{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sr', 'rr', 't', 'lm', 'bo', 'rem', 'srh', 'hr', 'sl']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('SaYoPillow.csv')\n",
    "col=list(data.columns.values)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(data[\"sl\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(data[\"sl\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "  \n",
    "# Create features and target\n",
    "X = data\n",
    "y =  target\n",
    "  \n",
    "# Convert to categorical data by converting data to integers\n",
    "X = X.astype(int)\n",
    "  \n",
    "# Two features with highest chi-squared statistics are selected\n",
    "chi2_features = SelectKBest(chi2, k = 3)\n",
    "X_kbest_features = chi2_features.fit_transform(X, y)\n",
    "\n",
    "data['pca-one'] = X_kbest_features[:,0]\n",
    "data['pca-two'] = X_kbest_features[:,1] \n",
    "data['pca-three'] = X_kbest_features[:,2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SOHAIL~1\\AppData\\Local\\Temp/ipykernel_9024/2804007799.py:1: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
      "  threedee = plt.figure(figsize=(6, 6), dpi=300).gca(projection='3d')\n"
     ]
    },
    {
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Wo7USt0KbZK3EExFeAu5SDK9jZ7Dt8/lGBNmTDbb9fr98Ph/BNgAAAACgqBBkAwAAYNIMw1A0Gk0Z1oxVhe3kvJ61DhQnTmooXMUeuDpP2kl3sG2F2gTbAAAAAAC3I8gGAADAIbNCmFStxJ2BzHg554glvATgBpMNtq33UsMwFA6H7fU659cm2AYAAAAAuA1BNgAAAA6JaZqKRqN26GK1Fpc0oVbiiajChYXnAtwqXcG2czoHa10E2wAAAAAAtyDIBgAAwISls5V4IsJLJMNzAW5GsA0AAAAAwEgE2QAAABg3q+raCkrS0Uo8EUH2+BTDY0PohmKVKthODLVTnUREsA0AAAAAcAOCbAAAAIxLYitxwzDSVoXtRJANC88FYJiz+lpSymDbGVpPJNj2+/1pew8HAAAAACBdCLIBAAAwJsMwFIlEJI2swk5niG2tz0J4CQAjpTvYDofD9uV+v18+n49gGwAAAACQcwTZAAAASGm0VuJWkDLZVuKJCLJHV0zBEs8FYHzSEWxLinu/93g8cW3ICbYBAAAAANlGkA0AAICkDMNQNBodcy7WdCO8BNyF13H2TSbYdr63R6NRRaNR++8E2wAAAACAbCLIBgAAQBwr7LDCi0y3Ek9kBS+S7OAcxYmTGgoXAWd+GSvYtv7rrMhua2tTJBKRx+NRIBBQfX09wTYAAAAAIKsIsgEAAGAzTVPRaNQOkLPRSjwR4SUAZNZowbb1nr9//3719PRIkqZPn65AIDDiRKZUwbbf78/4ZwUAAAAAwP0IsgEAACApd63EExFkj591goFb8VwAsiNZsJ3I+jywKrad82snBtuhUMgOtv1+v3w+H8E2AAAAAGDCCLIBAACKXGI72cRW4lZIka0AgvBydDwmADIt8T3fCqQT25FL4wu2rXU6g23nNBIAAAAAACRDkA0AAFDEEluJW21lpexWYTsRZMPCcwHIHedrzgqhnX9LnF97osG21+sdMcc2AAAAAABOBNkAAABFKrGVuLMKO1chtrVtizU22tECQP5wBtvW58ZEg23rxKlIJGL/nWAbAAAAAOBEkA0AAFBkRmslbrWTzeVcpoTWsFCRDeSH0d6Xrb9NJNh2zslt/T/BNgAAAAAgEUE2AABAETFNU5FIJC5oyHUr8USJYQUV2QBQOEYLtq1Q29kJxLrNRIJtv9/P5wIAAAAAFAGCbAAAgCIRi8XsuUnzqZV4osQxGIZBJV6RoiLbHdh3hSld+80ZbPt8vhFB9mSDbb/fL5/PlxefXwAAAACA9CLIBgAAcDnTNBWNRu2AIN9aiSdKHAchWLx82U/ZxvOgsBTr89St0rk/nZ836Q62rVCbYBsAAAAA3IEgGzlnGIZC/f2KRSPy+UtUVlVF1dUkRaNRrV+/XqtXr9bevXvV3d2tsrIy1dXVaf78+TrhhBMUDAZzPUwAQBYYhqFoNJoyGMiXKmwngmxY8u25CSD90hFse71eGYahcDhsr9c5vzbBNgAAAAAUJoJs5MT+rZu1/qXn1bJ9q9r37FJ4cMD+W2lFpRpmHqZpRxypJR85VdPnzsvhSAvHli1bdP/99+v555/X3/72N/X19aW8rs/n0/HHH6+rrrpKl1xySRZHCQDIFuuAf6pW4s6qtnxDkA0LrcWB3MnVa26ywbb1+RaLxRSLxez1EmwDAAAAQOEhyEZWbVr5st548mG1bN+a8jrhwQHt37JR+7ds1Kpnn9K0I47UBz96sRYc9+EsjrSwfPSjH9WTTz457uvHYjG9/PLLevnll/WLX/xC99xzj2bOnJnBEQIAsimxlbjVflWKP9ifrwiyASC/5DL0JdgGAAAAgOJFkI2s6O/q1B/v/E9tf/P1Cd+2ZftWPfUft2nDy8fo9C9draraugyMsLBt37496eVTp07V4YcfrsbGRkWjUe3atUubNm2yD/BI0v/+7//qxBNP1EsvvaRZs2Zla8gAgAwpxFbiiQiyYaEiG0CiVMF2Yqid6vOPYBsAAAAACgdBNjJu/5ZNevSHN2qwp3tS69n25mva9w8bdeF131fTkfPTNDr3WbRokT772c/qggsu0OLFi0f8fceOHfqnf/on/f73v7cve/fdd3XhhRfq9ddfz+sKPQBAaqZpxh2UL6RW4okIsgEg9wrlvTex00iqYNsZWk8k2Pb7/QVxEhgAAAAAuBGJFTJq/5ZNeujm7046xLYM9nTroX+5Qfu3bk7L+tzk5JNP1ksvvaT169fr+uuvTxpiS9KcOXN033336bbbbou7/M0339Tdd9+djaECANLMNE1FIhH7ALxhGIrFYjJNUx6PRz6fr6AOwieO09lJBMMKZV9OFhXZACbKCqf9fr9KSkrsf87PQivYjkajCofDCofDikaj9menNDwdUzgc1sDAgPr6+jQwMKBwOBx3HQAAAABAZhFkI2P6uzr16A9vVHhwIK3rDQ8O6NF//YH6uzrTut5C9vDDD9stwsfruuuu05lnnhl32X/913+le2gAgAyzDrQ7K84KrZV4IiqyASC/FNrniFM6g+1QKKSBgQH19/drcHCQYBsAAAAAMowgGxnzxzv/M22V2IkGe7r1pzvvyMi6C9GCBQsO6XZXX3113PJf//rXdAwHAJAFpmkqGo0qGo3ay1YbVeugfSGG2BJBdiqF0ho+najIdgf2HfJJOoJt6zOYYBsAAAAAMosgGxmxaeXL2v7m6xndxrY3X9OmlS9ndBtud+yxx8Yth0IhtbW15Wg0AIDxMgzDbiVuBdjOVuKFHGJLsuc5tRAIAIWnUN9/8J5iee8dK9iWlDLYds7DTbANAAAAAOnnz/UA4E5vPPlwdrbz1CNacPz422kjXklJyYjLQqFQDkYCABgPK7ROVoUtyQ6vCz1AoiIbFiqygfxQ6J8rE2F9jjpDbOvz1hlcx2IxxWIxSe99/jpPInN2TfF4PPL5fPa/Qj7ZDAAAAACyiSAbabd/62a1bN+alW21bNuiA9u2aPrceVnZntusX78+btnn82n69Ok5Gg0AYDRWtZc1/7V1EF1676C7G0JsiSAbAJA/kgXbVqDt/K+kCQfbfr9fPp/PNZ/fAAAAAJBuBNlIu/UvPZ/V7b3z0p8Jsg/R/fffH7e8dOlS+Xy+HI0GAJCKVYVtVYE5D5onHih3A4JsWKjIBnKH11xyVghtIdgGAAAAgMwhyEbaZasa29K6fVtWt+cW3d3duu++++IuO//883M0GgBAMomtS93aSjwRQTYA5Be3fc6kU6aCbSvUds7VDQAAAADFhiAbaWUYhtr37MrqNtt275RhGPy4n6Cvf/3ramtrs5cDgYCuueaaHI4IAOCU2ErcmptTcmcVthNBNixUZAO5w2vu0KQr2I5EIopEIvbfE+fYBgAAAIBiQJCNtAr19ys8OJDVbYYHBxQe7Fd5VSCr2y1kv/vd7/Tb3/427rJ/+qd/0tSpU3M0IgCAk2EY9sHrxCpst4fYEkE2AMA9nMG29Xk2nmDb2XXFOpmNYBsAAABAsSHIRlrFopGcbDcajkhVOdl0wfnrX/+qK6+8Mu6yD3/4w/qHf/iHHI0IAGAZrZW4dTDbja3EEyUekCfIHsnj8RTF40JFNgA3sd7TxhNsO7uwOE9iI9gGAAAAUEwIspFWPn9JTrbrL83NdgvNli1bdMEFF2hg4L2q+ebmZj3wwAMc7ACAHDMMQ9FoVKZp2v+KpZV4osT7aT0OKG4E2YWLfVf4iuXzJ9uSBdvO7wCpvhNMJNj2+/3sPwAAAAAFiyAbaVVWVaXSisqsthcvrahUaQXl2GPZu3evzjjjDLW2ttqXTZkyRc8++6yam5tzODIAKG7WweloNBq3XEytxMdCCFa8ivl5X+jYd4WP997sc3Zd8fl8aQ22/X6/HW7z+gQAAABQKAiykVZer1cNMw/T/i0bs7bNKbNmU008htbWVv3d3/2d3n33Xfuy2tpaPfPMM1q6dGnuBgYARc40TUWjUftgdLG2Ek9k3WcrRCFMKV60FgfyQ7F9DuWLdATbXq9XhmEoHA7b63W2ISfYBgAAAJDPCLKRdtOOODKrQfbUI+ZmbVuFqL29Xaeccoq2bNliXxYMBvX0009rxYoVORwZABQ3qwo7HA5r27ZtdvXU7NmzVVlZWfRV2ATZ42Od9AAAcL9DCbY7OjrU19cnj8ejyspKNTc3KxaLKRaL2esl2AYAAACQrwiykXaLTzpFq559KmvbW3LSqVnbVqHp6urSqaeeqnfeece+rLq6Wk888YSOO+64HI4MAIqXaZpxB5Cj0Whcx4zm5uaiD7ElKnExjOcBkDu85vLfeILtjo4OtbS0SJJqamo0ZcoUu1Lb+r5BsA0AAAAgXxFkI+2ajpyvaUccqZbtWzO+rWlz52n63HkZ304h6unp0WmnnabVq1fbl1VWVurRRx/VRz7ykRyODACKV2IrcasKOxEHjAkwAQCYqGTBtvPz1Op2ktiK3NmGnGAbAAAAQD5hYmFkxAfPv8hV2yk0/f39Ov300/Xmm2/al5WXl+vhhx/WaaedlsORAUDxMgxDkUjErpCKxWIyDENer3fE9UCQjWE8D4D8QHBZmBL3m9frVUlJiXw+nx1aW99JrOlOwuGwotGoYrGY/b4bi8UUDoc1ODiovr4+DQwMKBQKxV0HAAAAADKBimxkxILjT9T6V/5X2998PWPbmLviGC047sMZW3+hGhgY0BlnnKHXX3/vsS8rK9MDDzygs846K4cjA4DilNhK3Nnu0+PxyOfzjbg+FBfwE+4DQPbxeeQOzv3obClu/S3Z/NrOauzRKrbD4bCk4Yptv98fF5ADAAAAQDpQkY2MOeNL16giWJORdVcEa3T6l67OyLoL2dDQkM4++2ytXLnSvqy0tFT33XefPvrRj+ZwZABQnEzTVCQSsQ8IG4ZhVy9ZB4QJspOjEhcSzwMASKfEgNn6LuL3+1VSUmL/O5SK7VAopIGBAfX392twcFDhcJiKbQAAAACTRpCNjKmqrdOF131fpRWVaV1vaUWlLrzu+6qqrUvregtdJBLR+eefr5deesm+zO/367e//a0+/vGP53BkAFCcrEolZ3VTqvkonag+HkaAOTqq3VBIeA0XPt5zCldiRfZo0hFsm6apaDRKsA0AAAAgLQiykVFNR87XxTf8S9oqsyuCNbr4hn9R05Hz07I+t4hGo/rYxz6m5557zr7M5/Pp7rvv1ic/+ckcjgwAio9VhR2NRu1lZytxq6WndTA58aAyB3iHEWRD4nlQyAg+gfwwmffOsYJta/3Jgm1nu3KCbQAAAACHijmykXFNR87X5T+6Q3+68w5te/O1Q17P3BXH6PQvXU0ldhKXXHKJnn766bjLvvOd7+hDH/qQNm3aNKF1zZo1S5WV6a2iB4BiYRiGotFo3DyTyaqwE1kVTtY6QIA5Fh4TAJnG+4w7TKQieywejyeum0ziHNvWf605tCWNmF9bGj4R2zrhz+PxyOfz2f+YYxsAAACAE0E2sqKqtk4f+/Z3tWnly3rjqUfUsm3LuG87be48ffD8i7TguA9ncISF7ZFHHhlx2U033aSbbrppwuv6wx/+oHPOOScdwwKAomEdvE1WhS29dxA31YFZr9drH/AlOBhGkA2J5wGQLwgW3SHd+zFZsJ0Yalsn6B1KsO33+0f9/gQAAADA/QiykVULjj9RC44/UQe2bdE7L/1Zrdu3qW33ToUHB+zrlFZUasqs2Zp6xFwtOelUTZ87L4cjBgBgdFbLTOtArWEYcVXYyebBTuQ8QEtF9jACTAAAJi+dFdljsUJo57YnE2yHQiF7nX6/Xz6fj2AbAAAAKDIE2ciJ6XPn2QG1YRgKD/YrGo7IX1qi0oqqMQ/4AwCQDw61lXgi5+ceoe0wgmxIPA+AXOI15w653I/pDratdVqhtnOubgAAAADuRJCNnPN6vSqvCkhVuR5J4eIgEwBkV+L8jxNtJZ6IsG4kqtQBIH9QAVu4slmRPZZ0BduRSESRSMT+e+Ic2wAAAADcgyAbAABgAkzTVCQSsQ8MJ7YSH28VthOh7UhUqUPiJA+3YN8B+SHXQXYiZ7Dt/F41VrDtPGHQ+h5GsA0AAAC4E0E2AADAOMViMbu1ZWIV9qGG2BKhbTIEmKPLtzAiUxLvp2maRXPfgVzjvdcd8qkiezTW2MYTbDtPIHR+/yLYBgAAANyHIBsAAGAMo7UStw6cTqSVeCIqskciyAYAYPIK9TM0WbBt/bO+gzmXrdtMJNj2+/15He4DAAAAIMgGAAAYlWEYikajKQ+YHmoVthMV2SMRZEOiIruQsZ/chf1ZuAqlInsszhMGfT5f2oNtv98vn89X0I8RAAAA4EYE2QAAAElYB0JTtRJ3HhSdLELbkXhMkjNNUzt27FBbW5sqKytVV1en+vp6lZaW5npoAIA856aQNh3BttfrHRFsO9uQE2wDAAAAuUeQDQAAkMA0TUWjUfvAZ7pbiSeitfhIBNkjDQ0NadWqVerq6pIkdXd3a//+/ZKk6upq1dXVqa6uTrW1tfL73fE1P1lFNoDs4PXmDm6pyB7LZINtK9x2TiVjrYtgGwAAAMgddxzhAgAASJNstBJPRGvxkQiy4x08eFBr1qyxK8YS9fX1qa+vT7t375bH41EgEFB9fb3q6uoUDAbtOUYB4FAQ3hWuYv0MJdgGAAAA3IEgGwAAQMMHep0HKzPZSjwRFdkjEWQPMwxDW7Zs0Y4dO+IuLy8vl8fj0eDg4IjbmKapnp4e9fT06N1335XX61VNTY1dsR0IBOJOnshnVGQDwOQUS0X2WA412Ha2ISfYBgAAALKPIBsAABS9xFbi1nyJUuaqsJ2oyB6JcF8aHBzU6tWr7VbilunTp2vevHnyeDwKh8Pq7u5WZ2enOjo6FA6HR6zHMAx1dnaqs7NT0vBBdyvUrqurU1VVFQfeAYzA55H78F7/nlTBdmKo7QytCbYBAACA7CPIBgAARc0wDLtdc2IVdjZCbGs7FoKDYcX+mLS2tmrt2rVxrcS9Xq8WLlyopqYmGYahWCym8vJyVVZWqqmpSaZpamBgwA6tOzs7FY1GR6w7Foupra1NbW1tkqSSkpK4YLuioiJvDrxTke0O7LfCly/vCZg4Xn/j42wrLimtwbbf75fP58vKd0oAAADAbQiyAQBAURqtlbh1MDNTrcQTOSuyi7X6OFGxVqmnaiVeVVWl5cuXq6qqKuVzxOPxqKqqSlVVVZo5c6ZM01RfX58dand1dcUdXLdEIhG1traqtbVVklRWVmbPr11XV6eysrL031G4HmENkB9oLX5oMhFsW5cTbAMAAADjR5ANAACKjmEYikajY86HmC3FXn2cTDE+JqlaiTc3N2vx4sXy+/0TOtHB4/EoEAgoEAjosMMOk2EY6u3tVUdHhzo7O9Xd3Z30sQ2FQtq/f7/2798vSaqsrIyr2C4pKZnU/ZwIKrKB3OC15h4E2emRjmBb0ohg29mGnGAbAAAAGIkgGwAAFA3rgKPVbjlXrcQTMR/0SMUWZKdqJb548WLNmDFjxHPyUJ6jXq9XNTU1qqmp0Zw5cxSLxez5tTs7O9XT05P0dgMDAxoYGNDevXslSYFAwA61a2tr5fP5JjwWAIWFcM0d2I/pM1awbf03WbDt/K4ZjUbt76UE2wAAAMBIBNkAAKAomKapaDRqB8W5bCWeqFjbaI+mWIJswzC0efNmvfvuu3GXW63EA4HAqLefzGPj8/lUX1+v+vp6ScMtxru6uuxgu7+/P+ntent71dvbq127dsnj8SgYDNrBdk1NTdzzebKoyAaAyaEiOzsmEmwnthon2AYAAABSI8gGAACul2+txBMVS2g7EcXwmAwODmrVqlXq7u6Ou9zZSjybSkpK1NjYqMbGRklSOBxWZ2en3Yp8aGhoxG1M01R3d7e6u7v17rvvyuv1qra21g62A4HApF5bHLAHcsOt77vFiH2ZG8mC7cRQOzHYTpxfW0odbPv9/pydgAkAAABkE0E2AABwrcQDhImtxK0Dhbk+COisYKW1+DC3B9ktLS1au3atfXBaGq6QXrRokWbOnJnDkb2ntLRU06ZN07Rp0yQNB+9WtXZnZ6fC4fCI2xiGoY6ODnV0dEiS/H6/HWzX19ersrJyUq83Nz4XgEKQ689JHDoqsvODFUJbkgXb1nfA8QTboVDIXqff75fP58uL77QAAABAuhFkAwAAV0psJW4YRl5VYTu5PbQ9FG6dN9wwDG3atEk7d+6Mu7y6ulrLly9XdXV1jkY2toqKClVUVKi5uVmmaWpgYCAu2HaG8pZoNKq2tja1tbVJGg7HrWrturo6VVRUjLpNWosDQPrky/ceHFqwLQ1P72G1I6+urra/71rrtEJtqxU5AAAAUOgIsgEAgOvkeyvxRG4NbSfDjeH+wMCAVq9ePaKV+IwZM7Ro0aKstxKfDI/Ho6qqKlVVVWnmzJkyTVO9vb12qN3V1ZX0uRwOh9XS0qKWlhZJUnl5eVywXVZWlu27AiAJt7zvgn1ZKMYTbIdCIa1Zs8a+ztFHH63a2tq477WRSESRSETScEV34hzbAAAAQKEpnKNlAAAAYxitlbjVbjEf2y46DyxywHmY2x6TVK3EFy9erBkzZuRwZOnh8XgUDAYVDAY1e/ZsGYahnp4ee37tnp6epPtxaGhI+/fv1/79+yVJVVVVdqhdW1tLRbZLsN+A3KG1eGFyBtvWPkzW+WS0ObatbkQE2wAAAChkBNkAAMAVTNNUJBKxD/blcyvxRG6sPp4stzwmhdxKfDK8Xq9qa2tVW1srafhAe1dXl12x3dvbm/R2/f396u/v1549eyQNB9soTPn6fotDw/50B/ZjYbL2W+L+s9qIJ2tFbp24aX3/JdgGAABAoSLIBgAABS8Wi9lVKs4qbCn/Q2wpvvqY1uLD3BBkDwwMaNWqVerp6Ym7fObMmVq0aFFcC1G38/l8amhoUENDg6Th1qdWsN3R0aGBgYGkt+vv749bfueddzRlyhTV1dUpGAxy0B3IkEJ938VIVGS7R+Lr0u/3y+/3jziJM9XUOhMJtv1+P88XAAAA5AWCbAAAULBM01Q0GrUP0hVKK/FEbght063QH5MDBw5o3bp1I1qJL1myRM3NzTkcWX4oKSlRY2OjGhsbJUmhUMiu1u7s7NTQ0FDS2/X29qq3t1c7duyQz+dTbW2t3Yq8uro671/rAJBthfgZiuQS92VipbYVQicG2ZMNtq3Kbz5jAQAAkAsE2QAAoCAZhqFoNJry4Fy+V2E7OcdJRfawQg2yDcPQxo0btWvXrrjL3d5KfLLKyso0ffp0TZ8+XaZpamhoSB0dHWpra1N7e3vS28RiMbW3t9t/9/v9dqhdV1enysrKgnkPAPIdr6XCRUW2e6QKshM5T+JMd7BthdoE2wAAAMgWgmwAAFBQrINuqVqJOw/AFQpne+RCCm0zqRCDbFqJp4fH41FFRYVmzJih2trauCC7trZWvb29isViI24XjUZ18OBBHTx4UJJUWlqq+vp6O9guLy/P2n0AgHww3uATheFQ9+dkg23ru7VhGAqHw/Z6nfNrE2wDAAAgUwiyAQBAwUhsJW5VikjxB9wKTSGGtplWaI8JrcQzI/Gg+Lx581RVVaW+vj51dHSos7NT3d3dSTsZhMNhHThwQAcOHJAkVVRUxFVsl5aWZuU+AIWoEN53MXEEjYUtXScmpCvYjsVicSeWEWwDAAAgEwiyAQBAQXBTK/FEzvCd1uLDCiXIjsVi2rhxo3bv3h13eSAQ0PLly1VVVZWjkbmX1+tVMBhUMBjU4Ycfrlgspp6eHnt+7Z6enqTPmcHBQQ0ODmrfvn2Shtu9W6F2bW2t/H5+GgGpFOrna7GjIttdEr8jpuvkTYJtAAAA5DOO1gAAgLxmmmbcgTE3tBJPVCihbTYVQrjf39+vVatWqbe3N+7yWbNmaeHChbQST4PE13Wy14fP57MDaWm4xXhXV5cdbPf19SVdd19fn/r6+rR79255PB4FAgF7PTU1New/AAWP7xTukq0TE1IF24mhdqqTSgm2AQAAkE4E2QAAIG+ZpqlIJGIfuEtsJV7IVdhOzvuQr6FttuV7uL9//36tW7duxAHapUuXqqmpKYcjg9/v15QpUzRlyhRJwy3GncH2wMDAiNuYpqmenh719PRo586d8nq9qqmpsYPtQCBQkNMW5It8fA1jdOwzd3LDd6ZilqsK+8Tpe1IF287vRBMJtv1+v2u+0wMAACD9CLIBAEBeisVi9nzDiVXYbgqxpZGtIU3TdM19O1TJKnHz4TFJ1Uo8GAxq2bJltBJPs/FUZI+ltLRUU6dO1dSpUyVJQ0NDdqjd2dmpUCg04jaGYdh/l4YPttfW1trBdnV1dV48H/MZj4+7sD8LE63F3SVf9me6g+1wOGxf7vf75fP5XPU9HwAAAJNDkI2cMwxDoVBI0WhUfr9fZWVlVLxMUn9/v1avXq3t27dr79696unpUSQSUU1NjRoaGrR8+XKtWLFCJSUluR4qAIwwWitx68BZobcST5SvoW0u5eNjkqqV+GGHHaYFCxbQirpAlJeXq6mpSU1NTTJNU4ODg3HBdiQSGXGbWCym9vZ2tbe3S5JKSkrsULuurk4VFRU5f34CQCIq690lX4LsROkItiXFff/3eDxxbcgJtgEAAIoXQTZyYu/evVq9erX27dungwcPxlXClJWVqbGxUc3NzVq2bJlmzJiRw5EWjttvv13/+7//q7ffflu7d+8eszVtZWWlzjvvPH3jG9/Qcccdl6VRAsDoDMNQNBodc+49t0k8gcswjKI/qSsdlbjptG/fPr3zzjtxB2H9fr+WLl2q6dOn53Bk7pbp54HH41FlZaUqKys1Y8YMmaapvr4+O9Tu6uqK2+eWSCSi1tZWtba2Shr+/mqF2vX19SorK0vrOIFsy/V7LtIjX4NPHJrE3/j5uj8nE2w7v+tHo1G7OxPBNgAAQPEiyEZWrVu3TitXrtS+fftSXicUCmnPnj3as2ePXn/9dTU3N+uEE07QkiVLsjjSwnPzzTfbB1PHY2BgQA8++KAefvhhfe5zn9PPfvYzKrQB5Ix1cCtVK3HrYJVbD1jlW2ibD/LlMYnFYtqwYYP27NkTd3kwGNTy5ctVWVmZk3G59bWQKNvPA4/Ho0AgoEAgoMMOO0yGYai3t1ednZ3q6OhQT09P0pMFQ6GQDhw4oAMHDkgaPmHQWbHNdywA+aBYPjvcyvkZWEjfi8cKtq3/JlZkE2wDAABAIshGlvT29urJJ5/U5s2bJ3zbffv26b//+7+1Zs0anXfeeQoEAhkYofuUl5dr1qxZmjFjhgKBgAzDUFdXlzZt2qS2tjb7erFYTHfddZd27dqlp59+Wn4/bwsAsss0TUWjUTscKoZW4okS79tYXTWKQT4E2X19fVq1apX6+vriLj/ssMO0cOHCoq+aLwZer1c1NTWqqanR4Ycfrlgspu7ubrtiu6enJ+ntBgYGNDAwoL1790qSqqur7VC7traW71soOG7+DHYzKrLdJTHILlSjBdvOqm1nsJ04v7aUOtj2+/2u/+0AAABQTDiCgozbs2eP7rvvPg0MDExqPZs2bdLu3bt16aWX0m48icrKSp166qk688wzddJJJ+kDH/hAyrk6n3/+ed1www169dVX7cv+9Kc/6cYbb9SNN96YrSEDQNG2Ek+UGIhSkZ37IDtfW4kXw+vBKdfPg0Q+n0/19fWqr6+XNHwQvaury67Y7u/vT3q7vr4+9fX1affu3fJ4PAoGg3awXVNTw0kRADKCINtd3BJkJ0oWbCdWa1u/D8YTbIdCITvY9vv98vl8BNsAAAAFjCAbGbVnzx797ne/i5sDezIGBgZ0zz336LLLLiPMTrBx48Zxz8d4yimn6OWXX9bFF1+sxx57zL78//7f/6vrr79eFRUVGRolAAxLrLIotlbiiajIHinZvOHZkK+txJGf/H6/pkyZoilTpkiSwuGwXa3d2dmpwcHBEbcxTVPd3d3q7u7Wu+++a1d9W/NrBwKBonnvQ/4iAHWHXJ/8g/Rya5CdyAqhLZMNtp3rtIJtTiADAAAoHATZyJje3l7dd999aQuxLaFQSPfee6++8pWv0GbcYbwhtsXn8+nuu+/Ws88+ax9k7evr09NPP62LLrooE0MEAEkjW4lbbQSl4qrCdqIie6RcVOKmaiU+e/ZsLViwgIOeOZBvFdljKS0t1bRp0zRt2jRJ0uDgYFywHQ6HR9zGMAz779u3b5ff71dtba1dsV1VVVV074kAMoP3ksLmPKmvmPZlJoJtr9c7Yo5tAAAA5CeCbGTMk08+Oel24qkMDAzoqaee0qc+9amMrL9Y1NXVacWKFXrllVfsyw5lHnMAGC/DMBSJRCSNrMIu1hBbKrywLhuy/Zjs3btX69evH9FK/KijjrJDSWCiKioqVFFRoebmZpmmqYGBgbhg2zqg7hSNRtXW1qa2tjZJUklJiV2tXVdXV5Cdc3hPKzzsM3egst5diqUieyzOYNt6TCYabFsn0lq/Swi2AQAA8hdBNjJi3bp1GQ9EN23apHXr1mnp0qUZ3Y7b1dXVxS339vbmaCQA3Gy0VuJWC/FiaiWeiNbiI2UryI5Go9qwYYP27t0bd3lNTY2WLVtGK/Ecc9NJHh6PR1VVVaqqqtLMmTNlmqb6+vrs+bW7urqSvvYjkYhaW1vV2toqSSovL7ertevq6ibclSdbivX9HMgnhfyeiZGc+5OgdZj1WTORYNs5J7f1/wTbAAAA+YsgGxmxcuXKrG2HIHtyEucAbW5uztFIALiVYRiKRqMyTVMbN25UOBxWLBZTU1OTGhsbi7YK24nW4iNlI8Ds7e3V6tWraSWOnPB4PAoEAgoEAjrssMNkGIZ6enrsau3u7u6kz/uhoSHt379f+/fvlyRVVlba1dq1tbUqKSnJ9l1BESj2z+lCRUW2u1CRPbbRgm0r1Lb+Oac2mkiw7ff7efwBAACyiCAbabd3717t27cvK9vat2+f9u7dqxkzZmRle26zdu1arVmzxl72eDw6/fTTczgiAG5iHSCyWueapqn9+/crFApJkoLBoKZNm8aBIFGRnUymg+w9e/Zo/fr1cY91SUmJjjrqKE2dOjWt28Khc1NF9li8Xq9qa2tVW1urOXPmKBaLqauryw62U3XNGRgY0MDAgH1yYiAQsKu1a2tr4+YVBcbLza+1YsZ3rsJGkD1xzmDb5/ONCLInG2z7/X75fD72BwAAQAYRZCPtVq9enfXtEWRP3M6dO3XxxRfHzQV61llnacGCBTkcFQC3ME1T0WjUPiBkHRxKrHDloM8wKrJHylSAGY1GtX79+hEn3dXU1Gj58uUFN/8wzxX38vl8amhoUENDg6ThFuPOYLu/vz/p7Xp7e9Xb26tdu3bJ4/EoGAzaFdvBYJBOA0ARoSLbXZwn37EvD41zKqN0B9tWqE2wDQAAkF4E2Ui7bFVjW6y2ihhdJBLRwYMHtWbNGj3++OP6r//6r7hWqjNnztQvf/nLHI4QgFs4W4knHghyBigEcO8ppqrT8crEY9Lb26tVq1aNCAAPP/xwzZ8/n4AvD/HaeE9JSYkaGxvV2NgoSQqFQnao3dnZqaGhoRG3MU1T3d3d6u7u1o4dO+yqb6tiOxAIcLAd48LzpDAV83umG1GRnX7pCLa9Xq8Mw1A4HLbX65xfm2AbAABgcgiykVaGYejgwYNZ3WZra2vSKr9i94UvfEF33333uK57zDHH6Pe//z2V7QAmxTRNxWIxu9OD8wCQpBFzYdM++z2Jn2E8Nul9TEzT1N69e2klDlcpKyvT9OnTNX36dEnS4OBgXLDtPKBuMQxDHR0d6ujokCT5/X471K6rq1NlZSUH2wEX4/Vd2JxBNsc/MmOywbb1e8f5m8haF8E2AADAoSHIRlqFQiF77tNsb7PQWoHmg1NPPVVXXXWVLrzwwlwPBUCBS2wlbrXbkzSiYsFCWPseqk5HStdjkqqVeG1trZYtW1aw3x+K5QAor43xq6ioUEVFhZqbm2Wapvr7++1Qu6urS9FodMRtotGoDh48aJ+IWlpaGhdsF+rrA5PHa80daC3uLlRkZx/BNgAAQO4RZCOtkh0gc/N2C92LL76oWCymiooKnXXWWbkeDoACNVorcWeILdFaPJXEg1eE/OkJMFO1Ep8zZ47mzZtHNVMB4n1jfDwej6qrq1VdXa1Zs2bJNE319vaqs7NTHR0d6u7uTvo+Ew6H1dLSopaWFklSeXm5Pb92XV2dSktLs31XkCcIWQoTQba7EGTnXqpgOzHUTvV7iGAbAABg4giykVZ+f26eUrnabj679dZb9e1vf9teHhgY0MGDB/Xmm2/qiSee0F//+ldFo1G98MILeuGFF3TZZZfprrvu4rEEMG6jtRK3DvI4D/ZIoiI7BapOR5rMY2Kapvbs2aMNGzaMaCV+9NFH23MMozB4PB5eE5Pk8XgUDAYVDAY1e/ZsGYah7u5uu2K7p6cn6WM8NDSkffv22R0Nqqqq4iq2x/u9kf0H5AavPXchyM4/zuprSSmDbWdoPZFg2+/3j5ieCQAAoNiQWCGtysrKVFZWltX24tY2EW/q1KlJ5/w844wzdP311+uPf/yjrrjiCvvA5D333KOhoSE98MAD2R4qgAJkmqYikYh9QC1VK/FEzJGdnHUQzHo8OfB86EF2NBrVO++8o/3798ddXltbq+XLl6u8vDxtY0R28NpIP6/Xa4fR0vDrxhls9/b2Jr1df3+/+vv7tWfPHklSMBi011NTUyOfz2dfl4PuhY3XmjtQke0uzu/O7Mv8NNFge2BgQHv27LHbkM+dO1elpaV2sB0OhyXJDrV9Ph/BNgAAKDoE2Ugrr9erxsZG++BWNkydOpXWoIfgjDPO0J///Gcdd9xx6urqkiQ9+OCDuuCCC/TpT386t4MDkNdisZg9pYPzwIw0eogt0Vp8NM6wjpD/0ILsnp4erVq1SgMDA3GXH3HEETryyCNd9X3B+XhYXRCAQ+X3+9XQ0KCGhgZJUiQSsUPtzs7OEa8pS09Pj3p6erRz5055PB7V1NSorq5O9fX1vMcDeYjPisLmfF9103caNxsr2I5EIjp48KB9/ZkzZ0rSqBXbHo8nrg05wTYAAHA7gmykXXNzc1aD7Kampqxty20WLlyoa6+9VjfccIN92b/9278RZANIyjRNRaNRO2QdTyvxRLQWT83r9cY9tsVuIkG2aZravXu3Nm7cSCtxF3I+F3htZEdJSUlcd59QKGTPr93Z2Zm0+5Jpmurq6lJXV5d27Ngx4sQlTrgobOy7wkRFtrvQWrzwJQbbzk4m1vJYrcil4U4q1onFBNsAAMDtCLKRdsuWLdPrr7+e1e3h0H3uc5+LC7JXr16ttrY2TZkyJYejApBvDMNQNBqNa4k3nlbiiWgtnhqPTbzxBtnRaFTr1q3TgQMH4i6vq6vTsmXLaCUOpEFZWZmmT5+u6dOnyzRNDQ4OxlVsRyKREbdxvo8ZhqFXXnlFtbW1dsV2RUUFB9rzGCeNuAP70V0Ist0n8TVaWlpqn9zqPGk4WbDt/P1FsA0AANyMIBtpN2PGDDU3N9tzL2dSc3OzZsyYkfHtuNmMGTMUDAbV09MjafhA4+bNmwmyAUh6r+o6VStx68DIeA+O0Fo8NR6beInPqWThfjG1Ei9mVGTnF4/Ho8rKSlVWVmrGjBkyTVP9/f1xwbbzgLvFap9qtVAtKyuz59euq6vjpBMgCwizChtBtvskfr/1+XwpW5E759l2thpPbEMuEWwDAAB3IchGRhx//PF66KGHMr6dE044IePbKAZ+f/xbwdDQUI5GAiCfJLYStw6gSCPb4o0XrcVTI6wbyTlveOKc0Lt379aGDRviLi8tLdXRRx/NyVhAFnk8HlVXV6u6ulqzZs2SYRjq7e3Vzp071dbWlvJ2oVBIBw4csLspVFRU2NXatbW1Ki0tzdZdwDgQehQmWou7C0G2+yT+Hkrcr8nm2E6s1rbWMdFg2+/328E5zycAAJDPCLKREUuXLtWaNWu0efPmjG1jwYIFWrJkScbWXywGBgbU1dUVd1lzc3NuBgMgb6SrlXgiguzUaC0+ktfrtQ/KWQdvI5GI3nnnHVqJFxFO8igsXq9XNTU1mjJlih1ke71ezZo1S52dnert7U26HwcHBzU4OGh3daqurrartWtra0eceAlgbLxnuovz+yHBozs4X6PjOUHYCqGdtyfYBgAAbsfRAGTM+eefr5/97Gcj2n2mQ2Vlpc4777y0r7cYPfnkk3E/iMvLy3X44YfnbkAAciqxVd1kW4knIqxNjdbiIyUGmN3d3Vq9evWI7xZz587V3LlzaSUO5CmPx6O5c+dKGj543tXVZbch7+vrS3qbvr4+9fX1affu3fJ4PAoEAnbFdjAYjDuQj/Tjc8gdqMh2l4mGnsh/zt9Dh7JPMxVsW6G21YocAAAglwiykTGBQECXXnqp7rnnHoVCobStt6ysTJdeeqkCgUDa1lmsYrGYbrnllrjLTjrpJKrZgCJlmqYikYh9kCyxlXg65lMjrE2NkH8k52PS3t6u9evX00r8/6+Ywggqsgufc7/5/X5NmTLFft2Gw2F1dXWpo6NDnZ2dGhwcTHr7np4e9fT0aOfOnXbVt1WxHQgEONCeYcX0nuMmBNnuQmtx90n3Pk1XsB2JRBSJROy/J86xDQAAkE0E2cioGTNm6LLLLtO9996blsrsyspKXXrppZoxY0YaRuceN998sz7zmc9o9uzZ475NKBTSpZdeqjVr1sRdfvXVV6d7eAAKQCwWs8/CT6zCTleILdFafDSE/CM5n3OJrcTr6+t19NFHc/IVkKfG+5lRWlqqqVOnaurUqZKkoaEhu1q7s7Mz6QmxhmHYf5ckn8+n2tpa1dfXq66uTlVVVYQ8QAJeE4WPINt9JluRPRZnsO08WXmsYNvZhcs6uZlgGwAA5ApBNjJuxowZ+spXvqKnnnpKmzZtOuT1LFiwQOeddx6V2En87ne/00033aSzzjpLl1xyic4++2zV1tYmvW5/f7/uvfde/ehHP9LWrVvj/nbhhRfq/PPPz8KIAeSL0VqJWwcv0jlPGkF2alRkj5Qq0J87d66OPPJIDuIWCSqyi0t5ebmamprU1NQk0zQ1ODhoV2t3dXXZB9KdYrGY2tvb1d7eLkkqKSmxq7Xr6upUUVHB+8UE8VpzB4JPd2F/uk825z231j+eYNvZlct5UjPBNgAAyAWCbGRFIBDQpz71Ka1bt04rV67Uvn37xn3b5uZmnXDCCVqyZEkGR1j4QqGQHn/8cT3++OPyeDw67LDDNHPmTAWDQZWWlqqvr0979uzRtm3b7KpLp7/7u7/Tvffem4ORA8gVwzAUjUZlmqb9L92txBMRSKXGY/Me0zS1c+fOEYFVaWmpli1bpoaGhhyNDEA2eTweVVZWqrKyUjNnzpRpmurr67Orsbu6uuwTsZwikYhaW1vV2toqaXhqImt+7bq6OpWVlWX7rhQ8QrPCVOzfJ9yGINt9cjnvebJg2/mbMNVvxIkE236/n+cqAACYNIJsZNXSpUu1dOlS7d27V6tXr9b+/fvV2toa1zKwrKxMU6dOVVNTk5YtW0Yb8UNgBQA7d+4c87plZWX6+7//e/3zP/8zB/WAImEdjEjVStx5YCLdqMhOjdbiwyKRiNatW6eWlpa4yysqKnTsscfyWVWEOMkDFo/Ho0AgoEAgoMMOO0yGYai3t9eu2O7u7k76HAmFQjpw4IA9RUFlZWVcxXZJSUm27wqQdYRJhS+b1bvIjky3Fp8I5+8/n8+X1mDb7/fb4TbPXQAAMFEE2ciJGTNm2AG1YRgKhUKKRqPy+/0qKyvL+Rf4QnPnnXfqkUce0Ysvvqj169crHA6PeZs5c+bo4osv1pVXXqkjjjgiC6MEkA9M01Q0GrUPPmS6lXgiguzUaC0udXV1afXq1RocHBzxt+bmZkJsEGQjjtfrVU1NjWpqajRnzhzFYjF1d3fbFds9PT1JbzcwMKCBgQHt3btX0nD3KCvUrqmpkd/Pz2Rea+5ABa+75LJ6F5mRzycnpCPY9nq9Mgwj7hiVsw05wTYAABgPfqEj57xeryoqKnI9jIJ20kkn6aSTTpI0XHGyatUqbdmyRXv27FFvb68ikYgCgYCCwaCOOOIIHXvssWpsbMzxqAFkWy5aiSeisjK1Yq7ItjqJbNq0acRB92J7LCaiWB4fDnBivHw+n+rr61VfXy9Jikajdqjd2dmp/v7+pLfr7e1Vb2+vdu3aJY/Ho2AwGBdsExjxOixUxfAZUUw4McF9CunkhMkG29ZvzVgsFjctCME2AAAYC0E24DJlZWU65phjdMwxx+R6KADyhGmacQcMstlKPBEV2akVa0V2OBzWunXr7LlsLQ0NDRoaGrKDJw7GFy9OgMGh8vv9amxstE/gDIfDdqjd0dGhoaGhEbcxTVPd3d3q7u7Wu+++a1d9W/NrBwIBDrKjYBB8ugv7033yuSJ7LATbAAAgWwiyAQBwscRW4tacZVL2qrCdCLJTK8awrqurS6tWrRoRJs2bN09HHHGEXn31VfuyYnlMxmIdAOTxACautLRU06ZN07Rp0yRJg4ODcRXbyabnMQzD/rs0HI7X1tbaFdtVVVWuPMjOe4z7uPF5WmwIst2nkCqyx3KowbazDTnBNgAASIYgGwAAlzIMQ5FIRNLIKuxchNjWdp2sublRXK3FTdPUu+++q82bN8fd17KyMi1btsxuC1yM4T5G4nlQ+PJ1v1VUVKiiokLNzc0yTVMDAwNxwXY0Gh1xm2g0qra2NrW1tUkaDsetULuurs61UybxWV2YCD7dpZCrd5Gcc58WepCdKFWwnRhqO0PriQTbfr8/J79nAQBA9hFkAwDgMqO1ErcOKGSrlXiixAM0hmHI5/NlfRz5qFhai4fDYa1du1YHDx6Mu3zKlCk6+uijVVpaal9GgAkUrkI7sOzxeFRVVaWqqirNnDlTpmmqt7fXDrW7urqSvjeHw2G1tLSopaVFklReXh4XbJeVlWX7rgA2PjvdxU3VuxhWTCcnONuKS5p0sG11UbFCbZ/PR7ANAIBLEWQDAOAihmEoGo2O2botVwiyUyuGiuzOzk6tXr06ZSvxxOcmQTYkngfIDY/Ho2AwqGAwqNmzZ8swDPX09Njza/f09CR9Pg4NDWn//v3av3+/JKmqqsoOtWtra1VSUpLtu3JIeK25AxXZ7sL+dJ9iPjkh3cG29XdnG/Jc//YFAADpQZANAIALWD/6rTao+dJKPFGy1uIY5uaK7PG2Ek9EgAkgX3i9XtXW1qq2tlZz5sxRLBZTV1eXXbHd29ub9Hb9/f3q7+/Xnj17JEmBQCAu2OZkLmQSwae7sD/dx82txScqHcG2NDwFiPWbmGAbAAB3IMgGAKDAmaapaDRqHwjJp1biiZJVZGOYW0PbibQST+TWxwQTw/MA+cjn86mhoUENDQ2SpEgkYgfbHR0dGhgYSHq73t5e9fb2ateuXfJ4PKqpqbGD7WAwmLdBRj58h8DksA8LmxXiWdif7lBMrcUnaqxg2/pvsmDbGVgTbAMAUPgIsgEAKGD53ko8EUF2am5sLZ6slbjH49G8efM0Z86cMZ+bzseE5wqAfFZSUqLGxkY1NjZKkkKhkF2t3dnZOWJKBWn4vb6rq0tdXV3asWOHfD6fampqVF9fr7q6OlVXV+fsM9wtn0PFjv3oHon7Mp++3+PQFXNr8YmaSLBthduJbcglgm0AAAoRQTYAAAUo8Ud6Yitx60d4vv0QJ8hOzU2txU3T1I4dO7Rly5a4A3Tl5eVatmyZ6urqxrUeKnEh8TxAYSorK9P06dM1ffp0maapoaEhu1q7s7NTkUhkxG1isZg6OjrU0dEhSfL7/Xa1dl1dnSorK/Pucx35jQpe9yDIdidaix+6ZMF2YqhtPb6HEmz7/f68/D0NAEAxIsgGAKDAJLYSNwwjr6uwnZgjOzW3VGSHw2GtWbNGbW1tcZc3NjbqqKOOGrWVeCICzNF5PB4eF6AAeDweVVRUqKKiQs3NzTJNU/39/XEV287WqJZoNKqDBw/aUzOUlpbaoXZ9fb3Ky8uzeh9Q2NiHhS3x857Q0x042SR9rBDaMtlgOxQK2ev0+/3y+XwE2wAA5AhBNgAABaTQWoknoiI7NTdUZHd0dGj16tUKhUL2ZR6PR/Pnz9fhhx8+4ecmQTYkngdwH4/Ho+rqalVXV2vWrFkyDEN9fX12tXZ3d3fSz4FwOKyWlha1tLRIkioqKuIqtidyohCKAyGZe1CR7U5UZGdOuoNta51WqG21IgcAAJlHkA0AQAEYrZW4dWZ4IZwhTpCdWiGHdaZpavv27dq6deukWoknKuTHBMB7eP2Ozuv1KhgMKhgM6vDDD5dhGOru7rartXt6epI+hoODgxocHNS+ffskSVVVVfb82rW1tfL7D/3nPvvMHdiP7kGQ7U7O30Ls08xKV7AdiUTs6UG8Xu+IObYBAED6EWQDAJDnTNNUJBKxD2AVUivxRInjJMh+T6G2Fg+FQlq7dm1aWoknIsiGxPOgUBXK51I+8nq9dpW1NFwN1t3dbVds9/X1Jb1df3+/+vv7tXv3bnk8HgUCAXs9NTU1cQfwJ4r9WZioyHYPgmx3cu5XQtDscgbbzt/ZYwXb1m9vj8dj/y4n2AYAILMIsgEAyGOxWMxuZeb8US0VXogtjTxAQyj1nkIM69LdSjxRIT4muWB1ZgDgTn6/Xw0NDWpoaJA0XA3mnF97YGBgxG1M01RPT496enq0c+dOu+rbml87EAhwgL3I8DlR2BJP/mR/ugOtxfOD9XoaT7DtPKHc+XucYBsAgMwhyAYAIA+ZpqloNGr/UC7UVuKJaC2emvOxyffHxWolvmXLlrjLy8vLtXz5ctXW1qZlOwTZkHgeAIlKSko0depUTZ06VZI0NDQUF2w7Ty6yGIahrq4udXV1aceOHfL5fKqtrbUrtqurq3mtuRAV2e6R+JokEHMHXqP5KVmwbf2zfpM7l63bTCTY9vv97HMAAMaJIBsAgDxjGIai0WjKH8iFVoXtRGvx1AolQAiFQlqzZo3a29vjLp86daqOOuoolZSUpG1bhRTuIzvy+bUB5Ep5ebmamprU1NQk0zQ1ODgYF2xbB9CdYrGY2tvb7ffykpISO9Suq6vjteYS7Ef3oLW4O1GRXRicJ5D7fL5xBdubNm2y25c3NzerpqYmZbDt9/vl8/l4XQMAkAJBNgAAecL64Zuqlbjz7O5ClTh2DrC+x/nY5Gto297erjVr1oxoJb5gwQLNnj077c/NQgn3kVmF/J4HZJvH41FlZaUqKys1Y8YMmaapvr4+O9Tu6uqy5/p0ikQiam1tVWtrqySNOCmJ12FhotrTPQiy3cn5nZ99WjjGCrYNw1BbW5t9/ZqaGlVWVsadlO71ekcE28425ATbAAC8hyAbAIA8kNhKPNX8W4XO+aNdyt/ANhec+zffQlvTNLVt2zZt3bo17vKKigotW7Ysba3EExFkQ+J5AEyGx+NRIBBQIBDQYYcdJsMw1Nvbq87OTnV0dKinpyfpZ3FiFfdrr72m+vp61dXVqba2VqWlpdm6C0gTApHCRpDtPrSLd4/EYNs6Md3i9XpHbUVuhduxWCzuZDOCbQAAhhFkAwCQY25uJZ5MIVQe50JiWGfNh55roVBIq1evVkdHR9zl06ZN09KlS9PaSjwRASYApJfX61VNTY1qamp0+OGHKxaLqbu7267Y7u3tTfp+Ozg4qL1792rv3r2SpOrqarsNeW1trfx+Di3kIyqy3YMg230SfwcRZLtH4uu1tLRUJSUl455jm2AbAIB4/NoEACBHTNOM+3HqxlbiyXi93rj7jGGJB6/yIchub2/X6tWrFQ6H7csy2Uo8EUH26HL9/MgWngeFqVien4XO5/Opvr5e9fX1kqRoNKquri7t3bvXnj87mb6+PvX19Wn37t3yeDwKBoN2sB0MBuXz+bJ1FzAK3jPdg9DTfRL3KZ+b7pHs9Wq9Zsc7x3ZiG3KCbQBAMSPIBgAgB8ZqJe62Kmwn54E3KrLfk0/zh5umqa1bt2rbtm1xl1dUVGj58uWqqanJyjgIMEfHY4JCkg8n52Bsfr9fU6ZMkWEYcUF2c3OzOjs7NTg4OOI2pmmqu7tb3d3devfdd+2q77q6OtXX16u6uprQLUeoyHYPKrLdh9bi7pX4Gzfx5K5Uc2wnhtrO0JpgGwBQzAiyAQDIslgsZs+blViF7fYQW6K1eCqJ+9wwjJxUtA0NDWnNmjU5aSWeiCAbEs8DIBcSX2sLFiyQx+PR0NCQPb92Z2dnXMcOi2EYdqvy7du3y+fz2dXadXV1qqqqcvX3nHzFY17YCLLdhyp795rovnW2FZeU1mDb7/fL5/O5/hgDAMDdCLIBAMiS0VqJWz9e3dhKPJHzhzyh1HuStRbPtra2Nq1Zs2ZEK/GFCxfqsMMOy/pzkwATAPKD9X5cXl6upqYmNTU1yTRNDQwM2KF1Z2enfaKeUywWU1tbm9ra2iRJJSUldqhdX1+v8vJy13/3yRUqst2DINt9aC3uXpM9SSETwbZ1OcE2AKAQEWQDAJAFhmEoGo2OOf9VMaC1eHLJKrKzxTAMbdu2bUQr8crKSi1btixrrcQT8VyBxAkNQC6M57Xm8XhUVVWlqqoqzZw5U6Zpqq+vzw61u7q64g6yWyKRiFpbW9Xa2ippOBx3VmyXlZWl/f4UK94z3YMg231oLe5e6a62T0ewLWlEsO1sQ15MxyMAAIWHIBsAgAyyfmCmaiVu/WAsph+NhJPJ5aoie2hoSKtXr1ZnZ2fc5dOnT9eSJUuy2ko8EQEmABQOj8ejQCCgQCCgww47TIZhqKenxw62u7u7k76XDw0Naf/+/dq/f7+k4ZOorGrt2tranH4OuUkxfdd0I6p33YfW4u6V6X07VrBt/TdZsO0MrKPRqH2cgmAbAJDPCLIBAMgQ0zQVjUbtH7LF2ko8EXNkJ5f4PMhGcJuslbjX69XChQs1a9asnD83CbIh8TwAcu1QPwu8Xq9qa2tVW1urOXPmKBaLqbu7255fu7e3N+ntBgYGNDAwoL1790qSAoGAXa1dW1srn893yPel2NBa3D2oyHYf9ql7ZfskhYkE24mtxgm2AQCFgCAbAIAMoJV4asyRnVziAY5MhvyGYWjr1q3avn173OWVlZVavny5gsFgxrY9EQSYAJAbmXjP9fl8qq+vV319vaThFuNdXV12xXZ/f3/S2/X29qq3t1e7du2Sx+NRMBi0K7aDwSBVjKPgs9M9Ek9KKNbfEW5CRbZ75XrfJgu2E0PtxGA7cX5tKXWw7ff7eR8CAGQVQTYAAGmU+IOQVuIj0Vo8uWxVZI/WSnzp0qXy+/Pn6yFB9uiK5X2E54E7WN1IAEtJSYkaGxvV2NgoSQqFQnao3dnZqaGhoRG3MU1T3d3d6u7u1rvvvmtXfVsV24FAgOeZAxXZ7sG+dJ9ch53IHGdLbyn3r1krhLYkC7at5+N4gu1QKGSv0+/3y+fzFf0xDgBAZuXPkUoAAApcYitxwzCowk6C1uLJJT43MvHYHDx4UGvWrFEkErEvy6dW4okIMIHClW/vJzh02dqXZWVlmj59uqZPny5JGhwcjAu2ndNgWAzDUEdHhzo6OiRJfr9ftbW1qq+vV11dnSorK4v6uUj46R7sS/ehtbh7OX/H5eMxgHQH29Y6rVDbakUOAEC6EGQj5wzDUE/MUNgwVer1KOjz8oUHQMExDEORSEThcFgbN25ULBaTYRiaN2+eqqqq8vIHbK7QWjy5xM++dD42hmFoy5Yt2rFjR9zl+dZKPBFBNiSeB0CxqqioUEVFhZqbm2WapgYGBuz5tbu6uuyD507RaFRtbW1qa2uTJJWWltrV2nV1daqoqMj23cgbfA8tbM5gjH3pDlRku1dikJ3v0hVsRyIR+4Rpr9c7Yo5tAAAOFUE2cuLtnn7994EOrekd1Kb+IfXG3vuSF/B5taCqXEcHKvSJ6Q1aHqzM4Ujd57zzztMf/vCHuMuam5u1d+/eHI0IKGyJrcRjsZj27dtn/33OnDmE2AloLZ5cplqLDw4OavXq1erq6oq7vKmpSUuWLMmrVuKJCDCT83g8Rft4FOv9BrIt315rHo9HVVVVqqqq0qxZs2Sapnp7e+1q7a6urqTfKcLhsFpaWtTS0iJJKi8vt+fXrqurU2lpabbvSlZRxese7Ev3oSLbvQotyE7kDLat5+lEg22rOx3BNgAgHfL3yCVc6fGWTv2/3a1a3TuY8jq9MUN/6xnQ33oGdPfedi0LVOiqw6bqo1PrsjhSd7rvvvtGhNgADp1hGIpGozJNM+6fE3OCjkSQnVzij/l0PDapWokvWrRIM2fOzPvnZmKQzeupOLHPgdzKx9egx+NRMBhUMBjU7NmzZRiGuru77WC7p6cnaRg/NDSk/fv3a//+/ZKkqqoqu1q7trZWJSUl2b4rGZVvJyTg0Dn3JQGQOyRW2efjey0OTaEH2U7W83Iiwbb1fLZCbYJtAMBkEWQjKw6GIvrWpt36Y3vPhG+7undQX3pnpx4+0Kl/WzBLjWXuOriQLW1tbfrGN76R62EArmD9ULNaWjp/wGUijHQbqmyTS2dFdqpW4lVVVVq+fLkCgcAhrzubkj0mHOQrPrxnABiL1+u1A2lpuMW4M9ju7e1Nerv+/n719/drz549kqRgMGivp6amJq7VaqHj87OwUZHtPm4KOxHPuW/d9DkijR5sW8dErH/W4zDRYNvv9/M+BwCIQ5CNjHuru1+fXbtD7ZGRc5hNxLPtPfrbG5v0X0fP0fuCVWkaXfG46qqrdPDgQUnDQUZ/f3+ORwQUJtM0FY1G7R9lzhDb4/GMaNNMkD0SFdnJJf5YP9THJlUr8ebmZi1evDivW4knyuS84QCA1Ar9/dbv96uhoUENDQ2ShufttELtzs5ODQwMJL1dT0+Penp6tHPnTnk8HtXU1NjBdjAYLLiwifDTPdiX7sM+da9iOknBGWz7fL4RQfZkg22/3y+fz8drBACKXOEcyURBequ7X5es3hY3B/ZktEei+sSqbXpw+VzC7Al4/PHH9eCDD0oa/nJ53XXX6Xvf+16ORwUUnmStxJ0/xpL9SCWoHYkgO7l0hLatra1au3btiFbiixcv1owZMwruAECm5g1HYaEiG8BklZSUaOrUqZo6daokKRQK2aF2R0eHQqHQiNuYpqmuri51dXVpx44d8vl8qq2ttYPt6urqvP9c5T3TPQg93aeYws5iU8z71tkmP93BthVqE2wDQPEhyEbGHAxF9Nm1O9IWYlt6Y4Y+s2aHXvjgAtqMj0NPT4+++tWv2stf+tKXtGLFihyOCCg8pmkqFospFovZy9aPMElxP7ik4R9izhZbiEcoldxkQlvDMLR582a9++67cZcXWivxRATZAJB7bjxYXFZWpunTp2v69OkyTVODg4NxFdvOE8IssVhM7e3tam9vlzRc9W2F2nV1daqsrMy7x4rw0z0S51NG4WOfulcxB9mJJhtsW8daDMNQOBy21+ucX5tgGwDcjyAbGfOtTbsn3U48lfZIVNdu2q3fHn1ERtbvJl//+te1b98+SdLMmTP1ox/9SC+++GKORwUUjsRW4taZwZLizhp28nq9duhNkD0SFdnJWT/YJ3oSxODgoFatWqXu7u64ywuxlXgigmxInPziFuy7wlJM+8vj8aiyslKVlZWaMWOGTNNUf39/XLBtfa9zikajOnjwoD19U1lZWVywXV5enu27MioO8hc252uy2IMxt2CfuhdBdmrpCradhQbWugi2AcC9CvfIJvLa4y2d+mN7T0a38Wx7jx5v6dQF0+oyup1C9txzz+mee+6xl++44w5VVdGSHRiv8bQST/YDyefzEWSPgiA7NWeQPZ4QoaWlRevWrUvaSnzmzJkZG2e2EGSPjgM0yGc8P1GoPB6PqqurVV1drVmzZskwDPX29tqhdnd3d9LvL6FQSAcOHNCBAwckSRUVFXHBdmlpabbvChXZLsK+dB/CTvdi344fwTYAYDwIspER/293a1a287PdrQTZKQwODuqLX/yi/UXvoosu0gUXXJDjUQGFYbRW4taPJucPrkQEtaOjujI152Mz2nPHMAxt2rRJO3fujLu8urpay5YtK9hW4okIsiHxnvH/Y+/fgyPZ0/rg85t1U91vune3Wt3q06Oj092SGgaGGYxhbAL8guM1lxfWXhbPGEd4vO8fXgfgsAPYnSVYG7N+1w5i7bDxeAGHsR3Ma8DzOsJjEwZmgDMwMEyX1Oqj03P6plarda+7VFJdMvcPTWZnZmWVqqS6ZP7y+4nomFEelVTKrMrKzG8+z0M0bG6/+OvxeJBIJJBIJHDjxg00Gg0Ui0VtvnapVLLcN1UqFVQqFa07VjQa1ULtZDI5kI4p3GeKg0G2eNhaXFwMsi+uVbBtDrVbFRkw2CYiEhODbOq5B8UjrJQqA/ldmVIFmeIxluPhgfw+J/l7f+/vabNSU6kU/sW/+BfDfUJEDqEoCmq1mqG9cydV2HoMstvj+mlN35a+1cXn4+NjrKysNLUSv3r1KhYWFhzdStzM/F7j68UagwoiosHxer1aID03N4d6vY58Pq9VbJfLZcvHlctllMtlbG5uQpIkxGIx7eckEgl4vd6eP1eGn+LgthQPW4uLi0F27+irrwG0DLb1oXU3wbbP5+voGg8REQ2XOFc6yTb+953swH8fg2yjd999F//qX/0r7euf//mfx+Tk5BCfEZEzNBoN1Ot1AMYqbKDzEBtgUHserp/WzqvI3t3dxcOHD7XXKXB2Ev7OO+/g6tWrA3mOg8SKbAJYkU00DHyvdc7n82FsbAxjY2MAgGq1qgXb2WwWlUrzTd6KoqBYLKJYLGJjY0Or+laD7Vgs1vPwgxfpnY3Vu+LhNhUXg+z+6XWwXa1WteU+nw9er5fBNhGRDTHIpp5bHVA1tmqldDzQ32d3tVoNf/Nv/k3toO3bvu3b8KlPfWrIz4rI3hRFQb1e1044u20lbsagtj2GUq3pXzv6ddOulfjy8jKi0ejAnuMgmS/88PVCRDR4vJjbnUAggImJCUxMTAAATk5OtGrtXC6H09PTpsfIsqz9d+DsJrVkMolUKoV0Oo1IJHKh7cAqXnGwelc8DDvFxW07OL0ItgEYKrYlSTK0IWewTUQ0fAyyqadkWcbjo5OB/s7HRyeQZZkHh1/30z/903j8+DEAIBQK4TOf+cyQnxGRvcmyjHq9fu6spW50OufYrRj0t2YV8h8fHyOTyaBYLBq+99q1a1hYWOhLK1K7YEU2Abz5hYicLRgMYnp6GtPT01AUBZVKRavWzufzqNVqTY9pNBo4PDzE4eEhAMDv92vV2qlUCqFQqKPjU+4zxcGbEsTDmxPEpQ9NuW0H6zLBtv7aT71e17qgMdgmIho+BtnUU8WGjFJjsKFE6eu/M8GDQ3z1q1/FL/7iL2pf/4N/8A8wPz8/xGdEZF/qyUyrVuLqyclFTlAY1LbH9dOa+SaInZ0drK2tNbUSv3PnDq5cuTKMpzhQDLKJiIaD+9v+kCQJ4XAY4XAYV69ehaIoKJfLWjV2Pp83XFxX1Wo17O3tYW9vDwAwMjKihdrpdBojIyOWv4/hpzi4LcXD1uLiYkW2fZwXbKv/a67IZrBNRGQvDLKpp6rycC54nA7p99pJo9HAj/3Yj6FarQIA7ty5g5/6qZ8a8rMisidzK3FZlg1V2PoTnYtgUNueuX222sKdjOtmb28PT548Mfx30VuJmzHIJoAV2UQkLkmSEIvFEIvFcP36dciyjFKphGw2i1wuh0KhYLnfOz09xc7ODnZ2dgAA4XDYULHt9/stfxc5F4Ns8bAiW1z6awAid89yonbBtr5qWx9sm+drA62DbZ/Pd+GCCCIiao1BNvVUwDOcD+qRIf1eO/mH//AfYmVlBcDZgfJnPvMZ+Hx8ixOZ9aOVuBmD7Paswkme6J3Rrwc3thI3Y5BNJA6+f52Ln9GD4/F4kEgkkEgkcPPmTTQaDRQKBa1i23xsoDo+Psbx8TG2trYAnN34lk6nm1qnknMxyBYPq3bFxW3rHFbBtrlaW92e6mdqrVZDsViEz+eD1+tFOp3WiiVOT0+1YFv97wy2iYgujykX9VTc60HM6xloe/HY13+nm62vr+Pnf/7nta//1t/6W/joRz86xGdEZD/mu2p72UrcjEF2e+aTeVmWeYL/dVbtRN3UStyMQTYBrMh2Kl6wcza+1+xBvUCeTqcBnFWAqaF2LpfD0dGR5ePK5TLK5bJh2cHBAcLhMBKJBI+7HIhBtnjYWlxcDLKdSw2hVVbBdqFQwOPHj7Xv+XN/7s911IpcDbb5miAi6h6DbOopj8eD+UgQXykeD+x3zkeCrj4IkGUZP/ZjP4aTkxMAZxV7/+Sf/JMhPysiezmvlXivZxoxyG7PKsh2u0ajgcePH+P42Pj5GYvFsLy8jEgkMqRnNlwMssmMrwEicjOfz4fx8XGMj48DAKrVqiHYrlQqLR97eHiIw8NDrepbna8di8UYojkAQ0/xsLW4uBhki8Mq2NbvgyVJ6roVucfjaZqxTURE7THIpp5bjIUGGmQvxcID+1129M/+2T/DH//xH2tf//N//s9dG3gQWWk0GtoJg7kKux8hNmCcg8WQtpl5fbt9HR0dHWFlZaWpXWgkEsG3fMu3uKqVuBmDbAJ4wZ5o2PgetK9AIIDJyUlMTk4CACqViiHYrlarTY+RZVn778+ePYPP50MymdTma0ciEW5zG2LoKR6GnWJSx5epuG3FogbXKrXSul0rcnOwrRZW1Go17b8z2CYiao9BNvXcD02l8ctbhwP9fW72j//xP9b+/7d/+7fj7bffNrS4sfL69WvD1/V6vekxs7OzCAaDvXuiRAPWrpW42kK8X7OKWJHdnvnEzM3h5Pb2NtbW1ixbiqdSKVeH2ACr9+kMW4sTDR7fa84UCoUQCoVw5coVKIqC3/u939P+m8fjsfwcrdfrODg4wMHBAYCzcFwNtVOpFEKh0MCeP7XG1uLi4TYVk3k/y1BSPPpzdzWEBt68p9vN2NbP5Fb/P4NtIqLzMcimnrsfj2ApFsJKqXVbs15ZjoWwHHd3RbbaUhwAvvjFL+Ltt9/u+mfs7e01Pe5LX/oS52yTY8myjHq9rt0NrT956FcVtp7+ZzN4a8Zw8uwk9v3338fm5qZhudfr1U6M3bhezFiRTUREdDHmz9D5+XlEIhGtGjufz1sea1SrVezu7mJ3dxcAEAwGDcH2yMjIQJ4/GTH0FA8rssXEIFt8+m2sv/Fc3TdbBdtqqG11faqbYNvn8/EzgIhciUE29cX/OjOBT7230f/fc32i77+DiJxDPSFo1Upcf3LQT6zIbs/trcWPjo6QyWRQKpUMy69fv45KpYL9/X0ADG0BBtl0hhXZRMPFC6bOZN5fejwexONxxONxzM7OQpZlFItFLdguFAqW+9iTkxNsb29je3sbwNnoEzXUTiaT8Pv9A/l73I5Btng491xMDLLFp6/IbtdBTR9se73epiD7ssG2z+eD1+vl/oOIXIFBNvXFX5lM4Td2c/jtw+L533xB3z0ax/88kerbzyciZ1EUBfV6XTsBGGQrcTMG2e25ubX469ev8ejRo6aT33v37mFqagoPHjzQlrtpvbTCIJuIaDi4vxWP+TPV4/EgmUwimUzi5s2baDQayOfzWrBtvuFOdXR0hKOjI7x69QoAEIvFDMG228ei9AtDT/FwjrKYGGSL76LdFPTXo3odbKuhNoNtIhIVg2zqm//P/Ay+408f47BW7/nPHvX78L/Nz/T85zpRqwsM7fzX//pf8b3f+73a11euXMHW1lYvnxbRQA27lbgZg+z23NhavNFoYH19Xbvoq4rH41haWkIkEgHAtvRmDLIJYEU2EdFFmPeX5x0Le71ejI6OYnR0FABQq9UMwfbR0ZHl40qlEkqlEl6+fAlJkpBIJLRgOx6PM8TpEYae4mFrcTExyBafeUb2RfUi2PZ4PJBlGdVqVfu5+vnaDLaJSBQMsqlvxkf8+LXFm/jhzFOUGr27GB/zevBrizcxPsIWZkRupygKGo2GdiIxrFbiZgyy23Nba/FyuYyVlRXLVuJvv/224fWi//8M7M5IkqStC64TIufi+9e5eAHUmS77nvP7/RgfH8f4+DgA4PT0VAu1c7kcTk5OLH9nPp9HPp/H8+fP4fV6tWA7nU4jGo3y9XRBbC0uHlbZi4lBtvhazci+rMsG2+r1L/01MvVnMdgmIqdjkE19dT8ewWeXb+H/svq8J5XZo34ffm3xJu7HIz14dkTkZOZW4mp7JWA4Vdh6DLLbc1NrcatW4j6fD3fv3sXU1FTT97MiuxmDbGJFtjPxIpmz8b0mnsu+J0dGRjA1NaUdv1QqFeRyOWSzWeRyOa3FqV6j0UA2m0U2m8XTp0/h8/m0au1UKoVwOMx9RYcYZIuHVfZiYpAtvk5nZF8Wg20iojcYZFPf3Y9H8IVvmsdPPt7Ef7/EzOzvHo3jf5ufYSU2ERnmAZmrsIcdYgMMss/jhtbi7VqJLy8vIxwOWz6OFdnNGGISERF1r9vW4t0KhUIIhUK4cuUKFEXB0dGRoWJbf9FcVa/Xsb+/j/39fQBAIBAwBNuhUKinz1EkDLLFw4psMZn3fQyyxTOssQCtgm1zqN1q1B6DbSJyMgbZNBDjI37828U5fG43h3+5uYdMqdLxY5djIfyv1yfwP0+k+vgMicgJ2rUSVw/qh9FK3IxBdnuiB9nlchmZTAblctmwfHZ2FvPz821PdhnaNvN4PNp7XrTXymUNe183KHxfEA2XW/Y1ohnk/lKSJESjUUSjUczMzECWZZTLZa1au1AoWH6GV6tV7O7uYnd3F8BZOK4PtgOBwMD+Brtj6CkWNWxSMewUByuyxTeoiuzz6KuvAbQMtvXPt5tg2+fzDb1IhIhIxSCbBuqvTKbwVyZTyBSP8b/vZLFSOsbjoxPDDO2Y14P5SBBLsTB+aCqN5bh11RoRuYssy6jX6+feZWoH+pMZ9bna5bnZgXldiBRMbW1t4b333mtqJX7v3j1MTk6e+3i2Fm/GEJOIiKh7/a7Ibsfj8SAejyMej+PGjRuQZRmFQkGr1i4Wi5af6ZVKBZVKBa9fvwYARCIRbb52MpmEz+feS1isyBaL+fXPsFMcDLLFN6yK7PP0OtiuVqsAoIXaXq/XVtfdiMhd3HsWQEO1HA9rAbUsyyg1ZJzKCkY8EmJej60OBIhouNQD73q9bvjaTq3Ezawqjod5p67dqCdY6jYUIbBtNBp47733sLW1ZVieSCSwtLTUspW4GVuLN2OQ3cxO+7tB4GuAaPD4XhPPsMfuqFXWwFmL8UKhoFVsm7vYqI6OjnB0dKSNaonH49rPSSQSrjq+ZvWuWMznP247thOZuXsC36/isUtF9nn6UbEtSZKhDbndrsURkbgYZNPQeTweJHhgN1Df8z3fw4tT5AiKoqBer2sng3ZtJW5mfj4MspuJ1C76Mq3EzViR3YwhJhHRcNntOIs6M8yK7PP4fD6Mjo5idHQUAFCr1QzztY+Pjy0fVywWUSwWsbGxAUmSkEgktIrtWCwmdGDEimyxsCJbXHat1qXe0Qe/TtrGvQi2gbOb0dRCEwbbRDQoDLKJiMiWnNRK3Ez0GdC9IEpg++rVK6yvr1+4lbgZK7KbMcgmvgaIiLpn5yDbzO/3Y2JiAhMTEwCA09NTrVo7l8vh9PS06TGKoiCfzyOfz+P58+fwer1IJpNaxXY0GrX139wtBtliYftpcTHIFp9+Gzu5YOG8YFv9X6tgW389jsE2EQ0Cg2wiIrIV9UBZPVg2txJXD4TtfDDMIPt8Tg9s6/U63nvvPW2GoyqRSGB5eRmhUOhCP5eBXTOuEyKiweP+1vmcvA1HRkYwPT2N6elpKIqCSqViqNiu1WpNj2k0Gjg8PMTh4SGAs3A8mUwinU4jlUohFArZ+vyhHTVcUDn176A32FpcXAyyxSfqNm4XbOurtvXX68xtyAEG20TUHwyyiYjINsytxNUDZsD+Vdh6DLLPp19HTls/pVIJmUwGR0dHhuU3btzAhz70oUudzDp5vfQLg2zia0AM3HZEg+Wkiux2JElCOBxGOBzG1atXoSgKjo6OtIrtfD5vqBZT1Wo17O/vY39/H8BZOK5Wa6dSKQSDwUH/KRcmyrakN9haXFyihpz0hlNmZF+WVbBtrtZWX+/dBts+nw9er9f2RSpEZB8MsomIyBac3ErcjEH2+ZzYWlxRFGxtbeG9994zPGe/34979+5p7TAvg4FdM64TMu/7FUVxzOeBm3EbiYPbUgyibEdJkhCNRhGNRnH9+nXIsoxSqaRVaxcKBctjy9PTU+zs7GBnZwcAEAqFtGrtZDKJQCAw6D+lYww9xcPW4uJikC02fbdAQOwg20wNoVUMtolokBhkExHRULVrJa4exDrtYJZB9vmc1lq8VSvxZDKJpaWlC7cSN3NiwN9vrFInIho8J3w2U3tu2YYejweJRAKJRAI3btxAo9FAoVDQgu1SqWS5LiqVCra2trC1tQUAiEajWrV2MpmEz2efy2WsyBYPt6m4GGSLjTehvNGvYFsNtdVW5EREAINsIiIaIkVRUKvVtBN5p7YSN2OQfT4nhZOtWonfvHkTt2/f7unJldMC/kFgRfb5RF8vrMgmIuqeW4Myr9eLdDqNdDoN4OwCeT6f14Ltcrls+bhyuYxyuYzNzU1IkoRYLKZVbMfj8aFW3bl1W4qMYZi4GGSLzTzKgtv4jV4F27VaDbVaTfvv5hnbRORODLKJiGgoGo2Gdtel/sAWcHaIDTDI7oQTgmxFUfDq1Susr6/3rZW4GUPbZlwnRETD5dTjMTJy63b0+XwYGxvD2NgYAKBarWqhdi6XQ6VSaXqMoigoFosoFot48eKFVvWtVmzHYrGBXkxnkC0eBtni0ged3K7iMb933dRavFv6YFtfvHJesK1eC5QkSSt2YbBNRAyyiYhooBRFQb1e1w5YRWglbsYg+3x2Dyfr9ToePXqE7e1tw/JetxI3c0LAP2h2f61Q/1lVZBMRUXsMP60FAgFMTk5icnISAHBycqKF2tlsFtVqtekxsixr3wOcBRdqqJ1KpRCJRPq6frktxcNtKi5WZIvNXJHNILsz6j6uk2Bb36VRX+TCYJvI3RhkExHRwMiyjHq9DkVRtH8itBI3M5/MMJBsZufAtlgsIpPJ4Pj42LC8H63EzcyhLVsoM8hux+2vDSLqH+5vnY/bsDPBYBDT09OYnp6Goig4Pj42VGyrHaT0Go0GDg4OcHBwAOCsW48+2A6FQj39jGb1rni4TcXFIFtsfO/2hlWwrb9G2OqaYTfBts/n4/kykUAYZBMRUd+pB5+tWonrD0RFYP477BbU2oEdg+x2rcQXFxcxPj7e9+fAWcDNGGQTK7KJhsvtn0NOxYrP7kmShEgkgkgkgmvXrkFRFJTLZS3UzufzTdV4wNk8z729Pezt7QEARkZGtPnaqVQKIyMjl3pe3Jbi4TYVF4NssbEiuz/01wO9Xm9Pg22fz6eF29zXEjkXg2wiIuorcyvxVq2CRKL+Xfp2SWRkt3CyXq9jbW0NOzs7huWpVApLS0sIBoMDeR7m94Id1s2w2e21QkRE5ES8eNs9SZIQi8UQi8Vw/fp1yLKMYrGoBduFQsHy2OT09BTb29vaiJpwOGyo2Pb7/V09D4ae4jGfH3KbioNBtthYkT0YvQi2PR4PZFk2jAzRtyFnsE3kLAyyiYiob9zSStyKx+PR7tZlkN3MThXZrVqJz83N4a233hroySkrT5sxyCa+L8TA7eYs3F7Ox/Cz9zweD5LJJJLJJG7evIlGo4FCoaDN1y6VSpaPOz4+xvHxMba2tgAAsVhMC7WTyeS5FX3cluIxh53cpuLQb1tW64rHXJHNIHswLhtsq/vZRqNh2IYMtomcg0E2ERH1nKIohgNE0VuJW2GQ3Z4dgmxFUbC5uYn3339/aK3EzdiWvpl+nXB9GOkvbLMNPdkNX49Ew8Xws/+8Xi/S6TTS6TRu3bqFWq2GfD6vVWwfHR1ZPq5UKqFUKuHly5eQJAnxeFwLthOJxLkdergtnU+/Tbk9xcKKbLHpQ1ARuws6BYNtIvdhkE1ERD11Xitxt9xxboeg1s6GXWVbq9Xw6NGjobcSN2Nr8WbDfq3Q8LEim2i43HDcJiLuKwfP7/djfHxcuxmyWq0im81qwfbJyUnTYxRFQaFQQKFQwIsXL7SqbzXYjsVibEMtIIad4uK2FRsr7u3posG2vg05g20ie2OQTUREPdNoNFCv1wE0V2G7KcQGGGSfZ5jrp1AoYGVlpamV+K1bt3Dr1q2hXnBgRXYzBtlERIPH/a143HIMbieBQABTU1OYmpoCAFQqFS3UzuVyhrmdKlmWkc1mkc1mAQA+nw+RSMTwPQzHnE9/jM/3plgYZIuN29cZWgXb5lDbqsK+k2Db5/O56vomkR04Psi2+tDgRV8i6her/YvP5/hd6aW1ayWuHkCK3krcjEF2e8NYP4qi4OXLl3j//fcNF+gDgQAWFxcxNjY2kOfRDiuym+nXCdeHO7Eim4ioe2xHbT+hUAihUAhXrlyBoig4Pj7WKrbz+bx2Q7BevV5HoVAwLPva176G0dFRpFIphEKhQT196iH9+5NhmFgYdIrNHGqSM+jbigO4cLD97/7dv8Mv/MIvIBKJYGpqCp///Od5TZhoQBz/TtPvLNTApFarYWRkZIjPiohEdXp6CoAnnnqyLKNer5/bqsdtGGS3N+i5x7VaDWtra9jd3TUsT6fTWFxcHForcTNWZDdjRTa58TOEyE74HnQmfmbamyRJiEQiiEQimJmZgaIoKJVKWrV2Pp9veRy4v7+P/f19AEAwGNTakKfTaQQCgUH+GXRBDDvFxW0rNn3Qye3rXBcNtnd3d/H8+XMAQD6fH8ZTJ3ItxwfZHo8HXq8XjUZDC7JLpRKi0eiwnxoRCahYLAJ4c2HI6/W69uBVPchr1UpcDbDdevGTQXZ7g6yyLRQKyGQyqFQqhuW3bt3CW2+9ZavXKCtPmzHIJjO+Doj6j+8z52NFtrNIkoR4PI54PI7Z2VnIsoxCoYBcLof9/X0cHR1ZPu7k5ATb29vY3t4GAEQiES3YTiaT8Pv9g/wzqENsLS4uBtli44xsMXUabJfLZe0x5rEfRNRfjg+ygbMdR7FYhCzL8Hg8KJVKmJyc5AEDEfVUrVZDqVQC8OYuzHg8PsynNDSKoqBer2sH8Wwl3oxBdnuDWD/tWokvLS1hdHS0L7/3MthavBmDbOINHkREl+fm43In8ng8WiAdj8exurqq/bdoNGq4mK53dHSEo6MjvHr1CgAQi8WQTqeRSqWQSCQYvNgEO7yJi0G22FiR7Q6tgu3j42Pte8LhMI+tiAZIiCB7dHRUC7KBs7Bpc3MTMzMz/FAhop44OTnBy5cvDXfjAcDk5OSQn9ngsZV4Zxhkt9fv9dOulfjS0pJtR5CwtXgzBtmtcV9LdsbXpzi4LZ2JFdniMB8PfvjDH0aj0dDakOdyOcPFdb1SqYRSqYSNjQ1IkoREImEIyHnNbDgYdopJ36EO4LYVESuy3Uk9htJ3R2E3YKLBEiLIVg++ZVlGrVaD3+9HuVzGkydPEIvFEIvF4Pf7eQBBRB2TZRmNRgPHx8colUqoVCpaaKu20g4EAgiFQkN+poOjzodR70BlK/H2GGS3189wMp/PY2VlpamV+FtvvYVbt27Z+jXKiuxmg56nTvbDimwxcLs5C7eX83EbisPqpgS/34+JiQlMTEwAAE5PT7VQO5vN4vT01PLn5PN55PN5PH/+HF6vF8lkUgu2o9GorY+TRaLfplzn4jCfq/A6tHj0FdkMst1HH2RHIhHuv4kGSIgg2+Px4Nq1a3j58iUURdHC7Fqthmw2i2w2O+ynSEQCUPcvwNl+5/r160N+RoOjbyX+7NkzVCoVNBoNjI2NaaMceABnxCC7vX6sH0VRsLGxgcePHxsuDo2MjGBxcdGWrcTNWJHdjBXZRERE3WNFtjjMoafVthwZGcHU1BSmpqagKAoqlYqhYls9j9VrNBo4PDzE4eEhAMDn82mhdiqVYtvUPmJFtpgYZIuP7113Y5BNNDxCBNkAtIvTaphdrVbh8XhYIUhEF2Zuna1eQPB6vbh9+7ZrqrHVbhfA2Tp5/fq1NpNtZGQE09PT3MdaYJDdXq/XT61Ww8OHD7G3t2dYPjo6isXFRdu2Ejdj5WkzBtnE9wXRcPE4z/m4DZ2t2+pdSZIQDocRDodx9epVKIqCo6MjQ7CtrypU1et17O/vY39/H8BZBzJ1vnYqlUIwGOzdH+VyDMPEZD6vZcWueFiR7W76MR6RSGSIz4TIfYQJsoE3YfarV68gy7L2j4ioV4LBIG7duoVAIDDsp9J3rVqJ60+0FUXhhbEWGGS318t20fl8HplMBicnJ4blt2/fxtzcnKNeo2wt3sy8zyEiov7j/tb52LpYHJfdlpIkIRqNIhqNYmZmBrIso1QqaaF2oVCwPB6vVqvY2dnBzs4OACAUChkqtt1wTtwvfH+KiRXZ4uNNKO6l3hSmYpBNNFhCBdnAWZidSqVQKpVwcHCAo6MjyztNiYg6IUkSQqEQ4vE4RkdHXXOyLssy6vV6U1U6wIC2U1xP7fUinFQUBS9evMDXvva1plbiS0tLSKfTl36eg8bW4s1YkW2t0WhgY2MDJycnSCQSQn9GsSKbiKh73FeKQ3882IvQ0+PxIJFIIJFI4MaNG2g0GigWi9p87VKpZPn6qVQqqFQqeP36NQAgGo1qoXYymYTPJ9wlxr5hGCYmBtniY0W2u+mD7Gg0yhuRiAZIyKNM/UE58CaQ4cVgIuqUx+OBz+dz3YmHGljX63XD1+qFDEmSDAfrvFGoNQbZ7ZnXT7fV/dVqFQ8fPtRaH6qc1krcjBXZzRhkNyuXy8hkMtqJtHpBORaLaS1AE4kEL64QUU/wIp0zseJTHPpt2Y/zU6/XqwXSc3NzqNfryOfzWsW2OlbKrFwuo1wuY3NzE5IkIRaLaT+HxyHt8f0pJgbZ4uNNKO6mD7LD4fAQnwmR+wgZZJt5PB5hK1SIiHpFURTDTT/6EFuSJO2f/oIEA9rWGGS3Z1Vh2elFnFwuh5WVFSFaiZuxIrsZg2yj7e1trK2tWd5IVCqVUCqVsLGxod3YmU6nkU6nHX3HOCuyiYgux6n7fzoz6NDT5/NhbGwMY2NjAM5uINUH2/oZofrnWCwWUSwWDccharAdi8UY+ugwDBMTg2zxsSLb3fSff9FodIjPhMh9XBFkExFRe+1aiUuSBI/Ho100YUDbGa6n9i5SeSxiK3ErkiRpfx8DOwbZKlmW8fjxY2xsbBiWe71ey1BblmXtgvPTp0/h9/uRSqW0iu1QKDSop04EwN3vXyfi9nI+VnyKY9jbMhAIYGJiAhMTEwCAk5MT7Rgjl8vh9PS06TH64xDg7HglmUxqwbaTb7DrBQbZYmKQLT6+d93LPCObQTbRYDHIJiJyMUVR0Gg0tBDE3EpcDbD1FxkY0HaG66k980mfLMtt72hu1Up8bGwMi4uLQnVe8Xg8hvek2zHIPrtgnMlkkM/nDctnZmZw48YNnJ6eolAoaBeMa7Va08+o1WrY29vD3t4eACAUCmmhdiqVgt/vH8SfciGsyHYmNwcUouG2dCbuK8Ux7CDbLBgMYnp6GtPT01AUBZVKRZuvnc/nLY9DGo0GDg8PcXh4CADaDXbqv1AoZIu/bVDstk2pNxhki48V2e4lyzIrsomGiEE2EZFLmVuJy7LcsgpbT38yxhnZrTHIbq+bFtpWrcQlScLt27dx8+ZN4S7+6P8evnYYZB8cHGBlZcVwUdjr9eKdd97B5OQkarUagsEgIpEIrl69CkVRUC6Xkc1mkcvlkM/nLV9HlUoFW1tb2NraAvBmvnY6nUYikeCFNyIigYh2rOQ2+s9xu21LSZIQDocRDocNxyHqzXX5fN7ynNF8g93IyIgWaqfTaYyMjAz6TxkoVnWKiUG22PRFHwC3r9uYx2pEIpEhPRMid2KQTUTkQt20EjfjjOzOMMhur5PW4oqi4Pnz5/jggw8M/z0YDGJpaQmpVKrvz3MY9OvGjcGtmVuDbEVR8PTpUzx58sSwPBqNYnl5GaFQyHJ9SJKEWCyGWCyG2dlZNBoNrVo7m82iVCpZ/j7zfO1kMqlVbA+7/ScrsokGj+8z52PFpzictC31xyHXr1+HLMsolUraDXaFQsFy/3J6eoqdnR3s7OwAAMLhsKFi286dYy7Czjcn0MWZtyu3rVjM13VYke0u5XLZ8DUrsokGi0E2EZGLtGslrp5onXfCxYC2M1xP7Vm1FterVqtYXV3FwcGBYfn4+Dju3bsnVCtxM1ZkG7nxvdTq9T89PY07d+7A5/N13BHD6/Vqlda3bt1CrVbTQu1cLodKpdL0GFmWkc1mkc1mAcAwXzudTiMYDF7+j+wCLwISEXWPNyOIw8kVgB6PB4lEAolEAjdv3jTcYJfL5VAsFi0fd3x8jOPjY61zTDQa1Y5FEokEfD5nX8508jal1lhpLzZW3Lubfj42wCCbaNCcfeRHREQdUxQFtVpNO2nutJW4GVuLd8aN4Vs32gXZuVwOmUwGp6en2jJJkvChD30IN27cED7UcmsFcituWx+FQgEPHjxoaqW/sLCAmZmZS7/+/X4/JiYmMDExAeCsxbgaanc7XzudTiOZTA68SsoNrwMiOxH9c1dUTqripfZE2pb6G+wAoF6vI5/Pa8ci5qBAVS6XUS6Xsbm5CUmSEI/HtWptJ45EYeApJm5XsZmvf7Ei210YZBMNF4NsIiIXaDQaqNfrAIxV2EB3ITbA1uKdMreHVqve6YxVq2BFUfDs2TM8efLEVa3Ezdha3MgtQbaiKNjc3MT6+nrT6//+/ftIJBJ9+b2hUAhXr15tmq+dzWZRKBQ6mq+tXkzu13xtthYnGjy+z5xPpPDT7UTelj6fD2NjYxgbGwNw1pVGvbmuVecYRVFQKBRQKBTw4sULrepbPRaJxWK2X09sLS4mBtliY5DtbuYgmzOyiQaLQTYRkcAURUG9XtdOqC7SStyMlcadsao45onOG+b1c3p6isePH7uylbgZW4sbuSHIrtfrePToEba3tw3Lx8bGsLi4OLDXf6v52mqVVKv52sViEcVisWm+djqdRiQS4QVaIqIh437Y2UQOss0CgQAmJycxOTkJ4OzmOX2wXa1Wmx4jy7L23589ewafz4dkMqlVbNvxWIStxcWkDzp57i8ethZ3N3OQHYvFhvRMiNyJQTYRkaBkWUa9XtcqXdUQG+i+CluPQXZnGGS3Z14/KysrhpbGbmolbsaKbCOralyRXhPlchmZTAblctmw/Pbt25ibmzv3b5UkqW+vE3P7T/187Ww2a2h/rrKar51Op7UqqYvM12ZFNtFwibTPdRM3hZ+ic3P1bigUQigUwpUrV6AoCo6Pjw3Bttp1TK9er+Pg4EC7Qdbv92vHIalUCqFQaNB/RhM3b1ORsSJbbKzIdjfz+TorsokGi0E2EZFg1MC6VStxNcC+6Amz/mCdM7JbazcDmpov2OhD7GAwiOXlZSSTyQE/K3tgRbaRyEH29vY21tbWDPtSv9+PpaUlrcWmnbSar61WbFtdTK7Vatjd3cXu7i4AIBwOGy4m+3w8HXEL3oBANFh8z4mD1btnJElCJBJBJBLBtWvXDCNRcrkc8vm85bFzrVbD3t4e9vb2AJyda6jV2qlUCiMjI4P+U7hNBcUgW2ysyHa34+Nj7f+PjIzA7/cP8dkQuQ+vHBERCcTcSlyWZUMVtlqJfRnmimyRQqVeYpDdnlXgBQATExO4e/euq1qJm7mhlXY3RKzGlWUZjx8/xsbGhmF5MpnE8vLyhaqWh8E8X7tUKmkXk1vN1z4+Psbx8bFhvrYaareary3ia8ANeGzgbHyfiYXvR2djdb0180gUWZZRLBa1au1CoWC5Lzs5OcH29rY20iUcDmvHIslksu/hhNotTcUwTBwMssVmLuTgNnYXfWtxVmMTDR6DbCIiQfSrlbiZ+WCdQbY1BtmtHR4eYmVlxbBMkiTMz89jdnbW9a8nthY3Ei3EPDk5QSaTQT6fNyyfnZ3F/Py8Yy+ISJKEeDyOeDyOGzdudD1f+8WLF/B4PFp1FOdrE9kH34fOxPBTHNyWnfF4PEgmk0gmk7h58yYajQby+bwWbLc6FlFvsnv16hWAs7mn6vFIMpnseftg83kht6k4GGSLzTwSgNvYXfStxaPRKPfdRAPGIJuIyOEURUGj0dDuDu11K3Ez88F6o9HgAbwFBtnNFEXB06dP8eTJk6b/9tZbb+HGjRuDf1I2xNbiRiK9lw4ODprmwXu9Xty9exfT09OX+tl2u6nIPF+7Wq1qF5Lbzdc+PDzE4eEhACAQCGgXkfWcfjMDEdEgcF8pDgbZF+P1ejE6OorR0VEAZy3G9cG2vrpOr1QqoVQq4eXLl9qNemrFdjwev/S5r/m9yXNpcTDIFpu+Ipvzsd2HFdlEw8Ugm4jIwc5rJd6rKmw98wG7k0OlfhIpfOuF09NTrK6uagGVWTgcHvAzsi9WZBuJUJHd6iaOaDSK5eVlRKPRIT2zwQkEApicnMTk5CQURUGlUtFC7VbztavVqmG+tqpQKCCZTHK+NlEfOXFfS0YMP8XBbdkbfr8f4+PjGB8fB3B2fqKG2rlczvImO0VRUCgUUCgU8Pz5c63qW63YjsViXW8TztkVF4NssXH7upt+RnY4HObnMdGA8eoPEZFDNRoN7cK/uQq7XyE2YF2RTc0YZL9xeHiI1dVVnJ6easvU16b6mnXz+jHjjGwjpwfZ1WoVq6urODg4MCyfnp7GnTt3XBnGSpKEcDiMcDhsOV87n8+33c4bGxt4+fIlYrGYVvXdiwopIiJR8WKrs5nb2VJvjIyMYGpqClNTUwCg3WSn/qtWq02PkWUZ2WwW2WwWAODz+bRQO5VKdRRumI9xuE3FwaBTbKzIdi9FUQwV2WwtTjR47rtyRkTkcO1aiastxHvZStyMFdmdYZB99tp88uQJnj59algeCoWwvLyMP/3TP9VuxnDj+mlF/9rhenF2kF0oFPDgwQNDhY8kSVhYWMDMzAxPfr/Oar622vozm80a5pGpFEUxzNf2er1IJpNa60/O1ybqHb6XnIlVvOLgthyMUCiEUCiEK1euaKGFGmrn83nL7jH1eh37+/vY398H8GYsivovFAo1PYYV2eJikC02fZDN7es+bC1ONFwMsomIHESWZdTrdSiKov3rdytxMwa0nXH7ejo9PcXKyopWraCanJzE3bt34ff7WXncAteLkRODbEVRsLm5ifX1dcPzDQaDuH//PhKJxBCfnf2ZZ1qenp7i3XffbfuYRqNhOV9brdgeGRnp+/MmEokT9rXUHrehOPTbkuHJYEiShGg0img0ipmZGciyjHK5rHWPKRQKlud35rEowWBQu8kulUohEAgwyBYYg2yx6bcvK7LdRx9ks7U40eAxyCYicgA1sG7VSlwNsAdxIOX2gLZTbl5PBwcHWF1dNbTjkyQJb7/9Nq5fv669Tll5bE3/PuZ6cV6QXa/X8ejRI2xvbxuWj42NYXFxEYFAYEjPzLnM6+z27duQJEmrkupkvnY4HNZCbc7XJiI3YBWvOLgth8/j8Ri6x8iyjEKhoB2LFItFy2PUk5MTvH79Gq9fvwZwVsUXjUYN38NtKg4G2WLj9nU3/Yxs836ciPqPV3CIiGxOURTU63XtoHnQrcTNOCO7M24Mss9rJW6uQmWQbU2/Xuwe2g6Ck4LscrmMTCbT1Ar79u3bmJub68t+2g0XP81/YyAQwOTkJK5du2aYr53NZlEoFCxfI8fHxzg+PsarV6+0VuZqxTbnaw+Gnd+71J4b9jOi4zZ0NgbZ9uPxeLQqa+DsRkY12G41FgU4q+jTV/UBwKtXrzA+Po5EIsEqT4dj0Ck2zsh2L/OMbI6xIho8BtlERDZmh1biZpyR3Rn15gL1wpPo6+nk5AQrKyvI5XKG5VNTU7hz5w78fn/TY9hC2xrXi5FTguzt7W2sra0ZLnD4/X4sLS1hbGysb7/Xruuj1/T7U/MFfav52mrrz1bztQuFAgqFQtN87XQ6zVZxPcJ16Gxu2beIjOGnOPTnEdyW9uTz+QxjUWq1mlatncvlDJV8ZltbW9ja2oIkSUgkEtqNdrFYjGGow3CGsti4fd1NH2SzIpto8BhkExHZkKIoaDQa2oHyMFuJm7mx0viiPB6Ptg1FXk+tWokvLCxgZmam5euUFdnWuF6M7L7PkWUZjx8/xsbGhmF5MpnE8vIygsHgkJ6ZO5nna1erVS3UzmazOD09bXqM1XxtdZ4l52sTMThzKt6MIA7elOA8fr8fExMTmJiYAACcnp5qxyOHh4eo1WpNj1EUBfl8Hvl8Hs+fP9dutFMrv6PRKLe/zbEiW2ycke1u+huSwuHwEJ8JkTsxyCYishlzK3FZlodeha1n/t1sLd6a6EG2LMt48uQJnj17ZlgeDoexvLyMeDze9vEMbK2xItvIzhXZJycnyGQyyOfzhuWzs7OYn5/nBawealWRfZ5AIICpqSlMTU1BURRUKhWtDXk+n285X3tnZwc7OzsAzlrHqaE252sTkVMw/BSHflvy2MKZRkZGMD09jenpae0mYJXf77cMts032vn9fi3YTqfTCIVCfG/bDINssbG1uHuZW4uzIpto8HgVhojIRmRZ1k5izVXYdgix1efh9XqFDmh7ReSgtl0r8bt373YU9Ohfy6Ktn8vgejGya5B9cHCAlZUVw4VHr9eLu3fvYnp6eojPjFqRJAnhcBjhcBjXrl2DLMvafO1cLtdyvrY6z1I/X1ttQ862nyQqu+xrqTeGff5Al8ObEsRi3r9+9KMfxcnJidY9Jp/PW94sXqvVsL+/j/39fQBn4bharZ1KpdgFyAYYZIuN29fdGGQTDReDbCIiG2jXSlxtIT6sVuJWRK807hVRg+z9/X2srq4aAjyPx4O33367bStxM/364QXzN7hejOwWZCuKgqdPn+LJkyeG5dFoFMvLyzyp7ZN+dCrweDxIJBJIJBK4efNm1/O11baf6gVkztcmIjth+CkObkuxmI9jvF4votEootEoZmZmtBvt1PnahULB8lzy9PTU0EEmFAoZOsgEAoGB/D10Rr2Go2LQKR5WZLtXo9HAycmJ9jXP+YkGj0E2EdGQKYqCWq0GRVG0f3ZqJW5F1IC210RbT7Is44MPPsDz588NyzttJW4m2vrpFbYWN7JTkF2tVrG6uoqDgwPD8unpady5c4ctpx3OPF/79PRUq47K5XIt52sfHBxorwm1Okqdsc352iQCux2HUmd4DCEO/XEy34/OZ96e5m2qv9Huxo0baDQaKBQKWrBdKpUs39+VSgWVSgWvX78GcBa0qDfbcTRK/1ndoEBi4Y0K7qWvxgbORk8R0WDxKIaIaEjUwFqdz2nXVuJW9CdlnJHdmkhBbatW4pcJ8ERaP73E9WJklyC7UCjgwYMHhjuxJUnCwsJCV50I6GKGcYPHyMiIYb728fGxIdi2+vwzV0dFIhEt1OZFZHIShqBi4WeUs7EiWyzdbk+v16uNNAGAer2OfD6vBdtWHWQAoFwuo1wuY3NzE5IkIRaLaTfbxeNxBq09Zj5vY9ApHlZku5d5P8uKbKLB45UUIqIhUBQF9XpdO9mxeytxMwZtnRFlPbVqJb6wsIBr165d+HXKymNrXC9Gww6yFUXB5uYm1tfXDb87GAzi/v37SCQSA30+Znb9nBCNJEmIRCKIRCIXmq+tXkTmfG0iGhSGn+LQb0t+bjjfZas6fT4fxsbGMDY2BuCsY5AaaudyOVQqlabHKIqCYrGIYrGIjY0NrepbrdjmMcnlMcgWHyuy3ctckc0gm2jwGGQTEQ2YWoXtpFbiZqIEtP3m9FnH7VqJ379/H7FY7FI/n68ja/r3P9fLcIPser2OR48eYXt727B8bGwMi4uLnD04QHa7wcM8X1tfHZXNZpsudgCt52urFduiz9e2w3ajixH5dSkyBtni4LYUS69bxQcCAUxOTmJychLAWSctNdTOZrOoVquWz0H9HgDaMYn6LxKJ8LXWJQbZ4mNFtnuxtTjR8DHIJiIakPNaiasBthNOGPUnZWwt3pqT11OlUsHKygry+bxh+ZUrV/DOO+/0pD0ug2xrTr8BotfM+8RBvVbK5TIymUxTG7Hbt29jbm7OEftqGhxzdZR+vnari8jt5mun02nH3yjB9wjRcPEYQhwMssXS7wr7YDCI6elpTE9PG0ajqP/U6xF65mMSv99vCLZDoRBfe+dgkC02/bU7gNvXbY6Pjw1fsyKbaPAYZBMRDYC5lbgsy4YqbLUS2yn0d58ygGzNqUHt3t4eHj582NRK/J133sHVq1d7dhHDbhWWdsH1YjSMiuzt7W2sra0ZbkDx+/1YWlrSgsphkyTJVa8Pp70vrOZrq23Iu52vnU6nkUwmWflBA+WE9xm1x/BTHNyWYhlke2LzaBRFUVAul7XjkXw+b3lMUqvVsLe3h729PQDGm+1SqRRGRkb6+rydiEG22Mzbl8fl7mK+uf2y3QmJqHsMsomI+kyEVuJmTg1oB81p60mWZXzta1/DixcvDMsjkQiWl5d7frDutPUzKFwvRurNPupF3H6GK7Is4/Hjx9jY2DAsTyaTWF5eRjAY7NvvJnHpLyLPzMwY5mtns1kUi8WO5mursyw5X5sGzWnHqdSM29C51PNHFbel8/W6tXg3JElCLBZDLBbD9evXIcsyisWiFmwXCgXLYxLzzXbhcNhQse33+wf6d9gRg2yxcfu6m7m1eDgcHtIzIXIvBtlERH2iKAoajYZ2h7O+lbgazDillbiZk1tmD5KTAslKpYJMJoNCoWBY3stW4mZOWj+D5LTK00EYRJB9cnKCTCbT1E5/dnYW8/PzvFgxZCK9L1rN11YrtlvN187n88jn83j+/Dl8Ph+SyaRWsc2Wn0RkxvBTDObPPB6POJ+d2hN7PB4kk0kkk0ncvHkTjUYDhUJBC7aLxaLl446Pj3F8fIytrS0AZ9WJaqidSCT6cu5od+brIsPettRb5u3Limx30Z+fhUIhV+7jiIaN7zoioj5QFAW1Wk07STW3EndiFbYeW4t3xilBbbtW4teuXevb79W/B+y8fgaN66VZv0PMg4MDrKysGN4DXq8Xd+/exfT0dM9/H5Ge1XxtNdRuNV+7Xq83zddW232KMF+bhs/pN4wQt6EozNvRyeeQdGaQrcW75fV6tZvkgLMW4/l8Xgu2rW62A4BSqYRSqYSXL19CkiTE43FDsG23v7MfWLErNt6o4G76fV8kEhniMyFyLwbZREQ91mg0UK/XARirsAExQmzAOQHtsNl9PQ26lbiZfv3wYusb5pNitYuDm/UryFYUBU+fPsWTJ08My6PRKJaXlxGNRnv2u+hyRKrIPs/IyAimp6cxPT1tmK+dzWZbzrI8PT3F9vY2tre3AZy9htVQm/O1idyJFdliYJAtHie9N/1+P8bHxzE+Pg4AqFar2o12uVwOJycnTY9RFAWFQgGFQgEvXrzQOtGoN9zFYjHb/90XwSBbbJyR7W76IDsajQq5DyOyOwbZREQ9oigK6vW6doArUitxM7sHtHZh5/V0fHyMlZWVplbiV69excLCwkBaJdl5/QyTeR/BILs/IWa1WsXq6qpW0aqanp7GnTt32C7MxkQPsvWs5msXi0XtAnKr+drlchnlctkwX1utsBL1AjL1D18vzsdt6FwMssVj54rs8wQCAUxOTmJychLA2XgqtVo7l8tZdpGRZVn77wC08ShqxXYkEhHidc0gW2xsLe5urMgmGj5eoSMi6gFZllGv16EoivZPpFbiZvqDds7Ibs2uFce7u7t4+PCh1jkAONum77zzDq5evTqw58EW2tbMFz1kWXb9hZBev1YKhQIePHhgqCKRJAkLCwuYmZkRan8tCm6TM/pZlgC6nq/97Nkz+Hw+7eIx52tTK3Y6bqGLcVLVJ7XGIFs8Tg6yzUKhEEKhEK5cuaJ1kdEH2/rzTZV5PEogENCOS1KpFEKh0KD/jJ4wb1e+V8XCGxXcTX+OFQ6H+f4mGgIG2UREl6AG1q1aiasnMKId5LCStjN2W0+yLOPx48fY2NgwLB9WG2W7Bv3DZlWR7Xa9qshWFAWbm5tYX183/JxgMIj79+8jkUhc6nkOg2ifL624qbV4N1rN11aD7Vbztff397G/vw/g7PWvhtqpVIrztYkEwX2lGMzbkeGJ84l6k4m+i8y1a9egKApKpZIWaufzectz4mq1it3dXezu7gJ4c1yi/hsZGRn0n3IhIt2gQM04I9u9FEVha3EiG2CQTUR0QVatxNWDWzW8FvXg1m4BrV3ZaT21ayX+zjvvDKU1lp3Wj52YT4q4bnoTYtbrdTx69EibIawaGxvD4uIiwzsSgnm+9tHRkTbLstV87ZOTk6b52mqofZn52rwpRxy8WOd83IbOZT4O5LZ0PrcEnpIkIR6PIx6PY3Z2FrIso1AoaMF2q/Eo5uOSSCSihdrJZBJ+v3/Qf0pH3LJd3Uq/fUW+1kfW2FqcaPgYZBMRXYDbWomb6Q/a2Vq8Nbusp52dHaytrTW1Er9z5w6uXLkytOfF1uLWzCfFDH8uX71fLpeRyWRQLpcNy2/fvo25uTkh9teiv05Ykd09SZIQjUYRjUYvNF/75cuXnK9N5GCiVn26DVuLi8et702Px6MF0sDZ+XE+n9eC7VKpZPm4o6MjHB0d4dWrVwCAWCxmCLbtMquYQbbY9Ndz7PKao8E5Pj7W/j9bixMNB4NsIqIuqFXX6kGsW1qJm+kP3BlAtjbsimNZlvH+++/j5cuXhuXDaiVuxtbi1liR3ewyIeb29jbW1tYMFx/8fj+Wlpa0dsxEbmA1X1u9eJzNZg0XaFSt5murFducry0ufi47H7ehGBhki4eB5xmv14vR0VGMjo4CAGq1GvL5vHbDndVxCQCUSiWUSiXDDXdqsB2Px4e2Trldxcbt627m1uJENHgMsomIOmRuJS7LsquqsPWGHdA6xTDX0/HxMTKZDIrFomH5tWvXsLCwYIu7iPk6ssaK7GYXCbJbzYRPJpNYXl5GMBjs6XOk/mJFdu/5fD6Mj49jfHwcwFkrTzXU5nxt0nPL8a1o3Fr1KRoG2eJhIGbN7/cbjktOT08NN9ydnp42PUZ/w93z58/h9Xq1TjKpVGqgs2y5XcXGimz3Ms/IjkQi/CwmGgIG2UREHWg0GlpbZnMVtttCbKC5ZbaiKK76+zs1rKDWrq3EzcwV2XwdneFc2WbdhpgnJyfIZDLI5/OG5bOzs5ifn+fFJSILwWCwab62Gmp3O187nU4z1CYaMgbZYmCQLR6+NzszMjKCqakpTE1NQVEUVCoVLdjO5XKo1WpNj2k0Gshms8hmswCgdZJR//WzJTCDbLFx+7obK7KJho9BNhFRG+1aiastxN3QStzMfAcqA0hrgw5qG40GHj9+3NRKPBaLYWlpyXYH3FaBLV9HbC1upZsg++DgACsrK4aLW16vF3fv3sX09HTfniP1FyuyB0s/X/v69euQZRmFQkGrijJ3+1CZ52vrcbs5C7eXWHh85Vzm40BuS+djINY9SZIQDocRDodx9epV7YY7fbBtdcOduZNMIBDQQu10Ot3TDk3crmJjRba76UcdRCKRIT4TIvdikE1E1IIsy6jX61oAqYbYgDursPXMJ2ayLPNkzYJVi+h+vWaOjo6wsrLSFC7MzMzg7bfftuXJFl9H1thavJn+fdMq2FcUBU+fPsWTJ08My+0yE57IyTwej3bhd25urqM5luZ914MHD7Rq7XQ6jVAoNKinT+RKrPoUg3lfymNl59Mfy/K9eTH6G+5mZmYgyzLK5bJ2XFIoFCzPGarVKnZ3d7G7uwsACIVChorty3STYZAtNm5f97JqLU5Eg8cgm4jIRA2sW7USVwNsN590WgWQ1GxQQe329jbW1taa7hK2ewUqX0fWWJHd7Lxq3Gq1itXVVRwcHBiWT09P486dO/D5eMjrdKzIthfzHMuTkxPt4nE2m23Z7tM8X1sNtVOpFPx+/0D/Buqcm495nYz7SjGwtbh49NuUgVhveDwexONxxONx3LhxA41GA8ViUavWLhaLlvvESqWCSqWC169fAzgLqNTjkmQy2dU5BINOsbEi293YWpxo+HhVj4hIR1EU1Ot17SSErcStmU/MrNp4Uf+D2kajgffffx+bm5uG5bFYDMvLy7a/U5SBrTVWZDdrt88tFAp48OABTk5ODN+/sLCAmZkZ1++viQYhGAziypUruHLlila1sLu7i42NjZaPOTk5wevXr7WLx7FYTGv1mUgkeJFwyPjZIxZ+FjoXg2zxMPDsP6/Xq1VZA2ctxvP5vBZsl8tly8cdHR3h6OgIm5ubkCRJOzZJpVLnHptwu4pNf82L29ddarUaqtWq9rXdr7MRiYpBNhHR17GVeOfMJ3AMIK31M8g+OjpCJpNBqVQyLL9+/Trm5+cdEQAwsLXGubLN9K8V/Y1Gm5ubWF9fN6yjYDCI+/fvI5FIDPx5Uv+wIts51HafXq/XEGRPT0/j6Oio5XztUqmEUqmEly9fwuPxIJFIaBXb0WiUx2BEXWJrcTEwyBYPW4sPns/nw9jYGMbGxgCcdXPSB9utRqQUi0UUi0VsbGxoVd/qTXexWMzyHAVg0Cki/fZ1wrUW6h3z/oEV2UTDwSCbiFxPURQ0Gg3tDku2Ej8fW0J3pl/r6fXr13j06JHjWomb8XVkjeulmTnErNfrePToEba3tw3fNzY2hsXFxUvNtyOi/rh69Sri8XhH87VlWdYuLj99+hR+v1+riOJ87cHjMbAz8aYfMZiPA/l+dD62Fh++QCCAiYkJTExMADjrEqMed+RyOZyenjY9RpZl5PN55PN5PH/+HF6vF8lkUjs+UUfTAQw6RcTW4u5lLh5hkE00HAyyicjV1ECkVqthZ2cH9Xod9Xodk5OTCIVCrMJuga3FO9PrQLLRaGB9fR2vXr0yLI/H41haWnJciyO2FrfGiuxm+nVSrVbxx3/8x00tAW/fvo25uTnuswXFimxxmOdrVyoVbbZ2LpeznK9dq9Wwt7eHvb09AEAoFNJCbc7X7g++z5yPFdliML8XGXw6Hyt37ScYDGJ6ehrT09NQFEU7NlH/WR2bNBoNHB4e4vDwEIBxP9toNLTxdCQGvm/dSz8fG2CQTTQsDLKJyLVkWdZOSOr1Oh4+fKj9t1gshkgkwhOPFthavDO9DPzL5TJWVlYsW4m//fbbjjyZYmtxawz4m+nXycHBgeG14vf7sbS0pLUKJCJnCYVCCIVC2nztcrmsBdv5fN5yH1ipVFCpVAzztdVQm/O1e4/Hw87HbehcPD4WD28ysTdJkhAOhxEOh3H16lXDsUkul0M+n7c8r9dv152dHeRyOUM3mZGRkUH+GdRjnJHtXubuUU4rICESBYNsInIdq1biVhWQPKlsja2PO9Or9WTVStzn8+Hu3buYmpq61HMcJr6OrLEiuz39+kgmk1heXkYwGBziM6JBYEW2O0iShFgshlgshuvXr0OWZRQKBa1a+7z52uoMS7XVJ+drk5sxLBODeTtyWzqbOsZMxUDM/qyOTUqlkuHYxOo89vT0FDs7O9jZ2QEAhMNhLdhmNxnn4Yxs92JFNpE9MMgmIldRFAW1Wg2Komj/ZFlmBWSXzAfubC1u7bJBbbtW4svLywiHw5d+jsPEINuaepFSvXDp9tBOnVlnNjs7i/n5eV4AdCG3vyfcxOPxaBd8gbMW42pFVDabRaVSaXqMLMvIZrPIZrOG+dpqxTbna3eG7zPn4zYUA29IEIv5fclt6jwejweJRAKJRAI3b95Eo9FAoVDAw4cP214XOT4+xvHxMba2tgCchWHqMU4ymYTPx0v0dsYZ2e5lHmkWi8WG9EyI3I2fkkTkCmpgXa/XDV+rJ5Iejwcej0cL0hjMtsfgvzOXCWrL5TIymUzTQbNI4R0rj1vzeDzafsjN76+DgwOsrKwY5tJJkoTFxUVMT08P8ZnRoPFCLwFnowQmJiYwMTEB4OLztdVQmxVRJCqGZeLQHwdyOzofZ56Lx+v1Ip1Ow+v1audv09PT8Pl8yGazTdWcqnK5jHK5jM3NTUiShHg8rh2bxONxhqU2w04K7qV/D0uSxJtiiYaEQTYRCU9RFNTrde3AUx9iq5WPkiTB6/Vq3+Pm4KgTkiQZgn+uL2sXnQG9tbWF9957T7hW4masyG7N7W2UFUXB06dP8eTJk6b/lkwmGWK7kNvfE2TNar62Gmq3m6+9tbWlVUSp87XT6TQSiQQvTlpgeOZ83IbOxYpssZg/l/iZIw79to3H47h69SoAoFqtat1kcrmcZTcZRVFQKBRQKBTw4sULrepbPyaFr5XhYkW2e+lnZIfDYW5/oiFhkE1EQlOrsM2txIE3Yax6QcDr9WqVPKzIPh8r2M/XbVDbaDTw3nvvaRfXVYlEAktLS45vJW7GILs1/YVKt62XarWK1dVVHBwcWP53XsQhIiv6GZazs7Naq0+1YrtUKlk+zmq+tlqx7eb52rxhxNlYkS0O/bbkMZDz8b0prlYVu4FAAJOTk5icnATwppuM+q9arVr+LPW/P3v2DF6v1zBfOxKJ8LUzQPpujgD3xW6j75IYiUSG+EyI3I1BNhEJqZNW4moltkp/MMpg9nxer1dbv24L2jplnnXcbj25oZW4GVuLt6bf3m5aL4VCAQ8ePMDJyYm2TJIkJJNJbU62m9YHvcGKbOcZ9j5ebfWZTqdx69Ytbb62WrF93nxtAIb52ul0GsFgcKB/A9FFcT8pDlZki4UV2WLSF0wA7beruZvM8fGxIdhWr7HoNRoNHBwcaDf6qscn6jFKMBjk/qGPzO9bVuS6i761OG8iIRoeBtlEJBxzK3FZlg1V2Goltpn+YJTB7Pn065Drq7VOZh23aiV+79497c5tEXUT9LuN20I7RVGwubmJ9fV1w98bDAZx//59bG1tMchugSfSRJ2xmq+thtrdztdOp9NIJpOuma/N/YzzsOpTHAyyxcIgW0wXnX0uSRIikQgikQiuXbumjUlRb7xrNSbFfHwSDAYNFdsjIyOX/6NIw/etu+lbizPIJhoeBtlEJJRuWombsSK7O1xfnWkXZNfrdayvr7umlbiVToJ+N3LTjSL1eh2PHj3C9va2YfnY2BgWFxcRCATw+vVrbTmDbHdy280d1H+hUAhXr17F1atXDfO1s9ksCoVCR/O14/G4Vg3F+dpkZ7zo6lz6fRG3o/PxJhMx9Sro1I9JuX79OmRZRrFY1G66KxQKlsfBJycn2N7e1s6nwuGwdnziphvv+sV8vYsV2e6hKEpTRTYRDQeDbCISgqIoaDQa2gGmvpW4WvVpbiVupj8YZTB7Plawd6ZVIFkqlbCystLUSvzGjRv40Ic+5JqL4QyyrbkltGvVUv/27duYm5vT1oNb1kc3eOGTqHdazddWK7ZbzdcuFosoFotN87XT6bTjKza4r3U2hmXiYEW2WFjZKaZ+bVf12CKZTOLmzZtoNBrI5/NasN3q+OT4+BjHx8fajXexWEyr1k4mkwxiu8T3rbuZZ2Tzs5hoOBhkE5HjKYqCWq1maE/caRW2HoPZ7ripYvQyzJXriqJorcT1683v9+PevXtay1O3YEBpTb9eRH1/bW9vY21tzXDjkN/vx9LSEsbGxgzfy9cJ8TVAg6Sfrw0A1WoV+Xxeq9g+OTlpeozVfO10Om2YX+lUvGDnPNxPikO/LRmeOJ/5uJ77VzGYCyH69V71er0YHR3F6OgogLMW4/pgW185qlcqlVAqlfDy5UtIkmToKBOPx7lvOQcrst1N/76KRqNDfCZE7sYgm4gcrdFooF6vAzBWYQPdhdgAW2V3i0F2Z/TrqV6v4+HDh4Y2ycBZK/Hl5WWEQqFBP72h4+vImn69iHYxWpZlPH78GBsbG4blyWQSy8vLlmEPQ0wiGqZAIGA5X1ut2FaPRfVqtRp2d3exu7sLwNjmM5VKwefjqTgNDsMy52JFtlguOkuZ7G1YFbt+vx/j4+MYHx8HAJyenmqhdi6Xs7zxTlEUFAoFFAoFvHjxQqv6Viu2Y7EY9zUmDLLdTT8jOxwO8/1BNCQ8eyYiR1IUBfV6XTthuEgrcTNWZHeHwX9n9Ovp1atXqNVqhv9+8+ZN3L5927UXMRhkWxO1Ivvk5ASZTAb5fN6wfHZ2FvPz8y3fBwyyia8BshPzfO1SqaSF2q3ma5vbfMbjcS3UtuN8bb7PnI2txcXBIFssbFEsJvN2HVbQOTIygqmpKUxNTQE4u/FOH2xXq9Wmx5g7yvh8Pi3UTqVSDO7A962bcUY2kX0wyCYix5FlGfV6HYqiaP8u0krcjMFsdxj8d0b/WtSH2G5tJW7GcMqaiBXZBwcHWFlZMbwPvF4v7t69i+np6baP5Q0PRM7jloueaovOeDyOGzdudD1f+8WLF/B6vVo1lAjztWn4GGSLg0G2WNhaXEx2DTpDoRBCoRCuXLmiBXJqqJ3P5y07ytTrdezv72N/fx/AWVcafbDtxi5yg2odT/Zkbi3O/TbRcDDIJiLHUAPrVq3E1QD7ogcV+mCWQfb5GCydr16vW7bzSiaTWFpacuVJoBlfR9ZECvgVRcHTp0/x5MkTw/JoNIrl5eWO5kyJtD7oYvgaIKewmq+dy+W0YNvquKDRaODw8BCHh4cA3lw0Viu2hz1fmxfsnIf7SXEwyBYLW4uLya5Btp4kSYhGo4hGo5iZmdE6yqjHKK06ylSrVcOolGAwaDhGCQQCg/5TBk6/XtTiGXIPVmQT2QODbCJyBKtW4mrYrIbXlz2YZIVxd1jB3l6pVMKDBw+a2ne5vZW4GYNsa6K0Fq9Wq1hdXcXBwYFh+fT0NO7cudPxjFiGmETO59b3biAQwOTkJCYnJ6Eoitbms918bfNF43A4rF0wHtR8bbduL1GwIlsc5gCFnI0V2WJyQpBtpu8oMzs7C1mWUSgUtIrtYrFoeSxwcnKC7e1tbG9vAzgL9tTjk2QyCb/fP+g/pe/017s4H9t99DOyO7kJn4j6g0E2Edlev1qJmzGY7Q6Df2uKouDVq1dYX19vWi/pdBrz8/NDemb2xIDSmgitxfP5PDKZjKH6UJIkLCwsYGZmpqv9Nl8n5xN9vfA1QCKQJAnhcBjhcNhyvnY+n7d8favztV+9eqVdeFaroeLxuCMumNNwMSxzLv0+ge915zPfmMD3phicGGSbeTweLZAGzrrL6YPtVqNSjo6OcHR0hFevXgGAdoySSqWQSCSECH7129eJ25YujjOyieyDQTYR2ZZada2Gyr1uJW7GYLY7rKRtVq/Xsba2hp2dHcv/7oa2W93i68iakyuyFUXB5uYm1tfXDRdfg8Eg7t+/j0Qi0fXPZIhJenwNkCis5mvn83mtYrtcLjc9RlEUFAoFFAoFw3xttWK7H/O1GbQ4DyuyxcHW4mLhjQliErHS3ufzYXR0FKOjowCAWq2mhdq5XM5QpapXLBZRLBaxsbEBSZKQSCS0YNupN9+xItvdzDOyiWg4GGQTkS2ZW4nLstyXKmw9zsjuDivYjYrFIjKZTNMJXSQS0Q58nRZIDgKDbGtOrciu1+t49OiR1mpONTY2hsXFxQvfzMEgm0S4IEh0Hq/Xa7horJ+vnc1mcXp62vSYdvO10+k0RkZGBvo3kD3ws1IcDLLFwlbxYjJX7Iq4bf1+PyYmJjAxMQEAOD09NYxKsTpGURQF+Xwe+Xwez58/126+U4PtaDTqiHXFimz3qlarhjFArMgmGh4G2URkO41GQztQMFdh9yvEBhjMdosV7GfU6tP333/fsB78fj8WFxexs7PDILsNJ1ce95MTg9tyuYxMJtNUQXj79m3Mzc1dar/txPUxKE64+NMLfA2QG1nN11YvGHc7XzudTiOZTHY8X5vvM7G45bNCRAyyxcKKbDG5MegcGRnB1NQUpqamtGMUfcV2rVZreoz55jufz6eF2qlUCuFw2Jb7OVZku5e+GhtgRTbRMDHIJiLbaNdKXG0h3s85UuZgVv29ZI2VtK1biadSKSwtLSEYDGJvb09b7tb11I5TK4/7zWkB//b2NtbW1gwn+X6/H0tLSxgbG7v0z2eISURup5+vfe3aNcN87Ww2i0Kh0Lf52jwedh62FhcHK3jF4sbA0w3cvl31xyhXr17V5gqrFdv5fN6yWKRer2N/fx/7+/sAzsJxfbAdDAYH/adYcvv2dTPzTfoMsomGh0E2EdmCLMuo1+tQFEX71+9W4mbmOytlWebdlm24Pchu1Ur81q1buHXrlrZ+WOnfnttfR604JeCXZRmPHz/GxsaGYXkymcTy8nLPLj44Ldin3uPNDERGreZrqxXb3c7XTqfTtq2Eou5xPykOVmSLhTcmiEl/ns+g8+y1HY1GEY1GMTMzA1mWUSqVtGrtQqFgeU53enqKnZ0drVAgFAoZgu2Ljqm6LFZkuxeDbCL7YJBNREOlBtatWomrAfYgTvLMJxyNRoMHqW24NaBVW4mvr68bLiwFAgEsLi42VZ86JZAcFgaU1pywXk5OTvDgwQMUCgXD8tnZWczPz/f0Ig5DTCLnMR+78b3bX1bztdVQu5v52ul0GqlUitvL4ViRLQ62ohYLt6eYWLHbnsfjQSKRQCKR0G6+KxaL2nFKqVSyPO6oVCqoVCp4/fo1gLMQUQ21uxmXclncvu5lLlzhjGyi4WGQTURDoygK6vW6dlA46FbiZlYV2dSaG2dk12o1rK2taXMnVel0GouLi5bVp6w4bo9BvzW7r5eDgwOsrKwYZp95vV7cu3cPU1NTPf995gsGHP3whv71IfJ64c0MRN0JBAJNsyvVNuT5fL7lfG19JZSqVCphbGxsYBeMqfdE/WxwA1Zki4WBmJi4Xbvj9Xq1QBo4azGez+e1im2rrjLAWXVsuVzG5uYmJElCLBbTfk4ikehbIQorst2LFdlE9sEzUSIaCju0Ejezqsim1sxBmyzLQp+0FQoFZDIZVCoVw/Jbt27hrbfeavl6ZZDdHtePNbuGdoqi4OnTp3jy5IlheTQaxfLyct9O7KwqO3kxl4ioM+b52mqLT7USqtV8bdXm5iZevXqFeDyutSGPxWJCH/c5HSuyxcEgWyzcnmJikH05Pp8PY2NjWne7arVqGJdivgYDnL2XisUiisUiNjY2tKpvNdju5XEKW8e719HRkeHrWCw2pGdCRAyyiWigFEVBo9HQDgSH2UrcjBXZ3TEfwIsaZCuKgpcvX+L999/vqJW4GYPa9pzQQnsY7LheqtUqVldXcXBwYFg+PT2NO3fu9LVSjy2Kya43dxA5kb7F582bN1Gv11EoFLSKbfNFO8A4X/v58+eGairO17YfBtniYPApFgaeYuJ27a1AIICJiQlMTEwAOBtppVZr53I5y3Epsixr/x04u76XTCa1kSmRSOTC+1D99mVFtrvoW4tLkmTZhZGIBoNBNhENjLmVuCzLQ6/C1mNFdnfcEPy3ayW+tLSEkZGRc38Gg+z27N5Ce1jstl7y+TwymQxOTk60ZZIkYWFhATMzM33fdzPIJiLqH5/PZ5ivfXJygi996UttH9NoNHBwcKDd3DQyMqKF2qlUqqNjJOoffk6KQ3/+wCDb+bg9xcSgs7+CwSCmp6cxPT1tGJeSy+WQz+cN465UjUYDh4eHODw8BAD4/X7tBrxUKoVQKNTxe5AV2e6lv7kzGo1y+xMNEYNsIhoIWZa1g0tzFbYdQmyg+YSDQXZ7VhXZImnVSvytt97CrVu3On69Mshuj+vHml0qshVFwebmJtbX1w0XxYPBIO7fv49EIjGQ58Egm1iRTTQ45hD69u3bkCRJu2hsdYx8enpqmK8diUS0UDuZTHK+9pAN+zyLLk7/mccL6M7H7SkmVmQPjnlciqIoKJfLWjV2Pp+3PE6p1WrY29vD3t4egDc34Kk34bW7AY83KriXfkZ2JBIZ4jMhIp5NElFftWslrrYQH1YrcTM3VBj3kqgV7IqiYGNjA48fPzZcaBgZGcHi4qJWrdQpBrXtcf1Ys0NoV6/X8ejRI2xvbxuWj42NYXFxEYFAYGDPhUE2EdHgmPexIyMjmJiY6Gq+9tHREY6OjrC5uQlJkrS5lZyvPRhsLS4OthYXCwNPMXG7Do8kSYjFYojFYrh+/XrHxynmG/DC4bChYtvv92vfq7/WxSDbXfQV2ZdpT09El8cgm4j6RlEU1Go17aDRbq3EzUQNZvtFxOC/Vqvh4cOH2l26qtHRUSwuLl6oTaY5qFVv4qAzdqk8tpthtxYvl8vIZDKGO5CBs6q8ubm5gb+Gzb+PrxX3scPNHURupX//Wc3XzufzyOVybedr5/N55PN5w3xttWKb87V7j/tJcTDIFgtbi4uJQbZ9mI9TGo0GCoWCVrFdLBYtH3d8fIzj42NsbW0BOGsjrR6nsLW4e+lnZDPIJhouBtlE1HNq1XW9Xjd8bbdW4mbqc9IH79SaaK3FrWYAA923EjczrycG2UbDDmztapgB//b2NtbW1gwn7H6/H0tLSxgbGxvoc1GxIrs17k+IaJh8Ph/Gxsa0z4fT01Mt1M5ms6hWq02PaTdfO51OD7Tjh6hYkS0OBtliYWtxMTHIti+v16sdXwBnxQvqDXi5XM7yBjzg7MbucrmMly9fGpZXKhXIsszt7AKKojRVZBPR8DDIJqKeUhQF9XpdO5C3cytxK16vVwvgWZHdnihBdq9biZtZrSee9LzB1uLWhhHwy7KMx48fY2Njw7A8mUxieXkZwWBwIM/DitUNIeQurMh2Ht6A4lyX2VYjIyOYmprC1NQUFEXB8fGx1t6z2/na6XQayWSSbTx7wK7nXnQ+VvCKhdtTTAyyncPv92N8fBzj4+MAgGq1qh2jZLPZpsIGs5cvX+LVq1dNI1P4fhaTeUY2tzPR8DDIJqKeUauwFUXR/tm5lbgV/UkHg+z2RGjFXq1Wsba21tNW4maiBP79wtbi1ga9Xk5OTvDgwQMUCgXD8tnZWczPzw/9ggwDMSIi55EkCZFIBJFIBDMzM4a5ldlsFsVikfO1+4QV2eJgBa9YuD3FxCDbuQKBACYnJzE5OQngrOJaDbZzuZxlZxlZlrX//uzZM/h8PiSTSW2+NgNPcbAim8g+GGQT0aWd10pcDbCdcCCnr/hgqNae02dkt2ol3usZwAyy22NrcWuDrD49ODjAysoKarWatszr9eLevXuYmprq6+/uFINsYkU20fD08pjIar62WrHdyXxt9WKxWrEdCoUccY4xaNxPioOtxcXCwFNM3K7iCIVCCIVCuHLlChRFQaFQwFe/+tW2j6nX64aRKYFAQAu1U6kUQqHQIJ469QFnZBPZB4NsIroUcytxWZYNVdhqJbZT6MNZJ1YYD5JTA1pFUfDixQt87Wtfa2olvrS0pM1O6hWnrqdBMbcW5wzxM4MI+BVFwdOnT/HkyRPD8mg0iuXlZUSj0b783otgkE163P5E/TWo95jVfG011G41X9t8sXhkZATpdFqr2OZ87TOsyBYHg2yxsLW4mBhki0mSpKYufQsLC1o78nw+b3l9p1qtYnd3F7u7uwCAYDBoCLZ70fmP+o8zsonshUE2EV2YCK3EzdhavHPqjQrqxRUnrK9qtYqHDx9if3/fsHxsbAyLi4t9ufjJILs9q4DSafuNfjBXn/Z6vVSrVayurmpBgGp6ehp37tyBz2evQ0QG2cT9ApH4RkZGMD09jenpacN87Ww2i3w+33K+9vb2Nra3twGc3Yylhtqcr32G+09nY5AtFrYWF5P+84nbVSzm6zexWAzRaBSzs7OQZRnFYlG7Aa/VyJSTkxPDsUokEtFC7WQyCb/fP5C/hbqnD7Kj0Sg/h4mGyF5XKYnIERRFQaPR0A7W9a3E1XDTKa3EzdhavDter1drKW/39ZXL5bCysmJoJS5JEm7fvo2bN2/27fXKILs98/phQHnGar306jVq1VZfkiQsLCxgZmbGlvtuBtnt6W8qEhVbixMNzzA+F6zmaxeLRa1iu9XF4nK5jHK5bJivrbYhj8VitvyM6weGn+LgthQLK3fFxO0qLvNNdPprhh6PB8lkEslkEjdv3kSj0UA+n9fmZ5dKJcufeXR0hKOjI7x69QrAWTiuD7Z5E559mFuLE9HwMMgmoq4oioJaraadUJtbiTuxCluPFdndMbeFtiNFUfD8+XN88MEHTa3El5eXkUql+vr7GWS3Z7V+eOLWfKFSluVLXxRRFAWbm5tYX183vBeCwSDu37+PRCJxqZ/fT1brg4iI3EN/sRiAYb52Nps1XGhU6edrP3v2DD6fT7tQLPp8bd7wIwa1K49K1Nerm7C1uJgYZIvLfF2w3fb1er0YHR3F6OgoAKBWq2nBdqtjFQAolUoolUp4+fKldhOeerwSj8f5mhoic0U2EQ0Pg2wi6lij0dCqb/VV2IAYITbAGdndsnuQ3ap98vj4OO7duzeQOYoMstvj+rHW6wrker2OR48eae3MVP1sq99LrMhuZh7vIDpWZBMNjhPeY63ma6sV263ma+/v72sjZtSZleqMbbt/FnaD4acYzO9FhhnOx9bi4jHfcMKbssVivj7Rzfb1+/0YHx/H+Pg4gLNjFbVaO5fLGTqkqfQ34T1//hxer1cLttPpNNtbDxBnZBPZC4NsIjqXoiio1+vaAZxIrcTN7B7M2o2dg/9cLodMJoPT01Nt2SBaiZsxqG2PAaW1XrZcL5fLyGQyKJfLhuW3b9/G3NycI/bdbEFPRDQ8TvicMM/XPjo60iqgWs3XNs+sjEajWqgtUmtPJ2w/smY+3uG2dD5W7orHfH7P7SqWXm7fkZERTE1NYWpqCoqi4OTkRLsBL5fLoVarNT2m0WhoN+o9ffrU0F0mlUohHA7zs6FPzEE2K7KJhotBNhG1Jcsy6vW6dpepGmID4lRh69k5mLUjOwb/rVqJB4NBLC0t9b2VuBmD7Pa4fqz1qpX29vY21tbWDPszv9+PpaUlrYrNCXjDA7Ei23n4vqVhkSQJ0WgU0Wi0ab52NptFqVRqO19bbe2ZTCa1CiinzddmRbYYGGSLh+9N8TDIFpv+PFq9BtoLkiQhFArh6tWruHr1quEmPPWf1TVJc3eZQCBgCLZDoVBPnh+dVdDr39+syCYaLgbZRGRJDaxbtRJXA2zRTr70QTYDtfPZbaa4HVqJmzGobY/rx9plK5BlWcbjx4+xsbFhWJ5MJrG8vIxgMHjp5zhIDMSIiAZHtH2sfr723Nwc6vW6dpG43Xxt9Xv087XVim27z9cWbRu6FYNs8bAiWzwMssWmv87Vz04tVjfhlctlrWK7UChYXiupVqvY3d3F7u4uACAUChmCbZHGpgyauaMdK7KJhotBNhE1sWolrh68qeG1qAfndgtm7c5OFdnZbBYrKytNrcQ/9KEP4caNG0O78GOeYzvs9WQ3DCitXaYiu1KpIJPJoFAoGJbPzs5ifn7ekftvvk6IFdlE1Cs+n88ws/Lk5EQLtUWZr82qTzEwyBaLeZayE4/JqRmDbLEN6+YTj8eDeDyOeDyOGzduQJZlFAoF7Sa7YrFoeU5UqVRQqVTw+vVrAGdVxOrxSjKZhM/HKKhT+rbiAINsomHj3ouIDNzWStyMFdndscP6UhQFz549wwcffGBYHgwGsby8jGQyOZTnpefxeLQbI/i6MmJFtrWLVmQfHBxgZWXFMF/L6/Xi3r17mJqa6ulzHCQG2UREwyPysT9wdsxonq+thtqdzteOxWLaheJEImGr+dqibz+RMSATC29MEBPfp2LTHwMMc9t6PB6tyho4u8Eun89rwba5elh1dHSEo6MjvHr1CgAQj8e1n2O34xW7Ma9TthYnGi4G2UQE4E3VtXqQ5pZW4mackd2dYVewn56eYnV1FYeHh4blw2wlboVBdmsMsq11G9wqioKnT5/iyZMnhuXRaBTLy8uOv3uYQTaxIptocNz8HtO39rx+/bqhAiqbzaJYLFo+rlQqoVQq4eXLl/B4PEgkEkOdr82KbDEw+BQLA08xcbuKTb997RT6+nw+jI2NYWxsDMBZi3F9sG01NgUAisUiisUiNjY2IElS0/EKX79vmNeh06+pEDkdg2wiamolLsuyq6qw9YYdzDrNMFuLt2olPj8/j9nZWVu9Zu3Ugt1uGGRb66a1eKvZ8NPT07hz544Q7cMYZBMR0TDoK6Dm5uZQq9WQz+e1im2rC8WyLLecr51OpxEKhfr+vPk5KQYG2WLh9hQTg2yxDWpG9mUFAgFMTExgYmICwJuxKeo//XUzlaIoyOfzyOfzeP78ObxeL5LJpHbcE41GXb2f4oxsIntx/pVNIrqURqOBer0OoLkK220hNmCPVtlOMoz11ary1E6txM0YZLfGgNJap63F8/k8MpkMTk5OtGWSJGFhYQEzMzNC7b/1s+b5OjHSrxtRsSKbaHhE+iy5LL/f3zRfWw21s9msYbSHymq+thpqp1Ip+P3+nj9PVmSLgcGnWBh4ionbVWzDmpF9WeaxKZVKxRBsWx2vNBoNHB4eah0P/X4/ksmkdrwSCoVc9TlknpEdi8WG9EyICGCQTeRa7VqJqy3E3dBK3Iytxbsz6IC2VSvxiYkJ3Lt3ry8XAnuBQXZrrMi2dl5FtqIo2NzcxPr6uuEiZzAYxP3795FIJAbyPAeJLfqJiMhugsEgrly5gitXrhjma2ezWeTzecvPq5OTE7x+/RqvX78G0P/52m47nxMJg2yxMPAUE7er2JxSkd2OJEkIh8MIh8O4evUqFEVBuVzWQu18Pm95/bNWqxluxBsZGdGqtVOpFILB4KD/lIHSd93xer22GV1I5FYMsolcSJZl1Ot1KIqi/XNrK3Ez/UmHul54ItLaIFuxHx4eYnV11RGtxM0YZLfGINtau4rser2OR48eYXt72/A9Y2NjWFxcFPYEixW57sbtTzQ4DM8uptV8bbViu5v52mrF9kXberIiWwzm42JuS2fjvlVMDLLF5tSK7HYkSUIsFkMsFtOOV0qlktZdplgsWl6XOT09xc7ODnZ2dgAA4XBYC7WTyaRw1yH0FdnRaFSY7U/kVAyyiVxEDWZbtRJXA2w3n1CZ77BkkN3eIFqLt2olHgqFsLy87IjKUwbZrbG1uLVWFdnlchmZTKZpXtPt27cxNzcn9P6bQSaRs3D/Tm6nn68NwDBfO5vNolKpND1GP1/76dOn8Pv92s/oZr42329iYPApFgaeYtLf0K8WhpA4RKjIPo96E10ikcCNGzfQaDRQKBS045FWN+IdHx/j+PgYW1tbAM7CXn2w7fM5O3bSX3OJRCL8DCYaMmfvUYioY4qioF6vaydPbCVuzXzS0Wg0HH/w1U/9DmhPT0+xsrKCbDZrWD45OYm7d+/atpW4GYPs9jwej7ZeuH7OWAVA29vbWFtbM5xM+/1+LC0tYWxsbNBPceAYZJ9P/UwXEbc/0eDwPdYf5vna6rxKtWLbal5lrVbD3t4e9vb2AJzdyKmG2u3ma7MiWwzm9yIDMmdjhb2YRKzYpTf0595u2b5er1frDAOcdYTL5/PaMYt5drSqXC6jXC5jc3NTq/pWj1fi8bjjbgTQ/53hcHiIz4SIAAbZRK7AVuKdMx9YcU52e/1sLX54eIiVlRVUq1VtmSRJePvtt3H9+nVHvWYZZLfHILuZ+SR5c3NTm02lSiaTWF5eFn42lYpBprtZ3dzhpM8BIiKzUCiEUCikzddW51W2m69dqVRQqVQM87XVi8St5mtzX+lcrMgWC29MEBODbLHpt6/Tgthe8fl8GBsb026er1arWrV2Lpez7DCjKAqKxSKKxSJevHihVX2rFduxWMz27xf9jOyLjnohot5hkE0kMEVR0Gg0tICRrcTPZ9VanFrrR0CrKAqePHmCp0+fGpY7qZW4GYPs9hhQNjPvl80h9uzsLObn521/8tdLfJ0QEQ0HzxX6z2pepTpfO5vNolQqWT5Ona+9sbEBj8eDZDKJVCqF09NTw88mZ2KQLRa2FhcTg2yxcfs2CwQCmJycxOTkJICzm+zU0Sm5XM5QjKLSj04Bzq69qqF2KpWyXetu9QZDVSQSGeKzISKAQTaRsMytxGVZZhV2B6xai1NrvZ6RfXJygtXVVce3EjdjkN0e10+zVvtnr9eLe/fuYWpqasDPaPgYZLsbK7KJyE3087Vv3bqFWq2mXQBuN19bDb71qtUqKpVKx/O1yT7Yilos3J5iYtApNjfMyL4stcPM9PQ0FEXB8fGxoWK7Xq83PabRaODg4AAHBwcAzsav6IPtUCg09H2kvrW43YJ2IjdikE0kIFmWUavVtJbitVoNXq8XPp+PIfY5WJHdnV62Fj84OMDq6mpTK/GFhQXMzMw4+jXLoLY9rh8jRVGaOhIAZ+2slpeXEY1Gh/Csho9BNhHRYHAfaz9+vx8TExOYmJgA0Nl8bdXJyQn+6I/+CKFQSGtD3m6+NtkHK7LFwtbiYmKQLTY3zsi+DEmSEIlEEIlEcO3atabRKYVCwfLaYa1Ww97eHvb29gAAIyMjhmOWkZGRQf8phtbiDLKJho9BNpFAzK3Ev/CFL2ih4Pz8PGZnZ9lK/BysyO6OPvhX5693+/qSZRlPnjzBs2fPDMvD4TCWlpYc2UrcjEFte/rXjNvXT7VaxerqqnZnsiqRSOCbvumb4PO599CNrxN3s6rIJqLB4LmD/VjN11arsQuFQsv52ltbW9ja2gIAxONxpFIppNNpJBIJXqC3IQafYmFrcTExyBYbZ2RfjtXolGKxqFVrFwoFy/O609NTbG9vY3t7G8DZ9UF9xfYgbsYzV2RTe7VaDe+++y5evnyJ7e1tRKNRXLlyBffv38eNGzd6+rskSboJYBnAFQBRANsANgB8SVGU1nd3kqO592ookWAURUGtVtMOAGRZbmr7zIPq85kPTBlkt2d+TZlfd+c5OTnBysqKNidHNTU1hbt37woT2jHIbk+/ftwcTuXzeWQyGZycnDT9tytXrgjzfrgoVmS3xpCJiMi99BeJZ2dn0Wg0sLa2hsPDw7aPKxaLKBaLhvnaavVTNBrlZ4sN8HhHLGwtLiYG2WLj9u0t9XgjmUzi5s2baDQaKBQKWoeZUqlk+bjj42McHx9rN+PFYjEt1E4mkz2/yUBRFMcH2c+ePcOf/umf4itf+Qr+9E//FF/96lcN63d2dhYvXry49O/Z39/Hpz/9afz6r/9603gb1cc+9jH8+I//OH7wB3/wUr9LkqT/BcCPA/hoi2/JSpL06wD+H4qiHLT4HnIod18RJRKAoihaC3H914qiMDy7AKtgllq7TJC9v7+P1dVVQytEj8eDt99+2/GtxM34XmzP7etHURRsbm5ifX295QVLXsjkDQ/tuGF9sCKbaHD4/nI2r9eLQCCgfZ1IJDAzM6NdJO5kvrY6qzKdTiOdTiMYDA7s+dMb+vciO6s5n357MhATB4NOcanXV1WsyO49r9erHWsAZ1W9+Xxeq9jWh8l6pVIJpVIJL1++hCRJWpeZVCrVsy4z+t/tlBv8vvCFL+Dnf/7n8ZWvfKVlqNxLn//85/HJT35Sawnfype+9CV86Utfwo/8yI/gl37pl7q+MUCSpCiAzwD4q+d8axrA/xXAD0iS9AlFUf57V7+IbI1BNpGDKYqCer2uHVjpQ2xJkgwHWWrQTe2pM8TVdcqK7PasWrGf1+KnXSvx5eVlxOPxnj/PYXN7UHseN7eMrtfrePTokdYySzU2NoZSqYTT01MADBUAVmQTOQ1vPhCHEy7ckZH+/eb1epvma6uhdqv52uZZlWpLz3Q6jWQyyfnaA6I/Lub70Pm4PcXEIFtc5uuBDLL7z+/3Y3x8HOPj4wDOWoyrxyu5XM6ye52iKCgUCigUCnjx4oVW9a0G27FY7EL7XPOMbCfIZDL47d/+7YH8ri984Qv4vu/7Pm2kKXD22fYN3/ANmJubQz6fx4MHDwxj8/79v//3KBaL+M//+T93vL+UJMkL4NcBfI/pP+0DeACgAOAWgPsA1A09CeBzkiR9p6Iof3jBP5FshkE2kUOpVdjqXGL9nYJqGKtvQ+u2cOgyvF4vg+wOmQ/kz3udtWolPj09jTt37gjbOplBdnturbQtl8vIZDIol8uG5bdv38bc3Bx+//d/X1vmpvXSCoNsd2MoSkR0Meb9ZygUwtWrV3H16tWO52ubW3pyvvZgmCuyydlYkS0mBtni4lz74RsZGcHU1BSmpqYAnN2Mpw+29SGqytxlxufzacF2Op1GOBzu6DPV6a3F9UZGRnDt2jU8ffq0Jz/v1atX+IEf+AHD+v/Wb/1WfOYzn8HCwoK27PT0FL/0S7+En/zJn9RunPwv/+W/4Gd+5mfwj/7RP+r01/1jGEPsGs7ai/9rRVG0JyBJ0jsA/g3etB0fAfCfJUm6pyiKsXKEHEnMxIBIYO1aiQNnB1Zq2zF9yMhAtnNer1f7gGXo2F43rdhbtRJfWFjAtWvXhL44wyC7PTeun+3tbaytrRn2zX6/H0tLSxgbGwPg7kp1KwyyiYgGg/tY5+s0ALWar93JrMpW87XT6TQikYjQx/WDxOBTLAw8xcTtKi5WZNtPKBRCKBTClStXtDnWaqidz+ctu5HW63UcHBxolcGBQACRSAR/+Id/iO/6ru/C/Px802PMM7Kj0Wj//qge8/v9uHPnDj784Q/jm77pm/DhD38Y9+7dw7vvvouPf/zjPfkdn/70pw0FSh/72MfwP/7H/2gaRTMyMoK/83f+Dq5fv47v//7v15b/03/6T/GpT30Ks7OzbX+PJElzAP5vpsU/pCjK58zfqyjKe5Ik/UUAv4M3YfYogE8D+Nsd/3FkWwyyiRzE3EpclmVDFbZaia1ikH0x+nXI9daeVWtxM1mW8cEHH+D58+eG5SK3EjdzY1DbDTetH1mW8fjxY2xsbBiWJ5NJLC8vGw783Vqp3gqDbHdjRTYRUecuuo80z6qsVqvI5/NaZZNVS0+r+drpdFqrfOJ87YtjRbZY2FpcTPprIAyyxcKKbHuTJAnRaBTRaBQzMzNQFAWlUskQbFtdX6pWq/jqV7+Kn/mZn8FP//RPY2pqCh/5yEfw7d/+7fhLf+kv4dq1a1AUxZGtxT/xiU/gb//tv93XY68PPvgA//bf/lvt60AggF/91V9t+zu/7/u+D5/4xCe0x52enuJnf/Zn8cu//Mvn/bpPA9DPs/lVqxBbpShKRZKkTwJ4CCDw9cV/U5Kk/7eiKM9aPY6cgUE2kUN00krcfDLEIPti9OtN9FDtss5rLV6pVLCysoJ8Pm9YLnorcTM3BbUX4ZbK40qlgkwmg0KhYFg+OzuL+fn5phNjt6yXTjHIJiIaDgYuznaZ7RcIBCzna6sV21aVT7VaDbu7u9jd3QVgnK+dSqVcc/zfCwyyxcIKezGxIltcrMh2FkmSEI/HEY/HMTs7C1mWUSgUtGC7WCxq++HV1VXtcTs7O/jc5z6Hz33uc/jxH/9x3LhxA9/0Td+Et956C1tbWzg+PnZMRXYqler77/gP/+E/GN4bP/ADP4Dbt2+f+7i///f/viEA/+xnP4tf+ZVfCSqK0nyXJABJkkIA/hfT4l847/coivI1SZL+M4Af/voiH4D/M4D/17lPkmyNZxBENqcoChqNhvYhoW8lrlZhq//MGGRfDCuyO9eutfje3h4ePnzY1Er8nXfewdWrV111MYZBdntuqDw+ODjAysqK4f3g9Xpx7949bd6TmRvWSzcY7Lfmhv0pK7KJBofvL+frVwBqnq9dKpW0ULub+dpqqM352u0xyBYLA08xcbuKixXZzubxeJBKpbRwt16va8F2sViEJEmWx7wvXrzAixcvALz57P3sZz+LeDyOb/3Wb0U4HB7Y32BHv/Vbv2X4+m/8jb/R0eMWFhbwkY98BF/+8pcBaDPIvwvA/9HiId8NQL+y/0hRlPc7fJq/gjdBNgD8ABhkOx6DbCIbUxQFtVpN+2A1txK3qsLWY5B9MVxvnbMKslu1Eo9EIlheXkYsFhvkU7QFBtntibx+FEXB06dP8eTJE8PyaDSK5eXltnf2sgLZiOvD3XgBn4ioc4P4nNRXPt24ccMwXzubzaJcLls+Tp2v/eLFC3i9XiSTSa1im/O1jdiKWiy8MUFMDLLFxYpssfh8PoyOjmJ0dBSf/exnsbu7i9/+7d/G7/3e7+HLX/4yNjc3mx6j7rc/+9nP4rOf/SwCgQA++tGP4i/8hb+Av/gX/yK++Zu/GX6/v+lxotrZ2cHKyor2tc/nw7d+67d2/Pjv+I7v0ILsr/uf0DrI/kumr7/Q8S8C/gBAHW+yz/uSJE0qirLbxc8gm2GQTWRTjUZDa9Wmr8IGOguxAVYWXxRbi3fOfKJWqVTwJ3/yJ02txK9cuYJ33nnHta0EzUGt2lGBzohaaVutVrG6uoqDgwPD8k5b64u6Xi6KFeqkx9cA0eDwmMV5hhGYWc3XzuVyWsW21XztRqOBw8NDHB4eAjhrZa5vQ+72+dpsRS0WBp5i4nYVFyuyxTY5OYkf/dEfxY/+6I8CADY2NvDf/tt/wxe/+EV8+ctfxv7+ftNjqtUqvvjFL+KLX/wiPv3pTyMSieDP//k/rwXbS0tLQr9O1tbWDF8vLi52NT/8Yx/7mHnRnTbfftf09R91+nsURTmSJOkhgPum38Ug28HcmSgQ2ZiiKKjX69oBUzetxM30IQmD7M7xBoDOqTdUqBdZHj9+bFhnbm0lbmY+kGWQbSRiQJnP55HJZAwXbSVJwsLCAmZmZjra/iKul8tgRba7sbU4EdHFDOuYMxAIYHJyEpOTk1AUBZVKxRBsW83XrlarTfO11VDbjfO1WcErFlbYi0m/XVmxKxb9tS21oIjENTs7i0996lP41Kc+hT/8wz/EX//rfx35fB71eh3pdBrZbLbpMUdHR/j85z+Pz3/+8wCAdDqNj3/841qw/aEPfUio/f17771n+Pqtt97q6vG3bt0yL3qnzbcvmL5+YvldrT2FMch+B8DvdvkzyEbcdRZAZHOyLKNer0NRFO1fN63EzRjIXgwrsrujD7L1rzM3txI3s2rBzpOgN0RqLa4oCjY3N7G+vm64+BgMBnH//n0kEomOfxYrso0YZDdrNdeLyI74WnUObivns1sAKkkSwuEwwuFw03ztbDaLQqFg+bpT52u/evVKa2WuVmzH43Hhj6ftth3pclhhLyZWZIvLXKRB7nF8fGzorPfw4UMcHh7id37nd/C7v/u7+OIXv4hisdj0uGw2i9/4jd/Ab/zGbwA4606phto/9EM/1FX1sh2ZR+Zdv369q8fPzs6aF41KkpRSFCWnXyhJUhpA2vS9L7v6Zc3ff7vLx5PNMMgmsgE1sG7VSlwNsLs9eeWs54vheutcpVKxvOjk9lbiZlZBNr0hSmBbr9fx6NEjbG9vG5aPjY1hcXERgUCgq5/HimwjBtnuxops52HoIg5uS+ex+z7Sar52Pp/XKrat5msrioJCoYBCoWCYr61WbIs4X5tBtlgYeIqJ21VcrLZ3r6OjI8PX8XgcV65cwb179/B3EE2N+gABAABJREFU/+7fRb1ex5/92Z9pwfa7775rOULl9evX+LVf+zX8x//4H/EDP/ADg3r6fWMeIzkxMdHV46PRKILBoHldJQDkTN+aNH19rCjKEbqzZ/q686oSsiUmDERDZtVKXA1P1fD6ogfD5spitjPuDCvZO7O7u4uHDx82XWC5e/curl69OsRnZj8MstsTIbAtl8vIZDJNF15v376Nubm5C+17GdwacX0QERF1xmkBqNfrxejoKEZHRwEY52tns1mcnp42PabdfO10Oo2RkZGB/g394LTtSO2xtbiYGGSLixXZ7nV8fKz9f7/f33RM4fP58JGPfAQf+chH8FM/9VM4OTnBH/3RH+F3f/d38Tu/8zv4kz/5E8Pr55u/+ZsRj8cH9vz7xXy9KxQKdf0zQqGQOci2auMZNX1d6foXNT+G7UIdjkE20RD1upW4mfmOwUajwQrZDrC1eHuyLOPx48fY2Nho+m8zMzMMsS0wyG7P6a3Ft7e3sba2ZjhR8fv9WFpawtjY2IV/riiV6r3CINvdWJFNNDh8f4nFiYGZ1XxtdbZ2p/O1I5GIFmwnk0lHngcz+BQLW4uLR72Op+J2FQsrst1LX5HdSTvwYDCIj3/84/j4xz+On/u5n0OpVMLv//7va8H2d37nd/bz6Q6MOcgOBoNd/4xQKIRczlCAbQ6trZY1l7ufzxxkW/0echDnHckTCUCtulZDj161EjdjkH0xbC3e2vHxMVZWVlAoFCz/O19f1hhkt+fUwLbVTR3JZBLLy8sXOqjXE6FSvZec+johIiIaNJEqefXzta9du9bxfO2joyMcHR05er62SNuReGOCiMznJE7Yr1DnWJHtXvrANhqNdr3PjsVi+N7v/V587/d+LwBxr+dctvNgFy6yAsVc6S7GxIFowMytxGVZ7mkVtp5VkE3nY2txa2orcX0FhNfrRSAQQKVydqMbwyVrDLLbc2JgW6lUkMlkmm7qmJ2dxfz8fE9OdBncGrEi291YkU00PAxcnEfkfWSr+dpqxXa387XT6TTC4bAtX+es9BQLW1CLh0G22FiR7V761uLhcPjSP8+OxxgXEY0ai5rVa8HdsHhM84Fb87Lue5g3P8bq95CDMMgmGqBGo6GFgOYq7F6H2ACD7Itia3GjVlWn0WgUy8vLePToEYPsczDIbs9prcUPDg6wsrKCWq2mLfN6vbh37x6mpqZ69nucGPD3E4NsIqLB4D7W+dxUyWs1X1sNtbuZr51Op7WKbbvM13bTdnQD3pggHgbZYtNfQ2WQ7S7m1uL8DD7DIJuGiUE20QC0ayWuthDvRStxM/OBlhMCIjtgRfYbx8fHyGQyKBaLhuXXrl3DwsICvF6v40LIYWCQ3Z5TXkOKouDp06d48uSJYbl6U4f5oP6yGNwaMdhvzQ0n1qzIdh43vC7dgtvS2dy2/QKBAKampjA1NWWYr62G21bnd9VqFTs7O9jZ2QFgn/naDLLFwtbi4jHvTxhki4VdFNxJURRDkN3r6zxOlkgkDF/v7+939fhyuWwVZOctvtU8zzIsSVJEUZQji+9tZaKD30MOwiCbqM9kWUa9XoeiKNq/frUSNzMH2fqW0NSauSJbveHAbXZ2drC2ttbUSvzOnTu4cuWKYZnK7cF/Kwyy23NCYFutVrG6uoqDgwPD8unpady5c6cvFzjZWtzICa8TIiIiO2AAesY8X1uWZW2+di6X62q+ttqGPBaLDSzQ4HYUCyuyxcOKbLGxItu9WJFt7fbt24avzZ07z2Px/VlFUXLmhYqiHEqSlAOQ0i2+DmC9i183a/r6gy4eSzbEIJuoT9TAulUrcTXA7ueHISuyL8ZqvbnpoFWWZbz//vt4+fKlYXksFsPS0lLT3YhOqaYdJgbZ7dn9NZTP55HJZHBycqItkyQJCwsLmJmZ6dt+nBXIRgyyzyfyemFFtvNxmzkHt5XzcRta83g8SCQSSCQSuHnzJur1OgqFglaxrb9wrdLP137+/Dm8Xi9SqZRWsd3P+dqs4BULqzvFwyBbbHzPupd5RjY/g88sLCwYvjZ3KzzPs2fPzIvea/Pt6wA+pvv6LXQXZM9Z/DxyMAbZRH2gKArq9bp20DOoVuJmnJF9MeYD1Eaj4Zog++joCCsrK02txGdmZvD2229brge7h5B2wCC7PXNga5cuCIqiYHNzE+vr64YLwsFgEPfv329qq9RrrMg2YpBNRDQcdvhMpovj9mvN5/MZ5mufnp5qs7VzuVzL+doHBwdal56RkREt1E6lUj2dr80KXrHwxgTxmM/R3HLdyC1Yke1ebC1u7e7du4avV1dXcXx8jHA43NHj3333XfOitTbfvgZjkP1RAP+lk98jSVIEwGIXv4scgEE2UY8Ns5W4mfr71N/PILszbq1k397extraWtPBurmVuBlnip9PvXFFvRjlltdUp8wX5uwQZNfrdTx69Ajb29uG5WNjY1hcXEQgEOj7c2BFthGDbHdjRTYRUefYkvpiRkZGDPO1j4+PDcG21bnO6elp03xtNdS+7Hxtbkex8MYE8bAiW2ysyHYn84zsSCQyxGdjL9PT01hcXMTq6iqAs+tmf/iHf4jv+q7v6ujxX/jCF8yLPt/m2/8bgL+l+/o7On6iwLfBmHs+UBRlt4vHkw0xyCbqEUVR0Gg0tJPbYbQSt+L1ehlkd8mqIltkjUYD77//PjY3Nw3LY7EYlpeXzz1oM88UJ2sej0d7LXE9GZn3i7IsD/VEsVwuI5PJoFwuG5bfvn0bc3NzA70ZScXQjhXqbscL+ESDw88c5+M2vDxJkhCJRBCJRJrma2ezWRSLxbbztTc3NyFJEhKJhFax3e18bQbZYmEoJh4G2WJjRbZ7cUZ2a9///d+vBdkA8Cu/8isdBdnvv/8+vvzlL2tfRyIRHB0d/Xabh/x3ABUAoa9//VFJkt5WFOX9Dp7mJ01f/1YHjyGb4ycsUQ+orcT1IVWj0dCqCr1e70ArsfX0B1uiB7K94qaK7KOjI/zxH/9xU4g9MzODb/mWb+nozkO2Fu8M11Nrdmq9vr29jT/6oz8yhNh+vx8f/vCHcevWrYHux/maMWKwT3p8DRARtcYAtPfU+do3b97EN37jN+Lbvu3bsLi4iJmZmZbnTIqiIJ/P4/nz5/izP/sz/MEf/AFWV1fx6tUrHB0dnftZxu0oFm5P8ZjP0bhdxcKbT9xLPyObFdlGP/IjP2K4bv6bv/mb+OCDD8593C/8wi8Yvv7hH/5hKIpy0ur7FUU5BvCfTIv//nm/R5KkDwH4ft2iOoD/cO4TJNtjRTbRJcmyjFqtBqC5CnvQrcStMMjunltmi7dqJX737l1MT093/HPYWrwzDCVbs0OQLcsyHj9+jI2NDcPyZDKJ5eVlBIPBgT8nBrdGvDDkbmwtTjQ83P86DwOz/vP5fBgbG8PY2BgA43ztbDaLarXa9Bir+dpqG/J0Ot00uoYzlcXCUEw85vcot6tYWJHtXpyR3drt27fxiU98Ar/8y78MAKhWq/jkJz+J3/md32l53exzn/scfvVXf1X7OhAI4NOf/nQnv+7/CeCvAvB//etPSpL0W4qi/B9W3yxJUhDArwDQH1D9/xRFedrJLyN7Y5BNdEHtWomrLcSH0UrcjEF290RvLd6qlXg8HsfS0lLXdxuytXhnGGS3NuyAqlKpIJPJoFAoGJbPzs5ifn5+aBck2ErbiO8hIiKi7g37fNQtrOZrq7O1283X3t7exvb2NoCzi+VqqJ1MJjlTWTC8MUE8vDlBbNy+7mSeke20IPvVq1eo1+tNy3d2dgxf1+t1vHjxwvJnRKNR7UY9Kz/7sz+L3/qt30IulwMAfOlLX8J3fud34t/8m3+Dt99+W/u+09NT/Ot//a/xEz/xE4bH/8RP/ARmZ2fP/VsURXkmSdIvAvhJ3eL/JEnSjwP414qiaHcNSpK0AODfAPiY7nsPAfzsub+IHIFBNtEFKIqCWq2mnVjKsqwd4NihCluPQXb3RG4tXi6XsbKyglKpZFh+/fp1zM/PX+guU4ZLneF6am2YFdkHBwdYWVnROmsAZ/uAe/fuYWpqamDPw4p+vbD6lBXqbjfsG17oYiRJ0rYVt5lzcFs5Hyuyh0s/X3tmZqbj+drlchnlclmbr81tJw7z9mYoJgYGneJSi5VUrMh2D1mWHd1a/M/9uT/X1GnQytbWFm7evGn53z7xiU8YKqjNrl27ht/8zd/Ed3/3d2sdaN5991288847+MZv/EbMzc2hUCjgq1/9Kvb39w2P/ct/+S/j537u5zr/g4B/AOAOgP/p61/7Afx/AfzfJUn6KoASgDkA3wBAf+BUBfD9iqJsd/PLyL4YZBN1QT2QUe9ssmMrcTO2fe6eqBXZr1+/xqNHjwx/j8/nw927dy8V2DGg7QzXU2vDCLIVRcHTp0/x5MkTw/JoNIrl5WVb3HXL4NaI64OIaDjsdG5DneHnpL2o87XVGdv1eh35fF6r2NZXfqkURTFsx83NTRwdHSGdTiOdTiMUCvG96SDm8xuGnmJgkC0u83VAbl/30IfYgPMqsgflO77jO/Bbv/Vb+OQnP6mF1Yqi4Ctf+Qq+8pWvWD7mr/21v4bPfOYzXd0YoihKQ5KkH8ZZtfX/SfefJgD8pRYP2wPwCUVR/qDjX0S2xyCbqEOKoqBer2sHqnZtJW7m8715m4sSyPabejOCeuHA6eut0WhgfX0dr169MiyPx+NYXl5GOBy+1M/nzRKdYZDd2qArLavVKlZXV7X5hKrp6WncuXPHsN8cJr5mjBhkuxsrsomILsZu56dkPV9bDbVbzdeWZbmr+dpkL+Zjeb4vxcAgW1zm9ywrst2jXC4bvmaQ3dr3fM/3YG1tDZ/+9Kfx67/+61qrcbNv+ZZvwU/+5E/iB3/wBy/0exRFKQP4q5Ik/ScAPwHgW1p8axbArwP4tKIo+y2+hxzKHldqiWxOrcJW74rWt5ixYxW2HkPGi/F6vVrlvZMDpHK5jEwm03Qgdv36dbz99ts9Odkyz8hWb+4gI4aSrQ2yIjufzyOTyeDk5ERbJkkSFhYWMDMzY6vXrjm4dft7i0E2EdFgcB/rfGwt7iwjIyOYnp7G9PS0Yb7206dPWx4Xnzdfm6GLvbC1uJgYZIvLfP2U+1T3MHdJcVqQ3Wrudb9MTEzgX/7Lf4lf/MVfxLvvvouNjQ3s7OwgEong6tWruH//fssW5t1SFOU/4WxG9k2ctRK/AiACYAfABoB39XOzSSwMsonaOK+VuBpg2/niAGdkX4w+yHbqeutXK3Ez8wmb28O2VhhktzaIIFtRFGxubmJ9fd1wISkYDOL+/ftIJBI9/52XZVWB6ub3FoN9d2NFNtHwcF/rPAyynUs/X3tjY0Orzk6lUpBlueP52olEQmtDHovF+DoYMrYWFxODbHHxPete5tbiTpuRPSyBQAAf//jHB/K7FEV5DuD5QH4Z2QaDbKIWzK3EZVk2VGGrldh2xyD7Ypxcyd6qlXgikcDS0tKlW4mbWc0Ud8J7Y9AYZLfW74CqXq/j0aNHWsWKamxsDIuLi7ZtxWh1k4ib8QKstYODA7x8+RJ+vx/pdBqjo6O2fU0TEdFguP2YQRT67Tg+Po5r164Z5mtns9mmC+7q4/L5PPL5PJ49ewafz4dUKqVVbHO+9uCZ35Nc/2JgkC0uVmS7F1uLE9kTg2wiC05uJW7GIPtizO2ynaJVK/HZ2VnMz8/35eTKfEDvpPU1SAyyW+tnRXar98Tt27cxNzdn6305K1CN2P3BSFEUfPDBB3j+/M2NyLu7uwCAWCymVWElEgkhLqzx/UA0OHx/icXNn5VOZ1VZ32q+tjpj22q+dr1ex/7+Pvb3z8ZFBoNBLdROpVK8AW4AWN0pJv01Nm5TsfA9617m1uKxWGxIz4SI9BhkE+koioJGo6EdjOpbiatV2HZvJW7GIPtinFiRvbW1hffee6+plfi9e/cwOTnZt987yPnGTqbfb3AdGfXrNbS9vY21tTXDe8Lv92NpaUm7+GdnfG8ZmT97ZVl27QWFarWK1dVVHBwcWP73UqmEUqmEjY0NeL1eJJNJLdgOh8OOOo4hIqLusbW4GDrZjub52kdHR8jlcshms8jn85bnsicnJ03ztdVQm/O1+8N8HM/3pRj025XvG7GwItu9zEE2W4sT2QODbKKvUxQFtVpNO1k0txJ3UhW2nlMri4fNSeutXq9jfX0dW1tbhuX9aiVuZj6gd0rwP2isyG7NKqC8DFmW8fjxY2xsbBiWJ5NJLC8vIxgMXurnDworUI24Ps4Ui0U8ePAAlUpFW+bxeDAyMmJYpmo0Gjg8PMTh4SGAswveagvyVCoFv98/sOd+Gdz+RMPjxHMgt+M+Ugzd3pAgSRKi0Sii0ShmZma0mdpqxXapVGo7X/vly5eQJAnJZFKr2OZ87d4wr3e33owpGrYWFxcrst1LH2SPjIw45nyZSHQMsolwdpG3Xq8DMFZhA84OsQFjyKj+jXQ+p1Rkl0olrKysNLVNvnHjBj70oQ8N5GCbVaOdcdLNEYOmdrpQ97uXufhaqVSQyWRQKBQMy/vZXr9feh3wOx2DTOD169dYW1szvBaCwSDu3buHUCiE09NTFAoF7YK11ef+6empoQorHo9r1drxeNxR7xGyv17t22mwuK2cjbN4xXHZynqPx4NkMolkMom5uTnU63XkcjmtYrvVfG31e/TztdWKbc7XvhiGYmJikC0u/XVA9bowuYM+yGY1NpF9MMgmV1MUBfV6XTv4FKGVuJk5PHP7TNFOOaEl+6tXr/Dee+8ZTp78fj/u3buHiYmJgT0PBtmdYUV2ex6PR3uvXXT9HBwcYGVlBbVaTVvm9Xpx7949TE1N9eR5DpLVTGg3c3OQ3arLwOjoKO7cuQOfz4d6vY5gMIhIJIIrV65AURSUSiUt1C4UCpbrrFgsolgs4sWLF/B6vdrFarUNuV24efsTEV0Gz/2cSVEUw2ddL0IUn8+H8fFxjI+PAzhrMa6G2t3M11ZDbc7X7hxbi4uJQba4uG3dS18oFI1Gub8msgkG2eRasiyjXq9rJ4hqiA04vwpbTx/Iqn+nCH9Xv9m5erZer+O9997D69evDcsTiQSWl5cRCoUG+nzMB/V2Df6HjUF2e5cJshVFwdOnT/HkyRPD8mg0iuXlZUSj0Z49z0FiRbaRW4PM09NTZDIZ5HI5w/K5uTncunWr6UK3SpIkxONxxONx3LhxQ6vCUoPtVm3IDw4OtNnbwWAQo6Oj2gVrn2+4pw6s7iUaDp47OAsrssUwiO0YDAab5muroXYul7M89jw5OcHr16+1c9FYLKbdBJdIJDhHtgW2FhcTw05x6a9rcb/mLvpuJXa6sZvI7Rhkk+uogXWrVuJqgC3KCb/V/GIeYJ/Prq3FS6USMpmModUNMNhW4mbm15jbw7ZWGGS3p9/ndrN+qtUqVldXteBNNT09rVWqOhUrso3cGGTncjlkMhmcnp5qy/RdBvTHL+cxV2FVKhUt1M7lcpZtyE9OTrC1tYWtrS0tGNe3IRflWImISCQMssUw6O2on699/fp1yLKMQqGg3QRXLBYtH1cqlVAqlfDy5Ut4PB4kEgnO17bA1uJiYpAtLv11QG5bdzG3FufnGJE9OPfqLtEFWLUSVw9O1PBatAMUqyDb7/cP6dk4h90qshVFwdbWli1aiZuxtXhnGGS3p18/nQZz+XwemUwGJycn2jJJkrCwsICZmRnHn3C4Mbhtx03rQ1EUbG5uYn193fB3RiIR3L9/X+sycJnXeCgUwtWrV3H16lXIstzUhtzqORUKBRQKBTx//twwMzOdTg+kGwgrsokGg+8vZ+P2E8Owb0jweDxa+/C5uTnUajXk83ntBjir+dqyLDfN11a7ugzqWMGuhr09qT8YZItLv21Zke0eancSFVuLE9kHg2xyDbe0EjezCrLpfHaakV2v1/Ho0SNsb28blieTSSwtLQ39goD5fTPs9WVXDLLb62b9tAr5gsEg7t+/j0Qi0bfnOUhsLW7klvXRaDSwvr6OV69eGZZPTExgcXGxL10G1AqqRCKBmzdvolarGdqQ628WUZlnZobDYS3UTiaTju6GQERGIp4juQm3nzPZLfj0+/1N87XVUDubzaJWqzU9pl6vY29vD3t7ewDezNdWw2033WCvP24VqQOg2zHIFhcrst1LH2SztTiRffAKEwlPrbpWD0JEbyVuxiD7YuzSWrxVK/GbN2/i9u3btjigliQJXq/3wvON3eKirbPdotP10+rGjrGxMSwuLiIQCPTtOQ4aW4sbuaEiu1Kp4MGDB03tO2/fvo25ubmBHav4/X5MTExgYmICiqI0tSG3+lw8Pj7G8fExXr16BUmSkEgktIvVvWotqv8ZIm5/Irvg+8vZ7BaA0sWYj4ftth2DwSCuXLmCK1euGOZrZ7NZ5PN5ztc2YeApJm5XcbEi2730HUfYWpzIPhhkk9DMrcRlWXZFFbYeg+yLGXZrcUVR8OrVK6yvrze1El9cXNTuhLcLj8fDIPscrMhur5PW4uVyGZlMBuVy2bB80CHfoLilArlTogfZh4eHyGQyhoomO+zzJUlCOBxGOBzGtWvXIMsyisUiDg8Pkc1mUSqVmh6jKAry+Tzy+TyePXsGv9+vXageHR3FyMjIEP4SIiJ3EO3z0a3M29HOIVmr+drqDXDdzNdWb4ITrZ2rfnuK9He5HYNscemvnTLIdhdza3EisgcG2SSsRqOBer0OoLkK2y0hNsAg+6KGWZHdrpX48vIygsHgQJ9PJxjSno/rqL3z1s/29jbW1tYM70e/34+lpSWMjY0N5DkOGiuyjURdH4qi4MWLF3j8+LFheSwWw/37923Xzszj8SCZTCKZTOLWrVuo1WpaBVY2m8Xp6WnTY2q1mqG1aCQSMbQh7/TiECuyiQbPDedLomFFthicvB3187UBGOZrZ7NZVCqVpsfo52s/ffpUuwlOlPnaDDzFxO0qLm5bdzLPyGZFNpF9MMgm4bRrJa62EBe5lbgZg+yLGdaM7GKxiEwmY2hlAwBzc3N46623bHsAbZdW7HZmDmrVfRKdadVaXJZlPH78GBsbG4bvt/ONHb0iegVyt0RcH/V6HWtra9jZ2TEsn56ext27dx1x97/f78fk5CQmJyehKAqOj48Nbcitbkw5OjrC0dERNjc3ha/AcjvefEA0fNynOpOTg2wz83ztSqWizdbO5XKW87XNN8GFQiEt1HbifG1WZIuJYae4OCPbvcytxYnIHhhkk1BkWUa9XoeiKNo/t7USN1NDe/XEiSFjZwbdWlxRFGxubuL99993RCtxs2G3YncCcyDFINvIqrV4pVJBJpNBoVAwfO/s7Czm5+eFP6E0/31uf2+JFmQfHR3hwYMHhlb5kiTh7bffxvXr1x25f5AkCZFIBJFIBDMzM5Bl2VCBZR4LADRXYAUCAaRSKYyOjiKdThvm3jMUJRoMvr+cTaQA1M1E3o6hUAihUEibr10ul7Vgu9V87UqlgkqlYpivrYbaTpivzcBTPPqujwC3q2g4I9u9zBXZRGQPDLJJCGpg3aqVuBpgi3Ty1yk1wFcDbAbZnTGHarIs9+3EpFVFXiqVwtLSkiMqTtk2+3xWoSRPdt8wv4YODg6wsrJiqNDwer24d+8epqamhvEUB0604PayRFofe3t7WF1d1Y5bACAQCGB5eRnpdPrCP9du68Tj8WiV1gBQrVYNbcir1WrTY6rVKnZ3d7G7uwvgbC6Z+jPs9vcRuYEbz5+cTuQA1E3M51SibkdJkhCLxRCLxXD9+nU0Gg0Ui0XtWKFUKlk+Tp2vvbGxoY09USu27djdhUG2eMzvUYadYuGMbHcytxbnjGwi+2CQTY6nKArq9bp2EOn2VuJWfD4fg+wumQ9U+xU6OrWVuBlbi5+P1bXt6ffRhUIBW1tbhv8ejUaxvLzsqhMJ8+eW218zIgTZiqLgyZMnePr0qWF5IpHA/fv3HXHj0mUEAgFMTU1hampKu0iQzWZxeHiIQqFg+Rovl8sol8t4+fKlYfnJyQk7WxARWXDi5yM1c+sNCV6vV5uNfevWLdRqNa1zS7v52mrwrZ+vrVZs22G+NluLi8d83OqUazfUGd584l4MsonsiUE2ORpbiXeGIWP3zAeqjUYDPl/vdplqK/H19XXDSW0gEMDi4iLGxsZ69rsGga3Fz8cguz39+jG3H56ensadO3d6+h50Co/HY7hRy82cHuzXajWsrq5if3/fsHxmZgYLCwuuu0AiSRKi0Sii0ahWgaVvQ66/gGDl5cuX2N3d1aq1U6mUoQ05EV2c2z9vRMPzYWcyvw/ddpyg8vv9mJiYwMTEBICLz9dWjxWGNV+boZh4GGSLjRXZ7tRoNHBycqJ9zdbiRPbhvivCJARFUdBoNLQDC7YSb09/0MUguzPmA9VerrdarYZHjx45upW4GVuLn49Bdnv6FssqSZKwsLCAmZkZ1+7PORP4DSdXZJdKJTx48MDQfcPj8eCdd97BtWvXLv3zRXh/eL1ejI6OYnR0FABwenpqaENudaH69PQU29vb2N7eBvBmXmY6nUYikeAFRaIeEGH/4jZureQVDbejNav52uqxQqvuLpVKBVtbW1rHp3g8rlVsD+p4Qf+8uC3FwCBbbLz5xJ3MN1OzIpvIPhhkk+OYW4nLsswq7HMwyO6eVWvxXigUCshkMk0t0W7duoVbt2459gCZVf/nY5BtTe1OcHh4aFgeDAZx//59JBKJIT0ze/B4PNp7yu2vGfN7yClB9vb2NtbW1gz7Rr6+zzcyMoLp6WlMT09rF6q/+tWvtv2M0c/L9Hq9SCaTGB0dRTqdRigU4vEhEbmCUz4fqT0G2efTz9eenZ1Fo9FAoVDQKrZbzdcuFosoFouG+dpqxXa/5mvrt6dTz/nJiEG22FiR7U7m7oAMsonsg0E2OYosy1pFjrkKmyF2awyyu2fVWvwyFEXBy5cv8f777wvRStyMrcXP5/S2yP1Qr9fx6NEjrZpS5ff78bGPfYxtgsGKbD2nVWTLsoyvfe1rePHihWF5Op3G8vIyX99dUC9U+/1+7fN4amoKPp8P2WzWUOmuajQaODw81G6SCQaDhjbkw2grSuQUdt+/UnsMQMXA7dg9r9erfdbr52urFdv6drEq/Xxt4Ow8RD1WSKfTPeuWxupO8TDIFhvfs+5kPq9ka3Ei+2CQTY7QrpW42kKcrcRbY5DdvV5WZNdqNaytrWF3d9ewPJ1OY3Fx0ZGtxM3YWvx8rMg2KpfLyGQyTXe8AmcnCwz5zug/19z+mjGzc9BSrVaRyWS0i6KqGzdu4EMf+hAvhvRAOBzGjRs3AAAnJyeGNuRWowpOTk7w+vVrvH79GsBZW1H1Ync8Huc2ISJh8RzZmczHfdyO3bOar63O1m51vFCr1bC7u6udu4fDYS3UTiaTF74Rjq3FxcMgW1zqNWcVK7Ldg63FieyLQTbZnqIoqNVq2gVrthLvHqtlu9erGdntWom/9dZbwrx2GWSfj0H2G1atlj0ej7ZO7BxQDpr+deP29aLesKauB7uuj0KhgAcPHhiqfrxeL+7evYvp6em+/E79ulBv8hNRq78rGAziypUr2rzMUqmEw8NDZLNZFItFy9eK2lb0xYsXhgoutQ059Z5d37PUnqj7E5GxklcM5u3IkOzyQqEQrl69iqtXr3Y8X/v4+BjHx8eXnq/N1uLiYZAtLm5b92KQTWRfDLLJttQ74Or1OorFInZ2dlCv16EoCubn5xlid0EfylrddUzNLttaXFEUbGxs4PHjx02txJeWljA6OtqT52kXnJF9PgbZZ3/z48ePsbGxYVieTCYRi8WwubkJgGGHHluLG9k9yH716hUePXpkeG7hcBj3799HLBYb4jMTQyfvB0mSEI/HEY/HcfPmTdTrdUNbUfONZcDZ59b+/j729/cBnF3o1rch9/l4ynRRPE4nGjw7fj5S93hDQn+1mq+tVmx3O187nU4jEom03E5sUywehp3iMl/TYkW2e5g7BvIcnsg+eFWGbElRFNTrde3AsFwu49mzZ9p/f/vttxlid4EV2d27TOjYqpX46OgoFhcXMTIy0pPnaCd8jZ3P7UF2pVJBJpNBoVAwLJ+dncX8/DyePn2qLXPbummH3Q6M7Brsy7KM9fV17WYM1fj4OBYXFzmPeYh8Ph/Gx8cxPj4O4E1bUfWf1c1XlUoFW1tb2Nra0oJxfRtyHn+S6Oy0f6XL4z7LmRhkD5a+OwtwNiYmn8/3bL42W4uLh0G2uBhku5d5RnY4HB7SMyEiMwbZZDtqFbaiKNo/qwN9Hvx3jjOyuydJErxer7a+Ol1v+XweKysrTRVfb731Fm7duiXs65Zh2/ncHGQfHBxgZWUFtVpNW+b1enHv3j1MTU0B4GuoFbsGt8Nix5nhJycnePDgQdNNGqLv94ehF+8HfVtRWZab2pCbKYqCQqGAQqGA58+fw+fzGdqQ6y9SE4mI+zDnYQAqBv125DYcvEAgYDlfW63Y7na+NiuyxaO/RqSOQCIx8CYF99JXZIdCIXbmIrIRvhvJNvStxPVfK4piOa+Yd8R1jkH2xXg8no6D7FatxEdGRrC4uChcK3EzthY/n9pFQn192CWE6ydFUfD06VM8efLEsDwajWJ5edkwb4hBtjU7BrfDZLdgP5vNIpPJoFqtast8Ph8WFxe1C59kXx6PB4lEAolEAnNzc6jVaoY25FbVV/V6HXt7e9jb2wNwdpFaDbWTySQvdhDR0DHIFgMreO3FPF+7VCppoXan87VV1WoVsiwzHHM43pwgLvM1LW5f99DPyI5EIkN8JkRkxistZAvmVuKyLGv/X5Kkppac9XodgUBg4M/TqRhkX4zX69UqSNsFSNVqFWtra9pFbZXIrcTN2Fq8M/qbI0RfT9VqFaurqzg4ODAsn56exp07d5rCHrsFlHbB9WKkv4gwzPXR6ualaDSK+/fv86S3T/r9fvD7/Vr1laIoTdVXVsdQ6kXqV69eQZIkJBIJpNNpjI6OIhqNMnwgR+LnjbNx+4lBvx0ZotiLOnYkHo/jxo0bhvna2Wy2acaq2f7+Pv7gD/4AyWRSq9huN1+b7IlBtrjM12pYSOUe5iCb+2Ui+2CQTUNn1UpcH2J7PJ6mwINhbHcYZF9MJ1XG+XwemUymqWrr9u3bmJubc81BD6tpO+OWINvqfSFJEhYWFjAzM2P5vuBryJpdglu7sEOw32g0sLa2hu3tbcPyqakp3L17lxW5gpAkCeFwGOFwGNeuXYMsy4aL1KVSqekxiqIgn88jn8/j2bNn2qxM9Z8bbmwj8bjlWFYkrMgWA1uLO4fVfG21w0sul7Ps8NJoNHB4eIjDw0MAZ63M1VA7lUpxdIkDMMgWF2dku5d+RjZvTieyF15po6FRFAWNRkM7QNC3Elfny6j/GGRfDoPsi2m33hRFwYsXL/C1r32tqZX40tKSdhLrFubQv9Vse7cTPaxVFAWbm5tYX183vC+CwSDu37+PRCLR8rGir5uLYmtxo2EH2cfHx3jw4IEhxJQkCfPz85idnR3afs8t+9thbn+Px4NUKoVUKoVbt24ZLlJns1mcnp42PcY8KzMSiRjakPOiGBENgls+I0TDINu5AoEAJicnMTk5qXV4+bM/+zOt25uVarXaNF9bDbVTqRRvlLQh/bkZj+nEwhnZ7qXvqMGKbCJ74ZEQDYWiKKjVaoZZseYqbP2HhfmgUJ2jTZ0xt31myNiZVsFatVrFw4cPsb+/b/j+sbExLC4uurLtvfk9yteYNZFDyXq9jkePHjVVqXb6vhh2QGlXrMg2GubrZH9/HysrK4ZjkEAggKWlJYyOjg70udDwmS9SHx8fG9qQW+3jj46OcHR0hM3NTW0+9+joKFuKku3w88bZWJEtBgbZYlA7vOjHll29ehUjIyPIZrMoFAqW+1zz6JJ4PK5VbMfjcQZrNsCKbHHpC1nUa9QkPkVRDK3FOSaKyF4YZNPANRoN7SKwvgobsA6xgeaQjFXF3bFaf7yj93xWFdm5XA4rKytNLZPfeustV7USNzMf2MuyzIN9C6JWHZfLZWQymaZ5cN202DevG94McUbkmx8uYhhBtqIoePbsGT744APD8kQigeXlZYRCoYE8D7LvDS+SJCESiSASiWBmZqajWZmyLCOXyyGXywE4C8b1bchFvCnOTtuMSGR8r4lBf9zHY2Ln02/PcDiMmZkZbb52Pp/XurxYHTMoioJCoYBCoYAXL17A6/UimUxqFdu8GW44GGSLi9vWvfStxcPh8BCfCRGZMcmigVEUBfV6XTsgaNdK3EwNt9WTcgbZ3TEfeDHI7ow5yH7+/LllK/Hl5WWkUqlhPEXbsAqyqZmIQfb29jbW1tYM+2W/34+lpSWMjY11/HPMryEG2WdYkW006CCzVqvh4cOH2NvbMyy/du0aFhYW2EaQLFnNylRD7Ww2i2q12vSYarWKnZ0d7OzsADirANC3IXfqRTTux52P29D5uA2diRXZYmm1Pb1eL0ZHR7XuPp2MLrGar62G2ul0GiMjI33+awhg2Cky/bUNnu+5i74imzcJEdkLkywaCFmWUa/XoSiK9q9dK3EzSZLg9Xq1Sm62Fu8OZ4xfjP5k5PDw8MItk93A6mYJaiZSkC3LMt5//328fPnSsDyZTGJ5eRnBYLCrn2f+DGBoe8auFajDMsgK9XK5jK9+9auGu7IlScI777yDmZmZvv5usubU90MgEMDU1BSmpqa0lnXZbBaHh4coFAqWr+VyuYxyuYyXL19q87nVYDscDvOiCvWVk95f1IytxcWg344MyZyv09DTar62OrYkl8tZXgsz3wwXiUS044ZkMskihj5hkC0u/fUsblt3MbcWJyL74NEM9ZUaWLdqJa4G2J2cXOuDbIZk3WHIeDH6Oy/11VOSJOH27du4efMmLwx9nfkuVaeHtP0iSpBdqVSQyWRQKBQMy2dnZzE/P3+hkz2rqn7e/czW4maDCjJ3dnbw8OFDw+flyMgI7t+/j2Qy2bffS+KTJAnRaBTRaBTXr1/XWoqqlVf6iycqWZYNlVcjIyOGNuR+v3/QfwYR2RhvRBADK7LFcpFW8ep87XA4jGvXrkGWZZTLZe2YodV87aOjIxwdHXG+dp8xyBaXftvymoR7mGdksyKbyF4YZFPfWLUSVy8Iq+F1Nwd7VvOKqTOcMd498wGMKhgMYmlpyfWtxM3YWrwzIgTZBwcHWFlZQa1W05Z5vV7cu3cPU1NTF/65fA1ZY2txo34H2Yqi4Gtf+xqeP39uWJ5KpbC8vGzrVo36ESyicmpFdjvmlqKnp6eGNuT6fa3q9PQU29vbWqeYWCymhdqJRIIXU6mneAHPeViRLQYG2eJQuxKqLvo57fF4EI/HEY/HDfO11YrtTudrp1IpLdhml5eLY5AtLm5b99J3Y4tEIkN8JkRkxiCb+uKyrcSt6NshMYjtDoPs7lSrVayurjZVm46Pj+PevXtsJW6BVf+d0b8XnRbUKoqCp0+f4smTJ4bl0WgUy8vLl267xNbi1liRbdTPYL9arWJlZUWrelVdptMAUbdGRkYwPT2N6elpKIqCcrmMw8PDtpVXpVIJpVIJGxsb2gVqNdgOhUK8QE1d42ewszEAFQO3ozjM+9ReHVNazddWQ+1287UPDg5wcHAAgPO1L4Nhp7g4I9u92FqcyL4YZFNPqVXX6of+ZVqJm7Ei++IYZHcum81iZWWl6aRvZGQE3/AN38CLCC2wtXhnnFqRrd7coV7wUE1PT+POnTs9mbvGimxrrMg26ldFbqFQwIMHD3BycqIt83g8uHv3Lq5cudKz39Nr6jGVW14bIlZktyNJEmKxGGKxGG7cuIF6vW5oQ66vGFCZL1AHg0Et1E6lUmxDTuQyPHdxLgbZ4jCf1/RrewYCAUxNTf3/2Tvz8Miu8sy/t/Z91b52q1vdLbWWkjEQMIsBmyQQErDZMhCWTBIgkzhACAMhDGGSMCEEMJMEhs0JYQthccgMuwGDbYyN3SqppW71ola3ttJWe6n2qjt/iLpd91aVVCXVcpfv9zx+HtfpW3WPzrnLOec93/uhq6uLl1+7IG6XWwei/NqHh4Rs+UI5spVJOWtxgiDEA41IiLohtBLP5/NHjsIuplgoK+TKJqqDhOyDYVkW165dw9WrV8sujqvValpA2AcSIatDitG1oVAIXq+XJ/AxDIORkRH09/fX7b6ga6g8UrxmGkkjhMy1tTXMz8/z2tdoNOKWW26B1WqtyzmI+qMEIVuIRqNBW1sb2traAACJRIKLugoEAmXHx8lkEuvr61hfXwcA2Gw2uFwuuN1uWK1WWpwjDoTGv9KDBFB5cJicyoQ4aVRE9n6Uy68djUY5UbuW/NqFDXE0buBDQrZ8oRzZyoWEbIIQLyRkE3Uhl8txi2fCKOx6iNgAWYsfhUIfFAZj1H58UqkUZmdnS+xkzWYzN4ihNtsfEiGrQ0oR2SzLYmVlBRcvXuQtchgMBkxNTcFut9f1fMJ3hNjbp1koLQL1IOrZHvl8HgsLC1heXuaVt7W1YXJykiJXRQgt5PMxGo0wGo3o6ekBy7KIRCKcqB2JRMreI5FIBJFIBNevX4dGoymxIScIgN43Uof6Tx7UI6cyIQ6aFZG9HyqVCna7HXa7HcePH0c2m0U4HObGDcXiTYHi/NpLS0uUX1sARe3KF+pbZZLJZJBOp7nPZC1OEOKChGziSOxnJV6wuzyslbgQshY/Gmq1moTsMpSzEmcYBqdPnwbDMLh48SIAEtUOgnJkV4dUhOxsNov5+Xn4fD5eeVtbGyYmJhqSJ154DdEi7B5kLc6nXkJ2MpmE1+tFKBTilZ84cQInT55U9KKcmKGNHZVhGKZkgbo4WjuRSJR8J5vNYnt7G9vb2wD2hPFiG/J624lSnxFE86H3mXShyHr5IJz3iUEY02g0vPzaqVSKGzcEg8Gq8mvr9XpO1HY6nYrLr00R2fKFIrKViTBtEwnZBCEuSMgmDk0+n0c2mwXLstx/9bQSF0LW4kdDrVYjk8kAELeA1iwKVuJXrlzhlRsMBng8HjgcDqyurnLlJMzujzDqn66x8khByI7FYvB6vYjFYrzy4eFhDA0NNWwhjaL6y0PW4nzqIWQGg0F4vV7eAp1Go8HExAQ6OjqOXEeCEAMajQbt7e1ob28HsLcwEwwG4ff7K+bJTCQSWFtbw9raGs9OtGBDfpjnP4kv0of6UHqQACoPqB/lQyusxWtFr9fz8msXxg375ddOpVIl+bULorYS8muTkC1fiq93ErKVQzQa5X0mIZsgxIW8RxVEQygI1pWsxAsCdr0nWxSRfTRoI8BNKlmJd3R0YHx8nLOTLW6zYqcBojxkX38wYheyfT4f5ubmeP2n1WoxOTnJ5WRtFCRkl4cisvkcRchmWRbLy8tYWFjgfddiscDj8dBEVQJQRPbhKeTJ7O3tRT6fL7EhFyK0E9VoNFy0tsvlgsFgaMFfQRBENdDzUR6QkC0fxGAtXgsMw8BsNsNsNpfk194vfUkhv/bKygrnFFOI2JZjfm2K2pUvtElBmQgjsilHNkGICxKyiZpgWRbZbJZ7qTfSSlwI5cg+GkJRVqn4/X7Mzs6WtRIfHBzkXbvCyUg+n6cJyj6o1Wpuk4SSr7H9EKuQXSlXsMPhgMfjaYpgQTmyy0MR2XwOK2TmcjnMz89jfX2dV97Z2Ynx8XHZR4wQRDEqlQoOhwMOhwNDQ0PIZDJc1JXf7y9rJ5rNZrG1tYWtrS0Ae8J4sQ05jY/kBQmh0oYEUHlQPO6jfpQ2UojI3o9y+bVDoRA3dqiUXzsUCiEUCvHyaxfGDXLIr01ip3yhHNnKRPgso43uBCEuaNWOqJpmW4kLoYjso6H09mNZFouLi7h69Sqv3Gg0wuPxwG63l3ynXN5nWqitjFhFWjEhxjZKJBLwer0Ih8O88sHBQZw+fbppEzfKkV0eikDlc5h7KB6Pw+v1lkSdnjp1CsePH5f8IpqSoPuhMWi1WnR0dKCjo4OzEy1EXYVCobLjxng8jng8jtXVVS7qyu12w+VywWKx0H0lI6gvpQ31n3ShDQnyQYw5so+CRqNBW1sb59hVnF87EAggnU6XfKdcfu2CqO1yuaDT6Zr6N9QDErLlC0XbKxMSsglC3JCQTRwIy7LI5XLcIlazrMSFkDX20VCykJ1KpTAzM4NAIMAr7+zsxNjYGGclLqRcRDZRmeLJm9KusWoRm5C9s7ODmZkZZDIZrkytVmN8fBxdXV1NrQtZi5eHrMX51CpklrvGm2WX32wYhqFrhDgyxXai/f39yOfzCIfD3OK0MHccwI+6WlxchFar5aK16ZokiOZDAqg8KO5HEsmkjdSsxWulXH7tQm7t/fJr+3w++Hw+AHuCUUHUdjgcohcPiwN7ALpH5QblyFYmsViM99lqtbaoJgRBlIOEbGJfhFbi+Xy+qVHYxQiFWMpXXBtKFbL9fj9mZmZ4u4IZhsGZM2cwMDCw7zVULiKbqAzZ1x+MWITsSg4FrcwVTNbi5SFrcT7VCtksy2JpaQmXL1/mldtsNng8HphMpobVkWgcFJHdfFQqFZxOJ5xOJ06cOIF0Os2zIS8XdZXJZLC5uYnNzU1e+e7uLrnbSAS6v6QN9Z88oA0J8kHq1uK1UG5DXDX5tWOxGGKxmGTyayupT5UIbVJQJsIc2UajsUU1IQiiHCRkExXJ5/NcBJMwCrvZIjaAkvyVlK+4NpQmZLMsi6tXr2JxcZFXvp+VuBDh9aWEdjsKYhFpxYwY2iidTmN2dpazdSvQ3d2Ns2fPtjRXsEql4tqFFmH3oIhsPtUImdlsFufPny8R0Xp6enD27FkaOxDEEdDpdOjs7ERnZydYlsXu7i7Phny/d9v6+jo2NjbgcDi4iG2z2UwCjcih/pEeJIDKA+pH+SA3a/FaqJRfuxCxXU1+bY1Gw22qc7lcMBqNLb8nlNynckcYbU9zR+VQ/Dwym83U9wQhMkjIJkrYz0q8YCHeDCtxIcIXSDabpZdKDShJyE4mk5idna3ZSlwIWYvXhhhEWrHT6jYKhULwer1IJpNcGcMwGBkZQX9/f8sXBIqFbLqG9qCIbD4HCdmxWAzT09O8SaiYrnHiaFBEtrhgGAYWiwUWiwUDAwPI5XI8G3KhPR+w9xwr/DuwJ4wXRG2p5sgkCDFD7z3pUjzuo36UNsIxi5L7s1x+7YKoXSm/djabxfb2Nra3twGII782CdnyhfpWuRTPXcxmcwtrQhBEOUjIJniwLItMJsMNtFtpJS6EomOPhlLyF+/s7GB2dvZQVuJCyFq8NpRyjR0FoZDdrBQJLMtiZWUFFy9e5C2kGAwGTE1NVeVQ0AxItC2FhDs++7XH5uYmZmdnec8fvV4Pj8cDp9PZtDq2Gkq9QrQKtVrNCdLA3uL0z3/+833HBOl0GhsbG9jY2ACwl+LC5XLB7XbDbrfT4mGLoPeNtKFIXnlA/Sgf5J4j+yjo9Xp0d3eju7ubl1+74PQi1vzaJHbKF+E1RwFUyqHYWtxkMtGzmiBEBgnZBEcul0M2mwUgDitxIUK7WxLKaqO4/eTYdvl8HouLiyVW4iaTCZOTk4cS6igiuzYoR/bBCCe4zRCcstks5ufnuYl+gba2NkxMTIgq+o1stEtpxTUjZsoJ2SzL4sqVK7h27RrvWIfDAY/HA4PB0NQ6Eo2DNnZIC71eD41Gw407+/v7odPpuMXp/XJkLi8vc/m5C+I4LSgRRHXQ81EeFPcjiWTSRphvl95l5SmXXzsSiXAR27Xk1y6MHaxWa0Pam4Rs+SJcL6W+VQ7Frm4Wi4We1QQhMkjIJsCyLLLZLC8vqRisxIWUsxYnqkfO0bLJZBIzMzMIBoO88q6uLpw9e7ZqK3Eh5AJQG622zZYCwklQPp9v6MQoFovB6/WW2LsODw9jaGio5c91IXQNlSLso3w+r+hd4UIhs1LO94GBAZw5c4YWHghCROj1egwMDGBwcBC5XI7LkRkIBMrmyMzn8/D7/fD7/dz3i23IDzu+I2pDbGMF4mAoklceUD/KB+rLw6FSqeBwOOBwOACAl187EAjwoicLFOfXvnbtWsPya5OQLV+EfavkubeSYFm2JEc2QRDigoRshZPP55HNZsGyLPefWKzEhZCoeDTkmiN7e3sb58+f51mJq1QqnDlz5sj5UAsbOAoTTzm1WyOQ82aJelFOyG4UPp8Pc3NzvL7QarWYnJzkcpKJDbIWL6VcRLaSKb5GMpkMHn30USQSCa5MpVLh7Nmz6O3tbUX1iAZDEdnyQa1Ww+12w+12A9jblFhYmA4Gg8hkMiXfEVqJ2mw2TtS22Wy0iEwQZRDLXJ6oHRI/5YMwIps4HJXyaxfGDtXk1zYYDJyo7XQ6D+1ORkK2fKG+VS7CHNn07iUIcUFCtoIpRGIXC9iFyVJBwBbTQ5usxY+G3ITsfD6Pq1evlljJmkwmeDwe2Gy2upxHrVZz0f8krO0PWYsfTDOE7Hw+j4WFBSwvL/PKpWCzTNbipZSLyFYyxe1RLGADgNFoxNTUVN2e/4S4oWeEvDAYDOjp6UFPTw9YlkU0GuUWp8PhcNn+jkQiiEQiuH79OtRqNc+GvF4RV0qF7i9pQwKoPCge81E/Shvqy8YgzK+9u7uLYDC4b37tZDJZkl+7IGrXkl+b8ijLF+pb5VLs8kAR2QQhPkjIVjAMw0Cr1SKVSnEv6oJ4LcYdZxQdezSEQraU86zuZyU+NjZWsunhKBQL2XTN7Q/ZQh9Mo4XsRCIBr9eLcDjMKx8cHMTp06dF+Wwvhq6hUoTPaaWLC5XeW263G5OTk6LK+U7UH6mOW4jaYBgGNpsNNpsNx44dq8pKNJfLYWdnh0szYDAYOFHb6XSSDfkRoPtOeih9rCAXaEOCfKB8542HYRhYLBZYLJaS/NqBQADRaHTf/NrLy8tQqVSw2+3cxrj98mtT1K58ISFbuQitxendSxDigoRshVP8UBablbgQhmF4oiLlyK4N4eBLqnlWt7e3MTs7y7OcVKlUGBkZQV9fX92vX7LLrh4SIQ+mkUL2zs4OZmZmePeGWq3G+Pg4urq66naeRkLW4qWQtfhNUqkU1tbWSsqPHz+OU6dOiXb80iyU8PeTtbgyEVqJJhIJnpVouTlBMpnE+vo61tfXOWG8IGxbrVZacCYUgxLeDXKFxE/5QNbizac4v/bQ0BCy2SyCwSAXsV1uU1w+n+eOKc6vXdgUV+z2QkK2fKG+VSaUI5sgxA8J2QTUajUXnSv2iS5Fxx6ecjnGpSRk5/N5XLlyBUtLS7zyeluJCyG77OqRm319I2iEkM2yLBYXF3H16lVeucVigcfjgcViOfI5mgVZi5dC1uJ7hEIhTE9PI5VK8co9Ho9kNmo0GrpnCKVgNBrR29uL3t5e5PN5ng15JBIpuRdYlkU4HEY4HMbS0hJvYbpgQ07woeeJtKFIXnlA/SgfqC9bj0ajQXt7O9rb2wHsbXgriNq15NcuiNrCTXTUr/KheC1LCuvkRP2giGyCEDckZBMApLPDjISyw1NOyJYKiUQCMzMzCIVCvPLu7m6cPXu2rlbiQigiu3ooIvtg6i1kp9NpzM7OclaqBZpxbzQCuoZKUXpENsuyWFlZwcWLF0v+dq1WSyK2wqCIbEJIwQbUbrfj+PHjXMRVIBCA3+9HMpks+Y5wYdpoNMLtdsPlcsHhcEju3dloaBFPepBoJg+oH+UDRWSLD4PBUJJfuyBqB4PBsnPRYreXYhiGkazjIVGK8H6l569yKHZqkFJACEEoBZqlE5KieGGJRMXakKqQ3WwrcSEUkV09JEIeTD2F7FAoBK/Xy1ukZxgGIyMj6O/vl+SEi66hUpQckZ3L5XDx4kWsrq6W/XcpXuMEoWSasflAGHEVj8d5NuTlxr+JRAKrq6tYXV0FwzCw2+08G3J61hBSgzb6yAMSsuVD8fid+lJ8FOfXHhgYQD6fRzgc5jbGRSKRfb/PsiweeughbvzgdDpp/CBhiseKtDlBWRRHZJOQTRDig4RsQlKDq+JBBOXIrg2pCdmVrMTNZjM8Hg+sVmtT6kEuANVD0esHUw8hu1KEqsFgwNTUFOx2+5Hr2SpIyC5F+I5WyuJ0IpHA9PR0ycJRe3s7F0WplLYgbkIR2dKj1fMMk8kEk8mEvr4+5PN5RCIRng25EJZlEQqFEAqFcO3aNWi1Wp4NucFgaMFf0Xzo/pI2JIDKA+pH+UD5zqWFSqWC0+mE0+nE0NAQMpkMQqEQtynuoPzawN7GuoKoTWlMpAU5KCgTypFNEOKHhGxCUpCoeHiEQraYRaJKVuI9PT0YHR1tquUjibPVQ9HrB3NUITubzWJ+fh4+n49X3tbWhomJCeh0uiPXsZWQSFWKEq3F/X4/vF4vz4lDq9ViYmICyWSSE7LpOUMQRC2oVCo4HA44HA5uYToYDMLv9yMQCCCVSpV8J5PJYGtrC1tbWwD2hPFiG3KK1CHEDgmg0oWieOUDCWPSRqvVluTXvnLlCjcnKUc2m+WNHwr5tQvitlarbUrdidqhiGzlUrxJhYRsghAfJGQTkoKsxQ+PcAAm1oj2ra0tnD9/vsRKfHR0FL29vU2fxJM4Wz3Fk3KWZcGyLC26CDiKkB2LxeD1ehGLxXjlw8PDGBoakkVbU0R2KUqyFmdZFtevX8elS5d45VarFVNTUzCZTDybcSWI+gQf2uxC1BOtVouOjg50dHSAZdkSG/Jyz9t4PI54PI6VlRUwDAOHw8EtTFssFlm8i4XI8W+SOxTJK32E7zgSP6UNbUqQFwaDARaLhROyDQYD+vr6EAgEEAqFqsqvbbVauWhtu91OgqmIoI0nyiSdTvPWoclanCDEBwnZhKQG0mQtfnjEHpGdz+dx+fJlXL9+nVfebCtxIRSRXT3CQX4ul2tq9LwUOKyQ7fP5MDc3x7sGtVotJicn0dbWVtc6thISsktRSkR2NpvF3NwcNjY2eOXd3d0YGxvj3mHCDTPETaQ0niMIscEwDMxmM8xmM/r7+7n8mAVhOxqNlnyHZVnORnRxcRFarZYTtV0uF/R6fQv+kvpAz1dpQ/0nfYR9SO94aUPW4vKjeK6q0WgwMDDAy69d2BRXKb92NBpFNBrF8vIyVCoVl19bzhvjpAJFZCuTYltxgIRsghAjpDAQkoKsxQ+PSqUCwzDcJEpM7ZdIJOD1ehEOh3nlrbASF0LXXPWIfbOEWFCpVFzbHNRG+XweCwsLWF5e5pU7HA54PB7Z5eqkaMtSlJAje3d3F9PT0zy3AYZhcObMGQwMDPDagK4RZUP9TzSL4vyYJ06cQDqd5kTtQCCAdDpd8p1MJoPNzU1sbm4C2FsAKyxKU7QV0SpIDJEmJGTLC4rwlB+V+rR4/ACAl187EAggkUiU/S3hxrjCb1B+7eZTvO5H96tyEDofkpBNEOKDhGxCUpCoeDTUajUXyS6W9tvc3MTc3FxZK/G+vr4W1mwPshavnqPmf1YK1QrZlTZ4DA4O4vTp07KcVFFEdilyv6+2trYwOzvLc1nR6XTweDxwuVwlx5cT9mlxlyCIRqPT6dDV1YWuri6wLIvd3V1uUbqSjWgsFkMsFuOirYptyM1ms2SeXVKpJ3ETshaXPiRkywu6J+VHtZsThPm1E4kEgsEgF7FdvA5WIJPJ8PJrG41GTtSm/NqNp7hvaROichBGZFOObIIQHyRkE5IaSFOO7KMhJiE7n8/j0qVLuHHjBq/cYrHA4/GIZvcbWYtXj9wFt3pRjVi7s7ODmZkZ3sRWrVZjfHwcXV1dDa9jqyAhuxS5RmSzLIurV69icXGRV2632zE1NVXRbYCEbGVDEdnSRi59xjAMLBYLLBYLBgYGkMvleDbkwogOYO+dVvh3YE8YL7Yh1+l0zf4z9kUufaVUSDSTPsJxMPWjtKGIbPlx2Khdo9EIo9GInp4esCyLWCzGCduVNsYlEgkkEglefu2CqE2OL/WH7ldlQtbiBCF+SMgmJAXlyD4aYoloj8fjmJmZKYk07e3txcjIiKjyKoulzaSAcAJF7VWe/cRalmWxuLiIq1ev8srFtsGjUZBIVYpw4VIOAn8mk8Hs7Cy2t7d55f39/RgZGdl3wUCuwv5RKE4bQhBE81Gr1ZwgDQCpVIpnQ14u2iqdTmNjYwMbGxsAbi5KF2zIaeGUOAr0TpA+FJEtL0gYkx/16FOGYWC1WmG1WrmNcZFIhBs/RKPRst8r5Ne+ceMG5/hSiNim/NpHh3JkKxOyFicI8SMetYggqoBExaMhhvbb3NzE+fPneRsR1Go1RkdH0dvb25I67QdFiFYPRWRXR6VrKp1OY3Z2Fjs7O7zjxZArvlnQ/VaK3ITbaDSK6elpxONxrqyWdBJyaw+iNmizCyEF9Ho9uru70d3dzUVbFduQl7t2ixel1Wo1z4bcZDK1dFGaFsSlDfWfNBE+J0j8lDbkkiA/GrE5Qa1Wc7mxT5w4gUwmw+XO3i+/dmGMUZxfuxCxTfm1a4dyZCuT4vUJgIRsghAj8l8VJw5ESgNpobU4WYrWRittsqViJS5EDOK/VCAhuzrKibWhUAherxfJZJL7N4ZhMDIygv7+fsU850jILoVhGF7ErZTFO5/Ph7m5Od6z1GAwYGpqCna7varfkGOEOkHIGaW8vypRHG01ODiIXC6HUCjELToLbQyBvfGm3++H3+8HsCeMu1wuuN3upuXGlPK7hiDRTA5QRLa8oIhs+dGMPtVqtejo6EBHRweAw+fXLojalF+7OihHtjIpHpMzDEObQAhChJCQTUiKctbFSohSrBetyjEej8fh9XoRiUR45X19fRgZGRH14LC4biSY7I9wAkfCf3mEG0qWl5dx8eJF3oJVreKeXKBoy/KoVCrufpLicyifz+Py5cu4fv06r9zlcsHj8dSUG5YispUNPSMIqaNWq+F2u+F2uwEAyWSSZ0NeLnVSKpWCz+eDz+cDANhsNi5a22azkShClEDPR+lDQra8oM0l8qMVYme5/NqF8UM4HK6YX3ttbQ1ra2sA9sYQhYhtSmVSHorIVibFQrbFYqG+JwgRQgogISlIyD4arYjI3tjYwNzcXImV+NmzZ9HT09OUOhyF4jZjWRb5fJ4GNBUQ3p9SFNyaQfH1s729XeJS0NbWhomJiZrEPblAEdnlkbJ4l06n4fV6EQgEeOXHjh3DqVOnan6eCo+XWnsQ9YP6npADBoMBPT093KJ0NBrlLUqXu84jkQgikQiuX7/O2ZAW25DXGxJdpAeJZtKHhGx5QRHZ8qPVfVrO8SUcDnMR25XyaxfGEMX5tQsR25Rfew+KyFYmxTmyzWZzC2tCEEQlSAEkJIVQtKaIz9popk12Pp/HwsIClpeXeeVitxIXUk6cpclnechavDqK20mYh2d4eBhDQ0OKnUCSkF2e4utBSu0SDocxPT3Ns8xXq9UYGxtDd3f3oX6TIrKVjVKfjYQyYBgGNpsNNpsNx44dQzabRSgUgt/vr5gbM5fLYWdnBzs7OwD2hHG3280tStOGX4Kem9JEON6jfpQ2rRY9ifojtj5Vq9Xcprbi/NqFzXHF87ECxfm1gT0r88L4weVywWAwNPvPEAXFa6UkZCuH4rU5k8lE712CECE0syUk9XAuF5FNVE+zhGwpW4kLIReA6hHm8qX7szzlcllptVpMTk6ira2tBTUSD1IVbBuN0BlCCqyurmJ+fp5XX5PJhKmpKVit1kP/LgnZykbK7gQEUSsajQZtbW3c2CCRSHALzsFgsKwNeTKZ5CxEC8J4sQ15tfM+ur+kDUVkSx/hPSgGoYw4PHRPyg+xCdlCyuXXLowfKqUyyWQy2NzcxObmJoC9uVtB1HY4HIrJry32viUag9BanJ7VBCE+SI0hAIAnPokZoahYbvBFVKYZQrbUrcSFUN7n2lCr1VzfkxDJp+BSILT5cjgc8Hg8it3xXIwUBdtmICXxLp/P4+LFi1hZWeGVt7e3Y2Ji4sgLICRkV4Ym2wQhb4xGI3p7e9Hb24t8Pl9iQy6EZVmEw2GEw2EsLS1Bo9HwbMiNRmNV56Vni/Qg0Uz6kLW4vCBhTH5IrU+LxxDV5teOx+OIx+O8/NqFiG255tcupBMsIKUgHOLwsCzLE7LJWpwgxAkJ2YSkoIjso9FIITuXy+HSpUslVuJWqxUej0eyAwHK+1wbZA1dnkQiAa/XW7LQbDQa8bSnPU2Wk8DDIBSyWZalhTtI575KJpOYnp4uuc5PnjyJEydO1KUvSchWNlLa1EGUQn1WP1QqFex2O+x2O44fP16VhWg2m8X29ja2t7cB7EVaFURth8NBjkMygu416UNCtrwoHr9TX8oDqQnZxVTKr12I2D4ov/b169ehUqngdDq5DXJms1kW17Zwri21viUOT3GObLIWJwhxQrNVQlJQjuyj0Sghe3d3FzMzMyVW4v39/Thz5oykdzHS5onakIrg1kx2dnYwMzNT1lLcbDbT5KiIcnnWpfz8qBdSEO8CgQC8Xi/S6TRXptFoMDExwVna1QMSsg+G2oQQE7QI1ByKLURZli2xIS83fi1EWq2uroJhGNjtdk7YpjGcfKB7UJqQkC0vivuT5n7yQMpCtpDi/NoAkE6nEQqFDsyv7ff74ff7Acgnv7ZwvERrEcqhOEe2xWJpYU0IgqgECdkEAOlYi1MO3qPRCCHb5/Nhbm6O93tqtRpjY2Po7u6uyzlaCVmL10Zxeym9rViWxeLiIq5evcor12g0ZL9eARIpyyPm3OEsy+LGjRu4dOkSr78sFgumpqbq7sYhvEbE1h5EY5HCpg6CaDUMw8BkMsFkMqGvrw/5fB6RSASBQAB+v79spBXLsgiFQgiFQrh27Zqo3zvEwZC1uPQR3nfUj9Kl4DJVQOqiJ7GHnIRsITqdrmx+7cLmuFrzazudTsm4vlBEtnIRWovTe5cgxIc03iQE8UsYhuHl4KUc2bVRLGQfdVEql8thYWGhJA+qzWbD5OSkZK3EhZC1eG3U8xqTMul0GrOzs9jZ2eGV9/T0QKVSYXV1FYCy26gc5SKyCfHmDs/lcpibm4PP5+OVd3V1YWxsrCELFrTZgSAIojZUKhUcDgccDgeGhoaQyWS4BelAIIBUKlXyneJnayAQwGOPPcazIacIJXFD70bpI+xDElOkC21KkB/CPMpyvz+F+bWj0SgnaodCobLvnOL82gUrcynk16aIbGVCObIJQhqQkE1IjmIhW+kRn7VSPAg7yiaA3d1deL3ekoiOgYEBnD59WlaDPbIWrw2KyAZCoRC8Xi/PgothGIyMjKC/vx+XLl3iykmo5UNCdnnEGBkXj8cxPT3New8wDIPTp09jcHCwYYt0wmuEFuuVBUVkE8TR0Wq16OzsRGdnJ1iWRTwe50ValXvP7O7uYnd3FysrK2AYBg6HgxO2LRZLXZ/5mVQS6wsXsBv0I51IQKPVwWC1ovPkKVjd7XU7j5yhiGzpQ9bi8oE2JcgPJUftMgwDm80Gm82GY8eO8fJrBwIBXp7hAizL8vJrq9VqOBwOUebXVnLfKp1ia3ExXZMEQdyEhGxCcmg0Gi5yQKlC2WEpFmULu0hrHZitr69jfn6e1/YajQZjY2Po6uqqW13FgnDwQtfc/ig5RzbLslhZWcHFixd5CxYGgwFTU1Ow2+0AlN1GB0G20eURW0T29vY2ZmdneXnfdTodJicn4Xa7G3puisgmCIKoHwzDwGw2w2w2o7+/H/l8HuFwGAsLC0gkEmW/w7IsgsEggsEgFhcXodPpuMVol8sFvV5/qLqEfGu48JMHcPmRnyKdiJcewDAYGPdg9PY70Tc2SYvLVUILsdKEhGz5QBHZ8oPEzpuUy68dDAa5zXHl8mvncjlefu3icYTT6Wxpfm2KyFYuxRHZlCObIMQJCdkEAGkNpusVVaxEytlkVzvozuVyuHjxImeJXMBms8Hj8cBkMtWtnmKiYGdfGNCSsLY/SrUWz2azmJ+fL7FYbmtrw8TEBHQ6HVdGQnZlKNq2PGKJQmVZFteuXcOVK1d45Xa7HR6PB0ajseF1ICFb2YjlXiAIuaJSqeB0OmGxWDgh22azwWQyIRAIIJ1Ol3wnnU7z8mJaLBZuUdtutx+4CJyMRvDgP/8fLM9O7185lsXy7DSWZ6dh6+jCc9/wB+g+NXK4P1TmUES29CEhWz5QRLb8ICG7Mjqdjuf6kkgkeMJ2uTVc4TjCZDJxonaz82sLhWzqW+VAQjZBiB8SsgnJUbwYQtGxtVHOJruaQWEsFsPMzExZK/EzZ87IfnCnUqm4a42uuf1Rokgbi8Xg9XpLbLSGh4cxNDRUsvCkxDaqFrIWL48YrMUzmQzOnz+Pra0tXnlfXx9GRkaatludhGyCIIjmYjAYMDo6yuUPLNiHhkKhsu+kWCyGWCyG5eVlLj93QdgWWjVuLy3iWx/9ANLxMhHY+xDZ2sC3PvIB3P7GN+Pk02878t8oN+jdKH2K7y0SsaUNiZ7yQ9inFLVbHoZhYDKZYDKZSvJrBwIBhMPhffNrr66uclbmhYhtm83W0HuI+laZUI5sgpAGJGQTkqNYeCVRsTYOk+9ZaVbi5VCr1ZyFLglr+6O0HNk+nw9zc3O8v1Wr1WJychJtbW1lv0NCdmXIWrw8rbYWj8ViOHfuHC9vFMMwGB0dRX9/f1PrQkK2sqGIbIJoDuXuL4ZhYLFYYLFYMDAwgFwuh1AoxC1IFy8AFsjn89y/A4Ber+dE7fjGKn7wjx8GDnkv57NZ/Pgz/wSNTo9jU7ce6jfkCkVkSx/qQ/lA1uLygzYnHI5y+bVDoRAXsV0pv3Y4HEY4HObl1y5EbNc7l3Hxug7DMHS/KgSWZUtyZBMEIT5IyCYASGswTRHZh6cWIVupVuLlUJo4exSUYi2ez+exsLCA5eVlXrnD4YDH49k3rxMJ2ZUha/HytDIie2NjA+fPn+c9+/R6PaampuBwOJpaF4CEbIKQMnS/SpNK80S1Wg232w232w0ASKVSnGgdCAS4TaDFpFIp+Hw+XHr0YWz97EdHrhvLsvjhp/8Bd/3FB+Ds6T3y78kRKc3ziZsUPy9JJJM2ZC0uP0jIrg/CcURxfu1AIIBUKlXynXL5tQuitsvlgl6vP1KdivtWpVLRO1QhpFIp3noHWYsThDghIZuQHJQj+/BUK2RXskoeHBzE6dOnFTdQp80T1aMEkTaRSMDr9SIcDvPKq70/lNBGh4WsxcvTiohslmVx+fJlLC0t8cqdTic8Hs+RFwkOCwnZyoYisqUHLQAqB71ej+7ubnR3d4NlWcRiMZ4NeeGeDcx7EZj5Rd3Om0un4f3ON/G8//qHdftNqUPRvNKH+lA+kOgpP6hPG0O5/NqF3Nr75dfe2NjAxsYGgL1I2oKo7XA4as6vXbzeR/2qHIRr3yRkE4Q4ISGbkBwkKh6eaoTstbU1XLhwocRKfHx8HJ2dnQ2voxhRSpRxPZB79PrOzg5mZmZ4UUZqtRrj4+NVW+2TkF0ZshYvT7PFu3Q6jZmZGW6newExbGYiIZsgCKLxHPXZyjAMrFYrrFYrBgcHkcvlEAwG8Yuv/1tdRewCi794FM945WthsNrq/ttShN6N0oeEbPkgvB+pP6UPCdmNpzi/dl9fH/L5PKLRKBexXSm/9u7uLnZ3d3n5tQsR29Xk1y7uW8qPrRyKbcUBshYnCLFCQjYBQFqDacqRfXj2E7Kz2SwuXryItbU13jF2ux2Tk5OKshIXIndxtp7IVaRlWRaLi4u4evUqr9xiscDj8dS0Y7Nc1DFNfvcga/HyNNNaPBwOY3p6GslkkitTqVQYGxtDT09PQ89dDYVcZYVrQ07PGeJgKCKbIJpPPeaJarUaF779H1j5xSN1qFEp+WwWCw8/CM+v/2ZDfl9qkAgqfagP5QOJnvKD+rT5qFQq2O122O12Xn7tQsT2Qfm1l5aWoFar4XQ6uYhtk8lU8nwtXu8jIVs5UEQ2QUgDErIJyUER2YdHOMAutB9Zie8PRWRXjxzbKp1OY3Z2Fjs7O7zynp4ejI6O1mxXRUJ2ZchavDzNshZfW1vD/Pw8r92NRiNuueUWWK3Whp23VoqFbBIzlUW5iHxa4CcI8fOdez+IlTlvQ89x5dGfkpBdBnpGSpPisRj1obQh0VN+UJ+2nnL5tQui9n75tXd2drh1nXL5tYU5sgllsLu7y/tMQjZBiBMSsgnJQTmyDw/DMFCr1ZyAncvlsLq6iosXL5KV+D7Q5onqkVv0eigUgtfr5UWnMgyDkZER9Pf3H2phicTaypC1eHkaHZGdz+exsLCA5eVlXnlbWxsmJyeh1Wrrfs6jQFG5BEEQjaVez1aWZfEff/NebF9frMvv7Udow4etrS24XK6aNxnKDYrmlT7FfUhiirQha3H5IZyPUZ+2Hp1Oh66uLnR1dfHyaxfE7XJrU+XyaxdDEdnKgSKyCUIaKHuGR3BIaeBF1uJHQ6VSce22srKCcDjM+3e73Q6PxwOj0diK6okSuYmzjUQu1uIsy2J5eRkLCwu8xQeDwYCpqSnY7fZD/zYJ2ZUR2kaTSLlHIyOyk8kkvF4vQqEQr/zEiRM4efKkKMcHJGQrF4rIJgjpkM/n8bX3vRMh39rBB9cBNp/H7PQ01DodlxPT5XLBZrMp7jlB70bpQ5sR5ANF78oPYdQu3aPiolJ+7YKovV9+7WJisRiWlpbgcrlgtVrp3pUxwhzZJGQThDghIZuQHMLoWFrErA21Wo1MJgMAJSL2sWPHcOrUKRqgCZCjXXajkENbZbNZzM/Pw+fz8crb2towMTEBnU53pN8nIXt/ijfbUNvs0SjhNhgMwuv18qzX1Go1JiYmRO3IQUI2QRBE8zjMPCuXyeBL77oHiXCo/hXaj19uhivOianRaDhR2+VywWAwNLdOLYbmydKEhGz5ILSJp/6UPmQ/LS2K82sfP34c2WwW4XCYi9gWCtgFstkslpaWqs6vTUiX4mtArVZDr9e3sDYEQVSChGxCcgjtXXK5nOLt46qFZdmywpBWq8X4+Dg6OjpaUCvxQxHZ1SP1iOxK+eKHh4cxNDRUl8kKCdn7Q0J2KfW2Fq/kOGA2mzE1NSX6HcgkZCuXchHZBEHUn6PcW+lEAl985x8hk4gffHAdYVQqMGVsQLPZLLa2trC1tQUAMJlMnKjtcDhkOY8kEVT6UB/KB+pL+UFCtrTRaDS8/NqpVIrLrb21tVV2vi3Mr63X6zlR2+l0kvApcYqFbIvFQvc1QYgU+c3aiEMhpQE1CdmHI5vN4sKFC0in07xyh8OByclJshLfB8qRXT3CiGwpOSb4fD7Mzc3x+lir1WJychJtbW11Ow8J2fvT6HzQUqSe1uK5XA7z8/NYX1/nlXd2dmJ8fFwS71MSsglCmtD9Kn9iwQC+8p63ISeYbzSDtoFjeMYznnFgTsx4PI54PI7V1VUwDAO73c4J21arVTLj1v2ge036kPgpH0j0lB/F7xbqU+mj1+u5/NrJZJJLuWUwGJDJZMqOJVKpVEl+7YKoLddNcnKmOJCFou0JQrzQk5WQHMIBAQmLBxONRuH1ekssc6xWK572tKfR4PsA5GCX3SzKibTCzSdiI5/PY2FhAcvLy7xyh8MBj8dTdwtKErL3p5H5oKVKvcT9eDwOr9eLSCTCKz916hSOHz8umQkbCdnlkUr/HQWKyJYeSrgu5U61fRjZ3sRX/uJPwbZobnbm2c8vyYlZbB0ajUZLvsOyLEKhEEKhEK5duwatVsuzIZdihJXwuUj3oDQR2lET0oX6Un4U96nY1zqI2iju27a2Npw8eZLLrx0IBBCJRCrm197d3cXKygq3Sa4QsU35tcVPcY5ss9ncwpoQBLEfJGQTHMwvc4qJnXIR2UR5WJbF6uoqLl68WFb8oAFVdZC1ePVITchOJBLwer0l+eIHBwdx+vTphtwfJGTvj9Tt6RtBPcT9nZ0dzMzMIJPJcGWNcBxoBhS1TxAE0VhqfdfsLF/HN/7qz4EWzSW1BgNOPv02XplKpeLyWZ44cQLpdJqzDg0EAkilUiW/k8lksLm5ic3NTQA3I6wKNuRiHtMWICFbHlBEtnwo7ktae5EHFGUvX4SbFMrl1w6FQtx4olx+7eJNcsX5tQsR2xTxKz6E1uLUPwQhTkjIJiSHcAEhm822qCbiJpvNYn5+Hj6fj1eu1Wo5IYNE2eqgiOzqEU7kcrkctFpti2qzP+WEPbVajfHxcXR1dTXsvCRk7w8J2aUcJQKZZVksLS3h8uXLvHKbzQaPxwOTyVSXOjYTisjmo6SJNkVkE3KFZVnsxNIIxDNIZvOw6NRos+hgN7Z+DHXQM2b5vBff/dgHm1Sb8px65nOgPcBBR6fTobOzE52dnWBZFvF4nGdDXm7MURxhVVjMdrvdcLlcMJvNknj+SqGORCkkfsoHisiWHyRky5eD+laj0aCtrY3bCF6cXzsQCJSkcgTK59cuiNoulws6na5Bfw1RDSzL8qzFKSKbIMQLCdmE5KCI7IOJRCLwer08exQAOH78OJLJJCduk0hUHRSRXT3C+1OM1xjLslhcXMTVq1d55RaLBR6PBxaLpaHnJyF7f0ikLOWwEcjZbBbnz5/nIssK9PT04OzZs5KILCsH2c8TBCEXIsks/nN2E/9+bh1L/kTJv9/Sb8Orn9KDO860QasW32L55UcfwoOf/XhL66AzmeF50W/V9B2GYWA2m2E2m9Hf3498Po9QKMQtRBcvaBbI5/MIBoMIBoN759XpeDbkYlmIpohseUAR2fKBRE/5QX0qX4rX+6qZKxfn1y7eJFcYL1TKr+3z+bh1WYvFwonaUnF/kRvFa+cUMU8Q4oWEbIJDKtbilCO7MpWsxLVaLSYmJtDe3o75+XmunKLZq0MYkc2yLA1sKiB2kTadTmN2dpbbDVugp6cHo6OjJc+XRiD2Nmo1FJFdymGE21gshunpaZ5NFsMwGBkZQX9/v6SfYbTZQblQRDYhF8KJDP7hwev4z/ObSGQqv+vOrURwbiUCl1mL1z61F2/4lb6mCNrV3FsXfvJDPPz5zzS8LvvCMHjRW98Fs8N1pJ9RqVScIA3sjRcLonalCKt0Oo2NjQ1sbGwA2FuILvyG3W5v2UI0PRflAQnZ8oGi6+UHCdnypXh9uda+LbdJLhKJcBHblfJrx2IxxGIxXn7tQsQ2pYNsDkJrcYIgxAkJ2YTkUKlUPNGdxNg9stks5ubmuMWUAk6nE5OTkzD80m6PbLJrp1yUMe2SLE85a3GxEAqF4PV6kUwmubJWCHvC89B9yIeE7FJqjcje3NzE7Ows7/7T6/XweDxwOp0NqWMzISGbIAgpsxJM4M1fPo/lYPLgg39JYDeD//3gdTy5HMaH7xqBWd/aafzj//Hv8P6/+1taBwC4881vRcfQybr/rk6n40VY7e7uIhAIwO/3IxwOl30XFxail5eXufzcBWG7ldE9JIJKE7Kjlg/Ul/KDhGz5IsyRfRRUKhUcDgccDgcvv3YhYvug/NrAXiCX0+nkxhRGo5GeIw2guC+kkjqGIJQICdmEJFGr1ZyALSahrFVUshIfGhrCyZMneYPr4sEYtV11lBNnScgujxitxVmWxfLyMhYWFniil8FgwNTUFOx2e1PrwzAMVCoV1zZiaCMxQSJlKdVGZLMsiytXruDatWu8cofDAY/Hw21okjp0jSgXisgmpM7FjRhe/6/efaOw9+ORa0G8+d/m8On/Mg6DtjljUeF99+BnP4HLj/60Kefejxe86U9w/ClPa/h5GIaBxWKBxWLBwMAAcrkcz4a83EJ0Pp+H3++H3+8HcDMfZiHCqpE25GQtLg8oIls+UES2/CAhW56wLFtXIVtIufzaBVG7kvtLNpvF9vY2tre3AVB+7UZQ2LBYgHJkE4R4ISGb4JDSBEmj0ZCQjb0X7srKChYWFngDLp1Oh4mJCW6AVAwJ2bUjRnFWrIjNNjubzWJ+fp7LP1Sgra0NExMTLRv4k5BdGYrILqUa4baSbf7AwADOnDkjq0UWErKVi5TGqgQhZGY1jNd/fha5/NGeW97VCP7i/17Gh152pun3xPf+6cO4Mf1EU89Zjt969/9E54nhlpxbrVbD7XbD7XYDuLkQXfgvk8mUfEeYD9NqtfJsyOv5jqb3ojwg8VM+kOgpP6hP5Ylw7aHRfavX69Hd3Y3u7m5efu1AIIBQKFR1fu2CsE35tQ9PcVAYRWQThHghIZuQJMUvZ6Vai2cyGczPzx9oJS6EhOzaEQ4Gqd0qIyYhOxaLwev1IhaL8cqHh4cxNDTU0sEpibWVobYpRRiRzbIs7/qNRCKYnp5GIpHgfefs2bPo7e1tal2bAQnZRAHqf2mh5P56bCmIN335PHJ1aoLvXdzGK27pwtOPNSZdhLCvWJbF//27/4mNKwsNOV+1MCoVXv6XH4Szp6+l9ShGuBAdi8Xg9/sRCAQQDofLXvfRaBTRaBQ3btyAWq3m2ZAf1TaUIrLlAUVkyweyFpcfJGTLE+E6XzNF4Ur5tQsR2wfl115eXubl13a5XLBarfTMqRLKkU0Q0oCEbEKSKF2MDYfDmJmZKbESP3HiBE6cOLHvYFrpbXcYxJz3WWyIpa18Ph/m5uZ459dqtZicnCzrVNBsSKytDImUpZSzUy6Ura+vY25ujncdtco2v1nUmjNcacj5viFrcelBC2jAAwvbeNvXL9b9d7/ypK9hQnYxLMvi39/7DoQ31ht+rv1QaTR45V99BLb29pbWYz8YhoHVaoXVasWxY8d4+TADgUDJ3A3YGyvv7OxwjioGg4FnQ67Vao9cJ0J6kJAtHyi6Xn6QkC1Pmh2RvR/F+bUBVDWeKM6vfe3aNcqvXQNkLU4Q0oCEbEKSKFWMrZTrdz8rcSHCthNG9hGlkLV49ahUKjAMw12fzW6rfD6PhYUFLC8v88rFliOYhOzKUNuUInxGF9rl0qVLuHHjBu/f3G43JicnZZ0vizY7EAQhFb5/cRt/+o36i9gA8KNLO9iMpNBp0zfk9wEgn8vh0U98GOlYtGHnqAa1To/XfPAfYLBaW1qPWhHmw0wkElwuzEAgUNZZLJlMYn19HevrexsHbDYbXC4X3G43rFbrgQvrFJEtDyiKVz6Q6Ck/qE/lSSsjsg+iUn7tQsR2Nfm1DQYDJ2o7nU5ZrxfUAuXIJgjpQEI2wSGlCZJGc/PSVYqQnclkMDc3h83NTV65y+XCxMRE1QJdOVFWTAM0MULW4rWhUqm4NmqmEJlIJOD1ehEOh3nlg4ODOH36tKgmmSTWVobaphThtZtMJjE/P49gMMgrP378OE6dOiWp9/lhEFqtE3vIvd8BisgmpMXnH1vF3z1wrWG/n2OB/5jdwJueNVj332ZZFtl0Gsvf/DLymdLF0WaiN1vwmg/9EzQyWHA1Go0wGo3o6ekBy7KIRqOcDXkl29BIJIJIJILr169DrVZz0dqF6CohJGTLA4rIlg/Ul/KDhGx5IqaI7IMQpjXZ3d3lNspVyq+dTCYpv3YZCvnJC5C1OEGIFxKyCUmitIjscDgMr9fLy38K7FmJnzx5sqYJUTlRVqkDlmoRi122VCgWspvVVjs7O5iZmUEmk+HK1Go1xsfH0dXV1ZQ61AKJtZWhaNtShM/4xx9/nLfrWszXeiOga4QgCLHz70+sN1TELjDvizXkdzPJBG7c/0WwudKI4WZiaWvHq//mo1DJcK7CMAxsNhtsNhuOHz+ObDbLi9YWzvuAvXF1cXSV0Wjk2ZBrNBp6L8oEsqOWDyR6yo/iNQ7qU/kg5ojs/WAYBhaLBRaLpSS/diAQQDQaPTC/tkqlgt1u5yK2lZRfO5FI8NqHhGyCEC8kZBOSpHhAUc6STS6wLIsbN27g0qVLJVbik5OTcLvdNf8mibK1Q9bitaFWqzlBudFtxbIsFhcXcfXqVV65xWKBx+MR7SCUhOzKUNuUInxuF4vYZrMZU1NTor3WGwEJ2cqFIrIJKfDhH17Dv/x8tSnniiTrPw+KbG9i7l//D9Di+8vZ24e73/dBxYgEGo0G7e3taP9lDvB4PM4TtsvN2RKJBNbW1rC2tsYJ41aB/bpSFqLlBkXxygcSsuVHcZ9KRewkDkZKEdn7UZxfe2hoiNsoVxhTlMuvnc/nuWOK82sXNsrJOb92LMbfFKqkdRWCkBokZBMcUnopKcFafD8r8cnJSej1h8uHV9x2gHzbr54wDMPL+0xttj/NEiLT6TRmZ2exs7PDK+/p6cHo6GjJtS4mSKytDLVNKZXEuo6ODkxMTIj6Wm8EJGQTBCFWPtpEERsAsrn6PgO3lhbxHx/4Hy0XsTtPnsZvvesvW1qHVmMymWAymdDb21sSXRWJREqOZ1kW4XC4JMXOzs4OtFpt1WmoCHFAQrZ8oL6UH7Q5QZ5INSL7IIQb5ZLJJG+jXLGrYYFy+bULorbc8msLhX3KkU0Q4kVZK5+EbJC7tXgoFMLMzEyJpdzJkydx4sSJI02AKCL7cKjVai76n9psf4qvsUa1VSgUgtfrRTKZ5MoYhsHIyAj6+/tFv0hAYm1livuO2mYv2mp+fr6kfHh4GENDQ6K/1hsBCdnKhSKyCTHz7m8u4P/NbTX1nFZD/RZZfZcv4f/+3V/W7fcOy9DTnok7/uCPW10NUSGMrspkMtwitN/vRyqVqvjdpaUlLC0twWQy8WzI5bJAL1dI/JQPJHrKD+pTeSJcu5Lrs9dgMJTk1w4EAlxEdrk1mGQyifX1dayvrwMArFYrF7Ftt9slPabY3d3lfaaIbIIQLyRkE5JErtbilazE9Xo9JiYmDmUlLqRcjmziYIqFbBLX9qf4Gqt3W7Esi+XlZSwsLPDuEYPBgKmpKdjt9rqer1GQkF2Z4rZRukjl9/vh9XpLdkmfPXsW/f39LapV6yEhuzzFbcGyrGwXXwhCbORZFu+8/yK+d3Hn4IPrTK+jPlG2S9NP4Af/9OG6/NZRGH3eC/Gs17yx1dUQPVqtFh0dHejo6ADLskgkEpyoXWkROh6PIx6PY3V1FQzDwG63w+Vywe12w2Kx0DtDZBT3IfWNtKG+lBcsy1IOe5kitIxXwv1anF97YGAA+Xwe4XCY2yxXzgEGAKLRKKLRKC+/dmGjnNTya5O1OEFIBxKyCQ4pvWjkGJGdyWRw/vx5bG3xIzncbjcmJiYObSUuhITsw9GMKGO50CiRNpvNYn5+Hj6fj1fe1taGiYkJSdkbkVhbGRL5966J69ev49KlS2X/3eFwNLdCIoOi9pULRWRLGzn2F8uyeMuX5/CzpWBLzv8bYx1H/o3zD3wHj/7bv9ahNkfD86KX4ml3varV1ZAcDMNwNuR9fX0Ih8N48skn9/0Oy7IIhUIIhUK4du0atFotF63tcrnqNu8kDg8JZfKB+lJeyCWPMlFK8TqfUvtVpVJx9uEFB5hQKMRFbB+UXxvYszIviNoulwtGo7HZf0ZNkJBNENKBhGxCksgtR3Y5m2SgMdaxJGQfjkZGGcuNRgiRsVgM09PTJbY/UrVXJrG2Mkpvm2w2i7m5OWxsbFQ8RontUgxFZBOEdJDa+7kWMrk83vj5GcysRVty/tMdZkz22o70G09882s493+/XqcaHZ5n/87vYeS5L2h1NWSB8J679dZbuYjtQCBQ1oY8k8lgc3MTm5ubAPbyQxZEbYfDIWnLUKlC1uLygSKy5QUJ2fJFGJFN7DnACPNrF0Tt/fJrb21tcUFahfzaBXFbq9U29W84CKE4T0I2QYgXErIJSSKMyJaqhWYh6u7y5cslVuKTk5NwuVx1P6dKpQLDMNz5SMiuDorIrp56t5XP58Pc3Bzvt7RaLSYnJ9HW1nbk328FShdr90PJIuXu7i6mp6d5u4IZhsGJEydw9epVrkxp7SKEHA2UC0VkE2Ihm8vjtf/ixYWN2MEHN4hXPaXnSPOfn/3b5zD3wHfrWKPD8cI/egeOeZ7S6mrIBuFzUavVwmazobOzEyzLIh6Pw+/3IxAIIBQKlR2H7u7uYnd3FysrK5xlqNvthsvlgtlsluS8W2qQkC0fKCJbXpCQLV+K15tIyC6PwWBAT08Penp6wLIsYrEYJ2pXGlOUy69dELXFkF+7OFhGq9WSKw1BiBgSsgkOKU2QykUVF0dpS4F0Oo3z589je3ubV15vK/FyFOd7JlG2Oigiu3rq1Vb5fB4LCwtYXl7mlTscDng8HhgM9ckL2QpoY0RllCryb21tYXZ2lns2A4BOp4PH44HJZOIJ2Upql3IoebMDQRCtJ5HJ4e5PP4mVYPLggxtEr8OA3xg/vK349//pI7g+/Ys61uhwvOjtf46+0fFWV0PWFL8zGYaB2WyG2WzGwMAAcrkcwuEwF60ttNcESi1DdTodz4ZcSql9pAQJ2fKheNxOoqf0ISFbvtC9WhsMw8BqtcJqtfLyaxcitg/Kr33jxg1efm2XywWLxdL0d16xkG02m5t6boIgakNayh9B/BKpC9nBYBAzMzNNsRIvBwnZtSPHvOyNoh5CZCKRgNfrRTgc5pUPDg7i9OnTkp9YKFWsrQaltQ3LslhcXOQJ1QBgt9sxNTUFg8GAdDpd8h0lQ0K2cqGIbKLV7KayuPvT57AWbp2IbdKp8Y+vPAuj9nARLP/5wfdj48pCnWtVG3mwUL3maSRiNwDhc3G/eaVareYWjwEglUpxkVWBQKBk/AHsbcbe2NjgUqBYLBaeDbnUx+higYRs+UDW4vKChGz5Qjmyj0Zxfm0AvPzagUAAiUSi5DvFm+UWFxeh1Wq532hWfm2hkK2k53Q0GsXDDz+M1dVV7OzswGq1oqenB2NjYzh16lTdzpPJZPDII49geXkZPp8PFosFPT09mJqawrFjx+p2HkL+SEf5I5pCseW0mBGK1lIRFlthJV4Oii6uHYqgrZ6jttXOzg5mZmZ4+XbUajXGx8fR1dVVlzq2GqWJtbVQPHGQe9tkMhnMzs6WOHP09/djZGSEu06Ekym5t8tBkJCtXJS0sECIj2A8g9/65C8QjGcPPrhBaFQMPvnb4zjZXnvECJvP4+t/9ecIrNxoQM2qJ4c8vv68daRC9+Pu5OvhNrhbWh+5UYuQLUSv16OrqwtdXV2cZWhhATocDpcdf8RiMcRiMSwvL3OL2AVh22Qy0XP7kJCQLR/IWlxekJAtXyhHdn0R5tdOJBLcZrlgMFg2v3Ymk+Hl1zYajZwNeaPyaysxIvuRRx7BX/3VX+GHP/whzxGwmMnJSbz5zW/Gm970pkOPQ7a3t/G+970PX/nKVxAIBCpWB8BHWJb9+qFOQigKErIJSSIcVFR68IqJSlbibW1tmJiYaKotW3H7SaHtxACJ/9Vz2LaqFJlqsVjg8XhgsVjqVsdWQ0J2ZYT5j1mWleUCXjQaxfT0NOLxOFemUqkwOjqKvr4+3rHCBRKli7ckZJdHjvfJQVD/E81iJ5rCyz79JEKJ1orY9712Ap4+W83fzeVy+Mq734pYYKcBNauejCqHrz5vDWk9C7DAf17/T7zxzBtbWie5cRQhW/i9gmXo4OAgcrkcL7KqeOG3QD6fh9/vh9/vB7AnjBfbkDdiAVquUBSvPGBZluyKZQYJ2fKFcmQ3FqPRCKPRWFN+7UQigbW1NaytrQFoTH5tJUVkZzIZ/PEf/zE++clPHnjszMwM3vKWt+DLX/4yvvCFL6C/v7+mc33nO9/BG97wBm5Twj48E8AzGYb5IoA3sSxbOsAkiF9CQjYhScpZi4uZclbiDMNgeHgYx48fb/qLkkTZ2iFr8eo5jEibTqcxOzuLnR3+AmtPTw9GR0cllTqgGkjIrkw50VZukwmfz4e5uTnes8RgMGBqagp2u73keLJT5kNCtnKhe4FoBSvBBO7+9JNIZFr3vtZrVPi33506VCR2NpXEF9/5x0jtluY/biZJTQ5fe94astqb9+03l76J3zn1O9Co5DXOayWNei6q1Wq43W643XsR9KlUCoFAAH6/v2JkVSqVgs/ng8/nA3BzAdrlcsFut5MAVAFhH1I7SZd6bSwhxINwLYjuT/lAm06ahzC/di6XQyQS4TbLRaPRst8T5td2OBycE8xh8muzLMsTsuUUPCMkm83iJS95Cb73ve/xyrVaLZ7+9Kejr68Pu7u7mJmZwfLyMvfvP/3pT3HnnXfikUce4caAB/Hggw/ipS99KS9FDcMwuOWWWzA0NIRQKITp6Wnh+u9rANgYhnkpy7K0SEqUhWaMBA+yFq8vLMtiaWkJV65c4bWrwWDA5OQklzuk2ZAoWztkLV49tYq0oVAIXq+3ZKPHyMgI+vv7ZTnhF0YdEzcpZ6Mtl4lkPp/H5cuXcf36dV65y+WCx+Op6MxB1uJ8lGQ/TxBEa7nuj+OVnz3XUhHboFHh33/vFhx3m2r+bjwSxr+9+63IplqX0xsAdnVZfO35a2AFr/OtxBYe33ocz+x6ZmsqpgAaNY7W6/Xo7u5Gd3c3WJZFNBrl2ZCXG98WL0Cr1WqeDbnRaJTlmP8wkPgpH2hTgvygiGz5QhHZraMwJnA6nThx4gQymQyXO3u//NqFcUdxfu1CxHa1+bWLHfLknBLlv//3/14iYt9zzz34y7/8yxJt4vvf/z7e8pa34Nq1awCAS5cu4a677sKDDz54YPusrq7irrvu4onYt912Gz796U9jZGSEK0ulUvjkJz+JP/mTP8kAKFj2vATAXwP488P+nYS8ISGbkCRSsBavFGHa3t6O8fHxplqJCyEhu3Yoir16qr2+WJbF8vIyFhYWSjZ6VIpMlQsUkV0Zudpop9NpeL3ektxAx44dw6lTp/ZdBGEYhrfRTC5tclgoIlu5UES2tJFaf82shvHGz8+ghRo2bAYNvvH7T0GnTV/zd2MBP/7t3W9FPtfaeVLAnMJ/PmcDqLDudTV8lYTsOtIKEZRhGNhsNthsNhw7dgzZbJZnQ168SFwgl8thZ2eHmysbDAZO1G5UHkypQEK2fCDRU35Qn8oXisgWD1qtFh0dHejo6ABwtPza6+vrGBsb43J1C1GCtfjFixdx77338so+/OEP4+1vf3vZ41/4whfikUcewW233caJ2T/96U/xla98Ba9+9av3Pdf73vc+BINB7vMzn/lMPPDAAzAYDLzj9Ho97rnnHvzJn/zJKwHcX/RPb2cY5pMsy96o+g8kFAMJ2YQkUalUvEV9sYmxwWAQXq8XqVSKK2MYBqdOncKxY8da/mIkIbt2KCK7eqoRabPZLObn5zmrwQKtyBnfCkjIroxw0iiH9gmHw5ienua5DqjVaoyNjaG7u7uq3yh+58mhTY4CORoQhHRo9Zj3sJxfi+D1n59BroWPW7tBg/98861wmWsfEwV9a/ja+94JtsXvC58zge/9ylZFERsAouny9pHE4RDDe1Gj0aCtrQ1tbW0A9hagC6J2MBgsuwk9mUxifX0d6+vrAACbzQa32w2XywWr1aooUYGEbPlAfSk/hPnrlfRskjsUkS1eyuXXLnaB2S+/9pve9CZsb29jeHgYz3rWs3DHHXfg+c9/PhexrQRr8Q9+8IO8NrrjjjsqitgFurq6cN999+H222/nyv78z/8cr3jFKyreH1euXMHnPvc57rNOp8O//Mu/lIjYxbAs+x8Mw3wOwOt/WaQH8D4Av3vAn0UoEBKyCR5SGlhrNBpuF5ZYhEWWZXHt2jVcvXpVVFbiQkjIrh1qs+o5SKSNxWKYnp7mDRgBYHh4GENDQ5J6Dh0WErIrIzcb7dXVVczPz/PeCSaTCVNTU7BarVX/DkUh34TaojJSSRFzWCgim2gGP7nixz1fnUe+hZdXl1WHb/zBU2A11B6VurYwj2/9/V83oFbVw4LFSnsCP3rq9oHH5lgaVzcSMYyrjUYjent70dvbC5ZleXkwI5FI2Wd5JBJBJBLB0tISNBpNiQ25nBGOfcXQh8ThoOhd+UFRu/KF+lYaFOfXHhwcRC6XQzgc5iK2i/Nrb2xsYHNzE8CePfalS5fw2c9+FjqdDh6PB89+9rN567smU+1pfMQOy7L41re+xSt7xzveUdV3n/vc5+KpT30qfvGLXwAAlpaW8OCDD+IFL3hB2eO/9KUv8drzrrvuwvDwcDWn+iBuCtkA8EqGYf6QZdnW5kYiRAcJ2YRkUavVohKyxWwlLoRE2doh4bF69ote9/l8mJub45VrtVpMTk5yURtKQBhRKqc80EdFLtbi+XweFy9exMrKCq+8vb0dExMTNVtmqlQq7r6RapvUCxKyCYJoFD+6tIO3fu0CWvlk6bHrcf8f3AqTrvZooKUnH8cPPvHRBtSqeliwuNQXw88nAgcfDMCqrX5TF3EwYo8AZRgGdrsddrsdx48fRzab5Raf/X4/z72mQDabxfb2Nra39zZGFOxCCzbkGo28lrUor7J8ICFbfpDYKV8oIluaqNVqbkxQnF87EAjgkUceKfuddDqNxx9/HI8//jgAwGq1IplMYmlpCSsrKxgYGGjmn9BQLly4wNMpdDodL8r6IH7t136NE7IB4Gtf+1pFIfv+++/nfX7jG99Y1TlYlr3IMMxjAJ7+yyIzgBcC+M+qK0ooAnmN+AlFUTywaHWO7EAggJmZGdFaiQshm+zaIfG/eoT5xFmWBcuyWFhYwPLyMu9Yh8MBj8ezr9WMHClnn00T4T3kYC2eTCYxPT2NcDjMKz958iROnDhxqHdC8Xek2Cb1hIRs5UIR2UQjud/rw//41pWW1mG43YQv/+4t0GtqHxNcfvQhPPjZjzegVtXDgsWTJ4OYO1W9XXifpa+BNVIeYheyhWg0GrS3t3O5K+PxOM+GvNy8q2AXura2xgnjhUVsq9Uq+r/5IKTWh0RlqC/lBwnZ8qX4fUN9K12K82u/973vxctf/nJ897vfxU9+8hP84he/4EVsFyiUfe9738Pg4CCGh4dx55134o477sDznvc8OByOJv8V9WN1dZX3eXh4GHq9vurvj4+P8z4Lo7sLbGxsYGZmhvus0Whw22231VBTPIibQjYA/DpIyCYEkJBN8JDSwFoMwmLBSvzKFf6il8FggMfjEe3LrnjXOomy1SEUZ4nKCAf9iUQCMzMzJaLe4OAgTp8+rchJghzE2kYhdWvxQCAAr9eLdDrNlWk0GkxMTKCjo+PQv0t5oW9CQjYfhmFkbylOEI3mi79Yxd9+/1pL6zDWbcHnXueB7hAi9rlv/wee+MZXGlCr6mHB4vEzAVwcilX9HYvWgmd3P7uBtVIexe8CKc3tC5hMJphMJvT19SGfz5fYkAthWRahUAihUAjXrl2DRqPhRG2XyyXJzbIkfsoHisiWHyRky5fivqWIbPkwMjKCkZERvO1tb0M2m8XPf/5zfP/738dDDz2E2dlZzum1mCtXruDKlSv4+Mc/DpVKhac+9am44447cOedd+JXfuVXahKCW00gwHdIqlWnEB6/srKCcDgMu93OK5+bm+N9npiYgNlsruVUPxN8PlvLlwllQEI2IVlaLcamUinMzs7C7/fzyjs6OjA2NiYqK3EhFJFdO2QFXT3Cdnn00Ud5g0O1Wo3x8XF0dXU1u2qigYTsykjVWpxlWdy4cQOXLl3i1dlisWBqaqrWQXwJFJF9ExKylQtFZBON4LM/W8a9P77e0jo8ZcCO+147AdUhBKuHv3gfLvz4Bw2oVfWwYPHTyR0s9cZr+t6LB18Mg0Z6QqOYkdNzUaVSweFwwOFwYGhoiLML9fv9CAQCPDe0AtlsFltbW9ja2gKwJ4y7XC643W44HA5JiBMkZMsH6kv5QUK2PCms8RWQwruCqB2NRoNnPetZeNazngUACIfDuOOOO7CxsYFwOFx2c3g+n8djjz2Gxx57DH/zN38Dk8mE5zznOVzE9vj4uKif7UJtotzYaT/KHX/hwgU84xnPKCkr5uTJkzWdB8Ci4PNorT9AyB8SsgnJ0sqIbL/fj9nZ2RIr8dOnT2NwcFDULzFAHNHsUkM4kM3lcjRxqYCwrYpFbIvFAo/HA4vF0uxqiQoSsisjxbbJ5XKYm5uDz+fjlXd1dWFsbKwuuRtJvL0JtQVBEPXiQz9YxL8+vtbSOrzgtBsfuXv0UCL2jz7zj7j68/L5/5oFCxbfefoGttzpgw8W8NLjL61/hQgOsc9Ja6XYLpRl2RIb8nJjxng8jng8jtXVVTAMA4fDwUVrWywWUbaR8O8QYx2J6qCIbPlBQrY8oXtVmZhMJly6dIn7/I//+I9oa2vDAw88gB/84Ae4fv16yXfi8Ti++93v4rvf/S6AvWC2O+64g/uvv7+/WdWvCrfbzfssXLM6iHLHX7p0qUTIvnr1Ku/zIfKM3xB8djMM42RZNljrDxHyhYRsgoeUJkmtyJHNsiwWFxdLHtBGoxGTk5OitRIXQkJ27QjFWSmIa62i0jXV09OD0dHRuoh6UkeKYm2zkFrbxONxTE9P83ItMQyDU6dO4dixY3V7r5K1+E1IyFYuFJFN1JMPPdB6EfvOM2348F0jh3pXfPvev8Xq3MzBBzaQPFh887Z1hO21z8V+beDXMGgdbECtlI3UrcWrhWEYmM1mmM1m9Pf3I5/PIxwOc8J2uRyYLMsiGAwiGAxicXERWq2WZ0MuFqtQiuKVDySOyQ8SsuWJcA2L+lYZxON8J6Hu7m687GUvwytf+UoAwOLiIh544AE88MAD+OEPf4hgsFRT3drawpe+9CV86UtfAgA8/vjjeOpTn9r4ylfJmTNneJ/X1tawurqKvr6+qr7/6KOPlpQJ00YCQCgU4n2uNa0ey7IxhmGSAIqtmuwASMgmOEhNICRLs63F97MSHx8fh1arbXgd6kWxKEs22dUhbB/aAFCeUCiE8+fP88oYhsHIyAj6+/tpIeaXSNU+uxlIKUf29vZ2SV4lnU6HycnJkp2vR4WsxW8iFPVZlqVnC0FIALG861iWxZ/dfxHfu7jT0nr8ztN68M47a7bdA8uy+Pr/fDcCK8LAheaSY1j8x7PWELXWPiY+4zyDd3re2YBaEWK5z5qNSqWC0+mE0+nEiRMnkE6nOVE7EAggnS51DMhkMtjc3MTm5iaAPeeogqhtt9tbZi0r7EOap0sX2pQgP0jIlifC+TVZiyuDWCzG+yx0jjxx4gROnDiBN73pTcjlcpienuaitR9++OGSsYXVaoXH42l0tWuiq6sLp0+f5kWef/7zn8e73/3uA7+7u7uLb3zjGyXl5TYLCtvSaDQeorZIgC9kWw/zI4R8ISGbkCzNjCr2+/2YmZnhvaSkZCUuhGyya4cisveHZVksLy9jYWGhZMI+NjaG3t7eFtVMnEgt6riZSEHkZ1kW165dw5UrV3jldrsdHo/nsIP2faGI7JuUi8qV2nuYOBwUkS09xHZvikXE/i+3Hk7EzuVy+Mp73obYznYDalU9WSaPrz1vDUlD7eOXYdswPvrMj1Ju7CYgtvuvmeh0OnR1daGrqwssy2J3d5cTtUOhUNmxdywWQywWw/LyMpefuyBsm83mprUniZ/ygSKy5QcJ2fJEuKZMQrYyEEZkm83miseq1WrceuutuPXWW/Gud70L8XgcjzzyCH7wgx/ggQcewPT0NG6//XZRBrm99rWvxXvf+17u89/93d/hda973YHrtO9973vLRl9XI2QbDIca5ycAOIs+KzsnJVECCdmEZGmGtTjLsrh69SoWFxd55UajER6PB3a7vSHnbTTlhGwxvmzFRLk2I/bIZrOYn5+vmGtlv8GgUiEhuzJib5tMJoPz589ja2uLV97X14eRkZGGTXopIvsmJGYqG4ZhuD6nvidqIc+y+N3Pz+DJlUhL6/GOFxzH63+l9vx5mVQKX373nyAZKV1QaiYpTQ5fff4aspra779ndT8Lf/W0v4JeLQ4LZzmiFGvxWmAYBhaLBRaLBQMDA8jlcjwbcuHiK7A31ir8O7AnjBfbkOt0uobVl4Rs+UD5zuUHCdnyhDadKJPd3V3eZ2FE9n6YTCbceeeduPPOOwHsufUJ7bXFwh/90R/h7//+7zlROhQK4dd//dfxne98p6KY/ZGPfAT33ntv2X+r5v445PuOJvfEvpCQTfCQ0sC60RHZqVQKMzMz3OS1QGdnJ8bGxiQt/JIoWztkLV6eWCyG6enpkgFgMdRWpYhdrG0lDMPwhCoxtU0sFsO5c+d4O3cZhsHo6Cj6+2sXJWqB8kLfhIRsgiBqJZPL479+YQbTq6URBM3k7c8/nIidjsfxhT/7b8imkg2oVfXs6rP4+u1ryNe4Z8uiseBDz/gQJtsnG1MxgoOE7INRq9WcIA3szfuLbciLU8YUSKfT2NjYwMbGBoA9+9BiG/J6ih4kfsoH4f1IfSl9itc2SOyUDxSRrUwOshavhfb2drS3tx+1Sg3B4XDgvvvuw913382VnT9/HiMjI3jzm9+MX//1X0dPTw8SiQS8Xi8++9nP4uGHH+aO7evrw+rqKu/3hAjbLpFIHKaqQmvD0p2GhKIhIZuQLI0Usnd2djA7O1tiJX7mzBkMDAxIfgJCQnbtkLV4KT6fD3Nzc7zrR6vVYmxsDNPT01wZtVUpJGTvjxgjLjc2NnD+/Hne9a7X6zE1NVV2IF9vyFr8JiRkV6b43pErYnw+EOImnc3hVZ+dxtWd+MEHN5C/+o1TeOlkV83fiwX9+PK73go21xgHqmoJmzK4/znrQI3r9m8afRNef+b1jakUUQI9F2tHr9eju7sb3d3dYFkWsViMZ0Nerk2j0Sii0Shu3LgBtVrNsyE3mUxHWi+giGz5QNG78oP6VJ5QRLYyEVqLH0XIFjt33XUXPvaxj+Ftb3sbd71Ho1F86EMfwoc+9KGK37vnnnsQDofxuc99jisjIZtoFSRkE5JFo7l5+dZLiK1kJW4ymTA5OSlZK3EhJGTXDrXZTfL5PBYWFrC8vMwrdzgc8Hg8JVZ7JNKWQkL2/qhUKq5NWt02LMvi8uXLWFpa4pU7nU54PB7o9c2xRyVr8ZuQkE0QRLXsprJ41X3ncCPQ2kjmv3/ZGfzqaEfN3/OvLuPr738X0OLn3JYtiW/ftgnUoKc9vePp+OAzPgidunEWzMT+kABaOwzDwGq1wmq1YnBwELlcDqFQiBO2y7lQ5XI5+P1++P1+AHvCuMvlgtvthtPprNnJTTiuIUFFuhSP2el+lAfFfUpRu/KBIrKViTAiW+5pEe+55x6cOXMG99xzDy5durTvsRaLBR/84Afxlre8BS984Qt5/9bVVboxV6iXbG9v11Q3hmEsKBWyQzX9CCF7SMgmeEhpcC2MyGZZ9kj1TyaTmJmZQTAY5JV3dXXh7NmzkrYSF0LRxbUjvLaUKmQXrGYKuVUKDA4O4vTp01CpVCWLL0ptq/2g62l/ihfsWvl8SqfTmJmZ4RYmCxRf782CIrJvQkK2siGbfaJaIokM7v70k9iIpg8+uEGoGOBjrziL24fdNX93Zu5h/PzefwRTi3rcAK53xPDgrf6DD/wlncZO/NOz/wk9lp4G1oqoBFmL1xe1Wg232w23e+8eTiaTPBvybLbUKSGVSsHn88Hn8wEAbDYbF61ts9kOHD9SRLZ8KO5L2pAgDygiW54I12PouasMijen6fX6kqAcOfLCF74Q8/Pz+OY3v4lvf/vb+NnPfobNzU1Eo1F0dHTg+PHjeOlLX4rXvOY1nGC9sLDA+41bb7215HeHh4d5n2/cuFFr1QYFnwMsywbLHkkoFhKyCclSLkK2OEq7FipZiY+MjKC/v192gxiKLq4dhmGgVqu5tlKi+L+zs4OZmRle3ji1Wo3x8XHejjxqq4NhGEZUUcdiQwxCVTgcxvT0NJLJm1F8KpUKY2Nj6Olp/uI8RWTfhIRsgiAOIrCbxks/9SSC8dJct81CBeBffmcSU/21OTrtZnbxv77xZ+j/QaClIjYLFld6Y/jZZKCq43WMDu9/2vvx3N7nNrhmxH7QO7GxGAwG9PT0oKenByzLIhqNcqJ2OBwu2/6RSASRSATXr1+HWq2G0+nk2ZALISFbPlBEtvwgIVueCCPt6X5VBsVCttyjsYtRq9W46667cNdddx147MrKCi8/dm9vL3p7e0uOGxkZ4X2+evVqrdUaEny+UOsPEPKHhGyiBKnkVxSK1ocRsvP5PK5evYpr167xyk0mEzweD2w225HrKUaEA24SsqtDpVJxbaWkNmNZFouLiyUDEYvFAo/HUzaPTHFbKV10qwQJ2ZVpdUT22toa5ufneec2Go245ZZbYLVam14fQBzivlgQLizQ/VPKUV1qxAzdC8RBbERSuPvTTyKSbF1Oaa2awX2vnYCnr3oRO8/m8c8X/xk//d6/4Znz7paL2BeORfCL0VBVx7/y5Cvx1om3NrRORHVQRHbzYBgGNpsNNpsNx44dQzab5dmQC3NvAntzyJ2dHezs7ADYE8bdbjdcLhecTic0Gk3JuIb6UbqQ6Ck/qE/lCfWrMhEK2fS+LeWHP/wh7/Ptt99e9rixsTHe59nZWcTj8bIb9ipwm+DzXLVfJJQDCdmEZBFGFWez2Zpyle5nJT42Nnbo6G4pIIyYVZIoexTUajUXjawU4SSdTmN2dpZbbCnQ09OD0dHRivdJq4VIKUBtVJlWtU2l/O9tbW2YnJxsaYoJsha/CUVklyefzyOVSsl6/EJIm2bcq6vBBF72qSeRzLbuvapXM/jq7z8Fx91VL9zg5xs/x3seew+GLmnwzMutF7EfOxPAwlDswGPPus7i3tvuhVmrnCgWKUELss1Fo9Ggra0NbW1tAPZSMhVE7WAwWNaGPJlMYm1tDWtra5wwXjzepD6UNmQtLj9I8JQnxWuilB9bOSg1IrsWPvvZz/I+/97v/V7Z47q7uzExMYHZ2VkAexrNww8/XJJfex9uF3z+Tk0VJRQBrXQRkuUo9tjb29uYnZ3lWSSrVCqcOXNGllbi5SAhu3aEednlTigUgtfr5VkrV2u5XzypU0JbHQYSJivTiojLZDIJr9eLUCjEKz9x4gROnjzZ8vcCWYvfRLhoRPfP3vN6dnYWqVQKer0eLpcLbW1tXHSXnKCIbGnRzGfn1a1dvPqfzyGVbd11Ydap8W+/O4VjVYrYG7sbePsjb8f12HVMXrLDs2hvuYj9k8kdXO8tjSQtxqlz4mPP/hhO2k82qWZEtVBEtngwGo2cBWc+n+fZkEcikZJ3GMuyCIfDJWVra2twuVwwGo3NrD5RB8haXH6QkC1PitesqF+VA0Vk78/DDz+Mhx9+mPt8+vTpihHZAPCyl72ME7IB4J//+Z+rErIZhjkD4OlFRbsAvl97jQm5I6+VLaIuSNla/CDy+TyuXLmCpaUlXrncrcTLoTRRth4oRZxlWRbLy8tYWFjgPQsMBgOmpqZgtx9sk1l8fSlddKsERWRXptltEwwG4fV6kUqluDK1Wo2JiQl0dnY2/PzVQBsfbkIR2XxWVlZw4cIFrh1SqRR8Ph98Ph8YhoHdbofL5YLb7YbFYqEJOiFLZtcieP2/ziCbb93zwGbQ4Ou//xR02Q52iErlUvjAkx/AD1Z/AAC4bdqFkz5Ly0XsH96yhdWuZMVj1FDjnVPvxEuOv6SJNSNqgYRscaJSqWC322G323H8+HFkMhnOhtzv9/M2Dgu5dOkSgL01i0JubYfDIbuNanKEIrLlBwnZ8kSYI5uQPyzLkpC9D/F4HG9+85t5ZX/zN3+z73de85rX4K//+q+59fJvfOMbuHLlCoaHhw863X8XfP53lmUrD4wIxUIjX0KylLMW349KVuLd3d04e/as4iaCJGTXjhLE2Ww2i/n5efh8Pl55W1sbJiYmoNPpqvodEmkPhtqoMs1qm0qbNsxmM6ampsrmf28VFJF9ExKy96hkhV8My7IIhUIIhUK4du0adDodJ2q7XK6W2uUfForIJoRMr4Txxi/MINfCR6PTpMV//MFT4DLvP05iWRZfXfwq/uH8PyDH7o2/73y8Hb071duQN4I8WHzrGT74nZmKx/zawK/hPbe8B2oVLfCKGXouSgOtVov29na0t7eDZVmeDXkgECg71ovH44jH41hdXeVtVHO5XLBarbQAL0JI9JQf1KfyhCKylUk8ftOBSO7W4tlstmrdIxaL4SUveQnm5+e5srvvvht33333vt8bHh7G61//etx3330A9lJVvuENb8APf/hDGAyGst9hGOa3ALyhqCgN4P1VVZRQHMpS7ghZUYu1eCUr8ZGREfT19Sly0kdCdu3IPSI7FothenqatysR2BuMDA0N1XSfyL2t6gEJ2ZVphlCVy+UwPz+P9fV1XnlnZyfGx8dFt7mJxLubkJC9Nymcnp4u2ZzX39+PbDaLYDBYNrornU5jY2MDGxsbAACbzcYJ2zabTZHjIULa/PDSDt72tQto5VOgx6bH137/KbAa9n9vzO3M4c8e/TOEM7+0DmaBX/tZJ7rC5Rd2mkUOLO5/7hpi5vLjtQHjAD7+vI/DZXA1uWbEUaFnujRgGAYmkwkmkwl9fX1YXl7G1atXuX8rN84RblTTarVwOp2csF1pwZhoLmQtLi/y+TxF2csUishWJkqKyP7kJz+Jr3/963jd616HF7/4xWhvby85JhaL4etf/zre8573YG1tjSs/duwYPv7xj1d1nve///24//77uXWKn/3sZ7jjjjvwmc98BmfOnOGOS6VS+NSnPgUAXxX8xIdZlr1R699HKANxrdISokAqD26VSsWb2JUTyypZiZvNZng8Hlit1qbUVYyQ0Fg7chb/fT4f5ubmeH+XVqvF5OQk2traav49JUSvHxUSsivT6LaJx+Pwer2IRCK88lOnTuH48eOifA+StfhNlC5kRyIRnDt3jidUazQajI2Nwel0IpfLgWEYpFIp+P1+BAIBBIPBsvdSJBJBJBLB9evXodFoeNHaev3B9sitgDZ1EAUevNx6EbvXrsf9f/AUGHWVp9WhZAjv+Nk7cCF04WYhC7zsJ92wx6tzumkUWSaPr92+hqSx9PlggAGvNb8Wvz7x6yRiSwiyFpcXOp0OT3va03jR2sWpcApkMhlsbW1ha2sLwN56R7ENeSOEmVwuh0AggGQyiWw2C71eD6vVqug1FiEkesoL4biT+lQ+FK+DkZCtHIqFbDG58TUClmXx4x//GD/+8Y/BMAyOHz+O06dPw+l0Ih6PY2NjA+fOnUM6neZ97/jx4/j+97+Pjo6Oqs7T19eHb3zjG/jVX/1V7rceeeQRjI6O4ilPeQqGhoYQDodx7tw5bG9vA0CxRdz/A/DeuvzBhCwhIZuQNBqNhouyFgqLiUQCMzMzCIVCvPKenh6Mjo6KLtqu2RT//XITZRuFHMXZSta0DocDHo/n0Lv5SaQ9GGqjyjSybXZ2djAzM8Nz6DjKpo1mQdbiN1GykO3z+XD+/HneNWA2mzE5OQmTycSlWSmO7urv70cul+Pl4iy2USuQzWZ5i+AWi4UTtu12Oy3WEaLiy0+s4QPfW2xpHc50mvGlN05Bqy5/b2TzWXzY+2F88/o3eeWqHPDyH/fClG7tXCSlyeHrt68hrRMszEOFOwx34DmG57SoZsRRUNI7Ua4Io3i1Wi06OzvR2dkJlmURj8c5UbvSRrXd3V3s7u5iZWUFDMPA4XBwwrbFYqlqkwPLsshms9jZ2cHOzg6CwSDW19cRDoeRSqV4Y+lienp64PF4MDw8rPg1F7KhlhfCe436VD7Qvao8Cu/TAiaTSTEbAFmWxbVr13Dt2rV9j/vN3/xNfOYznykbvb0ft99+O+6//3684Q1vKIjVYFkWTzzxBJ544olKX/sygN9nWZYECqIiyh5VEpJHrVaXFbK3trZw/vz5Eivx0dFR9Pb2KubltB8UkV07cmuzRCIBr9eLcDjMKx8cHMTp06ePNIAnkfZg5HY91ZNGXD8sy2JpaQmXL1/mldtsNng8HphMrc1PehAUkX0T4TtcCc8YlmVx5cqVkslme3s7JiYmoFKp9r0u1Go13G433G43hoeHS3JxlnsGxWIxxGIxLC8vQ61Ww+l0ctHaRqOx7n9jtVBEtnSpV3/9+5OtF7HPdlvw+dd7KorY377xbfztub9Fls3yyrUpBnf/pBeGbGujfRLaLL76/DXkBdV4dvez8f6nvh+PPvQo1180b5IWFJEtffaL4mUYBmazGWazGf39/cjn8wiHw9z7PBqNlv29YDCIYDCIxcVF6HQ6zobc4XAgHo9zorff70c0GkUoFCqboqQa1tfXsb6+DqPRiKc85Sl46lOfqlhhiO5HeSGcc1DkrnygHNnKREkR2c961rPwile8Ag888EBJerJiNBoN7rzzTrz97W/HHXfccejzvehFL8Lc3Bze97734Stf+cp+5/w5gL9nWfbrhz4ZoRhIyCZKkNIAu3jgmM1mkc/ncfnyZVy/fp13HFmJlyJnm+xGIac2KxeVqlarMT4+jq6uriP/vpzaqlGQ2F+ZegtV2WwW58+fx+bmJq+8p6cHZ8+elcQiBEVk30RpEdmZTAazs7PcbuYCQ0NDGB4eBsMwNT9njUYjent70dvby1sE9/v9iMViJcfncjkuIgvY27FeELUbZVlaDXLve6KU//3gEj79yEpL6/DMIQf+z6vHy86ZFkOLePsjb8d2arvk3/RJBi9/sA/afGsXSMPGNP7jdh/Your3mfpw77PvRY+5p3UVI+qOlOb1xE1qET9VKhWcTiecTidOnDiBdDrN26iWTqeRz+eRSqUQj8eRTqcRi8WQSqWQy+UaOqZMJBJ4+OGH4fP58OIXvxharfbgL8kMivKUF8LxNvWpfKAc2cpEmCNbzng8Hvz7v/87WJbF5cuXceHCBayuriISiXDOLadOncLTn/70umknHR0d+MQnPoGPfexjeOSRR3Djxg1sbGzAbDajt7cXU1NTOH78+DPqcjJCEZCQTUiaYquqVCqFxx57rCS6lKzEy0NCY+3IQXhkWRaLi4u4evUqr9xiscDj8dRtF6Ic2qrRUIRtZep5/cRiMUxPT/MmKQzDYGRkBP39/ZJZ5KXr5SbCRSM5t0e561elUmF8fBzd3d11OYdwETyVSnGidiAQ4OzKi4nH44jH41hZWYFKpYLD4eCE7UbbsknlniXqz70/WsJnH22tiP28YTfufcVoyXW4m9nFux59F57cebLs98xxNe76SS/UbGuv3y1bEt++bRP4ZTUMagPe/9T349k9z+YdJ+fnqtyhCFDpU2sfsiyLUCiE3d1drK6uYmdnB5FIBOFwuGwqkWazuLiIb37zm3jZy16mOIFIaBNPSBuyFpcvlCNbebAsq6iI7AIMw+D06dM4ffp0086p0+nwvOc9r2nnI+QLKXuEpCkeYKyvr5fseB0dHUVfX18rqiZ6SMiuHam3WTqdxuzsLBdRV6ARmz1IyD4YaqPK1KttNjc3MTs7y7tf9Xo9PB4PnE7nkerYbMhO+SZKicje3t7GzMwMT0g2GAy45ZZbYLPZGnZevV6P7u5udHd3g2VZRCIRTtiORCIlx+fzeS7yq1DHgqjtdDrrvpGQ7gVpUa+F+//x/y7j/pmNuvzWYXnJWAc+8FtneGUsy+ITc5/AF658oeL33EEdXvxoF1RorYix5k7gB0/fAgAwYPCaU6/Bm8++GSqGFuLlBD0XpU85ITuXyyEcDmNzcxPhcBhra2sIBoNIJBJIp9OtqmrV3LhxAz/+8Y+PZFMqRfaziSekBwnZ8oXcE5RJ8WYvuUdkE4QcICGbKEFKO0UriR0WiwWTk5NkJb4PUhdlW0Fxm0lNeAyFQvB6vbxcZ42MSqX8zwdDQnZljmqjXSmfsMPhgMfjgcFgOHIdmw1Zi99E7kJ2pXzuTqcTU1NT0Ol0TasLwzCw2+2w2+04fvw4MpkML1q73OJ5MpnE2toa1tbWuO8XhG2LxSKpcSbReliWxTvvX8B3L5ZadTeT19zag//+whO8sp/5foa/eOwvkMxXziPbvaXHC5/oBNNCEZsFi8XuXTw85QcA3OK+BX/7jL+FRVdd5Ands9KCIrKlSWHjWCwWw5UrV7C+vo5MJoN0Oo2HH35YFmO/mZkZeDwetLW1tboqTYPEMXlBQrZ8oYhs5ZHL5ZBIJLjPSonIJggpQ0I2IVni8XiJjTgA9Pb2YmRkhKzED4CE7NqRojjLsiyWl5exsLDAW9gyGAyYmpqC3W5vyHmlLPo3CxKyK3MUG+1KzgMDAwM4c+aMZBccyFr8JnIWsnO5HM6fP4+NDX7kabXXb6NFC61Wi87OTnR2doJlWcRiMU7YDofDJX1RsDsNhUJYXFyETqeDy+XihO3D5MukiGzlkM/ncc9X5/GTq8GW1uMNT+/Dn94xxH3e2N3APQ/fg9Xd1X2/N+Az4nnT7S0XsWePhzE9Eka7oR0fvu3DOGk/uf936L6SDSRki4t8Po9oNAqfz4dYLIaVlRUEAgEuqloJ997MzAxe8IIXtLoaTYOsxeUFCdnyhTadKI9iW3GAhGyCkAKk9BGSZHNzE+fPny/J2zg+Po7e3t4W1UpakJBdO1ITZ7PZLObn5+Hz+XjlbW1tmJiYaGhUH4m0B0NtVJnDtk0kEsH09DRvZ61KpcLZs2cl/24g8e4mchWyE4kEzp07h2g0ypUxDIPR0VH09/e3sGblYRgGVqsVVqsVg4ODyGazCAaDnLBd7ABSIJ1OY2NjgxPqbTYbJ2rbbDZa5CU4snkWv/v5WUyvltrZN5M/u+M4Xvf0vfsvk8vgLx77Czy08dCB3ztz3YKnX3C1XMQ+dyqEheEE3nPLe/DiwRcf6nfovpQWFJHdWqLRKGKxGDY3N7GxsYFoNIpQKIRYLEbjfQAXLlzAs5/97Ka6y7QSshaXFyRkyxeKyFYeQiGbrMUJQvyQkE2UIOYJbz6fx6VLl3Djxo2Sf7NYLJIXKpoJCdm1I6U2i8VimJ6eLhmcDQ8PY2hoqOH3uRSj15sNCdmVOYyN9vr6Oubm5njHN9p5oJnQ9cKHYRhucVAOQnYgEMD09DQymQxXptPpMDU1JZl87hqNBu3t7WhvbwfLsojH45yoHQqFyl63kUgEkUgES0tL0Gq1cLlc3H96vb7seWhTh/zJ5vJ4/b96Mbsea2k93lEkYn/x0hfxiflPII+Dn79PuWjH2JK95SL2o2f9GH3unfgnz9uhUdG0XynQc7Gx5PN57O7uclHVa2tr2NnZQTweRyqVovY/gHQ6jUuXLmF8fLzVVWkKFJEtL0jIlicsy1JEtgKhiGyCkB40oyUkQzwex8zMTFk7caJ2hNHFLMvS5OoAhEKSWNvM5/Nhbm6OJyBrtVpMTk42LSeZ1KLXWwEJk5WpxUa70gYnt9uNyclJ2UR8CMU7sT5/mkWxkC3l+4dlWaysrODixYu8a91ms+GWW26RZD53YK9/zGYzzGYz+vv7kcvlEAqFOGE7Ho+XfCeTyWBzcxObm5sA9hYTCtHadrudFpUUQiqbx92ffgI3ApXzTjeDj949gjvOtGN2ZxbvePQdiGWqE9V/5bwTp1esLRexLz5Xh4+84itwGVy1f5+EOElDEdlHJx6PIxKJYGdnB2tra4hGowiHw4hGo7RBtw6sr68rUsimcYz0EW5MoGesPBDOJSkiWxmQkE0Q0oOEbEISbGxsYG5ujmclrlar0dbWxi140qSyNoSDs3w+TwO2AxB7m+XzeSwsLGB5eZlX7nA44PF4miqIkEh7MNRGlam2bVKpFLxeL4JBfv7U48eP49SpU7JaXChnpy2nv69W5BCVm8/nceHCBayu8vPs9vT04OzZszW9X4qFfTGiVqvhdrvhdrsxPDyMRCLBidrBYLDsGC4WiyEWi+HGjRtQq9VcpHbx3ynmv5ko5aD+iqdzePlnnsRKsHUiNgPgb3/rDG49rsXvPPA7WIwsVv3d259ow7Gt1toS5sFi/A9fhzfd8qK6/aaS3zVSh/quPCzLIpFIwOfzIRKJwOfzYWtri4uqpnF5Y1lbW8Ojjz7KvdedTic0GnkuTZK1uLygjQnyRDgPob5VBiRkE4T0kOdokZANlYQ5i8UCj8cDv99PQvYhES6Q53I5UYmyYkTMbZZIJOD1ekscCwYHB3H69OmmD8ZJpD0YaqPKVGMtHgqFMD09jVQqxZWp1WqMj4+jq6ur4XVsNsJ7WOkCnkql4t77UmyLVCqF6elphEIhXvmZM2cwODgoe/HBaDSit7cXvb29yOfzCIfDnLAdi5VGvuZyOWxvb2N7e5tXnkgkRPUuJkqp9loOJTJ46SefgH83c/DBDULFAJ969Vl8P/Bx/PW3vlP9F1ngRT/rREe4xQ4KDIOXv/cDaBs43tp6EC2FIrL3SCaTCIfDCAaDWFlZ4aKqI5EIb3M80XwKGwnW1tawtrYGhmFgs9k4Ydtms8nm2iVrcXlBQrY8oYhsZUJCNkFIDxKyiRLEMsCOx+Pwer2IRCK88r6+PoyMjECtVvMWgGlCWhvlRFlif4STFbG02c7ODmZmZni5VVst6BVfX4WcQzTZ40NCdmX2sxavZMVsNpsxNTUl2wlIuYhsJSPliOxwOIxz587xNmE0O/2DmFCpVHA6nXA6nThx4gRSqRQnagcCgX3Hd9vb23jooYfgdDrhcrngdrthNBpFM5YlqmMnlsZdn34SwXjrRGy1isHv3bmBP/P+BbKoYU6RB37rp91wxlubxoJRq/Hb/+teWFxHf4ZI7ZlK8FFK/7Esi1QqhY2NDYTDYWxubmJjYwO7u7tIJpM0thYxwuhrlmURDocRDoextLQEjUbDvdddLheMRmOLanp0KCJbXpCQLU+E63okZCsDoZBtNrfWVYkgiIMhIZsoS6stKitZiZ89exY9PT28sgJizlksRkjIrp1y1uKthGVZLC4u4urVq7zygmNBKwU94cSOhOxSSMiuTKW2yeVyuHjxYokVc0dHByYmJmRrSwiUv6eUjFSF7PX1dczNzfH6z2Kx4JZbboHJZGphzcSDXq9Hd3c3uru7wbIsIpEIJ2oLNzcCe/eC3++H3+/HlStXYDAYuNzacrYrlQurwQRe/pknsZtu3TNNZ9xE28l/xpeWQzV9j8kDd/24B9aUtjEVqxKNTo/X/P0/QW+iBTiCj9TnxZlMBsFgEJFIBMvLy4hEIgiFQohEIrwNvIS0GBwcRFdXFwKBAJLJ0lQS2WyW58JiMpk4UdvhcEjqvU7Cp7woXjOj/pQPwnk19a0yKHYBMxqNknq3EIRSobuUEBW5XA6XLl0qsRK3Wq3weDwlO6SEL5pcLkcvnyohIbt2xBSRnU6nMTs7i52dHV55T08PRkdHW34fkOh2MCRkV6actXgikcD09HSJkDU8PIyhoSHJL9YeBEVk85GakJ3P53H58mVcv36dV66ETRhHgWEY2O122O12DA0NIZPJ4IknnkAikaj4nWQyybMrtdvtXH5us9ks+2eFlFgO7InYiUyr3oEpmAbug9p8A9EajZ00WeDlP+qDIdvaqB2j1YZX/e3HoNM3ztac7hlpISVrcZZlkU6nsbW1hWAwiJ2dHfh8PkSjUaRSKXJckyEMw+DWW2+FxWLhLMYDgQACgQCCwWDZ+XU8Hkc8Hsfq6ir3Xi8I21arVbTXOcuykrofiYOhjQnyhCKylUlxRDZtKCcIaUCrZoRo2N3dxczMTIlI0d/fjzNnzpQdTJQTY2kxuDpIyK4dsbRZKBSC1+vl7WBnGAYjIyPo7+8XxSRZTKK/WBHaZ5OjxE2EbeP3++H1ennRN1qtFhMTE2hvb29FFZuO8NpQ+uYHKQnZmUwGXq8Xfr+fV37ixAmcPHmS7vsa0Gq10Ol0nJDd0dEBi8WCQCCAcDhcNhVBKBRCKBTC4uIidDodF63tcrmg1bY2klbJzPsieP3nZpDKteL+ZaFr+zZ0bQ/hMLefIanC3T/uhZZt7SK2xdWGV33go1DT3IcoQmzCWTabRTAYRCwWw/LyMoLBIJevujjFBqEMhoeHOdcwhmFgMplgMpnQ19eHfD6PcDjMCdvRaLTk+8Xv9WvXrkGr1cLpdHLvdr1e37C6Z1kWwWwO0VweWoaBQ62CVVNZ8BKOSUj4lD4kZMsTishWJvF4nPt/i8UiijETQRD7Q7NeoizNthb3+XyYm5vjiV1qtRpjY2Po7u6u+D2hsJjNZhs6eZETYhFlpUSrrcVZlsXy8jIWFhZ496fBYMDU1BTsdntT67MfrW4rKVAuap12/+4hFLJ/8Ytf8P7darViampKUTtnhdeL2MXbRiMVITsajWJ6epo3UVar1ZiYmEBnZ2cLayZdivteq9Xi2LFjOHbsGCeWFGzGywkk6XQaPp8PPp8PAGCz2bhobTFHdcmN+fUoXvu5GWTzzb931eYLMPZ+CYz6cJGelpgaL32oB5oWi9jO3n68/C8/2JBrVszPVOJgmt1/LMtyVtB+vx/BYBBra2ucUE1R1UQxk5OTFf9NpVLB6XTC6XTixIkTyGQynKgdCATKvtczmQy2trawtbUFYC/HabENeT3mVrO7SXxxK4LvBmOIC95bQwYtXt1mw11tVtg1+89/SRyTPiRkyxOKyFYmxRHZlB+bIKQBCdlES8nlclhYWMDKygqvvJKVuBASYw8PRczWTivbLJvNYn5+nlt8L9DW1oaJiQnodLqm1aUayFr8YEjIrsx+CwPd3d0YGxtTXFtRRDafcvbzYmNzcxOzs7O8d4XRaMQtt9wCq9XakHMqWYjVaDRob29He3s7WJZFPB5HIBCA3+9HKBQqe51EIhFEIhEsLS1Bq9Vyi99ut1t071W58PBiAP/tK3NouoatCcI08H+g0oUPFYUNAPaIBr/1cA9UaO191js6jhe97d1Nu9+V/FyRIo2IyM7n8wgGg9jd3cXy8jICgQAikQjC4XDZHMcEUY7+/n709/dXfbxWq0VnZyc6Ozt57/WCDXm59/ru7i52d3exsrIClUrFsyGvNeLuB8Fd/O/1ABYS6YrHXEtm8IFVPz6yHsBLXBb8SY8Lnbq9ZVbhphJ6lkofErLlifBZQveqMijOkU3ppwhCGpCQTbSM3d1deL3eEsuogYEBnD59uiqRolyObKI6VCoVL/Ke2u5gWhVlHIvFMD09zdsxCIg7NzBtlDgYEvsrUy7igmEYnD59GoODg6K85hsNRWTzEXNENsuyWFxcxNWrV3nlbrcbk5OTJJAekWr6nmEYmM1mmM1m9Pf3I5fLIRQKwe/3IxAI8CLkC2QyGWxubmJzcxPA3qbKgqhts9lowbIOPOlL4UM/n0Nz79gsDH2fh8Zy6dACNgC0+/V40WOdYFosYg899Rm44033tLQOhHSodrxUiKoOBoPY3NxEJBLB2toaQqEQkskkL7ULQRwGp9OJ3/iN3zj0GF74Xs/n8wiFQpywXSxIFChswAgGg1x6kYKo7XK5Ko7Hsvk83rG0hW8Fd8v+ezmSeRZf3YniJ+E4PjXcjbMmPUVky5DiPlXapmo5I3QGVeJag9IobI4qQEI2QUgDErKJlrC+vo75+fmarcSFlLMWJ6pHo9FwCxMkNB4MwzBNF//L2e5rtVpMTk6ira2t4ec/LGQtfjAkZJdna2sLCwsLvDKtVoupqSm4XK4W1ar1CCdWYhNvm43Qfl4sZLNZnD9/nhNDCwwODuL06dNNW8QUU5s0kmr/TrVazVmIA0AikeCitYPBYNn3eTQaRTQaxY0bN6BWq3nR2gaDoa5/hxJ4YpvB56+W5jttHCy0zp9A3/ndIwnYAHBszYTnzrS1XMQeec7z8ezX/X7Dz6OU54dcqRSRXcgtHIvFsLa2hq2tLcRiMYTD4bKbewiiXrhcLtx99911TQmkUqm49zKwlzqk2IY8nS6Nok6n09jY2MDGxgaAvZyohd+w2+1Qq9V4KLyLP17cxO4hbUO2Mjm8ZmENXzrTi+MMCdlygyKy5Qn1qzIha3GCkB4kZBNladROpFwuh4sXL2J1dZVXbrPZMDk5WfPLg6zFj0bxII3arjrUajW3YaKRbZbP57GwsIDl5WVeucPhgMfjEf0iOom0B0NtxKdSFCsA3HrrraLKAd8KyFqcjxgjsuPxOM6dO8eLClKpVDh79ix6e3tbWDN5UY8xqtFoRG9vL3p7e5HP5xEOh7lo7XJRXblcDtvb29je3gZwMwen2+3mFr+J8jAMg4d8DL52vXkLgyrDEkz9/wxGU9kOtlpOL5nxKxfdLRexb33Zq3DLi1/aknNThIo0KERV7+7uwu/3I51OY21tDQ8++CCSyWRZYY8gGs3Jkyfxwhe+EEajsaHn0el06OrqQldXF1iWxe7uLrdhLRwOlx03x2IxxGIxLC8vI69S4avWTvyIPbprzm6exe9f8eGLx9y8cnqWSh8SPOWJMMCKUAZCIZue0QQhfkjIJppGLBbDzMxMWSvxM2fOHGogSPbYR6N4kEZtVx3FQnajhKREIgGv14twOMwrb3ZE31EgkfZgqI1ukslkMDs7y4lEQrRabZNrJD7IWpyP2IRsv98Pr9fLs1/V6/WYmpqCw+FoXcVkSL37XqVSwel0wul0AthLbVAQtQOBQFm3H2EOTqfTyQnbRqORFkKK+M5i8pcidhPaRB2DaeCTUOm3jxyFDQBnr1px62Vny0Xs57zhD3DmWc9raR0I8RCJRBCJRLC1tYX19XVEo1FEIhHs7u6K4n1IEFqtFmNjY5icnOScUJoJwzCwWCywWCwYGBjg0osU3uvCdF3XVDp8zNSBJFs/AWsrk8M/bkXwoqIyKczhif0hIVueFK+FUr8qh+J3gcViaWFNCIKoFhKyiaZQzkpco9FgbGwMXV1dR/ptssc+PCRk106jo9h3dnYwMzPDE0PUajXGx8ePfK80E8qRfTAkZO8RjUYxPT3Ns7Us3qAEiEOobDUUkc1HLEI2y7K4ceMGLl26xKuH3W7H1NSU6N0ziFL0ej16enrQ09ODfD6PaDTKCduRSKTk+Hw+D7/fD7/fjytXrsBgMHA25g6HAxqNcqdbH/3RNXzhQhKNF7Hz0Hd/FVr7dF0EbAD4lfNOnF6xtlzEfv4f/DFOPu2ZTT0nvXNbSzabRSwWw/r6OmKxGHw+H3Z2dhCPxymqmhAVDMNAq9XCZDLB7Xajt7cXDocDx44dE9UmVGF6kVQqhUAgAJ8/gE9Gs/iRxoy6vTyK+G4kiecwKljYvTE7bXKTPiRkyxPKfa48hDmyTSYTPaMJQgIod2WF2Jd6PcD3sxL3eDx1yZOkVqs50Y9yZNcGCdm1U9xm9RSSKtkqWywWeDweye0QZBgGKpWKayOli27lICG7fA54g8GA06dPY2ZmhitTYtsIoYhsPmIQsnO5HObn57G+vs4r7+vrw+joKC1wNYhm9r1KpYLdbofdbsfQ0BAvB6ff7+dtOiuQTCaxtraGtbU1MAwDh8PBRWsrybbu3h8t4b5HVw8+8IhobL+Aoft+MKr6vSee90Q7BraMLRexX/yn70HvyFhL60A0hng8jmAwCL/fj9XVVUQiEUSjUcRiMRrzEKJCpVLBZDLBZrPBarWiu7sbnZ2dsFqtkk37o9frkXC48ZbNJALaxt1vKRZ4VGvGnek9R0IaF0ofErLlCfWrMqGIbIKQHiRkEw0jFovB6/WW5Bqstz0yibGHh9qudhrRZul0GrOzs9jZ2eGV9/T0YHR0VLLRXMVCNl1fpShZyM7n87h8+TKuX7/OK3e5XPB4PCWbkpTUNpUQil8kZLdWyE4mk5ienualgGAYBmfOnMHAwIBixEqlIczBGYvFuGjtcDhcci2yLItgMIhgMIjFxUXodDouKszpdIoqYq1esCyLd31zAd+eL58qol4wOh9Mg5+GShM/+OBqYYE7H+tAb6CxuVwPhGHwivf/HZw9fa2txy+h51ntZLNZJBIJrK+vIxwOY3t7G5ubm1xUtdLf4YS40Ol0MBgMcLlc6OnpgcViQU9PD2w2GzQajayeAal8Hn+/GsDntsJoxl34U62VhGwZQYKnPKEc2cqEhGyCkB7SVEcI0bO2toYLFy40xEpcSLHIR2JZbZCQXTv1thYPhULwer1IJpNcGcMwGBkZQX9/v6QXDpqRT1zKKFXITqfT8Hq9CAQCvPJjx47h1KlTUKlUJfcWLfgq93qpRCuF7GAwCK/Xi1QqxZVptVp4PJ6W5IJUGq3exFBcD6vVCqvVimPHjiGbzXIRln6/n3d9FEin0/D5fPD5fAD2LOgL0dpWq1XS73ygSSI2k4Cx/z6oTSv1dYJlgd98qAuumL6OP1o7KrUGr/rAR2F1t7W0HsTBpFIp7OzsIBwOY3l5mRdVTS5hhJhgGAZGo5GLqu7s7ER3dzesViscDofk3z3V4ktl8dsLq1jLNG/dY0utxS5UMCOvmHaWM5RLWZ5QvyqPctbiBEGIHxKyibIcdpCdy+Vw4cIFrK2t8crtdjsmJycb8nIoFmNp0aA2SMiunXpZi7Msi+XlZSwsLPAW4g0GA6ampiRr1VZM8SRA6aJbOZQoTIbDYUxPT/M2bqjVaoyNjaG7u5srU2LbHARFZPNplZi5urqK+fl53jmtViumpqZoAqxwNBoN2tvb0d7ezi2OFKK1Q6FQ2edYOBxGOBzG0tIStFotJ2q7XC7odLoW/BWHh2VZ/N4XZ/H4jfDBBx+KPLTt34be/XDdU5kyeeDuB3tgSbY2Ql6j0+NVH/gozA5nS+uh9PdLgWw2i2QyiY2NDfj9foRCIWxsbCAajSKTydDYhBAVWq0WBoMBDocDvb29sFgs6Orq4tw/lCyi5lgW96768cnN5kRhC4kzKpjZPFKplCydWJQERWTLE8qRrUwoIpsgpAcJ2UTdaJaVuBASYw8PtV3t1KPNstks5ufnuYisAm1tbZiYmJDc4nUlSMjeH+GCktzbqJwAaDKZMDU1BavVyjuWhOxSlHa9HESzny/5fB4LCwtYXl7mlXd1dWFsbEyyKSCkiFgisveDYRiYzWaYzWYMDAwgl8shFApx0dqJRKLkO5lMBpubm9jc3ASwt0GiIGzbbDZRL5hmcnn8wRdn8cRKpCG/rzbPwdj3ZTCq+o9V1Rng5Q/2wZhp7cKlzmTCaz70cWj1rY0IL4ecBbBsNovt7W1Eo1EsLy8jEAggFoshFoshk8m0unoEwcNgMMBut8NqtaKtrQ19fX2wWCxwuVyyvk+PwlY6izdcXseVZOvu5wzDACzw+OOPw2g0wuVyweVywel00vhRYpCQLU8oIlt5ZDIZnnsWCdkEIQ1o1ETUhUpW4uPj4+js7GzouUmMPTzUdrVzVGvxWCyG6elp3u4/ABgeHsbQ0JCsFiHo+tofhmF4ecTlKkzm83lcvHgRKysrvPL29nZMTEyUjUyg6OPyFF8v1CY3aXRbVLLDl+Nzm2gMarWay40NAPF4HIFAAIFAAMFgsOw7MhqNIhqN4saNG9BoNHA6nVy0tsFgaPafUJFMLo/fvm8al7Z2Dz64RhjtDowDn4Ja1xiBXJtm8Iof9UKXb62IbXa68er/dS/UJGjUnVwuh2QyiZ2dHWxubiISiWBjYwORSATpdJrGp4So0Gg0MBgMYFkWZrMZOp0Oo6OjOHXqFHQ6HY03aoBlWXxmI4QPrQVaEoVdjIm9OcdLJBJYW1vD2toaGIaBzWaTVYoRuUNCtjyhiGzlUWwrDpCQTRBSgWbLRFmqHUBns1lcuHAB6+vrvPJGWokLoRzZh6deNtlK4iht5vP5MDc3x7tOtVotJicn0dYmv1yIFJF9MHIXspPJJKanpxEO861mT548iRMnTlR811BEdnmkEInaLJrVFtFoFOfOneNF0KrVakxOTqKjo6Nh5yUqI4f7wGQywWQyoa+vD/l8HqFQCIFAAH6/v2SjG3AzanR7ey/3tNls5ha+HQ5HyxZT4+kcXn3fOSz5SyPMjwSThqH3i9BYLtXdRryAManGy3/cCzXbWtHA2TeAl/+P/wVGRAviwvtK7MJKPp/noqpXV1exvb3NRVWn0+lWV48geOj1ethsNlgsFrjdbgwMDHBR1YV55o9+9CPu+Pb2duhF6NQgZjbSGfzh1U2cj6cOPrjBaNk8rGz5NSqWZXkpRjQaDRetLbZNa8Ref5GQLU+K1+dIyFYGQidZs9ncopoQBFELJGQThyYajcLr9ZYsuB07dgynTp1q2sCOcmQfHoqYrZ3DRGRXsqR1OBzweDyynaSSkH0wcm6jQCAAr9fLW0TWaDSYmJg4UAAkG+3yqFQq7rmj9DYpvncaJWZubGzg/PnzvGe9yWTCLbfcQru2ibqhUqm4ReuTJ08ilUpxubUDgUDZse3u7i52d3exsrIClUrFi9ZuVq72WCqLuz/9JNbD9RQK8tC6fgx9xw8aJmADgCOswW8+0gMVWivQdgwN46V//j9bWgcpkM/nkUgkEA6Hsba2hmg0iq2tLQSDQaRSKZrDEKJCrVZzYnV3dzccDgfa29s5QfqgjSHCMQ2JZbXxta0w/nx5p+VR2AWmMnGosdePo6Oj3Ls9mUyWHJvNZrG1tYWtrS0Ae2POwvjA4XCQDXmLEd6bJHjKB9qgoDyEOgbN7QlCGtBIiKgZlmU5K/HiF75Wq8X4+HjTI5RIjD081Ha1U2tEdiKRgNfrLYlIbXTueDFwVBt2JSBHIZtlWdy4cQOXLl3iTfgtFgumpqaq2u3KMAwYhuG+L9Woy3ojh0jUetHItmBZFlevXsXi4iKvvK2tDZOTk2Xt8InmIff7QK/Xo6enBz09Pcjn84hGo1xu7Wg0WnJ8Pp/n/h0Al3/T7XbD6XQ2ZKE1FM/gpZ96Av7d+uUcVRuvwtD/r1CpGxtB27mjx68+3tlyEXvQcyt+9Y/+tKV1qEQr7iuWZREIBBAKhbC5uYn19XXEYjHs7u6WFXwIopVotVrYbDZYrVY4nU4MDAzAarXC7XYfWWyUmiOCWPCls/jTa5v4RUxcz4vbM3tRf2q1Gh0dHejo6ADLskgkEgemGInH44jH41hdXQXDMLDb7dz73WKx0LXRZIRzdTmv4ygNishWHiRkE4Q0ISGbKEulQfF+VuIejwdGo7EZ1eNB1uKHh4Ts2qlFnN3Z2cHMzAwymZsLvWq1GuPj4+jq6mpYHcUCWdcfjNyE7Fwuh7m5Ofh8Pl55V1cXxsbGalrco+jjUorfzUpvk0aJmdlsFjMzM5yFc4Hjx4/j1KlTtGhINBWVSgW73Q673Y6hoSGk02lu4dvv9/PGFwWE+TcdDgcXrW02m498DW9FU7j7008ilKiPCxKjDsMw8BloDNsHH3xEejb1uPPJTjAtFrHPvuDXcNtvv76ldWg2LMsiHo8jFothZWUFkUiE24CRTCZpHkKICrVaDZ1OB6vViq6uLm5jUFdXF/R6fUMFLBKya+cHwRj+eHETYnuKnNAAQ7k915Lia4ZhmJIUI+FwmHu/l9u0xrIsQqEQQqEQrl27Bq1Wy7MhJ/v5xkNCtnyhiGzlIRSyyVqcIKQBCdlE1VSyEj9+/DiGh4db9sInMfbwUNvVTjVtxrIsFhcXcfXqVV65xWKBx+NRzG4/uYm0jUBObRSPxzE9Pc1bfGEYBqdOncKxY8dqXogjIbuUZthpS4VGCNm7u7s4d+4cb5yjUqkwNjaGnp6eupyDODpyj8jeD51Oh66uLnR1dYFlWcRiMU4MjEQiJe3BsiyCwSCCwSCAvWjv4mjtWt0F1kJJvOxTTyCRqcMzmclA3/V1aO3ehtqIFzixbMKz5tpaLmJ7XvRbeNpdr25pHWqllvd3NBrF9vY2/H4/VldXEYlEuKhqpd2vhLjRarWwWCywWq1wOBwYGBiAzWaD2+2GTqdrWb2EY14Ssiuzlc7iHdc28ajIorAL/Lbx5htnv34spAhxOp04ceIE0uk0gsEgJ2ynUqUpPDKZDDY3N7G5uQlgT4QptiGnqNL6Q0K2fKGIbOUh1DWsVmuLakIQRC2QkE1UpGDrKjYrcSGUI/vwFLcdy7LI5/M0ID+Ag6KM0+k0ZmdnsbOzwyvv6enB6OioonJbkbX4wchFyN7e3sbs7CwvOlCn02FychJut/tQvymXtqknShbwhNS7Lba3tzEzM8MbRxgMBkxNTcFutx/594nGoOT7gGEYWK1WWK1WHDt2DNlslhetXW7hO5VKwefzwefzgWEYTrRxuVywWq37LrRf29nFq+6bRvLIIjYLjf0xGLq+CUbVnP6bvGyH56q95SL2r7zytZh44YtbWoejULDD3d3dxdraGrdJYmdnB/F4nMZ6hKhQqVTQ6XSwWCzo7OxEW1sb7HY7enp6YDAYRCtWUER2dTwUjuMPr/qQFOkw4L922vGcbBRLv/xcyxqLTqdDZ2cnOjs7OTeLwrs9FAqVnRft7u5id3cXKysrnJtLPd1YCBKy5UphHbQA9asyEArZJpOpRTUhCKIWlKOoEIcim81ifn6+xCbW4XBgcnKyJVbiQoqFwXw+D5ZlaaBeJcJBWi6Xo4HbAewnzoZCIXi9Xl4uP4ZhMDIygv7+fsVdl2QtfjBSF2tZlsW1a9dw5coVXnk90k2QaFuK1K+XelIvm3WWZXH9+nVcunSJV+50OuHxeMiqUYQo7V1aLRqNhpd/Mx6Pc9HaoVCobLR2OBxGOBzm2ZQWFr6LIxLn1iN43edmkMkf7Vms0q/A2P/PUGnjR/qdWnjqvAOjN2wtF7Ff+N/ejmNTT21pHaolkUggEokglUohFovhxo0bSCaTSCaTin/3EOJCo9HAbDbDZrPBZrNhYGAADocDTqdTFOsUh0H4rKa5OZ9INoe3XtvEQ5FEq6tSkVe32/DOPjeWroW5ssP2I8MwMJvNMJvN6O/vRy6X49mQx2Kxku/k83meG4tOp+PZkLfScUDKkJAtT4T9KtZNTkR9KRayzWYz9TtBSAQSsomKRCIRTE9PIx7nLza12kpciPCFk8vlFBX1ehSE7ZTL5Wq2mVQaQnG2sNiwvLyMhYUF3uKD0qP5SHQ7GCm3USaTwfnz57G1tcUr7+vrw8jIyJEnA1Jum0ZB4v5N6tEWlXK69/f3Y2RkRDTjHIIP3QcHU7zwPTAwgFwux9mU+v1+JBKlAoDQptRqtcLtdmM9rccf37+I7FEew+oYDH2fg8a40hQb8QK3eV04uW5puYj9kne8F91nRltah2ISiQTi8Tg2NjawtbWFcDgMv9+PWCxG7laEqFCpVNBqtTCbzWhvb0dnZyesVit6e3thMplkufBMEdmV+Xkkjv+2uIlITpzzAgbA23udeFOXk3M35P6tTv2oVqs5QRrYc1optiFPp9Ml30mn09jY2MDGxgaAvXRnxTbkNN6tDhKy5Qn1qzIpFrIpGpsgpAOpfURZ1tfX8eSTT5ZYiU9MTKC9vb2FNStFOIHNZrMkZFdJuYhsYn+EduyZTAYXL14sEULa2towMTGh6B3PJEQejFTbKBaL4dy5c7yNTgzDYHR0FP39/XU5h1TbppHUKwpZDhxVzEwkEpienkYkEuH95sjICAYGBupSx1ZAC95EOdRqNdra2tDW1gYAPJvSYDBY9nkSjUbx6I0oPn1JDRxaCM5B1/Et6Fw/a6qADQAv/HkHegKtjchkGAa/+e73o3NouOnnTqVS2NjYQCQSwcrKCvx+P5ermsb7hJhQqVSwWCyw2WywWCzo6+vjbMAtFkurq9d0SMguJZVn8afXNvG90O7BB7eIYzoNPnOqG4OGm3P/ZtgV6/V6dHV1oaurCyzLYnd3F36/H4FAAOFwuOz7PRaLIRaLYXl5mcvPXRC2TSYTXXMVIMFTngjHRHLcIEWUIozIpuceQUgDUvuIsgjz5DmdTkxOTsJgMLSwVuUpF5FNVAe1Xe0IJyw///nPS1wLhoeHMTQ0pPjBUPH1RddWeaQo1m5sbOD8+fO8PtXr9ZiamoLD4ajbeUi0LaX4elF6JOpR2iIQCMDr9fKiVnQ6HaampuB0OutWR6IxUET20TGZTDCZTOjr60M+n0coFOKE7cLCzoUgg09fUuGwIrba6oWx56tgVE1+/+eBlzzcBXestWkBGJUKr/zrj8De0dmQ30+n04hGo/D7/VhbW0MkEkEwGEQkEkEmk2nIOQniMDAMA51OB6PRiLa2NnR3d8NsNqO3txcWi4U2oAsQjnmVPp98LBLHn1zbgj8rzrkkA+B1HTa8q78NGkFfNSIie9+6MAwsFgssFgsGBweRy+W493sgECjJCQvsXW+FNCTA3pyu2Iac3PpuQkK2PBGuU1G/KoPi56HFYlH8u5YgpALNGoiyWK1WjI2NYWZmBkNDQzh58qRoX+gkxh4earvaEbZZsYit1WoxOTnJRTwpHSmKtM1GSm3EsiwuX76MpaUlXnmjcgmTaFsKCXg3OWxbLC8v4+LFi7zv2Gw2TE1NSTafJkAL3cThUalU3IL1yZMnkUwm8YVHl/DJhe1D/R6j3YJx8DNQayMHH1xnmDzwsp/0wJZo7cK7WqvFf/nQP8FosR7pdwp276FQiLOF3d3dRSKRoDE7ISpUKhVMJhNsNhusViu6u7vR2dnJ5a4mqocisvfIsyz+4vo2vuqPtroqFenUqvGFUz04ZizvwNaMiOz9UKvVcLvdcLvdAPacOgqidiAQKLvpKZVKwefzcW5zVquVGyPY7XbRrgk2AxKy5QnlyFYeBfeKAmazuYW1IQiiFkjIJirS19cHi8UCq/VoizCNplyeZ6I6SMiunUqLCQ6HAx6PR5SuBa1CSiJtq5BKG6XTaczMzHC79QsMDg7i9OnTDZnIS6Vtmgm1yU1qFbLz+TwuXryIlZUVXnl3dzfGxsZo0UJC0IaOxvLdhSA+9rNDiNiqFAzdX4TGernpNuIAoMoBr/hRH4yZ1t7LOrMZ/+Vv/wG6KjbGZLNZhEIhzv47HA4jHA4jFAqVzXNKEK1Ep9PBYDDA5XJx0dTd3d2w2WwUtVlHhO81JYplT0YTuGdxA1tZ8Y51X+W24j0DbTCqK/dPq4VsIXq9Ht3d3eju7gbLsohGo5yoHQ6Hy46potEootEobty4AbVazbMhNxqNitpoQUK2PCFrcWUizJGtpGcZQUgZErKJfRG7iA2Uz5FNVAcJ2bWRSCQwPz9fUt5IMU/KFLcHXVvlkYIwGQ6HMT09jWQyyZWpVCqMjY2hp6enYecla/FSSMC7SS1tkUql4PV6EQwGeeWnT5/GsWPHaOJKEL/k4z+9jk88tFzjt/LQtj0AfduPwTCteS7p0iq8/Ee90OVbOw4zOZx49QfuhUa3F53Hsiyy2Sx2dnY469b19XVEIhGkUimyACdEBcMwMBqNsNvtsFqtaG9v5wRrh8NB78omofSI7A+t7OBTm+FWV6MiNrUKXz7dg1Omg52omm0tXgsMw3COCceOHeM2VhWEbWHqNGBvPr+zs4OdnR0A4Da2uFwuOJ1O2W9oKV7PUKlUoutT4nDQBgVlIrQWJwhCGpCQTUgeEmMPj3CQRm1XmZ2dHczMzJQsOp46dQpDQ0MtqpW4Kb43SYgsj9iF7LW1NczPz/PqZjQaMTU11XCrSLIWL4XE/ZtUK2SX24ih0WgwOTmJ9vb2htaRaAy0oaMx/OODS/jkIysHH1iEynQJxr4vQqVuXfSwMa7GXT/pgZZtzcIjCyCvM8DY3oFjd/wGvv3d7yIajSIUCvGeOwQhBrRaLQwGA+x2O/r6+mCz2dDR0QGXywWNRkPCjAhQqpB9fjeJ/3Z1A76MeNcj7nJb8N6Bdlj2icIuRmwR2fuh0WjQ1tbGpUhLJBIIBoPw+/0IBoNlg0WSySTW19exvr4OYC9Vj8vlgtvthtVqFf3fXCtS6k+ieigiW5kIrcWV8q4lCKlDQjYheVQqFVQqFTewJDG2ehiGgVqt5tqM2q4UlmWxuLiIq1evlv13h8PR3ApJCKEQybIsDRAFiFXIzufzWFhYwPIyPzKvra0NExMT0OnK54KrJ2Jtm1ZC4v5NqhEz19fXMTc3x7t+zGYzbrnlFsqFRRBFvP/bl/G16Y2qj2c0QRj774PacLg82vXCGtXgZQ91Q4XGLSizAMCokNfqkDOakdfqkDdZkNPqAbUGUKkBhkEcgP/xxxtWD4KoFpVKBb1eD51Oh/7+fpw5cwYWiwVOp5PEFwkgHPPKfe7Esiw+4Qvi3vUgxDqyNTIM/uV0N26xHJwyopjivpRaPxqNRhiNRvT09IBlWUQiES5aOxKJlB17RyIRRCIRXL9+HRqNpsSGXOqQkC1PlPbMJfagHNkEIU1IyCYqIqUXuFqt5gYgZC1eGyRkVyadTmN2dpazzyoHtVllykX8C3PaKx0xirXJZBJerxehUIhXfuLECZw8ebJp7waKPi6FIlFvst/1wbIsLl++jKWlJV55e3s7Jicn6Tkkceg+qB8sy+Ld31zAt+arFKSZDAzdX4XGNtuSPNjFuENavPhn3VChPhXJa3XIaXVgDWZkjWawGh3yWh2g0QAMLVgT4kGj0cBgMMBms6G3txcOhwNutxsdHR0AgIceeog7dnJyEm63u1VVJQ6BkiKyFxNp/NcrPqylxbt+8xsuM/56sAPmKqOwiynuSykLnwzDwG63w2634/jx48hmswgGg5ywnUgkSr6TzWaxvb2N7e298YXRaOTZkEtxLE5CtjwpXs9Tq9WyfuYSe7Asy0ufQBHZBCEdpDd6IIgyqNVqzvKZhMXaKLbOoba7SSgUgtfr5dlCMgyDM2fO4OLFi1wZCWyVEdoyUVuVIjYhOxgMwuv1IpVKcWVqtRoTExPo7Oxsal0o+rgUsV0vraSSmJnJZDAzM1OyAanZGzEIQuywLIu3fm0eP7ocqOZoaJwPw9DxHTCq1j97+n0GPH+6A0yVIjYXVa3RIGeyIq/WIm80I2cw8qKqCUIs6PV6Ln+tw+HA4OAgF1W9nwAkTIFE7zzpIRzzylEwy7Ms7tsM4e9XAxDr6oOeAf73UCee7zx87lS5Cp8ajQbt7e1cip54PM6J2sFgsOyaUiKRwNraGtbW1rj83MU25FJ4Vsm1P5UO9asyoRzZBCFNSMgmZEHxhJ7E2NqgPMZ8WJbF8vIyFhYWeAsJBoOBywtcLGTT9VYZ4USArq9SxCJMVrruzWYzpqamWjK4F0vbiAmKRL1JuY0OsVgM586d4+2wVqvVGB8fR1dXV9Pr2ErkfH3QfXB0cnkWv/+lWfziRvjAY1WGGzD2fw4qTfzAY5vByWUzbptzlxWx82oN8noj8jr9nmCt1YHV6MBqtAAtThIiQq1Ww2AwwGKxoKurCx0dHXA4HOjs7IROpzu0qKOkaF65Ivc+3Epn8ZpLa7ieEm8U9gscJtx7vBOGQ0RhFyNla/FaMJlMMJlM6OvrQz6fL7EhF8KyLMLhMMLhMJaW/j975x3e2FXm/+9V78Vyle2xxzOe4hl7LCcQkhA2WQJZygIJoYSSsksouySUBcLCsiGEDktnSUILnYQSyu6ymx9lA5tAILFsjyfjKR6Pe5Ul2bLV7/n94eiOriTbkq1y79X74cnzoDP3SsfnllO+5/u+Y9BoNIJbu6amBgaDoQJ/xfaQ4KlMMh3ZRHVAQjZByBMSsolNkdNgO33AQaHFC4Pa7gKJRAInTpzA7OysqDwzL3B6OHYS2DaHhOztkYJYm0wmceLECczMzIjKGxoa0N3dXbHQbxRaPBtqkwtkipkLCwsYHBwULUYYjUb09fXBarVWoooEIUmSPI+bvzOEgansxeV0OHUIhpb7oTZOScas3HXOjr6zdUhYLUjqDeB1BiQNJkCj3XBVA+SsJiSDVqsVwvHabDa0tLTA4XDA4XAIc4piQ5t75I9ShWyeMXx7IYhPTPog1RUHPcfhw221eFmtrSjfp5TQ4oWgUqmE91xHRwfi8bgQhtzn84mifqVIJBJYWFjAwsICgA1hPD0MuVTERRKylQld1+ojM7S4yWSqYG0IgigEErIJRUDhsXcOObI3CIVC8Hq9op15ANDZ2YmOjg7RIoJKpaK84nmQK0c2IabSQvb6+joGBgaydssfOHAAe/furejiGYUWz4ba5AKZon5/f7/o32tqatDb21sysUCqKGXBeyvIkb1zYgke13/tSYz5svNZXiAJfePPoXX8uSKasJpXwxl1wpgwoj5cD0vcAiNvhDFugIpTI9xZ/joRRC7UajX0ej3MZjPq6+vhdrthtVrR1NQEvV4vifexFOpAFIYSheyVRBI3np7BifVYpauyKc+0GPD1zqZdu7DTqRZH9lZotVrU19ejvr5eEI9Sbu1AIJBzfWB9fR3r6+uYmpoCx3FwOByCsG2xWCrWliR4KhNyZFcn5MgmCHlCQjahCEjI3jnUdsDs7CyGh4dFf79Wq8WxY8dQW1ubdXx6TvZqFv+3g3Jkb08lheylpSUMDg6K8iludd+Xm0qL/FKEHNkX2GoRq62tDQcPHqRFJoJIIxxL4BVf78f4cmTTY9S2J2Fo+glUZciDbYlZ0BhuhDVmRU2sBqa4CRqmAff0/7KoTg2AqDAajQYWi0UQUJqbm1FbWwur1Qqj0Vjp6mWhRBG02sgc38n5GjLG8IPFFXxkYglSlbC1AP5lTy1uqLMVva1J+BTDcRzMZjPMZjNaW1vB8zyCwaAgbK+urmadwxiD3++H3+/H6OgotFqtKAy5Xq8vW/3peioTuq7VRywWE62BkZBNEPKBhGxiU+Q0aaIc2TunmoVsnucxMjKCiYkJUbnD4UBvb++m+Zmquc0KgUKLb08lxFrGGMbGxnD69GlRuc1mQ29vr2RCK5Fomw05si8Qi2UviXIchyNHjqClpaUCNSLKBTmyC2c1EsdL730Si6HcUgKnnYVpzzeg0mUvIhcTjnFoWm/CvuA+1EfrS/pbBJEPKpUKer0eJpMJLpcLra2tgqvaaDTKaj4MkJCtBJQSjjqc5HHrmVk8Htp881SlOWzQ4buH3LBpSuPCVMq1LBUqlQpOpxNOpxP79u1DLBYTRO3l5eWcY/14PI75+XnMz88DAMxmM2pqauByuWC320vqqCXBU5mQI7v6yIzCSUI2QcgHErIJRUDC4s6p1rYLh8MYGBhAMBgUlefj5Ev/t2pqs0Kh0OLbkylMMsZKuuiYSCRw/PhxYfKfwu1248iRI5KavJFomw0JeBv4fL6sjRh6vR4ejwcOh6MylSIqQjU/B/niX4/j2vuegG8tnv2PqgiMzd+G2nyu5GHEnVEnnrnwTFgStFhElBe1Wi2IHVarVQgDnnIGKgl6J8qf9Gso140IP/et4n1jC5J2Yb+71YWb6u1QlbCNKbR4Yeh0OjQ2NqKxsRGMMaytrYnCkOfa2Ly2toa1tTVMTk4K+blTbm2z2VzUdichW5mkr1HRda0OQqGQ6LPSxoIEoWRIyCYUQbr4kkgkKlgT+VGNQnaukMpqtRrd3d1obGzc9nzKK54fFFp8e3K51kslJufKA89xHA4fPozW1lbJLbBQaPFsqt2lzhjDxMQERkZGshbrn/nMZ9IktEqQ2rtKysytRHDtvU8gFMt8XzDoav8butpHypIHu3G9EZcsXAINo6knUXw4jhNc1Q6HA3v27IHNZkNDQwOsVmtVvTPIkS1/5CxkR5M83j42j18H1itdlU1p02vw3YPNaNSVvj8iR/bO4TgOFosFFosFe/bsQTKZFIUhzxSigI25UerfgQ1hPD0MuU6n21WdSMhWJunXVUqb+onSQY5sgpAvtJpAbIqcJk4UWnznVJOQzRjD6Ogozp49Kyq3WCzo7e3NewBTTW22Gyi0+PaUS8ien5/H8ePHRRt99Ho9ent74XQ6i/57xYCE7Gyq2aXO8zxOnDiB6enpnP++28UppZB+X5Q6wkOloMgE+TEVCOP6r/ZjLUPEVpmfgrH5B1Cpczi0S4Ar4sKzFp4FNaPFQWLnqFQqmEwm1NTUwG63o6amBq2trTCZTLBYLIp81xUDahf5IVch+3f+EG47N4+oRLtlNYD3tNTglgZH2dqVhM/ioVarBUEaAKLRqCgMebpBIUUsFsPc3Bzm5uYAbKz5uFwuoR8p9JqQc1eZ0HWtPkjIJgj5QkI2oQhIWNw51RImOxaLYWhoCEtLS6Jyt9uNrq4u0WaI7aiWNtstHMeB4zhhQYbaKptSi/2MMZw9exajo6Oi8u3ywEsBEquyqVZHdiQSgdfrzUoFkQ7dIwRxgZH5VbzumwOIJtMEEY0fxtavQaX3lcWFDQC6pA6Xzl9KIjaRFylXtc1mQ0tLC1wuF2pra+FwlE94kjvkyJY/chOy15I8PjC+gF8ur21/cIVo1mnw1c4mdBrLu+lRbtdSTuj1ejQ1NaGpqQmMMYRCIVEY8lzzglAohFAohPHxcSE/d0ocN5lM214jcu4qE7qu1UemkE1R3QhCPpCQTWxJugglZSi0+M6pBjd7IBDAwMAAIpGIULabkMoUWjx/1Gq18ExSW2VTSiF7s80be/bswaFDhyS/45gc2dlUoyM7EAjA6/UiGo0KZVqtFh0dHTh16pRQVi3tQdAml+0YmVvFDd8cQIJPtU0chuYfQGN9qmwCdoqOlQ7oeX15f5SQLCqVCgaDQRAPbDYb9uzZI4SPlfq4RA7QO1H+yEn8fHI1jFvOzCLMS/O+4wC8w+3EW9w1Zf9txhiFFi8THMfBarXCarWira0NyWQSgUBAELYzRStgY27p8/ng8/kAbAjj6WHItVptznNS0PVUDuTIrj4y3wlWq7VCNSEIolBIyCYUATmyd46S3cWb5VM1GAzweDyw2+07+l4lt1mxITFya0olZK+srMDr9SIcDot+68iRI2hubi7Kb5QauneyqTYBb3p6GidOnBBdf4vFgr6+PpGwDVRHexDEdvzxnB9v+eFxpIzYWucj0Df8Nziu/M8HxzjsXd1b9t8lKoter4fRaITFYkFTUxMaGxvhcDhQW1tLC8QVQOpCKJFN+phHqtcvlOTx4YlF/MSXnadYKjTp1PjSvkb0mCsTfSpz7kLvv/KhVqvhcrngcrkAbER2Sonafr8/ZxjyaDSK2dlZzM7OAgBsNpsgattsNqhUKhKyFQo5squPdCGb4zgYjcYK1oaQAxzH9QI4CKARgBnAPIBvM8Y2zdXFcVwTgEsA1ANwAQgBWADwF8bYuSLXTwPgMgB7ATQBiAEYAfAIY2zLkDkcxzkAPBtAJwAjAB+AYQB/ZIxJbiGWhGxCEaS7inmeV2xeyFKg1E0AiUQCJ06cECYjKWpra9HT07OrfKpKbbNSQGLk1pRCyJ6ZmcHw8LDou3a7eaMSVGsY7a2oljbheR6nTp3C+Pi4qLyhoQHd3d3QaDSIxWKifyMhu3qotg0d+fLY6DLe/MAwGANUxvMwttwPlSay/Yklomm9CaakqWK/T5QGlUoFnU4Hu92O+vp6WCwWNDc3w+l0wmQyFZSqhyg+FFpc/kjdxXt6PYrXnJpGMCnd/vfWBhve3VJb0fufnkXpYDAY4Ha74Xa7wRjD6uqqIGwHg8GcY8mVlRWsrKzg/PnzUKvVcDqdormHFJ9NYmekr+eRkF0dpAvZFBGI4DjuSgC/Syu6izH2QY7j1ADeCeCNAPbnOPUnAAIZ36UF8PcA/gFA9xa/eQbApwF8gzG2bVhhjuNuBvDNtKJbGGP3cxxnBfA+ALcAaMhx6irHcZ8B8OHM3+E4rg3ARwC8EkB2GBJghuO4Oxhj392ufuWEZprElsgxtDiwMRihhZT8yAyTrYRNAKFQCF6vNytkTGdnJzo6Onb995E4mz/kXt+aYgrZm4l/LpcLx44d29XmjUqQGUZbCe+m3VINocVjsRgGBweFUH8p9u/fj3379gn3QOa9oNT2IIh8+M/hefzzz0+BqVZhbPs61Ia5socRz6Q11FrZChA7RqPRQKVSQavVwmg04uKLL0ZNTQ0aGhposU/iUF8of6QaWjyc5PGRyUU8sCRdF3aDVo3PdTTgYmvl3XXkyJYmHMfBZrPBZrOhvb0diURCFIZ8fX0965xkMpmVqmtxcREmkwkOhyNnGHJCPpDTvvpIXyc2mWjTLZHN027qnwC4tIBzLgLwIICOPA7vBHAvgLdwHPdixtj0DurYAeBXAA5scZgVwJ0ALuU47m8ZY7Gnz33+03XdyunkBvAdjuMOM8beX2j9SgUpfYQiyBSyE4kECdl5orRNALOzsxgeHhaJpjqdDj09PaitrS3Kb5AjO38on/jWFEvIjkajGBgYgN/vF5Xv3bsXBw4ckNRCWL5ktg0J2cp3ZK+urqK/v18UEl+tVqOnpwcNDeINppn3ghLbg8gNObLF/HRgFnf+5wj0jT+B1tFfcQE7hTlhrnQViE1IidRWqxUNDQ2w2Wyor69HfX09jEajsCE0RWdnp+w2w1Ur5AKVP1IUsscjMbz21Azm49Kd977CZcHd7fVQS6TNSMiWBxqNBrW1tcI6UTgcFoUhTyRyG+VWVlZw/PhxQRhPhSG3Wq10rWUGObKrj3Qh22w2S6avJSSDHsBD2AgLDgBJAE8CmHz68x4AF6WfwHHciwE8ACBzZ8QsgEEAy9gISd6FDRE7RS+AxzmOexZjbKqAOjoB/D9cEM1XADwOYAkb4vSzANSkHf98AJ8A8A6O4y4H8Iun/04AmAHgBRDERuj0ywCk52V5H8dxf2GM/ayA+pUM+apVBJFGLjGWyA+lCNk8z2NkZAQTExOicofDgd7eXhgMxcuPReJs/pAje2uKIWQHAgF4vV5RzmC1Wo3u7m40Njbuuo6VIlfbVPvCgJId2XNzczh+/LjoPWEymeDxeGC1WrOOJ0d2bmgiXl18589T+MzjD8By8CFwKmmNR7Q8OZQqiVqthtlshsVigdPpxJ49e2Cz2dDQ0LCte4zep/KFhGz5IyUhO8EYPj6xhG8trlS0HltRo+bwhf1NuEQCLux06FmUJ0ajEc3NzWhubgbP81lhyDNhjCEYDCIYDGJsbAwajQZOp1MQtin3rrRhjJEjuwrJDC1O72cig3/EhpOZB/BZAB9jjInCBXIc14qNnNfgOK4LwA8hFrH/G8CdjLE/Z345x3EeAJ8HcMXTRc0AfsBx3JWMsXwXzT+ADTE7AOA9AO5Pz9f9dIjz9wH4YNo5t3Ec9x1sOLH1AJ4CcDtj7DcZ9XMB+CqAa9OKP8Nx3M+ZBCaJ8lOriLIilxd6pvBKgln+KGETQDgcxsDAQNbkoq2tDQcPHiz6gJTE2fyhMOxbsxshmzGGyclJnDx5UrRYYjab4fF4YLFYilbPSkBCZTa52kQu/fRmMMZw9uxZjI6Oisq3C4lP90f1Qo7sDe7+zR/wn0sfgsG9JhkXdjo8R31+KeE4DlqtFhaLBfX19aipqYHD4UBzczOMRiOFOiUAyGcuT1wgfS5Qyes3GYnj787M4Hx029SNFeP5DhP+raMBBgmKT+TIlj8qlQp2ux12ux3t7e343e8upFDVarWIx+NZ5yQSCSwuLmJxcRHAhjDucrmEPlqOhhElk/mckiNb+TDGKLQ4sR0pF8XrGWPfz3UAY2wSADiOU2HDiZ0eiuyDjLG7NvtyxpiX47i/fvq8654ufjaA1wL4dp51dGLDQX0FY2w4x2/EAdzFcVwDgLc8XawG8Ounz/0LgKsZY1k7FRljPo7jXokNF3rP08V7Afw1gN9kHl9uqBclFEGu0OJEfshdyF5aWsLg4KBoIlFqNyo5svOH2mprdipkJ5NJnDx5ElNT4ugz9fX16OnpUcQkuZj5w5VCrjaR84Q7kUhgaGgICwsLovL29nYcOHBgy0W/XKHnCaIaWI+v45X/+Rb4kmegkrBWGVPFKl0F2aNSqWA2m2G1WmGz2dDa2gqn0wmXy1XWhTcSQ+UDuUDlT/o1rIT4yRjDl2f8+Pysf/uDK4RVxeErndJzYaeTOW+hZ1HeZL5bDx06BLPZLApDnmsdLRwOY2pqClNTU+A4Dna7XRSGnO6LykIbTqoTCi1O5ME3NhOxM7gOwNG0zw9uJWKnYIwlOI67CRsCdv3Txe9C/kI2ALwtl4idwccBvBlA6iZ3AogBeHUuETujfv8G4Ftpxc8FCdkEURzkLsZWErm2HWMMo6OjOHv2rKjcYrGgt7e3pG5UypGdP+TI3pqd5PndLAJBZ2cnOjo6FDMQJyE7GyW5kNfW1uD1ehEKhYQylUqFo0ePwu12b3u+ktqCKIxqdmR/YegL+OHZHwKAJF3Y6fj1ftRGaytdDcmjUqmg0WhgNBphNBqh0+lgNpvhcrmEvJ02m00xfTtRWqrtnahEKhlafCISwz+OzmMkLN2NSM+xGfFvHQ1waKS9kTPzWSSBTN7kEjxNJhNMJhNaWlrA8zxWVlYEYXtlJVsfYIwhEAggEAjg3Llz0Gq1ojDkxUyFR+RH5lqenDeIE/mTGVqcIHLw8TyPuz3t/zMA7833BxhjIY7j7sVGmHAA6OY4rp0xdj6P0ycBfDeP35jgOO40gINpxQ8yxs7l8RsPZ3zuzeOckkNCNrElclk0kasYKwXk2HaxWAxDQ0NYWloSlbvdbnR1dZXcjZopziohvG+pICF7aziOg0qlEtpmuzby+XwYHBxELHZhgUmr1aKnpwd1dXUlrWu5IaEym51sfJAiS0tLGBgYEEVPMRgM8Hg8sNvteX0H3R9ENfGH6T/gA3/5AGK8dMWFTMasY+hc6ax0NSoOx3EwmUyw2WywWq1obGyE0+lEIpFANBqF3+/P+S5fW1vD2toaxsfHodFoUFNTI4Qn1ev1Ja0zvU+VA81P5EelhOzvzQdw16QPUn36DRxwz/5GXG43b3+wBCCnp7LY7nqqVCo4HA44HA50dHQgHo/D7/fD5/NheXkZ0Wg06zvj8TgWFhaEyFRms1kQtR0OB4mqZYCe0+qEHNnENpxgjJ3Z7iCO48wAnpVW9BfG2FiBv/U7XBCygY282efzOO/hAvJpn4FYyP7vfE5ijM1xHBcCkNrt0ZDn75UUErIJRaBSqURiEIUWz5/MAbLUhZFAIICBgQFEIhGhjOM4HD58GK2trWUZhORqM5po5Ibc69uTj5DNGMP58+dx+vRp0QKX1WqFx+NRZG4fcmRnI3fxNnUfnzp1SlTucDjg8XgKEmfk3halhOM4RbdHNTmyZ9dm8Q+//wfMh+crXZWCqDPU4YvP+yL++F9/zEqBoUS0Wi2MRqOQo9pqtaKpqQl2ux0ajWbLsWkymUQgEMDy8jJ8Ph/W19ezjkkkEqLFbovFIgjbdru95AuvtMAnHyi0uPwpt5A9GY3j7aNzGFqX7kapy6xGfGpvPep18lm+pNDiyiJzHWO7fler1aK+vh719fVgjGF9fV0UhnyrDWyTk5PgOA4Oh0MQti0WC91DJYAc2dVH6nlMocR1NGLXePM87lkA0hN9neM4rr3A38rsTPbled7JAn4jmPG50HNTQratgPNKhnxGggSxDWq1WhgQkmCWP3JxZDPGMDExgZGREdEEv1AXXzHI1WY06M0NObK3Z7s2SiQSGB4extzcnKi8qakJR48eVey9R0J2NnLOC51MJnHixAnMzMyIyltaWtDV1VWwEENCNgEo97rH+Bje+9h78aeFP1W6KgWh5bT414v/Fc9tfS6S8ThWhv4C1DRVulq7huM4GAwG2Gw22Gw21NbWoqWlBRaLBU6nc1eLy2q1Gi6XCy6XC52dnQiHw8Ji9/Lycs5xeSgUQigUwsTEBNRqtZA3u6amBkajdHPGEqVHqe/EaiJ9vFtq4eqXvhW8e2wR0pz9A2oAn9/XgGuc8gv9SqHFlcVunLscx8FsNsNsNqO1tRU8zyMYDAr9/OrqatY5jDH4/X74/X6Mjo5Cq9UKonY5IrNUC+TIrk4yQ4vTJhEig4U8j2vN+Pzqp//bDTV5Hucv4DsznZ6BHZ6r3fSoMkJCNrElcnqhq9VqxONxANIVY6VIZmhjKbZdIpHAiRMnMDs7Kyqvra1FT08PdDpdWeuTOcCVYptJhfS2onbKzVZCdq48whzH4eDBg2hra5PVO7pQSKjMRq6hxSORCPr7+0X54nYbSYPuj+pFye89APjOqe/gnhP3gEk2wGtuXtHxCtzWcxs0Kg3ikTC+9+63gg+vQ60zIWkp32bDnaLVagWxurm5GU6nE7W1tXC5XNBqyzdvNxqNaG5uRnNzs2ix2+fzicYCKZLJJJaWloR0OyaTSXBr7zQ0Kb1P5Qs5suVPORzZs7EE3j46h/617HDHUuEZFgM+09GARhm5sNMhR7ayKKbgqVKp4HQ64XQ6sW/fPsRiMdEGtvQUYini8Tjm5+cxP78RoScVmaWmpgZ2u12xG9tLDTmyqxPKkU1sQ/buoty4SvDb1jyP281CoDwWETdBnqNCgshBel5kEswKQ8pu9lAoBK/XKxpsAEBnZyc6OjoqMimUWzj2SkKO7O3ZrI0WFhYwNDQkSpWg0+nQ29uLmpp8N+rJF3JkZyNHR7bf74fX683K6+7xeHZ1H5OQLYbjOMWHFE+R69orYYF4YHEA7/rju7CeyA4tLWW6nF34zGWfgU2/EW1szb+MH77vHUjGY+AAGKfPYb3tIHhD5UP36fV62O124b+2tjZYLBY4HA7RPEIqZC52R6NR0WJ3agNvOuvr61hfX8fU1JSQszPl1jaZTEXZOETIB7p28iO9Hy+FO/DX/hDefm4eUYkOF1QAPrSnFq+ql/4GqK0gR7ayKKVzV6fTobGxEY2NjWCMYW1tTejnA4FAzjlwemSWVF+fErYp52/+FBoynlAGJGQTRaIUjjp6eW+D9GbsBLFD0sVFypFdGFJ1s8/OzmJ4eFhUJ51Oh56eHtTW1lasXuTIzp/055KEyNxkCtmMMYyOjuLs2bOi4+x2OzweDwwGQ7mrWBFIyM5Gbo7syclJPPXUU1l53fv6+nYd/paEbEIpBCIBvOX3b8F4aLzSVSkIm9aGz1/+eRysOSiUrSzM4cEPvAt82riI45MwjZ9CuHU/kqZ8N5nvAMYAngeXTECrVuFw38VCnuqGhoayuqpLhV6vR1NTE5qamsAYw+rqKnw+H3w+nyjiRQqe54XFcGAjHU9K1HY6nZIU74ndQY5s+VMqR/ZyPInbRufw51CkaN9ZbLpNetyzv1FWubA3IzNEPD2L8iZzzlUq5y7HcbBYLLBYLNizZw+SyaQoDHmuyCyZfb1OpxOFIS939EI5QaHFqw/KkU0UkaWMz+9jjH2sIjWpIuQ/QiSIp0kfTJKwWBhSazue5zEyMoKJiQlRucPhQG9vb8WFPLnkFZcCFFp8e9LbKB6Po7+/H4uLi6JjWltbcfjw4aqaXJGQnY1cxNvN3uGNjY3o7u4uyuKP3ER9ongoxZGd5JO4+4m78fDUw5WuSkFw4PAez3vwkvaXiNp9eWoCP77rvRuCcuY5fBLGidOIuZoQd9aBaXYoKqfE6ngUqlgYqngc6vUQVLEIVIkYOJ5Hc1c3XviOO2R5TxQCx3FCzu69e/ciHo8LC9k+ny9naNJIJILp6WlMT0+D4zjY7XZB2E7PESjVvoXYnnKEpSZKSymu4e8Ca3jHuXms8dJ8tjkA72x24k2NTsXct+XMdU6UnkoJnmq1WhCkAeQVmSUWi2Fubg5zc3MANjYRp4chr6b1hO1IX59Sq9X0rFYBqagHKciRTeyC+YzPBypSiyqDhGxiS+TUkVNo8Z0jJSE7HA5jYGAAwWBQVN7W1oaDBw9KYuBNocXzh0KLb0/6e3ZmZkYUUUKlUqGrqwstLS2VqFpFkYtoW07kEFo8FovB6/XC7/eLyg8cOIC9e/cWbVxB9wchZ35x/hf4ZP8nwcssRdU1LdfgfRe/D1qVWIieHB7Erz738S3P5RiDfmkGOt8sElYnYs568KYci0d8EkgmoUrEoIpGoF5fBRePQR0OQbXNc7637xl43j+8s+C/SwlotVo0NDSgoaFBWKRLubWDwWDWO5IxhkAggEAggNHRUcHB5XK5aLwmY6gvlD/FFEDDSR5vHZ3D71fCu61Wydiv1+BbB5sV4cJOp9Qh4onyIhXnbmZkllAoJApDnqsPWF1dxerqKsbHx6FWq0VhyHeackQppF9Xek6rg2g0KlrzJiGb2AV/BMBwIRz48ziO4xgNxkuKskaLRFUjJTFWbkil7ZaWljA4OCjaWapWq9Hd3Y3GxsaK1SsTCi2ePxRafHvS76d0EdtgMMDj8cBul3eOuJ1CjuxspO5CXllZQX9/PyKRC2ErNRoNjh07hrq6uqL+VmZOaJovVA9y3sRwNngWb/39W7ESzw4DLWXaLG348nO+jBpDdl77U3/8PR75+lfy/i6OMWhXlqFdWQZTqcE0GjCVGhyfBJdIgON3Nqbquur5ePZrb9nRuUojPTRpW1sbEokE/H6/4NZOf0enyHRwEfKnmsUJOVMsR/bvg2v4p3MLCCSlNVZMoQLwhgY73tHigkaB9yoJZMoic84lhfcrx3GwWq2wWq1oa2tDMplEIBAQ+vr08MkpksmksMkN2BDGU5vYnE6nItKwFEL6Oh49p9VB5nNhNpsrVBNC7jDGFjmO8wLoe7qoGcALAPxX5WqlfEjIJhQD5cjeOZUO/7xZTmCLxYLe3l7J7ZIjR3b+kCN7a3ieRzic7ZKoqalBb29vVee0yhQq6f6RtiN7dnYWx48fF10ns9kMj8dTsnc4CdmEXFiLr+H2P9yOk4GTla5KQRjVRnz6sk/DU+fJ+e9PPfJr/N93vr7j7+f4JLjY7sedl7zitTh2zYt3/T1KRaPRoK6uDnV1dUJuwNRCdyAQ2LZ/ffTRR4WF7pqaGuj1+jLVnCgUCi0uf3br5E0whjvGFvCL5exculLBrVPjBwdb4NYrdzmSnkVlkbkxQYrXVK1Ww+VyweVyobOzE5FIRBSGPNcaaTQaxezsLGZnZwEANptNcGvbbDbFi7vp17VUec8JaZGZZ15qa82E7PgSgG+kff40x3G/Z4xJdxAmc5Q7ciSKghQHaJshFVexHKlkWPZYLIahoSEsLS2Jyt1uN7q6ukR1kwqZAhvdb5uTKWTLNZdpKYjFYhgYGMhyRrW3t+PAgQOKnzjmAwnZYqToyGaM4cyZMzh37pyovK6uDj09PSXd2Z/eHiRkVw9ycmQzxvCFoS/ggdEHKl2Vgnlz15vx+oOv37TPfuLnP0L/L39a5lpl8+zX/z26/urqSldDNnAcB7PZDLPZjNbW1rwcXIlEAgsLC1hYWACwseiXErUp36a0IPFM/uzmGv4xuI53nJuHT6IubA7AzfV2/FNLDfQKf2+QI1tZyPF6GgwGuN1uuN1uMMawuroqiNq5Uo4AG9G1VlZWcP78eajVajidTlEYcqWRmSObUD4kZBNF5tsA3gPg0NOfDwN4iOO4VzLG/JufJobjuDoAVzDGKj+5ljjSU4gIYodQjuydUylHdiAQyBLyOI7D4cOH0draKukFGLVaLexqpfttc3K512mSAASDQXi93iwR226349ChQ5ucVX2oVCph4UDKYlW5kJojOx6PY2hoCIuLi6Lyjo4OdHZ2lvwdTkI2IWUemXkE//rnf0Wcj29/sIS4pO4SfPzSj0Ov2dx1+7/fvAenH32kjLXKzXPf9Dbse8azKl0NWZPp4AqHwxgfH8fMzMym54RCIYRCISHfptPpFIRto9FYxtoTmVBfKH92ImQzxnD3xBK+syjdtBVOtQoPHGrGXmN1RJuSo/BJbI7cryfHcbDZbLDZbGhvbxelHFleXs4ZIS6ZTGJpaUkwnBgMBqGvdzqdkjScFIrcrytROGtra6LPJGQTu4ExluQ47noAjwGwPV18NYAhjuM+CuC7jLHVXOdyHFcD4HkAXgbgWgB/AkBC9jbIv+chSk66I07KUGjxnVNuNztjDBMTExgZGRHdW3LKCZwuZEvBFSlVcuU5rnYhe2pqCk899VTO+6aaQ4nngkLTi5GSEzUUCsHr9YomgyqVCt3d3WhqaipLHUjIzo1cxm07RUrPQS5mQjN4y+/fgsXI4vYHS4h6Yz3+/Tn/DrfZveVxD3/p33B+4Iky1WpzXvrPH0LDvs5KV0NxGI1G1NTUiITsPXv2YHl5OcvFAmQvdJtMJmGh2+FwVP2Yr5JIeUMwsTmFCtn9oQjeenYWiwnpjpNfV2fDHa0uGKpIKKLoCMpCaYJnesoRAAiHw4Ko7ff7c66nRiIRTE9PY3p6WhDG08OQy/E+J0d29ZEpZFOObGK3MMZOcBz3cgA/BpASM1oA/DuAL3IcdxzABIAVACYADgAHnj6GKBASsgnFQKHFd0452y6RSGB4eBhzc3Oi8traWvT09MhGyKt0XnG5kDnRSyaTJQ01LGV4nsfJkycxOTkpKjcYDIIzm8RaMekTYmob6YQWX1xcxODgoGiRw2AwoK+vDzabbYsziwvdH4SUiCaieO+f3ovHFx6vdFUKQsNp8OFnfhjPaX7OlscxxvAfn/owZk8/Vaaa5YZTqXD9nZ+As5nm/qUic3PIvn37sH//fkSjUSEE+Wb5NtfX17G+vo7JyUmoVCo4HA7B8W00GmW50C0nSDyTP/leQ8YYPju9jK/MBcpQq51hVXH4xoEm9FqqL1KD0oTPakfp19NoNKK5uRnNzc3geT4rDHkmjDEEg0EEg0GMjY1Bo9GIwpDLJTqL0q8rkU2mkG21WitUE0JJMMZ+zXHcxQB+AODitH9SA+h9+r/tyDsUeTVDQjahGEjI3jnlartcDj4A6OzsREdHh6wWXOh+y49cocWrkUgkAq/XmzURTC0Mp8Ttam2fzUifUErNdVkJKu1EZYxhbGwMp0+fFpU7nU54PJ6yb0QiR3Z1UunnIBPGGL516lv46lNfBYO87sNX7381buu+bdvxF2MMP/rXdyMwO12mmuVGpdHglR/6N9jq6ytaj2ojdX/o9Xo0NTWhqakJjDGsrKwIwvbKSnZIY57nhYXwM2fOKDIsqdSo9PuQ2D3pc4HN3s0n16O49cws5uPSnYO+otaKD+6pg04ln/l9McnnOhLyoZoET5VKBbvdDrvdjr179yIej4vCkGemRgM2zCqLi4tCuimTySSI2g6HQ7L9PTmyq4/0tWi1Wg29fvNUSgRRCIyxsxzHPRPAiwC8DcAVALa7wU4C+DWABwE8WtoaKgNp9iaEpJBLiMr0wRHP8+B5XvGDzGJRDlF2dnYWw8PDou/X6XTo6elBbW1tSX6zlKS3GYmPm5MrtHi1sby8jIGBAcRiMaFMo9Ggp6cH9fX1OHnypFBeje2zFRRaXAzHcaI+uZxtkkwmcfz48axoGnv27MGhQ4cq0t+SkE1Umv6FfrzrsXchwmcv6kkWBhx2duGLV3wBJq1p28OT8Th+8M9vw3qgspvE1TodXvuJL8JgLV/Uh2oln/cpx3FZC93pbu30MU+KzLCkdrtdcGubzWYSe4oMtac8SX/+suZRjOErs358YcYPqY6KzSoOX9zXiCvs2/cvSmar60jIj2oSsjPRarWor69HfX09GGNZYchzrR+morNMTU0J/X1K2LZarZLpn9LrXm3XtVpJF7LNZjNddwKMsf8FUJSXEtvo/P8DwH9wHGcAcAmANgAuAGYAa9hwXp8FcJIx5svze+8HcP8O63QzgJt3eG77Ts4rJSRkE4ohcwddMpmkTilPSilk8zyPkZERTExMiModDgd6e3thMBiK+nvlgkKL50c1C9mMMYyPj+PUqVOixQyLxQKPxyPk4yGxdnModHQ2KpVKeOeUS7wNh8Po7+/H6uqqUMZxHLq6utDa2lqWOuSCHPtbwxiTzEJRMZGCI9sX8eEfH/lHTKxNbH+whFDxZnzz6q+g07E/r+Nj4TC+/97bEVvLzo1cTvQmM177qS9DQ64JyaLVatHQ0ICGhgYwxhAKhQRROxgMZj2njDEEAgEEAgGMjo5Cp9MJbu2ampqqTUOzWyi0uPzZ7BpOReN47cgMZuLZIf2lwt/WWPCp9jqoaQ2mqoVPJUKC5wYcx8FkMsFkMqGlpQU8z4uis6TPFVOk9/fnzp2DVqsVwpC7XK6KOmLTn1NyZFcHmUI2jZWIUsEYiwB4pNL1UBokZBOKIZeQTYsg+VEqITscDmNgYCArnHJbWxsOHjwo60kAObLzI9dzWQ0kk0kMDw9jdnZWVN7Y2IijR4+KIkiQkL05JFRmU25xf3l5GV6vF/F4XCjT6XTweDxwOp0l//2tIEd2dVJJITvOx/GRJz6Ch6ceLttvFgPGODTFXo8fvepNUOcZ5jWytorvvfutSOZw1pYTi6sOr/7oZ6GiBcaKsJMFPo7jYLVaYbVa0d7ejkQiAb/fLwjbucKSxmIxzM7OCuMmm80muLWl5N6SOiRky5/Ma5hkDF+fC+Cz08uQqoRtUnH4WHsdXlhDuUZTUGhxZUGCZ25UKhUcDgccDgc6OjqE6Cyp/6LRaNY58XgcCwsLWFhYALAhJqaHIS9n+9IGheojU8gmCEJekJBNbItcBt6ZeVeqRTArBplCdjFcXEtLSxgcHBSJH2q1Gt3d3WhsbNzVd0sBypGdH9XoyF5fX4fX681yrx44cADt7e1ZzxYJ2ZtDbZNNucRbxhgmJydx8uRJ0e/YbDb09fVJIpoGCdlEuWCM4edjP8enBz4NXrIBXbNhDIgHPbjC/mZ89obuvM9bXVrAD9/3DrAKv3edza14+Z0fp8VFmaPRaFBXV4e6ujowxrC+vi64twKBQM7+fWVlBSsrKxgbG4NWqxUWuWtqaiif4RaQkC1/0q9hkGd4w/AkRqPxLc6oLFfYjLhnfyN09J4WQaHFlQU57PMjMzpLqr9PhSHP1d+vra1hbW0Nk5OTQn7uVH9vsVhK2pfRBoXqI13ILvX9RRBE8SEhm1AMmQOPREKqe5alR2bb7UbIZoxhdHQUZ8+eFZVbLBb09vbCYrHsuJ5SgkKL50e1CdmLi4sYGhrKcq8eO3YMLpcr5zkk1m4OhRbPphwudZ7n8dRTT2FqakpU7na7ceTIEclM9EnIrk7K7cg+tXwKb3v0bViJr5T0d4oJY0AyWofw+Jvw0qP7cPeLD+R97uL5c3joI/+y8SUVpGHfAbz0n++qaB2qlVI+UxzHwWw2w2w2o7W1FclkEoFAQHBrr6+vZ50Tj8cxPz+P+fl5ABtzilQYcrvdTqJCGtQXyh+e39gu9TutFT9diEnWhW3kgA+21eFaF0VMyAU5spUFCdmFk9nf8zyPQCAgCNuhUHbaGp7n4ff74ff7hbQjTqcTLpcLTqez6BvZ0tfxpDK/JUpHanNFCpPJVMHaEASxE0jIJhRDtYYwLgbFyi8ei8UwNDSEpaUlUbnb7UZXV1eWa17OUGjx/Mi8j5T6XDLGcO7cOZw5c0ZUbrfb0dvbC6PRuOm5dC9tDoUWz6bU4n40GoXX60UgEBCVHzp0CG1tbZJajCMhmyglwWgQ73j0HRgJjFS6KgXBJ3UIT94CPrwXr76oCe//m868z50ZOYn/+PTdACr7PHVc/Cxc/ea3VbQOxAalfuer1WohhDiwkZYo5db2+/05x42hUAihUAjj4+NQq9WCc8vlckkiWohUkFJ/TeRPmAGfNTVgTCPde7nXpMc3D7phUZOgtxnkyFYWJGTvHpVKJfTXwMbaYXoY8liOVDaxWCxrI1vqO+x2+67FZ7qu1Uf6BgrKkU0Q8kM5qhJRMuTyYqfQ4junGPnFA4EABgYGRHnvOI7D4cOH0draKpv7KF/IkZ0fKpUKHMcJk3klCrXxeBzHjx8X8jylaGlpweHDh7edYJEje3OobbIppbgfDAbR398vymem1Wpx7Ngx1NbWFvW3igE59quTUjuyE3wCXzr+JTw4+mBRv7fUMAZE5/8GcYr0IVMAAQAASURBVP+VAIC3XLEH//Cc9rzPHx/y4n++8MnSVK4Auq56Hp792r+rdDWICmE0GtHc3Izm5mbwPI9gMCi4tXO5t5LJJBYXF7G4uAjgQq5Nl8tVlEVuuUGhxeULYwwPLK3gLkszEpw0BRU9B9y5pw7X15ILeztIIFMWdD2Lj06nQ2NjIxobG8EYw9ramiBqb5Z2JLWRbWJiQsjPnRK2dyJKUo7s6iMztDhBEPKChGxCMZAje+fspu0YY5iYmMDIyIho8cRgMMDj8cButxetnlKCXLT5o1KphHtKaW0VCoXQ398vClHEcRy6urrQ2tqa13eQWLs51DbZlEq8nZmZwfDwsOg7LRYLPB4PzGZz0X6nmJAjOze0uLxzfjP5G9z1xF1IMKkGc82GMSAeOoDo1I1ITe1u+6s2vPHZbXl/x/Bv/wePff/+0lSwAI694CW45OU3VLoaVY9U3qcqlQpOpxNOpxPARsSQlFt7eXk5ZxqpzFybTqdTELaNRqPi349SuXZEYUR4Hm8+M4dHV8OAREXsAwYt7j/oRp2WlhDzgUKLKwsSsksLx3GwWCywWCzYs2ePkHYkJWyni48peJ4X/h0A9Hq9IGo7nU7odLotf5MxRjmyq5D0e4kc2QQhP2gUSigGjuOgUqmEwQjlyM6fnQrZiUQCw8PDmJubE5XX1taip6dn28GjnElvM9o0sTXpQraS2mpubg7Hjx8X/U16vR4ejwcOhyPv78l02O4mR73SIKEym2K3Cc/zOH36NM6fPy8qr6+vR09Pj6RTQtD9kRult0UpHNnjK+O47f9uw1JkafuDJQJjAB93YP38m4CkUyi/+8WdeNmxpry/5y8PPQjvfz5UiioWxOWv/Tscuep5la4GkYGUxiN6vR5NTU1oamoCYwwrKyuCqL2ykp3Dnud5+Hw++Hw+nDlzBgaDQQhj7nA4JN2/FQMpXTtic/7Lt4J3jy0iO6iuNNAAuHNPLV5VZ6N7qgAotLiyICG7vGSmHUltZEv9F4/Hs86JRqOYnZ3F7OwsAMBqtYrCkGdet8wN4SRkKx/KkU0Q8kfZszeiKMhpwqJWq4UBiZIEs1KzEyE7FArB6/Vm7Y7s7OxER0eHrO6bnUChxfNHrVYLkw0luGoZYzh9+jTGxsZE5U6nE729vdDr9QV9X65JFU2kNiBHdjbFDC0ej8cxMDAAn88nKt+3bx/2798v+fc45VAndstafA3vf/z9+PPCnytdlYJgvAbhqdcgudYllHEAPviiwkTsxx74Nob/369KUMPCeP4/vgvtnosqXQ1CRnAcB7vdDrvdjo6ODsRiMfj9fkHYzpVrMxKJYHp6GtPT0+A4TghJ6nK5FOPKodDi8mEtkcR7zi/g4cD69gdXiA69Fvd2NqLdoNzN6aWChE9lQdezsmRuZAuFQkJ/HwwGc84DV1dXsbq6ivHxcajValGfbzQas9YW6LpWB5mhxWmsRBDygoRsQlFoNBpBMCNxMX8KFbJnZ2cxPDwsOk6n06Gnp0eSeVRLAYUWzx8liZGxWAyDg4NZwl9bWxsOHjy4owlQ5uCZhOwLKOneKRbFCi2+uroKr9cr2pWsVqvR09ODhoaGXdWxXJAjuzophiObZzy+cfIb+ObIN8Egn3uHMSDmuwKxxReJyjkAX3n1EVy+z5X3d/363s/j3F/+VOQaFs4L3/F+tBw5WulqEGnI8X2q0+nQ0NCAhoaGvBa5GWPw+/3w+/0YHR2FTqcT3F9OpxNarbZCf8nuICFbHvxfcA1vPjuHqEQfNQ7A+1tduKnBUemqyBZ6FpUFCdnSgeM4WK1WWK1WtLe3I5lMwu/3C27t9LltimQymRWhJTMFIq2/VAeUI5sg5A0J2YSioHDPOyNfIZvneYyMjGBiYkJU7nA40NvbC4PBULI6Sg261/JHKWJkMBiE1+tFJBIRylQqFY4ePQq3273j790uzFU1U6p80HKmGC7k+fl5DA0Nid5dRqMRfX19sFqtu65juSAhm9gJTy48ifc9/j6sxlcrXZW8YQxIrrchPHkLwMRjLRUHfOlVR3H5vpq8v+8Xn7wLc6dHil3NwuA4vOz9d6O+fV9l60EojsxF7kQiIbi1fT4fotFo1jmxWEwUktRutwvOLavVKhshivpCabOa5PHB8UX8YjlU6apsSptegy/ua8RhU2ERpggxJHwqC7qe0kWtVqO2tlYw1EQiEVEY8lwpJyORiGhNB9gw6zDGYLPZ6BormPSNDmazuYI1IQhiJ5CQTWyLXCbugFhcpBzZ+cNxHDiOExY/cgmz4XAYAwMDCAaDovLdOFHlTGZoccprvDlKEP2np6dx4sQJ0STWaDTC4/HAZrPt6rtJyN4cCh2dzW7EfcYYRkdHcfbsWVG5y+XCsWPHoNPJK3QkCdnVyU4d2UmWxCf7P4lfjv+yFNUqCYwBLGHB+sQbwGKNWf+uUQHfucmDo+78NqDwySR+evf7sDw1sf3BJYRTqfDqj30OVlddRetBbI8SxrYajQZ1dXWoq6sT8iOm3NqBQCBnXxoMBhEMBjE2NgatVivk2XS5XLLpK5Vw7ZTE0FoEN56awRov3fHK7W4nbnPnvymK2Jz09wo9i/KHhGz5YDAY4Ha74Xa7wRjD6uqq0OevrKxsOm+YmZnBzMwM1Go1nE4nXC4XampqYDQay/wXEKWCMSZyZJOQTRDyg4RsQlFoNBduabkKZpWA4zioVCqhzTLbbmlpCYODg0LYdmBDnOzu7kZjY/bCajWQ6WInIXtz5OzI3iwKQW1tLXp6eoqymElC9ubI+d4pFTsVbxOJBI4fP475+XlRuZw3I5GQTeRLgk/g/Y+/H3+Y/UOlq5I3jFchMnsdEisX5/x3rZrDd2/2oKsxv7B4fDKJB973Dqz6FotZzYJRa3V4zSe/CKN1d5vACGIncBwHs9kMs9mMPXv2IJlMIhAICIvcuUKSxuNxzM/PC/2n1WoVRG2pObconLH0CCV5fHhiCT/xSTcKSItOg8/va0CPuXqiq5Wa9GdRSu8IYmeQkC1POI6DzWaDzWbD3r17hQgty8vLWFpayhmhJZlMYmlpCUtLSwA2zAupzWxOp1O05kzIi0whm0KLE4T8oDcwoSiU4PysFBqNJkvI3sy9Z7FY0NvbW9Udf+YEJplM0qRmE+QqRkYiEQwMDCAQCIjK9+3bh/379xdtgZCE7M2h0OLZ7MSlvr6+jv7+foRCF0JZqlQqHDlyBM3NzUWvY7kgITs3ShcvCnVkM8bw6YFPy0bEZgyIBfoQm7seQO5xhVGrwoNv6EN7jSmv74zHYvj+u/8R0bXKhrNV6w14/ae+DJ0pv3oTlaGa3qdqtVrIjQ1sRKBKidp+vz/nfHJ1dRWrq6sYHx+HWq0WubUrnWapmq6dHDgbjuI1p2bgT0h3DHtTnQ3v31Or+LFDuSHhUzkwxkTXk3Ipy5f0CC11dXUYGBgQ/k2tVm8amXJ6ehrT09OCMJ7q9202G707ZUQ4HBaNk6p5PZsg5AoJ2YSioNDiOyczVHYsFsPQ0JCwEzGF2+1GV1dX1e9EzJVXXKvVVqg20ibz3pIDfr8fAwMDol26arUaPT09aGhoKOpvkZC9ORRaPJtCxX2fz4eBgQFRRA29Xg+PxwOHw1GKKpYN2uhA5MOvp36NX5z/RaWrsS2MAXykCesTfw/wmy+smHQqPPTGi+G25yeYhVdX8YM7bkMilu06KScqgxE9r38Tidgyo9oWaI1GI1paWtDS0gKe5xEMBgVhO30zWIpkMonFxUUsLm5EOjCbzYKobbfbyy54kCNbGkR4Hh+dWMIPlqTrwm7QqPDl/U04ZiEXdimgZ1E5ZM4xaGOCMsi8rpdffjnW1tZEYcgzYYyJUo9oNBpB1K6pqan4ZjZia9Ld2ACFFicIOVLdShSRF3IaeJMje+ekt93a2hoee+wxRCIRoYzjOBw+fBitra2yuidKRebCFIkom5PeVlJvJ8YYJiYmMDIyIlqAMJvN8Hg8Jdm1SffS5sjVzV9K8hX3GWMYHx/HqVOnRMfZ7XZ4PB5FTLTJkV2dFOLIZozhe6e/V+oq7Ro+YUB48mbwkfYtj3OaNHjojRfDZc4vrcXq0iIeeP87wScru7lTY3Og7UXXQ1XlmyAJeaFSqeB0OuF0OgEA0WhUWOBeXl7OuWl6bW0Na2trmJycFM5P5dk0lXkTB83XKsN4JIbXn57BbEy6axEvrbHgo+310KnoHikFmQ5eEj7lDQnZyiRzvVitVsNut8Nut6OjowPxeFwIQ768vCxaG02RSCSwsLCAhYUFAIDJZBJEbYfDUfXmH6mRKWSTI5sg5Ae9VYm84DhOFovEJGTvnPS2m5mZEf2bwWCAx+OB3W4vd7UkS67Q4kRu5CJGJpNJnDhxIuv+b2hoQHd3d8kmIuTI3hy53DvlJB8X8mb3cktLC7q6uhSzAEOO/eqkECH7Kf9TOB08Xeoq7RjGc4gs/A0S/r/a9liXSYOfv/kZsBvzi/4SmJ3Bj+58N1iF3532ljbUXvE8EtVkBL1Pc6PX6+F2u+F2u8HzPFZXV7d0bvE8D5/PB5/PB+BCnk2Xy1WyBW5ygVaOJGP41JQPX58PVroqm+JUc/hqZxOOWYyVroqiyXyHKmXcXa2QkK1M0tfvVCpVVp+p1WpRX1+P+vp6MMYQDocFUXuz1CPr6+tYX1/H1NQUOI6D3W4X+n2LxUL9coUhIZsg5A8J2YSiSF8QIGGxMDYbkNfW1qKnpwc6XX7un2qBXLT5I4fQ4uvr6xgYGMhaiDxw4AD27t1b0kkHCdmbQ47bbLZrk0gkAq/Xi2AwKDrn0KFD2LNnj6Im0HR/iJHLpsNy8tNzP610FXLCGJBY7UJk+gYA2wvTrQ4DfnxrH0y6/KZuc2dP4xcfv3OXtdw9rd292PeCazE1NVXpqhA7REl9RjFRqVQi51YsFhMWuH0+nyidR4rMPJsOh0Nwa5vN5qK0NQnZlWEyGsMbzszhXCT7ukuF59tN+ERHAyxqEuFKTeZYjJ5FeUNCtjIpJO85x3EwmUwwmUyi1COpfn91NTuNBGMMgUAAgUAA586dg1arFYUh1+v1Rf+biK3JTBFDocUJQn6QkE0oCsqRvTNCoVBOJ0FnZyc6Ojpo8pUDcmTnj9RDiy8tLWFwcFC06KjVanHs2DHU1taW/PdJyN4ccmRns5ULOVdud61Wi97eXrhcrrLVsVyQkF2d5OvIZozhD7N/KEeV8oYxgI+5EJ54A1jCmdc5e2uMeOANfTBq88u1O/qXx/Gbez+3i1oWh87L/gpX/d2bcfq0dB3xBFEsdDodGhsb0djYCMYYQqGQ4MZeWVnJek8xxuD3++H3+wFsuL1Tri2n0wmtNr/IC5lQX1heGGO4Z86Pz0z7K12VTTGrOHytswkXW8mFXS5I+FQWdD2VSaYjuxDSU4/s27cPsVhMFIY8fS6eIh6PY35+HvPz8wA2RNT0MOTbienE7iFHNkHIHxKyibyQi8uHQosXzuzsLIaHh0XtxXEcLrroorKIeHIlc6BJ99vmSFWMZIxhbGwsa5HdZrOht7e3bLkMM0UZKbVRpZHqvVNJNgstPjU1hRMnToj6aqvVCo/HU/a8nOWChGxiK6LJKELx0PYHlgk+qUN46jXg1w/lfU5XowXfu7kXmjwddCN/+B1+/637dlrFotH3kutx8UteDoCeUzlC12l3cBwHq9UKq9WK9vZ2JBIJkVs71wJ3NBrF7OwsZmdnwXEcbDabIGxbrdYdbSqmjcilZSISw1tH53EyHKt0VTblSrsJH2+vhyvPjVBEccics9CzKG9IyFYmhTiyt0On06GhoQENDQ1gjGF9fV0UhjzXOsba2hrW1tYwOTkpRHkpdpQWQkymkG21WitUE4IgdgoJ2YSioNDi+cPzPEZGRjAxMZH1b3a7nUTsbaDQ4vkjRTEykUjg+PHjwo7YFG63G0eOHCnrjliO40SbhaTSRlKABJBsMh3Zm73LGxsbcfTo0ZLldpcCdH9UJ/k6stcSaznLyw1jQNT3V4gv/g2A/BelnrHHhntf05O3iD308H/hTw9+Z4e1LB6XvupGdD/vBZWuBkFIBo1GI8qzub6+Lri1A4FATrd2MBhEMBjE2NiYEI40tcC9VbonCi1eHn64GMS/ji9BqiMPHQfcs78JV9iVuZFR6lCObGVBQrYySV8vLubaD8dxMJvNMJvNaG1tRTKZFIUhzwxvDWzcY+lRWnQ6nSgMOaV5LA7pQrZWq6Xw7gQhQ5S7uklUJZkhjHmep4FmDsLhMAYGBkQ5VInCyFwcoo0TmyO1SAmhUAher1c0kOU4DocPH0Zra2tFFv5UKpXQNiRkXyBTtGWMVf3CbPrfn0wm8cQTT2B5eVl0TLWkhSAhm9gKg9pQ0d9nDEiu7UN46vUAK6wul3U4cc+rj+b9DD/6/W/ixG8f3kk1i8pzbnkTDl1+ZaWrQRQRpfcj5SZ9gXvPnj1IJpNCOFKfz4dwOJx1TmY4UqvVKgjbNptty5QjRHGZjMbxjtE5DK5L14X9bJsRn9xbjzotLfVVChI+lQVdT2WSfl1LeU3VarUgSANALBYTRO3l5WXEYtn9SSwWw9zcHObm5gBshMBOD0NO9+DOWF9fF/4/5ccmCHlCo1tCUeQK90ydvJjFxUUMDQ2J8gGr1Wq4XC4sLCwAICEtHziOg1qtJvExD6TkyJ6fn8fx48eRSCSEMr1ej97eXjid+eUrLQUkZOcmV/7was8flS4qhEIh0aK1Wq3GsWPHUF9fX4mqlZ3NwqwTyiZfR7ZJY4JOpUOML7/gkIzZEJ78O7BYY8HnvvhIHT760kN5C4i/vvfzOPeXPxX8O8XmRe98P5q7jla6GgQhK9RqNWpra4VIWJnhSHNtAF1dXcXq6irGx8eh0WjgdDoFt3Y6tAmhuPyHbxXvGltA5bfk5kYN4NN7G/BiF+X8rDQUWlxZkJCtTHaTI3s36HQ6NDY2orGxEYwxrK2tCZvZgsFgzjltKBRCKBTCxMSEkJ87JWybTCZ6x+RJuhuewrcThDwhIZvIC7m84DNDqCaTSWi12grVRlowxjA6OoqzZ8+Kyi0WC3p7ezE3NycI2VJwzcqBdCGb2mxzpCBkM8Zw9uxZjI6OisodDgd6e3thMFTWuSeFNpIi+QpW1cRmziuTyYS+vj5YLNWzgEmObGIrOI6Dp9aDxxceL9tvMl6DyOy1SKxctKPzX9Jdj4+8JL8c2owx/Orzn8TU8MCOfqtocByu+8BHULtnb2XrQRQNep9WDpPJBJPJhJaWFvA8j0AgICxwZ+Z2BDZS5SwuLmJxcRFAccOjEhvMxhJ4x+gcnlzLzm0uFZ5hMeALHQ2o1dHynhSg0OLKgoRsZVLMHNk7heM4WCwWWCwWIUpLqt9fXl7O2e/zPC+kJwE2TBnpYchp/Xtz0tuTHNkEIU9opEsoilyObGIjNM3Q0BCWlpZE5W63G11dXdBoNJIL/ywH0icx1GabU+l22uz+37NnDw4dOiSJySgJ2bnJ5ciuZhhjWWHEAaC2thbHjh2ruokrhVPNjVw2H+6UQja4XNtxbVmEbMaAuP8SROdfgg1fXOG88fJW3HZlfmIwYww/vfv98E2M7ei3ioVKrcb1H/wkHE3uitaDKB1Kf59IGZVKJSxM79+/H5FIRBSOND26UIr0cfb8/Dzi8bjg1jaZKF9yofzWv4bbz80hKtEhhgrAB1pdeF2Do9JVIdIgR7ayyFy/oOupDEqVI3s3pCJlulwuAEA0GhX1++lRNVNEo1HMzs5idnYWwIX0IzU1NbDb7ZJY65IKmUI2PcsEIT9IyCYUReYAJNcEv9oIBAIYGBhAJBIRynLlAyYhu3Ayc7ITuclsp3LmOV5ZWYHX6xXlHFSpVDhy5Aiam5vLUod8ICE7NyRkXyCRSGBwcBB+v19UvnfvXhw4cKAqJ2LkyCa247LGy9BgbMB8eL4k388YkAy3IDx1M5DceTSEN1yWv4jNJxL44b+8E6GlxR3/XjFQabS44eOfh9lRubQcRGmg96k0MRgMcLvdcLvd4Hkeq6urgitrdXU163jGmMi1ZTQaBVHb6XRKZuFeivgTSdw2OofHVyPbH1whjhp1+NoBN1xauo5Sgxy8yiIzl3I1zrmUSKVCixeCXq9HU1MTmpqawBhDKBSCz+fD8vIygsFgzvFaevoRtVotCkNuNBqr9v5ljFGObIJQACRkE3khl86OHNkXYIxhYmICIyMjogGOwWCAx+OB3W4XHU9CduFQm+VH5sSgXEL2zMwMhoeHRZPPze7/SkObInJDQvYGa2tr6O/vzwovptVqcfDgwQrVqvKQkL01Sm2TQhzZGpUG13Zci3tO3FP0evAJE8KTN4GPtO3qez7wgv14ZV9+juZ4NIof/vPbEF4J7uo3d4vWYMRrP/1l6AzGitaDIKoVlUoFu90Ou92Ojo4OxGIxLC8v48yZMzkdWwAQDocxNTWFqakpcBwHh8MhCNvkTLrA74NreNvoPEK8NPtQFYC3NTnxFreTrplEodDiyiJTyCaUgRRCixcCx3GwWq2wWq1ob29HIpEQhSFPF2lTJJNJLC0tCZEJDQaDIGo7nc6qi+ZGjmyCkD8kZBOKIleO7GokkUhgeHgYc3NzovLa2lr09PRAp9NlnVNJ16xcqXTIbLmQOeFLJpMlnQTyPI9Tp05hfHxcVO5yuXDs2LGc93+lSX/WqlWszUXmO6ga22ZxcRGDg4M5I4xU+zuahGwiH161/1V4dPZRHF8+XpTvY7wKkfkXIhG4HMDunsH3X7MvbxE7ur6G77/nNsQj4e0PLiFGhwOv+dgXoK6yxa9qpdr7Gbmg0+nQ2NiI6elpBIMbG11sNhs4jsPKykpWH8kYg9/vF6K8pHJsulyuqlzcBoBwksfto/P435VsMUAqdOg1+N6hZtRqaRlPylBocWVBQrYykft11Wg0qK2tRW1tLYCNzWp+v3/L9CORSAQzMzOYmZkBsDFOSPX9VqtVlu1QCCRkE4T8oREwoSjIkQ2EQiF4vd4s515nZyc6Ojo27axztV3mxgBCDLlo8yPz3iplW0WjUQwMDMgu/DKFFs9NLjd/tcAYw/nz53Hq1ClRuclkEnZcV1N75IKE7OqkEEc2AOjVenzi0k/gLb9/C8ZXx7c8disYA+IrxxCdvR5guxN5OACfuvYwrumqy+v49aAf37/jbeATuZ2W5cLe0ITrP/Spgpwr9JzKD7pO8iX92tntdnR2diIej8Pv9wvhSKPRaNZ56Tk2OY6DzWYT3NpWq1Wy4+di8WhwHe8Ym4c/Ic0xOAfgjY123O52QadS9rVQAuTIVhZyFzyJ3EgxR/ZuMBqNMBqNcLvdYIxhZWVFELVzbWgDNtLwrays4Pz589BoNFlhyJVGppBNEIT8IJWKyAu5TF45joNKpRIGm9WWI3t2dhbDw8OiQZlOp0NPT4+wU28zcrlmScjeGnJk50e5wkMHAgF4vV7RAp1arUZ3dzcaGxtL8pvFgoTs3FRraPFkMonh4WHMzs6KyltbW2GxWHDy5EkA1dMem0ECGZEvDr0D9zznHrzrsffghL8wZzZjAB9tQHjyZrBEcfJBf+EVXbjywNbjshRr/mU88C//VHER29W2F9f9y0dkMycgiGokV1+o1WpRX1+P+vp6MMawtraG5eVl+Hw+BAKBnG7tYDCIYDCIc+fOQavVCo6tmpoaSUY22ikJxvDesQX8fDlU6apsSqNWjR8eakazvvpc8nKFHNnKQm4hqIn8kEOO7J3CcZyQfmTv3r1IJBIit3Y4nB3dKZFIYHFxEYuLiwA2hPH0MORyXxtOjX9SkJBNEPJE3m8igsiBRqNBLBYDUD3iIs/zGBkZwcTEhKjc4XCgt7cXBoNh2++gsOyFQ47s/Mi1SaLYTE5O4qmnnhItxpnNZng8HlgslqL/XrEhITs31RhaPBwOw+v1YmVlRSjjOA6HDx/Gnj17MD09LZRXu3hLQnZ1UqgjO0UiYcTIwOsQ0T8KrfOPUOsXtzyeMYCPuBGZfxH48L4d1zcdNQfc95puPLM9P0E8Ho3gF5/4IBLRSFF+f6e0dvfiBW+7o6J1ICoDCTDyIv19mOvacRwHi8UCi8WCPXv2IJlMCovbPp8v5+J2PB7H/Pw85ufnAQBWq1UQtW02m2wFgMdXwnj7uTksSdSFDcZwS6MD/9RcA71M27haSZ+vcBxH71GZQ45sZVJNGxQ0Gg3q6upQV7cRCWp9fV0kbOdanwuHw5iensb09LQQqSU9DLkc32vpecQtFoss/waCqHZIyCYUR/ogpBrE2HA4jIGBASEfWoq2tjYcPHgw78F2OcRGpUGO7Pwopas2mUzi5MmTmJqaEpXX19ejp6dHNjtHScjOTbWFFl9eXsbAwICwGQvYiKrh8XjgdG4IXyTeXoDaonrhOE645vlc+/mVKK776hNYiTBg/TLE/ZdCbRqF1vk41MZxcOp1cKoEWFIPPmFHYqUb8cAzwRL2otVZzQHfu8WDI03WvM957AffxurS1oJ7qTn47KvwVze/saJ1IAiicPJZoFWr1aIcm+vr64Ko7ff7c45JV1dXsbq6KoQiTTm2XC4X9Hp90f+OYsMYw0cmlvCtxZXtD64QZj6J9yWW8crW/ZWuCrED0sclJHzKHxKylYmSHdnbYTKZYDKZ0NzcDJ7ns8KQZ5IeqWVsbEzU99fU1ORlnJIC5MgmCPkjjxV+ouLIaadSupCt9NDii4uLGBoaQjx+IdzkTkMpU37xwqm2TRM7pVQ5sjfbxLFdPngpQkJ2bqoptPjExAROnjwpWvyy2WzweDyiHFXpbcIYA2NMVvd6MUn/u5V8bxC747xvHa/6ej/W4+n3CIfk+n4k19NFAh5AaRay9BoO37vZg4MN+UcIWQss49Sj/1uS+uTL0ef+DS674aaK1oEoP7QxSL5s58jejtTidktLC3ieRyAQEITt9AXgFIlEAgsLC1hYWACwsTCccms7HA7JiQP9q2HcPjqHeam6sAFcGV3B9VE/rDIRBohsSPhUFnQ9lQdjrKoc2VuhUqngcDjgcDjQ0dGBeDwuitSSnrYvRWbfbzKZRGHIpdqeJGQThPwhIZtQHNUgLjLGMDo6irNnz4rKLRYLent7dxRKmYTswqHQ4vlRCre/z+fD4OCgyLmq1WrR09MjhEySEyRk56YaQovzPI+TJ09icnJSVN7U1ISjR49mvZtzhVUmIZuEl2ojX0f2uaV1vPLrTyKayOf+KM3ipEHD4aE3XowWp3H7g9MY+f1vN+KbV4hnXvdq9L7wpRX7fUIaVGv/IleK2ReqVCphYXr//v2IRCIit3auDeNra2tYW1vDxMQE1Go1nE6n4NZO35RXCT4/7cOXZgMVrcNWWFQc7rYApik/ABLM5MxuN5QQ0oKEbOWRuaYgVeG1Emi1WtTX16O+vh6MMSFSy/LyMgKBQM61vPX1dayvr2NqakrIz53q+6USwjszR7Yc0g8SBJENCdmE4kgPJaxEMTYWi2FoaAhLS0uicrfbja6urh2HUiYhu3AotHh+FNORzRjD+fPncfr0adEigdVqhcfjgclk2vF3VxISsnOj9NDi0WgUAwMD8Pv9ovKDBw+ivb190/yW6fA8X7WLKrnuDylMlAlpMDAVxC3fGUQljXc2vQY/urUPbnthzjo+kcDJ3/+2RLXanr9+0+3Y/4xLK/b7BEHsnmL3hwaDAW63G263WxSK1OfzYXV1Nev4ZDKJpaUlYc5qNBoFt3Y5HVsn16N405lZzMalO1e73mXF3e11mBgbw/mny2g8I19I+FQWdD2VR+Z6C13X3HAcB7PZDLPZjNbWVvA8j2AwKAjbufp+xhgCgQACgQDOnTsHrVYrCkNeqRQkPM9n5cgmCEJ+kJBN5IWcJlJKDi0eCAQwMDCASCQilHEch8OHD6O1tXVX14mE7MIhR3Z+FCs8dCKRwPDwMObm5kTlmzlX5QQJ2blRsiM7GAzC6/WK3ucajQbHjh3bMqqA0sX9QiB3evWynRt/YDKIm78ziGQFHw+nSYOH3ngxXGZdwefOnzuL9YB/+wNLwDW3vwdtPZ6K/DYhDaq5X5E75XKCZoYijcVigqi9vLwsSnuVIhwOY2pqClNTU1CpVLDb7XC5XHC5XDCZTEWvL2MM98358ZlpP6Q6ejSrOHyuox5XOjYW1MnJqwxI+FQWdD2VR+Zap5zXkcqJSqWC0+mE0+nEvn37EIvFRGHI06MlpojH45ifn8f8/DyAjZDeKVHb4XCUre3TRWyAhGyCkCskZBOKQ4mhxRljmJiYwMjIiGiCazAY4PF4YLfbd/0bHMdBpVIJA3WltF0pUeK9VgqKIUaura3B6/UiFAqJvvfgwYNoa2uT/WIPCdm54ThOFEJYKW0zMzOD4eFh0d9jNpvR19e3bb6mXOJttUJtQeTid6d9eNuPT1QyKjfqrTo8dOtFsBm1Ozp/ze8rco3y48Xv/gDcB7sq8tuENJH7+KraqJQQqtPp0NjYiMbGRjDGsLq6KojawWAw63ie5+H3++H3+3H27Fno9XrBrV1TU7PjCGMpJqJx3HxqBpMx6W5qf6HDhM90NEC9yRyAnj35QhsSlAUJ2cqDHNnFQafToaGhAQ0NDULo7vQw5LnWblIpSCYnJ0Wb2mpqamA2m0v2zkwPKw5UV47scDiMgYEBnDx5En6/H5FIBDabDfX19ejr68P+/fuL0u7xeByPPvooJiYmMDs7C4vFArfbDY/Hg/b29t3/IQQBErKJAkgXE6SM0sTFzVyotbW16OnpgU5XuNNnM9RqNQnZBUChxfOD4zio1WqhjQptq4WFBQwNDYkiLOh0OvT29qKmpqaoda0UJGRvjkqlEu4ZOfRBW8EYw+nTpzE2NiYqr6urw7Fjx/JauC1WhAMlQEJ29bKZI/vhk4t4109PopJ3wh6nAQ/+fR/M+p1Ps6Lra9sfVEw4Dq+8+9NwNLpL8NWUy54gyoUUnjGO42Cz2WCz2bB3717E43H4/X5B2I5Go1nnRKNRzMzMYGZmRjg/5dYuJL8mzxjunw/iU1M+SFXCNqs4fKS9Di+qsWb9GwmgyoCET2WRvnZB11MZkCO7+HAcB4vFAovFgj179iCZTIrCkKcbUlKkb2oDNtb40sOQF3OtO1PIrgZH9h//+Ed87nOfw89+9rOcbvkUzc3N+Pu//3u87W1v29H66uLiIu6880488MADWF5eznnMZZddhne+8514+ctfXvD3E0Q6JGQTikNJObJDoRC8Xm9Wp9vZ2YmOjo6iT3DVarUQCk7ubVcOKLR4/qSLkfm2FWMMo6OjOHv2rKjcbrfD4/HAYCgs56iUISF7c3Zy70iReDyOwcFBIVdkin379hW0C5bE2wtQWxDp/Ng7i7v+60xF69DhMuJHb+iDTrO7BTGNtngLN9uhUmvwmk9+ESa7o2y/SUgbepfKFykKoVqtFvX19aivrxc5tnw+HwKBQNb9xhhDMBhEMBgU8mumu7U3W9j2xZN47akpjEakKmEDz7YZcO9+N3Sq3NcmvS1IMJMv5KxXFrQxQXmQI7v0qNVqod8GNjaspW9qy5WCJBaLYW5uTjBxWSwWURjy3VynahKyE4kE3v72t+Pf//3f8xrTT09P40Mf+hDuvfde3H///fibv/mbvH/rV7/6FW6++WYsLCxsedxjjz2Gxx57DK997Wtx7733VpUjniguJGQTikMpjuzZ2VkMDw+L/gadToeenh7U1taW5DeV0nblIlPIpvysm1OoUBuPxzE0NITFxUVReWtrKw4fPqy4yQYJ2ZujhLYJhULo7+8X5WZSq9Xo7u5GY2NjQd+l5LzhhUJCdvWS6fL91p8m8enfjG1xRuk51mzFN19/DFr17vsnfZkWV7QGI274xBdhoMUEglAcUpyTZDq2EokEAoEAfD4ffD4fIpFI1jnxeFy0sG21WgW3ttVqBTgO31sI4mOTPmQvi0sDIwfc1VaPl7m2dpdLcSMCUTi0IUFZkJCtPMiRXX70er0oBUkoFBKFIc81jw+FQgiFQpiYmBDyc6eEbZPJVFA/WS2hxRljuOGGG/DjH/84698OHTqEw4cPw2g0YnFxEU888YTghgeA+fl5vPSlL8XPf/7zvMTs//3f/8XLXvYykdub4zj09fWho6MDgUAAXq9XZOT43ve+h5WVFfzsZz+j9ymxI0jIJvJGjqHF00MRywWe5zEyMoKJiQlRucPhQG9vb0ldqCRkF0Zmx5tMJned002pFBKGfXV1FV6vVyT6qVQqdHV1oaWlpWR1rCRKEGtLhdzD0i4sLGBwcFB03xuNRvT19W0swBZI5ntHjm1SLEjUJwDgB0N+fNvr3/7AEuJpteH+1x+DqkjCQ/3e/eBUKrAS3tMGqw2v+fgXoNHrS/YbhPwhMU1eyE0I1Wg0qK2tFTZpr6+vC25tv9+fs19fXV3F6uoqzp8/j4RGi8+ZG3CGSVeEOGbS45sH3bDmsclJbtePyA05spUFCdnKgxzZlYXjOFitVlitVrS1tSGZTCIQCAjCdqboDGxcs9SmN2BDGE8PQ67Varf8zWpxZH/ta1/LErGf85zn4Mtf/jKOHj0qKk8kEvjOd76Dd7zjHQgGgwA2XPE33XQTTp8+DbvdvunvTE1N4brrrhOJ2Jdffjm++tWv4vDhw0JZNBrFvffei3e9612CC/+Xv/wl/uVf/gUf/ehHd/33EtUHKS6E4pBzaPFwOIyBgQGhE0nR1taGgwcPlnyARaGyCyNz5ya12ebke2/likRgMBjg8Xi2HEjJHRKyN0eubcMYw7lz53DmjDjUcU1NDXp7e3ec84lcyBegtqheUtf+p2McHpmrrIh99UEXPvPyrqIuVpvsDrR7noGxJx8v2nemY6mtw6s/8lmoyIFCEIpC7v2gyWSCyWRCS0uLkF8zFYY0fRGaAfg/rRkPGGoQZ9IUIPQA/rWtDq+oteaf45sEUEVAwqeySL+e5NxVBpnrxPScVha1Wi1EWgGASCQiiNp+vz9nGPJoNIrZ2VnMzs4C2HAT/+Uvf8E111yDK6+8EvqMjbqZObqVKmRnisPPec5z8Otf/zqn0K/RaHDLLbegu7sbz372sxGNRgFsGDHuuece3HHHHZv+zp133ilyc1922WX49a9/nWW80+v1uP3227Fnzx5ce+21QvlnPvMZvOlNb0JbW9uO/k6ieqG3NaE45OoqXlxcxGOPPSYSsdVqNXp7e8sWSlmubVcpcjmyidxsJ0amIhFkOldrampw2WWXKVrEBuQr1pYDObZNIpHA4OBglojd1taGiy++eMciNpD93pFLm5QCcqdXNz8+x+GRucouKF5zuLboInaKI1c9r+jfCQC1bR244WOfJxGb2BR6lyoDuQuhqfyanZ2duOSSS3DZZZfh4MGDsLvq8EVTPb5rrEWck+Zy1gGDFr/tacMr62wFXQdyZCsDCi2uLGhjgvJIX29SqVT0vpUYBoMBbrcbR48exbOf/WxcfPHF6OjogMPh2PRa/fa3v8XXvvY1vOIVr8CePXvwohe9CJ/61Kdw4sQJ8Dwv2gyn1+t3tR4jVY4fP47z58+Lyr7whS9s61a/+OKLceutt4rKfvnLX256/JkzZ/Ctb31L+KzT6XD//fdvGT32ZS97GW666SbhczQaxV133bVlvQgiF9QLE3kjl8490/kp9UV+xhjOnDmDJ598UrTTzGKx4NJLLy04f+puICG7MDJ35FKbbc5WocVjsRieeOKJrEFXe3v7rkU/uZDePowxWkROI73vkfr7HNgIi/n4448LuRyBjb/h6NGjRdmURC7kC1BbVCeMMdx7gscf5isrxL7uGW58+rrSiNgA0HSwC053cdNpNHcdxbX/8mHZjOmJykP3irxQshBqMBjgNdhwU8KEExpjpauTEzWAD+2pxX8caUW9rvDghySAKgNy1isLErKVB7ns5QPHcbDZbGhvb0dfXx+uuOIK9PT0oKWlBSaTSThuYGBA+P/hcBh/+MMfcPfdd+PSSy/FoUOH8OMf/xgHDhwQIr8okXPnzok+t7a24tixY3md+9KXvlT0OdOQkc73v/990Zruddddh87Ozm1/I9Ph/eCDDyISieRVP4JIQaHFCcWRS1yU6oAzFothcHBQyPORwu12o6urq+z5lknILgxyRubPZqHFg8EgvF6vaACjVqtx9OhRNDU1lbWOlSTXvUSTqg0yRX4p4/P5MDAwINqUpNfr4fF44HA4ivIblBf6AiRk50bJi6aMMbz9x09hYKmy9bjxkma8++p9Jf0NjuNwxY234j8+fTf4RGLX37f3okvwvLe8ffcVIwhCsihVyI7xPP7p3AL+O5CdN1Mq7NVrcU9nIzoMO9+Aq9TrV23QhgRlQUK28qBrKl80Gg1qa2tRW1sLYEO0XlpaQnNzM6ampnLm156bmxNMBhzHIZFI4MMf/jBe8IIX4OKLLy77unupyPzbW1ry3xDd2toq+pweNjyThx56SPT5lltuyes3Dh8+jEsuuQSPP76ROmttbQ0PP/wwXvKSl+RdT4KgNzahODI7IakKsoFAAI899phIxOY4Dl1dXeju7q5IZ0pCdmGQIzt/coWHnpqawuOPPy4SsU0mE571rGdVlYgN0KaIrZBDaHHGGMbHx/HEE0+IRGy73Y5LL720aCI2QOG00yEhu7rgGcON3x7Eb0/7tj+4hNx+ZXvJRewUjfsP4Kq//4ddCxqeF72MRGwib+hdKl+UeO0eCazhIu+YZEVsDsD7W114uHvPrkRsgIRspUAimXLIjJRG11MZpK/bkXlA3hiNRrS2tuJnP/sZJiYm8POf/xxvectbcOTIkZzPK2MMsVgMH/rQh3DppZeirq4O119/Pe67776sCJFyIzOaayFu58xja2pqch43NzeHwcFB4bNGo8Hll1+e9+9ceeWVos+/+tWv8j6XIAByZBMKJHMgkiiCi6WYMMYwMTGBkZER0aDYYDDA4/FUNBfwVuGfiWwy7zWpimxSIPPeOnHiBCYnJ0XH1NXVoaenZ9scLkqEhOzNkXpocZ7nceLECUxPT4vK3W43jhw5UvTJMTmyL0BCthglL3rHkzxu+vYgjs+sVrQedzxvH173zOay/ua+Z1wKtVaL39z3RSRjsYLOVWk0+Otb34qOiy4pUe22J/2+rPZnVI4o+b2idOR+7cJJHndOLOIhX6jSVdmUPXoNvrivEV0mfVG+j0JSKwO6jsohc55FQrYyyMyRTSgDrVaLq666CldddRWAjWh5v/rVr/Cb3/wGv//977G4uJh1TiAQwE9+8hP85Cc/AQAcOHAAz3/+8/H85z8fV155JaxWa1n/ht3wjGc8A3q9HtFoFABw8uRJhMNhGI3bp2N58skns74rF8PDw6LPPT09MJvNedfxsssuE30+ceJE3ucSBECObKIA5DIIl7JLNpFIYHBwECdPnhQt5tXW1uKyyy6rqIgNiN3sUmo3qZI56KU225z059Ln82WJ2Pv370dfX19VitgACdlbIeXQ4pFIBI8//rhIxOY4DocOHUJ3d3dJdniTI/sCJGRXB7FEEtd99cmKi9gffGFn2UXsFO29F+O6f/kI9j3zUnB5vFc4lQr7Lrkcr7jrUxUVsQmCKC9KcfR6V8O4dPC8pEXst7md+E13W9FEbEA516/aIQevciAhW5lQjuzqwOVy4XWvex2+8Y1v4IUvfKEguLpcrk0F6tOnT+NLX/oSXvKSl8DlcuHKK6/ERz/6UTzxxBOSX6OzWq248cYbhc+RSARf//rXtz0vmUziS1/6kqjspptuynnsU089Jfq8f//+guq4b584qlnm9xHEdpAjm1AcUhWyQ6EQvF5vVt6Kzs5OdHR0SGKySo7swiBHdv6k31vpURI0Gg16enpQX19fiWpJBhKyN0eqocUDgQC8Xq+w4xXY2AXc29sLl8tVst8l8fYC5E5XPqFoAq/8ej8m/fmHRisFn7u+C889WFvROjjdLXjuG2/HpcEARv7wO5x+9BGsLM6LjrE3unHw8r/CoWdfCYPVVqGaEnKnmvsVOZN53aQwtyyU9SSPj00u4YdLld24tBV1SOIDVjUuM6vBGCtqO5MAqgwotLhyICFbmZAju/pYW1sT1uKf+cxn4qGHHsKf//xnPPzww3j44Yfxl7/8JWscFY/H8cgjj+CRRx7B+9//frhcLlx99dW45ppr8IIXvCArlLcU+PjHP47/9//+nxAm/T3veQ8OHTqEq6++Oufx8Xgcb37zm+H1eoWyv/7rv8bLX/7ynMefPXtW9HnPnj0F1a+trU302efzgeM4J2Ns86TcBJEGCdmE4pBijuzZ2VkMDw+L6qLT6dDT04Pa2soujKZDObILg+M4qFQqYYJDbZYbxljWBg4AsFgs8Hg8BYWiUSokZG+OFEOLT09P48SJE6L6WCwW9PX1wWQylfS3Sby9AIn6yiYYjuPa+57AYii+/cElQsUBn7v+CK46ULrNKYVisjvQ9+Jr0ffia8EnEoiG1wEAeqMJKg1N7QiiWpF7H3g2HMUNIzMIJCU6rmEMV8VW8KpoANwK8OT0ODQaDWpqauByuVBTUwO9fnfubHJkKwO6jsqBhGxlQo7s6iN9PdJsNkOn0+GKK67AFVdcgbvvvhs+nw+//e1v8T//8z94+OGHsyJIAhui6wMPPIAHHngA99xzD970pjeV80/Ii5qaGvzud7/DddddB6/Xi3A4jGuuuQbXX389rr/+ehw6dAhGoxFLS0v44x//iHvvvRenTp0Szn/mM5+JH//4x5v2XYFAQPS5UEOSxWKBwWDIzMltB0BCNpEXtNpB5I1cBuFSypHN8zxGRkYwMTEhKnc4HOjt7YXBYKhQzXJDQnbhkJC9NclkEsPDw/D7xeOSxsZGHD16NGvjSbVCQvbmSCm0OM/zOHXqFMbHx0XlDQ0N6O7uLsv9zHEcOI4T2qLSbVJJKMx6Nmtra8IO7NTiuhzfs0uhGK677wn4w5Ubw6k44Ds39aKnWbrOZpVGAyM5r4kSIpf5H5GNXK5dhOfxySkfvrOwUumqbIqDJfGmtQV08DFReSKRwMLCAhYWFgBsLNCm+l673V6w6EUCqDIgR7ZyICFbmaSv25GQXR2sr68L/z+XkcblcuEVr3gFXvGKV4AxhlOnTglu7d/97nei8wHgec97XsnrvFPa29vx+OOP4/7778d9992HJ598Eg8++CAefPDBTc9xuVx45zvfiXe/+91bpnsMhcQpX/LJv52J0WjMFLLlk4icqDjyW9kiiG2Qiks2HA5jYGAAwWBQVN7W1oaDBw9KchCcKWQXO1yaElGr1cJmCRIfxayvr8Pr9WJ1VRweUKfT4dixY3RvpUFC9uZIJbR4LBbD4OAgfD6fqHz//v3Yt29fWe/ndCG7mu8VcmSLWVhYwNDQkDDumZ2dBcdxcDgccLlccLlcMJlMkn/3TvnDeOXX+7EardzmMJ2awzdff0zSIjZBEEQ6cgwtPhWN44aRaczFpbsZ+KVOCz62tx7J2B74fD4sLy9jeXk55xpDKBRCKBTCxMQE1Go1nE6n4NbOZ7GXhGxlQEK2ciAhW5nQM1pdZEaINJvNW/axHMfh0KFDOHToEG6//XZEo1E89thjgrC9srKCjo6OclR9xySTSSSTSej1etHaUS5aW1vxoQ99CK9+9au3FLGBbCF7J+Y8o9GYaXSyFPwlRNVCQjahSDQaDWKxjR3TlRCyFxcXMTQ0hHj8QjhMtVqN7u5uSebRSJEr5zPtUNwacrHnJtczkEKj0dDiTAYkZG+OFITs1dVV9Pf3IxwOC2VqtRo9PT1oaGgoe33SN2tVs3hLQvYGjDGcP39eFBYs/d/8fj/8fj/Onj0Lg8GA2tpauFwuOBwOyfXxE/51vPy+fkQSlXsH6jUcfvyGi9DuKm2aAIKQKtX6LpU7chKy4zzDv00t4RsLK5Dq3eZUc7hnvxt91o1FWq3BgObmZjQ3N4PneQSDQSwvL8Pn82Ut7AIb88KlpSUsLS0BAEwmk+DW3qz/JSFbGdB1VA6Zc0+pjZuJnUGO7OojXci2WArTTfV6Pa666ipcddVV+NjHPiZaE5Iijz76KF73utcJUdq2Y3JyErfccgve85734KMf/Sje8IY35P1bO+njqF8kdgMJ2UTeyOllkz4YKWdoccYYzp49i9HRUVG5xWJBb29vwR1muckcxCWTSRrYbUO6yEZC9sYzcO7cOZw5c0ZUnp4HhdopGxKyNye976nE4vrc3ByOHz8uum9NJhM8Hg+s1spEQap0m0gFErI33hUnTpzA9PS0qFyr1ebcSBSJRDA1NYWpqSmoVCrBLeZyuXYUGqyYPDW7itd/awCxZOWuo0mnxo/+3oM9NSRiEwQgr/lftSOXPnAulsBNp2ZwLprdR0mFqx0mfHpvA8zq3E69VP/pdDqxb98+RKNRwam9vLycs/9dX1/H+vq60P+moqXU1NQI0VLSx//07MkXcnsqh8x1C7qeyoCe0eoj05G9Gyo9Z96K3/zmN3jxi18sCt3d3NyM2267Dddccw327t0Lk8mE5eVlDAwM4Ac/+AG+973vIZFIYHFxEbfeeiv+/Oc/49577805DsnUNHYi6uc4J3s3IEFsAgnZhCKphEt2s7CzbrcbXV1dsshRmUvIJrYmvc2qXXxMJBIYGhoS8sSlaGlpgcViwcjICABqp1yQkL05lXJkb7YxyeVy4dixY9DpdGWrSyZScKlLgWoXsqPRKLxeLwKBgFCWCoVWW1uL9fV1BAIB+Hw+BIPBrPbheR4+n08Yt5hMJkHUdjgcZV3YGZpewY3fHkSSr9w1tBs1+OmtF6Heqq9YHQhCClTbu1SpSE0IZYzhyzPL+MJsQLIubLOKw1f3N+EZtsIWqfV6PZqamtDU1ATGGFZXV4X+dWUlO/c3z/OC6A1sbPitqakRbcCX2vUj8oc2JCgHCi2uTMiRXV0wxrbNka0EFhcXccMNN4hE7L/927/Fd7/7Xdhs4nRZDQ0NuOaaa3DNNdfgzW9+M1784hcLawJf/epXsW/fPtxxxx1Zv0FCNlFppK+sEcQOKLeQHQgEMDAwIOowOI7D4cOH0draKpsJDAnZhUOO7A1CoRC8Xq9opyPHcejq6kJrayumpqaE8moW3jaDhOzNqYRou9mmjPb2dhw4cKDiixjkyN6gmoXs1dVVPPnkk6Jxh1arRU9PD5xOJ2KxGMxmM+x2O9ra2pBIJIQQqD6fT0i/kk7KLTY5OSnK7elyuXaU/ypf/jTmx5t+cBwV1LDhNGnxszdehBpz5TaoKBV6XxFEeZByaPHFWAJvODOLp8LZfY9UeI7NiE/ubYBLuztRg+M42Gw22Gw27N27F/F4XBCtN+t/I5EIZmZmRGXBYBCrq6uwWCySupbE9qQ/i5WeMxC7I3PuSc+iMkhft6NntDrYTWhxufCZz3wGi4uLwudDhw7hwQcf3HYe/6xnPQsPPPAArr76aqHsrrvuwi233IL6+nrRsXa7XfQ5/ffyIRQK5RKyAwV9CVHVkJBN5I2cBm3p7udSiouMMUxMTGBkZEQ0YTEYDPB4PFkvealDQnbhUI7s3KGX9Xo9PB4PHA4HAHKQbgfHceA4TniPUBtdoNwiyNraGrxeryjfoUqlwtGjR+F2u0v++/mQ3ibVfK9kjkuqpS3m5+cxNDQkeudaLBZ4PB7o9bndxBqNBvX19aivrwdjDKFQSBC1g8Fg1vGZuT3NZrMgatvt9qIt+vxmZBFv/8nJonzXTnHb9fjprRfBrKdpEUFkIqf5X7Uj1Y0i350P4O5JH6TaQ+s44J79jbjCXhqHllarRUNDAxoaGsAYw9ramqj/3ey6pY7R6XRCbu2amhpotdqS1JMoDowxErIVRKa7nq6nMki/ruTIVj7JZFIknipVyP7Rj34k+nzHHXfkvRn9uc99Lq644gr84Q9/ALDhmv7hD3+I22+/XXRcZ2en6PP4+HhBdcw8vqamBj6fz1/QlxBVDa3YEIqkHDmyE4kEhoeHMTc3Jyqvra1FT09PRcPO7hQSsgunmkOLM8Zw+vRpjI2NicqdTid6e3tFgkr6pI8xBp7naSKYgUqlEp65aruXtqKcmyCWlpYwMDAg6jekuDEp83mqZtI3gCi9LRhjGBsbw+nTp0XldXV1OHbsGNRqdV79NsdxsFqtsFqtaG9vF9xiqUXzXLk919bWsLa2homJCajVamFR3eVybSqeb8d/nVjAe382sqNzi0WLw4Cf3toHo46mRASRQunv0mqh0psQ5mNxvG10Hk+uRStaj624zGrAJ/c2oKFMfQDHcbBYLLBYLEK0FL/fL/TB6VFWUsRiMczNzQlrDjabTRC1bTZbxa8zIYYcvMqCcikrE7qu1UV6WHFAmaHF19bWslLiPfe5zy3oO66++mpByAaAxx9/POuYw4cPiz6fPXu2oN84d+6c6HNXV1dB5xMErdoQBZG+YCxlSu2SzRVGGdjYndTR0SHbCQsJ2YVTraHFN8sJ39bWhoMHD2ZNCDLvLRKysyEhOzflELIZYzh//jxOnTolKnc4HFu6XCsFheq9QPpzo+S2SCaTOHHiRFbo0fb2dhw8eHBX47NMt9h2uT2TySQWFxeFUGIWi0UQtW02W17v9u/9ZRoff3h02+NKyaEGM753cy90GnJiEAQhf6QUWvyXvlW8e2wBUp0ZqQF8pqMBL6yprCtLo9Ggrq4OdXV1YIzhkUceEca6m/XrKysrWFlZwdjYGDQajcitLbXxajWSec1ovitvSPBUHowxypFdZaRH2gOU6cgOBAJZZY2NjQV9R+bxqchs6Rw9elT0eWhoCOvr6zCZTHn9xqOPPrrl9xHEdpCQTSiSUoYWn52dxfDwsOh7dTodenp6UFtbW9TfKjckZBdONTqyg8EgvF6vyDWwXehlygG9PRR+PTelFm03EwhbWlrQ1dUlyUULulcuUA2ifjQaRX9/vygEOMdxOHLkCFpaWor6W5m5PWOxmMitnSvKTSgUQigUwvj4uGhR3eVy5YxO830JiNhHmyz4zs0eaFTy3HhIEOVCrptzqxEpCNnzsQTeMzaPx1azncVS4WKLHp/taESjxCJxZF6vzs5OGI1GoQ/OdJQBGxHiFhYWsLCwAODCxrKampqipgEh8occ2cqChGzlkfmM0nVVPpkGNCUK2amUjumsra3lLN+MfAT/pqYm9PT0YGhoCMDGOOT//u//8PznPz+v3/jf//1f0ecXvOAFedePIAASsgmFUgpHNs/zGBkZwcTEhKjc4XCgt7c379wTUkalUlGe3gKpNkf29PQ0Tpw4Ibo3jEYjPB4PbDbbpudlThCqoa0KhcTJ3JSyXSKRCPr7+0WuU47jcPjwYbS2tkp28akaxNt8UXpbrKysoL+/X7RxSKvVoq+vD06nM+c56e3AGNvVfazT6dDY2IjGxkYwxrCysiKI2qurq1nHZy6qW61WuFwu1NbWwmq14rO/HcM3/zS14/oUg2fvc+LfX3VUss83QRDETqi0kP1IYA1vGZ1DXKJdMQfgg60uvKbBUemqbEr6NdRoNMKmsM7OToTDYSwvLwv/5ZpLpW8sU6vVcDqdgrBtNBrL+adULeTIVhYkZCuPzPUEcmQrn2oQss1mM2w2m2hdy+v14qqrrsr7O5588knR580c3ddee60gZAPAN7/5zbyE7JGREVG4crPZnLcAThApSMgmCkKOocWLkSM7HA5jYGBA5IYCNg+jLGfUarXQZiQ2bk+1OLI328iRb074XKHFCTEkZOemVO3i9/vh9XoRi8WEMq1WC4/Hg5qamqL9TilIX5yu9ntFyUL2/Pw8hoaGRH2xxWJBX19f3uG7ignHcbDb7bDb7ejo6EA0GoXP5xPcYrnGDKurq1hdXcX58+fxn5MaPFxZDRt/fcCFz13fRSI2QWyB0t6l1UKlrttSPIH3ji3gkZVwRX4/H46adLhnf1PZcmHvlPRrmNlPGY1GNDc3o7m5GTzPIxgMCv1vposK2JjHLy0tCaFBTSaTIGo7HA4Sb0oEuT2VBQnZyiNzvkLvQuVTDTmyAeDKK6/EL37xC+Hzfffdl7eQPTc3JzoXAK644oqcx772ta/Fhz/8YeFZ+ulPf4ozZ86gs7Nzy9/4xCc+Ifr8yle+UhGGQKK8SHskTxA7pJihxRcXFzE0NIR4PC6UqdVqdHd3F5xzQg6QkF0Ypc7HLgUikQgGBgay8q7s27cP+/fvz0sQoNDi20NCdm5K0S6Tk5N46qmnRAuGVqsVfX19snCspLdJtQsOShSyGWM4d+4czpw5Iyqvq6vDsWPHRGOcSqLX6+F2u+F2u4VF9ZRbO3Pn+7fPqPBkdpqtsvKS7gZ85CUHK1sJgpAZtOlDvpTj2v15dR23npnDOi/N/pcD8Ha3E29pckr+Xi7EUa9SqeB0OuF0OrFv3z5Eo1FB1F5eXs65kX99fR3r6+uYnJyESqWCw+EQhG2TyST59pELFFpcWZCQrTxos0n1kbnZy2q1VqgmpeVVr3qVSIx+4IEH8KIXvQive93rtjwvGo3i9a9/vaidLBYLrrnmmpzHd3Z24qabbsI3vvENAEAsFsPNN9+M3/zmN5sK0z//+c9x//33C591Oh3uvPPOfP80ghCQxkoYQRSZYoiLjDGcPXsWo6PiPI4WiwW9vb2KDEcCVIcwW0yUHlrc7/djYGAA0WhUKFOr1ejp6UFDQ0Pe30NC9vaQkJ2bTKFyN6GSN4ss0NjYiO7ubtnsyFaieLtTlNYWyWQSw8PDmJ2dFZXv3bsXBw4ckOyCaPqi+v79+xGJRODz+bC0tITP/imAJ5cqW+/XPsON9z5/f0XrQBAEUUrKGVp8NZHEe8YW8Otgdt5mqbBPr8G3DjZL3oWdYjfXT6/Xo6mpCU1NTUIakJSwnR5mNAXP80KIcgAwGAyCqO10OiWzYU6OUGhxZUFCtvIgR3b1kbnBWqmO7Fe/+tX45Cc/icHBQQAb/dGNN96Iv/zlL3jve9+LpqamrHN+97vf4Z3vfCcGBgZE5XfcccemacwA4K677sJDDz0Ev98PAHjsscdw9dVX42tf+xoOHTokHBeNRnHffffhn/7pn0Tn/9M//RPa2tp2+qcSVQyNUImCkOoCaia7DS0ei8UwODgIn88nKne73ejq6lL05I6E7MJQamhxxhgmJydx8uRJ0YTcbDbD4/EUvJEjc4JA91Y2JGTnJnPRYKdCdiwWg9frFQbbKQ4cOIC9e/fKpn8D6F5JR0lh1iORCLxeryiNCcdxOHr0KJqbmytYs8IxGAxwu9248399FRex/+7SFrzjrzsqWodqRmmbTaoBuk7ypFzXbSAUwa1nZhFISrPP5QC8ocGOd7a4oJHR2K5YTt70NCB79+5FPB4XubXTU+qkiEQimJ6exvT0tHB+Sti2WCyyGiNXGnJ7Kov0NQu6lsqAntHqI91pbDAYFLuer1Kp8OMf/xiXX345FhYWAGyMDb/whS/gS1/6Enp6etDR0QGj0Yjl5WV4vV7Mzc1lfc8LX/hC3HHHHVv+VktLC37605/immuuEcYVjz76KLq6unDRRReho6MDwWAQ/f39WFxcFJ374he/GHfffXeR/mqi2lDm00tUPemiGWMMPM/nPUAJBAIYGBhAJBIRyjiOw+HDh9Ha2qr4iZzSHcbFRonCfzKZxFNPPYXp6WlReUNDA7q7u3c08CNH9vaQOJmbXPdOoRPOlZUV9Pf3i97rGo0Gx44dQ11dXVHqWU5IGLqAUtoi1z2q0+ng8Xi23A0tVeJJHm/43hD6J7OdYOXkXVd34KZLWipaB4KQM0qf9yiZYl+7KM/wz+fn8cvlte0PrhBunRo/ONgMt15b6aoUTKmcvFqtFg0NDWhoaABjDKFQSBC2g8Fg1u8yxhAIBBAIBDA6OgqdToeamhpB2NZq5de25aSckRGI0pM+JyfnrjIgR3b1kZ4jW6lu7BT79+/HI488gte//vV44oknhHKe5zEwMJDlvE6H4zjceuut+NznPpdXX3/llVfioYcews033yyI1YwxPPHEE6LfTueGG27AV7/6VXruiB1DQjahSHK5P7ebDDLGMDExgZGREdEExGAwwOPxwG63l6SuUqOY+cWrgUzhfzdhj6VAOByG1+vNCkO3W9cqCdnbQ0J2bjLvuULbZnZ2FsePHxedt9PIAlJBSS7k3aKEfOFzc3M4fvy4qM+VU872TOJJHq/+Rj9OL1Q25Ox7nteB1z+TRGyCIKqDUgpog6Ew/mF0Hgtx6c4Nb6y34d0tLhhk6q4rhwDKcRysViusViva2tqQSCTg9/sFYTt9M12KWCyGubk5wbVls9ngcrngcrlgtVplPe8tBeT2VBYUWlx50DNafaSHFq+GKCOHDh3CH//4R3z/+9/HPffcgz/96U9brpMYjUZcd911eOtb34pnPetZBf3WC1/4QgwPD+POO+/EAw88kBX9MMWznvUsvOtd78LLX/7ygr6fIDIhIZsoCLm88DMdo8lkcssdRYlEAsPDw1lhNWpra9HT0wOdTleSekoRcmQXRuamCTkL2UtLSxgcHEQ8HhfKtFotjh07htra2l19d+YEge6tbEjIzk2u0OL5wBjDmTNncO7cOVF5XV0denp6ZO0oUYJ4Wyzk7MhmjGF0dBRnz54VldfX16Onp2fHYc8q2Qetx5J41df7cX45XLE6AMCnrz2Ea7rqK1oHgpArcnuXEhuUQgjlGcNd40v4/lJlo2tshVOjwg8PNaPDIO/5eiWcvBqNBnV1dairqwNjDOvr64KoHQgEcs5HVlZWsLKygrGxMWi1WtTU1Aj/6fX6ktdZ6hQrRDwhDUjIVh6Z61B0XZVPupCtdEd2Co1GgxtvvBE33ngjgsEgnnjiCYyNjSEQCCAajcJqtcLpdOLo0aM7jrqZor6+Hl/5ylfw+c9/Ho8++ijGx8cxNzcHs9mM5uZmeDwe7N27t4h/HVHNkJBNKJJMcXGrPNmhUAher1fUuQFAZ2cnOjo6qm7yocRQ2aUkl0Art8EwYwxjY2M4ffq0qNxms6G3txcmk2nXv8FxHFQqlTAZJKE2GxKyc7MTN388HsfQ0FBWPp6Ojg50dnbK/r0uZ/G22Mi1LZLJJI4fP561gU7O9+haNIHrvvokZoLRitWBA3DHc+pwRbs8oy0QBEHslGILocdDEdx2bh7Tsc3n0ZXm1XVW/HNLLUxqec29clHpkNQcx8FsNsNsNqO1tRXJZBKBQEDIrZ0emjVFPB7H/Pw85ufnAWw43VIhyO12u+zmxMWgVCHiicpAQrbyyLymcpxzEYWRvtZvMpmq7prb7XY897nPLfnv6HQ6XHXVVSX/HaK6ISGbUCS5QovnYmZmBidOnBD9u06nQ09Pz64dqHKFhOzCyHWvycnpmUgkcPz4cWEBIoXb7caRI0eKmrskXcimeysbErJzU2ho8Vybk1QqFbq7u9HU1FSSOpYbulcuIEchOxKJwOv1IhgMCmUqlQpHjx6F2+2uYM12TjAcx0vveQK+9fj2B5cIDgy3dSXRFJ/Fn/40C5PJJIQ/dTgctABJEDug2hb75Eyx+kDGGD4/vYwvzwWK8n2lwKLi8K0DbvRYDJWuStGQmpNXrVYLfSiwkX4q5db2+/0553KhUAihUAjj4+NQq9Uit7YcU6XsBApbrCxIyFYe6e8uytOrfBhjVRdanCCUDAnZhCLJFVo8HZ7nMTIygomJCVG5w+FAb28vDAblTIoLhYTswsgc/MpJVMol+HEch8OHD6O1tbXoAzy1Wi1ER5BTO5ULEidzU0ho8cXFRQwODoqicBgMBvT19cFms5WsjuVGjuJtqZBbWwSDQfT39yMaveBa1ul06Ovrg8PhqFzFdsHCahTX3fckgpHKufZUHMM/diWxL+0xX19fx/r6OiYnJ6FSqVBTUyM4xaplQZ0giOplJ+P4p9YieNvoPM5L2IX9cpcVd+6phVEBLux0Ku3I3g6j0Yjm5mY0NzeD53kEg0HBrR0KhbKOTyaTWFxcFKIjpTaX1dTUwOFwKFZAktqGBGJ3kJCtPOiaVh/VGFqcIJQKCdlEQchlIL5VaPFwOIyBgQGREwoA2tracPDgwaofzJCQXRhyzf08Pz+P48ePi54NvV6P3t5eOJ3OkvwmCbVbQ+2Tm3xCi28WHt/pdMLj8UCnk3fexEzS++Jqv1fkJGTPzs7i+PHjomtmtVrR19cnW2F1JhDBy+57AuF45e5DrQr4xqsPwcGF4fP5sLKyknUv8DyPpaUlLC0tAdhYxEg5zao1/GklkPozSmxA10me7FYI/cZcAB+b8hWzSkXFqOLw7/sa8Gy7Mheh5RSSWqVSwel0CnPGaDQquLWXl5dzpnXL3FzmdDqFDWZGo1E260zbkX4dpXwNifwg0VN5kCO7+sgUspXS3xBENUJCNqFINgstvri4iKGhIcTjcdGx3d3daGxsLGsdpQoJ2YUhN0c2Ywxnz57F6OioqLwc0QhIqN0aap/cbBdafLNcw3v27MGhQ4cUueiQ/jdVu+AgByF7s/duQ0MDuru7s6LIFAOO40reHueW1vGqrz+JSKJy7a7lGD76V3b07m0AALS3tyMejwsL6j6fTzTmS7G2toa1tTVMTEwI4U9TwrZery/3n0EQkoUW++TDTt/5Z8Ix3H52Fmej0nVhv9Bpxifb66FXmAs7Hak7srdCr9ejqakJTU1NYIxhZWVFELVXVlayjud5Xuijz5w5A4PBILi1nU5nScZF5YKET2VB11N5pK9x0jWtDtbX14X/T45sgpA38h0hEsQWcBwHtVotDFISiQTOnDmTtYhssVjQ29sLi8VSiWpKEhKyC0NOjuxYLIahoSHBkZaiXIJf+vdLuZ0qBQnZudkqtHg4HEZ/fz9WV1eFMo7j0NXVhdbW1rLVsdzIQbwtF1Jvi802Wuzbtw/79++X1UJ1Ok/NruK19w8gwVeuzQ0qhjt6k3BbxO8IrVaLhoYGNDQ0gDGG1dVVYcE814J6ZvhTi8UiiNo2m40WuXaJXO9xgpA7+Tx7P1wI4oMTS5DqqNzAcfhEex1e6LJWuiolR85Cdjocx8Fut8Nut6Ojo0O0uWx5eRmxWCzrnEgkgunpaUxPTwvnp/phubnn0q+jnOpN5IaEbOWRfk3Jka18MnNky61PIQhCDAnZREHI6YWfLmSfP39e1HkBgNvtRldXl6x3/JYCErILYzP3v9RYWVmB1+tFOBwWylQqFY4cOYLm5uay1CG9rUiozYaE7NxsFlp8eXkZXq9X5LbU6XTweDwlC48vFSi0+AWk3BaRSAT9/f0i8VSlUuHo0aNwu90VrNnuGJhawS3fGUCigs1t1nK4oycB+zZZAziOg81mg81mw969exGLxUQL6rnc2qFQCKFQCOPj49BoNCK3ttLSFBBELqS4KYjYnkKE0KloHG8+O4tT4ex3oFT4K6sBX+5sgr5KxCOlCNmZZG4uC4VCQh8cDAaz/m7GGAKBAAKBAEZHR6HT6YQ+2Ol0QqvVVugvyQ8SPpUFXU/lQY7s6iNdCyATG0HIG1LwCMWSLpqld1wcx+Hw4cNobW1VzASxmJCQXRhyCC0+MzOD4eFhUd0MBgM8Hg/sdnvZ6kFC7dZQ++QmV9SDiYkJnDx5UrT4ZbPZ0NfXV9Lw+FKBQotfQKqO7GAwiP7+fkSjUaFMr9fD4/HA4XCU5Dc5jit5SPFHzvhw24MnUMmWrrNo8Ym/smB1ecNBXcjfq9Pp0NjYiMbGRlH4U5/PJ4rskCKRSGBhYQELCwsANnKap7u1aRxJKB26x+VDPkIoYwwP+UL4wPgCYtLpMkXoOQ4f3FOLl9daq+r+yxz7K/Fv5zgOVqsVVqsV7e3tSCQS8Pv9Qj+cPmZKEYvFMDs7i9nZWQCA3W4XNphZrdK7R9Kvo9TqRhQOCdnKgxzZ1QeFFicI5UBCNqFIGGM5RdhKiHdyI30wxxgDz/M0aN+CzAmqlMR/nudx6tQpjI+Pi8pdLheOHTtWdmcZhRbfGhKyc5P5jI2Pj8Pn84nK3G43jhw5UjWTUSm7kMuNFEX92dlZHD9+XHRtlLDR4pGzlRexG206/PxNz8D5s6eQkp13et0zw59Go1GRWzuRyM4Xu7q6itXVVZw/fx5arVbk1pa6S4wgCGWz3btwOZ7E35+exXA4WyyUCn1mPe4/0ARjlYzn0sm8ftUw/9ZoNKirq0NdXR0YY1hfXxf64UAgkHOMGwwGEQwGMTY2JvTDqb5YClFT0q9jNVxDpUNCtvIgR3Z1kSu0OEEQ8oWEbKIg5LCrNJFIYHh4OCv/Um1tLXp6eiQxwZEyuRzGNMDbnMx87FIRaKPRKAYGBuD3+0Xle/fuxYEDByryLFNo8a0hITs3me+fTBH74MGDaG9vl0X/VCykKN5WCik5shljOHv2LEZHR0XlDQ0N6OnpkfVGix/3z+KuX52paB06ak144O88MGjVJXne9Xo9mpqa0NTUBJ7nRW7tUCiUdXw8Hsf8/Dzm5+cBbGxWcLlcqK2thcViqap3EqEsKv0uJYpD6h3EGMMvl0P4l/FFhHlpXlsdgH9udeE19XaoqvTdqdTQ4vnCcRzMZjPMZjNaW1uRTCYRCASEzWXpjroUmf2w1WoVRG2bzVaRNQwSPpUDz/O0MUGBkCO7+qDQ4gShHEjIJhRFKBSC1+vNyodts9lw0UUXVd2EcCdkDuYSiQTlEd+GdCFbCgJkIBCA1+sVhWdTq9Xo7u5GY2NjxepFQu3WUPvkZrP3tlarxbFjx1BbW1vmGlUeKYm3lUYqbZFIJHD8+HFhMTXFvn37sH//flmPP342MFdxEftggxnfv9kDnSZ7EbEU112lUsHhcMDhcGDfvn2IRCIit3auTWsrKytYWVnB2NgYdDodampqUFtbK4ucngRByJ9cQuh6kscbz8zi8VCkQrXank6DFt8/1AyHproFhWoXsjNRq9VCxBMACIfDQh/s9/tz9sOpqCnj4+NQq9Uit3a5IuJQaHHlkDkfJyFbGaS/O0jIVj6JREK0LkpCNkHIG1KniIIpdf7FnTIzM4MTJ07knNSYTCaaSOSJHHI+Sw0phcyenJzEU089JXpGzWYzPB5PxQdtUmonKZLpsmWM0XsLG+/2TCwWCzweT9WGhqLQ4heQgpAdiUTQ39+PlZUVoUylUqG7uxtNTU0VqVOx+Pffj+Mrfxjf/sAS0tdqwzdf1yN6R5b73WgwGOB2u+F2u8HzvOAS8/l8OV1isVgMc3NzmJubE0KYpxbkzWYzvdsJ2UD3qnwQ9YEch/9eXsV7xxawJr1pO4CNhah3t9Tg5gZH1bqw0yEhe2uMRiNaWlrQ0tICnucRDAYFYTtX1JRkMonFxUUsLi4C2JgPp0Rtu91eMgGLHLzKgYRsZUJRE6qLTJNbta4fEYRSICGbkD08z2NkZAQTExOicq1Wi3g8DoBEs0LInNRR222PFEJmJ5NJnDx5ElNTU6Ly+vp69PT0SMJVL4V2kjKZEyme56t6lzDP8zh9+jTOnz8vKrdarbjkkkskcU9XCgotfoFKC9m5ImDo9Xr09fXBbreXvT7F5N9/fx5f+cPE9geWkGd3OPClV3VDpRIv6FfyuqtUKsHl1dnZKbjEfD4f/H5/Vv/GGEMgEEAgEMDo6Cj0er0gajudTsW/yyr9jBKFQ9dJnqSuWxLAfYZaDJxbqGyFtqBVr8Z3DzTDradoFSlIyM4flUoFp9MJp9MJYCOlVkrUXl5eRiKRyDpnbW0Na2trmJycFM5PCdtGo7Fo7U2ObOWQOZ6r5nm5kiBHdnWRudGp0uYegiB2h7JXTgjFEw6HMTAwgGAwKCpva2tDMpkURD0SY/OHhOzCSW+zSrTXZs9BZ2cnOjo6JDOJptDZW0NC9gVisRgGBwez8mEDQHNzs+KFn+0gR/YFKimSzczMYHh4WHQNbDYb+vr6yhbCslR88D9P4ycDcxWtw9UHXfjs9UcqWod8SHeJpef09Pl8CIfDWcdHo1HMzMxgZmYGHMfB4XAIwjZFECIIYjcMaQz4pqEW6yppjh/VAN7ZXIM3NJILO5NMAZT6gvzR6/WiqCmrq6uCsJ0eLScFz/NCP33mzBkYDAahH3Y4HLuaZ5DbUzmQI1uZ0DNaXWQ6sknIJgh5U90rwcSOkEpo8cXFRQwNDQmua0CcB3hkZEQoz7Url8gNCdmFU8mQ2T6fD4ODg4jFYkKZVqtFT08P6urqylqX7SAhe2tyCdnVyOrqKrxeb85wvQA5xQByZKdTCVGfMYYzZ87g3LlzovLGxkZ0d3fLegMKYwz/+h+n8LOhyjr5XuFpxAde0Lnpv0vV5ZuZ03N9fV1YLA8EAjnd2n6/H36/H2fPnhUtpjudTlnfS4QyIDFNHvA8j48tR/ArU0Olq7Ipbq0aXz3QhANGfaWrIknS+zJ67naOSqWC3W6H3W5HR0cHYrGY4NT2+XyitaMUkUgE09PTmJ6eFm0wq6mpKTgdCIUWVw4kZCsTcmRXFyRkE4SyICGbkB2MMZw9exajo6OicovFgt7eXqFjqrRLVq6QkF04lQiZzRjD+fPncfr0adGE2Wq1wuPxwGQylaUehUA5sreGhGxgfn4eQ0NDovvDaDSC53khdLOURKtKkSniVXM+9XILmolEAsePH8f8/LyofP/+/di3b59krsNONh0yxnDbj07gkTPLJapVftxwsRvvu2Z/RetQLEwmE0wmE1pbW5FMJuH3+wVhOxKJZB2fvpiuUqmy3NoEUQ6on5UXjwbX8fZzcwgkpXndOAC3uZ14a5NTMn2kFCEhuzTodDo0NjaisbERjDGEQiHBrR0MBrPed+kbzIANt3cqBLnT6YRWu3U4fAotrhxIyFYm5MiuLjLNEZQjmyDkDQnZhKzYLNys2+1GV1eXKAxU+v8n0Sx/Mgdz1HbbU26BNpFIYHh4GHNz4rCvTU1NOHr0qGR3llKO7K2pZiGbMYbR0VGcPXtWVO5yuXDs2DE8/vjjgpBdTe2yGZkLYyRkb1Bq8SUcDqO/vx+rq6tCmUqlQk9PDxobG0v626UmwTO85QfH8afzgYrW421XtuMNl+/Z9jipOrK3Qq1Wo7a2FrW1tWCMZbm1M/8OnucFF9mZM2dgNBpFoU+l2tcTBFEeGGP4yOQSvrWQHTpZKjRo1fjivkZ4LPJOt1EOSMguPRzHwWq1wmq1or29HYlEQrTBLDXXSCcajWJ2dhazs7PgOA42m00Qtq1Wa84xeQoSyeQNCdnKgzFGjuwqIzNHttVqrVBNCIIoBiRkEwVTqYlVIBDAwMCAyMHCcRwOHz6M1tbWrHqRI3tncBwHtVottBm13faUU6BdW1uD1+sVDcg4jsPBgwfR1tYm6YUPCi2+NdUqZG/mcG1ra8PBgwehUqkoJ3QGmfeKXIS8UlCuMOt+vx9er1eUxkGv16Ovrw92u71kv1sOkjyPG781gOMzoe0PLiG3X9mWl4itBDiOg9lshtlsxp49e/JaTA+Hw5iamsLU1BRUKhWcTqcgbBuNxgr8FUQ1IOVxZTUzEIrgTWdnsZyQ7pjoDQ123NFaW+lqyAYSssuPRqNBXV0d6urqRBvMlpeXN00HEgwGEQwGMTY2Bq1WK4jaNTU10Ol05PZUECRkKw+6ptVHZmhxinBFEPKGhGxC8jDGMDExgZGREdEEz2AwwOPxbLqAnC4uUo7swlCpVCRkF0C5Nk0sLCxgaGhIdD/rdDr09vaipqamZL9bLCi0+NZUo5C9vr6O/v5+0cYMlUqFI0eOoLm5WVSWoppF2xSZC5w8z1ftjvJyOHOnp6cxPDws+n673Q6PxwODQd4us2iCx6u/3o+zS7lz0peLj730IF58NP/crnJ0ZG9F5mL62toalpaWNg19yvO8IHoDG2HyUovpDoeDFuaIXaGEZ0qpJBnDZ6d9uG8uCKlepTqtGp/ZW49n2WixuBAoJHVlydxglkwmEQgEhL42HA5nnROPxzE/Py9sxrVaraINj3Qd5Q2Jnsoj85pW6/y5mkgXsk0mE11zgpA5JGQTkmazEMq1tbXo6emBTqfb9Nz0DooxBp7nafCZJ2q1GvF4HAAJjvlQaoF2s7DLchNSKLT41lSbkO3z+TAwMCC8a4ANh6vH44HD4RAdS25+MeTIvkApBU3GGE6fPo2xsTFRudTTOKSzVdj5cDyJ67/ajwl/9uJsueAAfPhvDxQkYisdjuNgsVhgsVjQ3t6OeDwOv98vCNvpi+Qp1tbWsLa2hsnJSajVakHUdrlc0Ov1FfgrCIIoNqPhGF5/agaLCenOza53WfCR9nqoSMArGHJkSwu1Wi30o8DG5ttUug+/359zzp+eegbYiOYzMzODmpoa2czXiQuQkK08SMiuPtKFbMqPTRDyh4RsomDKNbEKhULwer1ZoUA6OzvR0dGxbT3Sc2QDGwIjDT7zg8KyF0YpBdp4PI6hoSEsLi6KyltbW3H48GFZ3dOZrtpqzuubi2oRshljGB8fx6lTp/J2uJKQLSaXI7taKZWQnUgkMDQ0hIWFBVF5vmMQqbMaSeCl9z6BxVC2KFouOA6459VHcVlH4RFFlObI3gqtVov6+nrU19eDMYZQKISlpSX4fD6srGTnxk0mk1hcXBTGDRaLRViMt9lssho3EJVH7u86JRDnGb4y68e/z/oh1VlZjUaFf9tbj2fbaZF4p1BuZWljMplgMpnQ0tICnucRCASwvLwMn8+XtV6VIhwOY2RkBABFTpEjmVESqD+UP5lrm/QcKp9MIZueY4KQNyRkE5JkZmYGJ06cEA00dDodenp6UFubX66tzN11iUQCWq22qPVUKiRkF0ap2mt1dRVerxfr6xdCvqpUKnR1daGlpaVov1Mucgm1tAv2AtUgZCeTSZw4cQIzMzOi8ubmZhw5cmTTyWQ1iVb5kDkBq+Y2KcW9EQ6H8eSTT4pC3qvVanR3d6OxsbEov1FJltdiuO6+J+Fbj29/cIlQPS1iX7oDEbua4TgOVqsVVqsVe/fuRSwWExbSl5eXRREuUoRCIYRCIYyPj0Oj0Yjc2ltFNipmnVNU87uKIHbCbDSOG05NYzom3fnY1Q4TPtfRCL2KFod3Azmy5YNKpUJNTQ1qamqwf/9+RKNRoR/O3ACZIj1yikqlgtPpFHJrU85WaUL5zpUHCdnVR7qQbbFYqH8lCJlDQjYhKXiex8jICCYmJkTlDocDvb29BYVkyhTISJDNHxKyC6MUocVnZ2cxPDws+r7t8sJLnVzPJAnZF1C6kB2JROD1ehEMBoUyjuNw6NAh7NmzZ8tJBTmyxVBo8Quk3zfFuDf8fj+8Xq8ofLPBYEBfXx9sNtuuv7/SzK9G8bJ7nkCogqKIRsXh2zceQ3fzztuTxNENdDodGhsb0djYCMYYVlZWhHyemSFOgY1NnQsLC8JCu9VqFbm1aXGHAKr7mZIKCcbwtdkAPjezLFkXtk3F4d86GnClg1zYxYCEbPmi1+vhdrvhdrvxxz/+UcinrdPpcqYD4Xle6KsBwGg0CpvMnE4nzY8lAgnZyoNCi1cXjDGRKYg2DRGE/CEhmyiYUk2swuEwBgYGRCIHALS1teHgwYMFDx5zhRYn8oOE7MIoZmhxnudx+vRpnD9/XlReU1OD3t7esrinSoXShdrdouT28fv9GBgYQDQaFcq0Wi16e3uF3HNbQUK2GAotfoFiCppTU1M4ceJEVsj7vr4+ReQZnvKH8fKvPon1eOXuF40KeODvPDjQYCnad5LotgHHcbDb7bDb7ejo6EAsFhMWypeXl5FIJLLOWV1dxerqKs6fPw+tVisspNfU1Mh6vEEUDxLUyk8wnsBrTs3gdKRyUTO241KrAV/e3wSrmsSdYpEZxpiQJ+ljkpaWFrjdbiG3ts/nyxk5JRwOY3p6GtPT0+A4Dg6HQ+iLKRRu5SAhW3lkrm2SkK180iOs0fuUIOQPCdmEJFhcXMTQ0JBoYL/bMJ65QosT+UFCdmEUy5Edi8UwMDCA5eVlUXl7ezsOHDgg+wmUkoXaYpDKvZVaAFFK++QSB61WKzweT967Ysl9KYYc2Rcoxr3BGMPp06cxNjYmKm9qasLRo0cVschxaiGEG77ejwpq2DBqVfjR3/ehzbX73fC0CLE9Op0OTU1NaGpqAs/zIrd2+qJOing8jvn5eczPzwMAbDab4Na2Wq3U5gRRYnjGcP98EJ+a8kGqs1YjB3x2XyOeSy7sokOObGWQmes8M3JKKBQS+uKVlZWssStjDH6/H36/H8CG2zvdrU2p8spH+rqOEuYCRPb6itzX14jtyQwtThCEvCEhm6gojDGcPXsWo6OjonKLxYLe3t5ddTQUWnznkJBdGMVor2AwCK/Xi0gkIvreo0ePoqmpadd1lAKZEwW6t7JRqVRCu8hdyN4sVURjYyOOHj2aFTVjK8iRLYYc2RdIvzd2ImQnEgkMDg5icXFRVH7gwAHs3btXtovJjDHwPA+VSoWTc6t47TcHKipim7Qq/OItz0CDtfjO9mreyJEvKpUKDocDDocD+/btE/J5ptzaufrjlZUVrKysYGxsDFqtVhC1a2pqaCFd4dAzVX7WkkncfGoWA+vR7Q+uEBeZ9fji/kbUaWkJqRRkCqCEPNnKxctxHKxWK6xWK9rb25FIJERu7fTIVSmi0ShmZ2cxOzsLjuOETWY1NTW0yazEkCNbeVCO7OojXcg2mUz0ziQImUOzEKJixGIxDA4OCrmBUrjdbnR1dRUkcuSChOydQ0J2YaS3F2MMjLGCBkhTU1N46qmnRJMlk8kEj8cDq9Va1LpWksxnsprFt81QipC9WXSBzs5OdHR0FDyBICFbTGb7VbPosBtH9vr6Ovr7+0XuVLVajZ6eHjQ0NBStjpWA53nwPI9Hzy7hrT8aQSWfmhqTBj+59SLUWoonYtMixO5Iz+fJ8zyCwaAgbKcv+KSIx+OYm5vD3NycaCG9traWwvQpHLq2pYUxhgcWV/ChiSVINZC4FsC/dTTgBTXkZCol5MhWBoWEiNdoNKivr0d9fT0YY1hbWxNE7UAgkNOtHQwGEQwGce7cOUoJUmJIyFYemdeU3rXKJz1HNjmyCUL+kJBNFEwxOvtAIICBgQGR+5TjOBw+fBitra1F+Q2O46BWqwVRiATZ/CEhuzByOY3z2YjB8zxOnjyJyclJUXldXR16enoU53ii0OLbowTBdmVlBV6vF+FwWChTq9U4duwY6uvr/z97/x0fTXvX9+Kf2d6rVr2XW3dR2dWDjWPzo6SAKUkwHBwTE2Ps0PODE0xJcpIYp3DghEPgF0ow2AYSCAaCcQjBcEzCARtw8KNVvYuk+1av21e7K+1qd+b3hzLzzMyOpJW0ZWb2+3699Hqeve7d2WuvKVf5XJ/v907H1EO71BMKLf4GdxWyk8kkotGoJKWJzWbD3NwcPB5PXevYDPhNVOLXn9/J4h/8VmtF7JDTjN/5jjfBY2vclKOdr/96YDAY4Pf74ff7MT4+jrOzM2EhPZlMVj1z5QvpfNjTjo4O+P3+e29EJYh24azC4rteHuEz2bOb39wiZpxW/ORoFwas+pqTqBESsvXBXZ31DMPA5XLB5XJhcHAQlUoFqVRK6I/F8yoeeUoQt9stiNoej4fE13tCQrb+oHDx7QW/QYiHNt8ShPahlQaiqXAch52dHTx//lwyyLfZbIhEIvB6vXX9PrGQTTmya0c8qCPR6Gbu4jQ+Pz9HNBpFJpORlI+Pj2NsbEyXAywKLX4zWhdsj46OsLy8LDm3DocDc3Nz99oBSzmypVBo8Te4y7WhlLfd5/MhEonAaq1/6OtGw4vY/HXAcRx+byWGf/77L2/4ZGMZ8NvwW39/Dg5L/acbeuwj1YLdbkdfXx/6+vpQqVSQTqeFhXSxq4FHHvbU5/MJYcgdjvvnQyeaD/Wzjef3Eln8wGZMtS5sA4D/cziEr+vQ3sYurXIbJy+hXup1Ho1GIzo6OtDR0QHg0lXI98WpVEpx7H96eorT01NsbW3BZDLB7/cLwrbNZrtzXdoVErL1B53T9oNyZBOEviAhm2ga5XIZKysrODo6kpR3dHRgZmamIaGQyFl8N8TtRhsAbua2Am0ymcTCwgJKpZJQZjKZMDMzc2fHqhag0OI3o1Uhm+M4bGxs4OVLqXDW0dGB2dnZe0cX0Gq7NApyZL/BbYRsjuPw4sULbG1tScp7e3sxNTWlyQUNXsAWO7J/d+mg5SL2eIcdv/a+OdjNjXE70OaW5mA0GgVRemJiAoVCQQhBnk6nFd3aqVQKqVQKGxsbsNlsmtwcQhCNolBh8QObx/jDdPWmELXwxG7Bvx3rwoiNwhQ3E3Jkax95n1jPcaXD4YDD4UB/fz9YlpVsMlNKCVIulxGLxRCLxQBcOhF5Udvn82lyzNtsSPTUH+TIbj/Em3CdTmcLa0IQRD0gIZu4NXeZWOVyOUSj0apB9l3zpdaKOLwhCdm1Q47s21FrPnaO47C9vY0XL15IFitcLhcikYjuB1YUWvxmtCjYlstlLC4uCgslPCMjI3jw4EFdnu9abJdGQo7sN5ALmhzHKV5zV12nDx48wMjIiCYXjZVE7F/87DZ+8n9stbReT3pc+I/fHIbJSIt+eoNfSB8YGBDCnvLCtjhdEM/5+bmk/OLiAru7u+TW1hBafDaqlT9N5/GdL49QVPHem386EMQ3d/laXY225K4hqQn1IN9Y16jzaDAYEAgEEAgEMD4+jvPzc4lbW8mIkM/nkc/nsbOzI6QU4YVt6o+VISFbf9A5bS+UQosTBKFtSMgm7gTDMDU7YA4ODrC6uioR9ywWC2ZmZoRQSY2CHNl3g9rtdtTiNK5UKlhZWcHh4aGkvLu7G1NTU22RU5JhGMmzo53Ft6vQmmCbz+cxPz8vmSAYDAZMTU2ht7e3bt8jXkjXQrs0GnJkv4FcZFESsguFAubn55HL5YSy++ZtbzViEZt//Yuf3cZP/fF2S+v15iEvfv7vzsBkaKz4RY7s1iMOe8pxXJVbW+m8cByH9fV1rK+vw263C25vn89HzhhCt5xWWHxoO4ZPJnM3v7lFjNvM+JmxLozaKYJCqyBHtvaRz1GadR5tNht6e3vR29sLlmWRzWYFYfv09FSxnnx/DUDojwOBAPx+P/XH/wsSPfUHObLbDwotThD6Qv/KCdEyWJbF8+fPsbOzIyn3+XwIh8NNydNDIbLvhlzIvsrhRlxyU2jxQqGAaDQqmUgyDIMHDx5geHi4rdrWaDQK9yJtkqhGfC2pXZyJxWJYXFyUPFttNhsikQi8Xm9dv0tL7dIMlMTbduUmUT+ZTCIajeLi4o1MpHa7HXNzc3C73U2pY72Ru7BZlsWP/sEGfvXzBy2t19vG/Pi5vzPVVn0acQnDMHA6nXA6nRgcHES5XBbc2icnJ4pj8LOzM+zt7WFvb0/iDgsGg7Db7S34FQRPO/cp9WYxd45vXjtAnlVvm353tw/f2xegZ3eLISFb+zTLkX0dBoMBPp8PPp8Po6OjKJVKgqidTCYl42EeeX/s9XqF/tjhcLTt9UhCtv4Qrz/ROdU/pVJJ8swjRzZBaB8SsomGcHZ2hoWFBWQyGUn50NAQJicnmzZooNDid0PJYUw7Fq/mOkd2LBbD0tKSZABlsVgwOzuLYDDYtDqqBa05jpuNuH3U+sziOA5bW1t48eKFpNzv9yMcDjckHypdN1IotPgbXCfq7+7u4unTp5Iyv9+PSCQCi0V7uT958Zo/3xzHoVKp4F99agO/GT1qad3+5lQI//pvPWzaYic5stWNyWRCKBRCKBSC0+nE+vo6gDfOm/ycyd1hDodD4tamxcbW0a4Cxn3JVVj8yE4cv5modkOqhUGLER+e6MEYubBVAQnZ2qeRObLvisViQXd3N7q7u8FxHE5PTwVhO5vNKvbHqVQKqVQKGxsbsFqtgls7EAi0RRQ5HhKy9Yf4nNL6pv4R58cGyJFNEHqgfUYhRF25LrS4knBnNBoxPT2N7u7uZlVR+F4etYpCakQp5zMN9K5Gqb04jsOrV6+ExVser9eLcDjctm4jEiSvR+3tc1WI/IGBATx69Khhk3wKLV6NwWCQCJrtipKQzbIsXrx4ge1taZjtvr4+PHnyRJOLUUr5sFmWxQ/+znP8wbN4S+v2N5904Ie/cgwXFxcwGAxCGgkttjNRf8T3qMlkwlve8hZJbu1SqVT1mUKhgEKhgN3dXRiNRolbuxkRnQjiPmwUSvjGF/tIV9Q7Xnl3hwf/dKgDJhJMVYN4fEtCtjZpVWjxWmEYBh6PBx6PB8PDw7i4uBD642QyiWKxWPWZYrGIg4MDHBwcCJ/n+2OXy6W631hPSMjWH+TIbi/kqRVIyCYI7UNCNlE3OI7DxsYGXr58KSl3uVwIh8Mt6TQotPjduClUNiFF3l6lUgnRaBQnJyeS8v7+fjx69KitNwVowXHcStQsZJ+dnSEajSKbzQplDMPg0aNHGBwcbOh3U2jxakjcv0S+gHZxcYHFxUXE41Jxd3JyUrOpHOQiNsdxqLAsvuc3VvEnL1Mtrdu3vrUP3/G2AeG+5J/rYjG7EaI2ObK1i9lsRmdnJzo7O8FxHHK5nCBqyyM5AZfXVDweF+5pp9MpLKJ7vV5aiGwAdE/djbMKix/djePX4up1YXeZDPjYZC8myIWtOsiRrX3UEFr8Nsj743w+L7i10+l01e/hOA6ZTAaZTAavXr2C2WyWuLW1GO3oOkjI1h/kyG4vxPmxARKyCUIPkJBN1IVSqYTFxUUhJCBPb28vHj9+3LIQROTIvhvy80Vtdz38Ij0/MF5fX5fsaGYYBo8fP0Z/f3/bL0yI78l2Ft+uQq1CdjKZxMLCgsQ5Z7FYEA6HEQgEGv79am2XVmIwGIRnczuLDvJn6uc//3lJGDGj0YjZ2Vl0dnY2u2p1QSxi868rlQre/2vLeH0ne8OnG8v7/0ovvuNtAwCUnfF83fl/MxgMwr1MC4IEcHnduN1uuN1uwR3GO8MSiYRiLs98Po98Po+dnR2YTCYEAgFhIb0RqS3anXYft9bK7nkJf/fFAY4u1DtnekfQhQ8NhmA30vNXjYjHctRHahM1hhavFYZh4HK54HK5MDg4iHK5jHQ6LWw0Oz8/r/rMxcUFjo6OcHR0md7G7XYLG83cbremfr8S5N7VH+JzSkK2/pGHFqcc2QShfUjIJu5NOp3GwsKCZHDLu/QGBgZaugBCQvbdIEf27TEajcLkVSxiW61WRCIR+Hy+FtVMXZAgeT1qbJ+dnR08e/ZMssDm8XgQiUSaFiKf3MfVUJtcIh9jiCesdrsdc3NzcLvdza5WXVAKJV4qV/DuX1rA8+P8DZ9uLD/81RN4x0xXVR15lBzTlUoFlUqlLm5tcmTrE7PZXJXLMx6PI5FIVIUHBC6jLZ2cnAgRcMSL6B6Ph0RYouFcsBx+fC+Oj560dmPRdfiMBvzKgx48clJYfjVDjmztc91YSGuYTCZ0dHSgo6MDwOX4mt9klkqlFOcep6enOD09xdbWlrDRjN9spsWNZuTe1R/ksm8vcrmc5DU5sglC+5CQTdwJPkf2zs4Onj9/Lhm022w2RCIReL3eFtbwErGzmMTY2lHK+UxcDe86k+P3+xEOhzU5cWsUFFr8etQkZLMsi2fPnmF3d1dS3tPTg6mpqaZO6OUTTY7jNL04VA9IyLvkquvA7/cjEoloMsyh2M3Mv2ZZFsVyBd/40Sg24mctrd8Hv/oB3vlan/CaF7MrlYrEPc4jv1avc2vfZVGpna9/PSPO5Tk6OopSqSQ4w5LJpGLKIPkiOi9q6zHkaSOhe6o2DosX+Oa1Q2wWqyMHqIWv9jvxL4ZC8JhIhFE7JGRrHy07sm/C4XDA4XCgv78flUoFmUxG6I/l4XuB6o1mfFqQQCAAn8+nibYh0VN/kMu+vZA/m7S6uZ0giDcgIZu4E+VyGYuLi0IYIZ6Ojg7MzMyoZrGIHNl3g4Ts2uHD6svbaGhoCJOTkzRAlkGhxa9HLUJ2sVjEwsICUilp/t1W5RmW30csy7b9znjKG355HWxtbVWV9/f34/Hjx5p8/iqFEmdZFoVSBe/48Os4zBZvOELjYAD85Dc8wd94JA3TzrezODKJWNS+bnH+rm5tWuTXDvXadGOxWNDT04Oenh6wLItsNisI23LHBXA5Vzk+Psbx8TGAy0gi4pCndA0Rd4XjOPy7gyR++jANtfa+NnD45ck+zLmbEzmHuD8kZGsfPQvZYoxGo+C2BoDz83PBrZ1MJhXXjsRpQYxGI/x+v+DWblaEr9tCQrb+IJd9eyEWshmGUe2zhiCI2iEhm7g1+Xwef/EXf1G1aDQxMYHR0VFVTbzEgxMl5wahDAnZtZHJZBCNRqtyRoVCITx69KhFtVI3ahFq1YoaxEml69pkMmF2dhahUKgldZL3KyRkkyP74uICCwsLSCQSkvKRkRE8ePBAVWORWhGL2OK/7NkFvu4XXsdJrrWuv5951zS+9EHHte+R58BmWVby1wi3djte/+2OwWCAz+eDz+fD2NgYisWixK2tNG7NZrPIZrPY3NyE2WyWuLXNZnMLfoU20OKztJEcFcv49peHeFootboqynAcwuUC/qHLQCK2xiAhW/voKbT4bbDZbOjt7UVvb69ko1kymVRMC1KpVBCPxxGPxwFcur15Udvn86lijiePuEdCtj6gHNnthTjlmNPppPuYIHQACdnErbFYLJJBusViwczMjJA/R02IQ4vzg1HqvG6Gd0KJnVKElP39fayuriqKsQ6HowU10gYkZF9Pq9vn4OAAKysrku92Op2Ym5uD0+lsen14lEKLtzutvlZaST6fx+uvvy6ZnPL09vZqcuFQyYnNcRziuRK+9sOvI3veus14RgODj35TGG8a9t36s2IBWhyCXCxa89zGrU0QYqxWq2QRnQ95mkgkFEOeXlxc4OjoSIgs5fV6BWHb5XJp8hlST6iPVeZXjtP4V7sJ1bqwTeDwnYUTTFXO4fV0tbo6xC0Rj+Xa/RmkVdrFkX0d8o1mpVJJ4ta+uKjelFkoFFAoFLC3tyd8nhe2HQ5HS+4HeT/YjudSj9DmhPZCbL5r5VoWQRD1g4Rs4taYzWa89tpr+OxnPwu3241wOAybzdbqaimi5CymAUttGI1GwcVOQvYbsCyL58+fY2dnR1JuMpmE9mo3Uek2ULj/62mVOMlxHNbW1rC5uSkpD4VCmJ2dlWwKagVKocXbnXZ1ZMfjcSwsLFwZZUWLbSEPwc2/PsoW8fW/8DpOi617VhoZ4D+9bw5TvZ57H0scghy4/J1XhSAHrndri58BWjznROMwGAzw+/3w+/0YHx/H+fm5xK2t1H9kMhlkMhm8evUKFotF4tZudf9HtJ7D4gW+99UxovnWpXa4iS/1OvCe/AkuKpfRdEgI1R7ivozWK7SJvH+h+/DS9NLd3Y3u7m5wHIfT01OhP85kMlXvZ1kWyWQSyWQSGxsbsFqtQp/s9/ub1ifTpgT9wW+m5SFHtv4Rb2Z1Op30TCYIHUAzc+JOeDwevOlNb4LH41H1oE4+OCmXyxQ+sEZIyK7m/PwcCwsLSKfTkvKxsTGk02khxC2119W0s4u0FlrRPhcXF1hcXBTCu/GMjY1hfHxcFQN+ErKrEZ+XdmmPnZ0dPHv2TLLY6/V6JQthWhI1xcIs/5r//83EGd75i6+jWGnd77GaGPzG3/8CTHS6GnL8q9zaN4UgB6rPc6lUEqLJkGubEGOz2dDX14e+vj6wLCuM1xKJhGJUh1KphMPDQxweHoJhGIlbux0Xwdrt98r5ZPwUP7h1ArX2skYAPz7Sia8JurGwcIxkqytE3BkKLa595OeQzqMUhmHg8Xjg8XgwMjKCi4sLpFIpQdguFqs3CxWLRRwcHODg4EDok3m3diMjqJCQrT/IZd9+kJBNEPqDhGziTjAMA5/P1+pq3Ih8xyYJjLVDzlkpqVQKCwsLkgmW0WjEzMwMurq6EI1GhfJ2EZXugnjCQNdVNc1un1wuh/n5eclivtFoxPT0NLq7uxv+/bWilCO73VFDPvVmcVUkjP7+foyMjOBP//RPhTKttMV1IvareAHv/EgUpRaK2DYTg9/9ri9En685OVZv49YuFovY39+XfF4sfovFbBK1CTEGgwGBQACBQAATExMoFApCyNNUKlXVt3Ach3Q6jXQ6jZcvX8JqtaKjo0NwhunVzaOV52gjOSqV8YObx/jz0/NWV+VKvtBlw0+PdcFnvpzvkhCqbej8aR8KW3w7zGYzOjs70dnZCY7jkM/nBVE7nU5X9UXiPpmPoML36YFAABaLpW51IyFbf8jXVvQ6hiMu4TiuKkc2QRDah4RsQtcohRYnaoOE7Es4jsPu7m6VC9DpdCISicDlunSqkUBbG+TIvp5mts/JyQkWFxcl16vdbkckEoHHc/8wwvWEcmRX0y6hxS8uLrCwsCBEvOB5+PAhhoaGqtwbWmgLcT5sea7ohb0svuU/LKKFGja8NhN++9vfhB5v69LGXOXWzmazWF1dRalUEt7rdruF98pDkPP3CX88WogkxDgcDjgcDvT396NSqQhu7Xg8jvPzagGT30Sxv78vbOrlhW273U7ik07476k8/sGrI1yotDsxAvhnA0G8u8t35XvoWtQeJGRrH8pzfncYhoHL5YLL5cLQ0BDK5bIkgopSn1wqlXB0dISjoyMAl1Ejebe2x+O51zkgIVt/0DltP+Q5sum5TBDah4RsQtcohRYnaoOE7Mvf/fTp0yrnV1dXF6anpyWOf3F7kUB7NdRO19MMIZvjOLx69Qrr6+uS8kAggHA4XNfd7PWCQotX0w6bQpQiBphMJoTDYXR0dAC4OeS02rhOxF7cP8V7/8Mi2Bb+hIDDhN/9ri+E36Ge5wB/rScSCSwvL0uud5fLhUePHgmvr7oeKpUKKpUKubWJKzEajUIIcd6tzS+gX+UMS6VSSKVSWF9fh91uFz7v8/l04/Rpp0W/2EUZP/TqGH+qYhf2jN2Cjzzohc9cfX2REKptSATVPpTnvH6YTCZ0dHSgo6MDHMfh7OxMcGsrRVABgGw2i2w2i62tLZhMJsGpHQwGYbVab/X9JHrqD3Jktx/i0OK8AYkgCG1DQjZxJ7QyuSJH9t1pd8Hx7OwM0WgU2WxWUv7gwQOMjIxU3QMk/NdGO4hv90EeLprjuLo+b8vlMpaXl3F8fCwpHxoawuTkpGon6RRavBq9O7Lj8TgWFhYkG9AcDgfm5uYkE1EtCdliEZt/zf//p18k8IH//BStrH23x4rf+Y43wWMzt7AW1XAch+3t7arNN52dnXj8+DEYhgHLsjfm1ia3NlErDMPA6XTC6XRicHAQ5XJZyOOZSCQU83ienZ1hb28Pe3t7MBgM8Pv9grBttzcnRH+9UPNztFF8LlvAt24c4ayVO4muwQjge3v9+PYePwxXjAvb8bzpCdqIoH1oM0JjYBhGiKAyMDCASqWCTCYjCNtisYqnXC7j5OQEJycnAC5FLF7U9nq9N475SMjWH/I1Ojqn+ke8Gd7hcNBzmSB0AAnZhK4hIfvuiNuu3Zzs8Xgci4uLuLi4EMrMZjNmZ2cFF6AcCi1eG9RO16MUQrteA+5CoYBoNIrT01OhjGEYPHnyBP39/XX5jkZBocWrEV8XehL2OY7Dzs4Onj9/LjnPV0UM0MImh+vyYTMMgz96Ecf3/eenrawihgI2/Na3vQlOi7qmBnx+dHlklOHhYYyPj1fdB3zbKuXWJrd2a9DDphuTyYRQKIRQKCTJ45lIJJDJZKp+F8uywr8Dl4tnYrc2XVvqIVOu4Ic2T/BHmcLNb24RYzYTfm2yHwEFF/ZV0GKt9iA3r/ahc9gcjEaj4LYGgPPzc0HUTiaTimsMuVwOuVwOOzs7MBqN8Pv9grCttNmMRE/9IZ8jkiNb3/DjdR7KkU0Q+kBdq1UEUWcYhoHRaBQGoiSc1U47OrI5jsPm5ibW1tYk5R6PB+FwGA6H48rPtmN73QVqp+tRCqFdj4lzIpHAwsKCZHOG1WpFJBKBz+e79/EbDYUWr0bu3tcDLMvi2bNn2N3dlZQPDAzg0aNHiveC2h3ZN4nY/+nzB/iRP9hoZRUx0enEb33razCrbEHn4uICS0tLSCaTQhnDMHj06BH6+vqq3s9fH3w/wwvad3Vri4VtWsAkeJTyeCaTSUG4Fudv5ykUCigUCtjd3RUW0Hlh22ZrXS76WtCzIDqfO8O3rR8iU1FXv8HDAPiObh/+v70BmA03nwdy9GqXmzZeEdpAPD+hcUPzsNls6OvrQ19fH1iWRTabFYRt8QZunkqlgng8jng8DuBysxkvavOpQciRrT9oc0L7IQ8tTn0rQWgfErIJ3SMWstvNWXwf2i1U9lUhl3t7e/HkyZMbd2y2W3vdFQotfj31Fmyvcrd6vV5EIhHVL6DzaMF122z04HIUUyqVsLCwUCVaPnz4EENDQ1d+Ts1ufXk+bAASEftX/3IfP/qHL1tZRUT6PfjYN0dgNqprMYePICHPjz47Oys4cG5CLEDz56FSqVTlJgeUN0QonTNyaxNyTCYTOjs70dnZCY7jkMvlJG5tOfIFdKfTKYjatYQ7Je7POcvihzZP8N9S1eFo1UKvxYhff9iHHkvtqR5ICNUPdP60CYUWbz0GgwE+nw8+nw9jY2MolUoSt7Z4UzcPv9mMTw3i8/mqcmpT36x9yJHdflCObILQHyRkE3dCSwNzk8kkuCNIYKyddhJmc7kcotGoZKDDu74GBgZqut4pZHZtyIXseueA1jr1FLJZlsXq6mpVSN5aN2eoCTWLla1CT5tCcrkc5ufnq0TLcDh8ZToHHrU6suUitlgYNRgM+Mn//gq/+Ge7Nxylsbx5yIuPvidyZb7VVpFKparSezgcDoTD4TuHhVNya1cqFcUQ5IDyRhFyaxM3wTAM3G433G43hoeHcXFxIXFrKy2g5/N55PN5Idwp7woLBoNVC+nNQi3P0UawnDvHt788QuxCnWN1BsC3dHnxfX1BWGtwYYvR83nTO/JxHM2NtAmFFlcfFosFPT096OnpAcdxOD09FfrkbDZb9X6WZSWbankSiQQCgQBMJlpC1yrkyG4/xGsLFFqcIPQB9cLEnWEYRhMT5nYSZOtJuwizx8fHWF5elrj1rVYrwuEw/H5/zcehkNm1IRdPWZbVlKDaaOolZJ+fnyMajUrcYAzDYHJyEkNDQ5pbICNHdjV6cWTHYjEsLi5KnsEOhwOvvfbanSacamgLsYjNv+b/32g04v/6ww388uf2WllFfMWjEH7if3uiumfBwcEBnj59KjmPfr8fs7OzMJtrdybexFVu7ZtCkAPKbm3eqU1ubUKM2WxGV1cXurq6alpAr1QqiMViiMViAC7dI8FgEB0dHfB4PC25X9X2jLgrZY7DB7di+I1EdZhZteA3GvDrD3sxar//Bga9nLd24aZ+h9AGFFpc3TAMA4/HA4/Hg5GREWGzGb/hTCk1CM/KygoYhoHX6xU2nFGoYm0hvz/p3OkbypFNEPqEhGxC94hFMgotXjvi3aZ6FLI5jsPGxgZevpSGdfX5fAiHw7cOuSwX/slprIySUEtC9hvUQ8hOp9OIRqMoFotCmdlsRjgcRjAYvHcdWwEvEMnFo3ZG/HzRYntwHIft7W08f/5cUh4MBhEOh2sWLeXXRiuFbLn7Wp4P22Aw4J/+l+f4xOJRy+oIAF/1pBM//vVPWloHORzH4eXLl9jc3JSU9/b2XpkfvV7Uw63Nj5PEYjaJ2oQY+QJ6qVSSuLWV5ii5XA65XA7b29swmUwSt7bFYmnBr9AmT/Pn+M6XxzgoqXce+J5OD36gPwjbPZ4ZFFpcu8j7Geo7tAmFFtcW8s1m+Xxe6JPT6XTV+zmOQzqdRjqdxqtXr2CxWIR+ORAI1HXDJVF/xGuatP6kf+RCNoUWJwh9QEI2oXvIkX039OzILpVKWFpaEvIT8gwODuLhw4d3WjyQD4ZJyFZG3raVSoUmfSKU2uc27O/vY3V1VbKQ4nK5MDc3B4fDUZc6tgqDwSC0hxpct61GfK1orT1YlsXTp0+xtyd1Jd/1GawGIVsuXAPS3MoMw+B//81V/D/PYy2pH8973zKAH/zy8ZbWQU6lUsHq6iqOj48l5ePj4xgeHm56X3pXt7Z8AwP/b/zxSJggxFgsFnR3d6O7uxssy+L09BTxeByJRAK5XK7q/eVyGScnJzg5OQEAeDweQdR2u911vU+01qdcBcdx+Dd7CfzCcXWucrXgNjD42IMezLrs9z6WXs5bO0KObH1AocW1C8MwcLlccLlcGBoawvb2dpXhQU6pVMLR0RGOji43qHo8HkHYblUUFeJqKGJCe1EqlSRraeTIJgh9QEI2cWe0Elpc787iRqHXDQDZbBbRaBRnZ2dCmcFgwJMnT9DX13fn4yoJkDRArkYptDjxBnd1ZLMsixcvXmB7e1tS3tXVhenpaV3k8xIL2XTdaNeRXSqVEI1GkUqlhDKGYfDo0SMMDg7e6ZitDrMuz4cNSEVsAPiO/7SEz7xMXXmMZqBGEbtYLGJhYUESZtlgMGBqagpdXV0trNkbdQFqc2tfla+9UqmgUqmQW5u4EoPBAK/XC6/Xi7GxMRSLRcEVlkwmFcfh2WwW2WwWm5ubMJvNgqhdb1eYVhfinxWK+J6XR9gqqteF/Xc63Phng6Fb58KuBa2et3aFhGx9QI5sfWK1WhGJRIQ+OZVKKc69+H55a2tLEkUlEAjAar1/ygjifpAju70Qu7EBcmQThF7Q/so2QdyAXgXZRqPHdjs4OMDKyopk4mGz2RCJROD1eu91bPlgmJzGytQrB7ReuUv7lEolLC4uIpFISMrHx8cxNjamm4UUcdvQdaNNR/bp6Snm5+clG4nqEfa+laL+dSK2wWBAhePwnl9awOJ+dT7cZvKDXz6G977lbhsFGsXp6SkWFhZwfn4ulFksFoTD4Xv3yY1C7tYW/5Fbm6gXVqsVvb296O3tBcuyyGQygrAtX5gDgIuLC4krzOv1CsJ2O+bw/PBRCv9mL9nqalyJ08Dgp8e68UXe+kbKodDi2kU+dqHzp03Ika0fxPek0WiEw+GAw+HAwMAAKpUK0um0kB6kUChUfV4eRcXlcgnCttfrpeujBZAju72QRzciIZsg9AEJ2YTuoRzZd0PcbvzCq1YHfFe5VYPBIGZnZ+uSZ5CcxrVBQvb13LZ9lIRBo9GImZkZVbgZ60mrXbdqQ2vtEYvFsLCwUBXia25u7t6hvlrVFmIRm3/N/7/BYADLAe/6yDyeHVWHCW4m/+jLx/Getwy0tA5y4vE4lpaWJNeDy+VCJBKBzWZrYc1qRykEuVjUJrd2NVp4VqkNg8EAv98Pv9+P8fFxnJ+fC6J2KpVS3GyayWSQyWSEHJ5it3YtEVq0ep7Wz0r43o0jrBcvWl2VK/lbARd+dDgEcwPucRKytQs5svUBCWX64bpzaTQahX51YmICZ2dnSCaTwp9Sv5zL5ZDL5bCzswOj0Qi/3y/0y3b7/VNLEDdDjuz2goRsgtAnJGQTd0YrEyw9OoubgZLDWIsTMj5sqTiMLQCMjIzgwYMHdbuO75vbuF1Quq6IN5BfR9ctJh8dHWF5eVnShg6HA5FIBG63u2F1bBXkyJaildDiHMdha2sLL168kJQHg0GEw+G6RK5ohTtdLFaKc2PzImSxzOJr//3/xE7q/IYjNZYf/dpH+Fsz3S2tg5ydnZ2q66Gjo0PTaRCuCkEu3+zAc5NbWyxsa3HsJUYr8wWtYLPZ0NfXh76+PrAsi3Q6LQjbSq6wUqmEw8NDHB4egmEYiVvb6XTq5vz8p5MMPrgTh1oleLuBwf81HMLbA40bn5GQrV3kfYTWn/vtCoUW1w+32ZRgt9sl/XImkxHc2nIxDbhc/4jH44jH4wAu5++8qO3z+UhkbRDiNRN6xuof+ZiYcmQThD7Q5moRQdwCypF9N/QQKjudTiMajaJYLAplRqMR09PT6O6u78I+ObJrgxzZ11NL+3Ach42NDbx8+VJSXs8IA2qEhGwpWggtzrIsVldXsb+/LykfGhrC5ORk3RYRmunIlgvXSiJ2oVTB1/77/4n9TPG6QzWcf/u/PcFXPO5saR3EsCyLtbU17O7uSsoHBwfrurFMDSi5tcXCthglt7ZSmHqxuE0QwOV1EQgEEAgEBFeY2K0tv9Y4jkM6nUY6ncbLly9htVoFUdvv9ytuJFH7fbl1XsL3vDzCszP1urD/qseOnx7vbogLW4xaxwLEzZAjWx9QaHH9cFd3vTiKytjYGIrFouDUTiQSihEiC4UCCoUCdnd3YTAY4PP5BGHb4XDQ86BOyMPFE/qGHNkEoU9IyCZ0Dzmy74bWnbO7u7t4+vSpZELpcDgwNzfXkEGM1turWZCQfT28UCEXMXjK5TKWlpaEfFs8w8PDePDgga4XTUjIlqJ2R3apVEI0GpVEw2AYBo8fP8bAQH3DXDdLyL4uHzYvMGbOyvian/0ckoXWiSoGBvj33ziDLxq/e97xesM/uxKJhFDGMAwePnyI/v7+Ftas8Vzl1lYKQQ4oX8/8mKJdQpATd8Nut6O/vx/9/f1CDs9EIoF4PC7JRc9TLBZxcHCAg4MDMAwjLJ5rRRD9ROIU/2TzBGpNHGVnGPzwYAfe0eFuuhBBwoe2ICFbH1Bocf1Qr3NptVrR09ODnp4ecByH09NTYcNZNptV/F5e+AYuo7DwovZVG86I2qD7s73I5/OS13qMWEgQ7Qj1goTuoRzZd0OrwmylUsGzZ8+wt7cnKe/s7MT09HTDXOUUWrw2qJ1uxmAwCO0innDl83lEo1HJ7lKDwYCpqSn09vY2vZ7NRms5oRuNmh3ZSrnbzWYzwuEwgsH6i6vNuDbkIrY8HzbDMIjnS/jbP/s/kT5v3VjDwAD/8VvmEO73tqwOcs7OzhCNRiULCiaTCTMzMw25HtTOVW7tm0KQA9KIAPy/iY9HC3MEj1IOT7FbW36tcRyHVCpVlYonl8uhUqmoyr10WCrjuzcOsVwotboqV/IFLis+OtEDexPbjUKLaxcSsvUB3YP6oRFhqBmGgcfjgcfjwcjICC4uLgSndjKZRKlU3aedn59jf38f+/v7kvQggUAALpeLrrNbQDmy2wvxvNNgMMBqtbawNgRB1AsSsok7o5VBE4UWvxtaFLLPzs6wsLCATCYjKZ+YmMDo6GhDr1kKLV4bvOBzleOYUBay4/E4FhYWJJtxrFYr5ubm4PWqR7BqJOTIlqJWYf/k5ASLi4uSPsPpdOK1116Dw+FoyHc2ui3keY7lIrbBYMBOqoB3/Pu/xNlF665Ni5HBf/yWOUz1elpWBznpdBqLi4uSxTm73Y5wOEwh3lA/t3alUiG3NnElDMPA4XDA4XBgYGAA5XJZ4tYWp+CRs7e3h4ODA/j9fkEYt9vtTay9lN9PnuIHN2M4V1G/J8YK4P8Y7MC7Qp6mz5XVNBYgbgcJ2fqAHJ/6oRnn0mw2o6urC11dXeA4DrlcThC2M5mM4oYzcXoQi8WCQCAgCNtaSwPYbChHdnshzpHtcrnonBOETiAhm9A9YoGRX4ymTuxmtCZkJxKJqsVys9mMmZkZhEKhhn+/PCS02turlVzlOCYuET+fKpUKNjc38eLFC8l7fD4fIpFIW+0sJSFbitpCi3Mch62traprtaOjA7Ozsw1dXGmkkK0USpzjOIlguJXI4+s+/Hmct1DEthoZ/M53vhlDgcZsFrgLR0dHWF1dlVyfPp8Ps7OzsFgsLayZermrW1scJUDJrU3jXkKMyWRCR0cHOjo68ODBA+TzeSSTScTjccXFc5ZlBTc3cJmqhxe1fT5fU66vTLmC71g/wufz1SHS1cIjmwX/4WEvvKbWO71ICNUW8nEcPbO1ifg80j2obZq9KYFhGLjdbrjdbgwNDaFcLiOVSgnCtlJ6kFKphKOjIxwdHQEAPB6PIGp7PM3fTKV2KEd2eyGOYuh0Oul+IAidQEI2oXvkg5RyuUwLqDWgFYcxL56sra1JFt7cbjcikUjDHIByeFGDF2hJyL4aaqfrEU+WDw4OcHp6Kvn3/v5+PH78uO0WudTqQG4VagotzrIsVldXsb+/LykfGhrCw4cPGz5xbISoLxYF+dfyfNgMw2B5P4t3f+x1lFvYRbqsRvzWt34BBlUiYnMch83NTbx8+VJS3t3djcePH9PiUY2I3dr8tXeVW1spBDn/fnJrE9fBMAxcLhdcLhcGBwdRLpfxJ3/yJ9d+plAooFAoYHd3F0ajUeLWttlsda/j/5PK4wc3j5Fj1dn3mwH8o4EgvqnTC0MLF0oprLF2IUe2PhCfR+pntU2rRU+TyYRQKIRQKASO41AoFARRO51OK853stksstksNjc3YTKZJG7tdtr8fhXkyG4vxKHFnU5nC2tCEEQ9ISGbuDNamWBpzVmsFuSDOzXmFy+Xy1hZWRF2ofL09PRgamqq6ZMOo9FITuMaMBqNuLi4AEDtpIT43hOL2AzD4NGjRxgYGNDM87eekCNbiloc2cViEdFoFOl0WihjGAZPnjxBf39/U+pQ700OSqHElUTslYMs/u7H5lFp4eXotRnxX77zCxFyq2OBimVZPH36FIeHh5LysbExjIyMtOWzqx7Ic2CzLCv5I7c2US9MJpMkwtDo6CgACKFO5VQqFcTjccTjcQCXi4W8qO31eu91fZ2zLL574wh/kj278zEazZDVhP842YduS+uXVUjI1i4kZOsDCi2uH9R0LhmGgdPphNPpxMDAACqVCtLptCBsi0Mo85TLZZycnODk5ATAZWhlXtS+b9+sVVq9OYFoLuL7ghzZBKEfWj/jIogGI86RDZCQXSsMw6hamM3n84hGo5KQMQzDYHJyEkNDQy0ZqMhDQhPKkCB5PUptYjabEYlEEAgEWlAjdUDXjRQ1OLJPT0/x+uuvS8LdteJaraeQLRaxxX8ABAGbYRj88Voc/+DXl9HKK7HDZcF/+Y43w+dQR068UqmExcVFyaYGg8GAJ0+eoLu7u3UV0yFKIcjForZa3dqtjh5B3B6bzYbu7m4MDw/j4uJCWDhPJBLCpkQx+Xwe+XweOzs7MBqNgiMsGAzeyhH2x+k8vn/zBJlW7hS6BiOAH+gP4lu6WuvCFkP3l3YhIVsfUGhx/aAmIVuO0WgU+tWJiQmcnZ0JfXMqlVJch8rlcsjlctje3hb6Zv7Pbre34Fc0H3G7kJCtbziOI0c2QegUErIJ3UOO7LsjFrLV1G4nJydYWlqSuMQtFgvC4XBLhT7xtaam9lIbJEheze7uLs7OpM4jt9uNubm5tplkXoVaHMhqQUmcauai2fHxMZaWliTPOpfLhbm5uaaldOCpl6iv5MQWi9j89/zJehzf/evLaKVk0Ou14hPf/ma4beoYyudyOSwsLEieXxaLBbOzs/D5fK2rWBsgDkEOXD4frwpBDtzs1hYL2/VYvKXFfO1x1XPUbDajq6sLXV1d4DgOp6engqidzWar3l+pVBCLxRCLxQC84QgLBoPweDyK11eZ4/ADr07wX1O5qn9TC70WEz460YMxu3pTVdF9py1IyNYHFFpcP6hZyJZjt9vR19eHvr4+sCyLTCYjCNti0wePvG92OByCW9vn8+lW5NXSOSXuD+XIJgh9oo7VL0KTaKUjUMqRTdSG2oRZjuPw8uVLbGxsSMq9Xi8ikUhD8vLdBnF7kdB2NeRcr4ZlWTx//hw7OzuScofDgbe85S26nVDeBjU4kNWEfALOsmxTrhM+//Ha2pqkPBQKYXZ2tioKSjOohyNbLvqJBW2j0Sh8x2/O7+OD/3XtyuM0g+GAHZ/4jjfBalLHcyGRSFRtLnM6nYhEIm2/AacVKLm1xcK2GKUNMeJ7gD+eWNwmCODy2vF4PPB4PBgZGUGpVJK4tZXmW2JHmDh/ZzAYhMViwWcyBXzg1TGSKnVhMwD+914/vrPHr8p5MIUW1y7yeSOdP21Cjmz9oFXR02AwwO/3w+/3Y2xsDMViUeibk8mkYt9cKBRQKBSwu7srfJ7vn+12uy6uZX4szKOlc0rcDbEj2+VytbAmBEHUExKyCd1Djuy7oyYh++LiAktLS8LOUZ6BgQE8evRIFYNREmhrgwR/KaVSCdFoFKlUqurf/H4/idj/C3LyS7kqXHAjqVQqWF1dxcHBgaR8eHgYk5OTLVvouI+QLXaj8q+V8mEDwG/M7+OHWyxiP+l24Vff9xosptb3eQCwt7eH58+fS9o9GAxienoaZrM6Qp63M/d1awNvjGeaGYKcUA+1PtctFgu6u7vR3d0NjuOQzWYFUfv09LTq/eL8nRyA33J34tOMeje+dJuN+Jnxbsw4W7tp9irI0att5JsQ6PxpD3mfSn2kttGqkC3HarWip6cHPT09kr45mUwqRlJhWVbou9fX12Gz2QS3tt/vb8mG5Xog7yNpfUX/iHNkOxwO6lcJQidosxciiFtAQvbdUYuQfXp6img0KhmMGAwGPH78GP39/S2rlxwSaGuDBMk3yGazmJ+fl+QYZhhGEtqYuIRCi0uRT8Ya3SbFYhHz8/PIZDKSOjx58qTlz+G7CtlKocSvErF/5v/dxM/8v1v1q/QdmO3z4FffN6eKfKwcx2Ftba0qisTAwAAePHig6UU/PXNXt7ZSCHLx8eh864d6jDsYhoHX64XX68Xo6Oi1jrA1oxW/aO9AhlHvssR3dPvwgf5gq6txLTRe1Dbkptc+8nuQ+kVtoxchW4y8b764uJD0zaVSqeoz5+fn2N/fx/7+vvB5PpKKlsI1y9cyScjWN5QjmyD0i3pnjITq0cqghWEYSa5nCi1eO2pwGB8eHmJlZUXy/TabDZFIBF6vtyV1ugo1tJcWoHa65PDwEMvLy5KJstPphM1mQyKRAECCrRgKLS5FvqjSyDZR2nBhNpsxNzcHv9/fsO+tlbsI2WIRW/wHSEMpA8BP/veX+PBndq47XMP5kvEAfuYbZ1QhYpfLZSwvLyMej0vKJycnMTg42KJaEbflNm7tqyJAVCoVVCoVcmvrmHrM98SOMHH+zp9LFPBJxg6o4LmmRKfZiJ8Y6cQXehytrsqt0co8nbiEhGztQ+Hh9YUehWw5ZrMZXV1d6OrqAsdxyOVygqidyWSq5lQcxyGdTiOdTuPly5ewWCyCWzsQCKg6EpP8/tTrOSXeQB5anJ7JBKEPSMgm2gKxkN3OwtltEYcOana7sSyLtbU1bG1tScoDgQBmZ2dhtVqbWp9aIEd2bbR7O3Ech/X1dbx69UpSHgqFMDMzg6dPnwpl7dg+V0FOfinNCi1+fHyMpaUlSR/gcrkwNzcHh0Mdi/u3desrObHFIrb4WvsXv/cCv/76geJxmsWXPQjip//OtCom4Ofn51hYWJCECjYajZienkYoFGphzYj7Indri//u49YmCDEGgwF7Fju+K2fEkUEdfYgS7+5w44NDIVU8d2uBQotrGxKytQ85svWDOEIT0B7nkmEYuN1uuN1uDA8Po1wuI5VKCWHGi8Vi1WdKpRIODw9xeHgIAPB4PIJb2+12q+pZRo7s9kMczZMc2QShH0jIJu6FOASumjGZTEKoHBKya6dVztlSqYSFhQUkk0lJ+fDwsKpDlqolFLvaaWdB8qpc76Ojo5iYmKhysrVb+1wHhRaXIn8O1rtNOI7Dq1evsL6+LikPhUKYnZ1VVY602ziy5SK2+P+NRqNwLI7j8I9+5xl+d/m4QbWujb/zWg8++NUPW1oHnmw2i2g0Kgk9aLPZEA6H4Xa7W1gzot4ohSAXi9q3cWuLx0NamDO0O408RxzH4ecOU/jJgxTUeiUETUb83yOdeJtXvSK7EiRkaxvxGI7OnTYhx6d+oE0Jl+unoVAIoVAIHMehUCgIYcjT6bTivDObzSKbzWJzcxNms1lwageDQVgslhb8ijeg+7P9kDuyCYLQB+pZhSSIBiIWGCm0eO20QpjNZDKIRqOSELZGoxFTU1Po6elpSh3uCoXMro12badcLodoNCoZVBsMBkxPT0uubRKylaHQ4lIa6ciuVCpYWVkRdtjzjIyM4MGDB6pbZK1FyBY7RvnXV+XDBoDv/Y1lfPpFooG1vpl3vdaLf/7Vky2tA8/x8TFWVlYkzySPx4NwOKzKCClE/VAKQX5bt7aYi4sL4Z6jhcT24eVZCe9fP8B+Sb3jvq/xO/FvRrtgUlkfVws0LtI25MjWPhRaXD+Q6CmFYRg4nU44nU4MDAygUqkgnU4LYcjFzleei4sLHB8f4/j4ckOwy+USwpB7vd6mtyk5stsLypFNEPqFhGyiLSCn7N1odrvt7e3h6dOnksmDw+FAJBLRhNur3UNm10o7tlMsFsPi4qJkI43NZsPc3Bw8Ho/kve0q9N8ECfxS5Atk9WqT8/NzRKNRZDIZyXdNTU2hr6+vLt9Rb27a5HAbEbvCcnjff1jAX26nG1/xa/jeLxvBt/9/hltaB+Cyrba2trCxsSEp7+rqwpMnT2ghqA1RcmtXKpWqXPPAZR/GL2Ly8Pee0j3Y7ovFauS+YgzLcfjYcRr/Zi8JtY5oPAYGPz7ahS/z6Wehk0Q0bSF+btJzUJuQi1c/kJB9PUajUQghDgBnZ2eCWzuVSimuX+RyOeRyOWxvb8NoNErc2jabreF1lteJzqm+kQvZ5MgmCP1AQjZxL7QSWpyE7LvRrHZjWRbPnj3D7u6upJzPGWw2mxv23fWErrPaaCdBkuM4bG5uYm1tTVLu9/sRiUQUw2yR81gZCi0uRT4Br8e1ohQRw2KxIBKJwO/33/v4jeI6R7Y4lLhcaOPFM/7zLMfhH/7WSstF7H/4V0fxrV801NI6AG/0zQcH0hzhIyMjGBsbI6GEUHRrVyoVsCyLs7MzrK6uVuWoE4fv5//L59bm/8it3TrqOe44LJXxnucH2Cpd1O2Y9eaL3Xb89Hg37EZtX28UWlzbkCNb+5D4qR/IvXs77HY7+vr60NfXB5ZlkclkBLd2Lperen+lUkEsFhNSrTmdTkHU9nq9DWlv+f1J51TfnJ+fS/pVcmQThH4gIZtoC8S5PElgrJ1mCLNK7j8AGB8f19xCOTlpa6NdhOxKpYLl5WUcHR1JygcHB/Hw4cMrFzjapX1uCwn8UuodWvzo6AhLS0uSa87tdmNubg52u/1ex240VwnZtxGxAeAnPv0Sn34eb17FFfjhr57EO1/rbWkdgMuQgIuLi0ilUkIZwzB4/PgxentbXz9CnfACdDabxeLiYlU+9YcP38j3rvQME+eu548nFrcJbcByHH71JIsf3YujpNLu2g4O/9hrwt/u82lexAZIyNY6JGRrH7oH9QNtSrg7BoMBfr9f2ABdLBYFUTuZTCqmeczn88jn89jd3RU+zwvbdru9LvcSObLbC/kGCnJkE4R+ICGbaAvIKXs3Gt1uyWQSCwsLkoVOk8mEmZkZdHZ21v37Gk07hsy+C+0g+J+dnWF+fh6np6dCGS8CDQwMXPtZErKVoXaRIp+A37VNOI7Dy5cvq0JHd3Z2YmZmRrIRTK0oCdliEZt/zf+/0WisWhRZ3s/io38ujQrSbH7sHY/wN6e7W1oH4HJBaWFhQeKkNZvNmJ2dVbUzn1AHsVgMy8vLkv7d6/ViZmYGRqNRsrlEjNJ9zB9DLGaTqN1cbruAnC5X8Pde7OP5mXpd2GPlc3x3IQZnlsVf7L6Cw+EQFs19Pp8mnVq0wU/bkJCtfUj81A90LuuH1WpFb28vent7wbIsTk9PBWE7m81WvZ9lWSQSCSQSCayvr8NmswlhzH0+353npeJzKk4pRegTcVhxgIRsgtAT6l+dJFSNVgYA4gUJpV2AhDKNErI5jsP29jZevHghmbi7XC5EIhHNhn5pB4G2Huhd8E8mk4hGo7i4eGMR9zbhmUmwVYbaRUo9HNlXRQ0YHR3FxMSEZvp4edh5uQv7qnzYYn71L/eaVl85DICfeucT/PWHrd/AlUwmsbS0JHl+ORwORCIROByOFtaM0AK7u7t4/vy5pEwpn7o4t7Z4wwmPXNQW38v8v/Hub1pgVgccx+HjsSz+9W4C5yoVVS0ci285i2OufCYpLxQKKBQK2Nvbg8FgEETtQCCg+ogkV6GV/pu4RDyupXOnTUj81A90LhuDwWCA1+uF1+vF6OgoSqWS4NROJpMScwvP+fk59vf3sb+/D4Zh4PP5hD5anK7mJsRrc3Q+9Y9cyNbq+jJBENWQkE20BRRa/G40QsiuVCpYXV2tyrnZ3d2NqakpTbj/roKc/7WhV0GS4zjs7u7i2bNnkkV5j8eDubk52Gy2mo6j1/a5L0rCRjsv9sl/+22vFaW0DgaDAVNTU5oLHS2/Nvi2qFXEThVK+P3Vk+ZUVgYD4GN/L4w3j7Te6XxwcICnT59Knl+BQAAzMzMwm80trBmhdjiOw/r6Ora3tyXlw8PDGB8fr7rvrsutLXdrX7Vpp1KpoFKpkFu7ztxlU9Q5y+J9Lw7wl/liA2pUH6YdFvzMWDfcFz2Ix+NIJBLIZrNVv5dlWcTjccTjl2kmnE6n4Abzer2qvb4orLG2EZ8/tV5jxPXQPagfSMhuDhaLBd3d3eju7gbHccjlcoJbO5PJVN1THMchlUohlUrh5cuXsFgsQv/s9/uvnauIz6kWo64Qt4Mc2QShX7SrGBHELSCB8W4ouWfuMykrFAqIRqNV4ZYfPHiA4eFhzU/4xO1FQtvV6NG5zrIsnj59ir09qauzt7e3yol2EyRkKyNfRGj3+0upPWolk8lgfn4exeIbooPFYsHc3Bx8Pl+9qtg0lDY5AKhJxAaA3144wkWl+Q5Ck4HBL70ngrlBb9O/WwzHcdjY2MDW1pakvK+vDw8fPqQFPOJaKpUKVlZWcHLyxmYQhmHw8OFD9Pf313QMsbOaj6ogjq4ghtzazeW6fpbjOPxO4hQf3I7hTJ0mbJgA/OhICH876LkssJrhcrkwPDyMi4sLJJNJYeFcyQ3G5+7c2dmB0WgUnGDBYBBWq7W5P+YaSETTNhRaXPuQ+Kkf6Fw2H4Zh4Ha74Xa7MTw8jHK5jFQqJYQZF89ZeUqlEg4PD3F4eAjgMo0N30e73W7Js1S85kRCtv6hHNkEoV9IyCbuhVYmWhRa/G7IB3mVSuXOjulYLFYVrtRsNiMcDiMYDN6rnmpBPsm5T3vpmXpvkGg1xWIR0WgU6XRaUj45OXmnDRokZCujlBO6nRcW7hpa/PDwEMvLy5Jry+12Y25uThchVPnfxf+XF7Cvuw//civV2AoqYDYA//nb34TxUGtDnSmJkAAwMTGBoaEhTT+bicZTKpWwsLAgiexgNBoxMzODjo6OOx1TLmrzIjW5tdXFBcviu18e4X9kzm5+c4t4bLfgp8a6MGyzKP672WxGV1cXurq6wHGckLuTd2vLqVQqiMViiMViAC4XRnlR2+PxtPT6ohzZ2oaEbO0jn7PRedQuJGS3HpPJhFAohFAoBI7jUCgUhE1n6XRacY0kk8kgk8lgc3MTZrNZkiZEniOb0DdyR7bb7W5RTQiCqDeksBBtATmy74ZcyL6LqMZxHF69eoX19XVJucfjQSQS0axwokQ92qsd0JOzVsnZajabMTs7e69FfB49CP314r6htPWGkrB/Hbzr9uXLl5Lyrq4uTE9Pa3bTjXzxPpfL4c///M8RDAYRCoXg9/tvvH9ShYtr/73e2M0G/Mbf/wKMtVjELhaLWFhYkAg2BoMB09PT6Oxsfb5uQt3k83lEo1Gcnb0hZFqtVkQikbotGF0VgpwXt2/r1hYL27SQqUwtgujvJ3P4R5vHKKhUO2UA/IuhEN4V8tT+GYaBx+OBx+PByMiIkLuTXzgXb8TlyeVyyOVy2N7ehslkkri1LRZl8bxZ0LhRW5CQrX0oKoJ+oE0J6oJhGDidTjidTgwODqJSqSCdTgv9c6FQqPrMxcUFjo+PcXx8DACSsOM0/tM/4mvCZDKpKoIOQRD3Q5urlgRxS+Q5skkYqg25MFsul2+1MFMul7G0tFTl9Orr68Pjx491F9ZHyZFNVKMXZ+3BwQFWVlYkk12Xy4VIJAKn8+4ClZ6E/npyn1DaeuQ2juxKpYLl5WUcHR1JysfGxhTz12oBsUgln5wWCgUUCgXs7u7CZDIJovZV4sLZRfM2RbitRnzyO9+Mbo+tad+pxOnpKaLRqGQTjtVqRTgchsdTu/hDtCepVAoLCwuSKEd8/2ezNe7aVnJr8+P6Wtza8rQD4ogNWhyHNANJaE6WxQ9uxfBfkrlrPtFaHtjM+MmxbkzY7ycky3N3ZrNZwa0tTpHEUy6XcXJyIsx53G63xK3d6H6WRDRtI55L0LnTJnLHJ51H7ULnUt0YjUahfwWAs7MzQdROpVKKa3DizWinp6dYXl4W3NqNHLcSrUHsyKaw4gShL0jIJtoCyl18N+7jMM7lcohGo5JBBMMwePz4Mfr7+3XZ/kqh2Ilq6hmyvhWwLIu1tbWqfLKdnZ2YmZm592/Ri9Bfb27rQG4HGIapEmbknJ+fY35+vsp1OzU1hd7e3qbUs96IXZbApas8kUgI4V7FlMtlyY58r9eLUCiEjo4OuFwuMAwDl7U5m6r8DjM++R1vQoertbvCY7EYlpeXJX2U2+1GOBymxRziRg4PD7G6uioRzoLBYF36v9twlVtbKQQ5UO3WBt4Yp1EI8pv500wB3/PyCDlWvZvIfqAvgG/r8df9uAzDwOv1wuv1YnR0FKVSSRC1k8mkYtqq09NTnJ6eYmtrSxLiNBgMSpxh9YKEbG1DjmztQ5sR9AOFodYWdrsd/f396O/vB8uyyGQyQv8sz5UMXD5vxWlCnE6n0Ed7vV7dmW3aEfF5dzqd9EwmCB2hHeWAUCVa6RDu6yxuV+4qzB4dHVUtkvPhJn0+Xz2rqCootHhtaFmQLJVKWFxcRCKRkJTX09mq5fZpJBRavBqDwSA8Z5Uc2Uqh77X+LBaHFOZ/My/Ml8tlpFIpxONxxONxRXGBz5+2sbEBq9WKUCgEbxOGA50uC37nO98Mn73+AkatcByHnZ0drK2tScpDoRCmp6dp4Ya4Fo7jsLm5WZWeoK+vDw8fPmz5Yu9Vbu2bQpAD0s0x/L+Jj9fq39ZM5G1V5jj8s60T/Hq82oWsFoatZvy7sU48dDRnI47FYkFPTw96enrAsqzEra20aC4PcerxeBAMBiUbqu5Lu0ep0Tri89dOzxs9QedQP5CQrV0MBgP8fj/8/stNbcViEYlEApubm5L5sJh8Po98Po/d3V3h87xb2+FwNLP6RJ0Qm6nuEymRIAj1QUI20RaQU/Zu3LbdOI7D2toaNjc3JeV+vx/hcFj3uUkotHhtaLWd+FC84pw7RqMR09PT6O7urtv3kJCtDIUWr0a8+C2/Tg4PD7G8vCwp93g8mJub06zrVknEFocItlqtEnEhk8kIoraSuFAsFrG3t4cBAwOgcSJun9eGT37nm+CwtG7YzbIsXrx4gb29PUn50NAQJiYmNLMxkWgNLMvi2bNnODg4kJSPj49jeHhYdddPvdzalUqlrd3aL41WfP9WGqcq7m6/tcuLH+gPtuwaNBgM8Pl88Pl8GBsbw/n5uSS3ttIYN5vNIpvNYnNzExaLBYFAAB0dHfD7/XVza6vtniSuhxzZ2occ2fpB/Nxupz5fj1itVvT29uLk5EQQsvkNZEppQliWFTamAZdub96t7ff7adOvRhCv15EjmyD0BQnZRFsgD3WoFeGs1dxGyL7KqTo0NITJycm2mASQI7s2tCjUHh8fY2lpSXIP2O12zM3Nwe121/W7tNg+zYDapRpxm/CLoBzHYWNjo8o12d3drWnXrVjE5l+LHdny60O8I39iYgJnZ2eCqJ1MJiXXTzjI4RNbHPLl+k9yxzsc+Pi3fgHs5ta1+8XFBZaWlpBMJoUyhmHw8OFD9Pf3t6xehDZQun4MBgOePHlS101cjeSubm1x7m0lt7Zex7ZlAL9t9eGPLB5ApSJ2n8WEnx3rwmOnujZm2Ww29Pb2ore3VxLiNJFISBxCPKVSCUdHRzg6OhJCmPPC9m0WXym0uLYhIVv7kItXP4jPpVbnTYQU8RqO1+vF5OQkSqUSksmksPlMnEeb5+zsDPv7+9jf3wfDMPD5fIJbmwRSdcJxHDmyCULHkJBN3AutdNxKocWJm6nVOZvNZjE/P4/z83PJZ7Wcg/UukPO/NrQk+HMch5cvX2JjY0NSHgwGMTs725AUBSTYKkOhxauRCy7lchnLy8tC+FKeeoa+bwVKLmyO4yQuyZuw2+0YGBjAwMAAKpUKkskkYrEY4vE4UCziLZ0c/uigvu3zqNuFX3//HMwtXAQrFApYWFiQTOhNJhNmZmYQDAZbVi9CG5ydnWFhYUES0cBsNmN2dlYI26g1buPWVgpBDujbrb1xfoEfcvUhZ1DvMsE7O9z40FAIJpX3aeINVePj4zg7OxNE7VQqVTWO4TgO6XQa6XQar169gtVqFfJq+/3+a3PQk5CtbUjI1j4UWlw/0KYE/aG0OcFisaC7uxvd3d3gOA65XE7oo7PZbFW/ynEcUqkUUqkUgEu3t9itXa+IKsT9kQvZ1K8ShH5Q7wyVIOoICYx3g1+YEy/cydnf38fq6qpkcGi32xGJRODxeJpWVzWg1ZDZzUYr7XSVKNjoKAMkZCtDocWrEU/KisUi/uf//J/IZrNCmcFgwPT0NHp6elpRvXsjdkLyr/n/v42ILcdoNCIUCiEUCgkLF56eI/zp7+yhVKfb7S3DPvzCN4VhNLRu4pxOp7GwsCBxGPD9M+1OJ24im80iGo2iVCoJZXq8fuRubfFfO7m1iyyHnz5I4sNHabAqFbFDJgN+fqIH0ypzYdeK3W5Hf38/+vv7UalUkE6nhUXzs7OzqvcXi0UcHBzg4OBA4gQLBoNwOByKIfEJbUJhqbUPnUP9QEK2/rjpnDIMA7fbDbfbjeHhYZTLZYlbWym/drFYxOHhIQ4PD8EwDDwejyBsu91ueg60EBKyCUK/qHOWShB1hkKL3x2TySQsgovbjWVZPH/+HDs7O5L3d3R0YGZmpiFOVbXDCyv8QJkESGW04MguFAqYn5+XuND4UKp9fX0N/W4SspWhdqlG3CY7OzuSZ7TVasXc3By8Xm8rqnZvrsuHfR8RWw6/cPGFU278n4wHH/jPT+99zLeN+vHhd8+2dNJ8eHiIp0+fSu4Tn8/XsEgShL6IxWJYXl6uCsUYDod1ff0ohSAXi9p6dWvvn5fwrhcHOLpQ7/zoq/wO/NhIF2wqbsfbYDQaBVEauBx38qJ2Op1WdGvzTrCNjQ3YbDaJW1sOLdpqC3Lzah86h/qBhGz9IR7P1hIu3mQyobOzE52dneA4rqqPVnJrZzIZZDIZbG5uwmw2C6J2IBDQ9dhZjVBocYLQLyRkE/dCK5Nk+QCUQovXjrjt+AHg+fk5FhcXhbA6PFoPX1sPjEajMPmhDRPKqF2QTCQSVS5Gq9WKSCQCn8/X8O9Xe/u0CmqXasTPWvHzxuPxYG5uDjabNl1rchFbng+bF4jqzVc+6UIyf4F//an1Ox/jrw5a8INfHECxWGxJ+3Mch1evXuHVq1eS8p6eHjx+/JgW5Igb2d3dxfPnzyVlXV1dePLkSVvlirwqBLn4+STmJre2WNhWy314wXL4ucMUfuYwBbX2qF4Dg1940IuIS5v9Wa04HA44HA4h/UUqlRIWzcWpm3jOz8+FvJ0Gg6Fqobad52JahEKLax8SP/UDnUv9IZ4n3/acMgwDp9MJp9OJwcFBoY/m3dpKEVUuLi5wfHwsRPZzu92CqO3xeOi6aiCUI5sg9A0J2URbwDAMjEajMIAhgbF2xIuW/KBtYWFBEl7HaDRiZmYGXV1draiiqlAS/gkpas11zHEctre38eLFC8mCktfrRSQSaZooRYKtMhRaXArHcZLNFjzd3d2Ynp7WrOAkF4nkInajJ/7vfnM/gi4L/o9PPsfZLdyJBnB4xzCLL+4p4MWLF3jx4gVcLhdCoRA6Ojrg9XobvjhdqVTw9OlTHB0dScrHx8cxPDxMi+PEtXAch/X1dWxvb0vKh4eH236TIqDs1hYL22KU3NryyBLiTTmtWNBMXJTxjc8PsFms7kfUwl/zOvBjI53wmrTZn90Vo9GIjo4OdHR01OQEY1kWp6enkrJXr16ho6MDPp9Ps+OBdoKEbO1D51A/kJCtP5RyZN8VcR8NXEZU4cOQJ5NJxbWb09NTnJ6eYmtrCyaTCX6/XxC2tbrxXM0UCgXh/10uFz2TCUJHkJBN3BtxDmU1YzKZSMi+A+KBXiqVwsuXLyXn2+l0IhKJwOVytaJ6qkPcXiRAKiMPwa6G+7FSqWB1dRUHBweS8r6+Pjx58qSpk1gSspVR6waIVsDnbxfnrgUuBcuxsTHNTtaUQonz/280Gpv2u97+uBOvDXjxm/MH+I35A5yclq58r8vC4K90AW/pKKNDtg6Ry+WQy+WEEHP8okcwGITZbK5rnUulEhYWFpDJZIQyg8GAqakp2mRG3EilUsHKygpOTk6EMoZh8PDhQ/T397ewZurkKre2UghyoNqtDbwx9ml2CPIKx+EXj9L4t/tJtH70pYydAT480Yu3eOytrkrLkTvByuWyxK2tlLcTgMStzS+YB4NB2O3UpmqERFDtQ+KnfqBzqS/4jYc89T6nfESV/v5+sCyLdDotuLXFzmCecrmMWCyGWCwG4HI9lRe1fT4fXXN1QNzuDoejhTUhCKLekJBNtA1yZzFRG+J2SyQSkn/r6urC9PR0VQ7ydoYc2bUhDsHeakHy/Pwc0WhUIgDxC/iDg4NNX1AiIVsZfrFf7mprN87OzjA/P1/lvuro6MD4+HiLanU/xCF4+deNyId9G0JuK77rS0bwrV80hP+xFscfryWQyJeQL1bgsBjhd5jxV0b9ePvjTlhNBpyeniIejyMej0ueJTwXFxc4PDzE4eEhGIaB1+sV3NpOp/Nevy+XyyEajUrCz1osFoTDYc3mSCeah9ImCD7SDu82Ia7nKrf2XUKQy49XzwXNbLmCb147wErh6s05reaLPXb86EgnQmaaWyhhMpkQCoUQCoWE8JmJRAJHR0eKC+YsywqiN3C5oMuL2rRgrh5IyNY+JH7qBzqX+kI+DmtklBKDwYBAIIBAIIDx8XGcn58LonYqlVJMcZnP55HP57GzsyPZfBYIBEiEvSPi8RAZrghCX9AMkWgbxAMWypF9fx48eICRkRGabMugDRO1IZ4UtlKQVAqVbzabEQ6HEQwGW1InEmyvxmAwCPdVO7ZLKpVCNBqtcmID2t1trBRKvNUithiz0YAvf9SJL3/Uee37PB4PPB4PRkdHUSqVBFE7kUhUjTk4jkM6nUY6ncb6+jpsNpsgavv9/lstsMTjcSwvL0u+w+VyIRwOk/OOuJF8Po9oNCrJ72e1WhGJROB2u1tYM+1yG7e2Ughy4HL8WKlU6ubW5jgO/+Ekgx/bS6Ck0iBaZgA/O96NL/VRLsNaYRgGLpcLLpcLDocDy8vLwr+ZzWbF9COFQgGFQgG7u7swGo0IBAKCsG21WptZfUKEeExLc2ttQpsR9AMJ2fpCvibXzHQbNpsNvb296O3tBcuyyGazgrAt35QOVG8+s9vtgqh92zliuyLPkU1CNkHoCxKyiXujldDiJDDeHiVnmdlsxuzsLLl0roBCi9eGGpzre3t7WF1dlTy/3G43IpFIy0XBdhdsr0IpPGu7sL+/j5WVFcnvNhqNmr5OxCK2+A+Q5o/VGhaLRbJokU6nBWFbyTF3fn6O3d1d7O7uwmAwIBgMCmHIr8ubtru7ixcvXkiuiY6ODoqUQtQEv5FLvgkiEolQvr46Indri//u49audXH9guXwz7dj+K1E9YKpWnir+9KF3WOh59ZdkV9Lb33rW1EoFIQNVdlstuozlUpFEt7U5XIJorbH4yEBp4mQCKp9SPzUD3Qu9YV8jtyqc2owGODz+eDz+YSNz7yonUwmFTefnZ2dYW9vD3t7e2AYBj6fTxC27xvRS6+wLCvJke100gZJgtATNFsk2gbxoi4J2dfDcRw2NzextrYmKTeZTPgrf+WvtFzkUzNqEGi1QCsFf5Zl8fz5c+zs7EjKu7u7MTU1pQoBiIRsZdTi5G8mHMdhbW0Nm5ubkvKenh4Ui0Ukk0nhfVpCyYktFrH1snAkDjH34MEDQViIxWJIpVJV541lWYmw4Ha7BVHb6/UKmwfX1taqnmEDAwN48OCBbtqOaByHh4dVG7mCwSBmZmZU0QfqFbmoDaChbm2W4/ADWyf4vWSuUT/pXhgA/PhIJ/5mkNz/9cZgMMDtdsPtdmNkZAQXFxeCy+uqBfNcLodcLoft7W2YTCbBrR0IBMit3WDE9z714dqEXPX6gYRsfdFKR/Z1WCwWdHd3o7u7GxzH4fT0VBC2s9ls1RyR4zikUimkUikAlxGU+H7a7/fDbDa34meoDnGUKYAc2QShN2ilgmgbyJFdG+VyGcvLyzg+Pq76N7fbTSL2DZAjuzZaJUjyuUB58Y9nYmICo6Ojqll4aEfBthbarV3K5TKWlpZwcnIiKeev1/n5eaFMS0K2XLQRC9pGo1E192EjcDgcGBwcxODgIMrlMpLJJGKxGOLxuGLI+NPTU5yenmJzcxNmsxnBYBD5fF4Sjo5hGExOTmJgYKCZP4XQIBzHYWtrCxsbG5Lyvr4+PHz4kBZsm4g8B/Z93dpiYZs/5v/vIKVaEXvOacVPjnWTC7tOXHe9AJcRtcQL5tlsVhC2lcKblstlnJycCOMPt9stcWvruZ9uBeTI1j60GUE/kJCtL9TiyL4OhmGENFXDw8O4uLhAKpUSNp+J0+DxFItFHB4e4vDwUPg8v/nM7Xa3bV+Sy0nHvSRkE4S+oJkjcW+00kFSjuybyeVyiEajiqFPgfYQju4LObJroxWCZDabrcoFajQaMTs7i87O6/PfNpt2E2xrpZ1Ci5+dneH111+XTMaMRiOmp6fR3d0NQNoeWrhOxMIL/1pN+bCbjclkQmdnJzo7O4Wd+LyorRQG9uLiAkdHR5Iyg8GAyclJ9PX1NavahEZhWRbPnj3DwcGBpHx8fBzDw8Ntde+pEblbm+M4VCqVqrQLgLJbW+zYZhgGxxcV/PvDVPN+QI0wAP7ZQBB/r8vX6qroitsIoQzDwOv1wuv1CuFNxW5tpXkyv6lqa2sLZrNZ4ta2WCx1/z3tBgnZ2ofET/0gXsOhc6l91OrIvg6z2SyZI+bzecGtnU6nFd3amUwGmUwGr169aut+Wr6WTUI2QegLErKJtoFCi1/PyckJlpaWJIsXVqsVwWBQWPSkdrsZcv7XRrPb6ejoCMvLy5LvcjgcmJubU+XgloRsZdqlXVKpFKLRqMSha7PZMDc3B4/HI5SJ20Ptwj6J2Ncj3ok/NjaGYrGIRCKBWCyGRCJx5XOSFye3trbQ0dGBUCgEv99PC2+EhIuLCywtLUmikTAMg6mpKWFjDKEe+PuXHyuxLHtlCHJA2a39G4lTqG0U+thuwc+Nd6PXSuEv6819hFCLxYKenh709PSAZVmJW1vubAIunyfHx8dC9C7eBRYMBtvaBXYfKCy19qFzqB9oU4K+kM+htHZOGYaBy+WCy+XC4OAgKpUKUqmUIGzLQ2kD1f20OKqK2+3WXBvcBnF+bIByZBOE3iAhm2gbSGBUhuM4bGxs4OXLl5Jyn8+HcDiM/f19oUzPwlG9oNDitdEsQfKq67ujowOzs7OqzSXULoLtbWmHdtnb26vKXev1ejE3N1eVo1IrjmxxPmy5u5AXsGnRT4rVakVvby96e3sV8xnLOTs7w+7uLnZ3d2E0GhEIBBAKhdDR0UG5Tduc8/NzRKNRiSBlNpsxOzsLv9/fwpoRtXKVW/uqEOQXHIffSipHV2oFDIDv7fXju3r89KxvEPXazGYwGODz+eDz+SSbqni3ttIcOpvNIpvNSlJg8C4wtY6z1QY5srUPhRbXDyRk6wv5HFkLjuzrMBqN6OjoQEdHB4BL4ZYXtVOplOKagDiqislkQiAQEBzbepsnUmhxgtA3JGQT90Yrky0KLV7NxcUFFhcXEY/HJeWDg4NCrkTaAHA7KLR4bTRDkCyXy1hcXEQsFpOUj4yM4MGDB6p+drWDYHsXtCLc3gWO47C2tobNzU1JeU9PD6amphQn3VoItU4i9t25Kp8xLxDwCxbyc1+pVBCLxYRnn9vtFkRtym3aXvApNcTRHex2OyKRCDkUNEotbu0/yZ4hXlZHHzlmNeGjD3rJhd1E6vmMF2+qYlkWmUxGELaVUlHxKTCOjo4kOTuDwSBcLhf1P1dAIqj2IUe2fiAhW19oIUf2fXA4HHA4HOjv70elUhH66WQyqdhPl8tlnJyc4OTkBMClY5mfW/p8Ps23D4UWJwh9Q0I20TaQICvl9PQU8/PzklA0BoMBT548keTapHa7HdRetdFowT+fz2N+fl4ykDUYDJiamkJvb2/dv6/ekJCtjJZCad+GqzZdPHjwACMjI1cuiKm9PcQiNv+a/3+j0UgLfdfAsiyePn2Kw8NDSfno6ChGR0fBMAyGh4dRLpeRSCQQj8cRj8clgiUPvwv/1atXsFgswi7+YDAoSbtC6ItYLFaVUsPr9SIcDrdVrjy9o+TWfl7MtrhWly7sb+n04gcGgjDRs77hNMPRazAY4Pf74ff7MT4+jrOzM8EFlkwmq8ar8pydVqsVgUAAHR0d8Pv91P+IIEe29qHNCPpAnr5D6+5dojrnuZ6fsXxUrkAgAOAyKpO4n1Zad8vn88jn89jZ2YHRaITf7xfc2na7vdk/4d6QI5sg9A3NHoi2gQTGNzg4OMDKyopkwcFmsyESicDr9UreS+12Oyi0eG00sp1isRgWFxclkReuur7VCgnZyuixXQqFAubn5yWTLqPRiJmZGXR1dV37WbU61Ckf9v0olUpYXFxEOp0WyhiGwZMnT9DT0yN5r8lkQldXF7q6usBxHLLZLOLxOGKxGE5PTxWPfXBwgIODAzAMA7/fLwjb5NDVD7u7u3j+/LmkrKurC0+ePKFFWR3D95GnLd7X1Gs24j9O9mLARhsmmkUrNrPZ7Xb09fWhr69P4gJLJBJVOSoBoFgs4vDwEIeHh2AYBl6vV9hU5XA42nZcIBfO2rUdtA65ePWB3t277YhcyG4nbDabJKpKNpsVRG2leWKlUhE2RwOX/Tzv1vb7/ZqYQ4jHHxaLhTbvEoTOICGbuDdamWyJd31XKhVwHKeZutcLlmXx4sULbG9vS8qDwSBmZ2cVO3nxYIUXI9ptAHgbKLR4bTRCkOTD8L548UJS7vf7EQ6HNZX/R4+CbT1Qq3B7V5LJJKLRKC4uLoQym82Gubk5eDyeGz+vRkc2idj3I5/PIxqNSqKlmM1mhMNh+Hy+az/LCwNer1fIbcovRiQSiao+ieM4JJNJJJNJrK2tweFwCKK23++nvl6DcByH9fX1qnHe8PAwxsfH6d5rEyot6g4YAN8Y8uAfDwRho+dHy2jFfS52gU1MTKBQKAiL5Uo5OzmOQzqdRjqdxsbGBmw2mxCCXCuL5fVCKc89oT0otLg+ICFbf4jPaTv1LXIMBgN8Ph98Ph/GxsZQKpUkbm3xegTP2dkZ9vb2sLe3B4PBAK/XK/TVat2AJo7ISJu0CUJ/kJBNtA1Kgmw7DWSKxSIWFhaQSqUk5TflC5YP3iuVCg3or4Ec2bVRb8G/UqlgZWWlKgzvwMAAHj16pLlrloRsZdQo3N6Vvb09rK6uSn6Hz+dDJBKpedOF2oR9eT5sACRi34JkMlkVTcLpdCIcDsPhcNz6eFarVXDLsSyLVColuLXFQjlPoVDAzs6OEFouGAwKwraWNgK1K3w/yOe843n48CEGBgZaVCuiFXiMzR/zBE0G/NpkH0bt5HxpBWpz9PI5OwcGBlCpVJBKpQS39vn5edX7z8/Psb+/j/39fWGxXbxYrmdIyNYHFFpcH5CQrT/Ea03ttP57ExaLBd3d3eju7gbHcTg9PRVE7UwmU/V+fi6ZSqWwsbEBq9UquLUDgYBq0oWIo9y5XC7qUwlCZ6jjSUMQTUA+aKlUKm0zkEmn04hGoygWi0KZ0WjE9PQ0uru7r/2sfEBSqVRgNpsbUk89IBdo29H5Xwv1FPzPzs4QjUaRzb6RE5JhGDx69AiDg4P3OnarICFbGT20C8dxePHiBba2tiTlvb29mJqautWCifjZ0mphXy5ii8VsXsCmZ+HV7O/v49mzZ5LzGAgEMDMzU5c+12AwCKLAgwcPUCgUEIvFEI/HkU6nq66fSqWCk5MTQRT1eDzo6OhAKBSC2+2mc6kySqUSFhYWJAtPfIqCjo6OFtaMaAVDTQ7p/Q6/E/+kPwCPmZYWWoXahGwxRqNR2BTFcZzg1k4kEor9D8uyQrSQ9fV1IbRpMBiEz+fT3fxd/vtJONMmFFpcH5CQrT/o3rwZhmHg8Xjg8XgwMjKCi4sLYQNaMpmUrCPzFItFSboqr9cr5NZupYAsdmTrfSMcQbQjNNsk2gYlQbYd2N3dxdOnTyWTZIfDgbm5Obhcrhs/r+TIJq5GvrhCQrYy9RIkk8kkFhYWUCqVhDKLxYJwOIxAIHCvOrYSPQi2jUBtDuTbcnFxgcXFRSHvFM/k5CSGh4dv/axQi0NdLGLzr/n/NxqN9Ay8hqtCQff19eHhw4cNWXBhGAZOpxNOpxPDw8O4uLhAMpkUhG2l0HLZbBbZbBavXr2CxWIRRG017cBvV5TC0VutVkQiEbjd7hbWjGgVbw+48KHtE5yzje0XnAYGHxsJ4bHDAoATokmINy/RonFzaPVmtloR9z+Dg4Mol8sSt7bSYrk8tKnf7xeEbbvd3oJfUV/Ika0P1LyZhKgdErL1RzvnyL4rZrMZnZ2d6OzsBMdxyOfzQhhypQ1o4nQh/FyRd2oHAoGm5qkW58h2Op30PCYInUErT8S90UrHIBcYxaE79UilUsGzZ8+wt7cnKe/s7MT09HTNDi8lJztxNUrtRQPmauoh1O7s7FQ5GD0eDyKRiOYXtkjIVkYtwu1dKBQKeP311yW7hI1GI2ZnZ9HZ2XmnY7Za2Je7rykf9u2oVCpYXl5GLBaTlD948ACDg4NNazuz2Yyuri50dXWB4zhks1lB1D49Pa16f6lUkuzA9/v9CIVC6OjooJ3vTSaVSmFhYUEypnW5XIhEIrDZbC2sGdFKvCYj/mbQjd+MZW9+8x35Kp8d/6wvIAljzvfL/FxBLGaTqN1YtCqimUwmhEIhhEIhYbGcF7UzmYyiW5v/d+Byc7bYra3Fa4yEbO0j3sAJkFimZUjI1h+UI/t+MAwDl8sFl8slbEBLp9PXpgsplUo4OjrC0dERgMs1Ot6t7Xa7G3Zf8eMInlqMW0TtXFxc4LOf/Sx2dnZweHgIl8uF3t5eRCIRDA8Pt7p6RJtAQjbRNrSTIHt2dlYVYhIAJiYmMDo6eqsJcju1Wz1QcrBTKPZqxNfVba8plmXx7Nkz7O7uSsp7enowNTWliwkKCdnKaLVdkskkotGoxOlqt9sxNzd3L8dkK4V9uXANUD7s23B+fo6FhQWJUMyn/AiFQi2rFx8azuv1Ynx8HOfn54jH44jH40gkElX3HcdxQgjYFy9ewOFwCG5trYoKWuHw8BCrq6uSez8YDGJmZoZc8gS+qcvXECHbxgAfHuvCFzitNwpw4n6C/zeDwSA8F+j50Bi02veKF8uHhoZQLpcFB1gikZBEX+IpFAooFArY3d2F0WiUuLW1spmHhGztIx8b0TnULiRk6w/KkV1fTCaTkC4EuOyH+b46lUoprtHwkb22trZgMpkEp3YwGITVaq1r/cRCtt4c2e9973vxy7/8y3U51tDQUFWqu6uIxWL44Ac/iI9//ONIJpOK73nrW9+K7/u+78PXf/3X16V+BHEVtMpBtA3tElo8kUhgcXFRMtk3m82YmZm50+I4Cdm3Q95eWhLbmsldBclisYiFhQWkUilJ+V1DM6sVrQq2jUaL7aKU3sHv9yMSidw7zFarHNnyfNji7ycR+2ay2SwWFhYkIVTVGgraZrOhv78f/f39qFQqSKVSgrAtDmXNUygUsLOzg52dHZhMJgSDQWGxo5lh5fQMx3HY2trCxsaGpLyR4egJ7THttOGv+Zz4o3T+5jfXyNv9LvzIaBd8psuxLsuyqFQqVf0Bj7gfELu1K5UKubXrjFYd2ddhMpkkoU1zuZzErS2nUqkI/RNwuYDNi9per1e11xiJoNqH8pzrBxKy9QflyG4sDocDDodDmCtmMhkht7ZYVOYpl8s4OTnByckJgEvXNC9q16OvFocWdzgc1KdeQa0RLH//938f733ve4XzdRV/9md/hj/7sz/Du9/9bvz8z/88nE5nPapJEFWQkE3cG610DHoXZPmFzbW1Nclkyu12IxKJ3Dncp97brd5Qe9XGXQTJTCaDaDQqCV9kMpkwOzvbUgdjI9CiYNsMWh1K+zawLIsXL14o5j5+8uRJXSbSSiJBo7lOxBbnRSWUOTk5wfLysuT69Xg8CIfDdd+RXm+MRqMgSvOh23jRQClfWrlcxvHxMY6PjwEAXq9X+Lzb7abr5A7wEUkODg4k5ePj47razEXUh/97rBvvfLqLtbNqJ+ttMDPAvxvvwZcHpBttxO5qvl8QC9ti5P3VdW5tWmi+PVpLt3JbGIaB2+2G2+3G8PAwLi4uJG5tccQbnnw+j3w+L2ysaqQD7D6QCKp9SPzUD3Qu9Qc5spuH0WgU+lrgMgIZL2onk0nFtdFcLodcLoednR0hsgrfV98lXSCFFq+NWpzTf/zHf4yv/dqvlZjkGIbB3NwcRkdHkU6nEY1GhQ2EAPCrv/qryGaz+J3f+R16fhINgYRsoi4wDKP6CbT8IaqnHNnlchkrKytCDhKeeoRaVgqVTVwNtVdtyEMisyx77UDn4OAAKysrksml0+nE3NycLnf7kZCtjFZyZF9cXGBxcVEyqAfqHzmg2e0hFrH51/z/k/hwPRzHYXt7G+vr65Lyzs5OTaZEEIeA5UWFRCIhCNtKokImk0Emk8HLly9htVoFUTsQCFAo7Bool8tYXFyUhHRjGAZTU1Po7u5uYc0IteIxGfFLD/vwLc/38eKOYvaXeB34ybFueM3X36P8859/ll3n1lYKQQ6QW7tetMOGFrPZjK6uLnR1dYHjOJyeniIejyOZTCKbrQ6pL3eAud1uwa3t8Xha2mYUWlz70DnUDyRk6w9yZLcOm82Gvr4+9PX1gWVZZLNZQdgWp9fikUdWcTgcgqjt8/lunC/Lc2TrLbT4j//4j+OHf/iHb/25z3/+8/iGb/gG4TXDMHjf+9537Wf29vbwdV/3dRIR+21vext+4Rd+AY8ePRLKisUifv7nfx7f//3fL8z/f/d3fxf/9J/+U/zIj/zIretKEDdBq0ZE28AwDIxGoyAs6kVgzOfziEajyOVyQhnDMJicnMTQ0NC9O269tlujoNDitaHUTkoTC47jsLa2hs3NTUl5KBTC7OysbsUPErKV0UK75PN5zM/PSyZRRqMR4XC47pEDmulQF4sR4tzYFEr8ZliWxfPnz7G/vy8pHx4exvj4uC7azmw2o7u7G93d3eA4DplMBvF4HLFYTDI+4SkWi9jf38f+/j4YhkEgEBCE7btGkNEz5+fnVWM9s9mM2dlZ+P3+FtaMUDvdFjM+/ngA3/d8B/89X73B5CocBgYfGu7E14e8d/peuVtb/Edu7fqjx9DitcIwDDweDzweD0ZHR1EqlQSndjKZVNy8fnp6itPTUyFfJy9qBwKBpqfBIBFU+5D4qR/Ea120kUofkCNbHRgMBvh8Pvh8PoyNjQl9Ne/WVtoEXSgUUCgUsLe3J3z+xYsXePDgAWZnZxXvTz07ssW5yW/Dj/7oj0pef9mXfRlGR0ev/cwHP/hBSTrHt771rfj0pz8Nm80meZ/VasX3fM/3YHBwEO94xzuE8p/4iZ/At3/7t2NoaOjW9SWI69CnAkAQV2AymXQlyJ6cnGBpaUkyQbdYLAiHw0I4l3pAQnbtUGjx2pAPOpVEuKtcrWNjY7oRf65CSyG0m4na2yWRSGBhYUEyEbPb7Zibm2tI7uNmOLLlwjWJ2Lfj4uICS0tLVS7aR48eoa+vr4U1axwMwwgLFePj4zg/PxdE7WQyWXXvchwniA4vXryA0+kUJuo+n6/tFxGz2Syi0ahkR7zdbkckEtFlRBKi/mSPDvGuo5f4YsaIPzG78WcWF84Z5fvqicOKb+ry4W8G3XAY63PvKYUgF4va5Na+P+0sZMuxWCzo6elBT0+PxAGWSCQUN1bJ02B4PB5B2G5GGgwSsrUPCdn6gdy7+oPOqToR99V8ZBVe2M5kMlXvZ1kWiUQC/+Sf/BPEYjF0dnbibW97G/7G3/gb+Kqv+iph/VueI7vdOTs7w6//+q9Lyt7//vdf+5n19XX88i//svDaYrHgl37pl6pEbDFf+7Vfi2/+5m8WPlcsFvGhD30IH/3oR+9Re4KohoRsoq0Qi4xaFhg5jsPLly+xsbEhKfd6vYhEItd2MHdBL+3WDCi0eG3c1E65XA7z8/OSgajRaMT09HRbhFAV33NqFGxbhZpDi+/s7ODZs2eSevn9fkQikYa5exot7F+XD5tE7JspFAqIRqOS55jJZMLs7GxdN5upHZvNhv7+fvT396NSqSCVSiEWiyEej+P8/Lzq/Xxe0+3tbcEpxwvbzXbKtZpYLIbl5WVJH+n1ehEOh9uuLYjbw3EcXr16hVevXgEAergy/k4xhe/t9mA32I39UhnZMguLgYHXaETYZcO009rQ5/pVIcjlqSt4bnJri4Xtdl6gVtuYSC3IHWDFYlHi1laap2WzWWSzWWxubsJsNkvc2mazue51JCFb+9A51A8keuoPcmSrH3FklZGREVxcXCCVSgn9Nb+Zd29vD7FYDMClqesTn/gEPvGJT8BoNGJqagpf8iVfIlkL15sj+y781m/9lmRjgN/vx9d93ddd+5lf+7Vfk9w3X/d1X4eJiYkbv+uHfuiHJAL4b/zGb+Bnf/Zn665PEO0NCdlEXdBCjmxAOnDRao5s3t3Fd+A8AwMDePToUUMG3CRk1w4v7MhFH0LKdSHYT05OsLi4KLnWePeZx+NpWh1biRZCaLcCNbYLHzZ6Z2dHUt7f34/Hjx83dBFEfmyO4+q2eCYXseX5sHnxgFAmlUphcXFR4s53OBwIh8Nt7aI1Go2CKM3nMeNF7XQ6XfV+uVPO6/Wio6MDoVAILpdL19fg7u4unj9/Linr6urCkydPaCGOuBGWZfHs2TMcHBxIyh8+fIiBgQFMt6hecpTc2mJhW4ySW1s+3hb3T+0qQuj5uXhfrFYrent70dvbC5ZlkclkhIVycThSnouLCxwdHeHo6AjAZR/EC9v16oNuus4J9UOObP1AQrb+EK8p0TnVBmazGZ2dnejs7BTmi4lEAtFoFCaTqWotv1KpYHFxEYuLiwAu++psNotnz54hkUggGAy24meogo985COS1+9+97tvFJY/8YlPSF5/y7d8S03f9ejRI3zhF34hPve5zwG43Jz+h3/4h/hbf+tv3aLGBHE9JGQTbYU4n64WBdnT09MqdxfDMHjy5An6+/sb9r0kZN8Og8FAodhvQCm0OO8cWl9fl/xbIBBoO/eZXLCtp0CpZdQWWvzi4gILCwtIJBKS8ocPH2JoaKjh50xpUb8e3yl3xslFbFoEuJ6DgwM8ffq0yp0/OzvbEDeXVmEYBi6XCy6XS9h9H4/HEY/HkUgkFHOlZTIZZDIZvHz5ElarVRC1A4GAbsRdjuOwvr6O7e1tSfnQ0BAmJiaoLyBupFwuY2lpSdI3GQwGTE9Po7Ozs4U1u56r3NpKIciBarc28Ma4u91CkFNo8dtjMBjg9/vh9/uFNBi8qJ1KpRTncHwf9OrVK1gsFolbW7zOcBvk17Xer1U9QkK2fiAhW3+Iz6le5grthHi++IEPfADve9/78Ad/8Af4gz/4A3zmM58RNjuL4R3IH/nIR/DRj34Ub37zm/H2t78db3/72/GmN72pba6Dly9f4k/+5E8kZTeFFT86OhI2BACXGsrb3va2mr/zS7/0SwUhGwB+//d/n4Rsoq6QkE20FVoWZA8PD7GysiKpt81mQyQSgdfrbeh3a7ndWgHlFL8Z+cSQFwTlA9HBwUE8fPiw7SaSjXTaahk1hRbP5/N4/fXXFcNGh0KhptRBfk2wLHvve0UplDh//VEo8evh035sbm5Kynt7exsWMUVPmM3mqrymvFtbKa9psVjE/v4+9vf3BUEiFAqho6MDdru9Bb/g/lQqFaysrODk5ERSzrtoCeImisUiotEoTk9PhTKz2YxwOAyfz9e6it2Bq9zaN4UgB97YgMWHIJcfT2/P41aPifSAzWZDX18f+vr6wLIs0um0IGyLx3o8pVIJh4eHODw8BMMwEre20+msebxEYam1D51D/UBCtr6QR3ehc6p9/H4/3vWud+Fd73oXWJbF6uoq/ut//a/4H//jf2B+fl4IQ87DcRw+97nP4XOf+xw+9KEPwe/348u//Mvx9re/HV/xFV+Bnp6eFv2SxvPRj35U0j/Nzc0hHA5f+5mVlRXJ65mZmVtFk3vrW98qeb26ulrzZwmiFkjIJuqCVgbrWgwtzrIs1tbWsLW1JSkPBAKYnZ2F1WpteB0oX+/tUGP4Y7Uh3wW5vLyMs7Mz4XUzIg2oGSXHOk281HNvxeNxLCwsSPoRh8OBubm5puZiUtrwcFfEi/78a8qHXTuVSgWrq6tVm3HGx8cxPDxMbXdLxHlNJyYmcHZ2Jri1k8lk1f3PsqwgOACA0+kURG2v16uJ52epVMLCwoIkj5nRaMT09HTTNscQ2iafzyMajUrGU3xqFq2nNKiXW7tSqejerU39zf0xGAwIBAIIBAJCHyR2a8v7II7jkE6nkU6nJRFDAoEA/H7/tW5tEkG1Dzmy9QOJnvpCPj5oFyduu8BHG5qensY//sf/GGtra3jnO9+Jg4MDnJ+fK34mlUrh4x//OD7+8Y8DAGZnZwW39lvf+lbdRIGsVCqSfNXAzW5sAHj69Knk9fj4+K2+d2xs7NrjEcR9ISGbaCu05izmFzWTyaSkfHh4GA8ePGja4Fpr7dZqqL1uRn7tihddrVYrIpGI5pxD9URJyCbUEVp8Z2cHz549k0yKWxX+XsmRfReUQomTiF07xWIRCwsLyGazQpnBYMDU1BS6urpaWDP9YLfbMTAwgIGBAVQqFSSTSUHYVlqoyOfzyOfz2NragslkEvJyB4NBVS5QKAmQVqsV4XAYHo+nhTUjtEI6ncbCwoIkJL/b7UYkEmnKptdmc1e3Nr+ofZVbW6vCBYUWbyx2ux39/f3o7+9HpVIR3NpX9UHiiCEMw8Dn8wl9kN1uV9xowUPnT3tQnnP9QEK2vpCvxdE51Td8qkKe3/7t30YsFsOnPvUpfPrTn5ZEK+Lhc2v/2I/9GFwuF/7aX/trgrA9PDzcxNrXl0996lPY398XXtvtdvzdv/t3b/zcxsaG5PXg4OCtvndoaEjymt8A6Pf7b3UcgrgKErKJtkJLObIzmQyi0ahkcmw0GjE1NdX08CckzN4OcrDfzFUTfK/Xi0gkApvN1uQaqQsSspVpZch1lmXx/Plz7OzsSMr7+/vx+PHjlkyM6+HIFovY4j/++LxzjVDm9PQUCwsLkr7aYrEgHA43PO1Hu2I0GhEKhRAKhcBxHHK5nCBqp9PpqveXy2UcHR3h6OgIAARBoaOjAy6Xq+XXdyqVwuLiokSAdLlc1BcSNXNycoLl5WXJWCEYDGJmZubOeXu1hNitzbfBVW5tpRDk/Pu17NYmIbt5GI1GIYT4xMQECoWCsLkqnU5XjcU4jkMqlUIqlcL6+jrsdrvweZ/PR0K2DqA85/qBhGx9IV9DIUe2vpGnARkfH8c73vEOfNu3fRsuLi7w53/+5/jUpz6FT33qU4hGo1Wfz+Vy+OQnP4lPfvKTAICv+qqvwu/93u81pe715qMf/ajk9dd//dfXZBSSz6U7Oztv9b0ulws2m02yNpLJZEjIJuqG/me2RFPQyoRLK4Ls3t4enj59Khl4ORwORCIRuN3uptdHK+2mFsSTHmqvaliWxbNnz6rKe3t78eTJE5pggITsq1Bql2ZcL3wOdz5sMc/Dhw8xNDTUsj7wvo5sJSe2WMSmBZzricfjWFpakjznSYBsLgzDwO12w+12Y2RkBKVSCYlEArFYDIlEQjGNDB/+dWNjAzabTRC1A4FA0/ufw8NDrK6uShbB20mAJO7P7u4unj9/Linr7e3Fo0eP2vIZLs+BzbKs5E+vbm0SslsDwzBwOp1wOp0YGBhAuVxGKpUSwpAXi8Wqz5ydnWFvbw97e3swGAxwOBxVxyS0hXj8TRtAtQ0J2fpCvhZH60z6Jp/PS16LU76ZzWZ88Rd/Mb74i78YP/IjP4KjoyP84R/+IT71qU/hD/7gD6qioALVYbK1QiwWw+/+7u9KymoJKw5civli7Hb7rb/fbrdLhGwlJzxB3BVaISHaCrXnyOYFvt3dXUl5KBTCzMwMzGZzS+pFQvbtIEf21ZyfnyMajUpygAJAT08PpqenaeL/vyAhWxkl4bbRE9JcLof5+XnJDl+TyYRwOIyOjo6GfvdNXOUsqwW5U028wG80GulevIGdnR28ePFCUtbR0YHp6WkSIFuIxWJBT08Penp6wLIsMpkM4vE4YrFY1eIGcNkniQWFQCAgCNt3mbjXCsdx2Nraqgrf1tfXh4cPH9LiKXEjHMdhY2MDW1tbkvLR0VGMjo7SM/x/cVUIcnkEEkDbbu27RGQh6o/JZJJEDMnn84Konclkqs4Ty7JVi8YbGxuCW1tN1xhxNXIhm9AuJGTrC8pf317I+9PrTGDd3d14z3veg/e85z2oVCp4/fXXBbf25z73ObAsi7e//e2NrnJD+JVf+RVJpK+xsTF8yZd8SU2flbfhXTbn2+12pFKpK49JEPeBVtqItkLNguxVAt/4+DjGxsZaOikih/HtUPN11krS6TSi0aiiOyEQCNDEX0S9ch/rjXqE0r4N8XgcCwsLko1PDocDc3Nzkh2+reIu7SF2m/GvKR927bAsi7W1taoNZ4ODg3jw4AG1nYowGAzw+/3w+/2YmJjA2dmZIGqnUqmq5yrLskKIcuByF39HRwdCoRC8Xm/dzi2fpkCctwy4HO8NDw/TNUTcCMuyePr0KQ4PD4UyhmHw6NEj9PX1tbBm6kYcghy4bMerQpADN7u1xcK2mhbH6RmiDhiGgcvlgsvlwtDQEMrlMpLJpCBsl0olxc/t7u5id3cXRqMRfr9fCENOkV7Ui/jZoaZnAXF7SMjWF+TIbi/km5adTmdNnzMajXjzm9+MN7/5zfjn//yfI5lM4tOf/nTN4q/a+NjHPiZ5/b73ve/OY8O7fI7GoUQjISGbqAtaeVCpNUd2MpnEwsKCZEJrMpkwMzNz65wUjUCt7aZWSPivZn9/H6urq1U71sVuF+INyJGtTLPaheM47Ozs4Pnz55LFqUAggHA4DIvF0pDvvS233fCgFEqcROzaKZfLWFpakoSYZxgGDx8+RH9/fwtrRtSC3W7HwMAABgYGUKlUkEgkBOFaaYNVLpdDLpfD1tYWzGYzgsGg4Na+a4Scq66hqakpdHd33/m3Ee1DuVzG4uKiJASiwWDAzMwMQqFQC2umPa5ya98UghxAVUQT/nhicbuZUGhx9WMymdDZ2YnOzk5wHIdcLodEIoHDw0OcnZ1Vvb9SqUg2VzmdTkHU9nq9JLKpCHJk6wfxmgTdY9qHHNnthVjIttlsd46SFggE8M53vrNe1Woqf/EXf4HV1VXhtdFoxHvf+96aPy83ayiNT25C/hk1GEAI/UBCNtFWyJ2yHMe1dLLBcRy2t7fx4sULyQIEn2Oz1h1kjYaE2dtBocXfgGVZvHjxAtvb25Lyrq4uZDIZIXdKu7eTHPluYWqfS5ohZF+V4mFgYEB1OUdv48gWi9jykKrixXdCmbOzM0SjUckEmd9wFgwGW1gz4i4YjcYqQSEWiyEej1dFxgGAi4sLHB0d4ejoCAzDwOv1IhQKoaOjA06ns6Z7h4+8Iw6vZjabMTs7C7/fX9ffR+iTq66hSCQCr9fbwpppn/u6tYE35kitCEFOocW1BcMwcLvdcLvdMJvNQqoShmFgMpkkIUF58vk88vk8dnZ2YDQaEQgEBGHbarU2+ycQIsiRrR/Ec0ty72ofcmS3F+J5ulrW0pvNRz7yEcnrr/zKr0Rvb2/Nnychm1A7JGQTbYV44MIv6rdqMFOpVLC6uoqDgwNJeXd3N6amplSVY5NCZd8Oaq9LSqUSFhcXJc4z4I1w+Z/5zGeEMhJqpZAjW5lGt0upVMLCwoLE6cY7boeGhur6XfWgVke2khNbLGLTotv1pNNpLC4uSqKm2O12hMNhmpjpALGgMDo6ilKpJLjgEomEJLUAcHn/pNNppNNprK+vw2azCaK23+9XHFeenp5Wpdaw2+2q2rRIqJtcLodoNCpsAAQuU11EIhE4HI4W1kyf3NWtrRSCXHy8ZvS3tClNW4jHbiaTCV/0RV+E09NTIQR5Nput+kylUkEsFkMsFgNwuUjMRw3xeDx0DTQZCketH+hc6gtyZLcXYiHb5XK1XV+Yz+fx8Y9/XFL2/ve//1bHkG+M5ccZtZLL5aqEbJ/Pd6tjEMR1qEcpIzSNVjoIuThcqVRaImQXCgVEo1Gcnp5KyicnJ1WZH1HuMG61k13tkIP9ctF+fn5eMogxGo2YmZlBV1cXAGqn6yAhWxml0J71IpfLYX5+HoVCQSgzmUwIh8Po6Oio2/fUk1raQy5ii//faDTSs/wGjo6OqtIi+Hw+zM7OqibEPFFfLBYLent70dvbC5ZlkU6nBWFbnnsNuHTJ8jlNDQYDAoGAIGzbbDbEYjEsLy9L+jmv16uqNAWEukmlUlhYWJBsqqBrqHncxq19Vb9cqVRQqVQa5tam0OLaRX7uGIaBx+OBx+PByMgISqWSJLe2fHMV8EYqjO3tbZhMJolbm54RjYdCi+sHErL1hTxUPN2f+kY8T2vHTZ6/+Zu/KdEYurq68DVf8zW3OsbExITktTyy5k3I3x8IBCjyGFFXSMgm2gq5aN0K8SwWi2FpaUkSMsxsNiMcDqs2PKlSu6nJMa422j20+NHRUdWiPe8acrvdQlm7t9N1kJCtTKPaJRaLYXFxUbI46HA48Nprr6naLXldaHGxI4x/Tfmwa4fjOGxubuLly5eS8u7ubjx+/JhC07UJvDAdCATw4MEDFAoFQdROJpNVm0dYlpXkNLVarVX5tzs7OzE1NUXXEFETx8fHWFlZkfR3oVAI09PTdA21CLlbW/zXKrc2Cdna5aaw1BaLBd3d3eju7gbHcchms4KoLd8UDwDlchknJyc4OTkBAHg8HkHUdrvddH00AAotrh9IyNYXdD7bC3lo8Xbr7+Rhxd/znvfcet3+0aNHktcbGxu3+vyrV68krx8/fnyrzxPETZASRbQV8gUfpR3NjYLjOLx69Qrr6+uSco/Hg0gkArvd3rS63BYSsm9HuzqNOY7DxsZGlfATDAYV3YvidiKhVgrvyBC7aIn6C9kcx2F7exvPnz+XlAeDQYTDYZjN5nsdv9FcFVqcROz7wbIsnj59isPDQ0n52NgYRkZGqO3aGIfDgcHBQQwODqJcLiOZTCIejyMWi0lCz/PIRWyn04nOzs6WprYhtMP29jbW1tYkZf39/ZicnKQFWZWgFIJcLGo3y61NObK1y202ITAMA6/XC6/Xi9HRURSLRUHUTiaTivPObDaLbDaLzc1NmM1mQdQOBAKqH+dqBRLL9AOdS30hfibSuFvfcBzX1qHF19bWJKkbgduHFQeAqakpyeulpSUUCoWaHe6f/exnrz0eQdwXUqKItqJVjuxyuYylpSVhZzRPX1+fJpxdanCya4l2dBpfdY0PDw/jwYMHihPBdhX8a8VgMAjt0i7X0U3UM7Q4L1bu7e1JygcHB/Hw4UNNLF4oObLFocTFf/z7+YVyQplSqYTFxUWk02mhzGAw4MmTJ+ju7m5dxQjVYTKZ0NnZic7OTnAch9PTU0HUVsppClw6BVZWVsAwDHw+Hzo6OtDR0dGWrgHiajiOw9raGnZ2diTl4+PjqkxBRFyiFIL8Pm5tsbB905iEHNna5T7nzmq1SlJhZDIZQdhWSoVxcXGBo6MjHB0dAbhMUcAL2+226F9P6P7TDyRk6wt5aHFC38gd2e3ERz/6UcnrL/qiL8Lk5OStj9PT04OZmRksLS0BuFzn/cxnPoMv//Ivr+nzf/zHfyx5/ZVf+ZW3rgNBXAcJ2URd0MqAXSlHdqPJ5XKIRqOSTpVhGDx+/Bj9/f2aaDsSsm9Huwm0+Xwe0WgUuVxOKDMYDJiamkJvb++Vn2tHwf82kJBdTb0c2aVSCdFoFKlUSihjGAaPHj3C4ODgverYTJQc2SRi351cLoeFhQWcnZ0JZXzqD5/P17qKEaqHz2lqs9mEsOLXwXEcUqkUUqkU1tfXYbfbBVHb7/erfoMj0ThYlsXKygqOj4+FMn7ecN2YilAfSm7tSqVS1T8Dyhv15FF5xP34dQvy1M9ri3rlVzYYDPD7/fD7/RgfH8f5+bkgaqdSKcU5aSaTQSaTwatXr2CxWCRubYq+VjskfuoHOpf6Qnw+aWytfwqFgvD/DoejbcZDlUoFv/IrvyIpu4sbm+cd73iHIGQDwMc+9rGahOznz5/jc5/7nPDa6XTWLIATRK3Q6JSoG+IwuGql2YKsUq5gq9WKSCSiqUVxErJvh7i99N5W8XgcCwsLkjD9VqsVc3Nz8Hq9136WQotfD7VPNfUQsk9PTzE/P68oVgaDwXvXsZnIJ2f8AjkgXQQ3Go1tM5G7K4lEAktLS5JnmdPpVH3qD0I98Ju6xM8Wi8WC2dlZsCyLWCyGeDwuWWThOTs7w+7uLnZ3d2E0GhEIBARh22azNfNnEC3k4uICi4uLkk1WRqMRs7OzmuufCClKbu1KpaIYghyodmsDb8wplNza5AjVLo06dzabDX19fejr6wPLskin04KwrdQPlUolHB4e4vDwUAhhzgvbFDXkekj81A90LvUFObLbC3lo8Xbhv/23/yZJieZ2u/EN3/ANdz7eu9/9bvyrf/WvhPvnt3/7t7G+vo6JiYlrP/djP/ZjktfvfOc7aR5L1B0Ssom2Qj54aVSObD4k4ObmpqTc7/cjHA7DarU25HsbBQnZt0PcXvzilN4m/xzHYWtrCy9evJCU+3w+RCKRmq5xEmqvR3zNUPtcct/Q4rFYDAsLC5JnmNPpxNzcnCbDTynlUqd82Ldnb28Pz58/l1xPwWAQ09PTlD+SqIlUKoXFxUVcXFwIZS6XC5FIRJjABwIBTE5OolAoCCHIU6lU1XOsUqkgFoshFosBuFyM6OjoQCgUgsfjoftZp5yfn2N+fl6yCGexWDA3Nwe3293CmhGN4Cq39k0hyAFltzaNE7WL+Hw3SmgxGAwIBAIIBAKYmJjA2dmZxK0tv344jkM6nUY6ncbLly9htVoFUdvv95NbWwZtJNEH8ucvCZ/ahxzZ7YM8R3Y7bcD6yEc+Inn9rne9615rWxMTE/jmb/5mIVx5qVTCe9/7XvzRH/3RlcL0Jz/5SfzSL/2S8NpiseCDH/zgnetAEFdBI1CirWAYBiaTSRCwGyHI8vk1E4mEpHxoaAiTk5OaHBCTkH075Oe4UqnoasJfqVSwurqKg4MDSXl/fz8eP35c8zXeTs71u0BCfzVKobRr4aqNF8FgEOFwWLNiJb9Jhl90KZVKJGLfgqvy0A4MDODBgwea7K+J5nN0dISVlZWqjRAzMzOKfb/D4cDg4CAGBwdRLpeRSCQQj8cRj8dRKpWq3n96eorT01Nsbm4KoV9DoRACgYBmn12ElNPTU0SjURSLRaGMIkK0D/Vwa4vfw4viN4UgJ9RBK0RQu92O/v5+9Pf3o1KpSNza4qgiPMViEQcHBzg4OADDMPD5fIKw3U7hW6+CXLz6QP6spXOpfcRrTCRk6x9xtBEtmhTuwvHxMX7v935PUvb3//7fv/dxP/ShD+ETn/iEECXqz/7sz/DX//pfxy/+4i/i4cOHwvuKxSI+/OEP4wMf+IDk8x/4wAcwNDR073oQhBz9KCtEy9FCaHHgcgDTKCE7m81WhZWsJVew2pEP+khUux49txfvGMpms0IZn1t4YGDgVgsZJNReD7VPNfyi7G0cSCzLYnV1Ffv7+5JyLW8uAiA4r8X33Pr6Ovb29gT3pt/vb/vFxasol8tYWVkRXK88k5OTmsqTTrQOfoPMxsaGpLyvrw8PHz6s6dliMpnQ1dWFrq4ucByHbDYriNrifpZHHvrV5/MhFAqho6ODxASNkkwmsbi4KIkS5fP5NL3Jirgft3VrF4tFySYI4LKP458H/PG0Ot7RO6128xqNRkGUBi6FALFbW37NcRyHVCqFVCqFjY0N2Gw2iVu7HcWieuU5J1qLfF5Jz0ztQ5tM2gu5I7sd+OVf/mXJHGJqagpvfvOb733c/v5+/PZv/za+4iu+Qtho/dnPfhaPHz/Ga6+9htHRUWQyGczPz1etp3zN13wN/uW//Jf3rgNBKEFCNtF2iCdX9Qwtvr+/j9XVVclgyW63IxKJwOPx1O17WoFcPGpUSHa9oFcHeyqVQjQalTjGzGYzIpEIAoHArY9HQu31iK8jap83uE3I9VKphGg0Ksk3yjAMHj9+jIGBgYbVsdHwIjbHcXA6nRLBS5xr12QyCe7NYDAIi8XSwlqrh/PzcywsLOD09FQoMxqNmJ6eRigUamHNCK3AsiyeP39etUFmfHwcw8PDd1rI5nOSer1ejI2NoVgsCqJ2IpGoGkuIxYS1tTXY7XZB1Pb7/bRgpwEODw+xuroqEYo6OzsxNTXVlmIQUc1Nbu18Po/V1VXJ2NxisUg2mFcqFcGhLY7WQs8IdaA2EdThcMDhcGBgYACVSgWpVEqIHCLfMAFcjqn29/exv78Pg8EAv98vCNvtElGCwlHrAxKy9QflyG4f5KHF2yVH9sc+9jHJ6/e///11O/aXfumX4hOf+ATe+973CmI1x3H4/Oc/j89//vOKn/nGb/xG/MIv/ALNY4iGQUI20XaIwzzWQ2DkFzPloUk7OjowMzOjG+HAaDRSHrYaUQotrnV2d3fx9OlTyUTd7XZjbm7uzosU4nbSQxvVGxL6lTEYDML1cl0UkNPTU8zPz0siZJjNZoTDYcF1okXEIjYAPHr0CGtra0in01XtUS6XcXx8jOPjYwCXLj/erd1OeaPE8JFTxIv+NpsN4XCY8tASNVEul7G0tCRJIcMwDJ48eYKenp66fY/VakVfXx/6+vrAsixSqZSQW1sp9OvZ2Rl2dnaws7MjOOw6OjrQ0dEBq9Vat3oR94fjOGxvb2N9fV1SPjAwgMnJybZ8NhO1IXZWp1IpLC8v4+LiQvh3t9stPIfkIcj5P3E0F3Jrt55WO7Kvw2g0Cv3IgwcPBLd2PB5HJpOpGneyLCu4uYFLUZwXtX0+n26vM3J96gP5egSdS+1DObLbi3YTsj/72c/i+fPnwmuLxYJv+qZvqut3fNVXfRVWVlbwwQ9+EB//+MclBhExb3nLW/D93//9+Pqv//q6fj9ByCEhm2g76pmX9/z8HIuLi1UP89HRUUxMTKhuMnofjEajsFBCouP16Cm0+FUbNbq7uzE9PX2vCQE5jq+HhGxlammXk5MTLC4uSp5VTqcTr732GhwOR8Pr2AjEi9DixWiHw4FIJAKWZZFMJhGLxRCPxyUL2zzpdBrpdBobGxuw2+2SEOTtsFhzfHyMlZUVyXXj8XgQDodJ6CNq4vz8HNFoFLlcTigzm82YnZ2F3+9v2PcaDAZBDJicnEQ+nxfc2kqhXyuVCk5OTnBycgLg8jrnxQiPx6Or8anW4DgOL168wO7urqR8YmICQ0NDdG6ImojH41hcXJT0Zx0dHXj06JHgxr5OICW3tnrQipuXYRg4nU44nU4MDg6iXC4LG6wSiYRkgyBPoVBAoVDA7u4ujEajxK1ts9la8Csag9pc9cTdIEe2/qAc2e1DuVyWRA1ph9Dib3vb25qS3rWzsxM/93M/h5/6qZ/CZz/7WWxvb+Po6AhOpxN9fX2IRCIYGRlpeD0IAiAhm6gjWhm01yu0eCqVwsLCgqSzNBqNmJmZQVdX173qqEbquQFA7+gltLhSWGYAePDgAUZGRu59z5NQez3UPspcF1qcz1n74sULSXlHRwdmZ2c1m29ULFzzr/n/5xecjUajJNduJpMRRG2x6MYjDkHOuzf5sMR6iSTCc1Uu466uLjx58oQWNoiaOD09RTQalYz7+BQyzV4s4cWEoaEhXFxcIJlMCsK2kpiQzWaRzWbx6tUrWCwWQdQOBoOSSEVEY6lUKlhZWRE2GACNcfMT+kYpJH1vby8ePXokya1dqVSqorjw3OTWFgvbJOY0FjU7sq/DZDIhFAohFAqB4zjkcjnBjZ3JZKreX6lUhH4KuOzHeFHb6/Vq+jrTymYE4nrk80qaH2gfipbQPojd2EB7OLKbjcViwZd92Ze1uhpEm0MrF0TbcV9BluM47O7u4tmzZ5JJi9PpRCQS0W2HSUJ27eghtHg2m8X8/DzOz8+FMpPJhNnZ2brlkKXQ4tdDQrYy4nYRP4NZlsXKygoODg4k7x8aGsLDhw81tTgoRrwIzf9euYgt/20Mw8Dn88Hn82FiYgJnZ2eCqJ1MJm90b3q9XsGt7XK5NNt2wGVbPXv2rOq6GBkZwdjYmKZ/G9E84vE4lpaWJH2V1+tFOBxu+cYPs9ks2cSSzWaF+12cB56nVCrh4OAABwcHYBgGfr9fELbbwb3QKkqlEhYXF5FOp4UyflwVCARaVzFCU2xvb2NtbU1SNjw8jPHxcUl/Jhag+TGEWNgWo+TWlo83+LEGubXrj1aFbDEMw8DtdsPtdmN4eFjYYMUL20pRgvL5PPL5vJAOIxAICMK21qLkkCNbH5AjW3+QI7t9kG/c1+u6PEG0OyRkE23HfQTZSqWCp0+fYn9/X1Le1dWF6elpXbtaSMiuHa2HFj88PMTy8rKk3o3YqEGhxa/nOudxO6Mk8BeLRUSjUYlAwLvc+vv7m13FunGdiC1eVL4Ju92OwcFBIRSkOAS5knszk8kgk8ng5cuXsNlskhDkWloEuLi4qEr/wTAMHj9+jN7e3hbWjNASe3t7eP78uURs6OzsxNTUlOruB4Zh4PV64fV6MT4+jvPzc8EBl0wmq8ZvHMchmUwimUxibW0NDodDuN/1nM+02ZydnWF+fh6FQkEos1qtiEQicLvdLawZoRU4jsP6+jq2t7cl5ZOTkxgcHLz2s/x9zD+veLe2fHzBI3drA2/M/SgEef3Rg5AtR77B6vT0VBC1s9ls1fsrlQpisRhisRiASwGCF7U9Ho/qrzNyZOsDErL1Bzmy2we5I5s25xKEPtGv6kY0Ha1MvMRi820E2bOzM0Sj0arJV73CLKsdcs/WjlYd2fwi2atXryTloVAIMzMzdQ/LLHfWchyn+/voNpAjWxl5u5yenuL111+Zwq9cAAEAAElEQVSXRA8wm82IRCKadrnJw4GKF5vvE+rTZDKhs7MTnZ2dNbk3z8/Psbe3h729PSFPLy90qdkxk8/nsbCwIBGOmpHLmNAPHMdhY2MDW1tbkvKhoSFMTExoor+y2Wzo7+9Hf38/WJZFKpUS7vezs7Oq9xcKBezs7AgOOT7lgBYdcmohm80iGo1KNg05nU7Mzc3pKkcs0ThYlsXTp09xeHgolDEMg6mpKXR3d9/6eFe5tW8KQQ5IU53w/yY+HgkFt0ePQrYYhmHg8Xjg8XgwMjKCUqkkcWsrpXrL5XLI5XLY3t6GyWSSuLVbHQVFCRLL9AEJ2fqDHNntg3jOD5AjmyD0CgnZRNtxF2dxPB7H4uKiJCwWvyDe0dFR9zqqkbtuAGhHeIcCPxnSQntdXFxgaWlJ2AnPMzo62rAFe/nkkGVZmmCIICFbGfG1mMvl8Bd/8ReSe8zlcmFubg4Oh6MV1asLYpeU+O+qUOJ3Rcm9KQ5BLr/uWJYVHDPPnj2Dx+MRRG23262aBdhkMomlpSVJn+1wOBCJRDR9XRDNo1KpYHV1FcfHx5Lyhw8fYmBgoEW1uh/8RpRgMAiO41AoFIT7PZ1O35hyQK33u5pRCknv9/sxOztb982BhD6pVCpYWloS8goDl3PZ2dlZBIPBex+/Xm7tSqVCbu070m5hqS0WC7q7u9Hd3S1sqORFbaUNleVyWdIXud1uiVtbDW3WbudQr5CQrS/kqTTofOobypFNEO0BCdlE2yEWypR2AIvhOA6bm5tVucg8Hg/C4XBbLYiTI/t2GI1GYeCsdhEyl8shGo1KBn8GgwHT09Po6elp2PfKRetKpUJCtggSspURt4t840UoFMLs7Kxm0zyInU7865vyYdcTm82GgYEBDAwMoFKpCCHIY7GYYgjybDaLbDaLV69ewWq1CiJXIBBo2b18cHCAp0+fShbfA4FAQ6JKEPqkVCphYWEBmUxGKDMajZienkYoFGphzeoHwzBwOp1wOp1CPtNEIiGEIVfKZyq+3y0WC0KhEDo6OhAIBDT7zG0kSs+irq4uTE1N0WIqURNKzyI+4ozX623Id97VrS3edKfk1qZr/mraOSy1eEPl6OgoisWi4NZOJpOKazWnp6c4PT3F1tYWzGazxK3dqnFeO59DPSHfkECbErSNfDMWrTPpG3mObErdQxD6hFYdiLqhlYFerc7icrmM5eXlKjdOb28vnjx50nYDIcqRfTu0IvzHYjEsLi5KFgpsNhvm5ubg8Xga+t1KjmziDUjIVuaqvmZ4eBiTk5Oa6YvkXJcPuxkithyj0YhQKIRQKCTkN+Tdm0r5DYvFIvb397G/vw+DwYBAICAI280In3tVGOi+vj48fPiQFhaJmsjn84hGo5Kw2xaLBZFIpOF9Yisxm80Sh1wmkxFEbSWHXKlUEu53hmGE+72jo6OtNnkqwW+CffnypaRcSyHpidbDp7QSbzLlx+fNyvsodmuLo0wpubWVQpDz7ye39vXoPbT4bbBarejp6UFPTw9YlpW4teUiBXAZUez4+FhYr/F6vUJ/5HK5mtae5PrUB3Qe9YV8DY7Oqb6RhxZv9/kIQegVErKJtqMWQZZfyBRPmBiGwaNHjzAwMNCWk0wSsm+HuL3UKEJeFW3A7/cjEok0Jf8YCdnXo/ZrqBUUi8UqEZVhGDx58gT9/f0tqtX9kYvY8nzYrXYFiPMbjo2N4fz8XBC5EomEYghy/t+fP38Ot9stiNqNCANZqVSwsrIihJ3kmZiYwNDQUFv22cTtSaVSVWlkXC4XIpFIW+UyZhgGPp8PPp9PSDlw3f3OcZwgNLx48QJOp1MQtX0+X1stHLIsi+fPn2N/f19SPjk5icHBwRbVitAauVwO8/PzKBaLQhmfNqVVuerlObBZlpX8kVv77pCQrYzBYBD6In7syTu1k8mk4npEJpNBJpPB5uYmLBaL4NRudOQQOof6gIRsfSEfr7abEandEK/dOxwOOt8EoVNIyCbaDnlocT7vKM/JyQmWlpYkDlWr1YpwOAy/39/UuqoJErJvh5od2ZVKBcvLyzg6OpKUDw4ONtW5KP8etbVTqyFHtpRsNov5+XnJs9lgMOBNb3qTpp/NYhGbfy0WsdW4kGKz2dDf34/+/n5UKhWkUinBrX1+fl71fj4MJL+wKA5Bft+FxWKxiIWFBckGBz41Qmdn572OTbQPR0dHWFlZqQpJr+VUBfVC6X6Px+OIxWKK93s+n0c+n8f29jZMJhOCwaAgbDdjk1yrUMplbDAYMDU1ha6urhbWjNAS6XQa0WhUMtZRY151pRDkYlGb3Nq1QyJobdhsNvT19aGvrw8syyKdTgubqOROPOAycsjh4SEODw+FEOa8sO10OuvW1pSHVz/QedQX8rUlEjb1jTiCTbMi1xAE0Xzae2WGqCtamXjJFyRZloXRaBTCkspDAfp8PoTD4bZy4yhBQvbtUGt7nZ2dYX5+XhIqlGEYPH78GAMDA02ti3wyQWKtFBKy3+D4+BhLS0tV91IgENC0iK0USpzfXNXsUOJ3xWg0CiIVx3HI5XKCqC3O68lTKpVwcHCAg4MDSUjiUCgEu91+q+8+PT3FwsKCREzjN57pOQw0UT84jsPW1hY2NjYk5b29vXj06BEtZMoQ3++Tk5PI5/OCqJ3JZKpcmeVyuSrsK3+/NzPsa6MplUqIRqOSDTUmk6ntN8EStyMWi2FpaUky5uvs7MTU1JSqF+DFIciBy7HMVSHIgZvd2mJhux2ewfK8vMTN8ClsAoEAJiYmcHZ2JojaqVRKMXJIOp1GOp3Gy5cvYbVaBVHb7/ffa8Oa/Ppuh2tWr5CQrS/kzwE6p/pGLmRTf0oQ+oSEbKLtkC8E8BPtxcVFiYsCaL5DVc2oVZhVK2oMC51MJhGNRiVhU/ncn61YaKXQ4tcjHny3a9twHIdXr15hfX1d8d+1OkERL9ryr1uZD7teMAwDt9sNt9uN0dFRFItFJBIJxGIxJBKJqr5DHpLY5XIJIpfX6722DWKxGJaXlyXHdLvdtPGMqJmrwkCPj49jeHhYk/dgM2EYBi6XCy6XC8PDw7i4uJDc7+KxBg8f9pUXEnhRPBgMqlqou45CoYD5+XlJXnWbzYZIJAKXy9XCmhFaYn9/H8+ePZOIYn19fXj06JHmnkVKbm2xsC1Gya0t3tzHH08sbusNcmTfH7vdLokcInZri5/NPMViUbKp0ufzCcK2w+G41Xm46ZomtAMJ2fqCcmS3F+LIHCRkE4R+ISGbaDvkC2XpdBrPnj2TTHIMBgOePHmCvr6+ZldPtZCQfTvUFFqc4zjs7u5WLZB5PB7Mzc21TPSh0OLX0+6ObD7v8eHhoaTc5XIJOZDki0daQCmUuB5EbCWsVit6e3vR29sLlmWFEORXhSTO5XLI5XLY2tqC2WwWRO1gMCi4ZTiOw87ODtbW1iSfDYVCmJ6e1qwYRjSXcrmMpaUlJBIJoYxhGDx58gQ9PT0trJl2MZvN6O7uRnd3NziOQyaTEaIziPPW8RSLRezv72N/fx8GgwF+v//O0RlaRSaTqdog2I551Ym7c1VUiNHRUYyOjmp+PHBftzbwxvxAryHIxW2gl9/USoxGoyBKA5fiBi9qp9NpRbd2KpVCKpXCxsYGbDabxK1907iSXJ/6gYRsfUE5stsHjuMotDhBtAkkZBN1QysTbfkAZmFhQTLI4V0UXq+32VVTNXIhW55bnJCiFkc2y7J4+vQp9vb2JOW9vb148uRJSwf0FFr8etpZyD4/P0c0GpWEpmYYBlNTU0in04IoorV2EYvY4j9A6jjSIwaDQVgY5EMS8yJXOp2uev/FxYUkt6Hf70cwGMTp6SmOjo4k7x0aGsLExIRu246oL/zzRSyuUhjo+sI73Hw+nxD2NR6PIx6PI5lMVj27WZaVRGdwOp0IhULo6OiA1+tV5YKyUhhoyqtO3AaO47C2toadnR1J+cOHD5ue7qdZ3NWtrRSCXHw8NT4jaoUc2Y3F4XDA4XBgYGAAlUoFqVRK6G+UNlWen59LNlnJ3dpy5P0ZnUPtIt5Ur+VnCnEJ5chuL8TzOnJkE4R+oVk20XYo5cjmCQaDmJ2dhcViaXa1VI984EdC9vWowcFeLBYRjUarRKLJyUlVhE2l0OLXIxey2+We4x1u4sUlcQh8sbitpWtGyYktFrHbacFEHJJ4ZGQEpVJJEpK4XC5L3s9xHJLJJJLJZNWx9LzgT9Sf09NTRKNRFItFocxutyMSidDu/QZit9sxMDAgCAnJZFIQtpWEhHw+j3w+j62tLZhMJgSDQSE6gxrG6Ht7e3j+/LlEgOrp6cHjx4/b6llO3B2WZbG6uirZmMUwDKanp9HV1dXCmjWP27i1lUKQA5dzrEqlomm3NgnZzcNoNAopLTiOq3JryzdTsCwrjD/X19dht9sFUdvn88FoNFKObB1Bjmx9QdES2gt5aHGCIPQJCdlEXWEYRvWhXq8SFUdGRvDgwQOaQF6BXMgul8uqWExUK60OLZ7JZDA/Py9ZrOcdZx0dHU2vjxL8opM8Fx5xiXyy1Q5C9tHRUZXDze12Y25uTgg1K24Xtfc3PPJFWbGgbTQadX9eb8JisaCnpwc9PT1gWRbpdFoIQa6U21DMxsYG0um0sDBpNpubVGtCa8TjcSwtLUn6ZK/Xi3A4TOOZJmI0GhEKhRAKhcBxHHK5nCBqK0VnKJfLOD4+xvHxMYDLc8a7tV0uV1OfnxzH4dWrV3j16pWkfHh4GOPj423/LCdqo1wuY3FxUbI5y2QyYXZ2FoFAoIU1ay1yt7b4T89ubfGYl54hzYNhGDidTjidTgwODqJcLkvc2uI5NM/Z2Rn29vawt7cHg8GAQCAAt9steY/arzfiasT3Irl3tY/cYU/PV30jDy1O55sg9AkJ2URbkU6nEY1GJWUGgwEzMzPo7u5uUa20AYWBvh2tDC1+cHCAlZUVyffyORvVtjvRYDAIkwzKkS1FybGu18URjuPw8uXLqhyRnZ2dmJmZkUTS0FLIdfHCKv9ar/mw6wW/MBgIBDA5OYnDw0M8ffr0ynNdLpdxdHSEo6MjIZwxn2dXbc87onUoOWg7OzsxNTVFi5UthGEYuN1uuN1uSXQGXtiWR2cALjfqZTIZIZcpv4klEAg09FyyLItnz57h4OBAUk5RIYjbUCqVEI1Gkc1mhTKLxYK5ubkqQaydUQpBLha19eTWJke2OjCZTJJNVvl8HvF4HIlEAtlsVtGtzfdVYk5PT+F0OlV3nRE3Q45sfUHns70QC9kul6uFNSEIopGQkE20Dbu7u3j69GnVJGR8fJxE7BpQcmQTV9MKRzbLslhbW8PW1pakXEkMVAtGo1FoH7WLks2mXUKvVyoVLC8vV+U9Hh0dVcx7LH6t5jYhEfv+KInYDocDLpcLyWRSMQR5KpVCKpXC+vo6HA6HIGr7fD5axGhDOI7DxsZGVb9IedXViTw6QyaTEYQCce47nvPz8yp3HC9s81E86kG5XMbS0hISiYRQZjAYMD09jc7Ozrp9D6Fvzs7OMD8/Lwl/abfbMTc3p5h7l7hEKQT5fdzaYmFbDeMCcf3VUB9CmgJneHgYFxcXSKVSiMfjSCaTKJVKV372+fPnWF9fRyAQEMKQW63WJtaeuCskfOoL8RocbVrVN/zmIx5yZBOEflGfqkFoGjWGFq9UKnj27Bn29vYU/53CkNYGObJvR7NzZJdKJSwuLkoWWQFgbGxM1eEuteSubTbtIGSfn58jGo1K8l4bDAZMTU2ht7dX8TNaCC0uzoct/gPeCG2m1ntSDVwVvlecg5YXuWKxGOLxuGTyylMoFLCzs4OdnR0hzy4vclEoaf1TqVSwuroqhKTmIQetNjD8/9l709jYvvWs86nZNc+ex+PxHE9VdZIOuVEIHaKI3FajkABNCwkIUUvNl1aTBEG3EBA1QkIoIBSBFBpdaCRAEHUDAu4lgUsSSHK5JMdV5XmeZ9fosmuu2v3B7PXfa9e2XbbLNb4/6ejeWv+yvWrXHtZ6n/d9XrUaTqcTTqcTk5OTyGQyiEQiuLm5QTwer3gmyqvjLBYLu97tdvurg9K5XA7BYBCpVIqN6XQ6+Hw+OByOV38+orNIpVJYWlriBDCxdQo9j16GUrV2qVSqWG8BytXa8pZGja7Wpors5ken06G7uxvd3d0QBAGpVIpZkEvdFURKpRJrkQM8PI9EUdtms5FI2qSQkN1eyK3FifaGemQTRGdAQjbR1mQyGYRCIU4kAfgqUKosrg65kE020E9TT2vxVCqFYDDILd40Gg3m5+eb3m2g0b3Em5l2Tx5R6uMu2ms+JQ40e/LDcyI2baSfplQqYX19vaJCf2JiAqOjo1zvS1HkmpqaQjqd5kQueZKDvM+u3IKcgsftRT6fr1j/ic9Fr9fbwJkRr8VoNGJoaAhDQ0MolUqIxWIskUWpl+nd3R3u7u5weHgIrVbLRG2Px1N1Euv9/T2CwSAymQw3j2Zs1UI0L7FYDOFwmNtzulwuLC4uNqVbUiuhVK1dKpUULciBympt8X8bWa1NQnZroVKpYLPZYLPZMDY2hkgkguXl5Sd/RnweHR0dQavVctXalMjSPJCQ3V5Qz/POgqzFCaIzoJ0T0bZEo1GEw2Eu812n02FhYQEbGxtM9CPxrDpIyH4Z9RJor66usLy8zP0N0aawFXrtNbKXeLPTzhXZFxcXWFlZ4T6TWJn0nB1sM1uLS0Vs8bX4/zUaDQUon0FJfBQr9Ht6ep78WZPJhOHhYQwPD6NQKHAiV6FQqHh/IpFAIpHA7u4ujEYjE7WdTicFr1ocJfFRr9fD7/fDZrM1cGZErdBoNFwv07u7O5bIIk9eBR4SWS4vL1mCTDWJLIlEAqFQiLt/WK1W+P1+soklqubq6gqrq6vceqWnpwdzc3P0rHkHHqvWfs6CHFCu1pa66LzH9/VcBTnR/MjPi8XFRSSTSUSjUc7JQ6RYLOL6+hrX19cAHp4r0mptOgcaBwnZ7QV9n52DkrU4QRDtCQnZRE1phoW3IAg4PDzE9vY2tzEUg08mkwk7OztsnATZ6iAh+2W8t0ArCAL29vawu7vLjbvdbiwuLrZMdnezV9c2Evn9tB2Oj9ivdm9vjxvv6enB/Px8VZVJzWgtTv2w387d3R2CwSCy2Swb0+v18Pl8sNvtL/pdOp0OPT096OnpgSAInAW5Up/dTCaDk5MTnJycQKPRwO12w+v1kgV5C6IkPlosFvj9fnR1dTVwZsR7oVKpYLVaYbVaMTY2hnw+zyzGo9GoovOSNJGlq6uLVWq7XC5oNBpcX19XJFu53W4sLCxQBS1RNScnJ9jc3OTGhoaGMD09TWuCOlCLam1xvysVs2spaj8nrhPNj3x/Zrfb4Xa78eHDB+TzeWZBHovFFJ9HqVQKqVQKh4eH0Ol0XLU2tcCrLyR8thfUI7uzICGbIDoD2okTbUWxWMTq6mqFJWlfXx/m5ubYAqbe/YvbAXEDL9/YE8rIK7IFQahZcKJYLGJlZaWi7+fIyAimp6dbauNFQvbjtFtFdqlUwsrKSsX9+aV93JvtnCER++1EIhGsrKxwAT6LxQKfz/dshf5zqFQqOBwOOByOij67sVisIohcKpW4Shm73c4qNy0WC32XTczl5SXW1ta4+4LL5cLCwgIFgzsIvV6P/v5+9Pf3o1wuI5FIMGFbGuQSyWazOD09xenpKdRqNYxGY8X7+vv78fHjx5ZaXxGNQxAE7O/vY39/nxuXt8gg6strq7WlbWJEC3Lp73vLfUH+d+ke03o89R3q9Xr09fWhr68P5XIZt7e3TNhWSqwsFApcGxybzcZEbavVSveOd4aE7PaCemR3Dvl8viKJmSCI9oSEbKJtEK0kpZsClUqF6elpjIyMcAt/qZBNPbKrQxRkxAUhCdlPo9TfuBaZoOl0GktLS9x5rlarMTs7i4GBgTf//npDSSWP005CdjabxdLSEm5vb9mYaBnd39//ot/VTEK2vB+2dE4kYlfHyckJtra2uECgx+OpukL/pUj77BaLRc6CXNqKRCSZTCKZTGJvb49VbooW5JTd3xyITjxyhxISHwm1Wg2XywWXy4WpqSlkMhl2vSslspTL5QoR2+FwoL+/n+7lRFUIgoDNzU2cnp5y458+fWrJdXq78pJqbSULcuBh31Iqld5UrU0V2a2PfC/y2HeoVqtZYuX4+DhyuRxXra20D769vcXt7S0ODg6g1+vhcrng8XjgdDopQe8dICG7vaAe2Z2D2DZUhIRsgmhfSMgm2oLr62ssLy9zorRoSepyuSreT+LZ69BqtSRkV8l7CNnRaLTCMtVgMMDv98PhcLzpdzeKZhIlm412EbKTySSWlpaQy+XY2FvOW7m1eC3dDl6CXMSWBj6lPRUJZQRBwPb2No6Pj7nxoaEhTE1N1SWApNVq0d3dje7ubgiCgNvbWyZyKfU1lFduut1uJmxTz9zGUC6Xsbm5ibOzM26cKh8JJYxGI4aHhzE8PMwSWcRqbekzSkoikcDv/u7vQqfTMQtysnwllCiVSlhdXWWOHsDDemB+fh7d3d0NnBnxHPJqbem/96zWJiG79ZHuz16y9jcYDJx7iNhXOxqNKrqH5PN5XF5e4vLyEiqVilVrezwemM1mOndqAAnZ7QVVZHcO8n07CdkE0b6QkE3UlHovoB/rE2y325/shyit8iJBtnrkdtnE48gXy6VS6dVBT0EQcHR0VFG1+Nx53gqQkP047SBkX1xcVPQZtdlsCAQCrz5vlSpjGvHskQY3pSK2RqOhYNIziO0RIpEIGxMdVIaGhhoyJ5VKBbvdDrvdjomJCWSzWa5yU379lctl3Nzc4ObmBhsbG7BarfB6vfB6vWT/WCeKxSKWl5cRjUbZmEqlwuzsLPr6+ho4M6IVEBNZXC4X0un0o0K2SKFQwMXFBS4uLtj9wuv1kohAAHg4P8LhMOLxOBvTarUtnWzaqShZkEtF7VpWa5OQ3fpIv8PXimVqtRpOpxNOpxMTExPIZDKIxWKsWlu+BhUEgTkG7e/vw2AwMAtyp9P5Lo5GnQAJ2e0FVWR3DvKKbOqRTRDtC61wiJalUChgeXkZNzc33PjQ0NCzVpJUkf066LhVj1JF9msolUpYW1vD+fk5Nz4wMIDZ2dmW32RRcsTjtLKQLQgCdnd3sbe3x4339vZifn7+TZtJpeNSr+tAXn1N/bBfTiaTQSgU4tojaDQaLCwswOPxNHBmPF1dXcyCvFQqcRbkSoJXKpVCKpViAUVp5SYFT2pPNputaCej1Wrh8/ngdDobODOilVA6j/R6PWZnZ5HP5xGJRBCNRivaEAmCgEQigUQigZ2dHRiNRnbNU9uBziOXy1W0/TEYDAgEAlQV1OI8ZkEuT2gUea5aWypsq9VqErLbAHlFdi0wGo0YGBjAwMAASqUSEokEq9bOZDIV78/lcjg/P8f5+TlUKhUcDgcTtk0mE51XVUJCdnshjS3Ruqy9kbtY0NqLINoXErKJliSVSiEYDHKZV2IVzuDg4LM/Tz2yXwcJ2dUjXyy/5niJAdZkMsnGVCoVZmZmMDw83BabUulxaiWhth60qpAtVtteXV1x4+Pj45iYmHjzeduo4yIXrqV/m0Ts6kgmkwiFQlwv6q6uLvj9/qbecGo0GlZtLQgCUqkUE7Wlfd9Fcrkczs7OcHZ2xnr0ihbkreyg0SyIa0BpQoHRaITf76cMfKJq7u7uEAwGkc1m2ZjJZILf74fJZAIAZvmaSCQQiURwc3NTUfUBPCTonJyc4OTkhGs74PF46Jpvc+7v7xEMBjlxyWQyIRAIwGg0NnBmxHugVK0tFbalKFVri8K1+F5puyilnyGan1pUZD+FRqNhojTwUHkoitqJREKxWjsejyMej2N3dxddXV1ctTYJesrIE1PoOLU+lJjQOUgTCQESsgminSEhm6gp9dh8XVxcYHV1lRMGxUC43W6v6neQtfjrICG7epSsxV9CPB5HKBTiAvU6nQ4+n49tZNsBqsh+GrVazTZhrSBkZzIZBINBTtwT+0PWyur3MRvH90TeDxsgEfulXF1dYXV1lTuP7XY7FhcXW6q/tNiX0GazYXx8HLlcjona0WhU0YJc7MG7ubkJq9XKRG2bzUbnzQuJRCJYXl7mnhd2ux0+nw96vb6BMyNaCXGNJU1mfew8EpNRXC4XpqamkE6nmagdj8crnkHStgMA2DXv8Xhgt9vpmm8jbm9vsbS0xImRdD/qHB6r1layIAcqq7WByoR6cb1JokvrUG+xzGQywWQyMcegeDzOhG1pYpZINpvlkivl1drEA/L1O12DrQ/1yO4c5BXZVqu1QTMhCOK9ISGbaBnK5TK2t7dxeHjIjbtcrhcHwkmQfR103KrnLdbip6enWFtb4wIgVquVqxJqF6hH9tNoNJqWEbITiQSWlpa4alvRWrPaJKNqqHdF9lMitihgkzDxOIIg4PDwELu7u9x4T08PZmdnW77iwWAwYHBwEIODgyygKArbSgFF0YL84OAAer2eidoul4t6Gj7D6ekpNjc3uWdjd3c35ubmWv48IurH1dUVVlZWuPPI6/VW3fbCZDJheHgYw8PDKBaLXNsB6fNPRHrN63Q67prX6XQ1/WxE/YhGowiHw9x+yO12Y3Fxke5HHcpj1dpPWZDLW5WUy2Umbkt/H4kwzct7WItXi0ajYYlSgiBUVGsrJVrFYjHEYjHWFkMUtR0OR0ffu0jIbj+oR3bnIHdqJUccgmhfKGJGtAT5fB6hUAixWIwbHx0dxdTU1IsXmmQt/jpIyK6e11iLl8tlbG5u4vj4mBvv6enB/Px8W4ocZC3+NK0i9J+fn1dU29psNgQCgZrbqtZTyJb3QJSK2dIAI6FMuVzG+vo6Li4uuPEPHz7gw4cPbZcAIA8o3t3dMYFL2iJCJJ/Pcz0NpRbktAH/CkEQsLu7W5HIODIygsnJybY7j4j34+joCNvb29zY4OAgpqenX3U/12q16O7uRnd3d1VtBwqFAi4uLnBxccH6mIrXPPUxbR0uLy+xurrKiUR9fX349OkTrQsIANVVaycSCWxsbHA/p9Pp2HlVKpVQKpW4vtri/xLNw3tbi1eLSqWC2WyG2WxmiVbSam150gTw4KR1enqK09NTqNVqOJ1OJmx32jqUhOz2g3pkdw7Simyz2UzXL0G0Me2nihAN5T0CMMlksqKHnUajwdzc3KutauWCrCAIFDyqAhKyq+el1uKPJWtMTk62peAj0ipCbaOQfu/NeHwEQcDOzg729/e58d7e3qqr215KvazFpcFGae9DshKvjnw+j3A4jEQiwcZUKhVmZ2drZjPfzKhUKlitVlitVnz48AH5fJ7ZEUej0YpngiAILNi4tbUFi8XCBK5OtiMulUpYW1vD1dUVNz4zM4OhoaEGzYpoNQRBwPb2dkWi4MTEBEZHR2tyfSm1HRDbCjx2zYt9TMXKOPGadzqdFIRrUo6Pj7G1tcWNUVIN8Rzyau3r6+sK962+vr4Khzn5OlQ8x8TfR/eJxtOsfXi1Wi28Xi+8Xi8EQcD9/T1bZyaTScVqbfG/Aw/uI9Jq7Wb6bO8BCdntR7Nem0TtkfbINpvNDZwJQRDvDQnZRFNzenqK9fV1bhFiMpng9/vf1PdCXtlaLpcpS68KSMiuHlHoklsRK3F7e4tgMIhMJsPGNBoNFhcX0d3d/e5zbSQkZD9NMx+fYrGIlZWVCoFpYmIC4+Pj7xbQfe+KbLlwTSL2y7m/v6+4p+l0Ovh8PjgcjsZNrIHo9Xr09/ejv78f5XKZsyCXHieRu7s73N3d4fDwsGPtiMUEL2k1u0ajwfz8PLxebwNnRrQS5XIZq6ur3LNKpVLh06dP6O/vf7e/azAYMDAwgIGBAXbNi8K21AJRJJPJ4OTkBCcnJ9BoNHC5XPB6vfB4PC9qn0S8D4IgYG9vDwcHB9z45OQkRkdHGzMpoiW5uLjA+vo6NzY0NMTOI6nA+FjyJlVrNw9PfV/NgkqlgsVigcViwcjICAqFAletrdQWI51OI51Oc88kl8sFt9tdc7etZoCE7PZC3tKBYr3tjbQimxyOCKK9ISGbaErK5TI2NjZwcnLCjXu9XiwsLLw5iKtk+0yLm+chG+iXoVarmeD/mPB/eXmJlZUV7r+bTCYEAgFYLJa6zLORUHLE0zSrkJ3JZLC0tIRUKsXG1Go1FhYW0Nvb+65/+z2F7Kf6YZOIXR2xWAzhcJhr22E2m+Hz+WAymRo4s+ZBrVazKhexSkas1pZWsIvI7YidTidnR9yOKCVD6PV6+P1+2Gy2Bs6MaCUKhQLC4TDi8TgbExMF3W533eYhveanp6fZNR+JRBCPxysq40qlEm5ubnBzcwMAsFqtTNS22Wz0HKoz4r70/PycjXWSwwhRO5TaG0xMTGBsbAzAw7km/fdYb22AqrWbhVas+tTpdFxbjLu7O0QiEcRiMcVWOPJnksViYc80m83WMp/7KUjIbi/o++wspAmiFouF1skE0caQkE3UlFo8MLLZLILBYMUiupZVfnLRulgsQq/Xv/n3tjvUW/xlaDSaR4XsxyyZPR4PFhcXO6birlmF2mahGY9PPB5HMBjksvcNBgMCgQDsdvu7/335M6BWx0UuYsv7YYtVL8TjnJ2dYWNjgwu8ulyumiSgtSvSKpnR0VHk83lEo1FmQS5/1gqCgFgshlgshu3tbZjNZs6CvB0CNYlEAqFQCIVCgY1ZLBb4fL6O69lIvJ5sNoulpSWuSkOv1yMQCLzJ1akWiH1MR0ZGUCwWEY1GmbCtVBmXSqWQSqWwv78PvV4Pj8cDj8cDt9td4TJF1JZSqYSVlRUm4AAPa4LFxUV4PJ4GzoxoJR6r6P/48SMGBwfZa7kFubg2lSdZAlSt3SxI9yGtuE+QtsIZGxtDoVBgldqxWIxbi4mIrkFHR0fQarWsUtvlcrWsgwgJn+2FPPZGRUvti5gULkLW4gTR3tDOl2gqYrEYQqEQF8TRarVYWFioqcWyUkU28TxUkf0yHjtexWIR4XCYC4oBwNjYGKamplpyE/xa5EIt9avnaTYh++zsDKurq1wgzW63w+/3181mTh5YqEWPbKmILb6WitgUzHgaMTHn6OiIGx8YGMDMzAwdvxeg1+vR19eHvr4+lMtlJBIJZkGuZEd8f3+P+/t7FkwUBS6Px9OSyQOXl5dYW1vj7neUDEG8lFQqhWAwiFwux8bMZjP8fn/TJUNotVr09PSgp6cHgiDg9vaWOTRIXU9E8vk8zs/PcX5+zjk0eDweCt7VmEKhgFAoxDll6HQ6+P3+uiTuEe2BIAjY3NzE6ekpG1OpVJifn0dPT8+jPyeuncT9ZLlcRqlUqlizSn+n9G/Kq7Wlwjaty2qH9Htoh+Oq0+nQ29uL3t5eCIKAVCqFSCSCaDSq+EwqFou4vr7G9fU1gAcHEWm1dqvs60nIbi/k8V36PtsbeY/sVrnvEATxckjIJmqOtC9wtQiCgKOjI2xtbXE/a7FY4Pf7ax6YkVcvkJBdHWQD/TKkC2bxeN3f31dUCKnVaszNzb1rr8ZmRZ5UQv3qeZpFyBYEAdvb2xWVJH19fZibm6vrd1Zra3ElK3ExoYKsxJ+nVCphdXWVBbBEpqamMDw8TMfvDajVataTULQjFkXtRCJRsdYqFou4vLzE5eUlVCoVHA4HZ0HezN+FIAg4PDzE7u4uN97f34+PHz9SAIqoGqX2Bg6HAz6fr+mTIVQqFex2O+x2O8bHx5HL5VildjQaVXT3kTo0mEwmJmo7nU66bt6AUkV/V1cXAoEAJQwQVVMul7G6uoqrqys2plar4fP5XtzeQKlaWypsS1Gq1pa3zJG6DdG94vW0orV4tahUKthsNthsNnz48IG5BonV2koOfaKDyOHhIXQ6HVet3cwOiCRktxfy75PiS+2NNNmb1mgE0d6QkE00nFKphLW1Na7vGAD09vZibm7uXSzzlKzFiechIftlyCuyb25uKoKrXV1dHV3ZoSRK0kbjK5pByC4Wi1heXq4QKicnJ/Hhw4e6i2O1ErKllSria+qH/TKy2SxCoRBXoaHRaDA/Pw+v19vAmbUnoh3x6Ogos34UhW0lC/J4PI54PI6dnR0YjUZ4vV54vV44HI6mCtCVy2Vsbm7i7OyMGx8fH8fY2Bhdh0TVXFxcYG1tjUvy6O7urnvCVa0wGAwYGBjAwMAAyuUy4vE4q9aW9o8XSafTOD4+xvHxMTQaDdxuNxO2W9XutRGISafZbJaNicnV9XKfIVqfUqmEcDiMaDTKxrRaLfx+PxwOx5t+92PV2koW5EBltbY4P/G/kQX562l1a/GXIHcNur29ZcK2tCJSpFAo4OrqiiVy2Gw2Vq1ttVqb6niRkN1eUEV2ZyG3Fm+mewtBELWFhGyioaTTaQSDwQqbounpaYyOjr7bA4isxV+HXJglG+inkS6Y4/F4RTWr0+mEz+fr6OCifFNRKpWavmKqnjRayM5kMvjy5QsXnBCFyt7e3rrPB3i8J+BLkPfDBkAi9gu5vb1FKBTirHsNBgP8fn/D+892AlLrx3K5jGQyyURt6WZeJJPJMIFLq9VyAlcjK2TERBlpoF+lUmF2dhZ9fX0NmxfRWojOTjs7O9z40NAQpqen2+J+rlarmQAwNTWFdDr9pENDqVTi7F5tNhtzaGg2AaGZSCaTCAaDXF/YVqnoJ5oHJVt6vV6PQCDwLmukx6q1X2NBLv99JAA9TbtZi1eLWq2Gw+GAw+FgDiLSam2l+Nrt7S1ub29xcHAAnU7Hnmkul6vh91fpfGkf2PpQRXbnQD2yCaKzICGbqDnVWovf3NxgeXmZCxTodLpXWW29FHFxKs9IJp5GKQHgPSrm2wXp8ZIGMoCH4CrZpSpbixNf0UghOx6PIxgMIp/PszHRVtNms9V1LlLEyhG5AF0tchFb3g9b/P3E41xfX2NlZYU79larFX6/v6MTcxqFWq2G0+mE0+lkApdYtRmPxxUtyKUVMlIL8npmsWezWQSDQS5RRqvVwufzwel01mUOROsjCAK2trZwcnLCjU9OTmJkZKQt7+cqlarCoSEWizFhW7q3EhEFhP39fej1enbNu1wuWsv/NyKRCMLhMPds83q9mJ+fpyA4UTW5XA5LS0vcs81oNCIQCMBkMr37339JtfZjyaGlUgmlUomqtaugkyqyn8JgMKC/vx/9/f0swVIUtpUSLAuFAtcOR1qtbbFY6n4s29kivhOhiuzOQnqPacT9gyCI+kG7VqLuCIKA/f39iqoJm80Gv98Po9H47nNQqVTQaDTMipOsxatDqXqWgl8vQ6VS4ePHjxgeHm70VJqCWvc7bjcaJWSfnp5W2LPa7XYEAoGmECrVajXboL7kuEhFbPG1VMSmTe7TPFb12MrWve2IyWTC8PAwhoeHUSwWOQtyJYErkUggkUhgd3cXXV1dzIL8PXvsplIpBINBrqLfaDTC7/dTJj1RNaVSCaurq1zri06s6NfpdOjp6UFPTw8EQcDt7S275uWuVwCQz+dxfn6O8/NzqFQqOJ1OeL1eeDyeughtzcj5+TnW19e5dc/AwABmZmZobUBUjehkJLX+t1gsDV0/y6u1pf/eUq1N18UDJIBWIk2wnJiYQDab5aq15Xs3QRCQTCaRTCZZspW0Wrse8Sb6HtsLqsjuLKhHNkF0DqRAEXXlsV6rAwMD+PTpU10XGFIhmyqyq0O+iaDj9jixWAzxeJwb0+v18Pl8cLlcDZpV80FC9tPUW8gWK9sODw+58f7+fszOzjbNJlAaaKv2uChZiYv/X6PRUObuMzzWx3h0dBQTExN0/JoUrVbLCVxSC3KlfobZbBYnJyc4OTnheux6vd6aWZBHIhEsLy9zawi73Q6fz9dQm3Oitcjn8wiHw5zjjVarxeLiYkevs1QqFex2O+x2OxMQIpEIIpEIotGoooAQi8UQi8WwtbUFk8nErnmHw9ERAf3Dw8OKBK2xsTGMj4/Ts42omru7OywtLXEJWna7HX6/v+G2ySJyURsAVWu/kU61Fn8JXV1dGBgYwMDAAMrlMhKJBBO2pQKUSD6fx8XFBS4uLtgzzePxwO12w2Qyvct9mYTs9oKs4jsLeUU2QRDtCwnZRM15bJFwd3eHYDDIPWRUKhU+ffqEwcHBui8upIIMCbLVoVSRTVRyfHyMjY0NbmOr0Wjw/d///XVxHGgl6Jx6mnoK2cViEeFwGDc3N9z41NQUxsbGmmoDKD0uz7WykFaTiK+pH/bLKBQKWF5eRiwWY2Oiu8TAwEADZ0a8BJVKxfoZTk5OIpPJMAvyWCz2bI9dMZDo9Xpfbdt2enqKzc1N7m9RRT/xUjKZDJaWlrgAuMFggN/vf5f+s61MV1cXBgcHMTg4iFKphHg8zoRtadWoSDqdxvHxMY6Pj6HValkyi8fjabtEE0EQsLOzg6OjI258enqanJOIF6HUW93tdmNxcbFpn23yHthvrdaWCtudJASStfjLUKvVcLlccLlcmJycRDqdRiwWQzQaRTweV0y2kjsHidXaTqezZtcXCdntBX2fnQP1yCaIzoKEbKIuXF1dVVTfiAEnh8PRkDlJq4tJPKsOpR7ZxFeUy2VsbGxU9GkEHvrHkohdCfXIfpp6CdnpdLqin59Go8HCwgJ6enre7e++lmqPi5KVOInYLyOdTiMUCnEbRKp6bA+MRiOGhoYwNDSEYrHI9djN5/MV7xdtH/f29tDV1cXELZfL9WwgURAE7O7uVrg9jIyMYHJykq5Dompub28RDAa5c9RiscDv96Orq6uBM2t+NBoNu27FwJ8oaicSiQrhqlgs4urqCldXVwC+SmbxeDywWq0tfd2Wy2Wsr6/j4uKCjalUKszNzaG3t7eBMyNajWg0inA4zO2Le3p6MDc311ICirxaWxAElEolTrQWUarWllZsd1K1NlVkvw2TyQSTycQlW8ViMUQiEWSz2Yr3Z7NZnJ2d4ezsjLXGEIXtt7TGIOGzvZDej5s1mYioDYIgkLU4QXQQJGQT74qY6b6/v8+NO51O+Hy+hvZapYrsl0NC9uPkcjmEQqEKO3EREmiVIWvxp6mHkB2LxSqqSLq6uhAIBGCz2d7lb76VaqzFpSK2PAgnBtZaOQhfD+LxOMLhMHdumEwm+Hw+2iS2GVqtFt3d3eju7q6qx242m8Xp6SlOT0+hVqu5qk25mFgqlbC2tsbEMJGZmRkMDQ296+ci2gslW3qn04nFxcWmse5tFVQqFSwWCywWC0ZHR1EoFBCNRpmwLb3vi0iTWQwGA7vm3W53SwWKS6USwuEwotEoG9NoNFhcXITb7W7gzIhW4+rqCisrKxW91T9+/NjSa0xx/yFe1+Vy+VELcqCzq7VJAK0d0mQrsVpbtCBXSraStsbY2dmB0WhkorbD4XjRc4m+x/aCvs/OIZ/Ps5ahAFmLE0S7Q0I2UXPEjYzYu04aJAAeqm+mp6cbvqCQLmylDz7icUjIVka0k5NmDWu1Wni9XlbpQQKtMmQt/jTvLWSfnp5ibW2NCww4HA74/f6GJho9x3PW4kqV2FIRu9HPn1bg/Pwc6+vr3PElwagzeKzHrmhBLr8Xlctl3NzcsLYEVqsVXq8XXq8XBoMBy8vLXB9jtVqNhYUFeL3een4sosVRuie1YtVjs6LT6dDb24ve3l4IgoBkMsmue6lbi0gul2NVcWq1Gk6nkwkQb6mKe2/y+TyCwSBub2/ZmF6vh9/vb9rkPaI5OTs7w/r6Ojc2OjqKiYmJlhaxlXisWvs5C3KAX4OL6wdpQmmr37/JWvx9UKlUMJvNMJvNGB4eRrFYRDweZ8K2tBe9SCaT4ZIspdXazznjkfDZXlBFducgdY0DSMgmiHaHhGziXRBt/6S919RqNebm5tDf39/AmX0FWYu/HBKyKzk/P8fq6iq3+TGbzQgEAri8vGRjdKyUEQMZ8gAH8cB7CdmCIGBra6vC5re/v78lRIGnjotcxJb+f41GQ0GmZxAEAXt7ezg4OODG+/v78fHjx6Y/N4jaI++xK7UgVwokplIppFKpCjcegAQj4uUIgoCDgwPs7e1x42RL/36oVCo4HA44HI6qk1lEcWFrawtms5mJ2g6Ho2meG0q91Y1GIwKBQFOL70TzcXh4iJ2dHW5scnISo6OjjZlQHXlrtTbw1b64HSzIyVq8PohFAl6vl7XGEJ87yWSy4ryTPpeAB0cpabX2U65w9D22PvR9dg7yZEsSsgmivSEhm6g5p6enWF5e5hYPRqOx6QKXZC3+csQNpvjddvJxEwQB29vbFWKP1+vF4uIitFotd46RQPs4arWanUt0nHjeQ8guFAoIh8OIRCLc+PT0NEZHR1tCFFCyFpfaGIqvqR/2y3jMAnpiYqJlzg3ifdFoNFwgMZVKMVFbWuH4GGazGclkEnq9nvoZE89SLpexubmJs7Mzbnx6ehrDw8MNmlXnIU9micfj7LpX6mF6f3+P+/t7HB0dQavVcq0H9Hp9Az7BQ6BzaWmJS76xWq1N70BDNBeCIGB3d7ciEfTjx48YHBxszKQazGurtZUsyKW/r1XEJxLM6o+0NcbIyAiKxSJisRgTrvP5fMXPpNNppNNpnJycQKPRcNXaXV1d9D22GVSR3TnIK7Kp/RlBtDckZBM1JZfLVVSnejweLCwsNCxw8RhkLf46NBpNxwvZjwmB4+PjnJ0cJUtUBwnZj1NrITudTuPLly/cgl/sC9nd3f3m318v5NbiJGK/nVwuh1AoxImRopNKT09PA2dGNCsqlQo2mw02mw3j4+PI5XKIRCI4PT19VNSOx+OIx+PY3NyExWJhorjNZqNrlOAolUpYXl7m1lp0T2o80h6mYlWcKGpLWwiIFItFXF1dsQQpu90Or9cLj8cDi8VSl+s+Ho8jFApx+z2n0wmfz8c5dBHEUwiCgI2NDS6xRqVSYX5+nu5J/42XVGsrWZADD/f+UqnUMtXaT30moj5otVp0d3eju7sbgiDg7u6Oq9aWUyqVEIlE2PrCbDaTkN1mSGNv9H22N2QtThCdBe3ciJpiMBjg9/vxO7/zOwCADx8+NK3tH4mMr0Oj0aBQKADozOMmVnRIbQk1Gg3m5+fR29vLvVe6aO7EY1UtnX5OPUUthexYLIZgMMiONfCVpabVan3T76438uMiDZDJ+2GLgTDicVKpFEKhEFdZp9fr4fP5YLfbGzgzopXQ6/XI5/MVIrZGo1G8t9/d3eHu7g4HBwfQ6/VMHHO73SQudThKfYy1Wi18Ph+cTmcDZ0ZIkVbFjY2NoVAoMHEgGo1y6w2RZDKJZDKJ3d1dGAwGeDweeL1euFyud6maur6+xsrKCreG6u7uxtzcHFVpEVVTLpexurrKOdaIiaBut7uBM2tu5NXa0n/tUq1NAmhzoVKpYLVaYbVaMTo6ikKhwFVrKz2X5EJYMpnE+fk53G43OXa0KNLrkp717Q1ZixNEZ0FRIqLm9Pb2YnJyEhaLpamzk6lH9uvo5ASA6+trhMNh7nM/ZZsvPVbiZpwEtUreqw90OyA/Nq89h05OTrC+vs4FjZxOJ/x+f9O5ZVSD9BiUSiUSsd9AJBLB8vIyd1+zWCzw+/1k/UxUzWMW0OPj4xgdHcX9/T3rsatUHZPP53F+fo7z83OoVCq4XC4mcBmNxnp9DKIJSKfTWFpaQiaTYWNdXV3w+/0UnGpydDod+vr60NfXh3K5jGQyyYRteaAReHACOTs7w9nZGdRqNZxOJ6vWrsV1f3Z2hvX1dW5scHAQMzMztDYgqqZUKiEcDrNeu8BDHMHv98PhcDRuYi2GkgW5PBlVpFWqteXzJiG7+dDpdOjp6UFPTw9riSOK2o+5B+VyOWxubgJ4aEEhWpCTe1DrQAkmnQNVZBNEZ0FCNvEujI+PN3oKz9LJguxb6MTjJggC9vf3sbOzw427XC74fL5HhUB59mepVKIqMwVIyH4c+cbrpUJ2uVzG1tYWjo6OuPGBgQHMzs627MZO6ZyRBpM0Gg0FGqrg+PgYW1tb3JjH48H8/Dzdq4iqKRaLWF5e5oL8KpUKs7Oz6OvrAwBWHTM2NoZ8Ps9E7Wg0WrGWEASBBRm3trZgNpuZBbndbqdru41JJpMVziGUWNOaiMK00+nE5OQkMpkME7VjsVjFeq9cLrPrHgC77j0eD+x2+4vWK4Ig4PDwELu7u9z4+Pg4xsbG6B5CVE2hUEAwGOQSsPR6PT5//kzB8jfwmAW5KG6/tFpbKmzXc2/z1DyJ5kPaEkdcj4rPnevra8WfSaVSSKVSODw8hFarZaK2y+VqyWTwToF6ZHcOUqdMtVpN+wWCaHMoSknUnFZZwFOP7NchPW6dIDoWi0WsrKxwVnIAMDw8jJmZmSc3y/L/RkK2MmTB/jjyc6hcLlcdoHmsl/v09DRGR0db5l4tRy7m53I55HI5aLVa6oddJeVyGdvb2zg5OeHGh4eHMTU1RcePqJpsNotgMMhVW2q1WiwuLsLlcin+jF6vR39/P/r7+1EulxGPx1mPXWkVrsj9/T3u7+9xeHgInU7HWZDrdLp3+2xEfbm5ucHy8jK3tnS5XFhcXKS1UxtgNBoxNDSEoaEhlEolxGIxJmxL21qISK97rVbLXfdPiQeCIGBra6vi+fbx40cMDg7W/HMR7Usul8PS0hL3fDMajfj8+TM5hdQYpWptueOSiFK1tvjfxeeH1JXpPYVteSykVROEOxW9Xs9cRL773e+yyk6dTqdoQV4sFnF1dcXiQjabjQnbVquV9k9NBPXI7hykFdkWi4W+b4JocygqQHQs8spisn2ujk5KAEin0wgGg0ilUmxMrDKrJhgmz/7sBOH/NXRacsRLUBKyq+H+/h5LS0vcwl6j0cDn88Hr9dZ0jvVEyaI/Ho/jt37rt5glqdfrpUzcJ3isenZmZoaC/MSLSKVSCAaDyOVybExst2E2m6v6HWq1mgUBBUHgLMgTiUTF+wuFAi4uLnBxcQGVSgWn08ksyE0mU60+GlFnTk9Psbm5yYkVfX19+PTpEwWk2hCNRsOe14Ig4O7u7snWA8ViEZeXl7i8vAQAOBwOJmxbLBa2JlDqY6xWqzE/P4/u7u76fDiiLchkMvjy5QuXXGWxWBAIBKhn7jvzWLW2kgU5UFmtDXwlYr23Bbl8X0axpNZFel4NDAxgYGCAVWvHYjHFZPvb21vc3t7i4OAAOp2Oq9amRMvGQj2yOwdpspnZbKb7MEG0OSRkEx2LvLqjXC7TIqcKOkV0jEajCIVCXDauwWCAz+eD0+ms6ncoWYsTlZC1+OO8RshWOneNRiMCgQCsVmvN51gvpHaDFouFC1RLLUk3NzdhtVpZkJwy5L8ik8kgGAxyCQ5arRYLCwtwu90NnBnRaij1Vrfb7U+223gOlUoFi8UCi8WC0dFRZvkoWpDLk+cEQUAsFkMsFsP29jZMJhOzInY4HCSAtgBi65b9/X1ufGxsDOPj43Tv7gBUKlVF64GnrnsASCQSSCQS2N3dRVdXFzweD5xOJ05PTxGPx9n7tFrti9btBAE8BMW/fPmCfD7Pxux2O/x+P4lTDeCxau3nLMgBVFiQy3/fW9cJ8r9P647WRd5T2WAwcO5ByWSS7TXlPXmBh0RLacKV3W5nwrY04YqoD1SR3TlIrcUpqZkg2h8Ssoma0yqLNLnIWCwWSciugnbvkS0IAo6Pjysqg8QAxksqPZWsxYlKyFr8cV4qZB8fH2NjY4M7d51OJ/x+f0v38ZKK2IIgYGhoiNm7KVmSiv3M9vf3YTAYWMWmy+Xq2Pt8IpFAOBzmArNGoxE+n4/6PBIvQql6tru7G3NzczW9vqSWj+VyGYlEglmQS4MWIul0GkdHRzg6OuKsiD0eD4kPTUi5XMbGxgbOz8+58ZmZGQwNDTVoVkSjkV/3yWSSVWsriQfZbBanp6c4PT3lxnU6HT5//tzSCXxE/UkkEggGg1wChdvtxuLiYseuH5uJWlVrl0qlmlRrk7V4+/BUBa9arYbT6YTT6cTExASy2SwTtePxuGL8IplMIplMYn9/H3q9nqvWpnYp7w9VZHcOcmvxVtEjCIJ4HfQEJd4FlUpVsZFoNqha9nW0s5BdLpextraGs7Mzbry/vx+zs7MvXgSTtXh1dEqV/2uoVsgul8vY3NzE8fExNz44ONjy1qxSEVt8rVKp8OHDB4yPjyOTyeDm5uZRS9JcLoezszOcnZ0xG2OxarNT7CEvLy+xtrbGnT8OhwOLi4stneBA1BdBELC7u4vDw0NuvB691dVqNVwuF1wuF6anpyssyOVrTqkVsUqlgt1uZy4NJpOJghwNRqnFAVlAE3Kk4sHk5CQymQy77uPx+JNrxkKhgNXVVZbIZrfb6bonniQajSIcDnP7256eHszNzbX0OrqdeW21trT3tlK1drXfN1mLtw/yiuyn6OrqYvbjYqKlKGwrJVrm83muLY60WpuskN8H6X2chOz2RWxLJVJtayuCIFoXErKJjkWeCdluoux70a7Vs9lsFsFgkBPCVCoVpqenMTIy8qoNBlVkVwdZiz9ONUJ2oVBAKBTiBAHgoartteduMyANMkmDTQC4CgrRili0JBUrNiORSMXxKpfLTPQGwIlb7RhIEAQBBwcH2Nvb48Z7e3vx6dMn2tgTVVMqlbC2tsZZ+gONq541m80wm80YGRlBoVBgVsSRSETRgly0It7Z2YHRaGTJLE6nkwSKOpPL5RAMBpFKpdiYTqeDz+eDw+Fo3MSIpsdoNGJoaAhDQ0MolUo4PT3F7u7uo2vHu7s73N3d4fDwkPUvJZcGQomrqyusrKxU9Mn9+PFj260N25WXVGsrWZADL6/WJmvx9uElQrYUaaKlmHAlrdaWP5+ka9K9vT0YDAYmajudTqrWrhGv/T6J1oN6ZBNEZ0FPSaJjUbIWJ55HurhuF2FWtJHL5XJsTKfTYXFxER6P59W/lyqyq4OE7Md5Tsi+v7/Hly9fuOxvrVaLxcVFeL3euszxPZAL14+J2HL0ej3LkC+VSojH40y4ll7fIqLtm9hnUxS120HcKpfLWF9fx8XFBTc+Pj6OsbEx2uQRVZPP5xEOh5FIJNiYWq3GwsJCU9xndDodent70dvbW5UVcSaTwfHxMY6Pj6HVajlxixwK3pf7+3sEg0FkMhk2ZjQa4ff7qYqCeBHJZBJ7e3vcushoNEKn0+H29rbi/dL+pVKXBo/HQ4HPDufs7Azr6+vc2OjoKCYmJui8aGHk1drSf7Wq1qaK7PZAus8E3iZ8Go1GDA4OYnBwEKVSiVVrRyIRxXZYuVwO5+fnOD8/h0qlgsPhgMfjgdvthtFopHPqFcivcUrcbm+kcTDaSxBE+0NCNtGxkLX462i3iuyzs7MKy12LxYJAIACTyfSm300V2dXRznb1b+UpITsSiSAUCnFJOCaTCYFAoKV7Hsv7YQOoSsSWo9FomDg1MzODVCrFRG1pJaBINpvFyckJTk5OmLglBrlbrXLrMeFxdnYWvb29jZsY0XKk02kEg0EuSKDX6+H3+2Gz2Ro4M2XkVsTpdJqzIlayIL+6umKV5u3u0tBIEokEQqEQCoUCG7PZbPD5fB3T5oGoDUrVs729vZidnYVarUY+n2fOLNFo9FmXBjGRTXRpoKB353B4eIidnR1ubHJyEqOjo42ZEPEuKFmQS0Xt11Zry/etrZ4E26m8V2W9RqNh1dbSau1IJKLYFkcQBMTjccTjcfZsEkVth8NBz6Yqod71nYXcWpz2bgTR3pCQTbwLrdAjm6zFX0e7iI7lchlbW1s4Ojrixnt6ejA/P18TWydxkysuplv5eL0nVJH9OI8J2cfHx9jY2ODusy6XCz6fr6UrCp8SsUUB+zWbE5VKBZvNBpvNhvHxcWSzWSZuxWKxivNOKm7J++s2e6bv3d0dQqEQV/FItr3Ea1ASHs1mM/x+P4xGYwNnVj0mkwnDw8MYHh5GsVjkLMiln0tEyaXB4/HA5XJRIOwNXF9fY2VlhbvXut1uLCwskI0m8SJOTk6wubnJjQ0PD2NqaoqtD/R6Pfr7+9Hf38/6l4rCtpJLgzSRTbSJFa/9rq6uunwuor4IgoDd3V0cHh5y458+fcLAwEBjJkXUhccsyKV7EClPVWvL1xG0TmhN6iF8qlQqmEwmmEwmDA0NoVgsctXaSs5h2WwWp6enOD09ZYmaojDeKuvwRiCPuVECQPtCPbIJovOgyAHRsYiCiDTLlniedhCyxWpFeU/hiYkJjI+P1zSLT6PRsM0RibTKkJD9OEpV/Wtrazg5OeHGBwcH8enTp5YOoMgDSFIxW27l91a6uro42zepuJXP5yvmJa3cMplMTNS22+1Ndcyj0SiWl5e56rNWEx6J5uDy8rLCrcTlcmFhYaHlHApEtFotenp60NPTA0EQOAtyaX81Eam4JVbViC4PVEFcPUrCY39/Pz5+/NhU90+iuREEAfv7+9jf3+fGJyYmMDo6+ujaXdq/dGpqirk0RCIRxGKxCtGqXC6z/w48uDSJorbdbqdKnzZAEARsbGzg7OyMjalUKszPz6Onp6eBMyMagVK1tlTYliK//pUSYaWJt/SMaw0aUcGr1WrZmnJqagr39/est3YymVR8Non/HXhI1BRFbYfDQeeaBHJK6Czk1uK0TiOI9oaEbKJjUalU0Gg0LOBPPbKrQy5kC4LQUouFVCqFpaUlrlpRo9FgYWHhXYIX7WbF/h7QMXoc+cZrZ2enQnCZmZnByMhIS12HcqRV2NJ/L7ESfy0ajQbd3d3o7u6uStxKp9M4OjrC0dERdDodPB4PvF4v3G53QysLT09Psbm5yQU+3G435ufnW1Z4JOqPIAg4OjqqsFptN+FR7EPocDgwMTGBTCbDuTTIA4ilUgnX19e4vr4G8GCJLSa0WCyWlr7/vhePVTx++PABHz58oGNGVM1jwuPHjx9fXD0rd2mIxWLs2pcnsgEPLid3d3c4ODhgz3zR6pWera1HuVzG6uoqaycBPKwDFxcX4Xa7Gzgzohl4rFpbyYK8VCrh/Py84neIe1mpmE2idnPTaCtqlUoFi8UCi8WCkZER9mwShWulZ1M6nUY6nWbJltJq7U53EpF/n1SR3d5IK7Jbub0eQRDVQUI28S60SnBKKmSTgFYd8oVguVxumcXh5eUlVlZWuO/aZDLB7/fDarW+y9+UHhuqNlaGjtHjyDfSUmFVq9XC5/PB4/HUe1o1Q2rPJ75+TT/sWqEkbol9tZX66xYKBVxcXODi4gIqlQpOp5OJW/WqgBYEAdvb2zg+PubGh4aGMDU1RYEzomrK5TI2Nzc5sQgAxsfHMTY21jJru9dgNBoxNDTE7B5jsdijLg0AcHt7i9vbW+zt7cFgMHAW5K2yJnpPyuUy1tfXcXFxwcZeKzwSnU2pVMLq6ipLIgEe1kYLCwvwer1v+t1arZZLZEulUuy6v729rXi//JnvcDiYsE1VQM1PqVSqcOTSarUIBAKw2+0NnBnRrDxWrZ3P57G2tsbdJ8xmM5fQKt3jiPcG6e+j9Xnz0GghW4782XR3d8dVa8splUqck4jZbGaidrO5h9WDZvs+ifeFhGyC6CxIyCY6Gq1Wy/rRkJBdHfIAbalUavqgrVgVtLe3x4273W4sLi6+a0/hdrBif2/IWvxppC0QREwmEwKBQEsv1p/qh90IEVsJo9HIKrcKhQLrY3Zzc1Ph4iEIAmKxGGKxGLa2tupiR1osFrG6uoqbmxtufHp6GsPDwzX/e0T7UiwWsby8zAX4VSoVZmdn0dfX18CZ1R95APH29pZd96lUquL9uVyO62HY6f11i8UiwuEwYrEYG6uV8Eh0FoVCAaFQCIlEgo1ptVr4/X44HI6a/i2VSgWbzQabzYbx8XHkcjnWdiQajVas4QVBQDweRzwex87ODoxGI3NocTqdFDhvMgqFAoLBICcCGQyGll9LE/VDvKbFNk9SEburqwsfP37k3i/vrS3+bKlUomrtJqOZhU+VSgWr1Qqr1YrR0VEUCgWuWlvepx14EPbu7+9xfHwMjUYDl8vFhO1OaI1DPbI7B+qRTRCdBwnZREdDIuPLURKymxkxOC+t5ACA0dHRulQrkm3289AxUkYQBBwfH1eI2C6XCz6f710TMN4buYgt74ctBniaCZ1Oh97eXvT29qJcLiOZTLJqbWlvJhGpHaler+csyGuxoc5mswiFQpywptFoMD8/T2IR8SKy2SyCwWCF48Pi4iJcLlcDZ9Z4VCoV7HY77HY7xsfHkc1mOQtyefBT3l/XarUyUdtmszXdfa3WKJ1Ler0ePp+PKh6JF6F0LtVTeDQYDOjv70d/fz/K5TISiQSr1lZ65mcyGZycnDCbV5fLxaq1OzGhpZnI5XJYWlriziWj0YjPnz/XzT2HaA+UziWz2Qy/3w+9Xs/ZkEuRi9pPVWs3k4jaKTSzkC1Hp9Ohp6cHPT09zElEFLWVnERKpRLbrwIPFauiqG2z2Zr6s74W6pHdOQiCUNEjmyCI9oaEbOJdaJVAnVRMoB7Z1dFKQvb9/X1FEEytVmNubg79/f11mQPZZj+P/Bi1Wt/196BcLmNjYwMnJyfcuMPhwPd8z/e09IZMKmKLr6Uidit8NrVaDafTCafTiampKdzf37MggbRyTCSfz+P8/Bzn5+c1qdi8vb1FMBjkLI+7urrg8/nerU0C0Z6kUikEg0HmTgM8BPj9fj8FAxTo6urC4OAgBgcHUSqVOAty6TEUSaVSSKVS2N/fh16vZ9d9rRJamom7uzsEg0Fks1k2JrZvMZlMDZwZ0Wrc399jaWmJO5fMZjMCgUBDRGHxue1yuTA9PY37+3uWsKLUdkQuHFitVpbM1gkJLc1EOp3G0tISMpkMG7NYLAgEAh1RmUjUjkwmgy9fvnDnktVqRSAQYMnFGo2G7fcf660tv/6pWrvxtGoFr9RJZGxsDPl8nqvWVopvionWR0dH0Gq1XLV2KyfJS6Ee2Z1DNpvlvm9yWCGI9oeEbKKjkfYxamZBtploFSE7EokgFApxC3ixkqOeVUFU9f888g16pwvZ+XweoVCIs2UV6e7ubumAhpKVuPh9N4OV+Gsxm80wm80YHR1FPp9ndqSRSKTiun+sYtPr9cJqtT57DK6urrC6uspt2mw2G3w+HwVliRcRiUSwvLzMnaM2m41VFhFPo9Fo2LUrVsWI1dpKVTH5fB5nZ2c4OztjyTCisN3qVYHxeLxizWW321vePYSoP8lkEsFgkLNLtdvt8Pv90Ol0DZzZV4jP/JGRERSLRdZ2JBKJcAlmImJCi+jQ4na7mUOLdC9K1Ja7uzt8+fKF+04cDgd8Pl/TnEtEa6CUXPPYuSTvgV0ul7l/L63WlgrbrbwHbGZaqSL7KfR6PXMPE1vjiKK2UmucYrGI6+tr5lxotVrhdrvh8Xiq2pM2K9J9TSvHF4jnkRYsASRkE0QnQDsnoqMhkfHlNLuQLQgCDg8PsbW1xY07HA74/f66Cz1km/088s1iuVxu2Q3kW7m7u8PS0pKibSXQulX90qCM+LrZ+mHXCr1ej76+PvT19aFcLiMej7PKLGkATERasWkwGJgw5nQ6ufutIAg4OjrCzs4O9/M9PT2YnZ2lbHPiRZyenmJzc5MLqHZ3d2Nubo7OpVcgrYr58OEDcrkcE7Wj0aiiBbkYXAQeAi/itd9qFZtXV1dYWVnhziWv14v5+Xk6l4gXEY1GEQ6HufWyx+PBwsJC055LWq2Ws3m9vb1lovZjCS0XFxe4uLiASqWC0+lkFuTkglE7EokEgsEgl1zjdruxuLjYtOcS0ZwoJUSIbZ6qOZekArQoZJdKpYrWSoBytba0Ypuqtd+HdhGypUhb44jrUrFaOxaLKVZri3vSw8ND6HQ6VqntcrlaKvlH+n22w3dJPI60PzZA1uIE0QmQkE28C60SgCNr8ZfTzEJ2qVTC2toazs/PufHBwUF8+vSpIQtZshZ/HqVzqhMrVG5ubhAOh7l7kWjHKgrbrXgOKVmJt6uILUetVrMgwPT0NO7u7pi4lUwmK96fy+VwenqK09NTaDQalhXvdruxt7dXcW8bGxvD+Ph42x4/ovYIgoDd3V0cHh5y48PDw5