diff --git a/econml/_cate_estimator.py b/econml/_cate_estimator.py
index e22965e1f..172a01507 100644
--- a/econml/_cate_estimator.py
+++ b/econml/_cate_estimator.py
@@ -10,10 +10,10 @@
 from copy import deepcopy
 from warnings import warn
 from .inference import BootstrapInference
-from .utilities import (tensordot, ndim, reshape, shape, parse_final_model_params, get_feature_names_or_default,
-                        inverse_onehot, Summary, get_input_columns, check_input_arrays, jacify_featurizer)
+from .utilities import (ndim, shape, parse_final_model_params, get_feature_names_or_default,
+                        inverse_onehot, Summary, get_input_columns, check_input_arrays)
 from .inference import StatsModelsInference, StatsModelsInferenceDiscrete, LinearModelFinalInference, \
-    LinearModelFinalInferenceDiscrete, NormalInferenceResults, GenericSingleTreatmentModelFinalInference, \
+    LinearModelFinalInferenceDiscrete, GenericSingleTreatmentModelFinalInference, \
     GenericModelFinalInferenceDiscrete
 from ._shap import _shap_explain_cme, _shap_explain_joint_linear_model_cate
 from .dowhy import DoWhyWrapper
diff --git a/econml/_ensemble/_ensemble.py b/econml/_ensemble/_ensemble.py
index 9412b8737..345b5e93d 100644
--- a/econml/_ensemble/_ensemble.py
+++ b/econml/_ensemble/_ensemble.py
@@ -13,7 +13,6 @@
 import numpy as np
 from abc import ABCMeta, abstractmethod
 from sklearn.base import BaseEstimator, clone
-from sklearn.utils import check_random_state
 from joblib import effective_n_jobs
 from packaging.version import parse
 import sklearn
diff --git a/econml/_ortho_learner.py b/econml/_ortho_learner.py
index 3c14df2ee..b411314da 100644
--- a/econml/_ortho_learner.py
+++ b/econml/_ortho_learner.py
@@ -24,28 +24,22 @@ class in this module implements the general logic in a very versatile way
 
 """
 
-import copy
 from collections import namedtuple
-from warnings import warn
 from abc import abstractmethod
 from typing import List, Union
-import inspect
-from collections import defaultdict
-import re
 
 import numpy as np
 from sklearn.base import clone
 from sklearn.model_selection import KFold, StratifiedKFold, GroupKFold, StratifiedGroupKFold, check_cv
-from sklearn.preprocessing import (FunctionTransformer, LabelEncoder,
-                                   OneHotEncoder)
+from sklearn.preprocessing import (LabelEncoder)
 from sklearn.utils import check_random_state
 
 from ._cate_estimator import (BaseCateEstimator, LinearCateEstimator,
                               TreatmentExpansionMixin)
 from .inference import BootstrapInference
-from .utilities import (_deprecate_positional, check_input_arrays,
-                        cross_product, filter_none_kwargs, one_hot_encoder, strata_from_discrete_arrays,
-                        inverse_onehot, jacify_featurizer, ndim, reshape, shape, transpose)
+from .utilities import (check_input_arrays,
+                        filter_none_kwargs, one_hot_encoder, strata_from_discrete_arrays,
+                        jacify_featurizer, reshape, shape)
 from .sklearn_extensions.model_selection import ModelSelector
 
 try:
diff --git a/econml/cate_interpreter/_interpreters.py b/econml/cate_interpreter/_interpreters.py
index bd4443e58..5806979a2 100644
--- a/econml/cate_interpreter/_interpreters.py
+++ b/econml/cate_interpreter/_interpreters.py
@@ -4,8 +4,7 @@
 import abc
 import numbers
 import numpy as np
-import sklearn
-from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+from sklearn.tree import DecisionTreeRegressor
 from sklearn.utils import check_array
 from ..policy import PolicyTree
 from .._tree_exporter import (_SingleTreeExporterMixin,
diff --git a/econml/data/dynamic_panel_dgp.py b/econml/data/dynamic_panel_dgp.py
index ee4e84333..8ae72588f 100644
--- a/econml/data/dynamic_panel_dgp.py
+++ b/econml/data/dynamic_panel_dgp.py
@@ -372,7 +372,7 @@ class SemiSynthetic:
 
     def create_instance(self):
         # get new covariance matrix
-        self.cov_new = joblib.load(os.path.join(dir, f"input_dynamicdgp/cov_new.jbl"))
+        self.cov_new = joblib.load(os.path.join(dir, "input_dynamicdgp/cov_new.jbl"))
 
         # get coefs
         self.index = ["proxy1", "proxy2", "proxy3", "proxy4",
diff --git a/econml/dml/_rlearner.py b/econml/dml/_rlearner.py
index 010c83567..25621699a 100644
--- a/econml/dml/_rlearner.py
+++ b/econml/dml/_rlearner.py
@@ -27,13 +27,9 @@
 
 from abc import abstractmethod
 import numpy as np
-import copy
-from warnings import warn
 
 from ..sklearn_extensions.model_selection import ModelSelector
-from ..utilities import (shape, reshape, ndim, hstack, filter_none_kwargs, _deprecate_positional)
-from sklearn.linear_model import LinearRegression
-from sklearn.base import clone
+from ..utilities import (filter_none_kwargs)
 from .._ortho_learner import _OrthoLearner
 
 
diff --git a/econml/dml/causal_forest.py b/econml/dml/causal_forest.py
index d2cf7269a..1437b72ca 100644
--- a/econml/dml/causal_forest.py
+++ b/econml/dml/causal_forest.py
@@ -4,20 +4,15 @@
 from warnings import warn
 
 import numpy as np
-from sklearn.linear_model import LogisticRegressionCV
-from sklearn.base import clone, BaseEstimator
-from sklearn.preprocessing import FunctionTransformer
-from sklearn.pipeline import Pipeline
+from sklearn.base import clone
 from sklearn.model_selection import train_test_split
 from itertools import product
 from .dml import _BaseDML
 from .dml import _make_first_stage_selector
-from ..sklearn_extensions.linear_model import WeightedLassoCVWrapper
-from ..sklearn_extensions.model_selection import WeightedStratifiedKFold
 from ..inference import NormalInferenceResults
 from ..inference._inference import Inference
-from ..utilities import (add_intercept, shape, check_inputs, check_input_arrays,
-                         _deprecate_positional, cross_product, Summary)
+from ..utilities import (shape, check_input_arrays,
+                         cross_product, Summary)
 from ..grf import CausalForest, MultiOutputGRF
 from .._cate_estimator import LinearCateEstimator
 from .._shap import _shap_explain_multitask_model_cate
diff --git a/econml/dml/dml.py b/econml/dml/dml.py
index 8a1f88236..fd3a0dc35 100644
--- a/econml/dml/dml.py
+++ b/econml/dml/dml.py
@@ -5,36 +5,25 @@
 
 import numpy as np
 from sklearn.base import TransformerMixin, clone
-from sklearn.exceptions import NotFittedError
-from sklearn.linear_model import (ElasticNetCV, LassoCV, LogisticRegressionCV)
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.model_selection import KFold, StratifiedKFold, check_cv
+from sklearn.linear_model import (ElasticNetCV)
 from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import (FunctionTransformer, LabelEncoder,
-                                   OneHotEncoder)
+from sklearn.preprocessing import (FunctionTransformer)
 from sklearn.utils import check_random_state
-import copy
 
 from .._ortho_learner import _OrthoLearner
 from ._rlearner import _RLearner
 from .._cate_estimator import (DebiasedLassoCateEstimatorMixin,
-                               ForestModelFinalCateEstimatorMixin,
                                LinearModelFinalCateEstimatorMixin,
                                StatsModelsCateEstimatorMixin,
                                LinearCateEstimator)
-from ..inference import StatsModelsInference, GenericSingleTreatmentModelFinalInference
+from ..inference import GenericSingleTreatmentModelFinalInference
 from ..sklearn_extensions.linear_model import (MultiOutputDebiasedLasso,
-                                               StatsModelsLinearRegression,
-                                               WeightedLassoCVWrapper)
-from ..sklearn_extensions.model_selection import WeightedStratifiedKFold
-from ..utilities import (_deprecate_positional, add_intercept,
+                                               StatsModelsLinearRegression)
+from ..utilities import (add_intercept,
                          broadcast_unit_treatments, check_high_dimensional,
-                         cross_product, deprecated,
-                         hstack, inverse_onehot, ndim, reshape,
-                         reshape_treatmentwise_effects, shape, transpose,
-                         get_feature_names_or_default, filter_none_kwargs)
+                         cross_product, hstack, inverse_onehot, reshape_treatmentwise_effects, shape, get_feature_names_or_default, filter_none_kwargs)
 from .._shap import _shap_explain_model_cate
-from ..sklearn_extensions.model_selection import get_selector, ModelSelector, SingleModelSelector
+from ..sklearn_extensions.model_selection import get_selector, SingleModelSelector
 
 
 def _combine(X, W, n_samples):
diff --git a/econml/dr/_drlearner.py b/econml/dr/_drlearner.py
index b9a770e67..4bdd1a352 100644
--- a/econml/dr/_drlearner.py
+++ b/econml/dr/_drlearner.py
@@ -36,25 +36,20 @@
 """
 
 from warnings import warn
-from copy import deepcopy
 
 import numpy as np
 from sklearn.base import clone
-from sklearn.linear_model import (LassoCV, LinearRegression,
-                                  LogisticRegressionCV)
-from sklearn.ensemble import RandomForestRegressor
 
 
 from .._ortho_learner import _OrthoLearner
-from .._cate_estimator import (DebiasedLassoCateEstimatorDiscreteMixin, BaseCateEstimator,
-                               ForestModelFinalCateEstimatorDiscreteMixin,
+from .._cate_estimator import (DebiasedLassoCateEstimatorDiscreteMixin, ForestModelFinalCateEstimatorDiscreteMixin,
                                StatsModelsCateEstimatorDiscreteMixin, LinearCateEstimator)
 from ..inference import GenericModelFinalInferenceDiscrete
 from ..grf import RegressionForest
 from ..sklearn_extensions.linear_model import (
-    DebiasedLasso, StatsModelsLinearRegression, WeightedLassoCVWrapper)
+    DebiasedLasso, StatsModelsLinearRegression)
 from ..sklearn_extensions.model_selection import ModelSelector, SingleModelSelector, get_selector
-from ..utilities import (_deprecate_positional, check_high_dimensional,
+from ..utilities import (check_high_dimensional,
                          filter_none_kwargs, inverse_onehot, get_feature_names_or_default)
 from .._shap import _shap_explain_multitask_model_cate, _shap_explain_model_cate
 
diff --git a/econml/federated_learning.py b/econml/federated_learning.py
index fb2068e51..0c28d9497 100644
--- a/econml/federated_learning.py
+++ b/econml/federated_learning.py
@@ -1,7 +1,6 @@
 # Copyright (c) PyWhy contributors. All rights reserved.
 # Licensed under the MIT License.
 
-import numpy as np
 from sklearn import clone
 
 from econml.utilities import check_input_arrays
diff --git a/econml/grf/_base_grf.py b/econml/grf/_base_grf.py
index 8255ba243..dac530fa6 100644
--- a/econml/grf/_base_grf.py
+++ b/econml/grf/_base_grf.py
@@ -10,19 +10,19 @@
 # All rights reserved.
 
 import numbers
-from warnings import catch_warnings, simplefilter, warn
+from warnings import warn
 from abc import ABCMeta, abstractmethod
 import numpy as np
 import threading
 from .._ensemble import (BaseEnsemble, _partition_estimators, _get_n_samples_subsample,
-                         _accumulate_prediction, _accumulate_prediction_var, _accumulate_prediction_and_var,
+                         _accumulate_prediction, _accumulate_prediction_and_var,
                          _accumulate_oob_preds)
-from ..utilities import check_inputs, cross_product
+from ..utilities import check_inputs
 from ..tree._tree import DTYPE, DOUBLE
 from ._base_grftree import GRFTree
 from joblib import Parallel, delayed
 from scipy.sparse import hstack as sparse_hstack
-from sklearn.utils import check_random_state, compute_sample_weight
+from sklearn.utils import check_random_state
 from sklearn.utils.validation import _check_sample_weight, check_is_fitted
 from sklearn.utils import check_X_y
 import scipy.stats
diff --git a/econml/grf/_base_grftree.py b/econml/grf/_base_grftree.py
index 44359f57a..7635dd59f 100644
--- a/econml/grf/_base_grftree.py
+++ b/econml/grf/_base_grftree.py
@@ -12,11 +12,8 @@
 import numpy as np
 from ._criterion import LinearMomentGRFCriterionMSE, LinearMomentGRFCriterion
 from ..tree import BaseTree
-from sklearn.model_selection import train_test_split
-from sklearn.utils import check_array
 from sklearn.utils import check_random_state
 from sklearn.utils.validation import check_is_fitted
-import copy
 
 # =============================================================================
 # Types and constants
diff --git a/econml/grf/classes.py b/econml/grf/classes.py
index 6318f4cf3..46d0a22ab 100644
--- a/econml/grf/classes.py
+++ b/econml/grf/classes.py
@@ -2,7 +2,6 @@
 # Licensed under the MIT License.
 
 import numpy as np
-from warnings import warn
 from ..utilities import cross_product
 from ._base_grf import BaseGRF
 from ..utilities import check_inputs
diff --git a/econml/inference/_bootstrap.py b/econml/inference/_bootstrap.py
index 717d2d716..7993a9c74 100644
--- a/econml/inference/_bootstrap.py
+++ b/econml/inference/_bootstrap.py
@@ -6,8 +6,6 @@
 from joblib import Parallel, delayed
 from sklearn.base import clone
 from scipy.stats import norm
-from collections import OrderedDict
-import pandas as pd
 
 
 class BootstrapEstimator:
diff --git a/econml/inference/_inference.py b/econml/inference/_inference.py
index be2e0f681..140756da7 100644
--- a/econml/inference/_inference.py
+++ b/econml/inference/_inference.py
@@ -9,14 +9,11 @@
 import pandas as pd
 import scipy
 from scipy.stats import norm
-from statsmodels.iolib.table import SimpleTable
 
 from ._bootstrap import BootstrapEstimator
-from ..sklearn_extensions.linear_model import StatsModelsLinearRegression
 from ..utilities import (Summary, _safe_norm_ppf, broadcast_unit_treatments,
                          cross_product, inverse_onehot, ndim,
-                         parse_final_model_params, jacify_featurizer,
-                         reshape_treatmentwise_effects, shape, filter_none_kwargs)
+                         parse_final_model_params, reshape_treatmentwise_effects, shape, filter_none_kwargs)
 
 """Options for performing inference in estimators."""
 
diff --git a/econml/iv/dml/_dml.py b/econml/iv/dml/_dml.py
index 3a3a3e361..741162147 100644
--- a/econml/iv/dml/_dml.py
+++ b/econml/iv/dml/_dml.py
@@ -15,20 +15,17 @@
 
 import numpy as np
 from sklearn.base import clone
-from sklearn.linear_model import LinearRegression, LogisticRegressionCV
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 from itertools import product
 
 from ..._ortho_learner import _OrthoLearner
-from ..._cate_estimator import LinearModelFinalCateEstimatorMixin, StatsModelsCateEstimatorMixin, LinearCateEstimator
-from ...inference import StatsModelsInference, GenericSingleTreatmentModelFinalInference
-from ...sklearn_extensions.linear_model import StatsModels2SLS, StatsModelsLinearRegression, WeightedLassoCVWrapper
-from ...sklearn_extensions.model_selection import (ModelSelector, SingleModelSelector,
-                                                   WeightedStratifiedKFold, get_selector)
-from ...utilities import (_deprecate_positional, get_feature_names_or_default, filter_none_kwargs, add_intercept,
+from ..._cate_estimator import LinearModelFinalCateEstimatorMixin, LinearCateEstimator
+from ...sklearn_extensions.linear_model import StatsModels2SLS, StatsModelsLinearRegression
+from ...sklearn_extensions.model_selection import (ModelSelector, SingleModelSelector)
+from ...utilities import (get_feature_names_or_default, filter_none_kwargs, add_intercept,
                           cross_product, broadcast_unit_treatments, reshape_treatmentwise_effects, shape,
-                          parse_final_model_params, deprecated, Summary)
+                          parse_final_model_params, Summary)
 from ...dml.dml import _make_first_stage_selector, _FinalWrapper
 from ...dml._rlearner import _ModelFinal
 from ..._shap import _shap_explain_joint_linear_model_cate, _shap_explain_model_cate
diff --git a/econml/iv/dr/_dr.py b/econml/iv/dr/_dr.py
index b7d62b4c2..d9e5b2e31 100644
--- a/econml/iv/dr/_dr.py
+++ b/econml/iv/dr/_dr.py
@@ -15,7 +15,6 @@
 
 import numpy as np
 from sklearn.base import clone
-from sklearn.linear_model import LinearRegression, LogisticRegressionCV
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import FunctionTransformer
 from sklearn.dummy import DummyClassifier
@@ -25,13 +24,12 @@
 from ..._cate_estimator import (StatsModelsCateEstimatorMixin, DebiasedLassoCateEstimatorMixin,
                                 ForestModelFinalCateEstimatorMixin, GenericSingleTreatmentModelFinalInference,
                                 LinearCateEstimator)
-from ...inference import StatsModelsInference
-from ...sklearn_extensions.linear_model import StatsModelsLinearRegression, DebiasedLasso, WeightedLassoCVWrapper
-from ...sklearn_extensions.model_selection import ModelSelector, SingleModelSelector, WeightedStratifiedKFold
-from ...utilities import (_deprecate_positional, add_intercept, filter_none_kwargs,
+from ...sklearn_extensions.linear_model import StatsModelsLinearRegression, DebiasedLasso
+from ...sklearn_extensions.model_selection import ModelSelector, SingleModelSelector
+from ...utilities import (add_intercept, filter_none_kwargs,
                           inverse_onehot, get_feature_names_or_default, check_high_dimensional, check_input_arrays)
 from ...grf import RegressionForest
-from ...dml.dml import _make_first_stage_selector, _FinalWrapper
+from ...dml.dml import _make_first_stage_selector
 from ...iv.dml import NonParamDMLIV
 from ..._shap import _shap_explain_model_cate
 
diff --git a/econml/iv/nnet/_deepiv.py b/econml/iv/nnet/_deepiv.py
index 4a1b8f059..1f2b7ac9b 100644
--- a/econml/iv/nnet/_deepiv.py
+++ b/econml/iv/nnet/_deepiv.py
@@ -5,7 +5,7 @@
 
 import numpy as np
 from ..._cate_estimator import BaseCateEstimator
-from ...utilities import check_input_arrays, _deprecate_positional, deprecated, MissingModule
+from ...utilities import check_input_arrays, MissingModule
 try:
     import keras
     from keras import backend as K
diff --git a/econml/iv/sieve/_tsls.py b/econml/iv/sieve/_tsls.py
index 38b22ac87..ffed3350d 100644
--- a/econml/iv/sieve/_tsls.py
+++ b/econml/iv/sieve/_tsls.py
@@ -4,12 +4,11 @@
 """Provides a non-parametric two-stage least squares instrumental variable estimator."""
 
 import numpy as np
-from copy import deepcopy
 from sklearn import clone
 from sklearn.linear_model import LinearRegression
 from ...utilities import (shape, transpose, reshape, cross_product, ndim, size,
-                          _deprecate_positional, check_input_arrays)
-from ..._cate_estimator import BaseCateEstimator, LinearCateEstimator
+                          check_input_arrays)
+from ..._cate_estimator import BaseCateEstimator
 from numpy.polynomial.hermite_e import hermeval
 from sklearn.base import TransformerMixin
 from sklearn.preprocessing import PolynomialFeatures
diff --git a/econml/metalearners/_metalearners.py b/econml/metalearners/_metalearners.py
index d90b804b9..92ba4a337 100644
--- a/econml/metalearners/_metalearners.py
+++ b/econml/metalearners/_metalearners.py
@@ -9,15 +9,12 @@
 """
 
 import numpy as np
-import warnings
 from .._cate_estimator import BaseCateEstimator, LinearCateEstimator, TreatmentExpansionMixin
 from sklearn import clone
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
-from sklearn.utils import check_array, check_X_y
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
-from ..utilities import (check_inputs, check_models, broadcast_unit_treatments, reshape_treatmentwise_effects,
-                         one_hot_encoder, inverse_onehot, transpose, _deprecate_positional)
+from sklearn.utils import check_array
+from ..utilities import (check_inputs, check_models, broadcast_unit_treatments, one_hot_encoder, inverse_onehot, transpose)
 from .._shap import _shap_explain_model_cate
 
 
diff --git a/econml/orf/_ortho_forest.py b/econml/orf/_ortho_forest.py
index 71361b2dd..578c41336 100644
--- a/econml/orf/_ortho_forest.py
+++ b/econml/orf/_ortho_forest.py
@@ -20,28 +20,24 @@
 For more details on these methods, see our paper [Oprescu2019]_.
 """
 
-import abc
-import inspect
 import numpy as np
 import warnings
 from joblib import Parallel, delayed
 from sklearn import clone
 from scipy.stats import norm
 from sklearn.exceptions import NotFittedError
-from sklearn.linear_model import LassoCV, Lasso, LinearRegression, LogisticRegression, \
-    LogisticRegressionCV, ElasticNet
+from sklearn.linear_model import LinearRegression, LogisticRegression, \
+    LogisticRegressionCV
 from sklearn.model_selection import KFold, StratifiedKFold
 from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, LabelEncoder, PolynomialFeatures, FunctionTransformer
 from sklearn.utils import check_random_state, check_array, column_or_1d
 from ..sklearn_extensions.linear_model import WeightedLassoCVWrapper
 from .._cate_estimator import BaseCateEstimator, LinearCateEstimator, TreatmentExpansionMixin
 from ._causal_tree import CausalTree
 from ..inference import NormalInferenceResults
 from ..inference._inference import Inference
-from ..utilities import (one_hot_encoder, reshape, reshape_Y_T, MAX_RAND_SEED, check_inputs, _deprecate_positional,
-                         cross_product, inverse_onehot, check_input_arrays, jacify_featurizer,
-                         _RegressionWrapper, deprecated, ndim)
+from ..utilities import (one_hot_encoder, reshape_Y_T, MAX_RAND_SEED, check_inputs, cross_product, check_input_arrays, jacify_featurizer,
+                         _RegressionWrapper, ndim)
 from sklearn.model_selection import check_cv
 # TODO: consider working around relying on sklearn implementation details
 from ..sklearn_extensions.model_selection import _cross_val_predict
diff --git a/econml/panel/dml/_dml.py b/econml/panel/dml/_dml.py
index 63869580e..790b5a14a 100644
--- a/econml/panel/dml/_dml.py
+++ b/econml/panel/dml/_dml.py
@@ -1,23 +1,16 @@
 # Copyright (c) PyWhy contributors. All rights reserved.
 # Licensed under the MIT License.
 
-import abc
 import numpy as np
 from warnings import warn
 from sklearn.base import clone
 from sklearn.model_selection import GroupKFold
-from scipy.stats import norm
-from sklearn.linear_model import (ElasticNetCV, LassoCV, LogisticRegressionCV)
-from ...sklearn_extensions.linear_model import (StatsModelsLinearRegression, WeightedLassoCVWrapper)
-from ...sklearn_extensions.model_selection import ModelSelector, WeightedStratifiedKFold
+from ...sklearn_extensions.linear_model import (StatsModelsLinearRegression)
+from ...sklearn_extensions.model_selection import ModelSelector
 from ...dml.dml import _make_first_stage_selector, _FinalWrapper
-from ..._cate_estimator import TreatmentExpansionMixin, LinearModelFinalCateEstimatorMixin
+from ..._cate_estimator import LinearModelFinalCateEstimatorMixin
 from ..._ortho_learner import _OrthoLearner
-from ...utilities import (_deprecate_positional, add_intercept,
-                          broadcast_unit_treatments, check_high_dimensional,
-                          cross_product, deprecated,
-                          hstack, inverse_onehot, ndim, reshape,
-                          reshape_treatmentwise_effects, shape, transpose,
+from ...utilities import (ndim, reshape,
                           get_feature_names_or_default, check_input_arrays,
                           filter_none_kwargs)
 
diff --git a/econml/policy/_base.py b/econml/policy/_base.py
index 42546b1dd..844cff254 100644
--- a/econml/policy/_base.py
+++ b/econml/policy/_base.py
@@ -4,7 +4,6 @@
 """Base classes for all Policy estimators."""
 
 import abc
-import numpy as np
 
 
 class PolicyLearner(metaclass=abc.ABCMeta):
diff --git a/econml/policy/_drlearner.py b/econml/policy/_drlearner.py
index 3a1333647..e9263cce8 100644
--- a/econml/policy/_drlearner.py
+++ b/econml/policy/_drlearner.py
@@ -4,10 +4,9 @@
 from warnings import warn
 import numpy as np
 from sklearn.base import clone
-from ..utilities import check_inputs, filter_none_kwargs, check_input_arrays
+from ..utilities import filter_none_kwargs, check_input_arrays
 from ..dr import DRLearner
 from ..dr._drlearner import _ModelFinal
-from .._tree_exporter import _SingleTreeExporterMixin
 from ._base import PolicyLearner
 from . import PolicyTree, PolicyForest
 
diff --git a/econml/policy/_forest/_forest.py b/econml/policy/_forest/_forest.py
index 474bc71a4..92c4bdfb5 100644
--- a/econml/policy/_forest/_forest.py
+++ b/econml/policy/_forest/_forest.py
@@ -9,18 +9,16 @@
 # Copyright (c) 2007-2020 The scikit-learn developers.
 # All rights reserved.
 
-import numbers
-from warnings import catch_warnings, simplefilter, warn
-from abc import ABCMeta, abstractmethod
+from warnings import warn
+from abc import ABCMeta
 import numpy as np
 import threading
 from ..._ensemble import (BaseEnsemble, _partition_estimators, _get_n_samples_subsample, _accumulate_prediction)
-from ...utilities import check_inputs, cross_product
 from ...tree._tree import DTYPE, DOUBLE
 from ._tree import PolicyTree
 from joblib import Parallel, delayed
 from scipy.sparse import hstack as sparse_hstack
-from sklearn.utils import check_random_state, compute_sample_weight
+from sklearn.utils import check_random_state
 from sklearn.utils.validation import _check_sample_weight, check_is_fitted
 from sklearn.utils import check_X_y
 
diff --git a/econml/policy/_forest/_tree.py b/econml/policy/_forest/_tree.py
index 3b02c374a..46c6f4fa9 100644
--- a/econml/policy/_forest/_tree.py
+++ b/econml/policy/_forest/_tree.py
@@ -10,23 +10,12 @@
 # All rights reserved.
 
 import numpy as np
-import numbers
-from math import ceil
-from ...tree import Tree
-from ...tree._criterion import Criterion
-from ...tree._splitter import Splitter, BestSplitter
-from ...tree import DepthFirstTreeBuilder
-from ...tree import _tree
 from ..._tree_exporter import _SingleTreeExporterMixin, _PolicyTreeDOTExporter, _PolicyTreeMPLExporter
 from ._criterion import LinearPolicyCriterion
-from . import _criterion
 from ...tree import BaseTree
-from sklearn.model_selection import train_test_split
-from sklearn.utils import check_array, check_X_y
+from sklearn.utils import check_X_y
 from sklearn.utils import check_random_state
-from sklearn.utils.validation import _check_sample_weight
 from sklearn.utils.validation import check_is_fitted
-import copy
 
 # =============================================================================
 # Types and constants
diff --git a/econml/sklearn_extensions/linear_model.py b/econml/sklearn_extensions/linear_model.py
index cb999e14c..9bc5529f7 100644
--- a/econml/sklearn_extensions/linear_model.py
+++ b/econml/sklearn_extensions/linear_model.py
@@ -25,7 +25,6 @@
 from sklearn.linear_model import LinearRegression, LassoCV, MultiTaskLassoCV, Lasso, MultiTaskLasso
 from sklearn.linear_model._base import _preprocess_data
 from sklearn.metrics import r2_score
-from sklearn.model_selection import KFold, StratifiedKFold
 # TODO: consider working around relying on sklearn implementation details
 from sklearn.model_selection._split import _CVIterableWrapper
 from sklearn.multioutput import MultiOutputRegressor
diff --git a/econml/sklearn_extensions/model_selection.py b/econml/sklearn_extensions/model_selection.py
index 4d165a913..fc0c7a755 100644
--- a/econml/sklearn_extensions/model_selection.py
+++ b/econml/sklearn_extensions/model_selection.py
@@ -10,18 +10,16 @@
 import abc
 
 import numpy as np
-from collections.abc import Iterable
 import scipy.sparse as sp
 import sklearn
 from joblib import Parallel, delayed
 from sklearn.base import BaseEstimator, clone, is_classifier
 from sklearn.ensemble import (GradientBoostingClassifier, GradientBoostingRegressor,
                               RandomForestClassifier, RandomForestRegressor)
-from sklearn.exceptions import FitFailedWarning
 from sklearn.linear_model import (ElasticNet, ElasticNetCV, Lasso, LassoCV, MultiTaskElasticNet, MultiTaskElasticNetCV,
                                   MultiTaskLasso, MultiTaskLassoCV, Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV,
                                   LogisticRegression, LogisticRegressionCV)
-from sklearn.model_selection import (BaseCrossValidator, GridSearchCV, GroupKFold, KFold,
+from sklearn.model_selection import (GridSearchCV, GroupKFold, KFold,
                                      RandomizedSearchCV, StratifiedKFold,
                                      check_cv)
 # TODO: conisder working around relying on sklearn implementation details
@@ -29,9 +27,8 @@
                                                  _fit_and_predict)
 from sklearn.neural_network import MLPClassifier, MLPRegressor
 from sklearn.pipeline import Pipeline, make_pipeline
-from sklearn.preprocessing import LabelEncoder, PolynomialFeatures, StandardScaler
+from sklearn.preprocessing import LabelEncoder, PolynomialFeatures
 from sklearn.utils import check_random_state, indexable
-from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import _num_samples
 
 from .linear_model import WeightedLassoCVWrapper, WeightedLassoWrapper
diff --git a/econml/solutions/causal_analysis/_causal_analysis.py b/econml/solutions/causal_analysis/_causal_analysis.py
index f181c625e..6e17fbb98 100644
--- a/econml/solutions/causal_analysis/_causal_analysis.py
+++ b/econml/solutions/causal_analysis/_causal_analysis.py
@@ -8,16 +8,15 @@
 
 import joblib
 import lightgbm as lgb
-from numba.core.utils import erase_traceback
 import numpy as np
 from numpy.lib.function_base import iterable
 import pandas as pd
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.compose import ColumnTransformer
-from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, RandomForestRegressor
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
 from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV
-from sklearn.pipeline import make_pipeline, Pipeline
-from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures, StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
 from sklearn.tree import _tree
 from sklearn.utils.validation import column_or_1d
 from ...cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter
@@ -25,7 +24,7 @@
 from ...inference import NormalInferenceResults
 from ...sklearn_extensions.linear_model import WeightedLasso
 from ...sklearn_extensions.model_selection import GridSearchCVList
-from ...utilities import _RegressionWrapper, get_feature_names_or_default, inverse_onehot, one_hot_encoder
+from ...utilities import _RegressionWrapper, get_feature_names_or_default, one_hot_encoder
 
 # TODO: this utility is documented but internal; reimplement?
 from sklearn.utils import _safe_indexing
diff --git a/econml/tests/test_ate_inference.py b/econml/tests/test_ate_inference.py
index 7748ea481..f0d9b523a 100644
--- a/econml/tests/test_ate_inference.py
+++ b/econml/tests/test_ate_inference.py
@@ -4,7 +4,7 @@
 import numpy as np
 import unittest
 from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.linear_model import LinearRegression
 from econml.dml import LinearDML
 from econml.inference import BootstrapInference
 
diff --git a/econml/tests/test_automated_ml.py b/econml/tests/test_automated_ml.py
index a5d7bb6d6..27524d0cc 100644
--- a/econml/tests/test_automated_ml.py
+++ b/econml/tests/test_automated_ml.py
@@ -3,27 +3,11 @@
 
 import unittest
 import pytest
-from sklearn.linear_model import LinearRegression, Lasso, \
-    LogisticRegression
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, \
-    PolynomialFeatures
-from sklearn.model_selection import KFold
 from econml.dml import DML, LinearDML, SparseLinearDML, KernelDML, NonParamDML, CausalForestDML
 from econml.metalearners import SLearner, TLearner, XLearner, DomainAdaptationLearner
 from econml.dr import DRLearner
-import numpy as np
-from econml.utilities import shape, hstack, vstack, reshape, \
-    cross_product
-from econml.inference import BootstrapInference
-from contextlib import ExitStack
-from sklearn.ensemble import RandomForestRegressor, \
-    GradientBoostingRegressor, GradientBoostingClassifier
-import itertools
-from econml.sklearn_extensions.linear_model import WeightedLasso
-from econml.tests.test_statsmodels import _summarize
+from sklearn.ensemble import GradientBoostingClassifier
 import copy
-import logging
 from econml.data.dgps import ihdp_surface_B
 import os
 
diff --git a/econml/tests/test_causal_analysis.py b/econml/tests/test_causal_analysis.py
index 193f8383b..0cf3a1802 100644
--- a/econml/tests/test_causal_analysis.py
+++ b/econml/tests/test_causal_analysis.py
@@ -6,7 +6,6 @@
 from contextlib import ExitStack
 import itertools
 import numpy as np
-from numpy.core.fromnumeric import squeeze
 import pandas as pd
 import pytest
 
diff --git a/econml/tests/test_deepiv.py b/econml/tests/test_deepiv.py
index 2f755fbc3..ce8d4fcb2 100644
--- a/econml/tests/test_deepiv.py
+++ b/econml/tests/test_deepiv.py
@@ -19,8 +19,7 @@
 
 from econml.iv.nnet._deepiv import _zero_grad
 from econml.iv.nnet import DeepIV
-from econml.iv.nnet._deepiv import mog_model, mog_loss_model, mog_sample_model, response_loss_model
-from econml.utilities import reshape
+from econml.iv.nnet._deepiv import mog_model, mog_loss_model, mog_sample_model
 
 
 @pytest.mark.skipif(not keras_installed, reason="Keras not installed")
diff --git a/econml/tests/test_discrete_outcome.py b/econml/tests/test_discrete_outcome.py
index 91ddd6358..23efa54ba 100644
--- a/econml/tests/test_discrete_outcome.py
+++ b/econml/tests/test_discrete_outcome.py
@@ -4,20 +4,14 @@
 import unittest
 import numpy as np
 import pandas as pd
-from sklearn.preprocessing import PolynomialFeatures
 from sklearn.linear_model import LinearRegression, LogisticRegression
-from sklearn.ensemble import RandomForestRegressor
-from joblib import Parallel, delayed
 
-from econml._ortho_learner import _OrthoLearner
-from econml.dml import LinearDML, SparseLinearDML, KernelDML, CausalForestDML, NonParamDML
+from econml.dml import LinearDML, SparseLinearDML, CausalForestDML
 from econml.dr import LinearDRLearner, ForestDRLearner
-from econml.iv.dml import OrthoIV, DMLIV, NonParamDMLIV
+from econml.iv.dml import OrthoIV, DMLIV
 from econml.iv.dr import DRIV, LinearDRIV, SparseLinearDRIV, ForestDRIV, IntentToTreatDRIV, LinearIntentToTreatDRIV
-from econml.orf import DMLOrthoForest
 
 from econml.utilities import filter_none_kwargs
-from copy import deepcopy
 
 
 class TestDiscreteOutcome(unittest.TestCase):
diff --git a/econml/tests/test_dml.py b/econml/tests/test_dml.py
index 452a71307..9114e46d7 100644
--- a/econml/tests/test_dml.py
+++ b/econml/tests/test_dml.py
@@ -7,20 +7,18 @@
 from sklearn.linear_model import LinearRegression, Lasso, LassoCV, LogisticRegression
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, PolynomialFeatures
-from sklearn.model_selection import KFold, GroupKFold, check_cv
+from sklearn.model_selection import KFold
 from econml.dml import DML, LinearDML, SparseLinearDML, KernelDML, CausalForestDML
 from econml.dml import NonParamDML
 import numpy as np
 import pandas as pd
 from econml.utilities import shape, hstack, vstack, reshape, cross_product
-from econml.inference import BootstrapInference, EmpiricalInferenceResults, NormalInferenceResults
+from econml.inference import BootstrapInference
 from contextlib import ExitStack
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 import itertools
-from econml.sklearn_extensions.linear_model import WeightedLasso, StatsModelsRLM, StatsModelsLinearRegression
+from econml.sklearn_extensions.linear_model import WeightedLasso, StatsModelsRLM
 from econml.tests.test_statsmodels import _summarize
-from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
-from sklearn.multioutput import MultiOutputRegressor
 from econml.grf import MultiOutputGRF
 from econml.tests.utilities import (GroupingModel, NestedModel)
 
diff --git a/econml/tests/test_dmliv.py b/econml/tests/test_dmliv.py
index bb6c36252..1fd491e22 100644
--- a/econml/tests/test_dmliv.py
+++ b/econml/tests/test_dmliv.py
@@ -6,14 +6,12 @@
 
 import numpy as np
 import pytest
-from scipy import special
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.linear_model import LinearRegression, LogisticRegression, LogisticRegressionCV
 from sklearn.preprocessing import PolynomialFeatures
 
 from econml.iv.dml import OrthoIV, DMLIV, NonParamDMLIV
-from econml.iv.dr._dr import _DummyCATE
-from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression, WeightedLassoCVWrapper
+from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper
 from econml.utilities import shape
 from econml.tests.utilities import GroupingModel
 
diff --git a/econml/tests/test_dominicks.py b/econml/tests/test_dominicks.py
index 126084e84..1688b0ee1 100644
--- a/econml/tests/test_dominicks.py
+++ b/econml/tests/test_dominicks.py
@@ -9,7 +9,6 @@
 from econml.dml import LinearDML
 from sklearn.base import TransformerMixin
 from sklearn.ensemble import RandomForestRegressor
-from sklearn.linear_model import RidgeCV, LinearRegression
 import pytest
 
 
@@ -29,8 +28,6 @@ def test_dominicks():
     featnames = ["week", "feat"] + list(oj_data.columns[6:])
 
     # Preprocess data
-    import datetime
-    import numpy as np
 
     # Convert 'week' to a date
     # week_zero = datetime.datetime.strptime("09/07/89", "%m/%d/%y")
diff --git a/econml/tests/test_dowhy.py b/econml/tests/test_dowhy.py
index 4d7c37ddf..c21326cda 100644
--- a/econml/tests/test_dowhy.py
+++ b/econml/tests/test_dowhy.py
@@ -10,8 +10,7 @@
 from econml.metalearners import XLearner
 from econml.iv.dml import OrthoIV, DMLIV
 from econml.iv.dr import LinearDRIV
-from econml.iv.dr._dr import _DummyCATE
-from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso
+from sklearn.linear_model import LinearRegression, LogisticRegression
 from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression
 
 
diff --git a/econml/tests/test_driv.py b/econml/tests/test_driv.py
index 4f9ee6696..734cb4694 100644
--- a/econml/tests/test_driv.py
+++ b/econml/tests/test_driv.py
@@ -2,7 +2,6 @@
 # Licensed under the MIT License.
 
 from econml.iv.dr import (DRIV, LinearDRIV, SparseLinearDRIV, ForestDRIV, IntentToTreatDRIV, LinearIntentToTreatDRIV,)
-from econml.iv.dr._dr import _DummyCATE
 from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression
 from econml.utilities import shape
 from econml.tests.utilities import GroupingModel
diff --git a/econml/tests/test_drlearner.py b/econml/tests/test_drlearner.py
index b1a859429..9bd7ee6c4 100644
--- a/econml/tests/test_drlearner.py
+++ b/econml/tests/test_drlearner.py
@@ -6,21 +6,19 @@
 import unittest
 
 import numpy as np
-from numpy.random import normal, multivariate_normal, binomial
 import pytest
 
 import scipy.special
-from sklearn.base import TransformerMixin
 from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, RandomForestRegressor
 from sklearn.exceptions import DataConversionWarning
 from sklearn.linear_model import LinearRegression, Lasso, LassoCV, LogisticRegression, LogisticRegressionCV
-from sklearn.model_selection import KFold, StratifiedGroupKFold, check_cv
+from sklearn.model_selection import KFold, StratifiedGroupKFold
 from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, PolynomialFeatures
+from sklearn.preprocessing import FunctionTransformer, PolynomialFeatures
 
 from econml.dr import DRLearner, LinearDRLearner, SparseLinearDRLearner, ForestDRLearner
 from econml.inference import BootstrapInference, StatsModelsInferenceDiscrete
-from econml.utilities import get_feature_names_or_default, shape, hstack, vstack, reshape, cross_product
+from econml.utilities import get_feature_names_or_default, shape
 from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression
 from econml.tests.utilities import (GroupingModel, NestedModel)
 
diff --git a/econml/tests/test_dynamic_dml.py b/econml/tests/test_dynamic_dml.py
index 118f51563..44b017bb1 100644
--- a/econml/tests/test_dynamic_dml.py
+++ b/econml/tests/test_dynamic_dml.py
@@ -5,13 +5,13 @@
 import pickle
 import numpy as np
 from contextlib import ExitStack
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, PolynomialFeatures
-from sklearn.linear_model import (LinearRegression, LassoCV, Lasso, MultiTaskLasso,
-                                  MultiTaskLassoCV, LogisticRegression)
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import (LassoCV, Lasso, MultiTaskLasso,
+                                  LogisticRegression)
 from econml.panel.dml import DynamicDML
 from econml.panel.dml._dml import _get_groups_period_filter
-from econml.inference import BootstrapInference, EmpiricalInferenceResults, NormalInferenceResults
-from econml.utilities import shape, hstack, vstack, reshape, cross_product
+from econml.inference import BootstrapInference
+from econml.utilities import shape
 from econml.tests.dgp import DynamicPanelDGP
 
 
diff --git a/econml/tests/test_federated_learning.py b/econml/tests/test_federated_learning.py
index 6ed1c5185..dc2bb8c05 100644
--- a/econml/tests/test_federated_learning.py
+++ b/econml/tests/test_federated_learning.py
@@ -4,7 +4,6 @@
 import numpy as np
 import unittest
 
-from sklearn import clone
 from econml.dml import LinearDML
 from econml.dr import LinearDRLearner
 from econml.inference import StatsModelsInference, StatsModelsInferenceDiscrete
diff --git a/econml/tests/test_grf_cython.py b/econml/tests/test_grf_cython.py
index f3a879dae..ced0a6e02 100644
--- a/econml/tests/test_grf_cython.py
+++ b/econml/tests/test_grf_cython.py
@@ -2,13 +2,8 @@
 # Licensed under the MIT License.
 
 import unittest
-import logging
-import time
-import random
 import numpy as np
-import sparse as sp
-import pytest
-from econml.tree import DepthFirstTreeBuilder, BestSplitter, Tree, MSE
+from econml.tree import DepthFirstTreeBuilder, BestSplitter, Tree
 from econml.grf import LinearMomentGRFCriterion, LinearMomentGRFCriterionMSE
 from econml.grf._utils import matinv, lstsq, pinv, fast_max_eigv, fast_min_eigv
 from econml.utilities import cross_product
diff --git a/econml/tests/test_grf_python.py b/econml/tests/test_grf_python.py
index 147c5cabe..2f4a5a773 100644
--- a/econml/tests/test_grf_python.py
+++ b/econml/tests/test_grf_python.py
@@ -2,12 +2,8 @@
 # Licensed under the MIT License.
 
 import unittest
-import logging
-import time
-import random
 import numpy as np
 import pandas as pd
-import pytest
 import joblib
 from econml.grf import RegressionForest, CausalForest, CausalIVForest, MultiOutputGRF
 from econml.utilities import cross_product
diff --git a/econml/tests/test_integration.py b/econml/tests/test_integration.py
index 4c13f6b20..8b82f5563 100644
--- a/econml/tests/test_integration.py
+++ b/econml/tests/test_integration.py
@@ -20,7 +20,7 @@
 from econml.metalearners import XLearner, SLearner, TLearner
 from sklearn.compose import ColumnTransformer
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
-from sklearn.linear_model import LinearRegression, MultiTaskLasso, LassoCV
+from sklearn.linear_model import MultiTaskLasso, LassoCV
 from sklearn.preprocessing import PolynomialFeatures, FunctionTransformer
 from econml.iv.dr import LinearIntentToTreatDRIV
 from econml.iv.nnet import DeepIV
diff --git a/econml/tests/test_linear_model.py b/econml/tests/test_linear_model.py
index 336e8809f..bf28a5a5b 100644
--- a/econml/tests/test_linear_model.py
+++ b/econml/tests/test_linear_model.py
@@ -4,15 +4,12 @@
 """Tests for linear_model extensions."""
 
 import numpy as np
-import pytest
 import unittest
-import warnings
 from econml.sklearn_extensions.linear_model import (WeightedLasso, WeightedLassoCV, WeightedMultiTaskLassoCV,
                                                     WeightedLassoCVWrapper, DebiasedLasso, MultiOutputDebiasedLasso,
                                                     SelectiveRegularization)
 from econml.sklearn_extensions.model_selection import WeightedKFold
 from sklearn.linear_model import Lasso, LassoCV, LinearRegression, MultiTaskLassoCV, Ridge
-from sklearn.model_selection import KFold
 from sklearn.base import clone
 
 
diff --git a/econml/tests/test_metalearners.py b/econml/tests/test_metalearners.py
index 2a306eb19..beb60af4a 100644
--- a/econml/tests/test_metalearners.py
+++ b/econml/tests/test_metalearners.py
@@ -3,7 +3,6 @@
 
 import numpy as np
 import unittest
-from numpy.random import normal, multivariate_normal, binomial
 from sklearn.exceptions import DataConversionWarning
 from sklearn.linear_model import LinearRegression
 from sklearn.pipeline import Pipeline
diff --git a/econml/tests/test_missing_values.py b/econml/tests/test_missing_values.py
index 265a10069..0924f5811 100644
--- a/econml/tests/test_missing_values.py
+++ b/econml/tests/test_missing_values.py
@@ -1,6 +1,5 @@
 # Copyright (c) PyWhy contributors. All rights reserved.
 # Licensed under the MIT License.
-import pytest
 import unittest
 import numpy as np
 from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso
diff --git a/econml/tests/test_orf.py b/econml/tests/test_orf.py
index 2be8fbaef..262f6f0a7 100644
--- a/econml/tests/test_orf.py
+++ b/econml/tests/test_orf.py
@@ -3,13 +3,10 @@
 
 import numpy as np
 import unittest
-import pytest
 import warnings
-from numpy.random import binomial, choice, normal, uniform
-from sklearn.exceptions import DataConversionWarning
-from sklearn.linear_model import LinearRegression, Lasso, LassoCV, LogisticRegression, LogisticRegressionCV
+from numpy.random import choice, normal, uniform
+from sklearn.linear_model import Lasso, LogisticRegression, LogisticRegressionCV
 from sklearn.multioutput import MultiOutputRegressor
-from sklearn.pipeline import Pipeline
 from econml.orf import DMLOrthoForest, DROrthoForest
 from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper
 
@@ -165,7 +162,6 @@ def test_multiple_treatments(self):
             self._test_ci(est, expected_te, tol=2.0, treatment_type='multi')
 
     def test_effect_shape(self):
-        import scipy.special
         np.random.seed(123)
         n = 40  # number of raw samples
         d = 4  # number of binary features + 1
diff --git a/econml/tests/test_ortho_learner.py b/econml/tests/test_ortho_learner.py
index 686b6cad1..bb66bd019 100644
--- a/econml/tests/test_ortho_learner.py
+++ b/econml/tests/test_ortho_learner.py
@@ -3,12 +3,10 @@
 
 from sklearn.datasets import make_regression
 from econml._ortho_learner import _OrthoLearner, _crossfit
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression, LassoCV, Lasso
+from sklearn.linear_model import LinearRegression, Lasso
 from sklearn.model_selection import KFold
 import numpy as np
 import unittest
-import joblib
 import pytest
 
 try:
diff --git a/econml/tests/test_policy_forest.py b/econml/tests/test_policy_forest.py
index bf6a610aa..a329ffff3 100644
--- a/econml/tests/test_policy_forest.py
+++ b/econml/tests/test_policy_forest.py
@@ -2,19 +2,13 @@
 # Licensed under the MIT License.
 
