bNJ$kJ-1$M#(T zfy~6@%7oXBN_uM`iKnJ{oSMi(*EjZiIxM1}$-?3{xx8b2{Ay|>AeFrZGlw^uK!@rR zw0lQ$ott{209pk(Ry6)-sf;*kZv ziqFZ~oyw#6gklrK-Fcdz_jr#0HI=g*?{>%9&cBCD7g<%QB3d}xXT$SXzYQKgetd>{ z3OdHCh;Md)X&R78>(Np>Q>g9zgc5p3u1S7U(YRb%{aK%JxwiSh-hv<*=pB*$gCSo@ z7<1RlWR)RFUZ~ZLjtw97{PcW9VMDdE`^SQiCqa8w7~L&Az|upW+pN#R1Zt}OsN=sO zBQTbPs*4WH`%l+E_JJ0et$iTBF}{wtFp`m7Sw3WFMQqi`7{cS9e-<&RP;Pg;F)1wx z$n{Vhj=y1mw=mcND@zSbp+-J2+Gs++Xg+TaS86Ybd~-!8p6UGuI++to1vRx6Fz5h>UQFk@S`JohCXudt;;^{-H8MB}T9h+`S^# zT@_%M--7guC~#76fLf$&nyUu6>*mv&v`}cD-sFuX?>4=8*?4gzCnq4|t#^flJ@KdU zpC^Zhm-+bAb9+fmZbA0r=%Zs0_Qgl>xy)#J612jl?MCB%Bq;qw$LOr;ro8kwTimON zZegDovDC^7ySibz+YB@#*29Mn5jO#BKqJ#9Y0K_Zw3*piv{P+>*r3ZZ5$X#hMQT#1 z8ylLC&f#gKlqoh76_c@_UJ1e#afRr%ou~`}k?+Z})Etje*de}X3Ff9}QC ztD3pT1N?OhYto$BLY!rDFTc^DbkO=)?|k=6>iI^o1LN zss$!`dUp0|TNy)K8a?s|gS0_D2$TKQVN`ZWFEtevp7=yVKB;En{DfI~%jH|aw_ol-cp+Lg(w_kR zhaRPf6)_ujPCA+>?3fBqy;86wchhCm%PSUz)BcUJt{4K1}~=sy5kmp7I(yA@$;*%)QIaT zXB9~z0zU!WxoNeV(f z=SSwb1{GA$aR}071_w*tub!JZ968OI5@P(U(Iftvj2hs7AGOM~%Dv3*Gz;UOQl?ID zq}Or){iT_;N9%-lJwRw&I4YQ-X!-L+X=~t-U0s8JT3&yOsI)!?8GCI(dFVR-FMpYn zzv>zq=uqqd1HExpWCe31Sk0vL^z`=rFTUP89_zpV8$Ke@G7`xQMOL9mR+%X?JIYMS z%*rl%g@zQwy$4!WiGAwOrK9B9Oz1_gcUzFtCOB>Gu%vGNXP(<>z}17zPR!(_ znH6M3Z`krw|AzOx>Xi>ZIEk_h?+BBT%}F|7kaBm{DkEVU zZwlyRHB{Bws%{OlDFlSiKL4592cKaE1chwk zRusY^JzVS@C;h=+57T^}c|#n>K1ObC`U@*Jc?=Oz75VGeO|>tDmbLLOG|Cvv8%mA| zzsc(uJ^SmK7^NlKGNib~8`msFMmzjDn@70s z#vWQh-P_;w_if7;&@FtGig`iRE^|7P9i7#K)YwkEvZQp_=)UQpB{f)_m6sOj88js8 z39ussKPg_J9(p7=$J28!j6(#IP-;4oDLJpY!D@YncE& z!e0U!`wDUl@U}-Q?y|8I4fnltqGqE}CViX=_4`uCeR6Sc{)t286{Yw`oA$+$F*(7} zZ*(Quinw}UX#4(5_M+SQ)FzG-fkyZ&;OQx6YcZ8vQkY{SI6Zjd@7<-Z?PEa#v?UcN z_}MjYm54lad;|F%l*<^1xFh={IfttZSzIp^yFAPj&wsXnFCpr^>q3-e;k{BUX0qdgGyf-V zzIu244@SxcKK1k&e9gJ2KW?rp-xev48*Nfl3&j`w@O!p7NFh|{BDaEHl)UT+!j7f~ z6LhI4kYw3PHrMaF_AQ{Md8xy*{|PV+e*+6$n?A+S-n$GlzD7WxmS;^UMm@AA-Xk~Q z>&OU>r6<0d#?-XF?tt%a>xHXMetJ3$x9ZFup4zCaqB0GrVBq~Bo&uK)p(`51^%=0T z{F`p=+8m4XyR%wa^hD9FH(la6gq|2F9I0dDclLX%!Lx5K0l3`tz-~RtZ79&eTTV3E zDeE<@`=~E;)Ur#tT#Y+4Uf94n0!+;oAODL@P}6c_09l>2r_ z83RZ+CcKXJ&}%~nd}@e}>(-q+mb0uL^$vS@{sVsHdv+`bg%fSMioc#?Pjn>YUz$=Vg|et?uEP#47&S;D z8m%$|;Z0nZ9Y6pcdmPwe8cpT>6G0t!)h@DTXJ^-;bjCFnvktfSzB}m4-Y&^kN`M!` zW0&d0&!00=-LUyl;8}pSl%U$>3D;dG@X{J2tUN_tNJE=KNm{szx*RY1~Ch-o5_{D`c%TD2N77XuL$m# zQLSn_nZRKC;Nim(SIhQSg%I5ylaL(w%k4~bvpAHRO})~1ceyOc5D05gaScaUg)|14 zZscr%;=lF%NQQBq+x(Pu?mjiuroYEOdP}|P2ByZTK)LhZq^~&?JogcPqp1ao=Q{#m zvL70J49RrQFjui(w9<|+vWEa{&p#^<@V-vb**NYgdL=L1-Gz86{Nv%7OxJ?j1T|X5 z^1!GBkNBdJw+vk{-m&TMtZ+U+`@>&K^yj_(uL>C_U(u3N_>_X8x_0eal0Qbc&drp=W7$g^x4yW-BmP7SxHW{wCIiG!4bx_8Swz_?My>$ey&Tq$pqw1xXoSw)XY9r&)x4 zc$jr7im|h)%=psNR^0<}gM%l(_mne0{BaOiEORnp!u0 ze7sb_e=ZI7R{*Yu0Mb6^yxn^BNG4&6$<>B*D?{(Rz#Cz1{g*}Fp*09GmS*f!uMFwJ zzt`)Nhi1Fy%m$C|(*sRzvtvd;t;&7pi7o?Ow=F(2xoL^-eVuHdNIvsk?{~*yTR$l< z#QSg3Z*-1qD2xvTH(&`%z$vO#Q}> zaFnFCv){?SN+WG6xAlHTv;Ms`K)tzPDoZvQJ+l1l%UA0syLv)5%xVZw^_%d z(>pq$KTPW!r{QwU5$X)Rw)GGkAQkjS?~4t&@3#n;RwH@}q&@Bw5edC>hbJH~@OjM! z-gZ;jqnz^(|FloZbSs)}fo7ki8Tp-KdN6aOMP9VUDd5{bpPAS@qHkv>n2`@`z5C7j zd9Necvx~V9zM=P^IoLzX!^+GYihCFarfvK8Zv`@5z{1ZE9Twx}MeTK+j{(ezleW1} z>VD(*evBUZW(A~#t*Ur8rxc1GJ_FVd!lOeXtmT zf8=!TbdKImNY>R4M|52)l>hvFQ7uzA<;nH+6gGvgXE6QRLG?il8kEZ^%`LNsiqc)e z8u;+D8RxE>>9dU?e$i^Tat!JG+ZKp;iuPG z#SCS0r1w1Vqv=2*nuPVUurcFTP+i!`>hN75?ye4t>z5tK@Z;9v^ycZFcdt2{rzVlWBY#K9Fy+v@|4T%-a7Tw*8Xy_u`oX?(VO|QC3OqX&+VPa|=X` zdW;$s=q6xZZ0O>7u~JCHIG^&D+i^*j18N#UaAIQj!7H_nWLDY^J!NVs-aFSKl-{hM zyT5+pF@<2Su{I&(eU>+VKBl)!$9FxqS%27sYRA?x2Kud5mv}2{Fq{%0@5`Jm>8%W5 zZ|Vc|JCZ(?w7!VSAZ`0a$@+lMOSD@>-sHWvC%`8K$Nw6__DK4ZudlC+`ykz#4cV!V zk2$zvz?prPLFciJ;MCKLg54a?^3eDm~4R++{J$oSa~tz ze)80*7zvmDVN+Sn6U}c9SsbO=+~>Z5{jl!4=a5q!JrR2)lY>`A%r*+qw8bk-f zte5nUOtk7cwOhPzrn~Rn3TaYLxn=*F58!ZKPx#FTux`{WjOBsMuaKD4o+&M&SgkY%(fGh7JKz%3{omB3No{X0&lN|-P2IPvUh}O{GlkOe%@%Oid ztp+2hn6y)u)g$u^+&hL$8+6X>=b0MQN0^d~%%PVo4YT0T0>b~>C34p~~sA+R}Gzr86HB5-R5axu&pLdF^`E|yeX?fHw}6Z!r@vCWR2~T(NXlIo)%5)NM_W^u44ix- zIzt9zROpDu<(AJ&4;<{&B{w@q>T!wg8`@cU_wHRPMMWt=^Nl1X<-b82F|qR;QR&H?>Op{QTt-l+Sod$vSF8`m(2>@2}Owgk(Nw3(?nYu5($h_#|R$23Zr zdoM$=+`ka!Btd)cQD8CsZJ++NBnpnun~*6oGCl2k`F@`yYwoSXteTA!dAvG$sts+< z#SLdn6@uF#TOv0pq)K0Wxe4Jzx@)BMaKt6O9)jN2ZFY`dIX_eE6~!m@X@8XMhs}Mft-evavb<~2$Y9<95d{=1%x)(OMPPfhu&^L9H6nq8)4Dyz z`iJQAqs?bk;;)KCjknTuxetG=pq)9nK>W>^Uvced-4K7;{k8HMLRdF&%d??Bt;g>s z0ju+{EuK1>&=%I?rEGamJYocjKWU_PbZaL|W-iq3bM6j9sCJcewVus^Bi9#9E_9p* zo2Mjp-FX{6h-JVu%^dgsjpzOLUHokOws+=Bh2R^sEN|Q1y_+d#|IN$m z+{w9tfr*x9e9}?F%#0mLrRyq>#{-d$*}pew2pw%# zmno6Fq6ttJM4NE^!>7jw&6Pi0=Ew8xhFoy&JgY~|g?)3;*;0-jI3n?!1S+TFzLj?q zI2{`INc{J7H|wZ!xg|@qtiHW=7dlm?J_z+Bm3=toH)5&`u(w2#ew3|(q)Yu z`ZgA5hoGG(QqVFzEoAXUny24k^hropNZC&6mOL?9swo$idU@VwGXW^4q0A-sa+zX| z?`i@o@1Vf>sG-`8#mbC&}60n zu{P@UD#ZOf55|(nL&+%doPf5lUeV?D9vnOLvHirGk}O%x_j5|)M7IwiA3~z)pn!{Q zH1xXu(@OTvLbDJ+niNJ@-%3i(HC|s>P15~_XR@-g`cVjo{se4#0oQF!2hdEj4yWq3 z^B_cR4a0>poB3;dK1E`JgJin_vo#~e2x;d(es^C&Y;k_knF!&Dzzz>xuwzFw#|H_| zM?DvWnJFzr-nxE%E-DfI^R&%bNSN_+?+-=1 z{1S;|5$KJcdMegmXY9ZI_equ%K5U%#E_f{4!>KYo2Sn`XV=aCG-P;?|9H=gi_$oE| z?F$SOig%A#gn@A!BcYP1DVsFj!LF{Zu}%yWxBVygJ9QPWf;(y+_EtC=-BfM{ybj;O zkkU+{G{j}yA1OO|qfYi5FNC!nHZ_^eR~m>#zf9k#CUrTqq+}u##fAFa3{h&A9xM;Z zbPl6@>6~>No_T+r@i#QbT_N%^RQg2+A4=%|IGE9UM*IEv>^v_1ReHab48T{VgJ_u< z)`p%)xuxV$++}$!P)%^8*dSM%Zo}R`nnc=!z%|%tA3!zb5;5TUr9J$V?Ornl`qVt( zW{v0G&Ut8PNS>*%l-a%GFUr{73C*Cvb&b5jbcoE1G5?CeYI8nLghoWO@x@yA*nxUL z7Z=yqa-H$nT}6-oyFFY)4iOX*=f0c%8huZVtnUW6Q`C4Em@WzYko0L@QZcEGpMH}{ zyUGgY2vSU8Gyynd+{s(D-O}>&n9DuuP}aN z+hn5yZbS6KhGu&=oFNe$Y> zZyMlYAW6<&f!W1&WxI|^sV99Kn>_j^%oGx_Ia)%PK^w-&}mw&fh8QvoK*Nx|0 z(CIzVO%tac`r?Jg!asS`*PX*2?Fci~|DQWOKd*xSO<4W+PVbSQ+*UVEg8tqOzq$Y>{!^b4*DTimu{6_e$$M*5i~B^2fzx7d`jMB9pbnBO6Y<*Y2M9x<<9S{*J7d zF#I(V5I><);lXS{A~S`Bp9NFX5=GSD$Vh9Sl?@B%al0FJcz~-rqL?rXal`z)4yvzv zr?>RaDU`9Bn2o8Ac3|)^hckd8JD}Un!z%wpJEMq0Hg(7L?Jeu-WW}|tCXgSFm~M4j zdri8Md;gQ!`{o@FLINP(yxpe&MlTIbO=d5xdz05X6SN9zB)^Dg=D9~xBJRA!skEsJ zTIIF%?&}#Sio(`1Q~;E5d_wI{jki@K-3CNrHF_t$863K99X!njhYD(}~S)E#fxmzoiSN1dft zN|YB#qlF51o1h@=;_n|#QB3@?jlO@i1p8Uv9YUEl_&;3X`o}V=OG`|VPq{PEDdAg# zA}aV#^-Tt%j=LmG%U!rAUvRe}Otjm%^tB&+0@FyAutEPrN)AonyP9XRN*=#cp37Jm zta3gY@m^i5K`N@M$pO9qQy|x*%Oi4gY_B-j6wh@9enD&q=zsjXk9o$O5`-MiZD4MR z@BFrcKiOk~88SI`#Fv|7SRge&{k^>^E)5<>$6wT^C09`{dHx8&1L^iLY^W=a6W<>v z_HF$2dZ+!I{j10LF()>5v-iGkn7fQu2|!;ONGc-LyrJSDr=Rnv%Cv~Bht3N!724O@ z?T7wb+qe8Ra^Sx;eXKlpnHO_4KGPyxEy>hlt)9nUwlSamR`phSA8}=6Wj&;|L2@4UduLf?31W zls?FP32sKz4~omb460)j+BP~mde+Z9tvLF~z5X^@R`Cme0=DziQn)&KBy;|;tG-#Y z{_hyzr$5yE9mxFno=55=cc;Iafu0+EP>Y z%qIWIFM6c#%FAbGKM`@C7w&|{3=^QhihXG}`{Fz7f}jQqkFh#q#DAi+bBgdk27=aV z3mVJTXX2XOdM=O$%Uqoj^e2;Ft9(Db;G z7JJpIRTcifcfmoV-S~2eLn8Ikm=%-%ng79qfG|O6MMZ9m!|D*TV)q_9!?ROOQ zdrSg8Wh^PT)32y|gb5Kc1khi&WWI4vg&PXVTze@f+ViQ*HwHv@xo(l&+rM3Pxpo3? zvuUY=(QLz}cbyhekZ=%v*RaQJId6JMkoGL}ITk%$ljcxa6l~pYq!vE?@grt~VMpes zx)1EjpJuCfiW>25+eUf+{ykV3380g8unefnGj!i%Oz!^ZnJSKlF!)Zs zZej1;9DRJ%$}VQDGW7!tPb89gH|@wT;156m zcj8MR3rzT>rCE^?1Yo}1s{%ve6dFrj{3zh{pAs$&ujyC3#DDjlpx4~S7ZS+AP$HHX zM4Qzo)>VFK+*6i_@hP!7E}L4{FmT6$2*E8+tZrl*&P+*PTy^^Q52u2xS*{oUI>)7c zm@^q|B)W1!YsLvARUuD{_=6EF{m)N3;#kFXE6gFY}ydBXDVAeW%<KOZbi>VXK8jIalSL>CgIm_wBeRRh4s#xqh{=vU13a3=dayU17&}9l{WYY!jTA zNHd9uj#1In%vZZzBY+|*nFmD85Fj#lshfM))HfqO1>o=My&8*QjLdmB7U|j9bSUsJr7*$b zM(yglknTkzI^m@Gs$IrQY70f%syzvv!(D_EVUmyhpta@b`O8^fZpIx_<2)LO5%Ok( zecX9;wZ|*Yu7YV1TFpl{>OK7N4!7%eY?Nw@m1y7k(;8poZ!_(MtdF)0TyHi8#bMgfv$r%AQAmej+`SV%|lohKYxH!9OF7DLTd~`0VAsEv9A)s8~(o1xjoUjjx zh))|~zefR2gW6{vAadSk9;igGi2=;rsI?S_2r|=yCJk=b7;U9}>h?XnKiY!~ zA|*}S^%%zM+~ywh&O*T!LbLwt;0q>9w*8hX3np=QC*{t4uY=9z__&NmX=&8JW!ggr z$DIFAzxngfn*v&H;S^dd??F+S`1DgW_rtSV+#?V7n<1Tqv6Igj1!y&^GtZ+q49k={ zc(+iK9rrf2u&0KnOUV-4T;s%9hq7J@W!w5QwzG3exz&!3X{(vWX7dx`jB1`$*X147 zjnEhin2b@qRp*Cy`6yPt;KtxEDLfv0B$KMiO?`#&*%2sf%Inz@e$2>UT0Z@7)sj@O z@>rAYC51K6{$Nwr*CUEOMXb(OhNzYsN-;9oFlI>k!C*`rHk7uxKJ}Y>&xGI!Q&Ui5 zPwkB`%V4&Jwx_CPvv`}vg26%Ef!@hD3`>s!^|pi0v4^}5ey3cI62?u*f(9wVGsRy# zn41L5+AIINwkzxRts~nv9e)yVEdTYUEhfId@4lp0!{l{RQ*J<~d4w!_!?vmK^0Gg% zw`DiclVg6X4_@y(%GvRvUt+<%Y$Bq;i+tP`)(4+cZaBmNXg>!O6AFQrIS-B_&;kQF zmv-%og-z|f{f1I?LBb2=8~Of5B$tlFbHCMl!y{>XRO$3e1E=u)=;-NLKPg;lJ2|R8 z4|>rW^%Sv#m(vEwo$Z!V#r8LfKjRw>Q$r;Tk9qg*t;T?l3NF1UC@+5;^8@)Bd`I3b zIk;~vc8!$KkToi$afbiC?a!xGhOYctl?|JVgtYFAVjG;jwwEgQNGs$`Df(10Uv=oEtVfZa)zi=V#C8951m}X@9&HAG4{xhb}z- z+0`RMI;i#`Z{ z?qiV`R<=_`Z@q1Q4S6vT6d^l2nVDst;o4WT^m?8@5FHAx?Ke&TCe#q`s>$h0x|_fF;tvCzAbN^7JXA%;t{eM$hFR@3qG z=Vy3AD7gY!qiY)6V%a39U^>|NN9OG26~8L3w1+8%*S6B0p?a;2SIa9M5N}G{y@z^D%xq8@&HVix&}#4c6EmfzU3!IOoigFZoq@x9~) z6ubAT8HU0{`3X|s*JLjT24BLoWWSOxqxy3A{+?AWb4rKy*tcvWF&tGRB)jo3z>VHlM(#h6B-&x@{0Nut$;;8{STZfBf3etr2-Rg9dCle9rOG_)Fjo*$u zd^(eo_ENLo=lfjIK6u}VaukSfY3Gh;<_`!cVq53;W5r|9iYR>9*zCuxnSwXu^%JHv zhwsKWwzmppiQWxF{gY+b_c-cRiUGnA#1`3?gI>)9c=5V*E81HEAq!VdKX~q(bn|Tm zN6BUtre~gt2<^be8Z~wL zJ$!znd`;+$exHJ|clxUvl0SMalz5GI0CjjcvLia-Xx770?eoQdSlr|zE+k>H%-&zd zu-5Q-EE~=|0|G=dw{+}j@(=ftUiix-0Kv!kvW7v@X5%GssGxHCChbgVPRDv{&V_u! zdYd+*S8@1KYZ86GAD~@zRClfHZ*&c0!baDFa2Ku77CLHO-FIUyrQ@9sNBpQ^m)`Eq zlVuuz=9aA6$#bc7vM_Oi-T&@;xL*~RKw;O<{0jBFrgV>TN{|DOOg3t98u~r>zEvF} zBEGTdGVe=iSSBC06;IOh`YnG-P(y3yNjHo7hL^C}eHj@M?3kkCuiqGB&5|MkmHp2M zuH<*e-20nkFI|)oe8sXl+bs39(I>l@#5H?aY69Pgrz`i+I=?Qia)z_>i%i)mu{D^k z6vY-F((KXkWNO-BHce~rFjM{=jMG$*+|-dYIET%p3IdHrti@t03d!eaS%f#T!<&eq z90Y->3iJnP+K8*QWW`B(VV|+7eB=Fgh~2j4xZ;kJGCcO(d|eIuq2wAR2`$WzPZuOk zyK#y_chqon z%yD$E7m&H78-dRB;{OH+nttv9Q87Ct553?r|_1Dhe^WHKd?;k8<+;uJM z6?->B)EUG>19#~0SV^%fLc6}5Y!@!T45N{0820YyR)Jz1;?VFtE7V5n4?g+~l`34* z`a~I8$F~`3;6B^W-2I4V934kSzKqw{X0WSha78+%qA0w)yx>oi+}$OSY!-u9+qVc- zk)fKJTJy^xYexT&awjC>#VvSuWW+pbbo0)KG~WRTiWm)Kn&5WhATStS&Z+UDT4Fp6 z5iCY7_foaiK@<+LM)t|WqfhGhqsIo#BbpH1h=H@fyu1-Iz3c%73An^ZU;mJJE|A(x zh1r>__cE^vh9R(_QlDnN2dOiO%SEfb{M*|rpIPThW%WsCTVnDjMF_-zPJ$rUoM zSX8r-hM#P!P+tu|f))$5Wd0JL*gXPJlxdSq&3GEJ$g?Y|*4HdNdG6ugY_JY zUCW0ZBaVz+XC#fjdpKw^WB{c}P5X7dWH6IDCe3>V_ zbjPj1h}74oB%`{^Rd4BUssVyM2^dYez_LkjmLH3I@VrCe+(Ga4rg5Rnw&y{H$lEpV z){87?a;_(5ujyM|PS^QU+a+BS@;tQngWM&HIFT~oXYV2it`xyav{Rj9`y5AO_9XX1 zl4CG=R@qqBDh@-lV@I1s_X!A#Q&Li7H^x-(Xg~GJs#qRzCpGo7^^LkCM~;N#h83v9 zqJbBSRQ(=HD(PRx+PmUH0TWjN0Rgz}(}A%;sZ?NE&u?t=4b_6XPf2TJpfvY@i97RY z&?Q#)y#I4j&2Tv70o(mf4-G{xW=ypEFM@#}Ya^k(mT}vGh|GVhgR{J(&2G}c z+W&ZXp?E`-->X4?bk-$of4E2if+bT@2gul3+t(h29|9gT_jlncN&j`Etlb1jLGybj zca44mSN;dU24b6epr>er@&WEulh#8wY++q2s9KYApzT>)n+RI+b03$OO+%5Mb=0SJ zHQE{DiD-d2!NenZf*il^JboLHB0Iwv3i@@=Rs?r_ZM|B+oY?l-GXE?7Tu@fRGZ!V{mXmxwDVGW_@2T5W%`y!2 z($9LUe;oW7X-1OIUvZoh|ECZ0lityz!2~ZrLNZES8#ZrP z_o-*w%dOs2`D|&eLd{pEw&UcZaUMsuk>_t$an&PVdLI;hnc*m7z$v|6#?TRp_$aFE zn{|?GS7#w#|+S+{I4u)65qEX&vUU2wC#|{T;iDnS(K!T_atv2uL zRw=AwW74nT;=T>>knJxq^v1SWVH^*Vg%3O+f;%3Xjx_vx&jq*1wK$~+^Ct_G(7Cxe zohVp!?5yLw7u$~cCE%X$4@BT&^?I~1Llutj$TdN;y>t-3egDnLIA0msCPF@21a9(m zt;sgOZ@*eiA+0c!r;om7M~Sf+zuh4)8HQZOH%(_Qy8L~xv;y~hs`+fvUM`qGrQU?} zKN5Y7j}{{(&qswVy{Q}<)aKq!ulo^_w#xNbtbyJ3)&x)G<7|%8=aMaN7>(SY42}Q# z)FrILg&zviqsjg{Q&Jks*#^An*v)8G;H;^chbwb<_~w$eBd+%oyUauNa~gn-z+$=@ z$$L;O5CI8Uv@?L7jO_2@gE#>13EAx|LH49)?oB!kk$=|uaz)W^TJ7()Vmj493;Psn z28cmgL#5+jKQonu-9OY(Qfn{Da36ZX(oi`nvaXA22e;lME}Ngu^$jnOdkgJ!pj`vl5a?0F~a~WHwQ25Lr0?T z&FYb--nUNvJRK?{0I}-TpN~}S-F5b;nwiurKb;6|3oJe_w0Rvlge#C06+`@&Cs0hO ztL@o-O_KG@?%D?|u-fnRmXXO5Eav=rCh~tKUc$)&sCh^gtbPu@WCM{yNh3xnWbNss z$S5(Jp36d@%aoJmlEJP%rEvXdsPy=SgYRkWjh6i_|2LVfW3wx*!#SRSdBv+;pJ8#!6)ZM2{>F|757w!v)xX#; zmZVgLxkoY=`%#Gq2_1gpcH^FL9=2bc{-pp9W`Xg=a}$5YoI0|!IkMXwpm6M1*tU7g z786_P69vWtAZuVT&39g-tMx&$;|Wy|wMpLfb(c8zUzOF^Um{`KXsBzoycqgXt7*!N zWydDL(?w7v(mDn*``Vj7Oz4T|pCGePZEfvNTmF8MkO1O58Zz_&V#I&b=qDApI&r_# zTT9IALf$e=c$}OlOy|$+F~~pdvo9~Ed`4ckmzL`d{BY!WCnrZk7EC;nA@6vvXfU-* z97ZBgqRtYpyEF<)wM>BE-_zNb~=?tY-O*?!@z$%Jnp(**(v!ziJ`+O6;_LgFn3%{$`18wtvySLiK};NizbuwJ0c;1!%fV_Z!f#MfQ%~$Qvq5eRUxj2D ziz)3cBbI4y*zlZJxvh#Y@`QUM63P7Z{%^W-15NR{EC|g;&z1Y%WyM|&&ouI}D(c9)0=xK?0aJELx8kVqhL!k81|Idphsj(cej(^TgP=euG?O}+T_^BMR{c8W=To;;3@&Zq59?CBWc8T%6v_{OKl zKF#P^tb_`66d8mV#a+O88~8ap%VNioj=5`7 zXniEldDG=TC@UmNky7`-?sZ!0H)@Xpbn}>1;bnm z_Smm7T$Vdl9j6C~zsMuxxlE3Cno-_-6k?e-ZHJJa#3&%eTzuhAC{Nf1=41E%820Ud z%jrMpAcU+J2qFwJTDQBoASkyHMGOoCz5G9e32>=W9jiaLvwJ4^;{fhSy+<4_M&>7# zqXq`NuA3jH4vOS!_`V1$MmYG|q+KUa57t65w3b=G3bCa`#}9R_jdb6?Rg_|R=6oru zUi({!!2*PTSg2xwvQ~+rbHuZvRIgI^96X;r-wUe{&PT2vp!cR;+%Wy5iBxXHto@QL z`grh9P4l!-8FuX4Hjcm1b+xsqj;~pbOl#jKmzyYt+CI>ln{SNn`iO)93I)6-!ni0D zuMUYU+cP3`yoEXcP7yXydo63YC{PVkz7z?THSE{Ap;ww205=V6Dy##`)g`_5W0R-T zeoj7@jf-Qnu_QX2{x!os$(Aa4MP69Ofb9)Wk!X)2|7bzJ;oQ>FQtpcL*n>Dm3Rzux z;Zn|JANJ2W*}Gqd%Pz9T%moA?YPRj0S%b9dow`>j`Xn$9dErkM>TL-kfIQ3l&8r&z zL8qG%<9v}$zNT2@n>Z!+!jJ>RP?6YBPdotV{3%`KK8C}$9}cxdaS@V!ufkg3zp7PqdYJd*i$%Kc%Y4Y4xd{hSwN`>Dy++&Ge9H^VERab*hl|t=#MkjE z&$X1Z5dD$dOJd>18jOP#dAU@NoeJH#u-JhbhmM%gA~N{q;6ct^f0Dgy{)nB0R@0cK z$EZL94S}p|9v_|F@Z(g8&mv|9lCK0^P`bzIITx>ce-LdX|7U)H^2bc=w4y~!kc+Nl z>MesLJ#$Y%)yikY>S)wqJv?94CVF==w3!IOkvyb~4HM0G4p@%N5b5D+smLe4;y=n@ zQ)H`mWar@+ZFyj)i90G=eK`abI1STl7d(Njk(>H&ny&wK6;tLi^ z9a_RKL(iovl~N+DIlh9K!Cq@^9&7o(QSBUYF*Tqb`M-wS-b;XqE4g!*Avv(js!&1n1Vg> zH5l&4Ug8VGQ%g+0B)-*|#(K%?-rt0FoqPxtNM{c;6HJRON z;B&-Koc%d?Sk&?fC;F6z7#0y#E@q~488&AwMqw_%d%a3;S>$v(Yz8E<5q2oDQKeOa zJ7$fRTcWoPhJQX!%db!N`X+7V-!b(=f(1WE{u5R&9?xAiF8r%0ST>s_Qz*ZM^M2mU zY?KVx&MKd2z6b^$`U@8Zc*I%O(ZG)Zo?r(c^@R61N7(Lee=+ood>iB};jiApg56wC z0`t~Mz!%7cOBP*h7d@K7&3(mRN2A1uJ}JkrtIU~$WW+&3#Jhd_YS+iJtcY5rOU#`c zBvqeSuZX|}QVpWaz}R{gR|2~C+_Q;xT5cGT`&qBx-BZdjybCP>_J(h=^=W8E@CCM4 zFa$Ys@KQ9iH}?m!6&bWjD+?-W~vS?L`M%{K0q>Oj2FP-SFM|lCVRcGNzrYfIDNiNM;ZNGdjN}@ zoE7%!>u$&!M?f+aWy;0+$-}PDw(_nj)=f>edB@Yy#U>`&{k&wHM|K0{4*ZuA=#AM3 z^hxzKzB3Lfi|Akt4PLJK#X_UyrYOequtKe&WlbvDy?uZapbS2E|KU=D`54!W0GE!n z2Kpq66>A9{K9hs*&qaN_fpn~~n|^J9EtSu)5)G#T zW(W-(oieuF4Dms#&1LKIcBQ`h_br7^>ExvR=x4i-cCVrhe*=2PW_IFj<&N<4f%2kY zMq0V3MT6qRP-Ck3l@TG(RE|jLCw?*iACFDOBhtVm|EQUv2Qs0Su*oIc;brEKr8swA zFdWe5XK9U554wqD72N{ayMcOj8LLRoC`3(I{oUQD-E_#Q-nfzv7ZJL9sfE)}j#Kn( ztJA*LLcXJQ8|HyqH{^XKp}jROJbp&{I$cJy!leffYbBmP_>_%-oCh9F6$Gg=a&d_@ zG9FgA^73RmgN$i4(}I^yF`G$}j$Oka@@(zb7wviO8{w5>&?}PW5Pm29e0S(Zs;B`D zh&NCR$pnq9Y@OeZlQyPT@S*Cn>K%(ZOg83swXL+Vy$uF)vK$M4O&sFNX$~44t$|RP zA0~mogH4GnJlD>DF&G=ziDop%G^lJ}&eR4rL)%s{Oznc-s=h{L`x85=K!2?Oa_pxQc6|3S0h=F==c-RI~ z@u-UzRi3{M)wAe*>KSmbZI73Qy3SbAV|O)Q-~A-08kdGrUOOSHu|iDA>qI_rY~yP7 z!G6MNB(st6zSP0y*Y}Ui#B?$mh=dKb7{~@xQ zg%`SA%fML{Bo#vjABx7&212S*o89o1+sg(Wt!sJ-MsC2dThV!*gqN=W&6&TxMCnp>u#2199U$Bjl0FBnOd`Vm zo4{)2WA%^XtgA%~Up}u^-W@}O{+4DvA6>|ahC(Z)?>6Sug(pl-u=uNWKADSod~xIY zn7qO&VYSuk&k4l@wXLSA{J~T6Qup4D8-oIa0k$wnZW@ejHi~b|UC!$$jUf z-y+MBe}!d>@hGD42Lt?r8+sfEHwhl)v>REcc;`}35QStM!Dp)yzK%s4a3?w>0n3eQ zC2$jMU=t@sFzTe&;iaWgvgf`>t^8TkEp$t?Tk6tF7ATUD58dSIaBVwKWCVI;o0SSf z3$v^l$aHRWbX3Fm_-k8^bhf0tje&XBUuduBJIVd2wla%;&R3~zg~rMn(nzxZ<q+X@r1P94Px0nqa6x4DYY3CWSqj(ZC}NN%Js zU(zai`}XZFH$BmQK|V44g^7IC%FT%)tEpBg2F&XXUeenc78e(H?=ikK{=2@~3zlg` z=nP4y{XYxf9;DH{?p)lRAIzp6_UrWhlYVjXIy{^40MO9%{?fDUcMd~0t9-6(sVriB zLZ8xh4WW_~W?e_CqMJ8WzTJdZlNw*53CPbEWU={vs;i@5L(^aJ?$lS6B{+G0R&77( zmi%5xpDQxYyi!q>@5)IU`u5rnqIeu|2uVN%zTI0aZp@3iNIx*g;?9ox82V?5uUKws z-uOiyTRdtexl$ioQg4-0MhpCb18SaZy>u#PY9U)sPtO*ZCMgd-Tfd8{*=Z#AG}50j z+WlmF+|Ju~-PPb_!m3)sQC&T~%Y*DT{RM;1+n-YXETaMx%H-$g$EFxip(Sx9_s!_G zgWInaN+by;J%EjHYsFb!*Z!M2Jch0=E-vEx_8o#52>C9&<>%Foo%tYjQdMK)mC3z! ze3tHLh%rDPKXIZ2I#;voM@L5)Ik^@03tJu%HAN>5Ke|1)!5A@iQoi|SU3VvB>; z6;+K_*#&?Ix3sq#owQe_iM~|N;>!P){{)w)fLv%9Q!e>lLd(zX=$P-`bI3c*+dFp) zD+ckJuBlrQkk$D3q40zpe#fz6-qxk?&|}YY(yN9us&iv+B#zg}^pT!9GP9Xr9i> zdel-Ab!>$7r$#PfYwy-uPfKDM`$M9lY3gT9e3!0@rNzH-)WzZye3FH=onSpZ$6u_0To6|L4rimy6NlI{jGH9 zkVL%FEqY_TczaEb)Bkdi-W_;W)w=5y3wW#I2M{1S@ z;d8I)=p*@$g?k~bv2xbYv|M3Zz4fY(mi$jItxxEgJv=S5pC3a%08&eAe6LJ5!$(Bx;ucCzE;>)>D*;?Y{TfAgJuerTK2y+H;h z`gatT?%j2FE;Kg*@cue65eN8Nm{<9341y*@Vbyus*T-#bZEeNIw8^z#bwZ-q_wfq9 zEOC!pGq3M|U>r(2p6>hPzOXATfM++lO+Lzh@w^^8Zr*WJWylY691^BUcWqPiN*p08m9w{g4pJUuR;&^{^}(}krHCRBv%T|ecYJ(&1rof_ zTM@@OK2Cj03nSXH(1Zl$z^82#Qs}fTwzhqvJF#9)-hXaGk!5WJx#zuq6o%17=jNhz zb(gzdf9_`SHTS2bMFPrNt_0GizQf%&{LU;k9yqP=}|cW3%_c zK>cN|JLN;6^?SmO)?4V`Ie++CsJq(7r{a9~6y$aona9WT#&Y<*oL%gNaBkbC=$2d~ zk()Kbp~zyN`t{9#gtZkin`Y_MU;bq!=bn%$Cn_xVOWbNi+m@NeK%-R2-`~Hhr!|S9 zoxs8q{AP4qzqDQQjd`DtVjy;6H;YSW$*SKh#BcnMKygsbs;GEh>XU*`g2lgyQG!mN z+F0Oz{Yz~H)t@HX7RhW%=t6ILW@i1Vc~$jmBCmEuF!4$)Rs7oB^5M!tp`E(=8ekX{ zJzfj#_ZTL~)5H7P0H58Lp`jhKj{7-x{SyADTy)j>NeZhOTt zwtJ5(-HW$Za|^5$7%FusQ6?@6^YbrCE$_0!&lQE(<$lm_KC@#iPEJmP9p3wsj%sXJ zGRQ59y?m+dvdSYl)u)4s!HG9+M4{7B*}3^{c5gID)x_U+YhR;l!gJFv-?F7QtCjZU z|Dx+nps`;6x6ucMgb<|+nKNW8r8Jo5WF8_@l(D3UGG-piJXAXw%RCiQ$}B^JghT_P zLMoC_@?Q7e|8w5=oORA`t-aQ7wc9+;_j});;rd+H^@%6iL;(6Y;N-)Jgq;zgZHQF>(jRla&4F_q%t62)E-!Y914a0CJ zTULjRJUn+>j7I6`xZmfDJz1rb3<9<5N+4AuGmVgt5P*=9H6A7=DfLpZO;tvLLfVgQ9addFNqBXBrO=BkwI*!3HQ}$WeN#x8{jDngz z%)(H)aD+a3AIoXMdY_qp&2!OxaX1^l37r2@o~Y&L6)aL|!f91^Npu5>si2Ei_uIL7 z^|)y}&fT7aZl5w|W_w2AA0subK0`ED#A*<|07i`6*3w!iA_bH?g_(Psuc*9gzCz2p zBiGu{C$HfVl}M!YzgNKb#N6Al?KT1Za2!mLwzVk>*w{}TVkFGV$R0az#}lnbM_r*PU{*0Qi8j`@=PTV}7=kU#NnL@J2f0ewwVUmv@^zP^mCtYO#YZoU2s6Askn1T)z?>nbnGZodlF|Fg6-8tf)f5di&@M#H@naT z{M%TCpSXZadQnU4+4G=vJaI*yt*-9%-5Un#m8lX@bB-Ex+Hj7c%)3@6xy*@+iLFCo zCY(e0%ZNv=ugWfiP&I~gB)7U0Th*KA))AGk)oabI!=FEjTo*M@VS2yqTcnKg@;Z%N z+K1!&{Qf~uZi)QC@c89IQ&lPWNT^iXA+kO5?cIU9r`{edTe-fEiRO?LF)c zg{v=KWqY_QTdm_zI!@avQWXE~S8cY+bRk1b!XJj+UTY0`El)dtHmOJ}T*V%7C0tYZ9XR(DB!W35-#+Ro zW7kM2v8bd5$eHFxt!sT>Y3pm-Ol6@is`TOnqe?Y~6p6l@CCTRdHZ6F)e{>P*ESNUs zs)RGZO(xd*w@6VNS{nkGA^4sl#l#WV%m${mkIXS9F29#59J(E`O;deHNA$ec#XSuH zzawCk(s`hhehsz%;`i*1ZP^AHw zYvDIn*Xfq5D~gm6`~_s(khHW-2rT*cMxusTPYbRIMp5zcqSW@EcT57JdOz^7Y1dMR ziKge^Auv_F=?ShWd$Q5OB+C3wwC+LchZi`&-AtXV-hbz4a9Ua4Ov9leN#j*DICd87 z33M;eOE&Krh>UJgs(Z};Yzqx7WAGK`(kIf>t?!x8CsBncWhJMFMOfo)q@n#O#dXx3kP)%+ws;Pkq7i;RWxo@Nn$yF}`CJmQDa088{g24x1ejDji`TDM%aIo_*PYd$fw z$M7&3zKXkd)nG6xWs`F1RYZvbw^ZEJuY~&n%P&<~TEiuTHXRnOJrYAE`v3V(%uTUT zMy94>P|9pt{_yc5Et>S~jKzjniLad>W*a}u_7oTjGV<&*u@7pJS6+_o?zpl+qLL@_ zB%ftKn@pVJp=IbrIks*yJo50u&r5jGFhT{jLDJ8<+=r|1cgIsb6E)}Y&0^4aF385w z*BP;Q((2Tjwt_3EWb?KaW`JGX+r+q9&kwdqNI2U6Ti^2S{p}{Sct$wrG5?%qok~D{ z1TT%(oxD@4$NgCS_Mtdl^goDH_c>38scFJX_;`2kIK9i{(Lv?zR-^mvdQ!bA8wAm$ zg}3FpjMvmPfB{eJe?XKc&iB|9s$yRIJ%0#zr&rwxc+ucX5^Nb%A{xs7?#ZZUeqk3V zdel~eixT)WJ z@ZbZ6u?isQe|&ahJ*B7AVzbK#Mf8v8%r=)TCI+4+bM*d8XWdHE zFni9SgIbhvkG0nh2JGWK6c`5zSQaGIMh$5#qs<+U4)wNN(OJ0`7qzqJjc~M3}a|0-G)ue%1tti3fx!Tuw8!i=pa^Fli2^SQ277s)hlC7A2aq^e~u2%bM&*itCOkKQ~ELG+p{jzWfTguM!Wp9V*0S2jP#K( zw@J{mJELIDDbLH-HWEtn@Y++3Z9<7#xR4;t%>36Fspd7jw7ga-uwmOlH9*CTjEtY# zBO{6dz>yW(@W~z{dF_#ZonHB?@~Pl2_hcsHRiCpeWz6e;yx=kZ3OD5t(u~pj!A(0| z`>@ofg%rvN)e79qZ|8etz=90R%@u@6fG76eLA|2Q|LR4pYsi!IbpEFn7CUB^EI%I| ze8H4xi^o%fjX|eYk14fQW)HPj(~8}X9AB0xhD$8kZ^k-1B_SFMdgfKXcO{t+#E*sGj@ zzZW5R;`2WJWqq_yo&Mc>^}o`(+@D{$(&S#)C3bi)U#w0*ahA!5SNl9mjP^@I+#B-+ zH^-B?I;rAhXooH9C^m%7Zp(8Y{jo&g2&grIV+a>Ui?2@22!iJ&n zrRpv#%MK0{3F$Mf%HsfJ&+?Wf_`-t7-pMH()iBvXgwbb%N|~z3E0h`rrAOs4$^L+K zI}mq7H7^qk=7@59=SB!L2(OxgUBsrW>HIsoV&av|^rB2W zs=G|n|J3^a&Pq=Q0G!Ac9k2K6*RRLV&(5^}JYbOhLymLc2dGLPWlBC#-O<<0GVa`T zY0v;|1yxJbApLc%Xs__)?zB1fqZGTU*n>Rx6i}0>14%*0Ir6nEfv{hY%)Ri|ZoozCd&`yFB$;NtyojF3uzcZH>q?Lu$-vX|%o*a}ECc!h z#V>gix$eFSEBvWzlP+u*nAGQdF(AEx!om7uf3!~RF_XH~m$Z)mMoj7iZ{O!PgwMVO95tuDtMz`~l!-nShTN?F;N!ZN|k zEQo1Y?pC|F$#m{K_Zi+7cr}Sv#o9)x!ez?d^XyCI(t3MPa&G@yPDA*p7D?{>SokE_ zoMPutK6uS(Y+WqBY6QFG;?0x*7%EUxxKY^qAGFPW1=&SNWx%1Uw&|NLgtd$1^WmO@ zG)R89 z@gv*_Z{(;5IYotpP$An;#!H*UV|n$x>mfhST z03TZXLdxfP$_(zDI81<&=J56TUmxQ>$8pnYd;j{UQ%C*8IfKG-^*kg*ZzR&m z@-jiyh}qzbz|tCqQp0Nr34BZY7qJ_P-G?V#a_IL3WiGBEam~W5O)Ply( zi-_XU=;Ob=zxg$1V_|eHE0PrV>(^^nzmm^n*tWrD@!KV_y#@66qAkB3|K|5SYdp1Z z=p(fy_g*hzBZ-QZb?Y88y-M4h%zM^lLWj74Yo;&vmiKbD``8#&KaG2&Z?!Hp*el$X ztu_3^@MZOphC2Qa5_@~f`^`q>bv8g=o^3#X^5n_E;bDpSqiWS>>a58?GkC0)HD*iy zG;P2%vs}X!GlxzMe8pGgwwhQ(A{;&!cyMnXLNQb)l8}}i@;jC!x`i}jtMTV9SD&*V z`z$D?pN>fw8hW|UN zH?HK1n{>%A0$E#&;)!xYOCu0uF&WBgvEL5~pkqe0hx7e&JX0i0j-Y(m*5 zVK2{3te?U+bLRr&?Z2{YYLhOcBcgYKZd@n*?!0SF#~!;n&rxbvHX1ze@wj*Io~XE* z5iJPa+9C(m4{L~a=_Hp48Zyh-bYFO!(7}Zq(H_r~>M|FT>qZ^AFJGemFTfL&y~^h1 z=2g8B3ETQhtu|x89ik<)JYi~qQ{Q{UVd8{p!uPrKGR+vA$ZGnrW~YNLho2Oj337Is z?AV)giT9v?6p><2Pm^8p3=O=70rIYWN|?`6QLy{iv`ZeKN8HwV(SEm`x#fbB1L8*~ ztf+zIUcGiL&G*+d0cvGs6QL1J>hu~)(O*05zbgILVJ~F6_ha=PMR@CJ*Q^;qZ?}}0 zoG-Hr2*TUfb`WI!1b~;P`A_1cd^^*HG}mc93Zd33y(_FfapFY&RA@@Tn4|zcK)9C4 z^7He+HgcDHH|*cuiVyu>>)$Lh;7F?2jH9_$z8` z@p4@@;iSpCVjge(H(DShw}Y%h8JC<8QZqwOtpNok(A#jJv=bMvoHSeClQfsI#gMsb z<9ks)6#+>AK@#%w8GXf`A-$)0*38x1h@BPzCVAf^0M)?E4Ab%B$Lj-rZvqa!l>Z2z zT$YoA*syf#5FVX3-a=cnN_zk7c|#nnK91D}o-bnK)*WUmou zSZMppdW?i(a@_1A8D=)86p3u20H?{i{#6~{ZUFXLK<+QY7L_>2CMNn7dxHj0iBSM3 zze-kCT%i7?;=Qq<57rJCN~yeY{*mHW40WknWJpy|Qd%18C(IxI!Batf)AWSry`EZE zNpIUN(&b(9y9K!LJ;{j0@o}W~Dz)TWBYf9jJQIF-d5(dB0q0?}0poxu5!qe4TH*9S z>9}oBUKUrZ-YcMw7f2;)UJ+}r*5MmKQ^+5g_xJCrpz80xI@jx|gK?e&4;(mFRhq#f z%frK?{ZP$m+j8Tmr@iA#)^_Glr$CG^lguAQvNzhdp6u|I-!Y}!XyA35~FkE`}OKYZt;kBfWrR<@|6r`?}5!S%$@=rI; zty^QG-^rlMYYs{2xp;5U3Ix&- z$tT6_GP<3wdvlp>so&gK+j@8|Z^qa!>A|fqc6jNoU07Hk>;$|=S*NZPqhDB~ zR~gKHynho`Q(0$3H**a^e9nmsKgJCTZMsLdG!B*>DXhJcK0w4z*hmu)5P%)s3{Gxt z>oHUT!_OY^3xo2jy~kNTMG`>1LBsk}y1km;?Xr{kA;oTtnU=@ku4)uWtG9p(FF8$?q za&*ihB|YX-aFv|V-rleS!(E&sHIRxBk0KCaKV7sLXt;Ar>Nvwc8QScz=ybJW@2_dP z_?p4tN0CC#KP|zw_fMfO-ytbUOzYNfmTG+Y@<;lz;59+S3OovR4ULF5kIs&NJRvyQ z&W0KJvi$M@j~yQATka+G6p0k+e_M@C3qdWb4d~`AuHU;HAFp|Nt2`e&!z^;3!oGb~ zY;=YJX!yuS!{!J0E?vPE_{vS|TcQ%S@PuJ#pk5<;W}Yh8;juie_db>Gik$Y+uB2gx z5_}7tfLaN0aUGj8it8hIgy0$)5BbRyB-Z zR*K7nx5hgh#fScL#Nu9R_{DU<2z!?{W9WEo&uRT+^E?X-sfh&{jA|=AhIgB7uK<$7IPDW$Q>mUlJk#(N=I;nwmM?&{~9SOR( z-BfI?tVTvOMj>m>Zf%^zu|Ig`A&<+JVZX|KeyKoi$&#cF#p~`fqTN=(=`Y@{MfS zZ3{)0aJjn_`8Aiva{Lrf_G%dI{A|8I=r4Eeska1S!*~7Ny~6tR!}$0b)WQ1$|8No? z$r8&u_|7P3jz2he)QZAJQ_q>F6`H0ggn6NyE)^6E~_XC^lj*&MI&d(F?l&!Fy+N z<)pL&(YW$OF(uv)us`qKx{dz8g?T0X-0@9Z{7N>m=lQ-RP_irRJlH;9K4x4O(zlYY zd5qE$HNI(^7*7|+m(j-&e^+Op$&ENSmAquD)3c#km1rHto~-IJ7u7*|2=OUob$sIW z>k{b)y)kM0BEuQ%Ufg-L#00~GPGQ=)L)S7TFvj}Qx`(AT0^q^rH;)=T0WS4_Gj!() z-N$49ad+g;PKlAAy~iXcL+KZIJj{EFQjaKbNXt~aF+YY z`=4L=gr=qW>(63yT@w-(6$Px?B;A{8JDJdrLSpk603Is~3tw&E&!WMWLHmR!<=t_Q zuEf3C^I0UC+S#GZU_RL7RWmbU>&VpAj~D+bKFP3Aa~tQtOcq=59#k4|FIpp&t}Eda zt#|aUa|ql=!RoRDGr>L5SZFq;qnE`gUz@pR*-t&+w>W#>5eKoK8$Y8hG+D3RhWyxJ zQqort7*8Gj1wi=X-xWpIfs$x4A5c_NA;bcVA8C{-yMulf?~16rk!})j;!*NLXL&=$ zE{-n2sP%LJlDI3 zwTx<+*?YXbU)^(qyGZF8N22;8v&Sqr!7&~`iXR+duVypZ>Qx-_6V%a7%L*+r%P3t=XZLE>J7`fx0100=r!VA z-HCV4ONXtKP5@B!rbacf8LyT{z>pdBX!=yO$im8m&*n^f&Q7xgz=*DW!6~AIT1;l3 z(3*}+zPwFvrZ>w2#YyJ#<^vbbkzc>HM3Gdy_~OFMb;sL} z@%&#iMHJ+d2|BQu#Xn0s!G^nCCEwR7ArN%1utLG_1FwGcOxuGaIzPVcmpGWy@p5Bo z;(PFdpzV!vXgG&zPf$=$@8-c{2+dv#D@{J=?Tc!-k8R}aJ0tyl5u@4JktUi>!~8H& z9au?>e*74L<3|)6m?Roz&*Vw!Fc1V;red;ns&JnZ_8Ir%hJ@UW+NpF!sb(`~KY59@ z)(u%;Nz!0Q8U~IW2J+(r(l4yBSVk2iVB&^HVD+j4rZzw(LR&@!B**UR-QGw&cf(P% zThx4fyWWM{JyeDJUPH+Xw4a-Y$E8h5i~}e;VG2VW(*(rQ#D{vlsIQUzsrFij^SWF5 zzfp-O9xHmJYbO z8guu%F-h+~r*}CejhY*lbsQNAIpK0d%6Kz%h?3j@rfRgMgTnc{v6H0aXf*@I5sy*) zzW~RfMpeA`xr9LVWyzRhRH-)>HHreAB-zWqq-LonqCvw@WxSumP>vLL5ChJ{4(-c8 zoq)ECiW0cGa=xxjQ_B!!yiio!L@BBn&zsuUwtTfLoae4yk>H-6#?b+Dx#b}zEkbI1 zMd>FP7D(?1kS(LcWQ%Pl$c%iQ17dzLadCvfFxQ{Qr^ZWm3xaww20V!fVqj@2{>zPo z;o*y_8JtXpY3GI&gR2g8@{yyR?2~0Vl-%71*CoyG6WyCT#dTG5x|^G+@ys6MJXc~r zigmqj;!Hx`!jGaIWo5FyC#r>#HG01s6#K$glT^^9V{o)VZ|;ikvs5pxb?N4x2bB5B zGkmb18=r##u*F^tEv-@TD}_RBFs6{vdvUwKh08Rh%-HbC(9LJc9nV{JA35Eur;gjY z!Jq>mDFN-3-np)Kb32--GVTWL0OfD(gcd8ApKa|hb2i`K*?5B0I>#n&Z(_+V8YAy! z8Ae!7$#U z{j+qIbITTcNW$DBRl9-#x;Fv?A$tjcD~^2o_S{Fy%h-f!(YAAEQ59QC3wy0&HYa#Q z5^vY;90AKK2xiGr1iuBXdkBqjNV2+k9Uoxz0WA7?t+R4#%OXq)P3o|J*>O&VmQp&AJ zHK$ZKcCvxnKvoX%wtLcH*XR!8}T<#{n! zDfu0TZMAF7Q}4;Yc;a!zb)fNRomc+;52Dro(Jh$&jfv)Foi_U7%LmkK-On?0>#?B&b^2)MDMysnfSHeAa-t zLbx~`f;@W%hnzmer~U`G0T_p8Q`R_p5;#r{pL?>2q5u5PpX&qO!nF@HME+kJUHYQ` zg97qSl^fSl#Z6yiUw(ryISX9m^VF2ltWR?cPVB$_PZ#Dz@!v(NMl!70rMmw}Sor*d z`=DHIs58fav_ZIa_N3#nE)Vxl`8}Ny6w>ix6RRNrRICmnK=T~{lMdq@iR1}Gw#2?K zMA+<&kSdx2w%C!sT30ob@1QN8AK%Mox_NBfGmwUwdwY{6oV6@>p8sl1zCOT>HURtf zr%pZd(Gu5Xwc6-pIyG5|@QVU!L((dMkOR!z3Vi8VfkG_waHNhD2trGSQMb@cX=y1x z{tS6v<@d$!BUIQZZ}Nqqv`Pw4888`IHI>34H*;5t@ojtcgAJ%qD2H;KrY9>$lyuFD0s*_KvfQ=_=P`}0@>(l)ZC6`RNWJv7E>0nI6}o0K zpe=F@4aMBpA?QfR5d#D5;3yE*LcaB#0_q3(lBcfodS~lx_PcGZ9K*)Tdj;djOWhM= zSaNNr$ZbSh?J>NmtgMWMeoa#UXrA-^O+Mg>(io@9%ADHILh87WyER?*Sn8~l8Xh^a zp{c2fsI~yIVg3v+Z&ofYt1&l>M8to3Lo6bLWeX}dH{LvW0wX{_z4|F}LBU+5-_SWd zxxQz^_1zxgl^SN2!v7VUdgZbeu7=}hp=`Ma#U~(j$w}c7P%AE7zs^O_b(D?TB4+Db zCR|&x%(x0S$N-h6rZAM!nV5-5m?z|D-x(jy($~!QNdJl9-KXa_ao9raTVfvVRf(!6 z9Bz`R2uL+OZRJ?1<|OKIX82~jNczcR8%^PC%I@^{eX>lg}=9H;3Bau0D zc|`_)_CP~~FxXgZUQP_TL4YzAdnG)Mj<>+7Rtk^wJC>=#J;1D_mH#m7n;gzPyz~!? zn8jyLTfRe(5xu@IULs`_XO9eiI3vh{mM=4tikyawiCST(;lS3vLzW)sz_8cIrzSFapf>+@28G`OcZ2rxnZRE{`km5O1qMhx$4W1 zpdz+n&*HknGSr>FQ0GhlIR zglTciq0zNH|8Q7<6x2R_eWW3Aw0rAwiil4@%nIACiwo}LXA;O5D&%n%G5&^AL}Lz+ z(Y3E21oNfuzV!~nC+i>a|1?vP_2%I#pau=?=5tLjbI*JqQ6`$}#?_kJhY*50XbusM zAo#^(uN%Wt4s|B2wyv)CwQw2298M4(zkn24@iJdCM>}Vr&d9>S$55Co%!Cl>>CqkG zhmUTKEIt`C=W$YSukU4*cp3B5(rEX~5-Epy9&>cff#QjugjN7nBiuedQ#a|NUAw`{ z1gb z)lcO;c6LX1Ohwv74*rv%BfGp{2v6l`a=?y z8~#AwVZ0c!tvT;J15ZLx49YuV;iB{|IvYI6G(M%AGGJSIaM8FkP0W-ls!cz#oJtTehSv(>@PIuVvb5QaAQ_j3ScLdy5a z_8U7%3PxyF3wTTZA%r~Oe89L%j_ls}bZlZ`?dHv!5i;#i8>3of;D2?e81EA*|Devd zUz-aqR;qgw&bazNX)EE@9-IS`f3KJW%tgabOF?ZN{bcQ)b%u9wvWY;Eo{6aqzaPr1 zZw|xL$d`lm-&bGVOFCs0Q$o44^|ROEPH|HQnv&|8O-7sKy62%7)#9f`Ei zrVDg`d!OUkvuB<@J{myo5!y-;az4atO(z>t(JCjuoW!Yl}<@ z1!^sJSpvb4vVFwp;o)(VO4Wiu7O0PiTnsigs72;B)|Fz(*B4GkCMKfAQBYLuF19ja z08S(KzSG9!ScBOx+Y$5Mf0piyw`v>>P&avv-oL`Bdy|@)+6ZYpP{c=OMRYXtaB`SNQ)X$aRKnLvafq=HSdsn@2Ushiy0ka|g_>(HzPX=1oX5NwFM2?%3-& zLLq84sB4aowMOH#(gS>z1;B&{X88D^&zkdUQ#};tq^E2s#D-&lar7g371WM{9hw z(_`%bsKZG5%E{(CxS5s9qkHSs7I|KJaDa5&YyFqJKz_=_rX^^iq&+giM6EGT zBrBmPjd1ROmxo0Ew9Xy2^`eU5H5`{qt+R{h0;A~i+Qgm)o0#GPYSp~MuhblsZ=+8f zfGUn)g9PS6)$JZBD1U7I12#9OP8|;OOXA|@1#S=34xfP+1{tG5t8{lRyrQeAi3)>P zS_;OS=@ZT!@#8hr-L0)_hK9T(3)LMPWdsOgd}^+vdC=Z7TB4jXfC=FfEItM~O`e+1f*l`;hJn zpZ>a*E)0;z**7V(zB&;Vb<67hIWf?YLqHZnmghPrR}_;lEO`t=MfjAh zx3sJ({lniNMKV>bS=WBQwig58(Yf&^U@}#}QHnlX1?4XlCM;ry3&KQG;s59;whYTd zv;=v9rOG_#J-uVg<>z*KOW!SJOp#!?jp@+cdjY+F>rmU7uTyXz;Ld=nz&cVr%u zLZh{)4vs5ws~8lUNs=upO7Dt}xwuZTwSABgz9B1}PZUM4_d*tE!K<)yaxxuH?+3Z4 z`$JL*yelF>>(7K7dgK%%SL5+3MO2xHfw=*e;A~63=@8i<~KtK*E~2!~cNF;5igmaDZTh6)raDXdpD_1$uz?+AHup zv{Og2h6l_tye@DPkhJPNW<){gbB@5}&B2F9*7NpUvOj-`r4-6JygViLKY|ib$R}}lHMP)vqVPo z&y5=d_;q%1spMhfXG1gBP7K=co-uo}HsXq}Pz9l?cOZ=_ST$0o(m_kTz$m9v-#-)# zKBo&znvYr9nI1Y`t7l+jRD+6xP(%<9RgT#6tEEP(2_WVQ!Vm@ZY%@v&rwb2v$$xB3 zZZ0}UXT<@f-*5B=u(+57E>Bh?4n8<9JZ(b^ghc{%oO zM4+2PZ%3F$@CT{Fr(S|+re$DY6K*ZZs`&ha%-Xq~9@M(V_^_;)4@rJ0==-0+XYz$% zJ_-U}3p9;H%!8Vu5>;z&m=66k7&|?W0xqHkBaX^=!g9Y1d&f<*t(bK$s<0a#D#G-5 z2INsr1y4RC6+&gef#Y`2Bs*sy$rI2C+~Z--Icl%9zk6r5u2kf#_{1 z;~X7mfO44cSxaF{<{I>*G<3|^ev^UE{eqsDFoC#ih^ZumCYNSB>BWp7WPXvYO6Tb0 zH2Uj(kaWMor4G8oc7?LDm5+r-r>>^CPCN#9Q{g-7KxqVX$j()KINCj|;2LzyB-cZs z)r9z>)30f`{SfFtsTT29SWoat{!t(kw%!@jj+l5+Q^v4@BFwVKFnS>5m|I6~0z3`V zLijzwx$^`g#SW7Mbb=@L3aE6|pa<5iaUh`?e6BCFYFe^50>rNf#U@caApyBZ=2hxE z46nHBolqcC?w@+ssT0jS8E=?UJ%fnV9+8kF_yzz28?q!r=`GTh&KlBHS} zd+hp(%{R=>&Vm87b$HZSNPJ96(Db3P!1LqrnCf$LbALx0Mwpft7rrE(y&G0R^nKsw z=fi4hc4r<8h=J$(iq}o_qIj*srv!Jz6?StHJ=1(HgJwpuZEjJWiXp}VF?iEC4s9_o zx>5m3f&iOfWY0;98GI`!0571V!P^d{eWhlN(dy!;Bg;50E<6RAw_syt6hrmf33oI|S0J148I649(MYuFo@a z;7-41rlFxB$s0t3Qn=;>v!h=8nIK|uMhS5Fb?CA6RQ6vK%Eo{D$jNl8kSe05~@aF@S+g(cRw%^ zvY81A`|-j({XrhA>%vY3HA*k#}T_UieG3Dq`9b0^5&ua zAcQE;(4gj}*Sx|F1mH4^fC-_dnkzxdvT6-*BDh>UMp^ZmIg%xO8qOcEoCQHM6d0_4 zgoLitN=)til(`!Z&sCt-CS)cndXjt~Dr@A8aieIR}Di4sj{3*>LpF?b?o&H|+mAK=} zcA3Qm^c|q6Bgg<1m6c5xVUG7nPcW!wStamyllt2zdXQCeA>AaiB$Tk>V2l)azccpU6_U>v!=2ETtR>Wp zYL#{4Mj9?IuDY8wM0-A8uztxb_soC%ITgC6D83@n&0kp6qA2?j@EW`Jm^Mis4MV^p zdyRWuyb$|-4HDrJfJ3Ace)#YJkXVwL2xJJxDp~r-G53I7SW@zE*_}UCL%^JI27-}M z?E!Jf?A+OcQ$~V6VT@tU((~$-bmdJ*ScTQB>JcvOL6spR5MT{qfSIb=F#zUn~FbrZ}4v$We#H-VQnFHpo#Ou|Ur0y-Fj zP+*;OjF&T;X6jdkGepG>5P4QpOO$6RzL{(Eu0{92=|qNV^$c3j|p~ zYSP5dh508VA>7aXL9HU|``r#zZOhxYJY2io$?hRSeS_f_O~(T_tR0}{J8ktGuWJIO$Q2hTa zvf}@Qf|yP$0pTRJ9!qDSI}iMVP0Y~h5c~#bhI~ILekp$EEn3f8wz_+GkU1Izz*O*! zQ1D-@7+)dlxRD9AFWrHM|2m8~Xdxf0>u*V-jcf@AY|&ODi|+ZOwutv|m!8Rc5a5KW zb@3n2(a}ZPeogjTJHen-eO4W95g9;$it+}gNI36ObLU0E#ydMYf}xzk{D%Z60N80W z2`01?53ACD!7y$eRu6vn-M@y8j##+5-uM;BEm*h}iHikz^n@5zIR*WhrK8vZGj@J+ zuky{Z*<*nPSiF!$J-qvU4L<(>6y6Si;rsCE2C}XLgvSg#<%xZMH!aXbUR3y6Z8rYyl9BvYP5iXJ-?FzATV@;!HrA zX*?RxuEG$QvvB4Jsg6P%A@Lw7!EiCM!^`%?AP+0OLQu(!f&)nj21G_ifeM%}pgki` zhg@jJ7Cy*?K;KXYKsQVJ3Q&KbC)Y^))(?6u=80Euo;LaFqvA%9s)7*-WqgL|)2uHM zEtA>|EiwT|L3V0HnLGOBOB@Ok2LuWt^FcH+tUYxMwkUMSuT4Ny$if~RanA%h#U!Id6H4klwz_%I(~#>wgSb|93nam78ww>h~hCT?TuGR97$rjfjn1?;jBGW93G%@oi?