K-Neares Neighbors (KNN).ipynb

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    "\n"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>y</th>\n",
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       "   Id         X1        X2  y\n",
       "0   0   9.506128 -0.404162  1\n",
       "1   1   0.249723  1.234908  1\n",
       "2   2   7.311201 -4.120096  1\n",
       "3   3  -1.385372 -1.216265  1\n",
       "4   4 -11.565323  4.387536  0"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# import the training data from a csv file and show first rows\n",
    "file_train= 'train.csv'\n",
    "df=pd.read_csv(file_train, sep=',',)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
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       "      <td>0.249723</td>\n",
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       "          X1        X2\n",
       "0   9.506128 -0.404162\n",
       "1   0.249723  1.234908\n",
       "2   7.311201 -4.120096\n",
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       "4 -11.565323  4.387536"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "# create a dataframe with all training data except the target column y and the column Id which is not used here\n",
    "X = df.drop(columns=['Id', 'y'])\n",
    "\n",
    "# check that the columns has been removed\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0], dtype=int64)"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# separate target values\n",
    "y = df['y'].values\n",
    "\n",
    "# view first 5 target values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split dataset into train (80%) and test data (20%); random_state=1 makes it reproducible (could be any numer); stratify=y ensures that the proportion stays in test and training data sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use KNN with n=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9506172839506173"
      ]
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     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# define the KNN model to use it with a simple holdout method\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# train the model using the training sets\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# show first 5 model predictions on the test data\n",
    "model.predict(X_test)[0:5]\n",
    "\n",
    "# check accuracy of our model on the test data\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use KNN with cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.95987654 0.95864198 0.95061728 0.95555556 0.96358025]\n",
      "cv_scores mean:0.9576543209876544\n"
     ]
    }
   ],
   "source": [
    "# create a new KNN model to use it with cross validation\n",
    "model_cv = KNeighborsClassifier(n_neighbors=17)\n",
    "\n",
    "# train model with cross validation of 5 \n",
    "cv_scores = cross_val_score(model_cv, X, y, cv=5)\n",
    "\n",
    "# print each cv score (=accuracy) and average them\n",
    "print(cv_scores)\n",
    "print('cv_scores mean:{}'.format(np.mean(cv_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use KNN with grid search (n between 1 and 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 17}"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new KNN model to show how GridSearchCV works\n",
    "knn_model = KNeighborsClassifier()\n",
    "\n",
    "# create a dictionary of all values we want to test for n neighbors 1 to 25\n",
    "param_grid = {'n_neighbors': np.arange(1, 25)}\n",
    "\n",
    "# use gridsearch to test all values for n neighbors\n",
    "knn_gscv = GridSearchCV(knn_model, param_grid, cv=5)\n",
    "\n",
    "# fit model to data\n",
    "knn_gscv.fit(X, y)\n",
    "\n",
    "# check top performing value for n\n",
    "knn_gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9576543209876544"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check mean score for the top performing value of n\n",
    "knn_gscv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## use the model on the (unseen) test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "   Id        X1        X2\n",
       "0   0 -1.787139  1.565062\n",
       "1   1 -3.506047  2.317350\n",
       "2   2 -4.229388 -2.612948\n",
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     "metadata": {},
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   "source": [
    "# make predictions on the unseen test data\n",
    "file_new= 'test.csv'\n",
    "df_new=pd.read_csv(file_new, sep=',',)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": true
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       "         X1        X2\n",
       "0 -1.787139  1.565062\n",
       "1 -3.506047  2.317350\n",
       "2 -4.229388 -2.612948\n",
       "3  0.980246  0.090494\n",
       "4 -3.380691  4.589728"
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     "execution_count": 172,
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   ],
   "source": [
    "# create a dataframe with all training data except the target column y and the column Id which is not used here\n",
    "X_new = df_new.drop(columns=['Id'])\n",
    "\n",
    "# check that the columns has been removed\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_new)[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "model_cv.fit(X, y)\n",
    "model_cv.predict(X_new)[0:5]\n",
    "predicted = model_cv.predict(X_new)\n",
    "print(predicted[:10])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## store the results in a submission file (csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Id  y\n",
      "   0  0\n",
      "   1  0\n",
      "   2  1\n",
      "   3  1\n",
      "   4  1\n",
      "   5  0\n",
      "   6  0\n",
      "   7  0\n",
      "   8  0\n",
      "   9  0\n",
      "  10  0\n",
      "  11  1\n",
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      "  13  1\n",
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      "  16  1\n",
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      "  22  0\n",
      "  23  0\n",
      "  24  1\n",
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      "  26  1\n",
      "  27  0\n",
      "  28  0\n",
      "  29  1\n",
      "  30  1\n",
      "  31  1\n",
      "  32  1\n",
      "  33  1\n",
      "  34  0\n",
      "  35  0\n",
      "  36  1\n",
      "  37  1\n",
      "  38  0\n",
      "  39  0\n",
      "  40  0\n",
      "  41  0\n",
      "  42  0\n",
      "  43  0\n",
      "  44  0\n",
      "  45  1\n",
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      "  52  1\n",
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      "  59  0\n",
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      "  61  0\n",
      "  62  0\n",
      "  63  1\n",
      "  64  0\n",
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      "  68  1\n",
      "  69  1\n",
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      "  71  1\n",
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      "  73  1\n",
      "  74  0\n",
      "  75  0\n",
      "  76  1\n",
      "  77  0\n",
      "  78  0\n",
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      "  82  1\n",
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     ]
    }
   ],
   "source": [
    "# add calculated y value to csv\n",
    "df_new['y'] = predicted\n",
    "df_submission = df_new.drop(columns=['X1','X2'])\n",
    "print(df_submission.to_string(index=False))\n",
    "submission_file = 'submission.csv'\n",
    "df_submission.to_csv(submission_file, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visualize the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fa3cd5128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iL94qVuPp4W1U0ggqRS1C5s1Y8pi08zLN97BkTzKMpwlbT6P4DSginDONzOwlfPH5r0yauP1qZpdc+AUP3D+Wiy/4kpt6j6CwsHTKCwM3uXVXLvR7dHKF7l0pzdlnfErlzP7c1PubCp2zKyi9liLnRmw1mJjbh4hzftItNh1DNMGUbQnK25A0JmQ8gJSVEaIS4HkdxZyXKHL+g9JrkaIGpmhPiYdW6oo4ejK22vm9JNTneJ9dImls99l1BKgKvvYi/hGpK4QQjYCDgalALa29hPPJf8t1jxFCXCOEmCGEmLFx485z6P9b0VpjqxHE3bfQuijtmBACQ7YgYJyKQTe8lM3LiNgXbbc/02gJFNskJKboRtj83w7HYMiWhM3+BIyTvL9FGyCM61hsXF8VpTS27bJ5czTtPK01uRuLWLRgM1p5k/h778zhsouGlLqHEFvXv8pbr7Tl8vNOp1PH7butlub337fw49jlALz3zmyi0T+fs6l4zJs2RcvZecRw1ZTtnhcyryM7MCZl9/BKgvamwD6UuBqIrb4g4tyE1jE067HEmQiqYooTkTSj2HVXUI2EOxRb/bjdawXllXjZWDMx5fF/+p7/tWhRsddexB43KgvPUXwwcKvWOr+iHi9a69eA1wA6dOiwl23M/h60TiSNwFMBA1fNLjcACkCxquS8cjKQFmOItghCaBws2ZOw+cgujytDfsD0n4bj2gfw8P8MfhizlPPOGYxlSiZOu4JGjSqjtebUEz9i0sSV1K2XjRAl2YSXLd2a1l+9OodxzpltOaxjIYd2rFuhMTRokEPdutmsW1dIq9Y1CYW2/1NYv66QwohNkyY7VxNdeO4XjPxmMe0Ors2IMc+jxOe4ejYgCBrXAWCrH9F6PZY8DSGC5fbj6PE4+keK60GDQOmfybcPxvPwEkAGYfNpwCHmvoogiK3GYOtPAAibT2LJk8r0HTBOxZLHA+Yuufr6lELvfZlMK8Ie/TYIzz1iMPCB1ro4G9p6IUQdrfVaIUQdYMOeG+Hei6MmYavRuHo6xWkXveIu5ROU5yTTUYMgG62dcicLKaqRbY1Hk5tMDb1rfPLRPB7vN4HVqwoQYimDPjqTCT+uJBZ1kGGLaVNW06hRZTblRpk0cSWOo1m7ppB+/Y/h809/JZFweea5EwBvNX7fPT/w/MBpNNivEj9O6oWUFVtQBIMmU366ksWLNtOiZfXtut5On7aGU3p8iNZwz32HM23KavILErz0ykns17BSWtto1GbE8EVoDXN/2cDqFadywAHHpbWx1fcUOTfhJcybRth8stzrermONJCBoC6azcAW0pPyaTSFaL0hGfkt8FJyRPHUfyu2e/9ChHb4fHwqwD7oZbTHVEbC+wW+CfyqtR5Q6tBQ4LLk/y8Dvvq7x7a3Y6tRRJyrSKiP8YSBBKqSYfbb7jmeEdhbrWrWkG93wNW/l9tWiAykaLDL8QtLl27lpt7fsGTxFmIxB8dRzPtlI3fcdRiGIahdO5Njj/cqllWrnkGnzvWxLMkh7etw/Q2H8sP4y5g49Qo6dvJcPvvcMornnp2G1rBxQ4Tx45aXuaZSmqKi8tVBGRkWB7WphWVtP73HuLHLSCRcYjGH11/5iW9HLmHCj8u5956ytRpCIZNjjmuMZUmaN69Gw20EBoDSq/E+k9gOJ2xD7E+WNYSw+STZ1jBCRl+2tRsExG1IUR1XL0geiwL5eKqjxgTkhWnttfbySxXaF+Kon7d7bZ8KoPFtCLuZrsAlwDFCiFnJ10nA48DxQohFwPHJv312AaV/x3PiiiM5EFOcAGylyL4mGeFbFk+vX3oPXIijRu3WcVlm+tet8f5VuOSyNmRkmITDJjVqZhIKeZOzEILh317IvAW9+XbMxeWu/IcOKfGQkYagevUwg96dTe5GT82yeXOUg1q+TL2aA3jmqfKNzbkbi/hmxGK2bi1fTXbWOS2pUjWDYNDgtDNaYFmSYNBkv4Zl3TWFEHzx1bn88uv1fPPdRVzQczBtDnyF8T96gmrG9DW0a76Or4fUY+6c2hTk7rjusyGaYsmTECJE0LiQLHMUUBcIEpT3k2Fd7z1X2T35+RWv+iWWPLKMS6mrpxFXb+DqKRQ5vXd4bZ8KsA/aEPakl9EErbXQWrfRWrdLvkZorTdprY/VWjdN/rt5T41xT+KoCeQnuhGxr0Tr7ev0S9rPptC+lJjzHJY4H0N0QnIgYevppA1BoViDo8s3bHrF7Uvn+w9giiMrNNbFizdzRJe3OfH4D9i4YfvxBfUb5PDAQ0dimt6PZPOmIqrXCHP/f8dSUGAz95cNjBm91BuP0iilqV0ni7m/bGDIF78Rj6fXI7i1b2ekFDRsWIlRYy7inDM/4/bbRnPsUV6MwPhxy9m8KYrral58YUaZ8USjNp3av8EVl37FEV3eZv26Qk496SO6H/s+K5Z7aaj3378KS5bfxPrNt9Ov/zG88vopPP1sdx54qOTZ/PD9Mtq3fY2rrxiG62rq1svm62GLmDB+JcuWbuW2mz3B2u+R8axe7XL9ZcdxUrfT+PiDBA/dP5aaVZ7kovO+KNcFVpcqwWjIJlQKTKBS4FdCZi8AHDWVQvtUBFlkmV9jiA4YojNB45oyfQkq4S1tTUQpF2KtC3HVr17UtZrvu6JWFL9Ajs/uJuGOIT9xCIX2WWidn3q/yLkPzSocPQU7uVJ31S8U2KdRZN+WVglM60IizqW4egJx9SqKBWRZg8gOfI0hmiY9SUKAmUz9XJb0GgEGWeaIZPK2nfPQ/eOYM3s9U6es4uUXvYm3oCDOyy/NoEHtZzjztE9IJLx8/9177I9lGZimpCjq8NorP3HY4fUxLUk0apOXF2PZsq00afgctao+xcABUzj+6EFcd/VwLr1wSNp1b7qlI7l5dzDn1+v4cewKYjGHaNRh5Yo8tNZ06lyfQNAgGDToee6BZcadu7GIvPw4kYjNqpX5DHhqChPHr2DalFU88lCJl44QAikFQgjOOKsFF1/ahqW/b+Wk7h9w/dXDue7qr1m0cDNfD13I6FGemq1582porQmHLXJzI1TJ6o8GLMv7yVmW5NCOdXj6ySnEYi6jR//OwgWb0sYXcwaQbzel0O6J1p6rra3GUWCfQcx5HoCocy+KZTh6Eq6eQ5b1CVnWIKSoXuZ+DdmCTPM1QkYfMi3Ps1vrfArsYyh0zqbAPo1C5xwKnTNJuLvfFXffooK7A3+H4LMrxN1+aLbi6t+w1ejU+4ZohZdYTWOIJgAUOXei9FxsPSrN3zzq3ENJjh0HQbqqIMPoR5b5JTnWj0hRu9xxGMLL3FmMoydV+B5ata6BZUkcR/Peu3MY9e0SDmj4PHf2/Y68vDhTJq1iwnhPX960WTWGDDsPIaAoYnPv3T9w+hnNMaRAKfjP7d/Rv99EtmyOYtuKt96Y5WVvLbL5ZsRiPv5wbtq1TVOyckU+Dz0wDtf1BJrjaP571/fUrpPF/IW9+Wn2NfTrXxIbkbuxiOnT1lCnbha9Lm9LlaohrrzmEBo0yCEQMAmFLA44oAoXn/8FlTP7c8hBr7J8ebpn0029v2HC+JUMHvwrWVkBAgGDSMTmxuuHs1/dZ5g/byOjf7iEW/t2IhZ1cF3NlEmrGDHqIt4ZdDrTZ13D4d0acvAhtcnMtMjJCVK/QfrnFlevAQpX/5pKY1Hk9EbpOcTVsyTcEcnssBmATiu6sz1MeThB4zqkqAGAqxeiiQCxZGnRGBDH1RWL5/jXokErUaHX3oTvc7aHMWQnlFqPN/GXrGLD5gAc/T2ShqmVuhQNk/l0NFKUuFe6usSYaooTy6zshRAYO0grDWDKrljibGw9FDAwxSEVvof/3NWVAU9NARQb1ke47urhRKPJugQSXFdTq1ZJTp8uXRtQtVoGGzcUIQTUrZeDlALLkhRFbD4YVFJeMhQyicXc1N9vvzmL8y/0kroppRECpBTobbQtr786k379jyUzM0BmZgDw1C9ffvEbN1w7AttWtGlbizHjLqFevWwe+99EhISHHj6KWrWzaN2mJk88PgmlNIsXb+GKS4cyZtylqf7r1c8hFDKQQnDvA924qtdQADZu8GIpbrz+G2rWDHNw+zoYhiQcNjnq6EZ06lwPknmQAL4dczEzZ6zlwNY1yMoKpH8moiuOnowgI5Vy3FskRAFN3H2OLGsojv4OyX5/KNmdIQ5C0gDFQrwFQRAwSKhhCKcWIfOGnfTwL2Yf9DLyBcIeJsN4lIA8DSHqpNIwg5eLyBI90tqGzQHYaihS7JcqtOKqecnIXw9Hj//DhXEyzP4EdS+EqIYUtSp0zgfv/8Lbb8yiTp0slv7uraINKQiHLWzHxTK9cZx0wocUFMQ54IBqDB95AR98fBY/fL+Mbt32o2OneowYdRHPPTOVoV8tSK30ARYuLFGjCAGX9PLyLH09dCG9LvmKjAwTaQhaHlidX+ZsSJ3b7uBaKKXTjNH3/3csL780nUTcU+zOmL6G11+dyeDPfiUWcwgGDTRw5tktiMUccnKCbNrkTfDhzPQEci+8fCJdDquPIQWxmEPzFtVYsmQL0SIH0xQ4jmbdugjfDF+MlPDqG6dwTjlqq1DI5LDDG5T7bMPmayj9G1LshxDZ3mdkPEDU7QMIpKif/J6UjTWoOHEUxQsKRUnVtwhx9QwBdTZx9QKQQcjo67urFqPZ69RBFcEXCH8zrl6KIIAUxdkyJabojNKbcdRMDNEmzf9f6VyKnFtw9QJMcRRhsx9ClKwkvbQRpTV/W4m7L5XJg1MRvJ1E2Ulre2zaFOXmG77BTiikhBYtq6OU4vkXT6TtwbVZsmQL3bq8jeuWuH4uX76VQw9+nYKCBL2uaEeXrt5k2L5DHR5/8li+HrYwTSAc1KYmc2ZvQCmNFTC44doRfDN8MatW5pNIuCnbRCLu8uAjRzJ82CJm/rSW2bPWc3KPDznyqIb0ub0LgYDB5EkrU8KgmFUr8+l7Zxeu6jWMylVCnHpaM8CbqGfPv5Znn55CIqG4tY9XtyCRcHnisYnk58e59vr2dGr/Jrbtkp0TpF69bJYs3kKr1jWYM3tDateilGdnOe+CkhW86yqu7DWUH8cu574Hu3H5lQeX83kYSdVhCQHjFITIQOnlBGRJevGE+x0x9x6kaEqm+QZgYavPAQNLnrXdBYJXl6L09qoaAhuNiaAqMfdZbP0F3ncsRIbZt9x+/pXsZeqgiuDbEP5G4u5nFNonUWAfj6Mmpt5XOpcC+xgiziUUOddtc86ApD53M44eQtx9I+24KQ5HUjqrqUCxZreMd/q0NVx52VC+/OLXco+HQgbBgIlhePr/Zcu20Klzfbp0bUA4bPHeO7NTbevvlwMCokUOeXkxbFvx0Ydz0VqnPGmysgNlrrF40WaqVcsAvElfa2930LlLidqlOBzi7HMOpE3bWriuJhZzmTxxFU8/OZmXX5wOwIOPHJW20g8EJDfc3JEzz2rJhi23c9rpzel86Jvcl4wxyMkJcf9DR/HoY8dQvUYYgNde+YmBz0zlzdd/pu9to0gkvDHl58VZvGgLWsPsWRuoXj2cdh8btvG+mjF9LSNHLCE3N8pdd4yhoCBORbHksQSNKwBJod2TvEQ7Yu7daHJx9SxsNYa4+wJR92Gi7gMUOXcTdR73CheVwqtP8RkGR2DQlaC8kxxrFFnWSMLmALKtYclIagEkSKiXiLuflTekfyG+UdnnT+L59cfxCspMSL2v9CI8l89oGbdQr9JZ8cekiKvncNTMkuNColmb/MtA0pKQ0WfnY3EUI79ZzK/zy49e1lpz+skf89mn87n2yq/LGFUBMjMDfDf2EnqceACBgCQWdfnsk3nccuNIlNJsXB9BawgEDDp3rpdaiLquN4mfcWZzWh7wEjUqP8XXQxcydcpqDCP9BxSJ2ASChqevlwLD8Dx+XntlZkoQVKoUZMpPV1K/QQ533HkYXY9oQPUaYYQA19HEkzaIRQs3o0u5dhqmZOkSL7P6hvUR3nl7Fvl5cQY+M5VD2rzKkiXlZ10vFmLFgmpbpCHIyCjZ5QkBh7RPr0PduHFlDEMQDpskEi771XmWa6/6mh++X1Zun+Vhqx9w9XwgP2kY9oSQIZqh2UBx9lRHf0lCvUHEvnSb8z8jrgb+O3fpAAAgAElEQVTiMg4pqhIyr0WIHKSogSW7I0QlQsadGKIT3ndQk1DbT/v9r2I3BqYJIXoIIRYIIRYLIcpkdxZC7CeE+EEI8bMQYk4yXusvwRcIfyNB42ogjKAKAeOc1PuGOARDNAMChORtaG2TcL/AVmMJGjcSMu6FlOeQS9R5NBVFrLXGkl5FLgihWEHM7Z/mv14eN143gssv+Yoju75Dj+Pe57ijBrFwwSamTV3NqpWe+2vJ5CyQ24lKzghbzJm9Him9iToWc3n7zVkcevDr3HnP4XQ/YX/OPKs5ff/TJe28QNBg1s/ryc0t8tQwj0+iVesaSENiGIIOh9ZJ5S/KzY1wz31HsHjZTYRCBq6rcV2v0lvNmmFef/s0GjaszL33fM8RXd5my+YYW7d48QdKKX74fim27WLbbkqNYxgCyzRocoDnj1+1WgbVq4cxk8FzixdtKTeY7ZsRi3EcTTBo8tCjR/Hy6ydTs2aYY49rzEuvnUSt2pn06NGElStLXIjve7Abn37hqXfeeXsW+zcYyO23jWLClMu54KLWmKbEdTUffziX88/5nDHfLd3hZ1eMIYvVe2FM0Z2w+SJZ1jAM2Yyg0QdTHINBR7zEdwpdqhqbo2Yns696KiNXLyXqPIjS6btLIcJkmA/iCRurTPTzvxntigq9doTwdHkvAicCBwIXJMsAlOZe4FOt9cHA+cBLf8HtAL4N4W/FlB3JsUqqYBWTUEOSKz2JIdsTdR/GVoMBQdgcQNDohSFaEHF6A3koZlNkX0OWNSoZfzAZyeEovFTGthpB0LgWg6bbHcuMGWuIRGwMUzBl8iqUgjNO/YTNSSPq2AmX8cVX59L/sYmc1bMlDfbz0jDEYg6ffzqf+g1yOKhNLY7o8hb5eZ6PfChkEo87aA0rlucxdcqq1EQI8PmXPbn15pGsXlVAPOayKbcI0xRIaXLGmc35cNBcXMdFCC+BXfHkbScUh3VtwKZNRWkeR8GgwYhRF9GseTVWLM/j1Zd+Ih53WbeuRD2jFEyetJqLz/+SQR+dydo1BaxfH+H47k3o3KUeNWpmJvsymTz9Svr3m8A7b81CCFlmVT918irGj/PcZ+NxlwW/bebWG0di24pfftnA779voaAgwZzZ69O8nm6+tSOBgPdTu/uOMUQiNqNG/c71N3Tghps78vFH84gn1WG2rVi5Iq/cz+y70b/zn77f0eHQOrz06smY5v5kW9+i9EoM0THNTiBFdTKtV72xOu/g6EmEjBK7UpHTC00eYGDQAZfZJNT85M41gRS1yDTfQIhKGGJ/cqypuGoOMfUcSi8hZNz9hxwX9il2jzqoI7BYa2+FJ4T4GK8EwPzSV6JkRVgJdpNOuBx8gfA3s23+H6U3EnMfxMtPD47+EaWX4/mDWym9ryk7k2m+TsS5BE/tJNCsTyav0ygmIWmCYq1ntKb8eINinhzQncsv/Yotm6NoDaYpyNsao6jIJiPDZObMtbz1+s/Mm7uRKVNW0717E6rXCHPDdSO8QjUCjjqqUUoYADRpWpnq1TKZOnU1AmjTNt1TqVKlIBuSaiQh4PmXT6Rt21rk5cVp1rwaTzw+Ea29OILc3JLVbIMGlejYqR7PPzsVKQWuq2nYqBKfD+nJgCcn88svG+jX/xjCmRamKRECCgttKlUWHH/iAlYuz2bKlCDa+Iw77v+JoHFdKrajNFWrZtD/qeM5/YwWaDRdD98v7fj8+bkYhnd905IsXbqFeNwTUBvWR1LHEnGX6jXCFBYmaNAghxZNXkRIwdDh59P6oJrMmbMeKQSN96/C7X1GkYiXCDnXVRx7XNl4ggnjV3D5JV+Rlxdn7ZoCzu55ICf0aIIU9ZFix0kGg2YvgvTa5t3in76FZZyN687BS5a3Bojh6g0k1JcEDe88ITKIuvejWISrZ2GIg1H6NzT5BI3bkKLyDsewz1GsMqoY1YUQpUPlX0tmawbPB7m0cWcV0Gmb8x8ERgkhbsIrp3ccfxG+QPib0DpGzB0IJAgZt+Fl/QZHfUdJfLvEkqdgyZOJOv9BUJ2APD/VhynbEzIeQemZBIxrENRE0gzFIgzRirDxLi7TMUTrlJvi9jj6mEbcdEtH/vfweBxH4bqaSCRBMGhwQNOqnHRyU265YSTxuEs4bLF06Vaq1wjz+5ItRKMOgaBBUZFNMGikJsVFCzYzcsXFzJ61njp1s2naNL1Gc8J2SSRKvHwaNqxErdpZ1KrtPYubb+3E6lUFvPfO7JSnkWVJ3nzvdAB6nHQA/R/zag/f9d/DmT83lyFfLqCoyKbPzaOYMv1KpkxeTbejGpKRYeIafYnGJ6K0ZvG8y4m5DwMxHDWFnMB4L9mb+ziO+o6AvBEhwmjy6HzYqTh6LHmJ073azeY7CJHJ2T1b8tkn81i+bCuvvXkqhikwTYnjePdUq1Ym69ZFOKxrfT745CwmjFvBcwOnsWihl33lzr6jydsaw04omraqSs1aYYYOKakCZ5qQlRVKGbCLmThhBWef/inxuIuUAqU0jRpVIhZz2LA+QoP9cnY50WCm+T4J9QGm7IYpjgVdgMtclM7D1T8ANoL0HZIQ1SAZB+Oo6ck02w6Omk6m9XrKc+7fwS4ZjHO11h2231EZttX3XgC8o7V+WgjRBRgkhGittd7tiTF8gfA3EXdfJaHeJlmdO6mXBUO0x/sYDALycgyxPwl3KGBiyVPQRHHUdEzRESEyCRpnA2en+s2yvkSzBkE9hDCQHL3dMaxYnkefW0dRs2aYp5/tziWXtmHokAUsW7qVgsIEjq1o2qwaTz/TnYvO+4Kjjm7I1ClrOPLohhzS3ttxPPdiDy7oOZhVqwqYOmU1F1/ahrfe+Nlb2bsKIaDbkQ3Lvf7hR+xHdnaAgoIEgYBBcJsaBKGQybPPn0A4bPLSCzOQErJzAhxyiHftps2qsXj5zcTjDpUqhZgxfQ1aazIyTBrvX5k6dbM58+wSj6sCexnBkA2EaH+oSSKZ7kOzhkL7bAT1cPRoIE5M3ZE8SxHjf8ma0Hm4ej4JNQRD7E929iGMGJVePOjZ507g5qQRfc2aQgCmTV3DyhX59L4uvbrbhAkrUcmf8C9zNpCXl8CyJLbtvamU4O33TiMjIz3mYXkyr5JSmvoNcvj4s7OpWSuLtge+wqZNUc6/sBUvvLxrdkZDNidDPpz6O2he7j2zxMV431EDxe84aiZFzo0IqhE2n/OC4EQTlFqBrb3vsuI3IvZFZAfG7tIY9np2j9vpKqB0IEp9yqqErgR6AGitJwsvGKQ6f0FpAN+o/LdR+stT8tgN2YxsayxZ5peEjNvROo+oeweunk7U7UuBfTxFzo0U2meX7RIQwkwGLhnEYg733v09t938bbnZO/vcOorvRi3hs0/m8/abs6hZK5NxE3txXe/2OLY3mV959cFceP5gxv+4grE/LOezL3sy6MMzMQxvzAe1qUWzFtVRShOPO1SpGkodMw2DVSvLz6bqjVUwblIv+t7RhU+/6EmjRmXVDJo19Ll7Ea3bbsV1IT8vQSxWktQuFDKpVMkLjupwaF2GDDuPJ54+nnc/KFtpNWz8D0EDJA2x5PmULLw0rv4ZRxfvziTpmciiSHEAxalDYu7jRJyriTiXl7nGBRe3pmmzqoRCZpqH1JYtMfLy4qnJHkBIkfKMCmWYVK4cpMkBJbsopTTfJZP7RSIJvh25hHVrCznr7JacdEpTWrWuweVXtiMWc/ji8/nk58dJJFwGf1a+W/AfwZLt8FJhWBiiheegwDoUC3H0dwSNq7HkMQSMi7BET4qr52m2/7nvixQXa6rIaydMB5oKIRoLL8DofLwSAKVZARwLIIRoiZeY7C8pE+nvEP4mgsa1eMXsbULGjWnHpKiBwkGzCkF1vI/Fwfuxed4qioU7jUB+8bnpvPryT8mSlIoXXj4x7XitWplYloGRTBUNsGzZVp5+ynN1DQZN3n5zFnlbvZW0ZzjNTaZb8MjPj9OuXU1+GONNXDVqZNKgQQ7r1hWCgG+/WYxSmtYHlVv5lLytcU47ozkHH1LWxqG1ptA+g0BmHsPGwPmnXcN55x1TJqVDabp0bZAKbnP176CjydoOoEmg2YjGJabuICjvJa4ew6s45j3VgLyFhBpIelrKqmTIp3GZSdwdhGIq4ODqn9E6QZHTB1fPQIrGBI3eTJhyOUt/30pRkc1bb8zihB77c/gRDbAsQTzuzQh16mYx8LkTuOnGkaxfFyEWdTj6iHexLMGBB1ZnwYJNmKakStUQ8bjDicd/wKKFm5GGoHbtLPLy4gjgyf6TeOTBHwkGJeFwgGjU4cprvKA2x1EMenc28bhLryva7bAK3PYIGn0w5KEIKmPKtrjqJ1w9Fy+1SkmQnBAWYesR4k4jbD0+zWD9r8H98+tprbUjhLgR+BbvB/+W1nqeEOJhYIbWeijQF3hdCHEb3qqml96ZG+EfRPxF/f6tdOjQQc+YUTa98d6CoyYSca4CICQfJaHeQzEPb/e4Gm8Cs6gUKElLrPQatM7DdZoxY9oamreszgeD5vDoQ+NRSnNprzYMGHgCg96dw/3//YFGjStzyqlNkVJQr34OPc/zPNuaNn6BDes9r5xi/XSxu2cgaDDwhR5cdLGXITUSSXBQy1dS9QbA8/SZv7A3I4Yv5rH/TWDzpiKkFMyaex2162Sl3ed778zmjr6jQXupH3qelx6Fq7VDvt2S4glb0JYM81YEBoboWq6e3FZjUHoxgkZE3dsAMMUJBI2L0WyhyLkFL/dPDt4urbQHj4XFpdi8TclOwatNbIqOCKpg66/xfoPSC+CSdUmowZAqMxokx5qBEJlsy523j+blF38CICsrwJqNfRjx9UKuuGwoRUUlu55atTJ594MzOPPUT5BS0LlLfcaNXY7jqNRnUR6tWldn1PeXkJ3tFTa6+PwvGfqV9x3p0LEu34+7tPwTdwGtNa6ejKBKhbPf/tMRQvy0A51+hWjfKEtPeaBdhdoGrpj4p6/3d+HvEP4B2GosxaURPV22wJuEVuLtDhNp/t+Omk3EuQCAj9/ryoN3tcB1FaEMk86H1aPDoXXpc7vn93/Xf76jID/Bpk1R5sxeT48TD+C25LHRo5aQm+sJA8uSNN6/CgsXbEJraNioEl0Pb8A5PVvy8UdzufG6b6hTJ4u8bVRR9RvkUKNmJpdd3pbb+4wiHnPJyDDJzS0qIxAmjl9JtMjmxj6zqN9kEo7qjylLXGOFMLHEpdj6bQA0sylyrgAMQsbDBI3z0/pz1LRkOUoHqEbxJO3or3CcYZiciSUuROmZuPxCer0HABuHHylRJYnkK4aj5+AJJg1YSFriMgFXSUp2GMXnlKwUtXZx9XQE9fnvQwZTJoeZPauI3jd680GnzvXJzgmSSChcV6G1V5ehoCCOlIJIxGbWz+t45H9H8cTjkxASNm8qeebZ2QFc1wuMu/f+bilhADBjxurU/3+bn8vuQSNFEwQ1dlN/+wp7XzW0iuDbEP4BmPJY0m0MutS/mgxjIBnmA6mjnqupC8Ro0eoXIhGbWMxl65Y448etoM/tXVJqlnbtaqcK0ti2SqVQWLumgIvO+xLlepG1Dz16FEceXWIMbtqsKjk5QerWHMAN144gkXBZuSovpRPPyDC5tnd7Rn9/SeqcN946lQNbVeeGmw+lVeuyE8htt3fm3Au3cNtdP9HyoFlEnJNQOt0uZskztzlLAw6OO4GIfRMR+yqUXu89BzUXb5J3oIwOW+EwGFd/jxQHUVYYAAhCRh+Csi9QFZMLMEV3BI3wVHUFgMbiOgTVUtcyOAZDHIbJGWSagxCiJGI56v6HiHMVhc6xaOsGHnrqc4JBeHbAVL768jeqVQ8z97frmb/w+tQ5CVtRrVoG3Y5sSI2aYZ4ccDw9z2vF7PnXMWTY+WRkeGrChg1zkh5hijp1s1m0qKR21MsvzWDdWs+obZqSp545vpz7rThaOyTckRTaZ1BgH0HEORut3Z2f+G/CT13h81fglbws8SyRHJrMT+RNBKWzoAJY8hQEtYAM1q28jFCGiWVJwpkmdetlEw6X9PX5kJ4plYMQcMddh3nXVBqdFDzBoME5PQ9EkCzgImDM6KW88tJP2AlV4gWTnA9CGSbjJvbiyaePT3ORPPn0BKMnvU+f/z6PJj3VhdaKxk2H8+zLeQSCKmlcVdjqu7R2jv643Gfk8D2OHo6jfyDmPI6jlhJT/8ObpAWmOAYvdbNF6Y2vVyVuTLl9GnTCkocnDcgWDu8jRIhsa0SpPjSIomS9CAAXl+9w9TQUizFl+7Q+vZoWRXgCO8HIYXWJRjWJhMt7785JPm+T2nWy6dS5HpmZFtlZAZo1r84ng89hyfKbSdgurZq/RPMmLxCPu4RCFoGAZP2GCPG4Qzzu8vuSLTz26ASmT1vNicd/wJ19v0t5MN18a0cuvLj8QkgVJereT9Ttg8ITuq6ej2bdn+pzX0NrUaHX3oSvMvoHYMpOpQxUkqBxNaY8GFsNxhAHpoykxUhRm5yAV9HrvHO918YNEaZNXc1hh++XlvI5FDKpXj3M+mRA2PKlW8jdWES9+jm8+fZpDPlyAVdc1Y6Te3zIiuV5CAGhoJEWEVyMEN7qs3LlEA32K1tTOOb2Q7EC9FpsNZigcVXqmK2GElOPU6waS44OU3ZO/eXlCCrCE4Se3cTT7Xu7oWQrlI4Tca6kZCclkRyCZ5fTlAgFb1LWFKtSwniTtXeOKU6gwO5R6jjY6lMC4hq8gFDPkcPWb5S6FhR7JGnS1TJuKqDQayNozGlnOLzzmoljK668qk1a+69HXsjPP62lWYvq5OSUqH4+HPRLMuYA3n17Flu2lKiMmresytLf83AdhQY2b4ry04y1af1mZQWYPm0NBx9Sm48/nEtGhsVx3RuTmRlIpebYGV7kfIzinash2pSJSyiNZ4tMJJPh/QvQ7HXlMSuCLxD+BHF3MHH3GUx5NBnGw7scHOSqX3H0JCzZg4C8ioR6GXBJqNcIGJ+WWxd3e9SomcnJpzYr874Qgl5XtOWpJybjupo7+o7hrv98T7/+x3Lt9e3JzS3i1BM/8nYM2lMFZWYGcFWMnuceiJSCuXM3MHfOBqQUXHxZGx565KhU0ZnSGKJtsrKXTlbyKk2CdGFgkWl+gKQxWscRIkjUeSJpxHWRHIJCALPwJqUw4Km7XEam3yPtUfwMqVTOnoeWQUdcSvIRmeIoBI1w9VgM2Q5LHk7c6bftXRBxtw0ELc+qKwkYvXDUVKLuY5gcjNY5FKeJ9jzKltKq3VJm//4rrmOSkz0PKEkOFwgYdOpSNsr4ut4dmDJ5NcGgwYoV+WnH1q2JMG5CL0YMX0THTnVZu6aQcNjEtr2UH+0OrsWT/SfxZP9JHNFtP8b/uALHUTiOonr1MBOnXlHGtlMeGcaDRJ0+QF3CZj+kaLDd77fWNhHnAlw9E0ueR9h8bKf97wvo3eBl9E9j37ujv5GY+180a7DVFyg9b6ftld5KkXMHEftmEs4oCp2ziblPUGCfDDqKt7IN/uGCJzOmr2HQu7PTgqEATjujOcGg6aV1kJ4t4Z03Z5G7sYjHHp2QNFJ6bVu1rsGvi3sz99frefm1k6lWPYxAcPIpTbnp1k70f/K4VBzAtoSMOwmbL5BpfoQljwBA6wK0trHkWRSrwEBgciaCEPn24eTbLclPHIqjR1Gs61fMBEobdhOUVquVIJDCSgaYiVLXcHFZlNbS0d8C68myhhE2H0WK/THEtnUIytOTi1LXDuKto0wk9ShybkHpOST0B9i8AoCkfVo/oVCUzKwCXD2t3Oe2Laec1oyV625l2epbiMfstGN5eXFuuHY4N9/akc2bYtx607dEIjbdezRhzcY+5OUliEa92tK//ZZLPO5i257xOhKx+f77iiXOM2U7sgPfkx14H0Put8PFjtKLkzsKsNUnaO2i9GYKEseTl2hFwvUEuNYaW31Hwh2C1uXZdPYiKmo/2MtURr5A+BNIGuEF8QhEBSqMxdwnsNUQHP01UXUDxemJIZ+EHoSkEVnWCIJmrwqPYfWqfLp2eosDm73Eicd/wO23jeaCnoNJJFwGvTubEV8vovVBNZm3sDfDRp5PdlYQy5JcdkVbOh7yOps3R9P627A+QjBoUqduNhMnrOT1V2by88x1TJ60mgceOpJgcPubSiEkljwaQ3iFYOLOm+Tb7cm3u6D0ajKMpxHshynOweU7Cp3TIJm6W7OpnOCm0ioKG+9ZpxMQt+Lqn/HUGyEMjsabsAMU54cqwcXWQ7DV8OR4BZnmexh0o/wMAk0wOJIsYxqVAgvIsZaQY80DqgA2UfcOhKiavJZOvRRzSN9VFAsThaMqVqs4I8PCsgyeeLo7zZpXpU7dLGTy1zpr1nouPv9LcnOLUMqzT8RiDiO+XsyK5SVutVpBtyP3o3mLqliWREqvvsSm3KLtXHXnuPp3ChI9KEicmsqMKkXjpBdSEFMcgRAGjhqFYjUQJe4OAMBWX1Hk3ELU/S8x96k/PIZ/CvuiDcEXCH+CLOtTMozHyLK+ThUt3xGC0r7qLoL6SY8Wb/LSbMKoQKH00rz2ykzmzd3IqpX52I4iGnVYsngzN143gr63juKKy77iqy8XUK1aBl277sfi5TexdNUtHHNsYwoL7bRIWoCGjUuih2vVLuKDIYMZOWEwB7W1t710ucScZ8m3m1GQOJmYehpvxb+ZQudolF5BtvUNSi9Es4Wynj/buko6pGs189kWU3YhKG8EDAzRgrD5JEHZh6C8B5NLyrT3VDkldQ6ECJAVeAdRbjLAFbhMJpHMQyaESNoNtpLyAJMPEDLuIWy8RUCeR0Ceg6faKhYIlZAUG3hdbHdqqnet47juZly1ku2lpTn4kNrMmHUN8xb05uRTmyWLEWlmz15P9xP2T070gnN6tqRevWxKL+Q3bIgw8MUTmf7zNXz2xTk4rmbgM1PpedYfK3KjdT5R5z4Ui1D8Stx9M/lcQmRbo8iyhhM23wLAEO3whGwAqIzSuclEjTYQQ+mK7VT+0eymegj/JHyB8CcQIoeAcRqGaFSh9iHjdgLyaqAqUBnJAUhaY3A0kraEzed3eQyLF29CKY1hCA46qCbNm1elWfOqfPLxPGIxT10wa+Y6Zv3seYgEAgY5OUGaNqvKWWe3ICcnyFFHNyQYMggEJK1aVSce9ybqeo3epX3HdRzYehNvffJVudfXOo+4+1rKWyiuPAOs4nfSbQYQVwOI2NcmPVe8CTMgeiNJN7aWsJUMY2A57xcbLk1sNZSgcS2VAovIsj5HykoEjctJqDdweLH8brdZtbl6yf/ZO+8wKYr8/7+quntmNpKzCEgGUaIioiIYwICoGMCIijnr1+yZznSnZ44gKioIoiQlCgoCIhmULDlnNk7o7qrfHzVhZ3dBvPPO8OP9PPvsbofqmu7p+lR9wvt9gAyahL5w6nMUe/+HcQdJLLoRVQOJ+Z+i2Y4UdQhat5TK2c9Dl6xTYDMAMX8M+W4LCv32FHqnUOj2PKBRABPMf3/weZx8Sj2qVM3gxZdO58uxq4lGfXxf8+brc7mi7yg00Kix0Xg4odMR1KtnaMvHj19DLOrjxhQ7d/76FYLWMQrcHvg6UQAaRKZVLgeR1EBjUmEt2YwM61UAFAsp9voTtK7EFidhibZk2A//6j784fAXdBkdDir/DyFEkAz7PjK4j0L3CvxkOqRDhvUitjwunustDylAPX3aBsaONn5y39e4rseqVXtZudK8lFIKjqiby79enM1L/5rNAw+dyIOPnBTvi+DtgecARif41Zd/YO3afXz4/hI+eH8JI8dcTJvj7eSMU1jp1ClaKzw9k6j3Ej5LAYss+z1scWrcn5/w/afDJ6UUZ3EalmhCTJfU+xDJ8yR1CVg9cFVPPD0mvrdpXCEuCni4ejiWakPQStUv+HpFCRW50gggZS2U3oKrvkXSioj/WLl9BYklOhKy7kFrF1eNRev9JGIVms342qSShv37AZuoPxBJE3z2kXBZaX4isQpUrAcgqt5Nu6ZiOa76moB1xgH6DZs25TPnhy1oYOQXK7jltuOwLEFGpk2VKpksX2YmB7Vr5/D93GuT7r0tm/N5f+DCZBX6gEHnEIv57NsbpnKVDL4YsYLqNbI4tWv9A15bk4dmF2bVJsmwXiVgdStxz9dQ6PYCYoTkwwTtK+Pa3xYQQ+t9CFGBLOe9A17jzwSt+UXxmz8jDq8QfjeU/DL5KL2RmD+WfLc5BW4nlP7lnO8Z0zem/b9s6Z60moOGjSoRjRPDaQ2fDUtpbkwY/zOX9/mCKV+vIxCwuPe+TkghCYc9ImGPDwYtJmTdh6A2YHz1JVHs3UKxdx0+izEzaYFmHxnWS5hZ9S8FDW0C1tmEVWm5T0GiniBkmWK8LOdlcpwfyHWWELKuIj02EMNVww2VtTeAItdwRlFuZW0uIetRbNGJArc7Ef9Riv1eKH4sdVwVEnEIR3bD098T9p4g7D+CYjWSRoCKr3RKwkOzF5/ZpBuYCJLWSOoRsh4FICAuoPTrF/bvJOaPOuAdW/qjKeIrLnKZOWMT7drXYt6i/kyacjkPPtKZYNAilGFzTs8mabGezKwAtm04rCpUDNGkaRWObfE2LZq8SeeO73P7reO5tPcIxn1pJhcrV+zmofun8PXktck2jKzmpUAWAXlVmjEA8NR0EjGxmB5uzuEobNEVSUcyrJeI+cNw1YRfVPP7c+DQ4gd/thjC4RXC74RM+0Ui3tN4eh6WaEzQuoxC9xLMoLIfV00maJXnAzdFZaNHriwTEC6JevUqsGN7IccdX4dt2wrRGvrf2BYwTJxX9BlJNOozcfwa1m+5g6ysAL0vbsjwYT+ileCiS1ogZQ45zhS0VkhpArpax3DVjPgqIOHiqIolmmDRFc160o1BJmY2LzEDRgBBXUAR9UuTyjlAFhnySbQoKJXTHiOmJiFFA8ysM1GrYGanvp5HVL0MhJ3DLMIAACAASURBVIlGlvPVZ69jO4VccFEuUX1vvF/5uGpS/DolRe81CcZTR/Ymw3oWX8/HVd8S8f8BEM/Bj5j+i2qg18TPE5jXKJHumkB6zEWxBijG9SeZnH5RgaC8CYseRNXL+HwNxOJpu2WZWwG6nd6AVsfUYNnSXTz1tKE5r3tkBb6evI47b5tAZqbD8M97c1Ip+vFKlUJ8OaEPkyet5aJLWrBo4Q7274/guoqVK3ajlOGtWrfOFBOedcYQdu0qZuC7C5m76Drq1TNxpUz7SeBJSkPrIpTOI7FyCsh+KL2HQq8HoJA0xNWjianhgCDDihKwziv3M/6p8Ccb7A8Fhw3C7wQpqpHpvJy2zZHnEFVvAFZawVYs5jPqixXUOSKHEzsfySv/ms3zz85Ca03jxpXIy4tyz32dGPrJEhYtNLPIDRvy0NqwmY7+6lJCIZsVy3cz54ctNGteFREvXktoIYe9Z2lz4kBWba2JXziCKlVr4anZFHnXAEZQxVXjiKlBmGyfxEDuAPvx9SwKvBYIqpNia7XJtqYjpUWB2wvNBkzBVgjFUjQWZiC2yZCvIWVFpGhMzB9G1H8V0GTaL2CL0yh0z41rAgd4+oGH2LlnPi+/+0XcpbUXtBGBB8mr/2zGW69MBWDf3lPof/Pdcd+/Aqy4Ql3KNWVmvX0JytuR0lRe2+K4OImdUaezxTEochFUI8N6jLD/GJoigvJWiv07SQXEJcYIFpZ64nvMs9TvIv0a8SI9H8GnSNECoRsgCBG0rjngdyYrK8CkqZeX2X7/vZOTaaVPPTGdSVPTJxKrV+2h17nDCBe75OQEePWlHygqMgZLKahePZO27WtxxVUmlpOgG/d9RYfWA2neoiqvvNGd9Wv3c0b3hmmV8ABF3i34+rv4/QzhyFNQejPGYIdRrETpavF7af81Kp416D9ZwPhQcNgg/IEQsm/F0WcjqIgUlZLb77x1Al98vhzf15zYuS5K6fhLq2neohp3/98JHHNsDR575Ju09jIzHU7t2oAup9anx+mfMH/eNoSAqdOvZPTYS/ji8xVcdEkLgqE8CtwBAAi5ndzKi4FaxPyhJCpvY2oErhoRbzmxMhGUnglrdhOQVyBFfRx5OlJUjm/fV+KcxG9TjZxh/Z2AleLe8fWi+DUku/bMZdSnGfS9vhAhPJTy+fLLn9m3L5fCggA5uRJBJpZsTaZ4GU8tYufWBsRiaxFCsGVLAbboQaYNSu9GUIOw/wOgEVSNp7oWsnDxaG7tV5nRX16S1I8OWXfEs2FsQvaDaZlkWXJAvK9rwU9xCplBsbQxSHsqaGIY4+Si2YOvfyBk3U/QKqu3oLRhuxXUJOI/j9I7ybAfQopUSm4waCcry/PyUlXNsZjPiuW7GT9uNQX5UbSGl16Yze5Saae7dxfTtWsDrrpsFPc90IkvRl/MKy/P4btp68nLi7F8+W5O7/IRti1p3aYm4yeniwQZydeEcVVovQ1LtMIRZ+Dp7whad+LIkyn2ihFUJiDTz//T4vAK4TAOBVr7eHoSglxseWI5+zWuGoPSawla1yBEheS+0mmn+/dHGD1qZZIueeqU9VgWtGpVg7z8CJMnrWXypLW8+945SS4bKWHQBz2pVTsnWQm7fv1+IhGPzCyHzZsLOLN7w5SOgFpGSiRGo/wohf7VCHIwwVBBQJ6N0hvjxHouxs+fnkVk4ODIs7FlabbfxMvjolhNagBxzaBaAiHrToq9lWgyOO8Mydo1c1m1thNPPLsFt+h8owMN3Hvz9Xw4tCaO7IIQFo44A0eeQa/z1zDt210cUSeHu+89ASFEsthPKZ+ivL5kZa8iFDiPSV+/wUN3H0dWtsuan/cy+MMlPPyoCbxLUZtsZ3iZT+jr1RR79yCpSkj+HfMaxUgVxpUU2zGw6Y2UuQStGxDkoliHp6bH01glUtROHqu1j6tG4+vVxNQH5nyRCKy7hL0ispxByeOHf96bnmd/itKavpcfg1Iaz/Npe8wAdmwvJCvLSYstBUtRkygFjz7yDdGIz7x5W9m07S6GnHAEN/b/ilFfrEAplWRiXba0rC5Lpv1Pir3b0ezDFmcjRUuEkGQ6L6Udl+0MKXsv1VI8PQdHdk8zcn8G/NniA4eCwwbh34SnFhPxn8cW7Qhad6dlBUX8F4mpD0m4PByZXnkc9QYQ1c8BEFNDyQ0cuIL1k49+JBxJD9D6PhQWxTjqqEqsX2cKkYZ+8hOfDLuAt16fS5/LWnHBRS3Sznn3vXO4/94ptO9Qi2bNq3DX7RNo1rwa19/YFimaI+iAxuTIR/Rd8bMyyLCew5Zd8NS3JVhWq8Zn1yuS7QuOJWQ9gC2PKKOtG/PHkuL4gfSgsMQW6cbDkk3JCUwlHHZZu+YlXFfx8aCW3ND/n2zZUoDWX2DbFrfdcSHF+dWpVClVOb1vX5jL+3xBJOKzc2cR5/YYQlGRy0dDzueYY2tw3z1TGPxBkFCoDd/PPY17b9nI1i2mb1IKmjWvesBnkUDYewqlf0LhxF0/CYNQkaC8iqgaTkoFURCSb6GYDxQhCCBEkEz76Xg8ZhRCVMWRXZPtR/1X4+m7qRiR0itJGBxjqEHpHfh6JR07Hc+IURdx8QUjePbpGWzdWkBRYSxZpOZ5CmmBQNDhuDo8+89uPPHot3w+IvX8fM8M+rk5QXxfsWTxTp5+9lSuua41tWvn8OjD3zB71maeeT7VzwRs2Z7cwKxfvG+lofROCr2LAZ+oP5DcwMxf3cbvBi3+kllGhw3Cv4li7xY0W/H1YmzZEVuYlYDWUXy1AvMy2/hqLU6pXC6f+cm/NXvQWiOEIOoPI+Z/jC06ErIfQAiLps2q4NgSyxIce2wN5s3dhlKa/fsi9L38aL6Zuh6tYd68rVx0aUtq186hcpVURe+GDfuRQnDSyfWYNcf4p7t1Gcz8udsIhWzq1a9A9x6NcOQJxNQPJXppARpPzSHsP4Ygl5R7aLeZDXIxHsPjn2MxrhqFq/KwRYekRi8YAaDyVxMgacau7a3Zs2cHR7eqnmZYMzIcBgw6l9dfnUPDRpXo3PF9lNL4vgngnn3GUCxL8NGQ8+lxdiP27Ytw6kkfJme/0YjPsqW7UApuuv4rWrasxqSJa4hEPCxLcMN1X7Jta8pQCQED35nPhb0PLgRjiYZxGooYnp4Qv1eZOOJkQvbtCK8BEXVH/GgbpecR04MxhHhRMqzHKXB7o1mJoA5ZzmC0Vghhviimwjf9fjnyUqQQKL2ToHU1Su+iwD0D8LFES5b+dC++0sTCHuO+XJ3m5w8GbaJRFykFfS8/mrp1c3nvw/PYnxdhyuT15hl5mvN6NaFKtUzaHTuAbVsLCIZs5i3sT/UaWbw/+LcPAmvySdCSlHwP/gzQHF4hHEYJCCqj2YnxRRt/v6fmUORdFd9f3wRYRQaemoUtOyXPDVoP4HlzgQIk9Sn2rsMSnYiqZwBNTK/C1h1wxBmcdvpRnNqtAV9PXsu8uVuNLq82GgZz52zFsgSep9m5o5h+V5jiseHDlrJ89S3MmLGRG641NA0fDT2fM7s3NB0okfWnlcJTCwnI8/HURBSrsemOFE2QojoR9UD8FBNcLckwasma+OpINCb91dPDAY2nv8VSx2DLdkS8l3F1eZWxEqjI/FnPcd5Z76I1HN+xDu3a1+KyK1rR6hhDBXJB7+acdsZRnNTxfdy47nNCRUwpjVKavpd8Tk5OkIKCGEqlpzQm3GhLf9rJj0t2EgpZSCmoUSOLH2ZvQWuwLGGUwXzN97O2sGrlbpo0PfBKQUXv48JzYyxdEuSJf8zi8ita4MhLkKIaWhcQtM8Fr5iYGowjLwa8uL9ZIAji6dlo1sTv6xYK3d7Avnis43UceS6umg9sx6zIsvDUlzjWWYRsI1Xpq5WmXcL4+kf6XNaK0SNXsmTxDrZuKUBagvr1K9C0WRXWr89j5Yo9SAlX9B1JrVo5zPjhGl5+tTutmr+d/Fz790eZMH4N0agxqAliw641fl31/KHCEo0Iyltx9QSC8rY/jTFI4i8YVD5ch/BvIssZRMi6m0z7HSxp3DMmCBsFotjyeKTIJeq/SJHXH0+l3EK2bECFwAIC8nIU6/H0dFxlBlMDhaASRUUx8vIi/PD9ZmLxl9T3DBHdrp3FTJq4Jj5bTofrKt54bS7Dhy4jEvGIRDzGf5Uievvw415c2e9YHnvyFE46/VWKvMsp9M4hZD+GIy/Etk4iaF9ORD1DQkVMkIXgSMxXxkLSmah6J24MBIbhMzG/iOCpxQDxrKlEH21scS459nyynUnkOtP4+MN1Sf/2D7O38Obr8+h+2ifJgd3zFG2OfieZEpkQLi85dihlSN9KG4OSSBiGjAyH6bOupnW7GsmBz/d1cr9Smuv6fZk8b/KktdSs8iItm77J5k35LF60nWNaDGDB3GyKigI89/jxBKzL8PRUCr2zyXdPwlMLiKhnUawhql4hpj4yRXgcR0D2xxItSJ+LmYC7pyfj6QUUezdhdNWjmNVHBJ9ZRPynkvEWS7TGoj2QQ0g+QKVKISZOuZwOx9U20pvAVf1ac9c9JzBg0Lmc0qUeti1RysSlpk9bn0ZhUeeIHDocb+IYUoKQ0LxFVU7oVJaN9d+Fp74nP9aOgtiZKG1iESH7ZnKcMWlJBX8K6L8ml9HhFcK/CSmqErRuTNtmy1Nx/fEkgrBh72mM7zwDpU3lrNK7KfbuBGJY4jgSxGe27IKrBLCXoHUXN16zjeHDphAISGIxM1oFAqWCgeUQcyZmzy+9OJu6R1YgOzuAZQmuuS7F6nlE3Vxefb07APmx/hj3VibF3u3ALlzGkKobMKmc2c5ECtwTSKRuwnpSbg0NZEEJjqCoeg5bHY0l2uLrpRi663ew5XHxI8yq6trrWjP803Sm2IKCGM89MwPPU/Q6vxm7dpVPtZCd7VBYeGgcS2AGun37InTu+P5Bj9u0aT+fDvmJn3/ey6QJaygudglvcrnyspHkVgiya2cxIHAcSefOZiVk7p2pt4j5YzCz9xjGHbIfP76KKvKuJjfwDTnODIrcfmlFcZIG+P4iUjTeYIxtIobkE3YfJ2TfhdLb8DGTDEs2TLbx8uvduefOSVSoEOTFF75HK03Xbg0YO74Pjz70DW+9MY9gyLz2q+IV7ULAw4+exOlnHoVlSWJRn2uua8327YVcd/VYjju+NrffdfwhzeA9tRil1+LI7mlKcgAR/wU0+9AU4qrRaXoZSu9D6VVYovWfR1PhTzbYHwoOG4T/AFprYv4HKDZgiS6E/QcBCIo7sGVnMu1/EPYfRYpGOLIHAFF/IL6Opz3qOmRYT2MKonqRYT+I1pqCgijDh5kahYQxAJIz2tKwLMHJXY6kT99W3HLjuCTd8b69xVSslEGrY6rTtFmVtHM8tRhfzyYg+xFVbyGpi2JlfG8YtMQR3XD1d4TkrUhRFUE9NGsxVbqls03ySc+skSi9liz7I3z9I5ZolJZNBUZL+InHpmFZosxK5/lnZiKE4Msxq6lUKZQmEmPuPb/KGEBqlVAeqtfIJBb12b8/yp7dEW7s/xVSCurVy00a2QXzt3Fi57pkZjoopbj/oRO54y5TLxKU/YmovyOoTNC6Cs+biqYYY/BT/dQY+U8pKuPIc4kqw+skaEGW/QEF3kkkDICkNRBCMTfxCfCZQZE3G0kbEgbZVV8ns9mOOqoSI8dcwsQJa5g4fg1FRS4zZ24C4KFHO+N5CssWVK2aieNIolGfChVDvPyv2dx283iuuOoYTulSj3atB+DGfLSGKV+vo3XbmpzSpf5B76+vlsa1vgWu+rIMTYUtTsTXKwAdJ7+L3xOdT6F7GpoolmhCtvPFQa/zx8Cfb/Z/KPhdDYIQYhBwDrBTa310fFtlYBhQHzMNvVhrve9Abfye8PQEIuoFIIbgW8wLqojqQQT1FViyFVniEzw9E80uBHWQoh4QYNH8Kli6Pp07GQ4e31eM/HwZD943hV27imnYqBLr1u4/qBskgTO6N+TDj3sRDFo8/OBUdu0sRkpBIGCzeVM+e/eGmTh+DT17GdEapXdR5PUFXASVyHWWEfGfIaZWJtt09edkOx8n/9c6iiUa4unNmJlvIamYgo1ZUWQgaU9xeA3LfszgyQcLWLvmTU474ygGDGqLEJCfH+XtN+axZu0+WrSsxsIF28t1e0lpjMTPP++lSZPKeJ6PUpqiovIpMaQ8+ID/S9hVivDNCAZp8vJjSZeWUlC9Rjbvf9STKZPXohREox6bNhbR/bRCIpFbGPrZhVSrlkXtOl+TmSnw9VLC3msojMJdhvVc8hpB62ogjGYXQetORDyQbxAgy36DsP80SidYQxMTAg/Fovg2iSPKultOPuVIjm1dk0WLtvG3J07iycemMfDdhezfbwzr+wMX8crrZ7J69T7at6/Ntf3G4PuaTz76kVEjVyZdlMYYajIyHObP28aYUSs57/ymtG1XNkVUJd2HYXwdj5FoF7ARQhC07sKWXRCiIpY4KnWe3owmEj9vyZ8juPwX5TISvyeviBDiZMzIMriEQfgHsFdr/ZwQ4gGgktb6/oO10759ez1v3ryDHfJfgavGxStgo0jRCqWXYwZLiS3OJst5hYJYzzjzpyTHmYqgCp9+Opw7b1kPwuK5f5xGv2tbc8+dk/jwg8XJFzEYsohGfBxH4LrmGQkBVapm0rhxZb6ftTm57fiOdahYMcTV17amdp0cxo1dzSmn1uO1l+fw7Tcb0Frz7YyraN7CFFb5ei2F7tkYAxYg11mOr+dS5F2OmZ0GCMhLybAfB8yAUOj2jNMvZGAMwF7AwhHnY8sT8dQiXD0WW3ShWd0a5OWlUkuDQYtOnetyZveGDP5gMcuW7k5uN6Lxqe9gy6OrUaNGFsefUId331rAnj0HpucoCduWeN6vswihkEWVKhls21ZYrjEp6a5LIDfXIRz2k7ThdY7IoVLlDH5aYirEs7MdPE9TuXIGcxZeR25uEK01inVIqiNENr7abIL3ohMQwPNn4+qvsGRrpKhEzP8cW3Yk4j9D4vskaU5AXkNE3YcxDCX1HjLIdkZjiUZpff1q7Cquunw0ObkBCgtiaStMyxIM/qQX557XlKKiGCd0GMTmTXnUqZPLxo2myl1K6Ht5K844syGnn3kUDY98jeJil8xMh7Wbbi9Tsax1jLB3L75eQYb9FJ76nqh6DUkTsp3PESKT8qC1T7F3M56eQVDemAyc/7cghJivtS5dKPOr0LpWJT31mrIpuOWhyjNf/MfX+1/hd10haK2nCyHql9p8HtAl/veHwLfAQQ3C7wVbdCcoN6JYT8i6k6g3iJh+D1B4ehy+ujHuhjGFXEpvxZZVmftDRSIRjdYes2dtpt+1rZk3d2vSGFiWwIsPOAljAMZtEQpaFBW5OI4kM9OhcpUM5s7Ziu9rJoxfQ25ukJlz+lGvXkXad6jNxPFraNFqBzXqXkRBLGjkOq1LCMl7cfV4gtbNaHZQ7N2NmW1ejy1bJl1cWmvC3osoEn7+CCkeIRtb9Cbi35CkPfb0lxzT5jy++zaluRyN+nwzZT3fTl2fdv9cV3HTze144/WUMT/m2BqM/HwFefsjh2wMgF9tDAAiEZ8tWw5cVVzaGADk56e7qbZsLqBS5VQdRFGRi9awPy/C8mW7Ob5jHYQQWJgZcdh7kZh6AxBIjseS9XHVpwC4/hBC8nGynDeJ+G+QGvAVip1E1VNI6iM4Ep+5Jfa7eGoOlpVuEP75/PfEYj55+yNIKZOuLzDB9Y7xosWsrADzF/fn7jsm8emQn9DaxKtuvaMDjz/ZBYD9+8PEYub76Xp+ufdbiACZzqupe+Fdh6FC34SvF2CLzmnHJ/SzTV3GW/h63p+rOO0v6DL6I2YZ1dDaRGDjv6uXd5AQ4nohxDwhxLxdu8pWT/43UOw9Ql6sKcXuHSilgCKC1g1k2s8hRU0CVp8SR0vC/tOkbK6H1pIr+ozk/fcWkZMToF79Ctx1r/FBP//P06h7ZC7HHV+biVMvo2atbDIzHVq0rEqH42tz7LHV6XFWI9q2q81PP+5EKc0JJ9Zl29bCtBl2fn6Ue+6chNaaYNCmZ6+m1DzyHRQbUawmqt6g0L2EoH0t2c4IHNkVV42LV8zGjJqbfy+F7nloXYivZ+Hq0pTFiZlmlLC6JO4rj18/z+H7GSljcMyx1bHiqpZagxOwkvsaNa7Ew4+dTCCQ+hoO/eQnIhGP+fN/G76bUMimS9d6v3zgvwnHkTRqVJnqNTI5tWt9ro8TCFasGGL3bkOgt27dfpo2fJ3a1f7FtGnj4mcaZTVPpdONeGoBMX8SvlpCSg4UYBea/SjW4PMN6eR8lXBkaR1ouKB3M0IhGydg8d4H53LTLe2pWDHIUQ0rsWT5DVSrbgSbJk1cQ/06r/DpkJ+IRn0cR9LhuFp4rmL7NmMwZ83YjLQEQkL79rXJzf3lwK8jTgOCCDLStBMAYv4E8t025LutKXafIuw9RJHXjwK3O75a/ott//4QaHVoP38m/GmDylrrd4F3wbiM/vvXy4/P5BSunoDy9uMzE0u0x6Ibrv4YKeqT8qtn4ev5pDJxAuzctZTx4zbge5r8/BjLf76FnBzzYnXsdARLV96cvN7chf1ZtXIPRUUxZs3cTO+LW9CwYSVee2UOX09eAwga1K/ANGmW9iUzkCZPXMvY0avo2aupKXiiAikXg0pTDAND5GYGn1B8pu+i2ICnZ8RZPkuiZC0CgMaiFT6LsUVXvOIb8P3xyb1LFu8kJydAYWEMywI3lvJN/9/9nVjz895/a3Z/qFBKMWvGprRtwaBVboBeSnFIMZuScF3FqC9M7KWocAsjRl3EsmW7+eH7zVzRdxRdu9Znw4b9bI8zzr76z5Np33EjjqMIynsRIoOI/zCJgLzHFDx/AmXlP0tDAxXIsacgRKVyfe6333k8Pc5uTIXcINVrZPH6q3PJy4sSi/l8PXkdl/YxUqdPPT6dgoIYjiPJzQ1QvUYWC+ZvY/b3W5jzwxYmTb2CnTuLkEKgFezaWcSWzfnUOSK3zDVLIsP+F0FuRVITIbLw9Woi3itYohWu+oZEsN3VH2JckWEghK9/wuLgxYG/O/ThwrT/FXYIIWpprbcJIWoBO3/vDhlkI2mIYjOCynGhF42vF8Yrj318vQWTVSIwaZj58f8dbNGRKpXOwbLeAkyB1T+encngD5ZQuXIGX07ok/aCZWcHqFkzmzat3iEa9Xn37fn8vP42br29A/XqVUAII8betn1tnvn7d+zZXZw0CFLCjh1FaB0m7L2Ap6cBYNEVRB5B63YAPDUbX6/EEm3IskcQ8wfh6hQfv6QZEf9JQCNpjy3bYIk2RPzH40V5AkdcQIb9j+SA1KAe3Hn3XgYOWEhhgQnIhsMeWoNXIh6sNQz+cAlbNucfUjD4YIN1SVdI2jkWyYyrBNq0rUHrNjV5/73FZY5PtB8IyDLnHQqKilxO7vQB0YiXdDdNmlhCU0DCgnnZNKl5He8M7Ebvi5sT8V9F0h5Fok6liPSVQWkDHG+L5oBHoXc6GfYLOOLUcvvUuHHl5N8VK4awbWlMSYWUm+u00xuwatUe0DBmXB92bC+k35VjDO9TkYvvK/pcdjSzv9/M8E+XsWlzPp1PeJ9Va2/FcaxyrhrvuRBYpFJii92bUKzF0xNLfTaF4Ag025AciSPPPGCbfyRo9Ud0sPxn+CMahDHAVcBz8d/lazf+jyGEJNsZja+XIWlCsd8fXy/AvLwuKXK4KIIWaFYBYIljyLI/TuZWd+lan4njf0ZKwUcfLmHfvggFBTGGDV3K3f93AlprPhi0iO9nbeakU45MVuQWFsTi/RDJbCGAatUz2bmjiKIil+ycAI0aVaJ5i6pceuVO8t1LSSmXWTjW8QSt/gD46qc4tXWMBG2xoDKJgGVI3oVmD57+AaP09SMZtqGpcOSpaHYaTehSM9OvJ6/lnbcWEI15yQH1QCuA76ZtLHd7eVBK07HTEaxbs48dO4rS9pU3cFuWoMFRFdmypYBwccoSLVywg7POaUIowyISLj+N998xBgmsWL6bS/u0JBbz2bQpv1Q7gnCxiTG88q8lnHPheGLqY9IZY20C8jY8NR7FZiw6oFicjNEYBJB0xsPIlRZ7f6NC4LsD9ika9bii70gWL97B+Rc248zujejeIzVQ/+2JUzinZ1NmztzIOd2HcnSratxx13FM+3YDc+dspX6dV3j1je70PK8Jnw1bRjTi43tRwmHvoAahLEKY9yRx3wU2F4HYR4b9KFLU/RVt/f44iOLpnxa/d9rpUEwAuaoQYjPwGMYQDBdCXIsp17zo9+thOoQIYQvjI84SnxD1B8RFWaBkDr5mNYlba4uOSWOweNF2bripHYUFMWbO3MTevRGkNH7o4zoaQrjr+o3hs2HGh/rpkKVcc11r1vy8j7vu7Zg2+OblRcjJCSZngFlZDscdX5tRYy8FID92OumCLT6uPxtHXkDUfxmlE2mjiX67WOIYPL0NCGLLM3HVWIzBCCCoTpF7HVI0ZNXSTkwcb3P2OUFaHp0e4nnmqe8oLv519QGHitnxzKpDge9rHnviFGrVzuHiCz5j795UHcNzT88oN9U1gdLGwHYEnvvLFkIIc91hny7j3ffOZsK4NQwvoVKXWIEEgxYX92kJbInvSUwmwKi0dYkHnmP4fEdAXF9KZtTHY0CJ//eX6YuvluPr5TjyDCZP3Mp30zaamoTvNjHw/Z6l+i1o174Wl/YeQWFhjB+X7OSOu45n5sxNuK4iLz/KNVeNIRi0Oe2MBqxauZcbbmpXJo7gqTkUe/djiQZk2m+UKUzLcgYQ9d4jpj8DfCQ1yXSe++OnmJYHzV8yqPx7Zxn1OcCubgfY/oeBEBJHnkRUvYJxC6WqSy06E7B6oIkRkMaejfhsGbfcYAKKJ518JCo+IAkpmDX3Who2NJW7077ZkHadQNDi0xEXkpUVSG574P++k4Yf9QAAIABJREFU5u0351GzVg7zFvXnhwW98a2HqVJ1MUp3QYqaSGrgx/lyEvCZQdh7GE9PBWws0Qatt8fdYBUxA5MAiin2bo9X0ZpsIs1uPL2J4sJpnNnNo7jI5sV/fM/6LXeQkeEQjXpIKTizRyMWLdzxX40LHAxCpmZuI4YvIxr1qVkzO80gHMwYlIdDMQZpx3uKkV+s5OOh5zPis2VJl9iR9Srw9HOn0rpNTerVq4jWR6O1zfAhgnN6D8JxzHfI6DabV1OQTdC6i5j3BSSFZdJXNoJ0eglPraHIOx+QxNRQGjd5C601mVkOx7auccB+n3hSXSaMXwMCWraqTp++RzPzu034vuF5Ki52CQYdFv54Q7nnh71H0WzA0zuI+WMJ2hen7ZeiFhnOI4T0bSZOIFqjyaPYfRDwyLCfSdOc+CND/0UL0/56TrD/ISzZgmxnHCHrfow+QAibM8hy3iVg9SZo9UUIh6KiGF+NWU047BEOm4HTssyXqWLFUNIYADzy+MnI+FOpXDnElMnrqFP9Je69a1LymAHvLEAp2LqlgMce+YZqtYdRudoctJhFxP8nAJnO84hSgTnB0fj6ZxKGKyDPIycwhVxnOTnO7HgaoA/4KJaQGniyMQMVxGIWbswYjmjUJxbzmThhDXWqv0T9Oq9watd6KRK9/zGycxwyMxyEMKuuvXsjTJq4luXLd6cdl7j3JSH/wzdBCBPnMG0JhDD8SjVqZgNg24K33j2L83o1S0pSCpHFd1Mu5oG7bR67/wTy8zKxRVds2Yls+wtC1gNkO6OR0iIgT8e4XEpdl1pk25+g9A60LsJXP1HsXYtZ2UXw9TqaNqvK9Fn9GDjobAYM9oj5Ixg9cimXXjSC49sNpHqlF3jnrfm8+c5ZPPNcV2Z834/69SuSkxNMGk8hjHvy/gc7lelD8r6K5iS4lyLqWZTejdbFaJ3u4hOiArY8ESGyiPpv4ekpePpbIv6L/9lD+B/jr8hl9LsWpv1W+L0K00rCV8tQ7MAWJyNEyq+alxehQ5uB7NsbxrIElatkMmLkRWzYkMeUyWu57vq25XLwb9mcz0knfJBUt3IcyZ78+wBo2vB1tm016YDBoMWm3TWJqucATVBeS8i+B60LKXSvRrGgRKshzEARIiCuIGTfm9bXQveSuOYBJFYLkpZkOq/gqyUsXTaEwuI1vPLPNkz6qoHhu9lWyPhxPyfdLC2PrsbyZbv+o6phgMpVMthbqg4hMytGJGyj4sG8VsdUp3r1LJo2r8LO7UV89aUxukKYdNOL+i7h5G6beP2FNmzf1oDt28zAZFkJtlQATfceDZkwfi2lkZMTiFNk/FLGT1k4jqTXBc0YM2ol0aiPbUtWr7uVKlXTi7NGfr6Cm67/inDYpV372kydfmW57Wntke/2hKQGRYBsZyKWqEfEe5eoepHUSjXRX0HQehhbNEfpTfh6MzE1kIXzqtD/si7s2J6VbD83N4DrKcLFHrm5QdZsvI0zun7MwgVmVZKd7bBk2U1UrZbqv1KaG/t/yeSJa7nvwU7cePOx5LvtMbWmmQTlzUTVa4CHI84jw/47QqQbtYg3iKh6AYCgvJ6Qfeevvte/Fr9FYdqxNaroiZf2OKRja736yeHCtP/fYMkWWLQos3350t0U5EeJRn0CQYtlq0xqaaPGlXjib9MY8M4C7rq3Y7IAKIHRo1aSl2/cHELAWec0Tu7rcXZDBg2IZ8kIcMRlSKsCJqBdnyK3H1oHShmDIGawUEAxnp6KEOn1fgHZh7C/CBPsOwUhK5NhPYgQuVhWPRo3OJO7bp/Ilg07uOiS6pxwwhH0vzbFDOo4gg3r9/9qY5Cd41BYkB53KMhP1wO4+6G53H7vQrZsyqH7SRdSWBBACM3fnvmWRk1XES28kEkTfaS0CWWEuazffG69exEZmT4dOm7n3psepnLVQp5/eQyhTJ/Rw/rTqctgTjxlC6uWdWfHjg4sXLAj7ZpH1M1h3dqy/vlDxZJF25PprZ6nuOfOSbz/0XmM+3I11/UbS50jchk3sS+33XEcK1fu4Ym/dzlgWxH/OVLGQBKyHsISpr7CVZ9hAtNG8Y7kUUdj0Zgi79r49qqAy+39O6cZA1PvUo3Z35uYRn5+lM2bC2jWvGrSIPS+pEWaMdizu5gJ439mxPDleJ7i/nunUK9eRbp270dUvWlU4fRaknxLeiTCr0KG/WDa5wpaVyNFRcDFkRf+mtv7+0KD8v96Dpa/3if6neGr5YS95/GUmWm3bluTxk0MsdxNt7QnLy/Cjf2/5OROH7Bs6a6kzi2YQWPDhjwuvWgET/xtGm5MISXcdnsHBn/SK3mNRNDZsgSPPnYyliUJWD0JWBcR9m/E09PwmZrWL4vTKBlkNuIk6bBkS8yS30LILDLtZxEilQrr+4oLezfj55/3MWL4Mm68/qu08wNBu1zCuVDIonnLKjhO2eXzOT0b89nIsnkDCWoIgJvuWMKd9y3EsuCIIwt54NE8pBQEM36kxhFTUKzDyX6Br2d/zCsDxjFt/lBuu2chTsBkC1WsFOXdj9/jq8lhjm27k6bNd3Pf4/+g08lbkRKaHT2B1948k1DI5+rrf6JDx+20aWfzzsdv0bjpdspL+ywPWVk2dY/MoWatLJTSrFy5N23/F5+vYMTwZTz1xHSKilxWr9rD0c3eZO6cLdSvn0vVqhkHaBlianDyb8lRBGTKPx+wriGlUa2Aykg6AzKeCediBuZtWHSidp0AjiOxbcGz/+jKlVcfw/kXNiMjw8wPq9fIokGDilSvkZVUPv3ogyWsXGFcb1+NXUXThq9z0/Xj0mJF/a4czfIlfch15pPjTCdgXUwqhVZSnkiSZhe+Xo8QlRGi7PxU6V14ahH6j5jSow/x50+EwyuEXwml9+GrOfh6CwHrLKSomdyntU+hdwlQSEx9SI4zg1CoMtNnXY3r+jiOxd8e+YbPhi1LC2xWrZpJcXGMzh0/YN1aUzSW8t0K7r6vU1omRqcT6/LNlHW4rmLaNxu4+dYO2HbCtidoqH0Sb7NNTxCRdGEcokS8lwjZd6F1Ia76GqV3kiAn89Q8fF9x+y0TmDN7Cw883Jm775xIcZFLzDUsmIk+SimoXj2T7dtTvuKup9UnNzfAmFGriER8li/dQ2amTTAk0lYD305dzzelKC1KomKlAA88PjupfyCl5or+I3n7tf7s3JETr0GwEcKjVp0iatUpKtOGECADG4DdpG6Cn6ygBod6zU9i1daqKL0fhMCSGSDyeH/YBO6/vTNTJv2ySExRkUdxccFBU1ZffukHTj+jIStX7MH3NZGIz9Qp65k6ZT2DP/yRn9ffVuJZGmhdiCXa4etFCLKwxHH4elky4y1o9SHmj0qyokqaxyU7w8RUEeY1N+nEAesiPh1+GkOHzKBZ8wqMGbWXjz9cgpSCJ58+lVO71adRo8pIKbjk0pa8+dpcXFdhWZLp0zbStFlVhg75qVxaDyEEhYVRRPydsMVx5NgzifgvI4QkZN1d9p6516BYRUw5ZNlDseWxyX1Kb6bA7YFhA+5Opv1HijH8dvEBIUR34BWM9RyotX6unGMuBh7HfIEXa637/iYXL4XDBuFXwNfrKHTPBYoxGRyDyHGmE/YfwVPTCUqjc2CgKJlfnsjXrlgxhGVJpNTUqJFFboUgvS5oTp3qL8UZNktfVXPlZSOpWCHES6+eSbXqWQwZdgFH1nqZWEwxa+YmZs3cxMmnGPdBtvNJ/CUqBkJxP+6b8dSbCqSI6fYTVW8T0JdR7N2UpCWWojFabyZkPcw3U9fzxYjlFBW53HXHBNyYKrfCt+XR1Vi7Jn02PPO7jWRmBdLcR+GwR40aWWkG4ZcorF9+64cyAV8hYPiXk/l67CMU7OpGxfo/EdP/Omg7BmWNhYHpg7B2YSUFgBqg9GKq1wzz9uCpnHPq+axcXvkA56fwSyG5VSv2MH3m1WzdVsBnny5L27d3T5gb+n/JwEHnApqo/xq+XoKnFwLheEX4Elw9BNf7ghz7B4TIiktv5sVbEWj2YDinHCR1sOWFRNULCCpgy05k5kyj73XGV79gYR88L4hlSX5cvIPRI1dwTs8mXHHVMZx3zqfJQHnFiiHOOttwJV3bvy0Tx69Ba02LltU5t2cT5szZQseOdeh80pFpn0nK6mTKZw5yR4pIkfWlno/WMSL+W5hnE8NTMw5+Y//H0Pw2lcrCBPHeAE4HNgNzhRBjtNbLShzTGHgQOFFrvU8IUS6dz2+BwwbhV8BXCZF5MBQQ2yly++KzBJNZ8Tcy7QHE/PdxZC+kSE/x+2DQIl751w80bFSJvpcfzQ03tScQsGjZ5E18XyflIc8+twm1a2fz4fuLiUZ9pn+7EdsWVKqcwWtv9sBxLNq2q8WC+dtAwFFHZVPk3obSK8iw/44jzsLVnwMyzjCpMfGFimgKSc2Ss+K+3nUY2oAMMqwHsOUJANStuxulNBkZNk2aVKGo0GXp0rK8UT8u2UnpVPJoVBGNRsoce/a5jfnkI8NXlEDJSmMhIDsnSlaWy949GZx46pJyn8UR9dZz9a03AIKY/uWB+tCRiaQ+IetRwv6NwC58JajXID9pEA5UGV0SBzomGvW56ILhfDz0ArIyHb6evJZdO4uIRo3l/OzTZZx/flPOPGczUfUu5rmYil6fBaTqFWIUeG0xNSL147UvABqdjDW4KNYStN4gaF2OSSoQuGo0CffN1dftZ9G8k6hQIchnw5YRDnvMm7uNChWC5OeZ2Jdh1K3NlZeN4o23z+LUrvXZtP0uhDB6zf8JMu23iPgvYok2WOKE5PaI/wKu+hxjEByC1j3/0XV+c2h+K56i44CftTZSeEKITzEEnyVnC/2BNxIyAFrr/xp7w+EYwq+ALbsiqIp5QXMAGWedNJzvoNA6QpYziIDVs8z5D94/hX37Iqxbt5927WoTiBO9nX1uYzIybLKyAkybeTVDhl3A5Vceg5RGlUtKgW1bVKueidYenprF8JEnMnjoqcxbcgzVa3+Hp6eiWEOx9wCunoQZ9D0sWpF4zEacJTULE9Qn322BTm5zARM4XLZ0F5MmrOG9D3vyr1fOYOTYS7j2+jbJgd+2BVWrpXzeh5KspjWMHb2KwUN60f+Gtsn0T9tOvVgNGxczd8UnzP5pKCu2fkgoVNbvnIKPiYv8lu9HIYplFPtXYtETtEU04rB+XWWuvHYHtq1p2qwCN97c9qCtJGbW5eHrSesZ+O5Chnz8I5s3FSSNQQLXXj2WLZsTAWIL80wEiWdjoDGfP4xmOQdyVmt24alv4ysJi2LvJjw9Jd5eNhWz+/HRkPN5/a2zqFgxFNeVUNxz5+S4q0jQtVt9xo/7mblztnJmt4+JRDxCIZtg0EZrzfivVjNpolkxAGzamJeUPP0lWLI5Wc5AQvYtaW5RrXdgnq1NUN5E0Lr4gG38XtBKHtIPpvB2Xomf60s0UwcoSba1Ob6tJJoATYQQM4UQs+Mupv8KDq8QfgWkqEqOMx1QuGoEYf8JQGLRIS5nGCPs30PAKn9W27pNTRYt2I6UgqMaVmLWjE1s2VLAU8+cytXXtCYjZLN7j6EZbt2mJuMmXcaqlXviflnBVf2Opdi7DU9PB1tz/ClBNDGifi3MgJARF7FJ8PREiar3MBlGZWUoNYvifyXcNh6uGkFxYTO6nTKYWMwnKyvAmo234TgWQz/5KalnfGLnulx2ZSuuv+arMu0eDDt3FnPbzeO58eb2nNylHk5gNw8+MZ7164q4+6YuXHzZNrKyzOrBwsNIbf6v9ZEMk63HeyCgcuUwk2eNQkr4+4sxbNGJBjVStNm2LQiFTEBdCJNBtnbNwQfEtWv2l+uHB2M4p06qyuX9/omvV+HI7hR5F2DSOZNH/cJnqI6JmUQo9u8mA4+A1TNuDBSQQZb1EZZslTzjo6Hn07vXZ+TlRfA8hVLQsFElrr+xHVO+Xg8YCdKb+n/J+x+ZJIfXXpnDM08Zd85z/+zGEXVz6XuxUTx74+0eXHRJOsvpoSJkP4j24umr1jX/Vhv/XfyqGMLug6SdltdI6YdrA40xrA5HAN8JIY7WpoLxN8Vhg/AroLWL0muRoj6OvAiw0ORjiy4UeudifM8H5mMZOeYSvpu+kRYtqrJixW4u6T0CAXwzZR3PvXAabVq9Q1GhS5u2NRk/+TLata9Fu/bpbKPh2HwS8QEzszdugUzrM5ReBQTxkrUE4OlvsDkdj9IDd4KALzHI2ICFLc8gv9jFdRWuqygqirFndzE1a+XQ57KWzJu7lcxMh5df78Edt4zn38GO7UU88bdpfD/nGho0fxHXX0mDRjD8y0nUr59V6ug/gFieACkigEAIic8ypsz+kTuuP4X5c2qiFEliQa3h59X70lZMlgV+qdDLh+8vPODlKlQI0rZdLYQ6hgznbHxdskZCYMQEN2MU75okebMMAlj0RoiteHo5sAOIEfHfRultOOIiXD0cyVFYslnada+7ekxSUU1rIzp0/0Mn0uPsxuTmBsjPN/GxWTM3U7PKC9x+5/Fs3JhHJOIihGD5sl0sW7Yr6Q4cO3pV0iAovR+l1wABYv4HWOJoAtbVaasCX69H6XXY4kSkqJkmwWmK2+w/jt6yPrRV8SFgM6QNGkcAW8s5ZrY28nPrhBArMQZiLr8xDhuEQ4RRDeuNYjWCamRYT+LI85OFXdn2CHz940GZGkMhm9PPMEIpg95bhBvz8TzNihW7WbtmH4UFMcJhj1kzN1FQECUnJ0hRUYyMDCfpgghZDxL2H0fSDCkq4enpBGV/HKstebFroEw6aQyPrxDUQ1ANRaKAzyLDegqt92HJ9khRlZ+W7OH5Z1fQ87z1PP9CNwa+u5BVK/fQtOEb3HRLeybHmTujUZ+ePT5h06ay4jI5uQEi8SKrRF47mBTZChUC7N2bcgHNnT+Fo5pngwhgWVGatdyOGfAkqUnS/zJvryLl8QKloDH920e9Bpq3PljEqR3OpagoPTCeGCjqHpnDju3F+KWtAWUNRAL33n8C27cV0u2UwdSuk8OsOdeQk3MUAXEdMW2YcmEnQXkHUfUemg2kqM1tQOAzpMxt06whql4mIK8h1/4RyEgbjDdvymfjxrzk/23a1uSb765ExiP6k6ZewRV9v2D/vig7dxbi+/DcMzN574NzWbF8D44jufPujuzdG2bYkKW4rs8tt3Uwn1WtpdDrQSr+4ePqkfhqJUJWNvdVZxPTb2DUBjsRtG5GijpIUY2YP4mwfxvgkG0Pi6dH/774rYLKmEG9sRCiAYbc6lKgdAbRKKAP8IEQoirGhVS2kvI3wGGD8AvQOoZxqfhx1TCFZhPF/k04uheZ9jP4ahXF3l0IkYstu5W7Bly2dBfPPPUdHY6vg+NIXnh+FlqbgfKZ57txdKvqVKwYIhw2g+x990ymStVMXn9lLg0bVWLazKvJzg4QsHoRsHqVaV+pGCWNgeDY+GBhBjjNBjKtjyjyTyERX4AQQfvq+PmaLp0/xPM0Y0evZtKUy6lVK4elP5kg8sABC8nOcuI01qpcYwBQEJ9FljQGAGf2OIpxX64BNMGQx+AR4zmh8w5iSgA1SU2Kfs/k7UNZgaeC4bVrpYoFtdaEQsanrjW0bVeTOT9s+dVFemNHr2Tt2n24rmb3rmJ+XLyTTp3rErQvJua+h0kOSGQW5VMyvdigvJhLiAQlCRSVK2V5843jkoas22n1+Xz0xUljANCiZTVyc0Os+Tm98HDEZ8v5dsZVyf9r1c5h/ZY7AJIGJ+L/g3RGVwOXcaAimHtqkSis8/QMPG8mRnb2K2Lqk/j5LjE1gYw/gEEA4DcIKmutPSHErcBEzE0YpLVeKoR4EpintR4T33eGEGIZ5iH+n9Z6z3988XJwOKh8EPh6HfnuceS7bXDV1zji7PgeCUTw1TxcNYGw/wCKVfh6ETF/SLlt9b3kc8aMXsWzf5/BewMWJl8+39fUrJmNbUvOv7BZMjtl2NClvPv2ApTSbN1SwPx5pVeRBlrH8NRSCr10oXVBXSxOKLFFxgV7UoOt0qlYllIaz0vtW758F40apziWKlUMpRHE/VokjAFAZqbHCZ23I4Thwi+7Qv5zwHMVr78/5v+xd95xUlPrG/+ek2RmttF7BymKggJSpAkWpCkKAoK9YwErlmsv91p+FuzYReyKoCBFRJpIk44oXXqHZcu0JOf8/sjM7M42FkTFe30+n/2wm0lOMiE55X2f93k4te1OtIaTT6nCqjU3svfgcLKyIijl5VvKlQvg95dOJnr1r/tRrjdRqFI1Dcd12b8/hBS1STPfwCeHkGZ9FGPkxF/fwqspkVQ1H8YQbbBk3yJrAQDS0yxMU5KSanLp5ScnDQYA30/byI4dOWitMU2JkJ5synl9GhdqSwiRtPqQHE980BKcSH4XwfyrBu/zSngrnhCe38hKfPKi2LYULJn8nP+VOFpaRlrriVrrxlrr47TW/45tezA2GKA93K61bqq1bqa1/uSP+k7/rBCKgat+JqI+wHswHSJqFBnWVyj1MEH3WpTehCGOJ+jcgTd78QESQzYqsr142EejGTT4JB59eBbgdRiO670UDz/WhfffWxZLTgq00kjpaeo0P7kqmzcdpErVNAIB779N6b3k2L1ivPPkqahiCskzbUVYPZa0T1SNJsA1gBcv/tcDHXnmqbk0bFSeiwafhJSCmrUyWPTTjoQr2JEiEHC45+H5LFtchSHDlhWiqf4dYfinc9Y5kHXQh2Eo1vwS5vprJ/Dm271Z/as3gdMaJk4dzIL52xh+29SkCuzi4LoayyfJyoowoO8YAgGTxSuuo0KFjpjS8yXWaDztovjMuywGbWL2mk6MeZRKnEzg6vVI6qHYi0Fhp7NXXu/Fqy8toGatMlzQLzm38OMPm+l73qco5SXQ49XJBb05ikPAvBlDVQckluxLlt0MbzAQGHRCcwBNFgHjdnxGbyLOaMLqcQRVPW0wmY4pT0NgIkT6Ic/3p0CLhKbWfxP++77R70DU/Zps+wJC9r/Jcfpjq3EQe/G8WQpIWR6fvBRBBo5eTnwmY4pzSDPfw5JFM8I+/7I/w25tw+tv9uaOu07jrG71MQzByadUo149T/0yEDD5fGx/2rariS9m8+gPmDz17Nk8+K8ZtGz+Bs2bjmT/fk/0zVELYnaYRXUyNsl+CJBXvORBkqxKete99dlxoB1zF15OSoqF329yy23tKFe+sMpmaVC3bhnM2JTj8ut+5qohP/Pim9Np2mw/yXORwzFZOfbQ5cytvP/FZKYt+Axp7OPaqyckVaI/8fgPzJ65+bBkt+2oYt/eEMGgTSTisObX5AiB4y4kP3NMUgGfPDffHppkZtlObP0hOXbvIs9XvnyA+x7szBVXnVLIn2DevLzQV/5VpO247CsgQFgUhDDxGQPxGf0RwsCkG/F8R6r5ZMyTvAqOnovWNrb+CpBodiRWsVKUO3YGgxj+G9VO/xkQYtA6SMi9E6WXEdXv4S1hI0iakmHNwRD1iLjvofVBQu5wFL+h2YqgHoZoQ4r5EKZsU2z7tWqX4dF/d+X8vscjhGD0RxcwfdZlzPjh8iSpgg4d6zB1+qV06lyHtDQLAZx4YmXGjFlBNOqSlXWQJYs9zwSRULfMD4FHWrCKuIr8mcy6pFkjEn8pvYVs+xyCzg0EnZuTjurcObn6tLTYtCkL0zQpU9bPLbc3zbcqEBi0y7fn30zwpQDKV4iQmuZQtlyUF19tkvC6AG9GPXHCWr4a9+sh/ZoNI//vAn/ASKwQN29Ozm+E1QP5j0TSgZAaRuHnoSDCOG5h+9CScNHgk5JWdIGAgWVJrryqBfXqlWPb1izuvnMq772zlGg0+fw5OdFEfUIcphH38HYIqxcJOsNx9QJsNQ5bTUTpHXi5EIE6Vhx0i4IWpfv5G+GfASEBC2+JbeJZFDZFUJMU80G03kmuczVh90ly7IvI06VXmOJk0q0PEZTHVtOIup/FEtHFY9fOHE5q8hpdO7/PuT0/xrYLU05Gf3QBb717LrN+vJLGTSpy2dVrkIaiStVcTmjxMFnRC4i6U/FqDHz5jrQoY43HFCV7DPlk12ThOh2nLgZjgmgeFi7Yzo3XF00vPfOseoVcswoiHHZwbMX2LQ3ybU0lOcl4DAqXHQbinWWKvzWVKnRi27bshAR3z96NMAxZqjFPSonfb+D3G3ToWBs3Vguwc2cuQ2+czMwZm1i6ZCcrlu/m7A4tGXx+Dw5mBpCcisMH+a+I4l9tM8nJTOsIIeeRWKX7riKPqFEjg/GTBtG4SUW6ndOANRuHsi/rLp78vzP51z3T6ND2XUa+uohhN02meqXn+H7aRgBuvmEitao+z+kdRhGNuvnOmU28sE7rrFhFv/ccSVGZVPN5pGiGTw7AFJ0OfeP+AmjtqcGU5ufvhP/5HIKjlhNVH2LJbqRbY3HUd5iiM4bMi43aajreSxZFsRrvZfM0b4Qo67WjJxF0huOxJJaTaj5e5Pl+WbWHB++fwcGDYVxXM3vmZp5/dh533dMhsY/rKr77dgNVqqbTuImnlPrgo2kMG/4ZKak5SKliPKFlSJrjMwYgKIutJsSW5WUIGEPJcX4guZgpD6boUuDvjhiiBUr/TMC4L7F99KhlSTIT+TFj+qYiwyDNmldhz54gTZpUZO7crbTvWJsmJ72Vb49cXOYX2ebfFxWxRH+Wr7qVjRtroLVJOOww4qVzeK/FMvbuDfLqSyV7djw3ohvrNxzgpRELmD1rcxLPXQMD+32ObSsCKSbZWWXx+TL4/MOBXHNTco5RcgIpxhOE3EdRbMLTrlJAOVLNZzBkXiI4qj4lqj4BbEJOhDTrjSKvrfPpdXnj7d5ccO6ntGz2OhMmD+bHOVt46/UlSc+HbStefXkhXc+ox+hRy9EaVq/eyy+r9nDyKZ7ond+4DMVmHDUbR0/GFGfjl3djyLqY0jPgyZDHhJV6ifi7hYNKg//2h7EzAAAgAElEQVTpFYLWilznYmz1OUHnZgQB/Ma1icFA6yxy7WsIOy9h0CrfkQrJifjljTG3NGLLXBcIJ7F3ks+n6dHtI6ZMWp8Ui124IJmiedcd33H1FePp3f0jZkz/DYAU81kql3sJn+yT3KhQ+I3B+IxepFmvYckzCDvPkutciknR8WIIIERySEkIP+nWaMr4FifRWvtccDx+v4FpCjLK5K1EAgGjyMFACI9ie/4FTRg/aRB7M4fz5VcDEPLIGUqHi1DIQClvFrfm13Kcd2YfdmxPTXSw+ugVFeXDPsJqOLXrf0+zk/cipSI1zaRSpTTuvKs9FSumUrFiSlJYqCCG3jSZl0bMx3WTRQ79foNLL21GNOpi24rsrCiWT2KYPo5v3Jrk5YeFYge5bh8US5HUJW/edxClkgv9BGl4kx0jEaPPyYlybo+PaXLcy/Q46wMG9vuCXTtzeOXFhRw4EGbv3hBvvr6Y1DTPnc40PUaUaXornIEXnYgQgu49G+L3G1Srmp6QgAcQIoUU42403jvj6Gn4jHOwZNcjv/1/Af4bcwj/8yuEZCT3ElH1MY6eDdhImuKFisKAj1RrBIbIk0T2yYE4ahGavaSYDxd7hkgRs+2qVZOTZcuW7Yr51xr8smovXbrWQwgTS3TCME8l21mIV7xYgVTjpaRjlc4kol4HHBw+L+IKJAbn4KiVuHojPjkQIQzmz9vGZRePpWLFVMaNH0iVqmns3RPkyX//kFA4jdcYQF5lbkHEZbFHvrqIx/7TNaZ3k40uFcf/8BGX0gBwHMHFF3Rn3pya3DJ8MSecuI//PNSW3zaU5fSWAxj5/nd0PXsrWsPe3X4qV40cZbaTxu+PMmby1yycV4Vbr7uU++/9no8+XMG+vaFSDUIFC9bKVwjwzPPd6NmrIV988QsH9oeREnr0bMj1N7SiQ6cMcuwUdGwF64Xi4p2+GzNJyqOlRtRTSFWFiDsCU5yB37gBsFF6L37jCgC+GvsrCxZsIxR0Es58Hdq9wxln1vcYbgLO7taA7j0bEgo67N6dy01DW2NHPUvV6jUyAPjk835s2nSQGjUyErpdeUhH0iDm5105Rjf9O+G/k2X0Pz0gCCFJM0cTVaMxxTlIUSPpcylq490iE0Ociik64OrVpJj3JQ0GXlvppFmvHuJ8gpFv9uLyS8YlWBv1G5TjtjtPS9rvqf87i2uu/JoaNTIYdPFJydckUyhjzUSzH0GFQowQQRqCimiy8RJ36cAOIAWPEeUDdhNRkwAJ2sZvXs4jD85kx/Yc9u4JMurdpdx5d3ueemIO8+clr16KQ7PmlVm5Yk+i00vPsPD5DLTWOHoR+W0dfTxClEc5dAK0dMjNkQgheOie9vw4qzagef7JuHSMV10cCllc3r87Z3XfTPMWu+lw+g4qV92VNKAcDXjeylC9ZpBtW7N5ccSCxGceZfPwliYHMyPcPGQi48YP5Kcl13L7rd+Smmrxf8+dncjfpFvTCNq34DKniBZM8udrDNGRoHMjkIurf8GSXfEZFyUdcULTyoVyHrt3Bfnko59JSTG59PJmvP3WYq65cjxly/l5bkS3InNJQogEg+6D95cz98et3HJbWxo3qYgQklRzJLb6Gkv2K7RiPeah+dsljEuDfzyVDwFbzUDrTCzZGyFMtM7B1esRVEKzHUO0KNLpqSCU0rzz1hJ27syhQvkUJk1cx7Db2nJ2twbMnbOFO277lhYtq/PSqz1KVMosDo5aSa4zCAhh0hfTaEPYvY+4fHCaOYaw+zBCl8XhRzwWh4FfDsMOXcedt33LuLGriUZcXFfTuUtdunSty6MPzSrV+WvWTGfbtrx8RY2a6SxfdQOOeBpbv08ewykDryL49z138cc2HDY4tcnF5OaYaCTKFRStF5aMKtWyuX7YcqpWDnJe/41HaVCQxBPkWsPVg85m6qS8icMVV53Mj3O2cmB/CClh165kwUGfTxZjPAPdezZk3dr9DLjoRO6+t0OhfXLt63D0d/mu5GwUCzBFG7S2cJkY294cxX5gJ2CSYU1FUBbNQaTIE9n8cc4Wup/1Yam+tRDwzZRBdOxUt8jP583dSu/uHxGNKurVL8fyVUPQOpdsuwMaG0EZMqwfEMIg4n5O2H0UQ5xAmjkqKQF+tHA0PJVPLF9Vf3ZG6TxqTvpyxD+eyv8tsGSXxO9aB8m2z0ZzkLhZvSk6kWa9dsh2Pv5wBfcMn0Y06mJZkrkLr6Zxk4oopel3/ufk5ERZuWIPdeuV46572h/2dYbdJ4hLWzuMIUXcQhiD+Oww5NyLzxiE0jtBzQC8qlBDX0Hbtu+ya1cOaWk+wiGvk5o/dyt33NUOy5KlKqbasSPZfGbnjhxuuPFa7rh3ITVr55dTODpidUrB9q3pdG09gEjk8B/jcNCiRavdtGy95yiuEKoAu0B73havvPM9E8bV586bugCSe/7VgRo1y7B40Q56dy9c0W47Rd9nrWHSN+sAeOapH7mw/wk8/eSPLF2ykxde7k6702oVCrkoPFVTR39PfgqyYgVp5mc4ajZCVEapKLluByCCX15PwLyNzMwwTz8xJ7GikdIrXCxJnXVAvzFs3110FfTLLy5IHBuM6T5pDqIJA1E0EVy9GlM0JeI+jbd6WYWjf8Q6BFvur8TfLT9QGvz3BcEOAaU3E3W/RuuDh945BkctI9s+n6B9K5r9eHkEz6ze1QuKPS4cdvh55W5s2yU3107QS21bMXrUcq67ejwd275DTk5ebP7nFYfPu444k3F1fsltSbZzJYK4UqqN4mfC7iMYoikexc+P3xhKVpbJli1ZBIMOmQdCtGxZHb/foF79ckyasLbEwcCjVnqx4TjHvkLFFFq2qoZSmi8/aciFPQr7QvxeaA3339me9s0HH9FgAJCV5UdpWDjPMzE6OvTAmP9ynIaa6tLzvN/odf5GhBAsXOBJdFwyaGyRTnElXYNhCPx+AyEEH3+8ko8/XMkvq/Zybo+PAfAZV5P3Opt47ngGcaJDHjIQVCOi3iTsPkiueyaQDUSJqFew1SxGvbOM2bM3J8JbWlPkYGDku/W5OVF6df+IDTEL2MzMMP95bBZfj1tNdrb3fEsJF1/qhUClqIFfXosXyjTIdS7EVSsxRFvi/g+GSK6YPqbwD+307w+l95Nt9wIUEbcGGb6ppTou6NyMZhsqKRRRBcjBb9yZtK/WBwk6txO1szizXRe2bQ1x/AmVGPVBH95uWolffvaMyj//9Gd2785NYur4fAZ33JWcT0huWxNx30GzEb8xNOHIFlZDyQvJ1MJLOK9DFxrvXcLu/UgakWI8gGmcir8iXHn1KXwwejnn9WnC3r1B2raryZvvnMs1V40v8b5onZxcTkuz+PTzfqxcuYfFi3biuoLMA/HYsklcUC+5gyo9tAbHFti2wbQpRYcnSg/BxX16sXbXewBEbYlhKKSkkGXnEUNbGCasWlGRSpVT6djZu+Zy5QJs35Z1WMJ3liWJRDwv62eenJvYHom4hKKjiPIckmYYtAEZxlWrUCwnuRgRPBbcJrx8UkEows7THNfwOUxD4hpeqjr/M2oYIvF3q1NrsiCWY9Iafpi1meG3T2XMuAE0P+E1MjO9lWH16um0aVuDatXSufPuvNVvwLwDOzoFxTrAwNW/kGqOwNWLEdTG1T+j9KYEFfXYwj9J5b89PBemGDWUTWitCyVlC8JVv6KTlDgBBOnmRxiyQaH9I+77OHo227alsHlTS6JRwZLFO2nT8m1cV2GYAtfRhMIOUkqE1AT8Jo0aV+CVkT3ZuCGTevXL8fabnqPWTUNbc8VVpwDg6ClE1HNABFevx6AFUf0WyS99/uKignF6hWY/mv3kupeTIb9Hiqo8O6Ibz47oxk1DJjLj+98QQjD0pknMmV00fbZipQDRqEpiHaWnWzRoUJ59+0JcfW0Ldu3MYdq0Bdz+rymxPeIJ5COnn+7f5+O5J09lwZzq7NiWccTtxBGJWMyZVYNWrXexb28Kfc46l9vuWcolV/3yu9sGkKIBlTPeZOYPlUlLs7wCNeDLrwbw2qsLef6Z0tdiRKNuIm+ilKb/4E088vT3nmx0LAynWINPnkdYPUMegaAgDCQtKTwwS8CPKU+j93mN+XTMhWzflk0waGNZkocemAlac1KzKsyauRmAjQV8tP0Bk9q1vWLH+GAAsHdvkM/H9qf5ycmWsgAB836Czu1IKmOIUxDCxBRtCDsvxCxEIcV4Ep9xLkrvI+gMBXJJMV/AEPVKff+ONo6i/PUxhRIHBOGVslbWWq8vsL251rpoW7BjGFIcjyX746hp+I1hRQ4GYec5bDUZvzHUewjZgffyhMhLGgpynUFkWPMSbdhqEo5aFAvTWNSs7XD6GTB1shdaiRfvpKf7yCjjo2+/48nNsTlwIEyvcxuycsUeOrd/D6U0tWqXYceOHKIRl1uHTqFn70ZUqVLQOEYT1SPz/Z0So/H9nG+b58VbNBSuXpnk+1yrdhl8PgOlNNOmbkjMYCtXTmHq9Eu449apfD/tN7IORgslvnNzbZYv380Vl47j3POaoIHPxlyNL2NhrDjqd0LDXcM68e03xx1631JDMLhPTxo1ySSYa/DNzK+oUrWws9yRQrEfW80jI6Nf0rNWrXo6mQcOb2AsuJoYfv88MjLyU0w9SNk4trPEWy3mAnvIqwZ30OInMqwZRN0JQBBBRUx5KpqDGMKrt+nStV5Su5df6U1K1q7dT9dOo8g6GGH/gTBSCpTypL/PPLM+7U6ryb59IS6/6mRGveNJZDRqXJEmx1ekKFiyM345kIh6mxznHKACGdY4XP0r3jtnJKroI+57uHoh8ZXMoVh9fzT+pwYEIcQAYASwW3icsCu0TlhxvQeUbCp7DEIIQar5CPBIkZ+7eh0R9RYQJuTejqvX4+qFGKIDrl6G92KBN9PeE0uEHY+rVsZUT6MI6pJqvoDWOXzxZW/O7voRC+Z7KwzLkjw74mxuu+Vb3npjMZGIF6L4alyykuim3/LyG0ppbr15Mh991g9TnINfbkuEjLLt9sQ7fL+8DlO2Ide5kjyKpxX7vYgCMqpiirZJ2+66pz2Oo3j+2XlJfPgDByIMHjiWVbFwV1F5BZ9PAgLLMhg39le0hgoVozzw5KICe5bBE107PMqpBvz+uM+w9w2OBrSWrPm1Auf3X0tGmehRChfFY/d7CLv3s+YXlwVzG/P8M3OJ2ooL+x/Pu28fnp4QgD9g0LhRRSJRl4DVAZhGfv+DNPNTJHVi548AGzC5DIfvyJMYVyh9AFNWIWCW3ppSa83mzQd56YUFtG1Xk+nTNuK63uSlWrU0li/bzTcT1jJl8jqqVk1n5eobeOmVHhw4EKZsWX+JzDlbfU/eM3uAoPMgrp4HpCE5Hr/h+S0Y4jjiqx5DFJbd/lOh/8cGBOBfQCut9Q4hRBtgtBDiX1rrLzlab+MxBkF5vJlVAEgnqt4EQgiqY4lO2PrLpP0j7lOY8l00Nt4tUUAES56V2Ofa61uxZPFOAgGTl1/rQTBoe7mAmLF6aeLIO3d6dE4hBAHz6nzX2wLNYrw6idYYoh1+OZSo+hjNLvL45wVXCj7SzHcKqUcahiQlxUQXEGFzHJUYDIpCuXIBxo4fwMIF29m86SCvvvyTZxTDTvI6xzhySTFeIuTeeOgvng9CwPMjpzP+y+P4I7gQ83+sjh2VqAAI+ftqEwTNEn7VytU89+z3fPnpxkTI5+UXj4wi7diK9z7oQ6PGFdFa4ahFRN3RuPyITw5Bikrk2F3Ir3Lq8H6BVhQh9x5cvQVbjcUn+xIwbyjxvEsW7+Tcnh+Tk+1NLgIBE8OQOI7Lvr1BOnSszdIlXqjScTTbtmXz28ZMjmtYgfKlUMr1G7cRcm/Ge058uHou3oCWSoo5HCk8BpXPOB8hKgBBTNGtdDftD8OxW4UcM9z5UGt92JS+kt4sU2u9A0BrvQDoCtwnhBjG312eshhIUZF0aywBeQ+W7EHcyUlQLpY8Lpdv7wAyNksxZQsC8m5M0ZM0883EHq6r2Lc3yHVDWvLYE10Zcu033H7LFNq0qekpmRbzPJmWJDXVomatDDqfXofX3uiV+EzrbHKil5IV7YjfGICBt5QPuXcA2QTMm0gzPfnguAOVNyDkP5lNjtMXrQsnFmfP3JRgDN33QEcM49APfWZmmFtunsyQG09lzep9KOVJL4z59CCGaILHJEncldjLf/jpK58PrrxuBX/E45eeHmX1L+WZ8V0toiVrE5YIgz5o8mb/2dnlmPtDpaMmlVGjppc7EUJiyPqkWk+Qbo3Fku1x1HQ0hwp5eY50UfVMzFbzWZQuufjwrTcWk3UwglI6NoER1G9QLiFfMviSk+g/sCn+GONMSpg4YW2pv5PP6EZZ3xrSzUmkm98giT8zJlIk5+ks2RlLdo85xv3FUKJ0P38+qgELhRCfCSG6i0MlSvOhpLuaJYRIBGxjg0MXoA9wjHjYHX0YohEuq7HVZ4DAFN1Is97DkNWw5BnEhe1McRoB467EcX7zMtKslzHkCYltH32wgkcemskbIxfz8gsLCIcdQiGX9HQfF/ZvWuSAICUMvaU1Y78ewPJVQ5gweTBNjs/jmEfVp7jMQbOdsPsvXFbjGaJk4igvraP0GpLVRKsC+XMQGsgi1x6WpMy6bOlOZs7YlLD2rFO3DM2bVynVfVu+bDdTJq1j3bq8ROP+/QY6/BllrILhEcWRKpw+8vR8ep2/7oiOLQlPjJhFm9N20f70HfiP2MfdwmUV+QesMmX3ced9yWGzww1LxZ8Tw/AICgAR92Oy7Q5k2aeSbZ9JjnMBjtpI8istKHrgzZ+QV7iqZHveHj0bEgiY+P0GXc+ox2P/6cLk7y6m6UmVEUJw6aBxPPTI6dxyW1tSUz0fjVNb12DL5oPM+3HrIWW/4zBkY4LObSjWAj7Sza8Tq4OC0NpF/4WcTq1BKVmqnz//2vT9QCPgbeAKYK0Q4j/5+/PiUNI07W4KhIa01tlCiO7AvUd+ucc+tN6L16GamLIFUlQGQNKAuEyvEHVx9CxMuhTLVIpGVezB0aSm5BUH7dkbTPjt+nxGAWlgCAQsTutQu8g2pchPtxQYnIzLPCCKo2ZhGR2RNC9w1A6STWi81YPLjzj6eyzhmfrcf+/0RAjLdTWTJ25g2bLi6yKqVEll9+68GelF/cckURRPbV2d1LR9KCIIqsVCSHEc2cssBKz9tcIRHVsSdu9MJxLeh2EeaScj8EIeBWbG2sfWLcmMqMOlm57WvjazZ3nKsi8+P58TT6qCmf4pBX2KHT2FZOZQ3Du7YMgwv9mOAaJkk5ve5zVm4dJrWPTTDsqXTyEadWjS4JUE88kwBCtW7Oa+BzpxdrfjqFAxBeUqWrd4CwQMvOhEXni5aOOo/FAqiGJF7K8Qrl6MQWEvDkctINe5AjBJNz/BkE0L7fNn4FgWedBaayHETrziGAcoD3whhJiqtb6ruONKGr5GAf1EPl0GIURV4F3g3GKP+i9AivkgpjgdS/bGJ73ydEfNIaJeBkxM0QNbfUrQuZmImycX7Kj5ZEVPI8fuh9ZZXHZFc24aeiqDLmnGldecQkqKFyYSwE1DWyOloEwZf5ICptaebEBRHgneDi55q5S+mLITXmevcVlJ1B1LjtsC7/8/P+KDVhoGnfCW5JpIqCZ3D/+Om4ZM5LiGFZJmr9nZESyr+Eck/2AAeXx1n0/Qf8AJ3Da8EjnOmeTY3QoMBoePhFRFyGDL5sIWkL8PmhuvOpPmDS5j4bwqR/iix03i8yBoxf6dw3n9hdZHfGWntq7Oli2ZCdHAqd9u4MUR8/HLyyn4+oqkkGZJUHih0Kb45CUJ7wyvAPM8gs7tiZVjTk6UG6//hksHjeW6qybQ/4LPufP2qYmaCCkFTY6vSMdOdRBC0LZdTRo1qsCSJd7/dzDXTij2loSI8z7Zzinkn7hEivEnj7jv4Q18OURVUQKOfw6OVbVTIcQwIcQi4GlgDtBMa30D0AroV9KxJa0QWgFPAEuEELcAzYDbYye57Ghc+LEKKWqSZr0NeIVmufaNOHo+xErtlf4NL+mlUTqPsx5yH0ezC1dnElVf4zMvpmPnupQrF+CUFlXZsSOHdWv28/DjXahXrxw33HwqoZBD00avEAzmsW4WLtjOooU7aNe+VtJ1Kb2boDuUeMdjiJr4jP44ejJK7ydg3E3IuQ1vQhAkeWaYhmehqfDJgbjUw7ErMrj/cmbM2ALakyfIP3vduiULf8zK83AQjWq+GreGnxYfZNoZUSzr6EylHAe6dehHKHg0y2c07TtvY+7smoSCJm+90pzTOpauYPHQLS+ies223HVPJ8Z88Qv794fZuiWryH2FKHrGuXHjQXbvypMFkVJQs2YGluxIyL0Xj50jkLTAoDGKVRQuRivq/gtSjIcRIoKr52HQnpBzN4o1KL0eU5yOz+jDqy8v5KMPViQ9F3t25SKlt7qdMHkwrdvUYPr3v1GmjJ9TW3sCkT16NuTVJgtZv+4ADz/WJenMYedVouojfHIwAfNGHLWIsHo43x4mYGEV40Boyd447vTY73+VtMWxm1QGKgF9tdZJfG+ttRJCFKeJD5QwIMQy1ENig0Gct9ZOa731KFzw3wZh9/WYaJhLPCbrN64hqt5A6yh+89bEvoZoidIbAI0hTuDxR2fzyosL0Vrz/kcXcP+DnZPaTkmxSEmxeOnVHgy59htsWyGlwGdJqtfMoO95n7JhwwFGvtGbdu1r4eoVeLNC74U3ZQdsNRWl92LKszBEc0xxFlE9KraPEfupgCGOizmhaRy9Alt/RjjqMGvm5aC9mabrJs9w168/QHp6cnHTmWfVY9p3vx3yvkWjLg2O24dSJdVClB6e7j40PmE/G9eX/d3t5cfypZXx+V2E0JzVY/MRMoyKptFG9WsMu6M/PXo1omO7d4vcxzQFp7Sswk8Lkh3LhIDdu3JQyvu9YqVUHv9PVy4afBKgEaTEGG4+FKtiUtd+vNc6UvhE+dumHI47mygvAxqfvAEp6qD0ZkAnhO7KlvUXCnHFJy8tWlRjwbxtjHp3GWM+/wWlFF3PqE/7DrW4dkhLZv14ZaHzKn2AiBoBOETUCHx6UJJDH4BPXoclO2GIogcEn9ETU7YADKQoXY7rj8CxOiBorR8s4bMSqy6LjQcIIcoJIV4HrgS6A18Ak4QQZxzphf4dIalKXALbu10CTRbp1pdk+CYkVUumGA+Rao4k3RyLKVvx04LtBIM2tq1YWUCjyLZdJny9hmVLdyKESPgqW5bkgYdPZ9aM35jzwxY2rM/kjlu/BcAUbZHUBwR+MQxDtCTsPohmB7b6DKVXI6hBXuzYRVCTDGs8aeYbpJr/R7r1NULkgo7g89vUruvZGXpaOXnzAym9moJ77+tIamre9nhNRaH7VMSTNGdWTX79OW6P+Ptn9ULAmx98x6vvfc/RGGSq18yhWo1c7IjBjbcs5cvJXzP48tWHPvAwsHtXOps3/8iSJTsxTK8DKTjgSClJTyucyW7YqDxKeSu3Hj0bsmTFdQy+pBlSCk+63RqDTw7FJ24hL3cQIY/T78EQ7TBFH/LChhKD07CZSvw+2mocqeYLpBgPkWa+iyk9cc5zujcs9rstWrSDhx6YwYejVxAM2oTDLpMmreOB+2bQoPaLbNxY2ANDkIqgDB61WxJ2no9RSOP0VAMh0jBl2xJVBKSo/hcPBsduyOj3oKS3dDHwKnCT1toBvhVCnAK8KoTYpLUe9EdeWCx5/QLeFPctrfWTf+T5ioJSCktehBBliLpfxJK3ERw1G78xgKg7hagajU8OxGecixASK58H7COPd+G3S8ZRoUIKl13enH17g+zbF6Jxk4oMvWES48auRimN4yqcWLGXEIJTW9cgGvFmYX6/wa+/7qV+rRf4Zspgmp44Ga1VgnYnqI5mKxAhx7kQj0ESDxloQBJyhqPJJtV8Binq4DduwlY/INjAuKnj+ODtNvTv9380OK4CE79Zx66d2ZimkdCgee7ZeQSDXi1EXKis8L3yBoX8s8lwyGTQef34bWfzmAb/74cQcO4F63nuiRasW11cnuTQOKfXRl5+53vQMOy6LlwwcC31j8v+/dfHiWg2ACHmzKzBxRf0xHU30aULNG5cgY0bMqlYKZWNG/I6y2jUZeaMzUntpKdbHNjvdfKVq6QmZNEvv2Qc+/aFePm1HtSr14AU81Zy7IIR3PyDZQUCxu3kOpfhvUqeP4LDZAxa4cbumSXPRYgUfMbApJbeeWtJ0d9TFBa8S0kxCYW859a2FdOmbuCkk6qQlm7RrHnV2HF+0q1vCNq347IQW3+GVBVINz8j6AxFiHL45IDkb6MPotiNpGGhQULpTAQBhDh0vcPRhnKPAerrUUZJA0LnguEhrfVSoL0Q4to/8qKEEAbwCnA2nlLbQiHE11rrVUf7XFrb2OprNDZa78GUbTFlG6LulFjxlMYnryRg3kyuMy92VAitg4TcYYBNyF2IKTsgRTL75ZQW1Vj28xAAFszfRs9zPgINt97elmVLPVe0QIqZmDEGAiZTpl1Mi5bViEZdJk29mLvumMqC+dvZty/Eu28v5f+eOzuJg+2TFxFRz+MNAsHYT1UEfiR1kbIetvoYcAk6D5NuvYMUlTFFZ2w2UKlyhFvvmUuGWQkpJX37JStMvvn6YvbtLZ2cw8BBJ/HxhyuTtgWDUVy1maNlhhPHd3O/4J2RTaldJ4dvJ9Zj8oT61D/uICuWVkTrEnwqY+hx3kYCAW/gfH7kDNLSDi9PUhw0qzA4AymqMOf7E3Fdr6OfNWszS1Zej99ncM2VXycNCFA4f5CTYycUUUMhh8pV0hjx3DwmjF+DYyvuvuM7Ph1zIUrvQOnkUJOHdAR1kZQn6n5FfAUhqBWzrlQYsiEp8gE02RiicOJ78aId1KqdnMD3+QxatKxKrdplGfN5cvThksuas3TJTn76aUd7E3YAACAASURBVDtpaRZbthzk/nuno7Vm1Ifn072Ht9qQoiqGPB5XLYp9d4khTyLDN73QNSi9jWy7J2Bjyf4xpQEPEfcTwu5DgJ90axxGgZqFPxZ/v9l/aVBSDqHYXIHW+s3iPjtKaAOs01pvABBCfIJX/3DUB4Sw+x+i6lO8F0YQUX4yrKlE3OeIz7Si6kNM2RqPmROMxfINvDJ6G4+xUbLj06WDxhKNJWc//+xnXnu9N0Ou+4YGDcpx593tmTn9N3x+g0sHj8VxFNu35WCakltvb8vyGPXz7HO8B15rha3GoLExRDu8/8a8Ds2gAek+z9wk4o7GjnXGrp6BrWZjyU7YekrexeEQcZ8mRd5T6Lpbt6mBU4xOf36cdlqtQoOBaXmhDlPUJ1KirtLhwzDg2pu8x+HMczbz4H/mUaZsFKUE997WkZnTalCrTi4L51aj78B1VK4SYvQ7JxDMtbjmpuU0bbYX1/VWNX7/0RkMPGhcfsDVmqF3j+eVFwehlCAQMKhUKYUWzV5n967S6yUZhqBy5VS6n/UhzU6ujGUamIakdh2vow7a96MLUl29T9CswcUFHcVbOUYIGPfh6iWx328pVK0ex4Sv13D1FV8jhOC6IS2ZOf03srKivPpGL848qz533vZt0jX6fAYDLjqRZ0d04+DBMCkpFoMHjiEYtDEMwaKfdiQGBICAMRyl9+PoCUT1SKRbDqV34TP6xiQqPDhqKXFBSkd9S37Zmaj6kDj11lEzMIw/cUD4H5Su+CtRE8gvtbkVSBLeEUJcB1wHUKdOYa5yaeEl0eIJuJj+O0FM0YuoHgGAIU7DFB2R1EexFr9xK0L4STM+IejegGYrQede0qyXiz1PVlZeku+iwSfRvmNtlq8akth28ilVqVN9RBKP33EU305Zz48Lr8I0ZcKOMOK+R0Q9AzgxuY24dIZ3rMtPXhhB+2MS2HmfBZ1rEdSDhLm6t93R04B7CIVsfD4jocx58GDJyUnw8g1z5ybPHwwDJn17Ma3b1ECIEMKtEpPTOPqwfFDWisbsKzVPjpiNY0sOHPDz3H9a8fCT8zBMRdNm+1i2pBIPPD4/Ed6KJ6uPLlKATL7/ti5+vyAUggoVU+nacdRhDQbgUU3XrtnP2jX7mT9/CyPf6E0o5DD4kmYAXqikiHHWCwetAARS1iXN+BBQCGGh9elE3fFk2a2Q1CDN+gIpksXnFi3aQTjsoDV8N3UD0aiidp0yfPbJzyxZvCOxwpESrhvSiipV0zj/3E9o164Wn315IZZlcN8Dnfl55R4y0n1cfsXJSe17IZ4wXmfvEnYfAxS2+pgMa1EiNGTJTkTcqig24ZfJEhs+eQlh90E8ldYuh3Vffy/+J9VO/0IUdaeTHnut9RvAG+BZaB7piQLmA4ScrJi3rsQS3TFEI1KsRphue2w1EUO0ANLI8CX7A0hZBu16s3dHT0LrnGJnXG++cy6PPDSTM86qzz3/6ljoc5/PwO83kuinAF3PqE/DhsmhKM1e4iEAzR68ZHdcM0gDNq7+oZhvHEWzJvZ7dQQRNNn4jWF8OHo5Q2+cTNlyfmbNuYLadcpy8cAxh+TlF1WJ6vcbBFIMbrjuG4JBm2ee+4oyFUcS1aMQ1EXzW8mNHgHi1+nVdSiqVQ/x+LNzME3vg6Yn7eOs7r8lKJ5SctQ9lQE2rOnAO28cpGYtG60lUmp27cwpNkmalmaRm1vYMKcgBJLz+x6flPxPMZ4h19mAYnW+/U7Eb9xH0B0AaCzRFy8Ka+Cq1eQ4/QEvJ6TYhaO+K5Q7OLtbA577P893YcN6r/Pfvi2bhQu2EwiY3HJ7W35auJ2DWRHeeP0ntPJUT2fO+I0F87fRoWMdWrSsxi9rbwLA1RvIinqhnzTzHQx5Ij7jIhxnOnnMOTfmohaXWwEhypDhm5aUN4vDbwzEJ7sDAYQ44vLyI8Y/A8Kfh61A/lLdWuTJNR5VGKIe6ZZX3KK1Juw+RXa0O37jdhw9DVt/ha0/RQjwGcn1eIJqSOqg2OgZ22un2M6l93mN6X1enkLjnt25PPrwLNLSLKZO2cDefUGee6EbE75ei5SCs86uT41aGZzdrXC1ecAYEivIiVecxmf6hzsu7ogdIYi4Ixk96mwcR5GVFWHSxHVcN6TVEfk7AwSDLp3avRebtQtSUyXPv2YDgVgS/Ogi/32Pd/YAPp9ODBTHn1hY6+toDwYgGXRBDbZvrUlKisnJp1Rhwfzt2Hby/02FCgH27w9jGIKatcqwZvW+YtrLw403n5oYDJTeQ8QdjSEaY4hmKJ03IEiREctveQSAiHoey/gEgKiaQHww8J4biSFbFTrXCU0rk5buS1CRXUdh2wohwI0xB+66twMP3T8jVmkfW2k6OsGYy4+oOzqWu9CE3ZGkyZew5OmUsRYBkqgaj6Mm4pOD8CY7qQBonYWr12GIE4mrBOSHEEeXglx6/GOQ82diIdBICFEf2AZcBJTO0fp3QOmVRNVoIETIvS2WaIsCFprCL6wQFpa8iIh6Cs0+wupRUuVzJZ5j3br9LFuyi08/XsnUbzcghEAphVJeAnf67CsOeZ1ClCHNfItc50Lvb5rG4siliYUXFcvXKH7hX49V5PIB9dm3NyVBkx07fiBdOxVUzEzGwMFNOfPMepQrl8qAfl8kt6w9FdUdu2aTE/7qKMfri0bBTv7od/oCqAzkUYmVW5ZtW1NYu7oGkVAKEMZ1FT8t3FHkCkspTbXqaezbG2LtmuRny8trmGitCIe9Tjg11aJb97zJQa59WUzzp2CFtJ+AvJ9c9+K8TTpPx8oUHYnySuwvg3RzPIaoX+j6ypcPMHvulfz4w2ae+PcP7NkdJCXFolnzKixetIMXnpvP8LtPwzQlQpBUvBgOFSYQmLJdLFcHpsxbIQvhXZvf8JhFQXcYuD7SzTFIUTPmYZ6DIZqQbn1ZqN2/DBr0XyNc94fimBwQtNZOTMJ1Ct4T/47W+udDHPa7IRJCWn4kNQgYN5HrzAVc0OXIta/E0YsIyDvxm5fFjjHwlrzeTFTpPQjKk0/xI4FtW7Po1O5dNF6IKD77jlM116ze77mqGXkzj8WLdmAYgpNPqZbUlilPJt2ajKuWEHKfw2PxpJM3+ysKBinGi3gWoq8BKSiW4HUokmanzGPO8nmcd8b5zJyxiUH9x3DgQIgTmlZk/brMhOZS7doZ7NyZi20r/AGDTz9axacfraJOnbKUK+8n80Be3qF+/XJUrJzKxvUHUOqPCdH8+dBQYILw04IAl/TtgVaCcNjzAGjWvAo/FOM6l5kZgXyuYvkrlZWCKdMu4YfZm3nmqbns3x8iN9fm3rumMWf+VUTcD1GsIX9oJdYKfnkPue4gPJ/kOOxEyMWbacftTCVCFC93Ub58gLFjfmXrFq8tv9+rk7Fthetq9uwJsuKXIRzMivDxByt45um5ND+5Km3a1SzUliXPId0chyYTKWqi9H6i7vsIquEz+iOEga3i+kwaR0/H5HQ02UAYVy9Fazf2vv31+CeH8CdDaz0RmPhnnlOK6qSbX+DoxViyO7YaG/tEEVWvxNzTgoTV0/hj6h0+ORits9BkovQesu0OSGqTbk1AiDzZ52+nrOfeu6YRjji4jiaYa1OzVgZly/pZ/es+XFfHinsc0tK86uBR7y7lrju+A+DVN3rS78JkES9DHIcWB/EkKRxKHgwAXGw1njTrVSzZE1BonYWtpxNxn0Ma2zEMk7YddpOb2Z7xX69JmOFYlsQ0BYGAxeld6/LF579i24pIPk/lzZsPFursL7m8GRkZfu69ewcXdj+f4fdZdO58HDLwFHlU1KPLQPpzkLzSGfVmU0LBPKZZTk6UbVu9jrQ4WYr8yP+5zyd5/71lfDh6RYLXD3DSSZVx1S+E3afJu19xbSsAX8zuNbmewuVHsu1uSBrgErftlPjlfUhRsJYjD+f3+oSlS3clrsk0JQvme5TSevXLcdPQNlSukkblKmk88PDp3Pdg55JDjMIi174asBFUjYUONRH1PBnWd/jkpYTclXhJ4rOQ1MMU7XH0LAR1CLuPEzDuRYiirEH/fBzL4nZHiv++INjvhCFPwG9cjBQVMcVpeGOmH1P2xluspMaSzB4U24mqMUTVhzh6AuCg2Imrkyter7jkK9au2Y/r5D1F27Zms+rnvQgBlSql8sjjXUhL87Fl80G2bslixvTfCIUcwmGHOT8kzzS3bc0iMzOMIZrGXLLyoyQ1UD+Ou4Fs+zSy7OOx9WT8Rl8Cxj2ASUqgMi+++DQ9ezdKOsq2FY6jycmJ8sH7K3niqaI1ZAq+JOPG/sqihdtxHc3ypRW5clBZOraOEAnHZ6Y+PBe1vyP87NhWi21bKtD7/P1YPk/+Io7snAh+v1Eqk5g4qlRNo2evRmzblp00GKSmWbz0ei1ynasp2OHnhYwiMVOnglBoNuDyXexYB/AXqpvJj0jESQwGAGd2a4DWXhFdJOIyd+HV1KlbFtdVTPpmLUuX7DxkvslV8/AGsCia3eSx+jJx9TJ8xvmUsRZRxlqIIY5DCIM06y1M0QHNb0TVJ3+pmF1BKC1K9fN3wj8DQgnQZCKpjyX6EjCGkWF9R5r5Jmnm24l9ou4baDaTJxcgEVQn6n7DrxvOol/f4dx37/dUqZqGz2dgWbKQzIOQgo8/68uwW9vy9bjVtDr5TU4+cSRjx/waEzNL58ab8wqHXn5xAc2bjuSEhq+w+tcc0q2JQI18LRbv1+voieS658ReSDcWOgJNLiDRHEDzG/0HNuXa61sU2864Mb/g9ycv38uU9RdaIWSk+/j0k7zyEcfRbNmcxaV9L+XlZ64kK/NEvBXO3w9Tx19Jl1P70LX1RVQs8wxoMxFGcF3N3j0hIhGX40+oRP0G5Qrdm0DA5P4HkhlnP8y7gvc/uoBLL2+eZE5UsdIBQmpwMaqxh/8aS9EAS3Yp9nOfz6DdaTXx+QzKlPWzcvnuxP/39Te0Ijs7wg3XfUOLZq9z+aXjOKPzKG66/pskenVBmPIMBBmAgSUuI544Bpuw804srJVeiDHkSV14oVlB0Sy+Px3/g9IV//MIOsPQHEDpjfj0BZjy1IQ3AoDSexE0Ji8ma5JiPI6gDrnO1VSvFeKpF3fQtmllZs65gpkzNtHutFqkZ/h44rHZfP21V3WakeGn7Wmesun4r9cQDudPymkyM6P4LIOvxq0mJyfKk/+eg217rI9xX/7KPfd1JM18maAzNMbkKInrnkxv9JzhwFYTiStn2uoHDKM1749aXmwrM2duLrQt62AkQeWMY/nyov0U5s7JZe4ci3k/VuODYyhXWDqkkiJf5p3XtxEMeiu3e4ZPK8QkiuPHOUWzqlxX0fXM+jz+WB5FuFe3j8g8GMG23aSalJatiltlVCLVfBq0JOK+i8vM2PZ4GC4NyM23fwCfGEKKNazEbyiEYOK3F7P61730Pe8ztmzOIiXF5MNPL+Dc85rwxL9/4PNPVyV5eXz04UpCIYd33u9TZJtSVCXDmgvYZNvdgDwfBpeZRNVYfLIHQqQmHZdi/hvpNkaKKljyvBKv+8+C/odl9L8HQZXYzFkRch9FuJJU8yWkqE3U/ZaQexMez3sIQliYsiWW7ISr1yGEJhox2Ls7lQoVU2jcpCInNcsT47rvwU5MnrQen2XQq3cj9u4JUqasn+uGtOKb8WuIRlXiZYtGHU495U3CYQfTFKh8PW6duh7tzpSnEDDuiEkiH7qYDMCkD6bogtYOAeM6cp0FCFLwGecDJPSV8sMwRMIisygUVMaMHkI6e++eQKItbwZtkmcSf2wiGlVkZ+Uw8KITE0njtWv2H+KoIiDAKEDRXLfuQKHEe/cex/Hya+fikxlE1VvkdfACKIvtfoekGSpf4Z+gEWV8k8mN3oJD/vqZKAFzaNI5t27J4tsp6+lyRj0aNMjLKZim5MSTqhCKTVBCIQfX1USjLmUyfLEKZYmUImGWk1WM1lUcHlvPxct35X+IBGH334Tdu7HkxUkSFUKkEzBvKrHdvwJHa/ZfWt02IcSFwOdAa631kRlzHwL/DAglIM36AFuNx3Zn4epZAITdEaSazxJ1RxJPLNr6M8paCxLHGaIhaeYoVGQROXtbMe+nk/D5ksMrjRpX5Kel17J1SxYTJqyh8XEvU6lSKnMXXs223bcD8OB903n15Z8wDJkwzHEcTaPGFdi86SCntKzG+f3q4apfkKIhluyDqzcSVSMpuBIo4tvhMBXHnYqpTifNeoUy1s+ASBRQVa+RkaS5k5pqJoxRSouCAmhxSAk1amRw2TWzPGE8AQiJoDaCU1EcO7Higli+uCwDeq/kvD4uKSkGodCRUWnT0ix6nfNh0rb4vfX+1ZzS6gCDL2tD2TIBtL6GqHop394msB5bry/UtsUgHLWMgHkbOc5sIBMQCBonFcg5jqJT+3fJzbHx+SSrN9ycIDUAbNhwgMwDeSHInxZu5/qrJ6C15rIrTmbRTzs4pWVVLNNgz+4g/36yeDFkR80h17kGTx/sZly9EIMWRPVo4EDsGolpbz1SbDvHBI4S7bS0um1CiAxgGCRYAX8I/vvWPEcRXmK5HS4/4CXuBBIv2WoaZyf2K8rmz5StKZ8xhLO6taZixZRCnwOEIw7XXj2el0YswLEV2VkR5s/dihBep/zYf87g59U3Mn32ZaSkWkgJtWpn8Mnn/diTOZxvpw3AMXqR4/Qj1xmMEIKAMRSDwoVGyRB4A4bC02Za6G0VEiEEWmfhqCUYRl5H5/NJHnm8Cxll/FiWLDTAeceXjlLq8xnce38nDmSGGfX6mQSzW+G4Pr78tAGzpgtkiZaOf25MVtIasAALpQzsqGTl8orYNkybtpH0jCOvkM08ECE3t3jRvzv+tZQvJn5F1x4346hlQKCAK1pxg74kyiPkOheQ61xCGWsh6eZ0UozXybCSB9pw2OFgZoRw2CEcdsnKJ1WitUY5GsvndROGIdgRS3aHwy5TJq9n2dJdfDBqBTVrZvDu6D7UqJlsF5oftvqWuDy30r+Qbo0ixboVmfhOnt+IJ4d97OMo5RASum3as6mL67YVxGN45mTFJwiPAv5ZIRwCHlso/p9q4JNXAeCXQxC6Go5agMuP5NgXk2aOxBvIPWidjaPnY4jmRWq3v/j8fDb9lpdQTUk1GXbzZIbfMZWvvrmIhg0rUK16OtWqp7N6/c1kZ0WoXiOv/dzQJp54vBbRaC1uv3cRaRWiCOFDygYxRkdx0EAUQQM0u/HJGwg5T4PW+IwB5DoD0eQyaVZjenXpwbq1+8nI8NPn/OPp1LkOE79Zx4EDYV4asaBQy30uaMLmTQdZsnhnsSuJc/s04j+PzUZrWPUznNywNd161f9/9s463Iqqff+ftWZm1wm6u0tSpFNEEFsEEYMWO1DB7kTsAOxAMVBsQkCQEhBQKUEaDnXoc86uifX7Y3acfQLR1+/7ij/u6zoX7D2z1qyZvfd61nrivpkzMwOU4JW3l9DqNC+L5lfi7PO35jMyFXEzkjbk6608+QvE/jx0vPJebPUTlkq6VjS64NcfZd8eL1Om3kso5GHBXB1fIJeZ37i61vlXzn+EsuX87M8OUa5cgGDQLJaqwufTCEdsDF1y5dBDeH0mYJJnDSJNfxOffIaQczXHnheSuzLFLhT7kKIqpjOFoPUlfn00UrgxK49HY8TIVnzz1QaOHInQtNF43nznfM44szY9u7/Hql/30aBhafr1b0L/S5uwaeMhvvzCff716pVh964chBD4/ccmdwTwyH5Enc8AG682KPF+QH+JsD0OKZrgkRcjRdF64v8kqD/HdlpWCJHfxfNqjHoHjo+3rSVQTSn1tRDitr865uPBSYPwBzBkT5IeAYHNfCQ9EELg1S8kar6NUjuw1V7yrBuQlMSnj0aKyuSafXHYBRhkGvMQIjW98uDB5Eq4RauKlCzhZe73rurdqxOWM3ZcT3Zl5eDz65Qu7ScQMMjLiya29C88s4fXXjnFXcnZDXn2Wfd9TTT4Q4eRW8Q0GI/Wl6NmO+KpjFHrS9yiK4Uv8CvLVn7GwQMhvvl6A0Ou/IJlS3ehaYJIpOiV7eefFS0w075jVX5esYdQyOLzz9anGAvTdPj2i9IoBV6vZNsWyd239uXQQS