 import unittest
-import logging
-import time
-import random
 import numpy as np
 import pandas as pd
 import pytest
 import joblib
 from econml.policy import PolicyTree, PolicyForest
 from econml.policy import DRPolicyTree, DRPolicyForest
-from econml.utilities import cross_product
-from copy import deepcopy
 from sklearn.utils import check_random_state
-import scipy.stats
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.dummy import DummyClassifier, DummyRegressor
 from sklearn.model_selection import GroupKFold
diff --git a/econml/tests/test_random_state.py b/econml/tests/test_random_state.py
index 8fe45ebce..2ae5f3912 100644
--- a/econml/tests/test_random_state.py
+++ b/econml/tests/test_random_state.py
@@ -1,25 +1,13 @@
 import unittest
-import pytest
-import pickle
-from sklearn.linear_model import LinearRegression, Lasso, LassoCV, LogisticRegression
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer, PolynomialFeatures
-from sklearn.model_selection import KFold, GroupKFold
-from econml.dml import DML, LinearDML, SparseLinearDML, KernelDML
+from sklearn.linear_model import LinearRegression
+from econml.dml import LinearDML, SparseLinearDML, KernelDML
 from econml.dml import NonParamDML, CausalForestDML
 from econml.dr import DRLearner, SparseLinearDRLearner, LinearDRLearner, ForestDRLearner
-from econml.iv.dml import OrthoIV, DMLIV, NonParamDMLIV
+from econml.iv.dml import OrthoIV, NonParamDMLIV
 from econml.iv.dr import (LinearDRIV, IntentToTreatDRIV, LinearIntentToTreatDRIV)
 import numpy as np
-from econml.utilities import shape, hstack, vstack, reshape, cross_product
-from econml.inference import BootstrapInference
-from contextlib import ExitStack
-from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
-import itertools
-from econml.sklearn_extensions.linear_model import WeightedLasso, StatsModelsRLM, StatsModelsLinearRegression
-from econml.tests.test_statsmodels import _summarize
+from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
-from econml.iv.dr._dr import _DummyCATE
 
 
 class TestRandomState(unittest.TestCase):
diff --git a/econml/tests/test_refit.py b/econml/tests/test_refit.py
index 6a8217a30..4669d8e79 100644
--- a/econml/tests/test_refit.py
+++ b/econml/tests/test_refit.py
@@ -5,10 +5,10 @@
 from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.pipeline import Pipeline
-from econml.dml import (DML, LinearDML, SparseLinearDML, KernelDML, NonParamDML, CausalForestDML)
-from econml.dr import (DRLearner, LinearDRLearner, SparseLinearDRLearner, ForestDRLearner)
+from econml.dml import (DML, LinearDML, SparseLinearDML, NonParamDML)
+from econml.dr import (LinearDRLearner, SparseLinearDRLearner)
 from econml.iv.dml import (OrthoIV, DMLIV, NonParamDMLIV)
-from econml.iv.dr import (LinearDRIV, IntentToTreatDRIV, LinearIntentToTreatDRIV)
+from econml.iv.dr import (IntentToTreatDRIV, LinearIntentToTreatDRIV)
 from econml.sklearn_extensions.linear_model import (DebiasedLasso, WeightedLasso,
                                                     StatsModelsRLM, StatsModelsLinearRegression)
 from econml.inference import NormalInferenceResults, BootstrapInference
diff --git a/econml/tests/test_shap.py b/econml/tests/test_shap.py
index 0761d565b..4c6983dc7 100644
--- a/econml/tests/test_shap.py
+++ b/econml/tests/test_shap.py
@@ -9,7 +9,7 @@
 from econml.dr import DRLearner, ForestDRLearner
 from econml.metalearners import TLearner, SLearner, XLearner, DomainAdaptationLearner
 from sklearn.linear_model import LinearRegression, LogisticRegression, Lasso
-from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import PolynomialFeatures
 
 
diff --git a/econml/tests/test_statsmodels.py b/econml/tests/test_statsmodels.py
index ba9b4dfd3..cf4dfc2e1 100644
--- a/econml/tests/test_statsmodels.py
+++ b/econml/tests/test_statsmodels.py
@@ -7,7 +7,6 @@
 import pytest
 
 import scipy.special
-from sklearn.base import clone
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.linear_model import LinearRegression, LogisticRegression
 from sklearn.preprocessing import PolynomialFeatures
@@ -23,7 +22,7 @@
 from econml.sklearn_extensions.linear_model import WeightedLasso, StatsModelsLinearRegression
 from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression as OLS
 from econml.sklearn_extensions.linear_model import StatsModels2SLS
-from econml.utilities import (ndim, transpose, shape, reshape, hstack, WeightedModelWrapper)
+from econml.utilities import (ndim, transpose, shape, reshape)
 
 
 class StatsModelsOLS:
diff --git a/econml/tests/test_treatment_featurization.py b/econml/tests/test_treatment_featurization.py
index 1127e3284..fdf0bd75a 100644
--- a/econml/tests/test_treatment_featurization.py
+++ b/econml/tests/test_treatment_featurization.py
@@ -4,19 +4,17 @@
 import unittest
 import numpy as np
 from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LassoCV, LinearRegression, LogisticRegression
+from sklearn.linear_model import LinearRegression
 from sklearn.ensemble import RandomForestRegressor
-from joblib import Parallel, delayed
 
 from econml._ortho_learner import _OrthoLearner
 from econml.dml import LinearDML, SparseLinearDML, KernelDML, CausalForestDML, NonParamDML
 from econml.iv.dml import OrthoIV, DMLIV, NonParamDMLIV
 from econml.iv.dr import DRIV, LinearDRIV, SparseLinearDRIV, ForestDRIV
 from econml.orf import DMLOrthoForest
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
+from sklearn.preprocessing import FunctionTransformer
 from econml.sklearn_extensions.linear_model import StatsModelsLinearRegression, WeightedLassoCVWrapper
 
-from econml.utilities import jacify_featurizer
 from econml.iv.sieve import DPolynomialFeatures
 from copy import deepcopy
 
diff --git a/econml/tests/test_two_stage_least_squares.py b/econml/tests/test_two_stage_least_squares.py
index befc18f28..3d2cf9e77 100644
--- a/econml/tests/test_two_stage_least_squares.py
+++ b/econml/tests/test_two_stage_least_squares.py
@@ -5,7 +5,6 @@
 
 import unittest
 import numpy as np
-import warnings
 import pytest
 
 from econml.utilities import shape, reshape
diff --git a/econml/tests/test_utilities.py b/econml/tests/test_utilities.py
index 676f498b7..467d74573 100644
--- a/econml/tests/test_utilities.py
+++ b/econml/tests/test_utilities.py
@@ -2,7 +2,6 @@
 # Licensed under the MIT License.
 
 import unittest
-import logging
 import time
 import random
 import warnings
diff --git a/econml/tree/_tree_classes.py b/econml/tree/_tree_classes.py
index ffc15861e..f65db4722 100644
--- a/econml/tree/_tree_classes.py
+++ b/econml/tree/_tree_classes.py
@@ -1,4 +1,3 @@
-import abc
 import numbers
 from math import ceil
 import numpy as np
@@ -9,8 +8,7 @@
 from ._criterion import Criterion
 from . import _tree
 from ..utilities import deprecated
-from sklearn.model_selection import train_test_split
-from sklearn.utils import check_array, check_X_y
+from sklearn.utils import check_array
 from sklearn.utils import check_random_state
 from sklearn.utils.validation import _check_sample_weight
 
@@ -238,7 +236,7 @@ def fit(self, X, y, n_y, n_outputs, n_relevant_outputs, sample_weight=None, chec
                 criterion_val = criterion
         else:
             valid_criteria = self._get_valid_criteria()
-            if not (self.criterion in valid_criteria):
+            if self.criterion not in valid_criteria:
                 raise ValueError("Input criterion is not a valid criterion")
             criterion = valid_criteria[self.criterion](
                 self.n_outputs_, self.n_relevant_outputs_, self.n_features_in_, self.n_y_, n_samples, max_train,
diff --git a/econml/utilities.py b/econml/utilities.py
index df51625f4..618ac8f38 100644
--- a/econml/utilities.py
+++ b/econml/utilities.py
@@ -8,27 +8,18 @@
 import scipy.sparse
 import sklearn
 import sparse as sp
-import itertools
 import inspect
-import types
-from operator import getitem
 from collections import defaultdict, Counter
 from sklearn import clone
-from sklearn.base import TransformerMixin, BaseEstimator
-from sklearn.linear_model import LassoCV, MultiTaskLassoCV, Lasso, MultiTaskLasso
+from sklearn.base import TransformerMixin
 from functools import reduce, wraps
 from sklearn.utils import check_array, check_X_y
 from sklearn.utils.validation import assert_all_finite
 from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures, LabelEncoder
 import warnings
 from warnings import warn
-from collections.abc import Iterable
-from sklearn.utils.multiclass import type_of_target
-import numbers
 from statsmodels.iolib.table import SimpleTable
 from statsmodels.iolib.summary import summary_return
-from statsmodels.compat.python import lmap
-import copy
 from inspect import signature
 