}{)5&E zsYC$7GjjjpABR48ph)3^Eg*T4xbUQ7;^g9L0k%aNZT!Q+${K=@4*0(C;d7`wZPVet_|R2IQ%-lSy)W0aBw5%G+IEIR%#7FD!2`^MD-IVZaKQl z$FF6sdsBbtq_Oc!NSD~66X3vt+xKa>#>LSw422TmUZJ2{J0)Xz^r#9S3!kiPWzS+@ zea)i2IS!+Pg9C+v3aR^*4T25P3nAPa050+m+CWBl1qB5v;5>LaLHe-g{c#eakR}j~ z9>P%%8wr=^*SQUpG=UpT)UHUYI`2Yn!k1R-uT6|BBZ*^}p2JpU1oW9^RK0d5PDG$v zghR|F`wW#2JIKahDeHT|c&ANo-qbh#02`24EmoYHG>YRruoXRicyV!Y-^yyy7y&>H zvC8%Y>-*1;T}0N^?S-}W2As#b`NMSrM2FnT0fgN5*H<3=i+D&+$Izvkq+Cnb_GPGg zJu>;)g!4Nq@H2O4<{cINHX&B*SB{Y{>O8qe{!5p@ov=s5#GbSi@+NZ!Bq~r4LM}lX z1~B@VImAsG(fgnI_+$(4IO>@@ckYn6;oxhr^ApLwN=4@VR6u_%Z`8BVuR(2*?x>BW ziYN2M1-46gTQ4O-NqbS}WI{qf_? zuWeyV$il@Hi!a7;@fcnP;tn}&q;4cgF={=I*YCiIgY>3j^Kp=iq-^TxckbM|xU|HN zTT1jcFmn34JYDWj<06%Qz1%t1H@=P-6%eHc!5eYJ6%_9EDByIDBG-l}1;ZzQU}pfv=x5_#8|rL6(S&M$wAD>24F&%ATZmo95-sI((vEBg8G3VfI#bo zE_zNJJ{B_=`_O-h4aH-`<;zUq_4qfwhx-kgreO))CiG_HhXES+cX-oRnEd3GJNO5n zvCtp~pzqi-WxYA8)?x zaKm5&z5{!))Q%q#5p=XIV8sEBCnj<&GJ~in5ihZvBLp99)9ah~aHPT{95obzNt~oW zM8+oi@&c1WHo$=}wM4=M){C(S5wm$Qew4Bk(vdD0Mr5-k9x#UV<3jd)}~gmpoFgwdJ<;P?zUN)Q)VnBY-2 z+=Pq((m@7IO-+=u8~`C8Zuw_dP>Hx^YOUPH{#tkfcZ$CytqgL6*UWz>qQIsM>^(J9 z_$2%gxE}u)c{K6o?RbDWKWVyQ_l|@6!k$h&tKPh-D&ZHT!tr2lU3&b%9`QUw;Y<#> zzBzLnEs%mfW3-li2q&3B0hpqFan?1bd5ZXvHbKC3@ZdpUyzQu0iCqnGCfM7+7|R*a z1pc(=^1KU)T&P+8)9K%ylE6myz#WAl@YoB(<0YUj>Kmk9iFtWM?-tZc6)0;+UO-aM zfU_#0)}WX=_L1Zd+&<><@o{494;eQP-V9U+bQma-i4X84dZ5}kFfx#zlI_7@JFH3g zMKy_Eh6k-Hulop;D=>Pp@I;&kO16LlR|#8V;sq%pB0?rq#99?n+!4@a)KJF7&YXsu z_agQ)k|Gh)%0@E!g3~pw7OA?3-zzOGEhHjqu>z93D44LY17uJGXhs4X#qVMnx`uyc zm>nJTL;-@=^#V(U$fyY2Hc@v|>~Q3f%t_sV-klZxi-^2B%TvHe07Ro#*?=yY73hvK zf*X~R(RWX@hbTUXMK2obSa6ou@7E9D7Z98cW+~qR-uvHL)t9_zVYGo;w~H&gkHkdhr0#@*C%OBUIKs z`MR6&n>6_Iw`jf2`wE<&$O&+Di4F~ueNEh$6|My8i33kDVAw}Yq?F!0ZNjsy;7+_? zJT34QDdhhkHSwLUfcFlWon@^+82lYF6&wepe@$o*EK)eCda*^#0V~u7L64`{p-&_d zSCczY2l%ThYHA2ij~oPSt*6}B=iQ8m^MfN)C~t=);341$YbYd!;YHs5?q-CqQiO<0 zcycH!uK;1qK}x#kIeKYGUxP&;7JUhcabPxJ{Mdw4;WDSAlk#iVV@${s5)-KqPf4kb z5d-N=OO192SO49dQ(hjA&6T7*fTjfn)Fl*IkZCa7NI^Ldn7$RLGGGvq|&lDK3UR2R(dG?GM9hWMAcG8q$pp_Z3kR-Pq zRUDe0Bt09PP93pia|bMmy$_2IRXaOAz>fc(q<3EWQFjsW$mzfI{8KIx**&j`m^bLH`TRKw z&)^k3Qyym27l5F`hI zrQ19?gT<%^k&%Qn;HKnyV5{en{!yV`!|AOS?go($asPurJ3Tm3xE&gMK!3}krxR7h~FDEQ$cU2i?sK7 zpp=W`ICKi5=(?O=R|yZ>vvTC?*HL%G{sN`A1=B7HkvBD?>u8!NTDeBAlJXHBmJHI6 z3%|_Foca1@Z(mXV*4z3;G|P9zj^mZlf{^+Lm<9|1n(%C#=VDm)7TGo&L~F}{LWg8Q zKpmX>&WXNN=69V$VF}X`WqiaZPoJ`KbHgGm><_h~q9VBv1nGgV9~l0jNNq3Hg{#ew zQUa}_!kMu(Bol!4;SM){k#hP(D4-vbho5o%x*%w2coyn7Rm1j$Ov|leszj%XXfX_MGRhtRM7@BAo{<#G>p zXoW>Y(;MdpC%}n_XVIS*_`MJ5+1zo zD4?jd0S_33$ruXADxOT8oOL_oe_%9BY~9n+(ntvgeGDCnc=8R9(a2gkxQ4d*WT>@0 z3|h^cVd+Z29}&_jsW9+N@WNkUgak@UCC}CuoNz!Uj4W4XxZS5Y+Ac+uSadFWOGF3TSV^iEMdwj4Yjs&<25QZ^!=EP%` zU%|usLIxd68;S%&KviVPApS1~P2}uVU{NTklwlFz9@#10#nF~)&2I}j9dJX8Bi8IY z_n89YM^ury0NudRIH9;drwJt?>4pL8fsRg1raZtl8z;PEu?Md6y=Bf{(Qh2_GxfL8 zNnylE_R51d9gL(l|H(qnc`SCBRdc{0Zd$aGWf(pg}($zzd#T8K{z10 z@L(b|oL%(K$cP;x`1KhXlU{`X*th1#s$EU-Lb3oqFdX_N@}78s36>~6DO*8c*R-}W=Z72IIs~N81lO&n0w-8B~heJLD0H){u{5x z74CHh4jiayFGa44^vggpFP-Ir0efWEuq1&F`3{eL!tPE(FMZ9bKK8;FF?=cY8o#AEVU2iRCh&Dg1AsH20s($-y&@|J5M;JXR($L_{f?UeX?W2T zJ9vPSaXy|B10p19RSq2Rg!vVeprjB-t)T)`67Id>P})!$N$FkVVMRnkNb!SM4u&R; z4Alu546AwPxA)}{^Q9)AV_HB4G)TTZ_s$$A!I1gOI0nFv5{&3dwo?$S4_BlTZ5#+L>5C^x z>qDXt!d@7DD9L_+TBs;+?Tb3J`e!2`JWp@$HsG5-9{P~)zVoF1qsRY)|5N=@xlbnCkfBQy%I_mv=ZX}2{r=;?{e$6f2*%WnM20Uz?g!OlM(aD zc_Dous=eg-+suAP$~Xm*k>M~Yj?mWCO?GP#NQ|lrT0Qs@)E*68-B_wdiXCoj8%B1S zOMh1$SeKg*aa!GV=8ryXc?kW5Blb%4ox#_a!TJe1jEI`=dJLyw6VVCI2z#K-PcfIc zV$T_&t)t_BkDY;xChLFj=F;*{3F3=(0xvzOX7}#hPcR9{z)k{@9XsOAczW6aH0qFH zY$51@9xiMKUW2SkcEa9^3p-vSj*INucMV2*aR9JCK-34UKn|7s6WqTHU?FmhE&qNT zD3CJn8D%gt(!;&^Fg@K30$UtPdwWcx&;Fb;CEwd`_3wce8OE2WF>c|t>w#gNoR-#( zw-tTn+&M=SYh9?+WQ}j>*<-Sbua!|yAlm-!UFJGQMn}vqGtjunpbkSjah{=m1AaG} z0ehkzfRKJCwwgRa$qdk(>mvvVaR#D}kP<|hGbL&P3W#2;5x6rq5*z#of`KCmR%oFfRo`K_% zw6rk8m7JORjQsdXG|SN73C$kKkO?8*87LPg%FYu_m)0izdjIU%74kEauV3%RU}`7k zy^b~wfluE&3b=m8%gX_AxD~*H3{qTNXeiAO6c&X*LU!!glfKt$oZcAOsq;PXyY6#e z-(1}bFuUc`58bzW43B~M>i}fw3Hm5bbMsgJH`7rzzaqygJE-!n!joQx{2Y|cTi~%xOLc-Bvf`(y#%IH z2>hdfPK)rZ0UnN_?If`Q60XUsH66)<)M zbUtKh9zQ=7CF}O>|3%!JKy&@RYolKjGGxk-3@MdRRGKqnN{N(0nKh6?lFUOHGbBYS zWiB)rDrA;9B~*rxc?g;3`MsWg|NZ{=e$RW>yY@O~owL_}?ez;^KEv}o_kCa2bzip+ z7)*?#AbA=w+@BtL7RQ1V`NlvUGpzjy9Snm_!f}HEB-K2?5_wz>AXSYdy-aG`d%&O2 zcPRX7fQ;%yMv7IV0Crf;V2~vhNk{>rHr$$ESnLBD6zB)RJi*Hz)tV$3C#lmBI!VVS z>=B}na)9r=7Cd|gT!)dRQ^F(S%miP5Wz*`0!cFv)RXjpxj@s=5fBA9QYyHqAFt0o zsMXo3t$|ZjD3??m>(^6oaw3RI6A>H^V$vPY#l`g-4o5h?eXzz29Z&f7kQ@s&5|}h< zEFmH#LJCcszi>-rhy=2;Hi)yy$53qjTzz)%Bo5j5gq*VWYep6p+Y;|sT)en)dl&&8 zRFkHmoHIc_ZvFrZ(B)jJFcu4u<^ptMY8L-dEazG($f(n>e*&~yJu2p~iVd%Q|8y+IL6MFQZ;5RlkrK8NMZvhMXv@1XD z{rfRB^Xr*@o*n|QE9Gg+mX5`O;} zYs*!q$XsPSURW>(BDI0@9u3hKEiKJk5pdkBAw>xDFNtKjdPl8d2eQKvG&lm#7%1D@f}J@qd}^dPxIj7_MHuKvM7w@h4f#+BP6#C0 zbyKJ)l%tA=Pou>W!oT1{Q#cAuw5Qk9W+4AZrA4}f1gpvZL3x1}u>H)T=ft{tS%$J6 z6XM5OJ8sRPMjCUO0ni(ZO6h*Kf>PqC=f`e+SC|_5#fTIXh$;b>A)pHA%Ea9COzxZ{ zV5NjH$4M-Na)Us?NL)ssF8=alH=|9@m%l>|kXC}#Q*}+a_p*l^3=9l~c;D6they>` z!r%=3X51(rlz}3inYt^{?YA7Qw?prx{ju2BJ$^vnRr{TAtR36=m=jepXc}V2k8P*) zw_$r7Kw?#nbG)LM=0(HME!Pq|wlZ6{ z5S|<}Fa3c@kd%@L>TpqXps3K8h%AJz8r?n6|eIGc;zr-#? zAr2^_4Mx;Zm=TEz5L%7dPhz%ktK3VDGfeIYJ5-0PoZ=4j5mgUir%(qGyF4V64_>yC zY8RO{#y@<7bb;tw5lh#D8q(XYJ;rhXYaDg6_>{s%dopcHCrl%PXT>&^G`~(w^ zB@y`?4v9^=a^M?@bs!Oq6J!tX7RR5lxj6%l6_PY#_Y$==q3n@`!Ax3z$eQiuMf^Ci zV+MqX`q3Mc0oP*+3BXg7*`)}WIPZOsgcj7+hK{Y=x(1q{2h+s1nkz-t^eBy1B)6r?~Z zOD1eYEiHiKK-lmc#?O)VX;`ov8$0|f&&3HsWZiz5XV0FcUa8+s^f~DLB^4hk$||dyd3_VP2HZ~}CT)OCS3nL!Rvkc?V=r3MkO1lAyGp}X zBnX#5a9KE~ld8l$=kW{!r)U%`DmD%2_KeyS+#kP+3i%OQ6o@A|_{N7@H=msx{wr!C zJh_&!UZ_)yQ1RN2cB~|p6UgQvp5FlE5ESI$0s){pkX=$Jnwpwq=rwsuNX*XOJ-+L2;_<>S zGX^LU^gV1?Q==#bN#chFLekbqns{etXWuKYw@P#0Hznvs*j@aeSE*W;6%?ZP5AUUr ztqwT~89SqaWQ}X%#(wWHgP#>n@cQ|P`VjI?0)T$1Xoe)@lV3MNJ#nx>81m@HATxAp zg27TELL7`#LWZMqeI0twv8~Ro+qf7|RQ<;3F~RN0=%t85w;(e5k<5tpQS4;JI77O0kuP#RYIcM@NVIAcW(E_z z9M^_g@enb3m>?v0+-d7D-Kf`XR@J-#Ua_y74Gi(MuD&wrAC$9yxR_W#$n@b3hvCj}e+ z<45U*L<2Xc{OhB<#3CN4JmfKAI5Z{E2ncX%&Cex73nGmo)N70qBk8;apnELf5v#lu zqnZ($JpI;jGY$N~IAPT3N+_TS6*}0sySStTUc1TF>3IL-JS8S>#5M#PIYK8v+8foC zuu4eLCSh>}7xOV(Mu;cvCS02zs!29hR(FDBqRoIzp1kz+=^Y%8?p3|qW^UkGNN_^_ z1ceysp2zC1LEPnv+2YAs88yYBBPBwf5g%^|Zk&pOK!xW1HM1xljnALoc-hLrf*D@} zGRgg03=9bn2-0wIF8%5q;EQdjR^idF(P5=XFZ4tye30VmWv_7^^fpoKPzabyz%;}y z@4P(MyeZk$$0#baoA_2A`5Db2PanJyzZ&(znmNA6Dv6sKr?8Ek2i^EWnsXdDkT*lw z6(nqj>pFP?Vgk@VX3I1ehl}h}RdU znyeUnBUt5qD^3{IMB!nn9R1hB2MA%0LNmGw4x%PNdJ(uGd{{yGB&rYB5$Zu(4DJM| z3Hc%o1)JQ5bRYt@$GR6n66i|p|M%G+llQjs_>U+tT4YDu5C#Uby@>p0eRG0Do`2T!1oQgq2B?_tlmo&tVWY8F(-pRU1085=WaJJ^oes!KAt--;+}u0e^(Cn`zY04KINnJrmU!-fpE1c&74;=CQwj-bJ6zfjyO@&prhm}IJcz62>SiFbC-=kS+T*&H2|O5A+g5RcX%Rkf;J^pWWCJh60+b^zE)M5j(U&g) z_z{mtqQxb|BhZ41oBO;FvuOaid4lXe zjPwlEZyDUsuVDwLD~Qz~N!@`X4YRrf2Q}ijAnrrPWekBcQS*{u1uS?0T<29EEpc#w zP_YVvn1{xdSZbn{IlVOQQGOSe)VO|eSZBiV7wvi)m?i9o(UJ~7q_xdRht3B0Kw&M& zN4|oD3_%X!DYWWf1P>A*57_CdJr?468TSE0#2*Z&g0eY{(ibB53i@P~O(ptB%t=#C zR#sL+hE?SK*au%x1)T((0ghW$;9Yt;@o_-80a&Wg zF0Tx6sRr=z!wvT%f`i#8Q8+*d5lL2RcX!G4({WOGE8N`-H*qW5F$;nYFDM3HiMXrh zUo&rSPU#Xsa*xz(+e9X5xWa!?+_5-FRS$Ni(3frFHN+8-bBuf^1YOyU-QeI*u9f-EjfDf6~IHbV5>zt>lejfvPJp85=(O;FuPg3N(D zlp0@CL@pb`YTO=o2bzL)M9qTNLC933o5zpVaH=&<>xQo7;lWbtbvnMVOdtu*I5xrv z!3m5w`2x*LNlQc1TjCvAS)2l1fUhyE!)r48q2eC27UQb6!tmcy?ce`hp|m?CqzhyT zq=scU5;p~1G&QAhuUOs$O5ir?Y)}{0&}4_I?7XOi+JbB$V8Lo*4Jc)RlTcwi0M|yb zY#G-DUxWuT!Uvo3l)vyeC@r`ODvdpc%Fz|3K-6}PzK*eahZ(T`W^sC+QHGRJ z>~$J~N=?*>!RNqF*t(I<9AKFq=(NExU~cm?B?}@gCYaJ$qcI4s8oOI<09T7Eo!JRx zGZEGhvuK>G;530}ZC0yL0e_foPJa#X+IItr{H(CD`eMnxp#w(qb zAz6F z66zinUyqZBjR5%$PUz>%4>-Xf6cNu_*iEBWB8&(c9)&zP{}RKF~N2fg;;TX`SL~Qt~aZE z!#}@-UPzdw+b;%hVN6*p#aH0E$T_3J;j`*GzBTh=g8x!-3JQtX5Y z0q){IT?w9kjn1ZR)0JqfMpb2W^=bgr7&H`|)G5SI4W%}?UNQy~5-rk(l>?%)_)1oG zHnAW;V!txtU0RwF_ydMR0_Y<|v@ZJjb1hT{zv0J8x^ZFLGC4d4fe7J^fo*s`dqx=D z6tuP=<3HL+T5tYwcL**9XQg`~63fyHWEN@S0t4<`g~+}NKY@qf|6>Qcysu~sA%^~` zW-v~1H4+%!&!wfb&^gPaZiE#y!y!Rj3q;qkkoiHu;)4teFmfs^S0NT79XW`!q@Nae zB$?_6iIP&%jv;9DAxiiN#JeWb#uOw14?2Xg@{17U(Zznph3J70%|Q0<1orBQlsF0p z8)1lu_y;Kpg#si{5=M0Zt!TRFAy!Pa8M+=U2wV)pAf|CW&dG{ zkS95$$(z}_igbb~9_5jjYQVv=76t7kl0HVPgDeThTM_0J2Y@5Zgf5=^6Xj;SToaJJ zohVBJ^7He%Qqk+@fxl&p)OCvkLE~_}@GSTDl&peA18^ll zwV(~_azYa4N``qM-jCu#r%KGgJmD?hHs&F%!UQjdIT$$ufxkU`c>+hy?hrAOKC2x4 zd!c?u;#t`PFQIt_U50-Nfr;P2!p3mwWs{HAcpJ%2!5I(P>PmDoVDt|GL`l~Qz$GC6 zG!%3bCpDYEJzy=R4LVxr^XzZ+PEJg~q-CYIF(mK|g9^@LKIprQuG)*vE^JwOuq((Z zR5?wN6i8kuXC*AWoPlu#NYRHcC2)lp+h*puH%9)Qf0pDejQi@JgAv<6PPZx7?Sxo!^eBsjZ}ZqO(7Vz-Dye!s*{O7@I`V z=o*JUfC5pFVWT8f*Pt$U8I<1>sxEB*mmMcgc3c&7o#^{hL>WqOD=1k=&?h6&FwyA! zL3ltBV-^HF6Ns0H^B^8LEn3C^!*}3_Ib}HB`#=U7A;$L@bF+d%5=BT4L~4gOt4k98 zXJR%aWDP|Q9cjmEW$)SDu0WlQ42>DeM|bZU!|`7R*a{s5DmtB_5uCaC?yqZYw5nBG9@QH1maPIL=^c`-D|#4&iP{aq1PP6TYCK}334~oG^E0vCbL>Z{iFg+ejf{B0>C5f(49I3X zo(eIl0U~aU?kg~xq&)fdZKq+3IeJ#f{1)6GnTqqTfJnMO342UDso;}h@MjV{G%33Y z4h=yPF*yCl_s9$Se{MuwVgTYVUZ?XE$S=XV{~Ai6L5G@xolcFUg-EkV>bH`2ZxxX|CTki?v{kn@KpACW;=cYd{T9a*_uKSv3rRn69Bd zGdaO@8Px-fBbm^ajf8O}G+_jzB-keEM=mp2(6+?%f*6cKt3_<#fs+xtmwk>C38$SJ z&{*IPWSdw!5?elyi0HIB0{v4P_V@iwC2s*Vtwnl9I9BH^fuvUhMG)axV0Y8yc$)#i zBM>r)zZd{pH^WaHx8FJ>#}2i)|ED^Ce=O(!XKB~}Kgr(zdw(@V2rQ;IQV?L6u#nmZ zK>HmWJv>=C6=#um(96xbCvcN9a1favshsh1M8Nt5O%LG0j}4rMr~y`8YM13DLR5rr z$H#90=1f$_pr1a!vD$Gl-_&h!k{ih=<0523A!W(H4mZU(3uV8ZiU2~YF(uPfVJ>qo zT{>|04@Mt^`@!|}#vWk!i1#Y-DX*+#wzjq=&2bPGlBIb%*+4(qH}> z0D%|Mh~ubh+!nd#j$(Q#$yCtpD!Cz8WxLDU$q2ytetp%^9(t~HfIvCB8GnXOP6x2Y zJg%5C)jj)(?rnN}s<&hGHc!Rw=W%gH|MI**Ze?U;6@$%j8lh(t&Bd7$pGutw)P#gvEWOb)L8gN4t(e%X+`}oUIt^J=0H@CZwkAvy&hm|b!ND*1;7}sd zwt;csDbY~chK3BfTDk?@iirRnu*|-L2f067LZE>4Gvp~6SFb+rBVuxNz`3Kf{3eV% zo@6MBLX3}0ikd>KuvSKdO)gS5I8-5}r9{bzd$Nx0s^EUEsWrATDu+C-MiIdfb@I60 z<8jT^TslaR_j`w)d9as-_4V8+;VY(}VgafMQ&R{?hnRS3@-h0?a8Ab}8q8kmE`cJN ziUM}zP4hf~?SWm=Kv$G#vW*!gOS9v>AZV$}RplUF_)@R@aGKF*Sej!?C>$%`?!|?2 zg>+s)aRD*9Kj{)%JV-D`vW8S=sWrYjDbt^H!4lHe8oTv+@bc?uSI`Ry?~(n|P|&Tb z)wX|o7~}T6R&4Df?M2UrcA;u!Dk}K#<9&56I7zCg2A1F7rT`3z+CF`53rrn086Tl> zf`t?M0ngmp^EStj;xzntU*eyp3>I)Cw_vP*%HI=)a0J=|U0wju*Tp$~yi^YIQlI0c z_WtLWTKDgl3JDoh@>CKM5+IIaP!uE>* zqtP5&h)Sisvs2TI`aY-b@MBm-5pEaF(!?_Qei_okyO0c=hmao%*|aW825ha9dH2-L ze&+UM-Jm=xx98$k!Scq4Qr>=_%_+XeNTv@xg*6a22<+BTs*y5L52DFXqV|3rVk19r zEz0_{8|L2uz$6LY;o!ycSQaG&S#)J@CT|c53n<8(ApyZS()*97ezr)+$>dvIo`Vgw zZL|fnLcX9)jgcslT87_Bl|I^8DE93`3FIyTW4EqWoRYbhMh+hqWG(|Bn1%9+QPT674aAs~3w?+1OXR>n@T2R@(~2}Y#TL@DT@T3NisQDnkL5A4jqPswUh=m{I)k?@ z$-{K-bN8O;^j-xoT(0kzr5F z+6Nf*z$B_vio51is{f*0U2}5)ilL6M{Dn=!JKG>9pfU^R7xJ7?!_z2R*SS*2ld^^- z>$7g>VeKZ(r1r;V6#oyKK98o8-YHktykCn*{&bI&ICX$mqN6MfFE2^lIPh$84yDR9 zBs@gjzH{fdZo{IrBv2vRore?>K9#AJx!3|U*VHNO^TXok{6S$Xuc^t=;?T<`S@YF5JzMWtg-u2r zcvPOBA}T#JX5p?CtgUyLU%7%7-7C_x8Cwh-`!+exc88Ivk=PlcVq&A;Psj@4@1s3t zcT8;T8;f6oE=j-cR09eQ(iBUQ^7ERp5miycrF47o|K^c{c z{r%=76UEsFO^Q+dk_q4M{>b{1%h}_0TBlCk&K}NNS6f@lbMU4(;ibNPJ4G~AqM;0e zE)#5(1sw7s1{yhSnILu!soUh?nl>jqssO@oh4{dbL<~dG-X6m7qK~I?Dwi z0JK=pbkc%hvsE>W?A%B0Xr?FJN&|lk=N!oSPAH!h(n6s^71d_FH;Y}V+L z#E;2~XyLnkOeFwZ`UH3j`d{?)TtdfuG}?VKvhf4OYA0Xg(!Qf+zqOO}4?)O&9 zagFYqkbOu#U98r;Fk52DVvttvJh!g7d6r$L?su^PUWfITy>^G{2S2`=x}oHxk+yi& z?DL5O$(o+N?f`+lsp`~uZamw1{`=m@&ObkG5?c8omUnLSUZuKzJVy~ns9yJ_^x%PQ;Xjz0SE zSt()mxtsV)R^4Abi{-W73clzG4}oUEm%D8r*O{hB+qwGP%{lIk=a=P2KF~c|N?T>U zr^0s?Tg{}4RY#iJ0=Ly-Fa5sN8s%}1_6o6aoQkQI{UTU-j%IB8nYJX3YvKRFPDEF1(X_9mBjNG!W^ zgo(1UN0q+N9xm?K>XALMG(+{VVXjn~XYY;m{o&_QI;LBrM$DOwzIF6OXzMpSVPz&= zACH&yT{^D)LmhY7aUgfpCLDKiFc#}VFK(*3wdO|um4*Ci*V4Qk_L+pPnf~XTDr!Tm zZ<0CMUKQ65Xs_SBpFJ(`Ky${@K`HZd6#Z?MLr&k7av>Y(5VbXRTVeEPYJT5cg$G(J z4-$LtDAh*fPt4?EOBLJLv@TCIzI%0ZIkP@O&37YC0bj-+{L3p!yRpj^!u=60%nG}Bzb-etn`ZBjH`w8RMqfV=OfTs?gM)S1XNi2K z*kHFy8AL*IgCmrdok_YF_x3k;vF}0Oeh0)wnVstGbEmX0A4nL#*B0x zyQ7~CM7+InZ)@?DiXBMxhTMc7WBe?|Ez* zrS*!a;qeB?lU~0>2;UfV=r-sB4`Uu81w~bb5_-TZg2U>DO_@K%on>!i!Kd2;^do0F za!t7oi`uqiG)iy>ef&|gqdJ{ubfz^uJcFG&VR80^Iu098)?Smd8*glyKWGtVGbGA& zXmaKD5G^6`YC6~{E5b=Yz;(tLAW6~RQR%@%9F;fMFNVHWS^!(h543`g7&Zmi?dj3! zyj`>;rRVi2&aPB25Qpo78@sh`tdYqdzrUfK1Fi#omwW=u5=DRCbfdTC3=%956%iqm zD=~2Ubv=~L8U_Y;y}fC}(?LcR#sG~Pe>sJtriPIb|)wl zgE@P_mYM!*VRNUmtrWr<5&PJVAfw7&6!_d0svJvfWk(yYB)5Os5?PEM|DYE-P|&qz z=wG_qL_&Y=oJ}}Yy3?k^@;$H1;Ab#WpTEQ@=sF`L#T%%<4mQGYdOkGWY(uik!rpxE zaM?Ela|#p8Ys7q;MrJs5;EYpkp0=l6JQ;w$L#quSE*N%G1N53HlU<^E{H3#OR7K)d znE_FY%krt@!iS1fExUfxB=ekb-~)+x^_x5`T+6}OFr>7Om=CU{G5J5MJ+ysLET z2K~$a-3=qd=^SrvY@#c8`%5ZQ4m9Vxav-2cTK<+>81M?!7sxRm z&a9U$)C_6?P}1}4Swooiw?3yQ>aI$YIML$5QeU&piP2?GZ@#!*W@+Yc>nuE~_ek+F z6K7Fi0PQF+O08VS*CAE+=-jt3@%;oDsYrH>)b8CvW_A9*9M_aEbZkH5N)dzCoUN^G z!b@{8zjHb|gkyrNttqV)%!Pn`8&xwPZ~M-`y-^ws+*upr4)%XtqEB(W#^)pJ@*cRi z`H%|J9qSNhmCBMe2is*u`0vq;wP=<<*I72(OldRJRgZc7w#6}dbRt84NEREreh06u z`$t#h=G(ZgsY(lxTmzrOY`11Qfi^A29U)5r|fYczf!7jF;MHaLtDv7(}41fMkq zd~(x*$oyrOMuTSNTbJ+p2skEiJF4a|f95q7*+uuXl1^g7w8={6)7M|?@BZ07pS9Ee zqQ$|1X;#q>`)rPjzqrf0BDcn~KF=&Xtw#H4)LC}<0|`kwHJp^?Qu;}STa z4Kg!rG^AKWSBavls|=tg-?8$h?D`{qoW;!c3luI&4$dD*-azze6+?`<1BCo*gIr$9 zJ;Y};c(PGOz8)MdxUkb0a&8qhwX`M2b}w$nck{&_wSRVSt4Tf98K8VSpAlrI^FjK? z(C1&ScQrmL;Z-zye5ax;S03QunLcna}Y(3bIQJub2Xk z`6-SR*Jdo}$>w8gC7&qJ-c}a<{o+i0-65aB{TAEKB}L|T_d+YuCsUcmK{5S(N;mhT zr{U7YT~qwhEYN4*F-h2w>gBugkM~L_X66kv)o#^dwQJ*~ zA>Lc|^Me`0BO5r`Yu${mb81h+!ib9@g+eBMq2qkLghW+Oy{g2dbD~UNc7QVfzTDZk z&u@GrqN85Z-f}&*;P_ML<8*?_=JC3meun7W{=`p{aLk8hXDqm z6|nOHn(ozz zz@lf~p+uly|`;WWnlQV!Q0sqd}2d!saQbIEQmv zf9=PjT3m7tIsM~o^4=boS}f=eH3kL-V(EoWlYunS;};ed8~t%ls$rTrr+~}E$$<{=ZrM|t}T~X6Q62Yib^sm{~QZ0e53J((QDFqrG45rX!6YW3uJCn8#6?(|njWZJ&?r&w z%+0uT@c^am)icv;v2}+&(B4%VjLvTzvspEIecMUzl2%atO@-^d{ z?JBLBk<(TmoxHTKjMQARIXNjSeV^*_>DvWA&TJp7(#+4|Fuo=8!8wgKa`8rmannnE zx9gV;>8(x4O?q|eNIhG!3Tw=po2A0$AdXVYbM^W5CwTuP(h1lTqj&1hZ)bkB+C`UC zr`<~IkDR?S&fZ~;Qcz{@ZqE1oHuG+8Xq4=}3JBScMY&b%jGq>|F?ODE)q}f8Qpd)} zfwQhmS3N2^bFfS7rI*rN)_*fB9$JgAn6RcrW@apY-dHO2(eCX6yGo1Y_e)k@_G>2%d_oR>axNO#YSv5x0|aQA7#XC zdD;zW;0;H^7RP~YWQO=`N%u@^vX?eXlTl!T?6SxAd3hC6K*skhfth3#ic{qJCJ+26 z&txjl%Xt=dno7y+_V@XZlI$zuTF%?KSUPT?WnoZ?RSrsawN#A?I5Vq0Od}N01CJc< z8Z$-UwP+%|17(v5&Qgjyv4@^4yDaGx7?9J#@wGS9YXzgU4`w3 z^+uVm?!U2$i6&>Hnu+3WSaCgU`smt+o=&}i%EKwK)9%9qcq3mI5O3TuT)!Pl<41JrQccG`TsL#566+Z^_6@k0sR zv}tjDu_h?2cHE;f>LvtKV(tM5n<8gmAn;IpMMQ3jxu|@W7W+=Mu&K*Pt4Qlvz-BJDg=5G|qmHfok(K)kIiqaOm>HJLV`rx81JBpsWjPa)z4>kG)w2w@mj7rGinpc0Q zlN`>QWIG#&$RzZu-d=g;!i$A(qE}^ZAb4aStfV=5t5cBcxgn)3JmgN(i`9qmCi|ock`~d1YqllAL$_gHI{rW zMTWjP0m4ls??L}`Ytr2xrLV^mtjrHzU0(l%w=7?&+Nz6xd-9bS2EnV#dTG@8k1i(V zk0j$gOe5zvOOz1u{6Hy7tqx;kVOSg-xO$B*<=Uy8-X&hy6J3X8m^R)rHxiA=`Tmw+ zfaeL{>q3)tV!svTzx8V5Dt4FyUVWOB^!?`YyzL%T5-N9tO3w#L_srd;B0bZ~3suXg z2N#T30$zMQPifLn$Mf_f&y(8bWXt!u`bf)zt0?wnCq;$X=O4%k=PgDJ$yRhPn((X& zq4_glCH+B?lR|E?LUh1lsP9;|&eLFM)57{J4c6q?&6@AtGnkLMb}Z=*rETm1-Rz2h zb9bJ9_g=q4bOnGa=S^b3^Gg_Ao z>MCjIVJ%21ch~`Ug=w`n<@RQetX{qC%*yS<+(KOMkA~mdZkE5TYodE#pY9DBhR18f zcFmr6vMOTigO0vOt8cg2){JfC;cNK)5@XL_YrC*DufdhKsztWeF}b@(X{K0haONU_ zzUJGP=(bERp8sxrjZbo~G}T7-4eJN?Hg}8Nm{k$pnLry*nj98b^4+cV4vTAf3Gjzo z$17U72SYb%cyGGXtSavq_S|4YWKK?c=pceG*DKXx|KDN<1=l#5cAoq_p&TG6y@rw7 z&q+iM7ICPseb64*9_S`mI=IVwRVlrH52LQV9e6c{_tgIP)NeF%jdiac511_cbd-LT zMpIfCt+IdCHdxZ!I=Qk*Ki?|CVP(VK?5e^ama9@yU)*~*2U!${H6Nq8Zjn>+i^KlQ zLo##}om}Tbl!7gnV$56yL?kD-HwW+Fi>5NyOMG3HUKF;XiC47hBq24nwD043npa~S zUh!n}fO=4vfeQ!I`TXxiwS-AQvtn#q%vaugK4Y*rci1=ZlEBU}nBirn%T(9>-mYrx zr>2&@4oM=5<`QQ895?HG)Q&5r{5QR?>^al3C*q^NuiLR9 zT5xlmB2~Z&KaHivJ0?avtEgi%M+-KLYmK{IJunot+s8Y{&7UCpqt`1}W*@W||60mN zp5nU4p8IWztF}_c?}S6LK|{$Itu^Kec%j~^sT%)`Be)uy>={d2_;an@Ifp9!OwNHmzoM>aQ z{&pm{E!KXsMkbuMQO~X5i0O|V9HVW8rvVmr^@Mf)p1pG0Vlvd!>=FucOE&CJu2m;) zQXbZ7$5SW+%X2rAU+2A@skCzSK;B87_zRcWXX&QlqQ*P>lzy-68$jq`r8{8BIBPc` zSFQi<$OxB}cA466TCwnj(gWF7>?#F--EE!Ui6Brn;9c?V{Wf5=PYX0OnRwE2rdG$#Ufk*Q1$-Opv)-}^ccjQ&d)d9J`HHypR#i$2W zEz8piNe+`|j_D=8$$N`=j2}CL!mrHbP5(N1jSt9E|LXkSeoHoa;Pv~yx$3H%bl3(k zo~DRm_q9i?KFrH%VPk<@R9Se!qs6Syj$fTU@$-_;qhA|6S(AU9 z{%~TXrA+tB%1A+-eZ0pqnOqgP{cE@R@F-K;E-)Q>-*D(r`nGJ!Ui0vkO#`3D&Axfy zAs#MDT;7+{8dux-E>fMEf=$x&>Q%D1D=N<-2t<1QtxT*8j>ZrEe8F%oRaNgU4l1^4 zYu)lvaT~UxwdXq|B@Y!O*7s&+|N4uQTe@(Q8gB zwK#Dfv+QW_x^nO6rVkmD8qA5J8efL?cj35aTg9fbu33YK=NwSyEGZ!r~D$`J8=KI#@XxaPw4mGa&=!E6qx%*k`pOL&z z#T|=vG`n4G=hvt_?|62p+^{e9M>#i?~58GP!Q!Rhm z*_v&a=P=0`J25jmHN-r6RU!|2el}Y^}qWA5|iG4-{aZLP$PTvjMhz=>#8bNyAb=I zByO*NU8x=g^DFCGw*Dw6~jo302Qg7Q+e~q%S zUoH}?q*0iz6b$sWw)3rdZt1Wv3M*9EKUEp~*Z8rRR?F#HAM-Q_&a1dgqr)OIv;O7F zmwf3z;V2HBX2>WERxzlyrtahf(xaK=huFNt;40;$fTk87mbi7@ew!iPr$jMLE z&)*7^jeoWnsySu2VKbnSaQbHz6SFtReWsmy0^dgH46Kt?dhcq>S)>wm?9r|Ho*)!h z&hJ@6cdJpq*)P?#sQW%II4=-)%LRIk`t`NSpSRzd^BvHqz2o_jMs>5C=o#OJ?4-x& zN>qN%-c@iO96FvM|1x zwPX84tyf*E-~mnViYJ#=%)gSf30Rclj9q;_+d}B&(nbq|`GhVV$sMaGReR*ICKMI% z1pOZlm{P?qE`L|mei;myJ&v8$E-gYwY}DUVu|sCJTI0CtlfbUS#wzP}QZ_#<%HUUj zZE?cT!8`pK$4QOX>s#DWkwh%k8HZQ7?mS`tMF4RAJ3-D^-Cw1WOj^rR)|7(yCiMfa z!{{k($8U_#S+9-1_zOss_>=JP(eTy3qmV#dwis8A=DJ5@XSPI>RzWDNF>TWN!JPKsCxs5mo#vj#v9&!Go~^@GJ~cn~h{>zE z++GeB%&FSf)}y{mlXR37YL3U>=h<@O?fCNCJ!txvt|@PJ_H|puPIugj zHMtczL;V$Bqu6qiEq1hp>-RjeUM`eMD5UeRO|#TZ>lautlXz0kz{h2a>H&+^QxUy$ z>cKLfu%e*PmL<+P81Y14N8&5)x;)?7shvN^39EzAVvZAS7>ZK@soAqh6?wgw?zyuC z7g}B=eP5S48#U`Y(d3Z9Lf!AGpfsSXjjX!=L*JhI7xASVgyjNX9$;Sm^sTv^av->Av_#1{#f+}5xT`OBe24_+{(6?(O-vgX1% zU1z5Hj=!bVd4wIwuP*~jfd$KA%izr$?|TOZ{O1Mi=_$xjpRuT0mvU*_-I+M4zPKHI zZ^T776@6almRN;88+q^{=(+GTM74SE*r0YYYlr&2yGq}^(!bN$@r%K+F^I}|NkvfL zV!c6)TR;=-I-4DKrE}-DKQQ@pdi=}R7rE(8^p_JQ*f@YV_dSuY?9GjENoPW80XE2F zYG#Fsvpn0QoB8Fr12Rfxho~1_QjP9Ayh5Qqb3%IsMe|)}mZIlAMtu0wzPn!S-XjmM z7Ub}=P=*Zm@XOE~xl{d0_nl*!!|qXapGo#gJSlB!^PbAL;xfw%Hdk9MmyEd4s^8dn zE(dj+$kwgaJ+buGtAG#b=;uyOPQRIqd8j66zOA?^V!6Bt@4*i5p@QoC)uglP8=J5H zW_za@MjY|A;yKHvV z{;Qw&w7P#1*2{^w7jYfi9-u67H-TF(|FP%nZdhEz0iU27P>y^UOm7-muWnG zbCSX~dApu4sde8q&$HnJPiWulUK)))pi^9n=YPU9ArBGl;`4z&qpNuKi5LWI-(RA@ zvdB&unM~TXdSDZ^drzU(jx5DFo_J;>GRzQ7ZN&MU%mya|DPh+=^Vx9ewq1(@9fr|> z0ot2tQo>*={jL9hOR=u9()IZ0j^mwjLT2_teCv%kD`dmlw6DKTo_;Fg{i4JjfrTe@ z&E@Y}9rc2)3S7RwN-P0f-J>nbaH`Zfv9Z~4=DwOP&c)*D@YLI^rmQci(ssqO2J%YB z6w%d1RWsWOv!t8rruMv^w zQ0#|Y5*j&w99zjyVSepUezUReVx@C1!Z$l^d28sgh1n{%QVP&AFx#4z%WJyfrm75K`{WTNqPqnPQQiUI=p99JGb=6$Xq1eL)KRnvZRQ+yBOSFv_xv(UYAyh zN@k;t^V=iLMk`mWFzVB@xjH}ByK8mR+pGhP*VUhX#i=N}Fyc{k7FH;%IrBr=_T8b{ zPbXC#WnzkOVc+I)1vWc#nZL4K|WzNR#f*m}Md2T_%0lGQ0)3=(7hYLlC;^4YIU9w~$ zcw$<;`pDZKLFe|kh)_S+)jTC9B9yN;?nEg-)^Ap~KVrf1!2B~F{S7X@-}7{1V*Z>k z>wJeX;-=Ehk1o!G53jA8GzHiqggTHN4Z^ za%S$Fo5X`-l5X-FC^kE$r*OVlr?o))wa=sHI#s=Bb`yjM@!KtIX?Aat!-4=YOP{P@kq-hn?L(c1~e*fb30#Wrv43;x(@2J z%8TusFT+_3Jk_lffzNEduzkE-M<`h3TK%9Z<Ma^B;?0)^Ah&dQY=4@M}fAQRXdLnB-M254tvj5MA%ooj&L9@fR8 z1*$~$v3vV=WcQx-tG0I!Hu?GfYX;lx-7zJHpK*iUQpF=uU$2Gna}|z!GMcpBRF+jd zeKn~jzpLAMLJzTSe)z8R@DU(*O20Y#7{LlNQd(T+EqDTzA@BS<=mswBWCAD!BwtC8 z>{7#LY2l%Oi-FaKRtMRg=h?=6MrS$Lg+d!${S#ug*f;l!F-AW6v?rN6Eopm`w z7&vge<}mZm9Jchz%ks6G&31H=38 z7Tx8u8F{?-W8T-$BUX%e9-ECjUOFfK0PNv)!bM{&MHboZI&z`v2U}`s`PC z@X~6JLqFd2_l=#M97r?bS>M}yCge=r!Kll2x$9*?a0&;S$Zps<|dho+Q zQZ~*{c}oJXzucCdCj)@2-f~ajk*#GRC;7DuBu@ny^os?T7bGtiTu!my1uWk*`N|5fc`SR@ZF}=78PwNfB%T_c#r}1BwNsDVi${EOY%4Qv zrxLGzXwyL*qRo1u$Xfik?ep8y5+TjSC1SPUpLHa+uFWgry3=s59xvcyS542O-fP+W zDe2FHkA<+t-6JV;5sXw@PANewW@9ViKITkI~GzfAFOek{iCJhkjqK7yyzFS*abcHrudI4$TBO`leW$zghB72972xVktN3zRG_J~5+ zM0WXQ?-AiX&aV5u{&`-n=l{m}Jkl#h$-XKeOS6P;=+<3`r(I!}mJ zT-Qs9Z~~P#24mK5%0A)87Oex*u;p(geZ3HIGk}f>I>#g9S zkh46=iNO1E-sRtJ_%{FfmIdwDvrNjYz|5udg2T9foG30E6SjDRBg`Kzw6%RQ;LVvX zJKVh2$Hg+bZdm?BfmF~>=Nf6De4bcl^?LflXD^H2&&R@DyonT3OTQP`5|+yktJ1XS zp6122k(8Mgm^3=E{JepC-Lmx4Yu<6fnR@-~0^6;hY2xR}Y08Ci{{9lcyD4u2)9LWe zPI)jZU4p6U!y`7_)#pbV!E~bh;(lL(JaOhxX<`qUYLlRN$84y0s~&?V*V%4O9nW|4 zlX9lEj_;78`#bUo&@BHADu;WmtGe-F_Yn~~yXOh}y_gvZl56tI9jf*d2KUSeadVYb z)o?v9p+O_7)z2nv_I7l$BJ< zs*c(96CYo{#zg%0K#J1-ETvAt*8n~i=@F08?VKOB@KB^!SZqQ(W5pG$wvm&r zaXKoAN_MVhJL$BN`qMS+mLeVBN|>wBzy)df&4dh&JEF~R^{x{sB^H05OmRL0oVk#Q3Nl5HgYLOk507+vT2&O+APv7{aK zQMA3iePkmBK*A~+GNM95&rOCqcwz#rTqzk_L83iSi)rI`!dWPOsiG~vv35+ExGTy9 z1kgx)9Z2hU4=1hcAnXl!=+-|gbclz6?|iV>+p;nUC)xmLZ^UlXar_P^<;TPR@h3De;=3a|mJDfj3RXw7=GI{Y=D7;7@<^e$ zch6DnWyz$midZH(@!oWc6J4D%IRAsM+!e9i>ZCmPOV&r`;YL-Gc%Z62 zC?!LQ&$$t%uk(_Q+|}wIsIvGN1rAp%kXDQm1glt3QlX<@z_|0{(KjTs)GBEA$$LgM z)kLpsgKsUykAVUj{#Tz`Y|O{Gs^RNf;rJ^d+^)SX{Y9kq3BubJ4cVWj7?HQ04{sd} zMi&&Dzgi|k&X~IdQPfph>Zo=Y=Xj%BRK7Y;$8*m46c_UdeN<{j`c>S2AV$W&Lj)$t zI0T7DU(|Mwwi@>tQSB32Yu2mo(@Y6KK$F<~7_%(yl7topGPL}6??{1Zw}P$44tce} ze!)Ykl7OPp1;?z(k7n8inHiM$rDI2Comr0K;~2^cOJw(grG1XY;B;mW@jD29c@SSche93T#WE5Jhx(j4@=a`c>krA{CWaC8dBJN?ZbR z6RF3Aqu6LjTZ3|0EV388ziq|e_~5&zDm&>8EkUjjOOnqEYJ$Rtr>lgtPVp(`z1 zW&$9^f*IBRtKXHYg_|5t-{_W%DabUI5VAna%}E~yF)p?YyIa>CQ~l1%o91`w4UB=Y zAO|;dY5DZ^-$Uh+|6nvS>bC;>mfs|NDMYWD0=SwaBZY@zCOX`Ls?})olz{rNNd*~0 z5U=;3_;2<1J?qrFrm(8HhAc@g{EY%y2p#d5mfi$@a7(CqvARoU1$y-+|QMkB*XyS(W&rD0MVo}F2Pj_LhylmC5kr>d1b%Z3L`*~vLLChJcv=568> z*Nxn5?ePX`2(2x!dcRlcV4#wtu*mx=3XSD{F!a-QIJ;cgHe2c`f=(Oj^ftr#iM@<_ z4f`W{l=Z$`uXlAV2X@bI!d-c-{;xoKL$gS^(01cgyiy9AqoA z$jT!A4Zag_kk==?Uo+_)Axke3vE$aBYT=*XPcj-rmLqL9e(4Y z`;N9KSu7bGiCdNLHzUl5vX*V5pkhj^85=vRHA>?)j)BQ}Yb zEh~f@67k>D@d`_-+}100FU~N+>h2X0pFe}JX*T($j#7mV#3mcUd)$K=AZrB-x{4v+p)a-aM+&6U1+;dc7y0Iul(6p-38KEg0)8H^sEr{K+m`uw*PK~1gc27 zn`!8c7#Wt3Od?+{qh_&bp=DDAuiayXff1t%AsZrI~s(u}N2etpZ-rjro0LK%tmMo5x-WF<$+Hhprk?H%9>SK3i1 z>L@woOsFk;C5@qkG>5^;sw;hOxNB2K;5ScsKpoQ*6-P>50NzOV4z+(TbG?!Nn}CXU zi*j|vkorw|H;Nv-ks0v>%#t#)X7}$hjX=dmUU8?54oM&a}Je}hb7~$F-e2YKXwwCpNjkFC7g)o7V zRH@B*%9KqUS}e>m4Y(rxc%dOX4w%@rg>gZ?kWEYi&K1;~)2k%!UECB32s^S5CPr}* zcP2Z~uYpsqpMas`<)0~AxRd|4V5$95Ue?koS$19b1ceI!Ye0RgTPl6rgN_A7omHl|Nd=0aC%PxQ zmY09h{m&No){o?hz0bVo3uEXLKCH%~!lb#LZW8{j7M!riF9Kal>VZLL#EmqJ>-Q41 z!}!=+kw+O`GP#RNZ0?5qhB7HL>Dq?^RM2(naNNXO znWy1muIZWJq)gsJEzg+tqBa-m0~V=3Q?_r$`7gBcS~52vLULT|3qG- z=S*phViyah#iv*7M(<=zPHaXvGc*t#{W3WuU*8t)%`|$wDQj|H?4#TLw?!u2@-dTu zOiz=Z5e>sYQFpRPA<1fHP2b^T?#tk3imOC059D)cQCyTX&Rhy62XVZm5=^G5vE~9l zQ1R-_<|*ank}gMIjnt>O=DGbNy2*<4-s>!FuglV8*T|@>Kh`D}yD_b` z31>l~$kqs&EgEN!q&mhE7TwdhX)#|`qF9U~9;iO5lxMs({ZROv(lB*oJ&Y%oH~*%? z_E*@|5qjN$TED(jnHBg!VPl^0t6VrNA-z5 zPtOYu4aB0p&;BU18Kli%esC2*M4)#@>;S>I*(7Q-$Ua->7;bNb~`^Rar> zBuiT@o*r(*K+Ues)l6nsPui*jIGT*2ILM*C|JxxN!`NISB1W1mX8;NmVEYT%-=IN! z^kGbK<;oTDbq!x+9&;OtEkyDLb_9P{#M(Nza)ZL7nL?(DE!5O-L(XRAmk{k6GTwo3 zJ}H=qBDVfe0bOa8-1RDN{l0QI2fHRs{ugDu=6H$YINEKJG$)4kOc_o}5KYAyz4bU$ z#wa-b9-=+!99hH_AZ$p?s%YdvTYSpvGa2eO%P0oInJZ^mM1S*&ioAO&6{Fx6iF-8> zd`zHNd>PlfQ}5tcMY@Dkz6opl7gxhVb;jSBiUs?ThlgY2U0B(mv(&@oRt9==LJXdK~F(b!!Na_Fug0+_V zPa6fnvO2Gb5U8vz>Vq$`m#5)tY)c-e}nYyk7=8HGL8N{`c zgOih0R@N$|m2_7$dmdeDvOd`6^0OYQy{{W567zJb)KeCmVM0bsM}#~H1taR!TufI5^*jV_yazOluPl*&-1!F@=iya~2!_ieV>7*D}4*jcJnY_IJv)DU8)0?nwkc zRK8z6=shjt^a(axC$^c@H%;NoSM*W_c+k)WX1(T*EW-Zx+}%2Zq0Pq~cV7>!K0{{c zN$G+pSZNF}tESHQVoLXbYF^y}mED^0f^@_cw%EsE%5AFVhGRus0%giwjPU2w6nOrE zzsM<}YI;=i3Ss4n8b+G{&0kludS`^3fGM7i%OJvIL*DCZQI88%k zZ?K?uT=0n8KM?n*=`3a#e4Cu}yTTYPo#-zzL_~~pz_zUU?_R$k?VkoQFwGzWwdf~M zb|Tq|AP4GQJ!jdH3;cz%?cE{$w#nKXkM!}T7SG8V){gL*l$4~c zwLxP!RTYG#YI*Ws`mciyiPe?yrnM=xf`k6Me3l+WDCTuPA!EbFE3x(ZeyMaUlv=z? z7gKhL2j`;^;=W&x ze#U@dCL0Gwk25@`g70SJ;5t5sL8I;r0n=;x<*2K*AGi+jsi4}q1NLyuB+;J;DIH3$J$OeuQi>Im zGKefRj?N5uhw?RG7yczHPddM(4yy^1V^FURp*wM7?vB^mg&4Tag z{9+Yo?~rjKF_>IK>|yiX7hb3P_vKAYXh4r94Qj;B%1yXe5aScX;7z%Z1YTbksBS%G zTpJ|nw2O{G8Cd+uGs`7#f8cXpH_5Qh7gvlp&)aXEf*<*&nRv(1*bVR!@|B3yPwJ^L z(W)IE7?uN+j+0xQY_)GGaJ{N0b^S1@pFLg5lNfcIL@4~l3&Ni)9y|C1J&`HbQ0-gG zf6XxY)o@uX#?4?(E)#&A>-$K+Ft1524rRshiHI%+lnks#1H#wH)$KKDujcK$Mh-f* z6*4e(vy=pr_J8@}Iq`C>{{M^j{;#G?_Iy0-JFgWSZW?a<3x zOG{1~VGYCexBQ3Xoz9rqZ(QqoVLs1IR1iar<+>_|YCo6=u;IFHnAfTneOorQ?p5Um z&flo(MVl{+`e8SF`7EfM-L3rHp4i%@Gr8wEEgfDFrGlZ0w`b`pXjFv8LF3 zM#Kxy%h|#*|9gMcc&;9PMg))Nl^*;Nj+P7~eIuh~hzsh4ch8clUSt266y#onH!^~! zAwgXRi3owWCR zN$9Z_#REs~6|GxRF~n8r>IC@sUBDh13Qegwwg7=BX1yE|k5MEW*@%XM$wUg(^?i|sA57t!Rqj6yAh`Sp8 zmGG~VY?u;u$iY?mUi7GYbAf@TP$^-X++I}ikVmoIwVr77Yu@Q#w9n-RvjKO81DC`X zq)XgRA0?c%tp-DHKk0GPXzBLAO?pD;GgU8;b&HG4?}8z_)jSsH$3T6=truNgQ`(@} z716pBsSiH$kvdt3K%{6O$P{v&7Loe9(?xh(MYi;yynbTE`|F1?b0fdWRjtEzB?2vK|d_M!=^N8|Iq z6e>9EA5{&Oe=aC^Md-QQ5ZxVUtMse+IXH?w!e6PK&Gxc~VaT4L$!O$zB{5VwS06=w z4eQ~T{q6*d$YHe={&Zn zN9bw_&L1;TCJ_F;cCGq+#|siM*g1v`?c7;YQb4JObtew(JUm!P>T6Itl=nB>&Co#% z2xhYO38F+txa0qtwppN$6};bE*&OzHWNT7!OOl&(QLZ(qG$QbW%9aM}bDOW^j|LaN z0+7e6HS$xOgw~%GKHyMMyUSTW;G=R!4 zUqpM8GMqhDaEW5$>sut4kr_?Qr*e-KgO48*DIPDMB&wAZMS2;y_dNYtpXED|R-YBf zytv3B61X*rxf5Pj3%mk*{03m(?_tq22lH}qJ!93lb+;io~zkaCUtdH zBjg6zTq$Y+RliN6|KsDFKVDmuSN@a=I)7!*Ib`DY+?Gn@ks`@(UZHSJ8>M@ap6T`! zHrHdAb35F~08x3a8izZqWc?!o(5 zUtX!zokOCnR1QCDs@jcp51eTsg7;C(|YMMm9Lgh>U%=M zi1T3i-EsefH|?f{x^3n;wRm2CYP4%@M%(lIg^}^!{{NVh)ph(TD3yL4d3*DV7bXzK zvNppTtb<|yTIh|A=q+drZQh2_b@z{SEFznyQxr-V)J+B@x;1F z*_>$FQZov+j-YF;KLzEbbL8L8cr2SR`e?axjaw+gc%z>e6J=lP=8<&m+<%5N@Pf@v z#nbB=R^%Ur5;4{@zR+N@e;@uH^5L(bOnFYYBvFroXYan`MMDYqcI%b@jshECO={P< z#YMO=P__CwRD~MdU0|Z}Wb4C!jEGbiqS8J=DiUO)VL;HJzMfr@ogE+w=0)H*0OT=XJy( zD*UPcm11#t02_Vv4zMJ~yexpc=-V3;84`noUu-;lXP@eNN5_w#4H?TwtLuzxQ(C^8 z)JReBBYf)N^d(lTVmJ_incyR^gKwsezJ4Tx%`Sp_9pJZa=P0xlrGufDCMI}F^i9?@sz=>#`X<-1kX62CZESkrSr{}4gY2)AP zsCJ}19L2ws?wXPp^@(LDsc1(Z=phGNI9;jJ--Sa?B2Et;M1sjH1DA&4u!o@q@_jXF z!U!>cI2*Q@AwrKaaVHHTTWdemi$VpD(sEjCtyJ6Hl#ZLO&W5kNFyS%H;4w`qFpSd- z6xJd$;08A9Q&W#oo38+rU2l zPMFomgPNvaqTpTklo|yi$4#a!nG8psLYfe7-qx% zo9fn&W=yh#^(>%}bgxnFqQifZPQN)OZt{=;RBcyIc!XRRTT<>Gpt=OpMP2E`>TOTt zHSdUZ|0dmKmbQEhZn8W-Le4ggTZ29vdK*D9ia_g0K}jMsqP* zKGovByRVOFpGBx}?)?o?I^d~%8650^Kr>fM-zRt8X~ZP&z!EgXFB@j+T-Ul4W9LOOhhV00U(r!)bZb-C3sPHn`;BD5zx?Mqcu{?lZV6QQX&2!kA+zEB z{aaB#hkKEg;gEU=(DYCDKH6bD1C+su^~G7yx7A(L1dPETUCnwQ*g_+4g24>rD@BH* zZ%G~Rg4X*rX|BU{Y<=`r9?X$wrNxC+-l@QXZZ}liJ$2PU1mIfKP!~^dF}mboyx?va z{Z?g`2Xd;l1v#9(yZM!B(Xi9Nz$-6U0pv&1FgyJL6Kf}_Gyd%s;0W#E4a^KuRRrk_ zxcR?IO45nfh*kNzDvyvCLDWI{U`8)T1+dk(y%@$A*wZ1@Hq=;XXlOh8`(#1i!0|~( zN5}fu66OnK?)CmHm-e`*{oVRSxs?E1k+Kk%pDegTl=sjCtMmkktSOfBl}hWcnm`-d z{SqI06Pn6KuvA*!=0Oxz|@$2CG%S>h-UEZJo>*1DY2}c+*8(X4Ua%M*}_b6 zFAWFYRX`lH#U{a`&fUy!iuex3_09hp3f*|}fm$X^x?(q-sH}{vM4@{b#$GK-z8q#d zIj=7aluhXgL=Q}k4F?gMbVRy&fOQtsDbJ``(wlq}_EIX?WxRWLW8EcT83`u>Th8YV zEyGUTFyjwA6_>x_tqI@rJ)w17RcKFNu;NyRuMIV>z1*e=P3_55b^~Ic?2iBmI zMX7wReO(l3jEvpnuW{FqC%yVEDd_%&8xUTk_Twn!>5~jg^({rOdxng0{bxwfkv|K} z;ooJ~cjxTB*Z*aG^q@JWWzUj~N;<5%eLnxKWyJEINkRC9v5wwI`RH=r_cByie2qxgI^W)qA_2~RFPw2u zO46R~t9#7UvK(DCGdd)dhaH9iC%^T`{@ktLKa!vf-nq*w7&3LcPr-?J-S9I^cr()y zs>HTCwqOv7Vchfr16td^QGCQ7qPrOm{ZKBzp6~|1o7>pVBgG zCX)I5i21pW+vMcz$Yt2at)~9dGssM`0Z-yaFzI#sa5vJ6!bf#Ytqh^;O`_~QfDs`ioLsRSi@8Me1P}3AJx1M%tBs{t_z$;sXr>>q2H^z?k2L|EM zuZy|!OueuTAJRKoSd3bBt-2Fa3dP^oCq-ONVBj)4Zr*X`S~C7gjz%Ipln8bk%$JVsOinVGbX(oc!Y z64YXW8GS_a=;{;TVS#dmP(u$Lh0*!*D{6P=5G2MZ| zOaH2EXR$C$IEK0Ol#8ubmICwj`gC+>BLPbPLzj5~c4x#5kGheH&sJFb>AK(c_|5yU zf#{I|q*RLf3vmqy1lNoHgzj`JQX{5vsi^-<;YaRW+1J4^RqRy*2RI<-rbED ze&p7Fv;>00lgax~})MO9J)e(u1i zhq)$;kPS68BMMZX-8uek1GrtN+E;tmB3<7WefC&tMk~(^ImHFj>D$J})TE@-hE2fA zM5Gj8q3Z`FXf6$E`TU}WNi z_lL~fMLKx`YLE{^92{Dl2H0i|j*Jq46w{Pvj}=iU$AY5BDdYo;!L5rEWa(h?l~w=% zsV?^^z3K5qdosuCib=V6P&xx>`EV-D)tS81sM`X#Kvi$v;2^mp*36f;}Va>R$bXhJLSRF}!YlU1!wkeE%xF|pbv!a6Wvxf-19 zr4EJ^hSoO|$2fBy3vhoAeRl?~VP$br*oiRhrptG2Y%)vB&Z<%Z<||>=%WL-Tc}|0vNbU&?Y%}0 zUrJ^!5U?h3RC{l4Z@`@jLyRzMhH{8&aFfn$WNIy9hj}fiSaf(DCNecq92}KJYqF$K zO*ek4AK6X+rVeV8Wdh7BN#=)-&)eOB(ENDK{Ktyu?cOy7!nDt@AUQ+D(2z1-nGFe| z09>V6JR`b|xeC#R>jV2y4J zQ8W-)HosYw0is5DTYGc2LNBBcLm;O*Qx{U(?~=d&`)_hz7RSRSNG;akY@s{F=?%H- zWd+C53_tFz>`r~7Q{GJRCN5iRhki`ef;#4xNg-NuUk046orS6&+@XLgpQ6^ohjzzl+JWx_nGDZ@9!Awv0R4qH&;hCWRul>21(I(Lj&(pm7Vd9rWXE$O;n4lP{ zUYw5x*JoVt?YY4{|JIzaY2*R;u28fjc1y?7z$sD4J4o5w>p}m{b7sxTL!Y$kSMgU1 zLfJZW*!9`m<=t=C7hl~44+AhiFO&+Ejv& zY!s@9N0M@O9Qs0~1(YTeL)`!E3*+e|lN$KT%BVj0c3T`OTgshM78aZCm#Ai6^`p-j>?kt3{r9BKWwG^C)q*N68ubKL<}KT!yplvX!u3M@d9no1|l7hYjOJ= zg1`1Gef7=a8U@I-^J>O~Gr*rA%6W$Oj#}(GD5Xi;RI8s&3bKh(GF|v}o^B75aBy6C zXi*MEKt(|DFCBDpg+FgCY}msa@qI{- zZ|9v4oMh*^47+6({5u6*F^V?=SM(RYF)jLqNUYI%vkR~`h;n!w7KU;Rc9t*KRDLxp+Ingfuc@RKt6KCHg*>9OqKLg7F3?``8w)5s z&Ldo>b@k+t9jh-;&8@6q$oxD0Z-WMa&8~oHNb@@33Yb*s5lB50i0hH z-M<^)lP;JbRDr+eNPI^7n!0z%hDPM+UWE)HG2n(dqjLI;O zlanJ(y2cPd2to`}fEjh%0`*iK?vMetk*JV6VX8`x`nlNkyQ@l--E)5+CO46E*|g#3 z#lewd0h;N)iX;Cw%o>m*r45t$Vn9SbH7de9cfvJWo-bPQd25d|O)Icj zteIWEvsT(^scYVxJj&(6w3Z>1=But~eWqAR{@x`x({q7(eVbqD;CPRd6TtPkI0^v1 zSRpH{#&6davZaZ1b#?u~0kr&>>biLhprTd>Ate6+UJU=(lh|KL)D+=zBrBH@DJd~3 zB$+sZdIR(>31n&G4};)L2Gtot&5ojTgu|1;eMSaH84n!Xj4jspsg=Vq<6fc(=F)EX z(Frk-j#I(cciH>-S={S4v2mKAG4bSrhWhkm+kNddvip;1jZqxjXkn`rmxS2b7LSMR zFW*BuG-CGeNCbumVx4)h_p)t@+5ip#dioQVL=v`yq0$?qP(;-3LNnKlb`Bp0am>#dw-lBH|()XGeUDy}*D9pKE+gv05zHw`^9YlGDYn<7< zK0!F-R>Ag5v)MHsYTp@(S;VgjQ2h4!ohO;6)g56V?bj0TiZaLpK33h&_RFGD#+gI4H#vDqo-U zbR9kT?B&drW7%zYb}Ru*`cg&G__@VuyBBHum?e@AQH@*VZs{3$N? zFrWZx0*F(T?I+tWZ{(&xY;N+QM>Tf4cP7nH?--%az?dArezgfsf+L_d7vvlAIK6fg zFsa=`doUl04xBEVLG?vH*K%&Gep>G1b%pFB0ShZDq9~go2m*HuR+BG30kHC@LyfI4 zABF}-M|W~~4I5N>!JgrJsEFy8M?%4xe8|H>t+X@n_Op*G;H@*D88QN9=IOgaP}TP4 zYr}{Oth&Ph#jsowvz544931mX1;2)?0mo%a^*>j~k@2`6!b@7nUlW=fqnK#DNbRm- zf(al8u)ff%vB2a(Avl@vafp2MR=Te*a#Ow!EJ9W-FE4MuiPckLI(ao*AYrSk2gLpJ zH4lodWZZr4d*3O1( zi1ODDE>0u0?caJY7APz}%bgJBsJvebM$DagqB7+>0`CBS3WN)&B#E1ZG7@EMa6(fL1OODbqsI(EBn4Y{A4m>j`epeD;MxvqcT&CN#pB z5v=GiTc-w5RmWYUjp+e^uU@>EU)VpsfawTH6){U|+3J1;7&z0xD_qVLgXlC9A`si}u9$DWP z{!f2n8I~h6woI@0Je5JPSj+@F1~--;vu-feWr^VI3H)A=O0@JU@iJnzHxATM)KQB7 zTIV6m(RfHC*I5uEEhz?sbd$w`fsE-6`j$iu<01?MHn;2*Pv z?LtMMRrSHDfZa?y6IzRuPp61Q69s{2&w?rKMx|A0xr&_0RWLUK3RpXr2PFrG83lT+0Tcnu= zh*;?R>3-kv#x0VUJoi|^s2B|u4f|-ucEQ!LoVz&L1y=z2y^m0Yw&iPVREjwt_5r+v zs{UDNHCoh27aJF+;`tyn-B|0P-j7b5<&#?ZB?iUwEh$K?PEmiIp2*HvY4@(9{%mnP z1O))dasE@GTr3eFv4H>em{{F{eTVl)9{|)?;1~PC$doRgbK@H?ruFrO>$;dD4BhU-QhIC*#rQZL4=q)nd;DX8z=X> z@PHXMf7wsf`ITB)V+fP@+49wc9t^2!AX)+9DoNq=emKFV#$>IgZfqfqS^#%f{c-1o z!WVWMNss@1N%+r`Fu725PtqlMu*?W8I$Oy&sBhWdj!vT=?^ugKZUngCf};84mp={FxUcN@$nqbo9d}&@uYEek)S_z-07R2Mqze!Q zGz_eT(vmkrH&>egJccEl@;o9hNdvOjNyXUrx3TS9S?9N{;}&pJ0Bz|GT@xTJV(f=PxtyXcbxCKt_>)6JKf+N~ zGp{e8BtqWP`#N*kxTxlDo0Ty3=5O32d`jk~4u=Bl@!LE$Iy#0Cys3xO zatcE8{m8)S`#Xn|wX!NWGDJo5o16cpNh;x3>DgwmeAbMEymxEw!vl;j&$!v5(8BK6 zc6wx6+)`I>rb9{@xPHt6l8)h2wuWLH^$Jc64&76>N97V)^x`IXEgju60D5dARj0bf zFNu!8K}l)2`_Tlo8%Ii4ws@Tn^x2i7JI1yY16c0-G3!|+U!{QI_>zVq+5#{%Afo+V z;w?pZm`(sNrU#P{e7cp72^|62)maq$4cPWH$~K^5T$=q?@_l>jvRRp>$#+y=`7;A_ zuP8ubg#1Z?(nVTLeaA_c;T4@FA1Gs89xG*#C#zh2XITP=HrU>^_l@iD z{z?~Nr48FTB8HZfWtKyvEC;AA-O98{co zuCo;NBmJujakD!O4m>xu!I<&ete-2C^M}_^`<3+ha_(^}`)2fWTsd*A8nH7lONXM9 zt4OiK=rszZuKt4_JoFMWKqAh-2WN`B>alpH!Mt}ZF-j2fE`HwNCO?mYceH}ECPFt! z!j>hCt)C(Drc$HGf3{$Jeu>b-e0Am5zHyWAOU$CJcih2Ht@G$&ncf02=ck-~I5jc# z(4feYq{ja*m*g|$2*=!D%GK3XRSk_U2*Mox!Ds%)9YmRk_nF|m0cuEqTzKU>ht{vR zv~eBE^?LNU-G9KCEbND{=VlGTj8Gjw6?_&2R|+U4hs#gD^$uVdzyAbAkw5ce5yJz5 zF4ZKVU{-1@#C#ZB8^c|_C$*qsl8@mM9>XJwvEnKF1k7l#hEJE|e9qY;9i~^>JyxTD zs^xbhW_@1T^P6-PlQH)kB7GT=`9X~xw$Ij&*r!kNfC~J$VH2{&5!DYOei4UT5J}w# z0tbzgpUK+Yn<6CLFH^kus-5K2V$bZTaf=>ffITTSFj&ERh+ZoANdqugBa3)}A2nerpa6d-M7zIF`<#*luet0lXDxyzN+ z32X#xPK*BV|L;}0#3N32QiPN(7AIp0JlB10-MgZ$rE($jKlX%B+fmo&9#nXns-A6@_TRI%iqmEptd}pImX- zp8fa_cJ~a8aapbWHEr>pot?&RxkE7i4-9Y^72Xm?l~$62^zFn?gAr^cB9~l+;wheW=m=U`-SCLjiR6qK=l6_TT3Qlf=Vttasf1h^uScWzYL|wBJZ>Sg{g%+~ zLpNvCI^e!tuyZ~I3b3(797}cyiiftYb0xfz^u~jQ$F$u7d|-Oi!t%0ROR-_r@khzG z5tyhuckcN8-R;fSE8S_wAoBVk+wHB%DczWcmF2>K&u0o}q6CcLhK#bif9gVAp@#UP zzO0&H=9V0*WZ2cR?SA^TcHYhHM#c^pUs?loAE6g)PMg+eT_Ra6kX^28ZXU0~=zj$= zA^d*lM`urPjR;?Lyt!uy0c|w`NX`7wn}cZZZ6KkN6RR@2N}*M?0MHlN&K}T!E#`FH zGJ(ACR`=)!1@PFlymYclCLWsm0 z@4wO4b1XUc)m;wh`Bh*URZb%58eiT}Bk-+MG^+?n5)Wvib6GziJ%nf6CRX)p7Ymb$SCD@wDumoDtkli zz39da#!Hub4h|CUnWRansIY%WS{PzWl{lDw#uywJ-sD0fa=1Yx7}8l`>&6lF7cd815s#()OnTP zxdmy9g7J~})SKgtYr^ZZj<2p1b$17Tdy!-hyhX*%+e>8X@7EZWX)?ll+Wd&Ewt`4+ zB~sd+7t;9545H;h38kYrq}Dyx&9H|TX1E@JZ)hao_sz++;^QquEWBN<1lNi9XJEUL zkdlXGHeu1<3cU(M0{q&adAx&B+CTJl&s5C&3)$NKe`XIG)49+FBuXDqBMtzd>PWz< z`Vljv8T%_{>}Up?Bjo(ILRqw~a&f!+n8n0h_V>^k6$($)!F$E-r|sEd+(j?ZdesTU z55G_o(!v}!vCxg0UZ;;AvL2?SY`hz5qjkKHD2%7&QqPTf8s3Fxt!YCc68ZSuF}Lo|bl~w8Vw>A@Qb>$#`Zf|< z)TiF8Vm-Ip%IsNV8|I^X0AdF7M}mpNZYL#4fMWO3u0|3hbytU+2b5d2{^F z1A#rsF;EZS(J=ux0yod32Nf^bAM*rNH@;%ivKi3_?Uj8(TrW5kFa2ltBwczgg!hfw z-C6t=wz+svClDnUryZYe*{yWM<8~DS-ViAjgfrf}WBtCD1M#uhkTi`P@9*8OcPpK3c(F)}qImx@-?~yP+zS!vGT;x0dYVw*Txlxcl_vTVd%89G53T)I_@r6I zb{*Ka;xw*e$Ar+e$@~5y-fqbS5MfZ(Drl)V78IA}2sxA1-O@Yw=MMuBk%pamJJE6Q zN@#ip`$i1Z?bjaa@uPq|vZQAs3S*IbTGUwns@c|NOLO573PRlD&2MZR6P8^gYrDEW z4ymG6z5hYgn`>5eu1xyML+>7FL*IjgLWE+7L*DFz?hi{@KATg;pihk%1zpNFaMYyz zcG(_SrB>FvIJ&aA^8QngLIEC(<0H7`mr1B7VvBwgQ{UnH=Q&=+D{uwTw72gh8XCQUFM0QHYBI(O&hF6*fYtGAI81Bj*Z$FSZ(Gk z*fOmw`>3EOKl-J}2mnbCYhNpUgWysSF^M$p(MVE}Vgh<)ZYRu3AIN&r+`m>rOCUCuyEeMfmybW(laQ)BM^sXkUvVxpuxY6VAmSO zgd`8exvBXOk^f@wqKRoFJq^P4FkBMoPyOB`oDiU;MPl(r? z8Ix#op51-QZ_11qo%f|HXIGOsPKi^oAjC08bZn2!5* zwvSRPH}`H3>1$b(wa>cHF&89GL1ll6c{W|=3VXz@8;`d3Ko5a-e!soGYx$oz;m3sO ziMDrtvsrR9U}EdRjSIfe1t2MG4qpdg1qGg=IKkd5DK=_LJ}+YAH;m=Vyro})OF`I< z_0b&As^a;Uu&=N=_X(Z`t#YLCeZ=?!xVq0z^H1+q6&UJYM-((i+--X#3wF?amd(^m z|A(&kj_2}!--lna_ujH)7a}vVM@BYfM@GorE1T>sL?~oSnb{>HA}cdwrtF>k&g=dA z-hbTp{rf#0pU;2w?s{F<^LdWrJdWcG*g+$@DiQ9!HiU)(8{HbDFo&dOVf>tf&BtLUaIObkBB=N1Mrmn2($Lp2>$3=rF~Ml2!_}!I>bt#$TJ$kc(wf7-bhck>7Z{jL zWR&9?;1w?jb4KRmZxzkdfC7aoSHtk=bYX8zb2Eo#eTTRjXj&doe`1%%MPwRgq)Rtt z?uRpy3L?*iO7d)_QNk|wM7vW+L<)L|k09{y1c??R)Qygb>5L?#cbPQxHs0pHUylQh zioIGg7vZq?al#l7wFG#O^50tVQ-Q5Gd;^wtj$(yu_n%p+S;G%Ni%jmgVMB|u(DY+P zS@dczrpj}1?@A+|K>WG><%S9xiWv}R#L2oH=ehnd*(O?%plv_sJ%=79VAq^z)^of- zJXVN68-!5s7J#{5GCstZkMi7NuWeQ`aa`(&!NDbx0~1s5Z19}QxVfpYN$JW_64-yf zUX(}zel*qX|LC4k@yprSl^)L5Sa$C~lxG18*Gte;fsfSE;-G5GMU*DYZJIWm%B!uP zj=+QC9)^rqjJ=!~_k_Vx!N-o^SPBk~c?34S4nqz}zFn(O-#aGXgVVSIS(l7zwwwhB zT&mWwVD5!Eqxw^t^(Z#_De~N~H$!Y%Itw_$`kr)1mwY>n2zT!qY(#Xo)x`R0P;4Ik zNA1?Q`oR@VdQvg}BR=3Xf-L2qlNr>hz|B*zw=V-Ko)iOs6#~yI6i*%@-%c}&8&c%2 zGHeY*1P`NqWSiXA4WW6j1Uyml;s3sLk?|qmP{qqJgkPIu0`P>v1^J29StSv z9&Y%n?#iKX%o-pB&GOzNlZzMmb62dwy0J4n1hAFGtkf|a}hr6x>3S4VyKV@Zmxm01M^yUD)TVTTK zk6Z~UowHd%HrWk8mpr0Cpg0hsbXM~eES=>+>-%W@L!1JoovCTVMf48J;Ob+8)NG-?d=i}AO&(s z%xBzm1Xv7GJ|wQLdcDy&oE#aynUNg8BXW7H*E?Knv24T6bzc3hp75IxQMX%;gOQ%-`p|Kyz;uk%`>H z?qX8L`bIs2_@zAwE^D2^7mAV+t=jkQ3A~OI%_KSL>$Jwc6BX)YywQmW$5DKoG!9O44Ui>_h;@mK9kQ0F~CO2<`Nh zeyfxH>z)=s73Wxl%gq*w<&S9hCzhMQ1a}v=D2C`pj{sR*mH;+n4`|~Tbsx*tcFn+_ zHXZk|v4hH7kR%D%R-vFxW1kOD+~>hO5Hx5T?VNX@u$o*_!99@bs@i=fgB%|bP^8Lcm#m- zo`V!FnZcjZw8LtC8IgLeQdrqJHiA_U_wIEh1mJvmvyHg;#e4*sc{CFVf&Gs=$^J?` zps0>KB~RTszFhrL!iH%i5yJ9^HLQ$1hWY^zwSaUT z_K>>jCG378WeGZpSC<&u3Sfk#2H?YV=dUJNiOclo5CzswAihN*u#TXhAPA5!dR-gA zW&$?n?Y1mj5J7yFEjjS_6p&*A-@2d`?*p6#oD1G*Ji&mh- z@x^f>-nj-p!w@1_!TkDNw?@nDw-#e!y@;e z!fx-45;(^ZMWR8*NWJ#)gOQ#d1!&N)6VSbWnz>II0JmJ@bVgy5*7a72fn>T0dm|Lk zuCUHezI?n8=-OaRzq7wgi^dUXFt@9PN*(&s{EE4ng9Qy@>m!i(1^55XG7Y-wb8HJx zVB{M=!mcYb3G$gy>|oe<*0E>Rb?0EzHDAXUkr@MG@ zc?B`$S|6kwWcX%j6$bZ{3@F~T@|DZe-Wh!W8)I4B!*gOf@EXYA?}iym-ePM8J3D$J$yH(u3y*|7)^fQ5yBm`fs;-O^ca+ zwd|1ZzFm`zA`mO*wV)4s>CV_4-Sh8<*#4HEKmSTo>HIGCbB;mFrznO=(U-=a+aOf%_%t?XE;C+uaU^?xWcvc-rdX({ zx3h-@FivR5<|jH%z76%`k610x6*1xiFgSbaBt3z8l- z#6RBfN8`pBZ-+{U^OA57bh{3!Rt?WE)11F4}@V#1mG$4=(leOMU72r_K3X}wCqynTW>*B zOaQ6_WEl>zwS)cpmWfjP&VQ|-VV4^~W$4MOsxk?Qsf59fX2dz4vq7N-Mn*3Z;Z4Os zb|cT^0cimS>{o?V5HDkMpn(qz2XM3^h8raze(frNeuT(L$I*y=U=*au_nAh^wUF4j zEkGQ#Z~t)`z_m}gxM!O~h7P31OZD5U0P4ZgaWTi6eg}4X2GjY{%4b*^BB4$^4J~aD zy$+Pf!E`oBii-mEBeO0yRD;Q?g10kNb=A*y=Dy+bE-=paVYHs-DqP;S@>LPlF}oXc zkDFrW?q;zX?=H_Xp;0uPC!!4n#-78lchEy=lbpv#tLUcDuQ%5}IK>#5vN|*K^Ap0h zg|F)bQ^9rh-;SDjl@-=0U9XkIlVD(DgSGfB&Lc47fK3KS*O6q_0~c=)A`ZwM3e&DG1R#`FT?M1l>}YOia^40(QmcnNTPqSzYid9NP-!)Huk z-~$K^{{zEi7+iY}!@Nao6n0BJ*9_$F3EuAnudmUF>?E}nNNJ6@kl3$^XIct-3WMU2C@7Nz7u?tiJ^Ct=Qc5nzi@yD z6mQ#3i7W%MP)_izb04psh=B%P3F%0ISP$kOY#@_;osi%5mU{*NNlRYJu%Mpx$I)$7+*@V&$W&xD8aW&R&} z_U9xPn977H$l=`JQUhFW1RF54O0239-hf56DI*6!m6q*C}fPwFkm>~YOSk^bv zSi2=F9#|Rufo%vX>m9){vFjX`*;|cHN8uBFuA|hICQW3>s|P3Wa6`l4$T(Y_)(;tb zQgj`AB-d%V5u0ET7;nVha8mxiBKVhk12+(vRAn)DEt-u#qd8iR~!XarnOOsVRcf~=tlh1cOZ3a%yNI2r60|CG?W{&v*R+` z>PwDju2*UPjj~ha)+!diNPe(#fK~zfu1!ApQDTL4HV-xwxh{Va|Lu!V{1p{35o727 zH?7@n@)j>I{*w=fRMP)W9zuZq0a$$F+6(`rtSl8pW>audsm&!26unI~3@g$ldGVi*ZIQTRFcG0UgMlI;UXY(QY^H<*7#OEZAeEFKBzeWKv z=xmY={UT_5xOl1+fp6OR{yF}G#r!(;o_SKl9(oH`%j^&A$N)kFtc=PMhPvxqC|f-e zTyCx+mefJB`3oB;DmLAiv72t?(Eq*~vD-m7)z~QvO)iU}x-ZJc9cp1;ZNSlY+lhxB zpz^Yx1=nYz7BBuWhj$w1;7n(2W79c2Obj6ByLYf)0TJkGZV)d5c?>jQPTyB)4>40y zDt^P2n*p~ht4~={CtNFBEh8ad2_wMD657Dee|V@4!WX{bSL4ku%61$G!D3JLTDpaq z#77*fc_lF$Jp#}>TI@f4=JS`jD>W&AlbQT@5)6?V8(^c_OMVJz!9TqdeF-1GMaolZ z9&VpCr}G*vT^uj;PIP1X{H_(h_xTmwuc3rCF?hg{|tl`}^|R+N7`}90NW( zFwsb0^wP2tl=7#q#pCLKt0Fi+12HxKhsc#2)`Ogsrkz}F&3PN?+R|g~NZy%Z4n?Ol z)OW!TEo^ipo-Jx@m@qXQe`C~OQ0YJZq0;{e#c@acl~{Dl9<7Dpk6bp)?{n-}y0?(4YTCrg%TSp^4ja#2NC(!uc&KV* zOe633MKNbzB3Xs_>iw~(;fqNzKao4m=u{mdE^e$&D|n-(#=oVrvT{RjURG6}yDu!7 zJPN(n9uzn=J`Y$HvE}8{6(Kecatylm`bx3lMtMynp`*?0od8ZeC0t zGhK-a81);t-{_oBfn~ zF7;J^QYXuUwx%z=nJ8j3EbZ7X@wrwAWfEURx#GWH-o-!thLg(nf<ZAk4GrOxJUaVUV=0fa7vHvj ztB^1S=sRZ@T?P@=Klzp>sh6S${-0l>fnd3!wARGTwzfLGXvi80_cSf4%;B z^o^wTS@hFa*`##hCH$qmIb>=jCAJ}<>Ca!f#@CbjDqqL`%r}selB;amw6xeAWd}{{ zYnl1^c~qUueQ&Vq6mwm|0MpCry+&M>gaAxcIlb4tMCO|Gl@CfBaMN6zd$onrp00(| zC!Z)+_YaYGT>6i5uYLU#h`CYq^RppI%CZfkmbzwV_5=Ilusq57Z+b>)=wRiA4GRwy z$0lh^;1Ix%ed+D{GkflcmiUwQx zEFAw{9I#STQ|HPAGjeLZ2#3CUuEo37Y37!XpPvKYYOBHo7*6Kb-*{f>kQtUdg?eq1 zv-dh&NZ`f-UY^u*bX0-~Zc@`e$rCjT>_eD^~i4%c4KhWSm$P*Y> zMVFA^Vfyq6=GTHM-qygX<*D>Lahp8;g2z4H$ChRsndj2R*Mj|Ql_$Nv zysWOSn|aXB0p?4QV28eV@MSGpOFc3s*~s~{f=*qvMem4uzRBG-T7eZOG{kABjL?b75u9B0Vpr6*l~ zs)fvA@v(4cJlkJo{_n@KtHvr$CloFue!is3;mVVqvOQt{f~6!b2l3)wp#m;QAF<}W z&0oID`7pcJDmv~2;hIi5K&+vGgWFh?BkFQ4utviaJDTu-MM!vA1GkS^uEmcCd49N? z77hz%*}+d&kl#>g(mB%HtZHzh1cKSuc0!}8^adf?q@1^ObYD-4l!uM@u}TH)qRFz5 zay_FI3t|y_Al6Zs)yBzE+ssZ&1 zLlXmY1KQnTF{W(|&)j~x8Ymab-n`+W{=9Y9O2>^S!8{lXWoItqbSyicmc%EgL-2fz z3;ia!;CyRh*VRjbu?i!;Z*{HQed6`$ykhqaMPGdi>Oa_>`S%j%^n?;Tz3p_CIcX?Y ziIUQqI`qM{RqTFyL84m6y7&=`1bqK`^=)c4Si}| zRS1OkgovcqB_K^&ij1D{g)wEYKm>>GV%A(^5a;^gj%^BFXMq8ba_ zvl;I)1&%i-+#cMcw0OeR$Nv6${Piy;sN%WL{nH0wkiw|p4^vYABviFpZ5g}L(V{id zc*aG8`uf4&TO&eIJn7jp{Qh*_sKyfopD2ou&tLhvU2iW3)Oc)ujQ{9{O21_hc)EuP z&FLMmAsKBBI)Yma5J(@YB3KD59i&sXgT%IpzZpR(-)=^kTLr){f@S9QH?H(SLEcoE$no$J}F)$ zy*%TQ&fk~on#oZ~@JR@75GD2XqAUF4Jnq($Mw>o&SP86`YO@br;PU(wb|4f%WEg!2 zWn?$_jgnAPM;-o}EZaM!dpN zA*C%3k`Gw+ZPZevrEJ(Th;)q6CxAh4uiEUk2V59b+XIDa{c;1luLWp-W}AxM+)YVH z_SUT%maJ5+H@(<{Ap2F6x1jGU7xvA%$~d%keoV0OJtQl8|ISx8B}~xpr&B1iY+U;m zrtdOYitC=zwB1|+A+KAYq)t{#i=?BI6Y1$GepOpq$84K;99~+4LQ1GzyL>KHO1FUF zPgD520XyS-16NNPbl)nbo^jnPC}{33G+_;-TZD+4l#;^rc9+L=G^%8fUP8%+&}e(1 zT{!}e@@EFO?rQ?+fRh4vU?O;6GE&N@eQ&cXsODRmk6nIf;Bt45zoY6;;|_y6X5vE! z)eA#h6YG<^NO=+pPdhw|Cubrjq&BrGAMdcz)OM9y-Q3)~IeH(?E2{XgLit7B(OiAf zjkX8%G~C>wO|08$@K;&H&75gt6q0UP9By7dyVg}`Qg}D>x`2>S7o-Z8pxG7`dTKdXr$-Zd-z+0d*fFQ!)-r`Cjk?m zw~V#^ttSpoQna|T{I;n2+`=9+Vw}k1YZk&P&L0g zTqA~(ds@R3QVNG^RB(CDAFXe0#=@kRg_)W8+cE@~(1r$46H`-tPt{SDJ!iGiCn}@L z=B^`7{Rs#3sW)vk*-kbp)dyUvnvrL0!H^9NuQ?Y=Y(J-nzkBIC_SO3bi6-f_SK<;9 zg;pP9Rn*mcI_2w1-dwrUKS|Ov;472j1*gU|#@NFH)giNycQsF{A#vDpw zXfnFKaI)aw^({rpr-T+qQ$NRzOY1U8K2~Apjt=$|A8_lf!eG56jSHoY;s?J&VG`Pv zThI}`c<}-i@?kBX%Ojz!EM7vMm4l|VuI~Ojht8Q9dJt`efD2w*&;=eCW$BgPL(_OJ z{1i+;d2T36$W53Wj_KhZUACNCP#>!aT)&?u%5fk+J;oCgb1l;6;t#&_$%lvs>15d% zck|1uN=il(#dJs^)o=>VYW=Cw@&4Pr!+LO2J-%ms!aW3wt!VZ&)U0;*TkwWV{KK!> zGFpBLQF0sLRBj8`e(FXSn~>INlH5?NF_JPTp6#5dH65J|R!qSSk{ro}XGg!E!3$mf z`Gq+kHdg*=z-u?PXBH-P?>||wo$3X7+uGSh!*rz(e*BxN=N@SlO`C>{ZJjOAG(T^O z%H5n465uh>?oQTrerRbKK}6X9@O(pg%6GMP5Q~rb_p>B>`=h}P9(Z&pfuoNknHvV@ z_w9?Sr*Bmd{7TUmCZ$@jLXRT&nEklB<~Wz>+Hn;&o1o-PH{@@yi5%%>*qFiM__GhF z^^FbHNkP_rB2G@u#>U2u$w}(-CjlK_sa{!Uh;WiaMWT<396+z;ga(_uKC_jxrZGz15gj^EWgs>>s7s8xQGEY7A2P zYU%Xc#p!buO6=ueUrris|CLR^*q7XV&^>)2g?itvso zY7ba+uIWvLjMv-iMnt)uou8L)))g!6t-etXitmNG5)E}b$8cpRte8v*v%mBEOD+br zDQ0LMh)z$6w0L{f2i)LRLVEL0VGI4qC=>LX)@mvBUY(Jwu>FEE^h?gysOSr!vF0;6 z5`CW^#Pt^KF8i&BmtVA?=b%6i1bgH;i=f`WssipZvW;ufU^X(TV3am+0q7Wwq!%B| zBYx03M@D|JWn($P(phFLc;bLwL{_D8u}6h#NnBuSjRy78{O~3!UBqI%RGaNvFDS!y zkA6Q;P*m*f>%&b*NPrM-pWWKDdHx}vnVW-Bzn?-b9;>oxpSdhtr5GDuS+%xms42*1Ru~* ztZ3tX2++V2B@YTHT#}*KtUhkAHU6_SXNZ#+EsK`(cV}nk>mBQ~ne3b#*ss8dV0yse z!}j*}-q>S{SqwUH^Ce}+)gIkrt)(60eHZ1L46A^%3%al>e$Uth??C&6U;rDVud<|k z>mXd-zxKGQnHguw_ZomKiL2wxqvVf;`1)7WVhLjLFHb1|L3sBE2|~O0xbd=4;6VNkS#XCR6w?kHF{8z3orcGIah>t@_g2VV!m@ z%s1lwmdQ6uIFDhhkJ(?(cx|Gg3T&&T1%!q59_poK63_}^AbSF#c?CivHU*EK%A=w; zNes`{u_cqqD7)+xKBr_t|n@s)Aurq*DW6j-vKLZ`zHy(%H|^O{LfM-A3N~y z^|QflD-6_>KR3>!@$>G&=bU@v3~_%6uU)}XiuU!w5*!|4-qj_+kS$!XQ`H`%Y*IFW zUx9zRL%H^+6$1rZKlSD;wHcSz*Q3E(h*eyi7GWX)ezP2`_eXrUIv{E1Px7o#u{7OP zQPVOV4k@3VJE@uMBFUnLw=W`limK4}vgzd)VxK)u*(9XB2|B?AP8`+;)LH3I&~sMb ze)mbohiZvP5RH@cM#(OZv>>hjunZdNz*(0(@|KBuj$S!*#Ypx6tD`7)QFIHFF;Y1< zDb^2bKYryAMh7p(pHR=A_^mxrFv*I5R?jI)T@9**Q?mKld!@4pZ-bIlFdAE0x;r~% zzi4Zl-2}HJR0m=hak8NpX&+XnaQ@r2H!8!f9*vio`OYFHt*W4^<-r0=yTV9NAt0gaBXdW+$L(kZKb5}+xlofcxGN5EK1ogV z=&iopotcF}al<<1ec&}0epVfEd3pJR-!s&x9iZUeZEv@}wIr{S&^>uR^F2B}4Bmhv z)Qh9NA$$WICB;^2P4z=W32m&N>l2mqeEj^odn1hhF58Q;U)-0JV<+oum3#0{J9U}>=5B~Y7YK^3 z=R4+H=5-z3HlzOI{CR%0q<695?FPZyUGhJDEH4$nNMwE~5KTxRa4@+bLP5Ktcyx4R zH&w#|XjOP#?-L8TLyA)QJP`?=!^O7N&VZYi0(S2u)C(JA*a)WPeaL7@(-n|20sM0Yh-5I0ZfV1>t4U7JNx_9 zpcvv$5;eT&@3v?qI45u?m-ld9>cK+SY0-J524Uhz%;g931k=&Slo5XG44dj^XE@Lo z00g6z^|=i=N_j!e{8C>Y5OD`qzW#2&WaFSxx11@%rkQ(3Mey+q%89n%E-XIeGt9x? zlT}F=uA5CYWO6yQ6gW6M+yz329kWn>`5S}ESSW%Pp;(&={>2t4%3Ju=cehh?D&&Mg4^D_VZE9@ZgP0A!E8!zDEAaEe#%FYPOvaoltIje%M13!-%eSE zcHKn8{E!>xpuopVpYhp(`1!Cmfy*p6M=sfq@Tvp_;sf?6c*wHGpE#QEr?(gC&R!H} zG_F{mMzJtguk9+}AL=ipXsfEKT)ezKAp!t;`kWPCCh35XoE&;0Myb-LT<+LVVRTC? zE+2ehM%`JM&~#*Hj(4pzAksW~ucEH`aUwV>dw?wSW8tX6!NdOw|3U9kjfVXLHuh^J zU1e7GV^#dY64c2&G<0-UV_D{CsK-RJk<6dX^9fU|*%ezMcPaR$e*P*oAVjL|Iu$Hf zDQ^uzicO*zoVjMtu*yn)xvnnP_078x!_R_O!ot1xGBugU`;Krbpjxx}C6%mFJWBi$ zHiRxhCm`m&N&x(PK@hm{0o&8etj^z)gA5m(uo8#!Wy<*yX0MkwZ9dSPjZ$Fks8n-J zyDa0PuTgn;5YI2zn1>&9x-WJsuQ8F-)S*iy_f+}8UM~s?r<(Ak$_Dr^Y=E#>V_rF zcZ1c>ri7o5OlKj1)Oo((A=dzkAoQY<5ZP7NIue;=7(e(QBj3%^$|_~Ux7?sI6y8@X zg|?$JZ(n{nV@^^y|7Ah|?bs0Q>sRzUiJ8Jmj;3@@^x{^lukZR$lZMGlqq&ttv12TR z*l+mJ#R)w57GZ)`96yl<@M-IZ56LMhp}_6x9vHv}rKj8Q`OYHxbizq>#Df-W`|r>3s1E97Zdt>bs03TYR~0j!F#YL@8c7RuAy zb33lCRt-Zp%rZmu4%MFay}mjVH#MGH`FrT7IL6!0^R5;P0&e~hh(U{Mmkg6~U$iH$ z|H?K+F|&VkD!%hcAxUZ6+HTklei105AzT-+WqT|4c+ z)qzSJ$ojA%G;#GvvEfs?b>WBG~J|gi0Dw&=UCgYTsl?}b?0i}Jey|MR!Lm=K=uZ51(OeUA#BlKgH214KJQnFl* z-aJlv?G_=$P+3lEtbyNj$d2KSzR;=S7^1x#JZWLAU=K5;2*?K(!!JGMZx1<{+mP)G;~oG3w|6e1nXzlJ;7F2kY?HZd`yGymmJ@wG7YO~*Lo|36IiF57Xn zNHElM#Ymx`O%Ke6ifyW* zPS~&k&JO{9NsqXH&AYL1p!FqJk6kr*QBr2YqUC4X3%A|E#s-s~o<0t4Fsw@$bgAAo z-`e_o-{2Qp(7<`9C^&gzlarIj(Fn?YNhS@f_Ss$9IZ%JBV0v`y+`+t`FmxH~TTdLb z7#lxPxHWKu!}svv!;_Pf0>B$SXWn4KknI3Km6VDqW2H&)*_+j8kD&%%JiQ z?TreJ%SjQ5`h!B9)w_i|5o#UXOPR=`@a)+MnyXpqLC^hgapi_Q+pezQTYp#|_xWwRQdwqQc(QWA83ZK|6A6Vq)^OKo%Vy+oF4CLFO*1Po#%n0OTT3{@1|G_nNH;0W}>UL(PWj=>av1Gop=*<-X% z{DLL<^87kKXP`eCt6MLxq$Le3rYoUSH$8X$g6%R-Cs#KUNbFyLjYy}Gb=)}a# zTPm*gRSr0z7$b?tw#Lq1I_rlLj(*P|HTIk(X`cu%i;(y6s}K9+aBaFqAYDc==9}D@ zmTpsM-qq)vIArYMbB3i`MU&c%KBp2;kO(@@iis#Iu|G)X(f?Ygh@UGRNLT)zsW-*G zo%w7R4Trl<`FY8+AU}-A2;RC>6OFpL5D{1-G&MEV0mLy_8P@2&MifiM9}B2lnNS-a z7|&XEhGIfXdB@F7h)&cw&i>nL)x-wpIXZ})G!h=AO3VElkmc7)lz(QZn#Ud6YH6i; z{T!1WB`k_0?D|gMZZ#|}qrRz6Y(P~<9s8)d`D{b3>W7`_VGRBrmJaz!25w^XC+TfY z)Tp0uOnoJsI4<)xDOjIgvp>j2@cW*;^J$jyc}T3&ckL1-I6zxlUt6=*)+k>{H=gND zdzeD6pT?JcdjGY;b0OCy1>^_ARJRMZz1%D46YlxY9NO5C3ZI#*o%Q->`bR6z?YKr; z71?n@3u0sYGjAl4vMK#MYw8CIHB$1x*%xW6DZa*`*m`H|_n}bYdHcWU$Zk=2l8}~6 ztpdLf26S-=D(}a}to8T}3AD@ud+LJ-1?rxa57uRfV0D$=5hqkqp6;ha#xg!WjUM%X zU>h9Lzr^S8qOrhlcE&>y|73W=eGjFb$u~j@&*B#&HaA#svVnQPj7FLQas&MlY2@1+hv%u!+#$e;Fg<%e{k^e zk)G-%p8z9l1A*W<_D28i;!wiu3*NR9XW2dF;E4XVnBhdayplU47*c)*I6!aEc87lt zH#7t)3h*(dHZwCrtVGDV7@5x&og;>9hwc%l$3KQnmGQZ-CGSJ$WT;>{TB_wqP>Ib% zrACp@c2oX*ouq!)6Y>WY(w;c4D5n!USZCs>y${p7*4Ea0Z}hl*e0&OE4pQ^>Eh1fh z)%&2qIUejY9i*yq`JA-PPU8*p3zup(e5S{eV^T|?6laY%*!oTm5Wbv>55~|3{-g~h zkxR;p0~~0spRTLWH!aP)QU#tnDJ^aG$|yT9$VcwHU`D>%$|CXr>j4U6AEB9EeWh)B}yt?uJvl8Ez5rZlLQPEz|IlCb8 z{4o2LwzyioYtBm$45u2_hYhEeDV($zQhJLkYpysZI1JFb{AD*6|(BY)z;Q} zcnpuWA9reMA;ZHf z&K^hnuB6IF;(l+$yIK`(ydMMU6It4{^Y_%9hL-lFN-}HqKWNq7!1(Oip5Hk7LO@qC zY|067otv{Td&&RgOqG+6Mga9OmQsGIq+#LBko=p31qK$69!0|O?)vyqQGYxoQgLoB zwwSoizixE0&0)oWNF?Xbf|z}S4=yOuYq2TCb<}+j9r<8P*;G`s6gt$|(Uq}nF9IFs zcZxhJvLkM~2U>6lVACx*3>+Z++YEy*My=IHyp!96Jux#+7PZS2CO9u7`Fk1*!p_hK zivYx`YBV&YxKZ&W{PdTm90`WU<`~jp7@Ause7Cl;3S9`kyo&t8u%mJWeiaR6S!821 zi*Y`mY9|0G;%!jJxL2_5><%^AwWGK8-XjG#))?yEjypE!%_`mODHQCjZF~*tXY1t| z?*=+6f8W!*%ARKngOtAZlHUREeU3eQdN})Jo<8G$a~060SL^jj4ckS@F#!RA zz{5`j?>;FP<3TMP8Wx5QVittCk(K!bGj%jJcsRyCFukZo4+)OY)kTz*aU&=d07Gox z#{tuZF9%T`&zMz`X#S}*3kV1#ISV5jg~KBwyIKQyjV5Rcj4-)( zql4=6#h0;TFl6sllu6-(DT@MBFVGaOXj1?1YG~%%{CAx_LMF|3cIKEP=tUco75Z%x z<4b&pBh#Ucu(To-cCs;-ifI2tbis}yiPeiy_5t_CsX{-%zog2_RHLONuf`r!9KY}- zDS3kR5^@Zf(KMcgvFW@7o8LlsK{djnA1m9>yRYS!$*pB)IN`l@GhU1k|5y6V>L zwyupFS$Mr2plvGq_aj=b!m!#5@!VyU@(How3>AFqj~Oxa%s-IwKcrDF8t(0hn|q~B zykQ6q>&zz2w^>}2fZIZOdo{i1TR#0&x1ac_#Ep+OxL$Kr4i%McZCK%ma zwb*gb^z_|(niBh!ELJGRKa~7@K&FbvI;0-aN}sCHyZvx<)4k@s@Y8LXo7&B%K3_0Z zimkqEw)~h)Z?S{8`rx(K=Q=4M4Ys(+S0!oUYu^DBfi7V-X03DaxzCTbEhGPv-&N@mMC~& z1gC$km0tyMZ)^a#YH+T=7#%Mtr-?v7z+k-<2u3PCoWZxX3ujpu=h!&ts}%&`kLl@psI{18YCd-buD+LF>$~*)^KJF13G+_-S@t5f&?(PCM<*M+mV62k$iG zP&q)b@NN0*o~!Iy!4tFSZvVG2P|(ESe~3!E(TSIt-*6~YxSaB1Yby?>M##_j;^N}c z0T7y??GS@{Zab-=!C`2bY^>bcr*^T#aM8)8JJl(WVIxm!u%mq&mCM}YVkKnF7f6sD zlW52%f_2vHTQAYgBJX3VD+a&{`F;B8ny~;XbQj%t_~`q*TiqS5T0g1@YgY z$^;knUjUE}zyVNfm2!s1)X?Osc#pKqaIz8J57s|w{e92)uXL=@m%rUIHV>Yn)}8e4 zp6wxNM%4-!ra;#H%BS82?M<^P1h}A z^c&WtjmaBEKA!qcgnI=?pySggEI3$UAYY&>WFJjHa46s*m+F^OL0rYUefu^t3!dH% zNqN?c?x%EWVOGnpywl$q_dJSAi2vu8bFRTTKTMQ>{}K{LtBT>nwqO3EkBqd+ZP_O~ zZ>7u$>PR+9d+;&p_ML+;Q?hG3`;01M&fHVuK%tfi&pQ>gMGHBzEbY%~2p z?IOHpla@opr5d+{GtXCp#+!K0`hGMs4o&+K!4g@jcEfvoB~9}xI48o@@Yi!jkXK#LmI-67n1TBv}|(eXV>TDx#=Xd>fuI^}3dj{V4m% z`8oQXX%D&c*EKF%FFo>ruP&Z<59^l23R*1D`9<$J?)5o;Lbh7zTaNQX@6@>_a|A4} zyfLg!D9ut(H}a*59V%UQW=VU%-s&HIVkoVamkJ67bHh_`nk|4~HUJU#bUhTN4U1Js zk*OC-V7vMdXSAH?PSeMdq6;E9R&7U8A6C=kwvE&zhVn6As)XAX_bVh&7O)k==U|-_ z=rSQt3A-28DYjUYZ%enpJ)sM>Nt%+dG)b+Uk z8R(f=ZPHFz=TF)_o`%W$dPXS~dje*LG-5`MA>Ty9Tt4@jzuXt&4|hX{27k5B^40JI zzZ2RkX03{gT@eWMJ&vtZ{4nU>sV!2Eb8~akNCn*@;Po)r3}6wbN~T_OW2ai7ft**E z&!D%*rycoD4yZcMfQfenWl(JvFBgGVCZ@(Z#{vlzHtPHw2C7Y4TNbdbB9|c#zqC!$ zDwE)dF@7SPPF;(}OAU{$;_wEen>w#!Z5>08{{f9Il^e@^?h=p?r->G$K#jHpW3~G& zK`?z2^xPCib^H`rBR$(Kms@kakF(;TN_r#xk8yC8MdGU&O*EAj7HF8XmFp%`R?k{X5^l!yMHCl$3if zJXl@&_>2bH9f$EZ5%B*`$jji{)DcC@MA0^ce29)X{J zso4h!X=P4}oxcx(V5`3_rHTJCmq&1x7{WD|r>0KK#JZ>h7L2Aw-r8z;Y)xsu5)?Fq zx;Mgsg3pKqt|mH)&!`4v__xdQn-3kN7+!tiNf8#)j5+(TYp>`#ic5JzX|&B2ZZ|ee83Zl24Rq@T-t#k3?&4r3+8{zUY;xy zH;EcvzkYqHEwJU_7*d`Eq_y>LCB%SQV#~{SZx4w>tcLAdEC6N%4Uv+H3J!ACkxv3`u7F@)v)}=A z9(2(L(zQl$!T(ON;rjMoy}UR0-4F97mhj+SE5uZ)bP2bX2mPebbV5Sy!OYWL;v*d_ z_bnXwu|P^jb_FIu_QXYR1Pn`w-6J+V*1v+oFF^;4vKBWsV!$G+v&zAiT^U0*g(lq- z^W?JmTI4O8F$Vr*H>8WbEb7m*wjg-cpI6LQ4aP4CS3&QD4xK6DO&N-b|AtR#74Oip z>Rui^+A@T2$X~Q)$J>eUw93u@a*~~FV49z`;w`w3=fpy`VgLJf^?=wFUTPhqj_!e9<&bAJ`y4T6y} zT)hg#2E29o`X>vF?SJ?13kyeIEW@D}0MYl~qnRhjA>5p-PK1fEC0ubAcXxTXvNvZ> zA|Q!@$fwk_<<{m{Ss2JeY}Rdd4i6Q9+4*B>=?ZX#_)MEA01rlrhW~gkaILx^O`)Mk zNl9r$94LVNsomggx;3?XNs1UyAl8<^nFs-S{4!YOeFT)dxECxUqcnlLIWj^D9EY27 za&p#fW^e<0;3e&@o2&6!#aqQ=Ui%5}-=G7C zVENL4h$JE@H@EPho}}urTr-#W9#HeT_o90qK{Dr;UrujtmqDx$U>{hZ#B*W$!n%_z z-Ynjb++^O0%F5c{%OH?wA$oMsD)rxs0`k~fHw}x8F?_#Iw38o4Zw_JKEin_QtIRE$ z-+$hNRbiq87y(p_1TTUqp3E!1^iY*_oN6|FB^kyw`mE@2_xk?IKok^U%aHZ583D%< zcb;#JfvbR=X`jC*f+?w~f&nKx1h{7~?6DgwCH({W2RPQqwsx>idYYI>j4Z2zNh6D} zFhyTq-}*wk42&IIkG70+CDPwl4Z>0oWJsKO!@Ll<{U8FPtuk*k5c! z@OZ(VFyVN6Au2VM9Q6Yp=HzrO-*tagBaU8_2;jC)@TJjEy06t`U0ei!b$~z*-Lta{ z09l1FiTqcbIZ~}Mc1mQ}k!(0fe63{H>Nl-$X z=G4sXfF%DFqV3>w5qwBwkT;2-5fuO)Q-x^@m8+}k`b@(OIG9n0btAM7jX@U@1{Fr& zlLKOUcnOinhXsrr*;5J^-F0iKmhUp~E(ot~+_-_T3!pGUbXPEC4u_GY$^a79&(2F< zt?jISSTg_V@%uvkfPbStN#Wy!&s|%cW;ACEg{6jtAPDtv+2Dfe8k>Mg38gr{VOCZa zQmZ0kb2z60&_i5m;gjar7|V2cYN&{E&(t_*H?bWY{FBLEgKBzxw&^CgnSO0_eW;#u zOT(sC&=9bSdK2vyFQ*M9(bQ`iT@m=yZROpc-wRdHYs$4f53|(%LXIdjJiArl1XX%Z1-IM4HUF6q)` zV(833Du+IND0cJKtsP)is%z^7{J$fEVu?;(t$GflcUJXB0?-4E{QSApXd2g)H~;1F z6*q9*2MzIQ5Ijb9=A_ICZVaR8UI_te;5^pCS3iV2}|x& zxhSGWg0%gZI?57q&=a;AFyZPY?Co8T9z9BbwOb68nFd~5bEb}cftxHY5DOehm>)JL3Qye8%CHy!>Lyyt+6EPzT_u_^jx{e%O#HklcPZ571%@z<|M5_%u+d3f!?(pcARd0=iF6`cHqVGb&!%L$Q3by1QKh2spjlbBsyb7y_`XR~Z zITQ^@Mt2KF5dTS%5y4hQCf*!Wa6D04XZsIT(HG|=7rZcK6duk%ifD`nwoKxGaN6Lj zCJHq4@N@48mbRk&d{rn%@cW%HW9<%^O+`9~4yoZQD0iSk<*gqc9$vp;11%Xh!Ta19 zT`8uUN;|)>u`u&@?vyUMFe8#A&i z0+Ej~lDf0%d7v^o5=JyW7}QbUP*YaIg4D{%iAYy)Un3+7fthby@ST~_45?u^1D)y~ zi{)Fp_CQ;e?~FK0+LBDf>9Qvwx6NDEuSS#a)!S>9G6G}qoJU`oBSJ&oW*M);04&G} zy1=D{q5~R3%Nw``foGU!*>#ji5oZpVY})`Y5;ZLpN0PVFy}!QnYv=9#G4@Zh7zejP z{S2}0*Wc0#UN z7*$pZ&}3j{uf|aBzS#`U)C)wubR@j|N!H5p2e1f1a$auGSQcijA%9Z%Wq9u~c3yc9aD< z@s(>MN(xID>`jFiPpy29;;Evo&2Y{GBk{XWy=N#aEM#D1eKX!IC?Ft_Uy7kpVVrXW zvdd>XzJEVXRLZ!wwF+T-yh(BC4&|dRk6HUvkCcQFS!s7iLDfAsYwa_?j7frEyiP(w>zfuvJPb!(BEYCG# zjFtJ=-mdlf%9bN5h)fG%&+9ZDEiLYWq(i>N8{@ma*CiH5_BH>W`@;#h)=ZB3?3-(n zKd&DV1X9f!D{v}m+Y4@u+b&^m9ZW4Pg+PPTp{_4*y5TeF++Mg1gu(?Ba3V4?YtdUX zFff!uVEPtw{u$ZE83(u$wwbO0ITIcmtM2K!`$WUlbE`HUxa0e4i0pgw=icY=#zeMk zVI=e(hV{C-yO*Jk;Nj(6rWE?a*meu4F7`NvOMCQnp&Ze~?Xlp@n=x|iw=l;AW(VUs zVn-h->FKQ^{jf`?!nI$Y+S_fW2Hujn0`y0lW4aO3jdCoPk*W%P1JQtEEpTo^F+OW# zbhSWZi@Y!n{(&YJ)L!H^INa(8$fX-8eg@YWkE6JST32|}rWK$IFiEaqZ7s6L>2vVO zrrZ06$pjIU027M~XE$uvV2OHychwSTaS~%81hPWCLhwk1fcdL~9*-=_6hLUMq!<8SP{8_xA^L zNRLs683+Kc?{SI*E+c5(wn1WmKUHwF(c_(p=AYH}K?|)IRoijuJsdDmPkw0K(P(y2 zs*kDT4HuMb)yPa_un7_02EDj(K&Jk8`Q&R$+8J+RrZ>nahCt*WHUL4!-8xW2tQ1Kp zsYHdOuc|jcecl8UpnJyooAB~N@STWs1+913?b`z2sqTdie9*dPFPz=!uRP65PiXf| zhpt;f9@b)&IfSeB(q2mXI-zz*lsJ43nBH!ixO2O=#yXlhc4sar*^9Wq@+wJm|9}NfBqY|@56>Qg8Di3MZ;Vd(0{4M2@7Zd1EkPBHzpNyxX zLQOC+lsNK)Hej}wTmRuvR-$u45dlpuGk!r6OM}yU`FDB7c1dZVhp1UdUIL5sH&$ZC zlLP@E6l5j~is=wC)&WwJG_L?+5!=Zf#*}ghJ|y9L^|ZLS;{E&8jg5_VZCUGZYn?Hmn`2N|qG_^eCce>uHfe#sDawvd^H5`OuJVRGn` zYYH-ZA0!M8SJ(@Gt}zq7A@WWos9^fOvCqorTTfP?%nwUR5oQxqU^#j6V=6`Mo+Di+VJ;-r*=63%)Tmf`a!+*3^>$BI}8WTWRb3ybnXNX#;zb54vWBrjUK_b zM8f@lL}AopnE_29FT1V0yIUGTU?%Iv~8qSbpjAlfAD`8rKmg*~#zw zk){r(INWUuoE|+E(wF_@Dh@7%d%d$flmlcYw{>&pBUGj*6RhU^vke6h`gE=aud1{^0U(+{pccx_Ua zm^z?nl|TF2uc$OcrpG4h&dm|j2=`2jBr2<_bRQnwMh0uvtm%vk(4TtN9yo}}8r1kE z&y+X+I=shlp#EeAx;T>pyK`{AAu=1NaSR;lyk(C0EK)x`K@m=-E0jLRM&_S*bIDX8 zf7Oi=gu9W!NRZG&7TJuiK{~cQYwb@>*F^!#fZ(3 zKb4!FzJZ62&tdEFL_NUNOT%#k^M4d)W~A4!*=qHmqF?LqeEI6J8sQcLk&6t03%|Gt z4eimd&2shE$}{sXR{yyE;njG+QO{?l!czNX8g2#BUndJPz68N_=?vfMM?u|H8kWel zl$O@rZ}oc&eEk}@5-ukq!{F{SRb&{_QtozWWdT%OpTn>-e&O_?mdud!xFNl1QT`VB zJQGLFh7=FFUO%$o4<2MukLEdtqDWQbB0-UNYFPXK zxvg_3T*a~6jbVSD*V|S!;uq#OPJB9<@@1d(d|YLYYmMX554&CTy#p)6q1!BQ{6y?V zF0q|90|WHA!2a@JOsEjURj8`q+Yvzq2QUNivCxTclg{l&gUtpa5AB6lCWoc97x;(I zuZ??<$nV#5?F>XNoM#BRMm|4bmR}KDi$hAR1MGQi3!l+_w7It* z^+5XNUp2VskPF+T8YdbuDB{x0A|PkfGqJ<)u(n~byDix+sK~3(qrXM}NPfF#&z_c@ zk=lcA4mu~VQOVoVvj2D5-t@?idA7_%2taxRZ*NPK@H;bK-e<;2c@p^$wCI}-UJU|V zErJAv7kZ^3w9BRQr|(>-v3ACxS^k^0v9m6j;X4a%U2ymFc!E|La4j7yN{C^>nKNgA zf|UHVD9Mno2lZ2fnv%rBebUlaX!Hqj3~(v)@pxeP^9~j0E|u$Eon@bu5mt@Jh zbk^K3a6(WVXv-EP-4?nv5)QUKd$+2W4ymmU&a)H>&N%9I`JE#|TcSkxwu$=cq>j8` zrrdg|EbYrG?$rF`^r4_JQ{|?PfOE<}<3uEcc~J<};luBiaf%n+t99s5%~)CHV@_HM zN`G=!y_F45*uD1Q#H_gC>)*Adt&Uv=s&)sW)~D{hOc1iV!tWZpPH$VANWBAElD81H z!gjIhDdm8_Gu}}Xjtd^q$-cpK8;<@{r{Y?ugZaXWO24%P#+N8>!05bb!$a>r!>p&2f&cH z_S{C;Dwto-{>5LdbK6YF|JjMxYcJ%~Zf28T+86#b`1s%)GsJm%>rhTW9w~?=4h{*S zBmTjEF{;b#iH=@cK5=_|^O1yD$U7-^r$d)doTd4)!}afh5P9k~CV&HV0n_2w);u_| z7f1cBFLrFRcmuzwKE`)>hk9ExM+)Lc>SIi^`r+w!E2%@O_4w@EFR>EOK@q3tCMd{Y z`BhyjCc-uN;UTx5pP%K)lVNc6B#H$@k(Z~U-v(;D4{WzgP%t=ld+L{WVhZzmHsEl=&RXJ1~z}%!oz$_h*wAVRKavb-y!`Rk!4G%|Qj|;l#Bq`a+}(!IWhb%6_V{zEL!BpNHT0-ycNq74%IS7#N9a>lf@!O zKD-etFz`)CDDj9-#X!@tq36O_=PiJRgHb|vK_ha`NW2zh5%HQt`TAf)hB_sW()>%w zX=V(JYhwG-W{2?PF7Eh7+k57r*4Pg6{Au^UK0+wrSMcMBSu1rOHXsDTO5e|kTY$hl zPlOF^68QvVSQ6lq_t~>)5CD4P$DVqB?;P|O0fxWJ((*`V8VH+n5IWhW0Ck`cs4bnRi|00Ox8zhT3 z*ly7>U9Y?(rZJ{+mGhp>F-|x+A+w1a=g|VyPEs&WQ)eZJowKvEBL7~FEww<30H#op zipYrFh^+hCfTr`gC*SEMh?y&;{BW*m%{YDHtYeG#>FE==E8gne-9s2%r!#+Xz=8H1 z{GfsvImvxc_$VhInv3XZW7AD^RnT~jeO-rsG-obOGZF*0HG|re+_Y=oUW@Ct6v2&jBBet!^y^* z6@YgM>*?GfL(0_7P7tjhq?j}eTcuJS*-9PIF$SPZ%*P+--zNh~J?I_cH8q|@BQHXjFD+f0c=F|>(ctVX9&>rWq2 z@Sh7t;h6&E1VOpc)DdAPq1-^0W3Z3{<|NJYp`&weS62|ycVN+)xA<50=xA2PJ&j*6|K-%JpCqbd+1ND>-=hjJk3%*ej4MfjL! zUSp}s7gmtV3W(-0^N_`tD35sAU_;KmE6q%`_TG0tc6sV;F?h;zh`+$7&p|Dt|Hy~~ zyrC)xw7#a~IB=B@x6bp&6sstJoFcAc|F)!2I=7sdRgtBFWd@q8mBzQTW9@}Y^0FAW zsB`U2C;l}v6FpXlf>@+)2qBWYc2$wf22c)@`T){@vq$TICxG)>iLn*pG=lo(Jj&SL zqa9H&LA8dU8enIOiP_Lg<@#@{Rit$LLiJk@PvB~Ka&%C1ssUPqmq{ZVFrP|1CPrW~ zhH^Yu1qB637X^J|%NbFbexy!!?zP(iLXLKpW&hqs7z=pE61jC~C|bRTS3YOoX9Oq% zB0vrxMJSp4!+WrM>q9}g5G8#?Jtg|q8!uIFsa(lynY;YWaav<<>K*6oN*?*+TC@kC2X+j0PLsPO0E!V&;PayRbHyrq@HRLis62a}`N^v; z_5ULxDanpO{aP%WBs52!KRz3X#WXx<#39Ttxqf^)bhmsl0kCiuNbiXNO+mLr%09xR zA$AlbS<)&iVdhLdFfae1DcMXA8+>`@rE}sM(ij=)B@#$P6;e(OISn=78E0$5G-i18Cv`e*SGapYzz}#5Do1 zc_r2$y%#tC#gc;od=hv}Eii(Nw%?PJmwtyn4_p!e4PS{?vl+e%yB$BWpF4LBVl5@? zcuE(b6&}z70Okql0di9{C20xa&=P$zk79rvs2<{e`S*s99GLqN9+9B!XoKCbGA#Yt z*LO3b)Ttl8iZg}KMn@s%54go!OeGQ3c-i8d7=i*V1$I5;+!4wfMCm?R*)&jH@VULx zdKxdCyee&NZM#o-8=-{TB7X+6hA5ZIKYcQmkvB9ZHZ*wmBm&~}DO*?wYQ*w~9O+0J zPz(^xlJkyn>1;JaXYktD6t(hTqHes?YH!%T0_%3MF)bY5kQvhGL@J(5J@sj1bp%IpX} zTd=CkP*kqIbm>wsdF0oxwLmh(v2$^41Rv6kOm!ZgeSGeh1E8Z6z}P=t7mePsk{ zrGisI3(C10=S#)fI+c(>DL|RS7DI-}q zJVMfT3yFRE_A#J3U8Sd|7YqiT5j+{;_HAo({bt<k2L6RyU3fdAzP4Kep|Ykzv9^9hq6Uat+p-42U)jGf#~M6a4|7-^1;3|VV`JHq!&{egG?x>v4Hm;pN8fAh}0Lx7Ia#;G?SZ9KF|e>!#4No_3PF^Pl;57+@gf{I@mC^ z@>ql_N7=$}KG6R=>|4^ud`%_Ho>Bm|fHbK3_>qTnq*!JwUJRzams6r+Vs=3M0GR+f zFfP2umSfK;cMw?YK0M%p!kHX1{VX$HVr+^U9lMS=1p-ng{5>dGGA(aw_7j{pZ-7AmqqhWrx{;>rNh z2-HptG4LqBQylJ^p2>U&l$nWJf<;T~HA@^xA-E9o>P}lG_*Pij*jzviA_@?u1wKN5 zB7%pNmMRbt3Bd8iah16LTS9i%!DM_HJHRyEJ4(x*K6|E>eHL#01nWUE3IY&JWG~QO zrnq*Kmq#xrFOQSRN zohsJukqt6WC0#~0XYUmQLqI|w5*GO5P^3f=DJd?J0TD&mF;9!OVhAttZ}?>Dg@Y5} zMPNbw@u}N-w6*l09e15<>;f;TPDo(KkCGT{-)y>GGcT2S_0R*U8*7y7{W*%pFO zYSf_ayV(6AvE7wbTK^!5h$6eK7Tg3+`W~GC9g|Q>U&!rX^-r__nDKAAKya=bY?s+Aa^S{2Zu-6CXofqk^NP3w{J<#G56yCTiD) zM=;;8eQJ)U8%JcolJ|gOC%id|t;2Ga|8@v@S}>x1UsN|W7?O{6qtaja0ffN?6V+1JVzF6m z-m8CYuF=hIX? z=6r9N#@|9D!lrnc1yqEH&55Qc%lt(o%BXK>(JN3$lkyLh5e>Gu2EtHth8DMx(f8X@ zET4ih)CXS}4@PZfJ|Dyv;F8-nj_GY)QU4xU85tt}Apj|46i@h{_#sn6)bN>2)rw5- zVfvBu=YORx-XK=d=Y*oEx6cFP#2QqvvJOh16UNhlXBDt}yGBQ2p_a4;Q%7R%ZYzMT zgU^!2T`qq_(~8b17Asu|55Hu^z*>OF>+_$>p|U4RO$>o;@gG62;(pfA_JfmyLlg>v z^$H5+*B!xmb>k`?RMJ~l>zu)f6W*~yckb-*Spbqx+DDk-hACC9hu zrN8u&O~Fqmc3t2|36s9Vd)#s`7xoDNt(TgA&TfYjAHm0gol*}UW+%5hBR>g&vK?kK zt1F>t(|vr2=7H={wT+t%^$)Np*F+|Xnm>Q4XHkMs(fDiN?Rs)U7I1-t#cdc{v_`fh z{(}wM!%EHA&|A2^qZBU0$MK+z;Q`cp9J+*McBl=8IxMDf(9Zp$J2BXoAsEDt5D9(1Ml2GyZcCm=jkLqipV-E-YjfI%GF z3lEJh4?*=rm{Md2O#c2=%g0NM-it=F(1XgA;1M9Th|32eyEPUCqWSgfQ5UY?sP^0Q zi6@Z&gQOsCkoZ4)fMlS^HV<7Ha9Z@g@un@uF=JsxIt-7#h6?aoXpP9#-gm$P&Y-YM zI_(8g2qeShQUweZ|7H82*2h!-UC>gD) z+I;Tga}PmcMMJCx0W;(j6a+)rLna@v`B9zTEC(`j{I`Oe)z_Q-Xw;FnGyoOm*uUGf zVbdm};Xv%tfJP1p;O@^DLuoD#I@Cp;TmBkTVNxGs;+v84!$9_@;6^9{#E~o1i6bUR z9@zYlA9)K_0>F0AYT2MsBl?WV(97;?0s{ld;5INXWqahW-Z0_X-*fqfp%wN}Fb2MdYUtM zW#E>1H!o~k6Gd*NCj}lW-=1KgAUG3}puID!^^T%i9-o=8A#E?gD`rhVKEXe4SNvRN_ONW9sN@{Ti-FTy(zhMtLNBLOTyD%K1g zR*YkfgQaB<(rg+&I%+z3aGSxX*$)KbFodFTCvZuTOJ}2N7KVAY>fZ~(3p_EiM0bD>D}urjy8za&81mhE^wCwT0t{_&K~d#)>xQ}dWQ?GqdC5Ct^h87$ zfQl`8DRyloX=1evJ=DO9?*JDK;XNZhZY|gbBIm8J(18_t0boIs!F+Xz%MF^vSuH)oYRW0cwb4MRRzR~8s zA;A#CP8ev_{CwvZ^nFD5kMF+oY2(|Ivkb zL5@tp{B!#<>yd$Ov#W+0@Y@~wUO7Td*hQ))G~7@xydL*9Gvi-QLsRDP#TWfQsx;TJ zukr*wG6?{)1+?Pk-Kx!WA3!}I2EISTLrtJT{|8kB!Eb?-y>cBvR%tPtaH?tri^0Bb zT@2D*7b0`ZBoW>r#AUyo`4pr^O}N0z!&CnCs{pZz>nwdSHGx_IS~wZis#b*mw~zdF zB$~CT33cd>Dv|NfX7CE=Kx%_;yK}dr|NIOGj$DggI+dEf4V%&R^y!d?4;@15K(b~O ziwFr(P-)N1OsogkBqgvulYQ%!XR7`Y;i6-fSwbSIk0cG*IktrT)Cm9Qmu6<}vZ#Om z#&a!S&cFZr|IHtWwnarnRiTUM9vWgohf7R`07vh9O;1Zpqw7#zUO)=`2G}08r|w-_q%b111n5or<{5Mi zu7fQbQ7sx&>2h*h+_VD^G+4;r8B{}6rBWwJW<$F}^!W%4q{Twt*n?|XU^cUqHIFT! zi|9xsYLG;OaV7PjO6VlmPrxGbm}!kg zO;(QR4h8O|J~k1N`fKFpy6=Hn%sN#VF9~oBJNl}|DN+yNm(1OPle`!2NPW|!z1JYw?8rW>A~=S3M7bZhcf=Hh8Z0?dfcIB zldvPJKuNw+ZWD~^_O6Y@#!P)4NpOU4QL#^NxMVtmHpcB)bqEU5uAZK>cQ3T4RH&A8 zkGaP+yNIPBqwKf-R_?Wd@JZ{IureI84R&>Lz}#x^9^u$N!Ko=YwuR$ z5cNXIU}9uoP$~;pAP6C$Oi`jy(Pk7@)wiH=A?=iY zw)r{?G*_T40#1MDM~)`zRj~rhWWBg5w)+AM;w`h9*}wo;XEjG--W+VIFf?SOIVL#t zaO*a5r3)#NQQtgSXOa}UQZN>KkeF*jC`!fwK*JQQOHv{DG0GYoZTin_YIo7ul|#Hm zic!3MltrwLpYqh&t+sr}un>`P0S8X|ad`D599CFmk=P2%~_C}i{Hoo>SqKpP+c9rqSrn1)N}E$!WaBHy23P^7Zc5<-tm zzoX+RSQlGKcEP=Sq9kvC+MAPbyM#Lc{{8vb;A9f_AD>Uw9SsCNUvWtZSTM=g?zOeG zL`IIo)eUqF}>0- zJ0oNfv(9CpTNNfaA@GXyI4H4~1vQot&nf6)x665tQ?M`EA~sw$O%O9=!>oM;nRknO z0Utd87CAXN&DO_@+t8?xYp5VJC&N6_BCkl3jA}JL-~`1!SkW{;=un7|h6D?Nh_{W5RH7bpkS#FVvUMvH5w<}|1eCCrKi?Ds=5z5-(YasZ{eAcu^MjLI?A2P~-V%->n$4a^=PeUgUpSvFZh#)z|F z#VWE?Fe;zfFyZm9Uz%&Qg*W^=nfz~a$-lqoza^RfM}LqiXOGbd;vfMqAW!