Week9 - Decision trees for regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees for regression tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, r2_score\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_validate,\\\n",
    "                                    cross_val_score, train_test_split, ShuffleSplit, validation_curve\n",
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.tree import DecisionTreeRegressor, export_text\n",
    "from sklearn import tree\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(306)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "features, labels = fetch_california_housing(as_frame=True, return_X_y=True)\n",
    "\n",
    "#train - test split\n",
    "com_train_features, test_features, com_train_labels,  test_labels = train_test_split(features, labels, random_state=42)\n",
    "\n",
    "# train ---> train + dev split\n",
    "train_features, dev_features, train_labels, dev_labels = train_test_split(com_train_features, com_train_labels, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean absolute error on the training set: \n",
      "0.590 +/- 0.005\n",
      "Mean absolute error on the testing set: \n",
      "0.593 +/- 0.007\n"
     ]
    }
   ],
   "source": [
    "from numpy import std\n",
    "\n",
    "\n",
    "dt_reg_pipeline = Pipeline([(\"scaling\", StandardScaler()),\n",
    "                            (\"dt_reg\", DecisionTreeRegressor(max_depth=3, random_state=42))])\n",
    "\n",
    "dt_reg_cv_results = cross_validate(dt_reg_pipeline,\n",
    "                                    com_train_features,\n",
    "                                    com_train_labels,\n",
    "                                    cv = cv,\n",
    "                                    scoring = \"neg_mean_absolute_error\",\n",
    "                                    return_train_score=True,\n",
    "                                    return_estimator=True)\n",
    "\n",
    "dt_reg_train_error = -1 * dt_reg_cv_results['train_score']\n",
    "dt_reg_test_error = -1 * dt_reg_cv_results['test_score']\n",
    "\n",
    "print(f\"Mean absolute error on the training set: \\n\"\n",
    "        f\"{dt_reg_train_error.mean():.3f} +/- {dt_reg_train_error.std():.3f}\")\n",
    "print(f\"Mean absolute error on the testing set: \\n\"\n",
    "        f\"{dt_reg_test_error.mean():.3f} +/- {dt_reg_test_error.std():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising the tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As a tree diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                (&#x27;dt_reg&#x27;,\n",
       "                 DecisionTreeRegressor(max_depth=3, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;dt_reg&#x27;,\n",
       "                 DecisionTreeRegressor(max_depth=3, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaling', StandardScaler()),\n",
       "                ('dt_reg',\n",
       "                 DecisionTreeRegressor(max_depth=3, random_state=42))])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg_pipeline.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ihs/1+Y9P+yxoXfzrBFT65LylpaVhZKCHFy8TPtuHpEhJSaGajRUCz13GnBXrsXjqGKzfsR+2VuZwcbSTdDwiIiIiop8KCxRERERERN+pcytvdG7ljfcfUjB0ynxMWbgKW5fPgb5uecTFv4K1RWUAQOx/hilSVlJEWnqGsPwqIRGG+roAgBUbdyIy+inOH94GfZ3yuHM/Ai7NukEsFgvtpaSkhO+NKuihXs3qOLlzbb58IpEIsrIyiIt/BcvKJgCAZ88LHjLpU5/rs6AMBa0z0NVBeMRjYVksFiMu/hUq6Ot8to9POTbuiNjn8QVu69K6CVbPm/zZ/b9FTk6OME/H2YvXcP7KTfifvQgAePvuHe7cj8Dd+xFYMXtCsR+biIiIiOhXwfeTiYiIiIi+Q2RUDM5evIbMzCwoKihASVFBGAaofTMPLF67DUnv3iMu/hXW/bk3z7721hbYe9QPIpEIASEX8wzhlJKaCiVFRWiUU8Pb5HeYt3LjZ3M0da+HR09i8dffJ5CdnYPs7BxcvxOOh4+fQEZGBq293DF3xXqkpWfgwaNo/PX3iS+e2+f6LKr2zTxw6uwFBIVeRXZ2DlZs3Al5eXnUru5Q5D5uBexDYvj5Ar8+V5zIyspGRmYmxGIxsrNzkJGZidzc3HztcnNzsXHXQSS9ew+xWIxrt+9h/Y79aFinJgBgo+9M3D69H1dO/oUrJ/+Ck501powYgFk+Q4t8DkRERERElB8LFERERERE3yEzKxvTFv0OQycPGNf0QkJiEuaMHwYAmDJyICoZ6sOqXks07zksz9BIALB0ug9OnDkPPYeG2H34FFo0dhO2DevbFekZGTCs7oH6bfrA0632Z3OoqargxPbV2H88AKa1vGFc0wtTFv2OzKwsAMDyWeORkpYO45peGOAzEz3at/jiuX2pz6KwqGyCrcvnYMzMJTB0aoQTZ87j703LhAmyf6RmPYdCw8oVl2/cxdDJ86Bh5YrzVz8WgXYfPgXHxh2Ftkf9g2Ht1hrlbeujz+jpGNyrE4b07gQA0CinBn2d8sKXnJws1FRVoV5O9YefAxERERHRz0xK/Ok74kRERERE9Fl2NtbYtnQGbK3Mv3rfkMvX0Xf0dERdOvkDkpGkbT9wDKF3o7Bt+w5JRyEiIiIiKhP4BgUREREREREREREREZU4TpJNRERERPQLu3D1Flr1GVHgtsTw8yWchoiIiIiIfiUsUBARERERlRC3WjVK3fBOdWs6shBBREREREQSwSGeiIiIiIiIiIiIiIioxLFAQURERET0i+nvMxMzfNdKOgYREREREf3iWKAgIiIiIiKJOXA8EA3a9YVmVVd4dh6Yb/uQSfNg594WSmbO2H7gWL7t0bFxaNNvFMrb1odh9UaYvGClsG3dn3tRp2UPlLOsjf4+M/PtGxR6FfaN2kGzqisadxmEp3HxxXpuRERERET0eSxQEBERERGRxGhqlMOwPl3gM7h3gdvtq1bByjkT4WhrlW9bVlY2mvUYiga1a+DpVX9EXTqJLq2bCtsN9HQwcVg/9OrQMt++b94mo/PgcZgxZjDibwfByc4aPYZPKrbzIiIiIiKiL2OBgoiIiIioBPn+sQ1mtZqgvG192Lm3RVDoVQDAtdv34Na2D/TsG8CkphdGTV+ErKxsYT9F0xpYv2M/bBq2QXnb+pi5dB2insahQbu+0LFzQ7ehE4X2IZevo3Ltpli0ZgsMqzeCRd0W2H34VKGZTp45j5pNu0LPvgEatOuLsAePvpi3uDSq64L2zT1hoKtT4PbfenaEu2tNKCjI59u2/cAxGOjqYGT/7lBRVoKiggLsqlYRtrf2dkfLxg2gpaGeb9/DfkGwrlIZ7Zp5QFFBAVNHDcTdB48QERVTbOdGRERERESfJyvpAEREREREv4rIqBis274fF45sRwU9HcTEvYBIlAsAkJGRweJpY+BkVxVxL1+jVe8RWL9zP4b37SrsH3juEi4d3YG4+Feo1aI7Lt+8iy3LZ0NbQwNu7fpg7zF/9GjXHADwMiERiUnJiL50ClduhaF135FwsqsKi8omeTLdDn+IQRNm4+DG5XCyr4pdh0+h3YAxCDtzEE/jXhSa97+WrNsG33XbCj33V3eDv+uzK8jV22EwNjJAy94jcONuOKwtKmP5zPGwtTL/4r4PHkXnKWaoKCvBzNgQ9yOjYPmfz4iIiIiIiH4MvkFBRERERFRCZGRkkJWVhQePopGdnQMTowqobGwEAKhuVxUujnaQlZWFiVEF9O/aFuev3Myz/5hBPVFOTRXWFpVhY1EZHvVcYFbJCOrlVOHlVgd3wiPytJ8xZjAUFORRv5YTmjSsiwMnTufLtHn3IfTr0hY1HW0hIyODHu2aQ0FeDlduhX0273+NG9wbr+4GF/r1IzyPf439xwMwtHcnPLnshyYN66L9wLF53jwpTEpaGtTVVPOsK6emig+paT8kKxERERER5ccCBRERERFRCalsUhFLpo3F3JUbULGGJ3oMn4QXrxIAAI+in6JNv1EwdvaCjp0bpi9Zgzdvk/Psr1deW/heUVEBuv9ZTvnk5rqmuhpUlJWE5UqGBoh/nZAvU+zzeKzctBN69g2Er7j4V4h/nfDZvKWBkqIC6tSoBq8GrpCXl8PogT3wNjkZDx8/+eK+qsrKeJ+Smmfdh5RUqKko/6i4RERERET0HyxQEBERERGVoM6tvHF2/2ZEXjgGSElhysJVAIDh0xbC0swE987+jYSwEMweNxRiiL/5OEnvPiA1LV1YfvbiZYHzPBgZ6GHC0L553nZIehCKTi29P5v3vxat2QJtm3qFfv0ItlZVICUl9U37Vq1ihrsPIoXl1LR0RD+Ng7VF5eKKR0REREREX8ACBRERERFRCYmMisHZi9eQmZkFRQUFKCkqQFr645/kKSmpUFNTgaqKMiKiYrBh54HvPt6c5euRlZWNC1dv4WTQebRr5pGvTd/ObbBx19+4eusexGIxUtPScSroAj6kpH42739NGNoXieHnC/0qjEgkQkZmJkSiHOTmipGRmYns7Bxhe1ZWNjIyMyEWi5GdnYOMzEzk5n6cB6Nr6ya4eisMZy5cgUgkwu9bdkFbUwNW5qYAgJycj+1FubnCcXJyPvbdyqsh7kdG4dCpM8jIzMT8VRthZ1WF808QEREREZUgTpJNRERERFRCMrOyMW3R73gYFQM5WVnUcrLHmvlTAAALJo/C0MnzsGz9djhYW6J988YIvnTtm4+lr6MNDXU1mNbyhpKSIn6fO6nAm+9O9tZYt2AKRs9cjMdPYoVhk+rWdPxs3uLy16GTGDhulrCsYeWK7u2aY5PvTABAs55Dhbk4Lt+4i6GT58F/9x9wq1UDFpVNsHX5HAyfugAJiUmoZmOJgxuXQV5eDgCwYPVmzFu5Ueh79+FTmDJyAKaNGgQdbU3sXrsYo2csRp/R0+FczQbbf59frOdGRERERESfJyUWi7/9vXEiIiIiol+MnY01ti2dAVsrc0lHKVTI5evoO3o6oi6dlHSUX8r2A8cQejcK27bvkHQUIiIiIqIygUM8ERERERERERERERFRiWOBgoiIiIiIiIiIiIiIShwLFEREREREPxm3WjU4vBMREREREZV6LFAQEREREREREREREVGJY4GCiIiIiOgnE3L5OirXbirpGERERERERJ/FAgURERERURElJiYiNS1N0jHKnEfRT9F+wBgYOXnAoJo7mvcchsiomELbZ2ZmYeD4WdCxc4OxsxdWbtqZZ/uB44Fw8GiP8rb1Uc2zA44GBAvbdhw8jtotukPHzg2VazfF5AUrkZOT84POLL+XL18ijf+NEBEREREVCQsURERERESFyMnJQWhoKKZPnw4XFxeYmZkhNTVV0rHKnOT3H9DMoz7unjmI2GsBqOFgg/YDxxbafs7KDXj85BkiLxyD/+4/sGz9DgSEXAQAPH/5Gn3GTMPiqaOREBaCBZNGotfIKXj95i0AID09A0umjcXzG6dx7tA2nL14Dcs37iz0WMXtYUQE9PT04OnpCV9fX9y7dw9isbjEjk9EREREVJawQEFERERE9InY2Fhs3LgR7du3h66uLoYOHYqsrCwsXLgQr1+/hq6OTonk8P1jG7oMHp9n3dhZvhgzcwkA4M/9R4W3CKzqt8LGXQcL7UvRtAaiYp4Jy/19ZmKG71ph+eSZ86jZtCv07BugQbu+CHvwqFjPxbmaLfp0ag0tDXXIycliRL+uiIx+isSk5ALb/3XwOCYN7w9N9XKwMjdFn86tsePAMQDA8/jX0CinBq8GrpCSkkIT97pQUVZCdGwcAGBg9/aoW9MR8vJyMNTXRedW3rh0/U6xns/nNHBzw/PnzzFs2DBERUWhZcuWqFixIvr27Yu9e/fi7du3JZaFiIiIiKi0k5V0ACIiIiIiSUpPT0dISAj8/f3h7++PhIQENG7cGC1btsTq1auhr68vkVwdmnth3sqN+JCSCjVVFYhEIhw8EYi9630BALraWvh78wqYVTLE+Ss30arPCNSwt4GjrdVXHed2+EMMmjAbBzcuh5N9Vew6fArtBoxB2JmDUFCQz9e+hndnPHvxssC+OrXyxqo5E794zAtXb0FfRxvamhr5tiW9e4/4129gX7WKsM6+ahUcCwwGADjZV4VlZVMcDwxBE/e6OHHmPBTk5WFnVSVfX/8cy9rC7IuZilO5cuXQqlUrtGrVCmKxGI8fP4a/vz927NiBAQMGwNraGl5eXvDy8kLNmjUhK8vLMiIiIiL6NfEvYSIiIiL6pYjFYty/f18oSFy8eBGOjo7w8vLC9u3bUb16dUhLS/5FY2MjA1SztcIR/7Po3q45zl68BiUlRbg42gEAmrjXFdrWr+UEj3q1EHrt1lcXKDbvPoR+XdqipqMtAKBHu+ZYvGYLrtwKQ/1aTvnaX/fb8x1nBcTFv8Ko6YuwaOroArenpH6cv0G9nKqwrpyaKj6kfFwvIyODbm2boteoqcjIzIK8nCx2rVkEFWWlfH1t23cEN8MeYN3Cad+V+XtISUmhSpUqqFKlCoYNG4bMzEyEhobCz88PgwcPxrNnz9CoUSOhYFGxYkWJZSUiIiIiKmksUBARERHRTy8pKQmnT58WihIyMjLw9vbGb7/9hn379kFdXV3SEQvUuaU39h0LQPd2zbH3qD86tfQWtvkHh2Leyo149CQWubm5SMvIgI2l+VcfI/Z5PHYePI51f+4V1mVlZyP+dUKxnMOnEhKT0LznMAzs3j7PuXxKVUUZAPD+QyoUFRQA4P9vkXxcf+bCFUxZ+DsCdq+Ho60VboY9QPsBY3Bk2yo4WFsK/RwNCMb0xWtwcudalNfSKPZz+VYKCgpwd3eHu7s7Fi9ejPj4eAQEBMDPzw8TJ06Enp6eUKyoX78+lJTyF16IiIiIiH4WLFAQERER0U9HJBLh2rVrQkHi3r17qFevHry8vDBu3DhYWFhASkpK0jG/qG1TD0yYtwJx8a9w1P8sQv7eCgDIzMxC58HjsXnpLLTwbAA5OVl0GDi20MmYlZUUkZaeISy/SkiEob4uAMDIQA8ThvbFxGH9ipTJsXFHxD6PL3Bbl9ZNsHre5AK3Jb17j+Y9h6K5R/3PHktTvRwMdMvj7oNIeNSrBQC4++ARrKt8HKbp7v1I1K3pCCd7awBADQcbOFezRdCFq0KBIiDkIoZMmotDm1fA1urrizYlycDAAL169UKvXr0gEolw8+ZN+Pv7Y86cObhz5w5cXV2FgkXVqlXLxH+3RERERERFxQIFEREREf0Unj9/Dn9/f/j5+eHMmTMwNDSEl5cXZs+ejbp160JRUVHSEb+ajrYm6tdywsBxs2BSsQKszE0BfHzDITMrG+W1NCErKwP/4FCcPn8Z1haVC+zH3toCe4/6wdrCDGcuXMH5KzdR3a4qAKBv5zbo+Ns4uLu6wLmaDdLSM3Du8g3UrekINVWVfH3dCtj31efx/kMKWvQchtpODpg7YfgX23dr2wwLV2+Bk701Xr95i617DmHDkhkAACd7a/j+sQ137kfAwdoSt8MfIvTabQzs3gEAcPbiNfQeNQ17/1gC52q2X51VkmRkZODs7AxnZ2dMnToVycnJCAoKgr+/P1asWAGxWCwUKxo1agRNTU1JRyYiIiIi+i4sUBARERFRmZSRkYHz58/Dz88P/v7+iI+Ph6enJ5o0aYLly5fD0NBQ0hGLRaeWXug3dgbmTxwhrFNTVcGyGT7oPmwSMrOy0LRRPTT3qF9oH0un+6C/z0z8sWM/Wni6oUVjN2Gbk7011i2YgtEzF+Pxk1goKSqgTo1qqFvTsdjO4UhAMK7fvY/7j6Kx4+BxYf2tgP2oZKiP3YdPYfHarULxY9qoQRg+bQEs6raAkoICxv7WE43d6gD4ON/G1JED0WXIBLx+8xbltTQxfkgfeNb/+LbFgt834d2HFLTuO1I4jquzI45uW1Vs51NSNDQ00LZtW7Rt2xZisRgRERHw9/fHli1b0KdPH9jZ2cHb2xteXl6oUaMGZGRkJB2ZiIiIiOirSIkLew+ciIiIiKgU+fQGrZ+fH0JDQ2FnZyc8UV5SN2jtbKyxbemMUj90EJW87QeOIfRuFLZt3/HDj/VPge6fYcxevHgBDw8PeHt7o3Hjxj9NgY6IiIiIfm58g4KIiIiISq13797hzJkzwlsS/wxx069fP+zatYtD3NAvS1FREZ6envD09ISvry/i4uKEybbHjh0rDHHm7e1dZoc4IyIiIqKfHwsURERERFRq/DNJ8D8FiU8nCR49ejSsrKwkPkmwjIwMsnNyJJqBSqfs7BzIyMpJ5NhGRkbo27cv+vbtm2eS+OnTp+PevXuoW7euULAoK5PEExEREdHPj0M8EREREZFExcfHC8PUBAYGQk9PTxi2qX79+lBSUpJ0xDw6tm+HRjVt0LtjK0lHoVJmxLRFMLWpjokTJ0o6Sh5JSUk4ffq08O9MRkYmz2Tb6urqko5IRERERL8oFiiIiIiIqERlZmbiwoULws3SZ8+eoVGjRsIN04oVK0o64mcdPnwYE3zGIHD3H9DT0ZZ0HColbt17iOa9huPqteswMzOTdJxCicViPHjwQHhL6eLFi6hWrZrw78/JyQnS0tKSjklEREREvwgWKIiIiIjohxKLxXj06JFQkDh37hysra2F4WacnZ0hK1u2Rh6dPXs2lvr6orZzNehoaXK4nF+YKFeE2LiXuPfwETZt3ox27dpJOtJXSUtLw7lz54R/nwkJCfD09ISXlxcaN24MAwMDSUckIiIiop8YCxREREREVOzev3+PoKAg4aZnZmamUJDw8PCAlpaWpCN+t6SkJAQHByM5OVnSUUiCpKSkYGBgADc3t59iIurY2Fjh3+2ZM2dgbGwsvF3h6uoKBQUFSUckIiIiop8ICxRERERE9N1yc3Nx69Yt4cbmzZs3UatWLXh7e8PLyws2NjZ8y4CojMnJycHVq1eF4aAePnyI+vXrCwULc3Nz/rsmIiIiou/CAgURERERfZNXr14hICAA/v7+CAgIgJaWllCQcHNzg7KysqQjElExSkxMRGBgoFCIVFRUFP7Nu7u7Q01NTdIRiYiIiKiMYYGCiIiIiIokKysLFy9ehL+/P/z8/PDkyZM8k1sbGxtLOiIRlRCxWIx79+4JPw+uXLmC6tWrCwWLatWqcbJtIiIiIvoiFiiIiIiIqFBRUVHCDciQkBBYWloKBQkXFxfIyclJOiIRlQKpqakICQkRfl4kJyfD09MT3t7eaNy4MXR1dSUdkYiIiIhKIRYoiIiIiEiQkpKCs2fPCmPOp6amCgUJT09PlC9fXtIRiagMiImJEYoVZ8+ehZmZmfB2Re3atSEvLy/piERERERUCrBAQURERPQLE4vFuHPnjlCQuH79OmrWrCkUJezt7TkJLhF9l+zsbFy+fFkoWDx69AgNGjSAl5cXvL29YWZmJumIRERERCQhLFAQERER/WISEhIQGBgIPz8/BAQEoFy5ckJBokGDBlBVVZV0RCL6if3zM+ifybbV1NSEn0ENGzbkzyAiIiKiXwgLFEREREQ/uezsbFy6dEm4Gfj48WPh6WUvLy8+vUxEEpObm4u7d+8KP5+uXbsGZ2dn4eeTg4MD3+IiIiIi+omxQEFERET0E3ry5Ilww+/s2bOoXLmycMOP478TUWn1zzw4//z8SklJQePGjYV5cHR0dCQdkYiIiIiKEQsURERERD+B1NRUBAcHCzf1kpOT89zU09PTk3REIqKvFhUVJfxcCw4OhoWFhVBsrVWrFuTk5CQdkYiIiIi+AwsURERERGWQWCxGWFiYcOPuypUrcHJyEiaddXBwgLS0tKRjEhEVm6ysLFy8eFH4uffkyRM0bNhQKFiYmJhIOiIRERERfSUWKIiIiIjKiMTERGFi2YCAACgqKgo35tzd3aGmpibpiEREJebVq1cICAgQfiZqamrC29sbXl5ecHNzg4qKiqQjEhEREdEXsEBBREREVErl5OTgypUr8Pf3h5+fHx4+fAg3NzfhBpy5ubmkIxIRlQq5ubm4deuW8HbFzZs3UatWLaGIa2try8m2iYiIiEohFiiIiIiISpHY2FjhBtuZM2dgbGwsDNtUp04dKCgoSDoiEVGp9/79e5w9exZ+fn7w9/dHZmamUKzw8PCAtra2pCMSEREREVigICIiIpKo9PR0hISECG9JvHnzRpjcunHjxtDX15d0RCKiMk0sFuPx48fCz9lz586hatWqwttoNWvWhKysrKRjEhEREf2SWKAgIiIiKkFisRj3798XbpRdunQJjo6OwpO91atX5+TWREQ/UGZmJkJDQ4W3K2JjY9GoUSOhYFGxYkVJRyQiIiL6ZbBAQURERPSDJSUl4fTp0/Dz80NAQABkZWXzTG6trq4u6YhERL+s+Ph4BAQEwM/PD4GBgdDV1RWG1qtfvz6UlJQkHZGIiIjop8UCBREREVExE4lEuHbtmvB0bnh4OOrVqycUJSwsLDhZKxFRKSQSiXDz5k3hLbc7d+6gTp06QsGiatWq/PlNREREVIxYoCAiIiIqBnFxcXkmtzY0NBQKEnXr1oWioqKkIxIR0VdKTk5GUFCQ8PNdJBLlmWxbU1NT0hGJiIiIyjQWKIiIiIi+QUZGBs6dOyfctHr58iU8PDyEya0NDQ0lHZGIiIqRWCxGRESE8HP//PnzsLOzEwoWzs7OkJGRkXRMIiIiojKFBQoiIiKiIhCLxXj48KFwYyo0NFS4MeXt7Q0nJyfemCIi+oVkZGTg/Pnzwu+FFy9eCIVqLy8vFqqJiIiIioAFCiIiIqJCJCcn48yZM8LNJ7FYLBQkGjVqBA0NDUlHJCKiUiIuLg4BAQHw9/fH6dOnUaFCBaFYUa9ePQ71R0RERFQAFiiIiIiI/k8kEuHGjRtCQeLOnTtwdXWFt7c3vLy8YGVlxclRiYjoi0QiEa5duyb8PgkLC0O9evWEgoWlpSV/nxARERGBBQoiIiL6xb148UJ44jUwMBB6enpCQaJevXpQUlKSdEQiIirjkpKScPr0aaFgIS0tLfyuadSoEdTV1SUdkYiIiEgiWKAgIiKiX0pmZiYuXLgg3CR69uxZnsmtK1asKOmIRET0ExOLxXjw4AH8/Pzg7++PixcvwsHBQShYODk5QVpaWtIxiYiIiEoECxRERET0UxOLxXj06JFQkDh37hxsbGyEYTacnZ0hKysr6ZhERPSLSk9Px7lz54SCRUJCAjw9PYXCuYGBgaQjEhEREf0wLFAQERHRT+f9+/cICgqCv78//Pz8kJ2dLRQkPDw8oKWlJemIREREBYqNjUVAQAD8/Pxw5swZVKpUCV5eXvD29oarqysUFBQkHZGIiIio2LBAQURERGVebm4ubt26JRQkbt26hdq1awtFCRsbG05GSkREZU5OTg6uXr0q/H578OAB6tevLxQszM3N+fuNiIiIyjQWKIiIiKhMevXqlfCEaWBgILS1tYWChJubG5SVlSUdkYiIqFglJiYKk237+flBUVFRKFa4u7tDTU1N0hGJiIiIvgoLFERERFQmZGVl4eLFi8IY3TExMXB3dxeKEsbGxpKOSEREVGLEYjHu3bsnzLF0+fJlVK9eXShYVKtWjZNtExERUanHAgURERGVWlFRUUJBIiQkBJaWlsKNFxcXF05uTURE9H+pqakICQkRChZJSUl5JtvW09OTdEQiIiKifFigICIiolLjw4cPOHv2rHBzJTU1VShIeHh4oHz58pKOSEREVCbExMQIv0+DgoJgZmYmvHVYp04dyMvLSzoiEREREQsUREREJDm5ubm4e/eu8JbE9evXUbNmTXh7e8PLywt2dnac/JOIiOg7ZWdn4/Lly0LBIjIyEg0aNBAKFpUrV5Z0RCIiIvpFsUBBREREJSohIQEBAQHw9/dHQEAA1NXVhRskDRo0gIqKiqQjEhER/dQSEhIQGBgoFCzU1NSE38UNGzaEqqqqpCMSERHRL4IFCiIiIvqhsrOzcenSJeEmyOPHj9GwYUPhRoipqamkIxIREf2yxGJxnrcZr127BmdnZ+H3tIODA99mJCIioh+GBQoiIiIqdk+ePBEKEmfPnoW5ublwo6N27dqQk5OTdEQiIiIqQEpKCoKDg4WCxYcPH4Tf4Z6entDR0ZF0RCIiIvqJsEBBRERE3y01NRXBwcHw9/eHn58f3r9/j8aNGws3M3R1dSUdkYiIiL5BVFSU8NBBcHAwqlSpIswVVatWLT50QERERN+FBQoiIiL6amKxGGFhYcINiytXrqBGjRp5hoOQlpaWdEwiIiIqRllZWbh06ZLwdkV0dDQaNmwoFCxMTEwkHZGIiIjKGBYoiIiIqEgSExPzTKiprKycZ0JNNTU1SUckIiKiEvTq1SsEBgbCz88PAQEB0NTUhJeXF7y9veHm5gYVFRVJRyQiIqJSjgUKIiIiKlBOTg6uXLkiPCUZEREBNzc34SnJypUrSzoiERERlRK5ubm4ffu2MNzjzZs34eLiIhQsbG1tOdk2ERER5cMCBREREQliY2OFGwtBQUEwMTERbizUqVMH8vLyko5IREREZcD79+9x9uxZ4e+KzMzMPPNTaWtrSzoiERERlQIsUBARFUIsFuPt27fIzMyUdBQqxTQ0NKCsrCzpGN8sLS0N586dE96SSExMhKenJ7y9veHp6Ql9fX1JRyQiIqIyTiwW4/Hjx8IwkSEhIahataowVKSLiwtkZWUlHfOHyM7Oxtu3byESiSQdhX4gGRkZaGlpcdJ4IqJvwAIFEdF/iMViLFiwAOv/+APvP7yHkqKipCNRKSUWi/H+QwpqOtfAgoWLUKtWLUlH+iKxWIz79+8LBYlLly7B0dFRGLbJ0dGRk1sTERHRD5WZmYnQ0FChYPH06VM0atRIKFhUqlRJ0hG/29u3bzFy+FAcO3YccrIykJWVkXQk+oFyckTIzhGhRYvmWPn7GmhpaUk6EhFRmcECBRHRf8ycORMnjx3F+mXzYVvVkmPl0mdlZGRi/5HjmDBrIS6EhsLCwkLSkfJ5+/YtTp8+LdwEkJOTEwoS7u7uKFeunKQjEhER0S8sPj4eAQEB8Pf3R2BgIHR0dIRihZubG5SUlCQd8avk5uaiZo3qcDRUxbgO9aGrqSbpSFQCXid9wJL953DreQquXr/Jh36IiIqIBQoiok/k5OSgQgUDXDhxEGYmZf/JLSo5k+csgoyKBhYsWCDpKBCJRLh69apQkAgPD0f9+vWFC/0qVaqw8EZERESlkkgkws2bN4W/Y27fvo06deoIf8dYW1uX+r9jQkNDMaBnF4Qu/63UZ6XiJRaLUWfUOmzeuRd16tSRdBwiojLh5xzkkYjoGz158gSqysosTtBXa1i3Npas2yKx48fFxQkX8mfOnIGRkRG8vLwwd+5c1K1bFwoKChLLRkRERFRUMjIycHZ2hrOzM6ZOnYp3797hzJkz8Pf3x6pVqyASiYRihYeHBzQ1NSUdOZ8rV66gvq0JixO/ICkpKdS3M8Hly5dZoCAiKiIWKIiIPpGRkQEVldI94bGcrikeXD4LczMTSUehTygrKyMjI6PEjpeRkYFz584JRYmXL1/C09MTTZs2xYoVK1ChQoUSy0JERET0o6irq6Nt27Zo27YtxGIxIiMj4efnh61bt6Jfv36wtbUVChbOzs6QkZH8XA8ZGRlQVpB8jn9otpyMG3+MhVkFbUlH+SUoy8uW6HUBEVFZxwHxiIh+EHOnulA2tMCbxLd51tdwbwY5XVPExMZ99zH6DvfB9AW+392PpL1NSkb7XoOgbmKNytVdsfvgkS/uk5WVBTtXD5g41M6z/rexk2BT2x3yemb4c8+BPNvuPYhA0449oW9VHXK6psV6Dj/6CTmxWIwHDx5gxYoVaNKkCXR1dTF79mxoaGhgy5YtePXqFXbv3o3evXuzOEFEREQ/JSkpKVhaWmLkyJE4efIkXr9+jdmzZ+PDhw8YMGAAdHV10bFjR2zZsgXPnz+XdNyvZt9/MXTbTkPi+9Q86+uP/B2aLScj9lXSdx9jyIoDmLsz4Lv7KUlZ2TnotfAv2PdfDM2Wk3EhLPqL+xw8dwcuQ5bDsMMMOA70xcXwJ0Xqa+2RC6g2YAkqdZqFqr0XYPKmE8gRib4qL1+cISL6OixQEBH9QKaVjLDn0FFhOez+Q6Snp0sw0fcRi8VIeJNY7P2OmDgd8vJyeH7vGv5cuwLDxk9D+MPIz+6zdM0GlNfWyrfe3roqfl80B472tvm2ycnJon2rZtiwfGGxZf+RkpOTcfDgQQwcOBAmJibw9vbG/fv30b9/f8TGxuLChQuYNm0aatasWSqeFiQiIiIqSYqKivDw8ICvry/CwsJw584deHt7w9/fH/b29rC1tcXYsWMRGBhYZp5oN9bTxMFzd4Tl8JiXSM/KlmCi7yMWi/HmXcp391OrqgnWj+kIvSJMOH721iPM/NMfq0e0w7O9M3BiwQCY6P973fC5vprUrIqQ5cMQu3cGLv4+EveexGP9sUvfnZ+IiArHAgUR0Q/UrUMb7Nx3SFjesfcgundsm6dNZmYmxs+YBzNHVxha18AQnylIT//3Amrp6vWoaFsTlexcsHXXvkKPFRMbBzldU2zfcxBmjq7Qt6qOBctXC9tFIhEWrlgDS2c3aJraoqZHCzx7/qJI5xEdE4tZi5ajSo162LZrf1FPv0hSU9Pw93E/zJw4BqqqKqhbyxnNvRrhr/2HCt3nydNn2HXgMCaMHJJv25B+PeFe3xWKBcy5YGleGX27dYK1pUWxnkNx+Wdy6zlz5qBu3bqoVKkSNm3aBBsbG/j7+yMmJgYbNmxAu3btoKGhIem4RERERKWKkZER+vbti7179+L169fYvHkz1NXVMWPGDOjo6KBJkyZYuXIlHj58CLFYLOm4BerUoBr2BN0SlvcE3USnho552mRm52DalpOw7bsIFj3mYfTaw0jP/LeIservc7DqtQBVey/AzsDrhR4r9lUSNFtOxu4zN2HbdxEqd5sL331nhe0iUS6W7guG40BfVOw0Ew1Gr0ZcQnKRziPm5Vss2HUaDgOWYOfpG0U8+4LJy8licCtX1LY2gYz0l19PWLD7DMZ3coezVSVIS0ujgrY6KmirF6kvUwNtqKsqAQDEYkBaWgrR8cX/gBYREf2Lc1AQEf1ALk6O+Gv/ITyIfAyLyqbYd/g4Qo7vx/QFS4U2k+cuRnTMU1wPOgE5OVn0+G0k5i5dhXlTx8M/KATL1m6E/8G/YFqpIn4bO+mLxwy9eg3hF88gMioadbzboHUzb1S1MMeKdZuw59AxHN29BRaVzXA3/AGUlZQK7SctLR0Hj53En3sOIOz+Q7Rv2Qx/bfgdLk7/XiCVN7cvdP/xIwZj/IjBX8wbGf0EsrIysKhsJqxzsKmKc5euFLrPqMkzMWfyOCgpKn6x/9IgNjYW7du3x759+2BiYpJn24sXLxAQEAA/Pz+cPn0a+vr68Pb2xowZM1CvXj0olpFzJCIiIipNZGRk4OLiAhcXF0yfPh1JSUnCZNu+vr6QlpaGl5cXvL290ahRI6irq0s6MgCghmUl7A2+jYhnr2FeoTz+Pn8XpxYNwrydgUKbWX/648nLRJxfORyyMjIYsHQvFu8JwoxeXjh9IxKrD53H4bn9YKynhZGr//7iMS8/iMG1dWPw+MUbeIxdixa1bWBZURdrjlzAwfN3sG96L5gblse9mJdQVpAvtJ+0zCwcDb2Hv87cQHjMS7R2tcOWcV1Qw7Ki0Ma4y+xC9x/Vzg2j27sV8ZMqmEiUi9uPn6NJzaqoPtAXmdk5aOpijdl9mkBJQa5IfewPuY2xa4/gQ3omtMspY07fpt+ViYiIPo8FCiKiH+zjWxR/o34dF1hVqQxDA31hm1gsxqYdu3Hz7CloaWoAACaMHIqeg0di3tTx2H/kBHp16QDbqpYAgGnjRmLP30cLOoxgms9IKCkpwsHWGvY2Vrgb/gBVLcyx5a+9WDB9IizNKwMAHGytC+1j4OgJOHzCHy5OjhjUuxtaentCoYA3Et48vvu1H0c+qampKKeqmmdduXJq+JCSWmD7wyf8IRKJ0LqZF0JCL3/38X+0e/fuoUmTJhgzZgxMTEyQmZmJCxcuwN/fH35+fnj+/Dk8PDzg5eUFX19fGBkZSToyERER0U9HU1MT7du3R/v27YW5vfz9/bF+/Xr06tULDg4OQsGievXqEh0+85+3KFxtTWFhpIsK2uWEbWKxGH/6X8WFVSOgqaYMABjToQEG+O7FjF5eOHwhDF09nGBt/PGaY2IXDxw89/m/2cd3bgQlBTnYmRrA1tQA957Ew7KiLnYEXsesXt6oYqQDALAzNSi0jxG//41jl8JRw7Ii+jZxQVMXayjI5b/l9HT39K/+PL7G6+QUZOeIcPTiPZxcOBBysjLoOncHfPedxbQejYvURwe3aujgVg1RL95gT9At6GqofnknIiL6ZixQEBH9YN06tIF7q06IiX2Wb3inhDeJSEtLh4tnC2GdWPxxqB8AiH/5CtU/mUvB2Mjwi8fT19URvldWUkJK6scb/c9exMPMxLhImcMfRkJeTg4Ottaws65aYHHiWzXv3BsXLl8DAKz1nYeqllXwPiXvuLQfPqRATVUl376pqWmYNHshju7eUmx5fqTz58+jffv2GD9+PGRlZdGsWTOcP38eNjY28Pb2xoYNG+Ds7Mz5I4iIiIhKkJSUFKytrWFtbY3Ro0cjPT0d586dg7+/P3r16oXXr1/D09MTXl5e8PLygoFB/hvzHTp0wKhRo+Dq6lrs+To2dESzSRvx9PVbdHLPO7zTm3epSMvMRoMxa4R1YrEYubkfh6x6+fY9HMwrCNsq6mp88Xh6mv/egFdSkENqRhYA4HnCO5gY5J/zrSAPnr6CvKwM7EwNYGOiX2BxoiT885bEwGa1oa/1sbAztHVd+O4teoHiH5UrlIdVJV34rDuCHZO7F3tWIiL6iAUKIqIfzLiiEUwqVcSp08HYsHxRnm3ltbWgpKSIO+cD8rxZ8Q99PV3EvYgXlmOLOGdEQSpWMEB0zFPhbYzPCT31cViqP3fvR+O2XWFYQR/dO7ZF5zYtoa2lKbTTMLEptI+Jo4Zg4qih+dYf37Mtz3JqahpyckR4FP0EVcxMAQB3wh8UOE/Eo+gYxDyLQ8MWHQEAWdnZePf+A4xsnHHh1CGYVCo9bx+sXLkSPj4+0NTUxPLly+Ht7Y3evXtjx44d0NIq2oUeEREREf14SkpKQjFi2bJlePbsGfz9/XHixAmMHj0alSpVErbXrVsXCgoK6N+/P9q0aYPNmzejRYsWXz7IV6ikqwljPU2cvh6J34e3y7NNu5wylOTlcGn1SGFehU/paanh+Zt3wnJR54woiKGOOmLi3wpvY3xOoO9gRDx7jb9O30CrqZtRQVsdnRo6on19B2iVUxbaGXWcWWgfo9s3wNiODb45LwBoqCqhQnl1SH0yvcSXZ60oXI4oF09evv2uTERE9HmcJJuIqARsWL4IAX//BRUV5TzrpaWl0a97Z/hMm4PXCW8AAM/jXyIgKAQA0KFVM2zfcwD3Ix4hLS0dc5as/OYMfbt1woyFy/Ao+gnEYjHuhj9A4tukQttXtTDHwhmT8OT2RUzzGYnzF6/A3Kketvy1V2iTHBNe6FdBxYmCqKgoo00zL8xatBypqWkIvXIdx/xOo1uHNvna2la1wJNbF3E96CSuB53E+mULoadTHteDTqKi4ccn27KyspCRkQmxWIzs7GxkZGQiNzcXwMenyzIyMpGV/XESwYyMTGRmZhb5M/waDx8+hKGhIVJSUqCkpIQPHz4gOjoaKf95W4SIiIiISpeKFSuif//+2L9/PxISErBu3TooKSlhypQp0NHRQfPmzREZGYm1a9di4MCB2LKl+N/u/X14WxyZ2w8qinnnfJCWlkZPrxqYsukEEpI//l35IvEdztyMBAC0rmuH3Wdu4mHsK6RlZmHR7qBvztDDswbm/XUaUS/eQCwW496TeLx9n1Zoe8uKupjdpwnubZ6A8Z3dcfHeEzgMWIwdAf9O1B23b2ahX58rTmRm5yAj6+Pf8Fk5ImRkZRc60Xm3RtWx4fglJCSnIDklHWuPhsLL2apIfW0PuCZ8rg9jX2HFgRC4OVQu2gdGRETfhG9QEBGVgMqmhQ+ttGDaRMxdugp1m7TFm7dJMDTQw8De3dDY3Q3ejRpgxKC+aNy2K6SlpTFr0ljsPnjkmzKMGtwfmVlZaNqxJ94kJsGySmUc2PbHF/eTkZFBU093NPV0x9ukZLx8nfBNx/+c3xfNwYCR41HBpga0NTWxevEc2Fh9fIPiwuWraN65D5JjwiErKwt9vX+HsNLS1IC0tHSedU069sS5ix8n2L507QYGj52M04d2w821Fp4+e44qNeoJbdUqWcG4oiEe37hQ7Oe0bt06AEBOTg4iIiJw69Yt3LhxA48ePUKlSpWK/XhEREREVPxkZWVRp04d1KlTB7NmzUJiYiJOnz4Nf39/LFy4ELKyshgxYgRCQ0OxefPmYjuuqYF2odtm9vLG4j1B8By3Dm/fp8FAuxz6NnFBo+oW8HSyxG8tXdFq6mZIS0thSjdP7A+5/U0Zhraqi6zsHLSdvhVvP6SiiqFOkYY6kpGRhpezFbycrZD0IQ2vkj580/E/5Tx4GZ69TgYAtJuxFQBwZ+M4VNLTxNJ9wbh0PwYHZvYGAIzr5I7E92moMXgZFOVk0bquXZ7ix+f6uvLgKebuCEBqRha01VXQytUOU7p5fHd+IiIqnJS4sJIzEdEvKCwsDF07d8St4FOSjkJlzMWrNzBx7hJcvFT6J+4mIiIiom+Tm5uLY8eO4fr167h58yauXbuG1NRU6Ovr49GjR5CWlsb8+fPx5nYgpn/lnAf0c5i9IwDlq3li8uTJko5CRFQmcIgnIiIiIiIiIqIiePnyJXbs2AEpKSkMGDAA169fR0pKCqKioiAtzVssREREX4tDPBERERERERERFUGFChVw4MABSccgIiL6abC8T0REREREREREREREJY4FCiIiIiIiIiIiIiIiKnEsUBAR/aT+3HMAbs07SDoGERERERGVUbvO3ID3hPWSjkFERD8xFiiIiKjMuh12HzU9WqCccVXU9GiB22H3C20bExuHFl36QKeKA4xsnDFi4nTk5OTka7dj70HI6Zpi8849+bZlZWXBztUDJg61i/U8iIiIiIhIMkatPgTnwcug1WoKdp258dm207achNOgpajYaSZqDl6GPUE3SyglEdHPiwUKIqIyQCwWIzc3V9IxIBKJ8iwXdIP/c762/edkZWWhXa8B6Nq+NRIib6NHp7Zo12sAsrKyCmw/fMI06JTXxrOwq7gedBLnLl3Fuq078rRJSn6HRSvXwsbKosA+lq7ZgPLaWsV2DkREREREJaX0XFPkzZDzn2uML/na9l9iY6oP399awqFyhS+2VVaUx+5pPfB093SsHdUBEzcex5UHT4s1DxHRr4YFCiKiIliy6g8Y29eCpqktbGq7I+hcKAAgPT0DfYf7QKeKA+zremLp6vV5nq6X0zXF4+gYYbnvcB9MX+AL4OPN8Fbd+sGgqhN0qjigVbd+iHsRL7Rt1Lozps1fgvrN2qOccVVEx8Ti4aMoeLfvDl2LarCp7Y79R44L7RPfJqFNj/7QMrNDba9WiI4p2h/Kn+uz73AfDB03FS269IG6iTWCL1yCuVNdLFn1BxzdvKFuYoOcnBwc8wuEQ73GKG9uj0atO+NB5GOhj4LaF4eQ0MvIyRFh5KC+UFBQwPABfSAWA2cvXCqwfUzsM3Ro1QyKigrQ19OBV8P6uP/wUZ42U+YuxtD+vaGtpZlv/ydPn2HXgcOYMHJIseQnIiIiol/LioMhsO69EBU7zYTz4GUIufPxb+b0zGwMWXEAJl1mo9bQ5Vj19znY9Fko7KfZcjKiXyQKy0NWHMDcnQEAgOSUdHSa/SfMu8+FSZfZ6DT7Tzx/805o23zyRszZEQCv8X+gQocZiHmZhMi412gzbQtMu86B8+BlOHThrtD+7fs0dJm7HZU6zUKjsWvxJP5tkc7tc30OWXEAY9YeRodZ22DYYQbOh0XDvv9irDgYAtfhq2DYYSZyRCKcvPIAtYeugHGX2Wg+eSMinr0W+iiofXEZ0Kw23BzMoSAn+8W2k7p6wMJIF9LS0qhhWRG1bUxw7WFssWUhIvoVffmnLxHRLy7icRTWbtmOSwFHUEFfDzGxccKbBHN8VyI65ikiroYgNS0NLbr0KXK/ubm56NW5PXZvXA1RrggDRo7HyIkzcHD7BqHNX/sP49jurbA0N0NqWhqq1ffGjAmjcXzPNoQ9iECTDj1gY2UJa8sqGDFxOhQVFPAs7AqexD5Ds069YFKp4mczpKamoUmHHoX2CQB7/j6Ko7u24Mhfm5GVlf1x3aGP68praSH6aSy6/zYSB7eth5trLaz8YzPadO+PuxcCIC8vn6+9rGz+Xz2Obt549vxFgRk7t22F1Yvn5FsfHvEIdtZWkJKSEtbZWVvh/sNIeLm75Ws/fGBf7D10DG51aiHp3Tv4B4Vg5oQxwvarN2/jxp0wrF48BweOnsi3/6jJMzFn8jgoKSp+7iMlIiIiIsrnUVwCNp64jDNLh8BAuxxiXyVB9P+3GRbtOYMnLxNxa4MPUjOy0GHWtiL3m5srRjcPJ2yd0AUikRjDVx3E+PVH8deUHkKbvWdvYf/M3qhiWB6pGdmoM3wlJnf1wP6ZvXA/5hXaTN+CqpX0YFVJDz7rj0BRThYP/pyEp6/eov2Mbaikl//hnU+lZmShzfSthfYJAAfO3cG+6b2xd1pPZOV8vJY6eO4u9k7vCe1yKoh5mYQBvnuwc3J31LUzw9ojoegyZzsurxkF+f8XDj5tLysjky+H6/BViHuTXGDG9vUdsHRwqyJ/rkWRnpmNW4+eo1+TWsXaLxHRr4YFCiKiL5CRlkFmVhYeRDyCjrYWTCoZCdsOHDmB3xfPgZamBrQ0NTC0fy/MW/p7kfrV1tJE2xZNhOWJo4bBs22XPG16dmonDDfkH3QOxhUN0bvLx4mvHe1s0KaZNw4ePQnLMcPw93E/3Arxg4qKMmyrWqJHp3Y4f+nqZzOcCAwqtE/rcSMBAC28PeDqUgMAoKioAAAYNqA3Khp+fAV6/+HjaOrREB4N6gEAxgwdiN83bsOlazfh5lorX/uC3ArxK8InlldqairUy6nlWVdOTQ0fUlILbF+vdk1s2rEbWpXtIBKJ0KNTO7Rq2hjAx6Grhk+YjpULZkFaOv/LhYdP+EMkEqF1My+EhF7+6qxERERE9GuTkZZGVnYOIp69Rnl1lTw3/Q9fCIPvb62gqaYMTTVlDGpeB0v2BhWpX61yymhZx1ZYHtuxAVpM2ZSnTddG1VH1/4WCMzfDUUlXA908nAAA9pUroEUdGxwJvYcqhjo4djEcob+PgIqiPKyN9dHZ3REXw2M+m8H/2sNC+/ynQNHUxRq1rI0BAIryH//eHtS8Nox0NAAAh87fReMalmjo+PEhqeFt6mL9sYu4+jAWde3M8rUvSOjvI770cRWrMWsPw9ZEH42qVynR4xIR/WxYoCAi+gJzMxMsnTMNs5esxP2ISDRuWB9LZk9FBX09vHj1CkYVDIS2xkaGRe43LS0dY6fNQcDZECQlvwcAfEhJgUgkgsz/nwgyMvy379hncbh68w7Km9sL63JyROjWoQ0S3rxFTk4OKn6SpVIRsnyuz39U/CSDsO6T47x4+TrPsaSlpWFkaIDn8S8LbF9cVFRU8P5DSp51H1I+QE1VJV/b3NxcNO/cC/17dMH5EweQkpqGAaPGY9LshVg4YxLWbd0BO2sr1KrhmG/f1NQ0TJq9EEd3byn2cyAiIiKiX4NZBW3M798MC3efwcPYV3CvXgXz+jaDgXY5vHz7AYY66kLbiroaRe43LTMLkzedwJmbj/AuJR0A8CE9EyJRLmRkPhYCDMv/29+z18m4ERkH4y6zhXUiUS46NqiGN+9TkSPKzdO+oq4GEP75DJ/r8x+G5dXz7ffpuvi372Gk+2/RRlpaGobl1fEi8f1n+5CUaVtP4UHsKxyd1z/PG91ERPT1WKAgIiqCLu1aoUu7Vnj/4QOG+EzBpNkL8efa5TDQ00Xci3jhLYfY/wxTpKyshLT0dGH51esEGFXQBwAsX7cRkVHRCD11GPp6Orgddh/OjZpBLBYL7T/9Y9fIsALq164JvwM78+UTiUSQlZXFsxfxsKpSGQDw7PnzL57X5/osKENB6yro6+LegwhhWSwWI+55PAwN9D/bx6cc6jXG02cF5+3avjXW+s7Lt97GsgpWrNsEsVgs9B92/yEG9+2Zr+3bpGTExr3AkH49oaCgAAUFBfTq3AEzFi7FwhmTcPbcRZy7dAV+p89+bJ/8DrfD7uPOvfvo260zYp7FoWGLjgCArOxsvHv/AUY2zrhw6lCeN2qIiIiIiArTwa0aOrhVw/u0DIxecxgz//TD+jEdoaephucJ74S3HOIS3uXZT1lBDmmZWcLy66QPqFC+HABgzaELePz8DU77DoaephrCol+g/qjVEOPTa4p/+zIsrw5XG1McmtM3Xz6RKBeyMtJ4/iYZFka6BWYpyOf6LCjDv+v+XWmgVQ73n/77gJNYLMbzN+9QQbtcge0LUnvoCjxLSC5wW4cG1bB8SOvP7l9UC3adxukbETgxfyDKKXP4VyKi78VJsomIviDicRTOnr+IzMxMKCooQFFRURgGqH3LZli0ci2Skt8h7kU81mz6M8++DjbW2PP3UYhEIvgHheDcpSvCtg8pqVBSVISGejm8TUrGXN+Vn83RrLE7HkU/wc59fyM7OxvZ2dm4dusOHkQ+hoyMDNo088KcJSuQlpaO+xGPsGPv3188t8/1WVTtWzXDydNnEXQuFNnZ2Vi+diMU5OVR27l6kfu4cz4AyTHhBX4VVJwAADfXWpCRkcbvG7chMzMTazZ//Owb1q2dr215bS2YVqqI9dv+Qk5ODpLfvceOvQdhZ20FANj8uy/CLpzG9aCTuB50Ek4OdpjqMwJzJvvAtqoFnty6KGxbv2wh9HTK43rQyQLfLiEiIiIi+q9HcQk4dycKmdk5UJSThZK8HKT/f8O9dV07LD8QjOSUdDx/8w4bjl/Ks6+tqQEOnLsDkSgXp29EIjT8ibDtQ3omFOXloK6iiKQPaVi05/NDQ3k5W+HxizfYc/YWsnNEyM4R4eajOEQ8ew0ZGWm0qG2DhbvPIC0zCw9jX2FP0M0vntvn+iyq1nXtEHA9AiF3HiM7R4TVhy9AXk4GNa0qFbmPS2tGIW7fzAK/PlecyMrOQUZWNsRiMbJzcpGRlY3c/88P8l/L9gfjQMgdHJ7TD1rllIucjYiICscCBRHRF2RmZmHy3EXQt3KCkW1NJLxJxLyp4wEA03xGopKRIarUqIemHXui+ydDIwHAsnnTcSLgDMqbO2DXgcNo1aSxsG3EoL5Iz8iAvlV11G3SBo0LmNj5U2qqqji5bzv2HT6OSva1YGRbE5PnLELm/5+mWrlgFlJS02BkWxP9RvigV5f2Xzy3L/VZFJbmlfHnmuUYNXkm9K2ccDzgDA7t3CRMkP2jyMvL48CfG7Bz398oX8UB23btx4E/NwjHXbhiDZp37i2037f1D/gHhcCgqhOsXBpATk4WvrOnAgA01MtBX09H+JKXl0M5NVWolysHWVnZPNu0NDUgLS0NfT0dYSguIiIiIqLPyczOwazt/jDvPheWvRYg4V0Kpvf0AgBM6OKOirqacBiwBO1mbEGnhtXy7LtwQHP4X30Ik66zsT/kNpq6WAvbBrd0RUZWNsy7z4PnuHVfnA9BTVkBf8/qg7/P30XV3gtg2Ws+Zm7zQ1Z2DgBg8aAWSE3PglXPBRi68iC6NnL64rl9qc+iqGKkg/VjOmLChmOo3H0u/K4+wO5pPYUJsn+ktjO2wqD9DFx9GItRaw7BoP0MYd6NfcG3UXvoCqHtnB0BiEtIhtOgpTDqOBNGHWdi6b7gH56RiOhnJiX+dCwRIqJfXFhYGLp27ohbwae+af+Q0MvoNWQ0Yu5c+nJj+qlcvHoDE+cuwcVLnESbiIiI6Fc2f/58vLkdiOk9Gn+5cQEuhEVj0LJ9CN86sZiTUUmYvSMA5at5YvLkyZKOQkRUJvANCiIiIiIiIiIiIiIiKnGcJJuI6Cd34fJVNO/cp8BtyTHhJZyGiIiIiIjKmovhT9Bx1p8FbovbN7NkwxAR0U+FBQoiomLk5lqr1A3vVLdWTRYiiIiIiIjKiLp2ZqVueKc6NqYsRBAR0Q/BIZ6IiIiIiIiIiIiIiKjEsUBBRPQL6jvcB9MX+Eo6BhERERERlVJDVhzA3J0Bko5BREQ/ORYoiIhIop7Hv0TbngOga1ENJg61sX7bX3m2/zZ2Emxqu0Nezwx/7jmQZ9ufew5AQb8yNExshK+Q0Mt52qzasBVVatSDuok17Fw9EBkV/cPPiYiIiIiIis+0LSfhNGgpKnaaiZqDl2FP0M0C2+0JugnNlpOxPeCasG7hrtPQaTMVRh1nCl8xL98WaV8iIvrxOAcFERFJVK8ho2FvUxV7N6/F/YhH8GzbFZbmZmhQtzYAwN66Kjq2ao5JcxYVuH+tGtURcnx/gds279yDrX/txZG/tqCqhTmiY2KhqaH+w86FiIiIiIiKn7KiPHZP6wHzCuVx89FztJ+5FaYG2nCpaiy0SU5Jx7L9wbCqpJtv/zZ17bFhbMdC+//cvkRE9GPxDQoiohK2ZNUfMLavBU1TW9jUdkfQuVAAwNWbt1G3SVuUN7dHRduaGDFxOrKysoT95HRNsW7LDlR1aQhNU1vMWLgUUU+eol7TdtAys0OX/kOF9iGhl2HiUBsLV6yBvlV1mDvVxa4DhwvNdCLgDJwaNkV5c3vUa9oOd8MffDFvcUhJSUVI6GVMGjUUcnJycLC1RtsWTbB11z6hzZB+PeFe3xWKCgpf1Xdubi7m+q7C0jnTYG1ZBVJSUqhsagwtTY1iy09ERERE9COsOBgC694LUbHTTDgPXoaQO48BADcin6HxuHUw7jIbVr0WYNwfR5GVnSPsp9lyMjadvCy8bTBvZyCexCei8fg/UKnTLPRZtEtofyEsGjZ9FmLpvmBU7jYX9v0XY1/w7UIz+V17iHojf4dxl9loPP4P3HsS/8W8xWVSVw9YGOlCWloaNSwroraNCa49jM3TZtZ2fwxsUQfa5VS+uv/v2ZeIiL4P36AgIipBEY+jsHbLdlwKOIIK+nqIiY2DSCQCAMjIyMB3zjTUqGaHuBcv0aJLb6zbuhMjB/UV9g8MPocrp4/i2fN41PRogUvXbuLPtcuhraWBek3bYc/fx9CzczsAwMvXCXiTmISndy7hyo1baNG1L5yq2cHSvHKeTLfCwjFg1AQc2rERNarZ46/9h9G25wCEXzyDmGdxheb9r8Wr1mHxqnWFnvubx3fzrRND/PF/xeJ/14nFCH8YWcRPFLh9Lxz6VtWhpaGObh3aYMLIIZCVlUXci3jEvYjHvYeR6DdiHGRlZdC9Q1tMGzcS0tKszxMRERFR6fQoLgEbT1zGmaVDYKBdDrGvkiDKzQUAyEhLY16/ZnCsYogXb96jw6xt2HzyCga3chX2D7r5CGeXD8XzN+/QYNRqXH34FBvGdISmmjK8xq/DwXN30aVRdQDAq6QUvP2QivvbJuJ6RCw6zvoTjuaGqGKkkyfT3agXGL7qIHZP7QlHc0PsC76NrvN24Nq6MYh9lVRo3v9afiAEKw6GFHruT3dP/+Lnk56ZjVuPnqNfk1rCuhuRz3D78XMs/a0lDl8Iy7eP37UHMO06B/qaaujfrBb6NS36vkRE9GOxQEFEVIJkpGWQmZWFBxGPoKOtBZNKRsI2Jwc74XuTSkbo37Mrzl+8kqdAMXboIJRTU4ONlRpsrCzg6VYPZiaVAABe7m64fS8cPdFOaD9r4hgoKCigfp1aaOrREAeOnMCUsSPyZNq0fTcG9OwCFydHAEDPzu2waOUaXLlxCxX09QvN+1/jRwzG+BGDv+rzUFNVRZ2aNTBv2e9YNGMy7kc+wqHjftDR1irS/vVq1cTtEH8YVzRE+MNIdBs4HLKyspgwcgjiXrwEAJwOPo9bIX5IfvceTTv2gGEFffTv0eWrchIRERERlRQZaWlkZecg4tlrlFdXQSU9TWFbNXND4ftKepro7VUToeFP8hQoRrStj3LKiihXSRFVjfXQ0LEKTPQ//n3tUd0Sd6NfCAUKAJjczRMKcrJwtTVD4xqWOHwhDOM6u+fJtM3/Knp71UQNy4oAgC6NqmPZgWBcj4iFgZZ6oXn/a3R7N4xu7/Zdn8+YtYdha6KPRtWrAABEolz4/HEUiwe2KPBBpNZ17dDLqyZ0NVRxPfIZei3cBXUVJbR3c/jivkRE9OOxQEFEVILMzUywdM40zF6yEvcjItG4YX0smT0VFfT1EBkVjXHT5+HG7btIS89AjigH1e1t8+yvp1Ne+F5JURG6up8sKyni5esEYVlTQx0qKsrCciUjQ7x4+Tpfpti459ix7yDWbPpTWJeVnY0XL1+jfp1aheYtLtvXLceIiTNg6lgHpsYV0bV9a9yPeFSkff8pzgCAnbUVpowdgaVrNmDCyCFQUlIEAPgMGwgN9XLQUC+HAT27wu90MAsURERERFRqmVXQxvz+zbBw9xk8jH0F9+pVMK9vMxhol8Pj528wZfMJ3H78HOmZ2cgR5cLBvEKe/XU1VIXvFeXl8i4ryOJ1UoqwrKGqCBVFeWHZSFcT8W/f58v0LCEZe4JuYcOJS8K67GwR4hM/wNXWrNC8xW3a1lN4EPsKR+f1h5SUFABg86nLsDHWh7NVpQL3sar077WLS1VjDGpRB0cv3kN7N4cv7ktERD8eCxRERCWsS7tW6NKuFd5/+IAhPlMwafZC/Ll2OYaNn4ZqttbYuX4l1FRVsXL9Fvx97NQ3Hycp+R1SU9OEIsWz5y9gY2WRr51RBQNMGjUUk0YP+6q8/7VwxRosXLG20DzJMeEFrjeuaIQjf20Wlnv8NhLOjg6fPbfCSElJCcNFWVY2g7y8vHDh8v8G39QvEREREVFJ6uBWDR3cquF9WgZGrzmMmX/6Yf2Yjhi77jDszSpgk09nqCkrYN2RUBy5eO+bj5OckoHUjCyhSPE8IRlVK+V/GMmwvDrGdGwAn44Nvyrvfy3dF4zlB4ILzRO3b2ah2xbsOo3TNyJwYv5AlFNWFNaH3IlC6L0nCLwRAQBISknH3egXCIuOx5LfWubrR0rq3yFmv3ZfIiIqfixQEBGVoIjHUXgR/wp1ajpBUUEBioqKwpwOH1JSUE5NDaoqKnj4KAobtu1EeW3t7zrerMXLMXfKOFy9eRsnAoMwffzofG369eiMDr1/g3t9V9SsXg1paekIuXgZ9WrXxIuXrwrN+18TRw3FxFFDvzrjg8jHMKqgDwV5eew/cgKBwecRdiFQ2J6VlYXcXDHEYjGys7ORkZEJeXk5SEtLw+9MMBztbKCnq4OHj6Iwf9nvaNeiKQBAWVkJHVo1g+/qDahmZ4N37z9g847dGDN04FdnJCIiIiIqKY/iEhCf+B4u1sZQlJOFkrycMKdDSnoW1JQVoKokj8i419jid+W7J3ZeuOs0pvVojOuRz+B/7SEmdm2Ur02vxs7ovuAvNHAwh5OFEdIys3EhLBp1bEzx8u37QvP+19iODTC2Y4OvzrhsfzAOhNzByYUDoVVOOc+2tSPbI+OTicJ7LPgLrerYortnDQDAycv3UcfWFOoqirj5KA4bjl3CtB6Ni7QvERH9eCxQEBGVoMzMLEyeuwgPI6MgJyeL2s5OWLd0PgBg8czJGDx2MnxXr0c1O2t0aNUcZy9c+kKPhdPX1YGmhjoq2deCspIS1iyeC6sqlfO1q1HNHn8sW4CRk2bicfQTKCkqwtWlBurVrvnZvMUl4Ow5LFyxBmnp6ahma4Pje7ZBp/y/hZkmHXvi3MUrAIBL125g8NjJOH1oN9xcayHoXCj6DfdBSloa9MqXR9f2rTFx1BBh31ULZ2Hw2MmoZFcLGurl0K97Z/Tpmv9JLiIiIiKi0iIzOweztvsjMu41ZGVkUNOqElYMbQMAmN2nCUavOYRVf5+HnZkB2tS1w7m70d98LD1NVairKqFq74VQUpDD0iGtYWGkm6+dYxUjrBzaBuPXH0VUfCKU5OVQq6ox6tiYfjZvcZmzIwDysjJwGrRUWDe6/cdih7qqEtQ/aSsvKwM1ZQWoq3x8y+Lg+bsY9vtBZGWLUEG7HEa2qy/MwfGlfYmI6MeTEv/zXhsRESEsLAxdO3fEreBvH1qpNAgJvYxeQ0Yj5s63Fzjo61y8egMT5y7BxUuXJR2FiIiIiCRo/vz5eHM7ENP//5R+aXUhLBqDlu1D+NaJko7yU5m9IwDlq3li8uTJko5CRFQmSEs6ABERERERERERERER/XpYoCAiIiIiIiIiIiIiohLHAgUR0U/IzbUWh3ciIiIiIqJC1bUz4/BOREQkcSxQEBERERERERERERFRiWOBgoiojAsJvQwTh9qSjkFERERERKXYhbBo2PRZKOkYREREebBAQURExe63sZNgU9sd8npm+HPPgc+2dajXGBomNsKXooE5Wnfvl6/djr0HIadris079+RZf/PuPTRs2REaJjYwtK6BVRu2Fuu5EBERERFR8Ut8nwqv8X/ArNscGHeZjcbj1uHy/aeFtp++9RRs+i5CpU6zYNdvMZbuCy5yX7vO3IB26ykw6jhT+LoQFv0jT4+IiIpIVtIBiIjo52NvXRUdWzXHpDmLvtj2zvkA4XuxWAwL5/po16JpnjZJye+waOVa2FhZ5Fn/JvEtmnfuDd/ZU9GuRRNkZWcj7sXL4jkJIiIiIiL6YVQU5bF6RDtUrqANKSkpnLzyAF3mbsejHZMhKyOTr30PzxqY0KURVBTl8SLxHdpN3woLo/JoUce2SH05W1aC36JBJX2aRET0BXyDgoioFFiy6g906js4z7rRU2Zh1OSZAIBtu/fDztUDmqa2sKhRHxv+3FVoX3K6pngcHSMs9x3ug+kLfIXlEwFn4NSwKcqb26Ne03a4G/6gWM8FAIb06wn3+q5QVFD4qv3OX7qCN4lJaNu8SZ71U+YuxtD+vaGtpZln/Yo/NsOzQT10bd8aCgoKUFNVRVUL8+/OT0RERERUGq04GIJeC//Ks27ixmOYsOEYAOCv0zfgMmQ5KnaaiWoDlmCr35VC+9JsORnRLxKF5SErDmDuzn8fHvK79hD1Rv7+8Y2E8X/g3pP4Yj0XRXk5VDHSgbS0NMRiMaSlpZCcko6kD+kFtq9ipAMVRXlhWUpaCtHxb7+pLyIiKj34BgURUSnQsU1zzFm6Eh9SUqCmqgqRSIQDR05g/7b1AADd8to4vHMzzEwq4fylK2jepQ9qONqjur3tVx3nVlg4BoyagEM7NqJGNXv8tf8w2vYcgPCLZ6BQQDHB0c0bz56/KLCvzm1bYfXiOV9/sp+xfe/faNPcGyoqysK6qzdv48adMKxePAcHjp7I0/7KjVuwrWqJek3bISrmKWpWd8CqhbNRyciwWHMREREREZUGbevZY/HuIHxIy4SasgJEolwcvnAPOyZ1AwCUV1fBnmk9YaKvhYvhT9Bh5p+oXsUIDpW/7u/ju1EvMHzVQeye2hOO5obYF3wbXeftwLV1Y6Agl/9WkuvwVYh7k1xgX+3rO2Dp4FaFHst1+Co8ep6A7BwRejauAR0N1ULbLj8QgqX7ziI1IwvGeppo7+ZQ5L7Col+gcre50FRTQqcGjhjdwa3ANzWIiKhksUBBRFQKGFc0gqOdLQ6f8EePTu1w9vxFKCspoVYNRwBAU093oW39OrXg6VYPFy5f++oCxabtuzGgZxe4OH3st2fndli0cg2u3LiF+nVq5Wt/K8TvO87q66SlpePvY6dwaMdGYZ1IJMLwCdOxcsEsSEvnf+nv+Yt43Lp7D6f274BdVStMnL0A3QeNxLkTn5/3goiIiIioLKqkqwn7yhVw4nI4OrtXx7m7UVBSkIOzVSUAgJezldDW1dYMDR3NcSk85qsLFNv8r6K3V03UsKwIAOjSqDqWHQjG9YhYuNqa5Wsf+vuIbz6n0N9HICMrG8cv3Ud2juizbUe3d8OodvURFh2PE1fuo5yyYpH6qmNjiou/j0RFXQ08iH2Nfkt2Q0ZGGmM6NPjm3EREVDw4xBMRUSnRuW1L7D308dXs3X8fRed2LYVtfmeC4dqkDXQtqqG8uT1OnQlG4tu3X32M2LjnWL5uE8qb2wtfz17E48XL18V2Ht/q0Ak/aGmqo34dF2Hduq07YGdtJRRq/ktRURGtmnrB2dEBiooKmOYzEpeu3cC79+9LKjYRERERUYlq7+aAA+fuAgAOnLuD9vX/fYsg8EYEPH3WwbTrx8miA29EIvF92lcf41lCMtYcvgDjLrOFr+cJ7xCf+KHYzuNTivJyaO/mgBUHQxD2haGkpKSkYF+5AhTl5bBw9+ki9WWirwVjfS1IS0vDxkQf4zq54+jFez/kXIiI6OvwDQoiolKifcumGD9zHuJexOPISX+cP/k3ACAzMxMd+w7G1tVL0dLbE3JycmjXcyDEYnGB/SgrKyEt/d+xVl+9ToBRBX0AgFEFA0waNRSTRg8rUiaHeo3x9NnzArd1bd8aa33nfc0pftaOvQfRvUNbSElJCevOnruIc5euwO/0WQDA2+R3uB12H3fu3ceqhbNhZ22Vp/2n3xMRERER/Yxau9pi2paTeP7mHY5fvo+Axb8BADKzc9BrwS6sG90eTV2sIScrg27zdkCMQq4bFOSQlpklLL9O+oAK5csBAAzLq2NMxwbw6diwSJlqD12BZwnJBW7r0KAalg9pXaR+snNEePryLexMDb7YViQS4Ul84Q9tfa4vKSkpFHI5RUREJYwFCiKiUkKnvDbc6tRC/xHjYFKpojDZc1Z2NjIzs6CjrQVZWVn4nQlGYMh52FS1KLAfBxtr7Pn7KGysLHA65ALOXboCp2p2AIB+PTqjQ+/f4F7fFTWrV0NaWjpCLl5Gvdo1oaaaf6zXO+cD8q0riqysLOTmiiEWi5GdnY2MjEzIy8sVOEwTAMS9iEdw6GWsWZK34LH5d19kZGQKyx36/Ia2LZqgb7eOAIDeXTqgY9/BGNa/N2ysqmDest/h6lID6uXKfVNuIiIiIqLSrry6KlxtzTBs5UEY62rCsqIuACArW4TMnByUV1eBrIw0Am9E4Oytx6hqrFdgP7amBjhw7g6qVtLD2duPERr+BNWqfBwKqldjZ3Rf8BcaOJjDycIIaZnZuBAWjTo2plBTzj933aU1o776PK49jEVObi6cqhhBlCvG+uMXkfAuBU4WFfO1zc3NxZ8B19Cmrj3UVRRx81EcNp28gtHt3YrUV+CNCDiYVYCuphoi415jyd4gtHa1++rMRERU/FigICIqRTq3bYk+w8Zi4fSJwjo1VVUsnzcDXfoPQ2ZWFpo1boQWXh6F9rFs3nT0G+6DdVt2oGUTT7Rq0ljYVqOaPf5YtgAjJ83E4+gnUFJUhKtLDdSrXbNYz6NJx544d/EKAODStRsYPHYyTh/aDTfXWth14DAWrVybp/jx1/5DqFXDEZVNjfP0o6FeDlD/d1leXg7l1FSFAkTDenUwZ/I4tOrWF2np6XB1qYEdf6ws1nMhIiIiIipt2rs5YPDy/ZjV21tYp6asgIUDmqPPot3IzBHB29kK3i5WhfaxcEBzDFlxAJtPXkZTF2s0dbEWtjlWMcLKoW0wfv1RRMUnQkleDrWqGqOOjWmxnUNWTg4mbDiOp6/eQlZGBtbGetgzrRcMtD/+rb8v+DaW7w8Wih8nLt3H7O0ByM4RQV9LDQOa1cbA5rWL1Ne5O1EYuuIAUjOyoKOhio4NqnH+CSKiUkJKXNgYIUREv6CwsDB07dwRt4JPSToKlTEXr97AxLlLcPHSZUlHISIiIiIJmj9/Pt7cDsT0Ho2/3Jh+OrN3BKB8NU9MnjxZ0lGIiMoETpJNREREREREREREREQljgUKIiKiYsAXEomIiIiIiJcFRERfhwUKIqJPKCoqIjU1TdIxqAxKS0uDoqKipGMQERERkYQpKioiLVMk6RgkIWlZObwuICL6CixQEBF9wtTUFClpaYiOiZV0FCpjzl64BJdatSUdg4iIiIgkzMXFBefuxfAN21+QWCzGubAY1KpVS9JRiIjKDBYoiIg+ISsriyFDhqL7b6NwN/wBLyroizIyMrFj70Fs230Affr0kXQcIiIiIpKw2rVrQ1ldC+M3nsTrpA+SjkMl5FXSB4zfeBIqGtosUBARfQVZSQcgIiptZsyYAQUFBbTt9RvevX8Hpc+8nisWA9nZ2cjJyYGcnBzk5PhjtazLzRUjOzsLubm5kJeTh4yszGfa5uJDSipcajrj2PHjsLCwKMGkRERERFQaSUtLI+B0EEYOH4qaw36HnKwMIM5FdnY2ZGRlIScnBykpKUnHpO8gFouRnZ0N0f+vAyEljewcEVq0aI6AP9dAWprPAxMRFZWUmI8HExEVSCwW4+3bt8jMzMy3LTMzE1u2bMGaNWvQqlUrjBkzBtra2hJIST/KxYsXMWvWLMjJyWHGjBlwdnbO10ZKSgrq6upQVlaWQEIiIiIiKs3EYjF27dqFyZMnw8rKClOmTIG5ubmkY1Exevz4MebNm4eHDx9iwYIF6NKlC4tPRERfiQUKIqKvIBaLsX//fkycOBG2trZYvHgxrKysJB2LfpDc3FzhotLFxQULFy5E5cqVJR2LiIiIiEq5ixcvYuzYscjKysLSpUvRoEEDSUeiH+js2bPw8fGBvLw8li5dijp16kg6EhFRmcF3zoiIiujixYuoU6cOFi1ahM2bN+Po0aMsTvzkpKWl0b17d0RERMDR0REuLi4YM2YM3r59K+loRERERFQKRUVFoUOHDujcuTOGDBmCa9eusTjxC2jYsCGuXbuGIUOGoHPnzujYsSOioqIkHYuIqExggYKI6AsKusho2LChpGNRCVJSUsLkyZMRHh6O9PR0WFlZYfny5QUO/0VEREREv563b99izJgxcHFxgaOjIyIiItCjRw/ORfALkZaWRo8ePfDw4UNUq1aNDzcRERURf1MSERXivxcZDx8+5EXGL05PTw/r1q1DcHAwgoKCYG1tjQMHDoCjJRIRERH9mjIzM7F8+XJYWVkhPT0d4eHhmDx5MpSUlCQdjSREWVm5wIebsrKyJB2NiKhU4l02IqL/KOwigxMh0z+sra1x7NgxbNy4EfPmzUPdunVx+fJlScciIiIiohIiFotx4MAB2NjYICgoCMHBwVi3bh309PQkHY1KiU8fbjpz5gwfbiIiKgQnySYi+j+xWIyDBw9iwoQJqFq1KhYvXgxra2tJx6JSLjc3Fzt27MDUqVNRp04dLFy4EKamppKORUREREQ/yOXLlzF27FikpaXB19cXjRo1knQkKgPOnDmDsWPHQkVFBUuXLkWtWrUkHYmIqFTgGxRERPh4kVG3bl3MnTsXGzZswPHjx1mcoCKRlpZGr169EBERATs7Ozg7O8PHxwdJSUmSjkZERERExejJkyfo1KkTOnTogIEDB+LGjRssTlCRNWrUCDdu3MDAgQOFOQ6fPHki6VhERBLHAgUR/dKePHmCzp078yKDvpuysjKmTp2Ke/fu4cOHD7CyssKqVas41iwRERFRGZeUlIRx48bB2dkZdnZ2iIiIQK9evTg3HX01GRkZ4eEmW1tbODs7Y9y4cUhOTpZ0NCIiieFvUyL6JX16kWFraytcZMjIyEg6GpVx+vr6WL9+PYKCguDn5wdbW1scOnSIY80SERERlTFZWVlYtWoVrKys8P79e9y7dw9Tp07l3HT03T59uOn9+/ewtLTkw01E9MtigYKIfimfXmS8e/eOFxn0w9jY2ODkyZNYs2YNZs6cCTc3N1y9elXSsYiIiIjoC8RiMQ4dOgRbW1v4+fkhKCgI69evh76+vqSj0U/mn4ebzpw5g1OnTvHhJiL6JXGSbCL6JYjFYhw+fBjjx4+Hubk5lixZAltbW0nHol+ESCTCn3/+iWnTpsHNzQ3z58+HiYmJpGMRERER0X9cvXoVPj4+ePfuHXx9feHp6SnpSPQLCQgIgI+PDzQ0NLB06VI4OztLOhIR0Q/HNyiI6Kd39epVuLm5YcaMGVizZo3wZApRSZGRkUHfvn0RGRkJS0tLODk5YcKECRxrloiIiKiUiImJQdeuXdGmTRv07t0bN2/eZHGCSlzjxo1x69Yt9O7dG61bt0a3bt3w9OlTScciIvqhWKAgop/W06dP81xk3Lp1C40bN5Z0LPqFqaioYMaMGbh37x4SExNhaWmJ1atXIzs7W9LRiIiIiH5JycnJmDBhApycnGBpaYnIyEj07duXc9ORxPzzcFNERAQsLCxQvXp1TJgwAe/evZN0NCKiH4IFCiL66bx79w4TJkxA9erVYWlpiYiICF5kUKliYGCATZs2ITAwEMeOHYOtrS2OHDnCsWaJiIiISkh2djZWr14NS0tLJCYm4t69e5gxYwZUVFQkHY0IAKCqqooZM2YgLCwMiYmJsLCw4MNNRPRTYoGCiH4a/1xkWFhYIDExEWFhYZgxYwZUVVUlHY2oQPb29vD398eqVaswdepUNGzYENevX5d0LCIiIqKfllgsxpEjR2Bra4tjx44hMDAQmzZtgoGBgaSjERWoQoUKwsNNR48ehZ2dHY4ePcqHm4jop8FJsomozBOLxTh69CjGjx8PY2Nj+Pr6wt7eXtKxiL6KSCTC1q1bMX36dDRq1Ajz5s1DpUqVJB2LiIiI6Kdx/fp1+Pj4IDExEb6+vvDy8pJ0JKKv5ufnBx8fH+jo6MDX1xdOTk6SjkRE9F34BgURlWnXr19Hw4YNMWXKFKxcuRIBAQEsTlCZJCMjg/79+yMyMhJmZmaoXr06Jk+ejPfv30s6GhEREVGZFhsbix49eqBly5bo3r07bt++zeIElVne3t64ffs2unXrhhYtWqBHjx549uyZpGMREX0zFiiIqEz69CKjW7duuH37Nry9vSUdi+i7qaqqYtasWbhz5w5evnwJCwsLrFu3Djk5OZKORkRERFSmvH//HpMnT4ajoyPMzMwQGRmJ/v37c246KvNkZWWFh5tMTU1RrVo1PtxERGUWCxREVKZ8epFhamqKiIgIDBgwALKyspKORlSsDA0NsWXLFvj5+eHgwYOws7PD8ePHOdYsERER0Rfk5ORg3bp1sLCwwMuXL3H37l3MmjWLc9PRT0dVVRWzZ8/G3bt3ER8fz4ebiKhM4hwURFQm5OTkYOPGjZg1axaaNGmCuXPnwtDQUNKxiEqEWCzGqVOnMG7cOOjp6WHp0qVwdHSUdCwiIiKiUkUsFuPEiRMYN24cDA0N4evri2rVqkk6FlGJuX37Nnx8fPD8+XMsWbIEzZo1g5SUlKRjERF9FgsURFSq8SKD6F85OTnYvHkzZs6cicaNG2Pu3LmoWLGipGMRERERSdytW7cwduxYvHr1CkuWLEGTJk14Y5Z+Sf883OTj4wMDAwP4+vry4SYiKtU4xBMRlVq3bt2Ch4cHxo8fj6VLlyIwMJDFCfqlycrKYtCgQYiMjETFihVRrVo1TJ06FR8+fJB0NCIiIiKJiIuLQ69evdC0aVN06tQJd+7cQdOmTVmcoF+WlJQUmjZtirt376Jjx45o2rQpevfujbi4OElHIyIqEAsURFTqxMXFoXfv3mjatCk6duyIu3fv8iKD6BNqamqYO3cu7ty5g2fPnsHCwgLr16/nWLNERET0y/jw4QOmTp0KBwcHVKxYEZGRkRg0aBDnpiP6v38eboqIiICRkRGqVauGadOm8eEmIip1WKAgolLj04sMIyMjRERE8CKD6DOMjIzw559/4sSJE9i7dy8cHBxw8uRJTqRNREREP62cnBysX78eFhYWePbsGe7cuYO5c+dCTU1N0tGISqVy5cph7ty5uHXrFmJjY2FpaYkNGzbw4SYiKjU4BwURSRzH1Sf6fp/O12JkZARfX184ODhIOhYRERFRsfhnXP1x48ZBT08Pvr6+qF69uqRjEZU5N2/exNixY5GQkIAlS5bA29uboxUQkUSxQEFEEiMWi+Hn5wcfHx/o6upi6dKlvMgg+k7Z2dnYtGkTZs2ahaZNm2LOnDkwNDSUdCwiIiKib3bnzh34+PggLi4OS5YsQbNmzXhDleg7iMViHD9+HOPGjUPFihX5cBMRSRSHeCIiibhz5w4aN26M0aNHY+HChQgKCmJxgqgYyMnJYfDgwYiIiIC+vj4cHBwwY8YMpKSkSDoaERER0Vd5/vw5+vbtCy8vL7Rt2xZ3795F8+bNWZwg+k5SUlJo0aIFwsLC0KZNG3h5eaFv37548eKFpKMR0S+IBQoiKlEvXrwQLjLatGmDsLAwtGjRghcZRMVMXV0d8+fPx82bNxEdHQ1LS0ts2rQJIpFI0tGIiIiIPislJQUzZsyAg4MD9PX1ERERgcGDB0NOTk7S0Yh+KnJychgyZAgiIiKgp6cHOzs7PtxERCWOBQoiKhH/XGTY2dlBT08PERERGDJkCC8yiH6wSpUqYceOHThy5Ah27NiBatWqwd/fX9KxiIiIiPIRiUTYtGkTLC0tER0djZs3b2L+/PlQV1eXdDSin5q6ujoWLFiAmzdvIioqig83EVGJ4hwURPRDiUQibN26FTNmzEDDhg0xb948GBsbSzoW0S9JLBbj6NGjGD9+PExMTODr6ws7OztJxyIiIiKCv78/fHx8oKWlhaVLl6JGjRqSjkT0y7p+/TrGjh2Lt2/fwtfXF15eXpKOREQ/MRYoiOiH4UUGUemUnZ2N9evXY86cOWjZsiVmz54NAwMDScciIiKiX1BYWBjGjRuHJ0+eYPHixWjZsiWHfyUqBT59uMnU1BS+vr6wtbWVdCwi+glxiCciKnZhYWHw9vbGiBEjMHfuXAQHB7M4QVSKyMnJYdiwYYiMjISWlhbs7Owwa9YspKamSjoaERER/SLi4+MxYMAAeHh4oHnz5rh37x5atWrF4gRRKSElJYVWrVrh3r17aN68ORo1aoQBAwYgPj5e0tGI6CfDAgURFZtPLzKaNWvGiwyiUk5dXR2LFi3C9evXERERAUtLS2zZsoVjzRIREdEPk5qailmzZsHW1hZaWlqIjIzEsGHDODcdUSn1z8NNERER0NTUhJ2dHWbPns2Hm4io2LBAQUTfLTU1FbNnz4adnR00NTURERGB4cOH8yKDqIwwMTHBrl27cPDgQWzduhXVq1dHYGCgpGMRERHRT0QkEmHLli2wtLREREQEbty4gUWLFnECbKIyQkNDA4sXL8b169fx4MEDWFpaYuvWrXy4iYi+G+egIKJvJhKJsH37dkybNg316tXDggULYGJiIulYRPQdxGIxDh06hAkTJqBKlSpYsmQJbGxsJB2LiIiIyrDAwED4+PigXLly8PX1hYuLi6QjEdF3unLlCsaOHYuUlBT4+vrCw8ND0pGIqIziGxRE9EUikQi9evXC69evhXWnT5+Gk5MTNm/ejIMHD2L37t0sThD9BKSkpNC2bVuEh4fD29sb7u7uGDRoEF6+fCm02bRpE/bt2yfBlERERFQazZo1C6GhocJyeHg4mjZtiiFDhmDGjBk4d+4cixNEPwkXFxecP38e06dPx+DBg9G0aVOEh4cL20NDQzFr1iwJJiSisoIFCiL6Il9fXzx79gzly5cXLjIGDx6M6dOn4/z587zIIPoJycvLY8SIEXj48CHKlSsHW1tbzJ07F2lpaahZsyaGDh2Kp0+fSjomERERlRKBgYHYvHkzbGxs8PLlSwwaNAju7u7w9vZGeHg42rZty7npiH4ynz7c5OXlhYYNG2LQoEF49eoVbGxssGnTJpw+fVrSMYmolOMQT0T0WWFhYXB3d8epU6ewceNGHDp0CFOmTMHgwYMhLy8v6XhEVEKio6MxefJkhIaGYu7cuXjx4gVOnz6NwMBASEvzeQciIqJfWXJyMuzt7bFmzRrcuXMHK1asQJ8+fTB58mRoampKOh4RlZCkpCTMmzcP27Ztw6hRo2Bvb49hw4bh7t270NDQkHQ8IiqlWKAgokJlZWWhRo0aMDc3x7lz59C7d29MmTKFFxlEv7DLly9j7NixSE1NRXZ2Nn777TcMHz5c0rGIiIhIgnr06IEXL14gMjISrq6umD9/PszMzCQdi4gkJDo6GpMmTcKlS5dQpUoVGBoaYvv27ZKORUSlFAsURFSozp07Y//+/bC2tkbdunUhJyeH1NRU9OjRAw0aNJB0PCIqYTNnzkR8fDxUVFQQGxuLgIAApKam4sKFC6hdu7ak4xEREZEErFixAqNHj4aRkRE8PDygrq6O1NRUuLq6onfv3pKOR0QlbNu2bQgNDYWKigqSk5Nx+vRpPH/+HMuXL8eoUaMkHY+ISiFZSQcgotJLQUEB7dq1g7m5OVRUVKCqqgoVFRVOhk30i6pTpw6ioqKQmpoKdXV1GBsb4/r163jz5o2koxEREZGEpKeno0mTJrCzsxOuF1RVVWFnZyfpaEQkAXZ2dsjKykJKSgq0tbWhp6eHu3fvIj09XdLRiKiU4hsUlEdOTg5CQ0MRExOD3NxcScehUkpaWhomJiZwdXWFrCzrnEQ/u5cvX+L8+fNISUmRdBQqxRQUFODs7IwqVapIOgoR/WC5ubm4fPkyHj9+DJFIJOk4VIppamqiQYMGHHue6BeQlpaG4OBgvH79GrzVSIWRk5ODra0tHBwcICUlJek4VEqwQEGCK1euoE2bNjDQ14e1lSVvPFOhcnJycP9hBOJfvsThw4dRs2ZNSUcioh8gNzcXw4YNxe5du1C3phM01NXAvyGpMOnpmbhw9SZs7ezx96FDUFNTk3QkIvoBwsPD0bypN5TlZWFX2QiyMjKSjkSllBhivH77HlfuPcLkyVMwcdIkSUcioh9kx47tGD5kCOwqaaOChiKkec1AhcjKEePm00SU09LBqcAgVKhQQdKRqBRggYIAfKx0m5iYYNMfa9CiWVNJx6Ey4tiJk+j/21A8ffo/9u46LqqsDeD4jwZpQQFBDBQVuxURWxGxu9dYde3u7l5zXbu7sTuxuwFFRVJEUToG3j94HWUBYxcd1Of7+czn5d577rnPmXedO3OfE8/R09NTdThCiAy2YMECtqxfjfuqeRga6Ks6HPEDUCgU9Bw1DXSMWCMLIQrx01EoFOTNnYvRHVxoVauCqsMRP4jAV2HUHfgnC/9ejqur/NYU4mdz9+5dalSpxI7u5ShgaaTqcMQPICkpiXnHH3P+lS7nL11VdTgiE1BXdQAiczhy5AjFihSR5IT4KvXruVKsSBEOHz6s6lCEEN/A5o0bGNWnqyQnxBfT0NBg0uCe7Nm7l9jYWFWHI4TIYBcuXMBEX0eSE+KrWJmb0LtpNTaul8S1ED+jLZs30rKMjSQnxBdTU1OjT3U7Hj16hK+vr6rDEZmAJCgEAN7e3pQoUUzVYYgfUIkSxfD29lZ1GEKIb8D78ROKO9irOgzxg8lunhUjQ32CgoJUHYoQIoN5eXlR1M5G1WGIH1Cx/Dnx9vJUdRhCiG/A++F9ilgZqDoM8YPR1FCnkI0Zjx8/VnUoIhOQBIUAktcU0NLUUnUY4gekpalFQkKCqsMQQnwDCQkJaGnJekTi68m9QYifk0KhQEtT1pwQX09LQ1PuC0L8pOLjE9DU+DEWnbDsv5unIRGqDkP8n6aGmtwbBCAJCvEvVKvlQlZLmwybuiGPvQNZTMwxNLPAKldeOnXtTkSE3DA+tmnLNnLnL4RB1uw0bt6K169ff/acdRs2oa5rwIpVa1Idi4uLw6F4KXLapewZffLUaUpXqIRxNivsChZh2YpVGdUEIcQvoFbr7liWqE5sbFyG1bluxz5Ku7TC1MGJXOXq0Gf0dMLehWdY/T+i2w88qdigPaYOTlRs0J7bD9LukRobG0f3YZPI71Qf86JVKFevDUdOeyiPP/MLQDdvWcyKOCtfUxeuUB4vWadFimP6+SvQpOuAb94+IcTPxbX/LGzr9yM2Lj7D6tx42IMKncdj4dKLfE0GMeDPDYRFRGVY/T+iO499ce42CQuXXjh3m8Sdx+lPmbF090mqdJ+Mee0/6DE95ff9Kw+e0HDwXGwb9CNPowF0GP83QaFhyuOLth+jWJsRWNfrg32zwQxfvJUEheJbNUsI8YsqM+EIOQftITQi5XOnmrNOYtl/N76hkf/5Gn03Xmf6gQf/uZ7v6fqz17T46zwFR+7HYdQBuq6+TPDbmHTL91p/jWJjD5Jv2D4cpxxl48VnymM7r70g71B35SvPEHcs++/m9os3AJz3DqHJonPkH76PMhOOfOumiV+cJCjEV3n27DnnPC6gpqaG+/4DGVav+87thIcGc/PyRW7evs20mbMzrO7vKTg4OMPrvP/gAT1692XdquUE+fqQRU+PXv0+/YDozZs3TJs5i8IOhdI8PmvuPLKZm6fYFx8fT5OWbejWpTNhLwPYsmEtg4aN4PaduxnWFiHEz+uZXwAeV2+hpqbG/hNnM6TOeSs2MHrGIqaN6MvL26c5s2s1vv6B1Gvfi7gMfND1rUTHxBAe8d9/PH0sLi6eZt0G07phXYJunqRdk3o06zY4zfcjQaHAxsqCY1uW8vL2KcYP/IO2fUbyzC8gRbngWycJvXeW0HtnGdmnq3L/zSPblPtf3T2DjZUFTVxrZmh7hBA/t+dBr7hw1xvU4OCF2xlS58JtRxm7bCeTejTDb/98Tvw1At/gUBoOnktcfObvhRkdG0d4VPoPk/6NuPgEWo9eTItaFfB1n0frOo60Hr043ffDysyEIe3r0b5upVTHwsKj+M3NmXubp3N/y3QM9HT5Y8Ya5XFXx+KcXTYG/wMLubRqAveevODvnScytD1CCAFgm1Wf3Tf8lNsPA94SHffjJkSTkpJ4FfHfOvqGRcXRzjEPV8fW4dq4OhjoaNJ/8/V0y/epac/VsXV4PKM+a7tWZPrBB8oERNMyOfGZ2UD5mtasOLnM9ClmYwJAFm0NWpfPxdgGRf5TzEJ8CUlQiK+ybuMmKpQrR8f2bVm3YRMAsbGxmFpYc+/+fWW5kJAQspiY8/LlSwD2HzxEyXIVMbWwplLVGty5ey/N+i0tLahdq2aKh+Lu+w9QpGQZTC2sqVbLhYePHimPPXz0iGq1XDC1sKZIyTIpkiadunanZ9/+uDZojKGZBU5VaxIUFEz/wUPJamlDoWIluXnrww+lGbPnYpM3P0bmlhQsWpITJ0990XsSHx/P7r3uNGzagvyFi3/ROV9j4+Zt1Heti3NlJwwMDJg4bgy79rgTHp5+D+IRY8bRp9cfmJuZpTr29OkzNm7ewvChg1Psf/36Ne/evaN929aoqalRtkxpChUswIOHDzO8TUKIn8/GXQcoV7II7Zu6sWFn8mdxbGwcFsWrcd/zw7yiIaFvMCnkxMtXySPBDp44R7l6bbAoXo2qzTpz92HymjbvwiOYNG8Zc8cPpnYVR7S0NMltk4ONi6bx3D+QTXsOAcnTjcxYvJpCVRthXrQKFRu050VA8toHD7ye4Nq+F1Yla2Bbtg4zFq8GoOuQ8Yybs0QZ05lL17FzrKfctq/cgJl/raZE7RZYlqjO70MmEPMVowYv37xLr1FTyVPBlfteT/7N25muM5evo1Ao6NO5NTo62vT6rRVJSUmcvng1VVn9LHqM6d+N3DY5UFdXx7VGZXLb5ODm3Udp1Pxp567cIPRNGI1dqmdEM4QQv4jNRy5StlBe2tZxZNORCwDExsWT060vD576K8u9Cgsne52ehLx5B8Chi7ep1HUCOd36UrP3dO49SX5A9S4ymqmr3ZnVpzW1yhVBS1OTXJbmrB3XHd+gULYeuwSAQpHI7A0HKNZ2JDlce+PcbRJ+L5PvOw+f+itHCNg1GcjsDcn3rB7TVzFx5W5lTOdueVKw+RDldpFWw5mz8SBlfxuLbf1+/DFjNTFfkSy/8uAJ/easp0CzISnanhHO3fIkQZFIr2Y10dHW4o+mNUhKgjM30/68b+BcCjenkpga6ac6Vrt8URpXLYORvh5ZdHXo1rgal+99uI/ntc6OiUEWIPlhm7qaOk/8QzK0PUIIAdCsbE62X/0wGmzrVV+al7VNUSY2QcH4vXcpPf4wRUYfZOi2mymSGItPelFs7EGKjz3EpkvP0r2Wb2gklv13s/XKc0qPP4zDqAPMO/phlLIiMYn5xzwpP+kodsP2UXv2KfzffNnIveevIpl56CFlJx5l86XnX9j6tNVwsKRBCWsMdbXIoq1J58p2XHma/gwbBa2M0Pn/1IxqgJoaPHuVdgeqbVd9aV42J2pqydN1lcqVleZlbclllvpeIURGkwSF+CrrN26mTesWtG3VkiPHjhMcHIyOjg6NGzZg89btynLbdu6iSmUnsmfPzs1bt+nS/Q/+XrSAVwG+dOvSmYZNW6Q5RZSfnz+HjxzFzi4vAF7e3rTp0Ik/Z83kpd8z6rrUoUGTFsTFxREfH0+DJi2oVbMGwS+esmDubNr91gVPLy9lfdt37mLS+LGE+D9HR0cbxyrVKVWiBCH+z2nauBGDhg4HwNPLi8VLlnLl/FnevQri8L495M6V65Pvxd179xg4dDg2efMza+48XF3q4Pv4w4+A6bPmYGphne7rSz14+JBixYoqt+3s8qKtrY2Xd9oLCV25eo3rN27S4/euaR7vO3AwUyaOR09XN8V+CwsLWrdozup161EoFFy8dJnnvr44OTp+caxCiF/Xxt0Had3AhVYNXTh27iLBIaHo6GjTsE41tu47qiy388AxKpcvSXbzrNy670n34ZNYNGUkATeO06V1E5p2G0RsbByXbtwhJjaORnWqpbiOgX4W6lStxInzlwGYv3IT2/YdYe+qeYTcOc3SGWPIoqdLeEQkru17UbtKRZ5eOsj9U7uoVqnsF7dni/th9q1ZwIPTu/F+5su0RZ+e8i7w5SvmLF1H8VrN+W3AGKyyZ+PivvVUKFUMAI+rt7AoXi3dl8fVW18U10MvH4oUzKf84QBQtGB+Hnj7fPbc4JBQvJ/64mCfN8V++8oNsHOsx+9DJvDqdVia527YeYBGLtXQz6L3RXEKIQTA5qMXaVGzPC1qVuDE1Qe8fP0OHW0t6lcuxY4TV5Tldp26RqXi9mQzNeK2ty+9Zq5l3sD2PNs7j05uzrQatYjYuHgu339CTFw8DZxLpbiOgZ4utcsX5eT15Kk6Fm0/xo6TV9k5rS/+BxayeOhv6OloEx4VQ4PBf1KzXBG8dszm1oapVCmV9ojjtGw7fpndM/tza+NUHr8IZub6/Z8sHxQaxrzNhynTcQxdp6zE0tyYs8tGU76wHQAX73qT061vuq+Ld72/KK6HzwIonNcmxb2hcF5rHj0L+MRZX8bjjjcFc+dIsW/b8ctY1+tDnkYDuPvkBZ3rO//n6wghxD+VzpWViJgEvILeoUhMYu8NP5qWyZmizJR99/F5GcHxIdW5NLoWgW9jmHsk+bnMyYfBLDn5mK1/OHFhVC3OeX0+mXrFJ5TzI2uxvacTc488wisoOXH+92lvdt/wY2O3ijye7safrUuhp53+mkxRcQlsu+JLk0XncJl7ilfhsSzrWJY+NT9Ms20/fH+6r4XH057C9Z8uPXlFAUvDT5YZtv0WeYa44zTtOBZGutR0sExV5sXrKC49eZUqASTE9yIrX4ovdt7jAs99fWnRtAnm5ubY5c3Dpq3bGdC3N21atqBH775MmTgegM1bttOta2cAlq1cRbcunSlfLvnBUMf2bZk2czaXLl+hinNlABq3aIWamhoRERFUr1qFCWNGAbB1+07q1a1DrZrJPTYHD+jHgsV/ceHiJTQ0NIiIjGD4kEGoq6tTvVpV3Oq6sHnrdsb///xGDRpQulTJ5L8b1mfJ0uV0aNcGgJbNm7L472UAaGhoEBsXy4OHj8iWzZzcudNPTpw8dZpho8YQHPySdm1ac+7kMezz509VbviQQQwfMug/vOPJIiIiMDY2SrHP2NgozREUCoWCXv0GsPDPOairp84/7t7rjkKhoHHDBpw+k3oKllYtm/P7H73oP2goAH8tmEfOnDb/uQ1CiJ+bx9Vb+PoH0rReLcyzmpDX1oat7kfo26UNLRvUoffoaUwc3BOALfuO0LV1EwBWbt5Nl9aNKVciedhw+6ZuzPxrNZdv3eXVmzDMTY3R1Ez9VcUqmxk37iX/8Fi9dQ9Th/fFPm9uAIoVSv7Sv9X9CBbZzOjftR0Aujo6yut8iT/atyBnjuQv78N7dmbAhFlMGPRHqnK+/kH0GzuDi9dv06B2VRZNGYFT2ZIpHhIBVCpbguDbXzYy71MioqIwNjRIsc/IUJ/wz8y9Hh+fwG8DxtCuaT0K2OUGwNzUBI89aynuYE/om7f0HzeT3waMYf/ahSnOjYqOYffhk+xYNuc/xy+E+HVcvOvNi+DXNKlWBjNjQ/LkyMa2E5fp3bwWzWuWo9/cDYzt2hiA7Scu0+n/D7nX7D9Lp/rOlHVITqa2dXFkzqaDXH3gQ+jbCMyMDdDUSP1QyNLMmFteyT1T1x48x6Ruzchvm/w5XjRfTuV1LLIa0adFbQB0tbWU1/kS3RpXwyZ7VgAGt6vHkIWbGdulcapyL4JDGThvI5fvPcHNqSTzB7bHsVj+VPeGikXz82L/gi++fnoio2Mx0k+ZQDbS1/vPU0nde+LHjHX72DK5V4r9yUmn8jz2C2bz0Ytkz2qUTg1CCPHfJI+ieEHFfObktzDEyvhDR8ukpCTWX3zGqaHVMdXXBqBfTXv+WH+NUfUL437Tj1blbSlklfwZNdilYIopo9IyyKUQetoaFLY2xsHamAcB77C3NGLTxeeMaVCYfBbJyYDC1sbp1jFwyw0O3gmgVK6sdKyUB5eiVspRDB/zmu721e/Hxx4EvGXu0Ues6VLhk+VmNC/B1KbFufYslAuPX6GtmfpZ0farvpTPay6jJYTKyAgK8cXWbthE7Zo1MP//2gWtW7Zg3YaNAFSr6kxUdBSXr1zl2bPn3Lpzh8YN6wPg6/uCufMXphg98MLPj4DAQGXdu7dt4d2rIE4dPcQjLy9ehYYCEBAYiK3thwyuuro6OW1s8A8IJCAwkJw2NikexNvmssU/4EO9FtmzKf/W09XDInv2D9t6esrFuPPZ2fHnrJlMmDwVi5x5aN2+IwEf1fOxlyEhPH7iQ5HCDhQvVgTbnDnTLPdvnDvvgaGZBYZmFhQpWQYAAwMD3v1jQdh378IxNEydJf9r6TKKFilChfLlUh2LjIxk2MgxzJ87K81rP/L0pHX731i7cjmx4W+4d/Mqs+bO48ChwxnQMiHEz2zDrv3UdKqAeVYTAFo2qMOGXcm9SqtWLEN0dAxXbt3jmV8Adx540bB2VQB8/QOZv2JjipEEfoHBBAa/wtzUhFdv3pKQkHr+7MCQUMxMk6/lFxhMXtvUo9KS9//7BKuNlYXyb1trSwKDX6VZLio6mgfePlhbZqdYofwUtMuT6gHUf/HxItW+/kEYZMnCu/CUw7LDIyIx/P90G2lJTEyk86CxaGtpMW/8UOV+A/0slC7mgKamJhbZzPhzwhCOn7uUat2MPUdOYmpshHP5Uv+sWggh0rXpyEWql3HAzDj5O2vzGuXY/P9pnpxLFCQ6Jo6rD3x4HvSKu09eUL9y8meMb3Aoi7YdSzGSwP/lGwJDwzAzNiD0bUSaizIHhb4lq3FyAtf/5RvyWGdLVcb/5Rvy5Ei9/0tZZ8uq/NvWIitBr8LSLBcVE8ejZwHkyGZKETsbCuSyytB7g1Xd3srXi+BQ9PV0CI+KTlEmPCoGwyy66dTweU/8X9J0+Hxm9G6FYzH7NMvks7GgUO4cDJy38V9fRwghPqVZGVt23XjB1ivPU/XufxURR3ScgtqzTytHHrReekG5sHbwuxhymHz4jmyTNf3vy+9lN9RR/q2npUFkbPJvkYCwaHKZG6R3WgqPAt+hpaFOEWtjHHIYp5mc+K+ehkTQZukFJjUuRgU788+W11BXo3xecwLDoll7/mmq49uv+tKinIyeEKojIyjEF4mOjmb7zl0oFAqsciX3MoqNjSMsLIzbd+5SvFhRmjdtwuZt27HInh03VxflA3QbG2tGDhvCqOFDP3UJAKo4V6Zju7YMGT6K3du3kMPKKsXaFklJSbzw88M6hxUaGhq88PMjMTFRmaR44fuC/Pnz/as2tmnVgjatWvDu3Tt69OrL8FFjWLd6RapyrVo0p1GD+uza486qNevo2XcATRo1pGO7NjhV+jAd0tQZsz652Hd4aOoFtSs7VUq136FQIe58tCaHj89TYmNjsU+jnSdPneHMufMcOnIEgNev33Dz9h1u3blD106/8ez5c5xrJPcYi4uL5+3bt1jlysvFM6e4d/8B9vnzUadW8gKoBeztca1bh0NHjlKvrsun3johxC8sOiaGnQeOo0hMJFe5OkDy/OJh78K589CLYoXsaVqvJtvcj5DdPCuu1Z0wNEjumWOTw4JhvTozvFfnVPW+fReBjrYWe46colm9Wsr9EZFRHD1zgQn/H5FhY2WBj68/hQuk/Ey0sbJg+/6jpEVfT4/o6A+9SoNDQlOV8Qv88Fn8IiAIK4u0v/gXzJeHR2f2cObSddbt2MekectwKleSto3rUa9GZXR0kntznb9yk4ad+6VZB8DeVfNxKlcy1f7QeylHuxWyz8v8lRtJSkpSPuy6++gxPdo3T7PepKQkug+bRPCr1+xdNQ8trfS/+qmRXF9iYlKK/Rt2HqBtE9cMfbgmhPi5RcfGsfv0NRSKRPI1SR5RHBefQFhEFHcfv6Bovpw0rlqGHSevkN3UCJcKxZQP022yZWVwO1eGtKuXqt63EVHoaGvifvYGTap9mLYvIjqGY1fuMe7/IzKss5vy1D8EhzwpE9jW2U3ZeSr1mj0AWXR1iI6JU24Hv36bqox/yId5vl+8fI2luUmadRXIZcWdTdM4d8uTDYc8mLbGHcfi9rSuXZG6FYuho60FwIU7XjQdlv4Iip0z+qaZHAg8tCjFdqHcOVi07WiKe8N9Hz9+b1Qt1blfwjcolIaD5jK0fT1a1674ybIJikSeyhoUQohvJGfWLNhm1efEg2DmtkrZWcZMXxs9LQ3ODK+BlUnqaUizG+kSEPZhlLH/m+hUZb5UDhM9nr+KUI7G+JSDA6riFfSOLVd8abb4PFYmejQvm5PGpWzIqv8hAZJ3qHu6dfSrVYB+tQqkeezF6yia/+XBgNoFv3pKpoTEJJ6FpuyMdMUnlKB3MdQvniOds4T49mQEhfgie9z3oaGhwf2b17h5+SI3L1/kwa3rVK7kyLqNyYtlt2nZgm07drJpy1Zat2yhPPf3zp1Yunwll69cJSkpicjISA4cOpzuIs/9+/Ti2ImT3L5zlxbNmnDg0BFOnDxFfHw8c+YtQEdbB8eKFShfrixZ9LIwc86fxMfHc/rMWfYdPESrFs2+un2eXl6cPHWa2NhYdHV10dXTS3OKpPd0dXVp06oFRw64c+vKRXLnsqVztz/IV+jDWhEjhw0hPDQ43deXatu6BfsOHuLceQ8iIyMZN3EyTRo1SHMExerlf/Pg1nXl/0dlSpdi7KgRTJkwjiKFHfB97Kk8tnzJIiwssnPz8kVy5rShZPHieD9+wslTp0lKSuLJEx8OHDxMsSJfPiWKEOLX4370DBoaGtw8so3LBzZy+cBGbh3bRqWyJdm4K3nh0ZYNXNhx4Bhb3A/TssGHhGfnlo1YvmknV27dS74/REVz6OR5wiMiMTYyYFTf3xk4fjZHz1wgPj6BZ34BtO09AmvL7LRt5ApAp5aNmDD3bx4/9SUpKYm7D70JfROGa3Ungl6+YuGqTcTGxhEeEcmVW/cAKOZgz+HTHrwOe0tQyCsWrd6cql1/r9+OX2Awr8PeMn3x6hRJkn9SU1OjasUyrJozgcce+3GrWYWFqzeTu0Jd5aLfTuVKEnrvbLqvtJITaalSvjTq6uosXrOF2Ng4lqzbBkDVimmvr9Fn9HQ8nzxj1/K5qdYeunLrHl4+z0hMTCT0TRiDJs7GuUJpjI0+9A7zCwzmzKXrtGvy34agCyF+LfvP30RdXY0raybgsWIsHivGcnXtRByL5Wfz0YtA8oiKXaeuse34ZZrXKK88t6NbZVa5n+HqA5/ke0N0LIcv3iE8KgZjgywM71CfIQs3c+zKPeITEnge9IqOE5Zinc2UVrWSp7no6FqZyav38NgvmKSkJO498SP0bQQuFYsTFPqWxTuOExsXT3hUDFcfJK/hUyxfTo5evsfrd5EEv37LXzuOp2rX8j2n8Q95zet3kczecJCmVcuk+x6oqanhXLIgy0Z24cG2mdRzLM5fO46Tv9lg5aLfjsXsCTy0KN1XeiMX/qlyiQJoaKizZOcJYuPiWbr7JABVShZMs3yCQkFMXDyJiUkoEpOIiYtXjkoJCHmD26A5dGtcjS4NqqY6d+2Bc8rFzB89C2DuxoNUKZX2dYQQIiP82bokO3o5oa+TsqONuroabSvmZuyeu4SEJ4+aCAyL5tTD5OctDUpYs/WKL55B74iKS2DO4Uep6v5SbSrmYsbBh/iERJCUlMSDgLe8jky9rup79pZGjG1QhBvjXRhUpyAXH7+i7MSjKRbq9pnZIN1XesmJwLBomi0+T+fKeelYKc8nYw4Jj2XPDT8iYxNQJCZx6mEwu2/4Udk+5UjCbVd9qVcsBwa6Win2JyYmEROvIF6RSBLJf8clJH7mnRLi35ERFOKLrNuwid86tMPWNuV0Rr3+6E6/QUOYMWUS5cuVRT+LPgGBQdStU1tZpkzpUixbsog+Awbh/fgJenq6ODlWxNmpUprXypYtG+3btmbS1Gns2LKJ9atX0HfgYPwDAilRrCjuu7ahrZ3cI9V91zZ69R3A9FlzsM5hxdqVyyhYIO0P8k+JjY1jxJhxPHzkiZaWFo4VyrN08cLPnwjkzGnDqOFDGTV8KOc9Lnz1tT+nsIMDSxbOp91vXQh9/Zqa1auxatkS5XHXBo1xquTIyGFDMDExSXGutpYWRoaGGBsnz49oaflhypKspqaoq6sr99nZ5WXl0r/oN2gIz31fYGxsRJtWLena+bcMb5MQ4uexYdd+OjRzw9Y65WJrf3RozqAJs5kyrA/lShQhSxY9AoNfUafKh5FmpYs5sGTqKAaMm8njZy/Q09XBsUwJ5cP6Qd07kNXEmOHT5uPz3B8jA33q167CmnmTlCMT+nVpQ2xcHPU69iH0TRgF8uZi69+zMDM14cC6RQyaOIcpC1ago61F706tKVeiCG0bu3LK4woFnBuSy9qKDs3qM39lyukpWjaog1vHPgQGh+BWqwojenf5ovfD0ECfTi0b0qllQ548e4G+fsYuKq2trcX2pbP5Y8RkRs9cTMF8udm+dDba/++NO2Pxajyu3cR99QKe+weyYvMudLS1yVX+Q2Jo0eQRtG5Ul6e+/oyd/Rchoa8xMtCnulN51s2fnOJ6m3YfpHzJotjlkvWIhBBfbtORi7RzqUROC7MU+7s1qsbQhVuY2L0pZR3ykkVXm8DQMGqV/9AhplSB3CwY3IHBCzbh4/cSXR1tKhbNR6XiyQ/r+7d2IauRPqOXbOdpQAiG+rq4VSrJilFdlSMTejevRWx8PI2H/Eno2wjy21qyaVJPzIwN2Dt7AMMWbmHG2n1oa2nSs1lNyjrkpVXtipy+/pCirYdja2lGW5dKLNqWciRe8xrlaDRkHkGvwnCtVIIh7VOP8kiLYRZdOtSrTId6lXni/xIDPZ3Pn/QVtLU02TSpF31mrWX88l0UyGXFpkm90P7/qLnZGw5w4e5jds1IHsk3c/0Bpq/dpzx/67FLDO9Yn5G/NWDtwXM8Cwhh2pp9TFvzocz7URuX7j1m4srdREbHYm5sSKOqpRnduVGGtkcIIT6W+xNTK42uX5i5Rx5Rb95pXkfEYWWsS8dKealWyIIaDpZ0q2JHs8XnUVdTY5hrIXZef/GvYuhRNT9xCYm0XOLB68g48mU3YPVn1n6A5KmVahW2pFZhS95ExvEy/L+tDbTx0jOeh0Yy+/BDZh9+qNzvM7MBAPOPeXLpSSibeziipgZrPHwYuu0WiUlJ2GTNwqTGRalTxEp5Xky8Aveb/qzsnHqa8ItPXtF08Xnldu4h7lS0M2d3n8r/qQ1CpEUtKSkp6fPFxM9u6tSphIe9Yeqk8aoORfxgRo4Zj6GJKSNHjlR1KEKIDGZibIzn2T2YGKUesfUzs6/cgCXTRlHDqfznC4s0OVRrytETJ7Gzs1N1KEKIDLRs2TIuHtjCggFtVB3Kd1ek1XAWDulAtdIOqg7lh3TT8zn9/9rDjdt3P19YCPFDaexWF7fsr3ErnnpdOCE+pdWqm4yYtZTatWt/vrD4qckUT0IIIYQQQgghhBBCCCGE+O4kQSGEEEIIIYQQQgghhBBCiO9O1qAQQgghhPiI1zl3VYcghBAik7m3ZbqqQxBCCCGE+CnJCAohhBBCCCGEEEIIIYQQQnx3kqAQP5016zZQuVotVYchhBAik1i3Yx/VmndVdRhCCCEykY2HPajdZ4aqwxBCCJFJbLn8nAbzz6g6DCF+SZKgEOIHdOv2HcpUdELfNBtlKjpx6/addMtWq+WCnrEZhmYWGJpZULBoyRTHQ0JCaNuhEybZc5DV0oZ2HTsrjxUpWUZ5nqGZBVr6xjRo0vybtUsIIcS/c/uBJxUbtMfUwYmKDdpz+4FnumWf+QXQsFM/LEtUJ1e5OvQfN5OEhIRU5TbsOoBu3rKs2rpHuW/Byk0UrNKQbMWqkqdCXYZMmpvmuUIIIVTvzmNfnLtNwsKlF87dJnHnsW+6ZZ8HvaLp8PnY1u9HviaDGDR/EwkKhfL4oQu3Kd9pHFZ1e1Oz93QePQtQHus/dz1WdXsrX+a1/yCHa+9v2jYhhBBf755fGLVnnyLPEHdqzz7FPb+wdMvmHeqe4pVjwG5G7rytPH7O6yVOU4+RZ4g7TRad48XrKOUx5+nHU5xrPXAP7Zdf/JZNEz84SVCITC0pKYnExERVh4Hioy/nwFc/jMnIhzdxcXE0ataStq1b8TrIjw7t2tCoWUvi4uLSPWfhn3MIDw0mPDSYR3dvpjjWtGUbLCwteO79kOAXTxk0oJ/y2L2b15TnvXsVRE4bG5o1aZxhbRFCiK8l94XU4uLiadZtMK0b1iXo5knaNalHs26DiYuLT7N8vzEzyGZuyrPLh7h8YCPnLt9g6YYdKcq8efuOmX+txsE+b4r99Wo6c2nfBkLunOb64S3ceeTN4jVbM6wtQgjxb2See0PKGBL+ca/4nK8t/ylx8Qm0Hr2YFrUq4Os+j9Z1HGk9ejFx8WnffwbO20g2E0O8ds7GY8VYPG57sXzPaQAe+wXTdcoK5g1sx4v986lbsRgtRy1SxjtvYHsCDy1SvppVL0ujKmUyrC1CCPG1ku8LSaoOA8U/YkhQfN296mvLf0pcQiIdV16iaZmcPJpWjxZlbem48hJxCWlfw2dmA+Xr7iRXdLU0qF/CGoDQiFg6r7rMUFcHHk6tR/GcJnRfe0V57tnhNZXnPplRnxwmetQvbp1hbRE/H0lQiE+aMXsuNnnzY2RuScGiJTlx8hQA0dHRdOranayWNhQuUZpZc+eR085eeZ66rgGPnzxRbnfq2p3R4yYA8ObNG+o3bkZ2m1xktbShfuNm+Pn5K8tWq+XCqLHjcapaE33TbPg8fcojT09qu9bHzConBYuWZNuOncryoaGhNGzaAuNsVpR3qsITH58vatun6uzUtTt/9OlHvYZNMMianVOnz5LH3oEZs+dSvEx5DLJmJyEhAff9ByhSsgymFtZUq+XCw0ePlHWkVT4jnD5zjgRFAv379EJHR4e+vXqSlJTEydNfPxTx6LETvPDzZ9a0KRgbG6OlpUXJEsXTLHv23HlehYbStHHD/9oEIcQPbPbfa8lb0RXzolUoWqMpJz2Sv4hGx8TQdch4LEtUp0TtFsxdth47x3rK83TzluXJsxfK7a5DxjNuzhIg+WF44y4DsClTC8sS1WncZQB+gcHKsrVad2fs7L+o2rwLpg6Veerrj+eTZ7i274VVyRoUrdGUHQeOKcuHvgmj6e8DyVasKk6NOuLj6/dFbftUnV2HjKfP6Ok07NSPrIUrc/riNewrN2D232spU7c1WYs4k5CQwP7jZyhZpwUWxatRq3V3Hj1+qqwjrfIZ4czl6ygUCvp0bo2Ojja9fmtFUlISpy9eTbP8M78AmrnWQldHB8ts5tSuUpEH3invnWNmLqZXx5aYmZqk2G+XywYTI0Mg+YefupoaT56/QAjxa/tz8yEKNB9CDtfelOowmtPXHwIQHRtHj+mrsK3fj7K/jWX+liMUbD5EeZ5Rtd954v9Sud1j+iomrtwNwJvwSJqPWECeRgOwrd+P5iMW4B/yWlnWtf8sJq7YTa3e07Fw6cXTwFd4+QbScPBcbBv0o1SH0ew69eFzMPRtBC1HLcK6Xh+q/jEFn4CQL2rbp+rsMX0VA/7cQNPh87Gs24uztx5RpNVw/tx8iIpdxmNZtzcJCgUHPW5R7rex5HTri2v/WXg+D1TWkVb5jHDulicJikR6NauJjrYWfzStQVISnLn5KM3yzwNf0bhqWXS1tbDIakzNcoWVoyROXL1PxaL5qVg0P5oaGgxo7ULgqzDO3/JKVU9kdCzuZ2/Qpk7FDGmHEOLHtPC4FyXGHcJu2D4qTTnGOa/kz/roOAV9N16nwIj9VJ52nMUnvSg57pDyPMv+u3kaEqHc7rvxOtMPPAAgLCqOdssu4DDqAAVG7KfdsgsEhEUryzZeeI5pB+5Tf/4Z8gx153loJN7B4bT46zwFR+6n0pRj7L354XfB68hYOiy/SL5h+3CZe5rnoZFf1LZP1dl343WGbrtFm6UXyDPUHQ/vEMpMOMLC415Um3GCvEP3kaBI5Mi9QJynH8d++H4aLzyHV9A7ZR1plc8IFx6HoEhMolsVO3Q0NehaxQ6S4Lz35++H+2/7Y26oQ4W8ZgAcvBNAAUsjGpSwRldLg8EuhXgQ8Bbv4PBU5158EsrryDjqFc+RIe0QPydNVQcgMi9PLy8WL1nKlfNnyZHDimfPnit7jE6YMo0nPj48fnCHyMgoXBt+ea/6xMREfuvQjq0b16FQKOjS/Q/6DBjE7u1blGU2bNrCQfddFLC3JzIykqKlyjFh7GgOuu/m7r371K5XnyKFHXAoVIje/Qeiq6tDwLPHPH32DJf6jciTK/cnY4iMjKS2a4N06wTYvHU7B/bsZN/uHcrRCVu2bWf/7p2Ym5vh8/QpbTp0Yve2LVStUpk/FyyiQZMW3L91DW1t7VTlNTVT/3MrXqY8vi/SfnDWumVz/lowL9X++w8fUqxIEdTU1JT7ihUtwv0HD3GpnfbaGyPHjmPEmLEUyG/P5AljqVrFGYBLV65QwD4/v3XpxqGjx8ibJzezpk2hinPlVHWs3bCJpo0aoq+v/4l3VgjxM/PyecaS9ds4v2ctOSyy8cwvQNlbdMr85fg89+fB6d1ERkXTsHO/z9T2QWJiIh2a1WfjomkoFAq6D5vEgPGz2L50trLMpt0HcV89H/u8uYiMiqFU3ZaM7d8d99Xzuef5hHodelHY3o5C+fPSf9xMdHV0eHbpEM9e+FP/t77ksvn0F+LIqGhcO/RKt06Are6H2bNqPrtX/klcfPLohG37jrB75Z+YZzXhqa8/HfqNZtvfs6lSoTQLVm2iye8DuXVkG9raWqnKp3VfKFO3NS8CgtKMsWWDOiyYNDzV/odePhQpmC/FfaFowfw88PahdhXHVOV7d2rNtv1Hca5Qmjdv33HkzAXGDeihPH719n1u3H3IgknD2HHweKrzt+w9TJ8x0wmPiMQ8qwkzRvX/xDsrhPjZefsGsWz3KU4vGYWVuQnPg14p7w3T1+7jaUAItzZOJSomlqbD5n9xvYmJSbRzqcTacd1RJCbRa+YaBs/fzObJvZRlthy7xM7pfclva0lkdCwVOo9nVKeG7JzRj/s+/jQc/CcOeawpmDsHg+ZvQldLE68ds3ke9IrGQ+eRy9L8kzFERsfScPCf6dYJsP3EFXZM68v2qX2IS0j+rbTjxBW2T+uLmbEBTwNC6Dx5OZsm9aJyCXsWbz9Oi5ELubpmItpamqnKa2popIqjYpfx+AW/TrUfoFmN8vw5oG2q/Q+fBVA4r02Ke0PhvNY8ehZArXJFUpXv2bQmO09eoXIJe8LCozh2+R6jO3/omJTEh17ASUnJSeoHT/2pWrpQinr2nr2OuYkhlYrbI4T4NT0ODmf1eR8OD6yKpbEevqGRJCYlf4bMOfKQ56GRXBpdm6i4BNosvfDF9SYmJdGqfC6W/VaOxMQk+m++wcgdt1nTtYKyzI6rL9jY3ZF82Q2IilNQdcYJhtQtxKbujjwMfEfLJR4UtDKigKURI3bcRkdLg9sT6+L7OorWf3tgmzXLJ2OIjE2g5RKPdOsE2H3jBRu7ObLh94rE/f9+uOeGHxu6VSSrvg7PQyPpse4qa7pUwDGfOUtPP6bDikucHV4TbU31VOU1NVL3La824wT+b6JT7QdoXNqGGc1LpNrvGRROISvjFPeFQjmM8Ax6R/VCFp9s97arvjQvY6s81zMoHIccxsrj+jqa5DLTxzPoHfktDFOee+U59YrlQF9HHkGL9MkICpEuDQ0NYuNiefDwEfHx8eTOnQs7u+SHNNt37GLk8KFkzZqVnDlt6NPzjy+u18zMjKaNG5ElSxYMDQ0ZOWwoZ86dT1GmY/u2FHZwQFNTk8NHj5E7ly2dOrZHU1OTkiWK06RRQ7bv3I1CoWDn7r1MGDsafX19ihQuTIe2qb+g/9P+g4fSrfO9hm71qORYEXV1dXR1dQHo0/MPcua0QU9Pj63bd1Kvbh1q1ayOlpYWgwf0IzommgsXLynr+Lh8Wm5fu8ybYP80X2klJwAiIiIwNjZKsc/YyIjw8NSZaoDpUybx5OE9/Hy8+b1LJxo0bcGTJ8k9Zf38/Tl6/ARVqzgT+PwJA/v1oVHzVrx69SpFHVFRUezcvYeO7T//3gohfl4a6hrExcXz0NuH+PgEctvkwC6XDQA7Dh5neK9OZDUxJmcOS3p2bPnF9ZqZmtC4bnWy6OliaKDPsF6dOHf5Rooy7Zu54WBvh6amJkfPXiCXdQ46Nm+ApqYmJQoXoJFLdXYePIFCoWD34ZOMHdAd/Sx6FC6Qj7ZN6qVz5Q8OnjyXbp3vudWqgmOZ4sn3BR0dAHp2bEXOHJbo6eqy/cAx6lZzombl8mhpaTLg93bExMRy8caHdYI+Lp+Wa4c2E3z7VJqvtJITABFRURgbGqTYZ2SoT3hEVJrlK5cryUNvH7IVq4qdYz1KFS1Eg9pVgeSpq/qNmcGfE4agrp7218RWDV0IuXOaeyd20rVNE7KbZ037TRVC/BLUNdSJjU/g0fMA4hMSyGVpTl7r7ADsPn2Nwe3qkdVIH5vsWenRpMYX12tmbEDDKqXJoquDYRZdBrdzxeN2yh77beo4UiiPNZoaGhy/ch9bSzPa1a2EpoYGxfPb0tC5FLvPXEOhSMT97A1GdW6Ivp4ODnmsaVP78z38D1+8k26d77k6FqdC0XzJ94b/J6N7NKmBTfas6Olos+vUNeqUL0r1Mg5oaWrSt2VtYuLiuXz/w2jzj8un5eLK8bzYvyDNV1rJCUhOrhjpp/wNYqSvR3hUTJrlKxW359GzAKzr9aVgi6GULJALN6fkteuqlS6Ex20vzt3yJC4+gdkbDxKXoCA6NvUUs5uPXKRV7YopHoAJIX4tGupqxCYo8AoKJ16RiK2ZPrnNk7+rut/yp1+tApjqa2NtmoWuznZfXG9WfR3ciluTRVsTA10t+tcqwMUnKZ9dtChnS0ErIzQ11Dn5MJicWbPQunwuNDXUKWpjQr1iOdh3yx9FYhIHbgcwtG4h9HU0KWRlRIuytp+N4dj9oHTrfK9OESvK5TVDXV0NXa3kpHMX57xYm2ZBT1uDvTf9qelgSZUC2dHSUKdntfzExCu4+jRUWcfH5dNyalgNvKa7pflKKzkByckVI72USQIjPS0iYj49qvvF6yguPn5Fi3If3p8vrSsqLoH9twNoWe7z7634tUn6SqQrn50df86ayYTJU7n/8CF1atVgzozp5MhhRUBgIDltPswflyvXl3/YREVFMWDIMI4cPc6bsDAAwsPDUSgUaPy/x1BOGxtl+ee+L7h89RqmFh+ul5CQQLs2rQgJeUVCQkKK8rlsc3LuMzF8qs73bGxSz4/38XUCAgOxtf3QbnV1dXLa2OAfEJhm+YxiYGDAu3cpkxHvwsMxNDRMs3z5cmWVf3ds35Yt27Zz8MgR+vT8Az09PXLnykWXTh0BaNWiOVNnzMLj4iUa1ndTnrdrjztZTU3THFkhhPh12OXOyazRA5k8fzkPvUdQy7kCM0YNIIdFNgKDX2Fj9aHnTS5rqy+uNyo6hiGT53L0zEXC/v/5Fh4RmeK+8HHdvv5BXL19D4vi1ZT7EhQK2jRyJeT1GxISFCnK21pbASnX3/mnT9X53sd1prUvMPgVttaWym11dXVsrCwICHqZZvmMYpAlC+/CUw5JD4+IxNAgdQ+wxMRE6v/Wly6tG3N6+0oioqLoPmwSo2YsZOrwvizdsIMiBfNRvmTRz143Xx5bHPLb0W/MDLb+PSvD2iOE+LHYWWdneq+WTFuzj0fPAqhetjDTerbAytyEwFdh2GQzVZbNafHlCc2omFhGLN7G8av3CAtPTriGR8WgUCSi8f/epDbZP9T9IjiUaw+fktOtr3JfgiKRVrUr8OptOAkKBdbZPlw/p4UZ4P3JGD5V53s22VO3yfqjfUGhYeS0NFNuq6urY53NlICQN2mWzyj6ejqER6XsXRseFYNhltQJ8sTERJoMm8dvbs4cWzSciOhYes1cw9ilO5nUoxn2tlb8Pbwzg+dvIuj1W1rWrEDBXFbk+Oj/W0h+v87d9mTB4A4Z3h4hxI8jTzYDJjUuxuzDD/EMCqdqwexMaFQUS2M9gt/GkMPkQ/LUxvTTIxY+FhWXwLjddzn5KJi3UcmjmSNiE1AkJqGhnpwUtTb9ULffmyhuPH+N/fD9yn0JiYk0K2NLaEQsCYlJXx3Lp+p87+MYPuz7UHfQ2xhsPiqjrq5GDhM9gt7GpFk+o+jraBL+jwRCeEwCBrqffjS845ov5fKakcvsw2waX1rXwTsBmGTRxjHfp0csCiEJCvFJbVq1oE2rFrx7944evfoyfNQY1q1egZWlJS/8/Cns4ACAr2/K+aezZMlC1EdfiIOCg7G2Th4GPWfeAry8vLl07jSWlhbcun2HUuUdSUr6MGz44x43OW2sqVLZiaMH96WKT6FQoKmpyQs/PwoWKJAcy4vPz4X9qTrTiiGtfTmsrLh3/75yOykpiRd+fljnsEqzfFqKlCzDc9+0423buiV/L1qQan/hQoWYO38BSUlJyvrv3L1Hzx7dPnmtj2N6/14XK1KE/QcOpTr+T+s2bKR929bSE0oIQauGLrRq6MK78Ah6jZrGqBkLWT13IpbZzfELDMbBPrkXlO8/pinKoqdLVPSHL93BIaFYWyY/rJ+3YgNePs85t3s1ltnMuf3Ak/Ju7dK9L9hYWVC5XCkOrl+cKr7k+4IGfoHBFLDLDZDulEkf+1SdacXwYd+Hv60szLnv+aFHbFJSEn6BweSwzJ5m+bSUrNMCX/+0423dsC6LpoxItb+QfV7mr9yY4r5w99FjerRvnqrs67B3vAgI4o/2LdDR0UZHR5sOzeozfu4Spg7vy6kLVzl3+QZHTnskl3/7jtv3PbnzwIt5E4amqi9BkYCPr3+q/UKIX0uLmuVpUbM87yKj6Td3PWOX7WT5yC5YmhnjF/KGQnmSO/74vUw5TVEWXW2iYz70wg9+/U750HvhtmN4vwji5F8jschqzJ3Hvjj9PinFVEMffy5bZzfFqbg9e2cPTBWfQpGIpoYG/iGvsbe1SjOWtHyqzg9BpLHro32WZiY88PkwpWtSUhL+IW9SPNz/3L2h3G9jeZHOFE8ta5Vn3sD2qfYXyp2DRduOprg33Pfx4/dG1VKVfRMeyYvg13RrVA0dbS10tLVoV7cSk1buYVKPZgA0qlKaRlVKAxAWEcX6g+cpXTB3inq2HLtEhSL5yJMj26cbJIT46TUpnZMmpXMSHhPPkK23mLzvPovalSG7kS4BYdEUtEqeFcI/LOWIXz1tDaLjP6zFExL+IaHx96nHPH4ZwaEBVclupMs9vzBqzj71/98MyZ9zah99KFub6FHRzpxtPZ1SxadITEJTXY2AsGjllET/jCUtn6rzPbU0bgwf77E01uVh4Ic1J5KSkggIi8bSWDfN8mlxnn4cv9dpx9usTE5mtiiZan8BS0P+PuWd4r7wIOAtnZzyfvJa26/60rtGymn7Clgasu2qr3I7MjaB568ildNcvbftii/Ny+aUZ0nis2SKJ5EuTy8vTp46TWxsLLq6uujq6Smne2jerAnTZ87mzZs3+Pn5s2jJ3ynOLVGsGJu2bkOhUHD46LEUUziFR0Sgp6eHiYkxr1+/ZuKUqZ+Mw821Ll7ej1m/cTPx8fHEx8dz9dp1Hj56hIaGBk0aNWDC5KlERUXx4OFD1m3c9Nm2farOL9WiWRMOHDrCiZOniI+PZ868Beho6+BYscLnT/6/ezevER4anOYrreQEQNUqldFQ12DB4r+IjY1VvvfVq1ZJVTYsLIwjx44TExNDQkICGzdv5ex5D+VaFY0b1udNWBhr129EoVCwY9du/PwDqPRRG/z8/Dl15iwd28n0TkL86rx8nnHqwlViY+PQ1dFBT1dHeV9o5lqTmUvW8ObtO/wCg1mydluKc4s52LPV/TAKhYKjZy5w7vKHEQ0RkVHo6epgYmTI67C3TFmw4pNxuFZ3wvupLxt3HyQ+PoH4+ASu3b7Po8dP0dDQoFGdakyev4yo6BgeevuwcdeBz7btU3V+qWb1anLo1HlOelwhPj6BeSs2oK2tRcVSxb64jptHthF672yar7SSEwBVypdGXV2dxWu2EBsbx5J1ye991YplU5U1z2pC7pw5WLZxBwkJCYS9C2fDrgMUKZAfgOWzxnHr2DYuH9jI5QMbKV20EKP6/s6EQT0BWLV1Dy9fJT8ke+jtw6wla6jmmPo6Qohfh7dvEGduPCQ2Lh5dbS30tLVR//+DiMZVyzB34yHehEfiH/KapbtPpji3qF1Otp+4jEKRyLEr91JM4RQRFYOujjbGBll4/S6S6WvT71gE4FKxOI/9gtl89CLxCQnEJyRw/dFTPJ8HoqGhToPKJZm6Zh9RMbE8ehbApiOfn/f8U3V+qSZVy3Dk8l1OX39IfEICC7cdRUdLk/KFv3xakytrJhJ4aFGar7SSEwCVSxRAQ0OdJTtPEBsXr3zvq5QsmKqsmbEhua3MWel+hgSFgrCIKDYduUDhvB9Gg9/0fI5CkcirsHD6zV5HXcfiymTPe5uPXqRtndRrHwkhfi2Pg8M57xVCbIICHU0NdLXUlYnYBiWsWXDck7CoOALColl51ifFuUWsjdl1/QWKxCROPgxOMYVTRGwCuloaGOlp8SYyjjlHPv38plZhS56ERLD9qi/xikTiFYnc9H2DV9A7NNTVcC2Wg9mHHxIVl4Bn0Du2XfH9ZH2fq/NLNShhzfEHQZzzekm8IpElpx6jralO2Txmnz/5/84Or4nPzAZpvtJKTgA45suGuroaK84+ITZBwcpzyR2rnPKnn1S++jSUwLcxNCiRcoaRusVy8CjwHftv+xMTr2DukUcUymGUYv2JgLBoPB6/kumdxBeRBIVIV2xsHCPGjCObdS6sctkREhLC1EkTABg3agS5bG3JW7AIddwa0K5N6xTnzpszk/0HDmJqYc2mzVtp9NF0Qf179yI6Opps1rmo6FydOrXSXtj5PUNDQ44c2MvW7TuwzpMPq1x2DB81htj/z3m68M85REREYpXLjk6/9+C39u0+27bP1fklCtjbs371CvoOHEw261zsP3AQ913blAtkfyva2trs3r6F9Rs3Y2phzeq169m9fYvyulNnzMK1QfKi5fHx8YwZP5HsNrnJZp2LRUv+Zve2LdjnT34QlTVrVvbu2MqcefMxyZ6DGbPnsmf7FszNPwy/W79pMxXLl1OuPyKE+HXFxsYzZuYirMvUIld5F0JC3zBpSPJipaP6/Y6ttRUFnRvi1rEPbRq7pjh3zphBHDh5DosS1dm89zD1a31Iqvbu1JromFisS9fCuUlnajl/el5wQwN9DqxbyPZ9R8lTsS65yrswauYiYuOSP8P/HD+UiMhocpV34fchE2jfzO2T9X1JnV/CPm9uVs+dyMAJs7EuXZMDJ86xa/lc5QLZ34q2thbbl85m4+6DWJSoztrt7mxfOlt53RmLV9Og04fpSbYumcnRsxexKVObwtUao6WpyazRAwAwMTLEMpu58qWlpYWhoT7GRsnzBl+8dpsydVuTtXBlGnbuj0vVSkwc0vObtk8IkbnFxicwbvku8jQaQL6mgwgJe8f435sAMLxjfXJaZKVY6xE0GjKPVrVSfr7P6NOKQxduk7N+X7Ydv0w9pxLKYz2b1SQmNo48DQdQo9dUapZNvbDzxwyz6LJn5gB2nryKfbMh5Gs6mHHLdhIbnzwNyOx+bYiMjiF/08H0mLGatnUrfbZtn6vzS+S3tWT5yC4MWbiZPI0GcujCHbZO6a1cIPtb0dbSZNOkXmw5epGc9fux4ZAHmyb1Ul539oYDNPlo0fINE//g+JV75G00kBJtR6GlocH0Xh/Wkxq2aAs56/elVIfRmBjqs/Af0zhdvv+EgJA3NKpa5pu2SwiR+cUpEpm8/z4Oow5SbOxBXkXEMsqtMACDXApiY5qFchOP0nKJB83K5kxx7qTGxTh6Pwj7EfvZdf0FLkVyKI91q2JHTLwCh1EHqDfvNNUKfnrqVANdLbb2qMSem34UH3uIYmMOMnnfPeISkheuntqsOJGxCRQbc4h+m27Qqnyuz7btc3V+iXwWhixuV4aRO+/gMOoAR+8Hsq5rReUC2d+KtqY6a7pUYPvVFxQYsZ8tl5+zpksF5XXnH/Ok9d8pk/fbrvhSr1gODHRT/p4xN9BhZafyTD/wgIIj93Pj+Rv+7pCy09KOq76UyZ1Vuf6IEJ+ilvTx/AnilzV16lTCw94wddL4f3X+6TNnad+5Ky+eeH2+sPipjBwzHkMTU0aOHKnqUIQQGczE2BjPs3swMUp7jZtPOXPpOp0HjuXJhc+PXhA/H4dqTTl64iR2dl/eQ1gIkfktW7aMiwe2sGBAm391/rlbnvw+ZQWPtsu6Nb+am57P6f/XHm7cvqvqUIQQGayxW13csr/GrXjqdTw/x8M7hN4brnFzQt1vEJnI7FqtusmIWUupXbu2qkMRKiYjKIQQQgghhBBCCCGEEEII8d3JItnip3XuvAeuDZukeSw8NPg7RyOEEELVzl+5ScPO/dI8Fnrv7HeORgghRGZw4Y4XTYelvfZb4KFF3zkaIYQQqnbpySvaLE17nSKfmQ2+czRC/BokQSEyRNUqzplueqfKTpUkESGEECpSpULpTDe9k1O5kpKIEEIIFapcokCmm97JsZi9JCKEEEJFKuXPlummd6pgZy6JCCG+M5niSQghhBBCCCGEEEIIIYQQ350kKMQvo1PX7oweN0HVYQghhMhEug4Zz7g5S1QdhhBCiEyix/RVTFy5W9VhCCGEyMT6brzO9AMPVB2GED8NSVAIoUKvX78mu00uKlerlebxiVOmoa5rwPETp77o3Li4OJq3bkseewfUdQ04fUamMhFCiB/N67C32JSpRbXmXZX7Nu85hFkRZ+XL1MEJ3bxluXH3IQCnL16jdpseZC9WFfvKqYek21dugEkhJ+X59Tr0/m7tEUII8e/sOnWVmr2nY+HSC9f+KafGunDHC6u6vVO8jKr9zt4z15VlngaE0HzEAnK49iZ3wwGM+XuH8tjS3Sep0n0y5rX/oMf0Vd+tTUIIITLeWc+X1Jp9kjxD3Sk57hB7b/opjx29F0iV6cfJO9Qdt3ln8Ax6pzy254YflaYcI//wfRQefYA+G68RHhOviiaIX5ysQSGECg0bNZZCBQqQmJiU6tiTJz7s2LUbKyvLrzq3kqMj/Xr3okXb9t8kZiGEEN/WqBkLKWCXh8TEROW+1o3q0rrRh/l51+3Yx7RFKylZpCAA+ln06Ni8AS3q12bmX2vSrHfn8jnUcCr/TWMXQgiRcUyN9OnZtAZevkGcvfkoxbF/rp1x7pYnLUcupGa5IgDExSfQcMifdGtYlTVju6Ohoc7jFx/W57MyM2FI+3qcuHqf6Ni479MgIYQQGc4z6B1/rL/KgjalqVIgO+9i4nkXnZxk8AmJoOf6a2zsXpHSubLy10lvOq64xPkRNdHUUKdsnqy493PGzECHyNgEhmy7yfQDD5jStLiKWyV+NTKCQnwXM2bPxSZvfozMLSlYtCQnTiaPCLhy9RqOVapjamFNjtx29O4/kLi4D1+Q1XUN+GvpMuwLF8fI3JIx4yfy5IkPlarWwDibFS3btleWP33mLDnt7Jk6YxbZrG3JY+/Axs1b041p/8FDlCxXEVMLaypVrcGdu/c+G29GunDxEvcfPOC3DmknEnr3H8j0KZPQ1tL+4nO1tbXp36cXTpUc0dDQyPCYhRAiI83+ey15K7piXrQKRWs05aTHFQCu3r5PlaadsShejdzlXeg/biZxcR968ujmLcvS9dspXK0J5kWrMH7uEp4896Nqs85kK1aVtr1HKMufuXQdO8d6zFi8GuvSNbGv3IDNew6lG9PBE+coV68NFsWrUbVZZ+4+9P5svBnp4vXbPPDyoUMzt0+W27DrAG0b10NNTQ2AssUL07axK3lyWmd4TEII8b38ufkQBZoPIYdrb0p1GM3p68mjxK49fEqNXtPI6daX/E0HM2j+JuLiE5TnGVX7neV7TlGi3ShyuPZm0qo9+Pi/pGbv6VjX60PH8X8ry5+75UnB5kOYveEAuRsOoEir4Ww9dindmA5dvE2lrhPI6daXmr2nc+/Jh16p6cWbUaqVdqBJtbJYmZt8tuymIxdoWKU0+no6AGw8fAErM2N6t6iNvp4OutpaFLGzUZZv4FwKN6eSmBrpZ2jMQgjxrS087kWJcYewG7aPSlOOcc7rJQA3nr+m3p+nsR++n2JjDzJix23iEj50+LHsv5vV532oOPkodsP2MePgA569isBt3hnyDdvH72uuKMt7eIdQctwh5h/zxGHUAcpMOMLOay/Sjeno/UBqzDyJ/fD9uM07w4OAt5+NN6PMO+pJB8c81HCwRFNDnaz6OuQ2NwDg1KNgytuZUT6vOZoa6vSuYU/Q22guPnkFgLVpFswMdJR1qaup8fRVZIbGJ8SXkBEU4pvz9PJi8ZKlXDl/lhw5rHj27DkKhQIADQ0N5s6cTpnSpfDz88e1YWP+Wrqc/n16Kc8/euwE1y6e44WfH6UrOHHx0mXWr1qBmVlWHKtUZ/PW7XRs3xaAoKBgXoWG4ufjzaXLV6jXqCllSpekgL19iphu3rpNl+5/4L5zO2VKl2LDpi00bNqCR3dv8uz583Tj/afps+YwY/bcdNv+Jtg/zf0KhYI+Awax7K9F3L13P9Xx7Tt3oaOjg6tLna8+VwghfgRePs9Ysn4b5/esJYdFNp75BaBQJP8g0FBXZ+boAZQuWgi/oJc07NSPpRu206dzG+X5x85d4qL7OvwCg6nQoD2Xrt9h1dyJmJmYUKVZZ7buO0L7pskP+YNCQgl9E4bPhYNcvnWXRp37U7pYIezz5k4R0637nnQfPomdy+dSumghNu05RNNug7h7fAfP/QPSjfefZi1Zw+y/16bb9uDbaSe9FQoFA8bP4q+po7jn+Tjd85/7B3L+yk2WzhiTbpm0dBowlsSkRIo7FGDaiL4UK2T/+ZOEEOI78fYNYtnuU5xeMgorcxOeB71KcV+Y1qsFpQrkxj/kDU2HzWf53tP0alZTef6Jq/c5u3Q0/i9fU7n7ZK7ce8LykV3IaqRPzd7T2X7iCm1dHAEIfv2O0LcReG6fydUHPjQbsYBSBXKT3zblyOXb3r70mrmWrVN6U6pAbrYcu0SrUYu4vm4SvkGh6cb7T3M3HeLPTeknx1/sX/Cf3rvI6Fj2nrnO1ql9lPuuPvDB1tKcJsPmc+PRMxzy5GBW39YUzmvziZqEECJzexwczurzPhweWBVLYz18QyNJTEqeVUJDXY2JjYtRPKcJAWHRtF16gTXnfehWNZ/y/NOPgjk6uBr+b6KpPfsUV5++ZnG7Mpjqa1Nv3hl233hBy3K5AHgZHsvriFhuTnDh+rM3tF12geI5TchnYZgiprt+YQzYfIP1XStS3NaUHdd86bD8Eh6javIiNCrdeP9p4XFPFh73TvMYgNf0tDswXX/+mtzm+lSdcYLXkbFUzp+dyU2KYaqf3Nn148sl/X/7UeA7KttnB+CyzyvaLbtIeEwCetoarO4sI67F9ycjKMQ3p6GhQWxcLA8ePiI+Pp7cuXNhZ5cXgNKlSlKhfDk0NTXJnTsX3bp24ey5cynOHzKwP0ZGRhR2cKBIYQdq1axB3rx5MDY2xqVObW7evp2i/KRxY9DR0aGKc2Xq1a3Dth27UsW0bOUqunXpTPlyZdHQ0KBj+7bo6Ohw6fKVT8b7T8OHDOJNsH+6r/QsWLyEcmXLULpUyVTHwsPDGTV2AvNmz/zqc4UQ4kehoa5BXFw8D719iI9PILdNDuxyJT80KVW0EOVLFk2+N9jkoGvrxpy7cjPF+QO7dcDI0AAHezsK29tRs3IF8traYGxkQJ0qFbl93zNF+XEDe6Cjo41z+dLUrebEjgPHU8W0cvNuurRuTLkSRdDQ0KB9Uzd0tLW4fOvuJ+P9pyF//Ebw7VPpvtKzeM1WyhYvQqmihT753m3cdYBKZUt81WiJNX9OwvPcXrzO7aNKhdLU79iHsHfhX3y+EEJ8a+oa6sTGJ/DoeQDxCQnksjQnr3Xyw5OSBXJRzsEOTQ0Nclma06l+FTxup/yc79/KBSN9PQrlscYhdw6ql3UgT45sGBtkoVa5Itx57Jui/OjOjdDR1sKpRAHqlC/KrtPXUsW0Zv9ZOtV3pqxDXjQ01Gnr4oi2tiZXH/h8Mt5/GtimLi/2L0j39V+5n7uBmbEBTsU/JJ79X71h58mr9GhSHa8ds6hToRitRy9OMfJECCF+NBrqasQmKPAKCidekYitmb5ytEDxnKaUzp0VTQ11bM30ae+YRzlS4L1e1e0x1NWioJURBa2MqFIgO7nM9THS06J6IQvu+b1NUX6oqwM6mho45jOnpoMl7rdSP+dZf+EZHRzzUCp3VjTU1WhZLhc6mupcf/bmk/H+U5+aBfCa7pbuKz2BYdFsv/qClZ3KcXFUbWLiFYzalfyczNk+OxefvMLDO4S4hETmH/MkTpFIdNyHTrjl85rjPb0+N8e70LNafnJmzfJl/2cIkYFkBIX45vLZ2fHnrJlMmDyV+w8fUqdWDebMmE6OHFZ4eXszaOgIrt24QVRUNAkJCZQuWSLF+RYWH77o6+nqYZH9421dgoI/DI8zNTVBX//DMGVbW1sCAgNTxeTr+4J1GzaxaMlS5b64uDgCAgOp4lw53XgzQkBAIAsXL+HaxXNpHh8/eSrt2rQid+5cX32uEEL8KOxy52TW6IFMnr+ch94jqOVcgRmjBpDDIhvePs8ZOmUeN+4+IComloSEBEoWSfnQ3sI8q/JvXR0dsn+8ratLcEioctvU2BD9LHrKbVtrSwKDU/5YAfD1D2TDrv0sWbtNuS8uPp7A4Fc4ly+dbrwZISA4hMVrt3LRfd1ny27cdZChPX/7qvody3yYR3Zoz05s2HUAj6s3qVfD+WtDFUKIb8LOOjvTe7Vk2pp9PHoWQPWyhZnWswVW5iZ4vwhi5F/buOn5nOjYOBIUiZSwt01xfrasRsq/dXW0yW768bYWL19/WBTUxDCLciokgJyWZgSFhqWKyTc4lE1HLrJs10nlvrgEBYGhYTiVKJBuvN/b5iMXaVW7onLaPwA9bS0qFs1H7fJFAejbsjazNhzA83kgRfPl/O4xCiFERsiTzYBJjYsx+/BDPIPCqVowOxMaFcXSWI8nL8MZt+cut1+EER2nQJGYRLGcJinOz2b44bNfV0s9xbaelgYvw2OU28Z6WujrfHhsamOqR9DbD8ff83sTxfarvqw866PcF69IJPhtNI75zNONN6PoamnQqrwtdtmTR3b0rWVPi788AMhvYciCNqUZufM2L9/F0LSMLfYWhliZpL6+lYke1QtZ0GPdVY4Nrp5h8QnxJSRBIb6LNq1a0KZVC969e0ePXn0ZPmoM61avoGef/pQoUZxN61ZjaGjIvIWL2blrz7++zps3YURGRiqTFC9evKCwg0OqcjY21owcNoRRw4d+Vbz/NHXGLKbNnJ1uPOGhwan2Xbl2jcCgIAqXLANAdHQM0dHRWOXKi5+PNydPncbP358ly5YDEBLyipbtOjB00AAK2Of/5Lmy7oQQ4kfSqqELrRq68C48gl6jpjFqxkJWz51InzHTKVG4AOvmT8bQQJ+Fqzax69DJz1eYjjdvw4mMilYmKV4EBOFgb5eqnE0OC4b16szwXp2/Kt5/mrF4NTOXrE43ntB7Z1Ptu3b7PkEvX1GydgsAomNiiY6NJVe5OvhcPKj8fL9w7TaBL0NoUrfG5xv+CWpqaiSlM7xcCCFUpUXN8rSoWZ53kdH0m7uesct2snxkFwb+uZFi+W1ZNaYbhll0WbzjOHvPXP/X1wkLjyIyOlaZpPALfk2hPKlHpdlky8rgdq4MaVfvq+L9p9kbDjBnY/pTPH282PXX8nv5mnO3PJk3sF2K/YXtbLh8L/3pAoUQ4kfVpHROmpTOSXhMPEO23mLyvvssaleGYdtvU8TGmL87lMVAV4tlpx+z/3b6M1t8ztvoeCJjE5RJCv830RS0MkpVztpEj361CtC/doGvivef5h/zZP4xzzRqSOYzs0Ga+x1yGKPGhwT1x38D1C9hTf0Syfe4t1FxbLr0jBK2pmnWlZCYyDNZg0KogCQoxDfn6eWFv38AlRwroquri66eHon/X9MhPCICI0NDDAwMeOTpyd/LVpDN3Pw/XW/cpClMnTiey1eusv/gYcaPGZWqzO+dO9GkRWtqVq9GubJliIqK4vTZczg7VSIgMDDdeP9p5LAhjBw25Kviq1unNk89Hyi3t+7Yweat29mzfSsaGhocP7Sf+I+GXper5MycmdOoW6c2WlpanzwXIDY2VvnQKS4unpiYGHR0dFL0qBJCCFXz8nmGf1AIjqWLo6ujg56uDorE5Lm7IyKjMDTQx0A/C55PnrFs407Ms6b9JfpLTZq3lImDe3Hl1j0OnjzPmP7dU5Xp3LIRLf4YQvVK5ShbvDBR0TGcvXQdp3IlCXwZkm68/zSsVyeG9er0VfHVqeKI57m9yu0d+4+x1f0I25fNTpF83rBrP41cqmNokHJR08TEROLi40lISCApKYmY2FjU1dTR1tbC1z8Iv8BgyhRzIDEpkb/WbiP0TRgVSxdHCCEyC2/fIAJevaFCkXzoamuhp62t/JwNj47BMIsuBno6ePkGstL9NObGhp+p8dOmrnFnXNfGXHvow+FLdxjZKfWDn45ulWk75i+qlipEmUJ5iIqJ49wtTyoVtyfoVVi68f7T4Hb1GJxOkuNTFIpE4hUKEhQKEpOSiImLR0NdDS3NDz/jtxy9SPkidqmml2pVswKLth3j1PUHOJcoyJJdJ8hqbECBXMmjwhMUChIUiSQmJqFITK5bU0MdTenwJITIxB4HhxP0NoayebOio6mBrpY6iv8//4iITcBQJ3nUg3dwOGs9nmJmoP2frjfr8ENG1ivMjeevOfYgiCF1U0/F2rZibjqvuoyzfTZK5jIlKk7BhcevqGhnRtDbmHTj/ad+tQrQr1baSY5PaVnOlj+PetKsTE6yGemw8IQXtQp/WFPp9os3FLE24U1UHCN23KZOESvy/38djZ3XXlDezgwb0yy8eB3FtAMPqGyfMSPEhfgakqAQ31xsbBwjxozj4SNPtLS0cKxQnqWLFwIwa9oUuvfqw6y58yhZvBgtmjXh1OnUPUu/lKWlBaYmJljnyUcWvSwsWTifggVSf8CXKV2KZUsW0WfAILwfP0FPTxcnx4o4O1X6ZLwZQUdHB0tLC+W2sZExWppayn1mZmYpymtoaGBqYoqBgYGyjemdC1CwaEme+ybPsetSvyEAPo/upzlllBBCqEpsbDxjZi7i0ZNnaGlqUqFUMRZPHQnAtJH96DVyKnOXrae4QwGaudXi9IXUc4N/KctsZpgYG5GnYl30dHVZOHkEBexypypXupgDS6aOYsC4mTx+9gI9XR0cy5TAqVzJT8abEXR0tLHM9iFBb2RogKamZop9MbGx7DxwnM1/zUh1/rkrN6nTpody26SQE5XLl+LY5qVEREbSd8x0fHz90NXRoVih/OxdNR8zU5MMi18IIf6r2PgExi3fhdfzQDQ1NShf2I4FgzoAMKVHc/rOWc/8LUcolj8nTaqW5ezNR//6WhZZjTAxzIJ9syFk0dXmzwHtsLdNPZ1rqQK5WTC4A4MXbMLH7yW6OtpULJqPSsXtPxlvRtly7CJ/zFij3M5epydt6lTk7+EfRvptPnqJfi1rpzo3v60ly0d2of/cDbwKC6d4flu2Tu6FtlbyI4CZ6w8wfe0+Zfmtxy4xvGN9Rv6Wdg9dIYTIDOIUiUzefx/v4HC0NNQokzsrs1smr885rmERhmy9yeKTXhS1MaFBSWs8vEP+9bWyG+pgoqdNiXGH0NPSYGbzEsoH+x8rYWvK7JYlGbHzNk9DItHVVqdcHjMq2pl9Mt6M0qZCbvzeRFP3z9MAVC9kweQmxZTHx+y6w/2Ad2hpqFG/uDXjGxVVHvMKfsfkffcIi47HRE+LGg6WjHRLPQuJEN+aWpKM7xfA1KlTCQ97w9RJ41Udyr92+sxZ2nfuyosnXqoO5Zcycsx4DE1MGTky4x7UCSEyBxNjYzzP7sHE6L/1UlWlM5eu03ngWJ5cOKDqUH4pDtWacvTESezsUk+lJYT4cS1btoyLB7awYEAbVYfyr5275cnvU1bwaPssVYfyS7np+Zz+f+3hxu27qg5FCJHBGrvVxS37a9yKp54q70fl4R1C7w3XuDmhrqpD+am1WnWTEbOWUrt26kS7+LWoqzoAIYQQQgghhBBCCCGEEEL8eiRBIYQQQgghhBBCCCGEEEKI704SFOKnUbWKs0zvJIQQIoUqFUrL9E5CCCGUKpcoINM7CSGE+KRK+bPJ9E5CfEeSoBBCCCGEEEIIIYQQQgghxHcnCQrxwzp95iw57exVHYYQQohM5Myl69g51lN1GEIIITKpc7c8Kdh8iKrDEEIIoUIe3iGUHHdI1WEIIf5PEhRCZKBuPXtTsGhJNPQMWbNuwyfLDh05Glu7AhhnsyJ3/kJMnZFyqLlCoWD0uAlY58mHkbklpco7EhYWBkBSUhKjx03AJm9+TLLnoFotF+4/ePCtmiWEEOJf8vZ5TrNug7ApUwurkjVw69gHL59n6ZbfceAYVZt1xtTBiVqtu6c6fvuBJxUbtMfUwYmKDdpz+4Gn8tiClZsoWKUh2YpVJU+FugyZNJeEhIRv0SwhhBAZxPtFEK1GLSJPowHYNuhHoyF/4u0blG75gJA3tBq1CNsG/SjYfAgr3U+nOK5QJDJx5W7smw0mh2tvnH6fSFhE1DduhRBCiIyy7Yovlv13s/HiszSPxyYoGLD5BqUnHMZu2D5qzDzJiQcp7xt7b/pReeox7Ibto/K04xy6E6A8NnTbTfIOdVe+bAftxW7Yvm/ZJCE+SxIUQmSg4sWKsnj+XEqVLPHZsl1+68DDOzd4GxKIx+kTbNqylV179iqPj5s4mYuXLnPh9EnehgSybtVydHV1Adi+cxer163n7PGjhAa+oEL58nTo/Pu3apYQQoh/KSw8gno1nLlzfAe+V45QprgDzboNTre8qbERvTu1ZnCPjqmOxcXF06zbYFo3rEvQzZO0a1KPZt0GExcXD0C9ms5c2reBkDunuX54C3ceebN4zdZv1jYhhBD/3duIaFwdi3N93WSe7JpD6UJ5aDV6cbrlf5+6klxW5jzZNYft0/oyYcVuzt58pDw+Zc1ertx/wvFFI/A/sJBlI7ugq631PZoihBDiPwqLimPBcU8KWBqmWyZBkUQOEz12966M9zQ3htUrRLe1V/ENjQQgMCya3huuMb5RUR5Pd2NsgyL0XH+NkPBYAGa2KInPzAbKV6NSNtQvnuO7tE+I9EiCQqjUjNlzad66bYp9/QYNoe/A5Ic3q9eux6F4KYzMLbErWISly1emW5e6rgGPnzxRbnfq2p3R4yYot/cfPETJchUxtbCmUtUa3Ll7L4NbA716dKdG9Wro6uh+tmwBe3v09fWV2+rq6jx+4gPAmzdvmL/oL5b9tYhcuWxRU1OjSOHCygTF02fPcXKsSN68edDQ0KBdm5Y8ePgozesIIcSPZvbfa2ndc1iKfYMmzmbghNkArN3uTvFazTEvWoWCVRqyfNOudOvSzVuWJ89eKLe7DhnPuDlLlNsHT5yjXL02WBSvRtVmnbn70DtD21K2eGE6tWxIVhNjtLQ06du5DV4+zwl9E5Zm+RpO5WlWrxZWFtlSHTtz+ToKhYI+nVujo6NNr99akZSUxOmLVwGwy2WDiVHyj5mkpCTU1dR48vxFqnqEEOJH9+fmQ7QftyTFvqELtzBkwWYANhzyoEzHMeRw7U2xNiNY5X4m3bqMqv3OE/+Xyu0e01cxceVu5fahi7ep1HUCOd36UrP3dO498cvQtpQplIcO9SqT1UgfLU1NejWrhfeLIELfRqQqGxEdw7lbngxpVw8tTU2K5stJI+fSrD/kAcCb8EiW7DjBgkEdsLU0Q01NDYc81pKgEEL8lBYe96LL6ssp9o3edYdRO28DsPnyc+UognKTjrDO42m6dVn2383TkA+fu303Xmf6gQ+zVBy9H0iNmSexH74ft3lneBDwNoNbk2zq/vt0cbYjq75OumX0dTQZUrcQtmb6qKurUbuwFbZZs3DHLwxITlAY6WlRw8ESNTU1ahW2RE9bg+evUt9XImMTOHAngBblcn2T9gjxpTRVHYD4tbVq3oyJU6YRHh6OoaEhCoWC7Tt3sWtr8o+L7NmzsW/XDvLmzcPZc+dxbdiEsmVKf9EIhY/dvHWbLt3/wH3ndsqULsWGTVto2LQFj+7eREcn9Qd/8TLl8X2R9o+P1i2b89eCeV/b1DRNnzWHKdNnEhkZSZ7cuWnTsgUAd+/dR1NTkx279zBv4SKMDI3o2/sPevVInu6jVfNmbN+5Cy9vb/Lkzs3aDZtwqV0rQ2ISQghVa16/NlMWLCc8IhJDA30UCgU7Dxxn69/JU+FlN8vKrhV/ktfWmnNXbtCwUz/KFHOgZJGCX3WdW/c96T58EjuXz6V00UJs2nOIpt0Gcff4DnR0tFOVL1O3NS8C0p52o2WDOiyYNPyz1zx/5SaW2cwwMzX5qlgBHnr5UKRgPtTU1JT7ihbMzwNvH2pXcQRgy97D9BkznfCISMyzmjBjVP+vvo4QQmR2TauVY/ra/YRHxWCYRReFIpHdp6+xcdIfAJibGLJtah/y5MiGx20vmg5fQKmCuSlh/3UPYG57+9Jr5lq2TulNqQK52XLsEq1GLeL6uknopPHQv2KX8fgFv06zrmY1yvPngLZpHvvYhTteWGQ1xszYINWxpKT3/5v0YR/w8Kk/AA98/NHUUGfv2ess3n4cQ31d/mhSk26Nq31Ba4UQ4sfSqJQNc488IiImHgNdLRSJSbjf8mN15woAmBvosL5bRXKZ6XPxSShtl16ghK0pxXKafNV17vqFMWDzDdZ3rUhxW1N2XPOlw/JLeIyqiY6mRqry1WacwP9NdJp1NS5tw4zmJdI8duP5a269CGN6sxK43/T/4vhCwmPwCYmggKURAMVtTclvYciRe4HUdLDk6L1AdDTVKZTDONW5B24HYGagTUU7sy++nhDfgiQohErlymVLqRIl2L13Hx3ateHkqTNk0ctChfLlAKhX10VZtopzZWrXrME5D4+vTlAsW7mKbl06U75cWQA6tm/LtJmzuXT5ClWcK6cqf/va5VT7voXhQwYxbPBAbt2+wx73fRgbJ99Q/PwDePv2LV7ej/F5dB/vx0+oWbce9vnyU6tmdaysLKnkWDF5vQsNDXLa2HDi8IHvErMQQnxruaytKFG4IHuPnqZdk3qcunANPT1dypcsCkDd6k7Kss7lS1OzcgU8rt786gTFys276dK6MeVKFAGgfVM3Zv61msu37uJcvnSq8tcObf4PrQK/wGD6j5vJjFED/tX5EVFRGBumfGBlZKhP+Edzi7dq6EKrhi48furLht0HyG6e9T/FLIQQmZGtpRnF89uy79wN2tRx5MzNR+jpalPOwQ4Al4rFlGWdShSgehkHLtzx/uoExZr9Z+lU35myDnkBaOviyJxNB7n6wAenEgVSlb+4cvy/bxTgH/KaQfM3MbVn8zSPG2bRpUKRfMxYv5/JPZrz6FkAe89ex9zE8P/nv+FtZDSPXwRzd/M0nvi9pP6gOeTLaUH1Mg7/KTYhhMhscmbNQlEbYw7eCaRFOVvOe4egp6VJ6dzJ339rFbZUlnXMZ06VAtm57PPqqxMU6y88o4NjHkr9v96W5XKx4JgX15+9wTGfearyp4bV+Oq2KBKTGL7jNtOaFkddXe3zJ/xfvCKRnuuv0aKsLfktku8FGupqtChryx/rrhKbkIi2hjrLfiuHvk7qR8DbrvrSvIxtig5QQqiCJCiEyrVu1Zwt27bToV0bNm/dRuuWH76QHzpylIlTpuHl/ZjExESioqIoUvjrv1z7+r5g3YZNLFqyVLkvLi6OgMDADGnDf6GmpkbJEsU5cuw44yZNYe7M6ejpJU/lNHbkcPT09ChWtAgtmzfj4JEj1KpZnYlTpnHt+g18H3tiaWnBhk1bqOFSj3s3r5IlSxYVt0gIIf67Vg3qsG3fEdo1qcdW98O0bFBHeezIaQ+mLFiB91Pf5HtDTAyFC9h99TV8/QPZsGs/S9ZuU+6Li48nMPhVhrThYyGhb3Dr2Idu7ZqlaMvXMMiShXfhkSn2JY8ySf25ny+PLQ757eg3ZoZy5IkQQvxMmtcox46TV2lTx5HtJy7TvEY55bGjl+8yfe0+HvsFk5SYRFRsHA55rb/6Gr7BoWw6cpFlu04q98UlKAgMDcuIJqTwKiycRkPm0bVhVZrXKJ9uuRWjujJo/kYKtRhKbqtstKxZgUfPkhc/1dNJHtUxrEN99HS0KWJnQ9PqZTl6+a4kKIQQP6XGpXOy+4YfLcrZsuv6C5qUtlEeO/EgiDlHHuETEkFiIkTHJ1Aoh9FXX8PvTRTbr/qy8qyPcl+8IpHgt2mPkvg3Vp/3wcHKSJlc+RKJiUn03nANLQ11pjYrrtx/1vMlk9zvsat3ZYrZmHDbL4yOKy6yqZsjRWxMlOX83kRx4XEIc1qWzLB2CPFvSYJCqFzzJo0ZPGwkfn7+7Hbfx4UzJwCIjY2lWau2rF25jIb13dDS0qJx81YphjR/LEuWLERFfbhBBAUHY22dvNCPjY01I4cNYdTwoV8UU5GSZXjum/a83W1bt+TvRQu+polfJCEhAR+f5DkRixVJ7s37cRb7479v37lLi2ZNsLFJ/qH1W4d2DBgyjAcPH1GmdKkMj00IIb63Jq41GTZ1Pn6BwbgfPc2ZnasAiI2No1XPYaycPYH6taqgpaVJ8+6DSefWQBY9XaKiY5TbwSGhWFtaAGCTw4JhvTozvFfnL4qpZJ0W+PqnPcVT64Z1WTRlRJrH3rx9h1vH3rjVqPzF10pLIfu8zF+5kaSkJOU94e6jx/Ron3ZP2wRFAj6+Xz48XAghfiSNq5Zh1JLt+Ie8Zv+5mxxfnPwZHBsXT/txf7N0RGfqVSqOlqYmrUcvTv83hK420TFxyu3g1+/Ikc0UAJtsWRnczpUh7ep9UUzlfhvLi3SmeGpZqzzzBrZP89ib8EgaDfkTV8fin72WraUZ26f1VW53nrSc0oXyAFA4b/KDuY87wqohvWKFED+v+iWsmbD3LgFh0Ry6E8j+/s4AxCYo6Lr6CgvalsalqBVaGur8tuJSur8Z9LQ1iI5XKLdDwmPIYaIHgLWJHv1qFaB/7dQj59LiPP04fq+j0jzWrExOZrZInRA47xXCxSevODHmIJC8WPY9/zDu+b9l2kfJh/eSkpIYsOUGIeGxbOzmiJbGhyWG7/m/pYKdOSVsk+9lJW1NKWWblbNeISkSFDuuvqBcHjNymev/s3ohvjtJUAiVy5YtG1WdK9O5Ww/y5M5FoYLJU3TExcURGxtLNnNzNDU1OXTkKEePn6CwQ6E06ylRrBibtm6jsEMhjp04yZlz5yldKvmD//fOnWjSojU1q1ejXNkyREVFcfrsOZydKmFoaJiqrns3r/2rtsTFxZGYmEhSUhLx8fHExMSgra2NunrK9egTExNZvnI1LZo1wcTEhKvXrvPX0mUMHzIIADu7vFSu5MiUGTNZMHc2Pk+fsnX7DjatXQ1AmdKl2LFzD62aNyNbtmxs3LyV+Ph48tnl/VdxCyFEZpPNzBTnCqXoNnQiuXPmoGC+5IcvcfHxxMbFY25mgqamBkdOe3D83CUc7NMeQVHMwZ6t7odxsM/LifOXOXf5JqWKJvci7dyyES3+GEL1SuUoW7wwUdExnL10HadyJTE0SP1F/eaRban2fc678Ajqd+xDxdLFmTysz2fLKxQK4hMSUCQoSExMIiY2Fg11DbS0NKlSvjTq6uosXrOF39s0ZdXWPQBUrZg8feGqrXtwq+FMdvOsPPT2YdaSNdSqXPGrYxZCiB+BuYkhTiUK0HPGGnJZmVMglxWQPMIhNj4ec2MDNDU0OHr5LievPaBQnhxp1lPULifbT1ymUO4cnLz+AI/bXpQskDwVVEe3yrQd8xdVSxWiTKE8RMXEce6WJ5WK22OYRTdVXVfWTPzqdryLjKbx0HmUL5KPCd2afra85/NAcmQzRUdLk12nr3Hy2gOurU2+bl7r7DgWy8/sDQeZ2acVzwJfsfPUVVaN/v2r4xJCiB+BuYEOFe2y0X/TdWzNsmD//3UY4hMSiU1QYGagg6a6GiceBHHG8yUFrdIeQVHE2phd119QwNKIM54vufjkFcVzJj/gb1sxN51XXcbZPhslc5kSFafgwuNXVLQzw0A39XpEZ4fX/Op2zG9TitiEROV251WXcSuegzYVcqdZftj2W3gHh7O9pxN62inXwShha8qiE17c8wujiI0Jd/3CuOzzit+c8qQot/2qL71q5P/qWIX4FiRBITKF1i1b0LHL78yYOlm5z9DQkPlzZ9GyXQdiY+Oo71qXBm6u6dYxb85Mfuvajb/+Xkaj+m40qu+mPFamdCmWLVlEnwGD8H78BD09XZwcK+LsVClD21GnXgPOnDsPwIVLl+jeqw8njxykahVnNm7eyrSZs5TJjz3u+xg5djxxcXHksLKi9x896NPzD2Vdm9atoWuPnpjnsCV7tmxMHDuGGtWTF7gbNnggL0NCKFnekcjIKPLZ5WXHlo2YmJhkaHuEEEKVWjZwocugcUwd/qGnqKGBPnPHDqJd75HExsXhWqMybjWd061jzphBdB0ynr837KB+rSrUr1VFeax0MQeWTB3FgHEzefzsBXq6OjiWKYFTuYwb5rz36Gmu3XnAA28f1u/cr9x/88g2bK0t2bznEDOXrFYmPzbuPki3oR8ecJkUcqJd03qsmDUebW0tti+dzR8jJjN65mIK5svN9qWz0f7/Qq0Xr91m/OwlRERFYZ7VlKauNRg3sEeGtUUIITKb5jXK0X3aKiZ1b6bcZ5hFl5m9W9Nx4lLi4hJwcSxOXcfUvU/fm9GnFT2mrWL5nlPUcypJPacSymOlCuRmweAODF6wCR+/l+jqaFOxaD4qFbfPsDbsO3eTG4+e8ehZAJsOX1Duv7JmAjktzNh67BJzNh5UJj+OX73P7A0HiI6No1g+W3bN7KdcgwJg1ejf6TVrLbkbDiCbqSGjOzWkaum0O3gJIcTPoElpG/psvM6YBoWV+wx0tZjcpDjd1lwhLiGR2kUsqV3EMt06JjUuRt9N11l9/il1i1rhUuRDUruErSmzW5ZkxM7bPA2JRFdbnXJ5zDJ0YWnjLNoptrU01DHU1cJIL/l7/vxjnlx6EsrmHo68eB3FugvP0NFUp+j/R1wAzGpRkqZlcuKYz5xBLgXpuuYKIeGxmBlo07dWAaoWtFCWvfY0lIC30TQo8fXTHwrxLaglpTfWVfxSpk6dSnjYG6ZOGq/qUMQPZuSY8RiamDJy5EhVhyKEyGAmxsZ4nt2DiVHqkWZCfIpDtaYcPXESO7uvXxtECJF5LVu2jIsHtrBgQBtVhyJ+MDc9n9P/rz3cuH1X1aEIITJYY7e6uGV/jVtxedgtvk6rVTcZMWsptWvXVnUoQsXUP19ECCGEEEIIIYQQQgghhBAiY0mCQijJYBrxb8h/N0L83OTfuPg35L8bIX5e8u9b/BtJyH83Qvys1CDdxaeF+BT570a8JwkKAYCpqSnBL1+qOgzxAwp++RJTU1NVhyGE+AZMTIx5+eq1qsMQP5iEhARC34TJukhC/IRMTEwICYtQdRjiB/Ty9Tv5zSDET8rEzIyQ8FhVhyF+QCHvouXeIABJUIj/q1WrFgcPH+Hdu3eqDkX8QN69e8fBw0dkvkAhflK169Rh276jqg5D/GCOnLlA/nx2mJll3MKBQojMoXr16py/+ZBXYeGqDkX8YHaevk7tuvVUHYYQ4htwqdcA9/uvSEyU7vDiyz0KfMfLdzGULFlS1aGITEAWyRZKvXr14uIFD/r36U1hh0JoamqqOiSRSSUkJHD/wUPmLVxERcdKLF68WNUhCSG+gcePH1O1ijNNXKrhVtMZU2ND1FBTdVgik4qKieHMxessXLOFjZs2U6tWLVWHJIT4BsaNHcPWDWvp07QaRfPZoqWpoeqQRCaVlJRE8Ou3bD95jdvPQzh7/oIkr4X4CcXExFCnRjV0IgJoW9YKG9MsqKvLbwaRttiERK4/e8OSs8+ZMG0WXbp2VXVIIhOQBIVQSkxMZMuWLWzfto1nz5+hUChUHVIqoa9CUSQqyJYtO2o/8f0uKQlCXr5EQ1MjU36J19DQIHeu3DRv0YJWrVqhri6DsYT4WT1//pylS5dy5tRJwsMzX4/ZhIQEgoKCMM2aFf0sWVQdzjcVFxdH8MuXZMtmjq6OrqrDSUVHR4cy5crRtevvlC5dWtXhCCG+kaSkJHbv3s3mjet57O1NQkKCqkNKJTw8nPDwcCwtLX/676lhYWHExMRgYWGBWib8gZQ1a1bquLrRvXv3TPm7RgiRMaKioli2bBn7d2/n5cuXmW69osTEJIKDg9HV1cXU1ETV4XxTCoWCoKBgjIwMMTQ0VHU4qWhpalGkWDF+69qd6tWrqzockUlIgkL8MEaPHs3Ro0c5deoU+vr6qg7nm4uIiKB69erUqVOHSZMmqTocIYTIdEJDQ6lUqRI9e/akb9++qg7nuzhx4gStW7fmxIkTFC1aVNXhCCFEprNjxw769+/PuXPnyJMnj6rD+eaSkpL47bffeP36Nbt375ZR8EII8Q8JCQk0atQIMzMz1qxZkymTuRnt6dOnODk5sXDhQpo0aaLqcIT4rJ+7O4n4aSxevJht27Zx4MCBXyI5AWBgYMCBAwfYsmULf/31l6rDEUKITCUqKgo3NzcaNWr0yyQnAGrUqMGCBQtwdXXF19dX1eEIIUSmcubMGXr27Mn+/ft/ieQEgJqaGitWrCA+Pp4ePXpkul7LQgihSklJSXTv3h2FQsGKFSt+ieQEQJ48edi/fz89evTg7Nmzqg5HiM+SERQi09uxYwf9+vXj3Llz5M2bV9XhfHc+Pj44OTmxaNEiyXwLIQTJvaAaN25M1qxZf5leUP80b948li5dyvnz52XKDCGEAO7evUuNGjXYvHkzNWrUUHU4311ERATVqlWjbt26TJw4UdXhCCFEpjBmzBgOHz7MqVOnMDAwUHU4393x48dp27Ytx48fl9HXIlOTERQiU/u4F9SvmJwAyJs3L/v376d79+6S+RZC/PKSkpLo0aMH8fHxv1QvqH/q378/9evXp379+kRFRak6HCGEUClfX19cXV1ZsGDBL5mcgA+jrzdv3sySJUtUHY4QQqjcX3/9xZYtWzhw4MAvmZwAqFmzJvPmzZPR1yLTkxEUItN63wtq06ZN1KxZU9XhqNyxY8do27atzDsuhPiljR07lkOHDv2yvaA+lpiYSMeOHXn37h07d+6UeceFEL+k0NBQnJyc6N69O/3791d1OCr35MkTKleuLKOvhRC/tF27dtG7d2/Onz//y3Z2/diff/7J8uXLOX/+PFmzZlV1OEKkIgkKkSn5+vpSqVIlZs6cSevWrVUdTqaxadMmhg0bhoeHB7a2tqoORwghvqslS5Ywd+5cPDw8yJ49u6rDyRTi4uKoX78+uXLlYunSpb/siBIhxK8pKiqKmjVr4uTkxMyZM1UdTqZx/fp1XFxc2LVrF5UrV1Z1OEII8V2dPXuWpk2bcuTIEUqVKqXqcDKNIUOGcOHCBY4fP46enp6qwxEiBUlQiEznfS+obt26MWDAAFWHk+nMnTuXFStWSOZbCPFLkV5Q6QsPD6datWq4ubkxfvx4VYcjhBDfRUJCAk2bNsXIyIi1a9eiri6zF3/s2LFjtGvXjhMnTlCkSBFVhyOEEN/F+5k4Nm7cSK1atVQdTqaSmJhIhw4dCA8Pl9HXItORBIXIVKKioqhVqxaOjo7MmjVL1eFkWoMHD+bixYuS+RZC/BLOnTtH06ZNOXz4sPSCSkdwcDCVKlViyJAhdO/eXdXhCCHEN5WUlET37t15/vw5+/btQ1tbW9UhZUobN25k+PDhXLhwgZw5c6o6HCGE+Kbez8QxY8YM2rRpo+pwMqW4uDjc3NzIkycPf//9t4y+FpmGJChEpvG+F5ShoSHr1q2TXlCfkJiYSPv27YmIiJDMtxDip3bv3j1q1KjBhg0bpBfUZ7yfd/yvv/6iUaNGqg5HCCG+mfHjx7N//35OnTqFoaGhqsPJ1ObMmcPKlStl9LUQ4qf2+vVrnJyc6Nq1KwMHDlR1OJlaeHg4VatWpUGDBowbN07V4QgBSIJCZBLve0E9e/aM/fv3Sy+oLxAXF0e9evXImzevZL6FED+lFy9eUKlSJaZPny69oL7Q9evXqVu3Lrt27cLJyUnV4QghRIZbunQps2bNwsPDAwsLC1WH80MYNGgQly9f5tixYzL6Wgjx04mOjqZmzZpUrFiR2bNnqzqcH8L70ddDhw6lW7duqg5HCElQiMxh/Pjx7Nu3j9OnT0svqK8gmW8hxM9KekH9e0ePHqV9+/acPHmSwoULqzocIYTIMHv27KFnz56cO3cOOzs7VYfzw0hMTKRdu3ZER0ezY8cONDQ0VB2SEEJkiISEBJo1a4a+vj7r16+XmTi+wuPHj3F2dmbJkiU0bNhQ1eGIX5z8yxUqt2zZMjZs2MDBgwclOfGVDA0NOXjwIOvWrWP58uWqDkcIITJEdHQ0DRo0wNXVVZIT/0Lt2rWZM2cOrq6u+Pn5qTocIYTIEB4eHnTr1o19+/ZJcuIrqaurs2bNGiIiIujVqxfSR1EI8TNISkqiV69eREZGsnr1aklOfKV8+fLh7u7O77//joeHh6rDEb84GUEhVGrv3r388ccfnD17lnz58qk6nB+Wt7c3zs7OLF26lAYNGqg6HCGE+NcUCgXNmjUjS5Ys0gvqP5o9ezZr167l7NmzmJqaqjocIYT41x48eED16tVZt24dtWvXVnU4P6x3795RtWpVGjduzJgxY1QdjhBC/CcTJ05k7969MhPHf3TkyBE6duzIqVOnKFSokKrDEb8o+dUvVMbDw4Pff/8dd3d3SU78R/nz58fd3Z0uXbpw4cIFVYcjhBD/yvteUBEREdILKgMMGjSIWrVq0bBhQ2JiYlQdjhBC/Ct+fn7UrVuX2bNnS3LiPzIyMuLgwYOsWbOGFStWqDocIYT415YvX87atWtlJo4MUKdOHWbNmoWLiwv+/v6qDkf8omQEhVCJBw8eUK1aNdatW0edOnVUHc5P4/Dhw3Ts2JHTp09L5lsI8cOZNGkSe/bskV5QGSgxMZG2bdsSFxfHtm3bZN5xIcQP5c2bNzg7O9OxY0cGDx6s6nB+GjL6WgjxI3N3d6d79+6cPXuW/Pnzqzqcn8asWbNYt24d586dw8TERNXhiF+MJCjEd+fn50elSpWYPHky7du3V3U4P53169czevRoLly4gLW1tarDEUKIL7JixQqmTZuGh4cHlpaWqg7npxIbG4urqysFCxZk0aJFqKmpqTokIYT4rJiYGGrXrk2ZMmWYM2eOfHZlsKtXr+Lq6srevXtxdHRUdThCCPFFLly4QMOGDTl48CBly5ZVdTg/laSkJAYOHMiNGzc4cuQIurq6qg5J/EIkQSG+q/e9oDp06MCQIUNUHc5Pa+bMmaxfv14y30KIH4L0gvr23r17h7OzM82bN2fUqFGqDkcIIT5JoVDQokULtLW12bhxo0z5940cPnyY3377TeYdF0L8EB4+fEjVqlVZu3YtLi4uqg7np5SYmEibNm1ISEhg69atMvpafDfyTU98NzExMTRs2JAaNWrIEO1vbMiQIVSvXl3mHRdCZHoXLlygS5cuuLu7S3LiGzIyMuLQoUOsXLmSVatWqTocIYRIV1JSEn379iUsLIw1a9ZIcuIbcnFxYcaMGdStW1fmHRdCZGr+/v64uLgo10oQ34a6ujpr167l9evX9OvXD+nTLr4XGUEhvov3vaA0NTXZvHmz/ND4DhITE2ndujUKhUIy30KITOnhw4dUq1aNNWvWyA+N78TLy4sqVaqwfPly3NzcVB2OEEKkMmXKFLZv387Zs2cxMjJSdTi/hOnTp7Np0ybOnj0ro6+FEJlOWFgYlStXpl27dgwbNkzV4fwS3r59S5UqVWjRogUjR45UdTjiFyAJCvHNJSUl0bt3bx4+fMihQ4fQ0dFRdUi/jNjYWFxcXChcuDALFy6UuXuFEJmGv78/lSpVYuLEiXTo0EHV4fxSLl++jJubG/v27aNChQqqDkcIIZRWrVrF5MmT8fDwwMrKStXh/DKSkpLo168fd+7c4fDhwzLvuBAi04iJiaFOnTqUKFGCefPmyTON7ygwMBBHR0fGjh1Lp06dVB2O+MlJgkJ8c+97QZ05cwZjY2NVh/PLefv2Lc7OzrRs2VIy30KITCEsLAxnZ2fatm0rvaBU5ODBg3Tu3JnTp09TsGBBVYcjhBDs37+f33//nTNnzmBvb6/qcH45CoWC1q1bk5SUxJYtW2T0tRBC5RQKBS1btkRDQ0Nm4lART09PqlSpwsqVK6lXr56qwxE/MfnXLb6pVatWsWLFCg4dOiTJCRUxNjbm0KFDLF++nNWrV6s6HCHELy4mJoZGjRpRrVo1hg4dqupwflmurq5Mnz6dunXrEhAQoOpwhBC/uEuXLtGpUyf27NkjyQkV0dDQYN26dbx69Yr+/fvLvONCCJV6P7IrNDSUdevWSXJCRQoUKMDevXvp1KkTly9fVnU44icmIyjEN3PgwAG6dOnCmTNnKFCggKrD+eW9z3yvWrUKV1dXVYcjhPgFKRQKWrVqhbq6uvSCyiSmTZvGli1bOHv2rHQkEEKoxKNHj6hatap8R80k3o++bt26NcOHD1d1OEKIX5R8R81c5Pme+NbkyYD4Ji5fvkynTp3Yu3evfHhlEgUKFGDPnj107NhRMt9CiO/ufS+oV69eSS+oTGT48OE4OzvTqFEjYmNjVR2OEOIXExAQQN26dZk+fbokJzKJ96Ov//77b9asWaPqcIQQv6A1a9awbNkymYkjE6lXrx7Tpk3DxcVFRl+Lb0JGUIgMJ3PUZW4yv68QQhWkF1Tm9X5ki5qaGlu2bJHkkRDiu3jfU79Vq1aMGDFC1eGIf3g/smX16tXUrVtX1eEIIX4R79dJk576mdPUqVPZtm2brDErMpwkKESGCggIoFKlSowdO5ZOnTqpOhyRjlWrVjF58mQ8PDywsrJSdThCiJ/cmjVrmDBhAh4eHuTIkUPV4Yg0xMTE4OLiQrFixZg/fz5qamqqDkkI8ROLjY3FxcWFIkWKsGDBAvnMyaQuXrxIgwYNOHDgAOXKlVN1OEKIn9zly5dxc3Nj3759VKhQQdXhiDQkJSXRp08fHjx4wKFDh9DR0VF1SOInIQkKkWHevn1LlSpVaNGiBSNHjlR1OOIzpkyZwvbt2yXzLYT4pqQX1I8jLCwMZ2dn2rZty7Bhw1QdjhDiJ5WYmEirVq1ISkpiy5YtaGhoqDok8Qky+loI8T14eXlRpUoVli9fjpubm6rDEZ+gUCho2bIlGhoasq6gyDCSoBAZIjY2lrp16+Lg4MDChQulF9QPICkpid69e/Po0SMOHjwomW8hRIZ73wvK3d2dihUrqjoc8QX8/f1xdHRk4sSJdOzYUdXhCCF+Mu/XI7pz5w6HDx9GV1dX1SGJL7By5UqmTJnChQsXsLS0VHU4QoifTGBgIJUqVWL06NF07txZ1eGILxATE0OdOnUoUaIE8+bNk2eA4j+TBIX4zxITE2ndujUKhYKtW7dKL6gfiEKhoEWLFmhpabFp0ybJfAshMoyXlxfOzs6sWLFCekH9YB4+fEjVqlVZu3YtLi4uqg5HCPETmTFjBhs3buTs2bOYmJioOhzxFSZPnszOnTs5c+YMRkZGqg5HCPGTeD8TR/PmzRk1apSqwxFfISwsjMqVK9O+fXuGDh2q6nDED04SFOI/SUpKon///ty6dYsjR45IL6gf0PvMd8mSJfnzzz8l8y2E+M+CgoJwdHRk1KhRdOnSRdXhiH/hwoULNGzYkIMHD1K2bFlVhyOE+AmsW7eOsWPH4uHhgbW1tarDEV8pKSmJXr164eXlxYEDB2T0tRDiP4uNjcXV1ZWCBQuyaNEieRbxA/L396dSpUpMmjSJ9u3bqzoc8QOTBIX4T2bOnMn69es5d+6c9IL6gb3PfHfo0IEhQ4aoOhwhxA/s3bt3VKlShaZNmzJ69GhVhyP+A3d3d7p3787Zs2fJnz+/qsMR/2PvrsOi2PoAjn/p7pAORVrswu7Cbuzu7q5rd3d3d3cHdoACgkiqKEizxL5/rHdxJfTeF++izud55nnYmTNnf7Ps7JyZUwLBL+zMmTN07dqVy5cv4+LiIu9wBP/S372vVVVV2blzp9D7WiAQ/GuZmZl4e3uTlpbGvn37hJE4fmF+fn7UqFGDrVu3Uq9ePXmHI/hFCSUKwb+2bds2Vq1axZkzZ4TKiV+cvr4+p0+fZsWKFWzfvl3e4QgEgl9UamoqLVq0oGLFikIX7d9AkyZNmD59OvXr1ycqKkre4QgEgl+Uj48PnTt35vDhw0LlxC9OSUmJnTt3Eh4ezogRIxDaOgoEgn9DLBYzfPhwIiMj2blzp1A58YtzcXHh0KFDdOrUifv378s7HMEvSuhBIfhXhFZQvydfX19q1KjBtm3bhJpvgUDwj2RmZtKhQwdEIpHQCuo3M336dI4cOcLVq1fR0dGRdzgCgeAXEhAQQNWqVVm3bh2NGzeWdziCfBITE0OVKlXo2rUrI0eOlHc4AoHgFzN//ny2bdsmjMTxmzl27Bh9+/bl2rVrODg4yDscwS9GqKAQ/GM+Pj40bNiQo0eP4unpKe9wBPns5s2bNG/enFOnTlGmTBl5hyMQCH4Bf7eCevDgAefOnRPmI/rNiMVi+vXrx+vXrzl58iSqqqryDkkgEPwCoqKiqFSpEuPGjaNnz57yDkeQz8LCwqhUqRIzZ86kY8eO8g5HIBD8IrZv387EiRO5efMmVlZW8g5HkM/Wr1/PnDlzuHXrFoUKFZJ3OIJfiFBBYnVd0gAAqjBJREFUIfhHAgMDqVq1KmvWrKFJkybyDkfwkxw9epR+/foJNd8CgeCHLFiwgC1btnD9+nUMDAzkHY7gJ8jIyKBVq1Zoamqyfft2YdxxgUCQp/j4eKpVq0azZs2YPHmyvMMR/CQvXrygZs2abN++nbp168o7HIFAUMCdO3eOTp06cfnyZVxdXeUdjuAnmTZtGseOHePKlStC72vBDxMqKAQ/7N27d3h6ejJmzBh69+4t73AEP9natWuZP38+N2/eFGq+BQJBrnbs2MGECROEVlB/gOTkZOrUqUOFChVYsGCBvMMRCAQFlEgkolGjRhQpUoTVq1ejoKAg75AEP9GNGzdo0aIFp0+fpnTp0vIORyAQFFD379+nYcOGHD58mEqVKsk7HMFPJBaL6du3L8HBwZw4cULofS34IUIFheCHxMfHU716dZo0acKUKVPkHY7gPzJ16lROnDjB5cuXhZpvgUCQjdAK6s/z6dMnqlSpQvfu3RkxYoS8wxEIBAVMZmYmnTp1IikpiQMHDgjzEf0hjhw5Qv/+/bl+/TpFihSRdzgCgaCA+XskjlWrVtGsWTN5hyP4D6Snp9OqVSu0tbXZtm2b0Pta8F1CBYXgu0QiEV5eXtjb27NmzRqhFdQfRCwW06dPH0JCQjh+/LhQ8y0QCKSEVlB/rtDQUCpVqsScOXPw9vaWdzgCgaAAGTlyJHfu3OH8+fNoaGjIOxzBf2jNmjUsWLCAW7duYWpqKu9wBAJBAfHu3TsqVarEqFGj6NOnj7zDEfyHkpOTqV27Np6ensyfP1/e4QgKOKGCQpCnzMxMOnfuTEJCAgcOHEBZWVneIQn+Y+np6bRs2RIdHR2h5lsgEABCKygBPH/+nFq1arFjxw7q1Kkj73AEAkEBsHDhQjZt2sT169cxNDSUdzgCOZgyZQonT57kypUraGtryzscgUAgZ/Hx8dSoUQMvLy+mTp0q73AEcvDp0ycqV65Mz549GT58uLzDERRgQgWFIE+jRo3i1q1bXLhwQWgF9QdLSkqiTp06VKpUiXnz5sk7HIFAIEd/t4IaOXIkffv2lXc4Ajm6du0aLVu25OzZs5QqVUre4QgEAjnatWsXY8eO5ebNm1hbW8s7HIGciMVievfuzdu3b4Xe1wLBH04kEtG4cWNsbW1Zu3atMBLHH+zt27fSZ0nt27eXdziCAkqooBDkatGiRWzYsIEbN24IraAE0prvXr16MWzYMHmHIxAI5ODvVlCNGjVi2rRp8g5HUAAcOnSIgQMHCuOOCwR/sPPnz9OxY0cuXbqEm5ubvMMRyFl6ejotWrRAT0+PrVu3Cr2vBYI/0N8jccTHx3Pw4EFhJA6BtPf1zp07qV27trzDERRAQmlBIJWQkCD9e/fu3SxevJgzZ84IlRMCAAwNDTlz5gyLFi1iz5490vVff28EAsHvJSUlhfT0dEDSCqpVq1aULFlS6KItkGrRogWTJk2ifv36vH//HpC0oE1MTJRzZAKB4Gf5uuz38OFDOnTowIEDB4TKCQEAysrK7Nmzh9evXzNu3Djp+qSkJDIzM+UYmUAg+FkyMzNJSkqSvh47dizBwcHs3r1bqJwQAODu7s7+/fvx9vbm0aNH0vXC8yTB34QKCgEADx48oGHDhgBcuHCBoUOHcvr0aWxsbOQcmaAgsbGx4dSpUwwZMoSLFy8C0KBBAx48eCDnyAQCwc8wePBgdu/eTWZmJt27d0dNTY3Vq1cLXbQFMvr160e7du1o1KgRCQkJ+Pn5Ua1aNXmHJRAIfoLExEQKFy6MWCzm9evXeHl5sXbtWqpUqSLv0AQFiKamJsePH+fYsWMsWbIEgHHjxrFhwwb5BiYQCH6K9evXM378eAAWL17MiRMnOH78OJqamnKOTFCQVK1alTVr1uDl5UVQUBBisRh7e3uhYZMAECooBF+cOnWKcuXK8ejRI7y9vdm/fz/u7u7yDktQABUrVox9+/bRvn17Hj16RLly5Th9+rS8wxIIBPlMLBZz6tQpypcvz9ixYwkKCmLPnj1CKyhBjqZPn46HhwetWrXC3t6ewMBAoqKi5B2WQCDIZ1evXsXV1ZUPHz5Qv359Jk+eTPPmzeUdlqAAMjIy4syZMyxcuJA9e/ZQvnx5Tp06Je+wBALBT/D3PcOePXtYtGiRMBKHIFctWrRg4sSJ1KtXjw8fPuDq6sq1a9fkHZagABAqKAQAnDt3jmLFiuHl5cWaNWuoWrWqvEMSFGDVqlVj9erVeHl54eHhwblz5+QdkkAgyGd+fn4oKytLW0AJraAEeVFQUGDt2rWoqKjQp08fatasyYULF+QdlkAgyGfnzp2jevXqNGrUiPbt29O3b195hyQowGxtbTl16hSDBw9GTU2NK1eukJaWJu+wBAJBPkpLS+PKlSuoqqoyZMgQTp06JYzEIcjT372vvby8qF69uvA8SQAIFRQC4PPnzzx+/JgZM2YwbNgwwsLCKF26NJcvX5Z3aIIC6PLly5QuXZqIiAiGDRvGjBkzePjwIXFxcfIOTSAQ5KOzZ8/i4ODA4sWLWbBgAZMmTaJSpUqIxWJ5hyYogHr27Im3tze9e/fG39+fpKQkzp49K++wBAJBPjtz5gxnz57Fzc0NZ2dnatasybJly+QdlqAAev/+Pa6urhw4cIDly5fTr18/LCwsuHPnjrxDEwgE+ej27dtYWlrSr18/li1bxv79+3F1dZXOTSYQfG3ZsmXUrFkTFxcXXFxcOH/+PGfOnJF3WIICQEEsPGn44+3evZtu3bpRtGhRQkNDadiwIT179qR69eooKgp1WAJZmZmZXLlyhfXr13P69Gmsra0JCAhg69attG3bVt7hCQSCfFKuXDmePHmCg4MDCQkJ9OjRg27dumFtbS3v0AQFUGxsLLt27WL9+vXExMQQGxtLZmYmsbGxQllCIPhNhISEULhwYWxsbEhISKBEiRL06tWLpk2boqamJu/wBAXQ06dPWb9+Pbt27cLa2ho/Pz969+7N8uXL5R2aQCDIJwMHDmT9+vW4uroSGhqKt7c3PXv2xMPDQ96hCQqg1NRUjh49yvr163n8+DHa2tq8ffuW4OBgoefNH06ooBDQtGlTrl+/zqRJk+jcuTNGRkbyDknwi4iOjmbbtm389ddfVK1alSNHjsg7JIFAkE80NDQoV64c48aNo06dOigpKck7JMEvQCwW8+DBAxYtWsS+fft4+fIlDg4O8g5LIBDkgyVLljBmzBiGDBlC3759KVy4sLxDEvwikpOTOXDgANOnTycjI4OgoCB5hyQQCPJJ4cKFUVJSYsqUKbRs2RINDQ15hyT4RQQFBbFmzRqWLl3K3LlzGTp0qLxDEsiRUEEhQCwWo6CgIO8wBL844XskEPxehHNa8P8SvkMCwe9HOK8F/y/hOyQQ/F6Ec1rw/xK+QwIAZXkH8LWMjAxCQkJITU2VdyiCAkxfXx9zc3N5h/HTRUZGEhsbK+8wBAWYmpoatra2v33L9o8fP/Lhwwdh7gNBrhQVFTEzM0NPT0/eofxU6enpvH37VignCXKloKCAiYnJb98bViwWEx4eTnx8vLxDERRgmpqaWFtb//bDzEVHRxMdHS2UkwS5UlZWxtLSEk1NTXmH8lMlJSURHh5Oenq6vEMRFFAKCgoYGxtjbGws71B+KrFYTGhoKImJifIORVCA6enpYWFhIe8wpApEBYVYLGbq1KmsWbMaNVVVtH7zC6fg3xOLxXz4+BELcwvmzJ1Lo0aN5B1Svjt58iRjx44lMjISY2NjoSZZkKvExERSU1Pp168fU6ZM+e2+K0+ePKF/3948f/6CQsaGKCr+XscnyD8ZGZm8+xhD+XJlWbdhE/b29vIOKV9lZmYyYcJ4NqzfgJamBhrq6vIOSVBAZYoziXr/AXc3N1asXEXJkiXlHVK+2759G9MmTyIu7jP6OloIVwZBTsRAQlIKisoqjBo9hiFDh8k7pHzn4+PDoAH9efnqJaZGhij+ZuVAQf5JS88gOiaW+vXrsW79xt+uQUdsbCx9enbjzNlzGOlqoaL8ezfeEvx7mZliPsTG4+zkyPLV6yhbtqy8Q8p3G9avY+b0KSQnJqKjoYpwaRDkRCyGmIQUTEyMmTlvES1atJB3SAWjgmLGjBmcPX2Sa6cPU8TeTt7hCAq4zMxMLly+Rrdu3Th+4gTlypWTd0j55t69e3Tv3p1t27ZRp06d377Fl+D/FxgYSMeOHVFWVmbixInyDiffvHv3jnp1ajOpR1Paz+6LqkqBuFwJCrCklFTWHbpIzerVeOH38rdqJThhwnhuXr3MzRN7sLexknc4ggJOJBKx+/AJ6terx+MnT36rXqcnT55k3MjhbBzSmLKOlr9dxbwg/714847ui+ehpa1Nz5695B1OvgkNDaVRg/rMHtyBlktHoKIslJMEefv0OZ7pa/fRrIkXl69el3c4+aqpVwOKqCdyf2YLDLSFRhyCvKWlZ3L43mu8GtTj/qMnWFtbyzukfHPgwAFmTBjN6uY2FLewE8pJgjxlZoq59SaO/j27YmxsTNWqVeUaj9znoMjMzMTa2oqzh3bj7ChMoij4cQuWrSIo/APr1q2Tdyj5pk+fPjg4ODBq1Ch5hyL4hfj5+VG7dm1CQ0N/m0qt5cuXc/v0AdZN6CnvUAS/mMYjFjJw1CRatmwp71DyRXp6OuZmZtw6uRc7a0t5hyP4hfQcPoES5SszbNjv03Lcq0FdGjtq0qZqMXmHIviFXH0azIwjT3jw5Jm8Q8k3c+fOxd/nMsvGCOUkwY/LzMzEpdkgzl+6gouLi7zDyRd+fn7UrlqJR7NbCL2tBf/IiJ13ca3bgTFjxsg7lHxTs0pFvC0/0dD19x7qU5C/Nt+Nwk+nNDv27JdrHHJ/kvXu3TvSRGlC5YTgH6viWYEH9+/LO4x8df/+fbnXWgp+PS4uLqSlpfHu3Tt5h5Jv7t+7i6eHcF0Q/HOVixXh/m90bQgJCUFLU0OonBD8Y1XLl+Hhb3QuADx8+AhPVxt5hyH4xXi62vD4uS+ZmZnyDiXfPPC5S+XiTvIOQ/CLUVRUpGIJl9+qnHT//n0qOJkLlROCf8yziBEP7t6Udxj56uHjp1Sw1ZV3GIJfTAU7HR743JN3GPKvoEhNTUVTU0PeYeSLrbv2Ua1Bc3mH8cfQ1NRAJBLJO4x8JRKJfothSbZs2ULlypXlHcYfRVNT87eaOFckSkVTTVXeYfzfdpy6Tp1+M+Qdxh9FQ02N1JQUeYeRb1JTU9HU+D3KSdv2HqZ6sw7yDuOPoaGh/ltdFwBSRSI01VTkHcb/bdflJzSYuEXeYfwxVJSVUFJU/K0mzk1NTUVDXU3eYfzfdpy4TJ1ev88Qpb8CTTXV3+oeWiQSoaHye8w5sfuGP41mH5d3GH8MDVVlUlN+r3KSKC0dDRW5P+b9v+199J5mG5/LO4w/hoaKEqkF4Lrw639zBf+px89eUK5GA3QtHShXowGPn73INa2+taPMomZsw5AxWQXQS1dv4F6+GrqWDtRu0pqQ0DDptv2Hj1OlXlN0LR2o1bjVTz0mgeDfePz4MaVLl0ZTU5PSpUvz+PHjXNN27NgRc3NzdHV1cXR0ZMOGDdJtO3fuRFtbW7poamqioKDAgwcPAJg6dSoqKioyaYKCgn724QkEP2zQ3E2UbD8a3Spd2HEq7zGNx6/YTYl2ozCv05tS3mPYdfqGdNvNJ68wq9NLZtGp3JmjV3wA8A0Ko9nwedg26o9O5c4/9ZgEgn/r8XM/ytdviV6RkpSv35LHz/1yTVu7VWd0ChfHoGhpDIqWxq1KA+m2yHfvad61P7alqqJq6cKb0PAc8/gUE4tFMU+h4kdQ4Axdc5Jyg1dh1Povdl1+kmfa1LR0Bq48jk2neTj3XMzK43dktl99Gkz5waux9J5DkynbCf0QK902ZftF3PssxabTPDz6LmPRwRsIBAXJU/9gKncejUmVDlTuPJqn/sG5pq3fdwpGlb0pVK0jhap1pGSrwTLbV+89jVvT/pjX6EyVzmO49TjrGhMbn0jvqSuwq9cDu3o9mLlu3087JoHg33r29iO1ph/Gpt9mak0/zLO3H/NMf/juazwn7se23xbKjt3Lbf8oAA7cCcS2/xbpYtNvMyY9NvDkTTQAn5NSGbDxKi5Dd+AydAfzjj746ccmEPwTzyMTqb/mKUX+ukv9NU95Hpn43X2CPiZTeMYdBh0MkFl/+OkHyi16gMNfd+m++yUxSWnSbQEfkmi95QXOs+5RaelDTvvlfc7Jk1BBkQuxWFwgugFnZGTIvP6nLX/ys6WQSCSiZYfueLduwYegF3Rq14qWHbrn2gIjNtRfuoT5PUJDQ51WTb0AiP74idadezF13Cjev35O6RIeeHfvJ93X0ECfwX17MnrIgHyLX/DvCOdCdiKRiKZNm9KxY0diYmLo0qULTZs2zfVcGDduHG/evCEuLo5jx44xceJEaQVEhw4dSEhIkC6rVq2icOHClCpVSrp/27ZtZdIULlw4345F8OMKzrkgG0N6ekYuKXP2T9N/TzEHGxaP6EIJR9vvptXSUGPv3GGEn13D2gm9GbN0B3eeSQpYlYo7EXV+vXTZP3c42hrq1C7vAYCyshLNa5Zn5dge+Rq/4N8pOOdDwbo2tOo+AO8WjXnve5dOrZvRqvuAPFuqLv1rIjEBD4gJeMCL66el6xUVFalXvTJ71y3N8z3Hz1qIc1HhmiBPknNBrlP6ATlcGzL+2fn5T9N/j7ttIeb3bEBx++9P0j533zWCIj/xdPUgjk7txPKjt7nw6DUAH+OS6LzgAOPaVeP1lpGUKGJO90WHpft2rFmCu0v78Xb7aM7M7Mr+6885fudlvh6L4McU3OuC/MpJorQ02o6cR7v6VQm7uAXvRtVpO3IeorS0XPdZOLIH767u4N3VHTw6sEy63ud5AFNW7mTHnBFEXNpK5yY1aT96vvR4xy7eQlJKKr5HV3F182z2nL7G9uOX8+1YBP9Mgbk2ZBaca4MoPYPOy8/TuoIDAcs609azKJ2Xn0eUyzl35UUY0w/4sKxbNYJXduHYGC/sTHQAaFXBgZBVXaXL3A6VsDPRwcNWMvfCxD13SE5N58Hcdpyd2JT9twPZdcM/345F8OMKzrkgG0N6xj+L6Z+mz4soPZPuu1/SwsMY37FlaV3ChO67XyJKz/t8m3AymOIW2jLrXr1PYszxIJa1KMqTUWXQUFFk/Mlgaczddr+itqMBL8aWZW7jIgw6GMjr6OR8O5b8VGArKOYvXYmtW2kMbJxwK1eVS1clrWGSk5PpPmAYJvZueFSowcJlq7FzKyPdT8XQisCgrFYJ3QcMY/LMeQDExMbStF0XzIt6YGLvRtN2XQgLj5CmrdW4FZP+mkvV+s3QtXQg6E0IL/0Dqd+8PaaF3XArV5X9h7O63H38FENz724Y2jhTsXYjgoJDfujY8sqz+4BhDBgxjsZtOqFnVZQr12/hULwC85eupGTl2uhZOZKens7x0+coXrEmxnau1GrcCr9XWTVoOaXPD1dv3CY9I50h/XqipqbGoD49EIvFXL72/XH7Dh0/hamxMZUrlgfg8PFTuDo70qqZF+rq6kweM4KnL3x56R8IQK3qVWjdvDHmZoXyJfZf2dy5c7G0tERHRwcnJycuXrwISM6Frl27YmBggKurK/Pnz8fKykq6n4KCAoGBgdLXXbt2ZeJESQ+WmJgYvLy8MDExwcDAAC8vL8LCsnqwVK9enQkTJlCpUiU0NTUJCgri5cuX1KlTB0NDQ5ycnNi3L6tVzsePH2nSpAm6urqUK1eO169f/9Cx5ZVn165d6devHw0bNkRLS4vLly9jZ2fH3Llz8fDwQEtLi/T0dI4dO4abmxv6+vpUr14dP7+slkQ5pc8PV65cIT09naFDh6KmpsbgwYMRi8VcunQpx/Rubm6oqUm64SsoKKCgoJDrZ7R161Y6d+6MgoIwjuq3Fu04gWOzIZjX6U3J9qO5cl/Sgys5VUSfmeuwrt+XMh3HsmTXSZyaD5Hup1O5M6/Dsubn6DNzHdPXHQAgJi6RVqMXYuc1AOv6fWk1eiHh7z9J0zYYOItpa/dTu98MTGv1JDjiA69CImgydC42DfpRsv1oDl28K03/8XM8bcYsxqJub6r3mkpw+I/NC5JXnn1mrmPogi20HLmAQrV7cu2hL26thrNoxwkqdJlAoTq9SE/P4OSNh5TtOA6r+n1pMHAWL99ktbjOKX1+6d2yNtXLuKGm+v1hVyb0aIGTrQWKioqUdStCxeJO3HsemGPanWdu0LRGWbQ0JOeOo405Xbyq4WJvlWP6P838leuxK10NQ0dJy/tL128DkJycQo+h4zB1LY9HdS8Wrt6Ifenq0v1ULV0I/Kq80mPoOCbPXQJATOxnmnXui0UxT0xdy9Osc1/CIqKkaWu36sykOUuo1tQbPYeSBIWE8jIwiAbtulPIrQJuVRqw/1jWQ/aPn2Jo3rU/Rk5l8GzUhtchb3/o2PLKs8fQcQwcO5UmnXqj71CKKzfvUrR8LeavXE+p2k3RL1pKUk46d4niNbwwcSlH7Vad8QvI+s3NKX1+uHrbh/SMDAb36oKamioDe3RCLIbLN+9+f+dvFDIxpm9Xb8qUyH0y6Ns+j3jxMoAubVv8P2H/8pYevoVb76XYdJxHucGruPpUch+QnJrGgBXHsO+ygApD17Ds6G3cemdV+Bi2+ougyKzf+wErjjFzt+RhXmxCMu1m7aFo90XYd1lAu1l7CP8YJ03bePI2/tp1mfoTtmDZYS5v3sXgHx5N8+k7Kdx1AeUGr+LwLV9p+k/xSXjP2YtNp3nUHruJ4KiYHzq2vPIcsOIYI9ados3M3Vh1mMv1F28o3m85Sw/fovLwdVh1mEt6RianffypOHQNdp3n03jyNl6FRUvzyCl9funZoAzVPOxRU/3+0Ct7rjxlZKvK6Gtr4GRlTKdaJdn9pdfF8bsvcbYyoZmnK+qqyoxpU5UXIe/wD5ccR1FLI7TUs4aEVFRUIDjqU47v87tbtPUIRRv1xqx6J0q2Gszle5JJwZNTUukzbQVWtbpSuu1Qlmw/iqNXH+l+2uVa8zo0Uvq6z7QVTFu9G4CYuARaDZuNbd3uWNXqSqthswl/l9X6sn7fKUxdtYvaPSdiUrUDweHvePUmnMYDp2NduyslWw3m4Plb0vQfY+NpM2IO5jU6U63rWILCfrCclEeefaatYMicdbQYOgvTqh259uAFrk37s2jrEcp7j8C0WkdJOemaD2XaDsOyZhfq953Cy+Cse5+c0ueH6w98Sc/IYED7RqipqtC/bUPEYjFX7//zoUtCIt/jUtiKki5FUFBQwLtRNT7GxvMhRvLbdOr6A4Z1boqmuhq2FqZ0blKTbcdyvjf5Eyw79YRiI3Zh138rFcbv55qvpFycLEpn4MarOAzaRqWJB1hx5ikeI3dJ9zPpsYGgd5+lrwduvMqsQ5I5O2ITU/FeehbnITtwGLQN76VnifiU1eq56bwTzDzkQ8PZx7Dpt4U3H+IIiIyl1cJTFB20nQrj93PEJ6sn/KeEFDouO4f9gK3U/esobz5kXWfykleeAzdeZdT2G7Rbcgbbflu48TKSUqP3sOzUE6pNOYht/y2kZ2Ry5nEIlScdoMjAbTSddwL/iKzrUk7p88PNl5GkZ2bSp447aipK9K7tjhgx1/0ickw/7+hDRjQpSZkipigqKmBuoIW5gVaOaffeCqBNxaLSe+hzT94yqIEHmmrK2Bjr4F3FiV03XuXLcfxqVl4Pp/SC+zjOvEuVZY+4HiT5fienZTD0cCCus+9RfcVjVt8Ip/TCrJ4mllNuE/wx60H20MOBzL0oKcvHJqfTeacfxeb64Dr7Hp13+hHxOWu4rFabXzDnwluabniOw193CYlJIfBDMu22+uI25x5Vlj3i2POs8sinpDS67nqJ06x7NFr3lJBPPzZcb155Dj0cyNjjQXTa4YfDX3e5GfyZ8osfsvJ6OLVXPaHozLukZ4g59/ITNVY8xmX2PVptfkHAhyRpHjmlzw+338SRkSmmV0Vz1JQV6VHBHDFwM/hzrvscfRaNrroylQvryaw/9DSaOk4GVLDTRUtNiVE1bTjt94mE1AwCo5N5Fy+id0VzlBQVqFxYj7I2Ohx88iFfjiO/Kcs7gJy8CnjNqvVbuH3hJBbmZrx5GyptGTBj3mKCgkN49fAGiYnJNG7T6YfzzcwU08W7Dbs3rSEjI4Neg0YwZMwkDu7YKE2zc+9Bju/bjlPRIiQmJVHCsxZTxo3kxP7tPPN9SYMW7XFzccLV2ZHBoyagrqZGqN9DgkPe0qh1B+xs8p64LzExiQYt2ueaJ8CeA0c4tncbR/dslba623PwKMf2bMPYyICgN2/p2GsAB7dvpFrliixdvZ7m3l15evsyqqqq2dIrK2f/N5esXJvQsJwvBO1aNWPFglnZ1r946U8xVxeZB6fF3FzwfelPvdo18jzu7Xv207FtS+m+vi/98XB3lW7X0tKkiJ0dvi9fCROmf+XVq1esWLECHx8fLCwsePPmjfRcmDZtGq9fv+b169ckJibSoEGD7+SWJTMzk27durFv3z4yMjLo3r07AwcO5MiRI9I027dv5/Tp0zg5OZGYmIi7uzvTp0/n9OnTPHv2jDp16uDu7o6rqysDBgxAXV2dyMhIgoODqVevHvb29nnGkJiYSJ06dXLNE2DXrl2cOnWKEydOSM+F3bt3c/LkSYyNjQkKCqJ9+/YcOXKE6tWrs3jxYho3boyvr6/0XPg6fU7ngoeHB2/f5vzQzNvbm1WrVmVb/+LFCzw8PGTOBQ8PD168eEH9+vVzzKt///5s2bKF5ORkSpYsScOGDbOlCQkJ4dq1a2zatElm/fHjxzE0NMTc3JyBAwfSr1+/bPv+7vzfRrLu0AWubpiKubEBIZEfpC2CZm86THD4e57sW0BSciotRi744XwzxZl0aliVbdMHkpGZSf9ZGxixeBt7Zg+Vptlz9iaHFoykqI05iSmplO88ngk9WnBowUheBIXSZNg8XAtb4WxvyYhF21BXVSHg6HJCIj/QbPh8bM2N84whMTmVpsPm5ZonwP7ztzkwfwT75w1HlCZ5mHrgwh0OzBuOkb4OwRHv6T51FbtnD6VKSWdW7D1L2zGL8dkxB1UV5WzplZWzPzCq0GUCYe9y7vLZunYFFo/s+sOf649IThXx8GUQvZrXyrYtMTmVo5d92Dd3WL6+5+/iVWAwqzfv4tbJ/ViYmfImNDyrnLR4paTi4OZZEpOSadyp9w/nm5mZSee2Ldi1djEZGZn0Gj6BIRP/4uCmFdI0uw4e49iOtTgVsScxKZmSNZswZdQgju9Yx7OX/jRs1wM356K4OjoweMIM1NXUePvoGsFvw/Dq0As767wrmBKTkmjYrkeueQLsOXKSY9vXcmRrCUQiSSvUvUdOcXTrGowNDQgKCaVT/5Ec2LScahXLsXT9Vlp07c+Ty8el14av0+d0bShVuymh4ZHZ1gO0a9aI5bOnZFvv+yqAYi5OsuUkF0d8XwVSr0aVHPOaOHsxE2YtwrGIPdPHDKWaZ7k8P5+/ZWRkMGTiDNbMn8Fzvz+3RWBA+EfWn/HhwpzumBvq8PZ9rPTaMG//dYKjYni4YgCJqSLazNzzw/lmisV41yzOphEtycjMZNDKE4zZcIYdY9pI0+y99ox9E9pT1MKIpBQRnsPXMq5tNfZPaI9vyHtazNiJi7UJztYmjFp/BjUVZfzWDyXkfSyt/9qNjaleHhFAYoqIFtN35ponwIEbL9g7vh17xllJW6AevPmCPePbYqSjyZt3MfRacpjto1tT2c2W1Sfu4j1nL7cX90X1y5jtX6dXVsredq3y8HWERed8w9yqijsLev14+TMnsQnJRMUk4G6X1SjJ3a4Qp3wkD5Jehn7A3c5Uuk1LXRW7Qga8DP2Ao6Xk+rrk8E0WHrxBYkoatqb6tKzi/n/F9CvyDwln7f4zXNsyB3MTQ0Ii3meVkzbsJyjsHU8PLScpJZXmQ7Lf5+UmM1NMx8Y12DZ7OBkZmfSbsYoR8zeyZ8FoaZo9p69xaMkEHG0tSExJoVy7EUzs05bDSybw4vVbGg+cgWsRa1wKWzN8/gbUVFUJPLWONxHvaTb4L+wsTPOIABKTU2gycEaueQLsP3uDg0vGc2DRWGk5af+5GxxcPA4jPR2Cw9/RbeJS9swfRZXSbqzYdZI2I+Zwf+9iVFVUsqXPqZxU3nsEYVHR2dYDtK5XmSVjemVb7xcUiruDrcx1wd3BFr+gMOpULJljXlNW7WTyyp042lowuV97qpZ2A6BuxZIs2X4Un+cBlHIpzLZjl/FwtKOQkb50X7FYLPO3X1BoXh/tbyswKpaNl3w5P7EpZgZavI2Ol7aenn/sIW8+xOEzuw1JonTaLT77w/lmisW0r+TIhr61yMzMZPDm64zdeYttg+pI0+y/HcieofVxMNMjKTWdKpMPMqZZafYMrY9v2CdaLzqNi6UBThYGjNlxCzUVJZ4v8ubth3jaLD6DjbFOnjEkpqbRauHpXPMEOHj3NbuH1GPX4EKIvpQPD917za4h9TDUVifkQzx91l5m68DaVHKyYM35Z3RYfp6bM1qi+uW7/3X6nK4N1aYcJOxjzkPStCxfhHmdKmVb/yoiBlcrQ5nzwdXKkFcRMdQqZi2TNiMzk8dvoqlXwpay4/aRmpZOg5J2TG1dDg1V2XJbaHQ8t/2jWNqtqsz6b8+Hl+E/1jjgdxIYnczme1Gc7O2Bma4qoTEp/P2MffGVMEI+pXBzSEmS0jLptCP3YUm/lSkW07akKWtbO5IhhuFHApl4KphN7Z2laQ4+/cCOji4UMdIgKS2DmiufMKqmNTs6uvDyfRLttvnibKqJo6kmE04Go6asyKORpXkbk0qH7b5YG6jnGUOSKIN223xzzRPgyLNotnd0Zqu3M6IvB37keTRbOzhjqKlMSEwK/Q8EsKm9ExXtdFl/O5Kuu15yeUAJVJUVs6VXVsreiLT2qieEf855LpNmxYyZ7ZW9p/Or90m4FNKSORdcCmny6n0yNYoaZEsfn5LO/Muh7Oviyu6H72W2+b9Poox11u+GnaE6KkoKBH1MRjWHc1cslrx/QVQgKyiUlBRJFYnwexWAibERdjZZP1YHjpxg+fyZGBoYYGhgwIDe3Zg5f8kP5WtkaECLJo2kr8eOGEydJm1k0nRu3xo3FycAzl64gq2NNV07tAWgpIc7zRs35ODREzgVHcKh46d4dOMCWlqauLs606lda67fyruV3MmzF3LN09V5OACNG9alUoWyAKirS07Kgb27Y21lAcD+w8doWKcWtWtIfoCHD+zL8rUbuX3vPtUqe2ZLn5NHNy78wCcmKzExET1dXZl1uro6xCck5LlfSGgY127eYd2yrIeGCYmJmBgb5ZDX98dd+5MoKSmRmpqKr68vJiYm2NnZSbft27ePVatWYWhoiKGhIYMHD2b69Ok/lK+RkREtW7aUvp4wYQI1ashWMnXt2hU3N0mB+MyZM9jZ2dGtWzcASpYsScuWLdm/fz8TJ07k4MGDPHv2DC0tLdzd3enSpQvXrl3LM4YTJ07kmueUKZIHP02bNqVSJUnh5u9zYfDgwVhbS34T9u7dS6NGjahTR1IoHDlyJEuXLuXWrVtUr149W/qcPH369Psf2DcSEhLQ05N9sKCnp0d8fHyu+6xatYrly5dz+/Ztrly5Iu1R8bVt27ZRpUoVmcqdNm3a0Lt3bwoVKsTdu3dp2bIl+vr6tG/f/h/H/StTUlQkVZTGy+BwjPV1sDU3kW47fOkei0Z0wVBXG0Ndbfq2qsvcLUd+KF8jPR2aVi8rfT2qSxMaDZotk6ZDwyq4FJY8VL1w9yk2ZsZ0aiT5/S3uaEfTamU5fPkeo22acvTKfe5sm4mWhhquha3wblCZm4/zHmrizK1HueY5zr45AI0ql6Kih6QSW/3L5OF9W9XBqpDkd/TQpbvUq1iCmmUlD2WGtG/A6v3nuPssgCqlXLKlz8mdrTN/4BPLP0Pmb6ZYERtql8/eQvzY1fsY6WtTuaRzDnsKpOUk/0BMjAyws7aUbjt4/AzLZk3G0EAfQwN9BnbvxMzF2Stac2JkaECLRnWlr8cO7kPdNl1l0nRq0ww3p6IAnL18HVtrS2kL/pLurjRvWJeDJ87iNMSew6fO8/DiUbQ0NXF3dqRjq2bcuHs/zxhOnr+Sa56uwyUVFI3r1sSzrGQYPPUvk8QO7NERa0vJUDL7j52mQa1q1K4quX4M79udFRu3c/v+Y2kFwNfpc/LwwtHvf2DfSEhMQldHttu1rq4O8Yk5l21mjR+Bi6MDqioq7D16iuZd++Fz7jBF7PJu7AKwYuN2ypX0oJSH2x9dQaGkqIAoLYNXYR8w1tXExlRfuu3ILV/m92qAgY4GBjoa9G5Ylvn7854n52+GOpo0qeAifT2iZSWaTN0hk6Z9dQ9cvlQUXHj8GhsTfTrULAGAR2EzGldw5uhtP4paGHH87ktuLOqNlroqrjamtKtWjFt+efcoOvsgINc8/66gaFjGkQrOkjKO+pcHNr0blMXKWFJGOXzTlzqlHKhRXHJzPLBJRdae8uHeq1Aqu9tlS5+TG4t+vJLz30hIkTRA0dXMehChq6lGQrJkfWKKCGNd2RazX28HGNq8EkOaefIs+B0nfV6hq/nrTx79TykpKpKalsbL4DCMDXSx/eqh/6ELt1k8pieGejoY6unQr20D5mw88EP5Gunr0KxmBenr0d1b0LDfNJk0Hbyq41pE8j08f/sxNuYmdGosua8o7mRP05rlOXzxDo62Fhy9dJe7uxeipaGOWxEbOjSqzs1HvuTl9I0Hueb5dwVFo6plqVhcUmb4u5zUr21DrApJKrEOXrhFvUqlqFm+OABDOjZm1d6T3HnqL60A+Dp9Tu7uWvgDn5ishOQUdLU1ZdbpamsSn5jz8BozBnbE2d4KVRVlDpy7SZsRc7i1Yz6FrczQ0dKgaY0K1Ok1CTFi9LS1OLx0vPQBV52KJVi49Qjrpgzk/adYth+/TNJvNgHwj1JUUECUnsGryFiMdDRkHvof8wlmbkdPDLTVMQB61XZjwfGHP5SvobY6jctk3acN8ypB8/knZdK0q+SIs6XkAePF56FYG2vjXVlShvewNcartD3HfIIZ3liPEw+DuTatJVpqKrhYGdLWs6h0joXcnHvyNtc8RzWVvG+DEraUL2oGgLqi5NrQq5YbloaSMsoRnyBqe1hT3U1yfzOgngfrLrzAJ/AdlZwtsqXPydVpLXPdlpvE1HR0NVRl1ulqqJKQkn3Isw+fk0nLyOT4/WCOj/FCRUmRTivOsejEIya0KCuTdt/tQCo4FsLWJOv/XNPdimWnn7CiezXexyWz+4Y/yaL8G9bzV6GkAKKMTPw/JGGkpSzz0P/4i4/MamSPgaYKBkD38uYsvhqWe2ZfMdRUoZFr1n3l4KpWtNkiOz9tmxImOH2pKLjsF4u1vhptS0quTe7mWjR0MeTEi48MMdbglO8nLvYvjqaqEs6FNGlVwpS7IXn3KDrvH5NrnsO/vG9dZwPK2kieYaorSn4re5Q3x1JPUk449vwjtRwNqFpEH4C+nhZsvBPJ/dB4PO31sqXPyYX+xb//gX0jUZSJjrpsRbiumjKJopx7782/FEr7kqZY5BBHoigjh7yUSEjNoKyNJsZaKqy+GUGviubcCo7jTkgcnna62fIpCApkBYVDYXsWzprK9LmL8H3pT92a1Zj/12QszM2IiHqHlWXWg3fb77TE+1pSUjIjJkzl3MUrxMRKWgLFJySQkZGBkpLkH/p13m/Dwrj34BHGdlkt/dMz0unQpiUfoj+Snp6O9VfpbaytgLwrKPLK829f55nTuoiod9h89TBCUVERKwsLwiOjckyfX7S0tIj75gFsfHwCOtq5X7hA0iulUoWy2Ntm3XBra2kRFy9bsREXH4+Ods5d9v5UDg4OLFmyhKlTp/LixQvq1avHokWLsLCwICIiQubBu63t98d//1tSUhLDhg3jzJkzxMRIWhLEx8fLnAtf5x0SEsLdu3fR19eXrktPT6dTp058+PBBci78w1jyyvNvOVUsfL0uIiJC5r0UFRWxtrYmPDw8x/T5RVtbm7g42QtmXFwcOjp5t3hRUlKicuXK7Nixg9WrVzN4sOzEd9u2bWP8+PEy6/7uTQLg6enJkCFDOHDgwB9XQVHEqhBzB3dg1qbDvAwOp1b5Yswe5I25sQGRH2OxMjWUprUxy/0h/LeSUlIZu2wXF+4+JTZe8hAxPimFjIxMlL60OLD8Ku+3UR+57/saq/p9pevSMzJoV68S0bFxpGdkYGWa9f42hYz43iB4eeX5t69j+NvXlQ2R0bFYf3XcioqKWJoaEhEdk2N6eZuwcjd+QeGcXD4ux+HMdp2+Tvv6lYWhznLhYG/LgmnjmLFoJb7+gdSpVon5U8ZiYWZKxLv3WFmYSdPa5NFY4VtJycmMnDKHc1euE/NZ8hsXn5Aoe234Ku+34RHce/QUE5esVv/p6Rl0aNmYDx8/Sa4NX6W3tbLgxndGO8orz79ZWWSvWPj6mCPfvcf2q+NWVFTEytyM8Kh3OabPL9pamtkabcTFJ6CjlXPZplyprBuazm2ase/oSc5cusaA7h3zfJ+IqPes3LSDO6d/7AHj76ywuSGzutVl7r5rvAyNpmbxwvzVtQ7mhjpExcRjaZx1A2ZtknePha8lpaYxYcs5Lj56TWyiZJiBhGSR7LXBKCvvsA+feRAYjl3n+dJ1GZmZtKlajOi4JNIzMmXSW5vowXcaKuaV59++Pr6c1kXFxMsct6KiAhZGukR+is8xvTxofxmeKT45VVrJEp+civaXh1ha6qrEJ8s+ZP16+98UFBTwKGzGpSevmbP3GjO71uFPUsTanHnDujJr/T78gsKoVaE4c4Z2wdzEkMjoGCxNsx6825iZ5JGTrKSUVMYs3sKF24+zykmJybL3z1891A+NjOb+iwAsa3aRrkvPyKBdg6pEx3wpJ31VHrH+gVjyyvNvljmUcWTKSR8+YfNVj1ZFRUWsChkT+eFTjunzi7aGerbKiPjEZHS0NHJMX9a9qPTvDl7V2X/uBmdvPqJf2wZsPXqJHScu47NnEUWszbh49wmths/h1vZ5mJsYMn9EN0Yu2ETxloMw1NOhVd1KHDj3/aGYf0eFC+nxV7sKzDv6kFcRMdRws2JG2/KYGWgRFZsk8+DdyijvZxlfS0pNZ9LeO1x6FkZskuR3KSEljYzMTJQUv1wbvhqCKOxjAg+DPlBk4DbpuvTMTNpUdCA6PoX0DDEWhlnprY20uf2dGPLK829f5/k3y6/WRcUmYv3VcSsqKmBpqEVkbFKO6fOLlpoy8Smy83LFp4jQVs8+TOzf14OetVwx05c8bO5XpxiLTjzOVkGx91YAQxuVkFk3q31Fxu26Tbnx+zHUVqN5uSIcvvdjw1D/TuyNNJhW345FV8Lwf59ENQd9ptSzw0xXlXfxIpkH3lb6P165nyzKYMqZN1wJjOVziuShekJqBhmZYpS+VARY6GblFx6byqPwBFxm35OuS88U09LDhI+JaaRnirHQy7quW+mpfefJat55/u3rGKTrvnqfd/EirL56raiogLmeGlHxohzT5xctVUUSUmUrI+JTM9DKYVjM55GJXA/6zNm+HrnkpZRjXtpqSqgoKbKxnROTTgWz8kYExS20aOxmhGoOPUEKggJZQQHQvlVz2rdqTlxcPP2Hj2XctFlsXbMM80KmhIVHSHs5vA0Ll9lPU1ODpOSsQsC7d++lN7KLV67FP/A1N88fx6yQKY+fvaBstXoyXb++fhhiZWlBVc8KnDm8O1t8GRkZKCsrExoeIR2SKPSbWHKSV545xZDTOguzQjz3zWqRKxaLCYuIwNLcLMf0OSlesSYhYTnXjnq3bsGqRXOyrXdzdmTJyrWIxWJp/s9e+NGvR5dsab+2Y+8BRn0z2bWrsyPb92TdWCcmJhH0JgRXZ6c88/oTeXt74+3tTVxcHH369GHMmDFs374dc3NzQkNDpb0cvh2mSFNTk6SkrEJGVFSUdI6KhQsX8urVK+7evYuZmRmPHz+mZMmSuZ4L1tbWVKtWjfPnz2eLT3ouhIbi7OycYyw5ySvPnGLIaZ2FhQXPnj2TvhaLxYSGhmJpaZlj+py4ubkREpLz/DEdO3ZkzZo1Oe6zcOFCmXPh6dOnDBjwY5O6p6enZ5uD4ubNm0RERNCqVas891VQUJD5P/1J2tT1pE1dT+ISkxkybzOTV+9l/aS+mBnpEfb+k7SXQ+g3wxRpqquS/FULsvcfP2NpInngv3zPaQLeRnJ53RQKGenzNCCESt0m5X5dMDWkcglnji0Zky2+jIxMlJWUCHv/ESdbixxjyUleeeYUg3TdV3+bG+vz4nXWb7pYLCb8/ScsjA1yTJ+Tsh3HEfou56EL2tb1ZOmobt/J4cfM3HiI83eecnrFeHRzuEEPe/eR649fsnR0/rzf76p9cy/aN/ciLj6B/mOmMH7mArYsn4e5qQlhEVHSXg6hX821BaCpoUFScta4ru8+RGNpLhlaZfGazfgHBXPjxF7MTE14/NyPcvVa5H4+WJhTtUIZTu+RHZYOvro2RETh7CBpvf02lyGTvpZXnlkx5LDuq2+4eSFTnr/MmptLLBYTFhmF5VfzWil854woXsOLt2E5x+vdojEr507Ntt7VqShL1m6RuTY893tFv67eeb6XNKYf/H33efyUyPcfKF5DUmmTnJJCckoq1iWq8ObBFelDwz9FqyrutKriTlxSKsPXnmTajousGdyMQgbahEfHSXs5hH2QHaZIU02F5NSsVpvvYhOwMJI0NFh57A6B4R85P7s7hQy0eRYcRbVRG/j6v/P1uWBprIunqy2HJ3fIFp/k2qBI+Mc46ZBEYdHfH2c8rzyzYsh7nZmBDr5vs4YCEIvFRHyMw9xQJ8f0Oak4dE2uQzy1rlKMRX2yD1n5T+hra2BmoM3zN++kPT2ev3mHs5Xk/+ZsbcKeK1m9XRNTRLyJipH2IvlWekYmb35wjo/fTZv6VWhTvwpxCUkMnrOOSSt2sGHaYMyM9Al/Hy3t5fDttV5TXY3krx4avvsYi8WXxhbLdh4nICSCK5tmUcjYgKf+wXh2HM3XP1Vf/55aFjKicilXjq+YnC2+jIwMSTnp3Uec7CRl9dCo74+DnVee0hi+c89gbmLIi8Cs+xOxWEzYu2jMTQxzTJ+TMm2H5Rpv2/pVWTYue28jl8LWLNt1XPa6EBhC79b18nyvr2MSf/nleRoQTP1KpSn6pYxZp2JJzIz0ufP0Fc1rVcRQT4dNM7LmYJu6ahel3f7coZNbVnCgZQUH4pNFjNh2g+kHfFjVqzqF9DUI/5Qg7eUQ/lG2YYGmqrJMS/v3cclYfKl0WH3uGYFRsZyZ2IRCepo8e/uRmtMOy5wPXxcvLA218XQy48CI7L+TGZmZKCspEPEpkaLm+gCEfcp7ZIrv5ZkVQt7ng5m+Fr5hWZVzYrGY8E+JmOtr5pg+J5UnHSD0Y87xtq7gwILOlbOtd7IwYNW5ZzLng29oDN1ruGZLq6+lhoWB7BA4OYV0NyCKd7FJNCltJ7PeQFudNb2zRoj466APJe1/vHL2d9Lcw4TmHibEp6Qz5ngQM8+HsLxlUUy1VYn4nCrt5RAeK9sYQENFkeS0rPlHPiSIMNeVPKxfcyuSoI8pnOhVDFMdVZ5HJlJvzVPZa8NX/y8LPTUq2Oqyp0v2/3VGphhlRQUiPotwMJHcG+Y2ZNLX8sozpxhyUkhHlZdfDXckFouJ/JyKmc6PV0rUWPGYsFzibeFhwtzG2Yd4cjLVZO3tSJlzwe9dIl3LZZ+D9/abOEJjUym3WNLTK1GUQWamGP8PyZzt64GjqSa+UVnHEPIpBVGGmMJGks/S1UyLg92zhr5ssuEZrYvnPbSivBTISbJfBbzm8rWbpKamoq6uhrq6OopfaqRbNfNi7pIVxMTGEhYewcr1m2X2Le7uxp4DR8jIyODshctcu3VHui0+IQENdXX09XT5FBPDX/MW5RlHo7q1CXgdxI69B0hLSyMtLQ2fh4/xexWAkpISzb0aMGPuIpKSkvF96c/23fu/e2x55fmjWjVrzKnzF7l09QZpaWksXrEWNVU1KpYr8/2dv3hy+xKxof45LjlVTgBUq1wRJSUllq/dSGpqqvSzr1E1+/iCf7t19z7hkVG0auols76ZVwNe+L3i0LGTpKSk8Nf8xRRzdZFW9mRkZJCSkkJ6huTkS0lJIS0te9e/392rV6+4dOnSl3NBHQ0NDem50KZNG2bPnk1MTAxhYWEsX75cZt8SJUqwa9cuMjIyOHPmDFevXpVui4+PR0NDA319fT59+sS0abJdtb/l5eWFv78/27dvz/re+vjg5+eHkpISLVq0YOrUqSQlJeHr68vWrVu/e2x55fmj2rRpw8mTJ7l48SJpaWksXLgQNTU1PD09fziPFy9ekJCQkOOSU+UESCYRV1JSYtmyZaSmprJihWR89po1a2ZL+/79e/bs2UPCl95aZ8+eZffu3dSqJTvu/tatW2nZsmW2XhhHjx4lJiYGsVjMvXv3WLZsGU2bNv3h4/td+L+N5OoDX1JFaairqqCupiK9mDevUZ6FO44TE5dI+PtPrD0oO4RdsaK27Dt/m4yMTM7fecqNr4Zcik9KQUNNFT1tTT7FJTB705E846hfqQSBoVHsPnOTtPR00tLTeeAXxMs34SgpKdKkWmlmbzpMUkoqL4PD2XXmxnePLa88f1TzmuU5e/sxV+6/IC09nWW7T6Omokz5YkW/v/MXPjtmE3V+fY5LXpUTorR0UlJFiIG0dMnfmZk5T6a3YPtx9p+/zfElYzDSy7nH0e6zNynvXpTClrIFNLFYTEqqiLQvY0unpIpIFf151wWQzEFx+cYdUlNFqKupovFVOall4/rMW7GOmNjPhEVEsXLzTpl9i7s5s+fICcnv0eXrXLvjI90Wn5goKSfp6vIpJpa/Fq/MM45GtasTEPSGHQeOSn/H7z9+hl/Aa5SUlGjWoDYzFq4gKTkZX/9Aduw/8t1jyyvPH9WqcQNOX7zKpeu3JeWktZtRU1WlYpkSP5zHk8sniAl4kOOSU+UEQLWKZVFSUmTFxu2kpopY9eWzr1GpfLa0sZ/jOHflBikpqaSnp7Pr0HGu37lP3epZN/QpKamkfpl/KTVVRMqXitb6NaoScOcCPucO4XPuEFNGDqKEuws+5w79cZUTAeEfufYsmNS0dNRVlFFXVUHxy7WhWUVXlhy+SWxCMuEf41h/WnZ4MXe7Qhy48YKMjEwuPHrNLd+sh5cJKSLUVVXQ01InJj6Zed8ZGqpu6aK8jvjI3qtPSUvPIC09g4eBEbwKi0ZJSRGv8s7M3XuNpNQ0XoZ+YPdXD9z/TZ4/qpmnK+cfBnL1aTBp6RmsOHYHVWUlyjn9eO/S20v6ErpjTI5LXpUTorQMUkTpiMWQli75OzMz5wq4ttU8WHjwBrEJyfiHR7P9wmPa15D0MPIq54Rf6AeO3fEjRZTO/P3XcbUthKOlMZmZYrace0BsQjJisZgHAeFsPPOAqsXsfvj4fhf+IeFc8XkmKSepqaChpoqiguS60KK2Jwu2HCYmLoHwdx9Zs++0zL4ejnbsO3udjIwMzt9+xI2vhlxKSEyWlJN0tPj0OZ5Z6/O+321QuTSBbyPZfepqVpnGN5CXwWEoKSnRpEY5Zq3fR1JKKn5Boew6dTXP/L6X549qUbsiZ28+5PK9Z5Jy0s7jqKmoUOHL8Jk/4v7exby7uiPHJafKCYAqpV1RUlRk1d5TpIrSpJ99tTLZ50mJjU/kwu3HpKSKSE/PYO+Z69x85EedCiUAKO3iwNmbDwkOf4dYLObS3ScEvo3EtYhklIKgsCg+xkp6xJ+79YhNhy8wuts/H4bndxAYFct1vwhS0zJQU1FCXUUZhS+tupuWKczSU0+ITUwl4lMiGy7JDkvjZmPIwbuvycjM5OKzUG6/ymqskJAiQl1FGT1NVWISUlhwLO+hoep6WPM6Ko59twJIS88kLT2TR8Ef8I+IQUlRkUal7Jh39CFJqem8iohh763vPxPKK88f1bSsPReehnLNN5y09ExWnX2GmrISZR2yPxzNzY0ZrQhZ1TXHJafKCYBKzuYoKSiy7sILUtMy2HBR8tlXccm5t2/7yo5suPiCD3HJxCamsub8c+oWl71+7b0VgFdpu2y96oLfx/EpIYWMzEwuPAtl+7WXDPfKed6X31lgdDI3gj6Tmp6JmrIi6iqK0nJSYzcjVlwPJzY5nYjPqWy+Jzu8mJuZFkeeRZORKeZyQAx33mQ1rkgUZaCurIiuujIxSWksvpL3fDe1HQ0I+pjMgScfSMvIJC0jk8fhCQR8SEJJUYEGLoYsvBJKsigD//dJ7P+BSZzzyvNHNXY34qJ/DNeDPpOWkcnaW5GoKivKzOnwPZcHliBgQvkcl5wqJwAq2umipAAb70SRmp7J5ruS35lK9tl7+3YsbcqtISU519eDc3096FSmEDUdDdjVSTIUaQsPY877x3A3JI4kUQYLLofSwMUQbTXJPYFvVCIpaZkkizJYczOC9/FptClZMCvrCmQFRaoolfHTZ2FW1AMr55J8iI5m5qSxAEwaPRwbayuKlvCkYcsOdGwje9FdNHsaJ89cwNjelV0HDtO0YdaEtYP79iQ5JQWzoh5UrtuEurXynthZR0ebUwd3se/QMWxcS2PlXJLx02ZJbxaXzvuLhMRErJxL0mPgMLp4t/3usX0vzx/hVLQIW9csY+iYSZg5eHDi7HkO79osnfjxZ1FVVeXAjo3s2HsQY3tXtuzcy4EdG6XvO2fRcrxayw5LsH3Pfpp5NUDnmzGZTYyN2Ld1HZNnzsOksBv3Hjxi58asMbJ37D2IjoUDA0eM48btu+hYONBn6Gj+NKmpqYwdOxZjY2PMzMx4//49s2dLxsefMmUKtra22NvbU7duXZmhkQCWLl3K8ePH0dfXZ+fOnTRr1ky6bejQoSQnJ2NsbEyFChVyndj5bzo6Opw7d449e/ZgYWGBmZkZY8aMITVV8rBkxYoVJCQkYGZmRteuXaXzSvw/ef4IJycnduzYwaBBgzA2Nub48eMcP378PzkXjhw5wrZt29DX12fTpk0cOXJE+r6zZs2STlquoKDA6tWrsbKywsDAgJEjR7JkyRKaNGkizS8lJYV9+/bRpUv23kh79uzBwcEBHR0dOnfuzJgxY3JM97sTidKYsmYfdo0G4NBkEB9i45jWVzKH0LjuzbApZEyxNiNoOnwe7erJVlDNG9KB0zcfY1W/L/vO38KrSmnptgFt6pGcKsLOawA1e0+jTg7zIXxNR1ODI4tHc+DiHYo2G0KRJoOYvHqvdELGBcM6k5CcgkOTQfSdtZ6ODXOeGPef5PkjHG3MWT+pLyOXbMeu0QBO33rEvrnDpBNk/0xNh83DpFZP7j4LYPC8zZjU6snNx5LJTfeeu0XZjuOkaaet3U/ou48UbzcKszq9MKvTi/nbjsnkt/vMTbwbZL+xeRsVjUmtnpTtJMnPpFZPSnnn3uvkd5YqEjFh9iLMi1XEumRVPkR/5K9xknmsJg0fgI2lBY4V69DQuycdWjaR2XfR9PGcPH8ZE5dy7D50nCb1sipLB/fsQnJKCubFPKncuB31quf9/dXR1uLkro3sP3oK21LVsC5RhfEzF5Ka+qWcNHMSiYlJWJeoQs9h4+nctvl3j+17ef4IJwd7tiyfy9BJMzEv5snJ85c5tGXVf3Jt2L9pBTsOHMXEtRxb9hxk/6YVWeWkZWtp3FHyECstPZ0p85Zi4eGJeTFPVm3awYFNy3EskjW2tW6REhgUlfxeFavWEN0iJQBQU1PFzNREuujp6KCirIyZacG82fiZRGnpTN95maLdFuHcazHRnxOZ1EHSWGB0m6pYG+tRov8KWs7YRZtqsr/vs7vV5cx9f+y7LODA9Wc0LJv1oLJvo3KkiNIo2n0hdcdvplaJnG8w/6ajocbBSR04dNMX195Lce65hGk7Lkp/x+f1qEdiigjnnosZuPI43jW+P17x9/L8EUUtjVgzuCljNp3Fofsizj4IYNe4ttIJsn+mljN2YuE9h3uvwhi29hQW3nO45Svpsbr/2jMqDs1qBDK2bVXsChng0W85jSdvZ2DTCtQuWQQAYz0tto5sycxdVyjcdYGkEmJY1m/JiXuvKDVwJTad5tF32VF6NShD74ayw3/8CUSidKas3Ilt3e4UadCLD58+M22ApPfWuF6tsTEzwb3ZAJoMnkH7r4ZGApg3vBunrj/AslZX9p65jle1rCH2BrRvRHKqCNu63anZfUKuEzv/TUdLg6PLJ3Lg/E0cGvamSP1eTFq+A9GXhmYLR/UgMSmFIg160Xf6Sjp65X0//iN5/ghHW0s2TBvEyIUbsa3TnVPX77Nv4VjpBNk/i6qKCnvmj2b3qatY1urC9uOX2TN/tPR9528+RPMhkjnA0tLTmb5mD3b1emBbtztr9p1mz/xR0h4T3o2q0bKuJw36TsG8RmdGLtzMsnG9pb1RHvkFUd57BGbVOzNl5U42TR8s7TXzp0lNy2DGQR+chuzAbdguouOTmdRC0phzZJNSWBtpU3rMXlovOk3rirKNeWa1r8i5J28pMnA7B++8pkHJrOGE+9R2JyUtHachO6g/6xg13fMeblxbQ5X9I+pz+F4QxUbswm34TqYfuEdquqQhz5wOniSmpuE2fCeDNl2jfaXvV5h9L88f4WCmz6pe1Rm36zZOQ7dz9slbdgyuK50g+2dRVVZi68Da7LsdgMOgbey64c/WgbWl77v45GPaLj4jTT/CqyQl7UyoMH4/nhMPUMzGiGFeJaTbU9LSOeoTTFvP7A2ynoREU3XyIewHbOWvgz6s7lVD2mvmTyJKz2T2hRCKzfWh5IL7RCemMa62pFJzeHUrLPXVqLjkId7b/WjpITsHz/QGdpx/FYPL7HscehpNPeesHmc9K5iTkp5JsXk+NN7wnOoOeX+22mpK7OrsytFn0ZRa8IAS8x8w83wIqemShgszG9mTKMqgxIIHDDsSSNsS3y/Tfi/PH+FgrMHylg5MOhVMsbn3Of/qE1u8naUTZP8sqsqKbGrvzIEnH3CdfY89jz6wqX3W+y67FkbH7ZKGuxqqSpjqqEoXLVUl1JUVMdKSXEecTDWZ42XPwIMBeMy7T0JqBrMaZd1PHHzygVIL7uMx/z43gj6zu7MLaj/5+P4tBbGcxwp58+YN1atVJfDx90bby9nVG7fo0mcwb17kPemi4PfzzNePzn2G8uz5c3mHkm+KFSvGrl27KFYs74ekObly5QodO3YkLJehuwS/Nzs7O65cuSIzkfqvrH3b1tRxNqZN3R/vDfO36w/96DljDa8OL/0JkQkKumW7TxOtUohFixfLO5R84evrS6vmzXhy+dj3E+fg6q17dB00muAHV/I3MEGBt//YaY5cuMm+A7/PXBVGBnrcW9wTQx3N7yf+xo3nb+iz7Cgv1g35fmLBb6dQuzkkJCb+9IrK/0rTxo1oX9WNxtXLfT/xN649eEHPKcvwP7H2J0QmKOj6z1xH1cZt6dGjh7xDyRcbN27k8o6lLOmUvbfij7j5MoJ+G67wdMGPDcco+H2ceviGg0GKHD11Vt6h5BtNdTWejSyBRg5zGXzPreDPDDoUyIMRpb+fWPBbefMphY4HIwkK/f5wvD9Twaw2EQgEAoFAIBAIBAKBQCAQCAQCgUDwWyuwk2T/ym7cvotXm045bosN9f+PoxEI5Of69evSoY6+lZDw/UnABILfxc0nr2g5ckGO26LOr/+PoxEI5OvG3fs07tgnx20xAQ/+42gEAvm57fuWNrN257gtdMefOYSd4M9085EfLYbOzHHbu6s7/uNoBAL5uu0fRbslZ3LcFrKq638bjEAgR3dD4ui4I+c5SgMm/LteU4KC65evoKhW2bPADe9UuWJ5oSJC8J+rXr16gRveqUqVKkJFhOA/V6WUS4Eb3qlScSehIkIgF9U8yxW44Z0qly8jVEQI/nOV3e0K3PBOFV1thIoIwX+uamm3Aje8U6WSLkJFhEAuKjlbFLjhnSo6mgkVEYL/nKe9XoEb3qm8ra5QEfEHEYZ4EggEAoFAIBAIBAKBQCAQCAQCgUDwnxMqKP4j3QcMY/LMefIOQyCQu65duzJx4kR5hyEQyF2fmeuYvu73mbxWIPh/9Bg6jslzl8g7DIGgwBmw4hgzd1+WdxgCwX+uz7QVTFud8xBoAsGfbODGq8w6VLBGEREI5GXo4UDmXnwr7zAE+UCooPgD7T98nCr1mqJr6UCtxq2ybb987SZlq9fH0MYZx5KerN+S1d326o1bqBpZo2/tKF227d4v3V6rcSu0zYtIt7mVq/qfHJNA8G+MHDmSokWLoqOjg7OzM9u2bZPZrqCggJaWFtra2mhra9OzZ0/pttTUVPr27UuhQoUwNDSkcePGhIeHy+y/Z88eXFxc0NLSokiRIly/fv0/OS6B4J86dPEutfpOx7RWTxoMnJVt+9OAEKp0n4xprZ5U6T6ZpwEhMtsfv3pDvQEzMavTi8KNB7Jq31nptjvPAqjeayrmdXpTocsEbj159dOPRyD4f4z9az6Fy9TAyKkMDuVqMmeZ7FAoGRkZTJ67BNtSVTF0LE3Zui2I/RyXLZ96bbqhaulCenq6zPrlG7bhWKE2+g6lKFatEf6vg3/q8QgE+enK0yCqj9qAVYe5uPVeyuFbvtJtQ9ecpNzgVRi1/otdl5/I7Dd87SmsO86VLmbtZmPTUWi8Jfg1HDx/i1o9JmBSpQP1+07Jtn3grDWUbDUYnfJt2HEi9wrFRv2noV2uNenpGdm2XX/4Au1yrYVKGcEvZdr+uxQfuRv7AVspOWoPi08+ltl+3S+CmtMOYz9gK2XG7GXb1ZfSbTdeRlB18kGKDNyG4+DtdFlxnsiYxP/4CASC/PPXuRDKLHyA06x7lFv0gGXXZIdgH33sNVWWPcJq6m32Pnqfbf91tyIoMf8+TrPuMfxIIKnpmf9V6AXCLz8HheCfMzTQZ3DfnrwKCOTy9Zsy29LS0mjVqSezp02gV5cO3H/0hDpN21CuTCmKu7sCYGFWKM95P5bOnUGPzgVrHEeBICdaWlocP34cR0dHfHx8qF+/Pg4ODnh6ekrTPHnyBAcHh2z7Ll26lNu3b/P06VP09PTo3bs3gwYN4tChQwCcP3+eMWPGsHfvXsqVK0dkZOR/dlwCwT9loKvFgNb18H8bydUHvjLbRGnptBu7hP5t6tGreS02Hb1Mu7FLeLxnPqoqykTHxtN8xHzmDO5As+plEaWnE/7+EwCf4hJoM2YRS0d2o0m1Muy/cJu2YxbzdN9CDHS15HGoAsF3dWvXiknDB6ClqUl45DsaevfAycGe5g3rAjBtwXLu3H/MtWO7sbG04MWrANTV1GTy2HXoOGnpadny3rRrP5v3HOTItjW4FC1CUEgoBnq6/8lxCQT/r5ehH+i95AgrBzahRvHCxCWl8DkxRbrd3bYQzT1dmbbjUrZ9F/VpyKI+DaWvB6w4hoKCwn8St0Dw/zLU06Z/u0b4h4Rz9f7zbNuLFbWjZe1KTF6R+zwae89cJy2HigmAtPR0Ri/cTFn3ovkWs0DwX+hQxYmRTUqhpaZCZEwirRedpqiZHl6l7UlLz6TLyvNMaVWOztWcefwmmmbzT1KqsAnu1kY4muuzb1h9zAy0SE3LYM6R+4zafpMdg+vK+7AEgn+lXSlThle3QlNVici4VLy3+eFgrEFDVyMAXAtp0djdmFnnQ7LteyUwlpU3ItjX1ZVCOqr03POKhZdDGV/H9r8+DLn5I3pQzF+6Elu30hjYOOFWriqXrt4A4N6DR1Su2wRjO1esXUoxePQERCKRdD8VQytWb9yKS5nKGNg4MWXmfF4Hv6FKvaYY2jjTvltfafqrN25h51aGOYuWY+ZQDIfiFdi1/1CuMZ08e4HSVetibOdKlXpNefoi64FQbvHml1rVq9C6eWPMzQpl2/YpJpa4+Hg6tmmJgoICZUuVwNmxKH6vhEm/fwdz587F0tISHR0dnJycuHjxIgD37t2jYsWK6OvrY25uzsCBA2XOBQUFBVatWiXtbTBp0iRev36Np6cnurq6tGnTRpr+ypUrWFlZMWvWLIyNjbGzs2Pnzp25xnTixAlKlCiBvr4+np6ePH369Lvx5pdp06bh7OyMoqIi5cuXp0qVKty+ffuH9g0ODqZevXoUKlQIdXV12rZty4sXL6Tbp0yZwuTJk6lQoQKKiopYWlpiaWmZr/EL/r1FO07g2GwI5nV6U7L9aK7cl/zv7vu+pmafaVjV74tD00GMWLQNUVpW62edyp1Zf+gCJdqNwrxOb2asP0BQ+Dtq9Z2ORd3edJ60Qpr++kM/nJoPYf62Y9g26o9bq+HsPXcr15hO33yEZ9eJWNXvS62+03kemNVVNbd480uNsu60qFUeM2P9bNuuP/IjPSOTAW3qoaaqQr/WdRGLxdKKjBV7z1C7fDHa1vVETVUFHU0NnO0k3/W7zwIoZKhP85rlUFJSpF29Shjp63DsmtAtvSCZv3I9dqWrYehYGrcqDbh0XfI76PPoKVUat8PEpRw2JaswZMIMmWuDqqULa7bswrVSPQwdSzNl3lJev3lL1SbtMXIqQ/s+w7LKSbfuYV+6OnOWrcXcvSJFy9di16HjucZ08vxlytRpjolLOao2ac9T36yeN7nFm1+cHOzR0tSUvlZUVOT1G8n5GBP7meUbtrN6/nRsrSxRUFDA3dkRdfWsCorPcfH8tWglsyeMlMk3MzOTvxatYsGUsbg6OqCgoEAROxsMDfTzNX5B/ll6+BZuvZdi03Ee5Qav4upTSW+XBwHh1B2/GbvO83HpuYTRG84gSst68GjY6i82nrlPmYErsek4j5m7rxAc9Yl647dg02ke3RYelKa/8fwNbr2XsujgDRy6LaR4v+Xsv/Ys15jO3g+g6sj12HWeT73xW3jx5t13480vCw/eoEudUtQp5YCykiKGOprYmxlKt/dsUIZqHvaoqSrlmU9iiojjd17SvrpHvsYnyD+Lth6haKPemFXvRMlWg7l8T/KdvP8igJrdx2NZswtFGvRi+PwNiNKyKmO1y7Vm3YGzFG85CLPqnZi+Zg9BYVHU6jEB8xqd6TRukTT9tQcvcPTqw/zNh7Cp0x3Xpv3Zeyb33sanrz+gYoeRWNbsQq0eE3j+VW/O3OLNLzXKedCyjifmxoY5bu/Tuj41yhVDXU0lx+2fExKZvX4/fw3qmOP2ZTuPU6t8cRxtLfItZsHPsezUE4qN2IVd/61UGL+fa76SHvQPg97TYOYxigzchtvwnYzZeQvRVxVSJj02sOmSL+XG7cOu/1ZmH75P8Ps4Gs46hv2ArfRYfVGa/ubLCDxG7mLxycc4DdlOqdF7OHAnMNeYzj15S/WphygycBsNZx3jRejH78abXxzM9NH66nuvqKBA8HtJr9KYxBTik9NoXbEoCgoKlLQ3wdFcH/+IWABM9TQxM8hqsKSoqCjdV/BrWHk9nNIL7uM48y5Vlj3ietBnAB6FxdN4/TNcZt+j5Pz7TDgZhOir3gCWU26z5V4UlZY+wnHmXeZdfMubTyk02fAMp1n36LPPX5r+VvBnSi+U9EZwn+tD+cUPOfT0Q64xnX8VQ53VT3CZfY8mG57hG5XVKye3ePOLg7EGml+VgRQV4M2nrIYcXcubUaWwHmrK2R/F73/8nnalTHEy1URfQ5kh1azY9zj34/wd/fY9KF4FvGbV+i3cvnASC3Mz3rwNJSND8sOvpKTEgplTKFOyOGERkTRu3YnVG7cxpF/WMC7nL13l7qXThIZHUK5GA2773Gfr2mUYGRpQpW5T9hw8Suf2rQGIev+B6I+fCHlxn7v3H9K4bWdKlyiOU9EiMjE9evqcXoNGcHjXZsqULM7OfYdo4d2dF/eu8uZtWK7xfmvekhXMW7Iq12OPfuOb67bcFDI1oV3LpmzZtZc+3Tpx78Fj3oaGUal8OWma99EfsXQqgaaGBk0a1mP6hNFoaWXdyE+cMYcJ02fj6FCEGRNHU62yZ05vJfiPvXr1ihUrVuDj44OFhQVv3ryRORcWL15MmTJlCAsLo0GDBqxatYqhQ4dK9z979iwPHjwgNDSUUqVKcevWLXbs2IGRkREVK1Zk9+7ddOnSBYCoqCiio6MJDw/nzp07NGzYkDJlyuDk5CQT06NHj+jevTvHjx+nTJky7NixgyZNmvDq1SvevHmTa7zfmjNnDnPmzMn12GNjY7/7+SQnJ+Pj40P//v1l1letWpXMzEw8PT1ZtGgRdnZ2APTo0YMhQ4YQERGBvr4+O3fupEGDBoBk+I/79+/TpEkTHBwcSElJoVmzZsyfPx8NDY3vxiL4ufzfRrLu0AWubpiKubEBIZEfyMiUFICUFBWZM6gDpZztCf/wiRYjF7D+8AUGtKkv3f/CvWdc2zid8HcfqdxjMnefB7JhUl8M9bSp1Xc6+y/cpkODKgC8+/SZj5/j8T+8FJ8Xr2k5agElne1xtDGXiemJ/xv6z97AvrnDKeVsz55zN2k7dgkPd80lJCo613i/tXD7cRbvPJnrsYedWfOPPy+/4HDci1jLtHR1c7DGLzicOhU88HkRiFtha2r1nU5Q2DvKuBZh0fDOWJsZAyBGnD3PoLBs6wTy8SowmNWbd3Hr5H4szEx5ExouW06aOpbSxd0Ji3xH4469WbN1N4N7dZHuf+7qTe6cOUhoRCTl67fkzv3HbFk+DyMDfao0ac+eI6fo3KYZAFEfovn4KYY3D65y9+FjmnTqS2kPd5wc7GVievTcl94jJnJ4yypKF3dn58HjtOzWn+fXTvMmNDzXeL81b8V65q9cn+uxf/C7l+u2eSvWM3vpGhKTkrC3saJdMy8Anr/0R1lZiUMnz7Js/TZ0tLUY1LMT/bp2kO47ac5i+nRuRyFTE5k8wyKjCIuM4sWrAHoOG4+yshIdWjVl0vABKCr+EW2GfikB4R9Zf8aHC3O6Y26ow9v3sTLXipld61CyiAURH+NoPXM3G8/ep59Xeen+lx4HcWleT8I/xlFj1AZ8XoWxdkhTDLU1qDthCwdvPqd99eIAvI9N4GN8Mi/WDeG+fzhtZ+2hRBELiloaycT0NCiKQauOs2tsW0oWMWff9Wd4z93HvWX9ePv+c67xfmvJ4ZssOZx7hfmbbaNyXH/fPxy7QgZUGr6WT3HJVC1mx5zu9TDQ+Wdlm+N3XmKkq4mnq80/2k/w3/APCWft/jNc2zIHcxNDQiLey5aThnWllEsRwt9/pPnQWaw/cI4B7RtJ97945zHXt84l7N1HKncezd2nr9gwfTCGetrU7DGB/Wdv0sGrOgDvPsbyMTaOgJNruffcn5ZDZ1PSpTCOtrKNep68CqbfX6vYv3AspVwKs+f0ddqMnMuj/UsJiXyfa7zfWrj1MIu2Hsn12MMvbf3/PrxcTF21m54t61LISD/btreRH9h+7DI3ts9lxPyNP+X9BfkjMCqWjZd8OT+xKWYGWryNjicjU1LOVVJUZEa7CpSwMyYiJpF2S86w6bIffeu4S/e//CKMi5ObEf4pkVrTD+MT+J5VvapjqKVOg1nHOHT3Ne0qOQLw/nMyn+JTeLrAm/tB7/FecpYSdsY4mOnLxPQ0JJohm6+xY3BdStgZs/92IJ2Wn+f2zNaEfozPNd5vLT31hGWnnuS4DeD1is65blt66gmLTjwiKTUdW2MdWpaXjD5gqqdJi/JF2H3Tn67VnXkY9IGwjwmUL5rVUDbsYwLVphwiPkWEkqICizpXyfufICgwAqOT2XwvipO9PTDTVSU0JoWML18vJUUFpta3o7iFNpFxqXTc8ZKtPu/oVTHrHvhqYCxn+hQjIk5E/TVPuR8az/IWRTHQVKbJhucceR5NmxKmAHxIEPEpKZ0HI0rzMCyeTjte4mGhjYOxbPnjeWQiI44GssXbmeIW2hx8+oFuu19xbVAJQmNTc433Wyuuh7PyRu6VeX7jyuW6bcX1cJZeCyNJlImNgRrNihn/0Of56n0ydZ2zKsHdCmnyISGNT0lpGGrmXPn9u/nt74aUlBRJFYnwexVAWloadjbWFLG3A6B0CQ8qlC2NsrIydjbW9Ozakeu3ZFvhjRjUD11dHdxcnHBzcaJOjaoUtrNFT1eXerVr8PipbBfPaeNHoaamRtVKFWlYpxYHjmRvHbhh6056delI+TKlUFJSonP71qipqXL3/sM84/3W6KEDiX7jm+vyb7Vt2YyZ85egZVaYGo1aMH3CaKytJK05nIo6cP/qWUL9HnLu6F4ePnnGyInTpPvOmjIe/4e3CHlxn55dOtDMuxuvg9/861gE+UdJSYnU1FR8fX0l3y07O4oUkVSelS5dmgoVKkjOBTs7+vTpw9WrV2X2Hz16NLq6uri5ueHu7k7dunUpXLgwenp6NGjQgEePHsmknzFjBmpqalSrVo1GjRqxb9++bDGtW7eOPn36UL58eZSUlOjSpQtqamrcuXMnz3i/NXbsWGJjY3NdfkTfvn0pXrw49erVk667evUqb9684eXLl1hYWODl5SUdS7xo0aJYW1tjaWmJrq4ufn5+TJ48GYB3796RlpbGgQMHuH79Oo8fP+bRo0f89ddfPxSL4OdSUlQkVZTGy+Bw0tLTsTU3obClpKBc0tmecu4OKCsrYWtuQvemNbjxSHbOhKHejdDV0sClsBWu9lbULOuOvaUpetqa1CnvwVN/2S6bk3q2Qk1VhcolnalXsQSHL93NFtPmY1fo3rQGZd2KoKSkSIcGVVBVUcbnxes84/3WiE6NCTuzJtfl30hMTkFXW7bwp6elSUJSMgDh7z+x68wN5g3piN/BxdhZmNBt2moAyrk7EBUdy/7zt0lLT2fn6esEhb8nKUWU7X0E8iEtd/gHSn5rrS0pYid5aFjKw43ypUtIrg3WlvTq2IZrd3xk9h/Zvwe6Otq4ORXFzakotat5UtjWGj1dHerXqMKT57LlkamjB6OmpkrViuVoUKsaB46fzhbTxh376NmxDeVKFZeUk9o0Q1VVlbsPH+cZ77dGD+zFB797uS55GT2wF5/873P37EG8WzZBT1cHgLDId3yOiycg6A3+t8+zZ91SZixcyYVrkiEzHzx5zi2fRwzonr2VbHiEpKX7has3eXjxKOf2b2XvkZNs3n0gz1gE8qGkqIAoLYNXYR9IS8/AxlRf2lugRBFzyjpaoaykiI2pPl3rlOKWr+wEjYOaVURXUw0XaxNcbEyoUbwwdoUM0NVSp3bJIjwNfieTfny7aqipKFPJzZY6pRw4cit7WX7rhYd0qVOKMo6WKCkp0r56cdRUlLjvH55nvN8a2rwSb7aNynXJTcSnOPZde8bWka3wWd6fZFE6Yzae+acfLXuuPKVttWLCEE8FlJKiIqlpabwMDpOUOyxMKWxlBkBJlyKUK+YoKSdZmNKjeR2uP5Tt1Tm0U1N0tTVxLWKNaxFrapUvjr1lIfS0tahbsSRP/GV79kzq2w41VRWqlHKjXqVSHLqQvVfcpsPn6d68DmXdi6KkpEQHr+qoqShz77l/nvF+a0SX5oRf2prr8jM89H3NnScv6dumQY7bRy3cxMS+bdHWFBoxFXSKCgqI0jN4FRlLWnomNsY62JtKhmksbmdMmSKmkuuCsQ6dq7lw+5XsEL8D63ugo6GKs6UBzpYGVHezxM5EF11NVWoVs+LZ248y6cc2L42aihKVnMyp7WHNUZ/sveK2X3tJ52rOlC5sipKiIu0qOaKqrMT9oPd5xvutIQ2L83pF51yXvAxpWJw3K7twcXIzWld0QFcj62Fqi3KFWXj8IZZ9NtN47gnGNy+DpaG2dLuVkTavV3Tm1ZKOjGtWhqLm+nm+l6DgUFIAUUYm/h+SSMvIxNpAHTtDdQA8LLQpba2DspIC1gbqdCxTiDtvZHsr9K9sgY66Mk6mmjiZalKtiD62huroqitTw0Gf55Gy85GMrmmNmrIiFe30qOVowPHnsucLwI777+hYuhClrHRQUlSgTQlTVJUUeBgWn2e83xpYxRK/ceVyXfIysIol/uPLcbavBy09TNBV/7F+AUmiDHTVsnpf6KhL/k5Mzbkh1u/ot6+gcChsz8JZU5k+dxEWjiXo0KM/EZFRAPgHBtG0XResnEtiaOPMpL/mEP0xRmb/QqZZtV0a6uqYmmS1htPQUCchMeukMdDXk+lJYGNtSUSU7M0HwNvQMBavWouxnat0CQ2PICLqXZ7x/hde+gfSoWd/Nq9aQtK7YJ7cusTC5as5dU4ytI5ZIVNcnR1RVFTE3taG2VPHc/j4Ken+5cuUQkdHGzU1NTq3b41nuTKcPp99HFrBf8/BwYElS5YwdepUTE1NadeuHREREQD4+/vj5eWFmZkZurq6jB8/nujoaJn9CxXKeiCqoaGR7XVCQoL0tYGBAVpaWd01bW1tpe/1tZCQEBYuXIi+vr50CQ0NJSIiIs9489uoUaN4/vw5+/btk7lZrlq1Kqqqqujr67N06VKCg4Px8/MDYMCAAaSmpvLx40cSExNp0aKFtAfF370kBg0ahLm5OcbGxgwfPpxTp05lf3PBf66IVSHmDu7ArE2HKew1kK5TVhIZLfntD3gbSavRCynSZBAWdXszbe0BPn6Ol9nf1FBP+re6morMaw01VRKSU6Wv9XW00NLIGvrF2syIyOjYbDGFRkWzfM8ZrOr3lS7h7z8RGR2TZ7z/BS0NdeITk2XWxSUmS2+kNdRU8apamtIuhVFXU2Vst2bcfRbA54QkjPR02DN7KCv2nqFI40FcuPOMGmXcsDQ1+M/iF+TNwd6WBdPGMWPRSiyLV6ZDv+FEREkmbfN/HUyzzn2xLlEFI6cyTJqzhI+fYmX2NzXOauGtoa5OIeOscpO6uhoJSUnS1wZ6ujJDJ9laWRD5LvsEcW/DI1iydgsmLuWkS1hEFBHvPuQZb35TUFCgpLsrGurqTF+w/MsxSs7nCcP6o6GhjoerE22aNuTMxWtkZmYyaPx0Fk0fh7Jy9psR9S+/BSP690BfT/dLpU9bTl+69lPiF/x/CpsbMqtbXebuu4Zjj8X0WHSIyE+S60FgxEfazdqDc8/F2HSax1+7LvMxLklmf1O9rHKQuqoyJvpZrzVUlUn8qqJWX1sDLXVV6WtrEz2iYmSvPQChHz6z6vgd7DrPly7h0XFExcTnGW9+UVdVwbtGcRwsjNDWUGV4i0qcf/T6H+UR9uEzN3xDaCcM71RgFbE2Z96wrsxavw/7ej3pMmExkR8kc0sFhETQathsCtfviXmNzkxdtYuPsbmXkzTUVDE1+rrcpEpiUtZwF5JyUtYDIhtzY+l7fS00KprlO49jWbOLdAl795HIDzF5xitvmZmZDJu3nnkjuqGsnH3os1PX7xOfmEyrOpXkEJ3gnypcSI+/2lVg3tGHuAzbQa81l4j6Mqnz66jPeC89i+uwndgP2MqsQz58TEiR2d9EN6sSSl1FOdvrxNSs4dL0NdVkhk6yNtImKjb7BNKhHxNYfe4ZRQZuky4RMQm8i03KM978pqCggIetMeqqysw9+hCAgMhYeq+9zIoe1YlY250bM1qy/MxTzj15m21/A2112lYqSucV50jP+LMmBv5V2RtpMK2+HYuuhFF83n367fcnKk5StnkdnUznnX7SCZ/nXHzLp6R0mf2NtbK+3+oqihhry75OEmV9D/TUlWWGTrLSU+VdfPYGb+GfU1l7OxKX2fekS0SciHfxaXnGm98UFBRwN9dCXUWRBZdDf2gfTVUl4r+qjPj7by21vIfN/J389kM8AbRv1Zz2rZoTFxdP/+FjGTdtFlvXLGPgyHGUKObOjvUr0dHRZunqDRw6lvvQGN8TE/uZxMQkaSVFaFgEbi5O2dJZWVowbvhgxo0Y/I/i/dacRcuZs3h5rvHEhv7zeSNe+L2kaJHC1K1VHQCnokVoUKcWZy5comHdWtnSKygokJlLF9q/t4vFufSbEvznvL298fb2Ji4ujj59+jBmzBi2b99Ov379KFmyJLt370ZHR4clS5Zw4MC/b9EZExNDYmKitJLi7du3uLu7Z0tnbW3NhAkTmDBhwj+K91uzZs1i1qxZucbzdeXJt6ZMmcLp06e5evUqurp5T1T69ff58ePHzJw5E0NDSevEQYMGMXnyZKKjozE2NsbKykqmskNoJViwtKnrSZu6nsQlJjNk3mYmr97L+kl9GbZwKx5Fbdk8tT86mhqs3HeGI5d9vp9hLmLjE0lMTpVWUoS9+4hrYats6SxNjRjVuQmjujT5R/F+a/62Yyzcnvu4/lHncx/uJjcu9pYs33MasVgs/R6/eB1K7xa1AXB3sObrb/e33/XKJZ25ukHS0y49PYNibUYwqF19BAVH++ZetG/uRVx8Av3HTGH8zAVsWT6PQeOmU8Ldhe2rFqKjrcWy9Vs5dPLcv36fmM9xJCYlSSsp3oZH4OaUfUJQK3Nzxg7uw7gh2b/jecX7rTnL1jJ3+brc4wl48ENxp6en8zpEcnNR7Eu5Lqff97j4BB48eU6HfiMApENP2Zepwe61iynl4YaqqopwbfiFtKriTqsq7sQlpTJ87Umm7bjImsHNGLnuNMXszVg/rDk6GmqsPnGXY3f8/vX7xCYkk5giklZShEXH4WJtki2dpbEuw1tWZkTLyv8o3m8tOniDxYdv5hpP6I4xOa53szHl66/sv/n67r32jPJOVtgVEiqqC7I29avQpn4V4hKSGDxnHZNW7GDDtMEMnbue4k72bP5rKDpaGqzcfZIjl/79PECSclKKtJIiNCoa1yLZe8VZFjJiVLcWjO7e8h/F+635mw+xYEvu80S+u5r7JNf/RlxiMg/9gugyfjGAdOgpR68+bJ89gis+z3j0MojC9Xt+SZ+EkqIivq/fsndBzuehQL5aVnCgZQUH4pNFjNh2g+kHfFjVqzqjtt+kmI0R63rXQFtDlTXnn3P8/r+fByg2KZXE1DRpJUXYpwRcLLP3irM01GZooxIM9yr5j+L91uKTj1ly8nGu8YSs6vpDcadnZPLmg2QeCb/wGAoX0qOmu+Tex8FMnzoe1lx8Hkbd4tnP84wMMR/iUohPFmGgnXPLdkHB0tzDhOYeJsSnpDPmeBAzz4ewvGVRxp0Iwt1ci1WtHNFWU2L97UhO+mbv8fCjPqekkyTKkFZShH8W4WSqmS2dua4qg6tYMqRa9vvtvOL91rJrYSy/nvsQTwETyue67WvpmWJCPqV8PyHgZKqBb1QSTb48NvONSsJEW+WPGd4J/oAeFK8CXnP52k1SU1NRV1dDXV1dOs5vfEICujraaGtr8dI/kHWbt/3f7zdtzkJEIhE3bt/l5LkLtGzqlS1Nj87erNu8nbv3HyIWi0lMTOLUuYvExyfkGe+3xg4fRGyof65LbjIyMkhJSSE9I4PMTDEpKSmkfZmsrISHO4FBwVy+dhOxWMzr4DecOneBYq4uAFy5fpOQ0DDEYjGhYRFMmD6bxg0kQ+LEfv7MuYtXJHmnp7Nr/yGu375LvVo1/t+PVZAPXr16xaVLl758t9TR0NDIOhfi49HV1UVbW5uXL1+yevXq//v9pkyZgkgk4vr165w4cYLWrVtnS9OrVy/WrFnD3bt3v5wLiZw8eZL4+Pg84/3W+PHjSUhIyHXJzezZs9m1axcXLlzAyEh2nOcXL17w+PFjMjIySEhIYMSIEVhaWuLiIjkXypYty7Zt2/j8+TNpaWmsWrUKCwsLjL+0Hu7WrRvLly/n/fv3xMTEsHjxYry8sv8eCP57/m8jufrAl1RRGuqqKqirZT0wTEhKQVdLA20NdV6FRLDh8P/fA2zmxkOI0tK5+eQVZ249plmN7N1Cuzapxsajl/B58VpyLiSncubWY+KTkvOM91ujOjch6vz6XJfcZGRkkpIqIiMjk0yxmJRUEWlfhjOrUtIFJUVFVu8/R6oojbUHzwNQrbQrAB0bVuXEtQc8DQghLT2deVuOUtHDET1tSaHxif8b0tLTiUtMZsLK3VgWMqJ2eaHlbEHxKjCYyzfukJoqQl1NFY2vy0mJiejoaKGtpcnLwCDWbtvzf7/f9AUrJOWku/c5deEqLRtnr6zq0aE167fv5d7DJ5LzISmJUxeuEJ+QmGe83xo7uA8xAQ9yXXKSmZnJ+u17iYn9jFgsxufRU9Zs3UXNyhUAKGJnQ+XypZmzdC2pqSL8Al6z7+gpGtaujp6uDiEPr+Jz7hA+5w5xbPtaAO6cPkC5kh5oamjQunEDFq7aSHxCImERUWzYuY9Gtav/35+rIP8FhH/k2rNgUtPSUVdRRl1VBcW/rxUpInQ0VdFWV8U/PJrN536ssisvc/ZeQ5SWwW3ft5x7EEBTT5dsaTrXKsnmcw+47x8uOTdSRJx7EEB8cmqe8X5reMvKhO4Yk+uSG+8axdl1+Qlv3sWQlJrGksO3qFfaQbpdlJZBiigdsRjS0iV/Z34z3vneq0+lc28ICib/kHCu+DyTlDvUVNBQU0VRQfI7m5CUjI6WBtqa6rx6E86Gg2f/7/ebuW4forQ0bj7y48yNhzSvVTFbmm5Na7Px0Hl8ngd8KSelcObGA+ITk/OM91ujurXg3dUduS65ycjIICVVlHX//FU5CUCUlkZKqijru58qIjMzEz1tTQJPruX2jvnc3jGfQ0vGA3Bj21zKujswqU87Hu9fKt3esEoZujatzepJ/XMLRSBHgVGxXPeLIDUtAzUVJdRVlFFQ/HJdSE1DR0MFLXUVAiJj2XL531da/23ekYeI0jO47R/F+SehNCljny1Np6pObL3ykgdB7yXnRmoa5568JSFZlGe83xrWqAQhq7rmuuQkM1PM1it+xCamIhaLeRj0nk2XfaniIhkevJiNEUHvP3PdLwKxWEzw+zjOPQ3FzUpS0XLiQTCBUbFkZoqJjk9m0t47FLMxEionfhGB0cncCPpManomasqKqKsoSssdiaIMdNSU0FJVJPBDMtt8/v9RYRZcDkWUnsndkDgu+MfQ2M0oW5oOpQux/f47HobFIxaLSRJlcME/hoTUjDzj/dbgqlYETCif65KTzEwx233eEZucjlgs5lFYPFvvRVG5cFYvQlF6JilpmYjFkJ4hJiUtU1pOalXchD2P3uP/PonPyeksvRZGmxLZG6v8zn77HhSpolTGT5/FS/9AVJSVqViuDKsXzwVg3vRJ9Bs2hgXLV1OimDutmzXm8vXcJ4z7HjNTEwz09bBxLY2mhgYrF87G2dEhW7oyJYuzZsk8hoyZRODrYDQ01KlUvixVKpbPM978smPvQXoOHC59rWPhQKf2rdm0cjFF7O1Yv2whw8ZOJiQsDD0dHdq3bk6Pzt4APHr6gi59BxMT+xkjAwOaetVnxgTJjUxaWjqTZ83nVUAgSopKOBUtwsHtG3F0KJyv8Qv+ndTUVMaOHYufnx8qKip4enqybp2kZemCBQvo3bs38+bNo2TJkrRt25ZLl/79g1kzMzMMDAywsLBAU1OTNWvW4OzsnC1dmTJlWL9+PQMHDiQgIAANDQ0qV65M1apV84w3v4wfPx5VVVUcHBxk1o0fP553797Rr18/wsLC0NLSwtPTkxMnTqCiIqnBXrBgAYMHD6Zo0aKIRCLc3d05fPiwNJ9JkyYRHR2No6Mj6urqtGnTJteeIoL/lkiUxpQ1+3j1JgIVZSXKFXNg+ejuAPw1oB2D521mya6TeBS1pWWt8lx98O/n9ClkqIeBjhZFmw1GU02VJSO74WRrkS1dKefCLB/dnZGLt/E67B3qaipULOZIpRJOecabX3afvUm/WVkVGCa1euLdoDJrJ/RGVUWZ3bOHMHDOJqas2YeTnQW7Zw9BVUVShKhW2pUpfVrTatQiklNSqeDhyKYp/aR5Ldl5inN3JJPu1S5fjN2zhuRr7IL/T6pIxITZi3gZ8BoVFRUqli7BqnnTAZg7aRT9Rk9h4apNlHB3oXWTBly5mX0OlR9lZmKMvp4utqWqoamhwYo5U3DOoYxQurg7q+dPZ8jEvwgMDkFDXQ3PsqWpUqFsnvHml6NnLjBxzmJEojTMC5nQv1tHmTkltq9cSJ+REzFzr4ipsSFTRw2mZhXJAzWzrybGTkmVdBsvZGIkHfJp6cxJ9Bs9GdtSVdHX1aV7h1Z0bZdzi2CBfInS0pm+8zL+YdEoKytSztGKxX0lEwFP71yLYWtOsfzobYrZmdHM05Xrz9/86/cy1ddGX1sd195L0FBTYWHvBjhaZp9UsaSDBUv6ejFm4xleR35CQ1WF8s7WVHS1yTPe/NKxVglCoz9TZ9xmAGqVKMyc7llzd7WcsZObX+biuPcqjGFrT3Fsakcqu9tJ10V8jM+x8kVQcIhE6UxZuZNXb8JRUVaifDEnlo/vA8DMwZ0ZNHstS7YfxcPRnhZ1PLl2//l3csxdISN99HW0cGjYB011VZaO7YWTnWW2dKVci7BiQh9GzN/I69BI1NVUqVjcmUolXfOMN7/sPn2NvtNXSV8bV+lAh0bVWDtlIABNBv3FjYeS8uKdp68YNGstp1ZPpWppNwoZZ/UWShFJGgWaGuqjrKyEqooKOlpZw/xoqKmiqaGGoZ5OvsYvyB+paRnMOOiDf0QsKkqKlHUwZVFnSY+2qa3LMWLbDZafeUoxGyOalSvMdb9/PzyxqZ4GelqqFBuxCw1VZeZ3qpTj/Awl7ExY1KUyY3feIuhdHOqqSpR3MMPT0SzPePPLyUch/HXwPqKMDMz0NelZy41etdwAsDfVZWm3qozfdZvQjwnoaqrQqrwDHatIeqNGxiYxZd9douNS0FJXoZKTOVsH1M7X+AQ/jyg9k9kXQgj4kIyKkgKlrXWY11gyZ+ikunaMPv6aVTcjcDfToom7ETeD4/71e5loq6KnrkyphQ/QUFFkTuPCOJhkn7enuKU285sUZuLJYII/paCurEhZGx0q2OrmGW9+OfPyI3MuhiDKEFNIR5Vu5c3oXj5rTiTv7X7cfiP5HO6HxjP6eBD7u7riaa9HjaIG9KtkQestvqSkZ9LQxZARNazzNb6CTkEs5/F33rx5Q/VqVQl8/O+7hhYEV2/cokufwbx5cV/eofwxnvn60bnPUJ49//eF4oKmWLFi7Nq1i2LFisk7lH/typUrdOzYkbCwMHmH8kexs7PjypUr2NnZyTuUfNG+bWvqOBvTpq6nvEP5164/9KPnjDW8OrxU3qH8UZbtPk20SiEWLV4s71Dyha+vL62aN+PJ5WPyDuX/cvXWPboOGk3wgyvyDuWPsf/YaY5cuMm+/2PIxoLGyECPe4t7YqiTvVv/r+rG8zf0WXaUF+uEytufqVC7OSQkJqKqqvr9xL+Apo0b0b6qG42r5z1ZZ0F27cELek5Zhv+JtfIO5Y/Sf+Y6qjZuS48ePeQdSr7YuHEjl3csZUmnHxty5Vdw82UE/TZc4ekCb3mH8ls79fANB4MUOXrq/+8JVlBoqqvxbGQJNFR/n3kLbgV/ZtChQB6MKC3vUH5bbz6l0PFgJEGhkXKN47cf4kkgEAgEAoFAIBAIBAKBQCAQCAQCQcEjVFAIBAKBQCAQCAQCgUAgEAgEAoFAIPjPCRUU+aRaZU9heCeBAKhevbowvJNAAFQp5SIM7yQQfFHNs5wwvJNAkIPK7nbC8E6CP1LV0m7C8E4CQQ4qOVsIwzsJBF942usJwzv9IYQKCoFAIBAIBAKBQCAQCAQCgUAgEAgE/zmhguJfuHrjFnZuZeQdhkBQIFy5cgUrKyt5hyEQyN31h344NRdawQoEV2/dw750dXmHIRAUCDeev8Gtt9CbTiC49uAFjl595B2GQFDg3HwZgcfIXfIOQyCQi1vBnym98IG8wxAUAEIFxW+i79DRuJWriqqRNVt37ftu+otXrlO2en30rIpi51aG/YePZ0uzfc8BVAyt2Lgt62KZmppK/+FjsXQqgWlhN5q170p4hHxnehcIvtW7d2+cnJxQVFRky5Yteabdt28fnp6eaGpqUr169WzbMzIymDhxIhYWFujo6FCyZEliY2MB6Nu3L9ra2tJFTU0NHR2d/D8ggeBfCHgbSduxi7HzGoBNg340Gz4P/7e5/15/ikugy+QV2DTsh22j/vSYtpq4xGTp9oaDZmPnNQCLur2p2GUCJ67LFiQ/xMTRfeoqLOv1wbp+X3pMW/3Tjk0g+Kf6jZ6MW5UGqFm5sm3v4TzTfoqJxbvvMMzcKmDuXpHOA0cRF58g3f74uR81mnfE2Lks9qWrM3PxKpn99x87TbFqjTB0LI1HdS+OnrnwU45JIPg3PsYlUX/CFop0XYhd5/nUHb+ZOy9Dc00/YMUxCrWbhXXHudIlIyNTuv3q02DKD16NpfccmkzZTuiHWOm2iI9xdJizj8JdF+DWeymbzwoPIAQFR0BIBG1HzsW2bnesa3el6aC/8A8Jz3Ofy/eeUqnTaEyrdsTRqw8Hz9+SbsvIyGDa6t04NOyNWfVOeHYcRWx8YrY8GvWfhna51qSnZ+T7MQkE+eFjfAoNZx/DcfB2igzcRoOZx7gbEJVr+siYRDotP0fRQdvxGLmLLVf8cky391YAJj02sP3ay58VukCQr/Y//oDllNvsevAu1zRFZ96VWayn3mbiyWAAROmZ9Nr7ivKLH2I55Ta3gj/L7Ntxu5/MvnbT71Br5eOfeUgFmrK8AxDkDw93V9o0b8K4abO+m9b3pT+deg9k08rF1K5Rlc9x8cR+lj1RYmJjmbt4OW7OTjLrl6/dyB2fBzy8fh49XR36DhvD0LGT2L9tQ74ej0Dw/yhevDht27ZlzJgx301raGjI0KFDefnyJZcuXcq2fcqUKdy6dYvbt29jY2PDixcvUFdXB2DNmjWsWbNGmrZr164oKgr1voKC4XNCEg0rl2L1+F7oaKozZ/MR2o1dwsNdc3NMP2PdAWLjk3i+fxFisZiOE5Yxa9Mh5gzqAMC8IR1wtrNEWVkJnxevaTJ0Lo92z8PMWB+ADhOWUcrFHt+Di9FUV8U3SJiLRlBweLg60bpxA8bPWvjdtFPmLSX2cxz+dy4gFotp22swMxauYP7UsQB0HjiKpvVrc+HAVt6EhlOjeQc83JxpXLcm4ZHv6Dp4DAc3raBejSqcvniV9n2GEXD3AqbGRj/7MAWC79JSV2V5/8YUMTdEQQFO+fjjPWcv/huHo6yUcxlmcNOKTGhfI9v6j3FJdF5wgKV9G1G/jCOz9lyh+6LDnJ/dDYC+y47iZleILSNb8iosmiZTt+NgaUQVd7ufeYgCwQ/5nJBIwyplWD2pPzpaGszecIC2I+fxaH/OPZ78gkLpNmkp66YMpGY5Dz4nJPE5IasC4q91+7j79BWXNs7E2swY36BQ1FVVZPLYe+Y6aULFhKCA01JXZlm3qhQ21UNBAU4/CqHjsvP4LemQ43Wi3/oruFkbsqlfbV5FxtB83ikczPSo7GwhTRObmMqSk49xtjD4Lw9FIPjXYpPTWX49DCdTjTzTBUwoL/07MTWDEgvu4+WWVeYvZ6NLrwrm9Nnnn23fHZ1cZF632vyCSva6/2fkv64/9kna/KUradult8y6YWMnM3TsJAC27NxLsfLVMbBxwrGkJ+u27Mg1LxVDKwKDgqWvuw8YxuSZ86SvT569QOmqdTG2c6VKvaY8feGbz0cD/Xt2pWa1yqirqX037eyFS+nVpSP169REWVkZI0MDitjbyaSZMH0OA3p3x8hI9gISHBJK3ZrVKGRqgrq6Om2aN8H3ZfYTTfBrmTt3Lq1atZJZN2TIEAYPHgzA5s2bcXFxQUdHh8KFC7N2be4T2ikoKBAYGCh93bVrVyZOnCh9feLECUqUKIG+vj6enp48ffo0n48GBgwYQK1ataQVCXmpXbs2bdq0wcLCItu2mJgYlixZwvr167G1tUVBQQF3d/cc801MTOTgwYN06dIlX45BIB+Ldpyg48TlMutGL9nBqCXbAdh+8hqlO4zBvE5virUewaYj2Su1/qZTuTOvw7JaW/SZuY7p6w5IX5+++QjPrhOxqt+XWn2n8zzwbb4eSxnXInTxqoahrjYqysoMaFufgLeRfPwcn2P6kMhovKqUQldLAz1tTRpXLcPL4KyWhO4ONigrKwGgoABpGRmEvf8IwMV7zwh//4mZ/dujp62JirIyxR3t8vV4BP+t+SvX0/Z/7d1nVFTX14Dxh86g2Ch2QWwgIEVEkRp7FwsWohg11mgsmNhiEnuNPZpoNHaN3dhLQLFgV1TsDUHsHQRmgHk/TP6DBoiSF0Fw/9aalczM4d59XFzuuWef0uPtJcsGfz+BQaPGA7D0j43aWQJVPOqzcPkfmR7LsLQd129Fad93Hzic7yfP1L7fvjcUt/qtsLBzx6dFR85dvJK9lQH6fPE5dbw93quddDv6Li0a1qWQaUEKFzKlZeN6XLyadl+Lir5Lx9bN0NPTo4J1OWrXqM7FK5rv7967T5FCpjSq44OOjg5N6vlRwETBzdvZe32LnDVr0xG6TFv/1mfDFu9m2KLdAKwMOUvNAfMp12kKLn3nsmRP5jMFirUdx817T7Xvv5r7J+NXh2rf7z55DZ8hC7EOmkrDEUuIvJ35qL3/wthQn0qlzdDV1UGtBj1dHZ7HJfIsLuHdP/wPW49dxraMBf61q2JsqM/Qdj5ERj3g6t3HxCUoORQZRXBrTwz09XCwLk6LWnasDDmbrfUROWv60s18PmzaW59989NihkxbDMDyraG4thtICb/OOPh/xaKNezM9VkH3AG5Ep83s7DV6LqPnr9a+33nwFB6fD6F0nS7U7T6SC9eiMjrMf+ZmX4kuLetSrLApBvr69OvYlGtRsTx5nnE7acriDXRrVZ8GtV3Q19fDrIgpNmVKAPDsZRzz1mxn7sjelCtpgY6ODvYVymFsZKj9+Rdx8UxcuI5x/Ttlaz3Ex2H2jgi6znt7xuSIVeEMX6WZZbPq0FVqf7cO675LcRv6B0szmWUAYNH9N24+SBtA2m/RASZsPKl9vyfiDn4/bqRCv2U0mfAnkdFPsrUuxgb6VCxR5O37xOsknsUnpSsbl6ji8JV7DGrmjIG+Lg5lzWjuZs2qg2/3EY3bcIIede0pZvrudpjIe34+eJcef7zdfv9+xy1G7dD0k/5x5iG+c85SefwxPGaeZvmJzNs2pX8I59aTtDbJwE3XmfxXWjt675Vn1J8fgd3E47T47TwX76efqZYdJu67Q7eaJSlmYvDuwn/bfukJ5gUMqGmlWVXDUF+XHh4lcbcqhK6uzr/+bPSzRI5FvaStk8X/K+687JOdQdGudUvGTpnBq1dxmJoWJCUlhfVbtrFu2UIALC3M2bxmCTbWVhw8cpRm7Trj5uKEq5Njls5z5twFevQPZtOq33FzcWLl2o20DuxG5PEDGGXwkOziVY/omNgMj9WhrT9zp717hsS7HDt5Bhtra5w96/Lk6TM+8/Fk5qQxFCuqSUYcP3WGU2fPMXfaBNZvfnvpp66dOjB4+A/E3rtPkcKFWb1uIw3rph9RJfKWDh06MHr0aF69eoWpqSkpKSmsXbuWTZs0y2BYWlqybds2bGxsCAsLo3HjxtSoUQNXV9csnefMmTN069aNrVu34ubmxooVK2jRogVXrlzJ8HqoVq0ad+5k3KkTGBjIvHnzMvwuu5w/fx59fX3Wr1/PjBkzKFSoEAMGDOCrr75KV3bDhg1YWFjg4+PzQWMSH1bberWY9PtmXr1OwNREQUpKKhtDj7FqvKaj1qJoIdZNGUz5UpYcPnuF1kOm4Wpng3MV6yydJ+LqbfpO/I21kwfjalueNXsO0/7v2Q1GhukbQbW6jCTmQcYPHwH1