 MAX_RAND_SEED = np.iinfo(np.int32).max
diff --git a/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb b/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb
index d7b14dc77..03c09a114 100644
--- a/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb	
+++ b/notebooks/AutomatedML/Automated Machine Learning For EconML.ipynb	
@@ -48,33 +48,19 @@
    ],
    "source": [
     "import logging\n",
-    "import sys\n",
     "# Main imports\n",
-    "from econml.dml import DML, LinearDML,SparseLinearDML,CausalForestDML, NonParamDML, KernelDML\n",
+    "from econml.dml import DML, LinearDML,CausalForestDML, NonParamDML\n",
     "from matplotlib import pyplot as plt\n",
-    "import pandas as pd\n",
-    "import os\n",
-    "import copy\n",
     "\n",
-    "import azureml.core\n",
-    "from azureml.core.experiment import Experiment\n",
-    "from azureml.core.workspace import Workspace\n",
-    "from azureml.automl.core.featurization import FeaturizationConfig\n",
-    "from azureml.core.dataset import Dataset\n",
-    "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
     "\n",
     "#Import scikit-learn model AutoML wrapper\n",
     "from econml.automated_ml import addAutomatedML,setAutomatedMLWorkspace, EconAutoMLConfig\n",
     "# Imports for linear double machine learning evaluation\n",
-    "from econml.dml import LinearDML\n",
-    "from matplotlib import pyplot as plt\n",
-    "import pandas as pd\n",
-    "import os\n",
     "# Helper imports\n",
     "import math\n",
     "import numpy as np\n",
     "from itertools import product\n",
-    "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV,LinearRegression,MultiTaskElasticNet,MultiTaskElasticNetCV\n",
+    "from sklearn.linear_model import Lasso\n",
     "from sklearn.ensemble import RandomForestRegressor,RandomForestClassifier\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -178,12 +164,8 @@
    "outputs": [],
    "source": [
     "from sklearn.model_selection import GridSearchCV\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
-    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
     "\n",
     "class GridSearchCVList:\n",
     "\n",
@@ -983,7 +965,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from econml.metalearners import TLearner, SLearner, XLearner, DomainAdaptationLearner\n",
+    "from econml.metalearners import TLearner, SLearner, DomainAdaptationLearner\n",
     "from econml.data.dgps import ihdp_surface_B\n",
     "Y, T, X, expected_te = ihdp_surface_B()"
    ]
diff --git a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb b/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb
index 26e37fd82..8f261e707 100644
--- a/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb	
+++ b/notebooks/Causal Forest and Orthogonal Random Forest Examples.ipynb	
@@ -46,9 +46,7 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import econml"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -59,12 +57,12 @@
     "# Main imports\n",
     "from econml.orf import DMLOrthoForest, DROrthoForest\n",
     "from econml.dml import CausalForestDML\n",
-    "from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper, WeightedLasso, WeightedLassoCV\n",
+    "from econml.sklearn_extensions.linear_model import WeightedLassoCVWrapper, WeightedLasso\n",
     "\n",
     "# Helper imports\n",
     "import numpy as np\n",
     "from itertools import product\n",
-    "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV\n",
+    "from sklearn.linear_model import Lasso, LogisticRegression\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "%matplotlib inline"
diff --git a/notebooks/Causal Model Selection with the RScorer.ipynb b/notebooks/Causal Model Selection with the RScorer.ipynb
index c964252f3..d92d2198e 100644
--- a/notebooks/Causal Model Selection with the RScorer.ipynb	
+++ b/notebooks/Causal Model Selection with the RScorer.ipynb	
@@ -37,9 +37,7 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import econml"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -437,7 +435,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from econml.data.dgps import ihdp_surface_B, ihdp_surface_A\n",
+    "from econml.data.dgps import ihdp_surface_B\n",
     "Y, T, X, expected_te = ihdp_surface_B(random_state=123)"
    ]
   },
diff --git a/notebooks/Choosing First Stage Models.ipynb b/notebooks/Choosing First Stage Models.ipynb
index 7388e8c34..5087fdfc5 100644
--- a/notebooks/Choosing First Stage Models.ipynb	
+++ b/notebooks/Choosing First Stage Models.ipynb	
@@ -48,14 +48,12 @@
    "source": [
     "# Imports\n",
     "import numpy as np\n",
-    "import scipy.special\n",
     "from econml.dml import LinearDML\n",
     "from sklearn.linear_model import Lasso, LassoCV\n",
     "from sklearn.ensemble import GradientBoostingRegressor\n",
     "from sklearn.model_selection import GridSearchCV\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib\n",
     "\n",
     "%matplotlib inline"
    ]
diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb
index 90fc84b57..4f9a158d0 100644
--- a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company - EconML + DoWhy.ipynb	
@@ -57,8 +57,6 @@
     "# Some imports to get us started\n",
     "\n",
     "# Utilities\n",
-    "import os\n",
-    "import urllib.request\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from networkx.drawing.nx_pydot import to_pydot\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb
index 2e57078aa..709c17277 100644
--- a/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Customer Segmentation at An Online Media Company.ipynb	
@@ -2,13 +2,14 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "<img src=\"https://www.microsoft.com/en-us/research/uploads/prod/2020/05/Segmentation.png\" width=\"400\"/>"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Customer Segmentation: Estimate Individualized Responses to Incentives\n",
     "\n",
@@ -24,11 +25,11 @@
     "4. [Understand Treatment Effects with EconML](#interpret)\n",
     "5. [Make Policy Decisions with EconML](#policy)\n",
     "6. [Conclusions](#conclusion)\n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Background <a id=\"background\"></a>\n",
     "\n",
@@ -37,17 +38,16 @@
     "The global online media market is growing fast over the years. Media companies are always interested in attracting more users into the market and encouraging them to buy more songs or become members. In this example, we'll consider a scenario where one experiment a media company is running is to give small discount (10%, 20% or 0) to their current users based on their income level in order to boost the likelihood of their purchase. The goal is to understand the **heterogeneous price elasticity of demand** for people with different income level, learning which users would respond most strongly to a small discount. Furthermore, their end goal is to make sure that despite decreasing the price for some consumers, the demand is raised enough to boost the overall revenue.\n",
     "\n",
     "EconML’s `DML` based estimators can be used to take the discount variation in existing data, along with a rich set of user features, to estimate heterogeneous price sensitivities that vary with multiple customer features. Then, the `SingleTreeCateInterpreter` provides a presentation-ready summary of the key features that explain the biggest differences in responsiveness to a discount, and the `SingleTreePolicyInterpreter` recommends a policy on who should receive a discount in order to increase revenue (not only demand), which could help the company to set an optimal price for those users in the future. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Some imports to get us started\r\n",
     "# Utilities\r\n",
-    "import os\r\n",
-    "import urllib.request\r\n",
     "import numpy as np\r\n",
     "import pandas as pd\r\n",
     "\r\n",
@@ -62,12 +62,11 @@
     "import matplotlib.pyplot as plt\r\n",
     "\r\n",
     "%matplotlib inline"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Data <a id=\"data\"></a>\n",
     "\n",
@@ -114,46 +113,26 @@
     "Y &= \\gamma(X) \\cdot T + \\beta(X,W)\n",
     "\\end{align}\n",
     "\n"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Import the sample pricing data\n",
     "file_url = \"https://msalicedatapublic.z5.web.core.windows.net/datasets/Pricing/pricing_sample.csv\"\n",
     "train_data = pd.read_csv(file_url)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "source": [
-    "# Data sample\n",
-    "train_data.head()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "   account_age  age  avg_hours  days_visited  friends_count  has_membership  \\\n",
-       "0            3   53   1.834234             2              8               1   \n",
-       "1            5   54   7.171411             7              9               0   \n",
-       "2            3   33   5.351920             6              9               0   \n",
-       "3            2   34   6.723551             0              8               0   \n",
-       "4            4   30   2.448247             5              8               1   \n",
-       "\n",
-       "   is_US  songs_purchased    income  price     demand  \n",
-       "0      1         4.903237  0.960863    1.0   3.917117  \n",
-       "1      1         3.330161  0.732487    1.0  11.585706  \n",
-       "2      1         3.036203  1.130937    1.0  24.675960  \n",
-       "3      1         7.911926  0.929197    1.0   6.361776  \n",
-       "4      0         7.148967  0.533527    0.8  12.624123  "
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -260,40 +239,60 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "   account_age  age  avg_hours  days_visited  friends_count  has_membership  \\\n",
+       "0            3   53   1.834234             2              8               1   \n",
+       "1            5   54   7.171411             7              9               0   \n",
+       "2            3   33   5.351920             6              9               0   \n",
+       "3            2   34   6.723551             0              8               0   \n",
+       "4            4   30   2.448247             5              8               1   \n",
+       "\n",
+       "   is_US  songs_purchased    income  price     demand  \n",
+       "0      1         4.903237  0.960863    1.0   3.917117  \n",
+       "1      1         3.330161  0.732487    1.0  11.585706  \n",
+       "2      1         3.036203  1.130937    1.0  24.675960  \n",
+       "3      1         7.911926  0.929197    1.0   6.361776  \n",
+       "4      0         7.148967  0.533527    0.8  12.624123  "
       ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "execution_count": 3
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Data sample\n",
+    "train_data.head()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Define estimator inputs\n",
     "Y = train_data[\"demand\"]  # outcome of interest\n",
     "T = train_data[\"price\"]  # intervention, or treatment\n",
     "X = train_data[[\"income\"]]  # features\n",
     "W = train_data.drop(columns=[\"demand\", \"price\", \"income\"])  # confounders"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Get test data\n",
     "X_test = np.linspace(0, 5, 100).reshape(-1, 1)\n",
     "X_test_data = pd.DataFrame(X_test, columns=[\"income\"])"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Get Causal Effects with EconML <a id=\"estimate\"></a>\n",
     "To learn the price elasticity on demand as a function of income, we fit the model as follows:\n",
@@ -308,12 +307,13 @@
     "where $\\epsilon, \\eta$ are uncorrelated error terms. \n",
     "\n",
     "The models we fit here aren't an exact match for the data generation function above, but if they are a good approximation, they will allow us to create a good discount policy.  Although the model is misspecified, we hope to see that our `DML` based estimators can still capture the right trend of $\\theta(X)$ and that the recommended policy beats other baseline policies (such as always giving a discount) on revenue.  Because of the mismatch between the data generating process and the model we're fitting, there isn't a single true $\\theta(X)$ (the true elasticity varies with not only X but also T and W), but given how we generate the data above, we can still calculate the range of true $\\theta(X)$ to compare against."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Define underlying treatment effect function given DGP\n",
     "def gamma_fn(X):\n",
@@ -338,44 +338,44 @@
     "        return np.median(te_array)\n",
     "    elif isinstance(stats, int):\n",
     "        return np.percentile(te_array, stats)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Get the estimate and range of true treatment effect\r\n",
     "truth_te_estimate = np.apply_along_axis(true_te, 1, X_test, 1000, \"mean\")  # estimate\r\n",
     "truth_te_upper = np.apply_along_axis(true_te, 1, X_test, 1000, 95)  # upper level\r\n",
     "truth_te_lower = np.apply_along_axis(true_te, 1, X_test, 1000, 5)  # lower level"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Parametric heterogeneity\n",
     "First of all, we can try to learn a **linear projection of the treatment effect** assuming a polynomial form of $\\theta(X)$. We use the `LinearDML` estimator. Since we don't have any priors on these models, we use a generic gradient boosting tree estimators to learn the expected price and demand from the data."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Get log_T and log_Y\r\n",
     "log_T = np.log(T)\r\n",
     "log_Y = np.log(Y)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Train EconML model\r\n",
     "est = LinearDML(\r\n",
@@ -387,13 +387,36 @@
     "# Get treatment effect and its confidence interval\r\n",
     "te_pred = est.effect(X_test)\r\n",
     "te_pred_interval = est.effect_interval(X_test)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f533b30d68>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6bRKwic5HgjQhRJcQDGmqPUZzY7UncEDfMq01JTVe1uyuZm1xNWt2V7OhpBZf0IjgbGYTvTITOKJfJn0yE8hPSyA/3Ul2kj3ughmzSZGZZCczyc6Q3AO3B0OakmovOyrcbCt3saXMCD7fW7YTf3iqEJvFxIDsJAZ1S2ZQtyQGd0smO9keV02mXn+IEr8RgFotqj5bmWCTU5voHGTFASFEpxUKaaqaCcy01uyq9LBsR2X9ZW+tDzACsv45RmAyMCeJvlmJdE9zxl0w1t6CIc3OCjcbSmpYt6eGNbur2VhaUx+4ZSXZGZGfwsj8VEZ0TyUv1RFXQVsdu9VUH7BJk6joCJpbcUCCNCFEp6K1psYbqO+w3/grrsLl48dtFSzeUs7SRkFZeoKVkfmpDM1LYUhuCn0yE6LeRBmv/MEQm0trWb27mhU7K1m+s4pKtx+AzEQbBT3TGNMrncKeaXHZt81pM5GWYCMtBs3OQrSVBGlCiE7N4w9S7vJR4fLXz+4f0pq1xdUs3FLOoi3lbNhTgwZSHBYKeqYxMj+Vkfmp5Kc525wRMpsUVrMKd/I3YQ6vbWkKL1pu/DUWLTc1c8yQ1uj9FkcP1S2gHtQE6tfs1HE3D5vWmu3lbpbvNLKPRVsrqPYGUMDAbkmM7pXOhD4ZDMhJavb5x4JSkOKwkp5oJdkRf8Gk6NokSBNCdDp1HeTLXX7cPmNUpT8YYtn2Sr7dWMb3m8ood/kxKRjcLZkxvdMZ2yud/i0EEErRsA6lxYzNYiwkbjWbsJlNmKLc5BkKGQut+4MafyCELxjC6w/hCwbx+GO/7mcwpNlQUsOiLeUs3lrO2uJqQtrIsk3sl8kRfTMYmZ8aV1ksq0WRkWAjPdEma4yKuCBBmhCi03D5ApTV+OqbM/3BED9uLefLdaX8sHkvtb4gDquJsb3SOaJfJmN7pzeZPbGYVf00GI7wwuB2S8daX9JYqD28TqgvhCcQbHUakUiqcvtZuKWc7zaWsXhrOd5AiESbmQl9M5g0KJvCHmlxE7ApBckOC5lJdpLsMthAxI4EaUKIDi0U0pS7fOyt9eHxG82BK3ZW8sXaEr7eUEaNN0Cy3cLEfhkc2S+Tgp5p2C0NncaVAqfNmPg1wd4wN1lnFApp3H5j/jS3z1g/MxbNpt5AkCXbKvh2Yxnfbiyj1hsk2WHh6P5ZTBqUzfDuKXHTJGq3mshItJGRYIt6tlQICdKEEB2Sxx+krNZHhctHKATby118smoPC9bsYW+tD4fVxBH9MjluYDaFPRuyNEpBgs1Mkt1YYshpNXfpk683YMwvVreSQl2/vWipy3Z+vraU7zeV4Q2EyEqyccKQbpw4JIfuac6o1qc5JhNkJNrITLRjs3TOIF7EHwnShBAdSrXHT2mNjxqPkQ36an0JH6/aw6pdVZgUjOudweTB2Yzvk1E/zYLNYiLZYSHJYSHJZunSQVlrPP5wwOYxgrZongo8/iD/27SX+Wv28OPWckIahndP4aQh3Th6QFZcTExbN9AgK9km866JiJMgTQgR90IhY8ml0hovXn+ITaU1vLdsN5+v3YPHH6JHupMpQ7tx/OAc0hNtgNGEmeKUZYIORyikqfEZ62VWuf1RzbKV1XiZv3oPn6wqZmelB6fVzOTB2UwdkUefrMSo1aMliXZjSS0ZFSoiRYI0IUTcCoY0ZTVeSmt8ePxBvl5fyrxlu1i1uxqbxcRxA7M5eVg3Bucmo5QiwW4OLyRulSapCHD7guFF4X34A9E5R2itWbmrio9WFPPl+hL8Qc3w7in8ZEQeR/XPjIv+g06biewkB6kJEqyJ9iVBmhAi7vgCIcpqvZTV+Nhb4+O9Zbv4YMVuKt1+8lIdTB2Zx0lDupHksOCwmkhNsJLmtElgFkUuX4BKtzExcLQCtiq3n09WFfP+8t3srvKQ5rQydWQeU0fmxcWEuXariewkO2kJ1g41EljELwnShBBxwxsIUlLtpcLlZ3NpLXOKdrJgzR6CIc34PhmcNiovPAhAkZ5gIz3BFhf9lLq6Gm+A8lrfASs5REpIa4q2VvDu0p0s3FKOzWzi+CE5nFXQnZ4ZCZGvQCskWBPtRYI0IUTMeQNB9lR5qXD5WLq9kjcW72Dx1nJsFhMnDsnhrIJ88tOdJNjNZCbaSHFYpfN/HAqGNJVuP+UuHy5vMCplbtvrYs6SnSxYvQdfMMS43umcN7YHw7unRqX8ltgsJrql2ElLsMW6KqKDkiBNCBEzHn9d5szHD5vLeW3hNlbvriYtwcrpI/M4dUQe6YlW0hNsZCTaZABAB+LxB9lb66M8PEVKpFW6/by/fBdzl+6i0u1nePcUzh/bk9G90mKezXJYTeSkOOKiSVZ0LBKkCSGizhcIUVzlodzl45v1Zby2cBsbS2vJSbYzbUwPThpq9DfLTDKaNM2SNeuw6kbmltV48URhxQOPP8hHK4t568ftlNb4GJCdxPRxPTiiX2bMJ8h12szkpjpkFQPRZhKkCSGixh8MsafaS1mNl2/Wl/Hy/7ayba+L/DQn543tweRB2aQkWMlKskvWoROq8QYorfZS7QlEvCx/MMSCNXuYvWg7uyo99MlM4MKJvZnYNyPmmbUkh4W8VIdkhkWrJEgTQkRcMKQpqfZSUu3hf5vKefn7LWwsraVnupOfTujFUf2zSEuwkp1sJ1GyDJ2exx+ktMYYIBLpU00wpPlyXQn/+d9WdlV6GJCTxM8n9mZMHDSDpida6ZbiiItpRER8kiBNCBExWmvKan3sqfKyeEs5L363hTXF1eSlOvjphF5MGphNRqKN7GS7jNLsggLBEGW1PkprvBHvtxYMaRas3sMrP2xlT7WXobnJXHxkH0bkx3aAgVKQnWwnO8kug2HEASRIE0JERKXLz+4qD2t2V/HcN5tZvLWCrCQ7F4zvyYlDcshMspOTYpcmH7HPpMWRXvDdHwzxyapiXv1hG2W1Psb3SeeSI/vQOzO2qxhYzIrcFEf9ihlCgARpQoh25vIF2FnhYWuZi5e+38KC1XtItFuYMa4np43KIzPJRrcU6Y8jDhQMacpqvZRWRz5Y8waCvLtkF68v2obHH+Skod342YReZCbZI1puaxLsZrqnOiWzLIA4DdKUUqcCfwHMwD+11vfvt90OvACMBcqAGVrrza0dV4I0ISLHHwyxu9LDzgo3sxdtZ07RTkJac0ZBd84f25O8NAfdUhxy8hGtqsuslUShGbTS7ee1hduYt2wXJpPi3NH5TBvTI+Y/ItITreSmOLBIf7UuLe6CNKWUGVgLTAG2Az8AP9Var2y0z9XAKK31VUqpC4BztNYzWju2BGlCtD+tNSU1XnZXepi/ag/Pf7uZcpef4wZlc9ERvemTlUBuqlOmHRAHLRAMUVJjLA8W6VPS7koPL3y3mS/XlZKVZGPmUX2ZNDArpoMLTCbITXHEPLsnYiceg7Qjgbu01qeEb/8eQGt9X6N9Pgzv861SygLsBrJ1K5WWIE2I9lXt8bOzwsPyHZU8/cVG1hRXM6hbElcc258RPVLITXHIbOvisPmDxrx60RgNumJnJU9/uZGNJbUMzUvhimP7MSAnKbKFtsJpM5OfJk2gXVFzQVosf/LmA9sa3d4OTGxuH611QClVCWQCpVGpoRBdnD8YYleFhy1ltTz3zWY+Xb2H9AQrvzlxICcOy6FbioPsJHvMpzgQnYPVbKJHegJZSUF2VXqoieA8a8O7p/Lo9EI+XV3Mi99u4XevFTFlWDcuObIPKTGau8/tC7KhpIaMRBu5KQ4ZBSpiGqS1K6XUFcAVAL169YpxbYTo2LTWlNb42F3l5v1lu3n+2814/SGmjcnn/HE96Z7mJDdV5n0SkeGwmumblUi1x8/uSk/EVjAwmxQnD8vl6P5Z/PeHbby7dCffbizj0qP6cOLQbjFZuUBrKKvxUeXxk5fqlMmeu7hYBmk7gJ6NbvcI39fUPtvDzZ2pGAMIDqC1fhp4GozmznavrRBdhMsXYEe5mxU7q/jrZ+tZW1zDyPxUfjW5P4O6JdM9zUGCrdP8vhNxLNlhJcluYW+tj91VnogNLki0W7jsmL6cNDSHv362gcfnr+fjVXv41XH96ZsVmyk7/AHN1jIXqU4r3dNkYEFXFcs+aRaMgQMnYgRjPwA/01qvaLTP/wEjGw0cOFdrfX5rx5Y+aUIcvFBIU1ztYdteFy99t5W5S3eS4rDyi2P6cuKQHPLSnGTI3E4iRgLBELurPJTX+iNaTkhr5q/ew7+/3kSNN8CZBflcOLFXTEeBmk2KvFSZW60zi7s+aeE+ZtcAH2JMwfGs1nqFUmoWsFBr/Q7wL+BFpdR6YC9wQazqK0RnVjcw4JsNpTy1YANlNV5OHZHLxUf0oUeGk7xU+SUvYssS7q+WkRhgZ4Ubty8yaTWTUpw0tBsT+mTwwrebebtoB99tLOOa4wdQ0DMtImW2JhjSbC93U+H2k5/mxGaR/8WuQiazFaILC4Y0OyvcbC1z8c+vNrJgTQk9051cd8JACnqlkZ/mlDU2RVwqq/FGtAm0zrIdlTw5fx07Kz1MGdqNXxzdlyRH7P4nTCbIS5WsdmcTd1NwRJIEaUK0rtrjZ9teF5+vLeUfn2+g2hvgvLE9uGB8T/LTnGQny6hNEd/qRh9XuiPbBOoNBPnv/7bx5o/bSXVaueq4/hzVPyuiZbYmyWGhR7pTBu90EhKkCSEAI3u2q9LN5lIXf/1sPd9sKGNAdhLXnTiQ4fkp5Kc5Yz4LuxAHo9LtZ2eFm0AwsuezDSU1PD5/HRtLapk0MJurjutHsiN2oy9NJuie6pS+ap1A3PVJEyIiPJXgcwGahtkwNSjTgZf91e3fVPYoFAQdAl33N3TgY5UCVNN/lSl83WTc1qEDj1m/3dSwX329dMPf/etbp7msV6PH1Xr97C6v5ouNNTz2XQUuv+ayAifThznolraXdJMbasygzAc+D2ioV/3zUcbzCAXCF79xu37f/Z7PPnVufH3/urfwPBs/X2Uy6mpqXGca3iMdanhvlNnY3xSujw5BKPz6h4Lh96G59/GAwpvfr/Hn7oD67/dcWnwPFQd8jlt6Peo/l7rh81n/2pv3fQ+a2nf/eurG2+pex0avX/3rXvf/pPYro/HnIsgB/zP7POcm/l/2+V9Vjd7PYMPxwq9XKookO5TUeKlw1c2tZmzTzfxfqLo66pBxndC+r0H94xQ6/HyHOE38ZWoury+r4OUfS1i+fS/XHZPHhF7J+z2m/iCNjtFwu6G8EKrueSkFmMJlmfard+PrGqXD7xGaXbWaGofZ6Ddq2u97rdFzqLf//2ooaLyPJguYrcZfk6XhO6pufx1sONY+3wM0fD7qP0/hbft/PuqOE2rme7T++E18DqDp/6V9vh9p5vPT6Lj1r4em6e/Wxo9X4EgDc+xCJQnSROfi2gueiljXIu5oDaW1XrZX+HhqsYdPNvsZkG7ipokJDMu2kZNiwWb2gzeyzUZCRIoZyLVAiiNIcbUnYlk1M3DRIDgyO5EHv3Nz18fbObWvlatGO0i0xaZ7gKcWtpUrclLsJMr0OO3L6pQgTYh2sWc1vDIDzvor2BKg6BVY92FD1qTu196MF439//c0rP9032NYE2D6v8Pb/wk7FhqPC/qNX572FDj7r8b2T++GnYvDDwz/SkvJhzMfN+766HbYs6r+FzImE2T0h1PuNbZ/eBtUbGGfX7ndhsHk3xvXP/g91BTvm83oPhqO+a1x++1fgbsC4xdg+Fdr76PhiKuM7XN/C6EAIcx4Q+D3mfiwooD53ilcPMzMLeV3YlsawqIaZZ0G/wRGzQBvNbzxy/DzrvvVHYAxl8Don0NtKbx5efhXt9X4EjNZoWAGDDwZqnbB/LvDv1BDDVmZsZdAn2OhbIOxfR8KjvgV9JwAu5fD5/c3/NKvyzaecDvkj4Ut38CCexuyOXWZtJP+CDlDYPNXxvtb9wu+zpQ/Qlov433/8aVwsY1+XZ96HyTlwOp5sOz18P06nB3UxmfLkQJL/gvL36Qum1FvxstgscPCf8PaD4xjm+3GfRY7nPG4cV/RK7D1Wwh4IOiDgA/sSXDu08Zxvn0KdixueN/RkJABpz1q3PflI1C8Yt9f/Mnd4aQ7je3z74Gy9ftmCjIHNGz/8Dao2tEoAwbkDINjrze2v38L1JY0yrZp6DEOjrrW2P7WleCpZp9MXN/j4Mirjeuv/Mx4z5Q5nHkzw8Apxmcn6INXL2rIXNY9fsQ0Y7u3Gl67pOG5mUzG57tgBgw7C2r2wNzfhN+yRu9/wc9gwIkkuLbT59M7CPm96IAXFfIBJvYUXEVNj+OwV6wn94cHGmVYjPe+ZNQVuHLG4ChbQbcfn2jYFP7fKB77WzyZw0nc/T+yi56ijzJzgtlMZbKifIeJWbv/j3OOHMpRwUVkrP7PPlkmpUPsmngr/sQ8krd+SsaaV+tfVxX+u23SgwSdWaRueIeMdbPDj234fG058e+EbElkrH6F1I1zAd3wydaajT95kZ0VmvxVz+Lc+L6xzRTOjFkdcMF/Gj5bGz9vlCELGZ+t818wtn//DyheDsGA8V4FvZCQCWf8xdj+8Z3G9sZZ2vQ+cOqfwp+9e6F8477/u1mDGz57711vfD/UZctQkDcKJt1obH/nOnCVhp92+Luj15FwTPg9n30Z+N31yTAIQb/jYeKVxvaXzw9vaJSRGzwVxlxs/L+9+vN9kmagYOR5xveepwLevnrfLNy0fxmf/RiRIE10HruWQPlmI7DJ6AsJ6cYJub65q66ZK3w7tSd0L2x4vMZI99exJRgnZExgthnbnGkN23NHgC2x0RepBmdGw/ZuI8CR2qh5SUNybsP29N7GiRsaUvXJ3Ru2J2YbX7CNv2ySGj0+azD4avdtXkrKadhuceD3ufH5/JTU+qlwB8iwunj8pEQKcqw4vk3EZLY0OpGawJFuPNZsMwLCxgGYyQw5Q8PbrdD7mEZNJ+FAzhqe+NNkMl6bugDVOGOAxRmum914b+qbHcJvgNXZ8NpnDW4Ivurq5wzXL6kbDDq14SRQ99ceXnvRlmQExPVNKuHTmSn8/tqTjPe//ts6HIiYwn3x7MmQ2oP6AKZxUylASncjWIR9m7jqHp/aw3j9dCgchHmMOtbvGzL2dWaAxWa83nXPHYz76z4rdUFY489WYrZRh1CjZktHasP2pG5GuY2bEVN7NGxPyTfKbxwk7XP8zHD2oFGzU+PPVtYg40TZuOkrvdFKL/ljwoF9o+bJuvdOmY3/u/rm6fDnI713w/beR7NP1wIdMgIFMF6rnGHhl7HR+29LMO6zOFEZfTGbbfiVhdqgCR0Cf4LxeobMDrzpgxo1nRl/g+HPbsjixJvat+G5hJs7QxZje8CRgStnTLiZMohTBwl4/AQr7dw438XtfeB8RzomqG8i1sqEVsZnI2hPw5s+MFxq3f+GQoc/m/7EPGq7jWt47+tqaTJO196UPtTkH11ft8b1BKhMGUygjybBZsZC0Ai2Gu+W1tv47NY1carw91sds8243+Iw/k/NNuPzVqfbcOP/p75JO7TvZ8OZBv7cRv+75vD/eljWoPBnrdFnLyW/YXtGv/DxGn3vZTR6P3KGGv9Pdc9Z7ffZ7jFu3+9krSE5L7y7GXpMYB9aN3zvKrPx2ar7vtZB43WIIRk4IDqPhc8a2aMLZ0PioYy82q+povHJ91D6UrWlrP37sOzf7+wQBUKwp8rDqlIff/rGxYaKEGcMsHJloYPuqXbSE2zNdmHrXOqeZEf6ntu3/1HHsn/fvPao/+G9HsEQlNR4qI7gOqAAbr/mbz96eH+jn0EZJn5/pJMeybEbgKOAzCQ76QmxXFZq/35gsM97uH9/y4M99iE/9iCOlTXI+MEZYTJwQHR+frfxt/uYRhmvJoKhSEcnuomOqHXlHkzZrf2AaqrTuVLUeANsLatl7tZd/POrTTgsJm4/bTCTBmXTM82J02rigE6zbeocv3+5uol9acjs1e+7X6fe/d+Tw30/mnqdmjqm1s3UZ7/6N/f86vZvvN/+x6s/ZnOvWd1xD/I1CDVqdmxcxv6Pbemk12Sn+NY08X/TzOeuWVo3/ZgD6tXE56mlwTDNdSLf77UxK0Uu4Kj1sqPCTSi0/74t1HvfO/a73fj1VyiluDoPCtaX8uSC9fzqIzeXH9uPKUO7HdpUNi0NZGrjftuBKruF/MaTUbflB2dby2zqO64tj2/puI2PvU85jb6P9h8gccAxGnWxqKvj/oNbWv1shR9vim2YJEGa6Dy81cZfR0pDs1MsHGww1tJxDnJ7cZWHjXtqeXz+Or7dWEZhzzR+e9Ig+uck0j3VickUhXo13ica6bq2ltHW96W9j3cwx2xOSyel9iwnEmW09jodSp0P4X8sLdFOgt3KtnIXLm/wwCT4AWUcfLUAjh6QxeDcZP78yVqemL+eom0VXHP8gINf77adPltV7gBufy090xOMiamj8d10OMc93GMrZTRbtksdYj8VkQRpovNwZkDuqH37lXUR/mCIbXtd/Li1ggc+WE1ZrY9fHN2Hc8f0oGdGAqnOrveaCLE/m8VEv6xESqq97Kn2tpqsPlRZSXbuPmsEbyzezkvfbWH9nhpuOmUIA3KSIlNgK/wBzabSWnKS7eSkxLaPlTg40idNdB7eGqOzcl0H5S6i2uNna5mLNxfv4LlvN5OZaOOmU4YwpncaPdITZJ0/IZpQ6w2wrdyFPxDZc+CKnZU8/NEaKlx+LjumL6eNzDu05s92kuSw0DPdKWvxxhlZcUB0fu5yQO07ArOT21PlYf2eGv7y6Tq+37SXI/pl8OsTjOZN+cUsRMsCwRDby90RH1RQ6fbz2CdrWbilnCP7ZXLdiQNJiuGauFaLamj+FHFBgjTR+b13A1RshQtfi3VNIi4Y0mzb62LxlnLu/2A1e2t9XHp0X84Z3Z1emYkxPQEI0dGUVHsprvJErPkTIKQ1c4p28Py3W8hJtvP7nwylb1bkRw02RynoluIgO9keszqIBs0FaZLvFJ3H3o1GkNbJefxB1u+p4Y3F27n5zaVo4IFpo/jphJ4M7JYsAZoQByk72U6/7ESslsg1Q5qU4pzRPfjTOSPxBkLcMHsJ81fviVh5rdEadld62FJWSzDU+ZI1nYUEaaLz8Lv3nRC0E6pw+Vi5s4pHPlrDE/PXM7x7Kn8+v5BjBmbRLzsJq/QzEeKQJNgsDMhOItEe2RF9w/JSeGxGIYNykvjzJ2v562fr8QebWdc0CqrcATaU1ODxB1vfWUSd/OQWnYe/1pgpvhPSWrO7ysPKHVXc9/5q1pfUcP64nlx0RG/6ZCWQ7JDRm0IcLovZRN+sRIqrvJRUeyNWTnqCjXvOHskL327mzR93sKGkhltOHRqzpkevP8SGkhp6pMtI8HgjP7tF5+H3GGtvdjLBkGZzmYtPV+3hN68VsbPSze2nDeWKSX0ZnJssAZoQ7UgpRW6qg14ZCW2enu5QmE2KS4/uy+9/MoRte9387rUiVuysjFyBrQiFYGuZi92VntZ3FlEjQZroPLIHQ+7IWNeiXXn8QdYVV/Pf/23ljjnLSXNa+fP5hZw6Ipd+WUkyvYYQEZKaYKV/dhJ2a2T/x47qn8Uj0wtIsJm5/e3lfLB8d0TLa01JtZdNpdJPLV7I6E7ReexeDpkDwNo5pp6o8vjZuKeGpz7bwMcri5nYN4PrTx7EgJxkMhJtrR9ACHHYgiHN1r0uaiI8TUeNN8BDH65h8dZyfjIil8uP7RfTPqZ2q4leGQk4rLGfdb8rkNGdovMLBaKzLE4UlFR7KdpSwe/fXMbHK4uZMa4nd5wxjBH5qRKgCRFFZpOib1YiWcmR/b9Lslu44/RhTBuTz/vLd/OHOcupcPkiWmZL6vqpVXv8MauDkCBNdBbBAPz3Z/Dd32Jdk8OitWZHhZuv15fyu9eL2Fhay82nDuHySf0Y1C354Nf/E0K0i7xUJz3SnRH9HWg2KWYe1ZfrpwxiXXEN17++hC1ltZErsBWhEGwpc0V0EIVomQRponMIeKBqhzENRwdVN0Dg/WW7uPmNpYDiwWmjOH1UHv2yEmV6DSFiLD3RRr/sRCzmyGbsJw/O4f5zRxIIam6cvZSFW/ZGtLyW1M2ntm2vi87YPSreybe+6By8NcZfW8cc3ekNBFm/p5oXv93Mve+toldGAo+eX8BRA7LomZGAydQ5mnGF6OgSbBb6ZyfhiPCAgoHdknnk/ALyUh3cPXcl7y7ZGdHyWlPh8rOxtJZADOd064okSBOdg7fK+NsBp+Bw+4Ks2VXNox+t5dmvN3PUgCweOG8khb3SZMkWIeKQzWKiX3YSSY7Idj/ISrJz/7mjGN8ng6e/3MjfP98Q01GXLm+QDSW1MvFtFEmQJjoHX7jfhi12a+EdiiqPnyXbK/jDnOV8uLKY88f15LbThjAsL5UUmf9MiLhlNin6ZCaQkRTZAQVOm5lbpw5l2ph83lu2i1lzV+DyRXakaUt8AWNAQY03dnXoSiRIE52DUtBnEqT3iXVN2mxvrY+Fm/dy0+ylrNhZxW9PGsgVk/oxMCdZhr0L0QEopchPc5KbGtlpf0zKGFBwzfEDKNpmjPouq4ldZ/5QCDaX1rK3NnajT7sKCdJE55CYBafcA72PinVN2qS4ysOXa0u44fWllNZ4uevM4ZwzuocMEBCiA8pOttMrIyHiMwCdMjyXO08fzq5KDzfMXhrTkZ9aw45yt6xQEGFyNhCdQygAxH/neq012/a6+HD5bm55cxkmBQ9OG8WUYd3olSkDBIToqFITrPTNSozoUlIAY3qnc9+5IwmFNDe/sZSl2ysiW2ArSqq9MvIzgiRIE53Dpi/gudNg19JY16RZoZBmS5mLNxZt549zV9Itxc7D040RnN1SOscqCUJ0ZYl2Y+RnpKfo6J+dxEPTR5GZZOfOd1bw2Zo9ES2vNRUuvywlFSExCdKUUhlKqY+VUuvCf9Ob2S+olCoKX96Jdj1FB+KpMkZ4muOzs30wpNlYWsOzX23isU/XMaJ7Cg+eN4qxvdNlBQEhOhGH1RyVNT9zkh08MG0UQ/NSeOTjtbz9446IlteaWm+QjSU1+AIyRUd7ilUm7RbgU631QODT8O2muLXWheHLmdGrnuhw6kZ3Wp2xrUcT/MEQ6/dU8/in63nhuy0cNyibu88ewcj8NJJlBKcQnY7NYqJ/dhIJ9sgOAEqyW/jjmcM5un8m//p6E899symmzY4ef4iNpTUyRUc7ilWQdhbwfPj688DZMaqH6Cz8LuOvNb6m4PAGgqzZXc2f5q3mnSU7ObOgO7+fOoQhuSk4bTKCU4jOymxS9M1MJDnCc6lZzSZuPGUIPxmRyxuLd/D4/HUxbXb0BzQbSmqolSk62kWsgrRuWutd4eu7gW7N7OdQSi1USn2nlDo7OlUTHU4oaCwLBXGVSfP4g6zcWcUdc5bz+doSLj6iN9edOICBOcnYLNIdVIjOzmRS9M5MIC0hshlzs0nxq+P687MJvfhk1R7+NG9VTLNZoRBsKq2lShZnP2wRC/GVUp8AuU1suq3xDa21Vko1F/b31lrvUEr1A+YrpZZprTc0U94VwBUAvXr1Ooyaiw4nFIC0XjD4J3Gz4oDbF2TJtgrufGcF6/ZUc83xAzhvbA96yRJPQnQpSqnw0m5u9tZEbl4xpRQ/ndCLVKeVv3++gTveWcEdpw8jyR7ZTF5ztIatZS66pzml3+1hULFov1ZKrQEma613KaXygM+01oNbecxzwFyt9ezWjj9u3Di9cOHC9qmsiH/eGihbZzR1Zg+KdW2o9QZYvLWc295azq5KNzeePJipo/LIT3OiIj2RkhAibu2u9FBSHflJaL9aX8ojH62hV2YCs84cQaoztn1fu6XayUmWEewtUUot0lqP2//+WLW5vANcEr5+CTBn/x2UUulKKXv4ehZwNLAyajUUHUcoYPxsi4MAqNrj57uNZdw0eyl7qj3cecZwzizMp0d6ggRoQnRxuamOiK9OAHDMgCxuP20Y28vd3PKmMWF2LBVXetlV6Y5pHTqqWAVp9wNTlFLrgJPCt1FKjVNK/TO8z1BgoVJqCbAAuF9rLUGaOFAoCJ8/AC+eHdNqVLr9fL2+lJvfWEqVx8/dZ43glOG5UflSFkJ0DNnJdvLSIv+dMLZ3OrPOHE5ZjY+b31ga8yCptNrH9nJXTOvQEcUkSNNal2mtT9RaD9Ran6S13hu+f6HW+pfh699orUdqrQvCf/8Vi7qKDiAUgIAXVGz6XgBUuvx8vmYPt7yxDH9Qc985IzlpaDeyk+0xq5MQIj5lJdnJT4/8IKfh3VO59+wRuP1BbnljWUyXkQIor/WztUxWJzgYMsRMdHyhgDG6M0YjOytdfj5ZVczv31qGxax4YNpIjhuUQ7p0lhVCNCMj0UaPdGfEe2kM7JbMfeeMBOD3by5j/Z6ayBbYikq3n81lLkKyOkGbSJAmOr6QPxykRb9ZscLl48MVu7n97eUk2608MG0URw/IIjXCQ+6FEB1fepQCtd6Zidw/bSQOm5nb5yxjbXF1ZAtsRY0nwKYyWUaqLSRIEx1f3TxpUc6kVbh8vL98N3e8s5yMRBsPnDeSI/plyioCQog2S0uw0TMjIeKBWl6qk/vOGUmS3cIf5ixn9a6qyBbYCpc3yKbSGgJBWUaqJRKkiY4vFIB+k6H/iVErsrzWx9wlu7jznRVkJ9l5YNooJvTJJDFGcxIJITquVKc1KoFatxQH950zilSnlTveWcGKnZWRLbAVbl+ITaW1Eqi1QII00fGFAjBqhnGJggqXj3eKdjJr7kryUoxFjsf1SZdlnoQQhyzVaaVneuQDtexkO/edM5KMRBt3vrOCpdsrIltgK4z1PmvxS6DWJAnSRMcX9Btrd+rI/5NXuHy89eMO7n5vJfnpTh44bxRjeqfjsEqAJoQ4PKkJ0QnUMpPs3HfuSLqlOPjjuytZsq0isgW2wusPsbGkFl9AArX9SZAmOrZQENDw0nnw5SMRLarS5WdO0U7ufW8VvTMTePC8kYzulSYBmhCi3UQrUEtPsPGnc0bSPc3BrPdWsiTGGTVfIMTG0hq8gditORqPJEgTHVvdagMRHt1Z6fYzZ8kO7nlvJb0yE7h/2igKeqRjt0iAJoRoX9EK1FKdVu45eyS5KQ5mzV3JshgHav6AZlNprQRqjUiQJjq2UMCYgkOHwBKZ0Z1VHj/vFu3knrmr6JGewAPnjqKgRxo2i/z7CCEiIzXBSn5a5Eespzqt3Hv2CKPpc+5Klu+I7WACCdT2JWcZ0bHVrTYAEZmCo8Yb4N2incx6byXd04xBAgU9JUATQkReeqKN7lFYQiotwca9Z48gO9nOH+fGftSnBGoN5EwjOra6OdIAbInteuhab4B3lxijOLuFR3EWSoAmhIiizCR7VNb/TU+wce/ZI8lMtPPHd1fGfB41f0CzsUQCNTnbiI4t6AeLA8ZcAt1GtNth3b4g7y/fxR/fXUF2sp2Hpo1idK90CdCEEFGXnWynW0rk1wHOSDQGE6QlWLnz3RUxX0IqEJRATc44omMLBcCeDOMuhdyR7XJIjz/IJyuLuXPOCtITbDwYnmZDAjQhRKzkpDjITo5OoHbP2SNIslu4453lMV+UPRDs2k2fctYRHVtdnzRXGQQDh304XyDEZ2v2cNvby3DaLMZEtb0zJEATQsRcbqqDjCRbxMvJSXZwz9kjsJpN3D5nOTvK3REvsyV1fdS64jxqcuYRHVsoALuXwUvTYPeSwzqUPxjiq/Wl3PrmcswmxYPhtTglQBNCxIv8NCdpCZFfHzgv1ck9Z41Aa7h9zjKKqzwRL7MldYFaV1uZQM4+omMLBSAQ/pVnSzjkwwRDmv9tLOPmN5biD4W479yRHD0gSwI0IUTc6ZHuJNkR+XWCe2YkcPdZw/H4Q9z29jLKarwRL7MlvkCoywVqcgYSHVvjKTgshxakhUKaxVvLuXH2UlzeAPeePZLJg3NkolohRFxSStErI4EEe+S/o/pmJfHHM4dT5Q7whznLqXT7I15mS7z+rrUouwRpomMLBcB/6FNwaK1ZsbOSm2cvpazWx11nDefk4d1kqSchRFwzmRR9MhNxWCN/Gh/ULZk/nD6M3VUe/vjuCly+w+//ezjqArVgSMe0HtHQ6rurlMqMRkWEOGihkLHSQN08aYcwme36PTXc/MYytu51cdtpQzltZJ4EaEKIDsFsUvTJSoxKt4yR+ancfOoQNpTUcO+8VTHvxO8JB2qhTh6oteWd/U4p9bpSaqpSkV5JTIiDEAr/mssdCROuBHvSQT18295afv/mMlbtquL6kwcxbUwPEmyR7+chhBDtxWo20ScrAbMp8qfniX0z+fWJg1i6vZKHPlod80yW2xdkc1nnDtTaEqQNAp4GLgLWKaX+pJQaFNlqCdEGoXDfiOzBUPhTY1LbNtpd5eb2t1ewcEs5/3f8AH5+RG8S7RKgCSE6HrvFTJ+syC/IDnDCkBwuP7Yf323cyxPz1xHSsQ2Qar1Btu51oWNcj0hpNUjTho+11j8FLgcuAf6nlPpcKXVkxGsoRHPqMmmuMqjaBbTtG6q81ss9767i87UlXHxEby4/th/JjsgPaRdCiEhJsFnolRmdQO3Mgu78dHxPPl29h39/vSnyBbai2hNg2153pwzUWk0dhPuk/Rwjk1YMXAu8AxQCrwN9I1g/IZoXCs9AvfA52PIV/G5Vqw+p9vh59ON1zF22i7ML8/n1SQNJjcKcQ0IIEWkpDiv5aU62R2Hy2Z9O6EWVJ8DbRTvJSLRxzugeES+zJZVuP6rcmDakM2lL+863wIvA2Vrr7Y3uX6iU+ntkqiVEG9Rl0gKecFNnyz8h3b4gz3y5kRe/28IJg3O4deoQMpMiv8yKEEJES3qiDX8wRHFVZOc0U0px+bH9qHD5ePbrzaQl2Dh+cE5Ey2xNhcuPxewmL/XgB5HFq7b0Sbtda3134wBNKTUdQGv9QMRqJkRr6oM0rxGktZDn9wVC/PeHrTw5fz1jeqVx99nDyUlpex82IYToKHJSHKQnRr6FwGxS/G7KYEbmp/KXT9exeGt5xMtsTWm1jz3VsV0doT21JUi7pYn7ft/eFRHioAXDAweCLWfSAsEQ85bt4r73V9MvO4n7p40kP71zpcSFEKKx/DQnSVFYlcBmMXHb1KH0ykjgvvdXsa64OuJltqa40sveWl+sq9Eumg3SlFI/UUo9AeQrpR5vdHkOiO1MdvEm4AVPJXiqGvpJtYdQnM+oHAqCay/4W+n/oDV4q8HnMq4fLq3BW9MwP5rfA1YHqAM/zlprvlxXyh/eXk5moo2HzxvFwJzkw6+DEELEsbpVCaIx2W2i3cJdZwwnxWHlj3NXsrMitguyA+wod1Ppiu3qCO2hpTB7J7AQOBNY1Oj+auC3kaxU3AuFoHKrEZwFPMaEqo1ZHMbs97YkY4JVsw1MrUyQGgyvQelzgd9lBD5BL5gsxvEs9oa/yhxu2lMNTXxBf0N9Al7jsSgjeLE4jcdZncbxtDbqXHeh7rZueC5KgTXBeNz+/G6oLQF3ecP+Zjs4UsCeAvZkCPqMoNVbBb6aRsc1Gce1JoTX2lTGvvWX8D+V2WaUbbaDxWbs56sxgjN/7b6vecEFDXXeT9G2Cm56Yylms+LB80YxqkcaMt2fEKIrqJvsdv2eGgLByI58zEi08cczh3PTG0u5850VPHTeKNISbBEtszXbyl2YTAkdevS+am3IqlLKorXuUJmzcePG6YULF0augFAQdi89uMcosxF4mK1GoBQK7HvZP9CLFyZLQ1BltoF7rxEstUgBMRgKnVsApoZfjZtKa7js+YXsqvDwyPRRnDIiLyoTPgohRDzx+INsKKmJSuPM6t1V3PbWcvpmJXLP2SNivoKLUtA/OwmnLb5XklFKLdJaj9v//paaO18LX/1RKbV0/8thVma6UmqFUiqklDqgUo32O1UptUYptV4p1VTfuI5DB41MmbfKCHS8VUbGLOiL3wANjADSWwU1u43sYasBGkQ9QCtZAzXF+2TS9lR7uOH1pWwureW204YwZXiuBGhCiC7JYTXTKyM6c6gNyU3hhpMHsba4mkc+XhPzVQm0hk2ltXgD7dgVKYpaaqz+dfjv6cAZTVwOx3LgXOCL5nZQSpmBp4CfAMOAnyqlhh1muaIzev8mKHqlPkircvu44+0VLNpSzjXHD+C8sT2xmiPfL0MIIeJVssNKXmp0RrQf2T+LX4ZXJfjnVxtjPslsMKTZXOrCH4zjhEgzmu2TprXeFb5qAnZprT0ASikn0O1wCtVarwofq6XdJgDrtdYbw/v+FzgLWHk4ZYtOyO+pXxLK4w/yyMdr+WDFbqaP7cGVx/WPebpdCCHiQWaSHW8gRFlN5Ec+nlnQnT1VHuYs2Um3FAdnF+ZHvMyW+AIhtpTV0i8rCVMHalVpS3rhdaBx+BkM3xdp+cC2Rre3h+8TooEOGYMkrE4CwRD//noTz3+zheMGZXPraUNlPU4hhGgkL9URlak5AH5xTF+O6p/Js19t4uv1pVEpsyVuX4gtHWydz7YEaRatdX3YHb7e6pANpdQnSqnlTVzOOpwKt1DeFUqphUqphSUlJZEoQsSjgDGrtrbYeWfJTh75aC0juqdw37kjSY/xyCIhhIg3dVNz2KMwNYdJKX43ZRBDcpN59OO1rNkd+znUajyBqCyb1V7a8i6VKKXOrLsRDrJaDYm11idprUc0cZnTxrrtAHo2ut0jfF9z5T2ttR6ntR6XnZ3dxiJEhxcO0rbWmLhzzgq6pTh45PwCuqd1nmVBhBCiPZlNit6ZCVEZTGW3mLnttGFkJNq4572VFFfFfjWACpef3ZWxr0dbtCVIuwq4VSm1VSm1DbgZuDKy1QLgB2CgUqqvUsoGXICxsLsQDaxO9hxxKzevGQQKHjpvFINzU2JdKyGEiGt2i5nemdEZ8ZnqtHLH6cPwB0PMmruSWm/sZ/UqqfZSVhPZ9U3bQ6tBmtZ6g9b6CIwRlkO11kdprdcfTqFKqXOUUtuBI4H3lFIfhu/vrpSaFy43AFwDfAisAl7TWq84nHJF57PXa+KKlQUsdOUw68zhHNEvM9ZVEkKIDiHRbiE/Sq0OPTMS+P1PhrKjws2DH66O+dQcALsqPVR54ntVgmYns1VK/Vxr/ZJS6ndNbddaPxrRmh2GuJzMVrQ7ly/AHz7Yxu6tazlpfAEXnXkKFplqQwghDsrOCndURnwCfLhiN08uWM/UkXlcNalfzFeAiZfJbpubzLalIR6J4b9NLXQY+xBYdGn+kOYv31WxfctGXrXfh6/nExKgCSHEIchLdeDxB6n1Rn7C11OG57Kzws2bP+4gP83BmQWxnbRBa9hcVkv/7CRslvg7h7Q0T9o/wlc/0Vp/3XibUuroiNZKiBaEQvD6siqeLvLwfzkBqAKbPbH1BwohhDhA3YjPDSW1+AKRn/D1kqP6sLPSzb++2kR+WgJje6dHvMyWBIK6PlCLt5Vp2hI2PtHG+4SIiq+31jDr61oGZZi4fGT4d4Y1IbaVEkKIDsxiNkVtIIFJKa6fMpjemYk8+OFqtpW7Il9oK7x+Y7LbeJtDraW1O49USl0PZCulftfochcgU7iLmNhQ5uGGT6pJtCoeOzmVVGt4lJA1OsudCCFEZ+WwmumZEZ0fvA6rmdunDsVmNnH33JVUx0EH/lpvkB0V8TWHWkuZNBuQhNEkmtzoUgWcF/mqCbGvcref33xUSblX89AJyQzNcUIgPNeNZNKEEOKwpTqtdEuxR6WsnBQHt04dSkm1lwc+WE0gDtbWLK/1s6c6fuZQa6lP2ufA50qp57TWWwCUUiYgSWtdFa0KCgHGmpy3za9gWUmQO45OYHLfJGNDryPg1PvAmRbT+gkhRGeRk+LA5QtS7Yn8fGZD81L4v+MH8JdP1/HPrzZx1XH9I15ma4orvdjNZlITrLGuSpv6pN2nlEpRSiUCy4GVSqkbI1wvIeoFQ/DU/6qYt8HPhcPtXFSYiqnuk5vUDXodCWZZAkoIIdpLz4yEqI12PGloN84uzOe9Zbt4f/muqJTZmm3lLly+2E+625Z3YFg4c3Y28D7QF7gokpUSorF3V1fz1GI3R+dbuPWYVKzmRj1by9bDlm+ISm9XIYToIuqWjorWV+vMo/owtnc6//hiI8t2VEan0BZoDVvKXDFvgm1LkGZVSlkxgrR3tNZ+ZJ40ESVFO9384YtqeqeYeGRKGon2/VroV8+DBX8CJEgTQoj25LCa6Zkenf6+ZpPixpMHk5vi4IEPVsdFv7BAUOPrAEHaP4DNGJPbfqGU6o0xeECIiCqu9vHbj41fVI9OSSE3pYkmzYDHGNkpmTQhhGh3qQlWspKj050k0W7h9tOG4g+G+NO8VXgDkZ9cN961Ze3Ox7XW+VrrqdqwBTg+CnUTXZjHH+TGTyrYUhVi1qRkRndv5tdcwAMWB6j4mylaCCE6g9wUB4n26My81SM9geunDGZjSS1PLlgfd/OWRVtLy0LVU0qdBgwHGk9GNSsiNRJdXjAEj3xbyRfbAlw12sGZQ5pamSws4AWLHWnuFEKIyKhbkWB9SQ3+QOSDpgl9M7hwYi9e+n4r/bOTOLswtktHxVKr6Qel1N+BGcC1GGfC6UDvCNdLdGFvrarin0UeTuht4TdHpNLikpwBD1ic0twphBARZDGb6JURvYEE08f15Mh+mfz7600UbauITqFxqC1tREdprS8GyrXWfwSOBAZFtlqiq/pxp5s7v6ihX5qJ+09Mx2FtJcV+1HVwzG+QTJoQQkRWgs1Cbmp0VncxKcVvThpIj/QEHvxgNburYj+QIBbaEqTVrZHgUkp1B/xAXuSqJLqq4hpjoIBZGUs+5SS1YSLB9N6QOUAyaUIIEQVZSXZSndGZ5DXBZuG2qUMJac19XXQgQVuCtLlKqTTgIWAxxkjPVyJYJ9EFeQNBbv6kgq1VIe4+LomRuc62PXDDfNi9TAYOCCFElOSnO7Fbo/Od2z3Nye+mDGZjaS1//WxDlxtI0JbRnXdrrSu01m9g9EUborX+Q+SrJrqKUAie+L6Sz7YG+GWBg9MHtzBQYH/f/hXWvI80dwohRHSYTSqq/dMm9M3ggvE9mb96Dx+s2B2dQuNEs6M7lVLntrANrfWbkamS6Go+2lDNXxd7ODrfwu+ObGWgwP4CbrDKwAEhhIgmh9VMfpqT7eXu1nduBz+d0It1e2p4+ouN9M1KZEhuSlTKjbWWTodntHA5PfJVE13B2lIPty6oIS/JxENT0lofKLA/mYJDCCFiIj3RRnpidPqnmZTi+imDyEyycf/7q6lw+aJSbqw1m0nTWl8azYqIrqfKHeC3H1Xi8mue+kkK3ZtaUaAloYBxsciKA0IIEQvdU524fUE8/sgvn5TssHLrT4Zy4+ylPPjhGu4+awRmU+f+7m82k6aUeqzR9V/vt+25yFVJdAX+kOaPX1SwojTIzUcmckSPxIM/SCA8JNsSnSHhQggh9mUyKXpGsX9av+wk/u/4/izbUcmL322OTqEx1FJz56RG1y/Zb9uoCNRFdBFawytLq3hjjY9pg238vDDl0P7BLU6Y/hwMPFlGdwohRIzU9U+LlhOGdOMnI3J5Y/EOvt9UFrVyY6GlM5tq5roQh2XxTjf3f1PL0Ewzd0xKw3qo6WqTGdL7gDNNmjuFECKG0hNtpCVEp38awC+P6Uf/7ET+/MnaTj3RbUtBmkkpla6Uymx0PUMplQFEZ6VV0emU1Pq54ZNKLCbFo1NSSXW2afnYprn2wrLXoWoX8jtCCCFiKz8tevOn2SwmbvnJUAAeeH81vkDk+8TFQkuvZiqwCFgIpGBMZLsofDmIiayEMPiCIf4wv4LNlSHumpTI0JzD7EtWvQu+fQoqt0omTQghYswU5fnTclMc/PakQawvqeGfX22MTqFR1tLozj5RrIfo5LSGZxdV8sEmPxeNsHPWkHaY42afgQMSpAkhRKw5rGbyUh3srIhOE+TEvplMG5PPG4t3MCwvhcmDc6JSbrRIb2sRFV9vreXRH9yMzjFzy9FpBzdhbXMCXuOvxSEDB4QQIk5kJtlJOZyuLAfpoiP6MLx7Ck8uWM/Wva6olRsNcmYTEbe7ysfN86tJsioePimNRHs7dWlsnEmT5k4hhIgbPdITsFqi871sNiluPHkwTquZBz5YjcffeRZilyBNRJTHH+TWBRXsqglxz3FJ9M+yt9/B/eEgzSrNnUIIEU/MJkXP9Oj1T8tMsvO7KYPYttfF0190nv5prQZpSqn+Sil7+PpkpdR1Sqm0iNdMdHihEDy7uIr5WwJcOsrBKQPbebzJgBPgp/+FhEzJpAkhRJxJtFvITm7HH+atGN0rnenjevLxqmIWrNkTtXIjqS2ZtDeAoFJqAPA00BP4z+EUqpSarpRaoZQKKaXGtbDfZqXUMqVUkVJq4eGUKaLvu201PLbQzZhuZn53xEEunN4WFgck54LJgmTShBAi/uQk20lory4ubfCzCb0Y3j2Fv362nm3lHb9/WltOmyGtdQA4B3hCa30jkHeY5S4HzgW+aMO+x2utC7XWzQZzIv4UV/u5ZUENiVbFgye2Yz+0xnYsgh9fNq5LJk0IIeKOUkazpylKnavq+qdZzSYe/GA13kDH7p/WlpfNr5T6KcbSUHPD9x3WtMJa61Va6zWHcwwRv7yBEHd8VsG2qhB/PDaJAe3ZD62xbf+DH180rsvoTiGEiEs2i4keaQlRK6+uf9rmMhfPfLkpauVGQlvObJcCRwL3aq03KaX6Ai9Gtlr1NPCRUmqRUuqKlnZUSl2hlFqolFpYUlISpeqJ/YVC8HxRFR+G50ObOjgpcoUFPGCpCwAlkyaEEPEqNcEa1WWjxvXOYNqYfD5csZsv1nbcmKDVIE1rvRK4GWPFAbTWm7TWD7T2OKXUJ0qp5U1czjqI+h2jtR4D/AT4P6XUpOZ21Fo/rbUep7Uel52dfRBFiPa0aKeLR//nYlS2mZuOSsVyqOtytkXAE57IFmnuFEKIONc9zRm1aTkAfj6xN0Nyk3nqs/XsruyY63u2ZXTnGUAR8EH4dqFS6p3WHqe1PklrPaKJy5y2Vk5rvSP8dw/wFjChrY8V0VdW6+f3C6qwmRQPnJhCkiPCkxn6JUgTQoiOItrTcljMJm44eTAKePijNQSCHW99z7Y0d96FERxVAGiti4B+EatRmFIqUSmVXHcdOBljwIGIQ75giD99Vcn68hC3HpXAkGxn5AsNeMPNnRKgCSFERxDtaTm6pTi45oSBrCmu5uXvt0at3PbSpoEDWuvK/e47rHBUKXWOUmo7Rl+395RSH4bv766UmhferRvwlVJqCfA/4D2t9QeHU66IDK1hzsoa3ljj4+yBVqYNT4nOL6Upf4TTH5UsmhBCdCA5yXactuhNy3HMgCxOHtaNNxZvp2hbRdTKbQ9tCdJWKKV+BpiVUgOVUk8A3xxOoVrrt7TWPbTWdq11N631KeH7d2qtp4avb9RaF4Qvw7XW9x5OmSJyVpe4ufebWvqmmrj92DSs5mjlsu1gS5KRnUII0YEopeiZ4Yzq7+vLj+1HfrqTRz9eQ6XbH72CD1Nbzm7XAsMBL/AKUAX8JoJ1Eh1IldfPbQuqcAc0fzo+mayk6I3eoehlWP8J0twphBAdi91ipntaFLrFhDmsZm46ZTA13gCPfbIWrXXUyj4cbRnd6dJa36a1Hh8ePXmb1rpjDpMQ7cof0jz5fTWLi4NcM9bJhB6J0a3AqneNudKkuVMIITqcjEQbyZEeYNZI36wkfnF0XxZuKeedJTujVu7haPbVUUq9izFPWZO01mdGpEaiw/h8Yw3/WuLh2B4WLhuT3P7LPrVGBg4IIUSHlp/uZF1xDcFQdDJbp43M48etFTz3zWZG9Uijb1aUkwsHqaUQ9uGo1UJ0ODsqvNz1ZS0ZDsXdk1NIsEXv11A9vxssTsmkCSFEB2U1m8hPc7J1b3TW2VRKcd2JA7nmlcU8/NEaHj2/ALsleoMYDlazZ1at9efRrIjoONz+IH/6uood1SEem5JE73RH9CuhdUMmTQYOCCFEh5WaYCXNY6XCFZ0O/alOK785cRB3vbuC577ZzJWT+kel3EPRlslsByqlZiulViqlNtZdolE50QTXXgjFbsHYYAhmr6jmvQ1+zh9i4yeDkmKTyAr6jL9WB9LcKYQQHVu0VyMY2zudMwu6M3fpLhZu2Ru1cg9WW1IQ/wb+BgSA44EXgJciWSnRjK3fwcvTYfalsPPHmFRhZbGLh75zMzDdxI1HpWKLeke0MIsdLp8Poy6Q5k4hhOjgzCZFfhRHewJccmQfemck8JdP11Hh8kW17LZqyxnWqbX+FFBa6y1a67uA0yJbrU6sYhts+Qb0Qc4HXLwCPr4T0nsbzXtmm3F/wGM0/UVBucvPXV9W4w1q/jipjdNt+Gph42fw5SOw9FWjvu1FKTCZkUyaEEJ0fMkOKxlJtqiVZ7MYy0bVegM8Pn9dXE7L0Zbe3l6llAlYp5S6BtgBJEW2Wp2Irwa2L4TckZCQCbuXwRcPQl4BTL4FkvNaP0bldvjgFkjMgtMeAUdqQz+sb56EsvUw9HTIHQWpPSOSWfIGQjyzqIpFu4P8ZpyD8T0TWn7A9oVQ9B/YtQR00Fhj02KH4ecY2117wZneel1rS2Dxi0awd8LtDfvXlsDCf8Ows6DH+MN/gkIIIWIuL8VBjSeALxCddTb7ZCUy86g+PPPlJuYt381pI9twTo6itgRpvwYSgOuAu4ETgEsiWalOwVcD3/0d1rxvBCnH/NYIKPpNNgKORf+G2b+AI6+BwVNbDlaScqD/iTBqhhHYNJY7AnYshC/Cg3Gd6TDwZDjiV8btbd8bf5XZyDolZB50IKc1fLe1lmeWeDmiu4VfjEnGatrv8Z4KWD8fek6A1B5GnzH3Xhh1PvQ6EroNM14Ts83IIs65GqyJMHCK8Zj0vvvWyVNlBHkr3jSOdcIf9t3uLoc186D3UW1+HkIIIeKbyaToke5kY0lt1Mo8fVR3Fm0p59mvN1HQI5Ue6a0kIaJIHUx6TymVDlToeMwJNjJu3Di9cOHCyBUQCsLupc1v3/INfPWokS0aeib0P8EIUkyNYuLq3fDZ/bCrCMZdBmMuOvA4nvCSqY7UluujNVRuM+q0axmk5sOYi41t/zq5oZN9naFnwrG/Mx5XvskI7ErWQuka2LMaxl0KWQOhZg/UlrDT3p+fv1PBXrfmP2enMqxb+ANclyVc97HRX04HjeBw1Azj2M0FgqEArPkAVr0DpWuN+xKzYPzlMOgUWP+p8fr5XEbAOXYmpIR/3fhcYEuAXUvh3etg6sMw4CTI6NvyaySEEKLD2Fnhpqwmev3Eymq8XPvKj+SmOnhw2igs4f7W/XMSozLFlFJqkdZ63P73tzSZ7R3Aa1rr1UopO/ABUAAElFI/01p/ErnqdnCr3wNbMky5B3KGNL1Pcq6xOPjKOdB3knHfireNZY7sKWBPNpoxTWY4+2/hvlfNUArSehmXIafvu+2Mx43gKRQ0/lbthJR8Y1vVDiOb13AgSOvZEByufBuK/oOy9uIY1/GMnjCJwVY/0Ms43sszwF8LzgwYOc0IqDIHNNSpOSaL0Tw79HQjENz+g5Hxc6QY21PyIK8Qxl8GGf0aHrf8DVjyX5j2TEPfNotDBg4IIUQnk5vioDqKzZ6ZSXauPn4AD3ywmtcWbuNnE3tHpdzWNJtJU0qtAEZorbVS6grgp8BJwCDgea31hOhV8+BEPJPmrYanjzMW97Ymgi3R6G9VcIHR1OepAqsTzAe5juXq94wgzVttXEJBOObX0OfYyD2PLd8aTZVZAyFrkPFcwgKeWlZ88x6mtfMYadps3Jk5AKb907i+9kOjKTZ35L5ZwkgpXQdvX200jw46BT6+w6hLjwnGgAohhBCdRq03ENVmT4BHPl7DF2tLeOi8AgZ1S47fTBrga9SseQrwX611EFillIrB9PJxJBiApFyjua92D5TXGv3MLA446pqGjNDBGnKacYkWezIMOrnZzZtrTFy56ViwHsubk0vpXrPCCOTqDDolCpVsJGsgTLwCvn3KWG3A6pRMmhBCdFKJdgtZyTZKq6PX7HnlpP4s31HFox+v5bEZhVErtzktBVtepdQIoBhjfrQbGm2Ln151seBIgVPujXUtIqrSHeCR72rY49L85aQkcvvmgWlkrKsFI6YZzaM7i+CcvxuZS5mCQwghOqVuyUazp9cfnWbPJLuF35w0kNvfXs5z32zmoemjolJuc1qaJ+3XwGxgNfBnrfUmAKXUVCA2M6mKqPAFQ8xZXc37G41VBU7qn4gpXlZeUiY47hZjsMPejQ33CSGE6HTqRntGU0GPNM4q6M57y3bx1brSqJa9v5bW7vweOKDXu9Z6HjAvkpUSsaM1rC528+j/PPRLM3Hd+CSctjhbfDYhA85/wegHCNLcKYQQnViCLfrNnhcf2YfF2yq47e3lTOybSWrCQfYxbyddu2+ZOMDeWi8PfldLrV/z6ElJ5KbEYPH0tqgL0ABp7hRCiM6tW7KDKnf0RnvaLCaunzKI3ZVukh2xC5WknUjUc/uDzF7p4qvtAS4ZaefIXgnx08zZEsmkCSFEp2YyKfKj3OzZPzuJi4/qg2n/ydujqCOcgkUUhEKwYrebJxa5GZZp5pejE3Fa46yZs1kSpAkhRGeXZLdEdW3PeNBqkKaUmq6USg5fv10p9aZSakzkqyaiaU+1mwe/c+EPwa1HJ5CTFKfNnE2RgQNCCNEl5KY4sFq6zg/ztpzd/qC1rlZKHYMxme2/gL9Ftloimmp8AV5f5eZ/uwJcVmBnbPcO0sxZR5o7hRCiSzCbFPlp0W32jKW2nIqD4b+nAU9rrd8Dula+sRMLhDRLd7r522IPBTlmLhmVGH+jOVslQZoQQnQVyQ4raTEabRltbQnSdiil/gHMAOaF1/HsSHkW0YLiKg8Pfe9CAzcfmUB2R2rmrCOZNCGE6FK6pzmxmDv/d39bgq3zgQ+BU7TWFUAGcGMkKyWio8od4L8r3PxYHOTKQgcFec6O1cwphBCiSzKbFN1TO3+zZ1tOyQ7gM6BMKZUBeIEFkayUiDx/ULN0t4tnlngYm2vmguFOEqOwiGxESCZNCCG6nNQEKynODnreaqO2BGmLgRJgLbAufH2zUmqxUmpsJCsnImd3pZuHv3djAm6cmEB2cgf+RSKjO4UQokvqnta5W4Da8tQ+BqZqrbO01pnAT4D3gKuBv0ayciIyyl1+Zq/2ULQnyBWFDkbkOTF36A+5ZNKEEKIrsppN8bsyTjtoy6n5CK31h3U3tNYfhe/7DrA3/zARj7yBECuL3Txd5GF0NzPnD3OS1FGbOetIc6cQQnRZmUl2EuwdbVaCtmlLkLZLKXWzUqp3+HITUKyUMgOHtIiWUuohpdRqpdRSpdRbSqm0ZvY7VSm1Rim1Xil1y6GUJRpoDcWVbh75nxsN3DDBSXan+AUiQZoQQnRl+WnOTvl7vS1B2s+AHsDb4Uuv8H1mjJGfh+JjYITWehRGX7ff779DOAh8CqN5dRjwU6XUsEMsTwDlLh9vr/WyuDjI5QUORuQmYI3hmmTtpjP+ZwohhGgzh9VMTnLna9xrtZ1La10KXNvM5vWHUmi4ybTOd8B5Tew2AVivtd4IoJT6L3AWsPJQyuzqPP4Qa0o8/L3ImLR2+lAHqZ1lVIwMHBBCiC4vO9lOhduP139IjXxxqdWztFJqEHAD0Kfx/lrrE9qpDr8AXm3i/nxgW6Pb24GJzR1EKXUFcAVAr1692qlqnYPWUFzl5s8/eAiF4PrxTnJTO0MzZx3JpAkhRFenlLFk1MaS2lhXpd20JZXyOvB34J80LBHVKqXUJ0BuE5tu01rPCe9zGxAAXm7rcZujtX4aeBpg3Lhx+nCP15mU1fp4f4OP/+0K8KvRdkbkObB17OGc+5LmTiGEEECi3UJ6opXyWn+sq9Iu2hKkBbTWB72gutb6pJa2K6VmAqcDJ2qtmwqqdgA9G93uEb5PHAS3P8imvR7+utjD0Ewz5w91kO7sbEuvSpAmhBDCkJvioModIBjq+PmatqRT3lVKXa2UylNKZdRdDqdQpdSpwE3AmVprVzO7/QAMVEr1VUrZgAuAdw6n3K4mFII91V6eWuzBHdBcP8FBbqqj8yWeOt0TEkIIcagsZhN5naRLT1syaZeE/zZer1MD/Q6j3Ccx5lj7WBkn2O+01lcppboD/9RaT9VaB5RS12CsG2oGntVarziMMrucMpeXz7d4+WxrgEtG2CnIdeC0dsK5ZGTggBBCiEbSE22Uu3zUetvcSysutWV0Z9/2LlRrPaCZ+3cCUxvdngfMa+/yuwK3L8iOCh+PL/TQN9XEhcPtZCR2vuHJBsmkCSGE2Fd+upN1xTU02aGqg2g1BaGUsiqlrlNKzQ5frlFKWaNROXFoQiEorvbyzBIPez2a6yc4yUt1dPCln1ogzZ1CCCH2Y7d0/LnT2nLa/hswFmOdzr+Grx/0QAIRPWUuLwt3+nhvg59zB9kYk2cn2dFJ5kRrigRpQgghmpCdbMdu7bgZiracucdrrQsa3Z6vlFoSqQqJw+P2BSmu8vHoDx7yEhUzR9rJ7uC/JFomAZoQQoimKaXonuZkUwedO60t4WVQKdW/7oZSqh8HMV+aiJ5QCPbUeHl5pZedNSF+M95J9zQ7VnMnDmQkiyaEEKIFSXYLaQkds5dWWzJpNwILlFIbMdIWvYFLI1orcUjKXF7WlPp5bZWPk/taObKHrRPOibY/CdKEEEK0LDfVQZXHT6iDrRjVltGdnyqlBgKDw3et0Vp7I1stcbDc/iBlNUYzZ5JNcWWh0czZ6RNNMv2GEEKIVljNJrqlONhV4Yl1VQ5Ks2c4pdR4pVQuQDgoKwTuBh463MlsRfuqm7T23fV+VpcF+dVoBz1SbZ1zTrT9dfooVAghRHvISrLjtHWsH/Yt1fYfgA9AKTUJuB94AagkvEamiA97XT62VwZ4dqmHcblmpvSxkpnUmQcLNCZBmhBCiLbpnuaMdRUOSktBmllrvTd8fQbwtNb6Da31H4AmJ6MV0efxhyiv9fLEIg9aw3XjnGQlO7CYukjwIpk0IYQQbZRgs5CR1HH6arcYpCml6vqsnQjMb7StE0+61XFoDXuqPXy5PcB3OwNcPNJO3zQrqc6u9PZIkCaEEKLtclMcmDtIIqOls/krwOdKqVLADXwJoJQagNHkKWKs3OVjryvIk4s8DEg3MW2QjZyUrtLMGSYDB4QQQhwEs0mRl+pge7k71lVpVbNBmtb6XqXUp0Ae8JHW9atfmYBro1E50TxvIMTeWh/PLvNQ4dXMmpRARqINu6WLBS3S3CmEEOIgpSfaKKv14fbF97SvLbaLaa2/a+K+tZGrjmirPVVeVpUFeXedn7MG2RieZenEC6i3RII0IYQQBy8/zcn6PTWxrkaLuljapXMod/mp9QV47Ac3GU5j6aesJHvnXUC9JZJJE0IIcQicNnPcDyLoiqf1Ds0XDFFW4+WttT42VIT4vzEOshIsnXsBdSGEECICuiXb43oQgQRpHUxJtY/i2hDPL/cysbuFY3tYOvkC6q2QgQNCCCEOkcVsIjfVEetqNEvOcB1IlTuAyxfgqcXGnGjXjHGQltAFBws0Js2dQgghDkNGog2nLT5X6OnCZ/eOJRDSlNZ4+Wa7n292BLhohJ0eKeYuOligMQnShBBCHJ7uafGZTZMgrYMoqfZS4wvx5GIPfVNNTBtsIzOxiw4WaEwyaUIIIQ5Tgs1CeqI11tU4QFc/xXcINb4ANd4AL63wUuLS/Hq8gySbhZQutbJAcyRIE0IIcfhyUxyY4iwqirPqiP2FQkYWbXNlkDfW+Di1n5XhWV18sEBjkkkTQgjRDixmEznJ8dXsKUFanCur9eIPhHh8oYcEq+KXBXZSnVYcVnnrABndKYQQot1kJdmwx9H5NX5qIg7g9gepcPv5ZLOfZSVBfllgJ8NhJiMxviffiy7JpAkhhGgfShnresYLCdLilNZ1gwU0Txd5GZJp5tR+VjISbVjieOK9qJPmTiGEEO0o2WGNmz7fEqTFqXK3D28gxL+XeajyaX49zoHTaibVGX+jT2JLgjQhhBDtKzfVERc5AAnS4pAvGGJvjY+1e40F1M8caGNAupmsJHtcfGjiirwgQggh2pndYo6LAXrxkc8T+9hT5SWoNU8sdJPuUMwcYSfJbiEhTmdEjikZOCCEECICspPsBEI6pnWQM1ycqXIHcPuDvL/Bz+q9Ia4sdJBkU2QlxT6ij0+SSRNCCNH+TCaFLcbLLkqQFkfqln6q8ob411Ivo7LNHN/bQnqiDatZgpEmSXOnEEKITiomzZ1KqYeAMwAfsAG4VGtd0cR+m4FqIAgEtNbjoljNqCutMZo5/73MS61fc81YB1aLiXSnTLkhhBBCdDWxyqR9DIzQWo8C1gK/b2Hf47XWhZ09QHP5glR7AqzdG+S99X7OHmijb5qZrER73C1TEVckkyaEEKKTisnpX2v9kdY6EL75HdAjFvWIF1pDSY2XkNY8schDmkNx8Qg7DquZZIeM7WiZBGlCCCE6p3jI0fwCeL+ZbRr4SCm1SCl1RUsHUUpdoZRaqJRaWFJS0u6VjKRylw9fIMRHm/ysLgtyeYGdRJsiWwYLtE5GdwohhOikIpamUUp9AuQ2sek2rfWc8D63AQHg5WYOc4zWeodSKgf4WCm1Wmv9RVM7aq2fBp4GGDduXGzHzB4EXzDE3lof1T7NP5d4GZ5l5qQ+VpIdFlmfsy2kuVMIIUQnFbEgTWt9UkvblVIzgdOBE7XWTQZVWusd4b97lFJvAROAJoO0jqqk2ocGnl/modpnDBYwKUWmZNHaSII0IYQQnVNMUjVKqVOBm4AztdauZvZJVEol110HTgaWR6+WkVftCeDyBdhQHuTd9X5OH2BlQLqxgLpV1udsG8mkCSGE6KRi1Z72JJCM0YRZpJT6O4BSqrtSal54n27AV0qpJcD/gPe01h/EprrtLxgyptzQWvPUYg9JVsUlIxxYzIo0mXLjIEiQJoQQonOKydBBrfWAZu7fCUwNX98IFESzXtFUVuslENJ8tjXAspIgvxnnIMWuZMqNgyUDB4QQQnRScoaLAbc/SKXbjzugeabIw4B0E6f2s+KwyJQbB02aO4UQQnRSEqTFQGm1F4BXV3opcWv+b4wDs0mRlSTNnAdPgjQhhBCdkwRpUVbh8uMJhNhVE+K11T5O6G1hRLaFJLsFp80c6+p1PJJJE0II0UlJkBZF/pCmrNbIov3jRw9mE1xe4EABWTLlxiGSIE0IIUTnJEFaFJXVeAlpWLQ7wNc7AvxsmJ2sBBOpCVasZgk2DokMHBBCCNFJyRkuStzhBdQDIc1fF3vIS1RMG2zDrBQZCZJFO2TS3CmEEKKTkiAtCuoWUAd4Z52PrVUhrhrjwGZWpCfaMMu7cOgkSBNCCNFJSXgQBRVuP95AiEpviBeXexnTzcyR3S1YzSbSnNZYV68DkwBNCCFE5yVBWoT5Q5qycBbt+WVeXAH41RgHSikyk2ySCDoc8uIJIYToxCRIi7DSai8a2FQR5L0NxvqcfVLNOKxmku0yce3hkSBNCCFE5yVBWgTV+gLUeANorfnbjx4SrXDJCGOQgExc2w4kkyaEEKITkyAtQrSG0hofAN/uDPBjcZCLRthJsZtIdlhwWmXi2sMm028IIYToxOQsFyEVbj++QAhfUPOPH730SjFxxgAbCshIlCxa+5BMmhBCiM5LOkVFQOPBAnPW+dhZE+JPxyVgMSlSnVZsMudG+5DmTiFEG/j9frZv347H44l1VUQX53A46NGjB1Zr22Z2kCAtAspqjMEC5Z4QL63wMiHPwvg8CyYF6ZJFa0cSpAkhWrd9+3aSk5Pp06cPSn7ciRjRWlNWVsb27dvp27dvmx4jKZ125gqvLADGlBveAFw12hgskJ5ow2KSL4h2I1+2Qog28Hg8ZGZmSoAmYkopRWZm5kFldCVIa0fGYAGjmXNTRZD3N/o5Y6CNnilmLGZFmkOyaO1KBg4IIdpIAjQRDw72cyhnuXZUt7KA1pq/h6fcuGi4kUXLSLBjkle7ncmXrhCiY7j33nsZPnw4o0aNorCwkO+//77F/WfOnMns2bMPq8zPPvuM1NRUCgsL6y+ffPIJAElJSYd0zMceewyXy1V/e+rUqVRUVDS7/9///ndeeOEFAJ577jl27tx5UOVNnjyZwYMHU1BQwNFHH82aNWsOqd515V9zzTUH1Kspmzdv5j//+U/97YULF3LdddcdctmHSvqktRN/SLO31sii/bArwOLiIL8abSfFrrBbTKQ65aVud/LLWAjRAXz77bfMnTuXxYsXY7fbKS0txefzRaXsY489lrlz57bb8R577DF+/vOfk5CQAMC8efNa3P+qq66qv/7cc88xYsQIunfvflBlvvzyy4wbN46nn36aG2+8kXfeeWef7cFgELP54Ka1alyvptQFaT/72c8AGDduHOPGjTuoMtqD5HbaSVmNl5CGQEjzjyIv3ZOMKTcAMmTiWiGE6LJ27dpFVlYWdnt4MvOsrPpAZdasWYwfP54RI0ZwxRVXoLU+4PGLFi3iuOOOY+zYsZxyyins2rULgMcff5xhw4YxatQoLrjggkOqW01NDSeeeCJjxoxh5MiRzJkzB4Da2lpOO+00CgoKGDFiBK+++iqPP/44O3fu5Pjjj+f4448HoE+fPpSWlgLwwgsvMGrUKAoKCrjooosAuOuuu3j44YeZPXs2Cxcu5MILL6SwsJD33nuPs88+u74eH3/8Meecc06LdZ00aRLr168HjEzg9ddfT0FBAd9++y0vvfQSEyZMoLCwkCuvvJJgMAjAv//9bwYNGsSECRP4+uuv649VVy+A9evXc9JJJ1FQUMCYMWPYsGEDt9xyC19++SWFhYX8+c9/5rPPPuP0008HYO/evZx99tmMGjWKI444gqVLl9Yf8xe/+AWTJ0+mX79+PP7444f0njQm6Z124PY3DBaYt8HP1qoQdx3jxGpWOK1mkmzyMkeEZNKEEAfpj++uYOXOqnY95rDuKdx5xvBmt5988snMmjWLQYMGcdJJJzFjxgyOO+44AK655hruuOMOAC666CLmzp3LGWecUf9Yv9/Ptddey5w5c8jOzubVV1/ltttu49lnn+X+++9n06ZN2O32Zpsc6wKNOm+88Qb9+/evv+1wOHjrrbdISUmhtLSUI444gjPPPJMPPviA7t2789577wFQWVlJamoqjz76KAsWLCArK2ufclasWME999zDN998Q1ZWFnv37t1n+3nnnceTTz7Jww8/zLhx49Bac/3111NSUkJ2djb//ve/+cUvftHi6/zuu+8ycuRIwAgiJ06cyCOPPMKqVat44IEH+Prrr7FarVx99dW8/PLLTJkyhTvvvJNFixaRmprK8ccfz+jRow847oUXXsgtt9zCOeecg8fjIRQKcf/99/Pwww/XZyE/++yz+v3vvPNORo8ezdtvv838+fO5+OKLKSoqAmD16tUsWLCA6upqBg8ezK9+9as2T7fRFIke2kFptZG2rvFpXljupSDHzFH5xkubKVm0yJGBA0KIDiApKYlFixbx5ZdfsmDBAmbMmMH999/PzJkzWbBgAQ8++CAul4u9e/cyfPjwfYK0NWvWsHz5cqZMmQIYTXt5eXkAjBo1igsvvJCzzz57n6xUY601d2qtufXWW/niiy8wmUzs2LGD4uJiRo4cyfXXX8/NN9/M6aefzrHHHtvic5w/fz7Tp0+vD94yMjJa3F8pxUUXXcRLL73EpZdeyrfffttsH7ELL7wQp9NJnz59eOKJJwAwm81MmzYNgE8//ZRFixYxfvx4ANxuNzk5OXz//fdMnjyZ7OxsAGbMmMHatWv3OXZ1dTU7duyoz+I5HI4W6w3w1Vdf8cYbbwBwwgknUFZWRlWVEfifdtpp2O127HY7OTk5FBcX06NHj1aP2RwJ0g5TpTuAJ2CkVV9Z6aXKq7my0IFSiiS7LP8UWZJJE0IcnJYyXpFkNpuZPHkykydPZuTIkTz//PNccMEFXH311SxcuJCePXty1113HTA9g9aa4cOH8+233x5wzPfee48vvviCd999l3vvvZdly5ZhsRzcaf3ll1+mpKSERYsWYbVa6dOnDx6Ph0GDBrF48WLmzZvH7bffzoknnlif8Wsvl156KWeccQYOh4Pp06c3W/e6PmmNORyO+n5oWmsuueQS7rvvvn32efvtt9u1vm1R16QNxnseCAQO63iSijgMwRD1Kwvsqgnx1lofU/paGZhhluWfokGaO4UQHcCaNWtYt25d/e2ioiJ69+5dH5BlZWVRU1PT5GjOwYMHU1JSUh+k+f1+VqxYQSgUYtu2bRx//PE88MADVFZWUlNTc9B1q6ysJCcnB6vVyoIFC9iyZQsAO3fuJCEhgZ///OfceOONLF68GIDk5GSqq6sPOM4JJ5zA66+/TllZGcABzZ1NPbZ79+50796de+65h0svvfSg617nxBNPZPbs2ezZs6e+7C1btjBx4kQ+//xzysrK8Pv9vP76603WqUePHvUBndfrxeVyNfs8wchOvvzyy4DRDJqVlUVKSsoh178lkkk7DHtrvQTDnTz/ucSDWcGlI40oOtlhwW6RGDiyJEgTQsS/mpoarr32WioqKrBYLAwYMICnn36atLQ0Lr/8ckaMGEFubm59c11jNpuN2bNnc91111FZWUkgEOA3v/kNgwYN4uc//zmVlZVorbnuuutIS0s74PH790m7/fbbOe+88+pvX3jhhZxxxhmMHDmScePGMWTIEACWLVvGjTfeiMlkwmq18re//Q2AK664glNPPZXu3buzYMGC+uMMHz6c2267jeOOOw6z2czo0aN57rnn9qnLzJkzueqqq3A6nXz77bc4nU4uvPBCSkpKGDp06CG/vsOGDeOee+7h5JNPJhQKYbVaeeqppzjiiCO46667OPLII0lLS9vndWjsxRdf5Morr+SOO+7AarXy+uuvM2rUKMxmMwUFBcycOXOfvmx1AwRGjRpFQkICzz///CHXvTWqqZEkHd24ceP0woULI1dAKIh3exHb9rrQwMrSAL/+xMVFw21cPNKBAnpnJWKV1QUiKzEbUg+9rV8I0TWsWrXqsIIAETnXXHMNo0eP5rLLLot1VaKmqc+jUmqR1vqAOT4kk3aISmt8aIy28H8UeclwKKYPMbJoqQlWCdCiQl5jIYToqMaOHUtiYiKPPPJIrKsStyRIOwTBkMblMzoDfrU9wMrSIL8d78BpVZiUsbqAiAIZ3SmEEB3WokWLYl2FuCdnucPgD2r+ucRLn1QTp/Q15kFJT7Rhllc1OmTggBBCiE4sZuGEUupupdRSpVSRUuojpVST60QopS5RSq0LXy6Jdj1bMneDj501IS4vsGM2KSwmWUQ9uiRIE0II0XnFMufzkNZ6lNa6EJgLHDABi1IqA7gTmAhMAO5USqVHtZbNqPFpXlruY3Q3M+PzjFbj9ASbLKIeTZJJE0II0YnFLKTQWjdelyMRaGqY6SnAx1rrvVrrcuBj4NRo1K81r6z0Uu3TXBGeuNZqNpHqPPSlH8ShkCBNCCFE5xXTvI9S6l6l1DbgQprIpAH5wLZGt7eH72vqWFcopRYqpRaWlJS0f2Ub2VHu5q21Pk7qY2VAujHjcWaiTRI70SYDB4QQca6srIzCwkIKCwvJzc0lPz+//rbP52uXMiZPnszgwYPrj1s3D1rjRcQPRlFREfPmzau//c4773D//fe3+JijjjoKgM2bN/Of//znoMsUTYvo6E6l1CdAbhObbtNaz9Fa3wbcppT6PXANRtPmIdFaPw08DcY8aYd6nLZ45JN1KAUzwxPX2i0mkh0yUDbqJCoWQsS5zMzM+sW377rrLpKSkrjhhhvqtwcCgYNeyqkpTS2ddKiKiopYuHAhU6dOBeDMM8/kzDPPbPEx33zzDdAQpP3sZz9rl7p0dRFNRWitT9Jaj2jiMme/XV8GpjVxiB1Az0a3e4Tvi5lgSGM1m5g+2EZOovHypcvyT0IIIdqobub9iRMnctNNNx2Q8RoxYgSbN28G4KWXXmLChAkUFhZy5ZVXEgwGD6nMZ555hvHjx1NQUMC0adNwuVwAvP7664wYMYKCggImTZqEz+fjjjvu4NVXX6WwsJBXX32V5557jmuuuQaA4uJizjnnHAoKCigoKKgPzpKSkgC45ZZb6lc5+POf/8ykSZPqg1SAY445hiVLlhzSc+iKYpb+UUoN1FrXLWZ2FrC6id0+BP7UaLDAycDvo1G/5phNigfOHcGGZcYH02Exk2yXLFpMSCZNCHEo/n3agfcNPxsmXA4+F7w8/cDthT+D0RdCbRm8dvG+2y5976CrsH37dr755hvMZjN33XVXk/usWrWKV199la+//hqr1crVV1/Nyy+/zMUXX3zAvhdeeCFOpxOAKVOm8NBDD+2z/dxzz+Xyyy8HjKWh/vWvf3Httdcya9YsPvzwQ/Lz86moqMBmszFr1iwWLlzIk08+CbDP8k7XXXcdxx13HG+99RbBYPCA9ULvv/9+Hn74YebOnQtARkYGzz33HI899hhr167F4/FQUFBw0K9XVxXL6OJ+pdRgIARsAa4CUEqNA67SWv9Sa71XKXU38EP4MbO01geu2hoDKhwgZCZJFi12JEgTQnRM06dPx2w2t7jPp59+yqJFi+rX9HS73eTk5DS5b2vNncuXL+f222+noqKCmpoaTjnlFACOPvpoZs6cyfnnn8+5557bar3nz5/PCy+8AIDZbCY1NbXF/adPn87dd9/NQw89xLPPPsvMmTNbLUM0iFmQprVuqnkTrfVC4JeNbj8LPButeh0Mp9VMgq3lf7LDYnFAwBO543d0FkesayCE6IhaynzZElrenph5SJmzAw6TmFh/3WKxEAqF6m97PMb3vtaaSy65hPvuu++wy5s5cyZvv/02BQUFPPfcc3z22WcA/P3vf+f777/nvffeY+zYse2+CkBCQgJTpkxhzpw5vPbaa7LKwEGS4XGHIeJZtNQeYJJpPZpkcYJVgjQhRMfXp08fFi9eDMDixYvZtGkTACeeeCKzZ89mz549AOzdu5ctW7YcUhnV1dXk5eXh9/t5+eWX6+/fsGEDEydOZNasWWRnZ7Nt2zaSk5Oprq5u8jgnnngif/vb3wAIBoNUVlbus72px/7yl7/kuuuuY/z48aSnx8VUpx2GBGmHKMluwWmNYBZNmcGWBIlZkSujI3PKP7oQonOYNm0ae/fuZfjw4Tz55JMMGjQIgGHDhnHPPfdw8sknM2rUKKZMmcKuXbuaPMaFF15YPwXHSSeddMD2u+++m4kTJ3L00UczZMiQ+vtvvPFGRo4cyYgRIzjqqKMoKCjg+OOPZ+XKlfUDBxr7y1/+woIFCxg5ciRjx45l5cqV+2wfNWoUZrOZgoIC/vznPwPGQuopKSlceumlh/U6dUVK64jOVhET48aN0wsXLozY8YOBAIGdS7BbIhjjOlIhox8EA7BnBehQ64+JFxYnBNyRLSNnGFhkIXshROtWrVrF0KFDY12NLmvnzp1MnjyZ1atXY5JleZr8PCqlFmmtD+hUKK/WITCbVGQDNAB7SrgwSwfLGinI6BvZ/mLWRAnQhBCiA3jhhReYOHEi9957rwRoh0BesXhlT264ntj0aJ64lJBhBFCRrHOHClqFEKLruvjii9m2bRvTpzcxrYlolQRp8chs3zdTZHU0ZNbimoKk8AITznQwRWjwsDMtMscVQggh4ogEafGocRatTmJ2OxxYGQMSIiUxCyzhEa8mEyREYNCDLRnMMuJVCCFE5ydB2qFQpsgGO44msmaOFKND/uFIzoPswZHpL6ZMkNRt3/sSs2n3CWelqVMIIUQXIUHaoVDq0KfGUCajObP5HYxsUVOayqbZkiFrcOvNofYUSO5mNKNmDTJGj7anhKwDM1xmi9FHrd2o9q+3EEIIEackSDtUiTlGwNVmyghkcoZBeu/md7MlGU2FTWncz8tsh/S+kDXAmCE7va/x2KaYrJDWqEyT2Zjeo67/2OFS5gOzaHVaaqY9qNcPoxnYLOukCiE6nr/85S+MGDGC4cOH89hjj9Xff9ddd5Gfn18/x9m8efMA+Prrrxk1ahTjxo1j3TpjmeuKigpOPvnkfVYnaMzv93PLLbcwcOBAxowZw5FHHsn7779/SPUtKSlh4sSJjB49mi+//JKpU6dSUVFxwH77Lw4fLY0XfW/O5s2b+c9//hOV+tQtMN/e5Ix3qMwWI+iq3dP6vo40o6mxboZ8s9W4z1Nx4L5N9UerY2rUpJiYve8C4yaTEXiVrttvjjIF6X2aDm5SwnWq2AY62PrzaE5idvPBk9VpZPG8Vfveb7YZgWXQBxVb2jYPnDR1CiHawbLtla3vdBBG9mg5w798+XKeeeYZ/ve//2Gz2Tj11FM5/fTTGTBgAAC//e1vueGGG/Z5zCOPPMK8efPYvHkzf//733nkkUe45557uPXWW5udyuIPf/gDu3btYvny5djtdoqLi/n8888P6Tl9+umnjBw5kn/+858AHHvssYd0nFiqC9J+9rOftfkxgUAAiyV+QiPJpB2OpFayacoMmQONecP2X8IopTtN9tdqKUirKzMpZ98ArY7JDJn9jQCoTnIe2FuI8J3pkDvSaDJN7WncNh/EclfKbNSnJftn0+qaaG0JxkjNrEGtl6lMRmArhBAdzKpVq5g4cSIJCQlYLBaOO+443nzzzRYfY7VacblcuFwurFYrGzZsYNu2bUyePLnJ/V0uF8888wxPPPEEdrvRpaZbt26cf/75ALzyyiv1KwvcfPPN9Y9LSkritttuo6CggCOOOILi4mKKioq46aabmDNnDoWFhbjdbvr06UNpaSkA9957L4MGDeKYY45hzZo19cfasGEDp556KmPHjuXYY49l9erVgLFu6HXXXcdRRx1Fv379mD17dv1jHnjgAUaOHElBQQG33HJLi8dpTnPHv+WWW/jyyy8pLCzkz3/+M8FgkBtvvJHx48czatQo/vGPfwDw2Wefceyxx3LmmWcybNgwbrnlFp566qn649dlC2tqajjxxBMZM2YMI0eOZM6cOS3Wqz1IkHY4zFZwttDnKq1X8wGSxX5g8GKyGIHL4dYpc4BxrLp+aK1Ryig3McvIunUb3vbRpIlZRnDYksaDHhJzwoFko18qVmc4aGshmLSnNN8MLIQQcWzEiBF8+eWXlJWV4XK5mDdvHtu2bavf/uSTTzJq1Ch+8YtfUF5eDsDvf/97Lr74Yu677z6uueYabrvtNu65555my1i/fj29evUiJeXA/sk7d+7k5ptvZv78+RQVFfHDDz/w9ttvA1BbW8sRRxzBkiVLmDRpEs888wyFhYXMmjWLGTNmUFRUhNPZMGht0aJF/Pe//6WoqIh58+bxww8/1G+74ooreOKJJ1i0aBEPP/wwV199df22Xbt28dVXXzF37tz6YOz9999nzpw5fP/99yxZsoSbbrqp1eM0p6nj33///Rx77LEUFRXx29/+ln/961+kpqbyww8/8MMPP/DMM8/Ur5O6ePFi/vKXv7B27VpmzJjBa6+9Vn/s1157jRkzZuBwOHjrrbdYvHgxCxYs4PrrryfSqzbFT06vo0rqBq4yQB94f2vzeSXngnsvhALG7dayaG1lsUNG/4PLiO3PkQa1Ja3v19YmyLpsW3MDCcwWI7is2Gq8JvWUEURKU6cQooMaOnQoN998MyeffDKJiYkUFhZiNhs/bn/1q1/xhz/8AaUUf/jDH7j++ut59tlnKSws5LvvvgPgiy++IC8vD601M2bMwGq18sgjj9CtWxt+hAM//PADkydPJjvb+PF94YUX8sUXX3D22Wdjs9k4/fTTAWONzY8//rjFY3355Zecc845JCQYCYUzzzwTgJqaGr755pt9Jq31er31188++2xMJhPDhg2juLgYgE8++YRLL720/lgZGRmtHqc5TR1/fx999BFLly6tz7RVVlaybt06bDYbEyZMoG/fvgCMHj2aPXv2sHPnTkpKSkhPT6dnz574/X5uvfVWvvjiC0wmEzt27KC4uJjc3Hbq390ECdIOl8VmBB6usob77Cnh5sxWmMId7qt2NDyuvRxuRs6eZAw4CPmb38fiMLJgbdGWUZ5KGYMqUnsASjJnQohO47LLLuOyyy4D4NZbb6VHjx4A+wRal19+eX3AVEdrzT333MN///tfrr32Wh588EE2b97M448/zr333lu/34ABA9i6dStVVVVNZtOaY7VaUeHuM2azmUAgcEjPLxQKkZaWRlFRUZPb65pg657ToR6nOW05vtaaJ554glNOOWWf+z/77DMSExP3uW/69OnMnj2b3bt3M2PGDABefvllSkpKWLRoEVarlT59+uDxeA6qngdLzoLtIakb9f3LzLZ9R1K2JjG7YUqO9sqktZfWpruI1HQYJrMEaEKITmXPHmOQ2datW3nzzTfrO7Pv2rWrfp+33nqLESNG7PO4F154galTp5KRkYHL5cJkMmEymXC5XPvsl5CQwGWXXcavf/1rfD4fYIzQfP3115kwYQKff/45paWlBINBXnnlFY477rhDeh6TJk3i7bffxu12U11dzbvvvgtASkoKffv25fXXXweMgGjJkiUtHmvKlCn8+9//rn8ue/fuPaTjNCc5OZnq6ur626eccgp/+9vf8PuN5MPatWupra1t8rEzZszgv//9L7Nnz67P6lVWVpKTk4PVamXBggVs2bLlkOp1MCST1h4sdqM5zlNhjFg8mGkilDJGWVYXx99M+o5UcJW2sD0talURQoiObNq0aZSVlWG1WnnqqadIS0sD4KabbqKoqAilFH369KnvzA7GYIDnnnuOjz76CIDf/e53TJ06FZvN1uTUEvfccw+33347w4YNw+FwkJiYyKxZs8jLy+P+++/n+OOPR2vNaaedxllnnXVIz2PMmDHMmDGDgoICcnJyGD9+fP22l19+mV/96lfcc889+P1+LrjgAgoKCpo91qmnnkpRURHjxo3DZrMxdepU/vSnPx30cZozatQozGYzBQUFzJw5k1//+tds3ryZMWPGoLUmOzu7vm/e/oYPH051dTX5+fnk5eUBRjPxGWecwciRIxk3bhxDhgw56DodLBXpTm+xMG7cOL1w4cLoFur3gN916JO3usvjr9+V1rB7WdPTc5jt0G1Y9OskhBAHadWqVQwdOjTW1RACaPrzqJRapLUet/++0qbUXqyOw5tdP94CNDCyfM01acoi50IIIURESZAmWtZckCbLMwkhhBARJUGaaJk95cAJe802sCU2vb8QQggh2oUEaaJlJtOBU4PIgAEhhBAi4iRIE63bv/+Z9EcTQgghIk6CNNE6e2pDk6c0dQohhBBRIUGaaJ3J1LCupgwYEEKINisrK6OwsJDCwkJyc3PJz8+vv1036ezhmjx5MoMHD64/7nnnnQc0LAx+sOrW5azzzjvvcP/997f4mKOOOgqAzZs3NzmHW0s+++wzUlNTKSwsZMiQIdxwww0HXefOSiazFW3jTAdvlfRHE0J0fDt/bN/jdR/d7KbMzMz6JY7uuusukpKS9glCAoEAFsvhn4pffvllxo07YJqtQ1JUVMTChQuZOnUqYKzPWbdGZ3O++eYboCFIq1tRoa2OPfZY5s6di9vtZvTo0ZxzzjkcffTRh/YEOhHJpIm2caQaTZ32pFjXRAghOrSZM2dy1VVXMXHiRG666aYDMl4jRoxg8+bNALz00ktMmDCBwsJCrrzySoLBJiYXb4NnnnmG8ePHU1BQwLRp0+qXYnr99dcZMWIEBQUFTJo0CZ/Pxx133MGrr75KYWEhr776Ks899xzXXHMNAMXFxZxzzjkUFBRQUFBQH5wlJRnnhltuuYUvv/ySwsJC/vznPzNp0qR91uE85phjWlzmyel0UlhYyI4dO1qs98yZM7nuuus46qij6NevX/2i6aFQiKuvvpohQ4YwZcoUpk6dWr9t0aJFHHfccYwdO5ZTTjllnyW54pUEaaJtTGZIyY91LYQQolPYvn0733zzDY8++miz+6xatYpXX32Vr7/+mqKiIsxmMy+//HKT+1544YX1zZ033njjAdvPPfdcfvjhB5YsWcLQoUP517/+BcCsWbP48MMPWbJkCe+88w42m41Zs2YxY8YMioqK6hcXr3Pddddx3HHHsWTJEhYvXszw4cP32X7//fdz7LHHUlRUxG9/+1suu+wynnvuOcBYK9Pj8bS4xFN5eTnr1q1j0qRJLdYbjHVPv/rqK+bOncstt9wCwJtvvsnmzZtZuXIlL774It9++y0Afr+fa6+9ltmzZ7No0SJ+8YtfcNtttzVbj3ghzZ2i7WRUpxBCtIvp06djNptb3OfTTz9l0aJF9etjut1ucnJymty3tebO5cuXc/vtt1NRUUFNTQ2nnHIKAEcffTQzZ87k/PPP59xzz2213vPnz+eFF14AwGw2k5racj/l6dOnc/fdd/PQQw/x7LPPMnPmzCb3+/LLLykoKGDdunX85je/ITc3t8V6A5x99tmYTCaGDRtGcXExAF999RXTp0/HZDKRm5vL8ccfD8CaNWtYvnw5U6ZMASAYDNavyRnPYhKkKaXu5v/bu/vYrMo7jOPfi9dOXpxTsjRWhRh5K32RAjVUFxAhbiNjSTPdJlOMxJDM6GLiYFlcMpWoWTI0GnVmm4TMzLGy4SaLk2UlKE6grTCk1E1XGBVjuzZiSXzvb388x1poSynankN7fZInnHOe89znam+gv97nPufAMqADaAZWRMSRHvb7GNiXrP43Ik5+UtzMzOwMMG7cp1fJjxo1io6Ojs719957D4CI4IYbbuDee+/9zMdbsWIFmzdvpqSkhPXr17Nt2zYAHnvsMXbu3MmWLVsoKyujtrb2Mx+rq7POOovFixfz9NNPs3Hjxl7b/2ROWmNjI5dddhnXXHMNpaWlveYGGDt2bOdyX88hjwgKCws7R9bOFGmd7vxZRBRHRCnwDPCTXvZ7NyJKk5cLNDMzG3ImT55MXV0dAHV1dTQ2NgKwaNEiqqqqaG5uBqCtrY1Dhw6d1jHa29vJz8/nww8/PO6U6euvv055eTl33XUXkyZN4vDhw0yYMIH29vYe21m0aBGPPvookBuNOnr06HHv9/TZlStXcuuttzJ37lzOOefkz6meMmUKa9as4f777z9p7t5UVFSwadMmOjo6eOuttzqLumnTptHS0nLc6c/9+/f32V7aUinSIuKdLqvjgJOXwGZmZkNUZWUlbW1tFBYW8vDDDzN16lQAZs6cyT333MOSJUsoLi5m8eLFvU527zon7aqrrur2/t133015eTkVFRVMnz69c/sdd9xBUVERs2bNYv78+ZSUlLBw4ULq6+s7Lxzo6sEHH6S6upqioiLKysqor68/7v3i4mJGjhxJSUkJ69atA6CsrIyJEydy4403ntL3Y9WqVWzfvp2DBw/2mrs3lZWVFBQUMHPmTJYvX87s2bM5++yzGTNmDFVVVaxevZqSkhJKS0s7L3rIMvU1RDhgB5bWAtcDR4GFEdHSwz4fAXuAj4D7ImLzqbQ9Z86cqKmp+fzCmpnZGevAgQPMmDEj7RjD1pEjR1iwYAENDQ2MGDHwY0PHjh1j/PjxtLa2Mm/ePHbs2NE5xy0Levr7KKk2IrpNKhyw75akv0l6pYfXMoCI+HFEXAA8CdzSSzMXJaG/Czwg6eKTHO9mSTWSalpautV7ZmZmNsg2bNhAeXk5a9euHZQCDWDp0qWUlpZyxRVXcOedd2aqQOuv1EbSOgNIFwJ/iYhZfey3HngmIqr6atMjaWZm9gmPpFmWZGIk7WQkXdJldRnQ0MM+50gamyyfB1QA9SfuZ2ZmZjYUpXWftPskTSN3C45DwCoASXOAVRGxEpgB/EJSB7li8r6IcJFmZmb9FhFISjuGDXP9PXuZSpEWEZW9bK8BVibLLwJFg5nLzMyGnry8PFpbWzn33HNdqFlqIoLW1lby8vJO+TN+4oCZmQ1pBQUFNDU14YvKLG15eXkUFBSc8v4u0szMbEgbPXo0U6ZMSTuGWb/5AetmZmZmGeQizczMzCyDXKSZmZmZZVDqN7MdCJJayN3aYyCdB/xvgI9h/eM+ySb3S/a4T7LJ/ZI9g9UnF0XEpBM3DskibTBIqunp7sCWHvdJNrlfssd9kk3ul+xJu098utPMzMwsg1ykmZmZmWWQi7TT93jaAawb90k2uV+yx32STe6X7Em1TzwnzczMzCyDPJJmZmZmlkEu0vpJ0tWSXpX0mqQ1aecxkPRrSc2SXkk7i+VIukBStaR6Sfsl3ZZ2JgNJeZJ2Sdqb9MtP085kOZJGSnpZ0jNpZ7EcSQcl7ZO0R1JNKhl8uvPUSRoJ/AtYDDQBu4HvRER9qsGGOUlfAY4BGyJiVtp5DCTlA/kRUSdpAlALfNP/VtIlScC4iDgmaTTwAnBbRLyUcrRhT9LtwBxgYkQsTTuP5Yo0YE5EpHbvOo+k9c884LWI+E9EfAA8BSxLOdOwFxHbgba0c9inIuLNiKhLltuBA8D56aayyDmWrI5OXv5NPWWSCoCvA79MO4tli4u0/jkfONxlvQn/4DE7KUmTgUuBnSlHMTpPq+0BmoGtEeF+Sd8DwA+BjpRz2PECeE5SraSb0wjgIs3MBoyk8cAm4AcR8U7aeQwi4uOIKAUKgHmSPEUgRZKWAs0RUZt2Fuvm8oiYDXwV+H4ytWZQuUjrnzeAC7qsFyTbzOwEyZynTcCTEfGHtPPY8SLibaAauDrlKMNdBfCNZP7TU8CVkn6TbiQDiIg3kj+bgT+Sm/I0qFyk9c9u4BJJUySNAb4N/CnlTGaZk0xQ/xVwICJ+nnYey5E0SdIXk+UvkLsIqiHVUMNcRPwoIgoiYjK5nyl/j4jlKcca9iSNSy56QtI4YAkw6HcQcJHWDxHxEXAL8FdyE6E3RsT+dFOZpN8C/wCmSWqSdFPamYwK4HvkRgX2JK+vpR3KyAeqJf2T3C+dWyPCt3ww6+7LwAuS9gK7gC0R8exgh/AtOMzMzMwyyCNpZmZmZhnkIs3MzMwsg1ykmZmZmWWQizQzMzOzDHKRZmZmZpZBLtLMbMiTdKzvvczMssVFmpmZmVkGuUgzs2FD0gJJ2yRVSWqQ9GTydAQkzZX0oqS9knZJmiApT9ITkvZJelnSwmTfFZI2S9oq6aCkWyTdnuzzkqQvJftdLOnZ5AHNz0uanubXb2ZnllFpBzAzG2SXAoXAEWAHUCFpF/A74NqI2C1pIvAucBsQEVGUFFjPSZqatDMraSsPeA1YHRGXSloHXA88ADwOrIqIf0sqBx4BrhysL9TMzmwu0sxsuNkVEU0AkvYAk4GjwJsRsRsgIt5J3r8ceCjZ1iDpEPBJkVYdEe1Au6SjwJ+T7fuAYknjgfnA75PBOoCxA/ulmdlQ4iLNzIab97ssf8zp/z/YtZ2OLusdSZsjgLcjovQ02zezYc5z0szM4FUgX9JcgGQ+2ijgeeC6ZNtU4MJk3z4lo3GNkr6VfF6SSgYivJkNTS7SzGzYi4gPgGuBhyTtBbaSm2v2CDBC0j5yc9ZWRMT7vbfUzXXATUmb+4Fln29yMxvKFBFpZzAzMzOzE3gkzczMzCyDXKSZmZmZZZCLNDMzM7MMcpFmZmZmlkEu0szMzMwyyEWamZmZWQa5SDMzMzPLIBdpZmZmZhn0f6bVjVEiA92hAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Compare the estimate and the truth\r\n",
     "plt.figure(figsize=(10, 6))\r\n",
@@ -417,74 +440,22 @@
     "plt.ylabel(\"Songs Sales Elasticity\")\r\n",
     "plt.title(\"Songs Sales Elasticity vs Income\")\r\n",
     "plt.legend(loc=\"lower right\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1f533b30d68>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 10
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ],
-      "image/png": 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6bRKwic5HgjQhRJcQDGmqPUZzY7UncEDfMq01JTVe1uyuZm1xNWt2V7OhpBZf0IjgbGYTvTITOKJfJn0yE8hPSyA/3Ul2kj3ughmzSZGZZCczyc6Q3AO3B0OakmovOyrcbCt3saXMCD7fW7YTf3iqEJvFxIDsJAZ1S2ZQtyQGd0smO9keV02mXn+IEr8RgFotqj5bmWCTU5voHGTFASFEpxUKaaqaCcy01uyq9LBsR2X9ZW+tDzACsv45RmAyMCeJvlmJdE9zxl0w1t6CIc3OCjcbSmpYt6eGNbur2VhaUx+4ZSXZGZGfwsj8VEZ0TyUv1RFXQVsdu9VUH7BJk6joCJpbcUCCNCFEp6K1psYbqO+w3/grrsLl48dtFSzeUs7SRkFZeoKVkfmpDM1LYUhuCn0yE6LeRBmv/MEQm0trWb27mhU7K1m+s4pKtx+AzEQbBT3TGNMrncKeaXHZt81pM5GWYCMtBs3OQrSVBGlCiE7N4w9S7vJR4fLXz+4f0pq1xdUs3FLOoi3lbNhTgwZSHBYKeqYxMj+Vkfmp5Kc525wRMpsUVrMKd/I3YQ6vbWkKL1pu/DUWLTc1c8yQ1uj9FkcP1S2gHtQE6tfs1HE3D5vWmu3lbpbvNLKPRVsrqPYGUMDAbkmM7pXOhD4ZDMhJavb5x4JSkOKwkp5oJdkRf8Gk6NokSBNCdDp1HeTLXX7cPmNUpT8YYtn2Sr7dWMb3m8ood/kxKRjcLZkxvdMZ2yud/i0EEErRsA6lxYzNYiwkbjWbsJlNmKLc5BkKGQut+4MafyCELxjC6w/hCwbx+GO/7mcwpNlQUsOiLeUs3lrO2uJqQtrIsk3sl8kRfTMYmZ8aV1ksq0WRkWAjPdEma4yKuCBBmhCi03D5ApTV+OqbM/3BED9uLefLdaX8sHkvtb4gDquJsb3SOaJfJmN7pzeZPbGYVf00GI7wwuB2S8daX9JYqD28TqgvhCcQbHUakUiqcvtZuKWc7zaWsXhrOd5AiESbmQl9M5g0KJvCHmlxE7ApBckOC5lJdpLsMthAxI4EaUKIDi0U0pS7fOyt9eHxG82BK3ZW8sXaEr7eUEaNN0Cy3cLEfhkc2S+Tgp5p2C0NncaVAqfNmPg1wd4wN1lnFApp3H5j/jS3z1g/MxbNpt5AkCXbKvh2Yxnfbiyj1hsk2WHh6P5ZTBqUzfDuKXHTJGq3mshItJGRYIt6tlQICdKEEB2Sxx+krNZHhctHKATby118smoPC9bsYW+tD4fVxBH9MjluYDaFPRuyNEpBgs1Mkt1YYshpNXfpk683YMwvVreSQl2/vWipy3Z+vraU7zeV4Q2EyEqyccKQbpw4JIfuac6o1qc5JhNkJNrITLRjs3TOIF7EHwnShBAdSrXHT2mNjxqPkQ36an0JH6/aw6pdVZgUjOudweTB2Yzvk1E/zYLNYiLZYSHJYSHJZunSQVlrPP5wwOYxgrZongo8/iD/27SX+Wv28OPWckIahndP4aQh3Th6QFZcTExbN9AgK9km866JiJMgTQgR90IhY8ml0hovXn+ITaU1vLdsN5+v3YPHH6JHupMpQ7tx/OAc0hNtgNGEmeKUZYIORyikqfEZ62VWuf1RzbKV1XiZv3oPn6wqZmelB6fVzOTB2UwdkUefrMSo1aMliXZjSS0ZFSoiRYI0IUTcCoY0ZTVeSmt8ePxBvl5fyrxlu1i1uxqbxcRxA7M5eVg3Bucmo5QiwW4OLyRulSapCHD7guFF4X34A9E5R2itWbmrio9WFPPl+hL8Qc3w7in8ZEQeR/XPjIv+g06biewkB6kJEqyJ9iVBmhAi7vgCIcpqvZTV+Nhb4+O9Zbv4YMVuKt1+8lIdTB2Zx0lDupHksOCwmkhNsJLmtElgFkUuX4BKtzExcLQCtiq3n09WFfP+8t3srvKQ5rQydWQeU0fmxcWEuXariewkO2kJ1g41EljELwnShBBxwxsIUlLtpcLlZ3NpLXOKdrJgzR6CIc34PhmcNiovPAhAkZ5gIz3BFhf9lLq6Gm+A8lrfASs5REpIa4q2VvDu0p0s3FKOzWzi+CE5nFXQnZ4ZCZGvQCskWBPtRYI0IUTMeQNB9lR5qXD5WLq9kjcW72Dx1nJsFhMnDsnhrIJ88tOdJNjNZCbaSHFYpfN/HAqGNJVuP+UuHy5vMCplbtvrYs6SnSxYvQdfMMS43umcN7YHw7unRqX8ltgsJrql2ElLsMW6KqKDkiBNCBEzHn9d5szHD5vLeW3hNlbvriYtwcrpI/M4dUQe6YlW0hNsZCTaZABAB+LxB9lb66M8PEVKpFW6/by/fBdzl+6i0u1nePcUzh/bk9G90mKezXJYTeSkOOKiSVZ0LBKkCSGizhcIUVzlodzl45v1Zby2cBsbS2vJSbYzbUwPThpq9DfLTDKaNM2SNeuw6kbmltV48URhxQOPP8hHK4t568ftlNb4GJCdxPRxPTiiX2bMJ8h12szkpjpkFQPRZhKkCSGixh8MsafaS1mNl2/Wl/Hy/7ayba+L/DQn543tweRB2aQkWMlKskvWoROq8QYorfZS7QlEvCx/MMSCNXuYvWg7uyo99MlM4MKJvZnYNyPmmbUkh4W8VIdkhkWrJEgTQkRcMKQpqfZSUu3hf5vKefn7LWwsraVnupOfTujFUf2zSEuwkp1sJ1GyDJ2exx+ktMYYIBLpU00wpPlyXQn/+d9WdlV6GJCTxM8n9mZMHDSDpida6ZbiiItpRER8kiBNCBExWmvKan3sqfKyeEs5L363hTXF1eSlOvjphF5MGphNRqKN7GS7jNLsggLBEGW1PkprvBHvtxYMaRas3sMrP2xlT7WXobnJXHxkH0bkx3aAgVKQnWwnO8kug2HEASRIE0JERKXLz+4qD2t2V/HcN5tZvLWCrCQ7F4zvyYlDcshMspOTYpcmH7HPpMWRXvDdHwzxyapiXv1hG2W1Psb3SeeSI/vQOzO2qxhYzIrcFEf9ihlCgARpQoh25vIF2FnhYWuZi5e+38KC1XtItFuYMa4np43KIzPJRrcU6Y8jDhQMacpqvZRWRz5Y8waCvLtkF68v2obHH+Skod342YReZCbZI1puaxLsZrqnOiWzLIA4DdKUUqcCfwHMwD+11vfvt90OvACMBcqAGVrrza0dV4I0ISLHHwyxu9LDzgo3sxdtZ07RTkJac0ZBd84f25O8NAfdUhxy8hGtqsuslUShGbTS7ee1hduYt2wXJpPi3NH5TBvTI+Y/ItITreSmOLBIf7UuLe6CNKWUGVgLTAG2Az8AP9Var2y0z9XAKK31VUqpC4BztNYzWju2BGlCtD+tNSU1XnZXepi/ag/Pf7uZcpef4wZlc9ERvemTlUBuqlOmHRAHLRAMUVJjLA8W6VPS7koPL3y3mS/XlZKVZGPmUX2ZNDArpoMLTCbITXHEPLsnYiceg7Qjgbu01qeEb/8eQGt9X6N9Pgzv861SygLsBrJ1K5WWIE2I9lXt8bOzwsPyHZU8/cVG1hRXM6hbElcc258RPVLITXHIbOvisPmDxrx60RgNumJnJU9/uZGNJbUMzUvhimP7MSAnKbKFtsJpM5OfJk2gXVFzQVosf/LmA9sa3d4OTGxuH611QClVCWQCpVGpoRBdnD8YYleFhy1ltTz3zWY+Xb2H9AQrvzlxICcOy6FbioPsJHvMpzgQnYPVbKJHegJZSUF2VXqoieA8a8O7p/Lo9EI+XV3Mi99u4XevFTFlWDcuObIPKTGau8/tC7KhpIaMRBu5KQ4ZBSpiGqS1K6XUFcAVAL169YpxbYTo2LTWlNb42F3l5v1lu3n+2814/SGmjcnn/HE96Z7mJDdV5n0SkeGwmumblUi1x8/uSk/EVjAwmxQnD8vl6P5Z/PeHbby7dCffbizj0qP6cOLQbjFZuUBrKKvxUeXxk5fqlMmeu7hYBmk7gJ6NbvcI39fUPtvDzZ2pGAMIDqC1fhp4GozmznavrRBdhMsXYEe5mxU7q/jrZ+tZW1zDyPxUfjW5P4O6JdM9zUGCrdP8vhNxLNlhJcluYW+tj91VnogNLki0W7jsmL6cNDSHv362gcfnr+fjVXv41XH96ZsVmyk7/AHN1jIXqU4r3dNkYEFXFcs+aRaMgQMnYgRjPwA/01qvaLTP/wEjGw0cOFdrfX5rx5Y+aUIcvFBIU1ztYdteFy99t5W5S3eS4rDyi2P6cuKQHPLSnGTI3E4iRgLBELurPJTX+iNaTkhr5q/ew7+/3kSNN8CZBflcOLFXTEeBmk2KvFSZW60zi7s+aeE+ZtcAH2JMwfGs1nqFUmoWsFBr/Q7wL+BFpdR6YC9wQazqK0RnVjcw4JsNpTy1YANlNV5OHZHLxUf0oUeGk7xU+SUvYssS7q+WkRhgZ4Ubty8yaTWTUpw0tBsT+mTwwrebebtoB99tLOOa4wdQ0DMtImW2JhjSbC93U+H2k5/mxGaR/8WuQiazFaILC4Y0OyvcbC1z8c+vNrJgTQk9051cd8JACnqlkZ/mlDU2RVwqq/FGtAm0zrIdlTw5fx07Kz1MGdqNXxzdlyRH7P4nTCbIS5WsdmcTd1NwRJIEaUK0rtrjZ9teF5+vLeUfn2+g2hvgvLE9uGB8T/LTnGQny6hNEd/qRh9XuiPbBOoNBPnv/7bx5o/bSXVaueq4/hzVPyuiZbYmyWGhR7pTBu90EhKkCSEAI3u2q9LN5lIXf/1sPd9sKGNAdhLXnTiQ4fkp5Kc5Yz4LuxAHo9LtZ2eFm0AwsuezDSU1PD5/HRtLapk0MJurjutHsiN2oy9NJuie6pS+ap1A3PVJEyIiPJXgcwGahtkwNSjTgZf91e3fVPYoFAQdAl33N3TgY5UCVNN/lSl83WTc1qEDj1m/3dSwX329dMPf/etbp7msV6PH1Xr97C6v5ouNNTz2XQUuv+ayAifThznolraXdJMbasygzAc+D2ioV/3zUcbzCAXCF79xu37f/Z7PPnVufH3/urfwPBs/X2Uy6mpqXGca3iMdanhvlNnY3xSujw5BKPz6h4Lh96G59/GAwpvfr/Hn7oD67/dcWnwPFQd8jlt6Peo/l7rh81n/2pv3fQ+a2nf/eurG2+pex0avX/3rXvf/pPYro/HnIsgB/zP7POcm/l/2+V9Vjd7PYMPxwq9XKookO5TUeKlw1c2tZmzTzfxfqLo66pBxndC+r0H94xQ6/HyHOE38ZWoury+r4OUfS1i+fS/XHZPHhF7J+z2m/iCNjtFwu6G8EKrueSkFmMJlmfard+PrGqXD7xGaXbWaGofZ6Ddq2u97rdFzqLf//2ooaLyPJguYrcZfk6XhO6pufx1sONY+3wM0fD7qP0/hbft/PuqOE2rme7T++E18DqDp/6V9vh9p5vPT6Lj1r4em6e/Wxo9X4EgDc+xCJQnSROfi2gueiljXIu5oDaW1XrZX+HhqsYdPNvsZkG7ipokJDMu2kZNiwWb2gzeyzUZCRIoZyLVAiiNIcbUnYlk1M3DRIDgyO5EHv3Nz18fbObWvlatGO0i0xaZ7gKcWtpUrclLsJMr0OO3L6pQgTYh2sWc1vDIDzvor2BKg6BVY92FD1qTu196MF439//c0rP9032NYE2D6v8Pb/wk7FhqPC/qNX572FDj7r8b2T++GnYvDDwz/SkvJhzMfN+766HbYs6r+FzImE2T0h1PuNbZ/eBtUbGGfX7ndhsHk3xvXP/g91BTvm83oPhqO+a1x++1fgbsC4xdg+Fdr76PhiKuM7XN/C6EAIcx4Q+D3mfiwooD53ilcPMzMLeV3YlsawqIaZZ0G/wRGzQBvNbzxy/DzrvvVHYAxl8Don0NtKbx5efhXt9X4EjNZoWAGDDwZqnbB/LvDv1BDDVmZsZdAn2OhbIOxfR8KjvgV9JwAu5fD5/c3/NKvyzaecDvkj4Ut38CCexuyOXWZtJP+CDlDYPNXxvtb9wu+zpQ/Qlov433/8aVwsY1+XZ96HyTlwOp5sOz18P06nB3UxmfLkQJL/gvL36Qum1FvxstgscPCf8PaD4xjm+3GfRY7nPG4cV/RK7D1Wwh4IOiDgA/sSXDu08Zxvn0KdixueN/RkJABpz1q3PflI1C8Yt9f/Mnd4aQ7je3z74Gy9ftmCjIHNGz/8Dao2tEoAwbkDINjrze2v38L1JY0yrZp6DEOjrrW2P7WleCpZp9MXN/j4Mirjeuv/Mx4z5Q5nHkzw8Apxmcn6INXL2rIXNY9fsQ0Y7u3Gl67pOG5mUzG57tgBgw7C2r2wNzfhN+yRu9/wc9gwIkkuLbT59M7CPm96IAXFfIBJvYUXEVNj+OwV6wn94cHGmVYjPe+ZNQVuHLG4ChbQbcfn2jYFP7fKB77WzyZw0nc/T+yi56ijzJzgtlMZbKifIeJWbv/j3OOHMpRwUVkrP7PPlkmpUPsmngr/sQ8krd+SsaaV+tfVxX+u23SgwSdWaRueIeMdbPDj234fG058e+EbElkrH6F1I1zAd3wydaajT95kZ0VmvxVz+Lc+L6xzRTOjFkdcMF/Gj5bGz9vlCELGZ+t818wtn//DyheDsGA8V4FvZCQCWf8xdj+8Z3G9sZZ2vQ+cOqfwp+9e6F8477/u1mDGz57711vfD/UZctQkDcKJt1obH/nOnCVhp92+Luj15FwTPg9n30Z+N31yTAIQb/jYeKVxvaXzw9vaJSRGzwVxlxs/L+9+vN9kmagYOR5xveepwLevnrfLNy0fxmf/RiRIE10HruWQPlmI7DJ6AsJ6cYJub65q66ZK3w7tSd0L2x4vMZI99exJRgnZExgthnbnGkN23NHgC2x0RepBmdGw/ZuI8CR2qh5SUNybsP29N7GiRsaUvXJ3Ru2J2YbX7CNv2ySGj0+azD4avdtXkrKadhuceD3ufH5/JTU+qlwB8iwunj8pEQKcqw4vk3EZLY0OpGawJFuPNZsMwLCxgGYyQw5Q8PbrdD7mEZNJ+FAzhqe+NNkMl6bugDVOGOAxRmum914b+qbHcJvgNXZ8NpnDW4Ivurq5wzXL6kbDDq14SRQ99ceXnvRlmQExPVNKuHTmSn8/tqTjPe//ts6HIiYwn3x7MmQ2oP6AKZxUylASncjWIR9m7jqHp/aw3j9dCgchHmMOtbvGzL2dWaAxWa83nXPHYz76z4rdUFY489WYrZRh1CjZktHasP2pG5GuY2bEVN7NGxPyTfKbxwk7XP8zHD2oFGzU+PPVtYg40TZuOkrvdFKL/ljwoF9o+bJuvdOmY3/u/rm6fDnI713w/beR7NP1wIdMgIFMF6rnGHhl7HR+29LMO6zOFEZfTGbbfiVhdqgCR0Cf4LxeobMDrzpgxo1nRl/g+HPbsjixJvat+G5hJs7QxZje8CRgStnTLiZMohTBwl4/AQr7dw438XtfeB8RzomqG8i1sqEVsZnI2hPw5s+MFxq3f+GQoc/m/7EPGq7jWt47+tqaTJO196UPtTkH11ft8b1BKhMGUygjybBZsZC0Ai2Gu+W1tv47NY1carw91sds8243+Iw/k/NNuPzVqfbcOP/p75JO7TvZ8OZBv7cRv+75vD/eljWoPBnrdFnLyW/YXtGv/DxGn3vZTR6P3KGGv9Pdc9Z7ffZ7jFu3+9krSE5L7y7GXpMYB9aN3zvKrPx2ar7vtZB43WIIRk4IDqPhc8a2aMLZ0PioYy82q+povHJ91D6UrWlrP37sOzf7+wQBUKwp8rDqlIff/rGxYaKEGcMsHJloYPuqXbSE2zNdmHrXOqeZEf6ntu3/1HHsn/fvPao/+G9HsEQlNR4qI7gOqAAbr/mbz96eH+jn0EZJn5/pJMeybEbgKOAzCQ76QmxXFZq/35gsM97uH9/y4M99iE/9iCOlTXI+MEZYTJwQHR+frfxt/uYRhmvJoKhSEcnuomOqHXlHkzZrf2AaqrTuVLUeANsLatl7tZd/POrTTgsJm4/bTCTBmXTM82J02rigE6zbeocv3+5uol9acjs1e+7X6fe/d+Tw30/mnqdmjqm1s3UZ7/6N/f86vZvvN/+x6s/ZnOvWd1xD/I1CDVqdmxcxv6Pbemk12Sn+NY08X/TzOeuWVo3/ZgD6tXE56mlwTDNdSLf77UxK0Uu4Kj1sqPCTSi0/74t1HvfO/a73fj1VyiluDoPCtaX8uSC9fzqIzeXH9uPKUO7HdpUNi0NZGrjftuBKruF/MaTUbflB2dby2zqO64tj2/puI2PvU85jb6P9h8gccAxGnWxqKvj/oNbWv1shR9vim2YJEGa6Dy81cZfR0pDs1MsHGww1tJxDnJ7cZWHjXtqeXz+Or7dWEZhzzR+e9Ig+uck0j3VickUhXo13ica6bq2ltHW96W9j3cwx2xOSyel9iwnEmW09jodSp0P4X8sLdFOgt3KtnIXLm/wwCT4AWUcfLUAjh6QxeDcZP78yVqemL+eom0VXHP8gINf77adPltV7gBufy090xOMiamj8d10OMc93GMrZTRbtksdYj8VkQRpovNwZkDuqH37lXUR/mCIbXtd/Li1ggc+WE1ZrY9fHN2Hc8f0oGdGAqnOrveaCLE/m8VEv6xESqq97Kn2tpqsPlRZSXbuPmsEbyzezkvfbWH9nhpuOmUIA3KSIlNgK/wBzabSWnKS7eSkxLaPlTg40idNdB7eGqOzcl0H5S6i2uNna5mLNxfv4LlvN5OZaOOmU4YwpncaPdITZJ0/IZpQ6w2wrdyFPxDZc+CKnZU8/NEaKlx+LjumL6eNzDu05s92kuSw0DPdKWvxxhlZcUB0fu5yQO07ArOT21PlYf2eGv7y6Tq+37SXI/pl8OsTjOZN+cUsRMsCwRDby90RH1RQ6fbz2CdrWbilnCP7ZXLdiQNJiuGauFaLamj+FHFBgjTR+b13A1RshQtfi3VNIi4Y0mzb62LxlnLu/2A1e2t9XHp0X84Z3Z1emYkxPQEI0dGUVHsprvJErPkTIKQ1c4p28Py3W8hJtvP7nwylb1bkRw02RynoluIgO9keszqIBs0FaZLvFJ3H3o1GkNbJefxB1u+p4Y3F27n5zaVo4IFpo/jphJ4M7JYsAZoQByk72U6/7ESslsg1Q5qU4pzRPfjTOSPxBkLcMHsJ81fviVh5rdEadld62FJWSzDU+ZI1nYUEaaLz8Lv3nRC0E6pw+Vi5s4pHPlrDE/PXM7x7Kn8+v5BjBmbRLzsJq/QzEeKQJNgsDMhOItEe2RF9w/JSeGxGIYNykvjzJ2v562fr8QebWdc0CqrcATaU1ODxB1vfWUSd/OQWnYe/1pgpvhPSWrO7ysPKHVXc9/5q1pfUcP64nlx0RG/6ZCWQ7JDRm0IcLovZRN+sRIqrvJRUeyNWTnqCjXvOHskL327mzR93sKGkhltOHRqzpkevP8SGkhp6pMtI8HgjP7tF5+H3GGtvdjLBkGZzmYtPV+3hN68VsbPSze2nDeWKSX0ZnJssAZoQ7UgpRW6qg14ZCW2enu5QmE2KS4/uy+9/MoRte9387rUiVuysjFyBrQiFYGuZi92VntZ3FlEjQZroPLIHQ+7IWNeiXXn8QdYVV/Pf/23ljjnLSXNa+fP5hZw6Ipd+WUkyvYYQEZKaYKV/dhJ2a2T/x47qn8Uj0wtIsJm5/e3lfLB8d0TLa01JtZdNpdJPLV7I6E7ReexeDpkDwNo5pp6o8vjZuKeGpz7bwMcri5nYN4PrTx7EgJxkMhJtrR9ACHHYgiHN1r0uaiI8TUeNN8BDH65h8dZyfjIil8uP7RfTPqZ2q4leGQk4rLGfdb8rkNGdovMLBaKzLE4UlFR7KdpSwe/fXMbHK4uZMa4nd5wxjBH5qRKgCRFFZpOib1YiWcmR/b9Lslu44/RhTBuTz/vLd/OHOcupcPkiWmZL6vqpVXv8MauDkCBNdBbBAPz3Z/Dd32Jdk8OitWZHhZuv15fyu9eL2Fhay82nDuHySf0Y1C354Nf/E0K0i7xUJz3SnRH9HWg2KWYe1ZfrpwxiXXEN17++hC1ltZErsBWhEGwpc0V0EIVomQRponMIeKBqhzENRwdVN0Dg/WW7uPmNpYDiwWmjOH1UHv2yEmV6DSFiLD3RRr/sRCzmyGbsJw/O4f5zRxIIam6cvZSFW/ZGtLyW1M2ntm2vi87YPSreybe+6By8NcZfW8cc3ekNBFm/p5oXv93Mve+toldGAo+eX8BRA7LomZGAydQ5mnGF6OgSbBb6ZyfhiPCAgoHdknnk/ALyUh3cPXcl7y7ZGdHyWlPh8rOxtJZADOd064okSBOdg7fK+NsBp+Bw+4Ks2VXNox+t5dmvN3PUgCweOG8khb3SZMkWIeKQzWKiX3YSSY7Idj/ISrJz/7mjGN8ng6e/3MjfP98Q01GXLm+QDSW1MvFtFEmQJjoHX7jfhi12a+EdiiqPnyXbK/jDnOV8uLKY88f15LbThjAsL5UUmf9MiLhlNin6ZCaQkRTZAQVOm5lbpw5l2ph83lu2i1lzV+DyRXakaUt8AWNAQY03dnXoSiRIE52DUtBnEqT3iXVN2mxvrY+Fm/dy0+ylrNhZxW9PGsgVk/oxMCdZhr0L0QEopchPc5KbGtlpf0zKGFBwzfEDKNpmjPouq4ldZ/5QCDaX1rK3NnajT7sKCdJE55CYBafcA72PinVN2qS4ysOXa0u44fWllNZ4uevM4ZwzuocMEBCiA8pOttMrIyHiMwCdMjyXO08fzq5KDzfMXhrTkZ9aw45yt6xQEGFyNhCdQygAxH/neq012/a6+HD5bm55cxkmBQ9OG8WUYd3olSkDBIToqFITrPTNSozoUlIAY3qnc9+5IwmFNDe/sZSl2ysiW2ArSqq9MvIzgiRIE53Dpi/gudNg19JY16RZoZBmS5mLNxZt549zV9Itxc7D040RnN1SOscqCUJ0ZYl2Y+RnpKfo6J+dxEPTR5GZZOfOd1bw2Zo9ES2vNRUuvywlFSExCdKUUhlKqY+VUuvCf9Ob2S+olCoKX96Jdj1FB+KpMkZ4muOzs30wpNlYWsOzX23isU/XMaJ7Cg+eN4qxvdNlBQEhOhGH1RyVNT9zkh08MG0UQ/NSeOTjtbz9446IlteaWm+QjSU1+AIyRUd7ilUm7RbgU631QODT8O2muLXWheHLmdGrnuhw6kZ3Wp2xrUcT/MEQ6/dU8/in63nhuy0cNyibu88ewcj8NJJlBKcQnY7NYqJ/dhIJ9sgOAEqyW/jjmcM5un8m//p6E899symmzY4ef4iNpTUyRUc7ilWQdhbwfPj688DZMaqH6Cz8LuOvNb6m4PAGgqzZXc2f5q3mnSU7ObOgO7+fOoQhuSk4bTKCU4jOymxS9M1MJDnCc6lZzSZuPGUIPxmRyxuLd/D4/HUxbXb0BzQbSmqolSk62kWsgrRuWutd4eu7gW7N7OdQSi1USn2nlDo7OlUTHU4oaCwLBXGVSfP4g6zcWcUdc5bz+doSLj6iN9edOICBOcnYLNIdVIjOzmRS9M5MIC0hshlzs0nxq+P687MJvfhk1R7+NG9VTLNZoRBsKq2lShZnP2wRC/GVUp8AuU1suq3xDa21Vko1F/b31lrvUEr1A+YrpZZprTc0U94VwBUAvXr1Ooyaiw4nFIC0XjD4J3Gz4oDbF2TJtgrufGcF6/ZUc83xAzhvbA96yRJPQnQpSqnw0m5u9tZEbl4xpRQ/ndCLVKeVv3++gTveWcEdpw8jyR7ZTF5ztIatZS66pzml3+1hULFov1ZKrQEma613KaXygM+01oNbecxzwFyt9ezWjj9u3Di9cOHC9qmsiH/eGihbZzR1Zg+KdW2o9QZYvLWc295azq5KNzeePJipo/LIT3OiIj2RkhAibu2u9FBSHflJaL9aX8ojH62hV2YCs84cQaoztn1fu6XayUmWEewtUUot0lqP2//+WLW5vANcEr5+CTBn/x2UUulKKXv4ehZwNLAyajUUHUcoYPxsi4MAqNrj57uNZdw0eyl7qj3cecZwzizMp0d6ggRoQnRxuamOiK9OAHDMgCxuP20Y28vd3PKmMWF2LBVXetlV6Y5pHTqqWAVp9wNTlFLrgJPCt1FKjVNK/TO8z1BgoVJqCbAAuF9rLUGaOFAoCJ8/AC+eHdNqVLr9fL2+lJvfWEqVx8/dZ43glOG5UflSFkJ0DNnJdvLSIv+dMLZ3OrPOHE5ZjY+b31ga8yCptNrH9nJXTOvQEcUkSNNal2mtT9RaD9Ran6S13hu+f6HW+pfh699orUdqrQvCf/8Vi7qKDiAUgIAXVGz6XgBUuvx8vmYPt7yxDH9Qc985IzlpaDeyk+0xq5MQIj5lJdnJT4/8IKfh3VO59+wRuP1BbnljWUyXkQIor/WztUxWJzgYMsRMdHyhgDG6M0YjOytdfj5ZVczv31qGxax4YNpIjhuUQ7p0lhVCNCMj0UaPdGfEe2kM7JbMfeeMBOD3by5j/Z6ayBbYikq3n81lLkKyOkGbSJAmOr6QPxykRb9ZscLl48MVu7n97eUk2608MG0URw/IIjXCQ+6FEB1fepQCtd6Zidw/bSQOm5nb5yxjbXF1ZAtsRY0nwKYyWUaqLSRIEx1f3TxpUc6kVbh8vL98N3e8s5yMRBsPnDeSI/plyioCQog2S0uw0TMjIeKBWl6qk/vOGUmS3cIf5ixn9a6qyBbYCpc3yKbSGgJBWUaqJRKkiY4vFIB+k6H/iVErsrzWx9wlu7jznRVkJ9l5YNooJvTJJDFGcxIJITquVKc1KoFatxQH950zilSnlTveWcGKnZWRLbAVbl+ITaW1Eqi1QII00fGFAjBqhnGJggqXj3eKdjJr7kryUoxFjsf1SZdlnoQQhyzVaaVneuQDtexkO/edM5KMRBt3vrOCpdsrIltgK4z1PmvxS6DWJAnSRMcX9Btrd+rI/5NXuHy89eMO7n5vJfnpTh44bxRjeqfjsEqAJoQ4PKkJ0QnUMpPs3HfuSLqlOPjjuytZsq0isgW2wusPsbGkFl9AArX9SZAmOrZQENDw0nnw5SMRLarS5WdO0U7ufW8VvTMTePC8kYzulSYBmhCi3UQrUEtPsPGnc0bSPc3BrPdWsiTGGTVfIMTG0hq8gditORqPJEgTHVvdagMRHt1Z6fYzZ8kO7nlvJb0yE7h/2igKeqRjt0iAJoRoX9EK1FKdVu45eyS5KQ5mzV3JshgHav6AZlNprQRqjUiQJjq2UMCYgkOHwBKZ0Z1VHj/vFu3knrmr6JGewAPnjqKgRxo2i/z7CCEiIzXBSn5a5Eespzqt3Hv2CKPpc+5Klu+I7WACCdT2JWcZ0bHVrTYAEZmCo8Yb4N2incx6byXd04xBAgU9JUATQkReeqKN7lFYQiotwca9Z48gO9nOH+fGftSnBGoN5EwjOra6OdIAbInteuhab4B3lxijOLuFR3EWSoAmhIiizCR7VNb/TU+wce/ZI8lMtPPHd1fGfB41f0CzsUQCNTnbiI4t6AeLA8ZcAt1GtNth3b4g7y/fxR/fXUF2sp2Hpo1idK90CdCEEFGXnWynW0rk1wHOSDQGE6QlWLnz3RUxX0IqEJRATc44omMLBcCeDOMuhdyR7XJIjz/IJyuLuXPOCtITbDwYnmZDAjQhRKzkpDjITo5OoHbP2SNIslu4453lMV+UPRDs2k2fctYRHVtdnzRXGQQDh304XyDEZ2v2cNvby3DaLMZEtb0zJEATQsRcbqqDjCRbxMvJSXZwz9kjsJpN3D5nOTvK3REvsyV1fdS64jxqcuYRHVsoALuXwUvTYPeSwzqUPxjiq/Wl3PrmcswmxYPhtTglQBNCxIv8NCdpCZFfHzgv1ck9Z41Aa7h9zjKKqzwRL7MldYFaV1uZQM4+omMLBSAQ/pVnSzjkwwRDmv9tLOPmN5biD4W479yRHD0gSwI0IUTc6ZHuJNkR+XWCe2YkcPdZw/H4Q9z29jLKarwRL7MlvkCoywVqcgYSHVvjKTgshxakhUKaxVvLuXH2UlzeAPeePZLJg3NkolohRFxSStErI4EEe+S/o/pmJfHHM4dT5Q7whznLqXT7I15mS7z+rrUouwRpomMLBcB/6FNwaK1ZsbOSm2cvpazWx11nDefk4d1kqSchRFwzmRR9MhNxWCN/Gh/ULZk/nD6M3VUe/vjuCly+w+//ezjqArVgSMe0HtHQ6rurlMqMRkWEOGihkLHSQN08aYcwme36PTXc/MYytu51cdtpQzltZJ4EaEKIDsFsUvTJSoxKt4yR+ancfOoQNpTUcO+8VTHvxO8JB2qhTh6oteWd/U4p9bpSaqpSkV5JTIiDEAr/mssdCROuBHvSQT18295afv/mMlbtquL6kwcxbUwPEmyR7+chhBDtxWo20ScrAbMp8qfniX0z+fWJg1i6vZKHPlod80yW2xdkc1nnDtTaEqQNAp4GLgLWKaX+pJQaFNlqCdEGoXDfiOzBUPhTY1LbNtpd5eb2t1ewcEs5/3f8AH5+RG8S7RKgCSE6HrvFTJ+syC/IDnDCkBwuP7Yf323cyxPz1xHSsQ2Qar1Btu51oWNcj0hpNUjTho+11j8FLgcuAf6nlPpcKXVkxGsoRHPqMmmuMqjaBbTtG6q81ss9767i87UlXHxEby4/th/JjsgPaRdCiEhJsFnolRmdQO3Mgu78dHxPPl29h39/vSnyBbai2hNg2153pwzUWk0dhPuk/Rwjk1YMXAu8AxQCrwN9I1g/IZoXCs9AvfA52PIV/G5Vqw+p9vh59ON1zF22i7ML8/n1SQNJjcKcQ0IIEWkpDiv5aU62R2Hy2Z9O6EWVJ8DbRTvJSLRxzugeES+zJZVuP6rcmDakM2lL+863wIvA2Vrr7Y3uX6iU+ntkqiVEG9Rl0gKecFNnyz8h3b4gz3y5kRe/28IJg3O4deoQMpMiv8yKEEJES3qiDX8wRHFVZOc0U0px+bH9qHD5ePbrzaQl2Dh+cE5Ey2xNhcuPxewmL/XgB5HFq7b0Sbtda3134wBNKTUdQGv9QMRqJkRr6oM0rxGktZDn9wVC/PeHrTw5fz1jeqVx99nDyUlpex82IYToKHJSHKQnRr6FwGxS/G7KYEbmp/KXT9exeGt5xMtsTWm1jz3VsV0doT21JUi7pYn7ft/eFRHioAXDAweCLWfSAsEQ85bt4r73V9MvO4n7p40kP71zpcSFEKKx/DQnSVFYlcBmMXHb1KH0ykjgvvdXsa64OuJltqa40sveWl+sq9Eumg3SlFI/UUo9AeQrpR5vdHkOiO1MdvEm4AVPJXiqGvpJtYdQnM+oHAqCay/4W+n/oDV4q8HnMq4fLq3BW9MwP5rfA1YHqAM/zlprvlxXyh/eXk5moo2HzxvFwJzkw6+DEELEsbpVCaIx2W2i3cJdZwwnxWHlj3NXsrMitguyA+wod1Ppiu3qCO2hpTB7J7AQOBNY1Oj+auC3kaxU3AuFoHKrEZwFPMaEqo1ZHMbs97YkY4JVsw1MrUyQGgyvQelzgd9lBD5BL5gsxvEs9oa/yhxu2lMNTXxBf0N9Al7jsSgjeLE4jcdZncbxtDbqXHeh7rZueC5KgTXBeNz+/G6oLQF3ecP+Zjs4UsCeAvZkCPqMoNVbBb6aRsc1Gce1JoTX2lTGvvWX8D+V2WaUbbaDxWbs56sxgjN/7b6vecEFDXXeT9G2Cm56Yylms+LB80YxqkcaMt2fEKIrqJvsdv2eGgLByI58zEi08cczh3PTG0u5850VPHTeKNISbBEtszXbyl2YTAkdevS+am3IqlLKorXuUJmzcePG6YULF0augFAQdi89uMcosxF4mK1GoBQK7HvZP9CLFyZLQ1BltoF7rxEstUgBMRgKnVsApoZfjZtKa7js+YXsqvDwyPRRnDIiLyoTPgohRDzx+INsKKmJSuPM6t1V3PbWcvpmJXLP2SNivoKLUtA/OwmnLb5XklFKLdJaj9v//paaO18LX/1RKbV0/8thVma6UmqFUiqklDqgUo32O1UptUYptV4p1VTfuI5DB41MmbfKCHS8VUbGLOiL3wANjADSWwU1u43sYasBGkQ9QCtZAzXF+2TS9lR7uOH1pWwureW204YwZXiuBGhCiC7JYTXTKyM6c6gNyU3hhpMHsba4mkc+XhPzVQm0hk2ltXgD7dgVKYpaaqz+dfjv6cAZTVwOx3LgXOCL5nZQSpmBp4CfAMOAnyqlhh1muaIzev8mKHqlPkircvu44+0VLNpSzjXHD+C8sT2xmiPfL0MIIeJVssNKXmp0RrQf2T+LX4ZXJfjnVxtjPslsMKTZXOrCH4zjhEgzmu2TprXeFb5qAnZprT0ASikn0O1wCtVarwofq6XdJgDrtdYbw/v+FzgLWHk4ZYtOyO+pXxLK4w/yyMdr+WDFbqaP7cGVx/WPebpdCCHiQWaSHW8gRFlN5Ec+nlnQnT1VHuYs2Um3FAdnF+ZHvMyW+AIhtpTV0i8rCVMHalVpS3rhdaBx+BkM3xdp+cC2Rre3h+8TooEOGYMkrE4CwRD//noTz3+zheMGZXPraUNlPU4hhGgkL9URlak5AH5xTF+O6p/Js19t4uv1pVEpsyVuX4gtHWydz7YEaRatdX3YHb7e6pANpdQnSqnlTVzOOpwKt1DeFUqphUqphSUlJZEoQsSjgDGrtrbYeWfJTh75aC0juqdw37kjSY/xyCIhhIg3dVNz2KMwNYdJKX43ZRBDcpN59OO1rNkd+znUajyBqCyb1V7a8i6VKKXOrLsRDrJaDYm11idprUc0cZnTxrrtAHo2ut0jfF9z5T2ttR6ntR6XnZ3dxiJEhxcO0rbWmLhzzgq6pTh45PwCuqd1nmVBhBCiPZlNit6ZCVEZTGW3mLnttGFkJNq4572VFFfFfjWACpef3ZWxr0dbtCVIuwq4VSm1VSm1DbgZuDKy1QLgB2CgUqqvUsoGXICxsLsQDaxO9hxxKzevGQQKHjpvFINzU2JdKyGEiGt2i5nemdEZ8ZnqtHLH6cPwB0PMmruSWm/sZ/UqqfZSVhPZ9U3bQ6tBmtZ6g9b6CIwRlkO11kdprdcfTqFKqXOUUtuBI4H3lFIfhu/vrpSaFy43AFwDfAisAl7TWq84nHJF57PXa+KKlQUsdOUw68zhHNEvM9ZVEkKIDiHRbiE/Sq0OPTMS+P1PhrKjws2DH66O+dQcALsqPVR54ntVgmYns1VK/Vxr/ZJS6ndNbddaPxrRmh2GuJzMVrQ7ly/AHz7Yxu6tazlpfAEXnXkKFplqQwghDsrOCndURnwCfLhiN08uWM/UkXlcNalfzFeAiZfJbpubzLalIR6J4b9NLXQY+xBYdGn+kOYv31WxfctGXrXfh6/nExKgCSHEIchLdeDxB6n1Rn7C11OG57Kzws2bP+4gP83BmQWxnbRBa9hcVkv/7CRslvg7h7Q0T9o/wlc/0Vp/3XibUuroiNZKiBaEQvD6siqeLvLwfzkBqAKbPbH1BwohhDhA3YjPDSW1+AKRn/D1kqP6sLPSzb++2kR+WgJje6dHvMyWBIK6PlCLt5Vp2hI2PtHG+4SIiq+31jDr61oGZZi4fGT4d4Y1IbaVEkKIDsxiNkVtIIFJKa6fMpjemYk8+OFqtpW7Il9oK7x+Y7LbeJtDraW1O49USl0PZCulftfochcgU7iLmNhQ5uGGT6pJtCoeOzmVVGt4lJA1OsudCCFEZ+WwmumZEZ0fvA6rmdunDsVmNnH33JVUx0EH/lpvkB0V8TWHWkuZNBuQhNEkmtzoUgWcF/mqCbGvcref33xUSblX89AJyQzNcUIgPNeNZNKEEOKwpTqtdEuxR6WsnBQHt04dSkm1lwc+WE0gDtbWLK/1s6c6fuZQa6lP2ufA50qp57TWWwCUUiYgSWtdFa0KCgHGmpy3za9gWUmQO45OYHLfJGNDryPg1PvAmRbT+gkhRGeRk+LA5QtS7Yn8fGZD81L4v+MH8JdP1/HPrzZx1XH9I15ma4orvdjNZlITrLGuSpv6pN2nlEpRSiUCy4GVSqkbI1wvIeoFQ/DU/6qYt8HPhcPtXFSYiqnuk5vUDXodCWZZAkoIIdpLz4yEqI12PGloN84uzOe9Zbt4f/muqJTZmm3lLly+2E+625Z3YFg4c3Y28D7QF7gokpUSorF3V1fz1GI3R+dbuPWYVKzmRj1by9bDlm+ISm9XIYToIuqWjorWV+vMo/owtnc6//hiI8t2VEan0BZoDVvKXDFvgm1LkGZVSlkxgrR3tNZ+ZJ40ESVFO9384YtqeqeYeGRKGon2/VroV8+DBX8CJEgTQoj25LCa6Zkenf6+ZpPixpMHk5vi4IEPVsdFv7BAUOPrAEHaP4DNGJPbfqGU6o0xeECIiCqu9vHbj41fVI9OSSE3pYkmzYDHGNkpmTQhhGh3qQlWspKj050k0W7h9tOG4g+G+NO8VXgDkZ9cN961Ze3Ox7XW+VrrqdqwBTg+CnUTXZjHH+TGTyrYUhVi1qRkRndv5tdcwAMWB6j4mylaCCE6g9wUB4n26My81SM9geunDGZjSS1PLlgfd/OWRVtLy0LVU0qdBgwHGk9GNSsiNRJdXjAEj3xbyRfbAlw12sGZQ5pamSws4AWLHWnuFEKIyKhbkWB9SQ3+QOSDpgl9M7hwYi9e+n4r/bOTOLswtktHxVKr6Qel1N+BGcC1GGfC6UDvCNdLdGFvrarin0UeTuht4TdHpNLikpwBD1ic0twphBARZDGb6JURvYEE08f15Mh+mfz7600UbauITqFxqC1tREdprS8GyrXWfwSOBAZFtlqiq/pxp5s7v6ihX5qJ+09Mx2FtJcV+1HVwzG+QTJoQQkRWgs1Cbmp0VncxKcVvThpIj/QEHvxgNburYj+QIBbaEqTVrZHgUkp1B/xAXuSqJLqq4hpjoIBZGUs+5SS1YSLB9N6QOUAyaUIIEQVZSXZSndGZ5DXBZuG2qUMJac19XXQgQVuCtLlKqTTgIWAxxkjPVyJYJ9EFeQNBbv6kgq1VIe4+LomRuc62PXDDfNi9TAYOCCFElOSnO7Fbo/Od2z3Nye+mDGZjaS1//WxDlxtI0JbRnXdrrSu01m9g9EUborX+Q+SrJrqKUAie+L6Sz7YG+GWBg9MHtzBQYH/f/hXWvI80dwohRHSYTSqq/dMm9M3ggvE9mb96Dx+s2B2dQuNEs6M7lVLntrANrfWbkamS6Go+2lDNXxd7ODrfwu+ObGWgwP4CbrDKwAEhhIgmh9VMfpqT7eXu1nduBz+d0It1e2p4+ouN9M1KZEhuSlTKjbWWTodntHA5PfJVE13B2lIPty6oIS/JxENT0lofKLA/mYJDCCFiIj3RRnpidPqnmZTi+imDyEyycf/7q6lw+aJSbqw1m0nTWl8azYqIrqfKHeC3H1Xi8mue+kkK3ZtaUaAloYBxsciKA0IIEQvdU524fUE8/sgvn5TssHLrT4Zy4+ylPPjhGu4+awRmU+f+7m82k6aUeqzR9V/vt+25yFVJdAX+kOaPX1SwojTIzUcmckSPxIM/SCA8JNsSnSHhQggh9mUyKXpGsX9av+wk/u/4/izbUcmL322OTqEx1FJz56RG1y/Zb9uoCNRFdBFawytLq3hjjY9pg238vDDl0P7BLU6Y/hwMPFlGdwohRIzU9U+LlhOGdOMnI3J5Y/EOvt9UFrVyY6GlM5tq5roQh2XxTjf3f1PL0Ewzd0xKw3qo6WqTGdL7gDNNmjuFECKG0hNtpCVEp38awC+P6Uf/7ET+/MnaTj3RbUtBmkkpla6Uymx0PUMplQFEZ6VV0emU1Pq54ZNKLCbFo1NSSXW2afnYprn2wrLXoWoX8jtCCCFiKz8tevOn2SwmbvnJUAAeeH81vkDk+8TFQkuvZiqwCFgIpGBMZLsofDmIiayEMPiCIf4wv4LNlSHumpTI0JzD7EtWvQu+fQoqt0omTQghYswU5fnTclMc/PakQawvqeGfX22MTqFR1tLozj5RrIfo5LSGZxdV8sEmPxeNsHPWkHaY42afgQMSpAkhRKw5rGbyUh3srIhOE+TEvplMG5PPG4t3MCwvhcmDc6JSbrRIb2sRFV9vreXRH9yMzjFzy9FpBzdhbXMCXuOvxSEDB4QQIk5kJtlJOZyuLAfpoiP6MLx7Ck8uWM/Wva6olRsNcmYTEbe7ysfN86tJsioePimNRHs7dWlsnEmT5k4hhIgbPdITsFqi871sNiluPHkwTquZBz5YjcffeRZilyBNRJTHH+TWBRXsqglxz3FJ9M+yt9/B/eEgzSrNnUIIEU/MJkXP9Oj1T8tMsvO7KYPYttfF0190nv5prQZpSqn+Sil7+PpkpdR1Sqm0iNdMdHihEDy7uIr5WwJcOsrBKQPbebzJgBPgp/+FhEzJpAkhRJxJtFvITm7HH+atGN0rnenjevLxqmIWrNkTtXIjqS2ZtDeAoFJqAPA00BP4z+EUqpSarpRaoZQKKaXGtbDfZqXUMqVUkVJq4eGUKaLvu201PLbQzZhuZn53xEEunN4WFgck54LJgmTShBAi/uQk20lory4ubfCzCb0Y3j2Fv362nm3lHb9/WltOmyGtdQA4B3hCa30jkHeY5S4HzgW+aMO+x2utC7XWzQZzIv4UV/u5ZUENiVbFgye2Yz+0xnYsgh9fNq5LJk0IIeKOUkazpylKnavq+qdZzSYe/GA13kDH7p/WlpfNr5T6KcbSUHPD9x3WtMJa61Va6zWHcwwRv7yBEHd8VsG2qhB/PDaJAe3ZD62xbf+DH180rsvoTiGEiEs2i4keaQlRK6+uf9rmMhfPfLkpauVGQlvObJcCRwL3aq03KaX6Ai9Gtlr1NPCRUmqRUuqKlnZUSl2hlFqolFpYUlISpeqJ/YVC8HxRFR+G50ObOjgpcoUFPGCpCwAlkyaEEPEqNcEa1WWjxvXOYNqYfD5csZsv1nbcmKDVIE1rvRK4GWPFAbTWm7TWD7T2OKXUJ0qp5U1czjqI+h2jtR4D/AT4P6XUpOZ21Fo/rbUep7Uel52dfRBFiPa0aKeLR//nYlS2mZuOSsVyqOtytkXAE57IFmnuFEKIONc9zRm1aTkAfj6xN0Nyk3nqs/XsruyY63u2ZXTnGUAR8EH4dqFS6p3WHqe1PklrPaKJy5y2Vk5rvSP8dw/wFjChrY8V0VdW6+f3C6qwmRQPnJhCkiPCkxn6JUgTQoiOItrTcljMJm44eTAKePijNQSCHW99z7Y0d96FERxVAGiti4B+EatRmFIqUSmVXHcdOBljwIGIQ75giD99Vcn68hC3HpXAkGxn5AsNeMPNnRKgCSFERxDtaTm6pTi45oSBrCmu5uXvt0at3PbSpoEDWuvK/e47rHBUKXWOUmo7Rl+395RSH4bv766UmhferRvwlVJqCfA/4D2t9QeHU66IDK1hzsoa3ljj4+yBVqYNT4nOL6Upf4TTH5UsmhBCdCA5yXactuhNy3HMgCxOHtaNNxZvp2hbRdTKbQ9tCdJWKKV+BpiVUgOVUk8A3xxOoVrrt7TWPbTWdq11N631KeH7d2qtp4avb9RaF4Qvw7XW9x5OmSJyVpe4ufebWvqmmrj92DSs5mjlsu1gS5KRnUII0YEopeiZ4Yzq7+vLj+1HfrqTRz9eQ6XbH72CD1Nbzm7XAsMBL/AKUAX8JoJ1Eh1IldfPbQuqcAc0fzo+mayk6I3eoehlWP8J0twphBAdi91ipntaFLrFhDmsZm46ZTA13gCPfbIWrXXUyj4cbRnd6dJa36a1Hh8ePXmb1rpjDpMQ7cof0jz5fTWLi4NcM9bJhB6J0a3AqneNudKkuVMIITqcjEQbyZEeYNZI36wkfnF0XxZuKeedJTujVu7haPbVUUq9izFPWZO01mdGpEaiw/h8Yw3/WuLh2B4WLhuT3P7LPrVGBg4IIUSHlp/uZF1xDcFQdDJbp43M48etFTz3zWZG9Uijb1aUkwsHqaUQ9uGo1UJ0ODsqvNz1ZS0ZDsXdk1NIsEXv11A9vxssTsmkCSFEB2U1m8hPc7J1b3TW2VRKcd2JA7nmlcU8/NEaHj2/ALsleoMYDlazZ1at9efRrIjoONz+IH/6uood1SEem5JE73RH9CuhdUMmTQYOCCFEh5WaYCXNY6XCFZ0O/alOK785cRB3vbuC577ZzJWT+kel3EPRlslsByqlZiulViqlNtZdolE50QTXXgjFbsHYYAhmr6jmvQ1+zh9i4yeDkmKTyAr6jL9WB9LcKYQQHVu0VyMY2zudMwu6M3fpLhZu2Ru1cg9WW1IQ/wb+BgSA44EXgJciWSnRjK3fwcvTYfalsPPHmFRhZbGLh75zMzDdxI1HpWKLeke0MIsdLp8Poy6Q5k4hhOjgzCZFfhRHewJccmQfemck8JdP11Hh8kW17LZqyxnWqbX+FFBa6y1a67uA0yJbrU6sYhts+Qb0Qc4HXLwCPr4T0nsbzXtmm3F/wGM0/UVBucvPXV9W4w1q/jipjdNt+Gph42fw5SOw9FWjvu1FKTCZkUyaEEJ0fMkOKxlJtqiVZ7MYy0bVegM8Pn9dXE7L0Zbe3l6llAlYp5S6BtgBJEW2Wp2Irwa2L4TckZCQCbuXwRcPQl4BTL4FkvNaP0bldvjgFkjMgtMeAUdqQz+sb56EsvUw9HTIHQWpPSOSWfIGQjyzqIpFu4P8ZpyD8T0TWn7A9oVQ9B/YtQR00Fhj02KH4ecY2117wZneel1rS2Dxi0awd8LtDfvXlsDCf8Ows6DH+MN/gkIIIWIuL8VBjSeALxCddTb7ZCUy86g+PPPlJuYt381pI9twTo6itgRpvwYSgOuAu4ETgEsiWalOwVcD3/0d1rxvBCnH/NYIKPpNNgKORf+G2b+AI6+BwVNbDlaScqD/iTBqhhHYNJY7AnYshC/Cg3Gd6TDwZDjiV8btbd8bf5XZyDolZB50IKc1fLe1lmeWeDmiu4VfjEnGatrv8Z4KWD8fek6A1B5GnzH3Xhh1PvQ6EroNM14Ts83IIs65GqyJMHCK8Zj0vvvWyVNlBHkr3jSOdcIf9t3uLoc186D3UW1+HkIIIeKbyaToke5kY0lt1Mo8fVR3Fm0p59mvN1HQI5Ue6a0kIaJIHUx6TymVDlToeMwJNjJu3Di9cOHCyBUQCsLupc1v3/INfPWokS0aeib0P8EIUkyNYuLq3fDZ/bCrCMZdBmMuOvA4nvCSqY7UluujNVRuM+q0axmk5sOYi41t/zq5oZN9naFnwrG/Mx5XvskI7ErWQuka2LMaxl0KWQOhZg/UlrDT3p+fv1PBXrfmP2enMqxb+ANclyVc97HRX04HjeBw1Azj2M0FgqEArPkAVr0DpWuN+xKzYPzlMOgUWP+p8fr5XEbAOXYmpIR/3fhcYEuAXUvh3etg6sMw4CTI6NvyaySEEKLD2Fnhpqwmev3Eymq8XPvKj+SmOnhw2igs4f7W/XMSozLFlFJqkdZ63P73tzSZ7R3Aa1rr1UopO/ABUAAElFI/01p/ErnqdnCr3wNbMky5B3KGNL1Pcq6xOPjKOdB3knHfireNZY7sKWBPNpoxTWY4+2/hvlfNUArSehmXIafvu+2Mx43gKRQ0/lbthJR8Y1vVDiOb13AgSOvZEByufBuK/oOy9uIY1/GMnjCJwVY/0Ms43sszwF8LzgwYOc0IqDIHNNSpOSaL0Tw79HQjENz+g5Hxc6QY21PyIK8Qxl8GGf0aHrf8DVjyX5j2TEPfNotDBg4IIUQnk5vioDqKzZ6ZSXauPn4AD3ywmtcWbuNnE3tHpdzWNJtJU0qtAEZorbVS6grgp8BJwCDgea31hOhV8+BEPJPmrYanjzMW97Ymgi3R6G9VcIHR1OepAqsTzAe5juXq94wgzVttXEJBOObX0OfYyD2PLd8aTZVZAyFrkPFcwgKeWlZ88x6mtfMYadps3Jk5AKb907i+9kOjKTZ35L5ZwkgpXQdvX200jw46BT6+w6hLjwnGgAohhBCdRq03ENVmT4BHPl7DF2tLeOi8AgZ1S47fTBrga9SseQrwX611EFillIrB9PJxJBiApFyjua92D5TXGv3MLA446pqGjNDBGnKacYkWezIMOrnZzZtrTFy56ViwHsubk0vpXrPCCOTqDDolCpVsJGsgTLwCvn3KWG3A6pRMmhBCdFKJdgtZyTZKq6PX7HnlpP4s31HFox+v5bEZhVErtzktBVtepdQIoBhjfrQbGm2Ln151seBIgVPujXUtIqrSHeCR72rY49L85aQkcvvmgWlkrKsFI6YZzaM7i+CcvxuZS5mCQwghOqVuyUazp9cfnWbPJLuF35w0kNvfXs5z32zmoemjolJuc1qaJ+3XwGxgNfBnrfUmAKXUVCA2M6mKqPAFQ8xZXc37G41VBU7qn4gpXlZeUiY47hZjsMPejQ33CSGE6HTqRntGU0GPNM4q6M57y3bx1brSqJa9v5bW7vweOKDXu9Z6HjAvkpUSsaM1rC528+j/PPRLM3Hd+CSctjhbfDYhA85/wegHCNLcKYQQnViCLfrNnhcf2YfF2yq47e3lTOybSWrCQfYxbyddu2+ZOMDeWi8PfldLrV/z6ElJ5KbEYPH0tqgL0ABp7hRCiM6tW7KDKnf0RnvaLCaunzKI3ZVukh2xC5WknUjUc/uDzF7p4qvtAS4ZaefIXgnx08zZEsmkCSFEp2YyKfKj3OzZPzuJi4/qg2n/ydujqCOcgkUUhEKwYrebJxa5GZZp5pejE3Fa46yZs1kSpAkhRGeXZLdEdW3PeNBqkKaUmq6USg5fv10p9aZSakzkqyaiaU+1mwe/c+EPwa1HJ5CTFKfNnE2RgQNCCNEl5KY4sFq6zg/ztpzd/qC1rlZKHYMxme2/gL9Ftloimmp8AV5f5eZ/uwJcVmBnbPcO0sxZR5o7hRCiSzCbFPlp0W32jKW2nIqD4b+nAU9rrd8Dula+sRMLhDRLd7r522IPBTlmLhmVGH+jOVslQZoQQnQVyQ4raTEabRltbQnSdiil/gHMAOaF1/HsSHkW0YLiKg8Pfe9CAzcfmUB2R2rmrCOZNCGE6FK6pzmxmDv/d39bgq3zgQ+BU7TWFUAGcGMkKyWio8od4L8r3PxYHOTKQgcFec6O1cwphBCiSzKbFN1TO3+zZ1tOyQ7gM6BMKZUBeIEFkayUiDx/ULN0t4tnlngYm2vmguFOEqOwiGxESCZNCCG6nNQEKynODnreaqO2BGmLgRJgLbAufH2zUmqxUmpsJCsnImd3pZuHv3djAm6cmEB2cgf+RSKjO4UQokvqnta5W4Da8tQ+BqZqrbO01pnAT4D3gKuBv0ayciIyyl1+Zq/2ULQnyBWFDkbkOTF36A+5ZNKEEKIrsppN8bsyTjtoy6n5CK31h3U3tNYfhe/7DrA3/zARj7yBECuL3Txd5GF0NzPnD3OS1FGbOetIc6cQQnRZmUl2EuwdbVaCtmlLkLZLKXWzUqp3+HITUKyUMgOHtIiWUuohpdRqpdRSpdRbSqm0ZvY7VSm1Rim1Xil1y6GUJRpoDcWVbh75nxsN3DDBSXan+AUiQZoQQnRl+WnOTvl7vS1B2s+AHsDb4Uuv8H1mjJGfh+JjYITWehRGX7ff779DOAh8CqN5dRjwU6XUsEMsTwDlLh9vr/WyuDjI5QUORuQmYI3hmmTtpjP+ZwohhGgzh9VMTnLna9xrtZ1La10KXNvM5vWHUmi4ybTOd8B5Tew2AVivtd4IoJT6L3AWsPJQyuzqPP4Qa0o8/L3ImLR2+lAHqZ1lVIwMHBBCiC4vO9lOhduP139IjXxxqdWztFJqEHAD0Kfx/lrrE9qpDr8AXm3i/nxgW6Pb24GJzR1EKXUFcAVAr1692qlqnYPWUFzl5s8/eAiF4PrxTnJTO0MzZx3JpAkhRFenlLFk1MaS2lhXpd20JZXyOvB34J80LBHVKqXUJ0BuE5tu01rPCe9zGxAAXm7rcZujtX4aeBpg3Lhx+nCP15mU1fp4f4OP/+0K8KvRdkbkObB17OGc+5LmTiGEEECi3UJ6opXyWn+sq9Iu2hKkBbTWB72gutb6pJa2K6VmAqcDJ2qtmwqqdgA9G93uEb5PHAS3P8imvR7+utjD0Ewz5w91kO7sbEuvSpAmhBDCkJvioModIBjq+PmatqRT3lVKXa2UylNKZdRdDqdQpdSpwE3AmVprVzO7/QAMVEr1VUrZgAuAdw6n3K4mFII91V6eWuzBHdBcP8FBbqqj8yWeOt0TEkIIcagsZhN5naRLT1syaZeE/zZer1MD/Q6j3Ccx5lj7WBkn2O+01lcppboD/9RaT9VaB5RS12CsG2oGntVarziMMrucMpeXz7d4+WxrgEtG2CnIdeC0dsK5ZGTggBBCiEbSE22Uu3zUetvcSysutWV0Z9/2LlRrPaCZ+3cCUxvdngfMa+/yuwK3L8iOCh+PL/TQN9XEhcPtZCR2vuHJBsmkCSGE2Fd+upN1xTU02aGqg2g1BaGUsiqlrlNKzQ5frlFKWaNROXFoQiEorvbyzBIPez2a6yc4yUt1dPCln1ogzZ1CCCH2Y7d0/LnT2nLa/hswFmOdzr+Grx/0QAIRPWUuLwt3+nhvg59zB9kYk2cn2dFJ5kRrigRpQgghmpCdbMdu7bgZiracucdrrQsa3Z6vlFoSqQqJw+P2BSmu8vHoDx7yEhUzR9rJ7uC/JFomAZoQQoimKaXonuZkUwedO60t4WVQKdW/7oZSqh8HMV+aiJ5QCPbUeHl5pZedNSF+M95J9zQ7VnMnDmQkiyaEEKIFSXYLaQkds5dWWzJpNwILlFIbMdIWvYFLI1orcUjKXF7WlPp5bZWPk/taObKHrRPOibY/CdKEEEK0LDfVQZXHT6iDrRjVltGdnyqlBgKDw3et0Vp7I1stcbDc/iBlNUYzZ5JNcWWh0czZ6RNNMv2GEEKIVljNJrqlONhV4Yl1VQ5Ks2c4pdR4pVQuQDgoKwTuBh463MlsRfuqm7T23fV+VpcF+dVoBz1SbZ1zTrT9dfooVAghRHvISrLjtHWsH/Yt1fYfgA9AKTUJuB94AagkvEamiA97XT62VwZ4dqmHcblmpvSxkpnUmQcLNCZBmhBCiLbpnuaMdRUOSktBmllrvTd8fQbwtNb6Da31H4AmJ6MV0efxhyiv9fLEIg9aw3XjnGQlO7CYukjwIpk0IYQQbZRgs5CR1HH6arcYpCml6vqsnQjMb7StE0+61XFoDXuqPXy5PcB3OwNcPNJO3zQrqc6u9PZIkCaEEKLtclMcmDtIIqOls/krwOdKqVLADXwJoJQagNHkKWKs3OVjryvIk4s8DEg3MW2QjZyUrtLMGSYDB4QQQhwEs0mRl+pge7k71lVpVbNBmtb6XqXUp0Ae8JHW9atfmYBro1E50TxvIMTeWh/PLvNQ4dXMmpRARqINu6WLBS3S3CmEEOIgpSfaKKv14fbF97SvLbaLaa2/a+K+tZGrjmirPVVeVpUFeXedn7MG2RieZenEC6i3RII0IYQQBy8/zcn6PTWxrkaLuljapXMod/mp9QV47Ac3GU5j6aesJHvnXUC9JZJJE0IIcQicNnPcDyLoiqf1Ds0XDFFW4+WttT42VIT4vzEOshIsnXsBdSGEECICuiXb43oQgQRpHUxJtY/i2hDPL/cysbuFY3tYOvkC6q2QgQNCCCEOkcVsIjfVEetqNEvOcB1IlTuAyxfgqcXGnGjXjHGQltAFBws0Js2dQgghDkNGog2nLT5X6OnCZ/eOJRDSlNZ4+Wa7n292BLhohJ0eKeYuOligMQnShBBCHJ7uafGZTZMgrYMoqfZS4wvx5GIPfVNNTBtsIzOxiw4WaEwyaUIIIQ5Tgs1CeqI11tU4QFc/xXcINb4ANd4AL63wUuLS/Hq8gySbhZQutbJAcyRIE0IIcfhyUxyY4iwqirPqiP2FQkYWbXNlkDfW+Di1n5XhWV18sEBjkkkTQgjRDixmEznJ8dXsKUFanCur9eIPhHh8oYcEq+KXBXZSnVYcVnnrABndKYQQot1kJdmwx9H5NX5qIg7g9gepcPv5ZLOfZSVBfllgJ8NhJiMxviffiy7JpAkhhGgfShnresYLCdLilNZ1gwU0Txd5GZJp5tR+VjISbVjieOK9qJPmTiGEEO0o2WGNmz7fEqTFqXK3D28gxL+XeajyaX49zoHTaibVGX+jT2JLgjQhhBDtKzfVERc5AAnS4pAvGGJvjY+1e40F1M8caGNAupmsJHtcfGjiirwgQggh2pndYo6LAXrxkc8T+9hT5SWoNU8sdJPuUMwcYSfJbiEhTmdEjikZOCCEECICspPsBEI6pnWQM1ycqXIHcPuDvL/Bz+q9Ia4sdJBkU2QlxT6ij0+SSRNCCNH+TCaFLcbLLkqQFkfqln6q8ob411Ivo7LNHN/bQnqiDatZgpEmSXOnEEKITiomzZ1KqYeAMwAfsAG4VGtd0cR+m4FqIAgEtNbjoljNqCutMZo5/73MS61fc81YB1aLiXSnTLkhhBBCdDWxyqR9DIzQWo8C1gK/b2Hf47XWhZ09QHP5glR7AqzdG+S99X7OHmijb5qZrER73C1TEVckkyaEEKKTisnpX2v9kdY6EL75HdAjFvWIF1pDSY2XkNY8schDmkNx8Qg7DquZZIeM7WiZBGlCCCE6p3jI0fwCeL+ZbRr4SCm1SCl1RUsHUUpdoZRaqJRaWFJS0u6VjKRylw9fIMRHm/ysLgtyeYGdRJsiWwYLtE5GdwohhOikIpamUUp9AuQ2sek2rfWc8D63AQHg5WYOc4zWeodSKgf4WCm1Wmv9RVM7aq2fBp4GGDduXGzHzB4EXzDE3lof1T7NP5d4GZ5l5qQ+VpIdFlmfsy2kuVMIIUQnFbEgTWt9UkvblVIzgdOBE7XWTQZVWusd4b97lFJvAROAJoO0jqqk2ocGnl/modpnDBYwKUWmZNHaSII0IYQQnVNMUjVKqVOBm4AztdauZvZJVEol110HTgaWR6+WkVftCeDyBdhQHuTd9X5OH2BlQLqxgLpV1udsG8mkCSGE6KRi1Z72JJCM0YRZpJT6O4BSqrtSal54n27AV0qpJcD/gPe01h/EprrtLxgyptzQWvPUYg9JVsUlIxxYzIo0mXLjIEiQJoQQonOKydBBrfWAZu7fCUwNX98IFESzXtFUVuslENJ8tjXAspIgvxnnIMWuZMqNgyUDB4QQQnRScoaLAbc/SKXbjzugeabIw4B0E6f2s+KwyJQbB02aO4UQQnRSEqTFQGm1F4BXV3opcWv+b4wDs0mRlSTNnAdPgjQhhBCdkwRpUVbh8uMJhNhVE+K11T5O6G1hRLaFJLsFp80c6+p1PJJJE0II0UlJkBZF/pCmrNbIov3jRw9mE1xe4EABWTLlxiGSIE0IIUTnJEFaFJXVeAlpWLQ7wNc7AvxsmJ2sBBOpCVasZgk2DokMHBBCCNFJyRkuStzhBdQDIc1fF3vIS1RMG2zDrBQZCZJFO2TS3CmEEKKTkiAtCuoWUAd4Z52PrVUhrhrjwGZWpCfaMMu7cOgkSBNCCNFJSXgQBRVuP95AiEpviBeXexnTzcyR3S1YzSbSnNZYV68DkwBNCCFE5yVBWoT5Q5qycBbt+WVeXAH41RgHSikyk2ySCDoc8uIJIYToxCRIi7DSai8a2FQR5L0NxvqcfVLNOKxmku0yce3hkSBNCCFE5yVBWgTV+gLUeANorfnbjx4SrXDJCGOQgExc2w4kkyaEEKITkyAtQrSG0hofAN/uDPBjcZCLRthJsZtIdlhwWmXi2sMm028IIYToxOQsFyEVbj++QAhfUPOPH730SjFxxgAbCshIlCxa+5BMmhBCiM5LOkVFQOPBAnPW+dhZE+JPxyVgMSlSnVZsMudG+5DmTiFEG/j9frZv347H44l1VUQX53A46NGjB1Zr22Z2kCAtAspqjMEC5Z4QL63wMiHPwvg8CyYF6ZJFa0cSpAkhWrd9+3aSk5Pp06cPSn7ciRjRWlNWVsb27dvp27dvmx4jKZ125gqvLADGlBveAFw12hgskJ5ow2KSL4h2I1+2Qog28Hg8ZGZmSoAmYkopRWZm5kFldCVIa0fGYAGjmXNTRZD3N/o5Y6CNnilmLGZFmkOyaO1KBg4IIdpIAjQRDw72cyhnuXZUt7KA1pq/h6fcuGi4kUXLSLBjkle7ncmXrhCiY7j33nsZPnw4o0aNorCwkO+//77F/WfOnMns2bMPq8zPPvuM1NRUCgsL6y+ffPIJAElJSYd0zMceewyXy1V/e+rUqVRUVDS7/9///ndeeOEFAJ577jl27tx5UOVNnjyZwYMHU1BQwNFHH82aNWsOqd515V9zzTUH1Kspmzdv5j//+U/97YULF3LdddcdctmHSvqktRN/SLO31sii/bArwOLiIL8abSfFrrBbTKQ65aVud/LLWAjRAXz77bfMnTuXxYsXY7fbKS0txefzRaXsY489lrlz57bb8R577DF+/vOfk5CQAMC8efNa3P+qq66qv/7cc88xYsQIunfvflBlvvzyy4wbN46nn36aG2+8kXfeeWef7cFgELP54Ka1alyvptQFaT/72c8AGDduHOPGjTuoMtqD5HbaSVmNl5CGQEjzjyIv3ZOMKTcAMmTiWiGE6LJ27dpFVlYWdnt4MvOsrPpAZdasWYwfP54RI0ZwxRVXoLU+4PGLFi3iuOOOY+zYsZxyyins2rULgMcff5xhw4YxatQoLrjggkOqW01NDSeeeCJjxoxh5MiRzJkzB4Da2lpOO+00CgoKGDFiBK+++iqPP/44O3fu5Pjjj+f4448HoE+fPpSWlgLwwgsvMGrUKAoKCrjooosAuOuuu3j44YeZPXs2Cxcu5MILL6SwsJD33nuPs88+u74eH3/8Meecc06LdZ00aRLr168HjEzg9ddfT0FBAd9++y0vvfQSEyZMoLCwkCuvvJJgMAjAv//9bwYNGsSECRP4+uuv649VVy+A9evXc9JJJ1FQUMCYMWPYsGEDt9xyC19++SWFhYX8+c9/5rPPPuP0008HYO/evZx99tmMGjWKI444gqVLl9Yf8xe/+AWTJ0+mX79+PP7444f0njQm6Z124PY3DBaYt8HP1qoQdx3jxGpWOK1mkmzyMkeEZNKEEAfpj++uYOXOqnY95rDuKdx5xvBmt5988snMmjWLQYMGcdJJJzFjxgyOO+44AK655hruuOMOAC666CLmzp3LGWecUf9Yv9/Ptddey5w5c8jOzubVV1/ltttu49lnn+X+++9n06ZN2O32Zpsc6wKNOm+88Qb9+/evv+1wOHjrrbdISUmhtLSUI444gjPPPJMPPviA7t2789577wFQWVlJamoqjz76KAsWLCArK2ufclasWME999zDN998Q1ZWFnv37t1n+3nnnceTTz7Jww8/zLhx49Bac/3111NSUkJ2djb//ve/+cUvftHi6/zuu+8ycuRIwAgiJ06cyCOPPMKqVat44IEH+Prrr7FarVx99dW8/PLLTJkyhTvvvJNFixaRmprK8ccfz+jRow847oUXXsgtt9zCOeecg8fjIRQKcf/99/Pwww/XZyE/++yz+v3vvPNORo8ezdtvv838+fO5+OKLKSoqAmD16tUsWLCA6upqBg8ezK9+9as2T7fRFIke2kFptZG2rvFpXljupSDHzFH5xkubKVm0yJGBA0KIDiApKYlFixbx5ZdfsmDBAmbMmMH999/PzJkzWbBgAQ8++CAul4u9e/cyfPjwfYK0NWvWsHz5cqZMmQIYTXt5eXkAjBo1igsvvJCzzz57n6xUY601d2qtufXWW/niiy8wmUzs2LGD4uJiRo4cyfXXX8/NN9/M6aefzrHHHtvic5w/fz7Tp0+vD94yMjJa3F8pxUUXXcRLL73EpZdeyrfffttsH7ELL7wQp9NJnz59eOKJJwAwm81MmzYNgE8//ZRFixYxfvx4ANxuNzk5OXz//fdMnjyZ7OxsAGbMmMHatWv3OXZ1dTU7duyoz+I5HI4W6w3w1Vdf8cYbbwBwwgknUFZWRlWVEfifdtpp2O127HY7OTk5FBcX06NHj1aP2RwJ0g5TpTuAJ2CkVV9Z6aXKq7my0IFSiiS7LP8UWZJJE0IcnJYyXpFkNpuZPHkykydPZuTIkTz//PNccMEFXH311SxcuJCePXty1113HTA9g9aa4cOH8+233x5wzPfee48vvviCd999l3vvvZdly5ZhsRzcaf3ll1+mpKSERYsWYbVa6dOnDx6Ph0GDBrF48WLmzZvH7bffzoknnlif8Wsvl156KWeccQYOh4Pp06c3W/e6PmmNORyO+n5oWmsuueQS7rvvvn32efvtt9u1vm1R16QNxnseCAQO63iSijgMwRD1Kwvsqgnx1lofU/paGZhhluWfokGaO4UQHcCaNWtYt25d/e2ioiJ69+5dH5BlZWVRU1PT5GjOwYMHU1JSUh+k+f1+VqxYQSgUYtu2bRx//PE88MADVFZWUlNTc9B1q6ysJCcnB6vVyoIFC9iyZQsAO3fuJCEhgZ///OfceOONLF68GIDk5GSqq6sPOM4JJ5zA66+/TllZGcABzZ1NPbZ79+50796de+65h0svvfSg617nxBNPZPbs2ezZs6e+7C1btjBx4kQ+//xzysrK8Pv9vP76603WqUePHvUBndfrxeVyNfs8wchOvvzyy4DRDJqVlUVKSsoh178lkkk7DHtrvQTDnTz/ucSDWcGlI40oOtlhwW6RGDiyJEgTQsS/mpoarr32WioqKrBYLAwYMICnn36atLQ0Lr/8ckaMGEFubm59c11jNpuN2bNnc91111FZWUkgEOA3v/kNgwYN4uc//zmVlZVorbnuuutIS0s74PH790m7/fbbOe+88+pvX3jhhZxxxhmMHDmScePGMWTIEACWLVvGjTfeiMlkwmq18re//Q2AK664glNPPZXu3buzYMGC+uMMHz6c2267jeOOOw6z2czo0aN57rnn9qnLzJkzueqqq3A6nXz77bc4nU4uvPBCSkpKGDp06CG/vsOGDeOee+7h5JNPJhQKYbVaeeqppzjiiCO46667OPLII0lLS9vndWjsxRdf5Morr+SOO+7AarXy+uuvM2rUKMxmMwUFBcycOXOfvmx1AwRGjRpFQkICzz///CHXvTWqqZEkHd24ceP0woULI1dAKIh3exHb9rrQwMrSAL/+xMVFw21cPNKBAnpnJWKV1QUiKzEbUg+9rV8I0TWsWrXqsIIAETnXXHMNo0eP5rLLLot1VaKmqc+jUmqR1vqAOT4kk3aISmt8aIy28H8UeclwKKYPMbJoqQlWCdCiQl5jIYToqMaOHUtiYiKPPPJIrKsStyRIOwTBkMblMzoDfrU9wMrSIL8d78BpVZiUsbqAiAIZ3SmEEB3WokWLYl2FuCdnucPgD2r+ucRLn1QTp/Q15kFJT7Rhllc1OmTggBBCiE4sZuGEUupupdRSpVSRUuojpVST60QopS5RSq0LXy6Jdj1bMneDj501IS4vsGM2KSwmWUQ9uiRIE0II0XnFMufzkNZ6lNa6EJgLHDABi1IqA7gTmAhMAO5USqVHtZbNqPFpXlruY3Q3M+PzjFbj9ASbLKIeTZJJE0II0YnFLKTQWjdelyMRaGqY6SnAx1rrvVrrcuBj4NRo1K81r6z0Uu3TXBGeuNZqNpHqPPSlH8ShkCBNCCFE5xXTvI9S6l6l1DbgQprIpAH5wLZGt7eH72vqWFcopRYqpRaWlJS0f2Ub2VHu5q21Pk7qY2VAujHjcWaiTRI70SYDB4QQca6srIzCwkIKCwvJzc0lPz+//rbP52uXMiZPnszgwYPrj1s3D1rjRcQPRlFREfPmzau//c4773D//fe3+JijjjoKgM2bN/Of//znoMsUTYvo6E6l1CdAbhObbtNaz9Fa3wbcppT6PXANRtPmIdFaPw08DcY8aYd6nLZ45JN1KAUzwxPX2i0mkh0yUDbqJCoWQsS5zMzM+sW377rrLpKSkrjhhhvqtwcCgYNeyqkpTS2ddKiKiopYuHAhU6dOBeDMM8/kzDPPbPEx33zzDdAQpP3sZz9rl7p0dRFNRWitT9Jaj2jiMme/XV8GpjVxiB1Az0a3e4Tvi5lgSGM1m5g+2EZOovHypcvyT0IIIdqobub9iRMnctNNNx2Q8RoxYgSbN28G4KWXXmLChAkUFhZy5ZVXEgwGD6nMZ555hvHjx1NQUMC0adNwuVwAvP7664wYMYKCggImTZqEz+fjjjvu4NVXX6WwsJBXX32V5557jmuuuQaA4uJizjnnHAoKCigoKKgPzpKSkgC45ZZb6lc5+POf/8ykSZPqg1SAY445hiVLlhzSc+iKYpb+UUoN1FrXLWZ2FrC6id0+BP7UaLDAycDvo1G/5phNigfOHcGGZcYH02Exk2yXLFpMSCZNCHEo/n3agfcNPxsmXA4+F7w8/cDthT+D0RdCbRm8dvG+2y5976CrsH37dr755hvMZjN33XVXk/usWrWKV199la+//hqr1crVV1/Nyy+/zMUXX3zAvhdeeCFOpxOAKVOm8NBDD+2z/dxzz+Xyyy8HjKWh/vWvf3Httdcya9YsPvzwQ/Lz86moqMBmszFr1iwWLlzIk08+CbDP8k7XXXcdxx13HG+99RbBYPCA9ULvv/9+Hn74YebOnQtARkYGzz33HI899hhr167F4/FQUFBw0K9XVxXL6OJ+pdRgIARsAa4CUEqNA67SWv9Sa71XKXU38EP4MbO01geu2hoDKhwgZCZJFi12JEgTQnRM06dPx2w2t7jPp59+yqJFi+rX9HS73eTk5DS5b2vNncuXL+f222+noqKCmpoaTjnlFACOPvpoZs6cyfnnn8+5557bar3nz5/PCy+8AIDZbCY1NbXF/adPn87dd9/NQw89xLPPPsvMmTNbLUM0iFmQprVuqnkTrfVC4JeNbj8LPButeh0Mp9VMgq3lf7LDYnFAwBO543d0FkesayCE6IhaynzZElrenph5SJmzAw6TmFh/3WKxEAqF6m97PMb3vtaaSy65hPvuu++wy5s5cyZvv/02BQUFPPfcc3z22WcA/P3vf+f777/nvffeY+zYse2+CkBCQgJTpkxhzpw5vPbaa7LKwEGS4XGHIeJZtNQeYJJpPZpkcYJVgjQhRMfXp08fFi9eDMDixYvZtGkTACeeeCKzZ89mz549AOzdu5ctW7YcUhnV1dXk5eXh9/t5+eWX6+/fsGEDEydOZNasWWRnZ7Nt2zaSk5Oprq5u8jgnnngif/vb3wAIBoNUVlbus72px/7yl7/kuuuuY/z48aSnx8VUpx2GBGmHKMluwWmNYBZNmcGWBIlZkSujI3PKP7oQonOYNm0ae/fuZfjw4Tz55JMMGjQIgGHDhnHPPfdw8sknM2rUKKZMmcKuXbuaPMaFF15YPwXHSSeddMD2u+++m4kTJ3L00UczZMiQ+vtvvPFGRo4cyYgRIzjqqKMoKCjg+OOPZ+XKlfUDBxr7y1/+woIFCxg5ciRjx45l5cqV+2wfNWoUZrOZgoIC/vznPwPGQuopKSlceumlh/U6dUVK64jOVhET48aN0wsXLozY8YOBAIGdS7BbIhjjOlIhox8EA7BnBehQ64+JFxYnBNyRLSNnGFhkIXshROtWrVrF0KFDY12NLmvnzp1MnjyZ1atXY5JleZr8PCqlFmmtD+hUKK/WITCbVGQDNAB7SrgwSwfLGinI6BvZ/mLWRAnQhBCiA3jhhReYOHEi9957rwRoh0BesXhlT264ntj0aJ64lJBhBFCRrHOHClqFEKLruvjii9m2bRvTpzcxrYlolQRp8chs3zdTZHU0ZNbimoKk8AITznQwRWjwsDMtMscVQggh4ogEafGocRatTmJ2OxxYGQMSIiUxCyzhEa8mEyREYNCDLRnMMuJVCCFE5ydB2qFQpsgGO44msmaOFKND/uFIzoPswZHpL6ZMkNRt3/sSs2n3CWelqVMIIUQXIUHaoVDq0KfGUCajObP5HYxsUVOayqbZkiFrcOvNofYUSO5mNKNmDTJGj7anhKwDM1xmi9FHrd2o9q+3EEIIEackSDtUiTlGwNVmyghkcoZBeu/md7MlGU2FTWncz8tsh/S+kDXAmCE7va/x2KaYrJDWqEyT2Zjeo67/2OFS5gOzaHVaaqY9qNcPoxnYLOukCiE6nr/85S+MGDGC4cOH89hjj9Xff9ddd5Gfn18/x9m8efMA+Prrrxk1ahTjxo1j3TpjmeuKigpOPvnkfVYnaMzv93PLLbcwcOBAxowZw5FHHsn7779/SPUtKSlh4sSJjB49mi+//JKpU6dSUVFxwH77Lw4fLY0XfW/O5s2b+c9//hOV+tQtMN/e5Ix3qMwWI+iq3dP6vo40o6mxboZ8s9W4z1Nx4L5N9UerY2rUpJiYve8C4yaTEXiVrttvjjIF6X2aDm5SwnWq2AY62PrzaE5idvPBk9VpZPG8Vfveb7YZgWXQBxVb2jYPnDR1CiHawbLtla3vdBBG9mg5w798+XKeeeYZ/ve//2Gz2Tj11FM5/fTTGTBgAAC//e1vueGGG/Z5zCOPPMK8efPYvHkzf//733nkkUe45557uPXWW5udyuIPf/gDu3btYvny5djtdoqLi/n8888P6Tl9+umnjBw5kn/+858AHHvssYd0nFiqC9J+9rOftfkxgUAAiyV+QiPJpB2OpFayacoMmQONecP2X8IopTtN9tdqKUirKzMpZ98ArY7JDJn9jQCoTnIe2FuI8J3pkDvSaDJN7WncNh/EclfKbNSnJftn0+qaaG0JxkjNrEGtl6lMRmArhBAdzKpVq5g4cSIJCQlYLBaOO+443nzzzRYfY7VacblcuFwurFYrGzZsYNu2bUyePLnJ/V0uF8888wxPPPEEdrvRpaZbt26cf/75ALzyyiv1KwvcfPPN9Y9LSkritttuo6CggCOOOILi4mKKioq46aabmDNnDoWFhbjdbvr06UNpaSkA9957L4MGDeKYY45hzZo19cfasGEDp556KmPHjuXYY49l9erVgLFu6HXXXcdRRx1Fv379mD17dv1jHnjgAUaOHElBQQG33HJLi8dpTnPHv+WWW/jyyy8pLCzkz3/+M8FgkBtvvJHx48czatQo/vGPfwDw2Wefceyxx3LmmWcybNgwbrnlFp566qn649dlC2tqajjxxBMZM2YMI0eOZM6cOS3Wqz1IkHY4zFZwttDnKq1X8wGSxX5g8GKyGIHL4dYpc4BxrLp+aK1Ryig3McvIunUb3vbRpIlZRnDYksaDHhJzwoFko18qVmc4aGshmLSnNN8MLIQQcWzEiBF8+eWXlJWV4XK5mDdvHtu2bavf/uSTTzJq1Ch+8YtfUF5eDsDvf/97Lr74Yu677z6uueYabrvtNu65555my1i/fj29evUiJeXA/sk7d+7k5ptvZv78+RQVFfHDDz/w9ttvA1BbW8sRRxzBkiVLmDRpEs888wyFhYXMmjWLGTNmUFRUhNPZMGht0aJF/Pe//6WoqIh58+bxww8/1G+74ooreOKJJ1i0aBEPP/wwV199df22Xbt28dVXXzF37tz6YOz9999nzpw5fP/99yxZsoSbbrqp1eM0p6nj33///Rx77LEUFRXx29/+ln/961+kpqbyww8/8MMPP/DMM8/Ur5O6ePFi/vKXv7B27VpmzJjBa6+9Vn/s1157jRkzZuBwOHjrrbdYvHgxCxYs4PrrryfSqzbFT06vo0rqBq4yQB94f2vzeSXngnsvhALG7dayaG1lsUNG/4PLiO3PkQa1Ja3v19YmyLpsW3MDCcwWI7is2Gq8JvWUEURKU6cQooMaOnQoN998MyeffDKJiYkUFhZiNhs/bn/1q1/xhz/8AaUUf/jDH7j++ut59tlnKSws5LvvvgPgiy++IC8vD601M2bMwGq18sgjj9CtWxt+hAM//PADkydPJjvb+PF94YUX8sUXX3D22Wdjs9k4/fTTAWONzY8//rjFY3355Zecc845JCQYCYUzzzwTgJqaGr755pt9Jq31er31188++2xMJhPDhg2juLgYgE8++YRLL720/lgZGRmtHqc5TR1/fx999BFLly6tz7RVVlaybt06bDYbEyZMoG/fvgCMHj2aPXv2sHPnTkpKSkhPT6dnz574/X5uvfVWvvjiC0wmEzt27KC4uJjc3Hbq390ECdIOl8VmBB6usob77Cnh5sxWmMId7qt2NDyuvRxuRs6eZAw4CPmb38fiMLJgbdGWUZ5KGYMqUnsASjJnQohO47LLLuOyyy4D4NZbb6VHjx4A+wRal19+eX3AVEdrzT333MN///tfrr32Wh588EE2b97M448/zr333lu/34ABA9i6dStVVVVNZtOaY7VaUeHuM2azmUAgcEjPLxQKkZaWRlFRUZPb65pg657ToR6nOW05vtaaJ554glNOOWWf+z/77DMSExP3uW/69OnMnj2b3bt3M2PGDABefvllSkpKWLRoEVarlT59+uDxeA6qngdLzoLtIakb9f3LzLZ9R1K2JjG7YUqO9sqktZfWpruI1HQYJrMEaEKITmXPHmOQ2datW3nzzTfrO7Pv2rWrfp+33nqLESNG7PO4F154galTp5KRkYHL5cJkMmEymXC5XPvsl5CQwGWXXcavf/1rfD4fYIzQfP3115kwYQKff/45paWlBINBXnnlFY477rhDeh6TJk3i7bffxu12U11dzbvvvgtASkoKffv25fXXXweMgGjJkiUtHmvKlCn8+9//rn8ue/fuPaTjNCc5OZnq6ur626eccgp/+9vf8PuN5MPatWupra1t8rEzZszgv//9L7Nnz67P6lVWVpKTk4PVamXBggVs2bLlkOp1MCST1h4sdqM5zlNhjFg8mGkilDJGWVYXx99M+o5UcJW2sD0talURQoiObNq0aZSVlWG1WnnqqadIS0sD4KabbqKoqAilFH369KnvzA7GYIDnnnuOjz76CIDf/e53TJ06FZvN1uTUEvfccw+33347w4YNw+FwkJiYyKxZs8jLy+P+++/n+OOPR2vNaaedxllnnXVIz2PMmDHMmDGDgoICcnJyGD9+fP22l19+mV/96lfcc889+P1+LrjgAgoKCpo91qmnnkpRURHjxo3DZrMxdepU/vSnPx30cZozatQozGYzBQUFzJw5k1//+tds3ryZMWPGoLUmOzu7vm/e/oYPH051dTX5+fnk5eUBRjPxGWecwciRIxk3bhxDhgw56DodLBXpTm+xMG7cOL1w4cLoFur3gN916JO3usvjr9+V1rB7WdPTc5jt0G1Y9OskhBAHadWqVQwdOjTW1RACaPrzqJRapLUet/++0qbUXqyOw5tdP94CNDCyfM01acoi50IIIURESZAmWtZckCbLMwkhhBARJUGaaJk95cAJe802sCU2vb8QQggh2oUEaaJlJtOBU4PIgAEhhBAi4iRIE63bv/+Z9EcTQgghIk6CNNE6e2pDk6c0dQohhBBRIUGaaJ3J1LCupgwYEEKINisrK6OwsJDCwkJyc3PJz8+vv1036ezhmjx5MoMHD64/7nnnnQc0LAx+sOrW5azzzjvvcP/997f4mKOOOgqAzZs3NzmHW0s+++wzUlNTKSwsZMiQIdxwww0HXefOSiazFW3jTAdvlfRHE0J0fDt/bN/jdR/d7KbMzMz6JY7uuusukpKS9glCAoEAFsvhn4pffvllxo07YJqtQ1JUVMTChQuZOnUqYKzPWbdGZ3O++eYboCFIq1tRoa2OPfZY5s6di9vtZvTo0ZxzzjkcffTRh/YEOhHJpIm2caQaTZ32pFjXRAghOrSZM2dy1VVXMXHiRG666aYDMl4jRoxg8+bNALz00ktMmDCBwsJCrrzySoLBJiYXb4NnnnmG8ePHU1BQwLRp0+qXYnr99dcZMWIEBQUFTJo0CZ/Pxx133MGrr75KYWEhr776Ks899xzXXHMNAMXFxZxzzjkUFBRQUFBQH5wlJRnnhltuuYUvv/ySwsJC/vznPzNp0qR91uE85phjWlzmyel0UlhYyI4dO1qs98yZM7nuuus46qij6NevX/2i6aFQiKuvvpohQ4YwZcoUpk6dWr9t0aJFHHfccYwdO5ZTTjllnyW54pUEaaJtTGZIyY91LYQQolPYvn0733zzDY8++miz+6xatYpXX32Vr7/+mqKiIsxmMy+//HKT+1544YX1zZ033njjAdvPPfdcfvjhB5YsWcLQoUP517/+BcCsWbP48MMPWbJkCe+88w42m41Zs2YxY8YMioqK6hcXr3Pddddx3HHHsWTJEhYvXszw4cP32X7//fdz7LHHUlRUxG9/+1suu+wynnvuOcBYK9Pj8bS4xFN5eTnr1q1j0qRJLdYbjHVPv/rqK+bOncstt9wCwJtvvsnmzZtZuXIlL774It9++y0Afr+fa6+9ltmzZ7No0SJ+8YtfcNtttzVbj3ghzZ2i7WRUpxBCtIvp06djNptb3OfTTz9l0aJF9etjut1ucnJymty3tebO5cuXc/vtt1NRUUFNTQ2nnHIKAEcffTQzZ87k/PPP59xzz2213vPnz+eFF14AwGw2k5racj/l6dOnc/fdd/PQQw/x7LPPMnPmzCb3+/LLLykoKGDdunX85je/ITc3t8V6A5x99tmYTCaGDRtGcXExAF999RXTp0/HZDKRm5vL8ccfD8CaNWtYvnw5U6ZMASAYDNavyRnPYhKkKaXu5v/bu/vYrMo7jOPfi9dOXpxTsjRWhRh5K32RAjVUFxAhbiNjSTPdJlOMxJDM6GLiYFlcMpWoWTI0GnVmm4TMzLGy4SaLk2UlKE6grTCk1E1XGBVjuzZiSXzvb388x1poSynankN7fZInnHOe89znam+gv97nPufAMqADaAZWRMSRHvb7GNiXrP43Ik5+UtzMzOwMMG7cp1fJjxo1io6Ojs719957D4CI4IYbbuDee+/9zMdbsWIFmzdvpqSkhPXr17Nt2zYAHnvsMXbu3MmWLVsoKyujtrb2Mx+rq7POOovFixfz9NNPs3Hjxl7b/2ROWmNjI5dddhnXXHMNpaWlveYGGDt2bOdyX88hjwgKCws7R9bOFGmd7vxZRBRHRCnwDPCTXvZ7NyJKk5cLNDMzG3ImT55MXV0dAHV1dTQ2NgKwaNEiqqqqaG5uBqCtrY1Dhw6d1jHa29vJz8/nww8/PO6U6euvv055eTl33XUXkyZN4vDhw0yYMIH29vYe21m0aBGPPvookBuNOnr06HHv9/TZlStXcuuttzJ37lzOOefkz6meMmUKa9as4f777z9p7t5UVFSwadMmOjo6eOuttzqLumnTptHS0nLc6c/9+/f32V7aUinSIuKdLqvjgJOXwGZmZkNUZWUlbW1tFBYW8vDDDzN16lQAZs6cyT333MOSJUsoLi5m8eLFvU527zon7aqrrur2/t133015eTkVFRVMnz69c/sdd9xBUVERs2bNYv78+ZSUlLBw4ULq6+s7Lxzo6sEHH6S6upqioiLKysqor68/7v3i4mJGjhxJSUkJ69atA6CsrIyJEydy4403ntL3Y9WqVWzfvp2DBw/2mrs3lZWVFBQUMHPmTJYvX87s2bM5++yzGTNmDFVVVaxevZqSkhJKS0s7L3rIMvU1RDhgB5bWAtcDR4GFEdHSwz4fAXuAj4D7ImLzqbQ9Z86cqKmp+fzCmpnZGevAgQPMmDEj7RjD1pEjR1iwYAENDQ2MGDHwY0PHjh1j/PjxtLa2Mm/ePHbs2NE5xy0Levr7KKk2IrpNKhyw75akv0l6pYfXMoCI+HFEXAA8CdzSSzMXJaG/Czwg6eKTHO9mSTWSalpautV7ZmZmNsg2bNhAeXk5a9euHZQCDWDp0qWUlpZyxRVXcOedd2aqQOuv1EbSOgNIFwJ/iYhZfey3HngmIqr6atMjaWZm9gmPpFmWZGIk7WQkXdJldRnQ0MM+50gamyyfB1QA9SfuZ2ZmZjYUpXWftPskTSN3C45DwCoASXOAVRGxEpgB/EJSB7li8r6IcJFmZmb9FhFISjuGDXP9PXuZSpEWEZW9bK8BVibLLwJFg5nLzMyGnry8PFpbWzn33HNdqFlqIoLW1lby8vJO+TN+4oCZmQ1pBQUFNDU14YvKLG15eXkUFBSc8v4u0szMbEgbPXo0U6ZMSTuGWb/5AetmZmZmGeQizczMzCyDXKSZmZmZZVDqN7MdCJJayN3aYyCdB/xvgI9h/eM+ySb3S/a4T7LJ/ZI9g9UnF0XEpBM3DskibTBIqunp7sCWHvdJNrlfssd9kk3ul+xJu098utPMzMwsg1ykmZmZmWWQi7TT93jaAawb90k2uV+yx32STe6X7Em1TzwnzczMzCyDPJJmZmZmlkEu0vpJ0tWSXpX0mqQ1aecxkPRrSc2SXkk7i+VIukBStaR6Sfsl3ZZ2JgNJeZJ2Sdqb9MtP085kOZJGSnpZ0jNpZ7EcSQcl7ZO0R1JNKhl8uvPUSRoJ/AtYDDQBu4HvRER9qsGGOUlfAY4BGyJiVtp5DCTlA/kRUSdpAlALfNP/VtIlScC4iDgmaTTwAnBbRLyUcrRhT9LtwBxgYkQsTTuP5Yo0YE5EpHbvOo+k9c884LWI+E9EfAA8BSxLOdOwFxHbgba0c9inIuLNiKhLltuBA8D56aayyDmWrI5OXv5NPWWSCoCvA79MO4tli4u0/jkfONxlvQn/4DE7KUmTgUuBnSlHMTpPq+0BmoGtEeF+Sd8DwA+BjpRz2PECeE5SraSb0wjgIs3MBoyk8cAm4AcR8U7aeQwi4uOIKAUKgHmSPEUgRZKWAs0RUZt2Fuvm8oiYDXwV+H4ytWZQuUjrnzeAC7qsFyTbzOwEyZynTcCTEfGHtPPY8SLibaAauDrlKMNdBfCNZP7TU8CVkn6TbiQDiIg3kj+bgT+Sm/I0qFyk9c9u4BJJUySNAb4N/CnlTGaZk0xQ/xVwICJ+nnYey5E0SdIXk+UvkLsIqiHVUMNcRPwoIgoiYjK5nyl/j4jlKcca9iSNSy56QtI4YAkw6HcQcJHWDxHxEXAL8FdyE6E3RsT+dFOZpN8C/wCmSWqSdFPamYwK4HvkRgX2JK+vpR3KyAeqJf2T3C+dWyPCt3ww6+7LwAuS9gK7gC0R8exgh/AtOMzMzMwyyCNpZmZmZhnkIs3MzMwsg1ykmZmZmWWQizQzMzOzDHKRZmZmZpZBLtLMbMiTdKzvvczMssVFmpmZmVkGuUgzs2FD0gJJ2yRVSWqQ9GTydAQkzZX0oqS9knZJmiApT9ITkvZJelnSwmTfFZI2S9oq6aCkWyTdnuzzkqQvJftdLOnZ5AHNz0uanubXb2ZnllFpBzAzG2SXAoXAEWAHUCFpF/A74NqI2C1pIvAucBsQEVGUFFjPSZqatDMraSsPeA1YHRGXSloHXA88ADwOrIqIf0sqBx4BrhysL9TMzmwu0sxsuNkVEU0AkvYAk4GjwJsRsRsgIt5J3r8ceCjZ1iDpEPBJkVYdEe1Au6SjwJ+T7fuAYknjgfnA75PBOoCxA/ulmdlQ4iLNzIab97ssf8zp/z/YtZ2OLusdSZsjgLcjovQ02zezYc5z0szM4FUgX9JcgGQ+2ijgeeC6ZNtU4MJk3z4lo3GNkr6VfF6SSgYivJkNTS7SzGzYi4gPgGuBhyTtBbaSm2v2CDBC0j5yc9ZWRMT7vbfUzXXATUmb+4Fln29yMxvKFBFpZzAzMzOzE3gkzczMzCyDXKSZmZmZZZCLNDMzM7MMcpFmZmZmlkEu0szMzMwyyEWamZmZWQa5SDMzMzPLIBdpZmZmZhn0f6bVjVEiA92hAAAAAElFTkSuQmCC"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "From the plot above, it's clear to see that the true treatment effect is a **nonlinear** function of income, with elasticity around -1.75 when income is smaller than 1 and a small negative value when income is larger than 1. The model fits a quadratic treatment effect, which is not a great fit. But it still captures the overall trend: the elasticity is negative and people are less sensitive to the price change if they have higher income."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "source": [
-    "# Get the final coefficient and intercept summary\r\n",
-    "est.summary()"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "<class 'econml.utilities.Summary'>\n",
-       "\"\"\"\n",
-       "                      Coefficient Results                      \n",
-       "===============================================================\n",
-       "         point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
-       "---------------------------------------------------------------\n",
-       "income            2.427  0.075  32.189    0.0     2.28    2.575\n",
-       "income^2         -0.437  0.026 -16.907    0.0   -0.487   -0.386\n",
-       "                        CATE Intercept Results                       \n",
-       "=====================================================================\n",
-       "               point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
-       "---------------------------------------------------------------------\n",
-       "cate_intercept          -3.02  0.046 -65.081    0.0   -3.111   -2.929\n",
-       "---------------------------------------------------------------------\n",
-       "\n",
-       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
-       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
-       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
-       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
-       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
-       "\"\"\""
-      ],
       "text/html": [
        "<table class=\"simpletable\">\n",
        "<caption>Coefficient Results</caption>\n",
@@ -507,32 +478,61 @@
        "  <th>cate_intercept</th>      <td>-3.02</td>      <td>0.046</td> <td>-65.081</td>   <td>0.0</td>   <td>-3.111</td>   <td>-2.929</td> \n",
        "</tr>\n",
        "</table><br/><br/><sub>A linear parametric conditional average treatment effect (CATE) model was fitted:<br/>$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$<br/>where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:<br/>$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$<br/>where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>"
+      ],
+      "text/plain": [
+       "<class 'econml.utilities.Summary'>\n",
+       "\"\"\"\n",
+       "                      Coefficient Results                      \n",
+       "===============================================================\n",
+       "         point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
+       "---------------------------------------------------------------\n",
+       "income            2.427  0.075  32.189    0.0     2.28    2.575\n",
+       "income^2         -0.437  0.026 -16.907    0.0   -0.487   -0.386\n",
+       "                        CATE Intercept Results                       \n",
+       "=====================================================================\n",
+       "               point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
+       "---------------------------------------------------------------------\n",
+       "cate_intercept          -3.02  0.046 -65.081    0.0   -3.111   -2.929\n",
+       "---------------------------------------------------------------------\n",
+       "\n",
+       "<sub>A linear parametric conditional average treatment effect (CATE) model was fitted:\n",
+       "$Y = \\Theta(X)\\cdot T + g(X, W) + \\epsilon$\n",
+       "where for every outcome $i$ and treatment $j$ the CATE $\\Theta_{ij}(X)$ has the form:\n",
+       "$\\Theta_{ij}(X) = \\phi(X)' coef_{ij} + cate\\_intercept_{ij}$\n",
+       "where $\\phi(X)$ is the output of the `featurizer` or $X$ if `featurizer`=None. Coefficient Results table portrays the $coef_{ij}$ parameter vector for each outcome $i$ and treatment $j$. Intercept Results table portrays the $cate\\_intercept_{ij}$ parameter.</sub>\n",
+       "\"\"\""
       ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# Get the final coefficient and intercept summary\r\n",
+    "est.summary()"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "`LinearDML` estimator can also return the summary of the coefficients and intercept for the final model, including point estimates, p-values and confidence intervals. From the table above, we notice that $income$ has positive effect and ${income}^2$ has negative effect, and both of them are statistically significant."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Nonparametric Heterogeneity\n",
     "Since we already know the true treatment effect function is nonlinear, let us fit another model using `CausalForestDML`, which assumes a fully **nonparametric estimation of the treatment effect**."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Train EconML model\r\n",
     "est = CausalForestDML(\r\n",
@@ -542,13 +542,36 @@
     "# Get treatment effect and its confidence interval\r\n",
     "te_pred = est.effect(X_test)\r\n",
     "te_pred_interval = est.effect_interval(X_test)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1f533c69208>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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dW0lPSapHIulf9zwPwUO0PhlyHAi4AYKui+MGUomVNEi6avfVtLG2SVLJWtPPrDh+u17S/0y09mWctt2GbfnvV0VMqYzGqYolUpck1bEkCfUToWQSkgoJD6oSSkXUozKuVMaVqrj/OY8llbjnf95iSfBSz7mlBQM09U/Cg8q4R1UCquNKVcJ/PP6r2eitT3odegX3CEfAFQg4guv/isCR+tgbfXLTJl/a8pOS+l1a3t6kzV1ZoKFpWO1pIt2xd0FuOMQLc2bsdju7ypI0kzUWvb+Zr8xfWXe7V1gYmO8wME/on+vQN0fol/pZnOP/BRr3/F/C8dRla3WSzVUeO2qU7TUe22s0dfGvVydo1P6+vR327eWyX28XEXhtXYLX1yfYEVUEGJgnlEWVqkTjWGeMCHJJSZh+uX4i5gjkhQMURALkBgPNflfWxD3e2lTDopU1hF3Yr7fLiKLaL26fqrKpSlmxNcm7W5J8tD3JqrIk5Q3ynl5hYUCeMLKXw9TBAfrnCQFH+GBrkhVbk7y6zg806FRx8r4hvjguRL9cl165IQojQRKeX1HcWBHnruVVPPJBjP55DocODnLEsBCHDglTEHHZWhFl2cY4z62O8/zqOBsq/d+qE/q5HDcyyDHDEuxbnIMCdyzeyd1v1RBNwrkHhDhzdIjKuLKtRtlerWyr8Xhzc5K/vxvlvveizBhZwzkHhBjXP8y6sjivrU/wyjr/Z1Uc+uUIJ+4T5HP7hNi3r1+Ji3seO6sSPPtpDQs+ivHqugTJNn7RBx3oFRGKwkJhSCgMCwWpn4PyHA4ZFGBoUYickEte2CXUQoUvmvCojCWojCaJxpMo/h8CSzYkeGZVnJfXJqhJth5LbTw5ASEcgIjr/wy7QtiFvJDQK5y6RByKwkKfiFCc+rznBByCrkMw4FdXaruTg46z64UHTfrZUQOeBwn10n4PJz1lZ02SNTsTrN6ZYG15kq1V9clUZRwq4kplTKmIqX89Xp8Q7QoBIqnXKeRCKPV6hVxp1KXf/Ktc6/IAV6A41yE3IOQEpe49qM1dapM5gIDjJ0NBB4INjuGpXyVT9ZNJp0FyJKncu/axARdcEdwGuX/tMRTw1P/8JNVPCmv/eGgQRuqY/vaEpyRqH+P59yupwlNqP9Oygl69M3p8ycZlxyZPnqyLFy/OdBhmD/vPwkWMXHg5AyJJVuQfwt8is1hf6fE/ZT8k4EURPNzU5dHk4fwqeTYBEjwT+m5dGwlcErjcm5zOfHcmw8JV/My7hRyJE5E4YWIENc5rfc/gPmcmO7Zv4lfV1+LhoPhVrJArLBtyPgXjTmSQt56Bz1+HJw4JFRLqUJMUrq86l2eYwpX7rOeSHb9K/dGX+rYUh+qDv4o7bArvvbuMwW/+jqqY38UiKA4e349/heUyhrPy3uI73t14CJVJl6jnx/+j5FeJ996Hz4Xf4pTo44SDAXJCAQKuIF6CzRMvI5Hbn/w1z9H744frKnXq+V21Py28lvs/K+ArgX9zVfBhIpJANA7iElWXY+O3sSFewC3FCziy6hnKEwGiBEgQJBgKcmnyB6yvFi4NPM5Z4cUUhf1unPKoR2XS5QuJ6zl0SIDjt/2DSfGl5ASE4jyXSMBBIgWUTb8ZVSXvtV8R2rAEvCTJpB/bp4m+nBq7iZFFDtdU/YJJzgd44hJwA7iuy0c6mPPKvw3A3MLbGeuuoSYBFTGPpMJHzgheHHkFY/oGOX7lLRRGNyKOgyOCp8qG3DH8u/9X2Frtcf7qG8lLbE91h/mVp0XJifxPwl++8dmcOfRxqwi7QiBVpagYdjQ7JlyCCAx64lJUAnhuhKiE2ZEMscg5lF9sPZx4rJq/h2+mOBSjdyCB64CHy+ohM/lo8Clo9Q4Oe/vHqOehWlsxTfJU3qk8GzqW3Ohmrqz4BQl1SKrgpboU/5A4lSe8Q9hX1vLb4G8AJSAQcJSAI/y79wVsLZ7MWFnJtNV/IOg6/ucCEBFiU76GO7gUXb+c4Ku/R9U/fm130o4p3yZRPIbwulcpXHYH6vmfzWTSI6kedxbP4X0dwcTKFzmr8l4S6pLAIeGBaJIvx69hjfbjC+4zXBl4CAfFEfUrwKJ8r/BWvJw+nFzzONMrHgfHQcRNVauFFyb9klAkl/0+e4D+655GnYDfHekEUCfA+qP/j9yQS9+3/0xk5dOQTP2V4gSQUB6c91f/9it/gNWvpv7npzKiSC845Rf+XS/8EtYvB7T+/S8YCCf/3N/+7E2w+f1U92+qUtl7JEz7vr990S2wc019VVE9KB4NR6d+3yy4Gso3NKiSezBkEhx9tb/9ga9CzfYGmZfCyKPgyG/6N/9xPsRr6repwuiT4LBv+G3OPSXVjRkCNwBOEA48FSaeC7EKuH82JBN+su2GIRiBCefA2NOhais8/T+QqIFE1P+ZjMHkL/ttlK2Bx79T3/0tqddg8mw/xq0fw6Kb/Sqql0h1lXpwxFUw/DDY8DY893/1r3uto74NAyfCmtfhxV83qPSmth/3I+h3AKx6AV67I/WZbPD6fe4W6DUCPngSltzdoP1UG6f+EvL7wzsPwbJ/1B+7tpfgzNshUgRvPwArHvPL0WfeCUMnpf/S6UQiskRVJze93yppJmvkb3+HA5zVRIuncvioERx6YD+iySSBp4rxvCQ1nkNNQqj2HMbnDOTbfSKEHY/46gP9rgCBfMcjTIKvDevPl4YX4sWCDHo1B3WKUDeE54RIuiHGDB7Jdwbk4ER7EXrnECpjftdVYdjBEThon6HkDs4nXNMbhh0MqoRSX/J5ySTf6NuPys8CPPCRckB4ICMKHRxRauIe0aRyz0sxnqjaxKhEjG8He1MYcuid61Kc65JUmNW7iP2jIdicz9LyfQg4Sp+wR1EoSb+A8sepfcjp15fAmgiRN6v9bqeoP+bGkwCuxkkA6oZJBvNBHFQcwCEgDt8ozeOkcXksWTKMB7YeirpBBhcGWbcziZeMM6Z/Hr8+tBdTakbBpwcSjcXYXlFDWVWUaCzGyF4OF0wMc1o8l76bI4Cg4tAboTwZ4OS8EIs+jTPJyaewT38G5PqJggMQyqU4P7UkSP9R4CRTCawQ9KBYC7hQwryxMYGXP5bqYBG9gx4OHq4m2T9SzD0j8/nPJzE+/aSARLIIEegdEfrnuZw0aACnH1Hs/16u7gcVtX1UHiD06teHAyb38o+/aADU5OCJQxI/GTqt1wgG9c7j1XUJVqwcQ3U0iioEXYi4sHJNMcsrKumbI5zHPsRqqkjEagh41eSygx26gYMHuRw/LJ8xn+UQjvTFDUb842mSA4YWc8B+eRD1YF1xqk+qvov5S/sP5Usj++GVK/pcbzSZ8Cs1OCQVvj20gLMK84htzye8ciDRJFQm/bc/moTnNwR5fk2UMZIgP1CIg0fIFfKCQm4QPvw0Rm6snMC2JCOTRUQ9IZbEv6jD316JscLbwagYnJkYTtST1B8pgiL8+7MA5eEEBW4eH8ooQk6SkJMk4AihQICvj8ynV58CRteMpHDzFIKug+MIrvjjNe+cOgjCBbByH1hVWp/kpMwcnQ+BCCQGQGxEqjs1lQh4SQr6BvyEoXgEJA7xkxSoH3tVK68Yeg1vUApTCBc23l40LNUvmOrWzSuu395reN0x/RiSEMyr3+6k4qhLZBw/AWj4+HBBoz/O6DWyfvugEohXpbalkoni/eq3jzgCkvEGiYYD/cakNgqMmZlKkOL+fsm4fzzwE7ahh9THl4z5iVgk9bmvjTenDwTC/usdCEHRUH+7G4LBpfVjDeuef25qe9B/rWpfg7rnn2o/mAN99m3eDR7I8X+G8qF4f+qTrFSiVfv6hvKhzz7+/bXd+OL4cQLk9oEB46lLXmtfo9pxdwWDYMjB1CeAtePZUtvDhf5zVc9/3hlklTSTNZ7/58846r0bKf/8vRT0GdDifglPiSc8HEdSy0J0/FhJz19WIprw6n4KUBgJUhAJttlmNOGxdns1yzbG+cMbNXy4PTWOSKB/rjA432FYocuhQ0JMGxWhX16QQIPumcpYgu2Vcarj9V9eAhREAvTOC7XY9VZL1Z/pWR1PEkt6fhdYwCEccAi5DqqwozrOtsoo729NcvdbNby+Pklpf5dLSsJM2yefgnDzv/E8z5/NWZN6TeIJfyxXQwFH6qo3uSGXgnCwQ2O/Vf1jVMUSBFOzP8OuSzDV9et5UJVIUB1NUlaT4K1Nccb0DTK8T5j8TppQkPCUymiSymiCdTtjvLY+wbtbk2ytVrZUeWytVnZE/S6uwfnCgX0DHFjsMravy6heDkFH6JPnd8V29jwSz4O45xFLeiST/uD02oHpCc8f07ijxmNDpX9ZU+7x8fYkH+/wWLPTSzvcxxG/q7ww7Cdz+SEhLwh5QaFvjsPgfIchhS4jCgP0z3cIuq7fdecIrisEZDe6VY3JpOLREMpre7/dZJU0k/U0Xg1AuN9IyC+mpQHdAZE0H/wm+7U6EBxclFyE3OY71e+ntSNWGsSR+hlGGNxbiRdV8ot9lfc3VVEQdhiQHyQUEIrzQvQrCLc47DUvdamOJdlWWYPrOvTNjxB0GvzlKdJ8QHjqtqiSg5LTwoBuQehdBAWektMvyv8MT1IR9SjKdRjRJ49IoMGkBuqfmwMUilDY4DVIeH4iK0A44OI6LcwOazFbaXi/P9avIHVJN/jJAfJFyAf6IYzcz+/mq/urva57hMavVUPNBtI3fg8DQJEqRcBAz2P0fjEqYwmq4/5MYNXaRF7JDbl17bgO5AQd+hfmNJ7h2nRGYaNYauOuGwHVPKaGz1+VMBBuFH9zSa92pqI/SaYqlmR7ZZxPttawdmecgrBLn7wAfXODFEX8rvKA64+DDAXqx7WFXIdw0G08uaTu/4GmfY/SavT8Us+t2We5SXuN2m7htWn0/yDd49oMLP3jmn522pNtt3dGatP4WptN2dZ+6Z5rS8+/o38ttfbYRq9PKyP/W3xfm/weTVXTG30uWmqvrhvUq99W93inQRt1wTZ4vNcgJq++EpshlqSZrPF20bH8JpbHfcX7tj2dvBuIACNzClm5pZIxw/xultywy5BeOc2WqGhJTi4M6dV1MQaAwYXQO5ZkW1WMAQUtL4HRWhuZ/EXT1cd2gMJ8aNBR5i/DEveIJpN+EhNwCQW612K+buoCkJ/6OUKVMXF/5qSbWuYh6PpxBxzZ48vHGLO3syTNZI1tUsg77oFID0jQaoUDLqOK8/hsaxV98kL0zQ9nOqS0ckIuQ0I5mQ6jx/DXznOBnvNZBH9gfm4oYOvMGdNN2CgBkzWKy97iDPfFTIfRYeGAy/4DCrptgmaMMSYzLEkzWWPc1ieZw12ZDsMYY4zpFJakmazhJKqJSWYHeRpjjDGdxZI0kzXcZNSSNGOMMVnDkjSTNZxkDXGxcV3GGGOygyVpJmsEvCgJS9KMMcZkCZtnbbLGryNfo39ekP/LdCDGGGNMJ7AkzWSN1cm+RHIL297RGGOM6QGsu9NkjenRhRwUtXO2GmOMyQ6WpJms8aXkgxxa8XSmwzDGGGM6hSVpJmuENYrnRjIdhjHGGNMpLEkzWSNEDA1YkmaMMSY7WJJmskIykSRC3JI0Y4wxWcOSNJMVampqiBCDoCVpxhhjsoMlaSYr1MRiHBb9DR+MvDDToRhjjDGdwpI0kxVq4gk20gcnr0+mQzHGGGM6hS1ma7JCtGI7V7kP0L8yBIzIdDjGGGPMbrNKmskKifLNfCv4AH2rPsp0KMYYY0ynsCTNZIV4TSUAbig3w5EYY4wxncOSNJMVErVJWjgnw5EYY4wxncOSNJMVElE/SQuErZJmjDEmO1iSZrJCMlYNQNCSNGOMMVnCkjSTFdYUlDCx5k500MGZDsUYY4zpFBlJ0kSkj4g8JSIfpn72bmG/pIgsS10e3dNxmp6jJqHsJI9IxM44YIwxJjtkqpI2B3hGVfcHnkndTqdaVUtTl9P2XHimpynY9jZzAn8nJ1me6VCMMcaYTpGpJO104C+p638BzshQHCZLFJV/wGWBxwhLItOhGGOMMZ0iU0naAFVdn7q+ARjQwn4REVksIq+IyBl7JjTT43geGo8CEM6xiQPGGGOyQ5edFkpEngYGptl0bcMbqqoioi00M0JV14rIPsCzIvKWqn7cwvEuBS4FGD58+G5EbnocLwEJP0mToCVpxhhjskOXJWmqelxL20Rko4gMUtX1IjII2NRCG2tTPz8RkUXAQUDaJE1V7wDuAJg8eXJLSZ/JRl4CklES6hBwg5mOxhhjjOkUmerufBS4KHX9IuCRpjuISG8RCaeuFwNHAO/usQhNz6FJSMaJSijTkRhjjDGdJlNJ2s3A8SLyIXBc6jYiMllE/pTa50BgsYgsBxYCN6uqJWmmOS/B/UWzOTl0d6YjMcYYYzpNl3V3tkZVtwIz0ty/GPhK6vpLwIQ9HJrpibwkNQklGLSuTmOMMdkjI0maMZ3KS3JE+X+YligHjs90NMYYY0ynsCTN9HxeggnRpfTxtmc6EmOMMabT2Lk7Tc/nJQh4MRKOTRwwxhiTPSxJMz2flyCoURJOONORGGOMMZ3GkjTT86lHUGMkrZJmjDEmi1iSZno+L0FMXeKB/ExHYowxxnQamzhgej4vwRe9mzh9WG8OznQsxhhjTCexSprp+bwk0aSSE3AzHYkxxhjTaSxJMz2bl0TV43+d31NavjDT0RhjjDGdxpI007N5SaJJON15iQHRVZmOxhhjjOk0lqSZns1LUBOLExAPAjmZjsYYY4zpNJakmZ7NSxCN1gAgQVsnzRhjTPawJM30bJokGo0C4AStkmaMMSZ7WJJmejYvSSyeYL32gUhRpqMxxhhjOo0laaZn8xJUBPtyWPS3bBsxM9PRGGOMMZ3GkjTTs3lJahIKQCRoH2djjDHZw77VTM/mJXC3r+Tu4C30qvgo09EYY4wxncaSNNOzeQmo2cZ0dzk5Wp3paIwxxphOY0ma6dnUIxnzZ3cGwza70xhjTPawJM30bF6CRDyVpEVyMxyMMcYY03ksSTM9m5fASyVpobAlacYYY7KHJWmmZ/OSVGmYj71BhHLyMx2NMcYY02kCmQ7AmF3mJQHlnYLDuSw2kY97Dcx0RMYYY0ynsUqa6bm8BAA1CY+AA65rH2djjDHZw77VTM/lJQEYv/U//D14E4h9nI0xxmQP+1YzPVeqklZUs44J8jEgmY3HGGOM6USWpJmeK5WkOckYUUIglqQZY4zJHpakmZ5LPQAcL0qMEFZJM8YYk00sSTM9V6qS5nox4mKVNGOMMdnFluAwPVcqSVsv/UkEYIhV0owxxmQRS9JMz5Wa3Tk39AXygsJRVkkzxhiTRay7szMkExAt9y+qmY5mz/E8qN4OiVjb+8YqIRHtvGPHqiBRA0B1QgkHBBuTZowxJptYJW1XqEL5BoinEoVkgyRFHAjl+5dwPgRz2z9WKpmAWIWf0MQqIVENbggCEb+dYOqnuH6bDdtNJurjiVf7P8WBYE7qsTl+O01j8bzUAHz1f9Zeah+bjpeEyi1QuRm8uH9fMA9yekNOL3CD/msUq4DqHVBTVr+fE4RQHoQL/NdIxI81EYNktD6RC0RSzzv1EyC6E2p2+slwbXvA92tuQ6QPyLHte52NMcaYHsCStF2hHlRsaHlbdKd/KQcQCIRTyVYY3DA4rp/oJGN+spFM+NeTaSpNiRr/UrOjlYAEaKGCF6tosJsDTqBxMtYacRskVHl+7JWb/YsmG+8br/QvO9f6+yZq6saMNeLF/efS6vPBf/3aaYS3hgoFq6QZY4zJJpakdTmtT7Q6sbev2THatZvXuOrX5v7J+oSzI7E0TAz3gJDG8Byb3WmMMSa7ZGRMmoicIyLviIgnIpNb2e8kEXlfRD4SkTl7MkbTc4SJ4blhS9KMMcZklUxNHHgbOAt4rqUdRMQFfgd8DhgLfEFExu6Z8ExPoaqEiaFuJNOhGGOMMZ0qI92dqroCQFqvfBwCfKSqn6T2/SdwOvBulwdoeoy4B695B+DkDst0KMYYY0yn6s5LcAwBVje4vSZ1nzF1ahLKV+Pf5aOBJ2c6FGOMMaZTdVklTUSeBgam2XStqj7SBce7FLgUYPjw4Z3dvOmmahL+pIlIwMajGWOMyS5dlqSp6nG72cRaoGEf1tDUfS0d7w7gDoDJkyfvRSvK7t3ilWW8GL6CNdsuBo7IdDjGGGNMp+nO3Z2vA/uLyCgRCQHnA49mOCbTzcSi1QyRrUQk2fbOxhhjTA+SqSU4zhSRNcBhwOMi8kTq/sEisgBAVRPA5cATwArgPlV9JxPxmu6rJuovPucEwxmOxBhjjOlcmZrd+RDwUJr71wEzG9xeACzYg6GZHiYe88/f6YZsCQ5jjDHZpTt3dxrTpnjMr6QFLEkzxhiTZSxJMz1aBTn8OzkFye+f6VCMMcaYTmVJmunRtoaH8fX4t6B4dKZDMcYYYzqVJWmmR6uO166T5mY4EmOMMaZzWZJmerRBm/7LG+FLyanZkOlQjDHGmE5lSZrp2eJV9JYKwqFgpiMxxhhjOpUlaaZH04Q/uzMcyclwJMYYY0znsiTN9GyJGABBW4LDGGNMlmkzSRORvnsiEGN2haQqaRK0Spoxxpjs0p5K2isicr+IzBQR6fKIjOmA1YERPKTTwLHZncYYY7JLe5K00cAdwJeAD0Xkf0XEFqUy3cLrkancErgMsL8fjDHGZJc2kzT1PaWqXwC+ClwEvCYi/xWRw7o8QmNaEY17hAOAFXmNMcZkmTZPsJ4ak3YBfiVtI3AF8ChQCtwPjOrC+Ixp1eztv2ZY7GPgyUyHYowxxnSqNpM04GXgr8AZqrqmwf2LReT2rgnLmPYJeFFUXKukGWOMyTrtGZN2nare2DBBE5FzAFT1li6LzJh2CHgx4hLCxqQZY4zJNu1J0uakue/7nR2IMbsioDESTsgqacYYY7JOi92dIvI5YCYwRER+3WBTIZDo6sCMaY+gRklIPlZJM8YYk21aG5O2DlgMnAYsaXB/OfCtrgzKmPZ6msPol5vHeKukGWOMyTItJmmquhxYLiLzVNUqZ6ZbultPZkavMF8QO8OZMcaY7NJad+d9qnou8IaIaNPtqjqxSyMzph3cZDU5bhDr7jTGGJNtWuvuvCr185Q9EYgxu+JJ50o+3no0yG8yHYoxxhjTqVrr7lyfuuoA61W1BkBEcoABeyA200OVVScoCAdwurgHMukpEeJowJbgMMYYk33a8zV6P+A1uJ1M3WdMWlWxBGU18a4/TtQjQgzcsC3BYYwxJuu0J0kLqGqs9kbqeqjrQjI9XTThsaM6hjYbydi5quJRHFEkEO7aAxljjDEZ0J4kbbOInFZ7Q0ROB7Z0XUimJ0t6EE96JJJKRbRrJwVHa2oA/CTNKmnGGGOyTHvO3XkZME9Efos/8Gc1cGGXRmV6rJpEfWK2vSpGQaQ9H7FdU5V0+VXiLCYUHYCNSTPGGJNt2vwGVdWPgUNFJD91u6LLozI9VjThNbpeGUuQF+qaRK2KMLclPs/v+va2Spoxxpis09o6aReo6t9E5NtN7gdAVX/RxbGZHiga9xrd3l4Z77IkLRqN0Y8d5Dh2WihjjDHZp7UxaXmpnwVpLvldHJfpoWoSyUa3q+NJapokbp3F3fEJr0e+waDtb1glzRhjTNZpbZ20P6auPq2qLzbcJiJHdGlUpkdKeEoi2XxK5/aqGIOKIp1+vGQsCkAwFMYqacYYY7JNe2Z3plvK3ZZ3N81Em1TRalVGE8SSnV9NS8T82Z3BcMQqacYYY7JOa2PSDgMOB/o1GZdWCLhdHZjpeZqOR6ulwI6qOP0LOnc9s2TcKmnGGGOyV2uVtBD+2LMAjcej7QQ+3/WhmZ6m4czOpipqOn/NNC9Rm6RZJc0YY0z2aW1M2n+B/4rIXFX9FEBEHCBfVXfuqQBNz1ETT9/dCZBUJZb0CLmdd0LPdaGR/G/8C/y/gr5YJc0YY0y2ac835k9FpFBE8oC3gXdF5Ooujst0QMLr4vMvtUPc0zbjqIl17ri09YFh3JE8lXBugVXSjDHGZJ32JGljU5WzM4B/A6OAL+3OQUXkHBF5R0Q8EZncyn6rROQtEVkmIot355jZbEdVHK9rVrlot5YmDTTUdHmO3eVEyxgp6wk7HlZJM8YYk23ak6QFRSSIn6Q9qqpx/LHgu+Nt4CzguXbsO11VS1W1xWRub1cRTRBNdm4C1FGxdqyF1lp36K4o2fZvFoW/g6BWSTPGGJN12pOk/RFYhb+47XMiMgJ/8sAuU9UVqvr+7rRhfNGERzzptTizck9pz4K10YRHZ67EIckYcXXBCWCVNGOMMdmmzSRNVX+tqkNUdab6PgWm74HYwK/YPSkiS0Tk0j10zB6lMurPmmxPd+Puaq1Ltb3Hb3gC9t0liShRCaVuWJJmjDEmu7TrpIoicjIwDmi4bPwNbTzmaWBgmk3Xquoj7YzvSFVdKyL9gadE5D1VTdtFmkriLgUYPnx4O5vv+SpSSVqsleUvOsv6nTUMKAwTcBonRO2ZNFArGvfIC+1+LOU1CSQZJUZtY5akGWOMyS5tJmkicjuQi189+xP+GmmvtfU4VT1ud4NT1bWpn5tE5CHgEFoYx6aqdwB3AEyePDnz0x33gFjSq1ubLJrw0C4cmhX3lKpYgs3lNDvFU0fGmlV3UrfstqoYrsbqkzSrpBljjMky7RmTdriqXghsV9X/AQ4DRndtWCAieSJSUHsdOAF/woFJqYzWJ0dK64vJ7q6q1LEqognKmyxM25EqXk1897s7y2sSxBIezwSO4Z7w+f6dlqQZY4zJMu1J0qpTP6tEZDAQBwbtzkFF5EwRWYOf8D0uIk+k7h8sIgtSuw0AXhCR5fiVu8dV9T+7c9xsU9vVWatLk7RY/bE2ldcQb3Ai9fZMGqjl6e7FqepX0QCWOuN5OXIU1tVpjDEmG7VnTNpjItILuBVYil+0+dPuHFRVHwIeSnP/OmBm6vonQMnuHCebxT1t1s3oD95v1zDDDlFtnKR5CpvKowzp5Xd7Rju4tEZN3CMc2LUzD9RW0QAGxleTHwmBpBv6aIwxxvRsbX6jq+qNqasPiMhjQERVy7o2LNOWqmjzxKi9y3AkPCWe8IgllXjSIz8cIBJsOWmqjidpOi+gKpZge1Wc/HCApHZsCGB1PEFRTseTyYZVNICron9EkhHg9x1uyxhjjOnuWvymFJGzWtmGqj7YNSGZ9mja1Ql+Ja21yQMJT/lsa1WzpCrheQwMRtI/CKiKpa+Uba2IoruwrnFHukcb2lmTIN5gobWQxqhxCm08mjHGmKzUWjnj1Fa2KWBJWoYkPaiONU/SFH/GZ0tdiZXRZNqqV0VNgni+EnTSJztVaY5Ve7ytFbG021oTT3okPG22lEdr/CpatNF9IeJUuiFsTJoxxphs1GKSpqoX78lATPtVxRIt1q+irYz3Sld9Az/Z2lkdp2+aBcziSe2SCQnV8SQF4fZ3eZZVx0kkGz/rkMbwnLBV0owxxmSlFgciicgvG1y/qsm2uV0XkmlLS8kWtLzyf8LTtNW3Wjur46QbWlbb1Zn0lBVb27d8xoYKD6+NcWqtjZ/zPH8GaHnUH/e2qTzKtsrmFbswMTzHKmnGGGOyU2tT7I5ucP2iJtsmdkEsph08r/5UUOnUtFD1qowmWx09lvA0bfJX29W56LMEVz5VxXOr463G997WJBc9XsEf34i2ul91CzNCy6oTfLylgs+2VbGhrIYtFVHKquPNumlVlR/EL+HtPsdbJc0YY0xWai1JkxaumwxRhR01sVaTrVgLlbTWqm+1dlQ3TsBU6xPCxRv8n3cuqyGWTB+BqvKHpTV4Cg9/GOOTHS0vzRGNJ5tV7sqjCTaX17QZJ0AsCU94U9heMBr7eBpjjMlGrSVpjoj0FpG+Da73EZE+gLuH4jP4yVJZdYJPt1W1OVA/3WKxbXV11qqJJxvNvKyO+9U3VWXphgRDCxw2VCoPfpA+hoWfJXh3a5JLS8PkB4XfLK5BW+j2VKCmQUJZHUuysaym3XNFo4kkRzhvUextsUqaMcaYrNRaklYELAEWA4X4C9kuSV0Kuj40owo7quKs2lbpr/Ifj9P7w/kEy1e3+rimp2lqq6uzobLq+gSsMpXYfbbTY1uNcu4BIQ4dHOAf70TZXtP4GDUJ5U/La9ivt8NZo0N8pSTM21uSPL2q5e7R2oQwmvBYV1bdocU8krEa5oV+ygE7X8QqacYYY7JRa7M7R+7BOHoUVWV7moHsu9Um/uD8pKckPMVTJZH0Gi0i2/fdv9DnowcpH3xUbSBpq0g1iSQFDd7a9nR11iqvSVCcD65TP2lg6Ub/8QcPDDChn8tX/p1g7ptRvnVITt3j7n8vxuYqZc6hObiOcOI+QRZ8EufO5VEOHxIkL5QmzniSeDLAuh3VzRbLbUs85o95k0DIKmnGGGOyUuefQ2gv4Cls3cUkLW/9qwSqN7NzxImoG2z34/LXvkCfjx5kx6iZJPIGgHoMfvl6qvpPYsc+J4NT/1Y2nDnZ3q7OWgrsrPHPJFBbkVu6IcngfIcBeX7h9fT9Qzz8YYzT9g+xb2+XzVUe962IctSwABP7+3E4IlwxKcJPn1zFB68s4qCjTvGTKU2C+L3lNfEka3dUk+hohgYk436S5gTCWCXNGGNMNtq1EyiadpNknMiWt+tuF618nP5v3s7QF75HoGpzu9oI7VzFgKW/pLrPgWye8FUAnHgVIPR7+06GL7ySwk+fwq3eAjTu7uxIV2etsup4XfUt4Slvbkpw8MD6YYgXjA+TFxRuf8Mfc3bXm1GSCl8t8c9a4FZvoddHD3PsW9fwQuQqzt32Rzas/wyAQa/9lIGv3Ux4x0f+6amS7VyDTZPg1Y9hSyb8JNkNRqySZowxJitZktZVNEnv9+9l1L9nMfSF7+PWbANg48HfZP2UOQTLVzNs0TfJ2fxmG+14DFz8c7xADuunzAHHr755oXzWHXY966b+EPHiDHjjV+zzxGwiW94mqUq8aieUr6d6+3rcmu040TKcWAVpF0NrIp706rpz39+WpCoBBw+or9QVhIQLx4dZtinJ3LeiPL0qztljQgzKd8jd9Aajnvgy/d7+E+LFWTP6QmZ6v+SWd4tRzyNWMIzcTUsZvuibDH7ph+StfxVJpqlKapKilQvI3fA6Eq9i2HNX0/vjh+o211bS3KAtZmuMMSY7tdndKSL7AmtUNSoi0/DXSLtHVXd0bWg9lxvdwYDFPydv8xtUDDqUspGfIxkqBCAZ6UPFkCOJFo5g0Gv/y+CXr2fVCX8mGemdvjFx2DDpWzjJKMmcvk22CZWDplI58BBCO1eRu2kp0d77A+At/ye89XcGNmnuw9MeBgkQKluFF8xle6AfVXFlQK7ixCvxQoVIooaBr95I2YgTWLZ9CgKU9G/8UTllvyD/+ijGI++W8bXIC3yxqA8xplPddyzbxpxL+dBpxAuGAnBSOMYvXq/h2c+SzBh7Edv3/zxFK/9Nr48fZvCrN7J5/CXs2O9MP1lTj1DFGvov+x2RHR+yc9h0qgZOIRHpQ5/3/kH54CNJ5A1ke3Ags2NXc0XxgVh3pzHGmGzUnjFpDwCTRWQ/4A7gEeDvwMyuDKzH8uIM/e93CdRsZWPpFewccULaSk+8YBirj/45OdveJRnpjRsto2DNIpKhIpLhQpKhIpx4JdX9JhIr2qf1Y4oQKxpFrGhU3V01g6fi5Q9mZ3UNoh54SUSTIH7xtPdbd1G4ZSmeDqGaPuwT/Jjq4gmsP/SHqBtGEjUMWnwrX5d+FBecTJF7Ckptd+ZWCjcu4W95r9Enuox8aijffBQbRk5H3TDbDrygUXgn7hPkwQ9iPPxBlBkjg3jBPLaP/jzb9zud3M1vEk3Fnb/uRfov+y2SjJEM92LDpO9SPvQYADZPvIwRz3yd/st/z7rD/ocKyWWRdxDfzOtjlTRjjDFZqT1JmqeqCRE5E/iNqv5GRN7o6sC6NfXI2fwmyWA+XigfL5iPF4j4CZATZOvYC4kVDG0zudJgLlUDJgMQrFhHv7fubLbPhoO/RfnwGa22szPqkRMQgm59slJZNMa/NJk0UBlXHv4gypKNX+AQbyyn5bxF79gOXs85kuHD/VgQYc3RtxJc+xpbX7uPy3UuyScfZO3hNxLttS/9l/+e/A2vEs8pZsfwaWwbdTzRPmNajM8R4cRRQf64LMqnZUlGFKXGtzlBqgZMqtsvWjiC5yLTKfMijD/2C3jBvLptiZxithz4Jfq/dQf5a58jUDWGE5x3yNNjsEqaMcaYbNSeJC0uIl/APzXUqan72j8tMRvV7GToiz9odveGSd+hfNh0KoYeneZBbTTZZwwfz/w7brQMN7YTN7oT0SSVA6e2+rjqhPLlBZUcNzLIZQdF6u+PJ5oNP3t+dZzbXq+hPKYcPmQEk8ePJrf3+fzkpSpeXZfgb33zKazdWRxedCZxbWwsf570KVN2PoXTZyR4sO2AL7L1wAuIFY5sdxVrxsggf1oe5cmVcb5amn4t5NWBkXx524V4Cn+pyWFwk09Z2T4nU7h6IUUr/01xrscdodtYFx8Hsm+7YjDGGGN6kvYkaRcDlwE/UdWVIjIK+GvXhtXNhXJZc8T/4sQrcGMV/s94BersRu4qDl6oEC9USOtnx2xs4adxyqLKM6vifLUkjOv4SVPTVS281CmbinOEn07LZUyf+kRp1rgw//0swQPvx7h4Yn2it3RjkqADA0aNZ2NgAiN657J2ezXRXh1PinpHHKYODvD0qjhfnlgfZ0OPfuhPIHAEnlwZY3aDWAAQl/WH/IBkuAhdshCAcMiW4DDGGJOd2kzSVPVdEfkeMDx1eyVwS1cH1q25Iar7df055lUVaaVSpar866MYYRd2RJU3NiaZPCj9W/rWpiSbq5WvlkYaJWgAI4tcjh4W4OEPYpw9JkRh2B+39sbGBOP7uYQDQtB1CLkOvXJDbKlo/eTpLTlhVJCX1iZ4fX2CQ4c0TmirE8qCj2McOTRAVVx5clWcCyeEcZo8/0RuP/9ndRkAOZEcG5NmjDEmK7W5BIeInAosA/6Tul0qIo92cVx7tfUVHj9/tZpT5pfz2rqW62rvbUvy0XaPL08MkxuEhZ+1vO/Tn8bJCcBhQ9IncbPGhalOwAPv+9Ws7TUen+zw6pbeyAn6H5WiSBB3F5OiqYMD9AoLT6xsHufTq+JUxOHM0SFO3CfE5ipl2cYWThYfU07bfg8AuTlWSTPGGJOd2rNO2vXAIcAOAFVdBrQx3dDsitrkbPbjFTzzaZzcgHD7sijJFlbk/9eHfuJ10j4hjhwa5IU1cWLJ5vvGkspzq+McOTRIJJA+oRnVq76atjNVlQM4aKCfpOWGUmcScKBX7q516wYcYcbIIK+sS1AWrV/EVlV5+IMY+/d2GFfscviQAPlBeGJl+rM6PPphjHOiP2LNwOMglG+VNGOMMVmpXRMHVLWsSbdbO5eJz07/eH01H368a11+LVlX4fHMp3Fc8U+7dO6BId7fmuT6F6r5zydxTt4v1Gj/nVHlv6vjnDAqSG5QmD48yJMr47y2PsGRQxsnUS+vTVAVh+NGtp5czRoX5rnVCR58P8rWaiU/CPv18vP4SLC+i7QoJ8T2qliHz7cJcOKoIA+8H+PZT+OcOToMwJINST7b6XHN1AgiQsiF6SOCPLEyTmVMG533syahPPhBjNEDxxM97JBUgmZJmjHGmOzTniTtHRH5IuCKyP7AlcBLXRtW9zbv1dWs2NC5SVrYrU/OinP8xKjvEGFcscs9b0c5dkSQnGB9MvLUqhixJJyyr5+8HTTApVdYWPhpvFmS9syncfrmCCX908+qrDWql8tRwwI89EGMSEAoHRDAdfzxaA2X93Ad6JUbYtsunL90VC+X/Xs7PPFJfZL20AcxekeEY4bXx33iqBD/+ijOos8aJ6j/+cSfKPHFsaH68WpWSTPGGJOF2pOkXQFcC0SBfwBPADd2ZVDd3SPfOIxP3n65U9sUodkgeRHh0tIwVz1dxfz3Y3xpvJ/UqCqPfRRnbF+XfXv7iZfrCEcNC/DEyjhVcSU3ldCVRT1eW5fgrDGhtDMqm7ogVU2rSmjdeLRIsHmveFFOkO2VsQ6fFxTghFEhfre0ho+2J4m48Nr6BBeODxNqkAiO7uMwotDhiZX1SVo8qdz3XpTxxS7j+wUaFNAsSTPGGJN92hyTpqpVqnqtqk5R1cmp6zV7IrjuynWk0y9NE7RaY4sDHDU0wH3vRdlW7fcyv7ExyZpyj1P2a1wxO3ZEkFjS796s9dxnCZIKM0a0bxxZ7dg08KtzALnB5rl8wBGKdnFs2rEjggQdeGJlnIc/jBFw4OR9G7clIpy4T5AVW5N8ttMfH/fsp3E2VylfGOsnbVZJM8YYk81arKSJyL+g5UKJqp7WJRGZZr48McxLaxP89Z0oV03O4bGPYhSGGncPAowtdumXKyz8LM6M1Pizpz+NM7LIYZ9e7Zkj4vt/B0c4bEiCoYV+khYJpX9sr9wQZVXxDlfTCsPCYUMCPLMqTsJTpg0P0ien+TFmjKhfAPfiCQ73roixby+HKallRupTM0vSjDHGZJ/Wujt/tseiMK0aWuhy8n5BHvsozjHDgry4NsHZY0KNugfBryxNH+4PzN8Z9aiMw7tbklxSEm51vbWm+uQ4HDfSr1YFXCHkpk/Sgo5QlBNkR3VHlt/1nTgqyHOr/YrfmaPrx5zVRqmpOA4ZFOCplXH26+2yutzjusNz6p6LYJU0Y4wx2avFJE1V/7snAzGtu2BcmKdXxvnh81V4CifvG0q73/QRQe57L8bzqxNsq1EEOHb4rp8JISfY+mSDPnlhKmIJEmmW/mjNpIEB+uUIA/IcRjdYXDcSdFGgJu53cZ4wyl+y41evVzOkwOHIoQ0+sjYmzRhjTBZrc+JAakbnT4GxQN15elTV1krbg3pHHM49MMzct6JMGugypCB9dWvfXg5DCxwWfhZnS7VS0t+lf17LXZ2CP8Yu0cJ6GjlpxqM15DowsCDCmh3V7X4upI75ixl5hJs0nxt2SSa1Lkk7dHCAwpCwM6ZcWtp48oONSTPGGJPN2jNQ6W7gD0ACmA7cA/ytK4My6Z09JsQRQwN1szzTERGmDw+wfFOSteVe3di0dFwRhvTOoX9hy+3ltDAerfE+Lr1z01f2WjMw36F3pHH7eaEAoUB9ZS3oCqfsF2RIgdNsnTdLzYwxxmSz9iRpOar6DCCq+qmqXg+c3LVhGaF+lf9akYBw/ZG5jCtuvbo1PTWTM+jAUUPTJ2lB12Fonxxygi55oQD5TUta+DM4WxqP1lTfvBDhQPsnJ6QTcIVwwCHU5KwIsyeEuWtmXqO12qBBAc0qacYYY7JQe9ZJi4qIA3woIpcDa4H8rg1r7ybAgKIIIdfhs22JNvdvalihy8R+LoPznUar9deKBF0GFUUINOg6LC4IUxlNNJqpmRNqfTxao5gFBhRGWL2tapfWTgPITY1/C7suQv3UYhFJWzUTG5NmjDEmi7UnSbsKyMU/08CNwLHARV0Z1N7MERhclFOXILkiJLXjac+tx+amTV3ywwEGFERwmhS9go7QNz/Mlor6MylEAu1P0gDCAadZGx2RG64/P2gw4BBLtH72sfoczZI0Y4wx2afNJE1VX09drRCRbwM7VHchazBtCjjC4F45jboNc0IuFdGOV9PSLY4bcIVBRZE0e/t65QQpr4kTTSVHHamk1eqdG6QymqA6NfC/vYTGi+aG3LaTtPrnaEmaMcaY7NPiICIR+ZGIHJC6HhaRhcDHwEYROW5PBbi3CLoOQ3rnNBvXFWljCYyOKIq0vhSHCPTL9ycRuCK7PMZsQFGEgNuxxCkcdGk4/C2c5lRUzdiYNGOMMVmstUraedSfo7O2e7MfMBr4C/D0rh5URG4FTgVi+Infxaq6I81+JwG/AlzgT6p6864eszO5jrB//z0zLK+tdcraS4CCnLbXS8sJuRREAuxOrTToCIOLcliz3V/TrT3ywo2fZ6gdCaJV0owxxmSz1r4JYw26NU8E/qmqSVVdQfvGsrXmKWC8qk4EPgC+33QHEXGB3wGfw1+j7QsiMnY3j9vjRIIO7TgvepvywgGC7WyoOD+cdrZnR4QDDgOLIu1On/KazGQNt2M8nI1JM8YYk81aS9KiIjJeRPrhr4/2ZINtubtzUFV9UlVrB1q9AgxNs9shwEeq+omqxoB/AqfvznF7qkgbC8q2R0FO+9sIOEJBZPePmRcKUJzf8hpsDY/XtGs16AhuG8mXWCXNGGNMFmstSbsKmA+8B9ymqisBRGQm8EYnxvBl4N9p7h8CrG5we03qvr1OexaUbU3AEfI6IdHbFb1yg/TKbb2bNbeFCQrt6fIErJJmjDEmK7V27s5XgQPS3L8AWNBWwyLyNDAwzaZrVfWR1D7X4p/JYF57A27leJcClwIMHz58d5vrVnI6uBRGU4U5wYzmMf3ywySS2uIs1aaL9tYKB51WZ4namDRjjDHZrMvKK6ra6gxQEZkNnALMaGFJj7XAsAa3h6bua+l4dwB3AEyePDmrlgiJBBsv7tpRhW3M6twTBhREgJpmiVq6MyvUCrsuEG+xTcdmdxpjjMliu9ePtotSszavAU5T1aoWdnsd2F9ERolICDgfeHRPxdidiOz6Uhy5oUCz0yllguPAoKJIs3N8Nl16o6F2d3daJc0YY0wWykiSBvwWKACeEpFlInI7gIgMFpEFAKmJBZcDTwArgPtU9Z0MxZtxrS0s67Zw2iSAwk6YANCZivND9C+on/XZdOmNhsIBp9X0S2o/vVZJM8YYk4Xa/AYXkXOA/6hquYhcBxwM3KSqS3f1oKq6Xwv3rwNmNrjdrvFve4NIK4u79isIEwo4bNpZQ02DVfpdkd1eSqMrFOUECLo5rC+rbnSWgaZE/GpaNM2ZB1wRcgO1j7UkzRhjTPZpTyXth6kE7UjgOODPwB+6NizTVE4gkDYViQT9xWfDAYehvXPpmx+q268gJ9Bti0y5IZehvXNbTT6h5S7Pwpxg/flHJVMFYWOMMabrtOfbrXZ63cnAHar6OBBqZX/TBRwn/QKv/RqsQyYCfXJDDOuTSyTodosJA61pz2mn0j1nAYoaLuvRXTNRY4wxZje0J0lbKyJ/xD9N1AIRCbfzcaaTRZqsl1YYCaStRIUDDsPSnAe0JwoFmidg+ZEmZ0+wJM0YY0wWas+3+Ln4g/dPTJ1fsw9wdVcGZdJrOHnAEejbjtX8e7p0lbReOVbINcYYk/3aM6o8AiwCEJE+QBRY2IUxmRbkBOrfrj55YQKdcVLPbi7gCAFHSKTO1J4TdJtUD7P/NTDGGLN3ak8lbSmwGf9E6B+mrq8SkaUiMqkrgzONuY7flRl0HXrldO/xZp0p1KCaVtT0FFPW1WmMMSZLtSdJewqYqarFqtoX+BzwOPAN4PddGZxpLhJ0KS4I7VW5Se3YuqDrUNB0SRGb2WmMMSZLtecb7lBVfaL2hqo+mbrvFSD7B0V1M71yg+S3cBqlbFWbpKWvHu5F2aoxxpi9Snu+7deLyPeAf6ZunwdsFBEXaL7KqOlSoZbOoZTFQgEHR1o4B+neVFI0xhizV2nPN/4X8U9u/nDqMjx1n4s/89OYLhVyHXrlhuoXr23EkjRjjDHZqc1KmqpuAa5oYfNHnRuOMc2JQO+Wlt2wSpoxxpgs1Z5zd44GvguMbLi/qh7bdWEZ01j6KhpYJc0YY0y2as+YtPuB24E/UX+KKGO6B6ukGWOMyVLtSdISqmonVDfdlCVpxhhjslN7Jg78S0S+ISKDRKRP7aXLIzOmPaySZowxJku1p5J2Uepnw/N1KrBP54djTEdZkmaMMSY7tWd256g9EYgxu8QqacYYY7JUe2Z3BoGvA0en7loE/FFV410YlzHGGGPMXq093Z1/AILUn6fzS6n7vtJVQRnTblZJM8YYk6Xak6RNUdWSBrefFZHlXRWQMR1jSZoxxpjs1J7ZnUkR2bf2hojsg62XZroLq6QZY4zJUu2ppF0NLBSRT/DLFiOAi7s0KmPazZI0Y4wx2ak9szufEZH9gTGpu95X1WjXhmVMO1klzRhjTJZq+YyIIlNEZCBAKikrBW4EbrXFbE33YUmaMcaY7NTamLQ/AjEAETkauBm4BygD7uj60IxpB6ukGWOMyVKtdXe6qrotdf084A5VfQB4QESWdXlkxrSLJWnGGGOyU2uVNFdEapO4GcCzDba1Z8KBMV3PKmnGGGOyVGvJ1j+A/4rIFqAaeB5ARPbD7/I0phuwJM0YY0x2ajFJU9WfiMgzwCDgSVXV1CYHuGJPBGdMm6ySZowxJku12m2pqq+kue+DrgvHmI6yJM0YY0x2as8ZB4zpvqySZowxJktZkmZ6OEvSjDHGZCdL0kzPZpU0Y4wxWcqSNNPDWZJmjDEmO1mSZno2q6QZY4zJUhlZlFZEbgVOxT/t1MfAxaq6I81+q4ByIAkkVHXyHgzTGGOMMSZjMlVJewoYr6oTgQ+A77ey73RVLbUEzaRllTRjjDFZKiNJmqo+qaqJ1M1XgKGZiMNkA0vSjDHGZKfuMCbty8C/W9imwJMiskRELt2DMZmeQrrDR9gYY4zpfF02Jk1EngYGptl0rao+ktrnWiABzGuhmSNVda2I9AeeEpH3VPW5Fo53KXApwPDhw3c7ftNDWHenMcaYLNVlSZqqHtfadhGZDZwCzGhwXtCmbaxN/dwkIg8BhwBpkzRVvQO4A2Dy5Mlp2zPZyJI0Y4wx2SkjfUUichJwDXCaqla1sE+eiBTUXgdOAN7ec1GaHsEqacYYY7JUpgb0/BYowO/CXCYitwOIyGARWZDaZwDwgogsB14DHlfV/2QmXNN9WZJmjDEmO2VknTRV3a+F+9cBM1PXPwFK9mRcpgeySpoxxpgsZVPjTA9nSZoxxpjsZEma6dmskmaMMSZLWZJmejhL0owxxmQnS9JMz2aVNGOMMVnKkjTTw1mSZowxJjtZkmZ6NqukGWOMyVKWpJkezpI0Y4wx2cmSNNOzWSXNGGNMlrIkzfRslqQZY4zJUpakmR7MEjRjjDHZy5I003NZFc0YY0wWsyTN9GCWpBljjMlelqSZnssqacYYY7KYJWmmB7MkzRhjTPayJM30XFZJM8YYk8UsSTM9mCVpxhhjspclaabnskqaMcaYLGZJmunBLEkzxhiTvSxJMz2XVdKMMcZkMUvSTA9mSZoxxpjsZUma6bmskmaMMSaLWZJmejBL0owxxmQvS9JMz2WVNGOMMVnMkjRjjDHGmG4okOkAjNllVkkzxrRDPB5nzZo11NTUZDoUs5eLRCIMHTqUYDDYrv0tSTM9mCVpxpi2rVmzhoKCAkaOHInYH3cmQ1SVrVu3smbNGkaNGtWux1h3p+m57JetMaYdampq6Nu3ryVoJqNEhL59+3aoomtJmunB7BeuMaZ9LEEz3UFHP4eWpJmey37pGmN6iJ/85CeMGzeOiRMnUlpayquvvtrq/rNnz2b+/Pm7dcxFixZRVFREaWlp3eXpp58GID8/f5fa/OUvf0lVVVXd7ZkzZ7Jjx44W97/99tu55557AJg7dy7r1q3r0PGmTZvGmDFjKCkp4YgjjuD999/fpbhrj3/55Zc3iyudVatW8fe//73u9uLFi7nyyit3+di7ysakmR7MkjRjTPf38ssv89hjj7F06VLC4TBbtmwhFovtkWMfddRRPPbYY53W3i9/+UsuuOACcnNzAViwYEGr+1922WV11+fOncv48eMZPHhwh445b948Jk+ezB133MHVV1/No48+2mh7MpnEdd0OtdkwrnRqk7QvfvGLAEyePJnJkyd36BidwSpppueySpoxpgdYv349xcXFhMNhAIqLi+sSlRtuuIEpU6Ywfvx4Lr30UlS12eOXLFnCMcccw6RJkzjxxBNZv349AL/+9a8ZO3YsEydO5Pzzz9+l2CoqKpgxYwYHH3wwEyZM4JFHHgGgsrKSk08+mZKSEsaPH8+9997Lr3/9a9atW8f06dOZPn06ACNHjmTLli0A3HPPPUycOJGSkhK+9KUvAXD99dfzs5/9jPnz57N48WJmzZpFaWkpjz/+OGeccUZdHE899RRnnnlmq7EeffTRfPTRR4BfCfzOd75DSUkJL7/8Mn/729845JBDKC0t5Wtf+xrJZBKAu+++m9GjR3PIIYfw4osv1rVVGxfARx99xHHHHUdJSQkHH3wwH3/8MXPmzOH555+ntLSU2267jUWLFnHKKacAsG3bNs444wwmTpzIoYceyptvvlnX5pe//GWmTZvGPvvsw69//etdek8askqa6cEsSTPGdMz//Osd3l23s1PbHDu4kB+fOq7F7SeccAI33HADo0eP5rjjjuO8887jmGOOAeDyyy/nRz/6EQBf+tKXeOyxxzj11FPrHhuPx7niiit45JFH6NevH/feey/XXnstd911FzfffDMrV64kHA632OVYm2jUeuCBB9h3333rbkciER566CEKCwvZsmULhx56KKeddhr/+c9/GDx4MI8//jgAZWVlFBUV8Ytf/IKFCxdSXFzc6DjvvPMON910Ey+99BLFxcVs27at0fbPf/7z/Pa3v+VnP/sZkydPRlX5zne+w+bNm+nXrx933303X/7yl1t9nf/1r38xYcIEwE8ip06dys9//nNWrFjBLbfcwosvvkgwGOQb3/gG8+bN4/jjj+fHP/4xS5YsoaioiOnTp3PQQQc1a3fWrFnMmTOHM888k5qaGjzP4+abb+ZnP/tZXRVy0aJFdfv/+Mc/5qCDDuLhhx/m2Wef5cILL2TZsmUAvPfeeyxcuJDy8nLGjBnD17/+9XYvt5GOJWmm5xIrBBtjur/8/HyWLFnC888/z8KFCznvvPO4+eabmT17NgsXLuT//u//qKqqYtu2bYwbN65Rkvb+++/z9ttvc/zxxwN+196gQYMAmDhxIrNmzeKMM85oVJVqqK3uTlXlBz/4Ac899xyO47B27Vo2btzIhAkT+M53vsP3vvc9TjnlFI466qhWn+Ozzz7LOeecU5e89enTp9X9RYQvfelL/O1vf+Piiy/m5ZdfbnGM2KxZs8jJyWHkyJH85je/AcB1Xc4++2wAnnnmGZYsWcKUKVMAqK6upn///rz66qtMmzaNfv36AXDeeefxwQcfNGq7vLyctWvX1lXxIpFIq3EDvPDCCzzwwAMAHHvssWzdupWdO/3E/+STTyYcDhMOh+nfvz8bN25k6NChbbbZEkvSTM9l3Z3GmA5qreLVlVzXZdq0aUybNo0JEybwl7/8hfPPP59vfOMbLF68mGHDhnH99dc3W55BVRk3bhwvv/xyszYff/xxnnvuOf71r3/xk5/8hLfeeotAoGNf6/PmzWPz5s0sWbKEYDDIyJEjqampYfTo0SxdupQFCxZw3XXXMWPGjLqKX2e5+OKLOfXUU4lEIpxzzjktxl47Jq2hSCRSNw5NVbnooov46U9/2mifhx9+uFPjbY/aLm3w3/NEIrFb7VkpwvRglqQZY7q/999/nw8//LDu9rJlyxgxYkRdQlZcXExFRUXa2Zxjxoxh8+bNdUlaPB7nnXfewfM8Vq9ezfTp07nlllsoKyujoqKiw7GVlZXRv39/gsEgCxcu5NNPPwVg3bp15ObmcsEFF3D11VezdOlSAAoKCigvL2/WzrHHHsv999/P1q1bAZp1d6Z77ODBgxk8eDA33XQTF198cYdjrzVjxgzmz5/Ppk2b6o796aefMnXqVP773/+ydetW4vE4999/f9qYhg4dWpfQRaNRqqqqWnye4Fcn582bB/jdoMXFxRQWFu5y/K3JWCVNRG4ETgc8YBMwW1Wbzc0VkYuA61I3b1LVv+y5KE23ZpU0Y0wPUFFRwRVXXMGOHTsIBALst99+3HHHHfTq1YuvfvWrjB8/noEDB9Z11zUUCoWYP38+V155JWVlZSQSCb75zW8yevRoLrjgAsrKylBVrrzySnr16tXs8U3HpF133XV8/vOfr7s9a9YsTj31VCZMmMDkyZM54IADAHjrrbe4+uqrcRyHYDDIH/7wBwAuvfRSTjrpJAYPHszChQvr2hk3bhzXXnstxxxzDK7rctBBBzF37txGscyePZvLLruMnJwcXn75ZXJycpg1axabN2/mwAMP3OXXd+zYsdx0002ccMIJeJ5HMBjkd7/7HYceeijXX389hx12GL169Wr0OjT017/+la997Wv86Ec/IhgMcv/99zNx4kRc16WkpITZs2c3GstWO0Fg4sSJ5Obm8pe/dF1aIulmkuwJIlKoqjtT168ExqrqZU326QMsBiYDCiwBJqnq9tbanjx5si5evLhrAgfwkrDhza5r37RP4VDI75fpKIwx3dyKFSt2KwkwXefyyy/noIMO4pJLLsl0KHtMus+jiCxR1WZrfGSsu7M2QUvJw0/CmjoReEpVt6USs6eAk/ZEfKYHsEqaMcb0WJMmTeLNN9/kggsuyHQo3VZGJw6IyE+AC4EyYHqaXYYAqxvcXpO6L11blwKXAgwfPrxzAzXdlCVpxhjTUy1ZsiTTIXR7XVpJE5GnReTtNJfTAVT1WlUdBswDLt+dY6nqHao6WVUn1063NVnOKmnGGGOyWJdW0lT1uHbuOg9YAPy4yf1rgWkNbg8FFu12YMYYY4wx3VzGxqSJyP4Nbp4OvJdmtyeAE0Skt4j0Bk5I3WeMVdKMMcZktUyOSbtZRMbgL8HxKXAZgIhMBi5T1a+o6rbUUh2vpx5zg6o2X3zF7KUsSTPGGJO9MpakqerZLdy/GPhKg9t3AXftqbhMD2KVNGNMN7d161ZmzJgBwIYNG3Bdt+40Ra+99hqhUGi3jzFt2jTWr19PTk4OAPvttx/z58/n+uuvJz8/n+9+97sdam/ZsmWsW7eOmTNnAvDoo4/y7rvvMmfOnBYfc/jhh/PSSy+xatUqXnrpJb74xS/u+hMydey0UN1ZuBCinXsi4KwSzMt0BMYY06q+ffvWnXw7XdKUSCQ6fCqndNKdOmlXLVu2jMWLF9claaeddhqnnXZaq4956aWXAFi1ahV///vfLUnrJHZaqO6sYCC4u/9XVlYK5oFrf2MYY3qe2pX3p06dyjXXXMP111/Pz372s7rt48ePZ9WqVQD87W9/45BDDqG0tJSvfe1rJJPJXTrmnXfeyZQpUygpKeHss8+mqqoKgPvvv5/x48dTUlLC0UcfTSwW40c/+hH33nsvpaWl3HvvvcydO5fLL/cXYNi4cSNnnnkmJSUllJSU1CVn+fn5AMyZM6fuLAe33XYbRx99dF2SCnDkkUeyfPnyXXoOeyP7luuuxIFgLuT2hfL1mY6m+4kUZToCY0xPdffJze8bdwYc8lWIVcG8c5pvL/0iHDQLKrfCfRc23nbx4x0OYc2aNbz00ku4rsv111+fdp8VK1Zw77338uKLLxIMBvnGN77BvHnzuPDCC5vtO2vWrLruzuOPP55bb7210fazzjqLr371q4B/aqg///nPXHHFFdxwww088cQTDBkyhB07dhAKhbjhhhtYvHgxv/3tbwEand7pyiuv5JhjjuGhhx4imUw2O1/ozTffzM9+9jMee+wxAPr06cPcuXP55S9/yQcffEBNTQ0lJSUdfr32VpakdVehfH/MVU6fnpekOQHwEl17jEjXnMzWGGP2hHPOOQfXdVvd55lnnmHJkiV15/Ssrq6mf//+afdtq7vz7bff5rrrrmPHjh1UVFRw4oknAnDEEUcwe/Zszj33XM4666w243722We55557AHBdl6Ki1v9gPuecc7jxxhu59dZbueuuu5g9e3abxzD1LEnrrkKp8VaBUM8bm9ZrBOz4tOsSNTcEwZyuadsYk/1aq3yFclvfntd3lypnzZrJqx9TGwgE8Dyv7nZNTQ0AqspFF13ET3/6090+3uzZs3n44YcpKSlh7ty5LFq0CIDbb7+dV199lccff5xJkyZ1+lkAcnNzOf7443nkkUe477777CwDHWRj0rqrUH799dw+mYujo4K5fpUrp3fXHcO6Oo0xWWTkyJEsXboUgKVLl7Jy5UoAZsyYwfz589m0aRMA27Zt49NPP92lY5SXlzNo0CDi8Tjz5s2ru//jjz9m6tSp3HDDDfTr14/Vq1dTUFBAeXl52nZmzJjBH/7wBwCSySRlZWWNtqd77Fe+8hWuvPJKpkyZQu/eXfjdkIUsSeuWxE92akV6+V2IPUFe6pRcuX277hhh6+o0xmSPs88+m23btjFu3Dh++9vfMnr0aADGjh3LTTfdxAknnMDEiRM5/vjjWb8+/fCXWbNmUVpaSmlpKccd1/xkPzfeeCNTp07liCOO4IADDqi7/+qrr2bChAmMHz+eww8/nJKSEqZPn867775bN3GgoV/96lcsXLiQCRMmMGnSJN59991G2ydOnIjrupSUlHDbbbcB/onUCwsLufjii3frddobiapmOoZON3nyZF28eHHXHcBLwoY3u679UD4U79/4vrI1ULm5647ZGdwQ9B9bv37Z5vchXtW5xxAXBk6wNdKMMe22YsUKDjzwwEyHsddat24d06ZN47333sNxrDaU7vMoIktUtdmgQnu1uqNQmvW/OqMyJS4EIrvfTktyixsnT11RTQsXWIJmjDE9xD333MPUqVP5yU9+YgnaLrBXbFeI4yc8XaXheLRawZzGXaC7onAw9N1/99tJRxzIK258X05v//7OZOPRjDGmx7jwwgtZvXo155yTZlkT0yZL0naFyO4N5m91fJmkr6RB+spUIAeKR6dP7BoKFfhJlBuAvvu1vX9H5fYFp0ni6ridn1TZeDRjjDF7CUvSdlXtAPmOCOb6CVXRsFb2yWme7NRqVJkSKBgE/cb4SV3vkS2fnUAc6NXgmI4Lffbt3ISnpdejM7s8Q/l2lgFjjDF7DUvSdlUg3P4kRxwoHJKqeOVBTi+/ApZOS1U0qK9MBXP95KxgYP34LDfoJ2qkGa+VP9CPt1FbDvTZx585ursiRc3brxUuALeFbQWD/cV628uqaMYYY/YiVpbYHXnFbS8yGymCwqH+orQN5ff3F3xtqq1uyMIhfndpusHzoTy/Slf2Wf19wVz/WOmIQJ9R/mlOojshVrFrC9DmtdB+rdy+UL6u4YH9OPNSVTZxoGpL28exswwYY4zZi1glbXdEilquEoE/DqzPPs0TNPC7LtM9tq0kzQ22Prsxr68/yxIAgV7D254NmdfXT9YGToDiMX4i2NrzaiiYB+E2Ys7tQ32FT/yKX16DbtBew9pO9NywnWXAGNNj/epXv2L8+PGMGzeOX/7yl3X3X3/99QwZMqRujbMFCxYA8OKLLzJx4kQmT57Mhx9+CMCOHTs44YQTGp2doKF4PM6cOXPYf//9OfjggznssMP497//vUvxbt68malTp3LQQQfx/PPPM3PmTHbs2NFsv6Ynh99TGp70vSWrVq3i73//+x6Jp/YE853NKmm7K68f7FzT/P6m48CabRfIH9C46hWIdM6Yq6KhkKjxE76OJjahXP+i2qT61YL2TKBwg363Z6zSTwbDBWliHuK/ZhUb0rdhVTRjTCd5a01Z2zt1wIShrU+Qevvtt7nzzjt57bXXCIVCnHTSSZxyyinst99+AHzrW9/iu9/9bqPH/PznP2fBggWsWrWK22+/nZ///OfcdNNN/OAHP2hxKYsf/vCHrF+/nrfffptwOMzGjRv573//u0vP6ZlnnmHChAn86U9/AuCoo47apXYyqTZJ++IXv9juxyQSCQKB7pMaWSVtd+X2Sb/MROHQlsdpNXxsw8H+rY1H6wgR6D3KH7O2q9IlUmn3a2fylN/fn1XaWruFg/wqXu0SJ07Qf30Ckc4ZO2eMMRmwYsUKpk6dSm5uLoFAgGOOOYYHH3yw1ccEg0GqqqqoqqoiGAzy8ccfs3r1aqZNm5Z2/6qqKu68805+85vfEA773z0DBgzg3HPPBeAf//hH3ZkFvve979U9Lj8/n2uvvZaSkhIOPfRQNm7cyLJly7jmmmt45JFHKC0tpbq6mpEjR7Jliz8s5Sc/+QmjR4/myCOP5P33369r6+OPP+akk05i0qRJHHXUUbz33nuAf97QK6+8ksMPP5x99tmH+fPn1z3mlltuYcKECZSUlDBnzpxW22lJS+3PmTOH559/ntLSUm677TaSySRXX301U6ZMYeLEifzxj38EYNGiRRx11FGcdtppjB07ljlz5vC73/2urv3aamFFRQUzZszg4IMPZsKECTzyyCOtxtUpVDXrLpMmTdI9avtnqmuX1l+2fNT+x5Zvqn9c5daui3FXrH+r8fNqetn4bqYjNMaYNr37buPfVW+u3tGpl/Ycf//999ctW7ZoZWWlHnrooXr55ZerquqPf/xjHTFihE6YMEEvvvhi3bZtm6qqvvHGGzp16lSdNm2arl69Ws877zz94IMPWjzG8uXLtbS0NO22tWvX6rBhw3TTpk0aj8d1+vTp+tBDD6mqKqCPPvqoqqpeffXVeuONN6qq6t13363/7//9v7o2RowYoZs3b9bFixfr+PHjtbKyUsvKynTffffVW2+9VVVVjz322LoYX3nlFZ0+fbqqql500UX6+c9/XpPJpL7zzju67777qqrqggUL9LDDDtPKykpVVd26dWur7TTUML6W2l+4cKGefPLJdY/54x//WPf8ampqdNKkSfrJJ5/owoULNTc3Vz/55BNVVV26dKkeffTRdY878MAD9bPPPtN4PK5lZWWqqrp582bdd9991fM8VVXNy8tr8b1pqunnUVUVWKxp8pnuU9PryfKK6we+i+uPA2uv3L5+F5+X6Py1y3ZXuACqt7Wy3bogjTGmLQceeCDf+973OOGEE8jLy6O0tBTX9Zda+vrXv84Pf/hDRIQf/vCHfOc73+Guu+6itLSUV155BYDnnnuOQYMGoaqcd955BINBfv7znzNgwIB2Hf/1119n2rRp9OvnL5U0a9YsnnvuOc444wxCoRCnnHIK4J9j86mnnmq1reeff54zzzyT3Fx/UfTTTjsNgIqKCl566aVGi9ZGo9G662eccQaO4zB27Fg2btwIwNNPP83FF19c11afPn3abKcl6dpv6sknn+TNN9+sq7SVlZXx4YcfEgqFOOSQQxg1ahQABx10EJs2bWLdunVs3ryZ3r17M2zYMOLxOD/4wQ947rnncByHtWvXsnHjRgYO3I1eqzZYktYZgjn+JIFYuT8ezA22/7GO4w+ar9qSfoJBJrWZpLWzS9QYY/Zyl1xyCZdccgkAP/jBDxg6dChAo0Trq1/9al3CVEtVuemmm/jnP//JFVdcwf/93/+xatUqfv3rX/OTn/ykbr/99tuPzz77jJ07d1JY2P4/oIPBIJKaXOa6LonELszwBzzPo1evXixbtizt9tou2NrntKvttKQ97asqv/nNbzjxxBMb3b9o0SLy8hoPNzrnnHOYP38+GzZs4LzzzgNg3rx5bN68mSVLlhAMBhk5ciQ1NTUdirOjbExaZ8kr9sdN7cqZCPKKu+fpjlqrlIljSZoxxrTTpk2bAPjss8948MEH6wazr1+/vm6fhx56iPHjxzd63D333MPMmTPp06cPVVVVOI6D4zhUVVU12i83N5dLLrmEq666ilgsBvgzNO+//34OOeQQ/vvf/7JlyxaSyST/+Mc/OOaYY3bpeRx99NE8/PDDVFdXU15ezr/+9S8ACgsLGTVqFPfffz/gJ0TLly9vta3jjz+eu+++u+65bNu2bZfaaUlBQQHl5eV1t0888UT+8Ic/EI/HAfjggw+orKxM+9jzzjuPf/7zn8yfP7+uqldWVkb//v0JBoMsXLiQTz9Ns4xWJ7NKWmeJFO16d6Xj+mcP6G7cgL/ERjzNhziUbyc6N8aYdjr77LPZunUrwWCQ3/3ud/Tq1QuAa665hmXLliEijBw5sm4wO/iTAebOncuTTz4JwLe//W1mzpxJKBRKu7TETTfdxHXXXcfYsWOJRCLk5eVxww03MGjQIG6++WamT5+OqnLyySdz+umn79LzOPjggznvvPMoKSmhf//+TJkypW7bvHnz+PrXv85NN91EPB7n/PPPp6SkpMW2TjrpJJYtW8bkyZMJhULMnDmT//3f/+1wOy2ZOHEirutSUlLC7Nmzueqqq1i1ahUHH3wwqkq/fv14+OGH0z523LhxlJeXM2TIEAYN8r+fZ82axamnnsqECROYPHkyBxxwQIdj6ihprezYU02ePFkXL16c6TCyw8716ZfFKBrW/ITqxhjTDa1YsYIDDzww02EYA6T/PIrIElWd3HRf6+40rWupS9MmDRhjjDFdypI007pQnj9jtaFApPtNcjDGGGOyjCVppnUizatpVkUzxhhjupwlaaZtTZMym9VpjDHGdDlL0kzbGiZltvSGMcYYs0dYkmbaFghBIHWidlt6wxhjjNkjLEkz7VNbPeuOi+4aY0w3tXXrVkpLSyktLWXgwIEMGTKk7nbtorO7a9q0aYwZM6au3c9//vNA/YnBO2rZsmUsWLCg7vajjz7KzTff3OpjDj/8cABWrVqVdg231ixatIiioiJKS0s54IAD+O53v9vhmLOVLWZr2idcAJWbrKvTGNPzrXujc9sbfFCLm/r27Vt3iqPrr7+e/Pz8RklIIpEgENj9r+J58+YxeXKzZbZ2ybJly1i8eDEzZ84E/PNz1p6jsyUvvfQSUJ+k1Z5Rob2OOuooHnvsMaqrqznooIM488wzOeKII3btCWQRq6SZ9gkXQDAXAuG29zXGGNOi2bNnc9lllzF16lSuueaaZhWv8ePHs2rVKgD+9re/ccghh1BaWsrXvvY1ksnkLh3zzjvvZMqUKZSUlHD22WfXnYrp/vvvZ/z48ZSUlHD00UcTi8X40Y9+xL333ktpaSn33nsvc+fO5fLLLwdg48aNnHnmmZSUlFBSUlKXnOXn+2fcmTNnDs8//zylpaXcdtttHH300Y3Ow3nkkUe2epqnnJwcSktLWbt2batxz549myuvvJLDDz+cffbZp+6k6Z7n8Y1vfIMDDjiA448/npkzZ9ZtW7JkCccccwyTJk3ixBNPbHRKru7KkjTTPiLd89RVxhjTA61Zs4aXXnqJX/ziFy3us2LFCu69915efPFFli1bhuu6zJs3L+2+s2bNquvuvPrqq5ttP+uss3j99ddZvnw5Bx54IH/+858BuOGGG3jiiSdYvnw5jz76KKFQiBtuuIHzzjuPZcuW1Z1cvNaVV17JMcccw/Lly1m6dCnjxo1rtP3mm2/mqKOOYtmyZXzrW9/ikksuYe7cuYB/rsyamppWT/G0fft2PvzwQ44++uhW4wb/vKcvvPACjz32GHPmzAHgwQcfZNWqVbz77rv89a9/5eWXXwYgHo9zxRVXMH/+fJYsWcKXv/xlrr322hbj6C6su9O0X8TWRzPGmM5wzjnn4Lpuq/s888wzLFmypO78mNXV1fTv3z/tvm11d7799ttcd9117Nixg4qKCk488UQAjjjiCGbPns25557LWWed1Wbczz77LPfccw8ArutSVNT6OOVzzjmHG2+8kVtvvZW77rqL2bNnp93v+eefp6SkhA8//JBvfvObDBw4sNW4Ac444wwcx2Hs2LFs3LgRgBdeeIFzzjkHx3EYOHAg06dPB+D999/n7bff5vjjjwcgmUzWnZOzO7MkzRhjjNnD8vLy6q4HAgE8z6u7XVNTA4CqctFFF/HTn/50t483e/ZsHn74YUpKSpg7dy6LFi0C4Pbbb+fVV1/l8ccfZ9KkSSxZsmS3j9VQbm4uxx9/PI888gj33Xdfi+3XjklbuXIlhx56KOeeey6lpaUtxg0QDtcPv2nrPOSqyrhx4+oqaz1FRro7ReRGEXlTRJaJyJMiMriF/ZKpfZaJyKN7Ok5jjDGmq40cOZKlS5cCsHTpUlauXAnAjBkzmD9/Pps2bQJg27ZtfPrpp7t0jPLycgYNGkQ8Hm/UZfrxxx8zdepUbrjhBvr168fq1aspKCigvLw8bTszZszgD3/4A+BXo8rKyhptT/fYr3zlK1x55ZVMmTKF3r17txrnqFGjmDNnDrfcckurcbfkiCOO4IEHHsDzPDZu3FiX1I0ZM4bNmzc36v5855132mwv0zI1Ju1WVZ2oqqXAY8CPWtivWlVLU5fWp5YYY4wxPdDZZ5/Ntm3bGDduHL/97W8ZPXo0AGPHjuWmm27ihBNOYOLEiRx//PEtDnZvOCbtuOOOa7b9xhtvZOrUqRxxxBEccMABdfdfffXVTJgwgfHjx3P44YdTUlLC9OnTeffdd+smDjT0q1/9ioULFzJhwgQmTZrEu+++22j7xIkTcV2XkpISbrvtNgAmTZpEYWEhF198cbtej8suu4znnnuOVatWtRh3S84++2yGDh3K2LFjueCCCzj44IMpKioiFAoxf/58vve971FSUkJpaWndpIfuTNoqEXZ5ACLfB4ar6tfTbKtQ1fyOtjl58mRdvHhxp8RnjDGmZ1uxYgUHHnhgpsPYa61bt45p06bx3nvv4ThdXxuqqKggPz+frVu3csghh/Diiy/WjXHrDtJ9HkVkiao2G1SYsTFpIvIT4EKgDJjewm4REVkMJICbVfXhVtq7FLgUYPjw4Z0brDHGGGM67J577uHaa6/lF7/4xR5J0ABOOeUUduzYQSwW44c//GG3StA6qssqaSLyNJDulblWVR9psN/3gYiq/jhNG0NUda2I7AM8C8xQ1Y/bOrZV0owxxtSySprpTrpFJU1Vm3eKpzcPWAA0S9JUdW3q5ycisgg4CGgzSTPGGGOM6ekyNbtz/wY3TwfeS7NPbxEJp64XA0cA7zbdzxhjjGlLpsdfGwMd/xxmanbnzSLytoi8CZwAXAUgIpNF5E+pfQ4EFovIcmAh/pg0S9KMMcZ0SCQSYevWrZaomYxSVbZu3UokEmn3YzI+u7Mr2Jg0Y4wxteLxOGvWrKlbJNaYTIlEIgwdOpRgMNjo/m43u9MYY4zZE4LBIKNGjcp0GMZ0mJ1g3RhjjDGmG7IkzRhjjDGmG7IkzRhjjDGmG8rKiQMishnYtbPQtl8xsKWLj2E6xt6T7snel+7H3pPuyd6X7mdPvScjVLVf0zuzMknbE0RkcbqZGCZz7D3pnux96X7sPeme7H3pfjL9nlh3pzHGGGNMN2RJmjHGGGNMN2RJ2q67I9MBmGbsPeme7H3pfuw96Z7sfel+Mvqe2Jg0Y4wxxphuyCppxhhjjDHdkCVpHSQiJ4nI+yLykYjMyXQ8BkTkLhHZJCJvZzoW4xORYSKyUETeFZF3ROSqTMdkQEQiIvKaiCxPvS//k+mYjE9EXBF5Q0Qey3Qsxiciq0TkLRFZJiIZOSG4dXd2gIi4wAfA8cAa4HXgC6r6bkYD28uJyNFABXCPqo7PdDwGRGQQMEhVl4pIAbAEOMP+r2SWiAiQp6oVIhIEXgCuUtVXMhzaXk9Evg1MBgpV9ZRMx2P8JA2YrKoZW7vOKmkdcwjwkap+oqox4J/A6RmOaa+nqs8B2zIdh6mnqutVdWnqejmwAhiS2aiM+ipSN4Opi/2lnmEiMhQ4GfhTpmMx3YslaR0zBFjd4PYa7IvHmFaJyEjgIODVDIdiqOtWWwZsAp5SVXtfMu+XwDWAl+E4TGMKPCkiS0Tk0kwEYEmaMabLiEg+8ADwTVXdmel4DKhqUlVLgaHAISJiQwQySEROATap6pJMx2KaOVJVDwY+B/y/1NCaPcqStI5ZCwxrcHto6j5jTBOpMU8PAPNU9cFMx2MaU9UdwELgpAyHsrc7AjgtNf7pn8CxIvK3zIZkAFR1bernJuAh/CFPe5QlaR3zOrC/iIwSkRBwPvBohmMypttJDVD/M7BCVX+R6XiMT0T6iUiv1PUc/ElQ72U0qL2cqn5fVYeq6kj875RnVfWCDIe11xORvNSkJ0QkDzgB2OMrCFiS1gGqmgAuB57AHwh9n6q+k9mojIj8A3gZGCMia0TkkkzHZDgC+BJ+VWBZ6jIz00EZBgELReRN/D86n1JVW/LBmOYGAC+IyHLgNeBxVf3Png7CluAwxhhjjOmGrJJmjDHGGNMNWZJmjDHGGNMNWZJmjDHGGNMNWZJmjDHGGNMNWZJmjDHGGNMNWZJmjMl6IlLR9l7GGNO9WJJmjDHGGNMNWZJmjNlriMg0EVkkIvNF5D0RmZc6OwIiMkVEXhKR5SLymogUiEhERO4WkbdE5A0RmZ7ad7aIPCwiT4nIKhG5XES+ndrnFRHpk9pvXxH5T+oEzc+LyAGZfP7GmJ4lkOkAjDFmDzsIGAesA14EjhCR14B7gfNU9XURKQSqgasAVdUJqQTrSREZnWpnfKqtCPAR8D1VPUhEbgMuBH4J3AFcpqofishU4PfAsXvqiRpjejZL0owxe5vXVHUNgIgsA0YCZcB6VX0dQFV3prYfCfwmdd97IvIpUJukLVTVcqBcRMqAf6XufwuYKCL5wOHA/aliHUC4a5+aMSabWJJmjNnbRBtcT7LrvwcbtuM1uO2l2nSAHapauovtG2P2cjYmzRhj4H1gkIhMAUiNRwsAzwOzUveNBoan9m1Tqhq3UkTOST1eRKSkK4I3xmQnS9KMMXs9VY0B5wG/EZHlwFP4Y81+Dzgi8hb+mLXZqhptuaVmZgGXpNp8Bzi9cyM3xmQzUdVMx2CMMcYYY5qwSpoxxhhjTDdkSZoxxhhjTDdkSZoxxhhjTDdkSZoxxhhjTDdkSZoxxhhjTDdkSZoxxhhjTDdkSZoxxhhjTDdkSZoxxhhjTDf0/wEI1r04NmRBXwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Compare the estimate and the truth\r\n",
     "plt.figure(figsize=(10, 6))\r\n",
@@ -572,77 +595,53 @@
     "plt.ylabel(\"Songs Sales Elasticity\")\r\n",
     "plt.title(\"Songs Sales Elasticity vs Income\")\r\n",
     "plt.legend(loc=\"lower right\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1f533c69208>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We notice that this model fits much better than the `LinearDML`, the 95% confidence interval correctly covers the true treatment effect estimate and captures the variation when income is around 1. Overall, the model shows that people with low income are much more sensitive to the price changes than higher income people."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Understand Treatment Effects with EconML <a id=\"interpret\"></a>\n",
     "EconML includes interpretability tools to better understand treatment effects. Treatment effects can be complex, but oftentimes we are interested in simple rules that can differentiate between users who respond positively, users who remain neutral and users who respond negatively to the proposed changes.\n",
     "\n",
     "The EconML `SingleTreeCateInterpreter` provides interperetability by training a single decision tree on the treatment effects outputted by any of the EconML estimators. In the figure below we can see in dark red users respond strongly to the discount and the in white users respond lightly to the discount."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "source": [
-    "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\r\n",
-    "intrp.interpret(est, X_test)\r\n",
-    "plt.figure(figsize=(25, 5))\r\n",
-    "intrp.plot(feature_names=X.columns, fontsize=12)"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1800x360 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\r\n",
+    "intrp.interpret(est, X_test)\r\n",
+    "plt.figure(figsize=(25, 5))\r\n",
+    "intrp.plot(feature_names=X.columns, fontsize=12)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Make Policy Decision with EconML <a id=\"policy\"></a>\n",
     "We want to make policy decisions to maximum the **revenue** instead of the demand. In this scenario,\n",
@@ -659,46 +658,47 @@
     "With the decrease of price, revenue will increase only if $\\theta(X)+1<0$. Thus, we set `sample_treatment_cast=-1` here to learn **what kinds of customers we should give a small discount to maximum the revenue**.\n",
     "\n",
     "The EconML library includes policy interpretability tools such as `SingleTreePolicyInterpreter` that take in a treatment cost and the treatment effects to learn simple rules about which customers to target profitably. In the figure below we can see the model recommends to give discount for people with income less than $0.985$ and give original price for the others."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "source": [
-    "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\r\n",
-    "intrp.interpret(est, X_test, sample_treatment_costs=-1)\r\n",
-    "plt.figure(figsize=(25, 5))\r\n",
-    "intrp.plot(feature_names=X.columns, treatment_names=[\"Discount\", \"No-Discount\"], fontsize=12)"
-   ],
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
+      "image/png": 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       "text/plain": [
        "<Figure size 1800x360 with 1 Axes>"
-      ],
-      "image/png": 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"
+      ]
      },
      "metadata": {
       "needs_background": "light"
-     }
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {
-    "scrolled": true
-   }
+   "source": [
+    "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\r\n",
+    "intrp.interpret(est, X_test, sample_treatment_costs=-1)\r\n",
+    "plt.figure(figsize=(25, 5))\r\n",
+    "intrp.plot(feature_names=X.columns, treatment_names=[\"Discount\", \"No-Discount\"], fontsize=12)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Now, let us compare our policy with other baseline policies! Our model says which customers to give a small discount to, and for this experiment, we will set a discount level of 10% for those users. Because the model is misspecified we would not expect good results with large discounts. Here, because we know the ground truth, we can evaluate the value of this policy."
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# define function to compute revenue\r\n",
     "def revenue_fn(data, discount_level1, discount_level2, baseline_T, policy):\r\n",
@@ -706,13 +706,13 @@
     "    demand = demand_fn(data, policy_price)\r\n",
     "    rev = demand * policy_price\r\n",
     "    return rev"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "policy_dic = {}\r\n",
     "# our policy above\r\n",
@@ -733,32 +733,15 @@
     "\r\n",
     "## give everyone -10% discount\r\n",
     "policy_dic[\"Give Everyone Negative Discount\"] = np.mean(revenue_fn(train_data, -0.1, 0, 1, np.ones(len(X))))"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 18,
-   "source": [
-    "# get policy summary table\r\n",
-    "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\r\n",
-    "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\r\n",
-    "res"
-   ],
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
-      "text/plain": [
-       "                                                      Revenue  Rank\n",
-       "Our Policy                                          14.686241   2.0\n",
-       "Previous Strategy                                   14.349342   4.0\n",
-       "Give Everyone Discount                              13.774469   6.0\n",
-       "Give No One Discount                                14.294606   5.0\n",
-       "Our Policy + Give Negative Discount for No-Disc...  15.564411   1.0\n",
-       "Give Everyone Negative Discount                     14.612670   3.0"
-      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -816,23 +799,39 @@
        "  </tbody>\n",
        "</table>\n",
        "</div>"
+      ],
+      "text/plain": [
+       "                                                      Revenue  Rank\n",
+       "Our Policy                                          14.686241   2.0\n",
+       "Previous Strategy                                   14.349342   4.0\n",
+       "Give Everyone Discount                              13.774469   6.0\n",
+       "Give No One Discount                                14.294606   5.0\n",
+       "Our Policy + Give Negative Discount for No-Disc...  15.564411   1.0\n",
+       "Give Everyone Negative Discount                     14.612670   3.0"
       ]
      },
+     "execution_count": 18,
      "metadata": {},
-     "execution_count": 18
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# get policy summary table\r\n",
+    "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\r\n",
+    "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\r\n",
+    "res"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "**We beat the baseline policies!** Our policy gets the highest revenue except for the one raising the price for the No-Discount group. That means our currently baseline price is low, but the way we segment the user does help increase the revenue!"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Conclusions <a id=\"conclusion\"></a>\n",
     "\n",
@@ -843,14 +842,16 @@
     "* Make the policy decision beats the previous and baseline policies\n",
     "\n",
     "To learn more about what EconML can do for you, visit our [website](https://aka.ms/econml), our [GitHub page](https://github.com/py-why/EconML) or our [documentation](https://econml.azurewebsites.net/). "
-   ],
-   "metadata": {}
+   ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546"
+  },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.6.6 64-bit"
+   "display_name": "Python 3.6.6 64-bit",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -863,9 +864,6 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.6"
-  },
-  "interpreter": {
-   "hash": "2e5c6628eef985e7fd2fa2aad22c988c5b8aa1d2648cf9c51c543a2a2637c546"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb
index ab547773a..d04aebf4c 100644
--- a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company - EconML + DoWhy.ipynb	
@@ -67,8 +67,6 @@
     "# Some imports to get us started\n",
     "\n",
     "# Utilities\n",
-    "import os\n",
-    "import urllib.request\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from networkx.drawing.nx_pydot import to_pydot\n",
@@ -81,8 +79,6 @@
     "from econml.dr import LinearDRLearner\n",
     "\n",
     "# DoWhy imports \n",
-    "import dowhy\n",
-    "from dowhy import CausalModel\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb
index 42d18ce0e..2a0e95ba1 100644
--- a/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Multi-investment Attribution at A Software Company.ipynb	
@@ -54,8 +54,6 @@
    "source": [
     "# Some imports to get us started\n",
     "# Utilities\n",
-    "import os\n",
-    "import urllib.request\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb
index 4a0cfdc47..01b32bb1c 100644
--- a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company - EconML + DoWhy.ipynb	
@@ -79,20 +79,14 @@
     "# Some imports to get us started\n",
     "\n",
     "# Utilities\n",
-    "import os\n",
-    "import urllib.request\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from networkx.drawing.nx_pydot import to_pydot\n",
-    "from IPython.display import Image, display\n",
     "\n",
     "# Generic ML imports\n",
     "import lightgbm as lgb\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "\n",
     "# DoWhy imports \n",
-    "import dowhy\n",
-    "from dowhy import CausalModel\n",
     "\n",
     "# EconML imports\n",
     "from econml.iv.dr import LinearIntentToTreatDRIV\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb
index 6e047b3fe..783a70dc3 100644
--- a/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Recommendation AB Testing at An Online Travel Company.ipynb	
@@ -72,8 +72,6 @@
    "source": [
     "# Some imports to get us started\r\n",
     "# Utilities\r\n",
-    "import os\r\n",
-    "import urllib.request\r\n",
     "import numpy as np\r\n",
     "import pandas as pd\r\n",
     "\r\n",
diff --git a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb b/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb
index df0f53488..e964e75b0 100644
--- a/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb	
+++ b/notebooks/CustomerScenarios/Case Study - Using EconML to evaluate the treatment effect of training program - Lalonde dataset.ipynb	
@@ -47,19 +47,17 @@
    "outputs": [],
    "source": [
     "# imports\n",
-    "import os\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import statsmodels.api as sm\n",
-    "import seaborn as sns\n",
     "\n",
     "from econml.dml import LinearDML\n",
     "from econml.dr import LinearDRLearner\n",
     "from econml.cate_interpreter import SingleTreeCateInterpreter\n",
-    "from econml.dml import CausalForestDML,NonParamDML\n",
+    "from econml.dml import CausalForestDML\n",
     "\n",
-    "from sklearn.linear_model import LogisticRegressionCV, LinearRegression,LogisticRegression,Lasso,LassoCV\n",
-    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor,GradientBoostingClassifier\n",
+    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression,Lasso,LassoCV\n",
+    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
     "import lightgbm as lgb\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from tqdm import tqdm\n",
@@ -68,7 +66,6 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib.transforms import ScaledTranslation\n",
-    "import seaborn as sns\n",
     "\n",
     "%matplotlib inline"
    ]
diff --git a/notebooks/Double Machine Learning Examples.ipynb b/notebooks/Double Machine Learning Examples.ipynb
index 77488385e..d047f15f5 100644
--- a/notebooks/Double Machine Learning Examples.ipynb	
+++ b/notebooks/Double Machine Learning Examples.ipynb	
@@ -45,9 +45,7 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import econml"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -72,13 +70,10 @@
     "# Helper imports\n",
     "import numpy as np\n",
     "from itertools import product\n",
-    "from sklearn.linear_model import (Lasso, LassoCV, LogisticRegression,\n",
-    "                                  LogisticRegressionCV,LinearRegression,\n",
-    "                                  MultiTaskElasticNet,MultiTaskElasticNetCV)\n",
+    "from sklearn.linear_model import (Lasso, MultiTaskElasticNetCV)\n",
     "from sklearn.ensemble import RandomForestRegressor,RandomForestClassifier\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
     "%matplotlib inline"
diff --git a/notebooks/Doubly Robust Learner and Interpretability.ipynb b/notebooks/Doubly Robust Learner and Interpretability.ipynb
index 3d8e8fe93..2e0803f50 100644
--- a/notebooks/Doubly Robust Learner and Interpretability.ipynb	
+++ b/notebooks/Doubly Robust Learner and Interpretability.ipynb	
@@ -35,7 +35,6 @@
     "# Helper imports\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib\n",
     "%matplotlib inline\n",
     "\n",
     "import seaborn as sns"
@@ -97,7 +96,6 @@
    "source": [
     "from sklearn.linear_model import LassoCV\n",
     "from econml.dr import LinearDRLearner\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
     "from sklearn.dummy import DummyClassifier\n",
     "\n",
     "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
@@ -525,7 +523,6 @@
    "source": [
     "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
     "from econml.dr import LinearDRLearner\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
     "from sklearn.dummy import DummyClassifier\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "\n",
@@ -802,7 +799,6 @@
    "source": [
     "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
     "from econml.dr import SparseLinearDRLearner\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
     "from sklearn.dummy import DummyClassifier\n",
     "from sklearn.preprocessing import PolynomialFeatures\n",
     "\n",
@@ -1204,7 +1200,6 @@
    ],
    "source": [
     "import shap\n",
-    "import pandas as pd\n",
     "# explain the model's predictions using SHAP values\n",
     "shap_values = est.shap_values(X[:100, :4], feature_names=['A', 'B', 'C', 'D'], background_samples=100)\n",
     "shap.summary_plot(shap_values['Y0']['T0_1'])"
@@ -1704,7 +1699,6 @@
    "outputs": [],
    "source": [
     "import shap\n",
-    "import pandas as pd\n",
     "# explain the model's predictions using SHAP values\n",
     "shap_values = est.shap_values(X[:, :4], feature_names=['A', 'B', 'C', 'D'], background_samples=100)"
    ]
diff --git a/notebooks/Dynamic Double Machine Learning Examples.ipynb b/notebooks/Dynamic Double Machine Learning Examples.ipynb
index 391f491ba..7d86378f7 100755
--- a/notebooks/Dynamic Double Machine Learning Examples.ipynb	
+++ b/notebooks/Dynamic Double Machine Learning Examples.ipynb	
@@ -83,9 +83,7 @@
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import econml"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -99,7 +97,7 @@
     "\n",
     "# Helper imports\n",
     "import numpy as np\n",
-    "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV, MultiTaskLassoCV\n",
+    "from sklearn.linear_model import LassoCV, MultiTaskLassoCV\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "%matplotlib inline"
diff --git a/notebooks/ForestLearners Basic Example.ipynb b/notebooks/ForestLearners Basic Example.ipynb
index f43f845d6..75404c5df 100644
--- a/notebooks/ForestLearners Basic Example.ipynb	
+++ b/notebooks/ForestLearners Basic Example.ipynb	
@@ -33,7 +33,6 @@
     "# Helper imports\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib\n",
     "%matplotlib inline"
    ]
   },
@@ -60,8 +59,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import scipy.special\n",
     "np.random.seed(123)\n",
     "n = 2000\n",
     "p = 10\n",
@@ -202,9 +199,6 @@
    ],
    "source": [
     "from econml.dml import CausalForestDML\n",
-    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
-    "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
-    "from sklearn.dummy import DummyRegressor, DummyClassifier\n",
     "\n",
     "n_samples, n_features = X.shape\n",
     "subsample_fr_ = (n_samples/2)**(1-1/(2*n_features+2))/(n_samples/2)\n",
@@ -555,7 +549,6 @@
    ],
    "source": [
     "from econml.dr import ForestDRLearner\n",
-    "from sklearn.dummy import DummyRegressor, DummyClassifier\n",
     "\n",
     "est = ForestDRLearner(model_regression=model_y,\n",
     "                      model_propensity=model_t,\n",
@@ -850,7 +843,7 @@
    ],
    "source": [
     "from econml.orf import DROrthoForest\n",
-    "from sklearn.linear_model import Lasso, LassoCV, LogisticRegression, LogisticRegressionCV\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
     "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
     "\n",
     "est3 = DROrthoForest(model_Y=Lasso(alpha=0.01),\n",
diff --git a/notebooks/Generalized Random Forests.ipynb b/notebooks/Generalized Random Forests.ipynb
index a951c9212..ac3ef349c 100644
--- a/notebooks/Generalized Random Forests.ipynb	
+++ b/notebooks/Generalized Random Forests.ipynb	
@@ -399,7 +399,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from econml.grf import CausalIVForest\n",
     "est = CausalIVForest(criterion='mse', n_estimators=400, min_samples_leaf=40,\n",
     "                     min_var_fraction_leaf=0.1, min_var_leaf_on_val=True,\n",
     "                     min_impurity_decrease = 0.001, max_samples=.45, max_depth=None,\n",
diff --git a/notebooks/Interpretability with SHAP.ipynb b/notebooks/Interpretability with SHAP.ipynb
index 06b6594d8..a13a17a12 100644
--- a/notebooks/Interpretability with SHAP.ipynb	
+++ b/notebooks/Interpretability with SHAP.ipynb	
@@ -49,8 +49,7 @@
     "import scipy.special\n",
     "import matplotlib.pyplot as plt\n",
     "import shap\n",
-    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
-    "from sklearn.linear_model import Lasso"
+    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier"
    ]
   },
   {
diff --git a/notebooks/Metalearners Examples.ipynb b/notebooks/Metalearners Examples.ipynb
index 671a65c44..610c66ebf 100644
--- a/notebooks/Metalearners Examples.ipynb	
+++ b/notebooks/Metalearners Examples.ipynb	
@@ -61,8 +61,7 @@
     "# Helper imports \n",
     "import numpy as np\n",
     "from numpy.random import binomial, multivariate_normal, normal, uniform\n",
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, GradientBoostingRegressor\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "%matplotlib inline"
diff --git a/notebooks/OrthoIV and DRIV Examples.ipynb b/notebooks/OrthoIV and DRIV Examples.ipynb
index 2027002fe..4388fe39e 100644
--- a/notebooks/OrthoIV and DRIV Examples.ipynb	
+++ b/notebooks/OrthoIV and DRIV Examples.ipynb	
@@ -587,7 +587,7 @@
     }
    ],
    "source": [
-    "from econml.iv.dr import LinearDRIV, SparseLinearDRIV, ForestDRIV\n",
+    "from econml.iv.dr import LinearDRIV, ForestDRIV\n",
     "\n",
     "est4 = LinearDRIV(discrete_instrument=True, discrete_treatment=True)\n",
     "est4.fit(y, T, Z=Z, X=None, W=X)\n",
diff --git a/notebooks/Policy Learning with Trees and Forests.ipynb b/notebooks/Policy Learning with Trees and Forests.ipynb
index 85f212b81..10ee9a435 100644
--- a/notebooks/Policy Learning with Trees and Forests.ipynb	
+++ b/notebooks/Policy Learning with Trees and Forests.ipynb	
@@ -23,7 +23,7 @@
     "from econml.policy import PolicyTree, PolicyForest\n",
     "from econml.policy import DRPolicyTree, DRPolicyForest\n",
     "from econml.dml import LinearDML\n",
-    "from econml.cate_interpreter import SingleTreePolicyInterpreter, SingleTreeCateInterpreter\n",
+    "from econml.cate_interpreter import SingleTreePolicyInterpreter\n",
     "import pandas as pd\n",
     "\n",
     "%matplotlib inline"
diff --git a/notebooks/Scaling EconML using Ray.ipynb b/notebooks/Scaling EconML using Ray.ipynb