@m;6RWq zT^Iz=A6kp0A#OWhC~kL1KKTX+f*1;-Tj>T*iaWSY$>JkEw#IF|03IkokV@DZ|Arul5`==}zSO2;#G`^_3^M4{@oXa@FCVfSm2z-DX?*`02I5-&Xu*5re)MY9_x4@1PTOIp-2Y!%t4%R*OiI}Eob>B^; zey%bT=0gdgyS9%BdsXwpzU5Jq8xUwoPkUad3n0hN*Xy9-p^yy7s&>~ORUoB&gDTYr z0Ts_crD7Q5I9`r{4b7TfV`J9oLzw4)OPVD>6O_d?6gVOgZkP-ck!el%ju7cDk{Z}f z$=41ge{IK?LDk>6&I5!m_}a3wU+U`Ufe?g%?VbmIf6sWLo6*M45cR{fMOxa!z>$YYCXgaxOiqk|S@=~<70g2K#XFSALJGb=g~(18CKX%k60 zAy;n#oBTY@5? zwYaC3!9@H@ED|XcG-Sa0)?*=;32ieb{-7sbAixBLh0)_?mvIc@?icJV zauLMYr$Eydb!lSGwu8)At4O-B;3tCXSjou8R_nXFhgH;nJE6`9e~*eMX%hhn*Go$q%OtO(Ad-iGP7%hW2s+6c z)C!npKab*?IE_<+&^;&C{Kh+?l!HqfN56zwFg`w@5HLSf+OW#jL}RW|5Syf7${UTd z>!06F1n0nleTsbd<_#;+1*5NotJXn446hwOsQpjdeW(uG{U-rB)@#Iep|xzGdVpvn zAsrl5i#r|^@-V_}{Z&R_MnetQpe{@o{N`tLQ;K^c=ODDCXeyT5M#RK`R`j`B2X(?S zrM?MFq|#8}$(7HyDX73_c+gEoR(9#|F)Fnr{GV=!bS&xSB>rVXp8(Ew17Tk@I^39!kUo$NYHEKM`buvU{ixX&#P>kAJZ1Zh;L`&4$|w9fl3fo zpM0GM8T*h(t_j3NMlPoq85`R^3=ndnPXs4b1s(#t5)o;C`SK-we{{I$Uc$Jcm1H7^ z=RS8^xybTn;F>_Idq8-SiG`p7;fI|vD5fZuSWvr`bVgjJny%`2;r#j4C9y{0D}u%f z!&uqa7PA}yicu(_qUJ}9fLW221C`3l7v@;MWfZT`FR!L%&?i#Tf84xu4DbhmK$_ud zfm)po6Lgh9OA1X9Gv%rIaI4<6hVFbX-{Vp)>JN^5!J~4d1X0d1JQ$qXZK0+VjR&7b!G+ z09`N+v=7-Wsrw`F1h-#92ISf{=qnOTZ=FMW2sRNCOPn@A`WJ zw)Qb;6#sgOOWLSDj2jErJQUxf8sdZ< zcFjUWM-Tlk8SO$>auJe2z~JAALM;0oXl=r0V8ZL^>ESi#JKSa`ZGneWA$A z;fuvaX>$J*n16j`aJAR+zy6FU-e+J3K#CMJ`v(R~BEmg6{;z@7AE|S|0*#3}o)7i* zTQo9bv0gtubAf0evo%CDHqu_S62q*dHb9!EL&F5^s_|&7Fg`c|-bwZ%B@()&d;0fR zof-QYgHFTWFaV=wWY`j*D;bz4BkbWZF=SRZ_w2NDNMFugyJiYfq$;F(Bds|HW_@8A4?1=Rj2+5S&pbN@eoE76ru5`>_}gW{8*A%2VV#_hr! z92aA)8-+wf`j)q-k#N=xScar;G-83R2R8H4Ag_3zUn+M>fJCHkVjB<@-qMy4kYH~E}09KFH~bj91bMUNy+ z^Ofu;PRBol!r$9tcg_46DqhXMpM> z8VB>?cWDmY(JHg?z~YVEqGu0%br}#nC(3UXH;{eJsCTvr5xYp1*iOrZ7f2o^eAiGSl6!Of9)kthC^+ zDslMt_2-?<$AD_x8XxWWJG+ZGqYPO7R9jXI;#MkTTx8M#BcaPEXtZ|riv<3;+z2xY zKrH~RW=F5XaTX&*Pknyg{qiy4G2SGY8A`nHiPI<>c`@_JK>16YJl$h%VLSYG%0Zo4 z5+;2?TJ@Um@3GD}bobt{`UY&mB=05C;@?2mKo{AXHX`Lymhy~+fN~Uql@ikhXk(C7 zcky4=+<;=65(Iv6y^M@WiVOnP1jteFy5g!}*)bYGp_rHY-OrOr*p|H54O%@4=u#JA z3mH6y6pC1H;5F%_^gm!g>5C4COvEagEI)v3d2a4Ev%rb+>U zSd?G>hU)LKUCs=>{Z1A#;$I612#+iFa=1HN-}1iYYD2FY|E}K0AT=2x102Wg@=z6m zB!~pKmxcoeA(?mKaCB4@P0&Hp&t?8dD!R6(MNPk=@<5=kLRU>JcI=Ch5Xn5oRa`Sf zhDd+#zRbctBg%bZzqL63Q)db@-0x`9Sr8dM-PbrmHl9QuOJB6$y02IS z5MDbM<90-^PiaQd9;PAz$B`;y4S`=eS80 zN>ajPwq_lRf=S5Y_@2jPvKlN1in{La-)SLO!=e(PZSAf*vu)hX9f(z)+#c%cc#UnU zRB1sEW%UNSYi*bRo=PvUFa;7&R^TVoQXIcL<$yE!xr_|kyS7E}U1@G^79U1=yaAOm z9FI2tq>);fn6gT{rd!BnV7BWRyzVJoVP1d8Fggm8P)zf{)S671gAF2*R`h`4+a3<$ zbG-TIbM#%^mGBTtv#0NT!wzmgi?|0rSB&i~h+;KLKaFjm2IXyIvSM;CJ#_p)I%t0MGtekp{$XPilKe9eSPLWZ z?0~HS*bfQ+FBKK|(q%wDjYDbE2ipuk`R<;$&T-M%rqQzTu$nwIA%6$AB)6zZTwdGv zg;3{r@6J#XibBG|dVuT#SvaiaVvky$*hFCJ-rs8k>JZp0!0V~x!0_P$KSxJ#Vz35R zpW`y=zYxkOi=Fk!>mWR>gxEup=z>3f)D#2|Do2?gmpk*H$pkNGg-7VtVd2!95ye5p zN}qfaw!Hqs+5zaj<-ybsC!%Ty3JMyFRrHEyxnGv5L zsWGr$7nuY`R1w89aR*0#a^h(p$~NM50zC`}+%=zkN3jX(kcQr(`G(ry_`bfbE=pG& zXZ%)#sUh8Ia=K#3=}P>?uG?5`R&I93QNBK*7wf{wcwWd(mZ83#bk4Yz^&jaWaRI`$ z_2&U^cOzpxJv*J{$@gUhjv$-J&@Xmw3TipRk)yZ@!AZ1%$cg}G1-PZtg4`fNiEn&p zSjc8u52SxBm^ck8m6oW-ktRsv0VtY4lvwp$Q%Wx*$I8&p5PFxCNw5vY&vy;&0fB)I zQ59?n3gi!trFr#bdnUgbw&8h-naL{W{hGgmI^mx*xzy|-Eb_xj!cyXJp@3~Y64k$s zgpABuw7(ai3W9n{=%OZ>l0llRg7X|g(@e@7ZuZ0*&V?5baTNI%BT6(AWm^2eFNw`9 zg+M5#h$`4U(?;-H!+hKL;JeM}bt=#SqWRH)EM_&HgK%?bx~(ze3W!wet}Ve}pfdf2 zM$&1%+>3tG>fX2s=VLDfyy zk~qF82oF_?Q-UIFysmyadQP*t;un?bg9f!%>R&Wji?BtqfSUEVR7PV&U=H&HrQyMu z3--A~LyYj5@mzr~RCDy*h}N)$LvVY8-dyN+)7-e<-+De>i`UfC=i%bu7+lrIRR0i_ zy`~nflEEHvMVmrEaB?vipcT?^<5iREW&i=dKXVmCtgxWYp%p^26Onp09BzGHJXap9 zjAD8m_*?-$hZoOt1u#pLsc-K$v z*Nyt&gWmqUm!9gp?Ej+Cdm8k2A7+@%fl!FB9)Lyw!}J6wXE;w$%~ddYnxyu){*0(z z`SRR}S5KEl*2S8$QW4h40Q2J;t=7|uVTT`Gy;WGXSwuu?#`{#{=OOL6@zoP!0+s2S zQcsRu2oHZBhwZ>>L|w}XMFXnEE@U@KP*#kX)q&(WQIm2J)nf9S+Vaqko+Aot9Lplb z+Wd9XOz1{Bi`kstJabs@DI%hGxq$DI{Qh}xWt z3DwSMeqn6zkt;9ZY|K^qXO<1eGC?LkdgLCmRT4AWLaS(%o6oRI@Bc5#76)1WdSMcI z?#1pJRR~}^Vzc9Dv=bdJn#OT!UPA`Cg8fC!K>Y1hP@ngkN7X3issqpxdJI zl@;m@%byW{jGhn6D5%xEdIYUqu)v!KmUyh3S0}el`_h?8%v^Mh(5wMC$Z_57`P>>0 zy1|{X$J3lhC{zs^&8FSjq_y5`O#2B?Hxh{-aN$BYm6U1UtPaR zf)QiyHcK^|pH7am;{MiP4=;1;AJuzYAL^<6meM(-QjzrY!-OZ!0qghP$OgZdL)AZv zIF@2trKrZT!(%7T9``u}F2@QxUoU@|lS6s`DD%~|QHiIXA2?}$B3M3OChcd!i>FKC z{4Y+ftG0L8wfez(mcCxo`&&-oRb6@7d{$}x&%rpl5NcQd;Gc<2a!hyCtq%@&Qv6M% zE0q*`UbaVOFjwXqbA9n!bw2mC_Il9`tBwUM(TmkRQ4d?eb==HYsro}k&V5<+CptK- z`r77@#|bg)JNt*ORj@dUFRoK$qf`%DNN=QR+R2}%20rAiZ8)_0Q}@l{^Pb%O!W{SC z4lxRRbnD~58|wLf=D=R_jC@I{%EG~Qy4AJ(O`Yo2ACv1irYBde`YNyIR*iF}eI5pE zX)mq`oaJ2`QON1lbI5!30j}K2S*{l_ubXfVC;flOy)A*O_U&$#1U6#AD4oM z{XySXGlojYh06CY95=UEwZJxEHEpm^fTaz=(z;~y$CwB5S0~a}XzowE6CNXdbDdb4 zpAY@OaK!k$(}w4J%&3pg1@?UWpcInT1Y&5b;cmll2qdGz2RET_j$(=ii*QTBx`YA1$?=d@yafb^9rw@p0k&``Jb^VSphM){aA4%>fFy*A%z`uzaAu(CrT~kdNvv8-)~n?l?I;8vTlxf zxp|0e6bxh7y7$%cJHbjVsyHbon4~%WSPJR-KN|P-#Cv*`VQKoXN$`W zIAXkjt?rAhK6RnNh4zHg?~0CcFB06yqaR*e+G~Ez@DH!A@8lKnZAK?WDZswF14lZ% zyjSgW<#vb!J+LNTt+q&a>_wDvt^S6x(V}?{8AD6X4rFt*|hyv%Tgf zW7S)yf^Qcs0wN^R)P9c{@#$RX`EK}-Q-xt7ZrCN=ys^ zn39&3R+TmiF>PF(z}bdb9}kbeUNjgJ#9r+Lq%@p894Mnk{5dyAqhC5?CZ zk4Szgvzrf+bNG12PVVg3K}z|bjL5}cOK8g-wtSA6#g4qpD*S$jUD$1%s^vcRLS zEmqYYf$XK(0p4w^#joA&@r#K#s678@^PhQ^Bm$hcN8%*BkDmK~_O4 z_as-hTO^s49ZTTa5JbB`Nixq4duul(%BdI^9v=Tkj6mk2#r*=uk00-Pv$r!?&u1ZH zHh4Douky!CSa>AlUZF|c#WBUODn zeVPuSX`U;QbeJNB`4>x};V zWEgX~zeb%-oM_?Qi75fow#Ey?4Aa+V|NBIV0YfU$zSl~Ny`2EuZ)ix8%IDb zh5OI<4+T!X+B`GrRTeeo*DpKs!zGaUiOG-GeV5oj_&Bfk?va|G%6>+nOm7#6!-w5F zf7XpTFfyGt?m&IW1)o4xJKv?XrxKSt2#V2C=KZVO-tR4Bc9Ndm(ZQplxw4?+=1kO^ z6U$u&;}gat9#_=0>s+u3>Xd&%dn&PV)?_~Q$wGkn{LRc;f@L#Zj_DcI-&&q%&wqRI zBJY>a5n0DQN6L~lH1DNg)db0weH|#FV(i4ZqDHOjG51jwz>(Om&*TMPpNtLqP}}|1 z;<1L|cFuJaTRKL5WyPSx+qYxq=l$ltahE9>8gdT}4^Pzcxpdt=J9_bb2YhiB$CegX z2F_Foj*nNZ{FCxz!)mvitaq==8?g9YTmz?x%_2mDAE|k6$Rh|GeqP<_-MhaBhG6EIq&V=#6ztf0A7He5exJWHotD zxUucyfYo8qImP1IyaV_2LRcc3ALNcks8NDVGwV;?IKHHPa!~Qlf}Dy9tJB`ii$Yhp zQaduFW?WzImt-05=Qb3PR%j73P7zSxD+}~x)T38R`+a~9VWV_-aa}#rDu(j`6lRKf z;KJ#Tj(b+GS@Z2i=lmwZPk~3IP$nlQ$3GZW7ApnL_Xt`QwNHG%tRu30d(&+HX2sbE z#Tz>3&3+a>$!VAFr?m8V>AdK!`0*h9Z8Pib=FIHC7e9hos=De{zCobL9Eko|Kh=0S zd1}ysbCrbMGwBKXXC2Fpo^9+Ebe4{ooLjSjIy~MoBrT9#)8sdwl+1VI8Pj3h3GeZ< zCD&7(ZC`BN(u@2fg{6;YE4L|Ljz6r_{w6|kDv2v7=k&O7opHp%>_{hK47@$2Nz{p=2k`2t-BKYDj9uOBvL=e%FBa`dKOM_HOC|GAH^ zI)D7!WptGBSnU^cizdDuJYpweC`~lk3xDn$wi^t}?WQItD{J0MBl}QBOCdgqw_)Z@ zSY>fYQ->n+TaLB;4!WFU^i|&dkBwU@4Ji!$lBJU`mfuSdk38@|*R$Y&$+iCGlBJdA zJ`%ze(Sv*u8~GaWTvFXlhZu_1E-+e)+EA-U8lzPu}Ew9+^zs zBBn`Uh*&1BGOzsnbbP6s6OuVvCa@uBu(x zt>V;l^Iew46GLe~FS0*Y#o<)=_~lQ?;RLSgPCH6ee8A3DhFt^gar)f{KNwI4^u6D^ z^&D~e#=TI`*%Za+C0qXBxCP&p#OT>s-mZNDE`Db|>eg3z|1$fJbAP4ewU=hSiA?lN zR8PsIV4#~ZFL&MyO}%t#-eY)+oL8qf zOJtPN^q@|Pj=)*1rVShP!`t8VCL|@vC}OnfMfP>7%IYQ$$)g8kHwMm5in44HbKga8 zH5l#K#>^!3p3&Icg}y2${I<8C%mGCuvjn!dueakm7xHdHA4Q+Q)(FT-`MPipxQpJ+LivUk3@AxC{ z=2I227KwMhSu>menO1&2A3x8r32!Iaj&68q6;lO9}5QV9`h$1QGLyb(FCt7=*Jd7{uJR4~ceGtsG}w||ui!=S8{dWXjibB!Y2 zF~8F+K{;z9t;{ZH%taTQ%QmHNK6F5W=a?L&>yOvL(#5Ufjq3fIFXgD?z;AzLOGg=( zi+d8-&PsV2IN=@rN^`m7xN%DCflAas6v}*WRNB)o@3&Z7!;9fywd8QW?0s8xda#L7 zUd;8qow@GVbYDe!$U>*g$0J`7!YJw`-j}R+K*D&8`RB0=qP1>40m{&e|8&^ zSzIZjcrjRt?4gvRN0gu!n}=M#f7xiK&-C1NNj#-l!#LOO)S46D*)DuKRX6;t_wY_p zvGB6yH7Y93m!4#-?Rs?YMN8)M6a2jjY^{gWtK@**$m%y zrks4QhFw44?l-?6yQ$}KQ|vb!tPyGRsA8YNOLI88qK+x??g?oF;f(s?tsBBJF-K2qkyN|SSHFAojL6)mHTPse0* z`B*xe%c!&H3_K}Sut{obzoUODC0bz9mXpS{KJVUl?oE(!xxUk2?6)r;iW1G!GDinW z{?sL1wi}cbvC$NFE_>s|x35Y>&6@ zc`*tfr;rW7zt13yCUYp|=+<$IX$Dy_zgt}SwJYlia;q$nXzO&?rcIKu9cajQ;EcW! zcVYN>wDV;;K_`x^tNxxJ(#?k-Uh=5OqsedH;oQpE*q-sFV^^Sf>eMOyW6m|BJyI$u zZT&I2Q%&-7_uPGv4UiJ@QcSt2Et#km1KyBs{(bHQkF6sq5&SpUIV-tdJ~^ zO*7bM(YC>~)5xQI^AC~Wc-Qr0Nf(biwqU@be zviHnZls(HzNbRY|Xwq&5yyg1eXT$d$3e(YmvVa8T<#i)-f!-y~`q5cn zU-I>rrYuHh{kyWL=Ha%a_l|ZK3A{-*ZJKEN56F~NYsx3C%-*(-ciGV$J-HZjGgNnz z|8VhT9T<*__QnK2%8qIdi0qv+G^)-O7;%muS>XsJ6GY_m&w z&N#|#2WgU!FT6%6@M-Dc(+=F6Gu=<1)^n;`;~iATZZb%nc=Gr2WWA67HzO*7tE0~! zs(EYLVlHWDV0G>MaoSU}sm&`+t)ie%{)}4jNRL@5MpxncMvnwr(aAQn`qxX%|Dbb^ zQ_sVt0@u_*;cLQ=+s)xRICY(PJa%ldIN>)YtBi;5?E)F~e$*zC9-t%zEp|P1`P1S; ze)0&zZk|)PVdGPD$3V$10;d#j73;8ZjoDrb8>KJLhr9D#dh8g>`#|T8b46C-6|=^N zn5fx##(azFdTW(OCDM?sXXWjnb2mz&D@u|z`Z}Gu?4(hTL<#-ma|9wYKaL;mc&6OB zfND73cV5n8-qO&ie~c6{4XOeV&mE(?Q(&cSsV~`4XMF@=!5x;-X~XBI9a>}QD2OU}tSmS))Hyx9*k4d?tV4H(ZrDi2sUg=B((oD1mSs{h-O?u42of zfv*jW$xG@p;<04P%8AChC}mT>n#Btc+pCvly0KXJwv5C`;0opys4_o^Gu;h@uStd+ zRIDxEA&HGofzb}4;apwjy`f@b$8~7stiI%SDp@|BAMX=ue5Ywq%?j0ZRpbMv?hyNm zw{{uh%lCczawZ<#=#16x$rPG2D5g(euBLNWtl>0~d&r7HQgfr|qF>w6y&f9{>pjcz zwsf%j#gW|~uq_#KARsCdUL){~AQAn1@X4=PZ414wnPS&e83=RK^v$dbIFpAT&fA2^ z+_qbt8!t_7Wi_tZy0bT{R`XBSJ3)L}pCkC_M*Dx>SIDvyZDAz)BD19Rk8LwCCZhST zFIILA|Df#~Bj`P=_vVLgabc1P{H>W}H=%2$du>r|;2UDTSW|H_=9o)rwNT$R-e4YeBC3{G$GAzCfl=ce-uUoPJLNuZ%NeIQx~*O5;Oc z@684#D3z1=?LwG^Widp%@`C5R{5~$`+WoD79ZE*wvH+c8(a9z%khQtd=k2+? zSL}{_+2YKdnn;rQtZVVEoR%(`*&E0`i`)E5j=7>IBnUXI%pYlgwEL?#S~#8Hr8{!d z=uT~t%Zk9vR$4f@vMS!XFDk9#!liaIV~fU*7tlvU@lWeYr^y zOFz{3S&d%CvbSH=I(#9fu3Bw5?60Q#nOnTH#7XfTuRat?8u@=Sa6t3F)zJ?nb0OUD zAnc!4G_4p~YH&CW>c~v-e@|HsTb#jymL~EugsJjZQqTJf?)v5t1J06IbVp*8LRt0v zGh9Y)ZfQG<(;}#5tmuihpyr;)jvwW*mzaaziDQW~^KVzmTVc~ZLL$7NS7u0=Hf^)C z)>CYq6r=1P++}V)Rq%qw3%3LW)StC}d4&q>^UQMf4a=`6#ba5Zamz3f)puSGu{6hSG5>PG2Q+{8f1Pd_!RD$dLMYJ=Q6cUe=YQNLD;D}-Ld zG)`HJdn%;l1XyBx_tmktJU7<7N>XkqpuQ4_{PH{d@qKi3W&I|_fMDxvbZOGuy{@%F zjvp1|1tY3!IOE9sdM!gAC=|HZBp2G-${G||9()07y;4MNj7uhL59gC`0DmIE$OON8 z{OOuWzySM_q{7*7a2YC4@G$dP%fqC>MQH}VVFBgie?M4@p7oxt_5anvL7AT7f5Am} z@{^Y=EzYR#TN3xE&2slm`^qLedi-mq49z|(+0Xh12i>Nmkpc}WwT69FWgH?%jdchl z!bUeQHI1?nnNR6lX66m5(VLoj`*^JXiO%~cc3t1gyB$iLge3Nxh*HTMbI_NwL#toQ z$~$g~J?PT1I5^s3n)t@nRs7qa{y5S?|2+==X1csMbz6dEztzXt^#_jyoZg8`PlWy< z0$_kct^yOKsVg?;HlzdjoL`O;xsbRaA&$kjQ#iy9jl&;QPxM_n}a!kC(xn)YF5!Rj0^{(0(kUf?5E<;EtU{=z#! zsTZ~AfZkUy5=on?6eQ4n7fUw!sC^D$nMSAo!vC@~5ewuB{krqa^jrJ(f zW@Ik-C>4Y#XCF8do~WdV6T%jKSwXq)EXbH}z!tD?P_OERtX>&T%+CQLMad8$0d+1a z93@@9Zs&V*T@#Cfe}U|xWd1=yDq`Ls9=JbnOE9~>ooL*_XLc#2j1Lz#;_enw4SdC@(UW7%zpL$`7gQSc z>xG6l4&E1i))Yyg0jxM#gdOqQGC69d*p<-Mxs4~w{6TC4pAiqGzHJ;Zz&Yd_;g zWWEX6X*lOM)Wc*SDO zSDGGrHtjL`0WyV1D&Vn!o zjXt!zlI=T(G^eXY^kR#UTvb|y`lAGuCg+OJN3ZJl8RJ&0WDsc1WhyKT<=Bf{<-bFET8cEze z{P3h=$vd$ikpTUg?ZW(Wy)h%@^njA@P{bWA-B@#*Iu~{AgIDv1Ydy{dwDGiy{4tqM zj?F~_n>;R)CcQD+ZpOzui%hH8Z76g5q64)wylnL47EL7PD%@y2Xpvcp4p!&!W5&Iw zJGnAIikh{z&Z>M7da>jz(dp7NQu#k9=!n8WfnGYNvXWZi=*ykE_wLPqyGbr$j_hEM zpQRmwm%4qo(`D%SyH)x4_A%=9BAkv6tE}Iw2 z4CcJ-%CB$V3@IMc`x;8#pSy7U(f@Btxq2yi2nvOuFWn;CjZWA@!~@iIRR^Y3FUjVt z;A^EKg@PNPFUN068#O28X*CS8pLhSR`E9wUbW{|4u(fX1s;C;^ZI3EMqlpeDT}hwr zrOhbje4hoJ4-1*hz*8<{zF|fUdg_t65Qw2ZA|O*ZKkBonFgbr2 z4&E_uO7Hq;lHohb_GRz=&PSm_zC>=`44uez$YT4HE8)^A;XhoJ)*-}yKY~s6>$_;W z@WfD6H@YjM_treVUD?#(lAr5CUv6jM|E7bI{iM)$-weS>AhFf$_2?!P_tJfSJ(^sX zk~H!~^9I0PD6>r|!Gf+d8T0B}r0ksSX>i6$6S@gdqx!}qIb-4IZ0FR}1Xd~>E!}p_ zzfAhc&YHu1-@9#1icfgqMk0eKTeqC)>jXL_Dk>`Tws2xZ84Vq9JKbhy(1$k943dbT znZH|}HSoVd{LP1sMS7n`CQv1HaAo-g&&@q(IN7Q%cQ4ZAn|^v?FT2+x+BluA*qV}8 zOVBWcLXCQ(>2WHk3qsxsD-qFpVl?S7q6Q&W5GXga zta;ojQk8A+|2fFte%&@>5v|Tw!0>*uPqSfO{HI^SbZ?7D`ov11b)3)*1Et^$yd2`IEt*VSsFkxNA;b#w=VOmx`2)^3|#YV5xYp)PYB0f5!TrhD*%&QNm0P zrjjeb*da}4z}Pu&rJtr{pM5AQLT0J#z{EFTEm*+z>&Y;>G zpGrn{F}JLljZ=Mi*p7|3-azMzM7~3nk44$7_^j~@%Yp3qRTI@e?fwn!Ln&rQG_U+-f2n>e6wvJT2S+BC5~&|v z_9{C33;o!po;2*xtsijwBkR`-v(r|imSSfBfm_ zo8zxunQe0r?3mi);y=A)6VjN&gaQz!bpI{~Y9ug5=Eg(yyPG%1D_~2`|s-r*>r41Z!Y=9j39;WM^@N^sFwX7tk+6L+8&Ad038OBvp+d%?5edh z$t)xr{GOAYg9*~|_q1u=PdcVhT=7KnPTcpyxU{^MO3UYY{Lw!24?)o|$F%u-ZcND$ zblu58iw@JXYCiI~isq4T^;QI7^k{iVaxEL=sVAf9W4C^{3^&8Wyht*Wy4%IE_ z?)MO&AU(Rd8pdaC&{ zj}AoTSfykvR#h1i&{4!csuZZz0X+Gx!Ge|tl@e`-M_g7`j2SRED{E`Y{{C{)Uh~9tNuiTeaE;g(V4^k@3}UJTC@C!W zQ>Crn=N5}NU(nZGUFV#p&@m40TBc8;Ak4SMSotpeQwoYD|1mLTzhLCU4w*Dz&2BPT zF=I%e2T(7=|XRPGgpJB~K|<3s6BpYSX~Cf?JB31##uGZ`Ri=ELfW093oSY^ee21 zQI`b;NzMHCld_CdF*CB?;?0Kb5sNcZO}`h+$RrL^K4zncGnnmu=N&c~Swqb82RId>V8o6rhMR}PzKsPcc68D9$Hhl1q3=@2sYt`DSPed z7#c=`O_$nPFOuB6Wi>q3l&uZ}a2L?e&5&E~ZaH4CxPSbHhykim@_8v4NkIih=_m?_ zCXy!ZLk12{N}d(`%q>Y%ef~r7AvPR{>ec~c4g-Jp;&=E-DTaVamp=ONWGG(w;>C|f z%BVm{?46LqMHwrvZ~j_27X@#)_eqM~;1{@iKMR$B5920apL5Q@`GP5-@U4s zRmDd`t@L}|Hx!CLgGJ*Qc9qtdxbj_HvhCGFC3(b9-qp2Y&oy&d%gihWF6psj@iby^ zhYcLE-P~rw->J)-P;PIVAl&_t{o}nXUxoTg#qvqHw<(7mp@rPRqF`21NEEJs`Cf}l z!|x2K;;+&6EaU7)*EaB13l+aYtzDcB2B>4Z^;>m>o>T5ABNNKOFAS`8KBQipWAM}x zn$n(NupcLKOR+~?K0R#wz2tG}N_}W&vcU2h2G{G2%cf-920xn&4}omE@#F~~d`AD) zy-R4lZuZ4}hY8$_YEsO8l8lA!C1N}a29^*&05v{qc1A?1*h%RJ;U;Uu>b^r?HXYc; zK!cs7>`ptSIV}#VS%|3VboZ9u(PB#D8Dr+MFsM+@SsRYm3c30DFLamJ%ty<}>aL&t&R0m1%M1;bcyjCNLKSJ`#M-+U#wB5lH)@GY zIuv%a;bFF)uj>n@JUBv=OKN?89))ad7hSh~*&J||LO4cEnD^?mahBuJi6aYcL7~0rWfAc%(zoxay&!YYCV%mfq4ZmXgelApfk*nR)V4}1Uw5nfrG(nke?J2de5 zdeBz}uD$KRKy&<%(_2@0+4zIYeis`8%PUEOs&^v%X|F=EK2_tMXbyh$&>$M}{Xd_* zNAkt*jr?R1Hj+$o&giKK>@%s`9XeU{c9s z14Y%|gtQlsb|^0`MR9ARSItL(Ag*NNOa4YB;yjPB+QAj*^h3%DyH>~4tp1S@trK*_ z433P*oDU8SWtW#zKtY4(Hr6*0)CYEm46*fwj259H-1~!7VKv?KG%-*)l#WYo@E3SA zHcxyvX`1RPV*3e|_yw%!ik4~9Wv#0DN7QI>5~+fn&-66g{3BN1NMBuHVb$FnXR5X9 z+%Hkvmj3(d&iM6KLI6>4AfGH!^9ii4@pmdXSt~@h!UO9xlwx|;TBbex{@s@L+wMjF zySlMPimhN}wS&?NYEyS7 zXx=`j+xpI9BzA?t@u|lJBNCklL+M(*E|&Wa*BAKlYTt02U4xevQMMGAtFaNw?`-DZ z!A%B9lis&oI$ehOQ?H|*OAQCxA*0S82{>O68&)nGsk*k||C-;*Zn$i!g#4_rF0F>{ zhBT)Jk&I)Zm5!2sj<;iIS}!$iR>n&UweK}vMOgLGL#hs|*xY=di}uDndyO0$kx^`l z`9F}cMt6CW5Z`*l1`^!F;j)81w$tO0PY1(nuqwD^o960hrra|ldYU%KOxO4fWpAfS zjluJX*H19q=*78J;Z~2w@)s$2B5Hqzb}iEsGRi~~Jgck!B&>IxnBdNfE8AHHI(0vCh03lYeS|0%2pbr5wP?eD?YO+TOg}Q;qIK34OL>tbX3vELB z^6SSFcs%gKU-vbfl@ zH*jo}KS#cZcR3uYBWgq8O~JZ8JGWj*pA&C|!*5J05RCJg|MnOPoCU97j@&Q|As))~fn;W`xt#FJd z6cPz&!tR9{(R57E-f){`-Qh$Q!2gXJEv3j={bA59=yWJ)rP%2#+#uA zf!;@}ce!N)*b{{dSgMZ+$gGFlUtu#@y_k~blzTOabBb3>4Pc1ATc*kOj#S^CL-kOd zqlC@1Gs9o1T~e{GoViMcx^K^HaeIfvz6=sIHn5)|+a3c!TDRg8nWcR3uP~~#tJn(5 zNepSx9~+PhKMoa0)K z>3E1YMm@P&YGudjV8H;qbTkz9$Xd!?;jnEkRV?03P zE^jsLX(lEmSwOoD0zFGaElZ`YgIqySn%CGdO@b9$t&BV~iB3EhDv_P$8za2v)-QCZ zKv1j1_;@5NNAfElzd*viWybHQeR1SYzp9+eD6OU~A%J55ia%b<(FR#SU4^W1d9c<8^1KoAB}t2+hb0quk3Df$xk`x5iD8d3 z`~N0KhR!R6gcd5B`K)pv9`)c@-U9GvY)Z;AfSLvXa$b06;M0?6=ZO~MO;6`3=vE;j zILd3{FAU|huiA6nId99HuM{#ex#fHN>Isqr5Wrbok?h5pUXik#9DdwN-bGwQD7zbs zeaOZua0QG8Rb zxBulq{2^JwIb(oWLMXx+d?s2J|C;RRi5@mtsWQ|{x+HG^LmYy(%A@qft`Z71)-pcp zGgW?)PY$3=jE}J%n-%uz#jI0yXg!Qn4*$alXAd6V7q~*;w?MDe)}BtK=KptPvB@rT zrbh6m6)n!*UYLEToXzmgnSp^DbE3(7c%_VPlN!|H9HO%u*b(yoADm`ASDQcV3?m4A}*sSHg~_i`fk z^`(&St7@gL4I8#5z!m?#<3)r`iMfid`_t`a`V@zXppO|_g@G_kIm)SjFVLebgfo+r zv)ohsO`%;v-}ntYmNWD9nz-m8g)7}mWV+C2`po9<8v;%tjwhi#H$~1kh zv%YTX>Vjf#YTKT3H`3ozsdtEbkP{yIEtxEiljCCK`xbM95!=J1bZW3c$4p1zaJQ4zOmjHcb2(B~{=kgqLZJhh+VHL(Q z58sj+(MTo=w|>(On?vcw4ArrkRwa@t2WQ1nr{8=v)my8(UY8M&$0+tSS1;^*&wNG@tfNFN$T&z~1xm!A zq1q}6>1y(1RT)k>y>j4%-_L4n#+PqK)43m#;Gsnp2dQL;(4d4o<4X;zL9+oM zisM|lr!h{SYrG3k=N>KwT82+MY7ISsc21J zm*o+1@zP^>6%Xrl+5}rQ`oy{ZDy7Fj;7kpADcZo(hq#WL9uEaURgj!%fKkIKvs8;f zmMd+xCSM6$5?V`AG+QZbw%nfDm5n3O0NLkmD86(D0_XgfOmnA_5~6f!&g4R=Xi2xF z_?vRRBh;^X;I~~u(nFul9J_o=bL$711pip_af^F?<5h~Swr|s!UnD3tP6=7PpPo+; z2a>-80L;Clg!oiu|Fex$;kv#3 z6|^p&muX#Lk_9c;g+SQiSO~W>8A)emmiHx}QsD!zj5Z}{!$7M5zdlX*IzhE&gqPwr zNV6@wzVl9s3x7MJcNA?I*Ji9b&-z>hl}*s+ac$yJ4JNCJXi44mgVc??k1YL$ixqPs z`%qMd%?_=nU+DhqKRYSS|45^oHZ}iJ2vuszht)OK>WPh6%K@nhnZfHqRPUOWM_w&u zK5t2|g>gMC(AC@@pGRSG#?S}1*$7&|ANR8vyRL@3UeSgFlX7rOmp$32DE}$a|2i#g z2i&cY5pj39sSqyK)NkhochbOl12p?uf(63+l9Dbk*4$|&6f_6lOnFDq9e@nX(e8}0 zO>JeN?oXS9*k(0T#AWy)Z9z@2JtSnr(!X)D@Yj!dp6^p-L0B{|3_tU`@xQ6D8Nuzea<9jUC0({CtfLtO3LHON375BwW;Rq0 zrh@=@e>hoPZOo(V;Z4(CbM_7uyf6Y{t1vb`0xla2FXp4g>YqhVSYN9+P-`DZXlA; zujXfULV^!JGuf@j)uNfIvAfK_)T!L<TAN*y4s0 zyZqJop@F|D7+{UfGSX&HCnv7}tC1$AVeedtlXy^CVJ!DZV^q`ax!4y=iw{$z$yRrT zco?%bWM90^87jxp*R7`R5B*JztoER|FkS@o8o>p{Fy2`BS$S(5HQ~F5b1B=~?wLh} zT52bxiPX6zkdx_{nnokz4x?qZem(v?I6OQDU(^eRkpkk6j64Um`fN^&eEi^FzzYyl zaij-JJ&yvM$J-Kl?)}>7Vm?7PwjaV`7>w$i-l^kdN;yo-UEi|t4P|hCcb)2D-nBE; zpT_*Pd$%YXAdj`S>i-WAIT|C9RO6|`B8gq+a~c*$G9LUDE;p7?cl zcf(Nd^|D}IK0ZQd@>jX}Y?)N~#tm%nG5V>MoRJY#UM`aLG3n2|Hl&dWVVUMct<&KnwZO zb-DY1ZQx2Os@c_J%thH}7jjzg)+*#pHN{ago^NTy>zDh)za_?={nGn~N-+R%pdJT1@kKQMrnnV&Fv6X_^0 z7o%@+PTyd599Z8MqK{&-np*}wm1$8rgM>3-nfMxKf|_&<(z9#YnWgp)Gt zByxJpVQhy6?kL)ZxLtz5KzF~|7ZMvNAuu;qnO-+*V@o`Y%gu(z`l%kat z2h0>Bqh9pR{0_E+PD?MBn-(J@2^N-?e2V1QoqnhDNa_6uEi9Ts;BXP^_cm6#H!9Xh zUgyS@UQNM5Q4!Eo-_~eL@dfv)GFuJzlXK4A#Je`plSCjrje?9Kt4z7jz3oNbYH+Sb zgTK=>d!O;$4z)f_B0b3TrL8de^)$`T7BtW&T^JkkrdVkiA=*E`V)qV`0F-K<&mz%b zX24QBJO#sNx@@OrPM0L&yR#?t)-jvG{q7fFNk0)71LPH}mEv*D=5=lw+8z+$fH7+> zCtA{QS`6WhKBfZQta6eVy|Cmnz#Hd4D38EH&~1`1xY#pbzY_X;p)%73R3)_GEpUyL zoN#e)bRZ0CZhk(Sf&#QBbqMwk4TT*a`!qlk5yGCcyXzGhhJhD@CB&4{F+0SUorGEb zB@AMlDzs+}%3jv^QmmbQO~lgp!@>dKjfWz{?UFTpAFvdIa?n zPF$O__^3?Yr-}9?qKws4Eo<@p&waiR4`jZSyz_^LADW(p+Dbao`wQlK8GkN zL_{7X%2Ms8^VDt~w6swA^}n6H+?%Lhl7V%Z{|GzKa+v|(4`q#9zBiBGg--JS$9TB( z++K_?7ah|3K4={XC4S=fVx4@uL33^@TQhON*$9b4UMhZq-GQC4Emv-7!>GrW&%6gk zhnTM~tSpLYT|e&mkI}tk`R(c0ZbtO2=%vMcneN00w&rrv^#{)`==kB#-WDUse>k_i zz1cE7?=ECu*Z)E8T!HR`=d~<35RV({srq>$_H)3{Ap>=g5P8wekDMUPiOl`+V;&f{ zvWS)ee^!KZ;D&Z0uyI1hjRagxBYKl5$^md9?j5rWmlXmbY({ZvOVRk)cryHuv9*m9 z?@}YEQHZ_$QBT0i(F=riLiXcgh$mU~lm`Qo_d?wImKj(31kq5YggPTsVesTU_;Ivx zuV;7Yza^?hh;qCQmhBR6wahfaxuU1pI_NZzZ_k$9!fb$$d~Mbh*`=jq$XP?&YN1d7 z-jW(6skeT1)ECf++{DmG*Ka#TX0;yARMlW4d(JzvIg@6wntq**s7*QJvK>CTj1w0h zy>n%yk@kpwMGT26m&CUbge1~)YaFUuT+A$#Ki^`ik|OgNy9F|+28GekY#UfDgrQZ$*HKI z1ed%v1kC(3tAx-q(|+tMwzl>U zWIQ3z&F>3<*XMY`*V7~m*pJ@jdKQnx(9LUSYnQ*TABbMV=`-M-9=QFiHbEXq`dbJ1 zr`B}+J}}XwF@lsbR!Xebpjb28mBKeC(vw&eiHU{J-bEVYlu8RU9`pKS;yhyvx2_Rb009hSW$UI0`^fWH+%23zG+cq)*uD zE7daHeWEsVm;wF=n}jkThN(@E)myhsc*I#F0kB0N`P1kQwYMgD zS7*IVwMSfzLgDBIr$XZ2JIt}QQ2sWr?fJki4k~lm>@h_JNSDpo>?CZ+8M9j!w6394 zV>rnTN-}}W&DAqquZ4jHOxW1iV4{`aF9#ET;J@7}`RSaDV_FjBx}Km=^W``!kh1N( zP9|~OPC7wL&psv5jmj;)vBW8;ya1(}VpZXw^I4M7vkU0>szwuN1iLGBcCJAuO63zp zFk)EmT($H%x41GdSehgE4y2HmC+-P6hvajW82t59B$daEHOs=?-Po1q zPXO&)anoL#`TUUf!jTObvrG1$dMIK6Gp9ZZrV~l3Y}PfUE_=sdP0FnmxVfDTxeF1k zm!$+4>x#fF2=s!}hF^${Rael9&`CT5Y(gXvjT1Qr%< zj=P&WYUH7o4wXy%l27vuZF!Zy)9*?H%EeiYDd^Iy2K+QEL+`DC;D--#+WcE7@<@IN`y zan(&z2A> zk=mcbf$gC|XKh7J^%s)Sydxz%6nEo(7?am5#s=7TAhSfkS}is?IULLr#l*$uz=&>E zk_2YfHHVPOuYlNr)xuU1zngl5rEJcdQ?!ftnabj^hs}K-L_N^p-=dPvL+;4V4{8R% zgG7swGWv!;&2}+$V)vAhHT+@d8k2Pa4UR{zx9QuHDDBAS*UiMQyk8B!^X%uT%0to@ zUH!S-#&yFHAvOX&nOr!5qGA9Xt!>1ug9PJ@6BO;~g(cPt@01H^j%i>y`M1(A)`xzi zO7LK1Z40ykSo$Yx$d!E`T{M(PeLZ=ki4F1;go(4s^u_^g<24-}YH)}Sg2}#@LD|DLczRuQDHprnVPDOd*QN@$NH;oIv>x_O`{HaQ z#L@^M^4}6lE85Ha`|Zm^%HM87>r9~Xym66l!zsb*3F3I4hCH~kK10&QuPCXpQ`W`Q zW*H-g6Vejc+p7tt)ZpJV*BS6C)(HCjP?BkTt_dCCbNBX9xwthyG}LC`mPQGQ@zAqr zQzA?XWEc-Xm)+}wynhhi|4FFLKBU{1q8xa0U-Id*GIeRL9nMlT1+;FLn#w#2R-^m# zXuV*%ufbwgsG-~80;=aCZYejP13_D9KXFWdc&XJ&_Rq=Z6u(rUoul6OMuO>Lx8|s>%JxV{>!8 zQp*E6wG^N3NLam)(I#y&Rn&$>>+PjPYKc1;{2N=TDOn`Vl^=@WB(Ubs@Re?T0>2i} zMAWPo*QaM4Vz&wXL0b4v^p$cUqFZgm~;7x>Q?p8TKUpZ`C}F(DEhhgpU2>EF6F-Y z-AcW|Thvi~aluFT<135D#i5+ST?)h;7|-A|Qy`5;tjh;VdNaZ0b4w8hTCpiX67q7v zCV2z$*no7g7S3Sl0~3`hYzeY7f0m<8_IAS1SMh~VLlR`yIxte8?&iPDe-)pWtI0Hf zsrj|A05vbii@Hva`w|~nTvgL6GWW;5ozjW<*N3LQus;Xow_@w7yuzS^%=cj#^Ko+g zOa)(rm01dNC3^lg3-QBFEEUUCQ8En|3Uo&sN(W*$k89sJkBOj!kjW0+ZZ%bDGBKRl z=#`7YeD9V$6!W#_)Lk!v6M>MloSOE?eb3l4W=hh(TUJ{@p}`nOrYy=i!B&lkv7@7* zVTber3Y?D)>&}6`gEjU3<5hRx{0|hU4DBnAEyFElzDRFK>&^=YV*e5vqLHbNBU1+d zkci0xXXo|c8aaB@?^k<|-sWUc#f1c_{5)SiKXev8NF0VHrBd{=;7iAMX8o&kCw^UkM z#mo-j7Kn!-r1=NOH}kZ!-@WT(*yL=#*7u5HNBXz3K}am9VFZ3_vw8!~Bg9|AUtWgZJ5c z7HA^s2dj@)wyWkzd9~@i;w+i~ddB|lA!x2VwjKD)@;qWtTX&LGn6JHp%QJcKv);$F z)t4r7El;k!3JVMK+HO27Hd+SswjC*B5fR#;>$`UEGo6?T+BQyPlcD4HHY5`|32Joq3q3w=Uc7UQHXl(foWt z|LPzwwsj0M32KzblOcQFjuJ^e$O6`0fxI>FMuR_er49d+53X+9yY%1XV>DuIw{Ov{ z`eyx5I}yfTW;ZoW9|75n4AhlBTA=)=Kr^a#*xa7Ss}<-S*gPHeSDE^I%BRb4?OF8< z=KsjWPz%w5$2Mb)Jb?OT>ZUb_rG*xh-Mu@1o(K+D+gylWXFQuu9aa!OcG zA*8&O5L8yY@|0|nd?050>2i-N@{XAs30Yl5^$=DY=Sg-FJK4T3>V*X@q8RXC%KR)u zgxE`hx7ES1r&lJ@l5~8cs{9aX+7-=`QTTtabnEvOvJVCc;7Z5lFTB8zK~S8-1m-hU z4Pt&x^+fg2I=&?4LDC%UVaT)Ha!q=u;y+Txv}`tZ1(J28gw)OX;V>GN0vuG2>uG?T z+Y&|HZJO(khM4vx@1?~UsbC^ND%I4Y4642(2fg5j1t3xTjmN?)GOx;8Z&4k42@wGv zH?Z@4_}-|2Hf;DC&+9=ob~{6w8YzYVA8*-{{a32Pw0C5nyq$lNdKk=Rv4ok}e;xQ) zTzov#J!Bl1)8occdynLz;))1U^p zueY&N+hN<)l;DmswsArQR_4V^b5yHlM`5EL5=FHATA&@gS~Wjg@Pa!%p2sK#VJY#D zmN@H@T)&k0@)!i3;tu`d^fl5b|GDag%MX7+SKpV+qIJ_4vRN7&P|HsHZo5A{1%%BK zoHxNsa3ADhv1#8c?v-~72QEI%p;Ax&T}Yi(AHK8PdGIk3S})y9qLwR2d#dWH^WMm{V zH^g^(w}tF3ar* z!@kHt9+pN3Cuf)4)McwfCHvIV6G@V-(icDcVG7}2cvnCvl2MpRc*#cVE090vNs_&! zAMzm2hPg?D0MemG8X zaR@7D+IyL?2dv>?wpU(7k9EaQut{l$@u|OnE-+noq;%PX9YON{yUVpwf|rJCJ=IZq zv4=zqYl*Szz*{$Ue(9Os;$L+>qrQBpCoOg{M>k9$+=4vGR{~|;d%?o`uX3YM#q_~%7cz8kisw&N~RZSX+ z8-)2rGa{u1dDt#T$V`99Zfm7Tb6h8ZR1Wvny|Aw3+nYG)XewQIs+uX?LN&UZDgIs& zz`>69D7ULT28Kfj1tjd~6(NbFlPYKSS)|u>-kYRz94p?%S<%Y$XkF#! zN6QC2Q+Z&Jvv@+#TYLAUr!ccM;9rYg~d;vvE zgwybQ;1)8Vad&SIA<^E{*Pnz3lzjY11)7;XTchWW`x}YI_M4rn9|}={6T1VlYufqk z_3fH!aN~nE3^O*%AYT)I^$UP>ttp7Q9Sy0CqIw^eH?`r>_N@Vh^fI_?v>$suj@sy{ zYi!Ka-Jk$ksC|S#YG6CB;_3)EClF_-L;&h1)opM)Q80~Wm$sK6FBsg~l+W#L1gpti zOrqcie6jS2+TK*$^ZAt_^P*#}wy2vB@w5&P$FUnL$A%l}#x@(l%i5*KSpBO|6Kwlh^6k+>~qgs zfkN=U&-=O6T~bHwl{pkDL4`ym(KHIQWtuwT>U_H%7W-H%B=BG#he^z%JV+Hjjca8^Aqn?d!%?Rf%tmR}#W3o84hH3o&|R z7`^P#LIO;IxS_0!30_rFeg|VVz&4ZzEC-w)2JB>=pW@z;Kgt~$SMs~FlIWG`d-<$< zPcru6AP*hhgZvAqC28IIO^|=W&1et@Ndj)bLu^Gr;2|}^oRtd@Od1mZsqUKcP{kxg z{6*Dk-&Y>`@+if;`{q@a*8VAzQu?u<{=Ntm1ke`$&eA&#{92?{fUDA;Y@%6LeoMXZV6`mj~+h`8nV4{>lPt&&4xW=eo70Y zE!rSu_q=B|@=zojCa#Sr=oV6gW53gI-`B6fUmhyoz8$qO;=5LmkMMlRM5XH8tXrpO zn|?Ul(&rz#lF8N*y|PAmFan(ozC4c^&i-NEQnp7NUq>a@KBWj`10a&W9@goz@=$Wg zKwT+qGuXoYrZ025EjbL=U0J-D;5^3BjfBSj3(FP&ePUzenM77;>{eirXgQ@iBP!E+!3|l z4ts4jg!BOnppw{kzXMe%j3wx9IsoDg1NGadu|gyN2Zmya1x&%x}n%<{fLpZQwfm$Rw*-BBvNI#-wV}xpfu-?{DDciUiAB#(cp}(4`mOA@en>A z!J<%JKf^AT!US!?cno%CMLfShIRcEC57fBqA?_sUB$?3HrLogg%MTwB2V)R%4s_h1@M)dUs*m;z50`1 ztymRuYuf#>nK-O`8l05$3|76vL9v7*Mny89sQ)tK4I3;rK0X+7+9@|9Rr6Wz-uVW3 zl+f!U$(2+sPqU)J=X3-jLLWoA+QxNbh&GgtXY~igKK=xi0v>p5tNr-wN5G<*+z)#|s9n%o@Y1Kpmq`w^Z^(%~i?Lx4BB^fJQr zw1%QVEi5jwfC1ZtnXayi3eJ6AYF=JmA&b@v|L8dQCv-mf58%xC%Jv0`x_c#lVf;*3 zb8w$>n=+*Heg&>8ql5;d%uFD7C_Z4V?De4%yB=L#fiyN{LS7TUS3;8cuSRbH$X`mf z#h{o``pd{SLcZ5ZIp!b6LW0#+y*%fgp>krQYYv`5ps4E_(|^w%v*W8y_Yy!qhupr$ z;0-M`YE<8ZEn6r0ms(nDl660K&!sGaO6{QILdN%XgOO;9oy;U|zE;z_hurScjL8Bf zkudEhJ1-CW%9Shn)eelhXnl+#UEg^@#o+jUo-!!~2?INBc^y(kpEGLgZ_NWl6eM>T zfj|K#vKD9AgADW@dI67K87IERP;cvwk`e~W%XkZ#_^xGTcj>w|yXOqbf(J;c`Xh?R z<(8Kt7_aXP8}7_xb9E=(gFIs6NvKyF45&k(^)ZDTH(I0e;DnoM==G|8U8A4S@-Co; zwkYuJ!$5sa6+=iT2|={ek|t!~sp7i&)K?siwj;_}8@%1IhnxIQe!;CFO{%vHDDkuZ zgfvh3{NOnJS~okV2gE59q8l)0rcaNome;}PvkQa-J0Z-c%}@H+&xR`QxqKGR$sDj$ zF(H>r47Lue?K6z<9rk1_Y}tL4={wx<#=@XP@R(ic^9a(W*hbnlW^T`$khtwK&As?~ zqn{{Kk?%Tgsu#`N27X(%IURTy-2l4~a7a9O|E2A|5s`oArL5|>3z*9d`KU_pv33V(FemR zob74?k9=g#BOM6nydLxiR$DIB+y@fEw&Y`WT{bmc$ULMk{bbDn|6cx!seXPQO5Bk> z7aTVEad+AZWrq1e>#!@pyFFKX)>w(T$C*cnrQc=y5VzDz(9)Y%toSS|5pa{ zriD+$+z0122#|a4fd5+^bj^}v(y6->2b-l0m^}f#Yb-o6@>5>*X z#-FtT9@zpC%%yH$CI?Gv-PiT&WbC+z7n4~(_7HO)Fl2Ba*6 z*sMjw4RY^br8jB0xMuFRmRDDEdG`a?_;@|pq+$O+3ra>4;Q@gs?^)PhNM}c4w%!>i zx_0&Jc4$r4?Z!uvnNY(!3sD*wSv33N6=g_NbaG4az*sDlyJUbFBXXO$vgBUk63xld z2LU%?zRO6(YiMJ)b3ALiS~j!m)eX>r`Ut7eMbFa>ki#4onY78RgMUXBGV~^&;MjZ| zyR%$cl+_)z)3Qfl-Jbwu`so6k`(;J;e4#uvz&euCOemXnc-iQ_CKqWf>LrKn4n<}# zUFjM9KQg&w^s*qzOdJ1${!o6Av9dLx|d=SXy&3Rmw{D#RID`q`;QkaDerVwQApqW68Y;&#J zRK;LMO(!+FM&cU{w-kX=fBTqb-E~O$efnoLi zbp&jxDuJ9v)X=;B9htZ*sQ)+_<5sI_aSxeTOS0;|`CdMvLxE}yexSLcS`ka8t@sCM zqG$xgRGCeP!Bpf=ndxGrq2|wuAFYo+;_oqhi3&9Tp!nf@!C`Qm9K}VO-})ndxO;`?@emjLBkv&7+*|PL%B>&!TRa z%GAQl0=;1GubBc_=k7qU<=!l#uybqv=?ASgzkj+R>5TrcWO@AQ`UlLPhp4Bf(1uh{ ziy@wW=$T1xJ#>F*TfMZf=7}nKvn@@`*N7ZYc#ona#)kwZ1*t%Er+^%(l9-BaDb9s4 z6;iR=po&$UI08_PQEL*D)^>H)rHEW)Q0donJ-PQZzofi7!uk3o8vFbI;e?EGw^F>t zGY!g=j{1bF7<9>r=^eU#y6IHhx6!RujhOojRV3iXGs4T{$!m<6E+}yl1#9!`s#iQE z=&;QSDFJkO9Y?->h@&L2%oMY{<^Sh5H%jEkkGHwMRoP(G4W{}iqM=^;w}2obZ+6j( zi5Gsl?x}kykxt}8Se|qFYX?EV!(3@&rF#S8x~-ED5>yA)9AnM|8l=eb$TA_vmt->afhkIjCN?saxg;Qg*PD)&tOLk zQhICc5o3(%^)`P+%Q(hlL;E|vo^;jnn=t@sBZks1Dof-zVB;=VQGEvj5X~(<%mB2i zsw$|@TOrT;BR~W&H4enC%Uav~%VxTN?RI6zzHC9bA6YZF^k%D-y>FaFjAp^t(4|mD z;`#vcqQ~^&ILLbcO3n@$EdT?s7$qs9FcJ_V0LUg*K8Z z3RN4Z#LpFe)oHH?GmA6zD=q#hV(!hkw?2)gr)k}raZbvW;-L}7z~=UR%7H`PjnO~v&1%_~BSBe-FRja>x zrGL(lJ6#v?pu_t{cS3=uj=f>jmw3;L-ygxzJTcf*qpgXl~IJ z*uy5&$P~<`y!9gtnP|(9@$~oI+)FC7!DAqRVO< zlL|6{p9RPkYtZrXUI$vuW7akND{G&is#G#NrN&S;AROfXSfyc9lDq7_Kg$4eCPK=^ z#!-ENnLt}QJTBz#-oXp+8ZI1CIYC1iJbI4rPDT1g@Jjb?3Q zq6+h5d3-bTzH>us;V2Opv0Ge<=CQnHU7P@4Xc_oe zsy(x82T_V9w^g8=sL%}kUu2DQ9#WxuA4-y19SPP15`TXiFxElsxX)^#>UcJDpO(g~ zy#c2AKDW2ro}FZ?xtj@@${YhWkKXcPrP3)0g`yC1!Uk6+gotMA%%9US`%sH83ktWt zfARi$8?!*~*Gpg!voo~)%tuR$EcMMAJ_ecQmPEyc2_-wfaBzMbwUvd4rVU=s9#viz z_Wh+PM$}^gt=g07pB4#uRFt)(C&)}cxin3-w?w}M3a_;Gg;}CQm~AG5RIBGpgqLy#e7}psGDlodc?EZ_-;ku`6|2q5)>Fj8S=a#INJEQo{%iCl&Qp z`OPBKxv_d`s`weJqQ641IAe$4n8DN@ECO2*5@@@!CqCTaE+I_psCa2hvlf|50d;;{MZ!MZhxxf*+a86`BOg|ln?Cb>gM;_orZ7)LuV*0~4%Jsv%de!~ny7po3jWXw z@h*uSad+;f?ZXMj1rx+Vb2+trkYwbPm6gro(_Vg!)a(ORPjH!V(P@eK{KDa@oc{pz z59cq|4((M=XD&QQBU>!SBxMczJ1o6KhwRU87scw^gp9&~_F+aUhL$+#t7u$iJ}OFH zP2QMPSEVbE(;|dr@4C;UM(kHZF4DuPx8D3u`-S*23p^1n;`q(x72?B33}=Syj0{Yv9f-pUC>+v;D@h2V{_=l*g79;A4L+*{g>UP zA6>;U@M-e&zY8%ep~n4Lz8sqNgf%mo$+XbJ%Td{Y24Z1&o;Z^7K0s48-V{@)tF55( z8NEi|_7e@2R`(T{qu$7HZy|N#rwuB+#`(#QR{|gtLzCmQXnUQKA&f_;JyBJFtjw6S zpm-ywV>Qs>Pkw*bZ7Kt5MTXU&P1&VwO5bwY*P&Y6^xQb0kZjp&=1j2(j3)_veGVmY zK#d|+dwLwDt=8iCT*BoWyYsUDO~cI!mBA3~r3LN@8aohclAd{DL+c{-@}<}__eBh6 zH@72gACQkAhHu=@N9g-9#cv}%Xe|zWxi{s%`(po_kuYoP@J8X$)2Z- z1&aa_fWpnk!3yMMrBvG`6SKLsCQQRddxxJ6yL%VZU;r}KrZ+MfZ=&6XrepY5la_xIQBrxBiQa4}j~i zM`P$a6wNtV*w#S2P2#_Wdd*Ew0LCYX+k>zcQ-Re7ad{o7F5jH)s3E!u7D6mBoczZc zmvN84y~rE%5y9U$&pe>A4V=XTz0?PBb`~x%7eXKj!R2=KQ(Q(pzDkZBjD1q#&;?Ax z7~^deLdo?x>n70A;n;BYvgyM4m8qIL90DRp6;% zXyKZOJ%93>9`9KpqK6jcbd_DE7|_7j^x)270)^>PW-`f>PCOat)Df0JZ>c<+s>U@* zTYs>uTPTYCy(z~E=nNKFFdFJTQ8{!Tj(Y&^285Ds=-#c_v^Onv`7~$?bdQi|7P}QL zH&<81lIqQ$KY`rH$G7j9+x418>f|q8Tz6nchwRh@0MekZLqt04z5i!yfiD|PwW;E> zyVS1P;Qq050zz?aYLw_stscJiW4Ot;lZO36Lb$<8!paIVGe|W5&N~4l2^hX?H@k{HnD!vm_2}#R;TyKO$0C2p?x*ZJB>>744AuR>;N#Tei5Lo7+^^RnDP?zyP z?MnQlv2G^VOFT^qW=6B*8Z9~0T=+m$f^!l;VgJ7D-B{0RffP0F_bc9-3A}cx1@wqEF4Y_HpbH2OWCE*P}e`wik+q|nvzS(wueIyMG5#f4@}@~sK{a%E-{ z^~y03`7%XH+5_?jgUao5%T-7@HlIY+Y?L6B(xfaY(S%jjAUNY73g=QQtb(b&u-75` zGq26Nq>-|#eId0j5Jq1C|!LukBEZb07isTA9{Q-ge zd;FR@tYwgOa0rPmU{P~?P8nyq9y2rwR!xYGq&iohhleLiwcxReiXZlMMidI zuQM_@Pu-B1dxWcr2nlvKDh4GB8s5rSw1tC&zvT3TTnf0yXm8z-{xv$_h?BaIO__LA zsnr4Xh2}5JXpNwoRhYDRwQ`#6Vv_ZTl?`6}7hR_9Rn@lfiT6;bAFH1*iW}rqA?t{US_M7#-L7LIr=CBxnjST<5xY zdj*1=a*-jJ@g3D>$++a@8va;6lF{v70A&7u%`?~ve4zXn0i4Wz?QPP|QaIPhg1YaT zz(Hux0i+5Y$8blbL8kmzO%#uDaL>xUuzS^pZn4F?Txv9_JIe0G@~vcE zbAog5jiF@>uFl55fzOPVDzLke;WPtH53*@RW-BChWtiMriNXDcgy~^gGi30ZT!aKz zoaq&x0#x_a@*B#Cd4`^4ou}TGU;n4bMOcs!jgo58Wkw)rh%P=eA&8Xw@mfIBhQc*r z{P2By?&)8%rpjo%hAgV)OTPbqAn+wr!@;-?sGE*~svp(RAhMb%N9EC`-_Jw6PMr!fgPHxENn)Y|9SqcL7_kr0~}8Lim|9Orgf2DE>ss+ez4`A4NDU(!DDoW z^BXlagT=z;AMT~WZtu(va}mFG%cJ*DG1>_j5rl_POdfota=((ks1A@fu zyojrZD!ETXIG91kIB>>8p-9ZnpRo%nbHcm3FYn+j>z033CgQ)py7s7LFRk>A+!vIx zYanJ_5yUAGC)C;&l0aE^o0|vn+V#q zYA_6GR2bxu^Wos;UlDe$rYyMBsbI%)S7RHkbLz!6KAvU^?Uc)x2SzzroLuC2(rD08 zsh}Mn=UftvP}B@=a@W2wH{(n(L-5pJzt!vS%x?{J_Wp!QyL9(-fvJyh;!jQ7vz`6< zSCgWP)=!R;4LEsO#l3+5`Y}d3QeIZpB1|JqAYktWK_1>;uTC;+Vbv9nP+TZOoBz+C z=YA&Goq-YaZ6q8`|6Y>rD_sp3(ARIY+LY57_2GbfuzPetHR;z*9CLbotIdw?Ft%g? zBl~D=5s>5#nakT%GPH!hV^p}mz@A}_3qR_5uKupm>Lfx-%rJ*!h(3@bWUvC(gZzpIxF9_zSIl1WpblQ-MO^QnE2b=~*<7X{_p6t!Am4*Oj3sWS8zk~xJL|pi%dufDFD%YX0LvuiZfIKk#5qjiJ%VGz?99?xv zDBYSd9^l!8Q6sr(PZ+5M#fShN0y`|ZLeurIRmRw%g)y`u#K#(jwrwEJjD$?><-QbN zlrJyB{5YShJ@bD0Nf80^Z_#N{HyGm&UMaEB5)+08dZY!jB9RK%vg#T)_N+r;3G55z?s|9rxo=EB0C@ktt zgmV!C0uH1$L5Px#)kf_&i1`&_-Y#~w{{(?&QFS_!t`sDaW-yXajRU(J5NNhzdb}kzmF?j5NYVd#Vd$j=?VYl9O?-BZPu6-mUZTa5 z_=Lzj7Utt&5IFo7{`)12gzfd~*9$LffXsu`G%>)tK*U}MP>aTx@`eTuK5(_7_gzmS z#(sto)31<`5iqmPL7Jn$7;0J96BQ3xP9AMpPw3B0h|iOD%t@W9~RC_da}5{j+nIa z*8X^l94zu(PWGR(Ygb-TQTc60ao{P6gNv)8qXSN$vDA!0JieGoj6jBrzLr7hUJ6A-v>U{HgmD2OZ*Fk6@to6x$-r-;7VGO=Y@<@KdhH z-dt^nto``*7L-z5<8L1-`~;BcUg7$vdHKp47;9>3ByS86WFUUsXzwURsKO>6usal$ z%)BUTn82md)N24PCuYdR+={80Sx#+j?L;dv_=q2F&b*4iOC@8t^d#N6!#Ujebitn` zm0wcS+#o7mg+kZdP5CBn`)B7FR@4uj?HKziE1rFR$ zpsfHnq+U&B$NQ(&6nwP-9tDqVX%7#f( zK(PmIg(5}tW&o5qcYA6VQN94|W~t*EKG9&Sm=1fJK%5;6?l?