8rl63ljvtXcvhQPWpUfoCw/VysKCuO/X94p8eCRnt02YiI9VDiPaVAlz3RZBWeGfcdr79aE6XA79cSK/qC2g9/hP3Z7md95Ej4mMIvpulQqqSPlyf2oVy5DdhsjB05GpO43M4fuwMN3CQHhaAOggqYzldEnDeBCEFrL+mGWzXctePbrFmdHSP5c4vOnnh0Affc9T1bYwI36387yMoVuxl9Z0dq1izJut+vY+2afUycsILuPWrRvXtNho4oPiMtDk2eQqbxM6BQ7CXX7A/4COjPEtBfIGjdTsgehV8fi8Z/k8r6r+FPGIT9SqnWxRw7Jm+bcAmcngUG/6nB/UWcdBn9AYQIxKT8/IBAihopxzVxSuyYg61+xFRfE7RGo5SNwybcjJ9wSsAvjq7dauL1uaR23bvXYNHC5ELhlCblefftX2jeZAKN673CvO+30qzxBKpWeJaxjy90ry1d2m2BB0Nrn2jrkZdgiHOR1MMr3awnd4x+3LiVANIxtC4oDpGalbSf+PdRE63Js4awc+993DZqFgsX7CAatQmFLEqU9BEI6ClKb4ZR/Ndp88aDibz6jIzChUXuhKtTr34Zli3qxq6daYSCBm9OPIWGVQbRumEnKpaawVuvHya/EHtyNVzw2kWNpbDrzmZhTDzGJhqVPPFAa+68uRM/rRyPZTnUq18ar1fH59MTRXp/FbouyMjwJuJBRcG2FUePRujarQaI/IWGGq6qWP5ivuLWczYB+S3p+mIyjJkxV2D83iXJdE9YsyYbSMYufH6dpi3Ks31bairwpk3JWFLZcgGuGv4NX32xgR/mbqNdh6pF8hcVBddlHiRkPoatlmOrRYTMx8k1L8VSM7DVCkLW/cfV1/8UCmxHHtffH+CPeNsygFOAuUKIrUA74EshRHEG5j/CyR3CcSBNfxXT+Q5N1EMTqZKCGh0wEwFQlyxM4EcIDa+8k6gzHk10I2jeiMMO/NpYPFofAIaNaElGpodwyGLAwCZMHL+CaNTB45GccWZthg35kkjERkqHd9/5lQMHgti2YvwrPzH6zo7cNKotpmUTjdjcfkcH1q7Jply5AOXKpxEwngfAdtZhqXJI0Q6fvDOWlpqOFCVjGVQmgooosgCJTjcsFuBScVg4LACtFKi+iXv2+XRefeMclIIWLSpStlwAXZd8NHk1V4/4BikFtu2krKD35qN9rlOvFCt+2pPIpnFXpxq2bbN2zX5Wr4pTMihQAtuROI7CcSSP338ag4aviWWAZVN8hbODpDsCB0u5qZjKUZTwriRojcLme+KGMe5+mfRmI15/pRmWJVm6uDKTPjjAiJGtqFo1E9t2ePONlcz+bqvb11+oUrUsxcGDoUTbzEwPwaCJFStWrFAxDSEsRl7TlowML7ZzP3nWTSi24PJNrUdQOva5eTAYjMlLpHIZASiEVESdd7CcOXi1WzDkmfi0B3DUNrzasMSZDRqUYf1vB9A0wYzZl5GR4aV/308SDLZlyvqpUCGdF17unXIFr0dLuPKKiicBmM4PWM5iDNkXOBrTbP6dPGso8R0MKCw+xzV47ud+YriM/rYso2PytimljgBxTh2EEHOB205mGf0P4UoLFp3/bKqPia/aNDqhycZ4teEA+PTh+BhO1J5CyJ4GhInY4xIGQUrBgEtPSfT1xTcDeHXCT3Q/vRYVK6Vzw41tWLI4CyHcYLLfZxCN2NStW5rfNxygXv0y3HNfFwDG3P4dr01YgdQkC38cQoOG7ncoaN2Ow1pstRZdtEuklEbtTwnZo0mdUL14tBF4xIUIyhJ1vsBx1lKzdpA3JpVj/pyK9O3XnPoNyqZU3+7alcOZp08ie18e9z3QBcOjMfu7zcyetbXIZ7Z8WbK4ytWmBdOyUYUW4IKOnWqwefNhdmw/iuGxaHXaPkDHJ8cRdm7FUdm4OryFV90O8zCjDVAIdmelM292Vc7uZVO7zhvYaiOWMx9DnoGtNrD2t4ksWVQCy5bYtmB3Vjlq1S7Fc0//yLNPL6HP2XV59/0LqFL+ucS4/wri7aSEoSNaomuScWNdmumGjaJM+nw8gs9x1BdoshFpxsvkmufi1og4+UgWwzFjYFAwRVejG2H7SWw1Hzdl+hY82lq8Wr+U87ZsOYyUgho1SvDaW+fS6tTKLF60I2GgfD6N28d04LIrmlKihI9w2GLYoC9Zv/4A9z3YlaVLs+jYsVpKOnUcttpK0BoJRIk6b+PuTHQM0TXfeCWuMYvvWg0k9fBrD/y1h/tfRuHv61/ooxjeNiHEQ8BPSqkv//OrHD+E+hcQcrRu3Vr99NP/icFMIGpPJWyPQ5ed8GuPJ7jZXanNGwGDdP1DNHlKoba2+p1c052IPbI/ft3dEt9/71xeeHYJui554qkeDB3eknPPmsyihTupXack7dpX4eMP1xEOW/j9OpM/6cul/T4lGrVIT/eyeceNZO8LcuN105gze0vih3zFoKa8POFsAHKjI2JZUhbgIChHQJtMnn0BBSkQNNGBNP0tkm5Nm6jzMY7aTdR5A5Ck6W+jy5SYFy1OmcjmTS61dJmyfvJyo4TDfz4Vsyi/fIeOVbliUDMyM7340+fQvO0rBLynEtBf46mxk6hSczJVq+fQvNW+GG1Eadw8fXfS+fC9JsydVZHZM2oQjWiUr5DB+k3XJfz4r7+6gtcmrmDd2mQsIjPTw/JfrqJsuQClM8eiHFff4c13zuOyAVMT58WJ3o4XQoBhaIn6Er9fZ8ydHXnqyUWYpsOLry2mzwU/Ax500RW//hC2s5GgfSuFA+cCCCCpj8Nvsft1J1ePGExUvUOcwE5SgwzP7ELjuf6aabz3zi8IAVcOmI2LnQAAIABJREFUaYGyFR9/tAbTtGl9WmXWrc3GshRlywX4efVIPvl4LTddN51QyKJhozIsXVG8kq6ru3AhqcHvAJpoi61cqUxdXIGjFqPLjuiiF4ptGPK8FA6w/wsIIZYfw6d/XKjjq6GeqDrmuM7tv+m6//h6/y2c3CEcJ0L2PUAI0/kaj7wExzlAyL4VQRUC2rto4hSkTCuyrSbqkWHMwlH70EQzwCU/e3acmwlk2zajbprJ2efWY+GCHdi2YsP6g2xYn1rspGsC21bYNgSDJqbpcM+dc5j13ZakSI2AmjVLotRRcs2LcdiKLs7CUt/gBvP2kWdfQmEiPD9SNSNk3Y+pPgcEafp7eLUrCJqudjRA1P4ASy1FF6ehy3Zk7TzK3j1Jw3LgQOgv8dRJCVJKnAIWYdHCnSxetBOPV+PraQMpl+6qZh06FOaJR7KxrNOpXDXKB5+tRUqdymWfpmx5h5D1AGFzIc892YysHcnMrOx9Lkurx6Nx4ECIMbfNKjSpd+xcjfq1X6Jxk3I0qF+GTZsOEYnYDL7yC8qV85GdHRMnOo7FVNlyfr769lI+/+w3Fi3cSThisXmTm9svhKBGzZJs2n4jS37M4sCRX7AsDV2PYqnZ5JlZsThUUUV6Ejc2tY407S3y7CHEFchMNZU41YNGWwLG00WOrUXLCkz52M0OatWqIs89syQR51n+027ADXKHQkdYvXoflmknjucX6SkKmmyAT7sHy/keSU2i6n000RiV0H820GVNvFr+eoN2f/g8/zFQnHA8RceDkwbhOKCUiaQuDhtxfZxVCFljgFwUGwjaVyCphUcbikeeH5MHTIUUlZCiUuL1+t/2p7BgKgUL5++gU+fqzJu7LdlOQrXqJXj4se506VaTp587k/fe+ZVrr29NIGBQsXIqt0v79lW54ea2mM4MHLIAK1ZnEN+eg7uCLjiZRTCZkPJ21JmMLlvh0S7BtGYCFqaaBrZDBA8Hsj6l42nfYlmKQECnRAkvu3fnpfRarnyA7OwgUohCymoFKZ/jxkAIV0MhruWsFETCNpf2m8LgYS2oV78Mo26ckTi+d7eHbm1aAFCt+qesWX8tAf0VBpz/Olk7UkVx+l3SmGDQ5MvP19OkaXk0TWAWSNqZ9o1b6LV2TTbnX1Cfg4fC7Nubhxl12L8/ueK1rMIGoWPnqixZnJU4duRwhFE3zWDxoqzEOVJCnbqlGTK0BRf2bYiUghFDv2Lf3iZcPtTDY08vQEgLh3Ukd2sCSWMc4izw8R2YIuJMIqC9Q9C+EoigCOOR/ZGiLh55SbHsoMNGtKRu3VLs3Rek78WNKFPGz+WXTnWzrHQ3jjVn1hYqVc7gjG7vIYVA10WR910UvNpApKiMo7aSKZcgRAZRexoh+yYEJRGUJifaG022dClf/iHU1scDBf9K6op/3x39jVDKJte8gqNmA6SojV97knTja6SogC474yo4KVyG098J2w8QtG49rr4bNCxLpUrJyVwImPrZb7z5bmqswnFgx/YjiXMHDWnOrLlXcNHFjQB44KFunNq6EprmCrkPHtYCn09Hl81xP14fuuyBIa4imZlSlDunoOvDG6PIBl12xKfdRTKd0T13zSp3FRmJ2GRkePlwSr8UwjufT+eGG9swb8HgQkR4ULwfvvdZdVOI3eLYvz/EuCcXc/Xwr1Py+O18t7Nj+1Hq1XyRDm3f5oe5qcYgLd3gttEd6Nn9Pa6/dhqnd3knhYunqPF9PnUDBw8EU947FpYszuLSy5riD7hrLdN0UowBuDuhLl1rcP1NbRACQtZjfDn7TfpeuoEpk2thJz4KRfJzMRB4SaqGeXGZYh0sNRPT+TpWNKkBOl7tOrzaFX9IFf3owwu4/upv6dbxbfqcU49pMy+jQ8eq3HJrOy67vCmOo9j4+8EEzbnHo6PrgnPOq39MIjsAy/mRoHUdYfsJgtZNbnvtLDKNtWQYPxK2n8BhA6bzFVaxkq//XPwbye1OGoRjwGEbdiyYb6kv3RRT5f4IfNq9BLRJaCJeFSyBCE5KjUnxSE/3sGLVSOYvHkxamhETM9/C6xNXFKIRdhx4/bWVRfbj8Wh8+e0A7n+oK8++0IsBlzZxRyOqkmHMI13/FL/2EAHjNjKMpaTpX8XGWvxHb3Ajfu0xbGc1udHLOBJtjuUsxjUoHgRl8cpb6XK6SdPmOhmZHh56rBstW1VMcCt5PBqDhjTnplFtmfrpuhQq5z/C9GkbUxS4CiK/RGZR2Ls3jzWrslMm71q1S2KZDjffOJ1NGw8RzDPJyzWPK13yeFfE8XN37T7KJ5/1o2xZf6HPsl2HKox9tjIPjv2EsPUaYetlos7rVKl2hGfHz+Otj6aj665gUSqi2KzAdR/50cXpCBrFzjMx1fs4rAZAo22RhZAFcfhwhGVLdxGJ2KxZs49PP1lLh07VmD7rcipVzuCmG6anEC1WqJDGL2uvpkdPV2+55SkTU2ppCsIN+Ltjd1Q+niWhI0Q8m8gHOFjOKo5GuxCyHjsuV9z/HsdHfX2iuZVOGoRjQFIFSUVcBbUG5Fp9yLUuJGxNRAiBoZ1Kmv4xGfpyDHEOUjQloD2e0oejthM0byJsvYQqkJYQCBg0b1GRBg3L4vfrKKXo2Lk6ulH4S1RwAly7JpvbbpnJtG83kpbm4eZR7bjsimYpRU9SlEaTjRLvSVEGXTYgTX8Dj7wad4VZ+K4RuwnZdxJxnsVmMZCDpWYT0Mbh1x4mw5jvcul7RvHJtFfZmBXl0oFNAXjg4a4JsrP5P7iMqO++7bKm+vw6AwY2oWatVB3cgnVa+cnuqlRNJzOz8DhbtKxY6L1jYcvmw0QiNgt+2EGjxmUS7xfHtRSH9he8GLNnbkWTsHHbjQwa0pw6dUtyyYAmvP/hRcycfQX9r5yAknOIOM8RVS8n2gkBnbruAQKx2pHCymMuIlhqTiwdNV5TkLyPgmL0RcGyHLxeSe+zXFlO24bhQ75m9K3fkb0vj1tvnsnePXkoBRUrpXP3fZ0ZMrwlH01ezYxpmwiFLPKCJuvWZhd7DUP2xpAXook2BPRxhY6n6W/g1+4noL1P1HkexU6izqSYa/afjXh23PH8nUg4GUM4BoTwkm5Mx1HbMJ05RBxXqtJS3wEjcdR+gtZ1KJVLwHgRTdRGKYVSYYTw4agscsxzgFxQfjRZD0P0KnSdz7++hK++WE/zFhVp1rwCQ4e3ZMLLy1POWbww6XawbYezer7PoUNh3nx9JZmZPs4+tx4vjT/rmFWwceiyvfvntCFqTwUCWGpy7KiDqRaSWg0rgEw00RGHtZjOTGxna+wcG0clYx6tWlVKxArWrslm/g/bGTCwCRNeWU44ZLHht/1kZaVmNxX3o1EKsnbmFuk+WrG8KF2A48Pq1dnUrlOK7dsO/+Hq/89UI6e0U3Bgf5BnX+hVaJcg8KNisquSqjhsAywkHXH4ETc2lYv73L2kBpU1XJdfJPZXOtGrRlc0WRmvdgvgkskFrRsRlCfNmIQUZZj7/VZ+WraLl55fytGjUV54uRfrfzvApliW2PRvN3LvA10wDLf2Iy3N4KlnejJy2NeYlp0i8BQOWZQs5UMpG1utQoqqyIQMras5HtAfKfRslDpC2H4GyMCn3YAQXoRdJabRoSGSaff/aJzUVP7/EEJ40UR9hEgn6kxCcQiv5nLJR+x3sdUKwCZsPUlAf5Zc8wIcNuGRI3HUJpLZPCbu9rgwSpb0ccWg5onXn3/2W8pxw5AMvDyZznrBuR9xKMajEy92mvLxWq65rnWROeFxRKy3CTvPoIs2+LXxSFEJv34PUpThaHRWrNALDNETU30J2AhKojCBQ+RYHYlXNUuaodEZRA5+/a7ENSpUTE+kYwoBZcr4uePuTkx4xXW9rVixN/Zcj3/1lF8X4M+gTdvKeLwajq1SNAmUA3t251CmrJ+9e46lHXH8Y5QyGTSvVbskD943j2VLd9GgYRkWLR2G1LIImQ8jRBn82puY6iN02QJdtCVqf4GlfgQkTsouUuFO+nFtg0w88jKizvjEGbrohaVmA/tw2IFPPErQvBOHjbHPMw9FHqbzFcsX9+CSvlMwTSdW6Ke47upptGtXlc2bXYNwx92dyMjwMnvelcycvomzz6vP0h93opTCMh3S0z3k5ZmJe/32643Uqv8YpvoO0AhoL6DLlgjh7gKj9lRC9r0xBuD3ESKNkP0IpvMF7uRfAp8+gnTjMyxnHppsdUxZz38STrT4wPHgpEE4TkhRmUxPauBLE/WIu1000QhbrYxxFymizrt45MWgPICNIfqiiy7Hda06dUqxJ5atYxiSxcuGUb9BGVb9upctmw8z7/vkijxOGaEU7MrKOaZBCDsuWaKlFhO2x2Cq6bi1BZ8hRQNsdRiN3pjqXZLyhjmJ/+eHw6/4tWfRZXvyr2B1XbJw6VCef/pHzjq7Lk1OKY/jKBo2LMvWrUfQNEFOTvSYE22lSuns3v1H+tB/jGFXteTD99ew5rfC5HfBoEUwmBrX0HXBiJGnMv7lP1fTUjDzxnEUS5e47AO/rTvAho0fU7X22yg2gALL/pEMY05iNxd2niapGldwRwDJ538UHB/uwiIMePDIgVj25NhZmwja16L41W2jwHEE0ajDut/KkpXlEtFZlpNw0ykFy5btYtmK4VSplpngyWrcpByNm7hiUCVKeGMsuGDZDrVql2TH9qPouuD0HrViAeEQoBG0rwbbT7o+lZB9M7ZaBSi3EFDNwxB9cD3VScJIcN2bHu3CP/Xc/5dQCux/4Q7hZAzhL0IpK6YyZmCI/ni1G9HEKQjSAA+G7IVPuwu/dj9+7Tn8+mPH5c4BuPHmtmiaQNME/Qc0oX6DMnw3YxPdO7/LiKFfUb58koumabPytG3nMldeMXAq38/ZWmy/bg1EANCx1ArcScUhaF2DrRYAJjZfk2oAip+5bWc9OWZHcsxu5JnJ7KqGDcsy/rVzOO+ChoC7ep67cDDfzBjIyGuOTc1dv2FpLuzbsNjj6ekGfv/xOfZHDvuG7+dsZd++5C6gKPdTHJaleHXC8uOi8I7D69NofEq5FLfQ9m1HEn0IoShX9ZEYNYgLxbbYRBnH0Xz/j6BxFoYYQFHrtSgvEC9A0zgdQ2tCKrfT0cRr29H44ftq9OzQl6GXb+P8CxtwUd+GtGhZgVffPDcxRstyyMmNpugg5MfqVfsS54aCFnt25/Li+N6sWX8tp7auhFcbRTL7KQqYRJ1PsNXvxOkoAKRwEx782j145BC80s2EOlFxMsvoJBKw1TIsNR84iqk+ixGIlSDDmEu6MQO/Ng4hPHi0S/FoZx+3MQDYtSsXw9CwbcWqX/fx4487GdD/0wSx3JEjYYZf1ZL+AxrzzvsXkJsXJRSyUAo2/l68clea/g5p+gQyjFn4tJsAPeYSSrpTJPWPMbJUrvuoehXXn62w1FQcJ5lxopTCUYdwHBulgni9Go0al+XtN38u1Kvfn5z4wiGbCa8sL3RO4njY/suCNACVKqdTq3bJYo/btkIIKFXKS+8+dRg6vPkxjUgkbLNmVXZKjcWFfRvSvkNVqlaTvPvpLDyeIAXdhVKURqkoOdH+FDS6Nj+g2I8sslBL5Dvve3LNCzDIP6mG8crRbNvUh0fubs+Iy85gd1ZpGjYuh8ej0bZ9VVavymbEkK8SuzRNExw+7O5Kjh6N0PuMSdSv/RJzZm8B4LQ2lalQwU171g2JUoo2batSpqy7MPFqAynhWYdXjsatjK6FIc/ON14/fu1FtBgxpBAZ+PU78OnXI8Qf02b/M3Eyy+gk8kGKmrH/BZBUJWp/FAsm+9FEjeM2AErZBK0x5ER7YTkui2nffo1o1rw8lSqlM3hoc87p9QFmvmyYSMTh/fdWcevt7alRoyTjnjmTRo3L0qVrdXRdcMnFU1gwv7DmsRBedNkJRRCHPaRpU8gwZiMojSu+Uot043NcwsU4GuBqDkBBqouCtQs5VmtyoxeRa15Kjnk2OWZrcqyWHDWbErRGsH37HgaPXMRlQ9ai6za64WZP1apdkg8+vJD0DIPt247gOKrYdNB4MVpRSEvTaX1apWKPA+zckZOIv4Dr4io44TsOHDoUYeb0TTRvXpHRd3Ys1I9hSDJiMpIIV0AoENB57sVeHD0SYdHCnezc4bD4h8ZI6mOInsRX/JpoF9MnmIZDUe6pPCw1C4eF+d6Lj9FCpx+CUkAEW61HSB/xn7JiN17tCsY+eDqvj29MOKzTpWsN7rmvM+ec9QG33TIz5RkKAW3aVqFb9xo8/OAPdG7/Fj8t282e3bnccfssADIyvKxYdRW/b7me1988l7kLBlOnTmE/v08fQYa+GiFKkGcNRBfdYvccJOI8d8zP5USDS27378syOmkQ/iKkqESGMR2vvBaHzYTsBwlao1DKQanj1wO21A+Yzjc4/E7QcrUvSpb0MWvulazffD2W5RQ5CQohuP+eebz84jJatKzAkuXDueu+ztw+6jumfbOR88/+kG0FKIwBlHLIMy8iYj9Dnn0piiAZxkzS9PfJML5FiHjBUxzr8YrrSXVLFIcQNr9gqyUofsP92bjauZaaR9XaT3HtzSt54InFvPPRHmrWLIWmSbZtPUJObjRR/ARQpUp6cRcp4lnErh6y+C1WAX4sxIVthHD948WlsDoOTBy/HF8+mgYpYeny4VSunEFeXpQOHasx5s6OvPLq2bzx9nkMGdaCPXuS8Y9Z05qS4ZmOX78DXfRA0gZDnonlLCdiv/IHd1a0606JNbGiwQAgMER3BJUAP5poAXi4/sY2pKUZlCzp4+77OtHnzA/4Ye72lPoOXZc8MbYH0767jGVLdvHyC8vYsvkw0ahNWppBm7bJtFcpBRUqpnPBRQ158L55lMkcmzAY+WGzEFv9DBzFUgtxvzd+NOG6AZVShK3XCZq3YKstf3D//2z8G11GJ4PK/wHcNLsKuHY1hKM2k2O2Q3EIn/YoXq3/H/dBFdyVti+x61izeh/Tp22iz9l1KVs2kFhllCsf4Jnnz+Tq4d+Ql2cyfdpGpk/byJ7duRgeyfPPLEnw8pimQ6umE/lwSl/yck327s3jysHN8PlELKXRJi6hKUQZdJEUONGpicXmxOuIepSiq5vjMEimqRZcEhnEK6RtvsXwOBgYnHFmNUYOcnmFDI+G3+9qDkhpc0rT8gkunfyIU2UXTAXNT3+Rm2NSu05J9uzOIxg8tpCMUi73Uv7iqnr1S7Nt65FEBfPatfuZ/P4qpBRIKTjvgvqsXr2P7P1BHMetb/hmxkAcZwdB+zaCVglef+t2evX4Gst2mPhGG3KiZwO5BPTxhOwnCNtjiRPPudBJ6hyAO9FHibvjUuFBE23waaMx5JkIUQFN1CFDzsBRm5HC1Uxu16Equ7JHsXZNNpGInSJoZBiCu+7pTJt2VejcxXXjlCnrRylXLrN6tRKMfaYnp/eoWeiZbdp4kNmzttD30rWMuu9VjkbrkWF8nFAK1ETc5RhAF20xZB9s9Qte6WbmWWoBEedZIIxt/kaGZ9oxP6N/LBTY9ok12R8PThqE/xCGPAfLmY/NFnTROcYKahOxJwIhpKiKIXsU216T9Qloz+GwB4+8iLy8KGd0e49w2GLck4t47MnT8Xo1lHKzjxxbFZJffHXCciIRq9BEaZoOE19ZzoL5O7BtmxnTNvLaW+eRWeoVIvYEV0SnCO55v/4SOVbj2Cu3AvvYKGridWsXdM7Bq51Dnj0Qd3KTGOJcfNp1jH16E/ffM5eOnapxznkNaNe+Gps3HSIYMhPpkXH4/DoPPNSVgwdCjH1iUeL9SpXTOXQoRDRiJ+7fjNopFbZwbIUzf8CgTBm/q+nw6cVcO/IbfsxHN7F+/YFEdfST484gFLLwGIqHX5lP5665mHYbgvZQ4inGNeuXZ8ceN/8+NzoSm3UAhO2nsNVS3Mk+/ozyG9Pk8/RqtyOcyoTVDQWeaRoeOQIhJLroAMAbr61g2rebuO329rTrkNzdTf5gNTffMAMUVKyURtZOd3x16pbm9ddW8vPPe2nXviqGoVGnbmmuv6kNmzYe5LEne1C5ctHayFWrZVKmjJ9b71qO1+ug2I6l5qMck7D9FLpsT7o+HcU2BBXItVx5YFutIV1+hCAeM4jf+4mJv1EP4R+FkwbhP4QQXgKG6x+11WaizjuAQqATtp8AJAH9FQzZtcj2YetZIs6ruOyPXYlEyhM1bezYxH/n6Nmc2roy513QgAEDT+HSflNS2mdmesjNjRY52em6pGnzCsybu41IxGHWd1uoU/0Flq0cTr36nxRuEINLp5wkw9M5F4uvijgz/qP2Uji+oIAjWHyEctYhqIRiF7rogk+7F0stY+AVzbj8yhsTLeL60eGwRatTK7Fi+W5an1YJTZPcNqYDp/eoBUC/AY3p0v5tQiGL3buS7hm/X8PwaLwz6UK++mpDgk0W3EDvZ1N+QwgoWdJLTq6ZiMtEwhYPPdKNvv0ao1SUuvWO8tNSt7isadMK/LZuP5GIja5reL06FSqk8/O6+gjfm0gZJmTfRn72WFPNw1abkVTDJkk7baksksbAfUaSrjgUdL1YCOVH1+qC5SUpJuPg0qMsQcPlmVq3Npu7xswhFLJYsngnO/bckuhl9swthGOUIXFjYHgk2fuCHDgQ4vChzXw/Zytn9qrDqxOW89LzrkZ2WppB+fLpXNi3Ic2aV0gZmd9vsHTlcA7mbAA1C4REE03JsfoAeZjONDyyP7rsiOnMj7UKxWpyQJft8Mp7iTrP47CZiP1JIZ2GEwMnXsD4eHAyhvA3QhO1yTQWuoFakU58Za1U8eX9pvNl7Dwb21lC6dJ+XnzlLBo0LIPXqxEMWqxdk80117WmVCkfPXrWwuNNfmwVKqRTvkKSdrtECQ+GIeneoyZrN1zD6Ds6cHG/xonjjqP44P3Vx7wPQQA3jiAQVCPN8zzJtML855WlhOc3ChuD/LCx1UoUuwAPXvkQudb5BK3ryTV7o1RhsXifT+e776/gwNHRzJh9Bd/OvCxhDAAaNChL1+41CARSV5gvvHwWO/eMonWbytz/YFfO7O3q8l4xqBlTP43rOAsefqwHn33Rn3r1SqPFKMWff9adDMP249z7+ERuuXMljz9VgbkLBvHNjIGMGNmKqV/1p2RJN1soM7NGjE7EhxTV0Lk430iyCJrDcQuvSpP8mRWmZHBYlO9VcoIJO0+Qa52NIfrg08ZgiIG4PFJetHzqiT6/4SZ2CgikpT6PG29pQ7lygRRpUzPq0LBRWXx+HcdR1KnrVjpnZeVgmg6RiMUHk1bz9FOL6XXGpCK1szMyvFSv9BzpxkdkGN8jRTU0UYe4RKkUblKCLtpjyF4IquPTnki012S5WH1LkIg9tlD/JwT+pdQVJw3C3wSljuCobITIRIoq+PUn0ERbDHkuhjwvdo5F1P6IqP1pgtfIo12NO3FkosvuAAy8rCk/LBpMy1YV8ft17rk/WdB22+gOPPV0T7w+V8JQoRhzZ0d8PtcPP/WrARw4Opovvh5AxUoZ+P0G4187m06dXdeQpgn6xphSTdMukkhMk/UJ6OPxyOtJNyYB8XTUuK5vGiBRCI5Em/7RkyHpB7fJs09HsR0IojiKo/bhqOw/TWj27vsX8tKEjgy9yksgIOnUpTr9BzRJHJdSMGVqfw7njeH2Ozok+nccxaibplO6tJ93P7gAn0/H69UYMNBt66idBNLyuPG2FQwesRVNk7RpW4WnnzuTjp2qJ/rXZXPS9Lfxa/eSpo/HZxTkhhIojqDQKcwkmx8FK6V13OfsCt6YaiZebTgB4yEyjDlkGAtY8EOIl55fyv7sILVqleTDT/oy6rb2fDvzskQv2fvymDFtE8+/1Jv6DZLcTc2alade/dI4tuMSzMXqJ265tR29z6pD5641Eu+Z0aITGgAUWUSdyZjOTJRSpOmT8GtjSTe+StC8C6FjyDMx5JnoskGirRtgdgV+NHHaMZ7NPxcK/pVppycV0/4GWM5K8qzLACemmVy03KbrHnoNAK+8DY92EZCLoDxg/KlahXfe+pklP2Zxy23tqVevNB9OXs0rLy6jd5+63Hl3p0J9KaVYtzabdWv3U6Kkjx8X72Ts44uoWi2TRUuHJla+RUGpCEfNlriFbHEaheOBh1QXSQb5dxManXDYg2ILgvroohm2WoVPv71YF1t+5ER7xniAJF7tJnRas/wnxQ3X/EqdOqV4453zCAQMDh0Kc3bvh3n5rW9wHMHVV57D6DGX0u+Sxhw6FCY3J0K16i7VgqO2k2sOjhktD37tcTza+SnXddQ+LOdXdNECKZO8O1H7G8L2m0AUXTRHqaNYfH2MOxC4k