{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression with SciKit-Learn\n",
    "\n",
    "We saw how to create a very simple best fit line, but now let's greatly expand our toolkit to start thinking about the considerations of overfitting, underfitting, model evaluation, as well as multiple features!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Data\n",
    "\n",
    "This sample data is from ISLR. It displays sales (in thousands of units) for a particular product as a function of advertising budgets (in thousands of dollars) for TV, radio, and newspaper media."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expanding the Questions\n",
    "\n",
    "Previously, we explored **Is there a relationship between *total* advertising spend and *sales*?** as well as predicting the total sales for some value of total spend. Now we want to expand this to **What is the relationship between each advertising channel (TV,Radio,Newspaper) and sales?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Features (N-Dimensional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(16,6))\n",
    "\n",
    "axes[0].plot(df['TV'],df['sales'],'o')\n",
    "axes[0].set_ylabel(\"Sales\")\n",
    "axes[0].set_title(\"TV Spend\")\n",
    "\n",
    "axes[1].plot(df['radio'],df['sales'],'o')\n",
    "axes[1].set_title(\"Radio Spend\")\n",
    "axes[1].set_ylabel(\"Sales\")\n",
    "\n",
    "axes[2].plot(df['newspaper'],df['sales'],'o')\n",
    "axes[2].set_title(\"Newspaper Spend\");\n",
    "axes[2].set_ylabel(\"Sales\")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x216014fb648>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j89pibNp9yskrurO2BWtLckN6Xcdar/eU5mFWVjJYFqLJmNzW28iYDdUVhdhVaw9pAILrXRBT/Eump+GLs7Lo1ngAiNUp/b+qcnQNjcJsHYch3V3HSGkJ7Tr/viQPSY0NpnWpKZGGWwu7j7p3FfXHoORkf/O+OqwuzoVcBlxvSIdeo3I7NidViaqKQnAFYnYfbZW8eya2duLlJCbCj6hRTHHijdpWfKEwQ3JtYjHmZCfj8CUjNYqjBJmM4LYFOdCoFKht7oWNsXc42rRqbkif1rks6nnV5ZKSRaTElAZrvGIGmCFKlXUsIVY39f26To+JPr62hPY1eUhqBQtabYISaRzXwo7DzT7H9boikxHcMleHMRvj5BxxXS8Mw+Jc+xAfx8ytwSJdkiTdK7Z2dCnKqEqoE4OwrPRakT6dmJDpAF4FoAPAAtjGsuxWQsh/APg2gO6JQx9jWXbvxHt+DGADABuAKpZl/+rpGqWlpWxtbW1Ixj8VsYzZsPTnf8PP71qIjKTASnadaRvAntPtePvhLwRpdBHDb+soGuWT86qF+2m9sduEW2sOuilK1/hgseNeeXApMpMTJI1XqucwmB3zIkRAgwy3fEqVAUcZ5UpRdQwKy6vUc/pKoLIRrPbkMc6k0p2RINj6Wsp6ETtmz8Zyp8YbYjIeQ3pVcDCh9BSPA/g+y7LHCCHJAI4SQj6Y+NuvWZZ9xml0hMwDcB+A+QCmAfgbIWQWy7I0MDVM/K2uEzMy1QEbxABQpEvChQ+GaMvnKCNSZcWkbkeLHSefCIXzVqfTF4VMaw+HF6ky4CqjDMOCYe3vH7KMw2qzQau2PyCFKswhENmIIaOAEuUEW1+LrZfOQQv/95ExGyxjDHJSlVhTksfH8/cNjwK4tiY9yXgs69WQGcUsy7YDaJ/4fYgQUgfAU/DiHQBeZ1l2FMBlQkgDgKUADoVqjBRnXv/sCpYXBifcISFODoNWheNX+nDjTP+T9iiTA6nb0VnJ7scZtIlo67fg69s/82pk+FoCjtYeDh/+hCSIVQjhQn9m65JDFubgr2wEsz05hRJMxNbgmI3lvcPVFYUwaBOxrlTvFMdclJWEkok8EG8yHst6NSzVJwgh+QCWADgy8dIjhJBThJDfE0I0E6/lArji8LZWCBjRhJBKQkgtIaS2u7vb9c8UP7naP4JTrf1Ymp8etHPO0iXj88u9QTtfLEDlUxip7TzlMrhVKfjRqrl8DBzguRJAqCtWxDqRlE9/WrqKVQhZXZyLR3edgFyGqGsTS2XQP6juDD1Ca3Dz2mI88c5pXmZ31bbiR6vm8gYxcK2CBKdzJ7OMhzzRjhCSBGA3gO+yLDtICPlfAE/CHmf8JIBfAfiW1POxLLsNwDbAHncU/BFPTXZ+fgXLZmqhCGJ3syJdEj69ZER10M4Y/VD5FEbqllr7gAWvHmrGhuUFIARgWaC+yyR5i9zVE5KTqsQ9pXkYttrQ2G0KaBtvMsSJRlI+/dlWFbv56tMToVEp0DFo8WurNpRzSZP0/IPqztAjtAaN5lGn+sHtAxaPOjdfq4ZKIeerU3ANj6Ts+sSC/gypUUwIiYfdIP4jy7JvAgDLsp0Of38BwHsT/20DMN3h7XkTr1FCzLiNweuft+B7N88K6nln6ZLx279fgo1hIY9C4Z+qeEqQCERpeXu/lC01XYoSfcNWPP9hA/9adUWhZCPDtQTcA8sM2LrfuZTRqvnZAODTZ6VxosHBUQa8yQvDsFAp4gTnvq1/BA8sMyA7RSkqV5FKBKItoSnBxFGOc1KVfG15b3pLTP6F1ovrGrMxjOC6y0xSegxnkhHg0KUet7HFkv4MZfUJAuAVAL0sy37X4fWciXhjEEK+B+AGlmXvI4TMB/Aa7HHE0wDsB1DkKdGOZqgGh31nOvA/f7uIn94+P+jn/rc/n8S29aWYNy0l6OcOE5Mqg1pIOT1910JcZ0jD2atDfikthmFxuceMuvZB1HcNYVdtq98tebnxOdbSvLFAC6N5DN9/Q3hsrspfr1GhpW8Y3UOj+MZLnwlmUV/o9O2zhqrKQYDEVPUJ4NqN2mgexdV+i1NpqKfvWogSfRr0E/WLOTlwjW107Dr4+rfLMDJm8+km3GQ0h3wuI1XlJcqYVLozEri2WxZ7yHeVrfFxBp82GlHb3AuGBd492YZNq+YKHsswLA5c6MSp1gEwLCAnQIkhDeZRxu0+MUuXhLt/e8ht7ez8dhm6zaN8B1LXscWS/gylp/gLANYDOE0IOTHx2mMAvkoIWQx7+EQTgH8BAJZlzxJCdgE4B3vliodp5Ynw8OInjbh5ri4k556lS8bRlr5YNoonFUIxmo+9dRrP3L0IP/jzSZ+Tgzy1SvYnuUiolua2jxvx3P1LsGdjObpNzkaGJ+NHbOu9pdf3RCjazCFwHOdqw/ICbP+k0U0OK1cUYE52CublJPNztONwM3559yJc6BwCywI7DjfzzVwOXOjiW9A63oQ9JQKFYy5jOdGIEj04yvGakjzeIAbE9RbDsNhzpt2tUdPmfXWYk50sKJPWcdapLvGWexfjlrk67NlYjrqOQVzsHMIv/3oB95TmCa6d3mErbxALjS2W9Gcoq098AmFLfK+H9/wcwM9DNaZohmVZNHSZcLS5D619IxhnGOhSlFg0PQ2L8tJCFn5wunUAzT1mVFcUheT8MzOTUHu5F+vLDCE5P8U3jOZRPl4XuBYPZh4d90tpiSVCbVhegOc/bPBL6bX0Dbsl1j3y2nHsrSpHWYFzJRNPxo9YbKdKEefzZ6VxooHjOFeEQHAOGBZ4dNcJvPLgUv7v7QMWXOgcwosHG92+fxtz7b1Sb8LRNpexEmtJCS8Mw6J7aBQPlRcAAJIS5JL0VpPR7KY/OZ0spOPEdOjeqnIQAvzgjWvOEoZ1D7WQolOjbc15gna0izDjNgZvHm/D7/5+CYOWcczLSUFGUgLkMoLGbjNe+bQJw1Yb1i8z4MEvzEBSQnCn7LkP6/FPC7IRJwtNIZKirCT835n2kJyb4hsMw+Jqv4X30DnGg6kSrsVucvUp5TIgMT5OtB0zIO5BJcR/peeLV8HTsUvztYKxnbqUBJ8VNI0TDRzXuTJoE7G6OJd/QHv3ZBtY1j5/w9ZxpznafbQV1RVFTlvH3I4Eh9SbcDTNZSzFWlLCh5BcPLF6HgzaRFjHWb5+sJwA2SnOestTrXchHedJh3LrkUOs5bQ3nRpNa84b1CiOIPWdQ9j4p+OQywjuu16P+dNSBNsrNxvN2HO6Ha982oSf3D4ftxfnBNyGGQAudAzhs8u92HLv4oDPJUauJhF9w1YYTaPQBqEpCMV/xDwIW+5djN9+1IDqiiK8/nmLUwznto8bPd6kxYwPGYHfSk+KV4Hzro2M2VBdUYhdta38ljp3LBeKsbOyDO0D9iSV+TmpkMmIzwo61gvSRwOO8/rxhS58Z0UhfvbeWX4Ofrp6Pv70mb2dsz7d+SbaN2xFriYR1RVFMFttmKNLxi/fP8/POSD9JhyquRTy+AKeEzppTWOKEEJy8eR75/D8/UtwsdPk9HA4OzsF+vRrciWmP0sN6YI6zlPFHq06AQZtIl+don3Agp21Ldj57TL0DluhUsRBl5KAvDSVR50aS/ozZIl24SCWg/E/vNCF771+AveU5uGm2VmSjNz6ziG8+MllzJ+Wgl+sLUZqYnxAY3jwpc+Qp1Hh1oU5AZ3HG5v3nUdVRSFWzglN3HKImTTJIocu9eCrLxxxe/3RL8/Cbz5qwHP3L0GmOgHrXjgsOSFCLHFvZoYaqap4J2UtFW/eM6G/V1cU4dVDzU4JfgzDiiabAJgMiVAxlWjnKaYYsMsZF1MsNEd5qYk43NSL2uZeaBLjoVTE4WfvnnWSkXk5yWgfcE64lDrHgYQxCMnkc/cvgXWc9egFFluTr1fe4BYqFINMGt0ZbsTk4qVvluJf/3hMUD/na9V8EmtrrwU/futaTPHmtcW4bUEO4gRKrnpL5nvqzgV49kA9mo0j/LkS4+U4e3XAKan6lrk6n9ZbFBD2RDuKCH+/2I3vvX4C3/vyLMzSJUt+X5EuGU/esQCvfdaM22oO4rdfvw4LclP9GsPB+m6c7xjCN2+c4df7faEgQ41jzf2xahRPGsQ8CDfMSMfqYrtSPXLZ6FO8LeeN3ba+FLXNvbAxwNb9F7GuVM+X6fF1K9ibV0HIi7J1fz1eeXApMpMTeO+Et2QTmggVXhzn9WLnkKCcLZmehi/OyuLn2nWOlhdmQJ+eiGMt/di6/yI2LC+AXAYsnZEOy5gNq7YedDNApcyx2IMYd6P3ZigLyeSp1gE+eYl7zdULHEuxlpTwISYXo+OM4LrpHLTgfMe1ijoGbSK23LsYjd0mFOel4cYCraBBDDivS9eKPZYxBo+/fQY7K8swbLVhzMbiiXdO8wayY1I15ziJdZ0alo52lGtc6jah+vXjqL65yCeDmEMRJ8M3b5yBNUty8bUXj+DPtVe8v8mFYes4fvzmaXy9zBDUZh1izMxMQm3z1OpsF43oNSpsXlvs1v3r+vx0vjUn12bZEa4+pRgtfcOo3FGLmv0NeP7DBjQbR5y6jgl1nvMGl71fVpDBj41DLAaOBcsfKxYqsro4d1J0XYpVuHmdNdGe2RFlvAyGiQeaxm4TDl3qQWO3CQzDOr2fYYHH3rLfmJ//sAE1+xvw2eVewex3qbInZNRu3leHPWfacWvNQXz1hSO4teYg9p3tcBoPh5BMMi7xmNx5HeXPny5/lMmPmK4uykwSXDdqhdxJfpuNI3h01wmMjDGo3FGLlr5hj9fj1iXDsoIyy5U9rNxRy4dScDp1TUmem1zHMtRTHEbGbAwe/uMxrC3Jw5zswEqULZuZgTyNCr/+Wz0+b+7Dz/55PpTxcq/vY1kWj715BoVZSSjRa7weHwxmZiXhtx9f8piwRQktDMPi/bpO7Dh0Gf999yKMWMcxPV2FpAQ5moxm3gPGtVl23D6rriiC3MOzk6dkOykVLHxFinfN12QTSngRivndvLYYRvMo2vpG8LiDN8o15MBXA1SK7AlVZVldnCvYXlwo3ldIJuVEOFPfUf5iKdaSEh4YhsVH9V242j+Cn90+H2plHDSqeFyvT8fVwRFB/TxmEzZmfdXBnnRrKJKqoxFqFIeRFw82QqWQo2JOVlDONz1dhf+8Yz62f3IZtz/7Cbbet8RrPeDnDjTgZGs/frJ6XlDGIIXUxHioFXJcNpoxM8a3VmKVJqOZb4Tww4l6xFxG87aPL/FhDkJtll891Iwl+jTkZ/hWroxlQ6MsHQ2qWVlJqFwxEyyAIcs4xscZxMXJfE42kQItneU/Qt8dZwx2DlpEt2XbByySQg7EDNDsFCUau00euzdmJSthNFndqrKIec2EDAwhI39hXqqkhE5a05jiSEuvGfUuyXTVFUWQTTyxCennOdnJQdHB+Vo1nrt/CU61DkClkCMvTcVfJyfV96TqWNSZ1CgOEz2mUfz27434j9vnB6VyBIdKEYdHbirEx/XduP+Fw1h7XR4evqkQ6WqF03GWMRv+a28d9p/vwo+/MleSVzmYFGYl4URLPzWKI0TnoAWri3P5qhIAJqpLXML3b5mD8x2DyE1LRE6qe5tlf8qVcaXe/N0K9qRMOe/a/O+W48jlPr7pCJcUcueiXFFP5I0FWr+UMi2d5T+evjvOEHTsdsVty3K1rl0NUW5uHbseLp2RjoV5qU4dtZ67fwnOtbt3Lrxlrg7v13W6JWpqVAq0D1j46//+m9cLGgGJ8XK3XS8xjy8A6gWm+ETn4KhTkw6NSoGRMRt6TFZMS1VCEUfc9LNrtRYpOlhIxzIMi+FRG9450YZ1pXon3brl3sV47v4lTmvMsQulUKe8WNSZtPpEmHjyvXO42j+CB5blh+wa/cNWvHW8DZ9eMmLFrAzcMEOLpIQ41HcN4c1jbSjMSgpJrWMpvHfqKmSE4Mk7F4T92gEyKTKoG7tNePtEG2r2X1OmOalKrC8zuNWcVMQR0XadYjgqWJVCjjEbg3R1gkcjQMzwlapMT7b0CVbK2PntMizSazy22hVrDS3m0YilNqVSCZd8evvuxDLtH1lZiOcONAh+z+PjDPaeaccPHRIpf3XPYsyfloyOCe8vywK3Pet+3T9suAFf337E7XXOCOf46eq5GBljsLO2hTe+52anYPsnl/Ct5TOj/uYeBUwK3RluDpzvxLdetn92IR3tWg2C040APOpg192Ry0aT20OkQi5H5Y5a0eowezbaG3p0DVmQnaLEuI1FS98w1BOl2RyN4yjVmY7Q6hORYmB4DLtqr+C/7loY0uukqRR48AszsLYkD5839+KThm6MjjHITE7Ad2+ehRkZkUveKMxMwu5jrRG7/lQnX6vG9YZ0J8/XmpI8N8/xo7tOYM/Gcuz10bvl6xawkOH71J0LcZ0+DYRAUu3WtoERwe3ttoERLIJGdExCJYimpSWisdvkVGLIWxxrKOKlJyNi313noOdGG9zWr5Cnq7l3mDeIufN9/w277C7N16LJaEZ9l3CFi8Zuk2hspOP1e8xjOHixC4/cVIQn3jkjWsWEQgkmhnQ1vx6EdDRXDWJkzOamn8V0sJC+ffTLs5x2R061DvDXEOs42W2yN0ViWeBM2yDqu4Z4nVldUYQiXRJWztZBJiMxqzNp9Ykw8KfPW1Ci14SteUVKYjwq5ujwrS8U4F+/VIi7r5seUYMYAPIz1KjvMmF03BbRcUxVZDKCZQVap4xmuUxc8YlVfggWQtn+j799Gm+daMOxln5oVK7hP+7ZzRlJCYKZ2Blqz+uMu7ZGpcD6MntNzurXT+B3HzdifZkBGpXCrXIBZ7i5XmuyJJeEErHvbszGgmFY6DUqPHXnAqdM+5/ftRBfmp2BvVXlgh7Z5l6zoOy29Jqx72wHbq05iDNXBwWvm6iIE3yduwRn+L55rBXls7J4g5i7Bq1iQgklMzLU+NU99ookYsbpyJjNJ/18ucdd32754CK+doOeP4Zhr7VxBiC4RjKTlNh3tgO3PXsQj/zpuJPO3Lq/HqdaB3i9Gas6kxrFIYZhWPzhcDNunhuc5LpYRRkvR06qEhc6hiI9lClLXJwMtxdPw96qcvzp2zfgi0WZYVNaDMM6ldkS8yJw5bbuKc3jX89JVaKqohDDVptTiS5dSgJ+evt8JyX+09vnQ5fq2Sjmri3khRErMSRUOmvz2mLoNarAv5xJTr5W7VZeqmplEWr2X8C59gEca+lDr2kUv7x7ER798ixsWF6Amv0XoVUniN701SKGrVoRx9/8uZa0jtf93s2z8MLHl9xe33LvYqxZkovXK2/Azsoy7KxtQfuARdQooVVMKKFCJiP4yoJs7NlYjhtnpvuto510bo/w7oguRYmHbyrEIysLMSc7Ge+ebEPVyiL+X9c1Ipe57+I56kyGBXrNo2jsNsFoHhUsKxft5QZp+ESI+aypF3EyQhPMYN/aOdk6gOK8tEgPZcriGFIwPs5g89pipwYXW+5dDL1GJZix7y9CW3cvrC8V3TK3jDF8HVuhDktcaMN0jRrT0sx45u5FMFvHoVbEITlRjukaz0qX82CIGTxCJYaEmpRs+eAC4uUyGlvqBZmMYFqa0iljft+ZdqwtycPB+h6nua1aWYQ/HbO37O4asvBdulxlMSs5QbA01ajNxs9p+4AFOw7bM/X16Ylo6x+BMk6GbpPV3qpWYAs6PyMJDMNi06q5eHTXCQDCVS0CqWJCoXhDJiOYmZWEGRnuCXRP37UQMgKPJU5ddW51RaGgHGckKfidEIM2ERtXFuHZiZ2QOBnw0jevBwB+7Yk1d+J0plohR1u/BV/f/hl/zm3rSxEvJzFTfYIm2oWYf3vjJOLlMty+aFqkhxJxPjjXgf6RMWy5d3Gkh+ILkzJZhFOajhn8pYZ0lOWn428XuoKaMSyUcGHQJuK7FbPw47dOOxlEOw7bWzVzCR2uHZYA52QNT8l03j77hY5B/O7jRrdx/dstcwAAc3NSMCMjqhNHYiLRDnD/7h6+qRByGZw6vgHXEt62f9KIfdXlaOg24VTrABjWXnZtYV4qvlSUZa/j2jeCHrMVDAvICFCcl4oZ2iTB5DouiU4Z79xK2lsSaK95FG39FqcHR08tcylOTErdGU4YhsWBC538GpARQKtS4PefXvbYLdR1veWkKt2cC9UVRZAT4On/u8C/z6BNRM19SwTjlcXOe09pHnLTEpGSGI+MJAXWbxfX11EGTbQLN2M2Bn8924GfhzjBLlYoyEzCy/9oivQwKHCO6eUy7pXxMuysLJOU5OYLQqESzcYR5KUn4sVvlKJjwIKW3mHeIN5y72LeGPWUrCHmRfQGVz5rXk4yDFo1HpswzA3aRDx8U5FbGSLuxhOriSPRgGuJPLlMvOGGXGave8qyQH2niTecuRt5dooSj7x2HBqVAmtK8vgEucLMJNHSVDsON/Pn51pJAxDdEXHcUSlhWCzMTaVl1Shhp8lodurUCFx7yPOkl111FVd//pm7F+F85xBkBMhKScAzf73o9L5m4wjaByyYpUsWlHPHdSy0i/f0XQv55D2OWNOR1CgOIYcbjchOVSIjTAl20Y4+XYUrfcMYsdqQqAhvnWSKM2IGHpeJ7Pp6IEpNrLoAwwA5KYmYrknEdI0KS6bb6106emfF3sslfPjr0ea2yvXpaiyenoauIQsS4+VYt+2w6AOBlE56FGFc6/gmxsfhwPlOwe+zYk4WFuam4fOmXqd6rZYxBlv312NBbiovq44l1JYVaMGwQGayAjsry9BrtuL4lX6+CQh3fq6VtFT5oc01KJHC326hQrqqb9iK7FQl1AlyqBRxUCfI0TdsdXqfMl6G022D+O7OE4LrwXEdu+7iWcYYPPbWaVSuKHAq/RlrOjJk+z+EkOmEkA8JIecIIWcJIdUTr6cTQj4ghNRP/KuZeJ0QQmoIIQ2EkFOEkJJQjS1c7DvTgesM4WmlHAvEy2XQp6twrn0g0kOZkjAMi6YeE440GsGCFUzg4LoWub4eiFITSlKrrijCd3eewG3PHsTZq0O4Pj8dN83RYWaWc2KV43u5hDsuhnjzvjo3A9axYoQUOIOnrCADw1ab6AMBAOg1qphMHIkWHL/rhbmpWJiXiuoK92SehblpkMkIzNZx4cx7q020msWtNQdR9acT2H++C2M2FjMzk6CII3j4pkJUVRTihfWl0GtUgtVPvMmPa7Iol/BJoQSKq2yNj9tLB8oIEZR1b53qhHRu1coifP+NExgZY3B9fjrm5aTiufuXoKrCnmhXXVGIH6+agzePtXpcD9w6Fuv4yOWDcNeNNR0ZSk/xOIDvsyx7jBCSDOAoIeQDAN8EsJ9l2V8QQn4E4EcANgH4CoCiiZ8bAPzvxL8xCcuyOHC+C9+7eVakhxJVzMhQ4+SVAVxnSI/0UKYUXGwaF4epUsjxk9Xz8J/vnXPylM3Pkdaa1hccvQvNRjOOX+nHq4fs29kblhfw3fQW5qYKeulWzc/GvOpyHGvp50MdHLfFOS9gqDzaWclKMAyL9+s6seWDC9iwvICPwfa3Q95UxbGBwAxtEoqyklCi12DYOm7vqMUCRy4boUtRwqBRCc5Ha98wnrpzAR5/+1rt4M1ri/HEO6f5MntcVZFSQyqqKmbh3x3kZsu9i5GZrPBpRyRWu3NRoh9X2XJMeLvvej2eWD0PTzroaSndQjm9mVtZhiONRmSnqdDUY8bti3L5Gtv5WjWs46xTeJKjveJNn4rpyzRVPCpXFPAx0Iq42FofITOKWZZtB9A+8fsQIaQOQC6AOwB8aeKwVwB8BLtRfAeAV1l75t9hQkgaISRn4jwxx6VuE2wMizxNYqSHElXka9U4eaU/0sOYcjQZzWjsMsHGXktuMmgT8esJb4JBq+ZjyITa1QZ64+e8C52DFtTsb3Dr1LTt40aP29dcqTbXUkCOXciC5dF2NXz0GhVOt/ULxmBHaQJJVOLJsATcwxmeu38JfnXPYnz/jRNON+0XDl6GIo44VY8wmkfRbBzBwzcVOpXZu6EgkzeIgWse4Z2VZR4fgFxj1cU8y4HE2lMogHvN9tXFuXj2QD3WleqxdX89NCoFKlcUoCgrCYZ0Faw2BqsWZHvVyzIZgdXGYJwBfuiQJ1G1sgi95lEA7uXVfv23i05JqZ70qZC+3Ly2GP/+1mk0G0f442JNT4YlppgQkg9gCYAjAHQOhm4HAN3E77kArji8rXXiNSejmBBSCaASAPR6PaKVg/U9KM5LBSGx9ZQUagoy1figrjPSwwgZ0SqfnYMW5KSp8G8TyhGwJ1V8b9cJvPLgUieFFYwYSrEWzpx3QaybntTEEe498omdRc6IYlng0KUev8r/CD0Q6DUqvF/XifMdg5MiyS6S8unJsATcb9CPvHYc+6rL8cqDS3GwoQcsC7z8aRO/M8A1MOAQKrMnVnZv2GoTfQASMtw1qnjB83DGhadkT7G1QHEmWnVnqHHVbYTYDWNOP7YP2B0JUoxLV1lLkMsEa7HvrCxzu25OqhJrSvKg1ySiuqIQC/NSve4QKuKIk1c4MV7uZBBz14wlPRlyo5gQkgRgN4Dvsiw76GgksizLEkJ8CsxiWXYbgG2AvWxLMMcaTD660