#fU8ljwGXE=CB{MS< zt*WO=F`%5bk~ueMQ%uRM)@*#`aX$S%a*%<9rml~mP(8;F z%8EuNl@1*1d8;VsK9~1 z*Zzt8f95Y{eaU`URPN0HPfPTT==x>oNV0PB=l*hJgC+tdFmA}4P-Q5PG>HEO3jV|$ zaA1QEPf+nIR&F8D?%a&J#mlsErFk3@G+=W#iwBOn)K2$n1accEZDgTw+TXc>j>@uk zWP}f$Qx|+yHycDXmq`7VM?!_P&88^~A zn=5zrD+exiU+f2jB^}cfU z#=Z3I1>yNW1OOEX2?<>T$9Dbq9_cgDA_SBIZJ zzXee|NG@Y2w`Vjk8nZ!Hw4YF{=Sf+eOae1Wh$1Xw4Ir?)stlJ_L;lgklOGsUtv>=* zke-YIbP>q8vc^{TvR$TAOCCx%KjU&WLf+8{WjZlX1UWyih%xvU4C0)e<9!)xF-<=? zyTPPU5++%21^D$z6z~niVh*BChSr?rA3l^dF`=jO-dB0C(}MMxs!d)VF74I8GdT5Q z&>s&4^0n}p1OwbThr}Y;~oL%qL(%}E&W0cD7Gb= zIUt^ww#rvgO@ciHuR!A+eL;LLo#H%Q-C{v-OsmH= zYP-(6Qe|L`r(tf+gh;>sLh_-M!^iawYC!xDX(6C(zuwwKAMsg=X&ou&Y|f<_XVdh8Gkz)rwq8_ixr zq3+bAQ`MndsvsM!=vux)cYC1eBO_;7o(&4FsVT!oUH=~(;yEv6UL&@IaEx<<$w0&3 z-@WpRzZ(?T85uE=7nD&sH4vl_K+dPX(v6{<-x4AE_DfjNiY>Y(BtbgAQwaH*_nlT~ zTO#6iT)<*CH+Yspn@d+<4z_8A*J=M<^*GDa_v8)uP`dQE3&r1S3)TAQyanDQKtY7` zkj?l}7HmNFhUHX}~M!4P(2yyQoSN zZLP;!6~z| zJmm{s%A~|*x8~AOwb8ebZ5E*z}IEd8qx@v{O_fqel@K(fyT+iw*aPZj7P{ zSQ@ga6k=+eRmrD!M)vlCwb_)TgL!9|)|M7`2Tb12-A*^z&``or8Z9O<+rlu#b_YKo zHjs@cBqU4}KLKNJp!PyNcbP^9$~5^~ojy@hc9DyLk3l-+NbxH+!;jOZ>tt30 z&^VZ&kU#T!Jsx}kkWM>-<|AqPR_X$rzqj$w+0z-*T&fJkp%(?`>yevjeKNOU%I->8 zh_*o9Y!t68#KaX5p>W3-f@bg-5o*M~cJv8JOE&~ERG9UD$q@A_^E$Gfnz%|K;0xLd zgOmL=q-x`VwtsGFLB$M8#IyHh;vRJ~?ARE}?jNf)jC+1&lM>(bgRHkh!2yn+-Lv*z zIyva(fWdGgS@L8lRpmBzupwM^;4RSF7Nv1CJUyY} z+s#FE-u9u`TOI5S#J+xwgd_+=wrK(mmtwGIc9xqY=5I+vz!x;6pjxw9S2vyI zp)0hbL--kB^G_&loIqZbCVI)vN8P_4iw&YVvQ!jaiEdCP73dQNLS=dvoL3Pj0)d&z zT-G%ZzX-fmP_LryTUxTdJSfzs;f*V)CwMx3V@-7j+`Cb}ro2vRyubb-6nz%K5B>i* zvVey)W*MZHG+*dcT*HxxzQ0%mxW-owp8u{)H(%PeJV1zH@s+w|*5fiwF{jH2fN<+D zJ&CgUk~s`<9pPgxyAf=r07y?eL1(|y(xHTlh<9}0t^Of}WT79V;9v*ZjY!eARhk0o zoL2>$lkDWahscQ8(XEM>KAVXHzfbUGL+5Kw(k^pfdw*t66)H^4k|z-ID9!GzQsVZ2gZ8UN{Gk%ke};RS_o;-)5j? z`0wiF;0PASPlTfYR)4Ypk#)iZYzHoIcJ&?C} z!ekO)N&l3dwh+9o&tI22qdSjxm$81fv?* zO#p|1`Fk5%X@h@A$d#!R#O)NCGf~}7uJ|BVLJo99gAe9i?&n6}{znV(#UNKO^tAm@ zp$jK)Su4a!N&WWiWdPgGTNf-s0giiVhEvwj$=YBYpI2W-w^mw7y#U7 zSTwt(QoZI|=54=QX}!9^k~{J>;(4+vlUeJFy33GkjKqW*eD%Y1KN$8HD1Q4eEy17h z^05$t^t$hF#1m&WhJeb3$avp2U>csh9NdcV3(N;qrys3l3B+a{iPn!YVVC92=r`OrdF&N?b~FJ;r-L=|lvsjJ4y_kwDs2 z5s-Nq6IieX{m-@C=|`0<2F>8OyFnpepW(DR;IWbdyi6pk4u%-p4YEgds~N3ep@2-~ zzzE!96zo65fVFpxj12otfAILY28cAl#ILmSRTn79((}bPRXU8He6&iGCI+=!e_}9; z#w~EHhImCN_W$cfNS4cE8C>vhRzDuP%!252;wW3-tSleq&TTaDtlrZVf&PX|`L|}W z<&iPu5+Q-`)*_Fi^v@0a_WPuvmq`8t4r1kWI}lypfpF>IwuYaVQ8UmGfgz|Lzc&G` z9LJs%t^zec(vsO~#_eF(f7+q?qH6m^DY|B2q50aKBkH9pHy@wl*4t$c%d%~4ZPf5) zg64Z9;rf{YPs-5bB=c$Numv#Q+d~LTI54!dwCkf+s|CZAHlt6ae1d z$b&@8BRgu<6!d<@U*`JX8s4w{DOFEC2q_W4kgWcNn_KbAbuc4wDH_$k>gClCQjz2O@YpMs{!8NQZhtB21A zr(M;Nxr7fF@pjPfqoXinQ2QZ_o3ryhaJi^3ov$RfBA}+-;}@@tpAGZtLHOk_9n)i5 zL8b#8IfSd4J)$5$!F%9jq>%+b4`H%R)tMb9XR8n-0VQbzCNj@q6!s68mASojQ!2FP zYR?$mA_HyywXdDFQefk%Y=gNW`YilU+{#l!utQPRxQtz@tZ?`4UBlrVnP(SoMWMV) z6Zd&`_)mL5V=ukb850xp`K5A#A_iz19hN%`4}Oio!UQ~>c~BA!fudD4cqseMS(TTz z_}KJ>fa8J)tVR`KE5d(18?(wQ>jnneyZAx%$h*O75_>Jy!7FO0vLl@mqAY!|W z!0&*>-z=?8agSZMe$l9uyrIeObeWIU6i#S&!gBwXo+(LlW7ryip6tc+B`JAN@+@(n zCy+44VFOd~^LgEz$twzna^t_pyY3qwJMBT0Ibza@iu=@z2yY?RDrUizn^SO@2BIhx=0-vRZgOw9{Iy!WS+#n$#87S}RQGBn)3I=dW zznY+EcL!fi(H{02xWI7dccVUNP~X{^3b(M>l5`G4LXH5X{&E%OyB%iKs-+HFPc9rE zrC96DS;liP`PpQZ`@@sb9~=noA29s9-2WrpN$yBHI`B54F`x|8xPnW@g0h++X zVi1EZ@I6Mxm@U!6(h5Co-BAfnvO=7}IpN^>$LoGcLy%DzcJQk)MxS?7xuYlLTHkLV zJqD0cIubc_hK7a~in%UvR*v=TiCyJ-qZaxa! z0sWQ*;^X5N&W`3gkrbQ?{#Usf==ho2H!$fF`@0+l-hbT;kj@)Sxr(awjw%z{?)_|9 ze(&m(cA;UT<20~8Y6foqyVz{=d=pr6Bn=VFr9o=bu06^^g9>~YD*Lt#LeEhP6dN0% zLJTd>|67kY2stSIIA1Ph*xDt-#|1(rPa-)_sA=_19`upkqkvYp(lr%|s&s47-W6jg z_lN)i%Mf&8thSj6Mn=>zV6T(D}+*SjuFyy+ZF1r5IokpBc+VcK# zKBXosxL%GHL=JIMV8D}e;;2(vL+F}*iu3C`_x|VW(x700p**5kL(STkBicE{4-gSn zK(G!n$S;3&OtcNvdM!kZzbXOEZ^(aPC)jM}(QQn^gGQ}z5UkMwKO)1NQJeF2fS?Dv z9?^A^|9y;Svmdm^O4AWGAK zh942q!Z*LCu@OE5(SxhtN(%#PugyPsZjK0BEZsy_)N<);zav(F8xJCo=PxOWH~X__ zX*O+(G)_KY6!vXTnKra3csJ%H(FRK3I7&Z&$NMyH@l#zh8{KYDPTl}BONY*K#h*O3 zts>zhaI68Xsco_1X8P)UZSF&bon1MNArmJJ_y76fy`kB4nH@ZPI z2Vb*Ry~4xAv(iTf5H@&xu#9VJN0;|f#(oESRB(daid(l)*m;ec%0)p_VN`7;o31W* z++agr_FUC5Tj@>Y%xb*hS>xWZ<0G<{f4|L`_;i8>OuX{-eo_J zsF-&YBt7u=ts6DVmWwWo!3JV2*&VILx4UQEgUe}~q4!%4Wy;tYPokx{&uPJ~ADJfP1h_FoXyhVl>-+|b{&@=Y5gj%Xa|J($hxLwxI{O^Nl-~;4x5f??7s=Ccjke$zg?_xv3RYwf?Ju`uq{@ z;3o!tE-(SrX(w|5%PO$Nc~#?Ve0aQww2e^eK`3@&v3aAKq!wWw-~x8nNte7@g;%2h zNjE?=*eiqgXcr^Zsj zcLjym$Ti0@DB1u1u3#q?g;QinlXEa}4>&Dq9j9v#Ec5Cml zuz6rX!7fRMK%?2Bn#Ne3o$-4f3RFv9*omK2r>i@ ze2BRMW&jxZ?@v{x$8~|)&R1Pdh^a&T8;dw-Kf<=}uU5GQ*fsuP{7_i9i+F-U6l@2q zV_IpkRs|;FL2Yn{bSJ6lFPLD^qk&KxL6L29|D}U^j9x=!WzkH$1d`5u)@eFYAEz>s?G6JLeXG0|<6iK>3gLEaDH=c!4EQdKPatQ;iSCjV(2e z2OOx^vlQjlQ#^g?!Z#s+Vt1TQ!5175FNo98sEZnjn(i@MbR8g^a<_2a^>&h>Fct~XhQt9E8L{e?DgXoh%PTrZE;zkXONyx$kVYk9B>8NKi+k%(UCeB^RZ{6bd z+&?I-9q8%#GFIqptjP@cf4ZS_@d&@O_Y>`)QPmPyNENH6ZiA)1|YTwYO!wr z5db<;77KsKV?y)Xj(^c0mAnCmEDM|be2A7qG-&xjey^i6Xo1UStoc^@=teZZ)TZa|Kcpl=G~q zil78Me$zu^hIpbPcv|wD|BvNOqpsSQEO0)($%e^Q^!f21$~V2!awwr5hubN`?`;8v z&8vBd)wMyx8XaH*%2Ok+x@mip+Z8Q@L>6#8r~*8^yZVO+sG`Tj-3d@T3J&NlivFag zGVajr>8<7!I-@Og)%CuwpT&X5Z;}eROIdrHUHuA*ieHx6!_iO|QyQ0CByR`|1cT?% zlP20Lh*=-fH2{%n7M!|kw;N9%t20=W%>WfN>b7_k!czr*_+#LrZNnf61(ACH2+QL7 z6M@SsPqWm@2H0%TaL66^fQ}d{==8$?vHm`Nb8$OEGiK#Q|FkZ_lQW6;SdkJB&fWe_ zDmnu1c(J(VA$I5PaWqWfYAC=(6W|dKaAmlSND(Q79d4K=)pPTYW46{smfcFt9%8yL z?kPx&Q`PiF!4^jF4O&QCcAgFv+QRZ?K72ULqowm6DzDDOywn2N196)^n)AAP@tNoP zq1l8%-DK%whDh0s@7pJ(G2q~*VPI-%y4ZhjKJZy-CqywLw+``HNC+`97S0~CMC@mc z+d>EttdTl6jCMyOAn)&d6hSwP4CNz+(X#^76fQ*n?zEzOuZqntHawh3U-_j~nbfel zZWipzssU5X|KvRy82 z1H9r$_e{qR1NAlkk(`POA(Af*|C^hei%v*LNGj^dcY|HiH{^;?FD)^cCj4Vge;W|I z+IiaXVj@Ba1Ana)YFNrgMJl=PvhmyV*SVc$?G-2?N~LUugRhmU8FXV$?b2g?1gle# zkCihy30TRE_&`qJ6=e4R*K7IN-=8zfS?XSm-$;ZR94az)p5Q9z1H#G#bI5vK*3;>C zOSniR7cixG9iL>PYbaZ#g|cM%pG1aEnsD#f8p5qiX3#h#;#_fk?$H;riTYVs*hZplbU*#GqmMs*|4wfh_RtK|{?u_Pu`sv0mIh=ekJc z3R#;76zn#TFlQzQ%?~`smw_*3YBsEv7B}Zv`!EVaeZ{;QVZ1Ea_t~jp=VZMZ=IOQ5 z(~%MmzXO)^^2PjIcP^)@;UWpxA1*FtK5f5e92ARKo)7#?QG1W`+CBfAT89Z0EX!}& zH7-`g(|#^Ga#b3Iav zCO`d~AO(NN;TA41G?FZMa3JQn8${1m&S?`FD{8)VoxCK+y@f)JP3KBP9|K$4P3)$c zOgnOQgTwlYB0+b$VFLEovY}#eMP2ok{iEKyC+PVl;uuDcZu4`~~>ri-b#moT_2qT8*Xlyqg+Y zG(FqC^amFM^Zy>8RZ01(v3;38MrBxyIVCCV z?eE;zgf1~S5qn8;2R}H|Z#`}EUgI98tk14OXA>ILuSvMhLaP;X_qkR4tj~|Zc40A6 zW)f#h;!h@z$r+ZESn&IqQ@Mm)D6Iuv9$?=hm^ZOLYQy5oQ_0DMyQZ>XN|oAZtY0VS z$Pk;XQeY7LE|rJ#mB__H{ggnQQ>@2${wVG9pR`nx>+>1XKcm&wo3#Ni8n?2nN`Y^I7t2UWb(TuuSdl2{rx#^Paf&F-1Un|WUrXU+sVkSWyaOB`XurHPcv@P! zcZ_cuzN+weyjdivq>xGVemU*B!UvV|9|jv6g|cIcvEoSye2UT^?r^AJsr%m3BY*n+ zc#Nq?{6*Zbo~4lmb*2;#PIp)l)5wKN4pS;RMbk;(O*^sIUwh(z+_hPGeKm&lfn!p~ zNMC&QK~K5og^R}R+L9{t-mdz*C@ZGQ;cop#$?UABrOyO}gm~=cMD!Z+hR|#zoDSxc zw#2-{3qI0p{+cq@D9^U;U{ei>adEg)P3z|Blq!&P>HM1&hZx4YcFUjDj!WSR8YT~? z%V{M^+TZ1VWE$(>xSJ#!Dv$q>_d`5iu?o(*wOPad)gO}youP>dvI-q^S}}qJe?M0} z(>ZV`@OXu}(xs7CUdbl?;W2Z(rutUiAH4FTKJPgU29e|kh9*%CC2fybi0kveJ_-fj zon`Q^sDGgl6I_X*Jo}Q7!)hw#+X(UKaD7o?R~s=7!&FZdW|mSM4$AtlRw|H{^_gJB zl8XuY5#EDxTBqSelG8ykYIrj+9JQ8ysa;xrvGP`hR#iqfx2>(~^sKP0nrXk zM8N7Yc^6J!*hG7I-_td1xm{CwHlCX{WM!5<8wPUu9VA9ct_r!*d9+_Ik#Ms;OX9pO zH-1&mJw*S$eFM)3bN3|j&w~`IrElxI{bU^GwoL0K=_s+VjejTTtiv6%rZsgh|Izl` z{S9fnDs6n<5k6t~``rtv846V_I3Q70+IOhZR+g8)LPbTDCgzh= zkN?RH`Na8YBYJlcRv}s5Nx~$OjyjX6Qh9Gt%$I|;3QbN&YdTx9N%_G_%6LvU zq)2v>7f!MKCK*;c>J+=0<%E=~5}%bm`y5Mw0dJqCwltOm@#3o3T#m+QAmfV&lZ)R82z@ zJzYo1L3s@m%9dxRSCsPha2A?9>|V@c#t45BEVi8S);(m?qEXccroi+0M$y8jrDiLK z%W9?CL8xe!-NPL`ub+Njx+n^NZiX{?VIS7AoR#1JwlC4L7`ixJwKf`eGv zbPL?nCli^!yN)Om%{pqwz<5~CUp9~7)~#F9mCswjAuUC8EPdWn{dLwb@_^*vq<*pU zz&%#;vBl*#BDQEdHsP$ka8Ej5kZHIv=^*0qqqXGQsf8wOeDcoyewUyajKmdfW9=*> zp>fr}NhUJm;I}F>xg4im$~EIn;#;E3W@Ks2-1|}{_1AopIC3lB>Z>AbO&(@Y+`#U6 zub%y4N0&nh_p1t=^~#xIqE)sG`a3tX>OjR)3z z4g3AAjUT%uN<%+y!p@K=j#_Muj0}A?1N(-?kX7;K$9Jx8Czn4kut{t4+`DJK(Ci1f zKPiGvW{X0FN)79l>Q^Kt#)(LdB@Z8uUWfaZ+Ud9P6&kj3%WPPLtqI?SeG3(3vCdGw z)ukoLu@CBX&TB-yhwi_nCbYGCG3UPpIW(64o?@UhInH#quwVv%yw<5HGC4W9jtDX- zd3pKrL9eruyPTYy)-%=0SXF%@mEe9dLrw5vR{oQ@6V{zOFVdL;z;f0>%IXngC2o8F-z=wF`eAkTW&^hOJ7sHb5 za!kKaWm5k&{qhwj=vu*};wYLgk51=j%A>W0i)5cGs+BMa(u|}@W*cZWm7t0ymlZSv z6|JV=$-y>uQNO!;J5v@ z(5FwI##do3ghW!$efCPSJm|uS#YrXc5)BPifdGLkreP}`d0FuC428aTd-QXn$Ip+~ zTeEs1u4S{(&?tVVn1j>8Bqi2%O-Um#IG6`?cp@SqAz@)TP?pzz`ebYvWAAzF^ZDgj zDm-Rh_$$-ZHg7Bsy8A|dk$%ZYQ{10kTOX{wn7xh5ODDv~{OUQOc{fh>^}2{w3!RA! zd^y{X7N-p7R7Wm3EB?0DZS+1KWa<;#-tcdPKlpZDOLxbTFt8VR6FrBS<<`qes{MW3 zbWHljp^Nd&ujQ%AJ%Ls6A=;JnTAr+EQ`Tfk&3Z4z7eC?MlAG&BT5^*4}n z|9reTno2YksJY;xPTJSkN8~-{kAd5jDK3tILoA=*X+kpX&qyy4OLE)1asOw7 z&j$~rufeO$?J(u=>;$XgEb!($--8ATc8OfClh-8F0hIN`Z-BD#kqD3h7XmTt+cV<^6U<>E)H@T zJ)f7v6mj0D5H5pMip11|AyyF)3UTo!=~A$?1R+mZRn?e?M!AW?m)2H~-`}wh4-c_3 zoX-Dx9BzH%a_i1eF*eD10~VUbUF!KNwt!OkjL|m?_-Yc zZo_DSZbaNfM!E9@3FD!Qa;5d;@^#I4TV}0nssF8Bjz;ITt@V_jch%MS!XZJ>2&+RG zM@_omL6yy$Ao*A*PwHQ&N2XW}?hLI`zT))!TIspghTHi*WZx%5!Ion}CiQs3EdY3A z!$n%W{mcQSrCe}Vts^5mW2{*AXD2T8Zrd3@tH2=%?c*D#j!>%*`s%BRO-~iD6V*!7!ezd%y|Ca{Y*f5T1aPMdn%27)wnJHHFiv@ppm>&4 zQNarxgVKKCv(KLQtryxtPq#0wid1R$gMazY@#0%6y}d7QWh&|nQ8etl?rhxdHhkW5 zFR~|K^Vti~D{Kl~MSbedpw+IU@M^!Sd2K({=zmpU>7hL6V^<$~M0s?73Q=)4!Eg%j@we z=E&mUzT~%5mPq= zL)5)I6T-d0j-nP2NCuvMLIBp)a`)XOwSp&RZ#gW^PXu!2mJ53{lgW*>^XlQuM6N4P zOhmS+lv3;C5&0`lvF|aD?r*S1UtGV*H$~}{C#76LrDrNLYanL?vA?aLqP>EP`_);# z+dyu5D3PT1P?v#+mAsP&qd=qjnyg2Ufp%67^6Z7eNARzBeQ)vQe;Dz1nc8Q&K{8V# zij(Z$6SLOm+2CI~{l zNjt#Dvq-y&wR@F?7-wsCxbgXX{vBT4T&)T-c0(+j#c&P=tom*bU?r~sUuHa} z!n{hw{)Xe-eWQO32j}cdznJXUISxt`y@hl=bOOl<@8wty2+7qsS{9X}HOu#KzGB@~ ztZcX(x%E>@qs!pc7JVW~_9APdj|hSH@pU3oJ>+uS$Ml;G$G&p`~>}U zU@l*MlP5Is!rqv!ZZ9FhPmpp5R>MH<1lPwSAZupk`sZqcbs1DWUx|E)$RFdRbDS6g zcEx)J23|L-DL%E9JUKcpFsGIw5C-imjE@piCf06cPiVBK=bhnh0A|)Xm1|djDON-JW(t?DFh%{0vA|Ob2m%tgfYrXrt z@A+`fhqIsGe%2$xpF6H=&N0Urb6&5tx{xm6{ynE%B7*VOtjekKcRM&VCMPG+5%CyO z?Mub9JJ@(an}72Lop6>D;wkgCo%@wHRPHM+_jK3@7U(G5!KmKOn}H^`^e7Z=+e;`I}FYkv4^lzRlL zk{9S$E{Ew}w;RT#)dDqpVtDHsn5-Q528fv?EAKDL8ZG}`s9iayB zZAN}c@zPy#6u`e5`}s2k@5M4q#QyjGf^mOAh51IJiEL3q{JJE1SEIi_7Cgx=;^bTB z8`sct-hFNdI1%fa0J8SlQ{4)O7ysnPh^KD*_h5nwKk9ZjcRKEPqbD`~-XWu8Ag$T# z$(bJVkw!oUuf>u2G=^3UXU@gmK(k{$G=FD;3eAs-j7s8Eaq!D9xk_jK+;ioba&x_g z`>_Hl&5qnxrLL^G7i~jc9QyYW(t9s{-84N-veA9tbNUNQ)&N30M_DeT#T%mINUPK# z2m?;5ehwLesMWlb4G@%gE!ZP5SAYF?bg9GKLzj+Yw&p5Qxx&LcLjDf0YJH|GTFv=M z_qSjoc=6-cL#bQo#!`(LWu1%w$u9Bn@#UNS2u9vke0%Sf+sXh%!@^)gG^JsT zN*M;b%ge5hEjTxI=dS;v$)%u zyx*wyPUNGrTrr7{K97yuw2KXhb>&SD*&9yYDb>8Y?3PBtS@W@o}qAM2<|!Rnq5{pwc$VVIjD}b_NW|{jZ2^38s;(`+SXNSQ*!|gNxqQF= zuY~CNyOYme#30atSUL9}Vx`o7Xv^`A1cBtwUcuzT*(^sTGuhsrFPYnG7hhiex^?M) zxFl6tN=A(Y;=a8#X|KP{PU#rq{uZ~TcdXMcw21wjx6AG_j0OMLR3U5h{=t^|U$zg= z$*}1g>n9etG{?psuiH~-a;jb64WHTf2cDigo*p~SNd%S%`eLheI`E~>$&5gi+#j){{Ym#bP`5YQQP>ZU8zML zYK7aHD1r+ZW(Jj!rr6`JTF{s~Hh%@Nsxp`Urs-J@jf6|Y`$X}sNU5=$StcN)6jDgw|C&7n(E<= z>C}!ftGw*awt|#WgR|0mmD^O1gie0`{5h&MWAnSq!)Mc9PS$xl(TA_UX%SC%jj9hn zOYP;AR+1DhzUxr7#i3Ex3tvs~2~q7VGM>RJoV7|~Kv=v=N=oJQEqfpQ7;8FAlYGUD zG5CA@wM07bQVeOC>dw95?m5HQ7uX5W@mxfzW4cIDZyu`SC=M<}a$h^9VYvRZ7%P`LBwxk57!Qa)N+o;okj zcENUr;kL!)R!!N3&rkKTT?H~VIzYYTRDBXTv~hBzvAajGvD`XC_t;NGX2ZzhW@j{b zOE2F)Oftt8TP?)NGjdww#^IU8hX?M47bI%#>i81Gbe0U0S$khpyZc> zYke#1wFko7IX!39zZP5Vs38ry6`C<985rJ9vb+%A|LId|LHK@Wmq*W!Puxg*%5fSO zzN4or{LjK%k?y@eElm4DNyY4}KeUvGo#CdS6#n-5SE_RPzH13VNG<1n%>EXvX=*zh zf2w_Okk6v)c$j1rK;&B&jkPnoE>5Znd9LU%jjp$fWIJlPeF~Ba!+w2w{jLb zn@juc17YHoAkShI&lBtKO=UCjf!!{4_T_zT=d6LsxR|P!9`)y>+3}MR2p*p@s5P^z zKc%iXT8)>hgKdA|5_`b9B;^Yyj-1UK+K|BMTpH!yLIB!W6uxMhI)?R}Wz_i<+%uyb zdY4w+&aS1mE)})?BTI9+c}->8eUnmw|M;fRGiLsdX=1ZCSh$YnJtB|f%xB8-t~VGc zH=Q?~5TDt4@yJ=~NgEY`H!aVwe8QAfNu+Py_kG4mu41;gx!mm{-ox*sS@MAi+Em=; zBRZ$DFH3Mov}np@`|oo&Nvm!mp3B(3de3~@r__9%o_l{zeJfjk!^J=79k-v8V<5e< zbI;5G70;ckAx`ZV9sXVpQHznDo!8WVx!hYEWp?1#A8rLUiGl-}n7odV?Gcc+mF~K& z!^>}~6BUhRw>Q*pb3eV}C~tD|8WUGy2`|mP`)2~G!>a$THd(3uZ9DbVnoBXb5Xs}U zyd_zdhE?Sn`50Ms-2BC~3U@GH4_z*jL#Z=l=w?~hft9|0%&Iz)_^mN4R)RX?E`R5H+RpM3AmC7TO!3vFi zZ&>*@6xB$@tE!MJTYXhvRQak|x9e9Y8+FOvYvU~+UfjG{ zpwn}Il#arp;yq0Ly5(8B#8sd8)m>0yEjQ^CC;z#1yQkRfm{4`d8T!TL?Y}t+rk0pQ zR1YnphjD%Su{X!oW#=Jsl2Z$oBHf*j?<;dNw0!mxQVU{=-oanGuAVccnLBVjJ|?_S zlx-Q%WO1>RzfjuOtT0t5n2L4AX*Jy4t~xG?Gbf%M1*f$6-B-%Vo#{#pwvuT)pE4b2 z%cl-EcJ$lNxE*>Q92-G7J{WUnzlOP}bYL;J`e%|jClWN|;cUq`uBL1YJ`GTj17wx2HBO)}sb)gv<5M@mB z+OCi@b8|g(sW0{KC>LxuU;TU1orAHmE}EZ1DS7=ftOwQkDk*m~>HlrX=~-eV{EsIc(qU3g! zXWEx374$2|JHV7hr1Q<0IdWGwmJg%r-->O&c~1?|8#Z`ghOw`QC$5^1t+a@%{-Crb zR;yO(qA_cA@k{*&{~FcAU_;L@Jb1#lM`J|C1vI?h$@LJV%DxGCZPOL`smkN(gJCki zghCLWj&98Eyam-l;}n*!&>6p9*R7sJbB9H_r#wyHlW=6`&Yfqp-abP|LEgx)dAW&t zoMxCW^GeJM(=n2h9D*VBzv>@hB{>dKd}#~Ii7wfkHUIT};Dlq{aEY&QB5UjA!oPx} zrhfbPUvL>Y6n!P1L+Dx1;UMy~_VX@pa}>rFcB>q%9GX0RN=(N2n>A0W=2jbO3fd0& zC;1T-%H*dG+#!8$Y1-JkFmE0_@c4CBNa;ZhFNAja!xs+?Raf0`M9$}`vgUT{BFp`x zWhj7J{*`Wp8_B+S=LfybPYmM{P7QZz);w)OSk;hLWMW2e_&P^Mu#aLZA8jpFDdqJv zGreTTSVT0HSVEOww|afm)~D`*Uixx9ku_0)F*j~$h~k=UV#ip8<%Md*&p(Z~8WY`K zv-s7YZ&XqA<~_}3xYz*s_zaOU z_wnjyg|jx z$o;PnbALYVEitur>^M#1k~`g2z2z6(ZRSE+y9$mlx~gkvXRjZcB z@*8hl-uIxVBW`dqgumwUbzHNJufzSlo3BE4J3E~Gk$WVZt{+f`dGAeblh0lG*)a!4 zGTqi!F<$%lgF`lC(+nqzlRR=BO^%0Ne0l(jAI6G1tm90`- zIo|e~O;xrmagJ@91ALfHYQLX~-ncP`(>`n+%~9>9b*AoRD)-Sob!jr2X}NUe*(Yv1 zcdqQ|2!8#g8`2-=+f#}sCvMnD9WjP`rfa`jaAU^4tGRu33v^e)PVv^h@b$e#ERgiz z3t`htuqM=>oYW&LVB`6b>)hCNdm>S`OQBm977353d^wmkW(Jbw;$>_4L}y&7 zKQ&1y+6iO{^gWI-T-%eljeW29YlYdN@TW25Gy80gFdg$Zc^Kq#5n^6S=U7Jjd68A2 zlU++x@LRP0t&GXYU?*o&e*fVEL-)6$9=E;kaaTD-Mc*##Ai9PdMjBKH ze0^tcnXZ;CnZsGhKfAu-q818&l(YkNa?aYJ0?9NQwOd3gD&#u^z{8?yTpkr@st-mnp9_a19Jk0-ay9fT4fy?`*vN!TiAKM8aYC6b zOjdWU0xaWuoYb)b?|bBVgPk269iE0Y)ZduRS>(WXz%yj zO)pETuw6;F(s^t9lc_fO4M|YpuJjgbW{2MjJEIW{A~NK5Q(O+z5Rp2qVrx$>qgt45 zjDYJ=wdPy=ih8(h7rYK@R~qAm;vtF5;20$NBuR3ta^m=PjW>E*Abl!lsQTlf-kluj z@2{NxU74ovYA3I0hhbZ)!i*T@Io)#Uh;!QatgSz`w}%zm3>~JVtU=@M$j>4Uk^=`0 zI5;_N?VXxxf+%TaXP2y+r3s=~%qF|HSMQW;j5wMrh|j~(QN0sC-2=B*I4!VYvezr? zfm)Zj27bIy;jyMM%*-TC6|6@dz={aDWN(3cK=E&c^Tl~gT09;+AR+nt_wR8*mkY07 zzgA9H_Sw0g>Wxv|F%q}GGdFOF^>A%ca_W(gG{s#p03&kY(j}z|H&?gKb$do81G(!; z8SQ5@ay>xe_Wk)Ig8WFyd-v$?-@mW+y%M#vv(tKe?;)zqzTV#VFsASd1&aAoop)X| z19nK!&-+kMedwK;-R}-5nhTp($Ck))7+%R$R0_KUB+4fHA1MN9_3q&K(*a}sp4I1_ zZL$SF)Pl)_$)BCi-TFd1Fu{+G=F*)0a@V%YE{9$$tOXviHKjgZ6Vq|ZqukfWhjD(U z=4e=K;pkCdml_L`kF`Rl4OH6Bw~sPtFlcPSN%dX7ewnnT9!faGRvw!h@}fC(!khd^ zf_p{Ns+hnVuCzZ?%nD!6&qtR(B{avn_{WitqhH9gyh;ClUPVljDBPwdC3B^xe`!1M z%5XU^jw`1rdg2A1b}GZtL=RaUWMZ_DQ(1JE`bZ}AVP@&8Tb1sY)(_BK7GGaofQySX zOS3>9%VGPjT{eB6_Q0=SOPo$PGjlCTaR!b{eLcPR=*szCA!Jvw@LLiO(Jxw@n^f@|6BRF)8Q7#h>n_utjPp@qjp_3A# zIJarX8Bi1fNF9!IgGS5Kze%1SOBGLd`Un=IG zG#pJ=ikWtK6zRk8w7QVdIY?6dN0IoVBP;RVgTCf&)$5#knxQl;zvld3Zw6DfoMQ-0#&ZCWFs@3@1;Dm^z|c zvNnQ)-g5ukXa7z)t=cDwEi92GvnNkyU#UWIMlMuzD%x`oaF{;)BAF{eaO_sQb7xj5-saDjg!^Sq{#3l*aR7iPDLHZS+S}q{1$YLr zxck=|!`M`O!HvI^XGzt_JB4=EeaAUTAl!kp+A_VcFfcn%ciDX-1_B*9J3D)>QQc=G z9N<3cPK7sGr||{J?r6!)s?%%{&e#r9R}DPoGY1c&X+Y%V=q7!CA>;*-Zmg6f1vb zX=w;a=PyPw{;3Vyi~su^NZ*7;(qh*&zUn~w>iT*=oGPVnXy}2*f@sycYU8vpN{$s8 zxbz(@=Db2BLV(n~a*`dy zg}&->Dym$ov2(KnHC4Y(TOQBrmCxOORHOqX z6{pt%K^kM>LoM8kLCzv|J*AEz2ryrhWvFStDf5F;VjS)$JW0(wM)s@C?^!)!G%=9~pt*<$MSK-z;7`u$A3gUT zp`#0^sBnL|ur_5{omCVV5b(LWde;L?bosF z+g&P?5V@v2!*JrniS82j0!@F(q>nv4^ach7L}m)0z2sF;zzds3s&cv<)mK8Te`h!G z5(DJ^;FPit3uaX`8bwYQd8rD2mlV-gy7g4|(Q76svmGw}S>YcHlFP8Hgf1j=S*?r9 z?~g;bdkr=?$?~tpUAptg0^j6dRk$6f6+7qU*Nh!+cDBZ1ZmN{bMDZ&L47>vr#9XsqyuT zAP1**8>Y8?lw2OV5wwKdPo;b|=%sT?`;?7XL_#a~85Gd(?73}+<*~r8z1Auw4EDf> zOlQuVxwZn0brJx3Wi~P|Xk}`qu=9rG<#R&%f9P}e2PaI#nrMHc%c#{>ESMv`YN8*O zK4AMB9|oM!*Vh+3ynvl(;xxV`Gb_skaY8NIol_M^1drWI2!3VHMQv@_N2aU`6WbkoSwz2i_3b23h7T%JUu;qyo2Qi z%!e&b6-ZqDV`FuZ+_!^Ww8JDtk#knyGAh3Jdn=A(dwGLwB9DJ|s7Vg(*!}G@XgeSO zk+;j#)RgdG!IT2a1PwA$kNYO=-ly(?#;R;#Gkh=nZgSA01wR-E{dyidt9fs__Zl#g zOJtUL`>D(|Q!X(tNxQfbg5e4ND|jt=b(Fit*PS5IZJlrC4$!S^Z?1A>2N2MreagMQ zB%0RkxFeVx2_xuPxuGbIV}5?g{v1h2c)Z>>*6foNa?cd*yPndRi7yl(y_Ee(m}z*v z?pR#K#?>^%r0@Bza;20kX`;D|j=|)6VD@rBX9-7)pMEV*&&TFwy$5nnqJCn^`B1FswR-e#hi3&;r)#^i55r zGE}n;pKVQ7Vc%FC>VBT)yy)+DRua9>#>frm|2s#;D)16_fhlt0g>c7&Kb+lBI!3` zg2G#1Bsi`Bhv0GvNK0cS_1vnftK0BIQR@V;7t_Z0bL%r1t%_y-nzjb98)&y&nyHE-V3E|-bs$VPZjpsEzhU6$6&F7CR z;IbQ6hbJ&(c*`2&Hp+?0#B869LRkyP}NP=URBMdtSge(Wm z^;p6r2MpFr+SQNRiH8VCsGgL|6|TtB$K>>Ve+!S0JT{&j@E8g)bZ_KJk8?Z8o-b7+ zsI*C*;QMkYaiYsj#F}p@T^}q~t$8?w%){AdjOQUw&WpNE=?BJ4Uy1%2l(^ z-95g2^f==>{ozSxfALLk4Y&7NQxOMsD%@h>$vf?2jz&t7=h4xobWbnoaOW(H;w2Kb zh{dmx^S2vmCcDe(KzDj8rX@lLGLd)w_2ees^S1g7v@NtcepD5%R3YqU#g)-P4GBt7 zwmMrNrl1^UE|#upDOV|xNvLpH$u)i~oTMlLCyvSYHw=$=?*I7Z3(MCbzvJg00l^aR zj!wu8;%@T2-yeZUn18Hg`zItYAQ2d2h!U}s5v+h8)KQ>%d3hZ@r}Z2NkqehMh<$ zysrt@cfrzB?7j(#mLdh|Li#!9L$DP&7QX~=kmrZ_kyZ_?wDafBqYjz_7~8@LcPJv7 z_tU3)2L}hyDKNY6P1j~~!<9t9Zu|?Nk4bwvIf_O&LLS2-x%Cd8Wfywl@f9M|0mpD+ zv7x^xO3yyT_ZK!-@ zU0n4}M6hdepU92s>0k9_F(n)Or1sh@(qEQF;A+XV2qp(WZ=TBJ6qhagoY=Um8{`ufwu^Og$l1~4gCr!~O zYQw(Nz3veRD$+6lo1PT1T0WCU~Cg7B5OAyQ@8ir%wnu>7P{hb#`P<50hjulyva+I(p?VgJh}Bfh*Su9g&wO>tXK-`f zRrYmkyvkBf`x>RX1glQz1wdN|XJ;iWOcFXlmnX2U1HA63rN3zHQDoUOi}t`fbWi>K zKDD(etEtGUutttL!zR?_*7Ygco+0#{euEdQHp_4ecZ0Fcx)7Md^Nnh%f-ijb)#YE^ zam+%n=3TPN59|4zt|=4xGh}B?H0sXj6mQ0lvRBNlGG*Krl@x1V-*$audCu6vVGS)J zp=I6yimx^A)3(d!3LCndI(5pdr+nKtfpc2rc2Dj4`Eng4cd>=~RitJ6rH^H2#*wo| zQXWt9HEyoIGA`gsmZVq{#cOKYL8MK_eqbzmZ!-F1r0lNZA(@}?c5f_O6=w%{o-VD$ z1lzeaXPKwIV&XM_mN{$xETKY*o0}Uy{Tckm?%lfq=^nrwhxGqcr1E-}DmNNf8)Gkd z!zj|)8UixwYmPy+ZR&c2)a!pau+#6?d*YF>2Svo@IV%-bS_+Jn`o5B90e^nD^-&o9 zhpX@=$sxofd_BBChh5!F)V0B{Z6*0^85EEh!LkRc*;`z z`#=MOO57a_%4<{y2@274r9O@1Q%e__9|sbieSSXU;zBVm9pT+MN|v`V;S{AmTbM66 zo9VeGA=8d*e#`C{a4}qZ?@37DQF)L0Zf0(d2_Hrv5E$@WTE>h#(#jzRzQ|iU2I!Ke zfAhiqkWQt1SzXHZBYk^4R<%;pvg6DIT=qZM$clZ=nM!_svX_ zNz8Y)s!dr@SNNo4cPnwq@hw_v%T#sxSYBZF5RZKS{wnb{!Dw-wKW{u~YrndGeM?fy zi7F|l{x6wle%01$Z0RgIwP)Xflsb5BxHSX8JeLM zSkH}&*!b}M=$D$WtrV;Zj1zpn$r;)AkdnT{_!v0fsUKh*V6NVWt@|o+?BmBf%HY0$;X&WnCeph8wrmQut6{C%_g^g8C<#Mxu#k={u1V%w@SKI-$wQpk%S)_2 zrs%o(&%#?+b!kc|sftOY)r%IvY30HkRk7Xkx|igfX4=2-qL&PH$hpgl=0T0@42nTv zVdQwYdMK`dP>{qPB2|%`0ODQA)lTy354?nN&t6zuIkjSsdpCJ=|LD&)*+lZM!@Cqm;|E>t|5Y2z6-fzB09ngHCDC0D#0IL0Qg>wzE?e zP1g3&Mo5e#cDyZDjWlQk zH--F5oCOTXO!h5Xw>n9KVlmcrx@Kv06QodyZR-h@H!gLDsNOA9ErrpeC`84nnO9tP zkZ}PZDG#&6H#XKRrKP36=iMd?2$1c1x?CF4(7!0ykhwN1xl48D66e(#GE+Lmq-uQ> z5#I3OYFww{HdS`ERvTmG&h`+$4)jVqAK6%eL$tIFO;VSKL0*5j6~tQZ?U%k++hJ|- zEO*4@3(3HWWrMjLBw`09>k@2=SoL(dV@*$s3=RSlr=*+)#&_9GnmqJp0xq1G)L}X2 zsm-Z}`y#EBwNG+nQ76J;1Sc^mnrwFNJN`g2>819&e*|kwl2l5q>28L@iY!NgP`oj= zO##gcae}Ur>)G$}HcAeWhM2|MawPev$r>Bv_L~8K#WPCO{=WaFXLr@7bEasrSD%W)FC~o>H zQ3wEWA`K*hpF<%@;wgL$Q0JoSDi1}r)~k(gR|dp2f-+Cb#U!u^C;P1=xC`&;n5 zzh*9Q_UrG{wSJzAH#O<;MK4mwuZSh7oz1Pz2SuGO+V)COCtCKD8?_`#5I7l#Ivfxe z?L9>QljC&n7DApw>ORa#{{!@2wr<6C=R=4)`_yzv)vhdg!y9}_Q5Uu1m;n6sGbA)Gs#h$L>>uSu~D{|a(sU^*_U{Kqs5>eWXcNK@M zE{ydDlCw_j3ngw?;G&~NpB)3J^1;n5*&Gx3|lkCB#G=lgrVguOx4=Bl_XVedJ zaB!ed7+$KB-a5x8`R2pB7M^#5`JI~w{7y3`4Qxo!?^H0fpW<2Eyzy0doa1?tYldOO zvZJHpc{Dv^+LnZa!vzuuLB_+)3G)*_BVfHtG-~J0$2xywJEFL{I1$~g^7WrKYW->k zRYKp$NaWzc1^121f`WoDOdkTTMQEUd*=c*L*gE=`nK=ffZEkmt3l%R6^O{$iC&G&& zJ**vg*fX;~Y;#j#VUvPlx3!1UU}%fN{eBmtg2qhOm04!=W9R`_Ew5Fd(=N!(eF-9m zpqdUdOC~y%aqq1moxpPqkBz0JIH%Pcq3d=FRXWWTS5Jf6S4LVgNeu*4nBohV4{mZB zByLt7wbwsBv^wA5Uh#)JL;Pmi*Awg0zqf%1Fu+gY=+fOJD7?G6x(ICvyp~*8iLqEP zc_*Ymh#q%;kBrnjImD!1WI+o?f;ecKC>?{=1#X$pTsmlh4$--b=PmRuUt~Q?ahg{! zFL@{QJTJi%Z^bOt{@=f^!Fl_psOUCM8;eS!M;zz}P3?li6-GK7lTC=m6!{T9MckR)4-6YA8d9lLUd8rjl_ClJ^U24r1)-gS`~zcVJ zgdC9yKf{3L7ecB;hEjrlc=y}u2EbaLB^!v>C{y$Aw)=up`I@G5+M=girl;K1>fXKH zjO_A0{6N$!B*?0UhW)2cpN`_UY{ZePJlPYIldmcF1Jb?1CWSyAjy0{6t#bvL*&FYT zL#LFa7i>Dg@*_>$ZA<-tF~Cp!q}@Vh@nwQH#3^n5xVZbHZMPQ3e~=)d_~IkK(A2|TMXR z-AO6V)ngqx%nu&|^J0iQzwGw+Il3GYZw`e)>w(%cJ3H7=2lcYpVTKVv^3>(aG4NO` z6lxkIpX>bm^r~p-0VbxgzCFfw)+^p_nU@QS;=WelVXK!EB&VH02T>PX!@Ya=UZBkr zS{Vfr4%RELl#syvQ4erd=hEX@&|;2F0WGd=L=Cct&_y#WLXqkKTFQ&oCkfBQy>&3P zwlE?qCP`YsID$hGYq2mXxsCmj>#@6P!q_QNWMlQQABYkghq{=&MvRZe+$t|rW33hL z?p|c{jg{VHAQy>tV1j`GwQl|U;|>sewMYWVj+2n_SQK7vhauw?Fa!h#&^0=Q8hrW6 zl@%PpxQyY5U@Lo)ZX+8TgQoz)0@L_;kI z{sEk(P4FgYDZMLp9i_>xPdi(YvA*M^z+=ZYEe;$fCl^Ii2m$1ffDIuzV_TiAQnDta z6%fG#;#pXRELm(fL4~Igs76LX5fQo`xPAvyFE6{UhGH8M+w;uXvy_(|?v?h0;0fg! zR3Cue`sHVlrN-NP3uR+(eG@qvj7dZ}7QyVxj>@@!hkxA41Uk4aJ*?iVWAi zAdIHaJ05rjvisQCp7%z96Fiy5PubYK%ui4D@wqXzubwg5$kEfu$Q?U*&`{%v+iKfR zldcwm`&57%0E`oKiI<2PWUDyY(nG6`Dv0qgd`Eg6ZdqgWLoQuBEGHz$tSf>t<21?MO>~G=CsG9krpX zl?W=tco0Oj8C8=)cjU@gNltHMwXw|nJx1WL^0X63ZphBR-(Qny=__}+~o@)v6 zcXxg%K$p`(O#07_u_2=Fwgz6@38{Gg|V5k zSgQ-h?(D8f%%&-2?6STs;>4%_Us)R zqR2jAVPSEclJb^`NevFCBnbNO@bFTn1yYnKC+x1+$JkAD1_`;lVL7C!=P))lHo|YD zXJ#gig#`U-iyri5o;%GC5Nd&(u7cV|dfo=;)xMgSu9R{B@tNsn;c|68q8d3A3`m)Q zz`{`N`!HKC5pj)m)m_iP;5v5mc}`BEM8%B`J>_?r|9_D|3zM}F=4;U5fcp4zd%Mb9 z2~qk2jJ9RnqyVFeL_{P-{B{GNAluP4AG{^_0ItF@V1=}StJYN>s=Z$^*u3&C=2Grs zw_iU)u0)eJ>696?)O!+$?wG}|ANOmY?p9F_URvq8`mC&AUQLoZiXn&;=k<{xYTp5S zN*trt-24n_&(X2WTZ>GaBK$ zLsM;8E<1bgncwB~bNNyxk9imwg8{62dwYp%{ZVXl8g594&(^)~w-D(KLQnsFw#3sl zr1-~I?g;;f0eDgfLjdv$O!pVCw6V-yKqVxqm^!j;Iq6-zHD$SiDw_LWy0EP;Cr0mK z{?_cW%=n<{5=Y+5FujLsS5G=>4Pw7SYPw}^9{Xpm^|5e5FG3BP>__MCH#9T|vG+p2 z203}oyfgn6x|;vgM=IGgK)?jn!lQlRm(nvzWtw(QQ9=0r91-a=7l2ac2XSETVZ?#Sg_K>A*YV+OI(>rMC z<*As~*42Y`3w^x*&!5C%I$By8fUts%%#E87n`n33kHqt-zFvAxy{n0p86f1yR1a!} znevpTLra?}h~+bM{I~#6(oykBVBC4XZLH0?qfj!c3-@2@0-n7)={nOV+L5iBU3`p| zHkM6kjGUCYo!UiaVW;k#^BopcIQ`gxzkz3I1l__bt3L=tQ%-2twG>;4Vt&cVx79=2 zAB|@v_4J9g7x2=>ukDy^D*u^zCI9*R_usj2eTmRnV0By$*`T{Gnk$^U6|p4(0s?}` ze{Do?LsY{*Fg-tS*qYp6*0~)v?HQTMSz9yZxV|0MpPk*r=u{{qxNCIdpsybOm2&1n z#9sG}f=IZLOaxdlMPL{%5!`GNp67^)c^0t>;cT>_q@*MO60#+sHmVog#Tv`(MZNzQ zlo#>;-#a@AeP5v9uILuir`w&4TxU&B%|((6S|XB35Roi1Aq|n!e(a*V`MXaEPsx0o zd(yOuxnO5_iC^gCE`VTsAo`q%AHlwidJmCFqg3J86QNNa_!989@Lsud^6>mYzB22~ zmoQyP1P2w532~e_jHAH-<&vy*Nq(U-hRntj66>*J$$O%aunD%vX>mOA<*Lg81O*Wh zkHH=Y_feYx(Q&2jn;Q<-1}7$Df*3`4n6x{OvdEwKAi)CYO}M{^Ux1gfAW+KM~m3EPRzFu=$@xFCRg7$tQjrFmC4OlueV4>oNIuuZRG1K@9qjRDb>GH zxi4?~j?P`=a#kgBfy1;O+L--7ZQzz}Bb+UlyH>~SA9|kmn?_|~uR?;MPHpHJ=_CR3 z)XaJ*x`NdZs^L|x^mD!_d;Pt3b>Rrl8Y?-3B4p;~6gm1b znuD7hkZ~nc=cN@~1#cKP`63U_;uPB%z<2A>HddIU8bQ4gdXCV>GLz6|W?PKFT}#XC zmDRQ@1qH6g>)B0|A}h)*`Vvf=6{_pL5gh(rSuYpJs4qA$6wakf_;Wcp&ty8G}tf#Jo&FHd_U>qM~^spd8^*Pe*h!}-^;!iE1a{eD4Ym73f?(T z@m@ef&G1(NLhpRwv(qy=qUv9Y=rI9bqT`Pq2cPZZkNRJ=m*Kx!CC=aQM3 zNhCy6XDw!0Fki`F97!E0(3WxMvnuIF@m*DGoe~|gcdFISC7tJyw;uay+Cf+1v({qZ zGFNwu%|1$Eas1*vSQjxui!dOf_?lgwG01u)mwV=wY2#IDHlKXW40JXkaZ8f^o?RY%T)Naqu%q?*z~M?5}z5 z_|Bwd@+NNj20~<-_B%ctad;c76^(%H6TsLz=J%4@y($>ZA!b?#tz8X~txWBH!JDC4 zHR-ATN^Czs#+O$&0*?bxciZb2DHBG+%_PvnuucIDU;z)o)o|u!p&zQ(LtsHsa7IEl z#$A1`s`3O9R)my{zNz;>BkF*fK<|}JO&m3iHJff&XdD_*%V~x?RjF1s>1xp@WL^1_ zjQhxS0lqVG7v%tpuyDY5~51uR;T zcI&5`6C2Ozl+6!}``_+zo@{zdLGLil=C>;_?l28aJu!E}VMf1nC@MPIz|v9HT;A7=3yA5sp8Yr}flIPcU1H$QO$9$q+*v>TY-N z%ol3__3kIUTG7!reQz)CUT4-|uszGk3BE1cVcQ9-$0s^Vyz3hpSe26flCNi=^mSQi zS7Qk)q3wD`scv%SwRcNSp8-wOSh=_2?w}l&qqQQJLQr<_JpB4v`%*(cY%SoS#9PIE z#=2%_XBknr{|wZzS^HX%(kMNw6nM=%!(`)Zo$$OAKs{(jEhn|>yMwMpMn)=ttYHHW zIW_a;%NK{oC#?L#K>;BD6Hcu77Y1Vp`~afX3!^Fo1;q-H?hN2=QfrCSu5j2Jo{=(qis0hm`P9*I?B>mz znne~_j+X@lq7d=#t=GF<4z&EekyljeNH3Y?J0s^GWW*r6J7|Rx)|c@88)~+;JfN5Z z_;=5)E|^le{t^{@N6H{_%)r=K4Es72hUC&}6*h8AYx4b;gSiOQsQR+!1Zod|l$$SB za`imOSoZ0x(Z;4hk&%XYXHe!rd^k-FDTBu{57#!S-3(mC^WA0&yBR$27Yya0wt9Q; z=_jNjJ&8Yi9HflgSF!g$e@T2t*(qA}e_#L4zp^EEh5zUKh(9-*wmfA1_c{OmH6KNM z*#G>wEd2MSH*gA5@>|dll^P~4Vzd;DJ^A5Fv$HQD2olXAeSIJEO}sQL zJ8*5b61n)d2uNjIPX)B90)aC2nc|NhKc-@E3liWf;U?RNwdp2_%l-Iw3+d*suUfnx8{ zv1{a>gn9n5+&c-%_g@h=y8zo6)3c;3G#{dyoa$FJr1s3LtwCcADS zxq5hd0(ort6c9(^BU{Of&lL6~h2Lk3Jlv%vb`!N&{z^1FR$EkID3;sM!LMLy9b!I! zAq~3|=n06AuUw3eCU@$27#0fV#Q@&mq9T4&<68-gPZanD1|cs9TO1sM!ll(<^_D}; z-xHi1IGcGVTZ!EaEy7*9R{g|?3$?xRe_wD=!lgHORd+fvDPhCc$K{d`oEGw!-rc)V zFI-UY630~G6j6SpsgD{mVn7W3Q(TR~5|7&M7yHld)s;q`TN!W?kr=^lkmQ*5xUWs{BMcCBCW2oE?Z!2VhZ_0(|G92) z;s4zVj5bPG9RIT~7(B#%|Idx`|Fy@GuH&sHAzEP%^?}h{>Tz#+XFt{QPP4{orf&y2 z=#xQDfrjJ0(|m&H{_k#=%t5#!>`KsZTHdkw%q}h62I9DI@ghM9121C7Nlx8OQonkY z1YYw#0FDvuu%k2Bn8;>?vkZDav0t}t-Aeo(0sBdsiJwI&*fm724r)YzQ2d_5Cg{d> z6;~ye?XPEA^^Ph_vDbmlQM zP%y!)?BL+g3k3kk;5g46c_%e?QM$_xSJAWZ79&NR)I>AzV+Ix$jS$QTPdJz(1JFz( z+F3^t=1Fmk-yg(j;s!v45|a=RS?=NdV-UxcYfDpi?*?`XPM&kXimb&8h$P(Rq;yXr6%1 z!q{G7qkx_0R*_QF(>np~$CyA{Kq>IHLrEY?6nxi+cG*b`nhXGhB79V&$Id+OZkK4o z10cv}K))--#>NEZ1DA}0>^tXQYvvM{h9jsb&+CggsWDf%xVj==?(1<~qJ_x}Oh*VZ ztTn7x_}*Z6doSYnIS{3y3G&k$>(0X>f*M-g7Sm~-Y9;>?6nbKl$YPo1a}n}Z3}T^7nF5E3!_sxNQ`}Wytv^ywj zT0X#c=i%T4!Yiw-B9|eTR#Z+317Usr8HgjB2lnr8AA141T8?PsL=VkiV{9Eh0mm%L zIH{ekg8y9vZyP9*kpA{OQW_Tep9>nn!s&6ydTdQlEq6^i(OFI~kM@MjU zcy9mxyU39246P#~2V@>JTWZd!qyOST_hv8}Et<1#@`FzxPFtF1G~{{;Bf=aORd=~- zhT}MPAz}SyXV0+B)EHcVQ5ZGvjeGYvQ4ZG}WUv7u!hr?pj!h4~FM;KKwVlbF(E#o6 zK0%qyS1?wl35F@(%2;V}; zEk5@AygVkLM8t<9l$1V!fn*qP`5qD+nk?sE$BB!=+;$#o38`^D7SqK13_UHNXOd|M zr!tXxK0@gQta}u+54_OHXbbeCxyXfIlnD0J3NRZMlT>(|N=*@++kYxu;0gvS@fcaG z<;Asa@CJePZse@qbDJ>qSAumy0c(St;qVRHtTj#bpS*>QEFCKlhJdSYWF!j8X9dSy zwYm=kH+%_?&;Q>Do|V{!8wgZBK!pX7toTQwBZsMrL1jiw&@{^)`b58?Mwn$LaJ3@KoDu{7%@zUORv0KOZu%SPmq6t{?f+0Bp(&n_f)K%j6#PdhBG0zl?3xxvws z)Q}Ak269j^B7KsGh={y#S+Pcreu7vBgqwnP(0%lRF<42=Yf;#mc&LZS$-{c1paMhN zm6%?}x*`}=Sy@@4B^U<$DCmUHMnd|g7Vu>KsCa#`iWJLT90^v4qvG#Jgl1kN!hVg< zfbk$|6(4F+HU$6lo4h=tmBYis{-yo=x|G*=xJ8HoE+%qU; z*pyQr0LcN3$lO~;8wY61IFNBfZ33DCvC9bD2HaaN^}Y}^n=151cylkiF>{8IsWbe-diQqx&Kra zs4Zv}_6B)o=~n2No0?|8ROR%@bla{2%0D|h)6u>JX1RS7e@-D!haxq77q46QCG#dK(SxoUhZGL`e1Md zjPnA9HMK)e9rdT?Kee*HW7n<>2(+)yp-?S#Lb6%Fo&NiKJk<;gEj!$Ho*BWs9j{-3 z_^^qQku*2-mVoc5y^9505Ev-uJ4t|gEYg3Vp6EnlXo;(Te^^5kuoz=04IsbHBscEd zzWpAUjPZ#HR^9T;2QMw)Gt>6b3h;bzC9;j9&fOmn{$Ydnqq`CX-sUcy8e16JHN=!o zv7$kTNDZjKVejwL9s`F65waie0qV*?-stnQH~F9nd0}Iqf1mI$KvE$(nxQHQjm4oZ zu>4MiN zqEmMufj$J%0Ug#7O7utTmdChntauZ)2|Wu7S>$nyMHqtbmsRspoFay+k~Eh-G(B?o z|LX2+Uz!Y~Fg_zH%OWzuK=UOvtc*0&G&Nr;R$_`FOw?tmEmmSk%$oVaP)UoV)Y3G> zh53S+rfU&qMZ}yHp+-we(=kC)Gr|vvhW+lM!hfNC9S?h+^W5j$*E!d9-CL)rt4r0I zG#ZVhL;eO0wS?ZPCO*!({l5QxY6Qc+J0ALs(L*O*RfH1%@7NOrK7`m1-jbXjYTn^-XGd=wh1P(zx=cQen9W*iFX#Mjo5go# z+$0N=kf3gO#&8z4W=_kUR6U<_f0aUjeQ)craP$|=mB%Nhrfz9XUf$kIq-O#rfxEV4 zI=z0mV4SU6SopixR++PxS|-Xp<2U4qF``=d4nro59yO;x?t#p(g^-P@p{<==H$2zg zxg273Z_yo10nOKjFcKAf9!YN>e4zr$z+Lt!O{rX&hl8=xfP|)DU?#dQ#%(r$Df-gk z_if2H4O*?1neH(SzE2AdJtI}aoLCi<<;$WuG*dCOpu_>_pJ7R2S&|R#Lpu5l(N(Z( zgtCE^c9!_U@hHdyj?S$n8TuKVDc?04$NTH!Xhao*M^-q`l&9<;b)CM=-o4gk+tucV zYD;zp&Ao?n#EJqZ)oT#HZa|;z$g&X7;@Pr7qWc0>Jf_+{j&0^;7vkMTH{`}mvS|SE z=Uk80Bj&IVQm@B;OjnhMGmKTK?;ij$1E;RcyzXihm6tg zvt}4vX_<(V$-aI_f8s^;r*4PiiNlsT0xqqlU*VC3b?0uCkGmU^C-TfnRB8Z^qyp`b zXZ(W{Tw*kXwj2>0)um^4vsuuTwgS%deGU}@L+1K$2s@78R45GM zfD3NN9d7`!YtLQCVZhjl}WzI<$~jG1o~USy1i$PbG$ z`=mI&H==0c6$r&cqgqqU9L5hmn_`;ZT?{YE+mm_}1o)Kr{ef5%3zu@E&GvtwtJVah z{oC}1jroi}rLz3_UL6_G`WK%$=gUji{7;wa7OYMu22Yj6$utr^2lpS|*AkYb`3ch^ BhL->U literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/figures/monte_carlo_eif_samples_vs_error_expected_density.png b/docs/examples/robust_paper/notebooks/figures/monte_carlo_eif_samples_vs_error_expected_density.png new file mode 100644 index 0000000000000000000000000000000000000000..0f07fe2fe69d491574d72473687352d2406667c5 GIT binary patch literal 28477 zcmb@u1yojRw>G?Jq&q}HLIeRpML03C=i=nGsy)!*QRL=KJXg!7uFc5%QTKuVl@=Pd^Af*Usb-RVOPc z_r1=C7(-M}BY&%>XCLpY7Y8P*D1YNbuSinnV14hU5|5WZ!&yQz@LA!{!0BsFw34#$ zha(K*S#WT$Vi!L`K~7F?O^+*yUclReD8k=nSj(b7{aBDClr<(M2A(^fO$K+MK8=-z8}t16^vsOk+}z!2m9ua=?koTQKJel!;4*MLG+X#-}%|`rkn{sO6jJ0S>D~q-{v0a zx`~08bmZY3Ef`5T&0?-m-uTw3zsj_)A~EDtGIz zVhBV%&bKc6@|acB;C74!ys(ByGosgd=M=v8?1#x!S!B9@vx$cYdH3%!%_AG2d zhUCx-@Zf^Xn@c7~BSy%EJU-RYp_8jiptJ&>LNWH#;VG5Udx=6ZgO`+Rw9u{2q(fBF zjciYwwS=eV>UG%{YrA#yj2S-y|8%}+FJ-?t}oaes12SxDtP+@ltvg5I-kiKAD#7AfQM+x}E z6{mO6wM^(^nduP0o8uTIB?Ff(mHwH+IjG(x(j2&a79TUv)v+y{e61v(#n=$t&1(Y4 z{j_)3l5rbLZr$IUsqlKfVIW%k048quL22EF)br6xy6E|3V}QjeKR~dZikQdOycokOk|k}-2#~Zz58{o% zC8k_!Lyd(M)CF#!_xpr#lY@_ox?ydj>0EKa2OVzH+~TlPhZ7yTSfec?H;vbRqL<>L zmlDFI{Bs<)DAZ?5w>s3%LLv3NuvPE)nrqh0D?bMc+6mdZjWT$L-#58GXfEs1>S>}ju<9p3!4 zL+KWmK!7X_R-Cnb@`bTNd*o^$TqtIW3`ssF@Zl~!#kFVAamq&I5wUVMzKoZl>ghfv z%Ikj+d0cm=7wEfCn4p&u`Ec*1)Hl9yVXAW5k3uovM*?Eo_D@` za!Z0RxaBAfqTMs;9e#UNjEs%~1GzVKlbEn$r`dy;@orH#Jea^Az3MdLn^YlS5NXtM zQPh*{MeN|!*X#Qc{QHAzxkg{5CuY!HlmvU|TR?ItC{x2zU6UHWm=B)~Wewlj-uT?O zlhC*BRS^M!jyoH?pb^}wA5cCG1d+-G7f_4X%nPd zjY{s!wI_I}!*4tpYp&8X8^jro43yy?DDGrQW6FfyK;SIpln4(s4*z}s_7 z=46rx!$htW8Zx4uOSt&zB*17%{C$VLyOtK_{v8$vJG)Bkud}ca+(-8UnH5~*J}WO= z(gnBBs({D_FS{pKuiTnp%ULqmq5JLXLShpm|5{50f8Su-dok24Lcr*Vd^i>t{H+qI zckn&egD{XY`P$93y&lbVa)u`>b65C#61SDJDfWBD+iQ6{$RM{NOLH?