Trees Viz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(15,10)})\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/ProgramData/Anaconda3/Library/bin/graphviz/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AdaBoostClassifier, GaussianNB, QuadraticDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_count = 3\n",
    "plot_colors = 'brg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
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       "        [6.3, 2.5, 5. , 1.9],\n",
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       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sepalLen</th>\n",
       "      <th>sepalWid</th>\n",
       "      <th>petalLen</th>\n",
       "      <th>petalWid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>0.2</td>\n",
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       "      <th>3</th>\n",
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       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
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       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
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       "<p>150 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "     sepalLen  sepalWid  petalLen  petalWid\n",
       "0         5.1       3.5       1.4       0.2\n",
       "1         4.9       3.0       1.4       0.2\n",
       "2         4.7       3.2       1.3       0.2\n",
       "3         4.6       3.1       1.5       0.2\n",
       "4         5.0       3.6       1.4       0.2\n",
       "..        ...       ...       ...       ...\n",
       "145       6.7       3.0       5.2       2.3\n",
       "146       6.3       2.5       5.0       1.9\n",
       "147       6.5       3.0       5.2       2.0\n",
       "148       6.2       3.4       5.4       2.3\n",
       "149       5.9       3.0       5.1       1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['sepalLen', 'sepalWid', 'petalLen', 'petalWid']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [RandomForestClassifier, DecisionTreeClassifier, XGBClassifier, LogisticRegression, \n",
    "    #SVC(kernel=\"linear\", C=0.025),\n",
    "    #SVC(gamma=2, C=1),\n",
    "    SVC,\n",
    "    GaussianProcessClassifier,\n",
    "    MLPClassifier,\n",
    "    AdaBoostClassifier,\n",
    "    GaussianNB,\n",
    "    QuadraticDiscriminantAnalysis,\n",
    "    KNeighborsClassifier]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "    #GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    MLPClassifier(alpha=1, max_iter=1000),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "         KNeighborsClassifier(3),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.ensemble._forest.RandomForestClassifier'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.tree._classes.DecisionTreeClassifier'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'xgboost.sklearn.XGBClassifier'>\n",
      "[11:52:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\xgboost\\sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.linear_model._logistic.LogisticRegression'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.svm._classes.SVC'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.gaussian_process._gpc.GaussianProcessClassifier'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.neural_network._multilayer_perceptron.MLPClassifier'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.ensemble._weight_boosting.AdaBoostClassifier'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.naive_bayes.GaussianNB'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'sklearn.neighbors._classification.KNeighborsClassifier'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model in models:\n",
    "    print(model)\n",
    "    clf = model().fit(X_train, y_train)\n",
    "    preds = clf.predict(X_test)\n",
    "    display(clf.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "preds = model.predict(X_test)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(453.375, 504.7714285714286, 'petalWid <= 0.8\\ngini = 0.667\\nsamples = 120\\nvalue = [39, 41, 40]\\nclass = versicolor'),\n",
       " Text(383.625, 427.11428571428576, 'gini = 0.0\\nsamples = 39\\nvalue = [39, 0, 0]\\nclass = setosa'),\n",
       " Text(523.125, 427.11428571428576, 'petalWid <= 1.75\\ngini = 0.5\\nsamples = 81\\nvalue = [0, 41, 40]\\nclass = versicolor'),\n",
       " Text(348.75, 349.4571428571429, 'petalLen <= 5.35\\ngini = 0.165\\nsamples = 44\\nvalue = [0, 40, 4]\\nclass = versicolor'),\n",
       " Text(279.0, 271.8, 'petalWid <= 1.65\\ngini = 0.091\\nsamples = 42\\nvalue = [0, 40, 2]\\nclass = versicolor'),\n",
       " Text(139.5, 194.14285714285717, 'petalLen <= 4.95\\ngini = 0.049\\nsamples = 40\\nvalue = [0, 39, 1]\\nclass = versicolor'),\n",
       " Text(69.75, 116.48571428571432, 'gini = 0.0\\nsamples = 38\\nvalue = [0, 38, 0]\\nclass = versicolor'),\n",
       " Text(209.25, 116.48571428571432, 'sepalWid <= 2.45\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]\\nclass = versicolor'),\n",
       " Text(139.5, 38.82857142857142, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]\\nclass = virginica'),\n",
       " Text(279.0, 38.82857142857142, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]\\nclass = versicolor'),\n",
       " Text(418.5, 194.14285714285717, 'sepalLen <= 5.8\\ngini = 0.5\\nsamples = 2\\nvalue = [0, 1, 1]\\nclass = versicolor'),\n",
       " Text(348.75, 116.48571428571432, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]\\nclass = virginica'),\n",
       " Text(488.25, 116.48571428571432, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]\\nclass = versicolor'),\n",
       " Text(418.5, 271.8, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]\\nclass = virginica'),\n",
       " Text(697.5, 349.4571428571429, 'petalLen <= 4.85\\ngini = 0.053\\nsamples = 37\\nvalue = [0, 1, 36]\\nclass = virginica'),\n",
       " Text(627.75, 271.8, 'sepalWid <= 3.1\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]\\nclass = virginica'),\n",
       " Text(558.0, 194.14285714285717, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]\\nclass = virginica'),\n",
       " Text(697.5, 194.14285714285717, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]\\nclass = versicolor'),\n",
       " Text(767.25, 271.8, 'gini = 0.0\\nsamples = 34\\nvalue = [0, 0, 34]\\nclass = virginica')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TQgghXkIKhYKpU6cybNgwIltF4ufvR9u2bfns089eil3sOrzZgdjYWAYNGITZbKZ69eoMHT7UUb9g/gJmTJ/B8ZPHAWjbri1Wq5XJn00mJiaGIkWKMH/hfEewlVk92NMIvb29n++DCiGEeCA5tFcIIUSmnvehvbdu3eKvv/6iatWqjpmYgwcP8mb7N9l/cL9swPCCyaG9QghhJz8NhRBCvHQUCgU9PujBnNlzuHL5Cn8d+YtJEybx2muvSSAlhBDipSEzU0IIITL1vGemALZt28bUL6Zy7uw53N3dqftaXfp+1Bd394ybQojnS2amhBDCToIpIYQQmXoRwZR4eUkwJYQQdvLTUAghhBBCCCEegwRTQgghhBBCCPEYJJgSQgjxSnuzw5uMHTP2RQ8jg8GDBmcYV3JyMmNHj6V61epElIng7c5vc+bMGac2s2fNpmrlqpQuWZpPPvqEhISE5zlsIYR4pUgwJYQQQrxEbDYb06ZOY+WKlRnqRo0cxe+//86kzyaxbPkyfH19ebvT2yQmJgLw7dJvWbRwEWPGjmHR4kWcOXuGIYOHPO9HEEKIV4YEU0IIIcRLIjo6mnfefodvl35LcEiwU11qairr1q6jX/9+lC1blly5czF23FgSExPZ9fsuwH5Y8LvvvUuVqlUoUrQIY8eN5edNPxMVFfUiHkcIIf7zJJgSQgjx3H23/Dteq/MaxYoUo0G9BqxZs8ZRd+HCBbq9142IMhEUK1KMxo0as2XzFkd9rZq1WL5sOR3ad6B40eI0a9KMU6dOMW3qNMqXK0+lCpVYMH+Bo/2bHd7k888+p+s7XSlRrARNGjdh546d9x3bbzt/o8XrLShRrAQN6zdk6TdL+Xvj2+SkZD7u+zHly5WnVIlSdHm7CxcuXHDZz/Rp0ymYv6DL1+pVq11ec+zYMbJkycIPP/1AWFiYU51CoWDW7FmUKl3KUaZU2n+NJyQkEBMTw+XLlylbtqyjvkCBAnh4eHDwwMH7Pq8QQojHJ8GUEEKI5+rYsWOMGD6CXr17sWHTBjq82YGB/Qdy4cIFbDYb3d7rhsHdwLLly1j9w2ry5cvHoIGDMBqNjj4mT57MWx3fYvUae1DyZvs3uXbtGt9++y1t27Xl00mfEnXl7mzMwgULKV6iOKtWr6JatWp0e78b58+fzzC2s2fP8kH3D2jZsiU/rv2Rnr16MmP6DL5b/h0AX3zxBadPn2bBwgWsXL0SpUrJ4IGDXT5np86d2L5zu8tX/Qb1XV5To0YNxo0fR2BgYIY6nU5H5SqVnc7ZWr58OUajkQoVKnAj+gYAgVmcrw0ICOD69esu7yeEEOLJyDHyQgghnqu/U85CQkMIDQ2lTds2ZA/Pjp+fH2lpabR8oyXNX2+Or68vAJ06dWL9uvXcunWL4GB76lu91+pRu3ZtAOo3qM+M6TMYOmwoOp2Od7q8w/Rp0zl79iyhYaEAlC5dmu4fdAeg70d92b5tOytXrOSjjz9yGttXX35FgwYNaNO2DQDZs2cnJiaG+fPnE9k6kqgrURgMBsLCwvD09GTUqFH3TaFzd3d/pgcM79q1i0kTJtGxU0dCw0K5dv0aAG5ubk7ttFqtUyAqhBDi6ZFgSgghxHNVpUoVylcoT+tWrcmVKxfVqlejWfNmeHl5AdC2XVt++uknjh45yvnz5zl27BgAVsvdg4OzZc/m+Fqn0xEQEIBOpwPuBhNG090AonSZ0k5jKFK0CKdPn84wttOnTnPq1Ck2btzoKLNYLJhMJoxGI+++/y5du3SlUoVKlClbhpo1a9K8eXOXzzln9hzmzpnrsm74iOE0btL4/m9SJjb/spm+ffpSu05t+vTtA4DOzf78/wycjEYjer3+se8lhBDi/iSYEkII8VzpdDq+mvcVBw8cZNu2bfy65Ve++fobZs6eScmSJWkT2QaNRkOdunWoVqMabm5uvN3pbac+1GrnX18KpeKB91SpVE7fW61WVEpVhnYWi4XWbVrTrn27DHVqtZpixYqxefNmtm3fxo7tO5g+bTrLli1jxcoVjmDub5GtI6lXv57L8QT4BzxwvA+yauUqhg4ZStNmTRk1epRj3VRQ1iAAYmJinNIEb968SZagLI99PyGEEPcnwZQQQojn6sCBA+zYvoMePXtQslRJevXuRWSrSP738/8wmUycO3eO3Xt3O2ZT1v60FsCxCcTjOHr0qONrm83GX3/95UgTvFfuPLm5cOEC4eHhjrIf1vzAvn37GDlqJIsWLiI8RzgNGjSgQYMGnDt7joYNGnLy5EmKFy/u1JePjw8+Pj6PPWZXNm3cxOBBg2nfvj0DBg1AobgbRAYGBpItWzb27d1HoUKFADhx4gTJycmUKFHiqY5DCCGEnQRTQgghniuD3sCXc7/E29ub2rVrc+7cOc6fO09kZCQ+Pj6YzWY2bthIRLkIjh07xoQJE4CM6WuP4u/Zr4qVKvL9d99zNeoqb7R6I0O7zm93plXLVkyfNp1GjRtx/tx5xo4Z61hDdSP6BksWL8FgMBASHMKqVavw8PAgZ86cjz22h5WQkMDQIUOpXKUyXd7tws2bNx11Hh4e6PV62ndoz4zpMwjLFkaWwCwMGTKE1+q9RkhIyDMfnxBCvIokmBJCCPFc5S+Qn4mTJjJz5kw+/+xz/Pz86NipI81fb45CoaBX7158/vnnJCclkyNHDvr168foUaM5evQouXLneqx71m9Qn82bNzNp4iTy58/PV/O/chlgFCpUiGnTpzFt6jTmzpmLv78/7dq3c2xe0aNXD1LTUunbpy8J8QkUKFCA2XNmO9Z7PUu/7fyNhIQEdmzfQdXKVZ3q+g/oz1sd36LDmx2IjY1l0IBBmM1mqlevztDhQ5/52IQQ4lWlsD1J3oQQQohXghVr5o1eUm92eJMCBQowcNDAFz2U/wylnKwihBCAnDMlhBBCCCGEEI9FgikhhBBCCCGEeAyS5ieEECJT/+Y0P/H0SZqfEELYyU9DIYQQQgghhHgMEkwJIYR47mrVrMWihYte9DAAiLoSRcH8BSmYvyBd3u4C2LdA79mjJxFlIihfrjzDhw4nOTnZcU1cXByffPQJFcpVoFqVasyYPgOr9fFm7+bMnkNEmQiXdVarlbc7v/1E75Wr/s1mM2NHj6Vi+YqUK1uOMaPHOLaej2wV6Xg/EhISHvu+QgjxKpBgSgghhADmL5zPp59/CsCHH3xIUlISXy/9mrlfzmX/gf2MHDHS0fbDDz7k2LFjTJ8xnWkzpvHzpp+ZMnnKI9/z1KlTzJg+w2WdyWRiyOAh/P7b74/1PA/qf8rkKWzdupUZM2cwbcY0tmzZ4hj/nLlz+GLaF499TyGEeJVIMCWEEEIAPj4+eHt7Ex8fT1i2MEaMGEG+fPkoVqwYb7zxBn/s+gOAo0ePsnfPXsaNH0fpMqUpVqwYI0aNYNHCRaSmpj70/cxmMwP6D6B4ieIZ6s6cOUPb1m3Zs3vPY59hdb/+09PT+Xbpt3z0yUeULFWSiIgIBg4cyPJly0lLS8PHxwcfb5/HuqcQQrxqJJgSQgjxyD75+BO6vdfNqWzZt8uoVbMWNpuNmzdv8nHfj6lYviLFihSjbu26fP/99y77GtB/AB90+8CprHnT5kyfNt3x/dqf1tKwfkNKFCtBsybN2LRx033H9maHNx1pav98RV2JyvTZvL29+ezzzwjLFgbApUuX+PGHHylfoTwAly9dRqPRUKRoEcc1BQoUwGg0cuTIkUz7/9uc2XPw8/Xj9ddfz1C3b+8+Spcpzao1q/Dw9HjoPh+m/+PHj5OSkkLZsmUdZWXKliElJYXjx48/1r2EEOJVpX7RAxBCCPHv07hxY7p3605iYiKenp4ArF+/nkaNGqFQKOjfrz8Wi4UFCxeg0+tY+s1SRo0YRY0aNQgICHike/3222+MHDGSYcOHUbRYUfbs3sOA/gNwd3encpXKGdpPnTYVk8nksi8/P79Huvc7b7/Dbzt/IzQ0lD59+wAQEBiAyWTi1q1bjme5GnUVgNjbsQ/V74kTJ/h6ydesWrPKMeN1r8jWkY80zkfp/0b0DTQaDb6+vo4yb29vNBoN0dejn+i+QgjxqpGZKSGEEI+sYqWKeHl5sXnzZgBu3LjBvr37aNioIQDVq1dn+Ijh5C+Qn/DwcN577z1MJhMXLlx45HvNmT2Htzq+RcNGDcmePTstWragdZvWzJ8/32V7Hx8fAgMDXb5UKtUj3fvjjz/mm6XfEBQUROeOnTEajRQrVozs4dkZPnQ48fHxxMbGMn78eNRq9X2DuHuZTCYG9BtA7z69CQ4OfqTxPIzM+k9NTcXNzS1DuVardWxCIYQQ4uHIzJQQQohHplKpqN+gPhs3bKRZs2Zs3LCRfPnykS9fPgBat2nNpo2bWLJ4CefPn+f4MXv6mNXy6DvenT51mkMHDzF/3t3gyWw233eWqes7Xdm3b5/Lup/W/URISMhD3zt/gfwATJk6hepVq7Nj+w5q1a7FtGnT6NOnDxXKVUCv19P9g+4cO3oMdw/3TPucNXMWvn6+tIps9dDjeBSZ9a/T6VwGTUajEZ1e90zGJIQQ/1USTAkhhHgsjRo3okO7DiQkJLBhwwYaNW4EgM1mo8s7Xbh+7ToNGzUksnUkuXLlonHDxg/dt9lidnxtsVjo0bMHtevUdmqjUrqeZRo1ZhRpaWku67JkyZLpvWNvx7Lrj13Ur18fhUIBQGBgID4+PtyOvQ1Avvz5WLtuLbdu3cLDwwOLxcKkiZMIDQ3NtP8ff/iRmJgYSpcsbX9Wsxmj0UjpkqWZ8+UcypQpk2kfT9J/UNYgjEYj8fHxeHt7A/at3k0mE0FBQU90byGEeNVIMCWEEOKxFC9enOCQYFauWMmRw0eYPGUyYN+J7o9df7Bh0wZy5MgBwIH9BwB7oPVPGo3G6Qwnk8nE9WvXHd/nzpObK1euEB4e7iibP28+CQkJ9OrdK0N/TxoQ3Lx1k769+xIaGkrx4vad8KKuRHH79m3y5s1LfHw83d/vzoRJExzB07p16wgICCBPnjyZ9r9oySLM5rvB4v9+/h+zZ81m5eqVTyWYeZj+DQYD+/buo2atmoB9wwuDwUCBAgWe+P5CCPEqkWBKCCHEY2vUqBHTp02ndOnSZM2aFQAvLy9UKhUbN2ykcZPGXLhwgTGjxgC4TC8rXrw4P6z5gS2bt5Ardy6++vIrp7VHXbp0oVfPXuTJk4cqVauwf/9+pkyewuChg5/JM+XNm5eKlSoyYtgIRo4aidVmZfTI0VSpUoUSJUoA9u3Fx48bT9+P+hJ1JYrRI0fzQY8PUCrtS5Hj4uIA+/qtf/rn7JW/vz9KpdIpWMzMk/bf8o2WjBkzBi9vL2w2G2PHjqVN2zYu11IJIYS4PwmmhBBCPLZGjRsxc8ZMGjZu6CgLCgpixMgRzJw5k9mzZhMaGkrbdm1Z8f0Kjh49SpWqVZz6aNykMUcOH6HfJ/1Qq9W0btOaqtWqOupr1qrJsOHDmPfVPCaMn0BwcDCf9PuEVq2ezZojgM8+/4yJEybStUtXzGYzderUof/A/o76yVMmM3z4cFo0b4Gvry/dunejXbt2jvoeH/YAYPGSxc9kfE/af9+P+pKelk6397qhUqlo3Lixy1k+IYQQD6awucq5EEIIIe5h5dE3jvi3iLoSRe1atVm1ZhUFCxZ8Kn3evn2b/p/0Z+5Xc59Kf8+7/91/7uatN9/izz1/ujw0WCmbAQshBCBbowshhBCAPXUuPj7+qfQ1Y/oMmjZv+lT6et79x8XFERcf90z6FkKI/xqZmRJCCJGpV2FmCqBy5cp8Oe/LJ+7TaDSi1WqfuJ8X0X9kq0gOHzoMIDNTQgiRCQmmhBBCZOq/HEyJRyfBlBBC2MlPQyGEEEIIIYR4DBJMCSGEeG5q1azFooWLXvQwXioD+g/gg24fPHE/UVeiKJi/IMePH38KoxJCCPEwZGt0IYQQ4gUaOGigy8OMhRBCvPwkmBJCCCFeIE9Pzxc9BCGEEI9J0vyEEEI8VQkJCQwaMIjy5cpTPqI8H/X9iLi4uAztzGYzUyZPoVbNWhQtXJTKFSszdvRYLBYLADeib/D+e+9TtnRZIspE0KtHL27dugVAclIyH/f9mPLlylOqRCm6vN2FCxcuuBzP9GnTKZi/oMvX6lWrM7T/YsoXNG3svO34ju07KFWiFMnJyQAsWriI2jVrU6pEKSJbRbL7z92OtgP6D+CTjz4hslUk5cqWY+eOnezbu49WLVtRolgJqlauyqSJkxzP+c80vz179tCmdRtKFi9JnVp1WPrNUkfdpUuX+KD7B5SPsL+3A/oPuO927kajkRnTZ1CnVh2KFy1OZKtI9uzZ88BxCiGEeDQSTAkhhHiqen7Yk+PHjzNr1iwWLl7IxQsXGTJ4SIZ28+fN54c1PzBu/Dg2btrIRx9/xNKlS9m8eTMAI0eMJD09nWXfLWPJ10uIiopiwvgJAHzxxRecPn2aBQsXsHL1SpQqJYMHDnY5nk6dO7F953aXr/oN6mdo37hxY06dOsW5s+ccZevXr6dmrZq4u7vz3XffsXDBQgYPHczqH1ZTr149urzThVOnTjnar127lrZt27JoySJKlS5F9+7dKRtRlnXr1zFh0gRWfL/CZSB34cIFurzdhRIlSrBqzSp69+3NhPET2LJ5C/Hx8bRr2w6AxUsWM2PmDI4fO85HfT5y+dxjRo/hu+XfMXDwQFavWU3x4sXp+k5Xrly+ct9xCiGEeDSS5ieEEOKpOXv2LH/88QcrVq2gcOHCAAwfOZwtm7dkaJsnTx7Gjh9LREQEAKFhocybN48zp89Qt25drly5Qo4cOQgLC8PNzY1PP//UMTMUdSUKg8FAWFgYnp6ejBo1iqioKJdjcnd3x93d/aGfIVfuXBQqXIgNGzbQ/YPuGI1GNv+ymYmfTgRgzuw59Ozdk+rVqwP2YO3gwYMsWbyEUaNHAZAjRw6aNrPPbsXFxZEQn0BgYCAhoSGEhoXy1byv8PP3y3DvFd+vIDw8nH79+wGQM2dOEuITUKqUrP1pLSaTiYmTJmIwGACY9NkkmjRqwl9H/sLX19fRT2JiIitXrGTCpAnUqFEDsK/NOnjgIAsWLGDI0CEZximEEOLRycyUEEKIp+b0qdOo1WoKFizoKCtcuDAf9vgwQ9uatWqiUqn4dNKnfNDtA+rUqsOZ02ewWu1nWvXs3ZOtW7dSoVwFur/fnb179pInTx4A3n3/Xc6dP0elCpXo3KkzP//8M/ny5XM5pjmz51C6ZGmXr59+/MnlNY0bN2bjxo2APcVPrVZTqVIlkpOTuRp1leFDhzv18+uWXzl/7rzj+tDQUMfXPj4+dH23KxPGT6Bq5aoMHDCQ+Ph4pzaO9+/0aYoULeJU1rpNa6pXr86ZM2fIny+/I5ACyJs3L15eXpw5c8bpmvPnz2OxWChRooRTeclSJTlz+m5bV2MQQgjx8GRmSgghxFOj0Wgeuu2M6TNYvGgxr7d4ndp1a9Onbx969erlqK9Rowa/bv2VX3/9lR07djB2zFjW/rSWBYsWUKxYMTZv3sy27dvYsX0H06dNZ9myZaxYuQKdTud0n8jWkdSrX8/lGAL8A1yWN2jYgEkTJ3HmzBk2bNhAvfr10Gg0pKWlATBm7JgMQY9Wq3V87ebm5lTXq3cvmjVvxtZft7Jt6zbe7fou3bp3o/sH3Z3aaTQauM/Gfv/s829Wq9Wx/srRVvuAtta7be/XpxBCiIcjM1NCCCGempw5c2I2mzl58qSj7MCBA1SvWp3U1FSntt98/Q19P+5Lv/79aNasGSGhIVy7es2xTfgXU77g0uVLvN7idSZPmcyUL6bwxx9/cPPmTRYtXMSevXto0KAB48aPY+m3Szl75qzTff/m4+NDeHi4y5e7h+v0vyxZshBRLoJNGzexbes2GjduDNh33gsMDOTatWtO/Xy37Ds2/7LZZV83b95k5IiRBAQE0LFTRxYsWkDntzuzft16l+/fsWPHnMpGjRzFqJGjyJU7FydPnSQlJcVRd/LESZKSksiVK5fTNdnDs6PRaDh44KBT+YH9BzK0FY9PtrQXQsjMlBBCiKcmV+5cVK1WlWFDhjFk6BCUKiVjR4+lVOlS6PV6p7Y+Pj7s2L6DcuXKkZSUxIxpM0hKSsJoNAJw/tx5tm/fzvDhw/Hy9mLt2rWEhobi5+fHjegbLFm8BIPBQEhwCKtWrcLDw4OcOXM+tWdp3LgxY0aPwdfXlxIlSzjKu3TtwtQvphIUFESJkiX45X+/sHDhQubMneOyH29vbzb/spnk5GTee/89UlJS+O233yharGiGtm3atmHJ4iVM/nwyzV9vztGjR1nx/QqmTZ9G2YiyzJ45m36f9OPDDz8kKSmJkSNGUqx4MUqULMHVqKuOfvR6Pe3at2PihIkY3A3kCM/Bt99+y6lTpxg1ZtRTeX9MNkvmjf7jNArVix6CEOIFk2BKCCHEUzV+/HjGjBlDp46d0Gg01KxZk/4D+2doN3b8WEaOGEnTxk3x8/OjVu1atGjZgqNHjwIwbMQwxoweQ9cuXUlLS6NEiRLMmjMLpVJJj149SE1LpW+fviTEJ1CgQAFmz5mNl5fXU3uOuq/VZeSIkTRq3AiFQuEob9+hPWlpaUyZMoWbMTfJHp6dSZ9NonKVyi770Wg0zJ47m3Fjx9Hy9Zao1Wpq1KzBoMGDMrQNCQlh1pxZfDrpUxbMX0BIaAjDhg+jarWqAHw570vGjx1PZKtIdDodtevU5qOPP3Ia39969+mNUqFk6OChJCYmUqRIERYsXOC0nk0IIcSTUdhkjloIIUQmrFhf9BDES0SJUmamkJkpIYSsmRJCCCGEEEKIxyJpfkIIIYR4bEUKFOKL6VOpVbv2c7/30MFDMBgM9B84wGV91JUoXqtdx2Vds9ebM3rsGMxmM2VLlsZkMjnVf7PsW4qXKP7UxyyE+G+RYEoIIYQQ/yo2m40Z06azasVK2r/Z4b7tsgZnZeuObU5lWzZvYfzYcbRr3w6AixcuYDKZ2PjLz+ju2Sre28fnmYxdCPHfIsGUEEIIIZ4Li8XCls1bWLJoEYu/+fqx+oiOjmbwgEGcOH6c4JDgB7ZVqVQEBAY6vk9MTGT6tOl80ONDChYqBMCZM2cIypqVsLCwxxqPEOLVJmumhBDiEdjM6S96CEK8FKpVrsIPq9c8VNukpCSWLFpMw9fqM3TQYAoXsR94PGPadIoUKOTytWbVapd9HT92nCxZsrD6xzWEhj5aAPTV3C/x8vSkw1tvOspOnzpNzlxPb0t9IcSrRWamhBDiESjUblwdWfFFD+O5Chn6+4segngJzZg1i9EjH3xm1ZUrV1i65BtWrVxJUNYgOr7dmSZNm2AwGADo1LkTka0jXV7r4enpsrx6jepUr1H9kcebkJDA0q+/YdjI4Wg0Gkf52TNnSU1JpWOHtzh//hy5c+ehd9/eFC1W7JHvIYR49UgwJYQQ4oEsSbdQefi/6GGIl4jNZqNI0SKMGjOa5k2a3rfd2291IjUtjUmff0qVqlUz1Bvc3TG4uz/LoTqsWbUabx9v6tWv71R+9uwZrBYrg4YOxtPTi++/+47Ob3Vi5ZpVZA8Pfy5jE0L8e0kwJYQQ4oGiP2/8oofwzDztWbfMzl4qUqAQ8xctJKJchKOsXp3XaNW6Fbly5eaD97uhN+idrklPS6fLu135sGcPenT/AE9PL8aMHwtAWloaa39ay9G//uLC+QscP3aMpKQkNv3yP0LDQjPstNe0UWM6v/027h4eTBo/gU2b/wfAvC+/YtvWrcxbMJ9yZSL4ZtlSx5qiv/vIEhREmzci+d+vmwkOtq9VypsvL3q983jv1entzsz/ah7DBg+lVetI3ohshb//3cB87uw5fDl3rstrh40YTqPGT+/f3rq162jUuDFqtfOfPt+tXIHVanXMlhUuUpjDBw+xcsVKevft89TuL4T4b5JgSgghhHiO1Grng16tFgsqpQqLxYxKpeL7lStRKhVObTy9vDL0k5KSQrvWbdFoNNSuU5tq1avj5qalS+d37nvvshERHDlyBKVCQUT5co7ycuXLM2PadA4ePIReryd/gQL3fwCb7R/Pc/8/JVq3bcMbka34388/s2ThYubOnkO9+vVp/2YHChUuRGTrSOrVr+fyWn//gPuP4RHF3Ijh2NGjDB0+LEOdTqdz+l6hUJArdy6uX7/+1O4vhPjvkg0ohBDiX671mlhWnEh9qLYLD6fwzvq4p3r/r/9KoeWq2zRYfosxvyWSZLQ+sP26M2m0WRNLvWW3GPBrAjEpD57N+a85evSY4+sb0Te4ceMG+QvkJ3fuPFgsFhITE8geHk728HCyBgfz6aRPOXXypP0Cxd0ga8/uPZw/d45FXy+m63vvUr1GdW7fug2ADeeA529lI8py/OgxDhw4QPny5R3lhQoXQqfTsXrlKspGRKBUZvzzIFfOXGg0Gg4ePOQou3TxIomJiQ98XpVKRb369flm+bfMX7SQ9PQ02rSyr5Py9vFxPOs/X+4eTy/979ChQxgMBgoVLuRUnpaWRtWKldm4YYOjzGKxcPLESXLlzvXU7i+E+O+SmSkhhPiXm13PG51akXlDILKgnub5dZk3fEg/nErj+xNpDKrogbebks/+TGLSH8mMqOp684A/ooxM25vMx+U9yOGtYvaBFIZsS2RWPW8Uiod7hn+7L2fPITw8O1mzBjNxwgTy5M1D2YgIVCoVterUZtCAgQweOpQsWbIwa+ZM9u3Zy9BhQwEwGAxERUURHR2Nj48PZrOZTRs2ElEugmPHjjNp4kQAjEajy3uXjYhgUP+BmEwmyt0zM6VUKilTtgz/+/ln+nzU1+W17h7utHijJZ9NmoSPjze+vr6MGTXGZeB1PyVKlqBEyRJcjYp66GseVUpyMikpKU5bop86aQ+O/vlvTKfTUb5CBaZ8Nhk/P3/8A/xZMG8+cXFxREa2fmZjFEL8d8jMlBBC/Mv56JQPHUzpNQq83Z7ej/7vjqfSrrCeiBAt+f3VfFLBg+2XjVxPcj3b9N3xVBrk0VErhxu5fdUMruTBmVgLh2+Yn9qYXnbNW7zOpPETade6DQaDgVlz5qBS2VP/Ro8dQ8lSpejTsxdvtGjBjegbfDl/niMwaNGyJefOnaNF0+YULVaUHr16MmXyZJo2asLsmTP5+JNP8PL24tjRoy7v7efnR1i2MHLkzOEUbIA91S81NdUp/e+f+g3oT526dfmod1/e7tiZWrVr4eUiBTEzIaGhj3zNw1owfwHVq1RzKrt16xbe3t4u2w8fOZyq1arS76OPiWzxBteuXmXB4kX4+Po8szEKIf47FDabzXUugBBCCJee99boMSkWJu9O5kC0CS+tkk7F9Uz6I5lvmviQ1UNF6zWxtCygo2UBPeN3JaFTgdEK2y4a0aigcR4dnYvrUSgULDycws4rRr5q4JPhPgsPp7DoiOt0wX7l3amX23lG63aqlRarYpldz5v8/ncTHRp9d5veEe7UyuHm1N5qs9Fw+W36V/SgWva7dW+vi6NmuBvtitx/I4Nn5UVsQHHvhhDi302jUGXeSAjxnyZpfkII8RKzWG0M2JqIl1bBtLrexKfb+OzPJKwP+Bhs3dl0WhXQMbu+N7uvGpm+L4USQWpKB2sfeK/Ignqa5HWdAuiuyTjzFZNiXxsVYHCe6fLTKRx190o02kizQID+H+31yldu3ZQQQoj/BgmmhBDiJXbwhpkLcRaWN/fF/04Q8mEZdwZuvf+i/yB3JV1K2hfvZ/PSs/JkGsdvmTMNpvQaBXoXQdP9pFvsEZ32H1mDGpUCkyVjtJduvtNe5XwPrRJMD96zQgghhHgpSTAlhBAvsbOxZoLclY5ACqBo4IN/dId6OKceGTQKLA8RrHz9VwrfHHWd5tcnwoM6OZ3T9tzuBEX/DIRMFhtuLtZw3W3vHGgZrbhs/1/014ljmTcSQgjxryHBlBBCvMSUCjKk9GW26Z3GxTKOh1kc2ySvjhrhbi7rfHUZN60IvJPedyvVit89wd7tNFuGVD4ALzcFOpW9/b1up1opFaR5iBEKIYQQLxfZzU8IIV5iuXzUxKRYuX1PAHL85rPZ+c7LTUmop8rly+Ai/c9PryTEQ8nhGyZH2ZlYMykmG4VdzJ4pFAoKBqiddu6LT7dyMd7isr14NurWrM2SRYsfqu2MadNp0az5U73/nNmzqVGlGhGlytDv409ISEi4b1uz2UzJosUpUqCQ0+vQPWddCSHEiyS/vYQQ4iVWMkhNLl8V435P4r1SBpKMNqbuSX7Rw3Jonl/HoiOphHio8NPbz5mqll1LkLt9eizVZCPVbHPMXL2eX8/InYnk8VWT11fFrAMp5PVTUyyLzEw9L8tWfIde/3A7J3bq3Il27ds/vXt/u4wlCxczftIEfHx8GTFsGMMGD2Xy1Cku21+8cAGTycTGX35G53Z31tTbx+epjUkIIZ6EBFNCCPESUygUjKziyWe7k+m2KR5fNyVN8+mYezAFjerFrzNqkV9HQrqNCX8kYbZChVANvSLcHfXLj6ey6Egqv7bzB6ByNi3vlzLw1cEUkkxWSgZpGHWfA37Fs+Hn5/fQbQ3u7hjcM2/3sBbOX0CXd7tSuUoVAEaNHUPLZq9zNSrK5dlTZ86cIShrVsLCwp7eIIQQ4imSYEoIIV5isWlWLsRbmFjDE8WdxVLHbprQKMHHzf79sma+jvb9K3hk6OPeM6U6FjPQsZjhqY1PoVDQubiBzsVd9+nqfs3z62me//mfKfWqiI6OZuSwEeze/Sfe3t582KMHQwcPYf2mjYSGhVK3Zm06vPUmHd56k0H9B6I36ElPT+fnjZvQarW8EdmKD3v2QKFQMGPadLZs3szKNasz3GfGtOnMmjHT5RhGjx1Ds9ed0wNvxsRw5fJlypQt6ygrUKAAHh4eHDxw0GUwdfrUaXLmyvmE74gQQjw7EkwJIcRLTAEM3Z7Im0X11MrhRkK6jVn7U6iaXYtK+eJnpsTLxWKx0O3d9/Dx8WHJ0m+Ii41l+JBhWCz3P8drxfff81bHjixf8T07d+xg/NhxlI0oS4WKDz6culPnTkS2jnRZ5+GZcbYxOvoGAFmyZHEqDwgI4Hr0dZf9nD1zltSUVDp2eIvz58+RO3ceevftTdFixR44NiGEeF4kmBJCiJeYj07JyKqezD+cwtd/pWLQKKiaTcu7JZ9i7pX4z9izezdnz5zlf1s2E5glEIABgwfR/b3373tNSEgIvfv2ASBHzhx8vXgJRw4fyTSYsqcAPvy/w7Q0+7b7Wjfn8840Wi0mo8nVJZw9ewarxcqgoYPx9PTi++++o/NbnVi5ZhXZw8Mf+t5CCPGsSDAlhBAvufKhWsqHPvjAXSEAThw/QdbgYEcgBVCyVMkHXpM9e3an79093DGbM98xcu7sOXw5d67LumEjhtOocWOnMjc3HQAmo9Gp3GQ0otPpXPbz3coVWK1WDAZ7qmjhIoU5fPAQK1esdASAQgjxIkkwJYQQQvxHqNRqbFbnc7yUygefgqLVZgzUbbbMTyaLbB1Jvfr1XNb5+wdkKMuaNQiAmJgYAgLvBns3b94kS1CQy37+GWQpFApy5c7F9euu0wKFEOJ5k2BKCCFeUb3+F08eXzUflHnxKYPJJisz9qXw+xUjJguUD9XQvbS7Y0v1dLONOQdS2HYpHYsNauVw492SBrQvwY6GL5N8+fISHR3NzXsClsOHDj+Te3n7+DzSFuUBgYGEZcvGvr37KFioEAAnTpwgOTmZEiWKZ2iflpZG3Zq1GThkEPXq1wfsa8JOnjhJ42ZNnsozCCHEk5JDe4UQQrxwk/5I5sxtM+NreDGtrhe306yM2JHoqP9sdxLbL6fzSXkPPqvlxbk4MxN2Jb3AEb+cIsqVI1++fAzoN4CTJ0+yd89exo4aDYDiJYg723Voz8wZM9j661aO/nWUIQMHUbfeawSHhACQkpzMzZgYwD4rVb5CBaZ8Npndf+7m7NmzDBsylLi4OCIjW7/IxxBCCAcJpoQQQrxQFqsNvVpBrwh3CviryeWrpmUBPYdjzJgsNuLTrfzvvJEPSrtTLlRLbl81gyp68utFI1GJ99+l7lWkUCiYPPULFAoFbVu1ZkC/frzesgUAGs2LX3fXvkN72rRty5CBg3inU2dy5c7NiFEjHfUL5i+gepVqju+HjxxO1WpV6ffRx0S2eINrV6+yYPEifHx9nv/ghRDCBYXtYRKjhRBCOFwd+eBdzh7kp9NpLDuWSkyKlaweStoVNvBaLjcAriRYmLU/mUM3zBgtNkI9VbxTwkClMPsfwa3XxNK2sJ7NF9I5ftNMNi8Vgyt5sPWSkTUn01AqoE1hPa0K2s9w6vW/eAoHqDkTa+HgDROhHireK2UgIkTrqL83zW/PNSNfHkzhYryFrO4qmufX0TSvGwqFghSTjc93J7HnqgmjxUbRLBp6lHEnzEuV4RkXHk5h0ZFUl8/fr7w79XK73mzgb7dSrUz6I4lUk40v6npz4paZ9zfG811zHwINd+/3+srbdC1poF6uB/f3ICFDf3/sa10x2V5scHfr1i2O/vUXVapWdZxLdujgITp2eJM9B/ahVkt2/9OkUWT89y+EeLXIT1UhhHhOTt82M2VPMoMreVDQX82fV01M2JVE4QA1oZ5KBm5LIK+vmpmveWPDxuIjqUz8I4kVzX3R3FkbNO9gCh+X9yC7l4oROxPp+b8EKoVpmfaaN79eTGfOgRSqZtOS1cP+R973J9JoV1hP99LubDyXxqBticxv6EO2fwRBF+PNDNmWyPul3CkTrOFMrJkpu5NRKqBJXh3zD6VwPs7CZ7W8cFMrmLEvmYl/JDG1rneG54wsqKdJXtcBjrvmwblmn/2ZxNoz6XhpFUyp4wWAn85+TUyK1RFMpZpsJKTbiE+TzwPvpVAo6PVhT97t9h4NGzUiLjaOTydOou5rdSWQEkKIZ0DS/IQQ4jm5nmyftQhyV5LVQ0XTfDom1vTER6cg3QINc+voUdad7N4qwr3VtCqoJyHdRmza3d3ZqmXXUjmbluzeKmqEa0k12+hV1p3sXiraFNJjtcHF+LuzI0UC1bxVzEB2bxVdS9rbrT+blmFs3x5Lo0a4G03z6Qj1VFEtuxtvFtXz3XH7DNO1ZAt6tYKsHkqyean4qJwHXUsaXD6nXqPAT690+XJTPziYallAx6x63hQP0tB3cwJxaVayuKsolkXNzH0pxKRYSDXbmLo3GQCTVYKpe/n5+TFl2hds/vkXmjVqQrf33id/gfwMGzH8RQ9NCCH+k+RjKiGEeE4igrWUCtLQfVMC2b1UlA/VUC+XGx5a++dazfLp+OVCOidvmbmcaOH0bXtQdG+8EOJ5d0ZJp1bgp7sboPy9s53pnp2xi2XROI0hv7+a83EZU9HOx5k5F2dh66V0R5nVBmYrmCw22hfW0//XRF5fGUuxLBoqhmnum1739V8pfHPUdZpfnwgP6uR0u99bRLi3/dfSoIoetFoTyy8X0mlZQM/Aih6M/i2JVqvj0KqgaV4deXxVmc50vYqqVqtG1WrVMm8ohBDiiUkwJYQQz4mbWsHEmp4cvWnmjygTu6KMrD6ZxtjqnhQO1PDBpnjUSqiSTUuFUC1aFXy0JdGpD/U/8gky26HtnzuHW20ZywAsNnuA0jx/xgBJpYSCARq+bebLH1FGdl8zsfBwKj+eTmdOPe8Ms01N8uqoEe46YPLVZUyISDXb2H3VSESwFv2d4MhNrSDEQ+lI4wtyVzGtrjfx6Va0SgV6jYLmK26T1V3WrAghhHhxJM1PCCGek6MxJhYcTqVIoIZ3ShiY19CHPL5qdlw2cjDaxMV4C1/U8aZ9EQMVwrTE3gkkniSR7dRts+Nrm83GyVtmcvtm/Bwt3EvFlUQLoZ4qx+tojJlvj6WiVChYcSKVQzdM1MzhRv8KHkyv683FeAtn48wZ+vJyUzr1c+/L4GImyWqzMXJnEruijI6yZJOVywlWcvqosNlsfLwlgWM3TXi7KdFrFBy7aSLZZKNwoHwm+Dx07PAW48eOe9HDAOxnTU2fOo1a1WtSoWw5Pni/O1ejoly2HTp4yEszbiHEf5MEU0II8Zzo1AqWHk1lxYlUridZ2H3VyKUECwX81Xi7KbDYYOvFdK4nWdhxOZ1Z+++sC3qCDeJ+jzKx+mQqlxMszD6QwvVkCw3zZJw1al1Iz55rJhYeTuFKgoXfrxiZti8ZHzf7r4mbKVam7knmYLSJa0kWNpxLw12jILuL3fwelbtGSf1cbsw9mMLBaBNnY82M2plEkLuSqtm1KBQK3DUKZu1P4XycmaMxJsb8lsTr+XV4ucmvsVfNkkWLWbViJePGj+PrZUsxmUz0/LCHUxubzcb0qdNYtWLlCxqlEOJVIR/pCSHEc5LbV82gSh4sPpLK3AMp+OiUtCqoo14u+/bjbxfX8+XBFFJMNsK8VLxfyp2pe5M5edtMdu/HC1qqZ9fy2xUTsw+kkMtHzac1vQhykRqX10/NqKqeLDicwtKjqfjolDTPp+OtovZt1jsXN5BmtjFqZyKJRhu5fdWMq+7pWO/1pD4s4868QymM/i2RZJONssFaxtfwRK20z2T1LuvO5D3JfPhzAno11M99d2zi1bJ1y6/Ub9iAiPLlAOj6Xlfeav8mcbFx+Pj6EB0dzeABgzhx/DjBIcEveLRCiP86CaaEEOI5qhHudt/1RO2LGGhfxHmHvNr3bNawrJmvU13LAnpaFnAOKH5t5+/0va9OyZDK7i7vN6WO87bmFcO0VAxzfbCrVqWgV4QHvSJcVj8xN7WCbqXd6Vba9Vi9dUqGV/F8Njd/SX2//Dvmz5vH9WvXCQsL4513u9K0WVMALl64wKQJk9i3dy9paWmE5winZ+9e1KhZE4C6NWvzTtcurF+7jsOHD5MjZ04mTJrIpo0b+fabpahUKjq/8zYdO3cC7Gl8xUsU5+SJE+zZvYfs2bPz0ScfU6lKZZdj+23nb3zx+WTOnj1LSGgo7dq3I7JNaxQKBclJyYwcPpydO3ZiNBopVbo0AwcPJDxHjgz9zJg2nVkzZrq8x+ixY2j2evMM5T6+vmzfuo32b3bA39+fH3/4kbBs2fD0sv/7OH7sOFmyZGHchHF81OejR37fhRDiUUh+hBBCCPGSOX7sGKNGjKRn716s27iedm92YPCAgVy8cAGbzcYH73fHYDDwzfJvWbFmFXnz5mPIwMGYjHfXnX0xeQodOr7JijWrAHirQweuX7vG198upU27tnz+6WdEXbm71mjxwkUUK16c71evpGr1anTv1o0L5y9kGNvZs2fp+cGHvP5GS1b/9AM9evZg5vQZfL/8OwCmTZ3K6dNnmLdwPt+vWolSpWTIoMEun7NT505s3bHN5ateg/our+ndtzcqtYo6NWpRtmRpNv/vF2bMnolKZZ9xrV6jOmPGjyUgMPBx3nohhHgkMjMlhBBCvGSioq4CEBISQkhoKK3btCZ79uz4+vmRlpbG6y1b0Pz11/Hx9QHgrU4d2bB+PTdv3SI42J7aVrfea9SqXRuAevXrMXPGDAYPG4pOp6PzO28zY9p0zp07S2hYKAClSpei2wfdAejdtw/bt21n1cqV9Pmor9PY5n85j3oN6tO6TWsAsmfPTkxMDAsXLKBV60iirlzBYDAQGhaGp6cnI0aOJOo+G0QY3N0xuLuejbyfS5cu4eHhyYzZs/Dx8WH61Gn07dWbpcuXYTC4PvtMCCGeFQmmhBDiP+qfaXzi36NylcqUK1+etpFtyJkrF1WrVaVZ8+Z4eXkB0KZdW9b+tJajf/3FhfMXOH7sGABWy91DxrJlz+b4Wq/XExAQgE5n3/rezc2ePmq8ZyarVOnSTmMoUrQIp0+fzjC206dPc+rUSTZt3Ogos1qsmEwmTEYjXd97l/e6vEvVipUpXbYMNWrUoFnzjOl6AHNnz+HLuXNd1g0bMZxGjRs7laUkJ/NJ34+Z9PmnVK5SBYDJU6dQp0Yt1q9bT8s3WrrsSwghnhUJpoQQQoiXjE6nY+68Lzl44CDbt21j669b+fabpUyfNZMSJUvQrnVbNBoNtevUplr16ri5aenS+R2nPjRq5wOblYoHZ/ar1c5/ElgtVlTKjJuVWCxmWrdpQ9t2bTPUqdRqihYrxs+bf2H79u3s3L6DGdOn893y5Sxf8b0jmPtbZOtI6tWv53I8/v4BGcrOnjtHYmIiefPlc5R5eHgQniMHVy5ffuDzCSHEsyDBlBBCvECt18TSsoAuw0YSL8L1JAttfogDoGywhok1vbiZYmXa3mT2XTehUkC17FreL+XuOFw3Id1ev/uaCa0SGuXV0aGIHmVmpwnfYbHamLk/hc0X0rHYoE5ON94vaUDj6mThR7y+28Z4jt+yn4P10xu+T23nwefh4IGD7Nyxgw96fEjJUiXp2bsXbSNb88v//ofJZOL8uXPs2vMner393826n9YCYHuCU8mOHj3q+Npms3H06F+ONMF75c6dh4sXLpA9PNxR9uMPP7J/7z6GjxrBkkWLyR4eTv0G9anfoD7nzp2jSYNGnDp5kmLFizv15e3jg7ePz0OPMUuWLACcOnmSoKAgANLT07ly+TLZIls9yuMKIcRTIcGUEEIIJ5/W8iKfr31GYsj2RDw0CqbV9SLNbGPSH8lM3pPEwIqejvr4NBujq3qiVsKnfyZjNNvoUvLh1sF8dSiFP6KMjK7midkK43cloVHC+6We/PrxNTw5GG1i2I6kx3sjXiC9Qc9Xc7/E29ubmrVqcf78Oc6fO88brVrh4+OD2Wxm04aNRJSL4Nix40yaOBFwTtt7VFu3/MrSr7+hYqVKfP/dd0RFRblMm+v0didavxHJjGnTadS4EefPnWf8mLFE3llDFR0dzdeLl2AwGAgOCWbNqtV4eHiQI2fOxx7b34KCgqhZuxYTx0/EYDDg4+vLrBkzcdPpqH+fGS4hhHiWJJgSQgjhxFurwNNNSWK6lRAPJe+UMBDsYQ+uGuZxY9mxVABO3TZz+IaZWfW8KeBv/3XSt5w7vf6XQIeiBnTqB88uGS021pxKY0AFD4oE2lPSPijtzpjfE+lczIDbE17v5ab81x7qmz9/fsZPmsDsmbOY/Nnn+Pr58VanjjR7vTkKhYIevXoyZfJkkpOSCc8RzseffMKY0aM5dvQouXLleqx71qtfny1btvDZpE/Jlz8fX86bR3BISIZ2BQsV4otpU5k+bRpfzf0SP39/2rZvx/vduwHwYc8epKWl8XHfviTEJ5C/QH5mzpnlWO/1pMaNH88XkyfzSd+PSU1NpUTJkixcsuiRN7IQQoinQWGz2R4/J0AIIV5BV0dWdHw99s4hs2Oq3/1D8cdTaSw9lsq3TX2ITbMxc38ye6+ZSDbZyGJQ0rawnoZ57GtH7k3zG78riSSjldHV7vb1zvo4Kodp6VjMvkvZL+fTWfJXKteTLYR5qnizqJ5q2V2fW9Xrf/EcumF2WfdtUx+yejivh/k7ze/L+t7k8cv4WVtUooVROxPJ7q1iYEVPtl5MZ+zvSWxq7YfiTlpfutlGveW3mVzbixJBmgx93OvYTRPdNyWwpqUv3neCnsR0K01WxDK9rheFA5/8+oPRJnr/knDfNL+Qob8/8B6PymSzPNX+npeOHd6iQMEC9B844EUP5V9Fo3i8w7SFEP8dMjMlhBBPoHZONwZvSyTJaHX8sb7lYjq1crihUCgYtysRqw0+q+WFTq1gzak0puxJpkKoFj/9o82a7L1mZMqeZHpHuFPAX82hGybG/56EXq0gIiTjYbsjq9pT31zxdnu4NUl/+3hLAnuvmcjqrqRLCXtg56dXYrJCbJoNP729v+hk+w3j0+9z43vEpFjRKHEEQgCebko0Snvds75eCCGEeFL/zvwHIYR4SZTOqsFDq+C3KyYAbqVaORJjpnYOe3BTIVRDnwh3cvuqCfVU0b6wHrMVriQ++gzG13+l8kZBHbVyuBHqqaJBbh1N8un47niay/Zebkr89K5fKuWjBVPvlTQwtY4XAQYlH21OxGSxUdBfTainks93J5GYbiU+3crM/cmoFGB6iMdLN4PWxUYTGqUC00PEQk96vRBCCPGkZGZKCCGegEqpoHq4G1svpvNaLjd+vZhOTh8VOX3sP16b5tWx9ZKRlSfSuJxg4XSsPe3O+hgJ1ufjLRy7aXasWQKwWMFH5/pzsX5bEjgcY3JZt7CRD0HuD5+ilNvX/jwjqnjyxupY/rxqonI2LSOreDLytySarohFp4a3iho4dduMuybzYM1NbV/39E8mqw23hxjak14v7lq4ZNGLHoIQQvwrSTAlhBBPqHYOLb3+l0CS0crWi0Zq57CvYbLZbHzyayIxyRZq5nCjcV43snsb6LQ2/qH7vucMVixW6FzcQJVszil995tk+qi8O8b7zBAFPESKYXyalX3XTdQI1zrWRPnplXhpFY40vly+ahY28iE2zYq7RoHFBrMPpJDVI/P+Aw32NMHEdCued1L1EtKtmKwQYHj21wshhBBPSn7bCCHEEyoUoCGLu5L1Z9M5fstMrTspfhfiLey/bmJsdS86FjNQNbsbSUb7TIqrrX/USkgx3a0wW21Oa39yeKu4lmQh1FPleO28bGT9mXSX4wo0qJza3vt6mDS/22lWRv2W5DirCeybVMSl28jpoyIx3UqPn+O5nmTBV6dEq1Kw64oRX52CcO/Mp4Zy+6jRqeFwzN3+D98wo1NDHt/MP+t70uv/DerWrM2SRYtf9DAAiLoSRZEChShSoBDvvtMVALPZzLgxY6lSoRIVI8ozdvQYTI+5Pfvc2XOoULacyzqr1UqXzu880Xvhqv8Hjb9tZGvH8yYkJDz2fYUQ/23/jd82QgjxgtXK4cbCwykUy6Im0GAPJDy0CpQK2Hopndo53LiSaGHq3mTAnor2T4UC1Px8Lp3frhgJ91Lx7bFUp3ZtCusZviORHN4qIkK0/BVjZt6hFHqUfTZbQuf0UVMmq4bJu5P5qJwHVpuNqXuTiQjWUCjAvtOe0WI/NLdrCQPXki18sTeZjkUNjkN7E+7MYLnaotxNraBRHh3T9ibjqVVgs8H0fck0y6dzrIV60uvF0/fVgnkULFQIgKlTvmDb1m1MnTEds9nMoP4D0Gg0fNzvk0fq8/Sp08yaMROdTpehzmQyMXL4CHb9/jtVq1V9rDHfr/8HjX/WnNns3r2H3j16PtY9hRCvBpmZEkKIp6B2DjdSzfag6m+BBhV9y7mz9kw6b62NY9reFJrl05HLR8Wp2xm3LK+Tw416udwY93sS3X+Ox1+vpNw9u/RVCtPSO8Kd1afS6LQ2jsVHUnivlIFGeTL+Afq0DKnsQR5fNf1+TeDjLYnk8FYzpLKHo35YFU9SzTa6bIjj8z+TebOInub5745n6PZEhm5PvG//XUoYiAjWMHBrIkN3JFIpTMs7xQ1P7Xrx9Pn4+ODt7U16ejrfLl1K348/omSpkpSNKEv/QQP4bvly0tJcb4riyt9BTPHixTPUnT1zhvZt2rFn957HPqfqfv1nNn7vO88phBAPIjNTQgjxFGTzUvFrO/8M5Q1y62iQ2znYeT2/3vH1sma+jq81KgV9ynnQx3Wm0337e5a83JT0q+Bx3/pgDxWTat7/j9zhVTwZ93vSfeu1mTzzk17/ovX/pB9JiUlMnzXDUbZ82TLmzf2KTZv/x61bt5g0fgK///Y7iUmJZM0azDtdu9DyjZYZ+hrUfyCJiQlMnTHdUdaiWXNq1qpF9w8/AGDdT2uZPWs2V6OiCM+Rg/fef4+69V5zObaOHd5i7549Lus2/fI/QsNCH/hsJ46fIDUllbJlyzrKSpcpQ2pKKieOn6BEyRIPvP5vX86Zi6+fH/Ub1GfCuPFOdfv27qN06dJ0+7A7rzdp9lD9PWz/T2v8QohXmwRTQgghnMQbbU6bOjyJRUdSeC2X60OFn/X1CelWR5rgi9KocSM+7PYBiYmJeHp6ArBh3QYaNGqIQqFgYL8BWK0Wvlo4H71Ox7dLv2X0yJFUr1GdgICAR7rX77/9zuiRoxgybChFihVl7549DBowEHd3dypVqZyh/RfTvsBkcr3bo6+fX6b3uxEdjUajwcfXx1Hm7e2NRqMhOvr6Q435xIkTfLPka75fvZI/d/2Rob5V68iH6udx+n8a4xdCCAmmhBBCOPlocwJlgzVMfMCM08PqVsodzROsX3qS6/v/mui0ecaLUKFiRTy9vNiyeQtNmzUl5kYM+/ftY9CQwQBUq16NKlWrkD08HICu773LkkWLuXjhwiMHU3Nnz+HNjm/RoFFDALJnz87ZM2dZsGCBy2DK28fniZ4tNS0NN7eMga5Wq8X4EJtQmEwmBvUfQM/evQgODn6isTxO/086fiGEAAmmhBBC3JHVw3Wq4pN4kkDqSa+fWe/Fr3dRqVTUq1+PTRs20rRZUzZu3EjefHnJmy8vAJFtWvPzxk18vXgJ589f4MTx4wBYLI8+o3bm9GkOHzrE/HnzHGUWs+W+s0zvdenKvn37XNb9uPYngkNCHng/nZuby6DDaDSi0+ldXOFszqzZ+Pr58UZkq0zbPo7M+n/S8QshBEgwJYQQQjxTjRo34q32b5KQkMDG9Rto2LgRYD+H7L0uXbl+7Tr1GzagVetIcuXKSdNGTR66b4vF4vT1Bz0/pHbt2k5tlCrX29SPGD2K9PtsFBGYJUum9w7KmhWj0Uh8fLxjo4b4uDhMJhNBQZlf/9MPPxITE0PZUqXt4zdbMBqNlC1Vmtlz51C6TJlM+3iS/p90/EIIARJMCSGEEM9UseLFCQ4JZtWKlfx15AifTfkcsO9U98euP1i3cT3hOXIAcGD/AftFLg4i02g0JCcnO743mUxcv3Z3bU+u3LmJuhLlSBkEWDBvPomJifTolXF776CgoCd6rvwF8qM36Nm/bx81atYEYN++fegNevIXKJDp9QsWL8RsvpuG+cv/fmHOrNl8v2oFWZ5wbA/b/5OMXwghQIIpIYR4oVqviaVlAR0tC/x304qSjFZ2XDZS/znuQviyadCwITOnz6BU6dJkzZoVAE8vL1QqFRs3bqRx48ZcuHCRcaPHAGA0ZUw/K1aiGD/+8AO/btlCrly5mPfVPKcNJN7p+g69e/Yid+7cVK5ahQP7DzB1yheO9VlPm06no+UbbzBu9Fi8vLyw2WD8mHG0advWsRYpPi4OcL0+KyTUebdAPz8/lEqlUzCYmSftP7PxCyFEZiSYEkII8UwtOpLK6dvmVzqYatS4MbNnzqLhnc0hwD4zNGzkcGbPmMXcWXMICQ2ldds2rFyxkqNHj1K5ShWnPho3bsyRw0cY8El/1Go1kW1aU6Xq3UNsa9SsydDhw1jw1XwmTZhI1uCsfPTJx7Rs9cYze67effuQlpbGB+93R6VS0bBxI6dZsJ4f2r9euGTRM7n/k/af2fiFECIzCpvNRS6BEEKI+7o6suJT6+tVmJmavjeZM7FmptR58RtC/FPI0N+fan8mmyXzRv9SUVeieK12HVasXkmBggUf6prbt28zsF9/Zn8595mM6Vn3v/vP3XR+qyO/7/7D5aHBGoXr9WhCiFeHzEwJIcQzlmS0MmNfCr9fMWIDygZr6FnWHa9/nONksdpYcDiVXy6kczPFipebghrhbnQrZUClVHAzxcrnu5M4fMO+DqTMnX58dUpSTDY+353EnqsmjBYbRbNo6FHGnTCvjH/sLTycwqIjqS7H2q+8O/VczCDtvGxk3qEUohIt+OuVNM2no3UhvdO4N55LI9UE+f1VdCvtTh5fNQsPp7DypH2Tgxrf3OLXdv6YLDaWHktl0zn7c+bxVfNuSQPFgzQAXIg388WeZE7cMuOmUlApTMsHZdzRqxWZvkfi2YuLi3PatOFBZk2fSZNmzZ7ZWJ5l//F3nlMIIR5EZqaEEOIRPerMVJ9f4kk02uhZ1h2dWsGnfySRxV3FyKqeTjNT3/yVyg+n0xhY0YOs7koO3jAx6Y9khlX2oGp2NwZvSyDNDB+WMWC2wqQ/ksjupWJgJU+m703mQLSJARU8cFMrmLEvmRSTjal1M/7Bm2qykWp2/aPfXaPATe0clMSmWWm1OpZ3SxqoHKbl5G0zo39LYnwNL0pn1fDVwRR+u2KkZ1l3/PRKNpxN46fT6Sxu7INerWDG/mQuxFkYWdUTP72Sz/5M4o8oI30iPAj1VPHj6TTWnkljQSMfgj1UvLM+jpzeKjoWMxCfbmXUziTq5HSjc3FDpu/Ro5KZqYf398wUQKXKlZnzVeazQSajEY1W+8zG9Cz7bxvZmsOHDgPIzJQQ4r5kZkoIIZ6hi/FmDkSbmVPfm3x+9h+5fcp58NuVjBsM5PBR0a+CByXuzNDU81Cx/FgaF+ItVAWuJVnJ5qUi2EOFVqVgSGVPUkz2oOhasgW9WkFWDyUeWiUflfPgerLrP+z1GgV6zcPP4txMsWK2QqBBSVYPFVk9VPjqlIR5qkg32/j+RCoTa3g5ZpbeLenO/usm1p9No30RAzqVArUS/PRKkoxW1p9NZ1BFDyqE2f8I/qCMO0dvmvjueCo9y3pwPclKiSwasrorCfVUMaaaJ+o7501l9h6JZyc0LJS/Thx7pGueZSD1rPtfunzZM+tbCPHfIcGUEEI8Q+fjLKgUkMf37ifY+fzUjsDqXpXCtByMNjHnQDKXEyyci7NwLcmK9c4k0jvFDQzfmUjT729TKlhDpTAtdXLYZ2PaF9bT/9dEXl8ZS7EsGiqGaaiXy/WGD1//lcI3R12n+fWJ8KBOTucZnjy+Kurk1DJ8RxJB7imUD9FQJ6cbfnol5+PMGC3Q/9cEuCc+M1kg3CtjMHc5wYLVBoUCnJ+/SKCGM7H29MWuJQ18sSeZTefTKRusoUo2LdWzax/qPRJCCCGeJwmmhBDiGVI/wjqeRUdSWHEijfq53KiSzY2uJdQM35HoqK8QpuW7Zr78HmVk91UT0/Ym88v5dD6v7U3BAA3fNvPljygju6+ZWHg4lR9PpzOnnneGtL0meXXUCHedEuerU2YoUygUDKzoSauCZv6IMrIrysRPZxL4uJx9XRTA+BpeBBicr9WrMz67VuX6/bDYwGq9O74KoRp+u2Jiz1UjY39PYvdVN/pV8Mj0PRJCCCGep4y/NYUQQjw12b1UWGxwLvbuLM3RGBNvrIol7R/rllafTOPdkga6lXbntVxuBLkruZFi5e9W8w+lEJVkoX5uHcOqeDKiiicHos3cTrWy4kQqh26YqJnDjf4VPJhe15uL8RbOxpn5Jy83e/qcq5fBRfrfpXgLU/cmk9tHRfsiBma85k217Fp+vWgk1EuFSgG306xO/Sz5K5WD0fYzkBT3dBnqqUKthKM3ncd1NMZENm8VRouN6XuTMVqgWT4dY6p78WFpd7ZcTH+o9+jfqm7N2ixZtPhFD+OlMqj/QHp0/+CJ+4m6EkWRAoU4cfz4UxiVEEI4k5kpIYR4hrJ7qygXouGz3Un0LOuBUgHT9iZTNIsa3T9mbrzcFOy+aqJkkIZkk41Fh1NINtkwWeyhwqUEC39ctW/c4KlV8MuFdLK6K/HR2Xf6W3kiDb1aQZC7kg3n0nDXKMjuYje/R+WhVbDhbBpuKmicR8etVCvHYsy8ltsNvVpB8/w6Zu5LRqdSkN3bvqHElgvptCpgTzPUqxXcSrVyPclCVg8VzfPpmL0/GYNaQaiXih9PpXE+zsLH5TzQqhT8FWPiUoKF90sZUChgxxUjBfzVD/Ueif+O/oMGIHtkCSFedhJMCSHEMzagggdT9ybT95cE1CqoGKqle2lDhnb9K3gwZXcyndfF4eOmpFI2LQ1yKzl12z6r1TvCnal7kun3awLpZhuFAtSMre6JUqGgc3EDaWYbo3Ymkmi0kdtXzbjqnnhonzwBwU+vZEw1L+YeSGb1yTQMGgU1w91oX9i+NXrXEgZUCvj0zySSTTZy+qgYV92TXHdSAGvl0PK/8+l0XBvH0qa+dClhQKmASXfa5/dT81ktL/LcWUc2rIonU/ck8+HPCVhtNkpn1fBJeY+Heo/Ef4enp+eLHoIQQmRKtkYXQohH9DQP7RUv1vPcGj0hIYGJ4yfw6+Yt2LBRuXIVBg0ZhLePD3Vr1qbDW2/S4a03MZvNzJg2nXU/reXGjRt4eXtTv0F9PunfD5VKxY3oG4wYNpx9e/eiUCioULECg4YOwd/fn+SkZEYOH87OHTsxGo2UKl2agYMHEp4jR4bxzJg2nVkzZroc6+ixY2j2enOnsmlfTGXL5i2s/nGNo2znjh307tGLbTu3Y3B3Z8mixXy9eAm3b98mb7689OrTh4hyEYA9bc9sMXP50iUunL/ApM8+RW/QM3H8RE6fOoWXlxeNmjSmV5/eqFQqBvUfSGJiAlNnTAdg7569TP7sc06eOEFAQAAdO3eidds2AFy6dIlPJ05i7549ANSoUZNPBvTD29s7w2HDJqORr778ih9Wr+HGjRsUKFiAPh99RJmyZe47zkpVKrt8n2RrdCGErJkSQgghnoPePXpy4vhxps+ayfxFC7l48SJDhwzN0G7BvPn8+MOPjBk/jnUbN9D3o74sW/otWzZvAWD0yJGkG9P5Zvm3LPx6MVFRV5k0YSIA06ZO5fTpM8xbOJ/vV61EqVIyZNBgl+Pp1LkTW3dsc/mq16B+hvYNGzfi9KlTnDt3zlG2Yd0GatSsicHdnRXffc/CBQsZOGQwK9esou5rr/Fel66cPnXa0X792nW0btuW+YsXUrJ0KT7s9gFlI8ry47q1jJs4npXfr2DN6jUZ7n3xwgXefacLJUoUZ8XqVfTs09semG7ZQnx8PG+2aw/AwsWLmT5zBsePH+eTvh+7fO6xo8fw/fLv6D9oICvWrKJY8eK816UrV65cue84hRDifiTNTwghhHjGzp49y59//Ml3K1dQqHAhAIaNGM6WzZsztM2TNw9jxo2hbERZwH6+04L58zl75gx16tbhypUocuQIJywsDDc3NyZ9Nonk5GQAoq5cwWAwEBoWhqenJyNGjiQqKsrlmAzu7hjc3R/6GXLlykWhQoXYtGEj73fvhsloZMvmzYyfZA/k5s6eQ49ePalWvRoAHTt34tDBQ3y9ZAkjRo0EIEeOHDRp2gSA+Lg4EhISCAgIICQ0hNCwUObM+xJ/P78M9165YiXZw8P5uH8/ez85c5CQEI9SqWLdT2sxGU2MnzgBg8GePjvx04k0a9yUv478ha+vr6OfxMREVq1cxbiJE6heozoA/QcO4OCBgyyav5BBQwdnGKcQQjyIzEwJIYQQz9iZ06dRq9UUKFjAUVaocCE+6PFhhrY1atZEpVLz+aef0aP7B9SrXZczp89gsdhTCHv06sm2rduoXL4iH3brzt69e8mTJw8AXd97l/PnzlG1YmXe6fw2//v5Z/Lly+dyTHNnz6FsqdIuX2t/+snlNQ0bN2LTxo0A7NixA7VaTcVKFUlJTubq1auMHDbcqZ9ff93C+XtmskJCQx1fe/v40OXdrkyaMJHqVaoyeMBAEuLindr87fTp0xQpWsSpLLJ1a6pVr8bZs2fJlz+/I5ACyJM3L15eXpw9c8bpmgvnz2OxWChRorhTeclSJTlzT1tXYxBCCFdkZkoI8dIy2sxoFfJjSvz7aTSah247a8ZMlixaTPPXm1OrTh169e1D3569HfXVa1Tnl61b2LrlV3bu3Mm4MWNZ99Na5i1cQNFixfh58y9s376dndt3MGP6dL5bvpzlK75Hp3M+xDmydST16tdzOQZ//wCX5Q0aNuSzSZ9y9swZNq7fyGv166HRaEhLSwNg1JjRGYIejVbr+NrNTetU16NXT5o2a8rWX7eyfds23n/3Pd7v3o33u3dz7kOjue/Ofm5a12emWa1WLFbnNWxaN9dtLRYL1nva/nOcrxJzug2128Ofjyf+HeT/67Mjf6UIIV5aWoWaJtGTX/QwnPwY1DvzRkL8Q46cOTGbzZw6eZICBQsCcPDAQfr26s3ajeud2i79+hv6fNSXlq3eACAtLY1r1645golpX0ylWvVqNG/xOs1bvM7OHTt4r8u73Lx5kw3r1pM9PJz6DepTv0F9zp07R5MGjTh18iTFijvPxnj7+ODt4/NIzxGYJZCIchFs2riJ7du2MWvuHMC+815gYCDXr1+nQaOGjvafTpxEcHAw7Tq0z9DXzZs3mT1zFr369OatTh15q1NHJn/2OevXrc8QTOXMmZOdO3Y4lY0ZORqA/AXy88OaNaSkpDhmp06ePElSUhI5c+ZyuiZ79uyoNWoOHjjoNPt08MBBChUq9EjvxX+V2k3BmGrXXvQwxFM2aFvwix7Cf5ak+QkhhBDPWK5cuahSrSrDhw7nyJEjHDt6jHFjxlKydCn0er1TWx8fH3bs2MGlixc5dvQYH/XuS1JSEiaT/RDk8+fOMXrEKI4cOcKlixdZt3YdoaGh+Pn5ER0dzdhRo9mzew9XrlxhzarVeHh4kCNnzqf2LA0bNWLh/AV4e3tTomQJR/nbXd5h7uw5rP3pJy5fvszC+QtYvHAROVzsJAjg7e3Nll82M2bUaC6cv8Cxo8f4/bffKFq0aIa2rdu05sL5C0z5fDIXL1xg/br1rFyxgqrVq9KwcSP0Bj0D+vXn1MlT7N+/nwGf9KdY8WJO4wPQ6/W0a9eOTydOYuuvWzl/7jzjx47j9KlTtG7b+qm9R0KIV4fMTAkhxCO4nR771LfTFi+OzZyOQu069etpGzt+HONGj+Wdjp3RaDRUr1mDfgP6Z2g3etxYRo8cSfMmzfD186VmrVq83rIFR48eBWDo8OGMHT2G97u8S1paGsVLFGfG7JkolUo+7NmDtLQ0Pu7bl4T4BPIXyM/MObPw8vJ6as9R57W6jB45igaNGqJQ3E0batehPenp6Uyd/AU3b94ke3h2Jnw66f7bims0zJwzi/Fjx9OqRUvUajXVa9RgwOCBGdoGh4QwY/ZMPp/0GYsWLCQkJIQhw4ZSpWpVAOZ89SUTx02gbWRrdDodterUps9HfZ3G97defXqjUCoZNmQISYlJFC5SmHkL5ztmDIUQ4lHIOVNCiJfay5bmJ56e/2LK5IPOmRL/Pf/Vc6Ykze+/R9L8nh1J8xNCCCGEEEKIxyBpfkKIV5L5ZjIph6/hVTPPQ7U/WXU2IWNew7NKTq6N3YI1yUjoWNc7of0bmKITOffGNxnKc69+E7W/IUO5zWwhZu5uEn4+jTXFiKF4MFl6VEKbzedOvZXTr32FzWR1ui77rOboCwc9k2cQQvx7pFhucsN4hBz6Gg/VfvH16lT3GUV2XRV+ixuH0ZZEDd8xz3iUz8e51F/YGT+a1ll+Qqv0vG+7E8mrOJbyPWnWOPw1+Ynw/BBfTW4ArDYzS6PrY8XkdE19vxkEags/0/ELZxJMCSFeSdGTd6B01z50MPVfk37uNipvHTkWtnIqV/nqXbaP+XI3iVvPEjKsFuoAd25+tYcrH68n59eRKNQqjFfisJms5FzWFqXb3V8tKu/nsx5JCPFy+zNhClqF+0MHU/9VqZZb7E6Ymmm7i2nb2J/0JVW8B+OtDudw0hJ+ie1H88BvUCvcSLBcwYqJ5gHfolbcc/yA0vtZDl+4IGl+QohX0794tajNYiVx2zkufbDmsfswXohFG+6D2t/g9FIo73MOiQ2yfFgJQ8lQtNl88HuzFKarCZiuJdr7Ox+LOtAdbYiXc3/q/+aaEiHEo/r3/tC12ixcTNvOxlsZD9l+VLsSPsdXnfnumpfTfidEW5Zsukp4qcMo6fk2qdabxJsvABBnvoBBGYinOhi9yt/xUsrZjM+dvONCiH+lk1VnE9S3KrGrjmC6moi+aBBBfauiDbV/KmdNNXFj5i6Stp7DZrVhKBZMlh4V0QR7cW3sFpJ2XgAg5eBVcn/XHuPlOG7M2EXq4WvY0s1owrwJ7FIOj8o5HnlsNrOVmwv2EL/+JLZUE7oCWQj8oAK6PPaDUC/1+AFDiRDSz94mefdlVF5u+LUtgW+LjFtC38uSbCR+3QniVh7BkpiOd/38ANycv4dbC/e5vCbrgOp41y+QoTz93G202X0e+pmydKtwdxyJ6cR9fwRNqBeaYHuKSvr522jDH74/IcS/y+Lr1Snv1YcTKatINF8ji7YI5b364Km2n9dlsqayL3EWF9O2YsNGFm0xIjw/wEMdzG9x47ic/hsA128cpEWW5SSYr7A3cSbRxkNYbEa81KGU9OhCNl2lRx6b1WbmUNJCzqRuwGxLxV9TgDKe3fDT2DMPNt3qSZC2BHHmc0Sl78ZN6Ulh97YUdH/9gf0arcmcSV3PiZRVpFsTyKOvD8DBxAUcTl7k8pqKXv3IY6jvsu5s6iaSLNco4/k+0bGHHnhvndKbc2l/Emc6j5c6O2dS16NVeOCpCgEgznQeb3X2B/Yhng8JpoQQ/1o3v9pNll6Vccvpx42Zu+xpZ4tboVCruD5pG+aYZEIn1Eep13B76UEu9/qJHIsjydKjEpbYVJQGLVl6V8ZmsxE1YCNu+QLIPqs52ODWon1cn7CV3OU6oNA82uzKzQV7SNp5gZChtVD7uxO/7gSXe/xIzq9bo/azr0e6/c0BAt+vQOB75Yhff5IbU3/DPSKbYw3SvYxXE4hb9Rfx606gDnTHt00JvF/Lh1KvAcCvdQl8mrrOkVd6aF2Wp1+IReXlxsWuKzHdSEZXIJAs3Su4vP+9bn29n5tzd6PQqgid0MAx85R+PhZrqplLPX7AeCkOt3BfAt4th76QrJcS4r/iQOJXRHj1wkedg32Js/glth9NAxaiVKj5I+EzUiwx1PKdgFqh46/kZfwc24emAQsp6/UhqdZYNEp3ynn1xGazsSV2AH6afDTwn4kNOJy0iN/jJ9DSbSUqheaRxnUoaSGX03dSxXswepU/Z1I3sOl2T5oFLEGv8gPgr+SllPZ8j1KeXTmTuoE9idMIdYvASx2Wob9E8zVOpKziTOp6DKpACru3JpeuLhqlPQ26sHsk+Q1NXI5Fo/RwWZ5iucnehJnU9puIyZqS6TMVdm9DjOkYP97qhAIlSjTU8pvgWGMVZz6P2ZbGpls9ibdcwkedg1IeXQnQyhb/z5sEU0KIfy2flkXxqmX/5DF4YA3Otvia5L1RaLP7kLj5DLm+a4cmyP6LJ2v/6pxtsYSkbefwqpsPhUaFwk2F2kePNc2Ed8MCeDcogMpbB4BfZDESN5/BfDsVTZDrX46uWNPNxC4/TNinDTGUsH+CGPh+eZL3RxG/9jj+b5YGQF8sGN+W9pmogHfKcnv5IdJOxLgMZq70+glrupngYbXxKJ/xk0ilQYPS8PB/fNhsNowXY3HL7U/gBxVRqJTcWrKfSx/+SM7FrVB56e57rWeN3LiXzUbc2uNEDdxA+JzXccvhh/FCLDarlaDeVVB5aIn78Zg9eJ3/BtqwVyOH32az/We3yhau2Ww2l2dZ/VcVdG9JTn1NACp5D2BFzBtcM+7DS5Wd82mbeT1wGR4q+wcoFb0/YUVMSy6mbSeXvg4qhQY1WnRKH8y2NPIaGpJbXx/dnTU+hdxbcSFtC2nWWNxVWR56TGZbOseSv6O23ySCtMUBKO35LtfT93E6dR3FPDoAkEVblILuLQAo4fE2x5K/46bphMtg6n+xvTHb0qnqM5RQt3IZ6jVKAxoybtTzILsSPiWvoRH+mvxcTz+QaftkazQ2rFTxHoKnOpQTySvZETeahv6zMagCiDdfxIaVCK+eaJUenEr5iZ9je9PI/yuXzySeHQmmhBD/WoZiWR1fq/0MaII8MJ6/DVYb2OD8m8ud2tvSLRgvxWXoR6nT4PN6ERL+d5q0EzEYL8eRfuqmvdJqzdD+QUxRCdiMFq58sh7u+RvLZrTidk8anOaeAEOhUqLUqbGZXd/Lt01xbn97kOiJW0lrWhifJoVQ37NRxK0l+7n19X6X12btWxWvuvmcyhQKBXnWvAVqJUqt/Y//kBF1ONfyaxJ/PXvfWS7AkUYZlC+A1MPXiP/pOFk+rET4Vy3AanPMlgXlDyT16A3i1x4n8L3y9+3vv+RV+qNa2L1q/8+zaO6mIutVfrirgog1n8dmswA2frz5llN7i81IvPlShn7UCh35Dc05n/o/bplOEm+5zG3TaYA7fT28RHMUFoxsju33j3ub8DaHO773Ut0NMJQKFSqFGzbMLvss5N6ao0nf8nv8JPIbmpBX3xi9ytdRfyTpa44kf+3y2vJefcmlr+NUdjplPUmW61T3GfnQz7UjbiSF3CPJqa8FQEXv/vxw801OpqyhpOc7NAyYi81mdcyW+Xvl56bpGKdT11Ha892Hvo94chJMCSH+vVTOe+jYrFZQKrBZrKBSkOOrlvCPP3Zcpb1ZU01cfH81Co0Kz6o58agYjkKr4kqftY88JJvFHhCFTWyAOsDd+d76u7NHSpepg64XaPs2L4JPk0IkbjtH7PdHuL1kP541cuPbsii6/IH4NC2EZ43cLq9V+7nene+fM1lKNzWaYE9MN5JdPlPS7xfRF8zieCaFQoFbDl/McWmO6+9lr/fBFJOxPyHEv5PiHzOvNpsFJUqsWFCgpJH/lzh9igRoXaS9maypbLjdHRVqsuuqEuZWAaVCyy+xHz3ymGzYg69avhPQK/2d6jSKuz//XKUO2myuf+YWMDQjn74xl9K2cyzlew4nLSGHriYF3Vvgr8lHPkMTwnXVXV6rV/plKDuX9jOJ5issv9Hkzn3tvydWxLzhMvhKs8aRaLmKrzqXo0ypUOGnyUuixX6gslrh5vRWKxQKvNXhpFhiXI5LPDuym58Q4l8r/eTdXxrmm8mYY1Jwy+OPWw5fsNiwJKajDfNGG+aNOosHMbN2kX72tv2Ce34JpRy8ivFiHNmnNcG/Qyk8KoZjiU0F4D6/a+9LG+YNKiXm2ymOe2vDvLm1eB8pB68+9rMqVEq8auYhfFZzsk1pjM1o5uJ7qwBQeemc7nXvS2nIGDwaryZwut48Uo9FO8qsKUaMV+KdZs/uvXf0p9uJ33TKUWazWEk7dRO3nH5Y082cabKQhC1nnOvP3HLZnxDi3+m26e7PgBTLTVKsN/FV58ZHnQMbVozWJLzUYXipw3BXZWFf4mxiTefuXHH3h2608SDx5ou85j+Voh7tCdNVJM0aBzz6nn+eqjAUqEi13Hbc20sdxpGkJVw3PniThwdRKlTk0Neggf9MXvObgoV01t96DwA3pZfTve59aZQZ0/+qeA+iScBCGvl/RSP/r6jg/TEAr/lNI5tbxg03tApPVGiJNZ9zlNlsNuLNF/FUhWC2pfPdjWZcSP3VUW+1WYg1ncVbHZ6hP/FsycyUEOJf69aS/WjCvNFk8eDGjN9xy+WLoUQICpUSjyo5uT5uK0F9KtvPRVq4j5RD1wjqUxWwzxKZridiikmyrxGyWEn89SyGkqGknbrJjRm7ALCZXKecmGNTSf7TOX1F6eGGvnAQvi2KEDP9d5Q6DdpwH+LWHCNxy1n8Ios/lefWF8mKvkhWTNcTH+t6bYgX2lx+RH++g6A+VVC4qbk5909UvnrHDJc1xYQ11eQ4wNe3RRFuf3MAtxy+aEK9uf3tQaxpZnyaFkLppsZQJoybc/5E7atH5Wvg9rKDWBPSH5gyKIT4dzmS/DWe6jDclVnYmzgDH3VOgrQlUCpUZHerwm/x4ynn1QuDKoBDSYuINh6mvFdvwD5LlGS5ToolBjelFzYsXEj9laxuJbltOs2+xJkAWDG6vHeaNY6o9D+dyrQKDwK1hSlgeJ09iTNQK9zwVodzMuUHzqdtoZB75FN57kBtYappC5Nkuf5Y1xtUgU7f/z175KnK6gi+TNYUzLbUO9ubq8hnaMLhpEV4qLLiqQrlZMpqkizXyWdojFrhRlZtafYnzUWn9EGn8uNo8jLSrfHku8/GGOLZkWBKCPGv5d2wIDEzdmG6kYR72TCCB9ZEcSf1L+uA6sTM3MXVof/DajSjK5CFbJ81cgQH3o0KcnXY/7jYeQW5f3iLgC4RxMzZjTXFiDabN1m6VyB6yk7ST8bgFu6b4d5pR6O58vF6pzJdoSyEz36dwHfLgUrB9YnbsCYbccvpS+j4+rjl9s/Qz5PQZPV87GtDR79GzMxdRPXbgNVoxr1MGNk+b+zYufD2soPcWriP/Nvtn8T6tSsJCgXRX+zEcjsVfbGsZJvcGJWn/VDerB9VI2bun1wduRlrkhF94SCyfdHEsaGHEOLfL4++AXsTZpBijSFYW4ZavgNR3kn9q+jdj72Js9gWNwwLRvw1Bajj9yl6lf3nXh5DQ7bHjeCnm2/TKssaSnq8w4GkLzElpuCpCqO0Zzd2J3zBLdMpl7MrMaajGdZFBWgK0sB/FqU8u6JUqNiV8ClGazI+mpzU9B2HryZXhn6ehIcqa+aNHtPR5OUcTl7Em1m3AlDK8100Cnf+TJhCujUeP3VeXvOb6tico4JXX/YnfcmO+NEYrUkEagtT12+KY0MP8fwobPdLGBVCiJdAk+jJLstPVp1NyJjX8KyS+eGH4uX0Y1DvFz0EIYQLY6pdy1C2+Hp1qvuMIruuygsYkXhSg7YFv+gh/GfJmikhhBBCCCGEeAwSTAkhhBBCCCHEY5A1U0KIf6W/1/IIIYR49v5eyyOEcCYzU0IIIYQQQgjxGGRmSgghXDjb6mt8WxbDr1WxTNvenL+HpJ0XyDH/jad2/1uL9xG7+ijWFBMelXMQ1KuyY+e8B163aB9pJ2MIHVvPqTxh8xluLdiLKToJt5y+ZOlRCX0R+85UNrOV0699hc1kdbom+6zm6AsHPbVnEkKI+1l5I5KC7i0p5J75z9GDiQu4nL6TxgHzntr9Dyct4WTKGky2FLK5VaKcV0+0yvvvmHo6ZS1Hkr8m1RJLVreSlPfq49hpz2ozszS6PlZMTtfU95tBoLYwVpuZ/Ylfcj7tf5hsKQRpilPW60O81GFP7XnE8yPBlBBCuBA+twVK3cP9iPRrXQLfFkWf2r1j1xwl9vsjBA+uicpbx/VJ27k+cRuho+o+8Lq4tce5uWAvHhWdtxVO2nWRa6O3ENSrEoZSocR+f5gr/TeQa2kbVF46jFfisJms5FzWFqXb3WdWeWcevAkhxNPQMGAOasXDHaVQ2D2SAu6vP7V7n0z5gePJ31PZZxBuSm/+iP+M3+MnUd13pMv2V9L/YHfCNCp6f4KPOgd7E2ezNXYIDfxno1AoSLBcwYqJ5gHfolbcPTjd7c625QeSvuJi2laqeA/BoArgQNJ8fon9hGYBi1Eq5E/zfxtJ8xNCCBfUPnqUOs1DtVUaNE/1PKXYZYfwa18S93LZ0RXIQtYB1Unadu6+h/Rako1cHfkLN6b+hjYs4xkjtxbvx6dpIXyaFkabzYcsPSqh9tGTdsJ+cKTxfCzqQHe0IV6o/Q2Ol0KtemrPJIQQD6JT+jx0MKVRGp7qeUpHk5dT1KM9oW7lCNAUoKJ3Py6lb7/vIb3Hkr8jr6EhOfW18NXkporPYG6bz3DDdBiAOPMFDMpAPNXB6FX+jtffgZLNZqOs14dkdSuJlzobxdzbk2S5+tiHAosXS8JfIcQryRSTRPRnO0g5EIXKU0fA22W5PnEruZa2QRPs5ZTmd23sFpQ6DTajhcStZ1FoVHg3KUTAO2VRKBQPTPO7OX8PtxbuczmGrAOq412/gFOZ+VYKpqsJGEqEOMp0eQJQumtJ/eu6y4N6TdcSsKWZyTGvJbeW7MeaZHTUWVNMpB2Lth8kfIdCrSLn160d36efv4023Oeh3zshhHhUKZYY/kj4nOvGA2gVXpT07Mzv8RNpHvANHupgpzS/3+LGoVbqsdiMXEzbihIN+QyNKeHxNgqF4oFpfgcTF3A4eZHLMVT06kceQ32nslTLLZIsVwnSFneU+WnyoFG4E2M8iofe+aBem83KTdMx8huaOsp0Sm981Dm4YfyLIG1x4kzn8VZnv+97UcbrfcfX6dZEjqesxFMV8kwPBRbPjgRTQohXjs1iJarfBlReOrLPaIYlPo3oidvAcv8zzOPWHscvsjjhX7Yg+c/L3Jj6G4aSIbiXeXCOu1/rEvg0LeyyTumhzVBmvpkMgNrf4FSu8tNjjkl22Y8uT0CGNVJ/M16NBxvY0s1c7v0T6Wdvoc3pR5buFdHlCwAg/Xws1lQzl3r8gPFSHG7hvgS8Ww59IVkvJYR4clabhc2x/XFTelPPbzrp1nh2JXyKDet9rzmdspZC7q1o6D+HqPTd7EmcRpC2BCFuZR54r8LukeQ3NHFZp1F6ZChLsd4EQK8McCrXK/1ItsZkaJ9uS8RsS8Pwj/Y6pR8pd9rHmc9jtqWx6VZP4i2X8FHnoJRHVwK0BZ2uOZL0DQeSvkSFlpq+4yXF719K0vyEEK+clINXST8fS/DgmujyBOBeOowsvSo/8BpNkAeB75ZDm80H35ZF0QR7knb8Rqb3Uho0Tqlz977uXZ/0N2uaGQDFP+oUGhU2o+URnvJOfyn2BdDXP9uO12v5CJvUEG2wF5d7/ugI3IwXYrEkpOH/ZmnCxtdHE+rF5V4/YbwS/8j3E0KIf4o2HiTOfIHK3oPw0+Qh2K00EZ49HniNuyqIUp5d8VJno6B7CzxUwdwyncj0XhqlwSm17t6XWpFxHajZlg6ASuH84ZZSocFqM2Zob7nTXvmP9iqFBsud9vHmi6RbEyjq0YGavuPwUIXwc2xvEsxXnK7JoatOQ/+55NbX49e4gcSZL2T6fOLlIyGwEOKVk376FpogD9QB7o6yv3e2u59/rkVSumuxme//qerfbi3Zz62v97usy9q3Kl518zn362Zfp/TPwMlmsqB4yA0x7qVQ2z8z821RFO96+QEI+rgqKfujiN94Cv/2JQn/qgVYbSj19jViQfkDST16g/i1xwl8r/wj31MIIe5123wGd1UQBpW/oyyLtsgDr/FUhTp9r1EYsGLO9F5Hkr7mSPLXLuvKe/Ull76OU9nfQdQ/AyerzeRyDdf92lvuad8wYC42mxWNUg+Av1d+bpqOcTp1HaU93737jGr7M/p59eaG6TCnU9ZS1uuDTJ9RvFwkmBJCvHIUKgVY/5HSp1Q8+BqNi80YbPdPC/ybT9NCeNbI7bJO7afPWBZoT0Mx30pxSvWz3E51Cv4eltrffo1bDl9HmUKlRJPNG1O0fUOLf86QKRQK3HL4YLpPWqEQQjwKJSrIkNL34OQolSLjBkC2h/iZm8/QhHBddZd1eqVfhjJ3ZSAAKdbb6O8J9lKttzOk/gG4KbxRK3SkWm87ladabxGsLAlgnwG751eKQqHAWx1OiiUGq83ClfRdBGgKOoJLe30O0qxxmT6fePlImp8Q4pWjzeWPKSYZ860UR1nasehnci+Vlw5tmLfLl9KQcc2U2t+AJsSL1MPX7o7tzE2sKUb0RR59DZMmyAN1kAepJ+7m/tssVoyX49CGeGFNN3OmyUIStpxxqk87cws32ZRCCPEU+KhzkWyJIdVyy1F203T8mdzLTemFlzrM5UujNGRor1f546EK4YbxsKPstukMJlsKgdqM610VCgUBmkJE39M+zRpPvPkigdoimG3pfHejGRdSf3XUW20WYk1n8VaHo1So+CPhM86lbnKqv206jY8659N6G8RzJDNTQohXjqFUCG65/bg2ZguB3StgTUwnespOe6XiwTNUz4NviyLcXLAXzZ2tyq9P3IZn9dxoguw7+VlTTFhTTRk2qbgfv7YliJn9J9owb3T5Arj93WGsSUa86uZD6abGUCaMm3P+RO2rR+Vr4Payg1gT0u+7cYYQQjyKrNqS+KpzszN+HGU838doS2J3whf2ypfgZ25Bw+scSlqIp8q+lfmu+E8J11XHQ2X/AMtkTcFsS3XMXBUwvM72uBH4afLgp87L3sRZ+GvyEaS1H/KeVVua/Ulz0Sl90Kn8OJq8jHRrPPnubIxRwPA6fyUvxVudA091KEeTv8VsS7vvxhni5SbBlBDilaNQKAgdVZfrn27n0rurUPnq8WlemJuz/3SsMXqRfFoWxRKfxvVxv2KzWHGvEE5QnyqO+tvLDnJr4T7yb3/vofrzbV4ErDZuzvkT8+0UdPkDyTa5sSMYy/pRNWLm/snVkZuxJhnRFw4i2xdNnurZWUKIV5dCoaC6zwh2JXzO+lvvo1P6kt/QjP1Jc1DycOf5PUsFDC1Is8bzW/wErJgJc6tAOa/ejvqjycs5nLyIN7NuBSC7rjJlPN/nQOKXGG1JZNWWpJL3KEf7Cl592Z/0JTviR2O0JhGoLUxdvymOs7GKuLdFgYLdCV+QZo0li7Yodfw+R6vMePSFePkpbA+TgCqEEC9Ik+jJT71Pc2wqaSdu4F4+O4o7n4qmHo3mco8fyLvpnZcioHoV/BjUO/NGQojnbky1a5k3egSpllhumU8Sqi3n+JkbYzzKptu9aBu0QbYEfw4GbQt+0UP4z5K/GIQQrx4FXB3yM7eX7Md4NYG0EzeImbkLj2q5JJASQoinTPF/9u46PIprYeDwb2bdN56QhAQJ7u4OhVKgRqndr3Z76+7eW3e/dYGWuhulhRZ3d3eJ61pW5/tj2w3LLiRokPM+D0+TY3NmCmf3zDFJYnr5Q6xyTcQRyKfEv57FjrfI0fcXHSnhpCf+BguCcNpR2w00eHwYJR8sovSTZchGDZb+jcU24IIgCMeAXrYzIOFxljs+ZJVzIhrZSI6+P53M/6nvqgnCEROdKUEQTkvmnjmYe+bUdzUEQRBOC1m6HmTpxAsr4dQj5rMIgiAIgiAIgiAcBjEyJQiCcBztvPlH9E2TSb25d31XJUrVlE3kP/4nTX+9ApVFd8jxgiAIJ6LfS28hUdOUrtab6rsq+EIuFjv+x67qOYTwk6nrQVfLjRhUiRR4l/FHefxNedqbr6C9+bLjXFuhrkRnShAE4TQXKHVT+Orsw44XBEEQajev6nkcgT0MTnwWFVoWOl5jRsWjDE96jRRtG8amfBuVfr37eza4f6KpYUQ91VioCzHNTxAE4TRX+MJMdI0SDzteEARBOLiQEkQtGehuvZVkTQsSNI1pZRxLkX8lQcWPStJgUCVF/gQUL2tdX9PDehsmVWp9V184CDEyJQjCKa/ip7WUfb6cQJETTYaVxEs7YhveHADfrgqK/jcPz8p8FG8ATZaNlKu7Y+6TC8CWCyaSdEknqqZuonpdEdpsOxkPDcYxfQvl361GkiUSL+5I4oXtgfA0PkPrdLybS3Av24sm00bqDT0xdcuOWzfXwl0Uv7sA3/ZyNOkW7Oe1wX52ayRJIuT2UfDCTFwLd6H4ghjaZZB2S2+02faYcko+XETp+CVxr5F+3wBsI1rEjaucvAFffhWp1/dk94pfDzleEARhfxvdP7PG9TmuYDFmVTptzZfSxHAGAFWB3Sx2vEmhbwVBxYdVnUlH89Vk68NTn78tGkdb8yVs8/xJsX8tNnVD+tgeYEf1DNa7v0eWZFqbLqK1aRwQnsaXom1NmX8zhb7lWNSZdLZcR6auW9y67fUuYqnjPSoD2zGr0mluOpfmhjFIkoQ/5GZ+1Uvs8S4khI9UTVu6WW/Bqs6KKWe54yNWuibEvUYv6z00NUaPJsmSit62eyK/u4OlbPT8TKqmHSop9uDiZc73SNa2JNcwsA5PXKhPojMlCMIprXpjCYUvzSLj4cEYWqXhnL+TgqenYWidhibLxp77JqNrlkzDt84BBUonLKHg2ek06f4vJI0KgOL3FpB+zwC0De3kPzqVnTf9iLlPLjlvnkPVX5spfns+lv6N0GRYASj/agWJl3Yi5cZeVP22gd33/kajCRfEdIK828vZ88DvpNzQE1PXLLybSil8aSaSLGMf04qS9xfh21ZO9sujkPVqil6fS8Gz02n4xtkx95l4YQfsY1rHfQayWRs3PFDiovh/88h6YSQht++Q4wVBEPZX6t/EgqqX6Wt7iGRtS/Z4FzCn8hlSNK2xqDL5q/w+EjXNODPpTRRgpXMCcyuf5Xzdt5FOxVLH+/Sy3UUPdUNmVjzG72W3kK3vzYikN9hePY2ljnfI0fXDrA4fRLvW9TVtzZfQ1XoDmz2TmVZ+P6OTP8Kqjn6JVRHYwbTyB+livZ4G2i6UBTazoOplZGSaGUez3PkBFYFtDEt8EbWkZ5Hjf8ytfJbhSa/H3Gdr0ziaG0fHfQYa2XzQZzSv8kU2eX5GK1k5I/GVmPiqwC62V09neOJrdXjiQn0TnSlBEE5p/gIHAJp0C5p0Cwlnt0abaUVlN6B4A9hGtsB2ZgtUNj0AiePa4fhzM4EyD5q08AeiZUATLH0bhX8e1ISSjxaTdntfZJ2axIs6UPrhYrw7KiKdKUPbDJKv6AJAyrU9cM7fSeUv60m5Lnpb4LLPlmEZ3ISEs8OdIG2mjUCpm7IvlmMf0wp/vgPJoEaTYUFl1pF+d3/8+Y649ykbNcjG2LebB1Pw3Axso1qib56Ce9meQ44XBEHYnytYAIBJlRYe+TGOwaLKRC/bCOIlzziSJoYR6GUbAK1MF7C9+i+qQ+WR6Wy5+v401Pf9++eBrHCOp7v1NtSSjjamC1nh/IjK4M5IZypV25b25ssB6Gy5hj3eBWzyTKKz5Zqouq1xfkauYRDNjWMAsKgz8QRLWeP6kmbG0TiCBaglA2ZVBlrZTE/rnTj/vp/9aWQjGoyH9Yxams4nzziS1c5PmVJ+B6OTP0Iv2yPx690/kKxpSaq27WGVLxxfojMlCMIpzdQ9G2OnTHZe+z3ahnZMPXOwjWge2ZHOfm4bqqZsonp9Mb5dFXg3loQzhkKRMrSZ1sjPkk6NOtGIrAs3n//8V/EHI2kM7dKj6mBokYp3W1lM3bzbyvFuKcUxbUtNYFBBCYRQ/EES/9WR3XdPYsuYCRjaZ2DunYttRPO491n6yVJKJy6NG5d+Rz+sw5pFhVX+uh5/gYMGT5wRN09t8YIgCPE00HUjXduJ38qux6ZqSKauB00Mw9HKFgCaG89hm2cKpf4NVAZ3UebfBICi1LShZnVm5GeVpEMvJ6KWdJHfAYJKzWh5qia605GsaUFFYFtM3SoC2ygPbGVH9bRImKKECBEgqPhpZ7qUqeX38FXROaRp25Gl601Tw/C497nKOZFVrolx43pY76CxYegBn5FdHT7jsI/9Qb4tGss2z1Rams6P1Gd79V90MF95wPzCiUV0pgRBOKXJOjVZL46kenUhzvk7cM7ZQcX3q8l8ejiGNunsuO57JI0KS79GmHvlIGlV7L79l+hCVPvt1VPL1j3SfumVUAhkKTZhMETC2a2xn9smNk4lY2iVRpOvLsE5byeuBTsp+XAxFT+uJee98yKduH/Yx7TCMrBJ3PqoEw0xYZV/bMS3u5LNoz76uy4KAFvO/4T0O/rVGr9/50wQBAFALekYkvA8xf417PHOZ5d3Luvd3zMo4WlSNK35rewGVKhpqO9Hlq4nsqRlavmdUWXIqKJ+l4jTfu6bXopOryhBpDgNdYggzY1jaG48J7YMVCRrW3Jeyhfs9s5nr3cBK5wfsdHzEyOT3ol05v7RzDiaHP2AuPUxyLEb9vhDHvb6FtBA2w2NHB7RUks6zOoGVIcqIulK/OvxhirJ1vU56D0LJw7RmRIE4ZTmWV2Aa8Eukq/qiqFtOilXd2fHtd/hmLkNJRDCt6OCvN+uQNaHp8hVTfnnLenhX7N6Y3HkZ0VRqN5QHJkmuC9tbgK+XRVos2yRsMrfN+JZmU/6Xf0p+2ol2mwb1sFNsQ5uindHOdv/9SXeLaUYWqVFlaWy6lFZ9XWuY8aDg1C8NW+Cq9cWkv/EXzR8/Wy0WVYMHRscNF4QBCGeYt8a9ngX0MFyJanaNnS0/JtJpdexs3omIcVPZWAHF6X9iloKt1dbPVMBOIIml1L/xsjPiqJQGthAtq5vTDqbOpeqwO6oDSW2eP6gyLeSnrY7Wev6Gqsqi0aGQTQyDKIysIMfSy6j3L+FFG2rqLJ0shWdXPe2UCHEzIrH6GN7kEaGQUD43KmqwC5aGcdG0hX712BTN8SgSqhz2UL9Ep0pQRBOaZJBQ+nEZcgWHZa+ufh2VuDbWYF9dKtw5yMYwjFtC8aOmVRvLKHof/OA6Gl7h8o5Zwfl367G1C2Lip/W4S9wYBvVMiZd4oUd2HHNt5R8uAjr0Dx8OysoenU29nPCa6gCJS4qvlmFbNCgTjVTNWkDskkbdze/Q6VJiV4gHSh2hsMzLMhGLbJRe9B4QRCEeNSSnlWuT9HKFhrq+lAZ3EllYCfNDKPRyVYUgmz3TCNd15Ey/yaWON4EIMThb3Kz2zuX9a7vaKDrykb3zziDBeQZz4pJ19p0IZNKr2G54yMaG4ZSGdjJoqrXaG48GwB3sIT17m9RywZMchqbPZPRSCZs6vi7sR4KrWyiiWEESx3vYFAlopMsLHW8i0mVRo6+fyRdeWALNnXsyzfhxCU6U4IgnNL0TZLIeGgQpROWUPLuAlQ2PQnj2mMd0RxJkki+uhvF7ywk5PahzQ5vY174ymy8G4rR5Rzem0HLoCY452yj+K156Jokkf3iWWjSLLF1a5ZM5hNnUPLhIso+XYbKbiDhvLYkXd4ZgOSruqJUB9j76FRCTi+6JklkPjsist5LEAThRJOgaUJf24OsdE1gqeNd9LKd1qZxNDEMR5IkOpr/zTLne/gdbiyqLDpbrmdh1auU+jdi+3st0aHK0Q9kl3cOSxxvk6BpwtCEFzGr0mLSJWnyGGB/guXOD1nt+gy9bKeF8VzamS8DoKPlSoJ4mVXxGN6Qg0RNUwYlPB1Z73WkullvZrnjA2ZVPI5fcdFA25UhCc8iSzVfx6uD5Zji1F04cUmKciSTWQRBEI6t0YUv13cVDsnOm39E3zSZ1Jt713dVTng/pd1W31UQBCGOJ/vn13cV6uz30ltI1DSlq/Wm+q7KCe2BGRn1XYVTVi3LqAVBEARBEARBEIR4RGdKEARBEARBEAThMIg1U4IgCEdRw9fG1HcVBEEQThtnJL1a31UQTnNiZEoQBEEQBEEQBOEwiJEpQRBOO1sumEjC+e1IvKBdfVcFf34VW8d9BoCxWzbZL4xECYQo+t9cHFM3owRDWM9oRur1PZE0qlpKi1X6yVLKPl9O3qQrI2HHsvwd135H9doiAJr+eoXYeVAQBL4tGkdL0/m0Mo2tPfEx5gzk813JRQDh3fQSnyekBFjseIttnqkoBGlsGEZny3WoJM0hla0oIaaW302mrvsh3as7WMLCqtfI9y1BQkWuvj+dLddFDvcNKUFWOD9is+c3AoqHNG1Helhvw6hKZlLpdZT41wFwYerPR23nQaHuxMiUIAjCCSDr5bNo8MgQAEreW4hr3k4ynxpO5lPDcc7ZTvG7Cw+5TO/WMkrHL44JP5blZz13Jg0eH3bIZQmCIBxPQxNepK/9YQCWOT9gt3ceAxOeZEDCE+yqnsMyx3uHVF5ICTCv6gXyfbFtbm2mVzyEX3EzPPF1hiQ8S5F/NQuqXonEL3O+zybPr/S23cfwxDfwhiqZVfE4AIMTnqW//bFDvqZw9IjOlCAIwglAZdWjsugIeQOU/7CalOt6YGibjrFDA1Jv6k3FT2sIeQN1Lk8JhMh/6i/0raLPKznW5auselRWMRolCMKJTSdb0ckWgoqXDe7v6Wy5llRtG9K1HehqvYmNnp8IKN46lVUR2M5vpTdQ4FuOVjLXnmEf3pADsyqDHtY7SNA0JlnbkjzDWeT7lgLgC7lY5/qG7tZbaaDrQoKmMd2sN+MKFeINOcL3IYnRqPokpvkJgnDSyX/iT4JOH1nPjIiEVfy4htJPl9H4y0sIlnsoemMu7kW7Cbp8aFLNJF7aEftZLWPLeuovQk4fmU8Nj4Rtv/JrzH1ySb6yKwBVUzZROmEJ/gIH2mw7SZd1wjKgSdy67bz5RzzL45/R0vjLi9FkWA96b97NpSieAMYODSJhxg4NUDwBvJtKMLRJP2j+f5ROXIrKbsA6uAlFr889buULgnDqmV3xJD7FxaCEpyJhG9w/str5GeemfEF1qJzFjjfZ612EX3FhVKXS1nQxecazYsqaU/E0PsXJwIQnI2E/l1xFtq4PHSxXALDVM5VVzo9xBguwqrNpZ/4XOfoBcev2e+ktFPpXxI07N/lzzOqDn69U5t9MQKkmTds+EpambU9Aqabcv5kUbeuD5gco8q0kVduO9ubL+bnkylrT70snW+j39wgZgCOwh62eP8jQdoqUDZCl6xlJk6RpxrkpXxzSdYRjR3SmBEE46ViH5rHn/skEnV5U5vAoSNWfm7EOyUOSJPKf/AtCClmvjELWqan4fjWFL83C3CsHdaLxkK7lWrSLwpdnkXZ7X/QtU/Eszyf/qWnIRi2mbtkx6TOfOAPFH4pblsqur/V6gWIXkkZGZatJq7LokDQygWJXnepcvbmEim9Xk/P++biX7j6u5QuCcOppZBjCtPIH8YWcaOXwyMt2z180MgxBkiRmVz6NQpBhiS+hknRscP/AgqpXyNL1wqBKPKRr7fUuZkHVy/Sw3k6ypgUFvhXMqXwGtWQkU9ctJv2AhMcJKf64Zelke63Xc4dKkNGgl2375LMgo8EVKialDnVuZhxdh1S1m1J2F/m+RZhV6XSyXA2AI7gHkyqFPd4FrHCOpzpUQZq2A92sN6Gvw/0Jx56Y5icIwknH2CUL2azDOXs7AIESF56VBViH5AFg7pVD2h390DdJQptlI/FfnSAQwrer8pCvVfrJUhIuaId1SB7aTBu2kS2wj2lN2RfL46ZXWfWok4xx/0iq2pvckDeApI19zyVpVIT8wVrzK4EgBU9NI/nqbmjSYqebHOvyBUE49WRou6CVzezyzgHAHSylyL+KRvrBQHjUpIf1dhI0TbCqs2hrvpQQAaqCh/6yZZXzE1qZLqCRYTAWdSZ5xjNpZhjNWteXcdPrZCsGVVLcP7JU+6Y6AcWLStLGhKskDSHFd8j1PxJdLNcyPPF1DHIyU8ruIKj48StuPKFyVjjH09lyHf3sj+AM7uWv8vtQlPgv7oTjS4xMCYJw0pFUMpZBTXBM24JteHMc07aga5yIrnH4Dah9TGsc07dQ/s0qfDsr8G4qCWcMHfoHj29bOdVriyj7fHlNYFCJGtnZ1+67fsW9Mv40v0Yfj0OTdvC57bJOhRKnU6P4g8i62pvs0glLUdn12Ee3qpfyBUE49ciSilz9QLZXT6OJ4Qx2VE/Drm5EgqYxAM2No9lRPYN17m+pCuyizL8JAEWp/QXN/ioC2yj2r2WN6/NIWEgJHnAUZmrZ3RT5V8aNG508AbMqLW7cP9SSlmCcTlNQ8aOSap9NcDQlaMLTxwfY/8s3xWPZ412AjJqA4qGX7R6SNM0A6Gt7kO9LLqHEv65O0xCFY0t0pgRBOClZh+ax66YfCTq8VP21BevQ8KiUoijsvutX/EVOrIObYh/TCm2One3/91Wdy1aCoaifk6/qirlvo+hEshQ3b9rd/VG88b9AqJNMtV5bnWJG8QUJOryRbcWDVdUo/hDqlNrzV/2+kUCZm41nvB8OCCooviAbz3ifrOdGHvPyje0Pvj5BEISTUyP9EH4vuwVfyMH26mk0MgwFwm3u1PK7cQeLydUPoplxNDZ1Q34qubzOZSvUtJkhgnSwXEVDXZ+oNBLxR5l62e464EYRRjmp1msb5RRC+P/ezCH8sssbqiKEH6OcXNdbOGzVoQryvUvJ1Q9EksKfKwZVEjrZijdUgUEVroNdnRPJY1FnIqPBGSwkBdGZqm+iMyUIwknJ0CoNdaqZyl/XU72+iAb/DX+w+7aX416yh0afXog22w6AZ1VBOJMSW46kURFy18y3VwJBAkU1a4e0OQn486vQZtXMpy/7fDlBp4+Uq2Pn72tSjmzqm65pEpJBjWdFPuY+uQC4V+QjGdTomtT+xSD7tdEogZrOoGPmVso+XkrO++dHOkvHunxBEE49KdpWmFSpbPJMosS/jn72RwCoDGynwLeUs5MnYlVnAVDkW33AcmRJgz/kifweUgK4gsWR3+3qXJyBfKymrEjYGtcX+EJOOlr+HVOeUVWXVU0HlqBpilrSU+RbSba+NwCFvpWoJT2JmvgbDR1N1cFyZlU+hlmVToo2POLvDORTHarArm6EXk4AoMS/gTRt+GzEqsBuQvixqBscsFzh+BGdKUEQTlrWoXmUfLQYQ7sMNKnhToxs1oJKwjFtC9ZhzfDtqqDo1fA8/3jT2wyt0qj6fSPO2dvR5tgp+2x5VLqkSzqy5+E/0OYkYuqRjWdVASXvLyT11j4xZR0Nsk6N/ayWFL42G9miA0Wh6PU5JJzdJjINL1hVDYTXZ+1Pkx49jVCdYABZiuoMHuvyBUE4NTXSD2GFczyp2naYVKkAaGUzEjLbq6fR2DCUqsBuFla9CkCQ2OlzyZpWbPH8zq7qOdjUOax2fRa1gUQb08XMqHgEuzqHTF13ivyrWeb4gG7WW47JPaklHXmGs1hY9drfB94qLKp6nebGc1BJ4dF7b6gKCK/POhwHy2/XNCJD24X5VS/R03YnKAoLHK/SQNstMoUvRz+AeZXP09N2FxrJwIKqV0nWtCJZ0+Kw6iMcXaIzJQjCScs6NI/S8UuwDmkaCdOkmEm/sz8lExZT+vFSNOkW7Oe0pvKX9VRvKMbUvWF0GcPy8KwrIv/Jv5DUMvYxrTD1qElj7pNL+h39KPt8OcVvzkWdaibl+p7YRx27NUPJ1/Qg5A2y577fwuvDhjQleZ9RsD0P/g5Aw9fGnJDlC4JwampsGMJK1wQa6YdEwoyqFHpY72SlcwKrnJ9gVqXT3HgOmz2/UurfSKau+35lDKXUv47ZlU8ho6KZcQyZuh6R+Gx9b7pbb2eN63MWO97CpEqhs+U6msXZZv1o6WT5D0HFy1/l9yGjopFhKB3NV0Xip5c/BMAZSa8eVvm15e9nf5jFjjf5s+weQgRoqO9HV8sNkfjetvtY4niLaeUPohCgga473Y9R51I4dJKiKHEmvgiCIJwYRhe+XN9VOKb8+VVsHfcZOR+cjz6vbvPzAxUeCp78i6znRx6TOh1p+e5le9h1y880/fWKyLqseH5Ku+1wqygIwjH0ZP/4m+icCpyBfL4ruYizkt4jUZNXpzzVoQpmVzzFkMTnDuuaR5q/NgXeZfxRfhsXpv789+harAdmiPWsx4rYGl0QBOEEEKyqJuiIv4h6f6UfLcY6vPkxq8uRlB+sqiZYVbf7EARBqC/eUBXekKNOaVc4x9PEcMZhX+tI8x+MN1SFV6nbfQjHhpjmJwiCcALYfdsvGLtlk/1C7aNBqTf2QtLUfn7K4TqS8nffPYnqtUVHuUaCIAhH15TyO2ig7cqQxOdrTdvFcgMqSXPY1zrS/AfzZ/k9lPjXHZOyhboR0/wEQTihnerT/E5nYpqfIJyYTuVpfqcrMc3v2BHT/ARBEARBEARBEA6D6EwJgnDK2HLBRMq+Wlnf1Tih5D/1F3vun3zE5fjzq9jQ722qN5UchVoJgnAq+rZoHGtdX9d3NY7InIqnmVb+wGHn3+z+jc8L6755z8cFA9hZPeuwryfUP7FmShAE4RSWenPvuIcVC4IgCLG6Wm/iSBrNXMOgqK3eazM25dsD7sAnnBxEZ0oQBOEUpjIfeGtyQRAEIZpWNh9RfrWkQ62qe7trUCUd0fWE+ic6U4IgnFSCDi9F/5uLc/Z2UMDUPZu0W/ugsuqj0imBECUfLaJqyiYCxW5UVh2WQU3CO9WpZAIlLgpemIlnRT5IYOySRdptfVEnGAi5fRS8MBPXwl0oviCGdhmk3dIbbbY9pj4lHy6idPySuHVNv28AthHRJ9SXvL8Qx+ztNBp/QSTMtWAnex76g6Y/XIZs1FD21UoqvllFoMKDrnEiKdd0x9gxEwhP21OCCv49lfh2VtDg0aFIejXFb8zFu7UM2aLDOiyPlP90R1LJ5D/1FyGnj8ynhgPgXr6X4ncW4N1cijrRQMKF7Uk4pw0Avj2VFL85D/fy8OJzc+8cUm/qHfesKMUfpPTTZVT9toFAqRtd0yRSru2BsUODA9bT1C27Dv+HBUE4kflCDhY53mRX9RxAoYGuG92tt6CTrVHpQkqAFc7xbPVMwR0qQSdbydUPoovlemRJhTtYwvyqFyn0hadmN9B2oZv1VgyqBPwhN/OrXmKPdyEhfKRq2tLNegtWdVZMfZY7PmKla0Lcuvay3kNT44iosGWOD9jlncPo5A8jYXu8C5hR8QhjU75jYdWr+BQnAxOeZLP7N9a7v8OuzmWXdy6tTGNpb76cje6fWe36DE+wlAxdV8yqdPwhJ73t97HZ/RuLHG9wUdqvkTOt+tsfY7njAxzBvSRq8uhpvYMETRMgPM1vgP1xGur7oighVrs+Y6P7Z7xKJUnq5nS13kSipmmtz1OoP6IzJQjCSWXvQ38QdFST9fQIJIOawudmUPDsDDKfjD7Do+yL5VRN3kjGQ4PRpJlxL8+n4LnpGDs0wNK/MYUvzULxBWj41jkowRCFz06n+H9zyXhwMCXvL8K3rZzsl0ch69UUvT6Xgmen0/CNs2Pqk3hhB+xjWsetq2zWxoRZhuZR+vFSvDvK0eUkAFD152bMfXKRjRoqfl5L+ZcrSLuzH9psO87Z29l91yRy3j0PXeNEABxTN5F+/yB0TRLRZtrYMnYitpEtaPDfofj2VrH3oT/QZtuxn9Uy6tq+XRXsvvNX7Ge3JuP+gVRvLKHg6WloUswY2mew84YfMbROpeFrowm6fBS9PJu9/50ad7v2wldm45q3g7Q7+6PJtFL541p23zWJ3AkXoG1gjVtPQRBOftMrHsEbqmJQwlOoJT3zKp9nXuXzDEh4PCrdGteXbPH8Th/7A5jlNAr9K5hb+Rxp2vbk6PuxoOplgoqPEUlvoigB5lY+xxLHm/SxP8By5wdUBLYxLPFF1JKeRY7/MbfyWYYnvR5Tn9amcTQ3jo5bV02cUabGhiGscn1CZWAHNnUOANs9f5Gl641GNsakLwtsIkXbmpFJ7yBLanZUT2dh1Wt0t95KqrYtm9y/stb9FU30ww74zJY7PqC79VY0spl5lc8zv+plRiS9EZNulWsia13f0NN2BwnqJqxyTeTP8ns4N+Vz1rq+PujzFOqP6EwJgnDS8G4vx710DznvnYe+eQoAaXf0wzlne0xaXW4i6fcPjIyU2DKslH2xHO/2ciz9wZ/vQJNtQ5NhQdapyXh4CCG3HwjHSQY1mgwLKrOO9Lv748+PfyiibNQgG+t+foguJwFds2Qc07agu7wLij+Ic/Z2Mh4aDEDpJ0tJvrob5p7hD/nEC9vjWVNI+TcrSb97AADabDu2M5oB4UNyQw4v6kQj6nQLmgwrWS+ehTrBEHPtyl/Wo8mykXpjr0g5IacXVBJVf2xCCQTJeHAwsiF8PxkPD2b7ZV9Rvb4Ila1m5C/o9FI5aT0ZDw7G3Ctcz9Sbe+NZXRDuCN7WN6aegiCc/CoCOyjwLWVk0rskacL/tnvY7vh7lCqaXZ1Lb9u9pGs7AGBWZ7DG9QWVge1AP5zBAqyqLCyqdFSSjr72h/ArHgAcwQLUkgGzKgOtbKan9U6cwYK4ddLIRjTEdoIOxKbOIVHdjO3V02lvvoyg4mendzZ9bQ8eME87078i0/FmVzxJnnEkecbwS6Yu1uso9K846DXbmi8hXdcRgJam85hXGXu2laIobHD/QDvzpeTo+wPQzXILy6UP8Yaqan2eQv0RnSlBEE4avm1loJLR5SVHwvTNUyIdq32Z++SGp7S9NR/frgq8W0rDHaJgCIDkq7ux9+E/2DxqPKbOWZj75GIdlgdA4r86svvuSWwZMwFD+wzMvXOxjWget06lnyyldOLSuHHpd/TDOiy2M2EdmkflpPUkX94F5/ydSCoZU9csQm4/gQInhS/MpPDlmt2dFH8IQ6vUyO/q9JrFyiqrnsRLO1H8v3mUfb4cU/eGWAc3RdMylf15t5VhaBEd/s+oWuFLM9E1SYp0pAB0jRKRzVq828oxdqg5o8S3swKCCobWaVFlGdqmU725NG49BUE4+VUEtiGhIlHdNBKWpGkW6VjtK1vfmwLfcpY43qEqsJPywFacwXxCShCAjuarmFHxKF8WjSFd25lsXW8aG4YC0M50KVPL7+GronNI07YjS9ebpobhceu0yjmRVa6JceN6WO+IlLmvxoYhbPZMor35MvZ4FyCjpoGua9wyVGij1jWVBTaTZxwVlSZV0xpfyBk3P4BFVTM9USMZCRGISeNVKvGEykjS1EwN18gGulpvAMCoSj7o8xTqj+hMCYJw8lDX/TSHkvGLKf9qJbYzW2Du14jka7qz9+EpkXhzrxwaf3Mprrk7cC3YReFrs6masonsV0ZhaJVGk68uwTlvJ64FOyn5cDEVP64l573zkHXRzaZ9TCssA5vEr25i7OgQgHVwU4rfmo93exmOaVuwDGyCpFYRqg5/wKbfOwD9fp0eSVMzJ17WRs+PT7m6G7bhzXDO3YFr3k523z2JpMs7k3x5l/3KkFEOsEuVpD3Ax4EChKLzSLr4aZWQAsGatPvXUxCEk5t8CF8bVzgnsM71NU0NZ9JQ349Omv8wo+LRSHyWvhfnpX7F7up57PEtZJHjNbZVT2VY4kska1tyXsoX7PbOZ693ASucH7HR8xMjk95BLUWv4WxmHE2OfkDcOhjkxLjhufrBLHG8TUVgO9urp5FrGIgsxb83WYqeeSCjAkL7pZIO9ihQSbXPXqh5tvHb6Nqep1B/xDlTgnAC8Cmxb6mEWNqGdgiG8G6pGf3wrC5gy3mfEKr2R6Wt+HY1Kdf1IPXGXtiGN0eTbiFQVPPmsOT9hfj3VmE7M7zWKPOxYbiX7iFQ5qbsq5W4V+RjHdyUjPsH0fDNs/FtL4+67j9UVj3aLFvcP7Ixds0UgDrZhLFjAxzTtuKatxPr0PCImMqsQ5VkxF/kjCqn4qe1OGdvi1tWoMxN4UuzUCUaSRzXnuxXRpF4YXscUzfHPr9sO96N0edEFb48i8KXZ6HNsePdUkrIU/Mcq7eUEnL5ws9933IyraCW8ayOnnbjWV2INic6rSCc6EIBcXZAXdnU2SgEKQ9siYQV+9bwTdH5BJTqqLTrXd/RyXItXazX08RwBiZVOq5gUSR+meMDHIG9NDWOoL/9Efrb/0uBbymeYBlrXV9T6FtBI8MgetvvY0TSG1QGtlPu38L+dLIVqzor7p94a6AAjKok0rQd2FE9nT3e+TTSD6nzM7CrG1PiXx8Vtv/vh0Mrm9HLCZT6N0bCgoqfb4rOp9C3stbnKdQfMTIlCCcAraRmSP4z9V2NE87UjHujftflJGDq0ZDC52eQeltfJFmi6LU5GNqmI+uj3/ypbHpc83dh7JhJyOWj5KPFhFw+FF94SoR3ZwXO+TtJu6MfKouOqqmb0aRbUNkNBEpcVHyzCtmgQZ1qpmrSBmSTNu5ufofLOjSPwlfnoLbp0bepmS6XdElHSj5YhCbZhL5NOs6Z2yj/aiVZz50ZtxyVVYdz9jZCHj9J/9eJkNuPa9Fu9HGm+dnPaU35N6sofncBthHNqd5YQuWv62nwxBkYO2RQOmEp+U/8RfKVXQi6fRS9NBt9q1T0bdIIFNSsGZP1GhLObUPxm/OQjdpwh++HNfi2lpJ+d/+j9owE4XiQ1RIzPoi/JvJ01P+qA0/PtalzyNT1YF7Vi3S33oqEzMKq10jVtkUtRe+oqpOt7PUuIF3bAb/iZoVzPH7FRRAfAFWBnSzwLqCH7Xa0kpltnj8xq9LRy3bcwRLWu79FLRswyWls9kxGI5mwqY/ejqCN9UNZ6HgNvWwjRRN/E6F4WpkuYFbFYySpm5OqbctWzx8U+1fH3WnwULUyjWO161Msqkxs6oasck1EltQkaZrX+jyF+iM6U4IgnFTS7x9I0Wtz2H3bz0gaFabeOZENFaLS3TeQwpdmsf3yr1DZDZj75mIb2YLqjcXh+Dv6UfjqbPbcPYlQdQBD6zQynx2BJEskX9UVpTrA3kenEnJ60TVJIvPZEXG3CD9c5r93FLQMbYok1UwRsZ/XhpA3QPH7CwmWedBkWsl4aPABtxWX1Coynz2TotfnsP3f3yKpJMy9c0m9pXdMWk2ahcxnR1D81nzKv1yBOs1C2u19MfdoCEDWCyMpemMuO679Hlmvxtw3l5TrekbV7x8p13QHWaLguemEnD70LVLIenkU+n3WswmCcOrpbbuXhVWvM6XsdmTUZOl709VyQ9x0C6pe4eeSK9HJdhrq+9DUcCZlf4+8dLfdzsKq1/iz7G4CipcUbWsG2Z9GkmQ6Wq4kiJdZFY/hDTlI1DRlUMLTR/Vw24Z/7yjYSD8kbht34Hx96GS5huXOD/CGqsjUdSdb1xuZum9EdCCtjGMJhNzMr3oRv+IiRdOawQnPopZ0tT5Pof5IiqKI8W1BOAGIkalYUzPuZXThy/VdDeEY+SnttvqugiAAiJGpffS/ysKT/fPruxonrALvMkyqNCzqBpGwqWV3kaJtQ3vzZfVYs4N7YEZG7YmEwyLWTAmCIAiCIAhCHez2zuOv8vso9q3BGchnvfsHCn0ryNGJ7clPV6IzJQinuGCJG/e0+JsXxLNrwEe4Z+3APX0buwZ+RMjhjYovuvN3dg34CN+m6M0YSp+aSdFtv0WVUds1BEEQhFj3vT2KqYs/Y3v+Gv7zXBd2FUVP5fpk8pP857kuLNs4LSr8x1lvc/87Y6LKqO0awqFpb76CFG0bplU8yI8ll7HF/RsDEp7ArmlU31UT6olYMyUIp7jyV+YhmbQYBx5aQ6/7+1wh77oSDN0yAVB8QXyrClGlGKlevBdtXs3ZG77VRRiHh88eafDtOOSjuL5IEAThdNQwrQV6rYmte1eRnVpzltO6HQtJsKSxdvsCOjYbGAnfsnclLRqGj0S4//8+RquJfzyDcPg0soFetrvquxrCCUSMTAnCqe4wV0Wq7HrUOXZ8a2u2XvWuLkQyajCd2YzqJXsj4cFyD4G9DvSdwh0wVZIRSZwxJAiCcERkWUXTrA5s27s6ElZSsYeyqgKGdfsX63YsjISHQkG256+heU748FmLMQGdRh9TpiAIR5cYmRKEE9yuAR+RcHtPHN+tI5jvQNsmjcTbe6LOtAIQ8vipeGsRnunbQVHQtksj4cbuqDMslD49C8+cnQB4lxfQ4Mux+HdXUvHmIrwrClF8AdSZVuxXd8bQu2HMtfUd0vGtLY78Xr0kH12HDPSdM3B8sQrFF0TSqvCuLkIyqNG2SInUOenxQRj75hDyBqh4fQHuaduRtCrs13Su032HXGK7V+H0EPAqqHV1301MOD5mLv+O3xd+TLmjkCRbA87scQU925wFgNfn4Zvpr7B4w1QURSEvqwPjBt9Jsi28KcF9b4+if8fzWb5pOruKNpKVksdFQ+4mN6MVAFWuUr7662XWbp+Px+skwZrGiO5X0Lf92TH1aJ7dmTmrfor8vnb7AnLSW9IxbwBf/vkCpVUFJFnT2VO8mWqfKzIydd/boxjc5SKGdLmYUCjIj7PfZvbKHwgGA5zR/cTdKEEQjpej1faKzpQgnAQq319Kwq09UOcmUPnWIorvmUL6+HOQ1DLlL84lWOwi+dmhyHo1VV+souj2yWSMP4eEm7oTKvcgmTQk3NITRVEoue9PtM2SSHtzJABVE5ZT9uxsGnw7DkkTPZqk65CO+8+tKIqCJEl4l+7FNLIZ2lapIEl4Vxei79QA35oidO3SkNSxg93lL8/Dt6qQlKcHI2nVlL8y76D3Gsh34PhuHa5JG2Hpw0fvIQonpMd67a090Snu4bkNak8kHFc7C9fz6ZRnuHrUkzTKaMOqrXMYP+m/NG7QjrTEhnzy+5OUO4q4+fxX0WkM/L7gY1764jr+e9VXaNThKc6/zH2P8wfcwmUjHmbyggm88vWNPHn195gMNj789RFCSojbx72FVqNn2tKv+GzKM7Rv2herKSmqLs0bdua7Ga/jrnZg1FtYv2MRLXK6kmhNJ9WezbrtC+jTbgxb9q4kI6kRNnPs8QS/zvuA2St/5PIRj5BoTefbGa9RWiV27DvdiPY22tFqe8U0P0E4CZjPb4VxUGO0jRNIvK8vgXwH1Uv2Esh34P5zK4n390PXMgVNowQS7+6DUh3EPXMHslmLpFEhadWo7HoUbxDTyDzsN3dHk2NHk2PHckEbQlVeguWemOvqOmQQcvoI7Koi5PDi21CKvlMDJLWMrm0q1UvDH8be1UXoOsU2SiGXD/fUrdiv64quXTraFskk3N3noPdadNtk3FO3kPTwgKPy7ARBEA5VaWW4bUu0ZpBky2BAx/O55YLXsRgTKKnYw6J1v3PlWY/RKKMNDZKbcNmIh/EFqlm64a9IGV1bDGNAx7FkJDXi0mH3oZJVLFo/BYB2Tfpw6bD7yErNIzUhmzN7XkUwFKCwbGdMXRqmtUCnNbItfzWKorB+5yJa5nQDoHlOF9bvWATA5t0raPH3FL99KYrCjOXfcmaPK2jbpA+ZKU254szHUKu0R/25CcLpSIxMCcJJQNc2LfKzKtGAKs2Mf1s5BBVQoOCy76PSK74ggZ2VMeXIejXmc1rinrIV34YSArsqa3blC8Yurtp33ZRk0qJKNaFuED40UdcxA8+83Sj+IL4NpSTc0iMmv39XJQRCaJrVvGnVNk5A0h+46bFc2AbH56spf34O+UNKxFlEp7CyEjGVUzgxtW7UkxY5XXlm4uWkJ+bStkkferU5C6Pewubdy1FQePSDsVF5fAEvBWXbI7/nZXWM/KxR68hMbsreki0A9O94PkvWT+XPJZ9TULaDXYUbAAgpwZi6/LNuauveVViMCXj9HppktgOgRcOufDP9VSC8+cQFA2PbS6engipXKQ3TWkTCLEY7KfbMuPfurgiKM4lOQRX5gfquwilLdKYE4SQgqfYbRA4qSLKEEgyBLJH23mjY7wR32Rz71jHk8VN0w6+gljH2y8HQMxtJq6L4zt8PeG19h3R860tAkiIbTADoOzWg8qPleNcUIxvUaJokHvgG9u+n7X8/+7Cc3RLzqOZ4Zu5g2L/OxrepDOOgXCzntULbLJlglZdQlTduXlWiAdl48FPoi275DU3TRBJu6h43PlDkJP+Cr0l+diiG7lmR8OK7fkeVYSHx9l5x85U8Og1JLZP0YP+48VMz7j1ovY6UmL4hCEePVqPn1rFvsHXvSlZumc3KLbOYtvQrbjzvJYJKEFlS8eBlE5Gk6LbMoLNEfpbl6GnTISWILMkoisKrX99MeVUBXVueQf8O55GemMujH15wwPo0z+7Mhl1L0GmMNGnQLjKVsEVOVyocRWzdu4qyqgKaNTzwmlRlv4ZYpYrfVi761h35efXWuUxd/Bnrdy6ifZN+DOo8juYNu1DtdVHlLoub36S3YjLYou89FGTlllnkZrTBvs80xHd+vBe1SsNVZz1+wHoD/DT7HZZvnsHDlx94K/f+V1kOGHe0iHZWiEdM8xOEk4BvY0nk52CJm2CJC02TRDS5dggphJw+NFlWNFlW1KkmKt9ehH/r3x90+/SxvMsL8O+oIPW1M7Fe2h5Dr2yCFbHT+/al65COb2Mp3tWF6PbpTGmaJSHpVLh+24iuQzqSHLuIU9PQDhoZ35qaHQH9u6tQatlcQlLJGAc2Iu3Ns0h9ZTiKN0jhtb8AoLLqIve6/5/aOlJ1oU41o0oz4dtQ88yVYAj/zirUDSyEvAH2nP151NldSjCEf0sZmhz7EV//ZPdD+YWs93xTp7Qr3eOZVPHvo3r91e6JfFd2Pl+Wnskcx5P4Qs6Dpt9c/Ss/lF/EF6XDmVZ1H+5g8UHTC6eHLXtW8tPsd2iS2Z5z+t3AI1d8QXZqM5ZtnEaDpEaElCBur4PUhGxSE7JJsKTxzfRX2VO8KVLGjoJ1kZ99/mr2lGwhKzWP/NKtrN+xkBvPe4XRfa6hU7NBeLwOAJQD7L7avGFndhauZ8ue6Kl8FmMCDVKaMGPZN2SnNsOkt8bkNRvs2EzJbN27MhLm9jopLt9V63No07gXt17wBg9f/hkmvZXXvrmF0sp89DpT5N73/7N/RwrCHctPfn+K+Wt+iYSFQkF2FW6gQXKTWushHJhoc+uf6EwJwkmgauJKPPN34dtaRunTs9A0SkDXIR1Ntg1D34aUPTOL6mX5+HdVUvbcbLwrCyNf7CWDhmCBk0CxC9mqg6CCZ9o2AgVO3LN2UPFmeL694oudXgLhdVP+LWX4t5Sj71jTmZJkCX37dDwzdqDrGH9KiGzUYB7ZjIq3F1O9eC++TaWUPTML4nS8DkTXOpXkRweS8dn5dc5zqEJuP8HSmrexlova4vh8Fe4/t+LfXUn5q/MJuXyYhjZB1qnRd25AxbuLw898RwXlL8wlVOnFNLr5MavjyWK47W2a6s+qU9qWhnEMtr541K69sfpH1ld/TQ/z3QyxvUxlcAfzXc8fMP0e33wWu16ng/HfnGH7HwoBZjgeQjnQN1rhtKHTGPhtwUdMXfwZJZV7Wb11LgVl28nNaE1aYg4d8wYyftJ/2bBzMYVlO/h48uNs2rWMjKSa8/xmrfyeBWsnk1+6jY8nP4FGpaVL86EYdBZkScXiDVMorcxn7fb5jP/tMQACwfgvmhqmtcDnr2bt9vm0+Hu91D9aNOzKko1/0qJh7HopAEmSGNLlYibPn8CSDX+SX7qNCZP+iy8Qf4Q/ngbJTfi/EQ/xzLW/YDEm1DnfvgZ1Hsfk+RNYsXlm5Jl4/R76dzgPCHc4K50lhELxP4uE+ESbW//END9BOAmYzsyj4n+LCBa70HdpQNL9QyNT/xLv6UvFW4sofWQaii+ItkUyKS8MQ5VkDOcd2YzS/06j8KofafDDRdj+3YmK95aguP2os6zYr+9Kxavz8W0sjTuyorLrUWWEp0/8U+Y/dJ0z8MzZGTX9b3/2G7uDWqb0sekAWC5pF3c9V23U6eZDzlNXji9XUzVhOdnTrwDCUw0JKVS8t4RgqQdt8yRSXzojcv8Jd/Si8r0llD4xA8XpQ9s6lZRXRqCyiTNd9LK9zmk1kgGko3eo6DrPV7Q2XEIDbfjLZk/z3UyqvBpnsACzKj1u+qb6M8nVDQagt/lBvi0/j6LAStI07Y9avYSTT1ZqHv8+6wl+mfse3814A4sxgaFdL6VXm1EAXHbmI3wz7RXe/vEeAgEfOemtuPWC/0XtpNerzSimLv6MvSVbaNKgHbeNewu9zoReZ+LSM+7n17nvM2nehyRZMxjYcSyzV/7IjsJ1tGkcO5VYllXkZXVk0+5l5Ka3jIprkdOVP5d8TvOcLge8n2Hd/kUwFODzqc/h81fTt/05NEw99Jc/h9uRAhjR/XIkJL6Y+jxV7jKaZrbn9gvfwqgPf74sXj+F8b/9l6eu+SmyxbxQO9Hm1j9JOdm7g4JwihiS/0zc8H3PbBJOXqfKmil3sJiFrpcp9C9DK1tpb7iC+a7nGW3/FLMqnR/KL6SF/nxaGM5nnvMZVOgJ4WOHbwYqNDTVj6K94UokSWKlezy7fbM50/5+zHVWusezyjMhbh16mO6hiX54VJgnVMZ35ecx3PY2SeqaL4lflZ1FN9NtkQ/vfyhKiC/LRtLLfC8NdTXr3H6tuIoc3SDaGC45ksd0SMTW6PVvxgeOo1revmc8CcfHqbpmSrS5x87RanvFyJQgCIJQJyElyHTHfWglK8Nsr+NVKlngfBGF0AHzbPH+Sgv9BYywvc1e30KWuN8gTd2BDO3BD29uaRhHnn503DiNZIoJc4fC8+6NcvQZO3opMRK3L5/iIEg1hv3SG+TEU2IOvyAIJz/R5p4cRGdKEARBqJOiwHIqgts5J+FLDHJ4u/suppuY7rj/gHlMchodTVcDYDVks6H6W0oD62r9YNdIhvCUlDoKKuH1HzLRu1iqJA1BxR+TPvB3epUUnV5GS4jY9IIgCMebaHNPDqIzJQgnuH/W8QhCfSsPbMEkp0U+1AFS1G0Pmsesij7LRiMZCVH7AvPV7oms8XwaN66b+XYa6YZGhamk8FbR+38oBxU/6r/j4qbf70M/hC9uekE4FE9f+3N9V0E4BYg29+QgOlOCIAhCnUjIMdNLJA6+M6OKeNvV175UN08/mhzdwLhxejl2EbxRTgHAEyrFINeceVatlMVMKwHQSVZU6PGESqPCPaEy0jSdaq2fIAjCsSba3JOD2BpdEAT2jvsax9dr6pS28qNlFFz141G9ftUnK9hz3hfsHvEJpU/MIOQ4+Ja9zl82svfCr9l9xscU3zuFQJErEqcEQ1SOX8becV+xe+SnlD4584DlVX68nJIH/jyq93Iqs6sb4w4V4wnVHNZZElh3kByHTydbsagy4/7RSMaY9AY5EbPcgCJ/zVk65YHN+BU3KerWMeklSSJZ3ZKiQE16b6iSyuCOuOkF4Wi57+3wLn918dPsd3hs/NHdxOLXeR9w1/+Gc9PL/fjgl4dwV9dt841Nu5dzzfPd2FW44ZjEC7FEm3tyEJ0pQRBIe2cUplF12ybXMq4NKS+dcdSu7fxxPY6v15B4dx9SXx6Bf0cFZc/POWB6z/xdVLw+H9vVnUl98ywIhih56K/IORVVE5bj+GoN9v90Ie3NkSiBEMX3Tok5x8L5y0aqxi8/avdxOkhTdyRB1Zi5zqcpD2yh0L+CRa7X6rtaEc3157DKM4HdvnmUBjYwz/kcDbX9ManSAPArnqgvJc0N57Kx+ke2en+nPLCVOc4nSFTnkappV1+3IJwG7v+/j+nb/tw6pR3W7V/cPu7No3bt6cu+Yeriz7hsxMPcceHb7C3ZyseTH681n9dfzYRJ/0VR4m98cKTxQnyizT05iM6UIAio7Hpkfd1m/cpGzVE9T6nqy9VYL22HoXsW2hbJJN7TF8/MHQQK4p+i7vhqDaaRzTANboy2SSKJD/bHv7kU78rCcPy3a7H+X3uMgxujybGTeE8fAjsr8S7LByDk8lH6+Awq3liAOst61O7jdCBJEn0tjyEBv1dez1zn0zTRjwDCi47rW3P9eTTTn81857P8WXUHNlUOPUx3RuLXeb7ku/LzIr9na/vQyXgdy93v80flDcho6Gep/YulIBwJizEBnaZubahea8RssB+1a/+x6BPO7HEFbRr3IjejFZef+QhLN/5FaWX+QfN9P+N1bOaUYxYvxCfa3JODWDMlCKeBQLGL8pfm4V2Wj2zVYbuyI2XPzSHj0/NQZ1jYO+5rLOe3wjK2NaVPz0I2qFF8QdzTtyNpZMyjmmO9qhOSJFH50TI8s3eS/sGYmOtUfrSMqgnL49Yh8Z4+mEbkRYUFS90E9zrQta853E/bNBHJpMG7pijmoF4lpOBbW4x5TItImMqmR5Nrx7e6CE2OHcXlR9c6NRIv69Wos6x4Vxai79SAQL4TpTpA2vujqfpkJYrTdziP9LRUHSqnMridgZbnkKTwvP0S/1pkNOgkOwBnJ3wRSd/THHu21r7nm7QzXk474+VHrX6SJNHeeCXtjVfGjY93veaGc2huOOeo1UEQyh1FfPrH02zYuRij3sqYvtfx8W+P88R/vifZ1iDqDKqPJj2KTmPAH/CyZMOfqFUa+nU4lzF9rkOSJH6a/Q7LN8/g4ctjpwX+NPsdfpn7Xtw6XD7iEXq1HRUVVuksoaRiD82ya3Z1y05thl5rYsueFSTZ4h++vmHnEpZu/Isbzn2ZJz++9KjHCwcm2tyTg+hMCcIpTgmGKLl3KrJNR+obIwlVVlP2whwIHXhBqvOXjVguaE3aO6OoXriHitcXoOuQgb7LwQ+4s4xrg3l0/OmCklkbExYscQOgSo6ej61KNBAsdsWkDzm8KNWBA6aXLVrQyFF5lZBCsNhNqKIaCHfWkp+MPkxQqCuJmY6HaWv4P3J1g/EqVSx1v0VDbT9kSVXflROEehcKBXn921sx623cfckHOD0VfDL5SULKgXdTm7Xie4Z2vZQH/u9jVm+bx5d/vkCz7M60yu1+0GsN6/Yv+nc4L26cQWeOCatwhs/y2X+EyGZKotxRFLccr8/DhMmPcfHQezAZYkfyjzReqI1oc08GojMlCKc47/IC/NvLafDVBaiSwp2QhJt7UHLf1APmUaeZsP+nCwCabBuOb9bgW19ca2dKNmrAWPepB4o3AICkjf5QkDQqFF/sl48Dpefv9JJKxjiwEZUfLUPTJBF1hoWqiSsIlntQAmKu/pHSy3b6WR5jpftDVnsmopGMZGv70dF0TX1XTRBOCBt2LWFvyRaeufZX7ObwjmYXDrmLN7699YB5kqwZnNv/RgDSEnP4c/HnbM9fU2tnSq81otfGbgxwID5/+IWSRh39Ykut1hIIxh+h/2b6qzTOaEOHvAGUVO496vHCwYk29+QgOlOCcIrzbS5DlWaOdKQAdG1SD5ID1JnRbxBlo7ZOnZGqiSuomrgyblzCHb0wDW0SFSZpw03Q/h0nxR+Mu4brQOnxB5H+Tp9wU3dKn55FweXfg0rGOCAXfY+scEdPOGKZ2h5kanvUdzUE4YS0q3Ajidb0SEcKoGlm+4PmSUnIjvpdrzMRDAVqvdakeR/y2/yP4sZdOux+urceERWmUYfP8gkEojtOgYAPbZw1XOt2LGTZpmk8euWXca9xpPFC3Yg298QnOlOCcIqTVFLslD754OdUSJo40wdqP6YC0+gWGAY0ihunSow9WV2VEu7gBcs8UZ29YJknZiofgGzTIenVBMs8UeHBUg+6juH5/rJFR8pTQwg5faAoyBYdhdf+jKrzwUfVBEEQjpRKVsXsHPrPWpcDUatiX/TsX0Y8/TucR5cWQ+PGWY2JMWEJlvBLtEpXCbZ9OnuVrlLs5tgXbAvW/IbTU8F9b4fXXil/fwg88+mVjOx5FUXlu44o/sye8dfZCMLJRnSmBOEUp2mcQLDYRbDUHemw+NYVH5Nrqaw6VNa6n2SuSjKiamDBu7IQbV74hHff5jIUtx9t69gPd0mS0LZKwbuyEGPfHACCldX4d1RERttKn56JoXdDjP1yAQgUufBtKiXxnj5HeHfC8TSl8lYS1E3pYrqxvquCX/Gwwv0+O30zCSgektQt6WK8AZs6t76rJpxgMlOaUu4opNJZ02HZlr/6mFzLZLBhMtjqnN5mTibZnsmm3ctpmBbexGdX0Ua8PjeNM2O3pj63/01RHZ5yRxEvfnEN/xn9NE0z2xMMBY8oXqg/J1T7GnKxxP0/dvvmEsRPpqYHnU03RB0E/I9t3qnMdT7J2ISf0cqx6wLri+hMCcIpTtcxA02TREqfnoX9uq6EnD7KX50fjqzljenxYDm3JVXjl6HOsKBKMlD2whwMA3JRp4UbypDbj+LxRzqClnNbUvLfGWibJqLJS6TyrUVomyWhaxfeEVBlN1D53lLUqWZQy5Q9PwdDnxw0jWJPcBeEuljkeoXSwAZ6mx9ELyewyj2BP6vu4iz7RyfUB7pQ/5o37EJWSh4fTnqEsQNuxe118PmU5wCQqP/2dnCnC/l5zruk2DKxmZP5ePITdG4+hCRruP2s9rnx+tzYzMlYTYlYTTVfaGU5PGMh0ZIW6cQdabwgzHc9jyO4l4HWZ5DRssT1OrMc/2WY7dWodJ5QGYtdr9dTLQ9OnDMlCKc4SZJI/u9AJAmKrvuFsqdmYjqzWThOU/9NgPm8VpjPbknZs7Mouv338NlQd/aOxDu+XM3e82rm3Bv65GC/rguV7y2h6PpfQa0i+fGa3fmsV3ZE1z6N4rv/oPiOyeGzq+7re1zvSTh1BJRqtnv/pLPxetI07bGpGtLTfA8+xUmBf0l9V084wUiSxLVnP4eExNMTL+fDXx+md7vwMRKqONP5jrdBnS9kYMexjP/tv7z05XU0SG7Mv4Y/EIn/Y+En3PXm8HqsoXA6CSlB1JKBbqZbSVK3IEHdmBaG8ykOrCSo+KPSLnS+iF2VWz8VrYWk1GViriAIx9yQ/GeOSbnBcg++DSXou2dF5u571xRRdOtvZP32LyR1/XeoTgdTM2LP/ziaHut15Dtlbar+mbWeL3CHijHL6bQ2XkJj3RkAVAV3s9T1FkWBFQQVHxZVJh2M/yZLG+74/lB+Ia0NF7Pd+yclgXVYVdn0Nj/ITt90NlT/gIRMa8NFtDRcAISnmSSrW1Me3EyRfzlmVSadjNfSQNstEr/vNJR83yKWu9+jMrgDkyqd5vpzyNONQZIk/Iqbhc6X2OtfRFDxkappSxfTzVhVWTH3uNI9nlWeCXHvv4fpHproo79IBhQvRf4VpGjaopHC6/5CSpCvys6ii+lGmupHHvFzf3iuWM9X32Z84Dgq5VS5ythRsJY2jXtH2tute1fxwuf/4fXbZqGSxYSgY6X/VZZjfo0jaWdF+xrbvu7PEyplvvN5/IonamRqq/d31nm+opPxWv5y3H3UpvkdrbZX/KsWhFOdJFHy0DRs/9ce45DGhCq9VLy1CGP/XNGREiLKAptY5HqF3uYHSVK3ZK9/AfOcz5Ksbo1FzmRG1f0kqPM4w/YmKAqrPB8z3/kc5yR8g0oKv3Ff7v6AHua7sKoaMtvxX6ZU3UKWtjdn2F5nh3cay9zvkK3th1kVnlK0vvprWhsuobPpBrZWT2aG4wFG2j/Eqore3awysIMZjofoZLqODE0XygObWeh6BQmZPP1oVrg/pCK4jcHWF1FLOpa4/sd853MMs70Wc58tDePI04+O+ww0kikmTC3pIl9A/rHJ+xMhfKRrOsekF05vkiTx1g93M7LXVXRvORynp5Kvp71Cp2aDRUfqNCba1/jt674WOF9ks/cXtJKVodZXIuHuUAlLXW8x0PosAcVz4ALqkfiXLQinOJVdT/LjA6n8cBlVE1cgGTUY++di+/scKUEAcAULADDJaZhV6TRTjcEiZ6KX7ATx0kQ/kia64ejk8FqHloYL2OH7i+pQOSZVePOPhtr+ZGvDG33kaAeyyjOBrqZbUUs6WhkuYqVnPJXBHZEP+xR1G9oZLwOgo+k/7PUvYEv1pJgzVNZWf06ObiDN9OHpUhZVJp5QGes8X5GnH40rmI9aMmCW09HKZrqb74zcz/40kiEywnQ4CvxLWOZ6m5b6CyL3IQj/sBgTuO7s5/hx9ttMmvcheq2Jzs0Hc27/m+q7akI9Eu1r7Vroz6epfiRrPJ/xZ9UdjLR/iF62s8D5Ak31I0lSN6fQv/ywyj7WRGdKEE4Dhh7ZGHpk155QOG1laLuRpunE71U3YFU1JFPTg8a64ZGpFM30Z7PdO5XSwAYcwV2UBTcBoFBz/phFVTNlQi3p0cuJqKXw7o4qKXxQaIiaefCpmugdxJLUzakIboupW0VgGxXBrez0To+EKYQIESCo+GltuJRpjnv5tvxcUjXtyNL0ovEBppOsdk9kjefTuHHdzLfTSBd/q2mAXb7ZzHE8Tpa2Dx2MVx8wnXB6a9ukD22biN1DhRqifa29fbWpwzv09jI/wA/lF7DdOxWNZMQVKqCf4bED5jsRiM6UIAiCgFrSMcjyHCWBNezxz2ePbx4bqr9ngOUpUjSt+b3yRmTUZGv7kqntiQotfznujCpD3u8jpbbdyySizzNTCMWEhcOD5OnH0Fx/TkycjIpkTUvOTvicPb755PsXstIznk3enxhueyfyZeMfefrR5OgGxq2PXj7wjo9bqn9jgesFGuvOoLvpTiRJTJEVBKFuRPsav30NKB72+haSoe0WGdFSSzrMcgOqlUp2++bgCO7hm7IxkXsA+L58bK2ds+NJdKYEQRAEiv1r2OtfQHvjlaRo2tDB+G8mV17PLt8sQvipDO7ggsRfUEt6IHzeR9jh72FUFtgY+VlRFEoDG8jWxu68aFXl4AjuxqLKjIRt9f5BsX8l3c13st7zDRZVJrm6QeTqBlEZ3MkvFZdREdhCsqZVVFk62YoO6yHVc6d3BvNdz9Ncfw6djTfWegirIAjCvkT7Gp+ihJjtfIxe5gfI1Q0CwudOVYV20UI1lma60QTxRdKXBNYy1/kUQ22vRdW3vonOlCAIx0TRLb+haZpIwk3d67sqUVxTt1D2xEwyf74Y2RJ+qxZy+6n8YAmeGTsIeQJoWyaTcGN3NLn2+q3scaSW9KzxfIZOspKl7UNVcCdVwZ3k6Uahk2woBNnhnU6apgPlgU0sdb0FQBB/LSUf2B7/XDZ4vidD24XN1b/gChbQVBe7O14rw4VMrryWle7x5OqGUBXcyRLX6+T9PcffHSphffW3qCUDJjmNrdW/oZFMWFUND7tu//CFnCxwvUADTTdaGy6hWimPfL/RSMbIlx9BONG88Pl/yE5tzrjBd9R3VfB4nXz110us2DyTQNBP28a9GTf4DqympPqu2nEh2tf4NLKJxroRLHe/i0FORCtZWO5+D5OcRkNtP2QpupviDhUDYJYz0EjGI77+0SI6U4IgnDaCpW4qXlsQE17+6jx860tIfKg/Krueqo9XUHzn76SPPwfZrK2Hmh5/Ceom9DY/wCrPxyxzv4tettNSfwGNdcORJIn2hqtY7n4Pv+LGqsqik+k6FrteoyywAdthfqg21A5gt38Oy9xvY1c3ZpD1BUyqtJh0ieo8+lkeZ6X7I9Z4PkMv22mmP4e2hvDi6vbGKwko1cx2PI5PcZCgbsIAy9NHZevcfP8ifIqTvf4FfFd+XlRcZ+MNtDCcf8TXEIRT3ceTn6CofBc3n/8qarWOL6Y+zzs/3stdF79X31U7LkT7emBdTDexwv0BcxxP4FdcZGi7MtDyTExH6kQmzpkShBPEsTpnqr6ciCNTxQ9MRXH68K4ojIxMhaoD7Bk5keSnhmDoHj43Q/EF2TP6MxLv64uxf+5RufbJcM7U8bT/OSenM3HOVP07WudMnUhOlJGpUCjIx78/Qb/259K4QVsAlm+awZvf38Gbd8xDfZQOMz7Rz5k6nkT7WjfinClBEGI4f96A4/NVBIpdqNMtWC9th+mMpgD4d1dS8eYivCsKUXwB1JlW7Fd3xtA7/NZr77ivsV7SFvefW/GuLUHT0EriA/3xzNiO8/t1IEtYLmqLdVwbINxZ0rZOwb+5DO/yAtSZFmzXdcPQLf485upFe6h4bwmB7RWo0s2Yz22JeUwLJEki5PZT/tJcqhfuQfEF0bVNw35LdzRZtphyKj9aRtWE5XGvkXhPH0wj8uLGuX7fTDDfif26rhSv+KMmQoLkp4eia5NaEyaH18SEnD4EQRDqauby7/h94ceUOwpJsjXgzB5X0LPNWQAUlu3k62kvs2n3MvwBL6kJ2Zzd9wY65PUH4L63RzG8x+UsXPs72/JXk56Yw1VnPc6SDX8ybelXyLLMGd0uY1i3S4FwZ6lxg3bsKtrIxl1LSLFnMXbgrbRu1DNu3dZsm8/3M98gv3QbSdYMBnUeR/8O5yNJEtVeFxP/eJo12+bhD3rJy+rIhYPvIi0xdlTkp9nv8Mvc+CNKl494hF5tR0WFybKKy0c8Evm9wlnCzBXfkZfV8ah1pAShPonOlCCcInybSil/eR5JD/VH2zKF6gW7KXtmFtrWKagzrZTc9yfaZkmkvRmeM101YTllz86mwbfjkDThHX4q319Kwl29SWhoo/Sx6RTfMgl974akvjES97RtVL6zGGO/HNQZ4TeAjq/XYL2kHfYbuuGavJmS+6eS/tHZaLKjO0H+HRWUPPgn9uu7oe/SAN/mMspfnockS5hHt6Dyg6X4t5WT8uIZSHo1Ff9bSNmzc0h7/cyY+7SMa4N5dPO4z0A6wJS8YImbijcXkvLcMELu6Dnosk4d0wF0/rwBxRdE3ymjDk9eEAQBdhau59Mpz3D1qCdplNGGVVvnMH7Sf2ncoB2pCdm88d1t5KS14N5LxwMKv8x5jwmTH6NN48mRTsUPM9/k/0Y8RHpiLu/9dB/Pf/4fOjTtzz2XfMDi9VP4dsZrdGo+iGRb+I361MWfMqLHFVww6Hbmrf6ZN769jUev/JK0xJyouuWXbuPN7+9g7MDbaJXbg11FG/hsyjNIkkz/Dufx4+y32VuyhdvHvYlWo+fLv15iwuTHuPvi92Puc1i3f9G/w3kx4QAG3cGnfn3y+5PMWvE9Jr2NOy9659AfsiCcgERnShBOEYECJwCqNDPqdDPmMS1QZ1pQ2fQo3iCmkXmYRuShsoUXzFsuaIP7r20Eyz2oU8MfgIb+uRj7hj+EjQMbUTl+OQm39UTWqbFe2Iaqj5bh31kZ6Uzp2qZhu7wjAPZrulC9YDeuSRuxX9M1qm5Vn63COKgx5jEtAFBnWgmWunF8uRrz6BYEChxIBg3qDAuyWUvinb0JFMSfdiMbNWA8tLeZZS/MwXRWc7TNk6leln/QtNVL9lL51iIs41pH7lM4+obaXqnvKgjCUVVaGW5bEq0ZJNkyGNDxfFITsrAYE/AFvPRtdza92o7CbLADMLTrpSxa/wdVrlISreGDVjs3H0zHvAEAdGkxlJ/nvMvFQ+9Bq9FzRvf/46c575Bfui3SmWqa2YFRvf8DwLn9b2LV1jnMXvkj5w24OapukxdMoGvLYQzoGF7jl5qQRaWrhCkLJ9K/w3mUVO5BpzWQZM/EqDPzf2c8SGlV/Clteq0RvfbwFv8P6XIxfdudzW/zx/PyVzfwyBVfYDEe+EgC4fCI9vX4Ep0pQThF6LtlouuUQdH1v6BuaMPQIwvj8LzIjnXmc1rinrIV34YSArsq8W0qDWcM1iybVGfWdB4knRpVogFZp478DuH1RP/QtY1ezKptkYx/W0VM3fzbyvFvLcc9bZ8DA0MKSiCE4g9ivbQ9JfdMYc85n6Nrl46hdzam4fGn61VNXEHVxJVx4xLu6IVpaJOoMOekjQQKnCQ/Nihunn15Zu+g9LEZGPrmYLu6S63pBUEQ/tG6UU9a5HTlmYmXk56YS9smfejV5iyM+nC7OqDjWBasncyOgrUUlO1gZ+F6AEJKzcGsKfasyM9ajR6rKQmtJvwCTKMOt+WBYM3oetOsDlF1yE1vxZ6SLTF121u8hd3Fm1iyfmokLKSECAT9BIJ+zux5Ja99fQt3vjGUvKyOtG/aj15tRsWUAzBp3of8Nv+juHGXDruf7q1HHPAZZSQ1AuCqsx7nnrdHsnDtZAZ3ueiA6QXhZCA6U4JwipB1alKeH4ZvTRGe+bupnrsLx/frSXl6MNrWqRTd8CuoZYz9cjD0zEbSqii+8/eoMiTVfgeR1naejio6XgkqkfVGUYIK5jEtMJ/TMk4ZMrqWKWR8MZbq+bvwLNhD1UfLcP60gbR3RkU6c/8wjW6BYUCj+NVJNMSEuf/YQmB3FXtGfxYOCIU7j3vHfhXV+XL+tony5+dgGt6UhDt7I8W7D0EQhAPQavTcOvYNtu5dycots1m5ZRbTln7Fjee9RJMG7Xnm0ytQyxo6NhtIuyZ9Uau1vPLVDVFlqFTR7Z1cy+HQKjn6ENaQEoqbJ6gEGdBxLAM7XRATJ8sqGmW04elrfmLV1jms3jaXn+a8y4zl3/HA/30c6cz9o3+H8+jSIv5hqVZjYkyY1+dh9bY5tG7UKzKipdXoSbFl4vCUH/T+BOFkIDpTgnCK8K4ponrBbmxXdkLXJg3+3ZnC637BPXMHij+Ef0cFmb9eiqwP/7N3TY19e3mo/BtLIz8rioJvQ0lkmuC+NLl2Arsr0WTVHObn+mMz3pWFJN7ZG8fXa1BnWTEOaoxxUGP8OyoouOx7/FvK0bVKiSpLZdWhsur2v8QBJT7QD8VbM5rmW1dM2ZMzSX39TNSZ4fq4p2+n/LnZmM9tif3G7uJQ1r/9UH4hLfTnnxDbfzuDBfxYEX6DnaHpyiDrc4SUIEvdb7Ld+ycKQRrphtLReB0qqW7TQI80f3WonKWut8j3LyZEkDRNe7oYb8KoSmFy5fWUBtYBMDbh56O2jbBw4tqyZyWrt85lTN9raZLZnnP63cDTn1zOso3TCAQD5Jdu49VbZqD7u3OyYO3kcMYj2FR5x9+jW+FiFHYUrKVj3sCYdA2SGlFYvoPUhOxI2Lw1v7J513L+NfwBpi7+jNSEhnRtOYyuLYeRX7qdRz44n93Fm2ncoE1UWSaDDZMhdnOgAwkpQd776QGuOutxurYcBoTPnSos38mQrpcc6i2fUkQbe3DuUAmLXa9T4F+ChIqG2v50Ml2HRop9cbraPZG11V9wQeIvAMe1DRadKUE4RUh6NVWfrkS26DD0aYh/ZyX+nZWYRjdHtuogqOCZtg1dxwx8m0qpeHMRED1t71B55u7C8d1a9F0zcf28gWCBE9NZzWLSWS5sQ+E1P1P50TKMQxsT2FlJ+WsLMJ8dXkMVLHHj+HZteN1UmgnX5E1IJg2a7LqfpH4g6hRT1O/BYlc4PN2MbNQQcngpe2EO+m6ZWC9uR6jME0krmbSRzqdwYhhsfYEEVfjv2Ar3++zxzae/5QlCBJjnfAYZDZ1M19WprCPNP8vxXxSCDLA+jYyKpe63mO64j+G2dxhoeYZC/3JmOR+pvSDhlKDTGPhtwUeYDFY65A2goHQ7BWXb6dfhXMwGG6FQkCXrp9C8YRd2Fq7n62kvA+APHv7BrCs2z+SvpV/SOrcHM1d8T2llPn3anxOTbli3f/HUx5fx0+x36N5qBAVl2/ly6gv0/3sNVYWjmD+XfIFOYyDJms7cVT+h15pIT4x9OXaoDDozvdqO4rsZr2M1JWHSW/lu5uskWtPp1GzwEZcvHF0nUhs70/EQGsnMMOvrBJRq5rueZ5HzZXpZ7o9KVxHYxirPx6ikmk2ojmcbLL4lCMIpQtskkaQH+1M5YTkV7y5BZddhGdca0/CmSJKE7d+dqHhvCYrbjzrLiv36rlS8Oh/fxlI0OfbDuqZxYCM8c3ZR+fZiNE0SSHnxDNRpsW9/tHlJJD8xiMoPl1H12UpUdj2Wc1tivawDALYrO6J4A5Q+Np2Qw4e2aQIpTw+NrPc6lqoX70Vx+qhesIe9530ZFWe/oRuWsa2PeR2EutNKNnSyhaDiY2P1D/Q030eKJvzmvLPpRuY6nqSd8UrU0sH/7hxp/qrgLooCKzjLPiFyqGZP0718X3EB5cHNJKmbo5OP/GWAcPLISs3j32c9wS9z3+O7GW9gMSYwtOul9GozCkmSOLvv9Xw38w2qfS7SEhoyduBtfD71OXYWrCMjKfewrtm1xVBWbJrBt9NfIzOlKbde8D+S/t7MYl8N01pw3TnP89Pst5m8YDwWYyIDO41jVO+rARjT91p8gWre+/l+3NUOslLzuOn8VyPrvY7UhYPv5MdZb/H+zw9Q7XPRKrcHN5//GmqV+Bp6ojlR2lhvyIFZbkAH478xq8I76zbVjWSt54uodCElyDzn0ySrW1IerJlxo5Otx60NFn+LBeEUYhzYCOPA+OuJrJe2x3pp+6gw05CazRoafDk2Ks4ytnVMRyJ7+hVRv8t2PUkP9Y97vdRXoxchG3o1xNAr/knukk5Nwq09Sbg1/vkoR5O+Y0bUfRzsmZ3s5jiewq+4GGB9MhK2sfon1no+Y4z9c6qVcpa63iTfvxi/4sIop9LacDFN9SNjyprnfAZfyEl/6xORsEkV/yZL24d2xssB2OadymrPJ7iCBVhUWbQ1/B8NdfH/fkypvJWiwIq4cWPsn2NWxX4h3Fd5YDMBqknT1PydTlW3J0A15YHNpGgO3gk+0vx6KYEBlqewyjUbBkh/r1XxK86D5hVOXV1aDD3geqIze17JmT2vjArr3mp45Oenr/05Km5Il4sZ0uXiqLB3714c9bvFmMi/Rz1JPHde9G7U7+2b9qN9035x02rUOi4eeg8XD70nbvyR0mr0jB10G2MH3XZMyq8voo09dvl1soU+locivzuCe9jmnUK6pmNUujWeiehkOznaQSxxv3HQMo8V0ZkSBEE4RTXSDWGG40F8IWdkvvgO71/k6gYjSRLzHE+jEGKw9UXUkp6N1T+wyPUKmdqeGOTYheQHk+9bzCLXK3Qz3UaSugVF/hXMdT6DWjLQQNstJn0/y2OECMQtSyfVvh7DHSpGRoNOrkmrky3IaHCHio95fq1sJlMb3flf5/kajWQiSd2q1vyCIJz8RBt77PLv66+qu8j3L8Ykp9PBeHUkvDywmQ3V3zHC9h4F/qWHVObRJDpTgiAIp6h0TWe0kpnd/jk01p2BJ1RKcWAVXU23AJCp6UkDbXcsqvChxa0Nl7K++hscwd2H/EG/2jORlvqx5OrCayAsqkwqgztYV/1V3A/6I51+EcAbNT/+H7KkIUTta1CONP/+tnr/YF31V3Q13RJ3cbQgCKce0cYeu/z76mi8ljaKm+Xud/nTcSdn2t5HQmKe8xnaG/+NSZXKYTTbR43oTAmCcFj2n8YnnHhkSUWObgA7vNNprDuDHd5p2FWNsKvD0xrz9GPY6ZvO+upvqQruojywCQCF0MGKjasyuI2SwNqo+ewhguhle9z0f1XdQ7E//nlhZ9nHY1KlxY37hxodQcUXEx5S/Kiofa3dkebf18bqH1nseo1W+gtpph9zSHkF4XDtP41POP5EG3vs8u8rQR1ektDX8l++Lx/LXv8CygIb0cl28vTxz0M7nkRnShAE4RSWqx3ClKpb8YWc7PBNJ1c3BAhvozzNcTeuYDG5ukHk6UZhMzbkl8oraimxRohg1M/tjVeSre0blUYi/jk5PUx3EiT2gxbAICfXem2jnEIIP96QA50cXiTvDVURwo/xOOT/xyr3BFZ6xtPWcFlkXYMgCKcP0cYem/zVoUoK/EvI0Q6MHFdikBPRSla8oUq2eafgCZXyZWn4xW6IICH8fFk6goHWZ0nVtKv1GkeL6EwJwilu77ivsZzf6oTYlS6Q7yD/om8A0HfNJOX5YSiBEBVvLcQ9dStKUME0rAn267oiaVS1lBarauIKqj5fTdavNWeXHGn5wXIPFW8tonrRHggq6DqkY7+xO+pUE4XX/YJvXXjud+bPFx+X3QcPVbKmFSY5lS3eSZQG1tHXHN4mtjK4nQL/UkbZP8GqCm+iUOxfDYBC7Lk3Mmr8ijvye0gJRM17t6lycQbzI9NZANZ6vsSnOOlgvCqmPKMqJSbsUNjVTVCjpziwkixtbwCKAitRoydB3fSY5wdY5/mKlZ7xdDJeR0tD7GGognDf26MY3OWimI0k6kNJ5V7uf2c0AK0b9eSWsa8TDAX4etorLFw7mVAoSI/WZ3L+wFtRq+p2DtA/QkqIV7++ibaNex/SvVa5yvhm+ius2TafUChIs4aduHDwnSRY0nj6k8vZlh9uk165edpR21nwaBNt7LHJXx0qY47zcczWdJI14XWozmABXqUCu6oRQ6wvo+zT2dzpm8lqz0TOtL1bp87i0SQ6U4IgHHcpL56BtlkSAJUfLMEzbzfJTw5GCSiUPT0LSS1jvz52DvjB+LaWUzlhOZI2ulk70vJLH52OEgyR8sxQUMlUvLmQkvumkvbOKJKfHYp3eT6lD087pLoeb7m6wax0jydV3S7yAauVzEjI7PSG36Q6grtZ7HoNCE/D2F+yuhVbvX+w2zcHqyqHtZ7Po9K1NlzELMej2FS5NNB2oziwmhXuD+hquvmY3JNa0tFUfxaLXa+jlSwoKCxxvUEz/dmRefreUBUQf+3AkeavCu5mufs9murOIlc3BE+oLBKnlcxx1woIwongtnFv0jAtfMbfDzPfZOWWWdxw7osEggHGT3oUlUrD2IG31rm8QDDAp388xbrtC2jbuPch1eXdn+4lGApy8/mvIssqvp72Cq9/exsP/N/H3Hz+q2zYtYS3f7j7kMqsD6KNPfptrF3diHRNFxa6Xqa7+U4UJcRi12tkaLpFOlf70ksJSEhRnc3jRXSmBEE47mSrDtmiQ/EGcH6/nsT7+6JrE56/nXBTN0qfmIn1qk7Iuro1UUogRNkzs9C1SsG3ubwm/AjL9++qxLuigPQJ50TO4kq8ty/5F3yFf0sZ2ubJJ+Ro1P5ydUNY5fk4snAZwm8tu5vuYJXnY1Z7PsGkSqeZ/mw2e3+lLLAxZkFzrm4oJYH1zHWGD6jN04+hgbZ7JD5L25tupttY6/mCpe63MMopdDJeS1P9WcfsvjoYryageJnuuB8ZFbm6IbQ3/jsSP9PxMABDba8c9fy7fLMIEWCz9xc2e3+JiutneZxsbZ8jvDtBODbMehsmvRV/wMv0ZV9zxcj/0iQzvH31uMF38MEvDzGmz7VoNfpay9pbspWPJj2Kq7oSo+7QRo4Ky3awcddS/nvVN5Fztq448xHueWsku4s2kZPeEpP+5DirTbSxrxyT/H3MD7HU/RbTqu4hRIBsbV86G284Ojd3FInOlCCc4EqfnEnI5SPlqSGRMOeP66n6bBUZX5xPqLyaijcXUr1oDyGXH1WqCevF7TCf1Sy2rKdnoTh9JD9Z0+AXXPUjhj4NsV0RPrvBNXULVR+vIFjgRJ1txfqvDhgH5MatW9Etv+FdURA3LuPz81FnHPzD1be5DKU6gK59zXkXuvbpKNUB/JvL0LVOPWj+f1R9uhKVTY9xUCN8byw8auWr7HqSnx6COrtma1dJFZ67HXLGn4t+IrKqsrkkKXb0rIn+TJroz4wKa244N/Lz2Qk1C51Vkobu5tvpzu0HvE688o4llaQ9aJ36Wh5lrvPpY5K/teEiWhsuOvRKCyeVD355CI/XyY3nvRwJm7HsGyYvmMBT1/yEw13GV3+9zNrt8/F4nSRY0xjR/Qr6tj87pqyPJj2Ku9rBDee+GAl7bPzFdGjan9F9rgFgwdrJ/Dr3fUqr8klLaMjIXlfRufmQmLIAXvj8P2zcFX876Keu+YlkW4OD3tuuoo14/R6aZXeKhOVld8Lr97CraCNNMmtfc7Jp9zLysjowqvd/eOyjQ/v3YDEmcuN5r5CWWHP+oCSFp1+7vY5DKqu+iTb22OTXyVZ6mut29lkT/XCa6IfXnvAYEJ0pQTjBGYc0puTBPwk5fcjm8NC4+69tGIc0RpIkyp6eiRJUSHlpOJJOjfOHdZS/Mg9Dr2xUiYe2RXP14j2UvzyfhNt7omuRTPWKAsqemYVk1GDoFjt0nvT4IPAH45QUPtC3NsESN2hkVLaatLJFBxqZYLGrTnX2bS7D+e1a0t4fjXdJ/lEtX7boMPTMjgpzfLUGyaRF2/LI5qMLh8enVEYtaD6YVe4JNNadcdjXOpL83lBVZAqLcPLq3moEb35/B26vE6MufI7QovV/0K3VcCRJ4sNfHyGkhLh93FtoNXqmLf2Kz6Y8Q/umfbGakg7pWmu3z+ezKc9wydB7yc1ow8ZdS/ho0qPotSZaN4o90Py6s58nEIy/H7TFmFDr9codRahVGswGeyTMpLeiVmkodxTVqc79O5xXp3TxGPUW2jWJHsGduuhTDDozjTLaHHa5wpE5WdrY2hzPNlh0pgThBKfv0gDZrMUzZyemM5oSLHXjXVWI/ZYe4fie2ei7ZaHJCk+HsF7aHuc3awnsrjzkzlTVJyuxXNAa0+DGAJgzrQS2V+D4cnXczpTKemRT3BRvAEkbuxGEpFGh+GrfOlYJhCh7eha2qzujTjXjPcrl78/1x2YcX60m4daeyMZDW6AtHB1/Vt1JhqYrg6zP1Zq2k+l6VNLh/386kvzTHPdSGlh32NcWTgytcrtj0FlYsWk6PducRYWzhE27l3PRkPA6nnZN+tCmcW9SE8IvXc7seRV/LvmcwrKdh9yZmjTvQ4Z2uYRurcJv11MTssgv3cYfCz+J25kyGWo/ePVgfIFq1KrYtX1qlZZA8PiPvM9b8ytTFk3koqH3oNcaj/v1hbCTpY2tzfFsg0VnShBOcJJKxjiwEe5p2zCd0RT3tG1oGiWgbRx+82ge3QL3jO04v12Lf1cl/k2lACjB2N2CauPfVo53bTGOz1dFwpSggsoev9NUfPcfeFcWxo1Ln3AO6jTzwe9Nq47bqVH8QSR97bvtVX28HNmuxzyq+TEpf1/OH9dT/up8LBe2xTymxSHlFY6cWZUedxrNwRzph/SR5B9ue/OIri2cGGRZRdcWQ1m8fio925zFkvVTyExpSmZKeDey/h3PZ8n6qfy55HMKynawq3ADACEl/oj9wewp2cLWvav5feHHkbBgKHjAUaZXv76ZzbuXxY179KqvSbKmx437h1atizuyFQj66rRe6miavuwbPp/6HGd0/z8GdDz/uF5bCDvZ2tjaHM82WHSmBOEkYBzShKJbJhFyeHFP24ZxaHjkSFEUiu/+g2CxC+OgxphHN0fT0E7B5d/XuWwlGNrnZwXbVR0x9MmJSvPPOqH9JdzVG8Ub/0uDKqn2N4uqFCP4g4Qc3shGDsEqL/hDqJJNteZ3/bGFYKmH3cM/idQff5Ddwz8h5bmhR1z+PyonLKfqo2VYL+sQWVsmCMLpoVurEbzw+dW4qx0sWj+F7i3DI0eKovDq1zdTXlVA15Zn0L/DeaQn5vLoh3XfJj8UCuzzc5Axfa6lY7OBUWlkKf45Qv83/EH8gf3H48Ps5tq3hk6wpBEI+nBVV0U2enB5KgkE/djNdVuvejT8Muc9fprzDmf1ujqydkwQTiaiMyUIJwFdqxTUqSackzbhW1dC0iPhD9vA9gq8S/NJn3gumqzwlA/v6vgjRQCSRiboqXkTqQRCUWuHNLk2AvnOyJRBgKovVqE4fdj+3TmmPHVK3Tsk8WiaJiLp1XhXFmLoHV6E7FtZgKRXo21S+5z/1FdGoARqOoOeWTuo+mQFae+ODnek4IjKB3B8tZqqj5Zhv74rlgtOjXn8P5RfSAv9+bQwnLxvgOc5n8EXctLf+sRh5d9SPZkl7je4IPGX2hMDn5YOFLv0naYaN2hDoiWd2St/ZHv+Gq4ZE14wn1+6lfU7FvL4v7+LbKKwZc8KAJQ4EwPUsgavr+YcoUAwQFlVTXudkdSYksq9kSmDAH8s/AS318HZfa+PKS/BcmQdnqyUPHQaA5t2LaNDXn8gvKGETmMgOzXviMquqymLJvLTnHcYO/BWhna99Lhcsz6INvfUbnNFZ0oQThLGIU2oGr8MXbs01KnhToxk1oIs4Zm2HWloEwK7Kyl/dQEAii92xEjbKgXX71vwzNmJOseG47NV4K/pjFgvbkfJI9PQ5NgwdM/Cu7qIyg+WkvD3+qyjTdapMZ3VjPLXFiBbtCgKlL++EPM5LZD+3rY8WBV+8xpvfZY6PXoaoc+uB0mK6gweSfn+3ZVUvLsE06hmGAc3Jlha80VItujirscSjo/OxhuPKH+ObiCZ+2w7XJtzE75FKx182qpw6urWajg/z3mXplkdSLCEj1kw6CzIkorFG6bQo9WZFJbv4POpzwPEXXPUqEEb5q35leWbZpCR1IjfF0wguM/I1PDul/HOj/eQkdyINo16s2XPcn6Y9WZkfdbRptXo6dPubL788wVMeisKCl/++SIDOo5Fow63hy5PJXD467MOlr+wbCffzXiDvu3PoVvL4VQ6SyJxRr0VjVqc1XYiEW3ugYnOlCCcJIxDGlM1YTnGIY0jYeoUEwl39qJqwgqqPlmBKt2M+ZwWuH7diH9jKYbuWVFlmIY2wbeuhNKnZiKpZMxjWqDvUZPG0LshCbf3xPH5aireWoQ6xYT9uq6Yz4q/JulosP+nC4o3SPF9fyKpJIxDG2O7qmYUrPShvwBIfXXEcS/fM2snBEK4ft6I6+eNUXFJjw/C2DcnJo9wfGjlI/uQVUs61FLdN1AxyIlHdD3h5Na91XB+mfse3VvVbL2cYEnl0jPu59e57zNp3ockWTMY2HEss1f+yI7CdbRp3CuqjB6tz2R7/ho+/PVhVCo1AzqcH5WmQ15/Lhl2H78v/Jhvpr1CgiWN8wfcQt/25xyz+zq3/034A17e+O42ZFlF91YjOLtfzSjYWz/cBcCdF717WOUfLP+yTdMIhgLMWvE9s1ZET02/7pwX6Jg34LCuKRwbos09MElR4g1GC4JwvA3Jf6a+q3DMBfId5F/0DWnvjUabV7edroIV1ZQ9NZOU54YdkzodafnVy/Ipvm0ymT9ffNADfKdm3Hu4VayTx3rtjfrdF3KyxP0/dvvmAgoZmq50Nd2CTrZGTTkJKUFWej5iu3cq7lAJOslKjm4gnYzXI0sq3KESFjpfoiiwEoAMTRe6mm5BLyfgV9wsdL7EXv8igoqPVE1buphuxqrKiqnfSvd4VnkmxK17D9M9MeeDrHB/yG7fHEbaP4iE7fUtZKbjEc5L/I7FrlcjU062VE9mQ/V32FS57PHPpYV+LO2Ml7Gp+mfWej7HEyolQ9MFkyodv+Kip/neqCknzmABP1ZcRF/zf1nh+RBncC+J6jy6mW4nQd0EiJ5yoigh1ng+Y5P3F7yhSpLUzehiuokEddNan2c8D889+HlAwrE344OT61yjw1FSuZf73xnNQ5d9SnZa3V6QOdzlfPjrI9wy9rXDuuaR5t+wczEvfnEtr9w8DaP+4Ft197/q0A4NPhz7t7P7Em3uydPm/uNotb1iZEoQhOMuVOWN2hTiYKrGL8N0RtNjVpcjKT/4932ciGY6HsanOBhgeRK1pGe+8wUWuF6gn+WxqHRrq79gm/cPepnvxySnU+RfznzX86Sq29NQ149FzlcI4uMM6/8IEWC+83mWuN6it+V+Vrg/pCK4jcHWF1FLOpa4/sd853MMs8V+cWppGEeefnTcumqk2LV3ubohrPZ8QmVwJzZVeD3KDt9fZGl7oZFit/wvD24iRd2aEbZ3kFCx0zuDxa7X6Wq6hVRNWzZXT2Jd9Vc01h2407zC8yHdTLeikUwscD7PItcrDLO9HpNutWci66u/pbvpdhLUTVntmci0qnsZk/AZ66q/PujzFIT65qyujNp04mB+nvMuPduMPOxrHUl+lydcz5OFaHNP3zZXdKYEQTjuiu/4HX3XTFKer300yH5DNyTNsVubdCTll9wzBd+64qNcoyNXGdhBYWAZI2zvkKhuBkB38+3s9s2JSWtX5dLTfA9pmg4AmFXDWVv9JZXB7UA/nKF8rKpszKoMVJKWPpaH8CvhtWOuYD5qyYBZTkcrm+luvhNXsCBunTSSIe4H8oHYVA1JVOWx0zuNtsbLCCp+dvlm09v8wAHztDH+KzI1ZK7zKZrqRtJUH/4i18l0LUX+FQe9ZhvDJZHn0NxwHgucL8SkURSFjdU/0sZwCQ114UX7XU03s0L6EK9SVevzFIT69vKX19O6UU9uGRv7pXV/Fwy6HbXq8LevPpL8r31zC9vyVx/2tY8n0eae3m2u6EwJgnDcqDMsZE+/4pDyHMuO1JGWn/bWWUexJkdPRXAbEioSVDUjbonqZpEP+X1laXtT6F/OMtc7VAV3URHcijOUj0J4Y5L2xn8z2/EoX5eNIUPTiSxtb3J1QwFobbiUaY57+bb8XFI17cjS9KLxflNH/rHaPZE1nk/jxnUz306jv8vcV65uCFu8v9HWeBl7/QuQUZOh6Rq3DBXaqDn25YHNNNVH//9J1rTGrzjj5gewyDUHU2skEyECMWm8SiXVShlJ6pqzxtSSgc6mGwAwapMP+jwFob4k2xrw7t2LDynPkXSkjjT/ff8af0TXPp5Em3t6t7miMyUIgnCKkaW6N+2r3BNYX/0NjXUjyNb1pYP6P8xyPBqJz9L25OyEr9jjm8te/0IWu15nm3cqQ2wvkaxpydkJn7PHN598/0JWesazyfsTw23vxCw0ztOPJkc3kHj0cvxt6nN1g1nmfofKwHZ2eKfRUDvggPcm73f4oyypQIn+MJWIf15aTZ7adw+T//7YVIi/3Li25ykIwqlHtLmnd5sb/yQ4QRBOeHvHfY3j6zX1XY0TSunTsyh54M8jLieQ72DXgI/wbSo9CrU6/qyqhigEKQ9ujYQV+9fwXflYAkp1VNoN1d/T0XgNnU3X01h3BiY5DXeoCP7+4Frh/hBncA9N9CPoa3mEvpb/UhhYhidUxnrPNxT5V5CrG0RP870Ms71BZXAHFYEtMXXSyVYsqsy4fzRS/AOeDXISaZoO7PDNYK9/AY10Q+r8DGyqxpQGNkSFlQTW1Tn/gWhlM3opgfLApkhYUPHzXflYivwra32ewqnjvrdHMXXxZ/VdjSPy0aRH+d93dxx2/rmrfuaWVwfUOf1/nuvCsk3TD/t6JyrR5p7eba4YmRIE4ZSRcFP3+KdlnmZsqoY00HRnofNFuppvQUJmset1UtVtUUv6qLQ6ycpe/0LSNB3xKy5WuifgV1wElfDhzlXBnez1zaeb+Xa0koXt3qmY5HT0kh13qIT11d+ilgyY5DS2Vv+GRjJh/Xvx8tGQqx3CYtfr6GQbyerWdc7X0jCWOY7HSVQ3I0XTlm3ePygJrMGqyq49c61lX8Bqz6eYVQ2wqhqyxvMpMmoS1c1rfZ6CcCIZN/jOI2ozu7QYSpvGveuc/vnrJ2Osw8YXJxvR5p7eba7oTAmCcMqQzeKQx3/0NN/HYtdrTK26AxVqMrW96Gy8IU66e1noeoVfK65EJ9vJ1vamiXwmZcHwuVrdTLexyPUa06ruIaB4SVa3YoDlKSRJpr3xSgJKNbMdj+NTHCSomzDA8vQRn0eyr4bafixyvUKubjCSdPApI/vK1vahg/E/rPB8iM9VRQNtd7I0vSNTRo5EC/3YyBbFfsVFsqY1A63PopZ0tT5PQTiRGHVH9m9Vq9Gj1ehrT/g3mzn5iK53IhNt7unb5opzpgThBBHvnKmQw0vFm4vwzNkJioK+Wxb2W3qgsurYO+5rLOe3wjK2NUogROX4ZbinbCVY4kK26jAOaoT9+m5IKplgiZuyF+fiXVkASOi7NCDh1h6oEgyE3H7KX5pL9cI9KL4gurZp2G/pjiYr9sT6yo+WUTVhedz6J97TB9OIvOj0HyzFM2cn6R+eHQnzLNhN6SPTaPDdhchGDY6v1+D4di2himo0jRKw/acz+o4ZQHjaHsEQgT0O/LsqSXp4ALJeTcWbC/FvLUeyaDENbYLt6s5IKpnSp2ehOH0kPzkYgOoVBVS+uxj/5jLkRAOWcW2wnN0SgMCeKireWoR3RXgnJH2vhiTc2A3Zoos5D0vxB6n6bBWuyZsIlnjQNk3Edm0X9O3TD1hPQ7dM9nW8z5k63RX6l2OUU7Goas4R+avqblLUrWlrvKweaxZLnDNV/w50zpS72sFX015ixaaZKCi0adSTi4bcjclg4763RzG4y0UM6XIxwVCAn2a/w8K1kyl3FmHS2+jachgXDAwfhlvhKGbiH0+xcddSJEmiZW53Lh5yD1ZTItVeFxP/eJo12+bhD3rJy+rIhYPvIi0xdrThp9nv8Mvc9+LW9fIRj9Cr7aiosB9nvcXyzTN45IovImGrt87l7R/v5oUb/uDzqc/hrnZww7kvMnfVz/y55AsaJDdmxeaZDO16CaN6/4eZy79j8oIJVLpKaJXbnSRbAzxeJ1ec+ShzV/3Ml3+9yKu3TI+cY3Xt2c/x46y3KK7YTcO0Flw67H6yUsOfDf95rkvkQN6QEmLy/PHMXPEdTk8FOWmtuHDInWSnNqv1ecZT3+dMne5Opjb3H+KcKUE4DZQ8Mo1QlZfkpwYj6TWUPT+H8udnk/z44Kh0ji9X4/59M4kP9EedZsK7ooCy5+aga5+OsV8u5S/PQ/EFSXvzLJRAiLLn5lDx5iKSHuhH5QdL8W8rJ+XFM5D0air+t5CyZ+eQ9vqZMfWxjGuDeXT8wx6lOKNCxiGNqfpkBf4dFWhy7AC4/9qGoXdDZKMG5y8bcHy1moTbe6HOtuGZs5Piu6eQ9s4otI3DC2Tdf24l8b6+aBonos60kD/ua0xn5pH0yAACex2UPDINdbYN88joXZP8uyspuesPTGe3IPHevvg3lVL69GzUKSZ07dIovGkSulYppLw6AsXpp/zVeZQ+NiPudu3lr82net7ucD0zLTh/2kDJ3X+QPv4c1BmWuPUU6tfuvxdv9zDdhUFOYq9/PkX+FXQyXlffVRNOIm//cDeu6kpuPO8ltBoDH09+go8nP8F15zwfle6PhZ8wf80krhz5GEm2DDbuWsrHvz1Os+xOdGo2iE+nPIM/4OW+f40nGAwwYfLjfD3tZa4663F+nP02e0u2cPu4N9Fq9Hz510tMmPwYd1/8fkx9hnX7F/07nBe3roY4o0zdWo3g13kfkF+6nYykXAAWrf+DDk37o9fGrpvZVbSBJpntePCyiahkNUs2TOWLP1/g4qF30zSzA7NX/siURRPpcZCzo36c9RYXD70Hg87Mx5Of4NMpz3DPJR/EpJs070P+XPw5l55xP9mpzZg070Ne+/pmnrrmJ6Yu/vSgz1M48ZzOba7oTAnCCcq/owLv0nzS3h2Ftll4akTiHT3xzNkVk1aTayfx3r7oO4RHStQZFqq+WI1/ewX0g0CBA3WWDXW6GUmnJumh/iie8HziQIEDyaBBnWFBNmtJvLM3gYL4b2llowaMdd/qVpNjR9MsCff07dgu64DiD+KZvZOkB8NnP1R9shLbvztj6BmeU60Z1wbfmiKc364l8a7wPHx1lg3TsPB2s/8ckqtKNKBKN6POsJDywjBU9tizNFy/bkSdZSXh+m7hsrNthBw+kCVcU7ZAIETiA/2QDeH7SXqwPwVX/IBvfQmyrWZXpJDTh+vXTSQ90A9Dr3A9E27qjm9NEY4vV5Nwa8+Yegr1r73xCvwuNzMdD+FXXNhUjehneRy7ulF9V004SeSXbmP9zkU88H8TyUkPb8t86bD7WbF5RkzajKTGXH7mIzRv2BkIb0P+x8KP2VuylU7NBlFauZfUxIYk2xqgUeu4etSTVPtcAJRU7kGnNZBkz8SoM/N/ZzxIaVX8ERC91hi3E3QgGUm5NExrwZL1Uzir99UEgn6Wb5rOVWc9ccA8I3teFZmO98GvD9Gn3Rj6tDsbgPMH3sLGXUsOes0RPa6gecMuAAzufCEfT469lqIoTF/2NWf2vILOzcMvBy8acjc/zn4Ll6ey1ucpnHhO5zZXdKYE4QTl31YOKglN06RImLZZcqRjtS9D74ZULy+g4p1FBHZW4dtaRjDfCcHwLF7bVZ0pfXQae8Z8jq5zBobeDTENbQKA9dL2lNwzhT3nfI6uXTqG3tmYhufFXAOgauIKqiaujBuXcEevSJn7Mg1pjHPSJmyXdaB6wW4ktYS+ayYht59goZPyF+dS/vK8SHolEELbMiXyuzq95m2ryqrDekk7Kt5cRNXnq9F3z8Q4qDG6Finsz7+tAm2L6GdlHhP+QlT28jw0jRMiHSkATaMEJLMW//ZydH9P3wPw76qEkIK2dWpUWdo2qfg3l8Wtp1D/1JKBHuY767sawklsT/EWZFlFdlrNqHdOeotIx2pfHfL6s2HnEr6d/hoFZTvYU7yZkso9hEJBAM7uez3v/HQvt70+mJY53WjftD89WodH/8/seSWvfX0Ld74xlLysjrRv2o9ebUbFXAPCozm/zf8obtylw+6ne+sRMeHdWw1nzqqfOKv31azeOgeVrKZVbo+4ZWjUuqh1TbsKN9K3/TlRaZpktsftjf/CDSA1oWZ6ol5nJhiKPTvI6amgylVKbnrNBgc6rYELBt0OQAfLwZ+ncOI5ndtc0Zk6RrxKgP9n77yjoyqbOPzc7T2dlkBC7713CB3pKoKgIopiRUAFC1IUQRFFQIoiBsUPqaIooIIU6YQWCDX0DiFte//+WNiw7IaOAbzPOXtO9q1zb2Czc9+Z+SlvQXdA5MHjXv+OBdnNKxdkz9qJcX4q2valUTeJJ+wFn/N0BXWDohSe1x3rxpPYtpwia+JmLCuOUODztijLx1D4p8exbTqJdfNpcr7bgenXAxSc3hGJMvD6tJ3KoW4W+imTNDK00rqmRQmypiXjPJaFZdUxNM2LI8gkeG2+P7ARQxqhKBvo9AgKyVU/B8bHhz1fE03bUtg2nMS66RTpQ//C8Ew1wp6pFriGTJJnZdRr1/Tj9eL1eG9urMfre91onIiIyAOJTHrzn++/rf+GFcn/o2HlTtQo05xuTV9j+i9D/P1VSjVmbP/fSTm8ltQjG5m78jO27F3OoB5TKV64EmNe/JXdR9az5+gGfl3/NWt2LuK9p78PKu7QtNqj1CoXLLYKYNBEhmyvXb4tC1ZP5Ez6EZL3/0Wtcq3yvLZrRXalEim3mlovvwmhXunlMXlpB93ofoqI3E+I3/bvEUpBRtOzo/PbDJF7yJrC793T9WVFw8DtxXk4A0Vp3+mUPfUCl4avotDswJh506K9hPevha6DL5/JY3fhumD292d/ux1V/aLo2pVG16401s2nSB/yF+4MK5aVR5DFGdAklkCTWALn8SzOPfMzzsOZKCsEnvhIDUqkhkBhwBshjdKgrFYIy+qjWDedJOYT3xcBiU6BJEqN+4IZeYsS/vFZU7ciLahF361C0FruDCs53+8krF9N9N0roe9eiazpyVhWHglypmRFw7BtOR3QljnBdwImLxWJ5Y80PFan/3TKcTgDr9mJvFhg4Q1ZrAFkEuypFwJOn+x7LqAoE4WIiMjDScHIBDweN6cvHKJoQd9n6+HTKUz/ZSgf9lsUMPbv7XN5tNnr/lMch9NGRs45f/8v/0ylcsnGNKzciYaVO7HnyAYmLnidHPMltuz7gwIRxahdvjW1y7fm7KVjDP/2MU5dTKNEkUoB+2jVYWjVwcWBrke4LpqyxWqx7cAKUg6vY8DjE296bmxMKY6dTaVBpQ7+tmPnUgNOn24HjVKHQRvFifP7KVO0BgAut5N3p3emX8fRN7yfIiL3E6IzJSJynyKPD0dVL47M8RsIf6MegkQgc+JmFJULIlEF/teVGFTYNp9CWa0wXouD7KSdeM1OvA5fSITzYTa/5wAA8m5JREFURDbWzaeIGFQfiU6JZeURpIV0SMJVuNMtGBfu9eVNFdRiXn4IQStHXvTuaYFoWpUka+ImJGGqgHA5w5NVyJ65HWm0BmXFAlj+OY5xfirRY0M/eZUYlFjXncBjcWJ4qipeqxNb8mkU5YNDH3VdymFauJesb7ahbVcK54FLmH4/RPRHiSirFiLn+11kfLwWw7PV8ZqdZE7YiKJ8DIqKBXCfM+XuqZKh61ae7KlbkajlyOIMmH7Zj/Nopj+vS0RE5OGjcFQClUo05Ic/RvNkqyEIgpSfVn5GqbiqKK85MdKqw9hzZANli9XCajezZP10bA4zLrcDgLMZx9h9ZD29W7+DRmVgy77lRIUVQaeJIMt4kZXbfkIpVxNlKMSG3b+iUmgpFBl/166lboW2/LTiM3SacEoUqXLT81rV7sU3S94jvlB5SsVWY1PqUg6fTqFgxJ3b1qpWL5Zt+o6Y8DgKRSawbNN3SKUy4guVv+H9FBG5nxCdKRGR+5jIoY3JmrSZi4P+QJBJUDcsRvgrdUKMa0TmhI2c77sYSbgKdaNiaNuXxnHwEgARg+qTNXEz6W//hdfuQlGxADFjWiJIBML6Vsdrd3Fp1Go8RgeKUhHEjGmFRH9rJ1DXQ9MknqwvNqJpWSJAt0LXrTxeu4vsGdtxZ1iRxeqJer9JUFnxKwgyCdFjWpI1eQvnX/gVpBLUDYsS8Vpw/L+soM43dnoyxnl7kBXUETGoPuq6cQDEjGtN1uQtXHjpNwSlDHXjeML71wqpqxHeryaCIJAxbj1eswN52WgKjG/rPzHMb1x2r1he+wHGZfciU968novIv8ez7Ucyd+U4Pp/7MlKpjKqlmtA9cXCIcSP48c+xjPyuBzp1ONVLN6NR5c4cP7cPgN6t3+GnFeP4csHrOJw2ShapwmuPfoFEkNC5cX8cLhvfLHkXi81IXIHSvPbYl2hUd68qaI0yifzvr0+oU77NLWkHVSvdjG5NX+OXf6ZhtmVTqXgDqpZqivQWQiDzolXtXtgcFmb/8TE2h5kSRSrz+mMTUchVN7yf+YH4Ofvwcbc+e0WdqXuIGOb3cHO3w/xC6UyJPDzca50pERGR2ycvnan/OgdOJBNpKERMeJy/7cv5r1GySBU6NOyXj5YF82/oTImIhOLmM9xFRERERERERET+M6Sk/cPkhQM5fDqF9OwzrN4xn4Mnt1OjrFieXETkCmKY3wOKO92CY/cF1M0Tbmr8uWbfE/5hM1SNi5E1Zj1ek4OI0c3vrZH/EtYVR8j+aB0FlvRAog8WjgXwuj2Yf9iNZVkaXpMDZYOiGF6v7Q9l87o8nG/3P3B6AuZFftUORcXgstsiIiJ5k5iYyNNPP02fPn2YNGkSK1as4Jdffslvs24bl8tFtWrVcDqdAe1z586lWrVqIecsW7aMiRMncvbsWUqVKsXbb79NnTrBIboiIvcznRr1x+owM+XnN7E5zBSJLsnLXT+jSHSwDIaIyH8V0Zl6QMmZsBlBq7hpZ+phxX3JSs7ELTccZ5qVgmXBPgyD6yEvFYEpKYXMoX8TObktgiDgPpUDTg/Rc7oiKHL/W1wt3ioiIvLf5NixYzidTlasWIFKlVt4IDw8POT4lJQU3nrrLUaOHEnt2rWZN28eL774IsuXL6dgwYL/ktUiIneOUqHm6bbv57cZIiL3NWKY34PKA5zp5nV7sK09zqXXlt/xWjmfb0RWPOKG4ywL96F7ujLqFsWRxYcTNqQBrhPZOHb4Sq26jmUhidEgK6xHGqX2v25F60lEROT+ZMOGDbzwwgucOXPmtuYfOnSIQoUKUbRoUWJiYvwvuTy0ns7q1aspU6YMjz76KMWKFWPQoEE4HA527tx5B1chIiIiInI/Ip5M5RPnmn2PYVA9LIv24zprRFGpAIZB9ZDF+hIoPVYnxqnbsK0+Dl4viioF0L9aB1lhHVlj1mNffxKACzvPUWDuo7hO5WCckoxj13m8DjeyWAO6ftVRNSx6y7Z5XR5MSbuwLkvDa3UhLxeF/uVayEv5BAEvDfgDRbWCuI5kYd9yGoleifbJimi7lb/uuh6zA+vSNCyL9uPJsaNuVwoA43c7Mc9KCTnHMKQBmsvjrsX6x2HcZ03oX6pF5q7zee+bZfPpB11VkltQyZDFGXCmXEBZozDOo1nIit2adoeIyP3M3LlzmTFjBmfPniUuLo7+/fvTpUsXACwWC5988gnLly/H6/VSs2ZN3nvvPeLifEnmiYmJ9OzZkxUrVrBv3z7KlSvHsGHDqFy5MgDp6emMGTOG9evXYzKZKFSoEC+++CKPP/74bdmalJTE999/T0ZGBmXKlGHw4MHUrVsXgKFDh6JWq7Hb7SxfvhyFQsETTzzBG2+8cd2qZA6Hg19//ZVZs2Zx4sQJOnToQHh4OIsWLeKdd94JOefVV1/ltddeC2o/dOgQJUqUCDEjNBEREaSlpZGcnEzNmjVZtGgRgiBQpkyZm17jXiFWDhQR+e9i97pQCuJX/7uNeEfzEeOMHRjeqIMsIRzj1G1kDllBdFJnBJmEnPGbcF+0EPFJCwSVDPNPe8gc9CfRSZ0xvFYbT6YViVaOYUBdvF4vme/8jbxMJFFT2gNeTLNSyP5kA8qFjyHIpbdklylpF/Z1Jwl7vzHSKDXWZWlkDPiD6B+6II1UA2D+3x70/Wuif6EG1mVpGCdtRVknFllcsDaR66wRy6L9WJemIY3RoO1REVXrEn6xVO0TFdF0KhvSFoku9JNfd7qFnCnJRH7aEo/FGXLMFQS9AuQS3BdzRWy9Hi/ui2Y8WTafjUez8NpcXBrwB+4T2cgSwtG9UCOkfpGIyP3O3r17GTFiBOPHj6dq1aqsWbOGoUOHUq1aNRISEhg2bBjnz5/nm2++Qa1WM2PGDJ555hmWLl2KUukLbf3qq6946623+Pjjj/nmm2947rnn+PPPPwkPD2fIkCG43W6SkpJQq9X8+OOPjBw5kubNmxMdfWv/Z+bNm8d3333HyJEjKV68OCtXruT5559n4cKFfudj/vz5PPvssyxcuJB//vmH0aNHU7duXRo0aBC0Xnp6OnPmzGHOnDmoVCp69OjB448/TkSE7wS7ffv2NG7cOKQtGo0mZHtaWhpWq5WnnnqKI0eOULJkSd58802qVAmt1/PYY4+xceNGevXqhVQqxev18tlnn1G8ePFbujf3AplSYFSD2zuhe1gRy10/PDQ4Myq/Tbiv2VDkg/w24aFEdKbyEe1j5VEn+v64hr3TkIuPL8Cx7SzSYgZsK48S81M3pAV1vv63G3DxsQXY1h5H3aqEz0FSyJCEq/DaXGgeKY26XUkkYb54fm33Ctj+PoYn04a0gPambfLaXZjn7SVyXEsUVX2x/foXa2Lfdg7r74fQPeX78qCoXADto76TKN1z1TDP24tzf3pIZypzoE/bKPyDJijrBusHSTRy0IR2mvIi57ONaDqURl42CvuO66uiC1IJquYJmL7bhbxkJNLCOkyzd+PJtOF1+QpOuI5ng8eLYUAdJDoFliUHyRz4J1EzOoS8pruNw+sSS2c/5Di8LhT/0hPB06dPAxAbG0tsbCxPPvkk8fHxREZGcvLkSX7//Xf+/vtvihTxfYn8+OOPadKkCX/88QedOnUCfE5Hr169ABg5ciRr1qxh6dKlPPnkkzRr1owmTZoQH+8T7uzfvz+zZs3i2LFjt+xMTZs2jYEDB9KsWTMA+vbty86dO/n+++/56KOPAChSpAiDB/u0fYoXL86sWbNISUkJ6UwNHDiQPXv2MGLECDp06IBUGvgwSaVSBeQ93QxpaWm43W6GDx+OXq9n7ty5PP300/zyyy/+e3A16enpZGVlMWLECKpUqcKSJUsYNmwYpUqVomzZ0A+ORPKPnAtusaz2Q8BFh1jeXiR/EJ2pfEReOTfsTBqpRlpQi+toJl63F7yQ/syvAeO9DjeuE9lB6wgqGZquZbH+dRTngUu4T2bjPJThm+O+teQq12kjONxkDlkZuLfTjSs+NwxOepWDIUglCEopuELvpe1RAdOcVLLHbUDTqSzqjqWRRqj9/abZuzHP3h1yrmFwPdStAsNrLEsP4T5nInxUs5u+LsNrtckes570Pr+AVIKqWTzKerEIl5246K8fwevx+k/LDGWjcO5Nx/r7IfQv1rzpfW6Xf+tLtkj+8W/+jhs3bkz9+vXp3r07JUqUoFmzZnTt2hWDwcC2bdvwer088sgjAXNsNhtHjhzxv69Vq5b/Z6VSSZkyZTh06BAAPXv2ZPny5Xz//fccPXqUvXv3AuB2u2/JTrPZzOnTp/nggw8YOXKkv93pdAac+hQrVixgnk6nw+VyhVyzZ8+efPXVV4wePZoDBw7Qq1cvYmNzH+L8+uuvDB8+POTcF198kf79+we1L1q0CI/H4z+5qlSpErt27WL+/Pm8+eabQePff/99atSoQc+ePQGoWLEie/fu5ZtvvuGzzz7L63aI5BMTuuQdJn6/ca9P0cSTHRGRW0f8BpePCNLAuHWv2wsSCbg9IBGI+qYDXBPaLtEFl/72WJ1kvLIMZFJUTYqhrB+HoJCQ+eaKWzfqsvMV8UkLJFHqgC5BnXt6FCp0MC/9Z02Xcqg7lsG29gSW+Xsx/ZCCKjEB7aPlkZeJQtOpDKpmwU93ASSR6qA2259HcJ0ycqHTXN++Ht++Fx9fENL5ApDolUR8nIjH5AAvSPQKLvX/HUVN3x8mQSkLuNWCICCLD8N90RLSLhGR+xmVSsW3337Ljh07WLNmDX///TezZ89m2rRpuN1upFIpixYtQiIJLLCi1+c+nb/2RMfj8fhD1p5//nnOnj1Lhw4d6NGjByVKlAhyzm4Gj8d3Mvzxxx/787GuoFAoQv58hbw+b9q3b0/79u1Zu3Yts2bNonXr1jRv3pynnnqKunXrkpiYSNWqVUPODQsLnTd57UmWIAiUKFGCc+dCn4rv2rWLRx99NKCtUqVKbN++PeR4EREREZEHF7FUWT7iPJjh/9mdbsGTbkFWMgJZQjh4vHhMDmRxBmRxBqQFtBinbcN5JNM34apv/o6d53EdzyZqYht0vSujahDnzwW61bJ/0jg9SAXcGVb/3rI4A6YfduPYdf1wuushSCWomycQNaU9kRPagN3Npf5LAZAYlAF7Xf2ShAj/C3uvMdFJnYia0YGoGR0Ie6s+AJGT2qDMo+BG1ph12NYeR6JTINErcF8w4zyUgaJ6Ibx2Fxe6zMO66ph/vNftwXk4E1m8WJRC5MFjx44dTJw4kRo1ajBw4ECWLFlC+fLl+fPPPylZsiRutxuj0Uh8fDzx8fEULlyYTz/9lAMHDvjXSE1N9f9ss9k4ePAgZcuWJS0tjY0bNzJ9+nRee+01WrduTU5ODpC3g5MXer2emJgYzp4967clPj6en376iRUrbuNh0FU0adKEb7/9lsWLFxMWFka/fv04ffo0Op0uYK+rX6FKndtsNurXr8/SpUv9bW63m/3791OyZGitnQIFCgTcS/CFChYteusFgR52Fmf2YL91wU2NTbEksTTr+bu6/x7LbBZlPMbcS+1ZbxyNw2O67vg02+8szuzJT5fasirnHSzui3fVHpG8yeyxGOuC/Tc11pKUQtbzS2888BawzN5DxmOLuNR+LsbR630PZ6+D7fc0Mnsu5lLbn8h5Z5X4cPYhRjyZykfMs3cji9MjKaDF+FUysuLhKKoVRJBKUDYuRvbY9RjeqIs0WoNp1i4cKecxDKwH+E6J3OdMuC9akBiU4PZiXXUMZfVCOA9lYJyyzbeJwxNyb0+WDfvm0wFtgk6BomIMmm7lMH61FUEpQxYfhuWXA9j+Por2iQp35boVFWNQVGyK+9z1/2jlhTQmMEn8ygeUtJDe73x5LE68VhfSy6drknAVxm92+PLHZBKyx21E2agY8uLhPptqFsL09XYk4SqkkSrMP6Xiybaj6ZT/1bdERG4VtVrN119/TVhYGC1btuTIkSMcOXKEJ554guLFi9OqVSuGDh3K8OHDKVCgAF999RXJycmMGDHCv8a8efOoXLkyFSpUYMqUKSgUCtq1a4fZbEYqlbJs2TI6derEsWPH+PDDDwFfBb1QmM1m1q5dG9CmUCioV68eL7zwAl9++SUFCxakevXq/PXXXyQlJfH111/flXtRunRpRo8ezeDBg/MsMHE9VCoVDRo0YPz48URFRREdHc2MGTPIysqiR48egM/hMhqNREZGIpVK6dWrFxMmTKB8+fJUqVKF5cuXs27dOubPn39Xrulhom3YNGTCzeWwlVc/QVlV17u290HbL+y3zaeB7j2UkjA2m8azyTyOJvqRIcefdmwi2TyJerq3CJMmsMMyjTXGYbQNm3rdypIid4ewaW0RVDf3tVX9RHlUXe9efqLtl4PY5u9H914DJGFKTOM3Yx63Cf3IJiHHOzadxjwpGd1b9ZAmhGGZtgPjsDWETW0r/lt5CBGdqXxE3b4UOV8l47loQVGrMBHvtkCQ+g4Lw4Y0wDg1mazha8DhRl4uisjPWvmdA80jpcgauZb055ZQYHF3dM9Xx/TNDowWJ9I4PfqXa5Lz5RacBy+FPF1xpl4MyouSl48mamp79C/UQJBKyPlsIx6zA3nxcCLGJCIvcWM9p1tBWkh3V9e7GvPcVMyzUii0+mkA9H2r4TU7yXh7BQgCqmbxGF7KzQkxDK6P6ZvtZH/0Dx6TA0XFGCIntPYX9BAReZAoV64c48aNY8qUKYwfP57IyEieffZZunXrBsCYMWP45JNPeP3113E4HFSqVImZM2cSExPjX6Nr164kJSWRlpZGtWrVSEpKQqfTodPpGDVqFFOmTGHq1KnExsbSq1cv5s+fT2pqKk2aBH+5OHnyJP369Qtoi46OZv369Tz11FPYbDYmTJjAxYsXiY+PZ/z48XlW3LtdIiMjb3vuqFGj+Pzzzxk8eDBGo5Fq1arxww8/+CsELl26lHfeeYeVK1cSFxfH008/jSAITJ48mfPnz1OyZEm+/fZbKlaseLcu56FBJQm/6bFyQQ1CcOj37bLPOo+K6l4UUdQBoL7ubZZm98PkPodOWijk+FKq9iQoWwDQUPc+CzMf5YIrhYLy0KGjIncPSfjN/z0W1PK7+U8F67x9qHtVRFHHlxqge7s+2f2W4j5nCvldxjpvH6r2pVC2SPCNf78hmY8uxJVyAXlVUbj7YUPw3mpchshN0/Ts6Dz7zjX7nvAPm6FqXCzPMSL3N2sKv5ffJoiI3BMSExN5+umn6dOnT36bInIP+DdLo1vcF9li/oLzzh0oJAaqqp9lk3kcncJ/RCctxOLMHpRTPUY59WNsNI1FigoPDo471iBFTilVR6qq+yIIAimWJE451tE+fEbQPimWJHZbZ4W0oZ52CCVVbQParJ4MFmU+StuwaUTJck8w5mV0oI52oN9huoLX62FuxiM00A2lmLKpv/33rOeIVyZSSd3rTm7TTfMwF6BwX7Rg/mILzh3nkRgUqJ+tinncJsJ/7IS0kI7MHotRPVYO9WPlMI3dCCopODw41hwHuRRVx1Ko+1ZFEAQsSSk41p0ifEb7oH0sSSlYZ4UueqUdUg9V28DwXU+GlcxHFxE2rS2yslH+9owO89AOrON3mK7g9XjJeGQuuqENUDbN/Y6X9dzvKBPjUfeqdAd36c4QS6PfG8STKREREREREZG7jsfrZrXxHRSCgdZhk7B7s9lsGo+X0OHnAIftv1NO1Z12YdM449jCNstkCsqqUVhx/aqq5dVPUFrVKWSfXAiWB7F4fLlOGklgKX+VEOnvuxqH14gbG+prxqslkWLe1F3A6/ZgfGc1gkFB2KTWeLPtmMZvBk/ez/vtvx9G1b0cYdPa4dhyBsvkbciqFURRs/B191I/UR5Vp9Ih+wRtcJ6253IqgSQ6MExYiFT5+wKuxegAmxtJdODRmCRSLeZNPaSIzpSIiIiIiIjIXeeCaydZ7mN0jZiLWuJ7ol9L+xqrje/mOUcrKUh1rS8k1KAuygHbQi659t3QmZILal8Y4E3i9toBkBBYKVIqyHF7g4XgXZfHS4XA8RIUeLi+cLzIjXHtvID7WBYRc7v6KwlrX6uF8d3Vec6RFNSi7VcdAHVRA7aFB3Dtu3RDZ8oXAnjz2pZe+2XJB0VgzTZBLsXrDJaD8Np9sg2C4pqqxwoJOPN+kCDy4CI6U/nElVweERERkfuNv//+O79NEHkIyHQdRisp6HekAGJkla8zA3TSQGF3uaDBw431y/ZYZpNq/TFkXx3dIIorWwW0SQUlQJAj5PY6kV3uCzn+GkfLgyPkeJFbw3U4E0lBbYAki6xyzHVmgDQ2MFdJ0Mh90jI3wDJ7D9YfU0P26QbVQdmqeOC6ystO0TWOkNfpRlAGf42+Mt57rePk8IQcL/LgI/5WRURERERERO46ApKgkD7hWvHEa5AS6sTgxqndpVWdiFc2D9mnkgQXT9JIfF/UrZ5LqCW5xUls3oygUD4ApWBAigqr51JAu9WTQUF5jRvaJ3IDJEJwSN+Nqt6F0Lu8GTUYVafSKJvnoW0ZEVzkQnK5grDnkjVA+9KbYQsK5QMQDEpQSfFcsga0ezKsyGuIxSceRkSdqYeUC08sxDx/702NNX63k/TnltzV/U0/pHDh0fmcb/c/sj76B4/x+noMlt8OcbHHIs61+ZHMoStxXzCHHOdIOc+5xB9wHsrV6PK6PZiSdnHhiYWcf2QOWaPX4THa7+r1iIiI3ByJiYkkJSXd1NhJkybRuXPnu7r/1KlTadSoEdWrV+fNN9/0a2CFwuVyUalSJcqWLRvw2rlz51216b9KuKwEFs9FrJ7cz+t01757spdSYkAvjQ35kgvBJfHVkkh0kiJccKb42zJdaTi9FmJkwVUXBUEgWlaeC67c8XZPNtnu4yHHi9washLheC5a8GTkOiCufen3ZC+JQYk0Vh/yJYTQtpREqpEU0eFMuZBrW1omXosTWcXg0zNBEJCVj8Z11XhPth338eyQ40UefMSTqYeU6OmP3LQeg/aJimi7lbtre1t+OYB5/j7C32uEJExJ9vhNZI/bQMSoZiHH2zedImfSFsLeboAsIRzjtGQyh60malr7AD0Gr81F9icbgp5emWalYFmwD8PgeshLRWBKSiFz6N9EThb1HERE/m0WLFiAWn1zuSt9+/ald+/ed23v//3vfyQlJTFu3DgiIiL44IMPeP/995k4cWLI8ceOHcPpdLJixQpUqtwn0qHEe0VunYKy6kRIS7DBNIYamv44vCa2mkP/LvKDsqqu7LbOQictgloSyWbTeIopmqKV+k4PnF4rLq/Vf3JVVt2NdcZRRMhKESEtzQ7LVCJlpSkgr5Kfl/FQIKteEGmJCExjNqDpXwOvyYF54tb8NsuPqmtZrLN2Iy2iQxKpxjR+M4qmxZAW9BU38Vp92pZXTq7U3cpiHLUOWakIpKUjsEzdgax0JPIqBfLzMkTuEeLJ1EOKJFx1086URCO/q3pK5rmp6HpXRlk3Fnm5aMKGNMC+9kSeIr3meXvRPFIadYviyEtGEP5+Y1xpGQFPgQCMX28PqqYDYFm4D93TlVG3KI4sPpywIQ1wncjGsePcXbsmERGRmyMyMvKmnSmtVuvXarobzJw5k/79+9OkSRMqV67MmDFj+OOPPzh9+nTI8YcOHaJQoUIULVqUmJgY/0suv/nkdJG8EQSBxvpRCMAf2S+zwTSGkqp2gK/QQ35TVvUoZVRd2GT6hJU5gwmTxlNP+6a/f591LosyH/W/L6poRA3NS+y0zODP7FeQIKeJ/sP8MP2hQxAE9KMaAwLZL/+BacwGVO18JcqFUOF8/zKqR8ui6lIG0yebyBm8Eml8GNo36/n7rXP3kfnoIv97RaOiaF6qgWXGTrJf+RPkEvQfhhb4FXnwEU+mHlDcFy3kfL4Jx45zCAYF+r7Vyf50A9E/dkVWWMeFJxaifaw82scrkDVmPRK1DK/DjW31cZBL0HQsg+65agiCgPG7ndjXnST6245B+xi/24l5VkoIC8AwpAGadqUC7bpkxX3GhOIqUTp5qUgErRxH6kXU14jbeT1enHvT0XTO1fmQhKmQJYTj2HPBv45j5zlsa48T8XEil1743T/Wk2XDa3Yir5j7tEdQyZDFGXCmXEBZ4/pVfURERG6N8+fPM3z4cDZv3kxYWBgDBgzgvffe488//yQuLi5Ao2ro0KGo1WrsdjvLly9HoVDwxBNP8MYbbyAIApMmTWLFihX88ssvQftMmjSJyZMnh7RhzJgxfgHiK1y8eJGTJ09Su3Ztf1u5cuXQ6XTs2LGD2NjYa5fh0KFDlChR4g7viEhe2DyZZLuP0Vz/qT9KIN25FwlylEI4AF0ifvKPr68bGrTG1ZpSVTR9qKLpc9fsEwSBqpq+VNX0Ddkfar+y6q6UVXe9azaI+PBk2nAfy0b/aXP/vxXn3nSQSxDCfQU+In7q4h+vG1o/aI2rNaU0faqg6XP3TgwFQUDTtyqavqHFmUPtp+5aFnXXsiHHizxciM7UA4jX7SFz6EokYUoiJ7fFk20n57ON19VjsPx2CG33CkRNfwT7ltMYJ21FUa0gylrXFwDUPlERTafQHwYSXQg9hvQregzB+gqei8F5UF6jHa/NFXTiJLlKv8FjdZL96QYMb9RF0AdWTRL0CpBLcF+1ttfjxX3RjCfLdt1rExERuTXcbjcvvPAC4eHhzJkzh8zMTN5//33c7ryrrc2fP59nn32WhQsX8s8//zB69Gjq1q1LgwYNrrtX37596dGjR8g+vV4f1Hb+/HkAChQIDKOJjo7m3LnQp9RpaWlYrVaeeuopjhw5QsmSJXnzzTepUkUM27o7CKw1fkBl9dMkKFtg9+aw3TKVYoomSIT8P20QuY8QwPjBWtRPV0bZIgFvjh3L1O0omhRDkIpBVCL3N6Iz9QDi2Hke17EsYuY9ijTK54ToX69D1jt5lzOWFtSif8FXcUhW1IBlwT6c+y/d0JmSaOQQIiEzL/LSVxDkEryO4JKlV/QbhFD6DQ5fn3HaNuTlo1E1KobrbGCooCCVoGqegOm7XchLRiItrMM0ezeeTBtel6jnICJyN9myZQtpaWmsWrXK77QMGzaMF198Mc85RYoUYfDgwQAUL16cWbNmkZKSckNnSqvVotUGi63mhc3me3iiVAY+cFEoFDgcoQvgpKWl4Xa7GT58OHq9nrlz5/L000/zyy+/EB8futqXyM2jkoTTRD+KFMtM9lhnIxc0FFU0obo2738vIv9NJOEq9KOaYJmZgnX2HgSNHEWTomhfrJ7fpomI3BDRmXoAcaVlIC2o9TtSAIpK109qlMYGPskVNHK4CWfDNHs35tm7Q/YZBtdD3SowROaKE3Wt4+R1ekLmcOU93o2gkmHfdhb7PyeJ/i44BNFvx2u1yR6znvQ+v4BUgqpZPMp6sSGr8oiIiNw++/bto3DhwgGnPzVqXL8sdLFixQLe63Q6XC7XDfeaNm0a06dPD9k3cuRIOnXqFNB2xYm61nFyOBx55nAtWrQIj8eDRuP7LK1UqRK7du1i/vz5vPnmmyHniNwasYp6xCrq3XigyH8eRb1YFPWCw3FFRO53RGfqQUQqgWv9oBucgodK4PR6byzIoOlUBlWzPPQYIoO/oEhifE+SPRkWpFeJ73kyrKH1GMKUCCpZQDlUuKznUL0wtr+O4Mm2cfGJRQH9l15dhu6pKuh6V0aiVxLxcSIekwO8INEruNT/dxQ1r3/qJiIicmtIpVI8nsAPH4nk+h8+CoUiqO1mPnt69OhBu3btQvZFRUUFtRUqVAjw5U7FxOSWH05PT6dgwdDaLldX8ANfXkSJEiXyDAsUERERERG5FtGZegCRlQjHfdGM+5LV77A476Eeg8Rw8+ru0ig10iI6HCkXkJf2feFxpmXgtThR5KHHIK8QjSPlPKrGvifYnmwbruPZKCrFIG1XEm3vyv7xnnQLGW/8SfjwJv7TuKwx61A1LIqqic/pc18w4zyUgWFIw9u+bhERkWDKlCnD+fPnAxyWXbt23ZO9wsPDb6lEeUxMDEWLFiU5OZkKFSoAsH//fsxmM9WqVQsab7PZaN68OcOGDaN9e1/iutvtZv/+/XTp0uUuXIHIv8Vf2W8QIStFLe2r+W0KTq+VXZYZnHCsxeW1EiUrTy3NK4TJEvLbNJHLZL/xF7JSEWhfrZXfpgRgGrcJQS277+wSuTGiM/UAoqheCFnJCLLHrEP/Ui28Jgc5X24BbiwY/m+g6VYeU9IupIX1SKPUZH+2EVWzeKQFfZX8PBafHsMVR1DTrRxZI9ciLxWJrHQkxqnJyMtEoahy+WlyRO6JlutyIqo0Rut38iThKozf7EBaQAsyCdnjNqJsVAx58fB/76JFRP4D1KtXj7JlyzJkyBDefvttjEYjo0aNArgvNN2eeuopJk+e7C91PmzYMNq2bUuRIr5TarPZjMViISYmBpVKRYMGDRg/fjxRUVFER0czY8YMsrKy8ix8ISJyI7aaJ3DJdYCGuvdRSSLYbZnFypy36BD+HQqJ7sYLiPzn8Hq9WJN2Y196GNWjYvW/BxGxRMoDiCAIhI9sBgJcemkpWR+vQ93+colyef7/SjWPlkPTpSzZn6wnY9CfyOLDMLyZW8bUPDeVi4/O979XNSqG/qVaGL/ZQcbLyxBkUsI/bHbT++n7VkNRtSAZb68gY/BfyMtFEf6OeColInK3EQSBiRMnIggC3bt35+233+axxx4DuC+0mZ5++mmefPJJ3nnnHfr06UPJkiX56KOP/P0zZ86kUaNG/vejRo2iWbNmDB48mG7dunHmzBl++OGHu6p9JfLfweW1ccy+kpqalykor0qYtBj1dUNweE2cc27Lb/NE7kPcFy0Y3/4b2y8HkRQM1tEUeTAQvDcTvC5yWzQ9O/qerOvOtOI6cAlF3Vj/02BH6kUy3viDgsueRJDlv0P1X2BN4ffy2wQRkX+VS5cusWfPHpo0aeL/7Nm5cye9e/dm586dyGRisMODwKgGZ+54jUO2Jey1/oTFcxGdpBAVNb0ooWwDQI77FNvNU7ng2oXb60AvjaWa5nniFL6HXIsze1BR/STH7CtJd+3DIC1KQ937nHCs5oBtMQISKqp7Ul7dHfCF8UXLKpLpTuOCcyc6aSw1NP0poqjj7786zO+sYys7Ld+Q7T6OVlqIsqqulFZ2RhAEnF4LW0yfc8a5FbfXQQF5ZWppX8cgjQu6xhRLEruts0Jefz3tEEqq2ga0ubx2Ljh3ESOvjFzwRVR4vG7mZXSglvZVSqkeueP7/sGGe5sL3ODMqHuyrm3JIaw/7cVz0YKkkA5Nr4oo2/gKWLlP5WCeuh3Xrgt4HW6ksXo0z1dD0dD3O8nssRj1kxWxrzyGa1860qIGdO83xLH6BLbFB0AioO5ZEXX38sDlML6K0bjTMnHuvIA0Voemfw0UdYrk9l8V5ufYehbLNztxH89GWkiLqmtZlJ1LIwgCXosT0+dbcG49g9fhRl65ANrXayGNMwRdoyUpBeus0AW7tEPqoWpbMqjdsfEUjjUn0bxQDeOodfc8/HBDkQ/u2dr/ZcS/fA8ggiCQOWw1uqeroGpZHG+2HePUZFRN40VHSkRE5J4hCAKvvvoqL7/8Mh06dCArK4tPPvmENm3aiI7Uf4gM1yG2mifQUPc+UbLynHFuZqPpE6JlFdFLYlmT8y4RstK0CZsCXi+7rd+zyfQpXSMWIBV8J5g7Ld9ST/cWBmkx1hlH8lfOAOIUDWkTNonj9lXssEynqKIJOqmvsMh+23wqqntRU/sKR2zLWWN8j0fCZ2KQFg2wLdt1nDXGYdTQvkRheS0yXWlsMU9AQEJpVSd2WWaS5T5KC8N4ZIKSbeav2GT6lNZhE4Ous7z6CUqrOgW1A8iF4LL9MkHpd/CucMj+Kx4cFJLXvK17/TDgOpSBecJWdO83RFY+CufmM5g+2YisYjSSWD05765BVjqCsClt8HrB+v1uTJ9uImJBV3/xLMu3O9G9VQ9pMQPGkevIGfAXioZxhE1qg33VcSzTd6BoUhRpIV8opW3+ftS9KqJ9pSa25UcwvreG8JmPIC0a6AS5jmdjHLYG7Us1kNcqjCstE/OELSARUHUqjWXmLtxHszCMb4GglGH+ahumTzcRNrF10HWqnyiPqlPpkPdA0IY+uVfUj0NRP9iRF3mwEP/6PYBIwlVEfNgM48ydmGbvRqKRo2oaj+6F65coFhEREbkTIiMjmTx5Ml9++SXTpk1Dq9XSpk0bsYz4fwyz21ftUCspiE5aiDLSzuglsaiEcNzYKal6hJLKtiglYQCUV3fnuONvbJ5MtFJf4aBiiqYUVfhCLuMVzdltnUVt7RvIBCUV1D1JsSaR7T7ud6ZiZJWoonkGgOraFzjj3Mxh29Igzaq9tjnEK5tTRtUZAL00Fqsng33WeZRWdcLsPotMUKOTFEIh0VFX96b/eq5FLqj9J0y3wznnNnaYp1Fe1d1/Hf9F3OfMAEgKapEW0iHtXAZJrB4hXAV2N6pHSqJsWxJJmC8PWt29PI6/j+PJtPlyoQFF02IoGvkcZ0XzeKyzdqN9ozaCUoa6ZwWsSSmXT5Z8zpSsUgyaZ3zi29oXquPcfAbb0sNBulW2OXtRNo9H1bkM4JOR8WRYsc7bh6pTadxnzQhqGZJCOiQ6Bbo36/qv51oEtRxBnf/hziL/PqIz9YCirBeHsp74NENEROTfpWnTpjRt2jS/zRDJRwor6lBQXoM/cl7BIC1GrLweJZRt/QUWyqi6cMy+gkuuAxjdJ8lwHwLAe5Wmh16aG64mE1SoJJHIBN+XaangK6fvwekfU0BeJcCGKFlZstxHg2zLch0ly32EE/bV/jYvHjy4cHudVFT3ZpVxKAszu1FAXoU4eQNKXBOud4U9ltmkWn8M2VdHN4jiylZ53qOTjnWsN35InKIR1TT98hz3X0BRpzDyGgXJeeUPpMUMyOvFomxbAonO93tWdSmDfcUxXAcu4T5pxH0owzfRk5uFIi2Sq5UpqGRIIlUISt9X2Ct6lThz/33JqwRqb8rKRuE+mhVkm+toFu4jWdhXn8ht9HjB5cHrdKPuXRHj0FVkdluIvEoB5A3iULUtEbQOgGX2Hqw/pobs0w2qg7JV8dA3SOSBR3SmRERERERERG4amaAkUf8p6a5UTjs3cdqxkQO2n2mm/5gYeUX+yH4VCTKKKhoTq6iPFAV/GwNPLyXXfP0QuH41SIFArUQvnqA2X7ub0qrOlFV1DeqTICVaXp4uEXM47djEWecWUqxJHLL/Stuw6X5n7gqlVZ2IVzYPaY9KkneRksO2ZWw2f0YJZRvqat9EEP7b4feCUob+00Rcqek4N53GsfE0tp8PoP+4GfKKMWS/+gfIJCgaF0VRPxYUUoxv/h24yLUpDDeqHiq9pt/jDW4DcHtRdS6NqmuIKnpSCfLy0UTM6YJj02mcW85iTUrB/ushwqa39TtzV1B1Ko2yeR66nBGqkO0iDweiMyVyS1wa8AfyUpEYXqud36YEYF1xhOyP1lFgSQ8k+lyRUNMPKVgWH8BrcaJsWBTDgLoB/SIiIvcnTz31FOXKleO99/K/0IvZbOaLL77gzz//xGKxUKVKFd59911KlSqV36blCxedqZxxbqaqpi8x8kpU0zzP8uyXOen4Bw9Ost3H6R75GzLB9wXyqH3F5Zm3X+8qw3XQ/7PX6+WS6wBFFY2Dxhmk8Rjdp9BLY/1tR+x/ctGZQl3dm+y3LkAvjSVBmUiCMpFs9wl+y3qGLNdhouUVAtZSSgwoCS40cD1O2NewyTyOsqqu1NS8el9IBuQ3ztSLODefQdO3KvJKMWier0b2y8tx/HMSnB7cx7OJ/K07gsr3ldS+4vKJ4x2UR3MdzPD/7PV6fUW7GhcNGieNN+A+ZUQam3vyZf/zCM6Ui+jerIt1wX6ksXqUiQkoExNwn8gm65nfcB3OQl4hOmAtiUEJt6DLKfLw8N9+XCLyUOC+ZCVn4pagdssvBzDP30fY2w2I/KI1ruPZZI/bkA8WioiIPMiMGjWKDRs2MH78eObOnUtERATPPvssRqMxv03LF2SCilTr/9hvXYDJfY4zji3kuE8QJSuHUgjDi5vj9tWY3Oc4af+H7eapALivCtu7VU47N3DA+jM57pPssEzD7D5HKWVwdbwK6h6cdW4lxZJEjvsUpxwb2GaehFISDoDFk85W80TOO3dicp/liG0ZckGLQVrstm27gsNjYrP5M4rI61BR3QubNxOrJwOrJwOX13bH6z+oCCoZ1v+lYl2wH/c5E44tZ3CfyEFWLgohTAluL/bVx3GfM2H/5yTmqdt9E53u297TueE01p8P4D6Zg2XaDtznzCgfCX74oe5RAefWs1iSUnCfysGx4RTmSduQhPucIk+6BfPErTh3nsd91oRt2REErRxpsVtzskUebsSTKZEHnpzPNyIrHoFz1/mAdvPcVHS9K6Os63tCGTakAZee/w33OZM/SVVERETkelitVn777TemTp1K7dq+E/kxY8ZQp04dNmzYQJs2bfLZwn+fCFlJGureY7f1e3ZYvkYlCae8qjsllG0RBIGq6ufYafkGp9eCQRpHDe1LJJsnkuE6QNhtOi3FFM045VzPDss0wmUlSDR8hlZaMGhcpKw0TfQfkmL5jlTr/1BJwimj6kplta94RVVNX1xeG+uMH+LwGomQlaSZfsxdEdQ969yKw2vijHMzizIfDeirqXmFcurH7niPBxFZyQh07zXE+v1uLF/vQBKuQtW9PMq2JRAEAfVzVbF8sxOvxYk0zoD2pRqYJybjOpCBtFjYbe2paFYM5/pTWKbtQFYiHMNniUgLhqjAWDoS/YdNsHyXgvV/qT7bupZB/UxlADR9q+K1uTB+uA6v0YGsZAT6Mc38+V4iIiA6Uw8EliUHMc9JxX3RjLSQDl3vyqjb+PQKXKdyME5JxrHrPF6HG1msAV2/6qga+o6zLzyxEF2vylhXHsW59yKyYmGEvdcI25rjWH7ejyCRoO1ZEe0TFQFfGJ+iYgzOtAwcO88ji9Wjf6kmyjqxIW2zbz2D8ZvtuI75quhou5VF3bksgiDgsTjJ+XwT9i2nweFBXrkAhgF1kIXQZzB+txPzrJSQexiGNEDTLnQ4jfWPw7jPmtC/VIvMq5wp9yUr7jMmFFVz/9jKS0UiaOU4Ui+iFp0pEZEg5s6dy4wZMzh79ixxcXH079+fLl26AHDs2DHGjh1LcnIyNpuNhIQEBg4cSIsWLQBITEzkhRde4LfffmPXrl0UL16czz77jOXLl/Pjjz8ikUjo168fffv2BXxhfNWqVWPfvn1s2bKF+Ph43n77bRo3Dg7dAli3bh2ff/45aWlpxMbG0rt3b5588kkEQcBkMjF8+HDWrVuH3W6nZs2aDBs2jISEhKB1Jk2axOTJk0PuMWbMGLp16xbQJggC06ZNo0aN3GqpEokvqCMnJ+eW7u/DRLyyeZ75RJU0vamk6R3QVlzZ0v9zl4ifAvrKqR8LcjR6Ra0KeK+SRNBIOyzkfq3CJgS8j1M0IE7RIORYqaCgju4N6vBGyP474Xr35L+Osnl8nvlEmt6V0PSuFDi+ZW6xhoifugT0qR8rh/qxcgFtUat6BbyXRKjQDmtEKMImBBYOUTSIQ9EgdEEvQSFF90YdeKNOyP67ybV2iTw4iGF+9znOQ5fI+WIzun7VifmhC9rHypM9dj2uUzl4vV4y3/kbQS0jakp7omd0QFYinOxPNuC96njcOGM72sfKEz2jIwAZA/7Ac95M1OR2aLqWxTh9O66zJv948/y9KCrEEP1NB5T1Ysl8dxWuk8FfGlzHs8h8fxWaR0oT/V0n9M9Vw5SUgnWJr3KT6duduI5mETm+NVHfdECQCmR/EjrMTvtERWIWPh7ypU5MCDnHnW4hZ0oyYUMaBiWnetItAEiiA8vaSiLVeC6GLmsqIvJfZu/evYwYMYKBAwfyxx9/8PTTTzN06FCOHTuG1+ulf//+aLVa5s6dyy+//EKZMmV49913cTgc/jW++OIL+vTpw+LFiwHo3bs3Z8+eZc6cOfTq1Ytx48Zx6tQp//jvvvuOatWq8fPPP9O0aVNeeukljh4NrtB2+PBhXnnlFR577DF+++033njjDSZPnszcuXMB+PLLLzl06BBJSUn8/PPPSKVS3n333ZDX2bdvX9atWxfy1b59+6DxKpWKxo0bo9XmPtWeO3cuDoeD+vXr39a9FhERERF5eBBPpu5zrugZSC/rM2g6l0Uaq0cS5tNn0DxSGnW7kr73gLZ7BWx/HwvQZ1A1TUDV2BdaoWqegClpF4aBdRGUMrQ9KmH6bhfuE9nICvtOaxSVC6DrUxUA/Ys1sW8+jXXpIfQvBooOmv6XijoxAU1nXxUcWawe9yUr5rmpaDqVwX3OiKCWIS3s02cwvFkf9zkToZBo5KC5NX2GnM82oulQGnnZKOw7AnVCvHYXcFXJ1MsIcglehwcREZFATp8+DUBsbCyxsbE8+eSTxMfHExkZic1m4/HHH6dbt25ERPiqmD377LP8/vvvXLp0icKFCwPQpk0bWrb0nUC0b9+eyZMnM3z4cFQqFf369WPSpEkcPnyYuDjfU+CaNWvy6quvAvDmm2+yZs0aFixYwFtvvRVg2zfffEP79u158sknAShWrBgXL15k5syZ9OjRg1OnTqHRaIiLi0Ov1/Phhx/6r+datFptgGN0q2zcuJFPPvmEZ5991n8dIiIiIiL/XURn6j5HWacIihqFyHh5GdJiYSjrxaJuW9JfkU7TtSzWv47iPHAJ98lsnJf1Gbzu3DI4stjckDZBKQ3UZ1D6nA2vI/ckS145UJ9BXi4aVx76DK4jmdhWHfe3ea/SZ9D2rkLmkBVc6DoPRZWCKBvGoW4bOlzPNHs35tm7Q/YZBtdD3SpQ18Gy9BDucybCRzULOeeKE3Wt4+R1evwVg0RERHJp3Lgx9evXp3v37pQoUYJmzZrRtWtXDAZfWG6vXr1YsmQJu3fv5ujRo+zduxcAtzv3s6NYsdx8GJVKRXR0NCqV70GPUulL6HY6c4sQ1KpVK8CGypUrc+jQoSDbDh48yMGDB1m+fLm/ze1243Q6cTgcvPTSS/Tr14/69etTu3ZtEhMT6do1uDQ2wLRp05g+fXrIvpEjR9KpU6c879GKFSsYNGgQrVq1YvDgwXmOE7m7XBvGJyJyPcRwOZF/G/Fb5X2OoJQRMa4lztSL2Dedxr7hJJaf9xMxJhF5xRgyXlkGMimqJsVQ1o9DUEjIfHNF4CLSW9NnEK4Z73V7QRJKn8GDpnNZNHnoMyjKRxPz06PYN53CvvkMpu92Yf31IFHTHwnSZ9B0KoOqWR76DJHBCvS2P4/gOmXkQidfmI/3srjfxccXYBhcD0UN35NyT4YFaVTufE+GNSj0T0RExOf8fPvtt+zYsYM1a9bw999/M3v2bKZNm0b16tV54oknkMvltG7dmubNm6NUKnn22WcD1pDJAv9fX8ktygupNPDk2OPxhJzjdrvp2bMnvXv3DuqTyWRUqVKFlStXsnbtWtauXcukSZP46aefWLhwod+Zu0KPHj1o165dSHuioqLytHXhwoUMGzaMLl268NFHH93w2kRERERE/huIztR9jiP1IvbNp9H3rYaiUgH0z1fn0ktLsa09gdfpwXU8m4K/9/SftlhXHLk88/YFGpwHL/l/vqLPoAyhzyBLCMN1KiegoIT1z8M4Ui4Q9mZ9zPP3Io0zoE4sjjqxOK7j2aQ/8wvOw5koKsQErCUxKH0aDTdJ2HuN/aF8AM596WSPXkfkpDZIYw1INHKkRXQ4Ui4gL+37guRMy8BrcaKoGJPXsiIi/1l27NjB2rVrGTBgADVq1GDgwIF0796dP//8E6fTyZEjR0hOTkat9j2MWLJkCeD7jLhdUlNT/T97vV727NnjDxO8mlKlSnHs2DHi43MfuCxevJht27bx4YcfkpSUREJCAu3bt6d9+/YcPnyY9u3bc+DAAapWrRqwVnh4OOHh4bdk5/Lly3nvvffo3bs37733nqgdlAeLM3tQThVcTCI/MLnP8UtWTwAKy2uTaPgUj9fNdssUjtlX4sVNcWUrqmteQircXIj5nc63eTLZbp7KWWcyHtwUlFelluY1NNIYlme/zCXXPgAej1hyV6oL3s9k9liMKkQhifzAfc5EVs9fAJDXLozh00S8bg+WKduxrzwGbi/KVsXRvFQdQR4sFH09vB4vxiF/I68be0vXer39s19ejmuf73taxJLHxcqC9wGiM3WfI6hkmH/cjUSvQNmoGO4T2bhOZKPuVMbnfLi9WFcdQ1m9EM5DGRinbPNNvIO8IPuGU5gX7UdZuwiWJQdxnzOh6VA6aJy2R0UuvbgU43c7UbcqgetENjkTt6Lp4jupcqdbsCzcj0QtQ1JQi3V5GoJWjqzo7ZU6vRppjCbgvfuir+CEtJDel38FaLqVx5S0C2lhPdIoNdmfbUTVLB5pwYf7j5SIyO2gVqv5+uuvCQsLo2XLlhw5coQjR47wxBNPEB4ejsvlYtmyZdStW5e9e/fyySefAAQUoLhVrpx+NWzYkHnz5nH69Gm6d+8eNO65557jscceY9KkSXTs2JEjR44wevRofw7V+fPn+f7779FoNBQuXJhFixah0+koXrx40Fq3Sk5ODsOGDaNx48a8+OKLpKen+/t0Op3fuRS5P2lh+IwIaRkAdllmcNqxiab6j/DgYqNpLBLk1NC+dFNr3en8f4wj8eKmmWEMEqRst0xltfEd2oZNp7l+LOedO/nHNPy2r1XkzjB81gJpGV9OqGXGLhybTqP/qCm4PJjGbgS5BO1LNW6wSi5elwfz51twJp9DXjd0ReS8uN7++rHNce48j2n4P7e0psi9Q3Sm7nPkJSMIe78x5lkpGL/ejiRchfaJiqjblkQQBHTPV8f0zQ6MFifSOD36l2uS8+UWnAcvIYu/PadF1Twe+/qTGKdtQ14ygojxrUI6IPLSUYR/1AzTzJ2Y/7cHSbgKTbdy6J6pAoC+b3Wwu8ka9Q8eox15qUgixiT6873uNZpHy+HJtpH9yXpweVDWj8MwsO6/sreIyINGuXLlGDduHFOmTGH8+PFERkby7LPP0q1bNwRBYODAgXz++eeYTCYSEhIYMmQIH374IampqZQsWfK29mzfvj0rV67k008/pWzZssycOZMiRYoEjatQoQKTJ09m4sSJTJ8+naioKHr37u0vXvHGG29gs9kYNGgQ2dnZlCtXjunTp/vzve6EdevWkZOTw9q1a2nUKLDU8jvvvEOfPn3ueA+Re4dCCEMp0eP2OjhoW0x93TvEyH1luGtqX2WDcTRVNH2RCdePjLjT+Tnuk1xw7aJD+Cy/1lZ97VB+zupOpjuNKFlZlBJRCDY/EcIUSPRKvA43tsUH0b1TH3klXySL9tWaGEdvQNO3SlCaQihcx7Ixjd2A1+hAuMWToxvtf6uRPCL3HtGZegBQN09A3TwhZJ+ud2V0vSsHjm+ZW6yhwNxA4UDt4xXQPl4hoK3Q6qcD3kvC1YQPaxJyv6gvAwUqVQ2KomoQHAIIvuIWhjfqYnjj3jswyuqFgq5DEAT0z1VH/1z1e76/iMjDwJUwuVD079+f/v37B7R17NjR//Pff/8d0NenT58gR+PAgQMB7yMjIxk/fnzI/X744YeA94mJiSQmJoYcq1QqGT58OMOH3/2n+te7Jw8b640f4/SaaWYY7W87aPuVvdb/0Tl8DjZvJtvNUzjrTMbpNaORFKCi+klKqR4JWmujaSwOj4mmho/8bUuznidO0Ygqmj4AHLWvYI/1B8zuc+ilcVRWP00xZdOQtv2V/QYXXLtC9nUOn4NOWui615bpSsOFjYLy3LDPArKquLCR6UojRl7xns5XCRE003+MQZJbAVIQfHl3Tm/oKrf3M8aP1+M1OzGMbuZvs/16EOv/9hI+pzPeTBvmKdtxJp/Fa3YiKaBB/WRFVI8EF6Eyjd2Ix+TA8FHu7z7r+aUoGsWh6eN7OGtfcRTrD3twnzMjjdOjfroyyqahBaCz3/gL164LIfvC53RGegOdSVdaJthcyK/SqZRVLQA2F660TOQ3kSrgSrmAvHIB1H2qkP3c7zccf7f3F/l3EZ0pEREREREREYorW7LG+D4Oj8mfs3Pc/jcJyhYIgsBG4xi8eGhhGI9MUHHQtpit5gnEKuqjlkTe0l5nHclsNU+gjnYgUbJyXHDuYoNpLDJBTRFFsEBqE/0oPLhCrARK4cZRGBbPRSTIUUpyxyoleiTIsXgu3vP5ComOWEWgLtk+63zkgpYoWYU8Zt2/KFsWx/j+Gjwmhz9nx/73cZQtEhAEAeOYjeDxYhjfAkElw7b4IOYJW1HUjw1ZVOp6OJLPYp6wFe3AOsjKReHcdQHT2A0IahmKOsEn2fpRTcAVOtVBCLvxiY7nogXkEiRXjZXolSCX+PpuAlWn4NSIm+Vu7C/y7yI6UyIiIiIiIiIUktdEIeg45VxPCWUbrJ5LXHTtprZ2AACx8voUUdRFL/Xlf1RU92a/bQFG96lbdqb2WGdTXvU4CcoWAOilsWS7j7PPNi+kM3WnIXAu7EiF4HAriSDHgzPEjLs7/1qO2P9kn20etbUDkAsPXt6dvGYhBJ0C5/pTKNuUwHPJimv3RbQDavv668eiqFsEaaweAHXvitgW7Md9ynjLzpR19h5Uj5dH2SIBAGmsHvfxbGzz9oV0pu44BM7uCtKoBJ9OJc5/Qacyv/cXuWVEZ0okgGvD+ERERETuBdeG8YnkPxJBSryyGcftqymhbMNx+yrCpcUJl/kKeZRWdeaEYzX7bQvJcZ8k0+XTBPNy61/wst1HSXftZa/1J3+bBzcqSXjI8X/nDOGiMyVkX4fwJLTSgiH7riBDidsbXCzF43Ui5cZfvu90/tUctP1CsnkiFVQ9KKPqfEtz7xcEqQRls3jsq4+jbFMC+6rjSIuHIyseDoCqc2kcq09gW7gf98kcXIcyfRM9t1790300G9fedKw/7b2q0YMkXBVyfM6Qv3GmhD4tDE/qgLTgDUS7lbIA7c0reJ0eUN5aNb/bIr/3F7llRGdKREREREREBIAERUv+ynkDh8fEccdqEpS+UvVer5dVxrcxuy+SoEyktLIjYZpi/Jb97A1WzMWDO+Dnqpq+FFU0DhgjEFq/q572TdyErhyplkTfcG+NJAYPTuweI0qJ77TE7snBgxPNvzD/Crsts0ixJlFZ/Yw/d+xBRdEygZw3/sJjcuBYfRxlywTA92/F+PYq3BfNKBMTUHYsjaZYGNnP/nbzi7s9AT9r+lZFca1ESyj9S0D7Zj0I4YwAN6UzKYnRgNODx2j3hdcBnhw7OD1IojU3mH3n5Pf+IreOqDr4AHLhiYWY5++98cB/AddZE+eafc+5Zt+T8ZZPLNjr8pAzaQvnO8/lfIefyJm4Ba8z9AdbKNzpFjI/WM35R+ZwvtNPZI/fhMeSG0bhybGT9dE/nO80lwuPzcc0a5dftPdW8Hq8ZLz5V8C9vPTSUv/1eIy3X/JZRORhIDExkaSkpPw2A4BTp05RtmxZypYty3PPPQeAy+Xio48+ol69etSuXZuPPvrotkq1ezwe+vbte9vXGmp+9+7d/fbm5OTc1rr5QbS8AlpJAQ7bl3LJtY8EhS8ML9t9jHPO7TQzfEwVTR+KKZvguFw4wRtC11CCDKc3N7/D43UF5BaFSRMwuc+il8b6Xycd60izLw1pl0YaEzD26pdEuPHT+nBZSWSouOjKPd264EpBhooIWXBRhLs9H2CfdR4p1iRqaF564B0pAHmFaCQFtNiXHsa17xKKy2F47mPZOLefw/BxMzR9qqBsUgyv6fL/y1C6dDIJ3qv+xntdnoDcIGlCGO6zJqSxev/Lse4k9qVpIe2SxmgCxl79EqQ3/torKxkOKhmuq063XCkXQCVDVirixjfmDsnv/UVuHfFkSuSuEDG+FfIyvph507c7sG88RcTo5uDykDVmPcgkGF6udVNrZQ1bjaCVEzmpLV67m5xPN5AzYTPh7/rKEmcOW40ny0bER81ALiVn3Aa8djf6F25N/yHn8004ks+ivEr/IeKTFjh2niPrgzU3f/EiIiL/GklJSVSo4EvY/+KLL1i9ejVTpkzB5XIxdOhQ5HI5Q4YMuen1nE4nw4cPZ/369TRpErqK6e3M//rrr9myZQuvvfbaLa+Z3yQoW5BiSaKArAoaqa9ymELQISDhhN13WmV0nyLZPBHwhbpdS7SsAkfsf3LKsR6DNJ691jkB4yqqe/KPcQRh0gSKKOpw0bWHXZZvqa19/Z5ck0xQUkrVgWTzJBSCHi9etpknU0bVxZ8LZff4nN5Q+Vl3Oj/HfYqdlm8opexAgrIlVk+Gv08h6ELmYz0IKFskYElKQValgF//UdApQCJgX30CZcsE3KeMmCcmA5dD1a5BViEa+59HcKw/hTTegHXO3oBx6p4VMY74B2lCGIo6RXDtuYjl211oX699T65JUMpQdSiFeVIygl4BXi/mydtQdSnjz2Xy5NiB28/Put78m9lf5P5CdKZE7goSg9Knz2B3Y/n5AGHvNkRRqQAAhtdqk/3ROvTPVbuhPoPHaEdaWIeuX3VkhS8nrnYojXnOHgCcBy/h3HWeqGntkZfzhVYYBtcn440/0D1dBUF1M/oPWWSNWY/XaA/Sf5AYlAh6Ub9BROR+JTw8nLCwMOx2O//73//45JNPqFHD9yDl3Xff5a233mLAgAGoVKHzKa4mLS2NIUOGkJ2dfVuaVNebf8XOB5EEZUt2W7/3F4cA38lQXe1gdlu/Z4/1B7TSQpRRdSHN/jsZroNBRSMSlK1Id+1ng8knUFta1ZkiilyZjDhFQ+poB7LX+hPbLVPRSGKooelPKVWHe3Zd1TT9cHntrDa+iwQpCcqWVNU87+9fa/wAgFZhE+76/JOOf/DgIs3+G2n2wHC3JvoPKapoFDTnQUDZMgHr97v9xSHAdzKkHVwX6/e7sf6wB2khLaouZbD/nobrYEZQ0QhlqwRc+9MxjdkAUgmqzqVR1M0do2gYh3ZgHaw/7cUydTuSGA2a/jVQdbi5E8HbQdOvGl67C+O7q0EqQdkyAc3zuWXxjR+sBSBsQqvbWv9G82+0v8j9hehM5QNZo9fhNTuI+DhXM8XyywFM/9tDzE/d8GTaME5Jxr71DF6zE2kBDdonK6PpEFxqM2vMerwmh+8U6DLpzy1B2ago+merAWBdcQTT97txnzMhK2pA91QVVM3iQ9p2acAfOHedD9kXPacbssLX12dwpmXgtblQXKWPoKhaEK/NhTMtE8UN9BEkeiXhH+Q+3XWdNmL98wiKGoUBcJ8xglyCrGyUf4y8VAQ4PTj3p6Oodn2tEQBHygUUVQqg61OV9L5LbjheRORB5a233sJoNDJt2jR/25w5c/jmm29YuXIlly5dYsyYMaxfvx6TyUShQoV48cUXefzxx4PWGjp0KDk5OUyZMsXf1rlzZ1q2bOk/fVmyZAlTpkzh9OnTJCQk8PLLL9O2bduQtj311FNs2bIlZN/KlSuJi4sL2XeFffv2YbFYqF079+l07dq1sVgs7Nu3j+rVb6wvl5ycTK1atXjttdfo1KnTDcff7fn3KwZpUXpFrQpqL6lqT0lVoOZWWXU3/89dInKLSUgFOXV1g6jLoDz3CbXevUQqKK5rU2P9CDaYxtyT+RXVPamo7nnrRt/nSIsaiFrVK6hd1b4kqvaBYt7qbmX9P0f81MX/syCXohtUFwblrUkZar17iaC4vk36EY19zt9NcPW13uz8G+0vcn8hOlP5gLplcTLfXxWgz2D9+xjqlsURBIHsMevA7SXy89YISimWxQfImbAZZYM4pLdYUtSefIacLzZjGFQPebloHLvOkT12PYJGhrJObND4iA+bhTyGB5CE34Q+Q/oVfYTcp8K5+ghm4ObF5jLe+gvH1rNIC+nQ9/M9eZZEqn2JmZk2/71wnzP79s6y39S6mk5lbtoGEZEHmY4dO/Lyyy9jNBrR630nvUuXLqVDhw4IgsCQIUNwu90kJSWhVqv58ccfGTlyJM2bNyc6+uaT6gHWr1/PyJEjGTFiBFWqVGHLli0MHToUrVZL48aNg8ZPmjQJpzN0SenIyBuX2T5//jxyuZyIiNwcgrCwMORyOefOnbspm3v06HFT4+7VfJF7j8ObHVA04nrstsyihPL2K9reyXy7J8cfJiiSP3izHQFFH66HZdZulG1K3PZedzLfk2P3hwmK3B+IzlQ+oKhVGIlOgX39SdRtSuK+ZMG5+wKGAb4wCWX9OJR1YpHF+cJGdL0rY1mwD/epnFt2pkw/7EbbvQLqFr7StrJYPa5j2Zjn7g3pTN2pPoM3T30EKV7HrZXP1fevhfdpJ8bp28gY/BfR33ZAXj4aaayenPGbCBvaADxecqYkg1TA67r5IhciIv8FGjZsiMFgYOXKlXTp0oULFy6QnJzMsGHDAGjWrBlNmjQhPt53Ut2/f39mzZrFsWPHbtmZmjZtGn369KFDB1+YVrFixTh8+DAzZ84M6UyFh4ff0bVZrVaUyuDPK4VCcVtFKEQeTlbmvElheW0SDZ/ecGwN7ctIBflt73Un81cZh3LJte+29xa5c3LeXIm8dmEMnybecKz25RoI8tvPX7qT+cahq3Dtu3Tbe4vcfURnKh8QpBJUzROwrTqGuk1JbKuOIysejryE7wmrplNZbGuOY1m4D9fJHJyHfImqXvetV6xzHc3Cufci5jmp/jbvdfQZMt5egTPlQsi+6FmdkBa8fpifoMhLH8GNoLq1Dw55Sd/9CB/ZjIuPL8C++TSqRsUI/7AZ2aPWcqHTXASVDN0zVXEdvISgeTATeEVE7hVSqZT27duzbNkyunTpwrJlyyhTpgxlyvhOZ3v27Mny5cv5/vvvOXr0KHv3+ipbut23/mDi4MGD7Ny5k2+//dbf5nK58jxlev7559m2bVvIvt9//50iRYLFOK9GpVKFdJocDgdq9YMngipyd9FJC4UMVbwed+JI3en8tmFTbjxI5J4gLaQLGap4Pe7EkbrT+WFTQodOi+QfojOVT6haFidjwB94jA6fU9XKd3Lk9XrJfHsF7osWVIkJaDqVQVYsjPQ+v9784lc7XW4P+ueqoWxULHCMNLQ+Q9hbDfDaXSH7JFE31je4U30ET5YN+/azqJonIAg+G6VRaiQGpT+MT14iguikzrgzrUg0CrweD8Zp25AWuoEQn4jIf5COHTvSq1cvcnJyWLZsGR07dgR8nzXPP/88Z8+epUOHDvTo0YMSJUrwyCOP3PTaVztdbrebAQMG0KpVYEK1RBK6FPHo0aOx2Wwh+woUKHDDvQsVKoTD4SA7O9tf6CErKwun00nBgtcXcBUREREREblbiM5UPqGoEIO0gBbr0kM496UTPtxXdMF1LBvH9nNEz+7iD/Nz7Al9UgQgyCV4rIH6DO6LZv97WUI4rrMmtHG5labMP6XiMTnQPx+coH2ltOntIi8VgaCS4Ui5gKqhT2DPkXIeQSVDXvLGeRDuTBvZo/5BWkiHooIvv8p11oQny4aseDgeo53Md1cR/l4jpIV8p2S2lSeQRKiQJYTfke0iIg8jVatWpUiRIixYsICUlBQmTJgA+CrRbdy4kT/++IOEhAQAtm/fDvgcrWuRy+WYzbmfLU6nk7Nnz/rflypVilOnTvlDBgG+/fZbcnJyGDhwYNB6d+rwlCtXDo1GQ3JyMi1a+KrOJScno9FoKFeu3B2tLSIiIiIicrOIor35iKplcUxJu1BUKYC0gO9URXJZn8G26hjucybsyWfI/uRyxZcQ4XPyCtE4dl/Atv4krlM55Hy+KaCAhPbJSlh/T8O8aL+vMt4fhzF+u+OeneIIShnqDqXJmbgFR8p5HLvOkzNpK5quZRGUufoMeSVPyouHo6hVmJzPN+Hcn45jXzrZI9egqFMERcUYX/l1h5ucr5JxncrBvvUMOV9uQft0FYTLauhicqaISCAdOnRg0qRJ1KxZk0KFfBUvDQYDUqmUZcuWcfr0adavX8+7774LEDJ8rmrVqmzfvp2VK1dy7NgxRowYEVBAol+/fixYsIDZs2dz4sQJFi9ezBdffEFsbHBu5t1ApVLx+OOPM3r0aJKTk9m6dSsff/wxTz75pD+XKisri6ysrNve407nPywszuzBfuuC/DbjjthoGsuanPdve/5h23LmZdx82fYfLzXnpGPdbe/3oJDZYzHWBfvz24z7CtPYjeS8f+dale5zJi41/xFXWsaNB4vkK+LJVD6iblkC86wUVC2L+9ukMRoMb9bDNCsF0w+7kRbSoelaFuvvaTgPXgoQmAVQtyqBc98lsj9eB1IJms5lUNbLHaNqWBTDoLqY56RinJqMNEaL/qWaaDrcu4p2+hdq4LW7yXznb5BKULcqju653FOwzGGrAYj6MnTVo/APmmCckkzGkJXg8qBqUgz9K7nlj8NHNCHn801cev43JOEqdM9UQds190n0jdYXEfmv0bFjR7766it/iB/4ToZGjRrFlClTmDp1KrGxsfTq1Yv58+eTmpoaJGDbqVMnUlJSePvtt5HJZPTs2ZOmTZv6+1u0aMGIESOYMWMGY8eOpXDhwgwZMoTu3bvfs+t68803sdls9O/fH6lUSseOHQNOwa6UbP/hhx9ua/07nS9y/1BT8+odzY9XNidWcfNlqrtFLEQhXD/HWOThRPNqzfw2QeRfRvCGiucQuSs0PTs6v02457jOmkjvuYiobzogL33jMD7w5UVlfbyOyE9b3hOb7nR9+45zZA78kwJLeiDR513UYk3h927XRBERkVvk1KlTtGjRgsWLF1O+fPmbmpORkcHbb7/NjBkzbmvPO52/efNmnn76abZu3XpbosD3ilENztzS+MWZPSineoxy6sfukUUiN8sHG65fmOVOaXBm1C2Nz+yxGNVj5VA/JobW3m3c50xk9fyFsG/aISt1c9+vbsSGIh/clXVEAhFPpkTuCp4c+03rM5iSdqFuc+/E9+5kfU+OHa9RDBEUEblfycrKCig6cT0mT55M165db3uvO5l/xc4HBYfHxDbLV5xybAC8FJbXprZ2AEpJoBPo8bpJsX7HMfsKLJ50lIKBeGVzamheRiJIsXjS2WL6nAuuFAAKy2tRWzsAlSQCp9fCFtPnnHFuxe11UEBemVra1zFIgwWaUyxJ7LbOCmlrPe0QSqoCK5rtsszklGM9j4TnVpM849jCWuNwHo1cRLL5SxweE00NH3HYtpwDtkWESRM47dxAOdXjVNE8wyHbEvZa52D1XKKwvBZaaSGcXjP1dUM5bFvONstkukf+hsl9jl+yetJYN5Jd1pmY3GeIlJWmjnYQETLf354fLzWnif5Diioa4fV6SLX+j0P237B7somSlaGW9jUiZKVueD/vBzwmB5avtuHYcAq8IK9dGO2A2kFSKl63B+t3KdhXHMOTbkEwKFE2j0fzcg0EqQRPugXT51twXa4YLK91eZ0IFV6LE9PnW3BuPYPX4UZeuQDa12shjQt+CGFJSsE6a3dIW7VD6qFqG/j33zJzF471pwj/Nre4jmPLGYzD1xK56FEEtRzrgv3YFu7352drXqiOvJovr9M0diNetwfPaSPuk0Z0wxoiqGWYp2zHfSQLQa9A2TIBTb9qCFIJprEb8ZgcGD7yndw7d53H8s1OXGmZSCLVqLuXR9XFFx3kPm3EPG07rl2+e6JoEIfmlRohv0t5nW6s/9uL/Y8jeNItyEpFoHmxOvKqedupqHNvnW8RH6IzJXJXyBz8F4raRYgcd+PTIP0rte64rOi9Wj9zyEqc+9LvskUiIiJ3iz59+tCoUaOAEux5MXToUBSK25dMuJP5L7zwArt27brtvf9t1ho/wOE10kw/GpmgYpPpMzabP6OJPvCkYq/tJ47a/6SB7l20kkJccO5kk3kcBWRVKaZswlbTBNw4aGP4Cg8uNpnGsc08lYb6d9llmUmW+ygtDOORCUq2mb9ik+lTWodNDLKnvPoJSqs6hbRVLgTn/CYoW7LH+gPZ7hOESX3Va487/iZO0QC5EFwqP9N9iBhZRdqFTUdAygn7GpLNk6itHUABeWXSbEvZZ5tHCWXrPO/ZLutM6mjfQC5o2Wwax1bzBFqHTQoat8c6m/22hdTVDiJCVoo91tmsyhlK54j/sc82/7r3837A+MFavEYH+tHNEFQyTJ9twvzZZvSjAu2z/bQX+59H0b3bAEkhLc6dFzCP24SsagGUTYphmrAVHG4MX7UBlwfTuE2Yp25D/25DLDN34T6ahWF8CwSlDPNX2zB9uomwicH3X/1EeVSdSoe0VdAGl6dXtkzA+sMe3CeykRbzPYRx/H0cRYM4BLUc229p2ObtQzuwDtJiBhzrT5Hz9t+ETW+HrHi4b/zKY+iG1kdaIgJprJ7MHj+jbFcS/fBGeE6bMI74B2lRA6pHSgXs7T6VQ87bq1B1Lo1uSH1chzIwjd2IJEaDrEoM2a//iaxCNIYJLfGanZgnbMX04fqQWlfmick4Np1GN6gO0lg9tl8PkTNkFeHfdUBaWBfSTpF/B9GZErkjZIV1FFr99C3NuZeO1J2uHzW1/V20RERE5G4RFxfHgQMHbmnOnThSdzp/3rx5d7T3v0m26zjnXTtoFzadSJnviXld3SBOOdYHjQ2XJlBfN4SC8moA6KRt2WubS7b7GNAEk+csBmlRdNLCSAUFjfTDcHotAJjdZ5EJanSSQigkOurq3sTsPhfSJrmgDukE5UWYtBiR0tKcsK+isuYZ3F4nJx3raKjLOxy7kuYp1BJf+NQG08eUUj5CKZXv9KKGtj8XnNd3hiupe/nvQ1n1o2w2fRY0xuv1ctD2C5XUvSim9J1U1Na+zi5hJnZvzg3vZ37jOp6Na8d5n2NRxnevdIPq4lh/KmisNCEc3ZD6/hMdaVsdtrl7cR/LhibgOWtCWtSAtLAOQSFFP6wRXouviI37rBlBLUNSSIdEp0D3Zl3c58xBewAIajmC+uY1vaTFwpCWjsS+6gSaZyrjdbpxrDuJ7r2GAFhn70HzXFUU9X355uru5XGlXsS2cD+6N+sBIIkzoGxdArgSweJAEqlGUlCLtJAOw7hEhPDg0yTb74eRxunRvuzLo5IWNeA1OUAiYF9xDFwe9O/6TroAdO83JLvv77gOXEIIy13PY3JgX3oY3XsNUNT3neRqX62FMzUd67x96AbUDrJT5N9DdKZERERERET+w2S5jyIgJUKa+1Q9UlbG71hdTZyiIeedO9lhnk6O+yRZ7iOYPGfx4qsiW1XzPOuMI5if0ZnC8hrEKRqSoPRpj1VU92aVcSgLM7tRQF6FOHkDSqhCC5Duscwm1fpjyL46ukEUV7YKak9QtuSwfRmVNc9wxrkZCTIKy2uHWAGkKPyOFECmK41SqsBqfdHyiji9ppDzAfSS3GJPckGLh2CNRrs3G5s3gyhZbk6RTFBTU/sKABpF9HXvZ37jPpoFUgFpqQh/m6xMpN+xuhpFwzicO89jnr4D98kc3Eey8Jw1gceXmq95virGEevI6DwfeY3CKBrGoWyVAIC6d0WMQ1eR2W0h8ioFkDeIQ9U2tFNgmb0H64+pIft0g+qgbFU8qF3ZMgH7ssNonqmMc/MZkEmQ1y6M1+rEc96M6fMtvpOzK7g8yMpH+d9eXQFZYlCi7lURy9TtWH/ai6JuERTN41GUi+Ja3EezkJUNbFd18v2/Mn2xBWmJCL8jBSArHo6gU+A6mo28Wq7envtkDni8yCpEB6wlrxSDKy0zpJ0i/x6iMyUiIiIiIvIfRiLc/FeB3ZZZ7LctoISyHUWVjakme4F/jCP8/XGK+nSJmMdpxwbOOLeQbJ7EUfsKWoZ9TrS8PF0i5nDasYmzzi2kWJM4ZP+VtmHTkQmBT/VLqzoRr2we0gaVJCJke4KyBTss08l2HeO4fRXFFM3yvDaJIL/mvRS8gQ6MQGhx+9w5Nz65lFz+muUldK2vG93P/EaQ3byCjmXWbmwL9qNsVwJl46LIXqiGccQ//n5F/Tgi5nXBseE0zi1nME9Kxr7iKGGft0RePpqIOV1wbDqNc8tZrEkp2H89RNj0tgjKwN+hqlNplM3jr90eAEmEKmS7skUCluk7cB3Lxr7qOIpmxRBkEjw2nwOse7sesmudIflV164IjHjRPFcNZZsSODacxrHpNMZ3VqN+ujKaZyoHr5FHnTdBkUcUjdfrd0BvONbtBc9V/27zGidyTxF1pu4jLjyxEPP8vfltxn1F1pj1ZL636o7XcZ01ca7Z9zgPiXoNIv89EhMTSUpKym8z7iuGDh3Kyy+/fMfrnDp1irJly7Jv3767YFX+YJAWw4ubTPcRf9tFZyqLMh/H5bUFjD1g+5nqmhepqX2ZEso2aCUFsXguwGVnYZdlJib3aUqq2tFYP5zG+pGcd+3A6slgv3UBF5y7SFAmUl83lNZhk8l2HyfLdTjIJqXEgF4aG/IlF0KLy6slURSUV+O4Yw1nnJsprrz5iq5h0hJccgWGkaa77vx3qpDoUAkRZLoO+dvcXieLMh/ngjPlhvczv5EWM4Dbi/tI7umHM/UimY8vwmsLPImz/XwAzYvV0b5cE2WbEkgKavFcsPgvxTJzF+7TJlTtSqIf3hj9yMa4dpzHk2HFumA/zl0XUCYmoBtan7DJrXEfz8Z1OCvIJolBiTRWH/IlaEKH/0mi1MirFcSx5jjOzWdQXpakkegUCFFqPBfMAevYlhzCsS44lBHAk2HFNGELQoSvmETY5y1RPVEex9/Hgu9fUQOuQ5kBbaYvt2L6civS+DDcRzLxWnPvo+twJl6z03ffr14nVg8yCa7UwJxuZ+pFpEVvXIxH5N4inkyJ3NcYXqt9v/xNEREReYh47733EJVBfIRJi1FEXpctpvHU1g1AQEKyeRIFZJWRCYFP+pWCgTPOLRSUV8fpNZNimYXTa8bt9eW+5LhPcMaxiTq6QSgEPcfsK9BKCqESwrF40tlvW4hMUKOVFOSIbRlyQYvhcsGIu0GCoiXJ5kkoJWFEyyre9Lzy6sdZb/yQSFkZYuSVOWr/k3RXKgZp0Tu2qby6O3usP6KTFsEgLUaq9UckyIiUlb3h/cxvpMXCkNctgmn8Fl9ejkTAPCkZWeUCCKrAr5CCQYlzyxnk1QviNTuxzErBa3bidboBcJ/IwbHpDLpBdRD0CuwrjiEppEUIV+FJt2BbuN+XN1VQi23ZEQStPMipuBMULRMwT0pGEqZEVjE3XE7dswLW71KQRGuQVYzGse4Utvn70Y9pFnIdwaDEsf4UXosLzVOV8FpdOJPPBp9sAarOZbAtPIDlm50o25XAdTAD++9p6D9sgrxqQaw/7MY0ZgPqPpX9BShk5aOQVYzGcz43Z0xQyVB1LYN52nYEjQxprAHbrwdxH81C99bN65+J3BtEZ0rkvkaiu7MEchEREZFQ6PVipaurqa97h2TzRFbkDEaKjFhFA2pqXgkxbihbzBP4PasvSkk4RRUNKSlpT4b7IAB1tAPZap7IqpwhuLx2omUVaKb/GEGQUFXTF5fXxjrjhzi8RiJkJWmmH4NCcvfEbYspmrDVPIEEZQsE4fpheldTVNGIapoX2GWdicOcQxFFXeLkDf1hendCOdXj/rLwTq+ZaHlFmhs+QSYob3g/7wd079THPDGZnMErQCZF0SAWzSvBwrS6ofUxT9hCVt/fkYQrUTQsiqR9SdwHfREh2oF1ME/cSs6QVXjtLmQVotF/3AxBIqDpWxWvzYXxw3V4jQ5kJSPQj2l2V78DKJoUwzxhK8oWCQH/NlTdyoLDjeXbXXgyrEhj9b5CD3mUFRdkEgxjmmGevI2sF5chSCW+e/JaraCx0oJaDB83wzx9B9b5+5AU1KIdWAdFXV++nf7TRCxfbSP75T8QlDIUjePQvFg95L9dTb9qIBEwjduM1+xEVjYSw/gWd02DSuT2EUV77yGhRHs9RgfGKVuxrT8JXlDWKYJhQF0kBiUXnliI9rHyaB+vgNflwZS0C+tfPj0BiUGJKjEB/cu1EKQS3OkWcsZvwpFyHgBFrSIY3qiDNEKNx+Ik5/NN2LecBocHeeUCGAbUQRZCr8H43U7Ms1JC2m8Y0gBNu8Ayn8Zvd2Bff5Lombkla+2bT5M1fA0xix5HopFjnr8Xy1V6DboXaqCsXgjwhe3h9uA+bcR1MofwDxojqGQYpyTjPJKFRK9A3aoEun7VEaQSssasx2tyEDHaFzvv2HUe49fbcaZlII1Uo32iApouvsRe12kjxqnJOHb57omyQVEMr9ZColcGiQt7nW7M/9uDdflh3OkW5KUi0fevieKyXkMoO5V1Yq++FaJor8h9RU5ODmPGjOHvv//G6/XSuHFjhg0bRnh4OImJiTz99NP06dMHl8vFpEmTWLJkCefPnycsLIz27dvzzjvvIJVKOX/+PMOHD2fr1q0IgkCDBg0YPnw4UVFRmEwmhg8fzrp167Db7dSsWZNhw4aRkJAQZM+kSZOYPHlySFvHjBlDt27dAtomTJjAypUrWbJkib9t7dq1vP7666xfvx6tVktSUhLff/89GRkZlClThsGDB1O3ru+p7NChQ3G5XJw4cYKjR4/y+eefo1arGTt2LAcPHsRgMNCpUycGDx6MVCpl6NCh5OTkMGXKFAC2bt3K+PHj2bdvH9HR0fTt25devXoBcOLECT755BO2bvUlqCcmJvLOO+8QFhYWJCbscDj4+uuv+fnnn7lw4QLly5fnrbfeonbt2nna2bhx4zv75d8ityra+1/gvHMnGkkB9NLcL9B/57xNjKwilTXP5KNlgdxvor0iDxaiaO+9QcyZ+pfJGr4a56EMIj5OJPKL1rhOGcketzFonHluKtY/DhP+TkOiZ3dF/2JNLD8fwL7+JAA5X2zG63ATNaUdkRPb4j5nwjhlGwCmb3fiOppF5PjWRH3TAUEqkP3JhpD2aJ+oSMzCx0O+1IkJQePVLUvgOpKF63iuGKX172MoG8Yh0cix/HYQ87y96AfUIWpGB1TN4sl8ewXOq+KtbSuPoulalsgJrZFXLkDm+6tQVCtE9KzOhL3bCMtvh7AuD46hd53KIeOtFcgrxhA9oyO6ftXJ+SoZ2/qTeIx2Ml5bDkDkl22IGN0cV1oGWaP+CVoHIGfiFixLDqJ/rQ7RMzoirxBDxtsrcJ015mmniMj9zGuvvca+ffuYOnUq33//PceOHeP9998PGvftt9+yePFixo4dyx9//MFbb73F//73P1auXAnAyJEjsdvtzJs3j9mzZ3P69GnGjh0LwJdffsmhQ4dISkri559/RiqV8u6774a0p2/fvqxbty7kq337YAmCjh07cvDgQQ4fzv2/v3TpUlq0aIFWq2XevHl89913fPDBB/zyyy+0bduW559/noMHc5/g//bbbzz55JP88MMP1KxZk1deeYU6deqwdOlSxo0bx/z581m0aFHQ3seOHeO5556jWrVqLF68mMGDBzN27FhWrlxJdnY2Tz75JAA//PADU6ZMYe/evQwaNCjkdX/00UfMnTuX999/n8WLF1O1alWef/55Tp48maedIvnPKccGVhvf5aIzFZP7HAdti7ng3EVRRf6XJxcREbm/EcP8/kVcx7NwbD9H1NePIC/ji60NG1zPd0p1DbKEcMKGNkRRzXeiIyusw/xTKq7Leg3ucyakcQakhfQISinhwxrjtV7WazhnRFDLkBb26TUY3qyP+1zo8q4SjRzySNgMhSw+DFmZSGyrj6F7pipepxv7uhOEve97smr6YTf656ujuqyDIHuiIo7Ui1gW7iPsrQYASOMMqFv7FMqv1muQFtIiK6wj8rOWSMKDK/JYfz+ELE6P4WXfUbqsqAGv0afXYP3rKF6Xm7D3GiG5rD8R9n5jLj37K8796Qhhuet5TA6sv6cR9l4jVA18dhpeq40z9QKWuXsxvFE3yE4RkfuZw4cPs2nTJhYtWkTFir48kVGjRvkdpKspVaoUY8eOpU6dOoBPv+nbb7/l0KFDtG7dmlOnTpGQkEBcXBxKpZLPP/8cs9kXu3/q1Ck0Gg1xcXHo9Xo+/PBDTp8+HdImrVaLVnvzZXpLlixJxYoVWbZsGa+++ioOh4MVK1bw2Wc+7Z5p06YxcOBAmjVrBvictZ07d/L999/z0UcfAZCQkECXLl0AyMrKIjs7m5iYGGJjY/3XGRUVnNcwf/584uPjGTp0KADFixcnOzsbqVTKkiVLcDqdjBs3Do3GV/hg/PjxdOjQgd27dxMRkVtZzmg0smDBAsaNG0fz5r7T9Pfee48dO3b4HcFr7RS5P6iqeRan2cJa4zCcXjNh0uI00X9IuCy4zLaIiIjI1YjO1L+I67Jew9XxrfIyUX7H6mpUDYvi2HkO4/RtuE7k4DqSifusCa/bVwJT91x1skas4ULnuShqFkLZsCjqVj5NBm3vKmQOWcGFrvNQVCmIsmEc6ralgvYAMM3ejXn27pB9hsH1/GtejbplCaxL09A9UxX75tMgk6CsXQSPxafXkD1+EzlfbPaP97o8yMvnJntKC+XGx0sMSrS9KmOckox5zh6UdWNRJSYgLxeopXDl/l3brulcFoDsLzYhLxHhd6QA5Ff0Go5lI6+a60y5Lus1KCrGBKwlr1QAV1putb+r7RQRuZ85ePAgMpmM8uXL+9sqVqzod6yupkWLFmzZsoVx48Zx9OhRDhw4wKlTp2jTpg0AAwcOZMCAAdStW5f69evTokULOnXyhfW+9NJL9OvXj/r161O7dm0SExPp2rVrSJumTZvG9OnTQ/aNHDnSv+bVdOzYkYULF/Lqq6+ydu1aZDIZDRs2xGw2c/r0aT744ANGjhzpH+90OqlSpYr/fWxsbihueHg4L774ImPHjmXGjBk0adKE9u3bB4y/wqFDh6hcObCkcc+ePQEYMWIEZcqU8TtSAKVLl8ZgMJCWluYP3wM4evQobrebatWqBaxVo0aNgGp/V9spcn8gE9TU072Z32aIiIg8gIjO1L/JLeg1mGbtwjx/H+r2pVA1KYb8hRpkjVjj71c1iKPAvEexbTyFY8tpjBO3YltxlMjPW6MoH03MT49i33QK++YzmL7bhfXXg0RNfyRIr0HTqQyqZnnoNUSGVp9XtUjAOG0brmNZ2FYdQ908AUEm8ZdJDRvSAPk1InVXayRcq5egf7466rYlsW84hX3TKTKH/o3umSronqkauLHs9vQavB7PzY31ePFepe2Q5zgRkfsMufzmT5cnT57MrFmzePTRR2ndujWDBw/mjTfe8Pc3b96c1atXs2rVKv755x9Gjx7NkiVLmDVrFlWqVGHlypWsXbuWtWvXMmnSJH766ScWLlyIShV4mtyjRw/atWsX0oZQp0MAjzzyCJ9++ilpaWksW7aMdu3aIZfLsdl85bk//vjjIKdHochNUFcqA7WKBg4cSNeuXVm1ahWrV6/mhRde4JVXXuHVV18NGCeXy/Os7HftmlfweDy43e48bbl2rOeqz6G81hQRERERefAQc6b+RWRFw8DtxXU4N3/IkXqRC48tCNJrMC/aj75/DQwv10LdpiTSQlrcF3LLZBq/3YHrjBFNu1KED29K+MimOLafw51hxTx/L45d51EnFif8nYZETW6H61g2zsOBWgfgOxmSxRlCviR5hP9JozQoqhXEtvo49k2nUV2l1yCJUuO+YA5Yx7LkILZ1J0Ku5c6wkjNhM5IIFdruFYj8vDXaJypgXXksxP0z4DwYqBOVM2EzORM2I4sPw3kkE481t5ys87Jeg6xYoAaD7LJegyP1YkC7Y8/FoLEiIg8CxYsXx+VyceBArk7Ojh07aNKkCVarNWDs7Nmzeeuttxg6dChdunQhNjaWM2fO+J2JCRMmcPLkSR599FEmTJjAl19+yaZNm0hPTycpKYnk5GTat2/P2LFjmTNnDmlpaQH7XiE8PJz4+PiQL50u9KlvgQIFqFu3LsuXL2f16tV07NgR8FXei4mJ4ezZswHr/PTTT6xYsSLkWunp6YwcOZLo6GieffZZZs2axXPPPcfvv/8e8v7t3Ruo8Tdq1ChGjRpFyZIlOXjwIBaLxd+3f/9+TCYTJUoEntzHx8cjl8vZsWNHQPv27duDxoqIiIiIPByIztS/iCw+DGW9WHLGb8S5Px3nwUvkTNyCIoReg8SgxL75DK5TOTgPXiJr5Fq8Zic4fE9CXSdyyJmwGef+dFyncrCuPIq0kA5JuAp3ugXjl1tw7DyH66wR6/I0BK3c58zdJVStSmCem4pEr0R+Vbic9slKmGfvxvrXEVxnjJjnpmKZvy9kJcEr12lbd4KcL7fgOum7VnvyWeTlg59ca7qUxXUyB+M32/3XbPn9EMr6cahblkBQycj+eD3OI5k4dl8g++N1yMtHB9gHPr0GTbdyGKcmY9twCteJbHImbcV1NNMfNigi8iBRsmRJmjZtyrBhw0hJSSE1NZWPPvqImjVrolYHnjCHh4ezdu1ajh8/TmpqKm+88QYmkwmHwwHAkSNHGDlyJCkpKRw/fpzffvuN2NhYIiMjOX/+PKNGjWLLli2cPHmSRYsWodPpKF787uWVdOzYkW+//ZawsDCqV6/ub3/hhReYNm0av/76KydPnmTmzJkkJSWFrCQIEBYWxooVKxg1ahRHjx4lNTWV9evXhwzze/LJJ/2V9Y4dO8bvv//O/Pnzadq0KR07dkStVvP2229z4MABtm3bxttvv03VqlUD7ANQq9X07t2bTz75hFWrVnHkyBFGjx7NwYMH/UUsREREREQeLsQwv3+ZsKENyZm0hYxBf4FMgqphHPpXaoceN2Ez6X2XIAlXompUDHX7Uv6TmbBBdcmZuIWMt1fitbtQVIwhfEwigkRA37c62N1kjfoHj9GOvFQkEWMSkejvnl6Dqkkxcr7YjKpl8QA9BE23cnjtbowzduDJsCKLNRD2fqOgsuJXEGQSIsa0IGfyVi698BtIJSgbxmF4rU7QWGlBHRFjEjFO34Z53l6ffsOgeigv6zVEjmtJzuRkLr20FEEpQ9W4KPr+NUPqNej7VQcBcsZtwGN2Ii8bReT4VshLi3oNIg8mY8eOZfTo0fTp0we5XE5iYmLISntjx45l5MiRdOzYkcjISFq2bMljjz1Gamoq4Mtn+uijj+jXrx82m41q1aoxffp0JBIJb7zxBjabjUGDBpGdnU25cuWYPn06BsPdE9Zs06aN376r/+8+9dRT2Gw2JkyYwMWLF4mPj2f8+PF5lhWXy+V8/fXXfPzxx3Tr1g2ZTEZiYmLICodFihRh+vTpjBs3jpkzZxIbG8uIESNo2rQp4KuA+PHHH9O9e3dUKhWtWrXirbfeCvnZMmjQIARB4P3338doNFKpUiWSkpIC8tnyE5fde8/La4vcO1x2LzLlzetn3Qp2r0ssnf2QY/e6UAriV/+7jagzdQ8JpTMl8vAg6kyJiIiIiIiIiPy3EcP8RETukMTERJKSkm67/27jcrmYPHkyiYmJVK9enR49erBt27abmnv8+HGqVasWlIfSokULypYtG/AKlXsiIiIiIiIiIvJfQjzrExG5QxYsWBCUl5KfTJkyhXnz5jF69Gh/kv7zzz/PkiVLiIuLy3Oex+PhnXfeCSpYYLFYOH36ND/++CPx8bmVH+9maJeIiIiIiIiIyIOIeDIlInKHREZG3lVnasOGDbzwwgucOXPmtubPnz+fV155haZNm5KQkMDQoUOJiYnhzz//vO68WbNmIZEEfySkpaUhkUioUqUKMTEx/pdY3llERERERETkv85dcabsXteNB4mIPKRcHcbndrv5/PPP/aKmX3/99U2t4XA4WLBgAR07duSll14iJiaG8PBwFi1aFBRed+U1adKkoHU8Hg+ffPIJrVq1CmgXBIGcnJw89z969CjTpk3j448/Duo7dOgQcXFxeWro/Ju47GKKp4jIfxXxu4aIiMj9yF0J81MKMhqcGXU3lnpoECvi/DeZOnUqCxYsYOzYsRQuXJhx48Zx+vTpPMenp6czZ84c5syZg0qlokePHjz++ONEREQA0L59+zyrlWk0mqA2iURCgwYNAtpWr17NsWPHaNSoUch1roT3vfzyyxQrViyoPy0tDZlMxosvvsiePXuIi4vjlVdeoUmTJnle171CphQY1eD2TuweFsRKbP8NxL+pwYh/V0VERO5HxJwpEZG7hNfrZc6cObz44ov+ksqffPKJ/+dQDBw4kD179jBixAg6dOiAVCoN6FepVKhUqtu26cCBAwwZMoR27dpRq1atkGO+/fZbJBIJTz31VMj+tLQ0srOzGTRoEG+88QbLly/nxRdf5IcffshzTRERERERERGR/wKiM3WPOOvKEktnP+Rcq9eQmZlJeno6FStW9LdFRkZStGjRPNfo2bMnX331FaNHj+bAgQP06tWL2NhcTa5ff/2V4cOHh5z74osv0r9//zzX3rFjB/3796dMmTKMHTs25JjDhw/zzTffMG/evJD5UgBfffUVDocDnU4HQPny5dm/fz8//vij6EzlA6ZLbnRR0hsPFHlgueQ25bcJIiIiIiI3iehM3SMevTAxv024a9zr0Aqv18uOHTtYs2YNf//9N8eOHWPatGlYrVZef/11fv/996Av+nq9nsjISBITExkwYACdO3f29z311FOULVuW9957j2effZazZ8/SoUMHypYtS4kSJXjkkUf4/vvvqVu3LomJiTz99NP06dMHgBkzZrBlyxbq1avHmjVrmDVrFgAZGRk0aNCAn376iZ49e7Jp0ybCwsLyvJ6rkcvleV57+/btad++PWvXrmXWrFm0bt2a5s2b89RTT/ntq1q1asi5ee0PsHbtWl5//XVq167NpEmT8jzdWrZsGUajka5duwa0Dxo0iC5dujBq1CgUCkVQvlTJkiXZsWNHnvuL3Ds+73g+v024be51iKIYGiciIiIi8m8jOlMi+cqOHTtYu3YtAwYMoEaNGgwcOJDu3bvz559/0qdPH9xuN0ajkSpVqgC+Qg0DBw6kd+/e1K9fH4DU1FS/M2Wz2Th48CCdOnUiLS2NjRs38scff5CQkADA9u3bgWCH5wp16tQhKSkJhULhXx98J0ylS5dmzpw5lC9fPqQjExERQUxMDDt27KB27doAGI1GTpw4ccP70KRJE5o0acKhQ4dISkqiX79+LFu2jNjYWP+J0M2yfft2Xn31VZo1a8b48eOv68z17t2bjh07BrS1bt2ad955h9atWwPwyCOP0KVLF/r16+cfk5qaSsmSJW/JLhERERERERGRh42H3pnK7LEY1WPlUD9W7oZjLUkpONadInxG+7u2v2X2HmyLD+K1OFE0jEM7oDYSXd5V0Wy/p2GdvQdPpg159YJoB9VFGhNcaOBhQa1W8/XXXxMWFkbLli05cuQIR44c4YknnqB48eK0atWKoUOHMnz4cAoUKMBXX31FcnIyI0aM8K8xb948KleuTIUKFZgyZQoKhYJ27dphNpuRSqUsW7aMTp06cezYMT788EPA55SFomLFilitVtavXx/gPADUq1eP+fPn07Nnz5BzBUGgT58+TJ8+nfj4eEqVKsUXX3yBzWa76ftRunRpRo8ezeDBg0MWmLgRbrebIUOGUKJECd59912ysrL8fWq1Gp1Oh81mw2g0EhkZSXh4OOHh4UHrxMTEEBUVBfgEe2fMmEHJkiUpUaIEP//8M9u3b88z/PC/wuLMHpRTPUY59WM3HJtiSeKUYx3tw2fctf33WGZz0LYYp9dCnKIhtbUDUEjydrzTbL+zxzobmyeTgvLq1NUOQiONuWv2iFwf8W+RiIiIyMPJQ68zFTatLaoOpW5qrPqJ8hjGt7hre9t+OYht/n50b9cj7IuWuI9nYx63Kc/xjk2nMU9KRvN8NcK+agMuL8Zha/I8RXkYKFeuHOPGjWPBggW0a9eOYcOG8eyzz9KtWzcAxowZQ40aNXj99dfp1q0b58+fZ+bMmcTE5H4J7Nq1K0lJSXTr1o309HSSkpLQ6XQULFiQUaNGMX/+fNq1a8dHH31Er169KFu2LKmpqSHtkUql1KpVC4lEQqVKlQL66tevj9VqpV69enlez3PPPcdzzz3Hhx9+yOOPP07RokUpX778Ld+XyMjI2yo8kZqayokTJ9i3bx9NmzalUaNG/teXX34JwNKlS2nUqBFnz569qTVff/11nnzyST788EM6duzIhg0bmDlzJiVKlLhl+x4m2oZNo5Sqw02NLa9+ghaG8Xdt74O2X9hvm0893du0DPuCbPdxNpnH5Tn+tGMTyeZJVNM8T5uwr/DiYo1x2EP92XK/If4tEhEREXk4Ebx36dNRjFUPJrPXL6g6l0Hd3fdl2pWWSXa/pYTP6Yy0UPAT5OxBK5AlhKN93ZfU78m2k/noQgzjWyCvWvBftf1q7udytNfmPIk8/PzXS6MD/JLZizKqzpRXdwcg05XG0ux+dA6fg05aKGj8iuxBhMsSqKV9HQC7J5uFmY/SwjCegvLQOXn3AjFnKn8Q/xaJiIiI3Dse+DA/90UL5i+24NxxHolBgfrZqpjHbSL8x05IC+kCQitMYzeCSgoOD441x0EuRdWxFOq+VREE4bqhFZakFKyzdoe0QTukHqq2gfkjngwrnjMm5FUL+NtkpSL4f3v3HR5FtT5w/DszuzvbU+gkQOgg0jtIR0GUoj/Feq+KvV0RC1hQwQKKeLmACldFsBdQvHaliYAQeui9hV5Csr3Mzu+P1YVlFxJKTIDzeZ48z+bMmTPvTGDPnp0z75FsRsJrDiV0YHpEJ7zuEOa+dWJlcoqKUi2F8OqDJdqBCcLFyKsdJNvzb/aHlmOSnTS23MFCzyj6pH6MXakYN83vD/dIFMxECLIj+BsKRmqZe9PYMgBJkk45zS/HO5lVvilJY2hjG0xNc8+4Ml/kCO7IHsofNwhKM9TCKNk4FF6TMJjS9QiHwuuoYz6WpEWVU0hRqnEwvPpvHUxdyERfJAiCcHE6rwdTuhbB9dQcJKeJlHFXoOcHcI9eBJGT32wLfL8Fc/96pEy4kmD2Hrzjl2JoUgFT80qnPJblhvqY+9ROuk2yJT7gHznoBUAuGz/HXEo3x7bFnYsrCH4NuawlrlxOt6AlqS8IQvGJ6BpzXE9hkpxckTKOgJ7PIvdodCIn3WdL4HvqmftzZcoE9gSzWeodTwVDEyqZmp/yWPUtN1Db3CfpNqNkSyjzRg4CYJXLxpWbpfTYtuMFdRcafiwn1LfI6Xi1xPrC6RN9kSAIwsXrvB5MhVccQNt+lLTPr0EuE33jtz3cAtfTc066j1zBhu3upgBYqjjxT9tAeN3hQjswyWJEspw8K9qJ9IAWfWGKfyxNMiroIS1J/XB0u+mE9WNMMoRO/gHuYjdr1qySDkG4AB0Ir+Cotp1r0j7HIkcTcbSwPcwc19Mn3ccmV6CpLZq0xGmpwgb/NA6H1xU6mDJKFoyS5ZR1jqfpAQBk4pMHKJIRTQ8l1A//WV+R4uvLmIiQWF84faIvEgRBuHid34OpLXnIFWyxzgvA0PDU2amUjPgpDZLVCFrhHYT3o9X4Pk6etMA+qBXq5dXj21X/7IhO6Hz0kIakJl72v+rrJ3ZWwUjS+oIgFJ+88BZscoXYQAqgnKHhKfexKxlxvxslKxESP6yeaLX3I9b4Pk66rZV9ENXVy+PKFEkFSBgIaXoIw5/bktY/YaAVIZi0vnD6RF8kCIJw8Tq/3xllKXEahSSdeh+jklhWhBQc5j61UbtUSx5GWmLWNfnPFLKRwz7k9GMdrH7EnzB9AkByqmBWiBz2xZVHjvgwNhNz1AXh7yQhJ0zpkzj1e4tCsrsFhb+51Db3oZraJek2s5yWUGaVox/SfZHDWOT0WLlfP5IwlQ9AlZwomPFFDseV+yJHqGBsVmh8QhGIvkgQBOGidV6nRjfUSCVy0EvkyLE3/fC6Q8VyLNmpomQ4kv5I1sQPUXK6BbmynVDOgWOxbc5D94YwNEj8xlKSJAz1yxI+rn4kP4C2Iz9pfaF4dO3alcmTJxep7rhx42KLBZ8rb7/9NpdddhlNmzbl8ccfp6Cg4KR1w+Ewl156KXXr1o37WbFixTmN6WKUaqiBN3IQX+RIrOxQeF2xHEuVnTiUjKQ/RilxXR+LnI5drsyBUE6sLC+8mZDupZyhQUJ9SZIoa6jPgfCx+oFIPvnajqT1hdMn+iJBEISL13l9Z8rQtAJKjTTcIxZgva8ZujuIZ+zikg4rxnxNXXxTVqFUtiOnW3CPXoSpU1WUCtGHynVfCN0Xjn1baLm2Lq7h8zDUSkOpnYb37eUYaqdjbFT+VIcRzqGpU6disRTt+ZUBAwZw6623nrNjf/LJJ0yePJlRo0aRlpbGc889x7PPPsvYsWOT1t++fTuhUIgZM2bErUmVbBFe4fRUMDQlTanBAvcImlnvI6i7WexJ/ncoCXXN17DKNwW7UhmLnM4i92iqmjphU6J3DkK6j7Dui925qmu5lnmu4aQZapGm1Ga5923SDbUpb2xUkqdxwRB9kSAIwsXrvB5MSZKEY3gHPKOzyX/gZ+Q0FXPfOnj/uwIp2RSKv5n5/+qiFwRwv7oQwhGMbTOwDWwV2+77fB2+KasoM/sWAEyXVcF6fzO8764g4g5hbFoBx4sdSyr8i1J6enrhlf5ks9mw2RKzrZ2pSZMmcd9999GxY/RvPmLECPr27cvu3bvJyMhIqL9p0yYqVqxIlSpVzlkMQpQkSXRwDCfbM5qf8x9AldOoY+7LCu9/UaSiP/xfXOqa/4+AXsBC96tECJNhbEsr28DY9nW+z1nlm8ItZWYDUMV0Gc2s97PC+y6hiJsKxqZ0dLxYQtFfeERfJAiCcPE6rxftjeT5CW84jLF1ZaQ/56eH1h6iYOCvpP94A5JyXs9iLDUupIUS9+/fz/PPP8+iRYtISUnhkUce4ZlnnuGXX34hMzMzbhHgIUOGYLFYCAQC/PTTT5hMJm644QYGDhyIJEmMGzeOGTNm8M033yQcZ9y4cYwfPz5pDCNGjODaa6+NKzt48CCXXXYZ06ZN49JLL42VN2/enGHDhnH11VcntDN27FiWL1/O+++/f5ZX5fzydyza64/kcTi8gcrG1rH3lkOhtfxaMJAb0n9Elkr+A/L56EJdtFf0RX+PC6kvEgThwnFe35lCAtdzc7H8syFqtyz0ggDet5dh6lhVdF5CAk3TuOeee0hNTeXTTz8lLy+PZ599Fk07eca1L7/8kjvuuINp06bx+++/8/LLL9O6dWvatWt3ymMNGDCAG2+8Mek2h8ORULZ//34AypePn0ZTtmxZ9u3bl7SdzZs34/P5+Mc//sHWrVupWbMmjz/+OI0aialbZ09irus5Glr+SZbajYBewDLv21Q1dRQDKSGR6IsEQRAuWuf1YEpONeMY3hHvpBx8H61GshoxdayC7d6mJR2aUAplZ2ezefNmZs+eHRu0DB06lHvvvfek+1SuXJnHHnsMgOrVqzNlyhRycnIKHUyd7hRAv98PgKrGp6o2mUwEg8Gk+2zevBlN03j++edxOBx8/vnn/POf/+Sbb76hWrXk2b6EojHLqXR0DCfHO4nVvo8wSlaqmDrS1HbyfyvCxUv0RYIgCBev83owBWBqk4GpTeLzJIJwonXr1lGpUqW4uz/Nmp06NXTVqlXjfrfb7YTD4UKPNWHCBCZOnJh027Bhw+jTp09c2V+DqBMHTsFg8KQJMb766isikQhWazTj26WXXsrKlSv58ssvefzxxwuNUTi1DFMbMkxtSjoM4Twh+iJBEISL03k/mBKEolIUhUgkfu0gWT71FByTyZRQVpTHDG+88UauvPLKpNvKlCmTUFaxYkUg+uxUuXLH0g8fOnSIChWSr+1yfAY/iD4EX6NGjZNOCxQEQRAEQRDOLTGYOon8gb9iqJWG7aEWJR0KEU8I75tLCS7IhZCGsU0Gtgebx9LY6loE35RVBH7aGl07pHF5bA+3QKloL+HIS5c6deqwf//+uAHLypUri+VYqampp5WivFy5clSpUoUlS5ZwySWXALB+/Xo8Hg9NmjRJqO/3++nSpQtDhw6lV69eQPSZsPXr19OvX79zcAbC3+XX/IGkGWrRwvZQSYcSZ6F7FAbJUuriupiVpn7peO5RC5EshlIXlyAIwt9BPBl7HvCMWkh48xGcI7vgHHcFkSN+XMN+j233T9uA/8ct2J9qi/PNHhCK4HpubglGXDq1adOGunXrMnjwYNavX8/ixYsZPjya/euvDFwl6R//+Afjx49n9uzZrF69mqeeeoqePXtSuXI0A5rH4+HgwYNA9K5Uu3btGD16NIsWLWLLli08++yzHD169KSJLwShKHRdJ8f7PlsCP5R0KEIpp+s63vdzCPywpaRDEQRBKDFiMFXK6Vok+o3fwFYY6pXBUCMNy3X1COccRA9Fs9AF5+eids3C2LQihmopWG69FG1THpH8QAlHX7pIksTYsWORJIn+/fvz5JNPct111wFgNJb82kH//Oc/ufnmm3nqqae4/fbbqVmzJi+99FJs+6RJk7jssstivw8fPpzOnTvz2GOPce2117Jnzx4+/PBD0tLSSiJ84QLg1Q4yy/UkG/3fYJWTTy8VBADtoBfXk7Pwf7MRuYK1pMMRBEEoMaVqmp//2034PltL5KAXuaId6y0NUHvUAEDLLcDz9jLCKw+gBzWUDAfWu5pgap8JQN6N07Hc3IDAzO2E1x1CqeLE/mx7gnN24p++AWQJy00NsPSvD/w5XaJBWbTNeYRWHEDJsGO9rxmmVsnXQQku3ov3nRVoO/JRKtowX1MXtW9tJElC94Zwv5FNaPEe9KCGsWF5bP9qgZLpTGjHOzkH35RVSY9hG9wGc8+acWWSImMf3Db2e+SwD/+3mzA0KhdbDFJOUQn+sRvz/9VFTjUT+GUrcmU7kr3kBwilyeHDh9myZQvvvvtu7E7UihUrMBqNscV6Z82aFas/cuTIhDaOX1Pq4Ycf5uGHHz5n8UmSxMCBAxk4cGDS7Scez2azMXToUIYOHXrOYrhYbPJ/y1rfZ3gjB7HLFWlgvYUaag8ACrRclnne5kB4JZoexKFk0MR6F5mm9gBMz7uRBpab2R6YyaHwOpxKFdrbn2VncA4b/NORkGlguYn6lv5AdBpfWUMD8rTNHAitwK5k0Mx6H5VNrZLGtje4mBXed8jXdmBTKlLXfA211b5IkkRI95LtfoM9ocVoepDyxoa0sP0Lp5KZ0E6OdzKrfFOSHqONbTA1zT0Tyo9om7HKZWiX+hTzXCWzZlNpI/qlxH4JQNt8BLmMldSn2uEaPu+Mrq0gCMKFoNQMpsKbjuAZsxj7s+0x1C9DaNEe3K/+gaFBWeQMBwVP/4ahdhopb/VA18H3wSrcry0kbeo1sUGF970V2J9og1LViWvYPAoe+RVT+0xSxvUgMHsH3onLMXWsEnuWyP/leiy3NMD2YHP8P23F9cxvpE66CqVKfGcT3pGPa+hv2O5vhrFFJcKb8/CMyQZZwtynNt5JK9G2HcU5uhuSasDz5lLcry0kZewVCedpuaE+5j61k14DyXbqwY979CIC321Gcppwjrk8Vm69pwmuoXM5esN0kCUkuwnn2MvF+iYnkCSJhx56iAceeICrr76ao0eP8uqrr9KjRw8MhlLzX0EoZkfCm1jsGUN7+7OUMdRnT2gRf7hfpayhAQ45g98KnibNUJseKW+BrrPK9wEL3a9xTdpUFCn6f3SF9z3a2J/AqVRlnmsYvxY8QqapPT1SxrEjMJvl3olUMXXErkQTi6z3f0kDyy00tz3IVv9P/OZ6hqtSJ+FUqsTFlh/ewW+uoTSz3U8lYwvywpvJ9oxBQqa2uQ8rvZM4qm2jm3M0BkllqedNFrpf44qUsQnnWd9yA7XNfRLKAYxS8rT9maa2ZJraJt12MRL90sn7JVPbTExtEwfxgiAIF5tS8wlS2+cBQK5gQ6loR+lbBznDgZRqhoCG+aqaqD1rIqdEU0hb+tcnOGsHkTw/SvnoBwNTp6qYLot+ODF1qYZvyipsA1siqQYsN12Cb3LOn9/gRTstw6XlsN4WXeDUdk9TQov24P9hS8LaIP5P16J2qYa5bx0AlAwHkSM+fF+sw9ynNtpeD5LFgFzRjmw3YX+8dex8TiRZjEiWM7tjZL6uHuarauH7ZA0Fj80kddJVyKlmtN0uJJsRxyudkVJUfO+vxP3C76S81RPJUmr+xCUuPT2d8ePH85///IcJEyZgs9no0aOHSCN+kfFo0WyHNrkCdqUidZS+OOQMzFIqGgFqmq+iptoTVU4BoL6lPzuCs/BH8rAp0bT6VU2dqGKKTrmsZurCKt8UWtoGYpBULrHcRI5vMvnajthgqpzhUhpZbwOgqe0e9oQWscX/Q8K6VWv9n1JN7UIdc18AHEoGvsgR1vm+oLa5Dx5tLwbJgl2uiEm209r+eOx8TmSULBil5Gn1haIR/ZIgCIJQmFLzSdvUqhLGZhUoePBnlKpOjG0yUHvWQLZHU1Ob+9UhMGM74Q2H0Xa50DYdie4YOZamWqnsiL2WzAbkdDOSGj1FyRT9lpDQsdTYxkbH1hsCMNQtg7btaEJs4W1H0bYeJTBn57HCiA7hCHpIw3JrA1xDZpN37TSMjcpjbJeJuWeNpOfp/Wg1vo/XJN1mH9QK9fLqyS8QYKgW/XBnf6Ydef2nE5ixHfNVNXG/OB/7c5fFpoIoL3Tk6A1fE5i1HfNVtU7a3sWoU6dOdOrUqaTDEEpQJVMrKhib8XPBgziVqmQY21BD7YlJjn6YrWPux/bADA6HN+DSdnFE2wSAzrH3DodybNqVQTJjltMxSNEP1IoUfc+KEIrVKW9sFBdDGUNdjmrbEmI7Gt7GUW0rOwNzYmU6ESKE0fQQDSy3Mts1hGl511Le2IhMYztqJJmuB7Da+xFrfB8n3dbKPojq6uVJtwnHiH6p8H5JEAThYldqBlOSasDxWlfCaw4RWrib4B+78X+9AccrnTE2KEf+Qz+DQcbUoQqmthlgUnA9Piu+EcMJ09oKy9CmnLA9oieWAWg65r61MV9TN0kbMsb6ZUn7tB/BhbsJZe/FNzmHwP82kTKxZ6zT/Iu5T23ULtWShiOnmRPKdF+YYPYeTK0qxb45lFQDcmU7er4fbUcBuieEUj31WDs2I3IVJ5E97lOfvyBchAySSlfHaxwKr2F3aCG7g3+wwf81nR2vUM7YgJ/zH0LGQBVTBzJMbVEwMcsVf/dSPuGtU+LU7zUSStzvOpGEsmi5Rm1zX+qar0nYJqNQ1liffmmfsju4kL2hbHJ8k9kU+B89UybGBnN/qW3uQzW1S9J4zLJIUlIUol9K3i8JgiAIx5SawVRozUFCi/ZgHdAY46XlsN7VhPwHfiL4+y4IRdB25JP+XX8kczTkwIw/v9UtfP3UkwpvPBJ7res64Q2HMXWoklBPqeZEy3WhZBz7hjHwy1ZCOQexP94a39T1KBkO1K5ZqF2z0Hbmc/S27whvOYrxkrJxbclOFZzqiYc4KT2i4x4+D/sz7VC7ZgHRdaciuwpQrq+HVCY6jUfbmodSLppRSQ9qRPa4ka8W60yVhH/84x/Uq1ePZ555pqRDwePx8O9//5tffvkFr9dLo0aNePrpp6lV6+K9Y3kwtIY9oUU0tg6gnPFSmljv4qf8B9gV/J0IIfK1HfRP/w6DFP0QuS0w4889z/zN5kh4Y+y1ruscDm+giqlDQj2nUg2XlotDyYiVbQ38wsFQDq3tj7PeNxWHkkGW2pUstSv52k6+O3obR8NbKGu8JK4tVXaikphsQCg60S8JgiAIhSk1GQokswHfJ2vwTV2Pts9NMHsP2s4CDPXKIKWooOkE5uxA2+cm8PsuPG8vi+74Z3rwMxFasBvf1xvQdhXgnbAcbZ8HNcm0OMuNlxBavBfv5By03AKCC3LxjFuKnBrtfCKHvHjGLia0Yj/aXjf+H7ci2YwoVc/+g4xsM6JeWQPvf1cQWrGf8JY83C/OQ65gw9SxKko5K8bLMvG+tYxQzgHCO/Jxj/wDSVVQuyb/plG4eAwfPpwFCxYwevRoPv/8c9LS0rjjjjtwuVwlHVqJMUhm1vg+Yb1vKm5tH3uC2RRoOyljqIcqpaCjsSMwB7e2j12B31nmeRsA7bhpe6drd2gBG3xfU6DtYrl3Ah5tH7XUqxLqXWK5kb2hxeR4J1Og5ZIbXMBSzzhUORUAb+QQiz1j2R9agVvby1b/jxglG06l6hnHJpyc6JcEQRCEwpSaO1OGmmnYn2mP74NVeP+7HDnVjLl/fdSeNZAkCcudjfG+swLdG0LJdGK7vxmesUsIbziCUjXljI5p6lyV0PxcvBOWY6iRivP1rigVErNcGWqn43ixI973c/B9siYa2zV1sNzWEADrgMbo/jCuF+ehu4IYaqbhGNE5Nq/+bNkeboH3vZW4XpqP7glhalkJx8guSH9OH3E81Q7vuytwvzgP3a9huLQszjHdxQPFFzmfz8d3333H22+/TcuWLQEYMWIErVq1YsGCBfTo0aOEIywZaYaatLc/wyrfByz3/heznEp9c39qqD2RJInGljtZ4X2HkO7FqWTSzHY/SzxjORLeQMoZDlqqmjqTG5rPcu8EUg016Op8HZuSuI5TuqE2HR0vkuN9nzW+TzDLqdQxX0NDSzR5RWPrAMK6n3muFwnqLtIMNensGBF73ks4t0S/JAiCIBRG0nX9LCYkHNNuz/m1Jkn+wF8x1ErD9lCLkg6l1FtQ+bmSDqFQn3/+Oe+++y579+4lMzOT++67j379+gGwfft2Ro4cyZIlS/D7/WRlZfHoo4/SrVs3ALp27co999zDd999x8qVK6levTqvv/46P/30Ex9//DGyLHP33XczYMAAIDqNr0mTJqxbt47s7GyqVavGk08+SYcOHWLbj5/mN2/ePN544w02b95MRkYGt956KzfffDOSJOF2u3n++eeZN28egUCA5s2bM3ToULKyshLOcdy4cYwfPz7p+Y8YMYJrr702rszv97N48WKaNWuGzRb9MBYOh2nRogXPPPMM119//Vlf95IwvN2ekg7htPyaP5A0Qy1a2B4q6VBK3HMLkq+XdK6cb/3QiUS/dGrnQ18kCMLFp9RM8xOEM7V27VpeeOEFHn30UX7++Wf++c9/MmTIELZv346u69x3333YbDY+//xzvvnmG+rUqcPTTz9NMBiMtfHvf/+b22+/nenTpwNw6623snfvXj799FNuueUWRo0aRW5ubqz++++/T5MmTfj666/p1KkT999/P9u2JWZn27JlCw8++CDXXXcd3333HQMHDmT8+PF8/vnnAPznP/9h06ZNTJ48ma+//hpFUXj66aeTnueAAQOYN29e0p9evXol1DebzXTo0CE2kILooDMYDNK2rVhLSBAEQRAE4WyVmml+gnCmdu/eDUBGRgYZGRncfPPNVKtWjfT0dPx+P9dffz3XXnstaWnRDGZ33HEH33//PYcPH6ZSpUoA9OjRg+7duwPQq1cvxo8fz/PPP4/ZbObuu+9m3LhxbNmyhczM6CKVzZs356GHoncaHn/8cX777TemTp3KE088ERfbO++8Q69evbj55psBqFq1KgcPHmTSpEnceOON5ObmYrVayczMxOFw8OKLL8bO50Q2my1uYHS6/vjjD1599VXuuOOO2HkIgiAIgiAIZ+6iHUyljBFrrFwoOnToQNu2benfvz81atSgc+fOXHPNNTid0Qetb7nlFr799ltWrVrFtm3bWLt2LQCaduwh8apVjz0LYzabKVu2LGZzNJubqkYf6A6FjiUgaNEifhpOw4YN2bRpU0JsGzduZOPGjfz000+xMk3TCIVCBINB7r//fu6++27atm1Ly5Yt6dq1K9dck5gWG2DChAlMnDgx6bZhw4bRp0+fk16jGTNmMGjQIC6//HIee+yxk9YTzr3LU8aUdAjCeUL0S4IgCOefi3YwJVw4zGYz7733HsuXL+e3335j1qxZfPTRR0yYMIGmTZtyww03YDQaueKKK+jSpQuqqnLHHXfEtWEwxP9XkOVTz4BVlPg1giKRSNJ9NE3jpptu4tZbb03YZjAYaNSoETNnzmTu3LnMnTuXcePG8dlnnzFt2rTYYO4vN954I1deeWXSeMqUKXPSWKdNm8bQoUPp168fL730UqHnJgiCIAiCIBRNqR5M5d04HfN19bBcV6+kQ0Hb5+boTd8AYGxZCedrXdG1CN63lhGYuR00HfXy6ljvb4pkTFyMM5nIIS+ecUsILd0HioSpU1Vs9zeLZeHT/WE8E5YRnLsLNB1j60rYHmyBnFK09UBOFV/+Az8RXncYgLRvrz+vMzwtX76cuXPn8sgjj9CsWTMeffRR+vfvzy+//EIoFGLr1q0sWbIEiyW6Jte3334LRNdwOVNr1qyJvdZ1ndWrV8emCR6vVq1abN++nWrVjqWpnz59OkuXLuXFF19k8uTJZGVl0atXL3r16sWWLVvo1asXGzZsoHHjxnFtpaamkpqaelpx/vTTTzzzzDPceuutPPPMM0iFLRh6kZqedyP1zNdRz3JdSYeCW9vHN0dvAqCSsSVdna8R0TWWed9ie2AmOhrV1ctpar0fRSpaxs6z3f8vuh5hlmswGcbWsWv1U/4DHA6vA+D6tG8visyCF3rf9Bc9ouMaPAtj64zTOteLpe8RBEGAUj6YKo2cr3dDqRN99sb77kqCC3fjeKkThCO4R/4BRhnb/c2K1JZr6FwkuxHnuCuiA6dRC3H/ezGOp9tF2//vcsI5B3C83AnJIOMetQjP6EU4hncsUvunis8xsguhFftxP//7mV2IUsRisfDf//6XlJQUunfvztatW9m6dSs33HADqamphMNhfvzxR1q3bs3atWt59dVXAeISUJyuv+5+tW/fni+++ILdu3fTv3//hHp33nkn1113HePGjaN3795s3bqVl19+OfYM1f79+/nggw+wWq1UqlSJr776CrvdTvXq1c84tr8UFBQwdOhQOnTowL333suhQ4di2+x2e2xwKZRO3Zyvk6bUAWCl9112BxfSyfESEcL84R6JjJFmtvuL1NbZ7g8Q0cNke95gX2gJGcbWsfIujpHsD63gd/fzp3eCwjl1LvsmAD0cwfNGNqEl+zC2zih8h+NcLH2PIAgCiGx+p01KMSE7VPSghn/6Rqz3NsV4aTmMTSpge6g5/v9tQg+EC20n4gogV7ZjG9QaQ/VUjPXLol5Vi9DyfbE6wQW5qL1rY6xfFkPtdCzX1SW4ZG+R4iwsPtmpRle9vwDUq1ePUaNGMXXqVK688kqGDh3KHXfcwbXXXkuTJk149NFHeeONN7jqqqt48803GTx4MCkpKXF3l05Xr169mDlzJn379mXJkiVMmjSJypUT0z5fcskljB8/npkzZ3L11VczbNgwbr31Vv71r38BMHDgQDp16sSgQYPo1asX2dnZTJw4Mfa819mYN28eBQUFzJ07l8suuyzu569sgkLpZZJSUGUHmh5ko386Ta33Us54KRWMTWhue4hN/v8R1gOFtnO2+wPkh7fzc/5D7A+txCTF33lSZSeqLBaCLWnnqm8CCG/PJ/+hnwmt3I90mneOLqa+RxAEAYr5zpTrlegis86XO8fK/P/biO+TtaR+2hc9z4/nrWWEluxF94SQy1ux3NwAc5LV3t0j/yDiDuJ8qVOs7OhdP2C6LBPr7Y0ACMzYhu/D1Wj7PCiZDiz/bIjaKfkim/kDfyW88kDSbamf9kWpeOqpKuHNeeAPY2x8bOFNQ+Py4A8T3pyHsUG5U+4vO1QcQy+L/a7tdhH4dRvGphVjZZLTTHDODtSuWWCQCczagaFO+inbPVfxnW/+miaXzH333cd9990XV9a7d+/Y61mzZsVtu/3227n99tvjyjZs2BD3e3p6OqNHj056vA8//DDu965du9K1a9ekdVVV5fnnn+f558/9t/qnuiYXmvmuVwjpHjo7X46VbfT/j7W+T+ib+il+PY9lnrfYG1pCSPdglcvTwHIztcxXJbT1h3skwYibTs6XYmU/HL2LTNNlNLLeDsC2wAxW+z7Eo+3DoWTS0PJPqqqdEtqC6DpTB8Irk27rm/opdqVi0m1/yQtvJoyfCsZj0z7LGxoTxk9eeDPljA2KdX+AA+Ecyhsb0shyO9/n31lo/dJO9E2nFs45gLFheSy3NyL/zu8LrX+ujy8IgnA+KdbBlNq9Oq5nfyPiDsbmRQdm7UDtloUkSbhG/AERHefobkhmA/7pG/GMWYypbQZy+ulNQQou2YtnzGJsj7bCUK8MoZUHcI9cgGQxYGqVeMfAMbwjhCNJ25KK8ExS5KAXjHLc80uyQwWjHN12GgqemEVoyV7kijasdzeJldsGtsQ9/HfyrpkKkoRc0UbKuCuK1Oa5jE8QSrvqand+cz1LMOKOPbOzIzCLLLUbkiTxh2sEOhG6OUdjkMxs9E9nsWcMGaa2WOSifUHxl73BJSz2jKGV7VHKGOpxILSSBe6RGCQLlU2tEup3dAwnQvI7AqqUUujxvJGDyBhR5WN1VdmBjBFv5GCx7w9Q23zyTJHnI9E3nZq5T+0i1Suu4wuCIJxPinWan7F5RSS7idD86GKnkcM+wqsOYuqeFd3eNgPboFYYaqahZDiw3NoAwhG0XNdpH8v30WrM19dH7ZaFkuHA3Ksm5j518H+xLml92akip1uS/khKES5LIIxkSnyYVzLKEEreEZ6M9b6mOMdejlzWiuvxmeihaMpubUc+cmUHztHdop26zYTrpflFS5xwDuMThNKuorE5JslObmg+AL7IYQ6GV5FliiYFyTC2pZVtEGmGmjiUDBpYbiVCGJeWe6pmk1rt+4j65uvJUrvhUDKoae5FHXMf1vm/SFpflZ1Y5PSkP7JUeEKAMAEUKXGqlSwZiRBKsse53f9CJPqmYlTSxxcEQfibFeudKUmRUTtXIzBnB2qPGgRm70CpnoqheioA5r61Cc7ZiX/aerRdBYQ35UV3jJx+ljVtWz7htYfwfbb2uMIIcqo5af2CwbMI5ST/VjZ18tUoFQpZHFU1oAe1hGI9FAH19DImGWpGHxp2DOtA3vVfE1q0B6VmGp7XF5H6/lUoVaPfKDuGd+Dozd8QWrYPU/NKf1t8QrwTp/EJJU+WFKqpndkRmEMNtQc7ArNJVaqTaogm8qht7svO4BzW+6dRoO0iLxxdE0zn9D/c5WvbOBRey1rfZ7GyCBpmOTVp/VkFgzkYykm67erUydiUCkm3/cWAiqYnJkuJ6CEUCr9Tcbb7X4hE31SMSvr4giAIf7Niz+Zn6p5FwcBfibiD0ed//vzmT9d1XE/ORjvoQe2ahdq7NtaqKeTf8V3RG9cica+tAxpj6lAlvo6cPBW07fE2kOQNH0AuW/g0DrmcFUKRaCIJR/QDSaQgAKEIcllroftH8v2Elu7D1KVaLF21nG5BcpqI5AfQNxwGWUKucuzBbqWiHSlFJbLHDc2LNz5BON9kmbrza8FAghE3O4JzyFKjd6V0XWe260k82kGy1K7UVnuTYq3Kd/l3FNLiMRG0uNeNrQOoYuoQV0c6yY3+NrbH0UieOdIily302Fa5HBFCBCIuVNkBQCBSQIQQ1r9h/wuV6JuKR0kfXxAE4e9W7Nn8jJeURS5vI/DDFsLrDmPqlgWAtj2f0LJ9OF/pjPX2Rqgdq6K7//zAkWwam0FG9x6bkqKHI3Hzr5WsFLS9bpQMR+wnOG8XgR82J41LKWeNq3v8T1GmUhhqpoLZQPi4bxDDOQfAbMBQK63Q/SNH/LhfnB9bbwOi64XoRwMo1VORy1ii00p2Fhy3jw+9IIhS2VHs8V0IunbtyuTJk0s6DAByc3OpW7cudevW5c47ow/wh8NhXnrpJdq0aUPLli156aWXzihdeyQSYcCAAWd8rsn279+/fyzegoKCk+9cipQ1XoJNLs+WwA8cDq8jy9QNgHxtO/tCy+jsfIVG1tupqnYkqLsB0El8r5ExENKPvbdE9HDcs0UpShZubS8OJSP2sys4j82BH5LGZVXKxdU9/qco0/xSDTUxYOZg+NjdrQPhHAyYSTMkJkQ41/tfqETfVDxK+viCIAh/t79lnSm1WxbeyTkYGpVHKRf9Zkqym0CWCMzZido9Cy3XhWfsEuDP6QAnBnpJWQK/bCU4PxelmhPfp2vj6lluaoDrhd9RslIwtapMePVBvO+txPavlsVyTpJqwHx1LTzjliA5TKDreMYvxdyvTmy+eKQgmnY4WRpYQ/VUjC0q4vl3NvbHW6NHdDxjl2BsVQnjJWXRtQhK3XQ8oxZie7gFKBKet5ah1ErD0LRCoe0XJT7h7zd58mQuueQSAP79738zZ84c3nrrLcLhMEOGDMFoNDJ48OAitxcKhXj++eeZP38+HTsWbf2xouz/3//+l+zsbB5++OHTbrMkZandyPFOpryhEVYlmjXMJNmRkNkZiN6tcmm5LPGMBaJT3U5U1nAJWwO/kBucj1Opxlrfp3H1Glhu4nfXC6QoWVQ2teJgeDUrve/R0vavYjkng6RSy3w1SzzjMEkOdHSWesZTx9wv9ixUIBId8CZLUX62+1/IRN90ZtM8Rd8jCIJwzN8zmOqehe+DVah/fvMH0W/fbI+1xvfBKnwfrkapaMPcrw6B7zcT3ngkIcuRenkW4fWHcI9YAIqMuW9tTK2P1TG1z8T2aCt8n63F+/Yy5HJWrPc1w3x18X3zar27CXogjOvpOaDIqN2zsN51LP2w67m5AKSMuTzp/vahl+F9exkFg2dDOIKpQxWsD0bn70mKjPOVznjeXkbBU3NA1zE2r4Rt6GVIf04PKaz9wuIT/n6pqamkpKQQCAT45JNPePXVV2nWLLqQ5tNPP80TTzzBI488gtmc/HmK423evJnBgweTn59/RutSnWr/v+I832Sp3Vnl+4AstVuszKqUo7XtMVb5PmC170NsSkXqmPuxOfA9R8IbEzLwZamXcyi8ngXuEcgo1Db3pbLp2CK1mab2tLI9ylrfZyzzvo1VLkcz633UMl9dbOfVxHo3YT3AHNfTyChkqd1pbL0rtn2u6zkALk8ZUyz7X6hE35S87yiM6HsEQRCO+VsGU0oVJ2Vm35JQbu5VE3OvmnFllmvrxl6nfdYv9loyKtgHtYZBrTmZZO0VJ8l06pgcL3SIdrAnITtV7IPbnnx7ugXHM+1Pur2w9guLr7R74okncLlcTJgwIVb26aef8s477zBz5kwOHz7MiBEjmD9/Pm63m4oVK3Lvvfdy/fXXJ7Q1ZMgQCgoKeOutt2Jlffv2pXv37rG7L99++y1vvfUWu3fvJisriwceeICePXsmje0f//gH2dnZSbfNnDmTzMzMU57bunXr8Hq9tGx57Nvpli1b4vV6WbduHU2bNj3l/gBLliyhRYsWPPzww/Tpc/qpq892/9LIqVThljKzE8prmntR0xy/5lZdy7Wx1/3SjiWTUCQjre2DaM2gkx4nWXvFSZFMp4ypg+MFFrhHFNv+xzv+Wp3vRN9UuOPPtaj7n+99jyAIwun4WwZTFxI9Pxj3YO2peKesQu1Ro9hiOZv2IwWB2FSN0qp379488MADuFwuHI7oc2I//PADV199NZIkMXjwYDRNY/LkyVgsFj7++GOGDRtGly5dKFv29B6snz9/PsOGDeOFF16gUaNGZGdnM2TIEGw2Gx06dEioP27cOEKh5Gml09MLX7do//79GI1G0tKOPUOQkpKC0Whk3759RYr5xhtvLFK94tpfKF5BPT8uacSprPJOoYba44yPdTb7ByIFsWmCQsn5O/umC73vEQRBOB1iMHWaCh6fibFlJZyvdS20ru2BZkjG4psjfjbtu4bMjkt+URq1b98ep9PJzJkz6devHwcOHGDJkiUMHToUgM6dO9OxY0eqVasGwH333ceUKVPYvn37aQ+mJkyYwO23387VV0enalWtWpUtW7YwadKkpIOp1NTUszo3n8+HqiZ+6DGZTGeUhEK48MwseJxKxpZ0db5WaN1mtgdQJOMZH+ts9p/tGsLhcPI1k4S/z9/ZN13ofY8gCMLpEIOpIlIq2pNOBzmV4hxInW37KW8ln75WmiiKQq9evfjxxx/p168fP/74I3Xq1KFOnToA3HTTTfz000988MEHbNu2jbVro+u4aFrytMKnsnHjRlasWMF7770XKwuHwye9y3TXXXexdOnSpNu+//57KleunHTbX8xmc9JBUzAYxGIpPP2xcOGyKxWTTlU8lbMZSJ3t/j1T3iq8klBsSqJvutD7HkEQhNMhBlNCqda7d29uueUWCgoK+PHHH+nduzcQXQvmrrvuYu/evVx99dXceOON1KhRg6uuuqrIbR8/6NI0jUceeYTLL49/oFqWk6cifvnll/H7/Um3lS9fvtBjV6xYkWAwSH5+fizRw9GjRwmFQlSocOpFXAVBEARBEITSodjXmUom78bp+KauL4lDl1rukX9Q8OxvZ92Ots/N4S4fE9585BxEVfIaN25M5cqVmTp1Kjk5ObFpeJs3b+aPP/5g4sSJPPzww1xxxRWx9ZD0JGvBGI1GPB5P7PdQKMTevXtjv9eqVYvc3FyqVasW+5kxYwZTp05NGleFChXi6h7/YzAU/h1FvXr1sFqtLFmyJFa2ZMkSrFYr9erVK9rFEYpket6NrPcl/zueL/5wj+S3gmfPeP8t/p/44kjRsw1+fLgLu4Lzzvh45wvRFyUSfZEgCMLpEXemSgnrQ81LOoRS6+qrr2bcuHE0b96cihUrAuB0OlEUhR9//JE+ffqwfft2XnzxRYCk0+caN27M9OnTmTlzJjVr1uSdd96JSyBx991388gjj1CrVi06duzIsmXL+Pe//81zzz1XLOdkNpu5/vrrefnll0lJSUHXdV555RVuvvnm2LNUR48eBc78+ayz3V8oPZpbHzqr/aupXcgwFT2z2rVp0zBJ9rM6pnB+En2RIAjC6RGDqVJCtptKOoRSq3fv3rz55puxKX4QvTM0fPhw3nrrLd5++20yMjK45ZZb+PLLL1mzZk3CArZ9+vQhJyeHJ598EoPBwE033USnTp1i27t168YLL7zAu+++y8iRI6lUqRKDBw+mf//+xXZejz/+OH6/n/vuuw9FUejduzePPvpobPtfKds//PDDM2r/bPcXSg+TfHYDG4OkYpCKvkCrRS48I6VwYRJ9kSAIwukptsFUxB3E++ZSggtyQQdjy0rYHmmZsGK6rkXwvZ9DYMZ2Ioe8SE4VtUs1rA80Q1JkIoe8uN/IJpxzAABjiz/bSTOje0O438gmtHgPelDD2LA8tn+1QMlMXMDUOzkH35RVSWO1DW6DuWf8GiDeSSsJzs8l9b1jz+AEs/fgen4u6V/9H5LFiG/qevzT1hM56sdQPRXrPU0xNok+7+Ie+Qe6FiGy24W2y4V9aHskiwHPW8vQth5FcpiiCxne3QRJkXGP/IOIO4jzpegH/NDK/XjfWUF4cx5yugVL//qY+0UTL2i7XXgmLCO8MnpNTO0ysT7YLGlKXD2k4ftkLYGftxI55MVQKw3rvU0xNj55nCcuSlnSqlevzoYNGxLKr7vuOq677rq4sn/84x+x17NmzYq9NplMDB8+nOHDh5/0OMnaK06FxfSf//yHJ598skhtHX+u52r/80kw4map901ygwsAnUrGlrS0PYIqx78XRHSNHN/7bA/MwBs5hCo5qaZ2oZn1AWRJwRs5RLb7DQ6EcwCoZGxBS9sjmOU0QrqXbPcb7AktRtODlDc2pIXtXziVxDXFcryTWeWbkjTWNrbB1DTHP4S/0juJ3OB8rko9lgBlTzCbua7n+b/0r1ji+Q/BiJtOzpfY4v+JDf6vSFGy2B1aQD3z9TSy3sYm/7es9X2KL3KYSsYW2JSKhHQPbe1D2OL/iaXe8fRP/w63to9vjt5EB/swVvom4db2kG6oTSvbINIM0ffBjw93oaPjRaqYLkPXI6zxfcKmwHcEIvmUMdShhe1h0gy1Cr2epYHoi0RfJAiCUJyKbTDlem4uuiuI4+XOSGYD7tcX4nl9EY7h8XcM/J+tJfDLNuxPt0OuaCO04gCeUQsxNC6P2rEq7jGLIajhfLMHhCO4Ry3E8/ZSHE+3xztpJdq2ozhHd0NSDXjeXIr7tYWkjL0iIR7LDfUx96mdNFbJlpjJSu2ehe/D1Wg781GqRhMEBGftwNQuE8lixP/dZvxfrMP2aCuUqk6C83MpeHIWKROvxFA9NVp/5nbsQ9qi1EhDyXCQd+PXqFfWxPH8ZUR2u3G98DtKFSfmq2rFHVvLLaDgydmY+9bGPrgt4U1HcI/8A7mcFUOjcuT/6xcMl5TFOaY7uieEZ8xi3C/OT5oS1zN2CcGFu7EPaoWS4cD/v00UDJ5N6vtXo1SyJ41TKD5Hjx6NSzpxKuPHj+eaa64542Odzf5/xXm+mOt6jqDuorPjZQySmYXu11nkeZ2OjviB6lr/Z2wL/EI7+9PY5IocCK1goWcU5Q2Nqap2ZLF7DBpBejjfJEKYhe5RLPW8TXvH06z0TuKoto1uztEYJJWlnjdZ6H6NK1LGJsRT33IDtc3JF0I2SraEsiy1O6t9H5Kv7SRFqQrAjuAsMk3tMEqJ2R3ztE2UMzTgypSJSCjsDPzGEs84WtoeobyxIZv9P7DO/wU11MT3wr+s9E2ilW0gRsnGIvcoFnvGcEXKuIR6q30fsd4/jda2QaQZarHa9xGzC4bQN+0T1vm/POX1LA1EXyT6IkEQhOJULIOp8I58wsv3R9/M60Sni9gHtSY4PzehrpKVin1w29i3aEpPO/7P16Jtz4eOENnrRqniRKlkRzIpOIZehu6NPuui7fUgWQzIFe3IdhP2x1uj7fMkHANAshiRLEVP/6tUTUGpnU5g9k6stzVED2kE5+3C/kx7AHwfrcZ6Z2NMbTMAsPSvT3jNQfzT1mN/vA0AcqYT9YrowoaRggC6K4icbkGuYEOpaMc5qitSauI3eP7vt6BkOrA9EJ27rlRxoruDIEsEZmyHcATH09FvFwHsz7Ynf8D3hDccRko51l7EHSTwwxbsz7TD1Db67bntoRaE1hzC98U67I+0TIhTKF633347l112WVwK9pMZMmQIJtOZT7k5m/3vueceVq5cecbH/jvlh3ewP7ycK1Mmkm6IfmPe2j6I3OD8hLqpShZt7YOpYGwCgF3pyVr/5+Rr24GOuCN7cSpVsCuVUCQTlzmGEtK9AHi0vRgkC3a5IibZTmv743i05AssGyVL0kHQyaQoVUlXarMzMJuG1tvQ9BC7gvNob3/mpPtcav1HbDreAvcr1FKvopY5eveime0+DoRO/fe71HJL7DrUtfwfi9yvJ9TRdZ2N/m+41HILVdXonYqWtn+xUppEQC8o9HqWNNEXib5IEAShuBXLYErbdhQUCaVW2rED1UmPdWbHM7XPJLRiP56Jy9F2FaBtPUpkrxsi0Yxs1rsa43phHkf6fomxWSVM7TNRL88CwHJrA1xDZpN37TSMjcpjbJeJuWfyN2LvR6vxfbwm6Tb7oFaol1dPKFe7ZxH4cQvW2xoSWrQHDDLGlpXQfSEi+z2438iOflv5l3AEQ/0ysV+Vise+gZadKpZbGuB9exm+z9Zial0ZU5dqmOqV4UTatqMY6saXm/tEPyS6/52NUiMt1nkBGKqnItlNhLflY2xyLC23tqsAIjqGS+IXsDVeWo7w5rykcQrFIzMzM+lUxVM5m4HU2e7/xRdfnNWx/05HtW1IKKQpx75VTzfUiQ2sjpdpas/+0AqWeyZSoO3iqLYVd2QvOhEAGlvvYp7rBb480pdKxmZkmtqTpUbT5Tew3Mps1xCm5V1LeWMjMo3tqGFOvmbOau9HrPF9nHRbK/sgqquXJ5Rnqd3ZEviRhtbb2BNahIyBSsaWSdtQMMU915QX3kwtc3y2vrLGBoR0d9L9ARxyRuy1UbIRIZxQJ6Dn49ePUMZwLMOkQbLQ3PYgAFZT2VNez5Im+qIo0RcJgiAUn2IZTEmGomdc905ZhX/qetQra6B2qILhnia4Xvg9tt3UNpO0L/oRXLCbUPYePOOWEJixjZQ3umOsX5a0T/sRXLibUPZefJNzCPxvEykTeyKp8adm7lMbtUu1pDHIaeak5Wq3LLwTlxPenk9g9g5MnasiGWQi/uiHDvuTbTCc2AEZjzt3U/wzA9Y7m6D2qEFwwW6CC3fjemoOln82xHpbw8Q2kqT3BpBMJ3kOQddjnX6hdTUdIsd92DlZPUE4D8hS0d/GVnmnsN4/lRrqlVRRO9DEcA+/u16Ibc80taVf2hfsDi5gTyibJZ5xbAvMoHvKG5Q11qdf2qfsDi5kbyibHN9kNgX+R8+UiQnJHWqb+1BN7ZI0BrOclrQ8S+3Gcu9E8sPb2RGYTVVT55Oem3zCIruypIAeP4CRkE55LWSp8MG2/GcXoZP8/aiw61nSRF/0V/CiLxIEQSguxbLOlFLVCZqOtvXYN06hNQfJu/4rdH/8t5/+rzdgvbcptgeao/aogVzBRuSAl7/6bu+klWi73ZivrInj+Q44hnUgvHw/kSM+fFPXE1p5ALVrFvYhbUkZfwXajnzCW44mnqhTRclwJP2RrMmnXMhlLBibVCD42w5Ci/agdo9+YyjbTUhlLEQOeOLa8X+7ieC8xOkjAJEjPtxjspHSog/wprzRHfMN9QnO2p54/ao4CW/Kiytz/2cx7v8sRqmWgrY1D9137DqGt+She0LR6358OxkOMMiE1xyKKw+tOYhSpfBndkqLrl27Mnny5JIOo1QZMmQIDzzwwFm3k5ubS926dVm3bt05iKpkOJWq6GjkaVtjZQdDa/gq73rCevzCyhv8X9PUei/NbQ9QQ+2BTa6AN3KAv95wVnon4dZ2U9N8JR0cz9PBMYz94eX4IkdY75vKgdBKstSutLUP4YqU8eRrOzga3pIQkyo7cSgZSX+MkjXpeVjkMlQwNmFH8Df2hBZRXe1e5GuQotTgcDj+zueh8Nn/TU2yHbOURl54U6xM00N8lXc9B0I5hV7Pkib6okSiLxIEQTi3iuXOlFI1BWPryrhHZ0fnQssSnnFLMDQsj2SOP6TkVAll78HYtAK6J4R3Sg66J4Qe0gDQdhYQXLgH+6BWSA4TgRnbkSvakFLNRA558U9bH52rXsGG/8etSDZjwhv52TB1z8IzbglyioqhwbEpCpabLsH3fg5yWSuGBmUJzsvF/+V6HCM6J21HcqoE5+eie8NY/3Epui9MaMnexG8TAXPfOvinbcD7zgrUK2sQ3niEwPebcbzYEWPjCvg+XIV7xAIstzeMPfRrqF8GQ4OyRPYfm6cvmQ2Yr6mDZ8IyJKsBJcOJ/38b0bYdxf5E0decEUqfZ555JunixBejFKUqlY2tyXaPpqX9ESRklnjGUd7QEIMU/02/KjnZE8qmgrEpId1DjncKId2DpkeffSnQdrInuJBW9kGYJAfbAzOwyRUxS6l4I4dY75+GQbJgkyuw1f8jRsmG88+EEedClqk7SzzjUOUUyhoaFHm/+pbrme96kXRDHcoZG7It8AuHwmtwKlXOOqb6lv6s9n2MXamMU6nKGt/HyBhIN9Qt9HqWNNEXJRJ9kSAIwrlVbNn87E+1xTN2CQWPzQCDgqldBtYHExcDtA9pi2dMNkcHfI+cqmJqXwW5V020jdFV022PtsIzdjEFg2ejB8IYLimL45XOSLKEdUBjdH8Y14vz0F1BDDXTcIzofE7XyTB1rIpnzGLUbllI0rFpM+Zr60JQw/veSiJHfCgZjujDtSdJ5SoZZJwjOuMZv5Sj9/6IpMjRa/Jwi4S6SgUbzlc645m4HN+X65Ar2LA92gpT6+gzDo7XuuJ9cyn5D/yMpBowdcjEem/TuPj+Yr27CcgS7lGL0D0hDHXTcY7uhqGWWEfmfOZwiExXx2trf4olnrHMKHgMBQMZpnY0tz6YpN4Qsj1j+P7oAFQ5lSqm9tSUe3FE2whAK9ujLPaMZXbBYMJ6gLKGS+jseAVJkmlsHUBY9zPP9SJB3UWaoSadHSPOeg2o41U1dWSxZwxZarek/59PporpMppY72GlbxJBTwGVTa3JNLaPTdM7G/XM18fSwod0D2WNDejifBWDpBZ6PUsD0RfFE32RIAjCuSXp5+jr7XZ7Tr5+j3B+W1D5uWI/RkFBASNGjGDWrFnouk6HDh0YOnQoqampdO3alX/+85/cfvvthMNhxo0bx7fffsv+/ftJSUmhV69ePPXUUyiKwv79+3n++edZvHgxkiTRrl07nn/+ecqUKYPb7eb5559n3rx5BAIBmjdvztChQ8nKykqIZ9y4cYwfPz5prCNGjODaa6+NKxszZgwzZ87k22+/jZXNnTuXf/3rX8yfPx+bzcbkyZP54IMPOHLkCHXq1OGxxx6jdevot7JDhgwhHA6zc+dOtm3bxhtvvIHFYmHkyJFs3LgRp9NJnz59eOyxx1AUhSFDhlBQUMBbb70FwOLFixk9ejTr1q2jbNmyDBgwgFtuuQWAnTt38uqrr7J4cfQB9a5du/LUU0+RkpJCbm4u3bp1Y/r06dSvX59gMMh///tfvv76aw4cOED9+vV54oknaNmy5Unj7NChw9n98U/T8HZ7/tbjnQ/2h1ZglcvjUI59gJ5V8CTlDA1oaL2tBCOL99yC4l03SPRDF7a/oy8SBEE4XcXyzJQgnK6HH36YdevW8fbbb/PBBx+wfft2nn322YR67733HtOnT2fkyJH8/PPPPPHEE3zyySfMnDkTgGHDhhEIBPjiiy/46KOP2L17NyNHjgSii9hu2rSJyZMn8/XXX6MoCk8//XTSeAYMGMC8efOS/vTq1Suhfu/evdm4cSNbthx7fuaHH36gW7du2Gw2vvjiC95//32ee+45vvnmG3r27Mldd93Fxo3HvsH/7rvvuPnmm/nwww9p3rw5Dz74IK1ateKHH35g1KhRfPnll3z11VcJx96+fTt33nknTZo0Yfr06Tz22GOMHDmSmTNnkp+fz8033wzAhx9+yFtvvcXatWsZNGhQ0vN+6aWX+Pzzz3n22WeZPn06jRs35q677mLXrl0njVMoebnBBcxxPc3B0Brc2j42+qdzILSSKqaST08uCIIgCBeyYpvmJwhFtWXLFhYuXMhXX31FgwbR50SGDx8eGyAdr1atWowcOZJWrVoB0ZTj7733Hps2beKKK64gNzeXrKwsMjMzUVWVN954A48nOnc/NzcXq9VKZmYmDoeDF198kd27dyeNyWazYbMVPU1vzZo1adCgAT/++CMPPfQQwWCQGTNm8Prr0bV7JkyYwKOPPkrnzp2B6GBtxYoVfPDBB7z00ksAZGVl0a9fP+DYornlypUjIyMjdp5lyiQ+1/Dll19SrVo1hgwZAkD16tXJz89HURS+/fZbQqEQo0aNwmqNJj4YPXo0V199NatWrSIt7VhmOZfLxdSpUxk1ahRdukQz0T3zzDMsX748NhA8MU6hdGhsvYOQx8tc11BCuocUpTodHS+SakhMsy0IgiAIwrkjBlNCidu4cSMGg4H69evHyho0aBAbWB2vW7duZGdnM2rUKLZt28aGDRvIzc2lR48eADz66KM88sgjtG7dmrZt29KtWzf69OkDwP3338/dd99N27ZtadmyJV27duWaa65JGtOECROYOHFi0m3Dhg2LtXm83r17M23aNB566CHmzp2LwWCgffv2eDwedu/ezXPPPcewYcNi9UOhEI0aNYr9npFxbN2f1NRU7r33XkaOHMm7775Lx44d6dWrV1z9v2zatImGDeNTGt90000AvPDCC9SpUyc2kAKoXbs2TqeTzZs3x6bvAWzbtg1N02jSpElcW82aNYvL9nd8nELpYJAstLE/XtJhCIIgCMJFRwymhBJnNCZPB5zM+PHjmTJlCv/3f//HFVdcwWOPPcbAgQNj27t06cKcOXOYPXs2v//+Oy+//DLffvstU6ZMoVGjRsycOZO5c+cyd+5cxo0bx2effca0adMwm+Ozvt14441ceeWVSWNIdncI4KqrruK1115j8+bN/Pjjj1x55ZUYjUb8/mh67ldeeSVh0HP8wrqqGr9W0aOPPso111zD7NmzmTNnDvfccw8PPvggDz30UFw9o9F40sx+J7b5l0gkgqZpJ43lxLqR49aCOVmbgiAIgiAIFxvxzJRQ4qpXr044HGbDhmPr5CxfvpyOHTvi8/ni6n700Uc88cQTDBkyhH79+pGRkcGePXtig4kxY8awa9cu/u///o8xY8bwn//8h4ULF3Lo0CEmT57MkiVL6NWrFyNHjuTTTz9l8+bNccf9S2pqKtWqVUv6Y7cnz95Wvnx5WrduzU8//cScOXPo3bs3EM28V65cOfbu3RvXzmeffcaMGTOStnXo0CGGDRtG2bJlueOOO5gyZQp33nkn33//fdLrt3bt2riy4cOHM3z4cGrWrMnGjRvxer2xbevXr8ftdlOjRo24fapVq4bRaGT58uVx5cuWLUuoKwiCIAiCIIjBlFAK1KxZk06dOjF06FBycnJYs2YNL730Es2bN8discTVTU1NZe7cuezYsYM1a9YwcOBA3G43wWAQgK1btzJs2DBycnLYsWMH3333HRkZGaSnp7N//36GDx9OdnY2u3bt4quvvsJut1O9+rl7rqR379689957pKSk0LRp01j5Pffcw4QJE/jf//7Hrl27mDRpEpMnT06aSRAgJSWFGTNmMHz4cLZt28aaNWuYP39+0ml+N998cyyz3vbt2/n+++/58ssv6dSpE71798ZisfDkk0+yYcMGli5dypNPPknjxo3j4gOwWCzceuutvPrqq8yePZutW7fy8ssvs3HjxlgSC0EQBEEQBOGYczLNL6CHRcrSC1hAD6NKxTsjdOTIkbz88svcfvvtGI1GunbtmjTT3siRIxk2bBi9e/cmPT2d7t27c91117FmzRog+jzTSy+9xN13343f76dJkyZMnDgRWZYZOHAgfr+fQYMGkZ+fT7169Zg4cSJO57lbWLNHjx6x+I5fa+Uf//gHfr+fMWPGcPDgQapVq8bo0aNPmlbcaDTy3//+l1deeYVrr70Wg8FA165dk2Y4rFy5MhMnTmTUqFFMmjSJjIwMXnjhBTp16gREMyC+8sor9O/fH7PZzOWXX84TTzyRdC2YQYMGIUkSzz77LC6Xi0svvZTJkyfHPc9WksIBvdjTawvFJxzQMahFXz/rdIh+6ML3d/RFgiAIp+ucrTMlCIIgCIIgCIJwMRHT/ARBEARBEARBEM6AGEwJgiAIgiAIgiCcATGYEgRBEARBEARBOANiMCUIgiAIgiAIgnAGxGDqbxQOiFwfgnCxCujhkg5BEARBEIRzTGTz+5sNb7enpEMoMSKl9cWj3Z7hJR1CqSPSdguCIAjChUfcmRL+Nu7DWkmHIPwNDmvukg5BEARBEAThbyFWvxP+Nm/03l/SIZyV4r6zJu7mCIIgCIIgnF/EnSlBEARBEARBEIQzIO5MXaSm591IPfN11LNcV2jdHO9kcoPz6JX67jk7/mrvR2z0Tyeke8k0tael7RFMsv2k9Tf7v2e17yP8kTwqGJvS2jYIq1LunMUjnFrejdMxX1cPy3X1Cq3rnZxDcF4uqe/2OmfH9360Gv/0jejeEKb2mdgeaYlsN520vv/7zfg+Wk0kz4+xaQVsg1qjlLOes3gEQRAEQRBA3Jm6aPVMmUAt89VFqlvfcgPdnKPP2bE3+r9hvf9L2tifpHvKv8nXdrDQM+qk9XcHF7LEM44m1rvokfImOmF+cw1F5E75+6RM6In56lpFqmu5oT7O0d3O2bH932zE/+V67E+2IeXf3dF25OMZtfCk9YMLd+MZtwTrXU1IebMHhHVcQ38T/14EQRAEQTjnxGDqImWWUzFI5iLVNUoWVDnlnB17ne8LGlhuobKpFWUMdWlrf5Jdwbm4tX0nrV/L3IsstRtphpq0tz9LnraZA+GccxaTcGpyqhnJXLQb2ZLFiJyinrNj+75Yh+WWBphaVcZQtwz2J9sSnLsLbV/yRBe+L9Zh7lULtVsWhppp2J9tj7Y5j3DOgXMWkyAIgiAIAohpfhcsr3aQbM+/2R9ajkl20thyBws9o+iT+jF2pWLcNL8/3CNRMBMhyI7gbygYqWXuTWPLACRJOuU0vxzvZFb5piSNoY1tMDXNPePKfJEjuCN7KG9sHCtLM9TCKNk4FF6DXakYV1/XIxwKr6OOuW+sTJVTSFGqcTC8mgrHtSOcOe2gF8+/swkt34/sNGG5ozGeUQtJ/bgPSkV73DQ/98g/wKxAMELwtx1gVDD3roVlQGMkSTrlND/v5Bx8U1YljcE2uA3mnjXjyiJHfET2uDE2Lh8rM9RKQ7IZCa85hFIxfmqoHtEJrzuEuW+dWJmcoqJUSyG8+iDGxhXO5jIJgiAIgiDEEYOpC1BE15jjegqT5OSKlHEE9HwWuUejEznpPlsC31PP3J8rUyawJ5jNUu94KhiaUMnU/JTHqm+5gdrmPkm3GSVbQpk3chAAq1w2rtwspce2HS+ou9DwYzmhvkVOx6sl1hdOn65FcD01B8lpImXcFej5AdyjF0Hk5NPiAt9vwdy/HikTriSYvQfv+KUYmlTA1LzSKY9luaE+5j61k26TbMaEsshBLwBy2fjnnaR0c2xb3Lm4guDXkMta4srldAtakvqCIAiCIAhnQwymLkAHwis4qm3nmrTPschlAGhhe5g5rqdPuo9NrkBT290AOC1V2OCfxuHwukIHU0bJglGynLLO8TQ9AIBMfPIARTKi6aGE+uE/6ytSfH0ZExES6wunL7ziANr2o6R9fg1ymejf0vZwC1xPzznpPnIFG7a7mwJgqeLEP20D4XWHCx1MSRYjkiVx0HQyeuDPtclM8TOSJaOCHkpct0wPhKPbTUr8BpMMoZN/mSAIgiAIgnAmxGDqApQX3oJNrhAbSAGUMzQ85T52JSPud6NkJULhi+yu9n7EGt/HSbe1sg+iunp5XJkiRZ+lOXEgpOkhDFLiczax+icMtCIEk9YXTl94Sx5yBVtsIAVgaHjqTIlKRvz0OslqBK3wwYr3o9X4Pl6TdJt9UCvUy6vHt6v+OSg6YSCkhzQkNfHt66/6+okDp2AkaX1BEARBEISzIT5dXIAk5IQpfRLSKfdRSHa3oPDsZ7XNfaimdkm6zSynJZRZ5eiHdF/kMBY5PVbu148kTOUDUCUnCmZ8kcNx5b7IESoYmxUan1AEspQ4pU869b8XjEpiWRGS5Zn71EbtUi15GGmJCVHkP9OZRw77kNOPDfb0I/6EqXwAklMFs0LksC+uPHLEh7GZeF5KEARBEIRzSwymLkCphhp4IwfxRY7EBiyHwuuK5Viq7ETFWeT6Fjkdu1yZA6Ec0g3RZ2fywpsJ6V7KGRok1JckibKG+hwI51BF7QBAIJJPvrYjaX3h9BlqpBI56CVy5NiAJbzuULEcS3aq4Cz6HUU53YJc2U4o5wCG2tF/y+HNeejeEIYGiXfPJEnCUL8s4ZwDqB2qABDJD6DtyE9aXxAEQRAE4WyIwdQFqIKhKWlKDRa4R9DMeh9B3c1iz9iSDiumrvkaVvmmYFcqY5HTWeQeTVVTJ2xK9M5BSPcR1n2xgWBdy7XMcw0nzVCLNKU2y71vk26oTXljo5I8jQuGoWkFlBppuEcswHpfM3R3EM/YxSUdVoz5mrr4pqxCqWxHTrfgHr0IU6eqKBWiCU50XwjdF44NBC3X1sU1fB6GWmkotdPwvr0cQ+10jI3Kn+owgiAIgiAIp00Mpi5AkiTRwTGcbM9ofs5/AFVOo465Lyu8/0WRiv7wf3Gpa/4/AnoBC92vEiFMhrEtrWwDY9vX+T5nlW8Kt5SZDUAV02U0s97PCu+7hCJuKhib0tHxYglFf+GRJAnH8A54RmeT/8DPyGkq5r518P53BVKy6Xx/M/P/1UUvCOB+dSGEIxjbZmAb2Cq23ff5OnxTVlFm9i0AmC6rgvX+ZnjfXUHEHcLYtAKOFzuWVPiCIAiCIFzAJF3Xi/Ckg3CuDG+3p9iP4Y/kcTi8gcrG1kh/PvtyKLSWXwsGckP6j8hSyX9APh89t6Bysbbfbs/wYm3/ZCJ5fsIbDmNsXTn27yW09hAFA38l/ccbkBSxtve5sKDycyUdgiAIgiAI55j4lHRBkpjreo41vo9xa3s5HN7AMu/bVDV1FAMpIZEErufm4vt4DdpeN+ENh/G+vQxTx6piICUIgiAIgnAKYprfBcgsp9LRMZwc7yRW+z7CKFmpYupIU9u9JR2aUArJqWYcwzvinZSD76PVSFYjpo5VsN3btKRDEwRBEARBKNXEYOoClWFqQ4apTUmHIZwnTG0yMLXJKLyiIAiCIAiCECPm8AiCIAiCIAiCIJwBMZgSzsqv+QNZ4hlf0mEkWOgeVSrjupjlD/wVz/glJR1GAveohaUyLkEQBEEQSj8xmBIuKLquk+N9ny2BH0o6FKGU03Ud7/s5BH7YUtKhCIIgCIJwnhLPTAkXDK92kD88r5EX3oRVrlDS4QilmHbQi+e1PwhvykOuYC3pcARBEARBOE+JwdR5aJP/W9b6PsMbOYhdrkgD6y3UUHsAUKDlsszzNgfCK9H0IA4lgybWu8g0tQdget6NNLDczPbATA6F1+FUqtDe/iw7g3PY4J+OhEwDy03Ut/QHotP4yhoakKdt5kBoBXYlg2bW+6hsapU0tr3BxazwvkO+tgObUpG65muorfZFkiRCupds9xvsCS1G04OUNzakhe1fOJXMhHZyvJNZ5ZuS9BhtbIOpae6ZUH5E24xVLkO71KeY5yqZNZtKG/+3m/B9tpbIQS9yRTvWWxqg9qgBgJZbgOftZYRXHkAPaigZDqx3NcHUPvr3yLtxOpabGxCYuZ3wukMoVZzYn21PcM5O/NM3gCxhuakBlv71geg0PkODsmib8witOICSYcd6XzNMrZKvzxVcvBfvOyvQduSjVLRhvqYuat/aSJKE7g3hfiOb0OI96EENY8Py2P7VAiXTmdCOd3IOvimrkh7DNrgN5p41E8q1zUeQy1hJfaodruHzzujaCoIgCIIgiMHUeeZIeBOLPWNob3+WMob67Akt4g/3q5Q1NMAhZ/BbwdOkGWrTI+Ut0HVW+T5gofs1rkmbiiIZAVjhfY829idwKlWZ5xrGrwWPkGlqT4+UcewIzGa5dyJVTB2xKxUBWO//kgaWW2hue5Ct/p/4zfUMV6VOwqlUiYstP7yD31xDaWa7n0rGFuSFN5PtGYOETG1zH1Z6J3FU20Y352gMkspSz5ssdL/GFSljE86zvuUGapv7JL0GRsmWtDzT1JZMU9uzubwXlPCmI3jGLMb+bHsM9csQWrQH96t/YGhQFjnDQcHTv2GonUbKWz3QdfB9sAr3awtJm3oNkjG6Hpn3vRXYn2iDUtWJa9g8Ch75FVP7TFLG9SAwewfeicsxdayCUtEOgP/L9VhuaYDtweb4f9qK65nfSJ10FUqV+EFQeEc+rqG/Ybu/GcYWlQhvzsMzJhtkCXOf2ngnrUTbdhTn6G5IqgHPm0txv7aQlLFXJJyn5Yb6mPvUTnoNJJsxabmpbSamtomDeEEQBEEQhNMhBlPnGY+2DwCbXAG7UpE6Sl8ccgZmKRWNADXNV1FT7YkqpwBQ39KfHcFZ+CN52JTyAFQ1daKK6TIAqpm6sMo3hZa2gRgklUssN5Hjm0y+tiM2mCpnuJRG1tsAaGq7hz2hRWzx/5CwbtVa/6dUU7tQx9wXAIeSgS9yhHW+L6ht7oNH24tBsmCXK2KS7bS2Px47nxMZJQtGyXKOr97FRdvnAUCuYEOpaEfpWwc5w4GUaoaAhvmqmqg9ayKnqABY+tcnOGsHkTw/SvnogNXUqSqmy6KDZlOXavimrMI2sCWSasBy0yX4Juf8eWcpOpgyXFoO622NALDd05TQoj34f9iSsGaV/9O1qF2qYe5bBwAlw0HkiA/fF+sw96mNtteDZDEgV7Qj203YH28dO58TSRYjkiX5oEkQBEEQBKE4icHUeaaSqRUVjM34ueBBnEpVMoxtqKH2xCRHP8zWMfdje2AGh8MbcGm7OKJtAkAnEmvDoRybdmWQzJjldAxS9AO1IpkAiBCK1SlvbBQXQxlDXY5q2xJiOxrexlFtKzsDc2JlOhEihNH0EA0stzLbNYRpeddS3tiITGM7aiSZrgew2vsRa3wfJ93Wyj6I6urlJ71GQpSpVSWMzSpQ8ODPKFWdGNtkoPasgWyP/o3N/eoQmLGd8IbDaLtcaJuORHeM6LE2lMqO2GvJbEBONyOp0bcNyRS9e0Xo2L8tY6PycTEY6pZB23Y0IbbwtqNoW48SmLPzWGFEh3AEPaRhubUBriGzybt2GsZG5TG2y8Tcs0bS8/R+tBrfx2uSbrMPaoV6efXkF0gQBEEQBOEsicHUecYgqXR1vMah8Bp2hxayO/gHG/xf09nxCuWMDfg5/yFkDFQxdSDD1BYFE7Ncj8e1IZ/wZ5eQTnlMCSXud51IQlm0XKO2uS91zdckbJNRKGusT7+0T9kdXMjeUDY5vslsCvyPnikTY4O5v9Q296Ga2iVpPGY57ZTxClGSasDxWlfCaw4RWrib4B+78X+9AccrnTE2KEf+Qz+DQcbUoQqmthlgUnA9Piu+EcMJCT+lU/9bQTlhe0RPLAPQdMx9a2O+pm6SNmSM9cuS9mk/ggt3E8rei29yDoH/bSJlYs/YYO4v5j61UbtUSxqOnGY+dbyCIAiCIAhnQQymzjMHQ2vYE1pEY+sAyhkvpYn1Ln7Kf4Bdwd+JECJf20H/9O8wSNEPkdsCM/7cUz95o4U4Et4Ye63rOofDG6hi6pBQz6lUw6Xl4lAyYmVbA79wMJRDa/vjrPdNxaFkkKV2JUvtSr62k++O3sbR8BbKGi+Ja0uVnagkJhsQii605iChRXuwDmiM8dJyWO9qQv4DPxH8fReEImg78kn/rj+SOfo2EJjx593GM/+nQnjjkdhrXdcJbziMqUOVhHpKNSdargsl49idr8AvWwnlHMT+eGt8U9ejZDhQu2ahds1C25nP0du+I7zlKMZLysa1JTtVcKonHkIQBEEQBKHYiXWmzjMGycwa3yes903Fre1jTzCbAm0nZQz1UKUUdDR2BObg1vaxK/A7yzxvA6AdN23vdO0OLWCD72sKtF0s907Ao+2jlnpVQr1LLDeyN7SYHO9kCrRccoMLWOoZhyqnAuCNHGKxZyz7Qytwa3vZ6v8Ro2TDqVQ949iEk5PMBnyfrME3dT3aPjfB7D1oOwsw1CuDlKKCphOYswNtn5vA77vwvL0sumNIO+Njhhbsxvf1BrRdBXgnLEfb50G9qlZCPcuNlxBavBfv5By03AKCC3LxjFuKnBodFEUOefGMXUxoxX60vW78P25FshlRqooBtiAIgiAIpYe4M3WeSTPUpL39GVb5PmC597+Y5VTqm/tTQ+2JJEk0ttzJCu87hHQvTiWTZrb7WeIZy5HwBlLOcNBS1dSZ3NB8lnsnkGqoQVfn69iUxHWc0g216eh4kRzv+6zxfYJZTqWO+RoaWqLJKxpbBxDW/cxzvUhQd5FmqElnx4jY817CuWWomYb9mfb4PliF97/LkVPNmPvXR+1ZA0mSsNzZGO87K9C9IZRMJ7b7m+EZu4TwhiMoVVPO6JimzlUJzc/FO2E5hhqpOF/vilIhMfuioXY6jhc74n0/B98na6KxXVMHy20NAbAOaIzuD+N6cR66K4ihZhqOEZ1jz3sJgiAIgiCUBpKu62cxqUc4XcPb7SnpEE7Lr/kDSTPUooXtoZIOpcQ9tyD5eknnSrs95/faWPkDf8VQKw3bQy1KOpRSaUHl50o6BEEQBEEQzjExzU8QBEEQBEEQBOEMiMGUIAiCIAiCIAjCGRDPTAmndHnKmJIOQThPpIwRa38JgiAIgnBxEXemBEEQBEEQBEEQzoAYTAmCIAiCIAiCIJwBMc3vAjA970bqma+jnuW6kg4Ft7aPb47eBEAlY0u6Ol8jomss877F9sBMdDSqq5fT1Ho/imQsUptnu/9fdD3CLNdgMoytY9fqp/wHOBxeB8D1ad9eFGna826cjvm6eliuq1fSoaDtc3P0pm8AMLashPO1ruhaBO9bywjM3A6ajnp5daz3N0UyKqfVth7RcQ2ehbF1xmmd66mOn//AT4TXHQYg7dvrRap2QRAEQbjIicGUUCy6OV8nTakDwErvu+wOLqST4yUihPnDPRIZI81s9xeprbPdHyCih8n2vMG+0BIyjK1j5V0cI9kfWsHv7udP7wSFc8r5ejeUOmkAeN9dSXDhbhwvdYJwBPfIP8AoY7u/WZHb08MRPG9kE1qyD2PrjNOK5VTHd4zsQmjFftzP/35abQqCIAiCcGES0/yEYmGSUlBlB5oeZKN/Ok2t91LOeCkVjE1obnuITf7/EdYDhbZztvsD5Ie383P+Q+wPrcQkxd95UmUnquw8o3MUzh0pxYTsUNGDGv7pG7He2xTjpeUwNqmA7aHm+P+3CT0QLlJb4e355D/0M6GV+5FO885RYceXnSqyUz2TUxQEQRAE4QIk7kyVAvNdrxDSPXR2vhwr2+j/H2t9n9A39VP8eh7LPG+xN7SEkO7BKpengeVmapmvSmjrD/dIghE3nZwvxcp+OHoXmabLaGS9HYBtgRms9n2IR9uHQ8mkoeWfVFU7JY3t1/yBHAivTLqtb+qn2JWKpzy3vPBmwvipYGwcKytvaEwYP3nhzZQzNijW/QEOhHMob2xII8vtfJ9/Z6H1SzvXK/PRPSGcL3eOlfn/txHfJ2tJ/bQvep4fz1vLCC3Zi+4JIZe3Yrm5AearaiW05R75BxF3EOdLx/7+R+/6AdNlmVhvbwRAYMY2fB+uRtvnQcl0YPlnQ9ROVZPGlj/wV8IrDyTdlvppX5SKp55GGd6cB/4wxsYVYmWGxuXBHya8OQ9jg3Kn3B8gnHMAY8PyWG5vRP6d3xda/1wfXxAEQRCEi4cYTJUC1dXu/OZ6lmDEHXtmZ0dgFllqNyRJ4g/XCHQidHOOxiCZ2eifzmLPGDJMbbHI6ad1rL3BJSz2jKGV7VHKGOpxILSSBe6RGCQLlU2tEup3dAwnQvI7AqqUUujxvJGDyBhR5WN1VdmBjBFv5GCx7w9Q29ynSPXOF2r36rie/Y2IOxh7ZicwawdqtywkScI14g+I6DhHd0MyG/BP34hnzGJMbTOQ0y2ndazgkr14xizG9mgrDPXKEFp5APfIBUgWA6ZWlRPqO4Z3hHAkaVtSSuF3dCIHvWCUkY+rKztUMMrRbUVg7lO7SPWK6/iCIAiCIFw8xGCqFKhobI5JspMbmk8NtQe+yGEOhlfR0vYIABnGtlQ2tcahRJ/9aGC5lfX+qbi03NMeTK32fUR98/Vkqd0AcCgZ5Gs7WOf/Iulg6mynwIUJoEiJU61kyUiEULHvfyEyNq+IZDcRmp+L2qMGkcM+wqsOYnukZXR72wxMrSujZDgAsNzaAP/U9Wi5rtMeTPk+Wo35+vqo3bIAUDIcaDvy8X+xLulg6qynwAXCSKbERBOSUYZQ8kHaOVXSxxcEQRAE4bwiBlOlgCwpVFM7syMwhxpqD3YEZpOqVCfVUB2A2ua+7AzOYb1/GgXaLvLCmwDQOf0Pd/naNg6F17LW91msLIKGWU5NWn9WwWAOhnKSbrs6dTI2pULSbX8xoKLpwYTyiB5CofAP3me7/4VIUmTUztUIzNmB2qMGgdk7UKqnYqieCoC5b22Cc3bin7YebVcB4U150R0j+mkfS9uWT3jtIXyfrT2uMIKcak5av2DwLEI5ye8Ypk6+GqWC7dQHVA3oQS2hWA9FQD29bH5npKSPLwiCIAjCeUUMpkqJLFN3fi0YSDDiZkdwDllqdwB0XWe260k82kGy1K7UVnuTYq3Kd/l3FLntCFrc68bWAVQxdYirI50kF0kb2+NoJA5mACxy2UKPbZXLESFEIOJClaN3SgKRAiKEsP4N+1+oTN2zKBj4KxF3kOCcHajds4DovxfXk7PRDnpQu2ah9q6NtWoK+Xd8V/TGtUjca+uAxpg6VImvI0tJd7U93gaSDEYA5LKF3xWTy1khFCHiCkSn1wGRggCEIshlrUWL/yyU9PEFQRAEQTi/iMFUKVHWeAk2uTxbAj9wOLyODvZoqu58bTv7QsvonfohTiUTgIOh1QDoJN5pkDEQ0o892xHRw3HPFqUoWbi1vbEpgwBrfZ8T1N00sSYmZ7AqZ/fAfaqhJgbMHAznkGlqD0QTQhgwk2ZITIhwrve/UBkvKYtc3kbghy2E1x3G/nx0cKxtzye0bB+pH/ZGyYxO0Qyt/vPvrye5M2WQ0b3Hpkvq4Ujcs0FKVgraXndsyiCA7/O16O4g1jubJDSnlDu7AYehZiqYDYRzDmJqH/33Hs45AGYDhlppZ9X2+XB8QRAEQRDOL2IwVYpkqd3I8U6mvKFRbBBjkuxIyOwMRO9WubRclnjGAtGpbicqa7iErYFfyA3Ox6lUY63v07h6DSw38bvrBVKULCqbWnEwvJqV3vdoaftXsZyTQVKpZb6aJZ5xmCQHOjpLPeOpY+4XexYqECkAkj+fdbb7X8jUbll4J+dgaFQ+NoiR7CaQJQJzdqJ2z0LLdeEZuwT4c6raCQyXlCXwy1aC83NRqjnxfbo2rp7lpga4XvgdJSsFU6vKhFcfxPveSmz/alks5ySpBsxX18IzbgmSwwS6jmf8Usz96sSeZYoURFPin+nzWafavyjHFwRBEARB+IsYTJUiWWp3Vvk+iCWHgOidoda2x1jl+4DVvg+xKRWpY+7H5sD3HAlvTEgakaVezqHweha4RyCjUNvcl8qmY4vUZpra08r2KGt9n7HM+zZWuRzNrPdRy3x1sZ1XE+vdhPUAc1xPI6OQpXansfWu2Pa5rucAuDxlTLHsf6FSu2fh+2BVLDkERO8M2R5rje+DVfg+XI1S0Ya5Xx0C328mvPFIQtII9fIswusP4R6xABQZc9/amFofq2Nqn4nt0Vb4PluL9+1lyOWsWO9rhvnq4rsraL27CXogjOvpOaDIqN2zsN51LDW+67m5AKSMufyM2i9s/8KOLwiCIAiC8BdJ15PN/RGKy/B2e0o6hGLl1vbxzdGbuDLlHdKLOA3PHznKAvcIujpfPaNjnu3++0MrmFHwKNenfRtLTZ/McwsSs9edS+32DC/W9ksjbZ+bozd9Q8o7V2KoVbTMlJGjftwjFuB8tesZHfNs9w+t2E/BozNI+/b6WGr6olhQ+bkzOp4gCIIgCKVX8qwDgnCWgno+gYirSHVXeadQQ+1xxsc6m/0DkYLYNEGh5Oj5QSKuQJHqeqesQu1R44yPdTb7RwoCsWmCgiAIgiAIYpqfUCxmFjxOJWNLujpfK7RuM9sDKJLxjI91NvvPdg3hcHjdGR9bODcKHp+JsWUlnK8VfrfI9kAzJOOZP790Nvu7hswmvO7wGR9bEARBEIQLi5jm9ze70Kf5XcjEND/hbIhpfoIgCIJw4RHT/ARBEARBEARBEM6AGEyVYtPzbmS9b2pJh3FW/nCP5LeCZ894/y3+n/jiSNEzDX58uAu7gvPO+Hjnk7wbp+Obur6kwyhV3CP/oODZ3866HW2fm8NdPia8+cg5iEoQBEEQhAuVeGZKKFbNrQ+d1f7V1C5kHJfavTDXpk3DJJ08I59wYbM+1LykQxAEQRAE4SIiBlNCsTpVqvGiMEgqBqnoi7Na5KKl1xYuTKeTqlwQBEEQBOFsicFUCQtG3Cz1vklucAGgU8nYkpa2R1BlZ1y9iK6R43uf7YEZeCOHUCUn1dQuNLM+gCwpeCOHyHa/wYFwDgCVjC1oaXsEs5xGSPeS7X6DPaHFaHqQ8saGtLD9C6eSmRBPjncyq3xTksbaxjaYmuaecWUrvZPIDc7nqtT3YmV7gtnMdT3P/6V/xRLPfwhG3HRyvsQW/09s8H9FipLF7tAC6pmvp5H1Njb5v2Wt71N8kcNUMrbAplQkpHtoax/CFv9PLPWOp3/6d7E1rDrYh7HSNwm3tod0Q21a2QaRZqgJRKf5dXS8SBXTZeh6hDW+T9gU+I5AJJ8yhjq0sD1MmqFWodeztIi4g3jfXEpwQS7oYGxZCdsjLZGd8QNMXYvgez+HwIztRA55kZwqapdqWB9ohqTIRA55cb+RTTjnAADGFn+2k2ZG94Zwv5FNaPEe9KCGsWF5bP9qgZLpTIjHOzkH35RVSWO1DW6DuWfN+PqTVhKcn0vqe1fFyoLZe3A9P5f0r/4PyWLEN3U9/mnriRz1Y6ieivWephibVACi0/Z0LUJktwttlwv70PZIFgOet5ahbT2K5DBFF9W9uwmSIuMe+QcRdxDnS50ACK3cj/edFYQ35yGnW7D0r4+5Xx0AtN0uPBOWEV4ZvSamdplYH2yG7EgcvOshDd8nawn8vJXIIS+GWmlY722KsfHJ4zxxgWRBEARBEC48YjBVwua6niOou+jseBmDZGah+3UWeV6noyM+s9ta/2dsC/xCO/vT2OSKHAitYKFnFOUNjamqdmSxewwaQXo43yRCmIXuUSz1vE17x9Os9E7iqLaNbs7RGCSVpZ43Weh+jStSxibEU99yA7XNfZLGapRsCWVZandW+z4kX9tJilIVgB3BWWSa2mGULAn187RNlDM04MqUiUgo7Az8xhLPOFraHqG8sSGb/T+wzv8FNdQrTnrNVvom0co2EKNkY5F7FIs9Y7giZVxCvdW+j1jvn0Zr2yDSDLVY7fuI2QVD6Jv2Cev8X57yepYWrufmoruCOF7ujGQ24H59IZ7XF+EYHh+j/7O1BH7Zhv3pdsgVbYRWHMAzaiGGxuVRO1bFPWYxBDWcb/aAcAT3qIV43l6K4+n2eCetRNt2FOfobkiqAc+bS3G/tpCUsYl/A8sN9TH3qZ00VsmWmJ5e7Z6F78PVaDvzUaqmABCctQNTu0wkixH/d5vxf7EO26OtUKo6Cc7PpeDJWaRMvBJD9dRo/ZnbsQ9pi1IjDSXDQd6NX6NeWRPH85cR2e3G9cLvKFWcmK+KXyRayy2g4MnZmPvWxj64LeFNR3CP/AO5nBVDo3Lk/+sXDJeUxTmmO7onhGfMYtwvzk+ant0zdgnBhbuxD2qFkuHA/79NFAyeTer7V6NUsieNUxAEQRCEC58YTJWg/PAO9oeXc2XKRNIN0W/LW9sHkRucn1A3VcmirX0wFYxNALArPVnr/5x8bTvQEXdkL06lCnalEopk4jLHUEK6FwCPtheDZMEuV8Qk22ltfxyPti9pTEbJknQQdDIpSlXSldrsDMymofU2ND3EruA82tufOek+l1r/EZuOt8D9CrXUq6hljt65aGa7jwOhlac85qWWW2LXoa7l/1jkfj2hjq7rbPR/w6WWW6iqRu9StLT9i5XSJAJ6QaHXszQI78gnvHx/dGBRJ3q97INaE5yfm1BXyUrFPrht7I6O0tOO//O1aNvzoSNE9rpRqjhRKtmRTAqOoZehe0MAaHs9SBYDckU7st2E/fHWaPs8SWOSLEYkS9HX9FKqpqDUTicweyfW2xqihzSC83Zhf6Y9AL6PVmO9szGmthkAWPrXJ7zmIP5p67E/3gYAOdOJekV0kd1IQQDdFUROtyBXsKFUtOMc1RUpNfFukv/7LSiZDmwPRJ+jUqo40d1BkCUCM7ZDOILj6eidLgD7s+3JH/A94Q2HkVKOtRdxBwn8sAX7M+0wtY3ezbU91ILQmkP4vliH/ZGWCXEKgiAIgnBxEIOpEnRU24aEQppy7Bv1dEOd2MDqeJmm9uwPrWC5ZyIF2i6OaltxR/aiEwGgsfUu5rle4MsjfalkbEamqT1Z6uUANLDcymzXEKblXUt5YyMyje2occJ0vb+s9n7EGt/HSbe1sg+i+p9tHi9L7c6WwI80tN7GntAiZAxUMrZM2oaCKe65przwZmqZ47P1lTU2IKS7k+4P4JAzYq+Nko0I4YQ6AT0fv36EMoZ6sTKDZKG57UEArKayp7yepYG27SgoEkqttFiZoU56bGB1PFP7TEIr9uOZuBxtVwHa1qNE9rohEl1GznpXY1wvzONI3y8xNquEqX0m6uVZAFhubYBryGzyrp2GsVF5jO0yMfdMPijwfrQa38drkm6zD2qFenn1hHK1exaBH7dgva0hoUV7wCBjbFkJ3Rcist+D+43s6J2zv4QjGOqXif2qVDx2R1R2qlhuaYD37WX4PluLqXVlTF2qYapXhhNp245iqBtfbu4T/b/l/nc2So202EAKwFA9FcluIrwtH2OT8sfa2VUAER3DJWXj2jJeWo7w5rykcQqCIAiCcHEQg6kSJEtFv/yrvFNY759KDfVKqqgdaGK4h99dL8S2Z5ra0i/tC3YHF7AnlM0Szzi2BWbQPeUNyhrr0y/tU3YHF7I3lE2ObzKbAv+jZ8rEhOQOtc19qKZ2SRqDWU5LWp6ldmO5dyL54e3sCMymqqnzSc9Nlown/K6AHj+AkZBOeS1kqfAkA/Kf/7R1kq9JXdj1LA0kQ9FXLvBOWYV/6nrUK2ugdqiC4Z4muF74Pbbd1DaTtC/6EVywm1D2HjzjlhCYsY2UN7pjrF+WtE/7EVy4m1D2XnyTcwj8bxMpE3siqfF/R3Of2qhdqiWNQU4zJy1Xu2Xhnbic8PZ8ArN3YOpcFckgE/FHB8H2J9tgOHEwZDzu3E3xz7BZ72yC2qMGwQW7CS7cjeupOVj+2RDrbQ0T2zjJmuSS6STPxel6bABaaF1Nh8hx/3ZPVk8QBEEQhAuWWGeqBDmVquho5GlbY2UHQ2v4Ku96wro/ru4G/9c0td5Lc9sD1FB7YJMr4I0cgD8HCyu9k3Bru6lpvpIOjufp4BjG/vByfJEjrPdN5UBoJVlqV9rah3BFynjytR0cDW9JiEmVnTiUjKQ/Rsma9DwschkqGJuwI/gbe0KLqK52L/I1SFFqcDi8Ia7sUHhdkfc/GZNsxyylkRfeFCvT9BBf5V3PgVBOodezNFCqOkHT0bYeu/sRWnOQvOu/QvfH343zf70B671NsT3QHLVHDeQKNiIHvLHT8U5aibbbjfnKmjie74BjWAfCy/cTOeLDN3U9oZUHULtmYR/SlpTxV6DtyCe85WhCTLJTRclwJP2RrMmn/8llLBibVCD42w5Ci/agdo/evZLtJqQyFiIHPHHt+L/dRHBe4lRGgMgRH+4x2Uhp0WQSKW90x3xDfYKztidevypOwpvy4src/1mM+z+LUaqloG3NQ/cdu47hLXnonlD0uh/fToYDDDLhNYfiykNrDqJUSUkapyAIgiAIFwcxmCpBKUpVKhtbk+0ezeHweo6EN7LEM47yhoYYpPhv+VXJyZ5QNi5tN0fCG5nnGk5I96Dp0edeCrSdLPaM4XB4PS5tN9sDM7DJFTFLqXgjh1jsGcv+0Arc2l62+n/EKNlw/pkw4lzIMnVnne9zTJKDsoYGRd6vvuV6tgR+YIv/Rwq0XFZ6J3EovAYKuTtVtLb7s9r3MbnBBRRouWR73kDGQLqhbqHXszRQqqZgbF0Z9+hswusPE954BM+4JRgalkcyx98xkpwqoew9aLtdhDcewTV8HronhB7SANB2FuAZs5jw+sNou10EZmxHrmhDSjUTOeTFM3YxoRX70fa68f+4FclmTBhUnA1T9yx8n69DcpgwNDg2Xc5y0yX4Pl5D4NdtaHtc+L5Yh//L9SiZyRM4SE6V4PxcPP9ZjLargPDGI4SW7E28swWY+9ZB21WA950VaLkFBGZtJ/D9ZkxtKqN2z4om9BixgPDWPEKrDuB+ZQGG+mXi4gOQzAbM19TBM2EZwT9yo9dy/BK0bUcx90uejEMQBEEQhIuDmOZXwtran2KJZywzCh5DwUCGqR3NrQ8mqTeEbM8Yvj86AFVOpYqpPTXlXhzRNgLQyvYoiz1jmV0wmLAeoKzhEjo7XkGSZBpbBxDW/cxzvUhQd5FmqElnx4izXgPqeFVNHVnsGUOW2g1JKvpAqIrpMppY72GlbxJBTwGVTa3JNLaPTdM7G/XM18fSwod0D2WNDejifBWDpBZ6PUsL+1Nt8YxdQsFjM8CgYGqXgfXBxIVp7UPa4hmTzdEB3yOnqpjaV0HuVRNt4xEAbI+2wjN2MQWDZ6MHwhguKYvjlc5IsoR1QGN0fxjXi/PQXUEMNdNwjOh8TtdsMnWsimfMYtRuWXH/PszX1oWghve9lUSO+FAyHNFEDydJKy4ZZJwjOuMZv5Sj9/6IpMjRa/Jwi4S6SgUbzlc645m4HN+X65Ar2LA92gpT6+gzd47XuuJ9cyn5D/yMpBowdcjEem/TpP9+rXc3AVnCPWoRuieEoW46ztHdMNQS65oJgiAIwsVM0vWTPFQgFIvh7faUdAilyv7QCqxyeRzKsQ/PswqepJyhAQ2tt5VgZImeW1C86wa12zO88ErCeWtB5edKOgRBEARBEM4xMc1PKFG5wQXMcT3NwdAa3No+NvqncyC0kiqm0pGeXBAEQRAEQRBORkzzE0pUY+sdhDxe5rqGEtI9pCjV6eh4kVRDYoptQRAEQRAEQShNxGBKKFEGyUIb++MlHYYgCIIgCIIgnDYxzU8QBEEQBEEQBOEMiMGUIAiCIAiCIAjCGRCDKUEQBEEQBEEQhDMgUqP/jcIBHYN69ovRCiWjOP9+AT2MKolHGC9k4m8sCIIgCBceMZgSBEEQBEEQBEE4A2KanyAIgiAIgiAIwhkQgylBEARBEARBEIQzIAZTgiAIgiAIgiAIZ0AMpgRBEARBEARBEM6AGEwJgiAIgiAIgiCcATGYEgRBEARBEARBOANiMCUIgiAIgiAIgnAGxGBKEARBEARBEAThDPw/LDAu6gLnO1oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(model, \n",
    "          feature_names = df.columns,\n",
    "          class_names = iris.target_names,\n",
    "          filled=True      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- feature_3 <= 0.80\n",
      "|   |--- class: 0\n",
      "|--- feature_3 >  0.80\n",
      "|   |--- feature_3 <= 1.75\n",
      "|   |   |--- feature_2 <= 5.35\n",
      "|   |   |   |--- feature_3 <= 1.65\n",
      "|   |   |   |   |--- feature_2 <= 4.95\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |   |   |--- feature_2 >  4.95\n",
      "|   |   |   |   |   |--- feature_1 <= 2.45\n",
      "|   |   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |   |--- feature_1 >  2.45\n",
      "|   |   |   |   |   |   |--- class: 1\n",
      "|   |   |   |--- feature_3 >  1.65\n",
      "|   |   |   |   |--- feature_0 <= 5.80\n",
      "|   |   |   |   |   |--- class: 2\n",
      "|   |   |   |   |--- feature_0 >  5.80\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |--- feature_2 >  5.35\n",
      "|   |   |   |--- class: 2\n",
      "|   |--- feature_3 >  1.75\n",
      "|   |   |--- feature_2 <= 4.85\n",
      "|   |   |   |--- feature_1 <= 3.10\n",
      "|   |   |   |   |--- class: 2\n",
      "|   |   |   |--- feature_1 >  3.10\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |--- feature_2 >  4.85\n",
      "|   |   |   |--- class: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree \n",
    "text_representation = tree.export_text(model)\n",
    "print(text_representation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import graphviz\n",
    "#with open(\"tree.dot\") as f:\n",
    "#    dot_graph = f.read()\n",
    "#display(graphviz.Source(dot_graph))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier().fit(X_train, y_train)\n",
    "preds = model.predict(X_test)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#estimator = model.estimators_[5]\n",
    "estimator = model.estimators_[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "# Export as dot file\n",
    "export_graphviz(estimator, out_file='tree.dot', \n",
    "                feature_names = iris.feature_names,\n",
    "                class_names = iris.target_names,\n",
    "                rounded = True, proportion = False, \n",
    "                precision = 2, filled = True)\n",
    "\n",
    "# Convert to png using system command (requires Graphviz)\n",
    "#from subprocess import call\n",
    "#call(['dir', 'dot', '-Tpng', 'tree.dot', '-o', 'tree.png', '-Gdpi=600'], shell=True)\n",
    "\n",
    "\n",
    "#https://dreampuf.github.io/GraphvizOnline/\n",
    "\n",
    "# Display in jupyter notebook\n",
    "#from IPython.display import Image\n",
    "#Image(filename = 'tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepalLen', 'sepalWid', 'petalLen', 'petalWid'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_descriptions = {\n",
    "    'sepalLen': 'ignore',\n",
    "    'sepalWid': 'ignore',\n",
    "    'petalLen': 'ignore',\n",
    "    'petalWid': 'ignore'\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "import autokeras as ak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 3 Complete [00h 00m 18s]\n",
      "val_accuracy: 0.9166666865348816\n",
      "\n",
      "Best val_accuracy So Far: 0.9166666865348816\n",
      "Total elapsed time: 00h 00m 59s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "Epoch 1/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 1.0232 - accuracy: 0.5960\n",
      "Epoch 2/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.9747 - accuracy: 0.6256\n",
      "Epoch 3/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.9296 - accuracy: 0.6404\n",
      "Epoch 4/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.8866 - accuracy: 0.6490\n",
      "Epoch 5/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.8464 - accuracy: 0.6575\n",
      "Epoch 6/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.8083 - accuracy: 0.6960\n",
      "Epoch 7/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.7717 - accuracy: 0.7662\n",
      "Epoch 8/200\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.7362 - accuracy: 0.7662\n",
      "Epoch 9/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.7016 - accuracy: 0.7748\n",
      "Epoch 10/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.6682 - accuracy: 0.7685\n",
      "Epoch 11/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.6360 - accuracy: 0.7740\n",
      "Epoch 12/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.6050 - accuracy: 0.7740\n",
      "Epoch 13/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.5757 - accuracy: 0.7910\n",
      "Epoch 14/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.5481 - accuracy: 0.8058\n",
      "Epoch 15/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.5224 - accuracy: 0.8144\n",
      "Epoch 16/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.4986 - accuracy: 0.8231\n",
      "Epoch 17/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.4763 - accuracy: 0.8231\n",
      "Epoch 18/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.4556 - accuracy: 0.8283\n",
      "Epoch 19/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.4364 - accuracy: 0.8338\n",
      "Epoch 20/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.4185 - accuracy: 0.8477\n",
      "Epoch 21/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.4017 - accuracy: 0.8531\n",
      "Epoch 22/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.3859 - accuracy: 0.8585\n",
      "Epoch 23/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.3711 - accuracy: 0.8733\n",
      "Epoch 24/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.3572 - accuracy: 0.8915\n",
      "Epoch 25/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.3440 - accuracy: 0.8969\n",
      "Epoch 26/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.3316 - accuracy: 0.8969\n",
      "Epoch 27/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.3199 - accuracy: 0.8969\n",
      "Epoch 28/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.3087 - accuracy: 0.8969\n",
      "Epoch 29/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2980 - accuracy: 0.9108\n",
      "Epoch 30/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2876 - accuracy: 0.9256\n",
      "Epoch 31/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2776 - accuracy: 0.9404\n",
      "Epoch 32/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2679 - accuracy: 0.9404\n",
      "Epoch 33/200\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.2585 - accuracy: 0.9404\n",
      "Epoch 34/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.2495 - accuracy: 0.9404\n",
      "Epoch 35/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2408 - accuracy: 0.9404\n",
      "Epoch 36/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2323 - accuracy: 0.9404\n",
      "Epoch 37/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.2241 - accuracy: 0.9552\n",
      "Epoch 38/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.2161 - accuracy: 0.9552\n",
      "Epoch 39/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2084 - accuracy: 0.9552\n",
      "Epoch 40/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.2010 - accuracy: 0.9552\n",
      "Epoch 41/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1938 - accuracy: 0.9552\n",
      "Epoch 42/200\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1869 - accuracy: 0.9552\n",
      "Epoch 43/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1803 - accuracy: 0.9552\n",
      "Epoch 44/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1740 - accuracy: 0.9552\n",
      "Epoch 45/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1680 - accuracy: 0.9552\n",
      "Epoch 46/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1622 - accuracy: 0.9552\n",
      "Epoch 47/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1565 - accuracy: 0.9552\n",
      "Epoch 48/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1510 - accuracy: 0.9552\n",
      "Epoch 49/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1459 - accuracy: 0.9552\n",
      "Epoch 50/200\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1409 - accuracy: 0.9552\n",
      "Epoch 51/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1361 - accuracy: 0.9552\n",
      "Epoch 52/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1314 - accuracy: 0.9552\n",
      "Epoch 53/200\n",
      "4/4 [==============================] - 0s 6ms/step - loss: 0.1267 - accuracy: 0.9552\n",
      "Epoch 54/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1223 - accuracy: 0.9552\n",
      "Epoch 55/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1182 - accuracy: 0.9733\n",
      "Epoch 56/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1144 - accuracy: 0.9733\n",
      "Epoch 57/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1107 - accuracy: 0.9733\n",
      "Epoch 58/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.1072 - accuracy: 0.9733\n",
      "Epoch 59/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1038 - accuracy: 0.9733\n",
      "Epoch 60/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.1006 - accuracy: 0.9733\n",
      "Epoch 61/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0975 - accuracy: 0.9733\n",
      "Epoch 62/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0947 - accuracy: 0.9733\n",
      "Epoch 63/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0920 - accuracy: 0.9733\n",
      "Epoch 64/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0894 - accuracy: 0.9733\n",
      "Epoch 65/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0870 - accuracy: 0.9733\n",
      "Epoch 66/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0846 - accuracy: 0.9819\n",
      "Epoch 67/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0824 - accuracy: 0.9819\n",
      "Epoch 68/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0803 - accuracy: 0.9819\n",
      "Epoch 69/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0783 - accuracy: 0.9819\n",
      "Epoch 70/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0765 - accuracy: 0.9819\n",
      "Epoch 71/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0747 - accuracy: 0.9819\n",
      "Epoch 72/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0730 - accuracy: 0.9819\n",
      "Epoch 73/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0715 - accuracy: 0.9819\n",
      "Epoch 74/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0699 - accuracy: 0.9819\n",
      "Epoch 75/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0685 - accuracy: 0.9819\n",
      "Epoch 76/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0670 - accuracy: 0.9819\n",
      "Epoch 77/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0657 - accuracy: 0.9819\n",
      "Epoch 78/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0644 - accuracy: 0.9819\n",
      "Epoch 79/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0632 - accuracy: 0.9819\n",
      "Epoch 80/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0620 - accuracy: 0.9819\n",
      "Epoch 81/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0609 - accuracy: 0.9819\n",
      "Epoch 82/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0598 - accuracy: 0.9819\n",
      "Epoch 83/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0587 - accuracy: 0.9819\n",
      "Epoch 84/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0577 - accuracy: 0.9819\n",
      "Epoch 85/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0566 - accuracy: 0.9819\n",
      "Epoch 86/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0557 - accuracy: 0.9967\n",
      "Epoch 87/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0548 - accuracy: 0.9967\n",
      "Epoch 88/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0539 - accuracy: 0.9967\n",
      "Epoch 89/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0531 - accuracy: 0.9967\n",
      "Epoch 90/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0523 - accuracy: 0.9967\n",
      "Epoch 91/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0516 - accuracy: 0.9967\n",
      "Epoch 92/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0509 - accuracy: 0.9967\n",
      "Epoch 93/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0502 - accuracy: 0.9967\n",
      "Epoch 94/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0496 - accuracy: 0.9967\n",
      "Epoch 95/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0489 - accuracy: 0.9967\n",
      "Epoch 96/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0483 - accuracy: 0.9967\n",
      "Epoch 97/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0477 - accuracy: 0.9967\n",
      "Epoch 98/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0471 - accuracy: 0.9967\n",
      "Epoch 99/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0466 - accuracy: 0.9967\n",
      "Epoch 100/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0460 - accuracy: 0.9967\n",
      "Epoch 101/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0455 - accuracy: 0.9967\n",
      "Epoch 102/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0450 - accuracy: 0.9967\n",
      "Epoch 103/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0445 - accuracy: 0.9967\n",
      "Epoch 104/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0440 - accuracy: 0.9967\n",
      "Epoch 105/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0435 - accuracy: 0.9967\n",
      "Epoch 106/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0431 - accuracy: 0.9967\n",
      "Epoch 107/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0426 - accuracy: 0.9967\n",
      "Epoch 108/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0422 - accuracy: 0.9967\n",
      "Epoch 109/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0417 - accuracy: 0.9967\n",
      "Epoch 110/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0413 - accuracy: 0.9967\n",
      "Epoch 111/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0409 - accuracy: 0.9967\n",
      "Epoch 112/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0405 - accuracy: 0.9967\n",
      "Epoch 113/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0401 - accuracy: 0.9967\n",
      "Epoch 114/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0397 - accuracy: 0.9967\n",
      "Epoch 115/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0394 - accuracy: 0.9967\n",
      "Epoch 116/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0390 - accuracy: 0.9967\n",
      "Epoch 117/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0387 - accuracy: 0.9967\n",
      "Epoch 118/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0383 - accuracy: 0.9967\n",
      "Epoch 119/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0380 - accuracy: 0.9967\n",
      "Epoch 120/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0377 - accuracy: 0.9967\n",
      "Epoch 121/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0374 - accuracy: 0.9967\n",
      "Epoch 122/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0371 - accuracy: 0.9967\n",
      "Epoch 123/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0368 - accuracy: 0.9967\n",
      "Epoch 124/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0365 - accuracy: 0.9967\n",
      "Epoch 125/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0362 - accuracy: 0.9967\n",
      "Epoch 126/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0359 - accuracy: 0.9967\n",
      "Epoch 127/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0356 - accuracy: 0.9967\n",
      "Epoch 128/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0353 - accuracy: 0.9967\n",
      "Epoch 129/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0350 - accuracy: 0.9967\n",
      "Epoch 130/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0348 - accuracy: 0.9967\n",
      "Epoch 131/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0345 - accuracy: 0.9967\n",
      "Epoch 132/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0343 - accuracy: 0.9967\n",
      "Epoch 133/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0340 - accuracy: 0.9967\n",
      "Epoch 134/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0338 - accuracy: 0.9967\n",
      "Epoch 135/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0335 - accuracy: 0.9967\n",
      "Epoch 136/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0333 - accuracy: 0.9967\n",
      "Epoch 137/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0330 - accuracy: 0.9967\n",
      "Epoch 138/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0328 - accuracy: 0.9967\n",
      "Epoch 139/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0326 - accuracy: 0.9967\n",
      "Epoch 140/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0323 - accuracy: 0.9967\n",
      "Epoch 141/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0321 - accuracy: 0.9967\n",
      "Epoch 142/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0319 - accuracy: 0.9967\n",
      "Epoch 143/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0317 - accuracy: 0.9967\n",
      "Epoch 144/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0315 - accuracy: 0.9967\n",
      "Epoch 145/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0313 - accuracy: 0.9967\n",
      "Epoch 146/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0311 - accuracy: 0.9967\n",
      "Epoch 147/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0309 - accuracy: 0.9967\n",
      "Epoch 148/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0307 - accuracy: 0.9967\n",
      "Epoch 149/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0305 - accuracy: 0.9967\n",
      "Epoch 150/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0303 - accuracy: 0.9967\n",
      "Epoch 151/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0301 - accuracy: 0.9967\n",
      "Epoch 152/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0299 - accuracy: 0.9967\n",
      "Epoch 153/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0297 - accuracy: 0.9967\n",
      "Epoch 154/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0296 - accuracy: 0.9967\n",
      "Epoch 155/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0294 - accuracy: 0.9967\n",
      "Epoch 156/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0292 - accuracy: 0.9967\n",
      "Epoch 157/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0290 - accuracy: 0.9967\n",
      "Epoch 158/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0288 - accuracy: 0.9967\n",
      "Epoch 159/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0287 - accuracy: 0.9967\n",
      "Epoch 160/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0285 - accuracy: 0.9967\n",
      "Epoch 161/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0284 - accuracy: 0.9967\n",
      "Epoch 162/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0282 - accuracy: 0.9967\n",
      "Epoch 163/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0280 - accuracy: 0.9967\n",
      "Epoch 164/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0279 - accuracy: 0.9967\n",
      "Epoch 165/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0277 - accuracy: 0.9967\n",
      "Epoch 166/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0276 - accuracy: 0.9967\n",
      "Epoch 167/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0274 - accuracy: 0.9967\n",
      "Epoch 168/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0273 - accuracy: 0.9967\n",
      "Epoch 169/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0271 - accuracy: 0.9967\n",
      "Epoch 170/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0270 - accuracy: 0.9967\n",
      "Epoch 171/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0269 - accuracy: 0.9967\n",
      "Epoch 172/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0267 - accuracy: 0.9967\n",
      "Epoch 173/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0266 - accuracy: 0.9967\n",
      "Epoch 174/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0264 - accuracy: 0.9967\n",
      "Epoch 175/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0263 - accuracy: 0.9967\n",
      "Epoch 176/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0261 - accuracy: 0.9967\n",
      "Epoch 177/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0260 - accuracy: 0.9967\n",
      "Epoch 178/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0259 - accuracy: 0.9967\n",
      "Epoch 179/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0258 - accuracy: 0.9967\n",
      "Epoch 180/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0256 - accuracy: 0.9967\n",
      "Epoch 181/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0255 - accuracy: 0.9967\n",
      "Epoch 182/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0254 - accuracy: 0.9967\n",
      "Epoch 183/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0253 - accuracy: 0.9967\n",
      "Epoch 184/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0251 - accuracy: 0.9967\n",
      "Epoch 185/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0250 - accuracy: 0.9967\n",
      "Epoch 186/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0249 - accuracy: 0.9967\n",
      "Epoch 187/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0248 - accuracy: 0.9967\n",
      "Epoch 188/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0246 - accuracy: 0.9967\n",
      "Epoch 189/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0245 - accuracy: 0.9967\n",
      "Epoch 190/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0244 - accuracy: 0.9967\n",
      "Epoch 191/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0243 - accuracy: 0.9967\n",
      "Epoch 192/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0241 - accuracy: 0.9967\n",
      "Epoch 193/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0240 - accuracy: 0.9967\n",
      "Epoch 194/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0239 - accuracy: 0.9967\n",
      "Epoch 195/200\n",
      "4/4 [==============================] - 0s 5ms/step - loss: 0.0238 - accuracy: 0.9967\n",
      "Epoch 196/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0237 - accuracy: 0.9967\n",
      "Epoch 197/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0236 - accuracy: 0.9967\n",
      "Epoch 198/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0235 - accuracy: 0.9967\n",
      "Epoch 199/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0233 - accuracy: 0.9967\n",
      "Epoch 200/200\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0232 - accuracy: 0.9967\n",
      "INFO:tensorflow:Assets written to: .\\structured_data_classifier\\best_model\\assets\n",
      "1/1 [==============================] - 0s 177ms/step - loss: 0.0780 - accuracy: 0.9667\n",
      "[0.07799240946769714, 0.9666666388511658]\n"
     ]
    }
   ],
   "source": [
    "# Initialize the structured data classifier.\n",
    "clf = ak.StructuredDataClassifier(\n",
    "    overwrite=True, max_trials=3\n",
    ")  # It tries 3 different models.\n",
    "# Feed the structured data classifier with training data.\n",
    "clf.fit(\n",
    "    # The path to the train.csv file.\n",
    "    X_train,\n",
    "    # The name of the label column.\n",
    "    y_train,\n",
    "    epochs=200,\n",
    ")\n",
    "model = clf.export_model()\n",
    "# Predict with the best model.\n",
    "predicted_y = clf.predict(X_test)\n",
    "# Evaluate the best model with testing data.\n",
    "print(clf.evaluate(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 4)]               0         \n",
      "_________________________________________________________________\n",
      "multi_category_encoding (Mul (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "normalization (Normalization (None, 4)                 9         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                160       \n",
      "_________________________________________________________________\n",
      "re_lu (ReLU)                 (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "re_lu_1 (ReLU)               (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 99        \n",
      "_________________________________________________________________\n",
      "classification_head_1 (Softm (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 1,324\n",
      "Trainable params: 1,315\n",
      "Non-trainable params: 9\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_y = [int(i) for i in predicted_y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  0,  0],\n",
       "       [ 0,  9,  0],\n",
       "       [ 0,  1,  9]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, predicted_y)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SVC().fit(X_train, y_train)\n",
    "preds = model.predict(X_test)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  0,  0],\n",
       "       [ 0,  9,  0],\n",
       "       [ 0,  0, 10]], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = [int(i) for i in preds]\n",
    "cm = confusion_matrix(y_test, preds)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}