I1FeamRHkbUkatJRPvACEyj40hKmHx5ntEqn7oUJa70DosYCONBvZYnjyDnXZBqZHJKfmTMhuqKQuyqbXVKmqqYk4UbZ2qRnaLEufYhvhyXv9vbrg8Ejd0mPLrrBB4qL5gUSXaRlE9P1TtYgUoUGpUCnYOjMFvHISfArqPOc+/43XNydaFjUHCeXP+vS1Hihhlatx0RMcN9Z+Uyt/MYtIlONVmFZI6GXUgnWnVnqBEKQxDrNurJuBSStWfuXiRo+LYPWJCTmgiDNhHNxhG3nTtOTj0hVBlDrB5yLOnJkBZaJITEw24Q/5Fl2TcnXu4khORM/D0HQNfE620Apju8PW/itZhj3MbgaHMf5k+jRrErcTIZ8rVqnGmjyXbhJF+rRq4mUTBpQ58e3CQIT0lMnDe2Yo7Oa0Ifp+RvrTmIb71ci9993IgHlhn4ZEAuKausIAMM6+5p9CfxzhXOkBPqjhZrCSSRxlPbV9e/cXVVv/HSZ6Jz7/jdc3J115JcPH3XQv5c755sc2shzb3XMfGP65wnZriP2WxuiUtP3rGQr1/MHecqc/4k9FGmFq5Jce+ebMOS6Wk+JzwLyVp91xB/Hs7w3f5JI77zh2NYt+0QNq4sgkGbKLpz50lOhdbKrtpWp/UXi3oyZK46YncJbwdQx7LsFoc//QXANwD8YuLfdxxef4QQ8jrsCXYDsRpPXNc+hHS1AimJ8ZEeSlSSr1XhTNsAygq0kR7KlEEmI7ghX4v/XluMH7p0sJuRETyFxTAsuodG8d2bi5CbpsLlHjOsNrtR6bjVnJWsxHP3L+E9DULKU0jJb91fj1ceXIrM5ASnbehQ1RDmjDXH7mhyGfiyYdTbJx3XGESDNhFP3rFQUB7uKc0TLMf2yoNLoUtJgI25lpDHyYFQmT0uBKZEr5EUIy+WPJSuTkCJPt3JsyxF5mhta4o3hMK28lITBbuNihmXDMOiz2zFL+9ehOHRcZhGx2G1MRizsfjt16/DT/9yxikkA7DL4eNvn8HOyjK/SnGKlX0r0adhb5BzUsJJKPevvwBgPYDThJATE689BrsxvIsQsgFAM4B7J/62F8CtABoADAN4MIRjCylHLhsxNzs50sOIWgxaNU7QZLuwExcnw+riaVgQokYEjl3y1pXq+fhlZbwMj355Fowmq9NW86/uWYx91eXoGBQei5hBwYJ1U9SB1hAWi/t0NOTaByzY/kmjU9kwinccv9vZumTsqy5Hj8neKa5yR63TTZ+Th+FR4fJ4gyNj6Bi0uBkLjuEIQjHxUmPkxZItHT3LjufxJnO0tjVFCkKydXvxNElNY7jKQvWdJreOdX/6rAW/+ciKzWuLkegih8BEicMxG19GTWriqatudIrLT7+2VmKRUFaf+ATibR4rBI5nATwcqvGEkyOXjZitS4n0MKKWgswk7DvTEelhTElC2YiA8+xuWF7g5pHY8sFFVK4ocHrt+2+cwJ6N5U7JUo74YlB4Mma8IRb3OS8nGe0D1ww5MeOdIo7Ydztbl8w/IAHXtmv3Vtnl4eSVfsG5V8TLsPH1427vC1YVCLHqK4B79zspMheIXFKmNlJ1dZPRjFOtA04t07mdFa6SxKbdpwTj4jl96qnyjuv6ffquhSjR2xsthaJSUaSZfJlOEYZlWRxt7sftxbmRHkrUkpuWiM4hC4YsY0hW0hCTaEdq9jzn2RXL+HftdWAZY9DSa8bMLGGl74tBEUgpObG4T64zE02O8h+x7/Y3XyvxuF1rtdlQtbLIKfGnamUR+s3WkIcjuBojnpLlvMlcqEocUqYeYnq4c9Ai2jKdq2vgGBcvtgsiJKdC65frWjcnOwWr5mdPum6P1CgOMld6R0AAZCQpIj2UqEU+sfVy9uogjSuOcnzJnndMlhLySLjaAcp4GVQKcRXkq0HhrxdcLEyDM+KD7Y2cSoh9t+qEOI+7AFp1AnbWtmDD8gIQArAssLO2BT+4ZU7YwxEu9wgb9rM3lmNmVpJXmaNtoimB4kkP61KUkBNhncuy134Xiot31KdCcupJN05WnRjS6hNTkeNX+lCkS6b1ib3AJdtRohOu7ehHF7twoWMQGpX9Ic9TVjLn2X33ZJtbpYb/XluMDLXC6bXqiiLoUhI8jkOoQoDQOANpvStWFYF1OBXnjaT4htB3a9AmIjHOXi6quqJQsKJEvlaNTavmYvsnjXjuQAO2f9KIdaV6vPDxJZ+rgAQqI829ZkHDoKWXVpCghIcmoxmb99Vhw/ICPLKyEA+VF2Dzvjo0Gc3I16oFW6ZXVxThzWOtfFMNrr15pw9hYJ5042TVidRTHGSOt/QFNZt/skKT7aIXIa+EY0tl1+1q10SqVx5cih7TKHZWlmHYaoMuxV4B4KP6LqdC7/p0Ff9+f7aTg1UDVihMo7qiiG9FDdDkKH8RqjixcWUR7t12WDBG0dFrxe0SdA5aMGZj8cQ79k5ZA5YxbFtfing5cdpGFtpeBtw75fkqI2qFsFfb0y4HhRJMjOZRrCvVu4UTDY5Y0WS0y2h5UQYWT09D3/AYOgZGMDpur+JSakhHWX463q/r9HkdCOlG7l4wWXUiXdVB5nhLP/550bRIDyPqmZGhxl/P0mS7aEQojsyxpbKjMhQzTG+Zq0NL3zCGrTYAdiNn5WwdZmiTUNcxiIudQ3hqTx36hq1+x+sGq/Wua5hGZpISl40m9A1bAcRmrc1owrHr1RxdMn7g0FWRi1HcW1Uu2N6b285lGBYvfXOpaBjN+DiDTxuNqG3uBcPaa71uWjUX83KSA5YRXUoCqiuK3DL7ve1yUCjBQiHSmW7etOtQvfMEVhfnQi4D5uak4I3PW7Bwehpm6ZIxNzsFMzL8b1PO6cbZG8t5vb3jcDOvtyejTqRGcRAZszG40DmEGRmTK8YmFORqEtExaJm0ne1iFc7bJpa04WogCinbzfvqMGZjBMtmEQL84I2TTuf3NzbN1xqwnhIGXePpZmSoY7rWZrTg2vXqkZWFfiXKeYrLZRgWe860O8nbj1fNQbPRjDgZwS/vXoS2/mGYRm3YPdEVz5fEPH26GkW6JKddjiJdUtCb3lAojjjqqxGrcInCM20Dbh7kJ1bPw3MHGtA3bOUfNqXoSjH9KJMRzMxKwowMNeblpODGmdpJrROpNRJE6jtNyExOQKJCHumhRD1xMhkM6WqcuzqIpTPSIz0cCuzetj1n2tHYbRLcLi4vzMCaJblem2asLs4V7PTFbYUHq3qALyXbfA21oMlRwUFovoXmLDNJ6VbyTOoNt8lodpI3jUqB4TGbk2e3amUR3j3ZhvVlBuysbfFp25fb5SjISKIPSZSw4KqvxNon67Vq/NBl5+XJ987xu3qcXvWmK6Xox6miE2miXRA50zaAGZNwOyFUGGiyXdTAMCw+bTRi0+5T2FUr3NL4+vx0t2Q3oUQMuUy4PFDXkMVjq19fcW2P6inMQcyjfbqtP6AkPYpnuPnOSVXi4ZsKkZQgx09Wz3Obs8tGE26tOYivvnAEt9YcxL6zHZLnw9XwXlPi3g2v5kA939HryTsW+rzt6y3hk0IJJq4VT3bVtrol0m1eW4yO/mGPu3qcXvWmKx31Y06qEhuWF+B8xyBOtw1MOb1IPcVB5FRrPwzUKJaMQavGSZpsFxU0Gc2obe6FZYxxammcECdD2Yx0EBn4TGdHg0AoEeN6Q7qbV8KgTURivBydgxa8sL4Uj08kTQUSr+tLyTZXwyknVYl1pXqsc0j4orWIg0++Vo3n7l/i1G3LoE10SpSTEWDV1oNuDyy5aUo+UdOX1sxidbK51+PlhD+X1BrcFEo4Eap4AgDb1l8HG8NCn66GIV2FQ41GGLSJWF2cy9ckfvdkG2QETnrVm67k9GNOqhLrywx8OMa2jxunnF6kRnEQOdM2iNsX0yQ7qczIUOPA+c5ID4MC8AXgOeOifcCCN4+14oFlBjzw0mcet9Rcla1eoxKsOOBogG5eW4zcNCXS1Qk+GyJChozrlp7rMVzZL+5Gs6Ykzy1xZbLW3YwkMhnBDG2SU1xxs3EElTtqsbOyzB4vOWYL6IHF9cHMU81WZbwMuhRpSaLUUKZECseKJ5yh6hgOtOXexZiRocYN+el45KYiPPHOGf5v/3nHAtwwQ4PpGmG55UpNOupIlSKON66nul6kRnGQsDEsLnYNwTBRZorinemaRLT2jWDEaqNx2BFGl6Lk6wtzSvGeUvdtaCEFKRRr5mgoJ8bLeQOHO8+m3aew1w9FKyX2TeyY5+5fwhtnnkI8poryDxddQ8Jx5PvPd6Fmf4NbvKSvDyyuD2bZKUrMzk5xKyO1s7bFa5Loo7tOYNv6UlTuqKU7CJSI4VjxxNN6AMAbxNzffvLOGbdqLq46kXNUPP72NWP6qTsXoGMgeDkfsQqNKQ4SzUYzUpRxUNNKCpKJk8swPV2Fuo7BSA9lysM1S+C6iFVVFGJudoqogvSGYwzmsEjmtD+F38UMGcdmImLHzNAmYW9VOV6vvAEVc3RBi22meEYsjtw2IRKu8ZKeHljEcJS3/IwkrJqfjb1V5fjTt2/AzsoyLJ2hwUvfXOpk3IolfXJhRNz/xZrVUCihwrHiiV6TKLoePCUuO+KqE1cX5/IGMfeex98+g2UF2imvF6kFFyTOtQ/SeGI/4Drbleg1kR7KpMdbSbJV87MxJzuZD4NgWeFtaF8VpC9VIrwhpXqF2DHdJgufKMUwrFss9GStuxlphOLOv3fzLDAsi0dWFgIA/u90O155cClYsEiMj8O2jxsDkhcpmfJicmlzFp0p5ymjhB8h3cxVPOk2jXrUn1J0q6tOFIu7Z0H1IjWKg0Td1UHkaRIjPYyYQ59u72z3wLJIj2Ry40/JnWAZjkJGkb+KVoqBLeUYX5L0KIHh+l2r4uU4caUfT//feV4euGYY+Rnhe2ARksvNa4ux5YMLTsdNNU8ZJbx40s0FmUle9aeUtSKmE13/n65OQIk+fUrrRcKysVtuo7S0lK2trY30MAAA33zpMyyZrqE1d32kocuEPxxuxl+/tyLSQxHDb20QTfLZ2G0veeWqBL3F9XIejEAVZDDP429M8SSMCw3ow0RKPi91mXDbs+6yuGdjOWZmOTcSCPWN2fU6eo3Kr3a4FEEmhe4MNVJ0s6f1IGWtSIkpnoJyLvhBRT3FhJDnAbzGsuw//LoaIb8HsBpAF8uyCyZe+w8A3wbQPXHYYyzL7p34248BbABgA1DFsuxf/blupLjQMYQ7FuVGehgxhz5dhSajGaPjNiTE0WS7UOCpS10gncR8IZjn8ebhlXIMLcUVOcQS77pNFt4oFpOXYM+btyRRTnYA+N1chELxhJSQME/6U4puFasSVKLXTFSfkMNqYwTLbk41PIVPXATwDCEkB8AuAH9iWfa4D+d+GcBzAF51ef3XLMs+4/gCIWQegPsAzAcwDcDfCCGzWJa1+XC9iDFoGUP/sBVZKQmRHkrMoYiTYVpaIuo7TViQmxrp4Uw6OA/BhY7BoMX1RhpvLX8djaal+VqvXpMp6iWJCAzDQqWQo6qiEAwLvu2yFFkM17wJhRFReaGEAoZhMW5jw6KbZTLCP+B1DtoT8fQaFc53DOHBlz+nsj2BaPUJlmW3siy7DMAXARgB/J4Qcp4Q8lNCyCxvJ2ZZ9mMAvRLHcQeA11mWHWVZ9jKABgBLJb434lzsGII+XQ0ZmZpCFCgGrQqnaWe7kMBlHYt1qYt0AgXDsGjsNgWlqxxnvHjrjCalggUl+HDzs27bYdTsb8CLBxuxvswAgzZRVBYd5eN0W39E5o3KCyVUNBnNePyd0266efPaYp90sxQ9KqQf95xpx+Z9dVS2HfCaaMeybDOAzQA2E0KWAPg9gJ8A8Hev+xFCyAMAagF8n2XZPgC5AA47HNM68ZobhJBKAJUAoNfr/RxCcLnQOYRcmmTnN/p0FU63DuCrMfMYJE60ySe3NefYpY4QoLwwA9fnp0fUGxBsD5yY8eJa31bKduVkJZLyKTQ/NQfqsbOyDAtz07x69KsqCiMyb1NZXsJJtOnOcNA5aEGzccRJN7MskJumlKwDpepRofW3afcpbFhegOc/bOCPm+qy7bVOMSEkjhByOyHkjwD+D8AFAGv8vN7/ApgJYDGAdgC/8vUELMtuY1m2lGXZ0szMTD+HEVzOtw8hN40axf4yQ6ueNJ7iaJNPxxqx7QMWPP+h3UOXmZwQ8e0xqR44qd5kqTU7xermxmIoia9EUj7F5mdkzB4l5zrHrvLBdVx0JBzzNpXlJZxEm+4MB5xscbr5uQMN2P5JI9LV0kMxuXWiUSnw8E2FeKi8ABc6BtHS66xHxdaf3MUKnOqyLWoUE0K+PJEs1wp7ctweADNZlr2PZdl3/LkYy7KdLMvaWJZlALyAayESbQCmOxyaN/FaTHC+g5ZjCwS9VoWGLhPGXQuEUgKGK+fjujWn10S+86KYku41j/IG0qUuEw5c6PQaEgFIN16EvpNoCCWZ7IjNDwHBPxp68ODLnznNsdE86iQfu48GNwRI6sMWlRdKqAiGbHUOWqBRKbC+zIDtnzRi99FWsABOtQ7gUtc1uRZbf6WGdCrbDngKn9gH4F9wLcQhYAghOSzLtk/89y4AZyZ+/wuA1wghW2BPtCsC8FkwrhkO6jtNePALMyI9jJhFpYhDulqBxh4zZumSIz2cSYVMRnDLXB1e+ub1MJqsMI+Oo71/BB/Vd2HlbF1A3uLxcQZn2wfQPmBBTmoi5uekIC5OepNModqZBm0i2vot+Pr2z5xq2GpUCrRPtCAVa/krtR4yrU4RGYTmp7qiCN/deQJ9w1Y8sXoedn7WglNtg3h01wnsrCxzko/2AQt21rZgV2UZBi1jUMjlMFvHcbnHjBkZvs2PL6E73BraWVnmJOtUHijBQJ0gwzN3L4J5dBxqZRzUCc461FUX6TUqtPQN8//PSVXinlJ7K2jOOObaQjvKtZh+vLFAi71V5eg1jyJeLsOw1Talq1B4MopPsSz7or8nJoT8CcCXAGQQQloB/BTAlwghiwGwAJpgN7rBsuxZQsguAOcAjAN4OFYqT/SYRmFjWaQlxkd6KDEN19mOGsXBp7V/GCeu9GPr/nonY6QwMwn5Gf7FjY2PM3j7ZJtTncun7lyAOxfl8oaxN8NSSEk/ecdCVO6odQqp2Lq/3inuTSzmzZeGHN4qWNBqA8HHcX6ajWYcv9KPVw81o33AHt7y5HvnsOXexXjyvXMA7PP8zN2LUN81hF21rbzh3GUaRX2nyUmefZ0fqfHngF0eaO1iSiho6TXj3NUhN91sSFfzjWxcddGTdyzAcx/Wo9k4wsvigmmpsIwxWFOSxxvEgLtci+nHfK0a5zuGqIzDs1GcSQh5VOyPLMtu8XRilmW/KvDydg/H/xzAzz2dMxqxV55QgdDKEwGhT7cbxWtK8iI9lElH5+Aor3SBa4ZmiV4DhoVf3tCz7QO8Qcyd8/G3z6AoKwmLpmskd9BzVdJiIRWOy8tTzJvUesieDHZfDCaKb3Dz0zloQc3+Bqe/WcYYnO8YxNdu0EMuI/jGS9d2C56+ayFK9GmwMcA7J9uc2kD7Mz++JM9ReaCECjHdXJyXivYBe/1gV9l74p0zvJOAk8Wd37bvqoi1b3aVa9eebVTGr+HJKJYDoG47L1zsHMI0mmQXMPkZarx/rjPSw5iUmK3jIrG7VifDwxfPABfK4HrOjgELFk23e0DOdwziofICAPZ4UCElK2TECtXs5IYUjJg3bwY7rTYQerKShdvOKuQyZCUn4Cd/Oet0g37srdPYO/HwxLCeb/xSQl/Erp+Z5P6wReWBEirEdHNb3wh+uPu0U8WVnFQl1pTkgRBgti4ZOalKXg8Pj9mw5d7FHuvRe9J7VMav4ckobmdZ9mdhG0mMcqFzCNNSqVEcKPbtm0EwDDvltmtCjSFdLagoHbvccZ6B2Q6tdj2Rk5ooeM7sVLvyPdbSz3vzlPEyVK0swo7DzV6VrFjc27ycZNw4UxuUlr9iXpHcidJgQrHOUz0jO9jIZUB1RZHbtrGcALmaRNEbtC5FCTkRfnCKk8nweZMRV/st2LT7lMeHPdHrC4TEU3mghAox3dw1NArgWsUVoVhhTqf2DVuhS1HihhlazMtJhkGrxmNvnXbLqxDTe9oHl0KliKMyPoEno5haJhK42GnCqvnZkR5GzJOSGA9VfByu9A3DMIUzX0PBjAx3Q/Ppuxbil3+94HScZYxBXcegpKSl+TkpeOrOBW4xxfNzUtFkNPNKmTtvzYF6VK4o8KpkPcUF+xv/7IqYV2T/+S609Vtwy1ydpIQ9iv+0D1jw6iHn2qyvHmrGv982F7lpwg9cnCwszEt1M2j/4/b5+P4bJ7C6OBfbP/EeWiF2/SX6NDc5k5rASaH4ipBu/t7Ns/Dyp00ArlVcsYzb3GKFOZ06MzPJSUfq09VYPD3NTX+K6b2DDT1492Sbmz6fqjLuySiuCNsoYphLXSbkLqee4mAwI0OFs1cHqVEcZIQMTRkB+oatTscp42W42DmEeTkpXrfM4uJkuHNRLoqyktAxYEF2qhLzc1IRFycTVb6zdMmSlKzUuGBPeNpCF/P82Rjg0V0nsLeqXHLCHsU/dClK9A1bnZoGKONlmJudAn26uBEqkxGsnK1DYWYSivNS0dY3gmRlPH6xrw7NxhHJMZVi1xd6aPMlgdMbtKoJxRHX5FPzqA2/fP88n3zKVVx5/LZ5gnI9NzsFX57rXEWI05+cd/jIZSNfpUJI77Es0GwcwbMTjXRGxmxTWud5avMstUXzlMVoGsU4QytPBIvpE53tKMGHU5RlBRkoyLR7E56+a6FTfcqqlUV4o7bVrdmFGHFxMiyarsE/LcjBoukavuqEWD3MudnhKWPlrd2zUG3QqpVFePNYK29AuX5fU/HmEErE6rNyuxSr5mdjb1U5Xq+8gX9IcUzQzM9IgowQ/HD3aZxtH0SzcYQ/dyhqVQdDHqS2IadMLTjZUsbL8fO9dVhXqneSy+qKWSjISBKU61m6ZMEymEKydq59CM/dv0RQ7wF2w3hkzDbldZ7XNs8Uceq7TJhOK08EjXytGkcu02excCCTEZTo01C5ogAMa98+5uLTAo0jE9tunpERuh0ARw+cShGHzfvqRLfQOaMrt7IM+893wcbYP3v7gGXKxtGFG1fva3aKEjYGvFcrX6v2ulvg+PDFecC47WbXOq3+1KoONjTDn+IJbvfCseWzjAAl+jTR3RNXncrpwSajGRc6Bt3qu+/ZWI69DiUROb0HTN0YYleoURwA9V0m5KZRIQoWMzLUeGkilooSevTpaszJTnFTtHqNCo3dJr+3eB0NDruRKofVxvhVEF7KdrNQVjWXhMIpfNctdJmMYGFuGtr6LTRWNEJwHjK9RoU9Z9qdkuM2ry3GbQtyPDaD4R6+Nu+r4w1hbrt52/pSxMuJR/l1DNMJR1gDzfCneMLRmfDmsVbcU5qHWVnJ4Bq9rpqfjdkby9HSa4ZKEQddSoKT3OakKnGufUhUD1rGGHSbLCgryEC+Vo2RMYYPoaO67xrUKA6A+s4h5NDKE0EjXa2AjWH5GxMltAh5y/QalU+NCsSMCceC8A++/LlfZd+kNtEQ8sDVHHBu+CHkBYmEt3CqI9Sd69NGI28QA/b527T7FDQqBZYXZojOBz9/2cnoNY9iZ2UZhq02n43acDVroVUsKJ7g5HledTmOtfRj6/6LWF2ci4tdQ7jekI4b8tNxofOa0WvQJmLjyiI+Oa6qotCtfrejHnSUNar7xKFGcQBc6BjCTbOzIj2MSQMhBAUZapy9OkCN4jDhmtR2qcvkU6cvT8ZEoNvFUt8v5oHjymt58oIEI6mPIg0hedm8thidAyOC81fb3Is8TSI/N2IPYIHOX7jCGmgVC4oUBkbGsHX/Rawr1TuFAf3XXQvxP/sv8nK6ujjXqYGSWP1uQoR1INV9wlCjOAAau834ehn1FAeT6emJONM2iJVzdJEeypSDYVjUtQ9K3uL1ZkwEul0s9f1iHriKOVlBq21MCRwhedm0+xR+efci0Wogjg05QuXNDVdYA/XOUTzByfj5jkGsLs51K8H247dOO+1+CVVaEVpH5YUZWLMkl8qaRMQDtigeGbSMwTQ6Bq1aEemhTCoMWjVOt/VHehhTkiajGfVdQ5Ky9wFxY6LZaMahSz1QKeJg0Do/NPqyXSxWxUJqJYGFuWlTPpM6mvAkL0+snueWFf/eqTZ+rsUewJqMZq/XZRgWjd0mHLrUg8Zuk1u1B6lyFgxoVROKGJyMM6y9uYyn3S8OR7ndfbQV1RVFbnrw+vx0Kms+QD3FftLQZUKehlaeCDYztGrsPtoa6WFMSToHLdhV6569//RdCwW3eMVa5R6/0o+a/Q18Q49nD9Sj2Tji83ax1O1mXzxw/iRU0dqywUHMo1+cl4aa/Rew5d7FON8xCBsD7KxtwaZVc/m5luLNFZonAF49zEJy9vRdCyEjEO2wSWWCEmw4Gd99tBU/mXhIdF0rN87U4p0TbWg2jrg13OgbtqJIl4Q9G8vRbbIgM0kJucxe0SUnVcnvvFB59Qw1iv2kodOEaWk0dCLY6FKV6B8Zw8DwGFJVtP5zOPFUEkio4kNrv93D9+R753hj4tEvz8JL/2gCYDdaHn/7jN8F4X0xdqXEx/mzBR+uJKypgNhDTll+OlJXz0fnoAVfLMoEAxZrS5y3e70lqYnN07ycZK/xwpyczd5YjrqOQVzsHMIv/3oBfcNWwbmmMkEJBZyMtw9YsOvzFjx5xwI88c61DnNVK4uwafcpPPrl2chNUyJdnQC9RoUSvcZNP87IUPMyqlEp8MAyg1MHSCqv4tDwCT+50DmE7FSaDBZsZMS+oM9epU08wg1ntHCdvl482Ig5Ex3GXGnpNePc1SFs+/gSNiwvQFVFIX5972Io42R8GTTAboQEUhA+mNvN/mzBB7JtT3FGqCnHLXN1+NuFLqzbdhiVO47ha9uPoHvI6vbw463Zhtg8dQ6OinqYXcdGCPCDN06iZn+DU21X17mmMkEJBZyMG7SJuH6GFs99WM/r1i33Lsa+M+1oNo5g0+5TSFcnoCAzCXFxMkH96Cija0ryeIMYoPLqDeop9pOLnUNYmp8e6WFMSvTpKpy5OoAbCzMiPZRJj+s28C1zddgrwTPbOTjKK1rHsmfP3L3I6bhoKjnlT0IVrS0bXFw9+o3d0qqdeNs1cJynnFQl1pTkgRDAxtpLV7l2vPMlRt51rqlMUIINp4c1qnhsuWcxHn3jBJqNI066dcPyApxqG5Qka44yKrX1OcUONYr95FK3GXctzo30MCYlBq0Kp2i755DjaRvYm7I0W8dFEkEIv80drJJTwYrf9KdOLK0tGxq4Ob3YOST5hu0pRIabJ41KgfVlBj4m/sWD0uPapc41lQlKMJHafIhLX+JkzZNedJVRKq/SCVn4BCHk94SQLkLIGYfX0gkhHxBC6if+1Uy8TgghNYSQBkLIKUJISajGFQxGrDb0mEaRRWvphoR8rRpn2qhRHGq8bQN7yto3pKsFM/aLspKctscDjVvjbhi31hzEV184gltrDmLf2Q63CgJS8LYFH6z3UNxxlKWmHhM/p2euDgal8gM3T/eU5rmVsnr87TOouW+JV5mUOtdUJijBRKz50JqSPP4YZbwMLHtN1vQalUe96CijYlUpqLwKE0pP8csAngPwqsNrPwKwn2XZXxBCfjTx/00AvgKgaOLnBgD/O/FvVHKp24RpqUrIaZB6SMjVJKJ9wALz6DjUCXQzI1DEPAqeSmQZzaO42m9xar3rmJwxI0M4aYqLawvWtlwwGyv4UyeW1pYNHFdPmGPnrd1H3aud+HrD5uQ7M1mBeHmqoExzce2ekDrXVCYowWB8nMHZ9gFc6RNuXuPYfGjz2mLkpin5BFRvetFVRrNTlLhlXja6TVRevREyi4Nl2Y8JIfkuL98B4EsTv78C4CPYjeI7ALzKsiwL4DAhJI0QksOybHuoxhcIDV0m5Gpo5YlQESeTwaBV4Vz7IK6ncdsBwTAsDlzoxKnWAXv9SwIszEvFytk60W3g41f6YWOA7Z80elW686rL0Tk4CrN1HAaBhLxACXb8pj9dnGjnp8BwvYE7dt5qH7Dw1U6Kc1NQpEsOqEVzdUWhm0wbtIlIjJfj0KUer+E3UueaygQlEKxWG/7R2IPjV/pRlJUsKLOlBg1+87UlyE1TYX5OCuLiru2oSNGLQjI6M4vKqzfC7YbTORi6HQC4tmW5AK44HNc68ZqbUUwIqQRQCQB6vT50I/XAxc4h5KRSoziUGCZCKGLNKI4G+XSkpdeM+k4T75lTxstQXVGEwswk6DUqbFtfitrmXjAs8O7JNtx3vR6vHmrG2uvyJBmj59qHQlqaKhzxm1Op5mwk5FPoBm7QJmJ1cS4fJ/nuyTasWZLrs5HpanDvqrVvFXNJoAZtIjauLMK6bYe9yuhUkoNoJNp0Z6hgGBZ7zrTjf/ZfxOriXDQbzfjN/SX42Xtn0WwcgUGbiO+sKETljqOiMiumF7NTlGjsNlEZDoCI7U2zLMsSQnwODGRZdhuAbQBQWlrqe2BhELjQOYQF01IjcekpgyFdhVOt/ZEehs9Eg3w64lglArAbtlv312NpfrqbQfv0XQvx8j8u88kd3ozRYIY2iCG1gYe/TLWas5GQT9cb+McXuvCdLxbiZ++e5b/zn94+H3Fy38/tanC3D1jw6qFmvPLgUrBgkRgv5w1iQFxGp5ocRCPRpjtDRZPRjP/ZfxHrSvVOYUNPrJ6HIcsYpqWp8MM/n/Qos0J68bn7l4TcSTEVCHed4k5CSA4ATPzbNfF6G4DpDsflTbwWlTR0mZBLG3eElBkZapxuHYz0MGIahmExzjB4qLwAj6wsRM5EXW3LGAPLuM3NoH3srdP40pwsAPaWod+7eZbH5AxPW3jBQqi2bTCVPK05G3pcE9O+NCeLN4gB+3f+s3fPomNg1ON5hBI/hVo09w1boUtJQFaykq837IiQjFI5oIQaTn4vdg7hB7fMcUsIffK9cxi22jxWZOEQ0osztElUhoNAuD3FfwHwDQC/mPj3HYfXHyGEvA57gt1AtMYTj47b0N5v4Q0MSmiYnq7Clb5hWMZsUMb74UKa4ngq89M3bMXoGCOoeGfpknkjIzFehsoVBWBYe2c7dYIMl3vMfKtQsTbP/oQ2eNq6DmX8Jq05G3ock346By0YGBkT/M6HreOi53CVZ4M2EU/esRBxMoIX1pfi8XdO8yXXHD1mD5UXSJLRUMgBDcegcAglmwrJ28yMJGjU8YIym5nkHhrhqBcPXeqhuiwIhMwoJoT8CfakugxCSCuAn8JuDO8ihGwA0Azg3onD9wK4FUADgGEAD4ZqXIFyuccMXWoC4uS0GWAoiZfLkKdJxLn2QZToNZEeTswhVuanckUB5mSnwKBVCyreudkp2FtVju6hUXzjpc+cGiIkxsvxLzuOOW3XBSO0IZhb174aIrTmbHhwfLC51Dkk+J0LdU7kcJTnnFQl1pXqUbmjlpcXLjs/XZ0AlgVue/agT9UtclKVqKooBFfpb/fRVvQNW/2WAxqOQXFEKNlUaA1c7DLhvVNt+Ont853Ci7bcuxiXjSY88tpxaFQK3FOah1lZyZibk4IZGXYdR3VZcAhl9YmvivypQuBYFsDDoRpLMLnYaUKeRhXpYUwJZmTYk+2oUew7Yp6vJdPT8MVZ9hAJIYOWU7Cu7xdqFfrIa8exr7pcUgc8TwQrNtkfQyTUMcsUd2ZkJonKnhiO8rimxL0W8abdp7B3Ql4cPWZSqlswDItz7UNuyahFuiS/5SAc8faU2MFVnwo9rDk27Pjt3xvw6oNLwYBFVrISMgKs2nrQrTmNo46juiw40CKwPnKxYwjTaOhEWNCnq3HiSj8eWBbpkcQeYl4Dg4NB4KnWquv7xVqFdgxaUFaQEdCNPlhb1/4YIrTmbPjx5zt3lEdvbWtdZbd9wILtnzTyRrMrQnKzdX899mws91sOaFgOxREhmdxZ24KdlWVo6x/B2atDTh3smo0jMFvHcdMce4Eu7kFP6IHQUcdRXRY4NAbAR853DNIkuzBRkKnGadru2S+4cmtVFYV4ZGUhDNpEN68Bt6XNGbWOytM1OUpOEJTOY0IIJUv5c25/E/88fQ+U0CCTEeRr1chKVqJz0IImo9ljl0KhLnKOOMqLrx3nxOSm2+R/wmiwZJoyOeBk0qBNxMM3FaKqohBP3bEQ83NSMVuXgu2fNPIGMeAeTsTJk6cHQoDqsmBAPcU+crHThFvmZUd6GFMCfboKLb002c5XGIbF+3WdTttom9cW45a5OslKUqgj0uzslJBszQVr2y/YMXU0USp0+Brq4iiPveZRFGUluXVbdJSX2bpk/OZrJVAnxEGXnAB9uvjchSIWk25lUxyRyQhumavDmI1xk9tb5uq8hhNx8nShY1ByEh7VVf5BjWIfsIzZ0DFIK0+Ei3i5DNPTVTh7dRDXGWhcsVSEtoM37T6FfK0KC3PTfDKMCzKT+LaiGlU8dlYuw5jNhnR1gmTF6824dK1OoFLIwbAsTrf1Y9hqk6zk9RoVNq8t9mgsSUXMaJuXk4z2AXrj8RdOFpqMZlzoGIRGpeDLpj266wRmbywHADT3mqFWxEGXksB7zBxlaHGeBgtzU522iQGgqceEYy39eOyt007zJpTEx43FaB51k5vNa4thNNtLxPkzzzQsh+JKS98wL2OAXS9v3leHrOQEpCVe060aVQLkMuDIZSOSlXEYttowOm5DbpoSyQlyGO5a6CbfXBIeTeoMHGoU+8ClbhNyUpW08kQYmaFV4XRrPzWKfUBsO3j/+S609Vt8UpZixmGJPl3wHK4GsF6jcvNaCylsbjv9fMcQfvTmKbfC9t6UPOcd3/LBBWxYXgC5DCg1pOPGAq1fNwax+OTKFQWo2d9Abzx+4KlMIGBPoDvXPoiGriHsqrVXf+AS3gAI3vS5+Fzu3Oc7BvmEOUB6sw6DNhHb1pciXk4wZmPxhEOJN3/nmbaCpjjiqpe5Kipf336El+tf3bMYYzYWhxqNSIiTITkhDtv/cdlJHzrKqi5FCQLgKzUH3WR+9sZy2tbZD6h15wMXOoYwXUPjicNJfkYSjrf0R3oYMYVYPKONgc/F3KU2NWAYFk09Jrx9og231hzEV184gltrDmLPmXZs3lfn9f2O11pdnCuYTOJp3Nx7m40jeP7DBtTsb0Dljlq09A1L/qyOiD1YcGGvUsZEcUasTODXbtBjfZkB2z9pxMY/HcfvPm7E+jIDNCoFtu6vx6nWAZxqHfAoD9y5GVY85tKx+cfptn6nsTQbR1C5oxYqhRyVO2rRbBwRvRaF4g+uelkoae77b5zAoUYjavY34NkDDTBbbbjver3TcZys6lKUKMhMwmWjWVDmW3qpzPoDNYp9oK59CLlptBxbOJmZqcbJGGz3HEmEEo2qVhbhzWOtkhLPAOfuS96S1ziv25vH2/htPe64TbtPYXVxrtv7Owfdx8AZot6SSYQIRnc9R6NJpYgTfLBgHXLBfD3/VMTxO20SuXlnJiW4GQc1B+qxpiSPfxBxzcFz/e4d519o3jLUCdh3toN/YNt/vktwLFI74FEovuKWvCwT1nOOD95b99cjT6MSPK7ZaMahSz1IShDWVSoFDQTwB2oU+0Bd+yDy0qmnOJzkaVToHBzFoGUs0kOJGbh4xp2VZaiqKMSG5QV8uR8pCUSckXtrzUGcuTooqHAT4+V8tQBvXjrXaCNlvAxjNtat2oCjJ8XXzH0x77jjOKV+5q++cARVrx/DU3cucBpPdYX9wULqmKY6rt/pySv9gnM0TZMoKDdkouKJjNg7Krq+z/G75+ZfqD15dUURLnaa8PtPLvHX4ZonuJ4zJzVxUlaNEGqRTQkvXLLdS9+8HlUVhSjKSpb04D1sHRc87viVfnz1hSP4tz+fxE9vn+8m87qUhJB/pskINYp94GLnEPS0cUdYkcsICjLVOENLs/mETEawMDcNc7KvlfsRSjwTulk6bnNzReZdvc5Vrx/HvrMdYBjWq5euRK9xe/8T75x225LmPCnvnmxzu6a3hDkx77jjOD3hurXfbBzBswfqsbOyDK9X3oA9G8tRpEtC37BV8pimOq7f6a7aVlRXuM/rdI2wISojQHVFETKTEqBVKZze9/RdCyEj4OeVm/++YSsYlkXligI8stL+QPjqoWY8+sYJfGv5TD5JWkiut9y7GPNzUnwq5xYLuD6c3FpzUNKaoAQXhmFxtn0AP3rzFJRxcjzz/nk3GRR68O41WwWPe6PWflyzcQS//XsDnrl7ER5ZWYjKFQUo0iV57BBJEYewbOwujNLSUra2tjYs1+oftuLGXxzACw+UQkZoYk04+eORZszLScH/u6kwEpf3e7LDKZ9icEauUAa8WBKdRhWPr75whD9HTqoSa0ryYEhPRHPvCN481sob2Xs2loMQ4NYa8W5LOakJ+FtdNwgBWBb8+1+vvAFlBRmC4+01jyJeLvOp+gTD2CtW7D/fBRsDp3GKNW7gOHSpx+kzcziO0dN3GSECunio5VPoO81JVeJ/1i0GO9GpizM2XeXwqTsXIDUxHgwLXO4xAQDGbSwMWjXqu4bwxkQinmMSHDc/FzuH8J0/HHMbT1VFIWwM8PyHDQAAgzYRNfctwciYzWk+o3CeA6Kx24RbHRKxAEhaE0EgpnVnMHFMBK3Z38Dr1GSlHNPSVLCO29A9NIqslAT8+1tnnNaBRhWPcQaw2Rg0dJsxI0ONp/fWOdU1BoCXvlmKRIV8UshsmBD8gmjQiUTq2ofsgkYN4rBTkKHG0Za+SA8jJvGUAS+WRLezcplgR7ANywt4g4I7vq5jEKvmZfN1NnccbkbligLM0iVjbnYKZmTYy7lt/6TR7aYstCUdSMa+TEYwbLWhZn+D0+tSOolJqVVLqwn4htB32jdsRWZygtt36Fq+TC4D9p7u4FuLc96x9v5hp/l1rCzBzQ8Awbm0MeBDeZTxMmxaNVewROFkm2faXS/ycLr2ofICKONlaB+w8LpUGS/jdeuPvjIblSsKoNeo0DFowa/ev4i+YauT7n1kZSG/Y8WhjLd3K6XzGTg0fEIide2DmE7jiSNCYVYSTrT0I5Z3NaIRsZvlmM3mtoW8eW0xjjR24+Gb7B3yuC55FzuH0NI3jFXzs7G3qhy/XrcIdy7Oxa0LcjAzK4kvtRauLWl/O4mFc4xTBV++U9dOXOM28AYxcC3pyGpz1gFCSXD5WjU2ry12C6V571QbKuZk4c/fKcPOyjJoVPFeO+lNBmh3vcjD6VrHsJ2cVCWqKgrx5B0LoFLIYNAmYmFuKkoN6Xj2w3o8M2EQb15bjPdOtfHn2n1UOAyJ6qrgQD3FEjlzdQDTaTxxRMhISgDDsrg6YKEttoOILkUJgzYRq4tzwW2AvHuyDenqBJTo0508d3mpiRgdZ/CTd65t7f109Xz86bNm3DhTy3vWhDwVoWpkINQUxN9OYrTZQvAJ5DvtGhJ+YLPanF9TxsuQneLezeu2BTnQqBSobe6FjQF21rZg06q5mJ+TKqlu9mSCdteLPI66dpxhsf0bpTCarPihQ8OYn66eD7VCjuI8DV765lKnRkaPfnk23/ijb9iKIl0S9mwsR7eJ6qpgQ41iiZxtG8TXy/SRHsaUhBCCoqxkHGvuo0ZxENFrVNi4sgiPv+0cw6bXqNy2kBu7TbxBDNgNlJ+9dxaVKwokeZyCvSXtqU2wv4bYZNs2jwb8/U7FwlmWTE/jX1fGy/DLu4tx9uoQvv+GuxwsL8xAniYRXUMWrC3J5TszCoUMuTb3mEzQB77II6RrqyuKnDo6/uy9s9hZWebUyOjBlz8XbNjBzR9tzhF8aPiEBKzjDJqMZkxPp57iSDEzS42jzTSu2F+Eqky09A3zShqwGwiPv31GsOGFWKjFLF1yRDxOnpqKuG7F05t/7OEaemHQJuK3X78O9Z0m/PLuRfjRV2Zjw/ICtPaN8AYx4F0OglHPOhahayJycFUnXHXt1v32Wtwc9vJrNgDC1XAcG3bQ+QsdEfEUE0KaAAwBsAEYZ1m2lBCSDmAngHwATQDuZVk2Kqygi51D0KUokRAnj/RQpiyzspKx26FUDUU6nqpMSE3A4eLfuPDL3Uft2f9zs1MioqBp8tDkhqvpurOyDJ2DFiTEyfGTv5zhWy9zzWjWXpfnkxxISaikUIIFp3sbu02itbg5lPEy6FLsckj1W+SIZPjETSzL9jj8/0cA9rMs+wtCyI8m/r8pMkNz5tzVQeRrqZc4khRkJqGh2wTLmA3KePpw4gtSq0wAwgYCw7A41z6EbR83Om39FemSMCMjMnGJ4TBuhGKWqYcmOHj7bhmGdYv9rVpZxDehqTlQjw3LCyAnwpUmxOSAxtdSwkmT0YzN++rwo1VzBeWUE3lXORRzQtCHt9ATTTHFdwD40sTvrwD4CFFiFJ9s7YeBKs2IooiTwZCuxskr/bihQBvp4cQU3qpMeDMQhIzqrfvrsWdjecSMxFAbN55ilqlhHBhSvlshmeMM4ec/bOA7JS7MS/VJDmh8LSWcdA5asLo4F7/YV4eqlUVONdw3ry3GorxU3DhT61YnW8wJQR/eQk+kjGIWwPuEEBbA71iW3QZAx7Js+8TfOwDohN5ICKkEUAkAen14Et9OXunH2uvyvB9ICSlFuiR81tQb1UZxJOTTG2JeVaEqE0IGgphR3W2yBDXRwxfPrK/Gja9e38makBUN8inluxWTOX16InJSlegbtqJiThYW5qYBgE9GLpfIxF0HADWMo4BokM1go0ux19xuNo5gx+FmbFhewDcxyk1TIj/D3nnuco8ZH13sgloRB3WCPOqcEFOJSBnFy1mWbSOEZAH4gBBy3vGPLMuyEwazGxMG9DbA3vUm1AMdszFo6DbRJ7QoYJYuGYcbjdi4sijSQxEl3PIpBU9eVSnVAcIVquDqPfzvtcW4dUEO4uKE84GlVjbwx+s7WWP6okE+pXy3YjLX1j+CB7+Qj4JMtVPjDVc58PQQRHcBopNokM1gk69V43pDumDDjjVLctHUY8Kxln489tZpXhZ/snoeX5WCIxROCIowEak+wbJs28S/XQDeArAUQCchJAcAJv7tisTYXDnfPoTsFCWNY40CZmcn48SVfoy71CqleIbzqu6tKsfrlTdgb1W5TwZAOBpbXO5x9x7+cPcpHG4yBtxcwVOlCjFow4PQIeW7FZK5qpVFeKO2FVs+uIj8dM+7AvvOduDWmoP46gtHcGvNQew728HLkT/yQKH4g0xGsKxA69ZMZsu9i3HZaMKbx9t4gxiwy+J/vncO95Q670xT3RM+wu4pJoSoAchYlh2a+P0WAP8J4C8AvgHgFxP/vhPusQlx4kofZsawZ2gykaKMR2ZSAs5eHcSi6WmRHk5M4ehV9TWUIBxxmM29ZkHv4dW+ETQZzQF5Zx09kzmpSjywzIA8jQoXu4bAssCMDPfPQhOyQgPDsGBZ4Jm7F6G+awi7aluhiCN48o6FMJpHMWQZw7DVBl2KErfM1WH7N0pxqLEXLAs+yQ4ArvQNo1CXLHgNb+EZk3UXgBJdOOrZBdNSsa+6HB2Ddv0pI8CqrQfxUHmBk25aU5IHQoDrDBo89pXZeOnTZvQNW6nuCSORCJ/QAXiL2GuRxAF4jWXZfYSQzwHsIoRsANAM4N4IjM2N2uY+zMikwhgtzM5OxqFGIzWK/cTfreNQN7ZQK+IEt8sTFXEBGytcJrdKIUdmcgK6Bkfxb38+6fHz04Ss4CMke0/ftRAqhRxPvHMa60r1TolIW+5dDF1yAl482OgmFyqF+K3Lm9EbzHAgWqGEIoQ3PXvoUg8vf8p4GTQqBdaXGZzkv7qiCI/fNhfzp6VA72FnhBJcwh4+wbJsI8uyiyZ+5rMs+/OJ140sy1awLFvEsuzNLMv2hntsQhxv6Uch9R5EDXNzUvCPhh7vB1IEidatY11KAqoripy2GKsritDePxzQtqFjJveQxYZm4zC27q+X9Plpw4PgIiR7j711GufaB7G6OJc3CLi/PbrrBOLlRFAudCkJotfxFp4RrHAgb2EalKmLNz3Lyejuo62oWlmEe0rz3OR/6/56XOgcAsOC6p4wEk0l2aKOHtMojOZRTNfQGsXRwvycVGz7uBHWcQYKkQQsijjRunWsT1ejSJeEyhUF9psAAdQKOaZpEgPaNnS8ORECMCyi8vNPBcRkj2EBQoTnxWy1uclFkc6esS+Gt9CXYO0CTNYKJZTA8aZnHWV0x+FmfPfmItG1QXVTeKFGsQeONvdhti6ZPqVFEUnKOExLS8SJK/1YOiM90sOJOaKxoxe3BZ2aGI+K2VnoHbZCpYiDLiUh4G1D15uTr80eKIHDza+MENEGBgwrPC9cSEJBRpJPJde8Gb3BCAeK1gdMSuSRomfn5STjlQeXYtg6jkSFXHRtUN0UXqirzQOfXe5FIS2BEnUsmJaCjy92R3oYMQPDsGjsNuHQpR7ICEJeScLXsXFb0Pf89jDWvXAYI2MMrs9PR35G4CELjlvpu4+2Il2lcNuOp0ksocNxfr+784Tgd1+cl4p3T7ahaqXwvPgSxsLJ+pHLRgDA0nxtyEJfaIUSihh6jUqw4kS+Vs2viVVbD2LdtsP41z8eA1jg0S/PcgsTmpeTQnVTmCEsG7vxT6WlpWxtbW3Izn/r1o9xT+l0zMlOCdk1KL5z9uoA3jrehj1V5eG4nN9301DLpxSEEj6eu38JZmiT0G2KfAJZY7cJt9YcdPOQ7JWwBS0lyYn7/Jv31WF1cS5SlXIsMWjAMMCwdRz6dLVg9YkYIaBBh0M+Xec3J1WJe0rzsGR6GvTpashlQLdpFAq5DFYbA4Vcxlef4IyBll4zOgdHYbaOw+BhvsJdf5jWO/ZKTOtOf2AYFi29Zhxr6cfW/RexujgXchlQakjHjQVayGQEp9v6caTRiJw0FS73mGG1MTh8qRsPlc/E5R4z0lUKqBPioE1S4HpDumiddkrACMonDZ8QYdAyhss9w7QcWxQyW5eMph4zjKZRaJPEE24owvV/H3ntOPZsLEdZQYakc4Qyw15sC7rZaPbaoU7IIJmXk4z2Aedx3jJXhzEbg027T1HjJcy4zm/7gAU1+xvw5++U4ULnkEeDkmFYHLjQifpOE58cKXZck9GMJqMZFzoG+cYHYjG+wZJnTrZ2VpahfcCCnFQl5uekUpmaonA6qbXXDJPVhtsX5QIA3qhtxbaPG7GrsgwN3WZs3X8R60r1ThVwqlYWITslAbN0ybTaTYShjyAiHGnsxSxdEuLl9CuKNuLkMizMS8VHF2gIhTfE6v+29EqrNhHqDHuxLejjV/o9XkcsyenN421u42zpG+YNYsdjI11xYyogNr/xcpnXKihNRjNOtQ54rBbiKJ/ferkWv/u4EevLDMhJVfLHdw1d6wwWTHlmGBbv13Vi3bbD+M4fjmHdtsN4v66TVp+YojQZzdi8rw5qZTy2fdyI5w404MWDdnnUqBQ43zGEx946LVhppeZAPQih1W6iAWrxifDxxW7Mm0bDJqKV4rw0vH+uI9LDiHq4+r+OeKvz6kgwSrg5xjQ3dpucjAZPncs8XcdTJQPXcXpKiKKEFrHyZ8NWm8c5YRgW3UOjyE1NxEPlBbyR63qckHzWHKjHmpI8/nqOMb7BLEkYreUNKZGhc9CC1cW5ePK9c7CMMchJVWLD8gJYxm3499vmIlWl4KvgCMn+sNUWoZFTHKHhEyJ8XN+Nf1kxM9LDoIhwnV6DPx5phmXMRltwe4Cr/+u4/eytzqsjrt3guI5L3aZRp+09sS1poTCHzWuLMS1NCa06AflaNVbNz4b2waU42NDj1rlMLJNfLLvbMUVCo1Kge2gUI2M2VFcUYldtK39emhAVHsQqQTQZzaLZ+UIy84NbZmNmlhosA5itNqgVcWAYVvSBhxDhJEpPD0jcuKSGVdDqExRHclKVWJSXil/fuxg2lkVivBz/+d5ZNBtHoIyX4T/vWIDHvjIbDMQrrVAiDzWKBWjqMWPIMg6DltYnjlZSEuMxQ6vGwfoefHmeLtLDiVqE6v/Om5YMGwMcutTj9ebPGZ+uHZdePNjIx3YCEE04EvKmbdp9ChuWF2D7J9fOkSnSuUzMcBWqRVtdUYRXDzUDuNbO+Rsvfeb2d6G2qY5GfVayEnIZ3GKTKf4hVP7MUy1hIZnZcbgJlStm8l44ZbwMv7pnMeZPS+YNDO6hTS4Dlhdm4O6SXLeSfmIPU9kpSp+T5sTORUDQ2G3yW25ol7zYwXGuGJZF15AFT+2pc4oV5h7yf/LOGVSuKEBOaiJ+ftcC/PtbZ5wcBbTKRHRAq08I8PtPLuMfDT14qLwg6OemBI/3z3agxzyKZ79aEsrLxHwGNae4u4YsyE5R4ly75wQn1/fuO9uB8x2D2Paxu9G6d6ICiFgFic5BC776whG38z6yshDPHWjgj8vXqn02Shw/V2aSEpeNJjzy2nFYxhhUVRQKjveVB5ciMznBzcvtem1XAzpKk/KivvqEJxznzzGx6NClHjeZefimQmz/xH0+92wsx4XOIWzeVyfYJtp13sQSNGfrknHbs75VQQmF3EyyihYxrzs94Wn+HXekNiwvwPMfNgCw670XDzaiuqIIs3TJuNRtQlFWEr5QkAGFgu54hhlB+aQxxQLsPd2OxdPTIj0MiheWzkjHR+e7MUJjsQQRqtnKsPApDpLb/l48PU10q9jTNrJYohXLOh/HXWdvVTler7wBe6vKvRoCjvVrZ2YlYeVsHf/+JSLjZcG6JbEIeSa37rfHpdI40dAhVn9YSGbkMuE4zJZeM26Zq0PNfUsE20S7zhsnZ/uqy7Gzsgy/+VoJZuuS0Ts86nPcuaPMvvTNUlSuKOANIn/lhsYpxwYMw+J0a7+o3uDgQnmAa3rPMmbv1Pjwa8eQnZqI/3j3LFoHRiLxMSgCUKPYhR7TKOraB1GclxbpoVC8kKZSoEiXRBPuBBDLsvcn6UwmI8jXqkUbFWQlCxu+mUlK0US6N4+1Op2Du04g2dfc+5fmaxEnk0lurOApLpX7nSblhQ8hmZmbkyJapeT9uk6viXuunGsfwjde+gzferkWtz17EC29IzBoE93O7y3unJM5ZbwcNfsbeA+ht+uLQZNCox9Ot+6/0OVRbwDXDGFHvedoHF/sHEKzcYTObxRBY4pd+L/T7Vii10BBC2bHBDfOzMDrn13BHYtzIz2UqELM4/Tqt5b61ebYUwxoS69ZMJlPLnNOtOoctGDMxuKJd06jfcAi2k3OMU5PpYiD1Wbjk/KkGMpNRjMef+c0qlYWOW2nP33XQntLYYaVFGfKebMdE8BorKdvcM0MpDTf4HBNzstQJ+BSj8lNxn5wy2xs/+Qy+oat2Fm5zKtcc/PXPTTqtjb+/a3TePa+JTh9dQAMa28HPteHbmKeYpUbu02SZSYa27BTrsEwLE632T3ED5UXiLZm5n7/yep5SFbGoXJFAXYctofVcHHGjgYznd/ogRrFLvz5aCv+aSJ5iBL9XJ+fjh2Hm9FsNMNAExV4xDxOx1r63IwLKW2OxaoIyGQE7QMWvHqoGRuWF4AQgGWBVw81Y4k+jW/VzCVaMQyLl765VLRAvVCcXtXKIhw434GqitmIlxOvxkXnoAXNxhHsOOw8pq4hC1ZtPegWo+kpaY/7fvQa1WSK9QwL3ppvABB9yHBNzsvXqpGbNoj501IwMDKOzsERbP/kMu+ZHbPZRB/auLFw8/dQeYHg2hiwjPFx6NxDlFSEZOi5+5f4FL8vdh7ahjw6cMyvsIwx+PhCF366ej5+9t5Zfq5+9s/zka5W4JGVhWBZ4NkD9ljiNSV5eOzWuajvGnIyjnfWttD5jTKoUexAfecQrvSNYGFeaqSHQpGIIk6GL87KxMufNuGnt8+P9HCiBjGP05DFhjePtaJyRQGWTE+DQauW5PH05CXVpSjRN2zlk0m4awl5P4QqETgiVnd2y72LUbmjVpJxwX329gELPyYu4YXzmDt2OXM1+DOT7NUnlujTnEqICXneXbulUa7BNd9wTHi0NzEYhDLO3sDj8XdO8yWrPM1pXJwMC3LT0NhtQuUO94S4dHUCSvTpgg9t3Fgc509obbT0DjvN72Nvncbi6WmS5lfooZFl4ZS8J0VmPD18UsKHkL7jZGjjykIo42Uon5WF337c4PTg/ZuPGvCDW+a4VdLZ/kkj9mwsx/xpKVhWoIVKIceYjcGqBdl0fqMMGiPgwB+PNOOLszIRJ6NfSyzx5Xk67D7aikHLWKSHEjV4iuXlWu0mKuSSYne9dQETa9Dgj/dDzMPNeWe4/3tKPvIWxywUo+matJef4RzbTGM9fcdepupaglxOqhLrywzY9nEjvvVKLb69oxbrSvXISVVKTijzJGueYtId52/30VZUrSxyOseTdyzAG7WtTtfydX5dr9815J/MBBpbTwkMMX1nNNuTMWWE8OFhzcYRPP9hA5470IDnP2xAs3EEbf3DbvL19F0LMSNDjYLMJCybmYFF