%AE)lh{x}_X z;ybzD!4+Ze#9}lDHak)V^&alAUOKjAxLl!bpvpLaVcBlOAR{Zgb@k+SvBS?d?j6|I z(bg8@VIbTF2{RoUh61_fm;YKX+y@L!CFr@(n)w%Zbun)yV&vYM78YUqPN0gR!HJ1* zC!ZXGp8MUqmZsmLyY$|!QpIRK9Bn6B8#z-Zu}rWUYWa6JYW{B9R)||%Fpfc_`LNP; z#3wHr<2A{0ut43U|6gaofc4{JnJmwHTqnBzMjW#NuNOfgKNs?^|4LclXvZkSLn^)e~`?1-gke^ zql-y{%5mn8*Fn_;rMht|g4|gdHJcupxVNAsbCOt;asS?|Xses&&EX=2_oNrW$2@v6 zYz(lq$d_z2X#}X_Yl!@PjkoI-s+bG5B9V1D_LxX_I5Gc^*&B4<$^=g?s%|@Z*PpZQ zH_uxhxnNf^i{l|fc^dS;+P7po7H*u_B>P{FkG}}!@&3ji)1xo5!I3Jsi1Pz&((4gx z7(w^rPU4{AwZCSRN)ajYUA5UN&v+}@LxtVOm-EI=g|`{+-nE$Rt+VQ12)kag-p#vV52=rPm0cWjq|{tN zR!(kuy!v6TUTKC_B5%1HcF6&y9C=4E&UD9i3jSkyLgb!C@j05v9}(xu3%bzMuF-!^PRYk$&7$AFUJ0dng$j^dm0| zG5-xD>5A^eQ|k@Kkq?H8rgwgfA;^Ub7qs#;aRLJaACvOokJNj;*J^noY=o8j?QL1y z$ntDhfI$@7xwAM(U!9BB!Ic`k?*;muCog@(PVCdu(?f=x-sh@B;OEoWD47>{(UHgp z8*x<1(>vHzve~Hev&p8wb+1Y2D`u=ey@X3C<5k@SvCrK<3&Y{}&v`QJ>Etgjg&i}{ zhvG`Q8{SmJ_SReU{Z+|~h1k3Se3J96Z}+f${lG&CB2v-_zCOg7E#r*y&8y0YW$~`` zy}1jBNkpA)GCOovT`$Q#q{i~e~x-8D+dQodwY9HS((iK&JTh6L+ADM^c2NyLd=n@%EwI9 zLDh*-(^J)=h!4TJz&$%XE6;&UYG-PKlaOx9@fh*kn8K*vn<1=q+hpxcl){#h{G6{{ z>b`wnSX5L+#2LL$bItc-s>{RfQxk)<0C66DNx1%+cEWxp*XW$6LC6J+rr=<;r{&r7 z`oon$(r2x6z&cqG3cSYFR@trPLA;AbwLWoi)PHu@qXLN;nums{Ha9mnc6U3{WrD~U z86#Fc7tC%g7C;8xf9TOMOWbuisQrO$MhSs!>;53}P-4|rG%D2`66ezn_S_eh z|5-#h#i;^DkA>rD3#Ha8HN$x&_#m+1=nvt-!h$`|^JmX|O3gcFV80Tsxo}GyA3Xef z=2}$66t_0UdY+HWxln&q=Gby}dG<2f(_G}UR||tBkp(`KVMy>TO7 zUVQBAY<_dQ>{)Vh1niNDh?vI9e~KZt2E4XYA0B7jS^cxap+^n4$rWy(F7X@N($Z4+ z@lSU2JPqGJnqK?%9>P}pxA(544~F?XxB3;p*{`UoscnAbOjShh_;2;}%nF|hqnml~ z`5_d&ikdM(e)XQeFV!FXRLRQ9k|dirP=vsB?%X-O3LE;-3fl|weH@XtCW_op98NA_ z;Uj|F22(4ed3}TPw`9N>uC@IP>-S?Wwza9Jlt=!a|^cd3)oRFFGrx zDrc}*l$N(Sed(50O*`Kny2ZtYLXrtoI=uc2#RJ?55Ej%^B?9W|yo%VKeEj%PQCZn9 zBt)s2P>IM#E9p`4R*SC6Zj+)h>7ve!KtpYnY85zCC+Pvgu=+nM7o1myP8A!~X{uiq z5b!N87sR6I7d<^*NRI_GRV=ko4fdZ3wy&gwpN@_$&8#g{(=J}|!YMzW z$#Yu)^+lLD2TW6`gs$^^5HCAVDPg6Bgq(_1mUtVg$c<)Wi)ReTRFF$bu)1fk4XHlB$;1^u+@_nOz3P@ho4-pqi%Ht72 zD0Y$zL~wS#69YLS$Fi)(GWc4pWguH6Iw^?<-f_toOY}byljM%g%aHpK$B>( z!^Zwv>^0wA`^L}D|IDdOcI%=P*~ZulFVnP~ut9H9$E5?tlT*8-IDjO?#|Pg3oG+!T zJD@@svLsk;^JZh)Lq;>E_%=O2~Aw$D8vC%#9x_E@jNd z2-yD;sP2NSeMx>GQIeI7O~8HYo~(j``ACWBy@}f22K&h9=qS29u&wMlT}SGh)8Q_N z4_iaHOu?T=mvqreDj*A^1ia@%cekvkr>Czt^4Xsd6aHkKu5T4UV#e!@9wDMP#(f@s z;@-G@jr@;$?SWr#@WhRQTaEqw{lQUDc+Uoxe%l&*+VMwQ9_?BS>1p#z-Mq)H!&uAW zDgwJAujpPiHTUtkrd?$4L`KF-fd%ggO3L`}P3!N~)79V0=UUuST6+t%RqXSV2x8dz ziCp9Cmi;6)Ae!m%LWMoMZPV{B_pQ_Un!w_Ia4d6~uyQ~4W0@M>WNyA`OlxH?-VF7^ ziODKgv*I-t8dT2gZO+Oux&55`xCD?SdYCigShwpnMLqpEIkGY9u3oFG8eNU@6`w-S zAhdjUg5fs{qEF^NMC+B9NY($!%*fa{`28z`L|yh+Al+DWsJzf`$!!N0%*Bs-jB5&D zK-@CTMOll8`V>Cz{kInpllQyCcdB=_CSZ4Rhh_wQWakwjz0&oU;~*xZoiZOjbjO@L zF#?#j&X?f&xEBa1#qh(LL$Ma;_=19imCxczW;nGs58?m1zK(_J59+P5}bR+1Yze6!j@{hIJ@5n+p%bWy}kZ&V0nsp8i~!>pNIW8OmsjM0e3nraQ9%zon^9IAnHi;ahF zjL`haw}5Pqp0r6kwJAOE^~_&jY)90;srWak_~Upp&d7&vsUW1tc6@R!1`^J2hj z&Zx1->PvrS=z};)yC&9@GAMLnICq;7#Von+`TAy~eMzgwC_+;8eA^A_Ci)EQ{kv{j z<_bCS{rq4x)h6}C5r${MiVOk>g*CmnAr8%xue5$FCQ#Vixr^>?A#kc$0s6wjt+{r* z7;{_rs=fc5Jw$R@{rg{Xi*hyliK0>!P%!=X?8&fLqT??K3A8qs)*(pOPD)!#iQ>x( z5aDCcm9@E7+6MO&xkN|DMJyMP_}fZ_1izCh#gWX`40-RmJ_E1?AgCz9a``f)si|q3 zAUk;-8^oSpv|c7QX+bJS>ZSWxspA}gxlf&sN2;D2dpmz0A@|26Zr+`I5`{xZgCNZ< zE&YWCLaK>ZiLI@z+XSwV*JZx?LM`BR&kAWwict89zBHjC6Y_%Rw%_eC%jx;97XS?j z0;I*);lBdR2+YahM4`=Hi_IRHi~$py)T=1hQX0aQP}i>l#t@vF0NB0wPPL!Q>X)Xz zKC*`k-Aw@g`>Gtxw+{fAOb;7B35bp+_w@2A_1LxJ3gXOQb*~eeO1UNEc(h}F5RZW%f+E^`akOn$M#7Pz~ks=nKi|wX$ln&wpdj_HU z)WyZE^|AxV4VC3pZ%U03^d)usX&e*Bn*=-e@re%-kTyW z16VNoOiD0&Ux6Byv-Na15Gtw$kMWhWHP>PQIhaAlSooC4cJ?erN=gb2pXu=*6b6(_cEacKrV`M+^XY!8>QtDtkaHPK3*&HASJWEL-(V&`MUysVlVuv#_gQCkq z;s5|#4FOKP=T}m2n#q?2!X9C+KPbuLXk(Z$v_5usw*o%c+?bYOKXbg=nk5u^3+syJT@#I>Z`N7Ug&TPM-(3K#|vA)!AUk5=M^Z8}7W0SKD( zZ%?z`$e;Vz{%7-(gOMIU z-2QA?d;a<)(^ZIyG1zU( z7W!}w?EPuE6X<4UR*E&UGhUwfH#wC7qV=(-r#*m>)?shMG)cnibY5QG3X`w6`_}ov zA11IbV|OjAGRKBKSN#$`!(49fCpd`PWeBnmwNxoy<8b!t*Jfb6DEb8j2aE(_yUKmH zj{pa*)*LR0)Z8${$<}OLOO=odT5vhp`ibZ{A>TQUYu8R2RM<2v^=EE&U2I?x5urzJ zR@!}wp#v8f%6jDDIk75^G*%XnvI&ZxIOCGxGR<({Eltg-A3q}46e6cO<1XCfjw6#J zr{>m@1n=DJS71(uw{H^^Bd5+>52|jN9T0z78{WqJUfq1*1E13i05R*50H>gG0Pr>Y zA(~CtWrb>BU;q>;XJ_ZWB12KM7FZg}qukow&OQ?#telWnia`^x8Ugx>Iq=L&!}S6y zU$x`j^8g@VSHhawUvG%gEjE&Jxt*Sz%!t+(q}LC~zI^$jNg^kEX?3DTqNcJa3i1>` zL#I23G%+H5GHB(w-SfGpMv%VRFm2UN>v> z!I0zi4OYeokj7J~{~pG7OBXG~>BdV%uGI*CG^BxZPx}y&mYkdnXAPD&H8u6@!RUJv zwtE>rQSN4Mbwh6GT~YAE3+Gx!p1JOWujL*!f5kFlg&U0uu~8IWRCgY?u=&VVGd9o!lmDua(oM%o&8F0tOsT^N307wN`sz_z zM+X}Vi)7JsNG_eT?QK%=$p=1fY&c#Q3FBvLa-G}!S z^nuo$m4wYc$53z+sfR% z5SLK@lk9ET2_&$St3Snl9WEZJak;=G=H~P6-8-SvO_o+xY=VN+(b3U1xN2D(G~vqu zPy$T@ziSX_*kUDafJU5X9RU4c(HT#UkAg!&G%AD0vCrxj&`FUYbK_UcOiWmSYS2>F zF1#DbEbr13E~rpqvg)`6QCIuI%Puh8=r1u_R2#3=5`n-WmXVR!TAyTQlMnYfAL`)Z z(pKd-+gE9CoRGL!yIg5IMgvYA2YHpdO=9^A0f2+(gqTR?FONR}@P(CZ;ZH%#govy2 zM$fmxb_^3a`n||}o{GTpaC;c>Sz5a1`DZz&3aA1Ii2ZMFU*=L_$&wnA8wX9;nnNiH2@DEq>FiWyJn~6e#G0&rsNi*aq_dOY*TN@%&Ae9N zI#7I|TpdXAc|&e8X*Xzn_`rYe9TDpC12+I3Y;8Gf73vEPztbYqi9y z0Z`-HeXHR>_Xk&XzlzePKyT`{)*0`y$C@Z&c^3nPopb`kliW4+X&0P%^bRmNUkVLq0Y~B!65diTtE>z@vC7gPZSQf0owKp_!ST1)3No=>P-(J47BQkXcjy6!Z=D z?APWC#%SrUrHQ?{-QlcwUR0C;VD8~Dz@)vsy{WHdR=L^*G}j(|$7f``%`Eq(6aWtR zT;uRC?d#XCf$`8fhlAZ9yU2CnSzKZ3m)mT8h1%k1lIPwHg*E_ERvUH^bd}!gE4+2raAstK?91G9kmDoq?YDv6OL4$8-uKtgtN1wbOY(> zSqS|JH9yHBd6}R$f|CnmR~aa-GUURt-eeAKY;HCI71Q-&m5uuoTBvh+F;DGeE{-fc z#xei%E*hdnhEv`Lj1uDpZ_O_n+&o6LbP^I0_eS64LEncp2dF-Kj#|2^#x8V(l^&T^ ziMS=D`7ExtfdL}vEv|hHyko*;tH^N+GV5}3LxVKAx>t%fJ~f6nM4~>ZQLV*;!OzBq z_5RV1X7JsqwY5mFL@dP2+#HBBA_&sC!hyW8hx@y;Eu`1Gx1l`4K$b?!FBGe=$GnV; zBtc8D8Un^MbreW(7X}IrwAQG8Q#p92NO*;A&UUb|vt#Ge_~+lGW@3u;$0Kd->LOH$ z=aE|MNzP_EDnwKL(c%7!y((EPx9#uwGhI;=9jP9@W(%WidubtTsu3pc9%9Y_Qu3N7g zM@Hxtds92=z4k$?u&|k^y`5PatjGjHk|YkL%=qIok;F{8Xx*E82S5!-AVWh#(!2#) z-08)|#&e}4Ok%;-gRiHdnClS~hZ7qvGHd~hH@$lo+rYp8eFxD;-_QTllVwlkrv9|i zp#B~Up|QR0yQty#lLmH$1a<{>aO3+|P`ST)Y|h|7=pzypea{;88Q@@&up=g5;SYj` zz)zQyl=zgFm%G2p%w&TKIQrSMn{%7NiV`50_q|PF)Esbf$I8D(1fZfH?y4>1+MO&a zG%2(Mq2OSng%pq~qv#`^pL%f@bOVdl5)EwtGQ&b+AG}&Q=HlX#V~Pr%%goI5=b+q| z1_9Ds7i-!j0CngqXRVPlg7>L_ES~}i#TN_D4AMO$K@0>9ovXve3V?qdmj^h2Y%y&O zCf6h(MF3oS&t7@Aat!QrOq7BwN2Zh7bOY7;;L9rizfrU%eEv@^8%RznRuC?K_7Sgu=hbVexQ^)onBMS5)W2@A9YfpF@(={5Ed;EBdd){k6M&z_M%)Ncd@20)Iz z;Zb`8!_r_5>CvCni(G-6th~3rHdB=y{~+G$+M&b`^W;DaZHq8@Azvs>OxnZg0WrLF ze+AJA4KF~P)b%fkKxqV84#Up(uUOfdr=-ZP(5D2y8|g?+G~eaAey5r?oc=+i$ygNI zUWzo(Vt}?25)y*JMrMA_cah0y9L7PpQ|Ga32Dh({Z*ZC?aNqeJ0@6o8D&{5Y0rm)E zgxs1Fz$eeqr5vHO-E9`PJ2ksIalyg(KDZbTErGT{(Z86In`^2 z9(HwVem-EV(jE%|K0g>J5lH!%fTiWaXoNlY_|K^(uzV#v%lc#Xt^v;TKTi+@?OmXV zLE~aBf2b$H9?+)13Wd?|O0Q2GPlgpp`QxGnMZ zeX#_KlY;RZW2Nkb-PTA_>VumyeoW|HDT+FYJnr!Bd*8oaPDo5#BzVFt6G-F>k*z0L zG>Ahb9zj5!AtWUw4ZO}86PsSo25FSe_VyuY-4kH8cSk8Fr0`TqC{v_Zv~}SM+CD_Bg&AJyb#iyRzU%W}KXfk{dm*6{467 z@5qpm=iS!kZ%gWRM-cC^W&b{Wlu7PX2mUS&kQvo3nXD}0b2j-^rkV_1R+iPwzTk1% z8L{P<;9NZ_59q|?=7OAGd5T%uSQjIeVb^gkqv2$8l)-ehiiS!H{fcob@DS+!YC?sB z$9+p04gvxK!p=)K>wh70qQ=?wll8A+C1tk)i*es|G(B|P*n511#zs8Sxc$j_w7|*^ zWue(*rH9%fBK18Nl>C^OS4IPqfW`SwuR{6Zx`pQCj=iNIV%P_!hDaI0#o93)fVylE zH!vEGh^8_;x#E=bjPS@c_NS}$)dK$rOQbAv$R^ zczc06a(^~jC$9Rj=W>S_6&z3%xe>ZGgoTA0K^W-#L_toS_=Wfke{I+W&__c3|ItS> zW;m^pcV%TQK!;?Fvy*F-?r%P~E6F$U+c%BYYXj@f!U1H@?7epuK;iWKJO{{e!J*o6 zm(9Bm#h|VSRcTtuEgWU;sV@S+0zPOHUS_(3`AKU$uL0TV)1gpOosaAEYyeO7Kl+h4 z<^Q@oIzBpJU5A;kF$Z)F!<4|;Lc6OP(5y+Q3EJM>j$j^&5tlVn%ziKrL`HMoCRXITk zdjH)bprPFqNAVn1zZ9anSZ=G@(lE(>?|AcJi6UAUG^qbkL`|5T6Vxu-mRpUo^Mi37 zO-sKH^^wNw*Q2CXaY;}B$j+;zjseDjSI978E;~$v_*h>cJ(~>pA~z{|i>jn^wj;{% z_{b|OJG;&?$NtfytD3iNK^F5Zv+8|oK|~&>eBFD1SfLnqN=lDPhe%-&{8~Hr!U&xjc{K>!|Eh;_NYU)<;yAUS zD**Y94brTxE-gsle&<8^g@hEK<^+gZQ2(pasLmY}HWU++E9Fn(MNEHrLo#r=jNgkI z+)3j%V4|&!4WPa0M4Z^6ybXZ$%fPLEj8%33+dTzH6aFP_Y|IS2g2TgyC`-<&1oE|U z`>t<#Js;ZU3GGy9?uG>s`|Pey27#PV|9gP}>UhYZjUPXr0!P3>mR48I`_iNmB)qPf znVFT?j$K8aA|^?uTsh%t7A0b`gR9AVIHU{Pl_!O3m8vixGcR;DjLvIaA zJml&f3g=%yI{^EC-Q$^bJrO%XVZo@vu(Z5v3WX7Dvka8sC;{cZy)q328J)QM74P5k zw4Ogdaspd>4|40tI}{MSme=F2phZlUQ!cw5i0PwXG*o^7(JQl5J2={32jC?G$ryN8 z4CK~pRbLi-lufy6KM@YZ0Nv@+Gr$_h!jWc}?Er+{!}^0u4vlR8MdMq$b)xYtC7)tF_T+v0 z-)nVQj(fk49wt=&rsMe(?&!EZeA49v&PSm36L?G6(Wx-fv5XWi?bdUzr*hO#V!$HI zPDA(LSMPNUBvU@ZFCaj!2#8itn^B@w@v3_b0ngFfi|$_gv}oJL)QwO56&k!J0XY^3 z+DDHb1+w68{QT+X?=Mqi9RNH8bUT@tm}mrEq;?}n2(CADp-YT13V_U&WOVgZ;2-F> zhIRmybREf%BSb{hkN4KEu(H|;JkZj0Gd$HeyV$Q+vCdP^IC>k68WW*HzW00-kPv+2 zsM>)x_4udImJ79Toejmxh|Wn3kGXk{3>1fLyD_q4!_}iPh7Epni}TQonRWJm7YEEoR4{t_-w`i&;)(M z!$Omf^F@7sT_gVar?z=QKvhv*(_MqM3R4fx}E|$m1Ig z&dU@B2L~pmroK$#?r9J+&>*6(e;WQ5HlV#)&)*blYim%|UUZrU>=KbLWD>Rfpk#4K z4Q)SN7jldREiC}J^MsI62G7X z{D4yoo@CscH$3B2j#$t`B_}P7$-~0~ItHSvtGl!m8>K*Y8KKZH{dBO3|Hb1@pqHRZ zlA#2>JTB_YKe;N9Tw=g|xg`|02KwxpHEV;F8i)PI`ORf%(Gq}H$0le7_(I3;BLZ*q z%Ly6|ei9-q;^K_JOI#>A3!BXQAHv~Ge}?^>0qL83kLF$xLI z2nI7Uhh8E@6_s1+q(nrmkl*?XbT3}99i@Vb^l5f>cC5?`VI~ar3|DwZdESvoU<}fW z3nb-rD%?}lI39L0j}gLXMQo`w>i`WXrf4KYyCkz-zlJP<30(ThV@F3v*m&Sun}f;O zp1pd-y}bcm63>FaG^a_6I0alsP(idm>{0VK*WJSd70*iEy=$#;Sw)E( zXuK>~eE(NU0M>^hMeMO3^vR&?twvsaa$zfIaPuPSF5H%1uBoSxoXao+>^5HKAq2{N z42Q}F1pLM7@USq#cs@e{=$$^;b36f_pNp`u*~HGBo&3$}1AG|FO!1Yz3RV;a_V@7c zPy^WRcaZuU=kFLAYSg*AVB_IIKOj7khJ4@BeyA4IKi&o9V8t=G9^@T}5*oP{yu7^8 zad9XcE}Q+TJo|Qf{J!1x^@M{(?>of!7@{uZ8jgK&PUbo~I{2qPK5n7FK07&o>GEZu z?}(re;P0}~wo%w&IDJX)(i`c0Qtp;CM?0HslM?m0w=yqDJaTPHBl&bQ+iV(>oxFbx zU~h>(^;&~NSRi6UTL9=1peS!dzg{U9U$+MpNp}E0$^)+b|K)1GM zH$Is{Peu@QoVyMRTshpL4c9MW1l{0ST=Lk-P~~=;~<@_y)EF zW`sDsO1}Z263q04(-H-m9^~TSIrgmQ z&dQ0`K`Be*;wo2smDt24*O&qnKara}y_gcNYiGQV4_!fbsuSq#=^+84NIr^5Jd3FU z`pqabl)652H*Y@h9hv?SHYmiLZ%-j0Bp?gKwRyt(3$KQXjHX;hWM$hZ5bc!fPa=az znOOw{$ieCz92|TyGtC`|b!X5L?nZCsOv!2%^O3}KSlp z`pH-wO!mN!KNYl6oe}n6KzrkkpVO`^gAm7`RYoYWHs^O*n`iC<+%|v}PZF5jPk6N0 zNfKWAvp+rrpie6kjY=`^2w@m`vU3$0@{zgP^$L4^29$9ubn&8FQW_{%&ik^GBZR#^OTe{pgftpt0{v@_# zJB&jr%5tPbE(VrJ7+MB^J;XqOROU6PkcETlD}SI1WQ>abJ?mC!L?WH6qp@ffYh0mh@|>PAMBoni#pIPlSPvCD38Xq4=m7#a`lT3g9%;($^M`;62$aL+8{NK0gePw9ZSVRP| zkhH#jDJxx0)J8#t?HknoaEFWBI)7xeQOs=vfp#3>N8jnBrKLd+43CKDuYPE?wbYLV zDzCq^M#ZEonlEaVmDM_~)2^ApVOBBG<);{BXVBxi*aX6hJG?ns8}_2qSiZ^Xg&68A zPNmAIYQ2tiV_O2Ac?z1f6aYYvHIjjV_VV`Eje4f1$rC~j1M3*Eok>xox7e_@_R|Cc zYu;y6-y)t6me#_JRYYpN_OC`ViUxug&$flpu1xp}-XHRbic)tZHMT*$#h@-Yq+{*5 zQ0d?zuAgyhevaty8^83mBD#@udTwq0Xh=5PilsOwb*;=~*Ncmv(|g<7aiMQi4w_y2 zL8_Udt7(O!i>|oE9arUfkxsPMzHv6@`oJZp6qz?U1(KCI+vpoX!VZ*fS|p>4I|oG- z=*lxNIkK^{(?k6yU%TKniI>`|E7A9bzd|G+KWx~`(mEsT${?z~Hv@vl$}&25>5#Lq zS5-y`oqC;mXUX&Kok0!{4u;Y%va(7u{&`ba_&L28DApJ%gHd$QdH&P!G9h~EURHKz z`M9*j4-d~p(k-loGOd3OO0Q1=SFx^ymQhqZ85?7OqH?8Ax7x|V`Nt?Vkmvpo%d-OF zVP;NN7DuJ)Lu>y9j1k9X+C@8YP4!BCLFp%z=jc|}LgmYC?!I}ZC~AGcV-g_k3nfw} zAUsf@10s9g8!nnYcwi7at%3uxUq&-wgj+y^muG2L2W@ZW?B_uS(NS=|3x6u@{h)jT z=gShmLgI6pm=Bl-{Jwh(PG&$cxx2#LaZ=qOzG$8O)Y4~37-zylh_+q&%hY6u9=b{; z29m6MO8N#9sAnSQ1HikKD%kMQVe3z=t(@l2WHs`51{dh5p}`-};&{)BmS}nW%x7-x zXQk2^KLK}dK+v~0&Lzv&E#~|9Xbz2V?8K0=e@$QhzESV%zqrQ2CCG@tC}x`ZL)2%a zUeNUP4G+duYi9gRZ%xJ4OhLC>>7ocPmIB`&b8y@$r;B>x-(c7dosTm$yfb~*K&~D$ zq!Z@xU`P?@pW=?*%+g=2#NQ=cD5HxhJ`WluKR`pYgl7Y}Y6KB%Xs(SJI@Jb^4Ig1@ zHLy1V4}FarcN(H#ItGEX5>>x0)O^!b_nkx0M=&aCijx5>0Axn8Hkgl6YeHb7H}@XD zbFX=BYvDQmmD>N>RT&GydQM7ebY2%d)}PR+r;FuR+A>C~z&9bM?0ycB)J^w?P6q+7 zSH@SvzD3e!A7;yDlm2_}pw`$cXT)6bMAgTACjG5embH0531r=R==psO6CJpMbvwfh z?a8{ADd~Pvh}LOjMz=BQ`V2T43J(lryLqg6#d)?M~#Lzn3Y4{aOq?O5oG!!3k$2+ zcat*Tg&na@aMa=Y!(igAzW26lM5{A?5KX_{2<)W#=hoIQKIn#wc>JM(4eGIdldnd2RZU)lJ&|@n79n}OzaabHb49%`14?9VLR{G7yiR4;pW7Mza{Bq|1=y%-( zWF`v7w}pPG^1F>I*8YTdZeWu!F-CRmrqEQK(QrKZltYZ34Ti$8kYJpZ-{VFDBsNmWcwDTmi*$A2g& zcI_RnVnq;dZAcA{E{(ory7vBB)9660o-tOO`aj2+QQ5zx#KL7wUj_Z{FcCZ5y3JDS z#;b}cIXL=xdSZay&_FmdI+IwP`d0eMU@3e@LpkPr=*E+u(u}x=Ok!Pv4f)w3;|4YN@izys`wtKl5lQzFhj2eo9sbLr~dH3LZ0wum-vPNcT zfMa|4+&HB^81Seag|V1E9x| zdxb)dd{>5T+Dja^&Cg-h`+RKY@o`lpkmfx_BBp(Xw`~*X89McNKl=U?^t5OoEzE*c zf^O9~ErGFwBd7L1?jjGdRMC+@&a6n}0 z&E`&@T;CQF?b7rtjSkQk^$4cJ7aO#=s5$>Vo7=k(djAy7T-f!Cjw{saErW!qK+B09 zWhCE?5`Mv$Qq7ouLw2rMh`Msyav~{u`kY?tv)X3)?kO8_JMQeTxfv5ShcHON zXsu2!B{oJ;PlbpA9vDSRvYyP4qKMR-MmG5X!8awqQBfi-1(7=kG*s@@zloml|A~eA;`*6 zZgP`z+K&f;M4VLp8z04TQ_@20;Af|b=e1o-Oo=#^V-!~W9gT1QW` zO8>iE+I~Hmbq2JOF+&X>Rse%^m~khAS#0>~z$F8Jd zd#br}fQ4r7OY+wiPj-Z?N&)SBZP=4@tkc1MI=qNCd3xmzWeq`qYFxbczvmRw3w$Xo zNOui~?SesGik0d8r(lpv1I_e|hDj!-_u0*}|0CT8D+d7#O{)J*hWggPu7nZC>-N zoLk$@7WrBd|1O97P}-=yd2uqupLShL>V)Tk@%U?3ict{K|FP|q4)wd)UrGNMrL2XD zrt-C(3GXa#BS)<}G43fW_zrmw!q8WpXr_m0o~|ExX8*AmLzoP&WD*bBEeAu}RdOr( z4a19#@Oc5W&(`*|2uuvAzgEuvC;7v7OxVP(8F}5^IEkGfGXg8Oy7E_SwH%GRirl&3fav zL?iG9!ViCuYYto@xC2?~Q$D8Wu=+*89DfxBYRHnZ7EfK|E&~q4NwgyAI7}{s)}b2e zs@mYBRl;Iy>=%k&0WcN?*J;VZ>@{?*-OROMGouWve$|ZXywH*9Q=a`Yt1Axs?{|!d z(GLtIDldE}ws?m$w&!C))VbJ{Pkv^(+$3i%*tWRaCU zy$T5MYByad1T3N8B1`S`p>iJ##ITx>1)jrA>RxUgyjHmKB81Zy1ug&E#UFgwbEzh27(sRamV35E_7iNFE#UI@7}!|Jtzc=i#`|nO6mj}z!#)I!bgAb|M}BV z04V`w57A47XFwLg^1nZQEt9qvv#!f=kw*S9!8SjE5R z;kE>YTHey~^|-}N0Tt+J3Lv0HRm5r)Q-r4rx;mad`Y}4)moDSP(o&1^zV_UfL1{K|tnj0Y=u;#01s5F?M!# z-VQZCK)OnbfcIO*=+oMPR;T9HR&40-+XEW-v21SA;OEb6P*RtA?LPu)1?0S3I*Yq^ zO`tUq+Fjt=TsKd_x18FnQoRC!&Y23kSlr1wd_-m6J0C z{vMRBwk7D(ZHH-FjJYI|iqg{PLK-P5IgN^bC3D!vHfS~61nI5IkbpW<_i+l5v(BwvNas1LqDH}8}a=6lKg~NOZKl&+% z_IBy?nJWL90>q>+7C-WZ(iN z6%{(_4`Bw$%}p3)7?f*NWAOjWa>c&e<4V5NF1kZsQ)B;VRtZMFqN6p6tn-g^va(_o zZP~?PZB1T%$g4i#jA?T33n?Wab4I?RR1UTfH|WP`2R96{u5Q5=lOg( zU!ZNTw{mVEKG4oO^TODh%i(u{Zm}7R3L`!s#M63s$!$V(n1wmArEl+rXf7J@xQeu$ zgJuzEEcT6xA_e&phmepERUEPKNMP0-{u6BrTsejbku!!h&W~61p!2f@0vnR1C@f5x{JvSI#^%B0Ios?+j{f-$)WBe|}C)Q~k+dT{eGGalvbs0;ziPYkc&PWb{X>>SDoSPT zv^yPJA)-VZr(?;GI`&rkmLp@Ch&GW{orq8vm8HeLlT%NMiYCgM48v)%Ofn4b^*zt? z{PF(r{`vlOKFMi*zwdJ2*L~mDbxmemW2+V5^u7Z969Wq{Bsw!+$>2GzVeJ$5cYaX5 zNkkFqK1HF6G-L4*{nHP>i@1b^8LB&#+xHpV`-A;jsWC}U2J`RL%XdJ68RJ_yDb|#_ zO*}!?M^Q=X@~vAJU`${bGCC*p`*2OZYn>)Y-vgW*T}7TaFnGAiAbnZct3P>Hxz?S* zY?P-!;U9WWo%c(7n zm>$`UwsAT_HiEM#1~}F#my9sKw9_hw~E>3p`DB(dhCcDGG@X`eoZTa3Lgp$>J&&Hm1BI+ZcGrPFJHM5 z1s?d~;$e_Dg(ze~#)Qes$nI_z!dre0zYRP`w6ub5pCp5F@$rcVE9G-mYJ@OFhz*{EXK;D884k_#jlMZ^^m`$Wc#v>Y0-o2bRZeOY&v_@dFgFiRIxW!DWslB&Jm zR1_LSyV)u4tbdKZs|XFI^s^$zWEB)*G3JDGz{u^qSv8IRrqoNpCS{DnN>K`+4l@@qskw9KN=QqKQoPC+u@II*tt+L<))q;BdnW4?^TWlZTEg46 zZ$}GS3?w&cXwTt0Bj@4R`brdny%1$Ke}B~LDkZeNd%q@U{4qI^ovchTm6K8bim{V1F$H)vitgTP=vAd3XyE(VEa z1qjq&5S>m+Qo!sk4&rG!eyAKI74ziPDQZ{Ta@*{DBj{wIW#+_XL{kd@Apj*m!(m54 zMrOB%hZ?!7V0MplTIWH{(G+yToOdf}lY10+kghm$aOFA6WUhyDCze||m0Nufn=Lf)ljs;?hW5R#${M0*~;RJ#6+_1Vqpk`{=Xn&{ogP zO!<u=BG2>OBYfMKg4Ch`N<4iGN`^J_5J?ixH_Vt3-iCc>nK zdbk_msQJyC?Vz%g36+$B?uMp`Gsu;P1bkj`AbUP0n)AV+hsFWZtF2w!74Gm|y$}Zx zIKl$hn5V&#A^{YMW&Py>QxlVKJ!e&VLFCVNEYp>f6a@hw|LCjb(J?V;SjQiOS*b4I zogs7@hJKd`(FY-~!v3n>PuZHx98BzmWjWnS? zH)J`No>-4HmjD%o(7m;_7lFen0k#Fs;0TDI)bookAP)z~YRmY-+aizHhj_mNz<>}4q09SyVghG>1XagMJfeYt0b$$X zcq(rVI~S**=SCK)GE5)BKih1Ut=%#Og$z9Ox3RH0&}r=x z=>}Z_Z1DmP74*f%uV2|&SXe+(vfa;56S2l0k|JpFRyLgq0hI-ue`i7Ha%JV*h*@(p zv&hs`6>{axud4xk3L#L{V^-)d;0KXd0>d(2uRuHziEjvz))0+Ajd7oGhTI)Y+!k}D zK7*Y(S9Xo6y83)Nou2<+*`P9!pFoKIz`1HGYvK!5^2hMwri3e3gb^Td!3dK^M%fSq zI8~zjLFJW!SB}Yvlr&p)#R~k$fxf;~2KxHv2_{~5p%*c!%bca;nc87KCdij=!Sh&MEIK*FU*p4Ja6E~*(vf1 z7O4I)_;%-sfPh6)yrH#lv#R7xP3q2eSRzR+xSgC_4|voccVy+tl{<0J3oyY?UpF&5 z5d;ER$&I8f!MS8%biLbYL>EV=+u7YHM%l+3`uN1%CYML!ZNZu$R4jb=_HD}$ie!5u z4KT)8^vPBfMWl=(uilIMuwiS&H2!kMitWi-ht80Hwa}y09F7YcS|)~2FfS$#1E%Xa z^Lvl1hWb{kt6O-zNsl4F_&UYt_q_m6O}|=LEMdP&%8IDKURpJU3ML)`ee|NL#Zo^5 z=aM(^D{pLq-*m*yE&T&jSY$C24NvBS_L-9+zj*QLcm+KRvKWpI4gs%16>NV2y4Dz- zFtOzV{s;WV5In7X*>uA7Q(3U3VCp{KqqDqZGjygSc~vuu047&MaLg{NBbH#4&-NzYP{vf)BHdD)02K7!DgAY$K}Z_AC<{s2BO zMyrFOz{zevX<>f&6QcJQgB+rw3tx;l-&94#f*_a;@`#7wO{zX)^U`Oyr+ z_3KX#uzEnIBwh(5zd*yv3E^>C>wsrmj~_pWS$564cViDfhVIXy+h#>NgC?W}3cW+# zV3kv4*m>{(gectWvq*bP_If!rXs$=H5Tk zw;Bww+559u0i%6ppUOjIZF6Wf_&jTfCwYb&H*VZupHGoYixtU7kTdbN;RPOXbrssa zK=P@Dlh3E(NXQ9js9_%;eHDeMOHV-^5`pxL(`(By?wc{Md}!ESYpjZ%~gdOFCQawz!JNCNnbY)R7qPdi9zsU|FM(XTQra)(J8WQ94F zPa%NREB5Q-Ql*1Mk6&0c& zI!hgTBmq!C`1}LSX?0tQ?xq(VqVBq_l@KPfEF2ditm`ZfdA2n(Cgz!f{Beb4%b2Du z;woX^8VvHBtFB$UHr_Bj(HW}Iw5tYCR@tK^TL_#foI{c4`uCtGmq!1MrxP^Wnq{dn zFfc&6KL8xQjT*!5JFf%c^TZEFNc(wvd$(pCjs4u92*?R~;Yxk|1A+>xk=X4eakhez5~HJ|O)8e4OGp`mpYH1AHQf-l zeL+jj3`C+a?U`!X?(Je(sM(7{#@4{pY;;)WCWZ)=4yt*SaQVk37Skq+>f03DbpPH} zXvvVk(wyw3CpHDUwtf6aQmn6FpJ+;OD+*BIqTjFC#Nq)+mx=%Yva@KW*_`#EkXLH} z`I_;@AS7$EN*S{PJ*X3}`j)M3Z8AG}@L=4!X$rCQ z8oQ|^rvXJ~FY2z4-23VL+aUs(P8Jq`sS$os5WI_5rXTv<%K-?0#odjqE&#N^bP_{%+hP4>;!G}oNINgXJ&%#l&l9q1e%6U2{R~Iq z+mDe$R5cj6LVaP)_rIQb9cz+y3PV@i)y~a}5?D`7MM2WCsq%*|PP6L^#G8QYGKL5t zw+o%^5&L#qSuH@fA#;8bdKbi~=M8cVU{YUME=MF2mDFUXlBbuK6tR(Ok8`5>8)N$;(dhNL{mPl`)gH+cOj2ss}uwFh|hctn!?U z_TJhw7g&@4rT=)~pr@z0X_FjYvjJsO;KMBb%7&&U82}169GC(ep%5a6Se1R;$%)3| z&IE_5`GzuHzGQSUXb|YS96frbzJ3S#qilmkGo|=K1KzR(q?s=t`MaJr4!H&ZoD-of zfw-1%rbH!AJd4_&xQpoN6$Mu+|0#J^D8alDRUcu&!ahie*d*P*?^zJXD99xp_Vbo^{&6?!4(l6V+|@}k^n|(XOSG!Lq1`Q<`0DlRT>$|Bxi()z ztiw;D9}VcRj`d~EBzk}iNEuz5`C%lsIhKycW&DMZyd5lJKOAa9jU!eeiK%(D!-*{C z(13u^Euz(<#GVNNX(w?-3K>1zn5-`gG*lckv;?c1HU9;=u>WnG!&oz$#d`P76d@CP zRG}AtXEr!FO76>ORMagV+!B`+_5+#HMDBzh^Lf;UHh|wz% zTH~Yc08|%4q)Ys5*IhR!T>;QC&s@DKiz2^uir~t`yGK>oQ;I4o&fO6qMqW$U5tl#C z&P+fH93YC^QwKxCF~`@$G7Z}Pq9Q$zmq$Q<45E@XU4GyEs-r- zwh;Xe5UHqGb+1)(XpY*a#Vr@U@T}-4j!gt!RrsIiWP8X^1$7xI&WryoydW|wa*R<| zg`{L=ruXQaqNUTaq9T*mnkeF!H+`Y$d1Cm}r@u{G`_W0s4aO0yD(I5nWK3CA4Kn$` zqPQs`r_Xv+0HMRmV{;d4Qk~ZHljt3v!7K8u<-K9!Z9~7M)z#Mep(psnt8Ec7v1o3V zfyt7kOK)-q^MDs|*+kZLvhK8>&3QK&5&{uF(1qTaBQp3VhCyYSDu{wyCxKhF5I zyHRV91H^RjRM~<56a=y@AYV|#jDyB7K6d!<9{S_!9#wt8B0}G0x9x|w)H>GgS-1~k zwvk@h`B0T09?b^^zk~ww^PZM61Uz{HCEyRIgy3&CJ2NwJtL92VmvvclvlZ|1_o(>l zKaq7ARVqgGYuo_H%yzrD+~Lcd86NgRcIpO@p#tKgN=r+Lq^TYuGyl+E^LaM+)E!G{ zcUikakbW0s@$-deX6yd0qN%9Di5U4L936UjR%fOaZkY{9K6 zn(RI;a|ccl=pqC)RnNA=Fc6>*EdtuzzupTHIVai}mvryv{xUu~`meEL3!b~bMGSW# zc4jfkzBp5GYOP1cN4}X3JFQNUSr~?b)T#5bYjd);q12_ihli(karG$(j}!RItEXqE z5cwSLGqj#Y4Ie)6xFNkKw69>(zK(7b{Il;frr^A3?BL*#oAj18HUIh4!4616c}o14 z3MX4h@%f6+$BtP)bxzXt=D$xm0$wlGHyz*q^dTZ~&|?nvmU=!Zav!#6OQE4gJG9e2 z3xz0WfnzgQvSN#vv6_F5O$|cvWJ<3$ZpHZcIHcVGJlU0n>S$mQr!tJ;%pV=5^nDvK ztJ?2eFV*)X@CTYIlI$6@+{{%JE1qEhFXE~sRCNeUm@5+RC?UfevNOzzV?1c;A z4n)6pS#IE3UOjZi3@5Xj;2 z{`wa*xj4u+9ghyxM$g?@`SW;Cj!Ys(gAVUP#|MN^j;vaRFQXZFQxZRC&Fa;zm{*aj zI%m5Tcc>O^4>61^0gUr8V7s_%qZm{IjZZP3lfuiML$DO0;6#?;p!XXy{&@(Up5tpM z91w5e-BoGC8Y>zryE^U|Ha+qehDN*4tb(8}acI3diK39+z|V<7h(t;?`#fU)80w&Z zk1es#u5c?WF;Pl*tn!`PbqT&76t%`O<-v@8s9?I`{Wr6 z#x$@nN9(%z>O$YHTp#~&9?Q8ZNCDls7=U*&KKu7fE{AQzm{G)!p8@?N`DRw?ziw+= zhH@1ylMQ-ZVONJP^$-UREIVm&78LjwIO*laos?F6QTzV=5@5eqMXUE>B&!KaT;j{D ztgO75F=Gha8i~QcO0+1DLA5aJxXxZ!()%{eR2=QDg&4+IZ@W!QVkmgeBg>F4m{zI8P5Vg6ql4N=4+jTb%ybd9djVDI1d<@fiyw967Yd7Svmkc@i!7?PrO3dh5pxhG=7w zg@A_%M$*w}R7XWct`!lVGe(*=Db!0SwYurY5{0xB_d-Cag{pY1F?G=_q_7R}aq zPlwTIzkDp|>Xj!aN8HFT6pd2Sb}sbkkVMKSGlapxLC#A9Tusd&4oynQfL7syZ1)BO z1LB9HpyyMMJ_S($!J&kC`Y`gW|48pnA~0LOe(<55yjcwdy<~`mP7Wy@i8K(VG-$Kd z;3hO8*%xJF(n2Jis8hV$3O-1Xqm3pQVfPuj%nzrvNc{~2>TgB>s{z!kpTfd)(w>w zTtY7KX-Cc6(dAt8+pTEK4Qc^9+uGVlmKKvyz6h*Q;eV_^RpO?euX5=he_tR*=4iU2 zSqa^}&@El>3&_&0-@0qAh!+41rK5w$IVOZLua)3O4*4s%`-$obuA760Uw~Dn*5zZM zKL~L%G}i^|;-R6L4zq0x@ucy&8wNi&kly-Q%am>a7sC$fkxa)5F?N-$rK0|fQ(eg zNF2SFRrX6?+l-n(|C+eRSXr}o*iiS5tme4LT2ilP^7l7DUO*ta1rq^KmZ)+ zt`R|1oq?kOt@F5ok?p*WhX)iLSyr}Xt6^*ud;yF- z@hbl(ccya=`KqwHM^4W9|NJhu|3%1|+@{zv@=0>xG97%C9c8xDVn?otOVs}WRi--} literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/figures/runtime_causal_glm_vs_dim.png b/docs/examples/robust_paper/notebooks/figures/runtime_causal_glm_vs_dim.png new file mode 100644 index 0000000000000000000000000000000000000000..670a22a3a8a7c5f98f469015a636baa775ad5d24 GIT binary patch literal 37721 zcmbrmcR1I7_&@r#_l~S=g@lZhY?75dvlH2SWRqP)Dx}EF4%vGvdnGHIjO;C=bHDnW z@Ar>$&UOCzbzNN*dX49}@5g$+Bh^&y6XDb1qfjU!1$kKw6bd~Mg+iOf#fGm44gOq& zKkm5Q({s~wv~=?{bFn}@FmrRVb9A$_e#GQq;o@rT=peu&%yW~Q$;!>m$yJ1x*ZzNh zfXC703GXC6Za7>7&q-e26@?-(Lw=*>NMu{1P;ng!vQk=4KdeuA>Rr;M!`c?8y~K&n z!tn@)QcIRBsy>+AhFe{|q+^;~Q_x0PutU~dxhMPTO!eKmH(r=E2j|#`W$WE+n6YyTU*OJ7q%W^%j$+3uy9=>e8aQ0(rl6oW+xf~kU?pfZ zl1C|ix-Ounr^jK$fDD+XbUgDJ26GE5vwr-uMZGI=d8UWulr<=7TH5Cm<+d3z;lz4X zPRuPWE!!u{AI*m{74&M{a)*}wjV{~A+K^0lgTR69QMy8Iv)L)Sys(3}T-S}_NToT9 zWF3sEU9kNA>^Gn6Et@$wq?k?SWTSTO#vlFkmxp}pMPRv`7{>qh` zPP0v-h1Zyw(WIoLyd}0GDX#HZ_E4AEjg$BF^;u0;+_3EXkb7i%<6#-@%a<>o?ysnS z?sxyyLj%{c{Ik_-TYt_V=Dp7`ypXN@E-uTv;dJw^a z9?dZS`qIhSS!(pNQOT436hV{UhZ~cnTHWt8jK6NrwGP|Ab~$3o9{u(*oP=@h{KMej zpkB2L`*grxzpC2W4FB^#Pye1Dm6)|+fBRA}YFlnUd8@y%pMcBH|8K*_#rdh#_Z;<< zGhfyvTz!?zlWxhOnnF_V3-R7UX-|T&p&|0$KMI3GL$Sh$uRNcfH9gyIC0QBFz`-!( z!DDth-ZtCcm=qpqfYtax)Qi90_UYO2P4VMdj3`q`4rXz2@m});UXzzzd%x0Pl&JY1;}X-~4pbFC z!Hi>@oAR9L-Jj5R|Fdr`d+%P>cx}MS0JD`KZN1TTvB?Vi)=`sy=c^er$dFN7Q)z=Ib2~g( zAE&^;A#SU3UMRL5<&lre9JT%ZA|r#f#{GAEzN=QA7N_p=zlVYcYw7D#d2+fjS$Wg{ z?CH;H*K55CEUc_0Pj?=6fx@4up6e$D4%jtTbY_IjX)^IWkieT6+)|I(hT>D!y zj4(j$?Jq7LXqX3M6RCYtrlA*o$~V#g#pHvCJ3a#ggLk!#|HDRqF^8Fk=v>Y0wUEY3 zAN~JucD>;Y?tL$ufh#&>w5VO6NA~&iXT5Tpw?kQK#1RSV10O_RWX|X?Isd%G=C{Y% z7Fuv{s1)xt85Cy}%8#P56u(x~Y(FNtpLmEP*FO`&Fnkd>S?sla$LqoB(Cq4JWLujo zy@bEv6CYTiFR$DUgJO{F^`Ws*qT}V27hyznE$!`CuuD3EFmT)^l;81VUAgPg_V&6i zpJ~&-QF-wGRy(X{GCDc})L4VxU2Gy+OjM$XI~NqS@~`3H;pRUI^sNTdu|6ax59VrK zD|lGWHEiZ&omt#=LbGS7VW7(V3JTgN0{+7Ms9<7;~S>henPZbxi^h{TWvQAu- zG#E7)YM<7AZJC*2Oup;cu{l*cm?{-47*p?YRN_X!-*aJ`k-Si( z25@q6w$f^IQBG9Yzkm|J%*-tHWabrXXFwXvuSlHz(N*-Z}ii*yzuSb10s$Kg@&ImigV|^^m{_n5Sw+#&qP*Ea04$?`)kC~JzBaha{ zF*G$bVGrNwJ(rV{gLRJKu{B+|(M%<6+8Rc*GLnDU(8x%=(!t1WeT+;gfoJ3-#Tr_{ zC)JNHVTB-5bHkuQR$g8{Y4lFOMV-nyEUlJm*Jb`f2^JP~%}>fwP;Tee*9l2TNT3-> zX!EX0{5=as!@zFp?R{_>wOQx2=QiEVsre}tuD*S65Soy1+0f8%;Iq-)^|2C+$jC^& zVpFsvVMk(NVPT{QtEPw!GSpfPvV8w+M2FNl*f#1##$sjmlT?ite{4U^gzKN5ltOzz zD&N7{h)jlDG#^ax&l>mZBPTFANR=1$`y)8q&&a9tjeoJ0B=zLCBb&b%W8^}%Wa(qd zMa|`)6|1K2m_v7oPy)kIs*km47jY4}mKnrHAVQHG#Do?lSkDShqoeT6!v2Ds};VbuGLT-BRTXE}u z!GucS&r^ZTpANX>NGnilIxyvaH>>URFe9n~r3x5{Z$ zQ`YzO;^JaurcnZl^D`)BEp2U>mwAobOajhBp%=ovf`_*e^ntIB*@LE;8bANmiZU^Y82{!+e+URH^)XG_$|8V2q zixuckr!gISl#v(0WF9;Ps;uxmyNUHUzOjYDV|srsds*5Y)>=SMRO{Stp=q0rv+ z+I=h=HT8z_4Q&OK78PVf&BI* zYHzvUe7eqS;>mSQY^FPR?#yMxh{IE(S2)fPJ32ZR=#@w>$HWK&GJyUrXf=Qe3kzFq z*o2n3;(NB&FJayp$Bv+fKl`h}iHX!_2csrPXEif74+bz~HMu!niZha@-CAZnOiE6U zZfpBnYgxi=g|s7vF-Gw%ml@1aEE^v3g0ajp#ZGh5WzY(zCdALSWsXly%wV-}7*ufc zm^4ILbjLGua}&<^pWX;uM2Y+TaYGjTN54awxVX6DfWQ9SCJpqTK7FE=@UJ`c?f)q8 z6`pIb*3+@>XxazCPf*UV85tQvLPN~}AmQTUw|oq^h(yZ#ei2_-0)azUJ z6Q-L#tB;|K4sYDKwiJ=57hx;RY|=n{(nZ81ee|PHs~SDi+db7K7bDkONZ!W%S4LpH z4`tN}iUw7xfE$!}+-Ap^J@_rL;`=r2V+<6UB>Hx{24h))?iCyN!v=ZfXrhDN^(4hC z=~|DC;wSwKusW>*O15Si866xPtS=rViuv4PQ)sEMpER|#B|stT{LkUW3j|BS_L)G# zheezy?pNdCX7*9sZySLXU%%oYI}5>*?^R$QC9dS8m#J=@Xx08oQ$9($>W(9I=Lh8VN@l(S0 z!j2sw)50+szeAG%UANWFbpD<5`TH-h@3e8376z4`P{n!I#Wv7cXFYG3C zNnEs@W6rjv-?6+V;Z^Q*PhHy_@XY1iKcwdA;&3HQ#1<%Ki-hNy`B5uVzv>VT%mU_@ z-}C?tgAZl-f)w3I{O^@z2l0Ww)vtb1=+!$;1cX{HCO_0JK0STqjTZ!XZLG}tbp;MP z6l_=+9RQ>?%8Bssnqm12!t2`~W5mfc1EJc1inOrU`Fdq2>xG9&wo0;FpL)Jdq2T?u z4Xdz~bM#BE+S%E;c^5-(+(zL2w^yvI*iCQLU%~9@6O#!%>1(^s=$An6pt2 zTAI5zk$_L^DO}yj$w~g;Y#cCsvB$!tl}-fBwm+<4Fk&W;yisysGm0SswgG5GRWAu%cG5%l96wKQ}Dn+OO9_#RDp zifnjnOoRf`Pd=K3waTHLr!_z{++~pBl&769*jUM~APtLm_i-Lg7cJRk z9Onfc7v7nr-;!2|4#B%iaTq4`BC$3il3l4d;-Sd0s;PEjk4;yUZl-6?^{m3fI`!E!Pid&jbE|h0p3OW@7&f_3{c)#=(!Wie$$^T0iV;PY(Y5_sbtmEGcd@| z%GD4NV_sEQz0ylWNB5pSnU$OS1jk$j2lejVJ7yjpo{_^hDUFqm5`RG#QOnhec=4hY z3QVb(SWI^IwZqM+7ceWS#`WG~jR6wVUdzc^m4AXp=Wf*(%>k8U;^tO9b>BZ6-Fh#k zwCc6ZbVAYr_1+A6x^;yY@Z==4ftD6@=-N+r=I_-ktG-A}V}8jX_8bV_902mFfWHm5 zd>hVxKGbRO59(>`AI1*^9Goa*OVx0;tK@q;7tSXrC<`+^m?nb*OWJ6Xz|P7l!gaZ? z8H%Fk=FjF}9Fnr4YM7YZzEjIyPO&`4nKb$Fi$kA1k8euVeks#OW;y{EQFOw@2|PwE z>Gxg~L&Y7NoXoJFtXLV&R?RcmN0~Y}d_JE}ue%n4O2KNZDfGG0!b|#ekL0?HjC{Fm z!`f0lMy}o3U1NRbTrDh{yo4yI5W#E;W9R4J=%>)d{myy*^!E6E9}RmKJXoc&##lx( zIC{gB$(^JNNYm{6{IlJq?rfX)gQ%!Cj5UrkR9&5-7GJnq0f8^y8hFpY*6iZ$-VVgs z83qX<-o3Z{{r!)By?5k$x@}tKIKu$FsC}%&T)o0hU(n$vHG=tJX*$oh;hn)!&3)Ow zE@W}&)wMGI`5DvF!IE2u^~o-xZKb(hCl2%&+Nt#U2yAR;MAyXZTP!m$rK=YJpX`i^ zU`z1v^S8h#s@HnhBb)l8@2*0Ued(#thv6a<)bUD2j9R1rx!`0bW^ScI9Hy?Wu3^2m z(8%)EgBO>1|6cf@9EY?i((Lz3Q(F_^r=&xxxo8p*E0Im zyBeYz!T>xxr@HZQ7E1bU|FhdFDWZ|Duc+gO&$UeB^eQ|atJw1Jha1={Hv0w4&)2ALlvySWXu=glG#ScLDgjdKHd9-c`2Kx{_3)?LzQ=sDLN*~eIXQ0G z0SeWHeo%k3Fhb!r1i(Fk1Ux>OuvRz`zbPd2;6TWCsf!X(hEJj%D=OC2!N$@gGz^7Z~4;6y?2x(jN-BfYeo0XKlR*An(lv9aB;SC zbc7RoMsK6&m{wH8i%r51Rc<@_2_L}{)`t!nn-R;rWkISx_i@OlSBlm1neU7YOizT` zkE7o`vi0Xk6#n#rsr+H+IP2?=p+2YGHCWLjVx=um(FYUx*ePX1y49?)z{e?JLqz!B@yNyo;23`J5>vS#_Z<|o21 zLYil#Px>E?fBDW@hA2(2ELxkJrA|d!TU*VZJjtfG0CHHSIr-RgEglg1EFiGlU)f4D z>dDjJ*|6?)?P)qTd>tRm)4onkNB27_CEkMO_h`|5Xfs0_lyrAJxYyU$p+*1H>hH7Y zXl=y^3JL=0hmuy%Dt43UIb0DX&ycfcsX7X{*?D(S0Rgd-jZ7Nec$y7i$;rtG6B4rh z!6YK`aFU0M3m=jHzUOKRtmCZ#o#fKW31sv?W*!lU`ofLKGzcvzHfuF~{5X?n%`uD~ zh3v^kj~;zF5M_>~pS8EMgYRM|-;st%F7`k7-slf1J3c*aot&hD*&J~Y;O8GwL3?a! zn(F5`^ZBSPlA_Az$f4qQZ_j&^a8&W5c6@4T>Qq?g))oD0{U1pr&PZYN|2R2OgX#lX z{U`4LAzGpoi2&o-zsJ`JWi`kb=`Q3aim$-yNX=)PNR@h1v@O|o&h>YZ=iTZx-5!*u zz7Qb^`=}aNV{Rb7)J^$&8>bkD&&1t&Eow>g)clVIqo~)8x1N`mEkO^%p#C7I>GYc+ zP3OgLWZz-XTb~poQVNv63}mgsko5HSmcSz$)_M>@bLc&Y*O_woL_^Q02Bt?(6Go z3i4+t!DSoA>*XNqgFVu&YvLCQlIuM5?6EHvFAfuq-Z!?DvSeL=0g&!E(@|VhBTmD_YLmA*6IKRi4Y51=7B0_ z^YzI}lI8x7U!P$S3=v6Oy?QnAmOU-PfMM#KbH8R%- zXB>+CuV23s1)dOt+(Ae{KwwXjUQ*&*(dbV^M5H;wt3Y1iFok#r+=f+rQw9q}w1R=q zb*Q-Y0<%>=whRuEAxbN-Ix|Pdq7BZ{($dyAb|q3qMiK)918829la-GB>hJWdy?miXPgC?uMla`mH&r3dqZgc&y_xVxGq1DKUkB@d#t-2Z0t>q+S5=|=@@QM#8>C_Zj=TH&Z6F8n8wOCxUsxd88N3?lb)AAhAVe-ML+^Ik?$XoK z^Eu5*+RZdvfziDO3sDVB4n)!?xXjarhV}#J7xp2l`D^x~+NSpv&B6eVv_W@Uup!G8lerJJ#wt+7a z26`GL*IRtpolWr1PaB~<@`Vq}i_O0h7Z}&mf*jU56Y!TvKVBVZB=BVex~{-QgF_HN z+hNo0K$S;2HKH4-N}PuvvLSS?r$8zzKE*5K{so(-y{Pd59|HpeaRq=q+lOsMvMC%d zC72)!d=^F@s38T|QUeWsKEC^d_a~mv0{VOo!t5is6sdHo%TgC2kqgoiHJl$B7=pnF zP+b4BFe`wn4&KJIXYixxkJ_IjqIMsbkfYMcMeq8u>eh6<8NgrYGKdE>q;jX!Zk%7x z=DYmz2N0J5zFSS#RT~>?s$UbNjhH-&bNp@GK)f?sI5?c8cvZDuqlj3mubj!^7N-Cj zicP)e2l4ag&!55G0ZU`}!=?BLjask0t2Lfmhy=2iKt-tt%clkCxbw1a}({ zXymq(^Y}@%fdj6;3-gWi8h{Hu!;`(T_r~EOgP^RrjrQ@V$hr|O<_iO&`)r6~EVsIz zkf)_c>H8q4ksz)i==E=S?vY93Muf+h z@K#ejJt|}$Rg}#8jrq1aAO2>*rDN;d^aK?NHoa=Z*zE|qi=u1h!~~<`yxJUpYZ8jW z#4-ux+Ftwb2s{7)<;@IS0=qsXYFvW!sq3Z|D=>*GCPk&T$vYY$Z#=30UFWN;FM7yK z;b^^Y|IEYYtO6megXGtvWA1rqojInw0s4mj{C@R{h*{EaoU9&>*r-TH?rKZZ)Y8SI z$L}w^H!|t5CBabdij`Lm*=|h!+3mR`vMnfI8KiBUvo;!(mm-ikA;e5WcU_aig65x_ zpT#2G%4E`jcCrgEHBVu@rIpV#;wIlvXi%iGYf8*ht&{WLO?TsLgFIm%4!27jFkWaB zIsX3P{Oqw!x^i0P<$vjf_BRm`A-+o^akirl=r>D>JQc~=!t*+Vij1$wzrF6#SBR$?uQ8(rdicGZ^kU7ODGxDULQIge0mh@}MUB9Ki1;R9Xq4}98w zfOd+s=(@eWyLzRT(Z-*}p!9*vL=8py$Eh=dP1=do*6bB9bHfI-ODJX-vc4GxbtSUb+Wto1}L0><`l}Onw!xw?#Ia4 z*>OkY9Fs~*{tUo$UW#TPoi^O>&Xjf8GOia<8!05}9Fnenq;L}+NIO@99We}hQBT*$ zZi4IlhD%#9sLLfAEXI`|g$7~vAgj+VE@qzJ78RvOS_EiJZWHGqD8XokADU}w4lDN; zIy#!bOcqwTE~_NDgFvvXt1p|$L`Fg2Jhe3Ik2&yK zKa)uf_DPp}gh3@*^jS^NE3!-xPNmOxWVkdRRzJ#>)DU;SB(PhpB!9N3#MXPlDA-si zIrQN#^UiBt=8v_RZr0oT`?4TkzGRg6Tq*|2E@EaNx}j(v_V@o5EXUhF4gAdW{z$V10qWU5?;K(vNQ!p(aFuN4JN}0 z!W4*Ri7EzPd*krUWF4|B!4Ze9mNC_U2({I~qD$OXH4vHr0w9Q$a*(1J_N|BBDGPr? z9>jCT?=oU2BHREV)Y_6e5&dmaz`P^=q7eN+40EW}+q=8ZpcDa02?sxw>E=x`0xE7v zaA}Y%4tUOUAnAxK`~2DGy!s$11pM&~FdSEa8iK-=mGCf<+ij!NALv3@ zZS7rvW*9g)IBo6iSzaJ)-I9#AcQOjjWyeqv=-AzHi!32p%6FgRn&i5KCH3jTDp;RS42h z!gfMOVPavW0ntar1!n;w0n8Bsei`%a+ti?t_Ln9_KEF9I*jTUD%9z&{Y=u)F%-&~~8<@_nC!S9>%gK4a3o^>m zNCR(jtn>*9aObi)tG^fL;5RDmd3^zCR3?ti4x%m<6&0vJ;36475)y?S8AROIFabj$ zk)vC;ZjIG>l^!0tK>tRZO$rnQeTZyrZ4plwTq+cp1=yxAi^qqPj<8E!Sz2{}6Q1!6 zc5FDg3W^|ElVfQ`MHo!|uby`p_wV1|D0K%A*9t1OI@F6HNRg;VDokgoPq2 z9!!z3N=L>hlN)pYV+ov=Z^Lvk!|njY@Z;qrMm|#%5(YTi3ivA%!ypDJig%)g$vHU` zc#)(RK@$VvsisrdKXlYaTvhzzi*z2ycZ{EU$-jAq#zz-IDxY*tA={K(S1qY`J5J6o zexo&$_K+{}BGOno6-g`kMuc+7p<^QI`&nhi@8EI*!lU)k{#Hw@`s3w4h zFxg+fG6qD!VoZnmuY$xv7zyL&J-akmnrq*4gJED$l2%q6K=;F;2C(q(sAPNgf|u(0 zs|S&ehOZ31PeGh!=nc6QP^`ramOBxL9ZZ8j=ptx=71wsbOMZH?%MN)8kp5AKqk<5g z{x!up;7R9yA+UgkdRSqXi}(VmO#1O;;BW%NfhIu;WGW1#0+kpWD3x;y3&DRcPD#Mg zHclB;(fS?{tA@adqSN0V{5jfC%CAXuFV7Q>yQ zAd5GEN65(_p<;ZMgI%(&b9$Nqg^)Y@TG%z&HoAomg$(PSl1c8Kd83LUo8h)h;|mbI z!bB4g?;sekc8m#hoWq@&@~h=#xOnX0DgAao8-ghrc@$853W&=vX!;VsB)AvQTbBH%v_$QWW$6<|)ylxvsum$2#La&v4yvZc^m`}p>CWw{T*n@Jy@q;vmlU zEmv_u%q~5K=`9db7iTY*byZUd>cmT0J^d41peVCssI-Xr-34 z0lNf+s&c({aAODd0y@gh-d;UTdY7cM>{G1Q_Ur+_Ea$Rj{z$I(Iywf%4p=aWmtHl7 zXzE<9hWwWbNggRWZ?TgT}O-`9#+isd)1JuI_AgCMBg&noq0Uud(QNv!d2Elo`WL2H-? zK@w6~!{fqWY9We0xFq=6_qF)`-zlj@qrXuP9r*TFsSoG>uY@%E znSK|n6@<1x_D~8cIT={N&`Z++30S9Wfs1n==q&WeNQeo}f>Ut3Kdh=*-hVwlJ{}ky zqThlDh%iYcumM8|r5rH}hUxX~C^2BQ?Q)%ce|CX7%DqIXF41crwU*0HxQ`=7n> zCm-j)DTW3e22l*SloLcp>F>B8A(kBVjIbeg@}=v{-7wc#%EK$#H)o&_`J4y4$@i0u9%GmT50(pitgx52_dKv%YvPNiufg-ZWj7WGsuu87%2*2; zu&NO%?3S|N3TAGSknJ`o`twlkrE)d05WIkhYnOQp<=q>g)SAJ6g2gCZZZm>-k;&|1 zyGfCec;Xkw=18Ohz`Y!J(}>9AdpQ0CNq3ZMc7w^jd`!