iamqJzqUYYDAYMDAxgYGAApVIJ8XicXftKCS2i1aNYsSkKW81esXl0dITt7W1ubHBwENPT0xQ8JF7ExcUF1tbWOFGnr6+vYev31yAVDsbHx1lCSyQSQTQardgzCYKAWCyGWCyG7e1tGI1G5tLgdDpb5nM3G9FoFKFQiFsz9/T0YG5ujo4p8SKUHCLekqglnn/iz5bL5UctyAGq1q4X8v11O66FDQYDl2idTCZZQqVcDASAQqGAy8tLXF5eAnhwRhH3tBaLpamPkbwim2hf5OcuVWQTRPvTvJERgqgDVJH9cppVyM5ms1haWuKqH1QqFT5+/IihoaGGLbapIvt5lCqyOwmx0nZzc5Mbd7vd8Pl8+J3f+R021mrHRipiyysPRAG7mTfCtUSlUsFqtcJqtWJsbOzZis1SqcTZvcmZnZ1Ff39/PaZOtAlistfV1RU3Pj09jeHh4QbNqv3RaDRMmK4moSWfz+P8/Bzn5+dQqVRwuVysv26zWJALgoDt7W0cHx9z4xMTExgdHe2Y+zpRG46PjyuclEZHRzExMdHS55I0oUV0aBGvfWmfXZFMJoPj42McHx9zyWwej4dah1SJkkPE4OAgZmZmWvpcIupPPB5HMBjk9u+9vb2YnZ2tmTj2WLX2cxbkAF+tLe4hpHsrEvCqR17B2+73CrG9jdPpxMTEBLLZLBO14/G4YswqmUwimUxif3+ftcgQq7WbLeFS+n2SA0d7Q0I2QXQezfXEIYg6Q0L2yxEX9/KNUyMRN5pSyy+dTge/3w+Xy9XAmdE5Vg3yjXYnHadyuYz19XWcnp5y48PDw5iZmanIsG+G661alCqxpSJ2pwdY5BWbsViM9dXO5XLP/mwqlUI0GqWqLaIq8vk8wuEwEokEG1Or1VhYWIDX623cxDoMeUJLPp/nElqUKjbF4OLW1hbMZjOzILfb7Q0JtpbLZayurnIJESqVCp8+faLkGuJFPOYQMTU1hZGRkcZM6p2QO7Tc39+zau14PF4hWsmT2Ww2GxO1W639QL04PT3FxsYGN0bONcRrUKrqHxgYwMePH9/tXHprtTbw1R6aLMhfRqdbUXd1dXFJV4lEgq09ldqdyVtkSKu1zWZzw++30rU0CdntDfXIJojOg4Rs4l1o9OKlWqTZg50knr0VjUbD7I8bbcl+cnKC9fV1bnNntVoRCASaonKpVUXIetKpFdn5fB7BYBDxeJyNiS4C0urIVjyH5EEXqaCt0Wha5hlRLzQaDROnBEFAKpXC8fExLi4uFN+fy+VY1ZZWq4Xb7WZ2pK3Uw4yoD+l0GsFgkAtG6fV6+P1+2Gy2Bs6M0Ov16O/vR39/f1UVm/f397i/v2f9C6UW5PW49guFAsLhMPfc0mg0WFxchNvtfve/T7QP5XIZGxsbXMsMlUqF2dlZ9PX1NXBm9cFsNsNsNmNkZASFQgGxWIxd+1IbY5Hb21vc3t6yarhWaT9QLw4PD7Gzs8ONtWNCBPH+XF9fY3l5mYstNKL9ymurtZUsyKW/rxOF2ufodCFbilqthsvlgsvlwuTkJDKZDFetLY9FCIKARCKBRCKBvb09GAwGJmo7nc6GPJ/o++wc5IkWJGQTRPtDux6io6Fq2dchFbIbJayVy2Vsbm5W2Fr29va+um/Ve0Dn2PPIv6tWEWvfQiqVwtLSEidU6HQ6+Hy+CjGglYRsaeBEfN0p/bBrhUqlwt3dHetJJqLT6VAsFiuCV8ViEVdXV7i6uoJKpYLD4WCittlsrufUiSYkkUggFApxwojZbIbf72+KZC/iK6QVm1NTU6xi8+bmhqukFykUClxFjNPpZBbkJpOp5vMTW7hIbfz0ej0CgQCsVmvN/x7RvpRKJSwvLyMSibCxTk6I0Ol06OnpQU9PDwRBwO3tLXNoSaVSFe+Xtx9472u/mXmsqv/Tp08YGBhozKSIluXi4gJra2vcWrsZqvpfUq2tZEEOPNx3S6USVWsrQMLn4xiNRgwODmJwcBClUomr1lZKuMzlctzzyeFwsLWtyWSqy3VEFdmdg3RPotVqqQ0LQXQAJGQTHY10YdPoyuJWotHirFIlK/CQeT82NtZUQpn0WDW7CNkoOq0i+/r6GuFwmLt2zGYzAoGAovDYKkI2idhv57GA7MDAAGZmZlAul5kFuVLVliAIiMfjiMfj2N7ehtlsZsFth8NBx7/DuLq6wurqKnffcLlcWFhYoMr9JkelUsFiscBisWB0dBT5fB7RaJRZkMvXrIIgIBaLIRaLYXt7GyaTiSW0OByONwdmU6kUgsEg1/aAEiKI11AoFBAMBrn+8GI7ILvd3sCZNQeiTavdbme9S0UL8lgspth+QH7tS5/77SzKCIKAjY0NnJ2dsTGVSoX5+Xn09PQ0cGZEK6JkTT85OYnR0dHGTOgJ5NXa0n9Urf1ySMiuDo1Gw0Rp4KEaVhS1E4mEYrW2uC/d3d1FV1cXV639XiIzfZ+dg1TItlgsFOsgiA6AhGyio5FbiwuCQA+/KmikkH17e4ulpSVks1k2ptVqsbCwgO7u7rrOpRqki2eqyFZGvolp1+MkCAIODw+xtbXFjXs8HiwuLj4qLLWCkC3thy39B4AJ2HRvfZpSqYTV1VXWD1NkcnISIyMjLBmgu7sb3d3dEAQByWSSidrSjZyIaEN8dHTEbIi9Xi9ZkbY5giDg6Oiowma1v78fHz9+pKBOC6LX69HX14e+vj7Wv1Cs2FTqX5hOp3F0dISjo6M3tx+IxWIIh8OceO5wOODz+SghgngRSlX9XV1djybyEQ/HR6yGE9sPiNe+UjVcOp1mrUdE0UF87rdTpVK5XMbq6iqurq7YWCdX9RNv4+joCNvb29zYzMwMhoaGGjSj6lGyIJeK2lSt/TwkfL4Ok8kEk8mEoaEhlEolxONxJmxLY3Ui2WwWZ2dnODs7g1qthtPphNvthsvlqqmbCFVkdw7SHtnN0J+dIIj3h6KYxLvQKg8QJUtjWuw8T6PE2YuLC6ysrHCbDbEiqFn7oVBF9vN0QkV2uVzG2toaVzUCACMjI5ienn5y09zsQvZzIjYFBJ4nl8shFArh9vaWjanVaszPzz+aoCPatTkcDkxOTiKdTjMb4ng8XlGRIbchdrlcTNimisr2oVwuY2trC6enp9z4+Ph40zmWEK9D2r9wenq6woL8ufYDdrsdXq+X2RA/dU4o2ax2d3djbm6O1svEi7i7u8PS0hJX1W+xWOD3+9HV1dXAmbUO0vYDgiCwaz8SiShe+6VSCdfX1yxBzmazsee+1Wpt2edBsVhEOBxGLBZjY1TVT7wGQRBwcHCAvb09bnx2dhb9/f0NmtXrUbIgf0u1tlTYbuf9HAnZb0ej0cDj8cDj8UAQhIpqbfm5Vy6X2X8HHkRx8fn2VjcR+j47B2kyb6e1ViGIToWEbOLdUKlUFQuWZkMehCsWixSYqwJ5Jft7IwgCdnZ2sL+/z417vd6mt0iVV69T1X8l8uPRjGLtW1CywlepVPj06VNVmf7NLGRLRWzxtfj/NRoNnetVkEqlEAqFuMx1g8EAn88Hm81W9e8xmUwYHh7G8PAwCoUCsyGORCKKNsRi8GBrawsWi4UJWzabjb63FqVYLGJ5eZkFhYCHe83s7Cz6+voaODPiPTGbzTCbzRgZGWHXvihuKbUfSCQSSCQS2NnZgdFoZJXaTqeTPW8eq+ofGhrC9PQ03SOIF5FIJBAKhbjzkar634a8/cBz1z7w4Gp1e3uL/f196PV6du27XK6WcWlRsqY3GAwIBAJNm9RMNCdifOHo6IiNtZs1vVK1thiPqKZaW/zv4v5T6rLVTgIhCZ+1RaVSsbXp8PAwisUi4vE4IpEIotEo8vl8xc+k02mk02mcnJywhE2xWvulCddUkd05kLU4QXQerbFjIYh3Qr5pb1dL41pTz4rsQqGA5eVl3NzccOMfPnzA5ORk0y9WlKqNaUHNI26GxU1kO12HqVQKS0tLnP2jTqeDz+er2vqwGYVs6oddG25ubrCyssKd81arFT6f700VajqdDr29vejt7eVsiG9ubhStSO/u7nB3d4eDgwMuuO12u+l+1SJks1mEQiGkUik2ptVqsbi4CJfL1cCZEfVEfu0nk0lWra3UfiCTyTAbYtGC3O12I5FI4Pz8nHuvtM0BQVTLzc0NlpeXufWL1+vF/Pw8PV9qiPTaF1uPiKK29Lkgks/nmcWr1KXF4/E0bVVTLpfD0tISZyVqNBrx+fNncpYhXoQgCNjc3OTca9RqNRYXF+HxeBo4s/dDqVq7VCopWpADldXawFd79HazICfh833RarUsYVoQBNzd3bGE6tvbW8VqbfH5BTwkbIrrU7vd/uz5Jv0+W/3cJB5HdKYRoRY1BNEZkJBNdDSd0pu31tSrR/bd3R2CwSC3QBHtdluluozs66tDo9GwIGeziLVv5fr6GuFwmLtGzGYzPn/+/KIgYbMJ2SRivx1BEHB8fFzRj+89gvtSG+KpqSmk02kmaicSiYr3S4Pb4s+KwYd26q/ZTqRSKQSDQc6yt6urq6nbbhDvj9h/0Ol0VtV+QGpBLqdVbVaJxnJ+fo719XXuXBsYGMDHjx9pnfCOSFuPTExMIJvNsms/FotVrCXlLi1ms5mJ2m+1eK0V6XS6IjHUYrEgEAjQ2oR4EeVyGevr67i4uGBjGo0GPp+voxL/HqvWfs6CHOD3guJ/a+VqbarIrh8qlQpWqxVWq5W5icRiMfYMUnITub+/x/39PY6Pj6HRaFi1ttvtVrz/S79Piru1N9I48XPtkgiCaA9IyCbejVa1Fieepx5C9s3NDcLhMPeddHV1IRAIvMhut9EoJUuQjWIlzSbWvgVBEHB4eIitrS1u3OPxYHFx8cXffzMdG3k/bOmcSMSujsd6GI+MjLy7y4TU6m10dBT5fJ4Ft6PRaMX9XJoRv7GxAZvNxkRtsu9qDiKRCJaXl7nvzmazwefzUXCf4JC2HygWi1z7AaXAoZS9vT3c3t4yG2IK9BJP8Zg1/djYGMbHx+nZUWe6urowODiIwcFBlEolZvF6c3PDtTUREUWDo6Mj5tQgCtt6vb7u8xfdjaR2tGRNT7yGcrmMlZUV1jceeKgWDQQCHd1fvdOrtUnIbhw6nQ49PT3o6emBIAhIpVJctbacUqnEErKBh4QmUdS22WxQq9VUkd1BkLU4QXQeJGQTHQ1VZL+O9xSyBUHA/v5+RfDL6XTC7/c3JIDyFuSLZzrHlKmnXf17Ui6Xsbq6WmHJOjIygpmZmVctrptFyJaL2NLAhjQLn3gcsVVCLBZjYyqVCjMzMxgcHKz7fPR6Pfr7+9Hf349yuYx4PM6CA0rBbbG/5t7eHrq6uuDxeOD1eknYahCnp6fY3NzkAozd3d2Ym5ujCgTiSbRaLRc4vL6+xvr6+qMJndlsFicnJzg5OYFGo+GELUqYIKQIgoDt7W0cHx9z4zMzMxgaGmrQrAgRjUbDrt3p6Wnc398zUTuZTD7r1GC329mzvx5B40QigWAwyN2b3G43FhcX6TlHvIhSqYRwOIxoNMrGdDodPn/+DKvV2sCZNR+vrdaW7g/l1drS39lskJDdHKhUKthsNthsNoyNjSGfz7Nq7Vgspph0KbbHEhOvXC4X9z56TrQ3ZC1OEJ0HCdlER0M9sl/HewnZxWIRq6uruLy85MaHh4cxMzPTkhsLJWtxohLpcWrVY5TL5RAMBjm7ZpVKhdnZ2TeJlM0gZEtFbPG1+P81Gg0J2FWQyWQqWiVotVosLCxU3S/9PVGr1SyjfXp6Gnd3d0zUVsqIz2azOD09xenpKRO2xN7arZZw1GoIgoDd3V0cHh5y48PDw5iamqLrkXgRqVQKm5ubnFBkMBhgMpmQTCYrnjulUgnX19esoo2cGgiRcrmMtbU1bh2vUqkwPz+Pnp6eBs6MUEKlUsFiscBisTCLV9Gp4TGL12QyiWQyib29PRgMBiaKu93umgsGkUgE4XCYuwf19vZidna2JfeEROMoFosIhUKIx+NszGAw4PPnzyR+PMNLqrWVLMiBh3VDqVRq2mptErKbE71ej97eXvT29kIQBNze3rJq7VQqVfH+YrHIuS0AQCwWY+I4rU/bC+qRTRCdCQnZREcjLqLllkjE07yHkJ3JZLC0tMQtSlUqFT59+tTSFRxUkV0dzSDWvoVUKoUvX75wVaw6nQ5+v//N/dYaeWzk1dfUD/t1JBIJhEIhLihsNBrh9/ubctMl7V/24cMH5HI5ZkEcjUafFbYcDger2DKbzXSO1JBSqYS1tbWKPsbT09MYHh5u0KyIVkXJmt7pdLI2GMViEbFYjF3/UmtfEalTg8FgYAktLpeLKmE6iGKxiOXlZa7asRP7zrYyOp2OEw2SySS79u/u7iren8vlcHZ2hrOzM6jVajidTvbsNxqNb5rL1dUVVlZWOJFscHDw1e5GROdSKBSwtLTEJWUajUZ8/vz5zedpJyKv1pb+a9VqbRKymx+VSgW73Q673c72ptJq7ccchUThW6fTcb21qS1Fe5BOp9n/p5gDQXQGJGQT70arPEQ0Gg1b+FCP7OqotZAdi8UQDAY5kUev18Pv98PpdL759zcSsq+vjlYWsq+uriqEAIvFgkAgAJPJ9Obf36hjIxeupX+fROzqubi4wPr6OnccHQ4HFhcXW6Zy2WAwcP01RWHr5uZGUdhKJBJIJBLY3d2F0Whk1ZoOh4MCRG8gn88jHA5zrg9qtRoLCwvwer2NmxjRkpyfn2N9fZ0LPPf09GBubo5dp1qtFt3d3eju7mbVMKINsVI1TC6XY04NarUaLpeLCdtdXV11+2xEfcnn8wgGg5xQJK7jbTZbA2dGvBaVSgWHwwGHw4HJyUlkMhlEIhFEIhHEYrGK9Wi5XGaCwdbWFsxmM7v27Xb7i579p6en2NjY4MaovzrxGnK5HJaWlrhEDLPZjEAgQM+kGqBkQS4VtVulWpuE7NbDYDCgr68PfX19KJfLrFo7EolwVboihUKBa5Nhs9lYmxxyE2pdqCKbIDoPErKJjker1TIBm0TG6qiVkC0IAk5OTrCxscFtdGw2W9tsMMlavDres+/6eyEIAg4ODrC9vc2Ne71eLC4uVrQueC2NELLl/bClf5tE7OoQBAH7+/vY39/nxvv6+vDp06eWDZRoNBomTIvCllixpSRsZTIZHB8f4/j4GFqtltmQejweyoZ/Ael0GsFgkMs8J6GIeA3is2tvb48bHxkZweTk5KP3dmk1zPj4OLLZLBO1HxO2ROELAKxWKxO2yOKxfRAdlaT3JqPRWLNkPqI5MBqNGBoawtDQEEtoE69vqRuRyP39Pe7v73F4eAitVsvaj7jd7ieT+A4ODrC7u8uNTU1NYWRkpOafiWhvstksvnz5wt2brFYrAoFAyySSthKPWZDL21OJPFetLRW233vPREJ2a6NWq1ni1cjICP7Tf/pP3H9Tip+IbkIHBwfQ6/WsUtvlctUshkO8P1Ih22KxNHAmBEHUC7pDEx1PKwpojaYWx6xcLmN9fR2np6fceH9/P2ZnZ9vGjpKsxauj1SqyRXvf8/Nzbnx0dBTT09M1DdDX+9g8JWKLAjYJEE9TKpWwvr7O9QkFgImJCYyOjrbN8ZMKWxMTE6xiSxS25EGrYrGIy8tLXF5esmovURQnweNxlKzpzWYz/H4/2WISL6JcLmNzcxNnZ2fc+Gus6bu6uhSdGiKRCHK5XMX7U6kUUqkU9vf3odfrmaj9Hr11ifqQSqWwtLTEOXNYrVb4/X4YDIYGzox4T+QJbXd3d0zUljqGiBSLRa4Szm63s+tfrIQTBAE7Ozs4OjrifnZ2dhb9/f31+FhEG5FOpytaPtntdvj9fkqirBNK1dpSYVuKUrX2U3vQWovNJGS3D/JY28LCAgAwC3Klau18Po+LiwtcXFywva0obJNddfNCPbIJojMhIZt4N1rlgS8NnpG1eHVIj5m4GXnJoj+XyyEYDFYEO6anp9tK5AEq+7C3gkjbCFpJyBZt6pLJJBtTqVSYnZ3F4OBgzf+e/Np66fX2EuRZ89JAQqP7l7UK+XweoVCIOz/UajVmZ2fR29vbwJm9P9KKrWKxiGg0yoQtqQgLPJxb8Xgc8Xgc29vbzIbU6/XCbre31XPgLVxdXWF1dZW7L7pcLiwsLFAwlngRpVIJy8vLrEIaeLg3zc3Noaen502/Wy5spVIpltQitZsWyefzFb11RWGLkjNag3g8jlAoxO2dXC5XTR1piOZHpVLBarXCarVibGwM+Xye2btGIhHFvXUymUQymcTu7i66urrgdruRyWQQi8W437uwsIDu7u56fhyiDbi7u8OXL1+4BBu6NzWWx6q1lSzIgcpqbeArgfI9LMhJyG4f5HEkrVYLm80Gl8sF4MFFRmyDEY/HK94vCAJrkbW3tweDwcBEbafTSfeQJqJcLiOTybDXVJFNEJ0B3YWJjocqsl+Okl12tYv+ZDKJpaUlrlpHq9XC5/PB4/HUdJ7NglqtZucWnWPKSM+fZj5Gt7e3WFpa4jL8dTodAoHAu/Vzr5eQLQ0mSLPlyUq8eu7u7hAMBrnzQ6/Xw+fzwW63N3Bm9Uer1aKnpwc9PT0QBAHJZJL11VbKhpfakOp0Ong8HmZD2olBA0EQcHR0hJ2dHW68v78fHz9+pEAb8SKUehiLa69aP7tUKhVsNhtsNhs+fPiAXC7HOTXIn/HS3rrAQyBK2luXnj3Nx/X1NVZWVrgAsLy/OtGZ6PV6rm9pMplk17/Ssz+bzVY4RKhUKszNzZGITbwYcZ8mTZ70eDxYWFgg548m4rFq7ecsyAFUWJDLf99rnkEkZLcP8jWm/Lo3Go2cm1AikWBrUKkoKpLL5XB+fo7z83PmJiYK2yaTidaoDSSTyXD3CxKyCaIz6LzIIEHIkAbIm1lAaybkC8JisViV0HB+fl5RWWaxWOD3+9vaCkaj0ZCQ/QzSc6pZK7Kvrq6wvLzMfYcWi+Xd+0AqCdm1RC5ck4j9OqLRKJaXl7nqI4vFAp/P1/EVhuLG3+FwYHJyEul0monaiUSiImhVKBQ4izeXy8WqPbu6uhr0KepHuVzG1tZWReuN8fFxjI2N0fVIvIh0Oo2lpSUuQNfV1QW/31+XoI/BYMDAwAAGBgZQKpUQj8eZsKXUW/fu7g53d3c4ODiATqfjLMg7Maml2Tg9PcXGxgY3NjQ0VPO2KkTrI7otOJ1OTE5Ocu1HlCrhRARBwMrKCg4ODuDxeFhSCwlMxFMkEgkEg0FuHU4JNs1Praq1S6XSq6u1SchuH+TPlae+T41Gw0Rp4GG9LIraiURCsVpbdBOTOoqI1dqULFNf7u7uuNckZBNEZ0DRAOLdaJVgBlVkvxyliuynKJfL2N7exuHhITfe3d2NhYWFtg9MtoJI22ia2VpcEATs7+9XVEZ6vd662NS9p5D9VD9sErGr5+TkBFtbW1ywxePxYH5+vu3vb6/BZDJhZGQEIyMjKBQKzIJUyYZUEAQWVNjc3ITVamXV2jabre3Oz2KxiJWVFc7+WWxd0NfX18CZEa1IMplEMBjkqtPEBMJGJIVoNBomTE1PT7Peujc3N1w7BpFCocBVwogW5F6vt+MThOqNIAg4ODjA3t4eN04JNkS1SNuPZLNZfPnyBel0+tH3i0ktolOL2+1m9w9qrUFIiUajCIVC3B6pv78fnz59ontTi/Haam25q5i8WvsxQVPes5uE7NbmuYrspzCZTDCZTBgaGmKJl+IeVCnxUnQUEdvkyKu1ifdFvn5o58IogiC+gqKrRMdDPbJfjnxB+FQCQD6fRzgcZpaRIuPj45iYmOiIzWWr2GY3kmY9RqVSCaurq7i4uODGx8bGMDU1VZfz972EbLmILe+HLWa1E48jCAK2t7dxfHzMjQ8NDWFqaoqCIVWg0+k4G9JEIsGqtZUs3lKpFFKpFA4ODqDX65mo5XK5Wj4TPpvNIhQKIZVKsTGtVovFxUXW240gquXm5gbLy8sV/dWbpU+oUm9dUdSORqMVawFBEBCLxRCLxbC1tQWz2cyqtR0OBz2v3hFBELC1tYWTkxNu/OPHjxgcHGzQrIhWJZvNYmlpiQtCd3V1oaenB4lE4tGklsvLS1xeXgIAHA4HS2ozm810/XcwSs86coloD6TV2uL3+1i1tpIFufj+p6q1X1LBSzQ/8rXja79PaeKlIAgV1drypIpyuczWqDs7OzAajUzUdjgcLb9HbUbk7UqoIpsgOoPGRzEIosFQRfbLqVbITqVSCAaDXKBCo9Fgfn4evb297zrHZoIqsp+nGY9RNptFMBjkAmpi376BgYG6zeM9hGypiC2+lorYtIl/HqXKWQCYmZnB0NBQg2bV2qjVarhcLrhcLkxNTeH+/h43NzeIRCJIJBIV78/n81wmvFit5fV6YTAY6v8B3oD4vMzlcmysnvbPRHtxenqKzc1NLtDW19eHT58+Ne39Xa/Xo7+/H/39/SiXy5wFuVJSy/39Pe7v71m1phhwdLvdVK1ZQ8rlMlZXV3F1dcXG1Go15ufnqYcx8WLS6TS+fPnCVbdZrVb4/X723BaTWiKRCKLRqGKieSKRQCKRYPau4vXfDkltRPVcXl5idXWVe9aNjY1hfHycROw2Q94Du1wuc/9eW639lgpeovmQx0lq8X2qVCqYzWaYzWYMDw+jWCxy1drSvZtIJpPB6ekpTk9Puf2t2+0mR6EaQdbiBNGZkJBNvButsnmgHtkvpxohW6mfsNFoRCAQgNVqffc5NhOULPE8zWYtLtqxSgNter0efr8fTqezrnOptZCtZCUuCAJZib+ATCaDUCjEbaA0Gg0WFhbg8XgaOLP2QaVSwWKxwGKxVFWtWS6XWSX3xsYGbDYbq9a2WCxNfV5Ho1GEw2HuM9lsNvh8vpYT5InGIrbC2N/f58ZbLbAvJqa43W6W1CJe/0pJLYVCARcXF7i4uIBKpYLD4WDV2mQ1+HqKxSLC4TBisRgb02q18Pl8dV8LEa1PKpXC0tIS8vk8G3M4HPD5fFzyiTypJZFIMGFbXoEFPCSeygUD8fpvRAsFoj6cnZ1hfX2dG5uYmMDY2FiDZkTUEyULcqmoXW21tvR+RLQ+0r3Ue7nLabVatscUBAH39/dM1E4mk4rV2uIzDHiwwBbXuHa7vWkTTJsdqsgmiM6EhGyi4yGR8eU8JWQLgoC9vT3s7u5y73G73VhcXIRer6/LHJuJZrXNbiaaSci+vLyssKizWq0IBAINyaCtlZAtzUQXX1M/7JeTTCYRCoW4wAdVzr4/8sB2LBZj1dpKfctub29xe3uLvb09dHV1sYCD0+lsqoCBUuVsd3c35ubmqCqEeBHlchkbGxs4Pz/nxlvdJUKa1DI6OopCocAltcirNQVBQDweRzwex/b2NkwmE2dB3kzXfzOTy+UQDAa5VgcGgwF+v7/jElKJt5NIJBAMBrnr1ePxYGFh4clnndypJZ1OM0EgFos9KxhYLBZ2/dvtdlrntgnHx8fY2trixqanpzE8PNygGRGNRGpBDjzcBx6zIAd4YVu+rxYEAYVCge2Nac3QWki/z3rso6Rr1JGRERQKBeYoFIvFFBMlREeh4+NjaDQaVqntdrspgfkFkJBNEJ0JCdlEx0M9sl+OfEEvirOi1a7UfhAARkZGMD093bEbgWa0zW42miGh5LEkjO7ubiwsLDSsp2gthGx5P2zp7yERu3qurq6wurrKfQd2ux2Li4u08awjarWa61t2d3fHqrFvb28r3p/NZnFycoKTkxNoNBq43W4W2G5UcpV4vzk4OODGh4eHMTU1Rdcj8SKKxSKWl5cRjUbZWLvaP+t0OvT19aGvr4+r1ry5ueFa2Yik02kcHR3h6OgIWq2Wu/7JglyZdDqNpaUlztLdZDI1LKGPaG0ikQjC4TC3durt7cXs7OyL94YmkwnDw8PM3jUWizHhWsne9e7uDnd3dzg4OKAWBG3CwcFBxV7t06dPdW37RDQ3StXaUmFbpFwuV6zDVSoVu1eJ/yvuk6W9tYnmRBpHasR3pdPp0N3dje7ubrZHFdtkKO1RS6US28MCD2KsKGrbbDY6355AKmTr9fqOLJgiiE6EhGzi3WiVICxZi78clUoFjUbDjlepVGJBL6nVrlqtxuzsbMdvLKki+3kaXZFdKpWwsrKCy8tLbvzDhw+YnJxs6P3srUK2XMSW98N+L9utdkIQBBweHlYEznp6ejA7O0uVsw1EpVLBarXCarXiw4cPyGazTNSKxWIV10upVML19TWur68BgFkQe71emEymulwLpVIJa2trFUlfVE1EvAalylmdTgefzweHw9G4idUBebWm3IJcXoVVLBZxdXXFrj25BTk9Cx/cLILBIFdFZLPZ4Pf7KUhIvBilHsZDQ0OYnp5+8/Wm1Wo5wSCVSjFRO5lMVrxf3oLAbrfT9d9CCIKA3d1dHB4esjGVSoW5uTn09vY2bmJEU/NYtXaxWMTGxgbi8Th7r8Fg4PYCUgtyAJyYTaJ2c1LviuynkO5RxTZZsVgM0WgUsVgMhUKh4mfE5Csx+VJarU1rMB6pkE1thAiicyAhm+h45JWgYq9Y4mnUajVb1N/e3mJra4tbjIn2g+0eRK2GZqg2bnYaKWRns1kEg0Eu6KVWqzE3N4f+/v66zkWJtwjZUhFbfC0VsWkD/jzlchnr6+u4uLjgxj98+IAPHz7Q86LJ6OrqwuDgIAYHB1EqlRCNRpmwpWTvlkgkkEgksLOzA5PJBI/HA6/X+24WxPl8HuFwmOvz266Vs8T7c39/j6WlJc5e32g0wu/3d2RQx2w2w2w2M3tH8fqPRCKKAUPp9W80Gpmo1WwtCOpFLBZDKBTi1qput7uhrjRE63J6eoqNjQ1ubGxsDOPj4zVfO6lUKthsNthsNnz48AH5fJ5d+4+1IJBf/2K1ttPpbLgAQvAIgoCtrS2cnJywMbVajYWFBXi93gbOjGg11Go1BEHA+vp6hYg9NzfHPful9ylpMni5XGb/TdxPd+KaoRmRrl+a7T6u1+vR29uL3t5eCIKA29tb1ltbmowqUiwWueRrq9XKVWt3egxCLmR3+vEgiE6BdqTEu6JSqSqqIZoNpX7PFKx5Ho1Gw4KCx8fH3H+z2+3w+/3o6upqxNSaDrIWfx7p5k/cINZjQ5hMJrG0tMTZEer1egQCgaZJwnitkK1kJS7+f41GQ4v9KlASHVUqFWZnZ9HX19e4iRFVodFouGqt29tbZt8mdQ8RSafTOD4+xvHxMbRaLRO1a2VBmk6nEQwGOftjvV4Pv98Pm8325t9PdBaJRAKhUIgTaG02G3w+H7U6wENVujRgKLUgl/fVA4BMJsOu/2ZpQVBPlCpn+/r68OnTJwrQEy/iMRebqakpjIyM1GUOer0e/f396O/vZy0Ibm5uEIlEFFsQZDIZ1oJErVbD7XYzYZv2s41FFB3Pz8/ZmEajgc/ng8vlauDMiFakWCwiFApxIrbRaMTnz59hMBge7a0t3zdLq7VLpRJVazcJ0jhJM38HoiuI3W5nyVeiqB2LxRRbXqZSKaRSKRweHkKn03HV2p3YKoMqsgmiMyG1juh4SMh+HY8tDAcGBl7V86ydIWvx55Ffh/UQsi8uLrCyssJteKxWa9P1gJRvnJ8TsqXZ4uJr6of9cu7v7xEMBrkeoZ1i19uOSAMGExMTyGQyLKgdi8UULYgvLy9xeXkJlUoFp9PJRC2TyfTiv68kOprNZvj9/qa63xCtwfX1dcXzy+PxYH5+ntawCojXsNPpxOTkJNLpNBO14/F4xfUvb0EgtSC2WCxt9ww9OTnB5uYmNzY8PIypqam2+6zE+yIIAnZ2dnB0dMTGVCoVPn361DCXI2kLgunp6Wev/3K5zPUstVqtTNS22+10TdSRcrmM1dVVrhWLVqsl1zfiVRSLRQSDQS5B2WQy4fPnzyxhRdpbW/pPfp+gau3mpNE9sl+LXq9HX18f+vr6UC6XuWptpeTrQqHAtcqx2WxM1LZarR3xnKKKbILoTCjSQXQ88oAfCY3Pk81mORtL4GExPzMzg+HhYVpEyKCK7Od5ax/olyD2WNvb2+PGe3p6mlIEEMVn8Zg8dWyUrMRJxH45sVgM4XCYy4Y2m83w+XyvEjGJ5sNoNGJ4eBjDw8MoFouIRqNM2JZbEAuCgFgshlgshq2tLZjNZtZXu5qg9tXVFVZXV7lr1+VyYWFhoSMz6Im3oSQ69vf34+PHjy0VtGskJpOp4voXhS0lC/JkMolkMond3V10dXVxFuTNZl35EgRBwN7eHg4ODrjxyclJjI6ONmZSRMtSLpexsbHBVc42Y+sM+fUfi8XY81+pBYlYBXdwcACdTsfcWlwuFz3D35FSqYTl5WVEIhE2ptPpEAgEyMWGeDGFQqGilZjZbGaV2HKkArS4t5aK2lSt3Zw0U4/s16JWq+FwOOBwODA+Po5cLsdVayvFq29vb3F7e4uDgwPo9XpWrd2uzylBEDgh22KxNHA2BEHUk+aK1hNtR6taixOPE4/HEQqFKmx7Pn/+DLfb3cCZNS9Ukf088g3dex2nUqmElZUVXF5ecuPj4+OYmJhoWpG3GiFbKmJL/4k/L26iiac5OzvDxsYG9+wi0bG90Wq16OnpQU9PD7MgFoPaShbE9/f3uL+/Z9Zuoqjtcrm4RBhBEHB0dISdnR3u50l0JF6DmIR1eHjIjX/48AEfPnyg+/srkV//yWSSidpKVTDZbJZZEGs0GrhcLiZst5Kle7lcxubmJs7OzthYoytnidalXC5jZWWFuRgArWH/rNVquRYkqVSKPf9vb28r3l8oFHBxcYGLiwuoVCo4HA4mbJtMJroP1wgl+2eDwYBAIECCBfFiCoUCvnz5wvUhtlgsCAQCVT23xfW6GDd8a7W2VNimvUBtaeYe2a/FYDBwrTKSySQTtpX2qfl8nnMVE6u1PR5PW1UuS9uE0POXIDoHErKJjke+wFHqR0I8cHp6irW1tYrFusfjIRH7Cagi+3mUrMVrTTabxdLSEheYUqvVmJuba/qgrXSTq3RslCqxpSI2bZKf5zGRaGBgADMzM3QMOwSpBfHU1BTu7++ZqJVIJCqef4VCAefn5zg/P4darWYW5G63G0dHRzg9PeXePz4+jrGxMdpsEy+iXC5jbW2NS8JSqVT4+PEjBgYGGjiz9kIUphwOB2tBILUglj9/S6USZ0Fss9mYqN3M1o5iUp84b+BhrbCwsACv19vAmRGtSLFYRDgcRiwWY2M6nQ5+vx92u72BM3sZYsDfZrOxKrhIJIJIJIJoNFqRZCsIAuLxOOLxOHZ2dmA0Gpmo7XQ6ad34SpQqZ7u6uvD582dyRSJeTD6fx5cvX7jENLGVmF6vf9XvVKrWLpVKFYnkgHK1tvjflVzT6L7xNlqlR/ZrEfeaTqeTrVNjsRir1pavU8UEzWQyif39fRgMBrhcLuYq1GxOhC+BrMUJojNp3bsWQdQIshZ/HrFq4/j4WPG/06LhaaQiLZ1fyry3tXgymcTS0hJyuRwbMxgMLdNj7SkhW25xJhW0NRoNXZ9VUCqVsLq6ylUSAcDU1BS1S+hwzGYzzGYzRkZGUCgUmKgVjUYrEt/K5TLLkFdidna26ZNmiOZDSSQi0bE+GI1GDA0NYWhoCKVSibMgV7IgFq0d9/b2YDAYOAviZqkMKhQKCIVCXI9QnU4Hn8/XEushornI5/MIBoNckmi7VM4aDAYMDAxgYGAA5XIZ8XicCdvSSjCRTCbDuTWIFXCt5tbQSPL5PJaWlrjKWXkPY4Kollwuhy9fvnCCl81mQyAQqJnLllK1dqlUUrQgByqrtcX/pWrt2tCOFdlPYTQa2XOqVCpx1dpKz6lcLlfhKiL21m61qmayFieIzoSEbOJdaYUHIVmLP00+n0coFOICqMBDJqu4yaRj9jRkLf487ylkX1xcYGVlhfud4ia2VYIiSkK21KZMfE39sF9ONptFKBTigmYajQbz8/MkEhEcOp0OfX196Ovr44LaNzc3yGQyz/5sIpGATqdrKlGLaG6y2SyCwSBXSaTX6+Hz+Vqq0rEd0Gg0nAXx7e0tu/6lzw+RXC6Hs7MznJ2dQa1WcxbkjVp7KJ1PXV1d8Pv9FAQkXozodCQNJptMJgQCARiNxgbOrPao1WoW7J+enmZuLZFIBPF4vEKsKpVKuL6+ZgmSVquVXf82m43W5woonU8WiwWfP39+deUs0blks1l8+fKFE/Psdjv8fv+7top6rFr7OQtyQLlaW9oejITt52n3iuynENvduFwuTE5OIp1OM1E7kUgoVmuLriK7u7vo6upizzmn09nUe1V5j+xWE+EJgng9JGQTHY+4OBQXjWQt/hW3t7cIBoNcgF6j0WBxcZEL3JE4+zTSRaAoNnbawvo53qNHtmgVvbe3x4339vZifn6+qRfncuRCNonYteH29hahUEixUt9qtTZwZkSzIw1qixbkp6enOD09rQhUAQ9VkFJRy+12t2RfXaJ+3N3dIRgMIpvNsjGTyQS/30/2qg1GpVLBbrfDbrdjfHwc2WyWidpK1o7lcpmJXkBjRK37+3ssLS1x55PZbG6ppD6ieUin0/jy5Qt3Pr3VrreVkLq1FItF5tYQiUQU3RpSqRRSqRT29/eh1+tZpbbb7W5pa9dakclk8OXLFy7mUA/RkWhPlM4nh8MBv99f1+utFtXaYkxEKmaTqP04nVaR/RQmkwkmk4m5CsXjcSZsS5/dItlslturyqu1mw1pkgolYxJE50CrZoLAg714oVAAQKKsyOXlJVZWVrjjIWbZWywWrkKbjtnTKPV/ps0HT617ZBeLRaysrODq6oobHx8fx8TERMuJvHIhW7oBlvfDFje6xNNcX19XVOpbrVb4fD4K6hMvQqVSIZfL4fz8nAtKabVadr1KKZfLXF9du93ORC2LxULXL4F4PI5QKMQlV9rtdvh8vo4QiVqNrq4uDA4OYnBwEKVSCbFYjAnb0kQpESVRy+v1wu12v0vgNZlMIhgMsr0OQCIR8XpSqRSWlpY4wdbhcMDn83Xk+aTVatHT04Oenp6q3Bry+TzOz89xfn4OlUoFp9PJhG2z2dyAT9BY7u/v8eXLF+5e6XQ64fP5SOQnXkwmk8Hv/u7vckKdy+WCz+druLD52mpt6X5ftCCX/j6KK32FNC5Jx+UrNBoNe84IglBRrS0//8rlMmKxGGKxGHZ2dmA0Gpmo7XA4Gn4tAZU9sgmC6AxoZUgQeHiwk5D9gCAI2NnZwf7+Pjfu8XiwuLjIAhTSxUut+xm3G0rVxrQx56mltXgmk6no16dWqzE/P4++vr5X/95GIrenJxH79QiCgKOjI+zs7HDj3d3dmJuba4qNGdFanJ6eYnNzkwsCeL1ezM/PA3gQJUXhWknUSiaTSCaTzNbN6/XC6/XC6XRSEKYDubq6wsrKiuL5RPen5kej0bBreGZmBnd3d+z6l65LRKSillqthtPpZIkttbBnjkQiWF5e5vY3Ho8HCwsLdD4RLyaRSCAYDHJJNnQ+fYXcrSGXy7FK7Wg0WhFnEASBiQXb29swmUxMbOiENUAqlcKXL1+4JBu3243FxUU6n4gXo5QU0azn00uqtZUsyIGHmECpVKJqbQnSGFKzfefNgkqlYq4iw8PDKBaLXLW20l41k8kw5zFxrSoK241qJUI9sgmiMyElhXhXWkVQkS5yOtlavFgsIhwOsyoxkbGxMUxNTXHfJx2z6ql1tXE7Uitr8UQiUVElYjAYEAgEWrqf6FM9soGHc6xV7reNpFwuY3NzE2dnZ9z46OhoS1bqE41FEATs7e3h4OCAGx8eHuaemWJQemZmBqlUiolaSpVa2WwWJycnODk5gVarZRbkbrebKnE7gKOjI2xvb3Njg4ODmJ6e7ujAZKuiUqlgtVphtVrx4cOHZ0WtcrnMAonAQ2BOFLXtdvuLn1EXFxdYW1vjAuL9/f34+PEjnU/Ei4lEIgiHw9w+pre3F7Ozs3Q+PYLBYMDAwAAGBgZQLpdZYlskEuFsj0XS6TSOj49xfHwMjUYDt9vN1hDt1oZEKSmiu7sb8/PzdD4RL+bu7g5fvnzhYgCtlGQjr9aW/qNq7eqhiuyXo9VqWQKm2HtaXIsmk0nFam3pWtVkMnHV2vU47mJVuUgnVmRvbm4iHA7j9PQUmUwGXV1d6O7uxsTEBBYXF990TAqFAn7rt34Lx8fHuLi4gMViQX9/P/x+P0ZHR2v3IQjiFZCQTRDghcZOrcgWe+dJM9vUajXm5ubQ399f8X6qyK4e+eapU8+xpxCzh8Vz6TXn1Pn5OVZXV7mftdlsLd//URAEbsOay+VQKBS4rGsSYJ+nUChgeXmZa4ugUqnw8eNHDAwMNHBmRCtSLpextraGy8tLbnx6ehrDw8OKP6NSqWCz2WCz2arqq1ssFnF1dcVaJDgcDhZo6MQNezsjCAK2t7dxfHzMjU9MTGB0dJTu8W2CVNQS+xWK9wClfoV3d3e4u7vDwcEBdDodE7Wr6aurlBRBSVvEa7m8vMTq6ioX0B4aGsL09DSdT1WiVqtZsF8MwouitpK1a6lUwvX1Na6vrwE87GnENgRWq7Wlj3ssFkMoFOL2xH19ffj06ROJT8SLURKxWzkpQsmCXN5aTISqtXmoIvttqFQqWCwWWCwWjIyMoFAocNXa0mtMJJ1OI51O4+TkBBqNhqvWfq8YXKlU6sge2YlEAn/7b/9tfOMb36jYM0rRaDTw+Xz4w3/4D+Mv/IW/UPXvv7m5wV/+y38Z/+yf/TMuZibla1/7Gn7mZ34GP/mTP/ni+RNELSAhmyBAQvbNzQ3C4TCXEd3V1QW/3/9oFWunH7OXUKtq43bntUL2Y3b4vb29LW/FKm5WpedQJBLBb/7mb7JKTa/X25E9CV9COp1GKBTiEnW0Wi0WFxfhcrkaODOiFcnn8wiHw0gkEmxMbF/Q3d1d9e+R99WNRqMsqK0UKEgkEkgkEtjZ2YHJZGLXv91ub/vAVDtTLpexurrKEhaAh0DSp0+fFBMJifZA2q9wenoa9/f3zK0hmUxWvL9QKFT01RWFbZPJxN4nCAJ2d3dxeHjI/fxTSTYE8RQnJyfY3Nzkxj58+IAPHz60tJjaSKTWrqOjoygUCojFYmwNILXaFrm9vcXt7S329/eh1+uZqO1yuVqqZdXNzQ2Wl5e5vd7g4CBmZmbofCJejJI9fU9PD+bm5tpibfyYBbkobr+0WlsqbLfD8ZEiPx6tHANqFnQ6Hbq7u9Hd3Q1BEHB3d8dVa8splUrMeQh4EJhdLhfcbndN96tSEVv8O+3OL//yL+PP/Jk/wyrhn6JUKuHLly84PT2tWsj+1re+hT/1p/4US557jN/+7d/Gb//2b+OP//E/jl/6pV+i5Hqi7rTOipdoSVplMyLd/HWSyCgIAg4PD7G1tcWNO51O+Hy+Jy3M5EK2vGqU+AqyFq8OjUbDkimqvQ6LxSJWVlY4AQB4qGIbHx9v6XNSukE1m82c5b+0SkOlUnGVmtKANvHQnzgcDnMBDpPJBJ/PRwtv4sWk02kEg0FuA63X6+Hz+d7UvkCj0XCBgtvbWyZq3d3dKc7j6OgIR0dH0Ol0TBBzu92U2NJCFAoFhMNhxONxNqbRaLC4uAi3293AmRH1RFoBMzY2hnw+zwKBkUjkyb66W1tbMJvNrAXB2dkZ5xShUqkwNzeH3t7een8sosUR94m7u7vcOCVF1B6dToeenh709PRwa4BIJKLYhiSfz1eV2NJsXF1dYWVlhRObyCmCeC3JZBJLS0tcMUa7V/YrVWtLhW0pStXa4rUnvles1G6Ham3552/1z9NsSNvliAlY0WgUsVgM0WhUMQFLdBY6Pj6GVqtlorbL5XpTuwz53rjdheyf//mfx1/5K3+lYlxsZ+b1epHNZnFxcYGVlRWueKMafv3Xfx0//uM/ziXSq1QqBAIBfPjwgbUCERMUAOAf/+N/jNvbW/zLf/kv6Voj6goJ2QSBzqwuLpVKWF1dxcXFBTc+NDRUVe88JXGWsh6VoYrs6lDqA/0UmUwGS0tLXIBHrVZjYWGh5QO2UhFbEASMjIygVCrh6uqqolJTEATE43HE43Fsb2+znpperxc2m62jA0Pn5+dYX1/nAmZOpxOLi4sk9hEvJpFIIBQKcRt1s9kMv98Po9FYs7+jUqlgt9tht9sxMTGBTCbDRO14PF5RfVEoFHBxcYGLiwsuoO31ems6L6K2ZLPZipYuer0egUAAVqu1gTMjGo1er0d/fz/6+/tZX13Rglypr+79/T3u7+8rqrDVajV8Ph8lRRAvRqndATlF1Af5GkBsQxKJRBCNRiv2SPLEFtGxxePx1K1faTWcn59jbW2NGxsfH8fY2FhH71WI16HUY72/vx+fPn3qmPPpsWptJQtyoLJaG/gqLtUOFuTyGFsrfoZWQqfTobe3F729vRAEAalUij2nlBKwisUi1y7DarUyC/KXxqzkFdntXJzwC7/wCxUi9v/8P//P+D/+j/8D8/PzFe8vl8v4zne+g//3//1/8Su/8ivP/v7T01P8xE/8BBdj/IEf+AH83//3/42PHz+ysVwuh1/6pV/Cz/3cz7FYyL/+1/8af/Ev/kX8tb/211756Qji5ajkD7c60rA/TNSPcrmsmJnVbKysrODs7AwA4Ha78b3f+70NntH7kslkEAwGcXt7y8bEXrHVZthHo1H8zu/8Dnv9wz/8w9Dr9TWfaztQKpXw7//9v2evA4HAi+xnO4X//J//MwvoT0xMYGJi4tH3xuNxBINBbsFlMBgQCATeVBXZDMitwqT/X6VS4e7u7slKTSl6vZ4JWi6Xq2OSTQRBwN7eHg4ODrjx/v7+qhJ1CELO1dUVVldXuQCyy+XCwsJCXZMiisUiZ0H+3BpLTGzxeDyw2+0dE9xrdlKpFILBIHK5HBt7j6QIor2Q9tW9ubnh2hsoIXVs8Xg8bR3oI2pHuVzGxsYGzs/P2dhr2mcQtadUKnGJLdls9sn3a7VauN1u5trSqL26kj391NQURkZGGjIforUR4wBS4ZLs6Xmk1dpKFuRylPpuS6u/W2HvnMlk8J3vfIe9/p7v+R7YbLYGzqhzyefzzII8FotxCSdKiM8qsVr7uWdVMBjED/3QD7HXy8vLiqJuqxMOh/E93/M97PjpdDr8k3/yT/CH//Afrurni8Xis61Hfvqnfxrf+MY32Ouvfe1r+Pa3v/1of/N/+S//Jf7QH/pD7LXBYMDW1tZbn+d04yaqhiqyiXelVRaSnWQtHovFEAqFOAFQtEV9Sa9YqjKuHjpW1SE9Tk8do7OzM6yurnIbLrvdDr/f/+iCqxWQ9rGS9rMCwGVIS6s05AFt+SY1n8/j7OwMZ2dnUKvVrJ9eI4NZ702pVMLa2pqi3fzo6GjLPJeI5kAQBBwdHWFnZ4cbb5R1oVarZfaj5XIZyWSS3QPk2enAV5ZuBwcHXE9Nt9vdMYktzUYsFkM4HOaCOg6HAz6fj5wiiCdR6qt7fn6Ovb09xXWT3LHFZDKxe0AzVWoSzYPo2CXtkajRaF68TyTeB41Gw0Tp6elp3N/fM1E7mUxW7AOKxSKurq7Ymthut7Oft1qtdVkTHxwcVNjTf/z4EYODg+/+t4n2IxqNIhQKcYmlor0u7fG+olbV2qVSqWWqteXrINrnNA69Xo++vj709fWhXC7j9vaWCdtKhRjyZ5XJZMK/+lf/Cl//+tfxta99reK7lFtnt6O1eLFYxJ/+03+a2y/+0i/9UtUiNoBnReydnR38P//P/8Ne6/V6/MN/+A+fjKn++I//OP7kn/yT7OdyuRx+/ud/nhPDCeI9ISGbINA51uLHx8fY2NjgFq42m+1VFUDyh2I7H7e3Ii74xQ0XHStlnrMWF20O5VW2fX19mJuba+nNily4fkzElmMymTAyMoKRkREUCgUWzFLqqVkulzk7J2lf7Xap0srlcgiHw0gmk2xMrVZjbm4OPT09DZwZ0YqUy2VsbW3h9PSUG//w4QM+fPjQ8ICZWq2G0+mE0+nE1NQU7u/vn6zUlPbUVKvVcLlcLLGllZOAWomLiwusra1x67Du7u6Wf4YRjSGXy+Ho6Ih73ut0Omi1WkUL8nQ6jePjY9ar0O12s3sAJVEQxWIR4XAYsViMjel0OgQCAapqa0JUKhUsFgssFgvXr1S0IVdybEkmk0gmk9jb24PBYGCi9nsktym5I6lUKszOzqKvr6+mf4voDCKRCMLhMBcnGBkZweTkZMPX5M3OY721laq15aK2NE4hr9ZuJlGbemQ3J2q1Gg6HAw6HA+Pj48jlcly1tlJs9Lvf/S7+zt/5O/g7f+fvwOFw4Gtf+xp+9Ed/FP/j//g/wuv1dkSP7F/+5V/G0tISe/37f//vx0/91E/V9G/8k3/yT7jj/xM/8ROYnJx89uf+/J//85wA/s//+T/H3/27f5fiCURdICGbIMAL2c/ZnrQiokXcyckJN/4WAZCqjF+GRqNhi+tq+j93ItLzUH6MisUilpeXuQoRAJicnGwKQektyPthA6hKxJaj0+m4zNdYLMZELal9rUgikUAikcDOzg7MZjMTtVvVfjiVSiEUCnE2i6LbRKvbzRP1p1gsYmVlBZFIhI01e39QaaWmaOn2VGKLGOwGHvqUifeAelVpdRKPVfYPDQ1henqajjfxYpT6gzqdTiwuLkKn07FKzUgkgng8/mylptyCnM7JziKfz1e0nTIYDPj8+XPbJDu2O/J+pclkkiW4KlXA5XI5zrXJ6XQyYdtkMr1pLo/1WF9YWCB7euJV3NzcIBwOc8+ysbExjI+P0/PqhbykWlt+bJu5WpsqslsDg8GA/v5+9Pf3M3cxUdgWK62/fPnC3p9IJPDNb34T3/zmN/EzP/MzmJ2dZftxsQVKO65TfumXfol7/X/+n/9nzf/Gv/gX/4J7Xa1Q/vHjR3zf930fvvvd7wJ4qJD/1V/9VfzBP/gHaz5HgpBDQjbxrrTKorKdK7JzuRxCoRDi8Tg3Pj09/SabXfnCsN2OW62p1ja7k3nsGGUyGXz58oULwmg0GszPz6O3t7euc6w1T4nY4obwNdeoaCPu8XgwMzODVCrFRO1UKlXx/vv7e9zf3+Pw8LAl7YcjkQiWl5e588ZisbS83TzRGLLZLEKhEHetaLVaLC4utoy1qtzSLR6Ps3uAUk/NVCqFVCqF/f19GAwGJmo7nc6WuAc0M4IgYGtrqyKZcHJyEiMjIy2zViaah5ubGywvL3NJf/LKfjGxRXRsea5SU5rcZjQa2TrA6XRSVVObk81msbS0xFl1mkwmBAKBFzt2Ec2BSqViFXATExPIZrNM1I7FYhUJw+VymQkJW1tbMJvNbB/x0jYEgiBgfX29ose6z+eD2+2u2WckOofr62ssLy9zAuv4+Dg+fPjQwFm1D/Jqbem/VqrWpors1kPqLiY+q6LRKPR6Pbq6uir2rOVyGSsrK1hZWQHwsFbJ5XL4N//m3+AP/IE/0DbuMbu7u/iN3/gN9np0dBT//X//39f0b1xeXiIcDrPXWq0WP/ADP1D1z/++3/f7mJANAN/61rdIyCbqAgnZBIH27ZGdTCYRDAa5BYAYjPd6vW/63SRkv4x2TpaoFUoV2fF4HMFgkOvp3tXV1RY2h1IRW3wt/v9abv5UKhVsNhtsNhvGx8eRyWSYoKVUpaVkP9zd3Q2PxwODwVCTOdWS4+NjbG1tcWMejwfz8/PP9gUiCDmpVArBYJBzMejq6oLf729Z2zK1Wg232w232816aor3AKkNv0gul8Pp6SlOT0+h0WjgdruZqKXX6xvwCVoXpX6zKpUKc3NzLZ+IRTSGs7OzijZBAwMD+Pjx46NJEUqVmuI9QN5nEHhIIDw5OcHJyQm7B4jV2nQPaC/u7++xtLTE7RWtVisCgQB9121EV1cXBgcHMTg4iFKphFgsxhJblJLbxATXo6Mj1oZAFLafOi/K5TJWV1eZ0wPwEHvw+XxwOp3v8tmI9uby8hKrq6vcM29ychKjo6ONm1Qbo2RBLhW1m7laWx5jIyG79ejq6sLAwAD+/t//+8hkMvj3//7f41vf+hZ+4zd+o6LVF/DQMgcA/qf/6X+CVqvFD/7gD+LrX/86vv71rz+5Lm52fu3Xfo17/ft//++v+WdZXV3lXi8sLLyosv1rX/sa93ptba0m8yKI56AIL/HuqFSqCqGk2ZCLjIIgtOxDT+T8/Byrq6tcZqLZbEYgEKiJ9QoJ2S/jKdts4gF5j+zT09OKXqJ2ux2BQKApBdWXIN0MVtsPu1YYjUYMDw9jeHiYVWmJ9sPy1gpy+2G73c711W7kfbJcLmN7e7uiynF4eBhTU1Mtfw8n6k80GkU4HOaeZzabDT6fr+XvOSLSnppjY2PI5XKsSisajVY8n0qlEq6vr5kQ20z3gGYnn88jFApxyQKtVtlPNA+CIODw8BC7u7vc+IcPH17UYkVaqTk5OYl0Os3uAUrJbY/dAzweDywWC90DWphUKoWlpSUuWdThcMDn81HP9DZGo9Gw57ggCCy5LRKJIJFIVLxf3obgsXtAqVTCysoKbm5u2M9Sj3XiLVxcXFSILVNTUxgZGWnQjDqLxyzI5cn4Is9Va0uF7fcQmaX7t0bbnBNvx2g04g/+wT/IqnzX19fxr//1v8a3v/3tirUL8PCs+rVf+zX82q/9Gv7cn/tzGB4eZqL2D//wD7eU/fh//a//lXv9/d///QAerqtvf/vb+Mf/+B/ju9/9Ls7OzlAsFuHxeDA5OYkf+ZEfwR/7Y3+sqkSf9fV17vXExMSL5jg+Pv7k7yOI90LVQIGxuZVNombk8/mmF7Kvr6+xtLTEXv/Ij/xIy1byiT2pDg4OuHGv14vFxcWafq5f+ZVfYd/t/Pw8BgYGava7243vfOc7LJg9MjKCjx8/NnhGzcfKygrOzs4APCxcM5kM99/7+/sxOzvb0ja3cuG63iL2U1RjPyzFaDSiu7ub9dWu52ZR7JkejUbZmEqlwvT0NIaGhuo2D6J9UKpy9Hq9mJ+fb+l7zksQq7TEgLa0Kl0Jo9HIgtlkP8yTyWSwtLTEKgWAh55wfr8fVqu1gTMjWhGlfrMAMDMzU9NnXrFYZBbkNzc3ihbkUrq6urh7QKfcK9uBeDyOUCjEJTB6PB4sLCzQ99jBFAoFlsCqlOAqx2AwwOPxwO124+TkhGtlptfr8fnz55Z1syEay9nZWYUwUutnHvF6RCFbKmw/hVIlt7SNWi32EGdnZ8ylTavV4vf+3t/75t9JNCd/6S/9JfzyL/8yzs7OoNPpnl2v6vV6/L7f9/vwYz/2Y/j617+OycnJpk7E9Pv9CIVC7PVv/dZvob+/Hz/90z+N//gf/+OTP6vT6fC//C//C/7G3/gbMJlMj77vf/vf/jf84i/+Inv9cz/3c/gbf+NvVD3Hu7u7ij1tLBZ7rftK834ZRNPRmkodQdQYubhbKpVaUsguFAoIh8OselJkfHwcExMTNX9YazQatsGliuynoYrs55EeI7mIPTU1hbGxsaZecD7HU/2wGy1iA5X2w3d3d0zUvr29rXh/JpPB0dERjo6OoNPpuL7a73n/zGQyCIVCXM90rVaLhYUF6r1HvBhBELC3t1eR/NWJlf3yKq1UKsXuAdJ+4SKZTAbHx8c4Pj5m1qOiqNXJ1Xy3t7cVLTEsFgv8fj+6uroaODOiFSmXy1hbW8Pl5SUbU6lUmJ+fR09PT03/llarRU9PD3p6eiAIAm5vb9k9QPrMFclms5wFucvlYveAdnGxaEeUeqz39fXh06dPlJDU4eh0OvT19aGvrw/lchnJZJKJ2kr3gFwuh7OzM5aILKLX6/G93/u9TwbRCeIxTk5OsLm5yY19/PgRg4ODDZoRIeexam0lC3Kgslob+Cp+WCsLcmk8kp5l7U02m2XPnYWFBfyjf/SP8Cu/8ivMhlxerZ3P5/Grv/qr+NVf/VX82T/7ZzE+Ps6qtX/oh34IRqOxER/jUS4uLrjX6XQa3/u931sR51eiUCjg7/7dv4vvfOc7+Lf/9t+ir69P8X1yB5bu7u4XzdFisVT0MU8mk9RGhHh3Wk+pI1qOVrMWB1pTlL27u6uo/tFoNJifn3+3PowkZFcP9ch+HqXjotFosLCwUPNgbb2Ri9jyftjiBq5ZUKlUsFqtsFqt+PDhA7LZLAtmx2Kxint6oVDAxcUFLi4uoFKpuL7atRRuEokEwuEwtzkxGo3w+XxU8UG8GCWBCACmp6cxPDzcoFk1ByqVCjabDTabDePj49w9IB6PVyRkSa1HRetiUdBqJSu3txKJRLC8vMw9z5xOJxYXFzta3CdeR7FYRDgcRiwWY2P1sqdXqVSw2+2w2+2YmJhAJpPhLMiV2hCI9wjgoS2DeA+wWq1NtcbpZC4uLira9gwNDWF6epq+I4JDrVbD6XTC6XRicnKS3QMikQhisdiTidliaw3xHlBv5yaidTk+PmZVtSKzs7Po7+9v0IyIalDqrS0VtqU8Z0Eu/X0vuW9I70nkLNK+iC0xRKxWK2ZmZvDp0yf82T/7Z3F3d4f/+B//I775zW/im9/8ZkUbOgDY29vDL/7iL+IXf/EXYTQa8e1vf5vZdzcDcpH5p37qp5iIbTab8b/+r/8rfuzHfgyDg4O4v79HOBzGN77xDfzmb/4m+5lgMIif/MmfxG/8xm8o7kHlyWmvEfONRiMnZCslvhNErSEhmyBQudB5zkar2bi+vq7o62k0GuH3+9+1JxWJs9UjXYTTsaokFotVZB52dXW1RV81eR8puYjdCoGdrq4uDA0NYWhoiFmPivbDcisnQRAQjUaZ7bcYzPZ6vW/qp3l5eYm1tTVuk+pwOLC4uAi9Xv/6D0d0JPl8HuFwmNsoqtVqzM/PvzgjuROQ3wNEC3Il+2FBEBCPxxGPx7G9vQ2TycTuAe0czD4/P8f6+joXsOvp6cHc3Fzbfmbi/cjn8wgGg5wjil6vRyAQaIg9vdFoZPeAUqnEWZDLK1+AB2eC29tb7O3tMfthr9cLl8tFAeYGoVTl+NIe60TnIr8HXF5eYnt7+9G4yf39Pe7v73F4eAitVguPx8P+UWIXocTh4SF2dnbYa5VKhdnZ2UcrConm5CXV2vJnj7Rau1QqvahamyqyOwdp8Zbc+cNisbDe2oIgYH19nYnav/mbv1nxzCoWi5ifn6/LvKshl8tVtPc6PT0FAHz69An/7t/9u4oWC4FAAD/1Uz+FX/iFX8DP/dzPsfHvfOc7+Ot//a/jL/7Fv1jxd+RC9muKT4xGI9dSRMm5hSBqDQnZBAFla/FWQBAE7O/vcwt+AHC5XPD5fO8u7pCQXT1kLf44p6enFdUharUa3//939/y1pRKVuKCIDSFlfhrkVqPlstlJBIJJmjJLeEBPpgt9tT1er1wOBxVbTIFQcDBwQH29va48d7eXnz69IkC4sSLSafTCAaD3CZYr9fD5/PBbrc3cGatgVarRXd3N7q7uyEIApLJJLsHSDPkRdLpdEPaENSLx+5RIyMjTd+DjWhOlHqsG41GBAKBprDq1Wg03D3g9vaWidpK1RhS+2G1Ws1ZkJPd/vvz2D2K3EeI15LP53FwcMAJAgaDAQaDQbEdUbFYxOXlJXPAcTgcTNR+S5Ir0T7s7+9z96j3aqFB1B95tbb0Xy2rtakiu3OQ7jfNZvOjzxAxGWZ2dhZ/7s/9OSSTSfyH//Af8M1vfhPf+ta3cHFxgR/6oR9qKme/x+LqdrtdUcSW8rM/+7M4OzvD3/pbf4uN/a2/9bfwv//v//uzn/E1z2F6dhONoPWjR0TT0wo3t1a0Fi8Wi1hZWcHV1RU3Pjw8jJmZmbpkIZKQXT1UkV2JIAjY2trC4eFhxX/T6XQtLWJLN13i62bqh10rxIC0y+XC1NQU7u/vmaCVTCYr3i/vqSsKWh6PR1HQKpfLWF9fr6jWpwoi4rUkEgmEQiGuithsNsPv9zddf6xWQLQRdzgcmJycRDqd5uyHq2lDIN4HWvH4l8tlbG5uVvQHJYGIeC2pVApLS0tclbPVakUgEGhK9xGpBbnYhkC8ByjZD5fLZWZPDDx8NnEdYLPZ6LleYwRBwPb2No6Pj9mYSqXCp0+fyKqXeBX39/f48uULVzHmcDjg9/uh1WqRz+eZc1M0GlWs2E4kEkgkEtjd3UVXVxcTtcmxofMQCzP29/fZmEqlwsLCAjkktSFyURtAzaq1qSK7c5AK2S8Roe12O37yJ38SP/mTPwlBEBAOhyuqnxuNyWSCWq2uWD//zM/8zJMitsj/9X/9X/jGN77BYnGxWAzf+ta38Ef+yB/h3ic/bkoFKc8h/5lmSggg2hcSsgkCrWctLlaTSasexGyzwcHBus2Dqoyrh0R/nkKhgHA4zAKZcuTiRyuhZCXejiK2HJVKBYvFAovFgrGxMeRyOa6vtlJPXbE6QxS0xGrtrq6uR62fZ2dn0dvbW+dPR7QDV1dXWF1d5c5F6l9cW0wmE4aHhzE8PIxCocC1IZCvraRtCLa2tmCxWNg9oBUErVKphOXlZe45plarMTc3RxVExKuIxWIIh8PcteJyubC4uNgy7gVdXV0YHBzE4OAgSqUSYrEYE7aVgoWpVAqpVAr7+/vQ6/WcYwMJWm+jXC5jY2MD5+fnbEytVmNhYQFer7eBMyNaFaVEG7fbjcXFRXa96vV69PX1oa+vD+VyGclkkt0DlFxbstksTk9PcXp6yhJkxfsAOTa0N4IgYHd3l0tqV6vVWFxchMfjadzEiLogis21qtaWrp1o/dC+CIJQYS3+2mpin89Xw5nVDrPZXOFw9Cf+xJ+o+md/4id+Av/gH/wDNvbrv/7rJGQTbUNr7IgJ4p0RhSVpll+zEo1GK6rJDAYDfD4fnE5nXeciXSA2u/jfaEj0/4p0Oo0vX75wwQyNRoOBgQFWMdKqx0gqYkv/AV/dZ5pdnKkVBoOBC2aLgtZjPXVFQWtzcxNmsxm5XI67r+h0Ovh8Pjgcjjp/EqLVEQQBR0dHFW04+vr68OnTJ8rafyd0Oh16e3vR29vLtSGIRCJcAELk7u4Od3d3ODg4aHpBS6l/sVarbchajGgPlBJtWr3HukajYckpMzMzuLu7Y/cAJdeWfD6P8/NznJ+fQ61Ww+l0smrtVnRsaCSlUgkrKyu4ublhYxqNBj6fDy6Xq4EzI1qVZDKJpaUlbm3e3d2N+fn5R+9R4nXsdDoxOTmJTCbD7gHxePxJx4bNzU1YLBa2FrDb7R2zh+oElNwi1Go1fD4f3G53A2dGNAp5tbYgCCiVShUxFUC5Wlsaw1WpVCgUClX11iZaj9dWZLcKDoeDE7J7enowOjpa9c//nt/zezghe2Njo+I98nZq0vViNdzd3VUI2RSnI+oBCdnEu9MqGw6tVsvElWYUsgVBwPHxMTY3N7lFnN1uh9/vb0jGMomz1UPW4g/EYjEEg0FOyBT7PqZSKbaZbcVjpFSJLRWxO3kDJe+nmUwmcX19jZubG0VBS16xodVqMTU1BZvNVq8pE21CuVzG1tYWTk9PuXGyp68v0jYE09PTXBsCqeuCiFzQaqaeuul0GktLS9zmvaurC36/vy2DKcT7c3p6WhFkGhoawvT0dNvco1QqFaxWK6xWKz58+IBcLscEq2g0WrHuK5fLLMENAHNs8Hg8JGg9Q7FYRCgUQjweZ2M6nQ6BQIDWUcSriMfjCAaD3HX6mmRAo9HIXFvEJFfxPqDk2CAmuB0eHkKn08HtdrMEN3LSaV0EQcDm5ia3NqdEG0KKeF8R443lcvlRC3IAyOVyFW6VYmW3WEggCtqdHJNpF+Q9stuNqakpnJycsNd9fX0v+nl56xhxLS1lcnKSe310dPSivyF/v8vlomRuoi6QkE0Q/w2NRtO0Qna5XMba2lpFD8b+/n7Mzs42rFKJ7LKrh0R/4OTkBOvr69zGw+l0wu/3Q6/XcwtScYPSKoFK+aZKKmhrNJqW+Rz1QNpTV95XW0nQAh6Csmtra9ja2mJBLI/HQ0Es4kmKxSJWVlY462fqDdocmM1mmM1mjI6OIp/PM9vRxwQtaU9dm83GqjwtFktd76/JZLIiGctqtcLv98NgMNRtHkR7oNQbFAAmJiYwOjra1msHg8GAgYEBDAwMoFwucxbk2Wy24v1SxwadTsc5NrSK7Xo9UHKL6OrqQiAQaMtgL/H+RCIRhMNhbv86MDCAjx8/vukeJU9yfc6xoVAocC2J7HY72w+Yzea2vl+2E4IgYGNjg4traTQaBAIBquYjHuWxau1yuYxMJoOVlRWu5YE04U0eoxF/n1TcJloHQRDaviJ7dnYW3/72t9nrl+4x5e9XWld//PiRe727u/uivyHfu3z69OlFP08Qr4V2fQTx32hWm+xsNotgMMht6FQqFaanpzEyMtLQTRsJ2dXTycdKrIiUZ+0NDAxgdna2oj+S9OeazU5WjrQ3k/i6E/ph1xKz2QyTyYRcLveokC1SLBZxdXWFq6srqFQqZjvq9XrJdpTgyGazCIVCXHa+VqvF4uIiVXs0GXq9Hv39/ejv70e5XEY8HmfJLUob79vbW9ze3mJvbw9dXV1M0HK5XO8ajLq5ucHy8jIXzG+1/sVE86BUkQY8BIIGBgYaNKvGoFar4fF44PF4KhwbHhO0Li4ucHFxwa0FPB4PTCZTAz5Bc5DNZrG0tMQFeE0mEwKBAK2RiFdxdXWFlZUVLgl5ZGQEk5OTNd3fyB0bxAQ30bFBHpsRBAGJRAKJRAI7Ozvo6upi9wCn09n0+8dORRAErK2t4eLigo1ptVoEAoEKm1uCeAxptXYmk8Hq6irn6OB0OtHd3c1ey3trA1/F46RiNonarYPU0a8dk/QWFha418/FyOTI36/UrmFubo57vby8jHQ6XfU6+rd+67ee/H0E8V5Q1IV4d1pFxGlGoTGRSCAYDHILM51Oh8XFRXg8ngbO7AGyy66eTj1WhUIB4XCYq4gEgOnp6YpqI/nGoVQqNXUggkTs2lAsFrG6ulrRl2dychJms5kFs6VZ1sDD8Y7FYojFYtja2mK2o16vFzabjY59B5NKpRAKhTgRlKyfWwO1Wg232w23243p6WlWoXVzc8NVGIpks1mcnp7i9PQUGo2Gc2zQ6/U1m5eS9TP1WCdeS6lUwurqKq6vr9mYWq3G/Pw8F3ztRFQqFSwWCywWC8bGxjhBKxKJVKyh5WsBs9nMWZB3yvV5f3+PpaUl7rlntVoRCARqei8kOoeLiwusra1xIna92rLIE9wSiQS7B8jbDwEPa4GTkxOcnJw0XTsS4gHRYfDy8pKNUcsD4i1kMhn87u/+Lvfcc7vdTFCTOuSJyEVtaTxH/G9i9XenrB9ajWKxyH3n7bi3/7Ef+zGoVCp2/u7v7yObzVb9PFtdXeVeDw4OVrynr68PCwsLWF5eBvBwXH/zN38TP/qjP1rV3/j1X//1ijkTRD0gIZsg/hvSappmEBrPzs6wtrbGVf5YLBYEAoGmqTZotmPWzMitxVvJNvu1iEE1acBB7H/l9Xor3i8XrZvZgl3aD1v6D+CtqoinUaqa1Wg0mJ+fZ+eI1+tlfbVFQUspiCW1HTUYDPB4POju7qbKjA4jGo0iHA5zzySbzQafz0fWzy2GUk9d0XY0Go1WPCNKpRKur6+ZOCjajnq93lfbjj5m/Tw2Nobx8XG6zxMvRkzwk/Yv1mq18Pv9ZKuqgJJjg2hBLu1TL3J/f4/7+/uO6ql7e3uLpaUlruWB0+mEz+cjtwjiVSglb01OTmJ0dLTucxGFaZfLhampKaTTaSZqx2KxCqFK3o7EarUyxwep3TBRP8rlMlZWVrjkLZ1Oh8+fP8NqtTZwZkSroiRiezweLCwsVMTdHuutLb8XSKu1S6USVWs3KfI4UDsK2f39/fj+7/9+/PZv/zaAh73Dt7/9bfwP/8P/UNXP/7t/9++41z/4gz+o+L4/9If+EBOyAeAf/IN/UJWQvbm5ie9+97vstdlsrloAJ4i3QjsbgvhvNEtF9mM2zD09PZifn2+qgESnVhm/BiWRtp3FtWg0ilAoxAXVjEYjAoHAoxtWJWvxZuQ5EZs2OdVxe3uLYDDIVVp3dXXB5/NVnCPSvtqTk5NIp9NM1JaKASK5XA5nZ2c4Ozt71ypNork4OzvDxsYGF6Twer2Yn59v6/ttp2AwGDA4OIjBwUGUSiXEYrFHHRuAh37WyWQSu7u7MBqNTNR2OBxV3afL5TI2NjZwfn7Ojc/MzGBoaKhmn4voHHK5HJaWlnB3d8fGDAYDAoFAWwbiao3UsUEUtKQW5HJBS95T1+FwcD1124F4PI5QKMRZLysF8wmiWg4PD7Gzs8ONffz4UbGiqxGYTCYMDw9jeHgYxWKRrQUikYjiWiCVSiGVSuHg4AA6nY6J2u2c3NJMlMtlLC8vc85ber0enz9/puce8SoeE7EXFxcr1vfy3trSf1St3ZpI19BAe1qLA8BP/dRPMSEbAP7m3/ybVQnZ//k//2f81//6X9lrtVqNr3/964rv/eN//I/jr/7Vv8pi+f/f//f/YWdnB5OTk0/+jb/+1/869/qP/tE/Su4nRN1QyW/edaRhf5ioL+VymROzmpVQKMSsjrq7uxEIBOo+h3w+j3A4jGg0yo1PTEw0ZeXP8fEx1tfXATxUk/zIj/xIg2fUvEQiEfzu7/4ue/3DP/zDbSuoHR8fV4hJTqcTfr//yc+cSqW4Xis/8AM/0HRZ2lIRG+AtqzQaTdNdo83K1dUVVldXuWSF11bNirajNzc3iEajzybVOBwOdHd3w+v1No27BfE2BEHA3t4eDg4OuPHh4WFMTU3RddnmCIKA29tbFsiWOjwoodVqueQWpUB2sVjE8vIytx4j62fiLaTTaSwtLXFVxNS/uHYUCgXOglzeU1eOyWSCx+N5UXJLs3Fzc4Pl5WVuLUUtD4jXouRAolKpMDs7i76+vgbOrDoEQUAqlWJrAaV2JFLE5BZR2H6tcwvxOKVSqSK2ZTAY8Pnz57YVn4j35SUi9lOIMRypqP2UNqIkelO1dmPY3NzEf/ff/Xfs9X/5L/8F3/d939fAGb0PpVIJ8/PznDvKL/zCL+BnfuZnHv2Z6+trfO1rX8Pe3h4b+2N/7I/hn/7Tf/roz/z0T/80vvGNb7DXX/va1/Dtb3/7UWH6X/2rf4Uf//EfZ6/1ej22t7cxMjJSzcd6DHr4ElXTPKWdRNvSKhuCRttkp1KpigCXRqPBwsICenp66j6famiWKvZWoJVss19LuVzG5uYmjo+PufHBwcGqgmrNfIyoH3ZtEAQBR0dHFZUePT09mJ2dfVX1kNx2VFqlmcvlKt6fSCSQSCSwvb3Neml6vV6yG2xRlHruAcD09DSGh4cbNCuinqhUKtjtdtjtdkxMTCCTybDkFiXb0WKxiKurK1xdXXFVmmJySy6XQzAY5ARxnU4Hn89H1s/Eq1Cyfrbb7fD5fG2b1FhvdDod+vr60NfXx/XUvbm5QTqdrnh/Op3G8fExjo+PueQWt9vdEt+JUv/ioaEhTE9P01qGeDGCIGB7e5vbw6lUKiwsLLRM8pZKpYLNZoPNZsP4+DhyuRyi0eijia6CICAejyMej2NnZwdGo5EltzidThKm3kipVEIoFEIsFmNjXV1d+Pz5MyUSE6+iViI28JUToBh7EC3I5UULIs9Va0uFbbp3vC+dYC0OPJybf/tv/238gT/wB1jc8Wd/9mdxdHSEv/JX/gqcTif3/v/wH/4D/syf+TOciO10OvHX/tpfe/Lv/PzP/zz+xb/4F8zl8Ld/+7fxIz/yI/j7f//vY2Zmhr0vl8vh7/29v4ef/dmf5X7+Z3/2Z98qYhPEi6CKbOLdEQRB0eap2djY2GB23g6HA7/n9/yeuv3ty8tLrKyscBssk8kEv9/fdBWpUi4vLxEKhdjrH/3RH6WF2yMkk0l85zvfYa9/8Ad/sK0ykQuFAkKhUIWbwMzMDEZGRqoKqmWzWfz6r/86e/193/d9FQu0RkAidm14zKb3vXrNSqs0b25uKmyo5Oj/f/bePLix7Lrv/2IlSIDYCe5rN8nmioWjWbSONKMZbdZYGZfKchTJ5cjxGlt2FDtOLP/sKBWXbJdlV7yUI8cl24lcljfJ9kiaTRppJI00MwTAneyNzZ0EAXAnsb73+4N6V7ggmgBBEA/L+VR1Vb/bbOISfLjv3vM953u0WtZX22q1kiVnGSCtO7u7u2yMqmaJVBKJBAtkB4PBrA5BtbW1iMVi3H6strYWbre7op7ZRPEIhUKYmJjg7imbzQan00nPmSJxdHTEKrV3dnbOrboCcMaCvNT2eCsrK5ifn+fGenp60NPTU3JzJUofURQxNzeHtbU1NqZUKuF0OmG322WcWeGQklukvUCm5JZUVCoVrFYrq9Ymy9SLkUgk4Pf7ufZPtbW1GBsbIwcSIi8KKWJnQxKyJWH7ItXaqRbkqeI2UTi++c1v4n3vex+7vnfvXkULqX/0R3+E//gf/yM3ptFo8PDDD6O1tRUnJyfw+/1nWpNqtVr88z//M5588smsr/HSSy/hySef5HQbhUKBsbEx9PT0YG9vD16vl2sRAQDve9/78MUvfrEQ5xnavBI5QxXZBPF9UhffbHZ0hUIURdy+fZvLmgJ+EOAq9YqA9AdWMpmkjdp9KOVq48tydHSE8fFxLiigVqvhdDrR0NCQ8/cpxR7Z6f2wAZCInQfxeBwTExNcQEOhUGBwcBAtLS1X8pqZqjRT+2qnH0hjsRjW19exvr7O+nBKVZqlvhZXI8fHx/D5fNy6o9Vq4XK5YDKZZJwZUUqo1Wo0NjaisbERoihib2+PrQPpGf0AOFcc4DRQ0NXVdeGWBwQBnCZ8Tk9Pc88bsn4uPnq9Hnq9Hp2dnYjH4wiFQkzYzpTcIjm3lFqVpiiKWFxcPHNuJAcSIl8yudqoVCq43e6SSCYuFEqlElarFVarFf39/VmTW5LJJNsrAEB9fT1bB4xGI539ziEej8Pn82Fvb4+N1dXVYWxsjBICiLwopogN3L9a+34W5OnV2sAP3CrJgrzwVEtFtsTP//zPQ6VS4ROf+ASLe8Tjcbz88sv3/T+NjY34x3/8R7zxjW/M6TUeffRR/NM//RN+/Md/nD33RFHE66+/zrXHTOVDH/oQPvvZz1JSLlF0qCKbKAqZ7F1LjTt37jC729raWrztbW+70teT+i8GAgFuvKurC319fWWxyQmHw3j11VfZ9aOPPkoHlPtwfHyMb37zm+z64YcfrgiL0mAwCL/fzyV/SD0fL7qpTCaTeP7559m1x+ORtaoyXcROPbikZtkS53N0dAS/388JjhqNBk6nU7YgmdRLU7IbzJa8ZDKZWF9tqsqUn93dXfj9fk6A0Ov1cLvdVOlB5Mzx8TGrzkq1vsyEQqGA1WplVZp0nxHZWF5exsLCAjfW2dmJ3t5e2juUCLkkt6SiUqlYkpvdbi9qktv9rJ/LpX8xUXoIgoDJyUmuwkqtVsPj8VRVQqDk3CIJ29mcBLVaLdeKILU9XbUTj8fh9Xq5/uR6vR5jY2OUEEjkRbFF7GykVmtnsiBP537V2tLcyyHmW2r8/d//PX7iJ36CXR8fH1fFuezOnTv4zd/8TXzpS1/i2l+l0tTUhJ/+6Z/Gxz/+8bye44FAAP/f//f/4W//9m+5ApRUHn74YXziE5/A008/feHvfw50MCJyhnZdBPF9itnv+ejoCD6fj7O6VSqVGB4evrLqxKsgU0U2kZlKfK+Wl5cxNzfHbdCtVmvePR9LqSI7vT9SqoitUqkoCJ0j4XAYk5OTnOAotU2Qsz9aei/NnZ0dFshOPShL7O3tYW9vD7du3UJdXR2r1DabzXQvFJmtrS1MT09z64PFYoHT6YRGo5FxZkS5UVdXh87OTiiVyqxCtiiKCIVCrH2GwWBg6wBVZxGpiKKIO3fuYHFxkRvv7e1FV1eXPJMiMqJQKGA2m2E2m9Hb28ucW6TklkxVmoFAgCUhm0wmJmobDIYrWwcEQcDs7Cw2NjbYmFKpxOjo6IWcjwhCIplMYmJigmsJpdVq4fF4Srqt2VWQ7tyyv7/PRO1UQVYiFothY2MDGxsbUCgUsFgszIK8mpNdY7EYvF4vJ7AYDAaMjY2RsxWRF6UmYgOFq9ZOJpNUrZ0nqTH0urq6qkkmunbtGv76r/8aJycn+Pa3v43V1VVsbm5Cq9WioaEBTqcTo6Ojl3oNh8OBP/3TP8Uf/uEf4tvf/jaWlpawubkJvV6P1tZWuN1udHd3F+gnIoj8qI5PPCE7CoUia7aa3KQ+AK9SZMxUwVpTU1OW2c+VKM5eFZVkLS71Ol5ZWeHG29vbMTAwkPcmXNrMp1t4F5P06mvqh50/6+vrmJ2dPZPoMDo6WlKCo2QjbrPZ0N/fj8PDQwQCAWxvb2fMdj0+PsbS0hKWlpag0WhYX22bzUbWSleIKIpYWlpizikSZNNL5IvU3uXevXvceHd3NywWC3NtSLcbB06DKIeHh1hcXGQBhIaGBlitVloHqhhpf7S+vs7GqGq2fKitrUVHRwc6OjpyqtKUktxu374NnU7HWZAXah1IJpOYmpo6UzXrcrkqyvqZKB6JRAI+nw+7u7tsTKfTwePxVLUQC/Btia5du4ZoNMrWgFAodCbWIYoiwuEwwuEwbt68idraWpbcIncrgmISi8UwPj7OCUz19fXweDwkYhN5UYoidiZSq6uzVWuni9qpsab0au1S+hlLjVT3nGp8ZtXW1uLxxx+/0tfQarV4+9vffqWvQRD5QkI2QXyf9IpsURQLKliJooh79+6dsRk0m81wu91labdEQnbupG9Gy/W9isViZzL4AWBgYAAdHR2X/swolUr23hT7PUoXrgHqh50P9xOHWltbcePGjZI+mCkUCtTX16O+vh7Xrl1DJBJhldqZqrPi8TirypD670mCVjmu6aWKIAhYWFjA6uoqN97T04Oenh76XBIXJlNfUIVCgYGBAbS2tgIAbDYb+vr6WC/N7e1tLvAvEYvFsLa2hrW1NVoHqphMgqNSqYTT6YTdbpdxZkQ+ZKrSlPYDqWKNRCQSwerqKlZXV1mCnCRo5bsOJBIJ+P1+zt5Ro9HA4/HAaDTm/bMR1UssFoPP5+MqjWtrazE2NlYV1qwXpaamBq2trWhtbWUOTucluZ2cnGB5eRnLy8usFYFUrV2p+4FoNIrx8XFOXDIajfB4PCWVuEyUD+UiYqdzkWrt9LMrVWvnTmq7Or1eT3EAgqgySMgmiO+TSZQtlE1JMpnEzMwMV6EBAG1tbWVdSUZCdu5Im1A5q40vy+HhIbxeL7d5lKpCChWkValU7D4q5nuU3g879fVJxM6dZDKJ6elpZrsp0dvbi87OzrJ7D3U6Hdrb29He3s6qswKBAILB4Jm+2oIgsKqNubk5GI1Grq92uf3spUIikcDU1BSCwSAbUygUGBwcLKtWHETpEI/HMTk5ydmJ38+mV6FQwGAwwGAwoKurC7FYjAWxM1VnZVoHJFH7Kq2HCXmJx+Pw+/1cooNGo4Hb7S47tyXiLKlVmtevX2dJbpIFefp+VRAEJnoDp6KOVK1dX1+f0zqQyaaXqmaJyxCNRuH1erlEDOpfnDupDk59fX04Pj7mktyytSKQ1gG73V4xLUkikQjGx8e52IDJZILb7SYRm8iLchWxM5FerZ36h6q186faK7IJotohIZsoCuVmLQ4UTsiORCLwer1c5rNU9dPe3l7Wh5hKsssuBiqViolf5Sb6Z7LEr6urg8fjgcFgKNjrpG7Mi3U/nSdiSwJ2OX9Oi0U0GoXf7+fWOqVSiZGRETgcDhlnVhhSq7MEQcDu7i4LVGeqytjf38f+/j5u377NrAalvtrVfgDNlUgkAr/fzwXy1Wo1nE4nrFarjDMjypVIJAKfz8cF8rVaLVwuV06Co1arRUtLC1paWiAIAsLhMBO0UoNuEtI6cOfOHeh0OrYOVJPlaKUj7fNTA2skOFY2qUluyWSSWZBvb29ntCCX1oG7d++ipqaGidr3a0WQ6Z7S6/XweDzQ6XRX+rMRlcnJycmZZGSyfs4fhUIBvV4PvV6Pzs5OxONxbj8Qj8fP/J/UdUCr1TJR22azlWWP15OTE4yPj3NnIMlpsBx/HkJ+KknETieTBXmqqE3V2rmTvjeiOB1BVBe0wyCI73MV1cU7Ozvw+XxcUEOq0KiEIHz6pim9QpHgSX2/ykXIFkURy8vLmJ+fP9Pr2OVyFTz4Uez3KFXElq6lv1PGa+4cHBzA7/dzB8+amhq4XK6KtL+U7IOtViuzHt7e3kYgEOCEfIlUq0G1Ws3ErHINXhWDTPeUTqeD2+0uaPIMUT0cHh7C5/Nx91RdXR3cbjfq6uou/P2USiULRIuiiMPDQ5bckmkdiEQiWFlZwcrKCrMclayHSUgoT46OjuD1erl7ymAwwO12k+BYJahUKjgcDjgcDoiiiIODA7YOpCZhSUSj0YytCOx2O3Q6XcZ7igRH4jJkuqfMZjNcLhdVzRYIjUaTsRVBMBjMuA7EYjGsr69jfX0dCoUCFouFJbjksx8pNsfHxxgfH+fuKSk2kCk5hyCyUckidjr3syBPj0tJZKvWThW2K+29SkcURRKyCaLKoegpQXyf9E33ZUXZlZUVzM7OchsRKRBRKT2oFAqFbFbQ5UjqPVYO75UgCJibm8PKygo33t7ejoGBgSvZKBfzPUrNgE3tjU1W4hdje3sbU1NTXOJBfX09XC5XVQTyU62Hu7u7EY1Gub7a6fdxIpFgfbUVCgXXT7ca3q9cCIVCmJyc5J7DRqMRLpeL7C+JvNjZ2TnjKmIymQqWkKVQKFBfX4/6+nr09PQgEomwCs1M60C65ajZbGbrQF1dHT1/yoC9vT34fD6u8o7EoepGoVDAaDTCaDTi2rVrbB0IBoMIhUIZLcilfwdOE2sikQj3dRaLBS6Xi5LeiLw4PDzE+Pg4l1RPguPVkqkVgfQ5D4fDZxK1RVFEOBxGOBzGzZs3UVdXx0TtUnRxOjo6wvj4OKLRKBuz2WxwOp10TxF5UU0idiYyVWunCtupZKrWPs9RsBLfP7IWJ4jqhk5ERFEoh4BcJmvxfBAEAfPz81heXubGm5qaMDw8XHGBiFQhmyqyzyf1cFfqFdmxWAx+v5/rISpZ4nd0dFzZ6xbDWjxduCYROz+kav2bN29y4w0NDRW51uVKTU0N2tra0NbWhkQigXA4zPpqp1sNiqKIUCiEUCiE+fl51NfXo6GhAQ6Ho2r76a6trWFubo5LAmtoaMDIyAgFyIi82NrawtTUVFHvKZ1Ox9aBZDLJLEfvZz28u7uL3d1d3Lp1i1oRlAHBYBATExPcPoXWKSKdTOuAlOCSKgJJpNo+p/5/gsiHTMk2tE4Vn9R1QBAE7OzssGrtTK2Jjo+PORenVNcGuV0ZMiVGVJPgSBSeahex07lftXYmC3LgbLU28IM4Y6VakFNFNkFUN9UZZSaIDBTCWjwWi8Hn82FnZ4cb7+3tRU9PT0U+ZMutylhOysVa/PDw8EwfNbVaDZfLBbvdfqWvfdVC9nn9sEnEzh1BELCwsIDV1VVuvLOzE729vfQefh+1Ws1Zjqb21U4PWAOndtoHBwe4e/du1fXTFUURd+7cweLiIjfe3t6O/v5+uqeIvFhaWjqTbNPW1ob+/v6ifaZUKhX7LKdajm5vb3O9uiXSWxFIlVk2m40qfUuA9fX1M45LLS0tV+ZUQ1QGqevAjRs3WCuCYDCIvb29jP8nEolgamoKSqUSFouFiVmV4uxFXB1Se7PU82ZTUxOGhoZonZIRpVIJm80Gm83GLHKlau3d3d0zIlUikeDcW4xGI1sH6uvri7o3Pjg4wPj4OJcY4XA4MDIyQvcUkRckYmfnftXa2SzIAb54Q/q31O9Xru9xqpBN7cYIovogIZsgvs9lrcX39/fP9J9Sq9UYHR2Fw+EoyBxLkXKqMpabchD9t7e3MTExwd3/dXV1GBsbK4p1z1XeT+kidno/bClrlTifeDyOqakphEIhNqZQKHDjxg2qHDoHqQeexWJBb28vjo+PEQgEsL29nTGIndpPV61Wc/10K03MEgQBMzMz2Nzc5Mb7+/uv1AGCqFxEUcTNmzfPuONcv34dXV1dsq316ZajJycnnAV5piD25uYmNjc3y7KPZqVx79493Lp1ixvr7u7GtWvXaP9A5ExqK4La2tr7CtkSgiAw9xbgNHArrQMmk4nuPYIjFArB7/dzZ83W1lYMDAzQvVJCpLYm6urqQjweRygUYsJ2uosTcBpv2t/fx507d6DVatm5wGq1XqkTlhTnSp1TY2MjhoeHy1YMI+SFROyLU6hq7WQyWbbV2qIocsUA1I6JIKoPErKJolAODxfpIZ5uyZILGxsbmJqa4g6MdXV18Hg8FZ8lRkJ27pRyRbYoilhaWsL8/Dw3brPZitrv8aoqslNFbOk6VcQul8273JycnMDn83GZsFLCjs1mk3Fm5YVCoYBer0d3dzfrqy2JWZn6aCYSCWxtbWFra4uJWVJlV7lXZsXjcfj9fuzu7rIxpVKJkZGRik4CI64OQRAwPT2Nra0tNqZQKDA4OIiWlhYZZ3aW2tpatLe3o729HYlEAqFQiFVpZmpFkNpHU6/Xs3WAxKyrRRRF3Lp1C0tLS9w4JdsQl2F5eRkLCwvcWE9PD0wmE9sTpAb5JQ4PD3F4eIh79+5Bo9Fwrg3V2taFOCUQCGBycpITNDo6OtDX10fPiBJHo9GgqakJTU1NEEURe3t7bB3I5N4Si8WwtraGtbU1KBQKWK1W2O122O32gia67e3twev1cknuzc3NGBoaonuKyAsSsQtDvtXaqQUdmaq1S/13QBXZBFHd0EmHIFJQq9UscJiL0ChV/KRboTY0NGB0dLTiquYyUcribKlRqqK/IAiYnZ09YxPd0dGBGzduFHUzexVCdiYrcVEUyUr8guzu7sLv93PiSm1tLdxud1Gq9SuZmpoatLa2orW1FclkkhOz0vvppopZCwsLMBgMrK92sW0GL8vx8TF8Ph+XWa3VauFyuWAymWScGVGuxONxTExMcC1eVCoVnE5nySfbqNVqNDY2orGxkbUikILYqUEbiaOjIxwdHTExS6rMIjGrsEh7pI2NDTamUCgwPDyMpqYmGWdGlCuiKOLu3bu4e/cuN37jxg20t7cDOA3q9/f34+joiO0HUhO+JOLxODY2NrCxscEluhVazCJKn42NDczMzHACBjlGlCcKhQJmsxlmsxnXr19HJBLh3FvSz8iiKDLXhoWFBej1eiZqm83mvM/yu7u78Hq9XNyCqvuJy3B8fIzx8XESsQtMarW2tD7cr1o7kwW59PXlUK1NQjZBVDcU5SCIFFQqVc5Cdjwex+TkJLa3t7nxnp6equoRmxosLSVxthS56v7P+ZCpr7tCocDAwIAsVUaFTIxIzTSVrqkfdn5sbGxgdnaWu2/NZjOcTie0Wq2MM6s8VCoV11d7b2+P9dPNJGZJlVmLi4uoqalhFZpWq7XkDp6pZEqM0Ov1cLvdZV9lTshDJBKB1+vlPidarRYejwf19fUyzuzipLcikPpobm9vZ+yjGY/Hsb6+jvX1dVaZJa0FOp1Opp+i/Ekmk5iYmOBaaZRLYgRRmoiiiIWFBaysrLAxhUKBoaEhNDc3c1+baj3c3d2NWCzGbIdDodCZNljpiW6SmCW5NpTynoC4HKurq5ibm+PGent70dXVJc+EiIKi0+nQ1taGtrY2JJNJ7OzsnOvaICW6LS0tsfZEkrCd67ktU5/1trY23Lhxg87PRF6QiF0c0ntgC4LA/blotXaqsC3370kURe6cRwl7BFF9kJBNFIVy2eymVsye1yP78PDwjL2uZIWaHoSodKgiO3dKrSL74OAAXq8XJycnbEyj0cDlcskWoC1UH/H0ftip349E7Ny5X9VQc3MzBgcHZT/MVDqpFRmSmCWJ2pkqs6LRKFZXV7G6ugqVSgWbzQaHw1FyfbW3trYwPT3NfcYtFgucTmdJzZMoHw4ODuDz+RCNRtlYJSVG6PV66PV6dHZ2Ih6Pc60IMolZUmXW/Pw86uvrWYWm0WikZ1+OSIl++/v7bEyr1cLtdsNoNMo4M6JcyVTdr1QqMTo6ioaGhqz/X6vVoqWlBS0tLRAEAbu7u2xPkLqXl0gVszQaDWw2G7Mgp2dt5bC0tISbN29yY6nV/URloVKpmCid7tqwt7d3RqRKbU8EACaTiSW4GAyGjHuCTH3WyaKeuAyZRGzJxZLiCVfL/SzIU0VriUzV2qkV26VQrZ3q5EYV2QRRfZCQTRAp5FJdvL29jYmJCS5wqNPp4PF4qjKwVWribClTKJG2EAQCAUxMTHC/M71eD4/HI6tNdCGq1tNF7PR+2NIGnDifZDKJ2dlZbG5ucuPXrl1Dd3c3vYcyIIlZXV1drDJLErPS199kMolAIIBAIMAEcalCU67sZVEUsby8fCbgSokRxGUIh8Nn9mVmsxkul6sixRqNRoPm5mY0NzfnJGYdHBzg4OAAd+/eRU1NDQtgW61Wbl9C/ICTkxN4vV4uWFZbWwuPx0PVH0ReJJNJTE5OIhgMsjG1Wg2XywWLxXLh76dUKmG1WmG1WtHX14fj42POgjyTa8Pm5iY2NzfZnkBaC6g9THkiiiIWFxdx584dbnxoaAgtLS0yzYooJumuDVKim+TakOp6JLG3t4e9vT3cuXOH7QmktiQqlQrBYBATExPcObyrqwvXr1+nsx+RFyRilw6pFuTAabztfhbkQOlVa8diMa7tGgnZBFF9kJBNECmcJ8pKlYm3bt3ixi0WC9xud9Xa65KQnTulUL0uiiLu3buHhYUFblyydZI76H/Z9yhVxJauU0VsOizlRiwWg9/vx97eHhtTKpUYGhqinqAlQmplVjKZRDgcZmJWpr7aOzs72NnZwc2bN6HX6+FwONDQ0FC0Ck1BEHDz5k3OThU4bcfR09NDwTEiLzL1BHU4HBgeHq4KkTZdzEp1bUhdvyWi0SjW1tawtrYGpVLJKjTtdjtqampk+AlKj8PDQ3i9Xq66v76+Hm63m94jIi8SiQT8fj/Xxkej0RQsCVqhUHCJbqliVjAYzOjaIO0Jbt26hbq6OiZqX6afLlE8RFHErVu3sLS0xMYUCgVGRkbQ2Ngo48wIOUlNdEttTxQMBnF4eHjm69P3BHq9HgcHB9zX0D6duAwkYpc296vWzmZBDvBxNinxJbVo5Cp+v6kJpgAJ2QRRjZCQTRSFctn43s9aPJFIYHp6+kxlYkdHB27cuFHVmzASsnNH7opsQRAwMzODtbU1bryzsxP9/f0lcR9f5j3KZCUu/V2lUpXNOiQ3UuuE1AOnVquFy+WCyWSScWbE/VCpVKzaWhRF7O/vMzErU+Dq6OgIi4uLWFxchFar5fpqX4X4l0gkMDU1xVWiKRQKDA4OUtUQkReiKGJpaelMcmF7ezv6+/urcr2/Xz/d+7k2CILA1gkgN7vRSmdnZwd+v587A1gsFrhcLs61iSByJRaLwev1cuKQ5OR1VZXQ6a4NqWJWalssiePjYywvL2N5eZn105UsyKs1UbuUEUUR8/PzWF1dZWMXsagnqoP09kQnJycsuSUcDp85ZwuCcEbENpvNsFqtEEWxKvcExOUgEbu8uGy1NvCDePBVWZCnxzXIUYYgqg86kRNECplEWcleMHVjLwXgqfcUCdkXQc73Sur1mFoNUor3cT7W4qkWR9I19cPOj1AohMnJSS6IbzAY4HK5KqLPbDWgUChgMplgMplw/fp1HB8fIxgMIhAIZLQbjcViZyo0pb7ahQhgRyIR+P1+7hmqVqvhdDphtVov/f2J6kMURSwsLJyp7u/t7UVnZyet998n3bVhZ2eHCdep1cYSqXajOp2OJbhYLJaqCDgGAgFMTU1xe49qqu4nCk8kEsH4+DhXQSS18dHpdEWZg1KphMVigcViOWNBvrOzk7WfbroFOa2v8pKpz7pKpYLL5aI9FXEutbW1aG9vR3t7O3NykoTtVKExld3dXbz++uuU4EJcGBKxy598q7UzWZCnfr/L/P7Tk/GoIpsgqg8SsgkihfQe2eFwGD6fj+svpNVq4Xa78+pnVomQkJ07clmLHxwcwOv1cr0zNRoNXC4XbDZb0eaRCxcVsjNZiZOInR8rKytYWFjgDiZ2ux0jIyNUiVbG1NXVoaOjAx0dHcxuVApiZ6vQTO2rnU/Gc6bqfp1OB7fbTQdPIi+SySSmp6cRCATYmEKhwPDwMLU9OAeVSsX6YN64cQMHBwfss55egQWcCnArKytYWVlh/7eSA9hra2uYnZ3lxtra2nDjxg3aQxB5cXR0BK/Xyz3/jEaj7O2o6urq0NnZic7OTsTjcdaWJBgMZuynu7u7i93dXdy+fRu1tbVsLaiWBJdSQhAETE1Ncc8/tVoNj8dDjknEhUh1csr0/EsnPcHFZDKxtiTV6uBC3B8SsSuPi1RrZ7IgB07PcMlk8lLV2iRkEwRBkWmCSCFVlD06OsJrr73GPZSNRmNRs+jLARKyc0cOa/FAIICJiQnud6PX6zE2Noa6urqizOEiXOR+ShWxU/8AfH8e4nxEUcTNmzexvLzMjbe3t6Ovr48OnBVEut2oVKEZCAQyVmhKAWyph6bUV9tkMmX9bGWq7jcajXC5XNRnlsiLWCwGv9/P9X6m6v6Lo1AoYDQaYTQace3aNUQiEZbgksluNJlMnqnQvEyCSykhiiLu3buH27dvc+PXrl1Dd3c37SGIvNjf34fX6+WEYavVCqfTWVKJgRqNBo2NjWhsbMypn+7JyQmX4CJVaBbKwYW4P8lkEhMTEwiFQmxMo9FgbGwM9fX1Ms6MKGcyidi9vb2oqalh1dqp+3gJycHl9u3b0Ol0LFHuqloUEeUDidjVQXq1duqfq6zWJiGbIIjSOUkRFU25BIJSN97pFkstLS0YGhqizXkaJGTnTjHfKyk4u7CwwI3b7XY4nU5oNJorff18ybUiO1MldqqITQel3MjUuxgAbty4UVKW80ThkWzEbTYb+vv7s1ZoHh8f4969e7h37x40Gg0Tsmw225nn4traGubm5riDbENDA0ZGRugZSuSF1OYl1aK3pqYGbrebgviXRKfToa2tDW1tbUgmkwiFQkzMisViZ74+PcFFWgtMJlNZPXvvZ1E/MDCAtrY2mWZFlDvhcBh+v5/b55fD8y9TP11pHQiHw2cC08lkEoFAgFUHU4Xm1ZFIJOD3+7n2UDU1NRgbGyv7ZCJCPlZWVjA/P8+NDQ4OorW1FQBY0uve3h5LdksXkYDTmNnq6ipWV1ehVCphtVqZsE1tqaoLErGrk0wW5KmidiGrtUnIJgiChGyiaCgUijOH4FLjfvPr7+9HV1cXHcozkF5lLIoivU/3IXUjJm3wrmJTLwgCpqensb6+zo13dXWhv7+/pH8/6UJ2pvspfVOcKmirVKqS/vlKiZOTE/j9fq7qRqVSYXR0FHa7XcaZEcUmvULz5OSE9dXO1EMzHo9jfX0d6+vrLGglBbBXV1exuLjIfX17e3vJrz1E6bK/vw+fz8eJqgaDAW63mxxyCoxKpYLD4YDD4YAoitjf32cJLpkqNI+Pj7G0tISlpSWo1WrOgrxUE+aAH+yTpCpz4HT/MTw8jMbGRhlnRpQz29vbmJyc5BIxW1paMDAwUHZB/NraWtaWJJFIcBbkmRJcMlVoShbkpSzglzrxeBxerxf7+/tsrLa2FmNjYyQSEnmztLSEmzdvcmPDw8Nobm7mxpRKJSwWCywWC0twkUTtnZ2dM0nngiCwSm7gdK8midrlluxGXAwSsQkgswX5Zaq1U4VtpVLJCdkKhYKegwRRhZCQTRDfZ29v70xVhlqthsvlIlHnHNKDE8lksqRs80qJ9PfqKoTsaDQKn8+H3d1dNqZQKDA0NFQWFUaZ3iNpLHVjK11TP+z82Nvbg9/v54KR1LuYkKitrUV7ezva29sRj8e5Cs10i8H0oFU6/f396OjoKMa0iQokGAxicnKSq260WCwl7SxSKSgUCphMJphMJly/fp1VaEoB7PRgVCKRwObmJjY3N6FQKGCxWFi1dikFmhKJBCYmJhAOh9kYWdQTl2VjYwMzMzPc56KjowN9fX1lvzdVq9UZE1yCwWBGB5f0Cs1UC3JqLZI70WgUXq+XSyLS6/XU5oy4FPfu3cOtW7fYtUKhwPDwMJqamrL+39TzQTKZ5BJcMrUoOjw8xOHhIXNzstlsTNimPVzlcHx8jNdff527B0jEJoDM1drJZPJMW0Agc7V2auEKgDPPQ0qUI4jqg9QmgsCpDerMzMyZrNJHHnmELLuykEl4JDKTvpEvtOh/cHBwJhNWo9HA7XaXTXA2/T2ShGwSsQvH1tYWpqenuc+qyWSC0+mkACNxBo1Gg6amJjQ1NUEQBOzu7rK+2uktONLRarU4OTlBOByG2WymYAZxIdbX1zE7O8sFORobGzE8PEz3kgykV2imJrik9gIGTp/R4XAY4XAYCwsLrCpLsiCX63kdi8Xg9Xo58U2r1cLj8ZBFPZE3y8vLZ1r5VGqf9fQEl0gkwlmQZ6rQlBJgAMBoNLK1oL6+vuLen0IRiUQwPj7OtdOor6+Hx+OhfuRE3ty9exd37txh1wqFAiMjI3k5kahUKpasJooiDg8P2Vqwt7d35uvj8ThLdgMAs9nM1gK9Xk9rQZlCIjaRK5mqtZPJZEYLcuBstTbAW4vrdDoWT6X1gyCqBxKyiaJRitbigiDg5s2buHfvXsZ/r6urK+6EypB0ITuRSNAB+z5kql4vFFtbW2eq1gwGAzweT1ndx5mE7NR+2OnZm5KATZvX7Eh902/fvs2NNzY2YmhoiDJaiaxINuJWqxV9fX04PDzE+vo6VldXMyYxxWIxLC8vY3l5GRqNhrMdJucO4n6IoojFxUUu2AoAnZ2d6O3tpfW+BFCr1WhsbERjYyProSmJVanCi0RqVZZWq+XWgmI9e46Pj+H1enFycsLG6urq4PF4SqpinCgfRFHE3bt3cffuXW78xo0baG9vl2lWxUWn03EVmqFQiFkPZ7Ig39/fx/7+Pu7evYuamhq2FlitVtqHfp9MFr0mkwlut5uqWIm8EEURd+7c4Vr/KBQKOJ1ONDQ0XPr7KxQK1NfXo76+Hj09PYjFYsytKRQKnXFzAoDd3V3s7u5SO4IyJpOI7XA4MDIyQiI2kZX7VWvfz4JcFEU888wzbGx/fx9bW1toamqCVqul8yFBVAkURSSqllgshomJCYRCoft+DdlkZ4cqsnPnKt4rKeCf3ueqoaEBTqez7O7fTIkRarWaROxLIggCZmdnsbGxwY339PSgp6eH3kPiwigUCgiCgI2NDW4tU6lUGQ+g8XgcGxsb2NjYgEKhYH21GxoayCKTYAiCgPn5eaytrXHjZFFfuqT20Ozr68PR0RETtVPbnEjEYjGsr69jfX2dJcdIAeyrWgsODg7g9Xo5Yc1oNMLtdlPyJZEXoihiYWGBa0sltfJJ7zNbLahUKs6C/ODggFVopvZ4lohGo1hbW8Pa2hpbCyQL8mrdFxweHmJ8fJxbq6xWa1me6YjSQBRF3Lp1C0tLS2xMqVTC6XReWfs8rVaLlpYWtLS0MDcnSdhOraiUSG9HQGtB6UMiNlFIcqnW/t3f/V185zvfYf8nkUhgdXUVHR0dFMsiiCqCdsNEVXJwcACfz8dVjahUKnR3d3PViiRkZ+cqq4wrjUzW4pchmUxiZmYG6+vr3HhXVxf6+/vLckOX/h4lEgkmiKWK2CqVqix/PjmQknYy9U2v1mArcXkyWdRLvYsVCkVW2+FQKIRQKIT5+XkYjUYmahsMBvpsVynJZBKTk5Ncv3WlUonh4eG8bC8JedDr9dDr9ejq6kI8HmfVmZmqsgRBYMHt+fl51NfXs7WgULbD4XAYfr+f23PZbDaMjo7SHp/Ii0zJgUqlEqOjowWpbqwEFAoFjEYjjEYjrl27hkgkwlVoZrIgl/4dOLXRlhJcjEZjVewL9vf34fV6uT2T3W7H6OgoVagSeSGKIm7evInl5WU2plQq4XK5YLPZijKHdDen4+Nj9lkPh8NnEl/T1wKDwcBEbTlbkxA/gERs4qpJr9b+nd/5HfzO7/wO+3eFQoEHH3wQDz30EK0JBFFl0OmdqDoyWTDX1tbC4/FAEAROyE4kEtQzNgskZOdOISuyo9EovF4v14NKEifb2try/r5yk0nsp37Y+XN0dASfz8dZqWo0GjidTlgsFhlnRpQroihieXn5jAtEc3MzBgcH2Wc43XY4EAhge3ubuxclJKvRO3fuQKfTMSHLYrFQQKRKiMVi8Pl8XNWeWq2Gy+WitaqM0Wg0aG5uRnNzMwRBwM7ODqvWTrXNlTg4OMDBwQGzHZaC1/naDm9tbWFqaooLlDc1NWFoaIjWFiIvMiXc0FqVHZ1Oh7a2NrS1tSGZTCIcDjOx6ry1YHFxkWtHYLVaKzIBZXd3Fz6fj0v2aWxsxPDwMK1VRF6Iooj5+Xmsrq6yMZVKBbfbLetaVVdXh46ODnR0dCCRSHBrQaowKiG1JllcXGRtiux2O2w2G1ntywCJ2EQxEUURf/AHf4BPfepTbKy2thb/8A//gLe+9a10zxFEFVJ5pwCiZJFbeJJ6A6X3h7XZbHA6ndBqtTg8POT+jUTZ7JCQnTuFqsiWMvZTAz9arVb2g2khSF8nJEsh6d9IxM6dcDiMiYkJLiim1+vhcrnKqm86UToIgoCbN29yVqrA+Rb159kOpybiSEQiEaysrGBlZQVqtZrrpUsBq8okU+9inU4Ht9sNg8Eg48yIQqJUKmGz2WCz2dDf3591LYhGo8xqVKVSce0IcrEDX1lZwfz8PDfW0dGBvr4+2kcQeRGPx+H3+zmHG61WC4/Hg/r6evkmVmaoVCr2WRZFEYeHh8zBJdNakNqOILU1id1ur4j+9qFQCBMTE9y5sKWlBYODg7RWEXkhiiJmZ2c51za1Wg232w2z2SzfxNJQq9Vn2hFIonamtSC9TZHZbGbCtl6vp8/LFUMiNlFMRFHEn/zJn+CTn/wkG6upqcHf/d3f4V3vehd93gmiSlGkW7kUEdlemJCHRCIhm8iZSCQwNTWFra0tbryzsxP9/f1s4xWJRPDSSy+xf3/ooYfKXhi8agRBwHPPPceuXS4XmpqaZJxR6ZJMJvH888+za4/HA4fDcaHvkclRwGAwwOPxlL04KYoiIpEIvvGNb7Cx1D5ZDQ0N5JCQI2tra5ibm+Oq0KxWK0ZHR0kMJPJCeo6mVqEpFAoMDg6ipaUlr+8ZjUaZkBUOh891qVAoFLBYLGwtqITgNQHs7e3B5/NxVqr19fVwu9203lcR0WiUsyDP5lhjMpnYWpAevBZFEXfv3sXdu3e5/3P9+nV0dXVR4IvIi1gsBq/Xi4ODAzam0+ng8Xig1+tlnFllIa0FkgV5ttiBwWBgCW/laDu8vb2NyclJbs1rb28v2xZRhPxkan2gVqvh8XhgMplknNnFiMVibF8QDofPtCZJp7a2lonaFouF7PgLDInYRDERRRF//ud/jo9//ONsTKPR4Atf+AKeeuopej5WHvQLJXKGhGyiaMglZEuVPqnV1kqlEkNDQ2htbeW+Nh6P48UXX2TXY2Nj1OssB5599lkmmI2MjJx5X4lTRFHEs88+y66dTmfOPYqlwOytW7e48YaGBjidzrK32ZPsw5PJJL75zW9mPKxKmdcNDQ1wOBwkZGVAFEXcvn0b9+7d48ZbW1tx48YNOmgSeRGNRuHz+bgAvlqtxujoaMF67CWTSdZXe3t7+0xf7XSuopcuUVwyBfCtVmtFPNOI/Em1Hd7e3s5oNZqKFLxuaGiA2WzGwsIC1tbW2L8rFAoMDAzQ3pTIm5OTE3i9XhwfH7MxvV4Pj8cDnU4n48wqm1zaEaQi2Q5LLi6l/hzZ3NzE9PQ0l3Ta1dWF69ev056GyAtBEDA9Pc0Vb2g0Gng8HhiNRhlndjkEQcDu7i5zbkhdizMhOcBIwjat05eDRGyimIiiiL/8y7/Ez/3cz7ExtVqNz3/+8/iRH/kRej5WJvRLJXKGhGyiaCSTyayZlIUmFArB7/dzAfGampr72ipRdXF+vPjii+w9HhwcREdHh8wzKl2ee+45FrQfHh7OqZ91MpnE9PQ0l1kNAN3d3RVhkSmJ2NLzaGtrCzdv3swavDYYDHA4HCRkfR/pPgkEAtx4X18fOjo6qv79IfLj8PAQPp+PCyBfte2zKIrY29tjweujo6Nzv17qpetwOKivdpmwurqKubk5biy9zzpBSFaj0lqQmkyTCYVCwYlCSqUSo6OjlJRK5M3R0RHGx8e5PanRaITb7c7J4p4oDKIosnYEwWCQs3fPRKqLi91uLznXqrW1NczOznJj169fR3d3t0wzIsodQRAwNTXFnQMrtfXB8fExS3bb2dlBtph2fX09S3IxGo10Jr4AJGITxUQURfzN3/wN/sN/+A/sc61SqfBXf/VX+NCHPkSf3cqFfrFEzpCQTRSNYgrZoihiaWkJCwsL3MbWZDLB7Xafm5WZKjRSdXFufP3rX2eb2/7+fjqEn8NFRf9IJAKfz8f1iVIoFBgeHi77e1MUxTN/Uivzjo6OEAwGEQgEzvSvT0en07HqzGoUsiKRCPx+PxfkV6lUGBkZoQA+kTehUAiTk5Pcs1sO2+fUXrrZgtcqlYoFq+x2O1nplxiiKOLOnTtYXFzkxru7u3Ht2jUKUBDnEolEmJCVrR0BcLpeNTU1MQtygrgI+/v78Hq9XEI0uUaUBpLtsGRBni3GoNfrOQtyOc8Jy8vLWFhY4Mb6+/spEZzIG0EQMDExwbX/0Wq1GBsbu7Kk01IhkUggHA6zvUEsFjv361OdG6xWK50TzoFEbKKYiKKIv//7v8dP/MRPsP29QqHAX/zFX+CjH/0onRErG/rlEjlDQjZRNIolZCeTSczMzGB9fZ0bb21txdDQUNZNF1UXX5xvfvObzOLp+vXruH79uswzKl1eeuklVtmYTfSXeoemVkJqtVq43e6y792eLlyn/l2hUECpVHKb1ZOTE2xvbyMQCGB3d/fczGu1Wg273Q6Hw1EW1oKXZX9/H36/nztkSs4TlZaBTxSPTH3W7XY7RkdHZe07l9ozLxgMZu2rbTabmXMDtSOQF0EQMDc3d2Z/duPGDbS3t8s0K6JckYLXm5ubCAQCWSuy6urqWMKb3EIWUfqEw2H4/X6uLZbD4cDw8DD1Xi0xUm2Ht7e3cXJycu7XS+cEyYK8mELW4uIibt++zY0NDg6WfXIyIR/JZBITExMIhUJsrKamBmNjY1WXwCWKIvb391mSy/7+/rlfL50TpPWgrq6OxLLvk8mNhERs4qoQRRFf+tKX8JGPfITbd/3Zn/0ZfvInf5I+l5UP/YKJnCEhmygagiBk7Xl5We5XvXrjxo2crXVThca+vj709PRc2XwrhW9/+9usErS7uxv9/f0yz6h0yVX039zcPNM7tL6+Hh6Pp+zFmFQrcekZdJ6Inc5FhSybzcaC18WsIi0GgUAAU1NTZ+4Tl8tF/cCIvLhfxWx7ezv6+/tL6iAp9dKVgtfZqjAMBgNbC8hasLgkEglMTk5ygValUomRkRE4HA4ZZ0aUM0dHR/B6vVzCX7q9eCbKrZcuUVwy7a1aWlowMDBAAfwSRxRFHB8fcxbk560H6ULWVQl/oiji9u3buHfvHvfaw8PD1MaMyJtkMgm/349wOMzGdDodHnjggbKPFxSCaDTKOTekCmSZqK2tZWtBNTq8SZCITRQTURTx5S9/Gf/23/5bTi/4X//rf+Fnf/Zn6Z6rDigoQ+QMCdlE0bhqIXtnZ+dMVaJGo4HL5YLNZsv5+3zrW99iNsbXrl1Db29vwedaaXz3u99ldq8dHR0YHByUd0IlTKro39PTg76+Pu7fJREpPVvf4XBgdHS07IOt54nYkoB9EXFJErICgQC2t7ezrjEmk4n10i3nLHWpfcKtW7e4caoWIi6DIAiYmZnB5uYmN97X14fOzk6ZZpUbUhWG5NyQra+2VqtlorbVaqXPzBUSjUbh8/m41gfS/sxsNss3MaKskVxrUp/7UguhRCLBElyy9c9M7aVLzg3E+vo6ZmdnuXumo6MDfX19lPxUhsTjcSZkBYPBrO5wdXV1TMgym80FCaCLooiFhQWsrKywMaVSidHRUWr/Q+RNIpGAz+fjWu7U1tZibGyMnmMZEAQBOzs7LBk+m3ODSqWCzWaD3W6H3W6vuGT4+0EiNlFMRFHEc889hx/90R/lEtJ///d/H7/4i79I91z1QBtsImdIyCaKxlUK2aurq5iZmeGCDlIfz7q6ugt9r1deeYVVdHd1deHGjRsFnWsl8tprr7Eqq9bWVoyMjMg8o9Il9f7q7OzEwMAA+7dkMompqakzIlJPTw96e3vLPoCWKmID4P6uVCovvVEVRRF7e3sIBAIIBAJZD6h1dXXMcthkMpXN+ysIAubn57G2tsaNd3V14fr162XzcxClRTweh9/v5wJi5VwxK1VkSX21z9vvSsEqqa+2Vqst4kwrm0wVs7W1tXC73WWdTETISygUwsTEBFdddb/WB/F4HKFQiFVoZhOyyLmhesnUu/j69evo6uqi+6ACEAQBe3t7bC3IlvCmVqvZ3sBms+W1NxBFEbOzs1xLDZVKBafTeaFEe4JIJR6Pn3EhrKurw9jYGDly5cjR0RFLcMmW8Aacxhalc0Kl7g1IxCaKiSiK+NrXvoYPfvCD3Dnx05/+ND7xiU/QPVddVN6CSlwZJGQTRUMUxay2nxdFEnSWl5e58cbGRoyMjORVvfrqq68ye6a2tjYMDw8XZK6VjNfrRSAQAAA0NTXB5XLJO6ES5n73VyZbfKVSieHhYbS0tMgy10KSWoWdrR92IRBFEUdHR6w6M1uPrHKpzozH45icnOQs5BQKBQYGBqi/HpE3x8fH8Pl8rO0BcPqZcLlcMJlMMs6sMEgVWYFAICdrQbPZzNYDElvzZ3d3F36/n0tiNBqNcLlcVVPZQhSejY2NM8mrzc3NGBwczBr0knrpShVZqWteJrRaLWdBXqp7A+JyiKKIu3fv4u7du9z4jRs30N7eLtOsiKsm1YI8FyEr3YI829lFEARMT09ja2uLjanVarjdbnIjIfImHo/D6/VyZ1u9Xo+xsTHaW+VJIpFAKBRiwna2mKW0N7Db7RXTnoREbKKYiKKIl19+GU8//TS3F//v//2/47/9t/9G91z1QUI2kTMkZBNFo9BCdiwWO9MTCAB6e3vR09OTtzA2Pj6O7e1tAKeBMafTeem5VjoTExPY2NgAADQ0NGBsbEzmGZUuqfdXS0sLRkdHsbe3B6/Xyx0ctFotPB5P2Qc60oXrYojYmYhEIqw6MxwO51Sd6XA4YLfbodFornx+uXB8fAy/389VkKjVajidTlitVhlnRpQzmex59Xo93G53RVoTCoLA9dVOXXczodfrmahdTs4NcpOpx6zdbs87yZAggMwVs5dxI5ES3iTnhvNQKpWwWq2sIouq3iqDTLbP1Lu4+ojH42xvEAwGs7rIZeulm0wmMTk5iWAwyMY0Gg08Hg+MRuOV/AxE5ROLxeD1erlWLQaDAWNjY+QmVCCkVkVSwlvqe50JqT2JJGyXYwIsidhEMRFFEd/97nfxwz/8w6ylJwD8+q//On7rt36L7rnqhAIsRM6QkE0UjUIK2fv7+/D5fJx1sGTTdVkLVL/fz6ydHQ4HPB7Ppb5fNTA9PY3V1VUAgNVqxYMPPijzjEoXn8/HMvMbGxvR1NR0JthfX18Pj8dT9iLSef2wiylip5NIJNjhNJvNaKn0ztzZ2cHExAQXWKurq4PL5SrLAzNRGmxtbWF6eppbfywWC5xOZ8kkcFwloiji4OCAOTekHqYzQdWZubGysoL5+XlurKWlBQMDAxScIPJCFEXcvn0b9+7d48b7+vrQ2dlZkNeIxWJcL91szg1Go5GtB/X19ZTkUoYIgoCZmRmupY9SqYTT6YTdbpdxZoScSK2KpHNCtr1BensSpVIJv9+PnZ0d9jVarRZjY2MwGAxXPX2iQolGo/B6vdz9KMUMSMS+OiKRCGtPEg6Hs+4N6urqmKidKcml1CARmygmoijitddew1NPPcW5Svzn//yf8du//dt0tq5e6BBF5AwJ2URRyVb5lAubm5uYmpriNpF1dXXweDwFORymirI2mw1veMMbLv09K525uTksLS0BAEwmEx555BGZZ1S6TE5Osj5pdXV1Z2wtL2OLX0qki9ipYrYkYJdC0FcQBOzs7CAQCORUnSn1x3I4HDAYDEX5GdbX1zE7O8tVkVssFoyOjlLggsgLURSxvLyMmzdvcuO52vNWKicnJ6w6M5vNqFKp5ALXZOd4f7Gxp6fnUk45RHUjCALm5ua4HrMKhQJDQ0Nobm6+stfc2dlh60Fq775M6HQ6JmpbrdaqXUPLiUwVs2T7TGRC2hsEg8Gsrk7AqbCdGqfQ6XQYGxtDXV3dVU+VqFCi0SjGx8c5Vy6j0QiPx1MViaelQureIBgMckU1mZCSXCRhu9TOCiRiE8VEFEX4fD780A/9EOeC9PGPfxy/93u/RyJ2dUNBAiJnSMgmisplhGxRFHHr1q0z/cvsdntBq8dIlL04N2/eZL8Xg8GAN7/5zTLPqHRJTZRI59q1a3nbY5YSqSI2AO7vSqWyZA9GkpWYFLjOVoGh0+ngcDjQ0NAAs9lc8J9LFEXcuXMHi4uL3DhVNhKXIZONKkBiYzrxeByhUIj11T7PuQE43S+k9tWutvcxU2WjQqHAwMAAWltbZZwZUc5kEhtVKhVGR0eLVjEriiIODw9Z4Hpvb+/cr0+vzqSEs9IjHo/D7/dzgVSppU99fb18EyNKnkQiwVmQZ3ObUygUaGpqQlNTEywWCwXqiQsTiUQwPj7OJb+bzWa43e6yT3wvZ0RRxPHxMVsLdnd3sya5lJKTC4nYRDERRRHT09N473vfi1AoxMZ/9md/Fn/4h39IaxlRXYET4lKQkE0UlVgslnWDl4lEIoGJiQnWW1iiu7sbfX19Bd0Ekih7ce7cuYNbt24BOK0yfutb3yrzjEqXqakprK2tcWNKpRLDw8NoaWmRaVaFI5OVuCiKslqJ54t0OA0EAll7Z2o0GnYwtdvtlw5UJZNJzMzMMBt6ievXr6Orq6us3keidEgkEpiamuJEIYVCgcHBwYpYf66Ki1Zn1tXVMVHbbDZX/Oc1Ho9jcnIS4XCYjZE9L3FZ4vE4fD4fJxxrNBq43W6YTCbZ5hWNRll7klAoxLVmyES1J7mUGpl6zFLFLJEPqQmwgUCAq5bNhOTkIp0XSq06kyg9Tk5OMD4+zlX+WiwWuFwuEn5KDCkBVmpPktoOLBOp7YqsVmtRf5+ZROzGxkYMDw+TiE0UHFEUMT8/j3e/+91cPP9jH/sY/vRP/5TWMgIgIZu4ACRkE0UlHyH76OgIXq+XOxxepfB39+5dZrdaW1uLt73tbQV/jUrj3r17rB9mTU0N3v72t8s8o9Jkb28Pr776Kmc3V1NTUxE2hpJ4LQV0U/9ejiJ2OrFYjIlY2QLXSqUSVquVVWtftBorGo1iYmKCC95La15jY2PePwNR3USjUfh8Pi54r1arMTo6CpvNJuPMyovU6sxAIMC9n5lITXKx2WwVd1iPRCLw+Xycg4VWq4XL5ZJVbCTKm0gkcmbvr9Pp4PF4oNfrZZwZTzKZZNWZ29vbWasza2truSQXChgXl5OTE3i9Xq6yUa/Xw+PxQKfTyTgzopzJJDbmQilVZxKlx/HxMcbHx7nkSavVCpfLRZX9JY4oitjb22OidrazgkKhgMViYQnxV5lURSI2UUxEUcTNmzfxnve8h3Pt+shHPoI///M/p9YIhARtgIicISGbKCoXFbK3t7cxMTHBWXrqdLorrcZYWlrC3NwcgNNg7Dve8Y4reZ1KYmVlBTMzMwBOhZHHH39c5hmVHhsbG5iamuIEUIVCgbe97W1lHzxLtxKvNBE7nWQyiVAoxALX2TKuzWYzV411HgcHB/D7/VzQgkQh4rIcHh7C5/Nx95X0LDUYDDLOrPyJRCJsLcjWO1NKcpECVeW+9me6r+rq6uB2u6mykcibTPeVwWCA2+0u6c+MVJ0pVWtnC1yr1WrOgpyCeVfL4eEhvF4vF7w3Go1wu91k/07kTSZRyGKxYGRkhFVrB4PBrO3VampquOpMEiqrm0z3ld1ux+joKN0bZUgkEmGidi5OLpKzk91uL2jSG4nYRDERRRF3797Fu9/9bs6R8kd/9Efxl3/5l7T3IlKpnGAxceWQkE0UlXg8nnXjBpw+9BYXF1lltIRkpXSVVlyrq6uYnp4GcBp0fuKJJ67stSqF9fV1TE5OAjgVLp944omKEi4vgyiKuH37Nu7cuXPm3yqhej1VxE79A4AJ2JV8LwiCgL29PQQCAWxvb2etxtDr9WhoaIDD4YDRaOTem2AwiMnJSa5ivxyC90RpEwqFMDk5ySWE1dfXw+12k61lgUkkElySS7a+2kajkSW5GAyGslord3Z24Pf7uZ/RZDLB5XJRYILIm93dXfj9fi5BzGw2w+VylZ3Qe3JywkTtbEkuCoWCS3qjRJDCsre3B5/Px91XVqsVTqez4lwyiOJxcHCA8fFx7r6y2WxwOp2c2CiKIg4ODpiovb+/f+73lZLeJGGbzgDVxeHhIcbHxzmHj4aGBoyOjpLYWAEkk0ns7Oyw/UG2dkVS0pvdbofdbs97j310dITXX3+du69IxCauClEUsbS0hHe/+91YXl5m408//TT+3//7fxSDINIpnyAIITskZBNFJRchO5lMYnp6GhsbG9x4e3s7BgYGrnyjtbm5Cb/fz66ffPLJsgouy8HW1hZ8Ph+7fuKJJ2hDjB/0o03vcyyh0Wjw2GOPFXlWhSO9Ejv170qlsuruAVEUcXR0hEAgkJPlsFarZfbjh4eHrM+8hN1ux8jICAVZibxZW1vD3NwcJ6BQRUdxEAQBu7u7TNTOluRSTpbDW1tbmJqa4u6rhoYGjIyM0H1F5M329jYmJye5c0Kl3FdSkosUuM7m5CIlvUnVWHQOyZ9wOAy/388lCTocDoyMjJT0OkuUNru7u/D5fFwyV6731UWrM+vr65monZ4ES1QWmZIjaL2qXKTYgbQ32Nvby+peaTKZmKida0sCErGJYiKKIlZXV/Hud78bi4uLbPz9738//vZv/5aSs4hM0MaGyBkSsomikk3IPjk5gc/n4zKVFQoFBgYG0NHRUYwpYnt7G+Pj4+z68ccfJyEpC8FgEK+//jq7fuyxx8qucqbQZLqXlUolWltbsbKywq7LteI/tQo79RoAVCoVBVnwA8vhQCCAnZ2dC7VV6OjoQF9fH72PRF6Ioog7d+5wh0fgNCGsv7+f7qsiIwWqJFF7b2/v3K9Xq9UsaG2320tqD7K0tHTGLaetrQ03btyg+4rIm/X1dczOznLPydbWVty4caPigqxS70xpPUjtA54JjUbD1gObzVZS60GpEwgEzrT1aWlpKUpiNFG5ZEqOaG5uxuDg4IXvK6k6U6rWzladqdVqOQtyWg8qh/39fXi9Xk7EbmpqwtDQEK1XVUI8HmdJb8FgMGvSm9SSwG63w2azZUz6IxGbKCaiKGJjYwPvec97uCKNd73rXfiHf/gHchwi7gcFEYicISGbKCqJRII79KUiHQpTN1lSb1ir1VqsKSIcDuPVV19l148++ihljWVhZ2cH3/ve99h1tb9nu7u78Hq93L1cU1MDj8eD4+NjTExMsPFyq/iXxGspKFjp/bALRTweZ9nWwWDwvuughNVqZdXa1fxZIi6OIAiYnZ0942rS19eHjo4O+nyWANFolOurfV6Cn0KhYH215VwPRFHEzZs3OXs4ALh+/Tq6urroviLyQrIeTHck6e7uxrVr16rivjo+PmZ7g2xJb6nrgd1uR21tbRFnWl5kSo7o7OxEb29vVdxXxNWQyTmiUMlcoiji8PCQrQfZkt6k9UAStmk9KF/29vbg9Xq5Cv/m5mYMDQ3RelWlpCa9BYNBHB4envv1SqUSFouFWw9IxCaKzdbWFt7znvdgfn6ejT322GP44he/CIPBIOPMiBKHHnREzpCQTRSV+wnZy8vLZ+xPjUYj3G530Q9le3t7eOWVV9j1W97yFuj1+qLOodyg9+wHrK+vY3p6mgtwGI1GeDwe6HQ6BAIBeL1e9m/vfOc7y8Yyk0TswiAIAjY3N7GwsJC1hy7wgz66DocDer2e3mPivsTjcUxMTGBnZ4eNKZVKjIyMwOFwyDgz4n4kk0mur3a26ov6+nq2HhSrr7YgCJienubaZCgUCgwODqKlpeXKX5+oTO6XHHHjxg20t7fLNCt5kaqxpMB1tj2CwWBgSS5kOfwDMjlHUNINcVkytdXo6urC9evXr+S+ikajbD0IhUJZk2ANBgMTsUwmE93rZYKUAJ/6+21tbcXAwAD9DgnGyckJq9TOlgQLnLYsikaj3NeRiE1cJdvb23jf+96H6elpNva2t70N//Iv/4L6+noZZ0aUAfSwI3KGhGyiqKQL2YIgYG5ujlktSzQ3N2N4eFgWge/o6Agvv/wyu37jG98Io9FY9HmUE4eHh/jWt77FrqvxPRNFEbdu3cLdu3e58aamJq6/Y7oN+zve8Q5otdqizjUfUvthp/4BwARsOmznxu7uLiYmJrjsaLVaDY1Gk3MfXYfDQX0zCQ6pnUGqTa1Go4Hb7YbJZJJxZkSuiKLI9dU+Pj4+9+t1Oh0TsSwWy5UEpjIlR6hUKjidTthstoK/HlEdCIKAmZkZbG5usjGFQoGRkRE0NjbKOLPSQRAEbj3Itj/QarVsPbBarWWTJFlI7tdWY2BgAG1tbTLNiqgE1tfXMTMzw41du3YN3d3dRUsokyzIt7e3s1qQU0uC8iCTTT21ASKykUwmEQ6HmbCdbT0ATt0Br127hoaGhrKIPRHlRSgUwvvf/374/X429qY3vQnPPPMMxSGIXKAHHpEzJGQTRSVVyI5Go/D7/VxwFAD6+/tlzZiPRCJ46aWX2PVDDz0Ei8Uiy1zKhZOTE3zjG99g19X2niUSCUxNTXHVasBp9Ue6NWY52rBnE7Epqzd3Njc3MTMzw2VHm81mOJ1OaLVaro/u7u7uud9Lo9GwoPX9+mIR1cHe3t6Z1hx6vR4ul4t6UZUxF1kP1Go1bDYbsxzWaDSXfv1IJAKv18slR2i1Wng8HsqsJ/ImkUhgcnISoVCIjalUqqK3EionRFHE0dERa1GSbT1QKpVcS4KampriTFRGRFHEwsIClxytUCgwPDyMpqYmGWdGlDsrKyucTSpw2q6ls7NTlvlI64Hk3JBtPVAoFJzlMO0LS4NQKAS/38+dCTs6OtDX10ciNpEzF10PAMBkMrHzQrHcnYjKZXd3F0899RRXrPPQQw/hK1/5SlXFhIlLQYsQkTMkZBNFJZlMIpFIYG9vDz6fj8seVKvVcDqdaGhokHGGp9VHL774IrseGxuTfU6lTiwWw9e+9jV2/cADmEjragABAABJREFUD8But8s4o+JxcnICr9eLg4MDNqZUKjE6OpoxcFZuNuypIjYA7u8qlYoOPjkiiiIWFxdx584dbrypqQmDg4MZReiL9NFVKpWw2WxwOByw2+2UaV1FBAIBTE1NcfeHxWKB0+ksiJhJlAaxWIytB6FQKGtfbYvFwkSsfFq0HBwcwOfzIRqNsjG9Xi9LyxeicojFYvD5fNjf32djWq0Wbre76px8LkMsFmOidi6Ww1KLkoaGhooMWmeq8FcqlXA6nVVzHiGuhnv37uHWrVvcWKlV+MdiMc6CPFtLAr1ez1mQU0Jy8cnUa/0qbeqJ6uDo6AivvfZa1jZFqdTU1LD1oFrdXIj82d/fx7/5N/+Gi296PB48++yztP8iLgI9+IicISGbKCrJZBLLy8tnegjr9Xp4PJ6SEPQEQcBzzz3Hrl0uF2XyZyGZTOL5559n1263uyqsIXd2duDz+bgqyJqaGng8nvta6KTbsL/pTW8qyco26oddOARBwOzsLDY2Nrjxnp4e9PT05PQ+JhIJro9utiCV2WyGw+GgyosKRhRFLC8vn+kD2tzcjMHBQQpMVjCSpaC0HqQ+gzJx0T664XAYExMT3DpjNpvhcrkoOYLIGynxL9Uyv7a2Fh6Ph55Tl0AQBGYxmovlsNSSwG63w2q1lv2zIplMYnJyEsFgkI2p1Wq43W6YzWb5JkaUNZls6hUKBYaGhtDc3CzjzM7noi0J1Go17HY7+0PP+KsnEAhgcnKS67V+kTMhQWTi6OgIr7/+OncmcDgcaG9vRygUQjAYxOHh4bnfQ6lUskRYu91OiavEuRweHuLpp5/mYpujo6N47rnnqiIWTBQUevgROUNCNlE0RFHE9PT0mR7CDQ0NcDqdJdW76bnnnmOi3cjICFpbW2WeUWkjiiKeffZZdj06OoqWlhYZZ3T1rK2tYXp6mjuEmkwmuN3uc63Cj4+P8c1vfpNdP/zwwyUXaCMRu3DEYjFMTExwNl9KpRJDQ0N5J8hIQapAIJBT0FoSsRwOB+rr6+l3VwFkslAFgO7u7jPtDIjKRhRF7O3tsaB1qg14Jmpqarg+uuki1sbGBmZmZrhnm8PhwPDwMFVpEHlzcHAAr9fLBVjr6+vhdrurwva6WIiiiMPDQ7YepFa+Z0KlUnEtCcrNzSUej8Pv93N7LGp/QFwWURRx8+ZNLC8vszGFQoHR0VE4HA4ZZ3Zx0i2Hz4v9KRQKmM1mzoKc9pOFZWtrC1NTU9zv4fr16+ju7pZxVkS5k0nEbmxsxPDwMLfPPzk5YX21s7m9AacxBCnJhdwbiFSOj4/xwQ9+EF//+tfZ2ODgIF544YWSTvYiShbabBA5Q0I2UTRisRheeuklLjO4p6cHvb29JXdIevHFF5klz8DAgGw9sMqJ559/nlkbDg0Nob29XeYZXQ1ScCM1Qx84rYLMJdAfjUa5Dd8b3vAG2Gy2K5lrPqT3wwbADjmSgF1qn9dS5ejoCD6fj1vzNBoNXC5XwZIXpKC1JGqnWtxnoqamhlVqWywWOpCWIZmqzxQKBQYHBys+gYjIzvHxMba3txEIBLL2yUsVsWw2GzY2Ns5YqLa3t6O/v5/WfSJvdnZ24Pf7uQp/q9VackmslchFWpQAp84Lkqit1+tL+nMfjUbh8/m4fY9Op8PY2BhV+BN5I4oi5ubmsLa2xsaUSiVcLldJndfyIR6PM3enYDCY1d2ptraWJb6ZzWY6M1ySTImCvb296Orqkm9SRNmTq4idTqq7UzAY5FoJZYLcGwiJk5MT/NiP/RjnYtrf348XXnihpNpuEGVF6R44iJKDhGyiqOzs7ODb3/42gNNK51K17H7ppZdYlWNfXx96enpknlHp87WvfY1toG/cuFGRh7JEIoHJyUkEAgFuvLe390IW0S+88AK7LqUe7OkidqqYTSL2xQiFQpicnOSCRMXoL3tycsKC1js7O+dWXkgHUiloTYJC6ZMpcK9WqzE6Olr2AVai8Fy0j246vb296OzspHWfyJtAIICpqSlOQM0lwEoUnou2JChlESuTTb3Upuo8VySCOI9MvdbVajVcLhcsFouMMys8giAwN5dgMJjVzUWtVsNmszERq9zcG+RmfX0dMzMz3Fh/fz86OjpkmhFRCeQrYqcjJcZLZ4a9vb2s/0dyb7Db7TAYDHRWqBKi0Sg+/OEP48tf/jIbu3btGl544QU6MxKXgW4cImdIyCaKiiAIWFtbg06ng9FolHs69+Vb3/oW6yFz7do19Pb2yjyj0ucb3/gGqzzt7e3FtWvXZJ5RYTk5OcH4+DjXW0ilUl04IaNUe7CnitgAuL+rVCralF6A1dVVzM/PcyKyzWbDyMhIUbOX4/E4gsEgAoFAVhFLoVDAarWyam2yei09Dg8P4fP5OCt5nU4Ht9sNg8Eg48yIciCZTGJnZ4eJWNkqL2pqatDc3IyGhgaYTCZ6BhAXZnV1FXNzc9wYVfiXBqIoYn9/n60H2fpmpia+2Ww2WSuxDg8P4fV6uTVMau1DFWJEviSTSUxNTWF7e5uNaTQaeDyeko5ZFIrj42MmYmVLhAXAWZCXunuD3GR6Fg4MDFDlInEpCiViZyIWizH3hlAolNW9QafTMVHbarVSK6IKJRaL4cd//MfxpS99iY11dXXhhRdeyLmohyDuA908RM6QkE0UFVEUs1YAlAKvvPIKy0Ts7OzEwMCAzDMqfVLF/56eHvT19ck8o8Kxs7MDn8/H3bs6nS7v4Mazzz7LAgRy92BPr76mftj5k6mnHgC0tbWhv79f1mqmi1ZimUwmVolFASr5yVThT/1liXyRRKzNzU2srq5mtRvWarVcX20KUBHnIYoiFhcXcefOHW782rVr6O7upudJCSL1zZQsyLP10bVYLFwf3WKxt7cHn8/H2j8BZFNPXJ5kMgm/349wOMzGtFotxsbGqjJRMB6Pc5bDqZ+3TNTW1rL1gNoW8aysrGB+fp4bGxwclPXsT5Q/Vylip3NR9walUgmr1crWBHJJqQzi8Th+8id/En/3d3/Hxtra2vDiiy+WZKtQouygG4jIGRKyiaJSLkL2q6++yg6zbW1tGB4elnlGpU+liv+rq6tn+lmZTCZ4PJ68BaQXXniBCVJy9hNPF64BsKpdErEvRiKRwPT0NFfJAZzaxrW3t5fU+yiKIjuQBgIBzpozE3V1dWhoaIDD4aDKTBlYX1/H7OwstwbZ7XaMjIxQ4J7Im0zWvAqFImsVllKpZH21GxoayF6U4BBFEQsLC1hZWeHGqfqsfEgkElwf3Wwill6vZ+vBVe4RwuEw/H4/5y7jcDgwMjJCwhmRN/F4HH6/H7u7u2yMeq3/gNQzQzAYzOreoFKp2B6h2i3Il5aWcPPmTW5seHgYzc3NMs2IqASKKWJnQmpjFgwGsbOzkzUZ1mAwMFGb4gjlSSKRwM/8zM/g85//PBtrbm7Giy++iBs3btDvlCgEdBMROUNCNlFUykXIHh8fZ4JUc3MznE6nzDMqfSpN/JeCsffu3ePGW1paMDQ0dKmKtFLoJ57eDxsAVWLnSSQSgd/v5/oWS7bzpdL//DyOjo4QCARy6olFlZnFQxRF3LlzB4uLi9w4WfMSl2V/f/+My4jBYIDb7YZKpeL6amezEzSbzZx7A1G9CIKA6elpbG1tsTGlUomRkRE4HA4ZZ0bkiyiK2N3dZWtCtkosjUbDBCybzVawZKtAIIDJyUku0aalpQWDg4P0LCTyJhaLwev1cvv3uro6eDwe1NbWyjiz0uUi7g3AafK3JGJVUx/dxcVF3L59m10rFAoMDw+XRDsxonyRW8ROJ5lMIhQKIRgMIhgMZm1bpNFoWKKL3G1KiNxIJpP4hV/4BXzuc59jYw6HAy+88AKGh4erZk0nrhy6kYicISGbKDrZNjilwMTEBDY2NgCcPqg9Ho/MMyp9UsX/lpYWjI6Oyjyj/EkkEpiYmDhTXdvX11cQW8zUfuJ9fX3o6em51Pe7KOeJ2JKATZvS3MgkCOl0OrhcLtTX18s4s/yIRqOsUjtbgCq16qKhoYEOowVEEATMzs6y55BEX18fOjo66PNJ5E0wGMTk5CRX1WixWOB0Os98hgVB4Ppqp/Znz4Tk3tDQ0ACz2Uz3aRUh7ZtSrXnVajVcLhcsFouMMyMKyfHxMVsPdnd3s1qQW61Wtibkay+6vr6OmZkZbqyzs5OsLIlLEY1GMT4+ziVnGAyGSzluVRuJRIKzIM9WrFANfXRFUcTdu3dx9+5dNqZQKDA6OkoJXcSlyCRiNzU1YWhoqCRcSURRxOHhIVsPsiXHKxQK1srMbrdTK7MSRBAE/PIv/zI++9nPsjG73Y7nnnsOLpeLfl9EIaGbicgZErKJolMOQvb09DRWV1cBADabDW94wxtknlHp4/f7sbm5CeA0M9Ttdss8o/w4Pj6G1+vlrNNUKhVGR0fR2NhYkNdI7Sd+7do19Pb2FuT75kKqiA2A+7tSqSyJg1C5EAgEMDU1xVlqGY1GuFyuigiCSfaigUAAwWDw3MpMhUIBs9kMh8OBhoYGqmS5BPF4HBMTE9jZ2WFjVNVIFIJMNvW5VnJIASrJvSG1gi0TUmWmVHVRiQFr4pRoNAqfz8fdE1qtFh6PpywTuojciMfjF3JvqK+vZwFro9GYUwA0kzXv9evX0d3dfam5E9XNyckJxsfHWVIxcLp/d7vdVW2FfRlEUcT+/j4TsbLtEaQ2JVK1diWcmzI5KSkUCjidzrJw6CJKl1IXsTMRi8VYpXYuewSdTsf2CBaLhc4NMiMIAv7Lf/kv+OM//mM2ZrVa8dWvfhUPPPAAidhEoaEbisgZErKJohOLxbLaUMnN3NwclpaWAJxaYj3yyCMyz6j0mZqawtraGoDTTL0HHnhA5hldnHA4DJ/Px/UD1Ol08Hg8MBqNBXud73znO9jf3wcAdHV14caNGwX73ueRWoWd2hubrMQvhiiKWFpawq1bt7jxxsbGS9vOlyqplZmBQCBrQpIUsHY4HFVlJXhZTk5O4PP5uAohjUYDt9sNk8kk48yIckYURSwuLuLOnTvc+GWqGiORCKvMzObeoFQqucrMSghYE6dIyX+pghBZ81YfgiBgd3eXrQmp90MmampqmICVqTLzfq01qNc6cVmOjo4wPj7O7WPNZjPcbnfBrPCJ0z1CqgV5tj660rmhoaEB9fX1ZXduEEURt27dYvEj4HTv43K5YLPZZJwZUe4cHh5ifHy8rETsdKQ9giRsZ2tTkproYrfb83Z0IfJDEAT8xm/8Bj7zmc+wMZPJhK9+9at46KGHym59JsoCuqmInCEhmyg65SBk37p1iwV8DQYD3vzmN8s8o9JndnYWy8vLAE5tSh966CGZZ3QxVldXMTMzw92bUmCj0EH3733ve6zasqOjA4ODgwX9/umkC9ckYuePIAiYm5vD+vo6N97d3Y1r165VxfsoiiIODg5YZWaqe0EmpAxrh8MBs9lcNofuYrO3twe/388FKurq6uB2u1FXVyfjzIhyRhAEzM/Ps0Qzif7+fnR0dBTkNRKJBAtYZ3NvAMCsBKW+2tWwblYimVprUFUjIYoijo6OmKidzV5UClhLlVharRbz8/PMGQug/rJEYTg4OIDX6+XWLJvNBqfTWZFJqKVCMpnkLMizJcNKiS52u70sHF1EUcTCwgJWVlbYmFKphNvthtVqlXFmRLlTCSJ2Jo6Pj5monS0ZFjhNdJHWBJPJROeGK0QURXzqU5/Cpz/9aTZWX1+PZ555Bm9+85vpvSeuCrqxiJwhIZsoOuUgZN+9e5dZ2dXW1uJtb3ubzDMqfRYWFljlhNFoxBvf+EaZZ5Qb0uHz3r173HhLS0tOdqv58NprryEUCgEAWltbMTIyUvDXkDivHzaJ2Bcjk+WzQqHA4OAgWlpaZJyZvKT2zEx9bzKhVqs5u2Gqfjklk039/foWE0SuJJNJTE5OIhgMsjGlUonh4eGCtcpIJ7UyMxAIZO2rXVtby/XVLufAXDURDofh9/u5Xus2mw2jo6O0rhMckr2oZEGees9kQqPRcM5ISqUSTqcTdrv9qqdKVDB7e3vwer1copXD4cDIyAg9d4qIlAwridqSQ9n9kBxdJAeHUqvMFEURc3NzXLKgSqWC2+2GxWKRcWZEuVOpInY6iUSCS3RJ/XkzodFouEQXOicXDlEU8elPfxqf+tSn2Jher8c///M/4+1vfzvFDImrhG4uImdIyCaKTjwez2ovJTdLS0uYm5sDcLpZeuyxx2SeUemTWsWu1+vxlre8ReYZZUcSJlOD/MBppVpXV9eVbda8Xi8CgQCA0wOJy+W6ktdJF7FTxWxJwKYNaW4cHR3B7/fj+PiYjWk0GjidTgpUpCAFrAOBAEKh0LlrPdkNn35Gl5eXz/QAbW5uxuDgYEUFKojiEovF4PP5uCCxWq2Gy+Uq2pol9dWWEl2yBawp0aU82NzcxPT0NJeUSmsWkQvJZJK1Kdne3s5amQmcio1tbW2wWCx0fxF5sbOzA5/PxyVRVKIgVI5Eo1EmYGU7NwA/qMxsaGiA0WiU9RwriiJmZ2c5ly61Wg232w2z2SzbvIjyp1pE7HQumuiiUChgNpvZmlBXV0exrTwRRRGf+cxn8MlPfpKN1dbW4otf/CLe+c530vtKXDV0gxE5Q0I2UXTKQcheXV3F9PQ0gFOx5YknnpB5RqXP4uIiFhYWAJxaCT/66KPyTigLx8fHGB8f53r0qFQqOJ1OOByOK33tiYkJbGxsADgN0Hk8noK/RqqIDYD7u1KprOhDUKEJh8OYnJzkKoTI8jk7yWQSoVCIBaxT379MmEwmOBwOZjdc6WSyIgSqy6aeuBoy9S3W6XRwu90wGAyyzesiPTMVCgWX6FJqVVjVysrKCubn57mxjo4O9PX10ZpFXAgp0UVqU3JwcHDu16tUKlaFJVmQE0Q2gsEgJiYmuOdNa2srBgYGaM0qMVITXYLBYFZHF61WywQsq9Va1OQ3QRAwMzODzc1NNqZWq+HxeGAymYo2D6LyqFYROxPRaJTFEnJxdKmtrWVrAiW/5Y4oiviTP/kT/Mqv/Aobq6mpwT/8wz/gPe95Dz0riWJANxmRMyRkE0WnHITszc1N+P1+dv3kk0/SAzwL5VTFHg6H4fP5OGGttrYWHo8H9fX1V/76U1NTzILMZrPhDW94Q0G/fyYrcVEUyUo8D9bX1zE7O8tVnlmtVoyOjpKV1QUQRZGzG04V2DKh1+tZX225Ky6ugkyWzwqFAgMDA2htbZVxZkS5s7e3d+b5Vl9fD7fbXVKuB4lEggWngsFg1kSX+vp6luhiMBgqbk0odURRxJ07d1gLGYne3l50dXXJMymiIohGo/B6vTg8PLzQ/zObzSzRpRqS34iLEwgEMDk5ye3hOzs70dvbS8+QEkdKdJGS3/b29s79ein5TRKxamtrr2xugiBgenoaW1tbbEyj0WBsbKwocQSiciER+/4IgoCdnR3WWzvVJS8TKpWKrQl2u50SYu+DKIr47Gc/i1/6pV9iYxqNBl/4whfw1FNP0bOSKBZ0oxE5Q0I2UXQSiUTWbDq52d7exvj4OLt+/PHHyeIyC+VSxb6ysnJGmLRYLHC73UWr7pidncXy8jJ77Yceeqgg31cSr6VEkdS/k4h9MURRxO3bt8/0Tm9tbcWNGzeq/jB5GURRxNHREavCymYbptVqmahttVrL/r2PRqPw+Xxc9Zlarcbo6ChsNpuMMyPKne3tbUxOTnLJglarFU6ns6T3MIIgYG9vL+dEF51OxwQsqri4egRBwPz8PNcDVKFQYHBwEC0tLTLOjCh3Tk5OMD4+zn3mDQYDRkdHcXR0lHPPzLq6OjQ0NMBut8NsNtOaQGBjYwMzMzPcea+npwc9PT10FipDpNZFuVZmGgwGJmqbTKaC/c4FQcDU1BRrEQacnlPGxsZkdbwhyp/7idjDw8O0ZmXg6OiIido7OzvIpmuUUluCUkEURXzuc5/Dz//8z7MxtVqNz3/+8/iRH/kReo+IYkI3G5EzJGQTRacchOxwOIxXX32VXT/66KOUxZeFjY0NTExMsOtSq2IXBAELCwtYWlrixltbW4ue5bqwsMCqmoxGI974xjde+num98MGQCJ2niSTSUxPT3NBCuC08qyzs5PexwITiUSY/Xg4HD73ICpZi0oB63Krij88PITP5+PsEkvB8pkof1ZXV5krikQ59i2WEl2kNSFbFZZarWZrgs1mK7s1odRJJpOYmprC9vY2G1MqlRgdHUVDQ4OMMyPKncPDQ3i9Xq5Ptslkgtvt5j7Hoihif3+frQnZKrdpTSAyPQ/JPaJykCozpUSXbMlvGo2GsyDPd03I5Kak1WrxwAMPkCsEcSlIxL4cksuTJGxnS36T2hLY7XbYbLaSTva9KkRRxOc//3n81E/9FIu9qFQq/NVf/RU+9KEP0X1HFBu64YicISGbKDrlIGTv7+/jO9/5Drt+y1veQgeULAQCAXi9XnZdSlXs8XgcExMT3METAPr7+9HV1VX0jdqtW7dw584dAKcWym95y1su9f3SRexUMVsSsGkzmhvRaBR+v5+rElYqlRgZGbny3unE6Wc1FAohEAggGAye+6xQKBSwWCysWrvUk43C4TAmJiaQSCTYWClaPhPlxf0snyul13o0GuWqsLL11ZbWhKu2Fq0G4vE4/H4/dnd32ZhGo4HL5YLZbJZtXkT5k6kFQq7uEScnJ0zUzlaFRWtC9XHv3j3cunWLGxsYGEBbW5tMMyKuEin5TdonpD6vMiGtCZKwXVdXl9PrJJNJTExMIBQKsTGdToexsbGcvwdBZIJE7MIiJb9Ja0KqA1omUtcEu91eFTFfURTxd3/3d/j3//7fs3OVUqnE//k//wcf/ehH6b4j5IBuOiJnSMgmik4ymeQC+aXI0dERXn75ZXb9xje+EUajUcYZlT6hUAivvfYau377299eEuLM0dERvF4vjo6O2JhKpYLL5ZKtmuju3bu4efMmgNPe3G9729vy/l6pIjYA7u9KpbKsKvHk5uDgAH6/n6uWrampgcvlos+/DAiCgHA4zALWqVVbmSjlHrqZeq3b7XaMjIyUTMIPUX4IgoC5uTmsr69z4zdu3EB7e7tMs7o6kskk66u9vb2dta+2wWBga0J9fX1JrQmlTiQSgc/n46pfyT2CKAShUAgTExNcoprD4cDIyMiF96xSFZZUmZnLmnAVdsOE/IiiiLt37+Lu3btsTKFQYGhoCM3NzTLOjCgmsViMrQmhUChrzEmv13NrQqY1KJlMwufzYWdnh43pdDo88MADlBxDXAoSsa8eKSE2GAzm1JagtraWOb9VYvsiURTxxS9+ER/96EfZe6FQKPBnf/Zn+NjHPkb3HSEXdOMROUNCNlF0ykHIjkQieOmll9j1gw8+CKvVKt+EyoDd3V1897vfZddvfetbZc9QDoVC8Pv9XGCrtrYWHo8H9fX1ss3r3r17mJ+fB3BqbfSOd7wjr++TyUo81RqINqK5s729jampKe5wU19fD5fLVfKVvtVAqrVoIBDgElMyIR1CGxoaZO2XmSmwCgDt7e3o7++nzyiRN4lEApOTk1x1UDW5R4iiyPpqb29vZ10Tampq2JpgtVorLjBVSKQEwNSkLr1eD4/HQ89D4lIEAgFMTk5ySV2tra0YGBi49PNQEARuTTg+Pj736yVrUcmCXKVSXer1CfkQRRG3bt3i2kcpFAqMjo5WxfOQyIwgCNjd3WWJLtnWBKktgfRHo9EgkUjA5/Nxld61tbUYGxsjEZu4FCRiFx+pLYFUrZ2tLYFKpYLNZmNrQikU6VwGURTxzDPP4MMf/jAXH/2jP/oj/MzP/AydjQg5oUWPyBkSsomiUw5CdiKRwAsvvMCux8bGqBdgFg4ODvDtb3+bXb/pTW+SVSxeXl7G3NwcFyyzWCxwu93QarWyzQsAVlZWMDMzA+B0g/zOd77zQv9fEq8lK6DUv1M/7IshiiKWl5dZhbxEQ0MDhoeHqVq2REntoZvNRlCj0TABq5jBakEQMDs7i42NDW68r68PHR0d9Bkl8iYajcLn83F2edVu+XyRNUGlUjEBSwpWE6dksnzO1LeYIC7K2toaZmdnubGuri5cv379Sp6H0poQDAaxu7t7rgW5UqnkLMgpYaN8EEURc3NzWFtbY2NKpRJOpxN2u13GmRGlxkXWBIVCAaPRiGg0yiV11dXVYWxsjNYI4lKQiC0/oiji+PiYa0uQTRsxGo1M1DYajWX1uxJFEc8++yw+9KEPcffd7//+7+MXf/EXScQm5KZ8PkyE7JCQTRSdchCypQe9hMvlQlNTk4wzKn2Oj4/xzW9+k10//PDDsgTUBUHA/Pw8lpeXufG2tjYMDg6WxCZtfX0dk5OTAE4Pyk8++WTO/zfdSpxE7PwRBAELCwtYXV3lxjs7O9Hb20vvY5kQi8VYpXY4HD63h65SqYTNZmPB6qtKaonH45iYmOBsCJVKJYaHh9HY2Hglr0lUB5mqZWtra+F2u6uir1suxGIxrq/2eTaCCoUCZrOZrQlyO8nISTAYxOTkJPd+2e12jI6OUrUqcSky9S3u7e1FV1dXUV4/Ho9za0K2c2h9fT1bE6gtQemSKWFQpVLB7XbDYrHIODOi1InH41xbglxiU2q1GgMDA3A4HCURTyDKExKxS5N4PM5amuXSqkRydbHb7bDZbCVd/CCKIr72ta/hgx/8IHd+/PSnP41PfOITtJ4RpQAtfkTOkJBNFB1BELJuDEqB5557jgkiw8PDaGtrk3lGpU00GsXXv/51dv2GN7wBNputqHOIx+Pw+/2c1Spw2i+0s7OzZA4Hm5ub8Pv97PqJJ57IaQOZKmKn/gHABOxS+RlLnXg8jqmpKe5eUSgUuHHjBn3Wyxiph24gEMjpECoJWA6Ho2AC1snJCXw+H2d1rNFo4Ha7YTKZCvIaRHWyu7t7pl2G0WiEy+Uqe7u7qyKZTLLA1Pb2Nhc4zITBYGACVrlVW1yGjY0NzMzMcNUoLS0tGBgYoAAXkTeiKOLOnTtYXFzkxgcGBmTba0nWolKwOpu1qNSWwG63w2q1UlJHiSAIAqamphAIBNiYWq2Gx+OhvRZxIVLbEgSDwaytStRqNWc3LLfTG1E+ZBKxm5ubMTQ0VDX7zXIgtaVZMBjkHLAyoVAoYLFYmNtTKSXFiqKIl19+GU8//TTXXuFTn/oU/ut//a+0xydKBVoAiZwhIZsoOuUiZL/44otsngMDA+js7JR5RqVNuh27x+Mpal+yo6MjjI+Pcxs0tVoNp9NZcrbw29vbGB8fZ9ePP/541izO9Ers1L8rlUrahF6ATEKjWq3G6Oho0ZMviKsjtTdeIBDgMpAzodfr4XA4LiVg7e3twe/3cwGKuro6uN3ukjrUEuVHIBDA1NQU5zhgt9sxMjJS0lUApURqYGp7exuHh4fnfr1Wq+X6aleqgLW0tHSmvcZVWj4T1YEoipifn+dcbxQKBUZGRkrGmUQURa4twd7e3rlfn+rqUgn9MsuVZDKJiYkJLhlVq9XC4/HI2taKKH+i0Shee+21rAkuqZjNZiZg6fV6em4SGSERu3yJRCIIBoMIBoMIh8PnOj0Bp2d/aU0wm82yxelEUcQrr7yCD3zgA9yZ59d//dfxW7/1WxQ/JEoJWgSJnCEhmyg65SJkv/TSS0z46OvrQ09Pj8wzKm3ktGMPBoPw+/2cLVhdXR08Hg8MBkNR5nARQqEQXnvtNXb99re//dxgWGoVduo1cGqhR4ef3MlU0Ui2vJWPKIo4PDxEIBDA9vZ21sxqqQLL4XDAYrHkdNDLJDRaLBY4nU7qLUtcipWVFczPz3NjVC17eY6Pj7m+2tl66Kb21a6ECixRFHH79m3cu3ePG+/v70dHR4c8kyIqAkEQMD09ja2tLTamVCrhcrlKOmEwGo1yFuTntSoBTvvHS8kuJGAVh0QiAZ/Ph93dXTam0+ng8XhoH09cikgkAq/XyyU6G41GtLW1YWdnJyenJ51Ox9aEXM8PROVzeHiI119/nbt/SMQuT5LJJFsPcnF1UavVsFqtRT8/iKKI1157DU899RT29/fZ+K/8yq/gf/7P/1mxyblE2UILIZEzJGQTRUcUxazWjqXAt771LZa5du3aNfT29so8o9JHDjv25eVlzM3NcQFoq9UKl8tVsoHm3d1dfPe732XXb33rWzNWa0ritfSeUj/sy7GxsYHZ2VkuMGk2m+F0Okv2XiGuhpOTEyZg7ezsnCtgSRaCDocDdrs9Y/VrporGpqYmDA0NURCLyJv7CY09PT3o6emh9b+ApPbQDQaDWastUvtql6N4kqm3rEKhwNDQEJqbm2WcGVHuZKqWVavVcLvdMJvN8k3sgkhtCaR1IRqNnvv1JGBdPfF4HF6vlwvK19bWYmxsDLW1tTLOjCh3IpEIXn/9dU6UMpvNcLvdbN8viiJnQZ7N1UWlUnEW5OTgUJ2QiF25SK4ukqidLSkWOE2OkUTt+vr6K7kHRFGEz+fD+973Ps5p5uMf/zh+7/d+j0RsohShxZDIGRKyiaJTLkL2K6+8wh78nZ2dGBgYkHlGpU+qHfvg4OCVVvQIgoC5uTmsrKxw4+3t7SVfpXZwcIBvf/vb7PrNb37zmcpxErELhyiKuHv3Lu7evcuNNzc3Y3BwsKTvFeLqkQSsQCCAUCh0roClUChYVnVDQwNqamqwsLBwZh3q7u7GtWvX6DNK5I0gCJiZmcHm5iYbUygUGBgYQGtrq4wzq3wEQeD6amcTsPR6PVsTTCZTyX/uk8kkJicnEQwG2ZhKpYLT6Szpalmi9InH4/D5fFzgVKvVYmxsrCQdknJFFEUcHBywNSGbq4uUACcFq8mV5fJEo1F4vV5OPNTr9RgbGyOBkLgUJycneP3117kWRBaLBW63+1zB5+TkhCW6hMPhrAKWyWRizi4Gg6Hk9wrE5SERu7qIx+MIhUJM2M7m4CC1MLLb7bBarQVpFSWKIqanp/Ge97wH4XCYjf/sz/4s/vAP/5DaURGlCi2IRM6QkE0UnXIRsl999VX28G9ra8Pw8LDMMyp9Uu3Y+/v70d3dfSWvE4vFzlR7AKe9zDs6Okr+YHB0dISXX36ZXT/yyCMwmUzsOrUfduofAEzALvWfsVRIJpOYnZ3lxCDg1GWhu7ub3keCQ6rAkoLV2Z5VarWaa2lAQiNRCOLxOCYnJ7kAhEqlwujoKOx2u4wzqz4uKmBpNBomattstpKreojFYvD7/ZzQqNFo4Ha7uX0IQVyUTEJjbW0tPB5PRtehciYSibCqzHA4fK4FuUKhOGNBTlyMk5MTeL1eHB8fs7H6+np4PB5yVCIuxfHxMcbHxzkRW3J2u8jzO5FIsPNDMBjMen7Q6XSsUttqtZbcXoG4PCRiVzeSg4OU7JLNwUFKlpfWhXz2TaIoYn5+Hu9+97uxvb3Nxj/2sY/hT//0T0nEJkoZWhSJnCEhm5CFbNUtpYDX60UgEABwuul0Op0yz6j0efnll1lfqevXr+P69esFf43Dw8MzwQy1Wg2Xy1U2Af5IJIKXXnqJXT/00EOwWCwASMQuJJkC9kqlEkNDQ0Xr306UL6kWgtvb21zPvPvhcDjQ0dEBs9lMn1MiLyKRCHw+Hxfw0Gq1cLlcJDSWABdpS6BUKmG1WllbArmrBjP1/6TeskQhODk5wfj4OGfLazAY4PF4ZL/vr5pkMolQKJSzgFVXV8c5OJAr0PlkEhrNZjNcLhdVuhOX4ujoCOPj41xcym63Y3R09FLCsiiK2N/fZ2tCtgQ4pVLJWZDrdLq8X5soDUjEJtKJRCKcg8N5CXDAqeOI3W6HIAjo7u7OupcSRRE3b97Ee97zHq6A4yMf+Qj+/M//nJ6XRKlDCyORMyRkE7JQDkL2xMQE6x3ocDjg8XhknlHp853vfIf1Levu7kZ/f39Bv38wGITf7+cqIOvq6uDxeMrKsjAej+PFF19k1w888ADsdjsnYgPg/q5SqejgcwEODw/h8/m4wBeJQcRlODo6QiAQwObmZtasaqkq0+FwUKUFkTOZ1q26ujq43e6Kq2isBCQLQSlYnbo3yUR6VWYxn+lSEmDq/rtahEbiasl0b5lMJrjd7qoLnF60h65Go2FWwzabjaql0jg8PMT4+DiXHJBPtSxBpJPp3mpoaMDo6GjBk0suKmDV19ezvcJV9dAlrg4SsYlsJJNJ7OzssHUh9dyXzqc+9SnMz8/j4YcfxpNPPokPfOADaGlp4b5GaqP37ne/G2tra2z8Qx/6ED73uc+RcwlRDtDiSOQMCdmELMRisax9hORmenoaq6urAE4PzQ8++KDMMyp9vve972FnZwcA0NHRgcHBwYJ8X1EUsby8jPn5ee6+kYIZ5bY5SyaTeP7559m12+1GQ0MDV4FN/bDzJxQKYXJykhMVDAYDXC4XamtrZZwZUe6Ew2FMTExkFaxSUSqVLFBtt9vLbr0iisPOzs6ZRC2TyVSWz7hqRBAE7OzssGrt84JSwKnlspTsctVVmbu7u/D5fNy9ZbFY4HQ6q05oJArL3t4efD4fF7C32WxwOp0kNOJiDg4KhQIWi4UJWNW+X93f34fX6+XurYaGBoyMjNC9RVyKg4MDjI+Pc/dWY2MjhoeHr9whIbWFUTAYzFrckdpDtxTblRA8JGITF0UURRwdHbE1YXd3l/1bNBrFv/t3/45LuJHamD322GN46qmn8OCDD2JlZQXvete7sLKywr7uR37kR/B//+//pWRVolygBZLIGRKyCVkoByF7bm4OS0tLAE6DyY888ojMMyp9XnvtNda3urW1FSMjI5f+noIgYG5ujtuYAUB7ezsGBgbK0pJPFEU8++yz7HpkZASNjY3s30jEzp+VlRUsLCxw64vNZsPo6ChVuhCXYn19HbOzs9y9ZbfbMTAwwFmQnydyKxQKmM1mJmBVe6CaOGVrawtTU1PcvUUB+/JFFEUcHh6yNUFyqrkfV1mVub29jcnJSa4CzOFwYHh4mO4t4lKEQiFMTEwgmUyysWKJQeXIRR0cDAYDE7WNRmNVnQUyJXbRvUUUgr29PXi9Xu7eampqwtDQUNHvLVEUcXBwwNaEbHsFqV2JtF8gC/LSgkRsohDE43EEg0EEg0F85StfwW/91m+d+/V2ux2tra2Ym5tjgvf73/9+/O3f/i2tEUQ5QYskkTMkZBOyUA5C9q1bt3Dnzh0Ap8GEN7/5zTLPqPTx+XzY2toCcHoodLlcl/p+Uo/jcDjMxqQsxI6Ojkt9b7l5/vnnWfBvcHAQzc3NAEAidp5IfYGWl5e58fb2dvT19VHgi8gbya7r7t273Hime+uiVZkGgwEOh4PsA6uYpaUl3Lx5kxtra2vDjRs36H6oECKRCFsTwuFw1qrM1L7alwlCra2tYW5ujnu91tZWDAwM0L1FXIpMyTd0b+WOIAjY3d1ltqLHx8fnfr1Wq+WSXSo5CSUUCsHv93PJN3RvEYUgkztJS0sLBgcHS+LeikajbE0IhUJZLcirOdml1CARm7gK4vE4XnrpJfzzP/8zvv71r2NxcTHr/2loaMCv/dqv4QMf+AC6urqufpIEURhooSRyhoRsQhbi8XjWzbnc3L17lwWXdTodHn30UXknVAak9hVvaGjA2NhY3t9L6rmXGtxRq9VwuVyw2+2XnqvcvPjii+yw09fXh7a2NhYQlARsOvjkRiKRwNTUFILBIDd+48YNtLe3yzQrohIQBAGzs7NsXZPo6+tDR0fHuZ/R1EqLQCCQtVemTqdjASmLxULJFxXO/ZJvrl+/jq6uLlr/K5REIsFVZaYGPTNhNBrZumAwGHK6L0RRxL1793D79m1uvKenBz09PXRvEZdibW0Ns7Oz3FhXVxeuX79O91aeSLai29vbnK1oJqSqTMluuJIqrgKBACYnJ7kEiY6ODvT19dG9RVyKnZ0d+Hw+zkGilBMkpB660l4hW2KslOwiWZCTC1nxIBGbKBa3bt3CP/3TP+H555/H66+/zlmOZ2JoaAjvfe978b73vQ+PPPIIrQtEKUOLJZEzJGQTslAOQvbS0hLm5uYAnNo+PvbYYzLPqPQpVF/x7e3tM31o6+rqMDY2Br1eX5C5yokoinjppZdYXyyDwYCWlhbY7XbU1taSgHUBTk5O4Pf7OZFQpVJhdHS0IhIeCPmIx+OYmJjAzs4OG1MqlRgeHmatAC6C1CszEAhgd3f33KpMtVoNu90Oh8NBAakKJJlMYmZmhjmYAKeVuIODg2hpaZFxZkQxkaoyJQHr5OTk3K+Xkl0cDgfMZnPGvcL9EiQosYsoBPfu3cOtW7e4sd7eXqr6KSCxWIzZigaDQU54y0R9fT1LdilnZ5eNjQ3MzMxwe6Pu7m5cu3atbH8mojQIh8Pw+Xxc7Km9vR39/f1lcW9J7Uqkau29vb1zv15ydpFcHKiN0dVBIjYhB4FAAE899RSOj4+h1Wpx69atrImxFosF73rXu/De974X73rXu2Cz2Yo0W4LICVowiZwhIZuQhXIQstfW1jA1NQXgVLx44oknZJ5R6XPZvuKiKGJpaQnz8/PcuM1mg8vlgkajKdhc5UAURfbnlVdeyVihaTKZ4HA44HA4UFdXJ8Msy4e9vT34/X4uG1Wn08HtdsNgMMg4M6LcOTk5gc/nw9HRERvTaDRwuVwwm82X/v5SoFqqtDjveahQKGCz2Viguqam5tKvT8hHPB6H3+/nqu5UKhWcTicFFaoYURS5qsxsgWop2UWqylSr1RAEATMzM9jc3GRfp1AoMDIyklfyDUFIiKKI27dv4969e9z4wMAA2tra5JlUFXDRdiU1NTWcs0u5WJCvrq6y5HGJ69evo7u7W6YZEZVCJqv6zs5O9Pb2lq3QmHqGCIVCWZNd9Ho9WxdMJlPZ/tylxsHBAcbHx0nEJopKKBTCD/3QD2FiYoKNvfGNb8Tv/u7v4hvf+AaeeeYZvPLKK+fGFpRKJR555BG8733vw3vf+14MDw/TPUvIDd2ARM6QkE3IQiKRyLrplpvNzU34/X52/eSTT9IDPgs3b95kfWQv2ldcsvCVKrolOjo6cOPGjbKvUpYEbGlTubq6ekawT0fqfeVwOMq6yuIq2NrawvT0NLdJN5lMcDqdJPQRlyJTgkRdXR3cbveVJJckk0mEw2EEAgFsb29nzag2mUxsXagEh4pqIhKJwOv1cgkSWq0WHo8H9fX1Ms6MKDWi0SjXVztbsovZbEY0Gj3TjsXpdMJqtRZjykSFIooi5ubmsLa2xsYoQaL4SFWZUgJctmQXlUrFkuDsdju0Wm2RZnoxlpaWWCsvCXKQIAqB5PBWyVX+qckuwWAwq7OLRqPhLMjLvUhALkjEJuRgd3cX73//+zE+Ps7GHnroIXzlK1+BxWJhY6FQCF/96lfxr//6r/jqV7+atWVJR0cH3vve9+Kpp57Ck08+eVXTJ4jzoIWTyBkSsglZKAche3t7m9skPP7442TvmoU7d+4wy8Ha2lq87W1vy+n/xWIx+Hw+zsJXoVBgYGAAHR0dVzLXYiIJ2JKYDZwePI+Pj1lG9f7+/rnfIxdL0Wrgfr0/GxsbMTQ0VDbVJ0RpEggEMDU1xYlGZrO5aI4Qoihib28PgUAAgUAga0Cqrq4ODoeDqizKgIODA/h8PtZSAjitknG73WT7SJxLMplkfbVzSXYBTqsthoaG0NjYSOsCkTeCIGB6epprg0AOEqVBNBrlqjKzOZ1JSXANDQ3Q6/WyrwuiKGJxcRF37tzhxoeGhqjFBnFpMvVb7+npQU9Pj+z3/lUhObtI60I28UqhUMBisTB3F3KCy41MInZLSwsGBwcr9t4i5Gd/fx8f+MAH8N3vfpeNeTwePPvss+e200skEnjllVfwr//6r3jmmWcwMzNz3699/PHH8fzzzxd03gSRI7R4EjlDQjYhC+UgZO/s7OB73/seu3700Ueh0+lknFHpc+/ePVZlrNVq8Y53vCPr/zk4OIDX6+UEG8nCtxKCZPcTsYHTQLNCoYBCoUAkEmH9c3d2ds7tn6vRaFgwymazVY14K1Xtb2xscOOVll1PyMPy8jIWFha4saamJgwNDcmSOJJqNRwIBLImu2i1WrYuWK3WqlkXyoFwOIyJiQkkEgk2VswECaJykJJdpHUhtQI7E1ISnGQ1XK1JcMTFSSaTmJiYQCgUYmNqtRput7sgLTaIwiE5u0hVmakJU5mora1l64IcybGZrOqpyp8oFFtbW5iamuLO0tVoVR+LxVgSXCgU4vagmairq2MODtWcNH8eJGITcnB4eIinn34a3/rWt9jY6Ogonn/+eTgcjgt9r3v37uGZZ57Bv/7rv+LrX/86t1/4gz/4A/ziL/5iweZNEBeAFlAiZ0jIJmQhmUxm3UzLzf7+Pr7zne+w67e85S1k45qFlZUVluWnUqnwzne+89yvDwQCmJiY4JIa9Ho9PB5PRbzXqSI2AO7vSqXyvgfEeDyOYDCIQCCQtX+uUqmE3W6Hw+GA3W6vWFEkFothYmKCyy5XKBQYGhpCc3OzfBMjyh5RFLGwsICVlRVuvNQSJKRkF8lq+Lz9m2QpWunrQjmwsbGBmZkZ7vflcDgwPDxMyQbEpcgUUD0PlUrF9dWmdYG4H/F4HD6fj7Ov1mq1GBsbg8FgkHFmRDZEUcTBwQHbLxwcHJz79Wq1mrMgv+p1QRRFzM/Pc62klEolRkdH0dDQcKWvTVQ+GxsbmJ6e5sb6+vrQ2dkp04xKA0EQsLu7y5JdsiXBqdVqZkFO+4VTSMQm5OD4+Bgf/OAH8fWvf52NDQ4O4oUXXrh0DOzo6AgvvvginnnmGTzzzDN46aWXcP369ctOmSDygRZRImdIyCZkoRyE7KOjI7z88svs+o1vfCOMRqOMMyp91tfXMTk5ya7v11dcsodOr3602+1wOp0VcVhKrcJO7Y2tUChYJXYuXKR/rmQRJlkNV4qDwNHREXw+35mqfafTyfUDIoiLkkwmMTU1he3tbTYmtTVobW2VcWbnk0gkmHVgMBg893kqrQtS9RXZWBeH+7VB6OjoQF9fHwW9iEtxvyr/pqYmhEKhrFbDtC4Q9yMajcLr9eLw8JCN1dbWYmxsjO6TMuQiSXAKhQJms5mJ2oVOKs7krKRSqeByuWC1Wgv6WkT1sba2htnZWW6M+q1nJt2CPNu6kNqaoK6urur2sCRiE3JwcnKCH/uxH8Nzzz3Hxvr7+/Hiiy8WPE4hiiLdy4Sc0M1H5AwJ2YQslIOQHYlE8NJLL7HrBx98kA7ZWdja2oLP52PXTzzxxJmqY0EQMDMzg7W1NW68s7MT/f39ZW9jlS5cX0bETic1mzoQCCASiZz79UajEQ6HAw6Ho2wr3DMF6/V6PVwuF/XyIi5FNBqF3+/nLLvVajVGR0fLqq2BIAjY2dlhyS7ZLEXr6+vR0NAAh8MBg8FAh9Yr4H5V/r29vejs7KT3nLgUW1tbmJ6e5oTqxsZGDA8Psz1UqtXw9vY2YrHYud/TYDCwILXRaKR7tEo5Pj4+0+7HYDDA4/GgpqZGxpkRhSCRSHDrQjY3B71ez1wczGbzpdYFQRAwNTWFQCDAxsiqnigUq6urmJub48YGBgbQ1tYm04zKh3g8zizIsyXHAj9oTWC326uiZQmJ2IQcRKNRfPjDH8aXv/xlNnbt2jW88MILdJYkKhG6oYmcISGbkAVBEHK2QpSLRCKBF154gV2PjY2R5VkWgsEgXn/9dXb9jne8A1qtll3HYjH4fD7s7OywMYVCgcHBwYrIlj6vH/ZlRexMr3VwcMDEq9TKmUzo9XomXpVLkHptbQ1zc3NclrjVasXo6GhFVO0T8nF4eAifz8clg+h0Orjd7rK2TRVFEfv7+yxInW1d0Ol0zMGB+uEVhmQyienpaS5Yr1AoMDw8jKamJhlnRlQCmYL17e3t6O/vv+9zXVoXpP3C0dHRua9RU1PDxCur1UoW+FXC4eEhxsfHuaQHk8kEt9tNe64KRBRF7O3tsf1CtnVBo9GwdcFms0GtVuf8Wpn6rWs0GoyNjaG+vj7vn4EgAGB5efmMy9vQ0BBaWlpkmlH5IggC9vb2WLV2tnVBak0gWZCnxn0qARKxCTmIxWL48R//cXzpS19iY11dXXjhhRfQ09ND9x5RidBNTeQMCdmELJSDkC2KIp599ll27XK5KAidhZ2dHXzve99j148++iiztz44ODhT5aHRaOByucqq+vF+pIvYqWK2JGBf5abz+PiYBalT+0hnoqamholXpZhJLYoibt++jXv37nHjra2tuHHjRsnNlygvMlX519fXw+VyVYwdv8Tx8TFzcMi2LkhBaofDAZvNRuJVHsRiMfj9fq6vrFqthtPpJEcX4lKIooi7d+/i7t273Pj169fR1dV1of2FtC5sb29ziYWZUKlUXP/cSgtSE6fs7u7C5/Nxz0WbzQan00nPgirh+PiYiVc7OztZrYatVisTts+znE8kEvD7/dxaU1NTg7GxsbJ1iyJKh3v37uHWrVvsWqFQYGho6NK9Y4lTLrIuAOAsyPV6fVkLbiRiE3IQj8fxsY99DH//93/Pxtrb2/HCCy+gt7eX7j2iUqEbm8gZErIJWSgHIRsAnnvuOVZROzw8TPZUWdjf38d3vvMddv3mN78ZBoMBgUAAExMTSCaT7N/0ej3GxsYqwh46VcQGwP1dqVQWXXiNRqNMvMrWD0+tVrMDp91ulz1gmamaEQD6+vrQ0dFBm3fiUqyvr2N2dpb7TNjtdoyMjFyouqgcicViTLzK1j9XqVTCarWyhBcSr7JzcnICr9eL4+NjNlZTUwOPx1PWVf6E/IiiiPn5eayurnLjg4ODl+6RF4vFWJA6FApx+7RMSP1zHQ5HRezfCCAUCp3Zo6db1RPVxUWthu/XmiAej8Pn83HJXdRvnSgUi4uLuH37NrtWKBQYGRlBY2OjjLOqXBKJBLcuZIvl6XQ6Fl+wWq1l9TwhEZuQg0QigZ/+6Z/G3/zN37Cx5uZmvPjii7hx4wbde0QlQzc3kTMkZBOyUC5C9osvvsjmOTAwgM7OTplnVNocHR3h5ZdfZtePPPIIwuHwGbsvu90Op9NZEVaFmazERVEsuJV4viQSCQSDQQQCAQSDwXOD1EqlEjabDQ6HQ5bKq0gkAr/fj4ODAzamUqkwPDwMh8NR1LkQlcX9qhnb2trQ399fVsGVQpBMJlkwKpc+mZJ4JVVYEDz7+/vw+XycJa/BYIDb7a64Kn+iuGRK7lIqlRgZGSn4c1EQBK5/bjQaPffrpZYlDQ0NMJlMsu93iIuztbWFqakpLrmrtbUVAwMD9PskAJyuC7u7u2xdSHXWyoRWq2XtSu7du8dZE+v1eng8HnouEpci055eoVBgdHSUzotFQmpNICXCZWtlJLm7SBbkNTU1RZrpxSERm5CDRCKBX/iFX8Bf/uVfsjGHw4EXXngBw8PDdO8RlQ7d4ETOkJBNyIIoilzAt1T5xje+wQ7sfX196OnpkXlGpU0kEsFLL73Eru12O4LBIPc1XV1d5/ZyLBck8Vqqakz9e6mI2OlIQWrJgvy8z6BCoYDZbGYVmVddObG/vw+/388FzmtqauB2u6l/HnEpBEHA7OwsNjY2uHGq8j9F6ocnrQvZgtR6vZ6tC6mVV9VKMBjE5OQklyRksVgqJlmLkI94PI6JiQnOkletVsPlcsFisVzpa0t9tSXxKluQWqvVstYE1Fe7PMjUb727uxvXrl2r+nWdyIwoilxrgmwtS1LR6/V44IEHyOGFuBSZ2k8plUqMjo6ioaFBvolVOScnJ0zUzuYGBwBGo5ElwhkMhpJ55pCITciBIAj45V/+ZXz2s59lY3a7Hc8//zycTifde0Q1QDc5kTMkZBOyUC5C9re+9S0WvLt27Rp6e3tlnlFpE4/H8eKLL2b8N6lnVSXYs6dbiZeDiJ2OlEkdCAQQCASyilf19fVwOBxwOBwF73kVCAQwNTXFWR1Xas9iorhkEoKUSiWGh4fJejADoiji8PCQtSZIdUfIRE1NDQtElZttYCHIZFVPlrxEIYhGo/D5fNxnUE6r+pOTE66v9nnnR8ndRVobSLgqPdL7ygJAb28vurq65JkQUZZctDVBqYpXRHkgiiJu3ryJ5eVlNqZUKuFyuWCz2WScGZFKIpFg7i7BYDBrzE86S0gW5HIlwpGITciBIAj41V/9VfzJn/wJG7NarXj22WcxNjZG9x5RLdCNTuQMCdmELJSLkP3KK6+wvl6dnZ0YGBiQeUaljSAIeO65586MazQauN1uWK1WGWZVWFJF7NQ/AJiAXW4bTkm8kioys4lXdXV1rEfmZexERVHE0tLSmWCqw+HA8PAwVXQRl+Lk5AQ+n4+ztdRoNHC5XDCbzfJNrIyIRCJM1M4mXqlUKtjtdhaMquRqZFEUsbi4iDt37nDjnZ2d6O3tLbtnAFFaHB8fw+v1cglmdXV18Hg8JdFXNh6Pc+JVtv65JpOJuThQawJ5yVTNCBSm3zpR3QiCgPX1dSwsLHCJqfdDp9OxPUM1JsIRF0MURSwsLGBlZYWNqVQquFyuiogvVCqSu4u0Z8gWY1AqlbBarewsUayEdhKxCTkQBAG/8Ru/gc985jNszGw24ytf+QoeeughuveIaoJudiJnSMgmZCNb771S4LXXXkMoFAJw2kt1eHhY5hmVNltbW/D5fNyYwWCAx+NBXV2dTLMqHOmV2Kl/VyqVFROEOTk5YZXa2WwDpV54kp1oru+BIAiYn5/H2toaN97V1YXr16/Txp24FHt7e/D7/VzCVF1dHdxud0WsRXKQKl4Fg8FzK68UCgULRDU0NFSUs8L91q7+/n50dHTINCuiUtjf34fX6+WCqSaTCS6XqyQrmwVBwM7ODqvWjkQi5369lAgn9dClZ33xEEURc3Nz3NqlUCgwMjJCDiXEpcm0dtXV1UGlUmUVr6T+uZJ4VYprHSEfmdYulUoFj8dDiallRiQS4SzIsyW91NfXs3XhqtoZkYhNyIEoivjUpz6FT3/602ysvr4eX/7yl/GmN72J7j2i2qAbnsgZErIJ2YjFYln758iN1+tFIBAAADQ3N8PpdMo8o9JEqk67efMmN24wGPDwww9DrVbLNLPCkVqFnXoNnB6mK3WzGYvFWEVmtgOnWq1mPTJtNtt9f+/xeByTk5MIh8NsTKFQYGBggCqCiEuTyarebDbD5XJVdJVwMREEAeFwmLk4ZHNYkexEr6I1QTFJJpOYnJxEMBhkY2RVTxSKUCiEiYkJLknEZrPB6XSWhUPJRd1dNBoNt2coh5+xXBEEAdPT09ja2mJjKpUKTqeTLHmJS7O7uwufz8e5M6S22YhGoyzZJRfxymw2M/GqnPcMxOURRRGzs7NYX19nY2q1Gh6PByaTScaZEZclmUxyFuTZilyk5Hm73V6wPQOJ2IQciKKIT3/60/jUpz7FxvR6Pf7lX/4Fjz76KN17RDVCNz2RMyRkE7JRDkL2xMQENjY2AAANDQ0YGxuTeUalRzKZxMzMDHfAlOjv70d3d7cMsyockngtBV3KsR92oUgkEgiFQggEAggGg+faiUrWYJKdqFRdcXx8DL/fz9k9q9VqOJ1OsoYjLs3y8jIWFha4saamJgwNDVWMY0KpIdkGSuJV6mc7E7W1tUzULqeKzFgsBp/Ph/39fTamVqvhcrlgsVhknBlRCWxubmJ6eprbFzc3N2NwcLBs1y6pNYEkXmXrq53q4lBTU1PEmVY2yWQSExMTzGEK+EHLHxKCiMsSDofh9/u5BJzzhKBU8SqXRDhpzyC5OJTrekhcHEEQMDMzg83NTTam0Wjg8XhgNBplnBlRaERRxMHBAavWTt1rZ0KpVMJisTBhO5+2KyRiE3IgiiI+85nP4JOf/CQbq62txZe+9CU8/vjjdO8R1Qrd+ETOkJBNyEY5CNnT09NYXV0FAFitVjz44IMyz6i0iEaj8Hq9rI94Or29vbh27VqRZ1U4SMS+P5KdqGRBni0QZTabYTAYsLm5yQngtbW1cLvd1DeTuBSZeucBQHd3N65du1a1n1M5ODo6Yi4O93s2SGg0GhagLuWKzEw9i3U6HdxuNwwGg4wzIyqBTAk4ldZvXUqEk8SrbH21jUYj11e7Ut6HYhOPx+Hz+bi1uKamBh6Ph9Yu4tJsb29jcnKSq7Bub29Hf39/Tp9ZKRFOWhcODw/P/XrJ+Un6Qy47lUsmFwmNRoOxsTHU19fLODOiGESjUSZqh0KhrC4OBoOBidomkynr+kMiNiEHoijij//4j/Grv/qrbKympgb/+I//iHe/+9107xHVDN38RM6QkE3IRjwez7oplZu5uTksLS0BOO1R+Mgjj8g8o9JB6oWW2g9Rq9VCpVKxYH9PTw/6+vrkmuKlSO2HnfoHABOwabN5iiiK2NvbY+LV8fFxTv+vvr4ebrebKq+IS5FMJjE1NYXt7W02Rlb1pcFF7ESVSiVsNhscDkdJ9cjc29uDz+fjgl20dhGFQBRF3LlzB4uLi9x4b28vurq65JlUERAEAbu7u2xtSE0QyQRVZOaHlGyaKg7W1tZibGwsr+o1gkglk4tEV1cXrl+/nvf56OTkhOufe16cSqFQMAvyhoYG1NXV5fWaROkhCAImJye5fb1Wq8XY2Bgl4FQhyWQSOzs7bG1IjT1lQqvVsmSXTK3OSMQm5EAURfzv//2/8cu//MtsTKvV4gtf+ALe//73071HVDv0ASByhoRsQjbKQci+desW7ty5A+A00/PNb36zzDMqDba2tjA5OcnZyBkMBng8HkxMTLDKj87OTgwMDMg1zbzJJmJTEPX+iKLIVWRmswYrV5thojSIRqPw+/1n7J5HR0ep72eJcdGKTLPZzCoy5QpQZ6o2s1qtcDqdZwJjBHERBEHA3Nwc15ZFoVBgaGgIzc3NMs6suFx0zyBVZEp9telzmJlMLhLSPp0ScIjLsra2htnZWW7s+vXrBW0nlbpnCAaDnOiUCb1ez0TtXCoyidIkmUxicnISwWCQjdXU1GBsbIzcuwiIoojDw0MmamdzflIoFLBarbDb7WhoaEAikSARmyg6oijic5/7HH7+53+ejanVavzN3/wNnn76abr3CIKEbOICkJBNyEY5CNl3797FzZs3AZzaiD766KPyTkhmRFHE3bt3cevWLW7c4XBgdHQUarUar776KsLhMACgra0Nw8PDckw1b1JFbADc31UqFW00L0CmYMR5aLVaFoSyWq0lazNMlAaHh4fw+XxcZr5Op4PL5SLbwRJHqsiU+mpnq66QLAMdDgfq6+uLsg6vrq5ibm6OGyv3nsVEaZDJRUKpVMLpdMJut8s4M/m5iIuDFKCW9g06na6IMy1dDg4O4PV6uZYvZrMZLpeLrJiJS5OpFUJ/fz86Ojqu7DVTnZ+2t7dxdHR07tdrNBomXFHCS/mQTCbh9/tZHAE43dePjY1RxT2RkVgsxlmQpxZZ5EJraysGBgYovkNcGaIo4vOf/zx+6qd+iosp/vVf/zV+9Ed/lO49gjiFPghEzpCQTchGOQjZS0tLLJCt0Wjw2GOPyTwj+Ugmk5iensbGxgY33t3djb6+PrYJGx8fZ8HZ5uZmOJ3Oos81H6gfdmGJRqNcdT5w+j62tbUhEolk7XelUqlYEIr64BHphMNhTExMcFW99fX1cLlcJGaUGVJ1hSRqHxwcnPv1Op2OCVcWi6XgovL97J6p3zpRCOLxOPx+P3Z3d9mYRqOB2+2GyWSSb2IlSCKRQDgcRiAQyKkis76+niW8GAyGqvys7u7uwufzcc9Gm80Gp9NJyYHEpVlcXMTt27e5scHBwaK3cTk+Pmai9u7ublYLckp4KX0SiQT8fj92dnbYGLVCIC6CIAicBXm2tiVKpZI5P9lsNoo1EAVHFEV84QtfwMc+9jEW91IqlfiLv/gLfOQjH6nKfSpB3Af6MBA5Q0I2IRuJROLCWZPFZm1tDVNTUwBONx1PPPGEzDOSh0gkAp/Pd0aUHB4ePhO88Pv92NzcBHBaqe3xeIo613wgEbuwHBwcwO/3n+mf7nK5WKA+mUwiFAox8eo8m2EpCCUdNskWs7pZX1/H7OwsF7i02+0YGRmhqpsK4OTkhNkMZwtQSzbDUsLLZX//meyeAWBgYABtbW2X+t4EEYlE4PV6uWpCnU4Hj8dDlqlZEEWR66t9fHx87tdfdcJLKRIKheD3+7kkwcbGRgwPD1fFz09cHaIo4vbt27h37x4bk86BTU1N8k0Mp8lBqRWZ2dqW1NfXs32D0Wik810JkEgk4PP5uASvuro6jI2NUeIBkRdS25JgMIjNzc2sSbIKhQIWi4WtDeQAQFwWURTxxS9+ER/96EdZzFuhUODP/uzP8LGPfYyePQTBQx8IImdIyCZkoxyE7M3NTfj9fnb9xBNPVF0waG9v74x9r1arhdvthsViOfP1U1NTWFtbA3BaBfKGN7yhaHPNh/R+2AC4jEmFQkEbzQsQDAYz9k93u933DUak2gwHAgFEo9FzX8NkMsHhcMDhcNBBs4qQWhvcvXuXG29ra0N/f3/Vrc3VQDweZ8JVNsvAyya8JBIJTE5OIhQKsTGlUomRkRE4HI68fwaCAICjoyN4vV5uL5Xt2Ujcn9S+2tl6ZKrVathstop2eNna2sLU1BSX+NPW1oYbN27QHpa4FKIoYmFhASsrK2xMqVRidHQUDQ0NMs7sLNJ5Qto3ZKvIpJZG8hOPx88ky+v1eng8Hno2Epdmf38fXq+Xc3RRKBTnJskCp4kU0p7BbDbTGZO4EKIo4plnnsGHP/xh7t774z/+Y/z0T/803U8EcRY6rBA5Q0I2IRvlIGRvb29jfHycXT/22GMVGQC7H5ubm5icnOSqO+rr6+HxeO5r8zU7O4vl5WUApz35Hn744aLMNR/SRexUMZtE7IuTqW/eRStlRVHE/v4+C1Bn64MnR+9covgIgoDZ2dkzrQ16e3vR2dlJv/cqIJlMIhwOswB1av/XTJhMJhag1uv1594j0WgUPp+Pq9jQaDRwuVwwm82F+hGIKkVKCEwNZlHP4sIRi8W4hJdsfbUtFgtbGyrBsnZ1dZW1QZKgVghEIRBFEbOzs5xLiVKphMvlgs1mk3Fm2ZEqMqW1IVvCi1KphM1mYxWZ5P509cRiMXi9Xm7vZTAY4PF46P0nLk0mEbu1tRX9/f3Y29vD9vY2gsFgVoeXakiGIwqHKIp49tln8aEPfYg7q37mM5/BL/zCL5CITRCZoQMLkTMkZBOykUwms9p/yc3Ozg6+973vsetHH320KrKDpR6h6X3QHA4HRkdHzxUlFxYWWG9Ro9GIN77xjVc613xJFbEBcH9XqVQU/LsAgiDg5s2bXLUGAHR0dHD90/Ph6OiI2Y9nC0JJVqIOh4OypyuIeDyOiYkJrm+eUqnE8PAwGhsbZZwZIReiKLIgVCAQyBqEkiorHA4HTCYTtyZlqpStra2F2+0mu2fi0gSDQUxMTHDiakNDA0ZGRqj67wqQEl6kvtrZEl6kZLhytRnO1LO4r68PnZ2dMs2IqBQEQcD09DS2trbYmFqthtvtLssEr1gsxlmQZ0umNxqNbG0wGAxltzaUOrFYDOPj4zg8PGRjUrK8VquVcWZEJXA/EXtgYODMZ1myIN/e3s7a0kihUHCJsnV1dbQ2EAxRFPG1r30NH/zgB7lz5e/8zu/gP/2n/0SxKYK4P7SQEjlDQjYhG+UgZO/v7+M73/kOu37LW95S8YHtZDKJqakp1udaoqenB729vVk367dv32ZBtbq6Orz1rW+9srnmQ3r1NfXDvhyZ7HgVCgX6+/vR3t5e0NeKRCJMuNrZ2Tn3oKnRaNgh02azkWBQppycnMDn83GV+VQpS6Ry0aqrVCtRlUqFyclJLtBlNBrhcrmoGoi4NOvr65idneWeVS0tLRgYGKBgVhFITXjZ3t7O6vBSU1PD2QyX8u8oU89iABgcHERra6s8kyIqhmQyicnJSQSDQTam0Wjg8XhgNBplnFlhSCaT2NnZYWtDtpZGUqJsQ0MDLBZLSa8N5UA0GsX4+Di3JhuNRng8Hqp2JS7N/v4+xsfHuTjj/UTsdOLxOEKhEILBIILBIHc+yERtbS2r1Ka1oboRRREvv/wynn76aS7B+n/8j/+BX/u1X6N7gyDOhwLwRM6QkE3IRjkI2UdHR3j55ZfZ9SOPPAKTySTjjK6WSCRypk+VVPnY0tKS0/dYXFxk9tI6nQ6PPvroVUw1L9KFawAsI59E7ItzcnICv9/PZdOr1WqMjo5eueVgPB5HMBhEIBDIWlmhVCpht9vhcDjIEqyM2Nvbg9/v5yrq6urq4Ha7qTc6cV8ikQhbG8LhcNY+eKnY7XaMjo5S4gtxae7du4dbt25xY2T3LC+pCS+7u7vnfq1KpWJWog0NDSW1bxBFEXNzc1hbW2NjCoUCo6OjcDgcMs6MqAQSiQQmJiYQDofZmFarxdjYGAwGg4wzuxpEUcTh4SFzcdjf3z/361PXBrvdTtXDFyQSiWB8fJwTekwmE9xud0mts0R5chkROx1BELC3t8eqtbMlw0kW5Ha7ndaGKkMURbzyyiv4wAc+wMXFPvnJT+I3f/M3ScQmiOzQ4ZjIGRKyCdkQBCFrlqPcRCIRvPTSS+z6wQcfhNVqlW9CV8je3h68Xi+Xla7VauHxeC5U+bi8vIzZ2VkAp9n7jz32WKGnmhfp/bABUCX2Jdjd3cXExAQnMtbW1sLlchU90JVqJbq9vX3uuiL1x3Q4HGhoaKiKVgHlSCAQwNTUFJd0Qj1liYuSSCQQCoVYgDpb8lzq2lAJvXOJ4iOKIm7duoWlpSVuvL+/Hx0dHTLNikjnIjbDCoUCZrOZsxKVi0x2zyqVCk6ns+R7FhOlTzweP5PQrNPpMDY2VjUJhFIy3Pb2NsLhMLcPzUT62kBnyftzcnKC8fFxnJycsDGz2Qy3231u2zKCyIVCitiZOD4+ZmtDNmc4AJwFuV6vp7WhQhFFEa+++ip++Id/mEuE+pVf+RX89m//NonYBJEbtEASOUNCNiEb5SBkJxIJvPDCC+x6bGwMDQ0NMs7oatjY2DgjGkl9qi4azF9bW8PU1BSA00rYJ554oqBzzYfzRGxJwKbDRe5sbm5iZmaGu19MJhNcLpfs2ceCIGB3d5dZkKf2J8qE0WiEw+GAw+Go+LYB5cLy8jJzdZBoamrC0NAQHQaJvBEEAeFwGLdv38bBwUHWr6+vr2d9tak/JpELgiBgdnYWGxsbbEyhUGB4eBhNTU0yzow4DykZTqrWztZXW6/Xs7WhmH21M1XKajQauN3uinaLIopDLBaD1+vlno91dXUYGxur2qTPZDKJUCiE7e1tBIPBrGtDXV0d7HY7GhoaYDabac+awsnJCV5//XXuXGa1WuFyucgFh7g0Vy1ipyMlykprQ7aYptSewG63l3zrEiJ3RFGE1+vFD/3QD3EJYL/0S7+E3/3d36W1jSByhwItRM6QkE3IRjkI2aIo4tlnn2XXLperooKRUo+9O3fucOONjY0YGRnJKzt6c3MTfr+fXT/55JOyCgCpIjYA7u9KpZIOEhdAFEUsLi6euV+ampowODhYcpt1URRxcHDARO1Uq6dMyBWcJk4RRRE3b97E8vIyN052vEQhEAQBMzMz2Nzc5MZrampy7o/pcDgoOE1kJJlMYmJiAqFQiI1RpWz5IYoi9vf3maidbd+g1Wq5vtpXtQ/KVClbU1MDj8dTkXbPRHGJRCLwer2cda7BYIDH40FNTY2MMysdLro2qNVqJmrbbLaqdhM6OjrC+Pg4t9ey2WxwOp0ld3Ykyo9ii9jpiKLIWZBnWxuk9gSSBTmtseWJKIqYmprCe9/7Xi7B8Od+7ufwB3/wB+QyQRAXgwJ9RM6QkE3IhiiKWTObS4HnnnuOVZ4ODw+jra1N5hkVhmQyiampqTNB/WvXruH69et5b/y3t7cxPj7Orh9//HHZNnKpVdipvbHJSvziZKo0A4Cenh709PSUxXt5fHzM7Mez9cesqalhFsMWi4WEqytGWo+2t7fZmEKhwMDAAFpbW2WcGVEJxONxTE5OcoEGlUqF0dFR2O12HB8fs4SXbGuDWq1mwpXNZqNABYFYLAa/38+JjFqtFm63G0ajUcaZEZfl5OSECVfZrESVSiVsNhscDkdB+2NGIhH4fD4uOF5bW4uxsTFqgUBcmkx2z0ajER6Pp6rF12xcZG2Q2hpJFZnVYtMOAIeHhxgfH+diPna7HaOjoyRiE5dGbhE7EycnJ5wFebb2BEbj/8/em4c3epV3/18tXmRrtRbP2LPYHo/HuxbThKwMDQnJJJOkJAUCtARok3BB0hJalqssbdnhJQTe0hCgBPoLDS1ZICQkk5mQhSQ0JJbk3Z7VM57F1up90XZ+f8z7HHRsjSTbsrXdn+ua67LOyNKx/eg859zf+/7eWn6uIAeo/IAxhqGhIezbt0+IXfzt3/4t/v3f/53OhgSxemjhI9KGhGwia+SLkP273/2Oz7OlpQU7d+7M8ozWj5R5H9/HRS6Xo6OjA1u3bl3XawcCAfzxj3/kj9/+9rdveqbpcuGaROz1EQqF0NPTIwg8crkcbW1teetQsLS0xIWrQCCQNAAVL1yZTCYKvGSYpaUluN1uYT2iSkYiUyQSgUpLS2Gz2RLa8Uq9cz0eD/x+f9IAlFwuR1VVFV8fqKqi+FhYWIDT6cT8/DwfU6lUcDgcRSVWFAPhcJgHp30+X9K+2oDYO3etrUvm5+fhdDoFkZEqZYlMkahS1mAwwGazUSB+FazWZlhygDKbzdDpdAV7Jk0kYlssFnR0dFCCMLFuclHEXk4kEuGtS9JpT1BWViZYkFPMIfeQHOT27dsnFAR98IMfxI9+9CNKACOItZEbizaRF5CQTWSNfBGyX3rpJR5AampqQkNDQ5ZntD6mpqbgdDqFoEVZWRnsdjv0en1GXv8Pf/gDf3zllVduajA3WT9sErFXz9zcHFwulxBELSkpgc1my8j1kgtEIhEuXKUKTm9UxVWxMjs7C5fLJfTMKy8vh81mg0ajyeLMiEIg0fVVUVEBu92e1n0pvj+m1+tNGZzW6XTcyWGtwhWRP8zOzq7YT5HIWBzEYjEEg0G+NsSvMYmoqKjg7QnSFa5mZmbgdDqFs5Jer4fNZqNAKbFuZmZm0N3dLdzXyO55/cRiMUxNTfG1IT7JKRElJSVCe4JCSSCYnp6G0+kUrq/q6mq0t7eTiE2sm3wQsZcjtSeQEuJmZmaSPj8+WdZkMqG8vHyTZkpcCMYYjh8/juuuuw5nzpzh47fddht++tOfUlyIINZObi7cRE5CQjaRNfJFyH7llVd4JVdDQwOampqyPKO1c+7cOfT19QkVZpJ9XKY2xzMzM3j11Vf548suu2zTBKnlIna8mC0J2Ll6uMlF/H4/ent7hUNiZWUl7HZ7wdpZxmIxBAIBbkGebI2SyWTQ6/VcuCrU38lGEQgE0NPTI1xfGo0GNpuNDuvEugkGg3C73cL1pdPpYLPZ1hRoYIxhcnKSOznEJ/ckQqq4slgs0Gq1dO8pMBJdXwaDAVarlUTGIoMxhpmZGS5cpQpOxwtXRqMxoWg4OTkJl8slXF8kMhKZQkpqjr++qFJ2Y5ibm+PC1eTkZMr2BJIFudlsztu9cKLra+vWrWhtbaXri1g3+ShiJ2JxcZGvDYFAIKUFuUaj4aI2nSs2H8YYTp48iWuvvRZjY2N8/NZbb8XDDz9MCawEsT5oQSPShoRsIqvEV7HkKn/4wx9438OdO3eipaUlyzNaPYwxHD16FMeOHRPGt2zZgo6OjowGxebn5/Hyyy/zx29961s3pXI3XsQGIHwtl8vp4LxKTp8+jeHhYSHgYjQa0dHRUTRBesYYpqam4PF40hKuNBoNLBYLLBYLKisr6YCZhLNnz2JwcFC4vkwmEzo6OgqmGoXIHhMTE+jr6xOuL7PZnLH7HWMMc3NzPOEl3hY/EaWlpVzUrqqqovtRnuPxeFYkBVosFrS3t5PISPD+mB6PJ62+2svbE/h8PvT09AjX15YtW9DW1kZrB7FuAoEA3G634D5EIuPmEN+ewO/3C0JcIiThymw2Q6PR5MW5IlESTk1NDVpbW/Ni/kRuk0jE3rZtG5qbm/P6+opGo4IFeaoYqXSuMJlMF0yIIzIHYwynT5/Gtddei9HRUT5+00034Re/+EXeJh0RRA6Rvws4semQkE1klVAolDTAkwu88cYb8Pv9AM5vlNvb27M8o9URiUTQ19eHiYkJYXzXrl1obGzM+KZ/aWkJL7zwAn/8Z3/2Zxve5zaRlThjjKzE14DU9+fUqVPC+LZt27Bnz56iDXIxxjA7O8uFq1QVVyqViovahdz/brVIllzHjx8Xxov9+iIyx8mTJ3H48GFhbKODXIuLi7waMxAIJN3XKBQKmEwmHoAqlsSgQuHMmTMYHBwUxgohiEpsDOFwWOidm0q4UqlUK5Lm6PoiMoXX60Vvb6+QJEHXV3aQ2hNIwnaqZNmysjK+d8jV3rnBYBAul0tIkqDri8gUhSpiL0dyeZHWhlTJsvFODiaTiRziMgxjDOfOncN1112Ho0eP8vF9+/bhl7/85aa2UCSIAqZwFnFiwyEhm8gq+SBkO51OeDweAOcrImw2W3YntAoWFhbgcrmEDbBcLkdHRwe2bt26Ie8ZiURw6NAh/tjhcMBisWzIe0nitRSQif+aROzVE4lE0N/fD6/XK4zv2bMH27dvp99lHAsLC1zUDgaDSZ9L1ZjnicViGBwcxLlz54Tx3bt3Y+fOnXR9EeviQkk4jY2NqKur27TrSxKuPB4PfD6fENBdjkwmg8Fg4O0JKKM/d2GMYXR0VAhiAeeTAuvr62n9IlISi8WE9gSp+moD59v/7N69G3q9vmj3DkRmSORUsnPnTuzevZvWrywjubxICXGSE9yFkMvlMBqNXLjKBUtbv98Pt9stJEns2LEDTU1NdH0R66ZYROxELC0tCRbkyc4VAKBWq/naQMn062diYgL79u3D8PAwH7v66qvxxBNPoLKyMoszI4iCghYqIm1IyCaySj4I2T09PVx4MZvN6OrqyvKM0mNychJOp1Po8VtWVgaHwwGdTrdh78sYw4EDB/hjq9W6IaL58n7YAEjEXgeLi4twu91CpbFCoUBHRwfMZnMWZ5b7hEIhHphO1eNKqVQK1ZjFYqMdDofR09MjiP5yuRzt7e2orq7O4syIQiAajWJgYEBwHpHJZGhtbUVNTU3W5hWLxbhVoNfrTWkVKLUnMJvNUKvVdA/LERhjGBkZEXriAUBLSwu2bduWpVkR+Yzk8iKtDakqrkpKSvjewWg0Fs3egcgMZ8+excDAgDBGSTi5i3SukCzIU/XO1el0/FyRjb1DonYIlCRBZIpiFrGXE41GBSeHVAlxJSUlggU57R1Wh8fjwQ033CDcP/fu3Ysnn3wSGo0mizMjiIKjuBZzYl2QkE1klXA4nPJwlm36+/tx+vRpAEBVVRUuuuiiLM8oNWfPnkV/f7/wu9XpdLDb7ZtS8fXcc8/x925vb894oHe5iB0vZksCdrEdbNbD9PQ0XC6XkPRQXl4Om81Gm/RVEolEhGrMZDai8b0xLRYLSktLN3Gmm4fkDDE3N8fHSkpKYLPZoNfrszcxoiAIh8Nwu92YnJzkYwqFAlardcPbWqwGxhimp6d50kv85yERKpWKrw06nY6qMbNELBZDf3+/kCRBSThEpmCM4ciRIzh58mTa3yOTyYS+2uTkQCRjbGxMqCQDgKamJuzcuTNLMyJWgyRcpZsQV15eztcGg8Gw4XsHr9eLnp4eoTChvr4eu3btorM4sW5IxL4wUkKcJGqncnKQXKCk9YEsyJPj9/uxf/9+9PT08LHLLrsMTz/99IYWBRFEkVLcCzqxKkjIJrJKPgjZw8PDGB0dBXBeDL7kkkuyO6EkSAGx5f1nt2zZgo6Ojk3rp/X8888jHA4DOF+xlMlgSbyIDUD4Wi6XU7B/lXg8HvT19QmfQ61WC5vNlhNWdfmM1P/O4/HA4/EIiQKJ0Ov1vK92oRwup6am4Ha7hZ+9oqICdrudekoR62ZxcRFOp1MQhUtLS+FwOHI+CSfeRjRehE+EVFEhVWPmYm/MQiQSiaCnpweBQICPKZVKWK1WVFVVZXFmRCHAGMPg4CDOnj3Lx2QyGdra2iCXy3lfbWk/fSG0Wi1fH8jJgYhndHQUR44cEcbISSJ/kXrnSnuHeBetRCiVSsGCvKSkJKPz8Xg86O3tFUTsXbt2oaGhIaPvQxQnJGKvjlAoxEVtv9+f0oK8srKSrw16vZ5+p3FMTk7ixhtvRHd3Nx+7+OKL8cwzz8BgMGRxZgRRsNACRKQNCdlEVskHIfvIkSM4duwYgPM9Zy6//PIszygxkUgEfX19QtUQcL4/6GZnRb/44ovc6qipqSljB9pEVuLS1wqFgjbgq4AxhpMnT64IcFVXV6OtrY2EkgzDGMPU1BSvxpyfn0/6fLVazUXtfA1MJ0qS0Ov1sFqtBVt9TmweMzMzcLlcQnVSZWUl7HZ73iWCrKY9QXxvTLPZTJ+lDSIUCsHpdApCQb4kSRC5TywWQ19fHzweDx9TKBSw2WxCkkQsFhP2DgsLC0lfd7OrMYnchDGGY8eO4cSJE3xMSpLYiHZPRHZYXFwUeucm2zvIZDJuQW42m9fd23V8fBz9/f2CiN3Y2Ij6+vp1vS5BAOcToZ1OJ4nYa0RKppfWh1R7B6l9iWRBnumkl3xienoaN998M15//XU+1tXVhWeffRYmkymLMyOIgoYWdiJtSMgmskokEkmZLZhtjh8/jsOHDwM4HyDau3dvdieUgIWFhRUBV7lcjs7OTmzZsmXT5/P73/+eV8jt2rULu3fvXtfrSeK1dECP/5r6Ya+eWCyGoaEhoQoIICu4zYIxxqsxPR5Pyt6Y8RbD+ZIxferUKYyMjAhjW7ZsQWtrKyVJEOsmEAigp6dHCHDp9XrYbLa8D75Eo1GhPUGqakzJycFsNpPLQYaYn5+H0+kUAn8VFRVwOBx5lyRB5B6JKv1LSkpgt9uT2lXG7x3SsRFVKpVCX+18XxuJ9GCM4fDhwzh16hQfk8lk6OzshMViyeLMiI1E2jtITg6pXKAqKiq4qL3a9iWJeq6TXT2RKUjEzizS3kEStVO5QMlkMuj1el6tvd6kl3xidnYW73rXu/Dqq6/ysc7OThw8eJDunwSxsdDiTqQNCdlEVskHIfvUqVMYHBwEcD7QdNVVV2V5RiLBYHBFf+OysjI4HI6s9W957bXXuDhXV1eH5ubmNb/WcitxErHXRzgcRk9PD4LBIB+TyWRobW1FTU1NFmdWvCwuLsLj8cDr9SIYDCLZfVmyGLZYLKiqqso5UThRABWgJAkic5w7dw4DAwPC58RisaC9vT3nPg/rJRaLYXJykie9SE4nF0KtVvPAtFarpc/bGpiZmYHT6RT2VFqtFna7narfiXUTDofhcrkEEVras6vV6lW91tLSEnw+X1pODtQbszhgjGFoaAhnzpzhY3K5HDabDUajMYszIzYTyQVKEq5mZ2eTPn811ZhnzpzhcRGJ5uZmbN++PSNzJ4obErE3nnA4LFiQx/+uEyElvUgW5IXq9DI/P4+//Mu/xIsvvsjH2tracPDgQXIyIYiNhxZ4Im1IyCaySj4I2WfOnEFfXx+A88GAa665Jssz+hNnzpxZYeul0+lgt9tRXl6etXm9/vrrXCjdsWMHWltb1/Q68SJ2/D8AXMCmQ036zM3Nwe12C7bWJSUlsFqt1O8nRwiHw1y08vv9SQPTCoWCV1ttRO+71RKNRtHX1wev18vHZDIZmpubqR8jsW4YYxgdHcXRo0eF8R07dqCpqang7wWMMczOzvKkl1S9McvKynjSC1kMp0cgEIDb7Rb2pUajEZ2dnVAqlVmcGVEILC4uwul0csciIHOV/vHVmF6vN6WTg0aj4aK2RqMp+PWzGIjFYhgYGMD4+DgfUyqVsNlstMcvchYWFvjakCphNlnSy9jYGIaHh4XnU891IlOQiL35SAmzkrCdqvWZUqnk7Y1yIfaQKRYWFnDbbbfh4MGDfKy5uRmHDh1CbW1tFmdGEEUDLfJE2pCQTWSVfBCyx8fH4Xa7+eNrrrkm6wFhqeoxvvcZAGzdujUnqtLefPNN+Hw+AEBNTQ06OztX/RrLK7Hjv5bL5Vn/G+QbgUAAvb29QnCzoqICdrud7GhzlHiLYa/XmzRjWiaToaqqilsMl5WVbeJMz1eGud1uwSZdoVDAarVSFRCxbhhjGBkZwdjYmDBezFaWqwlMx1sMm0wmEmUTMDExgb6+PuH3uGXLFrS1tdF+g1g38/Pz6O7uFlwVNBoN7HZ7xu/XUjWmtD7EC+eJkJJezGYzqqqq6HrPQxIlEpaUlMDhcECr1WZxZkSuEQ6H4ff7uXCVqhpTcnqJRqMr3Jba2trIzYvICCRi5wbLLchTaQXLLcjz8W+1uLiID3zgA3jmmWf4WGNjIw4dOoQdO3bk5c9EEHkIfdCItCEhm8gq0Wg05QEq23i9XnR3d/PHV111VVazDyORCHp7e+HxeITx3bt3o6GhISc2Wy6XCxMTEwDOB4JtNtuqvj++Cjv+MXBeHMuFnzGfOHv2LAYHB4XDSFVVFTo7Owsmk7bQkTKmPR4PPB4PlpaWkj5fp9PBYrHAYrFseKLC7OwsXC6XEKAvKyuD3W6HRqPZ0PcmCp9oNIr+/n7hnieTydDe3o4tW7ZkcWa5g2QTKDk5JEsQlJJeJOEqm+4tuUKiKrNiqfQnNp5EdvV6vR42m21T9mDxfbVT9cbMNacXIjXRaBRut1vouV5aWoqurq5V29UTxUUsFhOSXlJVY8bT2tpKlYpERiAROzeJT3rx+XwpnV5UKhXfO+SLE1QoFMIHP/hBPPnkk3ysvr4ehw4dQn19PV1/BLF50IeNSBsSsomskg9CdjAYxOuvv84f7927N2uB34WFBXR3dwu9rhQKBTo6OnIqoN/b24uzZ88CAMxmM7q6utL6Pkm8luyUqR/2+mCM4ejRoxgdHRXGa2tr0dzcnBcHDGIljDFMT09zC/JU1VaVlZVc1M60hWggEEBPT4+wjms0GthsNhLIiHUTCoXgdruFfrJKpRJWqxVVVVVZnFnuEo1GEQgEeGA6XjxLhFar5U4O+VpNsVYYYzh+/DiOHz8ujDc2NqKurq6ofhfExjA5OQmXyyXcI00mEzo7O7PinhQKhXi1lc/nS9lXW6q2slgs1Fc7BwmHw3C73UKCQnl5ORwOByorK7M3MSIvWU3Si1wu50lxJpOJ9vzEmiAROz9gjAkW5KliDwqFQrAgLy0t3aSZpk84HMbf/M3f4NFHH+Vj27dvx/PPP4/Gxka6/ghic6EPHJE2JGQTWSUfhOzp6Wm89tpr/PHll1+elQz3YDAIl8slBKWlYEWu2cYNDAxwC9iqqipcdNFFKb+HROzMkqiKEThfub9z5076XRYQc3Nz3H48XvBLRHl5OQ9K6/X6dSUzJKr0N5lM6OjoIOtiYt0sLCzA6XQKFUJlZWVwOBxUZZYm8RbDHo8nZbWVSqXiSS86na6g7xOMMQwNDeHMmTN8TCaToaWlharMiIzg8/nQ09MjiMW5ZFe/2qQXyWLYbDZDq9UW9PqQD4RCITidTszMzPCxTPVcJ4ilpSUMDQ0JdvXJ0Gg0fH3IdNIsUZiQiJ2/zM/Pc1E7VXsj4LxTnCRqq9XqrP99I5EI7rrrLjzyyCN8rKamBocOHaLrjyCyA33oiLQhIZvIKrFYLKVNTbaZm5vD73//e/74kksugU6n29Q5nD59GgMDA8ImUafTweFwbHov3HQYHh7mVcA6nQ6XXHJJ0ufH98OO/weAC9i0oUyfRP2K5XI5Ojo6YLFYsjgzYqNZXFzkQelAIJD0YFlSUsKDTkajMe3qsAtVMW7btg179uzJiQA9kd9MT0+vSNxSq9Ww2+1U9bMOVpP0Iq0PFosFVVVVWake3SgSJXrJ5XJ0dnbCbDZncWZEoTA+Po7+/n7hHrx9+3bs2bMnJ/ez8U4vXq9XcH5KRGlpqdBXu5DWh3xgaWkJ3d3dQlWcWq3O2XMhkV8kcvSSyWQwmUyYmZkRWgkloqysjK8PBoOB1gdiBSRiFw6RSIRbkHu93pSxXSmpXrIg3+z1IRKJ4O6778Z//ud/8rHq6mocPHgQ7e3tdP0RRHagDx6RNiRkE1klH4TspaUlvPDCC/zxRRddtGmWpowxjIyMrLCGrqmpQVtbW84eDI8cOYJjx44BOB9Yufzyyy/4XBKxM8vMzAzcbveKfsU2my3nKveJjWU1fXPlcjnvi2k2my/YFzMWi2FwcBDnzp0TxqnSn8gUPp8Pvb29wvVqMBhgtVqpX2sGWVpa4qKV3+9PmvQSvz7kqkVguoTDYfT09CAYDPIxpVIJu90OvV6fvYkRBcPp06cxNDQkjNXX12PXrl15c4+cn58XLIaTrQ/5YCFaSEhtphYWFviYVquF3W6n3z2xbhhjOHz4ME6dOsXH5HI5bDYbjEYjGGOYnZ3lolWqpDhpfZD2EHSNEolE7FxO9CLSR3KCktaHVElxCoVCaFGw0YlYsVgMn/jEJ/DjH/+Yj5lMJhw8eBBWq5WuP4LIHvThI9KGhGwiq+SDkB2JRHDo0CH+uKura1MqdiKRCHp6elZYejU1NaG+vj6nN1rHjx/H4cOHAZy3Kn3b296W8HnxIjYA4WuFQpHTP2Mu4vV60dfXJwhA1K+YAP5kISpVYyZbd2UyGQwGA++bK1074XAYvb29CAQC/LlyuRzt7e2orq7e8J+BKHwS2dXnkhVvoSJVU3g8Hvh8vqQtX6S+udL6kE8WtktLS3A6nUJgjezqiUzBGMPo6CiOHj0qjDc1NWHnzp1ZmtX6kZLipL7ayZLiAPC+2mazmfo0Z5i5uTk4nU4hWVWv18Nut1NLF2LdMMYwPDyM06dP8zGFQgGbzXbBJP6lpSW+Pvj9fqGVQiIki2FpfaCzfnFBInZxsbCwIFiQp1oftFotF7Uz3aIgFovhU5/6FB544AE+VlVVhQMHDqCrq4uuvwwwMzODV155BadPn4bP54NGo0FNTQ3a29vR1NSU8febnZ3F66+/jiNHjnCLe61Wi507d6K1tRW7du3K+HsSGwZ9AIm0ISGbyCqMsZQ92bINYwwHDhzgj61WK7Zu3bqh7zk/P78i2KpQKNDZ2ZkXgtHo6CiGh4cBnLcf/PM//3Ph/5dXX1M/7PXBGMPY2BhGRkaEcbPZjPb2dgpuEQKxWAxTU1PweDzweDwpLQK1Wi0MBgM8Ho9QAVRSUgKbzUZVjMS6YYzhxIkT3MlDYufOndi9ezfdDzaRWCyGYDDIqzFTrQ9qtZqL2rncFzORAFRZWQmHw0GJXsS6YYzhyJEjOHnyJB+TyWRobW1FTU1NFmeWWWKxmNBXe2lpKenzKysruWil0+lydn3IB2ZmZuB0OoVzs9FohNVqzVmHLiJ/YIxhaGgIZ86c4WMKhQIOhyPtfb6UNCslvaRaH1QqlWAxTAmLhQ2J2MVNNBoVLMhTxYDLysq4k8N6W5jEYjF8/vOfx/3338/H9Ho9nnnmGVx88cV0/a2TV199FV/60pfw/PPPXzAZ2mq14q677sKdd9657t/37373O3znO9/Bs88+mzT52mg04uqrr8ZnP/tZdHZ2rus9iQ2HPoRE2pCQTWSVfBCyAeDgwYO8AqG9vR3btm3bsPcKBAJwuVxCxWR5eTm6urqg0Wg27H0zydjYGAYGBgCcPwRfffXV/P+WC9ckYq+PWCyGkZERIXseIAGISA/JIlAStVNZgEmUlZWhq6uLqq2IdROLxTA8PCwETwFgz5492LFjR5ZmRQDn14eZmRl4vd601gep753UFzNXgtJTU1Mr9lU6nQ42m41sTol1E4vFMDQ0hLNnz/IxuVyOjo4OWCyWLM5sY1nt+lBaWsqD0kajkcTXVZBoDTObzejs7MyZdZbIXxhjGBgYENoGKZVKOBwO6HS6Nb+mtD54vV7MzMwkfb5SqRRaFFArmcKCRGwiHsYYpqenuaidan2Qy+WCBflqElAZY/jXf/1XfPOb3+RjGo0Gv/3tb3HZZZfR9bcOwuEw7r77bjz44INpf8+VV16Jhx9+GNu3b1/1+/l8Ptxxxx144oknVvV9DzzwAO66665Vvx+xqdAHkUgbErKJrJIvQvbvfvc7Ps+WlpYNswg8ffo0BgYGBFtVyTJuo3vGZJKzZ8+it7eXP37nO98JmUy2oh82ABKx10E4HEZfXx/8fj8fk8lkaG5u3tBkC6JwmZ+f5/bjk5OTSZ9bVlYGs9kMi8WSU6IVkT9Eo1H09vbC5/PxMbKrz10WFhb4+hDfYzoRSqUSJpMJFosFRqMxa84gfr8fPT09gh2yyWRCZ2cnCWnEuonFYujr64PH4+Fjqax4C5WFhQUuWkkWjxdCLpcLolU+nXE2m2AwCJfLJaxh1HKDyBSxWAwDAwMYHx/nYyUlJXA4HNBqtRl7n8XFRb4+BAKBpOuD1MJEWh8oaTa/IRGbSMXi4iIXtQOBQEoL8meeeQYymQw33XQTLrvssgvu5xlj+PrXv44vf/nLfKyyshJPPfUU3va2t9H1tw4ikQhuuOEGwbkUOH//uPjii7Ft2zbMzc2hp6cHp06dEp6zZ88evPrqqzAajWm/3/Hjx3HNNdescG8rLS2F3W7H1q1boVKpMDk5iaGhIYyOjvLnkJCdF9CHkUgbErKJrJPKdioXeOmll7il7u7duzPeb4MxhpGREeGGC4D3FMm3QMXExARcLhd/fPXVV0MulwsidryYLQnYtJlMn4WFBbhcLszNzfExpVKJzs7OVW0KCeJCHD9+fMVh4UIolUpeiWkymUggIlISCoXgcrkwPT3Nx5RKJex2O9nV5wGhUEjom5ss6BRfSWE2mzdNtDp37tyK5MCtW7eitbU17/ZVRO4RiUTQ09ODQCDAx0pKSmC329dcxVgohMNh+P1+eDwe+P3+pNaPAPXNvRA+nw89PT3C+lpbW4uWlhb6HRHrJlEiTklJyYa7wEUiEaFFQbzTQCIqKyu5m4Ner6drP48gEZtYLVKLAumMsTxWzBjDHXfcAa/XC+B8n+srrrgC+/btw4033sgTcBhj+M53voPPf/7z/HtVKhV+/etf4x3veAddf+vkk5/8JO677z5h7J577sE///M/w2AwCOPPPfccPvrRj+L48eN87Morr8SLL76Y1t/B6/XirW99q/D9NTU1+PKXv4xbb7014f1qYmICTz/9NH72s5/hfe97H+68887V/ojE5kIfSCJtSMgmsk4+CNmvvPIKt8xraGhAU1NTxl47HA6jp6dHqEgDzmeq1dXV5eUmy+fz4c033+SP3/72t0OpVApV2PEiNgWUV8fk5CTcbrdw8FepVLDb7ZS1TqwbxhgOHz68InvWbDZDLpfD5/MJlUHLkSqtLBYLTCYTWfcSK5ifn4fT6RR6rpeXl8Nut0OtVmdxZsRakIJOUrV2qqC0JFpZLJYNu2edOnUKIyMjwlhdXR0aGxvzcl9F5BaJEnHKysrgcDhoDVtGLBZDMBjkolV8n/pEVFRUCH21i/WM4PF40NvbKyTi7NixA01NTbSGEesmFoutiD+Ulpaiq6trU9cwxhimpqb4+hCfoJ2IkpISoUVBttxeiNSQiE2sF6kFmrQ+TE9PY3R0FH//93+f8PlSIs4111yDWCwmVGKXlZXh8ccfx3XXXUfX3zoZGhpCe3u7kGT37W9/G/fee+8Fv2d8fByXXXaZIEY/8sgjeO9735vy/f7yL/8Sjz76KH985ZVX4qmnnko74SoSidC9IvehDyWRNiRkE1knFAoltZfKBf7whz9gamoKwPnewy0tLRl53fn5eXR3dwuHNoVCAavVmtd99YLBIF5//XX++IorrkBZWRn1w84A586dw+DgoLBx1Ov1sFqtJBgS6yYajaKvr49nOQMr7epjsZggWqVqD2EwGGCxWGA2m6FSqTZ0/kTuk6jXp0ajybsWGkRiGGOYnJzkfXPjkxUSUVFRwdcHnU637j0BYwxHjx5d4XDT1NS0YW1hiOJicXERTqdT2LtXVFTA4XDQPS4F8UFpj8eTsi9msYpWidwkGhoa0NDQQOcmYt1Eo1H09PQIranKysrQ1dWV9YTo+fl5XomZqkWBTCZDVVUVXyNo/c0dSMQmNoKlpSX8/ve/x3333YfXX389ZWKcQqFANBqFUqnE//zP/+Dmm2+m6y8D3H777fjZz37GH7/jHe/AwYMHU37fSy+9hL179/LH9fX1OHLkSFInv8cffxy33HILf9zS0oI33ngj6/cqIuPQB5NIGxKyiayTD0L2G2+8wQ9727ZtQ3t7+7pf0+/3J6yqdTgcG2rntRlMT0/jtdde448vueQSVFZW8ipOErFXD2MMx48fF7IYAbJJJTLH0tIS3G63UGEmJdZcyK5eqqTweDxpiVYajQYWi4VXYtIaUFx4vV709vYKiThVVVWwWq1FI1AUE4wxzM3NcdEqfm1JRGlpKa/ErKqqWnWLglgshqGhIZw9e5aPyWQytLW1YevWrWv6GQgiHikBNT54qtFo4HA4KJlwDaymb258iwKTyYTy8vJNnOnmcfr0aQwNDQlju3fvRl1dXXYmRBQU0WgUbrdbaIlQXl6Orq4uVFRUZHFmK5FaFEgtTFK1KFCr1XwPodVq6YyRJUjEJjaD+fl5PPPMM3j66afxwgsvCC0SEmE2m3H99dfjhhtuwDXXXJP38dZswRiDxWIR3DyeffZZvPOd70zr+y+66CK88cYb/PGhQ4dw1VVXXfD5zc3NgsPWyy+/jCuuuGINMydyHLo5EGlDQjaRdfJByHY6nXxztGXLFthstnW93tjYGAYHB4Wf22AwwG63F0QgbHZ2Fq+88gp/7HA4eL9AErFXTzQaxeDgIMbHx4XxXbt2ob6+nn6XxLqZnZ2Fy+USgvNlZWWw2+1pH/SkSiupUjtVpZVKpeKidiYqMYncJlFwnhJxiovViFYKhUJoUVBSUpL0taPRKHp7e4XAikKhQGdnJ0wmU8Z+BqJ4mZmZgdPpFFxI9Ho9bDZbyuuTSE0kEuGildfrTSlaabVaLlqp1eqC2EOMjo7iyJEjwlhzczO2b9+epRkRhUQkEoHb7UYwGORjKpUKXV1dOV/NHIvFuNuL1+tNmTgrJcaZTCYYjcZVJ8YRa4NEbCIbxGIxvPnmm3jggQfw+uuv49SpU0nPGCUlJdi7dy9uuOEG7N+/H/X19Zs42/xmYGBAKOoqLS3F9PR02q5qX/jCF/ClL32JP77rrrvwwAMPJHzuiy++iLe//e388RVXXIGXX355jTMnchy6QRBpQ0I2kXXC4bBQnZWL9PT04Ny5cwDOZ/N1dXWt6XVisRhGRkZw8uRJYby2thZtbW0FEcxnjGF+fh6///3vhXGtVitUYhLpEQqF4Ha7ubU9cL4qpa2tDVu2bMnizIhCIRAIoKenRwg6aDQa2Gy2dVU8LSwscFE7PmiWCCngZLFYUFVVVRBrIXEexhiOHTuGEydOCOP19fXYtWsXBbaKFKnSyuPxwO/3JxWtZDIZDAYDF62WB9zD4TBcLpdwnywpKYHdbudJdASxHiYnJ+FyuYTr1GQyobOzkwSSDWC1opVKpeLrg16vz7s9xIVcl9ra2lBTU5OlWRGFRKL7ZEVFBbq6uvLO3UCKNUjrw+TkZNLnF4ubQ7YhEZvIFowx/M///A/+5m/+BrFYDGq1GrFYDG1tbRgeHk6ZXN/a2or9+/fjhhtuwFvf+lZyCUvCgQMHcO211/LHbW1t6O/vT/v7f/nLX+Ld7343f7x9+3acOnUq4XM/8IEP4Oc//zl//JOf/AQf+tCH1jBrIg+gmwSRNiRkE1knH4TsgYEBjI2NAThvg3rRRRet+jXC4TB6enqEaiEA2LNnD+rq6gpigy/1wI5Go/j9738v2KbHo1arYbFYUF1dTfbCSUhUJVtaWgqbzUbBeSIjnD17doU7hMlkQkdHR0YPcaFQiNsLBwKBpGu+Uqnk/e5MJhMdJvOYRFbPwPn+VlLPdYKIxWIIBoM88WVpaSnp8zUaDU98USqVcLlcQr/i8vJyOBwOSpojMoLP50NPT49w39qyZUvBJKDmOvEtCrxeryDEJSLf9hCMMRw5ckRIcpbJZOjo6EB1dXUWZ0YUCuFwGE6nU2jvUVlZia6urrSr6HKZUCjE+2r7/X7eyuxCFKKbQ7YhEZvIFowxPPHEE7j99tuFNoY//OEP8ZGPfAThcBi///3v8dRTT+E3v/kNjh07lvT1qqqqsG/fPtxwww145zvfCb1evwk/Rf7wyCOP4H3vex9/fNlllwlOnKk4ePAgrrnmGmFscnIyYWyztrZWiCGMjY1R/KBwoRsFkTYkZBNZJx+E7OHhYYyOjgIAdDodLrnkklV9/9zcHJxOpxBoVSgUsNlsMJvNmZxq1ojFYmCM8X+BQADHjh1L2ROzoqKCV2pTP6s/4ff70dvbKxwI1Wo1bDZbztu/EbnPhap/tm3bhj179mxocF6yD/V4PCl73sVXUVgsloJovVAsRCIR9Pb2wu/38zG5XI6Ojg5YLJYszozIZRhjmJ6e5okv8fumRMhkMiERR61Ww263U8UVkRHOnTuHgYEB4Rqj4Hx2WVpaEloUJDtDymQyvocwm805ty4wxjA8PIzTp0/zMblcDqvVSi0RiIwQCoXgdDqFikS1Wo2urq6C3FNLiXHSGhGfDJ6I8vJynvhCjlBrg0RsIlswxvDUU0/hr/7qr4QCmu9///u46667VnyeGWMYGRnhovarr76aNPFFqVTibW97G5555hlqIfP/eOyxx3Drrbfyx295y1uEntepeOqpp7B//35h7LXXXlsRXz9z5owgWm/bto0Xlk1MTOC//uu/8MQTT+D48ePwer3QarWorq7GpZdein379uHGG2+k9Ty/oJsFkTYkZBNZJxKJpMyczTZHjhzh2XuVlZW44oor0v5ev98Pt9stbK5UKhUcDkfavWdzGUm4lgJJ8V/LZDKEw2EekA4Gg0n71ZSVlXFROx+tATPF2NgYRkZGhN+V0WhEZ2dnzleWELnPhapkd+/ejZ07d25q0CG+EtPj8Qi9RxOh1+v5GkEJHbnL0tISXC6XEDgtKSmBzWajzHZiVczPz/NK7VT2oTKZDBaLBVu2bKGemMS6GRsbw/DwsDDW0NCAhoYGCs7nCNFoVOirfSEnKAnJzcFsNkOj0WT17xiLxTA4OMhbVwF/SnKuqqrK2ryIwiEUCqG7uxuzs7N8TKPRwOFwFKSIvRzGGGZnZ/n6kCq5XqFQwGg0cjeHYvgdrRcSsYlswRjDgQMHcNtttwnxg+985zu455570oojBgIBPPvss3jqqafwzDPPJDxnXHzxxfjf//3fTE49r1net7q2tlZIxkvFj370I9xxxx3C2EMPPYTbb79dGPv1r3+Nm2++mT+++uqrceDAATz44IP4x3/8R+G+loiWlhZ873vfwzve8Y6050ZkFbphEGlDQjaRdfJByD5x4gRGRkYAnM/c3bt3b1rfd+rUKQwNDQmCpMFggN1uL4jDkSRaS2I2AEHElsvlwiEmXtT2+/1JqyhKSkqEnrnFEJBmjOHw4cMr+sRs374dTU1NRSvsE5kjHA6jt7cXgUCAj8nlcrS3t2fdwlKqxJRE7fn5+aTPl1oUWCwWsgbMISQHkvgqGJVKBbvdTlbPxLqQWhScPn06ZUBacnOwWCwwm80FseciNgfGGEZHR3H06FFhfM+ePdixY0eWZkWkgjEm9NVOtYcoLy/norbBYNjUPXYsFkNfXx88Hg8fUyqVcDgc1DqIyAhLS0vo7u4WXE20Wi0cDkfRVhZKbg4+ny9lHAI4nzwrVWtTK7SVTE5OwuVykYhNbDqMMTz//PN4z3veI5w3v/nNb+KTn/zkmu7n4XAYr732Gq/WlmK/X/7yl/FP//RPGZt7vjM+Po6tW7cKY6ux/P7whz+Mhx56SBi7//778Xd/93fC2A9+8AN89KMf5Y9vv/126HQ6fPe73017rgqFAt/97nfxsY99LO3vIbIG3TSItCEhm8g6+SBknzp1CoODgwDOC6xXXXVV0ufHYjEMDw+vECS3bduG1tbWghAkl4vY8WK2JGAnO8TE2wt7vd6k14BCoYDJZILFYsmLfndrIRKJoK+vb0UP9ebmZmzfvj1LsyIKiYWFhRW9ZHO1Sja+J6bH40kpWqlUKp74otfrKYCSJSYnJ1c4kGi1WthstoLow0hkn7Nnz2JwcFBIEJTL5WkFpKU1oqKiYqOnSeQpiRIKZTIZWltbUVNTk8WZEaslvq92KjcHpVIpVGJupNAXjUbR09MjtN0oLS0tGKcuIvssLi6iu7tbSObQ6XSw2+1FK2IvJxqNIhAI8DUilSOUdM4wm81F7RonkUjE3rFjB5qamugMRmwojDG8/PLLuPXWW4U17stf/jI++9nPZuyzeeTIETz99NPYt28fmpqaMvKahUJzczMX+gHgq1/9Kj772c+m/L65uTnU1tZiampKGP/Sl76Ez33uc8LY17/+deE1t23bJlR+X3rppfjIRz4Cm82GyspKnDlzBs8++yweeOABoVpbJpPh17/+9Qo7cyLnoBsHkTYkZBNZJxqNJu2RmgucOXMGfX19AM7fDN/5znde8LnhcBhut1sIUADnb/ibbdu7UcSL2ACEr+Vy+ao3kLFYDIFAgFdiJrMGLMQqq8XFRbhcLmHTpVAo0NnZST3yiIwwNTUFt9stBGoqKipgt9vzQtRZXFzkSS+pWhQUo5tDLuDxeNDX1ycIiiaTCZ2dnfQ3INYNYwwnT57EkSNHhPH6+nrU19dzNwev14uFhYWkr1VZWcn3EFqttiD2ZcT6SdR2Qy6Xo6OjAxaLJYszI9aL5Obg9XpTVmLKZDIYDAYuWmWyjUkkEoHL5RKE9bKyMnR1dZFjCZERFhYW0N3dLdwH9Xo97HZ7QSaCZwLJEUqq1o5vi5MIpVIJk8nE/xVbcgCJ2ES2YIzhD3/4A/7iL/5CiJt94QtfwBe/+MWiTzDZLL785S/j85//PH+s1+vR39+P2trapN9377334jvf+c6K8U996lP4xje+IYx95jOfWTEGnN+jfetb38InP/nJhO8xNjaGa6+9lhehAecdUUdHR6HVapPOj8gqdPMg0oaEbCLr5IOQPT4+DrfbzR9fc801CTdKc3NzKzKglUolrFYrzGbzZkx1w0lkJc4YS2glvhYYYwgGg7wSM94uaDkymUzomVteXr6u984GiQTG8vJy2O12qNXqLM6MKBS8Xi96e3uFwK1er4fVas3LRJDVtCiQ3Bw2o8qqmEnUS7a2thbNzc0UVCDWzYXabiRyLInvienxeFIGpMvKyrhgVVVVRddrkRKNRtHX1wev18vHqF9xYbLaSky1Ws3XiPUkvoTDYTidTsFhRqVSoaurK6NiOVG8zM/Po7u7Wzg7V1VVwWazUULhKlhYWIDP54PX60UgEEiaPCvFIqQ1Ih+Sg9fD5OQknE6n4KRHIjaxGTDG8Mc//hE333yzcB/99Kc/ja9+9au0f99EJicnUVdXJ1RWd3R04JlnnrmgmH3ffffhH/7hHxKup5/5zGfwta99TRj71Kc+hW9961srnvuJT3wC9913X9L5jY2NoaOjQ5hfulXjRNagGwiRNiRkE1knH4Rsn8+HN998kz++6qqrVggiPp8Pbrdb+FkqKirgcDgKQpCUxGtJNIr/OlMidqL3XE3PXJ1Oxysx86GyYWJiAv39/YIQp9PpYLVayYaXyAinTp0SrJ8AoLq6Gm1tbQUR1IpGo0KLgmT3EplMJrg50Gds/TDGcPToUYyOjgrjDQ0NaGhooKAWsW5isRgGBgYwPj7Ox2QyGTo6OlBdXZ3y+xcXF7moncrNQbIXtlgsMBqNlPhSJEQiEfT09CAQCPCxkpISOBwOqt4ocBhjmJqa4qJ2fOuVRMQnvhgMhrT3UUtLS3A6nUIFWWVlJRwOR14m4RK5h5RMv7S0xMeMRiOsVmtB7PezhdQKTarWTuYaB5z/XEtrhE6nK6h9MInYRLZgjMHpdGL//v2COHnvvffim9/8Jq1xWeDxxx/HLbfcIoxpNBrcdddduO6661BTU4OFhQW43W78x3/8B1555RX+vOU24V//+tfx6U9/Wnitf/3Xf8UXv/hFYUyr1eLMmTNpxdaXV423trZiYGBgVT8jsanQTYRIGxKyiayTD0J2MBjE66+/zh/v3btXCDycOnUKQ0NDQoBUyoDOx4rH5Sy3Et8METsRs7OzXNROVWWlVqt5pbZarc6pAxZjDKOjozh69KgwXkgCI5FdLlTBWF9fj127duXU5yFTxGIxTE5O8jUiPpiXCJ1Ox0XtfEh8yTUuJDC2tLSktBYjiHSIRCLo7e0VWrWsp0o2HA7zKiufzycEY5cjJb5IAWkSmwqTUCgEl8slVPeQ1XPxMj8/z0XtYDCY9LkKhULoq32h816ifsUajQYOh6MgzohE9pmdnUV3d7fgLmAymWC1WqlKMYOsNvGlpKSEu0IZjca8tnYnEZvIFowx9PX14frrrxcSDj/2sY/hu9/9LsXNssj3vvc9fOITn0jqjrece+65B1NTU/jZz37Gx37wgx/gzjvvFJ533333rbAP/+AHP4if/vSnab3PqVOnsHPnTmFsYmKCWgXlLnQjIdKGhGwi68RisZTZrdlmenoar732Gn98+eWXQ61W8356Y2NjwvO3b9+OlpaWgjg8xovY8f8AcAE7GweYhYUFLljF95pLhEql4qJ2trOjY7EYBgcHce7cOWG8kAVGYnNJZJEqk8nQ3NyMbdu2ZXFmm0d8vzuPx5My2CT1zLVYLNBoNPQ5TEE4HEZvb68QUFAoFOjs7ITJZMrizIhCIZHAWFpaCrvdnpEq2VgshkAgwN0cUtkLa7VawfGF1oj8Z3FxEU6nU7g/SE5KZPVMhEIhnvji9/uTJr4A4K2O4u2FE1k963Q62O12cnwgMsLMzAy6u7uFWIrFYkFHR0dBxCFymfjEl8nJyZQW5PmaHEciNpEtGGMYGhrCddddB5/Px8f/9m//Fv/+7/+e18khhcJzzz2He+65Z4UD4HLUajW+8Y1v4KMf/SiuueYaHDp0iP/fr371K9x0003C83/yk5/gIx/5iDD2wAMP4K677kp7bjU1NULM9aWXXsKVV16Z9vcTmwrdTIi0ISGbyDr5IGTPz8/j5Zdf5o8vueQSqFQq9PT0CJVCANDS0oIdO3YUxMZ+eSV2/NdyuTxnDshLS0tcsErVy6q0tJQLVgaDYVN/hlAohJ6eHkF4l8lkaGtrw9atWzdtHkThsrS0BLfbLYg/JDCet1yUBKt4S7JElJeXc8FKr9fnzDqXKywuLsLlcgkWqaWlpbDZbNDpdFmcGVEoLCwswOl0ChWMKpUKDodjQ/pPxrcxSafKSkqOM5vN0Ov1BbHfKzbm5ubgdDoFgZGqZIkLEY1GEQwGuWiVyvGlsrISOp0OHo9HcB2jfsVEJpmenobT6RTiKNXV1Whvb6e96yYjOb5I/1K5DarVai5qa7XanN1HkIhNZAvGGEZGRrBv3z5MTEzw8Q9+8IP40Y9+RMlgOUQ0GsWvf/1r/Pa3v8Vrr72GiYkJzMzMwGKxoL6+HjfffDPe//73Y8uWLQDOF33FW4ufPn16hZvbK6+8giuuuEIYSyR4J+Mtb3kLuru7+ePHH38cf/EXf7GWH5HYeOiGQqQNCdlE1skHIXtpaQkvvPACf9zR0YFjx44JQValUgmbzVYwYlF8FXb8Y+C8MJarhxfpIOnxeODz+ZJa3SiVSi5YGY3GDQ0szc3NweVyYWFhgY+VlJTAarXCYDBs2PsSxcPs7CxcLpcQmC8rK4PdbodGo8nizHILqWeu1+tNmfgi2QJuxhqRDyS6xioqKmC32zdEYCSKj5mZGbhcLkEo0mg0sNvtm9bXfm5ujifHpUp8KSkp4cFoWiPyg5mZGTidTqEK32AwwGazUXUPkZJ4xxev1yskdSVDq9XCbrdTogSREaampuB0OgXBdOvWrWhra8vZM3qxILU6ktaI+LN/IkpLS/k+oqqqKmf2ESRiE9mCMYZjx47huuuuw9mzZ/n4bbfdhp/97GckYucxY2Nj2LFjB39cW1sriNoSwWBwRRupZ555Btdee23a73XZZZcJrqr/9V//hdtuu20NsyY2AbqpEGlDQjaRdRhjKS0ds00kEhHsTxQKhbCpl6wI1Wp1NqaXUSTxWhKAs9UPOxNEo1H4/X5eZZUsO1oul3PBymQyZXSDHAgE0NPTI7x/ZWUlbDYbiT9ERkh0jWk0Gthstryyr9ts4hNfUlmHSmuEFGwqtkN0omtMp9PBZrNRYJ7ICMFgEG63e0UFo9VqzZrAKDm+SIkvyZLj5HI5jEYj30fQ5yL3SHSNmUwmdHZ25ox4QOQXCwsLQl/tZLEVaY2Q9hG0RhBrIZHAWFtbi5aWlrw5oxcLjDGeHOfz+VK2Q5PWCOm8sVkJfMshEZvIFowxjI6O4rrrrhPaN9566634+c9/TvfNPOenP/0pPvShD/HH73//+/Hwww8nfO7yyu1HHnkE733ve9N+r7a2NgwODvLHBw4cwDXXXLOGWRObAN1YiLQhIZvIOvkgZDPGcODAgYT/J9nEFcKmqpBE7OXEYjEEg0FMTEyk7Icp9bGSLMjX87c9c+YMhoaGhMBWVVUVOjs7i04IIzaGc+fOYWBgQLjGjEYjOjs7qbpsFUSjUaFnbjKnEJlMBoPBwO2FCz1ZYHx8HP39/cI1Zjab0dHRQeIPkRE8Hg/6+voEoTjXLFIjkQj8fj8XrVJZhxoMBi5YUdJa9vF6vejt7RWusa1bt6K1tTVnrjEiv5mYmEBfX19SMTsevV7P14jKysoNnh1RCAQCAbjdbkFg3LZtG5qbm/P2jF5MhEIh+Hw+eL3elAm0wHknB2mNUKvVm/I3JhGbyBaMMYyNjeG6667D6OgoH7/pppvwi1/8ouDP28XAFVdcgVdeeYU/fuGFF7B3796Ez73zzjvxwx/+kD/+3Oc+hy996Utpvc/S0hK0Wq0Q8x0YGEBra+vaJk5sNHRzIdKGhGwi6+SDkB2LxXDw4MEVgYnt27ejpaWlIAJg8f2w4/8B4AJ2oRxeGGOYmprCxMQEPB6PYJObCL1ej+rqapjNZqhUqrTf4+jRo8ImHDifMd/c3FwQ1wyRXRhjOHHiBI4dOyaMb9u2DXv27KFrbB3EYjFMTU3B4/GktUZotVqe+FJoweiTJ0/i8OHDwhgFTYlMcvr0aQwNDQlj27dvx549e3L2GpOsQ6XEl1RrhNQP02KxQKPR5OzPVagkSvjK9WuMyC88Hg96e3tXJHyVlZXB5/OlXCMqKiq4YKXX6+m6JFbg9/vhdruFZBwSGPOXWCyGQCDAq7VTrRHl5eV8jTAYDBtyziMRm8gWjDGcPXsW+/btw9GjR/n4vn378Mtf/pISQguA5X2v9+zZg+Hh4Qs+/+DBg0IF9Vve8ha88cYbab3XM888g3379vHHJpMJHo+H1rHchf4wRNqQkE3kBPG9EHONUCgEt9uNQCAgjLe2tgr9PfKZVCJ2IQtijDHMzMxwwWpubi7p8zUaDaqrq5MKVtFoFP39/fB4PMJ4U1MTduzYQRsoYt3EYjEMDQ0JfaMAYPfu3di5cyddYxmEMYbZ2Vm+RqTqh1lRUcFFba1Wm7d/C8YYDh8+jFOnTgnjjY2NqKury9ufi8gdLpSMs2vXLtTX1+fNNRa/Rni9XszMzCR9/mYEo4k/MTY2tiJQ1tDQgIaGhry5xojcJpFrSX19PXbt2gWZTLbqNaKkpISvEUajkZxPCPh8PvT09Agidl1dHRobG2kdKwCkNUJyfJmenk76fIVCwdsUZKqVCYnYRDYZHx/H9ddfL+zXrr76ajzxxBMFlyRejMzPz+Oiiy7CwMAAH3v00Udxyy23XPB7IpEIdu/eLRQGvfTSS7jyyitTvt9VV12F3/3ud/zxX//1X+NnP/vZ2iZPbAZ0kyHShoRsIifIVSF7dnYWTqcT8/PzwnhtbS06OjqyNKvMEi9iAxC+VigURXdwmZub44JVqkNkZWUlF6ykCqvFxUW43W4hSKVQKNDe3g6LxbLR0yeKgHA4jN7eXiG5Ri6Xo729HdXV1VmcWXEwPz/Pg9Gpet2VlZXxKsx8Eqyi0SgGBgYwMTHBx2QyGVpbW1FTU5PFmRGFAmMMIyMjQv87AGhpacG2bduyNKvMIPXM9Xg8mJycTGozrFQqYTKZYLFYYDQaqR1EBrlQosSePXsKJhGVyD6JHCUaGxtRX19/we9ZXFzkglUgEEjZV7uqqooL29nqmUtkD6/Xi56eHuE6oWScwmZxcZFbkAcCASGBIRHxbQoqKipWfV2QiE1kE4/Hg+uvv17oZ7x37148+eST0Gg0WZwZcSEikUjaZ5bZ2Vns378fL774Ih+75ZZb8Oijj6b83v/6r//C+9//fv64sbERr776atK46re//W38wz/8A38sl8vR19dHtuK5Dd1oiLQhIZvICUKhUNr9xDYL6dCYqAdiQ0MDmpqasjCrzFHI/bAzxeLiIhe1g8Fg0ueWl5dDr9fD7/cLvXXLyspgt9tpE05khIWFBbhcLsE5oKSkBDabDXq9PnsTK1KWlpZ4MNrv96cUrKQgk8lkytkKq3A4DLfbLYj0CoUCVqsVRqMxexMjCoZYLIb+/n4hUUIul6Ojo6PgEr7C4bCwRiTrhymTyVBVVQWLxUKC1TpJ5Cghk8nQ1taGrVu3ZnFmRCGRqPVGc3Mztm/fnvZrRCIRLlj5fL6E5854dDqd0Fe72M9qhU6ivuu7du1CQ0NDFmdFbCbRaBR+v5+vE6la8qlUKqFNQaokWhKxiWzi8/mwf/9+9Pb28rHLL78cTz/9NLRabRZnRiTj+9//Ph577DH89V//Na6//nqYzeYVz5mdncVjjz2Gf/qnf8KZM2f4eF1dHV5//fW0znyMMezduxcvv/wyH9u1axceeOABXH311cJzJycn8S//8i+4//77hfG///u/x3e+851V/oTEJkM3GyJtSMgmcoJcErIZYzh58uQKG0KlUsmDCzt37kRLS0s2ppcRSMRePaFQiFdYpRKsJFQqFRwOB/X0ITLC1NQU3G63EMCoqKiA3W6naywHkILRHo8HPp8vqWAll8thNBphsVgyZgmYCRIlSpSWlsLhcFAyDpERIpEIenp6BEcJpVIJm80Gg8GQxZltPNFolPfDTCcYLQlWyVqZECtJ1HpDLpejs7MzYaCNIFbLhar929ra1uVaEovFMDk5yc8bqXrmrlawIvKLc+fOYWBgQDhz7t69G3V1ddmbFJFVGGOYnp7m+4hU7Y4k1xepTUFJSYnw/4lE7J07d2L37t0UCyI2nMnJSdx4443o7u7mYxdffDGeeeaZgj8T5Dv/9m//hrvvvhvA+fhxfX099uzZA4PBgPn5eYyPj8PpdK4469TX1+O5555DY2Nj2u/l9/tx6aWXrkgc3LFjB2w2GyorK3HmzBn87//+74r3u+qqq/Dss8+S41XuQzccIm1IyCZyglwRsmOxGAYHB3H69GlhfMeOHZidneWB123btqG9vT0bU1w3y/thA+AitiRg08ElOasRrOJtQ3O5CpPIbbxeL3p7ewVrOb1eD6vVmjMiKPEnYrEYAoEAtyBPJVgZDAZehalSqTZpliIzMzNwuVxCq4/KykrY7faszYkoLJaWluByuYTWG8WaKMEYw9TUFF8jlrewWU5FRQVfI3Q6He3TLkA0GkVfXx+8Xi8fUygUsNlsqKqqyuLMiEKBMYajR48KPRtlMhk6Ojoy2t5ltT1z411fqE1B/nP27FmhlyhAbRGIlUitTHw+X8o2BTKZDAaDgTtDhUIhErGJrDE9PY2bb74Zr7/+Oh/r6urCs88+C5PJlMWZEekQL2Sny4033ogf//jHa0oqHRsbwwc+8AGhMjsVH/7wh/HAAw9QrCw/oJsOkTYkZBM5QTgcTtn7Z6MJhUJwuVyChbRMJkNLSwt27NgBp9MJj8cDANiyZQtsNluWZrp2lovY8WI2idirJ1HVTzLiqzDNZvOKrGiCSMSpU6cwMjIijFVXV6OtrY0SI/KAeMHK4/FgYWEh6fM1Gg0sFguvwtyMNTkQCKxopaHX62Gz2WidIjLC/Pw8nE6ncP1XVFTA4XAUfaIEYwxzc3NcsJqamkr6/NLSUi5YVVVV0X3g/xGJROB2u4V9fElJCRwOB9lTEhmBMYbh4WEh4Xmzqv1X0zNXalMgrRPl5eUbOjcis5w5c0boFQus3rKeKD4ikQj8fj8XtuNbnaUDidjEZjE7O4t3vetdePXVV/mY1WrFc889V3AthgoVt9uNr371qzh06FDSFoxKpRJXX3017r33XrzjHe9Y13syxvDDH/4QDzzwAHp6ehI+R6FQ4O1vfzs+//nP48orr1zX+xGbCt14iLQhIZvICbItZM/MzKwIsEp9Z6WeoL29vVywNJvN6Orqyspc10q8iA1A+FqhUNChZZWEw2H09vYK9qjA+er9WCwGr9crVDYuR8qKlgQr6oVJLCdRj0/gfF+hxsZG+szmIfEVVh6PR6hMTYRKpeJrxEZVYSayrrRYLGhvbyeBjMgI09PTcLlcgjOBVquF3W6nLPkESIKVx+NJWWGlUChgNBq5YFWsiSdSMmp81Wp5eTkcDgfZshMZQXLtOnfuHB/LVrX/agUrjUbD2xSo1WraP+YwY2NjK9qbtba2ora2NkszIvIRxhhvU+Dz+YSWQYmQy+Worq6GxWJBVVUVOToQG8bc3Bze/e5348UXX+RjbW1tOHjwILZu3Zq9iRFrQopXSa6m09PTkMlk0Ov1aGpqwsUXX7whrluHDx9GX18fzp49i5mZGRiNRmzbtg2XX345dDpdxt+P2HBoY0qkDQnZRE6QTSHb4/Ggp6dHsFWqrKxcEfwaGBjA2NgYAKCqqgoXXXTRps91LSyvvqZ+2Otnfn4ebrdbOBQqlUpYrVYezFptFaZer+eCVbFXpxHn7VH7+/u5CwRw/vPa3NyMbdu2ZXFmRCaRLAE9Hk/SbGbgT1WYUpBpvb0wGWMYHR3F0aNHhfEdO3agqamJ7gtERggEAnC73cIey2g0orOzkwKlaSC1MpGC0fGuCcuJtw3NZpuCzWZxcRFOp1PYk1VUVKCrq4sqUYmMEIvF0NfXJ+zJlEol7HY79Hp99iaG83Obmprie4lU543y8nK+RhgMBuqrnUOcPHlyRQ/Q9fZdJwjgvHjo8/lw7ty5lEm0crlc2EvQfZTIFAsLC3jve9+LQ4cO8bHm5mYcOnSIknUIorihwBORNiRkEzlBJBJJ2md4I5CC+Mste00mE6xW64qqluHhYd4PTavV4tJLL92sqa6Z5cI1idjrJxgMoqenR6h+UKlUsNvtF6z6kaowJVF7dnY26Xtkw1qYyB2WlpbgdruFyjKFQoHOzk7qGVXAhEIhHohOZRuqVCp5mwKTybRqQZAxhpGREZ6cJdHU1ISdO3euaf4EsZyJiQn09fUJFcVbtmxBW1sbiSdrIBaLIRgM8nUimesLUBxVmHNzc3A6nVhcXORjGo0GDoeDqv2JjBCNRtHb2wufz8fHSkpK0NXVtSFVRuthtW0K4vcSRqOxaB0dcoHR0VEcOXKEP5bJZGhvb8eWLVuyOCuikJicnFzRE1smkyV1fQH+tJcwm83QaDQFuZcgNp7FxUW8//3vx7PPPsvHGhsbcejQIezYsYOuK4IobmgBINKGhGwiJ9hsITsWi2FgYABnzpwRxnfu3Ik9e/YkDLAeOXIEx44dA3C+YvuKK67YlLmuleX9sAGQiL1OElnwGgwGdHZ2ripgOj8/z0XtVEGmiooKLmprtVr6mxU4c3NzcLlcQkVNWVkZ7HZ7zgVMiY1Dsg31eDxpVWFK1sIWiyXlWnShan8KmBKZJJE9KlX7Zw7GGGZmZuDxeOD1elMmyElVmBaLBXq9viASCaanp+F0OoXEQoPBAJvNRtX+REZI1He9rKwMXV1deWFZv7S0xB0d/H5/yr7axejokAscP36cxxiA83+Ljo4OVFdXZ3FWRCGRSMTeuXMndu3ahcnJSb5OpHJ0KCsrg8lkgtlsRlVVFbUgItIiFArhgx/8IJ588kk+Vl9fj0OHDqG+vp7OBQRB0CJApA0J2UROsJlCttRHLz4oIZPJ0Nraiu3bt1/w+06cOMGrt8vLy7F3796NnuqaSSZiSwI2bRjThzGGY8eO4cSJE8J4TU0NWlpa1hUQXlxcFKyFk63JZWVlXNQulEA08ScCgQB6enoE0VKj0cBms5GtWxEjVWFKyS/xfYYTkaxNQSgUgtvtFhJolrdFIIj1cKH75e7du1FXV5edSRUB8/PzfC8xOTmZ9LlKpZKLVUajMS9F32AwCLfbLdwvzWYzOjo6KLBOZIRwOAyXyyXcL1UqFbq6uvJS5I1Go0Jf7VR7CbVaDYvFQlWYG0ii+6VMJoPVaoXZbM7izIhCIhgMwuVyrRCxd+/eLXyuV+voIJfLeSKtyWRCWVnZhv0MRP4SDofxkY98BI899hgf2759O55//nk0NjbSvYUgCICEbGIVkJBN5ATRaDRpxVmmmJmZgdPpFLJNS0pKYLPZYDQak37vqVOnMDg4yL/nqquu2tC5rpV4ERuA8LVcLifxc5VEo1EMDAxgYmJCGG9sbERdXV1GN9+hUAg+nw8ejydl5URJSQmvrjIajfR3zXMSVftTH1liOYwxTE9Pc1F7fn4+6fOlQLTFYoFCoYDL5RK+p6ysDA6HA2q1eqOnThQBsVgMw8PDgtuNlChIPT43j9XsJeRyOaqqqriwnQ+BaK/Xi97eXuHn2rp1K1pbW2kvRGSEUCiE7u5uwemgsrISDoejIBILGWO8r7bX6xX6yyeirKyMrxFVVVX0OcsAjDEcOXIEJ0+e5GNyuRxWq5XaCBEZI10ROxFSyyOfzwefz5d0LwEAOp2Oi9qF2s6EWB2RSAR33nknfvGLX/CxmpoaPP/889izZw9dIwRBSNBiQKQNCdlETrAZQrbH40FPT4+wka+srERXVxcqKipSfv+ZM2fQ19cH4Hxg9p3vfOeGzXWtxFdhUz/s9bO0tISenh4hI1kul6O9vX3D7d4ka+GJiQn4fL6kjgUKhQImk2nN/XKJ7MEYw4kTJwRLQQCora1Fc3MzBQuJpMzOzvIqzPie6umgVqtht9sLIihPZJ9oNIq+vj54vV4+JpfL0dnZSZVlWSS+CtPr9Qo23InQ6XS8CjMXrZMTJX1t376dAqJExlhcXER3d7eQ9FXofdfjqzBTOTooFAreV9tkMlFf7TXAGMPhw4dx6tQpPiaXy9NKrCeIdFmPiL2caDSKYDDI14mlpaWkz5famZjNZhgMBjrPFiGRSAR33303/vM//5OPVVdX49ChQ2hra6M9G0EQ8dCCQKQNCdlETrCRQjZjDKOjo9wWXMJkMsFqtaZ9AJ+YmIDL5eKPr7nmmpzZlC8XrknEXj8zMzNwu91YXFzkY6WlpbDZbNDpdJs6l1gsxvvlpgpES9VV1dXVMJlMBRt0KwRisRiGhoZw9uxZYXz37t3YuXMnfWaJVbG4uMjXiFRtCmQyGaqrq7FlyxbqcUesm3A4DLfbLQggktuNXq/P2rwIEcYYJicn+TqRqhdmZWUld37RarVZvycl6rve0NCAhoaGrM+NKAzm5+fR3d0t7P11Oh3sdnvRCLaSo4PUVztZIq1MJoNer+eCVTqJ4cUOYwzDw8M4ffo0H1MoFLDb7TAYDFmcGVFIZFLEXg5jDDMzM1zUnpmZSfp8pVLJLciNRiPFJoqAaDSKe++9Fz/+8Y/5mMlkwsGDB2G1WmnPRhDEcmhRINKGhGwiJ4jFYimrRNb6uv39/SuEorq6ulVXb/h8Prz55pv88VVXXZUTQY1k/bBJxF4bPp8Pvb29wuEvV6oXY7EYD0R7PJ6kGdEymQwGg4FXV2V77sSfCIfD6O3tRSAQ4GObVe1PFD7hcBherxenTp1KGWCSHB0kO8BcuK8R+cPi4iJcLpdgwVteXg673U6W9TmM1AtTErVTOTqUlpZyUXuzrYUv5FyyZ88e7NixY9PmQRQ2s7OzcDqdwr7aYDDAZrMVrdNRNBpFIBDgglWqvtqVlZX8zJELyS+5BmMMg4ODQlxCqVTCbrdT0heRMRKJ2HV1dRvWj3hxcZEnvwQCgZQW5PHJL7no/EKsj1gshk996lN44IEH+FhVVRUOHDiArq4uui8QBJEIWhiItCEhm8gJNkLIXlpagsvlEiqEZDIZ2trasG3btlW/XjAYxOuvv84f7927N+vC4HIRO17MlgRs2iyujlOnTiWs3u/o6Mi5QNZq++VKlqEWi4WqJrLIwsICXC6X0JOQqheJTHIh4UculycNMMlkMt4v12Kx5EW/XCJ7zM3Nwel0CtWLhdRHtphYXFzkbQpSOTpsZvJLIgteaS+/devWDXtforiYnp6G0+kUzqImkwmdnZ3kWPL/kM4ckqgdn7yUCCn5ReqrXey/x1gshsHBQZw7d46PKZVKOByOTXf6IgqXzRaxlxPfzsTn86VMfqmoqODrhE6nyxm3Q2JtxGIxfO5zn8N3v/tdPqbX6/HMM8/g4osvprgkQRAXghYHIm1IyCZygkwL2TMzMyus4UpKSmC321FVVbXm13z11Vf548svvzyr1UbxIjYA4Wu5XE4HgVUSi8Vw+PBhjI2NCeM7duxAU1NTzm+846urPB5PyipMtVrNRW21Wp3zP1+hMD09DZfLJRzsKyoqYLfbKbmAyAixWAzDw8M4c+aMML5nzx5s27ZNsBaOv0cmItf75RLZY2pqCi6XS9i7FZsFb6ESDod5dZXP50tpLbxRzi+JhB/qu05kmsnJSbhcLqHFVXV1Ndrb2+kslYT5+Xmhr3aymJJcLheSX4rNWlhyiJuYmOBjJSUlcDgc0Gq1WZwZUUhkW8ReDmMMU1NTfD+RKvmlpKQEJpMJJpMJRqOR9pJ5BmMM//Iv/4JvfetbfEyj0eC3v/0tLrvsMoo1EQSRDFogiLQhIZvICTIpZE9MTCS0hXY4HOsSiubn5/Hyyy/zx5dccknWMqgTWYkzxshKfI1EIhH09vbC7/fzMZlMhj179mD79u1ZnNnaWVhY4KJ2vCtBIlQqFRe1dTodXT8bhNfrRW9vr1ARq9frYbVaiy6oR2wM0WgUvb298Pl8fOxClvXx1VUej0dwCEiEZBlqsVig0WhonShiErXfoOrFwiQWi3FrYY/Hk7K6SqPRcFF7PUly0WgUfX198Hq9fEypVMJms1EfWSJjBAIBuN1uYS2rqalBa2sr3eNWwWqSX4DishaOxWLo6+uDx+PhY6WlpXA4HNBoNFmcGVFI5JqInYiFhQWe/JLK+UVKkpPWCZVKtYkzJVYLYwxf+9rX8JWvfIWPVVZW4qmnnsLb3va2nLkGCYLIWWiRINKGhGwiJ2CMpQyOpfMaJ06cwOHDh4Vxs9kMq9W6blvopaUlvPDCC/zxRRddtObq7rUiideSEBb/NYnYa2NhYQFut1vIElYqlejs7ITRaMzizDLH0tISD0IHAoGkB8fS0lIuVhkMBqpGyRBjY2MYHh4Wxqqrq9HW1kbCD5ERQqEQXC6X0Ot2Nb0X5+bm+DoxNTWV9Lnl5eXcflyv19M6UUScO3cOAwMDwn2kpqYGLS0tdB0UOPHtTLxeb8rkF5VKxdeJ1ViGRiIRuN1uBINBPkbVi0SmSZRcuH37duzZs4fOUusgFoshGAzydSK+53giKisrBWvhQvrdx2Ix9PT0CMmFpaWl6OrqyqqrG1FY5IOIvZxwOAy/3w+fzwefz5eyoEWtVnNHh0JbJ/Idxhjuu+8+fOELX+BjKpUKTz75JK666ir6WxEEkQ60UBBpQ0I2kROsV8iORqMYGBjA2bNnhfG6urqMBSQikQgOHTrEHzscDlgslnW/bros74cNgETsdTI5OYmenh7h2lOpVLDZbAUbYJCqJjweD3w+X9J+uUqlkgehjUYjCa5rIFF/TyD3AwxEfjE/Pw+n04mFhQU+Vl5eDofDsaZqJ6lfrtfrTZn8IlkB0jpR+Jw8eXJFsiCtZcWLlPwiWQsno6SkhItVydaJUCgEp9MptEdZz1pGEIkYHx9Hf3+/cG+jtSzzMMYwMzPD14lUbY/SXSfygWg0ip6eHsHtq6ysDF1dXbSWERkjH0Xs5cRiMUxNTfF1Yn5+PunzS0tLeauCfF8n8h3GGP7v//2/+OxnP8vHysvL8fjjj+Paa6/Nm2uQIIisQ4sFkTYkZBM5wXqE7KWlJTidTqGCTCaToa2tDdu2bcvUFMEYw4EDB/hjq9WKrVu3Zuz1U713vIgdL2ZLAjZtFFfH+Pg4BgYGBCFXp9PBZrMVjc1zNBqF3+/nVRPx/QGXo1AoYDQauWXoeh0OioFoNIr+/n7BTlAmk6G5uTmjaxNR3CTqVazRaGC321FWVrbu149PfvH7/UktQ+P7YJrNZupvVyAwxnD06FGMjo4K43v27MGOHTuyMykip1haWuLrRCAQSJokJ5fLYTQa+Toh7bkWFxfhdDqFSu/Kyko4HI6M9t4mipszZ85gcHBQGGtsbER9fX2WZlQ8rMZaWC6Xo6qqiq8TmdjPbBbRaBRutxuBQICPlZeX4y1veQtZJBMZoxBE7ESsJkkufp0wmUy0V9hEGGN48MEH8clPfpKPlZaW4pe//CX279+f19cgQRCbDi0YRNqQkE3kDKmsxxIxPT0Np9OJxcVFPlZaWgq73b4hPfQOHjzIDwvt7e2bIkbFi9gAhK/lcjlZea4SyYL+2LFjwviWLVvQ2tpatFm9Uh9MSdROllgik8l4ENpisRSN8L8aEtk8KxQKdHZ2wmQyZXFmRCGRyBrVaDSis7NzQ5JNotGosE4kswKU+ttJyS8UXMpPYrEYBgcHce7cOT4mJQtuVjIfkV/EJ8mlYxmq1+uh1+tx9uxZYe+h1Wpht9tpj0FkjFOnTmFkZEQYo4Sc7CBZC0t9tZMl0wLnk43j+2rnqkgSiUTgcrkEAU6lUqGrq4tEbCJjFKqIvZxQKCSsE8mSaYHzibzSOqHRaArqd5FLMMbw0EMP4e677+ZjSqUSv/jFL/Cud72Lfu8EQawWWjSItCEhm8gZQqFQ0szs5YyPj6Ovr0/Y0KrVajgcDlRUVGzEFPG73/2OB9mam5tRV1e3Ie8jkchKXPpaoVDQJnGVJArIA0BDQwMaGhro9/n/YIxhcnISHo8HHo9HSBRJhCRWWSwWEqtwPpPc5XIJNs9lZWWw2+3QaDRZnBlRSJw+fRpDQ0PC2NatW9Ha2ropCU6SFWC664RWq+WidqG2big0otEoent7hf6eCoUCVqsVRqMxizMj8oVYLIbJyUl4vd601gkJjUaDrq4ucnUgMsaJEydw9OhRYay1tRW1tbVZmhEhIfXVlqowU60TKpWKJ9PqdLqcSeoOh8NwuVyCS1xFRQW6urrofERkjGIRsZez2nWirKyMi9oGg6FoixUyDWMMP//5z3HXXXcJccmHH34Y73nPewr6GiQIYsOghYNIGxKyiZwhXSGbMYbjx4/jyJEjwrjFYtmwKjSJl156iYtTu3fvxq5duzbkfSTxWqqyi/+a+mGvjVAohJ6eHiFDXi6Xo62tDVu2bMnexHIcqb+dJFbFW34mQhKrLBZLUfaACwQC6OnpESpL1Go17HY7BbGIjMAYw7Fjx3DixAlhvL6+Hrt27crKvYExhtnZWb5OzM7OJn1+RUUFXye0Wi3dz3KQUCgEt9stBORLSkpgt9uh0+myODMiX4lfJ9LplysFoS0WCwwGQ86IVUR+keieKZPJ0N7eTvv/HERaJySxKt7ZKBElJSVCv9xstT4Kh8NwOp3CfCsrK9HV1ZVXtuhEblOsIvZypHXC5/PB6/UKe9VEKBQKoVUBOb2sDcYY/ud//gd/8zd/w2OTcrkcDz30EP7qr/6qqK5BgiAyCi0eRNqQkE3kDOkI2VLP2eUVtfX19WhqatrwzdOrr77KA28NDQ1oamrK+HsstxInEXv9JKqQLSkpgc1mg16vz97E8pC5uTkuVqUKLlVWVnKxqhjsvc6dO4eBgQFhHdtIm2ei+IjFYhgaGsLZs2eF8ZaWlpzquz4/P8/FqlT97Uisyj0S9SouLy+Hw+EoygQlYmM4c+YMhoaG0kpiVSqVXKwymUx0TyXSgjGGkZERjI2N8TG5XI7Ozk6YzeYszoxIl8XFRS5qBwKBpOuFTCZDVVUVLBbLpvbLDYVCcDqdQnKOWq1GV1cXCWZExiAR+8IsLS1xUdvv9wstlxKRL60KcgnGGJ544gncfvvt/BqUyWT40Y9+hA9/+MP0OyQIYj3QAkKkDQnZRM6QSsheXFxcYdclZdRvli3c//7v//Kg/M6dO9HS0pLR148XseP/AeACNm0SV4ff70dvb69QIVtZWQm73U69ytbJwsICtwsNBoNJn1teXs5Fbb1eX1DX8YX6rtfW1qK5uZmEOSIjRCIR9Pb2wu/387F8CMgvLS3xILTf7096n1cqlTywZDKZyAYwC8zOzsLpdGJpaYmPSW1bqKqMyBQXSvxSKBTw+/1J+2BKYpW0VpDbCZEIxhgGBweFxC+5XA6bzUatEfKUSCQi9MsNh8NJn6/Vavk6oVarN+TssbS0BKfTKTjRaDQaOBwOErGJjEEidvpEo1EEAgG+TsTvZxOhUql4ohwl1CaGMYannnoKH/jAB4SY2r//+7/jzjvvpN8ZQRDrhW5kRNqQkE3kDOFw+ILZk1NTU3C5XEIvnNLSUtjtdhgMhs2aIt544w0uItTW1qKjoyNjr728Ejv+a7lcThvENXD69GkMDw+vCJR2dHRQ38UMEwqFeAVmKrGqtLSUV2BWVVXl9bV9oQrZ3bt3Y+fOnRRcIDLC0tISXC6XUO2TjzbPkUgEPp8PHo8HPp8vqVgll8thNBrJBnATmZychMvlEoJUBoMBVquV7plExjh16hRGRkaEsV27dqG+vh4ymUwIQnu9XoRCoaSvJ7U0ocoqQiIWi6G/vx8TExN8TKlUwm63kxNTgRCLxTA5OcnXiXjXrUSUl5fzs4der8/I2SORe4lWq4XD4aB7JpExgsEgnE6nECcjETs9pBZp0jqRqqWJUqnkZw+TyUSfY5z/HR44cAC33XabsB+7//77cffdd+d1HIcgiJyBbmZE2pCQTeQMFxKyx8fH0dvbK/yflOm82RW1LpeLB0W2bNkCm82WkdeNr8KOfwyc7+lDh5TVwRjD4cOHcerUKWF827Zt2LNnD224N5hwOMzFqlSVVflcgRkOh9Hb24tAIMDHqO86kWnm5ubgdDqFRC6VSgW73Z7XNs+xWAyBQIAnwKQSqwwGAxeryE0j83i93hV7LYvFgvb29rxal4nchTGG48eP4/jx48J4c3Mztm/ffsHvmZqa4u4v8/PzSd+joqKCi1U6nY72z0VINBpFb28vfD4fHyspKYHD4YBWq83izIiNgjGGubk5Llal6pebiVYFi4uLePPNNwUBXa/Xw263U+sDImOQiJ1Z4lsVBIPBpBbkMpkMer2erxX5fOZaK4wxPP/883j3u98tVLZ/61vfwr333ksxNYIgMgXd0Ii0ISGbyBmWC9mMMRw7dgxHjx4VnmexWLLWc7a3t5dXXprNZnR1da3r9STxWvq5qR/2+olEIujv74fX6xXG9+zZg+3bt9Pvc5OJRqPw+/1crIqv9FuOXC6HyWTive1yOQt6YWEBLpdLqMKgvutEppmcnITb7RbsM7VaLWw2W0HZPEtilcfjgcfjSVlZpdFoeKsCqsBcP4l6FdfW1qKlpYV+t0RGSNSrWCaToa2tDVu3bk37debm5vh+IpVYVVJSIri/UEJG4ROJRNDT0yMkGJaWlqKrqwtqtTqLMyM2k/iWJoFAIKVYJSXKmUymtBLlFhYW0N3dLexVDAYDbDYbidhExiARe2OJRCKC+0uqVgVSopzZbIZOpyt4EZcxhpdeegm33nqrsNZ95StfwWc+85mC//kJgthU6KZGpA0J2UTOEIlEeOVmNBpFX18fxsfHhec0NDRg9+7dWdu8DwwM8CCcwWDAxRdfvObXIhE78ywuLsLtdgu2UQqFAh0dHTndQ7ZYiMViCAaDXKxKVoEp9cCUKjBzSbSbnp6Gy+US5q9SqeBwOFBRUZHFmRGFhMfjQV9fnxDAMplM6OzsLGhBhjGG2dlZXoGZygZQpVJxUZsqMFcHYwyjo6MrEgYbGhrQ0NBAv0siI8RiMQwODuLcuXN8TC6Xo7Ozc117s3ixKlVLEylRTqrApFYFhUc4HIbL5RISHMrLy9HV1UV7syJGSqhNV6zSaDRcrNJoNCvug/Pz8+ju7hZccqqqqmCz2Qp6b0ZsLoFAAC6XSzgD1NfXY9euXbQ32wDi3V+8Xq+QqJ6IkpISvqcwGo0Fl8DCGMNrr72Gv/iLvxB+F1/4whfwxS9+kURsgiAyDd3YiLQhIZvIGSQhe3FxcUUgQi6Xo729HTU1NVmcITA8PIzR0VEA56viLr300jW9Tnw/7Ph/ALiATYeU1TE9PQ232y3YHpWXl8Nms0Gj0WRxZkQiVluBqdfruViVTVvhRPa7er0eVquVAuNExhgbG8Pw8LAwVltbi+bm5qILHiwsLHBROxgMJn1uaWmpUIFZbL+r1XChFhzJbJ4JYrUksnlWKpWw2WwwGAwZe59IJMLdX3w+X1L3F8kulFoVFA6hUAhOp1NIfKqoqEBXVxfKy8uzODMil1htq4KysjK+pzAYDLwSO/6saTQaYbVaScQmMgaJ2Nlnfn4ePp+PW5Ani5lLrg5SAky+7ykYY/jjH/+Im2++GdPT03z8M5/5DL7yla/Q2YogiI2Abm5E2pCQTeQMkr2P0+kUDoilpaVwOBw5Ydd79OhRXrlUWVmJK664YtWvQSJ25klUuViI9ruFilSBKYnas7OzSZ+fLVvhROJidXU12traKIBFZATGGI4ePcoTpiSoQvY8oVCIB6BT2YUqlUoYjUZuF1po1RLrIRaLYWBgQHC9kclk6OjoQHV1dRZnRhQSkUgEbrdbSEApLS2F3W7f0F7FkvuLVFkVXzmZCLVazUXtRBWYRG6zuLgIp9MpVI2p1Wo4HA46AxBJie+rPTk5mfS5CoVCcE8DzrcZ6+zsJGGHyBgkYuce4XCYuzqkSpQDzt9/JFFbq9Xm1d+NMQan04n9+/cLRUX33nsvvvnNb1K8gyCIjSJ/Fkoi65CQTeQMY2NjcLvdwsZdo9HA4XDkTGbjiRMnMDIyAuB8te/evXtX9f3xIjYA4WuFQpFXG91cgDGGkydP4siRI8I4iYv5jRRY8ng8KXtgVlRUcFF7ow6LF6pcpD5lRCa5kLjY0tKC2traLM4sN1ltBabRaOSBpWIWNxL1kFUqlbBaraiqqsrizIhCIlGFbHl5ORwOByorKzdtHowxzMzM8D1FqkS58vJyXoGp1+tJoMpxEvUq1mq1cDgcKCkpyeLMiHwjFArB5/PB4/HA7/cnTZQDzlsL19XVobq6OmfiFER+QyJ27hOLxTA5OckTYFI5ypWWlgoW5Lkcm2KMobe3F9dff72QgPjxj38c999/f07PnSCIvIduckTakJBN5AQzMzP43e9+J4xVV1ejo6Mjp6qoTp06hcHBQQDnD7BXXXVVWt+3vPqa+mGvn1gshqGhIZw9e1YYpwNfYbG4uCjYCie7Z21EADoajaK/vx8ej4ePyWQyNDc3Y9u2bet+fYIAzmf79/b2CuKiQqFAZ2cnTCZTFmeWH0gVmJKrQ3z/+kTE2woXU+/UUCgEl8slWAVKrjfUgoPIFAsLC3A6nYJtb2VlJRwOR9ZtnhcWFuDxeLhdaDKUSiVPfinEHpj5ztzc3AqbZ4PBAJvNRn8rYl1Eo1EEAgF+/kjVVzufKzCJ3IBE7PyDMYb5+fm0XR3kcjmqqqpgNpthMpmyvh+KhzGGwcFB7Nu3T2gFc8cdd+D73/8+3VMJgtho6EZHpA0J2UTOMDQ0hMOHDwMAdu3alZOVjmfOnEFfXx+A82LWO9/5zpTfs1y4JhF7/YTDYfT09AhBSJlMhtbW1qz3USc2DqlaYmJiIqWtcElJCRe1jUbjmkTtUCgEt9stVIWTuEhkmsXFRbhcLqFSsLS0FDabDTqdLoszy08YY5ienuaidqoemJKtsMVigVqtLtj7cSJxUaVSweFwFJWYT2wsc3NzcDqdgp23VquF3W5HaWlpFme2EmlPIdmFJttTxAegi93VIReYmZmB0+kUkpaoVzGRaaamptDd3Y1oNJr295SVlfEKzKqqKroeiZSQiF0YxO8p/H5/ynVDq9XCZDJl/fzBGMPIyAj27duHiYkJPn777bfjhz/8IbmbEASxGdDNjkgbErKJnCEajcLlcsFoNOasGDkxMQGXy8UfX3PNNUkFsuX9sAGQiL1O5ubm4Ha7hWB8SUkJrFYrDAZDFmdGbCaRSIRbAPp8vqSHRYVCAZPJhOrq6rSrqubm5uByuQTLsLKyMtjtdqpcJDLG7OwsXC6XIPpUVFTAbreTuJghZmdneVVVfCVyIsrLy7mordfrC+b+nEj0kVq35Jq4SOQv09PTcDqdQvViVVUVrFZrzlfzSBWYUrV2qgpMnU7HXR020yqdOC8uOp1OoZ2ExWJBR0cHWcETGSMYDMLlcgnni9raWuzcuZOLVZOTk0mdohQKBW9rYjKZ6H5LrIBE7MJEcoqSqrXjz3mJKC8vFxJgNutexhjD0aNHsW/fPsHl8H3vex9++tOfkohNEMRmQTc8Im1IyCZyhlgsljJwlG18Ph/efPNN/viqq6664AZvuYgdL2ZLAjYdUFZHMBhET0+PcJ2Q6EOsJgAtl8thNBphsVguGFQKBoNwu91CkFStVsNut+eUDRiR3wQCAfT09AjXmU6ng81mo2DnBrGaVgXxrg75XFWV6DrLF3GRyB8CgQDcbrcg+pjNZnR0dOTdZ4cxxntgejyelD0wKysreaW2Tqejvf0Gkug627p1K1pbW0nEJjJGouts+/bt2LNnj/D5DoVC8Pv9vK92qgpMvV7P1wpKgCFIxC4OGGM8qdbr9aZMqt2sBBjGGEZHR3Httdfi9OnTfPzWW2/Fz3/+czqLZoiZmRm88sorOH36NHw+HzQaDWpqatDe3o6mpqZsT48gcgW66RFpQ0I2kTPkg5AdDAbx+uuv88dve9vboFKpVjwvXsQGIHwtl8sp2LIGzp49i8HBQUF4qKqqQmdnJ2WLEpxYLIbJyUluKxzfO3E5MpkMBoOBV1WVl5fj3LlzGBgYEK4zo9GIzs5OEn2IjDE+Po7+/n7hOstX0SdfCYfDPKiUylZYcnWQgkr5cs+ZmJhAf3+/8LNVV1ejvb2d9iFExvB4POjr6xOus5qaGrS0tOT9dcYYw9zcXNquDqWlpVyoyucEmFzE5/Ohp6dHuM62bduG5uZmEn2IjOH3++F2u4XrbMeOHWhqakp6ncViMd5X2+v1Jj1/AJQAU+yQiF28LC0t8bOH3+9Pev4Azic5xyfAZOL6YIxhbGwM1113HUZHR/n4zTffjEceeYQS9zPAq6++ii996Ut4/vnnhWTieKxWK+666y7ceeedGf/c9/X1oaura0V8/aGHHsLtt9+e0fciiAxANz4ibUjIJnIGxphge5mLzMzM4NVXX+WPL7/8cqjVav54efU19cNeP5LlUfwmGyicICmxcay2V25ZWdmKwFNtbS2am5vpOiMyxsmTJ3H48GFhjILx2SUajfKqKp/PlzSpTiaT8V65FoslZ3vlnj59GkNDQ8JYoooyglgPiZIM0xF98hXJ1cHr9SIQCKRlKyw5wORLAkwuMjExgb6+PuH3vXPnTuzevbsgrzMiO3i9XvT29grCUl1dHRobG1d1nTHGMDMzw52iZmdnkz6/tLSUJ8sZjUZKgClwSMQmJCRXOWlfkSoWqlKpuKit1+vXFJ9gjOHs2bO47rrrcOzYMT6+b98+PProowmLdIj0CYfDuPvuu/Hggw+m/T1XXnklHn74YWzfvj0jc4hGo3jrW98qOIlKkJBN5Ch08yPShoRsImfIByF7fn4eL7/8Mn98ySWXQKfTAcAK4RoAtxgjEXttRKNR9Pf3w+PxCOO7d+/Gzp076fdJpI1UVSWJ2jMzMym/p6qqCrt374ZGo6FrjVg3jDEcPnwYp06dEsYbGxtRV1dH11iOEO/qkE5fu1zrlcsYw/Hjx3H8+HFhnK4zItOcOnUKIyMjwtiuXbtQX19fFNdZOBwWbIUvVHED/MkBRkqAoWqn9Dl79iwGBgaEsWK6zojNwePxoLe3V0iWaGhoQENDw7qvs4WFBS5UpWprIrVAksQqsvctLEjEJi6ElIAvVWunilUolUq+Vmi12rTPIOPj49i3b5+wf7v66qvxxBNP5MQ5Jp+JRCK44YYbcODAAWG8pKQEF198MbZt24a5uTn09PSsiAfs2bMHr776KoxG47rn8c1vfhOf/vSnE/4fCdlEjkI3QCJtSMgmcoZ8ELKXlpbwwgsv8Md/9md/BqPRuKIfNgCqxF4nS0tLcLvdgo2jXC5HR0cHLBZLFmdGFAILCwvweDwYHx9PaRWqUqlgsVhgsVjI/o9YE9FoFAMDA5iYmOBjMpkMra2tqKmpyeLMiGTEV1V5PB7Mzc0lfX5lZSVfK7KRAMMYw/DwsNDrDgBaW1tRW1u7qXMhCpcLJUs0NzdnrJok34jFYggGgzwBJpWtsEaj4aK2Wq2mfcUFGBsbw/DwsDDW1NSEnTt3ZmlGRCGSqOK/sbER9fX1GX+vcDgMn88Hr9ebMgEG+JOtsMViQUVFBa0VeQyJ2MRqWFhY4GtFMgcYxhjuvPNO1NbW4pprrsG73vUutLe3J3yux+PB9ddfj8HBQT729re/Hb/+9a+h0Wg25OcoJj75yU/ivvvuE8buuece/PM//zMMBoMw/txzz+GjH/2osJe+8sor8eKLL65rPThy5Ag6Ozt5IrZGoxGSIkjIJnIUugkSaUNCNpEz5IOQHYlEcOjQIf7Y4XDAbDZfUMSWBGw6nKyOmZkZuN1uoRKurKwMNpsNWq02izMjComFhQW4XK6U4lQ8ZWVlPKBkMBjIcpxISTgchtvtxuTkJB9TKBSwWq0ZybomNo/4XrlTU1NJn1teXs7XirXa/62GRA4mlPxFZBrGGEZGRjA2NsbHZDIZ2trasHXr1izOLHeIr6pKJwGmvLycuzpsxlqRL4yOjuLIkSPCWEtLC7Zt25alGRGFyLlz59Df3y+M7d69G3V1dRv+3lICjFStncoBpqKiQrAVpvhC/kAiNrEeIpEI/H4/r9aOb4F0/Phx3HvvvcLzd+zYgT//8z/HzTffjLe//e1QKpXw+XzYv38/ent7+fOuuOIKPPXUUxRfywBDQ0Nob28XPuPf/va3V/xt4hkfH8dll10miNmPPPII3vve965pDowxvO1tb8Pvf/97AMCtt94Kr9eLl156iT+HhGwiR6EbIZE2JGQTOUWqCoZswxgTrGI6OztRXV0tCNjS13K5nIJRa8Dr9aKvr4/bsgPnMwltNhtZMRIZY3p6Gi6XS0ieUalU6Ojo4Bbkfr9fOIwsp6SkhAeUqKcdkYhEyRKlpaVwOByU+Z7nLC0t8erLVL1yS0pKYDKZYLFYNmStCIfD6OnpQTAY5GNKpRI2m21FBQBBrJVYLIbBwUGcO3eOj8nlcnR2dsJsNmdxZrnN/Pw8XyviE5oSsdFrRT7AGMOxY8dw4sQJPkbJEsRGkMi2fs+ePdixY8emz4UxhtnZWb5WpLIVjl8rqqqqoFQqN2mmxGohEZvIJIwxTE1N8QSYn/zkJ3jkkUcu+HytVotLL70Us7OzePXVV/l55a1vfSueeeYZ6PX6TZp5YXP77bfjZz/7GX/8jne8AwcPHkz5fS+99BL27t3LH9fX1+PIkSNr2v99//vfx8c//nEA5908hoaGcNttt5GQTeQDdDMk0oaEbCKnCIVCSYPBucDBgwe5yCrZwsb3xiYr8bXBGMPY2NiKfotmsxnt7e10QCcyRqJkCZ1OB5vNJvSii0aj8Pl88Hg88Pl8Se3/FAoFDyiZTCa6XgnMzMzA5XIJCVqVlZWw2+1QqVRZnBmRaeKtQn0+n7C2LEcul8NkMvEkmJKSknW999LSElwulxD0Lisrg8PhgFqtXtdrE4RENBpFb28vfD4fH6NkidUTCoV48DlVspxcLkdVVRWv1i6GXrmMMRw+fFjoHSmTydDZ2UnOEkRGOX36NIaGhoSxXKr4X1xc5GtFqmQ5aa2Q9hVlZWWbOFMiGSRiExvN8PAwfvnLX+LAgQPo6elJuq+Q2LFjB5588klYrdZNmGHhwxiDxWIR9sjPPvss3vnOd6b1/RdddBHeeOMN/vjQoUO46qqrVjWHU6dOoa2tDbOzswCABx98EHfccQf27t1LQjaRD9ANkUgbErKJnCIfhOzf/e53vIqzqakJ27dvJxF7ncRiMYyMjKzo67lz507s3r2bfp9ExkjUb7G6uhptbW1JM19jsRgCgQCvlEjWBkEmk8FoNBZV8JkQ8fv96OnpEQRNvV4Pm822buGSyG2i0aiwVsTb/y1HJpPBYDDwtWK1riPz8/NwOp1YWFjgYxUVFXA4HJQsQWSMRO0RyFli/USjUfj9fp4sl2ytAM7fQ+J75RYajDEMDQ3hzJkzfEwul8Nms1EbDiKjJDoLtLa2ora2NkszSk68rbDX603ZV1ur1fJ9RWVlJZ2js0QiEbuhoQENDQ30NyE2BK/XiyeeeALPPPMMXn311ZStTWw2G/bv34/9+/ejq6uL3CTXyMDAgNCXvLS0FNPT02knFX3hC1/Al770Jf74rrvuwgMPPLCqOVx77bXcOfSKK67ASy+9BJlMRkI2kS/QTZFIGxKyiZwi14VsxhhefvllHjTesmULdu3ahdLSUhKx10g4HEZfXx/8fj8fk8lkaG5uzpmseCL/YYzhyJEjOHnypDBeV1eHxsbGVX1uGWOYnJyEx+OBx+NJ2dNOEqosFgvZ4xcB586dw8DAgHAvs1gsaG9vL0qb2GJmtWtFfPA5VTX19PQ0nE6nIH4lcpYgiPUQCoXgdDqFiv/y8nI4HA5UVlZmcWaFRSwWw+TkJBeq4pNTElFZWcnXCq1Wm/dnj1gshoGBAYyPj/MxhUIBu91OFf9ERjl58iQOHz4sjLW3t+eNbf1q1wqVSiX01SahanMgEZvINoFAALfffjvGx8d5cm0ytm7dihtuuAE33ngjrrrqKkqIXQUHDhzAtddeyx+3tbWhv78/7e//5S9/iXe/+9388fbt2wVnmlT89Kc/xYc+9CEA51253G43mpubAYCEbCJfoBsjkTYkZBM5RTgcTssOJxtI9uF/+MMfuGULcF501ev1sFgsqK6uJjuvVZCof6xSqURnZydVXxAZIxqNor+/Hx6Ph49lKlmCMYaZmRkuVKXKfJaEKovFQiJAgcEYw+joKI4ePSqM79ixA01NTRS4KnLi+196PB5hH5GIiooKvlYsF6oSVfwbjUZYrVZKliAyxsLCApxOJ+bn5/lYZWUlHA4HJWVtINJa4fV64fF4UvbKLSsr40JVVVVV3glV0WgUfX19QpC9pKQEdrsdOp0uizMjCo0TJ04IezSZTIb29nZs2bIli7NaO4wxzM3N8bVieno66fOVSiVvg2Q0GqkN0gZBIjaRbRYWFvDe974Xhw4d4mP19fV473vfi5dffhl/+MMfksZcVSoVrr76auzfvx833HBD3q6Rm8UjjzyC973vffzxZZddhldeeSXt7z948CCuueYaYWxycjKtPdD4+DhaW1sRDAYBAP/6r/+Kz3/+8/z/Scgm8gS6ORJpQ7tXgkgDScRmjEGv1wsBaMYYgsEggsEgRkZGuKhtsVgokzEJk5OTcLvdQjWZSqWC3W4ngY/IGKFQCG63G1NTU3xMoVCgs7MTJpNp3a8vk8mg1Wqh1WrR2NgoBJ8TBZSmp6cxPT2No0eP8ooqi8UCjUZDwY08hjGGkZERjI2NCeNNTU3YuXNnlmZF5BIymQwajQYajQa7du3C/Pw8XyviLZsl5ufnMTo6itHRUS5UWSwWhEKhFRX/W7duRWtra94JWETuMjc3h+7ubiwtLfExrVYLu91OFf8bTPxa0dDQgIWFBV59GQwGVzhXLS0t4fTp0zh9+jSUSiVvbWI0GnO+lUU0GoXb7UYgEOBjpaWl6OrqSulKQRDpwhjD8ePHcfz4cT5WCL3XZTIZ1Go11Go16uvrsbS0JPTVXi5URSIRjI+PY3x8HDKZTOirTclJmcHv98PtdpOITWSNxcVFfOADHxBE7MbGRjz//PPYvn07ZDIZvF4vfvvb3+I3v/kNDhw4sCK5dmFhAU8++SSefPJJAOd7OO/fvx833ngjOjo66FpexvJ9cfzeOR0SPX9wcBCXXHJJyu/92Mc+xkXstrY2fPrTn17VexMEQeQbVJFN5BS5WJEtCdjSZyUSiWBsbCytKgmqvkzMuXPnMDg4KPyt9Xo9rFYrBUiJjDE3NweXyyXY7pWVlcFut29KX08p+DwxMZFQqIqnvLycrxV6vZ4OiHnEhSr+87nKh9hc4oPPfr9/VS1Wdu7cid27d9OaQWSMRLb1VVVVsFqtVMGXZcLhMHw+H7xeL3w+n+DKsJxcF6oS9V4n23oi0zDGcOzYMZw4cYKPyWQyWK1WmM3mLM5sY4lEIggEAnxvEb+eJ0Kj0fCEObVaTXuKNXAhEXvXrl1ZnBVRTCwtLeGDH/wgfvOb3/Cx+vp6HDp0CPX19Qk/10tLS3jxxRfx5JNP4je/+c2KpOzltLS0oL+/n5Jn43jxxRfx9re/nT+ura3F6dOn0/7+H/3oR7jjjjuEsXQqp+MtyWUyGV555RVceumlwnOoIpvIE2jTQaQNCdlETpFLQrYkXkvzif9a6oe9uLiIiYkJeDweoeIzEWq1mtuPV1ZWFuUBMVFGPEDVZETmCQaDcLvdiEQifEytVsNut2clmBsKhbilcCAQSCpUlZaWwmw2o7q6GgaDgT4XOUyiin+lUgmr1YqqqqoszozIVyKRCHw+HzweT1pClVR9aTabKRGMWDeBQABut1u47iwWC9rb28m2PseIxWIIBAK892UoFEr6fK1Wy4WqbJ9DEvVeV6lU6OrqIjcrImMwxnDkyBGcPHmSj8nlcthstqJqYcUYE/pqx7eLSER5eTlPgKFzSHqQiE1km3A4jI985CN47LHH+Nj27dvx/PPPo7GxMa17PmMMvb29XNR+4403Vjznpptuwq9+9atMTj3vGR8fx9atW4WxsbGxtFvYffjDH8ZDDz0kjN1///34u7/7uwt+TyAQQEtLC0+k/9jHPoZ/+7d/W/E8ErKJPKH4xBFizZCQTeQUkUgkadB2s4i3Eo9/DPxJxF6+GVxcXORClWTvciGk3pfV1dVFYykcjUYxODiI8fFxYXzXrl0XzBAliLVw7ty5Fda7RqMRnZ2dOVFNJlVUSUJVsuQdpVLJA89Go5GEhBwiUf/YsrIyOBwOskQlMkIsFoPf78fhw4dTBp4BwGAwcFGbxCBitXg8HvT19Qn3pJqaGrS0tJCQkeMwxjA1NcWFqrm5uaTPV6lUfK3YbBeYpaUldHd3C3NUq9VwOBwoKyvbtHkQhU2ili9yuRx2u73oEw3j+2qnSsSX2hWYzWaYTKacb1eQDUjEJrJNOBzGnXfeif/+7//mYzU1NXj++eexZ8+eNd/jz549i6effhq/+c1vcPDgQSwuLuLHP/4xPvKRj2Rq6gVDc3MzRkZG+OOvfvWr+OxnP5vy++bm5lBbW7tiLf7Sl76Ez33ucxf8vr/6q7/Cww8/DOB8Bfjg4CC0Wu2K55GQTeQJJAYQaUNCNpFT5IKQHS9ix/8DwAXsVJvB1VRfSpbC1dXV0Ol0BSnoJqpalMvlaGtrI+tdImMwxnDixAkcO3ZMGK+trUVzc3NOBuKj0Sj8fj+vqIqvIF+OXC6HyWSCxWKhYFKWmZ6ehsvlEirgslnxTxQm0WgUfX198Hq9wnhpaWnK6kuNRiO0NinEvQWROc6ePYvBwUFhv0q29fnLaoSqkpISXn250QlzCwsL6O7uFlq+UO91ItMwxjA0NIQzZ87wMYVCAbvdDoPBkMWZ5R6hUEhobZIsuVYmk8FgMPD1ghLmSMQmsk8kEsHHP/5x/H//3//Hx6qrq/H888+jtbU1Y3u4+fl5PP/887jkkktgMpky8pqFxJe//GV8/vOf54/1ej36+/tRW1ub9PvuvfdefOc731kx/qlPfQrf+MY3En7Pb3/7W1x//fX88a9+9SvcdNNNCZ9LQjaRJ9Bhk0gbErKJnCLbQvbySuz4r+Vy+ZqEsHA4zINJqQ6IZWVlQp/cXBTeVsvs7CxcLhcWFxf5WGlpKWw2G3Q6XRZnRhQSsVgMQ0NDOHv2rDDe2NiIurq6vAjEx2IxBINBngSTTKiSel9KFVVUxbR5+Hw+9Pb2Cvcqg8EAq9VKyQVExkjUP7akpAR2ux1arRZzc3N8rYi3502EVH1psVgKNmGOWDunTp0SqkiA/Lp3EslZWlriQlUgEEh6DpHL5bxdgclkyqi4PDc3B6fTKZwH9Ho97HZ7TrjlEIUBYwyDg4PCeUCpVMJut0Ov12dvYnlANBrl7Qp8Pl/KhDm1Ws1Fba1WW3T3CxKxiWwTjUbxiU98Av/xH//Bx0wmEw4dOoTOzs6i+0xmk8nJSdTV1QnJgx0dHXjmmWcuKGbfd999+Id/+IeERU+f+cxn8LWvfW3F+PT0NNrb27nbyC233IJHH330gvMiIZvIE2ixItKGhGwip4hGo0krEjeS+Crs+MfA+SzuTGwEV9P7UqqQqK6uRlVVVV6K2n6/H729vSv6FNtsNsriJjJGOBxGb28vAoEAH8v3in/JJlQSquKrlxKh1+u5UEWfrY0jUdXili1b0NbWlpdrNJGbLC4uwuVyYXZ2lo+Vl5fD4XCgsrJyxfMXFhZ4wlyq1ialpaW8XUG+7i2IzMAYw/Hjx3H8+HFhvLm5Gdu3b8/SrIiNJBKJwO/3c2E71ZkrvvqyoqJize87MzMDp9MpCGNVVVWw2WzUMoXIGLFYDAMDA0IbK6VSCYfDQcnTq2S17QrKysr4WlEMewsSsYlsE4vF8I//+I/4wQ9+wMeMRiMOHDgAh8NBInYWePzxx3HLLbcIYxqNBnfddReuu+461NTUYGFhAW63G//xH/+BV155hT9v27ZtOH36NH/89a9/HZ/+9KdXvMddd92FBx98EACg0+kwNDS0oj93PCRkE3kCLVhE2pCQTeQU2RCyJfFaOoik0w87E6zGUjgf++SOjY1hZGQkZ/sUE4WBdBiIF3xKSkpgtVoLxj6QMYbZ2Vkuasf/rImItxSmXs2Z4UK29XV1dWhsbKRgAZExElUtrsa2fjU2oVLvS6n6ku7NxUOi/rEymQzt7e15mwBGrI5YLIbJyUl+DolfcxIhVV9aLBZoNJq073tTU1NwuVwIh8N8zGw2o7Ozs+DFLmLziMVi6O/vx8TEBB8rKSlBV1cXNBpNFmdWGMzNzfFk/HinmEQoFAreV9tsNhecWxGJ2ES2icVi+NznPofvfve7fEyv1+PZZ5/FRRddROfSLPK9730Pn/jEJ5Kev5Zzzz33YGpqCj/72c/42A9+8APceeedwvNefPFF/Pmf/zmPrz744IO44447kr42CdlEnkCLFpE2JGQTOcVmC9nZErGXE4vFuJWXx+MRgj3LUSgUQp/cXAs8M8Zw+PBhnDp1Shjfvn07mpqaKGhFZIxEfYpVKhXsdnvCqsVCYTW9LysqKrioXYy2f5kgFotheHhY6LUIAHv27MGOHTuyNCuiEEkk+Oj1ethstjUFgldTfSmTyYTAM7UrKFwSVS3K5XJYrVbqe1ikMMYwMzPD9xapEubKy8v5WmEwGC64tw8Gg3C5XIIDFbmYEJkmFouhr68PHo+Hj5WWlqKrq4sSOjeAUCgEn8/HE+aSOczJZDLo9fqMODvkAiRiE9mGMYZ/+Zd/wbe+9S0+ptVq8dvf/haXXnopnfVzgOeeew733HPPirY9y1Gr1fjGN76Bj370o7jmmmtw6NAh/n/L+14vLCygo6ODJ9VfccUVeOmll1L+vUnIJvIEWriItCEhm8gpYrFYUhE3k8T3w47/B4AL2NnYCMZXSExMTCTtTxXfyy4XMp4jkQj6+vrg8/mEcbKpJDKN1+tFX1+fEDzR6XSw2WwZ7emY6ywuLvJqqmAwmLDHkoQUeLZYLDAYDHTQTYNoNIre3l5hTZPL5Whvb0d1dXUWZ0YUGj6fDz09PUJw1Gw2o6OjIyMuLLFYDMFgkK8XS0tLSZ8vtSsohMAz8ScSrWnUP5ZYTny7gsnJyaR7C6VSyRNsjUYjT7BNJPjU1taipaWF9h9Exki0ppWWluItb3lLQSe15grRaFTYW6Tqq11ZWclFbZ1Ol1drAYnYRLZhjOFrX/savvKVr/CxyspKPPXUU3jb296WV5+nQicajeLXv/41fvvb3+K1117DxMQEZmZmYLFYUF9fj5tvvhnvf//7uQvS9u3bBWvx06dPC721H3zwQdx1110Azscinn76aTQ3N6ecx3vf+168/vrr/PG3vvUt3HrrrfyxVPRAEFmGFi8ibUjIJnKKzRKyU4nYuVIlIPWnmpiYgMfjSWr7J5PJUFVVxasvN1vMS9TTU6FQoLOzkyp8iIwyNjaG4eFhYay6uhptbW15Ybu/UUiWwh6PB36/P2nguaSkhK8VxdDLbi2EQiG4XC5MT0/zMRJ8iI0gUe/1mpoatLS0bMhnkzGG6elp7gIzPz+f9PlqtVpoV0CBsvwkHA7D7XYLtrClpaVwOBxkvUtckHA4zF0dfD5fUrtM6SyiUqmEgCwA7NixA01NTbR+EBkjGo2ip6cHfr+fj5WVlaGrq4tE7Cwg7S2k9SKVs0NpaanQVzuXz3AkYhPZhjGGb3/72/jiF7/Ix1QqFZ588klcddVVdG/NY8bGxgSXt9ra2hV7qPvvvx+f+MQnMv7eN910E371q19l/HUJYpXQAkakTW55EhPEJhAvYkuPpa8VCkVObQIlOy69Xo+mpibMzMzwSu3lgWfGGPx+P/x+P4aGhmAwGHjgOZ2+muthamoKbrdbyMIuLy+H3W4nSzciYzDGcOTIEZw8eVIYpz7F5yktLUVtbS1qa2sRiUR4Lzufz7fC9i8cDuPMmTM4c+YMFAoFr9SOr6YqZubn5+F0OrGwsMDHysvL4XA4KDhKZJTR0VEcOXJEGKuvr8euXbs2bE2TyWTQ6XTQ6XTYvXs3ZmdneRJMfOKGxOzsLGZnZ3H8+HGUl5fzvYVery/6dTdfCIVCcDqdmJmZ4WPl5eXo6uqiinsiKSUlJaipqUFNTQ2i0SgCgQAXqpZXX0pnkeVs27aNRGwio0SjUbhcLgSDQT5WXl6Ot7zlLVCpVFmcWfESv7dobGzkzg4Xco0KhUL8LCK5zEnCdi65a5GITWQbxhi+973vCSJ2eXk5HnvsMRKxC4Dnn39eeLx3797sTIQgCCIPoIpsIqdgjKW0pFrPa+dCP+xMwBjD3Nwcr9ROlfGs0+lgsVhQXV2d8cP9xMQE+vv7hcOdTqeD1WqlHptExohGo+jv7xf638lkMjQ3N2Pbtm1ZnFnuIwWeJdu/ZK4XudauIBsk6lOs0Whgt9tpTSMyxoUSc7Lde31xcZGL2qnaFZSUlPAkmFyvpipmFhYW4HQ6hQTIyspKOByODU90JAoXyTVK2lukcnaQ7Cvz0VKYyC0ikQhcLpfgLqFSqdDV1UUido4SDod5X+1ECbbLie+rnc0EUhKxiWzDGMMPfvAD/MM//AMfKy0txS9/+Uvs37+f7qUFwBVXXIFXXnmFP37hhRdWiNlUkU0UOLSQEWlDQjaRU2yUkF1IInYi5ubmuEVoomqqeDQaDaqrq2GxWNZ1MGSMYXR0FEePHhXGyeKZyDShUAhutxtTU1N8jGzr10YsFsPk5CRfL5L1yZXJZIKzQzGIuF6vF729vULAymg0orOzkyrViYwRi8UwODiIc+fO8TGZTIb29nbeKy0XWI2lsEKh4EkwJpOpKJNgcpHZ2Vk4nU5hrddqtbDb7TlV8UbkNxdKzLkQ+WQpTOQW4XAYLpdLOBNUVFSgq6uLEnPyhFgshmAwyPcXyVqnAef/vlLS3GYmwZCITWQbxhh+8pOf4J577uFjSqUSv/jFL/Cud70rr+OXxHleeeUVXHHFFfzxnj17VrTQWw979+7FSy+9xB8/9NBDuP322zP2+gSRIWgxI9KGhGwip9gIITtVP2yZTFZQm8CFhQUuUsVnqieisrKSV2qvpu9loiA8sPF2qETxMTc3B5fLJVg8l5WVwW63U0/PdRLfJ3diYkL4HSdCcnawWCwFaUV7+vRpDA0NCWNbt25Fa2sr9RAnMkainp4KhQJWqxVGozGLM0tONBqF3+/n7QqSOTtIfXKlwHMxJMHkIoncJaqqqmC1Wikxh8gYjDEcP34cx48fF8a3bt2KUCiEQCCQ1NmBkmCIdAmHw+ju7hZaJFRWVqKrq4vuM3kKYwwzMzNc1I7/2yZCcoIxm80wGo0blgRDIjaRbRhjePjhh/HRj35UaIP48MMP4z3veQ/F2wqA+fl5XHTRRRgYGOBjjz76KG655ZaMvQcJ2USeQAsakTYkZBM5R7IKwdVSbCL2cpaWlrioncoiVKVS8UptrVZ7wd9LKBRCT0+PIJLLZDK0tbVh69atmf4RiCImGAzC7XYjEonwMbVaDbvdTlUXGSa+XUE6gSS1Ws1F7dUkweQijDEcO3YMJ06cEMYpMYfINIncJUpLS2G326HVarM4s9UR7+yQTjWVlASTbYvQYiIQCMDtdgv2rRaLBR0dHZSYQ2SMRJXYMpkMHR0dqK6uBnDeBjreUjh+T7ccyQlGEqrIJpqQCIVC6O7uFtppqdVqdHV1kbtEASG1N/F6vSmTYORyOU+aM5vNGUtmIBGbyDaMMfz3f/83/vZv/5Zfh3K5HD/96U/xgQ98gM6mOUokEkk7UXR2dhb79+/Hiy++yMduueUWPProoxmdEwnZRJ5AixqRNiRkEzlHpoTseBEbgPC1QqEoug1gKBTifS/9fn/Sg2F5eTkXqfR6Pf9dJaqOLSkpgdVqhcFg2PCfgSgezp07h4GBAeE6JYvnzWN+fp4nwcSLbomQkmDyse9lLBbD0NAQzp49K4y3tLRQ73UioyTqU6xSqeBwOPLa4UCqppLWi7m5uaTPl5xgzGZz0qQ5Yu14PB709fUJQfiamhq0tLSQiE1kDMYYhoeHcfr0aT4ml8thtVov2PYl3lI4VXsT4Hw7JMnZId+T5oi1s7S0hO7ubuH+otFo0NXVRRX8BUw4HIbf708rCQY4nzQX31d7LetFIhF7165daGhoWPVrEcRaYIzh8ccfx4c+9CGejCiTyfCjH/0IH/7wh+k+mMN8//vfx2OPPYa//uu/xvXXXw+z2bziObOzs3jsscfwT//0Tzhz5gwfr6urw+uvvw6LxZLROZGQTeQJtLARaUNCNpFzhEKhpCJrKpZXXxdaP+xMEA6H4fP5uEVosr6XpaWlsFgsUKlUOH78uFDdU1lZCZvNltdBeCK3YIzhxIkTOHbsmDBeW1uL5uZmCsJngdU4O5SVlQlJMLn894pEIujt7RUsnuVyOTo7OxMePAlirSTqU6xWq+FwOArODnVubo6LVKmSYMrLy7lIlevrRb5w9uxZDA4OCuv0zp07sXv37qLf+xKZI1GLIYVCAZvNhqqqqrReIz4Jxuv1CpW2iZCSbM1mM60XRcTi4iK6u7uFJDCdTge73U4idhEhOcFI+4tUTjAqlYqL2umuFyRiE9mGMYannnoKH/jAB4TEjQceeAB33HEH3fdynH/7t3/D3XffDeB83Lm+vh579uyBwWDA/Pw8xsfH4XQ6V7TSrK+vx3PPPYfGxsaMz4mEbCJPoEMqkTYkZBM5x3qE7OXCNYnYqYlGo/D5fJiYmIDP5xOE6mQYDAZYrVYKIhAZ40LVsY2Njairq6PPbg4QDocFZ4dkSTBSHzuLxYKqqqoN62O3FpaWluByuQQL9ZKSEtjtduh0uizOjCg0ErVIKJb7p5QEk45FaElJCUwmEywWy4b2vSxkTp48icOHDwtjdP8kMk0sFkNfXx88Hg8fUyqVcDgc67p/zs/P8/1FfPuiREjrhdlshslkovWiQFlcXMSbb74pOIHp9XrY7XZyZypiGGOYnZ3lFuTT09NJnx+/XhiNxoTXDonYRLZhjOHAgQO47bbbBKHzu9/9Lj7+8Y+TiJ0HxAvZ6XLjjTfixz/+8YYl0ZOQTeQJdFAl0oaEbCLnWKuQvbwfNgASsVdJNBpFIBDg1ZfJLLwUCgUXqSiIRKyXcDiM3t5eBAIBPiaTydDe3o4tW7ZkcWbEhZCSYCRnh1TrhSRSmUymrAYg5+bm4HQ6hWoOlUoFu91O/XuJjJLI4tlisaC9vb3o7pmSE4xkEZosaU4ul/Ogs9lsLnjBf70wxnDs2DGcOHFCGKcWCUSmiUaj6OnpEZxMSktL4XA4oNFoMvY+oVCI7y9SJc1tVJ9cIrssLCzgzTffFPZqBoMBNpuNRGxCYHFxka8XqZLmZDKZsF6Ul5eTiE1kHcYYDh06hPe85z2Ce9P/+T//B5/4xCdIxM4T3G43vvrVr+LQoUMIBoMXfJ5SqcTVV1+Ne++9F+94xzs2dE4kZBN5Agk1RNqQkE3kHOFwOGnAIhHJRGxJwCYRe3WEw2G43e6UVRHAn4LOkuUfBRiI1bCwsAC32y3YSlLv9fwiFosJSTDhcPiCz5XJZDAajXy9KC0t3bR5Tk5Owu12C/PTarWw2+2bOg+i8Dlz5gwGBweFsW3btqG5ubno9yPxSXNerzflemEwGHjiXHl5+SbONPdJ1KeYksCIjSASicDlcgnngrKyMnR1dW1oElg0GuV9clOtF8D5it34PrlE/jE/P4/u7m5BxK6qqoLNZiu6JDBidUQiEaGvdqr1oqKiAgsLC4L4TSI2sZkwxvDSSy/h1ltvFdwnvvKVr+Azn/kMidh5CGMMhw8fxuDgIE6fPo3p6WnIZDLo9Xo0NTXh4osvzmjyH0EUAMUdHCFWBQnZRM6xWiE7XsQGIHwtl8tp87cGFhcX4Xa7BdtdqeJhZmZGyBRdTrxIZbFYqJKKSMr09DRcLpdgoUXVsfkNYwyTk5Nc1E7Vx85gMPD1YiNFqkTVsSaTCZ2dnRQYJTIGYwyjo6M4evSoMN7Q0ICGhoaiF7GXs9r1QqvV8iQYtVq9SbPMTWKxGAYGBjA+Ps7H5HI5rFYrTCZTFmdGFBrhcBhOp1Ow8FWpVOjq6oJKpdq0ecRiMUxNTfEkmPigfyIqKyt5EoxWq6X1Nw+Ym5vDm2++KZwLaK9GrAVpvZBaFqRaL4Dz/dd37doFg8FAMSRiw2GM4dVXX8W73vUuzM3N8fEvfvGL+MIXvkDXIEEQxQJt0Im0ISGbyDlWI2THV2FTP+zMMD09DbfbLYjVZWVlsNvt0Gg0YIxhenoaExMTKQ+FUiWVJFKR3R8Rj9frRV9fn2Axq9PpYLPZqDq2QGCMYWZmhotU8Yf0REgilcViyWgiw9jYGIaHh4Wx2tpaNDc3U5CAyBiMMYyMjGBsbEwYJ4vn9JD6XkrrRbxLRyIqKir4elFsIlU0GkVvby98Ph8fUyqVsNvt0Ov12ZsYUXAsLS3B6XQKn8fKyko4HI6sOiQwxjA3N8dF7VR9cktLS7moXVVVRff+HGR2dhbd3d2CiG02m9HZ2Ul/L2JdSOuF5OwwNTWV9PlKpVLoq02J+USmYYzhj3/8I2666SaheOQzn/kMvvKVr9CaRxBEMVE8h3hi3ZCQTeQckUgkae9EACuEaxKxM4PH40F/f7/w+9doNLDZbAmDVVLQWRK1U4lUer0e1dXVZA9KJBQWq6ur0dbWRhUXBczs7Cy8Xi8mJiaEQ3siKisrYbFYUF1dDbVavaY1nTGGo0ePYnR0VBin6lgi08RiMfT392NiYoKPyeVytLe3o7q6Ooszy1/m5+d5JVWqNidlZWVcpCr0SqpErV82ok8xQSwuLqK7uxvz8/N8TKPRwOFw5FzC4eLiIl8vgsFg0j65CoWCi1Qmk4lEqhxgZmYG3d3dghV0dXU12tvbC3o9JzYfn88Ht9uddI2IJ77Fidls3lQXCqIwYYyhu7sbN954o5BU8clPfhLf+MY3KBZCEESxQUE5Im1IyCZyjlRCdrJ+2CRirw3GGE6ePIkjR44I4xaLBe3t7WlvpuMrqVKJVFqtlovaFRUVa547kV8wxnDkyBGcPHlSGK+rq0NjYyN9douIhYUFvl6kEqlUKhXMZjOqq6uh0+nSuk4S2e7KZDK0tLSgtrZ2vdMnCE4kEkFPTw8CgQAfUyqVsFqtqKqqyuLMCodQKMQrL/1+f9IAtFRJZbFYYDKZCioguLS0BJfLJeyxVCoVHA4H7aWIjJKoT7FOp4Pdbs954TccDsPn8/E+ucnOlfHuUWazmRJts8DU1BScTicikQgf27JlC9ra2kjEJjKKz+dDT0+P4P5XX18PrVbLq7VT9dXWaDRc1NZoNHR2JVYFYww9PT244YYbEAwG+fjHP/5x3H///QW1ZyUIgkgTupESaUNCNpFzJBOyl4vY8WK2JGDTYWJ1xGIxDA8P48yZM8L4eoXF+fl5LlKlsu9Sq9Vc1C72npeFTDQaRX9/PzwejzDe3NyM7du3Z2lWRC6wtLTEK6kCgUBSkaq0tJTbCV+o8jIcDqO3t1cQFhUKBTo7O6l3LJFRQqEQnE6nICxSdezGEolE4PP54PF4UopUcrkcVVVVXKTKtSrS1bCwsIDu7m6hpUsuWDwThUcii+eqqirYbLa8C7LHYjEEAgG+x4j/mRKh0Wj4erFWNxgifSYnJ+FyuQQRu6amBq2trfS7JzJKIhF7165daGho4I8ZY7yvttfrTek2J7nBmM1mallApIQxhsHBQezbt09oDXPHHXfg+9//PpRKZRZnRxAEkTVow0ekDQnZRM5xISE7XsQGIHwtl8vp4LAGEok9G1GxuLi4yEXt+MzTREh2whaLhbKcC4hQKAS32y0kNZCwSCRCqqSSRKr4gNNylEoltxM2Go1QKBRYXFyEy+US+nmWlpbCZrNBp9Ntxo9AFAnz8/NwOp2CsFhRUQGHw0HWk5uEJFJJ1dqpRKr4yst8+hvNzs7C6XRiaWmJj+VLdSyRX0xPT8PpdApViSaTCZ2dnXknYi+HMYbp6Wm+XqQSqSQ3GIvFAr1eT2eSDBMMBuFyuYRzf21tLVpaWuh3TWSUdETsRMT31U7lHkUtC4hkMMYwMjKCffv2CW2Ibr/9dvzwhz+k64UgiGKGNn1E2pCQTeQc0WhUyMoGkNBKnDFGVuLrYH5+Hm63WwjibIYV6moqL1UqFRe107UTJnKPubk5uFwuQewpKyuD3W6nikUiKdFoFH6/nwedl98b4pHL5dDr9ZiZmREC8BUVFbDb7WS7S2SUmZkZOJ1OQTjVarWw2+15XfWbz0iVVFLiXPw9JxH5Unk5NTUFl8slrGtVVVWwWq1UvUNklETVsYXcp3g1IlVJSQmvvJQS54i1EwgE4HK5BGFx+/bt2LNnT86uxUR+kkjEbmxsRH19/apeJxQKCS0LkiXaymQy6PV6vmbQGaS4YYzh6NGj2LdvH86ePcvH3//+9+Ohhx4iEZsgiGKHNn5E2pCQTeQc8UK2JF5LB4X4r0nEXjvBYBA9PT1CUFSlUsFut6OysnLT5hEOh+H1ejExMYFAIJD0QFhWVibYCdPfPT8IBoNwu91CUFStVsNut5MVKrEqYrEYgsEgF6lSVV4C59c1m81GLQuIjBIIBNDT0yOsayQs5haMMczNzfH1It76PRG5mjgXCATgdruFikWLxYKOjo6CFBaJ7OH3++F2u4W9eDFZPC8tLXE3mFRnErlcDqPRCIvFApPJRMlLqyTRtbZz507s3r27KK41YvPIlIi9nGg0ylsWpOMGo1aruait1WrpOi8iGGMYHR3Ftddei9OnT/Pxv/zLv8TDDz9M9w+CIAgSsolVQEI2kXNIQvbyftgASMTOAOfOncPAwIBQCW0wGNDZ2ZnVjbTU83JiYiJllnNJSQkPOFM/qtwl0bVmNBrR2dlJYg+xLlZbeanX6/makU92wkTuMTExgb6+PmFd27JlC9ra2uhelMMsLCxwN5hULU5KS0u5nXA29xgejwe9vb3CtVZMwiKxeSS61oq5OjYSicDv93ORKpkbDPCnPQZVXqbG6/Wip6dHuNbq6+uxa9euorzWiI1jo0Ts5UgtC6T1Ir61USKkPYbUV5vcHQoXxhjGxsZw7bXX4uTJk3z85ptvxi9+8QuUlZVlcXYEQRA5A20AibQhIZvIOWKxGEKhkCBix4vZkoBNh93VwRjDsWPHcOLECWG8pqYGLS0tORWAl+yEJVE7WQApUY9cIrtImcdHjx4Vxmtra9Hc3JxT1xqR/4yOjuLIkSNpP1+yE7ZYLFSpTayKsbExDA8PC2M7duxAU1MT7UnyiFAoxAPOfr8/aeKc1PNSqrzcrCSsM2fOYHBwUBirq6tDY2MjXWtERhkfH0d/fz8JixcgFothcnKSJ8IsLi4mfT5VXl6YRAkTDQ0NaGhooN8TkVE2S8ROxPz8vNCyIFm8VaFQwGg08r7aVJ1bODDGcPbsWVx33XU4duwYH7/++uvx6KOPkjMdQRDEn6BNIJE2JGQTOUckEkEoFFrRDxs4L2KTCLZ6otEoBgYGMDExIYw3Njairq4up4MHsVhM6JEbb4e+HCngXF1dDaPRSFW/WSAWi2FoaEjo/wTkx7VG5BeMMRw+fBinTp0Sxnfs2IHS0lJ4PB5MT08nfY3Kykouams0Gro+iYQwxnD8+HEcP35cGKd1Lf9ZTeWlTCZDVVUVr7zcqEqakydP4vDhw8LYZgXgieIiUcIEXWsXhjGG2dlZfiZJ1bKgrKyMJ9saDIaiPsMmcjOha43YCLIpYi8nHA4LfbXj24QkIr6v9ma2eyMyz/j4OPbt24eRkRE+ds011+Dxxx+nvy1BEIQIBVOItCEhm8gpnn76aXzve9/DjTfeiH379sFoNIIxBpns/2fvvgPbru698b81PWVtKYkznOl4W3LZJWVm23IKpVzoQxm3BW5vaUspv85bWvrQ0hbaQgd0QHtpH8pD4pGE7ECggRSoJe/sOMOJo+U9JUvn90euzuOvI8uyI1nDn9d/OvlKOnbko/M9n3M+HxEkEgktFk/DyMgIGhoa0NPTw9vEYjEKCwthNBpj2LOpm0qN3ED9OqPRCJ1OB5lMNoM9nZ28Xi8aGxvR2dnJ20QiEQoLCzFnzpwY9owkm2Cbc0QiEfLz8zFv3jzeNjw8zMeLydIJp6am8qC2SqWi7xsC4FLg4vDhwzh//jxvE4lEyMvLQ3Z2dgx7RiJt7BzD6XRiZGQk5PWBBWeDwRCRdMITZc7Jy8vD/Pnzr/j1CRnr7NmzggV2AFi5ciUWLFgQox4lnkDJAqfTia6urpAnL6VSKXQ6HT95OZs223Z0dKC5uVnQtmLFCixatChGPSLJKp6C2OP5/X5BXe3J5hgZGRk8qK1UKum+JIE4HA5s2LBBsFHs5ptvRm1tLRQKRQx7RgghcYm+4EjYKJBN4sr999+Pv/zlLwAuna795Cc/iYqKClRUVGDu3LkAQJP4Kejr60N9fb0gDZ5cLkdpaSmUSmUMe3blGGPo7u7mQapQqf4Cp6iMRiP0ej2l7YqC4eFh2Gw2QV0wmUyGkpISqNXqGPaMJBuv14v6+np0d3fzNolEgpKSEmi12gmfF0gn7HA44Ha7Qy44y2QyHtSOZY1cEls+nw/Nzc1wOBy8TSwWo7i4GHq9PoY9I9EWqHkZmGMMDg6GvD4zM1NQsmCqc1XGGI4cOYL29nbeRhvBSDQwxtDW1iZIdQoABQUFgo1gZGoCJy8Dc4xQJy8D9yWBIFUyp5gNduo/NzcXCxcujFGPSLKK5yD2eIwx9PX18aD2ZNkd5HI53whDpdTim8vlwsaNG9HU1MTbbrzxRmzfvh1ZWVkx7BkhhMQtCvKQsFEgm8QNr9cLo9EY9NScSCTCddddh8rKSlRUVPCTKRTUnpjL5UJjY6NgISUzMxMmkynpFkymsuAsEomgVqv5gnO0UoPOJr29vbDZbIIT8mlpaTCZTJQ6i0TU0NAQbDYbBgYGeJtcLofZbJ7SDvfR0VG+4DxZqr/AKapAjVxaPJodvF4vGhoaBHMSqVQKk8kElUoVu46RmBgYGOBzjMlKFkw1u4Pf70dzc7Mgw4RYLEZJSQl0Ol1E+k8IcGm+fOLECZw+fZq3iUQiFBUVJVyWpnjm8/kEJy9DZZACgKysLF6yICMjI2nub9vb23H48GFBG2WYINHgcrlQX1+fsKnrp5LdQSwW8zInOp2O1jLiSFdXFyoqKmC1Wnnbddddhx07dtC9AyGETCw5Jr5kRlAgm8SNgYEBvPjii3jzzTcFk79grr76alRUVMBisfD6lMly0x8JwdIF6nQ6FBUVJX0qu7H16xwOh+CEcDAqlYovOKelpc1QL5OH0+lEU1OTIBCoVCpRWlpKJ99JRPX19cFmswlS8WVkZMBkMl3R325gwTmQTtjr9U54baBkQWDBmUoWJKeRkRFYrVbB90dKSgrMZjMyMzNj2DMSD4aHh3l2h8kWnGUyGU8/rtFoLtsI4/P50NDQALfbzdtowwSJBsYYjh49inPnzvE2yjARfYwx9PT08DFjsuwO6enpfMxI5HTCwe5F6dQ/iYZED2KP5/V64Xa7eV3t0dHRkNcrlUpBXe1EHTMSXU9PDyorK/HRRx/xtk984hPYtWtXyIxhhBBCKJBNwkeBbBJ3/H4/jh8/jurqalRXVwsmg8GYTCZUVFSgsrISy5Ytm9VBbb/fj2PHjgkWqQBg4cKFWLFixaz8vQROUdnt9knTdikUChiNRhgMBjpJHIZz587hyJEjgjaj0YiCggI6tUoiyu12o6GhQbBhQqVSobS0NKLBZL/fLyhZEKp+HWV3SE4DAwOwWq2CchUZGRkwm81Jl82EXDmv18tPUblcLkFK0/EkEgnfCBM4aT2+TMJ0MkwQMhm/34/Dhw/jwoULvE0ikaC0tBQajSaGPZt9AvclTqcTPT09Ia8NpBOeaCNMvDp9+jSOHz/OH4tEIhQUFPAyYYRESrIFscfz+/3o6uri84xQpdSASxnRApttlUollUaaIX19ffj0pz+NDz74gLeVlpZi9+7dMBgMMewZIYQkhNkXqCDTRoFsErcYY/D7/Thz5gwPan/wwav2amEAAQAASURBVAchT74UFhaioqICmzZtwsqVK2dVUHt0dBSNjY2CUz0ikQi5ublYsGBBDHsWP4aGhnhQe7LFoyutd5nMGGM4fvw4zpw5I2hftGgRli9fTr8rElEdHR1oaWkRjP0GgwGFhYVRXdQdW7LAbrdjaGgo5PVKpZKPGenp6VHrF4menp4e2Gw2wal8yjBBwuXz+eB2u3nJglDZHUQiEcRisWBzTlpaGsxmM40fJKL8fj+amprgcDh4G536jw8jIyP8pHZnZ+ek6YQDNXJ1Ol3cfie1tbXhxIkT/DGlrifR4nQ60dDQkLRB7PECWecCY8ZkG/RlMpmgrnayZ+WLlYGBAXzmM5/Bu+++y9sKCwuxd+9ezJkzJ4Y9I4SQhEELyCRsFMgmCYExBsYYzp8/j6qqKtTU1OC9994LefIlNzeXB7ULCwshFouTNsA2NDSE+vp6QRpUqVSK4uJiSmU0gUBqULvdHrQu+1jp6ek8QJWVlZW0n6Nw+Hw+NDc3CxZEAWDlypW0YYJEFGMMp0+fFiyIArHJMDHVkgWBjTBGo5HS/CWIYKf+dTodiouLE+YUHIkfY7M7hHOKSiwWY+HChZg3bx5lhCER4/P50NjYCJfLxdtkMhnKysro1H+cGR0dFWyECZVOWCQS8dJIer0+LkojMcZw6tQpnDp1ireJRCIUFxfTiUQScbMtiB1MYC3D6XSGtRFGo9HwjTCUYSgyhoaGcPfdd2Pfvn28LS8vD3v37kV2dnYMe0YIIQmFFstI2CiQTRJOIKh98eJF1NbWorq6Gu+8807IG/4lS5bAYrHAYrHAbDYnVVC7u7sbDQ0N8Hg8vC0tLQ2lpaVUyzNMHo+HB6gmuxFMTU3lAapErl03HR6PB/X19YLT7BKJBEVFRVRfkURUsFqeALBixQosWrQoRr36fwYHB/mYMVl2B9oIE/+CnfqfO3cu8vPzKS0juWKMMfT19cHhcODixYuTZnfIyMjgASoaM8h0jY6Oor6+XrBZMyUlBWVlZbRZIs6NTSc8WZkT4P9tntPr9VAoFDM+ZjDGcOLECZw+fZq3Uf11Ei0UxL5cYCNMILA9WV3trKwsXlebMs9Nz/DwMO69917s2rWLty1fvhz79+/H/Pnz6XdKCCHhowGThI0C2STh+f1+uFwubNu2DVVVVdi3b58gqDveggULeFD76quvhkQiSdiJ5sWLF9HS0iI4mU5pUK9MoN6lw+GA2+0OeepfLpfzoLZKpUrqgMfAwABsNptgAT4lJQWlpaXIysqKYc9Isgl26l8kEqGwsDAuU7QFTkQ4HA50dXWF3AiTkpLCg9rJPmYkirNnz+Lo0aOCtpycHCxbtixh5wYkPgVLXT8ZGjPIdHi9XthsNsFGK0pdn5jGboRxOp2TZoRJTU2FXq+fsTGDMYZjx47h7NmzvE0sFqO0tJSygpGIoyD25AIZYQJB7ck2z6WlpfGgNs0zwjMyMoLPf/7z2LZtG29bvHgx9u/fj5ycHLp/IISQqaFBk4SNAtkkqQR2sG/fvh3V1dXYs2dPyMn7vHnzsHHjRlRWVuL666+HVCpNiIknYwxtbW04efKkoH3OnDnIz8+nNKgRMjo6CpfLxdP8jU05O55MJoNer4fRaIRGo0mqm8Curi7U19cLdndnZmbCZDJRajISUcFO/UulUpSUlECj0cSwZ+GZykaYwJhhMBig0Who3J5hwU6QAfFz6p8kl2Cp641GI5YvXw6XyxVWatBAvUuDwQCtVktjBgnK4/Ggrq5OEPDMyMiA2WymOVsSGBoa4kHtyUojSaVSHqCKRo3cYNlzJBIJSktLE2LORhILBbGnjjGGgYEBHtSeLIuUVCrldbV1Oh3V1Q7C4/Hg3//937FlyxbetnDhQuzbt482wRJCyPTQwEnCRoFskrT8fj96e3uxY8cOVFdXY+fOnRgYGJjweoPBgI0bN8JisWDVqlWQyWRxORH1+/1obW1FR0eHoH3JkiVYsmRJXPY5Gfh8Pl67brKUXYGFo2RYbA6Wcler1aK4uJhubklEDQ0NwWq1YnBwkLelpKTAbDYnZJmEsfUunU5nyI0wEomEB6ho4Sj6/H4/Dh8+jAsXLvA2kUiEgoICzJ07N4Y9I8nIbrejqalJ8D2anZ2NvLw8wZzN6/XyoPZkm+fEYjG0Wi1PJyyTyaL6M5DEMDw8DKvVKrjfUSgUMJvNlKkpCXk8HsGYEWrz3NgauXq9HikpKVf03owxHD58GOfPn+dtEokEJpMJarX6il6bkPGCBbGXL1+OnJyc2HUqAY2MjAjqaocaM0QikWDMoI1Ql+ZpDz/8MN544w3elp2djX379iE3N5fW4QghZHpo8CRho0A2mRUCadl2796Nqqoq7NixA729vRNer9FosGHDBlRWVuKmm25CSkpKXExMPR4PGhoa0N3dzdvEYjEKCgriMuVusvL7/ejs7OQ1ckOlCU3UABVjDKdPn8aJEycE7dnZ2Vi5cmVSnTgnsdfb2wubzSYoC5FMp/6nMmYEFpsDASoKPkSWz+dDY2MjXC4Xb5NIJCguLoZOp4thz0gyOn/+PFpbWwVt4aSu9/l8fMxwOp0hxwyRSAS1Ws030CXDmEmmbnBwEFarVZCJSqlUwmQy0UaHWWAqYwZw6bMRmGdMtWY6YwwtLS2CTdVSqRRmsxlKpXJa/SdkIhTEjo7AJv1AYHuyMUOhUPCgtkKhiIu1sZk0OjqK//zP/8Rrr73G2+bMmYN9+/YhPz9/1v0+CCEkgmgAJWGjQDaZdRhjGBwcxL59+1BVVYXt27ejs7NzwuuVSiXWrVuHyspK3HrrrUhLS4vJRDVYjWKZTIbS0lKoVKoZ7w+5JFCHKhCgGhkZmfDaQIDKaDTG9QmqYKcVgUvp26juE4k0l8uFxsZGwelDtVqNkpKSuP0buRKMMXR1dfEU5MPDwxNeKxKJoFKpeI1cClBdmWB1Y2UyGUwmEy2+k4g7ffo0jh8/LmibzuI7Y0wwzwg1ZgBAVlYWD1AlYjYLMnX9/f2wWq2COaharUZpaWnCbKAkkRMYMwLzjMlq5GZkZPAAlVKpDDnP9/v9aGlpwcWLF3mbTCaD2WxGVlZWxH4GQgAKYs+UsWOG0+kUZMcKJjU1lY8ZarU66Te4+3w+fO1rX8Of/vQn3qbX67F3714UFxfT2gghhFwZGkRJ2CiQTWY1xhiGh4fx9ttvo7q6GrW1tYJTWuNlZmZi7dq1qKiowJo1a5CRkTEjE1e3243GxkZBOuuMjAyYTCakpaVF/f1JeBhj6OnpgcPhgN1unzRAFTh1aTAY4ubU5ejoKBoaGgSbO0QiEQoLC+nUP4m4CxcuoLW1VbBANWfOHBQUFCT9oghwaczo7e3lAarJFo6USiU/dTnVE1SzXbCUu6mpqTCbzfS7JBHFGMPJkyfR1tYmaM/Ly8P8+fOv+LX7+/v5mDG2DnIw6enpfJ6RlZVFi61JqK+vD3V1dYLTdDqdDsXFxQld2oZExtgauQ6HI2RGMgCQy+V8nqHRaARzMb/fj+bmZtjtdt4mk8lQVlYGhUIRtZ+BzE4UxI6dsXW1x2YCDEYqlUKr1fK62sm2Cdnv9+Mb3/gGXnrpJd6m1Wqxe/dumM1mmlcRQsiVo4GUhI0C2YT8D8YYPB4P3nvvPVRVVaGmpkaw23y89PR03H777aioqMDatWsn3cE+Xe3t7Thy5MhlNYqLioqS7kYhmQTS2QeC2pMFqNRqdcxPXQ4PD8NmswkWxmUyGUpKSqjeHYkoxhja2tpw8uRJQXs4KXeT2dgAVV9fX8hrMzMz+ZiRmZk5a39n4ejv74fNZhNsLkqm1PUkfgSrGysSiVBUVASj0Rjx9xscHOQBqskWm1NSUniAajacoJoNuru7YbPZBBtdjUYjCgsL6f+XBDU8PCyokRtqLShQHkmv10Oj0eDw4cNwOp383+VyOcrKyijzA4k4CmLHD4/Hw8cMt9s9aV3tQKkTvV6f8Acu/H4/vvOd7+CFF17gbSqVCrt27cLVV19N916EEBIZNJiSsFEgm5AgGGPwer344IMPsGXLFtTU1KC9vX3C61NSUnDLLbegsrIS69atg0ajueKJ7ejoKP7yl79g4cKFgvb58+cjNzeXFqgSSOA0hN1uD+sEVaBundFonLEbwGA1itPS0mAymei0Iokov9+PI0eOCAI9AJCbm3vZeDebDQ0N8aD2ZAGqtLQ0HtSO1qaqRNXd3Y36+nrBaUWVSoXS0lLaDEYiKthpRbFYjNLSUmi12qi/v8fj4fVx3W53yACVVCqFTqeDwWCAVqul9NMJqLOzE/X19YKyHHPnzkV+fj7dI5CweL1euN1uOBwOuN1uwYaIyaSkpKCsrIzuEUjEURA7fvl8PnR2dvLA9th1g2AyMzN5UDvRssIwxvDUU0/h5z//OW/LysrCjh07cP311yfUz0IIIXGOBlQSNgpkEzIJxhhGR0fx0UcfoaqqCtXV1ZelixxLJpPhpptugsViwYYNG6DX66c80e3u7sZnP/tZHDx4EF/+8pdx6623ArgU6FmwYAFNnBPc4OAgD2pPluJPoVDwoHa0FoucTieampoEi6FKpRKlpaVxk/KcJAefz4fGxkZBCQexWIzCwsKonFZMFiMjI/zU5WQnqORyOQ9qz/ZTl06nE42NjYLTI3q9HkVFRZRyl0SUz+dDQ0MD3G43b5NKpTCZTFCpVDPen9HRUbhcLjgcDrhcLsH3+3hisZiXOtHr9fS9nwCCjW0LFixAbm4u3SOQafH7/ejq6uKbYcbWWw8mIyMDc+bMgV6vp6wwJGIoiJ04AiXVAkHtsaV7gklJSeEb6NRqdVzPwxljeOaZZ/DMM8/wtszMTGzfvh2rVq2i8Y4QQiKLBlUSNgpkEzIFjDH4fD7YbDZs3rwZNTU1OHbs2ITXSyQS3HjjjbBYLCgvL+c1hkNNfk+ePIlPf/rTOHHiBIBLC6E//OEPcc8990Cv10f2ByIxNzQ0BKfTCbvdPumpy4yMDB7UjtSi0blz53DkyBFBm8FgQGFhYVzfYJLE4/F4YLPZBJs3YhnoSVRer1cQoAqV4k8qlfJUwlqtdlb9TQerv56dnY2VK1fO6uA+iTyv1wubzYaenh7eFk8pd/1+Pzo7O3mAarITVIG0oAaDIeHTgiYju92OpqYmwdg228tykMhijKG3txd2ux3nzp0LOc8ALmWFCZy6VKlU9B1LpoWC2IktUOrE6XSiq6sr5LUSiURQVzueNtAxxvDcc8/h+9//Pm9LT09HbW0tbr31VvqeJYSQyKOBlYSNAtmETFMgqN3c3IwtW7aguroaLS0tE14vFotx3XXXobKyEuXl5Zg/fz4AYVD7H//4B+655x50dnbyNr1ej7///e+49tpro/fDkLgwMjLCUwl3dXWFPHWZlpYGo9EIg8EwrVRdjDEcP34cZ86cEbQvWrQIy5cvp5s0ElGDg4OwWq0YGhribampqTCbzZSW8gr4fD6eFtTpdIZMCyoWi/lJCJ1Ol7RptRljOHPmDI4fPy5oX7x4MZYuXUpjG4mokZERWK1WQcmQtLQ0mM1mpKenx7BnwQVOUAXmGmPH5GACWWHo1GV8uHDhwmX3GsuWLcPixYtj1COSrEZHR2Gz2QSbbMVi8aRBbZlMJihbMJs20JHpoyB2cvF4PHC5XLzUSaisMMClkj+BuUYs506MMbzwwgv49re/zdtSU1NRXV2NNWvW0ByIEEKigwZXEjYKZBMSAYwx+P1+HD58mKcfr6+vD/mca665BhUVFbBYLFi0aBFee+01fPWrXxWclMnNzUV1dTUWLVoU5Z+AxBuPx8NTCU9W6zIlJYWf1FapVJPeZAU2YDgcDkH7ypUrsWDBgoj0n5CAnp4e2Gw2QY1ihUIBk8mElJSUGPYsuQTSgtrt9klPXYpEIp5K2GAwxNVJiCvBGMOxY8dw9uxZQTuNbSQahoaGUFdXJwgGZ2Zmwmw2J8TYxhjDwMAAD2r39fWFvD4tLY2PGUqlkhZ0Z9jZs2dx9OhRQVtubi4WLlwYox6RZBUsy0RGRgbMZjPEYjE/del2u0MGtqlsAQkHBbGTm8/nQ1dXFx83wilbEMjwMJNzDcYYXnrpJTzxxBO8TS6XY/Pmzdi4cSPNeSKor68PBw8eRHt7O1wuFxQKBebNm4fCwkKsWLHiil+/q6sLLS0tOH78ODo7OzE8PAyVSgW9Xo+ysjIsXbo0Aj8FISSCaIAlYaNANiERFghqnzhxgge1P/7445DPue6663Do0CFB280334zXX38dCoUimt0lCWAqqYTlcjn0ej2MRmPQ+rgejwf19fWCxSmJRIKioiJKXU8iLlgdT61Wi+LiYkil0hj2LLkFTl3a7XY4HA4MDw+HvF6lUsFoNEKv1ydsKmG/34+WlhZcvHiRt4lEIhQVFVH9dRJx/f39qKurE2wYUSqVMJlMCZvtIFDqJJAVJpTAXMNgMECj0VAq4Shra2vjJYcC8vPzkZ2dHaMekWTl8XhgtVoFG1sm2qAzNiuMy+USbFgMJhBIMBgMcZmxgsw8CmLPLoGyBYGg9thsNsHI5XKe4UGj0UQtwwNjDK+88goee+wx3iaTyfD3v/8dmzZtoiB2hLz//vt4+umnsX///gmziJWUlOCRRx7Bww8/HPbv3ev14u2338a2bdtw4MCBkFkyAWDevHl46KGH8B//8R+89CMhJKZokCVho0A2IVEUCGqfPn0a1dXVqK6uxqFDh0KergWANWvW4Ac/+AEKCgogEolo8kw4n8/Hg9pOpzNkqi6ZTCaojzs0NASbzSY4PZaSkoLS0lJkZWXNRPfJLNLe3o7Dhw8L2ubOnYv8/HwKeswgxhj6+vr4qcuBgYGQ1ysUCl62IFHSvo+OjqKxsRFut5u3SSQSlJaWQqPRxLBnJBkFyzKh1WpRUlKSNGl0A2lBA1lhQm2gk0gkgrIFtEkpchhjOHnyJNra2nibSCRCYWEhLb6SiPN4PKirqxMElxQKBcxm86Snqf1+P7q7u3mAarKyBRkZGfyk9nRKJJHER0FsEthAF6irHWqNTCwW87rakczwwBjDa6+9hv/4j//g7y+RSPC3v/0Nd911F41NEeD1evHlL38ZL7/8ctjPWbVqFf76179OmlHrww8/xLp16ybdgBmMSqXCiy++iM997nNTfi4hJKJooCVho0A2ITOEMQbGGNrb21FVVYU333wTH3zwgeAaiUSC5cuX48iRIwAupUOtqKjApk2bUFBQALFYTJNpwvl8PnR2dvIA1WT1cQOfwYDMzEyYTCakpqbORHfJLBFs4R2gGsXxYmwq4d7e3pDXBhaaDQYDFApFXP7feTwe2Gw2wc8il8thMplogw6JOLfbjYaGBsEmMqPRiMLCwqTdoBPYQBdYbA411xhbtkCv1ydEivV4xRjD0aNHce7cOd4mEolQXFwMg8EQw56RZDQyMoK6ujrBZresrCyYzeYpZ5lgjKG/v59neJisbEFKSgrfeBssmxRJPhTEJuMFMtAFyhaEmmsAl7LgBOYa0914yxjDG2+8gS984Qt8w55YLMaf//xnfO5zn4vL+55EMzo6io0bN2L37t2CdplMhmuuuQbz58/HwMAAGhoaLisNlZubi/fffx9arXbC19++fTvKy8sva5fL5SgqKsKcOXOgVCrhdrvxr3/9S7DpOeC5557D448/Ps2fkBASATTYkrBRIJuQGDh27BjKy8tx7NgxQfucOXMEaVHHWrp0KSwWCywWC0wmEwW1icBU6uMClyb3y5Ytg9FopNNTJGL8fj8OHz6MCxcuCNrz8vIwf/78GPWKTGR4eJgHtSfbyZ6amsqD2iqVKi6+f4aGhmC1WjE4OMjb0tLSYDabKW0piTi73Y6mpibBwnt2djby8vLi4u9hJgTmGoGsMJPVuqRUwtPDGENra6vgu1QsFqO0tDTkgi4h0zE8PIy6ujrBd2kkSyVM5dSlVCqFVqvl2aQStVQDmRgFsclkAnONwLgxWYmk9PR0XlNbq9WGtbbBGENVVRUeeOABvjlRJBLhj3/8Ix544IFZM6+Ltq9//et4/vnnBW2PPfYYnnrqKajVakH7nj178Oijj+LUqVO8bdWqVThw4MCE/x9jA9mZmZm46667cM899+D666+/rFwWYww1NTX46le/elnQfM+ePbj99tun/XMSQq4IDbgkbBTIJmSGvfvuu/j0pz+Nzs5O3rZixQr8+c9/RktLC6qqqrB///6QgciFCxfyoPZVV10FiURCk23CMcbQ3d0Nu92Ojo6OSU9PBRaMIpmmi8w+wdI7i8ViFBcXU/31BODxePjpKbfbHXKhOR7q4/b19cFmswkCaQqFAiaTiU6Bkog7f/48WltbBW05OTlYtmzZrJ1/BWpdBjbDjA2CBZOZmck3w2RmZs7a39tk/H4/mpubYbfbeZtUKoXJZIJKpYpdx0hSGhoaQl1dnSAVuEqlgslkispG17GnLl0uV8gSSYEMD4FUwpRBKvE5HA40NjYK5pgrVqzAokWLYtgrEs8CGR4CG+hCZXh455138Oqrr2LVqlXYuHEjLBZL0OxMjDFs27YN/+t//S/BOsnvfve7KdVmJqEdPnwYhYWFgvI0k51+vnjxIm644QZBMPv111/H3XffHfT67du346GHHsKTTz6JRx55JKzT+W63GzfeeKOgBNrKlSvR2tpK//eExAb94ZGwUSCbkBn05z//GV/84hcFdRVvvvlmbN68mdfxDOxA3bZtG6qrq7Fnz56Qu1Czs7OxceNGVFZW4rrrroNUKqUJGIHf78eRI0dw/vz5sJ8jEomgVqv5QjMFg0i4RkZGYLPZBIsLMpkMJpMJSqUyhj0j0zE6Osrr40620CyVSgX1cWeiRnBXVxfq6+sFi08ajQYlJSWUYYJE3OnTp3H8+HFBG50eu9xUyhYEMjzo9Xqo1Wqat/4Pn8+HxsZGuFwu3iaTyWA2m6lUAom4wcFB1NXVCe4zNRoNSktLZ+S73OfzCTI8TJZNKisri2+iy8jIoHEjwVAQm0TC8PAwP6nd2dkp+Dz95Cc/wT//+U/+WCaT4aqrrsLatWtx5513YtGiRWCMYdeuXfi3f/s3wZrcr371K/znf/4nlTaIoPvvvx9/+ctf+OPbbrsNe/funfR57777Lm666Sb+ePHixTh+/HjQ7yWn04n09PQpp5dvaGiA2WwWBNk/+ugjXHXVVVN6HUJIRNCEjoSNAtmEzAC/34/vfve7+PGPfyxof+ihh/Db3/52wlOwfr8fPT092LFjB6qrq7Fr1y5B7bLxDAYDysvLYbFYcOONN0Imk9FN/iwU7GSsSCRCfn4+FAoF7HY7HA5HyM8ScOlEhtFohF6vvyw1EyEBAwMDsFqtgoXQtLQ0mEymadcsI/HD5/PB7XbzRaOxiz7jicViQYaHaKQEdTgcaGpqEiw8JHuNYhIbjDGcOHECp0+fFrRTqYTJBRaaA2ULQt1vymQyQYaHmQigxaPR0VE0NDQIMjbJ5XKUlZUhMzMzhj0jyWhgYAB1dXWCrCZarRYlJSUx+RtkjKGnp4fPNSa7R0lLS+NzjXgpd0ImRkFsEg2BjbdOpxMXLlzAPffcE/IAyMqVK1FWVoa3334bHR0dvP3nP/85vva1r9F9RAQxxmAwGAQb83bt2oU1a9aE9fyrr74aH3/8MX+8b98+3HrrrRHt46c+9Sm89957/PGzzz6LJ598MqLvQQgJC03iSNgokE1IlA0ODuLzn/88Nm/ezNtEIhGeffZZPPHEE2HfePv9fvT392PXrl2orq7Gjh07Qp540Wq12LBhAywWC26++WbI5XK6yZ8FhoeHYbPZ0N/fz9tkMhlKSkouq0M0MDDAg9qh0nQBl05BGAwGGI1GqnNJuO7ubtTX1wuCm1lZWTCZTJSmPglNpT5uNDI8tLe3C9LAAcCCBQuQm5tL328kohhjOHz4sCCriUgkQlFREYxGYwx7lni8Xi8PTrlcLsEmlPEkEgnfDKPT6WZNfVyv1wubzYaenh7elpqairKyMppzkYjr7+9HXV2d4AS0TqdDcXFx3GwkGRgY4Jthxv5dBBPYDKPX66HVauPmZyCXUBCbzASv14vdu3ejtrYWb7/9Ni5evBjW8/793/8dv/nNb+i+NcJaWlpQWFjIH8vlcvT29oZ9P/hf//VfePrpp/njRx55BL/73e8i2sevfvWr+NWvfsUff+UrX8Evf/nLiL4HISQstJBDwkaBbEKiqKOjAxaLRbCbMC0tDX/729+wadOmab8uYwyDg4PYu3cvqqqqsH37dnR1dU14vVKpxPr161FZWYlbb70VqamptOifhILVjA33ZOzg4CBPCTrZglGgzqXRaKTUfrNYsJOx8bYQSqIncHoqMG6Mra8ZjEql4kHtqWZ4YIyhra0NJ0+eFLQvXboUixcvpjGIRFSwGsUSiQQlJSXQarUx7Fnim0qGh9lSH9fj8cBqtQo2FKanp6OsrCxpf2YSO8GC2AaDAUVFRXF7GnFkZESQSjjUZpixmWF0Oh0Fp2KMgtgkFvx+P+rq6rBlyxbs3bsXR44cCXm9Wq3Ghg0bUFFRgbVr10KhUMxQT5PX7t27sXbtWv64oKAAzc3NYT//zTffxF133cUfL1iwAGfPno1oH5944gk899xz/PGjjz6K3/72txF9D0JIWGgxh4SNAtmERElDQwPKy8tx7tw53jZ37lxs27YNZWVlEXsfxhiGh4exf/9+VFdXY+vWrYIUPuMpFAqsXbsWFRUVWL16NQUik4TT6URTU5Oglq1SqURpaemUF3GGh4d5cCrUBgng0mKr0WiEwWCAQqGgz9Isce7cucsWBbKzs7Fy5cq4XQgl0cMYQ39/Px83xmaECEahUPCg9mTfQYwxHD16VPBdClB6ZxIdPp8PDQ0NgtIcUqkUZrMZSqUyhj1LPn6/H93d3TzDQ6h0oMClOc3Y+rjJYHh4GFarVZBGOTMzE2azOSJZLAgZq7e3F1arVbCBJNFKc4yOjgo2w4yOjoa8Xq1W83GDyiTNLApik3jAGMP27dvxzDPPYHBwEKdPnw45bsjlctx8882wWCwoLy+ne41pev3113HPPffwxzfccAMOHjwY9vP37t2L1atXC9q6u7sjOhevrKxEbW0tf/zDH/4Q3/ve9yL2+oSQsNEiMgkbBbIJiYJDhw5h9erVgsX80tJSbNu2LaqTYcYYRkZG8N5776Gqqgq1tbUh0yqlp6dj9erVfPdpVlYWBSITULCgosFgQGFh4RWfjPV4PDw41dnZGbLOZaBencFggFKppM9SEpqoZuySJUuwZMkS+j8nAKaW4SE9PZ2PG+O/g4KdjBWLxSgqKoLBYIha/8nsFCy9M9UonhmMMfT19fFxY7L6uBkZGbw+bqLOXYeGhlBXVyfIZpGVlQWz2TxrUqqTmdPT0wOr1SoI4MydOxf5+fkJE8Qeb6qbYTIzM3lQmzbfRhcFsUk8YIyhrq4O5eXlvCSfSCRCeXk5VCoV3nrrLcHGxWDKyspQUVEBi8WC4uJiGjfCtGXLFtx555388Sc+8QlBlsrJbN++HeXl5YK2Dz74ANddd11E+tfb24u5c+dicHCQt02lhjchJKJoYCVho0A2IVHQ19eHG264AU1NTQCAiooK/O1vf5vRhVDGGLxeL95//31s2bIFtbW1aG9vn/D61NRU3HLLLaisrMS6deugVqtpoh7nGGM4fvw4zpw5I2hftGgRli9fHvH/v0CdS7vdPmlqv5SUFB6cos9ScvD7/WhpaRFsjhGJRMjLy0N2dnYMe0bi2fDwMK9z2dXVFXIzzNhxIzMzE01NTejs7OT/LpVKUVpaCrVaPRNdJ7PIyMgIrFarYANiWloaysrK6BRfDEylPu7YcUOlUiVEUG5gYAB1dXWCUjAqlQomkwlSqTSGPSPJqLu7GzabTRDEnjdvHvLz85Nmfh7YDBMYNybLDJOamsrLFqjV6oQYNxIFBbFJPGCMoaGhARs3bhRkmPvyl7+MX/ziF5BIJBgdHcWhQ4ewdetW1NbW4vjx4yFfc9GiRaioqEBFRQVWrVpFpQtCOHDgAG6++Wb+ODs7O+Ra5Hh/+MMf8MUvflHQ9uqrr+L++++PSP9+9rOf4cknn+SPlUolHA4H/Z8SEhvJMRklM4IC2YREydmzZ3H11Vfjc5/7HJ599tmY1oxljGF0dBQffvghqqqqUFNTg7a2tgmvl8lkPKXS+vXrodfrk2ahI1n4fD60tLQITioCwMqVK7FgwYKov//o6ChcLhc/BREqqC2Tyfgis0ajocWiBOT1etHY2CgIKkokEhQXF0On08WwZySReDwePm643e6Q44ZIJBIsgsrlcpjNZqpbRyJucHAQVqtVcDKW0jvHj0B93HAyw8hkMuh0OhgMBmi12pjOvSfS19cHq9UqqFGs1WpRUlISl/0lia2rqws2m01Qemj+/PlYuXJlUt/bDQ0N8XGju7s75LghlUoF4wZtJpk+CmKTeMAYQ2trK9atWyc4cf3www/j17/+ddC/8UApo9raWmzduhWHDh0KOW4olUo8+eST+Pa3vx2VnyHRXbx4EXPnzhW0nTt3LuzslA8++CBeffVVQdsvf/lLfOUrX7nivp0+fRrFxcXo6+vjbf/1X/+FH/zgB1f82oSQaUneCSmJOApkExJFLpcr7oI8jDH4fD5YrVZs2bIF1dXVIXefSiQSrFq1itcJMhqNAJDUix/xzuPxoL6+XnBKSSKRoKioCHq9fsb74/P54Ha7eVA7VN0pqVTK0/rF6yIzERoeHobNZhOcbpHL5TCZTMjKyophz0giC9S5tNvtcLlcgkX2YHQ6HebOnQudTkeLzCRi+vv7UVdXJwgqKpVKmEwmSu8ch7xeL1wuF5xO56Tjhlgshlar5SnI4+H/M1h6Z4PBgKKiItrkRyLO7Xajvr5esGlswYIFyM3NnVX3cYFNdIFxY7JNdBqNho8btJkpfBTEJvEgEJBet24dHA4Hb3/ggQfw8ssvhz0XsNvteOutt1BbW4u9e/cKNjsGRCqwmqxWrlyJo0eP8sfPPPMMvvWtb036vIGBAWRnZ1+Wkefpp5/Gd7/73Svqk8fjwY033oiPPvqIt+Xk5KC5uRkZGRlX9NqEkGmbPZNScsUokE3ILBYIajc1NfGgdmtr64TXi8ViXH/99aisrER5eTlPJzybFkNibWBgADabTXAzFU9BRb/fj87OTtjtdjidTni93gmvlUgk/AQEBafiU39/P2w2m6DuYHp6OsxmM6XbJRHj9/vhdrtx/vx5OJ3OkNeKxWJoNBoYjUbodDpKAUemLVi6XToZmzh8Ph86Ozv5JrpQ8w2RSAS1Ws030qWmps5gTy/p7OxEfX29IPie6DWKSfxyuVxoaGgQBG2jVXookYwdN1wul2ATUzBKpZKPGxTkmBgFsUk8YIzhxIkTWLduHTo6Onj7vffei1dffXXaG9oGBwexb98+bN26Fdu2beMB8lOnTmHx4sUR6Xsy+tGPfoTvfe97/LFKpUJzc/OkJckef/xx/OIXv7is/cknn8Szzz57RX2677778Nprr/HHEokEb7/9NlatWnVFr0sIuSKzd2JKpowC2YQQAJcm/n6/H62traiqqkJ1dTUaGhomvF4kEuGaa65BRUUFLBYLFi5cCJFINKsXR6Ktq6sLDQ0NgsXazMxMmEymmCzKTsbv96O7uxt2ux0OhyPkYlE8npya7To7O9HQ0CAI8iiVSpSWllLwkERcsCCPWCye9OSUWq3m40Y8joMkPgU7qWg0GlFYWEhBxQTEGEN3dzccDgccDodg81UwWVlZfNzIyMiI+tw1WFBxNqR3JrHhdDrR0NAgCCouXrwYS5cupc/bGIwx9PT08M0wg4ODIa9PT0/n44ZSqaTf5f+gIDaJB4wxnD59GmvXrhXUYr7rrrvw2muvReze1efz4aOPPsIHH3yAr3/96xF5zWTV3d2NnJwcwcnqoqIi7Ny5c8Jg9vPPP48nnngiaFr3b37zm/jxj3887f5873vfw49+9CNB209/+lN84xvfmPZrEkIigiZUJGwUyCaEXCYQ1D5+/DgPav/rX/8K+Ryz2QyLxQKLxcIXSugGP3IuXryI5uZmwaReo9GgpKQkIU4yBxaLAkHtUIvMY9P6GQwGCprGQLDPm16vR1FREZ1UJBFnt9vR1NQk+LzNmTMH+fn56O/v58GpyRaZlUolHzfS09Oj3W2SoIJ93rKzs5GXl0fzliTAGBOMG2PLYgQT7eBUsM8bnYwl0RIsqLh06VIsWbIkhr2Kf4wxDAwM8Lravb29Ia+Xy+XQ6/XQ6/XQaDSzdm4c7POWm5uLhQsXxrBXZLZhjOHcuXNYu3Ytzpw5w9s3bdqE119/nUoExFBVVRXuuOMOQZtCocAjjzyCdevWYd68eRgaGkJ9fT3+9Kc/4eDBg/y6+fPnCzYl/OQnP8H/9//9f9Pqxy9/+Ut87WtfE7Q9/vjjeO6556b1eoSQiKIbIhI2CmQTQkIKBLXb2tpQXV2NmpoaHDp0KOguyYCioiJYLBZUVlbyOmy0WDc9gd3FJ06cELTPmzcPeXl5CXlyjDGG3t7esINTarUaRqORTlzOkDNnzuDYsWOCNjo5RqLl3LlzOHLkiKBt4cKFWLFiheDzFlhkDowbfX19IV83MzOTB7UzMzPps0sAAO3t7Th8+LCgjU4qJrfBwUEenOru7g55bUpKCk8jrFarr3iOdeHCBbS0tAjali5disWLF9PnjURcsE2Iy5Yto9S30zA8PAyn0wmn04nOzs6Q970SiYRnldLpdLMmqxQFsUk8YIzhwoULWLduHU6ePMnbN27ciDfffJPWDuLACy+8gK997Wshs2yN99hjj6Gnpwd/+ctfeNtLL72Ehx9+eMrv/4c//AEPP/ywYKx69NFH8dvf/nbKr0UIiQq6KSJho0A2ISRsjDG+47W6uhrV1dU4ePBgyElpXl4eKioqUFlZiYKCAojFYlq8C5Pf78eRI0dw/vx5QfuyZcuQk5OTFL/HqZ6cUiqVMBqNMBgMVKM5whhjOHbsGM6ePStoT6bPG4kfjDGcPHkSbW1tgvbly5cjJydn0ucPDQ3xcWOy4FRaWhoPalM60Nnr9OnTOH78uKAt3M8bSQ4ej4enEXa73SGDU1KpFDqdDgaDAVqtdsrZb4Jt0qF0uyRagm2aoM9bZIyOjsLlcsHpdMLlcglK7owXKHkSOK2drPcqFMQm8aKjowPr168XbMJes2YNtmzZQnXt48iePXvw2GOP4ejRoyGvy8zMxLPPPotHH30Uq1evxr59+/i/1dTUwGKxTOl9X3vtNdx///2C9coHHngAf/rTn+h+kJD4QX+MJGwUyCaETEsgqN3R0YHa2lpUVVXhwIEDgvqm4y1btoynHy8tLaWgdgijo6NobGyE2+3mbSKRCAUFBZg7d24MexZdY09cTpbWT6FQ8KA23aheGZ/Ph5aWFtjtdt42Gz5vJDaCbdIRiUTIz8/HvHnzpvx6IyMj/MTlZCen5HI5D2pH4sQliX+MMZw4cQKnT58WtOfn509Yo48kv6kEp8RiMS95otfrJy15EmzTRF5eHubPnx+RvhMy1vnz59Ha2ipoW7lyJRYsWBCjHiUvv9+Prq4uviFmZGQk5PUKhYJneUiW7DAUxCbxwuFwYMOGDYLx75ZbbkFtbS0yMzNj2DMSjM/nQ21tLXbs2IEPPvgAdrsdfX19MBgMWLx4MSorK3Hvvfdizpw5AIAFCxYIUou3t7dPad7+97//HZ/73OcE65P33nsv/vu//5vu/wiJL4k/OSIzhgLZhJCI8Pv9cDqd2Lp1K6qqqrB//354vd4Jr1+0aBEPal911VUU1B5jeHgYNptNcDpZKpWitLQUarU6hj2bWVM5cUlphKfP6/Wivr5e8DuWSCQoKSmBVquNXcdIUvL5fGhqaoLT6eRtYrEYxcXF0Ov1V/z6Xq8XLpcLDocDLpcrZMYQqVTKF5i1Wu2srXGZzBhjOHz48GWbJoqKimA0GmPYMxJP/H4/Ojs7eXDK4/GEvD5w4nJ8dhjGGE6dOoVTp07xNtoURqIp2Ml/2jQxMwKlkgIpyCfLKpWamso3w6hUqoQMpFAQm8QLp9OJ8vJyNDU18bZVq1Zh27ZtyMrKimHPSCScO3dOMK5kZ2cLgtqT2bJlC+6++27BJsXPfOYzeP311+l+j5D4Q4u3JGwUyCaERFxgQXDbtm2orq7G3r17MTw8POH18+fPx8aNG1FZWYnrrrsOEolk1gYi+/r6YLPZBDv809LSYDKZZvWp45GRER7U7uzsDHlteno6D2pnZWXN2s9SOIaGhmCz2TAwMMDb5HI5zGYzFApFDHtGklGwTRMymQylpaVQqVQRfz+fzwe3282DU6FOXI6tcanX66ecRpjEH7/fj+bmZkGmCdqkQybDGENPTw+fcwwNDYW8fuyJy/Pnz+PcuXP830QiEYqLi2EwGKLdbTILnT179rI0rQUFBdPKbEKu3ODgIM8OM9kGXJlMBp1OB71eD51OlxCBFQpik3jR1dWF8vJy2Gw23nbddddhx44dUbmfIDPvz3/+Mx544AH++N5778Vf//rXsJ67detW3HnnnYJDNZWVlXjzzTfp/o6Q+EQLtiRsFMgmhESV3+9HT08P3nrrLVRXV2PXrl0YHByc8Hqj0Yjy8nJYLBZ88pOfhEwmmzWBSJfLhcbGRkH6I6VSidLS0knTWM4mHo8HTqcTdrt90jTCgdMPBoMBKpVq1nyWwhFs00RGRgZMJlPS1vQjsRMs00RqaipMJtOMpP+byolLkUgErVbLg1M0/iYen8+HhoYGQXkOmUwGk8kEpVIZw56RRMIYE5Q86evrC/u5IpEIJpOJNk2QqBifvp5O/scXj8fDs8O43e6Q2WGmWrogFiiITeJFT08PKisr8dFHH/G2q666Cjt37qTv2yRy44034uDBg/zxO++8g5tuumnS5+3YsQObNm0S3Odt2LABVVVVcTm2EkIAUCCbTAEFsgkhM8bv96Ovrw+7du1CdXU1duzYEXJRUKvVYuPGjaioqMDNN98MuVyetIHI9vZ2HDlyRLBAYDAYUFhYmBC79GMlkEbYbrdPulBEtXH/H7fbjYaGBsGmCZVKhdLSUshkshj2jCSjgYEBWK1WQWaOjIwMmM1mpKamznh/GGPo7u7mwalQGUOAS2mEA2NHLPpLpsbr9cJms6Gnp4e3paSkwGw2U81EckWGhob4icuurq6Q10qlUhiNRhgMBmg0mlk95yCRderUKZw8eZI/pnIJ8S2QHSaQgjxU6S3g0nxcr9dDr9fHRTYuu92OpqYmCmKTmOvr68OmTZtw6NAh3lZaWoo9e/ZEpDwRiQ8HDx7EjTfeyB/n5uZeVkIjmL1796KiokJwX7d69Wps3boVKSkpUekrISQiknORn0QFBbIJITHBGMPg4CD27t2LLVu24K233gq5KKhSqbB+/XpYLBbceuutSE1NTYqgNmMMJ06cwOnTpwXtixYtwvLly5PiZ5wpo6OjcLvdsNvtcLlcgiDteDKZTFAbdzYtMHd0dKClpUWwIGU0GlFQUECbJkjE9fT0wGazCRZulUolTCZTXGyaYIyhr6+PB7XHptkPJisriwe142GBmQiNjIzAarUKTv6npaWhrKyMMk2QiPJ4PHA4HDh58uSkNbUlEgl0Oh0MBgN0Oh2ltiTTwhjDyZMn0dbWxtsofX1iCWQqC2SHmax0QUZGBr9fiUW5JApik3gxMDCAO++8E++99x5vKywsxN69ezFnzpwY9oxE0uDgIK6++mq0tLTwts2bN+OOO+4I+bx3330X69atE4ypt9xyC7Zv307zf0LiHy16k7BRIJsQEnOMMQwPD2P//v2oqqrCtm3b4HK5Jrw+KysLa9euRUVFBVavXo309PSEDPj6fD60tLQI6ncCwMqVK7FgwYIY9So5TKU2rlQqhU6ng9FohFarTdpgLmMMp0+fxokTJwTtCxcuxIoVKxLyb4jEt2DlEnQ6HYqLi+P272xsGuHe3t6Q12ZkZPCgtkKhoL+hGBscHITVahUsYmVmZsJsNtNJDBJxwdLXi8ViiESikBvpRCKRII0wfTZJOIJtfBWLxSgpKYFOp4tdx8i0TbV0QSCzlF6vn5EsDxTEJvFicHAQd999N/bv38/b8vLysG/fPsybNy+GPSOTGR0dDXvzXn9/P8rLy3HgwAHedscdd2Dz5s0hn3fo0CGsXr1asIl11apV2LlzJ9LT06fVb0LIjKJFFBI2CmQTQuIKYwwjIyN49913UVVVhdra2ssCvWNlZGTg9ttvh8ViwZo1a2KyW306PB4P6uvrBalPJRIJioqKKDVWhI2tjetwOEKm9BOLxTyonUynphhjOHr0KM6dOydoX7FiBRYtWhSjXpFkFuzk/7x585CXl5cwGRCmkkY4NTWVB7VVKlVCfA8lk76+PlitVsHJWCqXQKLF6/Wivr4e3d3dvC01NRVmsxlpaWno6uriG+lGRkZCvlYgjbDBYKAFVxIUYwzHjh3D2bNneZtYLEZpaSnVhE0iw8PDgjlHqHW6QJYHvV4PnU4X8e85CmKTeDE8PIx77rkHu3fv5m3Lly/H/v37MX/+fJpvx7nf/OY32LJlC+677z5s2LAh6DpXf38/tmzZgu985zs4f/48b8/JycGHH34YMuOIzWbDzTffLFhTy83NxebNm6dcTig1NZVO9xMSGzSQk7BRIJsQErcYY/B4PHj//fexZcsW1NbWCia346WmpuLWW29FZWUl1q5dC7VaHZc3NwMDA7DZbIJTY3K5HCaTCVlZWTHsWfJjjPEFZofDEXKBWSwWQ6PRwGg0Qq/XJ2wwxOfzobm5GQ6Hg7eJRCIUFhbSzRqJijNnzuDYsWOCtpycHCxbtiwux+RwBNIIO51OuN3ukAvMcrmcB6aoNm70dXd3w2azCTJvaLValJSUxO3Jf5K4PB4PrFar4PTkROnrGWPo7e3lc47BwcGQr52ZmclPXFKWBwJc+gwdOXIE7e3tvE0ikaC0tBQajSaGPSPR5PV64XK54HQ6Jy2XJBKJoFar+diRmpp6Re9NQWwSL0ZGRnDfffdh+/btvG3JkiXYt28fcnJy6DsyAfz617/Gl7/8ZQCXxqrFixcjNzcXarUag4ODuHjx4mUbUQFg8eLF2LNnD5YtWxby9Z966in84Ac/iEhfP/WpTwlOgxNCZgwN5iRsFMgmhCQExhi8Xi8+/PBDVFVVoaam5rK60mPJ5XLcfPPNsFgsWL9+PXQ6XVzc7HR1daGhoUFwKjgzMxMmk+mKFx7I1DDGeJ06h8MRsk7d2FSgBoMBcrl8Bns6fcFO/kulUpSUlNACKIm4YKlPgeRbAA0sMDscDrjd7pALzFKpFHq9np+aosBqZLndbtTX18Pv9/M2o9GIwsJC2kBAIi5YDfaMjAyUlZWFlSJ8KqULAlke9Hp93G7MJNHFGMPhw4cFm3glEgnMZjNUKlXsOkZmVCCzVOC09viAz3hZWVl8M11GRsaUxg4KYpN44fF48NBDD6Gqqoq3LVq0CPv27cPSpUvpOzFBjA1kh6uiogJ//OMfw8pSSIFsQpICDegkbBTIJoQkHMYYfD4f/vWvf/Gg9vHjxye8XiqVYtWqVaioqEB5eTmMRiMAzPgN0MWLF9Hc3CxYHNBoNCguLk7Y077JgjGGvr4+vsA8MDAQ8vrAyQeDwRC3GxCGhoZgtVoFJ8BSUlJgNpunnGqLkMn4/X60traio6ODt4lEIhQUFGDu3Lkx7Fl0+Xw+uN1uflp77Kng8QKlCwwGQ1RSgc42wRbc58+fj5UrV9ICJ4m4oaEh1NXVCTa9KRQKmM3maW1um0oaYZlMJsjyQBtikh9jDC0tLYLvVKlUCrPZDKVSGcOekVgam+XB6XROer+SlpbGx47Jyp5QEJvEC6/Xi4cffhhvvPEGb8vOzsb+/fuxYsUKmuMlkPr6ejzzzDPYt29fyDJNUqkUt99+Ox5//HHcdtttYb8+BbIJSQo0qJOwUSCbEJLQAkHtpqYmbN68GdXV1Th8+PCE14vFYtxwww2wWCwoLy9HdnY2gOgGtRljOH36NE6cOCFoT7R6sbNJf38/HA4H7Ha74ORVMEqlkge146W+ZW9vL2w2m+DUBp38J9Hi8/nQ2NgIl8vF2yQSCUpKSmZV/U6/3y8oXRDq1NTYLA96vT6s05zk/2lvb7/su37x4sV0SodExcDAAKxWK4aHh3lbJGuwe71eOJ1OnkZ4bIaB8SQSCbRaLW2ISWJ+vx8tLS24ePEib5PJZDCbzVSCiAgMDAzwsaO7uzvktYENMXq9HlqtVrAhhoLYJF6Mjo7iS1/6Ev7617/ytjlz5mDfvn3Iz8+nOV6CYozh2LFjaG1tRXt7O3p7eyESiaBSqbBixQpcc801UCgUse4mISQ2aGAnYaNANiEkaTDG+OJPVVUVqqur0djYOOH1IpEI11xzDSwWCywWC79Zj+QNkt/vx5EjRy6r7b1s2TKq7ZQgBgcHeVB7slSgCoWCB7VjderZ5XKhsbFRkO5YrVajpKSEFrxJxAVLXy+TyWAymWb1qbGplC4ALgXFAmPH+Dq7RKitre2yjWErVqzAokWLYtQjksz6+/tRV1cn2Jii0WhQWloalZPRgSwPgeDU2FI040W6Ni6JPb/fj6amJjgcDt4mk8lQVlZGi/wkpJGREV72pLOzM+SGGLFYzDfEBFLYj10XXLlyJRYsWDAT3SaE8/l8+OpXv4pXXnmFt+n1euzduxfFxcW0bkIIIcmJBncSNgpkE0KSUiCofezYMR7UrqurC/mcsrIyHtResmQJRCLRFd0wdXZ24vnnn8eqVav468yGVLvJbHh4mAe1Jzv5kJGRAYPBAKPRiMzMzBm5+b5w4QJaW1sFi1Fz5sxBQUEBnfwnETc8PAyr1SpIbZmamgqz2YyMjIwY9iy+MMZ4lgeHwzFploexG2KmWt8ymU1Ugz0/P59nVyEkknp6emCz2QTBZL1ej+Li4hn5TvX7/eju7uZphMeeCA9GqVQKauOSxOL3+9HQ0CDIbiKXy1FWVkYlYciUjI6OCjbEhCp7Mh4FsUks+P1+PPHEE3j55Zd5m1arxZ49e2AymWguTAghyYsGeBI2CmQTQpJeIKh96tQpVFdXo6amBocOHQr5nOLiYlgsFlRWVvJaTFO5gTp58iQsFgva2trw+c9/Hps2bYJUKkVpaSnUavWV/kgkDoyMjMDpdMJut09a3zItLY0HtbOysiJ+M84YQ1tbG06ePCloz8nJwbJly+jmn0Rcf38/rFYrRkZGeFtmZibMZjOlyZ5EIBWow+EQnGQPJj09nQe1ozF2JIrAibGx2U1EIhGKi4thMBhi2DOSrLq6umCz2QTZTWK5MYwxhr6+Pr4hZrLauIHNdHq9flaPHYnC5/OhoaEBbrebt6WkpKCsrIw2JZArEtgQE5h3TLYhJjMzk2+IUSgUNHaQqPP7/fj2t7+NF198kbep1Wrs2rULV111FX0GCSEkudEgT8JGgWxCyKzCGANjDGfPnuVB7YMHD4ZMv5afn4+KigpUVlYiPz8fYrE45A3VoUOH8NnPflawGPX9738fX/rSl2gxKkkF6lva7Xa43e6QQe2UlBQe1FapVFd8cz5R+nqqbUeipbu7GzabTXDCh9LXT8/w8DBfXJ5sQ0xqaipfXFapVLMmy0KwVLuzsQY7mTlutxv19fWCuWF2djby8vLiZkF9KhtiAvOO2TZ2JAqfz4f6+np0dnbyttTUVJSVlSE9PT2GPSPJJpAhpq2tDXa7fdLrA2OHXq+HWq2msYNEHGMMTz31FH7+85/ztqysLOzcuRPXXXdd3HznEkIIiRoa6EnYKJBNCJm1AkHtjo4O1NTUoKqqCu+++67g9M14y5cv50HtkpKSy4Lab775Jh599FFBPdT8/HzU1tZi3rx5Uf15SHwYHR3li8sulyvkJgm5XA69Xg+j0TitBSKfz4fGxkZBGkqxWIzCwkIYjcZp/wyETMTpdKKxsVHwuTYYDCgsLIxKvdjZxOPxwOVywW63T1rfUiaT8aC2VqtN2sXl0dFRNDQ0CAI8VIOdRJPD4UBjY6NgU8nChQt5dp54FMgQE6iNG+r+XiqVCsYOGrdja3R0FPX19ejq6uJtaWlpKCsrQ1paWgx7RpKV3W5HU1OTYJxIT0/H0NDQpGOHTqeDXq+HTqeDVCqdie6SJMYYwzPPPINnnnmGt2VmZmL79u2C0myEEEKSGg32JGwUyCaEkP/h9/vhcDiwdetWVFVV4e233xbURRwvJyeHB7U/8YlP4Pnnn8cPf/hDQfDhpptuwhtvvEG17WYpn88Hl8vF61uG2iQhlUr5iSmNRjPp4rLH44HNZkNvby9vk8lkKC0thUqlitSPQAh3/vx5HD58WLDQGW+nFJPF6OgoHztcLlfIsUMikUCn08FoNEKr1SbN4rLX64XNZhOcNk1JSYHZbKbvVBIVHR0daGlpEYxxS5YswZIlSxJmjPN6vXC5XHA6nZOOHWKxGFqtlp+4pIwaMyvYGJeeno6ysjKkpqbGsGckWQULYgdqYgfGDofDAbfbHXLsEIlE0Gg00Ov10Ov19HklU8YYw89//nM89dRTvC09PR21tbW49dZbE+Y7lxBCyBWjAZ+EjQLZhBAShN/vh9vtxvbt21FVVYW9e/cKasGOV1JSgoaGBkHbfffdh1//+td02oUA+H+fqUBQO9QmibGBKZ1Od9lnaHBwEFarVXDyPzU1FWazmdLXk4hjjOH06dM4ceKEoD3RAjyJyufzobOzM6yxY2xgSqfTQS6Xz2BPI2d4eBg2mw39/f28jU4pkmhqb2/H4cOHBW3Lly9HTk5ObDoUAVMZO0QiEdRqNT+tTYGp6PJ6vbBarYLNiBkZGSgrK0NKSkoMe0aSVagg9niBscPpdMLpdMLj8YR8baVSyYPaGRkZNC8kITHG8Ktf/Qrf+c53eFtqaiqqq6uxZs0a+vwQQsjsQoM+CRsFsgkhZBJ+vx/d3d146623UFNTg127dmFwcHDC68ViMTZs2ICHH34Yn/zkJyGVSumGjAj4/X50dXXB4XDA4XCEXCASi8XQ6XQ8MDU4OAibzSZYkFYoFDCZTLT4SSKOMYZjx47h7NmzgvaJFj9JdAW+jwJjR6gNVoHAVOC0ZaIEpoJt1KExjkTTmTNncOzYMUFbso1xjDHB2DE8PBzy+qysLD52UGAqsjweD6xWK/r6+nhbZmYmysrKEnbzEYlvUwlij8cYQ09PDy9fEOoeGLh0qjawIUapVNLYQQQYY3jppZfwxBNP8Da5XI7Nmzdj48aN9HkhhJDZhwZ+EjYKZBNCyBT4/X709fVh586d+Pvf/45t27YJUomnpKRgwYIF/OSiTqfDxo0bUVFRgZtuuglyuZxu0IjAVBaXRSLRZfXrtFotiouLkyadMIkffr8fLS0tuHjxIm8TiUQoKiqiGuxxgDGG3t5ePnZMtrisVCp5+YL09PQZ6uXU9PX1wWq1Cjb3qFQqlJaWUtpjEnGMMZw6dQqnTp0StBcUFGDevHkx6lX0McbQ39/Px46xmQ+CSU9P50FtCkxdGY/Hg7q6OsHvXKFQwGw2UxCbRMWVBLGDGRgY4FkexqbFD0Yul/PNuOGUTSLJjTGGP/3pT/jKV77C22QyGf7+979j06ZN9N1CCCGzEw3+JGwUyCaEkGk4fvw41q9fL0i1KxKJoFKp0NXVFfQ5KpUKGzZsgMViwS233ILU1FS6YSMCYwNTdrtdcCIxGLlcjsWLF8NoNNJJRRJRo6OjaGhoQGdnJ2+TSqUoKSmBRqOJYc9IMIwxvrjscDgEJ/2CyczM5EHtzMzMuPgu6u7uhs1mw+joKG/TarUoKSmhxW8ScYwxHD9+HGfOnOFts3WjzuDgID9t2d3dHfJauVzOxw61Wg2xWDwznUwCIyMjqKurw8DAAG/LysqC2WymjTokKiIdxB5vZGSEjx2dnZ2XbbYdSyKRQKvV8hTk9JmfXRhjeO211/Af//Ef/HMikUjwt7/9DXfddVdczEMJIYTEBH0BkLBRIJsQQqbo4MGDqKyshNvt5m2FhYXYvHkzjh8/ji1btmD79u1wuVwTvkZWVhbWrl0Li8WC22+/Henp6XQDRwQCJ6bsdjva29tD1rYELm2UCCwuUw1ZciU8Hg9sNpugdqdcLofZbIZCoYhhz0i4hoaGeFB7ssBUWloaHztiddrS5XKhoaFBkOFkzpw5KCgooEAZiTjGGI4cOYL29nbeJhaLUVJSAp1OF8OexZ7H4+GBKbfbHTIwJZVK+WlLrVZLmWFCGB4eRl1dnSBzhlKphMlkooAeiYqLFy+iubk5akHs8UZHR+F2u+FwOOByuQSb0sYLbP4OZHqg+5bkxhjDG2+8gS984Qt8nicWi/GXv/wF9957L62BEELI7EZfAiRsFMgmhJApeOONN3DfffcJ0p7efvvtePPNN6FUKgFculkbGRnBgQMHUFVVha1bt8Jut0/4mhkZGVi9ejUsFgvWrFkDhUJBN3QEwKXUzocPH8aFCxem9LxAbUuj0Ri3KYRJfBoaGoLVahUstqelpcFsNtNnKUFN5cRUSkoKr205U6ctgy22z58/HytXrqTvQhJxfr8fra2t6Ojo4G0SiQSlpaWUbWKcqQSmxGIxNBoND0xRmuz/Z2hoCHV1dYIsOyqVCiaTiYL/JCpmOog9nt/vR1dXF597jIyMhLw+kCVGr9fTfXCSYYxhy5YtePDBB+Hz+QBc2sjwxz/+EQ888AD9XxNCCKEvAhI2CmQTQkgYGGP46U9/im9+85uC9gcffBAvvfTShKcpGGPweDw4ePAgqqqqUFNTEzIomZaWhltvvRWVlZVYu3YtVCoV3eDNUqOjo2hsbBSc/BeLxSguLkZmZibsdjscDsek9ekCi0NGoxEZGRn0eSITClafWKFQwGQyUer6JOH1egWnLceegB5PJpPxFKBarTYq6b3b29tx+PBhQdvixYuxdOlSGqtIxPn9fjQ1NcHhcPA2qVQKs9nMNyOS4Px+Pzo7O3lt3LHfE8FQlphLBgcHUVdXh+HhYd6m0WhQWlpKJRNIVMQ6iD0eYwx9fX187BhbHz6Y1NRUvqFOpVJRVpYExhjD1q1bcd999wk2Qv3ud7/Dww8/TPM8QgghAAWyyRRQIJsQQiYxOjqKL33pS/j9738vaP/Rj36Eb3/722HfhDHG4PV68c9//pMHtcfWZhxPLpfj5ptvRmVlJdavXw+tVks3fLPEyMgIbDaboM6tTCaDyWS6bLF9eHiYpxCeqD57QHp6Og9q04kHMlZnZycaGhoEC00ajQYlJSV0YixJ+Xw+uFyusE5bSiQSnkJYp9Nd8WeCMYbTp0/jxIkTgvYVK1Zg0aJFV/TahATj8/nQ0NAg2BxGJROmhzGGnp4ePvcYe9I4GIVCwQNTmZmZs2buMTAwgLq6OsFpVK1Wi5KSEgpik6iItyB2MIODg3A6nXA6nZPet0ilUsGGOpqPJg7GGHbu3Il77rlHUB7rhRdewJe+9CXaoEAIISRgdtwYkIigQDYhhITQ29uLu+66C7t37+Ztcrkcr776Ku65555pvy5jDD6fDx9//DEPao9f0B9LKpXiU5/6FCoqKlBeXg6DwQAAs2YxcDYZGBiA1WoVnN5JS0uDyWRCRkZGyOd6PB6+sDxZCuHU1FQe1I5VXVwSHxwOB5qamgSnc41GIwoLC2mhaZaYymlLkUgErVY77RTCjDEcP378so1cBQUFmDdv3rT6T0goo6OjqK+vFwRNUlJSUFZWNun3KgmNMYaBgQE+9xi7AS+YtLQ0flI7mece/f39qKurE4ylOp0OJSUl9L1KoiIRgtjjeTwevqFusiwxgfIFgcA2ZQqKX4wx7N27F3fffbdgI89zzz2Hr371qzQGEkIIGSs5bwZIVFAgmxBCJtDe3o4NGzagsbGRt6nVatTU1GDVqlURe59AULuxsRGbN29GTU3NZalWxxKLxfjkJz+JiooKVFRU8IX/ZF0MnE26u7tRX18v2LmelZUFk8k05WDRVFIIp6Sk8IVlSuM3uwRL7bxgwQLk5ubSmDJLMcbQ3d3NA1NjN9UEo1ar+fiRmpo66Wu3trYKSmyIRCIUFxfzDVqERJLX64XVakVvby9vS0tLQ1lZ2axOeR0tQ0NDfO4x2WlLuVzOT2prNJqkmXv09fWhrq5OMJczGAwoKipKmp+RxJdEDGKP5/P54Ha7+WntsX8/wSiVSr6hjjYkxQ/GGA4cOIDPfOYzgmwdP/7xj/Hkk0/SGEgIIWQ8WnQiYaNANiGEBHHy5El86lOfwvnz53nb4sWLsXPnTuTm5kbtfRlj8Pv9aG5u5ie1xwbSxxOJRLj22mthsVhQUVGBhQsX8naSWIKditXpdCguLr7iFJSjo6OCFMI+n2/Ca2UyGQ9KJdPCMhFijOHUqVM4deqUoH3ZsmXIycmhMYQAENa2dDgcGBgYCHl9VlYWHz/GLywHq08skUhQWloKjUYTlf6T2W1kZARWq1VQkzUjIwNms3nSTRfkyk3ltGWkyxfESm9vL6xWqyAIRxlOSDQlQxB7vMCGusCmmMnKF2RkZPCT2smc6SHeMcbw/vvv49Of/rRgvvjUU0/he9/7Ho2BhBBCgqEvbRI2CmQTQkgQIyMjWLNmDd59910AwDXXXIOtW7fO6ImxQFD76NGjPKhdV1cX8jmf+MQnYLFYYLFYsHjxYohEIrqZTwDnzp3DkSNHBG3Z2dlYuXJlxG/6AyceAimEQ9XFDdSmMxgM0Gq1VNMxSTDGcOTIEbS3twva8/PzkZ2dHaNekUTQ39/PF5bHnnANJiMjgwe109LS0NjYiM7OTv7vMpkMJpMJSqUy2t0ms9Dw8DDq6uowODjI2xQKBcxm85QznJAr5/P54HK5+GnLUHMPkUgEjUbDT1smSgrhnp4eWK1Wwc82d+5cFBQU0FycREUyBrHHG1u+wOl0Tjr3SNZMD/GOMYYPP/wQlZWVghIT3/72t/H000/T/wMhhJCJ0CSZhI0C2YQQMoGuri5cf/31yM/Px2uvvYb09PSY9SUQ1D516hSqq6tRXV2Nf/7znyGfU1JSwoPaK1asoKB2HGKM4cSJEzh9+rSgfcmSJViyZEnU/7/G1sV1OBwh0/gly2mp2c7n86G5uVlwKlYsFqOoqIhSO5MpCaQQttvt6O7uDnmtSCQSLLSnpKTAbDYjMzMzyr0ks9Hg4CDq6uoEafGVSiVMJhNkMlkMe0aAS3OPrq4uvilmbA3VYFQqFQ9MxXIuHkp3dzesVqsg4828efOQn59Pc28SFcGC2Hl5eZg/f34MexV9w8PDfENMZ2cnQq1nBu5d9Ho9dDodjf9RwhhDXV0dysvLBRsNnnjiCfzkJz+hjdCEEEJCoYkyCRsFsgkhJASHwwGdThdXu4gDQe2zZ8+iuroaNTU1OHjwYMgb+fz8fFgsFlRWViIvLw9isZgW1mLM7/ejpaUFFy9e5G0ikQh5eXkxORXr9/t5XVy73Q6PxzPhtWKxGFqtlp+WooWhxOD1etHQ0CCoWyqVSlFaWgq1Wh3DnpFE5/F4+IaYyRaWAWDOnDmYN28e1Gp1XH2/ksTX39+Puro6wXeYRqNBSUkJbcCKQ4wx9Pb28tOWk5UvyMzM5EFthUIRF3PZzs5O1NfXC4LY8+fPx8qVK+OifyT5XLx4EU1NTYK22RDEHs/r9fIsU5OVThKJRFCr1Xz8oPISkcEYQ319PcrLywX3F4899hief/55CmITQgiZDE2WSdgokE0IIQmMMQbGGC5cuMBPar/33nshb+RXrFiBiooKVFZWori4mILaMeD1ei9LsyuRSFBcXAydThfDnl3CGENPTw/sdjscDofgVNt4Y1OAGgwGStkap0ZGRmCz2QTp/uhULIkGr9cLl8uFjo4OuN3ukNdS+QISScHqE+t0OhQXF9NnK0EEUgiHU74gNTWVb6hTq9Uxmcu63W7U19cL6n8vXLiQZ0IiJNIoiB1cIMtU4LT2ZJkeFAoFHz8yMzPp73UaGGNoaWnB+vXrBfO9Rx55BC+++CJtHiOEEBIO+gImYaNANiGEJBG/3w+Hw4Ha2lpUVVXhnXfeCZkuevHixTyoXVZWRkHtGTA8PAybzYb+/n7eJpfLYTKZkJWVFcOeBccYQ19fHw9qj603GoxareZBbTrtEB8GBwdhtVoxNDTE29LT02E2m5GWlhbDnpFk1dfXB6vVKjgVOz69+HhisVhQvoAyPZCp6O7uhs1mE9QnNhqNKCwspFP/CSqQQtjhcKCrqyvk+CGTyQR1cWdi44LL5UJDQ4MgiJ2Tk4Nly5bRXJpEBQWxwxPI9BAYPybL9JCWlga9Xg+9Xg+VSkXfGWFgjOHo0aNYt26doFzRAw88gJdffpnmcIQQQsJFk2YSNgpkE0JIkvL7/XC73di2bRuqq6uxd+/ekLvT58+fj4qKCmzatAlXX301JBIJLcRFWH9/P2w2m+CEcyIFFBljGBgY4EHtscH4YJRKJQwGA4xGY0L8fMko2AlFpVKJ0tJSOj1PoiJYQFGn06GwsJCnEHY4HCHLF4zN9KDX65GSkjITXScJKtipWKpPnFwCmR4CKYTH/l+PJ5FIePmTaG2KcTqdaGhoEATXFy9ejKVLl9JnjkQFBbGnb3BwkJcv6O7uDnmtTCbjm+ooU0xwjDEcP34c69evR0dHB2//3Oc+h1deeYWC2IQQQqaCJs4kbBTIJoSQWSBQ/3j79u2oqanB7t27Q56snTt3LjZu3IjKykrccMMNkEqltDB3hTo7O9HQ0CAI7iR6QHEqKUAVCgWMRiMMBgMyMjJmqIezm9vtRkNDg6DUgFarRUlJCS3MkagIdkJxzpw5KCgoEJxwCpQvCIwfY7MFBKNSqXimB9oUQ8YKFlBcsGABcnNzad6SpHw+H9xuN08hHCrzUKAubmBTTCQyxdjtdjQ1NQk+c0uXLsWSJUuu+LUJCYaC2JHj8Xj4Se3Ozs6Qm2LEYjG0Wi0/rZ2o92uRxBhDW1sb1q1bh/b2dt5+11134bXXXqPfESGEkKmiGzYSNgpkE0LILOP3+9HX14edO3eiqqoKO3fuDHmyVq/XY+PGjaioqMCnPvUpyOVyWhyeoosXL6K5uVmw6KnX61FUVJQ0AcWhoSEelJrstENGRgYPalNduugI9pmbO3cu8vPzKWUiiYpgn7lwAoqMMfT39/PxY7JMD4G6loHxg8xewT5zdCp2dgls1Aycthyb8SYYpVLJU5BPZ1NdR0cHWlpaBJ+55cuXIycnZ8qvRUg4KIgdPYFNMYFMD6E2xQCXNtUFxo/09PQZ6mX8YIzh7NmzWLduHc6cOcPbN23ahNdff52y5xBCCJkOumkjYaNANiGEzGKBVNG7d+9GVVUV3nrrLfT09Ex4vVqtxoYNG2CxWHDLLbcgJSWFFosncebMGRw7dkzQNn/+fKxcuTJpf3cjIyM8KDVZXcu0tDQe1M7Kykra38lMOnv2LI4ePSpoW7RoEZYvX06/XxIV586dw5EjRwRt0w0oDgwM8NNSob6PgEulGQJBbRo/Zpfz58+jtbVV0LZs2TIsXrw4Rj0iscYYQ19fHw9qT7YpJiMjg5/UDmf8uHDhAlpaWgRtubm5WLhw4RX3nZBgOjo60NzcLGijIHZ0BDbFBOYfk22Kmer4kegYYzh//jzWrVuHU6dO8faNGzfizTffjEi2C0IIIbNScn+BkoiiQDYhhBAAl25Qh4aGsH//fmzZsgXbt2+H2+2e8PqsrCysXbsWlZWVuO2225Cenp70N/FTwRjDsWPHcPbsWUH7smXLkJOTM2t+V2NT+Lnd7pBB7dTUVB6UUqlUs+Z3FCmMMZw8eRJtbW2CdjotRqKFMYbTp0/jxIkTgvYVK1Zg0aJFV/z6w8PDPCg12aaY1NRUflJKrVbT+JHEgm3WoYAiGW8qm2JSUlJ4UEqtVl+WuaS9vR2HDx8WtK1cuRILFiyIeL8JASiIHUtjM8U4nU709fWFvD4lJUUw/0jGzEcdHR1Yv369YHP2mjVrsGXLFioZRQgh5ErQTTsJGwWyCSGEXIYxhpGREbzzzjuoqqrC1q1b4XA4Jrw+MzMTq1evhsViwZo1a2Z9umifz4eWlhbY7XbeJhKJUFBQgLlz58awZ7Hl9Xrhcrl4Cr9QdenkcjkPaifrolAk+f1+HD58GBcuXOBtIpEI+fn5mDdvXgx7RpIVYwzHjx8XpJeM5mduKptiZDIZHz80Gg2NH0mkra3tso0TBQUFNM6RkEZGRgR1cUONH1KplAeltFotLly4cFnGifz8fGRnZ0e722SWoiB2fBkaGoLT6QxrU51UKoVWq4XBYIBOp4NUKp3BnkaHw+HAhg0bBFlQbrnlFtTW1lKJF0IIIVdq9i4ckymjQDYhhJCQGGPweDz4xz/+gaqqKtTW1gqCZeOlpaXhtttuQ2VlJdasWTPrTtZ6vV7U19cL6kRLJBKUlJRAq9XGrmNxxufzweVywW63w+VywefzTXitTCYTLCpTUErI5/OhqakJTqeTt4nFYpSUlECn08WwZyRZBds4IRaLUVRUBIPBEPX3Hx0dFWyKCTV+SCQSwfiRDIvKsxFjDCdOnMDp06d5m0gkQlFREYxGY+w6RhJOYFOd0+mcdPwQiUSXBa1o4wSJJgpix7epjh8ajQZ6vR56vT4h0287nU6Ul5cL6rSvWrUK27ZtQ1ZWVgx7RgghJEnMnsVicsUokE0IISRsjDF4vV4cOnQIVVVVqKmpuSx19lhyuRy33HILKisrsW7dOmi12qQOag8NDcFms2FgYIC3yeVymM1mKBSKGPYsvvl8PnR2dsJut8PpdGJ0dHTCa6VSKXQ6HYxGI7RaLSQSyQz2NP4E2zghk8lgMpmgVCpj1zGStHw+H5qbmwVZOiQSCUpLS6HRaGLSn87OTp4C1Ov1TnitWCzmJ6X0ej1kMtkM9pRMF2MMR48exblz53ibWCxGcXEx9Hp9DHtGEl1g/Aic1g41fgCX6uLOnz8fBoMhIYNSJL5REDuxjB0/nE4nPB5PyOuzsrL4/CMjIyPu74m7urpQXl4Om83G26677jrs2LEDKpUqdh0jhBCSTOL7y5DEFQpkE0IImRbGGHw+Hz7++GNUVVWhuroaJ0+enPB6qVSKm266CRUVFSgvL+eLz/F+Ex+uvr4+2Gw2jIyM8LaMjAyYTCakpaXFsGeJxe/3o6uriwe1Qy0KicViHtROlvR9UzE8PAybzYb+/n7elpqaCrPZTPXqSFSMjo6ioaEBnZ2dvC2eNk74/X50d3fD4XDA4XAIxuPxRCIR1Go1T0GekpIygz0l4Qp2+j+WGydI8mKM8fHjwoULITfVAZeCUoFsD4kQlCLxjYLYiY0xhp6eHh7UHrupOZi0tDQe1I7H7GU9PT2wWCz4+OOPedtVV12FnTt3UoYxQgghkRRfX4AkrlEgmxBCyBULBLUbGhqwefNm1NTUXFZPcCyJRIJPfvKTqKioQEVFBa8bHW838eFyu91oaGgQpJdTqVQoLS2lE39XgDGGrq6usIJSYrEYGo0GRqNxVpy0HBgYgNVqxfDwMG/LyMiA2WymU2IkKjweD2w2G3p7e3lbSkoKzGZzXNZIZIyht7cXDocDdrsdQ0NDIa9XKpU8qJ2enj5DvSSh+P1+NDc3w2638zapVAqTyUSnwUhUMMZw8uRJtLW1Tel56enpPCilVCoTdj5LYoOC2MlnYGCAZ3ro6ekJee3YEkoajSbm2ab6+vqwadMmHDp0iLeZTCbs3r2bsqBEWF9fHw4ePIj29na4XC4oFArMmzcPhYWFWLFiRcTex+v14v3338fZs2fR0dGBzMxMzJs3DyaTCTk5ORF7H0IImQaaNJOwUSCbEEJIRDHG+OLzli1bUFNTI6irNZ5IJMK1116LyspKVFRUYMGCBbw9EXR0dKClpUVQQ9FoNKKgoCDmCxHJJBCUstvtcDgcIYNSgZOWgaB2sp207Onpgc1mE6RApY0TJJqGh4dhtVoFJ4zS09NhNpsTIuMEYwwDAwM800NfX1/I6zMzM3lQOzMzM2G+j5KJz+dDY2MjXC4Xb5PJZDCbzVSXk0QFYwzHjx/HmTNneJtYLEZJSQnS09N5UGpsKY9g5HI5Hz/UajXEYnGUe04SGQWxk9/IyAg/qd3Z2Qm/3z/htYFsU3q9HjqdDnK5fAZ7eikAf+edd+K9997jbYWFhdi3bx+MRuOM9iWZvf/++3j66aexf//+CbN/lJSU4JFHHsHDDz887Xmo0+nE97//fbzxxhuCbEpjXX/99Xj88cdxxx13TOs9CCHkCtGNNgkbBbIJIYRETSCofeTIEV5T22q1hnzOVVddBYvFAovFgpycHIhEorgMIjDGcPr0aZw4cULQvnDhQqxYsSIu+5wsGGPo7+/nQe3J0vepVCoYjcakqGnpcrnQ0NAgWATT6/UoKiqijRMkKoKd/lcoFDCbzTO+wBopg4ODPNPDZCel0tLS+KYYOmk5M0ZHR1FfX4+uri7eFs+n/0niY4zh2LFjOHv2LG8Ti8UoLS29LI2ux+PhQW23241Q6ylSqRQ6nQ4GgwFarXbWlUAhoVEQe/YZHR2F2+3mge3JShio1Wp+WjvaGwcHBwfx2c9+Fm+//TZvy8vLw759+zBv3ryovvds4fV68eUvfxkvv/xy2M9ZtWoV/vrXv/LN/uHauXMn7r//fjgcjrCuv/fee/Hyyy9TeSpCyEyjm2sSNgpkE0IImRGBoPbJkydRXV2N6upqfPjhhyGfU1payoPay5cvj5ugNmMMR48exblz5wTtK1aswKJFi2LUq9mrv7+fB6UmO2mZyOmDL1y4gNbWVsGi+bx585CXl0cnvkhU9PX1wWq1CmrVJ9vp/5GRET5+dHV1hQxKpaSk8PFDpVLR310UeL1e2Gw2wQaDtLQ0mM3mhBuzSWJgjOHIkSNob2/nbRKJBCaTCWq1OuRzA0Eph8MBl8sVMigVKIESSEGeqBuBSGQEC2Ln5+cjOzs7Rj0iM83v96O7uxsOhwNOp1OwYTCYQLYYvV4PhUIR0Xvi4eFh3HPPPdi9ezdvW758Ofbv34/58+fHxf13ohsdHcXGjRsFv2PgUraZa665BvPnz8fAwAAaGhoEm6oAIDc3F++//37Y9ckPHDiANWvWCObvIpEIZrMZS5YsQXd3N2w2myDrDQCUl5ejpqaG5reEkJlEXzAkbBTIJoQQMuMCQe0zZ86gpqYG1dXVeP/990MGEAoKCmCxWFBZWYm8vLyYBbV9Ph+am5sFu5tFIhEKCwsxZ86cGe8PEZrKSUuFQiFIHxzPTp8+jePHjwvaFi9ejKVLl9LiEomKrq4u1NfXCwIzOp0OxcXFSXv63+v1Ck5ahkr/GW81LZOBx+OB1WoVbEhKT09HWVlZwmfTIPGJMYbW1lZcuHCBt023Drvf70dnZycPSo0NIASjUqn4HCQRSjSQyKEgNhmPMYa+vj4+B+nv7w95fWpqKvR6PfR6/RWXMBgZGcF9992H7du387YlS5Zg3759PDsauXJf//rX8fzzzwvaHnvsMTz11FOXbZras2cPHn30UZw6dYq3rVq1CgcOHJj0/6O9vR3FxcWCrDY33HAD/vCHPyAvL4+3jYyM4OWXX8YTTzwhKFf1rW99C88888y0fkZCCJkG+pIhYaNANiGEkJhijIExhvPnz/OT2v/4xz/g8/kmfE5ubi7Ky8uxadMmFBUVQSwWz8hNtsfjQX19vSBAKpVKUVJSAo1GE/X3J1MzPDwMh8MBu90+aU3LjIwMGAwGGI3GuKqJG6xmJ3Dpb2DhwoUx6hVJdsFS2M+ZMwcFBQWz5pSGz+eDy+UK66SlRCLh6YN1Oh2lD56GYHXYMzMzUVZWRidXSVT4/X60traio6ODt0mlUpjNZiiVyit6bcYYenp6+Ma6oaGhkNcrFAq+MSae5iAk8iiITcIxNDTEg9rd3d1RK2Hg8Xjw4IMPorq6mrctWrQI+/bto82yEXT48GEUFhYK5tXPPfccHn/88Qmfc/HiRdxwww2CYPbrr7+Ou+++O+R7PfTQQ3jllVf44+uvvx779++fcENgTU0NNm3axB+npKTg6NGjlGWOEDJT6IuGhI0C2YQQQuJGIKhtt9tRW1uLqqoqHDhwQLBLeLwlS5agoqIClZWVMJvNUQtqDw0NwWq1YnBwkLdRzc7EMTIyAqfTCbvdPmn64LS0NB7UzsrKitkiTrBFdjr9T6Kto6MDLS0tgr+RBQsWIDc3d9YuaI49aelwOEJ+J4lEImi1WkofPAVDQ0Ooq6sTBPuUSiVMJlPSpLAn8cXv96O5uRl2u523yWQymM1mZGVlRfS9GGMYGBgIuwRKYA6i1+uhUqlm7bibjCiITabD4/HA5XLB6XTC5XKFzBYzMDCA3/zmN1i7di0+85nPhNz06vV68cUvfhH/9//+X96WnZ2N/fv3Y8WKFTT2RND999+Pv/zlL/zxbbfdhr179076vHfffRc33XQTf7x48WIcP358wixAx48fR15eHj8QIJfL0dzcjOXLl0+pfw888IAgGE4IIVFEXzYkbBTIJoQQErf8fj9cLhe2bduG6upq7Nu3DyMjIxNev2DBAh7UvvrqqyGRSCJyE97Y2Ai32y04kZeZmQmTyUTpThNQIH2w3W6H2+0OuyauWq2esUUdn8+HhoYGuN1u3iaRSFBSUhJ2fTRCpurcuXM4cuSIoG3JkiVYsmQJLWj+D8YYr2npcDgmrWmpVqv5GELfF5cbGBhAXV2d4LtdrVajtLSUTraTqPD7/WhqahKUiJHL5TCbzVAoFFF//6mctJTL5Tx9sFarnTUZMZIRBbFJJPh8PkEJg/Eb695991384he/AHBpY11eXh7WrFmDO++8E6Wlpfy60dFRfOlLX8Jf//pX3jZnzhzs37+fl/AikcEYg8FgENSj3rVrF9asWRPW86+++mp8/PHH/PG+fftw6623Br32Bz/4AZ566in++O6778brr78+6XscPnwY+fn5/HFGRgZcLhfNWwkhM4G+cEjYKJBNyDh333033njjDUHbokWLcPr06dh0iBAC4NLCY1dXF7Zv346amhrs3r07ZKrGuXPnory8HBaLBTfccAOkUum0bso3b96Mhx9+GOvXr8d9990HANBoNCguLqaTYklgdHQULpcLdrt90lMOgQVlo9F4xfXoQgmWwj5aJ8UIAS4tsrW1teHkyZOCdkphH1qgpmUgqD02LXYwWVlZPKidkZExQ72MX319fairqxMsxCd7HXYSW36/Hw0NDYKAglwuR1lZWUyy6wROWjocDrjd7pBzECphkLgoiE2iIVgJg5/97Gd4//33g14/f/583H777bBYLKitrcWrr77K/02v12Pfvn0oKiqiIHaEtbS0oLCwkD+Wy+Xo7e1FSkpKWM//r//6Lzz99NP88SOPPILf/e53Qa8tLS1FQ0MDf7x7926sXr06rPe59tpr8eGHH/LHtbW1qKioCOu5hBByBehLh4SNAtmEjLF161ZYLJbL2imQTUh88fv96O3txc6dO1FVVYVdu3ahv79/wuv1ej3Ky8tRUVGBT33qU5DJZGHdpL/44ov49re/zRcWv/jFL+LBBx+cVXViZxOfzwe3282D2qFq4kqlUh7U1mg0EQu6DA0NwWazCQJiaWlpMJlMFPgiUcEYw7Fjx3D27FneJhKJkJ+fj3nz5sWwZ4mnv7+fZ3uYLH1wRkYGL2EwG2vidnd3w2azCcZZg8GAoqIi+n4lUREs00lKSgrKysri4vvV5/Px9MFOpzPkHEQkEkGj0fAU5OEGRMjMoyA2mQmBEgbPP/88amtrL8uuM55CoeDzFI1Gg71798JkMs26uchM2L17N9auXcsfFxQUXDYmhPLmm2/irrvu4o8XLFggmLMHXLx4EXPnzuWPpVIpuru7w/5+++Y3v4lnn32WPw4VMCeEkAiiLx4SNgpkE/I/uru7UVBQgAsXLlz2bxTIJiR+BW7cd+3ahaqqKuzYsUNwknU8jUaDDRs2wGKx4Oabb0ZKSsplN+1+vx/f/OY38Zvf/EbQ/uCDD+KFF16gm/xZwO/3w+12T5i6b6zAKSmj0QidTjftoHZ/fz+sVqsgxW5mZibMZjMtUpOo8Pv9OHz4sGDuIxaLUVRUBIPBEMOeJb6hoSF+Sqq7uzvktWlpaXxjjFKpTPrvmM7OTtTX1/MajsClLCr5+fkUxCZR4fP5UF9fj87OTt6WmpqKsrIypKenx7BnwQWyEAVSkIcqqwMAKpUKer0eBoMhLn+e2YqC2CRW2trasHnzZuzYsQNWqzXkxpj09HSsX78elZWVWL9+PdRq9Qz2NPm9/vrruOeee/jjG264AQcPHgz7+Xv37r3sVHV3dzeUSqWgbd++fbj99tv5Y7PZjLq6urDfZ/yhnhtvvBHvvfde2M8nhJBpSu4bXxJRFMgm5H889NBDeOWVVwAId6gCFMgmJFEwxjA0NIR9+/Zhy5Yt2L59u2DRcrysrCysW7cOlZWVuO2225CWlgav14sHHngANTU1/DqRSITvfOc7+Na3vjUDPwWJN4EF5UBQyuPxTHitWCwWpP4MN/18V1cX6uvrBQtNarUaJSUllMKeRIXP50NTUxOcTidvk0gkKC0thUajiWHPks/IyAgPSHV2dk5aEzeQfjyaJQxixel0orGxUZBCecGCBcjNzU36AD6JjdHRUdhsNsGGkrS0NJSVlSEtLS12HQsTYwy9vb18Y91kJQwyMzN5UFuhUNDfVYxQEJvEi87OTlRVVeFvf/sbmpqaQpbmkkql+NSnPgWLxQKLxULlZSJgy5YtuPPOO/njT3ziE4Ka15PZvn07ysvLBW0ffPABrrvuOkHbCy+8gK985Sv88V133XVZycRQxqdA12q1gjIchBASJTRRJWGjQDYhEO5elEql+NnPfoavfe1r/N8pkE1I4mGMYXh4GO+88w6qqqqwdetWQcBmvMzMTKxZswZtbW2wWq28XS6X45e//CU+//nPz0S3SZxjjKG7u5sHtYeHhye8ViQSQavV8tSfcrk86HUOhwNNTU2CwI7BYEBhYSHViSVRMTo6ioaGBsFGH6rDPjO8Xi+vietyuULWxA2UMDAYDNBqtQk/HtjtdjQ1NQkC+Tk5OVi2bBkF20hUeL1e2Gw2Qaae9PR0lJWVITU1NYY9m76BgQE+B+nt7Q15bWpqKp+DqFSqpNsYE68oiE3iCWMM//t//2/8+Mc/hkQiwaJFi9De3g6lUhny3hi4dKrXYrGgsrKS6mdP04EDB3DzzTfzx9nZ2Whvbw/7+X/4wx/wxS9+UdD26quv4v777xe0PfbYY3jxxRf54yeeeAI/+9nPwn6f/v5+KBQKQVtnZyed0CeERBt9sZCwUSCbzHoDAwMoLCzkgeonn3wS69atE0w2KZBNSGJjjMHj8eC9995DdXU1ampq0NHRcdl1aWlpgl3qmZmZ+P3vf4+Kigq6cSeXGXtKym63hzzhIBKJoFar+UnLQKrw8+fPo7W1VXDt/PnzsXLlSvrMkajweDyw2WyCAEhqairMZnNc1ImdTXw+n6CEQajUn9PN9hAvLly4gJaWFkHb0qVLsXjxYhrrSFR4vV5YrVbBWJeRkYGysrKkKdcxPDzMsz10dXWFzPYgk8n4xhiNRpPwG2PiVbCxjoLYJFYYY/jZz36GH/zgB7wtPT0dtbW1uOWWW2C1WlFTU4OamprLPrfj5eTkoLKyEhaLBZ/85CchlUqj3f2kML52NQCcO3cO8+fPD+v5Dz74IF599VVB2y9/+UvB6WsAuO+++/Daa6/xxz/96U/xjW98Y0p9TUtLE2zSbmtrQ05OzpRegxBCpohuBEnYKJBNZr2xOxeXLFmC5uZmfPjhhxTIJiRJMcbg9XrxwQcfoKqqCjU1NTh37txl1wVOv/X09OCWW26BxWLB+vXrodFoaNGdXIYxhv7+fh7Uniz1p0qlglQqvSxl25IlS7BkyRL6jJGoGB4ehtVqFXw+09PTYTabEyLFbjKbSgkDkUgEjUbDN8ZMlO0hXpw7dw5HjhwRtOXm5lLKUhI1Ho8HVqtVUCoqMzMTZWVlcf/3Ml1TyfYgkUh4xphE3BgTryiITeIJYwy/+tWv8J3vfIe3paamorq6GmvWrLnsXuPEiROora1FbW0t3n///ZBjiEajwcaNG1FZWYkNGzYk7bgaKStXrsTRo0f542eeeSaskmUDAwPIzs4WZBUBgKeffhrf/e53BW2f/vSnUV1dzR+/+OKL+M///M8p9VOj0aCrq4s/bmxsRFFR0ZRegxBCpogWvkjYKJBNZrUPPvgAN954I5+k7927F7fddttl6X8okE1IcmKM4dChQ9iwYYOgdqJCoYBYLL7splEmk+Gmm25CRUUFNm7cCL1eDwAUdCSXCaT+tNvtgoX0UJYuXYolS5ZEuWdkthoYGIDVahWctFAoFDCbzbQAGWcYY+jp6eFB7VDZHoBLG2MCQe1425DQ1taGEydOCNoosEOiaWRkBFarFf39/bxtto11gWwPTqcTTqcTXq93wmvHZozR6/UJm3I91iiITeIJYwwvvfQSnnjiCd4ml8uxZcsWbNiwYdJ7V6fTie3bt6OmpgZ79uyZsJRSWloaXC4X0tPTI9r/ZPOjH/0I3/ve9/hjlUqF5ubmSceHxx9/HL/4xS8ua3/yySfx7LPPCtpWr16NvXv38sd/+MMf8O///u9T6md2djYuXLjAHwerxU0IIRFGi6kkbBTIJrPWyMgISkpK+M7Iz3/+8/jzn/8M4PI6NhTIJiQ5bd++HXfddZcgSHDttdfi2muvxc6dOwU7p8eTSCS48cYbUVFRgYqKCsyZMwcABbXJ5YaGhnhQe/zmiPEyMzNhMBhgNBqRkZFBnycSEX19fbBarYJTvmq1GqWlpZQaMs4Fsj3Y7XY4nU5BcC4YhULBg9qZmZkz1MvLMcZw8uRJtLW18TaRSITCwkL+fUlIpAXLOpGVlQWz2TxrTx37/X50d3fzEgYTBaQClEolT0FO5SbCEyyIXVBQgHnz5sWoR2Q2Y4zhj3/8I7761a/yNplMhjfeeAOVlZVTvrcYGBjA3r17UVNTg+3bt8PtdvN/q6ysFJwCJsF1d3cjJydHcB9YVFSEnTt3ThjMfv755/HEE08ELRnxzW9+Ez/+8Y8FbeMD2X/84x/x0EMPTamf8+fPx/nz5/ljCmQTQmYALXiRsNHKFZm1nnrqKR6k0uv1eO6552LcI0LITPr973+PRx99VJA27fOf/zz+8Ic/QCqV4uc//zmam5uxefNm1NTUoLm5WfB8n8+HAwcO4MCBA/j617+O6667DpWVlaioqOA1rygISYBLpxUWLVqE7Oxs2Gw2wen/8fr7+9Hf349Tp04hPT2dB7UVCgV9nsi0dHV1ob6+XlB/WafTobi4mGqkJgCRSASFQgGFQoFly5bxbA8Oh0NQ+zegr68PfX19OHnyJDIyMnhQeybHEMYYjh07hrNnzwp+juLiYhgMhhnpA5l9hoeH8a9//UuwOVGlUsFkMs3qDTtisRgajQYajQa5ubno6+vjQe1gG2N6enrQ09ODEydO8DFEr9cjKyuL5iFBUBCbxBPGGF577TV87Wtf421SqRR//etfpxXEBoCMjAxUVlaisrISo6OjeP/991FbW4uamhpUVlZGsPfJS6VS4ZVXXsEdd9zB25qampCXl4dHHnkE69atw7x58zA0NIT6+nr86U9/wsGDB/m18+fPR3t7u+D1xhu/eXGybD7BjH9OLDdEEkIIIePRiWwyK1mtVlxzzTV8Ufdvf/sb7rnnHv7vdCKbkOTFGMN//dd/4Uc/+pGg/bvf/S5++MMfXnaDzxiD3+/H4cOHeU1tm80W8j2uvvpqWCwWWCwWLFq0CCKRiBb/Zrlg9TplMhkWLFiAnp4edHZ2Bt1xH5CamsqD2kqlkj5PJCxOpxONjY2CDTtz585Ffn4+xGJxDHtGImF4eJgHtcfWNAwmMIYYDAaoVKqojSGMMbS2tgpSU4rFYpSWlkKr1UblPQkZGhpCXV2dYBGesk5MbmBgAE6nEw6HY9KMMSkpKTyorVar6TsEFMQm8YUxhr///e/4whe+wO8pxGIx/vu//xv33HNPxL/3A/fItCkyfC+88AK+9rWvhaw/Pt5jjz2Gnp4e/OUvf+FtL730Eh5++GHBdffddx9ee+01/vinP/0pvvGNb0ypf+np6YLv0VOnTmHx4sVTeg1CCJkiWtgiYaNANpl1RkdH8YlPfAINDQ0AgLVr12Lnzp2CayiQTUhy8nq9+MIXviC4ERSLxfjtb3972c1gMIEb9hMnTqC6uhrV1dX46KOPQj7HZDKhoqIClZWVWLZsGQW1Z6HBwUFYrVbBwkB6ejrMZjOvZ+v1evlistvtDrnAEVhMDgSkaDGZBNPR0YGWlhbBBokFCxYgNzeXxqAk5PF4BGNIqHs8mUzGxxCNRhOxMcTv96O5uRl2u523SaVSmEymoKeHCImEwcFB1NXVCVJmazQalJaWUoBlCkZGRvgYMtnmOqlUytOPa7XaWfl7piA2iSeMMWzZsgUPPvggfD4fgEuZUP70pz/h/vvvp3lfHNmzZw8ee+yxkCXMgEunoZ999lk8+uijWL16Nfbt28f/raamBhaLRXD9l7/8Zfz617/mj7/xjW/gpz/9adj96u/vh0KhELR1dnZCrVaH/RqEEDIN9AVFwkaBbDLr/OhHP8L3vvc9AJfSJDU3NyMnJ0dwDQWyCUk+fX19uPPOO7Fnzx7elpaWhjfeeAPl5eVTfr1AUPvMmTM8qP3BBx+EXPgrLCxERUUFNm3ahJUrV1JQexYIVps4KysLJpMJcrk86HNGR0fhcrngcDjgcrn4glQw0QpIkcR27tw5HDlyRNC2ZMkSLFmyhMacWWAqY4hUKoVOp4PBYIBOp5t2QMrn86GxsREul4u3yWQymM1mZGVlTes1CZnMwMAA6urqMDIywtu0Wi1KSkpmZXA1UrxeL1wuF5xO56RjiFgshlar5ae1Z0Mtcgpik3jCGMPWrVtx3333CcrIvPTSS/jiF79I87445PP5UFtbix07duCDDz6A3W5HX18fDAYDFi9ejMrKStx7772YM2cOgEsbUcemFm9vb7+stvYLL7yAr3zlK/zxXXfdhTfeeCPsPrW0tKCwsJA/1mg0gnrohBASJfQlRcJGgWwyq7S2tsJsNvPFjueff15QPyiAAtmEJJeOjg5s2LBBkBJcp9PhrbfewtVXX33Fr88YA2MM58+f5+nH33vvvZCnaleuXIny8nJs2rQJhYWFEIvFtNCQZDo7O9HQ0CBYVNJoNCgpKQk71anP54Pb7eb1LMe+1nh0QoowxtDW1oaTJ08K2nNzc7Fw4cIY9YrEks/nQ2dnJx9DvF7vhNdONyDl8/lQX1+Pzs5O3iaXy1FWVkb1FUnU9Pf3o66uTrBRTK/Xo7i4mDZ1RVBgDAmc1g41hohEIqjVaj4XSU1NncGezgwKYpN4whjDzp07cc899wj+Nl944QV86UtforEwCZw7d04wh8/OzhYEtQP27t2L1atX88dmsxl1dXVhv8+2bdtQUVHBH3/yk5/EP/7xj2n2mhBCwkaLoCRsFMgms4bf78cNN9yAf/7znwCAsrIyfPjhh0EX+imQTUjyOHLkCNauXYszZ87wtqVLl2LXrl1YtmxZxN8vENS+ePEiamtrUV1djXfeeSdkAHLp0qU8/bjJZKKgdhKw2+1oamoSnNCfM2cOCgoKpr2o5Pf7eUBqssVkiUQiOGVJNUKTH2MMx44dw9mzZ3mbSCRCQUEB5s6dG8OekXjh9/vR3d3Nx5Cxp1jHCwSkAhkfUlJSgl7n9Xphs9kE9XVTU1NRVlaG9PT0iP8MhACXsp3U1dUJvgcNBgOKiooocBNFjDE+hjidTkHJlGAUCgUfQzIyMhJ+bktBbBJPGGPYu3cv7r77bsH3+XPPPYevfvWrNBYmiT//+c944IEH+ON7770Xf/3rXy+7rqOjQzAWSaVS9PT0hD0X++Y3v4lnn32WP37kkUfwu9/97gp6TgghYUnsySGZURTIJrPGL37xCzz++OMALk3qPv74Y5SWlga9lgLZhCSP+++/X1AT+6qrrsL27dthMBhm5P39fj9cLhe2bduGqqoq7Nu3T3B6aLyFCxeioqICFosFV199NSQSScIv/M02wdI6L1y4ECtWrIjY/+VUAlKzMe3nbOP3+9Ha2oqOjg7eJhaLUVxcDL1eH8OekXjFGENvby8cDgfsdvukASmlUskDUoFFUY/HA6vVir6+Pn5deno6ysrKkvIkJokPvb29sFqtgiD2lW4UI1PHGEN/fz+fh/T394e8Pj09nc9DlEplws1tKYhN4gljDAcOHMCdd96J4eFh3v7jH/8YTz75JI2FSeTGG2/EwYMH+eN33nkHN910U9BrS0pK0NjYyB/v3r1bcEo7lGuvvRYffvghf1xbWys4oU0IIVGSWBNCElMUyCazwqlTp1BUVITBwUEAwJNPPinYbTgeBbIJSR6Dg4O49dZb8c9//hMbNmzAG2+8gYyMjJj0xe/3o6urC9u2bUNNTQ327NkTMngwb948lJeXw2Kx4Prrr4dUKk24hb/ZhDGGU6dO4dSpU4L2ZcuWIScnJ2r/d4wx9PT08IDU2AWt8UQiETQaDQ9ITVSnmyQOn8+HpqYmOJ1O3iaRSFBaWgqNRhPDnpFEMdWAVGZmJjQazWUnMjMzM2E2myc8vU3Ilerp6YHVahVkuZk7dy4KCgpofhRjQ0NDfAzp7u4Oea1cLufzELVaHfdBNwpik3jCGMPBgwdxxx13YGBggLf/4Ac/wHe/+924/3si4Tt48CBuvPFG/jg3N/eyzdJjPfXUU/jBD37AH9999914/fXXJ32fI0eOIC8vjz/OyMiAy+WiTYmEkJlAE3gSNgpkk6THGMNtt92Gt99+GwCwZMkSNDc3Iy0tbcLnUCCbkOTicrnw4osv4nvf+17cpFj2+/3o7e3Fjh07UF1djZ07dwoWI8YzGAzYuHEjLBYLVq1aBZlMRou2cYQxhsOHD+P8+fO8TSQSIS8vD9nZ2TPaj76+Ph7UDmzgmsjY1MG0WJF4RkdHUV9fj66uLt4mk8lgNpuRlZUVw56RRDY4OMgDUmNThoeSnp6Oq666ijbHkKjp6uqCzWaDz+fjbdnZ2cjLy6P5UJzxeDy8pnZnZyf8fv+E10qlUl4KRavVxs08PYCC2CSeMMbw4YcforKyUpAN5dvf/jaefvppCmInkcHBQVx99dWC8Wfz5s244447JnzO8ePHkZeXx78n5XI5mpubsXz58pDv9cADD+DPf/6z4PErr7xyZT8AIYSEhybxJGwUyCZJ7/e//z0efvhh/njv3r247bbbQj6HAtmEkJkUCD7u3r0bVVVV2LFjB3p7eye8XqPRYMOGDaisrMRNN92ElJQUWsSNIZ/Ph+bmZjgcDt4WD2mdGWMYGBiA3W4P65RlIHWw0WgMudmLxAePxwObzSYYK1JTU2E2m2OWdYIkn+HhYR6Q6urqQqh7x5SUFL4xRqVS0YI6iZjOzk7U19cLgtgLFixAbm4uzX/i3OjoKNxuNxwOB1wul+A0/XhisZhnjdHr9THfGENBbBJPGGP417/+hYqKCsHc74knnsBPfvITSCSSGPaOTGZ0dDTsjTr9/f0oLy/HgQMHeNsdd9yBzZs3T/rchx56SBCEvv7667F///4JNyzX1taisrKSP5bL5Th27BgWLVoUVl8JIeQK0USehI0C2STpzZ07FxcvXgQArF+/Hr/5zW8mfc4///lP/Nu//Rt/nJ2dLahLA1xK+Rvrm2tCSPJhjGFwcBD79u1DVVUVtm/fjs7OzgmvVyqVWLduHSorK3HrrbciLS2NFnVnkNfrRUNDg+BErFQqhclkgkqlil3HghgcHORB7VAbJQBAoVDAaDTCYDBQUDQODQ8Pw2q1CrI4ZGRkwGw208l6EjXBTsRORCaTQa/Xw2AwQKPR0AI7mTa32436+nrBqd6FCxdixYoVNN9JMH6/H52dnXA4HHA6nfB4PCGvV6lUfHPMTG+woyA2iSeMMdTX12Pjxo2C1P2PPfYYnn/+efqOTQC/+c1vsGXLFtx3333YsGFD0M3O/f392LJlC77zne8Isnzl5OTgww8/hMFgmPR92tvbUVxcLLg3veGGG/DHP/4RK1eu5G0jIyP4/e9/j69//evwer28/Vvf+haeeeaZ6f6YhBAyVTSZJ2GjQDZJeiqVKuy0iFNhs9lQWloa8dclhJAAxhiGh4fx9ttvo7q6GrW1tXC5XBNer1AosGbNGlgsFqxevRoZGRm0yBtFIyMjsFqtgpPOKSkpMJvNyMzMjGHPJjeVWpYZGRk8qJ2ZmUmfqRgbGBiA1WoV1EJXKBQwm820wY5ETbDaxFlZWUhLS4PL5QoZ3JZIJDx1sE6ni7vUwSR+OZ1ONDY2CoLYOTk5WLZsGX0XJTjGGHp6evhcZGhoKOT1CoWCb46J9lzk/PnzaG1tFbRREJvECmMMLS0tWL9+PdxuN29/5JFH8OKLL9J3aoL49a9/jS9/+csALpWfWrx4MXJzc6FWqzE4OIiLFy/CarVetsFn8eLF2LNnD5YtWxb2ex04cABr1qwRvJZIJEJZWRmWLFnC53ROp1PwvI0bN6KmpoY2RhBCZhJN6EnYKJBNkh4FsgkhyYAxBo/Hg/feew9VVVWoqanh2SaCSU9Px+23346KigqsXbsWSqWSFn0jKFgwMVFPxI6MjPCF5MlSB6elpfGgdlZWFn2mZlhvby+sVqvg5IRarUZpaSktZJKoCZbWee7cucjPz4dYLBacsnQ4HILP53jxljqYxC+Hw4HGxkbBd9KSJUuwZMkS+u5JMoFSKIExZGzt32DS0tJ4UFulUkX080BBbBJPGGM4evQo1q1bJyhh9OCDD+Lll1+muV8CGRvIDldFRQX++Mc/TqtU1Y4dO3D//fdfFqyeyL/927/hD3/4A2XiIoTMNJrUk7BRIJskPQpkE0KSDWMMXq8XH3zwAbZs2YKamhq0t7dPeH1KSgpuvfVWWCwWrFu3DhqNhhaBr0BPTw9sNpsgWKNUKlFaWprwQRmPx8Pr4brd7pBB7dTUVEE9XPpMRVdXVxfq6+sFJ2L1ej2Kioro5ASJGpfLhYaGBsGJ2Pnz52PlypVB/+YZY+jq6uLjyNjNPuOJRCJB6uBE2wREosdut6OpqUnwHbRs2TIsXrw4hr0iM2VoaIiPId3d3SHnInK5HHq9Hnq9HlqtFmKxeNrvS0FsEk8YYzh+/DjWr1+Pjo4O3v65z30Or7zyCmQyWQx7R6aqvr4ezzzzDPbt2ydI+z2eVCrF7bffjscffxy33XbbFb2nw+HA97//fbzxxhsTvue1116LJ554AnfccccVvRchhEwTLSKRsFEgm5AgDhw4gJtvvpk/XrRoEU6fPh27DhFCyAQYYxgdHcVHH32EqqoqVFdXo62tbcLrZTIZbrrpJlgsFl6fiwKQ4XO73WhoaBCcTNTpdCguLk66YKLX64XL5YLD4YDL5RIEssaTy+U8GKVWq69oIZlcLlh63bEnYgmJhmDBxEWLFmH58uVhfW8wxtDb28tPWQ4ODoa8XqlU8lOWdCJo9uro6EBzc7Ogbfny5cjJyYlNh0hMeTwePhdxu90h5yJXUsaAgtgknjDG0NbWhnXr1gk2K3/2s5/Ff//3fyf8xtnZjDGGY8eOobW1Fe3t7ejt7eUb+1asWIFrrrkGCoUiou/p8Xjw/vvv48yZM7h48SIyMjKQnZ0Nk8lEG8QIIbFGi5EkbBTIJiQICmQTQhIRYww+nw82mw2bN29GTU0Njh07NuH1EokEN954IywWC8rLyzFnzhwAoMD2BDo6OtDS0iII6syWYKLP5+MLyU6nM2Q9XJlMxoNRV3o6igT/3C1cuBArVqygv1USNRcuXEBra2tE0zr39/eHnTo4MzOTb46Jdj1cEj8uXLiAlpYWQVtubi4WLlwYox6ReOLz+eB2u/lcZGyGkvFEIpGgjEFKSsqE1wYLYhcWFmLu3LkR6zsh4WKM4ezZs1i7di3Onj3L2z/96U/j//yf/xPys0wIIYQkGLrJI2GjQDYhQVAgmxCS6AJB7ebmZmzZsgXV1dWXLQ6PJRaLcd1116GyshIVFRXIzs4GQEHtgLNnz+Lo0aOCtpycHCxbtmzW/Y58Pp+gHm6ohWSpVCo4HZVsp9ajLdjnbunSpVi8ePGs+9yRmXPu3DkcOXJE0LZixQosWrQoYu8xNDTEx5Du7u6Q16alpfGgtlKppM9+kmpvb8fhw4cFbXl5eZg/f36MekTimd/vF5QxGBkZCXm9Uqnk40h6ejpvpyA2iSeMMZw/fx7r1q3DqVOnePvGjRvx5ptvUgkOQgghyYZu7EjYKJBNSBAUyCaEJBPGGPx+Pw4fPszTj9fX14d8zjXXXAOLxYKKigosWrQIIpFoVgYPGGM4ceLEZd8BkQ7qJKrAQrLdbofT6YTH45nwWrFYzIPaer1+Sik/ZxvGGE6dOiVYxAToZCKJvtOnT+P48eOCtmgHE0dGRngwqrOzc9J6uFTGIPkE2zyRn5/PN9UREsrYMgZOpxMDAwMhr8/MzIRerweAy8rxUBCbxFJHRwfWr18vyKi1Zs0abNmyhUpuEEIISUazb5GRTBsFsgkJggLZhJBkFQhqnzhxgge1P/7445DPMZvNqKiogMVi4SeQZ0NQOxD8v3DhAm8TiUQoKCigRc4gGGPo7u6G3W6f9HSUSCSCVquF0WiEXq+HTCabwZ7Gt0DtvLHpJOlzR6It2OaJWHzuvF4vL2PgcrlC1sOVSqWCMgaU8SExnTlz5rIyKBRMJFdiYGCAZ3zo7e0N+3lUE5vEkt1ux4YNGwSZKW655RbU1tYiMzMzhj0jhBBCoib5FxZJxFAgm5AgKJBNCJkNAkHt06dPo7q6GtXV1Th06FDI03BFRUWoqKjApk2bkJubm7RBbZ/Ph8bGRrhcLt4mkUhQXFwMnU4Xw54lhsDpqEBQe2hoaMJrRSIR1Go1D2rP5tp/fr8fra2t6Ojo4G1isRjFxcX89BghkTbR5oni4mIYDIaY9Wsq9XDHZnzQ6XS0OSZBtLW14cSJE/yxSCRCYWEh5syZE8NekWQyPDzMMz50dXWFnOPKZDK+OUaj0dDmGDJjnE4nNm7ciObmZt62atUqbNu2DVlZWTHsGSGEEBJVybeYSKKGAtmEBEGBbELIbMMYA2MM7e3tqKqqQk1NDf7xj3+EPA23cuVKHtQuKCiAWCxOiqC21+tFfX29oG6rTCaDyWSCUqmMXccSFGMM/f39PKg9WcpPlUoFo9EIg8Ewq2oB+nw+NDU1wel08japVIrS0lKo1eoY9owkM8YYDh8+jPPnz/M2sViMkpKSuNq0M5UyBiKRCBqNhqcgl8vlM9hTEo6JMgDEevMESW5erxfHjh0TZNqZiEQigVarpc0xJOq6urpQXl4Om83G266//nq89dZbUKlUsesYIYQQEn2Jv4BIZgwFsgkhhBAiEAhqX7x4ETU1NaiursaBAwdCnoZbunQpLBYLKisrUVpamrBB7eHhYVitVkGwNTU1FWazmWrTRUh/fz9P+dnX1xfy2qysLB7UTk9Pn6EezrzR0VHU19ejq6uLt8lkMpjNZjqJQ6LG7/ejpaUFFy9e5G0SiQQmkymuN08wxtDT08M3xwwPD4e8PrA5Rq/XIy0tbYZ6SSbCGMPJkycFtYlFIhFKSkoo8wSJqvPnz6O1tVXQplKpMDAwAK/XO+HzApljDAYD9Hr9rNpkR6Krp6cHFotFUObpqquuws6dO6HVamPYM0IIIWRGJN6iIYkZCmQTQgghJCS/3w+n04lt27ahqqoK+/fvD3kabuHChbBYLLBYLLjqqqsgkUgSIqjd398Pm80mCIpkZmbCZDLRomWUDA4O8qB2T09PyGszMzN5UDuZagV6PB5YrVZBUJ82T5Bo8/v9aGxsFGQASMTME4wx9PX18XFksowPCoWCjyP09zXzGGM4fvw4zpw5w9vEYjFKS0spaEOiKlgQO1CL3e/3o7u7m5cxmGxzjFKp5CnIaRwh09XX14dNmzbh0KFDvM1kMmH37t20qYcQQshsEf8LhSRuUCCbEEIIIWELpHfdtm0bqqursWfPnpALftnZ2SgvL4fFYsF1110HqVQal0Ht7u5u1NfXC07kqFQqlJaWUjrJGTI8PMyDUWNPJgeTkZHB0wYrFIq4/EyFY3h4GHV1dRgcHORtGRkZMJvNtHmCRI3P50NDQwPcbjdvk8vlMJvNUCgUMezZlRsYGODjSG9vb8hrk2UcSRSMMRw9ehTnzp3jbWKxGCaTCRqNJoY9I8kuVBB7vLGbY5xOJ/r7+0O+dkZGBg9qZ2Vl0ThCwtLf348777wT//jHP3hbUVER9u7dC6PRGMOeEUIIITOKJk4kbBTIJoQQQsi0+P1+9PT0YMeOHaiursauXbtCnoYzGo3YuHEjLBYLbrzxRshksrhY8HM6nWhsbBTUA9fr9SgqKoJEIolhz2Yvj8fDg1GdnZ0INV9NS0vjwSilUhkXn6lwDAwMwGq1CjaCZGVlwWQyUU1fEjWjo6Ow2Wzo7u7mbcmaAWAqm2NSU1P5OKJSqRJmHEkUwWqxJ0Iae5L4phLEDmYqmWNSUlJ4+nG1Wg2xWDztfpPkNTg4iM9+9rN4++23eVteXh727duHefPmxbBnhBBCyIyjmy4SNgpkE0IIIeSK+f1+9Pf3Y9euXaiqqsLOnTtDnobTarXYsGEDLBYLbr75Zsjl8pgEDi5cuIDW1lZBoDQ7OxsrV66kBcg44fV64XQ6Ybfb0dnZKdhwMF5gEdlgMECtVsdtMKq3txdWq1WQAUCtVqO0tBRSqTSGPSPJzOPxwGazCcbmtLQ0lJWVJX3taI/HA6fTCYfDAbfbHXJzjFwu5ycsNRoNfRdcIcYYWltbceHCBd4mlUphMpmgUqli1zGS9K40iD3eyMgIH0cm22QnlUr5OKLVamljJAFwaYPVPffcg927d/O2FStWYP/+/Zg/f34Me0YIIYTERHwu2JC4RIFsQkhSOXLkCBoaGtDe3o6hoSF+wmbZsmUoKSlJutNGhMQjxhgGBwexd+9eVFVVYfv27SFPwymVSqxfvx6VlZW49dZbkZqaOiMByNOnT+P48eOCtsWLF2Pp0qVxGwCd7UZHR+FyuWC32+FyuUIGtQPBKKPRGFcnozo7O1FfXw+fz8fbKAMAibaRkRFYrVZBmtyMjAyUlZUhJSUlhj2beYFxxOFwwOVyCf4Wx5NKpdDpdDAYDNDpdPQ3OkV+vx8tLS24ePEib5NKpTCbzf8/e/cdGEWZuA/82U2yyaaXLZTQe4CQAoqIKD0gyQZED3tBED0VRfD0zjvbnadnQ6yIongKZyGb0JFQVJCWRu+97m6yKaRnd+f3h7+8XyZls4GUTfJ8/ntf3pl5QzYzs/PM+74tai12annOnz+PQ4cOyequJ8SuqqKiAjk5OS6dR5RKJcLCwsR5hLOutE1lZWW4//77sXr1alHXvXt3pKamomvXrvzuQUREbREvfuQyBtlE1OLl5eXhgw8+wOLFi3H27Nla23l4eCAqKgpTp07FCy+80IQ9JGq7JElCaWkpNm7cCKPRiBUrViA7O7vW9gEBAYiLi4PBYMDYsWPh5+fX4A92JEnC0aNHq50v+vbti06dOjXosajx2O125OTkiFDbZrPV2rZyZJRer0doaGizhVE1TWPfvn17REREuE3QTq1PSUkJ0tPTUVJSIuoCAgIQExPT5gOVyvOIxWKBxWKRzZJQ1dVhlFarhZeXVxP2tOVxOBzYv38/TCaTqPPy8kJsbGyLX4ud3Ftjh9hV2e12WK1WMVrb2XlEoVAgODhYnEda+2wY9Ify8nI88sgjMBqNoq5Lly5ITU3lC7RERNSW8QJILmOQTUQt2o8//ojHH38cOTk5Lm+j1+tlI0OIqGlIkoSysjL8+uuvSEpKQkpKitO/RV9fX4wbNw4JCQmIi4tDYGDgdT/oKSsrw4IFCzBo0CCxL4VCgYEDB0Kv11/Xvqn5OBwOMTKqrjDKw8MDGo0Ger2+SUdYXrp0CQcOHJBNRdq5c2f07t2bDzCp0dS0FntwcDCioqIYxFbhcDiQm5srziNlZWW1tlUoFAgNDRVTB7e1Ue11cTgc2Lt3LywWi6hTqVSIjY2Fv79/M/aMWrumDrGrkiQJeXl54jxy9QtENQkICBBLojTGy5vU/CoqKjBz5kz88MMPoi48PBypqam8ByQioraOF0FyGYNsImqxXn31VbzyyivV6iuDAa1Wi9LSUly6dAn79u1DUVERAAbZRO5AkiRUVFRg27ZtWL58OVJSUnD+/Pla2/v4+GD06NEwGAyYMGHCNa1/nJeXh8mTJ2PXrl247777MHXqVDFTQ2ho6PX+SOQmHA4H8vLyYDKZYDabUV5eXmvbyhGWlaF2YwV7Z8+exZEjR2R1PXr0QLdu3fgAkxpNYWEh0tPTZX8DoaGhiIqK4hTZdZAkCfn5+TCbzTCbzXWGUZUjLHU6XZsfYWm327F3717Z7CsqlQqDBw/mEj/UqJo7xK5KkiQUFhaK88jVSzvUxNfXV4zUDgoK4v1BK2Cz2fDEE0/gu+++E3Xt2rXDxo0b0a9fP/6OiYioreOFkFzGIJuIWqR3330Xc+fOldXdfffdePHFFzFw4MBq7R0OB7Zv347ly5dj/fr1OHDgQFN1lYjqIEkSbDYbdu7cKULtU6dO1drey8sLI0eOhMFgwO233w6NRlPng6BLly5h0qRJOHz4sKh77rnnMHfuXAQGBjbYz0LupTKMqgy1rx6VWlXlCEu9Xg+tVtsgUy5LkoSTJ0/i5MmTsnpOY0+NLT8/H5mZmbLZCbgW+7WpbxjVlkdY2u127NmzRzZTkre3N2JjYxliU6NytxC7JiUlJeI8kpeX57StSqUSoXZoaCiXH2mB7HY7Zs+eja+++krU6XQ6bNiwAQMHDmxT1wYiIqJa8GJILmOQTUQtzp49ezB48GCxHqqXlxeWLl2KqVOnurS9zWaDp6dnY3aRiK6RJEmw2+1IT0/H8uXLkZycjGPHjtXa3tPTE7fccgsMBgPi4+PF9OBXPxw6evQo4uPjZSO+27dvj5SUFPTv37/xfhhyK5IkoaCgQDxELi4urrWtQqFASEiICKOuZdpgSZJw5MgRnDt3Trbf/v37u9WDdWp9cnNzkZmZCbvdLuratWuH/v37MwxpAMXFxeI8kp+f77Rt5QhLnU7XIMtjuDO73Y7MzEzk5uaKOh8fHwwePLjNj1KnxtUSQuyqysvLxZraVqsVDoej1raenp7QaDTQ6XQICwvj99gWwOFw4LnnnsPnn38u6jQaDdavX4/o6OhWfS0gIiKqB14QyWUMsomoRbHZbLjxxhuRkZEh6hYvXoyHH364GXtFRI2hMtTet28fli9fDqPRiIMHD9baXqlUYtiwYUhMTER8fDw6duyI3bt3Y+rUqbLRYT179sSqVas4IrYNq+8Iy/pOG+xwOHDw4EFcunRJ1CmVSkRGRkKr1V53/4lqk5OTg6ysLFko0rFjR05h2khKS0tFGJWbmwtn3629vb3FeSQ4OLhVvVRgs9mQmZkpG2WqVqsRGxvLEJsaVUsMsauy2WzIycmB2WxGdna2eFm7JkqlEqGhoWK0dkPMHkMNy+Fw4MUXX8RHH30k6kJCQrB+/XoMHjyY12IiIqL/w4siuYxBNhG1KMuWLcM999wjyqNHj0Zqamoz9oiImoIkSSIcTEpKgtFoxJ49e2ptr1AoMGLECOzevVs28jYmJgYpKSlcE5tkioqKYDabYTKZcOXKFadtAwMDZdMGV1XT+rCenp6IiopCSEhIg/edqJLZbMbevXtlYWrnzp3Ru3dvPjhvAuXl5cjOzobZbEZOTo7TEZZeXl7QarVihGVLDrUrKiqQmZkpG53u6+uL2NhY+Pj4NGPPqLVrDSF2VQ6HA1arFWazGRaLBeXl5U7b1/dFO2pcDocDr7zyCt59911RFxQUhLVr12Lo0KG8FhMREcnxwkguY5BNRC3Kbbfdhl9++UWUN27ciFGjRjVjj4ioqVWG2seOHROhdlpaWrV2SqVSFiQMGTIEH330ESIiIqBQKPgwiWpUuYalyWSqc9pgf39/6HQ66PV6+Pn5wW63IysrSza1rkqlQnR0NNdip0Z16dIlHDhwQBZid+/eHd27d+e5rhlUjrA0mUzIzs6WTfNelYeHh5g2WKPRtKhpgysqKpCeni57AcjPzw+xsbHXtCQDkatqCrEHDhyIdu3aNVOPGp4kScjPzxezPjhbEgUAAgICxAsy/v7+PPc3MUmS8K9//Qv//ve/RZ2/vz9Wr16NW265hb8PIiKi6nhxJJcxyCaiFuP48ePo1auXKHft2hUnT57kl0KiNqwy1D516hSMRiOSk5Px+++/V2sXERGBQ4cOQZIkREZGIiEhAZMnTxYjFXkeoZpUThtsMplk4XRN1Go17Ha7bPSUj48PYmJiahy5TdRQagp0evXqha5duzZPh0jG4XCIaYMtFgsqKipqbVs5bbBer4dGo3HraYPLy8uRnp4uW5rB398fsbGxbt1vavnaQohdlSRJYvYYs9lc5+wxarVahNrBwcG8z21kkiTh7bffxquvvirqfH19sWLFCowaNYr//0RERDXjBZJcxiCbiFqMRYsWYebMmaI8ffp0fPHFF83YIyJyJw6HA2+88Qb+/ve/y+r1ej1MJlON2/Tr10+E2hEREVAqlXzYRDUqLy8XobbVanW6Fi7wx3TiERER0Ol0/ExRozlz5gyOHj0qq+vbty86derUTD0iZxwOB/Ly8kQYVVZWVmtbhUKBkJAQsRauO03TXVZWhvT0dBQVFYm6gIAAxMbGwsvLqxl7Rq1dWwyxa1JSUiJGaufl5Tm9J1GpVNBqtdBqtS1+KQN3JEkS5s+fj5deeknU+fj4IDk5GePGjeM9IBERUe14kSSXMcgmohZjxowZsuD6iy++wPTp0yFJEjZu3IjvvvsOO3fuxIULF2Cz2aDRaNCrVy+MGTMG06ZN48gkolbM4XBg9uzZ+Oijj0SdUqnEv//9b/j5+cFoNGLLli1Op3ft2bMnDAYDDAYDoqKiGGpTrSoqKpCdnQ2TyVTnWrgqlUpMPx4cHMwHyNQgJEnCqVOncOLECVl9//790aFDh2bqFdWHJEkoKCgQoXZd0wYHBQWJtXB9fX2bqJfVlZaWIj09XdbfoKAgREdHM8SmRsUQu2bl5eXIzs6G2Wyu856kJS9l4I4kScKnn36KefPmiTqVSoXly5fj9ttv5/cIIiIi53ihJJcxyCaiFiM6OhpZWVmivG3bNnTo0AHTp0/Hpk2bnG7r5eWFGTNm4O23327Wh39E1PDKysrwwAMP4IcffhB13t7e+N///ofExEQAfwTdFosFK1asQFJSEjZu3Oh0etcuXbqIUHvIkCEMtalGBQUFSE9Ph81mc6m9l5cXtFot9Ho9QkNDGWrTNZEkCceOHcOZM2dEnUKhwMCBA6HX65uxZ3St6jttsL+/vwi1m3It3NLSUqSlpaGkpETUBQcHIzo6moEYNSqG2K6x2+2ypQyc3Z8oFAqEhoaKWR+4rn39SJKEL774As8884yo8/Lywvfff4/ExER+byAiIqobL5bkMgbZRNRitGvXTjY98IYNG3D33XcjOzvb5X1ER0dj9erVaN++fWN0kYiaWEFBASZPnix7mSUoKAgrVqzAiBEjatzG4XDAarVi5cqVMBqN2LBhA0pLS2s9Rnh4OOLj42EwGHDTTTfBw8ODD6cIVqsVWVlZslH+Go0G7du3R3Z2dp0PkD09PcX6lWFhYfDw8GiKblMLJ0kSDh8+jPPnz4s6pVKJQYMGQaPRNGPPqCGVlJSIUDsvL89pW7VaLULtoKCgRrs+lZSUIC0tTXa9DAkJQVRUFENsalQMsa+Nw+FAbm6umILc2VIGgPvM+tASSJKEb775Bk888YSo8/T0xHfffYc777yT3xOIiIhcwwsmuYxBNhG1GD4+PrIv4OHh4eJBrp+fH2bNmoUJEyYgPDwcRUVF2LNnDxYvXoytW7fK9nPTTTfhl19+4fSHRC2cyWTCxIkTkZGRIerat2+PdevWITIy0qV9OBwO5OfnY/Xq1TAajVi3bp3T6V3btWuHSZMmwWAwYPjw4fDy8uLDqjbIbDZj3759suk7O3TogH79+olR1pUvTFSGUc5mAOBUn+QKh8OBgwcP4tKlS6LOw8MDUVFRCA0NbcaeUWMqKysTQZTVaq1zLdzKICokJKTBZn0oLi5Genq6LMQODQ1FVFQUX8KhRlU1xFYoFBgwYABD7Hq6eikDi8UiW9++Jv7+/uJlu4CAAN7rXkWSJCxbtgwzZ84U52OlUolvvvkG99xzD/+viIiIXMeLJrmMQTYRtQhlZWXw8fGp8d8iIiKwbt06dOrUqcZ/f/fddzF37lxZ3euvv46XXnqpwftJRE3jxIkTGD9+vGx92N69e2P9+vXo2rXrNe3T4XDgypUrWLduHYxGI9asWeN0eleNRoPbb78dBoMBt912G1QqFR9etQEXL17EwYMHZWFS586d0bt371p//5IkITc3V4TazkZFKZVKhIWFiak++dIVAX+cn/bt2wez2SzqPD09ERMTg6CgoGbsGTWliooKsRZudna207VwG2rWh6KiIqSlpaG8vFzUaTQaREZGMsSmRnXu3DkcPnxYlBliN5zKpQwsFgvy8/OdtvXx8RH3JMHBwW16WRRJkvDTTz/hkUceEedfhUKBxYsX48EHH+T3ACIiovrhhZNcxiCbiFqE4uJi+Pn5VasPCgrCvn37ag2xK82ZMwfvv/++KIeGhuLMmTPw9/dv8L4SUePKzMzEhAkTZEsNDBkyBKtXr4ZWq22QY0iShOLiYvz8889ISkrCqlWrnE7vGhwcjIkTJyIxMRGjRo2Cj48PH2a1QmfPnsWRI0dkdT169EC3bt1c/n1LkoT8/HyYzWaYTCan09pfvX6lTqeDSqW6rv5Ty2S327Fnzx7k5OSIOi8vL8TGxiIgIKAZe0bNqT5r4Xp4eMhekHF11ofCwkKkp6fLQmytVovIyMg2HWZR42OI3XRKS0vFrA+5ublOZ33w8vKCVquFVqttc8uiSJKEFStW4IEHHpCdbxcuXIgZM2bwvp+IiKj+ePEklzHIJqIWw8PDo9rIk1dffRX/+Mc/6ty2qKgIHTt2lL1x/sMPP+DOO+9s8H4SUePZvHkzDAaDbKT0uHHjsHz58kZ7MUWSJJSUlGDTpk1ISkrCypUrkZ2dXWv7wMBAxMXFISEhAePGjYOvry8fbrVwkiTh5MmTOHnypKy+b9++db5IVdd+r1y5IkJtZ9PaA3+sR1sZatc2Swm1LjabDVlZWcjNzRV13t7eiI2NrfEFP2qbrl7KwGKxyILnqhQKBcLCwsRo7dpekLly5QrS09NlyyLo9XoMGDCAITY1KobYzac+sz5c/YKMRqNp1TPISJKEtWvX4p577pGdEz/88EM88cQTPCcSERFdGz4oI5cxyCaiFiMwMLDaNL+nTp1yeRrhRx55BF999ZUoP/HEE/j4448bsotE1Mhuv/12rFmzRpTvvfdeLF68uMlGqkqShLKyMvzyyy9ISkpCSkqKbGR4VX5+fhg3bhwSEhIwfvx4BAYGMtRuYSRJwpEjR3Du3DlRp1Ao0L9/f7Rv375Bj1VYWChC7cLCQqdtg4KCoNPpoNfroVarG7Qf5B4qKiqQkZGBgoICUadWqxEbG8vfOdVKkiTk5eWJpQyczfoA1PyCTH5+PjIyMmSjDtu1a4f+/fszsKFGxRDbfVTO+mCxWGCxWGQBblUKhUKcS7Rabat62U6SJPz888+4++67ZUvDvPvuu3jmmWd4TqRWa8uWLRg5cqQov/zyy3jllVear0NE1Brx4Ri5jEE2EbUYnTt3lgUJer0ely9fdnn7zz//HI899pgojxw5Eps2bWrQPhJR4yooKMDIkSORkZGBZ599Fu+8806zPUCSJAnl5eXYtm0bli9fjpSUFFy4cKHW9j4+PhgzZgwMBgPi4uIQEhLCUNvNORwOHDx4EJcuXRJ1SqUSkZGRDTaNfW2Ki4tFqH11kFmTgIAAEWpzlG7rUFZWhoyMDNkLDX5+foiJiWlVAQE1rqtnfTCbzSgqKnLaPjAwEAEBAbh8+TLsdruo79ChAyIiInjNokbFENt9ORwO5OXliSnI63pBJigoSMz60JLvSyRJwubNm3HnnXfKfuY333wT8+bNY4hNrRqDbCJqAvxyQS5jkE1ELcaYMWOwceNGUY6KikJmZqbL269atQrx8fGiHBkZiT179jRoH4mo8ZlMJhiNRjz22GNu81BdkiRUVFRg586dSEpKQnJyMk6fPl1re5VKhZEjR8JgMGDixInQaDRu87PQH+x2O/bu3SubRt7T0xNRUVEICQlp0r6UlpaKUNvZWu3AH2FnZajt7+/Pz1ULVFpaivT0dNlU8wEBAYiJieE66XRdioqKRKhd1wsylbgmNjUFhtgtx9UvyFgsljpnkPHz8xOhdkuamUiSJGzduhVTpkyRXY9fffVVvPTSSzwnUqvHIJuImkDLuCkgt8Agm4hajNmzZ2PBggWifOONN2LHjh0ub79hwwaMGzdOlHv37o0jR440aB+JiCRJgt1uR1pamgi1jx07Vmt7T09PjBgxAgaDAfHx8dDpdADQYh70tUYVFRXIysqShcYqlQoxMTEICAhovo7hj5G6lUFUbm4unN3Lq9Vq6PX6FvfwuC0rLi5Genq6bORXUFAQoqOjW/X6o9T0SkpKxOjKq9dgr4mPj4+Yfjw4OJjnEmpQDLFbtsoZZMxmM/Lz85229fb2FtOPh4SEuG0YLEkSduzYgcmTJ8uWNvvb3/6G1157zW37TdSQGGQTURPglwpyGYNsImoxvvzySzz66KOi3KdPH9lDj7r8+OOPuOuuu0T5pptuwu+//96gfSQiulplqL1v3z789NNPMBqNOHToUK3tlUolbr75ZhgMBiQkJKBDhw4AGGo3pfLycmRkZMgeXPr4+CAmJsbtpscsLy8XQVROTo7TUJtBlPsrLCxEeno6ysvLRV1oaCgGDRoET0/PZuwZtXaXL1/G/v37nZ5DKqlUKjG6MjQ0lIEOXZeaQuyBAwdCr9c3Y6/oWpWVlYn7EqvV6vSc4unpKc4lYWFh8PDwaMKe1k6SJKSlpSEhIUE2c8XcuXPx5ptvuk0/W5OSkhJkZWXh0KFDyM3NRWlpKQIDA6HT6RATE4OePXs2yH3rmTNnkJ6ejkuXLiE/Px9eXl4IDg5Gr169EBsb2+wvq7obBtlE1AT4UIJcxiCbiFqMixcvIjw8XHwh9vLyQkFBgctrRb788st47bXXRPnOO+/EDz/80Ch9JSKqSpIkOBwOHDhwAElJSTAajdi7d2+t7RUKBYYOHYqEhAQYDAZ07txZ1FPjKCkpQUZGhmwKyZayLrHNZhMPj7Ozs+FwOGptq1KpRKjtziOi2pKCggJkZGSgoqJC1Gk0GkRGRvKhOTUqi8WCPXv2yAInrVYLhUKBnJwc2VrZVVUGUVqtFhqNhp9VqheG2K2bzWZDdna2uC9xdi5RKpUICwuDTqeDRqNptmU0JElCVlYWJk2aJJuVZ/bs2Xj33Xd5jmtg27dvx/z585GcnCx7ia+qjh07Yvr06Zg9ezZCQ0PrdYzi4mJ89NFH+OKLL5zOkKVUKhEXF4fZs2fLZvFryxhkE1ET4MMtchmDbCJqUW6++WbZKOpVq1bh9ttvd2nbG2+8Ebt27RLlBQsW4KmnnmrwPhIR1aUy1D569KgItdPT051uM3jwYBFqd+/eHQqFgqF2AyoqKkJ6ejrKyspEXWBgIKKjo1vcusR2u108PLZYLE4fHnt5eclGRDHUbnp5eXnIzMyEzWYTdXq9HgMGDODvgxqV2WzG3r17ZSF29+7dxTXGbrcjJydHnEuu/oxWpVQqodFoRBDFqfDJGYbYbYvdbofVahUv3F390lZVCoUCwcHBYgpytVrdJH2UJAn79+/H7bffjpycHFH/+OOPY8GCBZwZpQHZbDY888wz+OSTT1yaCaSSXq/H119/jbi4OJfab9++Hffeey9OnTpVr/7dfffd+OKLL+Dr61uv7VobBtlE1AT4QItcxiCbiFqUL774AjNmzBDlUaNGYePGjXVu99tvv2HEiBGirFQqcfToUfTo0aNR+klE5KrKUPvkyZMwGo1ITk7G9u3bnW4zaNAgJCQkIDExEb1792aofZ3y8/ORmZkpe7DaWqZ0djgcsiDK2cNjDw8PEWpzdGXTyMnJQVZWlmwEfYcOHRAREcG/aWpUNU0n3rNnT3Tr1q3G9g6HA7m5uWItXGej5xQKBUJDQ0UQ5e3t3eD9p5aLIXbbJkkS8vLyxH1JSUmJ0/YBAQFiFhk/P79GuTZKkoQjR45gwoQJMJvNon769On47LPPWvy9oDuRJAl33XUXfvrpp2r/1rdvX/Tr1w9qtRoWiwVpaWnIzc2VtVGpVEhJSakzzN6+fTvGjRuHwsJCWX1AQACGDBkCvV6PsrIyHD9+HPv37682k9Ho0aOxZs2aFvcya0NikE1ETYBfeMllDLKJqEWx2+0YOHCgbI3Zd999F3PmzKl1G7PZjGHDhuHEiROibtq0aVi2bFmj9pWIqL4kSYIkSTh79qwItbdu3ep0muiIiAgkJCRg8uTJ6NevH5RKJQOwerBarcjKypKNWtbpdBg4cGCrGw1bnyDq6tGVWq2WD3EbgcViwd69e2V/3506dUKfPn34N0yN6tKlS9i/f7+srnfv3ujSpYtL20uShPz8fHEuqSuIqhxdqdPpmmx0Jbknhth0NUmSUFhYKELtK1euOG3v6+sr7kuCgoIa5FopSRKOHTuGiRMn4tKlS6L+/vvvx5dffsnZJRrYokWLMHPmTFndiBEj8PHHH2PAgAGyepvNhv/+97949tlnkZ+fL+p1Oh2OHj2KoKCgGo9RWlqKAQMGyJ7/qNVqvPnmm5g5c2a15YJOnDiBZ599FitXrpTVv/HGG3jxxRev6edsDeoKsh0OB3bs2IGTJ0/i4sWL8PHxQceOHTFixAhotdrrOrYkScjIyMDhw4dhNptRVlYGrVaLjh07Yvjw4fD397+u/ROR2+CXXnIZg2wianE2bNiAuLg42YPfp59+Gq+88gpCQkJkbVNTU/H444/j+PHjoi4kJATp6em1jjghInIHlaH2pUuXYDQaYTQa8csvvzidJrpXr14wGAwwGAwYNGgQQ+06mM1m7Nu3r9po2MoXAlqzq0dEmc1mlJaW1tpWoVCItSu1Wm2bHp3SUGoaDdutWzf06NGDf7PUqC5cuICDBw/K6vr06YPOnTtf0/6uDqLMZnO10W9VNcXoSnJPDLGpLiUlJeJccvUa1TVRqVRiFpnQ0NBrum+TJAmnTp3ChAkTcP78eVH/pz/9Cd988w3vdxpBt27dcPr0aVEeMWIEUlNTnb4wkJaWhuHDh8uW/3nzzTfxl7/8pcb2X3/9NR5++GFRVigUWLt2LcaPH1/rMWoaKR4YGAiLxdJmPwe1BdmlpaV4++23sWjRIpw7d67adh4eHhg3bhzeeecdRERE1OuY2dnZeOONN/Dtt9/CYrHU2EalUmHUqFF45ZVXcOONN9bvhyIid8MvAuQyBtlE1CJ99NFH1da39vLywtChQ9GxY0eUlJQgKysLZ86ckbVRqVRYsWKF0y8xRETuyOFwwGw2Y8WKFUhKSsKmTZucThPdtWtXMf344MGDGWpXcfHiRRw8eFAWJHbp0gW9evVqc/9PkiShoKAAZrMZJpPJ6ehKhUKBkJAQEURxyuD6qylIdDalM1FDOX/+vGxWIwDo168fwsPDG+wYRUVFYh3cq0fQ1aRydKVOp0NgYGCbO/e2JQyxqb7Ky8vFucRqtTqdncjT0xMajQZhYWFQq9XVXm6vSeUMSHFxcTh79qyonzJlCpYuXcr7m0awb98+REZGyuqysrIwaNCgOrd96qmn8NFHH4nyzTffjK1bt9bY9o477kBSUpIoJyYmwmg01nmMy5cvo3PnzrLvVxs2bMCYMWPq3LY1qinInjVrFiZMmICsrKw6t/fy8sKCBQswa9Ysl46XnJyMBx54oM6ZGa42c+ZMfPzxx5w5iqjl4s0/uYxBNhG1WJ9++inmzp2L4uJil9rr9XokJSVh2LBhjdwzIqLGVbnu8apVq5CUlIQNGzbIRilUFR4ejvj4eCQmJmLo0KHw8PBo04HB2bNnceTIEVldz5490bVr1zb9/wL83+hKk8kEs9mMoqIip+2Dg4Oh1+uh0+mqTdVI1dX02bue0bBErqrps9e/f3906NCh0Y5ZWloqgqjc3Fw4e/bg4+MjRlcGBwe3+lkx2hKG2HS9bDYbcnJyYDabkZ2dDZvNVmO7AwcO4JVXXsGQIUNw++23484770THjh2rtZMkCRcuXEBcXBxOnTol6uPj4/HDDz/wfqaRpKSkIDExUZQ7deoke4nAmdTUVIwdO1aUdTodTCZTjW2jo6NlQeuXX36JRx55xKXjDB8+HNu2bRPlTz75BI8//rhL27Y2VYPsF154AT///DMyMjIA/LEMUWxsLLp27Yry8nIcOnQIR48erbafzz77DI899pjTYy1evBgzZsyo9sJKjx49EBERAV9fX5w9exa7du2qNjtZXFwcVq5cyTCbqGVq2w9fqF4YZBNRi3bixAm88sorSElJqfXNzXbt2mHWrFl45plnal1HiYiopXI4HMjLy8Pq1auRnJyMdevWOX3Bp127doiPj4fBYMDw4cPh6enZZsJbSZJw4sQJ2UNLAOjbty86derUTL1yb0VFRSLUrmuERGBgoAi1fX19m6iHLcepU6dkS50Af6xxX9NDdqKGdPr0aRw7dkxWN2DAALRv377J+lBeXo7s7GyYTKY6R1d6eXmJUDssLIyhdgvGEJsamsPhQG5urpiCvLy8XPzb4sWLsWLFClFWKpUYMGAAJkyYgLvuugt9+/YFAFy6dAkTJkyQnRfj4uKwfPly3r80oqVLl+Lee+8V5Ztuugm///67S9seOXJE/P6AP64TV//ur9a7d2/Z73b9+vUYN26cS8e5++678b///U+U//Wvf+Gvf/2rS9u2NlWD7JCQEOTm5gL44//p7bffrnYPu3v3bjz++ONIT08XdSqVChkZGejfv3+Nx8nKysKNN94o+31GRUXh448/rjYIxWKx4O9//zsWLlwoq3/xxRfxxhtvXNsPSkTNqW08iKIGwSCbiFqFkpISbNu2DefPn8fly5fFmlmDBg2qNn0VEVFr5XA4cOXKFaxduxZGoxFr1651Gj5qtVrcfvvtMBgMuPXWW6FSqVptqC1JEo4cOSJby02hUGDAgAFo165dM/as5SguLhYPjuuaMtjf31+E2v7+/k3UQ/ckSRKOHz8uWxOSnz1qKlVfoHCHINFmsyE7O1uMrqw6uupqHh4e0Gg00Ov1CAsL44irFoQhNjU2SZKQn58Pi8UCk8mEBx54oNZRusAfoztHjhyJPXv2YPfu3aJ+9OjRSE5ObvP3K41t06ZNGD16tChHR0eL0b112bNnD6KiokRZr9fj8uXLNbYdMWIEfvvtN1FOSUlBQkKCS8eZPHkykpOTRfnTTz91eWrs1qZqkF3pueeewzvvvFPrdsXFxYiLi5P9DoYPHy4rXy02Nlb2ORg+fDjWr1/v9KWSd955B/PmzRNlpVKJrKwsDBw40OnPRERup3U+fKJGwSCbiIiIqBWSJAlFRUXYsGEDli9fjtWrVyMvL6/W9iEhIZg4cSISExMxcuRI+Pj4tJpQ2+Fw4MCBA7IHXkqlEoMGDYJGo2nGnrVcpaWlItSuHJ1RGz8/P7EObkBAQKv5XLmiphcolEolIiMjodVqm7Fn1NpJkoSTJ0/i5MmTok6hUCAyMhI6na4ZeyZnt9thtVphNpthsVhka5NWpVQqERYWBp1OB41GA5VK1YQ9pfpgiE1NzeFwIDMzEz/++CPWr19f4xTHNRk0aBA2b97s0rradH2uXLkCrVYrlkPy8fGB1WqFWq2uc9vFixdj+vTpojxp0iSsXLmyxrZ/+9vfZKNzX375Zbzyyisu9bFLly6y6c7T0tIQGxvr0ratTU1BdmxsLHbt2lXnTCnnz59Hv379UFhYKOrS09MRExMja7d582aMGjVKlAMDA3Ho0CGXlj2Jj4/HqlWrRPnhhx/G4sWL69yOiNxK23kwQNeNQTYRERFRKydJEkpKSrBp0yYsX74cq1atQnZ2dq3tAwMDERcXB4PBgLFjx8LX17fFho92ux179+6V/byenp6Ijo5GcHBw83WsFSkvLxehttVqdboOrlqtFqF2UFBQi/1cuUKSJBw8eBAXL14UdR4eHoiKikJoaGgz9oxau5pmAWgJL1BULpVReT6pDDtqolAoEBISAp1OB61WyzVt3UjV9dgZYlNzOH78OH744QesXbsWWVlZTpcz0Ol0SEhIQGJiIkaPHs3zSSOaOXMmFi1aJMoffvghnnzySafb2O12DBkyBJmZmaLuxx9/xNSpU2tsf+rUKfTt21dMVd2pUyccOXKkzsDcaDRiypQpoty/f3/s37+/zp+ptaopyF65ciUmTZrk0vbPPfcc3nvvPVF+6qmnsGDBAlmbhx56CEuWLBHl+kwRfuDAAQwYMECUfXx8kJeXB29vb5e2JyK30HofBlCDY5BNRERE1IZIkoSysjJs2bIFSUlJSElJgdlsrrW9n58fxo0bB4PBgPHjx7eoEbUVFRXIysqSjURXqVSIiYlBQEBA83WsFauoqIDFYoHZbEZOTo7TB8fe3t4i1A4JCWkxnytXOBwO7N+/XzbFKV+goKYgSRKOHj0qG1GmVCoRFRWFsLCwZuxZ/UiShIKCAhFqFxcXO20fFBQkzidc47b5MMQmd3Ts2DE89dRTMJlMOH36dK1rKwN/LI1SOUPRxIkTERQU1IQ9bf2sVitiY2PFi1ZqtRorVqzAmDFjamxfUVGBWbNmyUbajho1CqmpqU7vG9977z0899xzomwwGLB06dJarw+7d+9GXFwcrFYrgD+um6mpqTVOrd1W1LRGttlsdnmJj/T0dAwePFiUa5pKvmfPnjhx4oQoHzhwABERES73seq05Nu2bau2rjYRubXW8wCAGh2DbCIiIqI2SpIklJeXY+vWrVi+fDlSUlJko0erUqvVGD16NBITExEXF4fg4GC3DR/LysqQmZkpWyPcx8cHsbGxDDmaSH3WwfXy8hIhVGhoaJ1TFrqzmmYB8PLyQkxMDAIDA5uxZ9Ta1TaVfXR0dIueBaByqYzKUPvq83pN/P39xfnE39/fba9TrQ1DbHJHBQUFmDJlCrZv3w7gj+txeHg4hgwZgg0bNjhdHsXLy0vc9xoMBrRr166put2qnT59GlOmTBEjrJVKJaZOnYqpU6eib9++UKvVyM7Oxvbt27Fw4ULZeeWGG27AunXrXJoK/p133sFf//pXsWRFly5dMGvWLAwfPhx6vR7l5eU4duwYUlJS8N1334l2Xl5eWLRoER588MFG+OlbjqpB9pgxY7BhwwaXt7fZbAgICEBpaSmAP2YlKi4uFsuC5Obmyu5NgoODYbVa63XNfuqpp/DRRx+J8vz58zF79myXtyeiZsebdHIZg2wiIiIigiRJqKiowI4dO5CUlITk5GScOXOm1vYqlQojR44UI1bCwsLcJiwoKSlBRkaGbASfn58fYmJiOF1kM7Hb7cjJyRHr4Npstlrbenp6QqvVQqfTISwsDB4eHk3Y0+tjs9mQlZUlezDu7e2NmJgY+Pv7N2PPqLWTJAmHDh3ChQsXRJ2Hhweio6Nb3dqvJSUlItS+esaNmrSl5QyaE0NsckeFhYWYOnUqfvvtN1E3cOBAbNiwAXq9HhUVFfj111+RnJyM5ORknD9/vtZ9KRQK3HTTTUhMTMTkyZPRs2fPpvgRWq2Kigp8/fXX+Pzzz5Genu50WRoACAsLw5w5czBv3jx4eXm5fJxDhw7hnXfegdFodPrSQqUxY8bgrbfeqraWc1tUNch+4okn8PHHH9drH/369cPhw4dF+eLFi2jfvj0A4OjRo+jTp4/4tyFDhmDXrl312v/HH38sm5r+r3/9K/71r3/Vax9E1Kx4Y04uc20+ECIiIiJq1RQKBVQqFUaMGIFbbrkF77zzDnbv3i1C7ePHj8val5eXY/369Vi/fj08PT1x6623wmAwYNKkSdDpdGKfTa2wsBAZGRmytVUDAwMRHR0tRgBQ0/Pw8BBhksPhgNVqhclkgsViESNgKtlsNly6dAmXLl2Ch4cHNBoN9Ho9wsLCXJ7OsDlUVFQgMzMT+fn5oo6zAFBTkCQJBw4cwKVLl0Sdp6cnYmJiWuW0uGq1Gl26dEGXLl1QVlYmXpCxWq3VwpCSkhKcOXMGZ86cgbe3t3hJJiQkpEXP/OBOGGKTOyouLsbdd98tC7H79euH9evXi89m5Yjr0aNHY8GCBUhLS0NycjKMRiMOHTok258kSfj999/x+++/4/nnn8eAAQOQmJiIZ555pkUt2+Au7HY77HY7vL29oVAonAbZnTp1wmuvvYZp06bVK8QG/rinVCqVLm334IMPYu7cubJ1l+n/XMusQlXvQaxWqwiyq75YcC33KzXtn4iIWieOyCYiIrdQUlKCrKwsHDp0CLm5uSgtLUVgYCB0Oh1iYmLQs2dPjqIhagaSJImpkn/66SckJydXe7h3NaVSieHDhyMhIQEJCQno0KEDgKYJtfPz85GZmSkLRkNDQzFo0CC3DkDbMofDgby8PJhMJpjNZqfrViqVSoSFhUGv10Oj0dT7YWZjKi8vR0ZGhmzKY19fX8TGxnIWAGpUDocDBw4cwOXLl0VdW53KvqKiAhaLBWazGTk5OXA4HLW29fLyglarhVarbXEzP7gThtjkjkpLS3HPPfdg/fr1oq5Pnz5ITU1FeHi4S/s4cuSICLV37txZYxsvLy9kZ2e3uXPt9dq2bRvuu+8+sU62q7RaLd544w08+uijdbYtKyvD3Llz8fHHH9c52ruq+Ph4fPLJJy5/VlqrqiOyX3zxRbzxxhv12sfQoUNlfz9Xr4G9Y8cO3HTTTeLfxo4di59//rle+//2229x//33i/Ljjz+OTz75pF77IKJmxYe85DIG2URE1Ky2b9+O+fPnIzk52WmA0bFjR0yfPh2zZ89u0es8ErVkkiTB4XBg//79YqT23r17a22vUCgwdOhQGAwGJCQkoHPnzqK+oVmtVmRlZcnWYdbpdBg4cCBH3bUQkiQhPz9fhNqVa+rVRKFQIDQ0FHq9HlqttllH25eWliIjIwNFRUWizt/fH7GxsZwFgBqVw+HAvn37YDabRZ2XlxdiY2MREBDQjD1rfna7HdnZ2TCbzcjOzna6nEHlzA86nQ4ajYYvPrmIITa5o7KyMtx///1YvXq1qOvRowdSU1PRpUuXa7oHvXDhAlasWAGj0YjNmzeL80lcXBzWrl3bYH1vCzZu3IhJkybJ7vE6duyIp556CuPHj0e3bt3g6+sr7uuXLVuG7777TnYOnzFjBhYuXFjr79JmsyE+Ph7r1q0TdQqFApMnT8aDDz6IwYMHQ6PRoKysDGfPnsWmTZvw4Ycf4tixY6K9RqPBhg0bEBUV1fD/CS1EU08tfsMNN9T60khtOLU4UYvHIJtcxiCbiIiahc1mwzPPPINPPvmkXm9J6/V6fP3114iLi2vE3hFRXSpD7SNHjohQOz093ek2gwcPhsFggMFgQLdu3aBQKBok1N6+fTuKi4tlo+86dOiAfv36McRuoSRJQkFBgVgH9+r1zmsSEhIiQu2mHAFdUlKC9PR0lJSUiLqgoCBER0e71Yhxan0cDgf27t0Li8Ui6lQqFWJjY7keexWVyxlUTkHu7MVJhUKBsLAw6HS6Zn9Jxp3VFGJHRkaKpUWImkN5eTkefvhhJCcni7quXbsiNTUV3bt3b5B7ztzcXKxZswZGoxEJCQl44IEHrnufbYXFYkH//v1l1634+Hh8++23Tke179ixA5MmTUJOTo6oe/PNN/GXv/ylxvYvv/wyXnvtNVFWq9X46aefMHHixFqPUVZWhieeeAKLFy8WdR07dsSePXva7NTxVYPsMWPGYMOGDS5vb7PZEBAQIF5a8PDwQFFREby9vQH88bd09QCF4OBgl9Yxv9rTTz+NDz/8UJTnz5+P2bNn12sfRNSsGGSTyxhkExFRk5MkCXfddRd++umnav/Wt29f9OvXD2q1GhaLBWlpadW+0KhUKqSkpDDMJnITlaH2iRMnYDQakZycjB07djjdZtCgQSLU7t279zWH2h9++CH++te/4pFHHsGkSZMAAF26dEGvXr24HEErIUkSioqKxEjtwsJCp+2DgoKg1+uh0+mgVqsbrV9FRUVIT0+XrcceEhKCqKgojuikRlW53EN2drao8/b2RmxsLPz8/JqxZ+5PkiTk5eWJl2SczfwA/PE3rdPpoNPpuEzA/8cQm9xRRUUFZs6ciR9++EHUhYeHY+PGjbwndBMvvvgi3nzzTVHu27cvMjMzXTq3bty4EWPGjBFltVqN06dPVzvvWK1WdO7cWTZLzldffYWHHnqozmM4HA6MGjUKv/zyi6ibM2cO3n333Tq3bY2qBtkhISEwm80u3+Omp6dj8ODBohwdHY2MjAxZm549e+LEiROifPDgQfTr18/lPg4ePFj2IvW2bdswbNgwl7cnombHizO5jEE2ERE1uUWLFmHmzJmyuhEjRuDjjz/GgAEDZPU2mw3//e9/8eyzzyI/P1/U63Q6HD16FEFBQU3SZyJyTWWoffbsWRFqb9261enMCxERETAYDJg8eTL69evncqj92muv4a233hLlZ555Bo8++ii6du3KB5atWFFRkQihCgoKnLYNCAgQoXZDBnxXrlxBenq6bD12jUaDyMhIrrVLjcputyMrKwtWq1XU+fj4IDY2Fr6+vs3Ys5ZHkiRcuXJFnE+uDj5qEhgYKELttvrCAENsckc2mw2PP/44li5dKurat2+P1NRUcV9Jza9qaOlqwFxpxIgR+O2330T5gw8+wNNPPy1rU3XN5G7duuHEiRMufwY2bdqE0aNHi3JYWBgsFkub/AxVDbIBYOXKleLF4bo899xzeO+990T5qaeewoIFC2RtHnroISxZskSU//a3v+Gf//ynS/s/dOiQWG8b+ONeKC8vT4z4JqIWoe2dXOmaMcgmIqIm161bN5w+fVqUR4wYgdTUVKfTsKalpWH48OGykW/OphQjouYnSRIkScLFixdhNBphNBrx66+/ytaxrqp3795ISEhAYmIiIiMjoVQqqz08cjgcmDNnDhYtWiTqFAoFXnnlFcydO7fRfh5yPyUlJSKEysvLc9rW399fhFD+/v7X/FAyLy8PmZmZsvUauR47NQWbzYasrCzZTDU+Pj4YPHhwo84+0FYUFhbCYrG49JKMn5+fOJ8EBAS0iZCDITa5I7vdjqeffhpff/21qNPpdNiwYQMGDhzYJv42W4KioqJqy16cPXsWnTp1cnkfr732Gl5++WVRvueee/Ddd9/J2sybNw/vvPOOKD/yyCP48ssvXT6GzWaDn5+fbAmKo0ePolevXi7vo7WoKciOjY3Frl276rzfPX/+PPr16yebRSktLQ2xsbGydlVfHAgKCsLhw4fRrl27OvuXmJiIlJQUUX7ooYfw1Vdf1bkdEbkVXqTJZXzSQkRETWrfvn2yEBsAFixYUOdaooMHD8aMGTNkdStXrmzo7hFRA1IoFFAqlQgPD8dTTz2F1NRUnD9/Hp999hnGjRtX49/90aNH8c4772D48OEYNGgQ/va3v2HXrl2w2+2QJAl2ux2PPPKILMT28PDA/PnzGWK3QWq1Gl26dMGQIUMwYsQI9O3bF6GhoTU+uC4sLMTJkyexY8cO/P777zh27Bjy8/OdzhZQldVqRUZGhizEbt++PUNsanQ2mw2ZmZmyEFutVmPIkCEMsRuIv78/unXrhhtvvBHDhw9Hnz59EBwcXGPboqIinDp1Cjt37sTWrVtx5MgR5OXl1et80pIwxCZ35HA48Nxzz8lCbI1Gg3Xr1jHEdjM1vWzoSljprP3Vy2vUdpz6HsPT07Pamtg1HaetSk9Pr3MgQUlJCe655x5ZiH3zzTdXC7EBYNSoUYiKihLl/Px83H333SgpKXF6jPfff18WYisUCjz77LMu/hRERNQS8WkLERE1qZMnT8rKnTp1wqBBg1za1mAwyMrHjh1rsH4RUeNTKpVo164dHnvsMaxduxYXLlzAl19+iUmTJtU4DdypU6fwwQcfYOTIkYiIiMDcuXMxadIk/Pjjj6KNSqXC4sWL8eijjzblj0JuyNvbG506dUJsbCxGjBiBiIgIhIWF1fggu7i4GKdPn8auXbtcDqEsFgsyMzNlMwqEh4ejf//+DLGpUVVUVCAjI0P2gN7Pzw+DBw/mus2NRK1Wo3PnzhgyZAhuvfVW9OvXr9bzSWlpKc6ePYvdu3fj119/xcGDB5GTkwOHw9EMPW94DLHJHTkcDrzwwguyFxtDQkKwZs0aREVFMcR2MzW9FFTXcg5VXR2MAqg2wrum49T3GK4epy0KCQkBALzzzju49957ceHChWptdu/ejVtuuUU2BbxKpcJnn31W636//PJLqFQqUd6yZQtuueUW7Ny5s1rb7Oxs/PnPf8acOXNk9c8//zwiIyPr/TMREVHL4dncHSAioral6pfJ8PBwl7etOvXY1aOSiKhlUSqV0Gq1eOSRR/DQQw8hLy8Pq1atQnJyMtatW1ftTfzz589j2bJlyM/PF3V+fn5YsmQJ4uLimrr75OZUKhU6duyIjh07oqKiAtnZ2TCZTDWGS5Uh1NmzZ6FSqaDT6aDX6xEcHCwCapPJhH379smC7q5du6Jnz558WE6Nqry8HBkZGbhy5Yqo8/f3R0xMDNeBbCIqlQrh4eEIDw8X5xOz2Yzs7Oxq55Py8nJcuHABFy5cgKenJ7RaLXQ6HcLCwuDh4dFMP8G1Y4hN7sjhcODll1/Gxx9/LOqCgoKwevVqDB48mNdlN+Tn54fAwEDZsg2ZmZnVpq52Jj09XVauabR1hw4dZOXMzMx69fPYsWOy621tx2mLHnvsMaxbtw5ZWVlYunQp/ve//2HIkCHo0qULysvLcejQIdn1otKCBQswYMCAWvcbExODjz76CLNmzRLX1PT0dAwdOhQ9e/ZE//794ePjg3PnzmHXrl2yWZEAYOzYsXj99dcb9oclIiK3wyCbiIiaVNUvgqWlpS5vW7VtaGhog/SJiJqXUqlEaGgoHnjgAdx3330oKCjA2rVrYTQasXbtWjEy4uoQOzAwEH5+fnj88ccxadIkGAwGjBgxAiqVig8wScbLywvt27dH+/btYbPZkJOTA5PJhOzs7GrrtZeXl+P8+fM4f/48vLy8oNVq4enpibNnz8ra9ejRA926deNnjRpVeXk50tPTZaPDAgICEBMTIxu9RE3n6vOJ3W5HTk4OzGYzLBZLtYfrNpsNly5dwqVLl6BUKqHRaKDT6aDRaOpcUscdMMQmdyRJEv71r3/hvffeE3UBAQFYuXIlhg4dyuuyG7vtttuwYsUKUf78889dDrIvX74s2xYAbrnllhqPcbVt27bh4MGDiIiIcOk4CxculJX79OkDrVbr0ratnbe3N9asWYPx48dj3759cDgc2LlzZ40jp4E/rpcLFizAY489Vue+Z8yYgZCQEDz88MOye57jx4/j+PHjtW73yCOP4LPPPmsR11QiIro+nAOPiIia1JAhQ2QjiA4dOlTnGkiVqr6FPWTIkAbtGxE1P6VSieDgYNx99934/vvvsWfPHnTt2lXWxtfXFyqVCpcuXYLFYsFXX32FxMRE9OjRA7NmzcKaNWtQWlraatcqpWvn6ekJvV6PyMhI3HrrrYiKikL79u3h6Vn9/d6KigpcvHixWojds2dPdO/enQ/LqVGVlZUhLS1N9kA3MDAQsbGxDLHdhIeHB3Q6HQYMGIBbb70VMTExCA8Pr/H343A4YDabsX//fvzyyy/IyMjA+fPnUVZW1gw9r9uZM2cYYpPbkSQJ//nPf/Dmm2+KOj8/PyQnJ2P48OG8Lru5P/3pT7Ly999/j2+//bbO7crKynD//ffLrof+/v4YP358tbZRUVHo3bu3KNvtdtx///3VRlnXZN26dViwYIGsburUqXVu15a0b98eO3bswD/+8Q907NixxjYeHh6Ii4tDVlYWZs2a5fK+p06dihMnTmD27NnQaDS1tvPy8sK4ceOwbds2fPnllwyxiYjaCEUzPuDjk0UiojZq5syZsvXMPvzwQzz55JNOt7Hb7RgyZIhserAff/yRXy6JWrHTp09j7NixsjfxO3fujBtvvBGbNm1CTk5OrdsGBgZiwoQJMBgMGDNmDHx9ffmAk2rlcDiQm5sLk8kEs9mMioqKWttWjqzU6/XQaDQ1huBE16O0tBTp6ekoLi4WdUFBQYiOjuYD2xZAkiTk5+fDbDbDbDbX+cJmcHAwdDoddDod1Gp1E/WydmfOnMHRo0dFmSE2uQNJkjB//ny89NJLok6tViM5ORljx47lPV4L4HA4EBMTgz179og6hUKBp556Ci+88ALat29fbZvNmzdjzpw5yMrKktW//vrrss/C1ZKSknDHHXfI6nr16oX33nsPt99+e7XPSk5ODj744AP8+9//ls2sERYWhuPHj9e4vjf98fvcvn07Tp48iYsXL8LHxwcdO3bEiBEjrvt64XA4kJ6ejsOHD8NisaCsrAwajQbh4eEYPnw4AgICGuinIKJmxos3uYxBNhERNTmr1YrY2FicPn0awB8PIVasWIExY8bU2L6iogKzZs3C4sWLRd2oUaOQmprKhxZErdTBgwcxbtw4XLhwQdTdeOONWLNmDUJCQlBWVobNmzcjKSkJK1asgNlsrnVf/v7+GDduHAwGA8aPHw9/f3+eO6hWDocDBw4cwOXLl+tsWzktvl6vh1arZchI162kpATp6emy8DM4OBjR0dF8aaIFkiQJhYWFItS+ekRhTQICAkSo7e/v30S9/D8MsckdSZKETz/9FPPmzRN13t7eWL58OSZOnMh7uhbk+PHjuPnmm6vdtyuVSkRGRqJ79+5Qq9WwWq3IzMys8V5s4sSJSE5OdnrPNWfOHLz//vvV6sPCwhAbGwuNRoPy8nKcPn0aWVlZ1ZaGqJxGe9SoUdf4kxIRkQt4ASeXMcgmIqJmcfr0aUyZMkWMsFYqlZg6dSqmTp2Kvn37Qq1WIzs7G9u3b8fChQtl0xvecMMNWLduHUJCQpqr+0TUiHbt2oUJEybAarWKujFjxsBoNFZ7sC9JEsrLy/Hbb78hKSkJKSkpuHjxYq37VqvVGDNmDBITEzF+/HgEBwfzASgJkiTh6NGj1aYT12q1KCwsdDqyUqFQIDQ0VIRQnP6Z6qu4uBjp6ekoLS0VdaGhoYiKioKHh0cz9owaSlFRESwWC8xmM/Lz85229fX1FeeTwMDARr9WMcQmdyRJEr744gs888wzos7Lyws//PADDAYD7+FaoMOHD+P+++9HWlpavbZTKBSYMWMG5s+fX+fsFZIk4a233sLLL7+M8vLyeh2nS5cu+Prrr6utt01ERA2OF3FyGYNsIiJqNhUVFfj666/x+eefIz09vc71bMPCwjBnzhzMmzePo96IWqlNmzbBYDDIRq3dcccd+O677+Dt7e10W0mSUFFRge3btyMpKQnJycnVAsmrqVQqjBo1ComJiZgwYQLCwsL4QLQNkyQJBw8elL0IoVQqERUVhbCwMEiShCtXroiRlUVFRU73FxISIkIoHx+fxu4+tXBFRUVIT0+XrZkcFhaGQYMGMcRupUpLS2E2m2GxWJCbm+v0PtjHxwdarRY6nQ4hISENfq1iiE3uSJIkLFmyBH/+859FnaenJ5YuXYqpU6fynq0Fs9lsWLp0KT777DPs2LHD6flPrVZjypQpePLJJzF06NB6Hefo0aP48MMP8d133yE3N9dp24iICEyfPh0zZ85slhkxiIjaIF7IyWUMsomIqNmUlpbi66+/xrfffovt27fD4XDU2rZTp0547bXXMG3aNAYCRK1UcnIy/vSnP8lGTkyfPh0LFy6sd5AjSRLsdjt27doFo9EIo9GIEydO1Nre09MTt912GwwGAyZNmgStVgsAfEjaRjgcDuzfvx8mk0nUeXp6Ijo6uta1ESunCzaZTHVOFxwUFASdTge9Xu8Wa+CSeyksLER6errs3KfRaBAZGckQu40oLy8XI7VzcnKchjpeXl7iJZnQ0FAolcrrOjZDbHJHkiRh2bJlmDlzpvh7UCqV+Oabb3DPPffw/qwVyc/PR1paGk6dOoW8vDyUlZUhICAAISEhGDBgAAYOHHjdS2tIkoTDhw9jz549yM7ORkFBATw9PREcHIyOHTti8ODB0Ov1DfQTERGRi3gxJ5cxyCYiomaxbds23HfffWKdbFdptVq88cYbePTRRxunY0TULL766is8+uijshdann/+ebz55pvX/bCyMtTes2cPfvrpJyQnJ+Pw4cO1tvfw8MDw4cORkJCAhIQEtG/fHgBD7dbKbrdj3759sFgsos7LywsxMTEIDAx0aR/FxcUi1C4oKHDatrnXwCX3UlOIrdPpMHDgwOsOKKllstlsyM7OhtlsRnZ2Nux2e61tPTw8xEjtsLCweoc9DLHJHUmShJ9++gmPPPKIuC9UKBRYvHgxHnzwQd6PERERtQ68oJPLGGQTEVGT27hxIyZNmiRbA7Jjx4546qmnMH78eHTr1g2+vr6wWq3IysrCsmXL8N1338Fms4n2M2bMwMKFC/kgg6gVeO+99/Dcc8/J6t5880385S9/afBjSZIkRt8uX74cycnJ2LdvX63tFQoFbrrpJhgMBiQkJKBTp06inlo+u92OrKws2XrsKpUKsbGx1xwyV04XbDKZkJeX57Stn5+fGKnt7+/Pz1UbU1BQgIyMDFRUVIg6vV6PAQMGMMQmAH+co6xWq5iC/OrPSlVKpRJhYWHQ6XTQarV1LsPDEJvckSRJSElJwQMPPCB7iWPhwoWYMWMGr5NEREStBy/q5DIG2URE1KQsFgv69+8vG/kWHx+Pb7/91unItx07dmDSpEnIyckRdY0VdBFR01m0aBFmzpwpygqFQjysbGyVofbhw4fFmtoZGRlOtxkyZAgMBgMMBgO6du0KhULBh6otVEVFBbKysmRhs4+PD2JjY+Hr69sgxygrK4PFYoHJZKpzDVy1Wi1C7cDAQH6uWrn8/HxkZGTIXtJr3749IiIiGGJTjRwOB/Ly8mA2m2E2m2XrqVelUCgQEhIiZn/w9vaW/TtDbHJHkiRhzZo1uPfee2UvbXz44Yd44okneG4kIiJqXfiFl1zGIJuIiJrUiy++iDfffFOU+/bti8zMTJfWvd64cSPGjBkjymq1GqdPn+ZDN6IWzGq14tZbb8X+/fvh5eWF7777DnfeeWeT96My1D5x4oRYU3vnzp1Ot4mKihKhdq9evRhqtyDl5eXIyMjAlStXRJ2vry9iY2Nduh5di4qKChFq17UGrre3twi1g4OD+blqZfLy8pCZmSkLsTt06ICIiAj+rsklkiShoKBAzP5QUlLitH1QUJAItS0WC0NscjuSJOHnn3/GtGnTZEstvPfee5g9ezZDbCIiotaHX3zIZQyyiYioSfXs2RMnTpwQ5a+++goPPfSQy9uPGDECv/32myh/8MEHePrppxuyi0TUxC5duoTx48fjnXfewbhx45q7OyLUPnPmDJKTk2E0GrFt2zanwWP//v1hMBiQmJiIfv36MdR2Y2VlZUhPT0dRUZGo8/f3R0xMTLVRi43FZrPBYrGINXCvXhu+KpVKBa1WC71ej5CQED7Mb+Fyc3ORmZkpmzI3PDwcffv25TmDrokkSSgqKoLJZILFYpG9oOOKQYMGMcSmZiVJEjZv3ow777xTtvTUW2+9hblz5/K6R0RE1Drxyw+5jEE2ERE1maKiomprjp49e1asOeuK1157DS+//LIo33PPPfjuu+8arI9E1Dzsdjs8PDyauxvVSJIESZJw4cIFMVL7t99+k4VQVfXp0wcJCQlITEzEwIEDoVQqGVC5iZKSEqSnp8tGLwYGBiImJqbO9WQbi91uR3Z2tlgD19lny8vLC1qtFjqdDmFhYXy438Lk5OQgKytL9uJCp06d0KdPH54jqMEUFxeL6cfz8/OdtlWpVOjQoQO0Wi2CgoL4OaQmJ0kSfvvtN9xxxx0oLi4W9a+99hr+9re/8TpHRETUevHGk1zGIJuIiJrMhQsXEB4eLqsrLy+vV3jw+eef47HHHhPlcePGYf369Q3WRyKi2lSG2iaTCSkpKUhKSsKWLVtk6zhW1b17dxFqx8TEMNRuRkVFRUhPT5etKxscHIzo6Gh4eno2Y8/+j8PhQE5Ojgi1nX22PDw8RKit0Wjc8kUQ+j/Z2dnYs2ePLMTu0qWLWJaAqDGUlZXBbDbj7NmzspCwJpVLGuh0OgQHBzNApEYnSRJ27NiBxMREFBYWivqXXnoJr776Kj+DRERErRu/BJHLGGQTEVGTqWlEdm5uLoKDg13ex3vvvYfnnntOlKdMmYLly5c3VBeJiFzmcDiQnZ2NlStXwmg0IjU1VRaSVtWpUycRat9www3w8PBggNVErly5goyMDNm6m2FhYRg0aJDbBsAOhwO5ubliZOXVfa9KqVRCo9FAp9NBq9W6TTBPf7BYLNizZ49seYJu3bqhR48ePAdQozt9+jSOHTsmq1MoFE6Xy7h69ofQ0FC3PU9SyyVJEnbv3g2DwYCCggJRP2/ePPz73//mZ46IiKj14xchchmDbCIialJBQUGyhxWbNm3CyJEjXd7+3nvvxdKlS0X5iSeewMcff9ygfSQiqq/K0HHVqlVITk7G+vXrZdNXV9WhQwdMmjQJiYmJGDZsGDw9PRloNZL8/HxkZGTAZrOJOp1OJ6Z9bwkkSUJeXp4Ita9eQ7QqhUKBsLAwEWqrVKom7ClVZTKZsG/fPllo2KNHD3Tv3r0Ze0VtRdUQW6FQYNCgQQgNDRVLGmRnZ8vOj1V5eHiIF2U0Gg1flKHrJkkSMjMzER8fj7y8PFE/e/ZsvPvuuwyxiYiI2gY+ACGXMcgmIqImZTAYsGLFClGeNm0ali1b5tK2ly9fRq9evWRTzy1btgzTpk1r8H4SEV0rh8OBgoICrF27FklJSVi3bp3svFWVVqtFfHw8EhIScOutt8LLy4uhdgPJzc1FZmambN3p9u3bIyIiosWE2FVJkoSCggKYzWaYTCanL0woFAqEhISI6YK9vb2bsKd0+fJl7N+/XxZi9+zZE926dWvGXlFbUVuIrdVqZe0cDgesVqt4UcbZkgZ8UYaulyRJ2L9/P26//Xbk5OSI+scffxwLFizgixJERERtBx96kMsYZBMRUZNaunQp7r33XlFWKBT45ptvcN999zndrqysDJMmTUJqaqqo8/f3x9mzZxESEtJo/SUiuh6SJKGoqAjr1q1DUlIS1qxZg/z8/Frbh4aG4vbbb4fBYMDIkSPh7e3NUPsa1bQmcceOHdGvX79W838qSRIKCwtFqF1UVOS0fXBwsAi11Wp1E/Wybbp48SIOHDggq+vduze6dOnSTD2itsTVELuq+sz+AED2ooyPj0+D9J1aL0mScPjwYUyYMAEWi0XUT58+HZ999hlDbCIioraldXwppybBIJuIiJqUw+FATEwM9uzZI+oUCgWeeuopvPDCC2jfvn21bTZv3ow5c+YgKytLVv/666/jpZdeauwuExE1CEmSUFJSgtTUVCxfvhyrVq2C1WqttX1gYCAmTpwIg8GAMWPGQK1Wt5oAtrHVNJ1zly5d0KtXr1b9f1hUVCRC7StXrjhtGxgYCJ1OB71eD19f3ybqYdtw4cIFHDx4UFbXt29fdOrUqZl6RG3JtYbYVUmShCtXrohQu64XZSrPKTqdDn5+ftfUd2q9JEnC0aNHMXHiRFy+fFnU33///fjyyy/h5eXVjL0jIiKiZtB6v5hTg2OQTURETe748eO4+eabYTabZfVKpRKRkZHo3r071Go1rFYrMjMzZQ87Kk2cOBHJycl86EFELZIkSSgtLcXmzZuRlJSEFStWyEYnVeXv74/x48fDYDBg/Pjx8PPza9WB7PW4dOkSDhw4IAuxu3fvju7du7ep/7OSkhKYTCaYzWanswAAf3y+KkNtfrauz7lz53D48GFZXb9+/RAeHt5MPaK2pKFC7JoUFhbCYrG49KKMn5+fOKf4+/vznNLGSZKEkydPYsKECbhw4YKonzZtGpYsWcIp6omIiNom3iCSyxhkExFRszh8+DDuv/9+pKWl1Ws7hUKBGTNmYP78+ZwWlYhaBUmSUF5ejl9//RVGoxHJycm4dOlSre19fX0xZswYGAwGxMXFISgoiCHB/3f+/HkcOnRIVterVy907dq1eTrkJkpLS8WoytzcXKdtfX19odfrodPpEBAQwM9WPZw9exZHjhyR1fXv3x8dOnRoph5RW9KYIXZVJSUl4pySl5fntK1arYZWq4Ver+f1qg2SJAlnzpzBhAkTcPbsWVF/xx134LvvvoO3t3cz9o6IiIiaEW8KyWUMsomIqNnYbDYsXboUn332GXbs2AFn1yS1Wo0pU6bgySefxNChQ5uwl0RETUeSJFRUVOD3339HUlISkpOTce7cuVrbe3t7Y9SoUUhMTMSECRMQGhraZkOCqiEOwJGwNSkvLxcBlNVqdXrt9fHxEaE2AyjnagoR+/fvX+OSKUQNrSlD7KrKyspgsVhcOqeoVCox/XhISAiUSmWj94+ajyRJOH/+PCZMmIBTp06J+oSEBHz//fdcV52IiKht45dLchmDbCIicgv5+flIS0vDqVOnkJeXh7KyMgQEBCAkJAQDBgzAwIED4enp2dzdJCJqMpIkwWazYffu3UhKSoLRaMTJkydrbe/l5YXbbrsNCQkJmDRpkggwWnv4WDll6dX/NwwRXVNRUSECqJycHDgcjlrbent7iwAqODiYAdRVTp48iRMnToiyQqHAwIEDodfrm7FX1FY0Z4hdVUVFBbKzs2E2m5Gdne30nOLp6QmtVgudToewsDB4eHg0YU+pKVy8eBETJ06UfT7j4uKwfPly+Pr6NmPPiIiIyA207gcV1KAYZBMRERERuTlJkmC325GVlYWffvoJycnJ1aYwvpqHhwduueUWJCQkICEhAe3atQPQ+kJtSZJw9OhR2XSlCoUCkZGR0Ol0zdizlsdms8kCKLvdXmtbLy8vEWqHhoa22VBbkiScOHFCNtKQnz9qSu4UYldlt9uRk5MDs9kMi8UCm81Wa1ulUgmNRgOdTgeNRgMvL68m7Ck1BpPJhNtvv1223Mfo0aORnJwMf3//ZuwZERERuYnW9XCCGhWDbCIiIiKiFkSSJDgcDuzfv1+E2vv376+1vUKhwLBhw2AwGJCQkCCm2m7pobYkSTh06BAuXLgg6pRKJQYNGgSNRtOMPWv56hNAtdVRlZIk4fjx4zh9+rSo4+ePmpI7h9hVORwO5ObmimUNysvLa22rUCgQGhoqXpZRqVRN2FNqCBaLBZMmTZLdm9x6661YuXIlAgICmrFnRERE5EZa9gMJalIMsomIiIiIWqjKUPvQoUNiTe3MzEyn29xwww0wGAwwGAzo0qULFApFiwu1HQ4HDhw4gMuXL4s6Dw8PREdHIyQkpBl71vo4HA5YrVYRQFVUVNTa1sPDQzaqsrUuCVLTTABKpRJRUVEICwtrxp5RW9GSQuyqJElCfn6+OKeUlJQ4bR8cHCxCbbVa3US9pGuVk5ODhIQEZGVlibqbb74Zq1evRlBQUPN1jIiIiNxNy3oIQc2KQTYRERERUStQGWofP34cRqMRRqMRu3btcrpNdHS0CLV79uzZIkJth8OBvXv3wmKxiDovLy9ER0fzIXkjczgcyMvLg9lshslkcjqqUqlUIiwsDDqdDlqtttVMFSxJEg4fPozz58+LOg8PD0RFRSE0NLQZe0ZtRUsOsauSJAmFhYUwmUywWCwoLCx02j4gIECE2pye2v3k5eXBYDAgLS1N1N1www1Yt24dXzIjIiKiqtz7wQO5FQbZREREREStTGWofebMGRFq//7773B27z9gwAAYDAYkJiaib9++bhlq2+127NmzBzk5OaJOpVIhJiaG05U2scpRlSaTCWazGaWlpbW2bS1TBdc0nb2HhwdiYmIQHBzcfB2jNuPUqVM4fvy4KLfkELsmRUVFYqR2QUGB07Z+fn7inBIQEOB216u2pqCgAFOmTMH27dtFXUxMDNatW9dqPp9ERETUoHjzRi5jkE1ERERE1IpJkgRJknDhwgUx/fivv/4Kh8NR6zZ9+/ZFfHw8Jk+ejAEDBkCpVDZ7SGCz2ZCZmYm8vDxR5+Pjg5iYGPj5+TVfxwiSJOHKlSsi1C4uLnbaPiQkBHq9HlqtFj4+Pk3Uy+sjSRIOHDiAS5cuiTpPT0/ExMRwJgBqEq09xK6qtLRUhNq5ublO2/r4+IhQOzg4uNmvV21NYWEh7rjjDmzdulXURUZG4ueff4Zer2/GnhEREZEb4w0buYxBNhERERFRG1EZal++fBkpKSkwGo3YvHkzbDZbrdv06NEDCQkJSExMRHR0dLOE2uXl5cjMzJSN0FOr1YiNjeWaqW5GkiQUFRWJULuuqYKDgoKg1+vdev1bh8OB/fv3w2QyiTovLy/ExMQgMDCwGXtGbUVbC7GrKi8vh8VigdlsRk5OjtPZRby8vESoHRoaCqVS2YQ9bXuKi4tx1113YfPmzaIuIiICqampaN++fTP2jIiIiNwcg2xyGYNsIiIianYVFRXYtm0bzp49i0uXLsHf3x8dOnRAdHQ0unbt2tzdI2q1HA4HsrOzsWLFChiNRqSmpjpd97hz584i1B4yZAg8PDwaPdQuKytDRkaGLBD18/NDbGwsvL29G/XYdP3qM1VwQECACLXdZZS9w+HAvn37YDabRZ2XlxdiY2M5nT01ibYeYldls9mQnZ0Ns9mM7Oxs2O32Wtt6enpCo9FAp9NBo9HAw8OjCXva+pWUlOCee+7Bzz//LOr69OmD1NRUhIeHN2PPiIiIqAVgkE0uY5BNRERE1Zw8eRK7d+9GWloadu/ejYyMDFy5ckX8e5cuXXD69OnrPo7FYsHLL7+M77//HlartcY2w4YNw5w5c3DHHXdc9/GIqHYOhwO5ublYuXIlkpOT8fPPP6OkpKTW9h06dEB8fDwSExNx0003wdPTs8FD7ZKSEmRkZMimqg4ICEBMTEyLXWe5LSspKRGh9tVTxNfEz89PhNr+/v7NMlWww+HAnj17kJ2dLepUKhViY2Ph7+/f5P2htqemEDsqKgoajaYZe+U+7HY7rFYrzGYzLBYLKioqam2rVCoRFhYGnU4HrVYLLy+vJuxp61NWVob77rsPa9asEXU9evRAamoqunTpwundiYiIqC68WSCXMcgmIiIiAMCWLVvw73//G2lpabWGypUaIsheu3YtHnroIdkoN2fuvfdeLFy40G1G6RG1Zg6HAwUFBVizZg2MRiPWrl2LoqKiWtvrdDrEx8cjISEBI0aMgJeX13U/xN6/fz9SUlJwww03iLrg4GBERUUxgGgFysrKZOvfOvteqlarRagdGBjYJAGJ3W7Hnj17kJOTI+q8vb0RGxvL6xA1CYbY9eNwOJCXlyfOK2VlZbW2VSgUCAkJEVOQc3aP+ikvL8fDDz+M5ORkUde1a1ekpqaie/fuDLGJiIjIFbxhIJcxyCYiIiIAwPz58/Hss8+61PZ6g+wtW7Zg/PjxsimMFQoFYmJi0L17d+Tl5SEzM1M2Cg4A4uPjkZyczPUOiZqQJEm4cuUK1q9fj6SkJKxZs8bpFNGhoaGYNGkSDAYDbrvtNnh7e9f7ofbu3bsxefJk5OfnY+7cuRg2bBhCQ0MRFRXFqWFbofqsf+vj4yPCp+Dg4EYJTOx2O7KysmQvdfn4+CA2Nha+vr4NfjyiqhhiXx9JklBQUACz2QyTyeR0dhEACAoKEucV/o07V1FRgRkzZuDHH38UdeHh4di4cSN69erFEJuIiIhcxZsGchmDbCIiIgJQe5Dt7e2N8PBwnDhxQtRdT5B9/vx5REZGIjc3V9TdfPPNWLRoEfr16yfqysrKsHDhQsydO1c2VeSLL76IN95445qOTUTXR5IkFBcXIzU1FUlJSVi1apXTGRyCgoIwYcIEJCYmYvTo0VCr1XU+5P7tt99w5513iuUMPD098cknn2DatGkMsduAiooK2fq3Doej1rYqlUqETyEhIQ3ykpPNZkNWVpbsGqVWqxEbGwu1Wn3d+yeqS9UQW6lUYtCgQQyxr5EkSSgsLBQjtQsLC522DwgIgFarhV6vh5+fH4PZq9hsNjz++ONYunSpqGvfvj02btyIvn378v+KiIiI6oM3DuQyBtlEREQE4I8g+/nnn0f//v0xePBgDBkyBIMHD8bAgQOxbds2jBw5UrS9niB7+vTpWLx4sSgPGzYMGzduhI+PT43tk5OTMXnyZFH29vbGkSNH0KVLl2s6PhE1DEmSUFpaik2bNsFoNCIlJaXaLApXCwgIwPjx42EwGDBu3LgaA4L169fjvvvuk62JPXbsWHz//fec+rUNstvtyM7OhslkQnZ2Nux2e61tvby8oNVqodPpEBYWdk2hdkVFBTIzM5Gfny/qfH19ERsbW+s1iqghMcRufMXFxSLUvvpvvSa+vr7iZZmmWtbAXdntdjz99NP4+uuvRZ1Op0NqaioGDBjQpv9viIiI6Jrw5oFcxiCbiIiIAAC5ublQq9U1PqzfsmVLgwTZx44dQ79+/UQYoVKpsH//fvTq1cvpdg899BCWLFkiyg8//LAsDCei5iVJEsrLy/HLL7/AaDQiOTkZly9frrW9r68vxo4dC4PBgPHjxyMoKAjJycl45JFHZEsOGAwGfPPNN/D09GyKH4PcmN1uh9VqhclkgsVigc1mq7Wtp6cnNBoN9Ho9wsLCXBrJX1FRgYyMDNm0+X5+foiNjeVLFNQkGGI3vdLSUrGsQW5urtNlDby9vWXLGrSlZW4cDgfmzJmDRYsWiTqNRoOff/4ZUVFRDLGJiIjoWvAGglzGIJuIiIjq1FBB9quvvopXXnlFlKdNm4Zly5bVud2hQ4cQEREhyn5+fsjOzuYIOSI3JEkSKioq8Pvvv2P58uVITk7G+fPna23v7e2NkSNHIjU1VRZO3n333fj888/bVFhArnE4HMjNzRWh9tUvP1SlVCpFqK3RaGp8KaK8vBwZGRliOnsA8Pf3R2xsLFQqVaP8DERXY4jd/CoqKkSonZOT43RZg6tngAgNDW3Vy144HA688MIL+Pjjj0VdSEgI1q9fj8GDBzPEJiIiomvFmwhyGYNsIiIiqlNDBdlRUVHYs2ePKK9fvx7jxo1zaduhQ4di586dopySkoKEhIR694GImo4kSbDZbNi1axeSkpJgNBpx6tSpau0UCoVsJFxCQgLef/996PV6PiQnpyRJQm5urpgquKysrNa2SqUSoaGh0Ov10Gq18PLyQnl5OdLT02Xr5gYEBCAmJoYhNjUJhtjux2azIScnB2azGRaLxemyBh4eHtBoNNDpdLW+LNNSORwO/OMf/8D7778v6oKCgrB27VoMHTqU12ciIiK6HryRIJcxyCYiIqI6NUSQffnyZbRv316UPT09kZeXBz8/P5e2f+GFF/DWW2+J8qxZs/Dpp5/Wqw9E1HwkSYLdbkdmZiZ++uknJCcn4+jRo9Xa9e/fHwcOHICHhwdGjBiBhIQExMfHo127dgDAB+dUK0mSUFBQAJPJBLPZjJKSklrbKhQKBAUFobi4WDaiOzAwEDExMfDy8mqKLlMbxxDb/TkcDlitVvGyTEVFRa1tK1+W0el00Gq1LfplGEmS8Prrr8vuvQMCArB69WoMHz6c12IiIiK6XryZIJe1nldFiYiIyK3t379fVo6MjHQ5xAaAYcOGycoHDhxokH4RUdNQKBTw9PTEkCFDMHjwYOh0OsydO1fWpkOHDuJv2263Y/Pmzdi8eTOee+453HTTTUhMTERCQgI6duwo9klUqTKcDgoKQq9evVBYWChC7aKiIllbSZKQl5cnq/Px8UFERARDbGoSDLFbhsrlCTQaDfr27Yu8vDwxUru0tFTW1uFwIDs7G9nZ2VAoFAgJCRFTkLek5XAkScJbb70lC7H9/PyQnJzMEJuIiIiImhyDbCIiImoSBw8elJV79uxZr+179OjhdH9E1DJIkoR//etf+Pvf/y7qlEolXnvtNdjtdhiNRmRlZcm2cTgc2LZtG7Zt24Z58+bhxhtvhMFggMFgQOfOnaFQKPhgnWQUCgUCAgIQEBCAnj17orCwUIyovHot7KuVlpZix44dCAoKgk6ng06ng6+vbxP3nNoChtgtU+WI69DQUPTp0wcFBQXivFJcXCxrK0kSrFYrrFYrjhw5Is4rWq22Xi9yNjVJkvD+++/j9ddfF3VqtRpJSUkYOXIkr7VERERE1OQ4tTgRERHVqSGmFn/66afx4YcfivLcuXPx9ttvu7x9YWEhAgICZHVWqxUhISH16gcRNR9JkvDCCy/gP//5j6jz8vLC0qVLMXXqVEiSBIfDgWPHjsFoNMJoNGL37t1O9xkTEyNC7R49ejDUJqeKi4uxe/du2XTizgQEBIhQ29/fv5F7R23ByZMnceLECVFmiN06uPKyTCV/f3/ZecVdrlmSJOGTTz7B888/L+q8vb2xfPlyTJw40W362dqUlJQgKysLhw4dQm5uLkpLSxEYGAidToeYmBj07NmzQf/vCwsLsXPnThw7dgy5ubmQJAmBgYHo0qULIiIiqr08TERE1Eh4Y0EuY5BNREREdWqIIPuBBx7Af//7X1H+z3/+g3nz5tVrH2q1WjaN46lTp9C1a9d67YOImofD4cCTTz4pW9vex8dHPCCvqjLUPn36tAi1t2/fDmffXwYOHAiDwYDExET06dOHoTbJFBUVIT09HWVlZaIuODgYWq0WFoul2lTjVfn5+UGn00Gv17tV+EQtB0PstqGkpESE2nWdV9RqtQi1g4KCmu28IkkSFi1ahGeffVbUeXl54YcffoDBYOD5rhFs374d8+fPR3JystOXqzp27Ijp06dj9uzZCA0Nvebjbdq0Ce+//z7WrVsHm81Wa7uwsDCMHTsWL774IiIjI6/5eERERHXgzQW5jEE2ERER1akhguwpU6bAaDSK8ocffognn3yyXvsIDQ1Fbm6uKO/duxcDBw6s1z6IqOnZbDZMnz4d33zzjajz9/fHypUrcdttt9W5vSRJkCQJ58+fR1JSEpKTk/Hbb7/B4XDUuk2/fv0QHx+PyZMno3///lAqlXwQ34YVFhYiPT1dFhZoNBoMGjQISqUSAFBWVgaLxQKTySRGqdWmMnzS6/UIDAzkZ4vqxBC7bao8r5jNZlitVqfnFZVKJULtkJAQcW5qbJIkYcmSJfjzn/8s6jw9PcVsKTy/NSybzYZnnnkGn3zyidPPQ1V6vR5ff/014uLi6nW87OxszJw5U/Y9zBWffvopZs2aVa9tiIiI6oE3GOQyrpFNRERETaKwsFBW9vHxqfc+1Gq1LMiuuk8icj/l5eW45557sHz5clEXHByMdevW4cYbb3RpH5Ujqzt37ozZs2fj6aefxuXLl5GcnAyj0YgtW7ZUG1106NAhHDp0CP/5z3/Qo0cPMVI7KiqKoXYbc+XKFaSnp6OiokLU6XQ6DBw4UBYUeXt7Izw8HOHh4aioqBChdk5OTrWwoaSkBGfOnMGZM2fg7e0tC5/42aKqGGK3XVXPK9nZ2TCbzcjOzq72MlZ5eTnOnz+P8+fPw9PTE1qtFjqdDmFhYfDw8GiU/kmShKVLl8peLvXw8MCSJUsYYjcCSZJw991346effqr2b3379kW/fv2gVqthsViQlpYm+95jMplgMBiQkpLicph98uRJjBs3Tnb+Af54aSI6Ohrt27eHWq1GXl4eDh06VO8XlYmIiIiaAoNsIiIiahbX8mCMD9OIWpaSkhLccccdWLt2rajTarXYsGEDBg0adE37rAy1O3TogCeeeAKzZs2CxWLBypUrkZSUhI0bN1abovPEiRN477338N5776Fz584i1B4yZAhD7VauoKAAGRkZshBbr9djwIABTkc7enl5oUOHDujQoQNsNhuys7NhMplqDJ/Kyspw7tw5nDt3DiqVClqtFnq9vklHVJL7qinEjoqKQlhYWDP2ipqDl5cX2rdvj/bt28NutyMnJwdmsxkWi6Xay1g2mw2XLl3CpUuXoFQqodFooNPpoNFo4OXl1SD9kSQJP/74I2bNmiVe1lEoFPjiiy9w991389rYCL744otqIfaIESPw8ccfY8CAAbJ6m82G//73v3j22WeRn58P4I+XHR588EEcPXoUQUFBTo9lsVgwduxYnDx5UtR16NAB//znPzF16lQEBARU28ZkMmH16tVYsmQJf/9ERETkNji1OBEREdXJXaYWDwsLg9VqFWVOLU7kvq5cuYL4+Hj88ssvoq5jx47YuHEj+vTp0yjHdDgcyM3NxcqVK2E0GvHzzz+jtLS01vYdO3ZEfHw8EhMTcdNNN8HDw4MPbluR/Px8ZGRkyAKi9u3bo3///tf8e64MnypDbWfrjFaOqNTr9QgNDW20EZXkvhhikysqr10mkwkWi8XpeskKhQKhoaFiFgiVSnVNx5QkCcnJyXjwwQdht9vFvhcuXIhHH32U18JG0q1bN9l3qBEjRiA1NdXpywlpaWkYPnw4ysrKRN2bb76Jv/zlL06Pdeedd8pC8xEjRmDVqlU1Btg1sdls8PTk+CciImo0vNkglzHIJiIiojo1RJD9wAMP4L///a8o/+c//8G8efPqtQ9fX1+UlJSI8smTJ9GtW7d67YOImsa9996LpUuXinL37t2RmpraZH+zDocD+fn5WLNmDYxGI9atW4eioqJa2+v1esTHxyMhIQG33HILvLy8+CC/BcvLy0NGRoYIaIA/RqJFREQ02O/V4XDIRlRePeq7Kg8PD2g0Guj1emg0GobabQBDbLoWkiQhPz8fJpMJZrPZ6ctYwB9Ldej1egQFBdU5QvfqY6xevRr33Xef7Lz10Ucf4fHHH+dMEo1k3759iIyMlNVlZWW5NEPNU089hY8++kiUb775ZmzdurXW9klJSbjjjjtEuV+/fti9ezf8/PyuoedERESNgl+2yWUMsomIiKhODRFkV30AM2/ePPznP/9xefvCwsJqIwisVitCQkLq1Q8iahpnzpzBLbfcgnPnzqFfv37YsGEDOnbs2Cx9cTgcKCwsxLp165CUlIS1a9eioKCg1vZhYWGYNGkSEhISMHLkSKhUKobaLYjVakVWVpYsxA4PD0ffvn0b7fdYOaLSbDbDbDY7HVHZWNMEk/tgiE0NQZIkXLlyRZxXnL2M9e9//xu5ubmIi4vDn/70J0RFRdW6z/Xr1+Puu++Wnafee+89zJ49myF2I0pJSUFiYqIod+rUCWfPnnVp29TUVIwdO1aUdTodTCZTre379u2LI0eOiPKvv/6KW265pf6dJiIiajz8gk0u4xwxRERE1CR69eolK585c6Ze21dtHxoayhCbyI116dIFGzduxOzZs7FkyRJotdpm64tSqURgYCDuuusu3HnnnSguLsaGDRuQlJSEVatWITc3V9Y+JycHS5YswZIlSxAcHIwJEyYgMTERo0ePho+PD0NtN5aTk4OsrCzZOtadO3dG7969G/X3plQqERYWhrCwMPTt2xd5eXkifKo6otLhcIh/UygUCAsLg06ng1arveZpgsl9MMSmhqJQKBAYGIjAwED07NkTRUVF4txx9ctYpaWlyMzMRHl5OY4ePYoFCxagc+fOGD9+PO644w7cfPPNUCqVkCQJmzZtwr333isLsd966y2G2E2g6osI4eHhLm/bqVMnWbnqfcvVtmzZIguxb7nlFobYRERE1KIxyCYiIqIm0a9fP1n5+PHj9dr+5MmTsnJERMR194mIGlevXr2wZs2a5u6GjEKhgJ+fHxITE2EwGFBaWoqNGzfCaDRixYoVyM7OlrXPy8vDsmXLsGzZMgQEBCAuLg4GgwFjx46Fn58fQ203kp2djT179shC7K5du6Jnz55N+ntSKBQICQlBSEgIevfujYKCApjNZphMJtnyGMAfoyOzs7ORnZ0ttqtc+9bb27vJ+kwNgyE2NSY/Pz9069YN3bp1Q2lpqQi1V69eXW0WiLNnz2LRokVYtGgRdDodxowZg0GDBuHVV1+VvVzz+uuvY+7cuQyxm0C7du1k5bqmjXfWNjQ0tNa2X3zxhaz88MMPu3wcIiIiInfEIJuIiIiaxIABA2TlvXv3ori4GL6+vi5tv23bNqf7IyKqL4VCAbVajUmTJuH2229HWVkZfv31VyQlJSElJQWXL1+Wtb9y5Qp+/PFH/Pjjj/D19cW4ceNgMBgwfvx4BAYGMtRuRmazGXv37sXVS2d169YNPXr0aNbfi0KhEGvX9uzZE4WFhSLUrjo6T5IkWK1WWK1WHD58GMHBwSLUVqvVzfQTkKsYYlNT8vHxQefOndG5c2eEh4cjKCgIq1atwu7du2Gz2WRtzWYzli5diqVLl8qWMnjxxRfx17/+lSF2ExkyZAi8vb1RVlYGADh06BBKSkpcOr+np6dX21dtNm/eLCtfPSU5ERERUUvENbKJiIioTg2xRjYADBo0CHv37hXl9evXY9y4cS5tO3ToUOzcuVOUU1JSkJCQUO8+EBHVRZIkVFRUYNu2bVi+fDlSUlJw/vz5Wtv7+Phg9OjRSExMRFxcHEJCQhhqNyGTyYR9+/bJQuwePXqge/fuzdirulVOE2wymXDlyhWnbQMDA0Wo7efn10Q9JFedOHFCNnMMQ2xqLlarFcuXL8eKFSvw+++/Ox31GxQUhEmTJmHKlCkYP348zy1NYObMmVi0aJEof/jhh3jyySedbmO32zFkyBBkZmaKuh9//BFTp06t1vbChQuyKcvDw8Nx7tw5AH9cK5cuXQqj0YiTJ0/CYrEgMDAQer0ew4YNw8SJE5GQkMAXG4iIqKnwCzO5jEE2ERER1amhguxXXnkFr776qihPmzYNy5Ytq3O7w4cPy6Ym9/PzQ3Z2Nnx8fOrdByKi+pAkCTabDTt37hSh9qlTp2ptr1KpMHLQpZWfAABme0lEQVTkSCQkJOD222+HRqNhqN2ILl26hAMHDshC7F69eqFr167N16lrUFJSIkLt/Px8p239/f2h0+mg1+s5vb0bYIhN7urKlStYtGgR/ve//+H06dPVZoG4mlqtxvjx4zFlyhRMmjQJISEhTdjTtsNqtSI2NlZ8j1Kr1VixYgXGjBlTY/uKigrMmjULixcvFnWjRo1Campqjef+lJQUJCYmivLYsWOxfv16LFy4EPPmzUNhYaHT/vXr1w8LFiyotT9EREQNiF9iyGUMsomIiKhODRVkHzt2DP369YPdbgfwR+Czf/9+9OrVy+l2Dz/8ML7++mtZ+eoHOkRETUGSJNjtdqSnp2P58uVITk7GsWPHam3v6emJW265BQaDAfHx8dDr9QDA4LGBXLx4EQcOHJDV9enTB507d26mHjWM0tJSWCwWmEwm5ObmOm3r6+srQu2AgAB+tpoYQ2xyV5IkYf/+/Zg4cSKsViuUSiX8/PzQpUsXWK1WXLx4sdZtPT09MWrUKEyePBmJiYnV1nam63P69GlMmTJFjLBWKpWYOnUqpk6dir59+0KtViM7Oxvbt2/HwoULceTIEbHtDTfcgHXr1tX6osFnn32Gxx9/XJQfeughBAUF4YMPPnC5fx4eHvjggw/w5z//+Rp/QiIiIpfwiwu5jEE2ERER1amhgmwAmD59uiyEHjZsGDZu3Fjr6OqqIwtUKhWOHj2KLl26XNPxiYgaQmWovW/fPixfvhxGoxEHDx6stb1SqcSwYcOQmJiI+Ph4dOzYEQBD7Wt1/vx5HDp0SFbXt29fdOrUqZl61DjKy8thNpthNpthtVrh7Pu7j4+PCLWDgoL42WpkDLHJXUmShMOHD2PChAmwWCyi/tFHH8Wnn34KpVKJXbt2ISkpCUlJSbK13atSKBQYNmwYpkyZgsmTJ6Nbt25N8SO0ehUVFfj666/x+eefIz093em5HQDCwsIwZ84czJs3T7bOeVVvvvkmXnzxRVEODw+XLY0ybNgwTJ8+HVFRUfDz88OFCxewbt06fPrpp7LR2gqFAikpKYiPj7+On5KIiMgpflkhlzHIJiIiIuH8+fOw2WzV6nfs2IG7775blDt27IitW7fWuA9/f39oNBqnx4iMjJSNMrv55pvxxRdfoG/fvqKurKwMn3/+OZ577jlUVFSI+hdffBFvvPFGvX4uIqLGJEkSHA4HDh48iKSkJBiNRuzZs6fW9gqFAjfeeCMMBgMSEhLQuXNnKBQKBo8uOnfuHA4fPiyri4iIEC8HtFYVFRWwWCwwm83IycmBw+Gota23t7dYUzs4OJhrnjYwhtjkriRJwtGjRzFx4kRcvnxZ1D/wwAP44osvqoWgkiRh3759MBqNSEpKwt69e53uPyoqClOmTMGUKVPQv3//RvkZ2oLS0lJ8/fXX+Pbbb7F9+3an5/NOnTrhtddew7Rp0+pcVumFF17AW2+9Va1eoVDg7bffxnPPPVfjdufOnUNcXJzshbyQkBCcPn0agYGBLv5URERE9cIvv+QyBtlEREQkdO3aFWfOnLmufTz44IOyacBrsmXLFowfPx7l5eWiTqFQIDY2Ft27d0d+fj4yMjJko0gAYNKkSUhOToaHh8d19ZGIqLFUhtrHjh0ToXZaWprTbWJjY2EwGGAwGNC9e3eG2k6cOXMGR48eldX1798fHTp0aKYeNQ+bzYbs7GyYzWZkZ2eLJTtq4uXlJULt0NBQhtrXiSE2uStJknDy5ElMmDABFy5cEPXTpk3DkiVLoFKp6tzH8ePHRai9Y8eOWtsNHDiwztCbarZt2zbcd9999Z7dSqvV4o033sCjjz5aa5vnn38eb7/9drX6Z599Fu+9957T/Z87dw4DBw5Efn6+qHvjjTdkI7yJiIgaEL/wkssYZBMREZHQVEE2AKxZswYPPfRQtbC6NnfffTcWLVoEPz+/6+ofEVFTqQy1T506BaPRiOTkZGzfvt3pFKKRkZEwGAxITExE7969GWpf5dSpUzh+/LgoKxQKDBgwoM2v32q325GTkwOz2QyLxVLjzCqVPD09odVqodPpEBYWxhfD6okhNrkrSZJw5swZxMXF4dy5c6L+jjvuwHfffQdvb+967/PChQtISUlBUlIStmzZInth5uWXX8Yrr7zSEF1vUzZu3IhJkyahtLRU1HXs2BFPPfUUxo8fj27dusHX1xdWqxVZWVlYtmwZvvvuO9l5fcaMGVi4cGGN9wavvfYaXn75ZVldYGAgLly4AH9//zr7989//hN///vfRTkiIgIHDhy4lh+ViIioLvySSy5jkE1ERERCUwbZAGA2m/Hyyy/j+++/l001frWhQ4di7ty5uOOOO66rX0REzUmSJEiShLNnz4pQe+vWrU6nE42IiEB8fDwmT56MiIgIKJXKNhtqnzx5UraOq0KhwMCBA6HX65uxV+7H4XDAarWKdbWvXpqjKg8PD2g0Guh0Omg0Gnh6ejZhT1sehtjkriRJwvnz5zFhwgScOnVK1CckJOD777+vczpqV+Tk5GDVqlVISkrC+vXrsXPnTgwaNOi699uWWCwW9O/fX/YSb3x8PL799lun03fv2LEDkyZNQk5Ojqh788038Ze//KVa2/fee6/a9OH1+W529uxZdOnSRVZnMpmg0+lc2p6IiKge2uYXW7omDLKJiIio2ZWXl2Pbtm04c+YMLl++DD8/P3Ts2BHR0dHo1q1bc3ePiKhBVYbaly5dQnJyMoxGY7XRblX17NlTjNQeNGhQmwm1JUnCiRMnZOGMQqHAoEGDoNVqm7Fn7s/hcCAvL0+E2mVlZbW2VSqVCAsLg06ng1arrbaOblvHEJvc2cWLFzFhwgTZjBUTJkzATz/9BF9f3wY/XmFhIfz8/NrENaghvfjii3jzzTdFuW/fvsjMzHTpRYONGzdizJgxoqxWq3H69OlqAfPixYsxffp0Wd2nn36KWbNmudzPDh064NKlS6L8yy+/YMSIES5vT0RE5CLeSJDLGGQTERERERE1I4fDAYvFghUrViApKQkbN250OpK2S5cuYk3tIUOGtNpQW5IkHDt2TDZTiFKpxKBBg6DRaJqxZy2PJEnIz8+H2WyGyWSSTWtblUKhQGhoqFhX25V1dVszhtjkzkwmEyZOnIjDhw+LujFjxsBoNLo0lTQ1nZ49e8pmFvnqq6/w0EMPubz9iBEj8Ntvv4nyBx98gKefflrWZuvWrbjllltkdcnJyTAYDC4fZ/DgwUhPTxflpKQkTJ482eXtiYiIXNT6vsBSo1E2dweIiIiIiIjaMqVSCb1ejxkzZmD16tW4ePEiFi9ejPj4+BpHap05cwYLFizA6NGjERERgXnz5uG3336DzWZzuv52SyJJEo4ePVotxI6KimKIfQ0UCgWCg4PRu3dvDB8+HDfeeKNYi7UqSZKQk5ODQ4cO4ZdffkFaWhrOnj3rNPxurRhikzszm82Ij4+Xhdi33norkpKSGGK7maKiIlmIDQCjR4+u1z6uHpENADt37qzWpn///tXq6rs+etX2bfHcT0RERO6Fi2ARERERERG5CaVSCY1Gg4cffhgPPvgg8vPzsXr1ahiNRqxbtw7FxcWy9ufPn8enn36KTz/9FO3atUN8fDwSEhIwfPhweHl5tciR2pIk4fDhwzh//ryo8/DwQHR0NEJCQpqxZ62DQqFAYGAgAgMD0aNHDxQVFcFkMsFsNqOwsLBa+9zcXOTm5uLIkSMICgqCTqeDXq+HWq1uht43HYbY5M5ycnKQmJiIAwcOiLqbb74ZKSkpCAgIaMaeUU3y8vKq1bVr165e+6jaPjs7u1qbkJAQhIeHy66fNR3bmartec4jIiKi5sYR2URERERERG5IqVQiJCQE9913H3788UdcvHgR//vf//CnP/2pxqDi8uXLWLRoEeLj49GrVy/8+c9/xvr161FWVtZiRmpLkoSDBw/KHsJ7enoiJiaGIXYjUCgU8Pf3R48ePXDTTTfh5ptvRs+ePREYGFhj+/z8fBw7dgxbt27Fjh07cOrUKRQVFTVxrxsfQ2xyZ3l5eZgyZQr27Nkj6m688UasXLkSQUFBzdgzqk1wcHC1uvqeO6u+aFTbqPuJEyfKyle/7FCXsrIy2VrrABAeHu7y9kRERESNgWtkExERERERtSCSJKG4uBg///wzkpKSsGrVKqcjroKDgzFx4kQkJiZi1KhR8PHxccuR2g6HAwcPHsSlS5dEXWWIzXCm6ZWUlMBiscBkMtU5os/Pzw96vR46nQ7+/v5u+flyFUNscmcFBQWYPHkyduzYIepiYmKwfv16Lrvg5oKCglBQUCDKmzZtwsiRI13e/t5778XSpUtF+YknnsDHH39crd2GDRswbtw4UR48eDB2797t0jHWrl0rC8I1Gg3MZnOLPqcTEZHb4sWFXMYgm4iIiIiIqIWSJAklJSXYtGkTkpKSsHLlyhqnG60UGBiIuLg4GAwGjB07Fr6+vm7xgNrhcGD//v0wmUyizsvLCzExMbWODqamU1ZWBrPZDLPZjNzcXKcj/NVqtQi1AwMD3eLz5aqaQuzo6GiEhoY2Y6+I/lBYWIg77rgDW7duFXWRkZHYsGEDdDpdM/aMXGEwGLBixQpRnjZtGpYtW+bStpcvX0avXr1ko7KXLVuGadOmVWtrs9nQq1cvnD59WtT98ssvGDFiRJ3HGT16NDZt2iTKDzzwAJYsWeJSH4mIiOqp5XxJoGbHIJuIiIiIiKgVkCQJZWVl+OWXX5CUlISUlBRZMFyVn58fxo0bh4SEBIwfP77ZQkeHw4F9+/bBbDaLOpVKhZiYGK716obKy8thsVhgNpuRk5PjNNT28fGBTqeDTqdDcHCw24bakiTh5MmTDLHJbRUXF+Ouu+7C5s2bRV1ERARSU1PRvn37ZuwZuWrp0qW49957RVmhUOCbb77Bfffd53S7srIyTJo0CampqaLO398fZ8+erXXJjarH6tmzJ7Zt2+b0hYd3330Xc+fOFWWlUol9+/YhIiKizp+NiIjoGrjnFwNySwyyiYiIiKhB5ebm4sCBAzh27BisVitKS0sRHBwMrVaL2NhY9OjRo7m7SNTqSZKE8vJybNu2DcuXL0dKSgouXLhQa3sfHx+MGTMGBoMBcXFxCAkJaZLQ0W63Y+/evbJR5CqVCrGxsbWu/0nuo6KiAtnZ2TCbzcjOzobD4ai1rUqlEqF2SEgIlEplE/a0dgyxyd2VlJTgnnvuwc8//yzq+vTpg9TUVK5f3II4HA7ExMTI1jZXKBR46qmn8MILL9T4QsLmzZsxZ84cZGVlyepff/11vPTSS7UeS5Ik3Hbbbfj1119FXY8ePfDpp59i7NixsrZ5eXl49dVXMX/+fFn9M888g/fff78ePyEREVG9MMgmlzHIJiIiImojTp48id27dyMtLQ27d+9GRkYGrly5Iv69S5cusmkIXVVRUYFNmzZh5cqV2LJlCw4cOOC0fYcOHTB9+nQ88cQTaNeuXb2PR0T1I0kSKioqsHPnTiQlJSE5Odnp37pKpcLIkSNhMBgwceJEaDSaRgm17XY79uzZg5ycHFHn7e2N2NhY+Pn5NfjxqHHZ7XYRalssFtjt9lrbenl5QavVQqfTISwsrNlCbYbY5O7Kyspw3333Yc2aNaKuR48eSE1NRZcuXdx2lgOq2fHjx3HzzTfLZiAB/jjvREZGonv37lCr1bBarcjMzMTly5er7WPixIlITk6Gl5eX02Pl5ORg2LBhOHr0qKy+c+fOiIqKgp+fHy5cuIAdO3agvLxc1mb06NFYt24dPD09r/EnJSIiqhNvYshlDLKJiIiIWrEtW7bg3//+N9LS0mC1Wp22vZYge+fOnZgwYQJyc3Pr3bfg4GB8+OGHdU6pSEQNR5Ik2O12pKWlISkpCUajEcePH6+1vaenJ0aMGAGDwYD4+HgxLen1hicFBQVITk6WjUDz8fFBbGwsfH19r2vf1PzsdjusVqtYV9tms9Xa1tPTExqNBjqdDhqNBh4eHk3SR4bY5O7Ky8vx0EMPISUlRdR17doVqamp6N69O0PsFurw4cO4//77kZaWVq/tFAoFZsyYgfnz50OtVru0zblz53DffffJRmbX5ZFHHsGnn34KlUpVr/4RERHVE29kyGUMsomIiIhasfnz5+PZZ591qe21BNmrVq1CfHx8tXqVSoWBAweiXbt2CAoKQk5ODtLS0mQjLyu9++67mDNnTr2OS0TXrzLU3rt3L5YvXw6j0YhDhw7V2l6pVOLmm2+GwWBAQkICOnToAKD+oXZubi4mTpyIw4cP46WXXkJkZCTUajViY2NdfjhPLYfD4UBubi5MJhMsFku1kX9XUyqVItTWarWNNhpQkiScOHECp06dkh2bITa5i4qKCjz66KP46aefRF2nTp2QmpqKXr16McRu4Ww2G5YuXYrPPvsMO3bsgLNns2q1GlOmTMGTTz6JoUOH1vtYkiTh888/x6effiqb1vxqHh4eGDlyJP7+979jxIgR9T4GERHRNeDNDLmMQTYRERFRK1ZbkO3t7Y3w8HCcOHFC1F1vkO3v74+77roL99xzD4YNG1YtkJIkCcnJyXjmmWdw9uxZ2b/9/PPP1dbsI6KmI0kSHA4HDhw4IEZq7927t9b2CoUCQ4cOFaF2586dRb0z2dnZmDhxoliCwNvbG2+++SYeeOAB+Pj4NNwPRG5JkiTk5eXBZDLBbDajrKys1rYKhQJhYWHQ6/XQarV1TqNbnz4wxCZ3ZrPZ8Pjjj2Pp0qWirn379ti4cSP69u3LELuVyc/PR1paGk6dOoW8vDyUlZUhICAAISEhGDBgAAYOHNhgL/UcPXoU+/btw8WLF3HlyhWEhYUhPDwcw4cPR1BQUIMcg4iIyEW8oSGXMcgmIiIiasXmz5+P559/Hv3798fgwYMxZMgQDB48GAMHDsS2bdswcuRI0fZag+zp06fj+eefx6xZs1xa1zYnJwe33HKLbORn3759cfDgQT6cJXIDlaH20aNHRaidnp7udJvBgwcjISEBBoNBTHlb9e/ZYrFg/PjxOHLkiKjr0qUL1q1bJ4JwajskSUJBQQHMZjNMJhNKSkpqbatQKBASEiJCbW9v72s+JkNscmc2mw1PP/00lixZIup0Oh1SU1MxYMAA3icRERFRa8GbGnIZg2wiIiKiViw3NxdqtbrGkY5btmy57iDbYrHA19fXpQD7anv27EFMTAwcDoeo27VrF4YMGVKv/RBR46oMtU+ePAmj0Yjk5GRs377d6TaDBg1CQkICEhMT0bt3bygUCly+fBlxcXGy9bi7d++O9evXiynKqe2SJAmFhYVipHZRUZHT9sHBwdDr9dDpdC6P5GeITe7O4XBgzpw5WLRokajTaDTYsGEDBg0axBCbiIiIWhPe2JDLGGQTERERtVENEWRfj1tvvRW//vqrKL/11lt4/vnnm+z4RFQ/kiRBkiScPXtWhNpbt26VvZBSVUREBMaPH4+kpCScOXNG1Pfq1Qvr1q1Du3btmqLr1MIUFRWJUPvKlStO2wYGBopQ29fXt8Y2DLHJ3TkcDvzlL3/BJ598IupCQ0Oxfv16xMbGMsQmIiKi1oY3N+SyhllkhYiIiIionqKjo2VB9sWLF5uxN0RUl8rpwrt27YpnnnkGs2fPxqVLl2A0GmE0GvHLL7/AbrfLtjl48CDOnz+PgoICUdenTx+sXbsWOp2uqX8EaiH8/PzQvXt3dO/eHcXFxTCbzTCbzcjPz6/WtqCgAAUFBTh27Bj8/f1FqO3v7w+AITa5P4fDgX/84x+yEDsoKAirV69miE1EREREbR6DbCIiIiJqFp6e8lvR8vLyZuoJEdVXZajdsWNHPPnkk3jiiSdgNpuxYsUKJCUlYdOmTaioqAAAWYjdpUsXnDlzBqNGjYLBYIDBYMDgwYOhVCoZ1lCNfH190bVrV3Tt2hWlpaUi1M7Nza3WtrCwEIWFhThx4gT8/Pyg1WpRXl4ue1GKITa5E0mS8M9//hPvv/++qAsICMDKlStx44038rxIRERERG2esrk7QERERERt09Vr5QJA+/btm6knRHS9lEol2rVrh5kzZ2LNmjXYvn07NBqNrI1Wq8Xly5dRWlqK06dP44MPPsCoUaMQERGB559/Hlu3boXNZkMzLn9Fbs7HxwedO3fG4MGDceutt6Jfv34ICwurMewrKirC6dOnZSG2QqFAVFQUQ2xyC5Ik4a233sJbb70l6vz8/JCSkoLhw4czxCYiIiIiAtfIJiIiImqzmnON7IKCArRv3x7FxcWibt26dRg/fnyTHJ+IGs+JEycwatQonD17VtQNGDAA3bt3R2pqquzvvqp27dohPj4eBoMBw4cPh6enJ8McqlNFRQUsFgtMJhOsVqvTddu9vb2h0+mg0+kQEhLCzxc1C0mS8P777+Pvf/+7qFOr1UhOTsbYsWP5uSQiIqLWjjc75DIG2URERERtVHMG2W+//Taef/55UQ4KCoLZbIZKpWqS4xNR4zhy5AhGjRolGwU7ceJELF++HCqVCleuXMHatWthNBqxdu1aXLlypdZ9abVa3H777TAYDLj11luhUqkY7lCdKioqcODAAVgsljrbqlQqaLVa6PV6hISEQKnkpHXU+CRJwscff4y//OUvos7b2xtJSUmYMGECz3NERETUFvCGh1zGIJuIiIiojWquIPv06dOIjIyUBVj/+Mc/8Oqrrzb6sYmo8Rw8eBCjRo2CyWQSdQaDAd9//z28vb1lbSVJQlFRETZs2IDly5dj9erVyMvLq3XfISEhmDhxIhITEzFy5Ej4+Pgw7KFqJEnCiRMncOrUKVGnUCgQEhKCgoIC2Gy2Wrf19PQUoXZoaCg8PDyaosvUxkiShM8//xxz5swRdSqVCj/88AMSEhJ4XiMiIqK2gjc95DIG2URERERtVHME2eXl5bjllluwa9cuUde1a1fs378ffn5+jXpsImo8e/fuxZgxY2SjYKdOnYqlS5fCy8vL6baSJKGkpASbNm3C8uXLsXLlSuTk5NTaPjAwEHFxcUhMTMSYMWPg6+vL8IdqDLGVSiWio6MRGhoKh8MBq9UKk8kEi8WCioqKWvfl4eEBjUYDvV4PjUbDUJsahCRJ+Prrr/Hkk0+KOk9PTyxbtgx33HEHz2NERETUlvDGh1zGIJuIiIiojWqOIPuBBx7Af//7X1H28PDApk2bMGLEiEY9LhE1noyMDIwdOxZWq1XU3XPPPViyZAk8PT3rtS9JklBWVoYtW7YgKSkJKSkpMJvNtbb38/PD+PHjkZCQgPHjxyMgIIBhUBtUV4hdlcPhQF5eHkwmE8xmM8rLy2vdt1KpRFhYmAi163oxg6gmkiRh6dKleOyxx1D5HM7DwwP//e9/MW3aNJ63iIiIqK3hzQ+5jEE2ERERURvV1EH23//+d/zzn/+U1f3nP//BvHnzGu2YRNS4du7cifHjxyM/P1/UPfjgg/jyyy+vexSrJEkoLy/H1q1bsXz5cqSkpMjW3q5KrVZjzJgxMBgMiIuLQ3BwMMOhNqC+IXZN2+fn54tQu7S0tNa2CoUCoaGh0Ov10Gq1UKlUDfIzUOsmSRJ+/PFHTJ8+HQ6HA8Afn9HFixfjgQce4HmKiIiI2iLeAJHLGGQTERERtVFNGWTPnz8fzz77rKxuzpw5ePfddxvleETU+LZt24YJEybI1rufMWMGPvvsMyiVygY9liRJqKiowI4dO5CUlITk5GScOXOm1vYqlQojR45EYmIiJk6ciLCwMIZFrdD1htg17a+goABmsxlmsxnFxcW1tq1ce1un00Gn01VbB54I+OMzlZycjAcffBB2ux3AH5+dhQsX4tFHH+V5iYiIiNoq3gSRyxhkExEREbVRTRVkL1q0SDaVJgA8/vjj+OSTTxr8WETUNH799VdMnDgRRUVFou7Pf/4zFixY0OAhdlWSJMFut2P37t0i1D5+/Hit7T09PXHrrbfCYDAgPj4eWq0WABggtXCSJOH48eOy65ZSqURMTAxCQkIaZP+FhYUi1C4sLHTaPjg4WITaarX6uo9PLZ8kSVi9ejXuu+8+2ZrsH3/8MWbNmtXo50oiIqL/1959h0dV5u8fv2fSCAlJICGhC4jSRbqChA7SMlFARYWIiosoKlFA3XUXyy7IimDBXRAFBDskoUgRkNCktwUBpRiqkEICAulzfn/45fwYUkgkyUyS9+u6vC6fT57nnM9IIpO5z3kO4ML4ZQwFRpANAABQTpVEkD1v3jw99thj5laakjR8+HB98sknhEhAKbZ//3516dJFSUlJkv7YYeGdd94p8Z/rq6H2//73Py1YsEAxMTE6ePBgnvPd3NzUsWNH2Ww2hYWFqXr16pIItUub4g6xc3P58mUz1L548WK+c/38/MxQ28fHp1j6gWszDEMrV67UkCFDHJ7B/u677+r5558nxAYAAOUdv4ChwAiyAQAAyqniDrK/+uorPfroo+ZWmpL0yCOP6LPPPuMDXKAM2LNnj7p166a//OUv+te//uX0MNgwDNntdu3fv19RUVGKjo7Wvn378pxvsVh01113KTw8XGFhYapdu7ZZh+vKLcR2c3NTy5Ytiy3Evl5qaqoZaqekpOQ719fXV8HBwQoJCZGPjw/fX+WAYRj64Ycf9MADDzg8c/3tt9/WSy+9xHsgAAAAgmwUAkE2AABAOVWcQfbChQv10EMPKSsry6wNHjxYX375pdzc3IrkHACc7/Tp06pRo4bLhXNXQ+2ff/7Z3H58586d+a5p27atwsLCZLPZVK9ePVksFpd7XeWdK4TY10tPTzdD7fPnz+c7t2LFiuad2n5+fnx/lUGGYWjDhg0aOHCgwzPW33zzTb366quE2AAAAH/gjTAKjCAbAACgnCquIHvx4sUaNGiQw/Mgw8PD9e2338rd3f2mjw8AhXE11D569Kiio6MVExOjLVu25LvmzjvvNEPt22+/nVDbBbhiiH29jIwMJSQk6Ny5czp//rzy+7ylQoUK5p3a/v7+fH+VAYZhaPPmzbrvvvscnqn+2muvacKECYTYAAAA/x9vflFgBNkAAADlVHEE2cuWLdN9993n8DzIfv36KSoqSp6enjd1bAC4WVdD7RMnTpih9saNG/MNHJs0aSKbzab77rtPjRs3JtR2gtIQYl8vMzNTiYmJOnfunJKSkmS32/Oc6+npaYbaAQEBBJ6lkGEY2rZtm8LDwx2eoT5u3DhNnDiRP1MAAABH/EKFAiPIBgAAKKeKOshetWqVwsLCHJ4H2atXLy1evFheXl430yoAFDnDMGQYhs6cOaPo6GhFR0dr/fr1ys7OznPN7bffrrCwMN13331q3ry5rFYroXYxK40h9vWysrKUlJSkc+fOKTExMd/vMQ8PD1WtWlUhISGqUqUKAWgpYBiGdu3apQEDBujChQtmfcyYMfr3v//NI1UAAABy4pcoFBhBNgAAQDlVlEH2unXr1KdPH6Wmppq1bt26aenSpfL29r7ZVgGgWF0NtePj47Vo0SJFRUVp7dq1Do9IuF69evVks9lks9nUunVrQu1iUBZC7OtlZ2crKSlJ8fHxSkhIUFZWVp5z3d3dVbVqVQUHByswMJBA1AUZhqF9+/apX79+Ds9If+aZZzRt2jQeqQIAAJA7fnFCgRFkAwAAlFNFFWRv3rxZvXr1cngeZGhoqJYvX66KFSsWRasAUKLsdruSkpK0ZMkSRUVFafXq1UpPT89zfu3atRUWFqbw8HC1a9dObm5uhNo3qSyG2Nez2+06f/684uPjFR8fn++FE25ubgoKClJwcLCCgoIISF2AYRg6dOiQ+vTpo4SEBLM+YsQIffTRR/wZAQAA5I1fllBgBNkAAABl3KlTp3K942vLli0aMmSIOa5Zs6Y2btyY6zF8fX0VFBSUo75792517drVYSvNhg0basGCBfL19S1UnxUqVFC1atUKtQYAipvdbldKSoqWLl2qmJgYrVixwmH3ietVr15d/fv3V3h4uDp27Ch3d3dC7UIqDyH29QzDUHJyshlq53fhhNVqVWBgoIKDg1W1alV5eHiUYKeQ/vjz+uWXX9S3b1+dPXvWrEdEROjjjz/mzwQAACB//IKEAiPIBgAAKOPq1q2r48eP39QxIiIiNGfOnBz1CRMm6PXXX7+pY1/VuXNnxcbGFsmxAKA42O12Xbx4UcuXL1d0dLSWL1/usBvF9apWrar+/fvLZrMpNDRUnp6ehNo3UB5D7OsZhqELFy6YoXZ+F05YLBZVqVJFwcHBCg4OlqenZwl2Wj4ZhqFjx46pT58+On36tFkfMmSI5syZw58BAADAjfFLEQqMfY4AAAAAACgAq9WqgIAADRkyRA899JAuX76slStXKioqSt99953D7hSSlJCQoNmzZ2v27NmqXLmy+vXrp/DwcHXt2lVeXl6E2tchxP6DxWJRQECAAgICdNttt+n33383Q+3Lly87zDUMQ0lJSUpKStLBgwdVuXJlM9SuUKGCk15B2WUYho4fP65+/fo5hNiDBg3S7NmzCbEBAACAIkaQDQAAAABAIVksFvn6+mrgwIG6//77lZqaqjVr1mjhwoVaunSpkpKSHOYnJydr/vz5mj9/vvz8/NSnTx+Fh4erR48e8vb2LvehNiF27iwWi/z8/OTn56cGDRro0qVLio+P17lz53LdDSA5OVnJycn6+eef5e/vr+DgYIWEhMjb29sJ3ZcthmHo1KlT6tevn06ePGnWw8LCNG/ePHl5eTmxOwAAAKBsYmtxAAAAAACKiGEYSk9P19q1axUVFaXFixcrPj4+z/m+vr7q3bu3wsLC1Lt3b/n6+pa7UJsQ+8+5cuWKGWpfvHgx37mVKlUyQ20fH58S6rDsMAxDv/32m/r06aMjR46Y9T59+mjBggWqWLGiE7sDAAAodcrXLzy4KQTZAAAAAAAUA8MwlJGRoQ0bNigqKkqLFi3SmTNn8pzv7e2tnj17ymazqXfv3goICCjzoTYhdtFIS0szQ+2UlJR85/r4+Jihdnm8cOLPOHfunPr27atDhw6ZtR49eigmJoYLAwAAAAqPN6AoMIJsAAAAAACKmWEYyszM1ObNmxUVFaWYmBidOHEiz/leXl7q2rWrwsPD1adPHwUGBpa5wNEwDB0+fFjHjx83a4TYNy89Pd18pnZycrLy+9zH29tbISEhCg4Olp+fX5n7HisK8fHx6t+/v3766Sez1rlzZy1ZskSVKlVyYmcAAAClFm86UWAE2QAAAAAAlCDDMJSdna1t27YpOjpa0dHROnr0aJ7zPTw81LlzZ9lsNvXv319Vq1aVpFIdOhJil4yMjAwlJCQoPj5eSUlJ+YbaFSpUUHBwsIKDg8vFbgAFkZSUpAEDBmjv3r1mrWPHjvruu+/k7+/vxM4AAABKNd5oosAIsgEAAAAAcJKrofbevXu1YMECxcTEOGxffD03Nzfdc889stlsCgsLU7Vq1SSVrlA7rxC7VatWCggIcF5jZVxWVpYZaicmJsput+c519PT0wy1K1euLKvVWoKduoaUlBSFhYVp586dZq19+/Zavnw5F1sAAADcnNLzywucjiAbAAAAAAAXYBiG7Ha79u/fr4ULFyomJkb79u3Lc77FYtHdd9+t8PBwhYWFqVatWmbdVRFiu4bs7GwlJiYqPj5eCQkJys7OznOuh4eHqlatquDgYAUGBpaLUPvixYu67777tGXLFrPWunVrrVixQkFBQU7sDAAAoExw3V9Y4HIIsgEAAAAAcDFXQ+1Dhw6Zz9TetWtXvmvatWunsLAw2Ww21a1bVxaLxaVCbUJs12S325WUlGSG2pmZmXnOdXNzM0PtoKAgubm5lWCnJePSpUsaOHCgNm7caNbuuOMOrVq1SsHBwU7sDAAAoMxwnV9S4PIIsgEAAAAAcGFXQ+2jR4+az9TeunVrvmtatmxphtq33Xab00NtQuzSwW63Kzk5WfHx8YqPj1dGRkaec61Wq4KCghQcHKyqVavK3d29BDstHleuXNHgwYMVGxtr1po2bapVq1apevXqzmsMAACgbCHIRoERZAMAAAAAUEpcDbWPHz+umJgYRUdHa9OmTcrvd/umTZvKZrMpPDxcjRs3LvFQmxC7dDIMQykpKWaonZaWludci8WiwMBAM9T29PQswU6LRmpqqoYMGaJVq1aZtUaNGmn16tWqWbOmEzsDAAAocwiyUWAE2QAAAEApduzYMW3fvl07duzQ9u3btWvXLv3+++/m12+55RbFxcUV2/n37dun1q1b59iKdvbs2XrssceK7bwA/ggaDcPQ6dOnzTu1N2zYkO/zjhs2bKiwsDCFh4erefPmslqtxRpqE2KXDYZh6OLFi4qPj9e5c+eUmpqa51yLxaLKlSsrODhYwcHB8vLyKsFO/5y0tDQ9+uijWr58uVlr0KCBVq9erTp16rjUFv1lSWpqqvbs2aODBw8qOTlZaWlp8vPzU3BwsFq1aqUGDRrw3x4AgLKJv+BRYATZAAAAQCkTGxuriRMnaseOHTp//ny+c4szyM7OztZdd92lHTt25PgaQTZQsq6G2ufOndOiRYsUFRWl2NjYfJ93XL9+fdlsNtlsNrVq1arIQ21C7LLJMAxdunRJ586dU3x8vC5fvpzv/ICAAIWEhCg4OFgVKlQooS4LLiMjQxEREVq8eLFZq1u3rtasWaN69eoRpBaDzZs3a9q0aYqJicl3+/qaNWvqiSee0PPPP68qVaoU2fm5CA8AAKfjDRYKjCAbAAAAKGWmTZumMWPGFGhucQbZkydP1vjx43P9Gh8GA85lt9uVmJioJUuWKDo6WqtXr1Z6enqe82vXrm2G2u3atZObm9tNBXh2u10vvviibr/9djVo0EASIXZZdfnyZfNO7Wt3BMmNn5+fGWpXrFixhDrMW2Zmpp588kktWLDArNWuXVurV682ny2PopOVlaUXXnhBH330Ub6PQ7heSEiI5syZo3vvvfeme+AiPAAAXAJvslBgBNkAAABAKZNXkO3l5aVatWrp6NGjZq24guzDhw/rjjvuMJ+ZWqlSJYcAgw+DAddht9uVnJyspUuXKiYmRitXrsx3a+gaNWqof//+Cg8PV4cOHeTu7l6oQM9ut2vUqFGaN2+efHx89Prrr6thw4aE2OVAamqqeaf2hQsX8p3r6+trhtq+vr4l1OH/l5WVpZEjR+rLL780azVq1NDq1avVqFEjQuwiZhiGHnjgAYeLBq5q1KiRGjduLG9vbyUkJGjHjh1KTk52mOPp6alFixbddJjNRXgAALgE3mihwAiyAQAAgFJm2rRpGjdunJo2bao2bdqobdu2atOmjZo3b65Nmzapa9eu5tziCLINw1Dnzp21YcMGSdKgQYOUkJCgdevWmXP4MBhwTXa7XRcvXtTy5csVFRWlFStW6NKlS3nODw4OVv/+/WWz2RQaGioPD498A75rQ+xrj7F161YFBwcX6WuBa0tLS1N8fLzi4+NzhJLX8/HxMZ+pXalSpWIPkbOysjR69Gh99tlnZi0kJESrVq1Ss2bNCLGLwccff6ynnnrKoRYaGqrp06erWbNmDvWsrCzNmzdPY8aMcbggIjg4WL/88ov8/f3/VA9chAcAgMvgzRYKjCAbAAAAKGWSk5Pl7e2d67NGY2Njiz3Inj59up599llJkr+/vw4ePKghQ4YQZAOlzNVnHa9cuVJRUVFatmxZvnfRVqlSRf369ZPNZlPXrl3l5eXlEPjZ7XY9/fTTmj9/vlnz9vbWV199pR49ehTra4Fry8jIMEPt8+fP57uttLe3txlq+/v7F3mobLfbNWbMGM2aNcusBQUFadWqVWrRogUhdjGpV6+ew/uR0NBQrV69Wh4eHnmu2bFjh+655x6HxyJMmjQpzzuq88NFeAAAuBTecKHArM5uAAAAAEDhVK5cOdcQuyScOHFCL7/8sjmePHmyqlev7pReANwci8WiSpUqadCgQfr888915swZLVq0SMOGDVOVKlVyzD9//rzmzZunQYMGqV69enryySe1ePFiXblyRdnZ2YTYyJOnp6dq1aqlVq1aqXPnzmratKmqVq0qqzXnx1Kpqak6fvy4tm/frg0bNujQoUM3DL8Lym63a/z48Q4hdpUqVbR8+XJC7GK0b9++HBfVvf/++/mG2JLUpk0bjRgxwqG2ZMmSP9XDRx99ZIbY/v7+ev/99//UcQAAAFCy3J3dAAAAAIDS46mnnjK3Ie7UqVOOD5gBlE4Wi0UVK1ZUWFiYBgwYoLS0NK1du1ZRUVFavHixEhISHOZfvHhRX331lb766iv5+PioZcuW2rhxo/l1QmzkxcPDQzVq1FCNGjWUlZWlxMRExcfHKzExUdnZ2Q5z09PTdfLkSZ08eVIeHh7mndpVqlTJNQTPj91u12uvvaaPPvrIrAUEBOi7775T69atCbGL0bFjxxzGtWvXVosWLQq01maz6cMPPzTHhw8fLvT5uQgPAACg9CLIBgAAAFAgc+bM0cqVKyVJXl5emjlzJh/8A2WQxWKRt7e3+vbtqz59+mj69Olav369oqOjFRMTo99++81h/pUrV3KE2C+99JLatGkjwzD4/wTy5O7urmrVqqlatWrKzs5WUlKS4uPjlZCQoKysLIe5mZmZOn36tE6fPi13d3cdP35cHh4eCg8Pl4+PT77nMQxDb775pqZNm2bWKlWqpCVLlqh9+/Z8jxazy5cvO4xr1apV4LW1a9d2GN/oeeu54SI8AACA0outxQEAQJkTFxcni8Vi/sOz7oCbd/bsWUVGRprjv/71r2rUqJETOwJQEiwWi7y8vNSzZ09Nnz5dcXFxWrt2rUaPHm2GUddu+ezt7a2qVavqzTffVP369TV48GDNnz9fSUlJRbI1NMouNzc3BQcHq1mzZurcubNatmypGjVq5Lr9dFZWlmbOnKmnnnpKt9xyi2w2m2bPnq2UlJQccw3D0KRJkzR58mSz5uPjo8WLF6tjx46E2CWgWrVqDuO0tLQCr71+bm6PPcgPF+EBAACUbtyRDQAAAOCGnnnmGfMuqKZNm2r8+PFO7ghASbNYLPL09FSXLl0UGhoqq9Wq9957z2GOv7+/Tpw4IemPbaGXL1+u5cuXy8PDQ126dJHNZlO/fv1UtWpV85jA9axWq4KCghQUFCS73a6UlBTFx8fr3LlzysjI0MWLF7Vv3z5JfzxTe/Xq1Vq9erUiIyPVvn17hYWFafDgwQoKCtLUqVP11ltvmcf29vZWdHS0OnfuzPdfCWnbtq28vLyUnp4uSTp48KBSU1Pl7e19w7U7d+7McayC4iI8AACA0o87sgEAAADk69tvv1VUVJSkP0KnmTNnytPT08ldAXAWwzD00ksvOYTYfn5++vTTTxUREaGGDRvmWJOZmalVq1bp2WefVYMGDdS/f3/NnDlTv/32mwzD4G5t5MlqtapKlSpq1KiRQkND1bZtW1ksFgUFBeWYm5GRoQ0bNmjs2LFq0KCBOnTooFmzZplf9/Ly0oIFC9SjRw9C7BJUqVIlDRs2zBynpaXpk08+ueG67Oxsh+djS1JERESBz8tFeAAAAKUfQTYAAACAPJ0/f17PPvusOR41apQ6dOjgxI4AOJNhGIqMjNTUqVPNmp+fn1auXKnhw4dr4sSJ+umnn7Rnzx797W9/U7NmzXIcIzs7W7GxsYqMjNRtt92m3r1766OPPtLJkycJtZEvi8WigIAA3X///Tp69KiWL1+uxx9/XDVr1swxNysrS//73/90/Phxc21ERIRatGhBiO0EkyZNUt26dc3xuHHjtHr16jznZ2Zm6qmnntLu3bvNWrdu3TRw4MACnY+L8AAAAMoGgmwAAAAAeXr++ecVHx8vSapZs6b+9a9/ObkjAM5yNcSeNm2aWbsaYt91112S/giM3Nzc1KJFC73xxhvas2eP9u3bp9dff10tW7bM9ZibNm3SuHHj1KhRI3Xv3l3vvfeefv31V9ntdkJt5MlqtSo0NFQffPCBDh06pLVr12rkyJEOYem1DMPQzJkzVbt2bXXs2FHvvvuu4uLiSrTn8qxKlSpau3at+f+B1NRU9e7dWw8++KC+/fZb7du3T0eOHNGWLVs0depUNW/eXJ9++qm5vl27dlqwYEGBLkLgIjwAAICyg2dkAwAAAMjVsmXLNH/+fHM8ffp0+fn5ObEjAM6SV4j9/fffq3379rmuuRpqN2vWTE2bNtVf//pXHTlyRNHR0YqOjta2bdtyrNm6dau2bt2qv/71r2rVqpXCwsJks9nUoEEDWSwW7qRFrqxWq9q1a6e2bduqZcuWeu2111S1alUlJCSYF2NdZRiGfvzxR/3444968cUX1bp1aw0cOFADBw7U7bff7qRXUD7UrVtXW7du1Zw5czRz5kzt3LlT33zzjb755ps81wQGBioyMlJjx46Vh4dHgc7DRXgAAABlB3dkAwAAAMjh4sWLGjlypDkeOHCgbDabEzsC4CyGYWjMmDGFCrGvdzXUbtiwocaPH68ff/xRR48e1TvvvKOOHTvmGlDv2rVLEyZMUMuWLXX33Xdr4sSJOnDgAHdqI1eGYeibb77R008/rfj4eP30009KTEzUO++8oylTpuT5fbZz5069+uqratiwoZo1a6Z//OMf+t///sf3WDHJzs5Wdna2vLy8bnhhSu3atfXOO+8oMjKywCE2F+EBAACULQTZAAAAAHIYN26cTp48KUny9/fXBx984OSOADjD1RD7vffeM2uFDbGvdzXUrl+/viIjI7V+/XodP35c06ZNU+fOnWW15vyoYv/+/frnP/+ptm3bqm3btnrjjTe0d+9eZWdnEzhChmEoOjpaI0aMkN1ul/TH99mMGTMUGRmpyMhIbdy4UadPn9b06dPVrVs3ubm55TjOTz/9pDfeeEMtWrTQ7bffrpdfflnbt2/ne6yIbNq0SY0bN9bTTz+tTZs2mX9WeTl58qSGDx+uOnXqaNasWTc8PhfhAQAAlD1sLQ4AAJCPhIQEbd68WadPn1ZycrICAwPVpk0btW7d+oZrf/75Z23btk1nzpyR1WpV9erVFRoaqjp16pRA58CfFxsbq5kzZ5rjyZMnq3r16k7sCIAzFEeIfb2r24XXrl1bzz33nEaPHq2zZ89q0aJFioqKUmxsrLKyshzWHDp0SIcOHdLkyZN16623KiwsTOHh4WrZsqWsVivbj5czhmHou+++0+OPP67s7Gyz/uGHH+rxxx93+H6oXr26Ro0apVGjRikxMVGLFi3SwoULtXr1amVmZjoc98iRI3r77bf19ttvq06dOrr//vv1yiuvKDg4uMReW1myZs0a9e/fX2lpaWatZs2aGj16tHr37q169eqpYsWKOn/+vPbs2aMvv/xSn3/+ubKyspSQkKARI0Zo27ZtmjFjRp4/41yEBwAAUPZYnHhVKZezAgCAYhEXF6d69eqZ44iICM2ZMyfXuV26dNG6devM8dX3Rnv27NGECRO0bNmyHB9sSlLTpk31/vvvq1u3bjm+tnjxYv3973/X3r17cz1nz5499cEHH6hhw4aFeVlAgcTGxqpr167m+JZbblFcXFyB16empqp58+Y6evSoJKlTp05at27dDYOh63+WZs+erccee6xQvQNwLa+88oomTZpkjv38/LRq1Sq1a9euRM5vt9uVmJioxYsXKzo6WqtXr1ZGRkae8+vUqWOG2m3btpWbmxuhdhlnGIZWrlypIUOGOHxvTJ06Vc8991yud/fnJiUlRUuXLtXChQu1YsUKh7D1Kk9PTyUkJLBN9Z+QkJCgpk2bKiEhwawNGDBA8+fPz/e/55YtW9S/f38lJSWZtUmTJmn8+PE55sbGxqpbt27me/kZM2boqaeeyrcv3rsAAOA0vElHgbG1OAAAwHXmzJmjdu3aadGiRbmG2NIfW0/27NlTn376qVmz2+165plnZLPZ8gyxJWnVqlW66667tH379iLvHbhZn332mRliW61Wvfrqqzp+/Lji4uLy/ef6D/0TExMdvh4fH++MlwPgJvTt21c+Pj6SSj7Elv74f1BwcLCefPJJLVmyRGfOnNHs2bNls9nk7e2dY/6JEyf04YcfqkePHmrSpIlefPFFrV+/XpmZmWwNXQYZhqE1a9bokUcecQixJ0+eXKgQW5ICAgL06KOPKjo6WgkJCfrmm2/04IMPytfX15zTs2dPQuw/6d1333UIsRs1aqRvvvnmhv8977rrLn399dcOtddffz3He4rU1FQ9+eST5s95p06dNGLEiCLqHgAAAM7E1uIAAADXiImJ0RNPPGE+s69BgwZq0qSJKlasqBMnTmjbtm3mFqd2u11/+ctfdMcdd6hNmzZ67rnn9NFHH0n648P3O++8U/Xq1ZPVatXBgwe1f/9+8zwpKSm6//77deDAAVWqVKnkXyiQh9TUVPPf7Xa7+vTp86eOM3bsWI0dO9Yc22w2xcTE3Gx7AEpQp06dtHz5cg0ZMkRRUVElGmJfz2q1KjAwUI899piGDRumixcvatmyZYqOjtby5ct1+fJlh/mnT5/WjBkzNGPGDAUHB2vAgAGy2Wzq1KmTPDw8uFO7lDMMQ+vXr9eQIUMcLqR666239OKLLxYqxL6er6+vBg8erMGDBystLU2rVq3SwoUL//Tfh5C+/fZbh/H48eNVoUKFAq3t3r27OnXqpA0bNkj6433KV199peeee86ck9dFeDeS10V4V1WsWJGt5AEAAJyMIBsAAOAajz/+uOx2u+6++2598MEHOZ6Fffz4cT388MP68ccfJUlZWVkaO3asRo0apenTp0uSHnvsMb311luqWbOmw9pNmzZp0KBBOnv2rCTp1KlTmjJliiZMmFD8LwwAgD+hU6dOOnLkSIFDp5JgtVoVEBCghx9+WA899JAuXbqklStXKioqSsuWLdPFixcd5sfHx+uTTz7RJ598osDAQPXr1082m01dunSRl5cXoXYpYxiGNm/erAceeEBXrlwx66+99ppeeeWVmwqxr1ehQgUNGDBAAwYMKLJjljeXL182Q+arunfvXqhj9OjRwwyyJWnr1q0OX+ciPAAAgLKLrcUBAACukZycrL59+2rt2rU5Qmzpj+cNL1u2TNWqVTNrsbGx+stf/iJJ+te//qXZs2fnCLElqWPHjoqJiXH4wHz27NlsdwoAcGmuFGJfz2q1ys/PT4MHD9YXX3yhM2fOKCYmRkOHDlWVKlVyzE9KStJnn32mgQMHqn79+hoxYoSWLFmiK1eu8PdxKWAYhrZt26aBAwfq0qVLZn38+PGaMGFCkYbYKBopKSk5ate+jy6I6+cnJibeTEsAAAAoRXiHDwAAcI3AwEDNmzdPXl5eec7x9/fXqFGjHGrJycnq0aOHXnnllXyP3759e3Xp0sUcnzhxQocPH76pnoGi9MILL8gwjEL/07lzZ4fjXL1I4+o/3NEEoLhZLBb5+PjIZrNp7ty5OnXqlJYuXarHH39cQUFBOeZfuHBBX375pR566CHVr19fjz32mKKionTp0iVCbRdkGIZ27dql++67z+Gu+zFjxuif//wnIbaLCggIyFG7/lEAN3LtRQuSHJ5dDgAAgLKNd/kAAADX+Mtf/pLrHVzX69mzZ47ayy+/XKBz9OrVy2G8Z8+eAq0DAAAFY7FY5O3trX79+mnWrFk6deqUVq5cqZEjR+Z6N+jvv/+uBQsWaOjQoapXr54eeeQRff3117pw4QKhtgswDEP79u1TeHi4Lly4YNafeeYZ/fvf/5abm5sTu0N+fHx85Ofn51DbvXt3oY6xc+dOh/H1P8NchAcAAFB2EWQDAABco6DP1Lvtttscxt7e3goNDf1Ta8+dO1ew5gAAQKFZLBZ5eXmpV69e+uijj3T8+HGtXbtWzz77rGrVqpVj/pUrV7Ro0SI9/vjjql+/vh544AF9/vnnOn/+PKG2ExiGoYMHD2rAgAE6f/68WR8xYoSmTZtGiF0KXLsbkSTNnDmzwGvPnj2rxYsXO9Q6depUFG0BAACgFHB3dgMAAACupHHjxgWa5+/v7zBu0KCBPDw8/tTaa7fHBArq1KlTysrKylE/e/aswzgrK0txcXG5HsPX1zfX7XYBoKyyWCzy9PRUly5d1LlzZ7377rvatm2boqKiFB0drV9//dVhflpampYtW6Zly5bJw8NDXbt2lc1mU9++fVW1alVZLBYnvZLywTAM/fzzz+rfv7/Dc5EjIiI0ffp0ubvzsVZp8OCDDzqE0V9//bX69eunRx99NN916enpGjp0qMPW4r6+vurdu3ex9QoAAADXwjt+AACAa1SuXLlA867/4DS35/8VdG1mZmaB1wJX3XPPPTp+/PgN550+fVr16tXL9WsRERGaM2dOEXcGAKWDxWKRh4eHOnbsqA4dOujtt9/W7t27tWDBAsXExOiXX35xmJ+Zmanvv/9e33//vdzc3BQaGqqwsDCFhYUpJCTEPCaKhmEYOnr0qAYMGOCwe82QIUP08ccfF/gCQjjfQw89pMmTJ2vv3r2S/vizHTZsmLZv366XX35Z1atXz7Fm7dq1ioyMzPEInvHjxxf4/ToAAABKP4JsAACAa1itf+7JK392HQAAcD6LxSJ3d3e1bdtWbdq00T//+U/t37/fDLV/+uknh/nZ2dlau3at1q5dqxdffFEdOnSQzWZTWFiYatasaR4Tf45hGIqLi1P//v115swZsz5o0CDNmTOHELuUsVqtWrBggTp27Kj4+HhJf/wZv//++/rwww91xx13qH79+vL29tb58+e1e/fuHDvMSFLfvn01fvz4km4fAAAATkSQDQAAAAAA8H+uhtp33nmnWrRooddff10HDhxQVFSUYmJictwharfbtXHjRm3cuFFjx45V+/btZbPZZLPZVKdOHVksFkLtQjAMQ6dOnVL//v118uRJs26z2TRv3jx5eno6sTv8WQ0aNNC6des0dOhQ7dixw6zb7Xbt2bMnx8/VtSwWi/lMdC5iAAAAKF8IsgEAAIBSKK/nXjtLbGyss1sAgCJnsVjk5uam5s2bq1mzZvrb3/6mw4cPKzo6WtHR0dq+fXuONVu3btXWrVv16quvqlWrVmaofeuttxJq34BhGDpz5oz69+/v8Pdc37599cUXX6hChQrOaw43rVGjRtq8ebO++OIL/fe//9WWLVtkGEae8729vXX//ffr2Wef1V133VWCnQIAAMBVEGQDAAAAAADcwNVQu1GjRnr55Zc1btw4xcXFmaH25s2bc4Ryu3bt0q5du/SPf/xDzZs3l81mU3h4uBo2bEionYtz584pLCxMR44cMWs9e/bUN998o4oVKzqxMxQVd3d3DRs2TMOGDdOFCxe0Y8cO/frrr0pJSVF6eroqVaqkypUrq1mzZmrevLnc3Yvvo0suwgMAAHB9BNkAAAAAAACFcDXUvvXWW/Xiiy8qMjJSp06dMrcf37Bhg+x2u8Oaffv2ad++fXrrrbfUuHFjhYWFKTw8XE2bNpXVai33oXZ8fLwGDBigQ4cOmbUuXbpo4cKF8vHxcWJnKC7+/v7q3r27s9sAAACAC7M6uwEAAAAAAIDSymKxyGq1qk6dOnr++ef1ww8/6OTJk5o+fbp69OiR6x2lBw8e1Ntvv627775brVq10t///nft3LlT2dnZ+W61XFYlJibKZrPpwIEDZu2ee+5RTEyMKlWq5MTOAAAAADgTQTYAAAAAAEARuBpq16hRQ6NGjdLKlSt16tQpffzxx+rTp488PT1zrDly5IjeffddhYaGqnnz5nrllVe0ZcuWchNqp6Sk6P7779f//vc/s9a+fXstXrxY/v7+TuwMAAAAgLMRZAMAAAAAABQDq9WqkJAQPfnkk1q6dKlOnz6t2bNnKywsTBUqVMgx//jx4/rggw/UvXt3NW7cWC+99JI2bNigrKysMhlqX7x4Uffff7927txp1lq3bq2lS5eqcuXKTuwMAAAAgCsgyAYAAAAAAChmVqtVQUFBeuyxxxQdHa0zZ85o/vz5GjhwYK7PgD59+rT++9//6t5779Xtt9+uF154QT/88IMyMjLKRKh96dIlDRo0SFu3bjVrLVq00LJlyxQUFOTEzgAAAAC4CoJsAAAAAACAEmS1WlW5cmU98sgj+uabb3TmzBl9/fXXevDBB+Xn55dj/rlz5zRr1iwNGDBADRo00DPPPKMVK1YoPT29VIbaV65c0YMPPqhNmzaZtaZNm2r58uUKDg52YmcAAAAAXIm7sxsAAAAAALieS5cuaevWrTp8+LCSk5NlGIb8/Px0yy23qEmTJrr11lud3SJQJlitVvn5+emBBx7Q4MGDdeXKFa1atUpRUVFaunSpkpOTHeYnJSVp7ty5mjt3rgICAtS3b1/ZbDZ1795dFSpUkMVicdIrKZjU1FQ9/PDDio2NNWuNGjXSypUrVb16dec1BgAAAMDlEGQDAAAAgAs7duyYtm/frh07dmj79u3atWuXfv/9d/Prt9xyi+Li4orsfD/88IOmTp2qFStWKCsrK895gYGB6tmzp1555RXdcccdRXZ+oDyzWCzy8fFReHi4bDab0tLStGbNGkVHR2vx4sVKTEx0mJ+SkqIvvvhCX3zxhfz8/NS7d2/ZbDb16tVLFStWdLlQOy0tTUOHDtWqVavMWoMGDbRixQrVqFHDiZ0BAAAAcEUWJ25BVfr2vgIAAACAEhAbG6uJEydqx44dOn/+fL5ziyrITkxM1FNPPaXo6OhCrfvPf/6jkSNH3vT5AeTNMAylp6dr/fr1ioqKUkxMjM6dO5fnfB8fH/Xs2VM2m029e/eWn5+f00PtjIwMRUREaPHixWatXr16Wr16terVq+f0/gAAAFBieOOHAiPIBgAAAAAXM23aNI0ZM6ZAc4siyD527Jh69eqlo0ePOtQ9PT3VsmVLVa9eXd7e3kpJSdHBgwcdzkeQDZQswzCUmZmpTZs2aeHChVq0aJFOnTqV5/wKFSqoe/fuCg8P17333qvKlSuXeGicmZmpJ554QgsXLjRrtWvX1po1a9SgQQNCbAAAgPKFN38oMLYWBwAAAIBSwsvLS7Vq1coRON+MhIQE9ezZU8eOHTNrNWrU0FtvvaVBgwapUqVKOdacO3dO3333nebOnUsABZQwi8UiT09Pde3aVV26dNHUqVO1detWM9T+9ddfHeanpaXpu+++03fffWeus9ls6tu3r4KCgor9ZzgrK0sjR450CLFr1Kih77//nhAbAAAAQL64IxsAAAAAXMy0adM0btw4NW3aVG3atFHbtm3Vpk0bNW/eXJs2bVLXrl3NuTd7R/bgwYO1YMECcxwaGqqlS5fmGmDnJisrS+7uXCMNOJthGMrOztbOnTu1cOFCxcTE6PDhw3nOd3d3V2hoqMLCwjRgwACFhIRIUpEGy1lZWRo9erQ+++wzsxYSEqLVq1eradOmhNgAAADlE28CUWAE2QAAAADgYpKTk+Xt7a0KFSrk+FpsbGyRBdlRUVEaOHCgOW7cuLG2b98uHx+fP3U8AK7haqi9b98+LVy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      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "threedee = plt.figure(figsize=(6, 6), dpi=300).gca(projection='3d')\n",
    "import numpy as np\n",
    "# Creating color map\n",
    "my_cmap = plt.get_cmap('tab10')\n",
    "scatter= threedee.scatter(data['lm'],data['bo'],data['rem'],c=data['sl'], alpha=1.0, marker=r'o', cmap = my_cmap)\n",
    "threedee.set_xlabel('lm')\n",
    "threedee.set_ylabel('bo')\n",
    "threedee.set_zlabel('rem')\n",
    "legend1 = threedee.legend(*scatter.legend_elements(), loc=\"upper left\", title=\"Stress Level\")\n",
    "threedee.add_artist(legend1)\n",
    "plt.tight_layout(pad=0)\n",
    "plt.savefig('stressfeature2.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SOHAIL~1\\AppData\\Local\\Temp/ipykernel_9024/1319021707.py:4: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  h.hist(ax = ax)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2100x2100 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = data.iloc[:64, :20]\n",
    "fig = plt.figure(figsize = (7,7),dpi=300)\n",
    "ax = fig.gca()\n",
    "h.hist(ax = ax)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0)\n",
    "plt.savefig('stressValuerange.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({3: 126, 1: 126, 0: 126, 2: 126, 4: 126})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(data[\"sl\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=data[\"sl\"]\n",
    "data.drop([\"sl\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sr</th>\n",
       "      <th>rr</th>\n",
       "      <th>t</th>\n",
       "      <th>lm</th>\n",
       "      <th>bo</th>\n",
       "      <th>rem</th>\n",
       "      <th>sr.1</th>\n",
       "      <th>hr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93.800</td>\n",
       "      <td>25.680</td>\n",
       "      <td>91.840</td>\n",
       "      <td>16.600</td>\n",
       "      <td>89.840</td>\n",
       "      <td>99.60</td>\n",
       "      <td>1.840</td>\n",
       "      <td>74.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.640</td>\n",
       "      <td>25.104</td>\n",
       "      <td>91.552</td>\n",
       "      <td>15.880</td>\n",
       "      <td>89.552</td>\n",
       "      <td>98.88</td>\n",
       "      <td>1.552</td>\n",
       "      <td>72.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.000</td>\n",
       "      <td>20.000</td>\n",
       "      <td>96.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>85.00</td>\n",
       "      <td>7.000</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85.760</td>\n",
       "      <td>23.536</td>\n",
       "      <td>90.768</td>\n",
       "      <td>13.920</td>\n",
       "      <td>88.768</td>\n",
       "      <td>96.92</td>\n",
       "      <td>0.768</td>\n",
       "      <td>68.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.120</td>\n",
       "      <td>17.248</td>\n",
       "      <td>97.872</td>\n",
       "      <td>6.496</td>\n",
       "      <td>96.248</td>\n",
       "      <td>72.48</td>\n",
       "      <td>8.248</td>\n",
       "      <td>53.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>69.600</td>\n",
       "      <td>20.960</td>\n",
       "      <td>92.960</td>\n",
       "      <td>10.960</td>\n",
       "      <td>90.960</td>\n",
       "      <td>89.80</td>\n",
       "      <td>3.440</td>\n",
       "      <td>62.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>48.440</td>\n",
       "      <td>17.376</td>\n",
       "      <td>98.064</td>\n",
       "      <td>6.752</td>\n",
       "      <td>96.376</td>\n",
       "      <td>73.76</td>\n",
       "      <td>8.376</td>\n",
       "      <td>53.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>97.504</td>\n",
       "      <td>27.504</td>\n",
       "      <td>86.880</td>\n",
       "      <td>17.752</td>\n",
       "      <td>84.256</td>\n",
       "      <td>101.88</td>\n",
       "      <td>0.000</td>\n",
       "      <td>78.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>58.640</td>\n",
       "      <td>19.728</td>\n",
       "      <td>95.728</td>\n",
       "      <td>9.728</td>\n",
       "      <td>94.592</td>\n",
       "      <td>84.32</td>\n",
       "      <td>6.728</td>\n",
       "      <td>59.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>73.920</td>\n",
       "      <td>21.392</td>\n",
       "      <td>93.392</td>\n",
       "      <td>11.392</td>\n",
       "      <td>91.392</td>\n",
       "      <td>91.96</td>\n",
       "      <td>4.088</td>\n",
       "      <td>63.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>630 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sr      rr       t      lm      bo     rem   sr.1     hr\n",
       "0    93.800  25.680  91.840  16.600  89.840   99.60  1.840  74.20\n",
       "1    91.640  25.104  91.552  15.880  89.552   98.88  1.552  72.76\n",
       "2    60.000  20.000  96.000  10.000  95.000   85.00  7.000  60.00\n",
       "3    85.760  23.536  90.768  13.920  88.768   96.92  0.768  68.84\n",
       "4    48.120  17.248  97.872   6.496  96.248   72.48  8.248  53.12\n",
       "..      ...     ...     ...     ...     ...     ...    ...    ...\n",
       "625  69.600  20.960  92.960  10.960  90.960   89.80  3.440  62.40\n",
       "626  48.440  17.376  98.064   6.752  96.376   73.76  8.376  53.44\n",
       "627  97.504  27.504  86.880  17.752  84.256  101.88  0.000  78.76\n",
       "628  58.640  19.728  95.728   9.728  94.592   84.32  6.728  59.32\n",
       "629  73.920  21.392  93.392  11.392  91.392   91.96  4.088  63.48\n",
       "\n",
       "[630 rows x 8 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n",
      "Counter({1: 102, 3: 102, 2: 101, 4: 100, 0: 99})\n",
      "test\n",
      "Counter({0: 27, 4: 26, 2: 25, 1: 24, 3: 24})\n"
     ]
    }
   ],
   "source": [
    "print(\"train\")\n",
    "print(Counter(y_train))\n",
    "print(\"test\")\n",
    "print(Counter(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 4000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "y = target       \n",
    "fig, ax = pyplot.subplots(figsize=(8,4),dpi=500)\n",
    "ax = sns.countplot(y,label=\"Count\",ax=ax,order=target.value_counts().iloc[:15].index )   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ETC\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "print(\"ETC\")\n",
    "dt = ExtraTreesClassifier(n_estimators=200, random_state=5, max_depth=20)\n",
    "\n",
    "X=data\n",
    "y=target\n",
    "# Training the model\n",
    "dt.fit(X, y)\n",
    "\n",
    "# Computing the importance of each feature\n",
    "feature_importance = dt.feature_importances_\n",
    "\n",
    "# Normalizing the individual importances\n",
    "feature_importance_normalized = np.std([tree.feature_importances_ for tree in dt.estimators_], axis = 0)\n",
    "plt.figure(figsize=(6,4),dpi=400)\n",
    "plt.xticks(rotation=90)\n",
    "# Plotting a Bar Graph to compare the models\n",
    "plt.bar(X.columns, feature_importance_normalized)\n",
    "plt.xlabel('Feature Labels',fontsize=12)\n",
    "plt.ylabel('Feature Importances',fontsize=12)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0)\n",
    "plt.savefig('FeatureImp.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting dataset into training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.20, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.99 accuracy with a standard deviation of 0.01\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=200, max_depth=50) \n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(rfc, data, target, cv=10)\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        30\n",
      "           1       1.00      1.00      1.00        26\n",
      "           2       1.00      0.96      0.98        24\n",
      "           3       0.97      1.00      0.98        28\n",
      "           4       1.00      1.00      1.00        18\n",
      "\n",
      "    accuracy                           0.99       126\n",
      "   macro avg       0.99      0.99      0.99       126\n",
      "weighted avg       0.99      0.99      0.99       126\n",
      "\n",
      "[[30  0  0  0  0]\n",
      " [ 0 26  0  0  0]\n",
      " [ 0  0 23  1  0]\n",
      " [ 0  0  0 28  0]\n",
      " [ 0  0  0  0 18]]\n",
      "--- 1.2815721035003662 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=200, max_depth=50) \n",
    "rfc.fit(X_train, y_train)\n",
    "# calculate accuracy of class predictions\n",
    "y_pred_class = rfc.predict(X_test)\n",
    "print(classification_report(y_test,y_pred_class))\n",
    "print(confusion_matrix(y_test,y_pred_class))\n",
    "\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
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5JSZMmBALFiyIhQsXRkREVVVVrL322jFy5MjYeOONvfcbnTQ88PDDD8eZZ56ZGBr49Pc+rrmTCT7+umw2G3fccUdMmzYtLrrooqiuri7YzwHkT1lZ7seAFDLV3diYe+4KN2bA/2evAkpa1+7Rdd/jo3ytETmHNS2cHbV3XhLR1NQubZWtxskDn5YpK4uKjbaLsrXWi9o7L4ns3Ol56AwA4LPc9wHQUXz5y1+OH//4x7HuuusWu5XPSAoP9OjRI4YPHx4REbW1tXHvvffGjTfeGM8//3zU1dXlnLNbt26x4447xr777hu77rprah8v3+muBhYtWhT/+7//G01NTZHJZBJPESgvL48xY8bE0KFDo7q6OhYtWhTvv/9+vPjii1FTU/OZ8R9/7EE2m40XX3wxDjnkkLjqqqti4MCB7f+DAqukpRuv8vLCPcWlpRuvpnb6xTpQ+uxVQMnq3uuDRxW0cOJAdvniqL3lvIiaZe3UWES2bkXUv/BANM2cHE1zp0d2xZIP1u/aIzLde0XZgCFRvu7GUT58k8h0r8o5V1n1oOh2wElRc/2ZkV0ws51+AgAgTdz3AdBR7LnnnsVuIdHrr7/e7Pc32mijqK+vjyuvvDKuuOKKWLRoUavnrKmpiX//+9/x73//O9Zbb7348Y9/HDvvvHO+Wu4wOl144OKLL465c+d+Ijjw8RBAnz594qijjor9998/+vTp85nX19bWxiOPPBJ//etf45lnnvnMSQQfzpfNZuOdd96Jb37zm/G3v/0tBg0a1E4/IbA6WkqUFTJZ3dJNXyET5UDHYq8CSlGmV7/out8JUdZvzZzjsjXLoubmP0d24ex26uwDDc/f33xhxdLIrlgajfPfj8YJz0RUdo2KsbtF5djdI9O1e+J8me5V0XX/E6Lm2tMjViwpUNcAQFq57wM+rSmbzk83Q1sknTywYsWK2HPPPWP69LadKDhp0qQ48sgjY9y4cfHrX/86evbs2ab5OpLCxRiLYNGiRXHttdd+4hiJD9/oz2azsdVWW8U999wT3/nOd5oNDkREdO3aNXbbbbf4+9//Htdee22sv/76zYYQPvzzu+++G4ceemjMnTu3wD8d0BZ1dfU56y09b64tWrrpq6vPfdMIpIe9Cig1mTUGR9ev/6hVwYHam/4c2TnvtVNnq6G+NhqeujNq/nlGNM3PfapAWa9+0WXnA9upMQAgTdz3AUDb1NbWxjvvvNNs7bXXXmtzcODj7rjjjjjwwAPj/fffz9ucpa5ThQduueWWqK2tjYiP3uD/8K+77LJLXHHFFVFdXd3q+caOHRs333xznHjiiSsv2poLEEyZMiUOO+ywWLx4cZ5/IiBfamtbepZN14Kt3a1r7rmXLVtesLWBjsVeBZSSsnU2jG5f+2GUVeW+h8ouXxI1N/4xmma/206dtU12wayoue530dRC0KFiw89H2ZBR7dQVAJAW7vsAoG0mTJgQDQ0N7bbexIkT4xvf+EZeQwmlrFM9tuDf//73yj9//LEFw4YNi7POOisqKytXec7y8vI46qijYquttooTTjgh5s2b94nTDD7881tvvRVHHnlkXHXVVdGtW7e8/UxAfixs4bk2vXpVxfvvzyrI2r165X627vJlKwqyLtDx2KuAUlG+0XbRZdfxkWnhk29NSxZE7U1/iuyC3J/kLzm1K6L2tgui28GnRqZH78RhlWN3i9r3JrRjYwBAZ+e+D6C4rr766rjmmmuK3UaLDjnkkBg/fnyx2yhJr7/+eqvHrrXWWrHnnnvGNttsE6NGjYq+fftGRMTcuXNj9uzZ8dRTT8XDDz8cL7zwQs55ZsyYEd/97nfjn//8Z/Tunfx7hM6g04QHampq4oUXXvjEIwsiPggR/PSnP23zsyi22GKLuO666+K73/1uvPPOO80GCF566aU44YQT4qKLLoqysk51qAN0ePPnLchZ71VVuOfVVPXKPff8+bl7A9LDXgUUXyYqd9gvKrfco8WRTfNnRu3Nf47skvnt0Ff+ZZfMj7qHb4iuex6eOKZs2MaRqeob2aUL268xAKBTc98HUFwLFixIPPK+lCxYYE9O0prwQP/+/ePkk0+OffbZp9nH9gwZMiSGDBkSY8eOjaOPPjqee+65OOuss+L5559PnHPSpEnxs5/9LP785z+3qf9S12ne4X7jjTeisbFx5d9/eOrAxhtvHDvuuGNe1hg8eHBcc801sdFGG33isQUfDxD897//jZ///Od5WQ/In3kt3Jj17dunYGu3NPc8N2bA/2evAoqqsmt0+fJRrQoONM6cHDXX/6HDBgc+1DjhmZyPL8iUlUfZuhu3Y0cAQGfnvg8A2uaNN97IWd9hhx3izjvvjAMOOKDZ4EBztthii7j22mvjiCOOyDnunnvuiXvuuafVvXZEnSY8MHny5M98L5PJxO67757Xdaqrq+Nvf/tbjB49OjFAcOONN8aFF16Y13WBtpk9Z17O+sBBaxRs7UEDc889a9bcgq0NdCz2KqBYMlV9o9vXfhgV623W4tiGSS9F7b/OjqhZVvjGCi4bDa89mnNE+ZrD26kXACAN3PcBwOprbGyMt956K7E+bty4uPTSS6O6unqV585kMnHKKafET37yk5zjzjzzzKivr1/l+TuKThMemDmz+Wdsbr311nlfq6qqKq666qqcJxCcd955cccdd+R9bWD1zJgxM2pqahLraw4aWLC1c809Z868qK2tLdjaQMdirwKKoWzgutH1oFOjbODQFsfWv/RQ1N1xUURD57lJbpzyWs56a/65AAC0lvs+4NOy2YyvdvyiY1u0aFHssMMOMWrUqM88sn7s2LHxu9/9LsrLy9u0xne+853Ye++9E+vvvfde3H333W1ao5S17qyGDmDJkiXNfn+ttdYqyHpVVVVx+eWXx/jx4+Odd95ZGRqIiJV//ulPfxprr712jB07tiA9AK2XzWbj3anTY9QG6zVbHzK4MHtFRMTgIclzT3l3WsHWBToeexXQ3spHjIkuex4RmcquOcdlm5qi/pEbo+GF+9ups/aTXTg7sjXLItMt4RnA3Xu1b0MAQKfmvg8AVl+/fv3iggsuWPn38+fPj/feey/ee++92HzzzaOysjIv6/z617+Oxx9/PBYsaP6RPtdff3185StfyctapabTnDyQlIrs169fwdasrq6Oyy+/PAYNGhQR8ZkAQV1dXXz/+9+P995LfoYm0H7emTQlsTZyZOGOo80199tvv1OwdYGOyV4FtJfyjbaLLuOOajk4UFcTdXdc1CmDAx/KLm8+jB4RkenWox07AQDSwH0fAORHv379YsyYMbHXXnvl9QPlVVVVMX78+MT6c889F/Pm5X4UUUfVacIDFRXNH6JQV1dX0HXXXHPNuPzyy6N3794R8dkAwYIFC+Koo46KpUuXFrQPoGWvvvZmYm39DUYUbN2R6w1LrL3+evKzeYB0slcB7aFiyz2i6/8cGpmy3Ef5NS2ZHzU3nBWN77zcTp0VR7Ymx/1aeX4+tQAA8CH3fQBQ+g455JDERyA0NTXF008/3c4dtY9O89iC7t27N/v9BQsWfOaZF/m23nrrxQUXXBCHHXZY1NfXrwwQZDIfPDtl0qRJcfzxx8dll13W5udsAKvv2eeSf+m93oh1o6qqZyxduiyva44cOTx6904+6vbFF1/N63pAx2evAgqtYutx0WXbL7c4rmnWu1F72wWRXbaoHbr6mK49ItNnjSjrM2DlXxtefTSaZk0p2JKZrjlOF2jw7F8AIL/c9wEUT3V1dYwYUbigVr5UV1cXu4XUq66ujg033DBee+21Zusvv/xy7Lnnnu3cVeF1mvDAGmus0ez333333RgyZEjB199yyy3j9NNPjx/+8IcrQwMfBgiy2Ww88cQT8fOf/zxOO+20gvcCNO+5515KrJWXl8cWY8fEw/99Iq9rbrXVZom1pqamePqZF/O6HtDx2auAQqrc5stRuc24Fsc1vvNy1N51WURDYU9yW6miS3T92g+jrM8aken22fB3dsXSwoYHevROrOV6pAEAwOpw3wdQPOPHj895HD183FZbbZUYHpg6dWo7d9M+Os1jC9Zee+1mv//444+3Ww/jxo2LY4899hOnDnw8QHDTTTfFBRdc0G79AJ80Zcq0ePfd9xLrO+20Td7X3GnH5Dlfe31CzJ+/IO9rAh2bvQoolIqxu7UqOFD/0kNRe/uF7RcciIhoqIuyvgObDQ5ERJStM6pwa/foHdEt+eSB7MLZhVsbAEgl930A0DGsu+66ibWZM2e2Yyftp9OEB0aN+uQvkz58w/7++++PbDbbbn0ce+yxsffeeycGCM4///z417/+1W79AJ90730PJdb22mu3vK+355d2Tazdd29yL0C62auAfCsf9fmo3PGAFsfVPXZz1D/4j4h2vIf6UNPc6Ym1skHrfvAmfwGUDxsdmUzyrXHT7GkFWRcASDf3fcCHmrIZX+34Bauib9++ibXly5e3XyPtqNOEB4YMGRIDBgz4zPenTp0at956a7v2csYZZ8Rmm232iQBBxEeBhl/84hdx3333tWtPwAfuvueBxNoWY8fEyJHD87bWTjtuE0OGrJVYv/W2e/K2FtC52KuAfCpbc1h02f1bOd8gzzY1Ru19f42GZ4r333zT+5MSa5my8qjYeLuCrFsxcvOc9cb3JhRkXQAg3dz3AUDp69OnT2KtoaGhHTtpP50mPBARscMOO3zilIEP36w/++yzY/bs9jtqskuXLnHRRRfFkCFDPtHHh39ubGyMk08+Oe6+++526wn4wL33PhTz5jV/DFtZWVkc/b1D87bWMcd8J7H2xptvx+NPPJu3tYDOxV4F5E33quiy9/ciU1GZOCTb1Bh1d18eja+33yPfmtM4+ZWc9YqNd4jI5PdTImVrDovyEWMS69nli6PpvbfyuiYAQIT7PgDoCFasWJFY69q1azt20n46VXjgK1/5yso/fzxEMGfOnDjuuONi6dKl7dZLdXV1XHrppSsTKZ8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Xx/67bBMbrpv5EYxv/WdMzFuwsFE1gMJzPwUAFBvhASBRKioq4odH/yDj+p/vfzgmTJjU6DrvvvdR/OWBRzKuH3ts5lQ5gFkFFDtzCmisUeMnxdQvZ2dcH3bAHg0+8j+bVhUVcfzBe2dcT6fT8XqW0wRW9sGnE7KuH7jbtg1prU4lJSXx3T22z7ieSqfjvU/GN7oOUDjupwCAYiQ8ACTKAQfsHT17ds+4fsstf8xZrZtvzrzXlltsGptuulHOagEti1kFFDtzCmisf38wOuv6gbs2/gP4r9tz282ipKQk4/qHYz5frX0+nzoz49rAvr2id7cuDe6tLjtvOTjr+meTp+WkDlAY7qcAgGIkPAAkysEH7Ztxbfz4ifH2Ox/krNa/X3srJk6cnHH9ewfvl7NaQMtiVgHFzpwCGuujLB/U9+vVPXp3z80H8Cvr1rljDOjTM+N6tlDAyubMm59xrVe3zg1tK6O1e3XLGnaYXjUnZ7WApud+CgAoRsIDQKLsvdduGdcefeyZnNd7/IlnM67tu+83c14PaBnMKqDYmVNAY42ZOCXj2mbrr5u3ut27dMq4NjtLKGBlS2vrfnZ4ROT0UQvlZWXRoV3bjOuLapbkrBbQ9NxPAcul/TTpD5Cd8ACQGIMHbxA9enTLuP7yK6/nvObLr7yWcW3rrYZEtxwdZwm0HGYVUOzMKaCxUqlUTJk5K+N6l46Veatd2bZNxrXFq/lhfPt2mfeYU72gwT1lkk6nY+HimozrbVpV5KwW0LTcTwEAxUp4AEiM7bfbKuv6a6+9nfOab775Xsa10tLS2HabLXJeE2jezCqg2JlTQGOl0xHXnHd8XHzSD+LkQ74d3/vmjrHzloNjw3X7RpcO7fMaHqiaOy/jWuvV/DC+a5b+vphR1eCeMqmaUx21yzKfctCxsl3OagFNy/0UAFCsygvdAEBT2WKLTTOuTZs2I6ZOnZ7zmuPHT4w5c+ZG5851H4259dabx1NPv5DzukDzZVYBxc6cAhqrrKw0dts68yzJp2wnHnRazQ/jNx7YL/71/id1rk2vmhPjJ0+PAX17rVF/K3v9o9FZ19dfp0+jawCF4X4KAChWTh4AEmOTwRtmXBs9emze6o4dNyHj2uabD85bXaB5MquAYmdOAc3VmIlTYubszCcPrLNWj9XaZ+uN18u6/thLbzaor0yeeDnzN49LSkpi8w0G5KQO0PTcTwEAxUp4AEiMQYMyv7Ey+tPP8lZ37NjxGdf6918nb3WB5smsAoqdOQU0V/98+6Os6xsPWHu19tlxyEbRs0vd39yNiLj3yX/GrLnVDert694eNS5efvfjjOvbDF4venbN3ANQ3NxPAQDFSngASISKioro0yfzsZFffDElb7WnTJ6WcW1A/355qws0P2YVUOzMKaC5Wlq7LP7y9KtZr9l+s8zfBF5ZWVlpHLn/7hnXFyyqiQuuHxnLlqUa0uIKs+fNjwtvvCvrNYfvu9sa7Q0UnvspAKCYCQ8AidC3b+8oLc088qZMyfziqbGmTZ+Rca1Ll87RoUNl3moDzYtZBRQ7cwporh576Y2YVjU74/rAvr1iQN/MH+Z93bD9dov+fXpmXH/tw9Fx4U13xZKlSxvUZ9WceXH6FbfFlJmzMl6z7SaDYq/tN2/QvkDxcD8FfF3KT5P+ANkJDwCJ0LtX5jd1IiKmTcv84qmxps/4Mut6t25d8lYbaF7MKqDYmVNAczRvwcK46c+PZ73mO3ts36A9KyrK49rzjo8O7dpmvOaJV96OYy6+IT4eN3G19nz5nf/E4RdcHR+N/TzjNV06VsYlpx3ZoF6B4uJ+CgAoZuWFbqAl+vzzz+PFF1+M999/Pz799NOYM2dOzJs3L0pLS6OysjLWXnvt2GCDDWLbbbeN3XffPTp06FDolqHFq+/Fz+zZc/NWe+6ceVnXu3TuFBNiUt7qA82HWQUUO3MKaI6uHvn3mDk78wzp0K5tHLLXTg3ed71+a8VNPzk5zvrN7TGnekGd13w09vM47ILfxje22Dj22Haz2Grj9aJ7547Rvm2bqF6wMKZVzYk3/zMmnv7Xu1lDAxERHdq3jRt+fGL07dmtwb0CxcP9FABQzIQHcui1116L2267LV5//fVIp9MRESv+e7nFixdHVVVVvP/++/HAAw9Eq1atYu+9946TTz451l9//UK0DYnQuUunrOvzqqvzVnv+/LrfRFquS5fOeasNNC9mFVDszCmguXnxrQ/jb8+/lvWaYQfsHh3bt1uj/bfcaGDce8V5cf71f4wPx2T+8P/V90bFq++NWqMaERHrrtUjrv/xiTFw7d5rvAdQHNxPAQDFLNHhgUWLFsWkSZNi3rz/Ji67dOkS6667bpSXN+x/lvnz58ell14ajzzySESsGhgoKSn5yr+vvF5TUxOPP/54PPHEEzF06NA477zzorLSs6Ug1yrbt8+6Xp3hWyK5UF09P+t6x05OHwH+y6wCip05BTQn4ydPjwtvujvrNX17dovjvrtXo+qs3atb3HXZ8Hjo+dfitr8+FdOr5jRqv5W1qiiPow/YM076/reiTetWOdsXKBz3UwBAMUtceGDChAnx8MMPx3PPPRfjxo2LVCr1lfVWrVrFkCFD4jvf+U4ceOCB0aZNm6z7ffnll3HCCSfE6NGjV4QCvh4W+Lq6wgTpdDruv//+ePXVV+PGG2+MjTbaaA3+dkAm7bI8hzLiv2GifFlcU5N1vaKiIm+1gebFrAKKnTkFNBdVc6vj9CtujeoFmedSSUlJ/Pzkw6J1q8bPj9LS0jhkr53iu7tvH9fd/XDc9fiLjduvpCSOPnDPOPqAPaJ7l46N7g8oHu6nAIBilpjwwKRJk+Laa6+Np556asWH9XWpqamJt956K95666244YYbYvjw4fG9732vzmsXLFgQRx11VIwfPz4ivhoKyLT/15WUlKz4c+l0OiZNmhSHH3543HzzzbHjjjs25K8IZNG6nm9o1NYuy1vt2trarOutcvBGFdAymFVAsTOngOZg3oKFceplN8cX06uyXnfMd/aMHYZsmJOac6sXxMjHXojHX34rpsyc1ej9Uul0PPbSm1FeVhaH7r1TrNWjaw66BIqB+ykAoJglIjxw3333xW9+85tYvHjxVz7Uz3RCwPJrZs6cGRdeeGG88sorceWVV0arVl+9sbv44otj/PjxX/nwv769M9VaOUSwaNGiOOWUU+KOO+6IbbbZZjX/lkA2ZWVlWdfre/HUGPW96Kto4KNSgJbLrAKKnTkFFLv5CxfFab+6NT6ZMDnrddtuMijOOOyARterXbYs7vz7s/HHh5+L+YsWN3q/lX05Z178/m/PxMjHno+jD9gzTvje3tGuTeuc1gCanvsp4OtSsXpfRgVoCqWFbiDfLrvssrjkkkti0aJFkU6nV3xIv/IH/iv/RMRXrkmn0/Hkk0/GaaedFkuWLFmx77///e94/PHHswYHvr53tlpf/11NTU2cfvrpMXHixPz/jwQJUFqaPdCTz1T3smXZ9y73wgz4/8wqoNiZU0Axm7dgYZx82c3xwZgJWa/r16t7XH3u8VFRnv0DvPrMmDU3jv/5jXHTnx/PeXBgZUuW1sbv//ZMHDz88hg1flLe6gBNw/0UAFDMWvTdwOWXXx533313RNT9SIGVQwQrq+uD/VdffTUuvfTSuPTSSyMi4qabbqrz+uX/Xl5eHttvv33suuuusf7660e3bt2ivLw8qqqqYsqUKfHKK6/EK6+8EnPmzFklQLD83+fOnRvnnntu/PnPf643kQpkV98Lr7Ky/GWp6nvhlUql8lYbaF7MKqDYmVNAsaqaWx2nXnZzvScOdOlYGTdfeEp07tC+UfWmzJwVR190bcyYNbfea/v16h67br1J7LTFxtGvV/fo2qky2rSqiFlz50fV3Op495PP4tX3RsXrH34atVk+2Jv65ez44c+ujyvOPCq+uf3mjeofKBz3UwBAMWux4YG///3vMXLkyFVCA18PDKx8YkBErHIqwfLfpdPp+Otf/xp77LFH9O/fP95+++0Vv19+zfI/s//++8fZZ58d/fr1W6Wv9dZbLyIiDjrooFiyZEmMHDkybr/99pg7d+4qAYKIiI8++ihGjhwZxx57bK7+p4FEWvnkkLrkM1ld34u+fCbKgebFrAKKnTkFFKMpM2fFKZfdHBOmzMh6Xcf27eK2i06Lddfq2ah6c6oXxCmX3VxvcKBvz25x6tB944BdtonS0lVnWO/uXaJ39y6xyXrrxLD9d4/JM6ri1geeisdeejOWZfgAb3HNkvjxdX+MWy86LbbdZP1G/T2AwnA/BQAUsxb52IJZs2bF5ZdfvsrjA77+7z179oyddtop9t9//9h9991j8ODBUV5evkogYLl0Oh2//vWv4+mnn17xu5VPDSgvL48rrrgirr766jqDA1/XqlWrOOGEE+KRRx6JTTbZ5Cs9Lt87nU7HLbfcEtXV1Y3+3wWSbMmSpVnX83m6R30v+pYszf6iEUgOswooduYUUGw+/XxyHH3htasXHPjZabHRgLUbXfNXt/+l3np7bLtZPPjbC+I7u21XZ3CgLn17dotLTz8ybrnw1OhU2S7jdUtrl8Xwq+6IL6ZXNahvoDi4nwIAilmLPHngmmuuiXnz5q348P3rIYD9998/TjzxxNhoo41W+bPV1dXx9NNPx4gRI2Lq1Kmr7DFx4sS49dZbv/Jn0ul0lJaWxo033hi77757g/vt1atX3HvvvXHqqafGv/71r1VqVldXx/333x8nnHBCg/cG/qumJvuLnzZtWuetdpvW2fdesGBh3moDzYtZBRQ7cwooJq99MDrOvfrOqF64KOt1XTq0j1t/dlpsPKD+L3rU51/vj4qn//1u1msO3fsbcdGJQ+t8VObq2GHIhnHflefFcT+/MaZVza7zmnkLFsaVd/41bvrJyWtUAygc91MAQDFrcScPzJw5Mx5++OEVL9BWPm2gsrIyfve738XVV19dZ3AgIqJDhw5xyCGHxKOPPhr77LPPig/xl59WkE6no6amZsX1y9fPOOOMNQoOLNe6deu4/vrrY9111/1K38trP/DAA2u8NxAxZ2724yQ7dKjMW+369l64IPsbXUBymFVAsTOngGLxt+f/Haddfku9wYGeXTvFHy45KyfBgYiIm/78RNb1HTbbMH56/KFrHBxYbu1e3eOG80+Mtq1bZbzmpXf+Ey+9/Z9G1QGanvspAKCYtbjwwP333x9Ll3716Kd0Oh1t2rSJP/7xj7HLLrus1j6VlZVx9dVXx/bbb/+VUwCWP6Zg5ReBffr0iZNPbnzSu0OHDnHVVVeteGzC8v+O+O+JB6NHj250DUiqWRm+rbFch8r2eatd2SH73rNmZe8NSA6zCih25hRQaKlUKq69++H4+S33Re2yVNZr112rZ4y8bHgMXLt3Tmp/+vnk+Gjs5xnXK9u2iV+f/cN6nym+ujYasHacf+z3s15zzxMv5qQW0HTcTwEAxazFhQf+8Y9/fOXfl3/w/5Of/CQ22WSTBu1VXl4e11xzTbRv/9+bquWnAHx975NPPjlnz6IaMmRI7L333nU+buG1117LSQ1Ioqp6Xph17twpb7Xr27vKCzPg/zOrgGJnTgGFtHBRTZx91e/jDw8/V++1mw5aN0Zednb06dE1Z/Uff/mtrOtHH7hHdOmY228Mf3f37WO9LOGH1z78NL6YXpXTmkB+uZ8Cvi7tp0l/gOxaVHhgxowZMXr06FU+dF933XVj6NCha7Rnt27d4uSTT/5KaGBlJSUl8c1vfnON9s7kiCOOqPP3H3zwQU7rQJLMmJn9zZSevbrnrXavntn3nj79y7zVBpoXswooduYUUCjTqmbHMRdfFy++9VG91+629aZxxy/OzPkH+e+NHp9xrbysNI7Yb7ec1ouIKCsrjWMP2ivjejqdjlff+zjndYH8cT8FABSzFhUe+OSTT77y78u/vX/wwQc36llzRxxxRLRp0yYiYsU+y8ME6623XnTr1m2N967LNttsE+3atfvK79LpdHz+eeaj8YDspkyZFosXL8643rtXz7zVzrb3zJlVUVNTk7faQPNiVgHFzpwCCuE/4ybGkT+5Oj6ZMLnea3+wz85x3f+dEG1bt8ppD7XLlsWozyZlXN98gwHRsX27jOuNscuWg6M0y/taH4zxfhE0J+6nAIBi1qLCA+PGjavz9zvuuGOj9q2srIxvfvObq5w+UFJSEn369GnU3nWpqKiIwYMHr6i3PLAwbdq0nNeCpEin0/H5xMxvNK3dd6281e67dua9J3ye+c0nIHnMKqDYmVNAU3vhzQ/j2J/fEDNnz8t6XWlJSZx39EFx4QlDo6ws9293Vc2pjsVLlmZc32aT9XNec7kuHStjowFrZ1wfPeGLvNUGcs/9FABQzFpUeGDOnDl1/n7ttTO/wFpdO+ywQ52/79o1d8/Oq2/f+fPn56UWJMVn4yZkXBs0aEDe6mbbe8yYz/JWF2iezCqg2JlTQFP52/P/jnN+e0csrlmS9bq2rVvFtf93Qhx94J5562XegoVZ13t1zd8zyiMi1uqR+f2nOfMW5LU2kHvupwCAYtWiwgMLFtT9YqlTp8a/gNtss83q/P2SJdlfwK6pDh06rPK7pUszJ9yB+n30n08yrq2/wcC81R20Xv+Max9//Gne6gLNk1kFFDtzCmgKd/ztH/HzW+6LZalU1ut6descf7z0rNhj27rft8mV+QszHzEeEdG106rv4+RStyz71xdsAIqP+ykAoFi1qPBA27Zt6/x9dXV1o/fu2bPu50HNnj270XvXZd68VY/ja9Uqt8/rg6R56+0PMq6tN3DdqKxsn/OagwYNiI4dM7/J8957H+W8JtC8mVVAsTOngHy75YEn4/p7H633uo0HrB33XH5ubDygX957ysejEBqiorws41rtsuwBC6D4uJ8CAIpViwoPdOnSpc7fT5s2rdF7d+zY8Sv/XlJSEul0OsaPH9/ovesyZcqUVX5X12kEwOp7++33M66VlZXF1lsNyXnNbbfdIuNaKpWKN958L+c1gebNrAKKnTkF5NOI+5+IW/7yZL3X7br1JvGHS86Knnl+XMBy7du0zro+a15+HzVZNTfzF2Pa1dMbUHzcTwErS/lp0h8gu0SEB15//fVG751KpaJjx46RTqdX/ET8N5hQVVXV6P1XVl1dHaNGjYqSkpKIiEin01FSUhJ9+vTJaR1ImgkTJsXnn3+RcX3XXXfIec1dd8m8538+Hh2zZuXn9BKg+TKrgGJnTgH58qdHn4/b/vpUvdf9YJ+d4/r/O7FJPzRfq3vXrOtjJ07Na/2Zs+ZmXGuqAAWQO+6nAIBi1aLCAxtvvPEqv0un0/H44483eu9WrVrFG2+8Ee+++248/vjjcfvtt8cll1wSJ598ck4ei7CyJ554IlJ1PNNv0KBBOa0DSfT0My9mXNtvv71yXm/fb38z49ozT2fuBUg2swooduYUkGuPv/xmXHPXw/Ve96MjDogLTxja5I8RaNe2ddYP6d/6eEzeai+uWRIfjZ2YcX3dtXrkrTaQP+6nAIBi1KLCAxtuuGF0NDuy4QAAb0VJREFU6vS/F3LLv7n/4YcfxosvvpiTGm3bto311lsvdtlllxg6dGicffbZ0b9//5zsHRGxdOnS+P3vf7+i95VtvvnmOasDSfXkU89lXNt6qyExaNCAnNXadZcdYu2118q4/vAj9X+jBkgmswooduYUkEsfjJkQF99874pTHutSVloavzz18Djh4G81YWdftcWGmWfbp59Pic+nzshL3Tc/Hhs1S5dmXB+yQe5mLtB03E8BAMWoRYUHSkpKYrvttvvKi82SkpJIp9Nx0UUXxcyZMwvY3eq56qqrYtKkSav8vqSkJHbbbbcCdAQty9NPvxhVVXUfw1ZaWhqnnvzDnNU67bRjM66N+mRM/Ovfb+WsFtCymFVAsTOngFyZNbc6zr36zlhauyzjNWWlpXHlWUfHwXvu2ISdreobW6x64uXK7nvypbzUvevRF7Ku7zBkg7zUBfLL/RQAUIxaVHggIuKwww5b8c/LQwQlJSXx5ZdfxnHHHRdTpkwpVGv1GjlyZIwcOXJF4CHiv3+HkpKS2HbbbaNnz54F7hCavyVLlsS99z2Ucf2kk4ZF//79Gl1n2222iO8dvF/G9TvuuLfRNYCWy6wCip05BeTKJbfdH9Or5mS95qKThsY+O23VNA1lsdf2W0SrivKM6w8++++YNC23X1x5/cNP47UPR2dcH9C3V2w8oPHzFmh67qcAgGLU4sID3/jGN2LDDTeMiPjKh/AlJSUxZsyYOOSQQ+Lvf/97ATtc1aJFi+Kyyy6LK664IuM1w4YNa8KOoGW7/fd3RyqVqnOtbdu2cfttV0dp6ZqPx7Zt28Rtt12VcY/58xfEyLseWOP9gWQwq4BiZ04BjfXEK2/H829+kPWa4w/aK77/zZ2aqKPsOrRvGwfuul3G9ZqlS+Pim+/NeopCQ1TNmRc/G3FP1msO//YuOakFFIb7KQCg2LS48EBExDnnnPOVRxes/M+zZs2Kn/zkJ/Gd73wnRo4cWdBHGdTU1MQDDzwQ3/72t+Oee+5ZccrA1/978ODBsffeexesT2hpPv7403jgr49mXN9jj2/E1b/9xRrtXVJSEn+48/oYstngjNdcfc0tMWtW3cfSASxnVgHFzpwCGmNRzZK4euTfsl6z7Sbrx4+OOLCJOlo9xx+8V9bTB94eNS5+cet9sWxZ3R8Grq4FixbHWb/5fUzLcKR5RETPrp0K/igHoHHcTwEREalI+2nCHyC7Fhke2G233eKII45Y8eF7xP+O/1/+ofynn34aV1xxRey6666xzz77xPnnn98kvVVXV8ezzz4bF198cey8885x8cUXx/Tp01cJDCxXVlYWv/zlL5ukN0iSX15ydSxdujTj+plnHB9X//aXX/l/j/Vp1apV3DXypjjk+wdkvGbq1Olx9TW3NqhXILnMKqDYmVPAmrrrsRdi5ux5Gdc7tG8bvzpjWIPmR1NYu1f3OPa7e2W95tF/vhFn/vp3UTW3eo1qfD51Rgz76TXxwZgJWa+74LhDonWrijWqARQP91MAQDHJHJVu5s4///x477334uOPP17xofzXAwTLTyT4/PPPo6ysLO89ff7557HffvutOIpq5UcqrPzvy/+5pKQkzjjjjNh0003z3hskzaefjosrrrwhLv7ZuRmvOetHJ8SQzTaOU08/P8aOHZ91vy222CRuu/W3sfVWQ7Jed/Y5F8fChYvWqGcgecwqoNiZU8CaWFyzJO5+/MWs11QvWBTfOvXnTdPQSj544IZ6rznpe9+KV9/9OD4aNzHjNa+8+3EcdPav4pRD943vfXPHaNu6Vb37zl+4KO5+/MUY+egLMX/R4qzXHrjbdrHX9pvXuydQ/NxPAQDFpCS98ifWLUx1dXWccMIJ8f7776/yAf3XTyTYd99945prrslrP4sXL44tttiizhMG6urru9/9bvz617/Oa09J0bdv39W+dnrhnmRBEysrK4tXXno4tt12y6zX1dbWxoMPPR5/ffCxeOut92LatJlRWloaffr0ih122Dp+MPS7sd++36z3GXQjbv5DnHX2Rbn8K9AM9OqR+z3NqWQxq8g3c4rGMqdoCvmYVZ89eWfuN2W1PPTcv+MXt95X6DbqtDrhgYiIqTNnxbALr8l6esJyHdu3iz232yx22nzj2LB/3+jZtVO0blURc+cvjNnz5scn47+If703Kl565+OYt2Bhvfttst468Ydf/ijarEYggaYzcN/jcr6ne6rkcD9FU1jd+6nJkyfntxFWcU7/wwrdQqJcM+HPhW4BilqLDg9ERCxcuDDOOeecePHFF1f5sH7lD/HPPvvsOPnkk/Pez8477xxVVVVf6WO5lYMD3/nOd+LKK6+s90aP1SM8QCZrrdUrXn350VhnndX/v5E18eqrb8Te+/wglixZktc6FB8fypELZhX5ZE6RC+YU+SY80LKcdMmIeO3D0YV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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[30 , 0,  0 , 0,  0],\n",
    " [ 0 ,26 , 0,  0 , 0],\n",
    " [ 0 , 0 ,23 , 1 , 0],\n",
    " [ 0  ,0 , 0 ,28 , 0],\n",
    " [ 0 , 0 , 0 , 0, 18]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConRF.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.99 accuracy with a standard deviation of 0.02\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "a = GradientBoostingClassifier(max_depth=200, learning_rate=0.2, n_estimators=50, random_state=52)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(a, data, target, cv=10)\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        30\n",
      "           1       1.00      1.00      1.00        26\n",
      "           2       1.00      0.96      0.98        24\n",
      "           3       0.97      1.00      0.98        28\n",
      "           4       1.00      1.00      1.00        18\n",
      "\n",
      "    accuracy                           0.99       126\n",
      "   macro avg       0.99      0.99      0.99       126\n",
      "weighted avg       0.99      0.99      0.99       126\n",
      "\n",
      "[[30  0  0  0  0]\n",
      " [ 0 26  0  0  0]\n",
      " [ 0  0 23  1  0]\n",
      " [ 0  0  0 28  0]\n",
      " [ 0  0  0  0 18]]\n",
      "--- 0.9627573490142822 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "a = GradientBoostingClassifier(max_depth=200, learning_rate=0.2, n_estimators=50, random_state=52)\n",
    "a.fit(X_train, y_train)\n",
    "# calculate accuracy of class predictions\n",
    "y_pred_class = a.predict(X_test)\n",
    "print(classification_report(y_test,y_pred_class))\n",
    "print(confusion_matrix(y_test,y_pred_class))\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[30 , 0,  0 , 0,  0],\n",
    " [ 0 ,26 , 0 , 0  ,0],\n",
    " [ 0,  1 ,23 , 0 , 0],\n",
    " [ 0 , 0 , 0 ,28  ,0],\n",
    " [ 0 , 0  ,0 , 0 ,18]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConGBM.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADA\n",
      "0.67 accuracy with a standard deviation of 0.12\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "print(\"ADA\")\n",
    "xgb = AdaBoostClassifier(n_estimators=200, random_state=5,learning_rate=0.5)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(xgb, data, target, cv=10)\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADA\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00        21\n",
      "           1       0.50      1.00      0.67        21\n",
      "           2       0.97      1.00      0.99        34\n",
      "           3       1.00      0.96      0.98        24\n",
      "           4       1.00      1.00      1.00        26\n",
      "\n",
      "    accuracy                           0.83       126\n",
      "   macro avg       0.69      0.79      0.73       126\n",
      "weighted avg       0.74      0.83      0.77       126\n",
      "\n",
      "[[ 0 21  0  0  0]\n",
      " [ 0 21  0  0  0]\n",
      " [ 0  0 34  0  0]\n",
      " [ 0  0  1 23  0]\n",
      " [ 0  0  0  0 26]]\n",
      "--- 5.998065710067749 seconds ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "print(\"ADA\")\n",
    "xgb = AdaBoostClassifier(n_estimators=200, random_state=5,learning_rate=0.5)\n",
    "xgb_pred = xgb.fit(X_train, y_train).predict(X_test)  \n",
    "print(classification_report(y_test,xgb_pred))\n",
    "print(confusion_matrix(y_test,xgb_pred))\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
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RwBrOvR9Q6uxTAKxJMplM7LbbbnH00UfH5ptvXux2VnjzzTcTa5ttttlnflZXVxevvvpqjBs3LubPnx8LFiyIiIiqqqpYd911Y+jQobHpppv67DfaaXjgkUceibPPPjsxNPDpn31ccycTfPx92Ww27rrrrpgyZUpccsklUV1dXbC/B5A/ZWW5HwNSyFR3Y2PuuSvcmAEAa4KOnaPj/idE+TpDcg5rWjArau++LKKpqU3aKluNkwc+LVNWFhWb7BBl62wQtXdfFtk5U/PQGVAM7v2AUmefAmBN8ZWvfCVOO+20WH/99YvdymckhQe6dOkSgwcPjoiI2trauO++++Lmm2+OF154Ierq6nLO2alTp9h5551j//33j9133z21j5dvd1cDCxcujJ/97GfR1NQUmUwm8RSB8vLyGDFiRAwcODCqq6tj4cKFMX369HjppZeipqbmM+M//tiDbDYbL730Uhx22GFxzTXXRN++fdv+LwqskpZuvMrLC/cUl5ZuvJra6BfrAACrrXO3Dx5V0MKJA9lli6L2tgsiapa2UWMR2brlUf/ig9E0Y2I0zZka2eWLP1i/Y5fIdO4WZX0GRPn6m0b54M0i07kq51xl1f2i00EnR82NZ0d2/ow2+hsA+eTeDyh19ikA1hR77713sVtI9MYbbzT780022STq6+vj6quvjquuuioWLly40nPW1NTEf/7zn/jPf/4TG2ywQZx22mmx66675qvlNUa7Cw9ceumlMWfOnE8EBz4eAujRo0ccffTRceCBB0aPHj0+8/7a2tp49NFH429/+1s8++yznzmJ4MP5stlsvPvuu/Gtb30r/v73v0e/fv3a6G8IrI6WEmWFTFa3dNNXyEQ5AEBrZbr1io4HnBhlvdbOOS5bszRqbv1LZBfMaqPOPtDwwgPNF5YviezyJdE4b3o0jns2orJjVIzcIypH7hmZjp0T58t0roqOB54YNdefGbF8cYG6BgrFvR9Q6uxTwKc1ZdP57WZojaSTB5YvXx577713TJ3auhMFJ0yYEGPGjIlRo0bFb37zm+jatWur5luTFC7GWAQLFy6M66+//hPHSHz4QX82m41tttkm7r333vjud7/bbHAgIqJjx46xxx57xD/+8Y+4/vrrY8MNN2w2hPDhn9977704/PDDY86cOQX+2wGtUVdXn7Pe0vPmWqOlm766+tw3jQAAxZJZq390/MaPVyo4UHvLXyI7+/026mw11NdGw9N3R82/zoqmeblPFSjr1is67HpwGzUG5JN7P6DU2acAoHVqa2vj3Xffbbb2+uuvtzo48HF33XVXHHzwwTF9+vS8zVnq2lV44Lbbbova2tqI+OgD/g//udtuu8VVV10V1dXVKz3fyJEj49Zbb42TTjppxUVbcwGCSZMmxRFHHBGLFi3K898IyJfa2paeZdOxYGt36ph77qVLlxVsbQCA1VW23sbR6es/irKq3PdQ2WWLo+bmP0XTrPfaqLPWyc6fGTU3/D6aWgg6VGz8+SgbMKyNugLyxb0fUOrsUwDQOuPGjYuGhoY2W2/8+PHxzW9+M6+hhFLWrh5b8J///GfFnz/+2IJBgwbFOeecE5WVlas8Z3l5eRx99NGxzTbbxIknnhhz5879xGkGH/757bffjjFjxsQ111wTnTp1ytvfCciPBS0816Zbt6qYPn1mQdbu1i33s3WXLV1ekHUBAFZX+SY7RIfdR0emhW++NS2eH7W3/Dmy83N/k7/k1C6P2jsuik6Hnh6ZLt0Th1WO3CNq3x/Xho0BreXeDyh19imA4rr22mvjuuuuK3YbLTrssMNi9OjRxW6jJL3xxhsrPXadddaJvffeO7bbbrsYNmxY9OzZMyIi5syZE7NmzYqnn346HnnkkXjxxRdzzjNt2rT43ve+F//617+ie/fk3yO0B+0mPFBTUxMvvvjiJx5ZEPFBiOCnP/1pq59FsdVWW8UNN9wQ3/ve9+Ldd99tNkDw8ssvx4knnhiXXHJJlJW1q0MdYI03b+78nPVuVYV7Xk1Vt9xzz5uXuzcAgLaTicqdDojKrfdqcWTTvBlRe+tfIrt4Xhv0lX/ZxfOi7pGbouPeRyaOKRu0aWSqekZ2yYK2awxoFfd+QKmzTwEU1/z58xOPvC8l8+fbk5OsTHigd+/eccopp8R+++3X7GN7BgwYEAMGDIiRI0fGMcccE88//3ycc8458cILLyTOOWHChPj5z38ef/nLX1rVf6lrN59wv/nmm9HY2LjiP3946sCmm24aO++8c17W6N+/f1x33XWxySabfOKxBR8PEPzvf/+LX/ziF3lZD8ifuS3cmPXs2aNga7c091w3ZgBAKajsGB2+cvRKBQcaZ0yMmhv/uMYGBz7UOO7ZnI8vyJSVR9n6m7ZhR0BrufcDSp19CgBa580338xZ32mnneLuu++Ogw46qNngQHO22mqruP766+Ooo47KOe7ee++Ne++9d6V7XRO1m/DAxIkTP/OzTCYTe+65Z17Xqa6ujr///e8xfPjwxADBzTffHBdffHFe1wVaZ9bsuTnrffutVbC1+/XNPffMmXMKtjYAwMrIVPWMTl//UVRssEWLYxsmvBy1/z43omZp4RsruGw0vP5YzhHlaw9uo16AfHDvB5Q6+xQArL7GxsZ4++23E+ujRo2Kyy+/PKqrq1d57kwmE6eeemr85Cc/yTnu7LPPjvr6+lWef03RbsIDM2Y0/4zNbbfdNu9rVVVVxTXXXJPzBIILLrgg7rrrrryvDayeadNmRE1NTWJ97X59C7Z2rrlnz54btbW1BVsbAKAlZX3Xj46HnB5lfQe2OLb+5Yej7q5LIhraz01y46TXc9ZX5r8XoHS49wNKnX0K+LRsNuPVhi/WbAsXLoyddtophg0b9plH1o8cOTJ+//vfR3l5eavW+O53vxv77rtvYv3999+PsWPHtmqNUrZyZzWsARYvXtzsz9dZZ52CrFdVVRVXXnlljB49Ot59990VoYGIWPHnn/70p7HuuuvGyJEjC9IDsPKy2Wy8N3lqDNtog2brA/oXZq+IiOg/IHnuSe9NKdi6AAAtKR8yIjrsfVRkKjvmHJdtaor6R2+OhhcfaKPO2k52wazI1iyNTKeEZwB37ta2DQGt4t4PKHX2KQBYfb169YqLLrpoxX+eN29evP/++/H+++/HlltuGZWVlXlZ5ze/+U088cQTMX9+84/0ufHGG+OrX/1qXtYqNe3m5IGkVGSvXr0KtmZ1dXVceeWV0a9fv4iIzwQI6urq4gc/+EG8/37yMzSBtvPuhEmJtaFDC3ccba6533nn3YKtCwCQS/kmO0SHUUe3HByoq4m6uy5pl8GBD2WXNR9Gj4jIdOrShp0A+eDeDyh19ikAyI9evXrFiBEjYp999snrF8qrqqpi9OjRifXnn38+5s7N/SiiNVW7CQ9UVDR/iEJdXV1B11177bXjyiuvjO7du0fEZwME8+fPj6OPPjqWLFlS0D6Alr32+luJtQ03GlKwdYduMCix9sYbyc/mAQAolIqt94qOXzo8MmW5j/JrWjwvam46JxrffaWNOiuObE2O+7Xy/HxrAWg77v2AUmefAoDSd9hhhyU+AqGpqSmeeeaZNu6obbSbxxZ07ty52Z/Pnz//M8+8yLcNNtggLrroojjiiCOivr5+RYAgk/ng2SkTJkyIE044Ia644opWP2cDWH3PPZ/8S+8NhqwfVVVdY8mSpXldc+jQwdG9e/JRty+99Fpe1wMAaEnFtqOiw/ZfaXFc08z3ovaOiyK7dGEbdPUxHbtEpsdaUdajz4p/Nrz2WDTNnFSwJTMdc5wu0ODZv7Cmce8HlDr7FEDxVFdXx5AhhQtq5Ut1dXWxW0i96urq2HjjjeP1119vtv7KK6/E3nvv3cZdFV67CQ+stdZazf78vffeiwEDBhR8/a233jrOPPPM+NGPfrQiNPBhgCCbzcaTTz4Zv/jFL+KMM84oeC9A855//uXEWnl5eWw1ckQ88r8n87rmNttskVhramqKZ559Ka/rAQDkUrndV6Jyu1Etjmt895WoveeKiIbCnuS2QkWH6Pj1H0VZj7Ui0+mz4e/s8iWFDQ906Z5Yy/VIA6A0ufcDSp19CqB4Ro8enfM4evi4bbbZJjE8MHny5Dbupm20m8cWrLvuus3+/IknnmizHkaNGhXHHXfcJ04d+HiA4JZbbomLLrqozfoBPmnSpCnx3nvvJ9Z32WW7vK+5y87Jc77+xriYN29+3tcEAGhOxcg9Vio4UP/yw1F758VtFxyIiGioi7KefZsNDkRElK03rHBrd+ke0Sn55IHsglmFWxsoCPd+QKmzTwHAmmH99ddPrM2YMaMNO2k77SY8MGzYJ3+Z9OEH9g888EBks9k26+O4446LfffdNzFAcOGFF8a///3vNusH+KT77n84sbbPPnvkfb29v7x7Yu3++5J7AQDIp/Jhn4/KnQ9qcVzd47dG/UP/jGjDe6gPNc2Zmlgr67f+Bx/yF0D5oOGRySTfGjfNmlKQdYHCcu8HlDr7FPChpmzGqw1fsCp69uyZWFu2bFnbNdKG2k14YMCAAdGnT5/P/Hzy5Mlx++23t2kvZ511VmyxxRafCBBEfBRo+OUvfxn3339/m/YEfGDsvQ8m1rYaOSKGDh2ct7V22Xm7GDBgncT67Xfcm7e1AACSlK09KDrs+e2cH5Bnmxqj9v6/RcOzxbs+aZo+IbGWKSuPik13KMi6FUO3zFlvfH9cQdYFCsu9H1Dq7FMAUPp69OiRWGtoaGjDTtpOuwkPRETstNNOnzhl4MMP688999yYNavtjprs0KFDXHLJJTFgwIBP9PHhnxsbG+OUU06JsWPHtllPwAfuu+/hmDu3+WPYysrK4pjvH563tY499ruJtTffeieeePK5vK0FANCszlXRYd/vR6aiMnFItqkx6sZeGY1vtN0j35rTOPHVnPWKTXeKyOT3WyJlaw+K8iEjEuvZZYui6f2387om0Dbc+wGlzj4FAKVv+fL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ZMqQRJwLS4qijjmpQ0Pukk06KH//4x9G8efMCTAWkycMPPxzTpk3b5vVmzZrFhAkToqKioghTAQBpITwApEp1dXVirRDhgVz3z23vt1YAANJo0aJFcf7550dNTU3WevPmzWPcuHFOHQDybsOGDfUODnTv3j3GjBkTn/vc5wo0FZAmVVVV8aMf/ShrbdSoUbHffvs18kQAQNqUFnsAgMaUKzxQXp7/PFWuNZPeEAcASKsVK1bEyJEjY9myZYnPXHzxxdGvX79GnApIi2xHhOdSUVERJ554YvTq1atAEwFp88Mf/jBWrVq1zeuDBw+OkSNHNv5AAEDqCA8AqZIrPFBWVpb3frnWFB4AAPhfa9eujbPPPjvefvvtxGeGDh0aZ555ZiNOBaRJfcMD1dXVcc0118SQIUPi9NNPj5kzZxZoMiANpkyZEo899tg2r++xxx7x05/+tCDvWwEA/CvhASBVtmzZklgrxMkDwgMAANu3du3a+MY3vhGvvvpq4jODBg2K8ePHN+JUQNp88MEHDf7a2bNnx9e//vW48MILY926dXmcCkiD+fPnx4QJE7LWfvCDHzjhBABoNPn/pAygCSstTc5M1dXV5b1frjUlxgEA/nFVwdlnn50zONCzZ8/4zW9+E82bN2/EyYC0+ejkga5du0avXr2isrIyKioqYvny5fHBBx/EvHnzIpPJ5FzjoYceitdeey1uvfXW6Ny5c2OMDeziamtrY8yYMbFhw4ZtaocffnicccYZRZgKAEgr4QEgVZo1a5ZYq62tzXu/XKcLVFRU5L0fAMCuZMmSJfGtb30r5s2bl/jMvvvuG7fccktUVlY24mRAGg0fPjzOPPPM6NSpU9Z6VVVVPProo/Hb3/42Fi1alLjOu+++G2effXZMnjw52rZtW6hxgd3E9ddfH3PmzNnm9TZt2sRVV10VJSUljT8UAJBari0AUiXX1QSFuEYg18kDwgMAQJq9/fbbceqpp+YMDvTo0SNuu+226Nq1ayNOBqRV7969E4MDERGdOnWKU089NR5++OEYPXp0zg/05s6dG5dffnkhxgR2I6+88kpcf/31WWs//OEPY6+99mrkiQCAtBMeAFIl11G3mzdvznu/TZs2JdaEBwCAtPr73/8ep59+es77xfv16xeTJ0/2pjnQ5DRr1ixGjRoV1113Xc7T7e6///6YOXNmI04G7Eo2bdoUF110UdZfZvnc5z4XX/nKV4owFQCQdsIDQKrkOjIy291yO2v9+vWJtRYtWuS9HwBAU/fggw/GN77xjVi1alXiMwcccEBMnjzZfeFAk/bZz342xo0bl/OZG264oZGmAXY1EyZMiHfeeWeb1ysrK7e7twAAFIrwAJAq7du3T6zl+qC/oXIFEjp27Jj3fgAATdmkSZPi+9//flRXVyc+c9RRR8Xvf//7nN+3ATQVI0aMiKFDhybWZ82aFe+++24jTgTsCp5++um4/fbbs9bGjRvnPSMAoGiEB4BUyfUm9OrVq/PeL9dv1FVWVua9HwBAU1RdXR3f//7349prr8353IgRI+KGG26Ili1bNtJkADvvggsuSLy+IJPJxGOPPdbIEwFN2erVq+OSSy6JTCazTW348OFx/PHHF2EqAIB/EB4AUiXX0bfLli3Le79ca+6555557wcA0NSsWLEizjrrrHjwwQdzPjd69Oi46qqrory8vJEmA8iP7t27x6GHHppYnzNnTuMNAzR5Y8eOjaVLl27zeteuXeOyyy4rwkQAAP/LuzJAquyzzz6JtcYOD3Tq1Cnv/QAAmpJ33nknzjnnnFi4cGHiM82aNYsrr7wyhg8f3oiTAeTXUUcdFU8++WTW2ptvvtnI0wBN2UMPPZT19SVLlsQhhxxSsL6TJk2KSZMmZa3tvffeMWPGjIL1BgB2HcIDQKrsu+++ibVcb2o3VK41e/funfd+AABNxUsvvRQjR47MeY1T+/bt49e//nV88pOfbLzBAAqgX79+ibVCXJEHAABQCK4tAFKlZ8+eibUFCxbkvd97772X9fWWLVtGt27d8t4PAKApeOqpp+JrX/tazuBA79694+677xYcAHYLHTt2TKwJDwAAALsK4QEgVdq2bRvdu3fPWsv3UZLV1dWJgYS+fftGSUlJXvsBADQFf/vb3+Lb3/52bNiwIfGZww47LO68887E78sACmHjxo0xf/78gqzdrFmzxFppqbffAACAXYNrC4DUGTBgQNYP9auqqmLp0qXRpUuXvPR5/fXXY8uWLVlrH//4x/PSAwCgKfnb3/4Wo0aNSvweKCJixIgRMW7cuJwftAHsrL/85S/x2muvxcKFC2PhwoXx/vvvR1VVVZSWlsYzzzwT7dq1y2u/ZcuWJdZat26d114AAACFIvoMpM5BBx2UWJs9e3be+uRa64gjjshbHwCApmDWrFkxevTonMGBUaNGxVVXXSU4ABTc7bffHr/+9a/j/vvvjxdeeCGqqqoiIqKuri6effbZvPdbvHhxYm2fffbJez8AAIBCcPIAkDqf/vSnE2uPPfZYDBkyJC99/vrXv2Z9vbS0NA477LC89AAAaArmzp0b//Ef/xHV1dVZ66WlpTF27Nj46le/2siTAWnVp0+fePrpp7PWHn/88Tj++OPz2u/JJ59MrPXv3z+vvYBd29y5cwu29qxZs+Kss87KWhs1alSMHj26YL0BgN2DkweA1OnVq1fi/bozZsyIjRs37nSPpUuXJp48cMABB+T9iEwAgGJZtWpVfOc734m1a9dmrTdr1iwmTpwoOAA0qgMPPDCxNn369Lz83PeRzZs35wwPHHLIIXnrBQAAUEjCA0AqJZ0usGbNmnjggQd2ev0pU6ZEbW1t1tpJJ5200+sDADQFmUwmLrrooli0aFHWeklJSfzsZz+LE044oZEnA9Lu8MMPj7Kysqy1devW5eXnvo/cdtttsWrVqqy1ioqKOOaYY/LWCwAAoJCEB4BUOvnkk6OkpCRr7dprr43169c3eO0lS5bErbfemrXWsmXLvF2LAABQbH/4wx/i8ccfT6xfeOGF8aUvfakRJwL4h8rKyjj88MMT6zv7c99Hli5dGjfeeGNi/fjjj3fyHAAAsMsQHgBSad99942jjz46a+3DDz+MsWPHNmjd2trauPjii2PDhg1Z6yeddFK0atWqQWsDADQlS5cujQkTJiTWv/jFL8Y555zTiBMB/LN///d/T6x9+OGHMXHixJ1af9OmTXHeeeclnjpQUlIS3/72t3eqBwAAQGMSHgBSa/To0Ym1adOmxaRJk+q1XiaTiR//+Mcxa9asrPVWrVrFeeedV681AQCaqmuuuSbxt3Y7derU4DAmQL4cc8wxccABByTWJ0+eHLfcckuD1l6zZk2cd9558corryQ+89WvfjU+9rGPNWh9AACAYigv9gAAxTJgwIAYOnRoPPjgg1nr1157baxatSouvvjiqKioyLnW+vXr47LLLovp06cnPjNy5MiorKzcqZkBAJqC9957L+67777EelVVVRx66KGNMstVV10VI0aMaJRewK7nv/7rv+LUU0+NmpqarPXx48fH+++/HxdddNF2f+77yEsvvRQXXXRRzJ8/P/GZvffeO37wgx80ZGQAAICicfIAkGqXXXZZdOrUKbF+2223xVe+8pV4+OGHY8uWLdvUN23aFPfee28MGzYsZ3Bg8ODBMXLkyLzMDABQbJMnT47a2tpijwGwXQMHDsx56lxExK233hpDhgyJP/zhD7FmzZqsz1RXV8fMmTNj9OjRccopp+QMDuyxxx5x7bXXRtu2bXdmdAAAgEbn5AEg1Tp06BATJkyIkSNHZg0HRETMmzcvzj///Gjbtm0MHDgwunTpEplMJhYvXhwvv/xybNiwIWePdu3axdVXXx3l5bZcAGDXV1tbG9OmTSv2GAA77Nxzz42333477r///sRnFi5cGD/+8Y9j7Nixsf/++0e3bt2ibdu2sW7duli+fHm89tpr2/3ZLyKioqIiJk2aFJ/4xCfy+Z8AAADQKHySBaTe4YcfHldccUVceumlUVdXl/jcmjVr4qmnnqrX2m3bto2bbropunXrtrNjAgA0CXPmzInVq1cXewyAevnpT38aEZEzQBARUVdXF6+++mq8+uqr9e7Rtm3bmDRpUqNd2wIAAJBvri0AiIgTTzwxfvWrX0WLFi3ytmbHjh3j5ptvjkGDBuVtTQCAYps5c2axRwCot7Kysvj5z38eF1xwQZSVleV9/U984hNx1113CQ4AAAC7NOEBgP/nC1/4Qtx7770xYMCAnV7r+OOPjwceeCAvawEANCWLFi0q9ggADVJSUhLnnXde/OEPf8jbtQJt2rSJiy66KO68887o1atXXtYEAAAoFtcWAPwfffr0iT/+8Y/x0EMPxe9+97t6HVVZUlISRx55ZJx11llx9NFHF3BKAIDiWb58ebFHANgpgwYNinvuuSf+/Oc/x+TJk+O5556LTCZTrzW6d+8ep5xySvzbv/1btGvXrkCTAgAANK6STH1/OgJIkblz58aTTz4ZL7zwQrz77rtRVVUVGzZsiNLS0mjfvn20b98++vfvHwcffHAcccQR0aNHj2KPDAAAQD0sXbo0HnvssZgzZ07MmzcvFi9eHGvXro3q6upo1qxZtG7dOrp27Ro9e/aMQYMGxWGHHRb77bdfsccGAADIO+EBAAAAAAAAAEi50mIPAAAAAAAAAAAUl/AAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApJzwAAAAAAAAAACknPAAAAAAAAAAAKSc8AAAAAAAAAAApNz/D99v4tR/SO+SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[ 0 ,21,  0 , 0,  0],\n",
    " [ 0 ,21 , 0 , 0  ,0],\n",
    " [ 0  0 ,34 , 0  ,0],\n",
    " [ 0  0 , 1 ,23 , 0],\n",
    " [ 0  0 , 0 , 0 ,26]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConADA.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95 accuracy with a standard deviation of 0.03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "# 2. instantiate a logistic regression model\n",
    "logreg = LogisticRegression( solver='saga',multi_class='ovr',C=500.0)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(logreg, data, target, cv=10)\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        30\n",
      "           1       0.87      1.00      0.93        26\n",
      "           2       1.00      0.79      0.88        24\n",
      "           3       0.97      1.00      0.98        28\n",
      "           4       1.00      1.00      1.00        18\n",
      "\n",
      "    accuracy                           0.96       126\n",
      "   macro avg       0.97      0.96      0.96       126\n",
      "weighted avg       0.96      0.96      0.96       126\n",
      "\n",
      "[[30  0  0  0  0]\n",
      " [ 0 26  0  0  0]\n",
      " [ 0  4 19  1  0]\n",
      " [ 0  0  0 28  0]\n",
      " [ 0  0  0  0 18]]\n",
      "--- 0.11104846000671387 seconds ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "# 2. instantiate a logistic regression model\n",
    "logreg = LogisticRegression( solver='saga',multi_class='ovr',C=500.0)\n",
    "# 3. train the model using X_train_dtm\n",
    "x=logreg.fit(X_train, y_train)\n",
    "# 4. make class predictions for X_test_dtm\n",
    "y_pred_class = logreg.predict(X_test)\n",
    "print(classification_report(y_test,y_pred_class))\n",
    "print(confusion_matrix(y_test,y_pred_class))\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[30,  0,  0,  0 , 0],\n",
    " [ 0 ,26,  0 , 0 , 0],\n",
    " [ 0 , 4, 19 , 1,  0],\n",
    " [ 0 , 0 , 0 ,28 , 0],\n",
    " [ 0  ,0 , 0  ,0 ,18]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConLR.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96 accuracy with a standard deviation of 0.04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "print(\"SVC\")\n",
    "svm = LinearSVC()\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(svm, data, target, cv=10)\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        30\n",
      "           1       1.00      0.77      0.87        26\n",
      "           2       0.80      1.00      0.89        24\n",
      "           3       1.00      1.00      1.00        28\n",
      "           4       1.00      1.00      1.00        18\n",
      "\n",
      "    accuracy                           0.95       126\n",
      "   macro avg       0.96      0.95      0.95       126\n",
      "weighted avg       0.96      0.95      0.95       126\n",
      "\n",
      "[[30  0  0  0  0]\n",
      " [ 0 20  6  0  0]\n",
      " [ 0  0 24  0  0]\n",
      " [ 0  0  0 28  0]\n",
      " [ 0  0  0  0 18]]\n",
      "--- 0.0760033130645752 seconds ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from sklearn.svm import LinearSVC\n",
    "print(\"SVC\")\n",
    "svm = LinearSVC()\n",
    "svm.fit(X_train,y_train)\n",
    "y_pred=svm.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[30,  0 , 0,  0 , 0],\n",
    " [ 0, 20 , 6 , 0  ,0],\n",
    " [ 0 , 0 ,24 , 0 , 0],\n",
    " [ 0 , 0 , 0 ,28 , 0],\n",
    " [ 0 , 0  ,0 , 0, 18]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConSVM.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n",
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.00 accuracy with a standard deviation of 0.00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "r1 = LogisticRegression(random_state=1000, solver='saga',multi_class='multinomial',C=3.0)\n",
    "r2 = RandomForestClassifier(n_estimators=200, random_state=5, max_depth=50) \n",
    "\n",
    "\n",
    "er = VotingClassifier([('lr', r1),('rf', r2)],voting=\"soft\")\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(er, data, target, cv=10)\n",
    "print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        30\n",
      "           1       1.00      1.00      1.00        26\n",
      "           2       1.00      1.00      1.00        24\n",
      "           3       1.00      1.00      1.00        28\n",
      "           4       1.00      1.00      1.00        18\n",
      "\n",
      "    accuracy                           1.00       126\n",
      "   macro avg       1.00      1.00      1.00       126\n",
      "weighted avg       1.00      1.00      1.00       126\n",
      "\n",
      "[[30  0  0  0  0]\n",
      " [ 0 26  0  0  0]\n",
      " [ 0  0 24  0  0]\n",
      " [ 0  0  0 28  0]\n",
      " [ 0  0  0  0 18]]\n",
      "--- 1.318474292755127 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "r1 = LogisticRegression(random_state=1000, solver='saga',multi_class='multinomial',C=3.0)\n",
    "r2 = RandomForestClassifier(n_estimators=200, random_state=5, max_depth=50) \n",
    "\n",
    "\n",
    "er = VotingClassifier([('lr', r1),('rf', r2)],voting=\"soft\")\n",
    "\n",
    "x=er.fit(X_train, y_train)\n",
    "\n",
    "y_pred_class = er.predict(X_test)\n",
    "print(classification_report(y_test,y_pred_class))\n",
    "print(confusion_matrix(y_test,y_pred_class))\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[30 , 0,  0,  0 , 0],\n",
    " [ 0, 26,  0,  0,  0],\n",
    " [ 0  ,0, 24  ,0,  0],\n",
    " [ 0 , 0  ,0 ,28 , 0],\n",
    " [ 0 , 0 , 0 , 0, 18]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConHB.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "import glob\n",
    "import cv2\n",
    "import tensorflow as tf\n",
    "from keras import layers\n",
    "from keras.layers import Dropout , Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
    "from keras.models import Model, load_model\n",
    "from keras.initializers import glorot_uniform\n",
    "from sklearn.model_selection import train_test_split\n",
    "import keras.backend as K\n",
    "from sklearn.utils import shuffle\n",
    "# importing all necessary libraries to run the code\n",
    "import re,string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras_metrics\n",
    "import tensorflow.keras\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import stopwords\n",
    "from keras.models import Sequential\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation,Embedding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    ">>> from sklearn import preprocessing\n",
    ">>> le = preprocessing.LabelEncoder()\n",
    "le = preprocessing.LabelEncoder()\n",
    "y = le.fit_transform(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "4/4 - 37s - loss: 1.5964 - accuracy: 0.5218 - mae: 0.3189 - mse: 0.1589 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 1.5799 - val_accuracy: 0.7778 - val_mae: 0.3176 - val_mse: 0.1576 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 37s/epoch - 9s/step\n",
      "Epoch 2/100\n",
      "4/4 - 0s - loss: 1.5640 - accuracy: 0.8909 - mae: 0.3163 - mse: 0.1563 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 1.5439 - val_accuracy: 0.9921 - val_mae: 0.3146 - val_mse: 0.1546 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 182ms/epoch - 45ms/step\n",
      "Epoch 3/100\n",
      "4/4 - 0s - loss: 1.5216 - accuracy: 0.9901 - mae: 0.3126 - mse: 0.1527 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 1.4924 - val_accuracy: 0.9921 - val_mae: 0.3100 - val_mse: 0.1502 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 223ms/epoch - 56ms/step\n",
      "Epoch 4/100\n",
      "4/4 - 0s - loss: 1.4565 - accuracy: 0.9881 - mae: 0.3067 - mse: 0.1470 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 1.4126 - val_accuracy: 0.9921 - val_mae: 0.3025 - val_mse: 0.1431 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 250ms/epoch - 63ms/step\n",
      "Epoch 5/100\n",
      "4/4 - 0s - loss: 1.3594 - accuracy: 0.9960 - mae: 0.2970 - mse: 0.1380 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 1.2886 - val_accuracy: 0.9921 - val_mae: 0.2893 - val_mse: 0.1312 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 233ms/epoch - 58ms/step\n",
      "Epoch 6/100\n",
      "4/4 - 0s - loss: 1.2060 - accuracy: 0.9960 - mae: 0.2793 - mse: 0.1226 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 1.0981 - val_accuracy: 0.9921 - val_mae: 0.2653 - val_mse: 0.1111 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 219ms/epoch - 55ms/step\n",
      "Epoch 7/100\n",
      "4/4 - 0s - loss: 0.9745 - accuracy: 0.9960 - mae: 0.2464 - mse: 0.0970 - precision: 0.7500 - recall: 0.0188 - f1_score: 0.0361 - val_loss: 0.8249 - val_accuracy: 0.9921 - val_mae: 0.2211 - val_mse: 0.0800 - val_precision: 1.0000 - val_recall: 0.0692 - val_f1_score: 0.1294 - 263ms/epoch - 66ms/step\n",
      "Epoch 8/100\n",
      "4/4 - 0s - loss: 0.6722 - accuracy: 0.9940 - mae: 0.1900 - mse: 0.0623 - precision: 1.0000 - recall: 0.1367 - f1_score: 0.2397 - val_loss: 0.5098 - val_accuracy: 0.9921 - val_mae: 0.1538 - val_mse: 0.0439 - val_precision: 1.0000 - val_recall: 0.1845 - val_f1_score: 0.3116 - 249ms/epoch - 62ms/step\n",
      "Epoch 9/100\n",
      "4/4 - 0s - loss: 0.3704 - accuracy: 0.9960 - mae: 0.1176 - mse: 0.0287 - precision: 1.0000 - recall: 0.2371 - f1_score: 0.3829 - val_loss: 0.2413 - val_accuracy: 0.9921 - val_mae: 0.0811 - val_mse: 0.0152 - val_precision: 1.0000 - val_recall: 0.2743 - val_f1_score: 0.4304 - 256ms/epoch - 64ms/step\n",
      "Epoch 10/100\n",
      "4/4 - 0s - loss: 0.1626 - accuracy: 0.9940 - mae: 0.0561 - mse: 0.0087 - precision: 1.0000 - recall: 0.3157 - f1_score: 0.4797 - val_loss: 0.0972 - val_accuracy: 0.9921 - val_mae: 0.0331 - val_mse: 0.0041 - val_precision: 1.0000 - val_recall: 0.3460 - val_f1_score: 0.5142 - 358ms/epoch - 90ms/step\n",
      "Epoch 11/100\n",
      "4/4 - 0s - loss: 0.0676 - accuracy: 0.9940 - mae: 0.0228 - mse: 0.0027 - precision: 1.0000 - recall: 0.3779 - f1_score: 0.5484 - val_loss: 0.0506 - val_accuracy: 0.9921 - val_mae: 0.0145 - val_mse: 0.0026 - val_precision: 1.0000 - val_recall: 0.4048 - val_f1_score: 0.5763 - 374ms/epoch - 94ms/step\n",
      "Epoch 12/100\n",
      "4/4 - 0s - loss: 0.0395 - accuracy: 0.9940 - mae: 0.0105 - mse: 0.0021 - precision: 1.0000 - recall: 0.4312 - f1_score: 0.6025 - val_loss: 0.0396 - val_accuracy: 0.9921 - val_mae: 0.0082 - val_mse: 0.0028 - val_precision: 1.0000 - val_recall: 0.4537 - val_f1_score: 0.6242 - 391ms/epoch - 98ms/step\n",
      "Epoch 13/100\n",
      "4/4 - 0s - loss: 0.0269 - accuracy: 0.9940 - mae: 0.0062 - mse: 0.0017 - precision: 1.0000 - recall: 0.4767 - f1_score: 0.6456 - val_loss: 0.0403 - val_accuracy: 0.9921 - val_mae: 0.0060 - val_mse: 0.0030 - val_precision: 1.0000 - val_recall: 0.4951 - val_f1_score: 0.6623 - 378ms/epoch - 94ms/step\n",
      "Epoch 14/100\n",
      "4/4 - 0s - loss: 0.0226 - accuracy: 0.9940 - mae: 0.0042 - mse: 0.0016 - precision: 1.0000 - recall: 0.5161 - f1_score: 0.6808 - val_loss: 0.0442 - val_accuracy: 0.9921 - val_mae: 0.0051 - val_mse: 0.0031 - val_precision: 1.0000 - val_recall: 0.5306 - val_f1_score: 0.6933 - 230ms/epoch - 58ms/step\n",
      "Epoch 15/100\n",
      "4/4 - 0s - loss: 0.0197 - accuracy: 0.9960 - mae: 0.0034 - mse: 0.0014 - precision: 1.0000 - recall: 0.5491 - f1_score: 0.7089 - val_loss: 0.0488 - val_accuracy: 0.9921 - val_mae: 0.0047 - val_mse: 0.0031 - val_precision: 1.0000 - val_recall: 0.5619 - val_f1_score: 0.7195 - 174ms/epoch - 44ms/step\n",
      "Epoch 16/100\n",
      "4/4 - 0s - loss: 0.0133 - accuracy: 0.9960 - mae: 0.0031 - mse: 0.0011 - precision: 1.0000 - recall: 0.5762 - f1_score: 0.7311 - val_loss: 0.0526 - val_accuracy: 0.9921 - val_mae: 0.0044 - val_mse: 0.0031 - val_precision: 1.0000 - val_recall: 0.5893 - val_f1_score: 0.7416 - 174ms/epoch - 43ms/step\n",
      "Epoch 17/100\n",
      "4/4 - 0s - loss: 0.0103 - accuracy: 0.9980 - mae: 0.0022 - mse: 6.9796e-04 - precision: 1.0000 - recall: 0.6024 - f1_score: 0.7518 - val_loss: 0.0560 - val_accuracy: 0.9921 - val_mae: 0.0043 - val_mse: 0.0032 - val_precision: 1.0000 - val_recall: 0.6134 - val_f1_score: 0.7604 - 243ms/epoch - 61ms/step\n",
      "Epoch 18/100\n",
      "4/4 - 0s - loss: 0.0073 - accuracy: 0.9980 - mae: 0.0019 - mse: 5.0927e-04 - precision: 0.9995 - recall: 0.6262 - f1_score: 0.7699 - val_loss: 0.0584 - val_accuracy: 0.9921 - val_mae: 0.0043 - val_mse: 0.0032 - val_precision: 0.9993 - val_recall: 0.6349 - val_f1_score: 0.7765 - 258ms/epoch - 65ms/step\n",
      "Epoch 19/100\n",
      "4/4 - 0s - loss: 0.0077 - accuracy: 0.9980 - mae: 0.0018 - mse: 6.2770e-04 - precision: 0.9992 - recall: 0.6453 - f1_score: 0.7842 - val_loss: 0.0602 - val_accuracy: 0.9921 - val_mae: 0.0042 - val_mse: 0.0032 - val_precision: 0.9987 - val_recall: 0.6541 - val_f1_score: 0.7905 - 230ms/epoch - 58ms/step\n",
      "Epoch 20/100\n",
      "4/4 - 0s - loss: 0.0036 - accuracy: 1.0000 - mae: 0.0013 - mse: 1.4974e-04 - precision: 0.9988 - recall: 0.6633 - f1_score: 0.7972 - val_loss: 0.0614 - val_accuracy: 0.9921 - val_mae: 0.0041 - val_mse: 0.0032 - val_precision: 0.9988 - val_recall: 0.6714 - val_f1_score: 0.8030 - 220ms/epoch - 55ms/step\n",
      "Epoch 21/100\n",
      "4/4 - 0s - loss: 0.0066 - accuracy: 0.9980 - mae: 0.0015 - mse: 6.2862e-04 - precision: 0.9986 - recall: 0.6799 - f1_score: 0.8090 - val_loss: 0.0620 - val_accuracy: 0.9921 - val_mae: 0.0040 - val_mse: 0.0032 - val_precision: 0.9984 - val_recall: 0.6871 - val_f1_score: 0.8140 - 243ms/epoch - 61ms/step\n",
      "Epoch 22/100\n",
      "4/4 - 0s - loss: 0.0028 - accuracy: 1.0000 - mae: 0.0011 - mse: 6.6096e-05 - precision: 0.9984 - recall: 0.6943 - f1_score: 0.8190 - val_loss: 0.0622 - val_accuracy: 0.9921 - val_mae: 0.0039 - val_mse: 0.0032 - val_precision: 0.9985 - val_recall: 0.7013 - val_f1_score: 0.8239 - 328ms/epoch - 82ms/step\n",
      "Epoch 23/100\n",
      "4/4 - 0s - loss: 0.0026 - accuracy: 1.0000 - mae: 9.6507e-04 - mse: 1.0506e-04 - precision: 0.9985 - recall: 0.7088 - f1_score: 0.8290 - val_loss: 0.0624 - val_accuracy: 0.9921 - val_mae: 0.0038 - val_mse: 0.0032 - val_precision: 0.9986 - val_recall: 0.7143 - val_f1_score: 0.8328 - 364ms/epoch - 91ms/step\n",
      "Epoch 24/100\n",
      "4/4 - 0s - loss: 0.0037 - accuracy: 1.0000 - mae: 0.0013 - mse: 2.5313e-04 - precision: 0.9986 - recall: 0.7207 - f1_score: 0.8372 - val_loss: 0.0619 - val_accuracy: 0.9921 - val_mae: 0.0038 - val_mse: 0.0032 - val_precision: 0.9986 - val_recall: 0.7262 - val_f1_score: 0.8409 - 300ms/epoch - 75ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n",
      "4/4 - 0s - loss: 0.0017 - accuracy: 1.0000 - mae: 6.6496e-04 - mse: 8.1269e-06 - precision: 0.9987 - recall: 0.7320 - f1_score: 0.8448 - val_loss: 0.0616 - val_accuracy: 0.9921 - val_mae: 0.0037 - val_mse: 0.0032 - val_precision: 0.9987 - val_recall: 0.7371 - val_f1_score: 0.8482 - 283ms/epoch - 71ms/step\n",
      "Epoch 26/100\n",
      "4/4 - 0s - loss: 0.0021 - accuracy: 1.0000 - mae: 7.9405e-04 - mse: 5.9439e-05 - precision: 0.9987 - recall: 0.7424 - f1_score: 0.8517 - val_loss: 0.0616 - val_accuracy: 0.9921 - val_mae: 0.0037 - val_mse: 0.0032 - val_precision: 0.9988 - val_recall: 0.7473 - val_f1_score: 0.8549 - 313ms/epoch - 78ms/step\n",
      "Epoch 27/100\n",
      "4/4 - 0s - loss: 0.0043 - accuracy: 1.0000 - mae: 0.0013 - mse: 2.9988e-04 - precision: 0.9988 - recall: 0.7524 - f1_score: 0.8583 - val_loss: 0.0620 - val_accuracy: 0.9921 - val_mae: 0.0037 - val_mse: 0.0032 - val_precision: 0.9988 - val_recall: 0.7566 - val_f1_score: 0.8610 - 498ms/epoch - 124ms/step\n",
      "Epoch 28/100\n",
      "4/4 - 0s - loss: 0.0016 - accuracy: 1.0000 - mae: 6.0897e-04 - mse: 1.8133e-05 - precision: 0.9989 - recall: 0.7615 - f1_score: 0.8642 - val_loss: 0.0626 - val_accuracy: 0.9921 - val_mae: 0.0036 - val_mse: 0.0032 - val_precision: 0.9989 - val_recall: 0.7653 - val_f1_score: 0.8666 - 208ms/epoch - 52ms/step\n",
      "Epoch 29/100\n",
      "4/4 - 0s - loss: 0.0012 - accuracy: 1.0000 - mae: 4.8957e-04 - mse: 2.2781e-06 - precision: 0.9989 - recall: 0.7698 - f1_score: 0.8695 - val_loss: 0.0630 - val_accuracy: 0.9921 - val_mae: 0.0036 - val_mse: 0.0032 - val_precision: 0.9989 - val_recall: 0.7734 - val_f1_score: 0.8718 - 166ms/epoch - 41ms/step\n",
      "Epoch 30/100\n",
      "4/4 - 0s - loss: 0.0013 - accuracy: 1.0000 - mae: 5.1769e-04 - mse: 5.2494e-06 - precision: 0.9990 - recall: 0.7775 - f1_score: 0.8744 - val_loss: 0.0633 - val_accuracy: 0.9921 - val_mae: 0.0036 - val_mse: 0.0032 - val_precision: 0.9990 - val_recall: 0.7810 - val_f1_score: 0.8766 - 191ms/epoch - 48ms/step\n",
      "Epoch 31/100\n",
      "4/4 - 0s - loss: 0.0012 - accuracy: 1.0000 - mae: 4.7913e-04 - mse: 6.1250e-06 - precision: 0.9990 - recall: 0.7849 - f1_score: 0.8791 - val_loss: 0.0636 - val_accuracy: 0.9921 - val_mae: 0.0036 - val_mse: 0.0032 - val_precision: 0.9990 - val_recall: 0.7880 - val_f1_score: 0.8811 - 219ms/epoch - 55ms/step\n",
      "Epoch 32/100\n",
      "4/4 - 0s - loss: 9.8825e-04 - accuracy: 1.0000 - mae: 3.9472e-04 - mse: 8.0142e-07 - precision: 0.9990 - recall: 0.7917 - f1_score: 0.8834 - val_loss: 0.0639 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9991 - val_recall: 0.7946 - val_f1_score: 0.8852 - 206ms/epoch - 51ms/step\n",
      "Epoch 33/100\n",
      "4/4 - 0s - loss: 9.4364e-04 - accuracy: 1.0000 - mae: 3.7695e-04 - mse: 7.2175e-07 - precision: 0.9991 - recall: 0.7980 - f1_score: 0.8873 - val_loss: 0.0641 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9991 - val_recall: 0.8009 - val_f1_score: 0.8891 - 212ms/epoch - 53ms/step\n",
      "Epoch 34/100\n",
      "4/4 - 0s - loss: 9.5528e-04 - accuracy: 1.0000 - mae: 3.8112e-04 - mse: 1.4041e-06 - precision: 0.9991 - recall: 0.8041 - f1_score: 0.8910 - val_loss: 0.0643 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9991 - val_recall: 0.8067 - val_f1_score: 0.8927 - 233ms/epoch - 58ms/step\n",
      "Epoch 35/100\n",
      "4/4 - 0s - loss: 9.4222e-04 - accuracy: 1.0000 - mae: 3.7610e-04 - mse: 1.0941e-06 - precision: 0.9991 - recall: 0.8097 - f1_score: 0.8945 - val_loss: 0.0645 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9992 - val_recall: 0.8122 - val_f1_score: 0.8961 - 255ms/epoch - 64ms/step\n",
      "Epoch 36/100\n",
      "4/4 - 0s - loss: 8.0877e-04 - accuracy: 1.0000 - mae: 3.2305e-04 - mse: 6.1182e-07 - precision: 0.9992 - recall: 0.8151 - f1_score: 0.8978 - val_loss: 0.0647 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9992 - val_recall: 0.8175 - val_f1_score: 0.8992 - 258ms/epoch - 65ms/step\n",
      "Epoch 37/100\n",
      "4/4 - 0s - loss: 9.3819e-04 - accuracy: 1.0000 - mae: 3.7239e-04 - mse: 4.9337e-06 - precision: 0.9992 - recall: 0.8201 - f1_score: 0.9008 - val_loss: 0.0650 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9992 - val_recall: 0.8224 - val_f1_score: 0.9022 - 281ms/epoch - 70ms/step\n",
      "Epoch 38/100\n",
      "4/4 - 0s - loss: 8.3067e-04 - accuracy: 1.0000 - mae: 3.3106e-04 - mse: 1.6270e-06 - precision: 0.9992 - recall: 0.8250 - f1_score: 0.9038 - val_loss: 0.0653 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0032 - val_precision: 0.9992 - val_recall: 0.8271 - val_f1_score: 0.9050 - 291ms/epoch - 73ms/step\n",
      "Epoch 39/100\n",
      "4/4 - 0s - loss: 7.9248e-04 - accuracy: 1.0000 - mae: 3.1626e-04 - mse: 1.0604e-06 - precision: 0.9993 - recall: 0.8293 - f1_score: 0.9064 - val_loss: 0.0656 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9993 - val_recall: 0.8315 - val_f1_score: 0.9077 - 319ms/epoch - 80ms/step\n",
      "Epoch 40/100\n",
      "4/4 - 0s - loss: 7.1595e-04 - accuracy: 1.0000 - mae: 2.8608e-04 - mse: 4.3125e-07 - precision: 0.9993 - recall: 0.8339 - f1_score: 0.9091 - val_loss: 0.0658 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9993 - val_recall: 0.8357 - val_f1_score: 0.9102 - 354ms/epoch - 89ms/step\n",
      "Epoch 41/100\n",
      "4/4 - 0s - loss: 7.5301e-04 - accuracy: 1.0000 - mae: 3.0010e-04 - mse: 1.4615e-06 - precision: 0.9993 - recall: 0.8378 - f1_score: 0.9115 - val_loss: 0.0660 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9993 - val_recall: 0.8397 - val_f1_score: 0.9126 - 367ms/epoch - 92ms/step\n",
      "Epoch 42/100\n",
      "4/4 - 0s - loss: 6.9247e-04 - accuracy: 1.0000 - mae: 2.7629e-04 - mse: 9.3452e-07 - precision: 0.9993 - recall: 0.8419 - f1_score: 0.9139 - val_loss: 0.0662 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9993 - val_recall: 0.8435 - val_f1_score: 0.9148 - 307ms/epoch - 77ms/step\n",
      "Epoch 43/100\n",
      "4/4 - 0s - loss: 7.5652e-04 - accuracy: 1.0000 - mae: 3.0125e-04 - mse: 1.8841e-06 - precision: 0.9993 - recall: 0.8455 - f1_score: 0.9160 - val_loss: 0.0663 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9993 - val_recall: 0.8472 - val_f1_score: 0.9170 - 181ms/epoch - 45ms/step\n",
      "Epoch 44/100\n",
      "4/4 - 0s - loss: 6.2295e-04 - accuracy: 1.0000 - mae: 2.4891e-04 - mse: 3.6289e-07 - precision: 0.9994 - recall: 0.8491 - f1_score: 0.9181 - val_loss: 0.0664 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9994 - val_recall: 0.8506 - val_f1_score: 0.9190 - 160ms/epoch - 40ms/step\n",
      "Epoch 45/100\n",
      "4/4 - 0s - loss: 6.3611e-04 - accuracy: 1.0000 - mae: 2.5375e-04 - mse: 9.2972e-07 - precision: 0.9994 - recall: 0.8524 - f1_score: 0.9201 - val_loss: 0.0666 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9994 - val_recall: 0.8540 - val_f1_score: 0.9210 - 235ms/epoch - 59ms/step\n",
      "Epoch 46/100\n",
      "4/4 - 0s - loss: 6.6181e-04 - accuracy: 1.0000 - mae: 2.6276e-04 - mse: 2.6347e-06 - precision: 0.9994 - recall: 0.8557 - f1_score: 0.9220 - val_loss: 0.0667 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9994 - val_recall: 0.8571 - val_f1_score: 0.9228 - 232ms/epoch - 58ms/step\n",
      "Epoch 47/100\n",
      "4/4 - 0s - loss: 5.3191e-04 - accuracy: 1.0000 - mae: 2.1266e-04 - mse: 1.4493e-07 - precision: 0.9994 - recall: 0.8588 - f1_score: 0.9238 - val_loss: 0.0668 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9994 - val_recall: 0.8602 - val_f1_score: 0.9246 - 217ms/epoch - 54ms/step\n",
      "Epoch 48/100\n",
      "4/4 - 0s - loss: 6.2503e-04 - accuracy: 1.0000 - mae: 2.4852e-04 - mse: 2.0566e-06 - precision: 0.9994 - recall: 0.8617 - f1_score: 0.9255 - val_loss: 0.0669 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9994 - val_recall: 0.8631 - val_f1_score: 0.9263 - 214ms/epoch - 53ms/step\n",
      "Epoch 49/100\n",
      "4/4 - 0s - loss: 5.3365e-04 - accuracy: 1.0000 - mae: 2.1320e-04 - mse: 3.3823e-07 - precision: 0.9994 - recall: 0.8645 - f1_score: 0.9271 - val_loss: 0.0670 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9994 - val_recall: 0.8659 - val_f1_score: 0.9279 - 241ms/epoch - 60ms/step\n",
      "Epoch 50/100\n",
      "4/4 - 0s - loss: 5.4375e-04 - accuracy: 1.0000 - mae: 2.1722e-04 - mse: 4.1575e-07 - precision: 0.9994 - recall: 0.8674 - f1_score: 0.9287 - val_loss: 0.0672 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8686 - val_f1_score: 0.9294 - 238ms/epoch - 60ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 51/100\n",
      "4/4 - 0s - loss: 5.0864e-04 - accuracy: 1.0000 - mae: 2.0327e-04 - mse: 2.5557e-07 - precision: 0.9995 - recall: 0.8699 - f1_score: 0.9302 - val_loss: 0.0673 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8711 - val_f1_score: 0.9309 - 263ms/epoch - 66ms/step\n",
      "Epoch 52/100\n",
      "4/4 - 0s - loss: 4.8264e-04 - accuracy: 1.0000 - mae: 1.9293e-04 - mse: 1.7728e-07 - precision: 0.9995 - recall: 0.8725 - f1_score: 0.9317 - val_loss: 0.0674 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8736 - val_f1_score: 0.9323 - 298ms/epoch - 75ms/step\n",
      "Epoch 53/100\n",
      "4/4 - 0s - loss: 4.7923e-04 - accuracy: 1.0000 - mae: 1.9159e-04 - mse: 1.4456e-07 - precision: 0.9995 - recall: 0.8749 - f1_score: 0.9330 - val_loss: 0.0676 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8760 - val_f1_score: 0.9337 - 438ms/epoch - 110ms/step\n",
      "Epoch 54/100\n",
      "4/4 - 0s - loss: 4.6500e-04 - accuracy: 1.0000 - mae: 1.8587e-04 - mse: 1.6679e-07 - precision: 0.9995 - recall: 0.8772 - f1_score: 0.9344 - val_loss: 0.0677 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8783 - val_f1_score: 0.9350 - 285ms/epoch - 71ms/step\n",
      "Epoch 55/100\n",
      "4/4 - 0s - loss: 4.6302e-04 - accuracy: 1.0000 - mae: 1.8506e-04 - mse: 2.0589e-07 - precision: 0.9995 - recall: 0.8795 - f1_score: 0.9357 - val_loss: 0.0679 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8805 - val_f1_score: 0.9362 - 422ms/epoch - 105ms/step\n",
      "Epoch 56/100\n",
      "4/4 - 0s - loss: 4.4388e-04 - accuracy: 1.0000 - mae: 1.7744e-04 - mse: 1.5530e-07 - precision: 0.9995 - recall: 0.8817 - f1_score: 0.9369 - val_loss: 0.0681 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8827 - val_f1_score: 0.9375 - 405ms/epoch - 101ms/step\n",
      "Epoch 57/100\n",
      "4/4 - 0s - loss: 4.6106e-04 - accuracy: 1.0000 - mae: 1.8420e-04 - mse: 3.2052e-07 - precision: 0.9995 - recall: 0.8838 - f1_score: 0.9381 - val_loss: 0.0683 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8847 - val_f1_score: 0.9386 - 183ms/epoch - 46ms/step\n",
      "Epoch 58/100\n",
      "4/4 - 0s - loss: 5.8330e-04 - accuracy: 1.0000 - mae: 2.3122e-04 - mse: 3.0799e-06 - precision: 0.9995 - recall: 0.8858 - f1_score: 0.9392 - val_loss: 0.0685 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8867 - val_f1_score: 0.9397 - 217ms/epoch - 54ms/step\n",
      "Epoch 59/100\n",
      "4/4 - 0s - loss: 4.1140e-04 - accuracy: 1.0000 - mae: 1.6447e-04 - mse: 1.3407e-07 - precision: 0.9995 - recall: 0.8877 - f1_score: 0.9403 - val_loss: 0.0688 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9995 - val_recall: 0.8886 - val_f1_score: 0.9408 - 249ms/epoch - 62ms/step\n",
      "Epoch 60/100\n",
      "4/4 - 0s - loss: 3.9073e-04 - accuracy: 1.0000 - mae: 1.5620e-04 - mse: 1.2235e-07 - precision: 0.9996 - recall: 0.8896 - f1_score: 0.9414 - val_loss: 0.0690 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.8905 - val_f1_score: 0.9419 - 220ms/epoch - 55ms/step\n",
      "Epoch 61/100\n",
      "4/4 - 0s - loss: 8.5714e-04 - accuracy: 1.0000 - mae: 3.2086e-04 - mse: 3.7055e-05 - precision: 0.9996 - recall: 0.8914 - f1_score: 0.9424 - val_loss: 0.0695 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.8923 - val_f1_score: 0.9429 - 218ms/epoch - 54ms/step\n",
      "Epoch 62/100\n",
      "4/4 - 0s - loss: 3.9530e-04 - accuracy: 1.0000 - mae: 1.5803e-04 - mse: 1.3344e-07 - precision: 0.9996 - recall: 0.8932 - f1_score: 0.9434 - val_loss: 0.0698 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.8940 - val_f1_score: 0.9438 - 220ms/epoch - 55ms/step\n",
      "Epoch 63/100\n",
      "4/4 - 0s - loss: 8.4676e-04 - accuracy: 1.0000 - mae: 3.1782e-04 - mse: 3.5371e-05 - precision: 0.9996 - recall: 0.8949 - f1_score: 0.9444 - val_loss: 0.0695 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.8957 - val_f1_score: 0.9448 - 242ms/epoch - 61ms/step\n",
      "Epoch 64/100\n",
      "4/4 - 0s - loss: 3.8597e-04 - accuracy: 1.0000 - mae: 1.5427e-04 - mse: 1.9994e-07 - precision: 0.9996 - recall: 0.8966 - f1_score: 0.9453 - val_loss: 0.0691 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.8973 - val_f1_score: 0.9457 - 241ms/epoch - 60ms/step\n",
      "Epoch 65/100\n",
      "4/4 - 0s - loss: 3.9119e-04 - accuracy: 1.0000 - mae: 1.5623e-04 - mse: 3.5030e-07 - precision: 0.9996 - recall: 0.8982 - f1_score: 0.9462 - val_loss: 0.0689 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.8989 - val_f1_score: 0.9466 - 244ms/epoch - 61ms/step\n",
      "Epoch 66/100\n",
      "4/4 - 0s - loss: 3.6310e-04 - accuracy: 1.0000 - mae: 1.4517e-04 - mse: 1.1001e-07 - precision: 0.9996 - recall: 0.8997 - f1_score: 0.9470 - val_loss: 0.0688 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9004 - val_f1_score: 0.9474 - 346ms/epoch - 86ms/step\n",
      "Epoch 67/100\n",
      "4/4 - 0s - loss: 3.3800e-04 - accuracy: 1.0000 - mae: 1.3514e-04 - mse: 8.8053e-08 - precision: 0.9996 - recall: 0.9012 - f1_score: 0.9479 - val_loss: 0.0688 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9019 - val_f1_score: 0.9483 - 340ms/epoch - 85ms/step\n",
      "Epoch 68/100\n",
      "4/4 - 0s - loss: 4.5166e-04 - accuracy: 1.0000 - mae: 1.7886e-04 - mse: 2.5210e-06 - precision: 0.9996 - recall: 0.9027 - f1_score: 0.9487 - val_loss: 0.0688 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9034 - val_f1_score: 0.9491 - 346ms/epoch - 86ms/step\n",
      "Epoch 69/100\n",
      "4/4 - 0s - loss: 3.1188e-04 - accuracy: 1.0000 - mae: 1.2473e-04 - mse: 3.5459e-08 - precision: 0.9996 - recall: 0.9041 - f1_score: 0.9495 - val_loss: 0.0689 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9048 - val_f1_score: 0.9498 - 384ms/epoch - 96ms/step\n",
      "Epoch 70/100\n",
      "4/4 - 0s - loss: 3.2187e-04 - accuracy: 1.0000 - mae: 1.2868e-04 - mse: 1.0237e-07 - precision: 0.9996 - recall: 0.9054 - f1_score: 0.9502 - val_loss: 0.0690 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9061 - val_f1_score: 0.9506 - 299ms/epoch - 75ms/step\n",
      "Epoch 71/100\n",
      "4/4 - 0s - loss: 3.5691e-04 - accuracy: 1.0000 - mae: 1.4260e-04 - mse: 2.2890e-07 - precision: 0.9996 - recall: 0.9068 - f1_score: 0.9510 - val_loss: 0.0690 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9074 - val_f1_score: 0.9513 - 235ms/epoch - 59ms/step\n",
      "Epoch 72/100\n",
      "4/4 - 0s - loss: 3.1539e-04 - accuracy: 1.0000 - mae: 1.2609e-04 - mse: 1.0773e-07 - precision: 0.9996 - recall: 0.9082 - f1_score: 0.9517 - val_loss: 0.0692 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9087 - val_f1_score: 0.9520 - 150ms/epoch - 38ms/step\n",
      "Epoch 73/100\n",
      "4/4 - 0s - loss: 3.1154e-04 - accuracy: 1.0000 - mae: 1.2457e-04 - mse: 5.7543e-08 - precision: 0.9996 - recall: 0.9094 - f1_score: 0.9524 - val_loss: 0.0693 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9100 - val_f1_score: 0.9527 - 240ms/epoch - 60ms/step\n",
      "Epoch 74/100\n",
      "4/4 - 0s - loss: 3.0406e-04 - accuracy: 1.0000 - mae: 1.2156e-04 - mse: 9.5267e-08 - precision: 0.9996 - recall: 0.9106 - f1_score: 0.9531 - val_loss: 0.0694 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9996 - val_recall: 0.9112 - val_f1_score: 0.9534 - 237ms/epoch - 59ms/step\n",
      "Epoch 75/100\n",
      "4/4 - 0s - loss: 2.8862e-04 - accuracy: 1.0000 - mae: 1.1541e-04 - mse: 5.2167e-08 - precision: 0.9996 - recall: 0.9118 - f1_score: 0.9537 - val_loss: 0.0696 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9124 - val_f1_score: 0.9540 - 253ms/epoch - 63ms/step\n",
      "Epoch 76/100\n",
      "4/4 - 0s - loss: 2.8858e-04 - accuracy: 1.0000 - mae: 1.1540e-04 - mse: 4.8561e-08 - precision: 0.9997 - recall: 0.9130 - f1_score: 0.9544 - val_loss: 0.0697 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9135 - val_f1_score: 0.9547 - 235ms/epoch - 59ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77/100\n",
      "4/4 - 0s - loss: 3.8297e-04 - accuracy: 1.0000 - mae: 1.5181e-04 - mse: 2.2554e-06 - precision: 0.9997 - recall: 0.9141 - f1_score: 0.9550 - val_loss: 0.0698 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9147 - val_f1_score: 0.9553 - 229ms/epoch - 57ms/step\n",
      "Epoch 78/100\n",
      "4/4 - 0s - loss: 3.1069e-04 - accuracy: 1.0000 - mae: 1.2402e-04 - mse: 3.4395e-07 - precision: 0.9997 - recall: 0.9152 - f1_score: 0.9556 - val_loss: 0.0700 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9158 - val_f1_score: 0.9559 - 233ms/epoch - 58ms/step\n",
      "Epoch 79/100\n",
      "4/4 - 0s - loss: 2.6001e-04 - accuracy: 1.0000 - mae: 1.0399e-04 - mse: 2.5972e-08 - precision: 0.9997 - recall: 0.9163 - f1_score: 0.9562 - val_loss: 0.0701 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9168 - val_f1_score: 0.9565 - 255ms/epoch - 64ms/step\n",
      "Epoch 80/100\n",
      "4/4 - 0s - loss: 2.6715e-04 - accuracy: 1.0000 - mae: 1.0681e-04 - mse: 6.4981e-08 - precision: 0.9997 - recall: 0.9174 - f1_score: 0.9567 - val_loss: 0.0702 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9179 - val_f1_score: 0.9570 - 247ms/epoch - 62ms/step\n",
      "Epoch 81/100\n",
      "4/4 - 1s - loss: 2.6402e-04 - accuracy: 1.0000 - mae: 1.0558e-04 - mse: 3.6064e-08 - precision: 0.9997 - recall: 0.9184 - f1_score: 0.9573 - val_loss: 0.0703 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9189 - val_f1_score: 0.9576 - 506ms/epoch - 127ms/step\n",
      "Epoch 82/100\n",
      "4/4 - 0s - loss: 2.6733e-04 - accuracy: 1.0000 - mae: 1.0690e-04 - mse: 4.5951e-08 - precision: 0.9997 - recall: 0.9194 - f1_score: 0.9578 - val_loss: 0.0705 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9199 - val_f1_score: 0.9581 - 402ms/epoch - 101ms/step\n",
      "Epoch 83/100\n",
      "4/4 - 0s - loss: 2.5761e-04 - accuracy: 1.0000 - mae: 1.0301e-04 - mse: 4.2102e-08 - precision: 0.9997 - recall: 0.9204 - f1_score: 0.9584 - val_loss: 0.0706 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9208 - val_f1_score: 0.9586 - 422ms/epoch - 106ms/step\n",
      "Epoch 84/100\n",
      "4/4 - 0s - loss: 2.5547e-04 - accuracy: 1.0000 - mae: 1.0215e-04 - mse: 5.2302e-08 - precision: 0.9997 - recall: 0.9213 - f1_score: 0.9589 - val_loss: 0.0708 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9218 - val_f1_score: 0.9592 - 218ms/epoch - 54ms/step\n",
      "Epoch 85/100\n",
      "4/4 - 0s - loss: 2.5089e-04 - accuracy: 1.0000 - mae: 1.0033e-04 - mse: 4.0012e-08 - precision: 0.9997 - recall: 0.9223 - f1_score: 0.9594 - val_loss: 0.0709 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9227 - val_f1_score: 0.9597 - 176ms/epoch - 44ms/step\n",
      "Epoch 86/100\n",
      "4/4 - 0s - loss: 2.6553e-04 - accuracy: 1.0000 - mae: 1.0614e-04 - mse: 9.3824e-08 - precision: 0.9997 - recall: 0.9232 - f1_score: 0.9599 - val_loss: 0.0710 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9236 - val_f1_score: 0.9601 - 164ms/epoch - 41ms/step\n",
      "Epoch 87/100\n",
      "4/4 - 0s - loss: 3.1348e-04 - accuracy: 1.0000 - mae: 1.2499e-04 - mse: 7.5105e-07 - precision: 0.9997 - recall: 0.9241 - f1_score: 0.9604 - val_loss: 0.0711 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9245 - val_f1_score: 0.9606 - 204ms/epoch - 51ms/step\n",
      "Epoch 88/100\n",
      "4/4 - 0s - loss: 2.8190e-04 - accuracy: 1.0000 - mae: 1.1236e-04 - mse: 5.6247e-07 - precision: 0.9997 - recall: 0.9249 - f1_score: 0.9609 - val_loss: 0.0712 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9253 - val_f1_score: 0.9611 - 226ms/epoch - 56ms/step\n",
      "Epoch 89/100\n",
      "4/4 - 0s - loss: 2.3660e-04 - accuracy: 1.0000 - mae: 9.4586e-05 - mse: 8.2050e-08 - precision: 0.9997 - recall: 0.9258 - f1_score: 0.9613 - val_loss: 0.0713 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9262 - val_f1_score: 0.9615 - 255ms/epoch - 64ms/step\n",
      "Epoch 90/100\n",
      "4/4 - 0s - loss: 2.4348e-04 - accuracy: 1.0000 - mae: 9.7336e-05 - mse: 1.0135e-07 - precision: 0.9997 - recall: 0.9266 - f1_score: 0.9618 - val_loss: 0.0714 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9270 - val_f1_score: 0.9620 - 244ms/epoch - 61ms/step\n",
      "Epoch 91/100\n",
      "4/4 - 0s - loss: 2.2026e-04 - accuracy: 1.0000 - mae: 8.8089e-05 - mse: 2.4460e-08 - precision: 0.9997 - recall: 0.9274 - f1_score: 0.9622 - val_loss: 0.0715 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9278 - val_f1_score: 0.9624 - 238ms/epoch - 59ms/step\n",
      "Epoch 92/100\n",
      "4/4 - 0s - loss: 2.1976e-04 - accuracy: 1.0000 - mae: 8.7888e-05 - mse: 2.2911e-08 - precision: 0.9997 - recall: 0.9282 - f1_score: 0.9626 - val_loss: 0.0716 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9286 - val_f1_score: 0.9628 - 254ms/epoch - 64ms/step\n",
      "Epoch 93/100\n",
      "4/4 - 0s - loss: 2.0933e-04 - accuracy: 1.0000 - mae: 8.3716e-05 - mse: 1.9899e-08 - precision: 0.9997 - recall: 0.9290 - f1_score: 0.9631 - val_loss: 0.0717 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9293 - val_f1_score: 0.9632 - 269ms/epoch - 67ms/step\n",
      "Epoch 94/100\n",
      "4/4 - 0s - loss: 2.1863e-04 - accuracy: 1.0000 - mae: 8.7427e-05 - mse: 3.2758e-08 - precision: 0.9997 - recall: 0.9297 - f1_score: 0.9635 - val_loss: 0.0718 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9301 - val_f1_score: 0.9637 - 307ms/epoch - 77ms/step\n",
      "Epoch 95/100\n",
      "4/4 - 0s - loss: 2.4406e-04 - accuracy: 1.0000 - mae: 9.7515e-05 - mse: 1.4939e-07 - precision: 0.9997 - recall: 0.9305 - f1_score: 0.9639 - val_loss: 0.0720 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9308 - val_f1_score: 0.9640 - 446ms/epoch - 111ms/step\n",
      "Epoch 96/100\n",
      "4/4 - 0s - loss: 2.0794e-04 - accuracy: 1.0000 - mae: 8.3163e-05 - mse: 2.0387e-08 - precision: 0.9997 - recall: 0.9312 - f1_score: 0.9642 - val_loss: 0.0721 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9315 - val_f1_score: 0.9644 - 383ms/epoch - 96ms/step\n",
      "Epoch 97/100\n",
      "4/4 - 0s - loss: 2.1410e-04 - accuracy: 1.0000 - mae: 8.5612e-05 - mse: 4.1799e-08 - precision: 0.9997 - recall: 0.9319 - f1_score: 0.9646 - val_loss: 0.0722 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9323 - val_f1_score: 0.9648 - 394ms/epoch - 99ms/step\n",
      "Epoch 98/100\n",
      "4/4 - 0s - loss: 2.0938e-04 - accuracy: 1.0000 - mae: 8.3732e-05 - mse: 2.8081e-08 - precision: 0.9997 - recall: 0.9326 - f1_score: 0.9650 - val_loss: 0.0723 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9329 - val_f1_score: 0.9652 - 215ms/epoch - 54ms/step\n",
      "Epoch 99/100\n",
      "4/4 - 0s - loss: 2.1944e-04 - accuracy: 1.0000 - mae: 8.7739e-05 - mse: 5.2312e-08 - precision: 0.9997 - recall: 0.9333 - f1_score: 0.9654 - val_loss: 0.0724 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9336 - val_f1_score: 0.9656 - 181ms/epoch - 45ms/step\n",
      "Epoch 100/100\n",
      "4/4 - 0s - loss: 1.9992e-04 - accuracy: 1.0000 - mae: 7.9941e-05 - mse: 3.5567e-08 - precision: 0.9997 - recall: 0.9340 - f1_score: 0.9658 - val_loss: 0.0725 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9997 - val_recall: 0.9343 - val_f1_score: 0.9659 - 176ms/epoch - 44ms/step\n",
      "--- 68.8960690498352 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from keras import optimizers\n",
    "import tensorflow.keras\n",
    "tokenizer = tensorflow.keras.preprocessing.text.Tokenizer(num_words=5000,lower=False)\n",
    "#tokenizer.fit_on_texts(X_train_res.values)\n",
    "#print(tokenizer.word_index)  # To see the dicstionary\n",
    "#X = tokenizer.texts_to_sequences(X_train_res.values)\n",
    "#X = tensorflow.keras.preprocessing.sequence.pad_sequences(X_train_res)\n",
    "#Deep Learning Network Structure\n",
    "model_conv = Sequential()\n",
    "model_conv.add(Embedding(5000,100, input_length=data.shape[1]))\n",
    "model_conv.add(Dropout(0.5))\n",
    "model_conv.add(LSTM(100))\n",
    "model_conv.add(Dense(5, activation='softmax'))\n",
    "model_conv.compile(loss='categorical_crossentropy', optimizer='adam',   metrics=['accuracy','mae','mse',keras_metrics.precision(), keras_metrics.recall(),keras_metrics.f1_score()])\n",
    "#Y = data.Reviewer_Score\n",
    "Y = pd.get_dummies(y).values\n",
    "\n",
    "X_train, X_valid, Y_train, Y_valid = train_test_split(data,Y, test_size = 0.20, random_state =2)\n",
    "batch_size=128\n",
    "#Here we train the Network.\n",
    "\n",
    "pred=model_conv.fit(X_train, Y_train, batch_size =batch_size, epochs =100, verbose =2,validation_data=(X_valid,Y_valid))\n",
    "pred\n",
    "\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred.history['accuracy'])\n",
    "plt.plot(pred.history['precision'])\n",
    "plt.plot(pred.history['recall'])\n",
    "plt.plot(pred.history['f1_score'])\n",
    "\n",
    "plt.plot(pred.history['val_accuracy'])\n",
    "plt.plot(pred.history['val_precision'])\n",
    "plt.plot(pred.history['val_recall'])\n",
    "plt.plot(pred.history['val_f1_score'])\n",
    "\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Accuracy', 'Precision', 'Recall', 'F1 Score', 'Val_Accuracy', 'Val_Precision', 'Val_Recall', 'Val_F1 Score'], loc='lower right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('accLSTM.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred.history['loss'])\n",
    "plt.plot(pred.history['val_loss'])\n",
    "plt.plot(pred.history['val_mae'])\n",
    "plt.plot(pred.history['mae'])\n",
    "plt.plot(pred.history['val_mse'])\n",
    "plt.plot(pred.history['mse'])\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Loss', 'Val_Loss', 'MAE', 'Val_MAE', 'MSE', 'Val_MSE'], loc='upper right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('LosLSTM.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        33\n",
      "           2       1.00      0.96      0.98        28\n",
      "           3       0.96      1.00      0.98        22\n",
      "           4       1.00      1.00      1.00        24\n",
      "\n",
      "    accuracy                           0.99       126\n",
      "   macro avg       0.99      0.99      0.99       126\n",
      "weighted avg       0.99      0.99      0.99       126\n",
      "\n",
      "[[19  0  0  0  0]\n",
      " [ 0 33  0  0  0]\n",
      " [ 0  0 27  1  0]\n",
      " [ 0  0  0 22  0]\n",
      " [ 0  0  0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "yhat_classes = model_conv.predict(X_valid, verbose=0)\n",
    "rounded_predictions=np.argmax(yhat_classes, axis=1)\n",
    "rounded_labels=np.argmax(Y_valid, axis=1)\n",
    "print(classification_report(rounded_labels,rounded_predictions))\n",
    "print(confusion_matrix(rounded_labels,rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "32/32 - 53s - loss: 1.3188 - accuracy: 0.5615 - mae: 0.2849 - mse: 0.1327 - precision: 0.0000e+00 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - val_loss: 0.6937 - val_accuracy: 0.9286 - val_mae: 0.1906 - val_mse: 0.0649 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - 53s/epoch - 2s/step\n",
      "Epoch 2/100\n",
      "32/32 - 1s - loss: 0.5261 - accuracy: 0.8869 - mae: 0.1381 - mse: 0.0497 - precision: 0.8953 - recall: 0.1015 - f1_score: 0.1724 - val_loss: 0.0949 - val_accuracy: 0.9921 - val_mae: 0.0324 - val_mse: 0.0042 - val_precision: 0.9715 - val_recall: 0.2795 - val_f1_score: 0.4340 - 712ms/epoch - 22ms/step\n",
      "Epoch 3/100\n",
      "32/32 - 1s - loss: 0.2501 - accuracy: 0.9167 - mae: 0.0655 - mse: 0.0228 - precision: 0.9601 - recall: 0.3941 - f1_score: 0.5575 - val_loss: 0.0342 - val_accuracy: 0.9921 - val_mae: 0.0089 - val_mse: 0.0020 - val_precision: 0.9671 - val_recall: 0.4761 - val_f1_score: 0.6381 - 830ms/epoch - 26ms/step\n",
      "Epoch 4/100\n",
      "32/32 - 1s - loss: 0.1457 - accuracy: 0.9603 - mae: 0.0416 - mse: 0.0134 - precision: 0.9748 - recall: 0.5423 - f1_score: 0.6966 - val_loss: 0.0294 - val_accuracy: 0.9921 - val_mae: 0.0048 - val_mse: 0.0024 - val_precision: 0.9800 - val_recall: 0.5930 - val_f1_score: 0.7389 - 1s/epoch - 36ms/step\n",
      "Epoch 5/100\n",
      "32/32 - 1s - loss: 0.1239 - accuracy: 0.9563 - mae: 0.0337 - mse: 0.0123 - precision: 0.9785 - recall: 0.6306 - f1_score: 0.7668 - val_loss: 0.0313 - val_accuracy: 0.9921 - val_mae: 0.0044 - val_mse: 0.0023 - val_precision: 0.9764 - val_recall: 0.6642 - val_f1_score: 0.7906 - 1s/epoch - 31ms/step\n",
      "Epoch 6/100\n",
      "32/32 - 1s - loss: 0.1053 - accuracy: 0.9643 - mae: 0.0299 - mse: 0.0101 - precision: 0.9773 - recall: 0.6916 - f1_score: 0.8099 - val_loss: 0.0245 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0025 - val_precision: 0.9782 - val_recall: 0.7180 - val_f1_score: 0.8282 - 618ms/epoch - 19ms/step\n",
      "Epoch 7/100\n",
      "32/32 - 1s - loss: 0.1049 - accuracy: 0.9563 - mae: 0.0260 - mse: 0.0103 - precision: 0.9775 - recall: 0.7375 - f1_score: 0.8407 - val_loss: 0.0289 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0028 - val_precision: 0.9778 - val_recall: 0.7541 - val_f1_score: 0.8515 - 657ms/epoch - 21ms/step\n",
      "Epoch 8/100\n",
      "32/32 - 1s - loss: 0.0886 - accuracy: 0.9722 - mae: 0.0253 - mse: 0.0088 - precision: 0.9796 - recall: 0.7692 - f1_score: 0.8617 - val_loss: 0.0357 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0031 - val_precision: 0.9812 - val_recall: 0.7821 - val_f1_score: 0.8704 - 969ms/epoch - 30ms/step\n",
      "Epoch 9/100\n",
      "32/32 - 1s - loss: 0.0546 - accuracy: 0.9821 - mae: 0.0163 - mse: 0.0054 - precision: 0.9808 - recall: 0.7943 - f1_score: 0.8777 - val_loss: 0.0453 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9806 - val_recall: 0.8065 - val_f1_score: 0.8850 - 828ms/epoch - 26ms/step\n",
      "Epoch 10/100\n",
      "32/32 - 1s - loss: 0.0716 - accuracy: 0.9802 - mae: 0.0165 - mse: 0.0061 - precision: 0.9807 - recall: 0.8172 - f1_score: 0.8915 - val_loss: 0.0542 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9810 - val_recall: 0.8259 - val_f1_score: 0.8968 - 664ms/epoch - 21ms/step\n",
      "Epoch 11/100\n",
      "32/32 - 1s - loss: 0.0795 - accuracy: 0.9683 - mae: 0.0211 - mse: 0.0083 - precision: 0.9808 - recall: 0.8340 - f1_score: 0.9014 - val_loss: 0.0574 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9789 - val_recall: 0.8411 - val_f1_score: 0.9048 - 641ms/epoch - 20ms/step\n",
      "Epoch 12/100\n",
      "32/32 - 1s - loss: 0.0806 - accuracy: 0.9623 - mae: 0.0211 - mse: 0.0089 - precision: 0.9783 - recall: 0.8456 - f1_score: 0.9072 - val_loss: 0.0587 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9778 - val_recall: 0.8504 - val_f1_score: 0.9097 - 731ms/epoch - 23ms/step\n",
      "Epoch 13/100\n",
      "32/32 - 1s - loss: 0.0597 - accuracy: 0.9802 - mae: 0.0171 - mse: 0.0060 - precision: 0.9787 - recall: 0.8553 - f1_score: 0.9128 - val_loss: 0.0556 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9791 - val_recall: 0.8608 - val_f1_score: 0.9161 - 900ms/epoch - 28ms/step\n",
      "Epoch 14/100\n",
      "32/32 - 1s - loss: 0.0606 - accuracy: 0.9742 - mae: 0.0166 - mse: 0.0067 - precision: 0.9773 - recall: 0.8660 - f1_score: 0.9183 - val_loss: 0.0586 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9770 - val_recall: 0.8702 - val_f1_score: 0.9205 - 819ms/epoch - 26ms/step\n",
      "Epoch 15/100\n",
      "32/32 - 1s - loss: 0.0519 - accuracy: 0.9782 - mae: 0.0137 - mse: 0.0052 - precision: 0.9768 - recall: 0.8735 - f1_score: 0.9222 - val_loss: 0.0577 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9775 - val_recall: 0.8768 - val_f1_score: 0.9244 - 611ms/epoch - 19ms/step\n",
      "Epoch 16/100\n",
      "32/32 - 1s - loss: 0.0421 - accuracy: 0.9861 - mae: 0.0127 - mse: 0.0041 - precision: 0.9767 - recall: 0.8806 - f1_score: 0.9262 - val_loss: 0.0646 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9774 - val_recall: 0.8845 - val_f1_score: 0.9286 - 704ms/epoch - 22ms/step\n",
      "Epoch 17/100\n",
      "32/32 - 1s - loss: 0.0518 - accuracy: 0.9782 - mae: 0.0138 - mse: 0.0053 - precision: 0.9774 - recall: 0.8868 - f1_score: 0.9299 - val_loss: 0.0669 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9778 - val_recall: 0.8890 - val_f1_score: 0.9313 - 684ms/epoch - 21ms/step\n",
      "Epoch 18/100\n",
      "32/32 - 1s - loss: 0.0535 - accuracy: 0.9742 - mae: 0.0137 - mse: 0.0054 - precision: 0.9779 - recall: 0.8920 - f1_score: 0.9329 - val_loss: 0.0699 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9782 - val_recall: 0.8947 - val_f1_score: 0.9346 - 981ms/epoch - 31ms/step\n",
      "Epoch 19/100\n",
      "32/32 - 1s - loss: 0.0376 - accuracy: 0.9881 - mae: 0.0104 - mse: 0.0035 - precision: 0.9788 - recall: 0.8966 - f1_score: 0.9359 - val_loss: 0.0668 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9794 - val_recall: 0.8986 - val_f1_score: 0.9373 - 1s/epoch - 33ms/step\n",
      "Epoch 20/100\n",
      "32/32 - 1s - loss: 0.0616 - accuracy: 0.9722 - mae: 0.0159 - mse: 0.0070 - precision: 0.9797 - recall: 0.9012 - f1_score: 0.9388 - val_loss: 0.0540 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9797 - val_recall: 0.9029 - val_f1_score: 0.9397 - 567ms/epoch - 18ms/step\n",
      "Epoch 21/100\n",
      "32/32 - 1s - loss: 0.0338 - accuracy: 0.9881 - mae: 0.0102 - mse: 0.0031 - precision: 0.9797 - recall: 0.9051 - f1_score: 0.9409 - val_loss: 0.0386 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9799 - val_recall: 0.9075 - val_f1_score: 0.9423 - 648ms/epoch - 20ms/step\n",
      "Epoch 22/100\n",
      "32/32 - 1s - loss: 0.0485 - accuracy: 0.9762 - mae: 0.0131 - mse: 0.0059 - precision: 0.9795 - recall: 0.9097 - f1_score: 0.9433 - val_loss: 0.0491 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9794 - val_recall: 0.9114 - val_f1_score: 0.9442 - 610ms/epoch - 19ms/step\n",
      "Epoch 23/100\n",
      "32/32 - 1s - loss: 0.0473 - accuracy: 0.9762 - mae: 0.0125 - mse: 0.0055 - precision: 0.9791 - recall: 0.9132 - f1_score: 0.9450 - val_loss: 0.0465 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9782 - val_recall: 0.9149 - val_f1_score: 0.9455 - 999ms/epoch - 31ms/step\n",
      "Epoch 24/100\n",
      "32/32 - 1s - loss: 0.0508 - accuracy: 0.9782 - mae: 0.0133 - mse: 0.0057 - precision: 0.9783 - recall: 0.9161 - f1_score: 0.9462 - val_loss: 0.0477 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9777 - val_recall: 0.9175 - val_f1_score: 0.9466 - 819ms/epoch - 26ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n",
      "32/32 - 1s - loss: 0.0485 - accuracy: 0.9782 - mae: 0.0115 - mse: 0.0053 - precision: 0.9776 - recall: 0.9188 - f1_score: 0.9473 - val_loss: 0.0582 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9774 - val_recall: 0.9204 - val_f1_score: 0.9480 - 500ms/epoch - 16ms/step\n",
      "Epoch 26/100\n",
      "32/32 - 1s - loss: 0.0312 - accuracy: 0.9901 - mae: 0.0089 - mse: 0.0032 - precision: 0.9778 - recall: 0.9220 - f1_score: 0.9491 - val_loss: 0.0638 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9777 - val_recall: 0.9235 - val_f1_score: 0.9498 - 664ms/epoch - 21ms/step\n",
      "Epoch 27/100\n",
      "32/32 - 1s - loss: 0.0404 - accuracy: 0.9782 - mae: 0.0110 - mse: 0.0049 - precision: 0.9779 - recall: 0.9247 - f1_score: 0.9506 - val_loss: 0.0484 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9779 - val_recall: 0.9255 - val_f1_score: 0.9510 - 603ms/epoch - 19ms/step\n",
      "Epoch 28/100\n",
      "32/32 - 1s - loss: 0.0339 - accuracy: 0.9802 - mae: 0.0093 - mse: 0.0036 - precision: 0.9778 - recall: 0.9265 - f1_score: 0.9515 - val_loss: 0.0453 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9778 - val_recall: 0.9273 - val_f1_score: 0.9519 - 1s/epoch - 36ms/step\n",
      "Epoch 29/100\n",
      "32/32 - 1s - loss: 0.0317 - accuracy: 0.9821 - mae: 0.0090 - mse: 0.0037 - precision: 0.9779 - recall: 0.9278 - f1_score: 0.9522 - val_loss: 0.0560 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9775 - val_recall: 0.9290 - val_f1_score: 0.9526 - 646ms/epoch - 20ms/step\n",
      "Epoch 30/100\n",
      "32/32 - 1s - loss: 0.0424 - accuracy: 0.9802 - mae: 0.0109 - mse: 0.0047 - precision: 0.9775 - recall: 0.9301 - f1_score: 0.9532 - val_loss: 0.0613 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9775 - val_recall: 0.9311 - val_f1_score: 0.9537 - 572ms/epoch - 18ms/step\n",
      "Epoch 31/100\n",
      "32/32 - 1s - loss: 0.0356 - accuracy: 0.9841 - mae: 0.0097 - mse: 0.0043 - precision: 0.9770 - recall: 0.9321 - f1_score: 0.9540 - val_loss: 0.0671 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9772 - val_recall: 0.9330 - val_f1_score: 0.9546 - 662ms/epoch - 21ms/step\n",
      "Epoch 32/100\n",
      "32/32 - 1s - loss: 0.0338 - accuracy: 0.9841 - mae: 0.0088 - mse: 0.0036 - precision: 0.9775 - recall: 0.9340 - f1_score: 0.9553 - val_loss: 0.0401 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9779 - val_recall: 0.9349 - val_f1_score: 0.9559 - 851ms/epoch - 27ms/step\n",
      "Epoch 33/100\n",
      "32/32 - 1s - loss: 0.0309 - accuracy: 0.9841 - mae: 0.0083 - mse: 0.0038 - precision: 0.9783 - recall: 0.9356 - f1_score: 0.9565 - val_loss: 0.0190 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0019 - val_precision: 0.9786 - val_recall: 0.9366 - val_f1_score: 0.9572 - 798ms/epoch - 25ms/step\n",
      "Epoch 34/100\n",
      "32/32 - 1s - loss: 0.0580 - accuracy: 0.9663 - mae: 0.0143 - mse: 0.0077 - precision: 0.9778 - recall: 0.9375 - f1_score: 0.9572 - val_loss: 0.0687 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9776 - val_recall: 0.9383 - val_f1_score: 0.9575 - 737ms/epoch - 23ms/step\n",
      "Epoch 35/100\n",
      "32/32 - 1s - loss: 0.0577 - accuracy: 0.9702 - mae: 0.0134 - mse: 0.0070 - precision: 0.9778 - recall: 0.9383 - f1_score: 0.9577 - val_loss: 0.0485 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9780 - val_recall: 0.9387 - val_f1_score: 0.9579 - 662ms/epoch - 21ms/step\n",
      "Epoch 36/100\n",
      "32/32 - 1s - loss: 0.0523 - accuracy: 0.9802 - mae: 0.0111 - mse: 0.0054 - precision: 0.9782 - recall: 0.9395 - f1_score: 0.9584 - val_loss: 0.1058 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9784 - val_recall: 0.9404 - val_f1_score: 0.9590 - 707ms/epoch - 22ms/step\n",
      "Epoch 37/100\n",
      "32/32 - 1s - loss: 0.0311 - accuracy: 0.9881 - mae: 0.0088 - mse: 0.0035 - precision: 0.9786 - recall: 0.9409 - f1_score: 0.9594 - val_loss: 0.0448 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9788 - val_recall: 0.9416 - val_f1_score: 0.9598 - 1s/epoch - 32ms/step\n",
      "Epoch 38/100\n",
      "32/32 - 1s - loss: 0.0508 - accuracy: 0.9722 - mae: 0.0127 - mse: 0.0062 - precision: 0.9789 - recall: 0.9422 - f1_score: 0.9602 - val_loss: 0.0346 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9789 - val_recall: 0.9429 - val_f1_score: 0.9606 - 955ms/epoch - 30ms/step\n",
      "Epoch 39/100\n",
      "32/32 - 1s - loss: 0.0383 - accuracy: 0.9821 - mae: 0.0096 - mse: 0.0049 - precision: 0.9790 - recall: 0.9435 - f1_score: 0.9609 - val_loss: 0.0350 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9791 - val_recall: 0.9440 - val_f1_score: 0.9612 - 653ms/epoch - 20ms/step\n",
      "Epoch 40/100\n",
      "32/32 - 1s - loss: 0.0332 - accuracy: 0.9821 - mae: 0.0091 - mse: 0.0038 - precision: 0.9793 - recall: 0.9445 - f1_score: 0.9616 - val_loss: 0.0385 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9796 - val_recall: 0.9452 - val_f1_score: 0.9621 - 692ms/epoch - 22ms/step\n",
      "Epoch 41/100\n",
      "32/32 - 1s - loss: 0.0368 - accuracy: 0.9762 - mae: 0.0087 - mse: 0.0045 - precision: 0.9796 - recall: 0.9457 - f1_score: 0.9624 - val_loss: 0.0655 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9797 - val_recall: 0.9463 - val_f1_score: 0.9627 - 661ms/epoch - 21ms/step\n",
      "Epoch 42/100\n",
      "32/32 - 1s - loss: 0.0342 - accuracy: 0.9821 - mae: 0.0092 - mse: 0.0040 - precision: 0.9796 - recall: 0.9468 - f1_score: 0.9629 - val_loss: 0.0430 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9796 - val_recall: 0.9474 - val_f1_score: 0.9633 - 912ms/epoch - 28ms/step\n",
      "Epoch 43/100\n",
      "32/32 - 1s - loss: 0.0290 - accuracy: 0.9881 - mae: 0.0073 - mse: 0.0033 - precision: 0.9797 - recall: 0.9478 - f1_score: 0.9635 - val_loss: 0.0088 - val_accuracy: 0.9921 - val_mae: 0.0021 - val_mse: 0.0014 - val_precision: 0.9799 - val_recall: 0.9482 - val_f1_score: 0.9638 - 949ms/epoch - 30ms/step\n",
      "Epoch 44/100\n",
      "32/32 - 1s - loss: 0.0460 - accuracy: 0.9821 - mae: 0.0100 - mse: 0.0052 - precision: 0.9800 - recall: 0.9486 - f1_score: 0.9641 - val_loss: 0.0078 - val_accuracy: 0.9921 - val_mae: 0.0020 - val_mse: 0.0012 - val_precision: 0.9799 - val_recall: 0.9491 - val_f1_score: 0.9642 - 702ms/epoch - 22ms/step\n",
      "Epoch 45/100\n",
      "32/32 - 1s - loss: 0.0442 - accuracy: 0.9762 - mae: 0.0107 - mse: 0.0052 - precision: 0.9801 - recall: 0.9495 - f1_score: 0.9646 - val_loss: 0.0102 - val_accuracy: 0.9921 - val_mae: 0.0023 - val_mse: 0.0017 - val_precision: 0.9803 - val_recall: 0.9498 - val_f1_score: 0.9648 - 610ms/epoch - 19ms/step\n",
      "Epoch 46/100\n",
      "32/32 - 1s - loss: 0.0290 - accuracy: 0.9861 - mae: 0.0077 - mse: 0.0035 - precision: 0.9805 - recall: 0.9502 - f1_score: 0.9651 - val_loss: 0.0032 - val_accuracy: 1.0000 - val_mae: 0.0011 - val_mse: 3.5473e-04 - val_precision: 0.9806 - val_recall: 0.9506 - val_f1_score: 0.9654 - 658ms/epoch - 21ms/step\n",
      "Epoch 47/100\n",
      "32/32 - 1s - loss: 0.0456 - accuracy: 0.9802 - mae: 0.0122 - mse: 0.0052 - precision: 0.9806 - recall: 0.9507 - f1_score: 0.9655 - val_loss: 0.0061 - val_accuracy: 0.9921 - val_mae: 0.0017 - val_mse: 8.9947e-04 - val_precision: 0.9805 - val_recall: 0.9510 - val_f1_score: 0.9655 - 976ms/epoch - 30ms/step\n",
      "Epoch 48/100\n",
      "32/32 - 1s - loss: 0.0491 - accuracy: 0.9722 - mae: 0.0121 - mse: 0.0060 - precision: 0.9803 - recall: 0.9515 - f1_score: 0.9657 - val_loss: 0.0105 - val_accuracy: 0.9921 - val_mae: 0.0023 - val_mse: 0.0017 - val_precision: 0.9797 - val_recall: 0.9518 - val_f1_score: 0.9656 - 803ms/epoch - 25ms/step\n",
      "Epoch 49/100\n",
      "32/32 - 1s - loss: 0.0409 - accuracy: 0.9742 - mae: 0.0105 - mse: 0.0051 - precision: 0.9798 - recall: 0.9518 - f1_score: 0.9656 - val_loss: 0.0144 - val_accuracy: 0.9921 - val_mae: 0.0027 - val_mse: 0.0021 - val_precision: 0.9800 - val_recall: 0.9518 - val_f1_score: 0.9657 - 540ms/epoch - 17ms/step\n",
      "Epoch 50/100\n",
      "32/32 - 1s - loss: 0.0206 - accuracy: 0.9921 - mae: 0.0056 - mse: 0.0020 - precision: 0.9801 - recall: 0.9522 - f1_score: 0.9660 - val_loss: 0.0188 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0025 - val_precision: 0.9802 - val_recall: 0.9525 - val_f1_score: 0.9661 - 666ms/epoch - 21ms/step\n",
      "Epoch 51/100\n",
      "32/32 - 1s - loss: 0.0375 - accuracy: 0.9821 - mae: 0.0101 - mse: 0.0042 - precision: 0.9803 - recall: 0.9529 - f1_score: 0.9664 - val_loss: 0.0261 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0029 - val_precision: 0.9804 - val_recall: 0.9531 - val_f1_score: 0.9666 - 749ms/epoch - 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 52/100\n",
      "32/32 - 1s - loss: 0.0316 - accuracy: 0.9841 - mae: 0.0080 - mse: 0.0039 - precision: 0.9805 - recall: 0.9534 - f1_score: 0.9667 - val_loss: 0.0408 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9805 - val_recall: 0.9537 - val_f1_score: 0.9669 - 910ms/epoch - 28ms/step\n",
      "Epoch 53/100\n",
      "32/32 - 1s - loss: 0.0299 - accuracy: 0.9881 - mae: 0.0077 - mse: 0.0034 - precision: 0.9805 - recall: 0.9540 - f1_score: 0.9671 - val_loss: 0.0481 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0032 - val_precision: 0.9807 - val_recall: 0.9544 - val_f1_score: 0.9674 - 888ms/epoch - 28ms/step\n",
      "Epoch 54/100\n",
      "32/32 - 1s - loss: 0.0267 - accuracy: 0.9881 - mae: 0.0069 - mse: 0.0030 - precision: 0.9808 - recall: 0.9549 - f1_score: 0.9676 - val_loss: 0.0452 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9810 - val_recall: 0.9551 - val_f1_score: 0.9679 - 576ms/epoch - 18ms/step\n",
      "Epoch 55/100\n",
      "32/32 - 1s - loss: 0.0255 - accuracy: 0.9881 - mae: 0.0065 - mse: 0.0026 - precision: 0.9811 - recall: 0.9555 - f1_score: 0.9681 - val_loss: 0.0413 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9813 - val_recall: 0.9558 - val_f1_score: 0.9684 - 736ms/epoch - 23ms/step\n",
      "Epoch 56/100\n",
      "32/32 - 1s - loss: 0.0255 - accuracy: 0.9861 - mae: 0.0062 - mse: 0.0031 - precision: 0.9814 - recall: 0.9562 - f1_score: 0.9686 - val_loss: 0.0431 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9815 - val_recall: 0.9566 - val_f1_score: 0.9689 - 591ms/epoch - 18ms/step\n",
      "Epoch 57/100\n",
      "32/32 - 1s - loss: 0.0411 - accuracy: 0.9782 - mae: 0.0095 - mse: 0.0050 - precision: 0.9816 - recall: 0.9569 - f1_score: 0.9691 - val_loss: 0.0231 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9816 - val_recall: 0.9572 - val_f1_score: 0.9692 - 937ms/epoch - 29ms/step\n",
      "Epoch 58/100\n",
      "32/32 - 1s - loss: 0.0253 - accuracy: 0.9901 - mae: 0.0069 - mse: 0.0030 - precision: 0.9817 - recall: 0.9575 - f1_score: 0.9695 - val_loss: 0.0209 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0019 - val_precision: 0.9819 - val_recall: 0.9578 - val_f1_score: 0.9697 - 964ms/epoch - 30ms/step\n",
      "Epoch 59/100\n",
      "32/32 - 1s - loss: 0.0443 - accuracy: 0.9742 - mae: 0.0111 - mse: 0.0055 - precision: 0.9816 - recall: 0.9578 - f1_score: 0.9696 - val_loss: 0.0267 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9816 - val_recall: 0.9580 - val_f1_score: 0.9697 - 593ms/epoch - 19ms/step\n",
      "Epoch 60/100\n",
      "32/32 - 1s - loss: 0.0311 - accuracy: 0.9802 - mae: 0.0079 - mse: 0.0041 - precision: 0.9818 - recall: 0.9584 - f1_score: 0.9699 - val_loss: 0.0183 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0020 - val_precision: 0.9817 - val_recall: 0.9587 - val_f1_score: 0.9701 - 671ms/epoch - 21ms/step\n",
      "Epoch 61/100\n",
      "32/32 - 1s - loss: 0.0354 - accuracy: 0.9782 - mae: 0.0094 - mse: 0.0044 - precision: 0.9817 - recall: 0.9590 - f1_score: 0.9702 - val_loss: 0.0209 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0019 - val_precision: 0.9817 - val_recall: 0.9594 - val_f1_score: 0.9704 - 760ms/epoch - 24ms/step\n",
      "Epoch 62/100\n",
      "32/32 - 1s - loss: 0.0412 - accuracy: 0.9742 - mae: 0.0103 - mse: 0.0054 - precision: 0.9815 - recall: 0.9596 - f1_score: 0.9704 - val_loss: 0.0296 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0021 - val_precision: 0.9814 - val_recall: 0.9598 - val_f1_score: 0.9705 - 1s/epoch - 37ms/step\n",
      "Epoch 63/100\n",
      "32/32 - 1s - loss: 0.0335 - accuracy: 0.9762 - mae: 0.0089 - mse: 0.0042 - precision: 0.9814 - recall: 0.9601 - f1_score: 0.9706 - val_loss: 0.0217 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0027 - val_precision: 0.9814 - val_recall: 0.9604 - val_f1_score: 0.9708 - 731ms/epoch - 23ms/step\n",
      "Epoch 64/100\n",
      "32/32 - 1s - loss: 0.0277 - accuracy: 0.9841 - mae: 0.0076 - mse: 0.0034 - precision: 0.9816 - recall: 0.9606 - f1_score: 0.9710 - val_loss: 0.0186 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0025 - val_precision: 0.9817 - val_recall: 0.9609 - val_f1_score: 0.9712 - 637ms/epoch - 20ms/step\n",
      "Epoch 65/100\n",
      "32/32 - 1s - loss: 0.0297 - accuracy: 0.9921 - mae: 0.0073 - mse: 0.0033 - precision: 0.9817 - recall: 0.9610 - f1_score: 0.9713 - val_loss: 0.0359 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9818 - val_recall: 0.9611 - val_f1_score: 0.9713 - 633ms/epoch - 20ms/step\n",
      "Epoch 66/100\n",
      "32/32 - 1s - loss: 0.0312 - accuracy: 0.9861 - mae: 0.0076 - mse: 0.0036 - precision: 0.9819 - recall: 0.9615 - f1_score: 0.9715 - val_loss: 0.0100 - val_accuracy: 0.9921 - val_mae: 0.0023 - val_mse: 0.0016 - val_precision: 0.9819 - val_recall: 0.9617 - val_f1_score: 0.9717 - 641ms/epoch - 20ms/step\n",
      "Epoch 67/100\n",
      "32/32 - 1s - loss: 0.0282 - accuracy: 0.9861 - mae: 0.0079 - mse: 0.0032 - precision: 0.9820 - recall: 0.9619 - f1_score: 0.9718 - val_loss: 6.7811e-04 - val_accuracy: 1.0000 - val_mae: 2.5998e-04 - val_mse: 1.9528e-05 - val_precision: 0.9821 - val_recall: 0.9622 - val_f1_score: 0.9720 - 873ms/epoch - 27ms/step\n",
      "Epoch 68/100\n",
      "32/32 - 1s - loss: 0.0267 - accuracy: 0.9821 - mae: 0.0068 - mse: 0.0034 - precision: 0.9821 - recall: 0.9623 - f1_score: 0.9721 - val_loss: 0.0024 - val_accuracy: 1.0000 - val_mae: 8.3727e-04 - val_mse: 2.1372e-04 - val_precision: 0.9821 - val_recall: 0.9626 - val_f1_score: 0.9723 - 892ms/epoch - 28ms/step\n",
      "Epoch 69/100\n",
      "32/32 - 1s - loss: 0.0353 - accuracy: 0.9841 - mae: 0.0087 - mse: 0.0044 - precision: 0.9822 - recall: 0.9628 - f1_score: 0.9724 - val_loss: 0.0021 - val_accuracy: 1.0000 - val_mae: 7.2985e-04 - val_mse: 1.3230e-04 - val_precision: 0.9823 - val_recall: 0.9629 - val_f1_score: 0.9725 - 573ms/epoch - 18ms/step\n",
      "Epoch 70/100\n",
      "32/32 - 1s - loss: 0.0217 - accuracy: 0.9921 - mae: 0.0058 - mse: 0.0024 - precision: 0.9823 - recall: 0.9632 - f1_score: 0.9727 - val_loss: 0.0030 - val_accuracy: 1.0000 - val_mae: 0.0010 - val_mse: 2.2982e-04 - val_precision: 0.9823 - val_recall: 0.9635 - val_f1_score: 0.9728 - 651ms/epoch - 20ms/step\n",
      "Epoch 71/100\n",
      "32/32 - 1s - loss: 0.0302 - accuracy: 0.9841 - mae: 0.0077 - mse: 0.0039 - precision: 0.9823 - recall: 0.9637 - f1_score: 0.9729 - val_loss: 0.0053 - val_accuracy: 1.0000 - val_mae: 0.0015 - val_mse: 6.2641e-04 - val_precision: 0.9823 - val_recall: 0.9640 - val_f1_score: 0.9731 - 909ms/epoch - 28ms/step\n",
      "Epoch 72/100\n",
      "32/32 - 1s - loss: 0.0254 - accuracy: 0.9881 - mae: 0.0073 - mse: 0.0030 - precision: 0.9823 - recall: 0.9641 - f1_score: 0.9731 - val_loss: 0.0165 - val_accuracy: 0.9921 - val_mae: 0.0028 - val_mse: 0.0024 - val_precision: 0.9824 - val_recall: 0.9644 - val_f1_score: 0.9733 - 1s/epoch - 33ms/step\n",
      "Epoch 73/100\n",
      "32/32 - 1s - loss: 0.0487 - accuracy: 0.9702 - mae: 0.0116 - mse: 0.0062 - precision: 0.9821 - recall: 0.9646 - f1_score: 0.9733 - val_loss: 0.0230 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0028 - val_precision: 0.9822 - val_recall: 0.9647 - val_f1_score: 0.9734 - 564ms/epoch - 18ms/step\n",
      "Epoch 74/100\n",
      "32/32 - 1s - loss: 0.0259 - accuracy: 0.9901 - mae: 0.0065 - mse: 0.0032 - precision: 0.9822 - recall: 0.9650 - f1_score: 0.9735 - val_loss: 0.0266 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0029 - val_precision: 0.9822 - val_recall: 0.9652 - val_f1_score: 0.9736 - 751ms/epoch - 23ms/step\n",
      "Epoch 75/100\n",
      "32/32 - 1s - loss: 0.0180 - accuracy: 0.9861 - mae: 0.0050 - mse: 0.0021 - precision: 0.9822 - recall: 0.9652 - f1_score: 0.9736 - val_loss: 0.0110 - val_accuracy: 0.9921 - val_mae: 0.0024 - val_mse: 0.0017 - val_precision: 0.9822 - val_recall: 0.9654 - val_f1_score: 0.9737 - 611ms/epoch - 19ms/step\n",
      "Epoch 76/100\n",
      "32/32 - 1s - loss: 0.0219 - accuracy: 0.9881 - mae: 0.0059 - mse: 0.0023 - precision: 0.9822 - recall: 0.9656 - f1_score: 0.9738 - val_loss: 0.0082 - val_accuracy: 0.9921 - val_mae: 0.0020 - val_mse: 0.0013 - val_precision: 0.9822 - val_recall: 0.9657 - val_f1_score: 0.9739 - 814ms/epoch - 25ms/step\n",
      "Epoch 77/100\n",
      "32/32 - 1s - loss: 0.0146 - accuracy: 0.9940 - mae: 0.0042 - mse: 0.0015 - precision: 0.9822 - recall: 0.9660 - f1_score: 0.9740 - val_loss: 0.0149 - val_accuracy: 0.9921 - val_mae: 0.0027 - val_mse: 0.0022 - val_precision: 0.9823 - val_recall: 0.9662 - val_f1_score: 0.9742 - 954ms/epoch - 30ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 78/100\n",
      "32/32 - 1s - loss: 0.0304 - accuracy: 0.9841 - mae: 0.0075 - mse: 0.0037 - precision: 0.9822 - recall: 0.9664 - f1_score: 0.9742 - val_loss: 0.0049 - val_accuracy: 1.0000 - val_mae: 0.0015 - val_mse: 5.9833e-04 - val_precision: 0.9820 - val_recall: 0.9666 - val_f1_score: 0.9742 - 554ms/epoch - 17ms/step\n",
      "Epoch 79/100\n",
      "32/32 - 1s - loss: 0.0352 - accuracy: 0.9841 - mae: 0.0085 - mse: 0.0041 - precision: 0.9821 - recall: 0.9665 - f1_score: 0.9743 - val_loss: 0.0020 - val_accuracy: 1.0000 - val_mae: 7.1982e-04 - val_mse: 1.1678e-04 - val_precision: 0.9822 - val_recall: 0.9666 - val_f1_score: 0.9744 - 653ms/epoch - 20ms/step\n",
      "Epoch 80/100\n",
      "32/32 - 1s - loss: 0.0324 - accuracy: 0.9841 - mae: 0.0083 - mse: 0.0040 - precision: 0.9823 - recall: 0.9668 - f1_score: 0.9745 - val_loss: 0.0015 - val_accuracy: 1.0000 - val_mae: 5.3188e-04 - val_mse: 6.7209e-05 - val_precision: 0.9822 - val_recall: 0.9669 - val_f1_score: 0.9745 - 597ms/epoch - 19ms/step\n",
      "Epoch 81/100\n",
      "32/32 - 1s - loss: 0.0210 - accuracy: 0.9861 - mae: 0.0055 - mse: 0.0027 - precision: 0.9821 - recall: 0.9671 - f1_score: 0.9745 - val_loss: 0.0100 - val_accuracy: 0.9921 - val_mae: 0.0023 - val_mse: 0.0015 - val_precision: 0.9821 - val_recall: 0.9672 - val_f1_score: 0.9746 - 863ms/epoch - 27ms/step\n",
      "Epoch 82/100\n",
      "32/32 - 1s - loss: 0.0179 - accuracy: 0.9921 - mae: 0.0047 - mse: 0.0018 - precision: 0.9821 - recall: 0.9673 - f1_score: 0.9746 - val_loss: 0.0119 - val_accuracy: 0.9921 - val_mae: 0.0025 - val_mse: 0.0017 - val_precision: 0.9822 - val_recall: 0.9675 - val_f1_score: 0.9748 - 935ms/epoch - 29ms/step\n",
      "Epoch 83/100\n",
      "32/32 - 1s - loss: 0.0318 - accuracy: 0.9821 - mae: 0.0076 - mse: 0.0040 - precision: 0.9822 - recall: 0.9675 - f1_score: 0.9748 - val_loss: 0.0039 - val_accuracy: 1.0000 - val_mae: 0.0012 - val_mse: 3.5185e-04 - val_precision: 0.9821 - val_recall: 0.9677 - val_f1_score: 0.9748 - 540ms/epoch - 17ms/step\n",
      "Epoch 84/100\n",
      "32/32 - 1s - loss: 0.0176 - accuracy: 0.9901 - mae: 0.0046 - mse: 0.0022 - precision: 0.9822 - recall: 0.9678 - f1_score: 0.9749 - val_loss: 0.0038 - val_accuracy: 1.0000 - val_mae: 0.0012 - val_mse: 3.4845e-04 - val_precision: 0.9822 - val_recall: 0.9679 - val_f1_score: 0.9750 - 531ms/epoch - 17ms/step\n",
      "Epoch 85/100\n",
      "32/32 - 1s - loss: 0.0336 - accuracy: 0.9802 - mae: 0.0089 - mse: 0.0041 - precision: 0.9821 - recall: 0.9681 - f1_score: 0.9751 - val_loss: 0.0046 - val_accuracy: 1.0000 - val_mae: 0.0014 - val_mse: 6.0439e-04 - val_precision: 0.9822 - val_recall: 0.9681 - val_f1_score: 0.9751 - 708ms/epoch - 22ms/step\n",
      "Epoch 86/100\n",
      "32/32 - 1s - loss: 0.0356 - accuracy: 0.9762 - mae: 0.0089 - mse: 0.0042 - precision: 0.9822 - recall: 0.9682 - f1_score: 0.9752 - val_loss: 0.0059 - val_accuracy: 0.9921 - val_mae: 0.0017 - val_mse: 8.7020e-04 - val_precision: 0.9823 - val_recall: 0.9680 - val_f1_score: 0.9751 - 889ms/epoch - 28ms/step\n",
      "Epoch 87/100\n",
      "32/32 - 1s - loss: 0.0283 - accuracy: 0.9821 - mae: 0.0071 - mse: 0.0039 - precision: 0.9822 - recall: 0.9683 - f1_score: 0.9752 - val_loss: 0.0300 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0030 - val_precision: 0.9822 - val_recall: 0.9684 - val_f1_score: 0.9753 - 1s/epoch - 34ms/step\n",
      "Epoch 88/100\n",
      "32/32 - 1s - loss: 0.0206 - accuracy: 0.9901 - mae: 0.0053 - mse: 0.0024 - precision: 0.9823 - recall: 0.9685 - f1_score: 0.9753 - val_loss: 0.0188 - val_accuracy: 0.9921 - val_mae: 0.0029 - val_mse: 0.0026 - val_precision: 0.9823 - val_recall: 0.9686 - val_f1_score: 0.9754 - 557ms/epoch - 17ms/step\n",
      "Epoch 89/100\n",
      "32/32 - 1s - loss: 0.0299 - accuracy: 0.9821 - mae: 0.0072 - mse: 0.0034 - precision: 0.9823 - recall: 0.9685 - f1_score: 0.9753 - val_loss: 0.0171 - val_accuracy: 0.9921 - val_mae: 0.0028 - val_mse: 0.0025 - val_precision: 0.9824 - val_recall: 0.9685 - val_f1_score: 0.9754 - 717ms/epoch - 22ms/step\n",
      "Epoch 90/100\n",
      "32/32 - 1s - loss: 0.0414 - accuracy: 0.9742 - mae: 0.0101 - mse: 0.0057 - precision: 0.9822 - recall: 0.9686 - f1_score: 0.9753 - val_loss: 0.0367 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9821 - val_recall: 0.9688 - val_f1_score: 0.9754 - 800ms/epoch - 25ms/step\n",
      "Epoch 91/100\n",
      "32/32 - 1s - loss: 0.0283 - accuracy: 0.9821 - mae: 0.0072 - mse: 0.0036 - precision: 0.9821 - recall: 0.9689 - f1_score: 0.9754 - val_loss: 0.0354 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9820 - val_recall: 0.9690 - val_f1_score: 0.9755 - 914ms/epoch - 29ms/step\n",
      "Epoch 92/100\n",
      "32/32 - 1s - loss: 0.0221 - accuracy: 0.9881 - mae: 0.0056 - mse: 0.0025 - precision: 0.9821 - recall: 0.9691 - f1_score: 0.9756 - val_loss: 0.0351 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0031 - val_precision: 0.9822 - val_recall: 0.9693 - val_f1_score: 0.9757 - 883ms/epoch - 28ms/step\n",
      "Epoch 93/100\n",
      "32/32 - 0s - loss: 0.0264 - accuracy: 0.9881 - mae: 0.0067 - mse: 0.0029 - precision: 0.9823 - recall: 0.9692 - f1_score: 0.9757 - val_loss: 0.0038 - val_accuracy: 1.0000 - val_mae: 0.0012 - val_mse: 4.5080e-04 - val_precision: 0.9824 - val_recall: 0.9693 - val_f1_score: 0.9758 - 473ms/epoch - 15ms/step\n",
      "Epoch 94/100\n",
      "32/32 - 1s - loss: 0.0314 - accuracy: 0.9802 - mae: 0.0079 - mse: 0.0037 - precision: 0.9825 - recall: 0.9695 - f1_score: 0.9759 - val_loss: 0.0064 - val_accuracy: 0.9921 - val_mae: 0.0018 - val_mse: 8.6343e-04 - val_precision: 0.9824 - val_recall: 0.9694 - val_f1_score: 0.9759 - 633ms/epoch - 20ms/step\n",
      "Epoch 95/100\n",
      "32/32 - 1s - loss: 0.0190 - accuracy: 0.9861 - mae: 0.0051 - mse: 0.0025 - precision: 0.9824 - recall: 0.9696 - f1_score: 0.9760 - val_loss: 0.0475 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9824 - val_recall: 0.9697 - val_f1_score: 0.9760 - 745ms/epoch - 23ms/step\n",
      "Epoch 96/100\n",
      "32/32 - 1s - loss: 0.0289 - accuracy: 0.9821 - mae: 0.0073 - mse: 0.0036 - precision: 0.9824 - recall: 0.9698 - f1_score: 0.9761 - val_loss: 0.0419 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9825 - val_recall: 0.9699 - val_f1_score: 0.9761 - 951ms/epoch - 30ms/step\n",
      "Epoch 97/100\n",
      "32/32 - 1s - loss: 0.0306 - accuracy: 0.9861 - mae: 0.0068 - mse: 0.0037 - precision: 0.9825 - recall: 0.9700 - f1_score: 0.9762 - val_loss: 0.0433 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0021 - val_precision: 0.9826 - val_recall: 0.9700 - val_f1_score: 0.9763 - 813ms/epoch - 25ms/step\n",
      "Epoch 98/100\n",
      "32/32 - 0s - loss: 0.0261 - accuracy: 0.9841 - mae: 0.0068 - mse: 0.0030 - precision: 0.9826 - recall: 0.9701 - f1_score: 0.9763 - val_loss: 0.0431 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0031 - val_precision: 0.9825 - val_recall: 0.9702 - val_f1_score: 0.9763 - 491ms/epoch - 15ms/step\n",
      "Epoch 99/100\n",
      "32/32 - 1s - loss: 0.0265 - accuracy: 0.9861 - mae: 0.0068 - mse: 0.0034 - precision: 0.9826 - recall: 0.9703 - f1_score: 0.9764 - val_loss: 0.0169 - val_accuracy: 0.9921 - val_mae: 0.0028 - val_mse: 0.0023 - val_precision: 0.9827 - val_recall: 0.9704 - val_f1_score: 0.9765 - 575ms/epoch - 18ms/step\n",
      "Epoch 100/100\n",
      "32/32 - 1s - loss: 0.0178 - accuracy: 0.9921 - mae: 0.0045 - mse: 0.0018 - precision: 0.9828 - recall: 0.9705 - f1_score: 0.9766 - val_loss: 0.0178 - val_accuracy: 0.9921 - val_mae: 0.0028 - val_mse: 0.0025 - val_precision: 0.9829 - val_recall: 0.9706 - val_f1_score: 0.9767 - 805ms/epoch - 25ms/step\n",
      "--- 133.72861170768738 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "#Deep Learning Network Structure\n",
    "model_conv = Sequential()\n",
    "model_conv.add(Embedding(1000,100, input_length=data.shape[1]))\n",
    "model_conv.add(Dropout(0.2))\n",
    "model_conv.add(layers.GRU(64, return_sequences=True))\n",
    "model_conv.add(layers.SimpleRNN(32))\n",
    "model_conv.add(Dense(32))\n",
    "model_conv.add(Activation('relu'))\n",
    "model_conv.add(Dropout(rate=0.5))\n",
    "model_conv.add(Dropout(0.2))\n",
    "model_conv.add(Dense(16))\n",
    "model_conv.add(Dense(5, activation='softmax'))\n",
    "model_conv.compile(loss='categorical_crossentropy', optimizer='adam',   metrics=['accuracy','mae','mse',keras_metrics.precision(), keras_metrics.recall(),keras_metrics.f1_score()])\n",
    "batch_size=16\n",
    "#Here we train the Network.\n",
    "pred1=model_conv.fit(X_train, Y_train, batch_size =batch_size, epochs =100, verbose =2,validation_data=(X_valid,Y_valid))\n",
    "pred1\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred1.history['accuracy'])\n",
    "plt.plot(pred1.history['precision'])\n",
    "plt.plot(pred1.history['recall'])\n",
    "plt.plot(pred1.history['f1_score'])\n",
    "\n",
    "plt.plot(pred1.history['val_accuracy'])\n",
    "plt.plot(pred1.history['val_precision'])\n",
    "plt.plot(pred1.history['val_recall'])\n",
    "plt.plot(pred1.history['val_f1_score'])\n",
    "\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Accuracy', 'Precision', 'Recall', 'F1 Score', 'Val_Accuracy', 'Val_Precision', 'Val_Recall', 'Val_F1 Score'], loc='lower right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('accRNN.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred1.history['loss'])\n",
    "plt.plot(pred1.history['val_loss'])\n",
    "plt.plot(pred1.history['val_mae'])\n",
    "plt.plot(pred1.history['mae'])\n",
    "plt.plot(pred1.history['val_mse'])\n",
    "plt.plot(pred1.history['mse'])\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Loss', 'Val_Loss', 'MAE', 'Val_MAE', 'MSE', 'Val_MSE'], loc='upper right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('LosRNN.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       0.97      1.00      0.99        33\n",
      "           2       1.00      0.96      0.98        28\n",
      "           3       1.00      1.00      1.00        22\n",
      "           4       1.00      1.00      1.00        24\n",
      "\n",
      "    accuracy                           0.99       126\n",
      "   macro avg       0.99      0.99      0.99       126\n",
      "weighted avg       0.99      0.99      0.99       126\n",
      "\n",
      "[[19  0  0  0  0]\n",
      " [ 0 33  0  0  0]\n",
      " [ 0  1 27  0  0]\n",
      " [ 0  0  0 22  0]\n",
      " [ 0  0  0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "yhat_classes = model_conv.predict(X_valid, verbose=0)\n",
    "rounded_predictions=np.argmax(yhat_classes, axis=1)\n",
    "rounded_labels=np.argmax(Y_valid, axis=1)\n",
    "print(classification_report(rounded_labels,rounded_predictions))\n",
    "print(confusion_matrix(rounded_labels,rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "32/32 - 17s - loss: 1.1741 - accuracy: 0.8889 - mae: 0.2685 - mse: 0.1168 - precision: 0.2812 - recall: 0.0247 - f1_score: 0.0439 - val_loss: 0.5612 - val_accuracy: 0.9921 - val_mae: 0.1664 - val_mse: 0.0473 - val_precision: 1.0000 - val_recall: 0.2639 - val_f1_score: 0.4167 - 17s/epoch - 521ms/step\n",
      "Epoch 2/100\n",
      "32/32 - 0s - loss: 0.2312 - accuracy: 0.9921 - mae: 0.0725 - mse: 0.0163 - precision: 1.0000 - recall: 0.5171 - f1_score: 0.6780 - val_loss: 0.0441 - val_accuracy: 0.9921 - val_mae: 0.0128 - val_mse: 0.0021 - val_precision: 1.0000 - val_recall: 0.6398 - val_f1_score: 0.7803 - 490ms/epoch - 15ms/step\n",
      "Epoch 3/100\n",
      "32/32 - 1s - loss: 0.0401 - accuracy: 0.9940 - mae: 0.0098 - mse: 0.0023 - precision: 0.9981 - recall: 0.7069 - f1_score: 0.8272 - val_loss: 0.0273 - val_accuracy: 0.9921 - val_mae: 0.0052 - val_mse: 0.0022 - val_precision: 0.9965 - val_recall: 0.7597 - val_f1_score: 0.8621 - 725ms/epoch - 23ms/step\n",
      "Epoch 4/100\n",
      "32/32 - 1s - loss: 0.0230 - accuracy: 0.9921 - mae: 0.0060 - mse: 0.0018 - precision: 0.9952 - recall: 0.7931 - f1_score: 0.8826 - val_loss: 0.0268 - val_accuracy: 0.9921 - val_mae: 0.0044 - val_mse: 0.0023 - val_precision: 0.9951 - val_recall: 0.8187 - val_f1_score: 0.8983 - 633ms/epoch - 20ms/step\n",
      "Epoch 5/100\n",
      "32/32 - 1s - loss: 0.0119 - accuracy: 0.9980 - mae: 0.0041 - mse: 6.4747e-04 - precision: 0.9958 - recall: 0.8398 - f1_score: 0.9112 - val_loss: 0.0275 - val_accuracy: 0.9921 - val_mae: 0.0038 - val_mse: 0.0024 - val_precision: 0.9963 - val_recall: 0.8554 - val_f1_score: 0.9205 - 796ms/epoch - 25ms/step\n",
      "Epoch 6/100\n",
      "32/32 - 1s - loss: 0.0065 - accuracy: 1.0000 - mae: 0.0024 - mse: 1.8981e-04 - precision: 0.9966 - recall: 0.8681 - f1_score: 0.9279 - val_loss: 0.0301 - val_accuracy: 0.9921 - val_mae: 0.0036 - val_mse: 0.0024 - val_precision: 0.9970 - val_recall: 0.8797 - val_f1_score: 0.9347 - 793ms/epoch - 25ms/step\n",
      "Epoch 7/100\n",
      "32/32 - 1s - loss: 0.0061 - accuracy: 0.9980 - mae: 0.0021 - mse: 2.9895e-04 - precision: 0.9972 - recall: 0.8892 - f1_score: 0.9401 - val_loss: 0.0298 - val_accuracy: 0.9921 - val_mae: 0.0034 - val_mse: 0.0024 - val_precision: 0.9975 - val_recall: 0.8971 - val_f1_score: 0.9446 - 577ms/epoch - 18ms/step\n",
      "Epoch 8/100\n",
      "32/32 - 1s - loss: 0.0027 - accuracy: 1.0000 - mae: 0.0010 - mse: 5.2914e-05 - precision: 0.9976 - recall: 0.9040 - f1_score: 0.9485 - val_loss: 0.0295 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0025 - val_precision: 0.9978 - val_recall: 0.9100 - val_f1_score: 0.9519 - 546ms/epoch - 17ms/step\n",
      "Epoch 9/100\n",
      "32/32 - 1s - loss: 0.0053 - accuracy: 0.9980 - mae: 0.0017 - mse: 4.0319e-04 - precision: 0.9979 - recall: 0.9154 - f1_score: 0.9549 - val_loss: 0.0290 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0025 - val_precision: 0.9981 - val_recall: 0.9201 - val_f1_score: 0.9575 - 678ms/epoch - 21ms/step\n",
      "Epoch 10/100\n",
      "32/32 - 1s - loss: 0.0027 - accuracy: 1.0000 - mae: 0.0010 - mse: 6.3714e-05 - precision: 0.9982 - recall: 0.9242 - f1_score: 0.9598 - val_loss: 0.0290 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0025 - val_precision: 0.9983 - val_recall: 0.9281 - val_f1_score: 0.9619 - 793ms/epoch - 25ms/step\n",
      "Epoch 11/100\n",
      "32/32 - 1s - loss: 0.0016 - accuracy: 1.0000 - mae: 6.3177e-04 - mse: 8.6092e-06 - precision: 0.9984 - recall: 0.9318 - f1_score: 0.9639 - val_loss: 0.0307 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0025 - val_precision: 0.9985 - val_recall: 0.9347 - val_f1_score: 0.9655 - 945ms/epoch - 30ms/step\n",
      "Epoch 12/100\n",
      "32/32 - 1s - loss: 0.0015 - accuracy: 1.0000 - mae: 5.9601e-04 - mse: 1.3943e-05 - precision: 0.9985 - recall: 0.9376 - f1_score: 0.9671 - val_loss: 0.0298 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0025 - val_precision: 0.9986 - val_recall: 0.9402 - val_f1_score: 0.9685 - 729ms/epoch - 23ms/step\n",
      "Epoch 13/100\n",
      "32/32 - 1s - loss: 0.0020 - accuracy: 1.0000 - mae: 7.5912e-04 - mse: 3.7314e-05 - precision: 0.9987 - recall: 0.9427 - f1_score: 0.9699 - val_loss: 0.0298 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0025 - val_precision: 0.9987 - val_recall: 0.9448 - val_f1_score: 0.9710 - 660ms/epoch - 21ms/step\n",
      "Epoch 14/100\n",
      "32/32 - 1s - loss: 0.0018 - accuracy: 1.0000 - mae: 6.6825e-04 - mse: 9.0220e-05 - precision: 0.9988 - recall: 0.9468 - f1_score: 0.9721 - val_loss: 0.0351 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0026 - val_precision: 0.9988 - val_recall: 0.9488 - val_f1_score: 0.9731 - 734ms/epoch - 23ms/step\n",
      "Epoch 15/100\n",
      "32/32 - 1s - loss: 0.0012 - accuracy: 1.0000 - mae: 4.5343e-04 - mse: 1.2996e-05 - precision: 0.9988 - recall: 0.9506 - f1_score: 0.9741 - val_loss: 0.0352 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0026 - val_precision: 0.9989 - val_recall: 0.9522 - val_f1_score: 0.9750 - 625ms/epoch - 20ms/step\n",
      "Epoch 16/100\n",
      "32/32 - 1s - loss: 5.8000e-04 - accuracy: 1.0000 - mae: 2.3142e-04 - mse: 8.9101e-07 - precision: 0.9989 - recall: 0.9537 - f1_score: 0.9758 - val_loss: 0.0343 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0026 - val_precision: 0.9990 - val_recall: 0.9552 - val_f1_score: 0.9766 - 934ms/epoch - 29ms/step\n",
      "Epoch 17/100\n",
      "32/32 - 1s - loss: 6.2071e-04 - accuracy: 1.0000 - mae: 2.4659e-04 - mse: 2.5392e-06 - precision: 0.9990 - recall: 0.9566 - f1_score: 0.9773 - val_loss: 0.0338 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0026 - val_precision: 0.9990 - val_recall: 0.9578 - val_f1_score: 0.9780 - 811ms/epoch - 25ms/step\n",
      "Epoch 18/100\n",
      "32/32 - 1s - loss: 6.7003e-04 - accuracy: 1.0000 - mae: 2.6690e-04 - mse: 1.6604e-06 - precision: 0.9991 - recall: 0.9591 - f1_score: 0.9787 - val_loss: 0.0328 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9991 - val_recall: 0.9602 - val_f1_score: 0.9792 - 599ms/epoch - 19ms/step\n",
      "Epoch 19/100\n",
      "32/32 - 1s - loss: 5.6985e-04 - accuracy: 1.0000 - mae: 2.2667e-04 - mse: 1.9117e-06 - precision: 0.9991 - recall: 0.9612 - f1_score: 0.9798 - val_loss: 0.0333 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9991 - val_recall: 0.9623 - val_f1_score: 0.9804 - 640ms/epoch - 20ms/step\n",
      "Epoch 20/100\n",
      "32/32 - 1s - loss: 4.8206e-04 - accuracy: 1.0000 - mae: 1.9207e-04 - mse: 1.1808e-06 - precision: 0.9992 - recall: 0.9633 - f1_score: 0.9809 - val_loss: 0.0334 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9992 - val_recall: 0.9642 - val_f1_score: 0.9814 - 615ms/epoch - 19ms/step\n",
      "Epoch 21/100\n",
      "32/32 - 1s - loss: 3.6306e-04 - accuracy: 1.0000 - mae: 1.4460e-04 - mse: 9.4722e-07 - precision: 0.9992 - recall: 0.9650 - f1_score: 0.9818 - val_loss: 0.0334 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9992 - val_recall: 0.9659 - val_f1_score: 0.9823 - 1s/epoch - 32ms/step\n",
      "Epoch 22/100\n",
      "32/32 - 1s - loss: 2.7634e-04 - accuracy: 1.0000 - mae: 1.1023e-04 - mse: 4.7105e-07 - precision: 0.9992 - recall: 0.9667 - f1_score: 0.9827 - val_loss: 0.0334 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9993 - val_recall: 0.9674 - val_f1_score: 0.9831 - 751ms/epoch - 23ms/step\n",
      "Epoch 23/100\n",
      "32/32 - 1s - loss: 2.9229e-04 - accuracy: 1.0000 - mae: 1.1656e-04 - mse: 5.8835e-07 - precision: 0.9993 - recall: 0.9682 - f1_score: 0.9835 - val_loss: 0.0335 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9993 - val_recall: 0.9689 - val_f1_score: 0.9838 - 563ms/epoch - 18ms/step\n",
      "Epoch 24/100\n",
      "32/32 - 1s - loss: 3.4065e-04 - accuracy: 1.0000 - mae: 1.3574e-04 - mse: 8.1571e-07 - precision: 0.9993 - recall: 0.9695 - f1_score: 0.9842 - val_loss: 0.0341 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9993 - val_recall: 0.9702 - val_f1_score: 0.9845 - 677ms/epoch - 21ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n",
      "32/32 - 1s - loss: 3.5074e-04 - accuracy: 1.0000 - mae: 1.3968e-04 - mse: 9.4879e-07 - precision: 0.9993 - recall: 0.9708 - f1_score: 0.9848 - val_loss: 0.0333 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9993 - val_recall: 0.9714 - val_f1_score: 0.9852 - 616ms/epoch - 19ms/step\n",
      "Epoch 26/100\n",
      "32/32 - 1s - loss: 2.7147e-04 - accuracy: 1.0000 - mae: 1.0838e-04 - mse: 3.2474e-07 - precision: 0.9994 - recall: 0.9719 - f1_score: 0.9854 - val_loss: 0.0336 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9994 - val_recall: 0.9725 - val_f1_score: 0.9857 - 891ms/epoch - 28ms/step\n",
      "Epoch 27/100\n",
      "32/32 - 1s - loss: 5.1400e-04 - accuracy: 1.0000 - mae: 2.0408e-04 - mse: 2.1900e-06 - precision: 0.9994 - recall: 0.9730 - f1_score: 0.9860 - val_loss: 0.0341 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9994 - val_recall: 0.9735 - val_f1_score: 0.9863 - 734ms/epoch - 23ms/step\n",
      "Epoch 28/100\n",
      "32/32 - 1s - loss: 3.0875e-04 - accuracy: 1.0000 - mae: 1.2299e-04 - mse: 8.8125e-07 - precision: 0.9994 - recall: 0.9740 - f1_score: 0.9865 - val_loss: 0.0347 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9994 - val_recall: 0.9744 - val_f1_score: 0.9868 - 775ms/epoch - 24ms/step\n",
      "Epoch 29/100\n",
      "32/32 - 1s - loss: 2.5878e-04 - accuracy: 1.0000 - mae: 1.0315e-04 - mse: 6.2410e-07 - precision: 0.9994 - recall: 0.9749 - f1_score: 0.9870 - val_loss: 0.0348 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9994 - val_recall: 0.9753 - val_f1_score: 0.9872 - 584ms/epoch - 18ms/step\n",
      "Epoch 30/100\n",
      "32/32 - 1s - loss: 1.9330e-04 - accuracy: 1.0000 - mae: 7.6980e-05 - mse: 4.7769e-07 - precision: 0.9994 - recall: 0.9757 - f1_score: 0.9874 - val_loss: 0.0350 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9761 - val_f1_score: 0.9877 - 729ms/epoch - 23ms/step\n",
      "Epoch 31/100\n",
      "32/32 - 1s - loss: 1.7061e-04 - accuracy: 1.0000 - mae: 6.8139e-05 - mse: 1.8112e-07 - precision: 0.9995 - recall: 0.9765 - f1_score: 0.9879 - val_loss: 0.0352 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9769 - val_f1_score: 0.9881 - 617ms/epoch - 19ms/step\n",
      "Epoch 32/100\n",
      "32/32 - 1s - loss: 1.9239e-04 - accuracy: 1.0000 - mae: 7.6772e-05 - mse: 2.9533e-07 - precision: 0.9995 - recall: 0.9773 - f1_score: 0.9883 - val_loss: 0.0351 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9776 - val_f1_score: 0.9884 - 622ms/epoch - 19ms/step\n",
      "Epoch 33/100\n",
      "32/32 - 1s - loss: 3.0878e-04 - accuracy: 1.0000 - mae: 1.2196e-04 - mse: 2.2973e-06 - precision: 0.9995 - recall: 0.9780 - f1_score: 0.9886 - val_loss: 0.0351 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9783 - val_f1_score: 0.9888 - 891ms/epoch - 28ms/step\n",
      "Epoch 34/100\n",
      "32/32 - 1s - loss: 1.9608e-04 - accuracy: 1.0000 - mae: 7.8247e-05 - mse: 2.8611e-07 - precision: 0.9995 - recall: 0.9786 - f1_score: 0.9890 - val_loss: 0.0351 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9790 - val_f1_score: 0.9891 - 632ms/epoch - 20ms/step\n",
      "Epoch 35/100\n",
      "32/32 - 1s - loss: 1.4979e-04 - accuracy: 1.0000 - mae: 5.9831e-05 - mse: 1.3983e-07 - precision: 0.9995 - recall: 0.9793 - f1_score: 0.9893 - val_loss: 0.0347 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9796 - val_f1_score: 0.9894 - 576ms/epoch - 18ms/step\n",
      "Epoch 36/100\n",
      "32/32 - 1s - loss: 1.4987e-04 - accuracy: 1.0000 - mae: 5.9814e-05 - mse: 2.1059e-07 - precision: 0.9995 - recall: 0.9798 - f1_score: 0.9896 - val_loss: 0.0350 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9995 - val_recall: 0.9801 - val_f1_score: 0.9897 - 665ms/epoch - 21ms/step\n",
      "Epoch 37/100\n",
      "32/32 - 1s - loss: 2.1310e-04 - accuracy: 1.0000 - mae: 8.4620e-05 - mse: 1.1471e-06 - precision: 0.9996 - recall: 0.9804 - f1_score: 0.9899 - val_loss: 0.0364 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9807 - val_f1_score: 0.9900 - 877ms/epoch - 27ms/step\n",
      "Epoch 38/100\n",
      "32/32 - 1s - loss: 1.3327e-04 - accuracy: 1.0000 - mae: 5.3242e-05 - mse: 9.7908e-08 - precision: 0.9996 - recall: 0.9809 - f1_score: 0.9902 - val_loss: 0.0365 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9812 - val_f1_score: 0.9903 - 819ms/epoch - 26ms/step\n",
      "Epoch 39/100\n",
      "32/32 - 1s - loss: 1.5312e-04 - accuracy: 1.0000 - mae: 6.1105e-05 - mse: 2.3981e-07 - precision: 0.9996 - recall: 0.9814 - f1_score: 0.9904 - val_loss: 0.0365 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9817 - val_f1_score: 0.9905 - 789ms/epoch - 25ms/step\n",
      "Epoch 40/100\n",
      "32/32 - 0s - loss: 1.0844e-04 - accuracy: 1.0000 - mae: 4.3331e-05 - mse: 7.4940e-08 - precision: 0.9996 - recall: 0.9819 - f1_score: 0.9907 - val_loss: 0.0367 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9821 - val_f1_score: 0.9908 - 494ms/epoch - 15ms/step\n",
      "Epoch 41/100\n",
      "32/32 - 1s - loss: 1.7815e-04 - accuracy: 1.0000 - mae: 7.0838e-05 - mse: 7.7983e-07 - precision: 0.9996 - recall: 0.9823 - f1_score: 0.9909 - val_loss: 0.0370 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9826 - val_f1_score: 0.9910 - 597ms/epoch - 19ms/step\n",
      "Epoch 42/100\n",
      "32/32 - 1s - loss: 1.0905e-04 - accuracy: 1.0000 - mae: 4.3530e-05 - mse: 1.6402e-07 - precision: 0.9996 - recall: 0.9828 - f1_score: 0.9911 - val_loss: 0.0370 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9830 - val_f1_score: 0.9912 - 671ms/epoch - 21ms/step\n",
      "Epoch 43/100\n",
      "32/32 - 1s - loss: 1.0846e-04 - accuracy: 1.0000 - mae: 4.3315e-05 - mse: 1.1135e-07 - precision: 0.9996 - recall: 0.9832 - f1_score: 0.9913 - val_loss: 0.0367 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9834 - val_f1_score: 0.9914 - 679ms/epoch - 21ms/step\n",
      "Epoch 44/100\n",
      "32/32 - 1s - loss: 2.2169e-04 - accuracy: 1.0000 - mae: 8.8145e-05 - mse: 9.4110e-07 - precision: 0.9996 - recall: 0.9836 - f1_score: 0.9915 - val_loss: 0.0361 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9837 - val_f1_score: 0.9916 - 846ms/epoch - 26ms/step\n",
      "Epoch 45/100\n",
      "32/32 - 1s - loss: 5.3004e-05 - accuracy: 1.0000 - mae: 2.1195e-05 - mse: 1.3976e-08 - precision: 0.9996 - recall: 0.9839 - f1_score: 0.9917 - val_loss: 0.0360 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9841 - val_f1_score: 0.9918 - 668ms/epoch - 21ms/step\n",
      "Epoch 46/100\n",
      "32/32 - 1s - loss: 1.2187e-04 - accuracy: 1.0000 - mae: 4.8664e-05 - mse: 1.4245e-07 - precision: 0.9996 - recall: 0.9843 - f1_score: 0.9919 - val_loss: 0.0364 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9996 - val_recall: 0.9845 - val_f1_score: 0.9920 - 671ms/epoch - 21ms/step\n",
      "Epoch 47/100\n",
      "32/32 - 0s - loss: 6.8069e-05 - accuracy: 1.0000 - mae: 2.7211e-05 - mse: 2.6376e-08 - precision: 0.9997 - recall: 0.9846 - f1_score: 0.9921 - val_loss: 0.0365 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9848 - val_f1_score: 0.9922 - 491ms/epoch - 15ms/step\n",
      "Epoch 48/100\n",
      "32/32 - 1s - loss: 7.5147e-05 - accuracy: 1.0000 - mae: 3.0031e-05 - mse: 4.9644e-08 - precision: 0.9997 - recall: 0.9849 - f1_score: 0.9923 - val_loss: 0.0363 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9851 - val_f1_score: 0.9923 - 604ms/epoch - 19ms/step\n",
      "Epoch 49/100\n",
      "32/32 - 1s - loss: 8.4494e-05 - accuracy: 1.0000 - mae: 3.3726e-05 - mse: 1.1584e-07 - precision: 0.9997 - recall: 0.9853 - f1_score: 0.9924 - val_loss: 0.0365 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9854 - val_f1_score: 0.9925 - 769ms/epoch - 24ms/step\n",
      "Epoch 50/100\n",
      "32/32 - 1s - loss: 1.7013e-04 - accuracy: 1.0000 - mae: 6.6942e-05 - mse: 1.7833e-06 - precision: 0.9997 - recall: 0.9856 - f1_score: 0.9926 - val_loss: 0.0383 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9857 - val_f1_score: 0.9926 - 898ms/epoch - 28ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 51/100\n",
      "32/32 - 1s - loss: 8.0526e-05 - accuracy: 1.0000 - mae: 3.2193e-05 - mse: 3.3589e-08 - precision: 0.9997 - recall: 0.9858 - f1_score: 0.9927 - val_loss: 0.0383 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9860 - val_f1_score: 0.9928 - 583ms/epoch - 18ms/step\n",
      "Epoch 52/100\n",
      "32/32 - 1s - loss: 7.6709e-05 - accuracy: 1.0000 - mae: 3.0659e-05 - mse: 4.0046e-08 - precision: 0.9997 - recall: 0.9861 - f1_score: 0.9928 - val_loss: 0.0382 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9862 - val_f1_score: 0.9929 - 675ms/epoch - 21ms/step\n",
      "Epoch 53/100\n",
      "32/32 - 1s - loss: 5.9923e-05 - accuracy: 1.0000 - mae: 2.3957e-05 - mse: 2.3111e-08 - precision: 0.9997 - recall: 0.9864 - f1_score: 0.9930 - val_loss: 0.0384 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9865 - val_f1_score: 0.9931 - 588ms/epoch - 18ms/step\n",
      "Epoch 54/100\n",
      "32/32 - 1s - loss: 1.0583e-04 - accuracy: 1.0000 - mae: 4.2240e-05 - mse: 1.5326e-07 - precision: 0.9997 - recall: 0.9866 - f1_score: 0.9931 - val_loss: 0.0384 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9868 - val_f1_score: 0.9932 - 695ms/epoch - 22ms/step\n",
      "Epoch 55/100\n",
      "32/32 - 1s - loss: 4.1556e-05 - accuracy: 1.0000 - mae: 1.6620e-05 - mse: 7.3801e-09 - precision: 0.9997 - recall: 0.9869 - f1_score: 0.9933 - val_loss: 0.0384 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9870 - val_f1_score: 0.9933 - 888ms/epoch - 28ms/step\n",
      "Epoch 56/100\n",
      "32/32 - 1s - loss: 1.0264e-04 - accuracy: 1.0000 - mae: 4.0918e-05 - mse: 2.6782e-07 - precision: 0.9997 - recall: 0.9871 - f1_score: 0.9934 - val_loss: 0.0377 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9872 - val_f1_score: 0.9934 - 916ms/epoch - 29ms/step\n",
      "Epoch 57/100\n",
      "32/32 - 1s - loss: 6.9637e-05 - accuracy: 1.0000 - mae: 2.7835e-05 - mse: 3.9870e-08 - precision: 0.9997 - recall: 0.9873 - f1_score: 0.9935 - val_loss: 0.0369 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9997 - val_recall: 0.9875 - val_f1_score: 0.9935 - 735ms/epoch - 23ms/step\n",
      "Epoch 58/100\n",
      "32/32 - 1s - loss: 6.8991e-05 - accuracy: 1.0000 - mae: 2.7583e-05 - mse: 2.6410e-08 - precision: 0.9997 - recall: 0.9876 - f1_score: 0.9936 - val_loss: 0.0369 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9877 - val_f1_score: 0.9937 - 750ms/epoch - 23ms/step\n",
      "Epoch 59/100\n",
      "32/32 - 1s - loss: 8.6097e-05 - accuracy: 1.0000 - mae: 3.4395e-05 - mse: 6.3842e-08 - precision: 0.9997 - recall: 0.9878 - f1_score: 0.9937 - val_loss: 0.0370 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9997 - val_recall: 0.9879 - val_f1_score: 0.9938 - 677ms/epoch - 21ms/step\n",
      "Epoch 60/100\n",
      "32/32 - 1s - loss: 7.2749e-05 - accuracy: 1.0000 - mae: 2.9057e-05 - mse: 7.9636e-08 - precision: 0.9997 - recall: 0.9880 - f1_score: 0.9938 - val_loss: 0.0371 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0026 - val_precision: 0.9997 - val_recall: 0.9881 - val_f1_score: 0.9939 - 769ms/epoch - 24ms/step\n",
      "Epoch 61/100\n",
      "32/32 - 1s - loss: 6.5007e-05 - accuracy: 1.0000 - mae: 2.5968e-05 - mse: 6.3574e-08 - precision: 0.9997 - recall: 0.9882 - f1_score: 0.9939 - val_loss: 0.0374 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9883 - val_f1_score: 0.9940 - 936ms/epoch - 29ms/step\n",
      "Epoch 62/100\n",
      "32/32 - 1s - loss: 4.3505e-05 - accuracy: 1.0000 - mae: 1.7398e-05 - mse: 8.3641e-09 - precision: 0.9997 - recall: 0.9884 - f1_score: 0.9940 - val_loss: 0.0376 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9885 - val_f1_score: 0.9941 - 986ms/epoch - 31ms/step\n",
      "Epoch 63/100\n",
      "32/32 - 1s - loss: 7.7940e-05 - accuracy: 1.0000 - mae: 3.1125e-05 - mse: 9.4387e-08 - precision: 0.9997 - recall: 0.9886 - f1_score: 0.9941 - val_loss: 0.0380 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9887 - val_f1_score: 0.9942 - 690ms/epoch - 22ms/step\n",
      "Epoch 64/100\n",
      "32/32 - 1s - loss: 7.5532e-05 - accuracy: 1.0000 - mae: 3.0123e-05 - mse: 1.6665e-07 - precision: 0.9997 - recall: 0.9887 - f1_score: 0.9942 - val_loss: 0.0376 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9997 - val_recall: 0.9888 - val_f1_score: 0.9943 - 536ms/epoch - 17ms/step\n",
      "Epoch 65/100\n",
      "32/32 - 1s - loss: 8.5433e-05 - accuracy: 1.0000 - mae: 3.4095e-05 - mse: 1.2508e-07 - precision: 0.9998 - recall: 0.9889 - f1_score: 0.9943 - val_loss: 0.0375 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9890 - val_f1_score: 0.9943 - 918ms/epoch - 29ms/step\n",
      "Epoch 66/100\n",
      "32/32 - 1s - loss: 4.5605e-05 - accuracy: 1.0000 - mae: 1.8236e-05 - mse: 1.4707e-08 - precision: 0.9998 - recall: 0.9891 - f1_score: 0.9944 - val_loss: 0.0376 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9892 - val_f1_score: 0.9944 - 1s/epoch - 33ms/step\n",
      "Epoch 67/100\n",
      "32/32 - 1s - loss: 4.7695e-05 - accuracy: 1.0000 - mae: 1.9070e-05 - mse: 1.6938e-08 - precision: 0.9998 - recall: 0.9892 - f1_score: 0.9945 - val_loss: 0.0377 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9893 - val_f1_score: 0.9945 - 805ms/epoch - 25ms/step\n",
      "Epoch 68/100\n",
      "32/32 - 1s - loss: 3.0333e-05 - accuracy: 1.0000 - mae: 1.2129e-05 - mse: 1.0867e-08 - precision: 0.9998 - recall: 0.9894 - f1_score: 0.9946 - val_loss: 0.0377 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9895 - val_f1_score: 0.9946 - 516ms/epoch - 16ms/step\n",
      "Epoch 69/100\n",
      "32/32 - 1s - loss: 4.8988e-05 - accuracy: 1.0000 - mae: 1.9581e-05 - mse: 2.8103e-08 - precision: 0.9998 - recall: 0.9896 - f1_score: 0.9946 - val_loss: 0.0378 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9896 - val_f1_score: 0.9947 - 676ms/epoch - 21ms/step\n",
      "Epoch 70/100\n",
      "32/32 - 1s - loss: 4.9806e-05 - accuracy: 1.0000 - mae: 1.9886e-05 - mse: 7.2297e-08 - precision: 0.9998 - recall: 0.9897 - f1_score: 0.9947 - val_loss: 0.0379 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9898 - val_f1_score: 0.9948 - 653ms/epoch - 20ms/step\n",
      "Epoch 71/100\n",
      "32/32 - 1s - loss: 7.0842e-05 - accuracy: 1.0000 - mae: 2.8171e-05 - mse: 3.2399e-07 - precision: 0.9998 - recall: 0.9899 - f1_score: 0.9948 - val_loss: 0.0382 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9899 - val_f1_score: 0.9948 - 874ms/epoch - 27ms/step\n",
      "Epoch 72/100\n",
      "32/32 - 1s - loss: 3.8575e-05 - accuracy: 1.0000 - mae: 1.5422e-05 - mse: 1.6589e-08 - precision: 0.9998 - recall: 0.9900 - f1_score: 0.9949 - val_loss: 0.0380 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9901 - val_f1_score: 0.9949 - 870ms/epoch - 27ms/step\n",
      "Epoch 73/100\n",
      "32/32 - 1s - loss: 2.9079e-05 - accuracy: 1.0000 - mae: 1.1629e-05 - mse: 8.7761e-09 - precision: 0.9998 - recall: 0.9901 - f1_score: 0.9949 - val_loss: 0.0381 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0027 - val_precision: 0.9998 - val_recall: 0.9902 - val_f1_score: 0.9950 - 825ms/epoch - 26ms/step\n",
      "Epoch 74/100\n",
      "32/32 - 1s - loss: 3.4713e-04 - accuracy: 1.0000 - mae: 1.2977e-04 - mse: 1.3485e-05 - precision: 0.9998 - recall: 0.9903 - f1_score: 0.9950 - val_loss: 0.0393 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9903 - val_f1_score: 0.9950 - 596ms/epoch - 19ms/step\n",
      "Epoch 75/100\n",
      "32/32 - 1s - loss: 6.4767e-05 - accuracy: 1.0000 - mae: 2.5868e-05 - mse: 6.5124e-08 - precision: 0.9998 - recall: 0.9904 - f1_score: 0.9951 - val_loss: 0.0400 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0029 - val_precision: 0.9998 - val_recall: 0.9905 - val_f1_score: 0.9951 - 502ms/epoch - 16ms/step\n",
      "Epoch 76/100\n",
      "32/32 - 1s - loss: 6.1785e-05 - accuracy: 1.0000 - mae: 2.4676e-05 - mse: 7.4478e-08 - precision: 0.9998 - recall: 0.9905 - f1_score: 0.9951 - val_loss: 0.0404 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0029 - val_precision: 0.9998 - val_recall: 0.9906 - val_f1_score: 0.9952 - 780ms/epoch - 24ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77/100\n",
      "32/32 - 1s - loss: 4.6652e-05 - accuracy: 1.0000 - mae: 1.8647e-05 - mse: 2.4314e-08 - precision: 0.9998 - recall: 0.9907 - f1_score: 0.9952 - val_loss: 0.0409 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0029 - val_precision: 0.9998 - val_recall: 0.9907 - val_f1_score: 0.9952 - 882ms/epoch - 28ms/step\n",
      "Epoch 78/100\n",
      "32/32 - 1s - loss: 6.8707e-05 - accuracy: 1.0000 - mae: 2.7301e-05 - mse: 3.2580e-07 - precision: 0.9998 - recall: 0.9908 - f1_score: 0.9953 - val_loss: 0.0411 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0029 - val_precision: 0.9998 - val_recall: 0.9908 - val_f1_score: 0.9953 - 1s/epoch - 32ms/step\n",
      "Epoch 79/100\n",
      "32/32 - 1s - loss: 4.3560e-05 - accuracy: 1.0000 - mae: 1.7374e-05 - mse: 1.0237e-07 - precision: 0.9998 - recall: 0.9909 - f1_score: 0.9953 - val_loss: 0.0408 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9910 - val_f1_score: 0.9954 - 657ms/epoch - 21ms/step\n",
      "Epoch 80/100\n",
      "32/32 - 1s - loss: 2.6168e-05 - accuracy: 1.0000 - mae: 1.0464e-05 - mse: 8.7119e-09 - precision: 0.9998 - recall: 0.9910 - f1_score: 0.9954 - val_loss: 0.0402 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9911 - val_f1_score: 0.9954 - 629ms/epoch - 20ms/step\n",
      "Epoch 81/100\n",
      "32/32 - 1s - loss: 1.0941e-04 - accuracy: 1.0000 - mae: 4.3314e-05 - mse: 7.7558e-07 - precision: 0.9998 - recall: 0.9911 - f1_score: 0.9954 - val_loss: 0.0416 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0029 - val_precision: 0.9998 - val_recall: 0.9912 - val_f1_score: 0.9955 - 563ms/epoch - 18ms/step\n",
      "Epoch 82/100\n",
      "32/32 - 1s - loss: 7.9209e-05 - accuracy: 1.0000 - mae: 3.1534e-05 - mse: 2.8358e-07 - precision: 0.9998 - recall: 0.9912 - f1_score: 0.9955 - val_loss: 0.0426 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0029 - val_precision: 0.9998 - val_recall: 0.9913 - val_f1_score: 0.9955 - 637ms/epoch - 20ms/step\n",
      "Epoch 83/100\n",
      "32/32 - 1s - loss: 3.2362e-05 - accuracy: 1.0000 - mae: 1.2934e-05 - mse: 2.3774e-08 - precision: 0.9998 - recall: 0.9913 - f1_score: 0.9956 - val_loss: 0.0423 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9914 - val_f1_score: 0.9956 - 779ms/epoch - 24ms/step\n",
      "Epoch 84/100\n",
      "32/32 - 1s - loss: 2.4813e-05 - accuracy: 1.0000 - mae: 9.9195e-06 - mse: 1.2909e-08 - precision: 0.9998 - recall: 0.9914 - f1_score: 0.9956 - val_loss: 0.0425 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9915 - val_f1_score: 0.9956 - 936ms/epoch - 29ms/step\n",
      "Epoch 85/100\n",
      "32/32 - 1s - loss: 2.1034e-05 - accuracy: 1.0000 - mae: 8.4108e-06 - mse: 8.1842e-09 - precision: 0.9998 - recall: 0.9915 - f1_score: 0.9957 - val_loss: 0.0422 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9916 - val_f1_score: 0.9957 - 755ms/epoch - 24ms/step\n",
      "Epoch 86/100\n",
      "32/32 - 1s - loss: 2.1222e-05 - accuracy: 1.0000 - mae: 8.4875e-06 - mse: 5.2018e-09 - precision: 0.9998 - recall: 0.9916 - f1_score: 0.9957 - val_loss: 0.0421 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0028 - val_precision: 0.9998 - val_recall: 0.9917 - val_f1_score: 0.9957 - 591ms/epoch - 18ms/step\n",
      "Epoch 87/100\n",
      "32/32 - 1s - loss: 2.0293e-04 - accuracy: 1.0000 - mae: 7.9217e-05 - mse: 3.3729e-06 - precision: 0.9998 - recall: 0.9917 - f1_score: 0.9958 - val_loss: 0.0458 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9918 - val_f1_score: 0.9958 - 664ms/epoch - 21ms/step\n",
      "Epoch 88/100\n",
      "32/32 - 1s - loss: 2.0216e-05 - accuracy: 1.0000 - mae: 8.0850e-06 - mse: 4.5916e-09 - precision: 0.9998 - recall: 0.9918 - f1_score: 0.9958 - val_loss: 0.0462 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9919 - val_f1_score: 0.9958 - 668ms/epoch - 21ms/step\n",
      "Epoch 89/100\n",
      "32/32 - 1s - loss: 4.0290e-05 - accuracy: 1.0000 - mae: 1.6097e-05 - mse: 3.3337e-08 - precision: 0.9998 - recall: 0.9919 - f1_score: 0.9959 - val_loss: 0.0462 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9920 - val_f1_score: 0.9959 - 781ms/epoch - 24ms/step\n",
      "Epoch 90/100\n",
      "32/32 - 1s - loss: 2.0658e-05 - accuracy: 1.0000 - mae: 8.2591e-06 - mse: 7.0720e-09 - precision: 0.9998 - recall: 0.9920 - f1_score: 0.9959 - val_loss: 0.0461 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9921 - val_f1_score: 0.9959 - 1s/epoch - 33ms/step\n",
      "Epoch 91/100\n",
      "32/32 - 1s - loss: 1.2013e-05 - accuracy: 1.0000 - mae: 4.8061e-06 - mse: 1.2660e-09 - precision: 0.9998 - recall: 0.9921 - f1_score: 0.9959 - val_loss: 0.0460 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9921 - val_f1_score: 0.9960 - 643ms/epoch - 20ms/step\n",
      "Epoch 92/100\n",
      "32/32 - 0s - loss: 1.2292e-05 - accuracy: 1.0000 - mae: 4.9173e-06 - mse: 1.2580e-09 - precision: 0.9998 - recall: 0.9922 - f1_score: 0.9960 - val_loss: 0.0459 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9922 - val_f1_score: 0.9960 - 484ms/epoch - 15ms/step\n",
      "Epoch 93/100\n",
      "32/32 - 1s - loss: 3.3330e-05 - accuracy: 1.0000 - mae: 1.3313e-05 - mse: 3.8582e-08 - precision: 0.9998 - recall: 0.9923 - f1_score: 0.9960 - val_loss: 0.0447 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9923 - val_f1_score: 0.9961 - 663ms/epoch - 21ms/step\n",
      "Epoch 94/100\n",
      "32/32 - 1s - loss: 3.8303e-05 - accuracy: 1.0000 - mae: 1.5268e-05 - mse: 9.7548e-08 - precision: 0.9998 - recall: 0.9924 - f1_score: 0.9961 - val_loss: 0.0449 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9924 - val_f1_score: 0.9961 - 763ms/epoch - 24ms/step\n",
      "Epoch 95/100\n",
      "32/32 - 1s - loss: 2.3572e-05 - accuracy: 1.0000 - mae: 9.4227e-06 - mse: 1.1735e-08 - precision: 0.9998 - recall: 0.9924 - f1_score: 0.9961 - val_loss: 0.0453 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9925 - val_f1_score: 0.9961 - 815ms/epoch - 25ms/step\n",
      "Epoch 96/100\n",
      "32/32 - 1s - loss: 1.4804e-05 - accuracy: 1.0000 - mae: 5.9212e-06 - mse: 3.1932e-09 - precision: 0.9998 - recall: 0.9925 - f1_score: 0.9962 - val_loss: 0.0449 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9926 - val_f1_score: 0.9962 - 662ms/epoch - 21ms/step\n",
      "Epoch 97/100\n",
      "32/32 - 1s - loss: 2.3910e-05 - accuracy: 1.0000 - mae: 9.5571e-06 - mse: 1.6366e-08 - precision: 0.9998 - recall: 0.9926 - f1_score: 0.9962 - val_loss: 0.0445 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9926 - val_f1_score: 0.9962 - 647ms/epoch - 20ms/step\n",
      "Epoch 98/100\n",
      "32/32 - 1s - loss: 6.3922e-05 - accuracy: 1.0000 - mae: 2.5517e-05 - mse: 8.7595e-08 - precision: 0.9998 - recall: 0.9927 - f1_score: 0.9962 - val_loss: 0.0437 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9927 - val_f1_score: 0.9963 - 582ms/epoch - 18ms/step\n",
      "Epoch 99/100\n",
      "32/32 - 1s - loss: 1.3422e-05 - accuracy: 1.0000 - mae: 5.3698e-06 - mse: 1.8207e-09 - precision: 0.9998 - recall: 0.9927 - f1_score: 0.9963 - val_loss: 0.0432 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9928 - val_f1_score: 0.9963 - 773ms/epoch - 24ms/step\n",
      "Epoch 100/100\n",
      "32/32 - 1s - loss: 1.4453e-05 - accuracy: 1.0000 - mae: 5.7809e-06 - mse: 2.9489e-09 - precision: 0.9998 - recall: 0.9928 - f1_score: 0.9963 - val_loss: 0.0433 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0030 - val_precision: 0.9998 - val_recall: 0.9929 - val_f1_score: 0.9963 - 908ms/epoch - 28ms/step\n",
      "--- 91.00913596153259 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation,Embedding\n",
    "from keras.models import Sequential\n",
    "model_conv = Sequential()\n",
    "model_conv.add(Embedding(5000, 100, input_length=data.shape[1]))\n",
    "model_conv.add(Conv1D(128, 5, activation='relu'))\n",
    "model_conv.add(MaxPooling1D(pool_size=4))\n",
    "model_conv.add(Activation('relu'))\n",
    "model_conv.add(Dropout(rate=0.5))\n",
    "model_conv.add(Flatten())\n",
    "model_conv.add(Dense(32))\n",
    "model_conv.add(Dense(5, activation='softmax'))\n",
    "model_conv.compile(loss='categorical_crossentropy', optimizer='adam',   metrics=['accuracy','mae','mse',keras_metrics.precision(), keras_metrics.recall(),keras_metrics.f1_score()])\n",
    "\n",
    "#Here we train the Network.\n",
    "\n",
    "pred2=model_conv.fit(X_train, Y_train, batch_size =batch_size, epochs =100, verbose =2,validation_data=(X_valid,Y_valid))\n",
    "pred2\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred2.history['accuracy'])\n",
    "plt.plot(pred2.history['precision'])\n",
    "plt.plot(pred2.history['recall'])\n",
    "plt.plot(pred2.history['f1_score'])\n",
    "\n",
    "plt.plot(pred2.history['val_accuracy'])\n",
    "plt.plot(pred2.history['val_precision'])\n",
    "plt.plot(pred2.history['val_recall'])\n",
    "plt.plot(pred2.history['val_f1_score'])\n",
    "\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Accuracy', 'Precision', 'Recall', 'F1 Score', 'Val_Accuracy', 'Val_Precision', 'Val_Recall', 'Val_F1 Score'], loc='lower right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('accCNN.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred2.history['loss'])\n",
    "plt.plot(pred2.history['val_loss'])\n",
    "plt.plot(pred2.history['val_mae'])\n",
    "plt.plot(pred2.history['mae'])\n",
    "plt.plot(pred2.history['val_mse'])\n",
    "plt.plot(pred2.history['mse'])\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Loss', 'Val_Loss', 'MAE', 'Val_MAE', 'MSE', 'Val_MSE'], loc='upper right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('LosCNN.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       0.97      1.00      0.99        33\n",
      "           2       1.00      0.96      0.98        28\n",
      "           3       1.00      1.00      1.00        22\n",
      "           4       1.00      1.00      1.00        24\n",
      "\n",
      "    accuracy                           0.99       126\n",
      "   macro avg       0.99      0.99      0.99       126\n",
      "weighted avg       0.99      0.99      0.99       126\n",
      "\n",
      "[[19  0  0  0  0]\n",
      " [ 0 33  0  0  0]\n",
      " [ 0  1 27  0  0]\n",
      " [ 0  0  0 22  0]\n",
      " [ 0  0  0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "yhat_classes = model_conv.predict(X_valid, verbose=0)\n",
    "rounded_predictions=np.argmax(yhat_classes, axis=1)\n",
    "rounded_labels=np.argmax(Y_valid, axis=1)\n",
    "print(classification_report(rounded_labels,rounded_predictions))\n",
    "print(confusion_matrix(rounded_labels,rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sohail_Comp\\anaconda3\\lib\\site-packages\\keras_metrics\\metrics.py:26: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  return object.__getattribute__(self, name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "WARNING:tensorflow:`add_update` `inputs` kwarg has been deprecated. You no longer need to pass a value to `inputs` as it is being automatically inferred.\n",
      "32/32 - 42s - loss: 1.3289 - accuracy: 0.5496 - mae: 0.2862 - mse: 0.1328 - precision: 0.1875 - recall: 0.0170 - f1_score: 0.0307 - val_loss: 0.6007 - val_accuracy: 0.9921 - val_mae: 0.1654 - val_mse: 0.0551 - val_precision: 1.0000 - val_recall: 0.2216 - val_f1_score: 0.3617 - 42s/epoch - 1s/step\n",
      "Epoch 2/100\n",
      "32/32 - 1s - loss: 0.1235 - accuracy: 0.9921 - mae: 0.0353 - mse: 0.0098 - precision: 0.9904 - recall: 0.4830 - f1_score: 0.6435 - val_loss: 0.0110 - val_accuracy: 0.9921 - val_mae: 0.0028 - val_mse: 0.0011 - val_precision: 0.9934 - val_recall: 0.6193 - val_f1_score: 0.7629 - 553ms/epoch - 17ms/step\n",
      "Epoch 3/100\n",
      "32/32 - 1s - loss: 0.0097 - accuracy: 0.9960 - mae: 0.0018 - mse: 9.2287e-04 - precision: 0.9953 - recall: 0.6969 - f1_score: 0.8194 - val_loss: 0.0233 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0020 - val_precision: 0.9964 - val_recall: 0.7489 - val_f1_score: 0.8551 - 570ms/epoch - 18ms/step\n",
      "Epoch 4/100\n",
      "32/32 - 1s - loss: 0.0023 - accuracy: 1.0000 - mae: 8.3132e-04 - mse: 1.3691e-04 - precision: 0.9971 - recall: 0.7855 - f1_score: 0.8787 - val_loss: 0.0298 - val_accuracy: 0.9921 - val_mae: 0.0035 - val_mse: 0.0022 - val_precision: 0.9975 - val_recall: 0.8126 - val_f1_score: 0.8956 - 663ms/epoch - 21ms/step\n",
      "Epoch 5/100\n",
      "32/32 - 1s - loss: 8.0591e-04 - accuracy: 1.0000 - mae: 3.2148e-04 - mse: 1.3350e-06 - precision: 0.9979 - recall: 0.8336 - f1_score: 0.9083 - val_loss: 0.0260 - val_accuracy: 0.9921 - val_mae: 0.0033 - val_mse: 0.0024 - val_precision: 0.9981 - val_recall: 0.8506 - val_f1_score: 0.9185 - 962ms/epoch - 30ms/step\n",
      "Epoch 6/100\n",
      "32/32 - 1s - loss: 4.6744e-04 - accuracy: 1.0000 - mae: 1.8678e-04 - mse: 2.8514e-07 - precision: 0.9983 - recall: 0.8640 - f1_score: 0.9263 - val_loss: 0.0234 - val_accuracy: 0.9921 - val_mae: 0.0032 - val_mse: 0.0022 - val_precision: 0.9985 - val_recall: 0.8757 - val_f1_score: 0.9331 - 708ms/epoch - 22ms/step\n",
      "Epoch 7/100\n",
      "32/32 - 1s - loss: 3.5526e-04 - accuracy: 1.0000 - mae: 1.4199e-04 - mse: 1.6823e-07 - precision: 0.9986 - recall: 0.8858 - f1_score: 0.9388 - val_loss: 0.0226 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0021 - val_precision: 0.9987 - val_recall: 0.8936 - val_f1_score: 0.9433 - 523ms/epoch - 16ms/step\n",
      "Epoch 8/100\n",
      "32/32 - 1s - loss: 2.8821e-04 - accuracy: 1.0000 - mae: 1.1522e-04 - mse: 8.7428e-08 - precision: 0.9988 - recall: 0.9008 - f1_score: 0.9473 - val_loss: 0.0221 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0020 - val_precision: 0.9989 - val_recall: 0.9070 - val_f1_score: 0.9508 - 680ms/epoch - 21ms/step\n",
      "Epoch 9/100\n",
      "32/32 - 1s - loss: 2.4658e-04 - accuracy: 1.0000 - mae: 9.8591e-05 - mse: 6.3000e-08 - precision: 0.9990 - recall: 0.9123 - f1_score: 0.9537 - val_loss: 0.0218 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0020 - val_precision: 0.9990 - val_recall: 0.9174 - val_f1_score: 0.9565 - 769ms/epoch - 24ms/step\n",
      "Epoch 10/100\n",
      "32/32 - 1s - loss: 2.1413e-04 - accuracy: 1.0000 - mae: 8.5617e-05 - mse: 4.7395e-08 - precision: 0.9991 - recall: 0.9219 - f1_score: 0.9590 - val_loss: 0.0217 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9991 - val_recall: 0.9257 - val_f1_score: 0.9610 - 843ms/epoch - 26ms/step\n",
      "Epoch 11/100\n",
      "32/32 - 1s - loss: 1.8868e-04 - accuracy: 1.0000 - mae: 7.5445e-05 - mse: 3.6541e-08 - precision: 0.9992 - recall: 0.9293 - f1_score: 0.9630 - val_loss: 0.0217 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9992 - val_recall: 0.9325 - val_f1_score: 0.9647 - 829ms/epoch - 26ms/step\n",
      "Epoch 12/100\n",
      "32/32 - 0s - loss: 1.6801e-04 - accuracy: 1.0000 - mae: 6.7183e-05 - mse: 2.9157e-08 - precision: 0.9993 - recall: 0.9354 - f1_score: 0.9663 - val_loss: 0.0216 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9993 - val_recall: 0.9382 - val_f1_score: 0.9678 - 466ms/epoch - 15ms/step\n",
      "Epoch 13/100\n",
      "32/32 - 1s - loss: 1.5038e-04 - accuracy: 1.0000 - mae: 6.0138e-05 - mse: 2.2482e-08 - precision: 0.9993 - recall: 0.9407 - f1_score: 0.9691 - val_loss: 0.0216 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9993 - val_recall: 0.9430 - val_f1_score: 0.9703 - 629ms/epoch - 20ms/step\n",
      "Epoch 14/100\n",
      "32/32 - 1s - loss: 1.3613e-04 - accuracy: 1.0000 - mae: 5.4438e-05 - mse: 1.8752e-08 - precision: 0.9994 - recall: 0.9450 - f1_score: 0.9714 - val_loss: 0.0216 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9994 - val_recall: 0.9470 - val_f1_score: 0.9725 - 567ms/epoch - 18ms/step\n",
      "Epoch 15/100\n",
      "32/32 - 1s - loss: 1.2358e-04 - accuracy: 1.0000 - mae: 4.9425e-05 - mse: 1.5468e-08 - precision: 0.9994 - recall: 0.9489 - f1_score: 0.9735 - val_loss: 0.0218 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9994 - val_recall: 0.9506 - val_f1_score: 0.9744 - 1s/epoch - 32ms/step\n",
      "Epoch 16/100\n",
      "32/32 - 1s - loss: 1.1265e-04 - accuracy: 1.0000 - mae: 4.5054e-05 - mse: 1.2591e-08 - precision: 0.9995 - recall: 0.9523 - f1_score: 0.9753 - val_loss: 0.0218 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9995 - val_recall: 0.9537 - val_f1_score: 0.9760 - 1s/epoch - 34ms/step\n",
      "Epoch 17/100\n",
      "32/32 - 1s - loss: 1.0339e-04 - accuracy: 1.0000 - mae: 4.1350e-05 - mse: 1.0596e-08 - precision: 0.9995 - recall: 0.9551 - f1_score: 0.9768 - val_loss: 0.0220 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9995 - val_recall: 0.9564 - val_f1_score: 0.9775 - 635ms/epoch - 20ms/step\n",
      "Epoch 18/100\n",
      "32/32 - 1s - loss: 9.5179e-05 - accuracy: 1.0000 - mae: 3.8069e-05 - mse: 8.8494e-09 - precision: 0.9995 - recall: 0.9576 - f1_score: 0.9781 - val_loss: 0.0221 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9995 - val_recall: 0.9589 - val_f1_score: 0.9788 - 665ms/epoch - 21ms/step\n",
      "Epoch 19/100\n",
      "32/32 - 1s - loss: 8.7980e-05 - accuracy: 1.0000 - mae: 3.5189e-05 - mse: 7.5840e-09 - precision: 0.9996 - recall: 0.9600 - f1_score: 0.9794 - val_loss: 0.0222 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9996 - val_recall: 0.9610 - val_f1_score: 0.9799 - 736ms/epoch - 23ms/step\n",
      "Epoch 20/100\n",
      "32/32 - 1s - loss: 8.1540e-05 - accuracy: 1.0000 - mae: 3.2615e-05 - mse: 6.4743e-09 - precision: 0.9996 - recall: 0.9620 - f1_score: 0.9804 - val_loss: 0.0222 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9996 - val_recall: 0.9630 - val_f1_score: 0.9809 - 1s/epoch - 35ms/step\n",
      "Epoch 21/100\n",
      "32/32 - 1s - loss: 7.5912e-05 - accuracy: 1.0000 - mae: 3.0364e-05 - mse: 5.5667e-09 - precision: 0.9996 - recall: 0.9639 - f1_score: 0.9814 - val_loss: 0.0224 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9996 - val_recall: 0.9648 - val_f1_score: 0.9819 - 1s/epoch - 35ms/step\n",
      "Epoch 22/100\n",
      "32/32 - 1s - loss: 7.0850e-05 - accuracy: 1.0000 - mae: 2.8339e-05 - mse: 4.7861e-09 - precision: 0.9996 - recall: 0.9656 - f1_score: 0.9823 - val_loss: 0.0225 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9996 - val_recall: 0.9664 - val_f1_score: 0.9827 - 617ms/epoch - 19ms/step\n",
      "Epoch 23/100\n",
      "32/32 - 1s - loss: 6.6257e-05 - accuracy: 1.0000 - mae: 2.6503e-05 - mse: 4.1530e-09 - precision: 0.9996 - recall: 0.9671 - f1_score: 0.9831 - val_loss: 0.0226 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9996 - val_recall: 0.9678 - val_f1_score: 0.9835 - 629ms/epoch - 20ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/100\n",
      "32/32 - 1s - loss: 6.2164e-05 - accuracy: 1.0000 - mae: 2.4865e-05 - mse: 3.6205e-09 - precision: 0.9997 - recall: 0.9685 - f1_score: 0.9838 - val_loss: 0.0227 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9997 - val_recall: 0.9692 - val_f1_score: 0.9842 - 772ms/epoch - 24ms/step\n",
      "Epoch 25/100\n",
      "32/32 - 1s - loss: 5.8437e-05 - accuracy: 1.0000 - mae: 2.3375e-05 - mse: 3.1987e-09 - precision: 0.9997 - recall: 0.9698 - f1_score: 0.9845 - val_loss: 0.0229 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0020 - val_precision: 0.9997 - val_recall: 0.9704 - val_f1_score: 0.9848 - 958ms/epoch - 30ms/step\n",
      "Epoch 26/100\n",
      "32/32 - 1s - loss: 5.4991e-05 - accuracy: 1.0000 - mae: 2.1997e-05 - mse: 2.7960e-09 - precision: 0.9997 - recall: 0.9710 - f1_score: 0.9851 - val_loss: 0.0230 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9715 - val_f1_score: 0.9854 - 828ms/epoch - 26ms/step\n",
      "Epoch 27/100\n",
      "32/32 - 1s - loss: 5.1814e-05 - accuracy: 1.0000 - mae: 2.0727e-05 - mse: 2.4266e-09 - precision: 0.9997 - recall: 0.9721 - f1_score: 0.9857 - val_loss: 0.0231 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9726 - val_f1_score: 0.9860 - 630ms/epoch - 20ms/step\n",
      "Epoch 28/100\n",
      "32/32 - 0s - loss: 4.9016e-05 - accuracy: 1.0000 - mae: 1.9606e-05 - mse: 2.1675e-09 - precision: 0.9997 - recall: 0.9731 - f1_score: 0.9862 - val_loss: 0.0232 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9736 - val_f1_score: 0.9865 - 446ms/epoch - 14ms/step\n",
      "Epoch 29/100\n",
      "32/32 - 1s - loss: 4.6426e-05 - accuracy: 1.0000 - mae: 1.8571e-05 - mse: 1.9391e-09 - precision: 0.9997 - recall: 0.9740 - f1_score: 0.9867 - val_loss: 0.0233 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9745 - val_f1_score: 0.9869 - 668ms/epoch - 21ms/step\n",
      "Epoch 30/100\n",
      "32/32 - 1s - loss: 4.4022e-05 - accuracy: 1.0000 - mae: 1.7609e-05 - mse: 1.7183e-09 - precision: 0.9997 - recall: 0.9750 - f1_score: 0.9872 - val_loss: 0.0234 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9753 - val_f1_score: 0.9874 - 884ms/epoch - 28ms/step\n",
      "Epoch 31/100\n",
      "32/32 - 1s - loss: 4.1831e-05 - accuracy: 1.0000 - mae: 1.6733e-05 - mse: 1.5395e-09 - precision: 0.9997 - recall: 0.9758 - f1_score: 0.9876 - val_loss: 0.0235 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9761 - val_f1_score: 0.9878 - 870ms/epoch - 27ms/step\n",
      "Epoch 32/100\n",
      "32/32 - 1s - loss: 3.9753e-05 - accuracy: 1.0000 - mae: 1.5903e-05 - mse: 1.3748e-09 - precision: 0.9997 - recall: 0.9765 - f1_score: 0.9880 - val_loss: 0.0237 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9997 - val_recall: 0.9769 - val_f1_score: 0.9882 - 711ms/epoch - 22ms/step\n",
      "Epoch 33/100\n",
      "32/32 - 0s - loss: 3.7855e-05 - accuracy: 1.0000 - mae: 1.5143e-05 - mse: 1.2458e-09 - precision: 0.9997 - recall: 0.9772 - f1_score: 0.9884 - val_loss: 0.0239 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9998 - val_recall: 0.9776 - val_f1_score: 0.9885 - 492ms/epoch - 15ms/step\n",
      "Epoch 34/100\n",
      "32/32 - 1s - loss: 3.6124e-05 - accuracy: 1.0000 - mae: 1.4450e-05 - mse: 1.1255e-09 - precision: 0.9998 - recall: 0.9779 - f1_score: 0.9887 - val_loss: 0.0240 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9998 - val_recall: 0.9783 - val_f1_score: 0.9889 - 558ms/epoch - 17ms/step\n",
      "Epoch 35/100\n",
      "32/32 - 1s - loss: 3.4497e-05 - accuracy: 1.0000 - mae: 1.3798e-05 - mse: 1.0253e-09 - precision: 0.9998 - recall: 0.9786 - f1_score: 0.9891 - val_loss: 0.0241 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9998 - val_recall: 0.9789 - val_f1_score: 0.9892 - 554ms/epoch - 17ms/step\n",
      "Epoch 36/100\n",
      "32/32 - 1s - loss: 3.2980e-05 - accuracy: 1.0000 - mae: 1.3192e-05 - mse: 9.2469e-10 - precision: 0.9998 - recall: 0.9792 - f1_score: 0.9894 - val_loss: 0.0242 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9998 - val_recall: 0.9795 - val_f1_score: 0.9895 - 834ms/epoch - 26ms/step\n",
      "Epoch 37/100\n",
      "32/32 - 1s - loss: 3.1576e-05 - accuracy: 1.0000 - mae: 1.2630e-05 - mse: 8.4781e-10 - precision: 0.9998 - recall: 0.9797 - f1_score: 0.9897 - val_loss: 0.0243 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9998 - val_recall: 0.9800 - val_f1_score: 0.9898 - 886ms/epoch - 28ms/step\n",
      "Epoch 38/100\n",
      "32/32 - 1s - loss: 3.0245e-05 - accuracy: 1.0000 - mae: 1.2097e-05 - mse: 7.7097e-10 - precision: 0.9998 - recall: 0.9803 - f1_score: 0.9899 - val_loss: 0.0244 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0021 - val_precision: 0.9998 - val_recall: 0.9805 - val_f1_score: 0.9901 - 614ms/epoch - 19ms/step\n",
      "Epoch 39/100\n",
      "32/32 - 1s - loss: 2.9000e-05 - accuracy: 1.0000 - mae: 1.1600e-05 - mse: 7.0029e-10 - precision: 0.9998 - recall: 0.9808 - f1_score: 0.9902 - val_loss: 0.0245 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9810 - val_f1_score: 0.9903 - 577ms/epoch - 18ms/step\n",
      "Epoch 40/100\n",
      "32/32 - 1s - loss: 2.7836e-05 - accuracy: 1.0000 - mae: 1.1135e-05 - mse: 6.4549e-10 - precision: 0.9998 - recall: 0.9813 - f1_score: 0.9905 - val_loss: 0.0245 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9815 - val_f1_score: 0.9906 - 679ms/epoch - 21ms/step\n",
      "Epoch 41/100\n",
      "32/32 - 1s - loss: 2.6758e-05 - accuracy: 1.0000 - mae: 1.0702e-05 - mse: 5.9774e-10 - precision: 0.9998 - recall: 0.9818 - f1_score: 0.9907 - val_loss: 0.0247 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9820 - val_f1_score: 0.9908 - 948ms/epoch - 30ms/step\n",
      "Epoch 42/100\n",
      "32/32 - 1s - loss: 2.5708e-05 - accuracy: 1.0000 - mae: 1.0283e-05 - mse: 5.4543e-10 - precision: 0.9998 - recall: 0.9822 - f1_score: 0.9909 - val_loss: 0.0248 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9824 - val_f1_score: 0.9910 - 873ms/epoch - 27ms/step\n",
      "Epoch 43/100\n",
      "32/32 - 1s - loss: 2.4740e-05 - accuracy: 1.0000 - mae: 9.8946e-06 - mse: 5.0274e-10 - precision: 0.9998 - recall: 0.9826 - f1_score: 0.9911 - val_loss: 0.0248 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9828 - val_f1_score: 0.9912 - 680ms/epoch - 21ms/step\n",
      "Epoch 44/100\n",
      "32/32 - 0s - loss: 2.3822e-05 - accuracy: 1.0000 - mae: 9.5268e-06 - mse: 4.6783e-10 - precision: 0.9998 - recall: 0.9830 - f1_score: 0.9913 - val_loss: 0.0249 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9832 - val_f1_score: 0.9914 - 476ms/epoch - 15ms/step\n",
      "Epoch 45/100\n",
      "32/32 - 1s - loss: 2.2939e-05 - accuracy: 1.0000 - mae: 9.1744e-06 - mse: 4.2445e-10 - precision: 0.9998 - recall: 0.9834 - f1_score: 0.9915 - val_loss: 0.0250 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9836 - val_f1_score: 0.9916 - 578ms/epoch - 18ms/step\n",
      "Epoch 46/100\n",
      "32/32 - 1s - loss: 2.2134e-05 - accuracy: 1.0000 - mae: 8.8521e-06 - mse: 3.9817e-10 - precision: 0.9998 - recall: 0.9838 - f1_score: 0.9917 - val_loss: 0.0251 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9839 - val_f1_score: 0.9918 - 713ms/epoch - 22ms/step\n",
      "Epoch 47/100\n",
      "32/32 - 1s - loss: 2.1345e-05 - accuracy: 1.0000 - mae: 8.5369e-06 - mse: 3.6743e-10 - precision: 0.9998 - recall: 0.9841 - f1_score: 0.9919 - val_loss: 0.0252 - val_accuracy: 0.9921 - val_mae: 0.0030 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9843 - val_f1_score: 0.9920 - 1s/epoch - 32ms/step\n",
      "Epoch 48/100\n",
      "32/32 - 1s - loss: 2.0603e-05 - accuracy: 1.0000 - mae: 8.2396e-06 - mse: 3.3859e-10 - precision: 0.9998 - recall: 0.9844 - f1_score: 0.9921 - val_loss: 0.0253 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9846 - val_f1_score: 0.9922 - 824ms/epoch - 26ms/step\n",
      "Epoch 49/100\n",
      "32/32 - 1s - loss: 1.9909e-05 - accuracy: 1.0000 - mae: 7.9621e-06 - mse: 3.1872e-10 - precision: 0.9998 - recall: 0.9848 - f1_score: 0.9922 - val_loss: 0.0254 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9849 - val_f1_score: 0.9923 - 532ms/epoch - 17ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 50/100\n",
      "32/32 - 1s - loss: 1.9224e-05 - accuracy: 1.0000 - mae: 7.6891e-06 - mse: 2.9358e-10 - precision: 0.9998 - recall: 0.9851 - f1_score: 0.9924 - val_loss: 0.0254 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9852 - val_f1_score: 0.9925 - 707ms/epoch - 22ms/step\n",
      "Epoch 51/100\n",
      "32/32 - 1s - loss: 1.8587e-05 - accuracy: 1.0000 - mae: 7.4344e-06 - mse: 2.7151e-10 - precision: 0.9998 - recall: 0.9854 - f1_score: 0.9926 - val_loss: 0.0255 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9855 - val_f1_score: 0.9926 - 574ms/epoch - 18ms/step\n",
      "Epoch 52/100\n",
      "32/32 - 1s - loss: 1.7986e-05 - accuracy: 1.0000 - mae: 7.1935e-06 - mse: 2.5184e-10 - precision: 0.9998 - recall: 0.9856 - f1_score: 0.9927 - val_loss: 0.0256 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9858 - val_f1_score: 0.9928 - 745ms/epoch - 23ms/step\n",
      "Epoch 53/100\n",
      "32/32 - 1s - loss: 1.7418e-05 - accuracy: 1.0000 - mae: 6.9667e-06 - mse: 2.3793e-10 - precision: 0.9998 - recall: 0.9859 - f1_score: 0.9928 - val_loss: 0.0257 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9998 - val_recall: 0.9861 - val_f1_score: 0.9929 - 863ms/epoch - 27ms/step\n",
      "Epoch 54/100\n",
      "32/32 - 1s - loss: 1.6862e-05 - accuracy: 1.0000 - mae: 6.7456e-06 - mse: 2.2345e-10 - precision: 0.9998 - recall: 0.9862 - f1_score: 0.9930 - val_loss: 0.0258 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0022 - val_precision: 0.9999 - val_recall: 0.9863 - val_f1_score: 0.9930 - 577ms/epoch - 18ms/step\n",
      "Epoch 55/100\n",
      "32/32 - 1s - loss: 1.6320e-05 - accuracy: 1.0000 - mae: 6.5293e-06 - mse: 2.0571e-10 - precision: 0.9999 - recall: 0.9864 - f1_score: 0.9931 - val_loss: 0.0259 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9866 - val_f1_score: 0.9932 - 610ms/epoch - 19ms/step\n",
      "Epoch 56/100\n",
      "32/32 - 1s - loss: 1.5830e-05 - accuracy: 1.0000 - mae: 6.3343e-06 - mse: 1.9707e-10 - precision: 0.9999 - recall: 0.9867 - f1_score: 0.9932 - val_loss: 0.0260 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9868 - val_f1_score: 0.9933 - 763ms/epoch - 24ms/step\n",
      "Epoch 57/100\n",
      "32/32 - 1s - loss: 1.5335e-05 - accuracy: 1.0000 - mae: 6.1363e-06 - mse: 1.8144e-10 - precision: 0.9999 - recall: 0.9869 - f1_score: 0.9934 - val_loss: 0.0261 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9870 - val_f1_score: 0.9934 - 1s/epoch - 33ms/step\n",
      "Epoch 58/100\n",
      "32/32 - 1s - loss: 1.4881e-05 - accuracy: 1.0000 - mae: 5.9549e-06 - mse: 1.7017e-10 - precision: 0.9999 - recall: 0.9871 - f1_score: 0.9935 - val_loss: 0.0262 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9873 - val_f1_score: 0.9935 - 976ms/epoch - 30ms/step\n",
      "Epoch 59/100\n",
      "32/32 - 0s - loss: 1.4443e-05 - accuracy: 1.0000 - mae: 5.7788e-06 - mse: 1.5969e-10 - precision: 0.9999 - recall: 0.9874 - f1_score: 0.9936 - val_loss: 0.0263 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9875 - val_f1_score: 0.9936 - 472ms/epoch - 15ms/step\n",
      "Epoch 60/100\n",
      "32/32 - 1s - loss: 1.4019e-05 - accuracy: 1.0000 - mae: 5.6097e-06 - mse: 1.4945e-10 - precision: 0.9999 - recall: 0.9876 - f1_score: 0.9937 - val_loss: 0.0263 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9877 - val_f1_score: 0.9937 - 706ms/epoch - 22ms/step\n",
      "Epoch 61/100\n",
      "32/32 - 1s - loss: 1.3622e-05 - accuracy: 1.0000 - mae: 5.4502e-06 - mse: 1.4120e-10 - precision: 0.9999 - recall: 0.9878 - f1_score: 0.9938 - val_loss: 0.0264 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9879 - val_f1_score: 0.9938 - 685ms/epoch - 21ms/step\n",
      "Epoch 62/100\n",
      "32/32 - 1s - loss: 1.3232e-05 - accuracy: 1.0000 - mae: 5.2945e-06 - mse: 1.3247e-10 - precision: 0.9999 - recall: 0.9880 - f1_score: 0.9939 - val_loss: 0.0265 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9881 - val_f1_score: 0.9939 - 815ms/epoch - 25ms/step\n",
      "Epoch 63/100\n",
      "32/32 - 1s - loss: 1.2852e-05 - accuracy: 1.0000 - mae: 5.1438e-06 - mse: 1.2334e-10 - precision: 0.9999 - recall: 0.9882 - f1_score: 0.9940 - val_loss: 0.0266 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9883 - val_f1_score: 0.9940 - 977ms/epoch - 31ms/step\n",
      "Epoch 64/100\n",
      "32/32 - 1s - loss: 1.2509e-05 - accuracy: 1.0000 - mae: 5.0049e-06 - mse: 1.1737e-10 - precision: 0.9999 - recall: 0.9884 - f1_score: 0.9941 - val_loss: 0.0267 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9885 - val_f1_score: 0.9941 - 568ms/epoch - 18ms/step\n",
      "Epoch 65/100\n",
      "32/32 - 1s - loss: 1.2157e-05 - accuracy: 1.0000 - mae: 4.8648e-06 - mse: 1.0978e-10 - precision: 0.9999 - recall: 0.9885 - f1_score: 0.9942 - val_loss: 0.0267 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9886 - val_f1_score: 0.9942 - 679ms/epoch - 21ms/step\n",
      "Epoch 66/100\n",
      "32/32 - 1s - loss: 1.1829e-05 - accuracy: 1.0000 - mae: 4.7335e-06 - mse: 1.0368e-10 - precision: 0.9999 - recall: 0.9887 - f1_score: 0.9943 - val_loss: 0.0268 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9888 - val_f1_score: 0.9943 - 689ms/epoch - 22ms/step\n",
      "Epoch 67/100\n",
      "32/32 - 1s - loss: 1.1497e-05 - accuracy: 1.0000 - mae: 4.6016e-06 - mse: 9.7389e-11 - precision: 0.9999 - recall: 0.9889 - f1_score: 0.9944 - val_loss: 0.0269 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9890 - val_f1_score: 0.9944 - 1s/epoch - 32ms/step\n",
      "Epoch 68/100\n",
      "32/32 - 1s - loss: 1.1195e-05 - accuracy: 1.0000 - mae: 4.4791e-06 - mse: 9.1790e-11 - precision: 0.9999 - recall: 0.9891 - f1_score: 0.9944 - val_loss: 0.0270 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9891 - val_f1_score: 0.9945 - 845ms/epoch - 26ms/step\n",
      "Epoch 69/100\n",
      "32/32 - 1s - loss: 1.0894e-05 - accuracy: 1.0000 - mae: 4.3596e-06 - mse: 8.6006e-11 - precision: 0.9999 - recall: 0.9892 - f1_score: 0.9945 - val_loss: 0.0270 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9893 - val_f1_score: 0.9946 - 509ms/epoch - 16ms/step\n",
      "Epoch 70/100\n",
      "32/32 - 1s - loss: 1.0614e-05 - accuracy: 1.0000 - mae: 4.2479e-06 - mse: 8.1825e-11 - precision: 0.9999 - recall: 0.9894 - f1_score: 0.9946 - val_loss: 0.0271 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9894 - val_f1_score: 0.9946 - 600ms/epoch - 19ms/step\n",
      "Epoch 71/100\n",
      "32/32 - 1s - loss: 1.0342e-05 - accuracy: 1.0000 - mae: 4.1385e-06 - mse: 7.7544e-11 - precision: 0.9999 - recall: 0.9895 - f1_score: 0.9947 - val_loss: 0.0272 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9896 - val_f1_score: 0.9947 - 743ms/epoch - 23ms/step\n",
      "Epoch 72/100\n",
      "32/32 - 1s - loss: 1.0078e-05 - accuracy: 1.0000 - mae: 4.0324e-06 - mse: 7.2931e-11 - precision: 0.9999 - recall: 0.9897 - f1_score: 0.9948 - val_loss: 0.0273 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0023 - val_precision: 0.9999 - val_recall: 0.9897 - val_f1_score: 0.9948 - 1s/epoch - 34ms/step\n",
      "Epoch 73/100\n",
      "32/32 - 1s - loss: 9.8247e-06 - accuracy: 1.0000 - mae: 3.9312e-06 - mse: 6.9072e-11 - precision: 0.9999 - recall: 0.9898 - f1_score: 0.9948 - val_loss: 0.0273 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9899 - val_f1_score: 0.9949 - 1s/epoch - 32ms/step\n",
      "Epoch 74/100\n",
      "32/32 - 0s - loss: 9.5851e-06 - accuracy: 1.0000 - mae: 3.8342e-06 - mse: 6.5574e-11 - precision: 0.9999 - recall: 0.9899 - f1_score: 0.9949 - val_loss: 0.0274 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9900 - val_f1_score: 0.9949 - 447ms/epoch - 14ms/step\n",
      "Epoch 75/100\n",
      "32/32 - 1s - loss: 9.3465e-06 - accuracy: 1.0000 - mae: 3.7393e-06 - mse: 6.2312e-11 - precision: 0.9999 - recall: 0.9901 - f1_score: 0.9950 - val_loss: 0.0275 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9902 - val_f1_score: 0.9950 - 673ms/epoch - 21ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 76/100\n",
      "32/32 - 1s - loss: 9.1116e-06 - accuracy: 1.0000 - mae: 3.6456e-06 - mse: 5.8588e-11 - precision: 0.9999 - recall: 0.9902 - f1_score: 0.9950 - val_loss: 0.0276 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9903 - val_f1_score: 0.9951 - 671ms/epoch - 21ms/step\n",
      "Epoch 77/100\n",
      "32/32 - 1s - loss: 8.8976e-06 - accuracy: 1.0000 - mae: 3.5591e-06 - mse: 5.5966e-11 - precision: 0.9999 - recall: 0.9903 - f1_score: 0.9951 - val_loss: 0.0276 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9904 - val_f1_score: 0.9951 - 775ms/epoch - 24ms/step\n",
      "Epoch 78/100\n",
      "32/32 - 1s - loss: 8.6811e-06 - accuracy: 1.0000 - mae: 3.4723e-06 - mse: 5.2714e-11 - precision: 0.9999 - recall: 0.9905 - f1_score: 0.9952 - val_loss: 0.0277 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9905 - val_f1_score: 0.9952 - 995ms/epoch - 31ms/step\n",
      "Epoch 79/100\n",
      "32/32 - 1s - loss: 8.4735e-06 - accuracy: 1.0000 - mae: 3.3897e-06 - mse: 5.0180e-11 - precision: 0.9999 - recall: 0.9906 - f1_score: 0.9952 - val_loss: 0.0278 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9906 - val_f1_score: 0.9953 - 593ms/epoch - 19ms/step\n",
      "Epoch 80/100\n",
      "32/32 - 1s - loss: 8.2712e-06 - accuracy: 1.0000 - mae: 3.3088e-06 - mse: 4.7890e-11 - precision: 0.9999 - recall: 0.9907 - f1_score: 0.9953 - val_loss: 0.0279 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9908 - val_f1_score: 0.9953 - 564ms/epoch - 18ms/step\n",
      "Epoch 81/100\n",
      "32/32 - 1s - loss: 8.0790e-06 - accuracy: 1.0000 - mae: 3.2315e-06 - mse: 4.5626e-11 - precision: 0.9999 - recall: 0.9908 - f1_score: 0.9953 - val_loss: 0.0280 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9909 - val_f1_score: 0.9954 - 687ms/epoch - 21ms/step\n",
      "Epoch 82/100\n",
      "32/32 - 1s - loss: 7.8869e-06 - accuracy: 1.0000 - mae: 3.1549e-06 - mse: 4.3132e-11 - precision: 0.9999 - recall: 0.9909 - f1_score: 0.9954 - val_loss: 0.0280 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9910 - val_f1_score: 0.9954 - 835ms/epoch - 26ms/step\n",
      "Epoch 83/100\n",
      "32/32 - 1s - loss: 7.6989e-06 - accuracy: 1.0000 - mae: 3.0813e-06 - mse: 4.1163e-11 - precision: 0.9999 - recall: 0.9910 - f1_score: 0.9955 - val_loss: 0.0281 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9911 - val_f1_score: 0.9955 - 1s/epoch - 33ms/step\n",
      "Epoch 84/100\n",
      "32/32 - 1s - loss: 7.5276e-06 - accuracy: 1.0000 - mae: 3.0111e-06 - mse: 3.9103e-11 - precision: 0.9999 - recall: 0.9912 - f1_score: 0.9955 - val_loss: 0.0282 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9912 - val_f1_score: 0.9955 - 663ms/epoch - 21ms/step\n",
      "Epoch 85/100\n",
      "32/32 - 1s - loss: 7.3500e-06 - accuracy: 1.0000 - mae: 2.9415e-06 - mse: 3.7301e-11 - precision: 0.9999 - recall: 0.9913 - f1_score: 0.9956 - val_loss: 0.0282 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9913 - val_f1_score: 0.9956 - 576ms/epoch - 18ms/step\n",
      "Epoch 86/100\n",
      "32/32 - 1s - loss: 7.1832e-06 - accuracy: 1.0000 - mae: 2.8747e-06 - mse: 3.5512e-11 - precision: 0.9999 - recall: 0.9914 - f1_score: 0.9956 - val_loss: 0.0283 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9914 - val_f1_score: 0.9956 - 669ms/epoch - 21ms/step\n",
      "Epoch 87/100\n",
      "32/32 - 1s - loss: 7.0229e-06 - accuracy: 1.0000 - mae: 2.8103e-06 - mse: 3.3925e-11 - precision: 0.9999 - recall: 0.9915 - f1_score: 0.9957 - val_loss: 0.0284 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9915 - val_f1_score: 0.9957 - 644ms/epoch - 20ms/step\n",
      "Epoch 88/100\n",
      "32/32 - 1s - loss: 6.8699e-06 - accuracy: 1.0000 - mae: 2.7480e-06 - mse: 3.2068e-11 - precision: 0.9999 - recall: 0.9916 - f1_score: 0.9957 - val_loss: 0.0285 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9916 - val_f1_score: 0.9957 - 979ms/epoch - 31ms/step\n",
      "Epoch 89/100\n",
      "32/32 - 1s - loss: 6.7062e-06 - accuracy: 1.0000 - mae: 2.6840e-06 - mse: 3.0278e-11 - precision: 0.9999 - recall: 0.9917 - f1_score: 0.9958 - val_loss: 0.0285 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9917 - val_f1_score: 0.9958 - 789ms/epoch - 25ms/step\n",
      "Epoch 90/100\n",
      "32/32 - 0s - loss: 6.5640e-06 - accuracy: 1.0000 - mae: 2.6261e-06 - mse: 2.8979e-11 - precision: 0.9999 - recall: 0.9917 - f1_score: 0.9958 - val_loss: 0.0286 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9918 - val_f1_score: 0.9958 - 387ms/epoch - 12ms/step\n",
      "Epoch 91/100\n",
      "32/32 - 1s - loss: 6.4278e-06 - accuracy: 1.0000 - mae: 2.5701e-06 - mse: 2.7656e-11 - precision: 0.9999 - recall: 0.9918 - f1_score: 0.9959 - val_loss: 0.0286 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9919 - val_f1_score: 0.9959 - 629ms/epoch - 20ms/step\n",
      "Epoch 92/100\n",
      "32/32 - 1s - loss: 6.2838e-06 - accuracy: 1.0000 - mae: 2.5134e-06 - mse: 2.6364e-11 - precision: 0.9999 - recall: 0.9919 - f1_score: 0.9959 - val_loss: 0.0287 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0024 - val_precision: 0.9999 - val_recall: 0.9920 - val_f1_score: 0.9959 - 678ms/epoch - 21ms/step\n",
      "Epoch 93/100\n",
      "32/32 - 1s - loss: 6.1423e-06 - accuracy: 1.0000 - mae: 2.4582e-06 - mse: 2.5275e-11 - precision: 0.9999 - recall: 0.9920 - f1_score: 0.9959 - val_loss: 0.0288 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9921 - val_f1_score: 0.9960 - 623ms/epoch - 19ms/step\n",
      "Epoch 94/100\n",
      "32/32 - 1s - loss: 6.0103e-06 - accuracy: 1.0000 - mae: 2.4049e-06 - mse: 2.3925e-11 - precision: 0.9999 - recall: 0.9921 - f1_score: 0.9960 - val_loss: 0.0288 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9921 - val_f1_score: 0.9960 - 934ms/epoch - 29ms/step\n",
      "Epoch 95/100\n",
      "32/32 - 1s - loss: 5.8821e-06 - accuracy: 1.0000 - mae: 2.3542e-06 - mse: 2.3022e-11 - precision: 0.9999 - recall: 0.9922 - f1_score: 0.9960 - val_loss: 0.0289 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9922 - val_f1_score: 0.9961 - 644ms/epoch - 20ms/step\n",
      "Epoch 96/100\n",
      "32/32 - 1s - loss: 5.7575e-06 - accuracy: 1.0000 - mae: 2.3042e-06 - mse: 2.1971e-11 - precision: 0.9999 - recall: 0.9923 - f1_score: 0.9961 - val_loss: 0.0290 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9923 - val_f1_score: 0.9961 - 530ms/epoch - 17ms/step\n",
      "Epoch 97/100\n",
      "32/32 - 1s - loss: 5.6328e-06 - accuracy: 1.0000 - mae: 2.2551e-06 - mse: 2.1053e-11 - precision: 0.9999 - recall: 0.9923 - f1_score: 0.9961 - val_loss: 0.0291 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9924 - val_f1_score: 0.9961 - 717ms/epoch - 22ms/step\n",
      "Epoch 98/100\n",
      "32/32 - 1s - loss: 5.5113e-06 - accuracy: 1.0000 - mae: 2.2069e-06 - mse: 2.0037e-11 - precision: 0.9999 - recall: 0.9924 - f1_score: 0.9962 - val_loss: 0.0291 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9925 - val_f1_score: 0.9962 - 575ms/epoch - 18ms/step\n",
      "Epoch 99/100\n",
      "32/32 - 1s - loss: 5.3954e-06 - accuracy: 1.0000 - mae: 2.1608e-06 - mse: 1.9109e-11 - precision: 0.9999 - recall: 0.9925 - f1_score: 0.9962 - val_loss: 0.0292 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9925 - val_f1_score: 0.9962 - 842ms/epoch - 26ms/step\n",
      "Epoch 100/100\n",
      "32/32 - 1s - loss: 5.2885e-06 - accuracy: 1.0000 - mae: 2.1174e-06 - mse: 1.8372e-11 - precision: 0.9999 - recall: 0.9926 - f1_score: 0.9962 - val_loss: 0.0293 - val_accuracy: 0.9921 - val_mae: 0.0031 - val_mse: 0.0025 - val_precision: 0.9999 - val_recall: 0.9926 - val_f1_score: 0.9963 - 954ms/epoch - 30ms/step\n",
      "--- 124.36310625076294 seconds ---\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation,Embedding\n",
    "from keras.models import Sequential\n",
    "model_conv = Sequential()\n",
    "model_conv.add(Embedding(1000, 200, input_length=data.shape[1]))\n",
    "model_conv.add(Conv1D(128, 2, activation='relu'))\n",
    "model_conv.add(MaxPooling1D(pool_size=2))\n",
    "model_conv.add(LSTM(100))\n",
    "model_conv.add(Dense(5, activation='softmax'))\n",
    "model_conv.compile(loss='categorical_crossentropy', optimizer='adam',   metrics=['accuracy','mae','mse',keras_metrics.precision(), keras_metrics.recall(),keras_metrics.f1_score()])\n",
    "\n",
    "\n",
    "\n",
    "#Here we train the Network.\n",
    "pred3=model_conv.fit(X_train, Y_train, batch_size =batch_size, epochs =100, verbose =2,validation_data=(X_valid,Y_valid))\n",
    "pred3\n",
    "\n",
    "print(\"--- %s seconds ---\" % (time.time() - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        33\n",
      "           2       1.00      0.96      0.98        28\n",
      "           3       0.96      1.00      0.98        22\n",
      "           4       1.00      1.00      1.00        24\n",
      "\n",
      "    accuracy                           0.99       126\n",
      "   macro avg       0.99      0.99      0.99       126\n",
      "weighted avg       0.99      0.99      0.99       126\n",
      "\n",
      "[[19  0  0  0  0]\n",
      " [ 0 33  0  0  0]\n",
      " [ 0  0 27  1  0]\n",
      " [ 0  0  0 22  0]\n",
      " [ 0  0  0  0 24]]\n"
     ]
    }
   ],
   "source": [
    "yhat_classes = model_conv.predict(X_valid, verbose=0)\n",
    "rounded_predictions=np.argmax(yhat_classes, axis=1)\n",
    "rounded_labels=np.argmax(Y_valid, axis=1)\n",
    "print(classification_report(rounded_labels,rounded_predictions))\n",
    "print(confusion_matrix(rounded_labels,rounded_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred3.history['accuracy'])\n",
    "plt.plot(pred3.history['precision'])\n",
    "plt.plot(pred3.history['recall'])\n",
    "plt.plot(pred3.history['f1_score'])\n",
    "\n",
    "plt.plot(pred3.history['val_accuracy'])\n",
    "plt.plot(pred3.history['val_precision'])\n",
    "plt.plot(pred3.history['val_recall'])\n",
    "plt.plot(pred3.history['val_f1_score'])\n",
    "\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Accuracy', 'Precision', 'Recall', 'F1 Score', 'Val_Accuracy', 'Val_Precision', 'Val_Recall', 'Val_F1 Score'], loc='lower right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('accCNNLSTM.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3),dpi=300)\n",
    "\n",
    "plt.plot(pred3.history['loss'])\n",
    "plt.plot(pred3.history['val_loss'])\n",
    "plt.plot(pred3.history['val_mae'])\n",
    "plt.plot(pred3.history['mae'])\n",
    "plt.plot(pred3.history['val_mse'])\n",
    "plt.plot(pred3.history['mse'])\n",
    "plt.ylabel('Score',fontsize=12)\n",
    "plt.xlabel('Epoch',fontsize=12)\n",
    "plt.legend(['Loss', 'Val_Loss', 'MAE', 'Val_MAE', 'MSE', 'Val_MSE'], loc='upper right',fontsize=12)\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('LosCNNLSTM.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BsC458MADY9SoUdG9e933K+bOnRtPPfVU3HDDDTFjxozEdT7++OM47rjjYuzYsdGhQ/IHM8vKyhJrmW70N0amdcvLy3OyZ74lRx0BqJeamclH9JQP2SJn+7YanHnt4op2ERnSeEDh6dq1c8b6woWVOdu7ctHijPXOQklAmFNA4TOngEJnTgGwLhkwYEBieCAionv37nH44YfH448/HqeeemrGEwQmTZoUF110Ucb9BAiahhMI6jB79uyYPn16TJ8+PWbPnh2LFi2KxYsXx+LFi2PlypVRXV0dq1evjurq6igtLY3y8vIoKyuL8vLyaNOmTXTp0iW6du0a3bp1i+7du8eAAQNi/fXXz/cvC8iRqg8mR/kWm9ZZK+vbJ0p6dIuaNSS810Z9wglFbVpHennmxywAhaNT58xv1ixesiRney9duixjvXPnTjnbG2g+zCmg0JlTQKEzpwAKQ+fOnWPAgAH5bmONOnfOHDxrLsrKyuKUU06JLbbYIn74wx/G6tWr67zugQceiIMPPjh22mmnOuuZbtivWrUqK702ZF0BgnXYW2+9Fc8++2y88cYb8e6778aiRYuyvkfbtm1j4MCBscUWW8Tw4cNjxx13jPZreH450DxUvf1exEH7Jdbb7r17LBl7d1b3LO7SOVpvs9Uarytq1UqAAJqRinbtMtaXLMn8Zk9jrOmYyg4dvW4BzCmg8JlTQKEzpwAKw6hRo2LUqFH5bqPF2X333ePCCy+Mc889N/Ga66+/PjFA0LFjchBv+fLlje6vLsuWJf/Z3KZNm5zsmW8tNkDwzjvvxP333x+PPPJILFy48Ivvp3P0rPVly5bFW2+9FW+99VbceeedUVxcHEOHDo1DDjkk9t1333X2XzBoCVY8/3KkU6koKq77qTDtj/x2LLnr7xEJibq1UXHw/lFUn2RbSUnW9gRyr23bzK8HVqzIXSBo5RoSupmOBwNaDnMKKHTmFFDozCngK1KpfHcATerggw+O559/Ph566KE66y+//HJ8/PHH0b9//6/UMgUIMt3ob4xMwYQuXbrkZM98q/tu1zrsH//4Rxx++OFx6KGHxu233x4LFiyIdDr9xVdRUVHOvr68T01NTbz++uvx85//PHbeeee47LLLagUZgOYjNX9BVL35bmK9pFvXaH/EIVnbr7hjh6g49MB6XVskQADNSqtWmYNB1dW5eY7Xf9auzlgvL/dGEmBOAYXPnAIKnTkFABGnnXZaYnAtnU7HM888U2etU6dOiWvm4oT5TOu2atUqKioqcrJnvrWYAMG7774b3/3ud+PUU0+NN998MzEwEBG1bvRn6ysiEgMFy5Yti1tvvTX22muvuOGGGyIlbQbNztJ7H8hY73jiMVE+ZFBW9upy3llR0qW+zz3KzakqQG6UrCH0s6Y3expjTW9SlZW22IOrgC8xp4BCZ04Bhc6cAoCIvn37xg477JBYf+ONN+r8fqdOnaJVq1Z11ubPn5+N1r5i3rx5dX6/W7duOdmvEKzzAYJ0Oh3XXXddfOc734k33njjK8GBz6/JdKM/G19f7qeufdLpdCxdujR++9vfxpFHHhmzZ89u+t8sYK0t/8czsfrT6Yn1otLS6HrZL6J0ww0atU+HY0dFm69/rd7Xp2sEkqA5KS4uyljP5SdRamoyr13qjSQgzCmg8JlTQKEzpwDgP0aMGJFY++CDD+r8flFRUfTq1avOWtKN/sZYsmRJrEp4BFD37t2zvl+hWKcDBFVVVfGDH/wgrr766qiurq4VHEi6kR+RuxMIvrzX5/63h3Q6Ha+//noceOCB8c477zTh7xbQKDU1UXnNjRkvKe3RLXrcNCbKtxq8Vlt0Ov3E6HjiMQ3uC2g+1vRGUUlJ7l66remNIickARHmFFD4zCmg0JlTAPAfG2+8cWKtsrIysbbBBnV/UHP58uVZP4Vg2rRpibUBAwZkda9Css5GCmtqauKkk06KF1544YvgQETtG/af+/IN/vXWWy/69u0b/fr1ix49ekTXrl2ja9eu0a5du2jXrl20bds2SkpKorS0NEpKSiKVSkV1dXWkUqlYuXJlLFu2LJYvXx6LFi2KefPmxbx582LatGkxderUmDp1aq0jqP43tPDlEMGiRYvi2GOPjVtuuSU233zzpvgtAxppxdP/juVPPRtt99gl8ZqSjh2ixx9+E8vufzgq/3hLpBYl/yH4ufItN49OZ/4gWm351VlQU7k4Sjp2SPzZ9MqV9WseKAhVVVUZ67n8NMia3qTK5adggObDnAIKnTkFFDpzCgD+o2vXrom1TAGC/v37x7PPPltnberUqRnXbahPPvkksTZw4MCs7VNo1tkAwSWXXBLPP/98nTfpP//niIgNN9wwdt111xg+fHgMHjw4unTpkrOeqqqq4v33348JEybE008/HRMmTIiamppawYGI/4YbKisr4/jjj48HHnggp30B2bPwst9G2SYbRdkGvROvKSotiYpvHxBt990rVj7/cqx49oVY/eGUqJm3IFLLlkdJ505R3K1LtB42NNrs+rUoHzIoioq/+he81LLlsfDXY6LbpT9P3Cu9su6jdYDCVFW1OmN9Tc/KbIw1vUlVtTrzm1xAy2BOAYXOnAIKnTkFQHOyYsWKmD17dvTr1y/ra5eVlSXWiuu4J/K5QYMGJdY+/PDDGDZsWKP6+rLJkycn1gQImpkXXngh7rjjjsRTB9LpdOy6665x3HHHxbbbbttkfZWXl8eQIUNiyJAhccwxx8Ts2bPjjjvuiFtvvTVWrFhR69EKn/c6f/78+OUvfxljxoxpsj6BtZeqXBzzfvTT6HHD76KkS+eM1xa3bRNt99o12u61a4P3SVdVxfyfXRyphYuSe1m2POJLJ6wAhW/Vqsxv1rRu3Spne7dulXntZcuW52xvoPkwp4BCZ04Bhc6cAr4i7fEhFIZ//OMfMXHixJg2bVpMmzYtpk+fHnPnzo3i4uJ46aWXomPHjlndb968eYm1ioqKxNqWW26ZWHvnnXfiO9/5TqP6+rK33norsbbFFltkbZ9Ck7sHKuVJOp2OCy64oNZ///In/Dt06BDXXXddXH/99U0aHqhLz54940c/+lE8+uijsf3223/lhITPe37yySfjueeey2uvQP1VT50ec044I6rnJP/h1xipFSti3jkXxMrnX44oL0++rnJxTvYHcmdRhqO5IiLat09+4dxYa1p7+bIVOdsbaD7MKaDQmVNAoTOnAChUt99+e1x77bXxwAMPxOuvvx5z586NiIhUKhXjx4/P+n4zZ85MrPXp0yexNmDAgOjcue4PcE6YMKHRfX2uuro63nzzzTprm2yySXTv3j1rexWadS5A8MQTT8TUqVO/uPn+5Rvyffr0iXvuuSd23333PHdZ23rrrRe33HJLfPvb367V85f95S9/yUNnwNqq/uTTmH3UybHqzXeyuu7qqdNjzjGnxsrnXoqI/5xikCS1KPNfSIHCs2D+woz19hXtcrZ3RfvMay9YkLk3oGUwp4BCZ04Bhc6cAqBQbbTRRom1Z599Nuv7Zfrw9CabbJJYKyoqiq997Wt11qZMmRLTpk1rdG8REa+99losXlz3BzWHDx+elT0K1ToXIPjb3/72xT9/OTzQuXPnuPnmm6Nv3775ai2j4uLiuPjii2PkyJG1QgSfByGef/75rP0LDzSN1Lz5MeeEH8WiMTdEakXjEtzpqqpYfPMdMfvI42P1lI+/+H5J1y6JP1MzNzcnIAC5M38NbyR16pTdY8IasvZ8byQBYU4Bhc+cAgqdOQVAodp6660Ta4888kisaOR9ji9btWpVxgDBdtttl/HnR4wYkVh75JFH1rqvL3vooYcSa7vssktW9ihU61SAYPny5fHyyy/X+gT/5zfjzz///Nhggw3y2F39XHLJJdGrV6+vfD+dTscLL7yQh46ARqlJxZLb7orPDvpeLL71rqhZuKhBP55asjSW3P63+Oxb34vK626K9P88J6+ke9fEn62ePXdtOgbyaM7c+RnrPXp2y9nePXtkXnv2bKEkwJwCCp85BRQ6cwqAQrXTTjtFSUlJnbWlS5fGgw8+mLW9brvttli0aFGdtfLy8th1110z/vyee+4ZbdrUfULzXXfdFatXr25Uf5WVlYm/3l69esVOO+3UqPUL3ToVIJg4cWJUVf335lo6nY6IiMGDB8c+++yTr7YapG3btnHKKad80fuXZfO5HUDTSi1YGJVX3xAzRx4Wc3/4k1h8612x8pXXovqz2ZFaviLSNTWRWrosqmfNjpXjJ8TisXfH3B/+JGbsfUgs+t0fomZO3X+BK+2XfKpKzcxZufrlADkyc+asWLlyZWJ9vZ49crZ3prXnzp0fq1atytneQPNhTgGFzpwCCp05BUCh6tKlS8Yb41dffXUsW7as0fvMnj07/vSnPyXW99prr+jYMfOpORUVFfGNb3yjztqMGTPi9ttvb1SPV199dSxfvrzO2iGHHBLFxevULfavKM13A9k0efLkr3yvqKgoDjnkkDx0s/b222+/uPjii2sdBZJOp+ODDz7IY1dAVtTUxMoXX4mVL76SleXK+m+YWFv98dSs7AE0nXQ6HVM/nRGbblL388b69F4/Z3v37pO89idTPUYJ+A9zCih05hRQ6MwpAArZ9773vcRHC8yZMyeuuuqqOO+889Z6/ZUrV8bJJ5+cePpAUVFRnHDCCfVa68gjj4z777+/ztrvfve72GmnnWLTTTdtcI8vvPBCYgChdevWceihhzZ4zeZmnYpHzJkzp87vr+k5GYWmVatWMXjw4C9OIfj8kQxJ/2cCWqaiVq2ibGD/xPrqjz5pumaArPloyieJtYEZ/j/fWJnW/vDDj3K2L9D8mFNAoTOngEJnTgG1pFK+8vFFnXbdddcYOnRoYn3s2LHxl7/8Za3WXrx4cZx88snxzjvvJF5z2GGH1fum/5AhQ2K33Xars7ZixYo48cQT47PPPmtQj++//36cfvrpkUr4d+T//u//omfPng1aszl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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "array4=[[19 , 0 , 0,  0  ,0],\n",
    " [ 0, 33 , 0 , 0 , 0],\n",
    " [ 0 , 0, 27 , 1 , 0],\n",
    " [ 0,  0,  0 ,22 , 0],\n",
    " [ 0,  0 , 0 , 0 ,24]]\n",
    "RF = pd.DataFrame(array4, index = [i for i in \"01234\"],\n",
    "                  columns = [i for i in \"01234\"])\n",
    "\n",
    "i=1\n",
    "def plot_sub_sentiment(Airline):\n",
    "    sns.set()\n",
    "    #tmp = rfc.fit(X_train, y_train.ravel())\n",
    "    sns.heatmap(Airline,annot=True,fmt = \"d\",linecolor=\"k\",linewidths=3)\n",
    "    plt.title(\"\",fontsize=10)\n",
    "\n",
    "plt.figure(1,figsize=(4, 4),dpi=500)\n",
    "plt.subplot(111) \n",
    "plot_sub_sentiment(RF)\n",
    "\n",
    "\n",
    "plt.tight_layout(pad=0) \n",
    "# Show graphic\n",
    "plt.savefig('ConLSTM.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t=inf, df=3\n",
      "7.203760432571475e-17\n",
      "Reject the null hypothesis that the means are not equal.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SOHAIL~1\\AppData\\Local\\Temp/ipykernel_3960/2583718234.py:23: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  t_stat = (mean1 - mean2) / sed\n"
     ]
    }
   ],
   "source": [
    "# t-test\n",
    "from math import sqrt\n",
    "from numpy.random import seed\n",
    "from numpy.random import randn\n",
    "from numpy import mean\n",
    "from scipy.stats import t\n",
    "\n",
    "# function for calculating the t-test for two dependent samples\n",
    "def dependent_ttest(data1, data2, alpha):\n",
    "\t# calculate means\n",
    "\tmean1, mean2 = mean(data1), mean(data2)\n",
    "\t# number of paired samples\n",
    "\tn = len(data1)\n",
    "\t# sum squared difference between observations\n",
    "\td1 = sum([(data1[i]-data2[i])**2 for i in range(n)])\n",
    "\t# sum difference between observations\n",
    "\td2 = sum([data1[i]-data2[i] for i in range(n)])\n",
    "\t# standard deviation of the difference between means\n",
    "\tsd = sqrt((d1 - (d2**2 / n)) / (n - 1))\n",
    "\t# standard error of the difference between the means\n",
    "\tsed = sd / sqrt(n)\n",
    "\t# calculate the t statistic\n",
    "\tt_stat = (mean1 - mean2) / sed\n",
    "\t# degrees of freedom\n",
    "\tdf = n - 1\n",
    "\t# calculate the critical value\n",
    "\tcv = t.ppf(1.0 - alpha, df)\n",
    "\t# calculate the p-value\n",
    "\tp = (1.0 - t.cdf(abs(t_stat), df)) * 2.0\n",
    "\t# return everything\n",
    "\treturn t_stat, df, cv, p\n",
    "\n",
    "# seed the random number generator\n",
    "seed(1)\n",
    "\n",
    "\n",
    "hb = [1.00 ,1.00, 1.00, 1.00]\n",
    "rf = [0.99, 0.99, 0.99, 0.99]\n",
    "GBM = [0.99, 0.99, 0.99, 0.99]\n",
    "ADA = [0.83, 0.74, 0.83, 0.77]\n",
    "LR = [0.96 ,0.96, 0.96 ,0.96]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# calculate the t test\n",
    "alpha =0.5\n",
    "t_stat, df, cv, p = dependent_ttest(hb, rf, alpha)\n",
    "print('t=%.3f, df=%d' % (t_stat, df))\n",
    "print(cv)\n",
    "# interpret via critical value\n",
    "if abs(t_stat) <= cv:\n",
    "\tprint('Accept null hypothesis that the means are equal.')\n",
    "else:\n",
    "\tprint('Reject the null hypothesis that the means are not equal.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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