ajFjyBfvPOfhs1coblYYs8IZL0PWo3Vdftv0F23reQCw5cAJmni5vFWm7bc/sf/kRZKUKuq5O+JqWx6AE5E3qFSuBL3GL2DP0QjKl7Jk/Fcd8XKxfWdc4uPUrmUTxk2fx6u4eEwLFtC0k7buZN1vmgSehVkxNi+dj41VWQ4ePUHzTr1wc3bAxdE+S+c5c+EiPYO/Y9OSeVR3cmDlhq206dqXC2E7MXqjY+d/XOu1JPpuxkuVdfBvypyJP2S9sv/Qu0tHfl22mvb+TQHYtH0vzRqktXX6fxnEivVbGP3N19yMiuHYqbMM6dsdgOpODthWsmHrnhCa1PVl295QjAwNcaxaJcNzibyhtVdVpqwL41VCEqYKI1JSUtly5BLLvtUM7rAoXIA1w9tjXbwoRy7eod341bhULIWTTcksnefczfv0n7eVVcPa41KhJGsPnidw8lqOz+6DkUH6xzavwQuIefwigyNBW28HpvVonOm5vAYv4FrsY1TJqXSu64xF4QKZll20+xSLdp/CyrIIg1p70qKWZsTf5ehHOFhbassVMDbEunhRLkc/omQxzb1G/cZx1Go1l+48+rd/AvGRa9vAk4m/reNVfAKmBRSkpKSwcV84q6d8A2jaSeunD6N86eIcOnOR1gMmUL1qBZxtbbJ0nogrt+gzbh7rfhqGq50Na3YepN2QyZxZNyvDdlLNwGBi7j/O8FgBDb2YObTHO895+MwlipsVwaxIxu2kExeuUb5MCdw7DubJ81f41XBkanBXihU2JfL6HfT19Nj811Hmrt5GoQIm9OnQhF4BjbQ//+O81XzZpgHFzYq83z+CyFNaudswbetp4hKUFFQYkpKaypaTN1n6VX0AzE2NWfl1Q6wtTDly9T4dZ+7CubwFTlbmWTrPuajHDPg9jBVfN8DZ2px14dfpPGcv4eMDMDLQS1fe94cNxDzJuAO3Tc0KTOnsmem5fH/YwLV7L1ClpNLJuwoWhTIYSa5Wv/kf7f9fuvtM+/70zYecvf2YKZ082XLy5nvWVOQlLR3NmX4ghrikFAoa6ZGSqmZr5BN+66Bp/5oVMGDp57ZYFTXiaNRLOq24jHPpAjiWKpil81y4F0/wlussCbTFqVRBNpx7RNfVVwjr74yRfvrx9/XmRXD3RfrEGoC/ozkTm2V8bzoT84pzsXFMbFqebZHvnwBcd/YRbZ00SeqsWh/xiJpWhShb9N2DbPOrTzZBYVW2DC7VHNm8fRedO7QlNOwwJgoFtWpUB6BJg7rasj6eHtT/zJdD4ceynKD4belKenTpRE03TUduUMcAJs+Yw7GTp/Hx9EhX/syhD79OcUzsPVau3cCODasoVaI43foOZMDQUSxfMJeUlBT6fzOSWZPHZrhUTaUK5SlbuhRW9m7o6enhUNWWWVPGffCYxYdlZWWFq6srmzZtIigoiJCQEExMTKhVqxYATZs21Zb19fWlQYMGHDx4MMsJigULFtCrVy9q1tRMg+vSpQsTJkzg6NGj+Pr6piv/IWZXZEVMTAwvXrzg6tWr3Lp1i2vXrlG3bl0qV65M/fr13yq7dOlSgoKC/tPNSHw8ypUwx6myFVsPnCKwsRcHTl/ExMgId4eKADSq7awt6+ViSx13B45EXMlyguL3P/fTreVn1LCvAMDnjb2ZtmwrJyJvZNipf3Tp+P9cJ4C7D58SPH0ZE/sFZlrGuYo1SlUyVk37AuBXvSo9WtV7q8z6KcGokpMJPRHJlahY7X3i7sOn/HX8AnOHdWf+iC/Zsv8kHYbP5OyaqZhn8qAvPm5WZUrj4liVzTv30jnAn9DDRzFRKKhZ3RmAJvX8tGV9PNyp5+vJoWOnspygWLRiLV92aoe7qxMAQe38mTznV46dPouPh3u68qf3bfnPdXpfLo72KJUqSjho2ml1vGrRu0tH7fdN6/nRbeAwZvzyOykpKYwc1Bc3Z037UE9Pj05tWxL01TckJiVhaGDA6l9nUMDE5IPHLT6cshZFqGZTgu3HrtDBrxphF26jMNKnRuUyADSoXklb1tPeis+cbAi/FJ3lBMXSfafpUt8Vt8qlAejo58SMjYc5efUunvZW6cofmt4z3Wfv69D0niQqk9l+/DLKf1lypmeTGoztUo9CJsaERtyk+/SNWBYpSC3bssQnKjEv9HZio5CJEXEJSkwVRtS0LcO09QcZ3bkeV2IesfXYZcwLybWQl5UraYGzbXm27j9OYFNfDpy8gImxEe6OlQFo5FVdW9bb1Z66NZ04fPZSlhMUizftpVur+tRw0FxbnzfzY9qSjRy/cBVv1/T3mWOr3r1837+5++AJg6f+xsSBmc+EvvvwCWt2hrFl9neUtChKz9E/M2TaYhaPHUDswye8iHvN9TuxRG7+mRvR92n61WgqlStJnZpOnL54g6MRl5k6uCt3H2bvaHfxcShrbopjOXO2n4mife1KHLwUi4mhPm4VNEncBk7ltGU9q5TEr2oZjl69n+UExfKwywT52lLdRnPcDp6Vmbk9gpM3H+JZJf0958DoNv+5TgdGtyFRlcyO01GZ3icKKgxxr1icn7ae4cd27lyJfc62U7cxM9V0sqakpvLtiiNM+rz2O0eQi7yrTBEjHEsWYOelpwQ4W3D41gsUBrpUL6t5DqxXOW1lFg/rwvhWKMyxqFdZTlCsOPmATtWL41pGc9x2zpbMCbvL6ZhXeFgXTld+X1+nLNclJVXNiO23GNekfJZ+Z2OeJ3H09kt+alkhy+cETYLia58y/+ln84tPdokn0MxI+GPDZgBWr99Mhzb+2u927Q3Bs35zLG3sMbeuys69ITx58izjA/2LO9ExzJj3K+bWVbWv6LuxxN7P3unaWaEwNqZLYDsqV7ShYMECDB3cn117NVPL5y9aimNVO22i5p/6fzOSJGUSD26c50XMVVo1a0yzgM45Gb74QAIDA1m9WjOtetWqVQQGpnVk7ty5k1q1alGsWDGKFCnCjh07ePw441FK/yYqKoqffvqJIkWKaF/R0dHExmY8ayi3KRSaUSLff/89CoWCatWq0aFDB3bs2PFWuTt37rB//36CgoJyI0yRzdrV92D9vqMArN0bTkD9Wtrv9oRH8FnP0ZRr3IcyjXqzJzwi0yWT/k30/cfMWbOLMo16a193Hz7l3uOs32fe5dGzl7QcPIUereoSUD99Yvx/gkbNpWLZEtzbs4DY3b9SvrQlX475JV05A319Gng4EXL8AtsPnQZAYWSIVUlzujTzxUBfn7b1alHashhHz8sSgHlZB/+mrN2i+Xu3ZtN27YwCgF0hYXg1a09x+1pY2LmzKySMx0//Qzvpbiwzf12ChZ279hUTe5/YB7k3yjqw9yAq2Vjz9OpJnlw5gY1VObr01+xp9PTZc5p16sGIgX14dfMsN0+Esnf/IX5ZsgqAv8KOMHzcNPatX0r87XP8tWEZvb8ZxdkLmS/lIPKGtl4ObDgUCcD6Qxdo4+Wg/W7v6evUH/47Nl9MwzpoKnvPXOfJy9dZPkf0oxfM23oU66Cp2tfdxy+5/yzr95n3YWyoTxsvB2ZuOsKFTJaScrIpSTFTE/T1dKnvWpG23g5sO6bZ4LSAsSGvEt4emfgqIYmCCs3spwUD/Il68BzH3rMJXrCTdt6OlCr26a6tnF8ENPRm3Z5DAKzdfYiAhl7a7/YcOcNn3UZQtt4XlK7Thd1HTme6ZNK/ib7/mDkrt1K6ThftK+bBE+49+hDtpBe0+HosPdo0pN0bdfknhZEhnZp9RiWrUhQ0UfDNF63Yc+QMgHYpp2FfBqAwNsKhkhVt63uy+/AZUlNTGTRlIVOCu2qXyhT5U5uaFdh47AYAG47doHXNtI7KfeejaTR+C5X6azae3nc+mqdxiVk+R/STOObvOU+Ffsu0r9hncTx4nvV7zvswNtCndc0KzN4ZwYVMlpL6pYcfdx6/wnnIGr5dfpi2HhUoVVSTvF4ceomqZYppEzUi//J3NGfLeU0f0aZzj/F3TEu+hVx7RrOF57GfdBy7iccJufacp69VWT7H3RdJ/Bp+D7uJx7Wv2JdKHrzK+rEys/T4feyKm2iTK+9rfcQj3MuZUu4/zIA4HvWSh3EqmlX9tPer+2RnUAC0bdmMb0eNIeZuLFu27+Lgbs2ovKSkJNp90ZPf582kRZOGGBgY0KZTd9RvTVJOY2Ki4HVC2hppDx48pEwpTfa6TOlSDB/8NcODv36vmJw86hAVk/HGooEBrZk3fVJWqpghR3u7t0Z5v/n/oWGHCTt8lF37NOuqP332nLPnIom4EMnsKeM5d/4iY777Vrsc1Fc9u/LjxGk8fvIUc7Ni/+/YRO4JCAggODiYmJgYNm3aRHh4OKC5Htq0acOyZcto2bIlBgYG+Pv7o1Zndj2Y8Pp1WgPp/v37lCmjyQSXLVuWkSNHMnLkyPeKyd7enqiojNeb7dSp01sbVH8I1apVA8j0evmf5cuX4+npiY1N1kaHiY+T/2fujJi7mrsPn7It7BT7ftHsTZSkVNHpuzks+K4nTb1dMdDXp8PwmWRyKWBibEhCYlrHzcMnLyhtofk7WdrSjG+CWvBNlxbvFVONTsOJfpBxUrB9g9rM+qZrht89exmP/+ApNPF0eee5zl+/w/TBXSig0Cy31t2/Dg36Zj5DLjklhVt3HwJgX7EsOw+feet7mU2U97Vp3ohvx04hJvY+W3btI+xPTRI7KUlJ+x4DWDxrEi0a1tG0k7r1y/y+oFDwOiHtIfzBo8eULlkcgDIlSzLs614MH9D7vWJy+qwZd2IyXuIpsHVzfp78YxZqmLGIyMvMGj9KO+uhR+f2fNZKs2b4rTsx6Onp0TnAXxN/qRK0a9mEXSFh9P4ikIiLl/Gq5UZ1J03ntZuzIzVcnAg5FI6zg12G5xN5Q0sPO0Yt28fdJy/ZfuwKuydo/u4mqZL5Ytp65vVvSZMalTHQ16PT5LWQ2XODkQEJSWkP0g+ex1HKTPMQXNq8EIPbeBHcJvNO0jd5DPwl0yWeArwdmd6ryXsdJzklldsPnuFgXfydZXV00pbysC1rwZr9abNd4xOV3L7/DNuymvWTy1oUYc2IDtrve8zchGul9Ht+ibylVd1ajJi1lLsPnrB1/3H+WqSZ5ZmkVPH50Gks+LEfzXxraNpJQ6Zkfm8wNiIhUal9/+DJc0pZajpnShc345uurfm22/uN/nZrP4jo+xkntts38mH28IxnGz17GUfL/uNo6u32znPZV7TizabNm+0ch0pWf3+W/vuX8QmcvnSTLiNmAJoR5QCVm/Vi+cRgPF3k3pBftKhRnh/WHiP2aTw7Tkexc0RzAJJUKXSbt4+53f1o7GyFgb4uQXP2Zv4MYahPgjJZ+/7hywRth3/pYgUZ2NSZwc1cMv7hf/AatZ7oJ3EZfhdQqyLTgt7vfqNKURP16BUOZdN3oJY1N2XVgIba970WhOJio7kPHLwYy5Gr99g3KBrQbJZ9/s4TLkQ/ZfLntd/r3CJvaG5vxtjdt4l9kcSuy0/580tNWzgpOZUef1xlVquKNLQtioGeLt1WX86klQQKA10SVKna94/ilJQspEkClyxkyNfepRng+34zDT6be5aYTJZ4al3NgsnN0/ffHLr1gqO3XxJyTbPvy/OEZC7ciyfyfjzjm2be37M+4hH9vEq/V1z/tO7sIxrbFaOA0aedxP6kExQW5mb4enrwZb9grK3KYldFM4VUqVSRlKTEwtwMfX19du0NYW/oAeztMl4/2MnBnjXrN2NvW4V9oWGEHTlKdRfNVKLuQYEEdP6SOr5euFd34fXrBA4cDsfboyampumnM0WEZ77h6r9RKpWkpqaiVqtRqVQkJiZiaGiY4TJNXQLbMWHaLALbtaaEpSVTZ/5Mk4aaJa0W/TydxDc61AKCetC6RVO6ddY8XFR3cWLFmvX4enlgolDwy6JllCpZXJIT+YCFhQV+fn507dqV8uXLY2enaSwrlUqSkpKwsLBAX1+fnTt3smfPHhwcHDI8jrOzM6tWrcLe3p69e/dy4MAB3NzcAOjRowetWrWiXr16uLu78/r1a/bv34+Pjw+mpukz1JGRkf+pLlm5HlJSUlCpVCQnJ5OamkpiYiJ6enoYGBhQoUIFvL29GT9+PLNnz+bmzZusWbNGO9Pkf5YtW8bQoUP/U6zi42NRtBDeLnb0mbAQq5Lm2FprGhrK5GSSVCrMixRCX0+PPeERhBy/QNXyGTeQHCtZsXZvOHblyxBy4gKHzl7G5e89G75o4UvgiNn4udnjVtWG14lKDp65hKdzFUxN0q/vemLFxCzX42V8Aq2Cp1LLsTJj+rR/Z3lXWxuWbtvP2L6av/e/bwnFvkJZAK5ExRIV+whvVzv09XTZ8NcxDkdc0ZZt7lOd7+auZuXOg3Ro4MnWsJPEPnxKrb+XfBB5k4VZMXw93OkxeATWZctgV0kzElCpUpGkVGJhVlTTTgoJY9+Bw9hXqZjhcZzsbVmzeRv2VSqyL+wIYUdP4FpNs0RH988DCOjen7reHtRwqcbrhAQOHDmOd60amBZMvyZ+ROi2/1QXzX1BrbkvJKtITEzC0NAgw/uCm5MDv69ez8SRQwBYtHItjnaa3+VKNtao1WpWb9pG+5ZNePj4Cev+3Ilv7Zran506dyFnL1zC2cGOMxcucvjYybeWiBJ5k3nhAnhWtaLfz1uxsixClTKakYHK5BSSklMwL6SZZbD39HVCI25iVy7jTQ4drIuz/lAktmUtCD13iyMX7+BSQTOwKaiuC52nrsPXsTzVK5XidZKKw5FReFQth6ki/V5d4TPfL7H3phNXY0hJScW1YmlSUlNZsOMEj57HU71Sxg/VW8IvUde5AiZGBuw/d5N1YRdYNawdAM3cq/DD8r/48+glGrhWYuq6g1S1Kk7l0pp/mysxjyllZoqRvj6bwy8SGnGTo/8hZvFxsShaGG9Xe3qP/RmrUpbY/t0OUqr+bicV/buddOQMfx2LwO7vtsQ/VatszdrdB7GzKUPI8XMcOnMRFzvNfaZry3p0/HYqn7lXw82+Iq8Tkzh4KhJPl6qYFkjfTjr5x4ws1+Nl3Gv8vx5HLacqjOn37o2rOzf/jMmL1tOhsQ/FzYrw09LNNPLSLHdrU6YEtZ3tmPL7RqYFd+PW3Qes33uY38cOpHBBE65v/1V7nJiHT/D9YjiHlk3GvKjMKMpPzE0V1K5Skq9/P0A5c1Mql9IM6lQlp5CkSsXM1Bh9PR32nY9m/8UYbEsXzfA49uWKseHYDWxLF2V/5F3Cr9zD+e+loDr7VKHL3H34Vi2Na3kLXiuTOXz5HrUrl9DOXnvTobFts1yPkzcekpyaimt5C1JS1SzcF8mjFwlUL5/xfe1q7DNKFS2AoYEeW07cZH9kDIfHac47p7sPiaq05aG6/ryP5tXL87m37M2V35gVMMDDujCDN9+gbBEjKlloBvqoUtQok1MxK6CPvq4OIdeeceDGC6pYZrzko32JAmw+/5gqliaE3XjO0dsvqfb3UlCfVy9O9zVX8K5QGJfSBUlQpXLk9ktqWRWiYAad+6H9nLNcjxn+FUlKTkuQfPnHFZpWNaOja+azgE7cecX9l0qa2adP4CUlp2qTkaoUNYmqVIz0dbRJ7ARVylv7dXzKPukEBWiWeeraZyCTfkwb0W1qWpAZk8bQsVsfkpKUNG1Uj+aNGmR6jOkTR9O97yDmL1pKiyYNadkkbTMsNxcnfpk5hQFDR3H9xi0UCmM8a9bA26NmttajcZtAwg5rliQJP36SPoOGsu/Ptfh61WbVuo1Mnj5Xm/zo2qkDd6Jj8Kyvyeg3qOvHzEljAChSuDC8sXSboaEBhUwLUriQpvE0ZewoBg4bhZ2bN0qlCnu7Kqxb9lu21kXknsDAQIKCgpgyZYr2M1NTU2bPnk27du1ISkqiefPmtGiR+UjsWbNm0aVLF37++Wf8/f3x9/fXfufm5sbChQvp168f165dQ6FQ4OXlle0bSzdo0IADBw4AcOTIEXr27EloaCh+fn6sXLmSCRMmaJMfy5cvp2vXtNHnCoWCLl26sGTJEgBWr15N9+7dMTMzw9LSkrFjx1K3btoeNeHh4cTExBAQEJCtdRC5K6C+Bz3H/crYvmkd+6YmCqYO6EzQ93NRqpJp7OmcbhPpN00Z8Dm9xi1k4ca/aObjSjPvtKXzXG1tmPNtN4bMWMaNmAcYGxng4VgZT+fsa5hsDTvJqUs3uXQrhpU7D2o/P7F8ImVLmPPHniOafS/+Tn7MG/4l38xcTpVWA0AN1e1s+HWkZsShWq1m4uJNdPn+Z/T0dKlQpjhLRn+l3XujWKGCrJk8iME/LSV4+jIqlyvJ6kkDZf+JfKC9f1O6DRjGxO+GaD8zLViAGWNHENh7MElKJU3r+b21ifQ/TR8zgm4Dh/HLklW0aFiXFg3T/oZWd3Jg/tQxDPhuHNdvRaEwNqJ2jep416qRrfVoEvglYeEnAAg/eYY+3/7A3nVL8a3tzqqNW5k851dt8mPB9PEMGjWe8m5+qIEazo4smqm5TgqZFmTtwtmMmPAT/YePRmFsTNP6fowYqOl09fFwZ1RwPzr2GsiDR4+xMCvG0P69qO+b+SaUIu9o621Pnzl/8mPntN9hU4URk7o2pNv0jSSpkmnkVolGbpknZyd2bUDfuX+yaNdJmrhXpkmNtLIuFUsxs3czhi7axY17T1EYGlDTtiweVctlerysUqpSGLZ4N1EPnqOvr0vVcpasGdFeu6n1urDzTN94WJv8+HX7cb6evw21Wo2VZRFm9G6Kl4M1oEnaLB3ShqG/7ab37C1Ur1iKRYNaac8VcvYG0zceJiFJhWP54qwb2RHzf9mMW+Qd7Rp60ePHuYzrn9axb1pAwdTgbgSNmIFSqaKxd3Wa+Lhleowpg7vSc/RcFqzfTTPfGjTzTdt3yLVqBeaO7EXw1EXciL6HsZEhHk62eLpUzbY6bN1/nFMXb3DpZgwrt+3Xfn7yjxmULWHBH7sOMvX3jdrkR1CLOty5/wi/rsMBqO/hzNTgbtqfWzJuAH3Hzadc/a5YFC3MqF4d+Mxdsz9RcfO0juhEpWYGlWWxIrLkUz7UpmYFvlp0gB8C0n6fCyoMmRDoQY9f/iJJlUpDp3I0dEq/r9D/TOjoQb9FB1gccokmLlY0dkkr62xtwfQuXgxbeYSbD15ibKhHzYolqF25RLbVISk5hZGrwrn96BUGerrYlSnKqgENKPH3LI71R68zc/tZbfIjJPIuM7edJUGZjEM5M/4Y1AhzU00isbCJ0ZvdSxjo61JQYUAhk/TJFJH3+VczZ8DG63xXP63dUtBIj7GNy9N77TWUKanUq1yUBlUyTs4BjGlszcBN11ly/D4NbYvR0DZtMLRT6YJMbWHDd9tvcetpIsb6utQoZ0otq+xL9hZWvN1Nbqini6mRHoWMNZ/PDovheNQrVnROm/227uxDGtsVyzBJ4jPnLDHPNYPAA5drlnw9OtBFuxn27svPKGSsh2d5SVjrqDObc5lDbt++jZ+vD9fPhudmGCIPOn/xEkG9BnL+woXcDiXbODo6smrVKhwds7YZuxDW1tbs378fa2vr3A4lW3RsH0B9W3PaNZCpvyJrZq/eyWOD4kyfkfXRlB+jixcv0raVPxGhf+Z2KCKPWffnTjbvO8za9etzO5RsY1a0MMdnfEkxU9loWWRN8Q6TiIuPx9Awf3SKtWzelI4+9jT3c393YSHe0Hf8Anyat6d79+65HUq2WLRoEaErZjGzc/YOABX5347Tt9lwU5ctO3bndijZxsTYiPNDnFEYSvJTvL/bTxPptOEeN6MzXkY3p3zSm2QLIYQQQgghhBBCCCGEECJ3fBQJilyexCHyqPz6e5Nf6yU+rPz4e5P/aiRyQr68FvJhncSHl19/b/JptcQHps6HrYr8eo2LDytfXgv5sE7iw8uvvzX5tV7iw/lY2hO5nqBQKBTExcd/NP8gIu949SoehcI4t8PIVgqFgri4uNwOQ+QxarWaV69eYWKSf5a8UChMiH+dmNthiDwoLiERkwL5Z41zExMT4uLjczsMkQfFxcfnq/sCgMLYmPhEZW6HIfKYRGUyarUaAwOD3A4l2ygUCuITpJ0ksi7udSIKRfqNxvMqhUJBfFLKuwsK8Q9xiap89cwAoDAyJF4p14PImnhlKiYfwX0h1xMUlpaWFC1SlFNnz+V2KCKP2Rd6AE8v79wOI1t5enqye3f+WQNR5IxTp05hZmaGhYVFboeSbbx8fNl36lJuhyHyGLVazV8nL+Pl5ZXboWSbcuXKoUaHyCvXcjsUkcfsOXAET+/81U6qXbs2IWdv5HYYIo8JjbhJbfca6Ojo5HYo2cbT25d9x/PPPnwiZyhVKg6euoCnp2duh5JtateuzaFLMSiTpVNWZM3+K4/w9K2T22FkK49aNdl//UVuhyHymP03XuDp45vbYeR+gkJHR4fgIUMI6tmf0LDDJCcn53ZI4iP38uUrflu6kl+XrKBnz565HU626tGjB/Pnz2fhwoW8fPkyt8MRH7nk5GRCQkIIDAwkODg4Xz14t27dmgs37zHut408ePI8t8MRecCd+48ZMnMlKh196tTJPw8burq6DBo8mM/7BBMWfpyUFHkAF//uwaPHjPlpLqfPX6Jt27a5HU62+npQMBPWHWHDoUheJ6lyOxzxkVOqUth98hpDFu9l0DdDczucbNWhQweORFxlyu8befhUOqPEv1Or1UTeuEPnkbPw9fPDysoqt0PKNtbW1vj5+dF9wUEuxTyVlTnEOz16mcD0bREcvfmE9u3b53Y42WrQt8OZEHqf7RefkKCSZwbx7+KSUvjjzEMWnnhKn34DcjscdNQfyV/wxYsX8/PcuUScO4ehoWFuhyM+Umq1GrVaTf16dfnhx9G4ubnldkjZ7uTJk4wePZq9e/eio6OTrzqdRfZSKpU4OTnRr18/unbtmtvhZLs7d+4wfOi3bNu+naQkJbq6ci2IjKWkpmJaoAD+rfyZPGUaZmZmuR1Stvvll1/4Zf48LkRelHaSyFRqaiqGhgY0b9qMCZMm5atOqP8JCQlh/JgfCDscjr6enrSTRIbUajWq5GRqVncleOhwWrdundshZbsbN24wYvhQduzYhSpZha5Oro89FB8pVXIyJYtb0jEwkDFjx2FkZJTbIWWrpKQkvv9uJKtWruD+w8cY6OvldkjiI5WqVmOgr0+TRg2ZMGUaFSpUyO2Qst2uXbuYNOZ7Dh8/jb6eLtJMEhlRqzXXQx3v2nw/bhIeHh65HdLHk6D4n+TkZJRKWVtWZE6hUHwSD6OpqakkJsrasiJzhoaG6Ovr53YYH5xarSYxMVFGRIlM6erqYmRk9EncG1QqFSqVjBwXGdPR0cHY2PiTuBaknSTexcjICD29/N9RKe0k8S56enr5LimRmaSkJJltKjIl7SQh0hgbG6Or+/EMbvjoEhRCCCGEEEIIIYQQQgghhMj/Pp5UiRBCCCGEEEIIIYQQQgghPhmSoBBCCCGEEEIIIYQQQgghRI6TBIUQQgghhBBCCCGEEEIIIXKcJCiEEEIIIYQQQgghhBBCCJHjJEEhhBBCCCGEEEIIIYQQQogcJwkKIYQQQgghhBBCCCGEEELkOElQCCGEEEIIIYQQQgghhBAix0mCQgghhBBCCCGEEEIIIYQQOU4SFEIIIYQQQgghhBBCCCGEyHGSoBBCCCGEEEIIIYQQQgghRI6TBIUQQgghhBBCCCGEEEIIIXKcJCiEEEIIIYQQQgghhBBCCJHjJEEhhBBCCCGEEEIIIYQQQogcJwkKIYQQQgghhBBCCCGEEELkOElQCCGEEEIIIYQQQgghhBAix0mCQgghhBBCCCGEEEIIIYQQOU4SFEIIIYQQQgghhBBCCCGEyHGSoBBCCCGEEEIIIYQQQgghRI6TBIUQQgghhBBCCCGEEEIIIXKcJCiEEEIIIYQQQgghhBBCCJHjJEEhhBBCCCGEEEIIIYQQQogcJwkKIYQQQgghhBBCCCGEEELkOElQCCGEEEIIIYQQQgghhBAix0mCQgghhBBCCCGEEEIIIYQQOU4SFEIIIYQQQgghhBBCCCGEyHGSoBBCCCGEEEIIIYQQQgghRI6TBIUQQgghhBBCCCGEEEIIIXKcJCiEEEIIIYQQQgghhBBCCJHjJEEhhBBCCCGEEEIIIYQQQogcJwkKIYQQQgghhBBCCCGEEELkOElQCCGEEEIIIYQQQgghhBAix0mCQgghhBBCCCGEEEIIIYQQOU4SFEIIIYQQQgghhBBCCCGEyHGSoBBCCCGEEEIIIYQQQgghRI77PyuZ6dmoQDTFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2016x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(28,8), facecolor='w')\n",
    "\n",
    "a = tree.plot_tree(dt_reg_pipeline[-1],\n",
    "                    feature_names = features.columns,\n",
    "                    rounded = True,\n",
    "                    filled = True,\n",
    "                    fontsize = 12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As a text based diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- feature_0 <= 0.62\n",
      "|   |--- feature_0 <= -0.41\n",
      "|   |   |--- feature_2 <= -0.45\n",
      "|   |   |   |--- value: [1.65]\n",
      "|   |   |--- feature_2 >  -0.45\n",
      "|   |   |   |--- value: [1.17]\n",
      "|   |--- feature_0 >  -0.41\n",
      "|   |   |--- feature_5 <= -0.26\n",
      "|   |   |   |--- value: [2.84]\n",
      "|   |   |--- feature_5 >  -0.26\n",
      "|   |   |   |--- value: [1.89]\n",
      "|--- feature_0 >  0.62\n",
      "|   |--- feature_0 <= 1.61\n",
      "|   |   |--- feature_5 <= -0.12\n",
      "|   |   |   |--- value: [3.51]\n",
      "|   |   |--- feature_5 >  -0.12\n",
      "|   |   |   |--- value: [2.66]\n",
      "|   |--- feature_0 >  1.61\n",
      "|   |   |--- feature_0 <= 2.37\n",
      "|   |   |   |--- value: [3.94]\n",
      "|   |   |--- feature_0 >  2.37\n",
      "|   |   |   |--- value: [4.72]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tree_rules = export_text(dt_reg_pipeline[-1])\n",
    "print(tree_rules)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  0.6005762942842664\n",
      "MSE:  0.6417557936098145\n"
     ]
    }
   ],
   "source": [
    "test_labels_pred = dt_reg_pipeline.predict(test_features)\n",
    "\n",
    "mae = mean_absolute_error(test_labels, test_labels_pred)\n",
    "mse = mean_squared_error(test_labels, test_labels_pred)\n",
    "\n",
    "print(\"MAE: \", mae)\n",
    "print(\"MSE: \", mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HP tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=42, test_size=0.2, train_size=None),\n",
       "             estimator=Pipeline(steps=[(&#x27;scaling&#x27;, StandardScaler()),\n",
       "                                       (&#x27;dt_reg&#x27;,\n",
       "                                        DecisionTreeRegressor(max_depth=3,\n",
       "                                                              random_state=42))]),\n",
       "             n_jobs=2,\n",
       "             param_grid={&#x27;dt_reg__max_depth&#x27;: range(1, 20),\n",
       "                         &#x27;dt_reg__min_samples_split&#x27;: range(2, 8)},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_absolute_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=42, test_size=0.2, train_size=None),\n",
       "             estimator=Pipeline(steps=[(&#x27;scaling&#x27;, StandardScaler()),\n",
       "                                       (&#x27;dt_reg&#x27;,\n",
       "                                        DecisionTreeRegressor(max_depth=3,\n",
       "                                                              random_state=42))]),\n",
       "             n_jobs=2,\n",
       "             param_grid={&#x27;dt_reg__max_depth&#x27;: range(1, 20),\n",
       "                         &#x27;dt_reg__min_samples_split&#x27;: range(2, 8)},\n",
       "             return_train_score=True, scoring=&#x27;neg_mean_absolute_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;dt_reg&#x27;,\n",
       "                 DecisionTreeRegressor(max_depth=3, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=42, test_size=0.2, train_size=None),\n",
       "             estimator=Pipeline(steps=[('scaling', StandardScaler()),\n",
       "                                       ('dt_reg',\n",
       "                                        DecisionTreeRegressor(max_depth=3,\n",
       "                                                              random_state=42))]),\n",
       "             n_jobs=2,\n",
       "             param_grid={'dt_reg__max_depth': range(1, 20),\n",
       "                         'dt_reg__min_samples_split': range(2, 8)},\n",
       "             return_train_score=True, scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'dt_reg__max_depth': range(1, 20),\n",
    "            'dt_reg__min_samples_split': range(2, 8)}\n",
    "dt_grid_search = GridSearchCV(dt_reg_pipeline,\n",
    "                                param_grid=param_grid,\n",
    "                                n_jobs=2,\n",
    "                                cv= cv,\n",
    "                                scoring='neg_mean_absolute_error',\n",
    "                                return_train_score=True)\n",
    "dt_grid_search.fit(com_train_features, com_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dt_reg__max_depth': 11, 'dt_reg__min_samples_split': 5}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;dt_reg&#x27;,\n",
       "                 DecisionTreeRegressor(max_depth=11, min_samples_split=5,\n",
       "                                       random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, StandardScaler()),\n",
       "                (&#x27;dt_reg&#x27;,\n",
       "                 DecisionTreeRegressor(max_depth=11, min_samples_split=5,\n",
       "                                       random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=11, min_samples_split=5, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('scaling', StandardScaler()),\n",
       "                ('dt_reg',\n",
       "                 DecisionTreeRegressor(max_depth=11, min_samples_split=5,\n",
       "                                       random_state=42))])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg_pipeline.set_params(dt_reg__max_depth=11, dt_reg__min_samples_split=5).fit(com_train_features, com_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE:  0.4248952183820017\n",
      "MSE:  0.4154957726373763\n",
      "R2 score:  0.6859960039130073\n"
     ]
    }
   ],
   "source": [
    "test_labels_pred = dt_reg_pipeline.predict(test_features)\n",
    "\n",
    "mae = mean_absolute_error(test_labels, test_labels_pred)\n",
    "mse = mean_squared_error(test_labels, test_labels_pred)\n",
    "r2 = r2_score(test_labels, test_labels_pred)\n",
    "\n",
    "print(\"MAE: \", mae)\n",
    "print(\"MSE: \", mse)\n",
    "print(\"R2 score: \", r2)"
   ]
  }
 ],
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