#eyNw2hL5cT)4ZI)@xP8r_ zac%bs&ph_Ydw=#%ADOPBLG%$AFC>nKs1{J4z28?Q<)l&!-UK$~aV6quZmCJ0A@iN> z>hD2X^|Wk4(5Ig%kkg2Dnqc0$cdxm(mk1DBZv0)=E?zu5Jg~@=TjE;oT93y@7S zm7m4?H7WZ1;A^0AXr6(>j2N6|gt;$=^Q|A+$b6a4qtm)wj1w!5M0=r}Gy!*i4tkGG zB_kLvkgtU_u6nUZ`uu~ob}7T7(Z1dUo9&e3jeH@G-;#N&D9&j016iyqjW&^&)S&w! zDoNwnE&+s@O&uL$fD%0dteXR|IwYf;rNqW{D}=PER5;#nj|6j?-pSGmH`k+Ax8B#C z>FtAhE^>7!r-bCV7liXY6w0wrpk4 zgLiPXe6(DYc!JU!I$vzyb^FKCVP*hV^Zxw&>^)slfU}bwv-gQYa6NOM$E`C|7Dv#~3s!g1>`V!16#jM)J zhxhIt?y8rxBboQm_yH>>^eyQImn-wT?h!aK?sR%@Lb{elBBn%CVtbU|a=yt4G()km zq)XqEZ3yu_klX#~tLun9c=I10Jfimxr}-71G-_pOxXkA3;>5{lAMTP_P#|2I zQw0JB+a<7H91nNja-ijY*4*9mg z?ZTbcgvf`dS}ybcnF9+;x-X&?U`jRYuIY>aoeY%Q`;R8QEc*2;pQy7Y%B7urDNv4e zVL#dj7vC?^MTdQ`%0oM61Z!^x6B)!V0Tywz`Co;juWyFe4cM#o{enjGa)gX)lXzag_@P5y;&juDrP0k89mmJ5jyZHE25M=T+KL0SL}CIx%-7Ki2^ z?OE3_t=g+><_o^wSh8NK8Lw{o<*Mdwx6L z&5>4aNx5#OLsr`@M}iiw+@QXW_hn9?tzj*fViN6wg|ctb>3{ZNmB)y0;q`y#I@+Tm zB15Zu80QbgzIkrS{H!Jkx&L^J4KMOtB{LUx1w-u{GeCi0S6$9QV?x17Q-5_eE{=&o zG_1;%alTLNTjf?3Llyywi!$@MN8nrbwk+FsDY{>{v;IDc#DCY?P`SO+vVjUD-um7$ zG8yBJ9rC8KqG_Ehnc(LozW*)<>+RV`nFgfUPc@M2%reI&zNsmBK$EkcFAZW@XBt-~ z*IOnUI;n5`+1gl|jAbch9J5jH4xs)p1AH}S5$(sLQ!$d})$<6J}b~HwsX8EcdKbjgy1hUZX%2=V>O|IkCO>9T2@%$^|Bv}bePy!XnBY|^lpyO z$%TdTs!%pOkBq=CF8G}CYV}TvXt4b1cf{2+y7yrwXZ2{BiAUaW@qA(w98G%q4hcWhGQi;C26V7W@|-8f@!MGTtDae+{4oJA@-$*X~coF(bx#GKw?|a zpR>A|(;q1w<5)I$%SpHV-cI1hQtB%FsjFE`s@`0>SQN1H`y~Y`@S}t;qy5zL4s)_l z5fl$3X~2L&T+KH;p%Kr?m@&6m2_RGf*6fJ?n1<2d5$hLJz z|Mbh*n4<99!eCCzrOVTf<MWvyp4^hMyTk8 zW0)t83q~=%5$cTv4U*f0@BvucnQnJvwIU)zWT9Eb-Ct_J_kBu5i2JI>`NTynOSwLd ze2cy5)P3pY-0Kx zH%_=FZZ!7%j;8$>g(rigs5hb2P@>`Z1inT&R}qFuxnis`e~M#F{u5G4e`YfE?W7X) z{?-h&+PoV&em6-wLY>~m6vT$e|5lxPmJg~KE-~?sPdimrw^5RjyB3l|kb zJ$j@me2Wt;*T!W)X?05G-H^9mML6;v;*kopQOEumJU_idhS(n<+vA?d8Cob5cJ;wm z8Ck<1@{5*U+nmN>8KU+z1C*n9&%P)n@1aFg1-H*agnT$D`bVDWpy1_mvqz63%1wym z4xZmqOnAGDq{x5H-!M&o#yka=xd|dzTq#9cJ&q%01CHdk7vH0`x#TIk8);M3DdYSs z8sW7jA?Uggx?YXUeHHV!=gEA%gAZVwfYOSzKIdY~+Hiuq_?m*E%a z-J^}nv6?)e(N;EC!$2kSH(^X~Oi_ORcP>!9fs~f0Z3>4}_$=*?JCD-W92_61sm~S+ zx^lAZy`A}uvB-Z&(^*yPy?&ADF7bw`1+OD1mtSGzDN_Jhr$w<6WQi`>Hv z>JOL<4e0wBQj&%SZ$_%3-Nk976V!x~*NVb}y*=H!C`BiH#_TYKO-|uX_qJSx*oU*; zm+GF6pE3)V$S+6HD529nsjRr-$8ncwM{_^9DPUn^FsU=MJ-&0l@pLF4}1b9^dn$#iKs zaZk?ZAc}at?agGH*$`6nrx)nE?g0_+i{#ei8orCA<>F;N6iUl8qR5+g8JKKe@`2(- z#PgT%GHr`99_T3X9h+e0WLi!`^=9A}S`mQavc%jKt!~Fr-3V?DA&a%qQretSqRFKr z&zt=5jZ~v|O>M+EL@WPlX5QP|E6*aHN0kb1nJ?B6#af^_s(SivZluB6kt1)%{P<4w zGB1WgBR9_DDJeR&d}i$;{M;Y;PmbyHr1`lUTusQ-2if$#BSR|!j3CNI6TBm%~23|u?re3mg*PNXpTsGAk%eLU$ zg;ZJTv7gG&d<0r-P9Qu_=WE_mEFe(QqE9pLp153PoBZOXcZE#7nLHsjqIu?Xd+(_f zk^rDhe!D;;6~ z*$3ogl$U6uqnst|n>%ei)rY=Q(^(QrExBa9<;hn=4Av(syB53NHJoC;F)5i2S`GAC zTcnndKji@rXE&t7^=^~0(+x0e{U>*vKp;3h?hgJ zGKdY(%$)XYehU2$#iZr6q&P*aT%BISKVG*sfYzf#oGqTCirN);%K z<80E#Y)9Kuby(og;t~(qGbzO8X@x8vcib=%(6o5Y&cY=PohQP~bySA6du6JFau~-` zttM0Y1zPNLEavVzH5Oa|_KYPPPUJ{Tz7b5x#l5Nf@UZK~R1z_vFQ7M%Q)k?|M&r=L zW7#rmH4KS>vDf*&i|80klwRO>SraA|hXi?UX$4;mA(h;A-AE-mu|z?6^pSoGJ-y2j zQ`|@X>Q%1`5un=nsl^G~e{8UfWjj*+5AgNGvm^+Ks=Dqw@S!wbUHw@5=CF)2ZBG7S z8Q~gUYg(RF_9>x>UX_DEIf4#iA>i0mf(=r4tg_;w(m9+rpXyG$z5l+Noa9-nT6rDi zk6vm*R|+1-cLs!kT95Jm9or4G+;Op*PI1#O3|mw?a3?W|wlP#nh>%Cai&UWAof0vu z-MjQ7-g0dJKy6I#mjme)c5_rFi_$U9+VtUr(pT~gSq7kYiuw|dFwwXYDX_4xcfv`G zfa&KH368BjZMbxngi__Z7H@d+znLQcG335wZnAfx@b??E%g#bTCawtRo&|4wEozc1 zZ272Jr(WcZm8Ha`aod%f(nu~$-{z{m!ICpG@`H_=w}aRN&IwS*GxxVL9t6|k>uT+T z-13~B8FMk9U$WH9=T~&$zpZn04^Gp^+lhJO3H&{yL-CngUE6n9$aA`P4;Vcq=e#v( zoQ^|a6-{hinzHrmJ-wO@qB+>?Iw&r6FGU> z&N$BaGAi~BCN+)pREB`ol?iFXx}^X6Ou_XLQ9$#VQP+cFZo2h}uwAtt$jBmSxJaeA z=G`X)L%z~EdLCQm^4f#QIM?_JHQ^dYXNSni!hggg0}xgO$JUhJA9TM)LokWe6~WH;(kG07E0y|J{(TE2nP*70mUf=%l9Oj= z2J>ovM>K|H~8y7!5GT< zln2K3A!vZ@$-{|B#ui$5-yja&``ye26`y9fF&VB@sfC7i_875 z)(Pfr|K4x)9}&k-tvuF4&-%?ImyzYsJL60qR|1B=eX&&8^6Su1r@j$f>Dr=z=!j&(nxB`-2C;%9&+Z0PT1k! zc}qAYf*fb1xT^dOPT91e^qe9FoY)E{(VaIncEk9y%=o?$aE;cTvj#>)?B2I}Ojx6A z{H*CKbB=Zvy+`jAJOWphF%#0o+bGChT!upk9gwm(AkPF*7Ksnof$j{pUhl!lP%A1& zl({>WgdyDWw*wOov085G+8EUu!cG|yvl)E}y`=Z1M@Ou4det9>EC%UZMDcI)wlD^Y5(Q+^lhNVydN6)FjX{@2 zeY&c4w2H0$+me1!N>(cbNW{r#@|foj680#<;$PiTgaKG;4JsX%DJij{_0Wn>Ut!Ou7aA9d2_$JU#O(;K!S>5Gmy&BvBpgx;R!$E++|=_)3T;e&g^((zc> z9##1Wr*x!0H(BC-IAFD~ zqELCCh+gA(z2VAra)$z+kP;%By4CYGBI0Fmt)>O{Ne=-Wl^(KCr&8gi9GVq|~Ju>QfUQKIFf709pr zSYA@-2g(bdn-Owf`U^K!XNd^gTYMG;46hk8sAXv6mp_m*F~hPw+JZV8KX0k-xr3x0oDR)tW=Nmn?C)_m=w?=v_C%mM{Fj;$H2LrIsISqNW{ zk&>bm;*d{XfKl9+75BtbcoCm9O-4)XWxGonkoaC|s_2?lFWrur2}}ATY1|-dB~SDI z+~0isYUU%CG1O|g?xOHn>xujA;PV)e6UnTstPoa&59=_8OwPm!id~@vGP%gOLNcLw zYRc$i!Zk6{FCTvHC6Jl3Qun+gc7M@97yuaZTmSo*NA1%BodW*86L2PH;<^#}QtY%Y zI*p(esrUX$5V+Jxg0=X|m!ZvkFsI;lVXzpQJ38cM60V9tT%)GaZxE4zf9Nn-H(5Y@p1|t^5sMZI%G>s;Z+S zQztuGzsFW*Ss=O6ONJJKVRs<=$;ZR5CHEEF5)1!u}83u{u7HHFuUzUawKw4as$Pj?0N6)VGYh%|8 zvVRaw3WieLW$CZxRxoTHE+X-eK=+R<+~Vn zNMKM98o-t3H_bbc5OwtpR;B0Ws7p}NT@#qL#BiOrq;};Lo-G=ata*w77L7l2UM&DF zDSJ=X_YwJ@e#1x#-OZ+sA$lKw>gc=*op+4#q0f!cO`oTxG9d>gAoAV@*=y1(SCaFh zb8|W2#3>{M36PKIn8^* z$}A0{h|Gk{lm;Q0CCXGJnWst%nMy=tNTv)Sh2q;!>-TKW^L*R;e*b>!pS9M8`@XJo zIL_ldj(r%ULPUX_0XyjX&CSQ~tCd*Kz)y)7<{0NRd5wM${e$lRORq&a9BIwZtTYeB z$}(=zj(j`YQOfEwBAI-)xBK+v!XrDGV}ym*#M0Dm6jWqYuf=k>z*?ZGlhIk=uP=7_ za{gKl-M&r!mC@I}SGAgKvpAphrZ|@_x!~gJwE7gKJLjlma{U7XQ?8DEBrq5P_`Y?E zorPD91?;xp;A-tuR*|MLFV@K@vy`z=&)ILTL8bTPq(E#1+rFhq7IceBbK_Qji0|Cj zmc_BzD%IHJ0dA=|ue&0wK89YSP%6AS=_dWHwmn; zOlqc8rF15g0Ky|ABEkSP=kTZe^psuJ7Z?kgWl+;QSYUH@`HzuN>UzrPo|>fmZ4YBv z<1zu+-f{J-8fB~0A=6dnhkkwIeqUBp&86!t^1)>P;;+ft9|N_?#~%1LrF5I1Bc z9RN^vz>P>gDQRS~K{8z@WAJJvI@A4wjd#}%FaH#K^=eC!_21ExzgrfM{kWZMnc7_T z9ykb9WWcv?-Ad|?x^;^JZZtA=krUAnF?*WV@KRSQ>RA1lj}{`XrhSJFDUJ$pv}9bo z9K@x!So8MjF%>Qi1^r>_B;Zv0`}+yhLKrC72*JH?&_9R(akuL2!6MxpltgGIdw5>_ z^KOj|s;^1Uts#%3A!zlpVYtDpCM*jE z1_o8&Cmql&{${jaH%E3TQ1a&V^9n--U+gzfe^LMo>a;83PdZlSe)SJjX1!=H=!tY@Qy#S|6Z`E0I6ap(9@o>o#iH{Vzk5zruC%@ zY}8P&QyE~Mf6JH=IM>uNukO0)PE-yRC8M)2uy}cq*7C6eAPY)ob9*APHKq4uAGer~ zTsnU&|4!?c+kUeN8;DjIM!XOCl*Wq8-a#YVulPFG-%~>63!~yhvS6Xy$@ig6J2hov zq7pp^?}PDy0EtZ(Mr?YmIUm+>B~85N$5f$qd~CNv(32B~1EJ$n*F3prxu)N)mWslx z@;$_P;wDm3?e`b2fIh84;KAtQtmp$pN$kTpQm{BaKHidPD9s>51hhcv)lZC;zO}h_ z)F)n&J&`hBk^f1>bZJQD)vmpjZ`tXrRD8~yiJ>mw ziL3Kd6>l3`>!-Wvn{7YQQjU52^3$)9J8FYXkuvgXu#0^=7$>V0ku1HCJz(_9`wZ*X>(pcdPPT1p9;{{{l?mJK&0XFD_VR- z)B8@tp=6tWR?025#J8>6V|ShQNiwgjTSF%}JG5I88|2gVBpFo8m{15gKWe@0+^!C|>Hgg-BP+)l_3T^}k`v6s}B5juRxU;l)HS$6Auk|clHb)YJJo{xgYRPab zue#&*V_pf-WTG@UIIumOi-FZ6{(ytUr`i#d7i9iEzu^q<>`Bbf!99{Az5H`;~zKUAN zYAyxb8q{A5(6|m&G3sXyw`@B0cbDm@7!o*+!5xk3x4&HW$z9$bDRsf`h8f$w`5o)D zqYuzVnr`!(_?XR06c~WoPDYoO=wnR~lqZ2FqZU9&6q~nfNhXX1#BGdK@8b<6`|~aK zu=7znjBni>t*E%TYtNk8svg=yhwohe`=pJ@KpWp0f=1ro>GiB}zcd*)(PYhEVYfLU zmg!`q?=wO3UKRYgN<{YCk&*rYv67m|vGw^rn*eKQ zE9Ic5I{r3`_sTP)eC=Mu6;$Z&#=$Ei@)$*xax)k9F}B}oXtX$8*;}fY8QQuHf3*7Z=g+fVpVanuKcdAIQ%K6#_glF1 zMrL%pVhwnygpzDpxJTc-{xt*rhfgy@3GaVjc+WwpWV0$swMspOtroB^A&ZuU0t6js ze|4Gf*Pl90dpb$Yt@nrCdGC2Wmvn2<4ElB5yY2v~QsH^TwC2MX7aGdeUzdY?`8S9e zqgofoDU$flkQ7x!;-j*iN^AO`n3aFTw*|Z(8FShGZ-X#nV9)R8mrtkZq^PM+D*F(v zBBDJo@Mbks4Xt0!-ruYR5dFP!9g(O02u9+f)zlmbLLRJsVwgD4(wZ@4n|gHu@Pc}ugwop|wxGfVCP5!=bPGy$w^M+u1kU5(1mknD z)xex4*_<>B$-}$)EMR?PEe%PKf1hvI;X?_S2 zJzKJ}vSzQkmj(aNMt;soe$*-dhc;@wGN$LD)p)t%?=wGGDV3jFCi|{8-az;bMfg>v zkd+MqtQv`%vkUZyH~v|EVoto4~MpB`}k89((lud>$bl+|b@a2VBYRBTWF6N&gg9_Mq3&Xd0*i3nqu`8_uJ4RPG@EMrj#X_KE8oD9 zzeWQyejw5`DE{MPJ$mFw8ft2ODo1s&LLR=}w$qJ?HFFNdmh~)ZjTi>9=Ec2veU6$yFjNz6s$zGJ{DsgKk za%fQaBFbsNm2qYFJpDajdS16_Uteg(%jS!e=lKI>lGt@d&we!1 z0#d+6nR++XINXw#fIQbB1tSS6x>v$YEUJTw-#wTGr9ZP$xZCq-HLhNKWPd>av;7Yv zf<$!s@o|%&WFRp3I=z@Ir5-#1+q@IRqgza6!&ar2D+)RIV%sQ+P@ zfV<;nEj3%)ti~7LdpiR$G<+*as|EvCcFsVt7bi~cwUTK#d;?is=rdf(5vumx*&Hcl za;n4*@_p{2p?Ix~g9i^)-%=Q}|Jk_qq_g2#it9}5i(q0w(gFd+UgX}$Py zm?+%yrh4I%pVaz7gOAt$kTeICKAdMKof^aVUW%e%`cF0r4z!l17W*MArzb7#hd zT?GUJzDdmyq(!(w%TBIi?tkmPLzoZ3NspTJ5IneC|6i!2BfOPUnzXT|6^cU*_fwnR zgPG3diWF;V$$i&^lAEu#wImW4_Q)OL$(RwIy8OLIq1N$ZdQb=4$1_gRpBfvbalO~{`#AVOVxvNB9EIUP}Snn+Lqf>6*K=p$iMQ|ksDJEi2yaJM{dGXJ3Bi=XemJDDDI2;8$bUKaL?|? z*A-HWuiRr3dyeP=Vq-7_6rek+otVA_n%^H%dsoim+c=N)OPIX>K4<%2Q?|skrOwd( z?dBM9}=Cg_Wx*i@O{d{Ev%Dr}b-t6#{oV) zW#V2{;DhJt5%#Yfsu-`{d+t(-|7`-cVORm%th?QV()8SL%l^c4oDs)g{kakJ?CckX z4E;6Uiw(>-#`Fz}Hw%~bvu{PX+f5E$?~~UUC^9ay9>bp>-o#vtB$|3qn&S6Fk6RH1 z;wG|?u6&<-zguR$FrMObw6xRw;7Z3JayN#Vo|P{q+m|2G(I9XxNcf_>t=ahbWrXft zTYm{=SC8TW$IEPKIskBE?o_9*=ii647lZ(gWwxOaf@t%i+nqby_e++BvD%u>-A>hP z1H}%Hn8aG~*?<51Y3*Lm%NvGygj%2=x#!~ipMrY9$jmg3f=_d^2UeAqpYol`IEh|p zb?ZM~7L}|_V*S&0>?m6^62&2oO&aEqeUXQ5INDgzX3>Z~+CV<6^E#LHYTy=wey;hA z0!-=!#!ZlJuX79yr=I965GRVnAhe1)xS=)r>fh@5OSioY=#g8l~2Z7|3kswYkgW3Rv&(*$@zxr1IxM`~zOsH`V zisBrDya%Xz(I|94tHr6Eq4A)xF(2-PtdODlgNi)ZhWlJy(x~6Y-ZtClWn1X)YFnhD53S$SNE&EzsdLHk*XF z#v5!5a&j8Shas1G0r29FA1VA4AI8bTSHZtx$w_bp(~st2e4jURT|81}E}rRGB!x#;DHIHoormKFO|n^uoya=CKwTwiDtH%FC3Mz&W>P~ClmsuVQ~=nhgS zI4BRGyuHKr8k99W=Le%9R4xDknV9U07(9NE`KzGPapT(3yym$tQ1i>bDutu5*!0b- zf6j{Flsft3&DwE$l7?D$i)7o|@l* z1XADp>r476yz6jx$!=&BH&RhB9NW-EF^TS`B7&Q7*JpiZlYB*qq(drB{OSC8kmtjHAe#38U4ch&tmeD>^F)G<(Uutx5m zqU5+`YP(8I39Hry9_3TTy<=>O4k_nS4t@Qk-i%7$jys9Q=;qY8sOGah+_RPZxm{RD z>$=q8vED8Ylb3b$w*^*iF21ue@+^PP=Dezpag}|n$5r9oNe{S)9mfbw#a$$xm5evS zecKJi**O>16g-Kn8O4RU`t30sBcUwh_91r6vVTpl@t`Fquv!X@^P{PJw~w5uoYE~My=iy^DhKLc1M$L;R^ zWm?Mb3>R^hu~v1Y!wiI$#4&h#28DzE#nsJkca(O@RX+C}zb6$EY)YP2xQy9zmXGFa zwuY^maA#55tD|e(?eDRrP_Jmr%#MBvJLvlEq;GTdcKd8yj%$ZT*!I)lsM5bh=Kgk% zx<+mC7Z7)3$ z!D#+%Po3JeD3vK8WzA2#uLg(uDS=-#wb@#M%q@rQ=;+r6V?_|p^I*Ew`gtbFWE zzH)o!wd*g@P{K!g_^gU|8@|NK5L;OvFLbCPrmEt89&CM!KN3Jt&A{kKt|q*pNJ*E3 zQq^TNp0`R-NsbxTR!8&%;iA4xJ%F*o3X7-Suf{ZGvVn^sBsehaI{%3yKLc+k`y$)Y zl_SemHww4!8jp`QpFK8&)(vV~b%#UkV;2ICa2?gsC&1<|w4Of`SMI>JFIn2cppLV` zcCT-}Z-nlnNKv__4WUHlr>j#6H{<+94cg~AY0vWY7D6uj&EvEz@%q8pPpw0?ikWF0?Ji7PM0G?b@eabJhiZ|WBUWe?xq9Rd@< zy};php2;>2zPbNa1O=t-@FSR_~LUUvmCFS7jyh*^sX0TUOAjVSm+;wrl4ihFJgfxarYR+pSjAMm)XvU(s&z*9)CW2ZKFw(A ztCI!CMO9@X{cOC$+E&SlYWF1Q`-RVBHFH1y>}$)RvN0@p_fetEhAXzMpxx!Mx;|y) zPmVh3dcX#sgWVAffm7M))zyD(UsLZiUuAInVrVUuPC^$3t z;^48sAaywW*&&=V@O<$zpLkWK360a`K58AuwX~U=Q~U_8B7>bEfFw1gEb+?>o_s0Y zm(JPw_SlWqWZKc_=bleB4^Y>SS$T+K+Z`A?JQnEW_=|l8qA%|c{;FkkD4eCG=mI~+ z&ga|JaRr&{!Fe~xr&2i7dS`!nklA(q>#-UIhfc4xi7hdFf2% zgtqeUx#SMduflSI<-sU)6m*G4i{DVOGigc3BNP-If2xLV6In5wYq-`%bZ^TgFDCCc zp5t7C+zuo$=G+&nn=_obGW1Jd>!>dnWYCxcPYV4p8IL(zqT4odB{(>#gsZ#UFD|=J@CqcHQmA&eJ5JWCrOIKM0qJ zm_S#bQaSXsK)*iy(>+m0o|Yt?;5tn{=s|?>tGl-t#+OF9vdAR57vw}R$we{gA69>@ z3wth<0V)C2+zw|hPjq>;qxY+?O1#@H14oVE!WVnpI6~OIWwn1gYjJ|Q`*d+71|Anl zzs*|nh9VpsOcy9XIVuntM=0T8c8|xQSiW#PQNM4}4tST8#30_ziUI#M8iDi2flN8Y zWKDVgtnW;^y1xDIxZ)hguHDK5UoQVsa5W@DRU{IVb5IMbo`4{6>2YZOweC`nM^xH? zpPJb>2Wylz0sL2wq}`W%Ti#QMf~_(DM0RO-NYkIB!pMBF`}6?ES_>lP=x)INE-w`5 zZ+5%Jr*w_ka8jw|lNo|jr)&lz_7fS?rjW27vj@(^Hc@F)>Q0|dau|Oyud9eYKbcKa z#*pA@i!T(u>BAF)%pN?M`Cke&4m@|v4ROA7GsCRRuknv}zNT~4Q-v`hDn%g~YtKp0 zYm3fGb8vCrIcQ{38uP#I6FFc6K7>AtgXq+D)`VZR7GF-kqb-t#P2J|A`<^ccC3sd${*Uv)9yXrPGmHbx^W=*NjY6sPy zH!c;=IiDXov3C43f8V#09CS8Uqnf(Y9`97#^(4NJP;zOhFdkeN@+1a2{&x3|7ZoR*%I?>fR@WzF6y4ivdH92@qN(kt{z&po zmj(37bdMO?7>EX;0X=1(84_6Vq_TPASQ+k^2UfnvU5(*$sh~fZz2VZmjXj=}8xj|P zJ&2M~t9NIevUgG6Qy6RP7#$p{jMX9*nPS=R@MqeNZ~AAg(3jKL?aBES8V7ivdvN^* zyNSta|I*7%!rE1d!Ril1ZfG>NCNT=Fq83nYpPP;^cOU_B+X|v-f0tCW9mmzpW?seT@cOb_F|%B=zm-@Zu)b2m*cY_BgWJ}qIZHb7(hT1 z^UquOZ0yaUwsD6dE5ID2c8bVkxO_w7;LC~SAa(UkN@8*|cd1Xx|L}4xumj?6$K^xV z0B=C8dV6N>b6hz%1XKM6Nge_m0k@`x+p8wtEnnmMzNUx!R2F~1F~BsVnG0D8Xvmy(iLeVNnc9lN&&-bho(l6+@wXOZw{dEMFK zC@xoqw+SxYoHRiCa~PWLa4Ofq~^Em5Cp&;LEk!tfDiLV56^nV$#$Yu|l(5{)$; zh^M_J?=_InNPN#UUt4~xySBsD>XK#L1vZoELKGx(L&P!i?`#-S7Fw;nRb=@ZCYz$6%AkiENKE1`t-wu2YQ(X z0b8Hq;kfW{Ol#G8T>cVU2bY5Tl^u^E9MS#21tp-6*31<|}H7O)S{@u$a`}J*0oz4D^ay4fj z_QJvKmzP%5N{f%)iyfp~>@dkgei40@z7gK0VFR&?A!sJ08jdYw=swQ!<3e_Z_%hh} zemw__*qF%gBK5W@JRNhd(&OD&ipzrq*Yp$jl#Fz*qhh)Z?{;OQ*j53R^c<_P79Dwq zc{%@s36ZaxRaZ9!-W+Z#R#E!QuUnmAo>jaLE!(YlUJ30(t-T#-su*aFYl-C{9scU$ z@%yy46?2_?c##=vHXZ#%sl*{>;Qorz*Bi zc7wwEkh?Xh$V1hq&2R49J#n!xT4sl8`kR85k{jZu+HzU<(_HnAK8j4%5Hz5#5bB7s z8Idj7ktXADX1%MNQ?;+7cZd7z8rFmvO@LV_D2&9GbgazKrQ^ILS20i_6?8&F`i49k z@*HeGKF)2E7XE>|fy^#B^+SKgs`v^rJ5vlXrWFRtkNIqy9W?%EWhb!I7bN0IX#jKd8uA9U zLLC2;xkjj&{`{t_sk*hyEs)NpBU+j!DD-27ZVMeNm+i6C6nw_hz<{EV7x73&mhV6K zUmE^UO)0C8Lq*9ioa$EVy?aG7dALw7wb^yH7uwPYDA#sxlXi;I^LS8@#`C0t$%U26 zG^ng@P{Jaq(NjugMNYoNyd&=CElNCl%nq*^ocfNlz%2(e?W9xH*3)ntLWUmq#7q0K zX>Tg~_>7xqp92!GB~r z-g}Zp?t%Et{%_Amg$r*(RTs<|V$D`miao}I?5p5Zh@fATn&Yz!pn z3uEtLSTN|uIZoxX-%$V6osgl7YH4I1Vb}~$p>kU`l7NhzjwmXM=YhJ%*sp}Xy7AhV zYksearV*@J-X|lQ7ni+3WwdRB^69pEt$DofLm4_%Wd*MR(?I2O)RjnD$^8O+3l;CzFWTBhJlQ zevsO!5O>WchzwZL>K@If&`0q@Gw;d;e!Q zGSj>?NeO2@I7V4kFG@Ooc3d|#>=xCzda_er`ufiQ961XAKFg~w-`pcx9ZxJ}o9^C! z{p(}hsPBK+!0gMr+SvS-o3#DSq`K~=%`Nc7fp^uMm7|OsDAB{=S;8U>WjL}bp1vpH z(d(WZ7BIJ1ekBG}BeAtzz9iqE&28;^6NjI~r+Go|FY|&amIG9jKpU<#yDl7Uzq3QW z=2wIkiwzz(QV3dLSHNvmJbKCx^_D3omsg2;ivy+0HTs2ysh0iEY=3F}DIQpb%N>gR zPwv$Y^#~l)ZZ+@t{FBlu5T|==TP$g>`2|qP2*%HN`24E)P2<3*^`B@};8-6mN?OVC zPD|^MQcx&-;`d&knRG@2j#dB3SJN`tA*!#vELgL3ba;}vcXijEp6~j>($&#*uj--q zcyn2RPHds0WBwX6;W;=osfIQoS%I<%vWW|q{H(%M{GZ*nD7HIJcMe!xj=@zWH>Pz@ ze4A#D2z~9g95k;_{)$VJ#b;h*Z&Vn%gu-Eo}WSLVMG)3!QQyt`@)>BDPPT8qBizus`VA=Zi+=%3Q!=6sRwF>C)>Xt(U zgK4<+15f;hwr|GXWa%@|sRr0f0q7}woT(&^d`l<~4pMxqCDJ1NZ$`{Y4NO?CC59NQ!g#70%pQ~g z$tNBYfx-x+zvMmUBmf~@1x-CT&oDw7uOPyD;1;kyz$l;r(E{qZh6`v`~{W{E)GQ&WhqAf#uB zyA8Ujuc2V*bh8_#1dxB!t?>4mg+nh)pY;2;aJoT+hnV$(g8IDccaMlTB%Vp11`@RZ ze(Wb-3omI3elmj2CXxDs5GjnYZi27232<=Am-T7*N_Zi!h2Ve7rTTXs$t~xnpgl+| zV(j5Q1iI$)FCO#Sw@g{MWmG*EM$(11Zq-n7ol*t$!ARot3v_ftOM$O!$uipcqXIs~ zuWD<K`HEpX{MD&2EYIeY-40EtO4nQDD08(gh;kt@pIwH|5Ia_872ipH_zh2 z1>#OdJWU`2JKR~2Ip+l_OJ+rv2^NUC!qCJLc1t2A#j-Hc%EXJF0s-p3G>kg!JTFIH z9q-&_T<*yOH0}YYBf^O4CM0<2$a?(3OM5_Fy%&~%x2%myE|qDg`Jrq7Wj!s?@Fl-M zBogDti@c;?z>*f7{3Or$X>5yHh%WmQt3gos<`)*YDL*&<-?p&$!uRoUp!=^4XHnpg@4ro1;qyS^YYzHVt8*Nf=#q3NwXIdcC5h0fQ4s=|6~|7S)EwYy)e~o`;_qD zS3eet>)iMIjoy$cCQ6rIzU+-K)#@bA5BHj(XkN;AEo|bnBlRU1g`PrOm}ppm`yU|98^{yXt^b#QP{90xPwKhH-zEMWw10mqETb3e*-qi)|u8x`Z~ zQ&gk?=UTEh`2bl`W55V1Kv5m4$U#_YHE1FeKMOSbhy{;#6X(VvH|gVkeO>qWh5QPo zI>tsUTcA7a@9%%Uup2~Y^mn1*`_S(m7aNpcD}k1X09cuv1b~=Re9)s6-ndaU-3}IB z_*1Hl^IrAm_YO9qrR*^G-3SVs#4a6<=tR*O(c}wnfB~fa^CUKH->zG7ILex$3QrA0 z971sDdm_+iC+f}o1n@{)$wv*sWxFxw%H9RMNifo3C#ciJOs<= zWU1%;@jXngeboUFK=}%#@i3rJ!hoe?xS^4nFD)ypkH-i@BQ_W@!vW-|zR^)JY@sdw z?47qy<3MY@`N5{+X)-{#dcF5Jz2GBL@8VKYcR#1MzENQBkdxC^yo20E+$5lVR08e6(?0zbrv>NC8ie@Jc&%D{1Ih z-u`NTg=7$T>GfcZvrm@J+&usJ1Qk#`gk^@$!5V}D>R z67}X_ym&C^a>3v!czQ^RZWc1Vu?sjKWEZ|Goe~bN6@f{=mp{~P$(&872fU&W?)G74G#xHp5V-U%@^ZD?lZP;Uu6Iftbje*4E=)k6_c%NwGV3 zF4}C%`h#G~5i0?f)vND8)2_8rKsCiO3CL864}oaZIE(hB0mb8Hs%MdUA`^<;A;cQ2 zp@9XC5^u0fvfjUGZe|DL9sZpnb5-et-)e8K3JT`GABR#O3*q!o;4Qqwa+zkbyJS6Z zAn}S<>vCI}_@RAI4k^mht79jKGddui!Fds=NO8kMJqU)*upP6Nb~insSFV8i zAhU|^o6j*V3&m-bjF3#sR-uf49TMf1IPXSSZ$Z8n^Ryq}B@7WOR$e%t!WtrE@O%kJ z6bWzY#G?RO&A?n);v^#Y)o;=&Y*>)I@X1CVDDV6|#@WODy6SPdA_;)`3BLfa)8;&D z9Vk1jhiw9J6o;-og@Rn93ek+DFfTEK5S9NumRr%9{Tk;E*~@cfB$Fa@fQe80LWKC|?^KN(ov{h;yUh-L*;j3@1 zcC*luCMhMnoB!&D{VVY!d_G}>fQs($if-59e-j0rrc8dy*-^{VRDLzbGVwEuI zLc-d4k=T=f9b_7%!bV&WVNwhkpKBg2f_Dx6E<5~jV=r2@ZR7Kp>(qJocAqiU{bv2t zR`t8E)r*)06P?c3w!Fs|-x+brnJCg}(-ioB)!2M^-`TgfLR9lEnMd!FoLN`Q!l0Ed zB6eld(vtbm+bu)=-+mofF6xQ6#y1q#q)_YFC@-zzQrp$_0G8&^VcutFCs|NX5cBTr zaXr1O4_esx+dVM?Z_=(cv9n~udjbAD(X#TrOyc(Lu2DZ1_oU7^Ycp6Z#R8tZ_h`TTyh0om??RA zcaVbLe(<0k+&WQF(LN+8+&46W3hsdZvT4sAUL0e1n#Z}hLs%srK7PEDkZ?07h#t06 z<1Zh1+!w)NMj^9eC8cL-dJ_`ggM)*SQBlWDOx|Hd`NG~BNU1?Zx zzM-Msi4$+|pu{^3jMsi_3{Ebt3te)%cHNGS4yrOY2T)Vrz(5x-jKReFU}M`4Hw$gx z<<%^sgT*_J33{M6@7%o$$e)>rm{`h-7f~27SVFMX)z`yQxJ5D%qrrQ*vR`Y9qx6Ny6ptUud@kUvu2wtJkZI) zOYzPo-QC@8@FS)=fAQh~V$-Q2h$>w9HDN+hb~ub*G90tsaUT{PJ7u_I5RVnVE8$T3 z=k42G*lD&!9njFQIs9V({&`41P;h8bj78P3ihb4f`>Nxb8m(q z%eG_@CZyDrQ!SM?GnYZYsF{{~aD%9{_`G_h-fb7Iu?d$g9~1?s%nMG9jE+{D%M8m& zd$_rc2oxqK`vRD|6Y(D=dRHUhqYwl`8-9L%0%c=u`nzvb93ckHB_hsVtK{H9Jx zM;Dh`xb~_RR(x*uaR#@@{Aq3<_%W34em{=E^lS$l7l)AV7JF?uymc+zb&HN^i~x0W zTU(kWGYiWqx<%ec4-u%yni6w8q{K4Y>TpOq;2LZBZ1~bzfTE!f z0-vSiM~}{SEI?#_#B^p`f~#eR8?zAKT@@cSd5-)~4an0UXJqu=J*v`T`jDt@n9Hzp zaScATb#%nMocwSrY1`(_xHZ#BN<3MnqoeC=<7V9jGgOOu#Bh2RI!R3nD!TM@Jh=T8 z72StkhQjAdDUU50-oJNjus+)mlU!n4CS}9USJ(ElC-*|Zh-sOe&qx2P^z?LkKwSf; z;Zz{GlgOf$Y-oez!1TgcuHLCr+DDGCVw>Q`VzuKwq?)i-t8*%nHjTQiqN7v%Y^uwN z)MUS=X3*SsmR+*!WATNl`wprxc&woW*bAe?3meDgo}MFCR+zfms1aD^e*)T@=f{>D zx`YQ}FDKC+_KIKDfTu+Z_kk_i9;}J7YWq0kO3g-@-9GaL=>0d~G zm%NGRRQ;}~i>Zd6)1};Z||*^l4tiD+(dEvi?d|S)01+ea|urqtMobS%m6;GE#lpF4IIu zM-TM%RlyHhLn3|d+33W?N1KY-jKn*7i#KTL>1nR(B@s{g!Uo*G@j&&TY~_Mzlwg`$ zPn3Dyo#nkssa8L($zM`bgv{V?^UciTxrfi4+Xk78D=*{QY|ec) zR%1ay!G^Ot;qID_kPoL&Dul&kj$f%N`=J3~|C_!FC&kA^J+DB2_O(Z;lh}`Jm6heZ zcI_HoUsOWkA%+@dw`{h1n{aTRntI#R)ZYCKML!V!mnajf&$}WI`SJ5-T5j%9m*S|2 ziA$`4DoVEV%+jTmFkgA;HuI=vLnYGKHEY*yfAEfNJdE$~;lp3QeWONXc+*C2pF&7L z%$lQ~?!WfHLU~;neAGL(A8y)lCp?^`$8+Xz_hTb?zsuY|R5|>2l7!yr>6D6!#Oi9b zZo9g=I%04dnsQ#RmjMI4N>wQCmf{A`_p&R8@9xb!k!!lT5)0<2hDM-C_GuFne&VJC z@{Zbr32PWfpDMz&;Mj>1kM0%0&Jnb^mRP-@>8YvaN2>GF_0pnJQZE)e)vc|!VEE|i zF76VX-F?o(-Caad(i%qBn5eMy1ou!J6;r+fj+;k!y{oGe-m|A`!t`YJ2|F*)4CW+< zmvcdr=RAOz5cvJOt=%eFmd5M9;T1u-z9~X;htsJCcuyu~=JAOO2Mz?trGEjizF@CB znT!P-HvX79Y|GE3ZfoHqRb5k4YdnDzRY+Mm9!vIaV&YNUEO6CJ&dy%#gEezEHr8gh zfhNu$Bz@N4r4k#+hiPf>b*aH-A08jyA||H2sQvaBj*g_XG*T6sLC$z5Hdb3zl|sTO z#;ej?Q0eN#%aEtAYb%MZgUc9#;Q<|;uyvk@&ET6`ZJ1w3DxK@R*kLQTW9LpS9UUvw zrjV_C>L$bxXx{wTD=3aIhFj^agoGofPHiwUGJ>v3b9eV)9UbPsfB!1MEjtVDy&t^F zV7K@D#fvktBY@NczrTi`Upa>( z8TLW~TknSa`Qw>&LL5e{Z%`1r`_C(!TF;B-Cw@WO0Rc@!M5GtKMo!Mo(+xYDrmE-# ziM3KUB}0S>(hMLKkW9a&qvI`(1;i9Y-#n6T(g8beC}+H0-7&t7YcT2rZ_tbDBs>&yuFErnKSef z(5$p9z>1l>GZdy^3$P%$cbD3`D?l&*!Vcn30YJV%?(t>-ci-8Eshc_3yWmX#c|iVw z_La;`aYmB%?m&xh;1A6Q=M{jW&=_7mfILhJ5Dcj81jT-5Pr}a59(Y24sJ|)$eEmav z7tHJ|oGqb{rF1L8 zQ^I{6w>s-b?;cunSZ`W+KUm+}Ng*~N@Y1Yw!(kmiws5Q3-P?P4{B`R>qMLnez5$QQ zQI#)`n)-TaJbMyYw(_RBXLPC~o;K=SB7gd3^y$@2^42WrsodDE+z0MED`T!b4kh!2 zSqD>WVg{@iEcmE=c(z$oVh2t-aoL6$h`vxO?^Ii`ZhsIKC%eh1;(9AfV1B?v$*lRD zmcZKUTi%jAx*hlitDy3WvTc4-YDNgEq)`io@ugX&pcpF;?EEu)df75-xy*!Q8sVb|~tM+hnT3`Ft z(~e(WBQ9w*UQxl=Vr7P- zwJ_(k1j8Beqs&-(kFbGA7KeiAi2k*;<6j1V@sPq>0~>z`mpjTws&5n+C3k=+&6j?f zx5LhQ(U_+$j47}1g`WRKLcQCtqjfj^;U-}o{!ZC8T$;3~bV12bzDsG(y{4xN=?KXrXDS*H1^h1bU?kBzBk+*92MkOL=(x)=HC%Bul)RrCsEy_b#PXx38z!ebmZUTb|8!FFMd=9-|~?m)`&nIWnq+sZN_zt-?gL zS*F|&#?Sfqo;^n-6$}yMyw101$mHUgx zG4;JN=W#TO)cm|eoaP)p`1-J^T*d*+=NIN$`lut6!yA!HABY@RT*5PCj59Z$JT4$A zY^HD$r<7HD^D&$xAwTrY4cS9R>_?urX3L}LPcDb1L(5`vA=cmsK z2ZVA3_lR?ztRj}FGDtu7+NO#(Tz&c=+^g)~eLR`pImU=>r-*17M~1Tp+nX}T-(CCM zoREH$ol*RpQ|mzbOa3}4GEQ!0>0=R%i{q<#(IG9&j(zecGSq1!Pbx-P@mu(@6lLls z!%E{%_mk@O@K`?_x1F)-YBTOh8q8KmY&F+wO}8^XdH`=z8Q(Z0Q&i6waWRIMA=;gm zp72SJ?0CG_JvsH4ZA9-xk_g>6PIamHWBrtB7*pk$!k?S6zOcfvSRZF`!XJ{>u&_ME zoXdmNb}Pk{CVS-`Gj&!znBIMOxMkZm?g=T8vcr~|+adOY?b`*KH}fou1|h8_T&|ir z7!}tcr=pXb_b_&M&(!XeUpcQgUp#@1axRbJqO5ztB9xOEn| z%WWr?%ElscU7Aus2WPcIhsjG=Qq)QMZ{KfVuNVwk#*K(^qO@0iruDyeD4c%r;1mw! zMuze-izdrImRFYid36$&V4#=$qzQ*rvJ2uhBoSV>Tg_C2%W-~;=aVTn3NX()|BA_X z=0tz9nQ~ONG?8lg^erN)ND9GfWwQWNmynn$Dq)+unLfzvu%G!JoH1jwZ1_i={w zXJu8D0uG+dS#T8(Inz}f8gp_Chr$U_$`kebwqs9**I4r+nRH<-DGmM1Cqug`7-WNPg}9-*lkBZ}72YcN6ZzbV9_(2W z2@Qc#5{rH9j%Kl&F=1AlyFQqaN9Ib^%q#Zx z?z#E(o1;U9XNA&y1F0S6K8CO}pJk%uxag|=*p~Y0Y?#Dr3%UqOmNjZE!;dnjHI_K; zi@26MTLqAod_!CtdpKEqd(-Y+mYz%EI|3VOTmICb;HQGY*S%iyeG|U!4WECiB>9rO z)PcV(U^qy))c@^0isr=_z7h?bzme{j5iZVphQgu zpK%A#R41@5T)RUb&B1u^bD}cLpccC59C*Ja=`MZ3Rl3Fdca&U~G>Ov?6PK3coV7K@ zDRl`JqF<^)O|zRkI^@ywtPRO}hH|vu$SwF+LPO(dxH>AM;A37=xOK9qI4f6cPhv^m22VtsDb|Vt99Grz`rYK6$&9oqf6OAh#B)3_sHJ#XoW6qBbSnTm$PzW&2fzC zEdq25ZDDLZGih7?k}MY;65@^D9KM`d$sX*Rl;FxCn`1PVT6HG$c7fr?TTXY)?vY#R zIbDzue4Kh(9Np>flF00SPCcPA>dd#()-+JVd&g^lyJ5f+qb}-jDtfH%q!0dpTKo*z zigtLSAX{z=65HbjE0*RJFP@Dmb+^}Kn8$nc^%p1DZo9sil;WqU zxg_&;taa$%>WFjMt#4Io0Zn5W!AbVvYHzYOY-(C=yBxgUDl|d29?CMfNaxmS6aN(+ zbk}q4)6~XYWl_D^@b2}dTdiBS7>{~pzc@Hg=W<;*@P$m~;d6`$`~+LC$j=a8AM7=> zFz{Cpp2@Q`pOdv~>P%bwie2pOWUON#Wsa7e((k`}s1xu+^X*1>-EKMfx_`_{)UFxg z9`*fPU%y2?w2|V%JCo|NgioJ2+lI@+mZ2D@OzpQDlEkA6+%`{R*IV0bd|A$Y5p~z; z5OeQidq~Tmb?eEK{s?s^>y3s=bRSz2N@0Der`?y=;7Vns=f<~hE4)X)*lwm6UB!xp zec&_L9P=N!e0p)K<7)9%)jBomfXQ5~giuhJ#Q_#8v>|)Zy{7P^ymme`@Mi!2?N(Uzq$WN3>xtH z|HJpks5UziOOS4;&_MhXm)ze@t(YY5^b+=pnoAQsxW;$wUSds7=-axxq8r-`aW6I; zLsfcq6phZKrKui#^c+7qPuF#_=TQ8OmUpc7_a36IoRPV~JS>o18EkTn;bN$PQ2hCq z4R;+MsT&TUmIGa*l+q*yB{dlt@TnQ_-fIwrCETL`E63C9Xzo{P!3qRf8vah&eS=WH!b2+xocX)vW4BpcSn7U9CK zhCU_JxzV}Ei75HhtaeJWlwUBqkFxggdy#;g#zCd1m77+f`9}~v8`Kt2-a7<6(wB|) z(f6-Q%Dq*T0OG&3sMKCa=?5nzd6jaOo`6uY=+=xb>qd!WhsMcmq7eIO`B|30!h27~ znhU<%xgA8-^pTmGqR{#_zBOOtcJa24ugekB?n5C%-qd!bXIi;V_3gvhKFd4)zvx~R z3atrOEr1hJWtwL~rUT6(X9M*OeRzWj$tE3YGoaik6DerxtD)03&5SJ%L}Adf#wSk2 zOPWz~N1pEcbUKeb4}D;clpgaZM@Vo1`rC-Z!2erSC}fE$z!CZ&RUjSRejE`&k6gT6 zf2a1&!2|bTGm)A0P?hd`Hn}4Z0epvtbt@)3hn>rk6yc_b@TX1m? z&wV-nG3{hj`1Ivez4Yv;>28m4^lV${V#v~fJ@weI0 zLKsXBv^^oSkla*t3Ssk$&U54(3zhnM(I%j~iiP`2LfdXP&aDxap2 z@y`-3_-At_8FzYeznL^Qw&kF>M*p@Y;G6hmYnL@9yH4vEwTU+twTB~a`dkUWXcO;0 zc**9T(&FM68zZH2Ou3-$&~^(;MMvxkqC3!-Z0*>vZH9$FZ2SepEWH?=`0kikU_ zS>d!U6P@lHjQs19p|jL;6z7;98+u-0$uhFfMdm&qCn)JEq?Ue|G2^}x(fjUZ;;bCs z`xtqghCMeu(NiPT4NZfV*nZb$q2gp~(iDovn~dw9jw4t{&Y!f9OL`RasQ;>h;%m*P z?33b*T*F1U0evJ=W07l8>6l)r_Itavj#in_*+ex#2CucxZjDV}&s?b)bCinPM;Bn= z@poOo|DtO!z=T#WPEO>dR7;uCd6%Yr0_rgKepJ>+;KqS*;xjB%M2}#Vk>r)QRm`cY zwzdu@ud8?7!m`)WA*{rv>}_Q>I=mbB zk~uR}(AG+pY(Lk=#-!_wCxO@(K}s9Kj)C4vp##bEDYjy_I!j`_bo$8d56#5C5#@5y z@a=4WkVJb?STwqt&cv~b^VFc_F)i2p#y8eqYzukInv6%4!|xN%(A=H5)^;glEL!f6 z-(+8L2G6?3dOFL>?B>Agz=<7}phPEs*%9SO0wHZi4@zcLH8|6Gti+4#1Gyy$s*4JF zD*cSohLhWcXS~^N7mpszBD^0$d0mc2VR}L*E!AS+G?g!kktxhSVR_|Eae9wNL}{}k z`c0^`UHhk1(t6Vjl9}?W)V|hHGQ(7XN6vA*Wty)u2;w)s-tsuW#uZa3-uYoom;=Fn z@v|JOg!rw3Pbmj-dlRWS9Rz4nv36g;(6OAPLtUXYnQRQZz-|9{G3Y85D8s-BUR}c zFB&EE$QKJtU*!;2porB7b(y$dWH9zr5Amq&x3q6Z7DsmuyjaNM-)98u<0B!_0{{K# zV$>AvND!ohGc?uW5BwD5J5W6u#}~IsFqN(dBv~z^dHYQ=gR5~&c}9-o-Lh?d!#7ja z?#3P;Wr^Ub2)-jP*Oy&up*l>`mC)dX5v}K_DC&a;&6P$hebHpVSoaU`O1{>A6?|;L z^}V!*`wQJ*=7$&AZSHiY7qaN~J7Rfz+4MQttDRIo+{IPn6j{>WFKnzbHa&QDNu*Y$ zF~dE(w{2E|m+7%bBg;W$C9aSeTTM+_ZFURFk=nYJ(76W9Cd`cer(p)Ir{kVRPs1MD zyB1YMR$N=uv!szXP@>Iv3p>{Qxm@)9qUylY`JF3Y!t$5Dk@!XHitVGoeSA422KyHZ zRBg5;Mv!k*K=_V{XWjB-*O@Gw+c|Z9#8CK{kz&TnHZp(M)of74q2{;ipZBWH zMa;T}hsTgb_YC$6UdF1yq?!5|D3hi(>$Q@(-$n?VYbRp5Ij`{(Oc-B2tHf|Ad_<}_ zTT@LrXZ1?6xA`RqcOF%;xNh>)8yC8Vq|^SWlFP%@okuQk9avf;`+P#|jiD!=-l0#1 zT!&w{YQx*fspB}i#uS5QL1KvQdWMP5(h(Ht{KA0qh)>km89_(n>*X5b?3(QZF9t5B z{HaUSK3*t-5B1-=R8mnY3IGP<7aA;~s!xlD^XcQBJCEnIXV&SGuOFl;&^v}6eRH$u zMn!~H?&v9D;{4aX^aIimF?ApL7RkeSLBZQ;rAKJxiX{4zMbh#(vma+5l+tXqbCk+H z@^O!E=2odBRSBp(m>Z9Fd=|rPsNf5W%Rk(2;_1^6JeZ29iq}bcOEM+;+6BIi61#R;sRZl#<(C~{%P zmnWWhmWk3tk+o41!``)Q7FhHmA>96yaADQ55I2=8B-IFO^dQUVeX*^;&0dEH?rU=Q zIg%o$Fm;m96+?_ML*J0FN_FF%Gm{@5dB)gXca>lmyV-X-|Clzm=Bo}a5HcT6?knPFn>aOkgshqF6Iz6! zn8$P4_#2BpZdUYuJ@G;8)iYhoeKfd_YmUPF#ndGdB@}T6L4IumdxID5mwk6}byFge zxy-jBqbl+Ib=&ZlDjGd}+(QP8gLvCV^lOJzj(Bx1j$Y&Rdh{OF^USR_GMB#oRVCq& z=+U+7_GuQ^4B4?513~+^3X*`g zTf~w6rZ?DhJ+37adfkh5^(qywX0d|f0v$9an4 zjJCF=)kjS(%Us#l6GhygxqC`!HHtp zS2kfD{L@2il+I-n=S^p|5I5JlnP2OvSk_U3>8rn@ zXXv_!?Yv9&s}2EX<2gQh;yw#Uo5|n|#WJnW)taBENd*R93hA{etLT4Y`qrrHeQxKB z@}0Wr7R!^@?+84zM6=uLvm1)3(JXv9u0#_gxKDTOvuff03s(b$M5@D8;V2@iz)bcZ zE~|KTd|I|7Z!iH>%UFPdqz<(CbX*SLqVg6H7*1sd#;VGY#u7*u66oMg9{9jc2j7RC zeLQIFU$|*XVYT4Gj%X)7fYOgR=)H!PcgBYAVI0@zYex%g+Zu=)EQ#KbILqs9Bu545 zHVoY2@>0DK)l6!|kXS6FrF13Mv3%&RRR-;|Q!zDpn`2K6rmh|c>OOUG9}f5Ns{d|H z{#{`NfWl;dDD2lL!7pp_xIXf``?S1|4~yjgn?& z!8P~E@EV=`v4YDv#b*gi!ld#NtKHjIRj#e3^%}4G-qCfYy%2jrU(z+s)Z{9MNG#{H zv^=w%Y6-b$>_9a~+yaHj>=3~VQz??WbSI|U{5%t~i4G39Y8m%l;u+78dGbCcpv>yf zoWq<2vo1>f^GJ73;0EWmRO3*EWadle#|M~?Cw)orJFg~Q>zT?_7EV-qlfk|24F5{` zSDhuiN@Q304)kaVzoL|#%=t;Vrgziks!us&nht5LYM4D|T(fODt=YH{zP5oZuz8|# zeuSc7HHDL2k?MuAZJ>(EM5~#yBn4~7+6#mG?IR%wPRGEql<7|Q$1~ryNkqJ@DfiLt zuTJ;(qY*#OIKVVqjGcE6OUT;eZJ{v9?*|EZ`1mkT9uyJ-MZ$rl1Og77e>PNU*8##B2**B*?&BLG1pa1Or;3Oo zLXa+OK~fBlxsQo7o94aTstJciUkG_hj4bL4X1S!ym~xl+*^rCU*61<475(|;`XokO zN#jTK_!sxQK0bT*ZAt%q+~FK=@y2!m6Xybtj%#*Vdf4XkjWE~84_98ST|^ScJ29%+ zzlmoWybzEBlbooZ4IJlYxQ$8bAGoV4n0QG@GU=46gnjJ9yD?+4Yb9pFStf$bPj#$2 zXVZwzJ%Hp(w2l<(S|o_A>-#P&6SC__^X#L6{hT}0UwjA{RS7&`q{}yGzlmFLlQl1U zb@k6Z*r}mQ`b2#5`T^~yO0C5(w{xQxv5SL;cBE}LXq&lMO2uHAA zWUQ1kW(ZX1w~;DK!_-b6hrQ$|JSq!Qrbvw57)I%VY=L-`QFR4^bTwL3a?G_qm@dHSDlz(EjKSr^P)!0taVLw#LRi}O{t$A#?*=En*wEM_)_qTX;KY&F`31|v zuY2X)Ny}KK^5mS5DmQju3=`%)GDiMX?L{v4M>~C1-H%qmY{FYHo?ll_(7lb0)#<*^ zLAzDVoiX5EeEI}|iELhgLJH#emc`;|juL-W4Ze%{)jrJc=a&imjbXU0Xa~%F(qBYL zV8_Ha&kL%A+yjGaqFhR^Zzy?6x05%;vq|MlW_l@DDSV5BRf)cORPlzVQ*pxgc=oWC zeB0S_@im(6+P26IvolfC#%4j?Yl#~^9%0?S`z= zad5%vScpWygyy5QSC>D$re?^@7Z%Z1$mL$V&_Jo1#hfw`ZpksxWPJLP-j<^H$0_p_ z-Kh>eVCN^s{Ovvp-p6f2VE!U{DWig=02Dm91@VFUxwL*zpmdhsskw+(b*55^&){T^ zbEvCz^4Eq6ET`9|AN_J7Ch!_sLQkJKm^8$R=7K$f(z5{rpCy_ZJkpAkV%IV z=>_8Gn3i|vi4NR#Aius(oBKFJ2=w3BqFWV|L~v3mFO^zS=<`&MsMZG#YxXSfY0la- zzZG#Tt$6rpatgtEc?!2!DaM9VbbS&mc06rLaCp|3Ix2@S1^x1Vt5mw@ zx{EmqDdau2)-|$FSGE#;Xm7mPcs`l)60YpPK+w7FO^RgFHT4rX1O7bZZ>=^sb4L*&e&PMSGCj7z`FR&*)zf>7&xu&eGc6?fi+ zk;z8sl6;9u+k=-0-#ZQ}-qvWiwbdN;!Q%O$*X8HNB&R3s91JD33SOm0$I)DdwAD3k z_6Zz+%jTp&-gz!a#;17f&ejoE+9*Qm5S)yeTxr;BTM1VJX6s2%^ApLI*}#L1l_MOc z8*3D-)Sv5GdZajISypFkwWql_Y!tYHoV5Ben(|zD5o?4l(>RPbQpKb)B*5i}C0A#aEwV6j+jr z9X0zobR?yCeYfd9%#5yX9WKd;KfF&T|LXJoIMDeQOV!=|HNScqDPZUBj3ANrK9u)! z()j;k_{gXNvken@#VW+tkkvRg^2-IS&#$(3^n$W8kK6P!@x40EWiu9c?^NYQ;amr9 z_jelVsLI0|&tHo_6Vdgm(`}1yD4%#`_(&)Fq|*bOHcDDb#pOs_gpBkg?%G)@Z%q=* zZT5Oe>9LU8ghAF%5qz69J>`;#&!{!DM za7j4O;i+BgJ|FGnPoC0BxspdOvayfQ`}k!jzQ52XCQ1=l`jcM&z5^y~n>uq#R@Zc? zCi$qKZt=bLM>)_ZrV4SJ>d~5a9&7t7)Q6^=8@=_7>JpbPV>nwvONk8T<|F;PohIkb zQm@7^d*t(_Hq8Z2s1->isc2_4rI$Rq-j%hcbE$-7vGESHGeYA@Bgc4~%QvAD%7Kf$ z$g8p+$B!o(L!DBC*{Tjt@^#~nRj);`S@gQ+@ep#3(Nl_0o>Z$mZM9_GU12Wlz<*!* zfJ$^%w!|Z=7&y1(rR2i4W%nzQIjB#Y`L8RTC2r5=agslKBSqXYN3q$}EFd#SNn*Z8 zr_*YR%4uA9s@tRE*CwkMX)t^8q05!!xvq@c8C^_X4{~uNF)LI|33u{8XX9>B&xZBA z@#9WsI#^s6cH?u``^$at@+zYic##SUT^qN_fEcW|jjSp673%3y&n@5_@{r-$)tQ+N z^$q!r>VdW49`}TT3D(3Tok`4hPdt8e$JcGsV_W!&7@2b@+n*f=tY81w9YON_MbrzU z)@s`btSr4C5r$if9tLWkBA0x59^4Z@&Gq_L1`E|G&-D;n&a$sjJ!6iW$_2~ZgG9L+ z{wMUmDb!lr7%Ng^nlC+6cQdtz5f6zvsC8zn*-#e7G3#Ge8=@of$SZ94jEMf&40#|I zirO)6I8>kk&pSFiaU4exE$Jz_AhT5HzGFkhW|l3z)IAhne3a}GQ>DSslOO_Z5gxAj z^h=zV~QNGec?8ak?#OIFd zL&T78ZMHwWqtLIfVW6_(dvvbx$Nlo&pf-20UwpWioJdUK!gwV!i| z{EH|PCPESLy?~v_qyUzN&8Va-t)9kD+k*t`m!T;(VZt)*1=APkh4^YDf~kBnc=U@G z511FAo8!U?6X=K%Jh`6fzKKe{k`WyJq*AoF<#CdUX&$=7u{!X*(--^bJni@k2R#?` zbH#6SEZR*BjA(48!@TVrbI*G1Bf);&Bl0i$5)&ar1xnzRDk=BQ`u!?vbic9F4qu%D zL|oS1RGm1$xY0GkPT1wss)+OPT;eZqkq(hJJ1v@H#1Fa0rrpXpRsZvw3LUFCS$3W~wn|+b?W9x%2xW7LV}UTO4DMpPs|)p~RGL1pjX358(K zX}xT3NiT;q@MWAhcb4Yaq$Nd1fDy#x24q{w31qr^gb13 z4|{hrziHAuVKcenq}Fl@x0PUKe8o^TIB`}eu1Uif}@aw9sJDciX$g3q+&>eG-9cBtUz2swWsdyqaTqSmfEGBlQ zKl<6TcTZY!KW`HyNSW^wl0f|G2!D?r?PklsB#oTRzL(@wWOY?_&q?5MMm9>Cpd*QQ za+b0*!UJb3x|Uf74Y}7bQuBz8B+zabbu_F-JBf=OvVmKY5{Y2{>dHs z0fNA!apvY`Kn4z&a&rla0vsJD2O|?88wili0Zh%YF*A4GE8=k$md-#54)AD($Jv`g z!HrE%GrT<%W^V@wBj=Gg(w z0<1t0fSO@=fN`L4D9iL`rj0Xowgl7x&IG?E1?~3EkD~?ubp0>= zx7(llbrv9vjgf^Dps&B_HVG#aFvSTAgc)HVb?|`)39d<`j2z_6cD2fn;Df+)_8u^x z1PVZU7tTb&&cenFU>v65Y-W2AEGQYd?@9z}2=i-()sLC(5};!E|HTFQ-_k(>y$fC_ z39~zVCNX0br+rbig}6cb67O2MBZj!^lqnD9O(UunH*BkA4T)0xu8&xFD!K z(z~R7*80u_YF9D4jRWm~WI?7uq#pwRxC6}oWQre%{-S`BR=e*2=0SWAh5;g2XpmoE z3m1g_xG=zBK@3>u9~cA$MDxM=0)QfZT%Z&`I|=NlU#?y5K`s6Y1j@TT2r~WS+8qr5 z-tLG2;J@B?H4VbR_rF|wG$9}ZKLq~$4t@%|JqR%U>$@NA{Op}%0k9yuh7kBvc5Apm zcb5|YH*Qd1Q18Orfie8Ov}-+qqv#;`pWI#WD8If3@qfIRK>TXge`fQ- zq$B_&n28Y-0TPBuApb`+z|Q`IhWa1Tz{Fb+5xk3v{=)4q9@IY~{4(PH0sb1bkwx(A zdQ?2NI6D`>l73g|Ke(W(e#rRUIDgM`hAHAqL01L%zCfkzCIPzu26b1=z03f>?EF(% z3P|JyoiR?pb8@h;cecS9LtRn4{JaQmsHL;BgOeZ(X8ZFMuRY%4ECdF4P^K;>Kwj{# zwH!>%p~glg)<7$J6adU!@;G}tX)uSHQ(6!SN21{fBnFN^p^ySRa5M)T&T;k^b-Cls z%pt(c#s>i=u14}$@Qw>k7D40PVWAJlFShQRQF-|cTO6lNb7FunYahXFsb-|B*M z>>n_|NBcJncmn3v-(jH9`3;6b0rU57FgOwpd?4WOryOt;3OIuFZx{k40G!wV9R^ON zzrzrKq5BPn#)9(?`1_>|H2imZa1{R^I)I}D{^S`A2WO{$(?f$B4!^@t;1K)`4-Fhu z`yGb;LpC@D@kjr{G5hEbgZZO>;e2r5n8$Bz@S(7O@W;mw&d~qnj}QBY{^0ybfj{c< z^Ml^l9@d#WXm2|C3|Gc%HBj& z$nU*U-@fJd`}X;N{#Q@W^PcBC=Q+=L#yQV<&HF3Abw;s&u~y$8Q22f@Hlu(iG! zh@T$}QE|3GgCSyi_Iei9#$bq|o-x`E3vD1N(CTSiZ5`W6%J1-`R)UK-*b6U<`oqVEG67 zmqQzx>WNxA0X;&2KW-G1ix-UGMslG5@(?j#U_fg-FyD9fM69f=fhQOi^;cnFtA9vO z7Hws0ZvuuL_9$s;0gM+6k+cAWAdWV$Hbi6DwzJ2e^(;Y7aoKU#!x+@49&GPjHX|h~ zu^QdhZuUHRyjFDFldx74_Exe%3S>xM|7!LsGBx^a*K>~Z9F4q2nJ-o^SPQl{wPqT> za^K74;mO@!