{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:23:47.126107Z",
     "start_time": "2020-02-10T18:23:47.120230Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:55:03.569038Z",
     "start_time": "2020-02-10T18:55:03.473748Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from scipy.stats import entropy\n",
    "import warnings\n",
    "\n",
    "from causalml.inference.meta import LRSRegressor\n",
    "from causalml.inference.meta import XGBTRegressor, MLPTRegressor\n",
    "from causalml.inference.meta import BaseXRegressor, BaseRRegressor, BaseSRegressor, BaseTRegressor\n",
    "from causalml.inference.nn import DragonNet\n",
    "from causalml.match import NearestNeighborMatch, MatchOptimizer, create_table_one\n",
    "from causalml.propensity import ElasticNetPropensityModel\n",
    "from causalml.dataset.regression import *\n",
    "from causalml.metrics import *\n",
    "\n",
    "import os, sys\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_palette('Paired')\n",
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IHDP semi-synthetic dataset\n",
    "\n",
    "Hill introduced a semi-synthetic dataset constructed from the Infant Health\n",
    "and Development Program (IHDP). This dataset is based on a randomized experiment\n",
    "investigating the effect of home visits by specialists on future cognitive scores. The data has 747 observations (rows). The IHDP simulation is considered the de-facto standard benchmark for neural network treatment effect\n",
    "estimation methods.\n",
    "\n",
    "The original [paper](https://arxiv.org/pdf/1906.02120.pdf) uses 1000 realizations from the NCPI package, but for illustration purposes, we use 1 dataset (realization) as an example below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:28.626279Z",
     "start_time": "2020-02-10T19:00:28.545662Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(f'data/ihdp_npci_3.csv', header=None)\n",
    "cols =  [\"treatment\", \"y_factual\", \"y_cfactual\", \"mu0\", \"mu1\"] + [f'x{i}' for i in range(1,26)]\n",
    "df.columns = cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:31.815390Z",
     "start_time": "2020-02-10T19:00:31.767892Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(747, 30)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:35.067834Z",
     "start_time": "2020-02-10T19:00:35.002035Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>y_factual</th>\n",
       "      <th>y_cfactual</th>\n",
       "      <th>mu0</th>\n",
       "      <th>mu1</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>...</th>\n",
       "      <th>x16</th>\n",
       "      <th>x17</th>\n",
       "      <th>x18</th>\n",
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       "      <th>x23</th>\n",
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       "  </thead>\n",
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       "      <td>0.383828</td>\n",
       "      <td>2.244320</td>\n",
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       "      <td>0.808706</td>\n",
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       "      <td>3.587662</td>\n",
       "      <td>7.787537</td>\n",
       "      <td>4.009365</td>\n",
       "      <td>7.712456</td>\n",
       "      <td>0.390083</td>\n",
       "      <td>0.596582</td>\n",
       "      <td>-1.850350</td>\n",
       "      <td>-0.879606</td>\n",
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       "      <td>...</td>\n",
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       "      <td>2.372618</td>\n",
       "      <td>5.461871</td>\n",
       "      <td>2.481631</td>\n",
       "      <td>7.232739</td>\n",
       "      <td>-1.045229</td>\n",
       "      <td>-0.602710</td>\n",
       "      <td>0.011465</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   treatment  y_factual  y_cfactual       mu0       mu1        x1        x2  \\\n",
       "0          1   5.931652    3.500591  2.253801  7.136441 -0.528603 -0.343455   \n",
       "1          0   2.175966    5.952101  1.257592  6.553022 -1.736945 -1.802002   \n",
       "2          0   2.180294    7.175734  2.384100  7.192645 -0.807451 -0.202946   \n",
       "3          0   3.587662    7.787537  4.009365  7.712456  0.390083  0.596582   \n",
       "4          0   2.372618    5.461871  2.481631  7.232739 -1.045229 -0.602710   \n",
       "\n",
       "         x3        x4        x5  ...  x16  x17  x18  x19  x20  x21  x22  x23  \\\n",
       "0  1.128554  0.161703 -0.316603  ...    1    1    1    1    0    0    0    0   \n",
       "1  0.383828  2.244320 -0.629189  ...    1    1    1    1    0    0    0    0   \n",
       "2 -0.360898 -0.879606  0.808706  ...    1    0    1    1    0    0    0    0   \n",
       "3 -1.850350 -0.879606 -0.004017  ...    1    0    1    1    0    0    0    0   \n",
       "4  0.011465  0.161703  0.683672  ...    1    1    1    1    0    0    0    0   \n",
       "\n",
       "   x24  x25  \n",
       "0    0    0  \n",
       "1    0    0  \n",
       "2    0    0  \n",
       "3    0    0  \n",
       "4    0    0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:39.094246Z",
     "start_time": "2020-02-10T19:00:39.037034Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.813922\n",
       "1    0.186078\n",
       "Name: treatment, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(df['treatment']).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:42.729205Z",
     "start_time": "2020-02-10T19:00:42.629694Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,'x1':]\n",
    "treatment = df['treatment']\n",
    "y = df['y_factual']\n",
    "tau = df.apply(lambda d: d['y_factual'] - d['y_cfactual'] if d['treatment']==1 \n",
    "               else d['y_cfactual'] - d['y_factual'], \n",
    "               axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:46.194268Z",
     "start_time": "2020-02-10T19:00:46.144813Z"
    }
   },
   "outputs": [],
   "source": [
    "# p_model = LogisticRegressionCV(penalty='elasticnet', solver='saga', l1_ratios=np.linspace(0,1,5),\n",
    "#                                cv=StratifiedKFold(n_splits=4, shuffle=True))\n",
    "# p_model.fit(X, treatment)\n",
    "# p = p_model.predict_proba(X)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:00:49.772817Z",
     "start_time": "2020-02-10T19:00:49.623306Z"
    }
   },
   "outputs": [],
   "source": [
    "p_model = ElasticNetPropensityModel()\n",
    "p = p_model.fit_predict(X, treatment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:01:04.047676Z",
     "start_time": "2020-02-10T19:00:53.141686Z"
    }
   },
   "outputs": [],
   "source": [
    "s_learner = BaseSRegressor(LGBMRegressor())\n",
    "s_ate = s_learner.estimate_ate(X, treatment, y)[0]\n",
    "s_ite = s_learner.fit_predict(X, treatment, y)\n",
    "\n",
    "t_learner = BaseTRegressor(LGBMRegressor())\n",
    "t_ate = t_learner.estimate_ate(X, treatment, y)[0][0]\n",
    "t_ite = t_learner.fit_predict(X, treatment, y)\n",
    "\n",
    "x_learner = BaseXRegressor(LGBMRegressor())\n",
    "x_ate = x_learner.estimate_ate(X, treatment, y, p)[0][0]\n",
    "x_ite = x_learner.fit_predict(X, treatment, y, p)\n",
    "\n",
    "r_learner = BaseRRegressor(LGBMRegressor())\n",
    "r_ate = r_learner.estimate_ate(X, treatment, y, p)[0][0]\n",
    "r_ite = r_learner.fit_predict(X, treatment, y, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:01:18.322939Z",
     "start_time": "2020-02-10T19:01:07.893094Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 597 samples, validate on 150 samples\n",
      "Epoch 1/30\n",
      "597/597 [==============================] - 2s 3ms/step - loss: 1270.6818 - regression_loss: 614.8568 - binary_classification_loss: 37.4892 - treatment_accuracy: 0.8509 - track_epsilon: 0.0425 - val_loss: 239.1906 - val_regression_loss: 97.6509 - val_binary_classification_loss: 40.0911 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0418\n",
      "Epoch 2/30\n",
      "597/597 [==============================] - 0s 83us/step - loss: 296.8994 - regression_loss: 127.7073 - binary_classification_loss: 29.0478 - treatment_accuracy: 0.8526 - track_epsilon: 0.0421 - val_loss: 239.8115 - val_regression_loss: 94.8186 - val_binary_classification_loss: 43.9986 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0405\n",
      "Epoch 3/30\n",
      "597/597 [==============================] - 0s 97us/step - loss: 255.4919 - regression_loss: 113.5166 - binary_classification_loss: 27.9721 - treatment_accuracy: 0.8526 - track_epsilon: 0.0396 - val_loss: 259.4397 - val_regression_loss: 108.6940 - val_binary_classification_loss: 43.2644 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0396\n",
      "Train on 597 samples, validate on 150 samples\n",
      "Epoch 1/300\n",
      "597/597 [==============================] - 1s 2ms/step - loss: 211.4195 - regression_loss: 90.2754 - binary_classification_loss: 27.2481 - treatment_accuracy: 0.8526 - track_epsilon: 0.0427 - val_loss: 219.0321 - val_regression_loss: 83.5967 - val_binary_classification_loss: 45.4129 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0484\n",
      "Epoch 2/300\n",
      "597/597 [==============================] - 0s 78us/step - loss: 204.8274 - regression_loss: 86.0335 - binary_classification_loss: 27.2709 - treatment_accuracy: 0.8526 - track_epsilon: 0.0478 - val_loss: 208.0823 - val_regression_loss: 78.5176 - val_binary_classification_loss: 45.1409 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0387\n",
      "Epoch 3/300\n",
      "597/597 [==============================] - 0s 88us/step - loss: 194.1909 - regression_loss: 81.2670 - binary_classification_loss: 27.0086 - treatment_accuracy: 0.8526 - track_epsilon: 0.0308 - val_loss: 205.8695 - val_regression_loss: 78.4999 - val_binary_classification_loss: 44.7895 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0213\n",
      "Epoch 4/300\n",
      "597/597 [==============================] - 0s 87us/step - loss: 189.5814 - regression_loss: 79.2617 - binary_classification_loss: 26.5288 - treatment_accuracy: 0.8526 - track_epsilon: 0.0173 - val_loss: 201.7048 - val_regression_loss: 75.6974 - val_binary_classification_loss: 45.4131 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0156\n",
      "Epoch 5/300\n",
      "597/597 [==============================] - 0s 84us/step - loss: 183.4325 - regression_loss: 76.0126 - binary_classification_loss: 26.6035 - treatment_accuracy: 0.8526 - track_epsilon: 0.0118 - val_loss: 202.8189 - val_regression_loss: 75.9546 - val_binary_classification_loss: 45.9228 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0086\n",
      "Epoch 6/300\n",
      "597/597 [==============================] - 0s 96us/step - loss: 182.2276 - regression_loss: 75.4041 - binary_classification_loss: 26.6296 - treatment_accuracy: 0.8526 - track_epsilon: 0.0053 - val_loss: 205.3384 - val_regression_loss: 77.1694 - val_binary_classification_loss: 46.2288 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0012\n",
      "Epoch 7/300\n",
      "597/597 [==============================] - 0s 83us/step - loss: 180.2415 - regression_loss: 74.4598 - binary_classification_loss: 26.5372 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 201.6832 - val_regression_loss: 75.4064 - val_binary_classification_loss: 46.1580 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0029\n",
      "Epoch 8/300\n",
      "597/597 [==============================] - 0s 85us/step - loss: 174.4812 - regression_loss: 71.7626 - binary_classification_loss: 26.1734 - treatment_accuracy: 0.8526 - track_epsilon: 0.0042 - val_loss: 201.3234 - val_regression_loss: 75.2171 - val_binary_classification_loss: 46.2252 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0049\n",
      "Epoch 9/300\n",
      "597/597 [==============================] - 0s 89us/step - loss: 171.0054 - regression_loss: 70.0748 - binary_classification_loss: 26.1567 - treatment_accuracy: 0.8526 - track_epsilon: 0.0066 - val_loss: 202.3993 - val_regression_loss: 75.6317 - val_binary_classification_loss: 46.5645 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0082\n",
      "Epoch 10/300\n",
      "597/597 [==============================] - 0s 91us/step - loss: 170.0682 - regression_loss: 69.5563 - binary_classification_loss: 26.2435 - treatment_accuracy: 0.8526 - track_epsilon: 0.0096 - val_loss: 199.6441 - val_regression_loss: 74.2625 - val_binary_classification_loss: 46.4598 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0109\n",
      "Epoch 11/300\n",
      "597/597 [==============================] - 0s 97us/step - loss: 167.0560 - regression_loss: 68.0373 - binary_classification_loss: 26.2824 - treatment_accuracy: 0.8526 - track_epsilon: 0.0118 - val_loss: 198.9716 - val_regression_loss: 73.9292 - val_binary_classification_loss: 46.5013 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0115\n",
      "Epoch 12/300\n",
      "597/597 [==============================] - 0s 96us/step - loss: 165.4987 - regression_loss: 67.3859 - binary_classification_loss: 25.9894 - treatment_accuracy: 0.8526 - track_epsilon: 0.0119 - val_loss: 197.8179 - val_regression_loss: 73.4053 - val_binary_classification_loss: 46.4631 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0103\n",
      "Epoch 13/300\n",
      "597/597 [==============================] - 0s 76us/step - loss: 166.0216 - regression_loss: 67.8836 - binary_classification_loss: 25.6995 - treatment_accuracy: 0.8526 - track_epsilon: 0.0100 - val_loss: 198.6716 - val_regression_loss: 73.6842 - val_binary_classification_loss: 46.4583 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0145\n",
      "Epoch 14/300\n",
      "597/597 [==============================] - 0s 77us/step - loss: 167.4158 - regression_loss: 68.3341 - binary_classification_loss: 26.0148 - treatment_accuracy: 0.8526 - track_epsilon: 0.0159 - val_loss: 196.3751 - val_regression_loss: 72.4750 - val_binary_classification_loss: 46.5908 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0161\n",
      "Epoch 15/300\n",
      "597/597 [==============================] - 0s 99us/step - loss: 162.3269 - regression_loss: 65.8252 - binary_classification_loss: 25.9338 - treatment_accuracy: 0.8526 - track_epsilon: 0.0133 - val_loss: 195.3277 - val_regression_loss: 72.2033 - val_binary_classification_loss: 46.3533 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0114\n",
      "Epoch 16/300\n",
      "597/597 [==============================] - 0s 87us/step - loss: 156.9714 - regression_loss: 63.4403 - binary_classification_loss: 25.4225 - treatment_accuracy: 0.8526 - track_epsilon: 0.0130 - val_loss: 194.1708 - val_regression_loss: 71.3998 - val_binary_classification_loss: 46.5003 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0149\n",
      "Epoch 17/300\n",
      "597/597 [==============================] - 0s 86us/step - loss: 159.4497 - regression_loss: 64.2904 - binary_classification_loss: 26.0263 - treatment_accuracy: 0.8526 - track_epsilon: 0.0151 - val_loss: 192.6951 - val_regression_loss: 70.8486 - val_binary_classification_loss: 46.3501 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0115\n",
      "Epoch 18/300\n",
      "597/597 [==============================] - 0s 86us/step - loss: 159.7066 - regression_loss: 64.6457 - binary_classification_loss: 25.7649 - treatment_accuracy: 0.8526 - track_epsilon: 0.0098 - val_loss: 192.5754 - val_regression_loss: 70.8909 - val_binary_classification_loss: 46.1461 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0097\n",
      "Epoch 19/300\n",
      "597/597 [==============================] - 0s 80us/step - loss: 157.0761 - regression_loss: 63.2936 - binary_classification_loss: 25.8146 - treatment_accuracy: 0.8526 - track_epsilon: 0.0080 - val_loss: 191.2655 - val_regression_loss: 70.1602 - val_binary_classification_loss: 46.1613 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0117\n",
      "Epoch 20/300\n",
      "597/597 [==============================] - 0s 81us/step - loss: 155.1142 - regression_loss: 62.3842 - binary_classification_loss: 25.4675 - treatment_accuracy: 0.8526 - track_epsilon: 0.0135 - val_loss: 189.5332 - val_regression_loss: 69.2598 - val_binary_classification_loss: 46.0150 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0122\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/300\n",
      "597/597 [==============================] - 0s 78us/step - loss: 154.5792 - regression_loss: 62.0438 - binary_classification_loss: 25.7474 - treatment_accuracy: 0.8526 - track_epsilon: 0.0108 - val_loss: 191.2697 - val_regression_loss: 70.2473 - val_binary_classification_loss: 46.1159 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0086\n",
      "Epoch 22/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 153.5248 - regression_loss: 61.7347 - binary_classification_loss: 25.3457 - treatment_accuracy: 0.8526 - track_epsilon: 0.0080 - val_loss: 189.2342 - val_regression_loss: 69.2679 - val_binary_classification_loss: 46.0097 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0079\n",
      "Epoch 23/300\n",
      "597/597 [==============================] - 0s 74us/step - loss: 155.1560 - regression_loss: 62.2260 - binary_classification_loss: 25.9918 - treatment_accuracy: 0.8526 - track_epsilon: 0.0065 - val_loss: 187.2720 - val_regression_loss: 68.3552 - val_binary_classification_loss: 45.7725 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0089\n",
      "Epoch 24/300\n",
      "597/597 [==============================] - 0s 72us/step - loss: 152.4445 - regression_loss: 61.0552 - binary_classification_loss: 25.4562 - treatment_accuracy: 0.8526 - track_epsilon: 0.0107 - val_loss: 193.3582 - val_regression_loss: 71.4724 - val_binary_classification_loss: 45.5549 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0080\n",
      "Epoch 25/300\n",
      "597/597 [==============================] - 0s 79us/step - loss: 153.9410 - regression_loss: 61.9316 - binary_classification_loss: 25.3986 - treatment_accuracy: 0.8526 - track_epsilon: 0.0067 - val_loss: 185.9885 - val_regression_loss: 67.8059 - val_binary_classification_loss: 45.6309 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0066\n",
      "Epoch 26/300\n",
      "597/597 [==============================] - 0s 82us/step - loss: 152.2284 - regression_loss: 60.9298 - binary_classification_loss: 25.6594 - treatment_accuracy: 0.8526 - track_epsilon: 0.0064 - val_loss: 186.9394 - val_regression_loss: 68.2406 - val_binary_classification_loss: 45.6855 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0080\n",
      "Epoch 27/300\n",
      "597/597 [==============================] - 0s 76us/step - loss: 151.0331 - regression_loss: 60.5148 - binary_classification_loss: 25.2446 - treatment_accuracy: 0.8526 - track_epsilon: 0.0091 - val_loss: 186.0575 - val_regression_loss: 67.8268 - val_binary_classification_loss: 45.5729 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0080\n",
      "Epoch 28/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 152.1600 - regression_loss: 60.9849 - binary_classification_loss: 25.4377 - treatment_accuracy: 0.8526 - track_epsilon: 0.0060 - val_loss: 186.1113 - val_regression_loss: 68.0358 - val_binary_classification_loss: 45.3451 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0039\n",
      "Epoch 29/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 150.9097 - regression_loss: 60.4212 - binary_classification_loss: 25.3437 - treatment_accuracy: 0.8526 - track_epsilon: 0.0047 - val_loss: 184.9033 - val_regression_loss: 67.3800 - val_binary_classification_loss: 45.3508 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0065\n",
      "Epoch 30/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 150.0184 - regression_loss: 59.7821 - binary_classification_loss: 25.6023 - treatment_accuracy: 0.8526 - track_epsilon: 0.0071 - val_loss: 196.9308 - val_regression_loss: 73.5301 - val_binary_classification_loss: 44.9814 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0060\n",
      "Epoch 31/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 154.4520 - regression_loss: 62.0705 - binary_classification_loss: 25.5867 - treatment_accuracy: 0.8526 - track_epsilon: 0.0048 - val_loss: 184.0948 - val_regression_loss: 67.0141 - val_binary_classification_loss: 45.3803 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0036\n",
      "Epoch 32/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 148.9450 - regression_loss: 59.4126 - binary_classification_loss: 25.3471 - treatment_accuracy: 0.8526 - track_epsilon: 0.0051 - val_loss: 184.9047 - val_regression_loss: 67.3940 - val_binary_classification_loss: 45.2953 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0065\n",
      "Epoch 33/300\n",
      "597/597 [==============================] - 0s 62us/step - loss: 147.7056 - regression_loss: 58.6979 - binary_classification_loss: 25.5380 - treatment_accuracy: 0.8526 - track_epsilon: 0.0052 - val_loss: 183.7653 - val_regression_loss: 66.8886 - val_binary_classification_loss: 45.2760 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0043\n",
      "Epoch 34/300\n",
      "597/597 [==============================] - 0s 64us/step - loss: 148.4505 - regression_loss: 59.2592 - binary_classification_loss: 25.1531 - treatment_accuracy: 0.8526 - track_epsilon: 0.0045 - val_loss: 184.1559 - val_regression_loss: 67.0669 - val_binary_classification_loss: 45.2174 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0061\n",
      "Epoch 35/300\n",
      "597/597 [==============================] - 0s 65us/step - loss: 148.2367 - regression_loss: 58.9319 - binary_classification_loss: 25.5943 - treatment_accuracy: 0.8526 - track_epsilon: 0.0054 - val_loss: 185.0724 - val_regression_loss: 67.6386 - val_binary_classification_loss: 45.0281 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0042\n",
      "Epoch 36/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 150.4369 - regression_loss: 60.2207 - binary_classification_loss: 25.2545 - treatment_accuracy: 0.8526 - track_epsilon: 0.0037 - val_loss: 183.1421 - val_regression_loss: 66.6007 - val_binary_classification_loss: 45.2055 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0035\n",
      "Epoch 37/300\n",
      "597/597 [==============================] - 0s 79us/step - loss: 147.9177 - regression_loss: 58.9068 - binary_classification_loss: 25.3180 - treatment_accuracy: 0.8526 - track_epsilon: 0.0045 - val_loss: 187.0380 - val_regression_loss: 68.6611 - val_binary_classification_loss: 44.9672 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0030\n",
      "Epoch 38/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 149.4905 - regression_loss: 59.7513 - binary_classification_loss: 25.2271 - treatment_accuracy: 0.8526 - track_epsilon: 0.0037 - val_loss: 185.0969 - val_regression_loss: 67.6783 - val_binary_classification_loss: 45.0082 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0016\n",
      "\n",
      "Epoch 00038: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
      "Epoch 39/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 150.4060 - regression_loss: 59.9812 - binary_classification_loss: 25.7022 - treatment_accuracy: 0.8526 - track_epsilon: 0.0020 - val_loss: 184.8061 - val_regression_loss: 67.4567 - val_binary_classification_loss: 45.1825 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0019\n",
      "Epoch 40/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 147.8911 - regression_loss: 58.7708 - binary_classification_loss: 25.5944 - treatment_accuracy: 0.8526 - track_epsilon: 0.0017 - val_loss: 184.7605 - val_regression_loss: 67.5390 - val_binary_classification_loss: 44.9553 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0020\n",
      "Epoch 41/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 147.4302 - regression_loss: 58.7775 - binary_classification_loss: 25.1217 - treatment_accuracy: 0.8526 - track_epsilon: 0.0030 - val_loss: 183.1474 - val_regression_loss: 66.6912 - val_binary_classification_loss: 45.0185 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0036\n",
      "Epoch 42/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 143.9032 - regression_loss: 57.0180 - binary_classification_loss: 25.1108 - treatment_accuracy: 0.8526 - track_epsilon: 0.0041 - val_loss: 183.1681 - val_regression_loss: 66.6652 - val_binary_classification_loss: 45.0685 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0045\n",
      "Epoch 43/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 145.9709 - regression_loss: 57.8910 - binary_classification_loss: 25.4062 - treatment_accuracy: 0.8526 - track_epsilon: 0.0033 - val_loss: 182.8127 - val_regression_loss: 66.5287 - val_binary_classification_loss: 45.0264 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0028\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 44/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 147.1118 - regression_loss: 58.4151 - binary_classification_loss: 25.5357 - treatment_accuracy: 0.8526 - track_epsilon: 0.0023 - val_loss: 183.9478 - val_regression_loss: 67.0817 - val_binary_classification_loss: 45.0465 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0033\n",
      "Epoch 45/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 146.6344 - regression_loss: 58.2201 - binary_classification_loss: 25.4157 - treatment_accuracy: 0.8526 - track_epsilon: 0.0035 - val_loss: 182.6564 - val_regression_loss: 66.4863 - val_binary_classification_loss: 44.9337 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0032\n",
      "Epoch 46/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 145.8090 - regression_loss: 57.9971 - binary_classification_loss: 25.0709 - treatment_accuracy: 0.8526 - track_epsilon: 0.0028 - val_loss: 185.5000 - val_regression_loss: 67.8478 - val_binary_classification_loss: 45.0701 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0031\n",
      "Epoch 47/300\n",
      "597/597 [==============================] - 0s 95us/step - loss: 149.4340 - regression_loss: 59.6320 - binary_classification_loss: 25.4013 - treatment_accuracy: 0.8526 - track_epsilon: 0.0026 - val_loss: 190.2017 - val_regression_loss: 70.3956 - val_binary_classification_loss: 44.6640 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0016\n",
      "\n",
      "Epoch 00047: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-06.\n",
      "Epoch 48/300\n",
      "597/597 [==============================] - 0s 77us/step - loss: 149.7208 - regression_loss: 59.8646 - binary_classification_loss: 25.2522 - treatment_accuracy: 0.8526 - track_epsilon: 0.0023 - val_loss: 183.3720 - val_regression_loss: 66.8341 - val_binary_classification_loss: 44.9590 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0031\n",
      "Epoch 49/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 146.4089 - regression_loss: 58.2020 - binary_classification_loss: 25.2272 - treatment_accuracy: 0.8526 - track_epsilon: 0.0028 - val_loss: 181.9638 - val_regression_loss: 66.2070 - val_binary_classification_loss: 44.8110 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 50/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 145.6402 - regression_loss: 57.7422 - binary_classification_loss: 25.4097 - treatment_accuracy: 0.8526 - track_epsilon: 0.0025 - val_loss: 181.8525 - val_regression_loss: 66.1349 - val_binary_classification_loss: 44.8418 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0024\n",
      "Epoch 51/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 143.4757 - regression_loss: 56.7868 - binary_classification_loss: 25.1549 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.7604 - val_regression_loss: 66.5569 - val_binary_classification_loss: 44.9187 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 52/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 146.1171 - regression_loss: 57.9757 - binary_classification_loss: 25.4103 - treatment_accuracy: 0.8526 - track_epsilon: 0.0019 - val_loss: 182.4569 - val_regression_loss: 66.4359 - val_binary_classification_loss: 44.8548 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 53/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 146.4711 - regression_loss: 58.1842 - binary_classification_loss: 25.3556 - treatment_accuracy: 0.8526 - track_epsilon: 0.0020 - val_loss: 182.3928 - val_regression_loss: 66.4065 - val_binary_classification_loss: 44.8446 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0024\n",
      "Epoch 54/300\n",
      "597/597 [==============================] - 0s 74us/step - loss: 145.7055 - regression_loss: 57.7771 - binary_classification_loss: 25.3857 - treatment_accuracy: 0.8526 - track_epsilon: 0.0025 - val_loss: 182.2147 - val_regression_loss: 66.3179 - val_binary_classification_loss: 44.8379 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0024\n",
      "Epoch 55/300\n",
      "597/597 [==============================] - 0s 72us/step - loss: 144.4526 - regression_loss: 57.3121 - binary_classification_loss: 25.0753 - treatment_accuracy: 0.8526 - track_epsilon: 0.0024 - val_loss: 182.0351 - val_regression_loss: 66.2530 - val_binary_classification_loss: 44.7874 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0024\n",
      "Epoch 56/300\n",
      "597/597 [==============================] - 0s 75us/step - loss: 144.1982 - regression_loss: 57.1205 - binary_classification_loss: 25.2036 - treatment_accuracy: 0.8526 - track_epsilon: 0.0024 - val_loss: 182.3136 - val_regression_loss: 66.3662 - val_binary_classification_loss: 44.8441 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0025\n",
      "\n",
      "Epoch 00056: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-06.\n",
      "Epoch 57/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 143.8369 - regression_loss: 56.8825 - binary_classification_loss: 25.3167 - treatment_accuracy: 0.8526 - track_epsilon: 0.0025 - val_loss: 182.3594 - val_regression_loss: 66.4025 - val_binary_classification_loss: 44.8163 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0024\n",
      "Epoch 58/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 144.2899 - regression_loss: 57.0475 - binary_classification_loss: 25.4382 - treatment_accuracy: 0.8526 - track_epsilon: 0.0024 - val_loss: 182.5412 - val_regression_loss: 66.5005 - val_binary_classification_loss: 44.8003 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0022\n",
      "Epoch 59/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 144.2426 - regression_loss: 57.2677 - binary_classification_loss: 24.9548 - treatment_accuracy: 0.8526 - track_epsilon: 0.0018 - val_loss: 182.6658 - val_regression_loss: 66.5357 - val_binary_classification_loss: 44.8637 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0018\n",
      "Epoch 60/300\n",
      "597/597 [==============================] - 0s 78us/step - loss: 143.0137 - regression_loss: 56.4687 - binary_classification_loss: 25.3253 - treatment_accuracy: 0.8526 - track_epsilon: 0.0020 - val_loss: 182.4833 - val_regression_loss: 66.4569 - val_binary_classification_loss: 44.8323 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0023\n",
      "Epoch 61/300\n",
      "597/597 [==============================] - 0s 79us/step - loss: 144.5388 - regression_loss: 57.2944 - binary_classification_loss: 25.1843 - treatment_accuracy: 0.8526 - track_epsilon: 0.0025 - val_loss: 182.4334 - val_regression_loss: 66.4388 - val_binary_classification_loss: 44.8183 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0024\n",
      "Epoch 62/300\n",
      "597/597 [==============================] - 0s 85us/step - loss: 143.6628 - regression_loss: 56.9668 - binary_classification_loss: 24.9783 - treatment_accuracy: 0.8526 - track_epsilon: 0.0023 - val_loss: 182.3477 - val_regression_loss: 66.4035 - val_binary_classification_loss: 44.8031 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0023\n",
      "Epoch 63/300\n",
      "597/597 [==============================] - 0s 77us/step - loss: 143.7592 - regression_loss: 56.7775 - binary_classification_loss: 25.4567 - treatment_accuracy: 0.8526 - track_epsilon: 0.0023 - val_loss: 182.3529 - val_regression_loss: 66.4033 - val_binary_classification_loss: 44.8094 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0023\n",
      "Epoch 64/300\n",
      "597/597 [==============================] - 0s 78us/step - loss: 144.4561 - regression_loss: 57.2867 - binary_classification_loss: 25.1253 - treatment_accuracy: 0.8526 - track_epsilon: 0.0023 - val_loss: 182.3060 - val_regression_loss: 66.3758 - val_binary_classification_loss: 44.8172 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 65/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 144.3765 - regression_loss: 57.3234 - binary_classification_loss: 24.9777 - treatment_accuracy: 0.8526 - track_epsilon: 0.0016 - val_loss: 182.4476 - val_regression_loss: 66.4529 - val_binary_classification_loss: 44.8072 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0016\n",
      "\n",
      "Epoch 00065: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-07.\n",
      "Epoch 66/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "597/597 [==============================] - 0s 71us/step - loss: 144.8187 - regression_loss: 57.5510 - binary_classification_loss: 24.9592 - treatment_accuracy: 0.8526 - track_epsilon: 0.0019 - val_loss: 182.6972 - val_regression_loss: 66.5565 - val_binary_classification_loss: 44.8517 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0022\n",
      "Epoch 67/300\n",
      "597/597 [==============================] - 0s 76us/step - loss: 144.8041 - regression_loss: 57.4736 - binary_classification_loss: 25.0938 - treatment_accuracy: 0.8526 - track_epsilon: 0.0023 - val_loss: 182.5234 - val_regression_loss: 66.4864 - val_binary_classification_loss: 44.8136 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0023\n",
      "Epoch 68/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 143.8252 - regression_loss: 56.9214 - binary_classification_loss: 25.2248 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4574 - val_regression_loss: 66.4647 - val_binary_classification_loss: 44.7915 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0020\n",
      "Epoch 69/300\n",
      "597/597 [==============================] - 0s 75us/step - loss: 143.1978 - regression_loss: 56.8314 - binary_classification_loss: 24.7805 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.3775 - val_regression_loss: 66.4219 - val_binary_classification_loss: 44.7960 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 70/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 144.5244 - regression_loss: 57.2129 - binary_classification_loss: 25.3473 - treatment_accuracy: 0.8526 - track_epsilon: 0.0020 - val_loss: 182.3961 - val_regression_loss: 66.4298 - val_binary_classification_loss: 44.8000 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0020\n",
      "\n",
      "Epoch 00070: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-07.\n",
      "Epoch 71/300\n",
      "597/597 [==============================] - 0s 76us/step - loss: 142.9321 - regression_loss: 56.5138 - binary_classification_loss: 25.1521 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4254 - val_regression_loss: 66.4404 - val_binary_classification_loss: 44.8086 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 72/300\n",
      "597/597 [==============================] - 0s 80us/step - loss: 144.3344 - regression_loss: 57.1887 - binary_classification_loss: 25.2010 - treatment_accuracy: 0.8526 - track_epsilon: 0.0022 - val_loss: 182.4085 - val_regression_loss: 66.4308 - val_binary_classification_loss: 44.8096 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0022\n",
      "Epoch 73/300\n",
      "597/597 [==============================] - 0s 82us/step - loss: 143.8520 - regression_loss: 57.0468 - binary_classification_loss: 25.0074 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4823 - val_regression_loss: 66.4695 - val_binary_classification_loss: 44.8077 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 74/300\n",
      "597/597 [==============================] - 0s 88us/step - loss: 142.4526 - regression_loss: 56.3840 - binary_classification_loss: 24.9286 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4594 - val_regression_loss: 66.4591 - val_binary_classification_loss: 44.8052 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 75/300\n",
      "597/597 [==============================] - 0s 93us/step - loss: 142.7227 - regression_loss: 56.3094 - binary_classification_loss: 25.3561 - treatment_accuracy: 0.8526 - track_epsilon: 0.0022 - val_loss: 182.4612 - val_regression_loss: 66.4628 - val_binary_classification_loss: 44.7980 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0022\n",
      "Epoch 76/300\n",
      "597/597 [==============================] - 0s 86us/step - loss: 142.5488 - regression_loss: 56.2381 - binary_classification_loss: 25.3205 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4939 - val_regression_loss: 66.4762 - val_binary_classification_loss: 44.8060 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 77/300\n",
      "597/597 [==============================] - 0s 83us/step - loss: 143.2987 - regression_loss: 56.7939 - binary_classification_loss: 24.9629 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4439 - val_regression_loss: 66.4569 - val_binary_classification_loss: 44.7929 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 78/300\n",
      "597/597 [==============================] - 0s 84us/step - loss: 143.1639 - regression_loss: 56.5955 - binary_classification_loss: 25.2236 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4254 - val_regression_loss: 66.4463 - val_binary_classification_loss: 44.7962 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 79/300\n",
      "597/597 [==============================] - 0s 79us/step - loss: 143.0552 - regression_loss: 56.6011 - binary_classification_loss: 25.1026 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.3916 - val_regression_loss: 66.4269 - val_binary_classification_loss: 44.8006 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0022\n",
      "\n",
      "Epoch 00079: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-07.\n",
      "Epoch 80/300\n",
      "597/597 [==============================] - 0s 77us/step - loss: 143.4267 - regression_loss: 56.6880 - binary_classification_loss: 25.2984 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4181 - val_regression_loss: 66.4422 - val_binary_classification_loss: 44.7968 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 81/300\n",
      "597/597 [==============================] - 0s 84us/step - loss: 143.5535 - regression_loss: 56.7715 - binary_classification_loss: 25.2593 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4084 - val_regression_loss: 66.4378 - val_binary_classification_loss: 44.7963 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 82/300\n",
      "597/597 [==============================] - 0s 72us/step - loss: 143.1737 - regression_loss: 56.5954 - binary_classification_loss: 25.2270 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4203 - val_regression_loss: 66.4446 - val_binary_classification_loss: 44.7944 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 83/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 143.4288 - regression_loss: 56.8222 - binary_classification_loss: 25.0314 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4125 - val_regression_loss: 66.4380 - val_binary_classification_loss: 44.8005 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 84/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 144.5514 - regression_loss: 57.2933 - binary_classification_loss: 25.2142 - treatment_accuracy: 0.8526 - track_epsilon: 0.0020 - val_loss: 182.3979 - val_regression_loss: 66.4331 - val_binary_classification_loss: 44.7956 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "\n",
      "Epoch 00084: ReduceLROnPlateau reducing learning rate to 7.81249980263965e-08.\n",
      "Epoch 85/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 143.5767 - regression_loss: 56.7675 - binary_classification_loss: 25.2862 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4303 - val_regression_loss: 66.4495 - val_binary_classification_loss: 44.7950 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 86/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 144.4275 - regression_loss: 57.1787 - binary_classification_loss: 25.3191 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4286 - val_regression_loss: 66.4496 - val_binary_classification_loss: 44.7930 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 87/300\n",
      "597/597 [==============================] - 0s 66us/step - loss: 143.0436 - regression_loss: 56.5908 - binary_classification_loss: 25.1099 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4225 - val_regression_loss: 66.4457 - val_binary_classification_loss: 44.7947 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 88/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "597/597 [==============================] - 0s 69us/step - loss: 144.7102 - regression_loss: 57.4436 - binary_classification_loss: 25.0714 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4230 - val_regression_loss: 66.4466 - val_binary_classification_loss: 44.7933 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 89/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 142.3754 - regression_loss: 56.2528 - binary_classification_loss: 25.1221 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4270 - val_regression_loss: 66.4474 - val_binary_classification_loss: 44.7957 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n",
      "Epoch 90/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 143.2687 - regression_loss: 56.5889 - binary_classification_loss: 25.3412 - treatment_accuracy: 0.8526 - track_epsilon: 0.0021 - val_loss: 182.4297 - val_regression_loss: 66.4488 - val_binary_classification_loss: 44.7961 - val_treatment_accuracy: 0.6600 - val_track_epsilon: 0.0021\n"
     ]
    }
   ],
   "source": [
    "dragon = DragonNet(neurons_per_layer=200, targeted_reg=True)\n",
    "dragon_ite = dragon.fit_predict(X, treatment, y, return_components=False)\n",
    "dragon_ate = dragon_ite.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:01:21.529087Z",
     "start_time": "2020-02-10T19:01:21.403211Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds = pd.DataFrame([s_ite.ravel(),\n",
    "                          t_ite.ravel(),\n",
    "                          x_ite.ravel(),\n",
    "                          r_ite.ravel(),\n",
    "                          dragon_ite.ravel(),\n",
    "                          tau.ravel(),\n",
    "                          treatment.ravel(),\n",
    "                          y.ravel()],\n",
    "                       index=['S','T','X','R','dragonnet','tau','w','y']).T\n",
    "\n",
    "df_cumgain = get_cumgain(df_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:01:24.778325Z",
     "start_time": "2020-02-10T19:01:24.682974Z"
    }
   },
   "outputs": [],
   "source": [
    "df_result = pd.DataFrame([s_ate, t_ate, x_ate, r_ate, dragon_ate, tau.mean()],\n",
    "                     index=['S','T','X','R','dragonnet','actual'], columns=['ATE'])\n",
    "df_result['MAE'] = [mean_absolute_error(t,p) for t,p in zip([s_ite, t_ite, x_ite, r_ite, dragon_ite],\n",
    "                                                            [tau.values.reshape(-1,1)]*5 )\n",
    "                ] + [None]\n",
    "df_result['AUUC'] = auuc_score(df_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:01:27.968324Z",
     "start_time": "2020-02-10T19:01:27.907217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>4.054511</td>\n",
       "      <td>1.027666</td>\n",
       "      <td>0.575822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>4.100199</td>\n",
       "      <td>0.980788</td>\n",
       "      <td>0.580929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>4.021592</td>\n",
       "      <td>1.113436</td>\n",
       "      <td>0.564887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>3.537158</td>\n",
       "      <td>1.901297</td>\n",
       "      <td>0.553742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragonnet</th>\n",
       "      <td>4.011624</td>\n",
       "      <td>1.162131</td>\n",
       "      <td>0.556887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>4.098887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ATE       MAE      AUUC\n",
       "S          4.054511  1.027666  0.575822\n",
       "T          4.100199  0.980788  0.580929\n",
       "X          4.021592  1.113436  0.564887\n",
       "R          3.537158  1.901297  0.553742\n",
       "dragonnet  4.011624  1.162131  0.556887\n",
       "actual     4.098887       NaN       NaN"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T19:01:32.050765Z",
     "start_time": "2020-02-10T19:01:31.648475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EfcrmVPg1OZsPNp5i/+lsAEwaJ33DsnjJKx/DJQ2g3lbCHhyErVYdlOBgdNe5fp4tj4nbx7D5/EZV7IGKHRhQdxBp//0f+fFdUa5a4wd/f7wWJqCrW+cvtPD2SQJUijz++ONkZWWVdDWEEELcAQfS8pj/2yk2Hc3AqYAehS5+eQwMzsZfAZzqxMfiZ0KpF4ejXLkbXv9s3hlGbH6H5EsnXI57aA30qt2XzpUfx/blV5QdNx7F7jp/XhsVhWneHLTh4X+pjX+FJEBCCCHEfSQ918rM9Sf56WAaNocTUHjE28ygoGwq6RzgZgMssxGstWuhqxwDN7HR6J6MXYzeOpwca7bL8UBjIGObTCLaJwrLhInYPl2m6l/St38A4+RJaLy9b7+RxUASICGEEOI+cC7bzIfbzrJi7/k/BjhDOZ2DicEXae1lcXuOVe8kLdIPv5j26PQ33hop15bLsiNL+eLoZzgU18ULqwdEM7bJZALNegq6v4Fjy1bV+Yb4Hpd3c/+b1/i5GZIACSGEEPcop6Kw6UQWy3aksuH4hSvWFFT4p08+I4Ky8dWqu3wcWoWTEQ50NeMI9Lnxxq0Op53VKav4v0Pvk2VVD6VoW6E9b9cfgsfxk+T37oty+rRrAZMJ48QJeDz80G208u8hCZAQQghxj7E5nHyz9zz/t/k0Z7Jde3eCdQ7GB2XR3tusOk9B4VQFGxeig4ko2waD1nTd+yiKwta0zSzaP1+1kemfXqn5Gs9Xfwn7z7+QP/gdKHCdBu8IDsZ3YQK6mjVurZF/M0mAhBBCiHuEzeFkxb403t94itScq19rKXTyLmBMUBYBOnWvT2YZO4k17YSFtCDSoxqaG4z1OXzxEO8dSGB3xk638UBjEP3qvkWLsFZYly7FMmmKqoyuYUPS+vYm4C5LfkASICGEEOKuZ3M4+W5/Gu9tPMXZbPV4njJaB2OCsvmHT4Eq5tAqJEaZya4SRA2fdnhqrz/4+EzuaT449C7rzvziNm7UGeka9RxdIp/FqPXEPG06tg8+VJXz+FcXPIcOxZmSfHONvMMkARJCCCHuUvlWB1/vOcfH28666fG57EFvM5NCswjAoYpd9LezL9ZMaFActT1rXbfXJ8eaw5JDi/ku+VvVAGcADRoertSRV2p2J8gUhGK1YR46BPuq/7oW1OnwHDYEwzPP3Fpj7zBJgIQQQoi7TEaulU93nOXznalcsqiTEQB/ncLMyvm0VtSDkp0ahSORZs5X8aKGzyP46AOveS9FUVhz+icW7pvrdoAzQKOQOF6PiScy4PKAaSUvj4J+/XFs3ORa0GTCNHsW+pYtbrKlJUcSICGEEOIukWuxs+j3UyzbcRabw82CPYBeC29Whm7685js6uQo29fB3lr5+AfG0MAUh/Yae3fB5cUMZ+2exo70bW7jkf7V6R4bT8OQxoXHnOnpFMT3wnnwoEtZTWBZTAkL0NWqdRMtLXmSAAkhhBAlzKkorNqfxqx1yWTm2dyW0Ws1dK3sSR9TKkEF+WC/6hoahWNVLJyL9Kaqd1sCPMpf834OxcHnScv46PAHWJ1WVTzMK4xXY7rTtkJ7tJqiNXsce/ZQ0G8ASlqaS3lNeDhe7y1CW6nSLbS6ZEkCdJ9zOp106tQJPz8/li9fXng8Pz+f1q1b06pVK2bOnFmCNRRCiNLt4LlcJv/vGHvOXHIbNxl0PFzNnx5eqURePA3qcc7kejs4UMtBYLk46hui0WiuvdBgrvUSE3aMYev5zaqYh9bAC9Ev8q/I5zDoDC4x61dfYRk7HmyuCZq2ViymhAVoA6/9mu1uJAnQfU6r1ZKQkECLFi1YunQp3bp1A2DUqFE4HA7Gjx9fwjUUQojS6UK+jXnrk/l6z3l3u1Pgb/KgdY0QniyTT9PT+zFcdL+g4fHKFszRkdTwboRee6092y9LzjnOyC1DOZN3WhWrH9SQfvXeItzHtRdHsdqwTJ6M7bPlqnN0rVphmjEdjbfX9Rt7F5IEqBj8evHdO3q/1mW631L5ypUrM27cOIYNG0abNm04ceIEH3zwAatWrcK7hPdiEUKI0sbmcPL5zlQSNpx0O8BZr9XQukYI7aPK0uTcHionp7u9TmqIjfOxgVQo2wwvXcAN7/vb2fVM2TmeArtrF5KfwZ/4Wr15MPwR1Swx54ULmPv2x7FTvRaQx6uv4Nm/Hxr9vZlK3Ju1Frfs1VdfZdWqVbzxxhucOnWKXr160axZs5KulhBClCqbk7OYsuY4xzPy3cZjyvvRqV55IsmjSdI6fMx2VZlL3g6SYzwJrNiaKI+wG97TqTj5v0Pv88mRj1SxmmViGB03kSBTkCrmOH6cgvieKKfcbGsxfiweHTve8N53M0mASpEZM2ZQv359qlSpwrBhw0q6OkIIUWrkWuxM+OkYqw+4780J8vWkc73y1Aj1oeb5Q9Q8k8LVo3icGoXjkU50NRtTxTPyhis5A5zIOc7M3f/hwIV9qljHiM70rfOmaqwPgH3rVgr69oecHJfjmooVMc2Zha7G3bey862SBKgU+fjjjzGZTJw9e5aUlBSqV69e0lUSQoj73t6zl3hnxWHVnl0Annot7WNDaRYZRBlrHk0S11EmT72HV663g1P1gykf1ga9Rp2wXM3isPBx4v+xPOlT1aKGOo2O3nX682jlJ9wmUbZvV2AeOQrsrr1PuiZNMM2cjibgxq/b7gWSABWDWx2TUxJ27tzJrFmzWLZsGYsXLyY+Pp6ffvoJnU5X0lUTQoj7klNR+HDzaRb8dhK7Uz2AuWHlMjxcOwxfo57q549R68wR3GzhxalwO0rdJlTyiryp++5M386s3dPcDnQu41mWUXHjqB1YVxVTFAXrvPlYExaqYh5P/RPPkSPQeHjcVB3uBZIAlQJms5kePXrw3HPP8eCDD1KnTh2aNm3K7NmzefPNN0u6ekIIcd9Ju2Rh+KojbEnJVsXCAow80bAilQK98TbnEZe4maBc9RR4i8HJidrehFbpgEF741lWFoeFuXtn8n3KKrfxZuVa0K/uQIJNwaqYYrFgHj4S+3//q4oZ+vfD8PprN/XK7V4iCVApMGbMGMxmMxMmTAAgNDSUadOmER8fT8eOHalZs2YJ11AIIe4fW8+YeW/1Li4WqAcwN4sMpGPd8hg0UD31KDGpSejd9A6dC7GRW78WFf3q3lTikZZ/nlFbh3IkK1EVCzQG0rv2AFqVb+P2WpdnevXDsXOXa8BgwDhpwj0/2PlaJAG6z/3++++8++67fPvtt/j6+hYef+qpp1i5ciXx8fGsWbMG/T06jVEIIe4W+VYH//n5OF/vUff6eBl0PNU4nJgK/pTNvUjDlD0EFKhngln1To7GaAms9ghhHuqZWe7sydjF2K0jVPt4adDwaJUn+HfMG/h4+Lg991ozvTRlymCaNwdd/fo3VYd7kXzq3edatGhBZmam29iSJUvucG2EEOL+tPdMDkO/O8KpLPUA5qrB3vyrSQSBnlA7ZR/V0k/hrk8nLcjG+brliSjbCp3mxmNtFEVhxYmvWbBvjmqgc0WfcAY1GEZs2Wvvy2XfspWCfuqZXtoqVTAlzL+ntrW4HZIACSGEELfJbHPw3sZTfLj5NFfvXarVQIfYcrSpEYK/JY+WB7fgY1HPBLN6ODkSZcM7qjlVPW9uoPMFcyZz9s7kt7PrVLHm5VoyuOHwa/b6ANi++hrzmLFuZnrFYZo1E42//03V414mCZAQQghxGzaeuMjEH4+57fUJ9vWka5NKVCjrRWj2eZod34mHm93dz4RZORcTSOUyrTFqr52w/ElRFL5P+S+LDswj15arir9U41VeiH7ZZQNTl/OdTiwzZmL74ENVTP/kExhHjUJjuH9mel2PJEBCCCHELcjItfKfn4/zw6EMt/Gm1f4Y6KzTEJW+nzonU9Aqri+98kwOEmOclA1vTrRH1Zsa6Hwm9zQzdk9ld4Z6WwovvRfvNBxBi7BW1zxfycvHPPgd7L/8oooZBvTH8Nq/77uZXtcjCZAQQghxExRF4Yvd55izLtntHl6+Rj3/bBxOjTA/NE4bDc/8TpVz+XDViJ/T5a2k16lIVZ9meGiNN7yvQ3Hw5dHl/N+h97E6rap4dEANBjccToRv5Wtew3nuHAU9e+M8fNg1YDRinDQRj4cfumE97jeSAAkhhBA3YLE7Gft9EqvcbGWhAZpGBvJQrTCMBh1ejjM0ObGboCzXxEdB4Wh1J8aYtlQ3hN/UfU/nnmLKzgkcvLBfFTPqjLxc8zX+WfVpdNprf5w79u+noFcflHTXumuCgjDNn4uudu2bqsv9RhIgIYQQ4joy86wM+PoQe86oFyssH2DiiUYVCS/rBViJyN9EvaQsDDbXMTh2nUJK/bKEVGl7U1tZOBUnK098w7sHFmBxqAdONwqJo3/dgYR5l7/udWw//oR5yFAwu45T0kZHY1owD23YjTdTvV9JAiSEEEJcw5G0PPp+eZDUHNckxFOv5cFa5WgaGYROq8GgTabBqX2Ep+rgqm1MC4wK2S3qUS7w5jYQPZeXyrRdk9mVsUMV8/Xwo1ftvnQIf/i643UURcG66F2sc+aqYrp2bTFNnYrG+8arS9/PJAESQggh3Pg5MYNhq45QYHO6HA/29eTFllUI8vVEq80ixLaTBgfy8C5Q7614qawBpWUHfI2+qtjVrA4rXxxdxidHPnLb69OsXAverDeIssbA617nettaeLz0Ep4D30Qj+0BKAiSEEEJcKbvAxtQ1x92O94kM9eG5ZhF4eVowmHYRkXGa2odMaBXXhMKpgUsxldHXbAxa91PSr7Q9bRtz987gdO4pVcxb703P2v14uFLHG87ScqZnUNCnL869e10Dej2eo0ZgeOqpG9altJAESAghhPjDL0cymfDjUTLybKpY08hAHq0fhNFrHwaPI9Q4aqDKSfVrpHyTFmeLtujLqjcdvVp6QRoJ++ay/uxat/H6QQ15u8EQQr3K3fBajr37KOjfH+XcedeAvz+mObPQN258w2uUJpIAlQLx8fEsW7YMAJ1OR1hYGA899BAjR44kICCghGsnhBAl72K+jSlrjvP9QXWvj1YDnetVoGVNG0bv/+LhsFJvjxfBmeoFA/OrViTJN4jIm0h+fk/9jSk7JpBnVy9o6G8I4PXYHjxc6R/XXNTwT4qiYPviSywTJoLNNXHTVq2CacH9v63F7ZAEqJRo27YtixYtwm63k5iYSO/evcnOzmbx4sUlXTUhhChRvx27wMj/JnEhX93rU87fyNONK1K5/AkMxn345GtpuNsH7/yrXnlpNVgbN0GJiEBJSrru/RxOOx8eep9lSR+rYho0dK78OK/GdMfP4HfDuitmM+Zx47F/860qpmvRHNP0aWj8bnyd0kgSoGLQ/tuWd/R+Pz+x4ZbP8fT0JDQ0FIAKFSrw5JNP8umnnxZ31YQQ4p5hsTuZufYEy3akqmJaDbSrGUq7mAC8fbegN5yl3DkPah0y4WF3HYfjNJmwtGiJs2zZG97zouUiE7aNdjvDKzqgBn3rvkWNMjVvqv7OM2co6DcA58GDqpjHSy/h+dYANHr5mL8WeTKlUHJyMj///DMeHqVjvxchhLhaUnoe76xM5Gh6vioWFmDk6caVqBhkxuj9PzzIpeYBE+Fn1ev3OAIDsTRvgWIy3fCeezP2MGH7aDLMrq/ZdBodr8fG889qXdBpbm52ln3nTsx9+qFcvOgaMJkwjh+HR8dHbuo6pZkkQKXEmjVrqFChAg6HA/MfC2JNmDChhGslhBB3lqIoLNuRysy1J7BetTmpVgPtYkJpVzMYo+kIBtNe/HKh3l4ffPLViYmtchWsDRvCDaaU51ov8d7BhaxKXqGKBRqDGNl4LLUC69x0G2wrV2IeMUo13kdTuTKmObPRRVa76WuVZpIAlRLNmzdn9uzZFBQUsGTJEpKTk+nRo0dJV0sIIe6YM1lmxv5wlM3JWapYGW8DzzStRESwgtFrLTp9OpVPGohOMqo2MlW0Wqx162GPjIQbLEb429n1zNs3k0xzpipeN6g+wxuNoazxxq/O4PJO7tbZc7C+974qpu/QHuPECWh8bryjvLhMEqBicDtjcu40Ly8vqlatCsDUqVPp3LkzU6dOZciQISVcMyGE+Hs5FUWBMS4AACAASURBVIXPd6Yya12yalFDgPoRZXisQXl8vE/i6bUDk9lBnT3eBF5Uf0Q6fX2xNGuGM6DMde+Zln+euXtnsvGc+8+Hf0U+x2sx3a+7h9eVlPx8zO8Mwb7mZ1XMEN8DQ6+eaG5ivSFRRBKgUmrw4MF06dKFl19+mbBSvBeMEOL+lnyhgDGrk9h5OkcV8/TQ8kTDitSPMODptQm9x2nKp3oQk+irGugMYKtSFWv9+nCDgcX7L+3lw1/eczu9vaJPOAPqvk294AY33QZnegYFPeJxHjrkGjAYME4Yj0enf9z0tUQRSYBKqVatWhEdHc20adOYPn16SVdHCCGK3bd7zzPxp2NY7Open6ohPnSJCyO47DEMxkN42pzE7vWiXJp6coji4YGlYSMcN1hLR1EUvjj6Ge+eXICC6/ginUbHs1Ev8Hz0ixh0njfdBmdyCvnd30A5fdrluCYwENO8uejq3vzYIeFKEqBSrHfv3vTq1Yt+/fpRSRbJEkLcJyx2J1P+d4yv9pxXxTz1Wv5RN4xm0Xl4ev0PrS6foEw9dfb74GlVv0JyhIRiiYtD8br+xqFWh4WZu6fx06nvVbGYsrV4s94gqvhVvaV2OPbto6BHT9VML210NKb589CWl977v0ISoFIgISHB7fEuXbrQpUuXO1wbIYT4+5zNNvPWN4c5eE79+ql6OV+6NPUjuOwedB5paJxQ/YiRqinqHhlFp8Napw72yKjrDnQGyDRnMGrLMA5dPOByXIuW12J70CXymRuu5nw1+4bfKejXHwoKXI7rWrXCNGN6qd/JvThIAiSEEOK+8PvxiwxZmUi22e5y3KDT8GiDCjSNzsDT6xc0GgemfA319nkRkKP+GHSULYslrgnKTaygvP7ML8zeM4Nsq+vMMm+9DyMaj6FxaJNbbodtxYrL09ztru3QP/EExjGj0MgabsVCEiAhhBD3NEVReH/Taeb/mnLVyBsI9vXkhZYhVArbg97jHMA1V3RWNBpsNWOwxcTccAf3C+YLzNk7g9/OrlPFQg3lmNJqBuG+tza0QHE6sc6ajfV99RZFhtdfw9C/3w13gxc3TxIgIYQQ96wCm4NRq5P48VCGKhZbwY/nWmrw9VuLRmtF64CYRBPhZ9QrOjtNJixNm+IMDrnu/RRFYe2ZNczdO4sca7Yq3igkjmfLvnjryU9ePubB72D/5RfXgEaD55B3MLzw/C1dT9yYJEBCCCHuSedyLPT/6iCHzue5HNdqoHP9MrStexIPwykAvHO11N/nhW+uetVme/kKWBo3Bs/rz85KzTvLgn1z2XjuN1VMr9HzYo1XeCbqeY4fO3FL7XCeOUtB7944E4+4BgwGjJMmyrYWfxNJgIQQQtxz9pzJYcDXh8jMc90OwtdTS4+HNYSHbkWjvRyrcNaDmEMm9M7bW9E535bPp0c+4stjy7E51TvGRwfU4O0GQ295lhf8sadX3/4oFy64HNcEBWGaO0emuf+NJAESQghxT/lmzzkm/HQM21V7ecVWhJfbn8NovPw6TOuA2EMmKqa6eeV1Eys6OxUnP538nsUH3+WCRb2VhYfWg5dq/Jt/RT5z0ys6X8n2/Q+Y3xmi2tNLW6MGpvlz0coitX8rSYCEEELcE6x2J1PWHOfL3eeuiih0bZFPi9hTaDSXFz30ytdSf48Xfu5eeUVEYGnQEK4zm+rghf3M2zuLxKzDbuM1y8TydoMhRPhWvr22LPkIy5SpquP6BztgnDQRzQ3WHRJ/XYltHDJjxgzatWtHeHg41apVo2vXrhw8eNClTHx8PAEBAS7/dejQwaWMxWLh7bffpmrVqpQvX55nnnmGM2fOuJQ5deoUXbt2pXz58lStWpVBgwZhtVr/9jYKIYQoHudzLLz66T5V8uOhczLwiQu0rJVSmPwEp+tpvsVHlfwoOh2WRo2xxDW5ZvJzwZzJlB0T6PNrD7fJT6AxkMENhjGndcJtJT+K04l5yn/cJj+GHm9gnDlDkp87pMR6gDZs2MC///1vGjRogKIoTJw4kSeeeIItW7ZQpkxRl2Tbtm1ZtGhR4dcGg2tX5pAhQ1i9ejWLFy+mTJkyDBs2jK5du7J+/Xp0Oh0Oh4OuXbtSpkwZVq9ezcWLF4mPj0dRFP7zn//csfYKIYS4PdtPZvP2t4e5kO/6qijYz86bj5/Dx+uPfb4UiDzuSdRxo+oaTj8/zM2ao/j7u72HzWnjm2NfsjTxQ/Lt+aq4h9bAvyKf4dnqL2DS316ColitmIcMxf79D64BvR7juDF4PP74bV1X3J4SS4C+/vprl68XLVpEpUqV2Lx5Mx07diw87unpSWhoqNtrZGdns3TpUubPn0+7du0Kr1O7dm3WrVtH+/bt+eWXXzh06BD79u2jYsWKAIwZM4a+ffsyYsQI/G5ioav7UdeuXSlbtuw1V4kWQoi7wWc7zjJ1zXGuGu5DvSpWXmp/Cr3OAoCHTUOdfSZCMtU9O/bwcCyNGrvt9VEUha3nN5Owfy6nck+6rUOr8m15I7YnYd7lb7sdSk4OBX374di6zTXg5YVp9kz0LVrc9rXF7blrxgDl5ubidDoJCAhwOb5p0yYiIyPx9/enRYsWjBgxguDgYAB2796NzWbjgQceKCxfsWJFoqOj2bJlC+3bt2fr1q1ER0cXJj8A7du3x2KxsHv3blq3bn1nGihKXKdOnYiJiZGePyHuAU5FYdbaZJZsdR3S4KF38lyrHBpGpaLRKKBA2DkPah4xqvbyUjQarHXqYq9e3e0sr+Sc4yzcP49taVvd1qGybxV61+lP/eCGf60tqakUvBGP8+hRl+OaoCBMCxPQxdT8S9cXt+euSYDeeecdateuTVxcXOGxDh068OijjxIREcHJkycZP348jz32GOvWrcPT05O0tDR0Oh2BgYEu1woODiYtLQ2AtLS0woTpT4GBgeh0usIy7iQlJamOGY1GPG+wTsS9wuFw4HA4MJvNqpjVai181egufq9yOp3Y7fabalNOTo7q+8Pd90RpJc+iiDyLIsX1LGwOhbnbs9l42uJyPCb8Ei+2S8fbdPln2FSgIfaQiWA3vT42vZ6UylXI1WrhqsQj136J79K/5bcL63Ci3inepDXxaMiTtCnbDl2WnqSsW2/Xn89Cn5xCmbHj0GW6ziKzV6zAxZEjcXjooRR8D5XEz0lUVNR143dFAjR06FA2b97MDz/8gE5XNGjtqaeeKvxzbGws9erVo3bt2vz444889thjf2ud3D247OxsjEb1u2Xvz5f/rXW5Wt6/ut5S+fz8fN566y1WrlyJl5cXPXr0QKfTodPpMBqN1K5dm+eee47Tp0/z3Xff0a5dO5YsWcLw4cP54YcfOH36NMHBwTz55JMMHTrU5RnMmDGDhIQE8vPz6dy5M1WqVOGTTz5h3759wOWkY9q0aSxZsoT09HQiIyMZNmwYnTp1AiAlJYW6deuyZMkSPvzwQ7Zs2UKlSpWYPHly4WvN3377jUcffZQVK1YwduxYDh48SHR0NLNmzaJevXqFddmyZQtjxoxh165dBAQE0LFjR0aPHo2fnx/x8fFs2rSJTZs28eGHHwKwZ88eIiIi3D4zPz8/wsPDC79OSkq64Q9TaSHPoog8iyLF9SxyzHYGfHWQ7VckPwHeNrq0OE+dKkVjfaqkGIg6ZkTnVPfsOAIDsTVrTpibwcTrz/zCjN1TybWpN0vVoKFjRGdejelOGc9rT4+/kT+fhX3rVgqGj4BLl1zi2vr1CJg/jzJXvfG4X92tPyclNgvsT0OGDOGrr75i5cqVVK5c+bplw8LCKF++PMePHwcgJCQEh8NB5lWZdXp6OiEhIYVl0tPTXeKZmZk4HI7CMve7ESNGsG7dOj766CNWrFjB3r172bhxo0uZBQsWUL16ddatW8fIkSMB8PLyYt68eWzZsoXp06fz9ddfM23atMJzvvrqK6ZMmcKIESNYv3490dHRLFiwwOW6CQkJzJ07l9GjR7Nx40Y6depEt27d2Lt3r0u58ePH88Ybb7Bhwwbq16/Pq6++Sm6u6z9QY8aMYdSoUaxfv56yZcvSvXt3FOXywIADBw7wz3/+k44dO7JhwwaWLl3Kvn376N27NwCTJ08mLi6O559/nsTERBITE11eiwohSt6ZLDMvf7yX7adyCo81jb7I8K7HCpMfrQPq7/WiRpJJlfwoWi3WmFjMbduhuEl+vj72BWO3jXSb/NQNrMfCth/wVv3Bfyn5+ZPt++8peP0NVfKj79Aer8Xvoyklyc/drEQToMGDBxcmP9WrV79h+czMTFJTUwsHRderVw8PDw/Wrl1bWObMmTMkJibSpMnlHXjj4uJITEx0mRq/du1aPD09XXoP7le5ubksXbqUMWPG0L59e2JiYpg/f75qQ73mzZvTr18/qlatSrVq1QB48803adq0KRERETz00EO8+eabfPXVV4XnLFy4kOeee44XX3yRyMhI3nzzTRo2dH1XPm/ePHr37k2XLl0Ke3+aNWvGvHnzXMr17NmTjh07Uq1aNUaOHMnFixcLe5H+NGzYMFq3bk316tUZNGgQR44c4ezZswDMmTOHJ598kj59+lCtWjUaNWrE9OnTWblyJenp6fj7++Ph4YGXlxehoaGEhoa69DYKIUrWuqRMun64i2MZf87AUujUKI3n26bi6XH5NZXeBo12eVMuTf3KyxEcTMFDD2OrVQuu+tlWFIXFBxcxf99s1XlhXuUZEzeB6S3nEhnw13spFEXB+8uvML/1tmqBQ4/nnr08zd3NmwRx55XYK7CBAweyfPlyPv74YwICAjh//jwA3t7e+Pj4kJuby+TJk3nssccIDQ3l5MmTjB07luDgYDp37gyAv78/3bp1Y9SoUQQHBxdOg4+NjaVt27YAPPDAA9SsWZMePXowfvx4Ll68yMiRI3nxxRdLxQywEydOYLVaXcZW+fj4EBsb61Kufv36qnO/++47Fi9ezPHjx8nLyyscN/SnI0eO8OKLL7qc07BhQ47+8b49JyeH1NRUmjZt6lKmWbNm/PTTTy7HrqxP2B+rn17dc3dlmXLlyhWWqVChAnv27OH48eN88803hWX+7B06ceKEahyYEOLuYHcqzPs1hQ83ny48ptUqPNMqlWY1sgqPGSwaGu/yxu/SVcmNwYC1bl3slau4HejscNqZtWc6q1O+czmu0+h4pebrPFXtXxh06pWib4ditWEeMwbfb75VxQxvDsDw71dlN/e7SIklQO+//z4Aj1+17sHgwYMZMmQIOp2OgwcP8tlnn5GdnU1oaCitWrXiww8/xNfXt7D8pEmT0Ol0vPLKK5jNZlq3bs3ChQsLf7vX6XQsX76cgQMH8sgjj2A0GunSpQvjxo0rtrbc6picu5G3t7fL19u2baNHjx4MHjyYiRMn4u/vz+rVqxkxYkSx3O/qfwQ8rpie+mfszwTmZso4nU5efPFFevbsqbpXmCwnL8RdKT3XyuAVh9lxxSsvg97Jqx1OExtR9JrKK19L453eeBW4vrRw+vlhbt3G7esuAIvDwoTto/k91XXzUqPOxOi48TQObVJsbVGysino3189zV2vxzh+LB5/87hVcetKLAHKysq6btxkMqnWCnLH09OT//znP9ed2hweHs7y5Xd2oPLdokqVKnh4eLBt27bCMVZ5eXkcPHjwumOuNm/eTLly5Rg0aFDhsVOnTrmUqV69Ort27aJbt26Fx3bu3Fn4Zz8/P8LCwti8eTNt2rQpPL5p0yaio6P/Ystc1a1bl0OHDlG16rU3IzQYDC49WEKIkrMlOYsh3yW6bGbqa7LzxiMniQgpmqnpe0lL450+eFpdf2lyBAZibtnqmju4ZxRkMHLLEBKzDrkc9zP4M6nZf6hRJqbY2uJMOUl+fE+U5GTXgJ8fptmz0DeJc3ueKFl3xSww8ffx8fGhW7dujB49mqCgIMqVK8fUqVNxOtVTP68UGRnJuXPn+Pzzz4mLi+Pnn392Gf8D0KNHD3r16kX9+vVp3rw5q1atYvv27S5rOfXp04dJkyZRrVo16tWrx/Lly9m0aRPr168v1nb269ePBx98kAEDBvDyyy/j6+vLkSNH+OGHH5g1axYAlSpVYseOHaSkpODj40OZMmXQakt8HoAQpYrZ5mDu+hQ+3n7W5Xh0hVxean8WX5O98Fhgpp4Ge7zQO1yTH3u5MCzNm4Pe/UdY4sXDjNjyDpnmDJfjIaZQpjSfQSVf97M/b4dtzRrMw9QzvTTh4XgtXIC2SpViu5coXpIAlQLjxo0jLy+PF154AZPJRPfu3cnPVy/1fqWOHTvSs2dPhgwZgtlspl27dgwdOpS33nqrsMxTTz1FcnIyY8aMoaCggM6dO/Pqq6+yevXqwjI9evQgNzeXUaNGkZaWRlRUFB999BG1a9cu1jbWqlWL1atXM378eDp37ozD4aBy5cqF0+3hcjIWHx9P06ZNKSgouO40eCFE8Tt0Lpehq45wPKPo3x+tRqFjo3Qerp/hMoSnwlkPah30QnvVCtD2iAgsjePgGr+8rDvzM1N3TsTicF1DqLJvFSY3n06wqXhm/ypWG5bp07Et/VgVs8bEUOa9RWjL/PXZZOLvo8nKylJuXEzA5XWA/K+xj8z9yGw2u1336Hqef/557Hb7Pf/K8eq/67t1HYuSIM+iiDyLItd7FnanwoebT7Nww0nszqKPnABvGy+3P021sIKiwtfZz8tWvTrWuvXcDnZ2Kk4+OvwhSxM/VMWahDZjaKNR+Hj43EbL1JxnzlDw5kCcV81UBdA/9iinX+xGVEzxvWK7192tPyfSAyRuW35+PosXL6ZDhw7o9XpWrlzJ6tWr+eijj0q6akKIu8SRtDxGr07iwDnXtXdqRVyiW7uzeHkWjcvTOKHWIRMVz7rOylIAa9162K8xdjDbms20nZPYeG6DKtYl8hlej41HpymeZS/sa9dRMGQo5OS4BvR6PAe+hUe3F1QrT4u7kyRA4rZpNBrWrFnDjBkzMJvNVK1alXfffZdHH320pKsmhChhVruT9zedYvGm0y69Pga9kyebnadlzEWX8mUv6Kh12BvvPPXihpYmTXFcsTL7lXan72TSjnFkmF2XzdBr9PSvN5COEZ2LpT2KomD94EOs02eoYpqwMEwzpqOrW6dY7iXuDEmAxG0zmUysWLGipKshhLjL7D17idGrk65Y1PCy8KACXu5wlhD/ovE5BouGGkeMVDinXotHMRgwt2yJM0i9jpfdaeejwx/w6ZGlKLiO5PA3BDCmyQRqB9YtlvYoDgeWSZOxfbpMFdO1a4tpwgQ0AaVneMT9QhIgIYQQxcKpKMz/NYUPNp/mik4fNBqF9nUy6RyXju7PUc0KVDptoPpRIx52N2N6vL0xt2qN4mbB2rN5Z5i0YxwHL+xXxWqUqcmIRmMp5108638pZjPmQYOxr/nZNaDT4TmgPx6vvCyLG96jJAESQgjxl1kdCoNXJPLTYdep56EBFrq1SyUipKg3SG+DBnu8Cbzo/iPIVqUq1jp1VGv8OBUnK098w3sHEjA7zC4xDRqeiXqel2u+hl5bPB9tzosXKejVG+fuPa4BHx9Mc2ajb1p8CymKO08SICGEEH9JjtnO+A0XOZhRtKihVqPQvm4mnRqlo9MVdQeZCjQ02uWNT556ULLD3x9rw0Y4g4JUsXN5qUzbNZldGTtUsbKegbzTcDgNQxoXU4uuvbihJjQU06IEdDexf6W4u0kCJIQQ4radzTbT6/MDHM8sSn4qlDXT7YFUKgQWuJT1y9bRaLe3alVnRa/HGlsLe1SUan0fRVH4b8pKFu6fR4Hd9XoATUOb83aDIQQUww7uf7Lv2Im5T1+Uq3Ys0EZFYVqUgPaPvQjFvU0SICGEELdlW0oWQ747QnquFbi8ienD9dN5uEEGuqvWKQxJ11Nvnze6q3ajcQQFYWnazO1+XhaHhUnbx/JbqnrleC+9Fz1r9+WRSp2KdQyObdV/MQ8brtrJXdckDtOc2Wiu2ItS3NskARJCCHFLUi4UMHPtCdYmXSg8dnmszxmXfbwAUKDqKS+qJ3pwdZpiDw/HEtcEdOrXYZesOQzf8g77M/eqYg2CGzGw/juEehVfT4yiKFgXLsI6d54qpn+0M8ZxY9EYimfXeHF3kARI/K3mzp3Lu+++yz43K6YKIe4tOWY7izac5LOdqYVr+2hQaF3rAo81ScOgd52OrrNDo8PBlE21qq5lja6BrU4dt6s6pxekM2TTW5zIOe5y3Kgz0aNWLzpXfrxYe30UqxXzqNHYV6xUxQy9e2GI7yEzve5DkgCVAvHx8Sxbdnn9Cp1OR1hYGA899BAjR4502bhUCCHcURSFL3afY976FLLNRZuVlvWx8mybVGpUzFOd45dvovFefwyXXMftKBoN1voNsEdGur3XyUspDN74JmkF512OV/GrytgmkyjvXaEYWlTEeeEC5n4DcOy4anC1hwfG8ePweLR4FlIUdx9JgEqJtm3bsmjRIux2O4mJifTu3Zvs7GwWL15c0lUTQtzF0i5ZGLU6iY0nigYEe+icdKiXQYd6mapeH4DICxWI3GNGc9WgZcXDA0vTpjjCyru9177MPYzcMpQca7bL8VqBdRjfZDK+BvWaQH+FI+koBb16o5w+7Rrw98c0dw76Rg2L9X7i7iIJUDG4FFPrjt7P96B68a8b8fT0JDQ0FIAKFSrw5JNP8umnnxbG582bx6effkpycjL+/v506NCB4cOHF26G+sknnzBo0CA+/fRT3nnnHVJSUmjQoAHz5s2jcuXKhdeZPXs28+fPJy8vj86dO7vEAJxOJ9OmTWPJkiWkp6cTGRnJsGHDCndtT0lJoW7duixevJjFixezc+dOoqKiSEhIQKvV0r9/f/bv30+dOnVYuHCh6vpCiOLzv8MZjPvh6BW9Pgp1Kl/in83PE+hrU5XXKR40OlWVsonnVDGnvz/m5i1Q3AwiVhSFb45/ycL983AorqOkm5drxfDGo/HUearO+yvsv22g4K2BkOu6R5mmUiW8FiagrRxRrPcTdx/tjYuI+01ycjI///wzHh4ehce0Wi2TJk1i06ZNvPfee+zYsYNhw4a5nGexWJgxYwbz5s3jp59+Ijs7mzfffLMw/s033zB+/HiGDBnC+vXriYqKYsGCBS7XSEhIYO7cuYwePZqNGzfSqVMnunXrxt69rgMdJ02aRP/+/fn111/x9/fntddeY9CgQQwfPpyff/4Zs9nM4MGD/4anI4S4ZLYzfNURBn57uDD5Cfa30PMfJ3n94dNukx+DNYCWZ+q4TX7slSpR0L6D2+SnwF7ApB1jmb9vtir56RTxKKPjxhVr8qMoCtalH1MQ31OV/OiaxOH92TJJfkoJ6QEqJdasWUOFChVwOByYzZdnaUyYMKEw3rNnz8I/R0REMHbsWJ577jmcTifaP9blsNvtTJs2jaioKAD69OlD7969URQFjUZDQkICzz77LK+88goAAwcO5LfffuP48aKBjPPmzaN379506dIFgGHDhrFx40bmzZvHu+++W1iuV69ePPTQQwD07t2bZ555ho8++ojWrVsD8PrrrzNo0KBif05ClHaJ53Pp/9UhzuYU7ddVMzyXVzucxmhwqsrrMFLNqwmm/efxSnXdBV3RaC7v4h4V5Xaw89m8M4zcMpQTOcdUsRejX+HFGq8W72BniwXLhInYvvxKFfPo8jSew4ehueIXQ3F/kwSolGjevDmzZ8+moKCAJUuWkJycTI8ePQrj69evZ+bMmRw5coScnBwcDgdWq5Xz588TFnZ5Tx1PT8/C5AegXLlyWK1WsrKyKFOmDImJiXTr1s3lvo0bNy5MgHJyckhNTaVp06YuZZo1a8ZPP/3kciw2NrbwzyEhIW6P5eXlkZ+fj5eb9UOEELduS3IWb35ziFxLUU9MsxoX6doqVbWuj6JoCNLHEO3TCOPRZDxTU13jHh6YW7bCGazeyBRgy7lNTNwxhlybay+Mt96bwQ2H0yKsVfE06g/OM2co6D8A54GDrgGtFs9Bb+PR7QWZ6VXKSAJUDG5nTM6d5uXlRdWqVQGYOnUqnTt3ZurUqQwZMoSTJ0/StWtXXnzxRYYOHUrZsmXZs2cP//73v7Fai6av6vWu3y5//mPhdKp/K7xVV//Dc+XruT9jV96/OO8thIDvD6YzfNWRwuntoNCpUTqPNMxQlXXYQ6jp05JypiD0x47iuXu3S1zR6zG3boMzMFB1rqIoLD/6Ke8fWKjaxT3CtzJjmkwk3KdSsbULwP7rbxQMGgw5Oa4Bb29M06aib9OmWO8n7g0yBqiUGjx4MLNnzyY1NZVdu3ZhtVqZNGkScXFxREZGknrVb3M3Izo6mu3bt7scu/JrPz8/wsLC2Lx5s0uZTZs2ER0dfXsNEUL8ZR9tPcM7KxMLkx+dVqFbu7Oq5EdRwGFuQH3fzpQzBqI/kojnVdPHFZ0Oc6vWbpMfq8PClJ3jee9Agir5aVO+HfPbvFusyY/idGKZN//yeJ+rkh9NpUp4ffqxJD+lmPQAlVKtWrUiOjqaadOm8corr+B0OlmwYAGPPvoo27dvZ+HChbd8zR49etCjRw8aNGhAy5YtWbFiBTt27HBZa6hPnz5MmjSJatWqUa9ePZYvX86mTZtYv1691L0Q4u/lcCrMXHuCpdvOFh4zGhy8/tApqlfIdymrKDo8bC1oGFwdo82C4bff0Z9zHfCsaLWYW7Z0+9or05zBqC1DOXTR9RWUFi2vx8bTJfKZ4l/ccMhQ7N//oIrp2z+AceIE2dailJMEqBTr3bs3vXr1ol+/fkyePJnZs2czYcIE4uLiGDduXOFg5pv1z3/+k+TkZMaNG0dBQQEdO3akZ8+eLtPte/ToQW5uLqNGjSItLY2oqCg++ugjateuXdzNE0JcR77VwZCViaw7WrSdhY/RTs9/nCQ82HU7C8XpSZC2PTEhFdCnnsVz2zY0FotrGcDSvAXOFXkRxQAAIABJREFUUPX2FAcy9zF220gyzOkux308fBjReByNinEXdwAlN5eCPv1wbNniGtBqMQzoj+HVV2S8j0CTlZWlXsVKuJWdnY2/v39JV+OOMZvNhesAlTZX/10nJSW5DAAvzeRZFLlXn0Vqtpm+Xx7kSHpRL4+/l43enU9SrsxViY3DlyjTw5T39MOwZzcex9QzthQPD06EhxPayDWRsTosfHhoMV8cXaZ65VXRJ5zxTacU+3gfZ3oGBW/0wHn4sMtxTdmyGKdPQ98krljv5869+n3xd7lbn4f0AAkhRCmy50wO/b86xIX8orV8An2t9H30JGV9Xffs0jiDaOD3CL5mO8Y1/0N79SBi/tjNvUlTcs6eJfSK44kXDzNl53hSLiWrzmkcEsfwRmPwMRTvKyhncgr53d9QreysqVwZr/cWoa1QvNtoiHubJEBCCFFK/PdAGqNXJ2F1FPXGlAuw0PfRk/h6uS5u6KGE0ajMwxjPpuG5dQsau90lrmg02GJrYatRA7RF82lsThsfJy7h0yNLcV61sCHA09W60j02Hp22eD9+HPv3U9CjJ8qFCy7HtbVrY1q4AG2ZMsV6P3HvkwRICCHucw6nwvzfUli8ybVnpEbFXP794FmMBtfkxktTif9n7z7jqyjTPo7/5vRJL6SThJLQQ++9uAr2xtpd26IUadIRu6KiCAoi+6y6u65dUVBRVhSwgUgvCRBqqElICJDk9DPPi2BgMqCyS04CXN9XZq4559z3fMLxn5m7tI3oi33TFmxbcgzvFwgLw92ps2GmV6GzgEd/nszWEuNr4tV4xrSZSLtzPN4HwLdsGc7RY8Cp33vM3KMH6ksvoshaYeI0JAAJIcQF7LjLx8TPtvL9jiOVxxQ0/tTmMFd2KDQs0BxlbkiWrSvq9z9hLsinKl96Ou627aDKisk7y3fw96VzKHYXGV7TP+1yBmcNJ8wadm46dQrPBx/ifuJJqLImmOXaa3A8/pis7CzOSAKQEEJcoHYVlTPi4xz2FJ+8M+Kw+bmzzwGy6h03nB9vbUJTTwsc3y3GVF5lGryi4GndBl9GhmFbi//kfcn03c/h0/R3kmLssYxuM44uid3OYa9OtEfT8Mx8Gc/f/s9Qs913L7ZRI2Wml/hNEoDO0q/7XokLl6bJxEhx/vtuezETP9uq29YiKdrFoMv2USfSU+VshXqO9tQrjsfx8xLDeJ+Aw1Exxb1OHd1xv+bn75tf44Pt7xo+v1NCV8a3m0yk7dzPnNU8XlyPPIJvwWf6gsmEffIkbLfcfM4/U1x4JACdhdDQUEpKSoiKipIQdIHSNI2SkhLCZYE0cR57f81Bpv5nh27ieav6x7iz7wFsFv2jIjN2moX2JW53ObZ1P1D1m80fF4e7cxc0VdUdP1C2n+fXPMPGovWGz7858zbuaTYIs2I+Rz06KVBUhGvEKPxr1ugLDgeOac9j7df3nH+muDBJADoLFouF8PBwjp1mKuiF6NixY0RERNR0M4IuPDzcsO+ZEOcDTdOY9d0e/q4b7KxxWduK8T5VhZrr0FztR8SG7Vh3bDfUvZmZeFq11s3y0jSNz3fP57VNs3H59YOOrSYbY9pM4JLUS89Zn07lz9mCc9iDaFW26lGio1FfnY25Vctq+VxxYZJv+bNksVgumsUQCwoKSE1NrelmCCH+AK8/wJNfbWf+xoLKY1ZzgNv6HKBdQ+MfbYm2xmRYOqAu/8W4pYWi4GnTBl+GfvG6QmcBL6x9llUFKw3vF2mJ4plu02gS3fQc9UjP+9UiXJMmg0u/SrWSlkbI3NcwpZ/bBRXFhU8CkBBCnOfKPX7GfrqFH3aenOkVGeJlUP+9pFXZ1gIUMkK6keJKwbFsCabSUl1Vs1hwd+mKPymp8pg/4OOrvC+Zu2k2Zb5SquqY0JkbIm+qlvCjBQJ4Zs3G89pcQ83cqVPFNPdT9hsU4o+SACSEEOexojIPD36UzeaDJ4NJQpSbB6/cQ2SofjCzGTvNw/5EbH4A+8rFxsHOISG4uvdAOxEoNE3j+wPLeDPn/8gr3WP4bNWiMrjFcC5Pv5Lt242P0P5XmtOJa/wEfIu/MdSst9+GfdxYFHlcLf5L8psjhBDnqZxDpYycl8OhYyf370qKdjH8qjzCVH24UU3RtAi9lIicPGw52VXfCn9sLO6u3dBUFU3TWF24ijey57K1ZIvhXIBWsa0Z23YSSaHJ57ZTJwQKC3EOHUZg02Z9wWLB/ugUbDfcUC2fKy4eEoCEEOI89FVOIY9+kYvLd3JWV0pMRfgJcejDT7Qljab2HoQuX42lygBiAG/9BnjatgWzmQNl+3lp3TTWFK467efaTDbubXY/1zcciEkxnfac/5V/27aKbS2qjE1SYmNwzJyBpW3bavlccXGRACSEEOeRwImZXlW3tUit42T4VXk4bPr9txJtTci0dET97nvMVfbJqhjs3BZfw4ZowFd7Pmf2xpk4ffrZXQAmTFya1p87m9xDQkjiOe/Xr3zf/4Bz9ENQVqb//MaNUGfPxpScdIZXCnF2JAAJIcR5osztY+Jn21i2XR9k0uMqwo/Nqg8/yfbmNDS1Q122DHNJia4WcDhwd+lKIC6Oo+4Spq97nh8Ofnfaz+2R1Iu7m/2V9PB657Q/VXk++BD3k0+BX98Pc88eqC++gBIaWq2fLy4uEoCEEOI8cOCoi+EfZZNbqN+iIiPJydAr9mIx60NDij2Lhkpr1GVLMR09qqv5o6Nxd+uOFhLCyvwVTFsz9bR7eLWu05a/Nh9cbVPbf6VpGp6XX8Ez92+GmvXWW7FPGCeDncU5J79RQghRy204cJyRH2dTVObVHW/XsJw7++7FZNKHn1R7K+rTAnXpEkzH9Xt++evE4erRg1LcvLb2Wb7c87nh8xxmBw+0GMaV9a6p9lXvNY8X16OP4pu/QF9QFOwTxmO74/Zq/Xxx8ZIAJIQQtdiX2YU88sU2PH79HnWXtirnyk57UBT98TRHG+oFmlbc+amyxo8/PgFX9+6sKl7DC2ufpdBZQFWNo5oysf0UUsOqf2FBrbQU54iR+Jev0BccDtQXpmHp26fa2yAuXhKAhBCiFtI0jbk/7mXOD3mG2s3dy+nWfLfheLqjPfUCjXEsXYKpyiBiX2ISxR1b89rGF1m45zPDa02YuK3xX7i98V+wmKr/fw2B/HycDwwmsHWb7rgSE1OxrUXLrGpvg7i4SQASQohaxusP8OjCXL7YXHX/Lo0h/ctpmm5clDAjpDsp3jQcS7/FVK4fJ+RLTmFVoxCeWXYXBae565MWls64tpNoGtP8XHbjjPzZ2TiHPoiWn687LttaiGCSACSEELXIMZeP0fNy+CVPP3DZYoKx15aSHLdXd1zBRJPQPsS74nAs+xaTUz+F3ZuSwuvRW/nXT/8kgH4neBMmbsy4mbub3ovNbK+eDlXh/c/XuCZMNOzpZcrKQp0zG1NMTFDaIYQEICGEqCUOHnUx9MNsdhzW38EJs5sYf/0RoiL2646bsNA87FJiysMr9vWqEipKk+N5yP8u67etN3xWalga49pOollMi3PfkdPQNA3P3L/hefkVQ83cpzfqC9NQVDUobRECJAAJIUStkHOolGEfbuZwlZleiZEWRl2TT4iqf1xkUey0CBtAZKkNddkSFLdbVz8Qr3J3yQsc9envJAHc2PAm7mk2CHuQ7vpoLheuKY/g+2KhoWa98w7sYx6Sae4i6OQ3TgghalBA0/hw7SFeWrILp1f/iKpRoo37B+zDZjusO25TQsgKv4Lwwx4cP32L4tWHps3RHu478gpalRns0fYYJrR7mPbxHaulL6cTKC7GOexBAuuq3IWyWLA/8jC2G28MWluEOJUEICGEqCF7jzh57MtcVuUdM9Q6NFS5rfcOzBb9Cs4OUwQtw64gdF8R9l9+QQnoQ9OPYYcZU/quIfy0j+/I+LYPE+MI3hibwO7dlN8/GG2vftwSkZGoM1/C0jF4QUyIqiQACSFEkAU0jXdXH+TlZbtxVbnrA3BFG5XL2uegmPRjgULNMWSFDiB0Wx62jRsMr1vo2MkT7i/glPBjVszc2+x+BmbcXG2bl56Ob/UanMMehCqrUJsaNEB9dRamNJnpJWqWBCAhhAiifSUupny+jTX7jHd97BYTd/S00ipjPSj6Hd0jzAm0CLmUkPXZWLdvN7x2ru0X3vSv0IWfKHs0j3V8iqzYVue8H7/F++WXuCZMgiqP5sydOqHOfAklIiKo7RHidCQACSFEkHy95TCPLsylzOM31DITQrmzt4eoqHWAfnXnaEsqzey9CflpJZZDh3Q1PxpPWL5mEVt14SczshGPd3qmWndur0rTNDyvv4Fn+kuGmuXaa3E89iiKzRq09gjxWyQACSFENXP7Arz47S7eX3PQULNbTVzVKpFeLfeANddQT7I3I9PfEvXbpZiO6e8aleJhnOVz1pj00+P7pPRjTJuJOCyOc9uR3+L34376GbzvvGso2R4chu2B+6t9XzEhzoYEICGEqEZ5R5yM/XQLW/LLDLVGieHc0D6ZBilrcbG7SlWhodqFusfiUH/6BsXj0VXzOc5oy2fsMBWd8gqFe5sN4ubM24MaNjS3m6hpL+JdvlxfsFhwPPUE1quvDlpbhPijJAAJIUQ1WZpbxKTPthkeeZlNCgNaJvGnZrFERa7gWEC/tYUZK03D+hG/14dt9TIUTf9IbLNyiHGWLyhSTg6SjlfjGdl6LJ0SulRfh05DO3YM57DhOFat0hfCw1Ffnomlk8z0ErWTBCAhhDjHAprG3B/yeO3HvYZadKiNW7qk0zU9DBzfccSnP8euhNIirD/Rm/Zi3bbV8Pr/mLbxtHkxbqUiVFkUCwMzb+G2RneiWoK7knLg0CGc9w8mkKt/dKckJKDOnYO5UaOgtkeIsyEBSAghzqFSt4/Jn21j6fZiQ61F3Uhu6phKh2Q7hdq3lPj0Y3ccpnBaOvoTuWIzlgP7Da+fa17Om6ZVlYOd28d3ZFjWSFLDgz+lPLBrF+X3DUI7qB/XZGrQAPVvczElJwW9TUKcDQlAQghxjuwuKmfkvBx2Fek3JDUpcHmrZK5qmUBWnIXtrv9w1KcPDqopklaWfkQsW4m5RL/4oQsvj1u+ZolpR8W5FpWRrcbSr+6famRgsX/zZpz3D0Yr1oc8c9s2qLNmoURFBr1NQpyt4K2KVcX06dPp06cPqampNGzYkJtuuons7GzdOZqmMXXqVJo0aUJiYiJXXHEFOTk5unNKSkoYNGgQaWlppKWlMWjQIEqqfHls3ryZyy+/nMTERJo2bcpzzz2HVuWZuhBC/C9+3HmE2/653hB+Qu1m7u3VkLs7pdA6IcBW5+eG8BNiiqatryeR3/5kCD+FlHK/5ePK8NMwIoPXer/BJamX1kj48a1cSfld9xjCj6tTR9S//5+EH3HeqLEA9MMPP3DvvfeyaNEiFixYgMVi4dprr+XIkSOV58ycOZPZs2fz3HPP8e233xIXF8d1113H8ePHK8+577772LBhAx999BEfffQRGzZs4P7776+sHzt2jOuuu474+Hi+/fZbnn32WV555RVmzZoV1P4KIS5cX2YXMvyjbEqrDHZOjlZ58JJGXNu8DvHhR1l3/FNK/UW6c0LNsbQ72pbwZT9hcurD0zalkHusH7DVVAjAFfWu5pVec6kbllq9HToD7+JvcA56AMr0M9qsN1xPyfhxKI4gTrsX4n9UY4/A5s2bp/t57ty5pKWlsWLFCgYMGICmacyZM4eRI0dyzTXXADBnzhwyMzP56KOPuPvuu9m6dSuLFy/mq6++ouOJPWVeeuklBgwYQG5uLpmZmXz44Yc4nU7mzJmDqqo0a9aMbdu28eqrrzJs2DBZl0II8T/5cO1Bnl60g6r3lFunRzOwfV06poSimXex/vh3aOgDUpipDh321MeRU2UGFfCDsosplkU4FS8Os8ro1mPpl3ppNfbkt3k/nofr0cegyt5jtnvvwTZ6FJxmdWoharMauwNUVWlpKYFAgKioKAD27NlDfn4+ffv2rTxHVVW6du3Kzz//DMDKlSsJCwujU6dOled07tyZ0NBQ3TldunRBVU/OjujXrx8HDx5kzx791FMhhDgbn2wt46kq4UehYrzPbZ3T6FY3lDJlDVvLlxjCTxxpdN4YiyNnm+F93zWtZZzlC5yKlxh7LLN6za2x8KP5/bimvYBryiOG8GN/aDT2h0bLH5LivFRrBkFPmDCBrKysyjs5+fn5AMTFxenOi4uL4+CJWQcFBQXExsbq/vEpikKdOnUoKCioPCc5OdnwHr/W6tWrd9r25OYaV2S9GMl1OEmuxUkX+7XQNI23N5Xy6Tb9ZqUmBQZ2TKN9WjjJvgJ2HMmhXD1geH1sUQrNs91YXfqNQn34edH8HZ+YNwEQbY1hROoYfPl+cvODf82V0lKipr2Ifd063XHNZOLY0ME4e/aAU34XLvbfi1PJtdCrieuRmZn5m/VaEYAmTZrEihUr+OqrrzCbzTXdHOD3L9zF4NfHiEKuxaku9mvhC2g8s2i7IfxYzAq3dqlHl/pRtIl3sM+ziWKPPvwomGhV2prEjXkoVTYKLaaciZYvWW+qeE1SSDIvdJtJYmjNTCf3b9+Bc+JktLw8fcFmQ33heSIuuUR3+GL/vTiVXAu92no9ajwATZw4kXnz5vHZZ5/p7sYkJCQAUFhYSGrqyQF/hYWFxMfHAxAfH09RURGaplXeBdI0jcOHD+vOKSws1H3mrz//eo4QQvwRx10+xny6hRW79TO17BYTd3avT8+G0TSLtbHL+SMHPfoZq1ZUOuQ3I2LjDsPKzluUAsZbviBfKQUgNSyNad1mEqfq74AHi2/JUpzjxhsGOyvx8aivzMSclVUj7RLiXKrRMUDjx4/n448/ZsGCBTSqsmJoeno6CQkJLFmypPKYy+Vi+fLllWN+OnbsSGlpKStXrqw8Z+XKlZSVlenOWb58OS6Xq/KcJUuWkJSURHp6enV2TwhxAdlX4uLOt9Ybwk+o3cxfezfkyqaxtIi1scv1kyH82DSVbtsbELlhuyH8LDJt5X7Lx5XhJyOyEdO7z6qR8KMFArhfm4tz2IOG8GNq3YqQD9+X8CMuGDUWgMaMGcM777zD//3f/xEVFUV+fj75+fmUllZ8CSiKwuDBg5k5cyYLFiwgOzubIUOGEBoayo033ghA48aNueSSSxg1ahQrV65k5cqVjBo1issuu6zydtuNN96IqqoMGTKE7OxsFixYwIwZMxgyZIgM3BNC/CFr9x3jtn+uY2eVNX6iQqwM7ZfJ9c1jSY+wssP1Ewfd+vXMQrwqPdYloO7apzseQGOW+UceNf8Ht+IDoH/a5bzccw4xjpjq7dBpaGVluEaOwvPyK1AlpFmvv46Qf7yJKa5m7kgJUR1q7BHY3//+d4DKKe6/Gj9+PBMnTgRgxIgROJ1Oxo4dS0lJCe3atWPevHmEh4fr3mfcuHHccMMNAAwYMIDnn3++sh4ZGcknn3zCmDFj6NOnD1FRUQwdOpRhw4ZVdxeFEOc5TdOYv7GApxZtx+vXh4LUmBDu7laXAY1iUC0KO5zG8BNVFkLH9RGYy47ojpfhYYplET+ZdgNgM9kY3mo0A9KvrNb+nElg9x6cDw4nsGOHvmA2Yx8/Duttt8ofjOKCo5SUlMiSyOK0auvAtZog1+Kki+ValHv8PL1oO59vLjTUWqZG8Zeu6aT4Cmia2ZBdzpXsdelnSiUXhpC1yYHJp5/+vp+jjLF8zi5TxUrKSSHJPNbxaTKiauaa+r77HufYcXDKArMASlQUjukvYunc6Qyv1LtYfi/+CLkWerX1etT4IGghhKhttuaXMnb+VvYUOw21vs0SuLZNEp2TQ8jbGSDPuVYffjRotCuUhjssUGXtnzXKfiZaFnJUqRiT2DWxB+PbTiLMFk6waZqG981/4H5xuuGRl6lJE9RXZmJKSQl6u4QIFglAQghxgqZpfLjuENMW78RT5ZGX1axwXftUemXG0jEpBJtZocyxj4Ouk+ubmH3QanMYCQXG5Tw+NW3iBfMyfEoAi2JhUPMhXN9wYI08WtI8HlyPPY7v0/mGmuWKy3E88TjKKYvHCnEhkgAkhBCAy+vnkYW5LMo5bKjFR9i5tUs9GsWH0DExBKtZ4ZB7C8fCToYf1anQbl0Y4aX6uSU+/Ew3f8c80yZQKh55TenwBI2jm1R7n04ncPgwzhEjCazVP7LDZML+0Gisd/1FxvuIi4IEICHERa/U7WPER9ms2nvMUGtfP4ar2qSQGGalXaKK1aRQ4NnBtvLvKs+JKjHTdl0Idq8+/BzBySTLQtaeWNywd0o/RrUeS5g1rHo7dAb+nC04hw5DO3RIXwgPR53+ApZu3WqkXULUBAlAQoiLWnG5lyHvbyInX7/ujc1i4rp2dWmdHk1CiIXW8Q5MCuQ517LbdXLz0sR8Cy03hWAO6O+abFMKGWf5gkPKcRxmB0OyRnB5+pU1dnfFu/gbXOMnQJUd55X0dEJenYWpfv0aaZcQNUUCkBDionXomJv739vE7iqDneMj7NzRrT51wu2khltpUceOHy/ZZUso8p7YRFmD+ntsNMk1jpVZbMrlKfNiXIqPjMhMJrd/jLTwmll4VdM0PH9/Hc9LMww1c5fOqNNfRImMrIGWCVGzJAAJIS5Ku4udPPDeJg4ec+uOp8aEcFeP+oTYLWRE2ciMtlEeOEJ26dc4AxWblyoBaLbFQdp+u+F93zT9wt/MK9AUGJhxM/c0HYTNbAtKn6rSPB5cjz6Gb/4CQ816+23Yx41Fscj/BsTFSX7zhRAXndV5Rxk1L4ejLp/ueEZCGLd3rYfdaqZZrJ16kTYKPTvZWraUABXnWrzQemMIcUVW3Wt9+JlqXsIX5hyi7TGMbzuZDgl/bA2d6hAoLsY1fAT+NWv1BYsF++RJ2G76c800TIhaQgKQEOKismBjPo9/uR1fQD/NvXlKJDd3TkO1mmkV7yA+xMJ+1yZ2OH+qPMfhVGi/NpTwMv009+O4mWBZyGrTPhpFNebpzs8T44gNSn9Ox791K85hw9H279cXIiJQZ7z0hxc3FOJCJgFICHFRCGgas77bw+vL9xlq7evHcG27utQ5MdjZYVbYWf4z+9zrK8+JPGqm3boQ7B79TK8DHGW09TN2K0doF9eBxzo+TYg1pNr7cybe/3yNa+Ik42DnevUqBjvXq1cj7RKitpEAJIS44Dm9fqZ8vo2vtxYZapc0T6Rvs3gyou1kRtuAAFvLl1Lg2V55TkK+hVanmem1WclnrOUzihUnfetewri2k7GarNQELRDAM/tVPHNeM9TMnTqhzpgug52FOIUEICHEBa2ozMODH2Wz+WCp7rjFpDCwYyrt6sXQOt5BXIgFn+Yhu/RrSnwnHx3V22OjyTYHCvrw862yncctX+NWfFzfYCCDsx7EpOjvDgWLVlqKa/xEfEuWGGrWm2/CPnECirVmgpkQtZUEICHEBWt3UTlDPtjM/qP6mV5hdgt3dK9Hs8Rw2ieqhFhNuANlbC79ilL/ibtEGjTOddBgj3Gm11um1bxq/glNgfuaPcDNmbfV2Po+gb17cQ4ZZtzJ3WLB/vBkbH8eWCPtEqK2kwAkhLggrdl7lJEfG2d6JUQ6+Ev3+jSJC6FVvAOLSaHUV8Sm0q/waBWLISoByMpWSTmon77uI8A081LmmzdjNVm5I+lubml0e9D6VJV//YaKlZ2Li3XHlZgYHDNnYGnXtoZaJkTtJwFICHHB+c+Ww0z+bKthQ9PMxHBu7ZJOiziVzGgbiqJQ7N1LTuli/HiBig1N22wwTnMvw8NEy0JWmvYSaYviiU7PYC+uuQ1DvYsX4xo7Htz6u1umZs1QX56JKTmphlomxPlBApAQ4oLhD2i8uWIfs77bg1al1r5+DDe0r0ubBJWksIpwc8CdzfbyH+HE2TaPQru1IUQd0381FlPOKMsCtpoKSQtL5+kuz5McmkJucS41wfOvt3A/9zxo+l5aBvTH8dSTspO7EH+ABCAhxAVhd1E5jyzMZf3+44baJc0TuKJlIu0TQ4iwm9E0jV3On9nn3lB5Tki5ifZrQwgt16/xs5cSRlrns185Rtu4djza4SnCbOHV3p/T0fx+3M89j/ffbxtqtvsHYRv+oOzkLsQfJAFICHFeC2gab/9ygFe+24PbF9DVTApc3z6VS5vG0SbBgc1sIqD52Va+TDfNPfqImbbrQ7BV2c19i1LAaMsCihUnPZN7M6n9ozU3zd3lwjVuPL7F3+gLZjP2R6dgu/HGGmmXEOcrCUBCiPNW3hEnj36Ry5p9xww11Wbm5s7pXNoohiaxdkyKgk/zkFO6mCO+k4shphyw0iJbxaTp75ysVPKYYFlIueLlsrTLeaj1OMymmvnKDBw5gnPoMALr1usLoaGoL03H0r1bjbRLiPOZBCAhxHnp+x3FjP10C05vwFBrmhzBde3q0i0tnLrhFXdsPIFyNpV+Ran/cMVJGjTabqfhbofh9QtNW3jG/A0+JcC19W9gaMsRNbbGT2DvXsrvH4y2e7fuuJKQgDpnNuYmTWqkXUKc7yQACSHOOx+uPcgz/9lBle28cFhNXNUmhQ71ommXFEIdteIrzuk/xsbShbgCFXeKTH5ouSmEpALj46zXzMv5h2kVKHBLozu4t+mgGhtX49+0CefgIWhF+mnupsxM1LlzMCUm1ki7hLgQSAASQpw3AprGK8v28MYK435ejRLDub59KvHhNjokqkTYKwYzl/mL2XD8C7xaxd5YNrdCu3XGmV4ufDxp/ppvzBVjg+5tdj+3Nrqjmnt0Zr6lS3E+NNawp5e5YwfUl2eiRETUUMuEuDBIABJCnBc8vgCPLMzly+xC3XFFgStbJ9Mlow7hNjMdElVUa8Xjqorw8zlezQVA2HET7deForr0j7OKKWes5XM2m/JJDEliSNZwuiX1CE7HqtA0De//2UBQAAAgAElEQVQ//4V72gvGae5XXI7j6adQbLYzvFoI8UdJABJC1HqFpR7GfrqFtVUGO1vNJm7pkkbT5Eii7WbaJarYzBWPq8r8R07c+akIP3GFFlptVLH69eFnu3KYMZbPOWr1cW+j+7mx4Z+xmY3bXwSD5vHifvJJvB/PM9Rs992LbeQIFFPNjEUS4kIjAUgIUautzjvKuPlbOFzm1R0Ps1u4s0d9UmNCiFPNtE1QMZtODT+fVz72Ssuz0XSrA1OVDU2XK3uYbPmSnul/4p5mg4h11AlOp05DKynBOWIk/l9W6QsmE/ZJE7HdekvNNEyIC5QEICFEraRpGv9auZ+ZS3dTZUcL4sLt3NWjPjFhdqLt+vBTfmr40aBRrp2Ge4wzvT40beB1dT1T2k+lY0LnYHTpjPy5uTiHDUfbu1dfCAtDnf6iTHMXohpIABJC1Dqlbh+PLsxl8dYiQy0jIYxbOqcTYrcQbjPRPvFk+Cn27mVL2bf4NDdKABquN9HwsD78+Akww/w92XEac9q/TnxIQlD6dCbehQtxTXnUMNhZSa2L+upszA0b1lDLhLiwSQASQtQqe484GfphNnuKnYZan6bxXNI8EZNJIcSi0DFRxWpW0DSNPNda9rgqHh/53F4a/gKZTv0jrTI8TLEsom6jLkxvdj+WGlrYEEDzenG/OB3vv94y1Mzt26O+PAMlKqoGWibExUECkBCi1tiaX8rgDzZTVGW8j8Nq5qZOaTRJrpj67TArdEwKwW4x4Qu42VK+hGJvHgB78w/SY1MMTQPxuvcoppzJjsVc32EwXZO6B6dDZxAoPIzroTH4V60y1KwDb8Q+eTKKrWa23BDiYiEBSAhRK6zZe5ThH2Vz3O3XHU+OUrmtazoxYRUzsyrCj0qI1USpr4jssq9xBY7hC/j5OXcT9+xtTgMtVvce+zjKu3ULmNBmJnXUmhvoDOBfvx7niFFoBQX6gs2GfcpkbDfcUDMNE+IiIwFICFHjvttezJhPtxg2M22dFsX17VOxWiqmfv865ke1mMh355Jb/h0B/BSVH+PrzT/z6NE+hvCz03yEAx1bMCR1UND6cybeBQsqxvt49Xe4lMRE1JdnYG7RooZaJsTFRwKQEKJGfbG5gCmfbzPM9OqWWYfLWydjOrENRR3VTJsEFbMSYHv5jxxwbwZg/aEd/LhtAzM8VxnCT16ol6i+t5GghgelL2ei+f14Zr6M5++vG2rmzp1xvPA8ppiYGmiZEBcvCUBCiBqzYGM+j3yRS5Xsw6UtEundNL5yD6664VZa1LHj1crZfHwxx/z5eP0+Ps9dQd6hA8zyXUfDKuGnJDaM2N79wWwOUm9OTysrwzluPP4lSw012733YBsxHMUiX8VCBJv8qxNC1IjPNhUYwo8CXNM2hU4ZJ8fpNIq20TDKxjF/PjmlX+PRnJR6nLy3aQllx0qZ5buODE0/rscdH4e1R68aDz+BfftwDn2QQG6uvmCz4XjqSaxXXlEzDRNCSAASQgTfF5sLeOSLbbrwY1Lgps7ptEytmPptUSArzkFSmJXDnl3klH2DRoDCshLe3vgNmsvHbN/1hvDjS0jE1717jYcf34qfcY4aDUeP6o4rdeqgznoFc8usGmqZEAIkAAkhguzL7EIe/nwbgVPSj0mBW7vUo3ndSAAi7SbaxFfM9DroziG3/AdAY+eRA3yweRn4Arzsu5bM04Qfdw2HH03T8P77bdzPTwO/fkabqVkz1FkvY0pMrKHWCSF+JQFICBE0X+UUMumzrYbwc0uX9MrwUz/SSuMYOwqQ51zDbtcqNE1j9cFtfLl9JQTgaV9/WmnJuvf2JyTg7tatZsOPx4Pr8SfwffKpoWbpf1nFTu6qWgMtE0JUJQFICBEUv872qhp+bu6cTou6UVhM0CpOJSHUgqZp7HAu54B7E06vm8+2LSfncB5o8JC/F320DN17++PicHXrDjU4mDhQWIhz+EgC69frC4qCbcRwbH+9r3JQtxCi5kkAEkJUu3dWHeC5xTt1xxQFbuqURlZqFCEWhfaJIYTZTAS0AFvLl1Lo2c7ukkN8suUHjrnLAbgj0JaBgZa69wlERNR4+PFn5+AcNgztUL6+EBqKOu05LL1710i7hBBnJgFICFFtNE3jtR/yeO1H/S7nigJ/7pRGy7Rooh1m2iU4sJlNBDQ/OWXfUODeybI96/k+b2Pla/r7GzPUr98VPaCquHr2ApstKP05He/ib3CNn2DczDQ9HXXWy7KZqRC1lAQgIUS1CGgaz369k/fXHNQdN5uUivCTGkVymIWsOAdmRakMP7tKN/Ph5mXsO3648jUdA6k87O+nex/NasXVoydaSEhQ+lOVpmmEzvsE17/eAk2/kpG5ezfUac+jREbWSNuEEL9PApAQ4pzz+gNM+SKXL7MLdcetZhO3d0unUWIEmdE2MqJsKJXhZzHri1ZVrO/jdVW+pnEgjqm+y7FwcnCzZjLh6tYNrYZ2S9c8XlyPPU74p8bBztY7bsc+dowsbihELfdf/wstLS2lpKQETau6hiukpqb+T40SQpy/jrt8jJ2/heW7SnTHVZuZu3rUJz02lKw4B3XDK3Y7/zX8fLv3Wz7bthy/dnI/sGQtghn+awhF/4jL3bETgfiE6u/MaQQKD+Ma/RD+1av1BbMZ++RJ2G6+qUbaJYQ4O2cVgFwuF8899xxvvfUWxcXFZzzvt2pCiAvXgaMuhn2YzY7D5brj4Q4L9/RqQEqUStsElbiQiq+egBZgc+l/eH/rJyzfl617TZTm4DXtJqI1h+64u1Vr/Glp1duRM/CtWo1r9ENohw/rC+HhqC9Nx9K1S420Swhx9s4qAD300EO8++67XHHFFXTp0oWoGrr9LISofTYeOM6Ij7MpKtPvdB4bZuOeng1IjHDQPkklyl7xKEvTNNYd+4q/rf8nucX7da9xaBb+YbmbeKf+K8rbqDG+xo2rtyOnoWka3n/+C/eL0w2LGyqpqahzZmNu0CDo7RJC/PfOKgB99tln3HnnncyYMaO62iOEOA8t3nKYSZ9vw+0L6I6nxoRwR/d6JITZ6JAUQqjVBFQEil+OLOSl1XMoKKvyqEyx827IUBJL9EHKl5aGp1Wr6u3IaWhlZbgmT8H3n/8Yau6WWcS+NgdF/hgU4rxzVgFIURRa1cAXkBCidtI0jbd+OcCL3+4y1FrUjeTPHdOIDbHQIVHFbjFV1r7Ln8/0NbMp9einjsfZ6/DP0MHEHNKHIn98Au4OHSvmzwdRYO9enEOGEdixw1CzDforh/pfRh0JP0Kcl0y/f8pJl19+OUuXLq2mpgghzif+gMa0b3adNvz0ahLPLV3SiQ+10ik5RBd+Fuz5N1NXzjCEn0YRmbwbOcoYfqKicNXAFhe+Vaspv/lWY/gJD0edPQv7yBE1vuGqEOK/d9ZjgO655x6GDx/OnXfeSd26dTGf5gsgLi7unDVQCFH7uLx+Jn++jcVbi3THTQpc264uHRrEEuMw0z5RxWKquGujaRpvbp3N21veM7xfl4TOTDXfiGP3Ht3xQGgo7h49wWqtvs6chvfT+bgeeRR8Pt1xU5MmqDNfwiQzXYU4751VAOrQoQMAGzdu5N///vcZz5NZYEJcuI57Atz//mbW7TumO263mLitaz0yE8OJVc20T1Axnwg/AS3A9PVP8+XuRYb3u6b+1Tzk64utyp2WQEgIrt590IK4eagWCOCZMRPP31831CwD+ldsZupwnOaVQojzzVkFoHHjxslmfkJcxPaXuHh4aTH7j+tnQkWoFu7q0YCkKJU41UzbU8KPL+DjiVXj+fHAz7rXKCj8tcW93HG8JdYdubpaQFUrwk9oaPV26BSa04lr/AR8i78x1GxDBmMbOkS+/4S4gJxVAJo4cWJ1tUMIUcvtPFzOoPc2UViqDz/xEXbu7tGAqFAb8SEW2iRUbG0B4PQ6mbRyBBsK9Wv82M1WRrcZzuWH6mLdWSX8OBy4evVGCwur3g6d+pnFxRWDnTds0BfsdhxPP4n18suD1hYhRHDIWu1CiN+1Jb+UB97fzJFy/dT0+nGh3NGtHqrNQsqJfb1MJ8LPMfdRxiwfwo4S/bieMJvKw+3G0X13OJY9+h3iNbu9IvxERFRvh04R2JNH+f0PoOXl6Y4rsbGos17B3KrlGV4phDif/WYAevfddwG4+eabURSl8uffc8stt/zvLRNC1Aob9h9jyAebOe7W3/nJSq2Y5m4xm6gXYaVprL3yEVFB+SEe+mkIB0oLdK+JdoTzZMfHaZ3jwbJfH4w0ux1nr95oQdxA1L9+A84hQ9GOHNEdNzVuhPrqbExJSUFrixAiuH4zAA0ZUvHM+4YbbsBmszFkyJDffUNFUSQACXGB+GVPCQ9+lI3Tq1/gsGODWK5pl4JJUXSbmgL8cGAp09Y+Q6lXvx1GQmgMUzs+S6P1hVgOHdLVAqoa9Ds/3sXf4Bo3Hlwu3XFzl86oM2egBPERnBAi+H4zAK1fvx4Am82m+1kIceFbtr2YsZ9uMazu3L1RHJe3SkJRFJrF2qkXWfH94PQ5eXXjDBbu+cLwXumRiTzXfhqpq3ZhLtTvEB8IDQ3qmB8tEMDz6hw8r84x1CxXX4XjiSdQbMGddi+ECL7fDEBpVTYcrPqzEOLCNG/9IZ78ajsBTX+8X7ME+jVPwKwoZMU7SAmrCApbjmTzzKrH2V+23/BeTWPr8Uyr54hfvglziX6Rw0B4eEX4CQmptr6cSjt+HOf4CfiXLjPUbPcPwjb8QZnpJcRFQgZBCyEqaZrG3B/3MueHPENtQMskejaJx6z56ZgcTrSjYhHUz3fPZ+b6Fwlo+jtFJkXhT/W6MjJtNJHf/4KprExX90dF4erZC4K0ro5/+w6cw0eg7d6tL5jN2B+ejO2mPwelHUKI2uGsA1BBQQFvvfUW69at49ixYwQC+i89RVFYsGDBH3qvH3/8kVdeeYX169dz8OBBZs+ezW233VZZHzx4sGHgdfv27Vm8eHHlz263m4cffpiPP/4Yl8tFz549efHFF0lJSak8Z+/evYwZM4bvv/8eh8PBjTfeyFNPPVX5aE8IAb6AxjOLtvPx+nzdcUWBa9rWpVPDWKLsJqLLC4h2RKFpGv/I+Tv/3vZPw3vFqhHc3uIarnJcQ+h3y1Hcbl3dHxuLq0dPCNK/Qe/ixbgmTIJy/bgkJSYGx0svYjmxyKsQ4uJxVgEoOzubK6+8kvLycjIyMsjOzqZJkyaUlJRw8OBB6tevrwsev6esrIxmzZpxyy238MADD5z2nN69ezN37tzKn6uGlokTJ7Jw4UJef/11oqOjmTx5MjfddBPLli3DbDbj9/u56aabiI6OZuHChRw5coTBgwejaRrTpk07m+4LccFyev2Mn7+VZdv1q7hbzAo3d06neUokqeFWmtWxs3N7AF/Ax/R1z7Mob6HhvdolNWJg46voWN4edekPKH797DFfUhLuLl3BEpwb0J43/4F72guG46YWzVFnzpCZXkJcpM7qG+jxxx/H4XCwZMkSwsLCyMjIYOrUqfTq1YuPPvqIcePG8cYbb/zh97v00ku59NJLAc44w8xut5OQkHDa2tGjR3nrrbeYPXs2ffr0AWDu3LlkZWWxdOlS+vXrx7fffktOTg4bN26kbt26lf0YPnw4U6ZMISKIs06EqI1KnF6Gf5TN+v3HdcdVm5k7u9enfp1QmsbaSY+woigKLr+Lh1eM45eClbrzLSYz1zfpTvfknmQVZuBYtRxF0w8i8tarj6d9ezCd1T7M/xUtEMA97QW8//yXoWa59locj05BsdurvR1CiNrprL6FVqxYwV133UV6ejqmE19g2okvuBtvvJHrr7+eKVOmnNMGLl++nIyMDNq1a8fw4cMpPGUGybp16/B6vfTt27fyWN26dWncuDE//1yx7P7KlStp3LhxZfgB6NevH263m3Xr1p3Ttgpxvjlw1MVf3tpgCD9RIVYe6JtBZnwoHZJU6kVWTHMvdhUxffezhvCjWuz8pdWl9EzqQ6u8uqi/rDKEH0/TZng6dAhO+PF4cI0Zaww/Fgv2hyfjePpJCT9CXOTO6g6Q1+slMTERAMeJgYtHjx6trGdlZfHee8adnv9bl1xyCVdddRXp6enk5eXx1FNPcfXVV7N06VLsdjsFBQWYzWZiY2N1r4uLi6OgoGIBtoKCAsPu9LGxsZjN5spzTic3N/eMtYuJXIeTLrRrseeol6d/KKHYpR/Hlxjp4O6eDYh1KCR7Czmyz88RoMhzmBl7plHo0f+7iXKEcXvWJaQoGaT/omA/vElX14D9dety2OGA7duruVeglJYRPXUqtk2bdccDqkrJhPF4Wrc6p+240H4v/hdyLU6Sa6FXE9cjMzPzN+tnFYBSU1PZt28fAKqqkpiYyMqVK7nmmmuAijFCoedw88Ibbrih8r+bN29O69atycrKYtGiRVx99dXn7HNO5/cu3MUgNzdXrsMJF9q1WJV3lMe+z+a4Wx9+GsSFcke3+tSNtNEmXsVqjgZgb2keM340hp+ksFhuzepLQzWLlhusWA4f0NU1kwl3p85Ep6YSXb1dAiBQVIRz3HgCW7fpjit16hA29zUimzY5p593of1e/C/kWpwk10Kvtl6PswpAPXr04IsvvmDSpEkADBw4kFdffbVyNtj777/PHXfcUS0NBUhKSiI5OZmdOyv2D4qPj8fv91NUVESdOnUqzyssLKRLly6V5/z6OOxXRUVF+P1+4uPjq62tQtRWS3OLGPvpFjx+/SOqrLqR/LlTGg2j7TSNtVfu6bXz6A7G/TSKI279AOmM6GQGNu9FPXNTWvwSwFxUJfxYrbi6dScQpH9ngfx8nPfeR2DnLt1xU/36qH97DdNZTNAQQlz4zioAjRgxgh49euB2u7Hb7UyePJmSkhLmz5+P2Wzmpptu4sknn6yutlJUVMTBgwcrB0W3bt0aq9XKkiVLGDhwIAD79+9n69atdOrUCYCOHTvywgsvsH///soZakuWLMFut9O6detqa6sQtdH3O4p56JMt+KqscNglI5YrW6fQKMZOZvTJbS22HMlhwk+jOe7VjxFqEVePa5t0J51GNPvZjfnYMV09oKq4evYK2r5egf0HKL/nHrS9+3THTW1aEzJ7FkpUVFDaIYQ4f5z1I7DU1NTKn+12Oy+//DIvv/zyf/XhpaWllXdzAoEA+/btY8OGDURHRxMdHc2zzz7L1VdfTUJCAnl5eTzxxBPExcVx5ZVXAhAZGckdd9zBo48+SlxcXOU0+ObNm9O7d28A+vbtS9OmTXnggQd46qmnOHLkCI888gh33nmnzAATF5Xlu44wel6OIfxc2iKR3k3jyYzWh5+1hat55OeJlPv0a+e0Tszgqkadqe9tRJNfyjBVWVsnEBmJq0fPoK3uHNiTR/nd96BV2V/M3L076syXUFQ1KO0QQpxfanQl6LVr13LVVVdV/jx16lSmTp3KLbfcwvTp08nOzua9997j6NGjJCQk0KNHD958803Cw8N1rzGbzdx9992VCyG+9tprmM0Vq9SazWbef/99xowZQ//+/XE4HAwcOLBa71QJUdv8sqeEER/nGB57XdeuLh0bxpIRZdOFn/k75zFr4wzD6s4dU5rQv0FHmnlakb7iIIqnygKHcXG4unUP2gKH/h07cN5zH1qV/cUsffvimP4Ciix2KoQ4A6WkpET7vZO++eYbQkND6dy5M1CxgOG4ceMM56WmpjJhwoRz30pRI2rrwLWacD5fi7X7jjH4/U2GHd2vaZtC54w6uvDjC/iYvXEGC3Z9anif7mlZXFa/M3X31qPJ9sMoPp+u7ktJwd25C5z446O6+deuxfngCLTiKos3DuiP49mpKNbq39D0fP69ONfkWpwk10Kvtl6P370D9P333zNw4ED+9a+T62m43W7eeecdHA5H5Z0WgPLycrp160aPHj2qp7VCiLOy4cBxhn6w2RB+rmydTOeMOjQ4Jfwc9RzliZVTWHd4jeF9Lqnflv71+9K6uCkhW9cbFzisXx9Pu+AscAjg+fhj3I8/CVVCmOXaa3A8+QRKkEKYEOL89bsB6O2336ZZs2aV425O9d5779GrV6/Kn7t27crbb78tAUiIWmBpbhETP9tGuUe/FcWAlkl0axRH3TALjU+En93HdvHwinEcLD+oO9dqsnB90+70i/sTTXNsWPcYFw/1NGmCN6tlxaZh1UzzenE/Pw3v2+8YatY/D8T+yBSUIIUwIcT57XcD0IoVKypnWP2eq666ig8++OB/bpQQ4r+naRr/Wrmfl5bspurz7UtbJNKzSTxxqpkWcQ4URSG7eBMTlo+hzFuqOzfKEcYtzfvSz9yX5B8OGgY7A7hbtcLX+NyurXMmWkkJzlGj8f+80lCzDforthHDK8cwCSHE7/ndAHTo0CHS09N1x2w2G9dff71hj66UlBQOVZmJIYQIHq8/wJNfbWf+RuMq532bJdCnWQKRdhNtElRMisKawlVMWTEBl9+lOzctMp7bm15Gn0NZhOXuNDzy0kwmPO3a46tfv1r78yv/zp04Bw8xTHPH4cDx9JNYBwwISjuEEBeO3w1AVqsVt1s/0yMsLIzXX3/dcK7X69WNCRJCBM+Rci+j5+WwZp9+TR6TAle2TqFLZh1CrArtE1UsJoUfD37PE788gi/g1Z3fJjGDWxoOoPPmOtgK9ho+x+lwoPXshRaktXV8y1fgHDkKjuvXIlKSklBfeRlzs6ZBaYcQ4sLyuw/L09LSWL169R96s9WrV5OWlvY/N0oIcXaKyjzc885GQ/hxWE38pUcDumTWwW5W6JgYgt1s4uu9i3hs5WRD+OmV3pIH6t9Mt9Xh2AoOGz7H26gR2xo3CVr48Xz0Ec77HzCEH3O7doR88J6EHyHEf+13A9Bll13GJ598wo4dO37zvNzcXObNm0f//v3PWeOEEL/vcKmH+97ZyM7D+jE6MWE2BvfLpFFiODaTQodEFZs5wD9yXufZ1U8a1vi5tGF77oq7mqwVbszH9IEjoKo4e/XC07oNWjB2cw8EcL3wIu5HHjPM9LLecD3q63/HVGUTZCGEOBu/+002dOhQwsLCuPLKK5k/fz5+v35Gid/vZ968eVx99dWEh4czdOjQamusEEKv4Libe9/ZyM4ip+54/bhQhvTLJD7CQajVRNeUEIrcexi6bBBvbX1Td66CwlWNunCbow9Nfi7F5NaPB/LHxuL806UEEhKrvT8AWnk5rpGj8L7xpqFme2g09iceR7FV/xo/QogL2++OAYqNjeWDDz7gtttu4+6770ZVVTIyMggNDaW0tJQdO3bgdDpJSEjg/fffJ1b+KhMiKPKPu7nvnY3kHdEHlkaJ4dzerR5Ws4kYh5lW8TYW7HqPN3P+D2+VR14mxcT1Tbpzg7MN9Ta5qDqHylc3FXfHjmAJzqLxgUOHcA59kEBOjr7gcOB4birWP/0pKO0QQlz4/tC3Wps2bVi+fDlvvPEGixYtYuvWrRw/fpywsDBatGhB//79ufvuu4mSDQeFCIr8Y27ue9cYfhonhXN713pYzCaSwyzUCSlmwvKn2VS0wfAeYTaVmzN6cO3+RtQpDBjqniZN8WZlBWV9HwD/ho04hz2Idlg/9kipUwf11VmYW7QISjuEEBeHP/xnXWRkJKNGjWLUqFHV2R4hxO/IP1bx2GtviT78NE2O4NYu6VjMJjKibFjM+xj+3QiOekoM79E8rh63JXSlS3YMqksfcDRFqZji3qBBtfbjVN6FC3FNngJVZpyaGjdCfXU2pqSkoLVFCHFxqNHNUIUQZ6ew1MN97xrDT/OUSG7unIbVbKJFHQeacoBRP4w0hB/VYuPyjE5c7mlM07UqpiorJWo2G67OXQgkBmm8TyCAZ/areOa8ZqhZ+vTB8fxzKKHB2VVeCHFxkQAkxHmiqMzDX0/z2KtF3Uhu7pyO3azQJkHFEzjEqB9GUOI+ojsvMyaFaxt2ofOOWFIOGXdJ98fE4O7SFS00tFr78SutpATnuAn4f/jBULPddy+2kSNkWwshRLWRACTEeaC43Mtf393EriqzvSru/KQTajXRPlGl3FfAmB+HU+TSj6PpkNyY61I60X5DOBHHjaHCm5mJp2Wr4O3kvnEjzpGj0Q7q9x7DasXx+GNYr70mKO0QQly8JAAJUcuVOL3c/+5GdlRZ56dpcgQ3d04jymGmfaLKcU8hY34cToFTvw1G26RMbo/pStuVYVj1k8DQLBbcHTriT02t7m5UfJ6m4f3gQ9zPTAWvvjFKbAyOGTOwtGsblLYIIS5uEoCEqMUKjrsZ8sFmcgv14adxUji3dkknNsRCx6QQth7ZyOMrp1DsLtKd1yq+IcPsvWi6VkWpMt4nEBGBq1t3tPDw6u4GAJrTieuJJ/HNX2Comdq0Rp3+IqYq+wsKIUR1kQAkRC21u9jJ4Pc2ceCYfmZURkIYt3WtR7RqoX2Cyld75jNrwwx8mn7F5FZx9XnY34/UbXbDe/tS6las72MNzoKCgX37cA4fSWDLFkPNeucd2B8ajRKktgghBEgAEqJWyj5UypAPNnOkXP+YqEF8GHd0q0+kw0ybODOzNk5j4Z7PDK9vGVufx92XknhYP9hZA7wtsvA2bRq09X18Py3H+dAYOHpUX1BVHE89iXWAbJ8jhAg+CUBC1DIrdpcwal4O5R79tjNNkyO4pXM64Q4zDaPKmLBiCjlHsg2vv6Rua0Ye6UydI/o7KprVirtTZ/zJydXa/srP0zS8b/4D9/SXIKBfaNHUoD6OmTMwN2wYlLYIIURVEoCEqEUW5RQy6bNt+AL6ATvt6kVzXftUQqwmUsIPM+bHURx2FerOsZos3JLRk1vzGhN1TP9POxAaiqtnr+CN9ykvxzXlEXxffmWoWf50CY5nnkYJ0nR7IYQ4HQlAQtQSb63cz4vf7qLKWGV6NYnjsqwkQqwmEkLzeXjFaMNg5xg1nLsyL3LSwxoAACAASURBVGFAThLhZfqp7IGICFy9eqOpajX34MTnFRTgHDzUuJ+XomAb/iC2QX9FCdLjNyGEOBMJQELUsICm8eI3u/j3qgOG2uWtkunROI4ou4mYkANMXj6KkiqrO2fGpHBXam+6boxCdenX+PHHxODq0RPsxoHQ1cG/bRvOB4agHTqkL0REoD7/HJaePYLSDiGE+D0SgISoQS6vn8mfb2PxVv0dHZMCN3RIpW29GJJCLYTY8piwfDTHPPqBxG0SM7grtjvt1oRh8evvqvjj43F16x60mV6+n5bjHDkKSkt1x02ZmaivzMSUlhaUdgghxB8hAUiIGnKk3MvIj7NZt/+47rjdYuLWrvVolBhORpSNADsZ99MoSr36YNE+uRH3O3rQfJ2KSdOHH19yMu4uXYO2srP343m4Hn8CfPqp+OZePVFfeEH28xJC1DoSgISoAXuPOBn6YTZ7ivVbW4Q7LNzVowEp0Sot4xwUubOZvGIs5T79QoidkpswXOtBwy0Ow3t7G2bgadMGgrCPlub345kxE8/rbxhq1ltuxj5xAopFvmaEELWPfDMJEWSr844y+pMcSpz6uyUJEQ7u6lGf2DAb7RJVdh9fwyM/T8Tt1y+E2C2lOQ+Vdyc537jGj6dVa3yNGgVljR/t6FGcY8bi//EnfUFRsI8dg/Uvd8pgZyFErSUBSIggWrAxn8e/3G6Y5t4gLpTbu9Un0mGhQ5LKxqIfeGrVY3gD+oUQe9RtwcjybsbwYzZXrPFTt2619wHAn7sd57AH0fbu1RfsdhzPPYv10j8FpR1CCPHfkgAkRBAENI1Z3+3h9eX7DLXWaVHc0CGVcLuZjkkh/HToa55f8wwBTb8QYt/0Ngwu70jKIX34CTgcuLt3JxATW619+JV9+QrKZ74MTv3jOyU+HvXlmZhbZgWlHUII8b+QACRENfP6A0xcsJWvq8z0ArikeSJ9m8UTYTfTIVHl670LmLn+RbQqqwH1b9CBu8rakLq/SvgJDcXVuw9aEBYV1DQNz5zXiJ4121AztW6FOnMGpri4am+HEEKcCxKAhKhGXn+AsZ9uYUluse64xawwsEMqLdOiiXaYaZfg4O1tb/Dvrf/QnaegcFWjzvy5rAX19urX8gmoasUCh8EIPx4vrscew/fpfEPNOvBG7JMnodhsp3mlEELUThKAhKgmvoD2/+zdd3wVRQIH8N++lvfSQyqQQguE0CEkoXcQOcXGYTsRRJAmTTonyqn03iyIqIcFIyqKwqmgdCIiRikhlIQW0iD99d37IxqYPAuQl8b7fT+f/JGZebuzc0/yu92dGcz8/JRD+PF00+Bfnesh3N8DPm4qtArSYOnRl/H1hR1CO5Uk4YGoLvhHcWM0OifO9lLc3ErCj6dnhV+HUlAA4/iJsB88KFZoNHCbOR3awYP5sjMR1TgMQEQVwC4r+PcXp/C/k9lCeYCXG4Z1bQA/Dx08tSpE+8t4/uAU/JT9o9BOo1JjUHQ3dLXWR1SKuIWFotPB2K07FG/vCr8OOT0dxmdGQ05JEcolX1/oVy6HJiamwvtARFQRGICInExWFLzwVQq+PC5uVlrLU4fh3RrCx10Ld42E+r75mLJ/Gs7lnxXaGTRueKR5T7RQQtDyaJnwo9HA1LUrFF/fCr8Oe3IyjCNHQcnMFMptIcHw2fAmVPXqVXgfiIgqCgMQkRPJioKXtp/G1l/E0ODnocPTv4UfvVqCp9sZTN43Bzkm8Q5RLYMXHm3eC2HwRcwPXlDfMF1eUalg6tylUmZ72Q7/COOYsUCBuEq1qmVL5Dw3CX4MP0RUwzEAETmJoihY8PVZfPxzhlDuY9BieLcG8PXQQa2yI6VgMxJ++i9kyEK7UK8APNy8J3wlAzocDoDWIi6AaImJgRwUVOHXYdv1HYyTJgNm8fya3r2gXzAfykXHqfxERDUNAxCREyiKguXfpeKDI+lCuZdeg+HdG6KWpxsKrenYeXkRUvJOOHw+KiAcD0R1hrvKA12OBkNXKG56aoluBlu9+hV6DQBg/eRTmJ6fA9jFNYi0jz0Kt+nTIFXS3mJERBWNAYjICdbtPY+Nhy4JZZ5uJeEnwMsNJ3O/wbeX1sJkNzp8tkNoNHo3aAtPVS3EnawDt2zxOLbwcFibNavQ/gOAZcNbMC9e4lCue3YcdCNHcKYXEd1RGICIyunNAxfw2j5xSwh3nRpPdW+AIG89jmQnYNflNx0+56kzYGCTjmhUqy78VWFofcwLuoti+LEHBMDcPrZC9/ZS7HaYlyyFdePbYoUkwe35f0M3+J8Vdm4ioqrCAERUDv/94RJWfp8mlOm1Kgzr1gAhPgb8mPURvkt33Cm9iX8o7mncER46PUJVUYg+YoM6Sww/sqcnTJ06AxX42EnJz4dxyjTY9+wRK7Ra6BcugLZf3wo7NxFRVWIAIrpNCUevYNG354QynUaFoV0boK6fOw5nbcb36W8J9RqVGv0atke72pGQJAm1bRGI/rEI6vx8oZ2s18PUpSvgJq7+7EzyuXMwjn0W8jnxGuDuDsPqVdDEx1XYuYmIqhoDENFt+OLXTLy0/bRQplWr8GSX+gj398APmZux+4pj+Hm0eS/U9wsBAIQYg9DyRzNUZTYVlb29YerStUK3uLDt3gPjlKkO09ylwEAY1qyCunnzCjs3EVF1wABEdIu+Tc7G89tOCduValQSnuhcD/UDPZGY+SH2XNkofEar0uDRFj1Rz7ck/ATn+6LVERkqqzjV3B4QUPLYq4Lu/CiyDMvrb8CyajWgiBuuqlq0gGHVCqgqYao9EVFVYwAiugX7zl7D1M+SYb8hO6gk4NGO9dAo2AtHsj752/BTq8AdrY+oobJahXa2unVhjosHNBXzn6V87RpM06bDvnefQ53m3nugf/EFSBX4yI2IqDphACK6SYfP52HilhOw3bA6syQBg+Mj0LSON1Ly9uO79DeEz2hVGjzWohcifIMBAD5FerQ/4u4QfqwNG8HSpg2gUlVI321HjsA0eQqUDHGRRqhUcHtuMrRDnuA0dyJyKQxARDch5aoV/9l3HGabuHrzgzFhaBnmi4ziFGy/sAjKDQ/GyoafWkZfxBzRQVV2hefoaFibNa+Qqe6KosD61kaYly13WNxQ8vWFftECaDp1cvp5iYiqOwYgor+RklmEl/deQ7FVfGfm3jZ10a5+LeRbsvD5+RdhkU2ldRIkDG7WvTT81LaFo+URC1Qm8YVnS5MmFRd+CgthmjETtm93OtSp27aBfvEiqEJCnH5eIqKagAGI6C+kXTVi5Ie/orBM+LmrZW10iAyA2V6EbefnIM+SI9QPiIxDw1p1AAAN5RZo9MNVqIrF8GNt2AjWlq0qJPzIqWkwjh0H+exZhzrdU8Oge3YcJK3W6eclIqopGICI/sSVfDNGfvArcorE93V6NA1Ct6ggyIod315eiMvF4jo6HUKj0a5OY6igQQtre4QcPAuVySS0sdarB0vbthUSfmx798H43BSgzNpC8PaGYd4r0PTo7vRzEhHVNAxARH8gp8iCER/8ivR88X2dDo0C0Kd5CBRFwcHM13DiWqJQHxUQjj4N2kEFDWKKO6DWoeOQbDahjS0sDJaY9k4PP4qiwLrhrZL3fWTxXSVVdDQMK5ZBVbeuU89JRFRTMQARlXGt2IpRHx5D2lXxkVW7en74R5s6kCQJJ3M/xYGMz4X6Ol7+eCCqM1SSCjFXW6HWkV8glVlrxxYWDnNsrNNneykWC0zPz4Ft6+cOdZp/DIB+7ouQ9HqnnpOIqCZjACK6weU8E0Z9eAypZcJP81Af3B8TBpUkIb34IL48/7pQ7+PmgYeb9YBWpUH7S1HwPy6uEg0A1sZNYGnl/Hd+5KtXYXp2POxHfhIrVCq4TZoI7dAnOcWdiKgMBiCi35zOKsKozceQWWARyiNDvDA4LhxqlYRi2xl8fG6BMN1dp9bi0RY94aVzR9y5Bqh15rLDsc2tWsPWpInT+2w/fQbG0WOgXLwoVnh7w7B4ETSdOcWdiOiPMAARATh6MR/jEo4j3yS+r9MwyBOPd6wHjVoFtSoH75+ZDbNdnO4+KLorgtz9EHsmDLXOibPBFJUK5tg42MPDnd5n2/79ME6c7LifV716cF+7Bqp6EU4/JxHRnYIBiFze96evYuqnJ2Eqs8hh81AfDI4LLwk/tkz8N20qcs25QpsBkXGI9Av7LfyIdYpWC1OnzpArYG8ty4ebYX7pZYfFDdVxsTAsWwbJ18fp5yQiupMwAJFL23YsE//+4pSwtxcAxDX0x71t6kKlkuCnN+LNkzNxuUjcRqJDaDS6hsYj5lRtuJ8TH0EpOh1M3bpD9vNzan8VWYZl+QpY1r/pUKd96EG4/Xs21/chIroJDEDksr46noXZX5yCXCb89GoWjF7RwZAkCQHuFrx1ahLO5V8S2jQNCMewpsPQ5IQaunPiYoOKmxtM3bpB9nVy+LFYYJr1b9i2bRMrJKlkP68nh/BlZyKim8QARC7p65PZmPV5shB+JAAD29ZFXKMAAECIhx1vnZqEU1dThc+GeQVjTrv5CE26DE2aY/gxdu8OxcfXqf1V8vNhHD8B9kPiukMwGGBYtBCanj2cej4iojsdAxC5nF2ncjB9a7Lw2EslAYPjwtEyvOSuTZiXjDdPTcbxnDPCZ2t7BmJp/BrU/vE0NJfFu0KyXg9Tt+5QfJz7/o2cng7jM6Mhp6QI5ZJ/LRjWrYW6eXOnno+IyBUwAJFL2X36Kp779CRsN9z6kSRg0A3hp74P8OapKfglK1n4bLCHP5bHrUadH05CnSm+DyTr9TB17wHF29up/bUn/QLj2HFQsrOFcikiAu6vvwpVWJhTz0dE5CqcuxztLdq3bx8efvhhNG3aFL6+vti0aZNQrygK5s2bh6ioKISEhGDAgAE4ceKE0CY3NxcjRoxAeHg4wsPDMWLECOTmirNxjh07hrvvvhshISFo2rQpFixYAKXMCr1059t/7homfXJCDD8AHmofhta/hZ8IbzvWHR+PnzKOC5/11/tgefuVCE084Rh+PDxg6tnL6eHH+tVXKB7ypEP4UbVuBff3/svwQ0RUDlUagIqKihAdHY358+fDYDA41K9YsQJr1qzBggULsHPnTgQGBuL+++9HwQ3rngwfPhxJSUlISEhAQkICkpKSMHLkyNL6/Px83H///QgKCsLOnTsxf/58rFq1CqtXr66Ua6Tq4cfzeZj48QlYy0z3eiAmFG3r1QIA1PEsxrKk0TiWc0poU0vvjel1piD80Amoc8R1fmRvb5h69ITi6em0viqKAvPqNTBNngKYxb3INL17wX3Dm1A5eXYZEZGrqdJHYH379kXfvn0BAKNHjxbqFEXBunXrMGHCBAwcOBAAsG7dOkRGRiIhIQFDhw5FcnIyvvnmG2zfvh2xsbEAgGXLlqF///5ISUlBZGQkPvroIxiNRqxbtw4GgwHR0dE4deoU1q5di7Fjx3LWjAtIulyAsQnHHdb5ua9dKGIa+AMAdOoMLDw6FRnFmUIbP70XlscuRdjeZKjL7Ohu9/ODqWs3wM3NaX1VTCaYZs6CbfsOhzrd8KegmzAekpP3ESMickXV9l/StLQ0ZGRkoGfPnqVlBoMBHTt2xKFDhwAAiYmJ8PT0RFxcXGmb+Ph4eHh4CG06dOgg3GHq1asX0tPTkZaWVklXQ1XlxJVCjP7wVxRbxAUD/9G6DuIaloSfAusprDn2rEP4qe3pj5VxK9DwcBoMZcNPQCBM3bo7NfzIWVkoHvKkY/jRaKB/5WW4TZrI8ENE5CTV9iXojIyS9ywCAwOF8sDAQKSnpwMAMjMz4e/vL9zFkSQJAQEByMzMLG1Tp04dh2P8XlevXr0/PH9KmRk3rqomj8OFfBvmfH8VBRbxsdddLWqjU+PA39ocxqdp/4FFEff/auhXB6Nqj0adfclQFxcLdfle3jhXty4UJwZozblz8HvpFajLvO8je3vj2oxpsEZHA9Xof4ua/L1wNo7FdRyL6zgWoqoYj8jIyL+sr7YBqKr93cC5gt8fI9ZEaVeNeHl7EvLLhJ+e0cHo1rRka4r0ohP47MJLDuGnVXADPNd8ChokZjiEH3twMNSdu6CRWu20vtq++w7GmbOBMudSNWoEj7Wr4RMa6rRzOUNN/l44G8fiOo7FdRwLUXUdj2p7Pz04OBgAkJWVJZRnZWUh6Le9lYKCgpCTkyPM6FIUBdnZ2UKbPzrG73V057mYa8LT7/+C7CKrUN6lSSB6Nyv5XuWYzuOztDkw28WXjDuHt8D4ZmPR4HC2wwvP9sBAmDp1BpwUfhRFgeXtd2AcM84h/Ki7dCmZ6VXNwg8R0Z2i2gagiIgIBAcHY9euXaVlJpMJBw4cKH3nJzY2FoWFhUhMvL46bmJiIoqKioQ2Bw4cgOmGdzh27dqF2rVrIyKCu2XfadLzTHj6vV+QUSDe1Ylv6I/+LWtDkiQUWLLwadosFNnEXdT7NYzB4w0GocnhAqizxPeBijw8YOrcBdA456apYrXC/OJcmBcsBMosyaB97FEY1qyC5MSZZUREJKrSAFRYWIikpCQkJSVBlmVcvHgRSUlJuHDhAiRJwqhRo7BixQps3boVx48fx+jRo+Hh4YGHHnoIANCkSRP07t0bEydORGJiIhITEzFx4kT069ev9HbbQw89BIPBgNGjR+P48ePYunUrli9fjtGjR3MG2B0mo8CM4e//isv54l2ddvX8cE/bupAkCSZbAbaen4lcs/iuTdfwlvhHnd5o8YPN4T0cu58fzjRsBDhpk1GlqAjGMWNh3fyRWKFWw232LOhnzYTkpKBFRER/rEr/lf3pp59wzz33lP4+b948zJs3D4888gjWrVuH8ePHw2g0YsqUKcjNzUW7du2wZcsWeHl5lX5m/fr1mDp1Kh588EEAQP/+/bFw4cLSeh8fH3zyySd47rnn0KNHD/j6+mLMmDEYO3Zs5V0oVbisQguefv9XXMwVZ2u1jvDDAzFhUEkSrLIJ2y7OwpVicef2trUj8XD4PWj1A6DOvybU2X19YeraDfL5807pp5yZWbKtxcmTYoWnJwxLl0DTuZNTzkNERH9Nys3N5ZLI9Ieq64trZV0ttuKpTUk4m2MUyluE+WBwXATUKgkWuxFfXpyOM3niIodR/mGY1GQ4mh82QVVYKNTZ/f1h6tIV0OmcMhb2lBQYR46CcuWKUC7VqVOyp1dko3Idv7LUlO9FZeBYXMexuI5jIaqu48H77FSjmax2jE847hB+mtW9Hn7M9iJ8cWEqUvPFndvDfYIwtcloRCXmQlV2tldQUMkLz0567GU7eAjG8ROAAvG9I1XzZjCsXQNVQIBTzkNERDen2r4ETfR3ZEXBrC9OIemyGCqianvj4fhwqFUSTLZ8fHZ+okP4CfbwwwvNp/5h+LHVrl3ywrOTwo/l449hHDHSIfyou3eD+8a3GH6IiKoA7wBRjbV8Vyq+SRanqjcM8sRjHSOgUatgtOXh0/OTcLnwstCmjpc/5rWag8jEdMfwExoKc1y8U6a6K3Y7zIuXwPr2Ow512ocHw23mDL7sTERURfivL9VIm4+k4+3ES0JZsI8ej3esB41ahWLbVXySNglXisSd20O9AjGv3ctoeDDN4Z0fW3gEzLGxgBO2m1AKC2F8bgrsu/c41LlNngTtsKGchUhEVIUYgKjG2XPmKuZ9fUYo89Jr8GTn+tDr1Ci0ZuOTtInILBans0f4hGB+zHyEHzwLVX6+UGcLDXNa+JEvXIBx9FjIZ8Q+Qq+Hfv48aPv2Kfc5iIiofBiAqEY5caUQUz49CfmGuYtatQpDOteHr4cOBZZMbEmbiGzjVeFzDXzrYH7MItRNPA31NXGqu612bZjj4pwSfmw/HoFp3LNQcnOFcik4GIY1q6COji73OYiIqPz4EjTVGBn5ZoxLOA6jVS4tkyTgkQ7hqFvLHXmWK0hIneAQfiJrhWFJm8Wou/+k4yKHQUEwd+jolHd+rF9sg3HYUw7hR9WiOdw3f8DwQ0RUjfAOENUIxRY7xiUcR1ahuMXFP1rXQdM6PrhmvoQtqZORa84T6qP862Fx1MuotfsoVEZxqrzd379kqns5X0RWFAWW116HZeUqhzrN3f2hf+k/kPT6cp2DiIiciwGIqj27rGDaZyeRnFkklHeMDEDHyEBkm1KxJXUqCiziNPNmAQ2wKGI2/PYchmSzicesVatkkcNyTnVXrFaYXngRtk8+dajTjR0D3ahn+LIzEVE1xABE1d7ib89i9xnxvZ2mdbwxoFUdnC/8CZ+nvQhTmV3dWwY1wuKASfA6eBRSmc1GbXXqwBzfofx3fvLyYJw4GfaDB8UKrRb6l/4D7T3/KNfxiYio4jAAUbX23uHLeO/HdKGsjq8Bg+PCkZz/DbZfWA5ZkYX6tkGNsdAwAp5HTzgczxoZCUur1uV+4dl+5gyMY8ZBKbtHmLc3DKtWQNO+fbmOT0REFYsBiKqt3aevYtG34grO3gYt/tWpHn7OfQ970t9z+EyXkBb4j3UQ9ClpQrkCwNK6DWyNG5e7X7bvv4fxualAkfhITgoLhfur66CqX7/c5yAioorFAETV0okrhZj6mTjdXadR4YnO4TicuwI/5+x0+Mw/w7phXHYnaPOyhHJFrYY5vgPsdeuWq0+KosCy4S1Yli4DyjxWU7VpDcOqlVDVqlWucxARUeVgAKJq52KuCWM+OuYw3f3h+DAcKZiPU7k/Cu3VkgrPRtyHQecbQGUSX4SW9XqYO3eBXN5gYrHANH0GbJ9/4VCluf8+6Oc8D0mnK985iIio0jAAUbWSa7RizOZjyCmyCuUDWtXBReV1h/Cj1+jw73r/Qs9kL0hlXoS2+/rC3LkLFHf3cvVJyc9HrTkvwnb8uFihUsFt2hRoH3+cM72IiGoYBiCqNkxWO55NOI7Uq+J6PV0aB0Ly2YqkjF1CuY+bB16MHIW4JDskuzjN3VanTsmmpuWc5i5nZcE4YiR0yafECm9vGJYsgqZTp3Idn4iIqgYDEFULdlnB9K3J+PmS+AirZZgv6oQdxLeXEoRybzd3LGg5Ha0O5zms8WNp0gTWFi3LPdNLTk1D8dMjoFwSN11VNagPw+rVUNWLKNfxiYio6jAAUZVTFAXzvz6DXSll9u8K9EDrqLP48sIbQrlBo8PsFuPQ8qdCSGbxsZe5ZSvYoqLK3Sf78eMwjngGylWxT+q2bWBYsxqSj0+5z0FERFWHe4FRlVIUBUt3pWLzT1eE8mAfPXq0zseOi0uEco1KjYkthqHTr1qoiouFOktUlFPCj/XbnSh+4knH8NO9GwxvvM7wQ0R0B+AdIKpSa/acxzuJ4iMmb4MWd7c3Y/vlubAr9tJyCRKejn4QA06HQJ2fI3zGWr9+yWOvclDsdljWrIXl1dcc6ow9eyBw2VJI5XyniIiIqgcGIKoyr+87jzf2XxDK3HVq3BNrxo70F2EpM6vr4Sb98OjlllDnZAjltjp1YWkXUzJX/jYpeXkwTp0O+549DnXaoU/iysB7EcTwQ0R0x2AAoiqx8dBFrNkjbiOh16pxb7wZu7LmwiKLu77fXb8jRuV0gfaK+KjMHhgIc3x8uV54tp86BeO48VAuiGEMGg3cpk6B7vHHgJSU2z4+ERFVPwxAVOk2Hb6MZbtShTI3rQr3xBvxffZLsJYJP70i2mFq0QDoLpcJP76+MHXqXK5NTa07/gfTzFmAUZx6L/nXgn7ZUmhiYm772EREVH0xAFGl2vLzFSz8RtzfS6dRYUBsMfbmvAyrLC6A2DsiBrPND0B/UdwQVfbygqlrN+A2V19WFAWWV1+DZdVqhzpVy5YwrFgGVXDwbR2biIiqPwYgqjTbT2Rh7lenhTKtWsKA2GIcuOYYfvrUa4+ZloegP39RKJc9PGDq1h3Q62+rH4rZDNPs52Hbts2hTjtoENxmzeC2FkREdzgGIKoUu09fxazPT+HGLUTVKgl3ty/GwdxXHMJPv/pxmGZ7CIZU8T0h2d0dpu49bnt7CzkrG8Zxz0JOShIrNBq4/Xs2dIMeuq3jEhFRzcIARBUuMS0Xkz85AdsNW7urJOCuGCMS8+c5hJ/+9eMx1foADGfLhB+DoST8eHjcVj/syckwjhoDpcyL1JKvL/QrlkHTvv1tHZeIiGoeLoRIFSrpcgHGf3wCFvv18CMB6NPWiJ8K5zm88HxX/ThMNd8Hw1lxRpas18PUrTsUT8/b6odt9x4UP/6EQ/hRNWgA9w/eY/ghInIxDEBUYY5cyMOoD39FscUulPdsXYxfjPNhkcV1fvrVb49pxffCkCoujKi4ucHUrRsUb+/b6ofl/Q9gHD0GKCoSytWdO8H9/U1QhYff1nGJiKjm4iMwqhC7T1/Fc5+ehNkmC+Vdm1twwrLQIfz0rReD6QX/gKHMVPfSOz+3sf2EYrfDvGgxrO+861CnffRRuE2fCqkcU+iJiKjm4r/+5HRfHc/C7C9OCe/8AEDnpiqkYQksskko7xXRFjMK/gFDerZQXvrOj5fXLfdBKSqGaeo02HbtEitUKrhNmwrdvx6/5WMSEdGdgwGInGrzkXS88r8zUMqU92zmgQual1FkzhPKe0S0xnTbAzCki9tb/D7V/Xbe+ZHT02EcMw7yyZNihcEAw5JF0HTvfsvHJCKiOwsDEDmFoihYf+AiVu9OE8olAAPaBOC8agGuFYmPt+JDozFF9zC8UsR3fmQvr5LwcxtT3e0/J8E4dhyUHHGzVCkoCIa1a6CObnrLxyQiojsPX4Imp9h46JJD+FFJwKC4UGRrX8XlInEvreaB9TDNdwhqHSsTftzdbzv8WLd9ieIhTzqEH1VUFNw/fJ/hh4iISvEOEJXb579kYPl3qUKZRi3h0Q4RuKS8hdM5h4W6ej7BmBH2NEISLwvlilYLU5eutxx+FFmGZc1aWNa96lCn6dkT+gXzIXnc3sKJRER0Z2IAonLZd/Ya4Nf3CAAAIABJREFUXiizvYWbRoUnOtfDedt7+Dlnh1AX5OGLGY1HokFiDiT5+gwxRaWCqVOnW57tpVgsMM2aDdu2Lx3qdMOfgm7CeEjl2CmeiIjuTAxAdNt+uVyASWVWeFarJPyrc32kywn4MftTob23mzumRT2FFj+ZIVnF1Z/N7dtDDrq1zUeV3DwYnx0P+2HxDhO0WujnvgDtwIG3dkFEROQyGIDotqReNWLcR8dgsl6/iyMBGBwXjizlcxzM/FBob9C4YWajJ9DhiASVuVios7RoAXtEvVs6v3zxIozPjIJ89pxQLvn5Qb9yBTTt2t7S8YiIyLUwANEtyymyYNSHv+Ka0SaU/6NNXZj1O7Ev/R2hXK/R4YV6j6Frkh6SXVwA0dqgIaxRt/Zysv2XX2AcPQZKzlWhXFW/PgyvroUqLOyWjkdERK6HL0fQLTFZ7Zjw8QlczhODTPemQXD3O4jv098QynVqLV6uPRhdT3hCsotbYtgiImBp2xaQpJs+v/Xrr1H85DCH8KNu1w7um/7L8ENERDeFd4DopsmKgue/TEHS5QKhPKZ+LdSt+yt2XFwjlGslDZb4D0b7M75AmaURLVFRsLZoedPhR7FaYV62HNaNbzvUae7uD/3LL0Fyc7u1CyIiIpfFAEQ3bd2e89hxQtyuolGwJ5o3SsO2C8uEcjdJg7Xej6DZRV+hXAFgadMGtsjGN31eOSMDpsnPwX7kJ4c6zvQiIqLbwQBEN+XzXzLw+v4LQlmQtxu6tLyGLy8ugHLDHR4DdHhT/ygaZIl7eCkqFcxx8bDfwmMq2/4DME2dBuWq+MgLGg3cZs+C7p+Dbv1iiIjI5TEA0d/68Xyew1o/Hm4a3BVjxleXXoJduf4ytJ9iwAbtI6id5yG0V7RamDp1hhwUdFPnVGQZltdeh2X1GkARH59JIcEwLF0CdevWt3lFRETk6hiA6C+lXjVi4hZxrR+NSsI9scC3V+bCJl9/Gbqu4o03pMGoVawXjiG7u5es8HyTixwqRUUwzZgJ2zffOtSpO3eCfsF8qPz8bvOKiIiIGIDoL1w12vHCB78izyROd+/T1oZ9V1+GWb6+nk+o4oO35UfgYdcKbe0+PjB37QbFYLipc8oXLsA49lnIKeLeYZAk6MaOgW7kCL7vQ0RE5cYARH8o32TDS3tzcTlfDD/x0Xn4uXgFLDeEHzdFg1XSg47hJygYpk6dAK1Y/mdsBw7COGkykJcnlEt+ftAvWgBNx463eTVEREQiBiByYLTaMS7hOM6XCT/RDS7hnPIa7IrleqECLHIbiNqF4js/togImGPaA2r1TZ3Tsuk9mOcvAMqsFaSKioJh1Uqo6ta5vYshIiL6AwxAJLDaZUz99CSOXswXyuuHn0K27m3IihhQJvr2RmyWGE5sYWEwx8bd1Bo/iizDvHQZrBvecqjT3NUP+pf+A+kWd4cnIiL6OwxAVEpRFMzdfhq7z1wTyuvWPYoC9w9RdjHDf9XujIcuRAllspdXyZ2fmwk/FutvO7lvEyskCbrxz0L39HBIt7BKNBER0c1iAKJSy79LxdZfMoWyoNqHYPb6xKHtAw06YmhaK6jl6wFF0Whg6nhz7/woBQUwjp8I+8GDYoXBAMOSRdB0735b10BERHQzGIAIAPBu4iVsPHRJKKsVvB+yz1ahTIKEB5p0wpD0FnA3irOxzDExNzXVXc7MhHHkM5CTT4nH9veH4dW1UDdrdptXQUREdHMYgAjbjmVi8c5zQplP0B6o/MRHU2pJhcFR3fHP9MYIyha/OtZGjWAPj/jbc9nPnIFxxDNQ0tOFcqlePbi//ipUoaG3dxFERES3gAuquLj9567h+W3imjuegd9BW0sMPxqVGo8364nBlxsjJLPMdPdatWBp9ferMtt/+gnFj/3LIfyoWrWC+6Z3GX6IiKjSMAC5sF/TCzCpzCrPHgE7offfLrTTqNR4PLoXHjzfCMFZYviRPTxg7tjpb6e7W7/dieJhw4F8cXaZpkcPuG9Yz5WdiYioUjEAuai0q0aM3XwMRqv8W4kC94D/wRDwP6GdVqXBE81644HzDRCUXSb8eHrC1KMnlL+Zpm75cDNM4ycAZrNQrh00CPoVyyDd5CrRREREzsJ3gFzQ1WIrxmw+hmvG3xc6VOAeuB3u/t8L7XRqDf4V3Rv3pdZDYE6Z8OPlBVP3Hn+5xYWiKLCsXgPLulcd6nTjxkL3zEhOcycioirBAORiTFY7Jnx8HBdyTb+VKPAI2gZDrb1CO51aiyHRvTHwXD0EXBW/JjcVfixWmObMge0zcRYZVCq4vfA8dA895IzLISIiui3V+hHYvHnz4OvrK/w0bty4tF5RFMybNw9RUVEICQnBgAEDcOLECeEYubm5GDFiBMLDwxEeHo4RI0YgNze3si+lWpAVBf/eloKfLxX8XgKP4K0O4cdNrcWw6D64/8wfhB9vbxh79Pzr8FNQAOOoUY7hR6+HYdVKhh8iIqpy1ToAAUBkZCSSk5NLf/bv319at2LFCqxZswYLFizAzp07ERgYiPvvvx8FBQWlbYYPH46kpCQkJCQgISEBSUlJGDlyZFVcSpVb8V0q/ncy+7ffZHgEfwqD3wGhjUGjw9PN7sJ9KRGolVsm/Pj4wNi9B6DX/+k55CtXUPyvJ2A/IC5wKPn6wn3Demh6dHfGpRAREZVLtX8EptFoEBwc7FCuKArWrVuHCRMmYODAgQCAdevWITIyEgkJCRg6dCiSk5PxzTffYPv27YiNjQUALFu2DP3790dKSgoiIyMr9Vqq0uYj6TcsdKjAI/hzGPwShTbuWjcMb9oP/0gOhXeBOKvL7ucHU9dugJvbn57DnpwM4zOjoWRkCOVSWBjcX3sVqnp/v04QERFRZaj2d4BSU1MRFRWFli1bYtiwYUhNTQUApKWlISMjAz179ixtazAY0LFjRxw6dAgAkJiYCE9PT8TFxZW2iY+Ph4eHR2kbV3AwNRfzvj5T+rt7wNcOd348tHqMaTIA9574g/Dj7w9Tt+5/GX5se/eh+PEnHMKPqmVLuL+/ieGHiIiqlWp9BygmJgZr165FZGQksrOzsWjRIvTt2xcHDx5Exm9/aAMDA4XPBAYGIv23hfYyMzPh7+8vzDSSJAkBAQHIzBT3vCorJSXlL+triowiO6btzMHvS/3o/fbCPWCn0MZTZ8Bz9e9Bz18CobOKmbjQ0xNn64ZCTkv703MYtu+A92uvQ5JlodwUF4fcyROBnJySnxruTvlOOAPH4jqOxXUci+s4FqKqGI+/e8pTrQNQnz59hN9jYmLQunVrvPfee2jfvn2FnvtOeDxWbLFj5rs/o9BSkn7cvH+EZ/AXQhuDRoc5Ifeh06/+UCnilPR8Ly+o+/RFQ80ff00UWYZ5yVJY39roUKd9/DF4TpuKwL9ZILGmcLVHpn+FY3Edx+I6jsV1HAtRdR2Pav8I7Eaenp6IiorC2bNnS98LysrKEtpkZWUhKCgIABAUFIScnBwoyvWVjhVFQXZ2dmmbO5WiKJjzZQpOZRUDAHSex+BZ+2OhjU7SYKnvIHQ5HeAQfmxhYTjXoCHwZ+HHaIRp4iTH8CNJcJs2FfqZMyDdIeGHiIjuPDUqAJlMJqSkpCA4OBgREREIDg7Grl27hPoDBw6UvvMTGxuLwsJCJCZef9k3MTERRUVFwntBd6KNhy6VzvjSepyCV533IUnXH1HpocUGw2Noc7mWw2ct0c1gju8ARfXHXw85IwPFQ56E7etvxAqDAfqVK6Ab8oTzLoSIiKgCVOtHYLNnz8Zdd92F0NDQ0neAiouL8cgjj0CSJIwaNQpLly5FZGQkGjVqhMWLF8PDwwMP/bbOTJMmTdC7d29MnDgRy5cvBwBMnDgR/fr1q5a345xl39lrWPFdKgBA654C77rvQFLZSuslRcKrHg+jUa638DlFpYI5NvYvd3W3//wzjOPGQ8nOFsqlgAAY1q6Gunlz510IERFRBanWAejy5csYPnw4cnJyEBAQgJiYGHz99dcIDw8HAIwfPx5GoxFTpkxBbm4u2rVrhy1btsDLy6v0GOvXr8fUqVPx4IMPAgD69++PhQsXVsn1VIbz14yY9tlJKAC07mfgHSqGHwB4xe8+RGX6CmWKmxtMnTpDDgj402NbP/kUphdeBKxWoVwVGQnDurVQ1anttOsgIiKqSNU6AG3YsOEv6yVJwowZMzBjxow/bePr64vXX3/d2V2rlorMNoz/+AQKzHZoDGfhHboRkkoMKxOC+qLHxVChTPbygqlrNygeHn94XMVmg3nxEljfedehTt21CwyLF0Hy9HTehRAREVWwah2A6ObJioLZ207hbHYxNIZU+IQ5hp8hdbviobTGQpmi08HUpeufh5/8fBgnTYZ9/wGHOt3wp6Ab/yxfdiYiohqHAegOsX7/Bew8dRUa/Xl4h26ApLII9feFx+PJiy2gka/P9lIkCaaOHaH8yd0bVVY2iidPgXz6tFjh5gb9S/+BdsDdTr8OIiKiysAAdAf4LiUHa/ech9rtMrzDNkClFsPP3eHtMTKrDQwmcVaXpW1byEGO24wAgP3kSfhPnQb56lWhXAoJhmHVSqibNXPuRRAREVWiGjUNnhydyynGzM9PQaXLhE/Ym1CpTUL9vaGxmHClPfzyxKxrbdQItoaN/vCYtv0HUPyvIVCXCT+qVq3g/uGHDD9ERFTjMQDVYAUmGyZ8fAJGJQveYeuh0hQJ9Q/VjsfEy+3hXVhmb6/gYFhat/nDY1o/+wzGZ0YBReKxNL17wf2tN6EK/PNZYkRERDUFH4HVUHZZwYzPk3E+PwM+4euh1uYL9Y/4x2PUpXbQ2cSMa69VC6YOHYEyixwqsgzLipWwvLHe4Vzaxx+D27SpfNmZiIjuGAxANdSib89ib+qFkvCjEx9VDfPpjKcyWkMtl9neonYdmDt0cNjeQikqhmn6dNi+FTdJBQC3qVOgHfKEsKEsERFRTccAVANtOnwZ7x85D5/wt6FxE3e1H+PeDY9ntYAEMbBYGzSApW07hzs/8qXLMI4dCzn5lHgSnQ65459F2JNDKuQaiIiIqhIDUA3zXUoOFn1zBp4hn0FruCDUTXLriX/mOr6gbGnWHNboaKDMXRz70aMwjnsWSk6ZmV7+/jCsXokr7u7OvwAiIqJqgC9B1yDHrxRi2tZk6HwSofc9fL1CAWZq+uKfBWL4USQJ5pj2sDZr5hB+rP/7GsVDhjqEH1VUFNw//ADqVq0q7DqIiIiqGgNQDZGeZ8LYj47Bpk6FZ8jW0nKVIuF5qS/uLW4itFdUKpg7dYatQQOHY1k/3gLTpMkOe3ppeveC+3/f4Z5eRER0x+MjsBrAbJMx8ZMTuGq6Ct96/4Uk2QEAGkWFF+190UsWd7aXNWqYO3eFHBTkcCzLWxthXrTYoVw3cgR048ZCUjETExHRnY8BqAaY//UZnLiSD5/w90qnu2sUFebZ+qOLIt7hses0sHTtAblWLaFcUZSSae6vvyEeXK2Gfu6L0N5/X4VeAxERUXXCAFTNfZqUgS0/X4Zn7Y+gdT8HAFArEuba+jmEH5teC0v33lC8vYVyxWaD+eVXYP1ws3hwrRb6JYuh7d2rQq+BiIioumEAqsZOXCnEyztOwqvue3DzOgag5J2ff9t7o6cibmNh8dDB1r2vw67uSm4ujJOeg/3gQfHgBgMMa1ZDEx9XoddARERUHTEAVVP5JhsmbPkZ+trvQOeZDACQFGCavQfukqOEthYPHWw974JiMAjl9pTTMI4dC+XCRfHgPj5wf+1VqFu2qNBrICIiqq74xms1JCsKnvvsKIp9XisNP1CAifauGCiLU92tBi1sPfo5hB/rN9+i+JFHHcKPFBIM93feZvghIiKXxjtA1dCq3Sdx0r4UOo/zJQUKMN7eGf+UxbV5rHoNrD36QrlhwUJFUWB59TVYVq12OK66bVvoly+FKoAbmhIRkWtjAKpmth47h48vvwCt+yUAJY+9Jtm7YZDcUmhn06lh7d4HiqdnaZliNMI0azZs23c4HFc7aBDcZs2EpNNW7AUQERHVAAxA1cgPF9Ox9Jdp0BouAygJP9PtPR0ee9m1aofZXnJGBozjnoX86zHxoBoN3GZMh/bhwdzQlIiI6DcMQNXE6auZmLZ/PNT6kvCjUiTMsvfCALmp0M6mU8ParRcUX9/SMvuvv8I4ZhyUrCyhreTnB/2yJdDExlb8BRAREdUgDEDVQHphDkbvHAdJVxJ+1IoKc+x90FduLLSzuWlK7vz4+JSWWb/aDtPMWYDZLLRVRUbCsGYVVKGhFX8BRERENQwDUBW7ZrqG4V+PgV1z/Z2fF+x90Kds+NFrYOnep/Sxl2KxwrJ6NSzr33Q4prpbVxgWLYR0w/tBREREdB0DUBWy2C0Y/r9JMEnXp6oPkWMcwo/VoC2Z7fVboJHPnYNx6jTIx447HFM79Em4TZoISa2u2M4TERHVYAxAVURRFIza8TJy5ZTSsg5yBEbY44V2VnddSfjx8ICiKLB+uBnmhYsAk0k8oEYD/QtzoH3g/sroPhERUY3GAFRFpn27EamWb0t/D1V88B/7XVDh+kwtm059Pfzk5sE4axbsu75zOJYUFAT94kXQxLSrjK4TERHVeAxAVWD+99/gcMFG/D4r3aBosch+DzwVXWkbBYC1Q2coHh6QL16EceQoyOfOORxL07cP9C/MgXTDrDAiIiL6awxAlezVA79gR9ZiqDT2kgIFmG3vjfqyn9DO0rIV5OAQ2I8dg/GZ0VBycsQDubtDP2smNPcN5Po+REREt4gBqBJt+jEN76W+DK2hsLRsuD0WvWRxZ3drWBhsTZrA9v1uGCdNBoxGoV7VsiUMC+dDFR5eKf0mIiK603Az1Eqy61Q21v66BFrD9RlfT9ljMVyOE9rZfbxhaR8LS8LHMI4d5xB+NP3vgvs7Gxl+iIiIyoF3gCrBySsFmLN/CfS+R0oKFOBpexyeksUVmhWtFqaOnWFeuw6WV19zOI522NCSKe4q5lYiIqLyYACqYNmFFoz733JoffeUFCjAM/Z4PCm3F9opGg2M8R1gfOll2D7/QjyIJMFt1kzoHn2kknpNRER0Z2MAqkBmm4zhX6yG7P3b7uwKMNreEU/I4nR1RaOBsW1bFM2cBfuhRPEgbm7QL1oIbe9eldRrIiKiOx8DUAVRFAWjt61HgX5LadkoewfH8KPVorhxExSNmwD5zBmhTvLzg2HtaqhbtaqUPhMREbkKBqAK8vyuj3BOfrd0rZ8B9qYYIscIbRStFoXBwSgeNQZKdrZQJ0VEwP3VdVBF8GVnIiIiZ2MAqgDv/3II+3LXQlIpAIAoOQhT7d2FNopWi3ybHcYxzzru5N6mNQyrV0HlJ64NRERERM7BAORkP6dfxBvJcyFpbAAAP8WA+ba74XbDUMuShLzTZ2F+a6PD5zX9+kE/72VIen1ldZmIiMjlMAA50bXiYjy3dwYkbR4AQK2o8LLtLoTAq7SNYrHi2u49sO7d5/B5TnMnIiKqHAxATmKXZTy9fS5k7fX9usbaO6KtEnq9TV4+rn7yGeypqeKHuZM7ERFRpWIAcpJp327ANdXe0t/726PwiNym9Hfrpcu4tvkjyHn5wuckPz/oV66Apl3bSusrERGRq2MAcoKNR7/DkcJ3Smd8xcphmGnvWVpvOnYcuZ98BthswudUkZEwrF0NVd26ldldIiIil8cAVE6HL6XhnTOvQFLLAIAmciDm2+6GFmooioKiPXtRuPM7h8+pe3SHYeECSB4eldxjIiIiYgAqh2vFJszY929IumIAQF3FG8ts98IdOig2G/K2fgFT0i8On9MOGwq3iRMgqdWV3WUiIiICA9Bts8sKnvlqOWTdWQBALcWA5daBqAV3yEVFuPbBR7BeuCB+SKOBfs7z0D74QBX0mIiIiH7HAHSbXtz5FbJV2wAA7ooWS2z3IAy+sGVm4dp7H8Cemyt+wMcHhhXLoImN/YOjERERUWViALoNn/56BvvyVgFqBWpFhVds/dFUCYb59BnkfvQxlLIrO9evD8PaNdzWgoiIqJpgALpFyRmFWJH0ClTuBQCAifYuiFciUJx4GPlfbQcURWivjo+HYdkSSD4+VdFdIiIi+gMMQLegwGTD2O1rofJJBgA8ZG+JB23Nkb9jB4oPJTq01w4aBLfZMyFptZXdVSIiIvoLDEA3SVEUjP38U8g+nwMoWetngrkD8j75BKZjx8XGkgS3qVOgfeJfkH5fHIiIiIiqDQagm7Rs74+4rHkVgIJ6ih9eKuyJ/A8/hOVcqtjQYIBh8SJoenSvgl4SERHRzWAAugl7z17G9syXIatN8Fb0WHStJyybPoQt/YrQTgoKgmHdWqibRlVRT4mIiOhmMAD9jcxCE17+cQ7s2iz4KHosz+wO/TtbYLt2TWinql8fhtdfg6punSrqKREREd0sBqC/YJdljN6xEBbtCQQqHlh1Nh7u72+FvbhYaKdq0QKGV9dC5edXRT0lIiKiW8EA9BemffMhrqn/h3DFFyt+joa05TPIZTY0VXfqCMPy5ZA83Kuol0RERHSrVFXdgerq4+M/4xfja2hiD8Ca7yMgbf7SYTd3zd13w7BmDcMPERFRDcM7QH/i3TNz0coShLlfuMH6w/cO9bqnn4Zu/DhIKmZIIiKimoYB6E+0KtJi+gcmWE+VWeNHpYLbnOehG/RQ1XSMiIiIyo0B6E9MW58N2+Uy09wNBuhXLoemU6cq6hURERE5g0s9v1m/fj1atmyJ4OBgdOvWDfv37//TtvYy4UcV4A/D+5sYfoiIiO4ALhOAtmzZgunTp2Py5MnYvXs3YmNjMWjQIFy4cOFvP6uuXx+GhI+gbty4EnpKREREFc1lAtCaNWvw6KOPYsiQIWjSpAkWLVqE4OBgbNiw4S8/p2nTBobNH0AVFFRJPSUiIqKK5hIByGKx4OjRo+jZs6dQ3rNnTxw6dOhPP6fu1R36jRsgeXhUdBeJiIioErnES9A5OTmw2+0IDAwUygMDA5GZmfmHn7l6dw9YRowDUlMroYfVV0pKSlV3odrgWFzHsbiOY3Edx+I6joWoKsYjMjLyL+tdIgDdjojFq6q6C1UuJSXlb79AroJjcR3H4jqOxXUci+s4FqLqOh4u8QjM398farUaWVlZQnlWVhaC+G4PERGRy3GJAKTT6dC6dWvs2rVLKN+1axfi4uKqqFdERERUVVzmEdiYMWMwcuRItGvXDnFxcdiwYQOuXLmCoUOHVnXXiIiIqJK5TAB64IEHcPXqVSxatAgZGRlo2rQpNm/ejPDw8KruGhEREVUylwlAADB8+HAMHz68qrtBREREVcwl3gEiIiIiuhEDEBEREbkcBiAiIiJyOQxARERE5HIYgIiIiMjlMAARERGRy2EAIiIiIpfDAEREREQuhwGIiIiIXA4DEBEREbkcBiAiIiJyOQxARERE5HIYgIiIiMjlSLm5uUpVd4KIiIioMvEOEBEREbkcBiAiIiJyOQxARERE5HIYgIiIiMjlMAARERGRy2EA+s369evRsmVLBAcHo1u3bti/f39Vd8np9u3bh4cffhhNmzaFr68vNm3aJNQrioJ58+YhKioKISEhGDBgAE6cOCG0yc3NxYgRIxAeHo7w8HCMGDECubm5lXkZTrF06VL06NEDYWFhaNiwIQYPHozjx48LbVxlPN544w107NgRYWFhCAsLQ58+fbBjx47SelcZhz+ydOlS+Pr6YsqUKaVlrjIe8+bNg6+vr/DTuHHj0npXGYffXblyBc888wwaNmyI4OBgxMXFYe/evaX1rjIeLVq0cPhe+Pr64p///Gdpm7/7e2o2mzFlyhQ0aNAAderUwcMPP4xLly5V9qUwAAHAli1bMH36dEyePBm7d+9GbGwsBg0ahAsXLlR115yqqKgI0dHRmD9/PgwGg0P9ihUrsGbNGixYsAA7d+5EYGAg7r//fhQUFJS2GT58OJKSkpCQkICEhAQkJSVh5MiRlXkZTrF371489dRT2LFjB7Zu3QqNRoP77rsP165dK23jKuNRp04dvPjii/j++++xa9cudO3aFY899hh+/fVXAK4zDmX98MMP2LhxI5o1ayaUu9J4REZGIjk5ufTnxj9krjQOubm56NevHxRFwebNm3Ho0CEsXLgQgYGBpW1cZTx27dolfCe+//57SJKE++67D8DN/T2dMWMGPv/8c7z55pv48ssvUVBQgMGDB8Nut1fqtXAdIAC9evVCs2bNsHLlytKytm3bYuDAgZgzZ04V9qzi1K1bFwsXLsRjjz0GoOT/vURFReHpp5/Gc889BwAwGo2IjIzEf/7zHwwdOhTJycmIi4vD9u3bER8fDwA4cOAA+vfvjx9++AGRkZFVdj3lVVhYiPDwcGzatAn9+/d3+fGoV68e5syZgyeffNIlxyEvLw/dunXDypUrsWDBAkRHR2PRokUu9b2YN28etm7digMHDjjUudI4AMDcuXOxb98+4c7ojVxtPG60ePFirFy5EsnJyTAYDH/79zQvLw+NGjXCmjVrSu8aXbx4ES1atEBCQgJ69epVaX13+TtAFosFR48eRc+ePYXynj174tChQ1XUq8qXlpaGjIwMYRwMBgM6duxYOg6JiYnw9PREXFxcaZv4+Hh4eHjU+LEqLCyELMvw9fUF4LrjYbfb8fHHH6OoqAixsbEuOw4TJkzAwIED0bVrV6Hc1cYjNTUVUVFRaNmyJYYNG4bU1FQArjcO27ZtQ7t27TB06FA0atQInTt3xuuvvw5FKbl/4Grj8TtFUfDuu+9i8ODBMBgMN/X39OjRo7BarUKb0NBQNGnSpNLHQVOpZ6uGcnJyYLfbhVuZABAYGIjMzMwq6lXly8jIAIA/HIf09HQAQGZmJvz9/SFJUmm9JEkICAio8WM1ffp0tGjRArGxsQBcbzyOHTuGvn37wmQywcPDA//973/RrFkjl3UAAAAL6ElEQVSz0n+QXGUcAODtt9/G2bNn8frrrzvUudL3IiYmBmvXrkVkZCSys7OxaNEi9O3bFwcPHnSpcQBKguCbb76J0aNHY8KECfjll18wbdo0AMCIESNcbjx+t2vXLqSlpeGJJ54AcHN/TzMzM6FWq+Hv7/+nbSqLywcgopkzZ+LgwYPYvn071Gp1VXenSkRGRmLPnj3Iz8/HZ599hlGjRuGLL76o6m5VupSUFMydOxfbt2+HVqut6u5UqT59+gi/x8TEoHXr1njvvffQvn37KupV1ZBlGW3atCl9JaJVq1Y4e/Ys1q9fjxEjRlRx76rO22+/jbZt26JFixZV3ZXb4vKPwPz9/aFWq5GVlSWUZ2VlISgoqIp6VfmCg4MB4C/HISgoCDk5OaW3fYGSW6DZ2dk1dqxmzJiBjz/+GFu3bkW9evVKy11tPHQ6HRo0aIDWrVtjzpw5aNGiBdauXety45CYmIicnBzEx8fD398f/v7+2LdvH9avXw9/f3/UqlULgOuMx408PT0RFRWFs2fPutz3Ijg4GE2aNBHKGjdujIsXL5bWA64zHkDJtX355ZcYMmRIadnN/D0NCgqC3W5HTk7On7apLC4fgHQ6HVq3bo1du3YJ5bt27RKe1d7pIiIiEBwcLIyDyWTCgQMHSschNjYWhYWFSExMLG2TmJiIoqKiGjlW06ZNKw0/N07vBVxzPG4kyzIsFovLjcOAAQOwf/9+7Nmzp/SnTZs2ePDBB7Fnzx40atTIpcbjRiaTCSkpKQgODna570V8fDxOnz4tlJ0+fRphYWEAXPPfi/feew9ubm548MEHS8tu5u9p69atodVqhTaXLl0qfUm8MqmnT5/+QqWesRry8vLCvHnzEBISAr1ej0WLFmH//v1YvXo1fHx8qrp7TlNYWIiTJ08iIyMD7777LqKjo+Ht7Q2LxQIfHx/Y7XYsX74cDRs2hN1ux6xZs5CRkYHly5fDzc0NAQEBOHz4MBISEtCiRQtcunQJEydORNu2bWvcVM7nnnsOH3zwATZu3IjQ0FAUFRWhqKgIQMl/xJIkucx4vPDCC9DpdJBlGZcuXcK6deuwefNmvPDCC6XX7grjAAB6vR6BgYHCz0cffYTw8HA89thjLvW9mD17dun34vTp05gyZQrOnj2LZcuWwdfX12XGASh5SXfBggVQqVQICQnB999/j5deegkTJ05Eu3btXOp7AZTcuRozZgz69euHgQMHCnV/9/dUr9fjypUrWL9+PZo1a4a8vDxMnDgR3t7eePHFF6FSVd59GU6D/8369euxYsUKZGRkoGnTpnjllVfQqVOnqu6WU+3Zswf33HOPQ/kjjzyCdevWQVEUzJ8/Hxs3bkRubi7atWuHxYsXIzo6urRtbm4upk6diq+++goA0L9/fyxcuLB09lRN8Wf9nTZtGmbMmAEALjMeo0aNwp49e5CZmQlvb280a9YMzz77bOl0VFcZhz8zYMCA0mnwgOuMx7Bhw7B//37k5OQgICAAMTExmDVrFqKiogC4zjj8bseOHZg7dy5Onz6N0NBQPP300xg5cmTpS82uNB67d+/Gvffei2+//Rbt2rVzqP+7v6dmsxmzZ89GQkICTCYTunbtiiVLliA0NLQyL4MBiIiIiFyPy78DRERERK6HAYiIiIhcDgMQERERuRwGICIiInI5DEBERETkchiAiIiIyOUwABGRS/H19cW8efOcesxNmzbB19cXaWlpTj0uEVUcBiAicprfg8DvP/7+/oiOjsbo0aNx+fLlqu5euRQXF2PevHnYs2dPVXeFiJyAu8ETkdNNnz4d9evXh9lsxsGDB/HBBx9g3759OHDgANzd3au6e7fFaDRiwYIFAIAuXboIdQ8//DAefPBBuLm5VUXXiOg2MAARkdP16tUL7du3BwA88cQT8PPzw5o1a/Dll1/ioYcequLeOZ9arYZara7qbhDRLeAjMCKqcF27dgWA0ndk0tLSMHToUNSvXx8hISHo0aMHvvji/+3dT0jTbxwH8LfZkNFlzdSlUxurNrNwDTsk/RlFLLGx0kgLpXKHJQWNXTw4Q0oiO8xiSQ7/tPJiQpH5A4eHKEfR6qDSoWhIlhDVsqZ5GITb7xB+f35baagp/PZ+3fbZs+f72Q7y8Xk+z/f7j+gzPp8PMpkMXV1duHjxIrRaLdauXYvi4mIMDw+LxhYVFaGoqCjmulVVVdiyZcusuX39+hW1tbUoKCiAUqlERkaG8FT4aW/fvoVarQYANDQ0CFt8VVVVAH7fA9Td3Q2DwQCFQgGVSgWLxYLR0dGYHNPS0vD+/XscO3YMGRkZUKvVcDgcmJqamjV3Ipo/rgAR0V/35s0bAIBcLkcwGITRaMTk5CSsViuSk5PR1dWFiooKtLS0xKwQXblyBZFIBGfOnEEoFILb7YbJZMLjx4+xevXqBec2MjKC7u5uHDp0COvWrcP4+Dg6Ojpw8OBBPHjwAJs3b8aaNWvgdDpht9tx4MAB4aHCKpXqt/Pevn0bVqsVOp0O586dw9jYGNxuN54+fYr+/n4kJycLYyORCA4fPgy9Xo8LFy7g4cOHuHbtmlA0EdHiYwFERItuYmICY2NjCIfD8Pv9uHz5MqRSKYxGIxobG/Hhwwf09PQIvTQnT56EwWBATU0NzGYzJBKJMFcwGMTz58+FJ2bv3LkTZrMZTU1NcDgcC85106ZNGBwcxIoV/y2InzhxAtu2bYPb7YbL5cKqVatgNptht9uRm5uL0tLSWef8/v07amtrodFo0NvbC6lUCgAwGAwwmUxobGxEfX29aLzZbEZ1dTWAH09i37VrFzo6OlgAEf0l3AIjokVXUlICtVqN3NxcVFZWIjU1FZ2dnUhPT0dfXx/y8vJEjcRSqRQWiwUfP37E0NCQaK6ysjKh+AGA3bt3IycnB16vd1FyTUpKEoqfcDiML1++YGpqCnq9HoODg/Oac2BgAJ8+fUJlZaVQ/AA/ijedToe+vr6Yzxw/flz0evv27RgZGZnX9YloblwBIqJF19DQAI1Gg6SkJCiVSiiVSiQkJAAARkdHhS2kmTQaDQDg3bt3yM/PF+LTvTczqdVq9Pf3L0qukUgEV69ehcfjienhyc7Ontec030+GzZsiHlv48aNuH//vigmkUigUChEMZlMhlAoNK/rE9HcWAAR0aLT6/XCKbClkJCQgGg0GhP/kyZip9OJ+vp6HD16FA6HA3K5HImJiXA6nULv0t82c/uNiJYGCyAiWlKZmZkIBAIx8devXwMAsrKyRPGfT3xNx2aOk8lkv9wu+vnE1a/cu3cPO3bswPXr10Xxn+8WPb2C9ScyMzMBAIFAAHv27BG9FwgEYr4jES09/ttBREvKaDRiaGhIdMw8HA6jvb0daWlp0Ol0ovGdnZ2iraBHjx7h5cuXMBqNQkylUiEQCODz589C7MWLF/D7/XPmk5iYGLN65Pf78ezZM1FsupfnT7altm7ditTUVHg8HoTDYSH+5MkTDAwMiHInouXBFSAiWlI2mw137txBaWmp6Bj8q1ev0NLSgpUrxX+WUlJSsH//fpSXl2N8fBzNzc1QKBQ4ffq0MKa8vBxNTU0oLi5GRUUFgsEgbty4Aa1Wi2/fvs2aT2FhIS5dugSr1YqCggIMDw/D4/FAq9VicnJSGCeVSpGTk4O7d+9i/fr1kMvlyM7OFvUrTZNIJDh//jxOnTqFwsJCHDlyRDgGn56eDpvNtsBfkYgWiitARLSkUlJS4PV6sXfvXrS2tqKurg7RaBS3bt365V2ibTYbTCYTXC4XXC4X8vPz0dPTA7lcLozRaDRobm7GxMQEampq0NvbC7fbjby8vDnzsdvtOHv2LHw+H6qrq+Hz+dDe3h6zEgUALpcLWVlZcDgcsFgsaGtr++28ZWVluHnzJqLRKOrq6tDa2op9+/bB6/WK7gFERMsjIRQKxXYOEhEtM5/PB5PJhLa2NpSUlCx3OkT0P8MVICIiIoo7LICIiIgo7rAAIiIiorjDHiAiIiKKO1wBIiIiorjDAoiIiIjiDgsgIiIiijssgIiIiCjusAAiIiKiuMMCiIiIiOLOvzMC0UFvvFqoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `causalml` Synthetic Data Generation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:33:52.406736Z",
     "start_time": "2020-02-10T18:33:39.032433Z"
    }
   },
   "outputs": [],
   "source": [
    "y, X, w, tau, b, e = simulate_nuisance_and_easy_treatment(n=1000)\n",
    "\n",
    "X_train, X_val, y_train, y_val, w_train, w_val, tau_train, tau_val, b_train, b_val, e_train, e_val = \\\n",
    "    train_test_split(X, y, w, tau, b, e, test_size=0.2, random_state=123, shuffle=True)\n",
    "\n",
    "preds_dict_train = {}\n",
    "preds_dict_valid = {}\n",
    "\n",
    "preds_dict_train['Actuals'] = tau_train\n",
    "preds_dict_valid['Actuals'] = tau_val\n",
    "\n",
    "preds_dict_train['generated_data'] = {\n",
    "    'y': y_train,\n",
    "    'X': X_train,\n",
    "    'w': w_train,\n",
    "    'tau': tau_train,\n",
    "    'b': b_train,\n",
    "    'e': e_train}\n",
    "preds_dict_valid['generated_data'] = {\n",
    "    'y': y_val,\n",
    "    'X': X_val,\n",
    "    'w': w_val,\n",
    "    'tau': tau_val,\n",
    "    'b': b_val,\n",
    "    'e': e_val}\n",
    "\n",
    "# Predict p_hat because e would not be directly observed in real-life\n",
    "p_model = ElasticNetPropensityModel()\n",
    "p_hat_train = p_model.fit_predict(X_train, w_train)\n",
    "p_hat_val = p_model.fit_predict(X_val, w_val)\n",
    "\n",
    "for base_learner, label_l in zip([BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor],\n",
    "                                 ['S', 'T', 'X', 'R']):\n",
    "    for model, label_m in zip([LinearRegression, XGBRegressor], ['LR', 'XGB']):\n",
    "        # RLearner will need to fit on the p_hat\n",
    "        if label_l != 'R':\n",
    "            learner = base_learner(model())\n",
    "            # fit the model on training data only\n",
    "            learner.fit(X=X_train, treatment=w_train, y=y_train)\n",
    "            try:\n",
    "                preds_dict_train['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_train, p=p_hat_train).flatten()\n",
    "                preds_dict_valid['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_val, p=p_hat_val).flatten()\n",
    "            except TypeError:\n",
    "                preds_dict_train['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_train, treatment=w_train, y=y_train).flatten()\n",
    "                preds_dict_valid['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_val, treatment=w_val, y=y_val).flatten()\n",
    "        else:\n",
    "            learner = base_learner(model())\n",
    "            learner.fit(X=X_train, p=p_hat_train, treatment=w_train, y=y_train)\n",
    "            preds_dict_train['{} Learner ({})'.format(\n",
    "                label_l, label_m)] = learner.predict(X=X_train).flatten()\n",
    "            preds_dict_valid['{} Learner ({})'.format(\n",
    "                label_l, label_m)] = learner.predict(X=X_val).flatten()\n",
    "\n",
    "learner = DragonNet(verbose=False)\n",
    "learner.fit(X_train, treatment=w_train, y=y_train)\n",
    "preds_dict_train['DragonNet'] = learner.predict_tau(X=X_train).flatten()\n",
    "preds_dict_valid['DragonNet'] = learner.predict_tau(X=X_val).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:33:56.690526Z",
     "start_time": "2020-02-10T18:33:56.560904Z"
    }
   },
   "outputs": [],
   "source": [
    "actuals_train = preds_dict_train['Actuals']\n",
    "actuals_validation = preds_dict_valid['Actuals']\n",
    "\n",
    "synthetic_summary_train = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_train)] for label, preds\n",
    "                                        in preds_dict_train.items() if 'generated' not in label.lower()},\n",
    "                                       index=['ATE', 'MSE']).T\n",
    "synthetic_summary_train['Abs % Error of ATE'] = np.abs(\n",
    "    (synthetic_summary_train['ATE']/synthetic_summary_train.loc['Actuals', 'ATE']) - 1)\n",
    "\n",
    "synthetic_summary_validation = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_validation)]\n",
    "                                             for label, preds in preds_dict_valid.items()\n",
    "                                             if 'generated' not in label.lower()},\n",
    "                                            index=['ATE', 'MSE']).T\n",
    "synthetic_summary_validation['Abs % Error of ATE'] = np.abs(\n",
    "    (synthetic_summary_validation['ATE']/synthetic_summary_validation.loc['Actuals', 'ATE']) - 1)\n",
    "\n",
    "# calculate kl divergence for training\n",
    "for label in synthetic_summary_train.index:\n",
    "    stacked_values = np.hstack((preds_dict_train[label], actuals_train))\n",
    "    stacked_low = np.percentile(stacked_values, 0.1)\n",
    "    stacked_high = np.percentile(stacked_values, 99.9)\n",
    "    bins = np.linspace(stacked_low, stacked_high, 100)\n",
    "\n",
    "    distr = np.histogram(preds_dict_train[label], bins=bins)[0]\n",
    "    distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n",
    "    true_distr = np.histogram(actuals_train, bins=bins)[0]\n",
    "    true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n",
    "\n",
    "    kl = entropy(distr, true_distr)\n",
    "    synthetic_summary_train.loc[label, 'KL Divergence'] = kl\n",
    "\n",
    "# calculate kl divergence for validation\n",
    "for label in synthetic_summary_validation.index:\n",
    "    stacked_values = np.hstack((preds_dict_valid[label], actuals_validation))\n",
    "    stacked_low = np.percentile(stacked_values, 0.1)\n",
    "    stacked_high = np.percentile(stacked_values, 99.9)\n",
    "    bins = np.linspace(stacked_low, stacked_high, 100)\n",
    "\n",
    "    distr = np.histogram(preds_dict_valid[label], bins=bins)[0]\n",
    "    distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n",
    "    true_distr = np.histogram(actuals_validation, bins=bins)[0]\n",
    "    true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n",
    "\n",
    "    kl = entropy(distr, true_distr)\n",
    "    synthetic_summary_validation.loc[label, 'KL Divergence'] = kl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:50:11.614036Z",
     "start_time": "2020-02-10T18:50:11.451083Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_train = pd.DataFrame([preds_dict_train['S Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['S Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['T Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['T Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['X Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['X Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['R Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['R Learner (XGB)'].ravel(),                               \n",
    "                               preds_dict_train['DragonNet'].ravel(),\n",
    "                               preds_dict_train['generated_data']['tau'].ravel(),\n",
    "                               preds_dict_train['generated_data']['w'].ravel(),\n",
    "                               preds_dict_train['generated_data']['y'].ravel()],\n",
    "                              index=['S Learner (LR)','S Learner (XGB)',\n",
    "                                     'T Learner (LR)','T Learner (XGB)',\n",
    "                                     'X Learner (LR)','X Learner (XGB)',\n",
    "                                     'R Learner (LR)','R Learner (XGB)',\n",
    "                                     'DragonNet','tau','w','y']).T\n",
    "\n",
    "synthetic_summary_train['AUUC'] = auuc_score(df_preds_train).iloc[:-1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:51:14.430442Z",
     "start_time": "2020-02-10T18:51:14.285976Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_validation = pd.DataFrame([preds_dict_valid['S Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['S Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['T Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['T Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['X Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['X Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['R Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['R Learner (XGB)'].ravel(),                               \n",
    "                               preds_dict_valid['DragonNet'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['tau'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['w'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['y'].ravel()],\n",
    "                              index=['S Learner (LR)','S Learner (XGB)',\n",
    "                                     'T Learner (LR)','T Learner (XGB)',\n",
    "                                     'X Learner (LR)','X Learner (XGB)',\n",
    "                                     'R Learner (LR)','R Learner (XGB)',\n",
    "                                     'DragonNet','tau','w','y']).T\n",
    "\n",
    "synthetic_summary_validation['AUUC'] = auuc_score(df_preds_validation).iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:51:38.876503Z",
     "start_time": "2020-02-10T18:51:38.811788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Abs % Error of ATE</th>\n",
       "      <th>KL Divergence</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actuals</th>\n",
       "      <td>0.484486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (LR)</th>\n",
       "      <td>0.528743</td>\n",
       "      <td>0.044194</td>\n",
       "      <td>0.091349</td>\n",
       "      <td>3.473087</td>\n",
       "      <td>0.492660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (XGB)</th>\n",
       "      <td>0.315706</td>\n",
       "      <td>0.060831</td>\n",
       "      <td>0.348369</td>\n",
       "      <td>0.423556</td>\n",
       "      <td>0.575274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (LR)</th>\n",
       "      <td>0.493815</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>0.019255</td>\n",
       "      <td>0.289978</td>\n",
       "      <td>0.610855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (XGB)</th>\n",
       "      <td>0.443124</td>\n",
       "      <td>0.359123</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>0.785408</td>\n",
       "      <td>0.544435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (LR)</th>\n",
       "      <td>0.493815</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>0.019255</td>\n",
       "      <td>0.289978</td>\n",
       "      <td>0.610855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (XGB)</th>\n",
       "      <td>0.364116</td>\n",
       "      <td>0.205326</td>\n",
       "      <td>0.248448</td>\n",
       "      <td>0.530261</td>\n",
       "      <td>0.554322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (LR)</th>\n",
       "      <td>0.473947</td>\n",
       "      <td>0.026080</td>\n",
       "      <td>0.021752</td>\n",
       "      <td>0.352075</td>\n",
       "      <td>0.613613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (XGB)</th>\n",
       "      <td>0.376447</td>\n",
       "      <td>0.499069</td>\n",
       "      <td>0.222997</td>\n",
       "      <td>0.847715</td>\n",
       "      <td>0.527988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DragonNet</th>\n",
       "      <td>0.408403</td>\n",
       "      <td>0.042945</td>\n",
       "      <td>0.157038</td>\n",
       "      <td>0.434239</td>\n",
       "      <td>0.612939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ATE       MSE  Abs % Error of ATE  KL Divergence  \\\n",
       "Actuals          0.484486  0.000000            0.000000       0.000000   \n",
       "S Learner (LR)   0.528743  0.044194            0.091349       3.473087   \n",
       "S Learner (XGB)  0.315706  0.060831            0.348369       0.423556   \n",
       "T Learner (LR)   0.493815  0.022688            0.019255       0.289978   \n",
       "T Learner (XGB)  0.443124  0.359123            0.085374       0.785408   \n",
       "X Learner (LR)   0.493815  0.022688            0.019255       0.289978   \n",
       "X Learner (XGB)  0.364116  0.205326            0.248448       0.530261   \n",
       "R Learner (LR)   0.473947  0.026080            0.021752       0.352075   \n",
       "R Learner (XGB)  0.376447  0.499069            0.222997       0.847715   \n",
       "DragonNet        0.408403  0.042945            0.157038       0.434239   \n",
       "\n",
       "                     AUUC  \n",
       "Actuals               NaN  \n",
       "S Learner (LR)   0.492660  \n",
       "S Learner (XGB)  0.575274  \n",
       "T Learner (LR)   0.610855  \n",
       "T Learner (XGB)  0.544435  \n",
       "X Learner (LR)   0.610855  \n",
       "X Learner (XGB)  0.554322  \n",
       "R Learner (LR)   0.613613  \n",
       "R Learner (XGB)  0.527988  \n",
       "DragonNet        0.612939  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_summary_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:51:59.996579Z",
     "start_time": "2020-02-10T18:51:59.909264Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Abs % Error of ATE</th>\n",
       "      <th>KL Divergence</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actuals</th>\n",
       "      <td>0.511242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (LR)</th>\n",
       "      <td>0.528743</td>\n",
       "      <td>0.042236</td>\n",
       "      <td>0.034233</td>\n",
       "      <td>4.574498</td>\n",
       "      <td>0.494022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (XGB)</th>\n",
       "      <td>0.341574</td>\n",
       "      <td>0.066174</td>\n",
       "      <td>0.331874</td>\n",
       "      <td>0.779572</td>\n",
       "      <td>0.567832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (LR)</th>\n",
       "      <td>0.541503</td>\n",
       "      <td>0.025840</td>\n",
       "      <td>0.059191</td>\n",
       "      <td>0.686602</td>\n",
       "      <td>0.604712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (XGB)</th>\n",
       "      <td>0.467758</td>\n",
       "      <td>0.303262</td>\n",
       "      <td>0.085055</td>\n",
       "      <td>0.942250</td>\n",
       "      <td>0.550549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (LR)</th>\n",
       "      <td>0.541503</td>\n",
       "      <td>0.025840</td>\n",
       "      <td>0.059191</td>\n",
       "      <td>0.686602</td>\n",
       "      <td>0.604712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (XGB)</th>\n",
       "      <td>0.364071</td>\n",
       "      <td>0.164907</td>\n",
       "      <td>0.287869</td>\n",
       "      <td>0.648777</td>\n",
       "      <td>0.555098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (LR)</th>\n",
       "      <td>0.526938</td>\n",
       "      <td>0.029887</td>\n",
       "      <td>0.030702</td>\n",
       "      <td>0.739050</td>\n",
       "      <td>0.607303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (XGB)</th>\n",
       "      <td>0.428291</td>\n",
       "      <td>0.324614</td>\n",
       "      <td>0.162253</td>\n",
       "      <td>0.732380</td>\n",
       "      <td>0.536405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DragonNet</th>\n",
       "      <td>0.460422</td>\n",
       "      <td>0.041291</td>\n",
       "      <td>0.099405</td>\n",
       "      <td>0.843017</td>\n",
       "      <td>0.606557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ATE       MSE  Abs % Error of ATE  KL Divergence  \\\n",
       "Actuals          0.511242  0.000000            0.000000       0.000000   \n",
       "S Learner (LR)   0.528743  0.042236            0.034233       4.574498   \n",
       "S Learner (XGB)  0.341574  0.066174            0.331874       0.779572   \n",
       "T Learner (LR)   0.541503  0.025840            0.059191       0.686602   \n",
       "T Learner (XGB)  0.467758  0.303262            0.085055       0.942250   \n",
       "X Learner (LR)   0.541503  0.025840            0.059191       0.686602   \n",
       "X Learner (XGB)  0.364071  0.164907            0.287869       0.648777   \n",
       "R Learner (LR)   0.526938  0.029887            0.030702       0.739050   \n",
       "R Learner (XGB)  0.428291  0.324614            0.162253       0.732380   \n",
       "DragonNet        0.460422  0.041291            0.099405       0.843017   \n",
       "\n",
       "                     AUUC  \n",
       "Actuals               NaN  \n",
       "S Learner (LR)   0.494022  \n",
       "S Learner (XGB)  0.567832  \n",
       "T Learner (LR)   0.604712  \n",
       "T Learner (XGB)  0.550549  \n",
       "X Learner (LR)   0.604712  \n",
       "X Learner (XGB)  0.555098  \n",
       "R Learner (LR)   0.607303  \n",
       "R Learner (XGB)  0.536405  \n",
       "DragonNet        0.606557  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_summary_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:55:09.540589Z",
     "start_time": "2020-02-10T18:55:09.083685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pR5xlCo0Drw4F2BpoWHzkVZKL5D5JKpWDxs9FULN1CO1DDGhVt1YMgRBEAoFAIBDc80iSREluIYWnUyg1ef4vPpD3FdsosZT7A+lyMQb4oApqiqR3zU/557ki5vyUyNGzRbJyheSg/8m9jP59A+5Wk7Nc7W3Fv282aVJtPjzwEgUW+Ty0emgyqhP+jXyJDDFgUMv9iW4VQhAJBAKBQHCPYjdbyTt1gpJcDWbJC/C7or1OkY+nOhWjmwlDjQY4fCJAVR7D7tKghbmlVhbuPsM3hzNc6urnn2Xib1/QIvO0vP8aJnwfzOFgXiRf/DXM5SaZMdBAs5HtcQ9yo12wEXftrQ/IeAEhiAQCgUAguMcoPZ9EcdJ5Cq0hOPC/oq1RmYmPOhG9wYSmdksc3j1AqcFxGXtJkvjmSAbzd56h0GST1WntVp4/upUnj/2Eyi6vMzYqwfP+fDYl/YPtKfLMAQA+zQNoMqwVGoOGNkEGfPS3TwyBEESCasDp06fp27cvcXFxVRqN/IcffmDGjBns2bMHpfL2bAELBILqg1SWR+Gp4xQVuFPqCADqXtZWoyjBV30KN10O6tph4NcflOrLiqALJOWUMmPbKQ6mFrrUNc9KYtaBzwjIPnvpzDBEFqJvbmPZ0bEcz3XNGVmzZ33q/V8TFEoF4QF6Ao23X56IT+W7nOzsbMaPH094eDiBgYE0atSI/v37s3Pnzsu2iYmJwdvbm5ycW5so705h+vTpjBgxwimGVq1aRe3atUlOTpbZzZw5k2bNmpGXdzFWRmZmJpMmTaJt27YEBQURGhpK7969WbZsGcXFxU678PBwvL298fb2xtfXl2bNmvHaa69RVHTxTL1Pnz6oVCq++uqrW7xigUBQbXBY8SxNJeu3GE4fKCAjr/7/xFCFxnipzlBHv5c69bPw6tALddunIaANKK8sQMw2B0tikhn08R8uYkhtt/HGie9Y9kO0ixhSaBxIA/KxhroRfXCSixhSqJU0frol9fs3RaFU0MhHSy0P+THa7ULsEN3lPPPMM5SVlbFo0SLq169PdnY2v/zyC7m5uVU9NRkOhwNJklCpbs0WqNVqRaNx/SVKT0/nu+++Y+bMmc6y4cOHs2XLFl566SW2bNmCQqEgLi6O9957jzVr1uDj4wNAcnIyffv2xcPDg7feeouwsDD0ej1//fUXn376Kb6+vjz++OPOfidMmMCIESOw2+2cPHmSl19+GYVCwbvvvuu0GTp0KMuWLeOJJ564Jc9BIBBUE8z5lCb+SV6ukRJ70yuaqhWl+CgTMfgr0DXogkLT+ZqHsTskvj+exZKYZPk1+v/RrPAs0Qc/xzs10XWKDcx4d8/jXG5TVsSNdEnDofXS0WxEWzzqljtV1/bQEOqtvea5VTZCEF2FVnP23tbxDr/Z5Zpt8/PziY2NZePGjXTt2hWAOnXq0LZt25uex+eff87ChQs5c+YMtWrV4vnnn2f06NHOo55FixaxevVqzpw5g5eXFw8++CAzZszA27v8jf3FF18wYcIEVqxYwdtvv83JkyeJiYlh4cKF5Obm0q1bNxYsWEBpaSn9+vXjnXfewWg0AuXn0wsWLGDFihWcP3+eBg0aEBUVxZAhQ4ByodKqVSs++ugjVq1axYEDB5g+fTqjRo1yWcemTZto1qwZtWvXlpUvWLCATp06sWTJEufann76aXr16uW0GT9+PEqlkp07d+Lm5uYsr1evHn379kWS5G6EHh4eBAWVZ3WuUaMGAwcOJDY2Vmbz0EMPMWHCBBITE2nQoMEN/d8IBIJqiiRBYQoFp0+TV1IDi3TlzxB3xTncDLkYG7dA49n7OoeS2H0ql4V7kjmVVepSr7bbePNcDH32rEdhk/sKoZTI/b9iWgQVsDu1B+sTBrtEnvao502z59ug9Sp32A4wqgjz16G4RVGorwUhiO5i3N3dcXd3Z+vWrXTs2LHSstmvWrWK//znP8ydO5dWrVpx4sQJoqKi0Gg0TtGhVCqZPXs29erVIzU1lQkTJjBhwgQ+/PBDZz8mk4no6Gjmz5+Pv7+/UyzExsYSFBTExo0bSU9P59lnnyU0NJRx48YB5UdXmzZt4p133iE0NJQDBw4QFRWFt7c3ffpcdMSbNm0aM2fOZOHChRXuDgHs27ePNm3auJSHhIQQHR3N2LFj+e2337BYLLJdpNzcXLZv387UqVNlYujvXOkXNzU1lR07dtCli1zg1q5dm8DAQPbu3SsEkUAguDYkO46M4+QnZ5FrboCDy+8IaSjFkzS0Ndxwb9AWhfL6d+UPpRXy7s4kDqcXVVj/QEEikw+uxZiW7FJXWsuKsU8eYdhYd/IJdqf1cLEJjKxJ6OAwlJryuXnplLQJNKCsQjEEQhDd1ajVahYvXkxUVBSrVq2iZcuWdOjQgYEDB9KuXbsb7jc6Oppp06YxYMAAoHxHJCkpiY8//tgpiF566SWnfd26dZk+fTpPPfUUS5cude4i2e12oqOjad26tax/Dw8P5s+fj0qlokmTJgwcOJDdu3czbtw4SkpKWLx4MRs2bKBz587O8Q8ePMhHH30kE0SjRo1yzvFypKWl0apVqwrrHn/8cVauXMnmzZvZtGkT7u7uzrrExEQkSSI0NFTWpnnz5hQUFAAwePBg5s+f76ybMWMGc+bMwW63YzKZ6NSpE1OnTnUZNzg4mJSUlCvOWyAQCLCbsaX9Qd5ZC3nW+lzpyry7IwODNhd908YYfLvd0HBWu4MlMSms+C3N5Ro9gL+5iDmJW2l6oGIf1TPdzLRrlA02LcuOjeFYziUxixRQf2BTanSt5/yD0lOrpF2w4ZZHob4WhCC6yxkwYAB9+vQhNjaW/fv3s337dhYtWsSUKVNuKMN7dnY2aWlpvPbaa7L2NptNdkS0e/du5s+fz8mTJyksLMRut2OxWMjIyCAkJAQoF2zh4a5BvJo0aSLzJQoODiYuLg6A+Ph4TCYTgwYNku3AWK1W6tSpI+unop2fSzGZTJfdOTtx4gRxcXEYjUZ+/fVX57Hjldi6dSsOh4OoqChMJpOsbsyYMTzzzDNIkkRaWhozZsxg8ODBbNmyRXarzGAwuLQVCAQCJ3YTltP7yc3SUmivc1kzBXa8HWnYdYX4t+mMWn/jWeCTckqZ9G08JzJKXOoUksSbpUd46McvUBa53i4rNELK46X01eaSZ/LmgyNjSS+WuymodCqaPNsG3+YXHb5ruKsJ99ff0vxk14MQRFfhenx6qgq9Xk/37t3p3r07EydOZOzYscyZM4exY8ei1V6fg5rDUX7p8t1336VDhw4V2qSkpDBkyBCGDRvGv/71L3x9fTl8+DAjRozAYrE47XQ6XYVO1JcebykUCqfYujD+mjVrXPx+1Gr52/VyR1l/x9fXl/z8fJdym83Giy++yMMPP8yjjz7KiBEjePjhh527SQ0aNEChUJCQkCBrV69ePQCnv9OlY104BmvYsCFGo5FevXoRExMjE1t5eXn4+185LohAIKh+SDYzplP7ycs2UuwIvaydmjK8pDS0tbxwrx/JqdOJBN+gGJIkia/+OM+7O5Iw2Vwv3ff3dxC1ZyW6uP0Vtt8ZqaZuZA59zUWkFtXmg8MvU2DxkdnovPU0HxWBW01PZ1lTXx31vTRV6jN0KUIQ3YM0adIEm82GyWS6bkEUGBhISEgISUlJPPnkkxXa/PHHH1gsFmbPnu0UPNu2bbvpeUP53HU6Hampqde0Y3M1WrRoQXx8vEt5dHQ058+f55tvvsHX15eBAwcyevRodu3ahVarxdfXlx49erB8+XJGjRolO067Vi48m9LSiw6JJpOJpKSkyx7jCQSC6odkt1Ly137y89wpdTS8rJ1OKsRDfR5DkwYY/G/+8zG/zMqULSfZczrPpS7QXcNC9UlqLlsEJa67RslBSr4eoOYlzVkam00czQ7nk2MjsdjlO/LutT1pPjLC6TytVkLrQEOVxBm6GnfejATXTG5uLsOHD+fpp58mLCwMd3d3Dh06xIIFC+jatSuenp5XbH/8+HGXQIUtWrRg0qRJTJgwAS8vL3r37o3VauXw4cOcO3eOcePG0bBhQxwOB0uWLOGRRx4hLi6OpUuXVsqaPDw8GDt2LFOmTEGSJO677z6Ki4uJi4tDqVTy7LPPXld/3bt359VXX8Vmszl3mA4dOsS8efP47LPP8PX1BWDu3Ll06tSJ//73v0yZMgWAefPm0adPH7p168abb75JixYtUKvVHDp0iD///JPu3bvLxioqKiIjIwNJkkhPT2fq1Kn4+/vLdtoOHDiATqe77O6bQCCoPkh2G0Xxf5CfY8Qk1b+snVHKxaDPxT2sGTr3K1+xv1aOpBfyxqZ4zhe6XqV/LETBq3s+RhH7q0udSQNrHtST1cbK9JxkvC12dqV1Z93JIS43yXzDg2jyTEtUuvLPXr1KQWSIEXftnRkCUQiiuxg3Nzfat2/P0qVLSUxMxGKxEBISwqBBg3jjjTeu2v6RRx5xKUtLS2PYsGEYjUYWLFjA9OnT0ev1NGvWjJEjRwLlomnOnDm8//77zJo1i8jISGbMmMFzzz1XKet66623CAgIYNGiRYwfPx4PDw/Cw8OJioq67r569uyJwWBg+/bt9OnTB7PZzIsvvsjgwYN56KGHnHY+Pj68//77DB06lH79+tG2bVvq1avHnj17ePfdd5k1axbp6eloNBoaN27MiBEjnM/jAnPnzmXu3LkA+Pv707ZtWzZs2OAUXQDr16/n8ccfr/DITSAQVA8cNjuFCcfIy9ZjlWpe1s5dykDvUYpHWCs0OtfozjeCJEl8fuAs7+06g80hd532Vjl43/w7DRd8AWVlLm2PNFCz5FE9/ZU5vJWVBZKCdacGszP1QRfbGt3qUX9AebBFKN8Zah9iuGPFEIAiPz+/ImfyakdBQUGVpnW41VzJufhexmQysXr1ajZu3Mi3335bpXPJysoiMjKSnTt3On2RLkdlvB8TEhJo1KjRTfVxt1Jd115d1w13x9odNhsFCQnkZWuxSYbL2nmQjsHXjmezCJSqq0dtvta1F5psvL01gR0nXbMUPFV6ihd+W4sqLdWlzqSBVX0N7Gun5L95WbQpycVs17Ly2D85ki2/RaxQQMNBzQnucjFtiFIBkcEGfA139h7MnT07gaASGD58OHl5eVUuelNSUpg3b95VxZBAILi3kGxmChNOkJPtjk263GeQAy9FKoZADR6NIlBUYr5Du0Niy5+ZLIpJJrPIIqurVZjB3ITN1DkWV2HbP+upWPSYkRB3K+uzzuFhLqTQ4sHSwy+TXCQ/5tPoVDR9tjVezQNl5a0D9Xe8GAIhiATVAJVKdUMhCCqbiIgIIiIiqnoaAoHgdmEtpjjhGNk53likwApNFNjw4Qy6IDXujTpWqhAC+DUpj/k7kjh5abRpSeKJxBheOrAOpcXi0q7IoOCLXnp+bK9hvF3D4LMnUUh2zpcEs+TwK+SY5Ddl3Xz0NP5nBG615L6rYf46gt2qJjfZ9SIEkUAgEAgElYk5j7LEY2Tm+GJ21K7QRIkFP06j9VNgbPoAClXlfh3HZxQzf9cZYpNcw454lxUy6/c1tDr9h0udXQE/RGpZ00uPh7uBb4pt1Mw4DEBCXmOWHR1NmU0e8sSntieh/2yLzlt+DBjqraWuZ9XlJrtehCASCAQCgaASUJZlYEk+yvlsf0odFafmUWDDnwS0Xmb0YT1RqXWVOofUvDKWxKTw/fGsCqNNdz53jH//9hnGIleh9Gc9FR89YuRMiIqB2mAmpv2JpuQ8AAfOR/LZiWexS3LZEBwWQL1hrVDr5btAjX20NKzCRK03ghBEAoFAIBDcBEpTFrYzBziX40eRvXmFNgrs+DkS0fpYMDTvgkpzeafqGyGr2MLyX1NZf+i8y+0xADdLGbOSf6Rd7PcudaU6WP6IkZ1tNChQME9ThwcSfkThsCFJ8HNKHzaefsylXZ37alPrseYoVReP+ZRAeKCemu53xzHZ3xGCSCAQCASCG0BhyceWFMe5bA+K7K0vY+XA156C1seEW1inShdCdofE1yeK2fRtHCara6RpheRgdN5hhuz9ClW+awDGv+qomD/YSIavilpqd5aXKQg4s7V85pKCdQmD2Z3W06Vd4/6NCUlGwDQAACAASURBVOjRQBZpWqOEiKA7/zbZ5bg7Zy0QCAQCQVVhKcKa/AdZmUaKL7MjBOBlO4vBkIOxfSfUhisHyr0RCk023vw2nl8SXSNJA/Syn+f1A1/hdvK4S51dAV931/FVdz0OlYLH3EJ5PTkOTVEaABa7hlXHR3Aoq62snVKtIOzplni1qSErN6oVtAu+c4MuXgtCEAkEAoFAcC3YSrElHyTzvJFi++VzjbnbsvG2paFo0wRDQLtbMpXT2aW8uv44KXmuiaIbGGFO4lZq/LQZJNfjs/M+St4bbOSvumqCDcHMMTQi7NDHKOzlt81KrEaWHRnD6QJ5bCOdUU3YPyMwNvSVlXvryjPWa1V3rxgCIYgEAoFAILgytjIcaQfJOqumwHb5XGPu9hz8StKwNPLE0KhvpV+hv8CuhBz+tfkkJRa7rNzHqOHNkFK6fPIOUnKySzuzGjZ01fPNAzoknY6R9R5leEoc+uMfOG0ySwNYeuRlMkpD5GvzNdDshQh0wfIksv4GFW2DDKjvkIz1N4MQRIJ7ntWrV7NmzRo2b95caX1mZWXRsWNH9uzZQ82alw+9LxAI7mIkB5yNIzvFRK61IVDxl767PYvAwnQstZWo7nsAD61bhXY3i0OSWP5rKktiUlzqWgcbmZ//K5rpy5Hsdpf62OYaVvTTk+mjIjKoI296t6NWzDSUJeecNn/lNuPjP0dResm1er9anoSObIvmkmv1wW5qWgXqUd1BGetvhrt7f6ua4+3tfcWf0aNHV9hu9uzZdOrU6TbPtmqwWCzMnDmTiRMnOsuutv5+/fo5n2FAQACtW7dm2rRpmM0XkyAGBATwxBNPMHv27Fs6f4FAUDUoSs5RdugHTp0OJNcaSkViyMOeSaPcP/DVJOHo3g5j276ob5EYSskrY/TaYxWKoce8Cln847toPlwKl4ihdD8l/37Ojf8+7UZRgDsTw6KYX2ylzrYXnWJIkmBnag8WH37FRQyFNPWj0ZhIFzFU20NDm3tIDIHYIbqriY+Pd/77hx9+4JVXXpGV3Um5yywWC1rtrYlJ4XA4kCQJlUrlUrdlyxb0ej1dunS5rj6HDh3K1KlTsVgs/P7777z88ssAvP322zKb7t27M2PGDHx8fG5uEQKB4M7AYYXUXzibZqTI3qpCE6Mjlxr5qUiGIkyd22IIvLw/0c1itTtYuS+dD39JwWKX+wMZHFYWlsXR5Ms1SBVEm/6+g5YVDxmwaBW08m/D20EPUmvPFJSFF0WVzaFibfxT/Hrufpf29TvVJOSxMJQa+WdrA28tTXy0shtm9wJVJoiWL1/OihUrSE0tTyTXtGlTXn/9dfr06QPA6NGjWbNmjaxNu3bt+Pnnn52vzWYzkydPZv369ZhMJh544AHmzZtXqUcYe/I+rLS+roUHfEZds21QUJDz3xdydP297EaxWCzMmjWLr7/+mry8PJo2bcrkyZPp2bP86qXdbicqKoo9e/aQmZlJjRo1GD58OGPHjkX5vzPz0aNHk5ubS6dOnfjwww+xWCycOnWK8PBwhg0bRnp6OuvXr8fDw4MXX3yRV155xTl+QUEBU6dO5bvvvsNkMtGyZUtmzZpFmzZtAPjiiy+YMGECK1as4O233+bkyZPExMTQvLnrbY8NGzY431PXg9FodD7L2rVrs27dOnbs2CETRM2bNyc4OJjNmzczbNiw6x5DIBDcQUgSyoIEShL/Ir24JQ5c/4DTOwoJKUzGYM+jIKwOxtA+GG6RnxDAwZQCZv5wisScSzLPSxK9M4/x5uENaM+fdWmX76Zg0WNG4ppq0Cq1jGv4FI+lxqE7MFxmV2xx58M/R3M6X+48rVBA2D+a4XV/XRfR09xPRz2vuyvg4rVSZYKoRo0aTJs2jYYNG+JwOFizZg1Dhw5l165dtGjRAoBu3bqxbNkyZ5tLdxgmTZrE1q1b+fjjj/Hx8eGtt95iyJAh7N69u8LdAsG1MWbMGJKSkli+fDk1a9bkxx9/5IknnmDHjh2Eh4fjcDgICQlh5cqV+Pn58fvvvxMVFYWPj49MGPzyyy94enqybt06pL/ddFiyZAmTJk3ilVde4aeffmLixIl07NiRyMhIJEliyJAheHp6snbtWnx8fFi9ejX9+/fnwIEDBAcHA+VZ7KOjo5k/fz7+/v6XFYL79+9n8ODBN/U8jh49yr59+6hTp45LXUREBHv37hWCSCC4i1EVn8GR8hvpeQ0psrveClNio0bRafxKMsit646pVT/c9B4V9FQ5FJlszN+ZxPrDGS51tQszmHL0G5qfPlRh2wNN1Cz6h5ECDyUR/m14W1uXkN0zUJgLZHYZpYF8cHQ8WSXy3W2tQU2r51qjaxIgK1cpoHWggSC3e/dgqcpW1q9fP9nrKVOm8PHHH3PgwAGnINLpdJf9oisoKOCzzz5j8eLFdO/eHYBly5YRHh7Orl27nLsZgusjKSmJdevWceTIEWrXLs/BM2rUKHbt2sXKlSuZN28eGo2Gt956y9mmbt26HD58mPXr18uEgU6nY9GiReh08tD0PXr0YNSo8p2wF154gWXLlrF7924iIyPZs2cPR48e5dSpUxgM5WfWkydPZtu2baxdu5aoqCigfJcqOjqa1q0vFwwN8vPzKSwsdIqo62HlypWsXr0aq9WKxWJBqVQSHR3tYhccHMwff7jmAxIIBHc+ytKzqNN2kp3rTYblPiRc/5B2t+VQJy8Rs7uZvB4dMPjXr6CnymPPqVxm/HDKJSu9f2keI/76iX4n9qC021zaFRgVfNZXz88RWrx0Psyp1Y9uf36FOuMzF9tT+aEsOz6OUpNcAvgEuxE2KgL85H5EOpWCdsEGvHT39kbDHSH17HY7GzdupKSkhMjISGd5bGwsoaGheHl5cd999zFlyhQCAspV66FDh7BarfTo0cNpX6tWLZo0acK+ffuEILpBDh8+jCRJdOzYUVZuNpt54IEHnK8/+eQTPv30U1JTUzGZTFitVqeAukCzZs1cxBBAWFiY7HVwcDBZWVnO8UtLSwkNlZ/Jm0wmkpKSnK/VajXh4eFXXIvJVB6f40Z8qR599FHefPNNCgsLef/99/H29mbAgAEudgaDgbKysgp6EAgEdyx2E7rUbZRm5pFo7oxFct3tUUo2ahQn4mU+T35YTYyN70N/C4/HCsqszP05kS3HsmTl/qV5DP3zRx49tRe1zeq6FAVs66BldS89JQYlD9V+iPFmB147JqOQXG+b/VbyKKuPPMSlmqpuiwAaDmuNRSeXBe4aJe1DDBjU9/4drCoVRMeOHaN3796YTCbc3Nz4/PPPnV+WDz74II888gh169YlJSWFmTNn0r9/f3bt2oVOpyMzMxOVSoWfn5+sz4CAADIzM684bkJCgkuZXq+v8Ms70nB7j0IufIlfL5b/OdRdqf2FOpvNhsPhqNDWZDKhUCj4/vvv0WjkuWj0ej0mk4mNGzcyadIkpk6dSvv27fHw8GDFihVs3brV2afdbnfa/50LR2d/L5ckCavVislkwmw2ExAQwKZNm1zm5u7u7hRfWq0Wq9WK1er6AXEBo9GIQqEgMzNTNt6V1g/lTtpubm7UqFGDGjVqsGDBArp27crKlSt54oknZLZZWVn4+Pjc8P/b5SgsLLzq+/haqOi9Xl2ormuvruuGa1u7u+08IWVHSS5rQ6E9skIbD2sOtfMTyfO1cKJBMySVEU6fruzpOtl/1sSHvxeRb76YesO7rJDhR79nQEIM2gp2hKA8GevyR4wkh6jw0fgS5TuQXn9+hkeu6661TWlkXdG/iNkX6FLXoL0ftYe0xKKRSwKDZCHQlEdaUkVpYquWRo0aXd3oOqlSQdSoUSNiYmIoLCxk06ZNjB49mi1bttC8eXMee+xiIrmwsDBat25NeHg4P/zwA/3797/pcS+loKDgjrqVdb1c8K+63BpMJpOzTq1Wo1QqK7Rt164dkiSRn58v2xH6OwcPHiQiIoIxY8Y4y1JSUlAoFM4+VSoVKpXKZQyFQoFGo5GVK5VK1Go1er2edu3a8Z///AeDwUC9evUqHF+j0cjGuhx6vZ7GjRuTlJQks73S+i+dz4V+xo8fz/Tp0xk8eDBGo9Fpm5CQQOfOnSv9vePp6emy43a9JCQk3JIPjbuB6rr26rpuuIa1O6xo0raTm2MmwfJ/SBV8/WkcJmoWJqFVZVPYMQyPmi24dZ5CUGy2MffnRDYd/Zt/jyTRK+kAUQfW4m2uOCVHlpeCT/saiGmpAYWCDl6dmBHYGp/dk1CYC13sSxs+xuq/nuaPfVkudQ880Rxtl7qYLtlM8jeoiAjyRaX0c2lzr1Klgkir1dKgQQMAWrduze+//86SJUtYtGiRi21ISAg1atQgMTERgMDAQOx2Ozk5Ofj7+zvtsrKyqk2MnZvBZDJx5MgRWZnRaCQ0NJTBgwfz0ksvMWvWLFq1akVeXh579+6lbt269O/fn9DQUNasWcNPP/1EgwYNWL9+Pb/++qvzptvN0K1bNzp27MhTTz3FtGnTaNSoEZmZmfz8889069aNzp07X3d/sbGxjB079prXXxGPP/44M2bMYPny5U4/ptLSUg4dOsSUKVOua04CgeD2oixJx5LwC8lF4ZilCj6nJAeBpen4mFMobhqMutFjGFW39uvxYEoBk787ydmCi/HN/EoLeP23L7g/7UiFbbK8FHzdTc+OCC02tQIPjScTmr9I+7i1+B5a7mLvMPiT3XkBq1brOXPJUZxKreSRl9tRHOqP6ZLr/IFGFW0CDajugejT18Md4UN0AYfD4Tz6uZScnBzOnTvndLJu3bo1Go2GnTt38vjjjwOQnp5OfHw8HTp0uG1zvltJSkpy2QFq3bo1u3btYvHixbzzzjtMnTqVs2fP4uPjQ9u2bbn//vI4Fc899xxHjx7ln//8J5Ik0b9/f8aMGcPnn39+0/NSKBR89dVXzJw5k6ioKLKysggMDKRDhw48+eST193f008/zYMPPkheXp4sVtCV1l8RWq2WkSNH8v777/P888/j4eHB1q1bqVWr1nWLNIFAcJtwWFCk/MK5s1oKbBXHInOz5FOj6BSl9fRYwx7G7RYFVryAxeZgcUwyq/al45QhksRDp2N5JW4dHpZSlzaXCiGA9oGRTPXpQNBPE1CWnHdpY633ICktolkZfYLcc9myOoOHlicndea8jxsmi0NWF+ympnWgHuU9FmPoWlDk5+dXyeHgv//9b3r37k3NmjUpLi5m3bp1vPfee3z11Vd06tSJOXPm0L9/f4KCgkhJSWH69Omkp6ezb98+PDzKNzHHjRvHtm3bWLJkifPafX5+/g1duy8oKKiUHY47lb8fmVUnTCYTL730Ek2bNmXChAmV2nePHj0YPXq0U5BXJpXxfhTHJ9Vv7dV13eC6dkX+GfITTpFZ1rjC4zG1w0zNwjM4fAtRtemC1u3WHw39kpjHf39OJDn34kUMg9XEv35ZRfcUV78fixrW9tCzqYvOKYTUCjUvNxrO4ORYtH995dJGUmkxdfk3f6n/wRezf6OsWL7J4FfDnWFTu5Cq1nC+RO6bVMNdTcuA6imGoAp3iDIyMhg1ahSZmZl4enoSFhbGunXr6NmzJ2VlZRw/fpwvv/ySgoICgoKCuP/++1mxYoVTDEF5CgaVSsVzzz3nDMy4dOlSEYNIIGPatGls2bKlUvvMyspiwIABDBo0qFL7FQgEN4dkK8OUcJDz2YFYJddgrUgO/MvO4W1LxtSuGcYa3W/5nNLzTURvT2RnQq6svHZhBv/Z+QH1C1x3eP6qrWLRY0bSAi9+n9Vwq8k7AT0IjZmNstT10oXdrxmlD33IgcPufLMoBsclR2H1WwQw9F+dSbPC+Xy5UAo0qmgVoL/nok9fD1W2Q3SnIXaI7k3u1nWLHaKbo7quvbquG8rX3sBLIuNUMcW2imOPGW151CxIorS+FkOLrijVrjeLKxOzzcHKfWl8HJuG2SY/muqcdoS3967AzSIP22HRKPi8l44tnXU4/ubD0zOkG1ML8nE75uqaIClUmNu/Rln71/n5ywR2fX3CxaZtj3oMfDmCLLODPzLlt2I9tEo61TDeExnrb4Y7yodIIBAIBILrxlyEd146iecaIOHuUq2WyqhRdAaVLhdLt464+dzcDc5rISWvjNe/+Yv4TPlNMYXk4Nkj3zPi8GaXNokhKqKfNHLO/+KukE6lI6rpCzx6dC2a5O0ubez+zYlv+iaBLR9m3XsHOBqT6mLTe1g4XQc1pcDi4HCWXAxplQoiggzVXgyBEEQCgUAguFuRJKxpf3I+WUGZo7FLtQI7AaZkfEvPUdS8JrrQx1DcwuCKF9h5Mocp352kyCy/y+5hLuG/cZ/T8rSrv9Cu1hqWDDRi0V4UJqFejXm7+Us0/nkcqkz5zTNJqcYcOR5z5Hhyj5xi8+TdpPyVI7NRa5QMeq0DLe+vTaHZzsHzZTj+diakANoG6zFq7v2gi9eCEEQCgUAguOuQSnPJOx5Pdmkdyr/a5Xg4MqiVm0xJsALz/b1wM3jf8jnZHBKL9yTzyW9pLnUdClOYufcjDNly3x+7ElY8ZGBLZ215VtX/8XjoE4wM7Ir3t0+iLJL35/CsQ8kjn+MIbElWWiGb3ztJca7cJ8jNS8czk7tQp6kf50usHM40cYlLES0C9PjqhQy4gHgSAoFAILh7kBxYUw9zNkWP2VHXpVqFiVrFp9Dbcynq2BJjjQocq28BOSUWJm6K50CKPIkqksSU3H30/uFzFDb5ra4CNwXRTxr5s8HFrAB+ej8mtJ1MB7MJt3X/55KU1RbYmtKBa5HcgjhzLIvPZv1C2SV5zwJqezJ8ahd8gtw4lWfmZJ5rOJv6Xhpqe2hcyqszQhAJBAKB4O7AnEfBsT/JKK4PuB7z+NhTqJmdTlGgAmuHfhhvYUb6v7M/OZ9J38aTXSJPJeRtLePDk+uocfAXlzbxtVVEP+lGtvfFdbQNaMdbEVMJPPEV+t1vueQis9bvQ+nDH4PWnaN7U/n63X3YrHJn7YatAnnqzc5ojRoOZZo4V+Ka9qOup4amvrfWofxuRAgigUAgENzZSBKOs4c4e0ZJqb2hS7WWImoXnsK9rIjssBCMTe+/Lb5CdofE8l9TWfZLisw3B6BrYRL//nUlmswMl3bf3qfj0z56Z2whgCcbPc1zjZ/BfcfraI+vcWljDn8OU49oJIWKvd/E8/0nh11sInrVZ+BLETiUCn47V0qBWS6WFECYv446ntobW/A9jhBEAoFAILhzMedRfOIw5wvr48D1iMffeoYauWmYdQ5yu7bDLbDi9DuVTWaRmUmbTxJ3yRGZ2m5jctKP9IzdjEKSq6QSvYIFjxnYF3ZRkBjUBia2ncwDHg0xfv1/qDNcHa5N903F3P41HA6J75b/QeyWUy42Dw4No/uQ5kjA7+fLXMSQRgltgwz4GcTX/uUQT0Zwz7N69WrWrFnD5s2u11xvJ1OmTMFkMhEdHV2l8xAI7gr+5yt0PkVd4Q0yNWXUKYjHq6yI3CANqUGNCL1NYmjv6Vwmf5dAXqn8iKxe/jne/+Nz/FJPu7Q5XUPF3KeMZPhevFJf270O0zr8hwYFZzF+0Q1lmTzFhqT1oLTvMmwNH8ZUYuGrefv468A5mY1SpaDz4Nr0eCIMSZI4mmUip0x+1OauUdIu2CBuk10F8XTuYry9va/4M3r06ArbzZ49u9okwLVYLMycOZOJEyc6y0aOHEnXrl2xWi9+mDkcDh5++GGXyNNHjhxhxIgRNG3alMDAQFq0aMHjjz/O5s2bcTjK/wJLTk6WPffAwEAiIiJYuHChrK+oqCi+/PJLzpw5c+sWLBDcAzhKcsg5uJ+kM4GUOVxTanjZz9E04xBu5kIyW9dCe/9AJNWtD8BaZLLx9tYExnx9XCaGVA47zyXs4NNtsysUQ5u66HjzBXeZGOpbpx+Luy4nNG0/bhsGuoghu08jip/cjq3hw2SmFrJk/HYXMaQzqBn+9v00al/+jBLyLKQXy32GvHVKOtU0CjF0DYgdoruY+Ph4579/+OEHXnnlFVnZnRSh2WKxoNXemnNrh8OBJEkVpmzZsmULer2eLl0uJnaMjo6mU6dOzJ07l7feeguAxYsXc/z4cWJjY51227ZtY9iwYXTt2pXFixfTsGFDLBYLBw4cYN68ebRt25aaNWs67devX0+LFi0wm83s2bOHV199lZo1a/KPf/wDAH9/f7p3787HH3/MjBkzbsmzEAjuZiSHjbLTf3L+vDs2qY5LvQozNYtO41uSQ7GXAmuHB3DzrnFb5vZLYh7Tvk8g45IbXWFZiUz9fS01M5Jd2mR7KlgwyMiR0ItHff76AMa1mUCHwI7o9r+D/tdZLu2sDfpS2ncZ6Lw4HpvOV/P3YSmTCx1PPwPD376fkPreJCQUkVJo4dQl6TiMagURwQY0IujiNSEE0VXoubHiDMm3iu0D916zbVBQkPPfF9I8/L3sRrFYLMyaNYuvv/6avLw8mjZtyuTJk+nZsycAdrudqKgo9uzZQ2ZmJjVq1GD48OGMHTsW5f8cGUePHk1ubi6dOnXiww8/xGKxcOrUKcLDwxk2bBjp6emsX78eDw8PXnzxRV555RXn+AUFBUydOpXvvvsOk8lEy5YtmTVrFm3atAHgiy++YMKECaxYsYK3336bkydPEhMTQ/PmrtdrN2zYQJ8+fWRl3t7eLFiwgCeffJKHHnoIo9HIrFmzWLRoESEhIQCUlJQwZswYevfuzeefy0PlN27cmKFDhyJd4h/g6+vrfP5PP/00H330EYcPH3YKIoCHHnqIGTNmCEEkEFyCNTuFrIRciq2BFdb72FKpmZuG2mElq5EvhpY90Klu/VdYsdnGvB1JbDgsd472MJcw+o+NPJKw18VXCOCXFho+GGig2HhxZ6ZvnYcZ3WIs7iodhh/HoD2+2qWdqcMEzJ3exCEp2P75n+xce9zFpmaoD0+/dR9e/kYAStByKtsss9EqFbQPMaJTiZ2ha0UIIoELY8aMISkpieXLl1OzZk1+/PFHnnjiCXbs2EF4eDgOh4OQkBBWrlyJn58fv//+O1FRUfj4+DBs2DBnP7/88guenp6sW7dOJh6WLFnCpEmTeOWVV/jpp5+YOHEiHTt2JDIyEkmSGDJkCJ6enqxduxYfHx9Wr15N//79OXDgAMHB5TmKLvjizJ8/H39//8sKwf379zN48GCX8l69ejF06FBefPFFDAYDffv2lR2X7dixg5ycHKKioi77nC6XBFGSJPbt28fJkycZN26crC4iIoKzZ8+SlJRE/fr1L9u3QFBdcJhLKPjrBNkFQUi4iiEdhdQqOI1nWTHFHpDftg1uQU1uy9x2nMxh9k+nyfz7rpAk0StpP68dXIdnWZFLmxI9LH/EyK7WGmegRT+9H+Nbv0mH4E5gysft20GoU/fI2kkqLWW9l2BtOgiLycbad37jxL6zLv237VmPAS9FoNGW74gXmO2cU3rzd0mmVEBEsAE3cUx2XQhBJJCRlJTEunXrOHLkCLVrl+f7GTVqFLt27WLlypXMmzcPjUbjPGoCqFu3LocPH2b9+vUyQaTT6Vi0aBE6nTzeRY8ePRg1ahQAL7zwAsuWLWP37t1ERkayZ88ejh49yqlTpzAYDABMnjyZbdu2sXbtWqdAsdvtREdH07p168uuJT8/n8LCQqeIupSZM2fSvHlzlEol33zzjazu9OlyP4C/J8o8duwYvXv3dr6eP3++TGw9/PDDKJVKLBYLVquV0aNH079/f1m/F+aSkpIiBJGgeiNJmNL+4nyyEovD9dhLgZ0g6ymCcjKxaSQyW9bE2KgTxtuwK5RZZGbOT4lsPylPheFXWsAb+76gS+qRCtvtaq1h5UMG8j0uCpEetR5kbMtxeGo9UWYfw7hlOKo8+S0xh96H0ke+wF6rM4W5ZXw2Yy/pp/JkNkqVgv8b2YYODzd0/jFWanUQd74MSSEXPq0D9fjoXV0IBFdGCCKBjMOHDyNJEh07dpSVm81mHnjgAefrTz75hE8//ZTU1FRMJhNWq9UpoC7QrFkzFzEEEBYWJnsdHBxMVlaWc/zS0lJCQ+W3RUwmE0lJSc7XarWa8PDwK67FZCpPYng5X6pvvvkGm82G2Wzmzz//lK2vIho1akRMTAwAXbp0kTllAyxfvpzmzZtjtVo5ceIEEyZMwM3NjcmTJzttLoi8sjJ5hmuBoDrhKCsg93gCuSUV+/94SOeplXcGrdVCTj0PtC0fwO02BFl0SBLrD53n/V1n5HnIJIm+ib/xWtzXuJlLXdql+ytZ1t8g8xXy0HgQ1ep1utcqdzXQHP8Sw/bXUNjkv/t2r/qUPvo1Dp9QzicX8Om0GPKz5GO4eet46s3O1A8LcJZZ7RJx58swX5KPo7mfjmA3EYH6RhCC6Cpcj0/PvYDD4UChULBjxw40Gvkv1QVhsWHDBiZNmsSMGTOIjIzE09OT5cuXs2XLFpm9m5tbhWNc2q9CoXAeqTkcDgIDA/n+++9d2nl4XPxA1Ol0FTpR/x1fX18UCgX5+fkudSkpKbz11lvMnDmT+Ph4Xn75ZX799Vfc3cszZTdsWB787eTJk0RGRgKg1Wpp0KCBc86XUrNmTWd9kyZNSEpKYtasWbz++uvOZ5eXV/5Xn7+//xXnLhDck0gSpuRjnEvVYZVcxZCaUmqVnsS7sIhCXyUlEZ0x+rg6V98KCsqsTPw2ntgk+eeFf2keE2O/oFP6ny5trGoFX3fTseEBnSzIYmRQR8a3fhN/gz/YTOh3TUJ3dIVLe1tIJKX9VyMZ/Un44zyr58RivuQqf0h9b4ZN7eL0FwKwSxIHM8ooviRKdX0vDfW8RNDFG0UIIoGMli1bIkkSGRkZl90xiY2NJSIiwnnsBch2b26GVq1akZmZiVKppF69ejfVl1arpXHjxsTHx8uOuiRJYsyYMbRr144RI0ZQWlrKzz//zOTJk3nvdPWBOgAAIABJREFUvfeA8mM9X19f3n33Xb788ssbGl+lUmGz2bBYLE5BdOLECTQaTYUO4ALBvYy9JI/c46fJK6toV0giwH6akJzz2FUOstrWw9ggEvVtiDYNEJ9RzGsbTpBeIHdM7n7mIG/uX42bqcS1TW01Cx4zkB548Q8zjVLLiy3GMKD+P1AoFCgKkjF+92yFwRYtLZ6hrPtcUBuI+zGRjUsO4rhkt6dJuxCeeKMjOuPFPyIlSeJIpolckzzWULCbWqTjuEmEIKqmmEwmjhyRn4MbjUZCQ0MZPHgwL730ErNmzaJVq1bk5eWxd+9e6tatS//+/QkNDWXNmjX89NNPNPh/9s48sIar7+Ofu+RukUjIKiKWIJbYRVCK1vZoqaqqpYpHaaTkqbbUY6uS6mNpq60udKGUl4ZWq4ulqtZaqrULISEksu93vzPvH6kwmUuLqG0+/+WcM2fOzL2Z+53f+S21a7NmzRp27dpVFul2M3Tq1Ino6GgGDRrEjBkzqFu3LpmZmWzevJlOnTrRrl27655v9+7djB07tqztww8/5ODBg2Uh9iaTiffff59evXrRp08fOnfujKenJ++++y7Dhg2jX79+xMTEUKdOHcxmMz///DNWq1VmocrNzSUjIwOn08mxY8f48MMP6dChA97e3mVjdu3aRdu2bTGZTCgo3A8ILhdFpxLJyjQhIBdDegqoUXAST4uFnBqeeDTr9I9sj13ih2NZvPr9KazOy9YWk93CS/tX0y1pt2y83UPF8q561rfTI1wRzl7TqxaTW71K7cql1mVtyk8YfxiJ2ir1BRI1BiwPzcPRaAiCILLhs4NsX5tIedr0rMMjo5ujKRcllphrl9UnM4h2mvpXumqgh8LfQxFE9ynJyckyC1CzZs3YunUrCxcuZN68eUybNo20tDR8fX1p0aIFHTp0AGD48OEcPnyYkSNHIooivXv3JjY2VhaefiOoVCpWr17NrFmziIuLIysri4CAANq0acPAgQOve74hQ4bw8MMPk5eXh6+vL0lJSbz22mvMnz9fkkMoOjqaMWPGMHbsWHbt2oW3tze9evVi06ZNLFiwgNjYWHJycvDy8qJp06YsXLhQFr3Wr18/oNQyFBQURNeuXZk6dapkzJo1a5g0adIN3BkFhbsLURQpSc8mO7kAu8vHzQiBAGcSwdmZWDxFcjq1xBhQ1824W4NTEHlnawpL916QtDfJSOK1X5fiV5AlO+ZYmJb3+hlJ85O+DPWp9TijG8ei1+hBFNDvmYd+92xUSC0+rso1MT/yOUJAE+xWJ6vn7+HYr9Lzq1TQY3hTHnisnkzgpBTYOVMgzTXk6aEm0JqHRi1PYKlwfajy8/PlCRTuQwoKCirEwnGnYrVa76hEjf8UVquVMWPGEBERwYQJE27rWjZs2MC0adPYuXMnWu2130Uq4vt46tQpSZTc/cT9eu13ynXbiu1kHj+PxeL+mWMgnxr5J/G02siq5YW++UNotDe33fN3r93icPHtkUxW7E8jOeeyg7N/SR5PH/mRx05uRy1KfXOcGlje1cC6B/SIV1iFfHQ+vNj8FdoF/5mvzpqP6cfReCRvkJ3XUbsn5u4fgMGHghwzy2buJO201HrkodPQf3wbGrevLjs+pcDOsZxyuYY0KtpVM3Eh5fQd8bnf7SgWIoV7nhkzZsgcvm8HZrOZhQsX/qUYUlC4WxFFkcILuWSeMSMiF0MabATak/DPzcVuEMl6oAmmav+MP11GkY1VB9JJ+P0iBdbLW04BJbk8ffhHeiXtQic4ZcedC9Dw5gATKcFSq1D3Gv9idONYKutKX1zUmYcwrR+KpiBFMk5UqbG1m4qtdRyo1KSdzuPz13ZQmCuNNvPyNfD0lAeoXq+KbA3JBXaOlxNDGhVKfbIKRnkyK9zzhIaGXrWu2z9J3759b/cSFBRuGS6nQObRVIoKPIDyEaAC/pwkKCcLrQNyqhvQtnoYk859JGpFciHfyqJdqaw/kolTuLwhUtVcwLBD3/FI0k48BJfbY79tp2NZdyN2j8tWoWBTMP9pNoFWAa1LG0QR3cHFGLZNQeWSbmcJxqqY//UprhoPAnDqwEW+eGOXrAxHcC0fnp76AD7+ct/CM/l2TuRKxZCa0sr1Pnol11BFoggiBQUFBYWbwlJg5uLRDBxO+baXt/osISVnMBRocGghs3U4nrVa3fI1XSy08fHuVL46mCERQgCt0o4xffun+NqK3R6b4+vBu310/FHvcnSXChX96jzJsAYjMWpL84mpLLkYNz2Px+nvZXM4g1pifmQpolfp9teBLSmsfWefLJIsonUwA16SRpJd4nS+jcRcqchSq6BloBF/k/LzXdEod1RBQUFB4YYQRZH85HSyzguAVAypcRCq2otvhg2Vy4MCXzVC2054VvJ3P1kFkVNiZ/GuVBL+uIijnPhQiQLPHPqBEQfXo0buPmsJ9GVpeyebm6kleYUq63z4b6vpl61CgOb8Tkw/jEJdfEE2j63JCKwPzgatHlEU2ZZwgg2fH5aNa9+7Lj1HNEVdLpJMFEVO59s5mScXQ60CjfgpYuiWoNxVBQUFBYXrxmmzkXk4hWKzF6WbOJcxqrOpaf0dQ64eQaUlK6Iqpsad0apv3RaPKIqsP5LJnM1nJD5Cl6hsLeb1PUtoelaeYJGQavzUoxrv1zyFq1w6jQa+DZnWeiYBpj/rJdoKMOyZi+7A+6jKOV+LOm8sD7+Fo35pxKngEli/+A9+/U5aqkOlgn/9uxnt+9Rzex3Hc2ykFEoTNGr+rE/mZ1R+tm8Vyp1VUFBQULgubOkppJ124RDk+YL8NUeplp2O2magxBMsrVviGRAun6QCySi0MXtXPgcuZrrtf7jkLK9s/QRDTrl+lYqSZwcyucERUixJgDTMvW/tJxjdOBYPtQcITnSHl6Lf/Tpqi7TGGfy5RdbzE0SfmgDYLA6+fHOvLKxeo1XTf3wbmnQIlc0hiCKHsqykFUsF3SUH6qqKGLqlKHdXQUFBQeFvITrMFB4/QkZ+NUBaIkKDlTBxH5XTXQiijqy6PhgiO2PU3rpSEqIosuZgBm/9nEyxTe4Y3VBt5tWk76m2Y7OsT1WlCodfepyZmm+xWaROywaNkfHNXuah0NIM99qUnzBsm4Im57jbddhaxWFtNwU0pX5AeZklLJu5g4spBdJ5PT0YMrk9tSMDZHM4BZEDGRayLdLr0KpLxVAVg/JzfatR7rCCgoKCwl8i5p7h4ok8ipzyHDmeqgxqFh1FV+xBkbcWe6soTH41b9lazHYX20/nsvpAOvtTC92sR2Cu+TeafLcSzPJirDRrwmfDa/J1SQKU01E1vWoxPWoWNbzCwGnDuHkcuuOr3K5DqBSCpdu7OMO6lLWdPZbN8td3UlKuDIh3VSPDXu1AUE15kkq7S2T/RTP5NukWnF6jonWQEW8lmuwfQRFECgoKCgpXR3DgPLOb1PQgHGKwrDtQOE5wdiYCWjIb+GFq+CAGTcX/tJjtLradzmXTiWx2nM6TlNq4kkG2ZEbvWokm9Zzb/qIBvXgl6gwXSrbJ+nrU+Bdjm4zHoDWArRDPb4egTZWPE7UmbK3/g63l8+BxOVT+t83JfL3wN1zl1natsHqHS2RPupkiu/QYk1ZFVLBJyTP0D6IIIoX7ghUrVrBy5Uq+/fbbCpszKyuL6Ohotm3bJikDoqBwr6AqSaf4xB9cKGlM+dxCWiyEFR/Du9hMoY8GZ1R7PH1uzf/B90czeX3jaYrcbItdolZJNnOTvyPogLz+GICqXl12PN2S+dpNCOXm0Wv0xDV9ie41epaOLb6I59f90WRJI8NEVDgaDsTafipipcvi0OUS2LDkEDu+Pik7b6O2IfQf3wadmy0vpyCy76JcDHnr1LQOMqLXKmLon0S523cxgiDQs2dPBgwYIGk3m820atWKF1544arHzp49m7Zt297qJd4R2O12Zs2axcSJE8va/ur6e/XqhY+PDz4+Pvj7+9OsWTNmzJiBzXbZDO7v789TTz3F7Nmzb+n6FRRuB+qL+8g8mMSFkqaUF0OeQjYRWX9gspSQGRmE+uEn0N8CMeQSRN76OZlJ3568qhgyOGy8emYDS7+Z4V4MeXtjf3ks0/8TzFzNjwiidJ6aXrV4/8HFZWJInZdEpVXdZGLI5VOH4kE/Y+n+vkQMFedb+XTqL27FUOcBDRn4Sju3YsglivyWYZFtk1U1aGhTzaSIoduAYiG6i1Gr1XzwwQe0b9+eZcuW8fTTTwMwffp0XC4Xs2bNus0rlGK329Hpbo2DpSAIiKIoq0APsG7dOgwGAw888MB1zTl48GCmTZuG3W7nwIEDPP/880Dp/b1yTOfOnZk5cya+vr43dxEKCncCogvxzBZS0qthFeQiJ8CWTLW8CxRUVSO27oynd+AtWUah1ckr3ySy80ye2/5gLx2jLSfo9MMStFluostUKjyeeILEIQ8y49Q8CvLzZUMeq9WPUY3HlBZlBTTp+zB9PQC1NVcyzhnUCvNjqxCN0gKqqSdzWDF7FwXZ0jIcWp2GfnGtadqxhtu1C6LIHxlWcso5UFcxaGgVZESjVqrW3w4UQfQXeK5270x3qyh5csBfD7qCmjVrMnPmTCZPnsyDDz5IcnIyn376KevXr8fT88bT4tvtduLj4/nyyy/Jy8sjIiKCKVOm8NBDDwHgcrmIi4tj27ZtZGZmUq1aNZ555hnGjh2LWl36ZhMTE0Nubi5t27Zl0aJF2O12kpKSiIyMZOjQoVy4cIE1a9bg5eXFc889x7hx48rOX1BQwLRp0/juu++wWq00adKE+Ph4mjdvDsAXX3zBhAkT+Oyzz5g+fTonT55k+/btNGwor4uUkJBA9+7dr/semEwmAgNLH/ahoaEkJCSwZcsWiSBq2LAhQUFBfPvttwwdOvS6z6GgcEfhNGM7tpXU/IYI5aLI1KKdsIKT+FjzyKxXBVOTh1Cpb40V40y2mbg1xziXZ5W06zQqnmwRTPc6lam76E2cX69ze7y6eTP0/53EV/rDLDoyDQGpFaayzoeXW0yibVD70gZRRHfwYwzbJsvKbzhqdcPc6zPwkD5P9288w7oPDsj8hbyrGhny3/Zua5KVnkrkcJaVDLM0tN5bp6alIoZuK4ogugcYMWIE69evZ/To0aSmphIbG3vT22GxsbEkJyezePFiQkJC2LhxI0899RRbtmwhMjISQRAIDg5myZIlVK1alQMHDhAXF4evr69EGOzcuRNvb28SEhIQxcuZYd9//30mTZrEuHHj2LRpExMnTiQ6OpqoqChEUWTAgAF4e3uzatUqfH19WbFiBb1792bfvn0EBQUBpZXs586dy1tvvYWfn1+ZeCnP7t276dev303dj8OHD7Nnzx5q1JC/8bVs2ZIdO3YogkjhrkZlySTvyBEyLc1kfQZXIbVyE9FiIbNNPTzDWtyydfxwLIuZPyZRYpdaT/wr6Xjz8QY0NjqxjvsPzt9+k1+Dvz/6F8fj6PkQb/zxP34+9ZNsTKuAKCa2mEwVw5/WHnsRxs3/QZe4RjbW3mgwlofeLgunh1J/ofWLfmfP96dl42s19mfgxLZU8pEXtoVSMXQ0x8aFcnmGKnmoiQo24qGIoduKIojuEd58802aN29OrVq1mDx58k3NlZycTEJCAocOHSI0tDR52KhRo9i6dStLlixh/vz5eHh4SM4TFhbGwYMHWbNmjUQY6PV63nvvPfR6aVr/Ll26MGrUKABGjx7NRx99xC+//EJUVBTbtm3j8OHDJCUlYTSW1gyaMmUKP/74I6tWrSIuLg4otVLNnTuXZs3kD/BLFBQUUFhYWCairoclS5awYsUKHA4HdrsdtVrN3LlzZeOCgoL4/fffr3t+BYU7BTH7FGknCyl2Rsj6fG1p1MhLxmoSKGzfHk9feULBiiDf4iB+w2k2nsiW9TWp5sX8vhFUzTyPeXgsYup56QCtluLejxI06RXSyWf69hjOFEoFiwoVwxr8m0H1hqJWlVq21NlHMa1/Bk2eNJM0gDXqJWztJpemlb7UVmJn5f92c+r3DNn4Bx6rR/dhTdBo3FvNhD8tQ+XFkFGrIirYiO4qxyn8cyiC6B5h+fLlGI1G0tLSOHv2LPXqyVPC/10OHjyIKIpER0dL2m02Gx07diz7+9NPP+Xzzz8nNTUVq9WKw+EoE1CXaNCggUwMATRq1Ejyd1BQEFlZWWXnN5vNhIdLs9tarVaSk5PL/tZqtURGRl7zWqzWUpO7weD+je1a9O3bl1deeYXCwkIWLFiAj48Pffr0kY0zGo1YLBY3Mygo3OGILhxn9nA+zV8WUq/CRUhREn4lmeT7a1G37YHeIM9MXRFsP53Lq9+fIrvEIevrExnAlO7hqPf+ivmFF6GoSLrOsDCM777DBcHB9rRvWHriE6wu6Vabp7YSk1tNp03Qn5ZzUcTj2BcYt7yMyin93xV1Xpi7vouz3mOS9rzMEpbO2E7mOWneIw+9hsfHXd1fCEodqP/IkG+T6TSlofUGxYH6jkARRH/B9fr03A4OHDjA22+/zcqVK/nkk0+IiYlh48aNbh2M/w6CIKBSqdiyZQseHtIKzJeExdq1a5k0aRIzZ84kKioKb29vFi9ezPr16yXjr+bHVH5elUpVtqUmCAIBAQH88MMPsuO8vC4/kPV6/V9eo6+vLyqVinw3DpV/ReXKlalduzYAixYtIjo6mi+++ILBgwdLxuXl5eHn53fd8yso3FbshRQePcDFonDKBxx7iGZq5Z3A6Cgmq4E/pkadb4m/UInNyfyfU1jzx0VZn06jYnyXWgxoGoDjs0+xvPMuuKTbaJqo1hjffptTpPPGr6+RapXnHqrpVYsZbV6neqU/X9ZsBRh/ehFdYoJsrMuvEeZHliL4Sl/GUk/msGzmTorzpULLJ8DE01MeILiWPNniJZxCaTRZeQdqnUZFm2AjnkqeoTsGRRDd5VitVp577jkGDRpE165dadKkCdHR0SxYsIDx48ff0JxNmjRBFEUyMjIkFqEr2b17Ny1btizb9gIk1puboWnTpmRmZqJWq6lZs+ZNzaXT6YiIiCAxMZFu3brd8DweHh6MHz+e1157jb59+2IyXU6wdvz4cdq1a3dT61RQ+CcR81PIOJZFoVNuSa7kyqJmThJOvYPcdi3xDKh7S9bw27kCpn53kgvlMjoDRAR6Ev9IPWpbc7EOG47rgHxL2qPf4wj/fZEPkz5n7ekvZY7TAB2rdWJCi/9i1Jb+v2ou/obpuxGoC8/KxtobP42l8xzQGiXth3em8uWbe3GW82kKrV+FIZMfwMv36tZnh6s0z1D50Hrjn0kXFTF0Z6EIorucGTNmYLVaiY+PByAwMJB58+YRExNDz549adCgwVWPtVqtHDp0SNJmMpkIDw/nySefZMyYMcTHx9O0aVPy8vLYsWMHYWFh9O7dm/DwcFauXMmmTZuoXbs2a9asYdeuXVSuXPmmr6lTp05ER0czaNAgZsyYQd26dcnMzGTz5s106tTpusVHly5d2L17N2PHjv3b1++O/v37M3PmTBYvXlzmx2Q2m/njjz+YOnXqda1JQeG2IIo4Un7jwnkv7KI8pD7Qeprg/DTygnVo2jyKUXfjkapXw+pw8e62s3yxLw2xXJ9GBf9uG8qz7aqjWreOktlvyEtvqFToX3qRAz3q8s62f5NhkVuXKnlUYlSjMfwr7FFUKhWIArr972LYNROVIN22ErVGLF3m4WgktfwKgsjmL46wdbW8flnj9tXp/0IUHvqr/4Q6hdIM1IXlki56/ulAbVS2ye44FEF0F7Nz504WLVrE119/LdlK6tevH9988w0xMTFs3rwZrdb9x5ycnCyzADVr1oytW7eycOFC5s2bx7Rp00hLS8PX15cWLVrQoUMHAIYPH87hw4cZOXIkoijSu3dvYmNjWb58+U1fl0qlYvXq1cyaNYu4uDiysrIICAigTZs2DBw48Lrne+aZZ+jQoQN5eXmSXEHXun536HQ6nn32WRYsWMCIESPw8vLi+++/p3r16oqFSOGOR7CZyT96mOzi6pSv6q7BRljBCTztBWQ1C8UU3vaWbJEdSS9iyvqTJOfIfe7CqhiJf6QejXQ2bP95AeeWLbIxKh8fbNNfYr7PPrbv+djtOTqFPERs5LjLUWQOM6bvR+Bx5kfZWJdfI8z/+hShan1Ju7XEzur5ezixL112zIP9I+g6JBL1NSLCBLG0UGt5MeStU9M62IhecaC+I1Hl5+eXF+n3JQUFBRVi3bhTsVqtN+RYfLdz6bpHjBhBREQEEyZMqND5u3TpQkxMDP3796/QeSvi+3jq1Cnq1r012x13OvfrtV/tum2ZaVw8WYhN8Jb1eQo51MxOojgQVC06ovOsKhtzs1gdLj7amcrSPedxufnFGdQymLEPhuGxaSO2+NcR3fj8qTt2YMvI1nx0cSUWp1xQVfGoykstX7nsOA1gzcdz3UC0afIM1rZmz2LtMBO00udi1vlCls3aSfYFqfO2Rqumz5gWtOpa+5rXKooih9xEk/n+mXTxVoTW36/f94pGsRAp3BfMmDFD5vB9s2RlZdGnTx+eeOKJCp1XQaGiEFwCeSeSyMkxAXIx5G9PxtdynvzohnhWv3bE5o2yJyWfWRuSZEkWAap565nRqy6tTE5sL47H+pPcKoTRSMHYocSH/c7J85/KutWo6VunPx08OhEZdPkaVCWZeH7VT1aCQzD4Yun2Hs46vWRzndiXxqp5e7CZpdFulXwMDJ7UjrCGfx08cTLPLhNDl2qTaZU8Q3c0iiBSuC8IDQ0lJiamQuf09/cv8yVSULjTsBZayDh6AZujkqxPRxGhBSexVtfjinwMT608NcbNUmBx8ObPKXx9SJ6zB6Bvk0Be7FITw6YNlMS/DoWFsjGqyMZ8MzKSz8wJCAXyWmb1fRrwQrOXqetTj1OnTl0+rvAcnmv6osmX5iJy+TWi5LHViF5S/ynBJfDTymP8vOqY7Bwh4b4Mmdyeyn7ySvXlOVto53S+NNO1SatSxNBdgiKIFBQUFO4hREEkNyWHnPNWQP4j7ickUaUgHUubZngG1ZdPUAH8lJhN/MbT5LjJKxTgpWNq93A6BOuxTp2M9bvv5RN4eJA1vA+v1j/KhRJ5+g1PrScjGo7i0VqPoVFJU2+ocxLxXNsXdXGapN0Z3IaSx1aBQRoiby60sWrer26TLTbvHMZjsS2v6Tx9ifRiB0ezpRFzOrWK1sFKoda7BUUQKSgoKNwjiE4VqQfSsJpVyHILqYqpYT6C4CHg6NYdo/HquXNulEKrk9mbTvP90SxZnwp4skUw4x4Mw3j2DCX9xyOmpMgniWzEysFhrBI2gzwin04hXYhpPA4/o3z7Snv6B0wbRqOySa1NjpoPY35kqawe2fmTuax4Yxf5WdJINrVaRY/hTWjfp15plNo1cIkiibk2Ugqk4k+jglZBSp6hu4nb9kktXryYdu3aERoaSmhoKF27dmXDhg1l/aIoMnv2bCIiIggKCqJXr14cPy4Nf8zPz2fUqFHUqFGDGjVqMGrUqBtKwKegoKBwt1OQXgJZhj/FkJSq6pNE5O7DGuSNttPjeNwCMbT9dC6Pf3zArRiqXdXIkiFNmNS1Nvr132AeMFAuhnQ6Mkf3Z+QzVlYJu2RzBBgDiI+ew9TWr8nFkOCi2okP8PxmoEwM2es9jrn3CrfFWT+auEUmhjx99IyY9SAPPFb/L8VQsV1g9wWzTAypgOaBRnwMN5YcV+H2cNssRNWqVWPGjBnUqVMHQRBYuXIlgwcPZuvWrTRu3JgFCxawcOFCFi5cSN26dZkzZw59+/Zl3759ZSHmI0eO5Pz58yQklGYcHTduHKNHj2bVqn+2Qr2CgoLC7UIURDKTcim4aKP8O65WZaYGezHkiOS1bo5ntYYVfv6sYjvvbTvr1ldIq1Yxsm11/t02FA+7FevkKe4r1IfXIWF0JMsdm0DqgoMKFY/V7seIBqMweci3AFWWXIw/jKTyWblDti1yGNYu80F9WZgILoEflxxix9cnZeNrNKjKoInt8K5qlPVdiSiKnC92cizbKouaUwGN/Q0EmJQNmLuN2/aJ9eol9fCfOnUqn3zyCfv27aNRo0Z88MEH/Oc//ymrHfXBBx9Qt25dEhISGD58OImJiWzevJkff/yRqKgoAN566y169uyphCAqKCjcFzjtLtKPXMRSLLdk+GjPEGo5QolQBfPDD2PyrFKh507OMbN0zwXWH83E4SaWvp6/iVmP1KN+YCWElBTMcS8gXOH4fIniXp2Y3CmDs46tsr5a3rV5sdlEGlRpJOsD0Fz8HdN3Q1EXpkraRZUGa8eZ2JvHSIqz2swOVs371W1+obaP1qXn8CZoPa5t1bE6BY5m22R1yaA0A3WzACO+imXoruSOkLAul4uvv/6akpISoqKiOHv2LBkZGXTp0qVsjNFopF27duzZs4fhw4ezd+9eKlWqRJs2bcrGREdH4+npyZ49exRBpKCgcE9jLbKSfvgiDqdO0q7GQahuB765JWQHhmFs1hWtpuIe9b+fL2TJr+fZmpTrtl+jghFtQxndPhQPjRrHpk1Y/zsFSkqkAw0G9o/syOvV9iM4pAkM1agZWG8IT0cMx0MtrXsIgCiiO/QJhl8mo3JJHY0EUyDmXp/hqi5NlpqXWcKymTu4mFIgaffQ/Vmc9cGrF2ctPaVIapGDE7k2nPIqIQR5aon0M+ChUaLJ7lZuqyA6evQo3bp1w2q14unpyfLly2nUqBF79uwBSsOar8Tf35/09FJln5mZSdWqVSV7vCqVCj8/PzIzM6953lNu3lIMBoPbquz3Epcqv99v3I3XXVhY+Jff47+Du+/6/cK9fO1asw1ngTciUjGkUxVRW/sTHtkmTobUwVKpBpypmBqDqYVOlh0u4sBF+1XHVPfS8HyryoRXsZNy6iSVli2n0ldfy8bZqldj4ZAAtnkVkkZpAAAgAElEQVTvlfUF6AIZFvIstT3qkHI6RdavsedT8+AsjBm/yPqKfJtypuUbOCx+cMXnn5lSwk+fnsFaLj+QyduDh/5dG1M12zW/L3Y0ZKgrY1XpZH0qUcBfLKJSoYUUeeaAf4x7+fvujlth9Litgqhu3bps376dwsJC1q1bR0xMTIUnz7vaectTUFBwT2dyvt8zVa9YsYKVK1fy7bff3tb1TJ06FavVyty5c685ztvbm9DQ0Js61/28dXyvXrsoiuSdSCK7QL79VUlzgVr2/diKfCns8CDVK1erkHPmlNj5cMc51vyR4zbLNEB1HwNPt65G36ZB6LVqhIsXsU58Bde+/bKxRV2imfBwBumkyPr61HqcZxvFYNS69+HRnN+BaesoWUg9QEatpzA8+i41NZctSqIosueH0/y4OAlXObNOSLgvQ6a0p3LVq+cXcgoiZ/LtpObb3ZSOhcp6NU39TVTS3d4qB/fq9/2f5rYKIp1OR+3apWnQmzVrxoEDB3j//fd56aWXgNJMwFf+KFyqaQUQEBBATk4OoiiWWYlEUSQ7O7tszL2OIAj06tULb29viSO52WymY8eOdOjQgbfeesvtsbNnz+abb75h9255Svt7DbvdzqxZs1i0aFFZ27PPPsvJkyfZvHkzHh6lD1BBEHjkkUcwmUxljvoAhw4dYsGCBezcuZPc3FwCAgJo0KABQ4cOpVevXqjVas6ePUvTpk3LjtHpdISGhjJs2DBJUdm4uDiaN29ObGwsNWvWvPUXr3DPIDjsZB5MotAsjxDz1xwlJC+VXH0VtA8/gr4CirI6XALL9qXx8a5USuzypIgAjYIrMaxNdR6qVxWNWoXocmFfthzbgnfkRVm1Wg4N68irtX5HKFfW1UfvyystptA6sA1uEZzof/0f+j3zUJU7VtR5YXn4bVLVTah7hRhy2Jyse/8AB7akyNfdrrQ4q87g/idQFEXSip2cyLVhc6MCNSqoV0VPTW+Pv4xEU7h7uKMSJAiCgN1uJywsjMDAQH7++eeyPqvVyu7du8t8hqKioiguLmbv3ssm171791JSUiLxK7qXUavVfPDBB+zYsYNly5aVtU+fPh2Xy8WsWbNu4+rk2O1XN7XfLIIg4HK5f2ivW7cOg8HAAw88UNY2d+5cMjMzmTNnTlnbwoULOXbsGO+++25Z248//sjDDz9MYWEhCxcuZO/evaxdu5bHHnuM+fPnl23hXmLNmjUkJiayf/9+XnjhBWbMmMHatWvL+v38/OjcuTOffPJJRV26wn2As6iA8/vOyMSQChc12EP19EyyAwM5F9YUbQWIoTyzg1Erj7Bga4pbMdSiujcfD2zMF0Ob0i3CD41ahev4CcyDBmNzV6E+IIAvJkYzrfYBBJVUYDT1a86izp9dVQyp8lPwXN0Tw565MjHkDGpJ8eBtOOr3k7TnXizmowlb3IqhTv0bMHBi26uKoXyri91pZg5mWd2KIX+jho7VPalVWaeIoXuM22YhevXVV+nWrRshISEUFxeTkJDAjh07WL16NSqVipiYGN58803q1q1LeHg48+bNw9PTs6xuVP369Xn44Yd54YUXePvttwF44YUX6N69e4WaDosaNq6wuf4OXseOXNf4mjVrMnPmTCZPnsyDDz5IcnIyn376KevXr8fT88YfjHa7nfj4eL788kvy8vKIiIhgypQpPPTQQ0CpI3xcXBzbtm0jMzOTatWq8cwzzzB27FjUf1bJjomJITc3l7Zt27Jo0SLsdjtJSUlERkYydOhQLly4wJo1a/Dy8uK5555j3LhxZecvKChg2rRpfPfdd1itVpo0aUJ8fDzNmzcH4IsvvmDChAl89tlnTJ8+nZMnT7J9+3YaNpSHFSckJNC9e3dJm4+PD++88w4DBw6kZ8+emEwm4uPjee+99wgODgagpKSE2NhYunXrxvLlyyXH16tXj8GDByOK0gdmlSpVCAwMBGDIkCF8/PHHHDx4kMcff7xsTM+ePZk5cyYzZ868oc9G4f7CevEcaaecOEVpLTItZmpZD2AqcJHRpAaV6ndAVQF+JMk5ZsZ+eYzUfLnvXaiPgRc616RLvcv+m6LViu3d93B8vgzcvJQIUS14vZ+a/S7ps02FisH1hjI0YjgatZufIlHE4/gqjD+/jMouLbQqosLW+j/Y2v4XNFKn65O/pbNq3h4sxdIXMJ1RS7+41kS2d78VbXeJnMixcr5YHj0GoNOoaFhVT7CnVhFC9yi3TRBlZGQwatQoMjMz8fb2plGjRiQkJJT94MbFxWGxWHj55ZfJz8+nZcuWrF27tiwHEcDHH3/MhAkT6Nev9O2gZ8+ekjf++4URI0awfv16Ro8eTWpqKrGxsbRt2/avD7wGsbGxJCcns3jxYkJCQti4cSNPPfUUW7ZsITIyEkEQCA4OZsmSJVStWpUDBw4QFxeHr68vQ4cOLZtn586deHt7k5CQIBEP77//PpMmTWLcuHFs2rSJiRMnEh0dTVRUFKIoMmDAgLKtQF9fX1asWEHv3r3Zt28fQUFBAGW+OG+99RZ+fn5lQqQ8u3fvLvuOXEnXrl0ZPHgwzz33HEajkR49ekgKtW7ZsoWcnJxr1iu72oNRFEX27NnDyZMnGT9+vKSvZcuWpKWlkZycTK1ata46t8L9jSgIFJ06RkZGZUSk/n9GVQ61846ickJO+yZUquY+LP162ZOSz4tfHafIJhU2XnoNzz1QgwEtgvHQXN5YEC6kYRkXh1AuaS4AlSphjhnKS8E/k2aVWlIr63yY1HLq1bfIrPkYt7yILnGNrEswBWLu8RGusE6SdlEQ2fJ/R/lpxVHKvafgF+LFkMntCQiVF7i9tD12PMeGXZBbhNRAzco66vjqbkmleoU7h9smiD744INr9qtUKiZNmsSkSZOuOsbHx0fiF3I/8+abb9K8eXNq1arF5MmTb2qu5ORkEhISOHToUJkP16hRo9i6dStLlixh/vz5eHh4SM4TFhbGwYMHWbNmjUQQ6fV63nvvPVkEX5cuXRg1ahQAo0eP5qOPPuKXX34hKiqKbdu2cfjwYZKSkjAaS50rp0yZwo8//siqVavKBIrL5WLu3Lk0a9bsqtdSUFBAYWFhmYgqz6xZs2jYsCFqtZqvvvpK0nf6dGlhyCstjpciIy/x1ltv8eSTT5b9/a9//Qu1Wo3dbsfhcBATE0Pv3r0l815ay7lz5xRBpOAWl81C9qFTFFjk5Sl8OEuNjBTMXhocnTpi8nb/3b5e1h68SPyG0zjLiYJWod7M69sAX5PUEuP8dQ/W8S8iuqkOoO3WlTPP9WHy6TkUWaWhV7W8axMfPYdAk/t1a9L3YfpuOOqi87I+R+0eWLq+h2iS3hdLsZ2fPj1D6jF5mFejdtXpF9cag0kevl/iEDiSbSXH4n67PdCkJaKqXim/cZ9wR+QhUrh5li9fjtFoJC0tjbNnz1KvXr0bnuvgwYOIokh0dLSk3Waz0bFjx7K/P/30Uz7//HNSU1OxWq04HA5ZZFSDBg3cpjNo1Ej6RhsUFERWVlbZ+c1mM+Hh4ZIxVquV5OTLIcRarZbIyMhrXsulkPurRdh99dVXOJ1ObDYbR44ckVyfOy5FRgI88MADOBzSlP2LFy+mYcOGOBwOjh8/zoQJE/D09GTKlCllYy6JPIvFcs1zKdyf2LMzSDtRgF2Qi6Eg1zGCsrLJru2DofnD6DVucvRcJ6IosnD7ORbvSpX19YkMYGqPcIlVSBRFHEs/xzZvPgjS2CtVUBCGqVPYWU9k9m8zcAjS/48W/q2YHjWLSh6V3C0E3eElGH6egKrccaLGgPXBeOxNRkgSLQKkJ+fzxexd5KYXS9eiVtF9aCQdHpeX4BBFkZRCB4m5NtwYhajkoaahnx4/o/ITeT+hfNp/wfX69NwODhw4wNtvv83KlSv55JNPiImJYePGjWg0N5YtVRAEVCoVW7ZsKYvAusQlYbF27VomTZrEzJkziYqKwtvbm8WLF8vSJlzNj6n8vCqVqmxLTRAEAgIC+OEHeZXrK7dM9Xr9X16jr68vKpXKbY27c+fOMXnyZGbNmkViYiLPP/88u3btolKl0od1nTp1ADh58mRZNvQrIyPdbZeFhISU9devX5/k5GTi4+N56aWXyu5dXl4eUOpgraBwCVEUKTp9iow0AyJSwaDGTpjlMCZzCVnREXjWuLpV9HoQRJG5m8+w4jd55ua4TjUZ3iZE8j0XLRas017F+d13svHah7pgmP0632X/xNv75iGWc4DuUeNfvNBsAlp3/kJOK8YtL6E7ulzW5fJvjLnnJwhV68v6fv/5LF+/tx9HOcdvz8p6nno5mjpN5dvoLkHkcLaVNDe+QmoVhPvoqO2jQ634Cd13KILoLsdqtfLcc88xaNAgunbtSpMmTYiOjmbBggUy35W/S5MmTRBFkYyMjKtaTHbv3k3Lli3Ltr0AifXmZmjatCmZmZmo1eqbDk3X6XRERESQmJgo2eoSRZHY2FhatWrFv//9b8xmM5s3b2bKlCllTvpdunShSpUqvPnmm/zf//3fDZ1fo9HgdDqx2+1lguj48eN4eHi4dQBXuD8RbFayjpyioKSqrM+oyqFW7glsRpGSrg/hWcnfzQzXj0sQee3HJFkNMoNWTfwj9Xg4QirYhbw8LDGxCIcOSSdSqdCNfR6PZ0ey8vQKPjn2kexcwxuMZHC9Z9y+RKgKUzGtH4o243dZn63FGKztp4NWamUWBJENSw+xfW2i7Jjq9aow6JV2+PjL8wtZnQK/ZVgosMmzClU1amjsZ1C2x+5jFEF0lzNjxgysVivx8fEABAYGMm/ePGJiYujZsycNGjS46rFWq5VD5R5uJpOJ8PBwnnzyScaMGUN8fDxNmzYlLy+PHTt2EBYWRu/evQkPD2flypVs2rSJ2rVrs2bNGnbt2kXlyjefoKxTp05ER0czaNAgZsyYQd26dcnMzGTz5s106tSJdu3a/fUkV9ClSxd2794tyQf04YcfcvDgwbI8TCaTiffff59evXrRp08fOnfujKenJ++++y7Dhg2jX79+xMTEUKdOHcxmMz///DNWq1VmocrNzSUjIwOn08mxY8f48MMP6dChA97el505d+3aRdu2bTGZrp4QTuH+wZ6dTnpiITaXXAz5iaeolpFOTrgvpiYPoamgEhwOl8Dk9SfZcDxb0u5t0PL+k42IrOYlaRcuXMAy6jmE8i89Xl4Y5/wPTccOfHR0IV8mSV8c1CoNLzd/hW41erpdh+b8Dkzrn0FtyZG0ix6emLstxFnvMfnabU5Wv7mXo7vkPkZRPWrzyKjmbuuR5Vtd/JZhkYXSa9XQqKqBapWU6LH7HUUQ3cXs3LmTRYsW8fXXX0u2kvr168c333xDTEwMmzdvRqt1/zEnJyfLLEDNmjVj69atLFy4kHnz5jFt2jTS0tLw9fWlRYsWdOjQAYDhw4dz+PBhRo4ciSiK9O7dm9jYWFl4+o2gUqlYvXo1s2bNIi4uriwhZ5s2bRg4cOB1z/fMM8/QoUMH8vLy8PX1JSkpiddee4358+cTEhJSNi46OpoxY8YwduxYdu3ahbe3N7169WLTpk0sWLCA2NhYcnJy8PLyomnTpixcuFDiUA2URbNpNBqCgoLo2rUrU6dOlYxZs2bNNYMFFO4PREGgJOkI6Rd9EJEKEDV2Qm2H8SwpJrddJJ4hFZf+w+YUePnrE/xSrhZZFZMHHz3VmHoB0m1u14kTWEbHIP7p41e2xjp1ML77DmKNEOb+PpsN576X9OvUOqZFzaRtUHu36/A4ugLj5jiZv5DLNxzzI8sQ/OQvc0V5VpbH7yA1Ubp2rYea6H7V+dfgVm7PdaHIweFsq8xfyNNDTctAI5V0ilVIAVT5+flXScZ+f1FQUFAh1o07lfu9dMeIESOIiIhgwoQJt3U9GzZsYNq0aezcufOqQhUq5vt4P6fzv9OvXbQWk30kkTxzsKzPoMqlVv4xrCY1tO2Ch+nvV6n/q+s+lVXC5G9PkpgpLbQa6KVj0cBIalaRlsxw7t2L5flxUCx1WNa0aYPx3QWU6ERm7Z/OvkxpTTJPrSezov9HEz83vk6igH7X6xj2zpN1OWr3xNzjQ9DLv/uZqYUsfXU7eeXW7uVr4OmpD2AhR3btoiiSmGvnTIE8Kay/UUOzQOM9EUp/p3/f7xYUC5HCfcGMGTP+kTp5f4XZbGbhwoXXFEMK9zaOzGTST1qxCnIxVFU8Q0jGeXLq+WGK7IxKfWOBEeURRJEv9qXxzi8p2MttGYX6GPjoqcaE+EhfmBw//Ij1lUlQLpJS27MHhtmvc952kSnbJnK+WBqd5qP35Y2286nr4ybS1WnBuGEMupNfybqsbSdha/MyqOTWmqQ/Mljxxi6sJdK1BIZV5plpD+AT4MmpU9JtN6cg8kemhUyzPKS+dmUd9asomaYVpChPZYX7gtDQUGJiYm73Mujbt+/tXoLCbUIUnJSc/IP0TH9EpBYQNQ6qW4/gaSki94GmeAZf3ffvekkvsDL1u1PsO1cg66td1chHTzUmwOuy07Ioijg+W1IaVl8Oj6eHoJ84gf1Z+5i5bzolTqnlKNAYxJz2b1G9kjwbtMqchembQWjT90naRY0eS/cPcdSX/2+IosjOdSf54bNDiOX2u8KbBTLolbYYPOUV6M0Ogf0XLRQ7pM7TahVE+hkI8br5dAUK9x6KIFJQUFC4xYiWPLIOJ5FvDZH1GcinZt4JbF4C1m49MBnlxVtvlE0nsnn1h1MU2+RWkh4N/JjcPRzvK2p6iS4Xttlv4FixUjZe9+J4PIYPY82ZL/noyEKEcvXfG/g2ZEab16lqkKeTUOck4vl1f9SF5yTtgskfc+8VuIJby45x2Jx89d5v/LH1rKyvVbda9IlpiUYrtyblWJz8nmGVZZ3Wa1S0DDTiY6gYq5vCvYciiBQUFBRuIc6MU1w45cQmyMVQFSGFalmp5EX4YWrUGZW6Ypx7nYLIO1tTWLr3gqzPS69hcvdwejaUhu+LZjPWlyfivKKoNgBaLYaZM1A92ou3Ds7l+7PfyubsGtqd8c0moNPIk7BqUrfj+e0QVDaphcpVJYKSx1YhVg6THZOfWcLy13eRdjpP0q5SQbehkXTsFyFPtgiczLWRlC/3F/LWqWkZZMToRkApKFxCEUQKCgoKtwLRhS1pL6npQQhII7fUOKhuPk4laz4FDzTDMziiwk6bU2Jn4rpEt1tk0TV9eO1fdQn0LpfXJycHy5hYhMPlEtFWqoTx7bewto7ktd0v81uWdLtLhYpRjWLoHz7QrT+Ox/FVGDc+L4skc9TojPmRJW6dp88ez2Z5/E5KCmySdoOnBwNeiqZ+K7nvVYlD4Ly6ClY3YijIU0tTfwOae8B5WuHWoggiBQUFhYrGXkTx0f2kFdWltDzoZQzkUys3EbvJgblrV4ye8vxDN8rJXAcLNv5BRpFUGGjVKsZ3rsnAVtVkGZiFC2mYRz6LeFa6NaUKCsT44QdkV/dm8vYxJBeekfR7aj2Z3OpV2gS5KSQtiuj3zMGwe7asy954KJYu82VV6gGO77nAyjm/4iyXedo/1JunJ7fHL0SankAURS4UOzmabcWlkvsShfvoqOurOE8r/D0UQaSgoKBQgagKz5J17By5dnmpiSrOs4Rmp5ITZsLQ6hF0FVCL7BJrD14k/pdcnOWSMAd46Zj/WARNQuSV3l1nzmAZ+SziRWm2anX9ehg//IAkfR6TfxlNrk0awRVkCiY+eg41veXFiVUlGRg3x+Fx5kdZn7X9dGyt/yOrRwawb8MZvn7/N5nzdIM21eg/vo2sOKtDEDmSZSW9RF6CQ69R0cTfgL9J+YlT+Pso3xYFBQWFikAU4eJ+zp/RUOKqU65TIMRykipFmWQ3r4Fn3evLtn4tnILIvJ/OsNJNPbJWNSozp099qrqJxHIdO4bl2dGIeVI/HU27thjeepMt+bt5a+8crC6rpD/CtwEz2/yPKgZ5fiTtyXUYf3oBtVWaOFHU6P6MJHtcdowoivy8+jibl8vrRnYe0JCHBjVCXW67y+YS2JduodAuL8ERYNIS6a9Hr1H8hRSuD0UQKSgoKNwsghNn0lZSM8JwiOX9hezUyj+OVlNIQac2ePrJrSo3SoHFwYR1ifyaIi9ePDQqhLhONdG68Z1x7t+PZczzsoSL2h7dsb42kf8dnc2O9G2y4zpU68QrLaZg0JZL8mrNx/jzBHQnVsuOEQy+pZFkIfKtNcEl8O2i39nz/WlJu0qtok9MC6J6lBeWpSH1ey+aMTukliSVKNLQ30ANLw9li0zhhlAktMJ9wY4dO2jZsiUulzz8+Eax2Ww0btyY33+XF6VUuI+wF2E+tIXki/VlYkgvFlE/6yD2ACfObr0xVKAYSs4xM+TzgzIxZPBQM6dPfV7sUsu9GPrlFyzPjpaJIY/+T7DvP73497YRbsXQk+EDmdb6NakYEpx4HP0Cr2Xt3IohZ3AUJQO3uBVDlmI7y2btlIkhrU7D4FfauRVDRXYXu9PkYshLp6aGkE2Yt+IvpHDjKILoLicmJgYfHx98fHyoWrUqjRs3Zvz48eTny98Yr+SLL76Q1PG615k2bRovvvhiWTHWv7r+v3Nf9Xo9Y8eOZfr06bd8/Qp3KMVpZB84yPnCxohI89t4ObOplXuYwhYhGKMfReNhvMok18+O07kM+fwg5/Kk21l+RjVLhzShewN/t8c5vl2PZWwc2KQRXAwbzJuPwIwD0yiwS58dHmodLzR7mdGNY1FfyiItOPE4tpJKS6MwbYxFXZwmOUZUe2BtP52SJ39A8JGLwItnC3h//GYS90u3+QyeHox4rSMN28r/N/OsLn5NM8uKs/obNbStZkJHxb3sKNyfKFtm9wCdOnXio48+wul0kpiYyPPPP09BQQGffPLJ7V6aBKfTiUajuWVvcA6HAw8PuZPqnj17OHXq1HVnif479/XJJ59k6tSpHD9+nAYNKi67sMKdj3jxCBeSXJiF2rK+AEsynuo0rF074VnJvTi5ERwugY92pvLxrlTKF6FsFuLF8830RARWcnus/YsV2OJfl7XnjHqSVxrsI+tCpqyvvk8DJracTJhXzdIGUcDjRAL6X/+HJv+0bDyAy68R5h4fIvhHuu0/vDOVNW/vw26VOkN7VzUyfEZHAsOkofiiKJJW7ORItpVyWohgTy1NAwyyyDkFhRtBEUR/gdfv/+zbf1HzGdd9jF6vJzAwEICQkBD69u3LihUrbmodoijyzjvv8Nlnn3Hx4kVq165NXFwcAwYMKBvz6quvsn79es6fP4+/vz99+/blv//9b1kR2dmzZ/PNN9/w/PPPM3fuXM6dO8e5c+cYMGAAERERVK5cmSVLlqBWq3nqqad47bXXUP+ZmM5utxMfH8+XX35JXl4eERERTJkyhYceegiA7du38+ijj7J69WreeOMNDh8+zLJly+jRo4fsWhISEujYsSNG4/W9of+d++rr60ubNm1Ys2YNU6ZMua75Fe5SRBeO07tJTQ/GKUq/U2oc1Cg4icvPgibqMdQVGEV2Pt/KpG8SOZRWJOvrExnAlO7hnE2WixRRFLF/8CH29xZKO1Qq9o98kPiamxAtUqWhVWkZGjGCp+oOQqMu/ZlQZx/HuDkObbq0kGvZedRabC3HYYueCFp5gkbBJbBp+RF+STgh6wutX4VBk9pRuapJ0m51ChzJtpFplkeShXl70LCqXtkiU6gwFEF0j5GSksJPP/3k1lJyPcyaNYt169Yxb948wsPD2bdvH3Fxcfj4+NC9e3cATCYT7733HsHBwSQmJjJ+/Hh0Op1EGJw9e5aEhASWLFmCTqcrE0tffvklo0ePZuPGjRw+fJiRI0fSrFkznnjiCQBiY2NJTk5m8eLFhISEsHHjRp566im2bNlCZOTlN89XX32VWbNmUbt2bSpVcv9mvGvXrpuuIXat+9qyZUt27tx5U/Mr3B2IjhJKju4hrbAe5T0O9GIRtbJPUBRRGVODhyos6zTAD8eymLUhSVaCQ62C8Z1rMaR1NbfCQBQEbG/8D8fyL6TtGg0rnqnNl7X+kB1TxzuciS0nU6fyn9XTnTb0e+eh3/e2LMEilAohR8PBWNu8iOhdw+36bWYH/zf3V9kWGZSW4ej9XAu0Hpe3HC9ZhY7lWHHIA8mU/EIKtwRFEN0DbN68mZCQEFwuF1ZrqU9BfHz8Dc9XUlLCwoULWbt2Le3alYYH16xZk99++42PP/64TBBNmDCh7JiwsDDGjx/Pu+++KxFEdrudjz76iICAAMk56tevz+TJkwEIDw9n6dKl/PLLLzzxxBMkJyeTkJDAoUOHCA0tLRI5atQotm7dypIlS5g//3LRyYkTJ9KlS5drXk9qaipBQUHXfR/+7n0NCgri3LlzsnaFe4zidLKPJJNnl2eVruy8SEj+aQqj6uEZ2rTCTllgcTD3p2S+PSLfzvLz9OD1R+vTpqb72mdiiRnr5Ck4N26UtLv0HswZXIk94VmSdhUqnqw7kOENnsVDXSr8Ned3YdwchybvlHx+lQZHo0FYo15ErFzzqteQl1nCspk7uJgizZyt0ap5dHRzmfO03SVwKMu9VUitgoZV9dTwlqcRUFC4WRRBdA/Qrl07FixYgMViYenSpaSkpPDcc8/d8HyJiYlYrVaeeOIJyRuYw+GgRo3Lb4Dr1q3jgw8+4MyZM5SUlOByuWRRXNWqVZOJIYBGjRpJ/g4KCiIrq/QBffDgQURRJDo6WjLGZrPRsWNHSVvz5s3/8nqsVmuZZep6+Lv31Wg0YrFYrnt+hbsHIeMYF045sQg1ZX1BliQq29Mo7twOk697C8l1n08U+eZwJm//nEyeRS4MOtbxZUavelQxubcEC6mpWMaOQzgpFTJWkwfTn9aTGCbdIqtq8GNSy6k0929Z1qbf+yaGna+5nd9RuwfWB2e7dZi+ktTEHJbN2klxvtT526uKgWcW+twAACAASURBVMGT2lEjQloI1uoU2Jsur1IP4KNX08TfSCWdEgukcGtQBNFfcCM+Pf80JpOJ2rVLHTvnzJnDI488wpw5c5g0adINzScIpQ+jlStXllloLqHVln5l9u3bx4gRI5g4cSKvv/46lStX5vvvv2fq1KmS8Z6e0jDkS5TfelKpVIiiWHZ+lUrFli1bZOPKC5urzX8lVatW/cuoO3f83fual5eHn5+8wrfCPYDownZqD+cvBuFC+t1TY6dmwQkwFuF4uCcGo7wu142QmFHM6xtP88cFua+QTqNifJdaPNUi+KrbRc5du7G8+BIUSC0yBV4apg0zcDZYGg33QHBHxjefSGXd5fXrf/2f27IbgikQS+c5OOv2dptt+koObT9Hwlt7cZYTNyHhvjw95QG8q0r9rywOgT3pZsxOqVhTq6Cer55alZX8Qgq3FkUQ3YNMnDiR/v37M2zYMIKD5YUQ/4r69euj1+tJTU3lwQcfdDvm119/JTg4WLJtlpqaesNrvpImTZogiiIZGRkyi9CNzpeYmHjT81ztvh47doymTStum0ThzkB0lFB4+HcyiuVRZHqxkNrZiRRXU6GvIOdph0tgwdYUVuxPk0VTAdT2M/G/3vWpF+D+JUAURUzr1mFZ8jkIUhGSHKRh9hATmVUuiyG9Rs+YyHH0CustERr63bMx/Po/2fz2xs9g6TADDO636K7kl4TjbFh6WNbeqG0I/ce3QWeQ/vSUOAT2pJmxlrtwxSqk8E+iCKJ7kA4dOlC/fn3mzZsn8bcpjyAIHDp0SNKm1Wpp2LAhY8eOZerUqYiiSPv27SkuLmb//v2o1WqGDRtGeHg46enprF69mqioKH766SfWrFlTIesPDw/nySefZMyYMcTHx9O0aVPy8vLYsWMHYWFh9O7d+7rm69KlC8uWLZO1X+v63XG1+7p79+4yfyiFewOhII2MY+kUOeRiqLIznRo5Z8htFIApokOFOE8XWZ28+NVx9pyVV6jXa9WMahfK0KgQdFr35xKLirC+OgPvH+T1w3ZEevBuPxM23WXRE165HpNbTaeGV9gVk4ilYmjPHOncHpUo6f0FrhruX44kY0WRDUsPsW2N/AXkwf4RdB0SKSvDUWR3sTfdIssvFGDS0jxAqVKv8M+hCKJ7lOeff57Y2Fji4v6fvfMMjKJq2/C1vaWRSgiQkEKAEEqkNwErggKKYkMBX6V3pfcOAq9IR1EBGygoIryK4EeTjkgnBEIKEEo6ye5m23w/IsHJJJDEKG2uX8l5Zs6cs5nM3nPOUwaJ/H7+isVikazAeHt7Ex8fz5gxY/Dz82PhwoUMGzYMd3d3oqOjGTRoEADt2rVj4MCBjBo1CqvVSps2bRg9ejTDhg0rl/EvWrSIOXPmMH78eC5fvkyFChWIiYmhZcuWpe6ra9euTJw4UZIr6HbzL47Cn+uBAwfIzs6mY8eOpR6XzL2J/dIpLsarsQuFV1ddVLLEUSH3KulNozBVLjrPTmm5diOPfmtPcva6WWJ7NNyb4Y+HUtmreB8454kTWIa9i5B8UTxaBXz+pJ71rXSi7a2XwvMdp7WqvzgmCwK6vdPR739f1IegdSe387c4KzW+4zxcThcblvzOwZ/F/z8qtZLO/RsQ81iI5Jw0i4Pfr1qxFyroKucXkrkbKDIzM4tYnH34yMrKwtOzfHwA7kXK6lh8v3Nz3hMnTiQ1NZWFCxeWa/9vvvkmderUKTcheJPyuB/j4uKIiIgopxHdX5Rp7oILa9wBLl4JxIV4C0yNhZDMM6DNxd60BTrPSuUyzvOpZvqtPUlKtjhzdKCHjpFPhNI6wqf44QoC9lWryZs7Dxxix+tcPcztauL3yFvz8NH7MDxmLA38G4o7ctrQ7xyL7o/l4v617uR2XoezUqM7zsNhd7J27n5O/CYWZTqDmm7jWhAaLQ6scLoEYtPzSMiWhvFXdlMT7acvtb+QfL8/nHMvT+QVIpmHgqFDh7J8+XKcTmdB+Y6/S15eHlFRUfTt27dc+pO5ewgOCzknDpCSHQ6Iv4hNwnWqXT9PZogeff3O6Mop2eLvyVkM/PYUNwrlFoqu5M6CLrWoUEwEGYCQmYllzFic/7ddYrsQqGT2KyZSfPPvcyVK2oV04K2a7+CpE/v/KHJSMP7YHXXKfnH/Wg9yn1+HM7CQeCoCm9XBFzP2EPf7FVG70UNHj0ktCQr3FrWnWx0cu26V1CMDOdmizN1FFkQyDwUeHh68++675dqnTqcTOZXL3KeYr5N2/CzpedI3bD97PH7Zl8hoXKPc8gs5XAKrD1xi8a5EbIX8ZlqHezOzYyQGTfGi3XniBJaBgxCuXJXYNjXR8lk7A3ZNvqBo6N+YXrX7Uc1D6gulurgb46aeKM3iHEf5Ymg9zsAGd5xLdpqF1VN3c+lchqjd09dAj8mP4l/FQzTvuIw8LmRJV4VATrYoc/eRBZGMjMzDS9pZUs5kc8NZOJ+Oiyq5p9FoMjE/+RgmU/mkVTh7LZcJm+M4dSVHYutSryKjngwrskL9Tew/bMQ6fgLYbKL2XD0sfN7I3tr5fkHVPELpFdWPhgFF+P4IAtrDC9HvnohCEK9Oudwrk/vs57gC6t1xLpfOZbB6ym6y08U5uHwqudFzyqNU+DMaziUIJGXbOZdpkwhAAINaQR0/PT4G+etI5u4i34EyMjIPH4ILV+I+kpIrYBPE/kAqbIRkniSvkhJV/c6oVX//MWl3uvh4TzIf772IwyUVBf1bBfOfppWLXR0RnE7y5v0X+6efSWyxVVTM7XorpL598LMMrDsMtbKIcTvtGH7pj/b0GukYg9tiafcRgqF4v6WbnNhzkW/m7sduEwuqwFAvekxqhZuXvqD8xtmMPCyOol1Vq3poqOGtu60IlJH5t5AFkYyMzMOF00remZ0kp1XHhbgEhFbIITjjFOY6QZhC7+w/UxIuZVoZtO4UcUVEkXnq1Yx+Koyna/oVe76QlYXlvRE4d++W2H5ormPl03qcqnxB8XzAS/StN6BoYWU3Y9zUHc2FLRKTtfF75DUZCcrb+9cJgsCOb06zZfUJiS2yQSBd32uC3qghO8/J0etWbtiKKEQG6NUK6vjq8TXKX0Ey9w7y3SgjI/PQoDBfJevUMa6Ya0tsJlcaFXNjyWvVCJN3+ZTgSMqw8PZXJ7hSKIoM4Mkavox8IhQfU/F1uZwnT2J5dzhCYqKo3aaGxZ2MbI/JP1er1DLykXFUMhe9yqSwZmD8/mWp87TOE/PTy3GEPnXHudisDr5beIijO6R1+5p3rE67HnVQqpRcMzs4ctVSZHJJlQJCPLWEemnRyKtCMvcYsiCSkZF5OEiP5eqZNDIdURKTjy0ZN9UlhMeeQq93L5fLXUgz8/ZXJ7ieI/b38TFpGP1kGI9HFu+XJDgc2D5egW3xEklIfbq7ghmvm4irkv/49tJ6MaXJTGp51yYuTlqEVZFzGdP6F1ClnRa1Oz1DMD+/HpeX1OG6MJnXcvl82m9cjheXwFGqFHTsE0PDp/ILtCZm2TiZJhV/CqCKh4YILy26YpJLysjcbWRBJCMj82AjCDiTD3Ex0USeEFLI6KJyThyCnwXtIx1R3GHLqKTEXc+l19cnSMsVR1Q9Vt2HCe3C8TQUH1LvSkrCMmo0riN/SGyxVVTMfM1Ehke+qKjqFsz0pu8TaCo6L5IyPQ7T+s4ob4jzAzn9apPbeR2CKeCOc7lw4jpfztxDbpZY6BjctLw6qhlhdfwRBIEz6UVHkAWa1FT31mHSyEJI5t5GFkQyMjIPLILLgfn0AS6nVUYo9LhTC1aCM09jreGJKbJNuV0z9moOvb4+IalS/2xtfyY9E1FsKQpBEHCs/w7rjJlglvobbWmo5aMOt0LqY/weYULDqbhpi17RUqUcxPh9V5TWdFG7I6gZuR2/At3tE38KgsC+zefY9NEfuArtf/lV8aDb2Ob4VnLH4RI4es3KVbND0kctHx0hnsVvCcrI3EvIgkjmoWD37t0MGjSIAwcOlFtixrLw0UcfsXXrVtaskUb5yJQvrjwzqcdOk2kJkdgMrkyqZJ7B3CgSUyXpFlpZOZCYybDvzpBtFYuD5+sGMO7p8GJLUQg2G3lTpmJft15iyzIqWPy8gf21bgmL20aSAer4nzBu6oHCIQ6Jt4e1x/zMClDfPmu9yyWw6aMj7P3xnMRWs3ElXhraGJ1Rww2bkyNXreQUqmivUkA9fwMBJvkrRub+QV7DvM/p06cPXl5eeHl54ePjQ+3atRk6dCiZmZm3Pe+LL74gKCjoXxrl3Wf8+PEMGzasQAxNmTKFqKgosrLExTTfeecdmjdvju0veV4SEhIYMGAAtWvXxt/fnxo1atChQwe+/PJL0XE3/w5eXl74+voSHR3NxIkTcfzFB+SNN97g6NGj7Nmz5x+e8cONxmYm+VASmZbC9cjAx5ZE5ZyT5LVujLGcxJAgCHyy7yK9vj4hEUNdYwJvK4Zc11Mxd+9RpBg6GKlm4CD3AjGkQEGvqH4MqTe8WDGkOb4K4w+vSsSQrXY3zB1W3lEMOR0uvv3vgSLF0GOvRvHa6OZoDWqSb9j57ZJZIoZ0KgVNKhllMSRz3yHfsQ8ArVu3ZtmyZTgcDmJjY+nfvz9ZWVmsWLHibg9NhMPhQKVS/WOZaO12OxqN1Ddj//79xMXF0blz54K2ESNG8NNPPzF8+HCWLVsGwIYNG/j+++/Ztm0bWm3+F9CRI0fo2LEj1atXZ/bs2VSvXh2lUsmxY8dYsWIFoaGhNGnSpKDfDz/8kKeeegq73c4ff/xB37598fLyYvDgwUB+dusuXbqwbNkymjVr9o98Dg871kvxONM8cKITtSuxUyXnDEr9DZyPP42unJynb1gdjNt0lv+LS5fYXm9YiXfbViv2nneeOIFlwCCEq+Ks01YNfNLewJaG2oLCrHqVnlGPjKdFpVZFdQWCQODZjzCeXS4xWRu/R17T0aIir0Vhz3Pw1ex9nDlwWdSuNah5aUhjajUNwuESOHndyqUc6RaZu1ZJg4oGDLLjtMx9iCyI7oDnf73ufFA5kjXk9is7RaHT6QgIyHeODAoKonPnznz55Zd/axyCIPDhhx/y6aefcuXKFUJDQxk0aBBdu3YtOGbixIn8+OOPXLx4ET8/Pzp37szo0aMLisjOmDGDH374gf79+/P++++TlJREUlISXbt2pUaNGnh6evLZZ5+hVCp5+eWXmTx5Mkpl/oPUZrMxbdo0vvnmGzIyMqhRowZjx47lscceA2DXrl08++yzrF27lpkzZ3L8+HFWr17N008/LZnLt99+S6tWrTAYDAVtWq2WpUuX8thjj9GhQwcaN27M0KFDGTlyJNHR0QWfQZ8+fQgNDWXLli0FYwMIDQ2lU6dOCILYt8LT07Pgb1G5cmXWrl3L0aNHRce0a9eOzp07YzabMRqNZf4byRRCEMg5e4zLV30ovPitJ4Nq6We5UVmPNqZz+TlPX8tl6HenScqwitoVQJ+WVXmnWZVixZB944/5WafzxM7Kl32UTH/dxMWAW2OsWaEW79YfRYhH4Yzaf2LNxPDrMDzPrhM1Cwol1rZzsdXpcce5WM12Vk/ZzYUT10Xt7t56ekxqRcUQL3JsTg5ftZJrl+YXCnJTE+Wrl5Msyty3yILoASMhIYFt27YVuVJSGqZOncqGDRuYM2cO4eHhHDx4kEGDBuHl5cVTT+XnLDEajSxcuJDAwEBiY2MZOnQoWq2WsWPHFvSTmJjIt99+y2effYZWqy0QS9988w29evViy5YtHD9+nP/85z/Uq1ePLl26ANCvXz8uXLjARx99RFBQEFu2bOHll1/m119/LRAskC/Kpk6dSmhoKG5ubkXOZc+ePaLVoZtER0czYsQIhgwZQnR0dIHou8mxY8c4c+YMK1asEImhv3K71a4zZ85w4MAB+vfvL2qvX78+DoeDgwcP8uijjxZ7vkzJEZw2Mo8d5foN6TawNxeodD2ZjLrVMIUXUcqijOxPyGTgulNYC4kDD72amc9F0jy0QtFjFQRsy5Zj+3CBxHYkQs2cl43kGvLvN51KR8+a79A5rAsqRdEiTnVxD8af3pFEkgkqPeb2K3CEtb/jXHKz8vhs4k5JTTLviiZ6TnkU74pupFscHLpqwVFICykVUNtXT2X38il6KyNzt5AF0QPA1q1bCQoKwul0YrXmv6lOmzatzP3l5uayaNEi1q9fX7CtExISwuHDh/n4448LBNFfC5sGBwczdOhQFixYIBJENpuNZcuW4e/vL7pGZGQkY8aMASA8PJyVK1eyY8cOunTpwoULF/j22285duwYVapUAfJ9e7Zv385nn33G3LlzC/oZMWIEbdu2ve18kpOTqVixYpG2wYMH8/nnn7Nz5072798vcrg+f/58wfhukpWVRa1atQp+Hzp0KMOGDSv4vVevXvTt2xeHw0FeXh4dO3akT58+omsajUY8PDxILJRsT6ZsCJZMrh07T1aeVAwFOk7ieSOVrFaNMPmFlds1T1/JYfD60xIxVDPAxNzONQnyKtpPR3C5yJsxE/sX0hXcDS10rHxKj+vPrNP1fGMYVn8ElUzF+Po57ej2z0J3YB4KQTwOl74C5o5f46x0ZwGYevkGKyfuIi1FXF8tINiTHpNb4eFtICXHztHrVgpXHXHTKKkfoMdde/cCFWRkygtZED0ANGvWjPnz52OxWFi5ciUJCQn07t27zP3FxsZitVrp0qWLaAXEbrdTteqtDL4bNmxgyZIlxMfHk5ubi9PpxOkU1zaqVKmSRAwBREWJnVkrVqzI9ev5S/VHjx5FEASRbw5AXl4erVqJ/Sfq169/x/lYrdaClanC7Nq1i+TkZNRqNfv37xeJn6Jwd3dn165dALz44osip2qAyZMn8/jjj+N0OomPj2fMmDH06dOH5cvFfh0Gg6FAvMqUHVd6IpdOZ2Nxip2nFTgIMR9DpTCT98RTGIze5XbNpAwLfdeexFyojtfzdQMY+URYsYkHBZsd65ixODZtErUXzjoN8J9aveka8SpKRdF9KTPjMfzvbdRXDktsTt8ozO0/w+Udcce5JJy8zuppv2G5Ib6Pq0R68+aElhjddSRk2ThVRLLFIDc1tX31xaYRkJG537hrgmjevHls3LiRc+fOodVqadCgARMmTBC9fffp04evvvpKdF6DBg3YunVrwe95eXmMHTuWdevWYbVaadWqFXPnzi23CKqy+PT82xiNRkJD87PNzp49mw4dOjB79mxGjRpVpv5crvy3za+++qpgheYmanX+LXPw4EF69uzJiBEjmD59Op6enmzevJlx48aJjjeZTEVeo/CWnkKhKPDHcblcKBQKfv31V8lxhYVNcf3/FR8fnyKj7rKysujfvz/9+vXDz8+PUaNG0bp164J7Jywsf0UhLi6OunXrAqBUKgs+65uO138lICCgwB4REUFOTg5vvfUWo0aNolq1W/4fGRkZ+PqWTwX1hxJBwHnxD5ISTNgFcTFSNWZCM0+Q5yWgbNYRjVpXTCelJzXHRu+vT5BuFicgHNw6hB5NKhc/XLMZy+ChknpkuXoFU98wcTrk1qO4b/RAXgh7qdi+1HE/YPy5Lwp7jsR2JfQ1DO3nQQnmfHRHEt9+cABnoT2w8HoBvDa6GVq9mtj0PM5n2iTnVq+gJcxL+48FSMjI3A3umiDavXs3b731FjExMQiCwPTp0+nUqRP79++nQoVbe+83I6huUvhLaNSoUWzevJkVK1ZQoUIFxowZQ9euXdmxY8ddzTdzNxkxYgQvvvgi3bt3JzBQGnZ8JyIjI9HpdCQnJxfr47Jv3z4CAwNF22bJycllHvNfqVOnDoIgcPXqVcmKUFn7i42NlbSPHDkSDw8PRo8ejUajYdOmTQwcOJB169YVnBcZGcn8+fPp3Llzme6nm+eY/5Jo78KFC1it1gKRJVNKXHZsZ3eTdK2apDirgXRCU2O5UlGFd5OOKIrx/SoLN6wO+q49yaVCGZu7Nw66vRjKzMLcpy+uQs716e5KJnU3kRh4674aUGcInUJfKKYjF7o909EfmCMxuUwVsTy1hIu2ykTcQQwJgsD2taf55XNpgdaYtiF06v8IqJQcuWblSq44kkwB1PbTU0X2F5J5ALlrgmj9enHOjWXLllG1alX27dtHu3btCtr/GkFVmKysLFavXs2iRYto06ZNQT/R0dFs3769ICLpYaNly5ZERkYyZ84ckb9NYVwuF8eOHRO1qdVqatWqxYABAxg3bhyCINC8eXNycnI4dOgQSqWS7t27Ex4eTkpKCmvXrqVRo0Zs27atQEj8XcLDw3nppZfo27cv06ZNo27dumRkZLB7926Cg4N57rnnStVf27ZtWb16taht06ZNfPvtt/zyyy/odPlfIIsXL6ZFixasXLmSN998E4VCweLFi+nUqRNPPPEEw4YNIzIyEqfTyf79+7l06ZJEJGVlZXH16lVcLhfnz59n9uzZhIeHExkZWXDMnj17CAkJKViBkik5ClsWuSf3cfFGFIUjyTxcKVRNPUdG3aqk44tPOYqhbKuDIetPE3stV9T+XG1/BrcOKfY8V1oalv+8jSv2rKj9so+SiT1MXPO+df8MqjuM56pJnf8BsGZi/OmdIivV28OewfLEAgSDDxRRy+yvOOxONiw+zOGtCRLb469F0aZrLXLtLg5fMksiyVQKqB9gwF+uUC/zgHLP3Nk5OTm4XC68vMRh7nv37iU8PBxPT0+aN2/OuHHj8PPzA+CPP/7AbreLnGorV65MZGQk+/fvf2gFEVCwFTRo0CCR389fsVgskhUYb2/vAt8XPz8/Fi5cyLBhw3B3dyc6OrogCqtdu3YMHDiQUaNGYbVaadOmDaNHjxY5GP8dFi1axJw5cxg/fjyXL1+mQoUKxMTE0LJly1L31bVrVyZOnMjp06epWbMmaWlpDBkyhGHDhlGvXr2C46pVq8bEiRMZO3Ysbdq0oWrVqjzyyCPs2LGDefPmMWLECK5evYrBYCAqKopx48bxxhtviK41cOBAIH8LMCAggGbNmjF+/PiCrUaAdevW8eabb5bxk3l4UeRcJP1kLNfzoiU2P9sF/G4kk9W8HqbAGncUBiXFJQhsOHaV+TsSySi0TdYyrALj24UXu23kunYNS8+3cMVfELWfr6Ri8psmstxvCbYh9d6jQ0jHIvtRpp3B+MNrqDLPi9oFpQZr6xnY6rx1x/xCAJYcG1/M2EP8sWuidpVayQuDGlKvdTApOXaOXbdKKtVrlQoaVDTgpX84V91lHg4UmZmZwp0P++fp3r0758+fZ/v27QVv3evWrcNgMBAcHExSUhJTp07F5XKxfft2dDod33zzDb179yY1NVX0UHr22WcJCwvjgw8+KPH1s7Ky8PS8fW2f+5nbORY/yNyc98SJE0lNTWXhwoV3dTynTp2iY8eOHDp06Lb3W3ncj3FxcURE3Nmx9r7g+glSzlq54SxcxNRFUG4cbs5rWFu2ROeZby+PuR+/fIOZv5znRIrUV6dukDvLXq6NQVO0QHBduoy551sIhbaRT1RTMa2bGxZ9/vNKpVDxXsxonqjyVJH9qM9uwLiln8RfyGX0x9xhFc4gceBBcfNOS8lh5aRdpF66IWo3uGl5fUxzgqN8iS2mOKu7VskjAQaM93hx1gfqfi8lD/Pcy5N7YoVo9OjR7Nu3j59++km0BfHCC7f20qOioqhXrx7R0dH8/PPPpd42+StxRbw96vX6gq2TB5WHNarJarXSr18/VqxYQW5u7l31LUtKSuLDDz9Ep9Pd9u+RnZ3NtWvXirWXlKLu9fsKQcDHfJ7sG6HYBHGkmBIHIZmncekyiatWE+FaLly7Nd+yzt1sd7Hy2A22JRT99wnxVDM4Rs/FhPgi7aqUFLzHjkeVmipq/z1CzczXTNi0f2aeVhroXaU/IdZQyVgVLgdBZxZQMV4anp/jFcX5R2ZjNxe9RVa4r6sXctj2yQXyCvkDuftoeeLtMKy6bHbEK7EopEEC7i4L/pYsLiUUOdV7jvv+fv8bPGxz/ycE4F0XRKNGjWL9+vVs3LiRkJCQ2x4bGBhIpUqViI/PfxD5+/vjdDpJS0sTRexcv36dpk2bFttPUR9kVlbWA72C8rCvEOn1ekaOHHm3h1NkJu2i8PDwkET4lZb7/q3RZcd8ag+XsqMkleo1gpnQtDNYApxomzxPuErs5FvWucddz2Xcd2dITJeKIZ1aSc8mleneOAh9MStDzrNnsYybgFBIDO2vqeb9V0w41PliyFfvx/Sm7xPmKU3zoMhJwbipJ+rLeyU2W9TrONvOIaSYemSF531sVzJblh7FUcgfKLimL6+PaY5dr+b3KxashfbIlEAtXx1V3N1QKKRpM+5F7vv7/W/wMM+9PLmrgmjEiBF89913bNy4kerVq9/x+LS0NFJSUgqcrOvVq4dGo+H//u//ePHFFwG4dOkSsbGxNG5cfhlpZWRk/mXybpB+7DipFulD3s2ZRkhaHBkRHhij25ZbJNmmk9eY/NM5SbJFgMcjfRjWthqVPIt/qXDs2Ytl8BDIEW9v7YrW8MFLRpx/JlwMca/GjKZz8DdKg0VUF3dj3NQTpVm8OlhafyGAPRvj2PTREQpVl6Huo1V5fmBDrua5OHHZLEm2qFcpiAmQ/YVkHj7umiB69913WbNmDZ9//jleXl5c/bO4oclkws3NjZycHGbOnMlzzz1HQEAASUlJTJ48GT8/Pzp06ADk143q1q0bEyZMwM/PryDsPioqitatW9+tqcnIyPwNhJxrpBy/SI49RGLzsyZQMTuZ9JhqmEIblcv1bA4Xc369wJrfUyS2UB8DI58Io3HI7Wsa2r/7HuuEieAQb0tti9Gy6HkDrj+TF8b4NWBCwym4aaWFZTVnvsXwUy8Ugjjho8u9Mub2n+EMbFCi+QiCwC+rT7D9m9MSW9uXa9HmlVqcSbeRmC31F/LRq6gX+3nmvAAAIABJREFUoEenurf9hWRk/gnumiD6+OOPAejYURxZMWLECEaNGoVKpeLUqVN8/fXXZGVlERAQQMuWLfn0009xd7/1MJkxYwYqlYoePXoUJGZcunTpQ5uDSEbmfsaZeoGLZ6zkucSlVhQ4qHrjDCZ7OpmtYjD533lFuSTEp5oZt+lskY7TnesEMPKJ0GK3x+DPumSLl2BbtFhi+7GplhXtDQhKBUqUvFmzJ69U71ZkTTLN6TUYfu4jKcFhr9oGyzMf54fUlwCXU2D9gkMc/kUc2aZUKXh+QENqtw7m4BUL6Van5NwQTw01vHUo5WSLMg8pd00QFZU5+K8YDAZJrqKi0Ol0vP/++7z//vvlNTQZGZm7QF7SCS4mGHEijq7TkkO19DO4tBbMbdti8Pj7Pi1Wu5OP917k030XcRTaM9KqFIx6Mozn6xZd/+4mgs2OdeIkHN9/L7F98oyeH5rrQKHAz+DP6EcmUMe36EScmpNfYtjSDwXicVgbv0dek5GgLNnLnc3q4NfP4kk+mS3uX6fitVHNCIz2Z8+lXMyOQv5CCoj21RMkJ1uUeci5607VMjIyDzmCQG7sYS5fC0BA/OVvElKpdj2OHB9QNu+AVnvnUi13Yu+FDKb9fJ7kTKnjdJCnjrmda1Kzottt+3BdvYplyFBcf4izT9vU8N8XjeyNzo/YalaxJe/FjMJD61FkP5qTn2PYMkAkhgSFCku75dgji8lYXQRWs51Vk3dJxJDRQ8ebE1qgq+zJ3ktmCmkhDOp8fyFPnbyiLiMjCyIZGZm7huC0kXX8CNeypRF1FZxJVL2eSFo1NwwxT6FQ/b3HVbbVwYwt59l86nqR9tbh3kzpUB0P/e2v4zh4EOvQYQhp6eL+jQqmdzNxJliNRqmlV1RfOoW+UGziRs2JVRh+GSQRQ+ZnVuCo3qnE8zLfyOPT8Tu5dC5D1O7lb6THpFbccNNz/IpFcp63XkV92V9IRqYAWRDJyMjcFQRbDtf/OE2mVSqGAvNi8c26Smr9YEwRxafQKCmJ6RYGfnuKhHSpMPA1aRj+eChP1vC9bbFSQRCwr1pN3py54BT74KR4K5nS3cRlXxXB7iGMaTCxyJB6AFwOdHtnoD8gLqsjKNWYn/kER0TJc6zdyLDy6fgdXEnIErVXDPHk5XEtSHIpuZourVRf2V1DbV/ZX0hG5q/IgkhGRuZfx5VznSvHL5FjDxK1K3AQnHsCgy2bzFYNMPkXIypKwb6ETN77/gzZVmmh0pdiAhnQKhj3O6wKCWYz1nHjcfzvJ4ntcHU1/33JSI5RScdqz9Ordj90qqKTvCpyUjD+7z+oL/4m7l+pwdz+UxzhHUo8r8zrZlaM3U7aZbFDeJVIb9oObcKRXBdOQZpCoKaPjhAPjVypXkamEPJa6X1Onz598PLywsvLC19fX8LDw+nQoQMfffQRdrs0rPZeJTExES8vL6pVq0ZWlvhtt3379rz33nul7uvIkSPlPUyZcsCZlsDFP66TYxc7R6uxEpF5BJUqB8vjT2L4m2JIEAS+PnyZvmtOSMRQdT8jq9+oy+gnw+4ohlypqZh79CxSDH3dVsfUN0wovSowpfFMBtYdWqwYUiXtwO2LVkWLoQ4rSyWG0lJyWD7yV4kY8g93p1afhpy3CpJ6ZGoFNKhooJqnVhZDMjJFIAuiB4DWrVsTGxvLsWPHWL9+PU8//TQzZsygXbt25ObmFnmOzWb7l0dZMiwWS6lq0MncXzgunyLxlAOrS5zXR0c21dOOYq0goGjbCa3Ju5geSobdJTBty3lm/BIvEQbto/z4/M16RFeS5gIqjPP8ecyvvIbr+AlRe64epr5h4uvHDYR4hbG09QqaBbYouhPBhW7vTEzrOqE0i/2XXKaK5L7wPY6wZ0o8tysJmSwf+SuZ18yi9ip1Awh7pwm5Culj3UunpFmQUa5ULyNzG+T/jjtwduflf/V61VsVLl55Z3Q6XUH27kqVKlGnTh3atm3Lo48+yvz58xk9ejQNGjTgtdde4+LFi2zcuJE2bdqwcuVKJk6cyI8//sjFixfx8/Ojc+fOjB49WlTmY968eSxZsgSz2UyHDh2oVq0aX3zxBcePHwfA5XIxZ84cVq5cyfXr1wkPD2fMmDG0b98eyF+xqVu3LitXruTTTz9l//79VK1alZkzZ9KmTRvRXHr16sXSpUt5++23qVSp6M9CEAQ+/PBDPv30U65cuUJoaCiDBg2ia9euANStmx/efLPv5s2bs2nTplJ/rjLliCDgSDxIYpIfTsQrKCbhOqHX4sgINmBo0O5vZ55OybIyYUcGZ9PFK6QKYGDrEHo0DirRColj/wEsgwZDtjhyKzFAyYzXTVzxUdEooAljG0zCpCkm+s1uxvhTLzTnNkpNVVtjeXo5gqnkaQTOH7vG59N+I88snlvVBoFUfqUOSrX4s1MrINJbR1V5i0xG5o7IK0QPKLVq1eKxxx5j48ZbD+LFixdTvXp1tm/fzvjx4wEwGo0sXLiQ/fv3M3fuXNavX8+cOXMKzlm3bh2zZs1i3Lhx7Nixg8jISBYvFiehW7JkCQsWLGDixIns2bOH9u3b061bN44dOyY6burUqfTq1Yvdu3dTv359evbsSU6hMgedOnWiVq1aTJ8+vdi5TZ06ldWrVzNnzhz27dvHkCFDGDJkCD///DMAv/76a8HYjx07xueff16GT1Cm3BCc2ON2kpAUIBFDXs6LhF89Q3p1r3IRQ7vPp9P10z8kYsioVfHBCzXp2aRyiYSB/YcfsLz9jkQM/R6hZmQvd674qOhU7QWmNp5ZrBhS5F7F9E0HiRgSUGBtMhJz53WlEkNHdybx2YSdEjEU3KwyVV6ViqGKJjWtqpgIlrfIZGRKhLxC9ABTo0YNduzYUfB7s2bNGDRokOiY4cOHF/wcHBzM0KFDWbBgAWPHjgVg6dKlvPrqq7zxxhsADB06lF27dnHu3LmC8xYuXEj//v0L6smNGTOGPXv2sHDhQpYvX15wXN++fWnXrh0A48eP5+uvv+b48eOSQryTJk2iY8eO9OvXj5o1a4psubm5LFq0iPXr19OsWTMAQkJCOHz4MB9//DFPPfUUPj75WX29vb3x9/d/KIva3jM4bdhObycxvaYkx5Cf/QKV0pJJrVsZU2Qx200lxOESWLIrkY/3XpTYKnnq+PCFWkT4lyyHkW3FJ+TNnSdp39JAy7KOBgSViv7RA+kc1qXYPpSppzFteAlldrKo3WX0w9zuY5xVHy3RWCB/RXT392f53ydHJbZqbUKo9FwNFMpbgketgLr+egJMcqJFGZnSIAuiBxhBEERvhvXr15ccs2HDBpYsWUJ8fDy5ubk4nU6cfwkpPnv2bIEYuskjjzxSIIiys7NJSUmhSZMmomOaNm3Kli1bRG1RUVEFPwcGBgJw/bo0J0yLFi147LHHmDRpEl9//bXIFhsbi9VqpUuXLqK52e12qlatWvQHIXNXUNhzsZzcTlJ2XQovRle0nsM/6zKpDcMxVStZja7iuJ5jY+QPsRxKypLYWoZVYGqH6ngZ7iwOBEHANv9DbMs/kthWPaVnfSsdGpWO8Q0nF+8vBKgT/w/jj2+isIlXl5y+tcjtuAbBQ5pmoDhcThebPznKnh/iJLbIzjXwfTRE9H+gEpw0CXLHQ060KCNTamRBdAfK4tNzr3DmzBlCQkIKfjeZxG/IBw8epGfPnowYMYLp06fj6enJ5s2bGTduXLlcv/AyvUajkdiEwqW4/2TChAm0bNmSPXv2iNpdrvww4q+++ooqVcRfLGq1fDvfKyjy0sk5sY9LuVIRHpQbi2feVTJaxWAKKHtNMkEQ+PlMKjO3nCfDIo4iUwL9Hw2mR5PKJcq1I7hc5E2fgf3Lr0TtdhXM72Jkd10tRrWRKY1nUs8vprgBoT2yFP3OsZICrfbgxzC3/xR0RWesLgqr2c6a9/cRe0hcdFapUlDr9Tp4xYifTW4aJb7Wa3jobl+IVkZGpmjkb5AHlFOnTrFt2zbefffdYo/Zt28fgYGBom2z5GTxEn/16tU5cuQI3bp1K2j7/fffC3728PAgMDCQffv28eijt7YB9u7dS2RkZJnHHxUVxcsvv8yECRPQarUF7ZGRkeh0OpKTk0XX+ys3j3c6pQUsZf55FLmXyThximt59QpZXFS9EYtOlYr58ccwuPmV+RqpOTambTnPr2fTJDY/Ny39Y0x0alqylRjB4cA6dhyOH8S+PhYtTOtm4kSYBk+tFzObzaG6V42iO7HnYtg6GO2ZbySmvOgeWNu+D8qSP27TLt9g1dTfuJ5cqC6ZQU2NnjF4VhcXe62gU/FIRQOJ8dK8QzIyMiVDFkQPAHl5eVy9ehWXy0Vqaio7duxg3rx51KtXjwEDBhR7Xnh4OCkpKaxdu5ZGjRqxbds21q1bJzqmd+/e9OvXj/r169OsWTN+/PFHDh06hJfXrbfQAQMGMGPGDMLCwqhXrx5r1qxh7969Iv+lsnAzOg7yncQB3N3dGTBgAOPGjUMQBJo3b05OTg6HDh1CqVTSvXt3/Pz8MBgMbNu2jYCAADw8PPD09LzdpWTKCWV2PNdOXiTdXkvUrsBJcNYZBM8cXE2fQ6sxlKl/QRD48eR1Zm+Nl+QWAmgc7MmM5yJJv5xYsv7y8rC++x6Obb+K2rMNCqZ0NxFXRY2/wZ9Zzf5LVffgIvtQZsZj3Pg6qtRT4r5RYG01GVtMfyiFU/O5o1f5auZeLDni1Bh6Lx013mmAW5B4lSnAqKaevx6VUnaclpH5O8iC6AFg+/btREZGolKp8PT0pGbNmowcOZLu3buLVlcK065dOwYOHMioUaOwWq20adOG0aNHM2zYsIJjXnjhBRISEpg0aRIWi4UOHTrQs2dPNm/eXHBM7969ycnJYcKECVy7do2IiAhWrVpFdHT035pX5cqV6dWrF/Pnzxe1jxkzBj8/PxYuXMiwYcNwd3cnOjq6wGFcrVYza9YsZs+ezaxZs2jatKkcdv8voEw7zuXYXLId4oSKSsFOtYzTWKupMEZ3LHMkWVqujUn/O8eOc+kSm0aloHfzqvRoUhmVUoH0CClCZhaWAQNxHj4sak93VzCxhxtJFVVUcavKrGbzCDBWLLIPdfxPGH96B0WeeCVH0HpgfnppqfILCYLAvk3n2PTRH7hc4q1kj2BPInvGoPMSBwhU89RQw1snR5HJyJQDiszMzKKdOB4ysrKyHuhVBKvVWm7RVq+99hoOh4M1a9aUS3//JOU573+T8rgf4+LiiIiIKKcR3R5FygEunteQ6woQtasFK9XST2KuG4AptFGZ+98el8bE/50jwyzNvh5dyZ1Jz0QQ5mssaLvT3F0XL2Lp1QfXhQui9qsVlEzomZ9jqHlgS4bHjMFN4ybtwGFBv3syuiNLJCanT03Mz67GVaHkmbadThc/Lj/C/s3nJTa/RwIJfzkalfaWo7RaCXX89FQsFEn2b/7N7zXkuT+ccy9P5BUimdtiNptZsWIFjz/+OGq1mh9++IHNmzezatWquz00mXsBQUCRvJ2kJF8sLrFfi9aVS3DmSSxNqmMKrFVMB7fHbHMy99cLfPvHFYlNp1bSr2VVXm8YVKrtIueJE1j69ENIE/sfJfspmdDTjUwvDb1q9eLF8FeKXHlRXjuK8X+9UKWfkdhs1Z/H8sSHoC1CRBWD1Wzn61l7Oft7oTkqIKRDdYIeCxWNw1OnpL6/AaNGTiMnI1OeyIJI5rYoFAq2bt3KvHnzsFqthIaGsnz5cp599tm7PTSZu43gRHFhCwmXQsgTxKtZBmcWlXNOkfdoQ4wVypYO4UTKDUZvPEtiERXq6wa5M7l9dUK8S+eL5Ni+Hcuw98Ai7vNEiIoZ3UxovXyZ03ASdX2l0XG4nOgOfYBu7wwULrH/kqBQ5fsL1e9bKn+hjGu5rJq8m6uJ4pQBap2KiDfq4lNbvOIW8ucWmVylXkam/JEFkcxtMRgMbNiw4W4PQ+Zew2WHuE3EX6uFXRCvhrg50vDPi8Xetg16k08xHRTPDauDhTsTWXskhUKuNKgU0LtFVXo2rYK6FKtCgsuF7ZNPsX0wH1ziSKyddTR82MVIpH8dJjSago/eV3K+MuMchp/7oU7ZL7G5PKpifnoZzqCmEtvtSI5NY/XU38jJtIra9d56ar7dANNfaq2pFPlbZIFucrJFGZl/ClkQycjIlA6HGc5uIj4tBocgXqHxsF3Hm3PwWDu02pJlhr6JIAj8dDqVOdviSc2V+gpVraBn+rORJSrKKuo3MxPLyFE4d+6S2Na10vH5k3paBLVmdIPxaAtXqnc50f6+GP2eaSicVsn5tqjXsTw6vVT5hQCO/JrAd4sO47CJU0O4B3tS8z+PoPW4NQ69SkGDigY52aKMzD+MLIhkZGRKjCIvHefpn7mQ3URalyzvChUU8SgfbY+qlGH1iekWpm85z76EzCLtL9avyNA21TBqSycKnEePYhkyDOGK2D/HqYCPnzXwvyY6nqvWif51hqBSiPtWpp7G8Et/1FfEUWgALoMPlsfn4wjvUKrxOOxONn38R5HO0771KhLxWh2R83QFnZKYAAM6tewvJCPzTyMLIhkZmRKhyknCfGYvSeYWkrpkPtbLeCgvoHy0Q6nF0Pa4NEb+EIvFLk0qWNlLz+gnw2geWqF0gxUEbCtX5dckc4j9fbINCj54ycjvkRp61PwPr1V/U+w87XKiO/hfdPtno3DaKIy92lNYnlhQqsKsAFmpZr6cuZfkWGkyycqPhxLcvrqoJlmQm5rafnpUsr+QjMy/giyIZGRk7ogq7ThZ585zOa+lxOZvTsakTiq1GBIEgVUHLvHf/0ugcO4PjUpBj8aVeatpZfSa0q0KCTYbnh/MJ2+7NDHomSoq5rxiIt1LzdB679E+RBwcoLCkY9jcE03Sdsm5Lp0X1jazsNd4qVSO0wDnj13j69l7yc3KE7UrNUrCXooioFFlUXsNbx3VPDVyfiEZmX8RWRDJyMgUjyCgubKTawk2rtuleYQCb8Sj16WUWgzZnS6m/Xye745dldgaBXsy5skwQnyMRZx5e1wZGVgHDMLwl/IyN9nQQsfqJ/UIGjXjGkyiVVBrkV157Simja9LKtQD2MM7YGk7F8EUILHdDkEQ2PNDHJs/OYpQyEPc4GMgsmcMbpVv+R+pFFDP30CASX40y8j828j/dTIyMkUjCGiSt3Ax2Z0spzjpm0JwUTUrFqVbBsqWpRNDWRY7Q787I6lOr1LAe4+H8nJMYJlWRpzx8fn5hQrV48vVw4ddjOyvpUWtUDO+4WRaVGolOkZz6msMWwdLHKddBh+sbeZgr96p1KtCDruTDYsPc3hrgsTmE+VHxOt1URtvRY0Z1PnO0+6l9JOSkZEpH2RBJFNmFixYwPLlyzl+/PjdHopMeSO4UCX8jwuXgjC7xL4yqj9LceQF2NA06oRSVfLHSGK6hf7fnCQpQyw83HUq3u9Ug6bVSukr9CeOffuxDB4C2eISGpd8lEx900SKrwq1Qs2ERlNpFtiiwK6wpKPbOwPd0Y+kfVZqgrn9ZwhuRZftuB03Mix8MX0PSWcK+QspoNrTEVR6MkzkL1RBryImQI9OJTtPy8jcLWRBdJ/Tp08fvvrqKwBUKhWBgYE8+eSTjB8/XlSAVUamxAhOFOf+x/krYdgKJVzUuCyEpZ/iRpgBY/STpapLdigpi6HrT5NVqChrZS89C7rUItS39FtkAPb132GdOEniPH28mppZrxnJMSrRKDVMbDSNJhWbAaDISkD3+yK0J75A4TBL+syr9zbWVtNAVXwtwOK4dC6dz6f9RlaqOPmj1qAm8o26eNYSC8xKbmqiZedpGZm7jiyIHgBat27NsmXLcDgcxMbG0r9/f7KyslixYsXdHprM/YbLgTP2ZxJSa+IslGPI4MimWsYpsupWwhTepFTd/nD8KpP+dw5HIT+amMoezHu+JhWMpU84KAgCtmXLsX24QGLb+oiWpR0NONQKNEotkxpPo3FAU5RX/0B3aD6auA0oBGlUm6DSY3n8v9hrvVLq8QAc25XMtx8ckOQX8qpoIrxnDPoAcRLLqu4aonzl4qwyMvcCsiC6A6OfXfuvXm/6xpdKfY5OpyMgIN/ZMygoiM6dO/Pll18W2BcuXMgXX3xBYmIinp6ePP7440yZMqVgBemLL75g+PDhfPnll4wcOZLExERiYmJYuHAhISEhBf3Mnz+fRYsWkZubS4cOHUQ2AJfLxZw5c1i5ciXXr18nPDycMWPG0L59ewASExOpW7cuK1asYMWKFfz+++9ERESwZMkSlEolgwcP5sSJE9SpU4elS5dK+pf5h3HZsZ3aRmJ6NEKhR4O7LY3KN86Q3TQKU6WS1yVzCQKLdyXx0R6po/Kztf0Z/3Q42jLk2BFcLvKmz8D+5VcS26qn9KxvpQOFAi9dBSY0nEId33pojyxFv30UCklM259j9ahCbofVuALqlX48gsCvX59i25cnJbYq0f5UfrUOqkKiL9RTQ6RcqV5G5p5B3rB+wEhISGDbtm1oNLcevkqlkilTprB3714++ugjDh8+zPDhw0Xn5eXlMW/ePBYuXMiWLVvIyspi6NChBfbvvvuOqVOnMmrUKHbs2EFERASLFy8W9bFkyRIWLFjAxIkT2bNnD+3bt6dbt24cO3ZMdNyMGTMYPHgwO3fuxNPTk//85z8MHz6csWPHsm3bNqxWKyNGjPgHPh2ZYnE5sJ7cTkJ6bYkY8rZepmr2KXKa18dYCjGUbXUw/PszRYqhAa2CmdI+omxiyGbD+u57EjGUp4ZZrxhZ/6geFApqeddmaetPqONbD92BuRi2jyxSDLlMgVhaTOJGtz1lEkP2PAdr5uwvUgxFPx1G1R71JWKoegWtLIZkZO4x5BWiB4CtW7cSFBSE0+nEas13Vp02bVqBvW/fvlitVvR6PcHBwUyePJlXX32VpUuXovzTB8ThcDBnzhwiIvKjiQYMGED//v0RBAGFQsGSJUt45ZVX6NGjBwDvvvsuu3btIj4+vuA6CxcupH///rz44osAjBkzhj179rBw4UKWL19ecFy/fv148sknAejfvz8vv/wyq1atolWr/Mift99+WyLYZP5BBCeWUztIzqhJ4XekiuYL+JiTyW7ZEINfWIm7PJSUxdgfz5KSLc67o1UpmNqhOk/V9CvbUHNzsQwYhHPfPlF7jl7BtDdMnA7Jf6S19n6MkS3GolGo0f02Bf2BuZK+nD61yHukP/YaXcrkKwT5ztOfT/uN5Nh0UbtKraDxm3VR1gmUnFPTR0c1z7JdT0ZG5p9DFkQPAM2aNWP+/PlYLBZWrlxJQkICvXv3LrDv2LGDuXPncu7cObKzs3E6ndhsNq5evUpgYP4DW6fTFYghgIoVK2Kz2cjMzKRChQrExsbSrVs30XUbNmxYIIiys7NJSUmhSROxb0nTpk3ZsmWLqC0qKqrgZ39//yLbcnNzMZvNGI1lc7SVKSGCE8vJHSSnRyIWQy6q3DiLZ941slo2wuAXWqLubA4Xi3YlsnL/JclajLdRw/wXalInqHR1vwpGlJyMZdAQXGfOiNrTPBRM6u5GUkUVOpWOIfWGE2INRaNQo98xCt2RpaLjBbUB89NLcYQ/V+pQ+r+SciGTVZN3k5Uqdso2umtp0q8htkDxPBVAtJ+eyu5ygVYZmXsRWRDdgbL49PzbGI1GQkPzv7Bmz55Nhw4dmD17NqNGjSIpKYmuXbvy2muvMXbsWLy9vTl69ChvvfUWNtutsgRqtfhWuLmU73JJHU9LS+Ftgb9u5920/fX65XltmdsguLCc2kVyenUKi6GQ7FOY7Olkt2qMwbdaibo7n2pm1A+xxF7Lldiq+xn54IVaBHnpyzRU+9atWMeMgxs3RO0XfZVM6uHG9QpKgkyVmdBoCmGeEcSdPYNh62C0J1aKjhe07uR2XIOzcrMyjeMmJ/deZO3c/djzxM7TvpXdienTALObeJ4qBdQPMOBvlB+5MjL3KrIP0QPIiBEjmD9/PikpKRw5cgSbzcbkyZNp1KgR4eHhpKSklLrPyMhIDh06JGr76+8eHh4EBgayr9BWxt69e4mMjCzbRGT+OQQXltO7SU4LpzgxdKNlY/QlFEMnUm7w+qqjRYqhNxsF8cWb9cokhgSbHeus2VgHDpaIobOVVYzqlS+Gngl+lmVtPiHMMwKFOZWI/YMkYsil8yL3hQ1/SwwJgsD/rTnFF9P3SMRQQJQf4f0bS8SQRqmgcaBRFkMyMvc48n/oA0jLli2JjIxkzpw59OjRA5fLxfLly+ncuTOHDh1i6dKld+6kEL1796Z3797ExMTQokULNmzYwOHDh0W5jgYMGMCMGTMICwujXr16rFmzhr1797Jjh7SmlMxdRHBiPb2T5NQIihJDBmc6Oa2bo69QpUTdJWdY6P/NKcyFQs393bVMbV+dxiFly4flupyCZegwXIWc8gH21tIw/0UjWncvJtUbUZB5WnX5AMZNPVDmXBL3ZfQj9/nvcPnVLtNYIN95et2Hhzi2M0liC2wVTGinGigKJVY0qBU0rGjETSu/e8rI3OvIgugBpX///vTr149BgwYxc+ZMPvjgA2bNmkWjRo2YMmVKgXN0SXn++edJSEhgypQpWCwW2rVrR9++fUXh/b179yYnJ4cJEyZw7do1IiIiWLVqFdHR0eU9PZmy4nJgO/0rSWmFHahdhGSfREcmljZt0XmUrJJ7htlO37UnyTDbRe1P1vBl3NPheOhL/4gRnE7sa9aS98F8yMkR2RxKWNlOz8ZmOhoGNOa9mNH46H1BEPLD6neNQ+ESJ2h0uVUi94XvcXlXL/VYbnIjw8rqKbu5GCd2nlYoFYR2qUVg86qSczy0ShpUNKAvQySdjIzMv48iMzOz6KQcDxlZWVl4enre+cD7lJtRZg8b9+u8y+N+jIuLEznK47LjOL2FC2mF8wy5CLlxEo2UfqKaAAAgAElEQVQyE8ejT6Ixlqx8htXu5J2vT3D0kngrq3vjIAa3DilbPbLTZ7BOnISriHIw17wUzHnZRHywnrej+vB82IsoFUqw3cDwy0C0Z7+TnOOo1BRz+08Q3KTRXiUl7fINPp2wk/Qr4u1AtVFDjZ718YrwEbW7a5WEemkJNKlR/sth9ZK/+UOEPPeHc+7libxCJCPzMODMw3XmfySk1S8khgSCb5xGpcnC2eoZNHr3knXnEhi98axEDD1Ty49BZRBDgtlM3qLF2FetBqdTYj9YQ838LkbcfSvzYcPJRFaoAYKA+vxmDNtHocxOlJyT98gArM3Hg6rsUV2XzqXz2cRd5GaJ0wcYAkzUevsRDH6mgjYfg4pQTy2+BpWcX0hG5j5EFkQyMg86DgvE/kh8WgNciMVBlRuxKIzZ0OI51JqSraQ5XQKzt8az7ay4cGnDqp5Meiai1KsiroREzL37ICRJfXMsWvj8SQObm2hpVbktQ+uPwE3jhjLjPPrtI9Ek/CI5R9B6cD56LH6t3inVOAoTd+QKX8zYg80i3oLzquFLje71UBvyP0ujWkFdfwMV9HKVehmZ+xlZEMnIPMAo7DcQTv/A+cymuNCJbEE5ceCTi6ZhRxQlrFifkG5hwuY4/rgoriof5mtk3vM1S5152nn0GJa+/RAyMiS2vVEaPu5gILuCnkHRA+kQ0hGFPRfd7knofl+EwmmT9ucbhbnDKjJTXZQt9WM+R3cm8e1/D+B0iFM/+DWsRMQr0Sj/dJ6uaMovzKpRyitCMjL3O2UWRDk5OWRmZiIIUhekKlVKFp0iIyPzz6Fz3cB1cj/ns1vgQpwZOTAnHqo40Ee1L1HFeqdL4MtDl1mwM5G8wiLBTcvil6JK7UDt+PX/sLz7HvyZXf0m1z0VLH/OyMGaGioaA/mw0VSqe0WizDiP8bsuqLIuSPoSFEpsdd/G2mICaIyQGleqsRT0Iwjs/PYMP6+S+jAFta1GyLORKJQKFORnnA720MjbYzIyDwileoJZrVZmzZrF6tWrSU9PL/a429lkZGT+eZTmFCpmn+CcpbWkNpm/ORFFpBZTWIsS9ZWYbmF8EatCAF4GNYtfiqKih66IM4vHtmYteVOmQqHkm/9XX8Oy54xYdQoa+jdmdIMJeGg9UF35HeP3L6K0pEn6cgQ1w9Jm9t8KqQew25x8t+Agf2yXbt1V61SDoDb5OZn0agUx/ga85C0yGZkHilIJomHDhvHVV1/Rvn17mjZtKspB8yBws26XjMzdpKhV19KgyknAeuY3Ei1tEBB/aftaktDU8Sxxxfrfk7Po/80pcm1SR+dWYRUY93Q4/u4lF0OC04ntwwXYPvpYYlvbWseXT+hRKJS8EdmdbjV6oFQoUSdsxfjjmyjs4igvlykQa6sp2CNf+FslOKD4mmQKpYKI16LxbxAE5DtO1/fXo1XJofQyMg8apRJEGzdu5I033uCDDz74p8Zz1zCZTGRmZuLl5SWLIpm7hiAIZGZm4u5esmivwqiyzpAbe5Qk66MUTkTvnxuPrkGlEtclO5iYSf9vT2G1i1dx3HUqRjweSofa/qX6X3FduYJ1xEicB8UZz50KWPacgS2NdZjUJsY0mEjjik0B0Jz6GsMv/SW5hWw1u2JpOxe0biW+fnFcOpfB59N2k5VqEbWrjRpqdK+HV6QvAKGeGqp76/71UHoZGZl/h1IJIoVCQd26df+psdxV1Go17u7uZGdLtwUeBLKzs/HwKFtRzfuZ+3He7u7uktpyJUGd9jvZ5+K5mNdSbBAEAnPj0DSpht47uER97U/IZOC3p7AW8hdq+eeqUEApVoXgT3+hMWMhK0vUnqeBOS+bOFhTg0ltYlazedT0zi/0qz20AMOucZK+rA2Hktd83N9eFQI4c+AyX83ai73QCpjB/8+wen8TSgXU8dNTyU0uyioj8yBTqqfuM888w/bt20ud5bgo5s2bx8aNGzl37hxarZYGDRowYcIEatW6tZQvCAIzZ85k5cqVZGZm8sgjjzBnzhxq1qxZcExmZibDhw/np59+AuDpp59m9uzZZdrOU6vVD2xyxmvXrj2Uzu4PxbwFAe3VXWQmXuNiXqE6XYKLSuZYNE2roythKY7f4jMYsv60xHl6QKtg3mpauVSrQkJeHnlz5mL/4kuJLdOkYNobJv6fvfOOrrJI4/Dz3V4SSAhJCGkkhBIg9F4tiKJIEVGxrSjqggULIE2QolgRWFFR7IKCdMVesNEEqQmE0EkgvSe332//iBuY3IRcFFfKPOd4zuadmffOuEn8ZeYtadE6HzFk3PISpl9nir5QsF/2LM529/v9+Wdi+w9HWTF3C16v+EQZ1Lw+zf/VFp1Fj1mn0CHcTB2jjBeSSC52zuoh/PHHH+fw4cM8/PDDbN26lczMTHJycnz+8YdffvmFe+65h6+++oq1a9ei0+kYPHgwBael386bN48FCxbw3HPP8f333xMaGsqQIUMoOa3J48iRI9m1axfLly9n+fLl7Nq1i/vvPze/MCWS8x7VizHjS4qOZvqIIUX1EFWWgr57ot9i6OeD+YxZkeIjhh69vBEju0ef3RNZQQHlt99RrRjakaDjkYcDK8XQ8z1ePnUz9Ns8XzGkNWC79u1zJoY2rN3PJ3M2+4ihhn0a0fK+DugsehrV0dMzyirFkERyiXBWN0SdOnUCYPfu3Xz44Yc1zvMny2zlypXC1wsXLiQmJoZNmzbRv39/VFXltdde45FHHmHQoEEAvPbaazRp0oTly5czYsQIUlNT+fbbb/nyyy/p3LkzAC+//DL9+/eXpcwlFz9eN6ZjqynI8nLcIWaMKaqHyLJkdD3bYagT7pe7LUcLeXTlXlweUSSMvSKOOzpHntXW1KIibCPvw7t3r2B3a2DxVSZW9zKiahSsugCe7zGH5sEVN8OGba9g/mWa6EsfQNmgJXiie5/VHqrdl6ry3ZJkvv84RRxQoPGNLYjoGUtdo4ZW9U3UlUJIIrmkOCtBNH78+L8t4Li0tBSv11v51HX06FGysrK44oorKueYzWa6d+/O5s2bGTFiBFu2bCEgIIAuXbpUzunatStWq5XNmzdLQSS5ePG6MB/6mII8A8erxAwpqofI8hROxoXR2E8xdCCnjMeqEUNP9I3n1o4Nz2pralkZ5feP8hFDWcEaXrzFQlp0xa8dHzH0+2uYf5oi+tJbKRvyCZ7Ibme1h+rwelU+e2M7m9YdEOyKVqHpHW2I6BBBs3pGYgJlbSGJ5FLkrATRxIkT/659MGHCBJKSkipverKysgAIDRXrzYaGhnLy5EmgIj4kJCRE+OWlKAr169cnOzu7xs9KS/tzRdsudOS5Lw4U1U1j+48U2II5Vo0YiihP5kSjcFRdgF9nL7B5mLg+nxKH+Ex2b9tAOtUtO6t/f4rdTvD0mRhSxBuYXfE6nr3dSrmp4mfVqg1gTMxYtLl60nLTCD28jNjkF4Q1Ho2RtI4vUVpeH/7E/4en79vt9PLT4iMc3S0GdWsMWhLvbkdY82AiXTk4szwcyDrrjzqvuNi+388GefZLh7/jwuO8aN0xadIkNm3axJdffolW+/dfU1+KN0eX6hPiRXdurxPTgY/IKY/kpLODMKSoHiIcKRgv60qCOcivs5c7PUxZvIvcclEMPdwnlnu6nV0wuupwYBv9AJ4qYii5kZan77TiMFSIoWBjPV7oMZe4OvGgqhg3Po0p+UXRl9aEbfBSImL6nNUe/sfpZy8ttPPBrF84niqKIZ1FT4v7OxDZNITODcwYdcF/6rPOJy667/ezQJ790jz7ueSMguijjz4C4JZbbkFRlMqva2P48OF+b2DixImsXLmSTz/9lEaNGlXaw8MrrvpzcnKELKGcnBzCwsIACAsLIy8vTyioqKoqubm5lXMkkosGjxPTwY/IyIsmz9VcGFJULw0c+zD17obO5F+ZAbdXZdzqfezLEgse3ti2AXd3jTqrral2O7ZHHsWzcZNgT43WMuvOgEoxVN8Uyos95hEdGANuO+avH8SQulz0pTVSPugjPH9SDJ1O9vFi3pv+MwVVzmioY6TlqE5ExQfRMdyMXiufyCSSS50zCqLRo0ejKApDhw7FYDAwevToWh0qiuK3IHriiSdYtWoVn376KU2bNhXGYmNjCQ8P54cffqB9+/ZAReuQjRs3MmPGDAA6d+5MaWkpW7ZsqYwj2rJlC2VlZUJckURyweNxYjjwMUdym1HsiRGGFNVDuHMf5t5d0Zn8K+ioqirPfH2QXw6JTVV7NQ5mYr/GZ5dNlpuL7cGH8e7aJdgPRWiZcZcV2x/PZOHmBrzYcx4NrZEo5blYPr0d3QlRQKlaI+UDF+OOvdzvz6+JQ7uz+fDpX7GXuQS7JSKAFvd1JDoqgPZhZrSyMatEIqEWQbRz504ADAaD8PW5YOzYsSxdupQPP/yQoKCgypghq9VKQEAAiqIwatQo5syZQ5MmTUhISODFF1/EarVy4403AtCsWTP69u3Lo48+Wlk9+9FHH+Xqq6+W14eSiwePE13aMg7ltqbcK958ar1OIhwpGC7vg85g9cudqqq8+P1hVuzIFOyJ4VaeH9Qc3VkIBM/Bg9j+PRo1I0OwHw3TMO1uK2XmisoekdYoXugxl3BLAzT5aVhW3+TTpNVrDqF84BI8Df/6HzMHfstjwyc78Lir1BhqFkLzEe2IDTWTFGqSVaclEkklZxREMTExZ/z6r7BoUUUvo/+l1P+PJ554ojJ4e8yYMdhsNsaNG1dZmHHlypVCW4NFixYxfvx4hg4dCkD//v15/vnnz9k+JZJ/FK8TXdonHMxpj0MVi40aPDaiyvbgvbzn2Ymh7w7z4dYTgr1hHSOvDGuJxeB/DJ9702ZsYx6B0+qCwR9i6J4ASqwVYiihbhNmd3uJeqZ66NLWYPnmIRSHWBHeU68pZYOWoQY18vvzq8PrVfn2wz38/Ilvg9bwblE0HtaSFqFm4urKTDKJRCLyjwVVFxYW1jpHURQmTpx4xuy2oKAg3njjjXO5NYnk/MDrRLd/OYdy2/iIIbOrhJjiPdgu64zJGuKXO1VVef7bQyzZdlKw1zXpWHBTS+oHGPzemmvlKuxPTQe32GNsR4KO5289lU2WFNKGWV2fI0DRYvruMYy73vbx5Y7uRdmAD8D015pFO+1uls/dwp5f033GYgc0pVG/eNqHWwi1nBe5JBKJ5DzjrH8zZGdn88EHH7Bjxw6Ki4vxesXsFEVRWLt27TnboERySeJ1oU9bzqHcJOzeesJQoCOfmOK9FHdPwuxnBWpVVXnu20N8VEUMBZl1vHFLK+LrW/zzU1aG/elncK9e4zP2dUcDCweZ8fwRoNwlvDtTO83AUnQUy+cj0Oam+KxxtrgVW9+5oPVfjFVHcb6ND2f9SnpalW71Og1Nb0sirksUHRqYsepll3qJRFI9ZyWIUlJSGDBgAOXl5SQkJJCSkkLz5s0pLCzk5MmTxMXFERl5dhVtJRJJFbwu9Ac+4VBOK2xe8fanjiOPRgUp5HVqjDWieQ0ORFRV5dlvDvHx76IYCjbreGN4Ek3D/Htu8+zZg23seNRjvs9R711tYlVvY2XD1SujrmJ8+8mY9y7F/P04FHe5uCeNHnuv6TjbjfrLTVpPHi7k/Rm/UJQrfoY+wEDive1pnFiftuFm9DJ4WiKRnIGzEkTTp0/HZDLxww8/EBAQQEJCArNnz6ZPnz4sX76c8ePH8/bbvlfiEonET7xuDAeXcyi7BTZvfWEo0JFHXMFecpMisMZ19M+dqjL764Ms2y4GUAebdbw5PIkmfogh1evF+c67OOfN93kic+pg3o0Wfm1dccOjU3Tc3uwubmt6O5ZfpmPc9h8ff566cZRf9w7e8LZ+neFMpG49yUfPb8RpE/dliQigxb0diIoKpH24zCSTSCS1c1aCaNOmTTzwwAPExsZWNmFV1YosjhtvvJFNmzbx5JNP8umnn577nUokFzuqF8Ph1RzOauaTTRbgzCe+YB+5rcKxJvrX08urqsz66qBPNlmwRc+bw1vRJLR2MeTNy8M+/gmf+kIAx8I0vHSzlaMRFYHYzYMTGdtuInGWCCzr7kZ/wPfp3Nl8GLYrXgKjf7WSzsSmzw/w6cLtqFUatAYnhtLsrjaEBZnoIMWQRCLxk7MSRC6XiwYNGgBgMpkAKCo6Vf01KSmJjz/++BxuTyK5RFBV9Mc+41hmNGXeBsJQgLOAxvl7yW3dAGvzXjU4EPGqKgt/L+a7I3bBXs+i5w0/xZB76zbsj49FzcnxGfuis4F3rjXjNCgYtUbuaj6SoQk3oSvPw/LJ9egyt4rH05mxXfECrha3/eUnMq/Hy5fv7uKX1ft9xiJ6xxI/uDlmjZuODaQYkkgk/nNWgig6Opr09IoMDrPZTIMGDdiyZUtl6nxKSgpWq3/xCBKJ5A9UFUP6V2RkBFPsEYOkra5C4gqSyW3TEGuznjU4EPF4VZ76Is1HDIVY9bw5PInGtQRQq6qK6+13cMydBx6PMFZsVlgw1MzmFhVPZM2CEpnccRqRAVFo8lKxrh6GpliMMfJawikbvPScPJE57W6WvbSZlE1i3SMUiL8hkYa9G1HHoKG+LQedxr/sO4lEIoGzFES9evVi3bp1TJo0CYBhw4bx6quvVmabLV26lDvuuONv2ahEcrGiP/kjmek6CtyNBbvZXUJ8fjL5baOwNunuly+PV2Xquv18lize6oQGGHhzeCviQmoRQ0VF2CdNwf3DDz5je+K0vHyTlby6FZlancO7MrXTTMw6M9qTv2FZNQyNQyyn4QlpQdngpah1zq4vWnUU5pSz+JlfyTggVtfWGLQ0/1db6rUKI9CgoXOEhaOH1Bq8SCQSSfWclSAaM2YMvXr1wuFwYDQamTx5MoWFhaxZswatVsvNN9/MzJkz/669SiQXHfrsjeQdKyXX1V6wGz1lxOfvIb9dNNaErn75cri9TPlsP1/vyxXsoQEGFt2aRKN65jOu9+zZg+2xsajpvnV8lvcxsqSvCe8fKfV9o65mXPuJ6DQ6tMd+xLr2VhSX2C/MFXM55QPeBWNdv/Z/Jg7tzuaj5zZSVuQQ7IY6Rlrc14GA6LrUNWroGG7GIPuSSSSSP8FZP5md3mjVaDQyf/585s+ff843JpFc7OizN1J0+ASZzm6i3WsnPn8PhR1isMZ18stXfrmLR1eksCNDrBodFmhg0fAkYs8ghlRVxbV4CY7nX/DJIisxK8wdZmFbc32l7cbGN3N/qwfQKBp0B9dhWTcCxeMU1jlb3VkRPK3V81dQVZUNn6bxxVs78VYJnrZEBNLy/g4YgysqTzerZ5StOCQSyZ9GlmyVSP4B9NkbKD6cTrpDjAvSep3E5++huGMs1tj2NawWOZRbzoOfJJNR5fakvlnD27cmER18BjFUUoL9yam4v/7GZywtSssLwy1kB59q53Fvy1HcnHAriqKg37sM81ejUFQxzsje9QkcXSf85eBpp93N6gXb2LH+qM9YcMtQmt3ZBrPFQJswE2Gy+rREIvmL+PVb5LvvvsNqtdK1a8XVfVlZGePHj/eZFx0dzYQJE87tDiWSi4z/iaHjVcSQRnUTV5hMWedGWKNa++Vr85FCHl+1lxKHKEriQsyM7WQ9oxjyJCdje+xx1OO+T2TrulZkkbl1FaJGQeHRtuO5rtH1ABh2voXp+7EoiLc2tj5P42z/gF97PxMF2WUsfvpXThzybfETfU0CMVcnEGLR0TbMhEknq09LJJK/Tq2C6Oeff2bYsGG8//77lTaHw8GSJUswmUxotaf+eiwvL6dHjx706uVfarBEcqmhz/qV4iPpHHeIPyOK6iG2MAV750ZYGrb0y9fa3VlM/+IA7ipPSZ1j6/LSkESyjh+uYSW416/H9shj4BSfulwWA3MG6diYdKqVhlbRMrHDVC6PuhK8bkw/TcG4/XVhnYqC7ap5uFrd6dfez8Sh3dkseXYj5cXijZfWpKPp7a0JSQonpo6eliFG2aBVIpGcM2oVRIsXL6ZFixYMGDDAZ+zjjz+mT58+lV93796dxYsXS0EkkVSDPusXSo6kc9whFlZUVA+xRSm4u8RhadDML1+f7clm6ro0quZSDWkdzuSrG6PXasiqYa3riy+wPzHRJ14oPz6USUPsZIac+iNHrzHwVOdZdG3QHcWWh2XdCHTHfxLWqRodtmvexNVsiF97rwlVVdn0+QHWvbkDr0c8mTncSuI97bGEBxBXV0/zelIMSSSSc0utgmjTpk0MGzbML2fXX389y5Yt+8ubkkguNvS5Wyk5ks6xasRQTHEKnq6NMYcl+OXrm325PLluv48YeuSyRtzVJfKMQsG5YgWOqU+BKq4+2L8tE7oexqU/JYbMOjMzuzxHu9D2aHJ2Y117m0+NIVVrovz693HH9fNr7zXhdnlY89rvbPvG91arXlI4TW9vjc6kIyHIQJNggxRDEonknFOrIMrMzCQ2NlawGQwGbrjhBsLDwwV7ZGQkmZlimwCJ5FJHV5hC4cGjZPiIIS/RJSmo3Zpirh/nl6+fDuQzYW0qp7+S6TQKswc2o1/z+jUvBJwffIBj9nOiUVHY+e9+TIveDJwSGQH6AJ7rPofmwS3Q7V+N5avRPg1avYFRlF3/4V8uuFhSYGfxM79ybF+ez1hM/wSi+yWgaBSa1TPQOMj4lz5LIpFIaqJWQaTX63E4xLf8gIAA3nrrLZ+5LpdLiCmSSC51NMWHKUg9yEmHmFqvqF6iSpOheyKmejF++dr0RwD16TFDGgWeHdiMq84ghlRVxbnwDZzzqzRa1WrZMWYAT9X/UTBbdVae6/4yzYMT0ScvwfL1aB+f7shulA94H9US6tfeayLraBHvz/iFgmyxhpHWqKXpHW0ISar4o6tFiJFGdQ3VuZBIJJJzQq3pGTExMWzbts0vZ9u2bSMmxr9f7hLJxY5SdoL8lDROOjqIdtVDZGkKmh6t/BZDvx8vYsyKFJynxdYowIzrmp5ZDDldOJ6c6iuG9Hp2ThjmI4bMOjOzu79E8+BEdGlrMH/zoI9PR5uRlA1d85fFUNr2TF4f/72PGDKFWmjzWDdCksJRgDahJimGJBLJ306tgujqq69m1apVHDx48Izz0tLSWLlyJddcc80525xEcsFiyyV3dxrZzlaCWaO6iS5JRtuzFcagSL9c7Ugv5oFPUrC7vIJ9yjUJXN8qrMZ1SlERtntG4lq5Shwwm/hq7BVMs34pmE1aE890fZGW9VqhO/wNls9HoqinPlPV6CnvOx/7FS+C9q8JlC1fHuS9p37GUe4S7EHNQmj7WHcsDQLRKtCxgZnIwL9W3FEikUj8oVZB9MADDxAQEMCAAQNYs2YNnirNHj0eDytXrmTgwIEEBgbywAN/vQaJRHJBYy8ge2caec4mglmrOmlUtAe6NcdYt6FfrnakFzNqWTLlTvHnbtyVcdzYtkGN6zxpaYSMHY+nyu2uarXw2qjGvBa4WbAbNAZmdX2e1vXboE3/Bcund6B4T4kVVdFQfu0iXEl/La3e61X54u2drF6wzafydIPu0bS4vyM6ix6DRqFLQwuhsuCiRCL5P1Hrb5uQkBCWLVvGbbfdxogRIzCbzSQkJGC1WiktLeXgwYPYbDbCw8NZunQpISGyw7Tk0kW1FZG54zAlLjERQafaiStIxt6tKeaQRn75qkkMPdQ7lts71Xy75F6/HtvY8ejKxSBoR8NQpt6qJbWe2ClerzEws+uztAttjzZzG9bVt6B47MIcW78FuJsM8mvfNe7L5WH53N/Y9ZOYqYYCcYOa0/CyRiiKgkWn0CnCglUvCy5KJJL/H379+dWuXTs2btzI22+/zVdffUVqaiolJSUEBATQqlUrrrnmGkaMGEFQUNDfvV+J5LzFU17EyR3HKXeLNzd6tZz4vD2UdY7HEu5fnaEd6cWMrkYMje4Vw8ju1XeOV1UV19vv4Jjzsk9afVarKMYOLqbEIoqMCEsEUzvPpGlQczRZO7CsHIriKhXm2K54EVeL4X7tuyYc5S4Wz97AgR1idSSNQUuzO9oQ0roiePp/DVqNsvq0RCL5P+P3fXTdunV59NFHefTRR//O/UgkFyTu0hIydmbi8NQT7EZKiM9NobhdpN/tOHZmVIihsmrE0P09qg/CVp1O7E9Nx716jc/YjssbMfOKAjxaUWR0b9CT8e0nEWiogzZjE9bVN6E4i4U5tp5P4Wwz0q9910RpoZ33pv9MxoECwa6vY6TlH53qASKsOlqHmtBqZI0hiUTy/0c+0EskfxFHcQkndmXj8gYKdjP5NM5OpaBlKNb4zn752nSkkEdWpGCrEkB9JjHkzcvD9vAYvNt3CHZVo2HtsEa80yaf02sMaRQtI1vcz00Jw1EUBe2xH7GuGe5TZ8jeeSzOTo/4te+ayM8s5Z2pP5F3Urx1MoVaaDWqE6YQCwBNgw00DpIFFyUSyT+HFEQSyV/AUVRC+q5cPKrYRDWATOKzD5DXLARrYu8aVot8m5rLhLWpuKq0rRjVs2Yx5ElNxTb6QdSTJwW7NzCAucPr8lOjfMFezxjCk51m0Lp+GwB0h77C8tmdKB6x1pi906M4uk/2a981cTw1jw9m/UppoRiPFBBdl5b/7oA+wIhWgTZhJhpYZSaZRCL5Z5GCSCL5kzgKC0nfXYBHFasnB3GM2Kwj5DaphzXpSr98rdqZyYwvD1Al8YpRPWP4d8/qxZD7h/XYxo4Dm02wu6IjmDQc0oLE+j5xdeJ5uuvzhFsqYpx0+1dj+WIkilfsaWbvPgVHl7F+7bsmtv9wlFX/+Q13lZuuoOb1Sby7HVqjDrNOoUO4mTpGWcxVIpH880hBJJH8CRwF+aTvKfIRQyFKGtGZJ8iNC8LSuq9fvt7bnM6cH4742MddGVdjNpnry6+wjxsPVcpgFLdvxsMD8ig0iSKnfWhHpnWeRYA+AADDrncwff+4UGcIwNbnaZzt/1d8NAsAACAASURBVHzpDK9X5ZsPdvPj8n0+Y6EdImhya2s0Og0RVh2t6pvQa+UTmUQiOT+QgkgiOUsc+TmkJ5f6iKFQZS+RmdnkRQdgbn81iqb2TKkFPx3ljQ3HBZtWgaeubcLApPBq17jWfY59wkRfMXRDP0a234ZTI9qvjrmWx9qOR6fRgapi3PgMps0vCHNUFOxXzsHZekSte64JR7mLZXM2s3fzCZ+xqL7xxF7XFJ1WoWV9E5EBOhkvJJFIziukIJJIzgJnXibpKTZfMcQ+IjOzyW9owdT5Or/E0JKtJ3zEkEGr8Pyg5lzetPp6Xq61a7FPmgLe0252FAXn+IcYE/YZTocohu5qPpLbm/2rQnx43Zi/fQRD8ofCHFXRYOv3Kq4Wt9S655oozCnn/Rk/k3mkSLArOg1NbmlFWKdI6ho1tA0zy/pCEonkvEQKIonET5y5mRzf6yuGwthHw8xs8mICMHW+1i8x9G1qLs9/e0iwWQxa5g1NpHNs9fW8XCtXYX9yqlhjSKNBO2s6U4I+o6BQDKC+o+EI7mh+1x+Ly7Csuxv94a+EOarOTPm1b+Nu3L/WPdfEiUMFvDf9Z0ryxeBpfaCBxJHtqdMomMZBBpoEG9DIWyGJRHKeIgWRROIHzrzs6sWQuo+GWdnkNK2HpXVfv8TQ9vRiJn26n9Pjp816DW/c0oqkhoHVrnEuXYZj+gzRqNVifHY2L4b/xv70VGHotqZ30kNfkd2m2AuwrBqGLnOrMMdrqkf54KV4IjrVuueaSN16ko+e24jTLsYsWaPq0GJke+qFWWkTaiLIJAOnJRLJ+Y0URBJJLTjysklPKfMVQ95UIrKzyElqiDWxj1++juSVM2Z5Cg73qScvrQIvDUmsVgypXi/OufNwLnpLHNDpML3wPCvic/gu+RthqFuDHtyVOJKDBw6ilOdgXTEYbW6yMMdbJ4ayISvw1hP7rZ0Nm784yNrXf0etkhoX0jqcpre3pnGomWb1jLLQokQiuSCQgkgiOQOO/NwaxNA+GuRkkdsxAWu8fzcsuaVORi1LpqjKbcrU/k3oER/sM1+12bBPnIT7a1HwoNNhmvMiv7eysmjTTGEoNrAREztMRaNo0NtzsX5yO9p88fbIE5pE2eBPUANqbg57JrxelS/e38WvK1J9xhr2aUTijYm0DTcTYpa/XiQSyYWD/I0lkdSAIz+P9OSSap/JwnOzyOvWCmtUkl++Cm0uRi9L5kSRWABxVM8YBrf2zSbz5uRie/BBvLv3iANGI+Y5L7GjpYXpmyeinvbwFqgPZGaXZ7HqrSgl6TTbcB/acjFo2x3Vk7KBS8BYx699V8Ve7mLJC5s4sFUsBIkC8UMS6XhdAi3rm9DLWyGJRHKBIQWRRFINzsIC0pOLqxdDOVkUdG+NtWFLv3zllTm5/+M9pOWIrTGGtA7n/h6+jVo9aWnY/j3ap/q0EhKCecErbKyfz6xNT+DyuirHNGiY0mkGkQFRKEVHCFg+EE0VMeSKvYLy6z8EvcWvffuc40QJ7878hbz0EsGu0Wto8a+29LkilshAWXFaIpFcmEhBJJFUwV1SSMaePDyqKBzC1L2E52RT0L01Fj/FUHaJg/s+3sPhPLGadM/4YCZf3dinFo9n507K7x8FxWKTVU2TJphfXcB33t08/9szeFUxvX5U0kN0DOuEpuAA1uWD0JRmCOOu+Gsov+490IkCz1/Sfs9kyfMbcZS5BLs+wECXBzpxWZcILDKdXiKRXMBIQSSRnIanrIiMnVk+jVrD+EMMdfNfDGUWO7j3o90cKxDT0TvH1uWFwc3RV+k+796yBduoB3xacWh79sQ850U+zfmGeTtf8vmcB5MeYUjjG9Gm/4Ll0zvR2MX0e1eTQZT3fxO0Br/2fTqqqvLrmv188fZOIdsfKjLJ+j7ShU5Ng2Q6vUQiueCRgkgi+QOvrYQTOzNweMU6QPVJIzz7DzEU6Z8Yyii0c+9Hu8moEjPUIz6YOUOaY9KLaejuH3/C9sij4BDn62+5GeOkiXxyeBkLk18VxjRoeLzdBK6JvRb97vcwf/+4T18yZ/ObsF39KmjO/kfd4/ay5rVtbP36sM9YaIcIrrqvHS0jrLLitEQiuSiQgkgiAVRHKSd3HMbmri/YgzlKw6wT5HdP8lsM7Ugv5vFVe8mt8rzUJ6EeLw5ujkEn3gy5vvq6oi+ZWxQzhtGjMDwwmo/TPmRRykJhTKfomNRxGn0iemFaPwHj9td99pETPRDD1a+B5uxrANlKnSyevYFDu7LFAQUaXd+My4Y2o0mwUYohiURy0SAFkeSSR3WWk7V9P2UuMQ29DieIzjpCXscErJGtavejqizfkcmz3xzCXaU2T99mITw7sJnwTKaqKq6Pl+J4+hmxFQdgHPs4hrtHsGT/B7xVRQwZNAae6vI0XeomYll9M/qj3/nsxd5tEkfrDabJnxBDuSdKeH/Gz+RmlAp2rUlHszvb0K1XFI2D/lwskkQikZyvSEEkuaRR3Q5yd+yh2Bkl2K1k0yjrIHktI7DG1V5nyOn2Mvubg6zcmeUz1r9FKLMGNEV3Wiq6NyMD+9RpeDZuEicrCsYnp2C45WYWp77H23vfFIZNWjNPd32OduYIrMv6o83bK55HZ6H8mtdwNxkEaWm17rsqh3Zns3j2BmwlTsFuDDHT8r4OdGxZn0Z1zz4WSSKRSM53pCCSXLp43RTt2kqBPU4wmyggPjuN/Lg6WFtcVqubrBIHj6/ax+4TJT5j93SL4oFesZXVmlWvF9dHH+OY87JP8DQaDaZnZqEfOJAPU9/lnb2LxH1pzczu9iJtdXWwLr0GTfFR8TgBkZQNWoI3rI0fh/dl18/HWTZnE163eLtVJy6YFiPb0Sm+LhEBMq1eIpFcnEhBJLk0UT2U7NlIdmljwWyghIScfRQ10GNuf3Wtbo7k27jvo91kVblRMes1zLyuKVc1PxWT5D16DPvkKXh+/93XkdmM6dln0F91VbViyKyrEENtVF2FGCoXb6LcDTpSPnAxqtW3yKM/bFyXxqcLt0OVTLLQTg1JujWJTtEB1DXKfmQSieTiRQoiyaWH6sW291dOFiYIZh02GuelUBakYuh2Xa2NWtNyyrj/4z3kVQmejgk28fINiSSEWittru++xz5xEpSWVnWDtktnTDOmo4mOZsn+D6oVQ892e4k2TgeWVYPQOAqFcVd8f8qvext0Zr+OfzqqqrLuwz1sWLbXZyz2uqYkDWhChwZmjDpZY0gikVzcSEEkubRQVZxpG0jPFZ/JNLhoXJCMy+JC6XUdGu2Zn4ZSMkv598d7fPqS9YwPZvbAZtQxVfxoqR4PzgWv4nx9oa8TqxXjuLHoh92IoigsS1viE0D9PzHUtjQXy9rbUdxitWtn4s3Y+i34U2n1dqeXj17ZStoPRwS7olFoclsS7S9vRMv6RrQyk0wikVwC/KN/9v3666/ccsstJCYmEhQUxOLFi4XxUaNGERQUJPzTt29fYY7D4WDcuHHEx8fTsGFDbrnlFjIyxCq9Esn/8BzZyLHMaFROPf8oeIgr2oOqs+HtfQ06g/UMHmB7ejH3frTbRwwNaR3O/BtbnBJDRUXYRj9YrRjS9u6Fde1qDDcNQ1EUVhzwrTNk1pl5rutLtD/6K5ZVw3zEkKPtfdiufu1PiaHcYievP/2rjxjS6DUk3deBK69pTJIUQxKJ5BLiH70hKisro0WLFgwfPpx///vf1c657LLLWLjw1H9QDAYxw2XixIl8/vnnvPXWWwQHBzN58mRuvvlmfvzxR7RaGfMgOYU3/TeOpofjRbz9iSlJQa+WYL+sLwbzmZuebj5SyMMrUrC7xDT5WztEMK5vfGXFZk9qKraHx6AeTxcd6HQYxz2O/vbbK2v4rD60glf3zBemmbQmZnd6mo7bXseQssRnH/auT+DoOgH+hGA5cLyYT57bSMnRInFrFj19xnSmZ5cIjFr5RCaRSC4t/lFB1K9fP/r16wfA6NGjq51jNBoJD68+ULSoqIgPPviABQsWcPnllwOwcOFCkpKSWL9+PVdeeeXfs3HJBYeauYNjhwNxq2KcTWRZKlZXAWWX98ZorV/D6grW7s5i+hcHfGoM3dMtiod6x6IoSkVtoRUrcDw926fqtBJSD9PLc9B17AiAV/Wy/MDHPjdDRq2RF1qPp9P3k9BlbhXPgYK9zzM42486q/NDRbzQ5u1ZfDlnM84qFbSNdY3cPLUXzZvWO2u/EolEcjFw3scQbdy4kYSEBOrWrUuPHj148sknCQ0NBWDHjh24XC6uuOKKyvlRUVE0a9aMzZs3S0EkAUDJTeb4AS1OVbz9CbcfItiWRXGfrpjqNqxxvVdVefXnY7y54bjP2IO9Y7m3e0XHerW8HPuMmbjXfuozT9OmDea5c9D8Ie7TS4/z0vbn2JW3Q5hn0BiYn3AX7b56GE1ZpjCmGupQ3v9N3PG1Z79Vd4bPPjvIlnd24K1yu1WnQQAjZ/amfoOAs/YrkUgkFwvntSDq27cv119/PbGxsRw7doxZs2YxcOBA1q9fj9FoJDs7G61WS0hIiLAuNDSU7OzsGrxC2p8oWHcxcCmeO9B1koxUCzavKHjqOdMJLUknrUkUznwX5Ff/78bhUVmwtYgN6Q6fsX+1DuCyUDtpaWnojh0j6LkX0KWn+8wrv7ofxfeOhOJivEWFfJf3NWuzV+JSxew0naLjWUMH2n39OBqvmMZvt8ZwoNNL2D2NzqrgYlpaGm4v/PxlPke+PeozHt48iL53xFBQcpIC3zJKFzSX4vc7XLrnBnn2S4kmTZqcc5/ntSAaOnRo5f9u2bIlbdu2JSkpia+++oqBAwf+ab9/x7/I8520tLRL7tya4sOc3KOl1COKoTruLBqUHKKod2diQ+NrXJ9X5uSRFXvZdUIUQwatwqwBTbk6seKm0vXZOuxTp4Fd7GqP2Yxp6hQCBw0iHDhWcpTnfp/FvgLfFHeL1sS7hsbEp7zpM+ZqdBWO/m8SbQryGTsTaWlpBIfH8OELm8je4VtBu8P1TRhyTxs0F2G80KX4/Q6X7rlBnv1SPfu55LwWRFWJiIigYcOGHDp0CICwsDA8Hg95eXnUr38q/iMnJ4du3br9U9uUnAdoSo6Rk3yEInczwW715BNRmkbZ5b0wB0XWuH5/dhljlqdwolgUQ8EWPfOHJtI6suL5zbl0GY7pM3w/v3FjTC/PQZtQUfhxZ+52ntw0gTJ3mc/cniHtmZWbQcCBpT5j9o6P4Ojx5J9q0JpVrOGTuespPSYGTys6DQNGtadbv5rFoEQikVxqXFCCKC8vj5MnT1YGWbdt2xa9Xs8PP/zAsGHDAMjIyCA1NZUuXbr8k1uV/INoytIpSEkhz9VasJu8JTQoT8VxxeUYA0JrXP9tai5TPtuPrUqsTXx9C/+5sQVRQSYAnIuXVDRmrYJu8CBMUyajWCwA/HziR57eOh1XlWewAH0AY+Nvpf/WhWhzk4UxVWvEdtV/cCXe5P/B/8CrqmzYkcN3c5NxFIq3VsY6Rm6f3J3GLWo+v0QikVyK/KOCqLS0tPK2x+v1kp6ezq5duwgODiY4OJhnn32WgQMHEh4ezrFjx5gxYwahoaEMGDAAgLp163LHHXcwbdo0QkNDK9PuW7ZsyWWXXfYPnkzyT6EpP0lJ8jayHGJDVr1aTrhjL94rrsRgrv7pSVVVFv56nNd+OeYz1rVREC8Mbl5ZY8j53vs4nntenKTTYXpqGvobhlSaPjuyhnk7XsKLKK56RPTisfjhRK8ejqZUrJvltYRRPnAxnojam8pWxe728uX3x9j2xjY8Do8wFhQVyMjpvakXduY6SxKJRHIp8o8Kou3bt3P99ddXfj179mxmz57N8OHDmTNnDikpKXz88ccUFRURHh5Or169eOeddwgMDBTWaLVaRowYgd1up3fv3rz++uuyBtEliMaWRXnyL2TYewh2reogwr4X7eVX1Vh0sdzpYdrnaXy9L9dn7Ob2EYy7Mg79H7E2jrfexvnSHHGSwYB53lx0fXoDFeLqw9T3eHffoqruGNnifoZHXkPAJ/19xJAnNImyQR+hBkb5fe7/kVnq4osVqaStSPHpSRbTJpy7JnXHZJHNWSUSiaQ6lMLCQrX2aZILnYs96E5jz8GR/A2Hy3pzegF2jeoiqnwPust6ojPXrXatzeXhvo/2sKtKt3qdRmHCVfEMaxdRaXO8vhDn/P+IDoxGzK/MR9ejQoh5vG5e2T2XtYdXi3tEw2PtxtO/QW+syweiyxZT7l0J11N+zeugP7sbHI9XZU+2jfVv7yBro2+WW/v+jRlyfzu0F2HwdE1c7N/vNXGpnhvk2S/Vs59LLqgYIomkOhRHAe69X3KkrA+niyFF9RBZloKmT5caxZCqqkz7PM1HDAWZdbw0JJGOMafWORa8inOBWEQRsxnzglfQda2IWSt3lTNz61S2ZG0Sphk0Bp7sNIPuoR2xrh7mI4acTQZju/atsw6eLnJ42HywiN8XbqP4UIE4qED/e9rSa1DTs/IpkUgklyJSEEkuaBRXCd696zhU0hv19G9nVSWydB8nG9Un/gwVqN/ccJyv9orPZE1CLcwdeip4WlVVnK8swPna6+JisxnzwtcqK09nl2cxedN4DhUfFKYF6AN4uuvztApugeWzf6E7/rMw7oq5HNs1C89KDKmqypEiF1t35ZCyaBuOAjF4Wm/S0fv2GCmGJBKJxE+kIJJcuLjLUfeu5WBxD5/+ZA1L96Pp1hRPnrOGxfBdai4LfhYDqBNCLbx7e2sCjH80aFVVnP95xbdBq9VaIYbatwdgf+E+pmx6gjx7njAtzBzGM91eJC4gFvM3D6I/uE48QoMOlF//AeiM/h/bq7I7x87uX9PZv3gXXmeV4OlwK/+a2pNiR83FSSUSiUQiIgWR5MLE40DZt4YDhd3wIoqJBqWH0HaKxFQvFvKqr966P7uMyZ/tF2xBZh3zhrYQxdDceTjfrBIYHRCA5c2FaNu0AWDDyV94eutT2D3iLU2zoObM7PosIfq6mL+8D0PqCvEI9ZpRPvgTMPjfMqPU6WVbZjn7vjjA0Sr7B4hvHcatE7phCTRSnCYFkUQikfiLFESSCw+vCyV1DQcLOuPBJAyFlx5B1yYIc1jNT0V5ZU7GLE8R6gzpNAovDm5e+UwGnEEMvYG2TUWNox8zvmfW1ul4VfGWpmdEbyZ0eBIzCpbP7kB/6EvxCIFRlN2wEtXsfzPVrDI320+Wkbo0maxNvsHT3QYkcO09bdHqLp3gaYlEIjlXSEEkubBQvShp6ziY186nc31Y2VG0iUYska1qXF5Q7uKRFXt9KlA/cVU8nWJP1SdyfvyxrxgKDKwQQ62TAFif8R1Pb53hI4ZuShjOvS1HoXGVY117G7rjPwrj3oBIyoauRg2suVJ2VQ4UOEjJKGPv29spShOf5TRahUGj2tPp6sZ++5NIJBKJiBREkgsHVUU5/C2HslvgVsXU9NCy4+jivQTEdahxeVpORTuOjCJRDN3cPoKbTkutd//yK46nZ4uL69TBsugNtK0qxNYP6d/xzDZRDCkojGnzONfHDQZ7IdbVN6M7uVlw46nbiLKha1Drxvp97MOFTnalFZL8xlZsWWLrD5NVz20Tu9O4Tbjf/iQSiUTiixREkgsGJX0DhzNicKlizE398nT0UeUENLu8xrXr0/KY+Ol+yqsEIHeKqcu4K+Mqv/akHcD22OPgOW2e2SyIoe/Tv2X2tpmCGNKgYXyHyVwVfTU4irCuHIIua7vwWZ56zSkbugo1IAJ/SS9xsXlbJnsX/Y6rVAwQr9fAyp1TexEWXcdvfxKJRCKpHimIJBcG2bs4ejQIpyr+xz/EfgJDaD4BSddUu0xVVd7aeJz//Hi0avFmOsbUZc4NiZUVqL15edhGPwClpacmKQrmF54XxdDWGUIrDg0anugwhb7R/cBVjnXNLb5iKKzNHzFDIX4fOavMzfdfHyb1w12obrH1R0xiCLdP7kFAXVMNqyUSiURyNkhBJDn/yT/I8f0Kdm+wYA52nMQceBJzu+uqXeZVVV7dVswPR32zrYa1a8ATfeMrxZDqcGB76GHUDLGVhnHs4+iuqLh5+ub4Vzy/7WkfMTShwxSujO4HHmdFnaGMjYIPd8OulA1eCsbqi0NWR165izUfJnOkmkyy1r2jGTqmM3qDbE8jkUgk5wopiCTnNWrJCTL2lmLzit3Z67qyCDBnYOoyAEVTfVbV3B+O8MNRMRVeq8ATVzXm5vannq1Ujwf7pMl4d+wU5uqHDUN/178A+OrYF7zw+zOop90zadAwseOTXBF1FXg9mL+8H/2RbwQf7ojOlA1Zflap9fllLhbP/Y3MajLJLr85kStvbYVGo/jtTyKRSCS1IwWR5LxFteVxcncm5Z4Ggj3QnU1d/RGM3a6vUQx9vO0E720Rb3vqmHS8OLg5XRqdyib7nxhyfyGmxWu7dcU4ZRKKovD5kc+Ys+M5UQwpWiZ2eJIrovqCqmL+7lEM+1cJPjyhrSgbvMxvMaSqKqnppayZs5miA/nCmEarMOTBjnToG1fDaolEIpH8FaQgkpyfOIvJ2ZlGqTtGMAd4cwlWDmPoMQBFW/237/q0PJ779pBgq2/V89ZtrWlU71SqvurxYJ84CfdnYvVoTXwc5pfnoOj1fHp4NXN3viiMaxUtkztOo0/kFaCqmH6ajGHP+8IcT1BjyoasBFMQ/mB3e1m/+SSbXtuKs0oWnMGi547JPWjcOswvXxKJRCI5e6Qgkpx/eOwU7NxBoTNBMFvUPOq7D6C97Do0Wn21S/ecLOGJNal4T4ugNus1/GdYS1EMud3YJ07GvU4UQ0qDcMyvvYpSpw4rD37Cgt3zhHGtomVqpxn0bNinQgytfwLjjjeEOd7AqIo6Q1b/BExGiZNv1qSRtjwF1SOGfgeEWhg5vRdh0f7HH0kkEonk7JGCSHJ+4XVRumsDObbmgtmoFhNm34/2imvR1tD3K73QzkOfpGB3nx70DC8Mbk6LBqeerVS3G/uEibg//0JYr0REYHn3bdTICF7ZNZdVh5YL4zpFx7TOs+ge0RNUL6bvHsO4+11x++b6lN2wCrVOdK1HVVWVHSfK+fGdHWRvzvAZb5hYn7smdScgSGaSSSQSyd+NFESS8wfVgz3lR06UtBDMOmw0LNuLcsVVaPXmapcW2Vw8sCyZ/HKXYB/ZLpBejU+1x1DtduwTJuH++mthntKwIZZ336Y8rC6zNo3nt+wtwrheY2B656fp0qBbRQD1Nw9iSPlImOM11aPshpV46zWp/aiqypb9hXw3bwulx4t8xrsObMJ1d7dBq5VtOCQSieT/gRREkvMDVcW1/yeO5zcTzBpcRJcko/bqgd5UfQHCI/k2HlmRwpF8m2C/u2sU/SJPCSRvdja2hx7Gu3uPME9p2BDLe+9wsq7KlJ/+zbHSo8K4SWvmqc6z6BTeBTwuzF/926dRq9cSStnQ1Xjrt/TjqCo//pbF+vmbfeKFdEYtN47pROteMTWslkgkEsnfgRREkvMCz5ENHM2KQ+X02jpeYoqT8XRvg6lO9fE4vxzMZ8LaVEocYgXqaxLr81CfWA4eOFDhPyUF2wMPoWZlCfOUyEgs777DbmMW036cTImrWBgPM4cxs8tzJAQ1Aa8byxcj0aetEeZ4rQ0ou3Et3no1N5T9H6qq8vnnh9j41na8LrHYYr2GAdwxqQfhsTJeSCKRSP7fSEEk+cdRT/zO0fRwvBgEe0zJXtztG2Gp59v3S1VV3t2cwbz1R3wrUEfXYeZ1TdEoFbV6XF99jX3iJLCLNYk0jRtjXvg6v2mPMG3DZFxesTVGYnALZnSZTT1TSEVq/beP+IqhwKgKMRQUX+s5PR4vS9/exZ61vsUWG7dvwG3ju2KyGqpZKZFIJJK/GymIJP8sOXs5dtCMW7UI5oiy/XhaBGNt2MJnic3lYfoXB/giJcdn7LqWoUy9JgGDToOqqliXLsO+5COfedrevTC/+AIbS3YwY/OTuLxi7NGVUVcxtt0EDNqKAG7Tz9MwJH8ozPHWiaX0xrV+NWp1Oty8/fwmjm054TPWZWATrr+7DRoZLySRSCT/GFIQSf45Co9wPNWFQ60vmEPtR1BiwBrX0WdJeqGdx1buJTVb7PquUeCRyxpxZ+dIFEVBLS/HPmkKgVWCpwH0//oXxrGP8XPmz8zaOg2PKj63jUgcyW1N/4Xyxw2T4be5GLfNF+Z4A6MovWkdamBUrccsKbTzxvSfyTtQINgVrcK197enR//GtfqQSCQSyd+LFESSf4ayTE4m52LzNhTMwa4MDMFFWFv291ny66ECJqxNpdjuFuyBRi3PD25O97iKXmfejBPYHnwIb2qq6ECnwzjtSQxDh/JD+nc8s22G0LEe4JE2Y7k+bnDl1/rd72H+5Slhjtdcv6LOkB9iKCu9hEXTfqKsioDTBxi4+YlutGgbXqsPiUQikfz9SEEk+f/jKCJn9yFKPI0Ec6Anm0B9BsYOAwW7qqq8vSm92o718fUtzBuaSExwRTq+e+tW7GMeRS2ochsTFIRp3svoOnWqtkmrgsJjbZ/g2kYDKm26tDWYv3tU3IshkLIhy/EGi0Ujq+NAcg4fzvoVZ6kYm2RtEMCdU3sSHV191pxEIpFI/v9IQST5/+JxULxnGwVOsfCixZtPiPsgul5is9Yyh5sp69L4fn+ej6u+zUKYcW0TrMaKb2Pn0mU4nn4G3OINkqZpE8yv/AdNVBQ/Znxfbcf6ce0n0i/m1K2UPnkx5m8eRlFPzVO1RsoGfYQ3vG2tx9z28zFWvbzFJ5MsuEk97pnak3qy2KJEIpGcV0hBJPn/oXqwp/xEZplYq8eoltCgPBWu6IfmtCrUWSUOHvokpdp4oQd7x3J316iKeCFVxfnyXJyL3vL5SHu3rtSfPx/FamFz5kae2TpDFEOKlgntJ3NldL8/9qhiS07h4wAAIABJREFU2DrP55lMVbSUX/cOnqietR7zy0/28dMHu6h6nRXZuSEjxnXFYpI/dhKJRHK+IX8zS/4/qCruAz9yvEAsvKjFTnRxCu4+vTCYAivtaTllPPhJCpnFYuHCuiYdzw5qVhkvpKoqznnzqxVDhgdGk9n3SkKtFnbmbuepLZNxq6dujzSKlin/a9IKFe04fpyEcfvr4tZRsPV7BXfja894RI/by5LXfmfv14d8xppd14Tb7m2DTmaSSSQSyXmJFESS/wtq+haOZcainvYtp+AhtnAvzu5thcKLm48U8viqvT7FFpuFWZlzQyJRpz03ORe8ivONN8UPM5sxzX4Gfb+rIC2NfQUpTN40HmeVOkPj2008JYbcDsxfj/apQK1qDZT3fxN3k0FnPF+ZzcXbz2zk5I5McUCj0PnO1gy84VRdJIlEIpGcf0hBJPnbUXKTOXbUilsV+5BFlaTi7BCLpX5cpe2zPdlM+zwNt1d8b+qTUI9nBzbDYjhVydrx+kKcr74mflidOlgWvYG2VSsAMuzHmbvhBWxusa3HmDaPc1XMNRX7K8nA8sW96DI2CHNUQx3KBi2p9ZksK6eMd2f8StGRQsGuNWq5akxnevWMqkzhl0gkEsn5iRREkr8VTfERMlLLsXsjBXu47RDeplaska0qbe9vyeCl7w/7+LipXQMmXNUYreaUqHC8uQjn/P+IEwMCsLy5sFIMbcnaxJwjz1HmEWOQ7m05ioFxQ4A/Msm+GYPGIYoZr7UBZUM+wRuadMbzHT1WxPvTfsaWWy7YjUEmbprUncTE+jWslEgkEsn5hBREkr8NpTyTrOR0SjxiW4sg50m04XasTfpW2j78rXox9MhljbirS6Rww+JY9BbOl+eKE63WCjGUlIRX9fJh6ru8v+8d1CqRzbc1vZNbmtwGrjLM6ydi2PO+z2d6ghMoG7Ki1grU+/flsWTmLzirxDkFRgZy91O9CG8QcMb1EolEIjl/kIJI8vfgKCR/dwoFrirp9Z4CrNaTWFqfClD+eNsJXvhOFEN6rcLM65rSv0VopU1VVZxz5+F8c5H4WWYz5oWvoW3ThiJnEbO3zuC37M0+WxoSfyMjEu9Fk70Ly+d3oy044DPHFXMZtv6LUC1nvtnZue0ky5/diKdKkcjwFvUZOaUn1kDZk0wikUguJKQgkpx73DZKd28mxyE+Nxm9pYQoBzB1vq6y1tCy7SeZ/Y2YlWXSaZh/Ywu6NAqqtKleL45nZuOq2pfsDzGka9+efQV7mb5lCtk2saO9Bg13t7iXW5rcju7EZqyrbkRxlQpzVI0ee8+pONs/AMqZM8E2/3SMtS9vQXWLNYbiukUxYlwXdHptDSslEolEcr4iBZHk3OJ1YUtez4nyNoJZp9oIc+3FcNnVKNqKb7uVOzN5+quDwjyjTsO8qmLI7cY+5Uncaz8VP8tiwfzaArQdOvDp4dUs2D3Pp0lroDaQaV1n0S60A9oaxJAnuAnl/d/0q+Di+nUH+Hrh7z41hlr0i2f46PZoZVq9RCKRXJBIQSQ5d6heXPt+4HhRK8GswUVDewq6y/ui1VekzK/elcWML8QnK71W4eUbEul6uhhyOrE/Phb3d9+Ln1W3LpY3XsfRvDEvb5vO9+nf+mynRb1W3FH/7j/E0JZqxZCz1Z3YLpsNemutx/tsSTIbPkr2sXcYlsgNd7SSmWQSiURyASMFkeScoR7bwNHcJsCpJyMFD5FlKWj79EJnqBAdq3ZmMv2LA8Ili05TIYZ6xAef8uf1Yh833kcMKaGhmBe9wdEwDTN+HMnx0mM+e7khfhj3tRrNkYNH0J78DeuqoSjOEmGOvfsUHF3G1n4uVWXpwu3sWlcl5kiBnne35drBTWv1IZFIJJLzGymIJOcEJXcPx44H4cV4mlWlYfletD06oLdU3Pqs/EMMnY5Oo/DSkOb0alxPsDvn/wf3N+LNjxIZieWtN/la3cP8n17C4REzvMw6M4+3ncDlUVcCYC3Yg/XrMdWIocl+iSG328MHL/9G2k+i6FJ0Gvo+2JHLr2xUqw+JRCKRnP9IQST5yyilGWTtL8LujRPsDWxp6Lq0wPBHFeoVOzKZ8aWvGHphcHMuaxIi2F1rP/WpQK2Jj8P05hssyl/F0rQlPvuIq9OYaZ1mEh0YU+H7yLc03fQAikesEWTvNhFHl3G1nsvhcPPWMxtI/12sPq01ahkwrhtdujSs1YdEIpFILgykIJL8JRRnEcXJ2yl0txfsQc4T6NpGYQyOAmD5jkxmViOGXhzcnMubimLIs2MH9ienip8TEoL5jTd4r+CzasXQNTHX8VDrRzHpKmKU9Lvfw/zdYyiq2P7D3vUJHF2fqPVc+XnlvPP0BvLS8gW7PsDAsMk9aNUqtIaVEolEIrkQkYJI8ufxOHGmfM1JR3fBbPYUYW6swRKeAFQ8k1Unhl4a4nsz5M04ge2hh8F1WraYwYD5lfl8VPI1H+5/T5hv1Bp5uPXjXBP7R10jVcW4YRamLS/5bNfeZRyOrhNqPdbundmsfHETjkK7+FnBJu54qjfx8UE1rJRIJBLJhYoURJI/h6rCgc84VtwROJVdpVUd1KmfT2BcDwA2Hi5glp9iSC0rw/bAA6h54q2MadZMVlhSeDtZfEIL0AfwQo+5NA36o/ij24H5mwcx7PtE9IuCvc8zONuPOuORPF4v61alsfmDXageMa/e2iCAETN60zBCVp+WSCSSixEpiCR/Ck3GjxzJaYJHCKL2Emw8Rt2kPgAcyi1n3Op9nK4t9FqFl4Yk0idBDKBWPR7s4yfg3Z8m2A3338e6FnYW7log2C06C891n1MphhR7AZZPb0eX/qswz6sxYrtuEe6E6894nuIyF4vn/8bxDek+YyFN6zFicg/q1TNXs1IikUgkFwNSEEnOGk3hPjKP6bF7xRueYPUowZ0rOsPnl7t48JNkShynYngU4LmBzXzEEIBz7jzcP/wg2HRX9eXrAVH8Z9ccwW7Smnim2ws0D25RsZ+8VCxrh/Pf9u4zPKpqbeP4f2bShhTSQ0lCSyAklNACBAREETmIiKCgYsECoh4FjwgIKirH0EQQEZEiysHXgpwjNhQVIdTQewklARLSe53MzH4/oBNWBlQQSMg8v+viA89ee2etTDC3e6+9liFPXfHaavTnaPuZNPiTMHTybCGfxW2m8HS+3bGofmHcO7Itzk6y+rQQQtRmEojEZdGXZZJ35LjdJGoPSzo+3dqj0xswma08v+owKfnqK/Fjbm7MLS3s9wir+O//MC1Zqn6dli1ZNSKSpfvVMOSid2Fql+m09ju/ErbTyTXU+f4Ju9fqLT5hlNz1BcWZ6l5jF9I0jc070lk7Z5vdBq0GFwP9nupArLxWL4QQDkECkfjrzKWUHviZNFMPpexmzcevYyhOLkY0TeO17xPZfbZAaTOoTRAPxzS0v+TOXZS9OkUt+vuz4qlWfJak7kTvrHfmtc5v0i6gA2gaLjvm4rbxNXRV9tEwN+hKyZ0r0Iy+kKk+gvudyaLx9epEdn28z25PMo9Adx6aHEtwE5+LniuEEKL2qdaNlzZt2sSwYcNo2bIl3t7erFixQjmuaRpxcXFERERQr149+vfvz+HDh5U2eXl5jBw5ktDQUEJDQxk5ciR5eXnXcxiOQbNiPfIVZ0q6KmUnrRzfFkZcPc8/Plu0+QzfHMxU2nQKrcukvs3strawnj1L2bPPgfmCuziurqwYHclnhWuVti56F6bE/JuYoC5gLsW4ZiTGjVPswpAp6gGKB//vfBi6hNwSM8vm72Tn0j12YSikbRBj5/aRMCSEEA6mWgNRcXExkZGRTJs2DaPRfsLq3LlzmT9/PtOnT+eXX34hICCAQYMGUVhY+Xjk8ccfZ9++faxcuZKVK1eyb98+Ro0adT2H4RhOreF0bjRWnG0lnWbBJ6gYj/qNAfhqXzrz49UVnRv5GnlrUATOVTY91YqKKH3qGbTcXKX++UPN+MK4T6l5Onsyq9tcutSLhYoS3P831P5NMp2e0p5xlPZ5F5xcuZRTGSUseT2epLUn7Y51urM5o167CaOHy6W/D0IIIWqlan1kdtttt3HbbbcB8NRTTynHNE1jwYIFjBkzhoEDBwKwYMECwsPDWblyJSNGjODo0aP89NNPrFmzhpiYGADefvtt+vXrR2JiIuHh4dd3QLWULi2BlHNBmDQvpV7XPQOfiA4AbDqZa7cKdV03J+YNiaSu0VmpayYTpWOfx3pcbf9T/xA+aZKq1AKMgUzrOovGXk3BXIr7V/fhdGaD0sbq6k1p/w8xN7r5kmPQNI3tB7P54e1tlGYUK8f0TnrufLoDMbc2ucTZQgghartqvUP0R5KTk0lPT6d37962mtFoJDY2lm3btgGQkJCAh4cHnTt3trXp0qUL7u7utjbi79HlHSf9ZDlFlvpK3UOfRkD7dgAcTiviX/89jNla+fjK5bed6xtVeVVds1goGz8Ry6bNSv1gpyDejVXnHYV6NuadmxbYwlCdr+7H6cx6pY3FtznF9/38h2HIbNX4dm0yX7+2wS4MGb1deSKul4QhIYRwcDV2UnV6ejoAAQHqFgkBAQGcO3cOgIyMDPz8/JS5KTqdDn9/fzIyMi557cTEi0+0re0ud9xu1nw8crLJM7euUs+hKNDI8RMnSC+2MGldDqUVlXNxdMA/O3rhVZ5BYuIFn4Om4fXeAur8qM4PSgn14rU7yuCCz7GJsRnPNBhDfkoBBZZMwna8gHPmVuW8Eq9wjnacjyXLClmXmDyt6Vi2cBcnvz9BlelG+IR60OeRRpgMuSQm5l70/Budo/6sg+OO3VHHDTJ2R3ItngDV2EB0LTnio7TLfYSoqyimcPdhUis6KnVXayFBHUJw9axLbkkF//rPPvLK1YnJ425tygMd7Tc+LZ/9NqYqYSi3vhcTh4PJuTIMNfduwaxu7+Du7A7mMup8PdwuDFn8ozAPWU1To7oW0oXO5Zfz6VvbyNydZncsqmco9z7bCWeX2ru+kCM/NnbUsTvquEHG7qhjv5pqbCAKCgoCIDMzk5CQEFs9MzOTwMDzu6cHBgaSnZ2Npmm2u0SappGVlWVrI66AtYLSA2tJLeuklJ20Mvxa1sHVsy7F5Wb+ufIQyTmlSpuHYxpePAwtWYpp8RKlVuznzrgHocCj8sltqEcjpnV9C3dnd3TFGdT59mGcUrYo51n8IykeshrtEmFI0zQOnSvh29lbyTuarRzT6XX0eaQNPe9qbvfWmxBCCMdVY+cQNWrUiKCgINZdsHpxWVkZW7Zssc0ZiomJoaioiISEBFubhIQEiouLlXlF4jJoGhVH1nKmsAMX7lGm1yrwbVCCR1AI5WYrY1YdZn+quhhiv8gAxtzc2O6Sps8+x/SWusBiuZeRFx/Uk+Vd+SMYaAxiRre3qevqjT59Dx6f3GwfhvwiKR586TBUbrGy8UQ+q+M22YUhFw9nHn6tB70GtZAwJIQQQlGtd4iKioo4efL8689Wq5WzZ8+yb98+fHx8CAkJYfTo0cyePZvw8HDCwsKYNWsW7u7uDBkyBIAWLVpw6623MnbsWObMmQPA2LFj6du3r9w+vELWM5s5nRWBxoWPkqz4eJ7DO7wLFRYr4786QkKyus1Fp9C6vP6PcPRVgoZp+XLK46YrNbPRhZceNJASWPk1vF19mNltDgHGQJwPf4Zx7XPoLOpu8xbfCIqHfIVWx361a4CcUjNbE/PY9W4CJeeKlGN163vw+Gs98JPNWYUQQlxEtQai3bt3M2BA5T5TcXFxxMXFcd9997FgwQKee+45SktLGTduHHl5eXTo0IFVq1bh6elpO2fx4sW8+OKLDB48GIB+/foxY8aM6z6WWiH7CGdP18WCm1L205/AN/omrJrGq98lsi5R3Y0+qr4Hcwa3xMVJveFYvvADTHPfUWpWZyemPODMiYaVP3ruTh7MiJ1NcJ36uK2fhOsudSNXAHNIT0r6f3jRBRfLzFaO55lIPJXP/vkJlGWrj/ECm3jz+Bs98KjrZneuEEIIAdUciG666aY/XFVap9MxceJEJk6ceMk23t7efPDBB9eiew5FV5LOuSO5lFkbKXVfy0l8YruCTse0tSf5tsoq1E396/DevVF4uFb+KGmahmnuO5g+WKS0tTobiLvPlQNNK9clcjMYies6k2buIdT5+kGcT35v17fy9k9RdtProFd/XCssGifyTSTlmyg8W8DB93fY7UkW2NSDJ+Nuxq2OuhaSEEIIcaEaO6laXEfmEnL376XA0kope1lS8eochc7gzNz1yXy265xyvGFdVxYOjcL7goUXNU2jfNp0Kpb/R2lrdXXmjfvd2B1e+ZjMWe/C1C7TiPJqRp2v7sP5tLrbvWZwpfTWOVRE3qfUKywapwtNnMgzYbZCxo5Ujn+6H2uF+rZbi071iRkcKGFICCHEn5JA5Og0CyX7fiKjXH293mjNpW7b+jgbvZm3IZkPt55Vjgd4uPDBfa0J9KzcJkMzmyl/7XUqvlyltLXUcWPKcFf2N66cX+Skc2JKzFTa1W2O+3+H4JSiLtRo9WhIyYD/YKnXzlbLK7NwusBEarEZqwZWi5Wkr46Suj7JbljRvUIZ/FwMJ0+duOxviRBCCMcjgcjBVRz5kZSiaKXmpJVSt6kON58GzNuQzJItahjyNjrx/tAogr0r5+RopaWUvjAOy7pf1eu7uzHpIReOhVzwxhp6Xur4Kl28I3H/8i6c0nYq51h8W5zfoNWjPharRmqxmdMFJvIvWO/IVFDOkWW7KThhv6Bi1zvC6P9EO/R6eZNMCCHEXyOByIFZzm7nbFYY2gU/Bjos+Pjn4hnSgXcvEoY8XQ0sGNqKsAB3W03Ly6Pkqaex7tmrtC3zcmPCw84k1VcnW7/Y/iV6+bTGfeUADJn71T4FtKL47v9hcvPjdF45SfkVlFvUJaYLk/M4vGQXpnx1vpCTs547R7enY5+ml//NEEII4dAkEDmq/NOcS3KhQnNXyj5uZ/CO7Mr8+NMsvkgYWjisFZH1Kl9dt6aeo3TkKKwn1d3j8/2MvPSgk/JqvR49Y6LH0dejKXU+vRVDfpJyjrleB3IGfMGp0jqcTi/CXGWrDYCM7SkkfnoAzazOF6rrX4cHJsYS3Nz+LTQhhBDiz0ggckSmfDIOnqHEqm5oWld3Gr+OXVi46QyLNp9Rjv0ehqLqVy55YDl2jNJRo9F+23fud+ca1OGlh5zI9aq8M+RmMPJyp9foVm7C/dNb0ZWrG7lWNOjK3p7LOZHhjJUKuy5rVo2kr4+S8sspu2NN2wQy7MUu8lq9EEKIKyaByNFYzeTtSSDPHKWUPbR0/LpGs2pfBgs2nlaOXSwMVaz5gbJJk6FUXfPneJgHr96np9hYGYb83Pz4d5eZRJ7ejPGXf6HTLMo5xQ178XPHRRSXuHIxWlkFp1bsI3Wf/Ya93e9qTt9H2mAw1NhF14UQQtwAJBA5Ek2j5MA6MsrUMOSm5eMb3ZAtp0v49w/HlWNVw5BmsZxfY6jKvmQAB6J9eW2QhYoLNmpt4tWMN2PiCN35Lq673rM7J6XpMDZG/RsrLnbHXPQ6fErLWT8vgayz6jYhTs56Bv2zE+1ubmR3nhBCCHG5JBA5EFNSAil5zZWaE2V4N9I4WVqHcf/bx4Xzl92c9Lx3b1RlGMrLp/TFF7Fs3GR37R03h/DmLQVYL3izq0NAJ6a0eQH/tc/hnKTucq+hY2/USxxuNgqqbPfhatAR5uNCyZEsvpi9jbJi9RGap68bwyd1I6T5pXe6F0IIIS6HBCIHYTRlkJLmj0blIoU6LPh4plLg3ZFnlu+l9IKFDfU6mHZnC9o09ALAkphI6TP/RDujTrTGyYmfhrfm3eanuHAz2LZ+0cSFPUTdL+7AkK/O+7EYjGzq8A4p9W9X6jqgcV1nwrxd2LzqKD8u349WZWJ1cLgvD0yKpa5fnSv/ZgghhBBVSCByBKYC9HkuVGieStlPdxxd81ieXnGA7Cp3Ycbf2pSbf7sDY96+ndJnnoVC9bGVzt+fr57pxFIXdUf68LrNmenXDZ/P+6MzlyjHSt2CWN/5Q3K9Wyt1HzcDUf6uuFk1Vs7axv6N6qRuOL/Y4qBnOuLsKj+2Qgghri75zVLbWc3k7tlBkSVCKftYT+HetQtPfXmMpBx1YvQjnRsyrEMD4LfJ0+MnQIUamPTRbfnvU+35MOu/Sj3UvSHv40/dH0bbdSXLpz0bO71PqbG+reakg0h/Nxp6OJGXUcL7UzeSlpSvnKfT67j9kTZ0v6s5Op0stiiEEOLqk0BUy5Ue3kBmmRqG3LVMPKOb8t7mNHacVsPH7S39ea5XYwBM/1lBedw0qj63cr7nHlbdE8qHx9WJ1c2c6vJhTj51Uu032z3e6AF2tn4Nq6HyTTIvFz3tgoy4O+s5m5jDR6/HU5ynLrZo9HBh2ItdCG9X77LHLoQQQvxVEohqMUvKHlKz1bWGnCjFK0TH5gwXlm1T9/lqH+zFG/2bowPKZ7990TfJXJ4fy7e9PFly4B2l3tUMs9OP4VySqdStehd2tH6DE43vV+ohns5E+rli0Os4kpDK/83YQkW5+jp+UKO6PDi5G74XLAQphBBCXAsSiGqrwnOknjJg4cK1faz41DlLrk8MLy/bozQP8HBh1qAInA06yt+YSsWnn6nXMxhwe+N11kYbmL9nWmVd03ioqJBnslPQV1lfyGSsx68dF5Lt277yMjpo5e9GQ8/zk7u3fX+c1e/vRrOqd6GiYoMZMqYTrkbZqV4IIcS1J4GoFtIqSsg6cJxSazOl7sdxXFt3Y+R/9lN0wd0YJ72OWXdF4OfuQvniJfZhyGjEOOdtNjQpZfaO123lOlYLr2ancUuR/QarBUGx/Bw9jzK3QFvNWa+jc30jXq4GrFaNHz/ez4Yvj9id22NwC257qI1sziqEEOK6kUBU22hWivdtILeijVL2sqbgFdOe19ee4lim+ubX8zc3JjrYi4rvv8c0+23lmM7XF+OC+az3SSdu51Q0zt/JaVVWwtTMswSbTXZdSG31DBua/AtNX/nj5azHFoZKi0x8OXc7h7amqF9Lr2PAyHZ06R/2d74DQgghxGWTQFTLmBN/4VyxuhK1q5ZPiZ+VTcdKWH1A3f7itgh/7u/YAPOuXZRNnKRezNMTl6UfsMj0E1/s+BQAg6YxIi+Tx/My7H54NBcvEru9w06vW5T6+TBUBy9XA6kncvlk2mZy0orVNq4Ghr3YlZYxDa588EIIIcQVkkBUi+jSd3M2PRiNC3eYr6BuYBFbCj2JW69Oom7iZ2RKvzC002coe+ZZMF1wt8fJiYqZr/Fy+jz2Z+8FoEGFiTcyz9K2XL3DBGDxb8X+m5Zw2KoGGic9xNSvg6eLnu0/nOTrhbswV6g71bt7u/LwyzfJTvVCCCGqjQSiWkJXdJa046WYtCCl7uOcBI1jeWvRdiou2JfD6KznrUEtqVNaRPGTo9Hy8pTzMsc9yvji+eSUZwPQvaSAqRln8dDUMANQ3vYJ9kS9xMli9cfp9zBk1DS+nLOdXb8k2Z3bMMyH+yfE4hPkfoUjF0IIIf4+CUS1gK6igMKDO8m3dFLqda2n8eoYy7OrjpJZogaZV/uF00RfRskTT6IlJyvHku6/mee9vsT628TrtmXFTM84g2uV9YisdQIo6fMu+7x6klygLtzopIeYenXQ8spY+OYmzp1UAxdATL9m9H88GmcXg90xIYQQ4nqSQHSjs1ZQcfBbUstvUspGay4+HcNZvDWVzafUMHJ/h/r0rVtByfARaElJyrHUm9swJmoXaOff8AqtKOet9NN2YaiiSV9K+szjQIkXp6uEIeff7gxlH8vmk2lbKClQF1t0djUw6JmORPeSneqFEELUDBKIbnD6U99zprAjoLfVDJoJ7zAXEtJ1LNyk7gnWtqEnYxpZKRn+IFp6unIsu1Uoz96cbNt93sdi5p20JLyt6vpCpT2mUhz9FPuzykmrsgeai15Hp3puHFp7km8X7cFaZX2hgGBP7p8YS1Bo3b87dCGEEOKqkUB0A9Nn7SElrT4Vmjr/xrtuFvkerZi4cg8XxhHfOs68FVaB6eFHIF/dsiOjbSOevSsPs9P5MORqtfJ2+mmCzWrgKesygaxWo9mVWkJJhRp2XAw6Ovi78NMHu9m5Vt3hHiCyS0PuGRuDax1ZbFEIIUTNIoHoBqUryyLneCqFlmil7qVLwT2yHU+v2E9BmdlW1wPzQgtwe+YlKFU3cz0T25yxt6fbwpBe03gzK5VWVd4mK4+8n8TIsRxKLaHKjR9cDTqi3HR8NiWe00ey7fp7y/1R3Dw0UhZbFEIIUSNJILoRWSsoO/ATGaYeStlozcEzJpKx/z3C4XR1nZ8xXuk0emOaXRg6cVtrxvU4jfW3oKLTNF7KyaBnsTrvyBTSi21tp5GSbb8Qo6eLnvpFZXw0ZTP5Wer1XYxO3Pt8ZyK7NLzi4QohhBDXmgSiG5DlyGrOlHRWak5aKR6R/jz3v5Nsr7KD/RBfEwOXvmUXho7e05Xx0Ydsc4YMmsbr2Wn0LVTv8Jj9Ivm1wwKyS+zv7oR4OmM5lMHyd7ZTYVLnGvk18GD4pG4yX0gIIUSNJ4HoBqOlbiU1pznWKpu2ejS0MPbHDHafLVDatzOaGPPFdChQ60dG3MqE8B3A+ZDjYrUyPescNxWr+5JZ3BuwvstHZGvqjvMGHUT5unJo9VF+/fywXT/D2wUx7MWuGD1crnywQgghxHUigegGohWmkp1UTqk1VKl7uGfx/GYd+1MLlXprLx1z174HKeqeYcn39vgtDJ3nZrUyJ+MsHUvV0GRxr0d89xVkONVTv56zntbeTnw3bzsHt6jXBuh+V3P6PtIGg0Fvd0wIIYSoiSQQ3Sgs5ZQc3EaOWX1UVkefyfP7NA6nFyn1Vv6uvLfpfXRH1d3k02/twHNt9/L7nSEPi4V5GadO8vRfAAAgAElEQVRpXabOObJ4NeLX2P8jwzVEqfu4GYhw0/F/U+I5eyxHOWZw0nPX0x3ocGuTvzNSIYQQ4rqTQHQj0DTK9n1DikkNQy4UEncWuwnUbeq7M3/fJ+i2bVPquZ0ieKbnSducoaamMmZmnKFRhbpwotmnOetiPyHLSd0GxNfNQKipgkUvx5OXob6B5u7tyvCXutGopf/fGqoQQghRHSQQ3QAqTq4npbA9Fy6+qKeCNaUVbExSH5O1C/ZiXupa+P57pZ7WyIfn+p+jwnA+DPUpyuflrBTqVNmbzBQQzc+dPybP4KPU/YwGfNIKWDx9C2VVFmMMalSXh1/pjneg7EcmhBDixiSBqIaz5JwkNTUQizKJWiPNKZcFu9XHZNHBXrxrOIK27EOlnhVo5MUHLJS76DFoGs/mpPFAgf1aQWX1u/BDh6WUGDyVeoDRgLYvjY8X7MRqURcgCm8XxH0TYnGTxRaFEELcwCQQ1WTlRaQfzqFca6CUnZ3O8swG9S5NiLcbcxvkoT33b6We72lg0kNOFHjo8bWYics4TYcy9XEXQEGL+1nbfAomvVGpB7jpyfjuGJv+d8zunJjbmzJgVHsMTjJ5WgghxI1NAlFNpVnJ2r2bIkszpexBCoO2VChbcni6GnivvQuGp58GS+VaQOXO8MaDRtJ9DUSVlzAj/TRBFrNyPc3gwrmucWzwvQdNp64zFGiAg4t3cXT7Obvu9RvRhu6DWqDTycrTQgghbnwSiGqogv2byTGpYagOWYw5bKLsgptDBh3M6RGA77gn0YorJ1dbdTB7aB2OBzsxoDCXCdmpdjvWWz0acrzXEnYaouy+fqDJzKZ3t5GerL6K7+xi4J7nO9OqW/BVGKUQQghRM0ggqoHKzx4gPU9da8iFIlZm5XI016DUX+7ZkJZx47GmpSn1D/u5saOlM+OyUxlaoL4eD1AR0oP9sQs4WuZld8w3p5gf5myjOF99+8zL18iDL3ejYZjvlQ5NCCGEqJEkENUw1uIs0k+BdsFHo6eCrIokPjxWR2n7cLsA+i6dhuXQIaX+bRcXNnYx8F7aqYvOFypr/ww7IiZwpli9Y6QDjMez+O79nZgr1LfPGob5MHxyN+r6qX0QQgghagMJRDWJ1UzO3gOUac2VsgdHGbzdTan1aFKXJ39agmXjJqW+o4UTP96mZ3nqSepZ1InXmsGN4j7vsN13AGnF6lwiPRqWzaf58TM1XAG06hbMkDExuLjJj4sQQojaSX7D1SDFe9aQY26v1Lw4zaCtrly4BlFTXzfePLYay7ffKW1PNDCw6B5XFmSesgtD5cb6lN71KQlaONlVwpDBqpH91WEO/Jps16ebh7bklvtbodfL5GkhhBC1lwSiGsJ0Yj1pRerkZhcKmHCkjBJr5cfk5ebEwpJt8H//p7RN9dMT95CRuLzThJpNyjFzyE3sb/kq6eYwCqrsSG8oN5P08R6SD2SqdSc9g/7Zkfa9G1+F0QkhhBA1mwSiGsCSe5zMc57K4os6LPyck8bunMqPyKCDxa5HMc5boJyf46ljyiN1eLbsHO3K1TlDpoh7Ses1j5OppVhM6rwgfUEZBxfuIOusutq10cOFB16KpWnrwKs1RCGEEKJGk0BUzTRzGQWHT1Bsba3Ui83JvHNE/Xhm+aTR4J231HZu8NojHtylz+IfBfnKMXNwd5K6vc2eNBMWnfp2GumF7Fqwg6LcMqXsW9+Dh1/pTkCw/dtnQgghRG1Vo5cYjouLw9vbW/nTvHnlhGNN04iLiyMiIoJ69erRv39/Dh8+XI09vnwlu34g06yGIRcyeCRBbfekfwmdFk4Ha+VdnnInmPqQB5GehYzMUx95WXzCONRjCTuzrFTZbQPtZDYJs7fahaFGkf6MnnmLhCEhhBAOp8bfIQoPD+ebb76x/d1gqLzTMXfuXObPn8/8+fMJDw9nxowZDBo0iO3bt+Pp6Xmxy9UopUfWca4sWqk5UcpjO4qVlah7elsZ/skMKC211Sx6mHm/O038Cpmcmapcw+rmy+4e/yGx2P4Vecu+c2z7cC9Wq5qSWncPYcjYGJxdDHbnCCGEELVdjQ9ETk5OBAUF2dU1TWPBggWMGTOGgQMHArBgwQLCw8NZuXIlI0aMuN5dvSzlmSdIz6yPlcpNUXVYeP9EClmmyht3TTz0TP1lPlRZePGjO1240zeNf2Spj8k0gwv7blpKIg3VusVKaXwSu/571K4v3e9qzu0j2sqbZEIIIRxWjX5kBpCUlERERARt2rTh0UcfJSkpCYDk5GTS09Pp3bu3ra3RaCQ2NpZt27ZVU2//GquplOyjuZg09dHU8cJT/JBe+ZF4uOhZdGwlugP7lXbbelt5tP4Z/lGshiGA413ncLhOB6WmqzCTvCzBLgzpdND/iWj+8Vi0hCEhhBAOTZeXl6f9ebPqsXbtWoqKiggPDycrK4uZM2eSmJjI1q1bSUxMpG/fvuzfv5+QkBDbOU8//TTnzp1j1apVl7xuYmLi9ej+JdXNTCHPrC6+aLAmcffWyvlBemBJ3s+Er/7iglYaBb1LaN4s1+7WnlXvyqHWk9nf+BGlbs4p4ujiHeSmlCp1g5OOHg80pnFb778/ICGEEOI6Cg8Pv+rXrNGPzPr06aP8vWPHjkRHR/PJJ5/QqVOnK77utfhG/lX5BzaTXiUMGcng7oQKoHL+zhyPZMI//kJpp7+5gMhm6ivyABb/SE70eJ/9liZKvSQpl6NLd9vtSeZe15XhL3WjUaT/3xxNzZeYmFitn3d1krE73tgdddwgY3fUsV9NNf6R2YU8PDyIiIjg5MmTtnlFmZnq21WZmZkEBtbM9XNK00+SlVNfqTlTzOtHsjFZK8PQ855ZtF80S2nn1L6IBmH2Yag8+gmOD1jDziphKDPhLHvm2W/QWq9xXZ6afatDhCEhhBDir7qhAlFZWRmJiYkEBQXRqFEjgoKCWLdunXJ8y5YtdO7cuRp7eXHWilKyE/PtFl/cnpNIQk5lbaBbPnd/PB0qKrfecA0roV6HPPV6bj4U3/kJpzvHsSencv6PZtVI+uoIR1fsx2pWn4ZGdmnIqBm98Ql0v9rDE0IIIW5oNfqR2eTJk7n99tsJDg62zSEqKSnhvvvuQ6fTMXr0aGbPnk14eDhhYWHMmjULd3d3hgwZUt1dV2ka+Ts3UWKNVMp6yyH+faRyYnVrfREv/O8tKKy8E+RavwyfXrnq5Vw8KR78FanuLdmdVmp7Rd9cZubY8r3kHMiw60LbPkHc80ysTJ4WQgghLqJGB6LU1FQef/xxsrOz8ff3p2PHjqxdu5bQ0FAAnnvuOUpLSxk3bhx5eXl06NCBVatW1bg1iEoOryPTFKHU3HWpDEio3MHez1LGOxveQ5dRGWacfCrw/EcOTrrKOz2a3omSOz7mbJ2W7E0vs4WhspxSDn2wk5Jz6mM1JxcDQ8Z0wlivXMKQEEIIcQk1OhAtXbr0D4/rdDomTpzIxIkTr1OPLp858ygZ2Q258OmkE6VMOZyHRTsfiJwtFXy0+0Ock07a2hg8zHgMzMJNr+4/VtrnXU75dGd/RuUq0wWncjm8eBcVReqmrp6+bjw4uTvB4b7V/madEEIIUZPV6EB0o9PKC8k5lolJa6rU9+SeICHn/CrSzpYKlu35CO9De23HDR5mvAZl4e6s7kxf2n0KiQ3v5lBWZRjK3JXKsRX70cxqcGrQzIcHX+5GXT/71aqFEEIIoZJAdK1oGiV7fybPEqOU9ZaTvHa4Mgy9m7CURom7bccNnma8B2ZidFPDUHnbJ9jf9ElOZJf/dnmNs2tPkvztMbsvHdW1Ifc83xkXN/l4hRBCiL9CfmNeIxXHf+JcWVul5kouw3ZYAT3OlgpmbVxMVPIFd4Y8zfjemYmrsUoYCh9EfItXyMw//+aZ1WLlxGcHSd921u7r9rq3Jbc+0ErmCwkhhBCXQQLRNWDJTyYrwxur8oq9mbnHMymxOOFsqWDGhg/ocKZySw5bGKqjhqGS8Lv5qfVsin9bTshcWsGRpbvJO5attDM46bn7nx1p17vxNRuXEEIIUVtJILrKNLOJ0kO7KLSoayElFSXzc8b5MBS3fiGdzh6wHTN4XfzOUEHYYH5oOQuz5fyijWXZJeffJEsrUtoZPVx44KVYmraumQtSCiGEEDWdBKKrzHxgFecqYpWawZrJuH06DFYLb2xYRJcLw5CnGd8B9mEoo8kQfomchaY7H4byjmVzZNluzMUVSjufIHcefvUmAkPUjWKFEEII8ddJILqKLGc3kV4UgXbBt1WPiWf3FqGzWnl544d0P7PPdszgYcbnIo/JzoQOZlPr82FI0zRS1ydz6qsjYFVXng5p4cuDk7vj4e2GEEIIIa6cBKKrxFqaTcmZbEqs6p5ia86dI6XEwvit/+HWpB22usHdjM+dWbhVCUOnQoawLfp8GLJWWDj++UEyElLsvl6rbsHcMzYGZ1f5CIUQQoi/S36bXg2ahrb/a85V3KKUiysyWHTSzHPbv+CO45ttdX0dy/kw5G5W2icFD2Jbu/NhyJRfxqEluyhKzlfa6HRw6wOt6HVvS3Q6eZNMCCGEuBokEF0F5hNrSDV1Aip3rNdrZTy7u4SRu7/iniOVG9DqjRZ8B2Ti5qGGoeSGA9jabjaazkBJWiEHF+6kPKdUaeNqdOLeF7rQMqbBNR2PEEII4WgkEP1NluJ0CjP0lFl9lPq7x7Ppu28tDx1YY6vpXS343pGFm5cahk7X/wdb2s9F0zuRfzybw4t3YS5V2/g39GT4pG4yeVoIIYS4BiQQ/R2ahuXAz2SaeynlsyUZFO7dw5SdK201nYsVn/5ZuHmrb4mdrdeHLR3noemdydyZSuIn+7FW2Yajeft6DB3XBaOHyzUbihBCCOHIJBD9DabEH0kxdVRqOq2Ydzaf5t0NizFo598K0zlb8f1HFkY/NQylBPVmU8cFWHTOpPx0gqSv7bfh6NS3KXeObo/BoLc7JoQQQoirQwLRFbKUZJCfaaRC87igqjHrQBqv/jyfuqZiAHQGK363ZeMWoO5En+bfnY2dFmLBmROfHyRt8xm7r3HbQ63pOSRCJk8LIYQQ15gEoiuhaZTviyfX0lUpHy9Iod83iwnL/e01eb2GX59s3BqUK+0yfDuxofMSTBVOHF22k9zDWcpxg5Oewc91IrpXo2s6DCGEEEKcJ4HoCpQfW8s5U3ulptfyOPj5tzyZvBM4P2fIv08Wrg3UO0PZ3m1Z3+UjSop0HPpgK8UphcpxN3dnhk/qJttwCCGEENeRBKLLZC48R06mDxZl41YLK37ewZgdq4DzK1D7356Fs4/6pliuV0t+7bqcvHSNQx9swZSv3jnyDqzDw6/eRFBo3Ws/ECGEEELYSCC6TCUHdlBo7aDUEtOO8sTqd9Gj4exnwv/2LAx11DfF8j3CWRf7CWnHKjj68XYs5eoK1Q3DfHjole54+hiv+RiEEEIIoZJAdBlKDv5IekUbpWawZtBw7kw8TSW4BZfie2sOemd1z7EM305siFnMqfhckr45BuphWnZuwNAXuuDiJh+HEEIIUR3kN/BfVFGQSk5uIBrOtpoeE8ffX0Jsbgp1wovx6ZGLrsrb8ckN7mBz61kc/fw4mTtS7a7b7c5w+j3aFr28Vi+EEEJUGwlEf4FmtVK0/wAl1lZKPXPvOmL3rcM9qgif2Dy78w6HjSKh4b84/N5uCqvsSabX6+j/RDRd7wi/pn0XQgghxJ+TQPQXlBz8hUxLpFJzLjtFywVv4RldQN1OBcoxK7Cz9WvscR7Modn2k6fd3J25f0IsYdFB17rrQgghhPgLJBD9ifLcs2TlNQAqH2k5UYLL1FfwjcnDs22R0r4CHRvaz+ZgRiyJ/7cVa4U6udq/oScPvdwd/4ae16P7QgghhPgLJBD9Ac1qpejgccq15kpdt+YjQlscwSOyWKmX6XR813YaR/e05uxPe+2uF96+HsNkTzIhhBCixpFA9AeKDsWTbVXn+NTJ3UujrKV2YahIp2d58/Gk/tiCnAMn7a7VbWBzbh/RRvYkE0IIIWogCUSXUFGURW6uH1C5j5izVkDD78fg0VINQ3l6A7ODHqf0v60pSctQjhmc9Nz1dAc63NrkenRbCCGEEFdAAtElFO7bQ5mmTqQO2jwV7xbpSi1L78QrPIjTlzFYytT5RB7ebgyfFEtohP81768QQgghrpwEoosoTtxCtrmFUvPO/pX6bt8otRyrMxMynsD1QDQW7FeeHj6pG3X961zz/gohhBDi75FAVIW5vIjcNCMaBlvNyVJAkzMTlXZppd68dmosrmn17a7Rpkcod/+zo6w8LYQQQtwg5Dd2FUW7N1OiqQswNkqegpOlcmHFxNwmzDnyHPpS9e6P3qDjH49F0/WOMHQ6HUIIIYS4MUggukDJmT1kmao8Ksv7GZ/cNba/J6TG8NGxEWA1KO28/IzcN74rjVrKfCEhhBDiRiOB6DdWcwX5ySasF+xVZrAU0uj0G+gATYNvTg5gTfIAu3Obtglk2LgueHi7XcceCyGEEOJqkUD0m8K9v1JojVJqwWdn4VKRgcnizPJDj7Ars5PdeT0GR3Dbg61kc1YhhBDiBiaBCCjPSiK7WF0nyLNwOwFZK8kvr8vCfU+RXKgeNzjpGfTPjrTv3fg69lQIIYQQ14IEIiD/6BnMNLP9XWc10Sj5VU4XhLJw31Pkm3yU9nW8XBn+UiyNowKud1eFEEIIcQ04fCAqPLiBPEuYUmtw7n32Jwex4shDmK3OyrHAEC8eeqU7vvU8rmc3hRBCCHENOXQgqijJJSdHfSvMpTiRrRuz+fn0Y3btw9vX474Xu+DmLpuzCiGEELWJQwei/D27KdcibH83lZvZsnI7RzP62LXtfldz+j4im7MKIYQQtZHDBqKiU7vINVfuZF9cUM66zzaTlReqtDM46xn0jEyeFkIIIWozhwxEVrOJvLM62/Yc2elF/PzlfkqK1flCHj5uPDi5GyHN/aqjm0IIIYS4ThwyEOXsjKdEawnA2ZM5/Lr6KOYKdXPWBuE+PDSpO15+xuroohBCCCGuI4cMRPnlTQE4ujeNrWuPo2nq8ciuDRn6r844uzrkt0cIIYRwOA75G9+subB7YxL7tp61O9ZtYHP6jWgjK08LIYQQDsQhA9HG7xM5cTBDLergjifaETsg/OInCSGEEKLWqjW3QRYvXkybNm0ICgqiZ8+ebN68+ZJtq4Yhg4uO4S91kzAkhBBCOKhaEYhWrVrFhAkT+Ne//sWGDRuIiYnhnnvu4cyZM396rqu7jpFxvYns0vA69FQIIYQQNVGtCETz58/n/vvv5+GHH6ZFixbMnDmToKAgli5d+ofnuXtbeGb27fJavRBCCOHgbvhAZDKZ2LNnD71791bqvXv3Ztu2bZc8z8engOfmDcKvgee17qIQQgghargbflJ1dnY2FouFgAB15/mAgAAyMjIuek7jerl0f64n5zLPQOb16GXNkJiYWN1dqBaOOm6QsTsiRx03yNgdSXj41Z/ze8MHoivxyLsjcHF1rA1aExMTr8kPUE3nqOMGGbsjjt1Rxw0ydkcd+9V0wz8y8/Pzw2AwkJmp3urJzMwkMDDwouc4WhgSQgghxB+74QORi4sL0dHRrFu3TqmvW7eOzp07V1OvhBBCCHEjqRWPzJ5++mlGjRpFhw4d6Ny5M0uXLiUtLY0RI0ZUd9eEEEIIcQOoFYHo7rvvJicnh5kzZ5Kenk7Lli35/PPPCQ0Nre6uCSGEEOIGUCsCEcDjjz/O448/Xt3dEEIIIcQN6IafQySEEEII8XdJIBJCCCGEw5NAJIQQQgiHJ4FICCGEEA5PApEQQgghHJ4EIiGEEEI4PAlEQgghhHB4EoiEEEII4fAkEAkhhBDC4UkgEkIIIYTDk0AkhBBCCIcngUgIIYQQDk8CkRBCCCEcni4vL0+r7k4IIYQQQlQnuUMkhBBCCIcngUgIIYQQDk8CkRBCCCEcngQiIYQQQjg8CURCCCGEcHgOE4gWL15MmzZtCAoKomfPnmzevLm6u/S3bNq0iWHDhtGyZUu8vb1ZsWKFclzTNOLi4oiIiKBevXr079+fw4cPK23y8vIYOXIkoaGhhIaGMnLkSPLy8q7nMC7b7NmzufnmmwkJCaFZs2YMHTqUQ4cOKW1q69gXLVpEbGwsISEhhISE0KdPH3744Qfb8do67qpmz56Nt7c348aNs9Vq69jj4uLw9vZW/jRv3tx2vLaO+3dpaWk8+eSTNGvWjKCgIDp37szGjRttx2vr+Fu3bm33uXt7e3Pvvffa2vzZ77Ty8nLGjRtH06ZNadCgAcOGDSMlJeV6D+WyWCwWpk6dahtXmzZtmDp1Kmaz2dbmWn7mDhGIVq1axYQJE/jXv/7Fhg0biImJ4Z577uHMmTPV3bUrVlxcTGRkJNOmTcNoNNodnzt3LvPnz2f69On88ssvBAQEMGjQIAoLC21tHn/8cfbt28fKlStZuXIl+/btY9SoUddzGJdt48aNPPbYY/zwww+sXr0aJycn7rrrLnJzc21tauvYGzRowGuvvcb69etZt24dPXr04IEHHuDAgQNA7R33hbZv386yZcuIiopS6rV57OHh4Rw9etT258JffLV53Hl5efTt2xdN0/j888/Ztm0bM2bMICAgwNamto5/3bp1yme+fv16dDodd911F/DXfqdNnDiRr7/+miVLlvDdd99RWFjI0KFDsVgs1TWsPzVnzhwWL17M9OnTSUhIYNq0aSxatIjZs2fb2lzLz9wh1iG65ZZbiIqK4p133rHV2rdvz8CBA3n11VersWdXR8OGDZkxYwYPPPAAcD5BR0RE8MQTT/DCCy8AUFpaSnh4OG+88QYjRozg6NGjdO7cmTVr1tClSxcAtmzZQr9+/di+fTvh4eHVNp7LUVRURGhoKCtWrKBfv34ONXaAxo0b8+qrr/LII4/U+nHn5+fTs2dP3nnnHaZPn05kZCQzZ86s1Z95XFwcq1evZsuWLXbHavO4AV5//XU2bdqk3AW9UG0f/4VmzZrFO++8w9GjRzEajX/6Oy0/P5+wsDDmz59vu6t09uxZWrduzcqVK7nllluqayh/aOjQofj4+PD+++/bak8++SS5ubl89tln1/wzr/V3iEwmE3v27KF3795KvXfv3mzbtq2aenVtJScnk56erozZaDQSGxtrG3NCQgIeHh507tzZ1qZLly64u7vfUN+XoqIirFYr3t7egOOM3WKx8OWXX1JcXExMTIxDjHvMmDEMHDiQHj16KPXaPvakpCQiIiJo06YNjz76KElJSUDtH/e3335Lhw4dGDFiBGFhYXTv3p0PPvgATTv///C1ffy/0zSN5cuXM3ToUIxG41/6nbZnzx4qKiqUNsHBwbRo0aJGj7tLly5s3LiRY8eOAXDkyBHi4+Pp06cPcO0/c6erPaCaJjs7G4vFotxmBQgICCAjI6OaenVtpaenA1x0zOfOnQMgIyMDPz8/dDqd7bhOp8Pf3/+G+r5MmDCB1q1bExMTA9T+sR88eJDbbruNsrIy3N3d+c9//kNUVJTtH3ptHfdHH33EyZMn+eCDD+yO1ebPvGPHjrz33nuEh4eTlZXFzJkzue2229i6dWutHjecD4JLlizhqaeeYsyYMezfv5/x48cDMHLkyFo//t+tW7eO5ORkHnroIeCv/U7LyMjAYDDg5+d3yTY10ZgxYygqKqJz584YDAbMZjMvvPACjz/+OHDt/63X+kAkaq+XXnqJrVu3smbNGgwGQ3V357oIDw8nPj6egoICvvrqK0aPHs0333xT3d26phITE3n99ddZs2YNzs7O1d2d6+r3/zP+XceOHYmOjuaTTz6hU6dO1dSr68NqtdKuXTvbtIa2bdty8uRJFi9ezMiRI6u5d9fPRx99RPv27WndunV1d+WaW7VqFZ9++imLFy8mIiKC/fv3M2HCBEJDQ22B8Fqq9Y/M/Pz8MBgMZGZmKvXMzEwCAwOrqVfXVlBQEMAfjjkwMJDs7Gzb7Wc4f2s2Kyvrhvi+TJw4kS+//JLVq1fTuHFjW722j93FxYWmTZsSHR3Nq6++SuvWrXnvvfdq9bgTEhLIzs6mS5cu+Pn54efnx6ZNm1i8eDF+fn74+voCtXPsVXl4eBAREcHJkydr9WcO5/8tt2jRQqk1b96cs2fP2o5D7R0/nB/Ld999x8MPP2yr/ZXfaYGBgVgsFrKzsy/ZpiZ65ZVXeOaZZxg8eDBRUVEMGzaMp59+mrfffhu49p95rQ9ELi4uREdHs27dOqW+bt065RljbdKoUSOCgoKUMZeVlbFlyxbbmGNiYigqKiIhIcHWJiEhgeLi4hr/fRk/frwtDF34CjLU/rFXZbVaMZlMtXrc/fv3Z/PmzcTHx9v+tGvXjsGDBxMfH09YWFitHXtVZWVlJCYmEhQUVKs/czg/7+P48eNK7fjx44SEhACO8W/9k08+wdXVlcGDB9tqf+V3WnR0NM7OzkqblJQU24TjmqqkpMTubr/BYMBqtQLX/jM3TJgwYcpVGkuN5enpSVxcHPXq1cPNzY2ZM2eyefNm3n33XerWrVvd3bsiRUVFHDlyhPT0dJYvX05kZCReXl6YTCbq1q2LxWJhzpw5NGvWDIvFwqRJk0hPT2fOnDm4urri7+/Pjh07WLlyJa1btyYlJYWxY8fSvn37Gv1K6gsvvMCnn37KsmXLCA4Opri4mOLiYuD8fyh0Ol2tHfuUKVNwcXHBarWSkpLCggUL+Pzzz5kyZYptrLVx3G5ubgQEBCh/vvjiC0JDQ3nggQdq9Wc+efJk22d+/Phxxo0bx8mTJ3n77bfx9vauteOG85OAp0+fjl6vp169eqxfv56pU6cyduxYOnToUKs/dzh/V+Ppp5+mb9++DBw4UDn2Z7/T3NzcSEtLY/HixURFRZGfn8/YsWPx8vLitddeQ6+vmfdCjkO41G0AAAhtSURBVB49ymeffUZYWBjOzs7Ex8fzxhtvcPfdd3PLLbdc88/cIV67h/OLWM2dO5f09HRatmzJm2++Sbdu3aq7W1csPj6eAQMG2NXvu+8+FixYgKZpTJs2jWXLlpGXl0eHDh2YNWsWkZGRtrZ5eXm8+OKLfP/99wD069ePGTNm2N7Yqoku1bfx48czceJEgFo79tGjRxMfH09GRgZeXl5ERUXx7LPP2l6hra3jvpj+/fvbXruH2jv2Rx99lM2bN5OdnY2/vz8dO3Zk0qRJREREALV33L/74YcfeP311zl+/DjBwcE88cQTjBo1yjZhtjaPf8OGDdx55538/PPPdOjQwe74n/1OKy8vZ/LkyaxcuZKysjJ69OjBW2+9RXBw8PUcxmUpLCzk3//+N9988w1ZWVkEBQUxePBgXnzxRdzc3IBr+5k7TCASQgghhLiUmnnfTAghhBDiOpJAJIQQQgiHJ4FICCGEEA5PApEQQgghHJ4EIiGEEEI4PAlEQgghhHB4EoiEELWGt7c3cXFxV/WaK1aswNvbm+Tk5Kt6XSFEzSKBSAjxl/weDH7/4+fnR2RkJE899RSpqanV3b2/paSkhLi4OOLj46u7K0KIaiK73QshLsuECRNo0qQJ5eXlbN26lU8//ZRNmzaxZcsW6tSpU93duyKlpaVMnz4dgJtuukk5NmzYMAYPHoyrq2t1dE0IcZ1IIBJCXJZbbrmFTp06AfDQQw/h4+PD/Pnz+e677xgyZEg19+7qMxgMdhtOCiFqH3lkJoT4W3r06AFgm2OTnJzMiBEjaNKkCfXq1ePmm2/mm2++Uc6Jj4/H29ubzz//nDfffJOIiAjq16/P3XffzYkTJ5S2/fv3p3///nZfd/To0bRu3foP+5abm8vLL79MbGwswcHBNGzYkP79+7N582Zbm+TkZJo1awbA9OnTbY8ER48eDVx6DtFXX31Fr169qFevHk2aNOGxxx7jzJkzdn0MCgoiNTWV+++/n4YNG9KsWTMmT56MxWL5w74LIa4vuUMkhPhbTp06BYCvry+ZmZn07duXoqIiRo0ahZ+fH59//jkPPvggixYtsruDNGfOHKxWK8888wx5eXksXLiQAQMGsGnTJnx8fP5235KSkvjqq68YNGgQjRs3Jj8/n+XLl3PXXXfxyy+/0KpVK/z9/Zk9ezbPP/88d9xxh23T5CZNmlzyup999hmjRo0iOjqaV155hezsbBYuXMjWrVvZsGEDfn5+trZWq5UhQ4bQvn173njjDX799VfeffddW4gSQtQMEoiEEJeloKCA7OxsysrK2LZtGzNmzMBoNNK3b1/efvtt0tLS+Prrr21zcUaMGEGvXr2YNGkSAwcOxNnZ2XatzMxMtm/fbtuF+qabbmLgwIHMnz+fyZMn/+2+RkZGsmfPHvT6ypvhjzzyCJ06dWLhwoXMmzcPd3d3Bg4cyPPPP09UVBRDhw79w2tWVFTw8ssv06JFC77//nuMRiMAvXr1YsCAAbz99ttMnTpVaT9w4EDGjx8PnN/BvkePHixfvlwCkRA1iDwyE0JclsGDB9OsWTOioqJ49NFHCQwM5NNPP6VBgwb8+OOPtG3bVpmYbDQaeeyxx0hPT2fv3r3KtYYNG2YLQwA9e/akZcuWrFmz5qr01dXV1RaGysrKyMnJwWKx0L59e/bs2XNF19y9ezcZGRk8+uijtjAE58NcdHQ0P/74o905Dz/8sPL3rl27kpSUdEVfXwhxbcgdIiHEZZk+fTotWrTA1dWV4OBggoOD0el0AJw5c8b2yOlCLVq0AOD06dN07NjRVv997s6FmjVrxoYNG65KX61WK3PnzmXZsmV2c4AaNWp0Rdf8fZ5QeHi43bHmzZuzevVqpebs7Ey9evWUmre3N3l5eVf09YUQ14YEIiHEZWnfvr3tLbPrQafToWmaXf2vTEqePXs2U6dO5b777mPy5Mn4+vpiMBiYPXu2be7TtXbh4zohRM0lgUgIcdWEhISQmJhoVz927BgAoaGhSr3qG2W/1y5s5+3tfdHHS1Xf6LqY//3vf3Tv3p0FCxYo9aqrWf9+h+uvCAkJASAxMZHevXsrxxITE+3GKIS4Mcj/ugghrpq+ffuyd+9e5bX2srIyli5dSlBQENHR0Ur7Tz/9VHl0tH79eg4fPkzfvn1ttSZNmpCYmEhWVpattn//frZt2/an/TEYDHZ3l7Zt20ZCQoJS+30u0F95jNWuXTsCAwNZtmwZZWVltvrmzZvZvXu30nchxI1D7hAJIa6aMWPG8OWXXzJ06FDltfsjR46waNEinJzU/+QEBARw++23M3z4cPLz83n//fepV68eTz/9tK3N8OHDmT9/PnfffTcPPvggmZmZfPjhh0RERFBYWPiH/enXrx/Tpk1j1KhRxMbGcuLECZYtW0ZERARFRUW2dkajkZYtW7Jq1SrCwsLw9fWlUaNGynyn3zk7O/P666/z5JNP0q9fP+69917ba/cNGjRgzJgxf/O7KISoDnKHSAhx1QQEBLBmzRpuueUWFi9ezJQpU9A0jY8//viiq1iPGTOGAQMGMG/ePObNm0fHjh35+uuv8fX1tbVp0aIF77//PgUFBUyaNInvv/+ehQsX0rZt2z/tz/PPP89zzz1HfHw848ePJz4+nqVLl9rdqQKYN28eoaGhTJ48mccee4wlS5Zc8rrDhg3jo48+QtM0pkyZwuLFi+nTpw9r1qxR1iASQtw4dHl5efazFYUQ4hqKj49nwIABLFmyhMGDB1d3d4QQQu4QCSGEEEJIIBJCCCGEw5NAJIQQQgiHJ3OIhBBCCOHw5A6REEIIIRyeBCIhhBBCODwJREIIIYRweBKIhBBCCOHwJBAJIYQQwuFJIBJCCCGEw/t/VqO3IyyiQ78AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-10T18:55:28.939721Z",
     "start_time": "2020-02-10T18:55:28.492964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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    "left": "10px",
    "top": "150px",
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   "toc_section_display": "block",
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  "varInspector": {
   "cols": {
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