index e05168150..0c2e3e3d9 100644
--- a/notebooks/Scaling EconML using Ray.ipynb	
+++ b/notebooks/Scaling EconML using Ray.ipynb	
@@ -44,7 +44,6 @@
    "outputs": [],
    "source": [
     "import ray\n",
-    "import os\n",
     "import numpy as np\n",
     "import scipy\n",
     "from econml.dml import LinearDML\n",
@@ -238,7 +237,6 @@
     "import time\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from econml.dml import DML\n",
     "\n",
     "\n",
     "def compare_runtime(cv_values, use_ray=True):\n",
diff --git a/notebooks/Solutions/Causal Interpretation for Ames Housing Price.ipynb b/notebooks/Solutions/Causal Interpretation for Ames Housing Price.ipynb
index 504d9e0f7..90f193371 100644
--- a/notebooks/Solutions/Causal Interpretation for Ames Housing Price.ipynb	
+++ b/notebooks/Solutions/Causal Interpretation for Ames Housing Price.ipynb	
@@ -33,7 +33,7 @@
    "outputs": [],
    "source": [
     "# Some imports to get us started\n",
-    "from lightgbm import LGBMClassifier, LGBMRegressor\n",
+    "from lightgbm import LGBMRegressor\n",
     "from sklearn.model_selection import GridSearchCV\n",
     "import pandas as pd\n",
     "import numpy as np\n",
diff --git a/notebooks/Weighted Double Machine Learning Examples.ipynb b/notebooks/Weighted Double Machine Learning Examples.ipynb
index ad49f02c9..ec30a78c2 100644
--- a/notebooks/Weighted Double Machine Learning Examples.ipynb	
+++ b/notebooks/Weighted Double Machine Learning Examples.ipynb	
@@ -1,737 +1,736 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<table border=\"0\">\n",
-    "    <tr>\n",
-    "        <td>\n",
-    "            <img src=\"https://ictd2016.files.wordpress.com/2016/04/microsoft-research-logo-copy.jpg\" style=\"width 30px;\" />\n",
-    "             </td>\n",
-    "        <td>\n",
-    "            <img src=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/MSR-ALICE-HeaderGraphic-1920x720_1-800x550.jpg\" style=\"width 100px;\"/></td>\n",
-    "        </tr>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Double Machine Learning: Summarized Data and Interpretability\n",
-    "\n",
-    "Double Machine Learning (DML) is an algorithm that applies arbitrary machine learning methods\n",
-    "to fit the treatment and response, then uses a linear model to predict the response residuals\n",
-    "from the treatment residuals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Helper imports\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib\n",
-    "%matplotlib inline\n",
-    "\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Generating Raw Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.special\n",
-    "\n",
-    "np.random.seed(123)\n",
-    "n=10000 # number of raw samples\n",
-    "d=10 # number of binary features + 1\n",
-    "\n",
-    "# Generating random segments aka binary features. We will use features 1,...,4 for heterogeneity.\n",
-    "# The rest for controls. Just as an example.\n",
-    "X = np.random.binomial(1, .5, size=(n, d))\n",
-    "# The first column of X is the treatment. Generating an imbalanced A/B test\n",
-    "X[:, 0] = np.random.binomial(1, scipy.special.expit(X[:, 1]))\n",
-    "# Generating an outcome with treatment effect heterogeneity. The first binary feature creates heterogeneity\n",
-    "# We also have confounding on the first variable. We also have heteroskedastic errors.\n",
-    "y = (-1 + 2 * X[:, 1]) * X[:, 0] + X[:, 1] + (1*X[:, 1] + 1)*np.random.normal(0, 1, size=(n,))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Creating Summarized Data\n",
-    "\n",
-    "For each segment, we split the data in two and create one summarized copy for each split. The summarized copy contains the number of samples that were summarized and the variance of the observations for the summarized copies. Optimally we would want two copies per segment, as I'm creating here, but with many segments, the approach would work ok even with a single copy per segment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from econml.tests.test_statsmodels import _summarize\n",
-    "\n",
-    "X_sum = np.unique(X, axis=0)\n",
-    "n_sum = np.zeros(X_sum.shape[0])\n",
-    "# The _summarize function performs the summary operation and returns the summarized data\n",
-    "# For each segment we have two copies.\n",
-    "X1, X2, y1, y2, X1_sum, X2_sum, y1_sum, y2_sum, n1_sum, n2_sum, var1_sum, var2_sum = _summarize(X, y)\n",
-    "\n",
-    "# We concatenate the two copies data\n",
-    "X_sum = np.vstack([X1_sum, X2_sum]) # first coordinate is treatment, the rest are features\n",
-    "y_sum = np.concatenate((y1_sum, y2_sum)) # outcome\n",
-    "n_sum = np.concatenate((n1_sum, n2_sum)) # number of summarized points\n",
-    "var_sum = np.concatenate((var1_sum, var2_sum)) # variance of the summarized points\n",
-    "splits = (np.arange(len(y1_sum)), np.arange(len(y1_sum), len(y_sum))) # indices of the two summarized copies"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Applying the LinearDML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<econml.dml.dml.LinearDML at 0x1dcfd784408>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
-    "from econml.dml import LinearDML\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
-    "\n",
-    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
-    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
-    "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n",
-    "                model_t=LogisticRegressionCV(cv=3),\n",
-    "                discrete_treatment=True)\n",
-    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
-    "        freq_weight=n_sum, sample_var=var_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.07157889])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Treatment Effect of particular segments\n",
-    "est.effect(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0.87415187]), array([1.26900591]))"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Confidence interval for effect\n",
-    "est.effect_interval(np.array([[1, 0, 0, 0]]), alpha=.05) # effect of segment with features [1, 0, 0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[['A' '2.0109841149829912']\n",
-      " ['B' '0.07212612018559361']\n",
-      " ['C' '-0.01525518572828466']\n",
-      " ['D' '-0.18928230056541373']]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Getting the coefficients of the linear CATE model together with the corresponding feature names\n",
-    "print(np.array(list(zip(est.cate_feature_names(['A', 'B', 'C', 'D']), est.coef_))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Non-Linear CATE Models with Polynomial Features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<econml.dml.dml.LinearDML at 0x1dcfd7a4448>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
-    "from econml.dml import LinearDML\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "\n",
-    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
-    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
-    "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n",
-    "                model_t=LogisticRegressionCV(cv=3),\n",
-    "                featurizer=PolynomialFeatures(degree=2, interaction_only=True, include_bias=False),\n",
-    "                discrete_treatment=True)\n",
-    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
-    "        freq_weight=n_sum, sample_var=var_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Getting the confidence intervals of the coefficients and the intercept of the CATE model\n",
-    "# together with the corresponding feature names.\n",
-    "feat_names = est.cate_feature_names(['A', 'B', 'C', 'D'])\n",
-    "point_int = est.intercept_\n",
-    "point = est.coef_\n",
-    "lower_int, upper_int = est.intercept__interval(alpha=0.01)\n",
-    "lower, upper = est.coef__interval(alpha=0.01)\n",
-    "yerr = np.zeros((2, point.shape[0]))\n",
-    "yerr[0, :] = point - lower\n",
-    "yerr[1, :] = upper - point\n",
-    "\n",
-    "with sns.axes_style('darkgrid'):\n",
-    "    fig, ax = plt.subplots(1,1)\n",
-    "    x = np.arange(1, 1 + len(point))\n",
-    "    plt.errorbar(np.concatenate(([0], x)), np.concatenate(([point_int], point)),\n",
-    "                 np.hstack([np.array([[point_int-lower_int], [upper_int - point_int]]), yerr]), fmt='o')\n",
-    "    ax.set_xticks(np.concatenate(([0], x)))\n",
-    "    ax.set_xticklabels([1] + list(feat_names), rotation='vertical', fontsize=18)\n",
-    "    ax.set_ylabel('coef')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import itertools\n",
-    "# Getting the confidence intervals of the CATE(x) for different x vectors\n",
-    "fnames = np.array(['A', 'B', 'C', 'D'])\n",
-    "\n",
-    "lst = list(itertools.product([0, 1], repeat=4))\n",
-    "point = []\n",
-    "lower = []\n",
-    "upper = []\n",
-    "feat_names = []\n",
-    "for x in lst:\n",
-    "    feat_names.append(\" \".join(fnames[np.array(x)>0]))\n",
-    "    x = np.array(x).reshape((1, -1))\n",
-    "    point.append(est.effect(x)[0])\n",
-    "    lb, ub = est.effect_interval(x, alpha=.01)\n",
-    "    lower.append(lb[0])\n",
-    "    upper.append(ub[0])\n",
-    "\n",
-    "feat_names = np.array(feat_names)\n",
-    "point = np.array(point)\n",
-    "lower = np.array(lower)\n",
-    "upper = np.array(upper)\n",
-    "yerr = np.zeros((2, point.shape[0]))\n",
-    "yerr[0, :] = point - lower\n",
-    "yerr[1, :] = upper - point\n",
-    "\n",
-    "with sns.axes_style('darkgrid'):\n",
-    "    fig, ax = plt.subplots(1,1, figsize=(20, 5)) \n",
-    "    x = np.arange(len(point))\n",
-    "    stat_sig = (lower>0) | (upper<0)\n",
-    "    plt.errorbar(x[stat_sig], point[stat_sig], yerr[:, stat_sig], fmt='o', label='stat_sig')\n",
-    "    plt.errorbar(x[~stat_sig], point[~stat_sig], yerr[:, ~stat_sig], fmt='o', color='red', label='insig')\n",
-    "    ax.set_xticks(x)\n",
-    "    ax.set_xticklabels(feat_names, rotation='vertical', fontsize=18)\n",
-    "    ax.set_ylabel('coef')\n",
-    "    plt.legend()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Non-Linear CATE Models with Forests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<econml.dml.causal_forest.CausalForestDML at 0x1dcfe8f9848>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from econml.dml import CausalForestDML\n",
-    "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
-    "\n",
-    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
-    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
-    "est = CausalForestDML(model_y=GradientBoostingRegressor(n_estimators=30, min_samples_leaf=30),\n",
-    "                      model_t=GradientBoostingClassifier(n_estimators=30, min_samples_leaf=30),\n",
-    "                      discrete_treatment=True,\n",
-    "                      n_estimators=1000,\n",
-    "                      min_samples_leaf=2,\n",
-    "                      min_impurity_decrease=0.001,\n",
-    "                      verbose=0, min_weight_fraction_leaf=.03)\n",
-    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
-    "        sample_weight=n_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import itertools\n",
-    "# Getting the confidence intervals of the CATE(x) for different x vectors\n",
-    "fnames = np.array(['A', 'B', 'C', 'D'])\n",
-    "\n",
-    "lst = list(itertools.product([0, 1], repeat=4))\n",
-    "point = []\n",
-    "lower = []\n",
-    "upper = []\n",
-    "feat_names = []\n",
-    "for x in lst:\n",
-    "    feat_names.append(\" \".join(fnames[np.array(x)>0]))\n",
-    "    x = np.array(x).reshape((1, -1))\n",
-    "    point.append(est.effect(x)[0])\n",
-    "    lb, ub = est.effect_interval(x, alpha=.01)\n",
-    "    lower.append(lb[0])\n",
-    "    upper.append(ub[0])\n",
-    "\n",
-    "feat_names = np.array(feat_names)\n",
-    "point = np.array(point)\n",
-    "lower = np.array(lower)\n",
-    "upper = np.array(upper)\n",
-    "yerr = np.zeros((2, point.shape[0]))\n",
-    "yerr[0, :] = point - lower\n",
-    "yerr[1, :] = upper - point\n",
-    "\n",
-    "with sns.axes_style('darkgrid'):\n",
-    "    fig, ax = plt.subplots(1,1, figsize=(20, 5)) \n",
-    "    x = np.arange(len(point))\n",
-    "    stat_sig = (lower>0) | (upper<0)\n",
-    "    plt.errorbar(x[stat_sig], point[stat_sig], yerr[:, stat_sig], fmt='o', label='stat_sig')\n",
-    "    plt.errorbar(x[~stat_sig], point[~stat_sig], yerr[:, ~stat_sig], fmt='o', color='red', label='insig')\n",
-    "    ax.set_xticks(x)\n",
-    "    ax.set_xticklabels(feat_names, rotation='vertical', fontsize=18)\n",
-    "    ax.set_ylabel('coef')\n",
-    "    plt.legend()\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tree Interpretation of the CATE Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from econml.cate_interpreter import SingleTreeCateInterpreter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<econml.cate_interpreter._interpreters.SingleTreeCateInterpreter at 0x1dcfef36348>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=1)\n",
-    "# We interpret the CATE models behavior on the distribution of heterogeneity features\n",
-    "intrp.interpret(est, X_sum[:, 1:5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# exporting to a dot file\n",
-    "intrp.export_graphviz(out_file='cate_tree.dot', feature_names=['A', 'B', 'C', 'D'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# or we can directly render. Requires the graphviz python library\n",
-    "intrp.render(out_file='cate_tree', format='pdf', view=True, feature_names=['A', 'B', 'C', 'D'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# or we can also plot inline with matplotlib. a bit uglier\n",
-    "plt.figure(figsize=(25, 5))\n",
-    "intrp.plot(feature_names=['A', 'B', 'C', 'D'], fontsize=12)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tree Based Treatment Policy Based on CATE Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from econml.cate_interpreter import SingleTreePolicyInterpreter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<econml.cate_interpreter._interpreters.SingleTreePolicyInterpreter at 0x1dcff1f9748>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=3, min_samples_leaf=1, min_impurity_decrease=.001)\n",
-    "# We find a tree based treatment policy based on the CATE model\n",
-    "# sample_treatment_costs is the cost of treatment. Policy will treat if effect is above this cost.\n",
-    "# It can also be an array that has a different cost for each sample. In case treating different segments\n",
-    "# has different cost.\n",
-    "intrp.interpret(est, X_sum[:, 1:5],\n",
-    "                sample_treatment_costs=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# exporting to a dot file\n",
-    "intrp.export_graphviz(out_file='cate_tree.dot', feature_names=['A', 'B', 'C', 'D'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# or we can directly render. Requires the graphviz python library\n",
-    "intrp.render(out_file='policy_tree', format='pdf', view=True, feature_names=['A', 'B', 'C', 'D'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# or we can also plot inline with matplotlib. a bit uglier\n",
-    "plt.figure(figsize=(25, 5))\n",
-    "intrp.plot(feature_names=['A', 'B', 'C', 'D'], fontsize=14)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Appendix: Amendment\n",
-    "\n",
-    "To make estimation even more precise one should simply choose the two splits used during the crossfit part of Double Machine Learning so that each summaried copy of a segment ends up in a separate split. We can do this as follows: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<econml.dml.dml.LinearDML at 0x1dc81fe0988>"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
-    "from econml.dml import LinearDML\n",
-    "from sklearn.linear_model import LogisticRegressionCV\n",
-    "\n",
-    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
-    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
-    "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n",
-    "                model_t=LogisticRegressionCV(cv=3),\n",
-    "                discrete_treatment=True,\n",
-    "                cv=[(splits[0], splits[1]), (splits[1], splits[0])]) # we input custom fold structure\n",
-    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
-    "        freq_weight=n_sum, sample_var=var_sum)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.07249425])"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Treatment Effect of particular segments\n",
-    "est.effect(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0.90614918]), array([1.23883932]))"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Confidence interval for effect\n",
-    "est.effect_interval(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table border=\"0\">\n",
+    "    <tr>\n",
+    "        <td>\n",
+    "            <img src=\"https://ictd2016.files.wordpress.com/2016/04/microsoft-research-logo-copy.jpg\" style=\"width 30px;\" />\n",
+    "             </td>\n",
+    "        <td>\n",
+    "            <img src=\"https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/MSR-ALICE-HeaderGraphic-1920x720_1-800x550.jpg\" style=\"width 100px;\"/></td>\n",
+    "        </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Double Machine Learning: Summarized Data and Interpretability\n",
+    "\n",
+    "Double Machine Learning (DML) is an algorithm that applies arbitrary machine learning methods\n",
+    "to fit the treatment and response, then uses a linear model to predict the response residuals\n",
+    "from the treatment residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Helper imports\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generating Raw Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.special\n",
+    "\n",
+    "np.random.seed(123)\n",
+    "n=10000 # number of raw samples\n",
+    "d=10 # number of binary features + 1\n",
+    "\n",
+    "# Generating random segments aka binary features. We will use features 1,...,4 for heterogeneity.\n",
+    "# The rest for controls. Just as an example.\n",
+    "X = np.random.binomial(1, .5, size=(n, d))\n",
+    "# The first column of X is the treatment. Generating an imbalanced A/B test\n",
+    "X[:, 0] = np.random.binomial(1, scipy.special.expit(X[:, 1]))\n",
+    "# Generating an outcome with treatment effect heterogeneity. The first binary feature creates heterogeneity\n",
+    "# We also have confounding on the first variable. We also have heteroskedastic errors.\n",
+    "y = (-1 + 2 * X[:, 1]) * X[:, 0] + X[:, 1] + (1*X[:, 1] + 1)*np.random.normal(0, 1, size=(n,))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating Summarized Data\n",
+    "\n",
+    "For each segment, we split the data in two and create one summarized copy for each split. The summarized copy contains the number of samples that were summarized and the variance of the observations for the summarized copies. Optimally we would want two copies per segment, as I'm creating here, but with many segments, the approach would work ok even with a single copy per segment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from econml.tests.test_statsmodels import _summarize\n",
+    "\n",
+    "X_sum = np.unique(X, axis=0)\n",
+    "n_sum = np.zeros(X_sum.shape[0])\n",
+    "# The _summarize function performs the summary operation and returns the summarized data\n",
+    "# For each segment we have two copies.\n",
+    "X1, X2, y1, y2, X1_sum, X2_sum, y1_sum, y2_sum, n1_sum, n2_sum, var1_sum, var2_sum = _summarize(X, y)\n",
+    "\n",
+    "# We concatenate the two copies data\n",
+    "X_sum = np.vstack([X1_sum, X2_sum]) # first coordinate is treatment, the rest are features\n",
+    "y_sum = np.concatenate((y1_sum, y2_sum)) # outcome\n",
+    "n_sum = np.concatenate((n1_sum, n2_sum)) # number of summarized points\n",
+    "var_sum = np.concatenate((var1_sum, var2_sum)) # variance of the summarized points\n",
+    "splits = (np.arange(len(y1_sum)), np.arange(len(y1_sum), len(y_sum))) # indices of the two summarized copies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Applying the LinearDML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.dml.dml.LinearDML at 0x1dcfd784408>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
+    "from econml.dml import LinearDML\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "\n",
+    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
+    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
+    "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n",
+    "                model_t=LogisticRegressionCV(cv=3),\n",
+    "                discrete_treatment=True)\n",
+    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
+    "        freq_weight=n_sum, sample_var=var_sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.07157889])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Treatment Effect of particular segments\n",
+    "est.effect(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.87415187]), array([1.26900591]))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Confidence interval for effect\n",
+    "est.effect_interval(np.array([[1, 0, 0, 0]]), alpha=.05) # effect of segment with features [1, 0, 0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['A' '2.0109841149829912']\n",
+      " ['B' '0.07212612018559361']\n",
+      " ['C' '-0.01525518572828466']\n",
+      " ['D' '-0.18928230056541373']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Getting the coefficients of the linear CATE model together with the corresponding feature names\n",
+    "print(np.array(list(zip(est.cate_feature_names(['A', 'B', 'C', 'D']), est.coef_))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-Linear CATE Models with Polynomial Features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.dml.dml.LinearDML at 0x1dcfd7a4448>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
+    "from econml.dml import LinearDML\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "\n",
+    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
+    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
+    "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n",
+    "                model_t=LogisticRegressionCV(cv=3),\n",
+    "                featurizer=PolynomialFeatures(degree=2, interaction_only=True, include_bias=False),\n",
+    "                discrete_treatment=True)\n",
+    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
+    "        freq_weight=n_sum, sample_var=var_sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Getting the confidence intervals of the coefficients and the intercept of the CATE model\n",
+    "# together with the corresponding feature names.\n",
+    "feat_names = est.cate_feature_names(['A', 'B', 'C', 'D'])\n",
+    "point_int = est.intercept_\n",
+    "point = est.coef_\n",
+    "lower_int, upper_int = est.intercept__interval(alpha=0.01)\n",
+    "lower, upper = est.coef__interval(alpha=0.01)\n",
+    "yerr = np.zeros((2, point.shape[0]))\n",
+    "yerr[0, :] = point - lower\n",
+    "yerr[1, :] = upper - point\n",
+    "\n",
+    "with sns.axes_style('darkgrid'):\n",
+    "    fig, ax = plt.subplots(1,1)\n",
+    "    x = np.arange(1, 1 + len(point))\n",
+    "    plt.errorbar(np.concatenate(([0], x)), np.concatenate(([point_int], point)),\n",
+    "                 np.hstack([np.array([[point_int-lower_int], [upper_int - point_int]]), yerr]), fmt='o')\n",
+    "    ax.set_xticks(np.concatenate(([0], x)))\n",
+    "    ax.set_xticklabels([1] + list(feat_names), rotation='vertical', fontsize=18)\n",
+    "    ax.set_ylabel('coef')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import itertools\n",
+    "# Getting the confidence intervals of the CATE(x) for different x vectors\n",
+    "fnames = np.array(['A', 'B', 'C', 'D'])\n",
+    "\n",
+    "lst = list(itertools.product([0, 1], repeat=4))\n",
+    "point = []\n",
+    "lower = []\n",
+    "upper = []\n",
+    "feat_names = []\n",
+    "for x in lst:\n",
+    "    feat_names.append(\" \".join(fnames[np.array(x)>0]))\n",
+    "    x = np.array(x).reshape((1, -1))\n",
+    "    point.append(est.effect(x)[0])\n",
+    "    lb, ub = est.effect_interval(x, alpha=.01)\n",
+    "    lower.append(lb[0])\n",
+    "    upper.append(ub[0])\n",
+    "\n",
+    "feat_names = np.array(feat_names)\n",
+    "point = np.array(point)\n",
+    "lower = np.array(lower)\n",
+    "upper = np.array(upper)\n",
+    "yerr = np.zeros((2, point.shape[0]))\n",
+    "yerr[0, :] = point - lower\n",
+    "yerr[1, :] = upper - point\n",
+    "\n",
+    "with sns.axes_style('darkgrid'):\n",
+    "    fig, ax = plt.subplots(1,1, figsize=(20, 5)) \n",
+    "    x = np.arange(len(point))\n",
+    "    stat_sig = (lower>0) | (upper<0)\n",
+    "    plt.errorbar(x[stat_sig], point[stat_sig], yerr[:, stat_sig], fmt='o', label='stat_sig')\n",
+    "    plt.errorbar(x[~stat_sig], point[~stat_sig], yerr[:, ~stat_sig], fmt='o', color='red', label='insig')\n",
+    "    ax.set_xticks(x)\n",
+    "    ax.set_xticklabels(feat_names, rotation='vertical', fontsize=18)\n",
+    "    ax.set_ylabel('coef')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Non-Linear CATE Models with Forests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.dml.causal_forest.CausalForestDML at 0x1dcfe8f9848>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from econml.dml import CausalForestDML\n",
+    "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
+    "\n",
+    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
+    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
+    "est = CausalForestDML(model_y=GradientBoostingRegressor(n_estimators=30, min_samples_leaf=30),\n",
+    "                      model_t=GradientBoostingClassifier(n_estimators=30, min_samples_leaf=30),\n",
+    "                      discrete_treatment=True,\n",
+    "                      n_estimators=1000,\n",
+    "                      min_samples_leaf=2,\n",
+    "                      min_impurity_decrease=0.001,\n",
+    "                      verbose=0, min_weight_fraction_leaf=.03)\n",
+    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
+    "        sample_weight=n_sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import itertools\n",
+    "# Getting the confidence intervals of the CATE(x) for different x vectors\n",
+    "fnames = np.array(['A', 'B', 'C', 'D'])\n",
+    "\n",
+    "lst = list(itertools.product([0, 1], repeat=4))\n",
+    "point = []\n",
+    "lower = []\n",
+    "upper = []\n",
+    "feat_names = []\n",
+    "for x in lst:\n",
+    "    feat_names.append(\" \".join(fnames[np.array(x)>0]))\n",
+    "    x = np.array(x).reshape((1, -1))\n",
+    "    point.append(est.effect(x)[0])\n",
+    "    lb, ub = est.effect_interval(x, alpha=.01)\n",
+    "    lower.append(lb[0])\n",
+    "    upper.append(ub[0])\n",
+    "\n",
+    "feat_names = np.array(feat_names)\n",
+    "point = np.array(point)\n",
+    "lower = np.array(lower)\n",
+    "upper = np.array(upper)\n",
+    "yerr = np.zeros((2, point.shape[0]))\n",
+    "yerr[0, :] = point - lower\n",
+    "yerr[1, :] = upper - point\n",
+    "\n",
+    "with sns.axes_style('darkgrid'):\n",
+    "    fig, ax = plt.subplots(1,1, figsize=(20, 5)) \n",
+    "    x = np.arange(len(point))\n",
+    "    stat_sig = (lower>0) | (upper<0)\n",
+    "    plt.errorbar(x[stat_sig], point[stat_sig], yerr[:, stat_sig], fmt='o', label='stat_sig')\n",
+    "    plt.errorbar(x[~stat_sig], point[~stat_sig], yerr[:, ~stat_sig], fmt='o', color='red', label='insig')\n",
+    "    ax.set_xticks(x)\n",
+    "    ax.set_xticklabels(feat_names, rotation='vertical', fontsize=18)\n",
+    "    ax.set_ylabel('coef')\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tree Interpretation of the CATE Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from econml.cate_interpreter import SingleTreeCateInterpreter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.cate_interpreter._interpreters.SingleTreeCateInterpreter at 0x1dcfef36348>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=1)\n",
+    "# We interpret the CATE models behavior on the distribution of heterogeneity features\n",
+    "intrp.interpret(est, X_sum[:, 1:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# exporting to a dot file\n",
+    "intrp.export_graphviz(out_file='cate_tree.dot', feature_names=['A', 'B', 'C', 'D'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or we can directly render. Requires the graphviz python library\n",
+    "intrp.render(out_file='cate_tree', format='pdf', view=True, feature_names=['A', 'B', 'C', 'D'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# or we can also plot inline with matplotlib. a bit uglier\n",
+    "plt.figure(figsize=(25, 5))\n",
+    "intrp.plot(feature_names=['A', 'B', 'C', 'D'], fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tree Based Treatment Policy Based on CATE Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from econml.cate_interpreter import SingleTreePolicyInterpreter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.cate_interpreter._interpreters.SingleTreePolicyInterpreter at 0x1dcff1f9748>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=3, min_samples_leaf=1, min_impurity_decrease=.001)\n",
+    "# We find a tree based treatment policy based on the CATE model\n",
+    "# sample_treatment_costs is the cost of treatment. Policy will treat if effect is above this cost.\n",
+    "# It can also be an array that has a different cost for each sample. In case treating different segments\n",
+    "# has different cost.\n",
+    "intrp.interpret(est, X_sum[:, 1:5],\n",
+    "                sample_treatment_costs=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# exporting to a dot file\n",
+    "intrp.export_graphviz(out_file='cate_tree.dot', feature_names=['A', 'B', 'C', 'D'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or we can directly render. Requires the graphviz python library\n",
+    "intrp.render(out_file='policy_tree', format='pdf', view=True, feature_names=['A', 'B', 'C', 'D'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# or we can also plot inline with matplotlib. a bit uglier\n",
+    "plt.figure(figsize=(25, 5))\n",
+    "intrp.plot(feature_names=['A', 'B', 'C', 'D'], fontsize=14)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appendix: Amendment\n",
+    "\n",
+    "To make estimation even more precise one should simply choose the two splits used during the crossfit part of Double Machine Learning so that each summaried copy of a segment ends up in a separate split. We can do this as follows: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<econml.dml.dml.LinearDML at 0x1dc81fe0988>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from econml.sklearn_extensions.linear_model import WeightedLassoCV\n",
+    "from econml.dml import LinearDML\n",
+    "from sklearn.linear_model import LogisticRegressionCV\n",
+    "\n",
+    "# One can replace model_y and model_t with any scikit-learn regressor and classifier correspondingly\n",
+    "# as long as it accepts the sample_weight keyword argument at fit time.\n",
+    "est = LinearDML(model_y=WeightedLassoCV(cv=3),\n",
+    "                model_t=LogisticRegressionCV(cv=3),\n",
+    "                discrete_treatment=True,\n",
+    "                cv=[(splits[0], splits[1]), (splits[1], splits[0])]) # we input custom fold structure\n",
+    "est.fit(y_sum, X_sum[:, 0], X=X_sum[:, 1:5], W=X_sum[:, 5:],\n",
+    "        freq_weight=n_sum, sample_var=var_sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.07249425])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Treatment Effect of particular segments\n",
+    "est.effect(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.90614918]), array([1.23883932]))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Confidence interval for effect\n",
+    "est.effect_interval(np.array([[1, 0, 0, 0]])) # effect of segment with features [1, 0, 0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/setup.py b/setup.py
index a47263961..b38004f2e 100644
--- a/setup.py
+++ b/setup.py
@@ -4,7 +4,6 @@
 import os
 import re
 from glob import glob
-from pathlib import Path
 
 with open(os.path.join(os.path.dirname(__file__), "econml", "_version.py")) as file:
     for line in file: