{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bef88828",
   "metadata": {},
   "source": [
    "# Propensity Score Matcher\n",
    "\n",
    "In this notebook, we show the basic usage of the PropensityScoreMatcher. Unlike the GeneticMatcher and ConstraintSatisfactionMatcher, the PropensityScoreMatcher does not directly optimize a  particular balance score. Instead, the PropensityScoreMatcher uses the given objective as a measure of \"correctness\" of the propensity score model. The matcher tries a (possibly large) number of potential models and returns the model with the best score according to the given metric. In doing this, we are essentially automating an often manual process of hyperparameter optimization that accompanies propensity score matching.\n",
    "\n",
    "We show that the hyperparameter search still leaves unoptimized balance by apply a ConstraintSatisfactionMatcher\n",
    "to the resulting population from the PropensityScoreMatcher. The residual unoptimzed balance highlights a major\n",
    "limitation of propensity score matching in general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "347d7457",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging \n",
    "logging.basicConfig(\n",
    "    format=\"%(levelname)-4s [%(filename)s:%(lineno)d] %(message)s\",\n",
    "    level='INFO',\n",
    ")\n",
    "\n",
    "from pybalance.utils import *\n",
    "from pybalance.sim import generate_toy_dataset\n",
    "from pybalance.propensity import PropensityScoreMatcher, plot_propensity_score_match_distributions\n",
    "from pybalance.visualization import (\n",
    "    plot_numeric_features, \n",
    "    plot_categoric_features, \n",
    "    plot_binary_features,\n",
    "    plot_joint_numeric_distributions,\n",
    "    plot_per_feature_loss\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dd76114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60.807949</td>\n",
       "      <td>173.610298</td>\n",
       "      <td>77.912924</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.810836</td>\n",
       "      <td>170.541198</td>\n",
       "      <td>112.416988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.876976</td>\n",
       "      <td>188.138610</td>\n",
       "      <td>108.789013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.398077</td>\n",
       "      <td>162.939196</td>\n",
       "      <td>65.345017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.890587</td>\n",
       "      <td>156.386701</td>\n",
       "      <td>78.140295</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>pool</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>39.662026</td>\n",
       "      <td>162.692755</td>\n",
       "      <td>54.607476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>49.130301</td>\n",
       "      <td>141.583192</td>\n",
       "      <td>103.798145</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>68.035281</td>\n",
       "      <td>168.744482</td>\n",
       "      <td>56.499644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>62.044564</td>\n",
       "      <td>177.796983</td>\n",
       "      <td>75.983973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>51.243734</td>\n",
       "      <td>161.013556</td>\n",
       "      <td>86.513956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x152634350>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = generate_toy_dataset(n_pool=10000, n_target=1000, seed=123)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3cc6d22-3960-462f-bbfb-095ce6964d6b",
   "metadata": {},
   "source": [
    "## Optimize Beta (Mean Absolute SMD)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417e9766-9503-4be1-a4ec-8ecd8031f4f5",
   "metadata": {},
   "source": [
    "Using the given objective function, search max_iter possible different propensity score models and take the model that gives the best match given that objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddfa32bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta',\n",
       " 'caliper': None,\n",
       " 'max_iter': 100,\n",
       " 'time_limit': 300,\n",
       " 'method': 'greedy'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that using a caliper can result in matched population being \n",
    "# smaller than target! If this is undesired, do not use a caliper.\n",
    "objective = beta = BetaBalance(m)\n",
    "matcher = PropensityScoreMatcher(\n",
    "    matching_data=m,\n",
    "    objective=objective,\n",
    "    time_limit=300,\n",
    "    max_iter=100)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecbbc9be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:179] Training model LogisticRegression (iter 1/100, 0.000 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.023702966007283093\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l2\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0444\n",
      "INFO [matcher.py:144] \tSolution time: 0.002 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 2/100, 0.002 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 3/100, 0.003 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 4/100, 0.004 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 23.61454798133838\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0373\n",
      "INFO [matcher.py:144] \tSolution time: 0.017 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 5/100, 0.017 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 6/100, 0.021 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 7/100, 0.022 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 8/100, 0.035 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 9/100, 0.036 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 2.490445640066153\n",
      "INFO [matcher.py:142] \t* fit_intercept: True\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0345\n",
      "INFO [matcher.py:144] \tSolution time: 0.039 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 10/100, 0.039 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 11/100, 0.053 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 12/100, 0.066 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 13/100, 0.070 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 14/100, 0.070 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 15/100, 0.072 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 16/100, 0.073 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 17/100, 0.074 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 18/100, 0.075 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 19/100, 0.076 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 20/100, 0.079 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 21/100, 0.080 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 22/100, 0.090 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 23/100, 0.100 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.28866833556559457\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0311\n",
      "INFO [matcher.py:144] \tSolution time: 0.113 min\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 24/100, 0.113 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 25/100, 0.114 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 26/100, 0.127 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 27/100, 0.128 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 28/100, 0.129 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 29/100, 0.131 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 30/100, 0.136 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 31/100, 0.137 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 32/100, 0.138 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 33/100, 0.139 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 34/100, 0.140 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 35/100, 0.141 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 36/100, 0.142 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 37/100, 0.143 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 38/100, 0.144 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 39/100, 0.162 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 40/100, 0.162 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 41/100, 0.164 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 42/100, 0.165 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 43/100, 0.166 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 44/100, 0.179 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 45/100, 0.192 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 46/100, 0.193 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 47/100, 0.206 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 48/100, 0.207 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 49/100, 0.212 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 50/100, 0.213 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 51/100, 0.215 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 52/100, 0.217 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 53/100, 0.221 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 54/100, 0.223 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 55/100, 0.224 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 56/100, 0.225 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 57/100, 0.226 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 58/100, 0.239 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 59/100, 0.240 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 60/100, 0.241 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 61/100, 0.242 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 62/100, 0.243 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 63/100, 0.244 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 64/100, 0.246 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 65/100, 0.247 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 66/100, 0.248 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 67/100, 0.249 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 68/100, 0.250 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 69/100, 0.252 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 70/100, 0.254 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.6191810056908827\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0307\n",
      "INFO [matcher.py:144] \tSolution time: 0.267 min\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 71/100, 0.267 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 72/100, 0.271 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 73/100, 0.272 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 74/100, 0.273 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 75/100, 0.287 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 76/100, 0.290 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 77/100, 0.291 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 78/100, 0.292 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 79/100, 0.293 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 80/100, 0.294 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 81/100, 0.305 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 82/100, 0.318 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 83/100, 0.319 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 84/100, 0.320 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 85/100, 0.321 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 86/100, 0.322 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 87/100, 0.323 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 88/100, 0.324 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 89/100, 0.325 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 90/100, 0.331 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 91/100, 0.332 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 92/100, 0.333 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 93/100, 0.334 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 94/100, 0.335 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 95/100, 0.336 min) ...\n",
      "INFO [matcher.py:179] Training model LogisticRegression (iter 96/100, 0.337 min) ...\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1381: FutureWarning: 'penalty' was deprecated in version 1.8 and will be removed in 1.10. To avoid this warning, leave 'penalty' set to its default value and use 'l1_ratio' or 'C' instead. Use l1_ratio=0 instead of penalty='l2', l1_ratio=1 instead of penalty='l1', l1_ratio set to a float between 0 and 1 instead of penalty='elasticnet', and C=np.inf instead of penalty=None.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1407: UserWarning: Inconsistent values: penalty=l1 with l1_ratio=0.0. penalty is deprecated. Please use l1_ratio only.\n",
      "  warnings.warn(\n",
      "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:348: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 97/100, 0.351 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 98/100, 0.352 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 99/100, 0.353 min) ...\n",
      "INFO [matcher.py:179] Training model SGDClassifier (iter 100/100, 0.354 min) ...\n",
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.6191810056908827\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0307\n",
      "INFO [matcher.py:144] \tSolution time: 0.267 min\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
       "        <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>binary_0</th>\n",
       "      <th>binary_1</th>\n",
       "      <th>binary_2</th>\n",
       "      <th>binary_3</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5781</th>\n",
       "      <td>74.382687</td>\n",
       "      <td>194.082038</td>\n",
       "      <td>118.760023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6714</th>\n",
       "      <td>66.581290</td>\n",
       "      <td>178.545534</td>\n",
       "      <td>102.566840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9937</th>\n",
       "      <td>61.860293</td>\n",
       "      <td>159.449219</td>\n",
       "      <td>108.945960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>9937</td>\n",
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       "    <tr>\n",
       "      <th>8223</th>\n",
       "      <td>46.656414</td>\n",
       "      <td>140.392554</td>\n",
       "      <td>65.453208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>8223</td>\n",
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       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>52.829914</td>\n",
       "      <td>137.725077</td>\n",
       "      <td>93.206007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>pool</td>\n",
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       "      <td>0</td>\n",
       "      <td>962</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>39.662026</td>\n",
       "      <td>162.692755</td>\n",
       "      <td>54.607476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>49.130301</td>\n",
       "      <td>141.583192</td>\n",
       "      <td>103.798145</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>target</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>68.035281</td>\n",
       "      <td>168.744482</td>\n",
       "      <td>56.499644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>62.044564</td>\n",
       "      <td>177.796983</td>\n",
       "      <td>75.983973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>51.243734</td>\n",
       "      <td>161.013556</td>\n",
       "      <td>86.513956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>target</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x15785af90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher.match()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a4a58de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x157858dd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plot_propensity_score_match_distributions(matcher)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f36dbce4-7fdc-464e-a0ca-2860675ce54c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:139] Best propensity score match found:\n",
      "INFO [matcher.py:140] \tModel: LogisticRegression\n",
      "INFO [matcher.py:142] \t* C: 0.6191810056908827\n",
      "INFO [matcher.py:142] \t* fit_intercept: False\n",
      "INFO [matcher.py:142] \t* max_iter: 500\n",
      "INFO [matcher.py:142] \t* penalty: l1\n",
      "INFO [matcher.py:142] \t* solver: saga\n",
      "INFO [matcher.py:143] \tScore (beta): 0.0307\n",
      "INFO [matcher.py:144] \tSolution time: 0.267 min\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dw7diyC+//OJioOzYscON/lK8VSxXzFPiOXbsWBfXQ93mUPQYDXdv06ZNto5P23z66acuhut64Nxzz7Xy5cu7x+gYFMcVOxcsWGAdO3a0d99917p06eIeq8fod92ubXQMitPPPfeclS5d2iXHul7xYr16sUXDzcuVK+d+3rx5s+us+OCDD1yDgRrpTzjhBGvSpIkb7j5kyBC3nRoCdL+eW4m3OhXUoHDOOeekq7ej16trmcsuu8x1gOj4dU0R2OCvWL9q1Sr3mtTRkBfoSQcAn1ILsJJqb86TWojvvvtuV6BMPcvqRVZS+vTTT7v7FYgVmO69916XwIq3jTc369JLL3XBJXCbQ1FLeGBvdajHJ4UKFXLblCpVyv2uXmr1divJ9S4EzjvvPJdYa5jd/fffn7Z/JcNei/6pp57qArZawdWqL0qS1fqtnnNdOEjGYeyNGjVy88Z0UVGgQAE3RE3HeeSRR6a1rqv3QedJCbnmq4ta8nW+Ap199tlpc/J1bJrbp5Z2r1FC1HuioJ6UlJTWog8AQGbUkzxq1CgXq70EVDFKiaNiluLOgQMHXIO2N7pLSaMalRWrlJSGsk0osV7b1ahRI1vH522jY1Djt2KtaIScGrvvuuuutH1p/7pO8JJ00TWERrbpekEUW1evXu2KtaoBXtc2agTQNmosF40cDBySr0Rb+/nhhx9cQ76SeV23KNkOLJanY1KCPm7cuLTbdM0SSKMQlKgXLlzY/b5w4UJ755130iXp3kgJ9cbnVZIe9eruOkn6Y9euXdudNA3RyMquXbvc8AINa1AvkHpWNOQBAGKNPucCi5KcccYZLslVIFXwVUu2PjczJpLqBZZQtgmFEs7AYXehHJ9HybOXoMu0adNs9+7dbo67tlcCraCu4Wfq8Q6kBgdPyZIlXat54G16bl2MqBffo+dXL71a37t16+YaI5KTk12Ld2YU1NXgoNb7QGoUCBTYuyCKW4HPLV5irnOG9FWF9XfWOdPfUD0nh6JRGJo2oFiv0R+6sNN7BwBiTeDILjUua0qa17ur72qoDpx+paR05cqVLpkNdZtDUdxSYhqs9kxWxyd6jJewez37us5Qo0FgrNeIv4yxXsPnvQTdS4CPP/74dMeh2wLjbUpKihvuruHz2rca+3XNE3j9kZEa3jV97uKLL84y1rds2TItQRfFoIyxvmjRou748jLWR7UnfeLEiW4ogeYtKGg/9thjblimhjMEzk0MpCGTKsqjOX8K9ppLqGGPuoDUmxEAYkXGwOH9rvnfSlaUfGouViD9rqFfEso2odAQdH0uZ+f4PIEJumi+mYKx5ntnbNkPHE4vgRcbou2D3abeA9Fr1SgBBXMl6dqfWtG///5718CbGc05U8AtUaJEpttkdjzecwc2EihwqyUe/1EDiYYy6sJIcf67775zwwwVt3XxFowa3zVkU0MY9VhdeOliTMMqNQwSAGKFYmLGhnDFUy+W6rsS40BeXFcsr169ekjbHIpiphqsFS8D4+Ghjk80ei4w0VZCrKRZybOmswXyetpzGuulf//+roFdI9kU8xXDNaruULFevGHyuYn1qqej0QMZr1tiJknX8EMNvdRYf1FBAhUC0pBDtZgHo4stJfYK2jJw4EAXsHU7STqAWKLe5UBeC7EaKBUw1ZOsoVaBQ7n++uuvtLnmgdsEDi0L3CYU6sVUY6qCVGBwzer4MqPnVQE2DafTfLFwUgu5jmnOnDkuYIuG1WeUsXFAx7Rnzx732OwU1AtGFwk6XxkvQuKZplqosUbvIZ17DZPUxZU3miIYxXSdR2/6gf5GGmqo6QUAEEsUE1XY1atvot9VU8WLpfr8UxwPpDju3RfqNoei0XH6jFYcCxy1dqjjC0adrRpZph7pUNZmz44DBw64OjWag67RA6IYHthoECzW65jU+KBOBw2Jzw2dI8V5xam8ErWrCLWw6EJKBXo8aoFRMR/1lGdGf2j1nnu9QBrKqQurcL8BACDalPR603kUFJXUKLB488NVuVXz0b0lRvRZqOFfKr7iCWWbQ1HBFj1/xiHyhzq+YNTTfdRRR7lCLoFzvvVZrh7W3NAFgXfxIGpRD7Y8ilq+VRwncBi7eiA0180bTq3HalhedqlR4KyzzsrV64g1iul6DwVeMOnCSn/zzIav634V5NF1gmzYsMEmT56crbXtASC/UMelep5FMVvDub1YonitYeP6zPRGjalSuQq0aSpYqNscikaAKUZ/9dVX2Tq+YJSca/STasiowKpHQ+9VzC43ChQo4HruAzsKgk2HyhjrlWfqPOmY1OgQOLUqJ7Fe07HKli1rMdeT7s0PzLgIvH7X3LXMaE06DW9Xa4jedPqDaDic15ISjFpX9OXxLgz1BtMX/MX7m/C3QbjeTwosann1hit537evXmb2X8zJ82VZvOfNOGQqMzrmY445xgVGLVGiJFtDqxSEvX2oPoc+L7WEiAqXKKHRfHONTsq4jZJQFVLRXLCM23iBN7Nj05A5fe5qaRbVEAn1+LSNd+49StRU/EXD0dUqr2rtigcqAPfCCy+k2zbY+crqNs1/V2+r5rfpZ10YeMcb+DiN4FIFWiWPupBQYTu1yKu4jobN6XxqmRktKXOo4wl8fWq0+O2339xUrmDnUrdpe70nM875i+XPO/19A5fO8WK9zoeGrwdWx/fowlIF/TQ3UI0vmt+oiysNl88MsT5niLmIhfdIsFgf6Xifk1iv7ZRw6vNOsUe5jQqVKR5qWLbuVyPnjTfe6KYEa+i44pNippYo854nlG0yfg9GU9Fuv/12tzKKN8z7UMcXuL/AmKhkWDmXCr5q5JxyNjWAB8bWYNcJodz28MMPu6J0SvjViKtj0QoygduowKuK2Wl+u+7XY/Tcei26ZtI1jOKTRmLrGier5854/t58882DrhHCHe8TUr1njjANNVBvioa8qdXGoyqEGs7mDdHISFX5tFavhrhrWKJa1vWm1EVfYK98oHvuuccVDwpWtI7qu0BsU8upApV6d715RiqkcnyrVrY7OTlix1EsMdF+mTEjy17mQOrVVYuzer31eai53Eo8varpgXS/Wos19CyztVmz2kbJpZIgr0J6MCqaol5y9XZrqHoox6ckWYlTxvlogc+r/SpJy3hMig2q3KopUB4l1VonPbBxV0u1KfhXqFDhoNeqIX5q0FWxOg3dC5xjp8C8ZMkSd3y6uBGFQzVi6Fyo8SBwPn2w49GyMNpGS97IoEGD3HvtzjvvDPp6NddP7z0dm3r8A6n4jBoudB4zzvXP7/S3VcNIYOV+9dQoZqsnJLBCr0dL++h86AJLlf/1N9UcRDXCeMvpZUSsB+JXsFgfjXif3VivnEafbVoKTIm1jlcJpJYwy0iNmvosVO+tYlSwiu1ZbaNRyGpI9mJeMIqDmh6nY1I191COT0myYqcaCIJRUqxjUk+9Ev3Auet6nJ5Tx+rRsmyFChVKVzVdnRCawqfHexRLdR2hBNyrNq/rk8Bzr5iqRgXFdQ3n9+rFqCddX4rfgWukBzsevW6dV2/1GFV6f+mll1wV+syq5ocj3kctSdcfVBdPSq7Vq+NRIFfQ1gVRRpproDeFWoVUdMaj0vzqxdFavqG2rntl+WPtYigWqIVJyy7oAi6wuiKQE2q51QelktPABFIfzt4Q8EhQEplZAh2MiqEoKKqHNxz0Ua8gpaQylKVYglGPvAKcXke4jy8WKPHU0PnMGn/1XtRQfMWfjI0tikt6j8Rikq45e7q4CVxzXtMkdJGi152xuKCoUUUXY2oE8qhg7FVXXeXex8FWGyDW5wwxF7HwHsks1kc63mc31isP0iihjEt+RpPOo86XGt5DPb5wXGPkF7///rv7O2fVEBOOeB+14e6aJ6B/JCXjgUm6ejwyKwCnoZR6E2SswKuhF1ktL6ACQl4RoUD6oPHrhw34+yA89LmhgKE5TIHFvPT5k1XRk2jTMXvHHQ7ekKzc7FOt0Hl1fLEgsDhfMDpXOmfBYk8sxyIl6Bkb3jUqQr0kwRJ0LykIFuv1Ps7YK+Eh1ucO10TIz++RzGK93+O9d6x+iqUa3eZNQwr1+MJxjZFfHBtCsbhwxPuonkXNe9BwgV9//dX9c40cOdL1zKil3OPNrxC1OujEaI6Dhk6IhjZo/p/WwwWAWKGh05mtcuEHfj8++MeAAQPccENNU9OF3PTp0908Ql0DeD799FM35NBbi1bFYFW9V9cHoiGDmo+u6XGZJfYAkN9o9JV6q/3K78cXy6K6BJsK92iMv6rjKXCrd10Jd+A6fxoKEDhE5d1333WBXS08aonQZHzNlbj11luj9CoAIPwC52L5kd+PD/6h4jwa3q4iP9dff72L26ruf/XVV6cbGqjrATXYy1133eUKDGk+uld8RyMVVFUYAGKFpv5mNUc82vx+fLEsqkm6hgGoV1xFYDSPQUUOMs5jUO96YBU8FQJSEQMl9XqMqg4DAAD/UgEiVdrdunWr6wkPLBwkZ555putF1wWhqPDTE0884b7UWK/HxPoQSgAAfJGkexSsVZkvmMwm1CtYk6ADAJA/qBE+s1iv+eSBFXYDEesBAPGGZmkAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEgzv3444/2/vvvR/swAABAHlm5cqWNGjWK85tP+KJwHABE2ooVK9It75jXKlSoYLVq1TI/+uSTT2zWrFl23nnnRftQAADIt/Hez7F+0aJFbhlMLYEJ/yNJBxCXAbtxo4aWlLw7Ys9ZPLGYLVi4yLfBGwCAWBPpeE+sR7iQpAOIO2pRV8Aee1Uza1y1ZJ4/34I1O63XC7Pd84aapH/33Xe2Y8cOO/744+2HH36wLVu2WNeuXa1GjRrpttu9e7d98cUXtnr1aqtbt6517tzZChYseNA2n3/+uS1ZssSOPPJIO/XUUw/aBgCAWBPJeJ+TWP/PP//Yl19+af369XNxf/ny5XbcccfZsccee9C206ZNs7lz59phhx3mrgeCLU8ZyjbIH0jSAcQtBewWtf0ZwCZOnOiS75SUFGvXrp2tWbPGrrvuOps0aZK1b9/ebaPbTjrpJCtcuLC1atXKRowY4daa/vrrr61EiRIHbdO8eXN7+umnD9oGAIBY5td4/+eff7oh6C+++KJVq1bNSpcu7WL9PffcY0OHDnXbpKamuuloqh/TrVs3W7hwoQ0ePNjF8SZNmoS8DfIXknQA8Cm1sKsX/cQTT3S/Dxw40AYNGmR//PGHJSQk2LBhw6xs2bKu5bxo0aKut71p06b22GOP2d133+0e423z/fff2549e2z//v3WrFmzdNsAAIDo2Lt3r51zzjlpMVmFXHv06OG+Dj/8cHvrrbds8uTJNm/ePKtdu3ZaQn711Ve7+C+hbIP8heruAOBTav32EnS55ppr3DA2DVuXjz/+2Pr37+8SdClXrpz16dPH3e4JZRsAABA9aoT3KLkuX768G00nitdnnXWWS75FjfTXXntt2lS4ULdB/kKSDgA+Vb169XS/e/PRV61aZbt27XKBN+Mc9Zo1a7plViSUbQAAQPRoOlqlSpUOiv+K9aJ4HSyOe/eFug3yF5J0APCpDRs2pPt9/fr17nvlypXdfPJSpUoF3UZzziWUbQAAQPSo9szWrVsPitOK9aJ4ndn1gBfLQ9kG+QtJOgD41O+//+6Kv3jGjh3r5qcdccQR7vdTTjnF3aa5Z968tgkTJrjbPaFsAwAAomfcuHFpP2sOuXq/O3To4H7v1KmTffLJJ+kS+ddff91NifN64EPZBvkLheMAwKfUiq4qrZdccomtXbvWXnvtNZdgFyr030f3I488Ym3atHHLrLRt29Y+/fRTt9zaHXfckbYPbxvtp2XLlvbVV18dtA0AAIjecPdHH33U5s+f70a/Pf/88zZgwAA76qij3P1XXXWVvfHGG3bCCSdY9+7dbcGCBW4OupZu84SyDfIXknQAcUtrmvr5ebRG+oMPPuiWXZPp06e7RNvToEEDF9TVAq910vv27esSegX5jNuo8uvSpUvt0ksvtd69e6fbRgm+eugBAIhFkYj3OX2OIkWK2MyZM+3dd99166S/8MILdv7556dL4tW7/vbbb7visS1atLCHH37Y6tatm61ttHa7lndD/kCSDiDuVKhQwYonFrNeL8yO2HPq+fS82aWWdK81PRgNYxsyZEiW+9A2Cszbt293a7AWKJB+ptMZZ5yR7eMCAMDvIh3vcxrr9ZjACu/BEnk1sGflUNuo0X7UqFHZPjZEB0k6gLij1uQFCxfZxo0bI/acCsB6XgAAEJvxnliPcCFJBxC3gdvPSXPHjh0PqvYKAABiJ96rEOygQYOifRjwIZJ0APChwPloAAAg9qj6ugq8AhmxBBsAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT7AEG4C4tGLFCtu4cWPEnq9ChQrZWqd1w4YNtmfPHqtRo4b5yfLly61UqVJ22GGHRftQAADwVbzPbqzftWuX/fvvv9awYUPzE12D7N6922rWrBntQ4lbJOkA4jJgN2rcyJKTkiP2nInFE23hgoUhB2+tmzp37lz74osvzG/rt19wwQU2dOjQaB8KAAC+ivfZjfXTpk2zM844w/bt22d+8vjjj9usWbPsq6++ivahxC2SdABxRy3qCtjtb2lvZWqWyfPn27Zym00dMdU9byiBe/Pmze5LLewLFy50t6lHPSUlxdatW+d+L126tFWrVu2gx+p+Bfvq1au759u2bZsdccQRafevXLnStfSXKFHC/ZyYmOh+D7Rjxw73/HrOggULpt2u1n61rKuF3TuuBg0aWIECzJwCAMR3vM9urFc8VVwVL6aWK1fOfS1ZssT9XrRoUdebXahQ+pRt586dtnr1aheD9fOaNWvs8MMPtyJFirj7dQypqalWsWJFF88V13V/oL1797p9VKlSxYoVK3bQNUhSUlLacemaQqPoEDkk6QDilgJ2hfrpE1Q/eO+99+yjjz5yw93POeccd9szzzxjq1atsoceesj9vnXrVtu/f7899dRTdvHFF6c99v7777fff//dJeGzZ892Q+jUUr948WK3r2XLlrn7jj/+eFu7dq1169bNHnjgAffY7du325VXXmmfffaZG86+ZcsW12N+++23u/vvvfde++eff+zNN99028jMmTMJ3AAAX/NjvFc8VVxVLPdifa9evax79+5pvyuRV+P7oEGD7NFHH7WEhAR3u3q4e/bsaQMHDrSXX37ZJdqffPKJS+i1jw8//NAl6GXKlLFmzZq52P/zzz+7xyp5f/DBB+2xxx5z8Vux/vTTT7cXXnjBba/rD12H6Lm94xg9erR16dIlaucqHpGkA4DPKFFetGhR0OHuffr0Sfv5gw8+cMG4TZs26Vrtf/rpJxe0J0+enG6f6nlXUq0k/Z577nEXB0rSA/et1nq1yCtw//XXX9auXTurV6+eu2h48cUXXQMAw90BAMido446ysVVDXf3eqw9gb///fff1qFDB2vRooVLzD3Jycmux1yj2woXLpzWUD9jxgz3mDp16ti3337rkutjjz027XFq9H/99dftt99+s7p167oG+nPPPdduuukmdzx9+/Z11yAMd48uxigCQD6jYWsKwI0bN7bKlSu7nvJARx55pPXr1y/td/WiT5061e6++243vF3UOx44zF1D69R6rlZ5tdpr/9K1a1d7//33I/baAACAudF06gFXT7uS9ClTphx0WoYPH56WoIsa6K+77jqXoEvHjh3trLPOSveYJ554wnr37m0HDhxwsV6j6i666CJivc/Qkw4A+YR61pV8//nnny451xwy9XprGHwgtYxnHFInmrvmUVCvX79+2u/z58933zWkLqPmzZuH/bUAAICDaY65Yr03ZF0j29RbHtgbLroGqFq1atrvqlujWjNqqM/YY+9dJ2gIuxrlX3rpJRs3bly67fRc6p33GvMRXSTpAJBPaAiaEmb1intFXjTnXK3hgQKLvYkXcNUqH0jB2OO1xH/zzTdBC9IBAIC8p/nimm6monKVKlVKa0DXEPSsYr2mqymWKxHPLNbrMSr2qlo0gdPn4D8MdwcAH1ISrlbxQAsWLHDD1rwEXcPgvAqwWdGweAVuzVX3aB5b4Jy34447zlWRnThx4kGPDzyOYMcFAACyTzFVDe2Bje2K9Rre7iXouu+777475L5UVK5JkyauIT9Q4JQ4XQuocCyx3v/oSQcAH2rUqJE9//zzLrhqCJqWQzvhhBNcdVfNJVd1d1VeV5XWQ9Hjr7jiCrv55pvdBUH58uVd4Tgt1eZVitVtmrM+bNgw1+OueWyam6756Bo6d+ONN6Ydl6rKnnnmmW5fLMEGAEDOaNqZ4vAbb7xhrVu3dsuvKdaPGjXKOnfu7H5/+umnXS964ND2zNx2223Wo0cPV/BVyfj48ePt119/dQ3xnhEjRrh9q6CsetPVCPD999+7KXXvvPNOWqzX6jFK+NVYwBJskUeSDiBuaU1Tvz6PirhoCTUlx6q8qmqsCuIKwAMGDHCBe/DgwTZp0iSXYHu0DEswI0eOdMPe77vvPpfkq1q71mEPXBtVCboC8yuvvGJjx451FeNVyV0FZjxK5NU4oKH3GkLHEmwAAL+LRLzPyXMo+VVFdcXdRx55xC655BK79dZb3RrlWoFFCfzJJ5/sYu/SpUvTHqd56prulpGqtGt/auRXgt62bVt3raCVWTxateWXX36xxx9/3K699lorW7asa5jXPHWPYr+qv6txf9u2bSzBFgUJqaF0w8QYXfBqHUC96UqXLh3tw0EGGkr7+eef22mnnZauYiWQE5qbpcCmSqdeQrpixQpr1LiRJSf9b55WXkssnmgLFyxMt1RapP+vlJTrM0/z0fT5p/VUtQyLgjqi8170EJfCj3MaGmIuYuE9ktnna6TjfbRjvSrBZ5yrriRf097U2J9X1Buvz1zvGiPe7Q5DvKcnHUDcUfBUENW87EhR73W0grZMmDDB9cyfd955roX+oYceckPYAtdJBwAglkQ63kc71mvFFy3B1r9/f9fbrt70H3/80Q2fR/5Ckg4gLimIRjOQRpqGt6vInIbLq6Vd89Pee++9g1p4AQCIJfEU71W/RvPMR48e7dY/15B4TUtr2rRptA8N2USSDgBxQEuzqHX9zjvvZCgaAAAx6uyzz3ZfyN+YNAAAAAAAgE+QpAMAAAAA4BMk6QDiQhwuZAGf4T0IAHzOIvalhuGakyQdQEzzliLZu3dvtA8Fcc57D2ZcHgcAkDve0nBavQSINu99mJslCykcByDmC6YVL17cNmzY4D4s43X9Tq1hqiRRa3fG6zmI9vnXe1DvRb0nAQDho8bPsmXL2vr1693v+qxNSEjgFEcI1xj/60FXgq73od6PuWmU50oBQExTkK5ataotXbrUli9fbvEcOJKTky0xMZELlyhR44iWAeLCEQDCr0qVKu67l6gjcrjGSE8Juvd+zCmSdAAxr0iRIla/fv24HvKekpJiU6dOtfbt2+dq+BVy9z5kFAMA5G2jfKVKlVzMQ+RwjfE/usYKx7Q2knQAcUHJUbFixSxeKWDs27fPnQOSdABALMc7an9E/pxzjRFeTEwEAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ+IeuG4v//+21566SVbt26dNWnSxAYMGGAlSpQ45Fp8EydOtG+++catg3jZZZdZ06ZNI3bMAAAgdDt27LBnn33W5s+f76ovX3nllVa3bt1DPm7GjBn27rvvusd369bNzjnnHE47ACDmRbUnffbs2da8eXNbvXq1tWzZ0t566y23PFBWyyTpPgXqYcOGWcOGDd3XVVddZXPmzInosQMAgENLSkqyE0880d577z07/vjjbcmSJS72K2HPyvDhw+2UU05xS9e1adPGJkyYYE888QSnHAAQ86Lakz506FBr166dvfnmm+737t27W61atezVV1+1/v37B33Mo48+ar/88ostWLAgbZH4fv362a5duyJ67AAA4NBeeOEFW7FihS1fvtzKlCljAwcOtJNOOsluv/12++CDD4I+ZubMmXbbbbe5+88++2x326WXXmobNmzglAMAYl7UetL37NljX3/9tV144YVpt1WoUMFOPvlk++yzzzJ93Isvvmi9e/dOS9BFa/6WLVs2z48ZAABkz+eff25dunRxCbokJCTYRRddZF988YXt378/6GNefvllq1evXlqC7qlYsSKnHwAQ86LWk65WdS16r57zQIcffrh9//33QR+zdetW1xKvofFPP/20/frrr1atWjW75JJL7Mgjj8yyQUBfnu3bt7vvKSkp7gv+4v1N+NsA/F/Fi1j+vPvnn3/s3HPPPSjW796929asWWM1atQIOh3uhBNOsC+//NLNSS9ZsqR16tTJzjjjjEyfh1ifM8Rc8B5BbvE5Ev54H7UkXcFZFHgD6Xfvvox27tzpvt9xxx1unpqGyquoTLNmzezTTz91LfXBPPzww3bvvfcedPvkyZNd4Tn405QpU6J9CEDM4f/Kv/O2Y5VierBY792XWbz/9ttvXYLfp08fV1y2V69ergbNiBEjgj6GWJ87fDaA9whyi8+R8MX7qCXp3rC3zZs3p7t906ZNmQ5d9x6jSu6vvPJK2nx07eO+++7LNElXkbkbbrghXU96zZo13falS5cO22tC+FqY9E/euXNnN5UBAP9Xsc4b4RWLFLuDxXrJKt6vWrXKDYn3EnqNnFNVeDXUB4vdxPqcIeaC9whyi8+R8Mf7qCXpSpIVnOfOnWunnXZa2u1//vmnW4otmFKlSlmdOnWsUaNG6W7X7xr6npmiRYu6r4yUAJIE+hd/H4D/q3gRy7FIDeuK9YEU61VbRrVogjnmmGPchUxgD7xifWpqqq1duzZokk6szx1iLniPILf4HAlfvI9a4TgVjunZs6crDuO1KPzwww+ucruGtAVWhb355pvTfld110mTJllycnLaUDkVmmvdunUUXgUAAMiK6sZMnTrVZs2a5X5Xr/rrr7+eLtZr6trFF1+c1uOuIe5//fVXuuReS7ipcNwRRxzBCQcAxLSorpP+4IMPWrly5VzRNw0979q1q91yyy1uvrlHSbuScs+tt95qtWvXtgYNGthZZ53l1kkvUKCAjRw5MkqvAgAAZObMM8+0a665xjp27GinnnqqHX300W403V133ZW2zcqVK+3tt99Om6unhnfVktH66qeffrode+yx9tZbb9n48eOtYMGCnGwAQEzL0XB3LZOmueDnn3++JSYm5vjJNdz9p59+sp9//tkVhRkzZoxbciWQ1ku/4IIL0n4vVqyYKxL3+++/u0rvqg7fvHlz1zMPAADCY9CgQa4Ku3q1A5c9zYlRo0a59dHnz59vVatWtVatWqWL20rKlYCXL18+7bahQ4e6XvjffvvNNegrUS9RokSujgMAgJhN0uvXr++Ct1rGNTzt8ssvd8ui5YR6wdu0aZPp/ZntV4m5vgAAQPhpXrgqpt9+++3WrVs31zivJdAKFcpZORuNfNNXMFqGTdcTGanHXV8AAMSTHA13f/75513hlqeeesoWLVrkWsRV7O2JJ56wDRs2hP8oAQBARGm5syVLlrgK6yrcqjoySqZVJ2bBggX8NQAA8NucdA1z7927t1vHdPHixW7OmYamVa9e3Q1P//HHH8N7pAAAIKI0JF11YjQffM2aNXb33XfbuHHjXC2ZE044wd2+b98+/ioAAPipcJx60lWB/bXXXrPixYu7lncNYVeBmHvuuSc8RwkAAKJG65qrIrtG0q1fv97OPfdcO/74423w4MEuid+/fz9/HQAAopmk79y501555RVr27atNW7c2KZPn24jRoxwrexPP/20vfPOO245tccffzxcxwkAACLowIEDbqj7RRddZNWqVXPxvUePHvbvv//a+++/b08++aQbDr906dK05dUAAEDu5aj6i6q8lixZ0q1Z/uqrr7pCchmphV1VYQEAQP5z3nnn2ZdffulWctH3Dh06BF2lRcuqpaamRuUYAQCIRTlK0u+44w678cYbrXDhwgfdp+XUtJSKzJ07N/dHCAAAIk6V3NVbHqzBXYXjVIOmdOnS9uKLL/LXAQAg2sPdhw0bFjRBFxWSAQAA+duECRPs77//Dnqf5qL/8ssvET8mAADiQa4LxwXatm2bGwYPAABil+K9lmUDAABRHu5+2WWXBf3ZKzDz559/ujXTAQBA/vToo4/avHnzbP78+e7nsWPHprt/7dq1roe9UaNGUTtGAABiWbZ60rUWqrceqvez91WwYEE755xzDgrmAAAg/9ByaorrKgbn/ex9qUH+qKOOsq+//trKlCkT7UMFACAmZasn3UvAK1SoYKNGjcqrYwIAAFEydOhQ9/3ZZ591a6A3aNCAvwUAAH6v7k6CDgBAbBs4cGC0DwEAgLgUcpLeq1evtN507+fMMOQdAID8Z/jw4W75VK3i8sknn2S5lKq20dB3AAAQpSS9UKFCQX8GAACxQfVlFOMTEhLSfs6MtgEAAOEXcrb92muvBf0ZAADEhptvvjnt5yOPPDKqxwIAQLwK6zrpAAAAAAAggnPSQ8GcdAAA8u+c9FAwJx0AAB/NSQeAvLRixQrbuHFjTJ/kPXv2WNGiRSP2fFrfWubMmWMFCkRmEJWW66xVq1ZEngvhcah56IGYkw4AgI/mpANAXibojRo1tuTkpNg+ySq6lZoasadLTEy08ePHW/v27S05OTkiz1k8sZgtWLiIRD2fzkkHAADRQfc4AF9RD7oS9Fb97rbSVWtbLNqwdKHNHveI3X9eAzutSaWIPOeBAkVslZlNHXqCFTiwN8+fb8Gandbrhdnu70lvOgAAQOhYJx2ALylBP6xWQ4tFybv+GyVQp0Jxa1G7TESeM8UKuyT9mFqlrbClROQ5kf+wTjoAANHHOukAAMBhnXQAAKKPddIBAIDDOukAAOTzOemqFvzvv/+6n2vUqBGxisFAXoiHiuJU2waQExs2bLCtW7e6+gKRXJUAAIB4lKMkfe/evXbffffZ6NGjbceOHe62UqVK2ZAhQ+zOO++0woULh/s4gbyvKN64kSUnRabqdbQkFk+0hQsWUsgLQEjGjRtnd9xxhy1dutT9rsb47t2722OPPWbVqlXjLAIA4Jck/frrr7ePPvrIRo4caS1btnS3zZw50+655x7bsmWLPfXUU+E+TiDvK4onJVv7W9pbmZqRKeQVadtWbrOpI6ZSbRtASN5//33r27evDR061E4//XTXGP/333/bI488Yl26dLE5c+a4OewAAMAHSfrYsWNt8uTJ1qpVq7TbjjnmGDv66KOta9euJOnIt5SgV6hfIdqHAcSMBQsWWCyL5Skkb731lt111112++23p93WuHFj69Spk9WpU8d+//13O+6446J6jAAAxKIcJemJiYnWoEGDg25v2LChuw8AEN/27N3jvvfq1ctiWSxPIcks1hcvXtzVoSHeAwDgoyRdw9wef/xxu//++y0hIcHdlpqa6m7TfQCA+LZv3z73vXmf5lajZQ2LRbE+hUTxfMyYMW6ouxJzz5QpU2zbtm1BE3gAABDBJH3AgAHpCsc9+OCD9u6777qhbkrQf/31V/vrr7/s4osvDsNhAQBiQckqJZlCko88+eST6aYoqN6MGiBOOumktDnpP/30k7Vt29b++ecfa9SoUVSPFwCAuE7SMy5Ndf7557vve/b8N6SxSZMm7islJSXcxwgAACJAPeSB8V51Zjw7d+60qlWrpsV/4j0AAFFO0idOnJhHh5D/xcP62mqMidTauAcOHHDfVTlYy/1EQqwXtwKAUKhQHAAAyIdz0pFhfe1GjS05OSm2T4tqD6SmRuSpVIxo/Pjx1r59e0tOjty65WoOSNoc439HAAAAALGZpO/evdt++eUXl6R6BYI8l112mcXV+trJSdaq391Wumpti0Ubli602eMesfvPa2CnNamU5893oEARW2VmU4eeYAUO7LVI+G3JBrvyzUW2d1dkng8A8ovffvvNFi9ebElJ6RsxTz31VDf8HQAA+CBJ//PPP+3MM8+0zZs3244dO6x8+fK2adMmd1+1atXiKkn3KEE/rFZDi0XJu/67MKtTobi1qF0mz58vxQq7JP2YWqWtsEWmxsGupF0ReR4AyC80kqlbt242ffp0t5KLKrxv3brVFYtV3K9Xrx5JOgAAeSBHE36vv/56u+iii1yBGa83Wa3sbdq0sUGDBoX7GAEAQBQqvatGiGK8qrm/8847LkkfOHCgNW3a1N0GAAB8kqRrubVbb73VtazrS0uyHXHEEfbyyy/b888/H/6jBAAAEaVYr4Z3Lb2mIp6K9aVLl7ann37aNcxrOTYAAOCTJF0t6RrqJhUrVrTVq1e7n6tXr27r1q0L7xECAICI27JlS9BYr4Rdc9GJ9wAA+LS6u4a433333TZ48GB77bXXrHHjxuE5MgAA4AuK9epBP/LII23RokVuiUzNSQcAAD5J0jXU3TNixAg777zzrGXLllajRg17++23w3l8AAAgCnr06GG1a/+3akm/fv3s66+/dktjFilSxB577DGrUqUKfxcAAPySpA8fPjzt5/r167tq7zt37rSSJUuG89gAAECUXH755Wk/JyYm2vvvv++WXy1cuLAVLFiQvwsAAH4c7q6qr//++6/7Wb3oAAAg9mzYsMHVo6lVqxYJOgAAfiwcpwqvd9xxh5UtW9YOP/xw96Wf77rrLktJicy61gAAIG+NGzfO6tata5UqVbIGDRq4tdJ79uyZVkQOAAD4aJ10FYkbOXKkzZ49233p51deecVuuOGG8B8lAACIKA1v79u3r/Xu3dtmzJhh8+fPtw8++MCWL19uXbp0sf379/MXAQDAL8Pdx44da5MnT7ZWrVql3XbMMcfY0UcfbV27drWnnnoqnMcIAAAi7K233nIj5G6//fa027SCS6dOnaxOnTr2+++/23HHHcffBQAAP/Skq4CMhr1l1LBhQ3cfAADI3zKL9Rryrjo0xHsAAHyUpGuY2+OPP26pqalpt+ln3ab7AABA/qZ4PmbMGEtKSkp3+5QpU2zbtm1BE3gAABDB4e4DBgxIVzjuwQcftHfffdcNdVOC/uuvv9pff/1lF198cRgOCwAARNqTTz5pCxYsSPt95syZrqL7SSedZKVKlbK///7bfvrpJ2vbtq39888/1qhRI/5IAJCPrVixwjZu3Gi5XfFL5syZYwUK5KgPOJ09e/ZY0aJFzS8qVKjgYqEvk/SMf7zzzz8/7SRKkyZN3BfV3QEAyJ/UQx4Y71VnxrNz506rWrVqWvwn3gNA/k/QGzdqaEnJu3O1H01/Gj9+vLVv396Sk5NzfVwFEswO/G/AdtQlFk+0hQsWRjRRDzlJnzhxYt4eCQAAiCoVigMAxAc1yipBH3tVM2tctWSO93OgQBFbZWZTh55gBQ7szdUx/bZkg1355iJr3qe51WhZw6Jt28ptNnXEVHeufJmkAwAAAABiixL0FrXL5PjxKVbYJenH1CpthS0lV8eyK2mX+16ySkmrUL+CxascTxr4888/7bLLLnPLsB1//PHu57lz54b36AAAQNSo5+DWW2+1Dh06WLNmzezcc8+1jz76iL8IAAB+S9I//fRTa968uS1btsw6duxop5xyivtZAVz3ZYfmtH/yySf20ksv2YwZM7L1WBWtefrpp+2HH37I5isAAABZ+ffff+3oo4+2Dz74wMX3s88+2xXyueiii+zGG2/M9slTwTnF+s8++8wVoM3OdcKzzz7r1m0HACAe5Gi4++23324jRoywG264Id3tI0eOdPedccYZIe1n/fr1rnVejjnmGLvlllvsvPPOc0E8lKCt4jWqNHvllVe6SrMAACA8nnjiCWvZsqV9+OGHVrBgwXQV31XtXTG7cuXKh9yPVoDp06ePffHFF9a5c2e3GkyxYsXsm2++sfLlyx/y8XoeXRfUrFnTLrnkkly/LgAAYrInXcuzXH755QfdrtsWLlwY8n6GDh1qhQsXdgFbFQEVsF999VXXs34oaiBo06aN1a9fP9vHDwAADh3rNZUtMEEXJe5HHXWUW3Y1FFqu9e2337Zp06bZuHHjXMxX9d9QitTpeuCrr76yK664gj8XACBu5ChJr1ixolsHL6PZs2e7+0JdT08V43UBoLL9ouF0SrwVzLOioXdK6B977LGcHD4AAMhhrN++fbstWbIk5HivmK6pcd6a6iVLlrRLL730kLF+1apV1r9/fzfM3btOAAAgHuRouLt6zLt37+6GtqtonGg++QMPPOACaihWrlxpO3bssMaNG6e7Xb/PmjUry8cNHDjQPv/8cytevHhIz6Wh8d567t4FhrfGa27XeVVjgy4eChcwK5RwwGJRkYIJ/10gFSrqqjfmNe85IvFcntSCRdxrLFKwiBVKjc1FDwonFHavUe9ZP69vzP9UbPxf8T+VPX78n+zbt6+deuqpLn6efvrpVrp0add7/sgjj1jDhg3Tku5DmT9/vttPxli/adMmN+2tUqVKBz1m//791rNnTzdqTg34EyZMiGqsj2XeueEcgfdI/PGuubSEWm6uD8J5jeG364fCYb5+DnUfCamaLJZNCp4K0o8++qht3brV3Va2bFm7+eabXRXYjEPjglEl+CZNmtj06dOtdevWabfr8e+9954tXrw46PNqDrvmvGs7UfDWbaNGjcr0ue655x679957D7pdw+5CTfQBAMgrSUlJLindtm2bS4b9QpXcFdtV/0UKFSrk5oUr/ofak16jRg03XF2x2PPtt9/aySef7PZbr169gx6jbTU8XkPdExIS3PQ4zY3PakodsR4AECvxPkdJuiq5165d2xWDWb16tQugVatWdd+zU5ldgXny5MmukIxHveQ//vij/fHHHwc95o033rBrr73W7rvvPitQ4L+R+sOHD3fz0lVEbtCgQUGPIVjrugrQaGmZ3F4MaShg+/btreNNY6xczdicH79m0R82fcyN9mLfpnZRy6p5/nxqhZtS7hLrvOWtXK+1GKrpi9ZY1yf/sDZD2ljd9nUtFm36Z5NNunmSTZ061RVq9Cv+p2Lj/4r/qexRXKpQoYKvkvR169ZZmTJlXJE3Nchv2bLFxU4l6tmhGH3WWWfZ448/ni75P+ecc9yQ9mrVqqXbfs2aNe551DhQvXp1d9vHH3/srgvuuOMO11Cva5BIxnpVuj/uuJaWnJxkfpKYWNxmzZrpGkJy06szZcoUdy2mOkFAXr9H9P+kkTR+oQKWufkfys+8a66pQ09wa5z74RrDb9cPm8J8/RxqvM/RGIIjjjjC9WorIfYCaHbVqlXLihQpYkuXLk13u+a5ZVYMTkG5V69e6YrVKCDrwkGt62o0CJaka8kYfWWkD5rcftiosUAFcFIOmO1LzfGy8762d3+qe422b0/EkmbRc0Xq+RL273Wvce/+vbYvYZ/FopTUFPca9Z7184UY/1Ox8X/F/1T2+PF/UhXZlSh36tTJjZbTV04opgeL9SVKlLAqVaoEPRcDBgxwU+K8nnNd0Cve63fNbw8mL2O9rjM2b95krfrdbaWrHtxAEA3b1yyzGa/c646tTp06ud5fOM4TYls43iMrVqywo44+ypKTks0vEosn2sIFC11uEm+8a64CB/aG5dogHNcYfrt+SAnz9XOo+8hRkq5Wb80NVwt1bg6wW7dubsi5llBTcq2Wte+++85eeOGFdEPi1q5daz169HAtPfoKpDXSDzXcHQAA5CzW55Z60W+66aa0+edq5NeKLmeeeWbaqDiNrps0aZKbB68ehqeffjrdPrzh7hlvjzQl6IfVahjVYwCymxRrNEluaC6u1+vq/c/mZtUIJejtb2lvZWqWsWjbtnKbTR0x1Z2jeEzS4V85StLVsq6h5Uqmg7WCh0rz2lXNXUPXWrVqZWPHjnW/B66DqqquP//8s0vSAQBAZGh6meKxar80b948x/vp16+fvfnmm65BXbFcc82XL1+erhjc77//boMHD3ZD4NXDDiA8CXrjRg0tKXl3rvajollqWFNHmRtZmUtK8wuXKGwV6lfI9b6AWJWjJP2hhx5yc9U0D71cuXJu2Hog9XyHQtVhVUBOwVv7u+2229wFQeB8NxWWqVs38/kIF198MWulAwAQZg8//LAr4tqiRQuXOGvptIzrn7dr1+6Q+9E1gkbFqSF+3rx51qVLFxf3K1eunLaNatSo8T/jc3jUgJ/ZfQCCU++wEvSxVzWzxlVz/v+jyt+rzNy8ZQ2Lzo3flmywK99cZHt35W4/QKzLUZIezvXJlejfcsstmd6v6ndZ0RA4AAAQXlpSVUPVM5NZ/ZjMEnX1qGdGvfVZDWXXcWR1LAAypwS9Re2cDy3XLGMl6Sosltv5xruSduXq8UC8yHaS/ssvv7glU1SkTeumapg6AACIHaq6/uuvv7qeuOOPP94uuOCCaB8SAABxI1tJuoq2KFBryJkKvWnY+8SJE90cMgAAkP+piKuWmdm1a5er6K410TU//cknn4z2oQEAEBeyVaLxwQcfdMPL/1uKZLMrIKfbAABAbHjmmWfc8PMNGza4GjNffPGFPffcc75a1xgAgFiWrSRd65MqMVcvur6UsHtrmAIAgPxPcf3qq6+2UqVKud9PPfVUa9q0qSsiBwAAfJak79y508qU+V/hCf2s2wAAQGzYsWNHulgv+l23AwAAHxaOe+211w5522WXXZa7owIAAFEzefJkNzfds2bNmoNuUw+7VmgBAABRTtL79u17yNtI0gEAyL9ULC6j+fPnp/t9ypQpJOkAAEQ7SdeyawAAIHZ99dVX0T4EAADiWrbmpAMAAAAAgLxDkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+EShaB8AAAAAkN+tWLHCNm7caH6xYMGCaB8CfP4+4T3iXyTpAAAAQC4Tr0aNGltycpLPzmOCrdm228zKRPtA4Nv3Ce8RPyJJBwAAAHJBPaNKvFr1u9tKV63ti3O5YelCmz3uEduatC/ahwKfvk94j/gXSToAAAAQBkq8DqvV0BfnMnmXn3pr4cf3Ce8R/6JwHAAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+IQvkvStW7fa4sWLbe/evSE/Zs2aNbZhw4Y8PS4AABAee/bscbF+27ZtIT9mx44dtmLFCjtw4AB/BgBA3Ihqkr5v3z67/PLLrXLlyta+fXurVKmSvfHGG1k+5plnnrE6derYsccea40aNXJf3333XcSOGQAAZM+LL75oFStWTIv1AwYMsP3792e6/bfffmvt2rWzGjVqWNu2ba18+fL2xBNPcNoBAHEhqkn6Qw89ZJ9++qnNnz/fVq9ebaNHj7a+ffva7Nmzg26vgL5gwQKXlGv79evX2+mnn25nn302veoAAPjQjBkzXFL+yiuvuNg9Z84ce+edd2zkyJGZPubnn3+2Rx55xI20U0/6m2++aTfffLN9/PHHET12AADiLkl/4YUX7Morr7QjjjjC/d6nTx9r0KCBvfTSS0G3L1iwoD399NN2+OGHp/1+00032fbt2+3XX3+N6LEDAIDQYn3z5s3tggsucL9rBJzi/fPPP5/pY4YNG2Zt2rSxhIQE9/sZZ5xhRx11lH3zzTeccgBAzItakr527VpbtWqVtWrVKt3tJ5xwQrYS7nnz5rnvNWvWDPsxAgCA3FFMzxjrlYD/888/Ic9P37lzpy1fvpxYDwCIC4Wi9cSbNm1y3zXPLJB+37hxY0j7UHC/5ppr3JB3tbBnVaxGXx71vEtKSor7yg0Vs0lMTLTCBcwKJcRmYZsiBRPca7RCRS3FCuf583nPEYnn8qQWLOJeY5GCRaxQatT+LfJU4YTC7jXqPZvb931e4n8qNv6v+J/KHj//T4Yj3geL9aJ4X6ZMmUPuY/DgwVasWDG77LLLMt0m3mK9jiUcn+neY2P5PRgJfnyPhOv6LZzxw2+xIdLXRn57n/Aeifx7JNR9JKSmpqZaFCxatMgNeVNxmA4dOqTdfu2119pXX33l5qlnJSkpybp16+bmq2kfhx12WKbb3nPPPXbvvfcedPu4ceOsePHiuXwlAADkjmJaz549XeNz6dKlY+p0qthr9+7dbfjw4Wm3ff75566BXb3jtWrVyvLxd9xxhz355JM2ZcoUa926dabbEesBALES76PWhFW9enU310xLqQXS74caup6cnOzmp23evNnNT8sqQffmtt1www3pWtf1HF26dMn1xZAK4Khabcebxli5mvUtFq1Z9IdNH3Ojvdi3qV3UsmqeP59aaqeUu8Q6b3nLCltkWvanL1pjXZ/8w9oMaWN129e1WLTpn0026eZJNnXqVDvmmGPMr/ifio3/K/6nssfr9Y1FirfBYn2hQoWsSpUqWT5WibcS9EmTJmWZoMdjrN+y8m/79rGrc/2Zrl4dNYB07tzZCheO3Ai2WOPH90i4rt/CGT/8FhsifW3kt/cJ75HIv0dCjfdRS9JLlixpLVu2dK3pPXr0cLdpmJp60W+99da07VQJVi0O9erVS5ega410Jeha0uVQihYt6r4yUjDKbUAqUKCAO6aUA2b7Un2x7HzY7d2f6l6j7dsTsaRZ9FyRer6E/Xvda9y7f6/tS9hnsSglNcW9Rr1n/Xwhxv9UbPxf8T+VPX7+n8ytjh07uuJxWnZVibl89tlnduKJJ1qRIkXSLlpUxV0j7Lxt7rvvPnv88cfddYKWYTuUeIv1OpZwfqaH4zzFMz++R8J9/RaO+OG32BDpayO/vU94j0T+PRLqPqI6GURD0JVwH3300a5gnNZALVGihFuqxXPXXXe5pVjmzp3rAryWW9PPEyZMsHXr1rkvr2e+XLlyUXw1AAAgI9WOUSX3Xr162aBBg9wyqp988olNnjw5bRv9fOGFF9rKlSvd2ugjRoxw1wha0UWxXXFfNH+dQrEAgFgX1SS9a9eubp10rY+uNVObNGliP/zwg5UtWzZtGyXf9evXT6vuqp519Z6riEwgtbifd955EX8NAAAgc4rZiu2K00OGDLGqVau64evqYfco+VYBWK+H4bfffrPGjRvbM8884748Grqe1frq8WjBggW5eryKIXnDcNVTlBsVKlQ4ZI0BAMChRb2sohJ1fWUmsOCbknevNR0AAOQPmrL2xhtvZHq/5kMHxneNlkPWUvbsdd81QiE3VLV4/Pjxbp6sGxqdC8UTi9mChYtI1AEgvyfpAAAAyB5NAZT7z2tgpzWplOPTd6BAEVtlZlOHnmAFDvyX+OfEgjU7rdcLs92yevSmA0DukKQDAADkU3UqFLcWtQ+91nxmVApMSfoxtUqHpbhYboffhxtD8AHkRyTpAAAAyJU9e/eEZfh9uCUWT7SFCxbSuw8gXyFJBwAAQFiG3zfv09xqtKzhi7O5beU2mzpiKkPwAeQ7JOkAAAAIi5JVSlqF+hU4mwCQC7lbawMAAAAAAIQNSToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATUU/S33jjDTv22GOtRo0a1q1bN/vjjz/y5DEAACA6fvvtN+vSpYuL2y1btrTx48fnyWMAAIgFUU3S33nnHbviiits8ODB9vXXX7tA3LFjR1u3bl1YHwMAAKJj5cqVLk43bNjQvvnmG7vyyiutT58+9vHHH4f1MQAAxIqoJukPPvig9e3b1y677DIXiJ977jkrVKiQjRkzJqyPAQAA0fH0009buXLlbPTo0dagQQO76qqr7KKLLnLxPJyPAQAgVkQtSd+2bZsbpt6pU6e02woWLGgnn3yyTZs2LWyPAQAA0TN16lQ75ZRTLCEhIe02DWOfNWuW7d69O2yPAQAgVhSK1hOvXr3afa9cuXK62/X77Nmzw/YY2bNnj/sKTPZl8+bNlpKSkotXYbZ9+3YrVqyY7Vz1l1lKssWi5LVL3GtctHGvfffXjjx/vgMFiljSUUk2bfFOK3Bgr0XC3NV73WtMWp1kG//caLFox5od7jXqPbtp0ybzK/6nYuP/iv+p7Nmx47/P1tTUVIs1it0aup4xbh84cMBNVTv88MPD8ph4i/Xhis3h+mzw4/98JOMe75H8+T6J9LWR394nfvsciYf3yI5Q431qlMybN09HlvrDDz+ku/3GG29MbdCgQdgeI3fffbd7HF+cA94DvAd4D/Ae8PN7YOXKlamxpmbNmql33HFHutumTJniXu+SJUvC9hhiffTfv3xxDngP8B7gPWBhifdR60mvWLGi+75hw4Z0t+t3775wPEaGDRtmN9xwQ9rvaolXy3r58uXTDaWDP6ilqmbNmq5wUOnSpaN9OEBM4P/K39Sirtb1atWqWaxRfA4Wt6VChQphewyxPmf4bADvEeQWnyPhj/dRTdLr1KljP/zwg51zzjnp5qGdd955YXuMFC1a1H0FKlu2bFheB/KOEnSSdID/q3hRpkwZi0WtWrVycTrQ999/b0ceeaSVKlUqbI8h1ucOMRe8R5BbfI6EL95Htbr7tddeay+99JLNmDHD9u3bZyNGjHDz0Pr375+2zfXXX2/t2rXL1mMAAIA/DBw40BYtWuQqtu/fv98Veh07dqxbStXzySefuB7yNWvWhPwYAABiVdR60uW6666z9evXu2rtKvZSvXp1e//9991yKx4NB9iyZUu2HgMAAPyhSZMm9s4777j4ralnKsBz00032YABA9K2UTxXQR4l5KE+BgCAWJWgienRPgjNEd+1a1fQIWw7d+50PeYZh6dn9Rjkb7pYe/jhh938wozTFADwf4X8Sw3vJUqUsAIF0g/k27t3r5vTeNhhhx10X2aPQXgQc8F7BHyO+I8vknQAAAAAABDlOekAAAAAAOB/SNIBAAAAAPAJknQAAAAAAHyCJB2+ofIIjzzyiB111FFWq1Yt69Gjh/3777/RPiwgX/v555/t0ksvtcqVK7vq2AAg27Ztc0vd1alTx+rXr28333yz7d69m5ODNCrm+Mwzz1izZs3SLZEIeJYsWWKDBg2yo48+2ho2bGi9evWyxYsXc4LCgCQdvnH//fe7JP3RRx+1SZMmuaX3Onfu7Kr+Asi+n376yYYMGWIdOnRwDV9aLQMA5MILL3SNeBMnTrTXXnvNfe/fvz8nB2n0fpg3b55dccUV6ZZIBDzqBFCCPmHCBPvwww9dQ1+7du3cctnIHaq7wzdLwFSsWNHuvfdeu/76691t69ats2rVqtnYsWNdrzqA7I9OSUhIcD+3bdvWBdLnnnuO0wjEuZkzZ9rxxx9vv/zyi7Vs2dLd9sEHH9j5559vy5Ytc416gBdDvvvuO+vYsaOtXLnSatSowYlB0OsMSUpKstKlS9urr75qvXv35kzlAj3p8IXZs2e7tXA7deqUdpuG5zZt2tSmTZsW1WMD8qvAwAkAnqlTp1qZMmXSEnTp0qWLu+D+4YcfOFEghiBH1xnJycnuc6RYsWKcwVwiSYcvrF69Oi0xD6TfmQMFAEB4Y26lSpXS3VaiRAn3RcwFkFNDhw519QvU6IfcKZTLxwNhoVY3KViwYLrbCxUqZAcOHOAsAwAQxpibMd4ScwHkxuOPP25vvvmmqyulkTrIHXrS4Quajy4bNmxId7t+9+4DAADhibkZ462KtKriOzEXQHY99dRTdvvtt9v777/v6hcg90jS4Qta3qNo0aLp5sJp6Y85c+ZYq1atonpsAADEEsVVVeteuHBhunnq3n0AECot06clHN977z077bTTOHFhQpIOXyhVqpRddtll9vDDD7s1F1V4Qv/wGi7TvXv3aB8eAAAx46STTnKFWW+88UbXe65edfWCnXLKKda4ceNoHx6AfEIrxuhzRAn66aefHu3DiSkk6fCNJ554wq2tqAsEJeczZsywzz//3C3lACBnSxuqgIu+9P/0+uuvu59POOEETicQxzQf/aOPPrJdu3a5zwQtd6ph7uPGjYv2ocFH1HGi98fZZ5/tflfDjn6fOHFitA8NPqFlk1U7Suule9cb+hoxYkS0Dy3fY510+E5KSoqbG6cqswByZ+PGjQfdpoKMZcuW5dQCcCPXChQo4KacAYG05rW+go1+5P0C0bQZr/hzoOLFi7sv5BxJOgAAAAAAPsFwdwAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHkGe0dubu3bs5wwAA5NKBAwdsz549vtlPKPbt22cpKSkReS4glpCkA8gzixYtssTERFu7di1nGQCAXHj//fetcuXKuT6HkydPthIlSkTkb3HNNddYjx49IvJcQCwhSQcAAADiRMGCBa1YsWLRPgwAWSBJB+Jo6PmhhqB72wQbGhf4mMy2C/X+7DyXhsqFsj8AAOJJsNjoxXh9KX4Gc8opp9imTZsyHf6u4ekZ961tsnKo+7PzGD1/4BD5vXv3ZnvfQH5Hkg7EuA8++MBq165tRYsWtaOOOsqeeOKJg4ag//nnn9apUyfXsl6mTBk788wzbdmyZWn3T5061T3mlVdesQYNGrjttK+ffvop3XN9+umnVrdu3bTn+vzzzw86nlCf68UXX7Tq1atbyZIlbfr06Xl2fgAAyE+eeuqptFh77LHH2h9//JEuxpYtW9Z9FS9e3I444gh79tlnsxzu7g2jHzVqlPteqlQp+/vvv919emy9evWsSJEi1rBhQ7etZ//+/Xb77bdbxYoVrVChQla/fn2bMGFClsceymP69u1rF110kfXu3dtdA7Rs2TLX5wzIb0jSgRi2dOlS6969uw0aNMi2bdtmb775pkvSA61evdo6duxop512mm3evNlWrlxpNWrUcMlzxmIvCqTffPON21fbtm2tZ8+eaa3tK1assAsuuMAGDhxoW7dutddff91GjhyZ4+fSsaoRQL0Bbdq0ybNzBABAfqH4+/3339vPP/9sW7ZscUn4ZZddlnZ/06ZN03rSk5KS7Pnnn7ehQ4e6xPxQ+/3qq69szpw57rFqkFfSrsc+8sgjtnPnTtcQ/+WXX6Y95vHHH7eXXnrJdQbouW699Va75JJLbMaMGZk+T6iP+fDDD61Zs2a2fv16d0xA3EkFELNuueWW1GbNmqW77bnnnlNWnbpmzRr3++23357aoUOH1JSUlNS9e/em7tmzJ3X79u2pRYoUSZ0+fbrb5ttvv3WPWbhwYdp+FixY4G5buXKl+33o0KEHPdeYMWNy/Fy//vprHp8dAADyj3fffdfFxw0bNqTdNm3atNSEhITUpKSkg7bft29fanJycmrfvn1TBwwYkHb7pEmTUgsWLHjQfpcvX55224EDB1IrVaqU+sADD2R6PBUrVkx9/PHH09122mmnpV544YVpv/fv3z/1/PPPz9ZjLrnkktTmzZsf8nwAsYyedCDGq6u3aNEi3W0Zf//tt99s2rRpbkiZhriVLl3aDUNLSEhwveOBNLzOo6F0ol5zWbBgwUH71jC8nD5X48aNc/XaAQCINYqbFSpUSBeLNaJNPeGiUWk33XSTG6WmIeq6/4033nAj17KiofG1atVKN/JNvdgnnnhi0O01p33Dhg0HDUVv1aqVux7I7WO4BkC8KxTtAwCQdwoUKBBS8RcNUz/UPDJRMh2p5ypcuPAhtwEAIJ5kFYfl4Ycfto8//tg++eQTO/roo10s1ZS3f/75J1sx13uezIq7KeYHu1+/q3p8bh/DNQDiHT3pQAxTS/TMmTPT3Zbxd/V2a37brl27cvVcKhT3yy+/pLst4xyzcD0XAAA4mOLwWWedZc2bN09LdDV/PbuqVatmVatWdTE7mHLlyrn7M+5btWR0PRCuxwDxiiQdiGEq4rZ48WK76667bN26dfbdd9/Zgw8+mG6ba665xn1XgTlVhd24caMrDte1a9e04XOhPteSJUtc1VZVjv/666/z7LkAAMDBlOyqwNv8+fPdkHUNfddUs5zQtYMKvb366qtu6PusWbOsR48eafcPGzbMRowY4VZy0f0qFquYfsstt2S6z5w8BohHDHcHYpjmpGnYm4L0008/7XrWVal1yJAhbukW0XIrWuLsjjvusC5durjlUVRRVY/REmneEDVtHzjMTj/rNm/4mlrddWFwww03uCVb9FwPPfSQS8y9x+X0uQAAiHcaEq7lSwNljJmKr0rOO3To4G7TkqeXX365S4gz20+w/cqAAQPcXHWtCnP99ddbo0aN0jW+K76rEvx1113nOgK0RJuuOY455pi0bdSbr7nx2XmMts9syDwQLxJUPS7aBwEgctQirqRYBVwAAAAA+AvD3YEYd+2117r5XkrKtb6pWtn79esX7cMCAAAAEAQ96UCM++GHH1xiPnfuXKtSpYqbT3brrbdaoULMdgEAAAD8hiQdAAAAAACfYLg7AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOAThSwOHThwwFavXm2lSpWyhISEaB8OACDOpaam2o4dO6xatWpWoADt5+FArAcA5Nd4H5dJuhL0mjVrRvswAABIZ+XKlVajRg3OShgQ6wEA+TXex2WSrh507+SULl3a8oOUlBSbPHmydenSxQoXLmzxjHPBeeD9wP9FrH1GbN++3TUee/EJ8Rnr401+/F8F/Iz/qdiJ93GZpHtD3BW080vg1j9d8eLF3fHGeyDjXHAeeD/wfxGrnxFMwYrvWB9v8vP/KuBH/E/FTrxn4hsAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BNxOScdQHwux7R3716L5XlohQoVst27d9v+/fstnvn1XBQpUoTl1QAgj+lzX3EgHvk1/sWTwoULW8GCBXO9H5J0ADFPyfnSpUtdoh7L625WqVLFVbKO9+Jjfj0XWg+1Tp06LlkHAIT/s3/t2rW2devWuD21fo1/8aZs2bLu75CbvwFJOoCYD1hr1qxxrZpa8kKJUixSA8TOnTutZMmSMfsa8/O50DFp3W69F2vVqsXFEwCEmZegV6pUya0aEI9Jqh/jX7xdcyYlJdn69evd71WrVs3xvkjSAcS0ffv2uQ/MatWquaAd68P5ixUrFveB2a/nomLFii5R13uS5aYAIHw0tNtL0MuXLx+3p9av8S+eJCYmuu9K1PV+zOnQd/56AGKaNyeLIcaINu89yDxBAAgvbw56LDfGI//w3oe5qY1Akg4gLsTjsDf4C+9BAOBzFrEvIQzXnCTpAIAsbdu2zUaNGmXbt2/3xX4AAED4vfbaa/bnn3/6Zj/xjDnpAIAsbdiwwa6//no755xzrHTp0iGdrS1bttjrr79uV1xxhStgk9P9AH6xYsUK27hxo8WyChUquMKGAOLTAw88YDfddJM1adIk5Me88sor1qpVKzvqqKNytR+kR5IOAAi7devWuYT8ggsuSEvStSTJddddR4KOfJmgN2rU2JKTkyyWJSYWt4ULF5CoAwjZfffdZ3fccUe6JL1v377WtGlTzmIukKQDgM+ox/mtt96yK6+80n755Rf7+++/rWHDhnbSSScdtO3vv/9uM2bMcEVKTjjhhHQJcCj70Vqq7733ng0ZMiTtNi3f8tJLL9mll15q5cqVO+g5VQjlmWeecT8XKlTIateubSeffHJaoZTdu3fbG2+84X5++eWXrUyZMla3bl33vNpWjwn2GpTMd+nSxVVDzfgarrrqKps1a5b99ddfbl96PiBS1IOuBL1Vv7utdNXaMXnit69ZZjNeude9VnrTgch48cUXrV27di6u/vrrry4OnnHGGa46e6BVq1bZV199ZXv27LE2bdrY0UcfHXQ/uv/HH390o2LOOuusdPsZPXq0nX322Xb44Yen3TZ27FiXXDdv3jzo8b377rvuuTXHWsuJnXjiiVa9evW0+8ePH++msOnYdO2givLXXnut26ZUqVI5eg1axuyXX35x56Jbt25xWwyQJB0AfEaBTL3Q48aNc0t56IL5lltusYsvvtiee+65tO2UWGuYmQKx1oe9+uqr7YMPPrDOnTuHvJ9Fixa5bQKTdC1jo9u6du0aNElXAF22bJn7WRcWGtY+ePBgmzZtmtWoUcNVL9dze40AGvpeokSJoMPdM76GAQMG2EcffWSnnHJKutfwzjvvuICv/es1dO/e3Z599tk8/TsAGSlBP6xWQ04MgLC48847XWxW3G3btq2Lo/fff7/77sXJDz/80Hr06GEdO3Z0jd6KmzfffLPde++9Qfdz/PHHu4bvBx98MN1+brjhBmvQoEG6JH348OFuWlpmSfqaNWtcvFfc//rrr922SuwVs737tazopk2b3HbecmMZh7uH+hrUkJ+cnOxew08//eRu++233w5qtIgHJOkA4FNq3X711Vfdz+pFVtBS77Z6zH/44QfXKj59+nQ3F0xroypJV5K7cOHCdOtwZ7WfnC4lpgJwgc477zwXlJX8KyEfNmyY602/5557XGItixcvTveYjK9BNBxeveYZX8Npp53mhtPJJZdcYp06dXJD7LT2OAAA+ZUasjWiTD3HO3bssGbNmtnjjz/uEthdu3a5uH7bbbe5hFUmTZrketvPP//8dEPKtR/1xut6QD3fLVq0SNtPTqlXPNALL7zgbvOSdCX+iuNqOFcCH0x2XoOScV0bFCpUyPXMq+FBo/0U9+MN1d0BwKcGDhyY9vNxxx3nEtmPP/7Y/a7v+t1Lbr3tlyxZYnPnzg15PzmllvMpU6a4gO0l7LrIyI5gr0Gt68FeQ8+ePdN+btmypWvVX7p0aa5eAwAA0da7d++02i0aMdanT5+0GK0ecdV40Wg1j4aA16tXzz777LOQ95Mb6slWo/uTTz5py5cvd19qEAhVdl7DRRddlDYlrmTJkta4cWP7559/LB7Rkw4APhU470vUIx04jNzroc64ve4LHLqW1X5yYv369W5YngKpEmwNpdPyaps3b87WfoK9hpo1awZ9DYFz7b0e9r179+b4NQAAkB9ivZJVFV7NGCt1X6j7yQk1hquBXPPINXpN89zVKy6K98GmwwWTndeQceWXwoULx22spycdAHxKc7gzJseVK1d2P1epUuWg+73fdV+o+/FarDWP3OMF4cyo91wBV73dGkav1vX27du7gJ4dwV6Dji3YawAAIB5jvYZ9JyUlHbRNdmK9F+8DY/2h4r2GzqsezOzZs12BuKeeesr69+/v7stOvM/Oa8D/kKQDgE+p4JtHQ8BVRMWraq5WbVVw1e0eBVFVRs9YMTWr/Xg91/PmzUvb5vPPP8/yuNRrfthhh7kqrqJKrXruYK3hGYNyoGCvQUXogr0GAABikRJhL3nWVLIJEyakxWhN71I8VbG2wOHnaiT3CqyGsh8v3gfGev2s5SWzivVK7AN7zF977bWDttPxZRXrs/Ma8D8MdwcAn5o4caKbx6VKrAqMqtqueVxy+umn25lnnul6sLUeqSqsas6Ytsu4XElW+zniiCPs1FNPtQsvvNDNZ1N1VhVtyYoKxKj3/LLLLnOPV1EXFbtRBXmPWsd1n4rK6CJBc8+OPPLIdPvJ+BpU3V1Jul5HvC65AgCIL5rf3aFDBxebJ0+e7HrEVXxV1CA+YsQIN597zpw5rjK6lipT/NVyaBn3o3irZcymTp2abj8yaNAgV7hNQ9VVWE6rwWQcXh5IxWU1ZF5Lo6p4q5ZFmzlz5kHbafrbmDFj3PKrKvyWsdhcdl4D/oeedADwqW+//datLa5lzlSdNWMBGCXfqqqq+1UBVdtrebXs7ke/aykUBVhtp+CuKusKrKJ5ZPrdC+YqPqdWcK1Xrseoqrta8JVoe9TL/v3337sLD82JUyNBxv0Eew0aXhf4GlS9XY8JbABQy75uyzj/DgCA/EZLrmlpUfWCax1zDS8PHAauIeYadeatZqLGdi1dGmw/WvZM+1H19Yz7UWFWxWrN81b8/Oabb2zo0KHp6r8ojnvV1tVYrqRcFdjVq66G9Z9//tnF38D55SNHjnTXEFqGTUXlMu4n1NeglV1UKC7QBRdc4NZUj0cJqdmdRBgDtm/f7lpx9IbLqgXJT3QBqyGoaskKXJYoHnEuOA/ZeT8oiVQV8Dp16uSbdTYVWBU01QquQi2h0JIr+mzTZ5o3DD0n+4kFwc6FH2T1XsyPccnvwnlO1Sh17LHHWufbX43ZddI3r1hkUx7s6xrKtHRTJBDPES75MdaLkujHHnvMevXqFZb9qNCbH+NfvNkdhnjPXw8AAAAAAJ8gSQcAnwk2xDua+wEAAOEXbIh3NPcD/6BwHAD4jOaKjRo1yjf7AQAA4XffffeFdT+a7oXYQE86AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AcW7Dhg22cuXKQ263Zs0a27x5s8Wjf/75x7Zt25blNosWLbJ9+/ZF7JgAAAhVUlKSLVy48JDbKdb9+++/cXli16xZ476youul7du35/mxsAQbgLi0YsUK27hxY8Ser0KFClarVi3zo8cff9xmzZplX331VabbbN261Vq0aGHffPONHXbYYZafLVmyxL2GsmXLhvyYk046yR544AG77LLLMt3mlltucdvdcMMNYTpSAEB+ivd+jvU//fSTde7c2VJTU7PcrkePHnbGGWfY1VdfbfnZunXrbP/+/VatWrWQHzNs2DDX2D527NhMt/nwww9t0qRJ9vnnn1teIkkHEJcBu1GjxpacnBSx50xMLG4LFy7wbfA+lIcffthOPvlka9y4seV3Xbp0sZtuuskGDBgQ1v3efffd7hxdccUVVrp06bDuGwDg/3if32P9lClT7I8//rCPPvrI8ru7777bNc5MnDgxrPvt37+/Pfjgg/b111/bKaecYnmFJB1A3NGHtgJ2q353W+mqtfP8+bavWWYzXrnXPW+ogVstwGrNrV69uu3cudP1ZNeoUSPotrp/7dq1VrJkyUyTQ22zfv1616JcrFixbA+Re/HFF+29997L1vEFbqPXriF0RxxxRNr9+l1DxnR/gQIFDhperh6JMmXKuP0UKVLEypUr5+5Ty7iG4lWqVMkSExMPuiDT8ezatcvq1auX9hjP8uXLLSUlxe3TG/bXqFGjtPt37NjhXkvNmjUzPR86j3reUqVKpbtdIw0OP/xwe/31123w4MEhnVsAQGzE+5zEesWr1atXW4MGDVxs0s+KiYUKHZyi6X7FPsW1zEaCaRvFOcWwnIx6GzVqlF166aVWuHDhkI8vcBv9rOHiioWK27Jnzx53W9WqVa1o0aLpns8bWq77FH+9awKPrm10LOXLl7dAmzZtclP1RNcJenwg/Q0Uy7VPL9brb1K8eHH38969e9Nei/daM9Jjdf1TuXLldLfrdV1yySU2evToPE3SmZMOIG4pYB9Wq2Gef+XkwuD++++3iy66yDp16mR169Z1CWerVq1cUPEoyFx11VUueLVv395td9ddd6Ubyha4Tdu2bV3QvvPOOw853C2QhrgryLZr1y5bx+dto57rI488Mm2ouILnWWed5YLjiSee6I7pySefTPecGjY+cOBAq127th133HEuIR80aJAbYqbb9Fp0kfLUU0+le9wdd9xh5513nvXs2dMF5JYtW7q54p5bb73VXRQ8++yzds4557gvUbDXzzpPrVu3dhcYet2BfvjhB6tfv74de+yxbrvLL7/8oHN16qmnpmvMAADER7zPSazXNLNmzZrZkCFDrGLFita8eXMXu7799tt02z333HPu/hNOOMEljWeffbZLQjNuo/sUc5W0BtsmK0qwJ0+e7OJYdo7P2+bGG290jfVnnnmmayg4cOCAGz6ux+kaRbG+X79+LvH16P6LL77YXT+owVzXEm3atLHffvvNjj/+eNf4rWuFCy+80O3P8+6776bF8GOOOca93sDe/7feesu+/PJLmz59etp2c+bMcY38t912mzsWnUtdW2jKXyBdI+japmHDhq5jQcehxvlAOkdffPFFutcSbiTpAOBTmj+mQKDgoCRSrbeBc8QUWD799FP7888/Xev6Bx98YCNHjrS333476DZKoBVMdVvgNocyY8YMO+qoow5q2T/U8XnbKABrm2nTprnbLrjgAhcgdZt6vqdOneqGpSmgZkyKdSGgIi0KvmPGjHEXCj/++KO77c0333TD1r3WdHnjjTds/vz5NnPmTHe7Euq+ffum3T9hwgR3gXHPPfe41nWvhV0B3Cugt2rVKvfcgcm96PjUSKBtfv/9dzdnLeOcNF1Q/Pzzz9lqBAEAxK/k5GRbunSpG+GlHmL10mpeuEaEieKZGqlfeeUV17OsuKnRZop/Hm+bl156ycWuZcuWHbTNofz6669u9JsS7uwcn7eNGuAVRxVX1aD90EMPuZg5b948d8z6UnxWvA+keKuYrNir59Hj27Rp46bZ6brlr7/+cvvRNY5H09W8GK5riccee8x69+6dlkxfd9111r17d9dg4W2npPy+++5z50iN8ErG9Zw69kDfffedXXvtte65tc3u3btt+PDhB8V6dYKonk/MJulqFVErhybhL168OKTH6GTqok4XbbpQAoBYpBZptUyLhlZrDpQ+9xQoRb3BCkQaYiZqie7Vq5e73ZNxGwU+BbLAbQ5FQUpDz7N7fKIedLWce/SZ/f3337tkXgHw77//dsPvNZf7/fffT7d/BeE6deq4n0877TQ3xFxzwbxhhOeee667oNAFQCAlyLpQULBXj71ijHoIMqOLGzUm6Jx4w9o0VFA9+RmPR638okYL9ShkjEHqMdCog3itgp8VXWgp1qvRJ9RGDPWm6D2lBh5dKAFALFISqKHgCQkJLpFUruP1DCs2KUZqlJgoTinRfe2119I+F0PZJpRYr6HfwabNZXV8gdt4Q8f1Ga8RchpBp5ioWK8E//zzzz8o1mvkXLdu3dzPVapUcSPsTj755LSh5N6ouGA5n6bNad9qkNdQdsX7rHJOjb4bOnSo6x0XPUYj8AKp11/XDt61jX7O+NwaTadpeoeqBJ9v56TrxHbt2tW1rOhCThdJmseXsbUikC7u1AujeZUa8qhgr2GJGnKQce4hAORnKtIWOFf76KOPdt/VOq45WOo9b9KkSbrHNG3a1PWciwJzZtt88sknIR+HesjVYpyd4/OSXQ2FD6SEWo/p06fPQftTYAyUsSKrgmngbboY0FdgAq754BrSrteuhNlLBr35csGohV378Y4/M4Hz5EQ1ADRnLZB3nry5ePjP9ddf73ov1JOhUR3qZfnss88OmtfvUSOLrg/Ua6TpDnpPqZdGPSnaBwDECo1S09BqjxqkNcza67zUd30OZozjGrqt1UqUQ4WyzaEobml7JbOBsf1QxydqbA+cF67Gen1ma467huEH8uaFZxbrS5Qo4eJrxtsCY716/VWkVaMGdL2hBoQtW7a4nvHM6Ji0jXrBcxvr1UGg85SXsT6qSfrtt9/uehs09EEXnBruoDmPWh4gs4n4WtpGrSveUE3NtVCwf/rpp908SwCIFRlbv725TwqQXoKacZiWfvcCYCjbhEINohnnxx3q+DwFCxZMt42OR4mzAqyCbjipwVfzxDWkXfPJ1BugAK6Lk8C5bBkpuCvgqihOxqI22aULBLWwZ5Z8xiM1Gqn3QtMAdBGp3hRdJKk35tFHHw36GF3YKUHX38+7WNM8Rw3d1HQHAIgVSowzxh/FUy+W6nuwOC5eLA9lm1BivWKleocDE9VDHV9msV5UXM3rlQ6nXr16uel26qz1EmUd86FivXfsueU1Bnij/WJquLsu0jSpXxdUStBFhYA0/CCrtek0ZEJvIo8KB2luY7BeHgDIz2bPnu0qnQbOk1KwVcOkAqJ6yDMmz1oSRMOwJZRtQqGhaEqW1AId6vFlRr2gGi4XrLiaLgJyO5xarf8aBh/4WjNSi3/gc6lQnI5Jc94CKXHPLl0wdOzYMduPi2WK6Rol4fXyqBFDQyAPFes1xSKwN0UXQ8R6ALFGOZGm8XpU90Sjh7y54YrXiq+B04QU25T/eNO/QtnmUFSATTmZGlSzc3zB6HNeheCCLX+W21i/f/9+F+9VS8ZL0BcsWJCucG2wWK9zoREBGWvJ5DTWa38arRBzPekagqle8IzDC3VBqQu/zDzxxBOuUrEuBDXMXVUIdZI0wT+rYK8vj3dRqT9cbt8okeIdZ3453rzEueA8ZOf9oNsVYNS66rWwprW0KpZFor7X/z9H4DEc8iGpqa4VXBVNNepIvYqa/63Cafr8035UaEXzu5S8aA6XRhgpIVfw8J4ncBs1hGq4sAqmBG7jBfXMjk3JlQKbEmtvfnkox6dtvHOfcR67pjYpBihpV4v0O++8Yx06dHDD1zzBzldWtymeKGBrvtkZZ5zhgraq3Wd8nBekNXJL2+tCQsdz5ZVXumHWulD55Zdf3CgvjdLK6rkDX58CvaYRPP/880HPpXdO9J7M2OsQy5/tGt6ecXScYr3eMxoOGazegUbN6b2son9qJNIFoc6tCgNGI9brb+dGsBQwK5QQ2v9wfqPXpteo1xqp9yPxHOF8L2WM9RGP9zmI9dpOjcSqeaIcR6OwFFM1x1qfm7pfcfWFF15w8VdxSrHt3nvvdXVgvH142yiGanUTjSzLuM1B10AZ6Dj0WCXWXmN3KMeX2X41Ukpz5NWhqpVeNPrOK9j28ssv/3fKglwnHOq2hIQE16mr16ZEXHFb09wUVwMfpyluei0aqe01VmhKtQrK6Zh0naAGB83nV1HdrJ474+vT9ZAK6GV2PsMR7wtFcz66ZFzDTy0vWS0XoF50zYNQ6X3NdVRCrwvQzNYGFlUH1Bs1IyX42Rny6QdTpkyJ9iH4BueC8xDK+0FzqVSIRHOZvF44ryLp1lX/2P792W9Bza4da5enPW9gz3NWdKya+qPEVYmmPjMVnDW319uHkm7NwdZcX1V91eejEhl9z842+vzUOcrq2K655hpXXV01QUI9PtUJ0XYZ96vlT9TbrsRcFxVqcNXnuC4KvG11fEqgAx+rz3z9PQNvUyE39Z7rNgVrNVToQkJJuLbXMev4FBS9x+kCQ8vDabicjk8JuQrsqCFDFeP1GBXhU7Kf1fHonOncebepKJpes5L/YOdSz+UVPs3Ycp+Xy7hEm94bwWK9KN4HS9J1AaUGnI8//tgNj9fcRzWmZLV+fV7H+vHjx///TxstJlUqZ1eNH+8azbKa15kXiOfIrWCxPtLxPiex3pt+ppVZNHdbSaN6xRW3vH0otqn2lpJedVQqzujzTklisG0Uh4NtI4q9WR2b4rgaRlWMTXO9Qzk+JaGKxRn3q+sPjVBTEqxCrPrc1zWDkmtvW8UG9YwHPlb1ZBITE9PdpmNRL793m64dHnjgAXe82ocK5Coh15B2bxtdU/zxxx/uPs0pV6O7nl+F6/RaVPhO50P7yep49Po1lN67TQ3MOteK5Zmdy3DE+4TUKK0Toz++Wjj0way5gx4tH6DicHPnzg3aKqEeEL15dMHpBX+9WXSBl9nctmCt6wr0asHPKrn3E11g6lzpotib5xGvOBech+y8H9Ryq6CiJEtBTP4rVnmUJSdHLjFKTCxu8+fPC3nYmXp2vWXVQqGPcgUhtXKrlTnctH/NK1Piql777B5fJOX1uciM4pBimOqmBKP3opbFUfzx3ouBcUnJqmJafolLoVLDui6WtESORw1Fej/pf1OjKzJS9X/1uGh5Gw15199UxQbVMK+e+UjHeq2vqyH7HW8aY+VqZj6dIz/bsvJv+/axq91FpUaTRALxHOESLNZHI95nN9arcVefbaEm9ZGIf14PtUa9hfv4YsFjjz3mYo0a/DMTjngftZ50vXnV6qV/nkDLly8/qBqwRy27ak0PHH6oVhWtgResqJFHrSrBigF5RZXyk/x4zHmFc8F5COX9oBZRBSr1tnrVShXEFy5c4C7eI0UfyKEGbdExe8cdCm+4VXYek12qxp3T44ukSJyLYA7VYKFj0TEFe6/G8ue6kvRgsT5jNeBAaqzXkjzenHSdNzWCaB57ZkPk8zLW62+nXpGUA2b7Uv33ng8HvTa9Rr3WSL8fiefIrWCxPhrxPrux3jvWcMWqcMS/wELc4T6+WHDLLbcccptwxPuoJekKpJrLoGHr3hxH/QOp5VxDFQPXr9VQNy3FoqEOemGai6GKfh79HqwlHgAyoyCanUAaaRq252d+Pz74h9a4V20E9RqoYV09PZrqoLjuzdXTqAzNGzz77LPdMEf1Pii2B9Lvui/j0HkAyK/xXj3egcub+Y3fjy+WRXUJNk3e13yF3r17u7lnGsKudXdV9dWjAjyqMqhg7hUE0qLzSujV4665Zrpfre4AECv0Oednfj8++IfmUKoegka9Kb4rXmvYemAFYf3co0ePtOHvw4YNc1PhdH2gavmaIvfkk0+69x09OgBihTostSSpX/n9+GJZVMcuqHT/b7/95oa7qdKwKgpqLlTgcDVV79OQN4/WVdWQQo31nzZtmisks3DhQreEDgAA8BcV3dHa5hqurmRcw0+VpGv9eo96zlVx1yvwptoz6jlXY7xivQrvqBigig4CABDrotqTLioeN2LEiCxb4DNSgSh9AQAA/9OQyazm8ama/oQJE9LdpmrBwaq1AwAQ66gCAAAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+EfXCcQAQDStWrHBLOUZKhQoVfLtO6yOPPGJ//vmnjR07NtqHAgBAvo33fo71WmXjkksusWXLlkX7UBACknQAcRmwGzdqaEnJuyP2nMUTi9mChYt8Gby3bNlia9eujfZhAACQr+O9n2N9cnKyLV++PNqHgRCRpAOIO2pRV8Aee1Uza1y1ZJ4/34I1O63XC7Pd8/oxcAMAEIsiGe+J9QgnknQAcUsBu0XtMuZHd999t61atcoaNWpkH330kevtPv/88+3OO++0QoX+99H93nvv2VNPPWWrV692DQC33XabnXzyyen2FbhN3bp1bejQodahQ4covCoAACLPr/F+8uTJdv3119ujjz5qo0ePdj3dxx13nD322GNWuXLltO3++OMPu/fee23u3Ll22GGHWZ8+fWzgwIHp9qVt7rnnHjd9TcPug22D/IPCcQDgQ5s2bbJXX33Vpk6dak888YQ9/PDD9uKLL7ok3fPxxx9bz5497YILLrB33nnHjj32WOvatav9/vvvQbeZOHGitWrVyrp06ZJuGwAAEHlJSUm2YMECGzJkiN1444322muvuSH6Z555pqWmprptNB2tXbt2LvGeMGGCXXPNNa6xXdcGnsBtXn75Zbv66qsP2gb5Cz3pAOBTJUuWtLfeestKlSrlft+5c6ddfvnlrrdct6nFvH///i5gHzhwwGrXru1a0O+//357//333WMCt5GmTZvarFmz0m0DAACiQ8n4c889lzYKbvz48S6eq5f91FNPtZEjR1qNGjXcNgkJCda8eXNbv3693XfffXbttddawYIF07Z59tlnbceOHda2bVs31D9wG+Qv9KQDgE81a9YsLUGX9u3bu8Ivf//9t+3fv98Ne8s4bF1Bfs6cOe7nULYBAADRo8T7xBNPTPu9WrVqdsQRR6TF6dmzZ9tJJ53ktguM41u3bnW97qFug/yFJB0AfKpIkSLpfi9WrJj7vnv3btu3b5/7Klq0aLpt9Lvul1C2AQAA0VOgQAErXLjwQfHei9P6HiyOe/eFug3yF5J0APApzVPTMPbAojBqJa9Xr54LvioUp9sCqeW9fv367udQtgEAANGjUW+K94Hz1P/555+0ON2gQYOgcVxD2OvUqRPyNshfSNIBwKdU3f2hhx5y89VU3V1z0c8991yrVKmSu1/zzFUNdt68ee73H3/80caNG2eDBw9O20fGbb7//ns3zz1wGwAAED0333yz7dq1yyXsw4YNsxIlStjZZ5/t7lMRuO+++84VjfOuDVTp/corr0wbYRfKNshfKBwHIG5pTVM/P48Kv0yfPt3Kly/vCsG0bt3annnmmbT7VQ1Wc820XEvx4sVt7969LrhrqbastlGyH7gNAACxLBLxPqfPodjcpEkTV/gtJSXFLbH29ttvu9ulRYsW9tJLL7kCcFpSTcm8GuxHjBiRtg9vG8V8Jezqjc+4DfIXknQAcUdLlBRPLGa9XpgdsefU8+l5s0PJ+Ycffmh79uxxSXrGx2u9dPWSa33VDRs2uOHtekxm22hZt4oVKx409+3WW291yTsAALEk0vE+J7Fe09geeeQRt9SqqrZrtJzmqQfq27evXXrppbZu3TorXbq062nPSNv07t3bFi9ebNWrV09XeNZr+F+6dGkOXxkijSQdQNzRPO0FCxe55UkiRUFbz5sTSr4zFoTJeL+qwW7fvv2Q2wRTrly5HB0XAAB+Ful4n5tYr8S8SpUqWd5ftWrVkPYRLInXsHct7Yb8gSQdQFxSEM1pIAUAAPkD8R75EUk6APjQfffd55ZPAwAAsalLly42f/78aB8GfIgkHQB8SIVjAABA7FJxOEb1IRiWYAMAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfII56QAAAHAWLFgQsTNx4MAB933OnDkHrQvtxyWyACBSSNIBAADiXMqeve57r169IvaciYmJNn78eGvfvr0lJydH5DmLJxZz62aTqAPwM5J0AACAOOct+Xj/eQ3stCaVIvKcBwoUsVVmNnXoCVbgwH+NBHlpwZqd1uuF2bZx40aSdAC+RpIOIC6tWLHCXahFSnaHWL733nu2evVqGzx4sPnJww8/bC1atLBTTz012ocCIA/UqVDcWtQuE5Fzm2KFXZJ+TK3SVthSIvKciD+RjPfZjfVaI/2ZZ55xX37y4Ycf2vLly+26666L9qHELZJ0AHEZsBs1bmTJSZEZXimJxRNt4YKFIQfv6dOn29y5c32XpKvxICEhgSQdAOB7kY732Y31Or7nn3/ed0n6zz//bLNmzSJJjyKSdABxRy3qCtjtb2lvZWrmfY/RtpXbbOqIqSEPsfzyyy/tq6++ss2bN9uAAQPcbQMHDrRNmzbZO++8434vXbq0NW3a1Hr06GEFCxZMe+zbb79t27Zts9atW9vEiRPdENbhw4e7+7TPSZMmuZb+M888077++murU6eOnXXWWWmPX7p0qU2YMMHWr19vDRo0sN69e1vJkiXdfU899ZS7oFAL+7Jly9xtTzzxhJtXCgBAPMf77MZ6xVPFVRVQ9GJ9x44drV27dnbfffe534sWLWpHHHGEqxVx2GGHpT1WxRZfffVVu+222+yVV16xlStX2u23325VqlSxxYsXuzgtbdu2dQ3r2v6WW25Je/yOHTts7NixtmjRIqtWrZpdcMEFVrdu3bRrBV2HbNiwIe24+vfvb82bNw/zGUNWSNIBxC0F7Ar1K5jfVK5c2SpWrGgpKSnWrFkzd1uZMmVc9WPv961bt9qIESPstddecwHV88MPP7je7vLly9sll1xiNWrUcLc/++yzrkV80KBBbr9K0nfu3OkCr5ekf/HFF9azZ0+7+OKLrV69evb555+751BruvanCwUl5FWrVk07jsAGAgAA/MiP8V7xVHFVSbQXU6tXr+5u937fvXu3a1B/8MEH7Y8//nDXB16DunrfP/vsMzv33HOtSZMm7nFKxk866STr0qWLa6y/++673dQ57ddL0v/9919XrFEN/WoQWLJkiZvGpsS+Q4cO7vpDz6PnDrwGQWSRpAOAzygoHnPMMS4B9lqxPQrEnquvvtq11n/77bcuKHuUfCtQe8E8KSnJ7rzzTnv88cfThs+fc8457jk8e/futb59+7pWfSX3csMNN1inTp1cov7II4/Yaaed5oJ3q1atDjouAMgvIrnMXDSwzFz+oHiquDpmzJiDYmrg70OGDHFJ9+jRo12y7tFIuUcffdTFc49Gv+l6QI31atjXYzUqLpBiu7ZRT7ynZs2aduONN9qvv/7qrg10HaL9E+ujhyQdAPKRn376ybWqr1271vbv32+FChVyF5yBSfqxxx6blqCL5rZrqHz37t3TblMLeuPGjdN+nzlzptvnN9984+bDp6amui8Nuf/tt98i+AoBIG/s2bsn4svMRUN250XDf1S07d1333W93urR1hS0jI1L6oHv1q1butumTZtmjz32WNrvGi5/9tln2y+//OJ+V1xX77uGwV9zzTVpsV7P8+eff7rEXNcViD7+CgCQT6gFfeTIka6lvH79+lasWDE3JH379u3ptitXrly63zWvTDRkPVDg717lW7WeFy5cOF0yH5jwA0B+X2aueZ/mVqPlf1OBYk1250XDf5Rod+7c2S688EIXg0uVKuWGpGeM9cWLF3dJuGfPnj1um6xivUbW6UvD7I8++ui02/U86tWHf5CkA4APqYU8Iw1Ff/LJJ9P1At11112H3Jfmoolayg8//PC02/W7R4VjpE2bNq4nPjvHBQD5SckqJX03PxnxKVhMVbV31YZRzRnP999/n9bgnhkl7ErIV63SwoYWNNaXKFHCzS/XtUBWQ9mJ9dFXINoHAAA4mHrDt2zZku42DUlTC7hHFV0PFbRFreWq2hq4xIt64L0K7aLEvFGjRi7pV2u8R0PgtRRLVscFAACyTzFVU9dUbd2jau+BsV4V2D/66KOQ9qdCsG+++aarM+PFcM1PD6QCsWrwD0zmFfd1XRB4XMT66KInHUDc0rBAvz6P5pnde++9dv7557viMlqC7eabb3ZFYDRvXNXdNX9MldYPRfPLlKCrAqwqtSv4zp4922rXru0Ky4i+K5ArwGuuunrU161b5+bFPffcc2n70v133HGHu0/D7VmCDQDgd5GI9zl5DhVpU692165dXWFYLcGmAq8q2qoh74rXivlacSUU99xzj5tvrmrtmr6mOjZqpPdivagYrBrp1YB/8sknu0YBFZvVdYY35F3XICo4e95551mlSpVYgi0KSNIBxB1VvlVhHc3bixQ9n543VFqPdOHCha6Im+aYaXjaTTfd5AK3EmwFbgXXKVOmpK1tKioOl5ycfND+dAHw119/uUrwOg4l4SeccEK6YzryyCNdYZqpU6e69Vs1n1HbBa6DrkIzqu4+f/589zwswQYA8KtIx/vsxnoNUdfSalpKVSPjND1NsVnxWkPcNexcK7NoGbXAkXNKwNUbnpGWXVViri899uGHH3aF5NTg7ilZsqTrNf/999/dc5ctW9Yt1xZYf0YNBurB1zXItm3bWIItCkjSAcQdJZ+qfOsVS/Prkjh16tRxX4EUmL11S0Wt3KKWcFELemCLuWfevHmutb5Pnz7ud1WIVyOA1kQNpKJxp5xySpbH1bJlS/cFAICfRTre5yTWly5dOi2We5Ssa1h64BJpgTQS7vLLLz9oXxqiriHuF1xwgbsWUFX4d955x2699dagnQH6yoyeQ1+IDpJ0AHFJQTSeKt9qvpuCsYbAaR11JekaFqeKrgAAxKp4ivca3davXz+X5Cv5V6w//vjj3VB25C8k6QAQBzSUTcPWtLSLitSMHj3aLcECAABigxJzTYPTHHP1omuaHCPf8ieSdACIExqGp+JxAAAgNmmeu+rXBJv6hvyDvx4AAAAAAD5Bkg4AAAAAgE+QpAOIC6mpqdE+BMQ53oMAkLe8lU6A/P4+ZE46gJimJcW0VqjWF61YsaL7OVYDwt69e2337t1xPw/Nj+dCCbreg3r/6T0JAAifIkWKuM97rSeuWK/fYzXe57f4F09SU1Pd+Ve81/nX+zCnSNIBxPxyJDVq1LB///3Xli1bZrEcGJKTky0xMTEuL0zyw7nQsei9qPckACB8lBDVqVPH1qxZ4xL1eOXX+Bdvihcv7pb9y01DCUk6gJhXsmRJq1+/vqWkpFis0mubOnWqtW/fPu57av16LnQsJOgAkDfUa6nEaN++fW6p0Xjk1/gXTwoWLGiFChXKdSMJSTqAuPnQjOUESa9NFybFihWL+8DMuQCA+ORNKYrXBJX4FzuYrAAAAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPuGLJF3FjrZu3Zrtx2mJAa1FBwAA/E+xPrtVl3WNsGvXrjw7JgAA/KZAtNfyu/XWW61MmTJWtWpVt2zCxx9/fMjH/fLLL9amTRsrV66cVatWzQYMGEAABwDAp9577z2rXr26i/Vly5a1O+6445CP2bBhg/Xs2dNKly5tNWvWtA4dOtjixYsjcrwAAMRtkv7EE0/YCy+8YN9//73t3LnThgwZYhdccIEtWrQo08fMnTvXOnbs6IK1WuTXrVtnLVu2tAULFkT02AEAwKH98ccfdvHFF9udd97pGtS//PJLe/LJJ23MmDGZPmbPnj3WqVMnF+P/+ecf27x5sw0fPtyt/wsAQKyLapL+1FNP2RVXXGHHHXecW9fvhhtusBo1atjzzz+f6WPU+t6kSRN76KGH3HrAetzll1/u9gEAAPzl2WeftUaNGrlRbwUKFHAj4Xr16mVPP/10po959dVXbeHChTZu3DjX+y6tW7e2fv36RfDIAQCIsyRdw9iWLVtmbdu2TXd7u3bt3HD2zOalTZ482fW2y7Zt2yJyrAAAIGcU0zPG+vbt27sRcDt27Aj6mM8++8xdD1SuXNmNtFP8BwAgXhSKZpIuFSpUSHd7xYoVbfr06Zk+RsXitm/fbvXr13fD4A4cOGB9+vSxxx9/3BITEzMdNqcvjx4vKSkp7is/8I4zvxxvXuJccB54P/B/EWufEfnpWLNLsTtYrPfuK1Wq1EGPWbFihTVu3NhOO+00N8RdSboS/eeee87q1asX8Vivaw1dYxQuYFYo4YDFoiIFE/67jipU1FKscESe03ueSD1fasEi7jUWKVjECqVG7RI4TxVOKOxeo96zsfy5gtiJf/EmJcS/TdQ+oRISEtz3jK3j+l1D2LN6jIbOffPNN27Y+/z58+3kk092H0hK1IN5+OGH7d577z3odvXKFy9e3PKTKVOmRPsQfINzwXng/cD/Rax8RiQlJVmsUuwOFuslq3j/9ttv2yuvvGKff/65q0Fz0UUX2TnnnGN//vln2vVAJGP9+PHj//+njRaTKlWza9v99xo/j/BTTyl3SWSeqLXZ+Nb///P/2nNiSw2zPuP72KpVq9wX4lN+in/xJinEeB+1JF1V2UW94YH0u3dfRmqJL1q0qKv2qgRdjjzySLv00kvto48+yjRJHzZsmJvvHti6rkqxXbp0cVVj80uri/7hOnfubIULR6bF2a84F5wH3g/8X8TaZ4TX6xuLVNU9WKzX/HQNZ8/sMRo517dvX/e7KsIrlqtRfvny5Va7du2Ixvo5c+a4Ifodbxpj5WrWt1i0ZtEfNn3MjfZi36Z2Ucv/6gDkNfWgK0HvvOUtK2x53/M3fdEa6/rkH9ZmSBur276uxaJN/2yySTdPciNQjjnmmGgfDiIsP8a/eLM9xHgftSRdy67pw0NvJLWOey3rX3/9tQ0aNCjdC9Ebrnz58laoUCE33C3jeqmar5ZVK7kSe31lpDdvfnsD58djziucC84D7wf+L2LlMyK/HGdOKLlVL7SWXfV6wFXh/fjjj3cFYEXD1Lds2WKVKlVyybtWcVFV+MDHKNZLZvE+L2O9jkmNBikHzPalRrXmbp7Zuz/VvUbbtyciCXMgPV8knjNh/173Gvfu32v7EmKzzkFKaop7jXrPxvLnCmIn/sWbwiH+XaIaaVSp/fXXX7eXX37ZDVu/6qqr3ByagQMHpm2jVvGTTjop7Xct4TJhwgR78803bcmSJS7wv/baa3bllVdG6VUAAIDMXHPNNa7QqxrgVSxOU9beeecdu/3229O2+eSTT1wV99WrV7vftfKLEnRdA/z999/2ww8/2NChQ+2ss85yiTwAALEsR0m6WrfDQVXatcyKllzr1q2brV+/3r777rt0AVg97oEFZ5SwT5w40V566SU37E3BXl+BiT0AAMidefPmuYbz3NLSqortWtGla9eurnFeDexnnHFG2jbqUdfQd2+Ouoa3f//9925OrYarX3vttW4++ltvvZXr4wEAwO9yNNxdw9SPPfZYt16p5ocrmObUJZdc4r4yE2yeuYK8vgAAQN4YMmSIW6tcdV80N/yII47I8b6aNWvmCsBlRgn72rVr092m51OPOwAA8SZHPekamq75Yvfdd58bnqYkW3PJNTQNAADkf+PGjXPDzT/++GO37GmHDh3cVDM3bxkAAPgrSdfapY8++qj9+++/bn64irmoZ7tu3bpu+RNVXgUAAPmX1jK//vrr3RS3GTNmuNivYedVqlSx/v37u9sAAED45apwnKqtn3322W4t0xEjRriCL/fcc49L1i+88ELWZwQAIAa0bNnSxowZ4+aSlyxZ0l544QVr3bq1u33atGnRPjwAAGJKrpL0WbNm2dVXX+2GvCtJV4u7qrAqYKt3XUVeAABA/rVy5Up74IEHrF69enbeeee5oq0q6qYVVtq1a2ennnrqQeugAwCACBeOGzVqlL3yyitubrqCs34+88wzXc+6KJC/9957VqpUqVwcGgAAiJZJkybZk08+aVOmTLGmTZu6+emqQRNYLHbkyJH2888/u+sBVWcHAABRStIVtFXpVdXdtbRKMMWLF3dLqwEAgPxHS53Wrl3bzT0/7rjjMt3uzjvvtIYNG0b02AAAiGU5Gu5+/PHH21133RU0Qb/44ovTfr7iiityd3QAACAqmjdvbtdcc03QBP3BBx+0uXPnup+7detm1apVi8IRAgAQm3KUpGe2bqmWYGNNUwAA8r/vvvvuoLXLPd98842tX78+4scEAEA8yNZw961btwb9WQ4cOGA//vijKyIHAADyp127dllKSort27fP/Zwx3itx1xx04j0AAD5I0suVKxf0Z0+BAgXskUceCc+RAQCAiNPSql9//bX7WVXcgzn99NPduukAACDKSfrMmTPdd62L6v3sKVy4sNWsWdMOO+yw8B4hAACIGK2Hvn37dhs0aJBdeumlrg5NYGN8xYoVXbwHAAA+SNK94jFLly51FV8BAEBsadCggfv+8ccfW5kyZaxYsWLRPiQAAOJKyEn6xo0b3fcKFSpYyZIl034PRtsAAID8RT3oe/fudcl5YmKi7dy5030Fo200ig4AAEQpSdfwNq+Cu/dzZrQNAADIX8477zw3H33KlCk2fPjwtLnpwWibTp06RfT4AACIByEn6b///nvQnwEAQGx4/vnnbceOHVavXr20nzOjbQAAQBST9GbNmgX9GQAAxIYjjjgi7WdNbQMAAPlgTnoomJMOAED+nZMeCuakAwDgkznpoWBOOgAA+XdOeiiYkw4AgI/mpAMAgNhzqHnogZiTDgCAj+akAwCA2J6TDgAA8tE66Yean86cdAAA8vc66cnJyVnOT2dOOgAAeYN10gEAgMM66QAARB/rpAMAAId10gEAiD7WSQcAAA7rpAMAkI+S9Ix27dpl48aNswULFrjfjzzySOvZs6cVL148nMcHAACiREuqfvLJJzZjxgzbunWr1alTx7p37241a9bkbwIAQB4pkJMHzZo1y+rWrWvDhg1zS7Ppa+jQoa4F/rfffgv/UQIAgIhSUt66dWu76KKL7Ntvv7XFixfbs88+a/Xr17cXX3yRvwYAAH7qSR8wYICdffbZNnr0aCtWrJi7bffu3Xbttdda//79bebMmeE+TgAAEEHDhw+3lJQUW7ZsmVWpUiXt9tdff91dB5xzzjlWsWJF/iYAAPihJ33evHn20EMPpSXoop91m+4DAAD5m+L5Lbfcki5Bl0svvdQaNmxof//9d9SODQCAWJajJF3D2teuXXvQ7bpNw+ABAED+llms37dvn23cuJF4DwBAtIe7azi754YbbnCFYx599FFr2bKlKyyjeeo33XST3XzzzXl1rAAAIA9pePv+/fvdz1dccYV17tzZFYTt1q2blSpVyvWeP/jgg9axY0erVKkSfwsAAKKZpCcmJh502+mnn37Qbf369bO+ffvm/sgAAEBEKRn/+uuv092mWjPBaNh7p06dInRkAADEj5CT9GnTpuXtkQAAgKgaNWqUq+oeiiZNmuT58QAAEI9CTtLbtm2bt0cCAACi6uijj+YvAABAfiwcBwAAAAAAfLJOuowfP97effddW7Fihav0Gmj27NnhODYAABBFKgo7evRoW7x4sSUlJaW77/nnn7dWrVpF7dgAAIhVOepJHzlypA0ZMsSOPPJI+/XXX+2CCy6wWrVq2Zw5c6xNmzbhP0oAABBRv//+u7Vr185KlCjhllw77rjj3Ndff/1lFStWtKpVq/IXAQDAL0n6c88953rRH3jgAff7HXfcYR9//LFL3tWzDgAA8rcXX3zRLav67LPPuob4iy++2F566SX74Ycf7I8//nCJOgAA8EmSvnTpUmvdurX7uVixYrZjxw73s5Ze+/7778N7hAAAIOICY72WYfVifYsWLVzSrkQdAAD4JEnXHPQiRYq4nxWoNeRdVq1aZYUK5XiaOwAA8ImUlJSgsV7XAOvWrSPeAwCQR3KdUV966aXWvXt369y5s3333Xd27rnnhufIAACAL/Tu3ds6dOjg5qMvWbLEJe+skw4AgI+S9DVr1qT9PGzYMKtcubJNnz7dFZMbPHhwOI8PAABEgWrPqGicaNj7pEmTbOLEiVa7dm277rrr0nrZAQCAD5L0KlWqpP2ckJBgl19+ufsCAACxoVy5cul+79ixo/sCAAA+He6+a9cuGzdunC1YsMD9ruXYevbsacWLFw/n8QEAgChJTU21Tz75xGbMmGFbt261OnXquCluNWvW5G8CAICfCsfNmjXL6tat64a6ax1VfQ0dOtSOOOII++2338J/lAAAIKKUlGuY+0UXXWTffvutLV682C3HVr9+fbc8GwAA8FFP+oABA+zss8+20aNHuyXYZPfu3Xbttdda//79bebMmeE+TgAAEEHDhw93Fd6XLVuWbprb66+/7q4DzjnnHNZKBwDALz3p8+bNs4ceeigtQRf9rNt0HwAAyN8Uz2+55ZZ0Cbq3qkvDhg3t77//jtqxAQAQy3KUpGtY+9q1aw+6XbdpGDwAAMjfMov1Wid948aNxHsAAKI93F3D2T033HCDKxzz6KOPWsuWLV1hGc1Tv+mmm+zmm2/Oq2MFAAB5SMPb9+/f736+4oorrHPnzq4gbLdu3axUqVKu9/zBBx90Vd4rVarE3wIAgGgm6YmJiQfddvrppx90W79+/axv3765PzIAABBRSsa//vrrdLep1kwwGvbeqVOnCB0ZAADxI+Qkfdq0aXl7JAAAIKpGjRrlqrqHokmTJnl+PAAAxKOQk/S2bdvm7ZEAAICoOvroo/kLAACQH5dg82zatMn++usvNyddlV7Lly8fviMDAABRl5SU5GK9etjr1Kljhx9+eLQPCQCAmJaj6u7JyclujlrlypWtTZs2duKJJ7qfdZvuAwAA+d/TTz9t1atXt+bNm7ticbVr13bfFy9eHO1DAwAgZuUoSVcF92+++cYmTpxoa9ascUu06GcVm8ludffPPvvMTj31VGvWrJn17t3blixZEvJjn3vuOWvUqJE9/PDDOXgVAAAgMx9++KFbJ/3ee++1pUuX2ubNm2369OlWtGhRO+uss+zAgQMhnzxVhe/Zs6eL9SpO9+WXX4b82D///NOOOuooO+200/hjAQDiQo6S9LffftveeecdO+ecc6xKlSquF10/v/vuu+72UH3++efucVri5ZlnnnG98O3atbMtW7Yc8rFz5sxxybkes27dupy8DAAAkEWsV4J+7bXXuh70cuXKWevWre3jjz92cXru3Lkhnbv169e7ujYJCQk2ZswYF+fPOOMM19gfylD7iy++2C0Dl51GfAAA4i5J37FjR9A5abpt+/btIe/nnnvucS3rWl9dQ+bHjRvnkm71kGdl165dLmgr2OuiAQAAhFdmsb5IkSKugT7UeK8h8wULFrQ33njDTZG77bbbXE+8GgAORQ0Ep5xyivsCACBe5ChJb9GihY0YMcIVjPPo5+HDh9uxxx4b0j527txps2bNsq5du6YL/Fpz9dtvv83ysYMGDbKTTz456DrtAAAg9xTrlWCrYTzQp59+6grJaQh6KBTTNWJOibpH8fvHH3+0vXv3ZtmT//PPP7vrDQAA4kmOqrs/9thjLrl+7733rGXLlu62mTNnumHnX3zxRUj7+Pfff11iX7Vq1XS3q3U+qyF0b731lv3yyy/266+/hny8e/bscV8er/U/JSXFfeUH3nHml+PNS5wLzgPvB/4vYu0zwo/HOmTIEFdvplatWnbSSSdZ6dKlXXI+Y8YMe/TRR0MeybZy5Uo3Wi5jrN+/f7+raaP9Z6Sh7YMHD7avvvrKihUrFvVYr/n3iYmJVriAWaGE0Ofi5ydFCia412iFilqKFY7Ic3rPE6nnSy1YxL3GIgWLWKHUXC1w5FuFEwq716j3rB8/V5C38mP8izcpIf5tcvQJpeFqKgLz7LPP2rx589w8s0suucQGDhzo5qeHQsHZ6z0PpII0+/btC/oYVZO97rrrbMqUKf8FkhBp7nqwYXWTJ09289zyE712cC54T/C/wWdEbH1eau613xx22GE2e/Zse+mll1xirnnorVq1cr3r6mUPleJ9sFgvweK9LmB69OhhQ4cOtaZNm/om1o8fP/7/f9poMalSNbu23X+v8fMIP/WUcpdE5olam41v/f8//689J7bUMOszvo+tWrXKfSE+5af4F2+SQoz3CamBY9az0bo+atQoyw21nqsX/aOPPnJz0zx9+/a1hQsXugqyGenCQJVmA1vdly1b5oJvpUqVXINB4HC6rFrXa9asaRs3bnQ9A/mBLlr0D6chg4ULR6bF2a84F5wH3g/8X8TaZ4TiUoUKFWzbtm2+iUtqiNdc8AYNGuRqP6rorsZ91ZEJHMqu2jJae71MmTIHNcjXr1/fPa86AWTDhg1umpzWaVfdmg4dOkQ01qtYbfv27a3jTWOsXM36FovWLPrDpo+50V7s29Quapl+lGNeUQ+6EvTOW96ywpb3PX/TF62xrk/+YW2GtLG67etaLNr0zyabdPMkmzp1qh1zzDHRPhxEWH6Mf/Fme4jxPkc96c8//7ybI5axZTw7NNStRo0abr5ZYJKu5FxvrGB69erl5qwHOvfcc+3444+3YcOGBU3QvRZ7r9U+kN68+e0NnB+POa9wLjgPvB/4v4iVzwg/HqdWYFHhuNwm6ZoWp1gf6KeffrJ69eodlKCLnnPBggXpbhs5cqQb+q5l4ZR4RzrWFyhQwBW2TTlgti81R+V8fG/v/lT3Gm3fnogkzIH0fJF4zoT9e91r3Lt/r+1LCD5qM79LSU1xr1HvWT9+riAy8lP8izeFQ/y75CjSaLhbKEunHEr//v3dMDoNnZdXXnnFtaBfeeWVadvceeedaUl82bJl3brogV8KyJoXp58BAEB4hCvWK6arJ3rChAnud416U6X3AQMGpBuSrjiu2ja6gMkY6zX0Xh0D+rlEiRK5PiYAAPwsRz3pGmamYWoKsEceeeRBPeq6LxSab7ZixQo7+uijXWu65qa99tpr6eagrVmzhrVRAQCIsOOOO866d+/uhpprbfOSJUumu79jx44h1aHRaLcXX3zRXTOoroyGuCtxv/7669MN/1u0aBHFjgAAyGmSrrnhhQoVcr3gwYSapGsfL7zwgqsWv2nTJjf8PeMQgAceeOC/4VeZ0NC3/Fb8DQAAv1NirZj82Wefua+M3n///ZCLxfbr189NWVMhK83FK1WqVLr7Tz31VDfEXVPhgrnxxhvd6DsAAOJBjpJ0FWEJJ02az2zifGYB21O7du2wHgsAADC3zKpqy3rF23JLo+5U+C0YJe1ZTVurWLGi+wIAIB5ka066hqP17t3bzQ3X8HT97K1DCgAAYsPw4cOtWrVqbrnTtm3b2vz586N9SAAAxI1sJem33367q66qOWVahk0l/u+44468OzoAABBRWhr17rvvdqun3HPPPW5ZM81NP3DgAH8JAAD8Ntz9448/tg8++MBat26dNoesZ8+eNnr06Lw6PgAAEEGK9VpZxWuEHzx4sJtatmTJErdsGgAA8FFPugq+aL3TwIqt//77b14cFwAAiIKMsV5Lnh111FHEewAA/Jik79+/3woWLJiuOrtuAwAAsUHLoQbGei/e63YAAODD6u7NmjU75G2zZ8/O3VEBAICo0Zrmgeui//PPPwfd9vzzz1urVq2idIQAAMSubCXpKhiXUYcOHcJ5PAAAIIrOP/98W7Ro0SFjfbly5SJ4VAAAxI9sJemjRo3KuyMBAABRN3DgwGgfAgAAcS1bc9IBAAAAAEDeIUkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwiULRPgAAAAAAQP6zYsUK27hxo8WyChUqWK1atSL6nCTpAAAAAIBsJ+iNGjey5KTkmD5zicUTbeGChRFN1EnSAQAAAADZoh50Jejtb2lvZWqWicmzt23lNps6Yqp7rSTpAAAAAADfU4JeoX6FaB9GTKEnHQDyyXytaMyJAgAAQGSRpAPwdXKa3xNTN1+rUWNLTk7K9b4SE4vbwoUL8vX5AAAAQNZI0gH4OjnN74mpm6+VnGSt+t1tpavWzvF+tq9ZZjNeuTfic6IAAAAQWSTpAHybnMZSYqpzcFithtE+DAAAEKNLlB04cMB9nzNnjhUoUCDPn2/BggV5/hzxiiQdQJ4iOQUAAPGaoDdu1NCSkndH5PkSExNt/Pjx1r59e0tOjsyyaGoKSNqc+yl9SI8kHQAAAADCTD3oStDHXtXMGlctmefn90CBIrbKzKYOPcEKHNib58/325INduWbi2zvrrx/rnhDkg4AAAAAeUQJeovaeb+OeIoVdkn6MbVKW2FLyfPn25W0K8+fI17l/WQFAAAAAAAQEpJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfKJQtA8AyGjFihW2cePGTE/MgQMH3Pc5c+ZYgQLB25kqVKhgtWrV4uQCAAAAyFdI0uG7BL1xo4aWlLw7020SExNt/Pjx1r59e0tOTg66TfHEYrZg4SISdQAAAAD5Ckk6fEU96ErQx17VzBpXLRl0mwMFitgqM5s69AQrcGDvQfcvWLPTer0w2+2L3nQAAAAA+QlJOnxJCXqL2mWC3pdihV2Sfkyt0lbYUixWhWPYvzD0HwAAAMg/SNKBGB72Lwz9BwAAAPIPknQgRof9C0P/AQAAgPyFJB3wMYb9AwAAAPGFddIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ9gTjpi1oIFC3K9D5YvAwAAABBJJOmIOXv27nHfe/Xqlet9JRZPtIULFlqtWrXCcGQAAAAA4PMkXetBv/7667Zu3Tpr0qSJXXbZZVa0aNFMtz9w4IB98skn9tNPP1mhQoWsbdu21q1bt4geM/xt37597nvzPs2tRssaOd7PtpXbbOqIqW45NJJ0wD8UN/R/mVWckDlz5liBAsFndTFKJrKSk5Pt1Vdftfnz51vVqlVdrK9evXqWj5k1a5Z98cUXtnnzZjvqqKPskksusWLFikXsmAEAiMskXcH6xBNPtA4dOljr1q1t9OjR9sYbb9h3331nhQsXDnrhpUS+QYMGdsIJJ1hSUpL16dPHJel6HBCoZJWSVqF+BU4KEGMJeuNGDS0peXem2yQmJtr48eOtffv2LjkMpnhiMVuwcBENcBGwZ88eO+mkk9x3JdrTpk2zJ554wqZPn27169cP+pjBgwfbjBkzrEuXLlajRg175plnbMSIEfbzzz9buXLlInHYAADEZ5J+6623WosWLez999+3hIQEl3DXqVPH3nzzTevXr99B22ubDz74wCXpHgX+k08+2W644Qb7P/buA7ypuv3/+N1CgSJThuwpSxAEUQEBQcWtiIooICgCiuJeOIHH7ePCBc7HwRAnThwggiIgDoYKCMjee7XQQvu/Pl//J7+0pCUtbXOavF/XlatNcnJykia9z/0d9/e4444r4FcAAChI6kFXgj564HHWpGqpkNukxRezNWY2bUhbi09LOej+Bet2W+9X5jBKpoC89tprrkbI8uXLrUKFCnb77be7Bvp7773X3nvvvZCPGTx4sD3//POB6wMGDHDnBy+//LINGTKkoA4dAIDYStJTUlLs66+/dq3jSr5FQ+A6d+7shrNnlaQHJ+jSqFEj91PD5QEAsUEJeqs6ZUPel2oJLklvUauMJVhqgR8bMlJMV4+4EnTRFITLLrvM7rnnHjdCLtSUBC+2e0qXLm3VqlUj1gMAYkLRSA5ZTE1NtTp16mS4XS3lGgoXrldeecVKlSplJ554YpbbaIidLp6dO3e6n3p+XQoD7zgLy/Hmlk7YNFRVPWE60Q7Fuz2r+9OLFHP7KFakmBVNz/1HPCEu4d9jSUvL0fu+evVq27Jlix2ORYsWHfb7IHp8bl5DXv4tE+LNisb9O0c4p/TYQx2/378befE+hPNe+P198NP/iEh+L7Lil+PID0uWLLGuXbseFOs1ZW3dunWHnJsuGho/f/58e/jhhyMS6/Pqe+xnxYrEuddoRYtnG1fyUjhxLC/l1fmBn+X23AWRi1l5ie+U/79T4e4jLj09Pd0iQMG2efPmLvBqPnrwEPgPP/zQBfVD+fLLL+2CCy5wiXqonnfPsGHDbPjw4QfdPnbsWCtZsuRhvAoAAA6fEtaePXvajh07rEyZMlH1lioJ13B1xWLPlClT3FS1xYsX29FHH53t49esWePOE9q1a2fjx4/PcjtiPQAgWuJ9xJoRvYPatm1bhttVxTWcE5TJkyfbJZdc4lrVs0vQ5e6773Zz1oNb12vWrOmG3xWWkyG1unz77bfWpUuXkEX1ooWqMavYk+aSaqhqVq2E35bvZV22jQk5lHXGonV21oh51u7mdlavY71cH8uWpVts4h0Tbdq0adaiRYscHX/rK4ZY6aNyv2zblhWLbd77z9irVzW3S0+omqv3wR3Pyp3W8bEZ9uqrrx40fDQnNExVxZtywnsvOt/+kpWvGbo41KFsW7XYpjx5XbZ/A79/N/LifQjnvfD7++Cn/xF59b3I7XcjFK/XNxopzoaK9d592Vm/fr2ddtpp1qxZs0MWiM3PWJ9X32M/W7dons146bZs405eCyeO5aW8Oj/ws9ycuyCyMSsv8Z3y/3cq3HgfsSRdgVNBUxXeg5dQ+/PPP10wzo5a4NWDfv/997ue90PRkm6hlnXTiWxhO5ktjMecE5qbqGrMKvZ0qICt+0NtE3cgxe0j5UCK7Y/7dzm23EhNT/33WOLjw37PveMvUamWla6R+5P/nbuS/q1KvX9frt8HSd27y+3ncNeMz8168d57kZpmtj899DJYh6LHhvs38Ot3Iy/eh+D3QlMhQs3h9ZYd0//UrJYdi4alx/Lif0RefS9y+90IxY+f3byimK7PZTDF+kqVKlnlypWzTdBVp6Z27dquaGx2y7Pmd6zPq++xn6UcSA877uS17OJYXsqr8wM/y825C/wRs/IS3yn/fqfC3UfEknS90B49etgbb7xh1157rR1xxBE2e/Zst7yKkm+P1lD/559/AsPVp06dauedd57dd999rtUcQP6uGc968f6Quu/fKuVZJZbhLDsmLD2WN98L4bsRnssvv9zFe01z0zKq6kVQr7hu9/z66682cuRI++9//+uWWNu4caMbDq8EfcKECayPDgCIKRGtmvHII4+4YWwaOqD56RrCPmjQoAw96yoip8RdSfru3btdgq5CcUuXLrX+/fsHtrvyyiutffv2EXolgL+xZnz0JJYPXtTQzjm2co6XHROWHsuI70XBuOiii6xv376uAUmJ92+//eamCQTXilm2bJm9/vrrbl65kvQrrrjCjRrRMq1ajs3TunVr17APAEA0i2iSrmGXv/zyixu+riXUHnjggYPWOlfyfc455wSGBzzzzDMh96VhcwAQ7epWLBly6TGWHYPf10q/7rrr3DB3NcZ36tTJihYtmiH5Vo2AI4880l3Xtt27dz9oP6oKDwBAtIv4+hNKvFXUJSvBveOaaxbcew4AAAoH9YrrEoqWYw2O75mXbAMAIJZEPEkHABSsBQsWHPY+CnsBOgAAAL8iSQeAGLEvZZ/76aeq5gAAAMiIJB0AYgRVzQEAAPyPJB0AYgxVzQEAAPwrPtIHAAAAAAAA/kWSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPlE00geAvLVy5UrbvHnzYe+nYsWKVqtWrTw5JgAAAABAeEjSoyxBb9yksSUnJR/2vhJLJtrCBQtJ1AEAAACgAJGkRxH1oCtB73hnRytbs2yu97Nj1Q6b9sQ0tz960wEAAACg4JCkRyEl6BUbVIz0YQAAAAAAcojCcQAAAAAA+AQ96QAAAAAKbcFjv1qwYEGkDwGFFEk6gEId5NLS0tzPuXPnWnx81oODWLEAAACfFTxu3MSSk5MsusXZuh17NSE10geCQoQkHb5qDaXFEcFS96W4n717987yjUlMTLRx48ZZx44dLTk565UNSiaWsAULF1EMEQAAvxQ8Tk6yk/oNtTJV61g02rRsoc0Z+7htT9of6UNBIUOSDh+2htLiiH/t3/9vUHvwooZ2zrGVQ74tafHFbI2ZTRvS1uLT/k3qM1uwbrf1fmUOKxYAAOAzStCPrNXIolHynmgfJYD8QpIOX7WG0uKIUOpWLGmt6oQeJpZqCS5Jb1GrjCVYKm+gjzHaBgAA4NBI0uGr1lBaHJGf8mI6BXPbc4fRNgAAAOEhSQcQ9fal7Dvk3PZwJZZMtIULFjK3PYcYbQMAABAeknQAMTO3vWWfllbjhBq53s+OVTts2hPTmNt+GBhtAwAAkD2SdAAxo1SVUlaxQcVIHwYAAACQpawXFQYAAAAAAAWKJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8omikD+DAgQP2008/2YYNG+zYY4+1Ro0a5ctjAABA5Pz111/uUrVqVWvbtq3Fx8fny2MAACjsIhrttm3b5oJur1697PXXX7fWrVvb7bffnuePAQAAkXPDDTdYmzZt7I033rAePXpYhw4dbOfOnXn+GAAAokFEk/R7773XBdw///zTJk6caF9//bU9/fTT9u233+bpYwAAQGR8+umnNmrUKJs6dap9+eWXNn/+fFu7dq0NHz48Tx8DAEC0iFiSnp6ebuPGjbOrr77aSpcu7W5r166dnXjiiTZmzJg8ewwAAIgcxeeOHTtay5Yt3fXy5ctb3759s43buXkMAADRImJz0letWmXbt2+3Zs2aZbhdc8x/++23PHuM7Nu3z108O3bscD+3bt1qqamph/lKzDZu3Ojmxx8OzbNLS0vL8n7dl5SUZD/88EOWc/IWL15sJUqUsF3Ldpntzf2x7Fq3y+1HIxa2bNkS1mO0rR6ze83fZqnJuX7u5PX/uP0s2pxi3/+9K+Q2afHFLKlpkv2wZLfFp6UcdP8fa1PcPpLWJtnm+Ztj9n3Iq/ciN+9DXr0Xfnof/P6Z4H0I/72I5Ochy33t2hVojI426gXv0qXLQXFbcXPTpk1WqVKlPHlMfsb6vPoe+1k4/2/zWjj/t/JSXn33/Swv/y/lN75XeY/vlP+/U2HH+/QImTdvno4sfcaMGRluv/POO9Pr16+fZ4+RoUOHusdx4T3gM8BngM8AnwE/fwZWrVqVHm2qVavm4nCw7777zr3exYsX59ljiPWR//xy4T3gM8BngM+A5Um8j1hPemJiovu5e/fug1oXvPvy4jFy991326233pqhV1ot6xUqVLC4uDgrDNR6U7NmTTeaoEyZMhbLeC94H/g88L2Itv8RalFXLKtWrZpFG8XnUHHbuy+vHhMNsT7WFMbvKuBnfKeiJ95HLEnXP+WEhARbsWJFhtt1vV69enn2GClevLi7BCtXrpwVRgpiBDLeCz4TfDf4HxF9/y/Lli1r0UjxOVTcVrKtpdXy6jHRFOtjTWH7rgJ+x3eq8Mf7iBWOUyA97bTT7L333gvcpnlm3333nZ177rmB22bOnGmff/55jh4DAAD8QfH5m2++cTVlvF6E8ePH21lnnRWosbJy5UobPXq0q70S7mMAAIhWcRrzHqknnzdvnp188sl23nnnubXPtRaqesqnT59uxYoVc9v079/fJep//PFH2I+J1uEranVRIZxYb23mveB94PPA94L/EYVHcnKyi9eK1X369HHLqk2aNMlmzJhhTZo0cdt88MEH1r17dzfsuUaNGmE9BoUf8RzgO4XQItoc3bx5c5szZ47VrVvX5s6da1deeaULxMHJtoL0+eefn6PHRCONIhg6dOhBQ/liEe8F7wOfB74X/I8oPDRE/ccff7TLL7/cxe/GjRu7n8HJdu3ata1Xr152xBFHhP0YFH7Ec4DvFHzYkw4AAAAAAP4PE7sAAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ+I2DrpCF9qaqqrXr9582Zr2bKl1a9fP2bfvn/++cdV923VqlXMFhA6cOCA/f77764KstYSbtGihcWqdevW2a+//uqKDx1//PF25JFHWizTMlbTpk2zZs2a2XHHHWexZNmyZe7/ZGY9evRwFcIB+IfKIc2ePdtWrFhhDRo0iLn/V0B+2Lt3r3311VcWFxdnXbt25U0u5EjSfW79+vV2+umnu+VoFMh++OEHGzJkiN1///0WSxYsWGC33nqr/f3337Z27Vp78MEHYzJJ//rrr+3mm292FZBr1qxpP/30k3sfPv3005hamk8neP369XMrO2jFBzVgqfLzU089Zddcc43Fon379tmFF15of/75p91yyy0xd9KrBH3AgAF28cUXZ7j9oosuIkkHfJZI6H+VGptbt27tGt7PPvtse+eddyw+ngGeQG7ceeedNmbMGLfaVZEiRUjSowBJus8pMVUv4S+//GIlSpSwiRMn2jnnnGNnnnmmnXjiiRYrdu3aZTfccIOdddZZVq1aNYtVSk6/+OIL14Mu27Ztc6MrHnjgAXv22Wctlt4HndS98cYbrsVYnn76aRs8eLD17t07sIxTrAVonfAmJSVZrCpbtqyNHj060ocBIBv6X61GVV2qVq1qixYtcqPj3nrrLbvqqqt474BcqFGjhs2bN8+ef/554mCUIEn3MfWef/TRR+4LpwRdlJg0atTIxo4dG1NJeiy91uyokSJY+fLlrWPHju4fcyxRb8ull16a4TZNA0lLS3O9NLGWpH/22WduiNtvv/3mhv3Hqv3797tGLDn22GOtVq1akT4kAJmot0/TUJSgi85pdG6j20nSgdy58cYbeeuiDOOKfGzx4sVuCKvmlwbTyef8+fMjdlzwD30+NAdZn4lYpPnoGiL52GOP2W233WZPPPGEVahQwWLJmjVrbODAge4EN9YaJ0I1bI4YMcJNe9D0II2s0KgLAP6psbNw4ULOawDgEOhJ97EdO3YEekuDqTiWiiQBmgKwZ88eN9Q5FqlWgaaAqKCgetdjraiiigj27NnTfQ401D2WqTaBPgdHHXWUuz5r1iw3ykQNWLFapwDwm927d7sRT6HOa7xzHgAAPem+lpiYGAhqmedne/chdt1zzz02fvx4+/zzz6169eoWizT/XFM/Zs6caXfddZd1797dli5darFCczhVKE5z0TQXW5edO3e62/S+xFqS7iXoctJJJ7n6Hfp+APAHb+oe5zUAkD2Gu/uYVxxMS5QE03XvPsQmVfd/4YUXXC+ykhGY61HWnGT1oMaKKlWquDoF33zzjZuTrotGVmikjVYCiHUa/q/K/wD8QR0MmovOeQ0AZI8k3cc0/KtNmzb23nvvBW5bvny56zU899xzI3psiBxVcte8WyXo7dq1i8k/xaZNm9zcxmBac1diqViYeoq9HnTvohPg8847z/Wyx9rc/GDbt2+3yZMn04gF+IzOX1QUV9N1RCtSaBlRzmsA4P8wJ70QLFXSuXNntya0hnOOGjXKzbO85JJLLJZorpoqWIuqd6uKtRISDfPt1KmTxQpV+tca8dddd53rLfVqE2jpqfPPP99ihQoPqZLpBRdc4NaLX7Jkib366qt2+eWXW/v27SN9eIiAm2++2a2H3rZtW/c/Qp+HcuXK2d13383fA/BZQ/MJJ5zgYpaquqsjQqNebrnllkgfGlBoaUTdxo0b3Wo/mk7iLUd60UUXWcmSJSN9eMiFuHRK3xaK4lj/+9//bMuWLW55pf79+1uxYsUslqxevdqGDBly0O1aI1xVvWPFK6+84qq5Z6a141XZPJasXLnSzbvWHHQN+z799NPtlFNOsVinIoL6P6EljmKJQtmECRNs6tSpFhcXZy1atLBevXq5xB2Av6xbt87FM/0f10oMKu6YuZgcgPCpA2fRokUhO3f4bhVOJOkAAAAAAPgEc9IBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAAqJJ554wpYtW5btNs8++6wtWrSowI4JQN4iSQeQ73r06GFDhw7lnQYA4DDdc889h0zAhw0bZvPnzy807/XmzZvtzTfftBEjRtjs2bMjfThAxJGkA8h3O3bssD179vBOAwBQAG655RZr3LhxoXiv586da40aNbK33nrL5syZY6eddprde++9kT4sIKLi0tPT0yN7CADyiwLeqFGjbPXq1daiRQt7/PHHrWnTpu6+tLQ0e+yxx2zs2LG2fft2a968uT388MPWsmXLwOM7dOhgvXv3tmuuuSZwW79+/ezII4+0J5980l3v2rWr26eS8MmTJ7v99urVy7X0x8XF2aBBg+z111+3okWLWsmSJd1j1Ep+8803u8fpub/66iv3vOedd5499dRTNm/ePIuP/782xP79+9v+/ftdKzsAALFM8fTzzz+30qVLuwS3QoUK1q1bNytWrFiG4e5nn322S369IfLdu3e3jRs32m+//Wbly5e3Cy+80EqUKBF4zKuvvmpbtmxx8bdatWrWuXNnq169eobn9vazatUqt5/WrVu7cwydF5xxxhkZtv3f//5n9erVs1NOOSXb19O2bVurWrWqffTRR+76F198Yeeff779/vvv7twFiEX0pANRSgn4jTfeaAMHDrRp06bZdddd524LHgr33HPPuYA7ZcoUq1OnjnXq1Mk2bNgQ2Gbbtm2WnJycYb87d+603bt3Z+glV/Jfu3Ztd9Kg/T300EP2/vvvu/v/+9//umT/qquusoULF7qLttXjdDy1atWy7777zgVznTAsXbrUvv3228D+lcSPGTPG3QcAAMzuvvtuu+GGG9yQdjWKt2/f3vbu3ZvlcHdto+T61ltvtb/++suGDx9u7dq1cw3gnl27drmYq6HnH3zwgR1zzDE2adKkDG+39nPxxRfbHXfc4RJ1PeeaNWtcg3xwv9/atWttwIABGW4LRQn+zJkzXWO859xzz3WNAzoGIFYVjfQBAMh7SqyVKCt5VnIsdevWda3qoqCqHusXXnjBzjnnHHebflcw1nywRx55JEfPpxZvBX5Rsq99KvG+9NJLrVSpUpaQkGCJiYlWsWLFDI/TkLYhQ4ZkuO2iiy6yN954w84880x3XT39ZcqUcb3sAADArHjx4vbDDz+4+KrEWkPbR44c6Ya5Z0W92u+99577XT3mSoS/+eabwHmAF8c9anRXMq4e7WBVqlRxvd0aLSfHHXecS96nTp3qGvu9kXxqkD9UL/qCBQvcT6/H39OwYcPAfUAsoicdiEJ//vmnG35++umnZ7jdC6hLliyxpKQk1/Lu0fA2Xc9NoRm1tgerVKmSOwE4FAX2zNSa/sknn9jWrVvddSXsffr0ccP7AACA2ZVXXukSdClXrpwr0KrEOTvqSfdoiLyS6OAq8er1/v77712yr0b+5cuXu/OJAwcOZNjP5ZdfHjifEDXAa+qb4rVHo+PUSRC8XSjeyLyyZctmuF2vKXjUHhBrSNKBKOQNX/MCeLj363pqamq2+w41dC14/nh224XqCchMrfA1a9Z0Peh//PGH/frrr24ePAAA+JfmcAfTHHINMc+O5rBnFfNTUlLs1FNPdcm/4q4a2tWYr/s1DD5zQ3yoBvYPP/zQTYlTD7+mrmlfh3LEEUe4n3pcME2J8+4DYhFdU0AU0rA39TzPmjXLDW/LrH79+lakSBE3hE3D0z0qAqM5ah61bGcOnGpZDxWgs6NjUUG5cKjV/eqrr3Yt8grybdq0sSZNmuTo+QAAiGYqABdM9WQ0DD23NN3tl19+cfPLNcVMVGdGw9bD0aVLF6tcubK9++67NmPGDFdErkaNGod8nDfMXSP8gs9XFP8vu+yyXL8eoLCjJx2IQhompoItmu+tRF292v/884/dd999gdZ09U7ruoa6qWf9+eefd0ufXH/99YH9nHTSSTZ+/HhXREbD3TQETol8Tmnem+aWhZuoq/Vdw+5feeUVetEBAMhk9OjRgZiq6W0q1qpEObc0tFyj21Q/RnTe8Nprr4X9eDWw67zipZdecscS7gg4DbnX1LfgxgAVs1WHgCrWA7GKnnQgSmn5FS15pgJs+/btcy3aKgrnefrpp13Fd/VSKxgrkVZgVbEWj9Yp1ZBzDaNTATgVnlOxt5xSBVoVhFPjgE4AtARbdtQboEJxqvJOSzoAABmtW7fOLZF28sknux5vxWjF2txSgq/l2FTLRsXetCpM8Hz1cCgxV9V4dRRojnq4lNjr+ZWUq8jt22+/7c5PTjzxxFy8EiA6sE46EOXU0q5q71nN7VIvuu7PPFctmJJ8zV3T3HPNTdNPb3+aN6b7vDXQvVZ9PW/mfWp+my5aT1X7yfy4YKo2q6FzrI0OAIBlaEBXQdXFixe7ddIVU3v37p0h5ipZ1lJpzZo1Czymb9++GRri1Zh/wgknuERfNGpu3Lhx7qcKwmo03ahRo9xybt566qH2E0yPU8Id3CkQjpUrVwbmtGvd9MxrrgOxhiQdgO9oXpxODtTj3qpVq0gfDgAAOASNvGvevLmbrta0aVPeL+AwMNwdgK+oFV4FZDSfngQdAAB/0zrtL774oisad+mll2ZI0OfNm2dffvllyMepQJ2GtQM4GEk6AF/58ccf3VD6UMuzAQAAf1FhWRWeu/baa93a6JmnyymJDyWcpVqBWMVwdwAAAAAAfIIl2AAAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyhqMSgtLc3Wrl1rpUuXtri4uEgfDgAgxqWnp9uuXbusWrVqFh9P+3leINYDAAprvI/JJF0Jes2aNSN9GAAAZLBq1SqrUaMG70oeINYDAAprvI/JJF096N6bU6ZMmUgfDjJJTU21b775xs444wxLSEjg/QHyAN8rf9u5c6drPPbiEw4fsd7/+L8E8J2KNTvDjPcxmaR7Q9yVoJOk+zNolyxZ0v1tSNIBvlexhClYef9eEuv9i3gP8J2KVXGHmHLNxDcAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdick46gNhz4MABN/8xVum1Fy1a1Pbu3eveCxQ81dgoUqQIbz0A5OPyVvv374/ZOEesjzzFeZ1vHW6NGZJ0AFFv9+7dtnr1ahe8Y5Vee5UqVdyqFhQniwy971pupVSpUhE6AgCIXikpKbZu3TpLSkqyWEWs9wcVwK5ataoVK1Ys1/sgSQcQ1dSargRd/zArVaoUswlqWlqaa6xQghgfz0ynSJw4bdq0yX0WGzRoQI86AORxjFu2bJn731qtWjWXHMVivCfWRz7Wq7FI8V6fR8X73J5zkaQDiPqhX/qnqQQ9MTHRYpUCtwJHiRIlSNIjRJ/B5cuXu88kw94BIO8ovinOaf1pNcrHKmJ95OlcU9PbVqxYETjvyg26UwDEhFhsUYe/8BkEgPzFSDFEy+eQJB0AAAAAAJ8gSQcAZEtDturUqePmU/thPwAAIO916tTJxowZ45v9xDKSdABAtjSHWgm2lrUJ19KlS11Cvn79+sPaDwAAKBhqRN+1a1eOHtO+fXsbP378Ye8HGVE4DgCQ50Il5EraVe1Uy5ABAIDCL1RCPnXqVCtTpkzEjika0JMOAD7z119/uYT2q6++sq5du9oxxxxj3bp1s8WLF2fYTklw3759rXHjxta6dWt75JFHMiTFwfu58MILrU2bNnbRRRdl2M/06dPdNsHU+63b1Bseitag1f26HH300Xb66afbuHHjAvdv27bNTjvtNPd727Zt3XZXX321rVmzxg2BC+5dD/c1TJ482R17s2bN7IILLnC3AwBQmJ1wwgn26quvWv/+/e24445zvdKff/55hm327t1r999/v7Vs2dKdDwwYMMCtBx9qP7qvQ4cO1rFjx4P2o3it5DnYmWeeaW+88UaWx9e9e3cXg+vWrWvt2rWzoUOHWnJycuB+xWMl6UOGDAmcE0ivXr3s008/zfFreP31123QoEFuO72ODz74wGIVSToA+IyW7FDyetVVV9mVV15p77zzjhUtWtQlvl5w1M9TTjnFdu7caaNHj7Zhw4bZSy+9ZLfddlvI/SgRHjVqVMj9aJtgSpJ1m3rDs1pe5Pvvv3eXiRMnuv1fd9119uGHH7r7y5Yta2+//bb7/f3333fbPfroowf1rufkNVx//fUuqKsxQM+vxguGzQMACrNVq1a5+Kb1tBU3zz33XNcg/fPPPwe2GThwoIt9jz/+uP3vf/9zI9LUOB4cA4P3M3LkSDvnnHMO2o+WAA1OsEWN54rBWXn++eddDP/uu+/soYcecon/jTfeGLj/5ZdftipVqtgdd9zhtlODeqje9XBfw0033WQtWrRw5wTqnOjRo4f9+eefFpPSY9COHTvS9dL1E/6TkpKSPmHCBPcTOFzJycnpf/31l/tZWPz+++/uf9Trr78euC0pKSm9YsWK6aNGjXLXX3zxxfQKFSqk79mzJ7DNRx99lF60aNH09evXH7SfAwcOpG/bti199+7dGfbz7bffum2CrVq1yt22YMECd33x4sXu+rJly7I85mHDhqWfc845get6rB6jfXky7ycnr0HH6dHjdduiRYvSo+WzSFzKe7yn/ke8RyzHejnqqKPSu3fvnuG2iy66KP3CCy8MxM24uLj0qVOnBu7fvHlzesmSJdPHjBlz0H68WK+fwfuRIkWKpE+cODHDczVt2jT9mWeeCVyvX79++siRI7M83pkzZ6YnJCSk79+/P3Bb7dq101999dUM2wXvJyev4bbbbsuwn0aNGqWPGDEivbDJi3hPTzoA+JSGennUe6zh4HPnznXX58yZYyeeeKKVLFkysM2pp57qWqUzDwXPbj+59cknn7gWfw1b0xC35557zrXS50ROXsOxxx4b+F2t9rJp06bDeg0AAERacIwWDVX3YvS8efOsSJEibhi8p0KFCta8efOD4nh2+8kt9WL36dPHWrVq5Ya8X3zxxW5U3Nq1a8PeR05eQ3Cs9+J9rMZ6knQA8KlixYpluF6iRAk3r0v0s3jx4hnu965724Szn9z4+uuv7fLLL7fzzjvPDV+bMmWKDR482Pbt25ej/eTkNSjAZ5aersZoAACiN9YnJCRYfHz8QbEyv2P91q1bXR0ZFYDTEPpJkyYFprLlJN7n5DUQ6/8PSToA+NQff/yRISGdP3++m28mDRs2dNeDE1WvRdrbJpz9lC5d2v3cvXt3YJuVK1ceMknv0qWLK+6iuWNqXc/cqu4F2uwS6Zy8BgAAolFwjPZ6noNjveaRBxd8VU/2ggULso31mffjxfvgWJ+WlubmpGfll19+cfPKNS/9pJNOsvr167vCsJkp3h8q1of7GvB/SNIBwKceeOAB27x5swt+Tz/9tAumKgAn/fr1c5VRn3zySXf/9u3bXeGWs846K1BdNdR+nnnmmQz7UVX1I444IlDdVftRBdbsVK9e3Q1V37hxo7uu6vFe63rwELW4uLiDKtIHy8lrAAAgGr311ls2a9Ys9/vMmTPd9WuvvdZd1/Q0rcxyyy232J49e1xirZiuJFcV1MPdj7cvFaLVY7UfVWpX3M1KtWrVXI+5VxFexd7uvffekNtlF+tz8hrwf0jSAcCnVJ21UaNGVq5cOVcdXcFVwVD087333rMXX3zRVVOvXLmyGzoWaikV7adJkyZWu3Zte+yxxzLsR49VdVYl5hUrVnRJe+Z5bZmpB11z0WvWrGlHHnmkG+p+6aWXZthGLfZKuPXctWrVckuwZZaT1wAAQDTq2bOnS1Y1T1vzyJVYX3bZZYH7x44dazt27HDxVrFSy5J99NFHVr58+YP2c8UVV1i9evXcMPXM+1Ej/cKFC91+FG/V0K7e8axoyVOtuqJl2rS9lkVTTM9Msf6VV15xMT2rBvZwXwP+T5yqx1mM0VID+oDow6J5FvAXtax9+eWX7h+B5rAAh0PzndT6qyHZmp9VGKiXWsFQxVKUOG/YsMEFby2fForWHVfxtcz/z4L3o8C4ZMkSV+Qt87w1Ucu25p/p+Q4cOOCWQlGPub6DKuSm5VRq1KiR4Rj0v1St7JUqVXJD4nRdjwmm+3UioORbxxBqP9m9Bi3BpqH0SvS9+WwKW1qWrWrVqgfNaS+sn0XiUt7jPfU/4j1iOdZ7o840mqx3795uKLnis0a3haJeb8VEJcxZ7UeJuqas6ZzBm86W2ZYtW1zjv4apK76WKlUqEHs10k6/Bz9Wz6nH6HkVf0PFcZ1D6FxF26pDINR+DvUadN6h84Tg179+/Xr399TxFiZ5Ee9Dn/EBAHzjqKOOyvZ+r9r5oSgoZpXoKwFWgi4K3ErmPXpM8HVPcHBRIA51QqAkWj3unlD7ye416IQl82M0jD6r/QAAUBgdqlc53ERV22WV6IsSeI83qs6TuaHdi8NqFPeEir86hwjeJtR+vGPLSvC5Qk7Pb6IRw90BAAAAAPAJetIBwGc031vDpDTsyw/7AQAAeW/27Nl5EqPzaj/wD5J0APCZUEO8D3c/mi8GAAD8I9QQ78PZD7E+ejDcHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHgBg3YcIEGzFixCG3e/LJJ+3XX3+1WHT33XfbjBkzst3m3nvvtaVLlxbYMQEAEK6FCxfatddee8jtPv/8cxs9enRMvrFvvfWW/e9//8t2G703eo/yG0uwAYhJK1eutM2bNxfY81WsWNFq1aplfjRz5kz75Zdf7Kabbspymx9++MGef/55u+GGG6ywe+CBB6xLly7WoUOHsB/zzjvvWKNGjaxt27ZZblOuXDm7+eab7bPPPsujIwUAFKZ47+dYv3r1anv55Zdt1KhRWW6zY8cOGzBggE2cONEKu7Fjx9ru3btt4MCBYT9mypQptn//frvqqquy3OaYY46xc8891xYtWmRlypSx/EKSDiAmA3bjxk0sOTmpwJ4zMbGkLVy4wLfB+1CGDx9ugwYNsuLFi1s0BO5q1arlKEkPh04E1ADw+++/W8uWLfN03wAA/8f7wh7rR44caU2bNrXjjjvOCrtp06a5xpmcJOnhaNWqlTVo0MA1eNxxxx2WX0jSAcQc/dNWwD6p31ArU7VOvj/fznXLbdYbw93zhhu4x48fb9u2bbOTTjrJvvzyS/d7t27d7OSTT86w3Zo1a1wv79q1a61evXrWt29fK1++/EHbvP3227Z8+XJr3LixXXnllQdtkx0N4Z48eXKGIWDhHJ+3TZs2beyDDz5wrdOPPfaYu08t0O+9955t377dmjRpYr1797YSJUpkGF6uluoNGza4Xn7d169fP6tevbq9++67Nnv2bJdoX3PNNRlasp9++mn7+++/LS4uzqpUqWKnnXaatW/fPnD/E088YZs2bXKJ+pw5c9xtXq+CHqfj3LJli3sdF1100UHvhXrJNey9ZMmSdsUVV1jt2rUD95UtW9bOOecce/XVV+2ll14K+/0FABT+eJ+bWD937lwXW4cMGeJi24oVK6x169bWs2dPF8c8e/fudbH+jz/+sCOPPNIuu+wyN7ormLbRcG01FCv+XX755QdtcyivvPKK3X///Tk6Pm+be+65x9544w1btWqVm/6lGK1zAA0P13lEzZo1rUePHlajRo3A/nW8aWlp7ji/++4715N/8cUXu/OGH3/80Q0rL1asmPXq1SvDa1FP/yeffBKIvUqcu3fvbvHx/87k/vTTT90IQL0n3hB/jXTTOZDOO8aMGWOLFy+2hg0bunMixfRgeg91bqOeeJ2LBJ9HiN7bZ555Jl+TdOakA4hZCthH1mqU75fcnBgouAwbNswuueQSS09Pt+TkZOvUqZNLfD0LFiywZs2a2fTp013QU4LZokUL27hxY8htFDA//PDDg7Y5lG+//dYFV11ycnzeNgquCoAaIiY6Tg0b14mMkm4l6wqwCoYenYwoUCuQavigjuGEE06wrl27ujn03us944wzMhzr0Ucf7XoAmjdvbklJSXbBBRe4YfoetX5rNIBei7bzegt03Mcee6z99ddf7uRGSfztt9+eYd/qJX/ttdesQoUKNmvWLHfMmYdQqnf+66+/Dvu9BQBER7zPTaxftmyZvfjii67BW8mtN21KDdCelJQUa9eunT333HMuPi1ZssTFODWeZ97mhRdesMqVK4fc5lD0GB1PcGN7OMfnbaPHbd261cXSxMRElwTrdyXbarTQnHgdkxreg4eXqwFAU+mUjKsRXfvp16+f3Xrrra5BQselhgF1RniOOuqoQAxXkq5zDXUUeKpWrepite7ztitdurTrINC5iBoctI3OkU4//fQM74MaC/r3729FihSxPXv22KmnnmpfffXVQbFer0+vPb/Qkw4APqUEUImuEktRQFGrrXp4ExIS7M4773TJrjcH+pZbbnGB6KGHHnLBXLxt1Kq8c+dOFwyVXAZvcygKYuqlz+nxiRJvtbIroMquXbtc8FPyrdZp77hPPPFEl0yrB92jQP3RRx+53zVCoFKlSq7XXI8VNRDUqVPH9SyoIUKUlAfTPvr06WODBw92rf4K4jpGBVivdd2bg/ef//zH7rrrrgzDJIMdf/zx9vHHH7vfdZKiXnQ1FAQX4qlfv779888/tm/fvqiYGgAAyF8aZXbfffe5OCRqfFbsUuKqJFdD0BWPFFu8kWNKOHW/GpbF20YJrWg7XYK3CSfWS+Z4f6jj87b573//axdeeGHgceo1V++2epw9SpwVZ4MbD9T7raHpRxxxhLuuc4bJkye7hNobYacebzXoK/aKzmN08ajRQI33GmWnBn1dlIzrPCU4Rivh1jGrJ97rdc8c69XxMHXqVCtVqpS7roYHNdCfddZZGWK96L2tW7euRWWSrj+KWl80pFFvmoZK6EQvK/oQvPnmm4HhlWqZ0Ycm8zAEACjsvHlPwcOrNAxNw8Y0ZOv77793gdmjxFhD4BTIPOFscyhKrL1glZPj8xJbL0H3/ucrKdYQNgVJBUOvJ/63337LsH8Vd/OoN11D9INbvJUk6/Wodd9L0lNTU13iPH/+fBcjdOx6vnXr1rmRBKHomLRd5qq3mYcrBvfaq4Vdr13PHcx7n9QgokYF/F/BokceecSd0CjG6+ROvT7Z0WfXGzqp3pTzzjvPNdZ4J1YAEC0uvfTSwO9qWFd8U6Ko3Ei9zfr/Fzy1S9OtFNuVPynGBm+j+BNqm0NRHFTjctGiRXN0fKJG8LPPPjuwjUayqUda+1MjuRfr1dCQOdYrFngJuii2JiQkZJgCp1FymeOtnl9xQiMDDxw44J5LDQ1K0ENRr7geo/Of4DiSOdbreILPeTQlTx0dwTRSQOcB3nudHyIa6fSB0nxB/THUs6FhA2qZ0YckK+oVUk/L+eef74YeatiHhlhqXwAQTdTiHEyJqiggKQCqlzpzIqjrCsgSzjbhUIKkOVw5OT5P5rnvatXWCYCG3CuxVoDX8DcF8czVVBUEgymohrpNwdlL0E855RR79NFH3bA57d8bYp9dIFUruYK7hsVlJ/NzK0B7z+3R+6STlZzM+Y92aizRSZ0SdcVwTTVQ3P7pp5+yfIyGFur8QCMlFOvVg6ERF+rNAYBoooQ0c/xRPPViqX6GiuPixfJwtgkn1msUmBrNc3J8oiltwaPH9H9f8VHzyINjvXraH3/88cOK9aJcUL30iu3qZdcoQj1/drFe8Vnz3w/VYBFOrFeuqtsynwdFTU+6gq3mHGrYpXTu3Nm1sKtowW233RbyMZqLqN4O72ROj/niiy/ccE/9DgDRInOrsTckS0O6FBAVUDMP01JRF69VOJxtwqGEWkVh1AoeXMgmu+PLinqzNSJKveTecLG8ojXcf/75Z1f4zTuh0Fz8zIJfg2hevE5MNN8tq972cKmnWCcjoXoiYpXmSOr91QgHNZ6ouJ7ep6FDh7paA6Go10IjNTQFQRTfNfRR5wDqkQeAaKEG5vXr17uOR1GsVXz1YqkaNkPFcfFieTjbhBPrRb3RwUPJD3V8oSiJ1/97dcQGz1/PC+np6W4UtgriesPrlXxnriOTOdZ7x6Te/MMdge1NIcjPKvgR60lXD4/W5lWPuEcnlBrKOGnSpCwfp8IBmm+gD4zoA6kPiuYzAkA0+fPPPzOMEhoxYoQLot78J8391hIgql4qSk4VtIKLp4SzzaEooVbrtIaQ5+T4QunYsaM7mVDlVyXrHv0vV5J9OBSkvaHzolbup5566qDt1MutVn6P5qcrUVdCqMd7+9J8+5zS0Dslofg/mlt45plnupMjjwoAatihF8tDxXoV5NE0BVGSr9hPrAcQrY2ZHhUyVa+vN3xccVydkUouvfikmjIakaRCbuFucyjqKFXSqTiWk+MLRb3a6ojVPHUVgwvO/7755hs7XGlpaW5fwcen4ezZxXodk859tApMcI97bkZj6z1SnFKRvvwSsaZ+JdZ6gzP3Wuh6dpUI1Ztz9dVXuxMqbaslhbSkjuZYZkXBXReP94fRyUFWJwiIHO9vwt8GefV5UuKl/ze6iPdz59rlZv/mZPm+LIv3vN5zH4qOWS3QGjWked0arqZiKho55M3tUlKphk0NIVOw0Nxq9U6rsIr3PN42CrxKoLV8WOZtghPTUBSEdAKgeiBPPvlk2MfnXYL3q2Fz6lFV8NZarJo7ph5sXTT/OHjbUO9XdrdpX0q4VYVWjQFaYs0bfhf8ODUOe0u1KXHUnD2ddOg16v3R0Dw9Vq3/wVVuQz138OvTa9DfQI0iod5LrxFBn0kNnwsWzf/v1Jujv0kwxW+9ZiXhoXp5VNlXsVpzATXkXUPlNd/SWy4vFGJ94UO8R37G+oKO97mJ9dpOcUirgqjzUnOhVa9Fo4yV6+h+FUlV0VLFOMVzTQ9WzNVjvOfxttH/Wk37Un6UeZvMP0MZNGiQW0LUK9AWzvFltV81EijWa9qZGgvUWaDGffV4e/VlQp0nhHPb7bff7tY/V8OEGgH0nqinPHgbNSKooV7D4jWq8KabbnKdCSowq9ii8wTFFk0L0HuW3XNnfn1aVi74PCo/4n3EknTvADNXv9U8gOwOXsMb1OLx8MMPuxNNtcZoXoJOEnWSGormJw4fPvyg2/XYzOviwT+yGgYJ5ISGHWuIluZma4kS7/9OicREm/W/g/8v5Bc936HmSwXTsSoJVuuwlvtS8bPXX3/dzaXy9qH/l2rNVY+khqJp3VIllcENkJm3UeGtzNuop7xly5bZHpsCogKe5o4r2IVzfEqq1NKdeb+aP6YlWDQUXUmaCtDo5CP4/VEBOg0bD37sgw8+6AJr8G1qpFXPvXeblphTjNBrVeOtGiZUJEZFabxtVLRMPbKqgqvETrerwUDFbPQ+aa6Zplypwm12x6Mq9Top8G5TrNHfQO9PqPdS75l6+ZXIB48ikOAegWijz1moWO/dF4o+G1pSR583VeNVg4r+Bqqwq9tCIdYXXsR75Eesj0S8z2msV0xQ47UauBVL1YmpOl2KScH7UAOlYpSGomskkpLK4LiWeRv1ImfeRsPTlbRmd2zqadY2WllFiXQ4x6eibvr/m3m/+ptoLXONktN0JcVGxd7gGOkt4xr8WBWhTUhIyHCb1jIPLoqnBFmJv4ade69VtUyCzxEUs1X7RO+JV+9Mfxttp9vVgKzzGJ1/ZHc8Ghqv9867TfmjPmNK9rN6L/Mi3sele80DBUw9DmqBUQuITuQ86iVXK4taazLTG6wJ+mrh0cmRR49Xi4Uqvofbuq7hlipgFFwpEf6gkzYFbCUO3jJOQG6p5VZBRb1xwZVCNbw68xrX+UnJXE7mgSuRVAuvt+TX4dK/ev0P1bItmedphUv/Y5W4qnJ7Xh9fNNBIAwVtnYBk9VlU74biT/Bn0YtL+oyosSPa4pJGeqjHIni45Pvvv++qBatoX6gie5qDrnivURcePV4nh3qPgofOe4j1hQ/xHvkd6ws63uc01qvOhpYJzasq4XkR6+fNm+eKwilJz+vjiwaTJk1yIwwV27KSF/E+Yj3pGuqmFi/NMQtO0tVKo+GKoah1TP/Qldxn3pc+UFlRq0mo9WqVAJIE+hd/H+QFzUtWoFJl0OAlNxTIdfErHbN33HnBG5J1OPsM/l+d18cXDTREOzt6r/SehfrfFs2xSAWIFOuDKdbr+5dVFXwl75lPgBTrdeKjXohQSTqxvvAi3iO/Yr3f4713rH6K9cHF0PL6+KLBGUHLseZnvI+P9AmNhq6tWbMmMExRwxaCT3Q0fKJXr16BggYaUqH5fl7PuFopNJQiq8QeAAojDdXW3DC/8vvxwT80Qk5JuuYxenFbxQt1u0dTFNq0aRMoMKSed50TaLSGaNjgq6++6uoFhFsECQD8Tgmx5kn7ld+PL5pFdI0YrX2qif5KvDUcQMFYw9mCF6FfunSpK0bkUXEfDbtQi7p61PV49e5o7hoARIvDXR4k1o8P/qFGdM1xVAEhxXrNA9S8v7vuuiuwjVYdUO+61wCvujM6J9B8QV30u4aQjh07NoKvBADylnr4gxss/cbvxxfNIpqka2iaCvpofroqECpZ1xyKYPfcc48b5h48bE7LAKn3XUFdQTur4XIAACDybrnlFneip4Z3TXXTyLhgKg6nyvrecjaap6d6Bxr2rnmmmr+nxvnczrEEAKAwiWiS7lHgzbwUm0dFijJTkFaVPV0AAID/KfHWKgKhqNiehruHuj2rQnwAAEQrqgAAAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AMU5LX/7000+RPgwAAJBPtMTlhAkTeH8LCV8UjgOAgrZy5UrbvHlzgT2fqlNrNQo/0prVv/zyi02aNCnShwIAQKGN936O9VrSulu3bpaenh7pQ0EYSNIBxGTAbtK4kSUl7y2w5yyZWMIWLFzk2+ANAEC0Keh4T6xHXiFJBxBz1KKugD164HHWpGqpfH++Bet2W+9X5rjnDTdJnzdvniUnJ9txxx1nf/zxh23bts3atWtnJUuWPGjbOXPm2Nq1a92SlY0bNw65P22zZMkSa9asmR1zzDGH/ZoAAPC7goz3uYn169ats19//dXOPfdcW7Roka1YscKaN29uVatWPWhb3afpaVqWsnXr1lakSJGQ22hknJapPvHEE0Nug8KBJB1AzFLAblWnrPnRK6+8YtOmTbOUlBQ3fG7jxo22e/du+/rrr+3YY4912+zYscMuuOACW7BggUu+FejPOOMMGzt2rCUkJBy0jRJ4DXfLvA0AANHMr/F+1qxZdvnll1unTp1s1apVVrp0afv999/dOUCfPn0C291yyy3uNiXny5Ytc9t9+eWXVrt27YO2UeO+t6/M26DwoHAcAPjU/Pnz7ZFHHrEff/zRFi5caCeffLINGjQocP+wYcNc8q7W9++++84l4Po5cuTIg7ZRkv7pp5+64J95GwAAEBl79+61unXrulFzM2bMsKefftquv/56V+hNvvjiC3vxxRfthx9+sKlTp9rff//tGu9vuOGGwD68bXS/ftc5Q+ZtULiQpAOATzVs2NAuuugi93t8fLwNGTLEpk+f7ubYiXrDBw8ebOXLl3fX69SpY3379rUxY8YE9hHONgAAIHLuvvvuwO/XXHONFS9e3D777LNAHNdw+FatWrnrJUqUsLvuusvdv2vXrrC3QeFCkg4APqU55sHq16/vfi5fvtySkpJcD7l3m6dBgwbufglnGwAAEDlFixa1mjVrBq5rHrka1L04reHtoeK45GQbFC4k6QDgU5pPHmz79u3uZ4UKFSwxMdEVkfNuC95G90s42wAAgMjZv3+/7dmzJ8s4rZ+h4rh3X7jboHAhSQcAn1IhOFVt93zyySdWqVIlO/rooy0uLs7atGnjbvNo7dMJEya4KvASzjYAACCyvKHtornp//zzj7Vt29ZdV7z+6quvXCFZz8cff+x6270q8OFsg8KF6u4A4FPqCT/zzDPtpptusvXr19vDDz/sCsporpo8/vjj1qFDBxs4cKD7qYCsZdbef//9wD68bTTH7fjjj3fV4TNvAwAAIkPD22+//XZbvXq1q8j+2GOPWbdu3dwSanLjjTfa66+/7lZmufLKK10h2GeeecbGjx/vGuODtznrrLPs0ksvdUPcn3322QzboHAhSQcQs7SmqZ+fR0uyXHfdda4qu4atjR492i6++OLA/VqKReuhvvrqq64VXoXmFJSD12f1ttGyLErQtc2IESMybNO0adNA4g8AQLQpiHif2+dQkTetuqIkW/Fald2Dq7IfccQR9vPPP7vq7RMnTnTrpE+ZMsWt+JJ5mxdeeMEmTZpklStXPmgb3da1a9fDfJUoKCTpAGKOliUpmVjCer8yp8CeU8+n580ptZzrkhUl2ErMs6Nt1Oq+c+dOK1OmjKsUH+yKK67I8XEBAOB3BR3vcxvr1YCukW9ZUWJ+//33Z7sPbXPfffdlGeubN2/upruhcCBJBxBz1Iu8YOEi27x5c4E9p4J2cO81AACIrnhPrEdeIUkHELOB289Jc4sWLax69eqRPgwAAAo1P8d7FXU7//zzI30Y8CGSdADwoQEDBkT6EAAAQD466aSTbNy4cbzHOAhLsAEAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT1A4DgAAADFh5cqVBbr8ZiSwDBhQ+JGkAwAAICYS9MZNGltyUrJFs8SSibZwwULfLjsG4NBI0gHEpILuTclpz8b+/fstPT3dEhISzE9SUlKsSJEi7gIAhYn+5ytB73hnRytbs6xFox2rdti0J6a510qSXvDxPqexPi0tzVJTU6148eLmJ349B4klJOkAYk4kelNy2rMxZMgQ++OPP+yrr74yP2nXrp1dcskl7vgAoDBSgl6xQcVIHwaiMN7nNNZ/8803dt5557mk2E/uu+8+++WXX2zSpEmRPpSYRZKeB5jfBBQuBd2bktOejQMHDriLWtj37t3rbitWrJhr1VaLu3c9Pj7+kK3f6vnWth7d59G+4uLirGjRg0OBnjvz/rW9Hq/n8I6rRIkSOXgnAACIznif01gfHNO9mKp4rJFq+/btC1zPKkYH98Dr98zbaf+K8d45RfC5QHaxPqtzkFDnHMg/JOl50ULXuIklJydZNEtMLGkLFy5g6BSiil97U5555hl7/vnnXYAtV66cu+3999+3FStW2O233+6uK/A2bdrUnnrqKTvllFMCj7355ptt0aJFVrduXRs3bpxVqFDBli9fbps2bbI+ffrYd99954bjXXTRRTZ37lzr1KmTPfTQQ4Egr9bzN99807Zu3Wr16tWz+++/33r37u3uv/jii+3333+3efPmBR6zYcMGK1s2OoeNArGmoDsdlASI/hcVRAKwYMGCfH8O+JMf473iqeKqEmIv1g8aNMgGDBhgrVq1CnxHatSoYbfeeqsNHjw48NhPP/3UxeZHH33UHn74Ydu+fbv99ttv1rBhQxe3Fcd1nqDRbzof0HPNnDkz8PjRo0fbgw8+aEuXLrXKlSvbFVdcYf/5z39c0q/zD52HBJ+D6HyiW7duBf4exTKS9LxooUtOspP6DbUyVetYNNq5brnNemM485uAAqJEfP369SGHu3tBOjk52UaNGmVdu3a1xYsXW6VKlQLbaHjaAw88YOvWrbNSpUq52wYOHOi+w3///bcdeeSRdscdd9j06dNdku7RyYHu/+GHH6x+/fr2/fffu6B81FFHWZcuXdxJQevWrRnuDkRpgt6kcSNLSv6356wgJCYmupP/jh07uv9pBUFNAUlbo7tjBYWDEnHFVQ1393qsPd51JfBTp051DetqOD/nnHMC2+zZs8e+/PJLl4BXrVrV3aYEe8yYMfbZZ59ZmzZt3Pfr6quvthNPPDHwuHfffdedZ+hn+/btbdmyZXbppZe6+x5//HHX2K9zEIa7RxZJeh5Rgn5krUZ5tTsAyJaGtSmpfvHFF13veI8ePQL3KZAPGzbMtaJ7J986EZg4caLVrl3b9Vg9++yzNn78+MBj1q5da//73/9szpw57vFqve/QoYP16tXL3nrrLZekA4heasRTgj564HHWpOq/jXv5LS2+mK0xs2lD2lp8Wkq+P99v/2yyAe8sspQ9+f9cQF5Rb7iS6I8//jhDki46B/ASdHnhhRfsmmuucQ1fivVXXXWVvffee7Zt27bANhoJp7oyStDVW67zgnvuucduuOEGl6TDH0jSAaCQWLVqld122202efJkN7RN884151y3B2vcuHEgQRf1jkuLFi0Ct5UsWdJt51FyrsT8hBNOOOh527Ztm0+vCIDfKEFvVadgprCkWoJL0lvUKmMJ9u/c3Py0J2lPvj8HcLhU90VJ89ixY92IOMV63Xb66adn2E7zxNWoHvw4DV9v3rz5QT32Om8QnTP8+eefdueddx5UAFbnDRrRohEuiDwqAABAIdG3b183b/zXX391PzUcrkmTJgdVhc28ZIo319Ob/+nJfF1Wr17t9ht8mTJlSr68HgAAkNETTzxhH374oX3yySeBOHz99dcfMtYrydYluEBs5ljv3ff2228fFOtJ0P2FJB0AfEjBV3PRgv38889ublmdOnVc4q1icGo1P5RGjRq5wK15a55du3bZwoULA9dbtmzp9qn5bTk9LgAAkHOKqUqcgxNrxfrzzz/fjj/++EAiHlz0LSuqCq/CcSogF2z27NmB31UYrlmzZsT6QoAkHQB8SNVYlURrKLtauNUSrmrumjeugi66r2fPnoFlWrJTvXp16969uxvapsrs2ue1115ru3fvDmyjOW1qqddwelWS1/xUDYlT9dfnnnsuw3HNmjXLDbfPXOgGAADkLNYrvmvEmmKqessV67/44gsXg1UvRrFbiXs4brrpJnvllVfs888/t40bN9qIESMCQ909iusaSq+q8Dof0Mox6llXJ0DwcWmlGNW08c5BULCYkw4gZmlNU78+jxLwb775xrWk79y50yXOWlLluuuuc3PJy5cv75ZfUY948NqoanUPtRbqyJEjXTGZU0891S3Bdskll7hqr94aq6JicgrMWoalf//+bslFbdevX7/ANvfee6+rFK/tNDSOJdgAAH5XEPE+N8/hFXq98sor3eg4NaAridbUs86dO7tRcIrbWpZNtwX3mpcoUeKg/Sl2K7HWuYJ651UcTrE/ePnBCy+80CXxWr7tySefdMus6bm0dJvnsssuc6vLqE7Njh07WIItAkjSAcQcJamJJRNt2hPTCuw59Xx63nBp6bQPPvjgoNszzw9XMA+mtU1DURB+9dVXrUyZMm5Yu1rG1dp+9NFHB7bR7bfccou7ZEUt/Fq6DQAAvyvoeJ/TWC9Dhw51l2BaVSU7Gg6vEW+haHk1NbZ79Wi0VGtwrJezzjrLXbKi4rKqCo/IIUkHEHPUQ7xwwcIsA1x+UNDW80aKgq16vdUznpSU5E4I1BJ/7rnnRuyYAACIpngf6VivoetPPfWUDR482MqWLet6wNVr/v3330fsmJA7JOkAYpKCaCQDaUHT2qrqIX/66add4bfWrVvbjz/+6HrWAQCIVrEU77XmeY0aNVyDvOrXqHCs5rd36NAh0oeGHCJJB4AYoOHzal33hrsDAIDoo3ntWgedWF+4caYGAAAAAIBPkKQDAAAAAOATJOkAYoKWIgH4DAJA9CLWI1o+hyTpAKKaKphLSkpKpA8FMc77DHqfSQBA3khISHA/tXoJEGne59D7XOYGheMARLWiRYu69T43bdrk/lnGaiGVtLQ0lyRqffRYfQ8i/f7rM6jPoj6TAIC8o8bPcuXK2caNG911/a+Ni4uLubeYWB/5HnQl6Poc6vN4OI3ynCkAiGoK0lWrVrVly5a59UNjOXAkJydbYmJiTJ64+IEaR7QMEO8/AOS9KlWquJ9eoh6LiPX+oATd+zzmFkk6gKhXrFgxa9CgQUwPeU9NTbVp06ZZx44dD2v4FQ7vc8goBgDI30b5ypUru5gXi4j1kadzrLyY1kaSDiAmKDkqUaKExSoFjP3797v3gCQdABDN8S5Wa38Q66MHExMBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCciXjhuy5Yt9t5779mGDRvs2GOPtW7duoVV/XbWrFk2ZcoUtw5i9+7dXTVHAADgPwcOHLAPP/zQ/vrrLxeve/To4ZaoOZSVK1fap59+art27bIzzzzTWrVqVSDHCwBAzPaka91iJebjx4+3PXv22G233Wbnn3++paWlZbv+34ABA+zss8+2devWuYsC96JFiwr02AEAwKFpVQHF6bvvvtuSkpLsnXfecbF/1apV2T5O2zVp0sR++uknt3zijTfeaG+++SZvOQAg6kW0J/2uu+6yOnXq2OTJk92SAYMGDbJGjRq5pP3yyy8P+ZiRI0famDFj7Pfff3fbevvZu3dvAR89AAA4lLffftsl2osXL7bq1au7dXxPPPFEu+++++ytt94K+ZiFCxda//797fnnn7eBAwe62x544AG3DwAAol18JFvWP/vsM+vdu3dgLcN69erZKaecYh999FGWj1PA7tmzZyBBFw2Zq1KlSoEcNwAACJ9i+umnn+4SdElISLBevXrZhAkT3Oi4UF5++WWrXLmyGznniYuLs4YNG/LWAwCiXsR60jXPTL3fRx99dIbbdX3GjBkhH6M5aWpd15C5zz//3H799VerVq2aXXjhhVapUqUsn2vfvn3u4tm5c6f7qdZ8XQ6HhuYnJiZaQrxZ0bish+kXZnpteo16rYf7foXDe46CeC4gVvC98rdo/n/3999/23nnnXdQrFcsXr9+fciaMj///LN16NDB/vjjD/vkk0+sVKlSduqpp1rz5s0jEutjgXc+kxZfzFItoUCe03uegnq+9CLF3GssVqSYFU2PeFmmfJEQl1Cg52zwF2K9/4X7vYxLz6oZO5/Nnz/fBVsl5G3atAncrqHrKi6zZMmSgx6zevVqq1mzprVs2dLKly9vJ598sisgp8ukSZOsdevWIZ9r2LBhNnz48INuHzt2rCs8BwBAJGmutkaJ7dixw8qUKRNVfwz1oKtHXLHYo8KvSro1fD1zY700a9bM/dQpSteuXV1xWU11e/zxx+2mm24K+TzEegBAtMT7iDUjqlVcdIDBtm/fHrgvq8foBWkeu8crSPPtt9+GfJzuu/XWWzO0rivZP+OMMw77ZGju3LnWsWNH63z7S1a+ZgOLRttWLbYpT15n06ZNsxYtWhRIC5P+ll26dHHDIgHwvYp2Xq9vNFLsDhXrvfuyeox60VVg1hspp8T9zjvvdHPU1VNYkLE+FnjnM9OGtLUWtQrm/VIP+rfle1mXbWMswfK/13fGonV21oh51u7mdlavYz2LRluWbrGJd0wssHM2+Avn0NET7yOWpNeqVcuOOOIIV5VdSbZH11XNNRTNPdewOBWcCXbSSSe5KrBZKV68uLtkpgTwcJNALReXnJxsqWlm+9Ojc9l5vTa9Rr3Wgkya8+LvA4DvVWEQzf/rFNMzr8Ci69nVkznmmGNcb0PwVDbFelV513S54Lo0BRHrY4F3PhOfllIgCXMwPV9BPGfcgRT3GlMOpNj+uP0WjVLTUyNyzgZ/4f+ef4X7vYxYVqlicVoTXVVfFXRF66f++OOPbt3z4IIzzz77bOD6ZZddZj/88EOg2Ix+Tp061S3nAgAA/EUx/bvvvrN//vnHXVcCoYb14FivXvMhQ4YEehgU6zXtbc2aNYFtvv/+e9e4X7du3Qi8CgAACk5Eq2Y89thjrjBM27Zt7fjjj7dPP/3ULrnkEpe8e7788kubOXOm3XzzzYElWDSPTb3pepyKyyiI6wQAAAD4i5ZU/eCDD1y8VwE51ZHRkMwHH3wwsI2Kwmq++eDBg93QdA1R79u3r4v1Kg6rOekTJ0601157zYoVKxbR1wMAgC+T9H79+rlL+/btD7uYzLx581xyrgB86aWXumVagl188cUuGfdoeJyS9i+++MJWrFhhnTp1srPOOosCcAAA5CEl0RpWrsJtoYaRh0vDbjUqTrVG/vzzT+vcubNdcMEFGeK2RsM9+uijVrZs2cBtI0eOtCuuuMJmz57thro/88wzbo45AADRLldJunqutZ65KrJeddVVrrU71BIq4VBxGFW4y8rZZ5990G1qRQ/ubQcAAHlL08nUIK8EXeuaX3311bkuRKU1ztU7rksoagzQcPfM2rVr5y4AAMSSXM1J//rrr2358uXWu3dve/XVV13L9vnnn28ff/wxazICABAFNL1M65g/8cQT9uuvv9pxxx3npqa9+OKLtm3btkgfHgAAUSvXheOUmN9///2usIuGsGkYeo8ePaxGjRp2++23u6HoAACg8NJoN/WgT58+3c0b15S04cOHW7Vq1dxcc90OAADy1mFXd1clVi2loktaWpqdcMIJbm1GDYV/5ZVX8uYoAQBAxOzfvz8Q69WLrmXVtESa6sJoKDwAAIjwnHTNU5syZYq98cYbrhiM1jnVvDUNd1cxOJkwYYJrfR84cGAeHi4AACgo6j1XrNdyqXv37nW95yreqmHvomXV2rRpY7///ru1bNmSPwwAAJFK0uvVq2dr1651y6J88sknbvibisIEUzVYzVkHAACFz4ABA9ySZyeffLJbMlUrsARXZPfOB7RM2o4dOyJ2nAAARJtcJemq5q61TCtWrHjQfSoyo551Je27d+/Oi2MEAAAFTL3lN9xwgzVv3vyg+zTk/YgjjnCrrXz++ef8bQAAiPScdBWNCZWgS26XYgMAAP7xwQcf2MaNG0Pe1717d1d/BgAA+LBwXLDk5GRLTEzMy10CAACfUbzPPPQdAABEYLj7sGHDQv4uquyudVRDDYsDAACFg4rEqSCcLvr9xx9/PGha25w5c6xhw4YRO0YAAKJZjpL0SZMmhfxdEhISrE6dOvbUU0/l3dEBAIACpUrts2fPtq1bt7rflax74uPjrVKlSjZ+/Pgsp70BAIACTNK91nRVddcSawAAILo888wz7ufQoUPt4osvZoQcAACFobo7CToAANFNRWIBAICPk/T77rvP/XzooYcCv2dF2wAAgMLlzTfftCVLlthVV11lP/zwg/s9K9qmfv36BXp8AADEgrCT9ODCMZmLyAAAgMJv/vz5rghst27dAr9nRdsAAIAIJunff/99yN8BAEB0CC7+evzxx0f0WAAAiFV5uk46AAAAAAAowDnp4WBOOgAAhXdOejiYkw4AgI/mpAMAgOhzqHnowZiTDgCAj+akAwCA6J6TDgAACtE66YhNCxYsKJDnSUtLcz/nzp1r8fEFVzahYsWKVqtWrQJ7PgAAAADIjHXScUip+1Lcz969exfIu5WYmGjjxo2zjh07WnJyshWUkoklbMHCRSTqAGIW66QDABB5rJOOQ9q/f7/7+eBFDe2cYyvn+zuWFl/M1pjZtCFtLT7t3waC/LZg3W7r/coc27x5M0k6gJjFOukACtLKlSvduVc0Y6QmcoN10hG2uhVLWqs6ZfP9HUu1BJekt6hVxhIsNd+fDwDwL9ZJB1CQCXrjxk0sOTkpqt/0xMSStnDhAjqBULBz0g8cOOB+FilS5HB3BcTMvPtIoTUXQG6lpKRYsWLFeAMB5An1oCtBP6nfUCtTtU5Uvqs71y23WW8MZ6QmCiZJT09Pt9dff92eeeYZW7x4sbutYcOGduutt1q/fv1ys0sgoval7CvQefeRklgy0RYuWEhrLoCw/PDDDzZ06FD7+eefLSkpyapVq2Z9+vSxu+++20qXLs27COCwKUE/slYj3kngcJP0YcOG2bPPPms33HCDnXDCCe622bNn2y233GKrV6+2Bx54IDe7BSI+775ln5ZW44QaUfmX2LFqh017YhqtuQDCXnq1S5cu1rNnT7v22mtdUq6G+eeff96mT59uU6dO5Z0EAMAvSfpLL71k48ePt7POOitwW9euXa19+/auhZ0kHYVVqSqlrGKDipE+DCAqUBCocHv55ZftpptusieffDJw29lnn+2S9gYNGti8efOsefPmET1GAACiUdHcrmPdrl27g25v27ZtYI46ACB2uYJATRpbclLBLaMYCdE8hSSrWK/aFkcffbS7P5ZEe6NTtNdkAYCoT9LVYz5mzBgbNGhQhtt1W4cOHfLq2AAAhbkgUFKydbyzo5Wtmf+rQkRCtE8hUawfO3asdevWzeLi4jIs07Zu3Tpr3LixxYpYqUJtFmfrduw1s+j8zgJA1CXpjz32WOB3nYxcf/319vHHH7s56Sok98svv9ikSZNs8ODB+XWsAIBCRgk6U0gKj3fffdeWL1/uftfIuK+++sqOPfZYO/PMMwNz0hX7O3XqZBs2bLDatWtbLIiFKtSbli20OWMft+1J/9ZoAQAUgiT9gw8+yHC9VatWtnXrVvv6668z3PbTTz/l7RECQBQq6KGz3tDkuXPnWnx8fL4/H0NnC28191mzZgWue73lwUXijjnmGNu4cWNMJemxUIU6eU+0jxIAgChM0tVTDgDImwS9SeNGlpSsYaUFIzEx0caNG2cdO3a05OSCmSeupoCkrZz4FyYvvvhipA8BAICYl6s56QCA3FMPuhL00QOPsyZVSxXIW5kWX8zWmNm0IW0tPi0l35/vt3822YB3FlnKnvx/LgAAgGiS6yRd89BXrVrleoS8NaY9mqsGAMieEvRWdQqmQFOqJbgkvUWtMpZgqfn+fHuS9uT7c6BgbNmyxZYuXWpJSRlHRbRo0cLKly/PnwEAAJfyTK4AAGAXSURBVD8k6StWrLAePXpkmLeWOYEHAACFlxrg+/fvb2+//XbIuP7tt9/a6aefHpFjAwAgmuWqetDNN99sDRs2tE2bNrnru3btssmTJ1ujRo1s5MiReX2MAACggL388sv222+/uSKAp556qn3xxRdu+bULLrjALrnkEjvttNP4mwAA4JckXdVftSRbxYoV3fUSJUq4AD569Gh7+umn8/oYAQBAAVOsv/32210DvFYE0KVZs2b23nvvufs05Q0AAPgkSdf8tGrVqrnfjzzySLcUizRp0sQNhQcAAIVbVrG+ePHiVrduXeI9AAD55LAXy9Xa6CNGjHDVip9//nmrU6dO3hwZAACImOB56Ir1b7zxhq1du9amTJlic+bMibk10gEA8HXhOBWN8zzyyCN2zjnn2BNPPGElS5Z06/ACAIDCrXPnzlalShX3+8CBA+3999+36tWru+t33XWX1apVK8JHCABAdMpVkv7uu+8Gfj/hhBNs9erVtnjxYhewy5Qpk5fHBwAAIuDee+8N/K6l1n7++We3FNsRRxwRGAYPAAB8tE6658CBA1a0aFFXTAYAAETvkmwNGjSI9GEAABD14nM7T+21116zpk2bWmJiorsoSdd8NQAAEB1UxV2rt5QqVcqt5FKjRg2755573NKrAADARz3pw4YNs2effdZuuOEGN9xdZs+ebbfccosb+v7AAw/k9XECAIAC9P3331uXLl2sZ8+edu2111rp0qXd1DYViZ0+fbpNnTqVvwcAAH5J0l966SUbP368nXXWWYHbunbtau3bt7c+ffqQpAMAUMi9/PLLdtNNN9mTTz4ZuO3ss892SbuGvc+bN8+aN28e0WMEACAa5Wq4e1pamrVr1+6g29u2bevmqAMAgMItq1hfsWJFO/roo939AADAJ0m6eszHjBlz0O26rUOHDnlxXAAAIIIU68eOHZthvXSZP3++rVu3zho3bhyxYwMAIJqFPdz9scceC/yupdauv/56+/jjj92cdAXwX375xSZNmmSDBw/Or2MFAAD5SEusLl++3P2ukXFfffWVHXvssXbmmWcG5qQr9nfq1Mk2bNhgtWvX5u8BAECkkvQPPvggw/VWrVrZ1q1b7euvv85w208//ZS3RwgAAAqsmvusWbMC173e8uAiccccc4xt3LiRJB0AgEgn6eopBwAA0evFF1+M9CEAABDzcjUnHQAAAAAA+GQJNtFctJEjR9qCBQvcnHQNfxs0aJAdddRReXuEAAAgIlJSUuz11193Q+C3b99udevWdUuttmzZkr8IAAB+6kmfMWOGWyP1nXfesbi4OCtSpIj7XbfpvpxSov/999+7xD8ntmzZ4oraLFy4MMfPCQAAsqa6M8cdd5zdc889LkEvW7asi/GtW7e2Z555Jsdv3fr1612sX7RoUY4ep46Ab7/91n788Uf+XACAmJCrJP22226zAQMGuCqvqgQ7btw497tuu/3228Pez549e1zFWK3Descdd1idOnUyVJHPjtZn7dGjh5133nk2atSo3LwMAACQhWeffdbKly9vK1assAkTJthbb71lM2fOtI8++sjuu+8+27ZtW9jv3fDhw10v/J133mknnXSSi9179+4N67FPPPGEnX322da/f3/+VgCAmJCrJP23336ze++91+Lj/+/h+l2t7b/++mvY+3nggQfs77//dgn+7Nmz3UnA3XffHVZr+aOPPuqWg2nWrFluXgIAADhErL/pppusTJkyGW7v2rWrNWzY0P7888+w3j8tz/qf//zHrQbz888/u9Fz2vfDDz98yMdqmL2m1l111VX8rQAAMSNXSbqS41WrVh10+8qVK9194Xr77bft6quvtooVK7rr6lXXPDe11mdHy7y9/PLL9uqrr+bi6AEAQG5jveapa3pauPH+zTfftLZt21rHjh3d9apVq9qVV17pbs/Ojh07rGfPnvbaa69ZhQoV+IMBAGJGrgrHXXrppW6o+ZNPPmknnnhioLVbw+B1ezhWr15tmzdvPqj4jK7PnTs3y8dpXlyvXr1cgu4l94eyb98+d/Hs3LnT/UxNTXWXw6Fh94mJiZYQb1Y0Ls2iUbEice41WtHilmoJ+f583nMUxHN50osUc6+xWJFiVjQ91/UUfS0hLsG9Rn1mD/dzD8uT/xtp8cUK7HNe0N8rvlM548fvpGL9FVdcYSVLlrRzzz3X9ahr9JuGrpcrV86OPfbYsPYzZ84c69y580GxXiPiNO/9yCOPDPm4gQMH2oUXXminn366640/FGJ94Yr1wv+l2I71sXAOrddWkH8P7zn8/rePZalh/m3i0lWRJYeSk5PdELg33njDDhw44G5T8bh+/frZiBEj/v0nfwh//PGHC/DqFVcLu0fz1T7++GM3BD6Uiy++2GrWrOnmyomK2nTq1ClwPZRhw4a5k4rMxo4d604+AACIpKSkJNdrrN7jzMPLI+n55593U9PUQO5RzFVD+dFHHx3WPmrUqOFGzQXH4e+++85OO+00W7JkidWvX/+gx7zyyituzXYNjy9evLgNGTLETYnLrlAssR4AEC3xPlddhkrCFUAfeeQRV6VVFd41Py3cnm0pVqxYIOHPfODefZl9+OGHNnnyZDdETlXdZdeuXa6oja5ruLyOJTPNc7/11lsz9KQr0T/jjDMO+2RIvf4awtf59pesfM0GFo3WLZpnM166zV69qrldekLVAmlZ/7Z8L+uybYwlWMG0BM5YtM7OGjHP2t3czup1rGfRaMvSLTbxjok2bdo0a9GiRaQPJ6Z5/zemDWlrLWoVTEJW0N8rvlM5443w8psbbrjBJdiK9UrU69WrZ7Vr187RPhTTQ8V6777MNMru5ptvdvPYp0yZ4m5btmyZKzarWH/88cdbpUqVDnocsb5wxXrh/1Jsx/pYOIfetmqxTXnyugL7e6iXVqthdOnSxRISCm5EKvI+3ucqSVeROA3bUFKek8Q8mJJk9b5nnu+mYfCq8h6KgnmbNm0yVHPftGmT/f77764nXUl3qCRdrfC6ZKYP7+F+gPVe6OQjNc1sf3qupvj7XsqB9H9PsPbvK7CkWfRcBfV8cQdS3GtMOZBi++P2WzRKTU91r1GfWf5xR5b3fyM+LaVAv1MF+b3iO5UzfvxOKqZqdJuGmx/OuuiK6aFivWK65qeHOsnUibuGuHvD3DXMXtXkFeuVvIdK0on1hTPWC/+XYjPWx8I5tF5bJP4eeZHjIH+E+3fJVZKu4Lhx40arXLmy5ZZ64xWENXytb9++7jZ1+ysga56aR9Vj1Xp/8skn2/nnn+8uwcIZ7g4AAHIX6w/XWWedZY8//rjrPfemmGlknJL/okX/PQ1RITo1uGvuuhJ3b7Scxxvunvl2AACiUa6arVTM5f77789QjC03NFx+4sSJbjid5odr3VT1sGtoneeZZ56xa6655rCeBwAA5IzWJVcMXr9+/WG9dYMGDXLF4RTjx40b52L69OnT7cEHHwxs88MPP7i10DU6DgCAWJernnQl1loPffz48W5+WuY5ZTNnzgxrPxq6PmPGDLcGqvZ1yimnuLnjwcXctA6619IeinrYmzRpkpuXAQAAsqAirn/99Zebg96gQQMrVarUQUXlTjjhhEO+f1qqTUVilfC/++67rqdcK8IEV4evUqWKqytTokSJkPto1KiRdejQgb8VACAm5CpJ15IouuQFzXNTEbqsqHhMdlT9FQAA5K2TTjop22ltoeaFZ7etRs9lpX379tkOZb/qqqvcBQCAWJDjJF2V1KtXr25auU3Lp+S0yisAAPC33bt3u+I2Gqau3vJweswBAEAEknQtH3DOOee4ZVDkiCOOsC+//NIVgAMAAIXf1q1b7cQTT7SlS5cGbnvsscfsrrvuiuhxAQAQK3JUOE7F4rp37+4CuC4XX3yxuw0AAEQHTSMrU6aMW/ZMSwdp2dPhw4f7di13AABiOkmfN2+e/fe//7Xy5cu7i36fP39+/h0dAAAoUIr1Wh9dxeJUyE3V2I8++mhbsmQJfwkAAPyWpGu98ooVKwauq6DMtm3b8uO4AABABCiuB8d6Id4DAODjwnHff//9IW/r1KnT4R0VAACImLlz52ZY/lSJ+5w5c6xIkSKB21q0aOFG1QEAgAgn6Z07dz7kbar8DgAACqfbb7/9oNt+++23DNe//fZbO/300wvwqAAAiA05StKXLVuWf0cCAAAibsyYMa5g3KFUqVKlQI4HAIBYk6MkvU6dOvl3JAAAIOKOOuqoSB8CAAAxLUeF4wAAAAAAQP4hSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPCJopE+AAAAAACIVgsWLCiQ50lLS3M/586da/HxBdMXW7FiRatVq1aBPFcsIUkHAAAAgDyWui/F/ezdu3eBvLeJiYk2btw469ixoyUnJxfIc5ZMLGELFi4iUc9jJOkAfGflypW2efNmi1YF1aIOAAAiZ//+/e7ngxc1tHOOrZzvz5cWX8zWmNm0IW0tPu3fBoL8tGDdbuv9yhx3zkZvet4iSQfguwS9ceMmlpycZNEtztbt2GtmZSN9IAAAIB/VrVjSWtXJ/3ifagkuSW9Rq4wlWGq+Px/yD0k6AF9Ra6wS9JP6DbUyVetYNNq0bKHNGfu4bU/6t4UdAAAA8JCkA/AlJehH1mpk0Sh5T7SPEgAAAEBukaQDAAAAAHIl2mvtVIxABXuSdAAAAABAjuxL2Veg1esjJbFkoi1csLBAE3WSdAAAAABArqrXt+zT0mqcUCMq370dq3bYtCemFXgFe5J0AAAAAECulKpSyio2qMi7l4fi83JnAAAAAAAg90jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8AnfJOlpaWk52j49PT3fjgUAAEQ+1gvxHgAQayKepD/66KN21FFHWUJCgh177LH23XffZbv9119/bV26dLGyZcu6y7nnnmsLFiwosOMFAAA5o9h9zDHHuFhftWpVe/LJJ7PdftGiRXbVVVdZlSpVrGTJktayZUv75JNPeNsBADEhokn6qFGj7JFHHrExY8bYjh077KKLLrLzzjvPli1bFnL7AwcO2DPPPGNDhgyxtWvX2tKlS+2II45wSfvOnTsL/PgBAIAdMuHu2rWr9enTx8Xq119/3e6//3578803s3zM888/b507d7a5c+fa9u3b7corr7SLL77YfvjhB95uAEDUi2iS/vTTT9vVV19tp59+upUqVcqGDRtmFStWdMl7KEWKFLGvvvrKTjvtNLe9tn3qqadszZo1NmvWrAI/fgAAkL0XX3zR6tSp4xrY1bB+zjnnWK9evVyje1ZeeOEFl9RrpF3x4sXtpptuskaNGtmECRN4uwEAUS9iSfqWLVts8eLFdsoppwRui4uLc9dnzJgR9n7WrVvnfpYvXz5fjhMAAOSeYnpwrBf1ks+fP9/27NkT1j5SU1Nt8+bNxHoAQEwoGqkn3rBhg/tZqVKlDLdXrlzZfv7557D2kZKSYjfffLOdeOKJdvzxx2e53b59+9zF4w2NV9DX5XCL4CQmJlpCvFnRuJwXxCkMihWJc6/Riha3VEvI9+fznqMgnsuTXqSYe43FihSzoukR+1rkq4S4BPca9Zk93M99fuI7FR3fK75TOePn72RexPtQsV4F4TZu3Gh169Y95D6GDx9uSUlJdsUVV2S5DbG+cMV64f9S7MZ6Id7nPb5T/v9OhbuPuPQIlU3966+/rGnTpjZ16lTr2LFj4PZbbrnFJk6caAsXLsz28XqjevfubdOmTbMff/zRDaXLiobRK8BnNnbsWFeQBgCASFIC2rNnT1efpUyZMlH1x6hVq5aL16pBE1xI7qyzznI1aLKL3/LGG2/Ytddea+PHj7du3bpluR2xHgAQLfE+Yl2Gqu4qakUPpuuq5nqoBF1FZL7//nuX5B8qwN9999126623ZuhJr1mzpp1xxhmHfTKkojZqZOh8+0tWvmYDi0brFs2zGS/dZq9e1dwuPeHfv1t+twJ+W76Xddk2xhKsYFqBZyxaZ2eNmGftbm5n9TrWs2i0ZekWm3jHRNew1aJFC/MrvlPR8b3iO5Uz0Vz8VPE+VKzXFDf1qGfn7bffdgn6O++8k22CLsT6whXrhf9LsRvrhXif9/hO+f87FW68j1iSrjnkWo5lypQpdskllwSSb13Xsiue/fv3u9uLFSsW2KZfv342adIkt22DBodOjFV0RpfMtBSMLocjPj7ekpOTLTXNbH96xFe0yxcpB9Lda7T9+wosaRY9V0E9X9yBFPcaUw6k2P64/RaNUtNT3WvUZ/ZwP/f5ie9UdHyv+E7ljJ+/k4erffv2By2fNnnyZLesmjeaTbFdU9hKlCgR2EaJ+YABA+ytt96yHj16HPJ5iPWFM9YL/5diL9YL8T7/8J3y73cq3H1ENKu866673DC2Dz/80C2ppt7u3bt326BBgwLbqAW9VatW7neNzFfA1nB4VXmvXbu27d271120PBsAAPCX66+/3tavX++qu6vY67hx49x0M50DeD766CM352/16tXuurbR6i8vv/yy60H3Yr3f59gCAJAXIpqka3kVLaGmIWpNmjSxX375xb799lurUaNGhtYGrxd869atgTXVVSyuXLlygYta3AEAgL/Uq1fPzUHXUMHGjRu7GjFalu3SSy/NsMSqYr2GwHtLsKnXQg31wbG+b9++EXwlAAAUjIiXsb7uuuvcJSsjR44M/F6hQgXXkg4AAAqPk08+2X766acs7/d6yz3Tp08voCMDAMB/onMSNQAAAAAAhRBJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDAAAAAOATJOkAAAAAAPgESToAAAAAAD5Bkg4AAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4RMST9ClTptjFF19s7du3t0GDBtmaNWvy5TEAACAyVq5caQMHDnRxu3v37vbDDz/ky2MAAIgGEU3SJ0+ebGeccYYde+yxdv/997uAfPLJJ9vOnTvz9DEAACAytm7dam3btrXNmzfbAw88YA0aNLDTTjvNfvzxxzx9DAAA0aJoJJ9cSbZax4cNG+aud+zY0apWrWovv/yy3XHHHXn2GAAAEBkvvPCCHThwwMaPH28JCQmuof2PP/6woUOHuob3vHoMAADRImI96Xv27LGZM2faueeeG7gtMTHRtZRnFYBz8xgAABA5is9dunRxybbn/PPPt2nTpllqamqePQYAgGgRsZ701atXW3p6ulWrVi3D7bqeVcKdm8fIvn373MWzY8eOwHC6ww32GmZfokQJ273mb7PUZItGyev/ca9x0eYU+/7vXfn+fGnxxSypaZL9sGS3xaelWEH4Y22Ke41Ja5Ns8/zNFo12rdvlXqM+s1u2bDG/4jsVHd8rvlM5s2vXv/9bFeOijaaltWnT5qC4vX//flu3bp3VqlUrTx5DrC9csV74vxS7sV6I93mP75T/v1Nhx/v0CJk/f76OLH369OkZbr/jjjvSjz766Dx7jAwdOtQ9jgvvAZ8BPgN8BvgM+PkzsGrVqvRoU7169fT7778/w22TJ092r3fJkiV59hhifeQ/v1x4D/gM8BngM2B5Eu8j1pNeoUIF9zNzi4SKxHj35cVj5O6777Zbb701cD0tLc31ousxcXFxh/U6kPfUUlWzZk1btWqVlSlThrcY4HsV9dSirtb1zCPFooFibai47d2XV48h1hc+xHuA71SsSQ8z3kcsSVexN11mz57t5pl5Zs2aZaecckqePUaKFy/uLsHKlSuXJ68D+UcJOkk6wPcqVpQtW9aiUatWrVzcDqa4Xbdu3SxjcW4eQ6wvvIj3AN+pWFI2jHgf0SXYrr76anvttddcj6moiuuCBQvc7Z6HHnrILrvsshw9BgAA+EP//v3tl19+sc8++8xd/+eff+ztt992t3u+++47a926tW3cuDHsxwAAEK0ivgTb0qVL3fqn6vJXcB41apQdf/zxgW2WL1/ull3JyWMAAIA/nHzyyTZixAjX4F6lShVXBPbyyy+3O++8M7CNpqD9+uuvlpKSEvZjAACIVnGamB7pg9iwYYNLtuvXr28lS5bMcN+KFSts9+7d1rRp07Afg8JNFXofffRRN78w8zQFAHyvUDgpli9btsyOOuooq1y5cob7tm3b5hrgW7RokWHZteweg8KPeA/wnYKPk3QAAAAAABDhOekAAAAAAOD/kKQDAAAAAOATJOkAAAAAAPgESTp8RUvqnXvuudaxY0dXOG7nzp2RPiSgUFNV7KFDh9pJJ53kCjICQKSpkn/Pnj1dFf8rr7zSFi1aFOlDAgq1PXv22KuvvmqdOnWybt26RfpwkAdI0uEbWkpPwVpJ+l133WXffPONnX322ZaWlhbpQwMKpd9++83at2/vfk9KSnKrZQBAJM2fP986dOhgFStWtGHDhtmBAwesXbt2tmrVKv4wQC41a9bMZs2aZVWrVnXfMRR+VHeHLyhI6x/L4MGD7YEHHnC3/fPPP26JvU8//dTOP//8SB8iUOgkJydbsWLFrEiRIi5ZVxBXYxgARMqll15q69evt2nTprnraohv1KiRnXPOOTZixAj+MEAu7Nq1y0qXLu0avkaPHm1LlizhfSzk6EmHL8ybN882bdrketE99erVs2OOOcYmT54c0WMDCqvExESXoAOAXyimB8f6+Ph4l6AT64HcU4KO6EKSDl9YuXKl+1mtWrUMt+u6dx8AACjc82a3bt1KrAeAQyBJhy+kpqa6n8WLFz+oJ9C7DwAAFF7EegAID0k6fKFChQru55YtWzLcvnnz5sB9AACgcA/JTUhIINYDwCGQpMMXWrRo4ebOzp49O3Db3r17XYXKVq1aRfTYAADA4VOcV7wPjvWiqtTEegD4PyTp8IUjjzzSrev43//+11WolCeeeML9vOyyyyJ8dAAAIC/079/fPvjgg8AyUVOnTrXvvvvO3Q4A+FfR//8TiDgtDXXxxRe7pdjKly9v+/bts/fee88qV64c6UMDCqWUlBS3/rAsWrTI/v77b/vll1+sTp067iQZAArawIED7a+//rLWrVtbjRo1bM2aNXb//ffbBRdcwB8DyCU1cs2ZM8fWrl3rijPq+yVffvkl59GFFOukw3dUzV296Q0bNnRz1wDkTnp6uv36668H3a6CjE2bNuVtBRAxSiSUoNeqVcvKli3LXwI4DGqI90aiBtP0Es6lCyeSdAAAAAAAfII56QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQDUeKnn36yKlWq2IEDB7LcZu7cuW6bnTt3FuixAQCAvFGjRg2bMmVKtts0atTIPv/8c95yoJAiSQeiREpKim3YsMHS09Oz3CY1NdVtk5aWZoXBqlWr7P7777f69etbhw4dIn04AABE3Pr1623fvn3ZbqNYv3fvXisMZsyYYZdffrlrWGjdurXde++9tmvXrkgfFhBRJOlADDnuuONs3bp1VrZsWSsMTj31VEtISHA/N23aFOnDAQCgUPj777/t/PPPN79bvHix3X777XbhhRfaZ599Zk899ZR9/PHHdtFFF0X60ICIIkkHosw333xjp512mtWtW9e6du1q//zzT+C+P//80yXqXgu1N0T+22+/tTPOOMOOPvpoO/fcc23RokWBxyip1za61KxZ00455RQbP358huf09vP111/bySef7IbizZw505o3b25vv/12hm2XLFliVatWtb/++uuQr0XH8cADD9hRRx2VB+8MAADRYdmyZda9e3dr2LChtW3b1sXfYBp9ptjuUVx+8803rWfPnq7Huk2bNi4pzvwYxfJq1aq5Hu3hw4cf1GOv/fzvf/9zSXSdOnXsySeftEGDBtmAAQMybKdRfccff7yNGjUq29ehkXLTp0+3Hj16uNeic4ynn37aJk2aZKtXrz6Mdwgo3EjSgSgzePBgu+eee+zTTz+1uLg4O/PMM90w91DD3b0h8kOGDHHJ8BdffOF6rhX4PUqQ58yZ4y4//vijXXPNNda/f/8MJwTeftQa/sgjj9js2bNdcNZzv/zyyxmO74033rDKlSvbMcccc8jXEh/PvygAADK77bbbXIxVon322We7XvMFCxZkOdxdQ+S9Husvv/zSLrjgAhfrV6xYEdhGPdiK9b/88otLvj/44AO77777Mjyv9nPLLbfYxRdfbD/88INde+21bp+jR4+2bdu2BbbTnHnVwVFnQU7jvNcwUKxYMf7wiF3pAKLClClTNBk9fcKECYHbtm/fnl66dOn0sWPHuuuzZ89222zbti3DY3799dfAY+bOnetuW79+fZbPdeutt6ZffvnlBz33tGnTMmy3cOHC9Li4OPdT9u/fn169evX0Z599Nkev7d57701v1KhRjh4DAEA0KlKkSPrgwYMz3Na5c+f0fv36Ba6XLVs2/f3338/wmMcffzzDYypXrpw+evToLJ/nyy+/TK9UqdJBz/3AAw9kuO3AgQPptWvXTn/hhRcCt/Xq1Sv9vPPOy/FrS05OTj/uuOPSTz/99Bw/FogmdFMBUUbDzT2ae96sWTObN29eto9p2rRp4Hf1csvmzZsDt2mInIagaQi9hsK98sortnz58oP207JlywzXNaSuffv2rvdc1PuuueW9e/c+jFcIAEBsC471olibk1gvlSpVyhDrNexcveKK3ZqWdsUVV7iYnZSUlG2sV294v379ArF+x44d9tFHH7nbckKr0+j8YMuWLe68A4hlJOlAlNFw9WAaLqbh6NkpUqTIQbd5VeLHjh3rhrZpSJuSbA2F0++h9lmiRImDbtPQeM1L379/vwvgGmJXoUKFXLwyAACQH7FeDe+qTdOqVSv78MMP7ddff3VzzyXzfkPFeiXkOj/QEPdx48ZZ6dKl7bzzzstRgt6nTx9Xz0ZD5atXr84fGjGNJB2IMgqQHgVWFWhTMZbcUvEWJdZaHkX7UU/60qVLw3685rwlJye7+WqaO3f11Vfn+lgAAEDGWC9KkA8n1qsAbLly5Vx9Go3AU/E4LYMaLhWU0xx5Ncbrol74zA0JWVGdnL59+9rUqVPt+++/d8XkgFhHkg5EGRWB0/A0FYnTGuPqwb7ssstyvT9VdFfLtvapQDpmzBibMGFC2I9PTEy0Xr16uYJ2GkqvlnoAAJB7L730kuvtFhWKVVy+7rrrDivWb9y40WbNmuWu//777/bQQw/laB+q8P7666+74rHhDnXXecVVV13lknNdtMoMALOivAlAdFGFV1VO3717t0uKVZ31cNZFv/XWW13Q1tAztYprrpp61YOXaQsncOuE4uabb85RxXYtB6eTEL0WValVL77Mnz/fzaUDACAWKbHVsmVaJlVx9bHHHrPOnTvnen9afk0V41V/RkPnS5Uq5WK9lkPLyfmHhrmrJz6cFVxEleQ1JU7Pp3n1wTSvvV27djl+LUA0iFP1uEgfBIDDp6HtW7duDSSyKtxSpkwZtwybR73qKhKjZdV0e+bHeK3aak2vWLGiFS36f+14GrKuOWMKpHv27HFJsze3PNR+MgfhE0880RYvXpyjYWzaZ6g5dmp8YHk2AEAs0vJq5cuXd8m0GrGLFy9+0NByxXE10Ou+zI/xqECbRruVLFkyw3nCrl273LaZzxmy2o9H8VrD3tUDP3DgwLBei0b96ThCOfLII1mGDTGLJB1AvlLSf+mll7p1TzUnHQAARJ8XX3zRhg4daitXrsyQ+APIOYa7A8g3GsJ24403uh79yZMnZ7hPFeKzmtuuIXwjRozgLwMAgM+pMvwJJ5zglmpTRfjgBF0rxGjaXCgqTvfbb78V4JEChQc96QDyjYbIayiehs4HD7v3huPr/lA0/O5w5tEDAICCoalwGhav4emZh90rziveZ7UkHPVlgNBI0gEAAAAA8AmWYAMAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfIEkHAAAAAMAnSNIBAAAAAPAJknQAAAAAAHyCJB0AAAAAAJ8gSQcAAAAAwCdI0gEAAAAA8ImiFoPS0tJs7dq1Vrp0aYuLi4v04QAAYlx6errt2rXLqlWrZvHxtJ8DABDLYjJJV4Jes2bNSB8GAAAZrFq1ymrUqMG7AgBADIvJJF096N7JUJkyZSJ9OMgkNTXVvvnmGzvjjDMsISGB9wfIA3yv/G3nzp2u8diLTwAAIHbFZJLuDXFXgk6S7s9komTJku5vQ5IO8L2KJUzBAgAATHwDAAAAAMAnSNIBAAAAAPAJknQAAAAAAHwiJuekh7tMW0pKSqQPI2bnpBctWtT27t1rBw4csFikufhFihSJ9GEAAAAAKGAk6SEoOV+2bJlL1BGZ9YKrVKniqu/HchGlcuXKufchlt8DAAAAINaQpIdIENetW+d6MbUcTnw8MwIKmhpHdu/ebaVKlYrJ91+fwaSkJNu4caO7XrVq1UgfEgAAAIACQpKeyf79+12CVK1aNbcMGCI31aBEiRIxmaRLYmKi+6lEvXLlygx9BwAAAGJEbGZA2fDmQBcrVizSh4IY5zUSaY4+AAAAgNhAkp4F5gEj0vgMAgAAALGHJB15TkO0P/30U9/sBwAAAAAKC5J05LnffvvNLrroohw9ZsOGDfbZZ58d9n4AAAAAoDAjSYcvzJ4927p3757htqOOOsq6du0asWMCAAAAgIJGkh4F1q5da1988YVbumvhwoX29ddf2/r160Nuu3z5cps4caL9/PPPgSJ5OdmPHj9p0qQMt23evNkmTJjgHhfKjh073P266LkXL16c4f5t27bZzJkzXVV3b7s///zTqlevbn379s3Va5C///7bHeuaNWuyff8AAAAAwC9Ygi0K/PTTTy6Zbd++vUtSjzjiCJs7d6699tpr1qtXr8B2N954o73++ut2wgkn2NKlS61cuXL25ZdfuvXgw93PV199ZU8++aQtWbIksN8//vjDunXr5qqQFy168Edq69at9uabb7rf9+7d63rN9Rwffvih237Tpk02efJkl3BrOyX7p59+ukvuNdxdy+Ll9DV07tzZvQatta5kXq+hd+/e+fhXAAAAAIDDR096lNDa7g0bNrT58+e7XunHH3/crrvuOpcgyyeffGIvv/yyTZ8+3b7//nvXy1y2bFm76aabcrSf3Khbt26gh1xJ/j///ON66pVsi57v3nvvtYSEBLfNxx9/bFdcccVB+8nJazj11FPdnPZp06bZsGHD7I477sj18QMAAABAQSFJjyL33HNP4Hcl1vHx8fb555+762PHjrULLrjAjjvuOHc9MTHR7rzzTpcUK6kNdz+5pd5x9bhrmPqUKVOsfv36NmPGjBztI9zXUKRIERs8eHDgunrlNWxfPfMAAAAA4GcMd48SJUqUsKpVqwauaxh57dq13fxtWbZsmetdDtagQQOXPK9YscKaNGkS1n5yQ3PCzzrrLDd3/ZhjjrHSpUu7eelZzWHPSrivQUPcixUrFthGr0mSk5NdzzsAAAAA+BVJepTQXG9dvIRUtm/fbhUqVHC/66euB/Oue9uEsx/1qmdOrvft25ftsT344IOuUrvmt+vxctVVV9m6dety9BrDfQ0AAAAAUFgx3D2KBA9JV0K8cuVKa9Omjbverl07N9Rcxd08mvutYeeVK1cOez/VqlVzybV6pT2a950dFXBr1qxZIEHX0PRvv/02wzYqUqdjU4X3rOTkNQAAAABAYURPepTQsPSbb77ZDftWwvvoo4+6dcePP/54d7/ue+ONN+zMM8901c81P3zEiBH2wQcf5Gg/qpquiuqXX365XXrppTZnzhx7++23sz22c8891xVuq1WrlnvsqFGjbOfOnRm2adq0qSsc98ADD1irVq1cz3tm4b4GAAAAACis6EmPEkqotYzZxo0bXeVzVTwPTp41D1xLn3Xq1MktWbZ7926bOnWqK8SWk/3ofi1zVq9ePVepvUqVKq7nvWvXroGeciXYuu655pprbOTIkS6h13PecMMNLrlWz7hHPeE6Lj2vnk+V2TPvJ5zXoLXV1SgQrEyZMm4/wUP4AQAAAMCP4tJzWr0rCqgXVwXEVO1bCVwwzcdWgTItG1ZYkjr1JPfv3/+g+dqR2s/h0pB3/Y30t/ES/1hUGD+L8C9NE1Hj1jnnnONGraDwxCUAABBbYjcDAgAAAADAZ0jSo0CoId6R3A8AAAAAIHcoHBcF2rZt6y5+2Q8AAAAAIHfoSQcAAAAAwCdI0gEAAAAA8AmSdAAAAAAAfIIkHQAAAAAAnyBJBwAAAADAJ0jSkWOpqam2f//+Q26XkpJiaWlpMfkO792719LT07O8X/ft27evQI8JAAAAgP+xBFuYVq5caZs3b7aCUrFiRatVq5b5Uffu3a1GjRr2wgsvZLnNmjVrrGXLljZ37lyrWrWqFfaEu1ixYhYfH16b1u7du6106dI2Y8YMa9OmTZYNGI0aNbJ33303y20AAAAAxB6S9DAT9MaNm1hycpIVlMTEkrZw4QLfJuqHcs8991jfvn0LfYKuEQOJiYk2ZcoU69SpU57tt3jx4jZkyBC75ZZbXDIPAAAAAEKSHgb1oCtBP6nfUCtTtU6+f3J2rltus94Y7p433CRdQ9Dj4uKsaNHw/qQahn6onuFwtgll3bp1rod4/vz5OTo+bxvvOXVdyWzmYeLaJlRvt7bVfaG2yeq1aMi5ttd96i0Pdb/X863nCLVdVsd0qPt79uxpt956q82cOZPedAAAAAAOc9JzQAn6kbUa5fslNw0BZ599tvXv39+6du1q5cqVs1KlStlNN91kBw4cCGyjJPOGG25w9yckJNixxx5rEydOzLCfcLY5lI8++sjq1KljDRs2zNHxedt069bNqlWrZuedd567fePGjS6h1WOOOOIIl9AG9z5reLl6u4cOHeqG2Ov36tWr2/jx4+2LL76wxo0bu8RaPzP3WmvIuXc8Rx11lF133XVuf5769eu7nzoWbXfCCScEjql3795WtmxZ99gLL7zQDfEP9sMPP1jr1q2tZMmSbnrAO++8k+H+MmXKWIcOHdxxAgAAAICQpEeRt956y0477TTXkz158mSX/D311FMZhqAraf3+++9t165d1qtXL5c0L1myJEfbHMr06dPt+OOPz/Hxydtvv+0S3mXLltnXX3/thpufeeaZLjnXbdu2bbM+ffrYWWeddVBS/N5777lEeOfOnXb55Ze74fb333+/TZgwwZKSkuzUU091twVbvny5a5hQT7mS6tmzZ9sDDzwQuH/16tXu5zfffOO20xx79fDrdej5f/nlF9uyZYv169fPPvvsswz7fvXVV+311193x6P3VQ0Q3v48SvqnTZsW9nsLAAAAILr5Iklfu3atzZkzJ0MPZnY0fFjzxJUwbd26Nd+Pr7BQD/ONN97oepJPOukku+uuu+yZZ55x9yUnJ9uLL75oDz30kB133HGud1dzotVT/txzz4W9TTiUvFauXDlHx+dR8q1E2hvm/vnnn7tE+vnnn3c92RpurmRXPdwff/xxhscqIW/WrJnrNVePuIaq33fffYGe9GuvvdYWL14csgCgPlOaWnDbbbcdtN/MPv30U1u0aJGNHTvWGjRoYCVKlLALLrjA7T+YevZbtGjhRiToeHQMSuqDqfc+c+IOAAAAIHZFNElXEtWjRw+XcKliuBKWkSNHZvuYP/74w5o3b+56atWjqmHE2od6OWNdq1atMlzXe7R+/XrbsWOH/fPPP6632Buu7VGyvGDBAvd7ONuEK9TyY9kdn6dJkyYZtvntt9/c/UrQVTFdFw0T/+uvvw5KbuvVqxf4XdtI3bp1D7pt+/btGXruldgr0db9+kytWrUq29emBiU916GK4gUfj2hofPBzizcfHgAAAAAkotnBgw8+aD/++KMbSq0eTiVM119/vRtynBX1Vmq+snpr1ZP+559/2ldffeV6gGNd5sTYW6NcSaCXCGZet1zXixQpEtjuUNuEo2bNmm5Ie06Oz6Ne51DJrhphMl8ee+yxDNuFKs6WXUG3WbNm2cCBA23YsGFuGL0aKD788MMM8+RD0T7DWf89u+f2qJFC7xcAAAAARDxJ13xdDV1WkS+5+OKL7ZhjjrE33ngjy8ds2LDBTjzxxECFbfWUapiybo91SjozX1cCqN5nJboaZq5K4sFUSK1p06bu93C2CUf79u1DNrRkd3xZUW+7evgXLlxoeU3HePTRR9sll1zihvZL5sJyapxQI0Jw4q5j0vx4Tbk4XD///LN17NjxsPcDAAAAIDrER3IeunoRQw2t/v3337N8nHo9//e//7kiZCpupoJcKnA2aNAgi3UaGv6f//zHNVio6NoTTzzh5liL5nhrTe57773XFSrTe6/fNYJBVdbD3SYcF110ketJD16C7VDHlxVVVddnQtMhVNhNRdqUXA8YMMD9/Q+HGh7+/vtvN+9d+9Uc8xEjRhzUG167dm333KqZoN52HZPmmmuaheaY6/VoFMizzz6bo+fX0HeNJFGROwAAAACI6DrpXsG3I488MsPtFSpUcAlTVlTpW0t8aX1p9cCvWLHCFQfTkl/ZzX331rsWVdsWVenWJZiua1i2hjN7Q5q9nzvXLjc7eKp1vqyT7j1vOMOqPUpclVCr4UNV0dVwoekD3j5UyEw071rDuzW3X1MFNK8/J9toSLrWO8/q2CpWrGhXXHGFjRo1yhV8C/f4vP16w+K99cX1/GqcUUE5HZOWTdMIjJNPPtk9Vtt5hea8fYVz2ymnnOKmXAwePNglzEq89VkaPnx4htem4nZqDFIleo02UCOSGhl0myrfa1t9Lv/73/9meTyiee/BQ+XV0KRielo2LtR76e1Ln8mcTDeIBqo3kN3/gWig/3X6XhUU739d5v958Af+LgAAwBOXHqrCVwFQdWxV3f7uu++sc+fOgdu1RreW51JhsMx0qBrq7q2BrURIlb+9quFKmkJRgqfEKzP1nHrDnD1KEqtUqeKGYXtD6lVI7MSTTrK9yclWUEokJtrP/384eDi0bJkqsuu1+sGmTZusU6dObuky/b38dnyRpjn17dq1szfffNM1hISiXnt99jSiQY0aAKKXlons2bOnK5TpFbkEAACxKWI96Uo+NddXw96D6bqGF4ei+zS8WEOmvZ5K9aBr+SuthZ1Vkn733Xe7nvfgnnQ9/xlnnHHQyZCSJyVGpUqVcj2f3rDoBX/9FXLprvyi3mjNtQ+XGhf0nvjl5E7HEVwlPSfHp8YYTWHQXPVwiq8VRnofDrX2vD6LqhGgOeveZzEWqCCkXnPrK4ZY6aPC/w4UJrs2rLRf3nnMTSvRCI6C6qn99ttvrUuXLiELNCKyvBFeAAAAEUvS1YOtHvDPPvvMevXqFehJmDRpklvv2qOiYZoLrN5GDY1XYq/K7pmTdyW1WVFy6CX1wXSimvlkVQXClBgGV0T3GgOyG1Ifaer118Wvy3nl5Pi8od/e3yFW6bXrPQj1OY32152cnGwlKtWy0jUaWTRKTTP3GvVaC/pvG2ufp8KCvwkAAIh4ki4PPfSQ681WUt62bVt77rnnXCKuZbE8jzzyiKs2rvXR1avYr1+/QI+55gdrOPWXX35pn376qcUyvQd+5vfjAwAAAACL9SRdc9E1//yFF15wwz6PPfZYt/xa8JDo+vXrux4nz8iRI928dBUTU2Epr/K2iogBAAAAAFCYRTRJF809zW6daM0nD6a5zaoSrgsAAAAAANEkdif8AgAAAADgMyTpAAAAAAD4BEk6AAAAAAA+QZIOAAAAAIBPkKQjx+677z4bMWIE7xwAAAAARFt198Ji5cqVtnnz5gJ7vooVK1qtWrXMj7RmfY0aNSJ9GAAAAAAQdUjSw0zQmzRuZEnJe62glEwsYQsWLvJtog4AAAAAyHsk6WFQD7oS9NEDj7MmVUtZfluwbrf1fmWOe95wk/TbbrvNGjZsaGlpaTZlyhQ7cOCAXX311XbOOedk2O7zzz+3MWPG2NatW6158+bucVWqVMnxNgAAAACAvEeSngNK0FvVKWt+NHfuXBs1apSddtpp1q9fP3f9wgsvtI8++sjOO+88t82bb75p1157rQ0bNswaNWrktj/xxBPtzz//tNKlS4e9DQAAAAAgf1A4LopUqlTJJeVKzocOHWrXX3+93X///e4+9awPGTLEJd/62a1bN/vss8/c7c8991zY2wAAAAAA8g9JehRRL3rRov83OOLss892Per79u2zFStW2IYNGzIMfy9WrJidccYZ9vPPP7vr4WwDAAAAAMg/JOlRJPNwdF1PT0+3nTt32o4dO0JuU6ZMmcB94WwDAAAAAMg/JOlR5O+//85wfdGiRVaqVCk3DL5evXoht1m4cGHgvnC2AQAAAADkH5L0KPLNN9/Y9OnT3e/bt2+3p59+2vr06eOuly1b1s0xf/jhhy0pKcnd9t1339mkSZPsqquuCnsbAAAAAED+obp7DpdG8/PzdO7c2Xr16mXly5e35cuXW4MGDew///lP4P7nn3/eFZXTsm41atRwPea6v0OHDjnaBgAAAACQP0jSw1CxYkUrmVjCrV1eUPR8et6cOP74423ixIkusd6/f78de+yxFhcXF7i/evXqNnv2bDd8fdu2bda4cWOX0AcLZ5uHHnrIihcvfpivEAAAAACQGUl6GNSrvGDhItu8ebMVFCXoet6cUnX3Y445JtttlHgfSnbbNGvWLMfHBQAAAAA4NJL0MClhzk3SDAAAAABAuEjSo8RTTz110NJpAAAAAIDChSQ9SrRo0SLShwAAAAAAOEwswQYAAAAAgE+QpAMAAAAA4BMk6QAAAAAA+ARJOgAAAAAAPkGSDgAAAACAT5CkAwAAAADgEyzBFqaVK1fa5s2braBUrFjRatWqFfb2H3zwgZUvX95OO+0085Onn37azj//fGvQoEGkDwUAAAAAfI8kPcwEvXGTxpaclGwFJbFkoi1csDDsRP3NN9+0o48+2ndJ+gMPPGD16tUjSQcAAACAMJCkh0E96ErQO97Z0crWLGv5bceqHTbtiWnuecNJ0r/44gtbunSpbd++3R577DF328CBA2327Nn2+++/u+sVKlSwk046yZo3b57hse+++65VrVrVqlSpYpMnT3a98Zdffrm7b9asWTZz5kyrWbOmnX766TZ69Gg75ZRTrGnTpoHHr1+/3iZOnGhJSUlu3x06dAjcN3LkSEtNTbWPPvrIFi5caKVKlbLBgwfn2fsEAAAAANGGJD0HlKBXbFDR/EYJspLhffv2uURdDhw4YMnJyYHrixYtsttvv92GDBlid999d+Cxr732mku09fguXboEkvjHH3/c9YL36NHDJep6zLp16+yll14KJOkTJkywK6+80s444wyrXLmyPfXUU9a6dWsbP368xcXF2c6dOy09Pd327NnjjkPHBAAAAADIGkl6FOjevbu99dZbbri715MuF154obt4Bg0a5Hq6+/fvb5UqVQrcrh57JfFly/47SkDJ+LBhw9wQeq9XXb3i1113XeAxW7ZssSuuuMLNhT/zzDPdbUrKGzdu7G7TMd1111324IMPuu2CjwMAAAAAEBpJepT7448/7Oeff3aJeFpamuvNXrBgQYYk/dxzzw0k6DJlyhRLSEhwveiefv36ZRiq/uWXX7re9/nz59u8efPcbeo113D5GTNmuCQdAAAAAJAzJOlR7J577nHD088++2yrVq2aS7zj4+Nt69atGbYLTti9nvSjjjrKbespXry4HXnkkYHra9eutRIlShxU8V6V3I8//vh8e00AAAAAEM1I0qOU5oFrXvmkSZOsc+fO7rZdu3a52w5FCfqmTZsy3KZe823btmVYIk5z4YcPH+4SeAAAAADA4fu/rlIUahqursTco981vD14GLuKxIWjY8eOLgH/7LPPMlSBDy78pt559bSrWFwwzVVfsWJFlscFAAAAAMgaPelRQkujqXK7esG11JmWYDvnnHPc3PCePXvaP//8Y99//70b8n4oWvbtzjvvdEXjNBd9//79bpm1I444IjAEXsPnX3/9dXe/5qC3bNnSli9f7n4fN26c1a5dO3BcKma3bNkyK1euHEuwAQAAAEA2SNJzuH65X59nwIABLnHWuujecmdaIk094EuWLHFV3Z9//nkbMWKENWzYMPA4JeLVq1c/aH8PPfSQe4ySbq2TPnToUJd4K9H29OrVy/W6f/rpp254vKq8v/DCC1amTJnANkrkdQxK0nfsKJj3DwAAAAAKK5L0MGj+dWLJRJv2xDQrKHo+PW+4tC75eeed5y7BtPxZMM0hD3b11VeH3J+SaiXd3vJqH374oUv8tQ56MCXw119/fdavIzHRrrrqqrBfBwAAAADEMpL0MGj498IFCw+qZJ6flKDreSPlq6++srFjx7qeclVyV2/4f/7zH6tcuXLEjgkAAAAAoh1JepiUMEcyaS5ogwYNcvPMp06d6ua533jjje46AAAAACD/kKQjS23atHEXAAAAAEDBYAk2AAAAAAB8giQdAAAAAACfIEnPQnp6esH+JYBM0tLSeE8AAACAGMOc9EwSEhLccmZa97tSpUrudxR8cpqSkmJ79+61+Pj4mGwg0uvXZ1Cvv1ixYpE+JAAAAAAFhCQ9kyJFiliNGjVs9erVtnz58oL6OyBTkpqcnOzWWI/lRpKSJUu6FQVisaECAAAAiFUk6SGUKlXKGjRoYKmpqQX/F4F736dNm+bWaNfIhlhtLCpatGhMN1IAAAAAsYgkPZskSRcUPL3v+/fvtxIlSsRskg4AAAAgNjGOFgAAAAAAnyBJBwAAAADAJ0jSAQAAAADwCZJ0AAAAAAB8giQdAAAAAACfiHiS/tJLL1mjRo2sXLly1qFDB/v5558P+Zhly5ZZz549rUqVKlavXj177LHH7MCBAwVyvAAAAAAARGWS/vbbb9utt95qDz/8sP3555/WqlUr69Kli61ZsybLx6xatcratGnjlumaMWOGu+zZs8f9BAAAAACgMItokv7444/b1VdfbZdccolVr17dnnnmGTviiCNc73pW7rnnHqtYsaK9+eabVrduXTvqqKPswQcftPbt2xfosQMAAAAAEDVJ+rZt2+yvv/6yzp07/9/BxMe769OnTw/5mLS0NPvkk0/s8ssvdz3pAAAAAABEk6KReuJ169a5n5UrV85wu67/+uuvIR+zadMm27Vrl+ttP/3009121apVs759+9ptt92WZeK+b98+d/Hs3LnT/UxNTXUX+Iv3N+FvE5vUGJeYmGgJ8WZF49IsGum16TXqtRbU55zvlb/x/w4AAEQ8SQ/uPc98PT09PeS2XnG4oUOH2ujRo90Q91mzZlmPHj3cCc69994b8nGPPvqoDR8+/KDbv/nmGytZsmSevA7kvW+//Za3NUaNGzfu//+22aJS5fI2cNw4V38juxoc+YHvlT8lJSVF+hAAAIBPxKVnlRHnsy1btri55R999JF169YtcPsVV1xhK1assGnTph30mJSUFNeLrnnso0aNCtx+88032+TJk23+/Plh96TXrFnTNm/ebGXKlMnz14bDowYXJRIqIpiQkMDbGWPmzp1rHTt2tM63v2TlazawaLRt1WKb8uR17v9cixYtCuQ5+V75m+KSYuKOHTuISwAAxLiI9aRXqFDBGjRoYFOnTg0k6Wov0HXNOQ+lWLFi1rp164OGtet6dm0NxYsXd5fMlACSBPoXf5/YpNE0ycnJlppmtj894qtE5gu9Nr1GvdaC/h/E98qfiEUAAMAT0TPgW265xV5//XXXC65l1IYNG+Z6t6+55prANoMGDbLjjz8+cP2OO+6wMWPG2A8//OCGv2vpNVV615B3AAAAAABiriddibSG5R0uJeCq8q6ecw1/b9KkiX3++edWr169DEM0g4eqX3TRRW7b3r17u7mcKhx30003uaXZAAAAAACIuSRda5qff/751q9fPzvzzDMPazk0Jde6qMpx5iJyornnui/YgAED3CWrxwAAAAAAUBjlKsOdMGGCxcXFubnktWvXdlXVlyxZcngHkkWyXbRoUTcXPSePAQAAAACgMMpVlnv22Wfb+++/b2vXrnVzxL/44gtXBO6UU06xt956i6VkAAAAAADIhfjDrdCu+eBz5syx5557zmbOnGlXXnmlVa1a1SXvu3btOpzdAwAAAAAQUw5rCbb169fb22+/bW+88YYtX77cDX/v37+/bdy40Z544gmXvGu9awAAAAAAkE9JuuakKzGfOHGiNWrUyC2Z1qdPH9ezHjwkvnLlyrnZPQAAAAAAMSlXSbqWP7v00ktt2rRp1rZt25DblC9f3u66667DPT4AAAAAAGJGruaka/kz9aSHStCHDBkS+P2hhx46vKMDAAAAACCG5CpJf/bZZ7O87/HHHz+c4wEAAAAAIGbl6ULjc+fOtUqVKuXlLgEAAAAAiBk5mpNerly5kL9LWlqaW3LtlltuybujAwAAAAAghuQoSX/ttdfcz+7duwd+9yQkJFidOnWsRYsWeXuEAAAAAADEiBwl6Zdccon7OWXKFOvUqVN+HRMAAAAAADEpV3PSSdABAAAAAIhgT3qpUqXcz927dwd+z4q2AQAAAAAA+ZSkjx49OuTvAAAAAACggJP0Cy+8MOTvAAAAAADAh+ukAwAAAACAApyTHg7mpAMAAAAAUEBz0gEAAAAAgE/mpCP2rFy50jZv3lwgz5WWluZ+zp071+LjC25GRsWKFa1WrVoF9nwAAAAAkOskHbGdoDdp3MiSkvcWyPMlJibauHHjrGPHjpacnGwFpWRiCVuwcBGJOgAAAICIYZ10HJJ60JWgjx54nDWpGn5tgtxKiy9ma8xs2pC2Fp+WUiB/oQXrdlvvV+a410pvOgAAAIBIYZ10hE0Jeqs6ZfP9HUu1BJekt6hVxhIsNd+fDwAAAAD8gnXSAQAAAAAo7HPS09PTbdKkSbZgwQJ3/ZhjjrHTTjvN4uLi8vL4AAAAAACIGblK0pcuXWrdunVzCXqNGjXcbatXr3aJ+oQJE6xu3bp5fZxAgfAanaIVFewRratCRArfKQAA4IskvX///la7dm375ptvrEqVKu62devW2cCBA919kydPzuvjBPLVvpR97mfv3r2j+p1OLJloCxcspDgeCiRBb9yksSUnFdwKDZHAdwoAAPgiSZ8xY4b9888/gQRdqlatai+//LLVr18/L48PKBD79+93P1v2aWk1Tvh3dEi02bFqh017YhoV7FEg1IOuBL3jnR2tbM38LzgZCXynAACAb5L0mjVrWkrKwUtj7du3z90HFFalqpSyig0qRvowgKihBJ3vFAAAQPjiLReuu+4669Onj/3111+B2/R737593X0AAAAAACAfe9KDh7bLhg0brGnTplayZElX6T05+d95h3///bfdfPPNuTgUAIgtBVmoMC0tzf2cO3euxcfnqn02R6K9CCMAAEDEk/Qnn3wy3w4CAGJJ6r6UAi9UmJiYaOPGjbOOHTsGGlXzm5oCkrYmFchzAQAAxFySHu1VrwGgoAsVPnhRQzvn2MoF8pxp8cVsjZlNG9LW4tMOrimS1377Z5MNeGeRpezJ/+cCAACwWC8cBwA4fHUrlrRWdQqm8nmqJbgkvUWtMpZgqfn+fHuS9uT7cwAAAESjXCfpP/30k33wwQduLVyvV8gzYcKEvDg2AAAAAABiSq6qB40ePdq6dOliGzdutA8//NAqVqxoy5Yts08++cRKlCiR90cJAAAAAEAMyFWS/thjj7kCRErW5bXXXrM5c+bY7bffbnFxcXl9jAAAAAAAxIRcDXdfsmSJ60mXhIQES0pKckux3XXXXdawYcO8PkYAAAAAAGJCrnrS9+3b55bzkerVq9tff/3lft+9e7elpuZ/QSIAAAAAAKLRYVd3v+SSS9zybPr5+eefB3rYAQAAAABAASTpM2bMCPz+8MMPu2Jxuq19+/Y2fPjw3OwSAAAAAICYl6skvU2bNoHfixUrZg8++GDMv5EAAAAAAERsuHt6erpNmjTJFixY4K4fc8wxdtppp1HdHQAAAACAgkzSly5dat26dXMJeo0aNdxtq1evdon6hAkTrG7durk9HgAAAAAAYlauqrv379/fateubatWrbJly5a5y8qVK61WrVruPgAAAAAAUICF4/755x+rUqVK4LaqVavayy+/bPXr18/NLgEAAAAAiHm56kmvWbOmpaSkhFw/XfcBAAAAAIACStKvu+4669Onj/3111+B2/R737593X0AAAAAACAfh7sHD22XDRs2WNOmTa1kyZKu0ntycrK7/e+//7abb745F4cCAAAAAEBsCztJf/LJJ/P3SAAAAAAAiHFhJ+m9e/fO3yMBAAAAACDG5WpOerB169bZ2rVr8+Zo/l979wJlVVn/D/g7IzMgJIKapAImLsREQOUvKrrwgpioJZhKYpp2WV7KUlC8rOKiZZpmmkWuTE1TlpoGoallhhiCiokKXgEvICoqXkCuAzP/9e7WzI+Ri8PAzNlzeJ61zjqz33P22e8+nK3nc95bAAAAwOarXiF91apV8Ytf/CLatm0bO+64Y+y0007Z36ksPQYAAAA00jrpo0aNihtuuCFGjBgR+++/f5SUlGRrp19++eXZBHKXXnppnV9r2rRp2Wuliei6desWQ4YMyQJ/XYwbNy7bd+DAgXHGGWfU51QAAACgabek33TTTXH33Xdns7inkL7ffvtlf6eym2++uc6v88QTT8QBBxwQZWVlccIJJ8Sjjz4aBx54YCxZsuRz933jjTfinHPOieeeey5eeuml+pwGAAAANP2Q/v7778fee++9RnkqS4/V1SWXXBL9+/eP3/72t3HyySfH/fffH3PmzMl+BFiflStXxkknnRSXXXZZtGvXrj6nAAAAAMUR0r/yla/Erbfeukb5LbfcErvvvnudXiN1i3/sscdiwIABNWVbb711HH744fHQQw+td9+f/OQn0b59+zjttNPqUXsAAAAoojHpP/vZz7Jx4OPHj49evXplZU8++WRMnDgxGydeF3Pnzs0mmevQoUOt8hS+J0yYsM79Hn744RgzZkw8++yzda7v8uXLs1u1hQsXZvcVFRXZjfWrrKyMLbfcMipLy6Miyhr87ao+RmMcq1rVFuXZOZZvUR7Nqup1WeReWUnZ//4dKytz/bmv/ryVlUY0K6mMYlS+RUl2jtGseaN9zhv7unJNbZg8X5MAQOMqqaqqqqrPjtOnT49f/epX8cILL2QTx+2xxx4xdOjQbPK3upgxY0b23MmTJ2fj0qsNGzYsxo4dGzNnzlxjnzS5XOpS/+c//zn69u2ble21115xyCGHxLXXXrvOY40cOTKb7O6zUthv2bJlHc8YABpGmotl8ODB8cknn0Tr1q29zQCwGatXk2EKxGmiuD/96U/1PnCbNm2y+w8//LBW+YIFC9Y5u/s999wTixcvjquuuiq7Ja+99lp8/PHH8fLLL8cDDzwQpaVr9uC/+OKLs1njV29JTy34RxxxhC9DdZAm5+vTp088dtEB0aNjw395TC19D7c9Ofp9dEeUReO0Lk155Z048rrno/e5vaNTn05RjBbMXhAPXvBgNsykR48ekffP26Hnj462HTpHMXrnledjyuihcePp3ePEfXdolGM29nXlmtow1T28AADqFdJTa/cPf/jDaNas/t2CU7f27bbbLluC7eijj64pT9s9e/Zc6z5f+9rXYtddd61Vllrcu3fvni3Bllr016Z58+bZ7bPSrPLpxvqlHz7SHAKllSsaLTQn6ViNdbySVSuyc1yxakWsLFkZxaiiquJ//46lpbn+3Fd/3ioqI1ZW1WvajNxbsaoqO8dYubxRr6nGvK5cUxsmz9ckANC46pWyUzf1qVOn1uqmXh+nnnpqNpP7mWeemQX2f/zjH1lI/81vflOr1T61kqf10Dt27JjdVrfVVlvFzjvvHEceeeRG1QUAAACaZEj/1re+FYMGDcqWUEtj0cvLy2s9ntZOr4tLL700G9veuXPn7Jb+vvzyy+Oggw6qNXY9racOAAAAxa5eIb16fPdZZ5211sfrOhddq1at4p///Ge8+OKL2aRwKfB/dt3z8847Lxtzvi6jR4+ObbbZZoPqDwAAAEUT0hctWrRJK5HCebqtTdeuXde7b+/evTdpXQAAAKBJhPS0jmvqop7WR0+t5ccee2wMHz7chDcAAADQ2CH9sssui2uuuSaOP/74bDutk55mYl7bGuQAAADAhtmg9Y3uuOOOuPfee+PWW2/Nbmnd8lQGAAAANHJInzt3bhx22GE124cffnjMmTNnE1QDAAAAKN3QMemrL7eW/k5lAAAAQAFmdx8wYMDnlo0bN27jagUAAACboQ1qSU+zudelDAAAAGjglnQt5AAAAJCTlnQAAACg4QjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA5IaQDAABATgjpAAAAkBNCOgAAAOSEkA4AAAA50azQFfj000/jvvvui/nz50e3bt2ib9++n7vPjBkzYsqUKdGsWbPo3bt3dOnSpVHqCgAAAEXbkj5v3rzo3r17XH311fHCCy/EySefHN/85jejqqpqrc9P5f3794/BgwfH1KlT45FHHom99947Ro0a1eh1BwAAgKJqSb/ooouibdu2Wat4eXl5vPjii1loP/HEE+O4445ba0j/8Y9/HEceeWRN2b333hvHH398Fu61qAMAANCUFawlfdWqVTF27Nj49re/nQX0ZI899oiDDjoo/vKXv6x1n9LS0loBPUnPT1577bVGqDUAAAAUYUv6nDlzYvHixWu0fqftJ598ss6vc88990RZWVnW7X1dli9fnt2qLVy4MLuvqKjIbhvrrbfeigULFkSxeuWVV2LLLbeMytLyqIiyBj9e9TEa41jVqrYoz86xfIvyaFZV8KkaGkRZSdn//h0rKzfJ576hpPqlepaVRjQrqYxiVL5FSXaO0ax5o33OG/u6ck1tmDxfkwBA4yqpWtcA8AY2ffr0rGt76uq+//7715RfeOGFWRf2WbNmfe5rPPPMM9GnT58YNmxYDB8+fJ3PGzly5FrHrY8ZMyZatmy5EWcBABtvyZIl2Xwrn3zySbRu3dpbCgCbsYI1GbZq1apWq3a19AWl+rH1SRPNpa7vabK5n/70p+t97sUXXxxDhgyp2U7H7NChQxxxxBEb/WXoueeey34o+H+nXBRbtesYxWjBmzPj+b/8Om48vXucuO8ODX681NL3cNuTo99Hd0RZNE7r0pRX3okjr3s+ep/bOzr16RTFaMHsBfHgBQ/GY489Fj169Ii8qr6mDj1/dLTt0DmK0TuvPB9TRg9ttGuqENeVa2rDfPb/hQDA5qtgIb1jx47RokWLrMU8heVqaXu33XZb775pgrnDDjssjj322LjhhhuipKRkvc9v3rx5dvus1E0+3TZGGie/dOnSaPHFjrFV++JcCm7hoiXZOcbK5Y0WmpN0rMY6XsmqFdk5rli1IlaWrIxiVFFVkZ1j+sxu7Oe+IVVfUxWVESurCroARYNZsaqqINdUY15XrqkNk+drEgBoXAX7BpzWOD/mmGPi9ttvzyaRq578beLEibVmdn/wwQfjpptuqtl+6aWXsoD+9a9/Pf7whz98bkAHAACApqKgM2T98pe/jN69e0ffvn2jV69ecffdd0e/fv1i0KBBNc9J49OfeOKJ+O53v5uN2UvPTcPoO3XqFFdeeWXN84466qhsjDsAAAA0VQUN6bvsskvMmDEj7rrrrpg/f35cffXVWSt66u66evjec889a7ZPPfXUmrHrq1t99nYAAABoigq+1tS2224bZ5999jofX73re5qJ/YorrmikmgEAAEDjKs5ZmQAAAKAJEtIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHJCSAcAAICcENIBAAAgJ4R0AAAAyAkhHQAAAHIiFyF97ty58fTTT8fChQsbdB8AAADIs4KG9GXLlsU3vvGN6NKlS5xyyinxpS99Ka6//vpNvg8AAAA0Bc0KefBRo0bFU089FbNnz44ddtghxo0bFwMHDoxevXrFfvvtt8n2AQAAgKagoC3pt9xyS3zve9/LwnYyYMCA2HPPPbPyTbkPAAAANAUFa0l/++23Y/78+dGzZ89a5alFfNq0aZtsn2T58uXZrdonn3yS3X/44YdRUVGxUeeRxsS3aNEiPp33akTF0ihGS999LTvHVz5YEY++uqjBj1dZWh5Lui6J/8z6NEorV0RjmPH2iuwcl7y9JD6Y/kEUo0XvLMrOMX1mFyxYEHnlmiqO68o1tWEWLfrff1urqqoa5N8DAGg6SqoK9I1gxowZ0a1bt5g8eXIccMABNeXDhg2LsWPHxsyZMzfJPsnIkSOzbvIAkGdpUtT27dsXuhoAwObYkl5WVlYzEdzqli5dGuXl5Ztsn+Tiiy+OIUOG1GxXVlZmrejbbrttlJSUbNR50DAtqR06dMi+rLZu3dpbDK6ropd+L0+t6TvuuGOhqwIAbK4hPYWw0tLSmDdvXq3ytN2xY8dNtk/SvHnz7La6Nm3abFT9aXgpoAvp4LraXGy99daFrgIAsDlPHNeyZcvo3bt3jB8/vqZs8eLF8a9//Sv69etXUzZr1qya8eZ13QcAAACaooKNSU8mTpyYheuhQ4dmY8zTeuevv/56PPvss/GFL3whe06ayf2JJ57IxqPXdR+afnf31KKUJvjTkg6uKwCAzUlBl2A7+OCDY8KECfHmm2/GddddF127do1JkybVCtudO3eOffbZZ4P2oWlLQxNGjBixxhAFwHUFAFDsCtqSDgAAAOSkJR0AAAD4P0I6AAAA5ISQDgAAADkhpJM7M2fOjGeeeSaWLVtW6KpAUVi+fHlMmTIlZs+eXeiqAADwOYR0cuO9997LltXr1atXDBo0KHbccccYP358oasFTdbHH38cF110Uey6667Z0pVXXXVVoasEAMDnENLJjTPOOCNWrlwZ8+bNy1rThw0bFieddFK8++67ha4aNEnpWmrTpk3WM2WvvfYqdHUAAKgDIZ1c+OCDD7JW8/PPPz9atmyZlZ177rmxxRZbxJ133lno6kGT1LVr16wlffvtty90VQAAqCMhnVx4/vnno7KyMnr27FlT1qJFi+jWrVtMmzatoHUDAABoLEI6ufDhhx9m99tuu22t8rRd/RgAAECxE9LJhbKysuz+szO6L126NMrLywtUKwAAgMYlpJMLO++8c81EV6tL2x07dixQrQAAABqXkE4upLHn7dq1q7Xk2quvvhovvfRStnQUAADA5qBZoSsASZrF/fLLL4+zzjor2rZtm7Wejxo1Kg4++ODo37+/NwnqoaqqKh5//PHs74ULF2bLGU6aNClbQWGfffbxngIA5FBJVfoWBzkxbty4uO2222LRokVx4IEHxgUXXBCtWrUqdLWgSVqxYkUcdthha5Sn4SV33HFHQeoEAMD6CekAAACQE8akAwAAQE4I6QAAAJATQjoAAADkhJAOAAAAOSGkAwAAQE4I6QAAAJATQjoAAADkhJAOReK9996LO++8M6qqqtb5nI8++ih7TkVFRaPWDQAAqJuSqvV9oweajEcffTQOPfTQLIA3a9Zsrc95+umnY999983Ceps2baIpmDt3bjz33HOx9dZbR8+ePaNly5aFrhIAADSYtX+TB4rSNttsE4MGDYry8vLIu0WLFsV3vvOdeOaZZ6Jr167x5ptvxrx58+K2226Lo446qtDVAwCABqG7OxSZ1Dlm+vTp8be//S1mzZpV67G2bdvGgAEDoqysbI0u8i+++GLcf//98eqrr9baZ+nSpdlz0u3ee++NadOmrdGlvvp1KisrY+rUqXHPPfdkZXfddVe8/fbbtZ67cuXKrPydd95Z73mk46aQPnv27Bg/fnzWmp5+YPj+97+/ke8QAADkl5Z0KDLHHntsvPXWW/HFL34xHn/88fj5z38eQ4cOzR5Lgfekk06q6e6egnnaHjNmTBam0z4TJkyotU8Ky+PGjcv+Xr58edZlvmPHjvHQQw/FVlttlZVXv87tt9+etXbvtttu0a1bt7j++uvjySefjGuuuaamfn//+9/jtNNOWyO8f9b2228f/fv3r1XWpUuX+PTTT7MfCUpKSjb5ewcAAIUmpEORadeuXRaEU4hNLdqDBw+OgQMHRqdOnda5TxrrnVqrk1tvvTXOOuusOOecc7Ju8amLfGolr7ZixYro06dPFrxHjBhR63V23333rDW+Wmr1vuCCC+LKK6+sab2/+eabs/qkVv26mDRpUsyZMyf7geGPf/xj/O53vxPQAQAoWrq7Q5E5//zza0Ls8ccfHx06dIixY8eud58UyqsdcsghWet5CsarS93cUxf6v/71r9G+fft46qmn1nidH/3oR7W2TzjhhCzU33fffdn2/Pnz44EHHsi6sddVaolPx0w/FGy33Xax66671nlfAABoarSkQ5H58pe/XGt7l112ySZdW5/UWl6tefPm2f2yZcuy+/fffz/69euX3ffo0SNat24dM2fOXOvkczvssEOt7TQTe+oGn1rPjzvuuGzSt5122in69u1b5/Op7nafXHLJJXH00UfHG2+8kdUDAACKjZZ0KDJpvPlnt1MLdH39+te/zoJ7CvqpFTy1aKfQvrbVG9c2Tjx1eU/j19MY9FtuuSUbj17f8eSnnHJKdj4zZsyo1/4AAJB3QjoUmepJ3pI0jjvNin7ggQfW+/XefffdrIt59drraR326vHrdbHPPvtE9+7d4wc/+EG8/PLLcfrpp9f5uJ/9IeC///1vdp+62wMAQDHS3R2KzBVXXJEtf5ZmR7/22mvj8MMP36Du5WubLT6NLe/cuXPWnT11WU9d3zeku3lqTT/77LOzuuy888512uff//53jB49Oo455phsMry0rNyNN96YjblPs8sDAEAx0pIORSKF8rSO+JQpU7KZ1FOrc5oQbvWW9TT2PD2nejx59T6lpf/3n4Itt9wyK0tLtFWH9DRhXJr0LS2/duaZZ2ZjzL/61a+ucezVX2d1aW32ZEMmjEuz0v/+97+PxYsXx3/+859o1apVtjzcVVddVY93BwAAmoaSqrUNLAXYhFKov/DCC2Pu3LnRokUL7y0AAKyD7u5Ag0lj4idOnBjDhw/PZmlfPaCnFv91zTqfZqTfb7/9/MsAALDZEdKBBvP666/HI488Euedd152W93UqVNj8uTJa93v4IMPFtIBANgs6e4OAAAAOWHiOAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAMgJIR0AAAByQkgHAACAnBDSAQAAICeEdAAAAIh8+P/EP1siettjVwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "objective = beta = BetaBalance(m)\n",
    "\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, beta, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d032db-fbd5-4b50-8501-c36ae0b3028f",
   "metadata": {},
   "source": [
    "## Improve upon PropensityScoreMatcher solution with ConstraintSatisfactionMatcher"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7070708e-18cf-491d-9dd7-56ed3951c6c6",
   "metadata": {},
   "source": [
    "Because the PropensityScoreMatcher doesn't directly optimize balance, it only achieves good balance when we find the right propensity score model. Depending on how we've parameterized the space of possible propensity score models, we may in fact never find the right model. This leaves us often with residual confounding that cannot be removed via propensity score matching. Here we show that the ConstraintSatisfactionMatcher is able to find a significantly better matched solution compared to the propensity score approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e69b60d2-ae98-46bd-a232-7fd1a5b74174",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:66] Scaling features by factor 240.00 in order to use integer solver with <= 0.2898% loss.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'objective': 'beta',\n",
       " 'pool_size': 1000,\n",
       " 'target_size': 1000,\n",
       " 'max_mismatch': None,\n",
       " 'time_limit': 60,\n",
       " 'num_workers': 4,\n",
       " 'ps_hinting': False,\n",
       " 'verbose': True}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pybalance.lp import ConstraintSatisfactionMatcher\n",
    "matcher = ConstraintSatisfactionMatcher(\n",
    "    m, \n",
    "    time_limit=60,\n",
    "    objective=objective,\n",
    "    ps_hinting=False,\n",
    "    num_workers=4)\n",
    "matcher.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08bd0619-cb3d-4179-b377-07d5ca9a401f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO [matcher.py:499] Solving for match population with pool size = 1000 and target size = 1000, optimizing balance (no max_mismatch cap).\n",
      "INFO [matcher.py:503] Matching on 15 dimensions ...\n",
      "INFO [matcher.py:510] Building model variables and constraints ...\n",
      "INFO [matcher.py:519] Calculating bounds on feature variables ...\n",
      "INFO [matcher.py:609] Applying size constraints on pool and target ...\n",
      "INFO [matcher.py:239] Solving with 4 workers ...\n",
      "INFO [matcher.py:91] Initial balance score: 0.2449\n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 1, time = 0.02 m\n",
      "INFO [matcher.py:102] Objective:\t480270000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2421\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 2, time = 0.03 m\n",
      "INFO [matcher.py:102] Objective:\t479744000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2418\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 3, time = 0.04 m\n",
      "INFO [matcher.py:102] Objective:\t479679000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2418\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 4, time = 0.07 m\n",
      "INFO [matcher.py:102] Objective:\t479629000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2417\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 5, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t479552000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2417\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 6, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t479486000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2416\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 7, time = 0.08 m\n",
      "INFO [matcher.py:102] Objective:\t479423000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.2416\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 8, time = 0.10 m\n",
      "INFO [matcher.py:102] Objective:\t29842000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0165\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 9, time = 0.13 m\n",
      "INFO [matcher.py:102] Objective:\t29840000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0166\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 10, time = 0.15 m\n",
      "INFO [matcher.py:102] Objective:\t29819000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0165\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 11, time = 0.19 m\n",
      "INFO [matcher.py:102] Objective:\t29801000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0165\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 12, time = 0.21 m\n",
      "INFO [matcher.py:102] Objective:\t29777000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0140\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 13, time = 0.42 m\n",
      "INFO [matcher.py:102] Objective:\t29771000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0140\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:97] =========================================\n",
      "INFO [matcher.py:98] Solution 14, time = 0.70 m\n",
      "INFO [matcher.py:102] Objective:\t29767000.0\n",
      "INFO [matcher.py:123] Balance (beta):\t0.0136\n",
      "INFO [matcher.py:128] Patients (pool):\t1000\n",
      "INFO [matcher.py:130] Patients (target):\t1000\n",
      "INFO [matcher.py:146]  \n",
      "INFO [matcher.py:252] Status = FEASIBLE\n",
      "INFO [matcher.py:253] Number of solutions found: 14\n"
     ]
    },
    {
     "data": {
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       "\n",
       "        <b>Headers Numeric: </b><br>\n",
       "        ['age', 'height', 'weight']<br><br>\n",
       "        <b>Headers Categoric: </b><br>\n",
       "        ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3'] <br><br>\n",
       "        <b>Populations</b> <br>\n",
       "        ['pool', 'target'] <br>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>weight</th>\n",
       "      <th>gender</th>\n",
       "      <th>haircolor</th>\n",
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       "      <th>patient_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>37.519341</td>\n",
       "      <td>178.337875</td>\n",
       "      <td>57.424543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>10000</td>\n",
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       "      <th>1</th>\n",
       "      <td>23.722325</td>\n",
       "      <td>128.347114</td>\n",
       "      <td>102.183004</td>\n",
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>64.523502</td>\n",
       "      <td>144.600598</td>\n",
       "      <td>90.061948</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "      <th>3</th>\n",
       "      <td>25.377578</td>\n",
       "      <td>177.986337</td>\n",
       "      <td>82.076883</td>\n",
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       "      <td>10003</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.922515</td>\n",
       "      <td>155.633760</td>\n",
       "      <td>76.929413</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>target</td>\n",
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       "      <td>10004</td>\n",
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       "      <th>9935</th>\n",
       "      <td>63.955700</td>\n",
       "      <td>164.060497</td>\n",
       "      <td>101.344670</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>9935</td>\n",
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       "      <td>90.463362</td>\n",
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       "      <th>9960</th>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>pool</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>9983</th>\n",
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       "      <td>175.593470</td>\n",
       "      <td>75.612613</td>\n",
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       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>pool</td>\n",
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       "      <td>1</td>\n",
       "      <td>9983</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "<pybalance.utils.matching_data.MatchingData at 0x1585194c0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matcher.match()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99890264-1264-4bcc-bab6-79b51e4a0228",
   "metadata": {},
   "source": [
    "As one can already see from the reported balance metric, the ConstraintSatificationMatcher finds a much better solution. We also confirm this result visually below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0f18f39-6a98-48d3-be63-2ffd8d1ecfa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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++mvzzjvvmNNPPz3dc+qx2759e/Pcc89l4VMCAPA/JIwpQlW+Kqk69dRT7XhwGky4W7duYbNl7Ny501x55ZV2YM46deqYqlWrmnHjxoWtJ55lMjJx4kTToEEDU6tWrbDHNRi6V3zqZdqvXz9TpkwZ+3oNJPrAAw+kJa3Dhw833377rXn33XdNq1at7E3J6WOPPWauuuoq+3e5cuVMs2bNzPfff+8Zo5JrN+mMdfNqA6hE7OKLLzZnnXWW/fuKK64wrVu3Ns8//3yWXqMSVH1mbXcvSqi17JgxY0zJkiWjLnPeeeeZCRMmeK4HAIB4kDCmkPfee8/07dvXlrj98ssvZtasWbb603XnnXeaRYsW2faFSphU2tWjRw/z888/J7RMRlQ61qJFi4TjU5u77777zj6nNnnz5883b7zxhnnxxRft86NGjTJnn322Lbl0k7q6devadXz55Zf2byW8SsqUjHklfC+99FJa0hnrtmDBgqivVXXyvHnzbPIb6rTTTouZqGbmNbEoge7Vq5e5+uqrzSmnnBJzuZYtW5r169ebP//8M6H1AwAQiYQxhbRp08Z07drV3lc7v7vvvjutSlKdT15//XWbAGoGG1V7XnfddebEE09MaJl4aKac8uXLJxSfqmiVGCpp1Cw7WofGt1PJqRLNeChBVNW0St6UPC5dujTmsnfddVeGJYwnn3xy1NcqKdV7qTQzlP5WNX52vSYWtd9U55+MOh6534GSRgAAsoJOLymkSZMmYX+ralaJhdr+qQOFBiVv3rx52DLHHXecWbZsmb2vUsWMlolHgQIFbNvCROJbsWKFncNbJZCR0zHWrFnT8/1UUqnqeMWv6uyCBQum9R6OVtIpKj3NaJgfJVzqmBLJjS/yM6pKXcluNJl5TTRKggcPHmwmT56cNiepqvVV6qjPq+p+d33ue+n7AAAgKziTpBAlXNH+1qw1buITuYxKvdzn4lkmHkrwopVqecXnJjmfffZZuoTVixJDVUFfcsklZuHChXbKNiVKmrLNnbUnVpX0U0895blutf+LVsqoNoNq4xk5ibxKN2O1PczMa6JR4q+2nWr/6VLSrdJgVaPPnTvXVKtWzT7ufgcZJdwAAGSEKukUog4hob755hvbxq948eK2A4uSlpkzZ6Y9r1IpLaOSPolnmXioXZ7aIiYSX6NGjWwipB7EkULHK1TiGlpKp6RIHUAuuOCCtPl9Z8+eHbWEM7uqpEU9k6dOnRr22Oeffx7WY1klmKGJczyvyUi7du3Sxal2nR06dLD33WRR1Dby6KOPNhUrVox7/QAAREMJYwpRtfFtt91mxz/88ccfzeOPP27+85//2OdUTXvffffZdm+qalUv5Geffda2Fbz99tvjWiaydCwWjQmoOH744QdzwgknxBWfEsGHHnrIdrpRAnn++efbqmqVOCppdQf91piCSrrUMaZEiRI2zipVqti2l4pXVds333xzjk/Xpu2kRE9xXXjhhbZzjnqBf/DBB2nLqJOO2hvOmDEj7teoNDe0TaO2gRJBbRNVtydi0qRJtvc4gLxl9bDw5j+x1Bi8OMdjQeogYUwhav+nql0lCSph04DO6rTiGjBggC2FU8KiRKRp06a2lC+0BMprGTdhVEcNr+RFQ+aoTaHGGXQTRiV2SpjUsSZWfIpfJWQaZkYdYJQIquRQg0+77rjjDtvrt2PHjnZdSsY++eQTmySqlE1t+NRretCgQbZaOqeo9PHTTz+17/Xaa6/ZRPa///2vadiwYdoyqoIOrW6O5zVKstXRR/RaDRmkmzoLjRgxImos+j4imwwoof7pp5/CklEAADIrn5OZkZlzOVUVqvpTU8zppB5KHQhU6qNZMtwqzmTSST6R6l/XOeecY44//ng7c0gQYtPQOSpNUwlhaDVpEGJLNj9ic2d4ueWWWwIVV7xyU2x+HzNCVapUKe6agGQKalypGlsyShiDut2CGleQY/PKiUJRwogcoVJGjecIfzz99NNsegBAtqHTS4pQla+uEAAAALIbJYwpQvMUAwAA5ARKGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGJEwzQftzu0cy7///mtOO+00Ow9yXrNnzx7TuHFjz4HLNTuIto9mCMnI9OnTzeWXXx73upF9EvmeACCVMQ5jNk+1lFWaoXn1R8GeFH7VqlXm0KFDnssooaxZs2ZSpgXMSfqcmgZu9OjRafNix/OaJUuWmN27d8dcRlPInX322aZ///6e8z0fPnzYTvM3ZMiQuNcdb4yXXnqpHb8z3s+Vk15++WUbi+YHP+OMM+xc4V6UMGuuck3Tp1mFLrzwQjt/eYEC0Q9pt99+u5k2bZqdOlPzk7s0b/fPP/8ctmynTp3sPOeJfE8AkOpIGJHtVLr43HPPmc8//zzXb11Nta4ETYlMdlOCU6tWLfPHH3+YevXqRV1m8uTJ5p9//jEXX3xxtn+u5cuX58jnSpQuLh577DG7z1SoUMEMGDDAfPXVV+aHH34w+fLlS7f8woULzTXXXGP69OljevToYf78809z5513mnnz5pnXXnst3fJKRGfNmmW/R81xHkrb/txzzzXXXntt2mNlyuiyLbHvCQBSHQljitBJrX79+mbfvn22ClMlSNddd5256qqrwpYbM2aMeeutt2wS0rRpU3P//fenOwnGs4yXSZMmmRIlSpiTTz45ofi0TIMGDewyn332mTn66KPN66+/bg4cOGCeeeYZ8/HHH5v9+/eb4447zgwaNMhUrFgxrZpWpWUqPfriiy9siZESjwceeMAUL17cDBs2zPz666+mYcOGdpnQUk+VZm3ZssUcccQRpnr16nY9V199dVqioupI0WNaV506dWwcBw8eNM8++6z55JNPbLXleeedZ+6++25TqFChtHWvX7/eJjVz5swxtWvXNgMHDjTHH3982vNVq1Y1J554ohk7dqwZOnRo1G2p70IlXvnz5w97fM2aNebVV1+1n7Vy5cr2s6ok1JXRNov1uTLaHtlN390jjzxiS/S6d+9uH9P3c+yxx5r//ve/5pxzzkn3Gj23YMGCtJi0n+nzKk7Noa3P41JSrGRS6zrmmGOixqBtomr+WOL5ngAg1dGGMUWojZUSO50glSBdccUV5oYbbjDvvPNO2jJKIG655RbTuXNn8+STT9qTtU62SgwTWSYjX3/9tTnppJMSjs9dZuPGjWbUqFE2wXGrDSdOnGhLnp544gmboLVq1SqtdEzJp9bbt29fc+qpp9rXSvv27W3JnP5XIrFp0ya7rlAvvfSSjUHJQLdu3ex7PvXUU2nPv/LKK/b/wYMH2+X0/nLZZZfZ5ZT0jhgxwiZl7vu6brzxRlvde9ddd5lKlSqZdu3apSvh0rZVaVoi21JUwtakSRMbT9myZU2bNm3sdnNltM1ifa6Mtkc02sZKuGLdTjnllJivVSmiSqQ7dOiQ9pgSuypVqtgLi2iUlEcmsG5VtKrwXUoir7zySjN8+HB7sRLLG2+8YVq2bGkuuugiu11C1xHv9wQAqY4SxhSiUiy1tdPJtHXr1mbdunW2RETJg0rE1A5OJ08lOaITuU6kSgyVxGW0jEqb4qHEL1ppjld8LpWEKWl1zZw500yZMsX89ddfpnTp0vYxJT4qLVRiE1qVqOSmV69e9v5DDz1kEypVd7olV/qMOvHv3LnTlCxZ0j6m9bhUQqckQ6WQavMmKuV0Y3dLoZQ4qDRO7ef0HqJEVa8NpRJFxafllCArMVFpo0ojQ0uvYnWo2LFjh03UlTxFUsmlOh+539HcuXPtd6TSuni2WbTPFc/2iGbkyJE2IY0lsnQ0sqRUVEoaqly5cmnPZUTvrSptJeTu9yr33HOPLa3U59W+HY2qmtu2bWtL0vU93XffffY7Usl2KK/vCQDyAhLGFKLqxNCSl7POOsue7FVKqORs69atYVV8OpGfeeaZtk2YqC2Y1zLxJowqbQutmo0nvqJFi9rHVPUX6ptvvrElPipBU7s70f9Khn777bewZZs3b552XyV6ElpN6z6mkkY3sZg/f75NULUuJWgqgdO6vXz77bc20XCTRVfBggXD/m7RokXafW0PJUF671CFCxe2VfCxtqP72kj6TlzapqHfYyLbLFJmtkdW2vW5iVzkZ9TfkQl4NPqcasf4999/22YMLiXMSo4z6k2uZdzSSSXV2kfUBEDNC9ykOqPvCQDyAhLGFOImXa5ixYrZREE9at2qyGjLuM/Fs0w8dNLdvHlzQvG5z+mxUHpOJWxqUxlJCVioaD1koz3mJlG///67Of30020VvKp41dtWyeBNN92UYYlWZJzReL23S9vJTWQjqapZSWi829L9jhLZZqEyuz1UJa3XxlKqVCkze/bsqM8dddRR9n8lfKGljNu3b/eM1U0WVaKsGFV1r1JA17vvvmsTPCXNoe69917z4Ycfmvfffz/qd+Qur7ahoQmj1/cEAHkBCWMKWbp0adjf6hWqKkmdlN12X3pMHRxcOjG67bv0eEbLxEOlhG+++WZC8cWiTjAay1EdE8qXL2+ykzpCaOgflXK6pk6dGraMOn9EJnqqtl2xYoUtgVMylBXqvBHaOSjyvdVJJlrprrZlaCmjtmXdunXj3mbRPlc82yO7q6TVBEH73HfffWdL9kSl3Kr+VbtCr2RRnVw0VI6Sxcj9U00SNBSOS+1cVQKtdq7umJbRuOOGRn6vXt8TAOQFdHpJITrhu9VyqkZUZwz1PBZVwaoTg9oMuh1Y1ClC7d169+4d9zLxUInTL7/8km7Qbq/4YlHnEpXsqB2aSp3cqlp1zlD1aVYomVKMGldSFPPjjz+eLrFSj2tV17suueQSO/SKkg83UVICN2HChITeXyVg2rZaXyx6Tj2/I6mDjZoZyJdffmmHMHK3ZTzbTJ9LJZihnyue7RGrStqr00ujRo1ivlYloRoXUW1N1bZUCax6fGtfDN0u+kxqEypaRm1s3WRRCXIklTaGxqCe1aI2jW5ivXjxYtvT3K36Vu9wjXlZo0YNW9KayPcEAKmOhDGFaPBi9cjVcCgqKdKJXL1gXeoUoWo+nUx14lQJjca+Cy3JiWeZjKi0Rz2T1cElkfii0RApSgzUu1iJmzppKD51UHBP/JmlEi3FqRJDrVcdV9RxIpJiVnKoKkr1pNWQQUrili1bZhNHfR71Kg/tMBIPVY2qFDDae7qU9GlIICU3oRS3OmrovbVd1aFH8SeyzXr27Bn2ueLdHtlNYycqZsWqpPWjjz6yCXFoKZ/GQHQvQHThoX1LpYZK4kITQ22reGkf1DbRtlGJuvZ5lcRr22nA7kS+JwBIdfmcyEZVeYCqElUVqtKXyKonlRipOkwnzNCTRrLoBBbaUSNe6qii6ktVJ6pjhToTROtd67bHUjKhE2a0DhWxlnFjW716tW1bF9mzNZRK3NSpRSdwbet44lNpl9rmuWMFRlI7Nzem0LZnqp7U2I8amsVt26dkQgmdklL3e1RJkuJRiVTo51bJkrtelcTp+48cl0+lXxpXUSVzodWfKinV+4e2n9Pf+vxKzhSPu9303vpsahuoZfSYejWHzjwSq8pXVbZqdxe6bnXE0HehksJYVeOxtpkoLiVKkZ8rnu2RE7Qt1QFKHYqUIIf+DjRskr5HbWcNwxNaMhoq9PuOpKYVSrDdnuMu7Yvqka39OfK10b6nyN+o38eMUCpZ3rBhgwmaoMaVqrHFOzNZVmYUC+p2C2pcQY7NKycKRRvGFKSSGi8qxcmoPaDXMqqyy4iG1VFnBLetXDzxKVHworaO0do76j2UKIR2BFG7ucgkR0lutMRHJUxuB4tYy6iKNFoJYrSkWfFEW0fo65WkqGNGrMGkQ91xxx1pJWeR687sNvP6XPFsj5zgdQESWjKqEt7MxBTrNUqklexFk8j3BACpjIQROSarVcapTCWc8SYhSmjcNngI7vcEAKmMhDFFqPF+5FArQRL0+AAAQGwkjCkio6pJvwU9PgAAEBu9pAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEYGg2V80TaBm1pABAwYkNM0bMk+z4tx///1m69atbEYAQFQkjCni9ddfNx9//LEJGiUiS5YsyXC5e++9105D584M88QTT9jp1pL1/smg6ewef/xxM2jQIDNlypRse01Wl9GsOJoaSs8BABAN4zDG6dRnTjVJM9OYb2/5NqGXTJw40U6Pd9FFF5kgeeqpp8wJJ5zgOVOJEsO33nor5vzAOf3+yTB79mxz7rnn2vmuNSZljx49zKWXXmpefPHFLL0mK8vceOONacvcfvvtplGjRjbB9pqiDwCQN5EwpgDNdbts2TLz119/mf79+6dV6c6cOdPMmTPH/q35hFu1amXOPPPMsNe+/PLLplq1avb5L774ws7z3KdPH/vcp59+aubOnWuqV69uOnbsaF555RVzzTXXmOOOOy7t9b/99pv56KOPzO7du03Tpk3tcm4p4cMPP2xLDd944w0za9YsO6m5qp0jab1nnHFGukTFcRy77p9++snGp0RHcx+Hct9/xYoVpm3btnG9/4gRI2wVuJarUqWKadeunU2WctJNN91kLrnkEjN27Fj79wUXXGA/89VXX21OOumkTL8mK8u0bt3aNGvWLG0axxYtWpjRo0eb++67L0e3BQAg96FKOgWULl3aFC5c2BQvXtxUqlTJ3lTNqMfdv//55x/TpUsXc/fdd4e99r333jM333yz6d27t20/WKZMmbQq4s6dO5tdu3aZxYsX2+RD1d5Lly5Ne+2YMWPs4yohVHI3dOhQm7SpTZyUL1/e5MuXz65TMejvaJSonnbaaekeV1wqITxw4IBNKk888UTz77//Zvn99b/+LleunPn555/tej/88EPPbfzcc8/ZZNzrFsu6dets0quE19WmTRtTu3btmO+r0taMXpPVZaZPnx72nnr8888/99wOAIC8iRLGFNC+fXvz7LPP2irp0MTlnHPOsTdXr169bInSHXfcEVaad/DgQVuSWKRIEfv3qlWrbBtCVXOrNEqUmF111VVpr9mwYYNNMr/66itzyimnpJVqHn300WbcuHF22euvv96+l0r9OnXqFDN+JaHREq4aNWqY//73vzbpUwKrdT/99NO2BCzy/ZUU/ec//4nr/VX6Fkola6qK9YpRJZx79uwxmaHt6ZbihapTp44tIY3G7fDj9ZqsLuPG5dL+ozm/AQCIRMKY4lQtrWRwy5YtaT2Qf/nll7CEUQmnmyzKN998Y0sr3WRRVDqphNPllkSpo42qhEWlfIUKFTLz5s0LSy69qMp479696aqapVu3bjZZlGLFipnLLrvMTJs2zSaMke+vKmaVHMbz/iqx1GtUja/OHuvXr7eJlRLnAgWi/yT0+TNLn09UJR5KJcAqwY1GVfwZvSaryyjpDqXvYPv27Zn4hACAVEfCmMJUAqeESomW2uoVLFjQttuLTArKli0b9vfGjRvTVR8rkQpNOrSMkjhV60a+Z+PGjeOOUQme1hMtUYmMoWLFiradZrT3VxKo9pcZvb8SqFNPPdVWW6sTiN5DSav+VnX3kUceGbNKOqNe2+qFHE3RokXt/9u2bQvbXvo7ctu7SpQokeFrsrqMtl8ofQdukwQAAEKRMKYItyTOpZIztftTD1l1dnGThLvuuivDdSm5VOmTSiTdDiT79u0LS+rUBlDvoZ62Ko2MN65omjRpYks9I6nkL9TatWttB51o768qabcDh9f7q4RSiZ8+n1uqqvZ+qur2kpUqaVX/ikoxVe3rlsbqb3UiiuaYY47J8DVZXeb8888Pe099B82bN8/UZwQApDYSxhShkrHQhE4lbkoMdHM9+eSTca1LPalVPave11deeaV97LXXXkur0haVzqnkTB1N1OvYtXr1alsF26BBg6hxRXPeeefZtoqR1GP32muvtaWQ6rSjDjpqy5iV99d2URLpbhd9TpUeZiQrVdIqGT355JNtAq+49f6TJ0+2CbFKf10vvPCC/Uyq+q9atWqGr8nqMqHtW2XGjBmme/fumf6cAIDURcKYIjQ0jErb1Fta1ZDqgNK1a1dz8cUX25uGndFNyVdGVHo3bNgwm7h88sknNqlatGiRLclzSxxVlTt+/HjbzlBtHtVxRD1yVXqnxC40LiV1CxcutNWk0YbVUecUDYGj16r3rkvVxBpDUR1upk6damrWrJk25E/k+2voH1UpZ/T+/fr1s1XXSqLUWebbb7+1ny+naVzEs88+2/ZE1liIkyZNMgMHDrRDEbmUoCvBdduKxvOarCzjJtVuxyOVMIa2UwUAwEXCGKdEB9LOrFhVqxlRyZCqHJUYqcODhtXRYNjqHPLHH3/YhEFVkCppCm3jpzZ/ShAjqdeyxupzx2HUeI1K0kLb3KlTjJLEL7/80mzevNn2RlbpZGhS+tJLL5nPPvvMJnJqQxmrClzJ7siRI83zzz9vH1OpocYOVFvFH3/80Q48rfWHrjv0/bWM4s3o/ZVML1iwwLbtVEcgDWCtQb21rdy2hjlBCZyqgTW2parSNVB26HiWom0Q2vkontdkZRntay5tez2uqncAACLlc0LrLPMInTTVS1RVlZG9R1Wd6ZZ0hZ68kyWzCWN20/iESqTcNoAag1EJjTqdxOqokRX6LjSuosZedEsxc+N2y02xuXGpJPeZZ54x1113XVonGb8FdZtFi83vY0YoXfxF9n4PgqDGlaqxrR7WJK7lagxebFJtuwU1riDH5pUThaKEEVGpFE5V2qq6VXs3dRZRh5mcSBZFO+utt97Kt+EDlUardBEAgFhIGBGVZgZR20FNqaeqWvUi1lUIAADIe0gYEVPDhg3tzRXa5g0AAOQdzCUNAAAATySMAAAA8ETCGEMe7DwOIBNCB7QHgFRFG8YIGqtPQ8loXEGNOxjP1HbZnahqmI4gIrbU2m5BjSu3xKb/NQ+5jhUaCiqeQfEBILciYYwyxIjmK9a8xRoUOtk2bdpkZ2sJImJLre0W1LhyW2zFihUzNWrUyNT4oQCQW5AwRqHBi+vXr2/nHU62q666yg5lE0TEllrbLahx5abYdIFZoECBpNdEAECykTDGoBOBbsmmkk2/Z4uIhdhSa7sFNS4hNgAIFupQAAAA4ImEEQAAAMFPGHfu3Gk7mCTaZnDPnj3ml19+sY3QAQAAkIIJ48GDB03v3r1NuXLlzIknnmgqVqxo3n777bhff80115hGjRqZhx9+OEfjBAAAyMt8TRgfffRRM2nSJLN48WJbSvj444+bnj17mkWLFmX42tdee802jG/cuHFSYgUAAMirfE0YX3rpJXPdddeZBg0apJUY1qtXz7zyyiuer1u2bJkZNGiQGTt2rC89mQEAAPIS3xLGDRs22BLCVq1ahT1+8sknm/nz58d8nWZY6NKlixkxYoSpVatWEiIFAADI23wbh/Hvv/+2/6v9Yij9/e2338Z8Xb9+/cwxxxxjunfvHvd77du3z95cO3bssP+rk40fg3N70XRjQYvJRWyptd2CGpcQW2ptt6DGlaqxHToivlmSsvK5g7rdghpXkGOLNybfEka3KllzsYZSYqeZE6KZOnWqGT9+vJkyZYrtHe0uv3XrVvv30UcfHfV1jzzyiBk6dGi6x7/88ks7rVeQaHt89tlnJoiILbW2W1DjEmJLre0W1LhSNrYmg+JabFEWPndQt1tQ4wpybLt3745ruXyOUl6fhtIpXbq07RV95ZVXpj1+xRVXmG3bttlkLpKWHT58eNhjGo5HSV+FChXMkiVLorZpjFbCWL16dbNlyxZTqlQpEyTuPNZBRGyptd2CGpcQW2ptt6DGlaqxrX3s5PjWP2BOJqIK9nYLalxBjk05kWp3t2/f7pkT+VbCWLJkSdOyZUvz+eefpyWMyr6nTZtm+vfvH9bWUeMt1q5d23Tr1s3eQjVv3tycccYZ5sknn4z5XoULF7a3SAULFrS3INGctEGLyUVsqbXdghqXEFtqbbegxpWqseU//H8FJF6y8rmDut2CGleQY4s3Jl/nklY18UUXXWSaNm1qO7uMGjXKzm3bp0+ftGUGDhxo5s6da37++Wc/QwUAAMizfB1Wp0OHDubDDz80X3zxhbnxxhttsjhr1ixTtmzZtGUqV65s6tSpE3MdKnnUgN8AAADIGb6WMMoFF1xgb7FEtlmMpIG/AQAAkOJzSQMAACC4SBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAMHuJQ0g91k9rElcy9UYvDjHYwEA5DxKGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4Im5pAEgj2NucAAZoYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ6Y6QUAACQFswrlXpQwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAUwHvpwEAQKTVw5rEvVFqDF7MBkSuRwkjAAAAPJEwAgAAwBMJIwAAADyRMAIAAMATCSMAAAA8kTACAADAEwkjAAAAgp0wvvzyy+bYY4815cqVM2eeeaaZP3++5/Lr1683t956q2nYsKGpWrWqadu2rfn666+TFi8AAEBe42vC+Pbbb9vkb9CgQTZRVOJ49tln26QwlgceeMA0bdrUfP7552bu3LmmZcuWpl27dmbJkiVJjR0AACCv8DVhfOSRR8zVV19tunTpYmrWrGmefvppU7RoUfPCCy94lkhed911pnbt2qZ69ermoYceMocPH7bJIwAAAFIoYdy2bZstFVSJYlowRxxhzjrrLDNr1qy41nHgwAHz0ksvmSJFipgzzjgjB6MFAADIu3ybS9qtdq5QoULY4/o7o3aM06ZNM506dTJ79uwxpUqVMhMnTjR169aNufy+ffvszbVjx460hFO3IHEcJ3AxuYgttbZbVuI6dEThuJbL7PqDus1SNTa+z5zbZn78DnL6+wxybKn4+8xp8cbkW8IYWqoY+bc2qhd1jtmwYYPZsmWLLWG8+OKLzTfffGNatGgRs+p76NCh6R7/8ssvTbFixUyQ7N+/33z22WcmiIgttbZbluJqMiiuxRZlcv1B3WYpGxvfZ45tM19+Bzn8fQY5tpT8feaw3bt3x7VcPiej7CyHKNkrX768LR1Uwufq0aOHWblypU0A46VE8aSTTjIvvvhi3CWMav+oGFRCGSTVqlUza9euNUFEbKm13bIS19rHTo7vPQbMSaltlqqx8X3m3Dbz43eQ099nkGNLxd9nTlNOpJFqtm/f7pkT+VbCqODq1atnZs6cGZYwzpgxw1xxxRUJrStfvnzm0KFDMZ8vXLiwvUUqWLCgvQWJPkvQYnIRW2ptt6zElf/w/12Aecns+oO6zVI1Nr7PnNtmfvwOcvr7DHJsqfj7zGnxxuRrL+nbb7/dvPbaazZJ3Lt3rxk+fLjZtGmT6dOnT9oyN998sznxxBPt/V27dtle1b/88ovtGa1s+OGHHzY//vijufLKK338JAAAAKnL1zaMffv2NX///bctYVTyV79+ffPxxx+HdWBRIunWrxcvXtyOudi1a1ezbNkyU6BAAdO8eXPbJkC9qwEAAJD9fO/0MnjwYHtTL51oxaLPPfecLU10acxG3VQFnT9//iRHCwAAkPf4njBmVIcere2hkCwCAADkkbmkAQAAEGwkjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAAASRgAAAPgwNeCOHTvMzz//bP755590z11wwQVZCAkAAAC5PmH89NNPTbdu3cz27dujzvW8d+/e7IgNAAAAubUN45133mluu+02s2vXLpscRt4AAACQxxPGtWvXmrvvvtsUK1Ys+yMCAABA7k8YjznmGLN8+fLsjwYAAAC5tw3jH3/8kXb/2muvNVdddZV59NFHTb169Uy+fPnCltVjAAAAyGMJY/369dM91qFDh6jLOo6TtagAAACQ+xLGlStX5mwkAABEWD2sSVzbpMbgxWw7IAgJY61atXIyDgAAAKRSp5dt27aZMWPGpHtcj+k5AAAA5PGE8Y477ojaTvHw4cN2jEYAAADk8YTxww8/NJ06dUr3uB77+OOPsyMuAAAA5OaEUcPobNq0Kd3jeuzgwYPZERcAAAByc8LYrl07Wy0d2l5x69atdrpAPQcAAIA82Es61MiRI02bNm1MzZo1TZMmTWx7xsWLF5vy5cubGTNmZH+UAAAAyF0JY7Vq1cyiRYvMuHHjzIIFC2wVda9evUzXrl1N8eLFsz9KAAAA5K6EsXXr1mbWrFnm+uuvj/kcAAAA8nAbxm+//Tbq44cOHTJz587NakwAAADIrSWMP//8c9T77hiMs2fPttXVAAAAyKMJozq4RLvvKlmypHn22WezJzIAAIA8bnWc86nn9JzqCSWMf/31l/2/cuXKafddBQsWNGXLlrUdYAAAAJA6EkoYK1WqZP8/cOCAKVAgU/1lAAAAkBc6vZAsAgAA5B2ZKiZUCeOTTz5pJkyYYFavXp1uOsAtW7ZkV3wAAADIjSWMw4YNM6+88oq55pprzMaNG83jjz9urrjiCrNz505z3XXXZX+UAAAAyF0J41tvvWXeeecd06dPH/t3z549zfPPP2+TyO+++y67YwQAAEBuSxhVDd20aVN7v1ixYmbHjh32/sUXX0zCCAAAkGIylTBqkG6340udOnXMjBkz7P0lS5YwlzQAAECKyVSnl/z586fdv/nmm03Xrl3N8ccfbxYsWGBuuOGG7IwPAAAAuTFhDO0V3bt3b1OrVi0zZ84c26axc+fO2RkfAAAAfJYto2+3a9fO3gAAAJB6spQwHjp0yA6lE+nII4/MymoBAACQ2zu9LFu2zJx22mmmSJEipkyZMuluAAAAyOMljBp3sXr16mbKlCmUJgIAAKS4TCWMixcvNlOnTjWlS5fO/ogAAACQ+6uka9eubTZs2JD90QAAACA1ShgffPBB06tXL/PII4+YunXrmnz58oU9X61ateyKDwAAALkxYSxZsqSd1eXMM8+M+rzjOFmNCwAAALk5Yezbt6/p2LGj/Z8hdAAAAFJbphLGtWvXmnnz5plSpUplf0QAAADI/Z1eGjRoYNasWZP90QAAACA1Shi7du1qb48++qipV69euk4vegwAAAB5OGEcMGCA/b9Dhw5Rn6fTCwAAQB5PGFeuXJn9kQAAACB1EsZatWqlK1H89ddfTeXKlZn9BQAAIMVkqtPLd999Z2666aa0vzt37mwaNWpkqlSpYmbMmJGd8QEAACA3Jox33XWX6d69u73/008/2Xmlf/zxRzNkyBAzcODA7I4RAAAAuS1hXLBggWnRooW9r2TxkksuMc2aNbMDeSuBBAAAQB5vw6gBu5cvX24aN25sPv74Y9O7d2/7+NatW2nDiFxl9bAmcS1XY/DiHI8FAICUShi7dOlih9Q5+uijzdKlS835559vH58yZUrafQAAAOThhHHkyJGmYcOGZtWqVWbUqFFp80nr78GDB2d3jAAAAMhtCWP+/PnTqqFDPfjgg9kREwAAAHJjwqhe0NK8efO0+7FoGQAAAOSxhNHtFa1But37sTA1IAAAQB5MGDdv3hz1PgAAAFJb3AljuXLlot4HAABAastUpxfXoUOHzM6dO9M97vaaBgAAQB6d6WXZsmXmtNNOM0WKFDFlypRJdwMAAEAeL2Hs2bOnqV69uh2om9JEAACA1JaphHHx4sV2DunSpUtnf0QAAADI/VXStWvXNhs2bMj+aAAAAJAaJYya0aVXr17mkUceMXXr1jX58uULe75atWrZFR8AAAByY8JYsmRJs2TJEnPmmWdGfZ6BuwEAAPJ4wti3b1/TsWNH+z+dXgAAAFJbphLGtWvXmnnz5plSpUplf0QAAADI/Z1eGjRoYNasWZP90QAAACA1Shi7du1qb48++qipV69euk4vegwAkm31sCZxL1tj8OIcjQUATF5PGAcMGGD/79ChQ9Tn6fQCAACQxxPGlStXZn8kAAAASJ2EsVatWtkfCQAAAFKn0wsAAADyjkyVMAIAAOTFTnM18miHOd8Txu+++848//zzZuPGjaZJkybm7rvvNuXLl4+5/Pbt282rr75qZs+ebQoUKGBat25tbrjhBlO4cOGkxg0AAJBX+FolPWvWLHPaaafZBPH666838+fPN6eeeqrZtWtX1OUPHTpkmjVrZjZs2GC6detmLrzwQvPss8+a8847zz4HAACAFCthvP/+++0Ug48//rj9u127dqZy5crmlVdeMbfffnu65fPnz29++uknU7p06bTHjj32WHPccceZH374wbRq1Sqp8QNAy7vejGsjzB/Zg40FINfyrYRx9+7dtoTxoosuSnusRIkS5pxzzjFffvllzNeFJovizmWt9QEAACCFShg1H/Xhw4dN1apVwx7X39OnT497PSNGjDAVKlQwJ510Usxl9u3bZ2+uHTt22P8PHDhgb0GiQc+DFlMqx3boiPjavmblcwd1u2Ulrpzebjn9fWZnbIXyx/e6lQ8eH9dy1QbMMan2O2BfC+bvwI/jGvvagRzbZpKZ7yTe1/iWMO7fv9/+X7Ro0bDHixUrlvZcRl566SXbAeaTTz4xxYsXj7ncI488YoYOHZrucZVk6v2CRJ/9s88+M0GUkrE1GRTXYouy8LmDut2yFFcOb7ec/j6zM7Z7Ti4d3/uZAO9rQf0+gxybD/taShzXgvp95uF9bXecNbT5HJ/m8Vu3bp2pVq2aTfYuuOCCtMevvfZas3jxYvP99997vn7MmDG2o8zYsWNN586dPZeNVsJYvXp1s2XLFlOqVCkTJNomKn0NolSMbe1jJ+d4qU9Qt1tW4srp7ZbT32d2xnb6oPFxvW5ciadzNK5oseX27zPIsfmxr6XCcS2o32de3td27NhhypUrZ0eh8cqJfCthVNWzqpIXLFgQljCq88rJJ3tvHCWJGkrnzTffzDBZFA25E23YnYIFC9pbkOTLly9wMaVybPkP/9+FhJesfO6gbresxJXT2y2nv8/sjG3/ody/rwX1+wxybH7sa/FiX2NfS0S8+5ivw+r06tXLVilrDEZRaaNKF/W4a+TIkebqq69O+/vtt9821113nS1h7NKliy9xAwAA5CW+DqvzwAMPmGXLlpl69eqZ2rVrm99//92MGjUqrITx119/taWOouLSnj172p7SL774or257rrrLnP++ef78jkAAABSma8Jozq8fPzxx2bFihW2lLFhw4ambNmyYcsoEXR7NauDyrRp06KuS68FAABACk4NKHXq1LG3jBJB1bOfccYZSYwMAAAAgUgYkdrindA9L0/qDgBAkPna6QUAAADBR8IIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8FvJ9GbrJ6WJO4lqsxeHGOxwIAAFIHJYwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8FjM82b95s3nnnHbNx40bTpEkTc9lll5n8+fN7vmbXrl1m/Pjx5pdffjE33nijqVu3btLiBQAAyGt8LWFcvny5TRI//PBDc+jQIXPvvfeaDh062PuxvP7666Z+/frm888/N0888YRZs2ZNUmMGAADIa3wtYRwwYICpV6+emTp1qjniiCNM7969TYMGDWyJY7du3aK+pnnz5mbZsmVm586d5oMPPkh6zAAAAHmNbwnjwYMHzaeffmpGjRplk0WpVauWadOmjS1xjJUwHnfccfZ/JYxZNWfOHFO8ePG4l2/YsKEpX7582GP79+8333//fULvW7p0aVuyGmnp0qV2fbNmzYp7XdoexYoVC3ts2+6D5vfN+2K+ZnWU9VepUsXUqVMn3eP6bIpJ4olN3+Upp5yS7vF12/ab9dsPeL42MjZdPFSoUCHs+QMHDpjvvvsu3eu8YitVqpRp2rRpusd14bF01a4MYwqNq0WLFun2ma1bt5olS5bEfG202CpXrhy1KcUPP/xg9u2L/d1Fypcvnzn11FPTPa6S91WrVnm+NjIuldxXrFgxru290WO7FS98hDmmUtGo2/vvv//2jCkytmjbe9u2bebnn3+O+rpYcVUoWcDULFs43ePz5s0ze/fujSsmz3h3/mP2b9/iucwPxcJjq1m2kKlQsmDYYwcPOQn9/qVEiRL2QjqSmuxs2eIdU+R2O6ZSEVO8cHiToB17D5lfN+6NeeyIplKlSrYwINL8+fPNnj17TCJqRHlsw44DZs3W/x2XXBnFpt+bfnehVJuV6DFX+6P2y0jLt+wz/+w6GPU1sWJr1qyZKVmyZNhj27dvN4sXL7b3441Nv1v9fiMtXr/H7D1wOObrosWl44mOK6HWr19vVqxYEfZYRrHpfKLzSqjDhw+bHxI85ur85p73Q/32229m06ZN6R73ikvnAZ0PQu3YscMsWrQow+NaqL2//WbPT5EWLFhgdu/ebWKJFpvOl24OFGt7xxNXjTKFTMVS4ccTx3HMt99+m+Fr1cwvLo5P/vjjD0dv/8UXX4Q93rt3b6dp06YZvn7NmjX29dOnT89w2b179zrbt29Pu7mvTfT25ptvOvv37w+7rVq1KuH1nH766enWo9v555+f8LoWL16c9voVw1va26vd6yW8nttuuy1qTFWrVk1oPcWKFUu3DsXU7+wqCcc0evTodOtat25dwus55ZRTon62jh07JryuhQsXplvP5MmTE15P3759o8ZUs2bNhNZTqFChqOsZPnx4wjG9+uqr6dazYcOGhNfTonrxtH0xdF2XXHJJwuuaN29eupimTJmS8Hq6n1g+LabQ2OrWrZvQevLnz+9UqFAhLJ5WA8Y4Nc7onHBMj3SsmS6mn+5vnvB6Wrb8v+0cGtsVV1yR8Lo+vrFRupjGXdMg4fVcd911YdvIjat+/foJrysyHt3ua18t4fU899xz6falrVu3Jrye5s2bhx1v3VvHpmUTXtfs2bPTxaRzWqLrufrqq9Mdc3WrX75IwuvavXt3uphGjRqV8HqeeuqpdOvZuXNnwutp3Lhx1GPcVVddlfC6Zs6cmW49eizR9Vx11VVRY1Ksia5L2yRyPdp2ia5ncIfq6Y65+i4TWYfyIy++lTC6WXhktq/St7iz3Tg98sgjZujQoVlez48//pgu3n/++Sfh9aiU5bPPPkv3eLSrpYzMnDnTtgW1mgyy//25b54x5sGE1rNy5cqoMSVa+qIr9nTraTLIbFj2njFmXELr+umnn0yZMmXSXX0nSiWA0T6bOlol6ptvvklXcrdw4cKE1/Pnn39Gjcnr6jQaXbFHW8+vv/6acEza3pHrykxJ/u5i1cyi/78vLgpZ34YNGxJel67G165dmy7ORP191PFmUZPeYY8ptkSPNbpiVylB6Ha65+TSZtKGImZMgjGtrXaBWdTk3Cjff9eE1qPfhBtPaGx//fVXghEZ83u968zhiJLv5UalXf/7PuO1evXqsG3kxpWZY7u7L4X6a8VHxpjRCa1HpdKR+3dmSpdVImXXExHXtmn/McbMSGhdKv2JPO6rpilRqlEI+2z/P7a9RW7Rswmta8qUKek6nmYmJtW6RG5v1VgkSsegaMe4yONCPGbPnp3unK2S+EStXbs2akyZOV6qP0bBguElg141VrGsr9LOLGpyQdgx16s/SGb4ljCqGsWtXgqlvyOL6LNKnWn69esX9oOvXr16wutRtY865YTKzEH5qKOOSrceeeWVVxJe1+mnn26rymXtYyfb/7f8mXhSVbt27agxFSlSJKH16EATuR7FNXNj4ttJ1TWR61Kv+kQp6Yz22UaPTuyEI6eddpo59thjwx4rUCDxn5GaX0SLKbJ5QUZUlRFtPW6VVla3d2YuiIrtXmuaLh5u71cbMCft8TfffDPhdbVu3Tpdc4LChdNXLWfkqL/nmaaLw0/Mii2RJimiqrpChQqFbafTB40361clnnhUWzvZNF08N+yxnXsTP8DrItuNJzS2ceMSu0CT+n+8ahrvDt8Hd69M/CRYo0aNsG3kxpXo9hZ3Xwr1/V+JX+w1btw43f6dmQRWhQZaj3u8dR25dWXC61L17/HHH5/u+0yUzmehn82NrcjexI+X5513XrpjWmR1dDx0nIzc3ok0t3EpH4h2jMtMHwZV/7Zq1SrssbJlyya8nmrVqkWNaeDAgQmvq3379umOaRk1J4qmyvovTNPFC8OOuWr6lxIJow4oOnioJEQbLDTbb9SoUba+l76MaCcZZfaJtmGMvBJQWx2VOiVCB4TI9ciIESPsFdDHH38c97rUTsRdV/7D//sxnlC1gHn/mtoxX1Px6vQnbrU1iRbTxIkT09owXnTRRRnGpgQmcj2K67KmJcwpNWPHFC02tRGJXJfakEbb3l6x6QAf7bOp5Pmqo/7XdiXeuNROKHJdOgh57QPRYlNbqmgxTZgwIeE2jNHW07NnT3PGGWd4vjYyrmifTQfTaJ9t4+genm0Y3X0xdH0PPvigueOOOzL4ROGxHX300eli0gE/1vaOFZfaMLoxubRedbBLtJRJQ3+FxrT/kDGlGrYyDSqlb7MX6uFi49K1YYyMqUR+J+HjiS6+3XhC9wfVqtxyi0qYMuZut3pl/u844mpSMX/a8STasSMaHRdDt5Ebl4ZDS7QNY/6vbkz3WMdji5vjqoQfTzKKTW0YI/clJSO6oEzkmKtzhtYTuZ1ua3OUuer46MlerNiUxEbGpPaR7j4QzzHXrr9ixbD1uLH955Kqnm0Yo8WlQoLINoydO3c2J5xwQthjGcUWem5Kiyv//+1LGXFj00V0tGOckrMbbrgh3eNeceniM3JdulB2t7fXcS1UkwEDo8Y0ZswYz1qiaLHp80W2YdQxJrTdZjxxqQ2jvvfQuJT4x3M80YVTaB4WSz7VSxuf9OjRw1YTqPOJEjrd15f3/vvvm4svvjjtBKrqjTvvvDNdkbCuqqZPn57hiTGSShiVtKkqJ7KK2W860Gam6k5WD0vfkSaaGoMTL33KSmzxxhXk2DIbV1a/05zEvpY9+1rLu+JLoiaVHBnYfS2ox44gx8ZxjX0tVfa1eHMiX4fVeeyxx2wV30knnWSz6cmTJ5srr7wyLVmUL774wsydOzctYVSvRpUK/Pvvv/bvF154wb6ubdu29gYAAIDs5WvCqGo5dWdXwqcOCCpxjCwtvOKKK2w7JlfRokVtli4jR45M1yYSAAAAKTY1oOrvlRTGEllqqIa0kZ0OAAAAkKJTAwIAACD4SBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAAQMIIAACAzKOEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAnkgYAQAA4ImEEQAAAJ5IGAEAAOCJhBEAAACeSBgBAADgiYQRAAAAngp4P41Iq4c1iWuj1Bi8mI0HAABSAiWMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBUwPtpAH5ZPaxJXMvVGLw4x2MBAORtlDACAAAg2CWMhw8fNt99953ZuHGjady4salXr16OvAYAAAC5sIRx+/bt5tRTTzWXXXaZee6550yzZs3MPffck+2vAQAAQC4tYbz//vvNP//8Y5YuXWpKly5tZs2aZU477TRz7rnnmrPPPjvbXgMAAIBcWMLoOI55++23zbXXXmsTP2ndurU58cQTzVtvvZVtrwEAAEAuLWFcu3at2bZtm22DGKpJkybmxx9/zLbXyL59++wttFpbVFJ54MCBhOLetj++TVb8779NZhw6dMj8ncnXBjW2eOMKcmyZjSvIsbGvZc92O+Lgnrhex77GcS2r+1q82NfY1xKxc+fOtEI5T45PFi9erMicOXPmhD1+9913O3Xr1s2218iQIUPs67ixDdgH2AfYB9gH2AfYB9gHTLptsGbNGs+8zbcSxsKFC9v///3337DH9XeRIkWy7TVy7733mn79+oX1slbp4lFHHWXy5cuXpc+xY8cOU716dbNmzRpTqlQpEyRBjS2ocQmxpdZ2C2pcQmyptd2CGpcQW2pttx3ZHJdKFlXKWKVKFc/lfEsYa9SoYQoUKGBWr14d9viqVatMnTp1su01bqLpJpuuI4880mQnfWlB2qFyQ2xBjUuILbW2W1DjEmJLre0W1LiE2FJru5XKxrjcfiGB7PSiBE69midMmJD22JYtW8xXX31lzj///LTHfvjhB/P5558n9BoAAACkyLA6jz76qO3lfNVVV5mTTz7ZvPrqq6ZRo0amV69eacu89NJLZu7cuaZ9+/ZxvwYAAAApMnB38+bNzYIFC0zlypXtzC1du3Y1M2fODKs+1pA55513XkKvSSa975AhQ3x7/9wYW1DjEmJLre0W1LiE2FJruwU1LiG21NpuhX2KK596viT1HQEAAJCr+FrCCAAAgOAjYQQAAIAnEkYAAJDnaea3/fv3J/xcMuzdu9fO/JPoc9mJhBFASkvGgRRA7qdRWUIn+Yj3uWTo3r27mTRpUsLPZScSxgRpzuqbb77ZDulTv359c8wxx5h27drZ4X40z3VQff/99+azzz7z5b3Vr2revHlm6tSpdoT6SP/973/Nn3/+aYJGY3w+++yzvr3/8uXL7XemOdQj/fLLL+bbb7/1bbuMHTvWzJgxw/6tuW4HDhxoh7p6+eWXA5egVa1a1WzYsMEEiWZoePrpp03//v3tNtu1a5cJqk2bNpnnn3/eBJGfx7WMvPfee2bp0qUmiEaMGGF2797t2/urpO6JJ54wnTp1Mo8//rh9bOXKlXabBdXevXtN0aJFTV6OzddxGHObMWPGmGuuucY0aNDAJoyaWnDfvn3mr7/+Mo899phNLmbNmmVq1aplgnhgVZLRoUOHpL6vkoeLL77YfPLJJ2kz7OhK7Yorrkhb5oUXXjBdunQJ3HZzE0ZdICSbDqZ33XWXTbbz589vr2x1UXLEEf+7xvv666/txcupp56a1Li2bt1qjj/+eJvE6rt97bXXbDKxceNGU7t2bfP+++/b/WzUqFEm2QYPHhz1gkRTXum5YsWK2b+HDx9uSpYsmbS4dCK87bbbzMcff2z/1jBgF154oZ2itFKlSvZi6T//+Y+9qCpevLgJasJ40003maDx67gWDyU/+r2qUCGICaPOZe5vItkuuugis27dOlOxYkXzxx9/2MeqVatmfxcaSi/Z5wIdS6dNm2Yvwjdv3pyWxLqUXL/xxhv2OJJsOnf++uuv5rfffrP3IwtXFK/OB7oAzWkkjHFSYqiD/ujRo02PHj2iPq+kZ+jQoXaZZNIP7/fff/dcJqPnc8o777xjf4yzZ8+2B4RnnnnGXHnllXYu7z59+hi/6ACgk42XyCkok1n6NGjQIFuKd+6555rJkyfbhFFJmh5TAunnRZOSnJ9//tluH8Wn71UHM13hKulp06aNue+++0y5cuWSGptK6nQCjJwqS6UZ2v80raj7dzLp2KDt41Li1blzZ3sxUqhQIft9K+FR0qiS2mRSyaZm08oo4fVDkI9rS5YssSdqLxk9n1OU9Ki9nZeMns9JmohD35u2oY4nCxcutI8XLFjQ1taNHz/e3HvvvUmNSQnZW2+9ZQsJ9HvV/VA6pnTu3Nl069bNJJsuMFU7p+OtLn5/+umntOfy5ctnj7M6lnhNj5xtNA4jMvbbb785lStX9lzm66+/dlq2bJn0zfnMM89oLM0Mb3379k16bH369HGefPLJsMfeffddp1ChQs5zzz1n/7700kudCRMmJDWuZcuWxbXNGjZs6CTb+++/71xwwQVhjy1dutSpWrWq07lzZ+fAgQPOCy+84PTu3TvpsV1//fXO888/n/Z3+/btnWeffTZsmdatWzszZsxIemwjRoxwateu7UyfPj3s8YoVKzp//fWX4xfta+5+tHXrVqdw4cLOnj17wpaZOHGi07Zt26THtnjx4rh+B8cee2zSYwvycU3HrHhiS/ZxTY466qi4Ytu8ebPjhzfeeMPp0aOHvf/SSy+FHcdGjhzp3HLLLY5fPvzwQ7vfBdHQoUOdmTNn+hoDJYxxUhavq48VK1bEzOTnzJlji9iTrWHDhuakk06y1YGxqDjdj3ZcqnaLbFuh6miNUK8rNl0h+dWuTU0Kvvnmm5jVk2pD2Lt370BsM01/qTaDZ555pm3gfNpppxk/lChRIqyNoias13YMpWYHuhJONlXha7uoFOCyyy6zVc8qwQsSVelrexUpUiTs8erVq9vnkk3vq3hUKhWrOlylL6pdSbYgH9cU2+23327uvPPOmMv4cexwY9P3dcopp8RcpmnTpsbPc6nOoxJ5/Ncx7owzzvApMmM6duxogmqwD9XhkUgY41SmTBl7otaP8LrrrrMHMh1oVb2lNoxqeK3qV7etXjKdc845pm/fvmbPnj22I040Oon7cWBVW08Vqd9www3pfpgTJkwwl19+uS3uV3V+MilJvPTSS+0BKlbV+L///mv8oH1rwIABNnF02yxK3bp1bVsVJY3Tp0+3bUOTTXO6qw2q265TVUeRF0lKMJo1a2b80KpVKzN//nxz/fXX2/vjxo0zQbB+/XqbxOo71f3IC091jmjRokXS41LCr9+iLpx0XIvGr858QT6uaf8666yzbFvj0N9oKL86SOh4pvayoe3EI8WKORm03ZRMq52gEka109bv4cknn7QdIP3uYLV9+3ZbJa0Cg8jmK23atLHnLL+ovecHH3xgm2voWBJKv19NnZyTSBgTbCP18MMP2xPmQw89lPa4dvoTTjjBJos6oSab3l+NmFetWhXzwKqDr2JMNrVXVEmPTpJVqlQJe04NnLXzK3Hzwx133GEPULFovvJHHnnEJFuNGjVsw28l1CqFDaUkwy1p9EPbtm1tY35dJGn7RCaGKg1Swqt2jX5REqFtp99p69atfSntjLzY1Jz3Lp0s1abSTRgPHjxoT5a64PSD2sfqoi4WfZehx7tkCfJxTZ0ybrzxRtv+tGbNmlGX0UWVOoIlmxKaxYsX20QsVg2O2rwls+NXZCL96aef2oTWbdurc6tKHvW7Vam3X3SsUNKlRFH/q11lqG0+joSiC2EVWOm3oGNHZNKvC6ucxlzSmaSrWg0noob0qt5UVV1uoSpFHUzcTgA5TVdCOnDFOnipZ6uq6FR9qMbY6tTh5xVwNNpeii1ZVZx6P91ibQd3oFZVIyb7+/SiBuP6/txY/P4+1bBepbHReoQqVjWNCNq+lex9LRFBji1Iv4NIfv8OvPj1O9B5QZ3klHTrokq1UX4PW6MLXhVwqCNY0Pbxm2++2V5cvvjii77FELy9N5dQT9Fjjz3WthfJTcmiO4yN2t8kiw6SXm0VVSXt/jhVIjlx4kQTNKpmTWa7H20vr5OLEmy3zdkrr7ziy9A/0ejEE3rC1tiMfo6tpt+otk204UNUMhSE8Rn1XYeenJK9ryWagB933HEmiJJ9XEtEUI9rogIPtc9PNh3fVJOiGiZVU/udLLqJvfbvoCWLbmxKqv0UvEuxXEptCzQIqYqLkzHiOgAAuY1KFTVUWKyLJ7WrVSLp1Wknp6g5zYMPPmirpIOWNLZu3dqOFdmzZ0/fYiBhzCYqwVAje12tAQCA9NSeXb3b1TZV7WN1U/Mujc2ojqTqRKcRD9RJTGMy5jS193RnrRK17dS5XCWfSl5DNW3a1Jx++ulJ+1o///zztIHN1dxCbT/VSU0xRDYjOO+882zHyJxEwphN1KHj1Vdfza7VAQCQctTTWM1WPvroIzvji2vBggU2SXNnyNFy6kiqzpE5adGiRVGngNUkCZG6d++e1IRxypQp5osvvkj7Wx2D1GRFt0iagY6EEQAApASN+amSutBkUdR2sFevXnZIIA3XpWFiNBtXTieMGrfVjxlc4vHUU0+ZIKHTSw7MuQoAANJTBxuNdRiNHnenVFRn0iD2eM/L+DZyIGHUXLEAACCcZnLRRA6ab14Ds6s5l2Y5evfdd81zzz2XNvmFJsMYNWpUUjefSjT1vl4dclq1amVvyaaqeg3wH42GbKpQoYIdlzRHq6V9nZgwF/n333/tHLVet9dff92XOVddX375pTNmzJgMl1u7dq2dGzuZnnrqKef777/PcLmff/7Z2bRpk5Msq1evdgYOHOgsX77cc7ldu3Y53333nZNMn3/+uZ1vW/MPe1m3bp3z66+/Osmk+aM/+OADZ//+/Z7LLVmyxNm4caOTTAMGDHBmz56d4XLffvuts2/fPidZgryvJXIcjOd37Ac/jmvxSvZxLRGzZs3K8Hec3T755BOnZs2adk7rI444wv5fpkwZ5+mnn7bPa+73jz76yEk2zeleo0YNG0/16tWdVq1aOfXq1bN/lytXzjnmmGOc/PnzO927d096bIMHD3ZKlSrl5MuXz85Nf9JJJzmVKlWysdWpU8epVauW3ZaanzunkDDGafHixXFN6O5nwvjaa6851157rRNEV111lTN27FgnaHRg0nemH+EZZ5xhY9y9e7cTBFOnTnUqVqzoFClSxLnyyivt34cPH3aC4LHHHnOKFi3qlC9f3unXr589IQbFxRdfbL/PRo0aOSNGjHA2bNjgBEGQ97WFCxc6ffv2TTtBatu1bdvWeeSRRzK8YPGTEutPP/3Ul/fWb/GHH36wF+rbt29P9/y0adOclStX+hLbsmXLnFdffTXtQnLRokXOjTfeaM8POo4EgZJUbb8PP/zQXrzt3LnT75Ccf/75xyZfSmhDKc6aNWva7apj3VFHHeV89tlnSY1N26hBgwZhhQPaB9944w17XNm7d68zfvx4p1ixYs7ff/+dIzGQMMZp27Ztdif55ZdfnDVr1kS96QDhZ8Ko2LRDqYQiaHTw6tKlixNUc+fOdW644QandOnS9ta7d+9AlPIcOHDAHlAvuugip0CBAvag9cADDzh//vlnIH4TL7zwgnPCCSfYi6UTTzzRefHFF6OePJNN22fIkCF2e2m7dezY0ZZYaHv6LWj7mk44Kpk4+uijnauvvtrp37+/c8sttziXXXaZc+SRRzpVq1b1LfHJyDPPPGMT3WQ7ePCgc+GFF6YVFGg7vfvuu2HLXHrppc6ECROSHtvMmTOdwoUL2/1eF3VffPGFU7JkSadp06ZOy5Yt7QXL5MmTkx5XbqBjxOWXXx71uYEDB9oLULnrrrvscTiZ7rjjDlvjFE3r1q3TSv51PP76669zJAbaMMZJbRc0/tE333xje29F4+c8k6LBRjWPrnqbaZ5OtWnwcwypUJp3+Msvv7RtLDSqv2Z3SfYYUhkN2Kqb5vTV/Navv/66/btx48bm2muvtTc/5l5Vo2/td7pt3LjRvPnmm2b06NFm6NCh5uyzz7ZtgS655BLbhsWP30SfPn3sTTOAaJsNGjTIztGtMdTUltePtj7uLC4PPPCAGTJkiJ0vXLFpXm79Pnr06GFjizUHcF7a1zQt3G233Wb3KW2XaM936dLF7m9aJpnWrVtnx+bzktHzOUXzfv/44492TnCNI/jMM8/Y2Vz++ecf+3vw02OPPWb375EjR9rvTPu9zllue0A9PmzYMHP++ef7PpWjpgXUGIwqvHLpvFWjRo1AdsjZt29fWoecZB9zFVv58uVjTq+blM5COZKGpigVRT///PMxn1fVjUqD/PLWW2/Ztg2xbsOHD/cttltvvdUzNl0FB4GqJNQ+77jjjrNX6Oedd55TrVo1p2zZsjl21RavP/74w7n//vttPCqZUqljiRIlnCZNmth2jH5SdYmqu1SSobY07dq1s219dLV+6NAh3+JSG8X333/f6dChg41H7X5UyqLvdtSoUU5e39fU5q9y5cqeyygWbTM/Sg/jaQbkRwljnz59nCeffDLsMZUwFipUKK0UyK8Sxtq1a6e15dRvTzGp9smlZhBq5uJn85b33nvPNmeJ9n2qxN0vK1assNtLpYk6pmobbdmyxf5OCxYs6Pz3v/+1j6npxrx585J+ftfxVU1ZVLuj71Yl/9oXdT7Q8UTtZFWlnlNNXUgYc4h2qmQ2qEfm6YenhLVz5862KkdtuB5++GHb5sytflKDYxX7J5uaF6gjU5s2bWxV0qmnnmqrEN1mB6r+VaKhA1yyrV+/3nn00Udtwq+DrKowtR3dE5ESXDUgV1ONZFObrdtvv902VFcCpipWPRbaoUjf9Z49e/L0vqaTjE6EXh1x1I5RCXeyqW2gEvxYTYB000WwHwmjmhRE61ygAgN9rypY8Cth1AWk2tq51JRq8+bNYcsUL1486ft+aOckJT7jxo2zVbxqBqF2eYMGDbKdOPS839XSbscXt0OOjiHP/f8LASWSkW0ck+XBBx+0hQShsen4615gqq3l/Pnzc+z9SRhzsJOMn+0ZER/9+HVwcDuWfPXVV1GvvJcuXWp/mMmkxvzqFed2LAk9CYTSySnZV+VqH6gSO7V7GzlyZMweoFdccYVtiJ1MZ599dlrHkrfffts2Bo+mQoUKaYlaXt7XdMJW5yqVXn/88ce2tFijPuiErt6gKv2cMmWKk2zaNvXr1/fs+exXG8bRo0fb7RaNtqGSRv1u/UgY1b4utCZMpWKhvaBViqbevn7R6Arnn3++va82z9ddd13acz169EhXcusHFfa4HXI04oJGBwgKlS5+8803NrFVZzVdZCYLCWMKJow64N999932pKSGsn5dDUU7Abzzzju2tKdr167Offfd5/z444++xqSTuIb8UUlL0OiqUVU3QSypVqmnDlpBpB7cv//+uxM0Qd3X1BFo6NChTpUqVcKqBpV0qyOTH8mia9KkSZ69enURpU5EyaaLEFX9xWoKok4lShr9SBg1jJWS2VhVzrq4VC2FX5Qkuhe4qsZXhzTXE088YZsvIZjo9JJi+vfvbxs3H3/88bZR//z5822D7A4dOphJkybZOTr9sGfPHtOuXTs7MOopp5xiypYtaydSf/TRR80jjzxi7r77bl/i0mCsa9euNWXKlDFBo0bh//77rylUqJAJmp07dwZ2FobVq1fHbBzup6Dua/oeBw8ebG8bNmywnRD0WNWqVW0D+lAqZDhw4EDS9slOnTp5Pn/00UeH/V4UXzL2y8KFC9sON/pOo1GHEk3kUKRIEfu3tpk6SSTj+KtOI+pIFYvOD0WLFk37Wx059HmSJbSDS7NmzWzHvZ9//tkO4P3+++/bTnzJpDms1SGzZcuW5qijjrL3Y2nZsqU599xzkxbbhx9+aH755Rdz8cUXm2XLltn7sWiZhg0b5mg8wTziI1MWL15sXnzxRTNz5kzTunXrtMd1YDvzzDNtj8zLL7/cl6376quv2l5eK1assAcGl0b1Vy++q666yvakTjYdyDWC/ogRI0zQKLmYMWOGufrqq/0OJR1diBQvXty3XtBeNFODLpzUiztIgryvuSpVqmRvsag3vHpN6wQfNC+88II9oT777LNJeb+Mkr/QkSDUg1rbTaMH+K1YsWJhf+vCQNutXLlySXl/FWQcPnzY3leC0717d9OkSRP7d6NGjewoAcn0xx9/2ERVCb1i0/1Y8ufPn9SEcc6cOXaUhxNPPDHtfixahoQRcfvuu+/MBRdcEJYsSv369e2wCnrer4RR733rrbeGJYuiieW1oysBUezJVr16dVsioWTbPWgFhUpihw8fbnbt2mWTsyBp06aNmTx5sunVq5cJGsWm0mtNOxYkQd7XgGTREGqhlOD369fPDkmkod+SXaOiIeh0C/07KB577LG0+xqOzm+UMKYQJRWqBolGVU0VK1Y0QYtN1RN63K+ESGNrKTFTkqFSzjp16oSNr6Xqw27duvkSm6qKdNXfokULWwob+f1p3D7Ny+oHlUJ9/fXX9iCm8SAjS/PU/EAXKn449thj7QlIV+TNmzdPqxZ0XXPNNelKWfL6vgb4Sb8F3RBsJIwpREXlvXv3tidLleZpQFlVA48dO9a2aVEpn1/ULkVtLFTtrP81gLKqpx966CE74PnJJ5/sS1ya9F7tKtXu54svvkj3vA5ifp3EVe2n+FT1NWHChHTPq4rLr4RR7XzU3mf9+vV2/4q23fxKGD/66CNTu3ZtM2/ePHuLtt38SBiDvK8B+L8JMJ5++mkza9Yse3GniQiWL19uFi5c6HuTAhX8PP7447aW4t5777XH/+nTp9sCF9XU5Ti/e93kNhr765577slwOXXDd6fqSSYNoVC3bt2w3o4a3kGDfvpNE8trmJjQ2DRmWLIHQA2lnoSxplL0ei5ZvVdjDQnj9Vwy6L1jTbPn9Vwy6DuL1UPU67m8vK+lwnBhfg2xEw+/xmSMR7RxGvOyc845x56XzjzzzLT9SSNVNGrUyFm1apVvcek70vBX7du3t8NuufuTBmVv1qxZUiZI8KfLbC6/+ohW2hNJGf8JJ5xgkk1VhOpNpTaB6mE1d+5c8+effwai5OKWW26xU0Hpyk2lQD/99JOdXks9z/zy66+/2qkUE30uWR2FNG1bos8lQ8+ePWM2Dvd6LhlUUqdpFBN9Li/vawCMPTetWrXK1kxceumlaZtE7SrPPfdc8+677/q2mdShVU19pkyZEtYGWh1d1LRL5/ycRpV0gmrVqmV7eC1dutQcc8wxJogKFixoTz5BPAGp5+Cpp55qcoO9e/eGDT8RJMSWWtstqHEBeYku3E477TSbIEYOmVS5cmXz119/+Rpb27Zt7X2/YiNhTJDa26nhunaqHj162AQytOG62nVpCIVkjyEVD7/GkIpHMsaQCqUeeWrXqQnbdV/tQiKHQNF2VQeKZFPJ69SpU23p8Lp169LFtnv3bjNmzBjbhiXZ1DNa36kOXrqvoX9Cqc2shn7wY+gYDauiHuXaPrpfsmTJtOd0kaeSd40rmOzhdoK8r7lUCzF69Gg7hJOOabGobaiWSyaVqCi+rl27en53Oobou08mjXGr3u8a4cFr/MehQ4fatqvJpLFt1XY8o6GvVNsTtCGo/KJOhmpbHy0pmzFjhmnfvn3gYtOYuMoDNCRQTiNhTJAGtVVxtQYG1oEsUoMGDZKaMLpjSMXDrzGk4pGMMaRC7dixw7z11lu2J7J7P5R61mpQYA1rk2waN1PxaF/TgOeRsamUVsMj6YLFjyqbzz//3FbbqBNH6Hh8OojpgunJJ5+0v4Nk0zijSli1zXQ/9ATuDkT9zjvvJD2uIO9rLpWo6DiiGDRmq3qSK9mILPX0o6mNOn098MAD5s4777QxKTbFGHlC1/ebbBpYX4msLk7U812xRat58uNiQBd2KtzQ2IaKS/FFS1pzS41PMpxzzjnmxhtvNP/5z3/shZyqetXhRYOdf/PNN+a1117zLTY1K9OQRJqU4+DBgza+H374wV4YqIOrhiTKcTneShIIsNWrV8ecE9ZvmtIxCPOqRvPggw/a+YaD6PLLLw/c9HtB39dcc+bMca6//nrbOa106dLOjTfeaOfU9ZvmQp44caJzwQUX2Lmta9eubacz9LMTQujcvpq7+fjjj7cd+Vq1auW8/PLLzo4dO/wOzc4bPWjQIDuHecGCBZ1OnTrZua6TOf9wbjN//nw7h3lkx9EpPk6R6Ro7dqz9XYbGpg4vy5cvT8r759M/OZ+WAgByC1XtqsRRVemqilMjew3+r5KqyOkC/Rha5M0337SxqTRepUKaXk5V0n5NfepSibtKoVSKrG2oYVhuuukmc9JJJ/kal5pkqLZH20xTxGpqVtVQKLYaNWr4GlsQaZpJdSJRJ01tK1XrFw1IG2M1u9EQeWoep+YQKnGMNUVldqNKOpM0xZfa6KmDidqTqfe02k1p/MNkfXmxzJ4927ZL0c6uAZbVc9qPWVQi6dpE2+3bb7+11a1qK6VR9TWfqN8HU50cNc6W2oOE0vYbOHCgb7G5VZiqFlGVZii1o9WA3n5RexqNGKCxGHWADaXEws9OV6o++uqrr2wVdeQ1seYuD23fmExB3tciO+FooHHdVJ2vKi+1Sx02bJg97kXOJpXsamDFpf/1PapKXzMOqTpdzSW8pjbMaTqJKzZtPw20r+RW1cI6zo0bN863c4OqMN3vU/fV3k3tZlXV+sQTT9gRLBDefEvNpJIytmGc9F3p+KGxFzU+ZGjfiWQhYcwEHZg0nZFmuHC7sqsNkNo26gTq59RCmkNXO5auOnRQUHxqmN2hQwd7ZenXFbjalWlIAA1crAOorto0fdujjz5qT+Bqh+EXtTlVLLqK9Lv0JJROiJrlRdtOM5ZETpmlNoR+0XBI2l5169a1t8j9KtmdD0Lpwu3mm2+2pTrR2mxFJrfJFNR9TXQyUmcrlULpglMlT5rXV8mYkh9ttyFDhphBgwbZwYKTXaqiY6tiUztaJawPP/ywbcurkh8lQjruvvTSSzbGZNIFk0o81RlInXM6depkJk6caEs+lSCqnblO8jNnzrQn+mR3oNM2e/vtt+3fmrd55MiRaW0qNe+62oX26dPHFn7gfzMyuRfpKggK1aZNG9+m11XbdQ2tc88999i2xCow0H6lm84TSUkgk1LxnUI0IHGJEiWc3377zfn555/DBrFVG5sOHTr4FtuiRYuc4sWLO998803Y44q1atWqznvvvefroN0a+HTdunVhj6s9TdGiRZ3169f71v6oUKFCSWsDkohJkyY5xxxzjK8DdMdy2223Oddee60TRGrTo7Y+QRPkfU3tK6tXr+4UKVLE6datW8z2qTrGJHvgbh0jSpYs6VSoUMHp37+/Hag41jEm2QN333///U7+/Pnt7/SJJ56IOQD2JZdckvSBu9u0aePky5fPOeuss5xx48bFPI6ULVuWgbv/P7U7rVWrllOlShV7Lu/YsWPY7eWXX3b8tn37dtu+/c4773Ratmxp9z+1OdakHTmNEsYEqXeoSi007ZnGYgzSOE1q16Cq58jqIsWq9kd63q+rI723quurVKkS9viFF15oi/1VEupHtbmuIPV9BnEeU/WCU7Vu4cKFTRBj82s6x9waW5D3NZWE3XXXXbYESnNax6K2jKE945NBJbEqJevYsaNnKZgf1ao6tqqNZ0Y9jdVrP9nUo1bbLaP9Tc2D8D+aslMleep9HFmjExSKr169erbJmW4rV660tQPJaO7ATC8JUlXqpk2bbJVbtHGakjEWUiwqplZs0agtjZ4PWmxqX6bH/YpNwyOpHVS840UmkxJpNXNQe6ig0UWJ2ggGUVBjC/K+5lateiWLflGbOx1vg1hlqurLoCYWqh5P9tiPuZ17kR7E73Ty5Mm2SYsKpnRBrGH91OxGzUiU9GuoqZxGCWOCdEBVJxL1ytMgt6KpxtS+RoOzjh8/3vhFYyz27t3b9OvXz5bmqaG6Gv2PHTvWXmmqlM8vaiejXoza2fX/kUceaTtNPPTQQ7ahuF8lQnpvjSuntilqrxVZAqrv269pFdWGUe+v9m4qGY4cXLdx48a2/YpfF046UF100UXm9NNPtx0PQqm9qkpf/KD31f6vZFtj4kW27VGHnGLFiiU9riDva+6Up2pPHDQqRVFHPj/GHc2I9jH9FvyYBjYjait7//33B66tbJApAXvwwQft7yFoSeMbb7xhO5xp3M877rjDtmtPdicqhtXJBGXz+tJCZ1jRzqXG1vfdd5/xk0pWlMyqwW5oyYYGIvV7Pml1vlEvUPX8Da3iUmNxv+aTVnG+qm5iUXWOGob7QfOWejXg79Klix3Q2A8aRFknpFjU8UodrfygxuCaVSUWdZrQrAnJFuR9TVVaquZSKUbQpjzVrEK6MFHPcj8SfS8aQmfatGm+FhTEooG6VdquQgTEtnjxYls76FKHFyWMmks68iK9adOm9gLZD+o8pVEAvv76a9uBSp3QdPGpkkUVHKgAIacTSBLGLB7INN2YkkVVH/pxEopVrK4fgTusjpKyoBxolSwqNndYHe3kfo+dBuR1mrJQpbKq5grClKehdLxQG2y3J3RkNaufJ3El+ErM1EtVNU9qXxZKFwgaRcAPGoFCF5zaZiqNimwLrW0aWTOQF6kHebyzLHXv3j0QQ1+pKdeSJUvMJ598YguDdIGs4bqU5OYkEkYAyOM0ALY6oMWiqR4//vhjE8QTup8n8dtuu812lIjl6aefNm3btjV+UDMfr6G3NFe9mgYh9/j3339tDYlKGTW0lTqLKunXUHWaoz6npwckYYyDOmU8//zzcW1QXf1q9Hy/qE2giqlDe0qr8bPG3nruued8LfVU1YjiUGmFS1dIa9eutfN3+kFtPDWmZiwqNdaYfn61j1IVYSwaa9OvAdl1NevVW1YzXKj02A86cOrA6jVWqR/tuoK8rwEItv79+5unnnrKlhQrQXTHYFT72WR1CKPTS5yDxiqjd+mqbdGiRbaRutr9qAffb7/9Zp/zs52gqm7UcD2yHaWql5T0auBudTjxgwbU1Uk6NFmU9u3b29ILdYrR4MDJpu9ObZAie2Xq+9RMHKrO8eskvm7dunSxKV4N56RtqQFc/UoYlSxGxqZqTfUArl27tm1H6FfCqFleIocK0XBYujBR1aGqXv0Q5H0NQLCdddZZ9jyZzAQxEgljHHQCDE0Y1S5F1TeDBw9O60mlk7tKVfzqtSoaOypWzykNFaBe0n4ljJrhRTt8JO34ShjVoF3JY7JpNgsV8Uc7uatn8vnnn2/8onHndIu0evVq2ws52nPJos420TrcKCHSeH5KGP2iWUqitfnRtHb6napXqx+CvK+F0qgP2k7qCOPSbCru7CB+USN/tcvWxUDodI+q1fF7PmQNfaWanMiSbbVf9HuoIrX91AVT5KwlOldo2kfkDh186kQYJseHBk8xGmVdMw5EM23aNF9netGMCE2bNnUOHToUdaaBxx57zPFLv379os7CsGfPHjuq/nfffecEjWbMOe+885wgGj58uK/fp5ezzz47kN+nZmnSrBaHDx92giYI+9rChQudxo0bKxNLd0v27C6Rxo8f75QrVy5qbMme3SXS3Xff7RQsWDBqbMme3SXUP//841x44YVR49It1qw0QCx0T81EOySVCOzbty/qlZye94vmtlZvKY0zp0bsqu5SNZyqqNWbz69ZXkTDEL388su2fZkGEVebRlVrqhpOQxf4NayOF33Pfn6fXogtcfrNqgTIz3mug/x9qjmNSjlVStyoUSNbKqXxW9W28sknn/QtLpUqXn/99bb9p8aJVO9eNX3QcU3jumqsQb+oCZCGv9KwLBr6R9tLNT26r7mk/awFUEcglXzqXKBSdTVl0bAsKilW7UBk8yAgI5RHZ6J6unr16jb50sGrYcOG9kSkKmv1mNOBzS8aOkc9GZWEjRkzJu1xHfDfe+89G7tflBC+8sordvouVVm61M5Ng5EmZeL0KNR2LNp4gpriUQ2MO3fubPyisTR18gmlakIN5aTY1PHEL3PmzLEJRSj3d6ALAXXI8bNKes+ePem+Zw1gr2o4v2YVCvK+pjbOupBTpzklY6ryVTWvJifQhafaICsB8mtf08Wwto867qkaVcddxapq4IkTJ5q+ffv6Ept6qt5+++22R7KaJ+kYrH1fv001tVH7VL+q8hXbuHHjbDt7DV2mpFEXAhrvU1MZeo3xCkRDwpggtQ9UgqNSPHcMQZ3EdbBQT997773X+EkHKx2klGhoqi9dRWr0erVB8lvPnj1to121pVRprKZRVCKZ7NHqI9trRXY0UDwa7FxtUv0aGFvU3k0DZIdSYq0LFo295TUIdE7TYOs6UYfS8A46kavnu59TkqnkSd9rKI2Pp7FSNTadX4K8r6lmQqV12r/U5i504PNmzZqZF1980dfYNGuVRItNM0b5GZvbNjs0NrXNVnKm2PxKGKNtN3Us1ExIKmnXBYymqgTiRcKYCbpy1MlcV+IqBdJVpZJHHfj9pCpfDSb+xBNP+DbVXiwa3FaDinbq1Mm3kopodPXtd1WgV4KtWxBpX9MtiDSgbRAFeV8L7USiwf5ViqeqVlWtqrZCFylBiE0JopJuVa8q+fnggw98G1A8WmyaJUTNb3SxrukMH3744cDEphoeXTBptAwVcJAsIlEkjFlw9NFH21tQqC2gDlRBpGFgIkt9AASDSmA1fquo1kTjpWpAbFVHq3TKa3DqnKamNG5vXpXWqcmNW2qn/1Vt7meNjkpmRRd3mu/XbRuo7acmEH7RSAruKB6DBg0y5557rh1IXKXIfo7Ji9yLgbszObyDGjv/+OOP6YZRqFKlim/zSWt8SFU/q92Kn23IotHVttoZ6cTjZ3WlV8N6HezVQFwlxhqvr1evXumm0/KDZmRQMwgNp6NSFc0dqpIfv6kEQ221VNqukjM1MXCnIQtCJxJ1QNB4qRpORLUC1157rS9jfeamfS2yXaOqVFW1Gjmnrt9Us7Nt2zY7s4VfY9JFowRbJdxK1LTdgjaesGKrWrWqvQGJImHMBFWtfvnll6ZVq1bpGtDrpKkG7H419lcDbHVGUBV5ZGKm6mC/BgbWGHijRo2yJ2/FFjnnqhpg+zV2n6po1ENUyb7m3VaHCbWzVEmBej/6eXDV4NwjRoywbT21b6kdkpJvlR7o+/ars5A6uCgGdUhQA3o1qNdJXAnagw8+6GtbXsWh0jLFqEFudfLWxZ3G79N37WeTiCDvawDghYQxQTrp6KCutoI6gQeJTt7RBi12aTohv0qmNB+sZqKJRdU3fswMog5L+j417VK/fv3SOuC4M2+op6h6iPpB7bSU8GhIJLe60E2IVMr42GOP+dZ+64UXXrBDrXz11VdhSY56AWsEAXW8chvcJ5t60+p7ffPNN9M6e+lvdSpRuzz9dv0Q5H0tyDUnok5yah+ofT9yAGr9NvwcMuyPP/6wbSkjBzsXDQHkZ4m7LkLUWzpysHPR8cOvEQOQO9GGMUE6WKnNStCSRVFHl6B1dnH5OWWiF1XzqhopsjeyGoRrfDc/p2rT7DjqCR2aLLqzR9xwww22ZMqvhFHvrSGSIkvENI6fLkw0D7ZfCaNi0xzcoSMDqF2eZmZS2zxVZR555JFJjyvI+5ooaY1Vc+JXSbabUCvp0rFX/0dWQev79Mv8+fPt/q6exxquRvtZqMjhnZLpmWeesTVO+j6jdciMTG6BjJAwJkjJoqq41P5IBwnkbjoxqjRF7XsiT5Ial87PK3C9t9qRRaPY/GxXFis2lWLo8SBuN7XxVSmahv/xK66g7msqgVLpcBBrTtTuWU1YNFSY24kjKDS8lDrd+DnsUCwqrVbNTpcuXfwOBSmChDFB//zzjx3P7fTTT7cHClXVhFJbpGSW+qjTgWZPUZWMDvS6H4uWiSzdyEmqwlKVpQY4nzp1qr0fi5ZRNWuy6cpbHYTUvlNDTqhaXLMjKFaVoKn60C8arFhVWhoQXiUFGtpEnUt0EtCQNmrL6BeNp3nBBRfYEkbdV0/alStX2u9RyZraNfoZm7abZgZRu1iVSKmaVdXAanfpV8IY5H0tyDUnmhXquOOOC1yy6MamEsYgUmxBrXFC7kTCmIkrcV3pqnQncuBiUW/MZCaMtWrVsidunXyUrOp+LMluI6iOGuoFqhORez8Wd2gKP6hXuZL/0J7lSjJ0Elei5hd1JNHMPZo9SMNhhM7c8+qrr/raE17JrMb7HDBggOnTp0/a4xrmRDF7fdc5TUOIKLFWe12VKIZWl7/22mvGT0Hd14Jcc6KRH9SRSklt0JLG1q1b26kUgzheqmLTxYifww4htdDpBfj/1FFDDdjV9k2JT1CG/3Hn3dZwLIpJvWuD0lhd7cvUmckdVkcXJX62d4ucck8dh5RoaAYatTELiqDta6o5UUm2kp8g1JyE0v6liyZ9jxqhIrIphobWUY2PH6ZMmWInJVCJumKIHBpJbZDV5tgP6tSijl5KZo855ph07StVCu9XaTtyJxJGAMjjVLJ44YUXxnxeNScqOfaDmmAMHz7cc4SFgQMHGj8oyfYa1Fw1A23btjV+UHW02u16je/qR+cv5F4kjJn03nvv2cGUVZ2kqbN09auhRm699VZf50aWmTNnmgULFqQbSkG95byqrJNRUqZ5hlW6Ejlsh0ov/JpzVY391f4utJeqerP27t3bXqWrCtgvahPoll6E9rbV488//7xv+5q7fdQWL7QHZrRtmWyxtk+0bZlsQd7XAMCTg4QNGzbMqVChgnPllVc6xx57bNrj3bt3d959911ft2jPnj2d0qVLO7Vr13aqVKniHH300U6+fPmco446ynnyySd9i2vnzp12W1WrVs0pV66cjUv3tQvWrVvX+frrr32J6/Dhw3Y7/fXXX+mee/HFF52bbrrJ8cvSpUudxo0b2xgjdenSxXnvvfccv7zwwgvOjTfemO7xAwcOOHXq1HHWrVvn+KVz585Rf4e//PKLc8wxxzh+CfK+BgAZodNLglQaoJk3VIKnUkUNEhzaO1OdETSemR809p3a1KgETyWgv/zyi+0pqvH81ODfr6oR0bATali/cOFCW5qooR4uu+wy29tXpVR+9TRUu0CVQlWqVCndc+qZ+corrxi/qHNVs2bNopYiKjaNN+jXgMXap6J9Z5rzV/Or6/cR2Q4umbGpk0QktWPULEhqd6mxD/P6vqZ2pzpeqYS4Y8eO9n4sWkZTKyaL9h+NCanOcmo/qfuxaBnNk5wsqlnSsfXiiy82y5Yts/dj0TLa75JFVeCaFlNV5dqfdD8WLRM6VimQERLGBOmEowbq6kmoBvWhlBCpob1f1DGiffv2Nj6duDU1mmgYoGuuucb26tYAwX7F1rVrV1uFHxqbBqBWz1Wd5P0YikU9kTUMzPr169MlOErA9bxfdJJWgq0BdiMbrCs2JZN+xqYY1HA+lL5XdVLwe7sptnr16oU9rpO7tqVfJ8mg7WuaPUXDcmk7nXHGGfZ+LFommQmjOgQpHnWgUmcqr9i0TDITRs2o9d///tceV937sWiZZCaMameqgcxvvPHGtPuxaBkSRiSChDGTB31duUWW/GgaJj/HMVO7QLfkpGLFiratlEvDnOgEQWzhSpQoYYdf0dh4mupOJT36btXWTPMha7YEv2hcyh07dthejhoqpnbt2nbfU2mtpoB86KGHfItNyb+mLVTP4x49ethSIJVs64JEJyGdKP2iXqsqPdGFieaN1lAs7sw0Kt3W434I2r6m3rtKVF2h9/2mWprQmhq/am2iUVtT11lnnWWCRL3co90HskWGldZI56KLLnK6devmTJs2zbbL27Bhg/PMM884RYsWdT788EPftphi6Nu3r72/efNmp0SJEs5DDz3kvPrqq0758uV9bfN26aWXOhMmTLD3P/nkE6dixYrO66+/7gwfPtwpXLiws2bNGt9i+/vvv51zzjnHtqd0bwULFnQGDhzo+G3BggW2jWdobGXLlnUmTZrkd2jO2LFjnVKlSoXFpt/DsmXLfI1LbQVvu+02J3/+/GGx6XertrR+CvK+BgBeSBgzYcuWLU7btm3DDvqFChWyyZmflixZ4syePTvtbyUV6jShziV33XVX1M4TyaLkeuXKlfa+4hgyZIhTq1Ytp0GDBs64ceOcIFAnk4kTJzqffvqps2nTJicoDh486MyZM8d5//33na+++srZvXu3ExRKwKZOnWpj+/77751Dhw45QbF+/Xp7caLfwe+//+4ESRD3tf3799uOcUr6ixQpYjvKKbmdMWOG36HZbaSLAHXmU4KtY9pVV13lrFixwu/QnEWLFtkL4kqVKtnzQL169ZxBgwY5O3bs8Ds05+OPP3Zat27tHHnkkU7x4sWdFi1a2At1P88FyL0YVicLNO+q2kWpyktVcAyJASC30niG6lyitm0a4krV5Zop5J133rHtn/0akktxNG/e3A6KrXaUmiJz8+bNZvz48WbJkiV22sdq1ar5EtuiRYvscGVqPqIOLhrXcMWKFbYzn9qSf/PNN74NZK9OTH379rUDsat9uJpiaFtpjmm1HVfnTSARJIwAkMepbazmBVcCpkG6Q6mt7PTp031rEzdhwgQ7MLeSndBOGqohUyc/zRfu18DdSsjUznjs2LHpZs7R7CrqrKMp+vygpF/zp0d2Vvr2229t216NFuBXe17kTuwtmTiwakDgaNQJRgc0HSjatWtnewQn29SpU+2V4/Lly+2wP6F0pek1Y0JOU6N+zaW7bt0621s1lK7IO3To4FtsQF6mQfU1ykNksigqPfOazSSn6Tim+aQje/TqeNumTRuzd+9eX2PT9onWOVJTFkYeg4MQm4bD0qgLkcdgICMkjAnatWuXmTRpkq2K0EFBPQ337Nljq6dFBwmNy1WrVi0ze/ZsU6pUKZMsShI1vZd6iapXYWTC2qhRI+MXXYGrFEAzXGibRQ4T49csLwCMLV3Ub1LDW0X2cFcpmaaZ84t64+vYoVK70KGHlBBp5qjBgwf7Fpu2i84HuhgPHTVDsw2ptNbPoa8U2wcffGDuuuuusMe1zTTnu5pSAYkgYUyQhjbRGGqXXnqpHQrDTco0/qIGUb7++uttWx8NhKuqnNAhGHKaqho0Hpmfg03HoiotHdjvvPNOv0MB8P+rTVXi71I7N43HqMkIVEuiC2H9bnVcee6555K6zTSWp4YpCx1bU0mOYlN7RQ06rmRI434WL148qbF9/vnndpxIUSmdCgZatGhhL9bVhlHDmal9pR7TOIgacipZNKatvjf3XHXPPfeYr7/+2iaPqn7+6aef7HZTNbVKZosUKZK02JD70YYxQWqvokG7N27cmO45DeA6atQoO67aZ599ZkaOHGkPuMkyefJkewIIPQkERf/+/W11lxpbA/Df77//bpOceOi3q4Ggk+Xtt9+Ou/mMOusksw2jxviMt4peM68kc4YtJYZbt26Na9m5c+faBBeIFwljgtQDTj32dIUbWaSvnoSa5k7VOjNnzrQHPLUpTBYNzK0ee6pCCloVrwZOvvXWW+2BloMUAAC5C1XSCVIxvxqHq2pEVc668la1iIr9deWptixuaV8yOnFoxo/QGSJUzaCqELWdKV26dNiymmFCbQiTZdiwYTZxdqmdp9p2NmnSxA6REWrIkCG2tyMAAAgeEsYEqWGzJp/X3MwqxXN7m6kto+bVVdXIwYMH7XOqKslpGuvr+OOPT/s79H6kGjVqmGRS5xaNoRZPbGXKlElSVAAAIFFUSWeBBu1Wz2TN06wEUfM3AwAApBoSRgAAAHgKHwwPAAAAiEDCCAAAAE8kjAAAAPBEwggAAABPDKsDAB7mzZtnp3vTGKxeQ0MBQCqjhBEAYujdu7e55JJLzIQJE+w8vNll3bp1dk5fAMgtGFYHAKI4dOiQKVKkiPnyyy/NmWeema3bSIP/a2D/f//9l20PIFegShoAImzYsMFO76lZmzQ3/MaNG81ZZ51lZ1aS9evX26pqTb953HHHmZIlS6a9duvWrXbOdNEUmPXr1zeNGzdOe17z0M+aNcuu+5133rGPHX300aZ48eLm999/D5tSVO87ffp006VLF/v36tWrzfz5803Hjh3N3LlzbUnleeedZ0qUKGGfVymoqs81q5PmvNdMVACQHUgYASCCErXPPvvM3lfCduSRR5qmTZvahPGBBx4wTz31lGnVqpUtIfztt9/M+PHjbULpJowqQZQ9e/bYxE5tH/WYphD9+++/zXfffWdLMN3lLrzwQvu6Z599NixhXLx4sbnyyivTEsbZs2eb66+/3rRs2dLOYV+zZk1z+umn23VdeumlNuHUPPKahapcuXLmk08+sf8DQJY5AIB0Nm/e7OgQuXDhwrTHJk6c6FSqVMlZs2ZN2mMvvviiU6VKFWffvn1Rt+KOHTucBg0a2OVckyZNcooXLx623DPPPOM0bNgw7LGpU6faGFzjx4+3f48cOTJsuR49ejidOnVy9u/fb/8+ePCgc/755zvXXnst3yyAbEEJIwDEafTo0bZ6WaWGjuPYW6FChWwV9a+//mqaNGlilzt8+LCtOl67dq0tCVQVsaq21Ykmq/Lly2duvvnmtL/37t1rq7bvvPNO89FHH6XFpfd0q8YBIKtIGAEgTn/++adNxt5///2wxzt37mwTOVEbwrZt29o2isccc4xt36i2h+pAkx3KlCkTti4lq/v37zcLFiwwK1asCFv2tNNOy5b3BAASRgCIU6lSpUyDBg3M66+/HnOZ4cOH22U+/fTTtMfUI3rbtm2e61YHFZVMhlLpYSQ3MQ2NSW666SZz0UUX8V0CyBF0oQOAOLVv395MnDjRbNq0Kexx9VYO7WHdsGHDtL937Nhhpk6dGra8ejWrqlqlla6qVava0kJ1lHGpw01G1KlFnWBefPHFdM+FxgUAWUEJIwDEqV+/frbk8IQTTrAleuo9rbaKc+bMsT2apVOnTrY9Yfny5W3p30svvWSrjEOprWP+/PnNPffcY1q0aGGH1TnnnHPsMD2XX365HSxcVcxKTuOh9zj33HPtTa/dvXu3TVL1PiNHjuT7BZBlJIwAEIXGUFTbRLUZdBUrVszMnDnTDqOjsRSV9J100knmmWeeSVvmhhtusK/RgN+qZh4yZIgdfkdD9bgqVqxoEzrNIKOOKhoWR+Mmarid559/3naq0fiO11xzjRkxYkTa6zSMjhLCSCphXLp0qXnjjTfsOjT8T//+/W0SCgDZgZleAAAA4Ik2jAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAE8kjAAAAPBEwggAAABPJIwAAADwRMIIAAAATySMAAAA8ETCCAAAAOPl/wGY2Mzs5JDLHwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1800 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "match = matcher.get_best_match()\n",
    "m_data = m.copy().get_population('pool')\n",
    "m_data.loc[:, 'population'] = m_data['population'] + ' (prematch)'\n",
    "match.append(m_data)\n",
    "fig = plot_per_feature_loss(match, beta, 'target', debin=False)\n",
    "fig = plot_numeric_features(match, hue_order=['pool (prematch)', 'pool', 'target', ])\n",
    "fig = plot_categoric_features(match,  hue_order=['pool (prematch)', 'pool', 'target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfbdf80b-9391-4bdc-bae7-83ff75a6b56b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pybal2",
   "language": "python",
   "name": "pybal2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}