`_Qti=D{!Ey1O`2p=_}~K0C~53H;y4SxmQk*Hvcs)0T?s6$ST-s{QO9 z$CcfPv+g@P^P6q+gLc&)8Mmu=KVi-~yb!x>CH?CC%B%1jU%oBx&7RvY-*+5+En5K}Tb6T&`(It%n^}A(behMsVe@lK+i*~>$NFx4tXj$KO1F&_-__#V zuf}u|badp;I>%qr;*`w#wlA0H5j<~vwSV-6|D4jd-1$l4CxU8*uP|+wFuCquH=*V( zai8X;$oO*dW#le@oeex;l-50T+Ntw)o6=4(v~v^&eR-fqjqHxI#n)luuIm2Y0xmG?MH ztYxc{YQP#1#&ZZegf$WEjo-6ie8$%`_Z!*|A#xL2)otZ(nFt<3E3td3dc zR_}{@6%&u$G7_4tMyK(VQ0A5hS+5owceGT|wRe48x-#xvShmk2KhJ+777-QMzcr!d zF}pR@E>!7+nQa>TGLae^b)2=hRC09xvR0P8PKDL3U@|4;orxf=*vk=pu4VlR&+mlW z8J84IK!$ti*-?qtS6>8|x`^o-X0X1ztDShhb0fw$y0Gm9&y<~X7u07c=aH}pBS~`c z>HdCCqL<*gHl6op)_Agm@@GC-(zYkf=%hq0@|ZkS0X<{rUBWxpYm@^UIln$zl}!a} zJ=WE&{&n7nPFZy=PKW1Kw!~+%3%7+X<1rAAlo=Z# z2nSMW?o&M<5zj?-nwAiuPNi;q`9yECcv^K{{QN~zR;klt)>9#llT8^>W`s@SrZ3;; zytpr_m*y`hchStEI4Z)-R&z}GQF*s!3kL=%|Ini)sB>YUt>l)PbaB(2)MvE)&uc!K z=^`AWC*R<2Qx)dDT~yAKvk;jw?6hBt2nV*J8tsE z7Ff{3b>ouj8Oo)WlCtX{H4-8_$2(%- zPu!Zw%IR&oED9(pq*P2_H-u>rdpF5$=~G@#4JnjQeHeHIxXB8KH^O;(piKI zDJFaG@79jIRvO~_WLhG&m)NA+OuO3=GuJReSeM7+`IzV#Iwe!yPL%=8<9>4>j4=zR zu~Z_s8%_U)bfuWI>{3SAr6~=c4=FqhyZv)z!&Dz@2&WU`CdBJsuL#z>ho%yOx|UA( zzYV;ga5L5KIibDM64wlvb&_>NDeyFow1`}ZH#L66hGwE66`AC8czH?=RgrVch4a)l zefI7rM1|SZUmK)LtMqVkO%24)RnP=P z2C~qMm;cCBPh~dGEnCvJpnCJ=*{9l81e+(-Uoj}07-~T1-fV(UXH$+R;ZypDz3Fll zCVSi(t=rA9jnlyL`a*@z6FlKU%^6{a5ZARx;*LeMJsBaHBIL@=2?1m}L%K1~W7d0$ zDa|f&2?>}F4BJ7#N zVLgAARx#W4{1fU0O#bx~S?aIxF}vr?8*<`~cd=yQTE0YV>V$?8ZTUpc$O}};)w-{3 z^lf_{54%<6&|S#23G>y$v#EA?T4urPO?l!OFR%1lx^4S&Y~lSl{zHoQdoO#K893@v znkocp7RrtL*y5l`XPv%I+eO|@WK;4PVSS5d5}r-x`%e6Z^qX8Jk|l9^3qhceP z9YWd`SO+OvTB-b+5Z~@edJTl}2;g~n&fqrF*FY#*q^U(~+5MNdN{ug!;-TEsXOp_) zP#HB`w$l?d?xd?QtpbgXN}D=@z$veK93PD478`uU!0*P@c-1d964$MQJ~WAk=gGvQ zkuoJ3bc92yI`BbH?|CY!=N%*Zyw{xxFA`TWTlE;_iKyi%=3l-@$WIw+W4gsS%zfOW zNaW;H1G%|657_|ai7OiM*1G6m7mXpNc;ZJL_tVJ+&@UW4Q@-*f4EQI{2>QYm`!?yA z%eL`lIy*}~Jmg9tXK049adegIy%?oRk}ep7X6o{? z9Hhb_j4xfhWFnHf4lR~cTWxh2R5=G{T|)SD@T&Co*NNt-%Qd8Hv{>Y+2e`&10JVt5}_Nl5!XOHZ#HTJr?;fUw`%#+1?spTCkiG%q@e{2R4WkxbihORAgM z_%eZ=;X1{`9Qe=TIi$UHGaF_C?DelnJMdn`F)owLKTRAiqB2Co-HXfTKP950dMU-* z;ha~9GD8Hestw^6tvC&O-()M|lIwUVpV>Paie^5iBJm@&C(BD@p|3?xK9r!inyWPH z-xgBO$0u>*a*~M;5`H68u(Z~7d1i_lQKu3M zp31&A?bVoE-)EtyQKY3#!M1;%+suuDws+gcz*_Nn{i#8pk*wxzC8AqJ+qxHM-E7oJ ziTfGQU(*0G#-93E@rMz-=dY>v#W+kS|(2&oV+pGz>-v#l1E{020&zukL zuT2%yobqRmofh;=O_|Db0OzSM5h={%GtWJkv)qa6G)r$P=piym!kBHSGFIYmxz=1& zArQV`F`znqM&Ze_UP^$3B@s&Z0etO&(`3tS3$b3%ZQFQ)szD0Snndlh!J1>^Z4#EI z#DPMWQe+k6whCHBKG<`3eVn4p7-bZWeDJ9Zax>|CwAOu1lJNeQD|%~XL&}t)mlvoV zs#NeOYfMADj5paMMG@IaK4*CK?m2p2xj(V^6648nS@jkFnzh-sjG!{DS!2zJR-v$Q zOS@w>8I2@9$JhG6%z{pl8`PoC9qs6M{C8*~))@s~q}b`bxZ>SEsnpeFTxlm&*~W z0#5T;`o|t|Ks7#94&BRb-bdH?jF6bi^QF&E6bMVK=p-jEitjDa6ZFVYF9>X9+^$@b z9j!W9cJW@&HLoWu>zzDy=_R%}LzcWMh4Z{?AMJ2nJyU=j6MZ{+b%{ds3z2&I12r(e zl;I~myrDE-BcD5Yrv1hW?BHaWuTg%$l#o?HZ*Fjc&GiDK3^TYs9y3Da$^ppK@q#U{^C%@z@mB>o4eadwlC9#^t6+`u%Fv zbJs-ui=At7Sab9)MH$hST{OQg0m4)4XR6|)1+o>G!n&dG?F^zz}}6#6LR2D$KuH(}&bkoJ245toKzv#;#|B zDUWMzuYxEkS~cNMvW#hGH0tvnKEu$#^jwV7t!c7TT@*+wwKxI{I&ng$_sS|wsFKMi-Imc zMJcIS3d(gC-;-bZ8mS(S@%4QV+{`;$57(Nf=s_4_DNQCK^C8Yfp9XE}$FLk_sbnyo6eI zOs+;})!m~(Ki5d3J3;<3Ie*#Hiygv$Ym0BwZz#U7IQW!Vi681&PSBmXg)Ozl*sv+7 zft%i>7Xxv2l@vdbvL~zhr2UqVg2zyF(E6$TA z$X|2d#2J5*P%%$c4Wcf7^t1(cQHeQBo@APTjh#xe^$UN*{H;?@?UIxoTqCj>8y`fp zWzDXRASNY_#lU<>#R<2G5r#}$;Y};g3wYic;ty$DGOfX&j2UImM;KFYJGou$s0dyA z>OEV{U-jPoLlJTQo_xpWg$Ib?faUoLTfyr4Fz{X7YONQu#ZEObd-rXL$Y+t&&7Pc`d_;F;>MV>{~DQA=}d0Dl} z)jL;>b8Z4JAiLXq&$Uv~1wS|aZHgpkF(*Y18`@6l497e$BMXq#VeuK!>Kfe2bn===D(={)WX z(h=UAZlpWDoS9TYdD?-^G`;>h8OW#`Vy&PXq5vs)cH`Uj`D^Kgu1;-TwU>iI=d0;7 z=6azDxA&A1s=rQtsZcA~|MIP^Z7X)n-Qi31qePeF#;rlfCNE=E=cG;Ry9am2KXryv zf04JiYR_(|t<$giiT=HIkJhPzQFyq23jsOT7J{ceE$rR&(^-#Cv?0mjH>1xa^UL~1 zlyq&CkO}C-oty0@c@#y5;*St1KSe9^45i_)%bz1c=YQHIW@ToxR0rzY9z9U4Z6T+2 zu6Qfy;v>4#Ok2^o3kp_}<_#;8O{KR~-noA_T-4rNZ*ssHkB56>jeY{kPY))_1m7t39gr7J7Y$w-aOD zw!HJeO?Pza#JbQ9{Xd7!v;$`m)`7&0*F1Y9g<2E();~T6E&&NUYysMO<46W@U4AtXdeshalRY%Y|LJ? zXQ4JVl61o2!+RdpAc;bu>u;IR+9t)m^2v2F6nwSXNtq;OCq+(8YBqx)zBevrfrrNp zm-&O9@9hx}4KTMJF~}plWN^N};GN}$bT9#oq+<5uw;CzEi>nz*<`nPtUlmKAlQ|B4 z9FOlVlX4VDc}`8&rOrta6C!-ynzlm$Gmu3K+x&2{c#PC z#ZR~^H;p&`ls6pfrTon+$qW1Mr6SjsPvr?EA?!@JbP5+wwBXUK2J=pdXuI;6Z}<R(7}Jug{P^WJcMpeShVO_UzIUUsDm~l6PCsx2 zW54=p4;L{H_kS~nKya%-74x8Y)X$8bfo6P+K$m=O>AG|I;gL?+)TV(s5C&r4md^JK z{%Cy@oKPgUr2c98Xi+p3M+n2fCPNN+4mZv`$tmO!9ATZczljP7{R@uZC5iw?+(gtq zw6sTYgbzM;^KpGdZ3Gcc5`_Hq5j|tw=D+m}`^)uL z-mq??^nGsvKRqImuj~Sk$@vEz_jU5TD=uw7>2V`8S|w`1aYSY{s=_e-m_;^pT|Dyf znqU&cw0Be42H6h$QlOvERvC?%^`*$-$I}h%Gtna+qswNwl+7}%7mwf_>(c+7cj#Y; z4H=YwtX`NL-&L-bDxo9lMB60TW@2ef(w+AK&If|C7pU=HKq^DXD@`|$^mi>SZBAbk zG>xn&4~yxd<0h8!6*ruKa-yWt6E{6m9H*g_0x|nH&C3y1>j{-hFFPnd_tWnk!7TQL z!*9%TbN|J5K!!?(GQvp2w;e!SomBdr_6e&tPF>aeeUOZ;6c96kA!}=yg*w*`req$C z@ZJ0PTyl_oXJ^XsL7@-d8(+B{ep??mxqzYgQwf$rk2*@iUDXFjQ{T@<_Xx7vDSLFZ zJ&ikglV32bn%2O!ij98Q@}drl3! zOym6G{3YeW$GP31jiJ-~7kp#wyd+2EU+_L`)q7epr>M-9!f7U4VC};pid$Wf&spiI zmpl^R#y{)E^7!Q#<#W8$hg9CzIHhMM)sqvAhZv|m5a*Zzy<*ls_Pk8#RSqs~y3XAb zAa2#Rxj|BIm_|5TewW6>JXB(Y+UMA1_CBVCIvrmgeedS%dlrt!O5u*xE&huz)|=Wc zqz1LBYeZsag7ml_)`SvL`i_RMZ55g&&ME|)>oi{OtI$591xA|)i3TLSVUo_>qL9Wt zJ6SyNK9IYgBx&U-sw$#p%fVBIV8OD`ZYl$0?l4jJH6hZ2hfU5NB%)!hrzk$_dP0tS zblVs?Q2CzSN5E{9rcBr~#E*}@mtf5DYx37)%VYaEZx){)XwiQwSvd9`({C#P&dvMZ zyBw)>-HH%KGCX^tTKK7_v{XBySD9{kuLN1?2!oT>o-=CmC)K(8gegbQ_QN~LR?iXt zM5U-my0Htv>=k|yQr8ADYK;{~2)kn%?2v->7b^+|puY2^!7JM;j7amrAuiFbTb+K# z7afwdwT>DNBnx8|NCI7HbLA zO1w^U%II&MljdT|c7A<KiliE=G1X{oU30*EgGwWE)_QYKE2GSF8^`8>uGOWNxdLZB3+6Xci$%U;yU&9=sjq!Wj!`RU zRIDjOMM*w$<93sqkrs#}hdN$ZBYwKafi|6F#tTuRJyP9qOqLyIWsCIFX`von7tAS} z0SR(-9{#EwH#@s_I#%UrMwPtShn8Q`4BS_aAxIV$hwMk)LnqE&v4wZ7ztYcm^$q9E zkaofmvOLO=Lm~cKmU0Sm1^0l(xP5{TPtm>E*`9&UWj}jfZT1Uoyf+bbp5}4xu^#_| z2Nl7pS!4A41i4)vr-sBqLO0x{nnlSlSFSuxE*k^u8LRT>HLQ<+v0EzU1<-cfSLBrD$$GH`2q6q_cLS6uq?Xl}me z9>37l>;Xk{ZPfx9`YNIF+VjW$iHNJQ*O5I-=)ajiK+c06$*pLzEr#?GK^@B&{))V2J4VH|Lxv6cWsd zfFr?hD4_pgQ0T$i^Fz}P!2|lw>&YY>S)d3-ao>154M*Z5Fg4`YX^!JF`|5hJAWFM& z@LqwAus;w-*-pHFaiNe?goR>lr#(=D?s2CHfhNP8w|lRGz+tiviwNKa1AZ5@h=#dBZlv;)SKImaqk8Lha>`1%*LUrh zAkF@=GhB!J)my1R9}WO-?H- zNbp3RmA7e{{n_y$#jIS)L%$Vo{s`wgnR*5 zH{-*T+ZHD*VP8DxSJ_O->pcCWpx&-I5y@%m)Gln3V&(ca=K}dTj!lpkDZRnA;s`5-`!5bt#NkzifZ9j6yR$}^>2(NMYsd6G(3H*TlssYX@aAzDe4Hp7a_=Jq zc7)=GasSOmdsIP%4ln$+H|>6 zh{g1}W1*iAUGnsmkvV;u&`i7p?zgG6>fXnlgdV(oAv@z>rLrq&2Kz*luEVi)g`hqo zfIB#JYf)GF69T6AQV#x1-!QK76O&*{Xd0Ox3r+kr*b;Nmm+Fd@EvsAz>jWJ4h>;zo zhX2AOJdmbm{RuJPN_qAQzSsCv_?0SPj>3QP|rAH z_taDkAuguiaI7_0LVU{f{v~QR6+)A5to5SeeR9w64BG|>A&$|5n;q_#r@lpx+zDEk zbFpHq3m!m^P<2P7_!O(O-v}n<%esflE@DHAp>eGHMA^?1KdqW@JLi>gjOC*r~FK$J37+k(Yvk`vYITw}KIPWv5R3H|op!U2mrR0To z_wy}vt&$7NuOq-6!ODfNFMeos_K*2I^;R&8 zalcazCm!3xDJlV~GfI^VW-I3JDvbDTcv8i26vDbQL|&MMLpe;e;`3YAoo~=TtO3YIc8ZFbRw-_QaUoRGE^s0NdN@|Aa%$-ShxV=8# zo%T4bo5}TQmMLNQM{1^+h}=&ZrjKam0tb3LIZ~L2Ue*OZ_|(0mJrFIWFlLMqsG!iW zxHEMxoX^cd(vage%}lAw9uRQ$UU%#6>}>mu9jTq_p{XZJ*S z+}U;h#(!Ig)INaah>1JG+JK|}#+UT(>nLr8taxxfO%i5c?|PJW zftuds%R@`Hvd^Kt6SlkZdFvd*_*u$cr*D0gt~GuzQJ}=MP)c0qpV)g20}3TlJv-5) zD+#$c=T%nwP+j1KYv9OPfm;)^L#7e%?z6CW4 zIzxQr-TQm`l%!frl{)VWeR0(UIN29cwAi$zAa;h2#ch)w2C5B7G_c;F;Ui`-EvIP& z&t|3(R-NayRgArRP5$aGYn&vXz?}f5y4IZhR5{F36Cs0c#(diLWv^o8MrLG8hvvx3 zgdz;rQwK>CHS>_PIXQu3x2nW_SNFb-uix;O<@Am)e7S$Mq<(m7`ToYpSN!G1iQ`q{ ztSR6CtJ>d+ZspG)Gc*)Ll@hb+Ph2OsOm_<;1n;x>wmMF6tGJNVE}@!{ zoD7azAl1@qGPQ+PiLRJkwhUu@Q`+cLZUtsWcS_bFamCrj(JWn=WUrOQ^!TMu1?>gq zG#G4bG+vDjnvL)O%$UpL5hT8Q#QHkQRD=J8B#^;ZsR2_>%r#N;+PL}B26Nb;zTG~m zP8uXEX>E9e;GX_#$22Q`hs_NEd%7%wZKy!IK+k@&$^=0=$HX)g;zY#3>oy6q2#u}_ zVMSSV4Tc4)?PBijS*!5_uTP9Er9%dlw}bf{x}x;Tdf&$}FR_^4d9f=>jePci zQW7`b#>-w*UNZ@nu6}W%0_St4=Mracj9!Jrx%VB?=liKp7sD(A9HOof<;q@(XXKhu zOL$_KUaw_=+?h}*I{9STCnVYZK5LJat%1yvij07&6ZCbt0$J#zr1?^e?@Zp9mQeF; zXEz-4aGB5}M012i1?Tw-1L22jrQ_0FFPUBG_KnQvRxbONa@`l(NT~B*@x)S8Jtj%1h)ougS zv76P8#g(46keCzp7YVWkj{D~}P8r4Q##fwCT~FXJyYei$eQm!?=+W24qNdi3&nfSv zT-O#uK&6>{YP1wr^;-G-8)FvK?S>m;m`D4{Mn~BAfMNQN*X8?l>>%D6BC2PH{$9GK zAgQ6Kaajaos%Igmf?bdpJ9{w`Jq*wiBCB^;h9i-{MGS~&7Dw9|U`%c7tubK0(~ON| z{^wqY?Eu)8@_Lp)?DW6sesBDLU-_`)<@D?^rUwDlTu>-huK<5POWGg^mN7*j&KroM zJ_r&AK_skzuxM-)_&?F-KR_UexT%p58i?V>25oDB5r73{XQO8T#FGOCyA2v+YHbL{ zI`Lf47;7-Z+6rLX{ti&GH^HERvJnvO4F+zf5D_p6xEG0mu}Vz>7z&sN7^XCs2LQ@} zc>!8C!F)hj56lN37yx%z;OdWU50rq`ra%UCVgZI>H@p?l5l{lGZVc$);$RPkH~l@+ zfN_YjJ=*dnwjihHbTA^UXpp}~-v2ljwZJ@pgW~@~0{y?zfb;Nzu?c}hf{{oRHi39~ z`M@Y(>S25^Faq!x93%jSer z=LS|amKJt!KbXY!@uQ5z`B}#D03>WZHpljVSod>)2XwIYKiUChERP2?v2=k1yp03H zQQvc{0I-vWrGw1@x(BqdbO3MAKaBhYU`O)v0ow#Sq#yH+Wec0I2tWcM4oM%7`q}C` z6Nm%B9CQx!gC&b)8jJK}z&~<;*`G}D08SDF&mms`^H_W=42gt;xv~6m^Zn@kJ1rhy zHDTL)=LnmF5Wum5rOgWn;zz=c;^!n`C+e4Uz&%!qzXE~s!3<)V{*ew=1Auq1VgUHB z`GKUdFl_xV>5wJ}%fOET|DI#F!odszO#fQFh}t?gGkz~g01m_NB}p(Gt15t6X<+KVm*l{Jg?IqF35Fk>q9EXW2E2?1 zFau!qeJ`N_$$c*wf#HXJ9Gv^`!){E$2hLr9xCIz-&9~c3@*8ezku^LkZ_N(Ij`N0)LOavebG0+3U zu!JEZ@c$JJ&~^WyA^ul1C>Nj#0Yq$mK#}|ZKn2w00V*$m`pdwu13grJ|BUt*6Xze` zuZu?x@G2ilP7=m>VCZmKnp!zvRpD=vJHY$5di^i=H7}Tx7x*BI7yHTAk82tN2B_Ho z2&%CR5uxw~lS^z1NI=%4uS=j8yUk0ZWk2kd;M)qzj&OHY^Sdj33tf|cTJxEi^tycE zl*_x2?sD0{Q!~8i1uoPF&Myy+dCHU{NWR2;ywJ1@B8O_kn5&~So;Y$9oCV8-aii^msskJ~{jWh8+^vuSVdXFzi0T{=im;Fc>$S z?++M)2YXEX8xO$)=(68oeAs>Y8y+9(PkMa3z+(Lu9`FFDhu>jXo%tIK3B?{>*xyf{ zp-?zrZ2krV4(>nt<%VJpmVdQ{!4arGVKBfT`V9|;;`?*F+}O?YFM3cUl=~0A{NM3VSVQ%c8Rf!BN0= z{dZeFZk|8-1<3sgaj&;M@8hh{@=tgNPBS29C68f(F?CkY0_6J)X5C;qaVquX` HkOch?)#)|q literal 0 HcmV?d00001 diff --git a/docs/examples/robust_paper/notebooks/figures/toy_normal_monte_carlo_eif_unknown_var.pdf b/docs/examples/robust_paper/notebooks/figures/toy_normal_monte_carlo_eif_unknown_var.pdf new file mode 100644 index 0000000000000000000000000000000000000000..cbfaa51cd6972a539287297f774bb61f9254079b GIT binary patch literal 20671 zcmb`v1z1(h6EIFoOA7*eLFu^n0+&XR?(XjHl8{aTky1jsLFrBj5ottPY3UF|KtbSt zF6#UGD&P0>`JVrUeRj{B*_qkdJv*~|_6&`ps5lFhl>?LJ`Fqf_MocIO0&+06#^mP* zfmJ;n%|KugBUd9^2MZ8b(a6Hg1;hp*D1+|Y!8Efsg(GtRQbF9o-W9}tfdJN2Ft#=` zaRqUGzZCOul~8dtay0{Sdz+r`z{%*YPYBef(=>XBKuQ^?UrjrV!g zh3|N-$|w_%6J(;&2fTM%X=Lm^zG-!ItlmxbPN}g~H^|q+M(~5~m$P@9M@I@1b@4tY zQI7*4ptIBFJFTthvuEv~&ON*@YUCDIVM8${G7|Eu^bHeB1NZ6_Uevc7Gv9zx7~J=H zw))!TG-Bx7^WuX)NABDlU^}{f*{dwk6CTr*0s}8MvUyeKJ1n-|LzYF`=>T@)7 z^U<82-+kYcZ(rY;6k9~pEJK~o5@*xzym}|W?_F&2S$E}2qW(?6j!l&Zw%zE^sPUBZ z->74j=5r2u*#MS5orwjEd6C z!MP$;picEs($LhaK6&8(?C@RAYSp8W_|tpeR{HABzioUu+g$Q%ChM-sL_653daPta zd6O2w;ww6Jx&EDQEibU$;P9QTnVTZV6HO+ZRLnKGDT0nG3rj66W-XZ?-Uuh%{N(Pq zztrMWTa~Y%-*uXi?*btH(ph}ZF8~W-SX$VV|xzWO-o~RUn}3T`RCBE z9B54+7`#9wGq+Qc+yAma{$hDAd)Yw$g{9}d&Y2?}l6J8E&{U`HIUjWrpR!cTC`Hcp zRt9p@*GY=0w46b1WyS-`(-LDt%e(ZFe4J z+1cg1QgqJ=*G!6|Gr;raUZSU2=IkgnaL)|6^Z7(dXSnvtlib{yEqQw4sN3q>bIIbE z0=5!Z6czdmhrApqNwg%4)fILrHa1QxG1d4FvktE-R&5*Fkl`5PA*DDUj%&?KFWHy( zHx2N=v$fR~s4(<7L@MPo@!wijH5n(R-atZ2m?4e3Z5T|(zWIRrB{oHFOsW|=FV%d8hAYseQI z*O3%ogG4rnew4%x6|o?r?$5;@C7H^!dJC%Ul!Nq+EAG)IwAHSXj<{Q6jD#Nxf6E!Q z=ofY|b$($}l5Lg+n!7JFA2X1=WKR}T>A#Vno8w;py2jA_T~UP%;a;ho^;^b`!L`UL zt4y|y8%Eao$JjTtHIMA3bTw~9=HGi6&bGJRK&bx&u_yf&t^2^MHh%Vz=CorLcA=ndMQ&^^UhMy2xv03Ogusv_u&4GhTYEZ~o!5HCA?=TymTO~feBJkOW zt!6sxR*P^b7oUW3WI%jWEGO(`Rn3rsr7LxeF)WaCAu+pw?%G= z6z5jfg1y|+aSb6BSmnWEv?YX(#5eX|mQ^wErzC`>9<&&{AKuC3Wlea67uCG|1Th;= zBy=DOG>2*27@cWy4bq%saN^HV&4|@%pf`?Q5uqG`o>IB&&Ac?BclVLLEq3{UA$o%Y zlfSIySJrjP%64RnvG&+OavehN#ZDCO>cEo*;su`Qv+ApsVplTwt=8HE4a2DPrBk)^ zuDEJnr2^GdO&tdYDzCS2R7cP0tab=AwB5hPGEG3KH8C5?O?Z>non-wz5>fVBWHhAS zqKukBJXMK>3I)PJDe=8#e!OSr24=istcHml*k%!sS=tFPQf zEb6B<>&FiI*v7CK^uj*U^d@Bb{^U`l9#+V<0WULNfLmA#Qr22YL~If&qjQ8+tAh@2 zevKQUVSx@o2E?ed$>PIt208MRg8%z(TaMYDbNP#@H*q2M=A@>D`eEyDvi{&+|FlN9xbVv)0Oe3SGgpkDS4` zLOlfQK&Ihlh+4q|x8mQ`dSGCfBrnsD2_UKM-Kg8iv(t_?mgCeHagn%H?%75eX?S+k zH8P_rNbWNtOGcle2qnm;s}q_^uluF7yhwFR)zCV6Unvi{@ZJjY({9pT(9sfcNSE17 z+Gxcy3iBBi{UVg0Hh$74)N_>+1JF)VNq*wj?mD)}M9~#|#ZSnJ19SV_jS*zZaK$8S zY6{Gl?X^vfYguzhX?L+*Qu773KaPey1J1pKa(>T8uub!IM=FR5ZU^Ns%D>f3u!dbkle`ptUjCfy; z!CyH>Z)c>{U^FUSL{le<*95nYgfHpoLph62DfHIeLoVdEj0e7ipgZfW%?a7PUpOUY z)C?WDh81!*XKD$g;lPLX;ePL0lJSUuHsXoPA=00499K>d2K{U-Kvxs$r2yyn#EfXj9DEeAzrJ$*7b&yRPtJWqA?nw zQ5h=Z`aA>oTVhg!&*%d7<*szIc=2B(8Eu-|Br&7$KTP0vzI`Ywp{0RJM#)RP%8J(V zBvEuP2TD;){Iy26ztNVQnbR2Q&>KJ3h(k`ihMJbJYON5Jx=Xy`%$#XA2l~D%_R4;M zje>X|Dc=fZ!Fwe+am;(EoLo8y7)(y75sIkGZte0xL{V*9hep)ui2AVut(^V38rTz6 zB%jG&QS&k*CItDoDEerR_T7&tSl-+#&ne?kpJb)z z&_cRh+<`DsgGTjop11QlBO&`RVJI-V*mDXL^2uIFx6BtPCcDiQ1L} zS51SUlZ*8)k?L+j%A#-YVky;DcTfspE|Eo-A}^>R#jxUq${g914+_^cYpzJij0YB~ zxJ6F`Jd|qNQOMDs_@i;`T`O48iyD|kKN(*@75tBWlS9fQKLGR&e^Ejg2 z;(6+UTwpf=$sMFsYGxq9n){GVk~k(H`5H|xA@4Sd!;~L%q80DT zax%neLcimByMoS~C0jniWUE0}SL7MH&r|8G;B9^XQ^Rnr&sK+5j_?m$@TA?Tjr%re zEZkmS=RxS|WfV1$gMKEqXnP<~aq?;|{{eD(QQ+te@eWS0>e2<-;1*s)`4Cf4Ns^;0 zUt$hw<z-YGxumZf4drDZN=ALqU2gAPnsYC(SCV|=EC5px||`+n%q@R{W775B%kUcChtT= zRu*s7kBMz*Uu&#~AG0Whd^}6!sUu=o5kP!}sw)IK#-xR8gN|kvZJb>x#?!t$cALum z(`^gaybr25Agl0B|6z<}5xvO3@;-tB=FY{f>%E~5BU~yF{e`DcujapTA809qnqn)y zD-frNwWOge@%0Q4P8@qcI=@R{AkIyvQ0*y)T9;5)tq|dG|HcQ zr?c-XV_535+E*0%6J~0pg+6%>CL>%R)4z|uT7`q!LNcMEP30X4(jcqj>^GZ8sF#X9 zZ5It(g|Qff4D57CQF8VTh%(V8ynWmm zZU&k(HQwr%PzK8l;#|;&#Uv)~Y{M&ulP(jx(L_^nG8FNPYct8DNr}PKW(>ZYOt4i$ zbaw%RWUXNQ)+tJQmZ%z$j^j%F7YA&jWi4l392<`QXBDxzf#5wk+Z$LcdM_eAo2jGvxk=gH6>Q8Lm1WxJODQeW3 zmkQl+4iKA~2+>X#y?fX=PDRfaJ>5{wrKU%wQ7ZnJHHYuBo#dO8dDjJ=Y`!YG^)1Il zB7K)CVwWvZ_1uR2o%=l^`b5S(X1_F1l+3Q>OE7vwL0nBkZSE2C zosArR5plU4ZMG?!Fnui0%TbkRCc&xJHBo~#M&Q1{Q*(QXtYo#ASc zV-{VBv?#22Tz4YNK}r%*q23wQpeC|oS7eQ-T2vKEwWwO4LXelqs-4Z8juUiO>bS-K zuKIFI+gND8-c80nSK$*&$1w4<0Oyu6AqOqH!RTSbEqIhKd`vs4&SFm+U#ODvKFv=&K%Wj^O)%IeTu9cjpzht=RMD~R%w(Z8^msf|91?uhNcFh| zqQ+rAQJkXevtt;hq}wjz`^P(pQ7hg{mR)-%N`*HnHH8gstq8QdCYzF>c>7jbFT8A; zQE28_gprZjI@@>$1|AFHq9lF?zh!V$f|R9HWh28SWA4?%ye;Vt7LK9Mc?ZS8nr+Sx4NBgioQR;1**fUbi+i#|KVV zI?uA|sTiF`w19E5;n|#VdTrxp;2WhcB{OE8vCEg!@lW!Jh%#2vgdFLx$UT*z&-B)# z*%%~M%_|go2@6ebx3s{{iPX#~7_Z1=DOMiob1s~{F0RBXDGq5&tW=X?(YH!V{3=(8 zKSqdk!2H$r-h!(0E55WK`D!D4ij zHT3s_rcdbQ5yV1(wWpVz6cOvn{B2)XMPf#Gr(Fs)n+;-nc{SFVr;ILiev>&^?eEVc zue-Nrz0M^sGU5FyeEfVv_9KN6<)@K-Z6jTl_Xu7jo$Q39o~n(F54f^c~A;7kdQDw#xvJxeUNj9eoAgW?F0 za!Px|)gi6b*NJ3GxRcVMN0TGFh^pAeo3_isUC&y(X4RP`TIk&f*lIV3>@6s()7&0v zo;Bg-@rt5oNQr^5%uFC$nLJ_{yuuUDsZT057D>!(6qXnaq|DCS-GhBzV$wcBUV|jz zwHlaHdOmX|=Z_UJXH_Y|yfa?mP;n<6v$+V(16JDTmo@s~19M!&X*}gU&W1I0)Fn;T zu2C^v51Ge&?qt|FD(=zh(!s+)vV8ohgguK>pM3b>2o;iuW zTW5N(Sj~O=hD-)Ug-Nc9Vdi3Eo{wu~gBZW%@C$}nE-cq@x{|I?Nz=zUW9Ua~t-i2y&I(C- z!q#&#Z8|R38s6(TTD8+QOne;|_ijMK_`tpTuqu?YavIHD8N>pcb?r~AH->3$7LSxK z@n1D>e9S9@JI2U3uO7U%Xx2-|lNT7#Nk87SrkLF8PI47jO@%(Raai%lynIg|ktL9# zWOkv0iY#sIC6e2FSM{`4l}e+f%7ODVGgK*0EcCXlHkcztj_+QX`R;r`4;_qKoYXY&L}hN!fU&NM)salvZ&F0z2Jq@+_uVybobD)l z7R_zHtZ#9e;M$Y;?77PqAN7KMJu+m(y|@ph}cS6`o5X+Ac3 zCTvS)=!-H!nL)y(!ZoiOU`H%ExIsH!718NfM}k$Jy;vc4bM1_Yv{C*6c|B;Px}=LN zxFcAK)3$<+JGr#i(O61Rpb4YU!1VSr{T`;CVJn2O=(4rv{rY2a1kvmVb<9l> zO{zzg-DX_}W41mFKest2 zVq-nocS~u}-YiDe!HOZWbsp;JxVJrA@<54+R}wlsPCQ{Ot2Yxjp43(63Qk%Qzl*uq zxMbxi?%1~$(jJIQRAeYyPTf_7svZM>bp6f_H9%s?tjzIF+gGQ28XO!}^Q=@mCbTxcY z!q8ex&9>)Nnuy$5Q%BK@S}Kpq@7rHQbuMSXd*M-cIB%|IcKWumqY+=Oj)(gRUGIwq zimeebBq-hBGLF)jh>llDGe~okLcECWo+(cW0yQb9_p!ii!O4~Uk?4C93emeHiSg3* zM5Y=byoVdoC=44bj%p1Gw=~81i!sryXAdy%iC#rP1#dTs~V_;2bz! zKVM#W`>OYr76IiICK5Fr-vkar11EziBp%!u{)budj(S(D(vDeMl+o;PxNQR5)C!Ri zlad+jZ3re3L}ZJ2=8I0_lwhM9jmZ*?%N|RN>nsP(6;L9ju;s&}$|8E!&DL_-`nL5E zc?IyyBdr8FH#0Zw>f!oMyW5w8x))$|X^w&^cKkMa{{WbgpQ7MJ;xm*mkb( zz3OQ#QC=ZbR*0@=vo?^ks?upJQZB%Mr|-?Y&Jm1M=b7S@z7@FxCXpj-##4N)8@gq=Bd6Oi?J0_4o&~?>Zk(7AL%fIAYV-0T z%;`$iZ6Y?6Uqw{+LOf5u9e&+os#|)sq>$a)*l_0I)7)%&e(0gGjZbDM)kw7UWe3fF zv-B!etiAh$lY`?5%g`zVogx=!be79(<9hsR#WOT*&#y$)egiub5*Tjr(NWZxp2e&l zlS@V9Y0IwkA5tme6VLaaeH{9HT&{@Z{8{6<&ooQk zK>CCsu}2gswSQ|}<8j%6>bl{x=l14?jkrofPcw@aR`XexD_zFCqUzVI`Npfbx)OI6 zJ~#K}DeZA@rmE6LA5QMy$JNu36cq8gws(E&>uAWData3v2mAP9R0q&@&i$169-$3#mBFmoNDXShpINRZv{*0{EG@dy}p_p+PUt%fI+imyOzp4<48 z@JQ!$q-E`d;kF0cciv4`gT1C+(%iT%KGTrDI?b(@?3hX6zF_^vlm?*(*K~zwyD^h! zdr$F`Yyh7C*xiNc5q>1uR;{ssdO2uH2g^ZB(1?4}bNQ6NRDGY;qe4Hka{x^xa)iFo znBxJtIc5FWQkLhvlO{BhXngHY&2RFe>8?E`lx+OKEajL`T}Lzg#`c}yONrQeQ>I7! zVrq?h_17LhSX;!_C7Lbfs1f)m>7y7zPR*cTJ?Sp95`ighcb*`Nx#^_ysA~J(xEsaz zDbXvgWbp@Ki*z>1g#K?H*Th|&31WJ5u#MB8K6Z_@2E*V@-q_o8jbw+J;h+MhcLK6| zRWHSrG#ENZQ&=*GRsFT~K7h=IOJwu;wee9=adH#DNqVc4X_#I~vb1OOH9f%P~ zej+58`_o}+&CSO3HQ~ZpyE0Iv;Zt0G$~ZqgzlrS1kXDt_)o|gz~>a0b2F!>L^oa>=RSW?9vE988272_p!rO_ zucV@IdsuuIriG@p(|CiKdE_8}D^&ooAt6rAv{=l-b{6JC=Ar9nnn;@cpf+BYpNejXHO zD)%@$EjIVW{Y+Ngp3EgdZ_%vMo`6K9h3v zZQ%N-!mSR`ho2YY&c1z$J5!kT`FiN*cYHXo7bfU?_|5#xpvmj}%lxw=hmSy^#b>s3 z8IM9Ch=K&2Ae%3FrK&(ACfD0(*qF~(33zYNExRuXYbh?S574MF6%#VV;O^lL=`JT8 z&zKx0e!P9w@cyLMVI2d~6TW~wl8&9ox-!-(Z09R0Q z2AEri!%x}#I3n`nyv@aVl8b{kTx{Gw58+%KM1cc!Kzjecem$A<^Dqh&2An|oA16^@ zQGGbbchN;Ys)WeQ4=E0bE8$9=Pe3d+B)7Ik$&6{rX zKP)&sy)r#{yZ@3#UUHCzjpr|9r%{%QMuDO?trDNLNgC08EK;(;eS7|*R_dlS9_W>O zv1$cAxghmx&IC?hGchg>l#ej6v5xazj_{ke6@2iB=hwBeLXn5S;cl~EnBT3MZ(xL_ zhNAXeqo&IKhN2gfvZq?uED01uF=8t90h`YXD;WgoA+Kqfc~o)x{y^{zyqSJVXp64p5g%UQS5MV>7Rd4n#gAn>(M^^4mN> z+)=mK|5M*=@Pkah9bDpu{_mk8H&B7^4?#yAPG=xRL=vuiw5oOcq)vEH<@y$4EYTiD17T z=lU^-xj6o}VC?K1su0C;2ogcQc|PV!s5vG>sIH+eYd8|d2gmkY5Hq`p2&ixPJ;ehv zV@rfM7>Bqq8F{j>89s9i#pGv-QtVO=gmrXM*d-Fd&vX5zDj4K1Bx0*m1SH}>BM2oS zxlAHt5RQ6m=i~OrXb72L?5;~V!%x=z#u@rI543=M1wJwqeN!MrT*$vYovz{iwT#nu zhTT#@!6@NIXm^hp1!A%Jr`*r=%Y&O3_95hmkG1;6+Ciyk)@^D+Q2vxndb5r+SnTed zOp4{;o`!v_6E=pZaKXa{LTd+xC$+K5F9%nWXZ&Wjt;??VO4HI^B0K!l-|wmj>Q?fAQvwybxGUTd2%DK8tBfI@xdh=HG(A6&!NgNqKcF>j-o()KslYOZ5T9Z^Ir-ooQWeVcDopN#-zn5bE^<1JG~G_{(NJB2x8@{9&imEm(L0PTDtZ{B}!Zk>etC#h?ltH|6#H^ zm*uPH*X&!L*1k>ePm`VB3VZ4jAU-Sqj60&=D6ei!QH4I6#agJ+A(UAdv9+>-rP<#o zYbI@gf7OR3wtDVr0a8u`ey{|K)XI`(R))ni1%V&?O{%DXl)X=*)!Abz(e*tt9HWt< z_5+{y(K}6ZQCA!96Z+Z2iOmp%;xI9eQ*CtUhjAGP_m%|Oy2F}LcRM{b}6ylAg z+R67(924l7J9(TfkJ}F2{H0Mg?5bQo6k*!9Ep!~GKk?(DmvhCutL0#%D7xxRB*!s( zWC5FOe$N;xU_DFNAmkqt&O<(iGG}+3b&Rt;caHFCvw-W85dc4i`r8O#s%CtXwJBqVITK6>X!l@ew%HO(sgMt3+IZ_|BC z)YIdYRycK`I<4*Fq3nuVT2oFutP?c4^t7!miXRiLTCHSmWp8bs?BDFptJM)`7wgLP zD4OVBlVYVR@$9;VCNIYrv1+HPDy~6mfj`^c(Hpt`Qneel>hO7nQmtmu%cv%*#KFC? zDW)lCTicRIQeTcZcN~n@bI^EaYg=);X5-x8D5`Al7}Y;sOYjl}!jJv_u0R;iUno$q z#|{OGebR)O>$ct9tq;Jl3O`O-(Gw@aKismDZ5eo9b2S%Y-ihq;(GL& z`@@F`81ZB8rg(wXE?AUmiV{C{`CF%28uNHGzq!U!*jxG_F2p5cJq9^Sorkj`Jw>WY z^2PfGJwE0-n9QXFX+m0Q%cE{2h3G2*>~(rGtwT7nj0ii27zbp6qlRA2q>htf*qU7Y zEhj!ME`!%-RhB7L%b!Nrea$uT-N9i;-`JdXo%M}dxWVhh_Ga&eanXx2gjdsg>6hs8 z5=V{$`WLzsP}Z>roS~Bp6~_SSg|t+QXT^fv=X2Bjws`{`C`h$bRi;vf_lu;9r8Z+7 zTB>#KJ9rvg4+j&gC6?3B(NHhDQQg`=_jP)ed!=-JD{e;FU6ds+VqKf{A#W(Jt#O)( zL8AAgZr_&S0Za!?rd@+tn%W%8_u~)oJ)H2?Nr_@l+b{)#*u$@#elrRT-^2P!wpip5 z`5GkY3IpXMkgV25j;&dhUrQp_hCHZwf`upX&~d!ZNAJFvg2}DYFDaiZJqJ!QE5?|S z=-SZ-@WgPqDnnD!88&e1X^s`SFEoVk$^Z37U znTubjN#|;{EB=B=5#fGE_)!@2%PU_B(cwDKwm_|OvsQfo9#H@Z`mm?He=ylg_1-(8 zPrqFMJoagU{r$J`Wr6hU3|@9e2tnHN$Rags`7F^WiRlu8-8i;2DXN>`#PNl4vj6YrMovMlG7y+}j)-`W6n%R=T`8`4otLa@tbQd) z3q~U-*T&9i=Opt7%IuP~M~FK=vCJr1!%<=v??fvi&c zqLN&wT$Y_iv0TG0C-dUB=Pe4UE!+xu>x=PDwF%6IQhwm1GOQ^RFW;Bp?=oO5$(pI- zsLOZWxIy-nzj;4W`)0DbR&thUX=u5$|;%8nX%2h&R7QrQp!> ztF=mRY-0HOXl;EokYA9mWKz*aPpwj#yhX6Fof_v7DKGOt|H2nwXJb=_C_>ng2}1K} zfBJhut;tzM!mQzl1icG^Qo@>`9*U%5z}sXkM>Jf{4}GF2hW-Rmv;tAn>N>(lS`x@5 z;`~3mpWnqn1jG^lDbBAB@h|RY(j<1`C7geCKfiw(^79)Hu!@_p>&3?+;ttMsAh7WF z4=GqUIbk3cb~YG@4FdRjPzdDWV~QVcJUbWW|6GbLvB4@8A)Fk_ub0_i$n4H-*fNB8 z$hX4`18CUmM-Y%+U|!;DvR~$Fa{tBRQBDyW2ltatG1Cq4JQf6cOiN#%zIX_VmyM`F zVXK@BzojF8Gd&Td_Pv|j=^UQ+C*6&`&eWS)!p6@?onK{o@7B&A@91tNVHNubbq#Qv zxR!ek2H6*A^Yln}f!#|AKfO9sXG2MLxvAtZnoRXhHn12hywtfCy2wl!3rn4vPSoOk zs>3IoO0Ia<;mOkcg0Wdpof&_D32#r0rp?e=7BW*FX4&1o*=j9|6v3~$ew%wpw3?zU zmvq%74lElx*Z-bHV2XF0UqY}OPl%6&ESNER_Pj@?*7MF^kfeS_c@T`CQ6tw^4Zg=T zr_Qte?#j8S-3f6I<1LBmh61C!nNAz0ITrqCTJ@XF62_FFQd71f4Oy`Em22SFbQM>{ z!Sc9I<4@kDdg+QfTYJ4*3@}CL$X$Z2tM2R)>aKKnzr0_}Z793a=-HF`IxBn7#-Yjk z8}`*^xvu*zRN&qRvMa3Yb7nSLrkaB0EM=wyaUTesQ#4vO9aE9fpGKfJ zEm%SxD>z`2$lgIo(xm0?S!T?~n^t@-k(1f5);mvj^=#t!8)AEyM*byQyTs{(LOK6J zYw|p}$ak?ffN?YbZt2LD$PW6|kNhLO6?TVzxFjUlyYz8Z?jC^`{gOzdvE$7serB8n z>>8z4&zX1ab#H6!T8Go{pC)*H*(W0zkAI^1HiwS*w3<10+M}9+4ADfqG*BuXdhOI= zd#+fHtL26B2=(wK!e8QWas0((%Kl?nib4=7G&0;KMAa38)jzZxi21>hEW2EYBcXR* z8W|zc5j*%2o|iZ#P>#QO&>l76BSX+dycOEfL2VhHaeaA?hYq*4h!|{p#s!?}n+iXF zSccx#TejM67NoI~ajyy&yq2}hJF{j?7*~vx&ha{@T{9TZLonp^Guah4d)05^*3bjO zLVe~#UX;$FNRH^Z!%ah}19qrlwH#Z%v1w}a0o9%CxLmAo8p1RQ=+^C;udPixhxTP) z+66YmOImh`8D@w5h5s+0{L}x}%~bvpzb~ui_U7KX9nSh@Z}_wJp&F&ATqOmvgwS z%6<4Pwhkk=ozBq>BI5h&#YZl#Q;>>Gj62-h1+U8s)0_HG@E)KnoXf^2L&IA%^Nk(C z*iBPK-w~vU*5B>s#q1mWB;FBPX*Pw$kG+zD)l<=4d(R=Cj)4ys1;3|?F?v|$hNejB z$M`#&IdN6;(vojnWD45(Ui4OVe7XLZm2`@HIV=N$_{F{X#^!Q9tcK zYn`MNWruqrspHKwrmo>Aw^G!2vvfjgo9hgJ~yC+Y5 z9`a@SaF#rC@8QOpmpx@i^rYDg{>bC(&|1|&CTI^tGrs(nFG&?LBNV5 z>6>!A>f;g(-_6d*bf&pS#{@czem5V|r1aK_!5%!*O&l^|Vj$d4p!O`|%;;VZT~ewP zNmbA&=+3Tt7CcgLsHszTYrE?)Xee5xs*7&1-|d)>Og?mbg6+Qe?&7tl#vqrBaGDmZ z51enE@mdd~X)Go@N?DNT7fA61@UJU1Q&{iVylpb)cjU?uMNo(zDZ2a2Isw9LsgqXG zzvp2PQ_TMPTiKgt*SoQ6rS#afqarB1>$u-Wdbq{b@lnmUNi_RR*^9e2YqZ8R{rknW zWAxXq7rGhjt&il!=8jN#=RLPVP54AWmGZdkpvdYG;ab$>s6TTy67DxXN6v`!z+jCrJ#FWr8M zGn7i5NLDiX*zevq&oh1lK@8VOnoBzFR~PMvo5jZY7b``WQlDKH1iQ%_GurTU+f!fZ zbIgt(OJ1fB1>>7Xxwi<&y}m}+(KmdF8(VPtCSShC{0{lKN&uPeu~fT7$U>zO)kgi5 zjt3cIH=Qx#(9~`$^caeR>DB@o+9Nato_R;j+z`-RSj7%yWG8egor&ar>C8Gevvkc0 zH(uCFcvEbr!sFbQfX1vybm#5+K;x?zI#kX2@2kQPH3V1~H?no;^(4VArm><v?52&InPqiWQ4MU zWN#xunFUX)UUDL%BWE;0V`#6uQ z+u1>d5$~z(*3*_73P=Ouvk!;kd7%hkx>isx5k=uvDB!M`Kx4qfDErpwLN9~U} zU2vRE6N`G{rD{{5UUAw_SSZ{NG1clNq}!|PGrXO@(HB`DTahG_`czt*->cgLr`DEO zB-6Yp#_CcLF#zT{6Bd@hhQhf1wFjG#>%r zS&MjQ2usY{Z0+BUpE7ni=j@Qe6cTqZRYnOk?sCty=Xd+OFW`FZIm!`4U{GN6yjOJr zrI2}H83Oh|W9If*g4)I1H>|0_NE=6C6N6mRJG;L;LiPkK65-d}hoshaIKGAEpuvwx-D+Vc6*HxB6wTxI zCef&2?0xF(EgGA9&%Oy0!16<`iX)~u2Dl2#YiB|WHR%?b5WW=qZ?TM}7&VFAd_N>b zJ%P_j7jGBomMDQ%Cd->f$-1JEo@83ssbdQ}Sx~JZPTCHQ$?^@N9kq8dk=|037Etqm zbTukq#XZVguXhexDWB63>^1Q8L9j`Xj=MxPm-vuuTz}!u{oM5&Vz?h5{Y`3czqr8O z>)cLmgl+(xpd2c1eittktd`4MZGV5WlU$+usQ@8a&!;*|QW6xtHZ$NkAa#(5p#5a)mE~he_@AKyP*<=1b08GA0J1+7Fhv zgDKn*1(k+k*iN*)tU0YdlLSvcx5kPp<@KT4pib1>p^chR@D{JRW?GXNe-dEl!65ct1zzgPahuh8M-<&0dNtuDeL zutFekF9`Vir=*7oh6|<$L}>t`AY6oazyyog13?YoF&zGl9q|){2^O_7H#Y+!Fu+4D z=z!P(56Z>S$OMQ60j#ea&77?qOhNGdbuTk#2N2l79uV909#C?%bT$LZ=0LCp5DZw( zxBw!-fGTEI7M8A50aSGm4^TD&@c;-W zz$OH+cLS#glz`e+KnAe2#e~AA0DFKn2nq%G06K-Q{9Hj`Hz0xoaLWS(_5|Lo0}&g* z8dj#RmO!)NX5rW14;Q`gbI<$VF8-yTF8b}VP66b$HL`F4^zt{|bJxWL9?F6T*iHe% zslzW=*noopK&%c&$qUthxS*VvV9g&FF!*k<6cEP4%H*!Sg{>JNI9SEi%uXF%kTdeQ zXc1gBuwNr-{4{$yATHnq=l>50^Z%6w8y7bSp4ee95De&kAYpQI^ME*ku7~nKLG0}8 z-xC0XadUjf1z)Pa|I0RQ3w2B#0u`ym7-Jm&yLHJlc_fj@w@?QNN@M>ET-Z6$q3sdJr!3&vY>w0KAJ41HgaHFEkB@!SDZ) ze$d2(3-GhSzvu8r;i3ltp?|&ilkd;`$Gv|>!>_n-#azncXD0*szekIHbrkM`fTi|@ z`4Isg#_uIDV8{1+NgTumHy(g-k^;K^dr1xi_>31Ybr9RdEQ$%t&%ieE1oZA7;V}GaUVcu;aO-*(`m2%sC$th+zBMBhF^9za`{})uikY1p21E{|k5_tQ6n8klv z3s=P-z+Z=z9Pq;6$0!kpvRpVtEOu7*Zh(>e4*r+c1>S!R@Bex;!3|>J20}k^!-G5h zJR8Rj{ogMn>|*ZX27|E0js(QOyHYY@W}726^~th}r*+6qLb5X)!>#~-9;2DH^5W47#cqg69O*RKivQp0U}Ysy-9k&^>eg!aJ98E2D!7dapnvd%K-d5`^bb7vv;_bC(l#i3uk*h!E{;EV09%yssq?>hoLulp>Ax^;jz8PM z0|o5RfAJtZ?3};r55mLs2VV#eH}@ZWArSZz`(M6*tNFXGpb+*ybq|64*}nkdA3A`t z!+>G_Umj3S?mzglL4oz`Z+L7R@JHmob=hFR0^)ZV7u=}*hQ|$`{{IVOhx|#89lj0s zJ06VdPZ$@l&-yzaH{i(p2IGK2{@}|2`%}*xoN!YM|NT3LIJp1h0b_^!Aq$N2Po2Vm z!SOpiz-{=02PZqv@8bo^!vU<|{*lGi*~rS)%=seFpo*0juo48;D9R2F@CoX|N=n(A f0|ph)#`i^*i>r~d>&27+ List[Dict]: - mean = theta_hat['mu'].detach() - scale_tril = theta_hat['scale_tril'].detach() + mean = theta_hat["mu"].detach() + scale_tril = theta_hat["scale_tril"].detach() fd_coupling = ExpectedNormalDensityQuadFunctional( # TODO agnostic to names default_kernel_point=dict(x=mean), mean=mean, - scale_tril=scale_tril + scale_tril=scale_tril, ) return compute_fd_correction(fd_coupling, **kwargs) def compute_fd_correction_sqd_mvn_mc(*, theta_hat: Point[T], **kwargs) -> List[Dict]: - mean = theta_hat['mu'].detach() - scale_tril = theta_hat['scale_tril'].detach() + mean = theta_hat["mu"].detach() + scale_tril = theta_hat["scale_tril"].detach() fd_coupling = ExpectedNormalDensityMCFunctional( # TODO agnostic to names default_kernel_point=dict(x=mean), mean=mean, - scale_tril=scale_tril + scale_tril=scale_tril, ) return compute_fd_correction(fd_coupling, **kwargs) def compute_fd_correction( - fd_coupling: FDModelFunctionalDensity, - test_data: Point[T], - lambdas: List[float], - epss: List[float], - num_samples_scaling: int, - seed: int, + fd_coupling: FDModelFunctionalDensity, + test_data: Point[T], + lambdas: List[float], + epss: List[float], + num_samples_scaling: int, + seed: int, ) -> List[Dict]: epslam = product(epss, lambdas) @@ -92,23 +104,20 @@ def compute_fd_correction( # Better ways to abstract this. functional_kwargs = dict() if isinstance(fd_coupling, ExpectedDensityMCFunctional): - functional_kwargs['nmc'] = int(num_samples_scaling / eps) + functional_kwargs["nmc"] = int(num_samples_scaling / eps) pointwise = fd_influence_fn( - fd_coupling=fd_coupling, - points=test_data, - eps=eps, - lambda_=lambda_ + fd_coupling=fd_coupling, points=test_data, eps=eps, lambda_=lambda_ )(**functional_kwargs) - result['wall_time'] = time.time() - st + result["wall_time"] = time.time() - st - result['eps'] = eps - result['lambda'] = lambda_ - result['pointwise'] = [ + result["eps"] = eps + result["lambda"] = lambda_ + result["pointwise"] = [ y if isinstance(y, float) else y.item() for y in pointwise ] - result['correction'] = np.mean(pointwise) + result["correction"] = np.mean(pointwise) results.append(result) @@ -116,7 +125,6 @@ def compute_fd_correction( if __name__ == "__main__": - import matplotlib.pyplot as plt import json @@ -130,30 +138,27 @@ def smoke_test(): lambdas=[0.001], epss=[0.01, 0.001], num_samples_scaling=10000, - seed=0 + seed=0, ) # Runtime for ndim in [1, 2]: theta_hat = th = dict( - mu=torch.zeros(ndim), - scale_tril=torch.linalg.cholesky(torch.eye(ndim)) + mu=torch.zeros(ndim), scale_tril=torch.linalg.cholesky(torch.eye(ndim)) ) test_data = dict( - x=dist.MultivariateNormal(loc=th['mu'], scale_tril=th['scale_tril']).sample((20,)) + x=dist.MultivariateNormal( + loc=th["mu"], scale_tril=th["scale_tril"] + ).sample((20,)) ) mc_correction = compute_fd_correction_sqd_mvn_mc( - theta_hat=theta_hat, - test_data=test_data, - **fd_kwargs + theta_hat=theta_hat, test_data=test_data, **fd_kwargs ) quad_correction = compute_fd_correction_sqd_mvn_quad( - theta_hat=theta_hat, - test_data=test_data, - **fd_kwargs + theta_hat=theta_hat, test_data=test_data, **fd_kwargs ) # print("MC Correction") @@ -165,12 +170,12 @@ def smoke_test(): for mc, qu in zip(mc_correction, quad_correction): plt.figure() plt.suptitle(f"D={ndim}, eps={mc['eps']}, lambda={mc['lambda']}") - plt.plot(mc['pointwise'], qu['pointwise'], 'o') + plt.plot(mc["pointwise"], qu["pointwise"], "o") plt.xlabel("MC Correction") plt.ylabel("Quad Correction") # Set xlim and ylim to the same range. - xymin = min(min(mc['pointwise']), min(qu['pointwise'])) - 0.1 - xymax = max(max(mc['pointwise']), max(qu['pointwise'])) + 0.1 + xymin = min(min(mc["pointwise"]), min(qu["pointwise"])) - 0.1 + xymax = max(max(mc["pointwise"]), max(qu["pointwise"])) + 0.1 plt.xlim(xymin, xymax) plt.ylim(xymin, xymax) plt.show() From 4025e8f6b24b581de72770f94c0854cfb43d180c Mon Sep 17 00:00:00 2001 From: Raj Agrawal Date: Thu, 25 Jan 2024 16:02:37 -0500 Subject: [PATCH 10/26] uncomitted changes --- .../notebooks/error_vs_dimension.ipynb | 14 +++++++------- .../figures/error_rate_causal_glm_vs_dim.png | Bin 50821 -> 50864 bytes 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb b/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb index 4543cd5b..c0b45e78 100644 --- a/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb +++ b/docs/examples/robust_paper/notebooks/error_vs_dimension.ipynb @@ -400,32 +400,32 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3" + "Fraction(7, 16)" ] }, - "execution_count": 51, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Fraction(int(round(8*obs_slope)),8).numerator" + "Fraction(int(round(16*obs_slope)),16)" ] }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 69, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "