{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Índice:\n* [1 - Introdução](#first-bullet)\n* [2 - EDA](#second-bullet)\n* [3 - Estratégia de validação e treino do modelo](#third-bullet)\n\n> referencia: https://stackoverflow.com/questions/21151450/how-can-i-add-a-table-of-contents-to-a-jupyter-jupyterlab-notebook","metadata":{}},{"cell_type":"markdown","source":"## 1 - Introdução <a class=\"anchor\" id=\"first-bullet\"></a>","metadata":{}},{"cell_type":"markdown","source":"O objetivo desse jupyter notebook é cumprir com o requisto vivencial da Trilha de Cientista de Dados da Petrobras.\n\nO caso de estudo escolhido foi: Santander Customer Transaction. Abaixo segue a tradução para o português do desafio proposto pelo Santander.\n \nNeste desafio, nós convidamos os Kagglers para nos ajudar a identificar quais clientes irão fazer uma transação especifica no futuro, independente da quantia de dinheiro envolvido. Os dados providos para essa competição tem a mesma estrutura do real que temos para resolver esse problema.","metadata":{}},{"cell_type":"markdown","source":"## 2 - EDA <a class=\"anchor\" id=\"second-bullet\"></a>\n\n> https://www.kaggle.com/code/gpreda/santander-eda-and-prediction","metadata":{}},{"cell_type":"markdown","source":"De acordo com a própria descrição do problema trata-se de dados fakes criados especificamente para esse desafio com as mesmas caracteritiscas dos dados reais.","metadata":{}},{"cell_type":"markdown","source":"### Importando os módulos necessários e os datasets oferecidos pelo desafio","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nfrom sklearn.metrics import roc_auc_score, roc_curve, accuracy_score\nfrom sklearn.model_selection import KFold, StratifiedKFold, train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nimport imblearn\nfrom imblearn.over_sampling import RandomOverSampler\n\nfrom collections import Counter\n\n#Import the lightGBM package\nimport lightgbm as lgb\n\n# Import the functions that performs sample splits from scikit-learn\n\nfrom lightgbm import LGBMClassifier\n\nfrom bayes_opt import BayesianOptimization\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df= pd.read_csv(\"../input/santander-customer-transaction-prediction/train.csv\")\ntest_df = pd.read_csv('../input/santander-customer-transaction-prediction/test.csv')\nsample_submission = pd.read_csv('../input/santander-customer-transaction-prediction/sample_submission.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Avaliando os dados","metadata":{}},{"cell_type":"code","source":"train_df.shape, test_df.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Os datasets de treinamento e teste possuem 200.000 amostras, 202 e 201 colunas, respectivamente.","metadata":{}},{"cell_type":"code","source":"train_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Tipo dos dados","metadata":{}},{"cell_type":"code","source":"train_df.info(), test_df.info()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*** Dados de treino**\n\n    200 features numéricas (var_0 até var_199), 1 feature string (ID_code), 1 variavel de saida (target)\n    \n*** Dados de teste**\n\n    200 features numéricas (var_0 até var_199), 1 feature string (ID_code)","metadata":{}},{"cell_type":"markdown","source":"### Análise se as features são discretas ou contínuas\n\nCom intuito de verificar se as features são discretas ou contínuas será verificado a quantidade de valores únicos por feature. Caso esse valor seja superior a 20, trata-se de uma variável continua. Caso contrário, discreta.","metadata":{}},{"cell_type":"code","source":"feats = [f for f in train_df.columns if f not in ['ID_code','target']]\nunique_df = pd.DataFrame(columns=['continua','discreta'],index=feats)\n\nfor feat in feats:\n    num_unique = len(train_df[feat].unique())\n    if(num_unique>=20):\n        unique_df.loc[feat,\"contínua\"] = 1\n    else:\n        unique_df.loc[feat,\"discreta\"] = 1\n\nnum_continuous_var = unique_df[\"contínua\"].sum()\nnum_continuous_var","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Todas as 200 features são contínuas.","metadata":{}},{"cell_type":"markdown","source":"### Avaliando missing values","metadata":{}},{"cell_type":"code","source":"null_df = train_df.isnull().sum()\nnull_df = null_df[null_df > 0]\nnull_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"null_df = test_df.isnull().sum()\nnull_df = null_df[null_df > 0]\nnull_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Não existe valores faltantes nos datasets de treino e teste.","metadata":{}},{"cell_type":"markdown","source":"### Análise estatística das features","metadata":{}},{"cell_type":"code","source":"train_df.describe()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.describe()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Tanto o dataset de teste, quanto daset de treino possuem os valores de desvio padrão para cada feature bem altos, indicando alta variabilidade dos dados.","metadata":{}},{"cell_type":"markdown","source":"### Normalizando os dados","metadata":{}},{"cell_type":"markdown","source":"Visando melhorar a assertividade do modelo foi feita a normalização dos dados de treino e de teste.","metadata":{}},{"cell_type":"code","source":"train_data_x_scale = (train_df[feats] - train_df[feats].mean(axis=0))/train_df[feats].std(axis=0)\n\ntest_data_x_scale = (test_df[feats] - train_df[feats].mean(axis=0))/train_df[feats].std(axis=0)\ntest_data_x_scale.describe()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Distribuição dos valores da variável objetivo que indica aprovação ou reprovação do crédito","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(1,2,figsize=(20,5))\nsns.countplot(train_df['target'].values, ax=ax[0], palette=\"husl\")\nsns.violinplot(x=train_df['target'].values, y=train_df['target'].index.values, ax=ax[1], palette=\"husl\")\nsns.stripplot(x=train_df['target'].values, y=train_df['target'].index.values,\n              jitter=True, ax=ax[1], color=\"black\", size=0.5, alpha=0.5)\nax[1].set_xlabel(\"Target\")\nax[1].set_ylabel(\"Index\");\nax[0].set_xlabel(\"Target\")\nax[0].set_ylabel(\"Counts\");","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"counter = Counter(train_df['target'])\nprint(counter)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Classe 0 {}% Classe 1 {}%\".format (100*counter[0] / (counter[0] + counter[1]), 100*counter[1] / (counter[0] + counter[1])))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Os dados da variavel target estão desabalanceados:  10% (classe 1) / 90% (classe 0). ","metadata":{}},{"cell_type":"markdown","source":"### Utilizando oversampling para aumentar a quantidade de amostras na classe 1","metadata":{}},{"cell_type":"code","source":"random_state = 111\n\noversample = RandomOverSampler(sampling_strategy=0.25)\ntrain_data_x_over, train_data_y_over = oversample.fit_resample(train_data_x_scale, train_df['target'])\n\ntrain_data_x_over = train_data_x_over.sample(frac=1,random_state=random_state).reset_index(drop=True)\ntrain_data_y_over = train_data_y_over.sample(frac=1,random_state=random_state).reset_index(drop=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Distribuição dos valores da variável target após oversampling","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(1,2,figsize=(20,5))\nsns.countplot(train_data_y_over.values, ax=ax[0], palette=\"husl\")\nsns.violinplot(x=train_data_y_over.values, y=train_data_y_over.index.values, ax=ax[1], palette=\"husl\")\nsns.stripplot(x=train_data_y_over.values, y=train_data_y_over.index.values,\n              jitter=True, ax=ax[1], color=\"black\", size=0.5, alpha=0.5)\nax[1].set_xlabel(\"Target\")\nax[1].set_ylabel(\"Index\");\nax[0].set_xlabel(\"Target\")\nax[0].set_ylabel(\"Counts\");","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"counter = Counter(train_data_y_over)\nprint(counter)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Classe 0 {}% Classe 1 {}%\".format (100*counter[0] / (counter[0] + counter[1]), 100*counter[1] / (counter[0] + counter[1])))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Com intuito de aumentar a quantidade de amostras para a classe minoritária e consequentemente a assertividade do modelo para predizer essa classe será utilizada a técnica de Oversampling. Foram testadas várias proporções entre as duas classes, a que menos causou overfitting e manteve acurácia alta foi 20% (classe 1) / 80% (classe 0).","metadata":{}},{"cell_type":"markdown","source":"### Avaliando feature importance das features com a variavel target","metadata":{}},{"cell_type":"markdown","source":"Visando conhecer a relevância de cada feature no resultado da calissifação binária foi utilizado o método de feature importance a partir de um modelo lightGBM. Essa metodologia consiste em avaliar a importância de cada feature com relação à variável objetivo.","metadata":{"execution":{"iopub.status.busy":"2022-07-01T12:46:35.734971Z","iopub.execute_input":"2022-07-01T12:46:35.735270Z","iopub.status.idle":"2022-07-01T12:46:35.741922Z","shell.execute_reply.started":"2022-07-01T12:46:35.735238Z","shell.execute_reply":"2022-07-01T12:46:35.741035Z"}}},{"cell_type":"code","source":"model = LGBMClassifier()\n\n# fit the model\nmodel.fit(train_data_x_over, train_data_y_over)\n# get importance\nimportance = model.feature_importances_\n# summarize feature importance\nimportance_dict={}\nfor i,v in enumerate(importance):\n    importance_dict['var_'+str(i)] = v\n    \nsort_importance_dict = {k: v for k, v in sorted(importance_dict.items(), key=lambda item: item[1], reverse=True)}\nsort_importance_dict","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Visando utilizar no treinamento do modelo as features com maior importancia, serão descartadas as features com importancia 0.","metadata":{}},{"cell_type":"code","source":"new_feats_dict = {k: v for k, v in sort_importance_dict.items() if v > 0}\nnew_feats = list(new_feats_dict.keys())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Distribuição dos dados classificados para as 10 features com maior importância\n\n> https://www.kaggle.com/code/allunia/santander-customer-transaction-eda","metadata":{}},{"cell_type":"code","source":"n_top = 10\nfeature_names = new_feats[:n_top]\nfig, ax = plt.subplots(n_top,2,figsize=(20,5*n_top))\n\ntrain_data_over = pd.concat([train_data_x_over,train_data_y_over], axis=1)\n\nfor n in range(n_top):\n    sns.distplot(train_data_over.loc[train_data_over.target==0, feature_names[n]], ax=ax[n,0], color=\"Orange\", norm_hist=True)\n    sns.distplot(train_data_over.loc[train_data_over.target==1, feature_names[n]], ax=ax[n,0], color=\"Red\", norm_hist=True)\n    sns.distplot(test_data_x_scale.loc[:, feature_names[n]], ax=ax[n,1], color=\"Mediumseagreen\", norm_hist=True)\n    ax[n,0].set_title(\"Train {}\".format(feature_names[n]))\n    ax[n,1].set_title(\"Test {}\".format(feature_names[n]))\n    ax[n,0].set_xlabel(\"\")\n    ax[n,1].set_xlabel(\"\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"A distribuição dos dados das features mais relevantes tanto do treino, quanto de teste possuem um desvio padrão muito alto. Isso já havia sido evidenciado anteriormente","metadata":{}},{"cell_type":"markdown","source":"## 3 - Estratégia de validação e treino do modelo <a class=\"anchor\" id=\"third-bullet\"></a>","metadata":{}},{"cell_type":"markdown","source":"### Utilizando modelo lightgbm com kfold para validação cruzada usando parametros default\n\n> https://www.kaggle.com/code/ashishpatel26/kfold-lightgbm/notebook","metadata":{}},{"cell_type":"markdown","source":"O modelo utilizado para classificação binária foi o LightGBM, pois possui uma excelente acurácia em diferentes situações.Para diminuir o overfitting a primeira alternativa foi utilizar o método kfold para divisão dos dados de treino/validação. Usei 10 folds estratificados. ","metadata":{}},{"cell_type":"code","source":"train_data_x, train_data_y = train_data_x_over,train_data_y_over","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Criando uma função para treinar o modelo lightGBM com diferentes parametros.","metadata":{}},{"cell_type":"code","source":"def continuous_to_binary(y_array):\n    return (y_array > 0.5).astype(\"int\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def kfold_lightgbm(n_folds, params, filename):\n    # Set number of folds and ensemble variables\n    ensemble = []\n    auc_single_models = []\n\n    # Initialize k_fold splitter\n    K_fold = KFold(n_splits=n_folds, random_state=0, shuffle=True)\n    n_fold=0\n    # Iterate over folds\n    for train_val_index, test_index in K_fold.split(train_data_x):\n        # Get train_val (K-1 folds) and test (1 fold)\n        x_train_val, x_test = train_data_x.iloc[train_val_index], train_data_x.iloc[test_index]\n        y_train_val, y_test = train_data_y.iloc[train_val_index], train_data_y.iloc[test_index] \n\n        # Partition the train_val set\n        x_train, x_val, y_train, y_val = train_test_split(x_train_val, y_train_val, test_size=0.25, random_state=0)\n\n        # Prepare dataset in LightGMB format\n        y_train = np.squeeze(y_train.to_numpy())\n        y_val = np.squeeze(y_val.to_numpy())\n        train_set = lgb.Dataset(x_train, y_train, silent=True)\n        valid_set = lgb.Dataset(x_val, y_val, silent=True)\n\n        # Train the model \n        boosted_tree = lgb.train(\n            params = params,\n            train_set = train_set,\n            valid_sets = valid_set,\n            num_boost_round = 10000,\n            early_stopping_rounds =  20,\n            verbose_eval = False,\n        )\n\n        # Save the model in the ensemble list\n        ensemble.append(boosted_tree)\n\n        # Make predictions on test and compute performance metrics\n        y_train_pred = boosted_tree.predict(x_train)\n        y_val_pred = boosted_tree.predict(x_val)\n        auc_single_models.append(roc_auc_score(y_val, y_val_pred))\n\n        print('Fold %2d' % (n_fold + 1))\n        # Print empirical risk on all sets\n        print('AUC % on training set:')\n        print(roc_auc_score(y_train, y_train_pred))\n        print('AUC % on validation set:')\n        print(roc_auc_score(y_val, y_val_pred))\n        print('')\n\n        y_train_pred = continuous_to_binary(y_train_pred)\n        y_val_pred = continuous_to_binary(y_val_pred)\n\n        # Print accuracy on all sets\n        print('Accuracy on training set:')\n        print(accuracy_score(y_train, y_train_pred))\n        print('Accuracy on validation set:')\n        print(accuracy_score(y_val, y_val_pred))\n        n_fold+=1\n    \n    # Print performance metrics on test sample\n    print('Test AUCs of models in the ensemble:')\n    print(auc_single_models)\n    print('Average test AUC of models in the ensemble:')\n    print(np.mean(auc_single_models))\n    print('')\n    \n    y_pred_ensemble=0\n    for model in ensemble:\n        y_pred = model.predict(test_data_x_scale)\n        y_pred_ensemble += y_pred/n_folds\n\n    test_df['target'] = y_pred_ensemble\n    test_df[['ID_code','target']].to_csv(filename, index= False)\n    print('csv impresso')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"params = {\n    'objective': 'binary',\n    'learning_rate': 0.01,\n    'metric': 'auc',\n    'nthread': 8,\n    'verbose': -1\n}\n    \nn_folds = 10\n\nkfold_lightgbm(n_folds, params, 'res_default.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Rodando o modelo com os parametros padrão do lightGBM, fixando somente o learning rate em 0.01, já obtivemos um resultado bastante bom com assertividade de 0.89678. Como podemos ver acima, a acurácia usando os dados de treino está muito superior ao resultado obtido ao submeter o modelo, isso indica que o modelo está com overfitting e não generalizando muito bem.\n\n![image.png](attachment:f48cde41-5375-4062-b769-34e6ba93e0e2.png)","metadata":{},"attachments":{"f48cde41-5375-4062-b769-34e6ba93e0e2.png":{"image/png":"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Otimizando os hyper-parametros do lightGBM\n\n> https://towardsdatascience.com/seeing-numbers-bayesian-optimisation-of-a-lightgbm-model-3642228127b3\n\n> https://towardsdatascience.com/kagglers-guide-to-lightgbm-hyperparameter-tuning-with-optuna-in-2021-ed048d9838b5#:~:text=In%20LGBM%2C%20the%20most%20important,the%20%27actual%20decision%27%20happens.","metadata":{}},{"cell_type":"markdown","source":"Parametros mais relevantes p otmização de acordo com as referências citadas acima são: num_leaves, max_depth e learning_rate. O learning_rate foi fixado em 0.01. No caso do num_leaves foi usada a relação 2^num_leaves - 1 = max_depth.\n\nForam feitas várias simulações com diferentes configurações de num_leaves e max_depth, o melhor resultado de score foi obtido com num_leaves=15 e max_depth=4\n\nVisando diminir ainda mais o overfitting e aumentar o score foi feita uma otimização de parametros através do método otimização bayesiana. De acordo as refs acima os parametros que mais ajudam a diminuir o overfitting são: bagging_fraction, bagging_frequency.\n\n","metadata":{}},{"cell_type":"code","source":"dtrain = lgb.Dataset(data=train_data_x_over, label=train_data_y_over)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def cross_val(bagg_frac, bagg_freq):\n    '''\n    A function to return cross-validated LightGBM model accuracy\n    \n    Inputs\n      eta: float, learning rate for model\n      num_leaves: int, maximum number of leaves in tree\n      bag_freq: int, frequency of bagging\n      feat_frac: float, feature fraction\n    \n    Outputs\n      float, model accuracy from best iteration of cross-validated model\n    '''\n\n    # set the model parameters\n    parameters = {\n        'objective': 'binary',\n        'metric': 'auc',\n        'learning_rate': 0.01,\n        'num_leaves': 15,\n        'max_depth':4,\n        'bagging_freq': int(bagg_freq),\n        'bagging_fraction': bagg_frac,\n        'verbosity': -1\n    }\n\n    model = lgb.cv(\n        params=parameters,\n        train_set=dtrain,\n        num_boost_round=500,\n        nfold=10,\n        stratified=True,\n        early_stopping_rounds=10,\n        verbose_eval=-1\n    )\n\n    # return accuracy rather than error\n    # as the optimiser seeks to maximise rather than minimise\n    return model['auc-mean'][-1]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"space = {\n    'bagg_frac': (0.4,0.8),\n    'bagg_freq':(1,5),\n}\n\n\n# set up the optimiser\noptimiser = BayesianOptimization(\n    f=cross_val,\n    pbounds=space,\n    verbose=2,\n    random_state=0)\n\n# Optimize\noptimiser.maximize(init_points=5, n_iter=50)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"params_opt = optimiser.max['params']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"params_opt","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"params ={  'nthread':8,\n            'n_estimators':15000,\n            'learning_rate':0.01,\n            'num_leaves':15,\n            'max_depth':4,\n            'objective':'binary',\n            'metric':'auc',\n            'verbose':-1}","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"params['bagging_freq'] = round(params_opt['bagg_freq'])\nparams['bagging_fraction'] = params_opt['bagg_frac']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Testando o modelo com os parametros otimos","metadata":{}},{"cell_type":"code","source":"n_folds = 10\n\nkfold_lightgbm(n_folds, params, 'res_opt.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:621a72cb-e3bd-4e82-b268-355e1b29dfc5.png)\n\nHouve uma mudança pouco significativa na acurácia do modelo considerando os parametros ótimos. \n\nPara avaliar se o modelo está classificando bem para ambas as classes 0 e 1, separei os dados de treino em dados de treino, validação e teste. Porém, isso diminuiu a quantidade de amostras usadas para de fato treinar o modelo, afetanto a acurácia do modelo.\n\nSendo assim, como no Kaggle os dados de testes não são entregues classificados, não tem como avaliar a matriz de confusão.\n\nO resultado final de acurácia 0.89716 cumpre o requisito de obter um resultado no top 25% do Kaggle para essa competição.","metadata":{},"attachments":{"621a72cb-e3bd-4e82-b268-355e1b29dfc5.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAB9AAAAGGCAYAAAAjPxqpAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFxEAABcRAcom8z8AAP+lSURBVHhe7N0FeBVXwsbx7la2tIUixS1E8ODu7hT3AkWLe6HUaKHFXYt7cXd39wT3kIS4u/6/mcsNDZAW2O1+ZdP31+c8oTP3zpw5M1ee+8455y1ERERERERERERERP4int6+nLvszAMXV6KioqxLReRVREdH88jVnfOXnHH38LIu/e9we+xp2c8j18dEx0Rbl4okf/4BgVy+epOrN+4Yr7kY61JJzhSgi4iIiIiIiIiIiMhfJjwignsPHnH+khOubh7ExcdZ14jIH4mPh8ceXly45Mztuw8ICw+3rvnvCA0L55axnwuXruLh6WXs36iASDIXZlz3127c5rLTdbx9/YiL02fU34ECdBERERERERERERH5SwWHhHLl6g0uXHbGw8tbwZzIS5ivEG8fPy5eucYlp+sEBAY9WfFf5m/s58k+r1n2r1eqJGfh4RHcvnufMxcu89DFlViF538bCtBFRERERERERERE5C9lBuZmMGeG6BcvX8PH15/YWAUVIkkxe8D6+Qdyyfm6Jcj29Q/4f+sVa+7H18/fst/Lzjcs9VCPXEmOnoyO4sLZC1e47/KISE0x8reiAF1ERERERERERERE/nJmiO7rF8CVqze5cPkqnl4+6u0n8pw443Xi42v2PDfD8+t4+/j+vwfY5v68jP2a+79k1MPXz89SL5HkwgzPb929z7mLVyxTjJj/L38vCtBFRERERERERERE5I1gCQf9AnC6epNzF514+MiVqKho61qRv7fomBhc3R5z/pITl52vW0Lsv6r3d0KIbtbjglEfs15m/UT+1wUGBnH1+i1Lz/N7D12IiIi0rpG/EwXoIiIiIiIiIiIiIvLGMPux+gcE4nzN7InuzP2HjwgKDtG86PK3FhwSyoNHbly8ctUy1YE5jPpf/Zp4MmqEv1Gf65Z6mfULCQmzrhX532LerOXp7cu1m3csN6mYnz0atv3vSwG6iIiIiIiIiIiIiLxxwsLDuffQlYtO17hx+x7ePn5EREYqSJe/DfNaj4yMsozKcPPOfS5eucadew8JCX2zQuqQ0FBLvS46XeeWUU+zvma99VqV/wUxMTGWa9jF1Z3LV2/ifP0WXsbnTWxsrPUR8nekAF1ERERERERERERE3kiRUdH4+Ppz7dZdzl68wvVbdwgIDLL0UhdJ7gKDQ7h55x5nLzhx9cYdy5DpZjD9JjLrZYaOV2/cttTXrLc5coTImywqOho3dw/LzSnnL13loYs7oWFhxP5FUyPIm0MBuoiIiIiIiIiIiIi8scyw3AzN7z14xNXrt3G+doubt+/h/tiTkLBwhemSrIQZ1/RjDy9LAG1e6+Y1f+e+C37+gW98j26zfn7+AUZ9H+Jsfa2aPdIfe3hbRpQQeRNER8dYpgmxfKbcMK7T67e4cesubsZnSkSEhmyXJxSgi4iIiIiIiIiIiMgbz+wR6O8fyO27D7jkdN0SLN6++xAX18eW4d0DAoMtIZ3ZozBOvQflDWeGzdHGtRoWHkFgULDlGn7k9tgyFLoZ6l26co1bt+/h6xdAbOz/1vVsDn1tGXbeqL95HE9uAnhoOT7zOM3jNY/bPH4N8y7/Tea1aE79YQ7R7usfwGNPL+4/dLVcm5edr+N09Saubh6EG9ejSGIK0EVERERERERERETkf4YZjptzQLu7e1qCufMXnS3D716/dZcHLq64e3hahn03A/WQkFDLcLyhYeEqKm9ECTauyUDj2vTx87eEeQ9c3Lhx+64laDavZedrt3F197A87n99Dmaz/sEhIZaA0jyuhNeqebzmcZvHb7aD2R7m8SbVXioqr1vMzwdz+gBz1AZPb58nN6bcf4jTtZucv+Rkea2ZN6r4GtdeZJTm6pekKUAXERERERERERERkf855jC8oaHh+AcE4eHpzUMXN0vvdLP37mXnG5y/7My5i1c4c/4yp1VU3oBiXovmXP4XjGvzkvN1y7VqDnF+38XVEiabw0qb4Z85ikJyYh6PeVzm8bmbPYCN4zWP27wBxmwHsz3MdtFrVeXPKGeNcu6SExedrnHl2pPh2e/ed+GRuwfevn6WmzXCIyI1Uon8IQXoIiIiIiIiIiIiIvI/zQxCzCF4zbnSzSGi3T28LL0OH7q4Wobrvf/wkYrKG1HMURLMa9O8Rr0sUw8EWeY9N6co+Dswj9M8XvO4vbx/e62a7ZJUe6movG55YLznP3R1s4zk4OHlY5kGITg4NNndmCL/XQrQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDArQRUREREREREREREREREREDMkmQI+NiyMyKoqwsHCCQ0IJCAzGPyAIP/9AFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRWVv1kxs0IzMzSzQzNDjIyMIjY21pouiiTtfzpANy9wS2geHkFgUAi+fgF4evny2MMbV3dPXFwfW8rDR+4qKioqKioqKioqKioqKioqKioqKioqKioqKip/o2LmhGZmaGaHXt6++Pj6WwL1UDNMj4oiNkZhurzofzJAjzN7m0dGWS5wdw8vXNw8cH/shZePH/4B1rtIwsOJiIy0PC4qKlpFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFReVvVMys0OyIG2Idvdrbx++ZbNHfP5CIiEhL9iiS4H8uQA83LmLz4nZz98DD0+e3O0VCzcA8iuiYGF3kIiIiIiIiIiIiIiIiImJhZocxMTGWjrdm7/PAoGB8ff3x9PLBzd3T0js9LCyC+HjrE+Rv7X8mQDcvaHOeAg/jQjbvDPEy/gYFhRAdHWN9hIiIiIiIiIiIiIiIiIjIy8XExFpGtTbDczN79PT0tcybbvZIByXpf2dvfIBu3hESHh5hXLx+lnkKzF7n5lAL8boFRERERERERERERERERET+A2bmaIbmZpDu8ugxnl5mb/RwjXj9N/bGB+jmnATuHt6WeQgCA80e59EKz0VERERERERERERERETkT2Fmj+ao18HBITz28MbtsRdBxr+VSf49vbEBelxcvGWIdvfH3k/mHQiP0J0eIiIiIiIiIiIiIiIiIvJfYQbm5sjY3j7+lowyIDCI2NhY61r5u3gjA/TY2DhreO6Ft7efZf5zEREREREREREREREREZH/tqioaHx8AyxZZUBAsGW+dPn7eOMC9N96nnsZF6a/5QIVEREREREREREREREREfn/Yobmfv6BluHczRBdI2X/fbxRAbo5LEJwSJhlbgGz57nCcxERERERERERERERERH5K0RHx1o6/JrDuQcGhVg6Akvy98YE6GZ4HhERyWNPHzy8fDRsu4iIiIiIiIiIiIiIiIj8pcwOv17efpbRs8PCIyyZpiRvb0yAHh0dY1x8vpZhEEJCw6xLRURERERERERERERERET+OmFhEZYA3cPLV52A/wbeiAD9ydDtobi4PsbfP1BzCIiIiIiIiIiIiIiIiIjIG8HMLgMCg3F59JhA4696oSdvb0SAbg7dbg594Onta/m3iIiIiIiIiIiIiIiIiMibItI6lLunl69lKHdJvt6IAN3PP5BHbh6Ehobpjg0REREREREREREREREReaOYGWZ4eASu7p74+PoZ/29dIcnOXx6gR0XF4Onlg7uHFzExMdalIiIiIiIiIiIiIiIiIiJvDnMod09PHx57eBEZGa2OwcnUXxqgmxeVOU+Ah3GhBQaFaO5zEREREREREREREREREXkjmdlmcHCopXOwf0CQss1k6i8N0M2LysO4wMwAPSpavc9FRERERERERERERERE5M0VExNrmQv9yejasdalkpz8pQF6VFQ0bo898fbx1zwBIiIiIiIiIiIiIiIiIvLG8/ULxNXdg4jIKOsSSU7+sgDd7H0eHBLGY09vAgKDrUtFRERERERERERERERERN5c5jDujz28CQoJ1TDuydBfFqCbQxqYcwOYQxyEhYVbl4qIiIiIiIiIiIiIiIiIvLnCIyLw9vHD1z+QaE1Tnez8ZQF6ZFTUkwvLL5Ao498iIiIiIiIiIiIiIiIiIm+66Oho/PwDLR2FIyIirUslufjLAvSIyEgee3pZeqFraAMRERERERERERERERER+V9gZpuBQSGWYdzDwiOsSyW5+MsC9PDwSFzdPIyLS/Ofi4iIiIiIiIiIiIiIiMj/jpCQUEvWGRIaZl0iycVfFqCHhUXg8ugxQcEh1iUiIiIiIiIiIiIiIiIiIm++0NAwS9YZHBJqXSLJxV8WoIeGhfPAxY3gYF1UIiIiIiIiIiIiIiIiIvK/I8yadaqzcPKjAF1ERERERERERERERERE5DUoQE++FKCLiIiIiIiIiIiIiIiIiLwGBejJlwJ0EREREREREREREREREZHXoAA9+VKALiIiIiIiIiIiIiIiIiLyGhSgJ18K0EVEREREREREREREREREXoMC9ORLAbqIiIiIiIiIiIiIiIiIyGtQgJ58KUAXEREREREREREREREREXkNCtCTLwXoIiIiIiIiIiIiIiIiIiKvQQF68qUAXURERERERERERERERETkNShAT74UoIuIiIiIiIiIiIiIiIiIvAYF6MmXAnQRERERERERERERERERkdegAD35UoAuIiIiIiIiIiIiIiIiIvIaFKAnXwrQRURERERERERERERERERegwL05EsBuoiIiIiIiIiIiIiIiIjIa1CAnnwpQBcREREREREREREREREReQ0K0JMvBegiIiIiIiIiIiIiIiIiIq9BAXrypQBdREREREREREREREREROQ1KEBPvhSgi4iIiIiIiIiIiIiIiIi8BgXoyZcCdBERERERERERERERERGR16AAPflSgC4iIiIiIiIiIiIiIiIi8hoUoCdfCtD/UBzRIYEEenkTEBhGVLx18TOMhbFhBAX68dg3mMCwOOvyxMIJdb/LDaeLXLr2gEd+8cRa1zwvPiaKqOAgggNDCY+KMWrwrNjoSMICgwjyDyE8MtH6+EjCw0II8DfqG2CWIAL9AwgMjiAiyXr/jjhjm+FBhAYZz/U39mNux9yeXzChUdbHPMOoQVwEYaHB1n0nPMf6PPOv0XYRUbFmSyUhmlg/V+5dv8JFp5vcdgshzLrmBbExxIQGEWJsN+CZ/RjFONagoFDCjJMUa9lRvHEoRr2MN60Qo94vnhVjfazRlkHGOQsMN9oyoXaxRIWFEmwcS1BC/RMVyzJjXUCQcc4j4ohN6nQnKYYI8xq4fIHzTre47xNJks0ZH0VkeDD+gSEEhscS/cIFYDzPbGtjfUBYDNEJF1JcHDFhxjkKTHwOjHr6GecyqR3FxxEXGWxcS/74B4cTHG1dnoR447HRYeY1abRxaCSRiS7e+KgIIoPMdWFExsQlfY5j/fF44MTlC5e5fMMdz1d4ycf6uHDnygXOXb7OncchJPmU6HAig/2N824ct9EQMdbFL4omyvshd64arz/nO9z3NF5j1jUviI02XvMhxvtSTNLn1nh9xBjtH+Jv7DPcuHati0VERERERERERERE5O9FAXrypQD9D8UR5X0V5x1LWTz/VzZcdMP9uZQuPuAm9/YtZdGclSw7dIfbQYkitRg/vO+e5PCaX5j+7QB6du5Ah+5fMmzcKlbsd+a6bwSR1ocmiPW9wa3di1mxYC9HnH1eCJNDHztxdv0SlszexaHL3jx5ScZD1AOuHdnKiqkzmTl5MhMnTGbCuIlMmDyL2b/uYsd5o+7Br5D2RvoTfG03O1bPY8rkaUwxtjNx/CQmjJ3GtDm/sv7QFaPeUYnqHW28Q9zm4u4NLJk8gxmTp1j2bSljxzF24iwmLDvGmftBz4WrkUT6XufynmUsGzecAV90ol3nXvT5ZgazVp/g1D0/Ap5LJ+OCXHE7vpT1S6Yx2ajbpIT9jJ/M2FE/MXPRWvbfCbM+L5pQDydOLVvBskX7OXIjkAjLVhLEEm6c2zMrV7Jw9UkO3U+IVD25tWM9K38ca7TfJMZPmMbUGb8wZ84v/DLN2O8EY9nPo5myeDNbroYQ+LtJbIJIgt2vc37bIub/8CX9+vahS/+hfDV6IYu3OeH0OIywpw1j/CP2EbeObWT1kg1sueiDZ7h1lYVx/oJvc2X/KuYsWce6c14YT38i3A+vc6vZtsKo75TpTDbaxDxv48dOYdov61l/5Da3fI02T9hXTDgRdw9yYN1cJs7ZyPJ9LjwOSSoOjiHYzZnjy+azaPoMVh24jNGUT29IiHI9zYVNi1i+9DgXXIKfDaajvPG4cZQ9S6cw8qve9O7di96DR/LjrI1sOncf1+Do525siCHc5z7XDq5mxZivGdy3N537DWHIj7OYs+YMZx8EkvjlhfdlLu/4lSUr9nHsYRBB1sVPxQcT5HqBU1uWMHek0fbdOtC+S38G/mici63nufw4+IVgPj7YFdejy1izZDkbjt3F7bkXaHzoY9yP/8q6eRvZfswVf+tyERERERERERERERH5e1GAnnwpQH+J+OCLnJ7XnWYVKlBt8ErW3kqcoMfieXQak5oXp1z5Pny17iZ3ExLB+EiCLy5h1sCGlClSkiJFS1KsuFlKUKhwGYqWa0vPmYc56ftsr93ou+vYMrQc1UsN5vsVt/C1Lk/gd2UFc9uXp0LB/ny96BqelqXGTkOOsfn7z/nUrgBFipSmWJkKlClTnjIli1GsVAUKtx7L2F33CXw2sXxR6EPcNw+j66dlyZmnGMVLG9spa2ynTDmKFSxIqZqt6b7wPBe8E+odAd67Wda3JbVy5adw8bKUMPdd1iglilO0fD1KfbGQFee9LY9+Ip5Y7xMcmtWb5uVLUbxwMYoYbVPULEVKUaRQbRoPWMjKS4HP9J6PdT/ByYkVaFoxD3YFylLS3IdZjOMsWrAAtdv2ZurJAB5bQtZwfC6vZlrlSpTLWpNWI7dwxD8+US/lSPyd1jCzdg1KthjP8KMJb263OPxjf77IV4RSpcpRoqxxPEWLUcixGI7GsZUsbSwr7EjV9kMZuc8Hj2dT+efEEOd7kh3jetCyWGFKlqxExUbNqfVpXSoULU7JMq3oOOkgh1xjrUGycbCRZ9k16jPaVm5B76U3cH4mFTYOzGMni4Y3IH+lJrRf4MSVhAQ38BbO8+rRqaYddvnLWM+Bed5KU6RAacpU78mApVeMx1sbNNqfoIM/81Xb4qTJWYvybZex/VbQi7244x9yc9sYehYpiGNWGxp9t4AtbpaaWISen8QvnUpTudI4fjni8VsgHRdO0LkFTOtTz2ivMhQrX5OGTevRsE55ihSrSPGGwxi59Tr3Yn47wfFhzpxa+BVdypegdNGylGvQlFqNG1C5dClKFKlHs2/Xs/FWol7ft1ay0LjuKtT8jvGH3PCwLn4ilqjbm1nzY0uqlixNoUIlKG6+/oqVMF6LpShUvBHtR21k56NY8xaQp+I8TnNqQjmalMlJ4fYTmHwylMT3ncT5nOfslJo0KdmOLqNOc8+6XERERERERERERERE/l4UoCdfCtBfJjaAx8dn8dOneShYvCO9FjjxyLqKgNPsGNWIao6FKdtzBeuvJgw1HUrU/R0sHVSfikUqUr79WCau3sXuvfvZu3cjqyf24bPSjhQq2oMB869wJ84SnVpE31zGmp4OFMnVjYHzrpE4djb5XJjPlIa5yZvxc/rMvMJjy1JjA0EH+LVXLarZl6PBsKXM37SbXbuMsnYOM/rUoFiRklTsv4hN9/idoeitQu/gsqoXjUpUpEC94YzesJsd5nZ2bWXJqPa0K5WXolW/Y+yuB9bAMgIeb2Z26/JUzFeTFiPXsmKrdd87d7B99wF2nr7Lfb+ErrxGXcNvcHxuf1oXL0bukp3oM3kNG4222bd3DzuWj+f7FiUola8GjYds4pB37NOe43Gu+zjwXX7K21WkescZLLXU60nZvm0r+4+f4YZ3FOGW4wvH6/wyxhTMQ/63PuaTyn3pufIGD58G3pH4XlzC+BJFsanxPb33B1qXB+F55Rynt25n585d7Ng5l9GNKlI0Z3kK9JrD7HXGsu3b2HviAk4ekYQn7hH9vChvXLcPYmC1whQt3Z0vF+5h+8nTHDtxhE2zBtGjch7ylR/CV2vu/XajRORJtgytT72CNWj/izOXAqzLLYyduW9iZp+yfFKgEg2nX+RCwhP9r3BhUmnq5ClKqU/H8UtC2+xeyZwhLWmYvyRFW/zC3DO+T3qJx/gRtOdrete35a23MpOjaHfG7XPl8fPXhsdOto+pR8FP0vDhex9RdcB01hgvgKcB+ukfmdTElgL5v2PSPnfr9R9LbPBFDnzbijqFKlKo3QRmbjnMiVPHOXF0OYuGN6OmfTFK9ljIopsJ10UkPidGM6ZpUYoWbEX3SRvZcPwUx04eY9eKnxlWPz8FinWk0wwnXKzP4NpCZnSsRZ5iA/l+zyPcrYstoyJ4HWPrT62pW7wkJZp+y3cLt7LT8vrbxpZ5X9OzWhEK5WtFh9EnuBQS9/R44tyPcnRUHqrlfZt/ZKpF3UEbOOD+2zUY73WS4z8Xo5JNPZoNPcot63IREREREREREREREfl7UYCefClAfwXxIbe4sawr7RxLU67VTJZaeuBGEXbsB4bWLkzeyv0ZecQDH2tX1viwu9xd05m2hYtStNYoZp7zIyGetQg6z8kpn1E3dzHKt53Gmoex1tAXom//yob+hSlboA/DFt/A58nip3wvLWZmiyIUy/UFg+c6W0NsM0A/xOo+dfi0QlsGbQ9M9LxQQk6MZnCV3OSo0p9eOwMI+v3Joo2H38VlzQCalWlG3SE7OWNdbPFoK+t6l6WIfVmaT9jHGUuqGAWeW/mlXUXq1+rLyJO/hatJig0h9OocRrYoTZ58zeg4/RyXnnlf8cJj9zf0rlKEQtUH8fMxXzyt44LHue/jwIiSVMnbmX4z7/PM6OYvCMf7wiomlS5GqUzp+cjGDFJ/Zv7FAAItbR2F3+UVTKlYjvwNfmLQoWeS6kRcOfNlG+oWakmVRQ9+u3niFcQF3+TUmCrUsytEpS7bOGFdbhF1B+f1Exg/ahkrDz6yXh9GxSJPs/3bZjQt2YCuC65x5ZkLx2jZx1uZO6gaOUvUpsWcy1zys64KuMLFaVVokL8pbb++aB2Z4Inos9MYVzM/tiW+YvgWlyftFutH8P4fGNC0Mp+kzUnBshVpPOs4B70TJ+iR+O0Zy5SmDtja5CJtxuzUHzqHDa6/nePQs2OY3qYgJUuMYvrBx9YAPYKoR+uZWr8sdg7tab7c9WkAbeFykO0Tx/DzvP3svp/wovHl+oKWdChgR7H689j4zIXvzcM9M5g2ag5zN936raf5jaX80v1TipYfxk/7Xa03kxiiffHaP5g+FQtRsOQARu52SRSuG2LucWN5H9oWKUSxmt8w53Iw/tYDint8nBPjSlC/ZHZSprQlT4V2dF1+jWvB1nbxPsOp8eWpbbRzu29PcOfJUhERERERERERERER+ZtRgJ58KUB/JRHEuO9medeqlCvRmnazLuB0+wxbv69DrZI1aPz9QS4EJKTS8US4HmDbl6WoWKARbUad5LZ1zW/CiLu7lAktS1CqSlcG7/DBw5oj/scBevnWDNjknSgwjCHu9mLG1SuOTbmefLbBg4DEY1Y/zxqgNy/TlDoDt3A8cRruf4IjP9WmVMFC1B+1lSOW94Mo8NrK3M8qU6dKF77c5olbqFGfuDhiY2KflNh44q35Y3y4K3d//Zy2pUtQtvVsNrmZMW1ixnMDL7Jv+Ri++Xkmi0944WVNX+Pc93Pwh5JUzdeGLqPPcM/YaKyxn7hYcz8xxn6Mv8Z+nuwqHO9zy5lYzhzOvg6lq1WmefmqNB6xk10PzAaIJ/DKSqa+NEC/xoH+Lajl2JRKs5y4+kyg/cfiQu9wZUZLWhQsimPt0Uw5cI2rrn4EJB6X3vynWWkL49j/7QDdyRKgNyxYj8b99nIpOobomCgiA924vG4EXzWqSo1O85h32udJD/RYH4L2/UDvT9tRpHAtmrWvSL7uM5l40LrerFjUdfaM6s4XRQtRu0Y17PPmo/agma8UoEe77mFBx8o45q9KheGr2XnpAS6+oYQbh/jUk8vE+m9f7q3qRa/SBclT+ku+W3+WCw+88Q2LtZ5Pq0RDvv9egB4XeJVjY2tQ19G4Jnvv4GzCPPFPGbX33s6SXpWpUKoRnZbf5XrwkzVxj49ybEwZ6harSOly1anbvBKFWoxi6kE3LJe73xnOKkAXEREREREREREREfnbU4CefClAf1UxAbhu/ZI+dStRsUYffvzpKz6tUYGKbX9g3oVoQp/mejEEXF3FL41LUqRcL/r8ehtrNpdIHIQfY0v3OlQo0pJa829wx9oM/1GA3q8eDUo3o8eymzj7hBsv3DDCAu9yZ+sIulcsTNHG3zPupLE8cYj5vIQAvdynVOu5gh2eYQSZ2zGK79lFLOhWnmKF2tB97jmuWbYTCZ7bmN+xOtWLNaTtmI2s23mEE4ePcHDvPvYdPMmBK564Bz1poNjAq5wcV5v69tWo23U75629y58VS3REMIFBwYREPAnFLUvdDnJ4ZAmqF6xB3S/msuLoMQ4ePszh/QfZt/swx8/e4EGIOTaAKRzv04sZX748ji1+ou/MSSztU4PKZbvTd4ETD8wmM87TjEovCdDjr7I/IUCfeQXngGfi3D8WG0z46dmM61QDB4fSFK/Zmc7fzGbu5uOcvfmYgIRhB54yKvVvB+hXuTS9Co2LlKNS62ksPnqSIycPcWDpDwxsU4fy9frz7YZb3Hk61IEXAXuN66JOXxo0/YbJU7tQqXp3ukw6yjXLAyLg+kK++6wN9Wp24adR/alUqQRV+kx5hQA93nKjxKVFvelRww6H4lWp2G4IQ6avYsPha9xwCSfihVEQYom5tprFgxtSOF8pCpZvTZuBk5i25iBHrrjgHRz9bJBuSjJAjyfS4wBrO1embLHWtJh2AdeEyj7DmaPft6Zh/lqUHnuKY15Pth73+BBHfipHDcd2dP1yJgsW9qBxpQo0GPIr21yN4wq4wIWJ5amjAF1ERERERERERERE5G9NAXrypQD9NcQHnWTrqI40tc9F8WIlSFuuO91+OcmjZ0LgCLzPL2Z82dLkLz2QAZvu8UIHWFPcFfb3ako1x+ZUnO3MTWvK/m8H6MGHWT+kKbXsSlCh+SB6Df+B74YOYFC3ZjSvXR4Hx8a0+3k754Msj/59ofd5tG4AbaqUxa58S9p//T3Dv/+aAb160bFOaSqVKk2pjotYdibAelwR4LWDRd0aUDGTPfmKV6RspRpUr2qG1WUoU6cVFUfvZ9vdJylmbMAFdn9bjRrZavFp9z1cTtyj+CVi3Y5wbHQx6hTPSba85SlbrSaVq1ajSuXKlCrWhFY95rH+bjjGIRrMAH0h48qVJ1+rGfx09hZu+8fyjXFcpeqPYcpZVx7dWMvM6hUp+N8K0M3IN8wDlwvbWDJ6IN0a1KKq2SblK1Op7me0HzSL5Wc98bI++j8L0K9zeWYVWpS2waF4Q5p36U6n7p3pVL8EJQoUokCjHxlvTjNgfbgZoPvv+Z6u1XvTovcydh6fzdf1atG4ywyWuht7injMw+UdadKsPc2/XszBdd/RrHZxyvd4lQDdFEuElxNO26czcchn1K9dhZKlKlC2fA1qt+xP70l7OPrwuVdGlC+eNw6yfsa39GtWn5qWtqpAxdotaf7FGGbvu8vDxMF7kgF6LOGu21lQrzJF83egzbzLeCQZoD/k3KgutMhbi8Ijj3LII1GAPqoM1fO3p/eUS1x33c2qoZWpWKY5neY4cdXVmbOTK9OgoAJ0EREREREREREREZG/MwXoyZcC9NcShc/RXxjbICc2Oe0p0H05K68+X/9o/K6sZGadUhQuNYC+a+4l0QM9FiLOsOWLBlQs0oI6868l6oG+kg39zAC9P8OX3sL/yeKn/K4sYXarIhTL0Y1Bs52soeGTAH3Dly2pmS0vjuUbUrNpG5pVKUDBjKlJm7Ue9YesZfs1bzPu/mOhD3BdP4B21YuSOV95qjRrwadNa1AiZ2ZSps1PnrZjmHLgEe5PDyocPLezoHMdqjqUoVq7QfQd+g1fDTfKkMEMHjGOYesvc9bzSWxvDq996Ocq1LGtTv3ue7gQY4mZX0ms22GO/FScWkWKUbzmF/QZ/rWxn+EM++orBvUfzbjZeznlEWUN9q0Betmy5GsyiR+coo1ju87V2V34tGgdagybwbytc5lcuRIlP/2Zgf+VAD2RwNs47V7F8qk/MXJ4Lzo2qEqJfBWo+flclp72eG4O9KY0K9WQHotv4vzkbgAr47ox2nrekBrkKF6bprMuctHXusrsgT6jCk1LFKZk7YGMmDWbmbNnMXvKeMYP70nPDp/TYdAyVp/1erKvGG8CzQC9ajeaDNjDabfT7PuhPo0b9Kf/ehfcH+3n177lqdNuOGN2OXF31/e0qVWIsq8coCeII+zeCfatm8fUn37gmx7NaVK1BHlKtaPlpP0cckviioxy5e6RTaydNZbR3/Xji1Z1KZO/JOUbj2bq7oc8naY9yQA9jsjH+/i1TSVKF/yMljOu4GZZnpi5gZscHfEZnxaoRdkxxznh+WSjTwL00lTL35ouE27habx6/S5OY0Sd4lSqN5LhCzezZlwF6pdowWcK0EVERERERERERERE/rYUoCdfCtBf16PdbPymIkWLFqH11COcfTEdJ+LhHrYOLEe5Uq1oNfkcd17IXKPhwQqmNi9D8fKd+WKzK+7WicCjb61kbd98lMrflSGLb5GQjyYIdlrKrCZFKOTQi8GLruNtWfokQF87sDF181ek4eBZTF2xnjVz+tGnTikKOfbiq413cHmhHkmw9kBvVakqReoN5ofla1nx6xzGdqhMiZL1qPjdXi48k+qHE+9hzoFeiXrVe/DtgTBrGJy0+DBXbi1tTqtipSjdeglbvCy1f1aUBw8uH+XQiYs4e0aTMOp4rFvCHOgd6DnJ2XrsvydxgD6Rr0+aDRwDXodY2bse1etUpULHTnQsVIIiLccz5E8P0OOIiw7A9/597jzwweuZUQqiCTi/jNnty1Aoe2PajDrElYRGiDrF1q+bUq9IfdrMuYrz8++5XttYMLACNsWr03quM5cTqp0wB3qBT2k55Dh3rYstPA6y++salMlbm1bjD3DR7MUd60PIPjNA70KDXoe5HOyJ5/Hh9G7Ulk87jmPhwu/oUKkObQev4aS7L257fqBF1YKvFqDHRxMX5MnDB3e46xVk3mLxm+gHXF83jB7lHMlS61uG7PCwbiuEwEcPuHvXA9fnOqZH3NnNmoHVKZ29KrV7rueosUFLc91Meg702IDLHBtZizola1Pjq71JzIFuCNjFip7VKFe0Ea0WGu1svWgTB+idfz5vDd/duLGwO92qNKRopQ50bl+YUoU/o/MIBegiIiIiIiIiIiIiIn9XCtCTLwXor8t1H9u+q0qx4kVpPf0wZ5KofnzAda4taE2zUoUo2W4Ki5wiCUqYyNsMVoPucnNVL1qXL0ypNmNZfD2WEOvq6Psb2TysIKXz1aTVT/s5GxL3Ww/tuCAe7BrLd5WKUqDGT4zc7WYNJ2OfzIHetx4NSzel5/LbXPULJzzkNEend6FusQpUHLSKdbejLY/+Q4nmQK/ecwW7je2EhIXgsfs7vmxYlKK1hjD+iM/TsDJxgF63eg++2RfC70TRT8QEEnJiEkMbFcahYgcGrr/L/aBExxgdQPjJaXxvbK9cy8FMORmIlzWtjXNPCNDb02OiE55PFv+OxAH6BL4+nnCnQyS+h8Yz6vMS2NjkJHsOB9K3m8rwPz1AjybK/xR7R/Sne88J/Lzf/dmh/H1OcWzCp1TMW4f6Q7dzIqEjduwl9o5qR408FSg+eCPb74X9doNBfCQxl+cx7rNC5C77KYM3PuJewvMSBegthhzjtnWxhc9Jjo2pR5l8eajx9Wr2m00R40vo/icBer3ue3GOMc5j8B4W9jaONa8jVavUJHfhQQxdeJ3geB88jcc2qfyKAXqsP+EXVzJmSGc6/7SMrQ8sty5YxRHktJTZzY1ruEofeq5+aM6ib7jB6anDGdj5O4ZsuMVj6w0lFmF3ub6kPZ8WrkT59ovZ7me2ruFW0gE6EZ64buhF9yr5cKz3NROP+eERFW+9xoy/ET647/mO3rULUbj2ICadDMLL2sjPBuhnefhkMXGe+9jwbQcq58iFbQ470mfoRM+fTjx7o4KIiIiIiIiIiIiIiPxtKEBPvhSgvy6XPWz5pgrFihWl5dSDnEqiBzqxYYTeXs30juUo5VCSqt2mMX3nea7cd+PR5f1sn9aHzrUcyVK6He1nneJBhBkrPhEXcgnnpZ/TwLEgNmU703feXo5fe4TL/Wtc2D6Vnz4rQdkCxan61XY234u0hoIxTwL0PnVoWK4V/TZ48siScEYTfHkpU9oWoFDxdnT/5TL3Xpb/WgP0ZmWaUGvAJo4mZO6hx9kzojF17StSafA2drgkxMFG5T22Mq99VWqWbU2PRRc5e9MVD9dHPHzwkAf3jb/3vfEPjrSGrmbY78yeCS1pVDQ7her0YvC8PRy66o7r/cuc2zGVSe3KksehHCV6LmSnS2SiHugHOPRjCarlb0TLr7ZxzM2dh49ccX3gwsN7D3jwyAM3/2giLDuKeC5AT9QvPvw+F5f2pVORD8mcPTvvtZnGN/+FHugxgZfYP6wmNfMVpmSHqSw5eosbLh74uTpzceskvmtaGvvcHfh88mnuJSTS8YHcWvsN/ctlJVephrQavZ5tF12NtryJ05FlLBxUn4p5S1Kk2QR+vR5MWEJ1/I1tTqvCp4WqUbvrKnY/NNrG3R3322c4uvxbBtcqir1NbdpNOcRFszd8oh7odbts50q8uaFALs/rRfdC75DN1pHsrZcw75yZZD/GZ993NKr0qj3Qg4m4vpSRjUuRL19Dmv28gz1OD3F57I3b9SPsnt2ZVmXzYNf0Z0YeDLJu6xHnJrahVcHcFPz0eyZtv8yVB174ut3k+uHFTOlcmfy2jaj/5W4uGlX6ox7oxMcQ7bGHFYNrU9nBqHPrUYxae4yzd41r7NoJDi35joGNipCjWAPq/rCTy/7xTwP+5wP0B9blxscg/qcWMqVdYbJksOWdFB3pPVoBuoiIiIiIiIiIiIjI35UC9ORLAfrrerCNdQOKkCuXDXXG7OHoM3NUJxLvx939s5nQoSZVipWjZM1WtOrUk64ta1O9ZH7yVWpLszG72HPn+fGlgwm7t49fv+9IzSKO5C1ak4btetC1S3uaVClGIccSVO38E3OOezwd9t3SHzdwL0s7laNSvtp8vvQeNxNeq6E3ub68PS3t8mBffQQ/nfEm+LfuwC8KucWDZV2pla8apTotZ2dAQu/hEHwPz2BcbUeyODah89zT3LHswwzQNzKrZTkKp7ajYM32tOnUi949etKtaxc+79CPLl0Wse7oI35rqhiCru9g4/dNaWS2Rck61G5tPL5za5rVLEXxYjWo1XM+i84adbU+wxT7aB8HvrGldM7sZCvalJY9e9Pti1580fULOrdvT49hY5ly4DEulksqAq/jsxiRLx+Zqo+k/6FnB5aPct3P3m8rUyVXRt6qNZK+e5+fbd4q/jK7utSmdLYaFJpwgUv+rxqgG0+NDsD96AymdqlOlZKVqNCoM5917UP/bm1oWrsShUo0oX6/1Wy45Gvthf1ExKOTHJ7RgY6V85G7SFUqNv2Crl070rp+WQoXKkWR+iP4YfUtHoYnxNjm5ebE+QlFqJ07PRnzNqRJN6NtjPbp0aEp9SqVpFDhBtTvvpxVF7yfjBAQ40nA9sG0KtqS8i3Xc8a6qZALM5ncwoZMOctTfdJxjpnNFu+C57ZBVCmUlXyfjeZXl98C9JCT3zGuflbs7YYzbo+bcZWYYogLvs7Z+V/Tp0Z5SpatTb123enSsz9ftK1L3cpFKVy3C13mn+K0h+UJhkh8L61gyaD61C1VjrJ1P6Nl5370+6I9bRtVo2jxOlTp+AvzD7v/dh1dncvUNlWwKdCHr3e5PDfXeShuZ1Yyr3dD6pQsS7HKTWnSwbjG2jWiXrkC5CvdgNrDV7P6cgCJR9ePc9vL/uF5KJ65Hi2+OflsQB7uwq1tw+hQLhcp32pA668Oc8u6SkRERERERERERERE/l4UoCdfCtBfl8dpjszpQfv2nzF81Xmck5pf+alAfC5vZfkPfejYqCE1qtWiWs16NPp8CN8uPc1Fr99LsmOIdTvCzmlD6N64LnVr1aJ6jbpUq9eejsMXs/KkFwHPjMZubCf0PHsn9GdQ56+YsPcxrk8nng4j6tEuVg7qRJN2PzB82318n5mP+znhrnjsm8xXnb+kz4T9nDVOz9Na+jtxafkgOrVtTa8Z+zlpmaDdqEjACbaOHkzXanWoX68Bdeo1pK6l1KZG9ebUqTuFBbvuvzC0e7j7GfZM/5LeLRtRq5rx2Gp1qdeoMz1+XsWOq77PDnluiPO+xOUln9G/o9GOtT617sModRtQs2oVPu00mB93POK+5X0qioCrW1jWuTMtv1zO7MvPX2f++F1YzrReHak/dAnTzyc1lIAh/h5nJ31F3zZD6b72DneDXz1AfyKYxxc2suC7rrRpUM9yDdQwjrVWsy/oMnYTO28Hv3Ccpmi/q5xY/CND2jWltvGcWtWNUq85jfpMYsqeOzx67hzGBz/gzroefNutBtVrG21T90m71KndgLrNe9Jz7Do2XQ/67RzE+BN0ei4/9fyJAT8d5UbCSQ46y6H5I+ncZQy/HHV9Mkx+nCd+p+YwpHtrOo/9lYOJ5q0Pv7aENd+2pmOHhaw57/PsfOchN7m+dQI/f9GUT+vWplr12lSvWZ/GxjX6w8rzOPn8dgPAE5H439rPuvH96Ny4ATWqGte9edyNOtLm2yWsvuhtnLVEHuxgg3HdfdZ1BovOeuFjXfybMELuHWDzxC/p0ayRcY08ef3Vb9OTATP3cvRhxNPjSBDnc56LCzvRs+U3jFp0LdFUBU9Eep7iwMTudG34NSPmXeaRdbmIiIiIiIiIiIiIiPy9KEBPvhSgv67YCMIDvfH09MQ3JJKol+WpcZGE+Xvx2OUeN6/f4uatB7h6+OIfFvtCePesaKKCffFyvcfD29e4du0Wt++54+EXRtgLubtRCXM/AT74ePkSYGw7JnG94o11vp64P/bGKziKp9OxJyU+htiwAHy9fPAOCCPSqORvD48hJsyok4ebUY9QjN0YzH1HEOpv1tUNt0cuuLi48PChUVwe8uDBI+PfxvGGRD/ttfybWMsxeru7cv/GPe5cvYfLI0+8AsKTbtfYSCKDPY1jdOXRo0e4mPtIKA8e4OLmgbexnyhLw8YTFxVKsJcXHr7BBJoH8ryYMIJ9jHPjE0RAUustookMMOponDMvY9vRf3zSkmZcM6H+j3F/cJsbV41zef0O94zj9AyMeKb387PiiQk1zoPR1g9u3uKW8w3u3DeO2zuI4KQaJy6G6BBv/LxdcXV1TdQ2j3jk5oV3YDgRiZ8Wb1x/EUH4e/vj45+4vaOJCAnE2yuQkMiEazTWOIRAo9098PIPJlHHd+Kjggnx9TBeD0GWxz9bs3jj0gskwPMhj+5c47pxDDdvPjSufz/LNZpkU8ZHG68vLzzN14vRVlev3uLOQw/c/UMJf/4JMaGE+PvgadQ1yNj3i9eXyTieIB/j2rzPrRu3uH7TuMbcvfExXgdJ3r5iXmNBRnsZ59svKInHGPWLCjKe/9hYHxiZ9DZERERERERERERERCTZU4CefClAFxERERERERERERERERF5DQrQky8F6CIiIiIiIiIiIiIiIiIir0EBevKlAF1ERERERERERERERERE5DUoQE++FKCLiIiIiIiIiIiIiIiIiLwGBejJlwJ0EREREREREREREREREZHXoAA9+VKALiIiIiIiIiIiIiIiIiLyGhSgJ18K0EVEREREREREREREREREXoMC9ORLAbqIiIiIiIiIiIiIiIiIyGtQgJ58KUAXEREREREREREREREREXkNCtCTLwXoIiIiIiIiIiIiIiIiIiKvQQF68qUAXURERERERERERERERETkNShAT74UoIuIiIiIiIiIiIiIiIiIvAYF6MmXAnQRERERERERERERERERkdegAD35UoAuIiIiIiIiIiIiIiIiIvIaFKAnXwrQRURERERERERERERERERegwL05EsBuoiIiIiIiIiIiIiIiIjIa1CAnnwpQBcREREREREREREREREReQ0K0JMvBegiIiIiIiIiIiIiIiIiIq9BAXrypQBdREREREREREREREREROQ1KEBPvhSgi4iIiIiIiIiIiIiIiIi8BgXoyZcCdBERERERERERERERERGR16AAPflSgC4iIiIiIiIiIiIiIiIi8hoUoCdfCtBFRERERERERERERERERF6DAvTkSwG6iIiIiIiIiIiIiIiIiMhrUICefClAFxERERERERERERERERF5DQrQky8F6CIiIiIiIiIiIiIiIiIir0EBevKlAF1ERERERERERERERERE5DUoQE++FKCLiIiIiIiIiIiIiIiIiLwGBejJlwJ0EREREREREREREREREZHXoAA9+VKALiIiIiIiIiIiIiIiIiLyGhSgJ18K0EVEREREREREREREREREXoMC9ORLAbqIiIiIiIiIiIiIiIiIyGtQgJ58KUAXEREREREREREREREREXkNCtCTLwXoIiIiIiIiIiIiIiIiIiKvQQF68qUAXURERERERERERERERETkNShAT74UoIuIiIiIiIiIiIiIiIiIvAYF6MmXAnQRERERERERERERERERkdegAD35UoAuIiIiIiIiIiIiIiIiIvIaFKAnXwrQRURERERERERERERERERegwL05EsBuoiIiIiIiIiIiIiIiIjIa1CAnnwpQH/DxcbH4RL4mOMu51l7bScLLqxl9tmVTD65iHHH5zH22FzGHZtn/P9iZp5dblm/7touDj84w/0AV2LjYq1bEhEREREREREREREREZE/gwL05EsB+hsmJi4G1yAPLjx2Zv/9k6x02sqoI7Nov/FLKi5qTYGZdbGbVo3ME8qRblwJ0owpTrqxJcgysTy5plaxrK+4qA1t1g9gxKHprLiyhf33TnDe3RmXQHeiY6OtexIRERERERERERERERGRf4cC9ORLAfobIiw6HPcgTw7cP8kPh6dTd3lnHGfVI/f0mthPq47NlCpkn1yRrJMqWMLyzBPLWUL0zBPKPvlr/L+53FxvPs58vPk88/mOs+pTa+nnfHtgCvvuHcc10INQY3/xxn8iIiIiIiIiIiIiIvIXiI0mMiyU0OBggkPCCIuMJibOuk7+NPExkUSEhhASbJTwSCJj/uRsJDaKqPCwJ+cxNNw4jzG87tjA8dFGvcJDjToaJTSSKKOOv1vLuFjj0jH2GfUaJTqa2Lg/SoXiiYuJJirSqEd0DNGxxmNfqZliiY4wrt2QYKPe4YRHxfJ3uoQVoCdfCtDfADd97jH19BLabxhC9aUdKDSnAVknlift2OJGKUH68aXJNKGsZVk2MyCfVJHskyuRI1ExQ3NzubnefJz5+AzG88znm8UM1x1n16fakva0Wz+YCccX4OR5y1oDERERERERERERERH5/xIfeIfrJ7axesYkxn39HV+Pms605dvYf/E+bsHWB8l/LM7vBs77V7F4/EhGfPcjP81ayeq917jjHf2nBL0xvje5eXIr6+dMZcK33/P12F+YuvoQx2544/9KO4gm0vMWzrtX8+vscUYdx/HzpBVsOHSZaz7RSQbeMd63uXd8Hds2r2PN+o1s2Pg7ZdMmo2xg7ZpNbNt7ytheOGHWbTwrBnyvc37fDjb8+ivr953g2L0wQl42oHG0H49vHGHr0ulMHfE9Iyf8wtz1pzh3K4Bw60OSOwXoyZcC9L/QFc+bzDu/mh7bvqPE3CZ8Mq4kqUYXJf34Upae5IkD8v+0mNszA3Vz++nGlqTonE/pvOkrfjm3ikse14w34T/5jitJhiIIcL/PzbNnOX/xMmdve+ERpDn2RURERERERERERF5VpPtFTq6azKTvetO5VQOqFy1Inqw5yGJXBMeKn9Los54MGjWbBbtuczfQ+iR5bXFeV7myaRYTvunF542qUT6fHTmy22JXtCrVm3Sj2/DZzN7pxL1/M6KKDbiN89bpTP2uB51a1DXOYwEcjPOYyb44Bas0p1nn4Xw3eT3bnHzxSzJINxYGOHNqwwwmDu5G50bVqVg0Dzmy5iZXvkpUa9yBrsPH8vOqU5x2jbA+54mIa2vZ9kNlGtetTJlKtahW47lSvabxtzbVa9enTv16VK/Skjbdp7Hqqh++1m08FXSdKztnMXl4T9o3aUztKsZ2e49k7AEfPH4vBY8NIODSBlZN/Y6+nVtSv2IxCtnaYVegDKVrtaXdF6MYNXcvxx6GJvsgXQF68qUA/S/gFuTBzjuH6b3jR8uc5Qk9zM0e5M8H3+Yys/d4xvFlLAG72Ss99Zhiv1vM9ebjzMebz/u9bZpDvptzp+ebUZue20ew8cZeXIMeW2sokhRPrm1ZxNSOHejeayAdZx1i561nP7hFREREREREREREJCmxRHqe5ejsHnQolZXMaVLzfkY7chWtSNnKtahSsQylCuQka7oP+ChDbvLV/4ZR225yP0Sd315XXOA1LqwcRu8q9mROl5Z/Ge1pX7IaVapXpULR3ORIn5Z3U+chd72vGLH+NvdCYqzPfDXxwfdx3jyCgbVtsE2fmlQ5HClQvg41a9ejbs3KlCloS9aP05E+Z2Vq9FvEuku+BD0ToscS4XeVcwuH0LWSA5k/Sk3arAVwLF+T6nXqUatyKQrZZiRNmkykLvEZHWYf5nSiHvPhzstYP9SRyqUdcShQgiJFnyvFSlG0WHGKFsqLrW02Uv4rHwVKD2PKKW88LFuIJSbEC/cbJzg4rz9f1HAgfap0pEybiXRpPsahRicGbPbgUVLpd4w/vldWs6BvFYrnTMt7KTOTyaEohUuUomTRgthlz8AHH2Uhfd5mtB65lwMPQom0PjU5UoCefClA/38UExfDVe/bjDoykwoLW5FjcmWyTCpPlonlLEOvJw64LfOcTyhrWWcus5tWjbwzalFgVl3LnOaFZpulwTPFHKLdXG8+zny8+bwn23kyP3q2RGG6uT9z22aQnmtKVUrOa8qIQ9O47n2XaKOeIi9y4/yUL+meNh02DkVI33c1c5x0rYiIiIiIiIiIiIj8sXiIcuPKgr70r5SNHFmzk8m2CAUbdqXLmKUsWruTDStmMvubVrStno9cWTKQPqs9eVuPZdx+T0KVob+6GD9ctn7P8Cb5yJYlCxlyFiRvvV70nraeDdvWsmJCL9pVL0qujJlIl70keev+xPTDLvi+chtH4rt/MpPbmWFxVtJmzU/J9l/z3fIDHDh0mON7V7Pg+/a0Km1D9gw5yZyjMm1+3sI+n/jfhoyPe8j1bT/To1xB8qQ36pGtFOWaDmXUsh3sOHyUg2unMbZPA8rntSFTxmykq96LzxdfwS3cciUR9egslzaMZcbksfw0biLjJz5bJkyZwcSxI/i2S3UqFslBig8K4Vh5BHPO++Fj7j/eF89Ti5jYrQblC9hiY58Xe4c85MqZiyxpU5OndhcGbvHE9YUAPZ6YW1tY9209SubNySeZcmFbtjGf9v6RnyZNYvrYoXzRugZFc2clY3pbo+078MWUY1yOTnTsyYwC9ORLAfr/k6i4GHbePkK3rd9QeE5DMo4vbel5njg4N4sZnptzmGecYKwfVwrbqVWpsaQDfXeOZObZ5Zae4gfvn+TEowtGufhMOfTwDJtu7GPmmeX03zWKmks7Yj+tOhnGl7EM325u9/ke6eb+zd7q5nqzN3y3Ld+w7dZBgqP+vkPry+/x4OLEAXR671+kz5KbFD1+ZeZV6yoRERERERERERER+R3hxLjuZm632hRL+S8y5itHpcGLWHLkNrc9AgkIiSA0yAffR9e4tGUiI1vkxiFzat76uAHNh+/hQphllmp5qXCiPfazqld9Kqf7gNQOpSnWdQZz993krncoYeEhBHne5sbuKYxsVwm7tJn4JEdp6ozezvZH1k281ANO/9yNzzN+RNocjmRqNpKxu27iGhRNTEwMcVHB+LsdYtfkbjTKkZ1sH2XCrv04xpwJJmFK8ej7G1j3XQUKZs3Gxx+VpXbX2Sw59xC3wAgiYsze4d54XN3N5p9bULNodv6ZqiCl205g3c0wzFH946PCCPP3xNvLEw9PLzy9ni1e3n64XtvLuv7VqJs3De/mrEXpHqs44Gq0j1mBQCeuLPyc2oWz8I/MFanZ8wcmTRxK1/IlyPdRSvLU7cagrUkF6O44L/yK3vk+IXM2e2wbj+CHVedxuufGY2O/Ph4PueO8mTWj2lItW2Y+/kcmCneYyBynKMKTaYKuAD35UoD+/yA4MpT113bTYm1fzF7nZnCedVL5Z4Jsc45yc0j1NGOKk39mHVqvG8CIQ1Mtc5Rvubmfy5438AsPsG7xj5mPc/K8ydabByxzrP9weDqfbRhs6aGedkwJ0o0raQnTE+/fDNLNudGzT6xA09W9WOW8/ZX390aJDcLtzEa2zBnFmF9WMfOoOx6BXgSYHxazJzN92kzWnbrDw2fe+GOMD9VLnNm6jEUzpzNl0hQmTpvFzCWb2HbiLq5/eImGEnj7JIfWLWLBrKlMmjSJCeOnMX3ur6w5eInrfrF/+p1VcX43ubhvFcvmzWDqlCnGPqcxddYyVuy8yDU/64OeYXwxcDvHgcXzmDHqJxZsPsxF43ExYa7cO7qeFTOnMmWKUfcZ81iy4wJOXs/VOMIb38tbWbXoZ/o0rkPVjJnJkSsvmWp2pc2XYxk/cylztjtz2/f1B2Ixj+XS88cycxnLzWN5YTKWxIwvITePc2DtQuYltPuE6Uyft5p1h524GZDoGOKjjM/1s+zftJgfRo9n2uzF7LnqhXcSJyYmyPiAP/gLy+eM48cxv7Jk8zXjWtE87yIiIiIiIiIiIvIfiPcn6tJs+jepSKp/piBvxU8ZsMMTd+vqZ4Tf4/aW0Xw/pC9tW37LmDkHuRDK0/D1NyEE3j7G/lXz+WXqNKZMnsakqTOZuXQL28658vzPvM+L872F0761LPtlBlOnTmXylClMmbmEFTsvccXSTflFMV6XcN45kTkzpzJq4RGO3PElNPgOl3YvZ+HUccxYtZuD9yIIS/yTapwPbpf3sGXFL8yYZuxjilHXmfNZuPYgJ24HEGx92AuC7nLv3AH2Hr3Bff8YS8/rl4r2xP/EOPrVKkGGtz4hT8XO9Nv4CNfn2yL+IecXDqRN7nTYZMlCxsbjGLnHl1ebsPQKO/q259O33id7gfJUGHeMI17WVU9F431yPj+VsaVQxrS82+Brum72tA5lHovX/jGMa5qebDa5SVlqCN+uv4e/ZV1iEUTeXMCYphXI91Yq7Mu2otuGu9wKs67+QxEEOy/ju0bFyP9xKrJX60X3NfdxjbS2osthzsxsT9NOfWgxcgt7rlzj4YUl/Fy7HI7vpSB3UgG6+Tu7504W9GtIgXfTk8u+AZ/NPc/FF/KTWKJv7WHpVwMZ+HkHek9YyYoLYSTXn9kVoCdfCtD/y8wQeqXTVuos72wJqc3h1BMH12aQbfb+NodSLzrnU5qt6c3IwzM48vAsYdEv3N7zbwmPieCk60VGH/uF5mv6UHxuE8uQ7mYPdzO4T1yfTBPKWOpUZ0VnllzagFfo73xSvqHiI104Oak57ezf4t18Vcg2dC2rf53Lur7VKZjybT78KC21R21mb0LQHOuN+6UdrPm5Ox0qOpAjVUree+d9/pkiDalsSlOxzbeMWHaKc24hxtt+YtHEBD/i5sGVzBvShkbFbMmWNg2p02UgVcpPSJOpAIU+7c2Aads5csuPoD9liJ1gvK8fYue0QXxRvzD2mVLx/r/e5513PiRFmtzkqdGT/lO2ceCGJ77PfCHwxOvkNIaULEDWt96iRKt+TD5wi73Lx/N1vYLkSpmCd//1Ee+ltSF39a70mbmfA3eDCUvYhv9lrs5pRrk8GXnrgwxkssuNTS47bDOn55MU/+CtNIXI2HoR66+9zg0X5rEcZodxLD0aFiF3lo9J8dyx9Ju8nQPXH+MTY5xX67Ms7R70kOt7lzFnYEsaFrEhq9Huacx2T/UJqbM4UqRJf4bM3MXxu8aXL/OJ8cbXnju/Mq5LNdK89S4ZbIrQdNYJjr9Q3VgCrm1iUWt7SmR6m7eyNqHOsANcCnrxq6mIiIiIiIiIiIjIK4vzIfLsJLo3Lst7735InrL16bnqDjeT7FYeTWyINy7373Hj2m0euvvgbzzut598YyHsIbcOrmDuEPM30uxk+OBDY7sf8M77aUjjUIEqXcYyaf15rj0OeXH+6ZhgAm7uYdecofSoW5zc6VOR4v0UvPve+/wrtT15qnWnz/Rt7Lnmh89ziXLE5bks7/oRDjkz8FbZIfSZtZb9K4bRq7odGd55i7QVuzBgRyDeUeajY4jwvcnlDdMZ26MW5fKkJ2WK93nvX0ZdU2Uha9FPaT5kEcsOueAeEZ/oN2DjSCM8uLF9EiM+b0T91t/wzeorOPtbNvqH4vyccJ7XhkblcvHPFMWp2Gwya+5F8GK0GY/3sdnMaJKdArly8C+HL/hi+llcjF2//Kf8exwZ8TktP/4n2QqUp9q4Exz1tq56Kgbvkwv4qWIuCqZLRcrGPzBgnz9PjiCS2xu/5ZsKKcmRuyAfd1/KjEtJ52TxEefY/nljGr37L9IVqUnJkQc5+OjlYxHEeh3n+IRmlClgx9upS1BnwCK2uZmxupXnVR4c/pW1x29yxZLcBxF0eT4jqpci33sfJB2gRwcScvB7hjTPzbupipG38mgWXAqybjOWuNBAfL398QmKJjI2lkg/D7weGNf4PQ9cfCKJ/VMykjePAvTkSwH6f1FQZAiLLq2j8uJ2lnDaDMkThlDPbhQzqDaL3dRqVF7Ujgkn5nPN5w6hUWHExv25t+PExscRGh3OdWP7U04totayz7GZUsUSoJsl+2RrvYz6ZTbqZM6XXnXxZ8w6uwKfsBfvfXpTmQH66Smt6VDgPT4uWIKsn/Xi83r1aV8wD7Y5cuBQoDAdp+/hmPleFudH+Pk5jOtQnaI5bLGzy4t9ngLkyWstDrkty7IXbcfnE/ZxIfi3eTriox7gsudn+tUrT/5suchlW4B8juWpUqcB1apVpFh+O3IZ28xhU5HGX69i88Noov+TD4j4YCJub2Ber8ZUsc2Fra0Dtrnzk9tSV+OvUddcNrmxsSlDrS9msPByOAFP9+eJ16kZDC1XGJu3P6BMvUb0//4n2taqS+ncecmbrwC5c+fBLmdOsmfLTuZiTaj51Ub2Pgh98qEWeJ1bK3rSrFoR0mexNfZjh00ue2zs8mBva0PWwnUo2XctO28HPdndyxjHEnlnI/P7NKFKLhtje8a2HPLh8PRYHLDJ6UDOHKWpaRzL/CuRBFqPJT7yLvd3jqRXrbLky2rUxbYg+QuVp6rZ7lUrUDSfUbfsttjYVqH59+vZ5hpjnDPjydHO7PupE59+/B6ZcuQn74C1/HrtuS9c8Z7c3zuJgY522L//IZkbfsmgHR74/UcnTkRERERERERERCSAqKuLGdGmEjk//Ij0DmUo1Hkei08/Iig0nIio2N8JGM2FicNliAu/w/1tI+hbpwz5stiQ0zY3drkTftfOi72dHdlzFMDesTU9ph3gVLDlF9In4mPwv7aR5f2qULNwLrLlzJ3od+YClt+ZbXPmxLZAGcr1mM/Ck/5E/5bcE3FlPiu/SEd+hyz8q9Jn1G7XgZ41C1GyQG6yZ8tG8ZZD+fFgKP4x8cRFXeHU/MG0L1GAvDZ25HTI93Q/efIa/7Y361mSkvW/Y/JBN9yeRiKR4L6N+QMbUTBNaj7JWYRsHeYz+3SSw68+I+LhQXYOK07Ngml4O2MjavfazKmA6GfaL0HM7Q1sGl6UonlseP+jT2nz1Q7OGLt+eTwdzO11Q/mqciqy2BYiS5uZzDjqTrjRUHHGjuKNNo4OucyJuQNpZJuDHOntqDF0MWvvJZyHcO6YAXrFj8jh4MjHXVcw8+Lv9H0PP8uGjo2plyIFqfNUJvcXa9lw/WVBbQSuB2cwulZ28mbPzgflB9F/xdVn53iPjSE2IoJIo82fLPbD/+Jcvv+DAD0+zJNbS1vQteL7vGdXl0J9tnHgYQjRkX54OR/m8JrFzJw8nxnL97Df+SHugRGWa8ey/fjEO09eFKAnXwrQ/0siY6NYe3Wnpee5GZxnmlD2mfnHs06sQPpxpSn+S2O+2jeBffdO4BHy/9Pb2yvUl+Mu5xm2dwJFZjew1M2sY0LdzDDdXGbOjW4G7SuvbMU/4hXD0b9YfOQjzsz4jM4l05Elly1ZHAuTu0RzWvWewuw1m9m5ZwenrnniHxtO4N2drOxRmyqZU5LaphyVu01j9tYjHD1+nGPH9rJz/jf0qmyPbZpPyFh7MD02PeCRdXiU8Pu72P5VScrZfsLbWapTpcdMFu05y/nLV7h48Sx7l37PkGqZyfHRB7xdvCddl13H498OYuMJdTvOnq8b0zhPBlKkNr5w1BrEj0t2se+YUdfjh9i97Ce+rJuPfJ+k4qMc9ajffz17XcKMj2KTFz5nZvNNlVIUSJ0JW3t7SpSvQY2uE/hp6QEOm9vYvpRFgxtQ0zET76XMTOaSzei85DznzWHUY0IJfXyNS2c3Mqt3WxqnS0/2XPlJ3/IHBi/cy6ET5zl13QOfsJd/tUg4lr3fNKFpvox8+IEduUp3ps+0TWw9arb7AXYuMb78VbLH4aN/kcKuJQ2/P85F/ye9wMNvb2HL4GKUtknPO9lqU73XHJbtO8cFs90vnGH3wuEMqJSebCk/4t1S/ejx6y2ejAQfyeOtIxhV42Ny2uQjbfnRjN726NnhgQLOcXxOF8rbOZD2gwLUHryYde5xmltIRERERERERERE/kMxxAU5sW9UC5rm+Zi0WezIkK8SFT7tSPdhY5i0ch+Hrrjj8dLhuX1xOTKPcQ2KUihjOv6VsRzl249l+qYjT37nPbKK+V+3pXrW9KRLkYXMjX7g672uT0dIjXU5wO7JjaleIAtpPrShYP0BDFu6l72W32aPs2/ZCIY3y0/OTBn4KHsZGn2zkm13sfachginRazqm52iebORzsGRXIXKU6Z2F/pOXMavm7ex79hlLj6OIjLGg1sbfuCbyjnInj4HaYt0oOu4dWwz62iW/WtYNKwh9fJ8RJoc5SjceSWrL/tZO7DFgPdOFvSpT75/vEXKDLnI1Hkh88xx7F8i+M4uVvcqSJXcqXgnTzsa/niYG6G/8wuv+34OTqxDifz2vP9+dZoNWs+BYLP//8vEE+F5kF1jW1HTLgcZ7cpQutMIRv96jGOXbnH57BZ+ndCTz8vmwS57XvIY52DKnof4Pe3PFY3Ltu8YWesjctrk5eMKY/hpq5v1t/zEwom/v5bRLapT4MNUpHGoRO5Oy1jr/JKRYP1Ps2Py55TKmoGsmRypPHQNq+69rPe+D74X/jhAjwm6w/Hx9WhT8J98UKotJcYd4cTJjeyc1peWtapTtUxpihYtS/Hy1anSsD2fDZrJsrNexpaTNwXoyZcC9P+CiJhIdtw5TLM1fSw9zLMk7nlu/M00vowlsK61rBMzzyznlu996zP/f5n7nXZqCfVXdLX0OP9kXKmnIbpZzAA92+SKNFz5hWU+9T9rSPn/pqcBeqnUZMjgQDb7RjT7fg2bnP2fHaYl/C431g+hmaMD6VIWp3KXWSy7Esgz30+ibnNt6QC6VczJB5kqU7jjWnY/jLAM5R5wfilzmmeiaK5P+GfpQXT/1e3ZD9aIe9zcPJGZY75j0PhVLD38AL+YfzNAj/XE5eBkupWyI8fHKchW6XO+mH+V24lPR6RxPCsH0r1qftK+kxX7kh35ZqcLDyzj83hbA/SSFEqdno9siuDQZBhTDrjg9rRK4YRdXcXcPnWomPVDMufIi13Hxcw9F5zouPy5PnMYPT5KSbZcjqTuv5H5xpen1xLnZTmW7mXsyWkcS/aybeg06RRnEw9xE36dy798Qacqufn4k9IUbziRlTeeDAUTcHoeM5ukxzFXBt4uN4ze6x4/uYMtQdgtrm0Yx7QxIxg8YS3Lj7qQcOtHzF3jS9hXJciZw54PMjSm57TDXE00flHkrV9ZNbQwtvZ2vJu1jbH+GHeM71b/7m0PIiIiIiIiIiIiIr+JJODyZjb/1JYWZe3IkvID3vpnClJkzkfBqi1o1e0rvvpxHOPnrGDl/qvcCLQ+LTH/E+yd1J6yGTKTMXUeSrYay6SDrvzWNzuGEKeNLP2iOhUK5OT93G1oPvIINyzd28Nx3/wTP1TKSKYstqQo3o0e889yK/EY75H3cFo7nG7l7LD7+AMyVe9FjzWP8LZm0JFmgN4vO0UcsvLxB/koUmsAQ9Zd4GLCAyzCwXMvC/rUxjFFOnI6tqLdtCOcfG6Y8+h76/l1aHXKZLMlba7W9Ft0jgeWH6ONuka5cn3PEmZ+2ZeBX41mwnYnrvu97JfaaHwurWV2XUfKZErNuxV60nSeM+7hv/O8wHOcW9iF0oVy8/4H9tTpNQPjUJMIspMSS9ij8+yZ0IeeNexwyJOb3JVa0qz9F3RoW5dKBXJgm8ORyp3HMOGQJx7PdDCPJ/jyQhb2LES2zDakzFiDJoOXsPlmcKJ9xxP1YC97fmxFjYK5SftxBtLlqUS+LktZf/WPAvRQ/PaPYUyzPKTMkIc0Rbvx/cZruL50wOOXB+iR/ufZObQejXN9RJpSdSnY4we+61KftuUcsS9Vn7qt2tGiUTXK2GYk1bsf8mGW0pTvOIPFx1wSXZ/JjwL05EsB+p8sOi6GU66XaLdhELmmVHnS8zxheHTjb0bj/83ljVf3ZO21XQRF/bUvquCoUDZc303rdf2xnVrVqF8Zy1ztT0N0y/9XpP2GIRx5cOZPH1r+z/Y0QC+WgtQ5ymHbaAGrLgW+EILGuexh78hKFM1vz9sOveg5+xJulhVxxMbEGiWe+Hjj2nywjqnda5Pjn3Y4lhzI1JOemB9PIc6rWdTBlsK50vPPIu1o/vMWjl9zwcUr0GjTl32Qv6bA05yZ/zkl8tnzwQcO1O49i7Wuv93194TxzcLvAMv7NaF8yvfJVrgiNSad4LCHWRdffM/O5pvKJSjwQUo+qdSeJvOuce+FSy+Eh+u/4ftqacmePR9pinzP9+vuWHtwmx5yesIAOqf6mGy5CpKm5wpmXHy1rxNPBZ2xHEvJ/A6k+CA3dXvPYd1DS+0TMb6xeR5l2+Lx9O85lB/GrWDn3SDLDRDBF5cxr21O8ufMyNvFOtJ67HZO3XjEI+8ggl/Wwz/iKmeXDaCqgy0ZP7al1peLLO34RCRuu35mdK2U2Nnakr72eMbtdv9tThgRERERERERERGRP0GU6ykOzP6Svm2bUq9qFSqWLEYB+5xk/iQ1KT/4gBSZCpK//kCGzN3FoXu++CXKpqMu/cLcXoXJlMWBtLZtGbjgHLee/8k+0hP/M0uYMf5bun8xkWmLjMfEGA+KuceR0T1pkToVOXIVJM/ANSy5ljg9fyLy0RG2DihLjTxp+WeuZtQcupvLQU+GkY92WsTqvpkp7JCdFOlb0vHng1x54cd3H8JPj6Nf82K8814xiteawLKrfpj3A8RZfns3iiXQf4jzmhF0zZ4T+zS5qfvdarZ5GI+xbMQUTlSwD+6eIUS+0uiuYTw+tYRxRQtQPFVq3q0zkOarH+D74iE+Ee6M06qBlCmSlxQfpKNat59YesuMoF9BXDDBns6cWDuR71o4Wm4oyORQlIKFilC4YAHssqQnV4FKNPhyOUuP38c9MCrRcRlCrnJx1Vc0LmlHxjSpSV+oLo2HLmLZnrOcPn2aUwdWsPCH7jQuU5HyRXNTOH9G0uWuRN5OS1n3uz3QjT0EnWfb981pmO19MjhUpFC/DWy5/cw4rL/jZQF6HOFeh1nTvja106cnY57c5CxRnJIl69Cmy49MWneYw5evcPbgOhb/2I2OlR1xyJSTtOnKUn/YCrZ6xPDv9i980ylAT74UoP/JbvjcY/j+ieSfWYcM40s/M+e52RM955TKtFrXn333jhMe83vv3P+/wmMiOHD/JJ02DbPUL8uE33rMm2G6WW9znvYhu8dw0/ee9VlvJkuAPv0zOhd6j1QFamD/1W72u7w4REvw5SUs6Z4DxzzGB32pLrT9eTU7Dh/h6KHDHNx3kAP7j3H08F6ObJ7IV82qYPf2h9gXrsWQzXe5b7zRxz8+xqFxdalZ3IaPM9hgU7AylZsPYuC4xSzfc5pztzzwCYwm6k+43yD+wVa2jSyHo4Md73/YjA7f7+eiufzJ6t/E3+PM5M50cUhB+rylydlvHatvmDG7P37n5lgC9DzGh1+Bxn347kAQPi82C+HnZzH3C3tssuUhTYYvGDjnAnes6+ABp8YnDtCXM/3CS8cUekb8w22WYymU245/fdCKziMO42xd96wYosJDCQwIND54Qo1rNM7yBSPW5SB7R9eiShEbUpvt7liVqi2HMGTiElbuO8OF215/0O7GF6jjCxhd2YHCGdOSs+0ofjgaag3v73BqSl/apf0Yu1yOVP5+K1vvP/OVRkRERERERERERORPEEdMWACB7je4eWgL66ePZFi3ZtSvUAjH3LbkyJmLrNlssS9QhtrDlrLyakL4GsvjXcMZ2SgVGW0K8UnNCUw+6P5cRyur2AjCQoMJ8A8mJCSS6PgY8DvEiqGtKfxhKnLlLkrrmcc48Vvvqd8E3uTaolY0rZCDf/6rKhWbLmb742jLfmKdF7G6d0YK5cnJv4p+yYAVCVNoJhL2iIcbuvF5bRveS1uOIrVHMG7NbvadOs7hfQc5uO8wBw8c4fiZzaz4uRetM2cmV+r0FOk1k1+c4y0jwP57ovC6uJLpNQtSOkNq3qnai6ZLruMR8TvJbdAlLizuRelCeXk/RX4a9P6FLa7mDOIvER9LxPUNrBzRgirFC5M3Tz4K1GpH0wGj+XnsGMaN/JI+rWpRvZADuQuUxrFOP4YtO8fV4MT1iCXy4XF2j2xCvVJ2ZDDOt41DQfI5FqNQkeIUKVSQgpVaUL7LVEZ/24DeDT8mtV0l7Dst+/0e6NG+hB6fzKCmJUn9fibyVOjEl1vuc+eVeom9LECPIczTuH6a16JGmgykz5SZnPlKUu/btWy+GkxopBmQxxMXE0lEwAXOLh5MmwJ25EiZnqyNhzNolweBz/aiSzYUoCdfCtD/RGZv7gUX11JiXhPLEO1m8GzpyT2pElknVbCE0S3W9mP33aNExrxszon/X2Z99t499kzP+YRe6NkmVyD9+NKUmteUqacW4x+R1Ngxb4ZnAvTCtbAfeZgDL4xPEo/n8VlMaZ6VQvbZ+MShGAXLVKZylepUrVqdKsbfylVqGv+uRpWKpSjkYEvad98he8FS9Fx1g6vmG310IP5X17Jo0KdUsU/Phx+mJVWG3NgXLk+5mg2p37g1rTv2pefPv7L2opel1/q/K9J5MasGZKegXU5SfNSZL0aeJOmR071wWtCD/iU+IJ1dSTK2mcfCC+bdZYH4mgF6pRI4vJuCUm0GMf1SLP5JfW+4vZpVw8tjm92BNGmb0X3SEc4/Ddr/8wA9ynmJ5Vgc7XPy/odd6TnqFK91S0akP95Oq5jfrx6Vcn3CBx+mI1WmPDgUKU/5Wma7t6H15/3pNXY1G6/4WO5qTCz6/kH2DClF1Xyf8G6xHrSfcwvvGOOEhuxj2bAm2L+XE1u7Vgxa68TNZPqBLiIiIiIiIiIiIm+I2HBCvV15cPMKF08f49jWhcwc2JR6edOQOW06Utu2pfPYozhHW/oAc2d1f4ZUep9MtkXJ0HQWs094WTf0MpHwcC2T+tUn3UepsclbjJ6Lz3AxqXgm5D4uG3rRunoe3vlnccrWnciyOxGWEULjr/4WoL9X/huGrLv77NSphvjA2zjPq0O7SulJkzk/tvnLUqpiNSpXr2n57b1KlRqWUq1GFcoWc8QhTRo+SZ2GAp0nM+38fxKgxxNwYwtL2jtSMVdq3inRicbTzvAg7Hc6Snmd5OSMNpQqmJv3U5SnyYBf2e37/MivzzNHTz3G5lEdqZErDemz5aFgh/FM3HaVK/fceezxGA+3B9x1PsKWKb1o45iJrBltyfrpSL7bdg//uEQ/ysdFEHn/MHsWfs3AFuUpkTMdqf71Ae+ntydXhfZ0HL+Ndeducmbp53xZ41+kzFsN2/4b2HY76UwtyusMx0fUo2HBDLyVtSpleixln0v4S44nwcsC9Hgi/S6yY3B9GuX+mI9TOZLHcRCj97vy4hVoXKk3NrCgQ0HK2qTiXeM8NJ1+noe/dx7+xylAT74UoP9J4uPjOfLwLG3WD7SEz0/Dc6NkNv7fDM+bru7NtlsHLHOkv4nMOc533j5Ey7X9sLGE6GWeHoN5A4B5XA1//YLDD84QFftmpovPBOiFamE/4rDxIfFigO5xdCZTGmfB0S4baYrWo3LbvgwYNJQvhwxl8MAvGTRwiFGMv4O/Zvg3P/DN8OH8PHEGmy554fE0UA7B7+JOtswbz4/DB9G/W2ua161CmdzZyJTiXd567xM+zmN8ERiwmEWnPAlO/OH4GiKcFrKyf1ZrgN6NHr8XOsf7cHVJTwaU+YB0tiXJ1GIeC86ZAXrAMwF6mXaD+eVKPIFJVefuWlZ/Uwnb7LlJk7YVPaYc49LT5vvPA/QIp8WWYzED9BQfdqXHyFO/czPAH4gPwuf8NjbOGc8PwwfSr1srmtWpRGn7rGR432j3f6UndYE61By8gmVnvJ4dcif0pnGInWlWzoF/pKxD3QF7OesXSpDzJMZ0yMN7GUqSs8YEFp/1fLWhekRERERERERERET+UJwZIFj//TLhBDitY9XwRlTJb0+qt22p/Nl4VntYBjTnwar+DCmf4kmA3mQWs46/RoB+fxUT+tYjTUozQC9O7yXnuJTUz7uhD3Dd1J82NfLx9tuFKVN3LMtuRWD+0vxMgF7uK4asufVigB5wiyu/1KJdhTSkzFaCfBXb0r7XIIZ8ZTx+4JfW39+NMmAoXw77lq+/+57vv/+RSWuOccw13trb/t8T8fAg24Y7UsPxY97J3IK6/XdzPigmyW1G39vMthElKZY3F++naESboTs4GWH2Df8DMX4EnxjF4EaFSPWPjNiXaEfv9fe4n8STYh7sYnWfUpR1SM9bGepQZ/AmzgXEPjedqcmNR6c3smr2ZCaOHc/4mUtYvOMSlz3jiDfWXRzXjo42b5OmSA2K/nyQQ65JDC0b54/HydkMMs5ZnlQpyVatLz3WuvD4laebffkc6LHBdzk5tR7tSr3Hhxmq41hvJZtvhxh1fFGMxymO/lyUOoU/5q087Wjw02Fuhvz7t0a8yRSgJ18K0P8kHiHefHdgCvlm1CHrxPJPh0DPYvzbDNMrLmrNaudtluHS32Sh0WGsu7aL2ss6kWFc6WfmQ08/rhQFZtXlmwOTuevvYn3Gm+XFAP1QEgE6BF1YzKLPs+OY2/igrzqCgeseEGh+iTHnQDeHGzFLlPEB9aqf1jE+eN45xfGdq1k6djjDOnxK7bL5sMuZlRQ5m9Dgq11cCIz9t+6eizE+yLeMKEt+B1v+9WEzOo04gFOSn3sPOTe1K13zvE/6PKWw6bGWX6+Z95f5PwnQLUO4pyB/k758dygEvyQqE35+Dr90y0POrHlJnaEng+de5L513dMAPaU1QO+98rXnQI+5t8VyLAUtQ7g35XPjWK5Y1z0jLgSfxw+57nyd23cf4Rn8ZIigF0R78/jWSY5t/5Ulo79i6GcNqFkmD7Y5s5HCpjmNv93LlVDjnFofbrZFsNNCvm1chhxv21G+zVTmXbjHmUVdGFLubT4uWJOS3+zjiKtmPxcREREREREREZE/QxzxEb743TzOhaN72b3nPOeveBP6u9mmB14HRtO6XCHSvvsuZdoOYtY1CDLW+O8azo8NUpMxZyE+qTiGifvckhhyPIb4UA9cHtzG+codHjz0IzguCnz3snxIcwql+Bgbcwj32Sc56W99SmIhd7i5pB3NK9rwj/crUL7ZXLa6WYdwTyJAf2GG7VAXHqzpQoca2XgrfXXKfLacnfeDLcFxXGTMb7+/J5EDJ5nGvoZ4v4tcmluLBmUz84+3y1Cx0XTWPYpOoo3i8Ts5ixktMlAgV1bes+lD9ykXeWRd+7vC3Li35nM61c7Ou+8XolCF75lzzo+kmpGgy1xe1JjapW1455/FqN56OusfxVg6bsUHPbL8rn3+wiXOOnnh9Xsxmd8BFnasR5l3PsCmbDParbyOcxIDBMc+PsKx8c0pmdeOd1IXp87ARWxzs9w28YpeHqDHhz/mzvK2dKv6Me9lrkiBpvNYdzMkyZsTYt1PsG94EWrlT8Vbjh2oP/4E90IVoMv/FgXof4Lw6Ai23z5E/RVdST/ut3nPzWLOg154dkN+PDKDB4Gu1me8GWLj44iNe/FNyyvUl5+OzKbQ7AZkmVT+aYhu3gxg/ruK8eG58caeJJ/7V3vVAD3u4S52jixP8by2vGvTje7Tf+/DMZrw8EjjzS+G6KhYnnQiN+fyiCIiJIxg/wBCIxLdwRZnPC48lOA7h9g9th5Vi2bn7XdLUan1HNa5RL1wN94r8T/HqbldKJHXng8+yEedXnNY/zD+uQ/9aOJ99rCiVyMqffAhWR2rUHXiEQ4+Nivsi+/Z2ZYAveAHKUlTtg21pp7Dye/5byO+XF02hEFl05ItW17SFfqaH9beTjSHzX1OjutPp4QAvedyZlz6nY9g49qKiYogIjKa6MTN73/eciyl8jmQIkVe6vQ02uXB88cSRty9rSwa3Y9P67Sly4AZrLniZ9TDaPfoKCJDjPb1DzTaPfa371Nxxn7CjOU397Htp1pUKGS2exmqfjaPze4xxhYTxBNttOeuYfWpn+Uj8tZtTddfljK1S0NaZf0X9pXb0HvDI+7qs05ERERERERERET+JPEBN3Ba3IRO9XKT2eZTGnRcwvZ7QSTZPSn8BjfXDKR2yfx8+N6HlGv7JXOuYwmq45xmMbtXQbJmdOCTrO3o88sprj6fYIbe59HOMQzv257qdQcyfOIhrkaaY8Df4sSYbrT6OBXZcxWk4MBVLHd+sdtS+MODbPyyFNXyfcy7OZtTe8g+zgXFW36LjXZ6hQA92o+wk5Po16wE777rSNHqo1noFJj0b+Mx0YQHhhEZHkFMTNyzYWycGbY/+Y3ZWPVqolzxPjCCTnWK8OE7aXGs0pFvt7vx4PkEPe4uFxYO5gv79NhlsSFNswl8v9eL3zL9JxlAZEQkkdGJ6hXqyt1VHfi8Zlb+9X5+HMsOZvLhx3haVz/D5wwnZ9anRomcvPNOSaq2nc1693jLOY+5NI/lAx0pWaIcDhV/ZPS6O/g9edZvot3x3jearrWK8uHbdhSv9y2zLgTg/UJbhPBo7yTG1spB7uw5SVF2IANWOuP7/M//f+jlAToxQUSeHMe3bQrz0Yf22Bfvzo/b7nD/hf1E4H1uOTPqF6BUptS8W6knzRdf4/HvzUX/P04BevKlAP1P8CDAlS/3jiP/zDqWwNwSnk+qaBn23GZKZdpvHML5x07Exr85gbN/RBBbbx5g/fXdhEW9+DF9yeM6Pbd9T87JlS3BuTmPu3lcGSeUwXZqVb49MMVy3G+aVw3QibzBtbX9aVwgN5+kyEPFThNZ4BSSKGg1BeB5aDqThneiYcO+9Bqwkp13gjDvV/M4tZa5XTrSrUVrPhu1ilVOzwXJkTe5PL0+tYpn559vVaBa84Vs9DC+DFhXv5ZYLx4Y7d2jSB7sPkpN5vKd6Dj7IlcTv3QibnN5URe6VLDnk7dtyVuyO1/vfsg9y/cfb+OzejbfVClJodTpSZm1CLlrD2fCfvdE85NEEHZ1JZO/qEnhDCnJbHyByt15KQvPhyYaUuYepyf0o0ua1GTPkZ80Lacy8WQSt7vFhxJ5dy9rJwxj8PCZzN/vyuOE72HGsTw0jqVXsbzGsaQhS6nPaD/xBBcTf8uKNIf46cBnFXLwfuqyFPt0BmtuBBk1NL4IHP2V2Z+3p2vLtrQfvY61154b8CbMmbOTalO1SHbefqsyNdsYX0R9Yp8J6OOjvXFf15d+NbORMZ8jBWvWMh7vSN4Pi1Kh0SRW3Akn6FW/kImIiIiIiIiIiIi8TPgDXDd0pkmZzLz1VjrS525Aw2/XsM0p6NlRS6PcuL1nMpM6lKKwOQ3mvwpRs9MM1ntaexMHH2LnhDaUzpCNzKnzkb/lWMYc9kz0u3YcodfWsaRzKcrkzsw/8rSl2ehj3I8xw8sI3Dd/x8iqqcmeLRdpCnSk54xTOCf+0Tr0BpdWDKRVuVzkSPshOWv1p9/ax3hZKxnptIhVLwvQ46PAZz8LvmhI0Xc+xsaxIS2nHeWUj3V9Ar+zHFvyLV3bdaR1q56M+fU4F59mkBH4XVjNqpH9GfTNL6w445FEcJyUKKLc9zKvey3Kpn+fjLYlKNNyCnOPuiXqJe7Do70/M7JlEfKnyUrWLCWp/dN2tngmOhMht7i0YRI/DvmaH+Yd46Rr5JPOXLF+BB0bydAmRUmfIiM2+WvT5IedHHB9PoMw9rFvOj82LEQhm0y8k74h9ftt5XSosQljbdy91aweUpxPPk7LWynyU7rtz8w46IJnws/d8Y+4e3AWY9tUpKhNRv5p9ym1vtrFRd/YRCG/le9Jto1rT+ks6cmWpRAVh65l1d1X73v+xCsE6Oaeg46ydmgLKnyYhmw5ilOy6yx+OeX9zM0Rcfd3s2VUC2rmyUXmVDko1GEc486FEpJMf3NXgJ58KUD/D0XHRrP7zlGqL+lA5gnlnvbWNv9mHF+Gakvas+zyJiLeoKHbzfB86ZVN1F3RhQYru7HaeTtBkc++uKPjYlhzdSdVFrczjqUiWSaUtxxX5onlyDqxAnWWd2bD9d3EGI97k7xygG68pQff2smSnjUpmTklqR3KU63HeMYs2MDazTvYtnkda+f+xIi2xcif7i3eer8oJdqvZPvdEONjIgrvI7P5uVwa0r9rrMtVk1r9ZjBv9TY2bt3Bjm3rWT3nOwY3LkC+rB/znk1rWo46xfWwxEOJv44Ywh6f4MDXTfnUPgPvpMpHrio96T9uMb9u2MyGjetYNOVLelW3I/cn7/JBzlrUH7KNI26R1sDeC29LgF6K/B9nIG++wlSr1pJ6n//A8HGz+WX+QubPmcDYvg2pXjQH73+YhYxFW9Lt18s4BVg2YOXH7eVDGFL4IzJnzkXqos1p3u9HJq/cwq8XvfC0fkuL8rlknIN2tC6WibQZHKnefSprb4ZZQ+lYwo1jOfRtMxo7ZOTdFA7kLPM5vcbMY9H69fy6dg0Lxg+jVw178qb5Bx86GI/76aJRD/M6C+Pxvin8UCoVad95m7fs61J3wCwWrN3OJku7r+PXmV/Tv2FecmdNzXu2HWg39ix3op6bNyc+kphb85n0RWVypstOxkyZyJw1Kx/afUbD4UdxDn3uTkcRERERERERERGR/0R8GDEuh1g+vCnlbVOSIl02UhdoSNPeY5g0ZyHzzd9o589j5sRvGdimCuVzpiJj6rSkL9KDfnMvczfa2IRlQ964Hl3AT40KYZ8xDW9lq0iZtj8yYf5K1m1czapVsxk3oCV17NKRMcUnZG00ih8Oe1qGDTdFue1n75RPqZ07I2nfsyF/1S70mbKMpRs2s2ndKtZMHkTvevnInD4D72QtT4MRa9jjFv/0d+2IVwnQLTy5s24U31TISfb0OchWvhOdf5zP8jUb2bp1M9vWrWD5z11oUzYD773zIe/aNqPPgrNct/zGbBys7wk2jG5NuZxp+SRXFSoM3WyZb/tVfreNj/Xg/tphDK2ck7TvZyJ1pnLU6PQtIxPaefqPfNWqNKVyvEuKT+yxqTacqXsf4ZHQQTo+BI/DUxjfIq/RxtmwLfsZ36y9xB3Lj+3mzQFn2TW6PfUdUpHyk+xkLNGWDsNmsHDlOjZt3cLW9StZOe9HvvysBsXSpydtypzkqvMjP2x2wSdhHxGPuLtnKn1r5SXHJx/yUa4ylG87nG8n/mLUcT7zp37N4LYVccz4CSlTF6Bwp+nMPOPDi6Ogh+G/dzQ/Nc7LRxnykK50D37YcoNHrx3bvEqAbgrg0e5pTKibH9t0GUmRozLVPv+B0U+v4ZlMHtKa5iUykPaTjHxc8MkoCddCzFs7kicF6MmXAvT/0ONgL8Yfn2/pfW7OEW4Jz42S0Pv8y71jcQlwtz76r+cXHsgKpy1UX9qBtGNLkG1ieWov72QJy4Min/2ou+vnwrcHp5BvZu2nPevN4enNmwPMXujmnO/eoS8MLPKXio98yMmJzWhj9xb/sKtApq/2s+vB78TW0f74nZ7DmN71Ke5gQ9b0xht61vzYOZamuGMecmf9hHQZbMiWuzR1+kxk1mk//BPuAPO5woVlPejUoAT2WTKSIUM2MtiXIH9R44O3eF4cbLORJqMtuQpWpOZQ4wPvQjDRCR+O/xbj+bfXsmBIG2rlt8c2UwbSpMmOTd7C5CuQx6j7J6TJYIdNPuNLzaDpzL8YTMjTw34SoH9duSS5U6SmcpM2fDV2Ct0b1KRYprSkTpWalKnSGB/ExjYy2ZKtREuaDFvHgQdBL9zNFuK0glXDylKhYDbSp0nHx++/wz9yVsBu0G72PnjSOGEP9rNxQHHK5/qIt/6Rnvw1ujP5lD+eTz8hg4m6s46FQ58ci13m9KROk5GMefKT08GOjB+nJ80nduTMV5YmQ+ew3Cn66d1p8V7nObu4G+3rFDeeZ7a78QXFviQFipntngc726xGO9hiV7gytb9ewqJLIUnctGAsCT3PtpHtqJ7xY9KlTMVHKdOQts4Qem71wJLVi4iIiIiIiIiIiPypogk6tYgFg+tRoXgBbDNnJF3qtHyUKi2pPraWVGlIlS4bGXMVoViVJnSaup3dLtanJwi5y61tI+jbsjIFbbKRKXV60qbLgUMBO2xsMpH64wyky1GEguU6MGT+Uc4/E15G4Xd9B0v7f0qjEvZky5CeNOmzkSVvERzz5CJXpk9InT4HNkVrUbH3AuYf93xmVNWIy3NZ1uUD7LKm463CA+mz4oZlbvYkBZ/n9NJhdKpcgkI5Mhr7yUImW0eKFjOOLXd2MmfITJrMDjhWa81nkw9z9GHCD7OR8HAt43tWJ/377/DhJw5kbD6Lacd9XzGENR7le4x9M/vSumxhCubMxCdpE9o5DSk/TkfKtEYb58xP8fpf0HP+RS57JfrxPs6PG8t706tcSj78IA0fZS5Ohym7OfX0QOMJu7iG1d81p3b5Ijhkz0LalBnJkC0vBYsWpVjeHGT7xNjXJ9nJnrccpWv35qulJ7jw/J0GfjdxXtKfno1Lk9smO5mMOn5sqV9aUqZMS+p0WcmWvyLFm/3EpG03eL4DP3GRRD0+yOoB1amc5i0+ylWBIkN3sPvBs+PsvhpvfM7OYFjZAmR/6y2yVG5Pr40euLwQoBsirnFt00j61atESftsxnlMZ1y3Rp3N9rXkDFlInzUvjpWa0HL0LnbeenGagOREAXrypQD9P3Ta9RKfbxpKrqlVLcObmyGzZa7wyRWpurgdq5y2ER//am/r/20BkcFPwvMl7clq1DHThLJkNkrOKZWos7wTK69sJSw6wnj7f/JhERsXw47bh6ixtINxTM/2rjcD9fYbhnDO3fmNmgs9PtKVc7M78UXJVGQo2YACo45w4NEf1C8mBL+r21k1ogNNiufDPpcDOe3z4WBvfNnIlZfcJVvy2Y9r2XE9gJDEwWp8DDGBd7m2ZQqjPqtCWfPLhW0ebB3ykdvBnpy2BclT7jM6/byF3TcSnhtHbFQUURHmvC2RlhL5OyXCeIz5Nzo2UW/o+DCC7x1m97RedK3uSF47O7LnyoOd8TdnrvzkLWPsb/Qm9twMfO5ONGuAXqkEDu9+QPmOQ5l+7D7H1xl1b1GGwjmND/LsNmS3yY99pU50mrSbwy5hRCZx2cZHB+B+dikz+1SnamE7cmQ2vnzYV8Wxw6+sv/HkG0Rc4E2uLutOu2r5SJOzDvUGrGKPu3E8lrVW8eEE3z9iOZZuNQoZx2JLtly5jTa0JYfxxSVvmXZ8PmYje28HEZb4WOKjiQ64jdPGifzQphJlcpvtnhc7a7vnML6A5a3YkW7jtrPvVuCz5+wZIdza9CPf1slFfrtcZMuRh1oDZrL0Vtyz9RQRERERERERERH5s0QH43drH1um9qNHzRIUts1Ftpy25LCxI6fxN2eOnOQoWIVKnccxcbsTN/wjiHyhY1YcceEeuBxdzIxeDamW3w6bnPbksnMgl40N2fOWoXTbHxm10ZnrAVEvDvkdG07IvcPsmt6fjtWLkTuH8XzbPNjb2ZPTxp58FVvSccwWdl4PIPS57DPCeSlrB+SkZKHcpKr6LcPX3/6dHuimGKKD7uC8bRKjO1ambH5bstkY+3HIg4Oxr2z2pSndfDijVp/lilcUUb/9EG7s6Cr7pvaked5M5C1cjXpj9rH3j37nf0EUkX7OXF4/im9bl6dEXluyZDd/e85FdqOtsuWtTJUOY5m67Sb3AmKxjHD/VDQh5+YyrXspbO2KY19lOON23cEj8e/lMeGEP77MmV9/ZFiLShR3sCO7TW7j2PJaji1nNnvyFm9Aq6FzWXrkIS6h0S+eB/M8Bt7l+u6ZjOlSh8r5cpHLUj+jnbLYkbtYXZp+OZ/lp9zwjEzi2CO9CDgynsHNy5I5dQ6K1fiCH/cY18Xv/ib+R3zxu7SAkXUrUiRVWhwb9WLYDi/ckgrQiSU2zM24/pYwZ1gj6pR2wCa7Ue8cuchhlOz25Snb9DtGLD/NpcfG9fs6p+1/kAL05EsB+n8gOjaG5Vc2U2ZBC0tgbobSZsBsDt1uM7UqA3f9zDXvu9ZH/7UCI0NYdmWTped5lgnlyGTUMaFHuRmkm6X+iq6svbrT+FD87Q6lu/4u9NsxirzTa1kek8M4TvM5ZoBeaVFbFlxYS0jUG3QO48Lxv3eeiwe3sP3gCfbe8ME7/IVvGM8xvjA8cubCob3s3LSR9WvWsXbNRjZs2sWeQxdxdg39/bnLQz1wczrJkV3b2Lp+vfHc9cZzNxnP3cPeo1e46pYQHMcTE3yNoxNG8kPbDnTq9gVduveg2++UTh078kXv/kzf5cyNZ24YMz74vW9y7eQ+9m/bxMa1xv7WbmTj5t3sPXKZq+7hSQTAiQP09yndfhizr8YTEPwYtytH2b91MxvXb2b9lj3sOObM1cfhSXyYJxIVgOeNE5zcv4MtG4z22riXXcfv4xJo/TYVF0GEp9me29m07TjHrvni/+w3EKvEx7KZTWs3sO65Y/ndiQ9C3Hl05QRHdprtvsHa7sZxbN7L/hPOXHOPTDR3e9IivW9z58wu9u3exY6dezh99QGexg5fdrWIiIiIiIiIiIiI/PuiifC8xY3Thzm4YyvbNm5i43qzbGbL1u3s2H+CE1fdefyynj4xfnjfPMeJPTvYunEj683fV9dvY/Puoxy94pLE8NuJxRDudZurpw6ye+tWNprPXbOBDZu2s+fYJZzdwkiq33BskLHdKzs5uG8PW45c46qbOeXpS0R44Hb1JMd3b2HrhvWW/axdu40t2w9z7NIDHgUl9YtsJEEPL3PBeM62Xcc5fS/g3xs5NOwRDy4d5cD2LWxcZ7bxRks7b9pxhGNO7k+nJX1BmDsPLx9g19Zd7Dh2l7t+UUmMdGow2uP+pRMc2bONbZue/E69bo2xn0072XP4HJfv+RD4sv6VEV48vnbaaJ+dbN+w2ajnRjYZ52HXwbNcuOtLks1jig0j0u0yp4/uY/2m3Rw+4YRLUNxLfxdPWhRRAQ+4cfww+7dsZd9J4xrwiCT8j8LvOH987p/nzOEd7Nxitqtx3Bu2sdVs24sPeRhofVwypwA9+VKA/h/wCfNn1JGZlt7nZq/z7NZw2ZwLvdjcxiy5tNHSo/uvZs55bgb9NZd2tATfZm9yMzxP6E1u9pw3699iTV9Lj/PEdQ6JCmP55S2WudDNIerNGwXM4zTnQs87oxaD94zGLcjD+mj5fbHG5+Bu5lZwJO9bb/HWq5R3P6L26B3sf2Ye8n9H4gA9BaXaDmTG5Xj+Jp9fIiIiIiIiIiIiIiIifzoF6MmXAvT/gJPnTXpu/94SPieE0ua/zbnPm6zqxbGH56yP/Ov4hQdYwvPqSzpYesYnDMOeEJ6bPdHN+cxbre3HztuHiXtuuHlz+PnLHjf4bMNgS9CebdKTAN2c493822b9AK4Y6+Vl4ogKvMjur/rSq1J1atX/lLoNGlH/d0qtGjVo2LQVP6w7x+X/+CXyfIA+iBmXY/G3rhUREREREREREREREZHXowA9+VKA/m+KiYu19NZuvKqXpTd24uHb882ozYBdP3PL57710X+NgIggVlzZQrUlnxn1q2DpGZ8Qnpvhtzkke64pVS09z/fcPUZkTFKDsoBXiA/D9o3Hdmo1S+hu9rJPGMa91rJOludGx/57A4P8ncTHRRLm442Xqxtu7u6W4v47xc3t/9i7D7CoskRd2HPv/9x7T5w5k86EM3nmnJkzsSf0dLRz7rbNOQsCEkUkKTlJEhBUwICAipgBEwqKApJzzjnnUKQKfP/aRYGA2LZOd4/N+d6ZemirdlXtvfbaofa311pNaG5pQdfgyJxjkT+ednSkHYDVS8/gp//r/+BPq3Zgf64SPeyrnIiIiIiIiIiIiIiI6IkwQJ+/GKA/oRHFKA5nncbLx1arW59Ptuz+lvuzePbwUgRmRqi7eP976R0dQFjeJbwVtlEd8Eutx6fCc0038z8U/73qrAniqpMxLJbnYUaVYwhID8dfg5eoW55PLus3xbI+d3g5QvMuok98Hz2t2tB+zxemf/wlvvOVr+C/F22Hd7YS3QzQiYiIiIiIiIiIiIiInggD9PmLAfoT6h8dgHtSEP4QuFDd+lzdMtvnFXzb41m8E74ZsZWJGFPO3aL78yaNeR6WL4Xnk2OeT7SOlx7qbtu9XlD/95pzO8R8JkE5q9v22aScNbosHh+f0sF/7HsJ3xcPdYAulvUPhxbC+95RtAy0T0xMT6EBDNYlIsbXAw7GO+B5/ALuNKkwxACdiIiIiIiIiIiIiIjoiTBAn78YoD+hdlkXrOO98cuAd6daZUvjg3/P6wV1l+iZzQWaKb9YXZoxz98I3fDQMc9/7veGeszzG5WJmnc9WnpTAbSirdU3C0gt2qXPk8L5Xx94H1Zx3qjsrtdMSURERERERERERERERDS/MUCfvxigP6H6vhboX3HAj3xfVQfTUrfo6r8+C7A5ygKlXdWaKb84vaP9CNeE5+qg+xPGPL/5CWOez6WsqwamsW5iGSduEpA+U/q8/9r/NrZF70F+W5lmSiIiIiIiIiIiIiIiIqL5jQH6/MUA/QlJLa61ondPa33+sjq0/k//t2By3QX1fc2aKb8YfSMDCM29iDfDNqhbiE920y49JkLvF/HDfa9g9bkdiKu+px7D/XE0DbTD4U4Afub3hnrM94lx1F9Qt2Zff2Hn363FPREREREREREREREREdEXjQH6/MUA/QmMi//lt5dhw8Vd6rB6eoD+3wHvwSrOE839bZqpH076nMqeemS3FGFYPqJ59vF1j/QhPO8S3grdiH/3eG5qjPKJ8HxizPMfi/+Wxjy/UfXoMc/n0j7UDffkIPyX/9szAvSf+b2O5WcMkVSfqZmSiIiIiIiIiIiIiIiIaH5jgD5/MUB/AqpxFdKa87DinBG+4/W8pvv2BeoA/VcB78Lipgea+ls1U89N+oy81hJYxXtja7S1ejzyMaVc8+qnJ415Hp5/Ca+Hrp9zzHPpOfWY5+dMEPsYY57P1ibrgltSoLqF/WSALv39ie+r+PDUNsTV3NNMSURERERERERERERERDS/MUCfvxigP4HJAH3lOeMnCtBVGEdRRwXMYvfiF/vfEo83se7CTtyty9BM8en0jvQjPC8Krx9frxnz/H637T8Uj4kxz1/DyrPG6m7bH2fM89k+OUDXQVxNimZKIiIiIiIiIiIiIiIiovmNAfr8xQD9CUhdrxe0l2PDRfMn6MJ9HPltZdgZ64ZfB7yLb7j/Bd/2fA4/3/8GNl+yQEJtuma6TyaF5+oxz0M3qIPz6WOeq7tXl0Ju8d/SmOfx1Sl/U3gukbpw3/vQLtyNkNyQpZmSiIiIiIiIiIiIiIiIaH5jgD5/MUB/QpXd9dCO3o3/0ITnkwG61Jrc+Loz6vuaNVPeJ409nt1SKF53wn/ufwvf9nhW09W6FHi/gB/7vqoO0e/WZUKpUmre9SBpzPOwPCk834h/9/yrmIeZY55/x0v6rFew9pypeszz8fFxzTufXNNAGxwS/NWB+fQA/ed+b2L9BTNkNRdqpiQiIiIiIiIiIiIiIiKa3xigz18M0J9QQ18LDK44qENvKbSe6MZd/PVZgE2XLFDaWa2Z8j65UoELpbF4K3wjvu/9krr796lW4+J9E4H069h40RzJ9Vlzjok+Oeb5G8fX47vSmOfe08c8X6AOz6Uxz1efM8HNqmTNu/52pZ012HHdVf0dk13FS63epRbpOjF7UNBWrpmSiIiIiIiIiIiIiIiIaH5jgD5/MUB/Qh2ybuyO36ce8/x+K/SJcHn5GUNkNOWru3qfTmoJ3jLYjuCsSLx8bLU6gP6PGQH4y+K55/Ff/m9h00VzJNZlat45Qeq2PUwKz0PXi2lnjnk+GWhPjnn+WXTbPl1aYx62RllNjLXu/aL6+77t+Vf85sD76nKo6q7XTElEREREREREREREREQ0vzFAn78YoD+h/tFBuCcF45nAj9Wh8kTr81fU3bK/HbYJ1yruYFQ5d4DdMdSNo9ln8WrIWnxLTC+9dzIEnwzRpa7St0ZZI6UhR/0emXwYx3Mv4I05xjyf6AJ+olv1NedNEVd97zMNz6UbAS6V3sTCU9vU3cV/X9Nl/DfFvP8hcCH2pRxDy0C7ZmoiIiIiIiIiIiIiIiKi+Y0B+vzFAP0JjShGcSTrDBaErFGPCT4Zgn/L/Vn8JXgJDqafVLdSf5ju4V7x/kgsOLYa3/F8Tj0G+v1A/GV1IC51xa532RYXSm4gOOs03j2xZc4xz6XA/ce+r2HtuZ3qbttnt3z/W0nLuj8tDH8JWqxpbb9A/d3fFMv6/JEVkLqU7x8d0ExNRERERERERERERERENL8xQJ+/GKA/IaVKheuVd7E00kAddkut0KVQ+Tuez6u7dTe+5oyyOcZBn65nuE8dwr98bJU6hP+e10TX6NJDas0ufeYv/d/Gs4eX4neHPsRPNOOtT04zc8zzHbhZdU/zyZ+ttsEOmN90x0/9Xld/v9TSXXr8u+dzeDd8K+KqkyFXKTRTExEREREREREREREREc1vDNDnLwbof4Oi9goYXHHA971fUgfgUqgt/fePfV/Fogg93K1N10z5cFIr9WPZZ9Vjokvh+/SAXAqp1V2mi89Uh/TTXpMeUqv1n/i+hlVnTRBffQ8jn2G37ZNU4yrktBRj7fmd+I6XNH8L8EMfab6kLusXYN0FUxS0lWmmfhqNq8een3honvpSEvOvHMPI8ChGR5Wa5+jJTKsTmmeeHmKOVHKMjoxiZETx+c7f1HbxNJbDU2jelJemjon9yIg4ZHxZl0Ull2NMnEeMyZVQsgITERERERER0ZfM1HUm9UPz5NNgXAnF2ChksjHI5SrNk/TZm7b+Nc88tqn682k/43GnF6a+Q/PvR5maftZD8/Jc7k+neeJLhgH6/MUA/W/QNdwL17uH1OOVSyG3FChLobc0RvkfgxYhJOc8ZGNDmqkfrlPWow7RXwlZg295SF20TwvRxWdOtvqeek76Du8X1a+tObcD8TUpGHvIeOt/q4FRGUJzL4p5W4tvi3mbWkbx/f8d8K66ZXrzQJtm6qfIcBNaEk/jpI8r7PbYYJfVHtg4++LI2WQUtozib4mgB4tjkH7jFK4V9Ig6oHny8yZrRvPtAwhwcoNfRCoqxfc+LacvQ825yD0XjIs3c5H3iKHwR2uTUHDzGGIyGlDXp3nyizJQjdq4cIR4usBGXSdsYOd2AKHRmSjvVj75icpnaawXvekhCNnrBPcj8cjvVUGueekzMyTK4VYYQtwcsGe3Dcyt7WC9NxDBlzNR0vmUlMNTRNVditxLh3Bwrx12794N892OsPU4jhM3y1Df/7dshWPozL2G1PMncLesHx2fzy78AarhVtTEin2JnQ/8TuahRv7JJ7BfHDmG2nORejQIV65ko7hfOqV/iJEalN08Aj9nDxyILkXJw0dLISIiIiIiIiJ6OowPQlEbhxOBztDV2w49HV3o6G6HtpEVjG284XviGu5UD2NUM/njGwOGipF0/CrOHs5E7ajyEdfApSsvnSiLvobLvteQ0SqHOqlpvoM7wVYw3RGA0MQGdEjP0WdnvBf1987giNNO6G3eCi29XdixLxqxZX2fPrMYrkbxtUDsMxP1SHsbNukYw3z/BUQXD0m1YA596CmMxmmPnTDR08HGTdthZHMQIfGVaHvINUlFdylSI/fB2dQAWpu2YKvhHuwJvonEZsWD3zHegab0Cwi0NoOx1jZo62xX1+1t23SgrbUVu/zO4lLpGEZmXEoVn1NxE5f998BcZzO2aBtAx9wPAZfLUNWvmeRLggH6/MUA/W+gUClxqiAGLx5bqQ6Wp3fjLrUMl7pxL2wv10z9yaTu3KVxzqUx0aUxzaePiT79MTnmufT5UqvwiTHPPz+V3XXqVvb/5f+2+saAH2kCdKn79teOr8Px3Auf6iaBL4woDFVnMfJi/OFnZQwzI1MYmeyCsYkJjPV0YbrdHHYHYnGjrBtPmn13XrVGqMsW2Ec1oOaLCoEH69F01RUuxrvgGHQbpZMB+kgjKjJzcPN2FZpls6PWEfSUpiL3XhpyGkYx/Dk1XO/JP4ezhh9hh9s5XHxEZwQDKYcQ7bYM1ieykdasefLzJrZTeZv4vrPe8NplhJ3GO0WdEAdzY1EndHVgargbTkfvIrG2/yEnGF+g0W70Ju6Hn9kOWHnFIKtbzLvmpb/Z+AgU7YVIPesLbwsD7NTfIbYLUQ6mpjDcrgt9oz1wDI5DWm2fqDkkbT+D1cm4d9QNriaG2GEkysvUHEai3ujrGYr64wz/synIax+D6ol2wkOoOmePo8Yb4H+zBRVf0G5UNdSE6gtOcNHfA7tDaaieDND7qlCbm4K4tDo09c2udUPoKEhH3s1klLSOQfa5HHSG0V12FseWfIA9e07jWsvEz7g5jVSgMMobDibmcI3IR36X5vnPi7Ib3ZUFiI8tQnHDgDi9JyIiIiIiIiJ6TKo2DKe4Y82br+GbP30NH6xZjzXrN2HtRl1s2qYHne3GMLINRkRyLRqe6OLcENBzDYdW28Lg45NIHZA/4rqidOWlCjct7LH7JTuEF8omwvKm67jhpYMNa11x4GYNA/TPkGqgHg0JAfCz1sW6VRuweuU6rF6+HB8u2YotHhdwsXLgEZmFHMq+KmRH+MJZZx3WrFqP1Ws3Ys3alViyagt0HY8huqgL3dMvcqt60HT3OEJ2rceqpcuxeOVarBQP6bu37NwL9ytlKOmefrVrHMONyUg6YYddOpuxeoX4/NXrsHbNcqzYaAwdjwRcLeubOZ+jOUg6ugsLfv8GnnttEVZsEvVaqt8r12DV8hXQdTuB04XTAnRlDzoLruL4nm3YtGwJlixbjdXiO1au3owN22zgHZmEwr7xv6kR5BeJAfr8xQD9b5TRlI+tUdbqVuhScC6F3FJr9B/4LFC3KD+RHwXV+Kfb1DuHe3A4O1Idokth/PQx0SfC88kxz99UtzyP+5zGPJ8kV8oRUx6P10PXq8Pzye7lpb9SgL7pkoW6e3eV6mlpCz2OcXkvWqIc4Ka3Duv2nMCFzDq09g5hTNaLzpJbiPM3wZYV+jD1uYliUfWeZM67bjjglKc+9l5tQcOTpvCPa1wOVX8rmirr0dTUdz/AaYvFSXsnLN96GvHNsxO4DpQe3wWfXebwutn1ubVwlZVEI2rXSlh5RyGmQvPkQwymH8FV7w1wiMxHdqfmyc/VOFSyJtRGmGP3tk3Y7HwWsYXN6JDqxGA32nKvINp9O9Yt3wGbI/dQLc4qP7ts8Ak+aVyBcVk7Wqvq0FDfjdEnS2bnpOqvQsNFW+zU0sNmh0jEFrWjq1eG0aFudOXFINplOzYt14XTqXSU/o9P0MWeYbQYSX4WsFyhA8sDcUgq6UCfTI7h3mY0ZZxD5O4N2KLlCKdzVehUPEkr7mHURLkhzEIHQQkdqPuiUlmVHOM9DWiqbkBty9D9fWDlBVzw3oUVe67gZuXs43I9sg5ZwGu1MY4kDaDls6uW0ygxUB2FE+uWw8npPG62fdIWpMBQdyuaKyrQ0inD0Od9CBrOR1GEl/jBEICAm7XSz1EiIiIiIiIiosejasFQii/WvWWCD/QuILWnHz0DAxjsbEVHVSpuhTjA9KPX8IFhIA5kjTxBo5phoPcmjm5xgdnqs8h65AUM6cpLDW7busHxbVecLhqYCMtVfRhsrUVlSQs6+8Y+w2ulT7vPe0nH0J5yAoe3vIO1xl7wi69Hu2wEAw3ZSDm2A+vXbMMKx+vI7lI8PDgea0T7HTeYrFiP5frBuFDUhfZBUVf6qpB6bBfM13yE5V63cLNOMzTo+BDkTVcQqLcMi97dDqvTecht7sOIrBN1iWEIst6IdzZ6wPNqFSY62hTvUrYh77AxrFctxBavq7he1INBuRzDDXeReNgMy9/YDn2fBBSMTSuxrjjEHLLFc5uOYv+tCnQOy9DX3YPezh70dHSju28Yg1PTqyCvu4Jrrmvx5vvboXcwHilNwxjqbUN7QQxOWy/B5i07YHmpAU2f+0W/zwYD9PmLAfrfqHWwA/vuHcNvDryv7uJcCph/KB5SN+w/Fn93xrqhuqdBM/Wjtcm6cCT7DF4+tmrGmOjSZ04f8/xWTSpGFE/eocunUd5VA6t4b/wq4F11YD4Z4kvz9Iv9b8I+YT86h3o0Uz8NlFDIcpBgsxEOeg44lNyHjhk3Tw1hsCEe5+wM4elwGHF14rRCvQ8eg0IaT1faic84Top/SK2XR+WQy+8ftrpuOiHC2wge11tQPyCeGFdgtF+GYdkwxhQzPmCCSi7+P4YxKZwVL4+L/xjtHxbTiwPZ9MmV4nmxXcgGRjE6NsfBYVyJsREF5GOaNynFtlMeiUDTHXh/1UFcKGlFr3hazPLEy4M1yPLTg6OuPmzPV6OyTdQXlerB1rLSMg4NYXhwCEPD4js+8bgk3aQwhqFBMZ9DE6dxfcUxiLZcjd0+0bhcqX7qoQYzjuGazyY4n81Hjrq7d3HAFN8rffeoXHX/oCtRjYqyH5koi9nzLBmXQzkmXhdlonpo0CwO7u13cd10FWxN9iEkewAD0ydV9qO7LAYnLPTh7XUaSa3SO2ZRifUrrZd+8V1zNeMXK1EpKo9CrjkxkQ+jv6kZHa1dGBSzfr84lWLdD4kTlCFRdmI9aj5qXPPe++M4izIZFa8/bKx7uSiXIRlkslEMi1X6sCWfbaj6Du44roWJzWEE3uvH8IxyGERnbhTO2unC7dAV3KzVPD+DWM6REYwMysRJgRwjD5k9pdheFCNjUM5abdI4NsrRiWVVl4lU+cfEsojyGJLq3tRDbBviMSJOIIdGRVncL0AN8X5RjlKdkQ2M3d8ePkvj4mSn+jx89M2xSe84bjaJ9aZ5SU3Rgb6M4wgwd8cejzsoFoWhLg6xbHJpGWcPyi0tq9hu5GOiLk2V2zBqot1xwkoPh++0o1a6GUcplnlAnDAOiro9R/mOKxRQiM+XylaqJ1L9l6nLSfxr6iulchUnzjJRR+ZaT9K8KEW5jykhqtkEpQJjOcdxzF4X7xudxNmsZnVIPFH2Yl4U+bjtoQv7hdrwjWlGmdTie44bp8YVY2K/JtZLvyivEfE+zfNzm749SP8eQ29VNE5tXAkXlwuI+8QAXfousfxDI2LexD5t4gmxXkbFflwlyl89yX3jYlpRdtL+U/XAgOli+tFRUe4T9W/sgS7txb/EPiT1kDUWfuAI1/O5kDrPkPbnD59B8RliBY6Jla0upun7WPGc+IpPMC6OF2J+xDYwJNXv2TsklVj/0n5x8rOnU4q6J5ZFKoMZ1PVPPC/eI6ogEREREREREf09qFogS92P9W9bY6V5Amo0T0+QY6Q1B/lHt2PpUgus25eFOs0rGBmFfHgUCvFzf/olBelam2JkRH29aeJSyAjQG4djWnthse48smVj4nkFVLIBDAyMYEi8PPOShPSvGiTYu8P5vb2ILO7XtDaXrmsoMDI813UUQSWeHxrEUL8MA0NKjH3yBaAHjSsxPizD0MAgBqTrrZ94MVq6TjICmfiuwb5BDI9+QrgsjMvlGB0YmJh+RDXnNRjpmpJCFIZCXaBjGO3rREtNO3rFvMyYE4V03XIAgwNDGBwen3bt9gmN5CM50Alr3zSH09k8VExdbBTz05uICPP12PqRPvYndqLpIddvVK2pyDz0Lt7Ws4Lh2bqJLvc1lPVXccXbCC9uCYVfYttEOcnq0HDVDGu1jbDG8w7K+6dd95J3ozb5KDxWLsQu5wjcUF+rF188kIiTulrQWWiDY3kDuD9yohyDVdE4vvktmFrsxxlRgacuW1WdxUmvnXhh52VcqHhUZjWI2ku28Nn4HjbvS0BsnajbmlcwPoD+whD4Gpli6eYIxNYOfCl6SmWAPn8xQP8bycXBJK46GW+Hb8b3vV+c0UpbCsBfPb4Wx3LOYUj+6Zsqt8u6cDDjpDpEl4Jr6XO/q+4+/RV1t+3x1dKY5w/EfJ8p6fMjCmOw4Nga9bJIreql75fGPpdax78TvgXni2OhnExrnwpyjA3cwUW9lXAwOIArTZqnZxhGX2kayrPyUdsj3iElEHW3cOtmDIKSWkXZTztMqgYx3ngP5y/ewsnb9w9IXTeccPagKTyispAQcQSRvs5wcnSBu50jbFyOIzK5Bh3Tj7bNGchJvoQDsSm4c+YoLh9whp29A3bv8YRLSBLSy8vQVBCNk94e2OdgD0cbO9h6Hkd4Yj1aJr9U2YfR2mScvpSIi5ndUMj7MVx0EVd9DbFp8WK89cFGmNi6wfVMKhLrejHWU4KcY75wX7sIK5Yuw4rtYh5dQnEzowatU/M2hO7MGFz0cYStvQv2ujjDcY8dXA6cR2zRwIOtOrvzkX3pEPbZ24v5d4OHozP8T8YgPCIMZy3XwDEg5tEBenoIbhzUgtu5JFw7dxaXA5zg4uQGd1Eee+wDceRKAeomj4pjpci9ch4hh24iq1GcIGmenqBEf+5FXI84imO3RTkNPuwMZhSDTZcRsWEpnCxP4dacYyWLE4HCeyjLLUVDv/TJGqONaE45iVBPFzg5iLJ1dISzrTvcg+OQUiO7f2BX9KA2/hTSrpzGnaS7iPB0w24tK7gfjUeumGn154mTm4zIfaKOiLJzFJ8l6kpA2EVEpxch/+IxpNxJQ740tsu4OMFoS0f0lQScEHWuf/qZWV8NKmKCcNDVCY5OzvBwcoCdnQ98T4j61fjo/cFA8VVE7ViMHZ7RuNyoeXI6ZT/6yu6hoKgGNTPKaRwjNfdw5/BeuDo4wdnZGW52trDdG4wQMY+NU3ezSvPagpKLMYgLvYXCbuWMExz5QD3KY44g7kYSCqRyHhtCb3KQKC8TmJjthpn5blhYioe1A6z32MPZzQsHYwqQ36b5AFHiytrbSAj1gKutA9xd98LHxQlOzgE4dCEdFb2aIPWzMN4L5B2G9SZLrLGKw5zVWtmBhtwiZGXUoV0d4o6gLe0SEs+dQmLlMKb3fDQ+JkNP+gncuh6Fm+XixFy9WkdQE+2JM3Z6OHYzD1dOn8aJvWK5nV3hJpbP3v0kzic3qm+KmaBEV1ECcs4FIeleIi6fikCIk62oU3tg5eCNoBslKK6oQvWNIwgU+xKpjjnaiPV14BxulfZiYLJw5AMYLb2GCzcScKZIugFFhv6c07jisRnb1izDm0t1YGzpgKBLaUivG8BweybSznjAZvXHWPXuYmwwcIOtwyXcSKnDVM/p8jpU3Q5HkJM97MW24iG2FXsbL3geT0B267ST4EntWcg85y/q0eT24IKgyFOIvHoCx9auhs/ei48I0AfRXpCKu0HRSC/unDiR7xf7tvRzCL6eg9vVs06tBytQmnIDB89mI0/sSyaMQSWWLfGEH7xsxX7XZS88XVxg73QYJ2KL0TS5I2hMQcYJUcZbVuDNt1Zhs745HEKicSqlHf0TK/JBo01oL76D4PPJuBJzFvFnvOHqLPYddnawsRH19nQCcjvEj1zN5FPaU5Ea6Qdve1GvxfzsVZdPAI5czkX91H6xHBV3YnAkMBFpVb33P0O6CSblFE77HULQ2SIx/f1j82hnCUovHcT5a2KdflFDZxARERERERHRTFMBuiVWmMXhwY48B4HSEOz62BJrt19DCaTGRm0oCLmA6INxyO0dn3Gtbay3GvnnPBF9PQ4Z6gtIY0B/PI5t84P9umO4k3sOh32dYbtjFyx37YShoR0cw9OQ1Tb9Ctq0AL1kEOoOQ0cqUJp+FR6RGUipnT4Y9Qj6c6Nx1s0CO7bvwC4rS5jo74CpYxhOpneh75EX5kYhK4nFZX9b7DIR82RtA5udJrB2CsSxu60P9p46Uo7s8/vgZGKI7Sa7YWlqBAMDRziGJCO3T7pSNo2qE9Wxgdi/xxTbTa1hZSkNWSnm0e8yrlYqpl0zHEZ3RT5uuUXiTtQlXDrrjZ0r9bBhZTDOprRMjD8vb0PjzaMIsDGDiakFrC3NYaQv/h64gbiq0RnXHwdrs5ByIRxhN/KQ+7DBxDUU5aE47LANz+ucx6Wy2VnRGBoid8B+w+tYcTwPaQ8ZrnC8/i4S972Kl2z9YZsxowREeWUhN9IGf91wBHvjmtTzOd6Zh4Kg97DB0RuOyXNcP+7JR5bzqzDY44i9WdLr4iG7iaBV+jBYHIQEUc4zVqssF3cMFsBupzOOV6prnJoiJxAH3bTxokcq7j6y19kWpHlbw+E9XQTc6xH/mq0Od70cYfSyGYKSWtQNWZ52DNDnLwbon4H6vmZYx3vjt1IrdM+JVug/2jfRCl367zXnTZHWlAuF6oHL5Q/VMtiOgPRwvHh0Bb7t8ax63HEpPI+rStZM8fnKbC6EdvRu9fxPhudSK/jveD2Pn+9/XSzvvsdqWf/FUEI5XIpsX33YbtaH+eEUpFR0YfCTil1qtShOXHy8bLH4SCEquqcdeBSdUOUehamVL7T8s6bCop5b7jjlshLGjn7wtnPAPkdb2Dk6w3W3OUw2boWO9TGEpXaifzJbKTmDc946eFtfTOPgglA/W+y2t4S50VasWmMFc/v9OHzEB97iMzztbOBgvgVb1mzCyl2RuFrUM3FiJG8WJ1iB2GZ2ADvCKjAk78NQ4SVE+Rlj45IVeOuDLdhh6wqXE0lIqOvBaHchso/6YO+6RVixbBlW6jnB0SUEV1OrJ7pfVsgwWHoJF91NYaxliB17nODi7Ah7cy3oaenBzOUiEssHMJFLK6EarEFZhCs8t4t5098Naztn7LW3hYeXJxzFiYnV1uXY4XcNsVXS9A8nyz6J655LxYmVO1zs3eDvuBt2Ti5wE8u9a/MWaBl7Y39sI9qkAVEUZUg6sBumS0xx6FYdGqaO8WKmxsXJ44EdcNyqB6uoOtQ9NEBXiLLIQrLzVlhqmcHmRDZy6/o0PQ88jPgsZSca74YjxFoXpkZiXYnldXES69lYD9objUXduoPEZs0pq7IBWft14b5V1Akbf9jtsoeFvgN8TiWjeEgOVV8lskLFCeHWtdhkaAtLUQdcbHbDy9MdHh6OcN78Lhz8TuNqq/gs1QBQfByWe/Zh4740tEzW3b4qFF87AA/x/QZGFrBxcMJeRxtYGxuJ9WUDd7Hecztn9Wgwy2hzBjL8tsFQfw92H7mHwoaBma3Q5yTHSEMSEoNFnd2mB8NdtrB3EnXFxgQ79LZBb2cAIm7XiPUlTSsVaiFuWNnCa/N+XKuXY/op9mhHFhLsVsDbIxhRTeKTx4bQk3wQ4R6GMNwpTrx3WWGnxR5Y7diMtR+8iD+9tAw6B5PFSb14s2oIoy0puOVvBvMtm6C9ww4uLq7wcbGFudYmaBnZwzehAfVDj1ygT2dcnMQ2xyPUagc2r7dBwPUCFLcM37+rck59KAm3RLCpDgIT+1A37WbL8aEu1Edsha+bNbxvyzRh9ijqrvkizHgFbFwOwM1B7APs9sDRWZSvtfhRs34r9GyP42ROLwbUuyUFGuMCcFznA7Gt+8DR9SCCbK3gtnsHdmxZgTWmjtiz7xjCfffCzcUNDjb2sNFdI34ArIdx0D2kNGtOaUfaMXjDASau+6Ab1QHZ8CD6sk4ixmMz9NaswJvL9LDD0h6BZ5KRJn4kDbeJH0unvGGzajFWvrcI641cRP07g8tJteJHlShveTeabgQhxGI79LZbwkq6GcfJHntE/dDeao7dIRnIERVkYs2ICj1QjrwTLnDW3YyNRpPbgw2899nB1UX8sPl4IWy9L+PWJwbo7SiLOoL9H1ni+PUqqDtMaEtD0SkzLHE7C78UcXY/XecdxB33wAc7z+Ni/sTdIfKeElScdYCzvi609fbAXtQnd2c7Ub+0YWhxEMdT29Al9W5Ql4iUk7aw0FqLN99aJ/Z/5rAPPIcT99rQ97AAfbAYlZfdsXirGTYYW8B3nxMcHN3gZiu22S0rsXGbFewiy1A1MFmjRiBvL0RqiD1cjXRhbGYHe2dpv2gJC+0t0DPxgM/VetRK0yvKkXPKA6aL7HHsesXUneEYrkD23g1Y/7uX8I72ccS1TN64MIaegks4o70SdvsuqceWJyIiIiIiIqK/g0cG6DKgMgKWi/dgvd51lEMpfvGXIGq9OXYv9MXFJtWMa22ypiTE7Hoee/Z6I0LdWEYBDMbjhMFuaL9mBFtfe+y0ssQuo52w2KkP7VWL8eFyQzicTEGp1Kuq2vQAXTYRoPfdxs1QWzyjF44jaZqWLdK15NpohLkZYN3itVirZQxTK3MY6a7Fkg+WYI2JHyJyOtH+sMbH46NQdWfgio8FtixegWXbTGFmtQc2pluwafVarDMJwsXcDnSpL5WMQzXUgKpIDzhvXoGPV2pD29gKFjsMoLN0MT5YthMGQdnIbp+4Njsua0ZT4gHs27EOK1ZtxRYTMa2lMfQ3L8eiJdowdL+ElIZRTLSbG0DTvdPweGYJti7bDl0bc2it0IfWljBcyuqGfKQZjZmH4W6ihZWrtKBjYg5LC1MYbFyGjxZuholPNNLalZjoEFOJpjg/uC56Cc8ZhuBI3ieHpwN3HLHPYQk+CEpHauus8FsYyd6Pg06L8bxLLGIqHxLG95ahInon1m23xPZD6aibatjUj47EIBw118HHzjdwrmxiBY+35yDnwBvY5OoD1/Q5rmP1lSJv/9tYaWYJ49gBUfJivpSVuO20C7tW7ILrtVpUT2b9Y22oTTwE+7VrYOVxDim998P1gSRX+Fq/gefNg+B99DwungrFkcATOHUhAfcq+u7nJGoNSNprDbs3DET96sNU26kpzcjwN8OuF1fCLaYOZZ94Hf/pwAB9/mKA/hmQulKPrUrEwggdfNvzualW6NJDakH+u4Mfwua2L6p7pjpe+VRaBtrhm3ocb4ZuwGppzPPqLyY8bxnogP1tf/z2wAf4/r6XppZHPba7+O83wzYguiweT8/Y59OoRjGSdxKhDlpYs8kUe/adREx8HorL6lDf1I6ufqnrmmmkAD0jEAF+zlgTWozKnukBehdU+WGwsjsgDso5U92V9N/1RqDuC/hwrTlcLpWivFfqnl0O+VAnOu/uh4uuEXStLyK9U33IAaou4ZL9Bny40haeVyrRIHXnPtaPofpruGCyFsvfkcb6SEFZn9TFs3hNnExkhDjAYKkdDsSUTNxlpWjFcPpRGFoHwyK8WJxOiaOOSglFw2WE7LHHkk3huFbdhWGFEkopRR1XQTXWiPzgHfAw3Ym9V5rQ0KeAUiV106LAaGsWkjwMsGePDw7dbb3fOlVRhbKLbnDT0oFDRA6ypGOtqgO9uaHw1taBmc0p3KgZw9CYWF4xr/LqW7jhuQ6rPvoY69yuI35m30MPGCmIxAXLV7B42VaYHrmH7DZN2UldUWeH4bCFATZtP47r4iAvF8vQevsgQo02wDIkHSltk0dacSbWcxenLcyw2+AAYusVmqD/IeT96E87jINWW7B6iwWcDpzF9TsFKKuoR31zJ3oG5TPvpBMndIraC4hwNoOR2TFcK5WJU6sJqo585IabQ09vL1wiytTdXI+jBYXB4qTx42VYu/sS4iv7MSiVj9QqebQdA4m+2KOnh82253CncQwyTdkNVibgpvtarF/4KrZ6XMItKQlTSXeanoStoz90ArLQrl4uOXrE9hhothWbnWIQV94t6o/4DKkr/eYsJPrqYYeJI5yiGtD7Sd2Zi/o8kB2OQ2Za2LLeVJTDRVxLLESxuhw60D27HMS/xsdqURBkDkc9sS1FiDLrHNWsr050ZEfi1I6N2GUfhtNFUvf10neXIt7OGX56gbjRKJ8qN8loZw4SXTZgv08IYuqlpRJlp5RDod4epHog6qdyCAM5p3DaVhfrjY/idEYrZNLHKjrQn30SAfau2HskAQV94kRa/anid0H6MYQ7bMZK7zjcqJx+H+4cVGMYlYn60DuIYfkjWqyP96DmcoBYlrXYuN0ensFXcTutDKU1jWhs6UHvA3cf9KPstC1CrIxw9F4/xHn5lPHhbjSe1ccBL3vsvzuk7tpfqsdNN/3ht+otLF65G96XK1AzrNkehjrQdMMdTsaG2ORwEzkdY+p57bgThENbF2GpkVjm9NaJbXC4A33Jh+C49SO8s9YG+641ok2sS6lMB4ujcWXvViw1E9tWsub2z7EuyOLdYOHpD8NLjRiW9gkqUfbFpxHpvhNLLS/ialGnqL9KqDT7EqWiEil+O+G20gAH49tRMyStK6k7r37IqmJw1MwENrbhYt8g9i0T3yLOdVOQFGQF3c3uCI6tnNh/Susx5SBc9I1gaH8eSU3TtofyG7jmugyr3n8DWl5XcefBs+dpOlB+JRSHVtji5M0aiOoEtGeg5Iw11npdxMG06T8nha4k3D7pg6VW0YgpnGjT35N/Dmf1l8NqbzSuTnYxoBrBYGYkTgccgOfJXFQOij34uNin9mUhK8wdK5b6wvdKqfgxp1B30//QrW1YrMsoR6xavBIfWYQjunBQs92MYSQ/AmHWRlilLbaDsp6JkHuoEnXX9sJiiylsAm4hb/KYIg1VUHABZxxMsW5bIE5ntkE6gnWknEKEzmb4hd9Dpvq8fETsm24j0tQAq367AOsM7BCe14cuqdKMN6Hu9nG4rbLGgYhsVD50pomIiIiIiIjoczUVoFth5a7bs7pwFwbL0XhxN9au2I01ezPRqL4aVIoY7T1wWHkAMS2qGdfahlpScG3363D03o+z6gvIYvrBWzhjvBSv/fIDfGAXg5t1fRPX3cYG0V91DWd2vIf1unZwShjWXE+tezBA77+LW6ec8aLJaYRmShcsx6FsyUSq3xqs0NkD41OFaJSuYUmfO1qHnJN74KqzETbny5B3vyvFmeQdGEnzhc46U3xoeQ2ZmuB3XNWJsrhjCLLajcPXy1AuhbVK6bpjGByXbIC2yVFcKJdhSL0MYxgtvYBTzruwXDcC5/KloWXl6MmORJjOO1iz6wAOpnRieHLawVIk++tj16rVYp7LkNsjleco2tND4PHs83jxNQtYxVSgbVRMK3UNr5JDlnMakbZr8bHVKYRkdd2/djhYgASP9TDQ2o6d0a1oUF8XlKMl6SgCtFdhicMFnCmZSrPnoELzFXPst1kEo/MVKJprVNzKM4jwNMALphdwumBW45Apw5A338ZJ023QWmUGh/AkJGTkIC/xOPwMN2DLUgv4JLSifqJVBdBXjvLIzViqa4nNR0rQNSMYGUNfYSSCN7+A36/aDb1rXVO5yWBuCIJ3r8OHW9zgdToRmYVFSDnvCw+zVXjLKBB+d1rvXwMUupK8cNT8RXy8QQerlyzHyndexgsvvo6X3lyDdXsicDGnE7LJeUIXikN3wXnJBzA5XoCMGWWhgrzxKsIM38Q7L34I66h6lH9yi6anAgP0+YsB+mdE6nbdMSEAvzn4wVTQLIXOUnfn3/N6Ud2S/ET+JcjGPmlH+qCankbEVoqdVHOhOFDM7trjszcwOoiIgst4K3Qjvu3xHH7gM7Ec0kMa4/13hz6EQ4I/anvn6gP6KTHWg86qFCREeMPbYht012/AqrVboWXiAJ9jt5FRLZvWHbjYc2cG4aC/K9aFlaBqdoBeEI7dDodgFJw7FaAPJOyFn8liLLY+L9bN6MxAfrQAt9wN4bBlN0LThtElHRiqz+OilymWWpxHlDiQTh14R8uQ7r4Ju3TFSUZst9RJj0YfWpJPIWCFFQ5H5qBcekrRipGMYzDecxhWJ6QAXaMrDhEOLli29SxuS2Ocz9CN8rBd8N1ljn3xveie+uJWdGSchPsWa+wNvI3i2W8TJ1NxPlrY6BuNCOlWyN48lESaYeMuP3hcrpvZlfp4NxrifOG9ZjkMXS/jerXm+YcYzTuByD0LsVAcaI+lds840EJZh9wwW7iu1ceBy/WoFS8q668j6YAR1tqfx+kczdFUKd6XeQB2JvYwdL+LGnEQfWQmNNqFttI7uHHcDXtNtaAj1Yl14q+ZGw6euoe8xtGpLmcw2o36ixbwsLOE7blatM44SA9jtC4KR8VJh6dDDIrF+h0bb0VRoAFcjM2x93LbjOnHu4pReHgrTHZ7Ya9YxzPiXXHS3HjLU5wAL4SJ03nES2eoUoBedgr2zgegdzBXnLRKtasaGT6mcN24CwG3uiDOlaeRYTT/GHx32MJgz00U9M26QWQG8cbRHnSUJOD2UTe4GWlDe+MmrNqoDZ2drgg4lYT8ZsW0VtaDUDZeR4ixMUx3HMblJnECo3lFbagaLRd2Y4eZBywiqqWpxZPluO3gCn/9INx8IEDPRZLbJgT4HYdYvTM/a0olso56wNPEE6FJzWic7IZ6fAyKgTY01jWjpXts5vquE/XOVw+LdkXiTPbkVjoX8S5Rl7MuHsNh/3NIKO8TW9onUw60oynnKqIPinqpK/Yhq9dh5UZ96FsEIDimEOXqDXxSvzghtcfx3SY4ljJHgH7OEIf2OSIgcVqAfn0ffLVWYJ1DFGKr5DOXazgN170sYb7eFRdyutTz2plwEKHG62AZnI4U9bhEElFGbddx3EAXBjoBuFQ7fr9sh4tRGe2B1Wah8LuhjpknAvRb7rDyPgDjqIb7vTFUX0LUPnMst7mGW9LGN0MzcgIt4LXaGEdThjD11T2lqIqyhr6VJ1wuVKpvKLlvDF1ZkQjV2QK/Y3eQK23PYnsoOqoN0z1in3Gj+/42J1E2o/6mO+zF9mDqFi32Z5rn59SBiqthCFplj1NxkwF6JkrP7sH6fZcQmD47QE9Gwik/rNh9GZeLpF9yKrEPDEXYhiWw87mFxIlm3BNU/ehrb0Vj6wCGJodQGC0W+8B9WLX8IA7EN8yc77kMiXK55Im1y21hGZqFhunVRJWHlCBHmC53xKl7Teou+uU1cbjtvR5brY/h2L3+mduwqh1VV/yxb50OfCPzUSo+S1l7Byne67DT5wxOl01MM1JwCrutg2AofsgF+RjDM7YWpVKlkWUh+7QnNugeQ+jdxodsd0RERERERET0uVO1YCh1Pza8a4ZFRpeRMzyCYbkC8mEFxvrrUXbDF15rXsXHuvux754ME1fAihGjYwun1Ydw+YEAPRXXbd6Cs08Azk0G6APxOKG3DCte04PTzY5Z3V93oveaDfS1zbDQLQdlY9IFi0bcfSBAT8TtCFcsMD2DsCypT9ZhtKeG4uD7H8HI/iKiZ12zGe+pQWN+FgrrB9E9+5LSpNEWDNx2wtp15vjAJRUzOjEd60V3fQ1auocgky6KtKciLUwX725ygnVk+bRr5pJ+dNeVIDOrFvU9crHI1cg76QGdBdqwCs1A4fRrMIKiMAynHLfgNYtLiCyUSk+BlpQw+Lz0IQwsz+Ly9GtCaEL+YRc4v6sLp6hKlM64BjuOsZxAeNlZ4A2nZKS2SFd5xXP97WipKkdZYze6PrHLUxlKTxnCzXApzC7VoWyui5JV5xHpaYQXjU4jIu8TrnEqO1F7fT/c1n2AV//8Kha88iZefekVvPjmBmi5XkRyq/z+tSVlD4byj8Jm5Tp8sMITIRntmOyAV9mcgkT/tVj60m/xr4udYXi94/77xkpReNEBxovfwMsvv4aXXn0DL768AM8t0sK2I2kQHzONCr2F13DzqDt8D0fh3I10ZGamIy3pMs4fMIf2h+9jrVkwIsvGNVmAAsM5IQjZ9SEWbNgL1yvThpwdKEd68A5o/fXf8cc3P4Tl1UY8rDH+04QB+vzFAP0zIrVFu9eQjQ0Xd+F73i+oxy2fDJ6lAF0K1BdG6OJCSSyG5I9oJTnLFzXO+MCYDFGlcVgaqY8f+7w6Ne669JC6o5fGP18SuR1J9VmQf85jsH8meitRlnwZ0afDcSTIHz6OZrDT1oORVShOJDVqTjjE3vkxA/S+eGcEOurCIKwCFQ/cVTeGhvO2OLpLBx5X2ie6Uak6h/OH7LAiIB0pDff3+ApZFTJ9t8HN1h1BaSMYmvrqQbRkRSNs7R6Ens5GqfTUwwL0lmsIs3XE0s2ncKO+f6I14yRVK4qO7oTXTjN4XG9Dy2S6Ja9G48190F6lhy0GTgg+FoyjgYE4LD2CjiDUew9cDJdjwfZgHEgdgbwlERmBG2DgHyNOpB48EegvjkaU8Wrs9orG5Ud04T6cdRwX3NZDOyAFd+pmxIXCODqTgnHOfDWcTuQjUzo/G6tA9Q0fmGxyR8C5YnU3+srBKtSE6cHazhduseIkQP3eT0MFZUcJChOi1N3IBB/ygbftDuzRNoCx/VlcyOqYCMZGG5F/QBfW2quxxT4QgYFBOBY0UT5HDou/B+xg+v6HMDI5CLGKxfbchuJD23HQeS8iSsS+bVoRDTekIMF5FdwCziFa3c/0dCMYqI5C2KZVcHCIxM3ZAfqhfHRJY6LL0xBlZQsX3WNI7pg51pEoDfGFibhgZ4ed2odxo2H4ft14KBVU7aUouBWFC6eO4/BBX3jv2YFdOobY4RCBK/ntE9uGqguKglDsMdmLHT5JeGDAhnFxpld4BHtEHdJ2Tka9uvZVIuFJA/TROpScdoDTdmvYBeWg9mH3C/VUID/+EqJDDyEkwBs+ttuhvWET3jOIwKXchwwQpCbqW81FnLRYjg8XGsI3vgnNn3ReO2VMrMds5Maex7mwwzi8fy88LXfCRHsnLPdfxY2Kye7JBx47QK+77IEjptpwulA9x4lzH8rPeCB4ix4O32oWP2fEz5zbAYi03IqDsaKspla02AI6b+G0qTUcd4bhjqhHU0U3XIH6W0HYaHkS+2M1FXBGgC61QJ94GmVncc7TDMusoxH7wMzUIj1gF9xXGSLoTs/UcArjrTnIDVyNTXp62GhzEGFi+5jclxw9cgwH3CxgufQtGIiT91ixrYw0JOOuy0q4HLyImAc6ZBlBX+VFhG5YCUeXC4gXP8Zm7yHue9IAPQaXC6UbccYx3JIm5t0M9gYm0Df3hE/gGUTFZ6Osc45vHcwVP8Y8sXLZfuy/Xjmju7Q5yYpQcdkfaw3CcCi+QTrKTFOO3BNecFxpi7CEevXNCAM5p3HReiXswsQPlzluHOjPv4jLRqvgeCB+4kabgWLUXTDHVotAeNwcxOhQGZqv2Yr6GIPjpyKRfNYJeoezkCCVcWM04g4YYb37LVwte7xzDyIiIiIiIiL6DEk3wGf4QXvxQvzsmY+wcpsutulth462LrS1tmDjho3YpOuIQ9dKUK2+7qMS/y9E9KcO0BXAQBxCtE1huOIQrrWNz7puKgeaLsFD1x5LN1xC+pB0naAZSQ8N0M8iLFu6jtKKipgDsPrLLuwPK8Dj9bGroRzCWGM8TrgYYPWyldho4QSnwLO4mFCO+lkXWuTlF3HT+20s8ziD40WPyCAGkxEX7Iw3lh/GkXtSz32zyFKQeGQPXvzQH343pXHBFWhOPgP/V/TgG5yKAs1kaqp83PYwhfZ/fwg9cze4HNyPfb4+4uGLfX77cdBNC0veW41ffxiCqPK+mdfhH2kUFWeM4GG6GGYXa1A6V0t9dYBujBeNzuBU3lxN1AVZExrTjsEvwB3mDmKZ9u3Hft/98PENgv9eO9g7OsAqIgMpTZPXgJSiojSi4OQ+2K9bgSXLN2GrrgF0dfVhtHM3LK0sYGu2Bn8xC8C2aGmwRlHnlA3IOX8UgdY2cPH2g7d/APbt84XPQV+4uzvC0sgDwWdzUDI8ee1uHPL+TrTX1aOuRTbz2nRnJm67LcHyjduxMbwKTdKwrZKhBlQkHIDd9tVYtGwj1mjrQ2/7DhgZ74KFvSssdN6HifZ7cLpRh+KHXSN+ijBAn78YoH+GZPJhhOZdxHNHlqnDZilE/7EUQGvGQ5dapi+O1EdM+S0MP2aI/nkbVozgakUCVpw1mgrP1fMuHlL4L7VGf/7IChzMOIG+0emH6i8LOYZrk5B82Aamq7Vg4nIGie0qjEmhdXYwDga4Yd2JMlTPCtDHi05ij2PgjAC9N84Jx9z1YXmhDtVzHOzab+xFuIt0p5p4XToBkAL0YDusCM5ESuO0AH2wHOn7tOFk5YoDyYPTxmrvQ1PaBRxbswfHI3MgNTD8TAN0WSXqr3tizSYtfLRaB7vNdsJqxw5YmuyAhYkprA31sdNgF/RsL+BK0TCGGxKQemA17I7G4spkV8fT9JVcweXdq7HHNxqX53h9uqHMEER7bcTO0CxRFponp+nLPIGYvetgE5YmDvTSMzL0FV9GmJYOPAOuIWtUheHGZNww3wx3n9O4Jo0b/sSGMVB6Ezf3W8BglQ4sfK8jt28cY8M1yPU3gM3qhVi/zQymxjux21QqG6mMTGFuZARD7a2wD7iAO+L7h0dbURiojyAXN3GiKc4xp1WhgdrbuLFnLQ4GXVZPO5McfXXXcGbzarg6ROLGXAG6Sqy0obs4Y+EDZ9MY5A5NbyEuEWt8LA3XXKxhu3UfLpYNTdXTT28Y/SVxuOpniR3iRN0k8AZuSf35KDugyj4GE+tDMDxa8ODnjovKX3YaTloO2GZxA8Xq2leFOw57EWBwGPHNswL0rnzcc9+EA/vnCNCHW9GScAj79PVhsfci4sWZ+IzQUTWCsY5y5Mefw6kgf/h77IWP7R7stbOBjfEmrF61Ee8YRSJaM7713MQpXUsa7pzwgovXEVzO74K656bHJX7sdOVG49xefWhvFOV+KAMN4uRPKcqxMtIeoXt24HjaAKZt6hgf7kHzBSME+s4M0Gti3BFurotDsc2oeuD8Shwjoz0Qvn0LDsY2QurcQQrQz1huRdDNOpRO/QIawnjbTZzcYQ6bHSGIb1Xd/3EkK0XNjQPYYHkS/uKEU+0zDNCVLTnIDlgKI60VWLF1l9iPiP2H2JdI24rVjp0w274dxoYmsDmWiFRRvwdrbyHOZi0OHL6Kuw9sD2PorbmM0xtXwc3lAuKeNEAX+6GgjFnHqO4U3DntjxW7xT6qcLKOyKCoj0fMQTuY6+nB2Mgc9ntcsd8/FOdv5aOkfQyTDdAfO0AfLETFtQCstY5EcIJ6R3bfeDEyQz1gu8oBoXca1GOYd6WH4cKu9Qi8UIC8B26kEPuRoiuI3bMa1n4xiJFuUlK2YiDrGGy0PbE3rBBVDeli/ejgwKlbyKyuRMm14zDZFYWYvE7xOyUEF2y3weVCKdQ9mxERERERERHR34eqQxOgf4if/vYdLF63AevWb8DateuweoMO9O2OICypFe1T15Sk63NSgO4A5zXBuNo2K0BvTccN+7fg4jcZoMs1AbozTFefxt1e+ayGOKNAdxwCtzhhy8cRuDcgJZOfJkCvQen5IOx4KQCHL1aqr2U8mWH0pIfhxO51WLd6A9bomMFstxNcfU4g/IbUsE2hvh44VhiBm+4vwOBIDKIeMVwoOm/iyjEXPGsShfC5rgvKs5ES5Io3/uQOj0tVokSlAP0iAl4xh19w2swAXZmBOK9dWPvrd7Fi1UZsMtDHNm0daImHtvY28d9rsOwjA2hvP487DYOzrtE+WnusFbztFmNDRDHy52gDNF56HKGem/GcdQzOF8+VmSkgK49F1I6PsVTPE063u0SJTtN4A5c9tPDREjvsvViG9hlBQROqbhyAi85arFm6DB8vXokNFoHwP5+MzPMGMNjvD5Mbg6IMZFDVn4Gr9lZ8vN4XZ8uHp9UhJUbKryB02xKs2u4Fj/R+SKMffjIxE4X7sVtfH6/oRyO9a3Ta9b5etCcFw3fXFqxctASLF6/HGj1H+MZn4upxL4RvXYODCXUofVivBk8RBujzFwP0z1hFdy3sEvbjtwc/UI9//oN9C9Qh9A+lVtzeL6n/Lj2jj6vlCRh8zO7cPy+DYzJcr7yLdRd2ivlboA7+pb/TW57/5/63YBXnhapudVTxlFJiXCxLf88ABoakMZkfND5aj9wD+nAz24N9iaPolo4AecE44OuCdcdLUN03LVFT9QJSC1yHAzNboMc5I9BpGwzCS1D5QCAhR8MFRxyx0IPb5WbUSNVbCtCD7LAiKAP3prdA/ywC9OarCLVxxJLN4sSiYUA6rbpP1YLCI1KAvguese1onTyiDpej7rofNm51hYVPFDLycpCXloGclHRkp2QgNy0L+ZkFKCxoRqdsHGO1t5HivxYm+6NxtujBUu0rESczZquxe9+jA3SpBfr5vWux7VAi7tY/mF52JR3FWcv1sAvLRpomYFO05yPbVwt2e8MQkVuHpsxT8NGyx/7jqXjE1wnixGtUqhODkE12Bz6Loq8YqZ5b4GDlicN5KvT11aLooC7cpRsPTmciNSUbhelS2YhHqlQ+2SjIzEJ5ZQu6xJmSYrQRuQf1EejshtPFMwN0WX0ibtmvwV6fSMQ80L39GAZqohG+aRUcHxqgiwo6koILoo44GZ5CSo9q1p2U4suGknDJwRrmWgG4Uj30kHBPnB6ODmKguw9Do6q5t43WAuQEiZNDx/1wSRL7JpWo8YXhsNyxDyb+6WjRTDdFaoFedBx24iRe2zYBVeoAvRIJ9o7w2y6Nga6a0cWSvCcPic6b4L9vVoA+LrbDrEicMt0MQ4cInM0dmBmeC+ODNWi+5gTL7TpYb+qHkOhkZOVWo6V9GEOll3H1wE4sk7pwz1Kf5j+cejxvOeTSeOvS2N+apx+gEuU11Iu+fpnYP2qem0E82ZOA89ZW2KNzSH2zwLBYopozdjhmZYKjKTMDdGlYicYzhjjoNTNAr43xwDEzbbiJXwMPtkDvQlmkCw5u0sGhm83qu3snA/TAG7UzWqB/NgH6mYkAfXcMbpTPrkU1SPM3UwfowXd70ajZVymbMpETuBomdi5wOZWCwoxM5It9ibSt5KRmqvclBdkFKKrpVt+sIKu9gzi7NfDwv4jrD/TIMIq+qiic2LASzk8UoGegJHI31nleRGD67AA9GXdO7psVoGsoB9DfXITipEu4HLgXHtvXYJOWHexPFKFBrCh18UgB+glPrFjmj/2xVTN+rM5pMkC3ikDQ7Vl3Cs0RoPdlnUKU5Rp4nsxA2hy/Qvvzo3DZbBVs/a9pym0EirZEnDewQqDPSVy6dQORxpY4G5MjymIYLfeicHyLBy7djMOV04EI1rPDpewOzNG4nYiIiIiIiIi+KJNjoL9jjiXGl5E/PIzhqccIRsYU92/mV1NiXFmAKG0rOKwMwOVWzLjWNtKehmuWb8HJMwDn1BfuFOoA/bj2LhitDMaNdqm5x3SjQMsFeG3djeWrzyFdJl0Y/zQBehPKowKx60UnBESWqHtJfNA4lEoVVLMv6s1luBc95blIiz2HEz7G0Fn6Ht5buBOuN2pRJxefVHEe8V6vY43fBZxUd806B5VSfJcorL47iD3shFfWn0BIRscD1xQxkoakIFsseMUH+67WS1dI0Zx8AQGv7IJvUCryNZOpr0IpMxHrag/d5y2w71Qc4vNykJ6cihT1Iw1paZlIv1eIgvwmdI3IH/yuRxjJ9IGPwzq86JKAhPoH4/eBRGfs3/Me3jmYjNuNc13HlqH+znHse3Ep7LxvIeWBYLkfrSmH4fXyQli7XMEd6dqhSgHFcD96ZcMYGB2DQtaL7vYOtHb2Y1Au6kdbETJ9P4C1lzcCpRBiuAPDt+ywydQeK4IK0DX74py8E+1XTbHJZBcWHyhE46g0gRyjA70Y6BnE6AOFIupkYwRctu3ES8tO4nb7sFSrIR8egKyvD8Oizo8N96G/ow0d7b3oHxkT67UKSb5esH/HCWey2u8PJfkUY4A+fzFA/4wpxM5bGq988yUL/NzvDXVL7skwWvor/fsnvq/iw1PaOFkQjZ6ROZqbfYF6xfefLoxRd9v+E6nlubeYX03oLz2+4/W8OvTXirJGSkM2VOOPe2j4Ao33YrQqHuf8QhF+Lge1c+alXSg/YggP011wvyVDl3QWUXgYB5ytsdojCQWd0w5eo+0YTPDELnN3GB/OnwrQB265ws9yHdZ43UXOzAGyhUZkBuyCp5YZDiXJ0CLNQ+WZzy9Ab5kWoDcOzjxwq1pReMQUnjvN4BnbgbbJ28VU9Wi+ewSWa13gHZKORrFOVQpxgiaFiuLAqRxVYHxYLJdi4gg53pyCnKO62LDnGALuzL49bhjtmeE4smEVdrrG4OqjxkDPCUOkw3IstzuPmPsJoEYvKi544sBGLXhdrELx5PFmpAX9tzxgsSdIHMzDcPOMA7Y4nEbI3dYHu+WZTdWB/qLrOOUdjtNXimeN+6OhqEO+3xY4WTjhQMa4ONC1o/GkEfba2WPvjU50y8RhfbJ8FOLkaEycbIgyUsgnAtjxsXpkB8wdoCvbC5F7cBP0d3vDPb5r1olVJzrTDsF97WLsdDyHuNkBunoMdFEpVGW47WoJd203XCqRq8fCvk+cKVVH4qD5bmjtPI+07llj8k/pQXvBFUR57Melm+Wom6vg+qtQHrIR62z3wSZeKnzxaLqMgwY2sLG5hGyxrcz47JFW9MY5wtTMFYZHSsXak5auAgkOO7B3iwsiCpTq8Z0niBOy1gREGa+Fm0Morogz7anq3nQXtw6YQUu858DN6ll3R05QNmcgL2gdjOz94XWtER39IxBVdUJLPO4EmWL5rtOIfFSA/mkNd6Ej+ShCjkUg9E7LrO6uNOTpuGJrBevNfrjSIBfbpBz15+xxcMd2uF9uRPW08yXVcC3yDupg7x5Rx5KHIFNvWqNouOKJAP2N0A/KRObsOxRUxbh30BH2ax1wMl3UFfFUe/xEF+6fS4BeegZnPXZiqXUMblTMjognAvS9K6UAXeyfJsu+Iw9lJw2w1e4g3GObxI868eNlalsR+xK5CvKRcYin1NuKvDlL7PM2wMz5CILT79+/OqEHnVlH4btmCaxcLuLWJ6a9cwTonVkoPWWBtebiM+JmFWbtFVwNsMZyq0u4UjxXH1nC2AAG2uvQmB6Cg+L4sF0/DLfqxLqSXpPlIPeEB5YvlQJ0abz/R5gM0C0/KUC3x/E7DepQW1l+Fbc81sDYT2yjpbO34F403TmMwxs3wjs0DVnqextEaQ7VImefIY46G8LB8xicNgUi9l6det0Plifizh4tnAx2g82eYNganUR68+Cj95dERERERERE9PnRBOjr3tqN1ZaJDwmipxO//5X5iNZZD/OFljhWIl0lmKTCYO01hK16FeZWB3FBfR1FBWkM9DCdDdj6kS1CC8cwo+3X+CAUmT4wM9yFD23uonxUuq7d9KnGQG9IOA731zbA5UAismZfYBjqQk99GUrretA9fWzL2cbVF8SmqIb70dNaiZyzznDXfg3LfK4gSiqUjkRkHtXCWxbH4CmNVTjDCIa6G1BVWoOGnlFRPtJ1Fndsfc0B+69U4oEOD+sv4qKPLl7QO43QzF5RoqNoSr6A/a+YzQrQhfFKJPo6wPKNnQi81YRaMb/S9S2FQiEe0jVzufq6uVwxd+OkRxlvu4xIN2M8t/ggjqa1z+odoBuFhw3htG4JLGKmD9043SBqbx2Dx1+XwXl/IrIfmIkxdOeE4eCLC2Bpew43pYtEI23oSDkC79AYsfyzr2ipMFp1FaGrVsPB9QxuSzMkppfFWmP9LlesPF714DUw1QBkibbQMt6F9zxyUCeX1ncLis/6I9z5AKKKR2aF7mIdFR6Cjak53jC7gdw+uSi7MTQnnUDUAR+czh2cqHPTdVzFMQd7fLz1HG7XiTqrefppxgB9/mKA/jkYU8oRV30Pq8/tUHd/rm7RPdkSXfyVQvTver2I10PXw/veMRR1VGje+cUq66yBT0oI3gnfjP/Y9xK+7fHXqeBcekgt6NUt5iMNcL0yESOKB25rerqMyzBaL04c9DZDb4MTApPb0DnjmN2D/qxT2K+7Btt3+uBshRzDUlbRcA4n7HWxeJM3TqS3aw4MvRgsOocYR20sWeMAs9DCqROOgbveCNB7Ce9ssoHntXq0TWXowxgpOgrvbVuwbfthxDWNT9zlV/Y5BujdNxHpYI01qw8hqlyK8KYZ70VluClcTXRhea4OzVO3HMowWBmHs0bbsNsqGGdLR2YEu8rqO0gMC8LZW+LER1ro4Ua03PWFxRYDGLteQdb0FpKt93DXbyu2frgIm1yuI+4R3eqMFpzGWctX8f4qPZiF5aByek5XexEnrbSxZa0bIrO70TuZI42Lo33rNRze44Ctby+B6c6t2BaajYTJZrCfZLwP/cVnEbhxPbbr+KjvmpxxGFO1ovXuYbhvXgUjm6O42gTI5SMYyQlAgLURttlfxr0ZPTAPYbjoKiKOXMQZsbDSSeu4shG5DwnQMdqJnjQfOBhsxTqTIFzIakOXtBkpOtGcfRanbTdA+903YegRhVvS2cL0AP1ANtrVJ5aDaIlyw36dzdjunYyU6cGirBhFQcYw3W4Py5NVaB172EmqDF25pxG2dRG0jfwQnNIunb5M04LmW8Hw11mKLc4RiCiTapIo35FypHrvgPVWMzjE1KJx6uOVGKu4iiir9TC0OYLDOTJNHepAwSFj2K7ZCJOQIpRozuqVrYXICLeF7fJ3sUN8/vVmUW7SO/qKkHnMEY5GVnCJLEblQw4HqoZ7SPNdgq02hxGUrXlSrQPNsV5w3boOHxicwoW8ObpqehJjXRhI8oCjzhasMQlDXMWsbpmG6lB12RUWGw2gtzsaef0q8boKvfFeOGiwGOvsL+FGhaaEh1vRHHcYB/QXQsvUE4H3hjCkPpEUJ+w3/OG79m2x73GEX1zztBbCoxhIPQDv7QbYaHoRyY0j6vCzOe6zD9CHJtdp9SVccDfF+9tP4XzB7BborcgLMofHam14XWvF1G5stBXdGYGw09+FnW5XZ3URLn4EZV3B2QNncD2tQX236PhQIzqu28FCRx/bPeJROO1rxluScdt9Pba8/yb0vK7izqcO0Ksnxt4aLkNjjD201hjD2Pc2KibLp78ApadtsHvLNiw0E+ulXNoDiO2hMA7XA8Q2n1A9807WrmiEmptg1cYjuFE/NLGdyAtReMYdmz52gNeFsgdP7Gf7tAF6QsPETT0DRai8aAlDLQvYHslEzfTK1nQNV7yMoLXJAxGJjfeHHVAMoO+mCwLNP8Brq8ywzPwakkU9lSg7i1FxdAvs9RdhwVoPmPrnoOH+AYaIiIiIiIiI/h4mW6C/bYnlO2+iXPP0JxpvQJrrUui+/wE2hpRPNThStOQj4ZAxdF/5LTbsOYpo9cUNFTB4C6eNl+CdX32IVc5xSJp+raYzFbFWi7BkgwWMYjrQK7XgRj0SHhqgRyJM01hlqCoe0UbvYvkGJ9jfaL1/7Qmj6EwIxkl7c+y/VoXS/gdS3QnD7ei4Fwz/kJM4lNQ5IzxWZQTg6IY/YoVrFC5IrSTkjWhK8MLmj9dirc05JE1vz9Wfhlsn/WDoGIWrZdKFxwG0J4UiYPk7WGkZiuNl069/tKD4qAUs16zDxuP5SJOGrIQMjUnn5w7QMYjm2AD4b16E1U5XEDWjI14lBjLCceZ4EA4ltEwNlTrUXIi8m9G4klyB8s7ZjSJmUTSh8LQT9F79CJs9ruHW1AUpOYbzj8Fl3XIsXemO85XDGJCKUS6+p+gOrkUnIKW8SyypHH0FFxG+/mUs19oL73vdM69Xdt3DbT89LH1xE6yPpKJSKgp5O/pTPWGwcguWGEYicXpZNsXimp8W1qxwgd/5yol1r+yDPO8QLDdq4e1Nx3C5emZL+/H6W4g2X4jFG/bANLoTvepr132oOmEKmyXvYZnbXdyafhdDh1hftsuweqMZtp9vRru6wZ4CvUm+CDR4D+/uPI1ThdNqw2A+Urx0obfZEtvPNKB26BFl+pRggD5/MUD/nMhVClwqjcPCiG34nvdL+K7X81PduUsPqWv0b3n8FX84tBCmsa64XH4bjf0P3CP1uWgeaFMH/LtuuOOPgYvwHa8XxDy+MDVv90P+F/D+SW2cK7qGvtEvx8avGqtDyWk3eOtshYHZPgSEX0H0tVhcvXYVMREHcchKD1u19sDlVBqqJxPE4RLknLSD+ZqV0Lbaj/3hlxF94TTOBDnh0E4tvLvCGfrBOVMt0HvinHHY+K9YrLUDO53DcPpcLC5fuYrL50MQbK0NHT0XuJwtRvPk/r3wBCL2m+P9/Sm4O63pr2KgBPdc18DaxBb77gxgYOr43ouG5NM4tHAngsIzUSw9pWjGcMohaO8MgMmxgvsh8Gg5sk64w369DnZ7h+FQbCFyamWalsIKdKcdwnH7rdhq4oMDxy4htbQZnWIWxoeaUBezF/vNTWHmeATHo6/j8lXxuByKEBcj7NIyFGWUhVx1WK6AojMTdzxNYKFlil3e53Hp6jUx/WVcPnVUlOkWbN/wNjZ7xuDKI878hrJCcdHqeazcshXb9gQj9NQ1sW6kzzqDMGcDGG8zh3lwGkp675eT+uRPVYv8YAvseP5PeHuRCXxvNaF5dgPWOY1DPlCKvGO2cNXaBiPLAARHiO9T1wmxnsN84LtLF1t0nOB7KR9TvePIxME63B12emZw3BeO01HSe8QjMhhBe/SxWdsdnmdL1N0vj6sakOW9GX677RBWIE4ZZhzXxUqVFSH1uCuc9Axh7nQYoZFXcfXCWZw95g1vS23seP9dWHlEIV4doA8AxcdhsXsfNu5Lm+jBQNKUhNuHd8NA1xoO/pE4e0Uqs4u4FLIXzvqGsPeNwq061WSnAXNSdJagINwGljrboGvhh6NnruPKteu4ej0GUWFe8BbloK/rhODLJaiZutliFH3Z4Qhz0oe2sQf2n4xBjFj3MZdP4Pg+a1jo2uLQmRyUT501K9CTHoIQi3VYsXE3nP1P4fylizgfdhiBdiYwX74AO91DcblZLKpyWNRpd7iueh2vv7UNewLP4vSlq4g+fxHnzojyOR+DyERx4t0lyrCvBrVXHLDH0Bgm9sE4I9W/azdwJSoSJ531oLtkKV7WCsWZ7DkGEHoiYufQkYq4ACuYbdKDpVhvx8+L77weiytXo3H+sDfcDLWhbeaPg3F1U116q2pv4oaPFjat0MZOl8MIuxSNSxEncMrDFc7ab2GLpRP87simWqDXXfbEgdUvY9MWU5h7nsLJi1fEehX7kqjj8N+lBz1Dd3hcqUO7el+lQt1Vb4Qbr4LflWoUTu0EZBhvuYoQHQOY6QTierNKPKMxWISqK/uw1DgEnlc0d7eMdWIw1gEmLvugfa4essk61lOA9JMe2L7OBFZ7xbZ5txBZDcOaOjWE1lvBiDDbBCPrg/A7fBsZJa1iT6WAqq8I9wLssdfAEk7+Yr3FiP2IqFcxEf7wN9eDvpY7jt2s1NxVLTba1kREuxnARMsItgejcU6sy2ix/4w5HYQD5puw/eOXYeJzETfn7C5i0hwBOnogKzmHI6brsHmTWIaAC4g8dwkXTgUiwtUQRhsM8LrhBVwpkVJ78eMqKxIRJlthaboXPqFX1dvClSsXcdbXErtMnWF9NBuVk6HzeCtaUk9i/zYd7LIQ2/7FNMTld6H/YTesDOShNNobi4xDsf/GjF9b4rMKkBrsiF0fWiDoZg0a1E8OYLjmJs7ZGcPC0A4eIVI9uCbq2kWc8jLHbhNLWIZkoXCiIkwYFz+Ras/jqMlbeObllVgWmI/8Ts0OYLQF3becsXPhX/GnJTZwvtWDgRm/qIiIiIiIiIjoC6dqwmCSB5Y8b4z39K6iRPP0JxtBR5IXvAzewYJlJtjlfhihJyIQGnwQe3dtxpZ3f4pNLgc1Y6ArgYEbCNXegrd/tBDLjB3gejwCp89E4vTpozjqZoTNH6+CvstFxDdKV5okVbhp4YDdL9kjvHBQfa0TfbdxI9QWz+iG43CapoXDUBPq411gpr0JS7e5wvvEOZw5cxZnTvnCVX8NdDabIvBuM2qnXbqYQdaC1ltO2GW0CR9u34t9YecReUZ8RuQh+Fptg9EmY/jFlKFcfVFrDKMtSbhsvQG6a7Wg5xGGcPFdkWdOI8zTFNt1jLHEIR63aicujqm6CpBzeju0tfWw3ioQR9XTiscxd5hv0oWe/gFElwxqGsf1oT4hAh5/3I69+5OQo35umvZ0pEXsxBZtY+jaBuFopFhG6TplmB88DBdhve5OWF6sRb36q5VoSTgEn9Vv490d4QjNfXTONVgZizjXtdi22QB6LiEIizyDyBMB8DZZhcVrrGF0rOB+ZtGTjAR/PSx8ayvMT+SiUlphvaUovGAC/c0bsHK7B/xDRRmeldbFSYS4m8Bo82YstbyEc4U90lVpQXxYVxqiHXZAZ4kWtrsexuEI8Z2RoQhy1IehjnjuWDKSpy6Ky8V35OKmpxUMFm3GNpsDOBguyvKseESG4JCzGbYtWg9T9yjclq7vat41Wh6Di57aWL52B0xcjiFcWq4zoTiydxe0F62GgcNpSJfIpi5PdaQhJdwc69dpY6uVH4JOS9OHq68366/aCjPnc7jTqp77LwUG6PMXA/TPkUw+hJMFUXgrfBN+rO4e/cUZIbrUOl0aF13qOv3FoyvhmngIOa3F6Bnph1w5laZ+JhQqBXpHBpArPt89ORivHV+nbl0uzYP0mOxmXvrv73lJY6C/om6ZfiT7DHqG/77dzD+ecYzLalB57SACdmzEptVrsHTlWixftRpLVm3GOv298I/OR2X/9K5WxqGou4PkQyYw3LgaCxcvx+Jtdti9/zzSr4bB1TcC9qeKprrI6U30x+Xje+B74RauHHLHPqMNWLp8JZas2IgVW93gf6kQNcPTksyKGFw54Q3dE3nIbL6fYihk1cgN3gVfN38cz5BBNrXKB9Cacw2nDTxwJrpQnMYIig6M5p2GrccpuF0on3aXnwojlbdwe58Otm9Yi7cNjiAgrnnq4DI+WoqSaB84rF+L1cu2wPFUMvIn076RelReD4a/2RasXbMOS1esxdJlK7BOzwZuJ9JR2DYybdydUajq4hC73wL6K1dh+fJVYnnXw8ArCiei4nD3mBEOnLmJm+qZfThZ3lncCdkBn/OxuBQiTmjEid6aVeKzxDpasn43nI4mIb9HpbkBYKaBBHFytfljLNoahmtV0j13n5YSqr5SFF3wgZf+OqxfLZZz5TpNndiKTaa+OBpXjoaJVFNjHKruYuSf94KL7iasE8u7dOVqLF21BRsMPXAgpgCVfdKILYKiGQXHdyPcLwBR5WI+55r50WY0JZ7GSWdzWJuIk5yd3jh85RbupZ9D2OZVcHOIRKx0hqoS+8OqS9jndxxWx/PRMZWIqzDWnInU41aw2LYOS5aKeRfrYdUWQ5j5xSJRnF0+JMq7T+qqv6cMued94CFOVteotwvxENvIomUbsdV0H47frkXLjHIQxgfQmReFs47boSPq0eLlq7FYrPv1hs7Yd6EQlV2zui0aq0f1jcPw1tsoym0lPlq2GUauQQi9EotrXjtwUpzY3RbLKo3J3nndFt4G7+P9xWuwbM1GrF63AavWisfKFVi6XhuLPK7gXPGwmAexXfdIwaMNbDdKd2OK9SG25xXbvOHrtQ9HDrpA2/UKrs4e3/pvooKiJQPpJ1xgpyWVl9iXiLqzbMVqrFwntjfbUJxPaUTP9MGpxgfRLcrqjPVWbFu9HB+IfclmUw/si7iH2+G7EXbcH6Hpw5jozWoMDXFBiPbYhdDLt3H+oDfct60Uny/2Jas2YJmhL/wvl6J16kYRBZrvhiDa1QzhdxpQMbUTGMZ4x11ctHeBp30kkjpUEz1fSIYq0XDnOIxdLyLkjqYltLwHQ8kH4RZ0HLaxLRie3O+MKzFQcRvXnbRhuHoJFlkeVd8RrO6lQ3q5twhll/bCZstGLPnIHHvD01GpXnSxrbRnIyt8L5y0N2DtKlFO4rFouahTO0Wdiq9G86DYBtWfIlFguPwKrvkZw2CNWO+iPi1cvkXU45M4E3cL1zxNcCLiKu6K32ezauI0nWIfH47gGQG6MNqClrj9CDBbj6Wi7Jes0cYaqxBcOn8WVyJCoOspflyVq/uNgGqsHb2ZpxCyZzs2L12J5ertez2WbbKE8/FE5HSK9T/xqcI4lD3lKI2wE8u4FG9tdFL3SlI358YuyMpQczsEhq6XcOLerO7kx8uRf/YQvLbvwxlRf6ZumxsfFvvyWMR4m8NA7BNXiHq2VNSD5VvFd4bcRXGXYtY2Lr57OAe3D5hh9zZrBMV3ommqrgyKzfAqTlroYc+eEMRUi0kfXphERERERERE9EVQtWMo+xiM1rhCx/EuHjES5n2jNSiK8YTVmjfwxksv4U/Pf4y1VodwMDoG55xXIzAkHFfVObcCGEzCGcsguOkeQUx8EFzNVmPhC8/hr8++gFfeXgktr2uIr5neqrgeKfsOwm/dAURXDE00IBtIw71L/ljqEI1z+ZP98I0DY82ojg+Ah95iMR+v4oUXXsYLz7+KdzY5wjWmCk1Dqodfn5SuS8obUX5lP/ZuXYg3FryK519YgBeeW4C31lrA5mwByns011rVxqBoSMCNACPofPwqFrz0Mp5/cQGee3sdNrucxfWqEQyqW9BLxqEcLEfGKUeYr34PL4v5ev7F1/DCm6uwenc4Tmd1T13fkq67N6dfxbFlTjhyInuOmxhUUHQXIv2kDXaseh8vifl7UXzvy6+8jQ+1HOEVnY/qQbEs6q8eQ+udYPit/RDv7wxFaN7sHh3nMD6G0aZE3PDSgdYHL+G551/E8wvexmsrLGEfmYPioYkhQ9U6U5Gw3wiL3teC5clcTDSuF6+P1CDnvBds1n+kXg8vvfgKXnrpNbz0kTa2ekYhtmpy+MhJKihb0hB/aBe2vvcqXn3hJfz1uZfx2mJDmB5KRmb72Kz1Ng5VZy5SQ21htPgdvPGyKIOXFuD5l9/Bm8t2wvxQIjIaZvZoKy1Xf/VtXN6rj80fvCXW60ti/b6J1z7UgYHvTdypHp71HaKcOwuQccIShivewQvPinJ44TUseHcrtH2uizo6Omv6pxsD9PmLAfrnrG9kAOeLY7HwlI46oJ4Ip2e2RP+O5/P4nveL+N3BD/HhqW3YHe+tbiHePzp7HNonMzA2iLt16bBP2I+PxOf/IXAhvu/9krqLdun7J+dFCs0nWsq/jIUROogoiEH38Iw+eb80VLI2tJbnIC8tCUl37iLhzj3cTclHTkkjWgcU0w7GGqohjLRXoiIvA6mpqbiXW4GyBnFwHehGc2sn6tplU2GKoq8JPZ2NaOsfwkBLDWpzxfR37+JuYgYyChrQ2n8/dlEb7kJ3eyMqxGcMjt3/5nGlONCL9zc1NKNtQDktrFZibLAbHZWN6OiSTXRpMy5ObGQdqGtsR0PnrAOOYhCy5lKU5GQgObdafM/0A5gC8r4W1BXkIiNNHITruzF99lSDbWgpz0VWWgru3kkSy5CKzKIa1HfLp4VHk4Yw2FqF0gxxEnU7CXeS0pFd3YW2vmGMdFWjrbMHPVPJ3dyUgx3o66gVZTSE/vYmNBemIz0pEXfvpiIlpxp1nSNzhueSkUQPBNtvx7aAHJSou9x5HOJEob8FTSVZyElNRGKCVCdSkJhWgLyKFnRO9WM9nUKs60bUFuYgI0mUzV1NHSoV635wWh0SJwiytlq0NzWrx9SfnqfOMNaHnrpyVJQUoaCkDs29veisj8XZLavgYheB663quRQL2oXGpjbUtMkgn/FZYxjrqkJlfjqS7yTizt1kpGSXoLxFhk/fm824WCZRDmW5yEwVyyPWubRtJKXkoqCiGaL45ybvR19tIQozkkXZJSExUZSjWIamPlFvNZPcJ520dqKlrAD56Wlie8pCYVWTWOcysb3Uoqu9A71yMZVKgbHOKjRU5iGvsBA5OTnIyspGpvTIzERGdi7SK9vQPBVSKjDaXoMasb2l3hV1JlF8dnYN6sT209HRhIrGbnTfvwvlMyLKvKcBNYViXqQ6cFts56K8UrKKUFTbhb65boMcGxBlVYySzDSkpaQip1jsI8Q2K+tuQLeYz7aByRtEVBjtaUZ3cx26B4fR21yLqkyxLqR1m5yN9JIWsa+aXrrjogq1oauhGu3ii6fGLpe2dnkfOuvq0VjXgX5RtvdfGsaoeE9VQ6fYTjUzO66Aqr8ZDS1tqOsRJ8jT65i0L2koRllmqtiXibLuGptWn+Xia5pEWeQh7V4RSmp7pp2MyzHaVY+a/ExkJYv6Iepm4r2ciTo1V3KrGhD7kjIUZ9wT9Uksb1IGCmrb0SkbgUyqIx3dmNEJxQO6UHU9HMEr7XDqZtX9AF3aKofa0CbqVHZ6ClIy8pBRKrbv7l4M9HaisrEXvUPT6oiiBx01xci7J8r9trRNibqVWyn2Q0MP7v/EPljRU4e6ogzcyxTvqeuHbOoGl1mUYp/Y24bKhi60989eEFEXOlvQKO3f+0dFDZtmXIb+pgoUi+3mntjO7iRlIiVf1J+O2T8yJg1joLVWfFYdOvuV0/YXKrGa+9FVW42m+nb0iS+Z+/1ERERERERE9MWRQzXYiqrSBlTU980aA/uTjEMx0IqGonSk37mNhIQUZJc3obl/EH3N5Whva0O3+gLDOKQuuNurO9BS0w3ZUCtqi9OQGHsN167G4fa9PJS0Dt1veKE2iv7GZjSVSdc1NY0JlAPo72pGcV0XOmWzmjANt6GlLAPJt+IRezUWN24mIrWoAQ2fNjcUy9FSlIp78bG4fvU6btxIQHJ+NWr65mpQNYqhtgoUp93Breti+mtxiEvOQ3FD76xlmKDoqUd1TiLu3JCmvY1biTnIr+1Gz4yLPEqMDXSjtbgere2D0xqpTSfKoacGlbn3cPvGTfFZNxAnyjy9pBEtM67VqTDW2yzWSz7yKlvQ8qmHzxvDkHQtPzUet2Jv4Hp8CpJya9HQNyuzGOtHT0M5CvJKUdU2oBkScoKyvxn1hWkT60HM3424O7iTUYLi5oE5y0b6zuGOahSnivK5FotronxuZ5Sjsm1sjgxAosBYTx0qslOQEn8TN6+L77iZhHvZFajuHJu7Ydv4CGRNpShKT0T89TjEXr+L5PQSlLcNP6SuK6HsqUZFbvLE+r0qyuNeAYpbZQ9ZL08vBujzFwP0L4BsbBgx5bew7vxO/MT3NXxbHVy/NBVcTwbp33R/Fl/f+2f8KuAd9bjj5jfdIY1RfrboGjKbC9Ax9OlaV3bIupHVXKh+n19qKCzjPLHirBF+c/B9fEN8/jc9nlW3fJ/+/VJoLn3/j/e9glXnjHGh5AZ6Rz7FXVNEX4TRKmTu3wkXCwcEZPSj6xPDtaeIchAjdfdw45w4aUjpnHVyMYSepEA4L9mCPV7XkSF2hQ+J44houvFqFJ70gdMiG4TeqkaT5mkiIiIiIiIiIiKiLxID9PmLAfoXRDmuQnx1CgyvOeLPwYvVrb+lMdCl4Hp6kC21Tpdah3/b86/4hvtf1M+9GrIWupdt4J1yFBGF0jjTCeoW6vGzHtcq7+J04WV43zuK7Zft1N20/9T3NfE9z+Lb4rukz53e+l16SMG9NC/S9/3+0EcwvOKIG1VJGJI/oikx0eduHCMd1ahMiMSVUEdY61nB1fsa8nule+a+JJT9GCq7hKOWVthp4IkjUbdwIyERCQm3cfP8YRyx2wkj40B1N9ftmrcQ0cMogL5qFN0MgOuOHTA2CkdsYRd4qxcRERERERERERH9PTBAn78YoH+BVOMqlHfXwjP5MN4IXY+f+r6uDrWlbt1ndKW+b4H639/3fnEq9JZarv98/5v4L/+38Uv/d/DLgLkf0uvSdFJwLr1PamkufYb0V/rcye+YGOv8BfV3S9O/dGwV3BIDUdldp55Por8/Jbpzo3B210psWb8Oup4xuFE0+iVrpT2O8bFWVF0NwgGjDdiwdiPWaRlCe6sW1qxYh02GbjgSX43G4Wnj2xDR3MaHgarLCHbQwVI9Z/hca0SrjNsOERERERERERER/X0wQJ+/GKD/HbQNduJ2TSp2xu7FHw4txHfVQfYL+MG+BerQWxqLfEaY7v2SehqpFbnUzbrUMv3r7n9+4PEN8fimeE2a7jtez0+E79NCc+lzpc+XvkcK56XppHHXd8S6qFuvtw52aOaQ6Gkwrh5HpjE/HVlZuShq7JsxfvuXxziU/a1oKclEZvJd3L2bjLu3k5F4JwPZJXVoH1IxACT6VJTAYAuqS/ORWVyLpsFxju1NREREREREREREfzcM0OcvBuh/RyUdVTiRF4Ud113w3JHl6i7dv+r2R3V36tNbpH8Wj8mu2qXPl0L4vwQvgf5le4TlXUJRR4VmjoiIiIiIiIiIiIiIiIjoURigz18M0J8CVd11OJQZAe3o3XjvpBb+GPSxuov1r+/9k3j8WR2sSy3QpRbl0vNSq/LZY5mrW66L56XXpekmW6xL75ce0nN/CFyId8O3YmuUNfzTwtUBPhERERERERERERERERE9Hgbo8xcD9KfEmFKOTlk3kuuzsDcpCIsi9PDnoEX4zcH38auAd/EzvzcmWpJ7S2Oav6gOyKVu2icfEwH7i+rXpel+5ve6eN876vf/JWgxFp7SgdOdA7hbl4F2WRdGlWOabyYiIiIiIiIiIiIiIiKix8EAff5igP6UUaqUaBnsQGF7OZIbsnC+JBYeyYfVrcZfD12P3x/6CP/l/zZ+4P0yvu35HL7l/iy+7fGcuvX5L/a/qX5dmm7jxV1wTQzEueLrSKrPQkFbGZoH2qFQfSkHkSYiIiIiIiIiIiIiIiJ6ajBAn78YoD/lxsX/pOA7o6kAMWW3cKogGsdzLyAo8zQC0k+ou2KX/gZnnUZIzjn169J0qY05aOhvwfj4uOaTiIiIiIiIiIiIiIiIiOizwAB9/mKATkRERERERERERERERET0GBigz18M0ImIiIiIiIiIiIiIiIiIHgMD9PmLAToRERERERERERERERER0WNggD5/MUAnIiIiIiIiIiIiIiIiInoMDNDnLwboRERERERERERERERERESPgQH6/MUAnYiIiIiIiIiIiIiIiIjoMTBAn78YoBMRERERERERERERERERPQYG6PMXA3QiIiIiIiIiIiIiIiIiosfAAH3+YoBORERERERERERERERERPQYGKDPXwzQiYiIiIiIiIiIiIiIiIgeAwP0+YsBOhERERERERERERERERHRY2CAPn8xQCciIiIiIiIiIiIiIiIiegwM0OcvBuhERERERERERERERERERI+BAfr8xQCdiIiIiIiIiIiIiIiIiOgxMECfvxigExERERERERERERERERE9Bgbo8xcDdCIiIiIiIiIiIiIiIiKix8AAff5igE5ERERERERERERERERE9BgYoM9fDNCJiIiIiIiIiIiIiIiIiB4DA/T5iwE6EREREREREREREREREdFjYIA+fzFAJyIiIiIiIiIiIiIiIiJ6DAzQ5y8G6EREREREREREREREREREj4EB+vzFAJ2IiIiIiIiIiIiIiIiI6DEwQJ+/GKATERERERERERERERERET0GBujzFwN0IiIiIiIiIiIiIiIiIqLHwAB9/mKATkRERERERERERERERET0GBigz18M0ImIiIiIiIiIiIiIiIiIHgMD9PmLAToRERERERERERERERER0WNggD5/MUAnIiIiIiIiIiIiIiIiInoMDNDnLwboRERERERERERERERERESPgQH6/MUAnYiIiIiIiIiIiIiIiIjoMTBAn78YoBMRERERERERERERERERPQYG6PMXA3QiIiIiIiIiIiIiIiIiosfAAH3+YoBORERERERERERERERERPQYGKDPXwzQiYiIiIiIiIiIiIiIiIgeAwP0+YsBOhERERERERERERERERHRY2CAPn8xQCciIiIiIiIiIiIiIiIiegwM0OcvBuhERERERERERERERERERI+BAfr8xQCdiIiIiIiIiIiIiIiIiOgxMECfvxigExERERERERERERERERE9Bgbo8xcDdCIiIiIiIiIiIiIiIiKix8AAff5igE5ERERERERERERERERE9BgYoM9fDNCJiIiIiIiIiIiIiIiIiB4DA/T5iwE6EREREREREREREREREdFjYIA+fzFAJyIiIiIiIiIiIiIiIiJ6DAzQ5y8G6EREREREREREREREREREj4EB+vzFAJ2IiIiIiIiIiIiIiIiI6DEwQJ+/GKD/nSiVSgzKhtDR2Y3G5jZU1zWivKoGJeWVKCqpQGFJOR988MEHH3z8j31Ix8LS8ipUVNehtqEJLa3t6O7pxdDICMY1x1IiIiIiIiIiIiKivxcG6PMXA/Qv0OjoGHp6+9Da3oG6hiZUVtehrLIGxWVV6rAgv6gMeYUlyC2QHsV88MEHH3zw8T/4USKOiaUoKC5DYWkFSsqrUF5Vi6raejQ0taC9o0t9YqpQKDRHWSIiIiIiIiIiIqIvDgP0+YsB+udMpVJhZHRUHZzXN7agsLgcWTmFyMkvRml5NeqbWtDR2YW+/gEMD49gbEwOhVKpfh8ffPDBBx98/E99SMfCUXFMlMmG0NvXr775rKa+EUWlFcjOLUR2XhHKKmrQ3NouziUGIZcrMD7OtulERERERERERET0xWCAPn8xQP8cKRRKdHX3oLSiGpk5BSgoKkNNXeNUq7mh4WF1YC51586L/kRERHNTB+oKBUZGx8T5w0SgLnXpLvXkkpNXrA7Ta2ob1MdWlYrHUyIiIiIiIiIiIvr8MUCfvxigf066e/rULcxzC0vU3c7W1k8E51IrcyIiIvrbKFUqDAzK1EF6VU29esx0qcv36toGdat1IiIiIiIiIiIios8TA/T5iwH6Z2x4ZAStbZ0oKaua6F62sga9/f2aV4mIiOizJoXpbR2dKCypQE5+kbplendPr7pbdyIiIiIiIiIiIqLPAwP0+YsB+mdE6oJdaglXWVOH7PxidXAujXuuUqo0UxAREdHnRToOy+VytLZ1oKCkDHmFJWhoasXIyJhmCiIiIiIiIiIiIqLPDgP0+YsB+mdAumjf3duH/MJS9Vjn9Y3N6o2G45oTERF9sRQKpXqM9IqqGqRn5aG8sgZDw8OaV4mIiIiIiIiIiIg+GwzQ5y8G6H8jlWocHV096pZuUpftDU0tGBuTa14lIiKivwdpHPSqqjpk5uSjtLwKgxwXnYiIiIiIiIiIiD5DDNDnLwbofwPV+GR4Xoqc/GK0dXSx1TkREdFTQurSvba+Ud07TFlFtXqoFSIiIiIiIiIiIqLPAgP0+YsB+hNSd9ve04f8Iik8L0JXdy9UKo53TkRE9DRRKBTq3mEysvNRXlUjzj/YEp2IiIiIiIiIiIj+dgzQ5y8G6E9I6hpWGvM8O7cA7R1dDM+JiIieUqNjctSJc470rFxUVNeqQ3UiIiIiIiIiIiKivwUD9PmLAfoTkMY4r6qpR3ZuIRobmxmeExERPeVGR8dQXlGN7LwiNLe2a54lIiIiIiIiIiIiejIM0OcvBuiPSalUoq2jEzkFxahvaFaH6URERPT0k8mGUVpehYLiMgzIZOrhWIiIiIiIiIiIiIieBAP0+YsB+mPq6ulDcVklyiprMCjjOKpERERfJp1dPSgqrUBldZ36BJeIiIiIiIiIiIjoSTBAn78YoD8GhUKJ8qpaZOUWoqunly3XiIiIvmQUSiXqG1uQnpWPltYOHsuJiIiIiIiIiIjoiTBAn78YoH9K0gX2ru5e5BeXoayyGnKFQvMKERERfZn09w8iv6gU5ZW17E2GiIiIiIiIiIiInggD9PmLAfqnJJfLUVZRjaLSSvT1D7DFGhER0ZeUQqFAW0cX8gpL0dDYonmWiIiIiIiIiIiI6NNjgD5/MUD/lKTQPDuvEJU1dU9feC7mRzk2hKHBPvTLxN8xFVTM94mIiB5qdEyuDtBLyirFf49pniUiIiIiIiIiIiL6dBigz18M0D+FsbExNDS1oLCkHK0dnZpnnx7jY/1oSj6EkL06MPc8ipDUTrSPaF4kIiKiB6hUKtTWNaK4tAIdnd3qfxMRERERERERERF9WgzQ5y8G6J9Cv5jHguJyVNc2YGj46Uumx4fbUH5iJXRe/Uf8/M0t0DvTgBpuq0REX3JyjHaUIfNGFC5cSUeG2LGPal75VBS96Km8i/joaEQnlqKilwHxdFJvMn19A+oW6NJY6AqFUvMKERERERERERER0aMxQJ+/GKB/Ch1dPcjMKURza/vTOfb5cBuqwxdj6/NfwX+8vA46ZxpR+1QU6xjk/Y2oykpDyq1buJVRiIyGQcjkmpeJHts4FMOdaMrOQGb8LcRLdarxKa1TY33oayhARmoybt26jcyiSohZhULz8udnBP315Si+nYCkxHu4XdyMur75HAyqMDbQhrr0VKTfvo341AJk1/RgUPHlH8dC2ZeHwkgTbF/0Cv6yYDvMgtJQPCIt8achh6w8HtEWK7H85Vfw/IZ98IlvwSCH95hBCs2l8DyvoATDI491ewIRERERERERERH9D8cAff5igP4ISqUSjc2tyMkvRk9vn+bZp4wUoJ9YAq0X/hd+uGA9dM8+LQF6J7qyQ+C68A289P0f4D/e18X7R4tQ3vs/JMFRKTDc3Y6OhjrU17aiubEfw6MqML/6W8gxUH8TYYvfx4ff/R6+K9WpY8VPZ53qyEBG8Fa8u+BP+P73f4wPtzshtBQY+NxntQnZh2xh8pOf4ve/+yt+ZHISAdlDmtfmo1F0FlzEwTdfwVs//Am+88pWLPNKQknfl/9OndGy04iy/C88+4vv4h+/ugAf74zAjU7p1iRhfARDvZ1orG1AQ20jmrpk6J8xjHc/6q4fhMPvfoZff/M7+Ic/6UP7UC6a2cj6AfWNzcgtKEa3OMY/lTfJERERERERERER0VOJAfr8xQD9EaT5rK5rQGlFNQZlT2kIpQ7Ql0L7xf+NH72yAXpPTYDejo7U/TB/9r/xk698BV95Zime8c1BYff/jIBCNdyA3HAr2G95C2+/o4v1WyJxo6Tn8bpgplnk6KuOwoEXnsGfJuuUXy6KnsY61ZqEJK+F+P3PvoOviHn90wpTBOQDfZ/7rNYh1d0A6//X/8bX/+0/8JX1QXBJmc8H7xG0Z53A3l//Ar/5yv/CV36+EC/viUNez5c/QFd2pSIjeC2WP/s7/OT3W6AfmIJSsQNRt0Dvy8DtcHss/XgRPvp4BfT9r+F6rfptGnIMFETjpPZreOE3v8HPl++Fx812tkCfQ3tnF4pKK9DU2oYxObtIISIiIiIiIiIiok+HAfr8xQD9Ebq6e1FeWYP6hmaMjD6l0edTG6B3oDMjELavP4vf/dO/4J9fXo+XD+ajpOd/SIIzXocU1zex6BdfwVe+8mt8/xdeCM97uuv700+O/tqrOPzWy3jlH/4R/yDVqUMFT2ed6khBiv8qvPiHX+Cf/ulreGWTNYKLgP7PfVYbkeFjhm3/9nX8+Ee/wle3hcAzfVjz2nwkR1duJHz++kc896//hv/3zCq865yAwt75EISOYLilCBlXL+PSjTzkNo/c775dloDzzh/jn/7f/8X//n9fxTOGx3FiRoAujHWjo/wurl25jMsZVajn7mdOff0DqKypQ1VtvfrchIiIiIiIiIiIiOjTYIA+fzFAf4SW1nYUl1airb0TcsXnP3rxE/kyBeiHClAqdbetkqG/rRZFRYXILyhAVVMXemfcnzCK4d4W1JQUoSi/ALmVjajrlUOlUonK04aW2mLk5ZWiuKwN3fJxjKsG0VNThLyMVNxLy0FBdSe6pj5PjrG+RlQX5SAzJR1pmYUobepF3/SBhMcGMNhWiTLxfTm5pahp7YNMzKa8txmNJVlIS01Fak4Jyup7IHvkAMQT816ZeR2hJm/hw999DV/7+p/xy9+bwz3yDtLL6lHTJsOIalaSOtaDzoYyFGRnIPVeKlIy85Ff1ozWfsWjxzxWiu/sqEJ1eQGy88TyVdShXaYQSz6TYqAZbbV5KCwoREFuJera+jGjqox0o726GIXZ6UhNEfOQkY+Cqla0Dc6eA/FvadqGSuTl56OwuBR1HTKIr7xPrOO+tnpUFIjXC8X6qm1Hq7rwxESydtRXlyEnpwjlNa1iHSox1t+G5qJ0ZOXkoqh5AP1z5p+fNkAfwXBXHaqKspGVLupEeg4yCipRNXt5Z1P0obu+AqW5GUhPuYd7qVnIKqxGdfuQ+MSHGcJgexXKckXdEvUrI68SNT2i7DvFew+txsvP/AL/+EkBumIAvS1VKM7LFN+ZhtTMPOSW1KOpe2zWeOkqqKT5q6lAeW4OCqsaUd+nhGKkBz11+cjKykdGRSe6R2uR5W8BHXWA/t/4qk44/HPEUo91orUiDzmiLt9Ly1Wv1/aHL5Sasq8JTeXiPdJ2JeYtLbcExfXdELP2CcahGmxFU2Uh8jLTkJIstp0MUefENtw6MLseDYvtrh61eXkoKilDuZgh2egoRjvKUZqXhXv5dajuHL0/7MFQJzqq8tXbeYpYpznlUt0ZRk/xRex//k94/lMF6CqxufSgo0J8X14ucioaUC0WaFzU15GOSpRmi3WQkqGuLzViJ/KJiyp9Ts2s7aWyFa2Dn9RP+qjYhdWiqiQXGZPlmi22x3KxbQ4oZw7xMDYIWacon9pqlJXXoKldbBeifAbEfNalBcJrx4f4wfe/j3//3k/w7Oa9cL1UIPaJlWjqGVP3dKEa6UVPo5i2WuzbxHbW3DUMsbucRanez9WX5iFbbCspog6ni31gWaPYR84+5I3LoexvRW1V6cS2W9uBXmlR5aIO103sf1PSxTZXVIdmsQE/cr/1lJDGPq9raEKJKLv+fp7oEhERERERERER0afDAH3+YoD+CNLYqPlFZejp7X96x0b9MgXogcUok4aSHylE2mkXrF61DB8uWgyroGtIalXHshpNqEo4CqcNa7D6o4/xlnkA3BK7MTI2ApREIMR5A977QAdrtU4gqqAEpUXnEGy8HO+88Gf89rn3sMj4EEISW9A9qsBoexqSju+G4dK38fIzz+Ovr67EBocTOJPbiX6lFPcJnbkoOGOJ7VtW48139WFz4DISygtwN9wJ1itfwp+f+SP+9NZ6bNkTivPpjWgb+6RoqAGVtw7DZuUSvPG7X+G/fvEj/McPf4mf/fwvWPD2+3hzqxusTxSiYVTzGeNKqPoaUH07BAE2m7H49efwh18/g9++sBAfbnGB+4lkZNT2YfiTvnK4Ha2392HvznV4+91F2Ghoj+DUNjTNCMDG0HL7II7sfB9Ll67BwpVu8L9Sinr1a0qM9Zaj8EoQfI1XYfGrf8HvfyvNw8dYpO+BfRcykNsqw+hULigXG0cczvia4/0PP8Ty9VvhHlWI4gHNy1KpjpUg5ZQHdi5aiKUr1uF913MIL5JivX6g+By8bHTx2psbsd3qCKIL8pF8yQ92H/8Rr4j53xqaj+zuiU+a6VEBuvgr70d70Q1EHzCH4YrX8fJfnsGvn30Dzy3aDn2PSETltKFrdHxmUCmWXzFYL+rcSRy10sKGd57DX3773/j1H17BK0uNscP3Eq4WtKJzbNb7FAPoL76GC75G2PTB8/jL7/6C597Vg2HwPSSlxSPefzVe/uMv8M9zBugqjMtaUJ98DiEu+lj93sviO/+I37/wAd5euxt2wbdxr24YQ1PrXY7RnlTE2u6A4ZtvYrW5B9yvVyAn/iROmL2LF15djgVWlxHfUoa8I3ug+/Vv4Mc//jW+pheGgLtFaEsIgKfWu3j1D2K9/kVsI2K9+l0vQ7XYCB6Ie2IovtgAAEsXSURBVJVyDDblIOWkExy1RJ396+/x69/9GX9+Zx1W7z6M4PgyVHTLoZhZiKL4x9TvuxfpAUfdJXjvpT/hN7/8A/7w/PtYst0eHqdTkdM4PK0e1aEgYh+c338fqzfowOREOm6n3MMdvy1Y995r+K/lPnC72SbmT5T7aB3Krh+Cr+FifPjiH/G7Z9/Ea5vcEJiciYyU0/B+/s948WufJkAfEfN4Gxd26MLg3Xfxtok7dp7PRHlmNOJ9tmHNK3/GM394CS8s1ceOwOtIqBzA5KZ6nwLy3goUXz8Cvx1rsOQ1zfby/EJ8rOcOr3NpyG6evr1IxqGSd6Oz4Dou+eyC/pq38YIo1/8W5frMq0uxUMsFnhEZyGsbux9yt2cj57QV9LU34IOFRnA6lorUxibknt8BJ+1n8Zc//xY/+vHPxOPn+Pkzr+PPr3yA1drG8L/dLPagYrdQEYsYh7XYvmEF3tXdD4+oSnROnyeVDIN1qUg47gzrDe/h9Wd/j9/87jk8+/ZGbLYNQXhiBWoG5Ji610fRCVn2CThbaOPVNzbC0P4Urlc2oSz1HI6ZLcN7Yv/7++fewmurrOB8Ig15nQ/exPM0UiqVaGppQ15hKbp7pAMUERERERERERER0aMxQJ+/GKA/QnVtPXILiv/O459L6UU3apu6Ud04R3vITxmgy5o70F7RAGkz/qQs9rMzV4BeognQ7+Ga7yb87Mc/wNe/9T18vCcE0U1SjDmpCkXn7bD5Vz/Hf37j3/EPH1pgU0w3hkbFesj2heOG3+Fr33wZv/i1Dey8rGGz+y0885Pv4+tf/Tr+9Zs/wHd+/hwW6ljj8JUrOOpojE1//hl+8t1v42tf/Ra+8Z2f4oe/eh0f6frhdOGgKFmhPRlp+5fixWd+iX/6t7fwwTIT7HbUxuK3nsFPv/VV/PO//Bv+9d9/ip/9bgGeX+YE58hCND6QOk4qQ94pC6z6ifief/sPfOcn/4mf/kx8/4++j+9/51v4P79Zj4Xu91CtWZXjtXdx118XGz9agN//8qf43je+hn/4f/+C//fV7+Lff/pH/PbZxVhpsB9HU1rQ/rDmsMoeyLJFWa98BT/8h6/hp8++ixVHs5DepXldrRFpXtux7WdfxXd/8nt8631PeMW3QYYxyBtvIdJOFxsW/Bl//OWP8J2vfxX/8A/SPHwf3/3ZH/DHN1ZgkVkIItJaMNHBsVxsHKexz+QDfO1rX8ePfvVHaB++h/SpAF3UsLEMxO7Vxgff/jZ+8KNf4qtbAuGSLsVposSz90N3+fP4P//8Zzz/ynbYe1lh27rX8dN/+gq+8bM/4X2/TKR0THzSTA8J0Hs1Lw83oP6WN6y0FuLF3/8Kv/iPb+Kr//zP+L///E38y3d+jh//+nW8s9oO7lcqUDG1jYgV2ZWMW0EW2PLGy3hBrIMffPNr+Cfx+f/3n76Jf/ver/DLP7+KBZtdYXO+FA3Dk2liF7pzz+CAFBT/8T/x4+99S3zXv+JfvvEz/PAPa7BeywS7DBfimd//UtTLf7sfoGvejY4C5IXuhMGKN/DMf/8CP/jWv+GfpTL/13/HN37wa/znnz7GMoMAnMpuF1uSRIGR9niEL3sbr4r68eu3F2G9ox+s1y7Bhz/8R/x///4X/GDLCVxuKEb+MRvofuPb+OlP/xvf1HGCkb0d/Jf9EX/98Tfxj//4VVHH/wPf+9kz+OsHm2ASdg+p7eov0BhCc9pZBBt8jKULfo///vF38Y1/FWUove9bP8YPf/08nnlfF1p7o5FQO3K/Zb5SBlnhKQTZrMfbz4t6+9Pv4Vtf/Rf83//7z/gHUZe/+7Pf448LPsQym5M4kTmk2darkeJuhA1f+yZ++as/4F3zvbDcaYatv/su/uOb38FXnreG5bVOjA7VoOK8CcyWP4vf/vxHYhsRZfUv38TXvvcbPPP/t3cXUFpV+xvH9f5VajqZbobukgYBSWkJaRAUEVFRQEEEEURBxUAFW66BdKd0dw4w9DDd3fP9n/My5DXgXgPH57PWby14Y94Te5931jxn7z1gKL3HPMuwypWoZ7S1Er8ZoGeRGLqID5s+SINSdtg170St5yYwqXc7+lT2NLbZhhJWzth7laVM9aZ0GvU+nx5MI+HazSgZZF/eyA8TnqBvo5pUD/ahtNFfStzYX5p2pd2znzJ3R/gNMx5cImTpTKZ0a06LGuXx83LBxqoU9xe3pridO65+VSlfx7xJ5wfWX8o2zoIhYiObp3egdsUgipdqRLeX17DqzGl2fNCYnrVLYeXsi7d5ffELxNvTB/tSJfCt0oARP4YSarw99dDXfNLDjxoBrjxQ8ykGfnKIiGvXrRjCtn/N9IGdaF+nEsHepbE3tqeYcf0o5eRn9JU61HnkSQa9tYrtl9Kv3DiSF0Hylqn0bluT+0rUpXHrF5n6yRRGDG5LdW9XHG3tjPbvjotvRcrWHsKQV9ezK9a4vlg+7+5mzjKz9+BRYuMSCh8RERERERERERER+XUK0IsuBei/4fSZ85Ypsc0pXv8aOWTFHmb3d1MZN3wko95eysawK5H6NbcRoOeHbWPVzJd49ulXmfS9OZr4N+Zu/l38SoCetY0V7/TB27001rbOtH3pU5ZE3BygH/9xHP0DfPC3tue+1i/Se2ki6VkZcOBdXu1dFTvHcrh5taJB6448MmQgvYcOYfCjD9OoYllKWzvhVyaYpp070aL9Y3R59AmeNF7Tq011KpTzpFQJB7wqt6f31yc4bCZV8TvZ/V5X6lYKpJRdZcqUa0KDNh1pO+RFnnt5ImOHdKZtTXfc3Rz4l10lavWbwech6aTcOsWxqSCWqMMr+Hr8MzzRohq1g90p7VaJwOAOdBvyAiPfn8eXmy+RaE5Hn3KaXZ+9QM+azjg4OWFXsT1tH3+VydNe57UxQ3i0Xln8SpXEwbMpTZ/8lmUnkgoD7FvlQOIuvn+uM83s78OpTB3KP7+QRaeunmdjQ1O2MPfpbtQrdj+uQVWpNnYlS422RMxe9r7/GF0reGL9L3d8qz5Ct2cnMWHKG0wZ2YMu9fywt3ehuHcT2k6az6pIs/UZP+/i97w14mFKlbTGPaAyA+dsY9e17wgzQN/NmtcH0sbO3tj/QEr2/5hJu80zHA8H3+XxTrV5wDqYIP+6NHmwChUfakO9Dn0Z9uJrTF95hpM/u675zwXoxwixBOjJRO/6N+/2qknZ0jYU86nPg93HMHbyW7w17TXGD2zNQ8HO2LpWo1ynj/lsW5wl/C3ICefs3Cd5omop7rnHkWIVHqHT05OYNv1t3nr1OZ42trOyrxP3OlSkRt/pLAzNsoSbeVHr2TCtLc0C3bAvboNH+UY06TOG58e9wpjHe9OjWUPqVK2On68/rk6OVwL048ZpMDc16xInl77JsKZ+uNvb4FipFW2HTWbiG8a2vvYSz3SqQw33kjj6Nabp2BWsOGue9QKyYzbxdYfmNCpmi1+FstRq2ogKFY02/9Cj9BwxmTFzNnEkMYS9n4xlqLM7gV5+uDZsSM0Wj9Cjy+M8O3oCr4weTM+HquFnZ4ujgwc+zScyaXEYseZ2GTLOLeL78S2oX9p43tqXMs360m/sDKa+9gqvPNmWpuW9sXrADdfavekz+wD7Y83bcfItMz2ser4F9ezv4Z4Sfng/NISh44z9mT6D157vTfd67vi5WFHCvzs9J+/kVGa+8a7L7J76NH1tnPD39KFK47pUqdGC6jU60HXQSAZPns/qA2e5tP9bpnYtRzk3a/7PoTz+dbvz2HPjmDDuOYYMaEfD+lWo4+9PsLs7NlW70+pXA/RsEk8v4cOGdXmwpD325cvh27AZzZr3ZehT4xj/yvM83aM5tQK8cSpeHIegNrR+aQs7oq58B+RE72HfB315tJInNv9yw6dSO7qOnMgrU6Yx9dledGvgj4ODC8U8G9J6wjyWRxjtON9o95GL+eq5llSwssOpdAXqdBnByMkzjOPzJm+9PJAetb1xvuce7vdrT/vPjnPQvE7Gb2XrjA7UKOfPfQ80pOOopawOj+PSrtn8OMW4rrWsh5eXDx7e/kb/6Ubnx19kyvRZrDgRb7kxKPHA13z0qDdVfR25t9qT9P/kENGWbpVL8vHv+W5EY2o4u2BVsgyVmvdn6IS3mDxpPOMGtqBB+dLcV8rTOM99GPbdQY6ZDbcgkrRtU+jZujoPlCpHcPBDtGj1MA917UPXwU/zeJ/OtK/hR5CXA8WK+xHc4BmmbI4g/Ffnwb87xMTGs2ufcXxibrrrSEREREREREREROQXKUAvuhSg/4ZToefYd/AoWdl/VQKQTfqFdcwb0Zh6Lg9gV6ktj769ji1hV9a4tfjVAD2X7PDdbHy3D10q2VPKqxG1nl/KyjN/xnH/tQB9Oyvf7Ye/tyf2jm48Mu5zlt4aoM9/hYHBAQTZO1O83Rj6LbsaoL/HpL61cXJ2o7RPJap2n8Jri44QcjGEkJ8+ZmqPJtTxdMXV1Q/3wCY8MuFL5h2M4NLJfez49lkGt6yKr7UdrpWbUH3yT6yLND4ueTd7PuhB/aqB2Dk44VG5JS2e/4H5R+JJy80l8egivnu1PS0rBOBmZYtH48fo/tVpTv/HgtaGwocy4w/x02vt6FHVCmvbupSv/haf7Yi4PgI5P52MPe/wWt9aOFi74+7TkJajf+CHo2ZYmk9+wlE2zxhE7zoexjEKwCO4J89/s58Tv3gvRxynvhzBc/XssPeuTumWM/hwc3jh6M80co9/wsS+DXG534mgyh155seTnM/PJGrD24xv7E05dxesgzrSeexCNl3OujKyOGwTK9/uT/Py3ng4OVG62UiGfx9Oaq7RHyLn8/bINtja2OMdXI3Bn25n900B+h7WTh1MOydnPL2CsRn4CZP3XA3Q32No1/pY27vi7h1EYN0u9H3jO+bvDDH63AXORKSQlGkGs7f6mQD945DCWQ12sn7GYzT08MHNqTqNh8/hq0PZ1282Md63aHwb6vsE4ujQloFvbuC4sZO52Rc4MHs043s8xMN9nqXPexvZdm1EdgSRC0fSo2kFHrjfigqNuzJubTSX84zjtmYarzYvTZC3O1beNWgy2Dy/cURlZJN9eTMr3xjMo9UCCPD0xMnRyRKgzw7BMgNEbuhcvhjZCHc3XxyDm9Nr2jI2XfvMfJJ2fsj7/StRxrss9lWeZ9wPpy2hfVbcZr7t/DDNbZxw9vDHs8JDNBs+iw9XHODI6YtcuBRDav5ZdpproLt6EFDaCxfvitTqOZ4pa85zOSWHgvj9bJo1nL51/fDx8MbG8SG6jF7AFuNY5OTFc3LOQJ6sY4N96TK4Vu3DsNnb2G/eoJCbSubphcx+ujWNPK1x8q6CR/t3eX9jLBnGNqeHb2b+qCcY2rI5nZ6ayKtLzhF29eDH/sSGiQ1oXMmD+++vTJNHZ/Dj5RyjjUVx4M1nGOhcGl83H5x9q1Gl4xhe+HwLW4+dJfRcNKmhq9nwUR8aGtcCFzs3vGs/Rs831rE5PJXc/CTCfvqItwfUo5a/L+7GNcH2dgL00KXMalqPBrZOxn4GElSnB4M+2sqOSKN9ZoZxYf0nvN6rIbUCXLGxq0yF+i/x8e4osgrMZRBmMrGpLxU8nLEObE+HF35kw6XMKze2XN7KmncH8HBFXzwdHXBt8jRPfhtOQqZxhA7MYGKfqpS08cQzqBVDZ+/juGV7DAWhHPhsPOO7tqBd/xH0/PQAe8xpBxK2se2dTldGoJdsTKeRP7DWaOuWwxq5iu8mdcfT3Q1nox3Vf+YTPjfau5nVXxW7/xs+KhyBfr85An32UeMqYbw77wI73n2cPmVtcPcsg1vtJ3n+0z2EmJ0+J4W0Q9/w7rCHqVTaweifwQQ+9jEf7TbPcgzpO9+kd7s6WNk54uZXlaqtX+T177ax72IYIbsWseCVLnSsGURpG1u8a7Wk3az97I691gvvWubI8517DlpGoouIiIiIiIiIiIjcDgXoRZcC9N9w8vRZ9h44Qnb2XzUJbQEFGZFcXD6ekY9UxNnBFe/qrej+7no2Xi5cuzg7mnNzbwzQL3PBkubkkxu+i83v9KZr3QAcbfyp3PoFJqwJ5/zPhpO/tz8qQJ/JpL41sbPzwSe4A898vodD6YUBTUYIW9/oTt/KVtiXroF3g8nM3HiBJMvTOeTGr+O7wR14uERJHCo2xnf8Wpabo7BTrgTo9aoEUsrKhdrdXuCtbanEXN2ggkRiDn3NWy0fpLa1FVYV2vPg6HVsDbfEzD+rICPU2JYO9KxeCisbM0B/m7n74woDbUPmZY580ovH69tgbVuFgMrjeHP1eSwDvC1yyT31bz545mE8HV1w9apAs3ELWHD2l8KoPLL2vs97Qyrh6lwWV5/BvPTvQ5i7R3YUEUuGM7SNL8VK1KJy47f56kii0UJOsP2joTTx8cDNyY3gPlN5fUvi9Wm5jW2I2fs173UOpqaPI8WcHqH9iDUcMvtt9CJm/o8BesmS1vhUa0LbN9by0/lMri4tX1BgtPuf3c3/DNAbzT7NqWTj9Sc+5qOhZfB2K4Oj52Ce/XAbJxLTycjKNvpvFhnJpzn0wwSGB/gRWMqFJs/M4odQ4ycW5JKdlkxKXCSR0VFciE0jOTOX/Dzj+KeeI3rlKzzWqgYlipcisH5Hhnx/irOJoez7aASd3Z3xcvPD7uGxvPDjSZKuNeBcMo8vYN4LD1KzrBdW1g406vcSn542A/RcopaM4uWHbHHyqYRL60m8ueIk8alZZGYb22q08bSYbax5exCNPf1xcWhIH+P4HDGOR3rsFn7o2pLmpayx9m9E+Z6z+XpfNKk5hWuzWw7aJXa/M4rHnVzwdQvAvs4zRh85wMVrDc+4LoQuZtErD1E12AcrK1caDZrGN6ezSIndzsLhj9DUqiT2Zevj//S3fH881TybhVK5uHgU49o7U9o1wNj+XoyYvYdjxgsK8rJIT0oiKfIS0VERRCVnkpFrXKNyc8gO28bWd7ryUM1AihVzom7Hkcw6mkmycY04NP0ZBjo44uURjH2D0Yyae5RLmcb7zF0xKvPwx3w1MoCyvp7YGP2k9fBPmHc25/pMDDmXCF31Bs/UrEh5O0dK/uYU7lcD9AdpYGVLyaBWPDj0e1afTzPOTKG0MM7+MJzHWwdTymgrQdXb8uLCU0QknGPfZ8NoHuCFm2NpyvSaxMSf4q9Mt26RR+z+f/Nht/LU8XEw+kt72j61mr3xKWQd/4hp/Wpia+2Bu38zHpu6jPUXs8nJzSUvJ8M4t9EkREYSm5hEfJrRDsy2FLmZLW/fHKCvMa6flvORuIH5U3vg6XYlQG/4/FfMPWU+cV3svlsC9M9CSCgwenfMcj5+ui1lra3xCK5BvXELWXD6+tWXgjiO/XsMzz3ogK+7H7Z+wxn16UkSjcdTdk+nX9vaFDf6Q1D9Dgz6/DiH443+Yr4vL4nUE//mre7NqW5eYys3I/jVDay5+HNTddxd4uIT2LH7gHENUIAuIiIiIiIiIiIit0cBetGlAP03nDx9dQT6XxWgmwooSLnAqeWTeb5DOVwdSuNWpQ2931nNBnPUam4cEd93KQzQ+zJsQdSV9bkjt7FxZj+61PbD2dqPcq1f4vVVZzhnHPI/J874IwP06tjYBOFX7nFeW37uSkhsEcHBWX0YUr04dr4NCOj+FfOPX1uY2ziUR1gzvCddipXAvmITvMetY9m1AP1R6lUOoljxarR58lOWxtx8nPLi97F8ZHPaBVpTwrEp1dp8xuJTidcD8VvkpZ5i89RH6HEtQJ/O13uiC4O/AvJS9rFieBs6uxXD1rchZR/9ioXHr41PvyJnH6veHEJjZ2c8PP0oPfgTpu28Htf9h8T1LJvagxqOHng6V6X3myvYlWX8mISDbH+1FZ3LW3FfcCceHL2VvebU2/ErmDe+Df7uHjg4e/Lwy1/xY3jhzyqUd3kj68ZVplllZ+57oD5Ne33B8svJ5EUu4f1n/4cAvUt9it1fknINH+G55eFcsCRwv+XWAL0PTT49yankNFLXjmNCG2e8vANx9W5E49Y96TNoIH379qd3n/70HdCPnh1aUN/LEx9He6oPfIOZe/O4PrdEDpmXdrJz+VzmzHiDyaNHMurJ3vRu35gqFcri4uBAUIOuDPr6OOcu7mTttL7UdnDBwyOIyk9/yWdHbgggTamHOfxZfxpWK0PxEjY06jeOz88YTa0gkkNvDmCgrx2ePsG4V2tNyy59GTSgP32M7ezddwD9Bvai88NGm3DzwM3Rh/YvzWW9cR5TY7YyzwzQixWnVLWOVHttB/vMubpvcok9ZoBu74CPedx7fcJb22/58s45xrHvnuJho707WllT9bGJzNwWRcyRuUx+7CHc7iuFV42WtP1w/y3r6BuOfszsp2vg4eqOs0tFekxdxAazT19VEE/s4bWs/GE2b059jZeeG8Yz/TrRsVlNypYJxMnOljqdnuTdgxkkEMuhGc8w0NbWOBYVse/3Be/tu/mmlLgtbzGzhwOB3j5Y2XXn8Slbro/cLpQeupjPOlengZstD1TqzsOTbiNAb1KXBiVKUaxuP5q+e4TQm76KjG04+gEv929AqVI2+FZpRK+P9rLvwAZWTWlPWW8vHBzdaTF6Dt9dv/hY5Eds4adXq9GyihP331+Pxt1ms+hSKlnJW1kwthPVHJxxcytDhfptadNtAP0Hj2DEhHeZOXcDW0+n3BDGG8J/PkC3tLT4tcyb0v16gP7sl3x9wvKua24N0Ad9cZr4nGRyj8xkXK+62JS0w6/yg/T/6gD7bvkqTt75NrP6uRLo5Yu1bSf6v7GNUxkJJO6awYB2tY19K0Hllr14dXMK5iQe12TtZf7jXWhltFHbSg/hM95oC+fv/gDdHIFuBugagS4iIiIiIiIiIiK3SwF60aUA/TdcWwM96xfnzf7zpIdyasXrPNuxMh52dthXbMeAj7ex+UwUZ+f1YESTe/FtMYRnlkZwLmQHe2YNoFM1Nxxsy1C+9VheX3mS0D81x7g5QC/ZoDf1Zp0g5OcC9PGfszTy5gD9xIJXGBwcQJlfDNDL4lfuSSYsOc3ZwneRfYnd7/dmYLUS2AU2IKD/V8w/Zlkg26Ig+xDLh/WgU7ESOFRsgu/4dTeMQO9WGKA3pcPweaw13nbjWc9OOMrGqS3pUsOaksXqUa3mO3x/NPaG0do3y005yaYpNwfoX+2OKgzI8slJ2MS33drw8P2lsC3XlPIj5rEy9Iaw3+IU2997lsecXfDxDOL+3u/z8qbr+/Ofojj64ySGVnIkyLM0FUZ8wTcXUsg4s5ivutWlnvV9uLccxMDFsVfWJT73LV8+3xAvTw/sXTzpNunfrIkrHOF6VfROdr7XgIdru/NAsZo06vkR884kk3N5CR88d0OA/tl2dl/rzsZPyNnDuqmDaf8rAfoD95ekUuMOTFgbwe1lbD8XoJ/gVHICUQtfYFRDF/x9fXHz8zH2yZ3SLq44Ol8tN5w9fPEMCMAvwJ/ag6fz7v4CsvIzSDm+mpWzxzPmiW50alqT8mUqEuBbiWplK1K5amUCywTj7uRImYbdGPLNcS6EbGDBa4/ia5wXN+8ytHxlPovOFW7iVZmnOD1vLI1rVqB4cSsa9RvP50ZDTSkIZcuL/ehm7YaPtz+evn64u7njdNO2uuPqFYC3n59lWzuO+4a1xvdvanRhgP5AcUrV7UK1d/exP8Yy9vwGhQG6rR0+PuWweWoubx+49fp1kTPLp9CzWnk8rWwp3+s1pq87R9S29xnRqwH/Z2xvYJ3WDP7qBEdubW5n5/P12LZ4lHbB2d2NTpO+Y43xmvzMCC5u/Df/nvIsT/VqTbM6lfAtU9moKlSuUI7KlcsRYBx7Vwd7ancaxruHbgjQbWzx9KuE/fBv+eDQjT0ql8urpvBGOycCPf2xtn+akdMP3HDDzBXpF9ex+MmatAiw4b4KdxCgFy9JsaaDafaFcQ256d4VoyGHfsMrQx7GqpQt3pUb88iMbWxcZ5zncc2MtmH0Fyd3urzyJStibrxuGWL2GNfeJrSp68YDD9SgQee3+SY00+j3iVzc/h0fj+hOl3rl8XYowb333MM99xrXRrfylG/QiY79RzF+1o+sDk0m0eyE0b8SoMet4YfXbwzQv+CrW+4s+I8A/cszxGUmkL3tNUZ1rUIJKzsCq9bn2XnHOHZLE8k+Opuvnw+kjNE+be1a0ev1nzhg9LOEnVcD9JJUe7gnr2+O5/INByAv6yDLhnSjY7Hi2FVuTsCE9ay+cPcH6FoDXURERERERERERO6UAvSiSwH6bzh7/iIHjhwnNe1XRv3+mTIucH7VJEZ2qYWXnS2edXrSffJcZk1oS5/6VgQ07k6vqV/zyfje9K7lhrVDBaq1Hc3rq89z5nfPMArIz80kNS6euKhoohJTSMy6McyLI+HAbF5tVpvKJaywMqfb/vgEp24I0P0KA/SOr3zFSnPN32sucmrRJPoHBxD4iwF6MH7lhjJh8amfD9ADGhDQ90t+PJpQ+KQZoB+8jQC9Du2f+prV8WbUdl1O/AFWv9CCDsFWlLBvTLUWs1kQkvCLI9D/M0Cfwdd7ogpHoOeTm7SbhYPa0t6uBLb+jSjX+xuWWO4uuNFxtswcQWdnZ7w9A7AbMIvJ2379QpxwcC6f9w2gUhlv7mv1JhOX7ePC9rcZ1awmXve6Ub/XRD49aYkI4cICvh3b6kqA7uxJ+/HGNkRYfsx10bvYPr0RLWu6c/99D9Kkx2csuphMXsQSPni2jXEergToQ7/Yzf4rO3dF/kHWvzmUNr8RoFds9Agvr7rM2dua5OGXRqCnErdsNKOb2eLjG4hruTa07/M0Y14ewwtjRjNqtFFjXmLMK68yYdKrvDpxAm98s44N4Tmkx+xn7WudaRdsh5NXEIG1W9G821D6PjmJya9N46MpQ3i4SQ3srG0Iuhqgn97E4sk9CXJ0wc2zDLVe+I5vjGN6k4zjhHw3nEY1ylK8uDkC/WqAfomdE/rTy9EOD78KeDTqTbeho3n5pdG8YG6nWWPH8dKESUwwtnPCq6/wyWJzfeoC0qM3812XwgC9dmeqTd/N3uhbh+4XBuh29vh4l8VmwGfMuH5nwxX5Z43+NZ4uVcvhZmVNpV6TmLExjOh9nzLusSbYP1AKn5qt6PjJYfbemieGfstXo5vh4eqGc+kguk9dyrbkLBKOfcf7jz1IdVdHXP2rUL1ZZzr0e4bHR09n6huTeNNoK/WqG+3AyvaXA/Qnv+G9/Tdea3OIXPs60zvYXxkJbd+H4dO2ceu9CunnV/NDn5o087bh/op3GKA3HECzj49z9qZuZTTkE3MYP7ApVqWs8a3SkO4f7GHPrrUsn9SWAF/juuXkTtuxn7HgcuFbrorZy653mtGmttlf6tKow0y+PZd9pb+ZYg+wbe40xj1pnPdOnejQrg0tGhnXyDIBlLY12kTF5rR/Yyvboo0LduJ2tt6yBvovBeiNnruNEehfhhKflUTu/rcZ06M2JUvZ4V+lHk/MPcyhW77isg7N4cungyjj44et9SP0nbKVYxkJJFwbgV6Sqi178trGOMKufbcUkJexj8WPd+WRv1mAbo4833vwKDFx178vRERERERERERERH6NAvSiSwH6b7gYFs7hYydJSEy2rMt8V0i/zPmVr/Jspyp4uAfiXbY2VauXp3xZX/zLVqJijdpUKhuEp3MZqrZ5gdfWhnEuy4w2fm85pMedYNtXn/Hpm9OYOmcB32y9RPy1kYxxxO2ZxUtNalK+WElcmval8zdnOW+e8sytrHinL77eXtg7udHquY+ZF1pwfeRz9nH2fjWKR8oE4O3gQsk/JUDvTr3KgRQvGUjT/m/x1anCkNkil7Tzq5jTpRGNHUtRKqg1tYYtYf2ljF88rrkpIWx8vT09qpW8tgb6N/virofyGaHsfqsr/aqVwtqpFkEPTmX2togbPtNgTtM8oQfVHF3w8Aym8rNf8umxG2P9/1QQtZ3tb3egQeVg7vMbxtCXP+L7zwbzUP1KFLduTddRS9hjHEbLdica52FGP8p6eeDk5E7dJz/gk0MFN42ozTi9jB9HVKRhOSfuL9mKlgOXsDnO+DIw10B/pjU2tg54lalK73d+Yuu1myCMn56wiR9feYx6Ts64ewVj94cE6L1pPCfUsgZ6zo5JvPGoLR5eZXGs+Dxjv97HueRUUtOMSjUriaT4GKIiIoiIDCcqIYXU7BiS9n3EqA51cHugGD61H6HfrN3sOp9IfFIaaSkXiNs0hT5t61C8hBWB9bvw+NwQzkYfZdd7Q2nr6oKXewAO3afxyvqom0cih61jzZSW1KnoS6lS9jTq9zKfhhpNjUTOfP4kI6pa4eJfndKPvs97P10kKdX4PHM709JITU4kITaaCGNbI42KS0onsyCPjOiNzO18mwG6oxM+HkHYtprA+CXnbp4aPGYLW97pTp3y/thYOVK7z2RmHUgi4eIavni8LTVLlMS5UhMqjFvDinM37lUBKdvfZPqAYNxcfXD2bMETs3YREn2BkO+e5pG6ZSh+vxfV273AG2vPcyY2icSUDNIu72bfx71pWceczt76DgL0AtL2vsfnQ9wo4+uFtV0Dur34Levjbhz1nUjUrtlMebAKNe3sKX67a6A3eZAGJa0oXq0LDV/dwP6YG0LevHiS1r/KyM7VLecusNpDjPg2hEthJ9j84UAqG9vi6OBG7SHv8OH+vJuWesg4s5KFz1ahaXlHo788zEO9vmV1dBbpuTnk5GSSnZFKckIC8fHxxMXFEXXuAPuWTGd0xwep5GSFXenqBLR8n0/3JZGfsYdt73Y22tDPBeir+X7yo3hcHYH+3Ff/cRPHf6yB/vlJEvOMzn9pHlMfb4ljSRu8y9fmkbc2suGmG2dyLTcuvNHJmQBPP2ycBvHkzCOEFRjHZfd0+rctWgF6Xl4elyOiOHQ0hPiEW29iEhEREREREREREfl5CtCLLgXovyEiMppjIaGWaV1zc++iECD9ImdWvs4LXSrh5uCCjbMPnn5l8PP1wd3F+L9VAJXaj2PK6rNcuHX25t9NLknnV/N1/7Z0rFKOMjU70vaJz1kemn0lnM0K4ei/R9Kpdnkci5WiQtsnGLMhGXNgJelbWfF2PwKM7XVwdieg8VCGz95/ZR3iggzCN7/HWwNrU8HXm9IuHlj/KQF6D+pXCcTGzgXfhn3p8/Fhjly95mWFcvzHsfSsWwlPKyuc6nal5cx9HIm/Nby8Li8lhC2vt6VnVTNAr025GtOYszf5+oj13ASilo3k+Ue8sbL1wSuoHQNm7WD/tVncUwhbPo6XHgmmtL0XHl7N6DPzJ7am/MatELlhXFo/mSF1quNj04h6TTrSrXNlylYMoESDMQz75hzXZqvOu8CBf4+jh78nfo6lcXnwSYZ+cpQLhU9j/Ovk/PEMqetHGVdHilV/nG7vhRBuLmkQtYC3n26Dg4Mrpb3LUanjRKYtP4flaKeGGcfrRZ5pXwFf19KU9jXawB8UoDf65BQnzR2KmMfcMQ3wdvfD3rMpXaeuYstNWVgckVu/YsYLLzJ63Cu8u3wvhy9eInrDa/RsUZ1/3etEzXbPMfOg+SlXRRH5/TC6Ny5L8VK2BNXrxOB/n+Z0RiphCyYwqporge6elApsQ6dXFrPl2vLJ0ZxeOJmRjYMI9vIwjpETjfqNZc4p49CQR+b2qbzZyx8njzI4VR3I8/8+zsXCd1pkneDwgumMfvYlXp7wDnM3hRhtvIC0mDsYge7ihn9pL5wC6vLwqC9YcG3YdjLha2bwWqdyBHl7Y21Vg9bDvmBpQh4Z2aFsmfAo3byN/uFZGY+mLzNtdbh5xq7IOsZP0/rTtYKDse3lcak9likrI0hPPsKBj7rToGog99xXx7gOzGX9jTOxx2xi9UuNaVTRi5JWdncQoEN++EpWv9GKioG+2Np7E9TqeZ798TzXluqP3Mi6aV1oHhSAu70rNtW60+p2AvSm9Whg64CNRzUqt36Z6T9FcO3+j+itrBr7CO0ru2NlVYZy1Z/irY3hpOYkETL/VfoGeuNvtHvnukMY9OGhG0bEX+L0ogk8WS+AYFcHilUdRNeZhwlNNtretq/4fMJgBg4ZzYgpa9lx44wbOcdZ+0o3ugRZYedSDfdaU3hvWzz5mXt/JUBfyw+Te1hGoDu5+VDj6Tl89hsj0Ad+etw4l8a7s/fx4+heNLKxxt27AoEd3uTtn64uL2FI3sWKyY/Rxt/OuO4EYd9yGhNXJ5JlnK+0XW/Rp4gF6JmZWZab5Y6fDCUpWb/oioiIiIiIiIiIyO1RgF50KUD/DXHxCZw6fc7yx/XMu2Ed9BtlhHJ6xes817UGfp5eOLh64+7qTmnXitTs8DJTVp76g9c8LyAzaic/jWlOM59S3PN/HniUa0ePF2fwzpzPmPPWGJ7tVpcyfu7c61KD5s/MYWUYV9YMzz7Nnm9G0b6MO+5Orlh71qJ66ycZ9fq7zHxrIiOHd6F5syrU8fEh0M2TUn/SCPT6VYOxcyqDd/kG1Oo8iGGvf8CHH3/Gp2+9yHOPPkiZAC/uf8Cbcsb2TNwRS+yvNIn89EucnNOT/vUcKW5TDt/gLvR+4U1eX7yFVWfSyCvIJffScr4d25bartY4lfYhuO1wnppiHDvz+H38BmN71qe+zwNYOQUT2GIyH20K57dX6M0i5cxyZveoQyN3dzx8jePk44yPtyeVhn/CzKM3jmDPIOHwAr7t24Dq7qW5174a1ds9zeiZnzHb3IaZY3i+e33KOpXGpmQQZQe+x4yjGca250PqTn6c0IO6pe1xdvXB3r8xTXuOZtKM93h78gsMGtSKJnXLU8PNDS//8tj/QQF6g4+Oc9IMynMvcvCHl+lR1QVXO1vc63Wn1ytf8sUPS1i08Ae+m/0SIztVw6ekLfc5N6D9G+vYdzmO+H3vMbx5dezvcaZs3S4MmvYtczduYvOGZSz9aALP9WhA3fL+uNs7EFSvAwPnniHEOO+Zp+bx7RO1KOPuRokSPlRo2Iu+r87hwzlz+OidFxnTpx3NKtfC39sfFycHS4D+yQkzQDfEbWb124/SyMsRJytfKrZ+gmfe/5Z/L1jM4nnmOuJD6FPfk5LFnSlZqQ/PfHOY88bbMmLvJED3INDDi9JlAynfsCs9hr3L+x9/yidzpvJy/zY09HPAw9kVh8pDGf7JIctNE/mkE7X+TaZ2Lo+HrRuOrnVp3v8VJs422+QnRp9+hn5Nq+H7gBWO3g2p9+Iqll8yLjJ5Jzj5zeO0qBxE8QcqUqv1c7z8zXJWbd7KxuXz+GLiQPo3rUB5fx8cjON4JwE62Zc5tWwy/Wp54+VgQwn/hlTrMobxM+cwZ/ZHTH/5Kfq2rUPN6n74+rphU/lOAnRnnPyCCajRknaPjWfS9I/56NNZvP/qEHrVKUuwfTHj2tSQ2gN+ZPXZdPKMI5R8bAnzBjSipofZX6pSpc1wXrzaX957iRd6NqC8i7EdxQMI7v8uM46nk5WdRPzG1xjb0jin9zhQ0qc9nUe/z5wfF7N8+TJW/PA+k/s0poHb/dgZ18Jqjy9k0ZkMClJ2/meAnlgYoGccYN27Q2jg7Iybiyd+LfrQc9RsvvluO4cjMy0zXcTvvyVAn32k8CaBBC4seoOJTcvg6uBJCa+mPDx4Am+Y+zBnNnNef5zeTSpS+gFHHLwa0cToK8ujzPdFkb59Gr2LWICeZPxye+bcRUuZv5uIiIiIiIiIiIiI3A4F6EWXAvTfkJaWztnzlzh5+uzdsw76jTIucm7lRJ7tWhtfV1ccXCpRs/1oXl97gbOWlOUPlp1A8p5ZTHviYaoE+ODp7Ii9rQPWZlk7YGfvhmdwLao+9iZTVpwj7trg6WxiD8/nq4FNaF7OA2dnZ+zszPc4YedenoqPD6PniCcZ6OZG2RKluKfZszy6KIE0M0Df+xYvdwvm3nuN93n1Y8z8EEILfyrZF9gxows9y9zD/7lWw7XbHL49fG38LAXZ+1g8sAOt7rmH+/1q4/TiKhabQ39TzQD9UepXLYuVfSMaP9SdvgOaUq+KNy62tpSytsfGwRlHr/L41xjMsLfWc9Bour8aC+Wmkbb/Q94e1ogy3h442dliU8rYlxoDaffxcWIs5yeVsA1z+OCxJjSqGICrkzO25mfZ2mNtYxwPBy9K+1QwzumTDP/8EEdifmP0eaGc+BPsfvsRHq3rhoObD66uxrnxfZD+76xmx7Xh54UyLxGx4V1GP9aCyr6eeDg5WqZlt3y+cQ5tHD1wDXqQ6i2eZ+K3+wi5lksmcHblO0x7pArV/d1wdDS23cbYbmsXnMvWofbzLzDwsR70KFWS0k6e3NPzfcbvNHc6DvZNp1+bqtxjnAe/ms0ZtSyM0NsM0JPOLuHDetWoabz3nmpdqPbuIY4X3iORfnYjayZ2oUejcni7uWDn7IVrYDUqVgjCx8MFW0d3PCs0pfajM5m1PpxM4wxmx/zED093o413aZwdXXDwLEOFh1rS8uGWtGrWlAYd29GgXDmC/u9fuFVtQY/PTnLYPIY5ZwhdNoX+bR8kyKM0Lo7GsbKxw8rGaMteNWn/aF9GPNGTMoF+3HfvvdR89DneP2KO/zalEbnnO2YPbkGrqgG4OTlhX9oHr/JVLWvXe7oY/cDFG/86HWg2dhELj1imZiArej1ftG5APXPfK7SmzJQd7Iq6NUC/wM5pw+nzQCljm9wo0aoHLTt3pW9lP/ydHLAy2pe1rfF5Tl741WhB20kLWRRyw3Dx9GPs/nYCg5rXoKqfG84OxnvMtmCeW1tH7Er74RHUmJZ9Xuf97dGY95+YI+7jd81hXNcmlCvtiLWT0WYCa9CwVXuaN29Jm4ea0qntQwT4+3G/cSwqtBrIm/sziCeG/VOH0vPefxnnyp/7BnzO9D3/+Z2Qdn4zq8Z1onttHzxcXbC3XCvssbL1wLNeT1oNeorH2/lTw6c49/g+QsOX13M44TYC9FL2ONRvTuVegxjQtA5Nvcz+Z5xDYz/NtmLOnFDjsZd5eVUYEVcv/1nhRG16j7F9jGuev5elv9j+R3+pS7WHRjJh7l5OXL1IxG1h1dtP0bFqMAHGPtg4euFVpgo1q1emWqAn7m6+uPhUpX6P55m09jLnzAQ8eiMbp7WignEe7rmnFq2emMsK43J2Zc/iOb/uI6Y8VJ1q7sYxsbfj/nucjTbUmwlrwog0XpG8/3Pe7+BMWdeS3FNuED0/2E/E1UtI3D52fvkC3ZvVJNhov852hdccy3k22p95nss/TMveM/hiV6Rxhg354aRsfo0uTStY+m5Qg46MWx/LxZsC9N3M79OW5sbz/xdQD9cxq1l+7u4O0KNj4yyjz8MiosjO/vUlMkRERERERERERESuUoBedClA/w3mtO1h4ZEcPHKChMS7dG3U9DDOr3iVp9o0oGarUUxafekPWvP85xifkptAxM7vmPNsF9pUL0OQrx+ePv54evgTWKEprYdOZ/amc1zOvDnoK8iJJXbnl7w/vC1Nqwbh4+2Ll391yrd9kdfXb+Gn1Z/zerUq1HPzwLrzOAavTLoyAv3Qh0wZ9CAuLpUpW3U4ry07fX0K5eww9n40gKF17SldvhnlB33DwmOJhU8an5l9mJXP9KW7rT3uNVoSPHEDKy8bT1hGoHejXuUyFC/ZlC7DZvHV2h95/5m2PFzRC1d3H7wCq1K51VM8++lOdkdlXRkF+quMY5MTTsiKtxnfvQHVggPw8vDCtuoAur6xg9CMK2eoICuRqP0L+WxsL9rUqICvhy/ePn54+QTiWa4JTfu/ycwVpziflEfu7Z5U49gm/zSRpzvVwN7JGWf3SnjVeok3lpwh/j9+Rr6xDbGE7fiaD4Z3oHmFQPyNz/f09sfb0w+fys1p+uR7fPTTeS6n51xfp94My9LPcHrZm7xs7F8t432ensY5LNuYeoOm8+mBnaz55DWe9fSkfFAlnIZ+yhv7zKOWAIdnMbxnM2ysHajRvCsT1oRz7jYD9OQLq/isTTOa29lj12wAzT45Skhi4U7lZZMZsY8dn49h2CO1CQ4IwNc/mMBAo315BhJcswM9Xv6K+fsiib/aHvOSSdj9NbNHtKWxue9+AfiXKUtQ7U50f+FtPl84m0m9OtDQ3oEKTbsyeO5pjlkuBXnGWy9wZOEUXjL3P8jHOF7eePpVp3qfd/hsyWp2zRtLq8Y1sbd3pvng8Xx63GhqhZtakJNKQsha5k8bQrcGlfH38sc3wNjWgEC8/cpSqcUAhr2/no3nUjEOuyGPzJjNfNujHW1s7HBs2IO67+5lf8ytAXoY+94fy7DSHgSXrYbb03OY8t081r3Ri641y+Ln7o27RwDl6nSkx+QFLD+VTMZNjTmPnORQTiybxuS+jXmwvD8exrZ5eRnn1jeIgLpd6fbK98w7FE9cTkFhe8gjP8Wc7n8yz3etR5lA47gbrw0yPr9s66cZNX0OS78YR8eHG+Bg50jD7s/wwZEMEonjyMxRDHVxJbBsTdxGfMusgz9zo1JeFhmXNrH0jf70qh9sXGN8jbYWgGeZ9vSZupB5W1Yy97lGdKjqik31PrSfstk4R78RoJtroBe3omTzQTSZsZSV30xk+mP1qezlhaeXsf3l69Bw4BRmrDpDmNFXr3cb41/ZcVzeNZePRnakZcWgm/tLpYdobK6Nvv4sYcaJu3ZoC7LIvLybDbNG8niz6lTw98fHaJtBRtsMMM57QKXWPDzsQz7efI6oLKNvme+J3Mq297rTsEZF7BxaGO1xPuuM7nMl4jVek3yaoz9MZETr2lT29cHVyZWytdsyauFpy6wcaUe+49O+ZalX3gfr+iMZ+tlhoq5tUA45iac4sGAKY7o0pGaAcX6NffDy8jOuP2Xxr9eTHq/NZ9mxBJKM82zZ/7xIknfMYECnhpabBeq068fUzfFcvilAP8CyYUZbs7WjdK02lH/tJ9ZeT9jvSuYMMwePHCcuIYn8gutnWkREREREREREROTXKEAvuhSg34aY2Hj2HjhKRFQ0d+3f1tPPc2LnVtbvCOXijWsP/1lykkgI3c+uDatZsWgxi+YvZNHCpaxYt4NdJyKI/6VBfXkJRIbsYce6pSxfuphlqzexft95IjKzyE6JIHTDejYuX8GSncfZH5lDXn4eJJ3l5P6NLF++jrXrDxISkcq1VpSfQcLZfRzYaPy8tVtYu/8i4ck3BGn5SUQd2svuJca2bdjKmhMxRJkz9sbuZNf7ZoAeRLHijehsTpUck0746X3sXb+YJQsXs3DZT6zbGUJoQu6vjzy/RX5yOBcObmbDyuUsnL+Uhct3s/t4DMk3peHpxJ87xJ6Na1i2aInxOuPzFi5n+eqt7DgWcWUb70gWnPmW1/u3xNPaBtegOpQb8iPzjmTcEATewjgXMaf2sXPtKlYvWcSiBUYtNrZhw052hEQRd1PIel1BegTnDmxh06rFLDWO67J1O9l2IpLE/GzSI0I5smIFa1evY9mB85xKMD/daAzGOTy4ewtLjNdv2LqLE1GZpN2aA/+sfHLTozi/bQtbly5l6ZZ9bDmXTMotWWle/DlO7dvMWuOzl85bwIIfFhjHcwWrN+3j0PmE62s9X5UbS3ToHratXsHyHxfw43fzmb9iB3tOG/uRGm2cv91sXrKEtZt3sf9iKjc2KVIvcf7gVn5aZmzP/EUsW7mJTcejiUvLICfuBDu2bDD2cxlb9x/nfIrxUYVvuyKHlPATHNq2jhWLF7PA2NYf5y1lybI1/LTrGCejsm++YSErlku7t7PNPM6bdrMxNIHErFvPqNEHzhzj4PLlrFmzgeVHI7mQkERm5BEOblzFMqNvzl+w1HIsDl5KNV79CzLCCTuyna1rjP1atJAFRptcsHQNKzYd4ODFFK4t1X+jpAuEHtjEqqWLLPvyw7wVLN58glPh8aTHnWHvzs0sWbyUTbsPcSYpz9j7bJJCj3DA2NbVazew4tAlzib+QkMzjlxq2FEOb1rOysULLedz+Zp9HL+cQmpOCtHHt7Br/TIWb9jD9pOxxjn6pQZ1NUCvS4PiJSnWdAgPfXaCMxGXCD+2nfVG2zf735JVP7Hl2GUifukrKj+BOOOad3N/WWbpL9uN9h/7sxeJXLJjT3N8x09sWGZ8xvz5zJu3kB8XGcd1rXFdOBVLzI1tKzOWuDO72bRhHUuXbmX3kctEG89f3zOjP6RcJGT3JtYtMc7Pj/NZum4z+y9dOT95yWGc37uWjWtXsmTjYQ6cN9rBrc0lLczov9vYtMK4Zi40rtvmvhvtb9XWQxwKS7m5fRRkkhN/mn07N1n67k/b93IyNpvCe4EsCvISiTD6yy7j+eU/bWNtSAzR6b941fnL5ebmcfrMeUuAnpHxV3x5ioiIiIiIiIiIyN+VAvSiSwH6bUhOTuXIsVOcvXCJjMy7bB10+X1E7mDntQC9Ae2GzWVVghlZ/s1kpZKREk9Y2FGOzR/HM+1q4lbcHu8KLegyex977tJJFET+PLcE6I0G0PSTk5y9C1fokD9YQYHl+/3EyTOcPH3OMuOMiIiIiIiIiIiIyO1SgF50KUC/DVlZ2ZYpXo+GnCYqJq7wUSlSorazY2YnapXz5d5/1aH1kK9ZHmsZx/03UkBu6Hp2LnqX1ya+wNA2lanha0dJ+3JUazuWj3ZGE1X4SpF/rmwSTy/mg/o1qPOv+7j3wT40+OgYp3RzyT9Ofn4+F4zfQ44Xfreb/xcRERERERERERG5XQrQiy4F6LehoKCAxORk9h86Sui5i4WPSpESdWUEev0qZSheogHth81lZSz8vSb0zSN+/QRmDChHhYoV8fMNwNfTm4AmAxk0aw8n4vNumPpZ5J+qcAR60wdpUKIUxRuZ6+gf57QC9H+crOwcDh8LsQTomVm/tM6IiIiIiIiIiIiIyM9TgF50KUC/TTk5OZw4dYbjJ0NJTk21hOpShMQd4cSCV3n+qUF07PwME2atYVeSZbXuv4+CNC6tncmskY/Q7dGedH1sGI+PeYf3Fu/jcGQ2OWqyIgZz3fmdLBr7HM917kzHUW8zZskFwlPVQf5JzLXPY+LiOXQkhAuXwgsfFREREREREREREbl9CtCLLgXot8kMzGPiEjh8/CSnz5zXWqlFTUYcief2sXfXNjZu2snBU5eIzjbHdP+NFGSQcO4Ex3ZsZtvW7WzdcYB9ZxNJ+FvthMgfLZ+c9GjOHdjL7k0b2bjnOPvPp5CuO0z+UczfPY4eP2VZ+zwl9e/xe4iIiIiIiIiIiIjcXRSgF10K0O9ATm4eJ0PPse/gURISkzUKXURE5G8mLy+fsMuR7N53mPCIaH2Xi4iIiIiIiIiIyH9FAXrRpQD9DsXGJXAs5LRlFLq5DyIiIvL3ERefaPkePxV6jrS09MJHRURERERERERERO6MAvSiSwH6HTLXTY2MjmH/4eNcuhxBTo6mchcREfk7yMjI5OTps5blWMxfavPzNfpcRERERERERERE/jsK0IsuBej/hazsbE6fPc+BQ8cID48kX9O/ioiI3NWys3MIPXOe/cZ3d5jx3a1vbhEREREREREREflfKEAvuhSg/5dSUtM4dOQEBw4dtUzrrhBdRETk7pSdk2uZNWb3voOWqdtzcnIKnxERERERERERERH57yhAL7oUoP+XCgoKLMH5oaMnOHjkOAmJyZbHRERE5O6Rl5fH5Ygo9h44QsjpM5Yb4ERERERERERERET+VwrQiy4F6P+D/Px8omLiOHjkhGU0ekxsfOEzIiIi8lfLzc3lUlg4+w4e4cTJUP0iKyIiIiIiIiIiIr8bBehFlwL0/1FeYYh+4PBxDhoVHhlFTk5u4bMiIiLyV0jPyODc+UvsPXCY4yGnSdbIcxEREREREREREfkdKUAvuhSg/w7M9c/j4hMsU7nvPXiEsPBIMjOzNKW7iIjIn8ycst38hfXMuQvs3nuIkFNnSE1LL3xWRERERERERERE5PehAL3oUoD+OzGnc09KTuHUmfPsP3yc02fPW/6PMnQREZE/hRmeR8fEcTTkFAePnuDCpXDSMzILnxURERERERERERH5/ShAL7oUoP/OzP26HBHF8ZBQDhw5QejZC+o4IiIifyBzJpiY2HhOnDxjWU7lVOg5YuLiyc7OKXyFiIiIiIiIiIiIyO9LAXrRpQD9j1AAsXEJHLOE6Mc5GXqWS5cjLNO8Z2ZlFb5IRERE/lvmzC9paemWEefm7xPmOucHzBlgzpwnVeudi4iIiIiIiIiIyB9MAXrRpQD9D2SOfLs6Im7P/sMcOXbSMp1sbHyi5Y/+Zpiek5tLXn6+1ksXERH5BeZ3pDk9e3ZODhmZmSSnphEVE8uZc5c4cOg4+w4eNf59kcSkFON1+YXvEhEREREREREREfnjKEAvuhSg/8HMP+Sb66+aI9LN/T16/JTlD/2HjpzgZOg5wiIiiY1PICU1jaysbHJz8yxT0YqIiPyTmd+FOTm5ZBjfoUnJKUTHxlpuQjtxMpQDh46x36iQU2cJuxxJYmKS5TtUN6OJiIiIiIiIiIjIn0UBetGlAP1PlJmZRVxcAmHhUZy9cNEyzezJ02c5ceqMZbr3oydOceT4ySt1TKVSqVSqf3AZ34XmTWfHQk5z/GQoIcZ3pXnj2emz543fH8KIiIwhMSnZErKLiIiIiIiIiIiI/NkUoBddCtD/Irl5uca+pxIVE8fFsAhCz12wBOlmiH74aAiHjp5QqVQqleofW+Z34bGQU5wMPcuZ85e4HB5lmbHF/P1BM7WIiIiIiIiIiIjIX00BetGlAP0uYE45e2uZ4YBKpVKpVP/U+s/vxsIvTREREREREREREZG7gAL0oksBuoiIiIiIiIiIiIiIiIjIHVCAXnQpQBcRERERERERERERERERuQMK0IsuBegiIiIiIiIiIiIiIiIiIndAAXrRpQBdREREREREREREREREROQOKEAvuhSgi4iIiIiIiIiIiIiIiIjcAQXoRZcCdBERERERERERERERERGRO6AAvehSgC4iIiIiIiIiIiIiIiIicgcUoBddCtBFRERERERERERERERERO6AAvSiSwG6iIiIiIiIiIiIiIiIiMgdUIBedClAFxERERERERERERERERG5AwrQiy4F6CIiIiIiIiIiIiIiIiIid0ABetGlAF1ERERERERERERERERE5A4oQC+6FKCLiIiIiIiIiIiIiIiIiNwBBehFlwJ0EREREREREREREREREZE7oAC96FKALiIiIiIiIiIiIiIiIiJyBxSgF11/WYCenp7JxUsRalQiIiIiIiIiIiIiIiIi8reSlpZuyTpTUjVYuKj5ywL0jIwswsIjSUxOoaCgoPBREREREREREREREREREZG7l5ltpqamEXY50jLrthQtf1mAnpmVRWRUDAmJyeTl5RU+KiIiIiIiIiIiIiIiIiJy98rLzycpOZWIyBjSMzILH5Wi4i8L0LOzc4iJjScuPtH4d3bhoyIiIiIiIiIiIiIiIiIid6/snBziE5KIjoknM0s5Z1HzlwXoubl5ltHnZsPS1AYiIiIiIiIiIiIiIiIi8neQkZFpGShshug5ObmFj0pR8ZcF6Pn5BaSmZVimNkhMSi58VERERERERERERERERETk7pWcnEp4ZAzJqenk5+cXPipFxV8WoJvMOzIuR0ZZRqGbi+2LiIiIiIiIiIiIiIiIiNytzEgzNi6RsPBIMrNzCh+VouQvDdDN0DwqJo7IqFiy1MBERERERERERERERERE5C5mDhCOio4jIiqGvLy8wkelKPnLA3RzioOo6FgSkpI1xYGIiIiIiIiIiIiIiIiI3JVuzDYTk1KUbRZRf2mAbsrNybM0sssRUeTmahS6iIiIiIiIiIiIiIiIiNx9zBHnEZExREbFkJ2dW/ioFDV/eYBuSkhMtqwTkJqapjs1REREREREREREREREROSukl9QQFp6BpfDo4iLTyx8VIqiuyJAz8rKITYuwbJWQEZGZuGjIiIiIiIiIiIiIiIiIiJ/vcysbCKjYomOiSczM6vwUSmK7ooA3ZSWls7FsAjLHRtacF9ERERERERERERERERE7gbmDNrmjNoXL0VY1kAvKHxciqa7JkDPzc21jEK/HBFNckpa4aMiIiIiIiIiIiIiIiIiIn+d1LR0wiOiiImNJycnp/BRKarumgDdlJ2dTWR0XOFU7pr6QERERERERERERERERET+OuZ07ZFRcURExhj/zqagQOPPi7q7KkA3mYvvm+sHRBkN0WyEmgNBRERERERERERERERERP5sWdk5ljXPIyJjSU1NR9n5P8NdF6Cbd22YDdC8i0OL8IuIiIiIiIiIiIiIiIjIny07O4fYuETCI2KurHuu9Pwf464L0E3mQvzmWgJmiG6ORM/IyCx8RkRERERERERERERERETkj2MO8DUH+prheUpKGnl5+YXPyD/BXRmgm66ORI+MjLVM6Z6SajTO3LzCZ0VEREREREREREREREREfj+WQb5mPhltrnkeawnPzcfkn+WuDdCvSjfXRI+O5XJENPHxSZY7PvLzNUWCiIiIiIiIiIiIiIiIiPzvzIG9mVlZJCQmEx4RfWXN87T0wmfln+auD9DNBpuVnW1ZY+BiWITRYKMtDTYvL09rDYiIiIiIiIiIiIiIiIjIf8XMGs3MMS0t3TIj9sVLEcTEJljCdOWQ/1x3fYB+VU5OrmWahJjYeC5HRFmCdPMuEDNcR+1XRERERERERERERERERG5TTk4OSckpRETGcDk8ipiYeJKTU8nOzil8hfxT/W0C9KuysrKJT0gi0mjM5p0gUTFxxMYnGg08lfSMDEujvrKQv1J1ERERERERERERERERkX86MzvMzskhPSOT5JRU4uITiY6JIyoq1hKgm//PzMwufLX80/3tAvSrzBHpZgM310cPC48iPDKaqJh44hKSSExKISU1zTLdQlp6hmUddZVKpVKpVCqVSqVSqVQqlUqlUqlUKpVK9c8pMys0M8OkpBTLAF0zNDczxbDwSCKjYiyPa8S53OpvG6Cb8vMLyM3LI8to2Oa66PGJyUbDj7fcKRJ2OcqyTsGFS+Gcv3hZpVKpVCqVSqVSqVQqlUqlUqlUKpVKpVL9g8rMCsMuR1qyQzNDNEN0M1A3Z7zOzc0jP9+c1VrkZn/rAP1GZphujkrPNBp8RkYmqWkZlg5grptujlRXqVQqlUqlUqlUKpVKpVKpVCqVSqVSqVT/nDKzQnMQrjl1u5khmlmiQnP5LUUmQBcREREREREREREREREREflfKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXERERERERERERERERERExKEAXEREREREREREREREREREB/h+BivteSZsIqAAAAABJRU5ErkJggg=="}}}]}