39yFS5pgC5OJ6o+BnIw5Lk44Ue5/NKpbNt6mNF3dOTmG6Zj24omp5Rj3sLBRfbcs/t7LF3ixkE6d63Orz/vpUrVTL78ZgCtW7zGoUNh/AGdl8f34eL+yR3kr7/soVePSeTlWQy8/BQmvHZOkf0fjfZG8TvgxRDnI0U5vNpIhEjuVm1nNblWfyCKpCYZnlkoZSKEgaP24KhtaOJUhPjveq7/DsW0alptNSrw8HGdOyr38pOKaf8/wXTmEJcKjDqf4tHOQ6kgjtqBFHVxRWrW46gs3ACiwFHryDE7ARZeeSs+PSlH6NYmjMRS8/HK4fj02wtdc9CQFgwa0iLxesytszh0KMyG9Qc5s1cdWp9WOeV8IQSfT13P2McXxQj43Pd37jjK5EmruOb6412pFWcMSiKog2IlkobojMJkLBBFEURSBkElbOYkWjjsjlXw2ijWYSo3+Bq0rsUvX8ZhLZpsjC5OxVa/oIlmCJGZbyQHiE+mEftZIlh8/uVp9D7P5qP3WvDZlHoMuFyhpd3Ax1+HSc84ilJw94MbOLNHPQDSSywjkLkZpS4iYn9ExJkAHIrdZxRbrQTORylF0LoVS03HXb27Fbh+7Tk82rk4ajchewzxFb+i3ck0AAAgAElEQVSp1pCKkggqo1gbe63jldcRdb5CxTK6BCXQRCuiaiKuofCi0YOPP17LooU7CIcsxj6+ECEF0ZDFvr15FIdtW48kPuMf5m5n3LM9uerqU3lt4gpyc10j7ffpnHFm7ZR2/ftOIS/Pfaaff7a+SINgO+tixsDlWjLVZy5NBgcI6I/iqIMI0lBEcY2eg0OIPHMYlvoeXZxNmvEiUvw5xtp/Gv6N1BUnDcLfAI/sExMSN/Fqg1EqlxyzJ4ojaKItSu3HYTNQMsZn5EGK2qAsIIrpfIGPpEFw1Aai9iKee7Ipa1dt5JFHsmnQsFyh6x49GuH72Vto2aoSZcsFyM2LopSidJmiNWnfe+fXQqplAHeMno1uaIwY2arIdkJ4CWgTCNqjcYOnhQnh/NpLeDQ368Wy15Nnn5U4lqbPQBMViNrzsJ2kQVDsjAWbt+bryZWGDDlDYw9DA0oBQQRuJXi8ujWgv0zQuhcVYwsFuOWO5UipaNM+m9JpgwhZt6DYSUYJcCuzdc7q0x+vZmA5S2NC8ArTmR4jKkxm/Aiq4dXccThqHZb6goK1AWH7GUL2Awgqc2zX0GEUNvHMGo1WRJ130UUvTJUFBAjokwlal8b6cQCTiPMoNWu9hxm1kVLQsHFZGjUuy6+/7OX5l3oXe7WCFOp3jp7N+++tonMX1+2laYKeveoU2hnmj8sUXFTEYaq5uJ+TDWTi8hg5QJSw9ToRZyzgJU1/F58cjaWW4pGXELTd77ilpqHUkQQB3omIEzE+cDw4aRD+BmiyEZnGCsBBCC+W8wuKo0A4xhHk4E4kNn79fqSojqOyiDpvojiCVxuZ0p8U1Zg9vR4TXmhOKKiTtf0rFi0dWui6vXpMYuuWw0gp+G7OFcyZvYXT2lSmZs2SLFywnYqVMlIqSjt3qc5Hk9fEUihdds74v089sYiz+tQlHLGoW7d0oWu5K/n9FDfpRR3XIFjOavLsvqnHrClYfIqi4Io2giZaootBRJwPkTSMcSnlv4Yduy4ooigOg8pECC+O2h+j3VAISqPIw+NxA/mtT8ugYulq5JqBxBwuqEhAfx5dujUXroCLAMIotR/352DGzq1AhjEdWy0j1+yLoBxF7Y5cYwSKQ7gV3jsLnZNEDro4B4/Wl6B1DRDGVB/hFeOIqDvIs/qii54x/WsT8KPL08jKykHXJZGIzbo12ezZk4tpObz80k+cfkYtXn91BatWZXPb7e2pXqMEX3+5gZ07UgP9punw88o9VKqUzrnn1ycYtHj40e6FRvjpF5cwbuwiqlXL5PYxHYq8C0P2ImJPAML45AMxWc88vNrt5JkX4RpnizzrUjKMOXjFIJSykHZNHLbH4lFFp7WeSDjR4gPHg5MG4W9Cfk4WTTSKsZ+uwCMHowhiOh+hi64I3OCuFFXIMJYAdqKt5SwgZD2EJltStsSjoL5F0xQZGUWTjm1YfwDTdPAHdEzL5vqb2qCU4pKLP2XOrC1ommD6rMuoU7c0GRkeXhrfh85dqlOqlJ+OnauxZfNh+vT6AMdRtGhVkVbNXwUFTz3Tk0FDmqdcyyUqK2gMkvEEW/3K0WjXWDZRgSI2oWLZRBFStZ0FlpqLo1bi18cStC4r3DYBHY2LCVl3Y6lZ6OJ0HHWIeIzCkGfh0x4gbD+DozaSUepOALxyCEHbzSDSZduEMXDb9MJSi3DUBrxyFJazEkUWumyPIbsghJeQ+QSKAyhy0bkQi6nFjA9gV+KZeORQbGcTNvMKnONFFy0ReFFIJJWxmExcW0KTdfDLuSgVRLEXTbSlatUdSCnw+XVKlfGzZ09M3zk7yOxZW7j3rrmEwxa/rNzDJ5/1Y8igL4ocnVLw3czN3P9QV0Ihq1BWEkCtWiV5eXyfY9xjPJvuJ8BCCD+QjLF45OWEnbgGgoatNiJFZYTQSTe+jBmEmscl4vNPx8kdwkkcF4TwkG5MQSk7IRyu1COFAr2WWohS+zDkOQjhJWjdjOIgjrOJDl3a8MrEc1m7Npuri2EIHfdsTx5/ZCE9e9Xm8UcX8N2MzXQ/vSbfzdyMUm4qYo+u72FZDjVqZHLqaZW5fUwHmpxSnpycCP0u+gSUonbtktSrW4rp37ppkV99uZ4u3WowbNCX+AM6b06y8GW+XcQI0nHdRzZu1lBR2gKd8WnX49grsNVKBFXR6YfFdzGa5kM4HCJkPUrRBW6Jp4omKhNVU2LP7nuSK3aJRw5ECA2/fjuO2oGj8nBUDobWnTTxOo7a4wZo1XZMZz666ELYfiwmdhQgaF+Ga6wMDNEbITJRysQhThZo4dH6Ydmfk7pTkLHXBskAegl82p0o7TB55k0pqaVebRhCZJJuzMBWv6KLtpjOHEL2L7Fr93CzdASAq2bWtVtNJn/Sl5Ur9vDW6yuxbYdWp1Zk4uvnsGNHfqZUN3kg//CkdNsvW5rl0l5LwSMP/kDUtHnj1ZVMeP3slJTeouCofYSsexEiHb/2IEKkxxYxhQ2KVx8KlkbYeQZNNEMXbZOfoPCiUe+Y1zqRcNIgnMSfQtwYuP9PNQamM5egdS3uKnkJAX0sKrFyVljOMvr260tfGhXb/5BhLRkyrCW/bzhAx7ZvYZoOM2dsTuTWK5Ukg9u27Sjbth1l8aKdlCjhY/MmN3AajTqsW7ufdWtdt4ymwc2j2vHw/fNY/tMupCbYset76mXmv7IHMEjTPsFUU4k6r1P0yl6QoY9HygABXiXHao1iOybPkT/n3r3jomojfCQDuCaKKJIGOPxOavZSeTTZCKVCWM5KgvYwUjQaRG8C+stAhBzzvNgxD3GepWRRmeu3d5wNIDsTdT4CkspsumwLtg/XZw6CZijW4bpIyuLuEACOYDnLQEQIaOPItbviGjsvkipE7Y8BDUNegBAaHu18DNkN8MRW3G586OuvNrB82W42bTzIvQ90xbIcsrJysG1FZgkvDRqWpX6DMjz8WDd+/WUft4/pQIWK6bz7wYXccO00Dh0MkZnp4Y67O1KjRklmz9rMnt15jH1iIY4Ne/bkcknfKezKHoVhFM9VFbYec0V4lIakFj79+mOc+wxR5xO8csQxzzvREU87/bfhpEH4L0Mpk4j9bkwhyw3EOWorAB4xhKh6CRBo8hSi9ofosjNSFK2tO3/eNoYP+Ypg0MTv15GaoE6dUlSrnukKza/eR7gAVbRpOmzb5hKYBQI6QgoqlE9j+3Z3pWnbcGrrSjzx6AJXBMRS7N/TlXoNNwIOBv3wGlchqQAowtbPFB1XMPDKG1Aim5D1CUrlH4eNW8RkFXivIHTimSygY8jW+OUN2GoLedYA4DDgxgbyondg8TGFU12J6Q/vwN3RhHAn53j1b0E4hJ2X8KihWM7ilPFF7XeIZ5MBsR1OHLvIj6B9DWAiyCBdn4OtlqHLtkTtt4nEFM8cdTCRXVYwwHr+2R+yetW+BBnd+t8OMPGNczA8Gh7gjJ61Y+1cUZ/p0zby4QerOe+CBpzVpy7rN11H04bj2bcvyAXnfMSS5cM5/8KG+Hw6WVlHefvNX3AchWFovDZxBc2aV6BT5+oUBSFKg3I5qVyW1cKwnAU4aicRZyJuMPwFPM7lSFk81fgJDQV/UcjvH43/SR2CEKIf8ADQCGijlPop37E7gWG4M8SNSqkZf9Tff6sOIWp/SdSZglcbVCQ/kaMOA0GkKDo7AyBsTYxRASsElZCiBH7tCTTZEKWUyxSqNEL2VSgiCLxkGD/GWEiTsG2HciXHJXYApUr5+GRqP5q3qIA3xs65efMhbrxuGqt+3UeZMn7S073cfW9HBl/xJUoprhjUnKee6clnU9Yy+IovAVf9S9MkmgbBoDsZPfJ4d667sRQIb4LT3n0eHxKyHyI5SSZjCjrnkeZ5jqPRDij2knQv2GiiCxoNiKoJx/3sBTUAE000IqC/hGnPJ+RcS6pRKa5tFTKMWQjhJWK9TdR5F4cQsLfYNhnGUmy1iqB1FXFjJSgVC4xHi23nIj9ZnSBdX4QmXQqIkHU3Uefj2DEDSTXSjPf4ZaXDNVd9Q40aJXjz3fNoVPflhEaBlIJGjcuyeNkwNm06RM7RCE2blcc0HXw+nSWLd3LOWZOJRGyEgLvv68zoOzpSudwz5OZG8fl00jMMDh+KcM99nRl1e3sWLdjBjOmbmP3dZtavP4CUgplzLqd5i8KpoEpFiDofIghgyL4p/n+lTML2E0Sd90nWucYV0UJooilp+mSEKL7O5b+Nv6MOobKoo0bqjx3XuQ9YA06YOoT/VWRnNXAR8EP+N4UQjYEBQBOgN/CKyO93+R/CUdmE7NHYagFB67pClAu2szamHnY6EXvSMXoK4k6a7oTh0+5Aky41sBACXbZDk3Vj/vgwijwUoSJ7su3kyrxUKR9t2lZJGIPv52zljG7vceRwhK7darB1yxHWrctm48ZDrPj1Ksbc1Ym6dUsRCpk0bVYhn3qWIhKxE8YAYNu2w2iyfooxAJAiXn3qQ9IAjxxF/Ctl8ztKRWITqEtqF9BfI01/nzT9VaLqWM8ov8SiC8U2FLuw1CIsNTtG+fFHxsBAF5egiVOJ2p/E+jkYiwsUbwwE1RCUxpDdkfl83pIm6KIrqRXJAaA6Gt1j7/tJ0z8kdXLcnzjbq90S0wnIAMI4bMF0PuOqYV+xdk02s77bzAP3zePliX2oXacUZ5xRi7vu7cTULy8BXILDeXO3Uip9LOVLjePRh37gkQfnJ4gAlXLTiwHennQ+LVtVpPdZdQjmmViWw8TxrvhQh07VePCRbhw+EiEScVNa9+7Nw1E7yDVdTQsnRrkihBevNgiP1q9QMDhojYmlXEeBMBqn4teeJM69ZKvfsVXxgkcnMhx1fH8nEv4nLiOl3AqkIipzzwc+VK6gwBYhxEagDbC44In/bQi8JH/kBgX1AVz1NNcVYTpT8WqXF9mPVxuJo/Zgqk9RbCPPGkKmsTJlBSVFKXxyDFHnAzzyMqQovO3WNMnV17Zmwvif0KRgfIEConvvmsP+7CC5OVE8Hg0h3Q2/rmssXZLFk48txHEcVq7cQ4OGZQsFyIRwRWCEFPQ5uz7Tv93IPXd9j6YJJk2+kHr1y6DLlqTrn+KwG110xVaLiTpeXLfMYY6arYj72yXNUWo/UrYgnupZ/LMujy56YKqPSJ30NVxCuNpYfJvSIun+ye8ycrDUFMDGUjPB8sQKz45lSAQ+7XYgB6XSUcT1mL0Y8iIs9QW6aIOgLqZ6G9fA7yVgfArkISjp0pdQD4cdgETECrCUigJRAvqrROwXYqSGio3rq7Lxd7eQzTQd3n5jJS1bVuTn1SMpCs88lSTte/JxN2AtpVtboBT0u8StPD6zVx3O7FWHPbtz+eGH7ViWYuDlp6T09cZb53LnmDmcelolzuhZm7A1Clv9Akgi9uv49TuLHIPpTMNyfo2lVcefvQ+/8QCaqIPpfIOl5iHwIEXxkqgnMk6wuf648E+LIVQBfsz3emfsvUIQQlwFXAVQvXrRvs+/E0JkkqZPxnRm45F9Ckn/6bIXwp7o5sLLEcX0AkIE8Ot3YZqfE2evNJ0leLRUqgavPhQvhWsP8uPJcWcwbERLMjI8VCpAV9ypc3U2/n4IpdxJoFQpH61aV2LYiJa8+fpKolEby3KYPWsLDz3anccenp8inlKnbknemXQh6Rlenh67iHffTvrLH3t4AW+956YaarIRWizwrdERr7wWW/2MoDymmpxo47CMkP0r2G51b/GQKPZgqvfxitFE1DMkJ3AHr7wNTTZE0CAW0IX4DkSnPzaLYrQTkBqXsGO0IQWNQUEtAojaHxCyb0FSDZ/2EBH7GTTZGtP5Gpv5uPGMCvlmBBvL+R6PliS4Szc+xlLz0UQzpCiDUnnkmGeh2A3o6OJ8Ato7SFmSn5eHgGRls20rNqw/UOwT6tW7Dh9OTq2ENjwa/S9pzEeT1/Dic0tp36EqPc90s5QqVkpn3e/XcvhQuND3pG37qsz54crEa+nUBeUuTqRIrWKOw3J+IWjdimt4S+IaZIFHDI6R3EFAfxFH/YYU1VKqy/8tcIPK/+tR/P34PzMIQohZQFG16f+vvfMOk6LY+vBb1d0zs5GcJQqCoICgqIgKgl4Vr4KoqKiAKGbuNVwMmBXErCDqZ45cDChgREFFchBRMpIzLGHZNKG7q74/emZ2l11gURR3b7/PMw/MdHV19exun6pT5/zOEK116YHSe/sJPEr92rXWrwCvgLeH8LsGeZCYsg2mbFPqMUM0IiMZm1163kACISphiGPisggQdq/DlNOQwtPFsdUk7wEjr8CQ+44yAooJlxXlsSe6ct75RxEOO1xx6ScA7NkT5Z57T+XSy1txx63fArBtaz4/zdvM9t13ULvaU4TDLimpJk88fRbHtvb83hPGLS8ydmh7XK2SF8Rb8YXMmwBw1K/Yzqd4K4FEaGYYr1Rn0ZDFDASh+IPSoOgGdVS/iJcJmwj91ETVSEJcTbr1FvnONWidHTcHATRbixiDBN5KzhL9sfUbpYzaIcV4nrD7JLAJELjMwZNb2E5MfYIiC4saICWusgCvFoEWURw9EQhgyOIuYiEysERhPL+rl6LZhWekXBz9GSHRB0M0Zc3qH5MZ5BkZAVq3qUX9BhlUzXgCx1H0ufIYKlcOMXvWZh59rAsvv9ad/te0ZeCAz8naXoBSGtt2+O/oRTi218/wodOTBgE82eqUlAPXHwgaN2DII4EApiiZuAbgqnkkCiwJ0kgxh6J1BEueR8R5mqh6F0teQIrx4EHpc5U3KuKm8p9mELTW3X7HaRuBohVbjmDv8I2/MZ5/df/GIEFAXkvYvTH+LkaufSbgEpAXE1OjgSgx9QWZ1uwSG8r7YuXKXZx/7hiiEYePPr2YTqc24LvJaxBSUJBvs3RJFo6jSE8PJjOU4yNHCMGAa9vx0qh51K2bwUknF9ZVvv7G4xk+zKvv+6/bTmTQrV5sudZ6n3/wpmxNpjUbpcMIYWGr74m6z2OIloTdu5A0JWBcRkB2J+K+Qky9DLhI2sZlPnIoWmMgQaKqnBQ1CMg+RNz7ABtFCEoYAzDFWQSNgSi9ErvUClcSTQ5p5nDynYF4LiADT4E2hBt3BUbVS2SYczFEcwSVseT5WLIHijUIqpbq1iuKIVohqYtiZbx/iYirgtapm0EoxURr6D+gLed2b5ZMGAR4/91FpKZZFOTbnH3m+1SqFGTcZ72Z/MOVLF++kwfum8K8OZsRRaasCb2ig0UIiSU8SQylt4NOKyZY56jZRNTTeEERNUkz/w9wcFmO1jviEVQKW40hZFwXl/SomFRAe/C3k7+eAFwqhAgKIRoDzYA5h3lMfwoB42yMYhr6e4A8YupNCmfJOeTYrYm5ZSsz+PorP7NpYw5ZWQXJ4jCnd27ISSfVQwiIRl3uvnMyUgo+GHsRLVpU44abjufsc7yZ5LDHuzJ23MX0ueJYdu0s3Mi++95ObM66jd15g3n40S5MGLecpo1GUin1ce4ePLnUsYA3SzZkTaSoQtC4kMzAFJRehWYbit8QmAiRgSlPJSEWp4ngrSRKGkFJB9LNMQBorYi5iQxf8FYiNt7D1ivZCSFM2ZGYeg+ld8SPBSi6/yPIwJKnYYi2SOri/UnUBSw0eQjSgVQkDRCiUnxz9QKE8IyoIZoc0Bh430UK6dbXZJjTSDWfJ8P6Mrki7D+gLc+/cDYX9DiKUSPn0vfKccXkwBs0zEQr7dVf0LAnO8qZXd6jVfOXyMuzubBXCwIBA9OU1K2bTkZGgKHDzzjgmLzvUZcqPR5xXiPXPpUc+2TceFg0gNKJbGwHQTUQJnnOJYTd+8h3BiGoB6R6eymUvnqtCCTKFZXlVZ44LAZBCNFTCLEROBn4QggxEUBrvRj4EFgCfA3cpIsHsFcYXL0at9QqZACZSFriPbhsYnHJ7ANx2ukNCIZMUlJMzujmZZ8ahqTbmU2wLINo1GXZkh1kbc9Ha82kKVfx+FPdkrP8jRtyuOySTxj26FT+0e09du+OcELbV6mW+SSffLwUw5AsXrSd/ldNSCptjho5lxHPzipVNK80DNkWrb3kKyMepaT0IhIbxppleA/2kmU7Fb/EtaJmkGMfE6/sVpTaBMTNBMW9pBrvkGZ+SsQdhq3GElXPkWI8R0g+iBTHAhYB+W8yrFlxP3cq6dZEMq1FCFw8A2MSMu4hxRhBqvnWH5ZbEEIiZV0seW6x0qVSCi697BimT92I62py9kSTm/xSwpTp/fhkwiVccmkrAkGJYQgcRxGNurzx2s90OrUBQnj9NG1WlU3bb+OMro25ddBEjm/7Kl9/VbIwD4CrV5JrH0+O3dpLpCuCrT7E+znYOGp68nNLnosl/4EhWpNqPh5X8PUECTXryLA+I9UcSbr1VZlXtuUVXcZXeeJwRRl9CqWLwmithwJD/9oR/fV4YmqFRdYlx6JYhJfMpEg1nyTPuQhwseSFZerznO7NmDqjH7GYm/T/A1zRtzWTJq1h+7Z87n/oNDq0e41IxKFO3Qx++uVadu+OcEH3MaxZnY3rahxHk707wrhPlrJ+wx4cRzF86HSu6teGWEwli/skePThqfz443p+W7GLe+8/lYt7t9p7aEmeHXYu8xfkk7unOu//twk1aibE0l6MCwJWAsLs3hVg+7YgR7XYlgyJNfB0iCLuKzhOFMPQ8YdOwnjsxDRaYskz499x8c1iUx7n6fZzaalj8wxjiBRzGGHnXgzRClN0Ic/pgWYr4dxeBMT9VK2aQl5ejHVrs2lxdHUMo6Sh+HHKOpYu2UHvy1rtt9ZEUc49rynvvv0rhilJS7OI2S4pIYu0tACndGrAKZ0aMOT+U1myKIt+V43HdTRX9W3Nls25GKakIN9m3bo9AMyYvoH/jl5EQb7NdQM+Z93mfxe7ltYOUeetuCgfRN3XMMTxxNw3cfVCTPlPYmpk/Ng7oBVB80pP+dZ8JtmP1E5ckG8yQtRGk4clS997qGiUt9l/Wfi7RRn9z2CIEwjIy3H0LFKMOzHEKXG9/bmEjHvjCqoz0BQkK1CVheYtqpf4rHLlEOM+8+LYV63aTV5+jGjEZdXKXcRiLuM+WcbyZTuJRBxq1UrjyKZVSEuzuHXQNyilMU3BOd2bAnBcu9pcf+PxjBpZmAho2y7ffO1p+g/o9xnnnteMtLTS91LefGMh27fVJy3NYvq0DfS4sEVc6G8O3kZljEWLZ3FW559xlcsV/VfxwGPTAZNU80UAPnq/E7fdfBQ1a4eZ/GNnMqs/HFc9NVFK8uqr8wmHbQZe3560wHvE1Oj4rLxQQlxr7W0Y698IGgOKHbPkaVgBL0XGUdPjm8EOSnzCMc2r8/nXl9P7oo/J2RPl9C4N+XBs8ZrA83/awsU9P0IpzfhPlxWrZLY/nnzmTPoPaEvt2uns2hVm4ter+Mc5TQmFCv9MGzasTMOGlVm59hYcR1OlSgjHUVzSuyU/z9/Kk097xrBuvQy01qSkmDRqXNylpXWUPPt8FKtI6DeZ8ixcPSO+PxBB6qYYdMdlPJrfiKhHsfQZJbLmhTAxRDMc/SVKLyLsPEqa9WKZ7rc8ozl0WkZCiLOB5/GWya9prYfvdfw24Bq8cLgs4Gqt9bpDc/Xi+AbhMCGEIMUcknzvqJ/jxVdiRN0XCBj/QIhKCDxJA63zKXDuQLOdFPNxDNH0d123SZPKXHbZMYwbt5ybbzmBYNCk7XG1kzV5e17UgieeOpPqlZ5MuoG0hn/d2iHZR8+LWhYzCO5eTr05czbTpUujUq9/9YC2PPXETNLTA8WkEjx3jGTpkmzO6DQ/GQL75v81IZyfzpB7b6TSEZVxHMWTw2K4riR7Vybff1udPld9SNR9GSma8sbLmTxw33dopdm0KZfHn+xWTOE0gaOnEXHvxwv7/ZQUc3ipM1vXacXuXSHSMyJ8NLoZrqsZ98kycnNihMMO336zGtt22ZFVwOzZm3hq+AyaNvPkw6NRlzVrssnOjhRbJezaFeaBe38gLc3igYdPT0b/CCFodUxNAKpVT6XZUfv2wWdkFLpjTFMyYtQ5xY43aVKFb7+7kl8WbOWfFzQvdszRM+PGwJvjpplfYMomOCphlDWKjcBve101tdSxeMbU2yiXouY+x1zROBS+7Hji7SjgTLygmrlCiAla6yVFmv0MHK+1LhBC3AA8AfQ+BJcvgW8QDgMx9zOi7vOYsish4y402UTcR/HiuhWUqBsAMfURjv4OsAk795JqPoPGLpE9fCCEEIx48RxGvFj4AGnXvg6PPdmVMaMX0bVrY7KzI5x9zpFMGL8CgGDQILNSiNmzNvHTvM2kpVmkpllEwg4pKSb5+cVVSnud/yFff9uHDid5s8ktm3PZti2fNm1rcc99p3LdjceTmRkoVVDttVd+LpYP4bow5r1aHNlkO3cMbsqlF3/M1i2e1r80JCefUh8pqpJiPgBATs50lKtRSrEnu+Q+RCGJYwpNFgXOjTw37Ek+GrOBQf/uwMDrPYXZD0av4+7BFxMIFrBzRwp16oa45tp2fDtxNYsXZyGAjie8weo1u7Fj3gN20aLttGxVg0ULs9iyOY9jW7zErHkDqHeEF49/z53f8eGYxRiGwHYU9Y/IpEevFjRqVBnbdhk+bDpZ2wu4/8HTqF6j9IdwWWjdphatjt1FvtON3FiQNOstpKhPxHmZhDEwaI0pvXwDU3agMPK7MKjAqyPxHFKUrmNkyYvwNuFzCchLfvd4yxOJTeVDQAdgpdZ6NYAQYgxegm7SIGitvy/SfhZQetbrIcA3CH8hMXciMfUerp4NOMTUZgKyBzE1AVcvJKFvlHCNFEN7ip8Arl5Ort0N0PGavz3QWlHgDIqX3by5WEnO0tBa8/yzs5nywzoG/etEBt8+iVjUpc+lnxAIGMRsl7792tCiZTW6dmvCzOkbuKrPOKJRF9OUnNyxHldf04527WvT5bR3ikUlOY6i75XjSE21WLVqF2gvxPf4lYQAACAASURBVLVmrVTq1cvEcRTvjO5ZrHhPghXLd5T4zDAkHTt6m7A/fLcWx9EEgpK2bWtxxqnvMPjujtw8qAPvv7uQ556ZReUqITqeUp9Hh3kz/uxsLyu66CzdFF0JyBuJqecAlw3r0njhuSVEo4r/3P4tzZtX4/QujahePRXXNcjNTef8Hk15d3QPwmGHnNxo/F41K1bsLOY+UAoWLcyK/18TiTq8/toCrr+hPTVrpSWFCAHeen0BWsMLI+bw29pbePvNXxj53BwcR7F7d5h3R/fc78/xQETckWg2o5FE3XdJMe9BiioobQImAWPgXmcUNe4mguqkm5P3q0UkhCBg9PhD4yyPHIRBqC6EKCq29ko8jwq8xNsNRY5tBE5k3wwAyhZ2+DvwDcJfhNZ5hN1BFP7BeVIYQlRHUgcvUcskIC9OahsVnptDVL1d5BOHhPSDrSYSMHqg9JJ4jYAwUfUEQX0NQgi0tnH1AqQ4EikKK6HNnbOZ4UOnUVDg8OuCbRgyMTMUOK4mFlVMm7aekS95K4nPP1uR1MtxHMW6dTn0uthLmlu17hauv+Zzxo9bTiTize5jUYdNGxNVu7yn5fZtBezICqO15oF7v+eCHi04pVN9fvxxHfcP+YFTOtXnkktbMWvmJmzbRSlPjuHa69rRsVN9HEeRkmoRi0VRrmbWzE24rubeu7/n5kEduH/I9+Tn2aDh0staUb1GKt9NXk3vXmPRWvPB2IvpGo++8lx2N2GpjkTdN6hT4wxCobXEYlG0ggvOG8PwJ7vx1hu/0PGU+nTu0oirrz0OIQQzpm9g547C2g8JY5DY/N7bt6yV5sWRc3j91fn8uuR6Hn2sCzVqphKNOLw06iciEYfduyNo7bl/hIhLh5h/PAjQFCfi6CmAxpReidRU8wmi7vtIURtLnrPXGdUhLtdhif4Yoim5ThsENUi3PkGKwmAFr0BR5KD2uCoSB7GFsGM/4nZlTsYVQlwBHA+cXtrxQ4FvEP4yEtm5DhAkZNyNKU/yEqyMKxCiMhDDkiVnWraahCYr2U+K8SAR91E0NkFjAOCV3RQE0AgMcWQylDTfGYCr5wGKFONxAoYnOZGREUBr74FbuUqI0R/2Ytwny+hyRiNuuekrsux8br39pOQY+vZrw5ef/8aypTupVi2FF14+h5HPz2Hxou3cNaQTr755Pq++CWNGL2LunM2ccmp9+l1RMiHdNAWxmGbCuBV88/UqhBA4jottayaMW07PXi2YOW8AGzfkcM+d31GlSog7Bp8MwPp12eTleglXSmlUfIpmxGfbp3SqzzffrPZ88cfWwLZd+l85IemCeuuNBUmDkByPPA5TjiStMkyfvYfWLV9Ga6/v+4d8TyTisnZNNrfecRLp6d5G+THH1ETt9dS3LE++wbYVUgqk9L7jatVTWL1qD7GYg9bwycdLqV49lbvu6YSUgp/nb+XHKesRAn6at4Ur+7Zmz54oO3YUcPt/vPtO5An8nqzfoNkXQ7VGEMCQreL9ZBAyry+1vSFa4OqdQADLOIWIMwxw0OzCVt8mNboc9Qv58frPIeNhgsb/hqsowSF0GZUpGVcI0Q0YApwe13r7Uzgs8teHmr9K/vqP4qqlxNTXBGQ3DHnsQZy3gjzHcx1Ysgep5tB4QpFCCJXUVVI6C1cvwxQnJJf4e2JHU+gvNzFER0zRlqAxiMmT1jJn9iau6tuGI+oX6s1orXEchWUZxGIuD9z7A1nb83n0sTOoXScdgK+/Wkm/K8YTidi0aVubKdP7sW1rHrt2hTm6pRex0/3s0UyfuiG5OW0Ygrr1MtiwvniVr6K0OLoac+aXdHc9MXw6jz40Nfm+7XG1WLZ0J0pr2rWrwzffXYHjKGbP2ki16qn857ZvWbc2m40bcpMS4YYhePPdC+jRswUFBTYLf93OMcfWKBYRdXa395g5YyNSerUlVq/OxnEUjRpXZsacq0lPD/Df0YsYdOOXRKOF/V50ydFMGLeCcNjhiCMyOaljPT7+0NNaatAwkz3ZUY5uWZ1fftmGQHDToBO474HT6NzpLeb/tJVA0OCRoV244abiE8kZ0zbQq+eHpKZYfD2pz343mktD6V1ovQUpjk7mUThqNhH3RSzRjaB5JVrn4ug5GMIzHFF3NIZsiCXPJeKMiGcfG6Sbn2DIowCIuCOIuiMAhSFOIt0afVDjOpwcCvnrGqKJvpCyyV+/wmX7vJ4QwgRWAF3xtFPmApfHc7ISbY4DPgbO1lrvvdN/SPFXCH8hhjyalANoE5V+3lFkWN+g9BYkTYg4L4BII+aORJNPijGCgPEPpKhRLHwSIChvJaoSUWwOrv4RV8/FkM3pduY5yUIrRRFCJDd833pjAa+/Oh/bVuTn2/z3o16A59pItBUCnn5yJo8Pm44Q8O/bT+LuIZ0Y81Evnn5iJiOem43jaFxXs3lTLkKCFALTlPEZdeGMP7EC2JsXRxZPnGp1TE2ef+EcFi/azgU9myfHdEqnBrw0ah6zZmwkGnWTqwfwROPefuMXKlcKcsN1X7J7d4TatdOZt+Da5P18+llvxo9bzpFHVqVps6oc1XgkjgNZ2/OZPWsTn09YwYRxy3HjQjaZmQFGvHgOp5/ekM5nNGbalPUMurUDvXp8mLzu5k157ModzLBHpjJv7hYcR7Fksbfiu/+h0+l3xXhq10njootL/m4898ws8vNsCvJt3n93IQ8+0rnU76c0lN5Ern0OXi7LeaSajwOQ71wL5Hm/B+pEws4NKLYhCJBh/UjIvCHZR8gchKW7I6iMFIXGyJLdibqvAxGCxjVlHlNF4lBEGWmtHSHEzcBEvFCtN7TWi4UQDwPztNYTgCfxqjt9FF8lrtdan38ILl8C3yCUE6SohxT1yLP74uqEGriXHB9TrxIw/lHqeSFzIKY6kaj7Mq7+DU0ifPnA0Staa7Zty0NrkIYgPaNwJt21W2OGP9WVJYuymP/TFoY9UqjJ/9n4Fdw9pBMZGUH6XNWaF1+Yh+N4SWKuqzEMwZV9WzN0+Bk8eN8UlNbkZEcY9+lyAkGTrO351Kjp6efk5ka5ddBEdu0qlMsOBg1uH3wyTZtW5bh2JfUTm7eohpSCYNDAtov/2cZiDr0vGks47I1nw/o97NoZpmattGTluUsvK5SI7nXR0Yz9eClp6QFcVyWTvaThuYUKwg5XXzUehODZ58/ipVe7ez8vWWiIqlTxVmsDr2/P9Gkb2LMnysOPdgbgjK6NWb/l38yYtoHfVuyiavX1uHoZljwLIdLo/s+j+OH7dVSuEuHc88PF6nQfCC9QQQFhHPUDAI5aTGEEkUZgoViLlyXuotiOQXqxfhIKpnt/lmnNB9wDijlWRA6hywit9ZdQTM8drfX9Rf7/e3Thfhd/Ny0jnwOSQ2H5Rw0EsOS+I1HC9jMUODciRRPSzDEE5HVYoie2+x1KlxSDK8rI5+cwasQ8lNL06NmCp587K3lMCEG//m154ukzWbt2D7atEMKbpd91zynJds2aVeW+B0/juHa1aNS4kqfHgxejn5ER5OGhnTnzzCYsX74L21Zs3JDDB2O81fLP87fSrNELfDjGi8ALBg1efeM8Nm2/jaZNCzfIt2/Lp98V47jlxq/4ZcFW3n9nIX2vbssjw7oQCBT/FZ8xfWPSGAgBF1/Skho1U7n/3u+pW+MZTjjutWLCcG3a1uKEDnV54+3zadGiOmhISTWpVjUF0zTiIa6gXM199/wAwI6sAjZtyk1e45N4UmD1Gql8MfFyps3qX8z18/abC7jwgg+44/aXyY70JOzeS77j7Q317d+GmfPOZ/aSj2je5g4KnFv2+zMriilOQdIAMAka/8J2p5Dv/JPE3NYUPTFkY4LyjrhgX08kjffbZ1GEMP4njUECLXSZXuUJf4VQzkgxn6HAHohiDSAwxZkEjdIzYSPOx/EazXirCHkBljwzvhkYw7WnkxGYhKMWEHEfxxTHEzRuS25eTv1xPQUFNlIKGjWqRGZm6do0o146h8F3TKJDh7q8+Er3YvkFy5buYOxHS6ldO4P3P7iQIXd9TyTs8FDc9XHuWaNZsXwntq2SM/qvvlxJv6vb8tmE5YTDXlSWEFC3bgYX9PSE3BYt3E5GZoAGDSpx9+DJjB+3HMOQfPH5b+zIKiA11WLMx72YOvNqxn+6nFde/ont2wtQWmOakoaNKjHu895YpsHq1dm8+vJ8lNJs2pjDvLmb6dylEXNmb+Seu77DdTWXXvQxF/duxaj/O5dIxKHLGQ35vxfn8+svW5k8aS0A6eneXk5mpSA1aqSRmxOlUqUQLVuWzB4vytw5WwiHHarVyMZ1wTTDKL0mebxB420UOC4QxtHT993RXgiRQUagcOKZE+tU7LgVl3IPmTcQ4gZ8Dg5fusLnsGOIxliyN1H1NKD2q7SpKZrs6JLvDMQUp+GtMBQ6XlmswLkJzRZc/SumPBlTdATg7iGd+HXBNtIzAvS7um2p15gwbjkjnpvDoH93YMC17Uoc/89t3zL/py0EgwbjP13B2+8Vj6JasjiLaNQlNdWiYcNMli3byexZm3jo/im89YYXo29akqGPdaH/gOMIhUxGPj+HRx/6Edt2cV1N7drpGIbAMASBgIGUAq01AcugeYvqDL67Oj0ubMFFPT9k/bocHEdRq1YaSxZlcXlvr1ZEYk8gEDD4ZuIqApbBpEmrk5/n5dm8+foCPvpwCRu33oqUgoce7YzWmofun8LP87cmFUYDAYOZcwcw9qMlfPH5bwwfOo0bbj6BUSPn0rBhJfr2b1MsYujWO05izqyNbN9aBTu2kGBwI4Yo/C5NcQKS2ijWEZQ3ez9bHQGsMrmPtHbRbMIQLXD0NjyZ8XYEjD8l2fV/gkPpMvo74RuEckjQ6AvE0BQQMkrO7Fy9hrDzAIJMoCmQDexGsz5excwLkZTCk44QVI2HtWoEhcli7drXYfnqm/c5jljMpf9V47FtxS8LtvKPs5sWi1YCaNqsKnPmbEIAjRuXNF7Dn+zK0Eem0bVrI1LTLNas3YMQsHjR9mS4aI+ezTm6ZQ1OPflN2revw7p1e5JuH4DsPREGXHscLY6uwRndGjNq5Bzat69Dx06F0XxHNa/Go8PO4Ko+4wCvPOh9Q75PPvATFBTYjBoxl9de+ZlHh3UhGPQ2vjWg466haNRh+/Z8GjSohBCi1I3eatVSGPbIVHbtijDl+3VM+mY1CxdmEQhIKlcO0ePCwlyTI4+swpyfr8XVq8mzvWpyjv4GraOeuqv+DsUWIIBmM1H3fSLugwiqkm59tl+5CK01+c4luHoxgiNJMZ7GEE0xZMt9nqP0dqLuCwgaEjSurtBFbv4IbrnTMj0wvkEohwhhETJvLPWYraZS4NwM5OLlO9xHQF5Ern0yushmoje/8WaXadYb2GosUrQ6YIW2ohiGID09QE5OFMOQpKaVrMj1xNPdOPHketSsmcYZXQv90xO/XsX0aevpd3Xb5MoiFnNp174u1aqlEAwazJ3jhWNfevkx3DDwCzZtzGXjhhxu+8/J/PrrNmJRFyE8XZ8h959GZmaQ66/5nNHvL6JL10ZePH9WATf/qwOZmUGU0liWJBp1WblyN82bV2PFcq8am5ReGUrlarSGSNjhnjsnU7VqCgMGtqPJkZWZ+OUqLuh5FA3qPEs0qjjm2BrMmDOg1O9Ga53cCFdKk5dvo5T26sZFSq/pLKmDoDKaXCSNSBRbUnoj3qrOxtVr4xvELppcHPUDgVJyAJTeha2+wuBIXP1rvP1KTHlCMpHMy5x/HUteSNAoVIAtcG7F1XPwKsHVKVb5zcfD28HzDYLP35wC5zqKFrCXohpCBEi3PsNRUzHEicTUR2i9mZA5GAClV2OrGVgiFeSpZb6WYUh+mH4uyrqeSpUcKqV1B1qwc2eY884ezYYNObz25j+LRe0ArFi+kysv+5Ro1GHsR0tZvNwzboGAQd/+hSVKl6zwPq9RM42mTauya1cYpTTdz2vGf+7siOMoFi3cTpMjq5CXG+O7SWsY/f4iAKZOWcfM6RtxXcXqVbt57a3zaXF0daQhSEuzaNeuDiNGnc1jQ6dRq3YavS5qSVqaxY3Xfcm333jKrbGYYs+eKLVqpXHxJa24+JJW3DDw82T+waKFWcyaubFYdbkEQghOPLke8+dtIS0twOgxF/LE8Bk0alKZS3qXPjsXIoUMayKuXu7lBMRn5gF5GY6ah2YXKeZ92O4XRNUoPJXSk0rtK9++HBWPKDPEibh6JqbogIhXtdU6msycd91fsWRnpCgasVXxHnaHGt9l5PO3R1ApvjcgCRn3YQovMkiKugSM3rhqLVrnIWiOwIvUKYxLn4epTkomH5WF2vU/I6Y2AC4R9xnS5CuM/3QZq1buJhJxePC+KZx9TnFl1kjEQQhP4iFcUPpsGUiGngL896NejB+3nKOPrk7LVl6uhWlK2h5Xm507w5zY/jUcR5GaaqIUpGdY5OV6s/JE1FCLo6szbWZ/Vv62i65nNiEQMHjq2bNwHEVeXozKlUP884KjmD5tA7GYN65YzGHqj+s5o2tjGjSsRLv2dXj/3UXJcX03aU2pBgHgy4mXs2hhFk2bVSEjI8jrbx84dFyITExxwl6fZZBmvZJ8b5i3ENC9EGQiRPreXQDEXUxRIIRJN1LMoUjRoIj7x0SQiiYPr1xoSvLcVPMZou4IBA0x4+U0fUqiy+pJK0e21TcIFYx062NiagKmODGpXZPA1b+R53THcz9IcHNJMf+DIA2Np80jxMGpa5qyFTFlAQEM4W08t2ufiRBRUlIVp3cpWRe5dZtaPPpYFyZPWsMdgzuW7b7SA/S5ovTs7o0b9mDbLgUFDqYpmT67P/WOyOTxx6azbWsejw4rLCfZ7KhqxUI+d2QV0PHEN9iRVcDguzty1z2daHZUVZRS9On9KdnZUT76YAnfTlzND9OvYsC1x/HGawtYvCgL05RkVtp3VTDLMjiuXW1+WbCVpUt38M/zj9pnnQitHQqcf+Pq+YSMewkY+3fTSFEXrSM4ajaGaIEQlYodTzWfJeI8iSaHqH6KqPMsGdZkBF7EkxAGadZYbPU1ljit2PlS1CLFrPA1qv4QntO1HD3py4gvXVFB0HoPrl6EIdoWK4pelJg7jrA7GM8gQEBeSYr5EEqvJ+Z+ihA1sNU4DNGKkHHvPiNYXLWUsDsMQ7QkZNyJq38B8r3SmKIArfNYsfpBtm8TtD9BUyk4+5Dfb3Z2hLsHe/WhH3joNAbfMYkpP6zjzrtP4foby65KMO6TZdww8Avy821q1Urjt7VenL/WmtrVni62eR0ISHpd1JIHHj6dwXd8S7VqqQx/smuynkFpLF2SRedOb3supJPqMf6L0qu1eXs/NwAFCKqQGfipRBtXr0Xp9bhqPTE1Eo0NxBCkkmF9X+rPfU+sFV4iWipp5puY8oQSbf7XOBTSFVVEE91FPlymtp+qK//w9f4q/BVCBUDrCLn2P9DkIalNuvVNqfV/LdmVqGqB0suRHE/IuA0AKRoQMv9FbqwbitW4ejGm7Iglziz1egXOIBSrcPUMbDWWFOMRlN5IRD0DSAKiD/UbRqnfMIYlexFzPyPsDkZyBGnWh/vU1S8rSu9izAdv8dUX+eTmBvlwzGKU0jw8tHPSGGgdQ7EOSaOk1lNpdDylPqmpFrGYy5V9Wyc/F0Iwdvwl/PuWr9m5M0xuTpRYTDFu3DL+7/XzeO+/ZStrunZNNkIKCvJtVizfmfxca42tPkHpLQSNqzBEI7x5ZypSNCHqvo4hjo3XKIiv7uxEyG6Eon4IjYOtvsQQXXH1j5iyfbJmc0j+h4h6BlOckFzB+fxxvLzu8j+Z3hvfIFQANDvQZAOxuAxBhNKkKYTIIMOaUOJzR/2C0isQohroDUCMmDsBU5y2j0LpCb+1RrOLAvemYkddlpNp/YhiO5IjybO7AlEUm7DVl2idBaQTNPohhEnUeTNeOvSWA0Y5aR0hzz6bXpfn0rFzkC4dLkvWgX791QXcdEsHtHbjBnI9UJkMcyZSlu6qqVkrjSW/3cie7Cg1axWfYXc6tQHzFgwkPz9G505v89uKXVwzsLgbTuntOGoyhjwRSQ1s9TWGaJUM6+x2VhO6d2/Gzz9v4alnCzO9Hf0VYfd+PH2pxaSaT5FmjkOzirAzkoj7JACWuIiA0QulN+NtYyaECgMUGgUn3tdj3nHXIMP6ESkqEzT7EaTffr9Tn99HRXQZ+QahAiCohyXPx1ZfEpBXHHAfwFaTcdUSAsalaL0znrkMkmORHIliBY6eREyNJmj0T56ntU4WQ4m4iyhd3ktgyc4IkYmBl5NgyjOIKS//wVGzcPQ3eKopBqZsTUQ9BYTJdxaSGZhaSp+FaHaiySEQjFG/YYz/3NmOF0cuJBDMZcC1bYq0SWg27cbR4wiwb3nmYNCkZq19/ymkpQWYM/8aCgrsYnsAWmvy7PPR7ABXIjgKjRehlG594SURWkapm8la5+E90F1cvZQcu2285sDnwE6IJw3aejS28wnp5iQEtZP3JahFujWaqDuGmHoJz1DE4n0GvTGx76RFnz9OmTeVyxG+llEFQAhBqvkElQKLSDHv2m9bRy2gwLmFqHqOXLsTtvoaL1EtgmZjXDPfAAQivsrQOpfc2D/IsZuRE+uOqxbgPWxEsq33qyQR1CpW00HpLbj6NwzRlnRzfHzFkdBhiiFIvJcI9l2VK3mv1MWSFwOZBI1e3HjHMyxcO4Z5y9+j7/XPepLg1AASWkcSQbOyfpX7vq4QpWwIu/GEPoVXM2ApiZBfpTawPyzZk4C8HFOciSY/3lcOjppJqjkCQdGVkvKub72OtzqzCBo3IUU9gkYfJM2RNCAgbkZQj4Dsg6SkIJ3PoSOxqVyWV3nCXyH8j6HJxXuAeSU5o2oMljwXV/1KivkAhmiDpD6CKvEHLzh6OopNeH8CS7H1KoLyVoLGlbh6BUpvRYq6SOohRCU8iXePiDMcV08HJLaaQED2Q+ndSBrHXUZBUs1ncNTPBI0rDzh+z/g9AjxCbuxMNKtAeCbJ1fPRZCFFLTKtH4i6YzFlc0x5XIl+XLWEfKcfIEkz3zmoUNvCsZgYdMYlUfJW443EJqpewzJO28+5FinmvQCEnceJqTcRBDDl8UhRC4GVfJSY9Ewmk2Vac4BoMipIitpkBL5I9pvCrQd9Hz6/Dz8xzafcY4pOWPISbDUasDDEUaSaTxZrEzIHFXtviNZ4KwGDxGze01CykDShwB2EZieSmgSNIViyazLeXYg6oL2ZtdaCfKc3IJCyOpodOGoppjgdyzz4eHcpGsWzeL28C6/AS434ddMJmX0pcO4j37kaQ3TE1fOQVCbN+i9R97W4WwUi7lNI1YyAvOCgDUOq+Ty5Tusin1hALF43u2ykmHcS1FciqIIQXj6Atyfk4TKbPbFjMUUnUs0X9lvf2OevoyImpvlhp/+juGo5rl4Zf3gf+AGjdDZKr8N2Z2PISkjRMr73kKgRnfg3RMh4iKDhrS60trHVJ0AKMXdCfDat8WqLZwMaQxxNuvVRmcattY2jJyOogyGOwlafg26AlCHPdSIL70XpXeTaJ5EIs/UwsEQfDNGGiBpCwo/vtckg0/q51Ait0nDUfGLuO2idicu3SE5F8wuKlRh0Q8qqBGR/pAghqJXcoPc2iFP2G20Vdp4jpkZRXEYthXTzAwx5zD7P8zkwhyLsNFM00h2MB8rUdrJ7tR926vP3Qes9aAqKFUM3ZHMMmpe5DykqI0VlzLhkctgZColkNqqhSYRU2ii9OnmeEFY8Q3o5LtNJRMYExKXE9ItAGFcv3ed1bTWVqDsCU3QjZF5H2B2Crb4ANGnmWwSMiylwbsN2vkBQkwzrq2T2riATQZ342Ew8/34MW7+Do2uSZnxAxB2By+T41WJ4D999GwStcwk796J0Hi4z430GybAmE3O/JKq+AAK4fI+rHGw1HlAIqpJhTSSmviTiPoDnqvpv8vvcm5BxIwIV1yv6CcVqBIFkOKnP4UeVs1oHZcE3CBUcVy0lz7kYcAjJewiaVx2Sfi15FjH1PqAIyoeQohZRNRRILbWkohDV8FxOIQQ1CJnXo5xluHoWlryMnFhHBOmkWW8XM1yJZC1XL8ZSp+GqpXiJVqG44emArb4EbDS7cdRibP0xrppPyLyPDOuLeMLeMYTd4XFXWSJUNwuXKYkRkmI8XWz/ozSi7pvY+msKV0RebyBRrKIwLDQRghKJt8jB1cuw1cd4hkfgqO+TBiHijCSqXsYUp5FqjkKIACHTyxPROoKrf0KK5iUykn0ODxU1U9mPMqrgOHoa3gMoRkx/esj6NeUJZFgzyLCm4vIdBe7luHorSm8k1+6Go2YWay9FdVKMYZjiTFLNN+LSCSPJDMxF6YVotqJYTcz9oNh5nhy395AWIpMU8yEEdYG0eAF5jSV7AxJJXSAXW32FYg1hZzBCpGPKk7w9BeM6IBjvtzKGaBcXe0uNh+6exYEQJAxbAoOgvAYpant5FKIdhmhPSA7Bkr0wRY/42I5A0hRRJIdD6Rzvf1oTVc/hFcCZgtLLi19ThDDlKUix/0I7Pn8tuoyv8oS/QqjgmPIshPsSmjyC8tpD0qfS2xFUShbn8Vw4NrATjcITunuWdHly8hxXryTs3gVolLOGjEBhgpwhTsLRcwCNsZcLJd36AFt9hiFOSNaV9h7IO4mqERiiNanmQ2h9B5DmJea5Cs9HX7wcpBT1ybSWoNmQ9Ol7K4iFGOLYUlcHjlqArcZiye6Y8iQCxmUIkYKtPsfRUxFUJRDP1ZCiLunWhyX7cK8i372SXCdRfMjE25hPbIALDHEsrl6JIIQUpYvl+fy9qIgrBN8gVHAM0ZAMay7g7CPr+OAIO48RU28hqES69SVSVI9HLb2PoDqa3UAAKVoRcz/GlN2QonI8OzmR77C1WJ9BYyCmbIcgvUSmshR1seQ55NlXnAeGOQAAB4xJREFUABFSzVcp/LWNEXGHYxmfI0SGd780Id0cmyxUvzdCCAQNirzPSFaI2xuto+Q7fYAwMfUxGdZMpKhMwLiQgHEhSu9GkF5MGkNrF6WXIUV9hPAS82z9A95+i0bpMAF5NYIQAdkHW/2AIZqRZn6Aq3+Ou4Uyyvrj8DlMaLzMk4qGbxD+B/BE6g5carEs2Gosnr++AFfPR4qz4jP024F0FOtRai0F7o3YgHRfJSMwEUOc6On667mkGMWT54QQJSSfixJ1x6DZBGii7ihSjP9Q4A4CHBSrS7Q35NEYlL3QT1G0zkWTXWTzNhHhU7JoYmlRQgXOdTh6BoJU0q1JSFEZS3Yhpl4FXILGpQSMC9A6hzz78nhtbEmG9e0+axv4/B3Rfh6Cj09A9iGqXkRQCaPIQzwxGzZoBCIXbzUQRrEhflwmE7H2hdJZcV2g9hjCyy7W2sFRiVBVA1N0wpTdMFUXHD2LkLztD92Prb7GVcsJGH2ACLl2dyBKUF5LyLydNPN1ou5oAkZPpKh6oO5w9HQgikai9G9IcQKmbEOmNRNNDCmqo3WUXPvsIiulVJReu1eBGp+/MxV1U9k3CD4HRci8laAeAKTuMyJHimMIyN446geCxr4f2EpvpsC5C0Eaqebj5NkXeVIQriTD+gEpqqP0qmTlL3AJGH3iG9L/t89+tS4g3xmI0mtINZ/AlKeU2s5RcyhwbgNsHD2VgLwcLx8hRkyNRztRYuptTNEFU3Qp0/cTlNcTVSORNEdQz5PSEBIhMpNxR16E067kOaY4EUO0L1P/Pn8TRMUMO/WjjHwOGiEy9xueKYQgxbyfjMB3BIzz9tku7AzH1TNw9GQizuvxGXMEUGgdr3UsGsYje0IY4sR91mgoiq2+xtU/o9lC2Hlwn+08GQ8BuGidiylPQ1AJMAjI/sTUa3jGYkrctXNgQua/yDBXIEUV8pzTybN7oHXxqnCeHtN5QIiAvJo06/X9SnT7/P1IOBDL8ipP+CsEn8OGFHWTshZS1iWFp4i6I7HkWci4y0iIEBnWNzj6V1z1E7b6AUt23m+/nrtJAyGkaLHPdqboQlBeg6sXEzIGI0V1MqxpeBFTAWz1aTwhLCMe0lo2hIjhaM/NpViBYgMGjYscF6SaTwFPlblPn78fvsvIx+cQEjJuxxBNgFQs2R1bTUSIqgjRpEjtX6/4fNQZjqsXgzL3m+ELYMhjCYh+xPRrOPr7uNpqScVTISQh89YSnyVyFdKtj+JJbUclNYbKRhBTdMbRU5E0Q+JnF1c0vAI55W3+f2AOi0EQQjwJ/BMvY2oV0F9rnR0/djcwAE9gZpDWeuLhGKPPn48na+HVKdA6TNj9F2Djuguw5GnFErG03o3n37fQ7Dlg3w5T4+0FjpqKYRy8BLYQQczf4dv3VgCeeJ6gWpm1kXzKFxVxhXC4flO/BY7RWrcGVgB3AwghWgKXAq2As4EXRVmcxj4VAANvZu4VzhEUz5lINUdiiI4EZX9MceoBewvKAYCJIA1LdvszBrxfhBBIUcM3BhUYvx7CIUJr/U2Rt7OAi+L/vwAYo7WOAmuEECuBDsBMfCo0QgRIt8Ziq4lYokuJ5CxDHku6fK/M/QWMHljybMA8oD6Rj8/B4oed/nlcDSQEbOrhGYgEG+OflUAIMRAYCNCgQYPSmviUMwzR7He5dvaFXzfA589EVcASmn+aQRBCTAJKy7QZorUeH28zBM/R+37itFLal2qGtdavAK+AVw/hDw/Yx8fHp4z4K4SDRGu9X8etEKIvcB7QVRdW6dkIxUIyjgA2/zkj9PHx8fl9aDR2BYwyOiw7XkKIs4E7gfO11gVFDk0ALhVCBIUQjYFmwJzDMUYfHx+f/eFvKh86XsALKfk2Hm8+S2t9vdZ6sRDiQ2AJnivpJq21e5jG6OPj47NPytvDviwcriijpvs5NhQY+hcOx8fHx+eg0GhccWhcRnGPyfN4sdevaa2H73U8CLwDtAd2Ar211msPycX3wg+S9vHx8TlINOCiy/TaH/E8q1HAOUBL4LJ4PlZRBgC74xPpZ4HHD/0defgGwcfHx+cg0UBMqDK9DkAHYKXWerXWOgaMwcvHKsoFwNvx/38MdBVFtV0OIX+HPIQ/zE8//bRDCLHuwC3/NjQA1h/uQfwJ+PdV/qio97a/+2r4RztXetPE3MjdZS1yHRJCzCvy/pV42Dx4eVYbihzbCJy41/nJNlprRwixB6gG7Dj4ke+fCmEQtNY1DvcYDgYhRJbW+vjDPY5DjX9f5Y+Kem9/9n1prc8+RF2VJfeqzPlZfxTfZXR4yD7cA/iT8O+r/FFR76283FdZcq+SbYSnw1IJilRYOoT4BuHwcGC5zvKJf1/lj4p6b+XlvuYCzYQQjYUQATxxzwl7tZkA9I3//yLguyLJvIeUCuEyKoe8cuAm5RL/vsofFfXeysV9xfcEbgYm4oWdvhHPx3oYmKe1ngC8DrwbF/vchWc0/hTEn2RofHx8fHzKGb7LyMfHx8cH8A2Cj4+Pj08c3yD4+Pj4+AC+QfDx8fHxieMbBB8fHx8fwDcIPj4+Pj5xfIPg4+Pj4wPA/wMy1MQupG9KLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fa3ce5978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show figure with colors\n",
    "plt.figure()\n",
    "df_new.plot.scatter(x='X1',y='X2',c='y', cmap='plasma', s=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.787139</td>\n",
       "      <td>1.565062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-3.506047</td>\n",
       "      <td>2.317350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-4.229388</td>\n",
       "      <td>-2.612948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.980246</td>\n",
       "      <td>0.090494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-3.380691</td>\n",
       "      <td>4.589728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id        X1        X2\n",
       "0   0 -1.787139  1.565062\n",
       "1   1 -3.506047  2.317350\n",
       "2   2 -4.229388 -2.612948\n",
       "3   3  0.980246  0.090494\n",
       "4   4 -3.380691  4.589728"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_submission= 'test.csv'\n",
    "df_submission=pd.read_csv(file_submission, sep=',',)\n",
    "df_new.head()\n"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}