0zUozdfS+Y1Cos5TTAhZBWArADmAF1mW/UU4rjswMobdx9p82jKjRAcZSQlYrNfglX80YWNFUaSHExU4epyajWYcv9Lv1BTDlzg2b17SYHq3xDzcNmfbgjcuhDxuYp8dsJf2ksuAxPg4t9hif8ZFYwHF0aUoISfXvLJrSvIE29v+8u5FuNA5hN1HW702vvBX1hznr33Agh2Hm512SwiAvmGr03sCnV8qM9GPJ4+wq77bWVkGZbwMZqsNu4+24rFb53rdjctNTUTbwAhK9GnU+I0RosolSgiRA3gewFcAzAPwVULIvHBc+9VDTVgyPQ0ZSdI6fVGiizsWTcP2Ty6j3+XGNpXhPE5fnJWFOdkp/E3fV0+uFC9psLxbQp7AJ1bPw3un2pyO82ZcuH52RRzB+jIDtn/SiJr9DVi37ZCTt9ufcdFYQM/ka9VYmJeK6gq710ysve2FziG8eLARDywzIFtCVy9/ZM11/vqGrZiTnYIvzspCQWYSDCGYXyoz0Y2vFXqGrfa4dTmxy8/Te+vcPMLVFfYdqb5hK5Rxcvzm7w2Yk50CfTqd81ghqpp3EEKWAfgPlmX/aeL/PwYAlmX/S+j4YBX4Hhgewxd/+SGeWD0P09ISvb+BEpW89I/LyElV4md3LAjmaSdFAXqxRglSaOw2iTbnCEU8rXP1CTlYlkVz7wg27T7lV5IblzG+btvhgD5DIN9hiIj65h2O1SdsLINvvVzrNgdccwOuGcfMrNDEaHubv1DMbxTKTDiJat0pptd2Vi7Dum2HBHUFV23nWEs/HnvrNDQqBe4pzUNRVhLmZKcgXm5PPOZihtN9qJpDCTuCkxJt4RO5AK44/L8VwA2OBxBCKgFUAoBerw/KRX/1wQUsnZFODeIY5+7r8rBp9ymsKcnDogg1XwmFfAYDbwlungh3RrzQWIvzWCzMTfXLuJDJiMc6tlK/k0C+w2gh3PIpkxHkZ9hjtBmGdZMjrjwVYJ+PbpMlZEaxt/kLxfxOBpkJF+GWTTGPsKdKJpw869PVWDw9TVAf5WfQuY5los0o9grLstsAbAPsT5OBnu+zy73Yc6od/7WGxhLHOsnKeKwvM2Djn45jT9VyJCvjwz6GYMtnNBANGfGBGhc0vtNOJOUzmHHulMlHuGVTTCd4q2QC0IedyUxUxRQDaAMw3eH/eROvhYSuQQs2/ukYvrV8RkQMKErwWTYzA3Oyk/HIa8cx7pqdRfGbWM+Ip/Gd0UGw4twplEDxt5IJZXITbZ7izwEUEUJmwG4M3wfg/lBcyGgaxde3H8EXZ2WiRK8JxSUoEWL9MgP+54OL+H9/PIaary6BMl4e6SFRIkw0eLsp16DzQYk0VAYpQkSVp5hl2XEAjwD4K4A6ALtYlj0b7OucvNKPO57/BxbmpuJO2h540hEnk6H65lkwW8ex5jefoqFrKNJDokQB1PsTXdD5oEQaKoMUV6LNUwyWZfcC2BuC86Ku3V7658CFLnxjWT7KCrTBvgwlSoiXy/Dwlwrxt/OdWPO/n2LV/Gw8sCwf86elgBCq+CgUCoVCoTgTdUZxsGjoGsL5jiG09o3g3NVBfHa5F9ZxBl+cnYn/umshkhLiaMzpFOCmWVm4Tp+Ov53rwDd+/xkIAa7PT8e8nBRMT1chIykBc3OSoaX1qSkUCoVCmdJEVZ1iXyGEdANoFvpb7sOvLoxLSlc4vsYyNgAI7gdmWQJCYvdLlMrk+JyEyNzji01nP+o2vvdMi8NLGQB6Jn7vYVl2lX8XE5fPMOL4WSJNtIwlWsYBBDYWv2UTCKt8RtP3LYVYGm80jzUadWe0fl90XL4T6NgE5TOmjeJogBBSy7JsaaTHEWqmyucEJtdnjabPEi1jiZZxANE1llARa58xlsYbS2ONBqL1+6Lj8p1QjS2qEu0oFAqFQqFQKJRIQI1iCoVCoVAoFMqUhxrFgbMt0gMIE1PlcwKT67NG02eJlrFEyziA6BpLqIi1zxhL442lsUYD0fp90XH5TkjGRmOKKRQKhUKhUChTHuopplAoFAqFQqFMeahRTKFQKBQKhUKZ8sS0Ubxq1SoW9rrD9If+hOrHb6h80p8Q/wQElU/6E+Ifv6GySX/C8CNITBvFPT3RWlOaQqHySYluqHxSohUqm5RIEdNGMYVCoVAoFAqFEgyoUUyhUCgUCoVCmfLEReKihJAmAEMAbADGWZYtJYSkA9gJIB9AE4B7WZbti8T4KBQKhUKhUChTi4gYxRPcxLKsY+DQjwDsZ1n2F4SQH038f1NkhhY5GIZFk9GMzkELdClK5GvVkMlI1IxHr1GhpW84asZHoVAoYnjSp77q2mjTzRQKRzTJ5vg4g7PtA2gfsCAnNRHzc1IQFxc7QQmRNIpduQPAlyZ+fwXAR5hiRjHDsNh3tgOP7joByxgDZbwMW+5djFXzsyMi4K7jMWgTsXFlER5/+ww0KgXuKc3DrKxkzM1JwYwM/282FAplchMJneBJnzIMi08bjaht7gXDAu+ebMOmVXNFdW206WYKhSOaZHN8nMHbJ9vw+NtneJvhP26fj4Q4OaalKWFjgK6h6LYLImUUswDeJ4SwAH7Hsuw2ADqWZdsn/t4BQCf0RkJIJYBKANDr9eEYq1eCpfCbjGZesAHAMsbg0V0nMKeqHAWZScEets/jWV2cyxvE68sMqDlQ77YIAUheoJPReI5G+aRQOCIhn5G6abvqL41KgfMdg0iMl0FGCH7ylzNoNo5AGS9D1coibN5XhznZyYK61l/dPBl1XKiYLLoz3HMeTXbD2fYB3iDOSVViXake//rHY9CoFHhgmQFb9wvbDNG0RiJlFC9nWbaNEJIF4ANCyHnHP7Isy04YzG5MGNDbAKC0tFS01ly4CKbC7xy08ILNYRlj0DVkiYhR7DoeQuzjWVOSxxvE3Bi5RQhA0gKNpqfbYBJt8ukrDV0m/NfeOpxuG0B2qhL3L9XjntLpkMfwnFCuEQn5jNRN21F/5aQq3R7kq1YWYcfhZrQPWFBzoB4blheI6lp/dPNk1XGhItZ1JxCZOY8mu6F94NpYHO2ENSV5vEHMje/RXScwr7oc59qHomqNRCTQg2XZtol/uwC8BWApgE5CSA4ATPzbFYmx+YqYwm8ymn0+ly5FCWW885Qo42XISlYGZazBGg9nHDvCLUJPC9SRYH5vlOBwpm0A9/z2U+RqEvH4bfNw64IcvHKoCfdtO4Qe02ikh0eJUaTqhGDjqL+EHuRrDtRjTUke/3+5DKK61h/dTHXc1CMScx5NdkNOaiI/Fkc7Qcxm6Bwcjbo1EnajmBCiJoQkc78DuAXAGQB/AfCNicO+AeCdcI/NH4Kp8PO1amy5dzEvVNxTU75W7XYsw7Bo7Dbh0KUeNHabwDDBf7DWa1TYvLaYH8+7J9vw1J0LICcQXYRSF2ikbpQUYYat4/jXPxzF+rJ8fGVBDjKTE7Boehoev3UepqercOfz/8CV3uFID5MSg0Tqpu2oT8VuyoRcG0+pIR16jQqXukw4cL4TRxqNaOqx61ZfdDMH1XFTj0jMuRTZDJW94HreubpkPHXnAqexcAjpALN1POrWSCTCJ3QA3iJ2bRQH4DWWZfcRQj4HsIsQsgFAM4B7IzA2n+EUvuPE+qvwZTKCVfOzMaeqHF1DFmQlC8fXhGOLhmFYvF/XiS0fXMCG5QWQy4BSQzrK8tNxdXAEBq0aj7112un63CLccu9it7G53jyC+b1RAud3f2+EXqvGsplap9dlMoJ7rpuOFGU81m07hD9/50ZMS0uM0CgpsQh30/amE4KNoz7tNo3ixYONbvqGZe3/bl5bjLL8dLxf1+k0zuqKIhTpkrBytk6SbnaE6ripRyTm3JvdECp7Qey8/7xwGoqyktBrHsXMuxbix2+dxu6jraiuKHKLKTakq6NujRCWjcnQHQD2uKPa2tqIjsFXgQtGEH5jtwm31hx0E6S9QYzR83YN7nOILUKxv3HEULyd34OJBvmUgml0HDf+13785x0LoEsRV0Z7Tl/FJ/U9eOv/fQEatSKMI6SIENBCCad8uuqEcJd2FNI3m9cWIzdNiXR1AvK1ajQZzYI6r3JFAe5cnOuzbo0hHRcqJr3udCVYcx7MZL1Q2QtSzuu47rNT7NUnuk3X7AJAemJ+CBC8QDSVZItJpHp3AekLxtuCCEdgvbdryGQEBZlJgtfz9DfHY3z1vFBCw59rr2B+bqpHgxgAbls4DYMj43jw5c/xemUZlPHyMI2QEus46oRQG4ti+tObvhHTeQwLv3Qr1XFTj2DMuT/rw5PNECp7Qcp5hWyBmVnO14y2NUKN4iAgxQgEpGVhS1kQUrZoAn3SDMc2kNTvjRJaXv/8CtZOJBx5Y9310/Hbv1/Cxj8dx+++fh29wVN8JpTVKLzpT0/6RkznyQiQnaJEY7fJZ31KddzUI9A593V9eJN5qfdyX22GYNkI0bZGYqfNyCRAShC+lOxVb4H13CK5teYgvvrCEdxacxD7znZ4Da53DJpnWeC5+5f4lFhCiT0auobQPTSKeTkpko6XEYJvlxegvX8ET+05F+LRUSYjoUxGCiT7X0ivVlcU4TpDGs5eHfJZn1Io/uDr+vAm81IT8bzZDK5JdXqNyufk01iAeoqDiLcnLSlPVlK3JDxtOfjjiRF72txXXY6OwejY1qAEn72nO7B0RrpP8xovl6H65ln4z3fPQp+uwje/MCOEI6RMNkK5CxXIVjGnV2dvLEdLrxkqRRx0KQkYt7FYveMTN306e2O501YwbdRBkUIw7ARHpIQ6egtR8GYziNkHt8zVYW8UhT4EA+opDhJSnrSkPLFJLV/EbTmUFWTwgs/hjydGbFEwLASvQZkcvH+2AyV6jc/vS0qIww9umY1nDzRg35mOEIyMMlnxp7yZVAIt/yaTEczMSsJNc3S4oUCL/IwktPQNC+rTlt5r3md/d+coU4tg2QmOSJF5T/YC4N1mELMPWvqGPZ43FqGe4iAhxTsr5YktGOWL/PHEeHvapF6QyYfRNIrLPWbMyU726/1ZKUo8+uVZ2LT7FLRJClyfnx7kEVImI74mJ/uid0JR/k2tiBPUpyrFtdtnNLXapUQvwbITHAmHzSBmH/SaR/m/Txa7gBrFQULqtp23oPJgZK/6s0g8LQpaWmhycqjRiLk5KYiT+79hVJCZhP/3pZmofLUWf3yoDPOmSYtNpkxtpCTX+KN3QlHxQZeS4FZjtbqiCLqUBP6YaGq1S4legmUnOBIOm0HIPjBoE9HWb8HXt382qewCahQHiUDj5IQ8Iv4qU38WiadFQb0gk5NP6nswV2KCnSeK89KwviwfD/z+CHb9yzIqE5Sg4K/ekckIfzPvHLRv/wZiGOvT1SjSJaFyRQEYFpARoEiXBH26e9hbNDUhoEQfwZITMXshVDaDkH3w5B0LUbmjdtLZBdQoDhKBbGGEwhPra5kTblHMqy5H5+AozNZxGNKv3ViEnm4vdg4BCOyGQ4kchxuNqFwxMyjnWjZTi9FxG7667TB2/ssy5GfEdgYyJXj4G3rlb0hXsPWpTEawcrYOBRlJIQ17o0x+giEnodq5FbMZuHWmUcVjZ+UyjNlsSFcnoGNgcu6OUKM4SMhkBLfM1WFnZRnaByzISU3E/JwUSUIaTZ7Yc+1Dbottti5Z8On2dNsgvrvzhE8LksYmRwd9Ziu6hkahT1cF7Zxfmp0FG8ti3e8O4bXKMsyMYcVICQ5CN/Cn71qIEn0a9On+10H1ZBiEQp+GI+yNg+rIyYmYcenr/IbTXhBbZ4vzNGjrG/HJ6x0rck2rTwQJhmHxfl0n1m07jO/84RjWbTuE9+s6JWUfe8v8dK0PGKqMZrHFJpfBLRu2amUR3jzW6lMdUJqhHT0ca+nDLF0y5EFWShVzdLirJBf3/vYQTl7pD+q5KbGHkE557K3TePN4m9e17ykL31NtVn/rIAeqZ71l+EuB6sjJieu8rtt2CF1DVr8MQ0/yHWxbQWydnW0fwOPvnEbVyiKn9bl5bbGg1zuW5Jp6ioNEIE9v0ZLkJrbYOgYtvBekocsEyziDq/3DWHtdHnYfbUX7gEXSlomn74i70UX7U+Rk4VhzH2ZmhmZr94uzspCUEI8Hfv8ZnrpzAW5fNC0k1/FEx4AF9V1D6B8eQ0piPIpzU6FRK8I+jqmOp9bJ3vSjJ++rJ8MgJ1WJqopCcPfb3Udb0Tds9Ri3GWw9669XLJp2DSnBI5jzKmYvZKcog24riNoEA6O4fVEuZDLgkZsKYRlnwLJAbppS8Fqun1+jUuB8xyCU8TLka9VRdb+nRnGQCCT7OJhJboFsUXgyzrnkldNtA9i0+xQ/zqqVRdhZ2+I1UYBhWHQPjeKh8gIA4I1prqzL+Q73sI1Yz2KNZo5f6ceNMzNCdv7rDBqkq+fg53vq8OmlHjx+2zyoE0KjbmwMi8s9Zhxr6cOnDT043NiLkTEb9OkqJCvjYBodR2O3GTfP1eEnt89DOjWOw4aYTmFZafpRLGxB7Ly6ZCXOtQ9h28eNTpUiinRJHuM2xfRsbmUZhq02n3RpIAY2rWIxOQnmvIrZCzYGQbcVxNbZ2fYBPHeggbcBuAfPtSW5guc2mkf5c+SkKrG+zICaA/VReb+nRnGQCCSr1F+PiFBAfCBPit6SAJqMZt4g5sZRc6Ae29aXerzhCI2ramURdhxuRt+wFfFymeBidu0YRQkOLMviTNsAvl5mCOl1ZmSo8fO7FuAPh5tx0zMf4dEvz8KdS3KhjJeLvqfHNIrTrQM41z6IS10mtA9YMGgZw7iNBSH2bnrxcgJCCMbGGfQNW9E5NIp0tQKFmUmYpUvCD/5pNqalKkHINZkfto7jreNtuLXmIP740A003jlMCOkUbu0HUp1B6LzVFUVoGxhx0yVb99djz8ZyjzpQTM8erO/BM+9f9EmXhmrXkBK7BHNexeyFI5eNQbcVxNbZq4ea+fPXHKhH5YoCzMlOQb5WLXjuzWuLUWpIxQ0FmZiTnYx/+/PJqN0NoUZxkAg0q9RXj4jQYgqGV9lTsojYjSNeTjzeKITG5biQrDYGG5YXgLNhOC9yXccgZmREz7bKZKG1bwTxcTJoVKH3mKoUcahcMRMXO4ewq/YKnt5bh+VFGSjOS0O6WgHrOIPOQQsudAzhzNVBDIxYUZiZhOnpKuSkJqJIlwy1Qo44uQwsy2Kcsf+AZREnl0GdEAetWuHR0ObG8bUbDJiWloivv3gE725cjoykBI/voQQOdwOfW1WOK33DaB+woMc0CkUcCag6g0xGMC8nmS+TxrLAq4eacU9pnqCO6jZZPD5gi+nZgswk5KQq0T5gEdWlnrxijmMIdNeQErsEe16F7IVg2QquoYyOrZwJCL678wTaB67F51vGGCyZnoYvzsqCTEbQ2G1yO/eWDy7g3/5pDi52DmF4dBwPlRfw93numGjZDaFGcZAIRdF4wLfF5Gi05qQqsaYkD4QA3aZRt7F4elIUy7KWsuiEDG0xY3rJ9DSUF2Ziz5l2bP/k2nYnF5JxsXMI83JSomKhTCbOXh3EjDDfZGfpkvFv/zQHRtMoTrcN4EzbAMyj45DLCFIT4zFLl4xb5mcjJ1UJGQndQ9BNs7PQOWjBD944iZe+eb2TN5kSOupcwqM2ry3GLXN1HvWjt+3d9gELavY3OL+HhaCOykxSorHb5HQuAPz5c1KV+O+1xfihS2jY5n11WFOSh+c/bBC8cYt5xQzaRDQbR5zGEOiuISV2Cce8SrEVuDV1sXNI0DD1FMpYkJmExm4T+oatTtflujte7jFDLgMudg653e/vu16PK73DTmFN3I5R+4AlqnZDqFEcRHytDSz1nFIXE2e0alQKp5idFw82Ysu9izEvJxntA/abAsv6Fn8EiG+lXDaaMGOiLq2QoS1W0s2gVaOlb1gwJGPLvYvx5HvncONMLTWKg8y5qwOYHsRSbL6gTUrAl2ZnReTaHHeX5OHxd85g7+kO3FacE9GxTAWEvFKbdp/CwtxU0bUtZXtX6CH93ZNt2Ly22CnvYcu9i3HZaMIjrx3nX3vu/iWwjrNO5//VPYuckoa4Gzb33KSMl4HA7gnjdLDYZ9u2vpRvbBCsXUNKbBPqefVmK3gKY+QMU4B4tAvEbIDv7jyBvmErqiuKwLCs27qcrlHhBy4hEzUH6rFheQG2f9IYVbsh1CgOE4EkwEldTJzAnu8Y5A1i4JpgV64oQM1+e3D8M3cv8nmLT2zLsm/Yir1V5QCEDe191eWiT7BicVANXSavGeMU/zhzdRDFuamRHkbEiJPL8ECZAU/vrcOX5+mgiKOVKUOJ2E5Rs9EsqgelhIIJ3aA3rZqLW+bqsDA3lTcMZARYtfWg07lOtQ7wXivute+/cRKVKwrw3IFr3mcuKdD15s8Z6J5CyvZSby/FB4JRx9eTrSAWxsgZplUri3C40XNcsqPh3Ww04/iVfrx6qJn3Nm/dX4/qiiJUrSxySqSTy4jgeedPS8beiZCNaFkf1CgOA6Eoqya2gFbNz3Z7SgOulUHifq/vGvIr8F9oyxIAuoYsfEa563UdS7q53iTEQjLGGSaqnh4nE+c7hrB64dT2kM6blorM5AS8eawV9y3VR3o4kxqxNX78Sj9GxhhBPSglwdiTZ8zRMDh0qcftXIyIrprlsKvFhUJo1fGQywqcbv6b99XZy08RIlwFI0VJvb0UyYTDRhBbU4b0RGxYXoAdh5ux9ro8r3YBt746B91tAcsYA7PVht1HW7FheQGKc1NQpEsGy7h7j5XxMszWRV94JHWRhIHLPeKF5v3BUyFsrnQaV1Cbg/N4cOyqbcXTdy0ULIzvCe4G53rurGSlx7+JFbYXKtD/9F0LsWZJbtSUaJlMmEfHYTSNQpdCPfCri3Pw279fisoC8pMJoTVetbIIb9S24tFdJ3C5x+zWcMCTLnFESsMMoXPJCQTPPzc7BXuryvF65Q3YW1WO24unQS6ToWZ/A28Q56Qqsa5Uj3XbDuO7O0+guqLIZz1KoTgitjNyuSd4NsK4jRWU+ebeETz/oV2+dx9tdWvIISbPYmuUZe3Os+2fNKJIl4yCzCTMyEwSbMTDhV1GExHzFBNC5ABqAbSxLLuaEDIDwOsAtACOAljPsqzV0zliAYZhUdc+6Hc2shDetha9lVEBgL5hK0r0aT5v8XkL5vc1w5YmloSX+i4T8jSJ9PsFMC8nBTJC8I9LPSgvyoz0cCYt3BrXPrgUBxt6nOJ1AaCuYxA/eOOkk864Za4uaNn6QjprYV6q4Pm5ajeesvrXlOTxW8PtAxa8eqgZlSsKsGR6GgxR1oiAEhuIeXH9rcAkZCM8/s5pt3j7zWuLseWDC/z72gcs2Fnbgp2VZRgZs3m8H3uyM1zXayzd5yMZPlENoA5AysT/NwP4NcuyrxNCfgtgA4D/jdTgvCE1/qfJaPY7VEEMb1uLrgKYmaTEZeO1rFFOYPXpaqdzAvAqqN6E21fBj5V+6JOFix1DyNNEJsku2iCE4Euzs/DqoWZqFIcYmYwgMzkBLx5sdNODjtnq3AP+3qryoNxEOf2iUcVjZ+UyjNlsSFcn8Ddrb+dnGBYsCzxz9yLUdw1hV20r5DLn0AsupOz1yhuibiuY4plouf+IhRj5W4FJyEZoNo4gN03p5AjTa1R8nwCNSoF7SvMwKysZSQnxWJib5pMtkJmkhFwGLNGn8ed2/W5jIaQoIkYxISQPwG0Afg7gUWKvi7QSwP0Th7wC4D8QpUaxL/E/nYMW7KptdQs8f+rOhdD7aZxIKY3mGlc3I0Pt5hUGhKtFeAtb8BTM70uGbThbWFPsnO8YxLRUGjrBceNMLapfb0H/sBVpYajbPJUR8iw9sXqeU2Ib4PyAH8hNVEy/lOjTAcCrMST0/qfvWojCrCSnJD2ANtiIRaLp/qPXqPDUnQvx+Nun3SpD+FOBScxGSFcnuK2pVfOzMa+6HMda+vHYW6cDtgXyM5Ki6rv1lUjFFP8PgB8C4GZMC6CfZdnxif+3AsgVeiMhpJIQUksIqe3u7g75QIUQC1843dYPhmHBMCwfI6dSxKFv2Iodh5uxYXkBHllZiMoVBeg1WfB+Xadf8YxCMXpSwhRcY+/EPoe/sc6+Eunrh4JokE9P1HeZME2TGOlhRA3qhDgsmp6GPafbIz2UsBBJ+eQ8S3uryvHSN0tRXVGEjKQEwbqnwTAwPcVpCuVkjI8zTrHNLb3u73/srdNIUcb5rH8p3gm3bAb7/uN43+di46Uce6nLhI/qu9BrsqByhd1G4BLf/K3A5IuNIJMRMCx4gxgI/LuI5Xt72D3FhJDVALpYlj1KCPmSr+9nWXYbgG0AUFpaGpEMGbHwhf3nu9BtGnWqf2nQJuKpOxfg8bfP4PkP7eXQvnfzLLz0aRP6hq1+tTYMVnxOMPux+0Okrx8KokE+PXGpy4S7lgg+b05ZbpihxV9OXMXXbght2+toINLyyT2cG82jYFng6b3n3HbRgmVgiukXIWN38746jNkYp3jLp+9aCI1K4da9y7GaTq95FPFyGYatNjR5KDFH8U64ZTOY9x9fPKNCx1ZXFOGzRiNunp+NJ987F/Ba8NVGCPa9OJbv7ZEIn/gCgH8mhNwKQAl7TPFWAGmEkLgJb3EegLYIjE0SYlsTNgZu9S+bjSN49kA9nrl7Ec53DkFGAOVEXdRAhCQYhcCD2Y89Fq8/1Rix2tBjprWfXVk8PQ0vHLwEo2kUWtr6OSwo5DLeEOZ20eQyYEVRJkr0mqAYlmL6RaWIc7thry7OdWsi9Nhbp/na7o7v56rp5GvVot2/qGEc/QTz/iOlrranY7futzes2ryvjl8LS/QalM/M8FuWfLERgn0vjuV7e9jDJ1iW/THLsnksy+YDuA/AAZZlvwbgQwB3Txz2DQDvhHtsUhErMfTmsVbB+pfNxhGc7xzCcwcaULO/Af+17zzWlORFXEj8CcMQwnXbyHUbUmwbKVjXp0ijsceEnFQl5PSG7YQiToaFeWk4cL4r0kOZMgxbbbyebB+w4PkP7bpxzMaIGgG+bE8D4vpFl5LgXqJN5q63HesWO77fsUW0kCH0eVOvpPFRIksw7z+ePKNSjz3fMYhm4wi/Fv71D0fR0jfs81j8wZfvgmFYXOoy4cD5ThxpNKKpx13WY/neHk3NOzYBeJ0Q8hSA4wC2R3g8onBbE7mVZdh/vgs2xl5iCADm6JJRVVEIhgXfV9y1RrBljIFchogLSTDCMBiGxYELnTjVOgCGBVIS5EhPSsDjb5/x6j2JpTItk4HGbjOmpdJ4YiEW5aXhr2c7cU/p9EgPZUog5kkSq589Ps7g00Yjapt7wbD2ds6bVs110itClQSE9AvgXjryekO64Hi4usVC+knMuDnY0IMXDzZSr3GUE8z7jyfPqKtc5qSK7zQ7Es5wA6nfxfg4gz1n2p3CjKorilCkS8LK2Tr++Fi+t0fUKGZZ9iMAH0383ghgaSTH4wsyGcHC3DS09Vv4ciYPLDPw/b057/HO2hbcd73eqUawMl6GijlZXkuehINAwzBaes2o7zTxISNVFYXYMmEQA563kYJxfYp0GrtN0KXQ8AAhlkxPwx8ON8M6ztC2z2HAW71zRxiGdbsRV60swuZ9dZiTbW8O4CmmU0i/uN6w9RqV5LrFHGKGENfZ05Peo0QHwbr/iMmzXqMSlMvn7l+CR147LlovGAh/uIG374JhWHzaaHQLM9q6vx6VKwpQkOH83li9t0eTpzjm4J6G5lWXo2twFA+89JmTsNQcqMfOb5eh2zzqViM4FAaxLzUXg1WfsXNwFFv31/OfW6x9aiwE2E92LnWbMS0t+mO6IkFKYjxyUpU41tKHsgJtpIczJZitS8ZvvlYCdUIcdMkJ0KeL13p3vRHXHKjHhuUFvF7xJabTEW4Hzx/PlpAhxJXR4sbgqveipS4uJbi4yk92ihI2Bjja0icol3s2lovWCw5Wwqk3WfNVFpuMZtQ29wre3xkWk+YeT43iIHCufQjnO4S71o2M27Byts5r57hAlWWg2a/+bvWZreNun1vIe5IYL+fbUFMiw6VuE64zaCI9jKhlwbQU/P1CNzWKQ4yY/nFsJuSIWJiCXAbek+ZLtruvXmUxHA2hZqMZx6/08536clKVuKc0D8NWGxq7TQHVhQ8G1BgPPZxnNF+r5uf5ofICQbnsNln48qgcnh7KfJ0/b/d4f2yAzkELGFb4/j5Hl4xskdCnWIPuEwYI56HghMURx0xl1xrBjgj1Kd93tsOnRA1f6gIGs4agIV3t9Ll3H21FdYVz7/SqlUWoev245M/ka0INxTssy6Kld3jSKK5QMD83FZ809ER6GJMeX/UPF6bgiDJehlJDOm9sih0jtP0cTP3H6fYvzsrCnOwU9A1bkZOqxAPLDNj2cSO+9XItr88v90SmLm4w7i8U6bjKl1S5FLMT/Jk/bzLuzxrQpSjx7sk2VK10v7//8v3zONc+5NanIRbv39QoDhDOQ7H7aKubsEjd/hAT0JZes2ThCkb2q9Cx3piR4Zxl2jdsRZEuCTu/XYaqimtFyJuNI5JuAFSBh4a+4TEwLItkJd0cEqMoKxkNXSYMWsYiPZRJja/6RyiTffPaYtxYoOUNB1+y3YWur1Ep0D006veN3LExyf+sW+wUUsbp8+Zec9D0ri96MpYbKcQijvIViF3A4Tp/GpUC5zsG8dHFLlFZ9bbG/LEB8rVqbFo1FztrW/Dfdy8SvL+39Ao3xoml+ze9QwYI56FoH7A41dv0JZFOTEn70nbRW11Ax+0XlSIOBm0imo0jgsdySNmyEYvFO3LZ6FTfE5AWW+xvbCDFM01Ge+UJe0d1ihCKOBmKdEn4/HIvKubqIj2cSQnDsFAp5IIVesSSiqTE+/oSE+yqKznP7jcmckL8DWvgPH2u+jwnVYk1JXmw2hhUVxRiV20r3xAkHHVxY7mRQiziKF+cXVC5ogBLpqfBoFX7HLriOH85qUqsLzO4NbtxlVUxeyA7RYnGbhNGxmyCspgYL8ehSz2C93t+jWUn42LnkOD9vXNwNObv39RTHCCOHor2AQu2f9KIOdkpSE2Mx5HLRkleB8etv5xUJR6+qRD/futcn9ouevKUuHoV1m07hI0ri2DQJrody+GLJ0Jo28eX7UxHgunFplyj2WgWLXdFucZsXTION/ZGehiTEk6nrNt2GDX7G/DiwUasLzPAoE30q029P8cA7rryntI8Qc+ulHrDQlvFrvp8fZkB2z9pxHd2HMPvPm7EA8sMfFmucNTF9VcXU/zDVb76hq2Yk52CL87K4g1DX8ILdClKGLSJePimQjx261zeIAbE7QIhe+C5+5fgXPsQbq05iG+9XOsmi0/duQBVrx/3eL/n1phj/W4OZbxMMMco1u7f1FMcIK4eiswkJS4bTVi19aBkrwMnwJv31WFdqR41B+pFA/TFnu49eUoau01uT2+Pv30GOyvLMDJmE/SqBOqx9aXkkiOx3AknmmnqMSOLlmPzypycFLx1rDXSw5iUCOmUmgP12FlZFtbylK660rGRCIdlzHu9YbFkpVvm6njdt6Ykz82I2bq/Hq88uBSZyQkhqYvrir+6mOIfnu7F/iS46TUqbFxZhMffPiPZLhAaA8sCtz17UFAWlfEyVL1+nN899na/F5MpLscolu/f1CgOAo71+Bq7TXz9QUCaMck3A0lTYt22w04B+lKFy1Oog5hXYWTMhrKCDMHzBbrl5m/xbqrAQ0Njtxm5GlWkhxH1FGYm4VK3GSNWGxIV8kgPZ1LhSQ8Bdu9ZuKojuOpsf+oNizkO9laV87rvYueQ4GdmwQa0neyLnozlRgqxiliNXn+cTS19w3wzLMCzXSBkB3DnPXSpR1QWh602p3BK7m++OuEA98Y4sXb/pkZxkPHXmJTJiJPHggvQd40dEitu7+np0x/vq78eW0+LUgpUgYeGZuMwSmg5Nq8o4mQwaFU4caUfy2bS0mzBREynZCYpI1aqDPCv3jDgXddzx4fCc+arnozVRgqTDX/sA6HEPSG7IFA7QGpHPk7OxGQq1u/fNKY4yAQSv+X4XscA/Ze+Wcp7H8SK23vKLvanD7k/7wlW5QipsYEU6VzpG6YxxRIpzEpCbTONKw42eo0KT925wEmnPHXnAsgIIlodwbFyxEvfLEXligK+3jA3TiH9LUXX+6NHfRk31ZOxhT/2gVS7IBA7QOxvXEc+X+7psS6X1FMcZALZ/nd9r2OAvifB8vb0KcWrIPQ06Ok94+MMzrYPTBSqT8T8nBS09A3HfObpZMQ0Oo6RMRvSEuMjPZSYoCgrGbVNfZEexqSjpW8Yz050oiPE3k3u2QP1+Ont80NaHUFItwEQ3NHK16oxMqF7AWfjwDW8Q4qupztfFEf8sQ+k2gVSvNDzcpLxyoNLMWwdhz5dzbcxB+we3tkby9HSa4ZaEYdRmw1n2wexeV/dlLqnU6M4yDi2fu4cHIXZOg6DSKcmsff6qkClhDp42j7ztcPT+DiD/zvbjvouExgWqGsfREuvGdmpSlr6JwppMdq9xLQcmzQKs5Lw+39cBsuy9DsLIp2DFjQbR/D8h86lnNQJcT5v3UplfJzBp41G1Db3gmGBd0+24YnV82AdZ0W3mV11sF6jwvt1nYLHS9HXNHSB4ojU9uYcUu0CT3aA2D1+RoazbXLZaEJ9pwlb99dDo1LgntI8PHJTEbqGLPjD4Ra0D1gm/T2dGsUh4lz7kNcYOTGF76sCDTQ5TUrwv+NYE+PlaO0bwbaPG/nrVVcUYbpGFfOZp5ORlt5h6JJp5QmppKsVUMTJ0GwcRn5G7CSIRDtiN21dcoKg/uK2bl1fn5eTjPYB70Yyw7DYc6Ydm3afcooVbuwyYcvf3Mtazd5YjplZSW46WKh6j6N+pAYvRQq+tjf3NT/Hkx0g5R5/uceMU60D2PZxIzQqhVs9ZC7Ovm/YOqnv6dQoDgFSjcxgJZcEukXnbdvFday//0apW13PrfvrMX9aCl5YX4rH3zmNZuNITGaeTkau9A4jcxIrsVBQlJWEE1f6qVEcRMRu2vp0NfTpajf9JaZHK1cUoGZ/g1ed2WQ08wYx9/6aA/X45d2LBPVdXceg03YyRyCVeAL1dFMmD75UnvDHPvBkB0i5x9e1D4KZqLgiVEqw5kA9KlcUYE52yqS+p1OjOARIUaLB7twWyBadt/AL17GaRep6tvdb8NN3z2Lz2mLkpimRrva/DicleDQbzchIUkR6GDGFQavGiSt9uHNJbqSHMmnw9vDuqr/E9CiX4+NNZ4qWgLOOC+q7i51DmJeT4nauQCrxRLKqBiW68OXhyl/7QMwOkHKPr+8aQpJCDmW8DIRAcKxLpqd5zXGKdWj1iRAgJcM00p3bHDsxsSzw3P1LRDOkXcfaNWgR/HzdplFYxhhs2n0K6eqEmMw8nYw09w5P6u2uUFCYqcbxlv5ID2PS4UtmupgeZR0S3z3pTLH3T9Mk4um7Fjrpu6qVRXijthVdQxa3LnV6jcqvChLeqgFQpha+VJ4Itn2Qr1XjufuXoKqiEI+sLER1RSGeu3+J0z1+V20rCnVJqK4ogpxAcKyGKeDkop7iECAlxjeSndvEPBj7qsvRMejuwXEd64ufXMa//dNs/PKvF/j3f+/mWXj50yYANLku2mjpHcbtxdMiPYyYYkZGEi50DmHMxiBeTn0HkUBIj1ZXFOHVQ838MZ50ptD7N68tRlm+Fq39w6hcUQCGtVfB4GIls1OEaybfMleHvT6GpwXaAIkyufAl9ycU9oF1nHXKA9py72Kn6/UNW3Hu6hD+9FkLHlhmwBOr5+HJ9875lacUy1CjOARIifGNZOc2T52YhDrcCZWEMWhV2DNRvuX4lX68/GmT17qelPDDMCzaByzIpIl2PpGokCMjKQENXSbMzUmJ9HCmJK56NDNJictGk1u5NDGd6UkP69PVmJOd4qZ/bYxwzeS9Dkl1UqEt6ymO+JL7E2z7wFs4Bne9Cx2D6Bu2YvO+C8hJVWLD8gLIZUDFnKywtmKPJNQoDhHeYnwjWb/SFw8GlyiiUcVjZ+UyjNlsTrHCMzKE63pOhSfKWKDbNAq1Qo7/z965x0dZ3fn/c2ZymcxkksxMksmQMAlDAgRIuDQiWkJroiy1tCqotO5aa/GXvRRDS7uldXXdrtaW1tIVsetirVW7LWBRqUipFmzBFdFwvwRICCQk5H6bZCaTSWbO74/J8zCX55n7NTnv14uXZuaZ5zmTfM/3fM/3fC+yZNayOFAM2QqcaR1kRnEMcdejM7IVAXlsJRLickQMgP+MkP49eqU3bN5d1rKe4Y6/uT/htg/E1nxuTnQaLZitVWLeNCUKNQo89tYZtA9a8PKHTdhy/8IpYxADzCiOKbGqX+mvB0MszGKxXs1PEFacPr651mdGLutkFxSFGgVOtQ7g/pumx3oojAkC1Zm+kt3c7xVO7y7TjYxQCKd9ICbXYzaKO7cedpkbXy6fhoXTs6aszEY9WI4QIiOEfEIIOUUIOUcI+eHE6zMIIUcJIY2EkJ2EkIRIl3dPygi0pXEs8Lf1qL+JImLJM4n4u5lstPaPICedhU4EQ9GEp5gRGfzVD6HokUCT3cLdljnRW94yhEm0tU1IrjevKccTe854zI2WfjMMOelYUqQBABy90psQ3zFcxMJTPAqgilI6TAhJBvAhIeRPADYC+AWldAch5EUA6wD8dwzG5zexKLkTjrqX/nowQq3PycoRxZ5rfWZoWDm2oCjSyNHQNQybnULKZDasjI/bPRpriDU4CkWPBKrDmHeX4Yt4WdsCsQWE5LrXNIrm3hGX67i5UaRRxMV3jAVR9xRTB8MTPyZP/KMAqgD8YeL1VwHcHe2xBUq0S+5wk/HOrYfx1ZeO4s6th7H/XIfXHZzYjtYfD4ZYCZm0ZKnPHTIrRxQfNPeZmac4SOQpSVDJk3GlZ9j3xQy/sdspPmrq9WissXHXSXx6tc9Fr4SqRwIpg8XBvLsMb8TD2iZmC1ztEfdeu8u1RpEqOjfi4TvGipjUGiKESAkhJwF0AXgfwGUAA5TS8YlLWgEIVs0nhNQQQuoIIXXd3d1RGa8Y0a41HKigBmNEOyN05PL03fNRu+OEz/v5+t0k2vGTv8STfAKOcmys8kTwFGUrcLbNGOthhI14kM+rvSbUNfcJ6oejV/pc9EqoOjac4RCTVWfFC/Egm/4Q6x4DgLgt8OaJNr/Xer1Kjqfvnu+xvutVctHv2NxrmvRyH5NEO0qpDcBCQkgWgLcAzAngs9sBbAeAioqKmP51ol1yJ9CjwFC65glVnUiWSlC74wR/5OLtfmK/m7RkKT692ovrAxafR6eJSDzJJwC09Y8wozgEpqvlOHfdOGk628WDfHYaLbBTCOqHomwFVPIUXOgwQimTIi05CbXVxbBTYPexVrQPWgLSseEKh4iXI/PJTDzIpj/EQ6m9YLs9OodcyFOSsOOTZqxbZgAhjnrdzx9swGK9SvQ7nrg2gJEx+6SW+5hWn6CUDhBCPgBwC4AsQkjShLe4AEBbLMfmD9EuuSMmqHkZMjR1D3vEFgUbEyy2AOQoU0RjkNzvJ/S74bzMq8rz8fKHTYLGepFGEXLMNMOBzU7RNWRBNgufCJpCtRyHG3piPYxJhTZDhndOtaG2qgRbDzbw+uHJL81DWrIEG++YhX7TKM5fH8Iv/nKJf7+2qgQ761qwaWVpQDrWPYuf8/gGomNCcTAwJhfxUGqPswVU8hSsXlwAQjDRhe5G6U33tVloXa+tKsHrHzfzPQYAoGvIggq9Gk/fPR+Pv33W49p+sxWah5cgR5k6KdfnqBvFhJAcAGMTBnEagDsAbAbwAYB7AewA8BCAPdEeW6BIJAQrSrXYWbMU7YMW6DJlmKfLjJiQCE3GbQ8swvn2IUEPRrA7WrEFYGfNUr/v5+6hSUuW8l5msb7qnUYLLnQIf5fJNvGiQafRgoy0ZNaRLQQKNQq8dLgJlFIQwmQwHBRpFNh4x2xsef8i3xygbFomOocsePT3J2AZs6O2upjvvgU49MPWgw3YWbM06JqpdjvFlR4T6tuNaOgawq66VvSbrX7pGNadjsHBrW2zJ5pXyVOSoM2IruOBa9vc0DmM5w7c2Fh++/ZZ0GXKBE9UhNb1rQcbsG6ZAS980Ajgxnre0m/G8wcb8LN7F+Bi5xDf9ZEzng839uBXh5sm5focC0+xDsCrhBApHDHNuyilewkh5wHsIIQ8DeAEgJdjMLaAsNsp3qvvDKsR5y2jVOgokFLgi88fFvW6BrOjFVsAzFabx/2euacMEgKXxBjnsXMemiOXe1y8zELGtTxFiu+/eZo/zgGAzfvrMSdPyRaeIGjtH2Hds0JEJU+GnQKdxlHkZbLfZTAI6bQvztdBJU9BXXMf0pKlGLdTPP1uPSxjdugyZcjPTBPUQSNjtqANYjEvma9jZm2GDLnK2B+ZM+KLi52Rd+CI2QMSCcEMTTrW/+6Ey9r/i79cwrplBr7phvNaL7auSyWALlOG+yoKMCtXCUqBPrOjMsXFziH86nCTh9xTOnlPS6JuFFNKTwNYJPB6E4Al0R5PKFztNWHz/vqwGXH+xK25HwUeudzj1YMRTDydmIdZmyHDzTM0mP1oJeo7jLjUOYSf/fki+s1WbHtgEazjVHTszvfcfazV4+h0y/0LYacUayv0Lq/XVpWgzzQ6qSZdtGjtNyOblWMLCUIIijRy1HcYmVEcBHY7xcGLnTjdOgg7dRzxlhVkomq2FsuKs6FXp+F4ywDqO4y8Qfzg0kJcHxwJqxHqy0vm65h52wOLYn5kzogfOHlyDl+42GHEXJ0SRdnhWat82QNdQ8JGbnl+BvZNOMWc13qxdb00LwPrq4rx1N7z/HM2rylHoSZNcK1+ctU8/P6TZv55k+20JCSjmBBSAOB5AMvgKKt2GMAGSmlrGMYW9/SaRsNqxAUTt+YrRCKYrjjePMwSCQEhwHffOOXyzNOtgx7HnRt3nUR+zVJYbXakSiV49t4F/LHlzroWbH+wAslSwu+Az7QN8L9L7h7ckSkjcFr7R6BRMKM4VKar5ahvN+K22bmxHkrC0dJnQkPnMK8bZMkSPLFqLv7vcjfys+SQEOCxt87gkUoDZMkSrF5cgK0HG6CSpwhunCXE4QgINN9AzEtGiKexLaSH1//uBPZvqAyoxTRj8tJptEAlT8GDSwtdZLRQo4BeHR658GUPiK39JVphp5zQul5bVYKrvSY+BIN7zpb3L+Inq8txpKkXEgmwoboEOemp6DBa8OKhRqwqz8fpNuOkPC0J1VP8CoDfAbhv4ud/mHjtjhDvmxCkSCUBGXG+im0HE7fmzYANttGHr4xtoXHaqXCc8NGmXozb4aI4nrmnDIv1WR7Kw2y1iYZtMAKnpc/MkuzCQIFKjnOTqCxbNOk0jnosuE/tPT9xxHsMz9xTBpU8hfdIWcYdOqB90ILXP76RGV9ZnA07pXjzRBvsFHjnVBs2rSwVPa72J/yhUJOGOVolnr13ASh1fMZbgnKH0cLXeGVMbbQZjnAD9/X/sbfOYOH0rIBkRGyd9mUPiK39epVcMJHUeV2/1DmEM21GvP5xM766RO/yHF2mDGsr9Hj4N5+6bGRfO3IVpyf0ILeZnIynJaEaxTmU0lecfv4NIeRbId4zYQjEiPMnNEIsbi0n3XuheSEDFkBIJYS8eZiFdqhSIhwnnJclx/f+cMpDceyrrfQYh7ewDUbgXOszo2QO826GSqFGjvfOdcR6GAmJyTou6qHldEHNcgO2HmjE6x8347E7S3kd0D5owQsfNEKWLEFFoQr/9NtjLh4usVA1f8IfCjVp+OZtJfjuhG4KR4IyY+pQpFFgVq4y5ORLMbtgRakW4zbq8xTYfe3Xq+Re85y4dR0AvrXTcc2MbIXLc7jTGqGNLOcdrizOxupF+ZPytCTUtPReQsg/TDTjkBJC/gFAbzgGlgiIdUsSMuKu9prw6w8v46f3LsDm1WX42b0L8OsPL7s03pBOHFM4F9PeUF0CX8UDhDowRbIjjVBB/LKCTI/XaqtKcLXHJKo4/LnvZNyJRou2gRHmKQ4D+VlpaB0Yweg4O7EIlEK1QlBHUqd6qrO0SsiSJWgftODZ9y54NBT45d8vwojVhh9+aR6e/+oizMpNx9aDDVhVni+oR8TCH+bqlNhXW4kdNTdj61cW4d/3nBXUj0wPMXwhkRCU6jJEO8L5g91OcaZtAK19Jvzs3gXYeMcsPFJpwOb99TjXPojH95xBbZWrPbB5TbmLHLqv/S39Zr/WfWcZbxswuzxHKhE+9XX2Dt9UpJ603R5D9RR/A46Y4l/AEVP8EYCHQx1UohDI8cXgiBVrFut5rykXsG4csfL3ax+04LUjrsW0/3SmHYv1KrQPBhYCEckSQt680+4l2L60ID/oMm4sbi94bBPdwJhRHDrJUgl0GTI0dg1j3rTMWA8noZiRLRzH+PrHjkQdLtFnn5u3a7Feha4hR5nLo1f6eQPWOdFHKoGgHvEV/lCkUeCvl7rCnqDsTrDha4zEQEi2xTZP7rLAeXQ376/H2go9/tXJLqitKkGvyVH9wTmESJEixbRMGY5e6RWVJ3/Xfee1tnt4FN/7wyn+OSW5SsE1ezJ7h50JySimlDYD+HKYxpJwBHJ8oVWm4od7z7ns4H649xx+u+5m/n7aDBn6zVa+ZqAuU4av3VKIh175JOAQiEgfAYqFV3Cv2e0Um1aWYvP+esGEGTGvSzCJgQxPuoYsSE9NQkoSq1EcDqar5bjYMcSM4gBx1pGdRgvGbBRP7DnD11Hdcv9CzMhWuBzrAjf0yKlr/R4e3R/uPYdn712AjLRkQT3iTfdxx9UXO4xhT1B2hnXAm/z468QRkoXtD1Zg466TWLfMIJiX9Nt1N/OnJy980MjbAg/+2rstEMi6z8l4kUaBTStLXUKL3Bt3cN7hqSC7QRnFhJDvUUp/Sgh5Hg4PsQuU0tqQR5YguCvPpu5hweOLLfctENzB9QyP8j+7e57vqyjwSFLxty5grLvu8AojT4k+0yh21iyF2WpjHpMo0do/wmKxw0i+Kg3n241YHeuBJCDOOtJup3jl60v89sC2Dwp7vigobjVoBD/rTfc5l9IKZLMeKKwD3tTAn82TkCzUNfe5xNY7YxmzY8xmD8oWCGbdF3Pucac1U+3ENlhPcf3Ef+vCNZDJgvPxhS5TxtcwzFamCu7gdE61T92FUyyRz58QiEiFIgRyJMi8vrGjrZ/FE4eT6So5jjSxds/BIKQzhJLjhPSKLjNNUG/qVXIkiZyCeNN9nH4Wqm4RTk8Y64DH4BCShdQkiUussFBy+c0zNAHbAt5k31djMPe1eqqu3UEZxZTSdyb++2p4h5P4OPckd65h+PHlbvznXfNdYuOevns+5ulcj2OdhbOpezikEIhwG6XsSDBxaO03sxrFYUSvluPVj4ZjPYyEwx+d4e2aeboMj6Pcp++e7zOMRUz3OR8vO1e3WL0oP6w6jFWwYHC4y4IuUwZlahI2VJdgx6ctoicWwdoCQrLP1m7/CTZ84h0IhE1wUEqnbJwxd3xxocPoEitU1zwIoBm/XXczeocdLWPn6TJFvR3O9wo1BCJcCR9iR4KzH63EzNypt6OMZ5p7zdAwT3HYyE5Pgdk6jn6TFSq22fAbMZ2RX7MUZflZXivlcK3qbypS4bWHl6DHNIr8zDTMm+Zdb3ojWmFlsQ5fY8QPQmGRz/zpAt8NTyIBnr13ARSpUhRqFILrsy958rXGs7Xbf4INn3h24r+rAeQB+O3Ez18F0BnqoBIZ7vjCfVcHOAzjcbsdfzdfF9C9QgmBCOcOkTsGcg4LAYDLPcN8sgwjPmjtH8HyWWwBDheEEOg1ClzsHMJSgybWw0kYxMIIDlzoQtuABSvn5Yle02m04ELHkIfuKivICno8wejUYJwKrJIOg0MsLJI7qeDYUXOz6Kmur7AIX2s814HPed3efawV9R1Gtna7EWz4xN8AgBDyc0pphdNb7xBCpnycsURCUKRRhOX4LNQQiFATPpwXBHlKEioKM1E1J8/luOeJVXPR0mcKW893Rui0DYwghx3VhpUClQyXmFEcEGJhBDY7eD3kHHLGLdpSAihlSXxXLSB8yWqB6NRQnAosp4LBEY6wSDF58rXG2+0USlkS1lcV46m953k53lBdguv9ZlztNTEZdSLUek0KQoiB+4EQMgMAc08hfhpReEv48AW3INy59TC++tJRrN1+BP9yW4lgt5tO46iPuzGihd1O0TFoQQ4Lnwgr+VlynL/O2j0HgpAerK0qwZvHW3k9VKRRYNsDi/C1Wwrx8odN2HawEf9zqAmXOoehkruGqviru8JFJJsgMaYm4bYNvK3x3Br+3vlO3iDm3n/uQAOsNhrV+ZQIhNq849sA/koIaQJAABQC+MeQRzUJiJfjM38SPsSOB4UWhJPXBgQnoNk6Hp0vxPBJ9/AoFKlSVqM4zExXpeGd0+2xHkZCwenB/JqlOHChCzY78PrHzXyd4lylDBIJwQxNOtb/7oSLrnFuAc3hzaMWiWYZrIoEI9yE2zYQO2nJy5Dxa/gjlQZBObba7Cz5041Qm3fsJ4SUAJgz8dIFSilzGU4QD8dn/gToix0PCi0IdipcQkavZgcE8UJrv5kpuggwXS1HY9cwKKUghMXg+YtEQlCWn4W2AYuoHuoaEjY+uRbQvpLVIpVdz6pIMCJBOG0D7qSloXOYr2UsS5Zgdl4GVPJkXnaF5LiiUM2SP90I1VMMACUAZgOQAVhACAGl9LUw3DfhiMe2nr52pd7ikYQWhHdOteFH95Th394647L4zMhmEyteaO0fQbaSVUgIN0pZMlKSHF2mpmWlxXo4CYUvPSRmfLq3gBbTqVd6Qs+uF9LfrIoEI1CibQeInbRs3HUSO2tugSxZgt3HWj1Kv21eUy7aAGcqE5JRTAh5EsDnAcwFsA/AFwB8CGDKGcXxXAfQ267U2/HgkiINNq8px6bdp/nvtLZCj98fvYqdNUsxMmZjWdVxSGv/CNRyZhRHAr1ajoudQ8woDgJvekjM+BRqAS1Ec59JUI+19Jn8Moq96e94CINjJAaxsgPETlqGLFZsqC7Bcwca8PrHzahZboBeLces3HTMnyiJyHAlVE/xvQAWADhBKX2YEKLFjfJsU4pEbespVFj84VsLYbNT/PVSF/KzZNhQXQKT1QZKb8QDjozZsNSQ7fP+8eg9n+w095qRo2RJdpFgWlYaLnUM4bbZubEeSsLjrhtWlGpFvcLe9IjdTpGekoTa6mLYqaPUFBezLE/xb4nzpb9jHQbHSAxiZQc4r+O6TBm+dkshpqvkMFltSJFK+DXcZgde++gq/u2Lc3H0Si9bkwUI1Si2UErthJBxQkgGgC4A08MwroQj0RIynBeZlx6swON7zsA6TvHPnzPAZLVh3at1/E534x2z+IUG8D+mLp6955OZa31mLJ+VE+thTEoKstJYBYow4E03CLWAFrsWgMd7tVUl2FnXgq/cpIc2w/vmkNODlzqH8EilwUXPxbP+ZsQnsbAD7HYKSh0NQFr7zVDLk9FrHsN3/3CKnxPfvt2xhgPA124pxEOvfMLWZBGCNoqJI9PkNCEkC8BLAI4BGAZwJDxDSyxCTciIpkdVaJHZvKYcBVlp+FtDN7YfanLZ6W55/xJqlhvwRl0r7qsowKxcJSh13MfbGBPVe57oOGoUM09xJJiuTsPhhu5YDyPh8afDFqcTu4dGRfUIAI/3th5swLP3LoAsxXsCsJAerK0q8aiOwWD4SzgSMwOxBYRk+IlVc7Hj0xaXOfGLv1zC+tuKYbXZ+WQ87j22JrsStFFMKaWEkCWU0gEALxJC9gPIoJSe9vY5Qsh0OGKOtXC0it5OKX2OEKIGsBNAEYCrAO6nlPYHO75ow8XEbd5fj1Xl+ZBKgJsK1dCr5D4/6yzYKnkKb3iW6jIi0m1GaEHatPs0Xn14CewUgjvdRdOzUKRR4DG3BDtvO0yxXXMzC6eIGKxGcWQpUMlxpdcEm51CyuTWK94WdzHdwHXYstspPmrqRV1zH0pylVDJU3gPLndt15AFVERfpSZL8PmS3IA37VsPNmDdMgNe/rCJJdQxAsY5Nt55LffHiQQ4rjl4sROnWwdhp47SamUFmaiarRX8rJAMP7X3PNYtM7h0y7OM2aFXy6GUJSXUiXYsCDV84jgh5CZK6aeU0qt+fmYcwHcopccJIUoAxwgh7wP4OoADlNKfEEK+D+D7ADaFOL6oIZEQrCjVYsxmd0lM8+doghNslTwFDy4tdMkQjcTRhtiCZLaOQ0qES7eoFSn45/89HtAOU2zXfOLaALYeaGRHNxGga2gU6bIkVqM4QsiSpciSp6Clz8wqrnjBV+iUmG641DmE+dMycKp10EWPbqguwWtHmgVDuITuc6ZtEGM2GtSmvTzfUfGCbdgZgcJVWZm7oRLHWwYCciIBQEufCQ2dw/xpLSf7xTnpgh1jxWRY6qb+ZckSNHYP4xaDhpUY9EGoK+fNAI4QQi4TQk4TQs4QQrx6iiml7ZTS4xP/PwSgHkA+gLsAvDpx2asA7g5xbFGnpd/MK3LA/+5HnGCvXlzg0S3On8/b7RRN3cM4crkHTd3DsNup1+u5BckZrtZwWUEmNlSXeHTbsdrsojtMMYQ692yoLsEbda0BfT+G/1zrNyOXhU5EFL0qDRc7WFyxN3x1givSKPDMPWUene7eqGtFp3HUQ48+d6AB91UU8NdyXlyxjnlv1LX61C1ierBEq4QhJ50ZxIygkEgI7BS8QQwEYguMeoQ3PHegge8Y677W5yqFZbi8IEtwTpxpHUBtlev6vnlNOTsRcSJUT/HfhfJhQkgRgEUAjgLQUkq5dlEdcIRXCH2mBkANAOj1+lAeH3aCDbLnlDMhwkeB3j4v5pFZUapFS79Z8OjSW/mjGdkKFOekY7FeBbN1HHq147WrvaaAd5jutUkJCL6186TgMehkObqJtXxe62OVJyLNtKw0XOwYwsr5ulgPJWCiJZ++dKFEQrBYn4Wa5QbYKfjKNv1mK/pMVsHPFmSl4ZWvV6Bwwhjm9NnKeXnQPLwEhxt7XCrkAPCqW1gN4vgi1roznARrC5is46KnuEKhFYsLswRlOD8rVXBuDVpsePN4K9YtM4AQx3v5WTK2AXQi1I52zcF+lhCSDmA3gG9RSo3OHaIm4pUF3Z2U0u0AtgNARUWFd5dolAk2yJ5Tzhc7jAF/Xsgjs3l/vdcwDl+F9IuyPY9qgl1AnGuTNnUPo99sdXl/sh3dxFo+r/WZka1gNYojSYFKjvPtiekpjpZ8+qML9WoFZuaku+ip2qoSdBhHBD87TZWGWw3ZHgu4REKQo0zFrw43hbRpZzWIY0usdWc4CdYWKFQrRDvGioVW3FmW51HKEACu9Ix4JNNvef8i2gctfLyxLFmCNYvzI/AbSFxiEnhICEmGwyD+X0rpmxMvdxJCdBPv6+Ao75ZQCB3l+Ws4rpyXh3sW5XscKfr6vPuOVJcpw3dWzMHl7mE8UmmALlMmeHTDGatLDdmCR4XuxzSAwyOzr7YSO2puxr7ayoBjgYP9/TD8p7nXjOxJtMmIR6ar5bjYMRTrYcQ1/sx1iYRgWpYM65YZsL6qGOuWGfD6x82wjNk9Qrg2VJcgPzNNVN84P0+XKUNtdTGevXcBn+Akhi896ItAQ9cYU4Ng17oZ2cKfm5GtQO+wFSNjNjxS6ZgvKnkKnjvQgI7BUV6GizSOU92jV3oxV6fEu4/eWK+/OF+HTStL2frrg3C0eQ6IiVJuLwOop5RucXrrjwAeAvCTif/uifbYQiUUz4NEQlCUnQ69WoGF07P8/rx70e4Hlxbie071CZ1LDAkd3QhliDtnftupo7XzppWlfA3RYEMdmGcm8rT0mVGqy4j1MCY10zJluD5ggWXMBlmyNNbDiUv8nesaRSpe/tDVwzsyZsMbda5HvK8dacYifRZm5KQL6iwAmK1V4r//fjEIIXjyj2fR3DsS0WReVoedIUawa53Y5wDgSq/ZxUvMre1jNhuauofRaxrFuI3iWHM/LON2l3XbOdSIrb/eibpRDOCzAB4EcIYQcnLitcfgMIZ3EULWAWgGcH8MxhYy3lqZRuLzzmENQol6ziWG3I9uhJT6tgcWwTxqw/fcjjQ376/HnDxlyLG/of5+GN5p7R9hiXYRJkkqQV6mDI1dw5ifnxnr4cQt/sx1obCsmwrV2H6oyaWkFHf0LGaIpiQRrP/dCUFnQKTqsLI67AxvBLvWCX2uqXsYj799xmNt33h7CXpNY/i3tz/B2gq9S+UqoXWbrb++ibpRTCn9EIDY1qQ6mmNJdDiPiUqejJ01t6DTOCJankXomERIqZ9uHfRo3sEZ1pMpIW4yYh23o9c0Cg2rURxx9Oo0R/kwZhSHhJBnTK+Si+YviBmiNcsNgjrrhQ8aI1YbPdG6mDISC+cTkZExm6CszdFloOb1Y1i3zCDqEGPyGBix8BQzRAi1k81LD1YIBulXz8lFWX6Wx72ElLpY8w6pBKJJAtHsxscQp21gBNnpqaypRBTIz5Kjvp3FFYcDiYTwG/ZOo6NqxIpSLfZvqESncRQm6zgK1TfeF9JP7qG8ljE7uNztSNVGD0f3MgbDGee1dNxG8fieM2juHcGG6mJBWRu3UV7WxdbtnHQmj4HAjOI4IdD4NCGPyeN7zmDzmnKPqhNCBjEgrNTFmncsmaEWDMh3H3ehJg1P3VWGZClhBnKUaekzQ5vBFGA0KFCn4WhTX6yHMSnwNyRiy/0LUaBKE9RP7ipGliwBpTeS9F474iiUFM4QB1bSjRFOvLUd31XXig3VJXwNY07WCjUKl8Q593lRXpCFyxOJ8pHojjsZYUZxnBBofJqQx6S5dwT5WTKP8iy+MradJ2GpLgMb75iFLe9fcin7Ipb57TxuXaYMayv0qHm9jiWexICWPjNylKwcWzTQq+R4vTPoipQMJ/wNidi46yQ23l6C2qoSl9jJb98+C2nJEt4o4MpP5WfJsLwkO2K10VniMCOceGs7/sIHjXjtSDNefXgJKKhLAh5Xas19XvzonjK8+NcG1DUPsrU4AJhRHCa4Y49e0yhSpBKYrbaAPKWBxqeJHd2pFal+B9ILKfVe0yh++M550cxvb+MW68jHEk+iw9UeEzQKFk8cDbKVqRgeHcegeQyZ8uRYDyeh8SckQpcpw+rFBchMS0H/iBXrbyuG1WZHca4SP95XDwAeBoNEQkRro6clS2G3U143BxsCxhKXpi7uMqNXyUUbZvl7L6F5wIUB9ZutyFGmesjatCwZVpXnQyIBfnrvAlztMWF03I7eIQvqmgf5+7C12D+YURwGuGOPzfvrPTJA/d2dce0a3Y1csXigcB3dCSn1frNVMPNbCK4mqJ06SiKp5CmTumNdPHO114T501jiVzSQEIJCtRwXOoy42aCJ9XASGrENPqcyuVKT7pn1b9S1Ys1nCtA+aHHoSgGDQUhP1laVoHbHCb5cFQBef68qz4dUAtxUqMYtBg2SkmJSyp8R5wiFDT5aVYLH3z4b8NrP3UuseRcXBiS2vguVNZQlS7D+tmKX69ha7B/MKA4D3LGHUAaov7szqQQeMUMbqksgFdHJ4Ty6c97x5ipl2PbAIo9YPrF44vPtQy61Ex/7whwYLeOwjDt+B++camOJJ1GiudeM6jmC3dEZEaBALUd9OzOKfeHLCyu2wU9JIpAlS0RLTW6oLkG+So7a6mLcVKiGXiX3eDanJ/NrluLAhS7Y7PAo1QZA0KGxeU05vlQ+jR03MzxwD3VYVZ7PG8RAYGs/dy+VPMUjBIILA1qzOF9wfbfbKSgFnr13ARq6hrCrrhX9Zis23jELM3MU2HjHLFhtduw+5nidrcW+YUZxGOCO/8QyQP3ZnbUPWvDakWbBsAX3lssc4Ti6E0ty2b+hEh1G78a2u2JQyVNgstqwbaIMkixZgqfvni+4WDHCC6UUbf0j0Gaw8IloUaBKw3lWgcIr/iQQe2tYsK+2Epc6hwT16rSsNJdGRWKeOYmEwGy1YeuBRo97dA1ZQKnDqHE3vDftPo2y/EzmWWN44B7yE8raz92rfdCC1z++YQNUFmfjpiJ1QBWonlg1F9YxGySE4JtOjq0N1SUo0aazJFA/YGdDYYA7/gPA/5fD3xI92gwZH7aw7WAjXvig0WVnJ9ZONNQ2o2JJLnYKn61P3RXD6sUFvKebu9fjb59FS785oDExAqd7eBQpSRLIU9g+N1roJzzFDHGENs4XOoz466UuF30l1G6Ze22WVimoVy93D3voLedW9hx2O4U8RYra6mKsryqGLlPG3yNXKYM2QwapRNyoYTDccV7zOUJZ+7nPtg9a8MIHjXjnVBsUqVJ8erUPBy904nKX59outHY/tfc8NEoZfrz/gsvrzx1owAxN4G3MpyLMKA4D3PHfO6faUFtVElRvcW+90rkd4Z1bD+OrLx3FnVsPY/+5DjT3DuPtk20er3OTZ3zcjlPX+rH/bDtOXRvA+Ljd47neEvx84a4YvO2WGZGlpdeMvEx2NBZN9Go5GgUWK8YNnPULFxu8/VATvvGbOl5fXe3xvqkX0o3P3FMGeYrUxchVyVPQPTTqci9Od67d/jG2HmjErw434cGlhSjUpPH6tUijwE2F6qCNGsbUw10m3znVhqfvnh+Wtb9Qk4bvrpiNww09eOiVT/CN39Thi88fxpsnWnG2bQBWq6Ots9gJyoh1XPD17mG2DvsDcyuFAf74L0+JPtModtYsDbj6hLcY4abuYUFv7vYHP4PH3jrj8fqc2kroVXK8farNJfD/6bvn4+4F+S7JI6EUoHePBRSrceztXqFW7WA4uNJjgpa1d44q8pQkZKQlobnPjBnZ7FhSCE6/qOQp+MGdpWjsGsIjlQbsPtbKx/VuqC7B8KhNNMHNWTd2Gi0Ys1E8MdHUgEuc23+2HV8o0+GhVz7h9d22BxYhR5GKCx1Gl2duPdiAnTVLXeq332LQCNZ4Z8fNDCHEOjEu1qsCzvFxv1dashQHLnR5dJZ9/O2zqFluwHnVEHZ92oylM3ME11t5ShJkyRLMyk3HI8tnYmR0HApZEr95ZHiHGcVhIhzxvWL3EPPmtg+Ie3mHLGMegf+Pv30WJbnpWDBdxV8fShUL98mclyHD7LwMv+8VjqodDAdXekzIZY07ok6hRoH6diMzikUo0iiw7YFFaOgcdon/5ZoStA9akJOeiucO3Ni8CyW4cboRAO7cetgj6e7ZexfguxP3Bxxe44bOYaw/cELwmSNjNpf7JyVJ8KXyaSjLz2Q1hxl+IbReB2sDON/ryOUe0c6ydgr8+56z+Om9C/DjffWCiXnTVTL88u8XodNodZlzT989H/mZclZRxQfMKE4AxLy58tQkUc+s2NFKx6AFC6bfeC3UKhbuikGvVvh9r3BU7WA4aOoxoZj9rqLOdFUazl834s4yXayHEpdIJAQzNOl8NRvAtSnByx82oaXf7HeCm5iDQOIWEyyU3+D8TKHTK1ZzmBEPyFOSRE9dKb0RIuGcmFeen4ESrZJfb0+19ONf/veET6cYwxO2ZUgAhGLqaqtK8NKhy6IxzLrMNMEYOaG4U6Ekl2AJ5F7+VO1g+MfVHhOLKY4BerUCZ9oGYz2MuKZrSNiQlUqAJ1bNxRt1rR7vic19sQSn/CxXfSemU6QSsLAIRlxjtdmglqdgQ7Xr2v7t22fhzeOtjuYzEwnV7YMWvPxhE0q0Spf1tl1k89gxyNZUXzBPcYzxp5OSsze3udeEE9cG+GPA7mErapYbUJqXgVlaJd/ffJ4uA0/fPd8jpnieLn6aO7hX7QgmrpnhKMfW0meGloVPRJ1CjRy/+4S1e/aG2ElX9excjIzZBDvO5SplgrpRLNxrni7Tr/yG6jm5LrHEDEasEFv7NYpU/PqjK/jKTXr8/L4FoACu9Znxm4+uot9sxX/eNR+vfdQEQDyhj3OKucs/c5z4hhnFMcSfGp4cnAe2SKPAyJidX0j6zVbMzEnHHaVal1ihpCQJ7l6Qj5LcdHQMWpCXKcM8XWZcxRNxC9zm/Z6xUcyb4z/dQ44kxfRUNp2jTY4yFcOWcfSbrFApUmI9nLhEzJAtK8iC3U4FE9z0KrmobhQL9/Inv4EZxIx4wNvaX6RRYNPKUpdueT/88nz88MvzoM1IRak2AxWF3hP6EsEpFq8QShO3nFBFRQWtq6uL9TB4/PH6OnO1ZxhvnmgDV4WI6zqzbyKWVux+3OuTISGE+y59plEkx2f1iaAHES35PHK5F0+/ex6Pf3FuxJ/F8ORH757HD+4sxWeLs6P96JAmSDT1pzedJfTe1V6TS0Id4ChVtfUri2C22pCrdNQWbh8U17VizwxUTzOCJu51Z7jxV7aauofx8G8+waryfBACpCVLkCwhKJ6IC9ar5GjpN4e0xo+P23GufTBunWJxgOAvlLmWQsB5AugyZTjfPuSX19dup7jSY8K564MguGEMP7FqLoYsY+geHoVeJcd79Z2i95ssCSGT6bvEiqaeYRY6EUOmq+U42zYYC6M4YXA+6braa8LRK70uRoO7Dug0WqCSp2D14gIQAqSnSkFAsHb7xy5dul470ox+s9VrtRpnv08gp3OMqUswGydvsgXA5X6DI1Y8sKQQv/jLJRd5/re3zrrIcyjrYlKSBAumq1wS6xm+YduGIHFvqPHmiTbBWsLuHZa4z33x+cOo3XES/3PIUUxeJU/BU3vPY8hiw0O//gQfNfX6dT8Go6nbxIziGFKoUeB0K0u284VYEyKhhh26TBm+dkshXv6wCdsONsJstfEGBHCjS9fqxQWCulHsWVd6hDt4Mr3K4AhETp0R6w57pcfkcb+rPWb87pNmv+WZET2YURwk7hNArK5gc6/JZTIJTZytB29MBqVMinXLDGgfHMEjlQaXgtuRrsgQastoRmy41DmEaSyBImbMyFbg7HVmFPviaq8Jm/fXY90yA9ZXFeORSgM2768XXPxtdriUVBPTr4Tc+P9LnUO83hIzUJr7TEFVumG6ceogJju+jFSxcoEtfZ73+8FbZ7CqPB+6TBm+eVsxPx+UMil/jbtMMhmMDix8IkiEJoBQtueJawMYGbPzx3NiE4cQR8ycUpaM//rLjYQz54LzkazIEMtjRRbjFxqXu024Z2F+rIcxZcnPSkOn0YLh0XGW7OiFXtOoR5OeJ1bNhXHE6nGtUBk3sbqt3P+faTPiWztPYsv9C5GjTBHUswovtd3FYCEXUwuxNbpryOI1nEGsyopUQgTvlymT4sGlhR7zQZcpQ7/Z6iKT/sggW0fDA/MUB4l7vczdx1o96grWVpXgjbpWl12mWJ1NCQE2rSzFU3vPC3qRI12RIdjdcaiMj9vxzunrAR9VMRxYxmzoGRpl3exiiFRCUKhW4Px1Y6yHEtekSCUeTXqe2nseAyPjHvPdH/26obqEr9taW+X4f05vpUglgnpWq0z1qPnuS6/GSjcyYoPYGu3LIVWkUWDzmnIPG+BCu1HwfqW6TMH5cF9FgYdM+pLBYEM+GJ7ExK1BCPk1gFUAuiil8ydeUwPYCaAIwFUA91NK+2MxPn9wLzPUb7aiRJuO176xBIcaekApeA8vAH6XKVSe6Jl7yqDLlOFYc7/gjnLeNCVefXgJtBmpYRu/+66y1zQa1O441DF81NTLl2Pinsm62fnP5e5h6DJlkDKPQEwpypbjdOsAlsxQx3oocYvZahPUMcdb+qFXy13mu14lx/YHK1DX3Ac7Bd451QZFihTrbyuG1WbHkhlq5GemYUa2AmfajC661jJmh9lqEy71plYE1HUTCN5zyEhMxEoI+nJISSQE07JkWLfMAELA2wAA8Mw9ZXjsrTMu9xsaHROUq7L8TNw2K9dFJntNo/x9AccmsX3QwsugWKz87EcrMTOXyWggxOqs7zcAtgF4zem17wM4QCn9CSHk+xM/b4rB2PxCrD3y1V4TfnW4SfR4TuxzgKO947YPGj0+e6FjCFsPnEChJg1P3VWGZCkJ6XhE6Chm85pyFGrS0Nw7IjjuSHC114S65j624IRAY9cwpqnSYj2MKc+M7HScvDYQ62HELXY7hTxFOHTBZofLfLfbqUflnSdWzcXOT1pwus3If25fbSVmaZX41s6THvccs1Fsef8i1i0zQCoBKgrVuNWg4fVlIBVvxI7FWXOhyYnYGu3PWqtRpOLlDz3X/8X6LOxzu9+VHpOgXI2O2XGkqRe3GDRISpLAbqe4PmDh78t5oHfWtfAyKBYr39JnYkZxgMQkfIJSeghAn9vLdwF4deL/XwVwdzTHFAxCLY2FWjK77zLFWiFnpiXhmXvKPI4I36hrhS5ThrUVetS8Xhfy8YjQUcyW9y/iJ6vLUVvtCPov1KRFvIFGp9ECO0VQR1UMB5c6WJJdPGDIVuAUM4o9sNspLncNY9+Zdhxt6sGTq+aiUJOGb95WjNrqYvzi/oU42tTtMt+F9NNTe8+jclYufw23cRbSt5vXlOOJPWfQ3DuCFz5oxNYDjah5vQ4t/eagvoM/Op0xuRBbo30hJit6tYK/H+c86zOPeoRbbKguwY/21eP/vV6Hd8+28ye6m3afhkqegm/e5kjIGx234Sery3kZVExsOJ2RJUsgT2E5DoEST78xLaW0feL/OwBohS4ihNQAqAEAvV4fpaH5TzC7TGfPrUqegprlBszSKpGrTMWGHSfRPmjBN28r9og/CjbMwP04kDO4H/7Npy6e4xWl2ogG6mszZHjnVJtHN7vNa8oTdsGJtnzWdwyhLJ91KYo1+Vlp6DVZMWC2Iksev53toimfQidSP109H49WlXh02tKr5PznvCUjc3AbZyF922sadTnx4j4f7OlTKJ5Dhv/E+9ruD75kxX1OFGrS8N//8Bn0Do+ipc+M147cCAPatPs0yvIz+Zrd7kl5hU5rpDYjFRuqS/iKLZyBHc6Qy6lCPBnFPJRSSggRdIFSSrcD2A44ut5EdWB+EmhDCmfPSPugBVsPNEKWLMHOmqV8O2dChEsScd6SQLJO3Y8DVy8u8DC4uQnpfKTp7RnBZL5y7Sy5Mk1Cx5yJRrTls6FzCHfO10X6MQwfSCQEM3PScap1EJ+blRPr4YgSTfkU8vg29pix/ZDjGFiXKcPqxQVo6TPjXLsRZfmZkEiIaLgCpxI475uEAEcu9/D6xlnfBlNhwpv+Yk2GIk8irO3+4E1W3OdEc+8I/vm3x/DsvQuw9UAjfx03Ny51DkGXmYb7Km6s0dx7V3tNONM2iLL8TOjVCpRo01Gz3AA7BSQEKNGmQ69OTOdSLIkno7iTEKKjlLYTQnQAumI9oGgh5hkxW218wD8grOjzMmQBlwtyTySQSsQNbq7dtLdOPS19JhxvGfBIJPBVsojfVecpmQcmCEasNnQOjUKbybwB8cCMbDlOtvTHtVEcTYT0GldvWJcpc/F8bT/UxOsMsUSnuTolbp2pQU66DFd6h7HyucOC+ibQRKlAOpEx/cQIBbG1Xioh/PruPjcKNWnYeMdsn/OmarYWhux0tpaGSDwZxX8E8BCAn0z8d09shxM9xDwj2gwZbp6hwZzaSvSZRlGSm+6RTT1uo4JZp97CKpyPeDqNFqRIJbz3xvn5nGdFrBzM3A2VON8+hAsdRpfPBxLawTwwwXOxcwjTVWlIkrDKivFAca4Sn151T5WYugjpNSlx6Bah0ylnnSGWjGynDn10pnUQKnkK2gctHp+VSAhWlGqxs2Yp2gct0GWmYZ4uQ9RAENNvsx+txMXOIVafmBE2xNb6ktx0bF5Tji3vX8R3VszB9/5wyqXNeWqSxOu84apMsLU0dGKymhJCfg/gCIDZhJBWQsg6OIzhOwghDQBun/h5SuAtkYMzGhfr1VhQkIlXH16CV75egXcfrcSKUi0udAyJenm9wXlU+s1j2PjGSdRWlQg+HxDf3XYaR7Fx10nRblOR7L7HAOrbjZjuFIvJiC0luek4dW2A1QadQEivzdIqsXlNudfTKcAz0QkAX4f1G7+pw/8casKDSwv5jp/On+WqV6zd/jH+6bfHsXb7EbxX3yn6dwmkExmrT8wIBaE58cw9ZUiSEnxhbh423jEbjV1DfAwx1+b8x3+qxw+/PE903rT0MZkMFzHxFFNKvyryVnVUBxInBBqczxmtANDQNRR0uSDn2oavf9zMx/VWz8lFWX4W/3yx3a3JOs6/JvR+WrIUdjtlXpUIcf66EQXMKI4bsuQpkKckoalnGMW5ylgPJ+YI6TW9So6/NnRhllYpqDMICJq6hz2OfoW8uVsPNmDdMgNe+KDRr5MtsZMrMf0mT0li5SIZYYWbE7MfrUR9hxGXOofwsz9fRL/Ziu0PVmDT7tN4pNLgEkMMOGKPf/nXRvxkdbngqS6rMhE+2LlrnOCtBIyYkm/uM2FXXauHl/eZe8p8Vm+w2ynq2438PdsHLXz5opExm8vzxTzZhWoFZMkS7D7mOYbaqhLU7jjBuupEkLPXB1GoYUZxPDE7T4m6q3HbcyjquOu1ln4z1v/uBLb/7TKeXDXPoxzVt3aeFCw36a0ihb8nW2InV2L6TZuRyspFMsKOREJACPDdN05h64FGPgSIq9m/+1grijQKDxlu7h2BcWTMo7PjxjtmsSoTYYRtLxIAMSWvSE1Cv9nKe3kJcWSdLtZn+fTOXu01+e1l9tZwhEtoef3jZjx77wJc6hqCzX6jmx/rThcZ7HaKix1D+JfPFcd6KAwninPTcfRKL76yJDFLSkUaTpdVzsrFi4casW6ZAXpVGtoGR1zKUbnrDTFvbmVxNlYvynfxLAfabMMf/eZPwh6D4S9iSaiyZAnaBy2QJUsEZXjMRvHaEdf1nqs+wQgPzFOcAIj1YtcqU7Hl/oXoN1vxwgeN+NXhJszJy/BrgnQaLQF5mYU82dxisq+2Er9YuwDyVCm2HmjECx80urRcZbHF4aepx4SMtGSky9i+Np6YrVXikyvMUywGp8sIAd9c49rACO8x43DXG2Le3JuK1B4na8E02/Cl33bU3Ix9tZUsyY4RFoTW9HdOtfHNPFr6zIKnr60DZv5Ud9vBRr6MG5PJ8MFW1BjhXBczVymDVOIIYRAq++NeYohr99w+aMFsrRL7N1Siw3gjZs+fEkLaDFnQXmZnnKtHNHUPs3aoUeJs2yAM2cw7EG/kq9IwPDqO6wMjmJbF2m+7w+myix1GF13hS284x2K29JkgT0nij4ztdoorPSY095mgSElCXmYqSvOUePXhJTBbx6FXKzAjO7jyVKw6DiNcjI/bca59EO2DFkzLSsO2BxZh/e9OuKzpSYTgf9fdjFGbDT948wy/NlMK7KxrwV0L813uyVWpYoQPZhTHAKHEuQ3VJXjtSDP6zVaPsj/Ox3t9plG0DVhQ83qdYE1Nf2sWOxvaXKIK147S3+/gbnwHWh+UETwnr/WzeOI4REII5k7LwNErvbhnUUGshxN3cLpsrk6JQo0Cj711BruPtXp04xLTG84l0go1afjJ6nK0D1pcaqQ/+aV5ePFvjWjuHeHvNYNtIBkxZHzcjrdPtXl0cvxTbSV6Bdb0H6ycg7+/uRBb3r8ElTwF91UU4F9XzIFKkYxCTZqLbLP1NbwQShM3CaqiooLW1dXFehgB09Q9jDu3HvbwjDhnUu8TicMV++y+2koAEH1P6F6cYetPsW9nI1iXKcP5ds/6nStKtWgdMKPTOBqyhyaOCHrwkZTPLz//Ie5aOA1zp7EWz/HGn891YMgyhp9PVIiJICFNrFjrT2f9k5chg80OdA+76iJnvSNPScJTe8/iZkMOlDIplLJkdBotgtn4nC7lfhbTgYyIEpe6MxacutaPtds/9pDTnTVLoZQlC67bG6pLICWAOl2Gx9++sen7yepy5Ktk0ChSWYOO0BD8xTFPcQzwlknN/b972R9ucbjUOYRHKg3YfazVI26XOnWL4op+A0CfaVRwQfD3aNDds11bXSzYrGP7gxUeHmzmoQk/1nE7LnUNsUU+Tpk/LRM/e+8CKKUghC1YQriHPADAjGwFZua66jz3k68nV83Di4casao8H//1lwY8Umnwqku5n1kZNUYs4SpMOKOSp8BoGUf7oEVwTVcrUpAhS8a33SpPff/N0/jVQxXMII4QzCiOAWLZ0ZzT3j2eTmhxqK0q4Ss8OF9fqEnD2go9X+OQ65azOIR6we4l4cSadZxuHQiqqx0jMM5dH4QuMw2yZGmsh8IQYFqWDDY7xdVeM9sUCiAWPlaiTUfVbC2vp4RKUf5w7zk+ztJbPLLzAag/eQ1C4WDM4GCEC4e+viGnukwZvnZLIf7fa3Wia3r30Khorezr/SO42mtia2sEYNUnwojdTtHUPYwjl3vQ1D0sWp9XKDt6Q3UJ3jzeKhgnJFa4fvXiApfrizQKPHVXmUcbyE27T4fUhUnIsy1UDcOQk47y/Ax887ZirK8qxiOVBvSZRv1+jr+/v6lO3dV+zNIyZRivEEJQlp+JQ5e6Yz2UuERInz13oAGnWwdd9JSvEzX3Gum6TBlqq4vxk9XlUKZKocuU8RV1JASi+oQz0u/cehhffemoYJ1kBkMIf9eseboMPH33fH7dvK+igI+hBzzX9G/fPgvK1CS+DrczsmQJstNTA1pbGf7DPMVhQqzrHJcA5+6FcK6LmZPuqD6xSJ8lGNsrtjiU52dgX22ly/XJUhL2Lkzunm2hxJjaqhJs3l+PTStLXX4H/nqpvf3+mMfGlY+v9GKuLiPWw2B4oSw/CwfqO/HQrUWxHkrcIabPkiQO7xinJzmjVsgL/OZxhzG89WADXv+4GRtvL4FKkYon9txIZHrqrvnoN43yHcPE9EmgHfAYDMD3muV++vDlsmkoyU1Hx6AFEonwOl2qU2LdMgPslOLH+y/gW7eXeKy1G6pL0NpvRuZoSkgnwAxhmFEcJsQU69wNlYJJaSvn5XnE8xZlCytgsXCLEq3SQ2kHWrjeF3Y7hYQAz9xTxmd495ut0Kvl+O6KWVCmJkOemoS2ATMyZcmw2ykeqTQAcBjPm3afRll+Jgw56V6PKNnC5B92O0Xd1X6sZpUN4pqygkz86sMmjFhtSEthYS4cdjtFilS4McHcaRl46JVPXPSkc9kqLmP/+YMNaB+0YGddC7Y/WIFkKYE8ReqSyGQZs+OJPWexbplBtCEIh7cOeNHQPSx0IzHxtmYVaRSiBvOC6US0fOl0VRq++8YpfOv2Eqy/rRjZ6anIVaZiQ3UJTFYbJARQpEhhstrwzJ9urK2M8MGM4jAhplg7jaMhG3uBlDoLZ1k0552wSp6CmuUGzNIqUZqXgSQp0NJnxpPvnHMphfSz9y7w5WK4GKmuIQuKNAocvNiJ062DsFNAShyGAxdDGOuFKVE4325ERloS1IqUWA+F4YX01CTMzEnHh409uGOuNtbDiQs4fbJ5fz3v5XU2dp/ae85DT/7qoQrULDfATh111LPkyXj14SV8XXbOgDxyucfvhLsijcLFCBXzSEejvjo7IUtcfLUT97bui63T83SZ2PbAIvQOW/HkH2+srU+smotCjRyK1CTUtxv5mHq2PoYfZhSHCTEPrck6HrKxJ9aGVEhp+ro2EK/ElZ4bO+H2QQu2HnCUi3v30UqM2+ARE/XDd87xpZC4GKma5QbkKmVo6TOhoXOYr1rBHQMV56SjKDs97B7uycqRyyx0IlFYOD0L+8+2M6N4AmfP2v6z7fjpvQtgsY6jUKPA+euDaO4dcbneMmbHJ1f6+K5dwI0yVkuKNC56y1fyMvdzXoZM0Ah190hHq/4rOyFLXLytWb6cPO7rdLYiFZZxG/5yoRM5ylQ8tfe8i0w8tfc8apYbYLMDL3/YNGEkp7H1MQIwozhMiO38CtWKsBh7gXRWErs2UK9Ec69JcGK39JkgS5b65ZmZpVWiSKPAp1f7PIzoHZ+24DN6FdoHHcZ7rBamROLAhU58dmZ2rIfB8IObitR4Ys9ZjNnsSJaynGbOUNBlyrByvg7f+8Mpfq7/YiLx2CNELFcJXaaMD4FQyVNwrW8E1/pGUKrL4OugC+lfrnwbd68t9y+EzS7swXv30Urs88PpEKnfiTPMA5gY+DqV9adLoyEnHXqV3KOxR21VCfafbUflrFx+TZWnSDE8auON5O0PVrD1MQIwozgMcN7XHGUKdtYshdlq472wAAQnjl4lR1P3cETjyJzbSuoy05AhSwrIKyFLEY79S0lyTG5/PDOleRmQSIiHx1yXKcPaCj2+5hZD6NyymsXWuWIaHcepa4P4x+UzYz0Uhh/kKFORlyHD/zX24POzc2M9nJjg3nxDlizB6sUFHhVyfrK/Hk+smst7yLgj42ffu4AHlxZi/9l2fKFMh4y0ZHz3D6f4Ll+zcpW8cbxyXh40Dy/B4cYeUAr8/pNmrCrPByFAZXE2bipS4+iVXkEjtHvYgqWG7KgbouyELLFwP2ldUaoV3EwJGczOVVCc17WLnUa09JldcnG2Hmzw+PwTq+by66tlzI5kKWHrYwRgRnGIiHlfbypU85Nnrk6Jdx+t5Ls16VVyvFffGdE4MqG2kk/dNR8qeQrvdQG8eyVSpVLBzFdZkhRSCTze49qrAjc8M1ydVnePudDCuHHXSeyrrcRSA/OECnHoUjdm5ylZfeIE4maDGm+daJuSRrG7bizUpOHpu+ejpc/sYZg2947AZBnDumUGSCXAnLwMvPhXR6vmrQcbsP3BChhHxniD+MGlhS4xyZz+zFGm4leHbzQWOt1mdBjii/IhkZC4M0LDmQPCiCzeTlq59ZMr0cat+3vXL8OFziFc6hwSrIIyPm7H+fYhl7BCLhfnQofRI4Ri/W3FABwyq81gG6dIwIziEBGKCdu8vx4S4kiKEkoqa+oeDjiOzN3rO0+XgaQk8SPZc+2DvEHMPeOJPWdRs9zgEaMntiBo0lOgSJG6JLooUqTQpDsM69eONPOF9CkFfn+0GT9dswAU1MPTOyPbVflLJcINQNixoTh7T7djsV4V62EwAuDWmdn47hunMDw6jvTUqaVu3XVjc+8Inj/YgC33LRRszTwtS45CqQSn2wbx1N7zLt296pr7+KZBYhtqzcNLkJeZiu0PVvDXv3OqDZtWlvJGZrwZoYHkizBii6/4b/fE9PsqClCcm47r/SN4o67VpQrK7EcrATjamnNlBLl7crk4NtflEZYxOyzjsZfZyc7U0tIRQCgm7Cs36XGlxySaVBZoHJmQ1/fpu+fj7gX5ooaxUFtJy5gdepWc95T4mlx6tQLTVMPoMVn516ap0qBXK2CnQL/Zihc+cDWwc5Spoi2lnZV/WnKS4MLIjg2FsYzZcKihG5vXlMd6KIwAyExLxrxpGXjn1HV8dYk+1sOJKkJ6rrl3BFIJ8NRd811qCtdWleBH++rxzD3z8fKHnnrBZgfkE+Fczt3sOCxjdhy90odkKXE5vdq8phwrSm90yYtHIzSQfBFG7PC1bnNGs9BJhnO3OsuYHfUdRnz3jVN4+u75gvecmZOOLe9fdHldlixBZXE2Vi/Kj7nMTmZY9keIcMdxzkxXyT2Syp470IBO46joZziDUKhDjpDX9/G3z+Jc+6DouLi2ku7P6DBasG6ZAbXVxdhZs9RryIZEQlA1W4u7F+ZjWbEGdy/M573dQl35fO1eOeW/1JCNsvzMgD8/lXn/fCdm5qRDJWel2BKNz8/OxasfXQWlU6tDmpieI4Sg3zSKmuUGrK8qxrplBrz+cTP6zVZMV8mxeU25i16orXJ0+5QQgg3VJZCKdPkqylZ46N1Nu0+jpd/scq2zHuIqATAYvvC2bgM3jGahkwyuWx33mev9ZqxbZkB+lvA6nZ8lw1du0nusjzcVqZnMRhjmKQ4RzjjcvL8eq8rzIZU4vENCsbtm67jLZ4SS74RiltSKZJcgfG632TFowYLpwuPi2kq6Z7Ryu1UAuHWmxufkEvNihOpxiUePTTyz89NruHWmJtbDYARBeUEm/vdoM45e6cNSw9T5G4rpObPVhlc+avbwpj1zTxmu9pmw5f2LfGzxYr0K2w5eQvugBWM2it9/0oKv3VLokZRXW1WC6wOescosJIsRLpzl2TnRk1JHLDFnNIudZChlUtRWF2OeLgN9Jiu2/KUBanmyR83u2qoSUABfmJ+HxXoVzNZx6NUKvtIKI7LEnVFMCFkJ4DkAUgC/opT+JMZD8opEQrCiVIsxm8Mr4Rwu8dqRGwaoLFkCvVrBf0bIIHQ+flm9uACEABc7jFCkSLHtYKOLYdtvtiIvUzzUIClJgrsX5DvaShpHce76oItBHI5QhVCP/dixoX+09Jpxpm0Q/29iY8RILCTEMd+3HWycUkaxNz3Xb7bytYpHrONQpCRhZq4c9/zyCCxjdj4sS5YswfYHK/DEnjMoyU1Hv9mKzfsvQpcp4w3nmwrV+NmfL2D57Ny4SqJjTC44eZ67oRLHWwb4Dq/cZm9FqRZb7l+Iix1GDznkago/d+ASinOVaDda8EilAZZxG3bVXXPJzdlZ14KV8/NQlJ0u2uWWETniyigmhEgBvADgDgCtAD4lhPyRUno+tiPzTku/mTeIgRvhElxSm3slBkDYIOw0WgTjkZ5YNZev1ckF4evVcszTZXodV1KSBAumq1Bmp7DZKfrNjthgFqqQWLx0uAmfn52DFC+JlYz4ZvmsHOw5dR3HmvvxmcKpkywppOeKNApse2ARGjqHXWoVP3NPmeAJW7KUYOtXFqF2xwneQ9w+aMHLHzahtqoEj+85g7sW5uONulaPijhMzzHCiURCYKfgDWLAtXLSynl5mKtTolCjcDGan7qrDE/sOYO1FXoXmf/Byjl4YEkhfvGXS0xm44S4MooBLAHQSCltAgBCyA4AdwGIa6NYLAB/0fQs7Ki52e/QAG2GDPdVeMYjPbX3vEunuLL8TNw2K9dr9QlnWKhC4tJltODtk20swS7BSZY6yoL9597zeOufb53Sc08iIZihSecb9QAOPffYW2cEq+NoMxwdwpp7RzA0UbaN86pxp1+Lpmfh1pka5GXIsGJuHl/+kuk5RrjxlXBXlJ0OvVqBhdOz+PW202jBqvJ8j7X9x/svYEN1CWqWG7BoehYKNQomszEm3ozifADXnH5uBXCz8wWEkBoANQCg18dHNrdY7ctCjSKg0IAijQKzcpWCE47raiNLlmBmTrrfBjEHC1WIDuGWz5+/fwmfn53DEuwmActLcnDwQhf+cKwV998kkgwQYeJFf3YNCRsWs7RK0eo4smQJhkdtgtUp3HXtzFym5xKNeJFNX/hT61povRUrQzoyZsOcvAx8blYuM4bjgIQ7j6WUbqeUVlBKK3JycmI9HAAIqhKDEBIJQakuQzAblVIW9pAIhFM+T7T04/3znfhyeX6YRseIJRIJwbplM/DMvnpc6zP7/kAEiBf9KZbJX5qXgX21ldhRczN/HO1c7eadU22orSphVWsmIfEim74IZr0v0ihwU6FaUOar5+SGtXEXIzRIPJUJIoTcAuA/KKV/N/HzDwCAUvpjoesrKipoXV1dFEcoDtf+MdTwBKGuOZvXlCM/Swa1IpUdrUSfoH/Zocin0TKGVVs/xD2L8qdUctZUYP/Zdhy90oc//POtoTb0CEkRxFJ/eusOJqbfOB3bZxpFslQCs9UGbQYLkYhjYqI7o0Ew6/34uB3vnm13ScgPdydbRkAI/tLjzShOAnAJQDWANgCfAniAUnpO6Pp4nzjBEi4DmxEWoq7YR8dt+MZvPkV6ahK+fuuMYB/PiFMopfj1/12BcWQMrzy8BIrgDeOENYoBpuemAJPWKA4WJvNxheAvPq7CJyil4wDWA/gzgHoAu8QM4skMKy4/dek3WfHQrz+F3Q48uLQo1sNhRABCCB6+dQYy0pJxzy//D5e7h2M9pJjA9BxjqsFkPv6JK6MYACil+yilsyilMymlP4r1eBiMaDBms+ONumtY8YtDyFWmYv1txZAyhTlpkUgIvvHZGVhWko3Vv/wIP95Xjy6jxfcHGQwGgxEx4q36BIMxJRix2nCt34wLHUM4crkH753rxLSsNKyvKsYsrTLWw2NEAUII7ijNw+LpKrxz+jqqfv5XlBVkYXlJNsryszAjRwGtMhVJ0rjzXTAYDMakJK5iigOFENINoDnGw8gG0BPjMUSDqfI9Adfv2kMpXRnMTcTkU5quTir45msLnF+zWYbGbUN91mCe4xVqTwKRjIf9vsEQL2OJl3EALmMhSSmSZJXOo/1az96fXzad+2BA4NNByyYQVf2ZaLojkcYbz2MNu+4MA/H6+2LjCpxQxyYonwltFMcDhJA6SmlFrMcRaabK9wQm13eNp+8SL2OJl3EA8TWWSJFo3zGRxptIY40H4vX3xcYVOJEaGzuXYzAYDAaDwWBMeZhRzGAwGAwGg8GY8jCjOHS2x3oAUWKqfE9gcn3XePou8TKWeBkHEF9jiRSJ9h0TabyJNNZ4IF5/X2xcgRORsbGYYgaDwWAwGAzGlId5ihkMBoPBYDAYUx5mFDMYDAaDwWAwpjzMKGYwGAwGg8FgTHkS2iheuXIlBcD+sX+R/Bc0TD7Zvwj/Cwkmn+xfhP8FDZNN9i8K/wRJaKO4pydeG60wGEw+GfENk09GvMJkkxErEtooZjAYDAaDwWAwwgEzihkMBoPBYDAYU56kWA+A4cBup7jaa0Kn0QJthgxFGgUkEhLrYTEYCQebSwwGYzLBdFr0YEZxHGC3U+w/14GNu07CMmaHLFmCLfcvxMp5eUzwGYwAYHOJwWBMJphOiy4sfCIOuNpr4gUeACxjdmzcdRJXe00xHhmDkViwucRgMCYTTKdFF2YUxwGdRgsv8ByWMTu6hiwxGhGDkZiwuZT4DI6M4fmDDfjOrpN4o+4axmx23x9iMCYpTKdFF2YUxwHaDBlkya5/ClmyBLlKWYxGxGAkJmwuJTZdQxZ86fkPcexqPzLTUvDqkau478Uj6DdZYz00BiMmMJ0WXZhRHAcUaRTYcv9CXvC5mKEijSLGI2MwEgs2lxIXSim+teMkKopU+MfPzcQdc7X4wRdKoVfL8Q8vH4VlzBbrITIYUYfptOjCEu3iAImEYOW8PMyprUTXkAW5SpZdymAEA5tLicuB+i609o/gXz5fzL8mIQRfuWk6XvhrI/7znfN4ZnVZDEfIYEQfptOiCzOK4wSJhMCQkw5DTnqsh8JgJDRsLiUmv/xrI+5ZlA+p22JPCME3PjsD33/zDO5qmoabDZoYjZDBiA1Mp0UPFj7BYDAYjJhyqXMILX1mVBSpBN+XpyThqzfp8e97zsFup1EeHYPBmCowo5jBYDAYMeWPJ6/jFoMGSRLxJWmpQQ0Kir1n2qM4MgaDMZVgRjGDwWAwYsre0+1YMsN7WAQhBPcsKsDWAw2glHmLGQxG+GFGMYPBYDBixrU+MwZGrDDk+M6mX1CQCbud4q+XuqMwMgaDMdVgRjGDwWAwYsaHjT0oz8+EhPjOpieEYMU8LV4+fCUKI2MwGFMNZhQzGAwGI2YcbuhGqS7D7+tvMWTjTNsgmlmbWwaDEWaYUcxgMBiMmHGsuR+ztUq/r09JkqCyJBu//6QlgqNiMBhTEWYUMxgMBiMmXB8YgWXMjrzMwFrWfm5WDnYfa4ONlWdjMBhhhBnFDAaDwYgJJ68NYJY2HcSPeGJnClRyZMmTcbiBJdwxGIzwwYxiRtxht1M0dQ/jyOUeNHUPs2L9jIBg8pM4nG0bhF4tD+qzt87Mxu7jrWEeEYPBCIVE178Ra/NMCPk1gFUAuiil8ydeUwPYCaAIwFUA91NK+4nDTfAcgDsBmAF8nVJ6PFJjY8QvdjvF/nMd2LjrJCxjdsiSJdhy/0KsnJfHer0zfMLkJ7E43TqImw3qoD57y0wNvvvGKZit45CnRGwpYzAYfjIZ9G8kPcW/AbDS7bXvAzhAKS0BcGDiZwD4AoCSiX81AP47guNixDFXe038hAIAy5gdG3edxFWWac7wAyY/icW564OYofFdn1iIzLRklOSm4+CFrjCPisFgBMNk0L8RM4oppYcA9Lm9fBeAVyf+/1UAdzu9/hp18DGALEKILlJjY8QvnUYLP6E4LGN2dA1ZYjQiRiLB5Cdx6BkexbidQq1ICfoeS2ao8faJ62EcFYPBCJbJoH+jHVOspZRyjes7AGgn/j8fwDWn61onXvOAEFJDCKkjhNR1d7Mki8mGNkMGWbKrWMqSJchVBpadHiuYfMaWRJefSBNP8nmpYwh6tTzgJDtnKorUOHK5B8Oj42EcGSMWxJNsMoJjMujfmCXaUUfz+oAjsCml2ymlFZTSipycnAiMjBFLijQKbLl/IT+xuJikoiCPWKMNk8/YkujyE2niST4vdQ4hPystpHukpyahVJeBA/WdYRoVI1bEk2wygmMy6N9oZyd0EkJ0lNL2ifAILhisDcB0p+sKJl5jTDEkEoKV8/Iwp7YSXUMW5CplKNIoEiZInxFbmPwkDvXtQ5gWolEMAJ8pVGHv6XbctVDwcJHBYESJyaB/o+0p/iOAhyb+/yEAe5xe/xpxsBTAoFOYBWOKIZEQGHLSsdSQDUNOekJNKEbsYfKTGDR2D4fsKQaAikI1PrrcA7OVhVAwGLEm0fVvxIxiQsjvARwBMJsQ0koIWQfgJwDuIIQ0ALh94mcA2AegCUAjgJcA/EukxsVgMBiM2HOlxwRdgJ3shEiXJaEkV4m/XWRxqAwGIzQiFj5BKf2qyFvVAtdSAN+M1FgYDAaDET8YLWMYsY6HVHnCmcV6RwjFF8pY0SIGgxE8rKMdg8FgMKJKU7cJ07LSQqo84cxNRSr87VI3RsdtYbkfg8GYmjCjmMFgMBhR5UrPMPLCEDrBkSVPgV4tx/819oTtngwGY+rBjGIGg8FgRJXmHjNy01PDes/PFKrw7mmWn81gMIKHGcUMBoPBiCpXek3IyQhvQf+bitQ4UN+FcZvd98UMBoMhADOKw4jdTtHUPYwjl3vQ1D0Muz3g3iQMBiMGsLkbXZp7zdCG2SjOUaYiW5mKT670hfW+DEY4YDomMYh2845Ji91Osf9cBzbuOgnLmJ3v5LJyXl7C1eljMKYSbO5Gn2t9ZmiV4Q2fAICKQhXePdOOW4uzw35vBiNYmI5JHJinOExc7TXxAg8AljE7Nu46iau9phiPjMFgeIPN3egyYrVhyDIOVZjKsTmzpEiNP5/rYF44RlzBdEziwIziMNFptPACz2EZs6NryBKjETEYDH9gcze6tA2YkZuRCkmYyrE5o8tKQ7osCcdb+sN+bwYjWJiOSRyYURwmtBkyyJJdf52yZAlyleGNm2MwGOGFzd3ocq1/BDkRCJ3guKlQjb2nr0fs/gxGoDAdkzgwozhMFGkU2HL/Ql7wuZihIo0ixiNjMBjeYHM3urT2jyA7PfyhExxLZqix7wwLoWDED0zHJA4s0S5MSCQEK+flYU5tJbqGLMhVylCkUUypIHq7neJqrwmdRgu0GVPv+ycC7G/kCZu70eVanxlqeeQ8xQUqOdJSpDje0o+KInXEnsNg+EswOobp6tjAjOIwIpEQGHLSYchJj/VQog7Lro1/2N9InKk8d6NNS58ZxRH+PS8pcoRQMKOYES8EomOYro4dLHyCERZYdm38w/5GjHigrX8E2WHuZufOzQYN3j3dARsLoWAkIExXxw5mFDPCAsuujX/Y34gRD7QPRjamGADys9KglCXh06uskQcj8WC6OnYwo5gRFlh2bfzD/kaMWGMdt2PAPIYseWSNYgC42aDG2yfaIv4cBiPcMF0dO5hRzAgLLLs2/mF/I0as6TRaoFakQBqFuMhbDBrsP9uBMZvd98UMRhzBdHXsiEmiHSHk2wAeAUABnAHwMAAdgB0ANACOAXiQUmqNxfgYgcMy+OMf9jdixJq2gcjWKHYmRymDLkuGQ5e6UV2qjcozGYxwwHR17Ii6UUwIyQdQC2AupXSEELILwFcA3AngF5TSHYSQFwGsA/Df0R5fIhPrEi4sgz/+mSp/o1jPBYYw7YMjUEegvbMYSw0avHm8jRnFjIQjVF3NdGBwxKokWxKANELIGAA5gHYAVQAemHj/VQD/AWYU+w0r4cJgOGBzIX65PmCBSp4ctectNWjwnV2nMDw6jvRUVoGUMTVgOjB4oh5TTCltA/AsgBY4jOFBOMIlBiil4xOXtQLIF/o8IaSGEFJHCKnr7u6OxpATAlbCJT5g8hl72FwQJ9byeX1gBGpFdMInACBDloxSnRL7z3ZE7ZmM4Ii1bE4mmA4MnqgbxYQQFYC7AMwAMA2AAsBKfz9PKd1OKa2glFbk5OREaJSJByvhEh8w+Yw9bC6IE2v5vD4wAnUUKk8489nibOyquxbVZzICJ9ayOZlgOjB4YlF94nYAVyil3ZTSMQBvAvgsgCxCCHe+VQCA1dIJAFbChcFwwOZC/NIxaIE6wjWK3VmsV+FChxGt/eaoPpfBiBVMBwZPLIziFgBLCSFyQggBUA3gPIAPANw7cc1DAPbEYGwJSyRKuNjtFE3dwzhyuQdN3cOws+5QjDiGk9dOowUvPViBQk0aAFbOKJ7oHBqNaqIdACRLJbjVoMEf6lqj+lwGI1YEYw+w9d5B1DMPKKVHCSF/AHAcwDiAEwC2A3gXwA5CyNMTr70c7bEFSzxkeYa7hAsL1GckEkLyunlNOfKzZFArUsMyJ+Nhnicy1nE7jCNjyJRFL9GOo7IkB7/862XUVpewvxkjosSDngjUHmDr/Q18GsWEECmA1yilfx+uh1JKnwTwpNvLTQCWhOsZ0SKehCmc5bbEAvXn1FZ63D8elABjauEuc5TCQ1437T6NfQLyGuzz4mWeJyqdRgtUipSY/L5mZCuQkiTBx029uLU4O+rPZ0wN4klPBGIPiK33+TVLUZafNaV0nM/wCUqpDUAhISS6Z14JQqSzPGN1pOFvoD6nBO7cehhffeko7tx6GPvPdUzZoxdGcAQi50IyV99ujGhiCcvmDp1OowXZUQ6d4CCEYPmsbPzuk5aYPJ8xNYiknoikLSC23h+40DXl1nN/wyeaAPwfIeSPAPi/LqV0S0RGlUB4Mx5D9VDFctfJBeo7fzehQH1vHuUijYJ5kBk+CVTOhWSuoWtIUF7TkqWw22nIchfJeT5V6DBakBUjoxgAls3Mwbd3nUS/yQpVDMfBmLxESk9E2hYQW+9tdky5E2J/E+0uA9g7cb3S6d+UJ5JZnrH0TvkbqC+mBDqNloA8yCzIf+oSqJwLydyuulY8c0+Zi7zWVpWgdseJsMgdy+YOnY5BC7LSoh9PzJEuS8JifRbePM4S7hiRIVJ6ItK2gNB6X1tVgjePt0bkhDie13u/PMWU0h8CACFETilldW2c4ITJfQcXjkz3WHqn/A3UF9thylOkePg3n/odkxwvcViM6BOonAvJXL/ZisX6LOysWYoDF7pgswOvf9yM9kFLWOQukvN8qtA+aEFWlGsUu/P52bl4/UgzvrFsBhzFjxiM8BEpPRFpW4Bb7/MF9GegJ8S+xhPv671fRjEh5BY4qkGkA9ATQhYA+EdK6b9EcnCJvmKJ7gAA4bVJREFUQLirPjjjbwhDpPAnUF9MCVhtdr8ncSgTjJH4BCrnYjKnVytw9Eovth5odLk+HHIXyXk+VWgfHIn5JmJOnhI2asfRK31YatDEdCyMyUek9EQ0bAGJhKAsPwttAxafRn0oRnq8r/f+xhT/F4C/A/BHAKCUniKELI/UoBINznjkYmiPXukNS4xNIninJBKCFaVa7KxZivZBC3SZMszTZaKl3+z3JGbxmlObQOXc28IjtngQEDR1D7vMSW+hP9z7zvM4nNVdpiKdxlEs1qtiOgZCCG6bk4vXPrrKjGJGRPClJ4KJxY2WLeCvUZ+rDN5Ij/f13u86xZTSa27HTbbwDydxicSRQCJ4p+x2ivfqOz2+94pSrd+TONYecUZsCUbOxRYeocVjQ3UJvrXzJPrNVpc5KSZ3YzaKO7cejsujvUSm02iBKsbhEwBQWZyDb+08ycsagxEtgrUTomkL+GPUX+kdxobqEjx3oCFgIz3e13t/E+2uEUJuBUAJIcmEkO8CqI/guBKOSAXCcwK61JANQ0563C3MYt+7pd+MlfPysK+2Ejtqbsa+2krRiR+JbnyMxCJccs4tHvtqK/HK1ytQs9yA1444YuPc56SQ3G1eU44n9pxhpdfCDKUUPcOjcWEUK1KTsNSgxo6j12I9FMYUIxQ7IV5sgau9Jqz/3Qm8dqQZ65YZsL6qGDXLDZirU/o1pnhf7/31FP8TgOcA5AO4DuDPAL4ZqUElIvF+JBApfH1vf46bE8EjzkgcuMWj02jxGl8sJHe9plE0946IfoYRHEOj4yAgSEuRxnooAIDbS7XY8v4l/MttM5Ek9dc3xGCExmSwE7jv0D5owQsf3NCvt87UoCjb93eI9/Xe3+oTPQDC1tFuMuJ8JKDLlGH14gJIJUBaclJY6qTGK+E4Cpms9Q4Z0cVdjnSZ3mVTSO64a+L1aC9R6TJaoEmPvZeYo1CjQHZ6Kt4/34kvlOliPRzGFEGbIUOhJg2ryvPBRaO+c6otofRLsGu+kL6Nx42Av9UnDHB4ipcCoACOAPg2pbQpgmNLKLgjgc3767G2Qo+tBx2xNtsPNWHbA4swQ5OOrqHEMfr8NVRDTQCI9/IsjOCI9kZHTI62PbAIT+09j1Xl+ZBKgJsK1dCr5KLXBxILz/CfTmN8hE44U12ai1c+usqMYkbU0KvkeLSqBI+/fZbXL0/fPZ/XSaHqzGjo3WDW/ERa5wmlvosmE0I+BvACgN9PvPQVAI9SSm+O4Nh8UlFRQevq6mI5BBfsdoozbQNYu/1jfhely5Tha7cUegSkx6MwcAQqwNxEDOYopKl7mE9q4pAlS7AvTsqzAAj6jxRv8hktYqEAxeToT7WVON02iE27T7uMZbZWiS8+Lyx3XBWZeDzacyOkQUVTPt883oq3T7Thnz9fHJXn+cO4zY4NO09iR81SzNKyXlQRgOlON8T01P4NlTjfPhSSzoym3g10zY/TdV5wwP4GU8kppa9TSscn/v0WQOL4+6OEREJgttpc/vCrFxfwBjGQGIk7gSYDhJIA4C3GipGYxKITo7gcjfIGsfNYmvtMonIXLwktk4lO4ygyY9jNTogkqQS3zc7Bqx9djfVQGFME8TKQoyHrzGjq3UB1ZCKt8/4axX8ihHyfEFJECCkkhHwPwD5CiJoQoo7kABMN9zaPhCAiwhDJNonRFGDWPnfyEQsFKCZHJuu44FiSpRImd1Gk0ziCzLT4Cp8AgKo5Wvzx1HUMWcZiPRTGFCBQPRWIznTXu7pMGdYtM+BS51DMWykn0jrvr1F8P4B/BPABgL8C+Gc4QiiOAZh8Zxwh4F5uREoQdmEIte+4L6IpwPFenoUROLFQgGJyVKhWCI7lbNsgNlSXMLmLEh2Do1Ap4stTDABqRQrm52fizeOtsR4KYwoQqJ4KRGc6611dpgwPLi3Eyx824Z9+ezzsNkKgJNI671dMcbwSr3FHzvE2eRmykGOF3Il0fE60Y0JDiUmOAiwuLkBilVQhJEcAPMZSW1WC1z9uBgDcV1GARdOzUKhRxJvc+UPCxBTf88L/YdWCaZiry4jK8wLh3PVB/O5oCw5853Nwa1DFCA2mOwXwV0+FElO8bpkBL3/YFFcxvHG4zgs+3O+OdoSQ+QDmwimWmFL6Wujjmny4d4TRqxVhrckX6VqH0a4jyNrnTi5iVYdSTI64sVzqHMKZNiNe/9jRzAMAth5oxI6am5nsRZiuoVGo4iymmGOuLgNjNjs+udKHm1nrZ0aE8aWngtWZznr3UudQ3NVDTpR13t+SbE8C+DwcRvE+AF8A8CGAoIxiQkgWgF8BmA9HibdvALgIYCeAIgBXAdxPKe0P5v7xRriFIRptErkxc5n4R6/0hrXEC6tNPLkJl8yHQ064sQDAt3aeDHreMJkNDq6bXVaclWTjIITgtjm5eO1IMzOKGTEjHDrTWdeFy0YIVO8lup7011N8L4AFAE5QSh8mhGgB/DaE5z4HYD+l9F5CSAoAOYDHAByglP6EEPJ9AN8HsCmEZ0xa/KkTGK6ah2JHOgCCvn8i1SxkxA5/5CQQOQ+kvqb7ffUqOd6r72QyGwRDo+OQkPjpZidEZUkOvr3zJPpMVqgV8Wm8Mxj+Emwt4Ss9JjT3maBISYI2IxUFWYHpvcmwtvtbp/gTSukSQsgxALcBGAJQTymdE/ADCckEcBKAgTo9nBByEcDnKaXthBAdgL9SSmd7u9dkjjvyhbf4HDHBnKtTon1Q3HgYH7fjXPsg2gct0GWmQZEixaptH4a9pmKc1iwUY8rHxcVq5+9LToJRwM7fRZ4ihdVmh0aR6nP+bF5Tji3vX3RpAR0HMpsQMcWNXUP4+iuf4mf3Loj4s0Lhv/92GZXFGvy/5TNjPZTJwpTXneEkWI+tr3AMu52ipc+E4y0DeOytM7zO21BdgrKCTDzyap2HDt5ZsxRl+Vke95sMa7u/1SfqJkIeXoKj4sRxOLraBcMMAN0AXiGEnCCE/IoQogCgpZS2T1zTAUAr9GFCSA0hpI4QUtfd3R3kEBIfb3UCxeoVvnmiTbRaxfi4HW+fasPa7R/jn357HGu3H8G5dqNgXFLvsBUXOox4pNKA9VXFUMlTAqqHmEg1CwNlsslnJCud+Cor6EtOgqnLKZEQFGkU6DePYe32j3Hfix97fKcrPZ733bT7NFaV54uOJVGIhXx2GUehksdnPLEzn5uVg99/cg2JnHyeyEw23RlOgtHD7jYCAA99y933zRNtvEEMOHTbcwca0NY/IqiDD1zoEnz+ZFjb/TKKKaX/QikdoJS+COAOAA9RSh8O8plJABYD+G9K6SIAJjhCJZyfR+GINRYay3ZKaQWltCInJyfIIUxuxASTk18h4+Fc+yDfepK7pql72KNMTKEmDVd6zY721Qcb8avDTVhfVYxv3V6C7uFRv4ylRKpZGCiTQT6djdUzbYPYvL8+7AXh/VHyvuQkWAXsy5gWa+whddOWiSizsZDPrqHRuKxR7E5pnhKWcRtOXBuI9VCmJJNBd0YKZ53F1R++0GHEmbZBv9ZcMX3b0ue4r50K91OQpyQJ6mCbHYLrwGRY2/31FIMQspoQsgXAowBCOV9qBdBKKT068fMf4DCSOyfCJjDx364QnjGlERNMZweIu/HQPuhpYOyqa8XTd893qS341F1lePxt1x3lU3vPY8hiw0O//sQvL6JQzcJn7imDhCCmBcYZnspz7fYjWFuhhy7zhlILx87fHy+vr9qWgSpgzti/1DmERyoNot9JIbIQLNKrEqLOZrzRabTEXTc7IQghqCzJwa5Pr8V6KAyGC5wDwLn+8NYDjVi7/Yhfa66Yvu00jvKvCem89gEzNq8pd9F7tVUlePN4q+A6MBnWdn+rT/wSQDGA30+89I+EkNsppd8M9IGU0g5CyDVCyGxK6UUA1QDOT/x7CMBPJv67J9B7MxwIBdlvqC7Ba0ea+WvcjQddZppHtmq/2Yo5eUrscyoTI+ad4zr3bdx1EnN8xA9xpWNmP1qJ+g4jLnUO4Wd/voh+szXhgvInG0LKc+vBBqxbZsALHzQCCM/O35+ygr5KuwWaOCdWr7h90OLynbQZqdhQXcK3Z+fmjyFb7jIXEi2rOlZ0Gi3ISoDwCQCoLM7GD946g//48jzIkuM3MZAxteAcAKsXF2DrwQYP49bXmiumb83WcciSJdh9rBXfvn0WfvGXS646Lzcdny/JRZFGjgMXumCzQ1BnckyGtd3f6hNVAEq5xDhCyKsAzoXw3EcB/O9E5YkmAA/D4bXeRQhZB6AZji56jCBwNyZy0mW40juMfrMVgLCXa54uA0/fPZ8PoZAlS/D03fMxR5uBpCSJy4QTKvVCnUIzhGohCiUJEAJ8941TLvfyZ4IzIoeY8uRCB8LlIfW3rKC3MkWB1EMWM/Y3VJdgZMyGWblKUOqQU71agRJtOmqWG2CngIQAJdp0TFcpXEoeMfyjw2jBjOzE+J1p0lNhyFbgQH0Xvliui/VwGAwANxwAFzqE83x81R8W07d69Q3Hwm8+uooN1SUo0iigVqRAm5EKvdqh88rys9A2YPHLASGREEglwKXOIdgpsOYzBdh9rDVh1nZ/jeJGAHo4jFUAmD7xWlBQSk8CqBB4qzrYezJccTcmZmQrvHq5kpIkuHtBPkpy09ExaEFepgzzdJlISnI9UhHyzjl3CBMybMSqBOQoU+KuwPhUR0x5Vs/Jxa0zNWHzkPry8vqbae1vbU8hY18lT0GOMtVlI8h5M6pma2HITmde4TDQZRzFoumqWA/Db26ZmY3dx1uZUcyIGzgHQH5WGrYf8uxU5+vkTkzfSiWASp6MnTW3YMxmg9qtEo/78/1xQNjtFMdbBvhxOtsIibC2+2sUKwHUE0I+mfj5JjgqUvwRACilX47E4KYikSp/5W48cPGVzs9JSpJgwXQVFkyH6DUSCcGKUi22P1iB060DMOSkY/P+ev44RWj3KBbPtLNmacSbkDACQ0x5CpXfCQVvStZXqTWxOeJt7ggZ+/dVFHgklzp7M7h/iV6MPtZ0DY1CFaeNO4S4qUiF149cxYDZGrcNRxhTC04Hma3jeOnBCjy+5wyae0f8Prnj1u2dNUsdJVczZBiwWLHyucMuOnaxXi2q24QcEEK68WqvyaOSxdaDDahZbkiItd1fo/jfIzoKBoDoFb72tymC2DUt/WbUvF7HB/6vXlwAqQSonpMraDyJxzPZAi4wzogs0WzRLOblFdtEzamtRJFGISiXK0q1XovMCxn7s3KVPk8qJkMx+ljj6GaXGDHFACBPScKC6VnYd6YDD9ysj/VwGFMcsbrp+VkyUc+u0D3c9eOG6hKo5Cl8kn2g4Q1iulElTxbUq7O0yoRY2/01iusAjFBK7YSQWQDmAPgTpXQsckObengzBvyJ0fV3kfbnOd6ucTZy2wctfALWrTM1gmMQO5LXZshw8wwNb4DlZchgsyPsLaUZgRHrHvW+Sq2JnTp4k2khY58AqK0u5ksV7j7Win6z1cWbcbXXhM3767FumQFkQhQ376/HnDxl3B8DxgPDo+OwUwp5HHezE2LpDA3eOtHKjGJGzBFaizftPh1QQwyh2uvPHXBNoA40dFH8BPgWwfV+ni4jIU7c/DWKDwGoJISoALwH4FMAawH8faQGlig4G6e5ShmkEnjtGucNMWOgzzTKvx+OlrP+ZP57u8bfJCkOb/GjnAEm5gFkHrnJhT+bOW/yJSaXQiUFOXnljvS4Zy4p0gAA9p/rwPZDTVDJU3BfRQG+s2IWdJmO+cXRaxrF2go9n/HNxcf1mUaZUewHXUYL1PIUEJJYc3jB9CxsP9yELqMFuRnxf+TLiD2u3TKTYLXZPLplBoM/67XQGDj9CgD1Io24nKdloKGLYuMas3meAG97YBHqO4LvghtN/DWKCaXUPFEZ4peU0p8SQk5FcmCJgNDxAVf6LJgSJELGQKEmDW0DFvzDy5+4HJ1sef9iwGVZvD3HfUJ4u4Yzcjfvr8d9n5mOHGUqdJkOz5vdToMK0g/ES84InXDHybq3CJ+ny/BI0vQ3FMFXEp6QXAqVFJQlS5CXIRNteb5x10mo5Cl4cGmhi9HrPKYUqcSjBNLWgw3YWbM06N/VVKJraBQqReLF5aYkSbC4UIU/ne3AQ7cWxXo4jDjEWYfqMmU43+5q9NVWlWBnXQs2rSwNyvjj7i8hxC8n1Pi4He+ebcem3ac9dF1D15DgPbghBRO6KGYjqBWpWKxXu6z3lAJffP5wQqzv/jbvIISQW+DwDL8b4GcnLUKG3HMHGrB6cQH/Rw+k85d74etCTRp+srocl7uH+WYD3NGJc8tZrsPNpc4hwXa5vp4jNCG8XcMF7X/njtnY9kEjNu0+g5rXj2H/uQ4cvNgp+HxvbamBydEeMlHw1U3OV/tld4RahL99qg3j465/T3/bMnObqH21ldhRczP21VZ6xAa7y+U8XQa23L8QhZo0fPO2YtRWF+OlBytAqXC4BVe0XqzuJzcms9UmGg/P8E0i1Sh2Z0mRGntOtsV6GIw4xF2HvnmiTbDk46ry/KA6gI6P2/FhYw/ePtmGuuY+PPaFOS52wfYHK9BptLi0a/6oqZc3iLkxcLpuV10raqtKPJpqrF6U76Fj/cWXjeC83ncNJc767q+neAOAHwB4i1J6jhBiAPBB5IaVGHhrZMH9fyAxOs4e1T7TKNoGLHj4N596lDVpH7TwdWO5Djdini5fzxHz3Pq6pqXfjO+5TcDnDjgyTA3ZgcejBhqSwQieYBLZvMmTUIvwx98+i5LcdCxwKsUVyDGgWFyzN7lcUarFmM3u4il55p4yPpnE+Zlc0Xqu6YzYmLzFwzN80z00iqwEaPEsRHlBJl7822UWQsHwwF2HirVJ5vRLIHaA3U49PL7fvn0WNt5egrnTMtBrGuMT3Z29wXXNfSIb+HH0m614/eNmPjdCQoDF+iwUZaejKMga4oEkZSfS+u6vt7eNUvplSulmAKCUNlFKayM4roTAVzvlYP7onDGgVqR67Pq2HnR4oWXJElQUqr12uPF3Z0q9OAG9eXfFDBw7RVC7P3+814zw4M049deb64xYPG+HkyFqtzuSrWqri7G+qphvsRzKHHGXy5Z+s8eceeytM7ivosDl885F66VEuL0pNyYml6HRPpgYLZ6FSJbeCKFgMJwR0qFitkCgOu5qr8lDj/3iL5cwaLEhWSr1eG/z/np0GUeRn5mGDdXFLu3rnXVdv9mKFz5oxK8ON2FOXgb06tB1mK8TYI5E0qP+eop/TQgpgCPB7jCAQ5TSM5EbVmLgrZ0yF/sb7B/dW2exLfcvxK0GDfbVVuJS55Df3jeOcJSZEtv5SQiC2v1FsxTYVCeYRDZv8iQazzuhnMVaLHPxduFSjGJjn6VV8uPjZH1GtgIzshWYq1OiUKPg62q6K2sml6HRabRAr5b7vjBOqShU4d3T11lcMcMFdx26+1irR2t4TscFavx5W/tN1nGX93SZMqyt0ONrr3ziYYP0m63YvKac13Wx1GGJpEf9MooppZ+baMl8E4DPA3iXEJJOKVVHcnDxjnuf72t9ZgCOpgAVhWrcahAuUeYNX8H1zrWAOSNF6LqcdBmu9gyj0zgKk3UchWrHxJBISFBJbe6JWXqVXHBDUKJND9rIiXUpsKlCMIls3jY6Yi3C5+kyAYi3WN5Zs1SwrrW/SYDu1+UqhY390rwMj26O3Lg6jRYs1mfh3Ucr0T0sHkrEVUi52mtiJQMDoMNowYKCrFgPI2gWFGRh+6HL6B0ehSY9NdbDYcQJ7jq032xFiTYd7z7q0DPyFCnGbHasnJ8XsJ4Qc1pUFKqRn+XqgBA6KX7uQAN++cBiJEkJpk9U0vFHh0W6SVGirO9+GcWEkGUAKif+ZQHYC4fHeMojkRDMyHYssvIUKRSpSdAqb/QMd8aX0Dl71FTyFI+dp1BnMTEDp3XAhPPXhzw+v3JeXsDeQCFP37YHFmFWbjp++cBiyFIkSJVKoUlPEfzejPjC267dl8EshK8W4WLyNjJmE5wj/pxicNdt3l+PVeX5kEqAWw0a/Py+hfjOGzc++8w9ZZBKgIIsOT8WCYFHprivkxLWxCM4uoyJWX2CIyVJgvKCLPylvhNrb2I1ixkOvOnQmbmuXTAB+DQw3StZuOvgzWvKeSeb83tSiXAs88nWAWw90OiipwCI6jDn97gSlbNylSjVZfDOtKmCv+ETfwVwDMCPAeyjlFojNqIEQ2yxdI/X8WdRdfaotQ9a8NqRZtQsN2DR9CwUahR+9ySXEODNE20uPdKdvcGBBr27e/pU8hQ0dA5j/e9OuHyXRXqVz3a7jPggmEQ2b7i3CHcmEHnz9xSDa6rhXEN4+6Em/Nfahdi7fhkudA7hUucQfvbni+g3W/H03fPx/MEGNPeOoLa6WHRuiHkxWBOP4OgZHoUqQatPcHymUIW9p9uZUcxwQUyHBrqBFnM6iZ1eOevntOQkF10GOHSrbeJHZ90GCFficX7PV4nKUEgUu8DfRLtsAP8J4BYA+wkhfyGEPBW5YSUOYov4mbYBl5JW/iQwuXvU2gct2HqgEWkpUq9B7O7B7u2DFtFsWK6ZQSBB7+7jWr24gPdAu38XX+W+fBFoOTBG+PE3ecJf/JU3bp4IyW3zhGxxdBotWFWe73F0+K2dJ2GyjuO7b5zC1gONfBLg42+f5csYepsbYnBNPF7+sAnbDjqSVdZW6PnGOgxPhixjoJQiLTmxutm5s3B6Fo4198NoYQ1cGb7x1ybwdv36350AIRDUwc76uSw/E8/cU+aiW2urSvDm8Vb+ek63eTsh5t7zlbgf7Pocql0QTfyNKR4ghDQBmA6gAMCtABJ7+x8mxATtwIUul+OLHGWKx3UqeQq6h0b5nZMuMzxlS7QZMj6rXuhegXoD3T193spYAeK7UffOYt7CR9gRdfgJdace7Of9kTfub3+xwygotyeuDWBkzM7LgjZDJnp0KFYNw717UyDzjDXxCJxO4yjUitSE62bnjjwlCXN1GfjgQhfuWpjv+wOMKY2/NsGKUi1a+s241DmERyoN2H2slS8d6R7OKKZ7JRKCxfos1Cw3wE6B2Volnn3vgksJSmfd5k3v+SpRGUrX2URqzuVvTHETgAsAPgTw3wAeZiEUDsSOht2PL3bWLHW5Tpcpw9duKcRDTlmjW+5fiG0PLPIISwg0ca1Io0BZQaZgTLJzVr2/Qe/ucabeDG4xhdBptOCCW5vHbQ8swgxNOt86WkLEDep4mziJRqgbjlA/70veOKWpkqegtqrEo63y6x87sqk5WSjSKHDTRFlCdzkUq4bBlR8UyhT3Nc/MVhtU8hSsXlzAG9e7j7WyJh5e6DJaoFZMDt/J4kIV9p1pZ0Yxwyf+2ASb99d71FR37kPgbKz60r16tQJz8jJ4/fm1WwpFdZu3fJEt9y8UdUrkKmUhGbbevNS+nGXRxt+Y4mJKqd33ZVMPvUqOzWvKBYWbwzLm6IDlLJD3VQiHILz7aKVHpnygAiKREFTN1qI4Jx2L9SqYrePQO1WfCBR3T19ehgyzJyah0OQSmlTyFCnfiAQQjksWa7QQSOFzhjCh7tQjvdPnlGb7oIUvMp+aJMGC6ZmobzdizWcKsPtYKy8LEgnBLQaNx9xz7m7nLJ9cTDEAl0xxsYoT7nCbWOfFZkN1iUtNUIYrHUYLsuSJm2TnzGf0Kvzv0WZYxmyQJXg4CCM8iHlvhZKV3W2CVeX5gn0I1i0z4OUPm1zWU1+6V2h9XjE3z2c8stB73kpUHr3SK2rY+loDxDYKeRmyuDsd9tsoJoT8NwAtpXQ+IaQcwJcppU9HcGxxj91O8V59J7a8fxHrlhkglQCL9SpsO3jJ4/hCmyHDzTM0vECKtY9t6TNBliwNecckkZCQutUI3c/Z06dXC9c9FKteYLXZfcYlP/bWGdQsN2DrgUb+umDCRxieBFN/OJDPhxqa4aw02wctePN4K752SyH+8fVjLkZonlNnsaQkCb5UPg1l+Zkecuiu/PUqORbrVYKZ4v5gs8NDXp870IAVc/P8/o5TjU7jKLIStHGHOxlpyTBkp+PQpW6smMf+5lMdX95b12Q4KWp3nHCxCcRCv0p1SuysuQXzdBm8/vRHdwudxAnpNm8ndpzNoFcrsHB6loeuDKUrnZhdYLPH3+mwv4l2L8HR5nkMACilpwF8JZQHE0KkhJAThJC9Ez/PIIQcJYQ0EkJ2TtRFjmu4HVxz7whe+KARWw804p9+ewy11bMFk4qcA+SLNAr+Gg4udjLeA9GdDSD33SanEPbVVrr0VNcoUl2+r1jsEtdoAYjvrjeJhlj3RX83HN4+H44kCvdkPKGTlOcONPBHkBxiSYHuryclSVx+BhBQwkjXkPDC1D0cePfGqUL74Mik8RQDDofHvjPtsR4GIw7wlTjvmgyXhU0rS13WNS70yxlZsgT17UNYu/0I3qvv5HVSqLo7UMR0qlDC9LYHFoFS+NSjYnaBmF4NpituuPDXUyynlH7iljAxHuKzNwCoB5Ax8fNmAL+glO4ghLwIYB0c8ctxi9gOLllKfIZAeOuGx93Hecck5IkDELZYHDFPn1DTjvfqO70edwjtRv2NSxZqtMCS7EInmPrD/nxer5LjTNsALnQY8UilAYcudqFyVi4udBiRn5WGsvzMoJLxhE5SVPIU9AyP8jHowc6BYOKjQ/GSTFXaB0cwd6KBy2TgpiIVHnvrDKzjdqQk+etPYkxGAjl5Ezu5Egux4GKOC7Jk6DFZkZ6ahP9auxDf2hmc7uYI1YYQCtM43z6ELz5/2C89KmQXxKNe9dco7iGEzARAAYAQci+AoLfMEy2jvwjgRwA2Eoe1XQXggYlLXgXwH4hzo1jsD6rNkPlMYnMXMAKCb+08KRhPK5b1mZJEPJLyAo3FsdsprvSYUN9uREPXEHbVtaLfbOWzY90N4J+uKUfH4Ai+dXsJ8rPkuNJjwsUOI+bqlF5DNfyNS+binlkMcXgJtc2mmGJ3l48nV83Di4ca0dw7gu2HmoJOxmvqHvYrMVWRKsGx5gHYKSAlQFlBJqpma712wLva65BZLn7dnyM7oU3B5jXl0KsSt4VxpOkYHMVnZ04eT7EmPRXTstJwpKkXn5uVE+vhMGJIoMackEHI6dNLnUM402bkk+x0mTI8sKQQ92//mNc1G++YhVe+fhP/7GAqBwnVQraOU/61Qk0anrqrDMlSwjvAWvrNHgazs44W8pbP3VAJO4VfhnaozppIQCj1fcRJCDEA2A5HKbZ+AFcA/D2ltNnrB8Xv9wc4GoEoAXwXwNcBfEwpLZ54fzqAP1FK5wt8tgZADQDo9frPNDcHNYSwEM4SYk3dw7hz62GPSbZvorC20HtC8bf7AojFERq/c6b/zpqlWDsxMZ2fsfH2Eozb4VIh4Jl7ynD3wvygDPKWPhPkKUnQZgh3AowxAQ0mnuQzktjtFGfaBnDgQhfsFHw5IVmyBOuWGfDCBw65DFQmne/vLJvuDTcAoFCThprlM/HU3vMupy1fmJ/nsUHzJuvcRnRHzc1YasgWHdP4uB0fNfWirrkPNjuw93QbNq0sjWVSSMAPjaZ8LvnRX/D4F0uRM4m86e+euY7RMTt+dt+CWA8lEZi0ujOSa/83byvGyx96NuSoWW7A3QvzA1rfOS+wPCUJtTuOo7l3hH/fWafqMmUeTTucGx4Jfb8jl3vw1ZeOujxTlynDv/7dbI9EPV/dQq/2mmJxOiz4EH89xW0AXgHwAQA1ACOAh+Bo6BHYKAhZBaCLUnqMEPL5QD9PKd0Oh4GOioqKmAbchup9c0avkmP7gxWoa+6DnQLvnHIsuN6yPt3DdwKt1CAUF8VlwL7wQaNovVddlhz/+odTHklyC6dnBVxe5WJnYO124514kk8xgk2K4z7XaxrF9QGLaDkh5yirYKuH+BNOsao8nzeIuWc9d6ABi/UqD6PYl6z7c2TX0m9Gzet1LuOIdVJIoERLPm12ij6TdVLFFAPAkiI1/n3POYzb7EiSshCKcJIIupMjHGu/sx5+6cEKPL7nDJp7R0ST8PKz0tA9PBp0iJi7E8C5iZFQ047H3z7L60eh0zQhb/l9FQW8Qczdx5eODKQ8bDTw1yjeA2AAwHEA10N85mcBfJkQcicAGRwxxc8ByCKEJFFKx+FoENIW4nOiQjj+oHY7xV8bunC6dZA/Bv7BF0px+xwtAEfxeKGjGvd5EWgsjlhcFCHe672aR8cFPydUi9ibkZtIBb0nC4F4OJyVti7TET+2cddJvmyQkIH58odNcD58CiU+zFs4BSCewW22eqY7+JJ1Lj66qXtYdLMQagWPqUTv8CiUsiQkTzLDMUcpQ44yFR839WFZifipAmPyE8raL6SHf3xPGTLlSciUpQi2bm7pG8GTfzzn0vhDTFf5cgIAcMnrEUt89+bgEAp9mJWrTHgd6a9RXEApXRmOB1JKfwBHJQtMeIq/Syn9e0LIGwDuBbADDi/0nnA8L54Q89C19JnQ0DnMTwTuGPhavxn1HUPYvL/eo6EBF1PMCbW/C7sz2gwZCjVpWFWezwv/O6faICEQrfe6oboEPaZRv2oR+zJymZERfa70eN+IOMvouI3y3gvnozYxBSqVgD9y02XKcF9FAWblKkGpQ/YD8Ub7U/tTrHmHXu0ZjyYWA1hZnI3Vi/L9SiCNx6SQeKV90AJNemqshxERlsxQY8/JNmYUM4JGyGj9wVtnsG6ZAXtPt/F6dFV5PqQSoDQvA7s+bcG6ZQY0dQ/j/1Kk+Pc/nhUNbRBbW7k9qixZgrKCTF6ncq+56zZvDg4hbzmlgXcLjTf8NYo/IoSUUUrPRHAsmwDsIIQ8DeAEgJcj+Kyo481D12kcdSk/pZKnYGTMhgudQ7jYYYR1nPINDaQS4PY5uchIS0b7oAU7a27BmM0GtSLVr4XdGb1KjkerSvD422f56//zrvm4eYYK01UOwyIlifAtJBUpUhhyFGjuMeGJVXNdYjmFahED3o1cZmREF7udor7d6PL71mXKsHpxAS51DkFCwHuD3Y/cnI/aAGHFVz0nF/N0magoVOF4y0BAcWXc+Pyt/SmWwc0la7ojltDxGb0KLf1mHGrs9ki+27y/HvlZMpitNj7xJN6SQuKV9kELNIrJFTrBsdSgweNvn8WPWBUKRpC4G62cHtar0nB/xXSkSOGRL+GcxOweDuGuq3KVwmtr9Zxc3DpTw4d7AMCc2kr0mUZRkpvuEhLn3PDIWdcJOS6c21Enuo70N9HuPIBiOBLsRuEIUKaU0vLIDs87FRUVtK6uLpZDEMVdcCQEWPmccCLd1V4TvvEbx/cQCnh3Fn5vgexXe02iyXpCRqm35D7uyNr5fV2mDA9/tghb3r8ElTzF4QnUKlGal4EZ2YqAnx/OZIUIEvRA4kU+nasumK02/OzPF9DcO+Iha0LJbFziHCHArw6LJ2U4/918yZUYwXwukCQN92uFNpHOnaeEviN3bBknJQNDenAk5fM3/3cFHzf14qFbZ0Tk/rHmqb3n8d2/m4WqiRA3hiAJrzuDxVfehrOuE9Knv7h/Ib7t5EkGbuhi5yTmdcsMePN4q8fn3StLBJPwxlWfcNZ1AHyu2TFMnAuUkBLtvhDGgUx6hIw9by2MC9UKflcnFPDuHAvkLZA90HAEX9e7v796cQG2vH8JljFH17GtBxp5o8VbNzuxXWI4ExUZwgjJIudxWFWe7yJr7t5g4EZc2e5jrdhQXYLnDjQ4TijqWrD9wQq+fI/z301Mri51DgGA6N84mHCaQOL63K8VKinEzTUAHvNw466TvIHOwnu8c31w8rR4FuLmGWrsPtbGjGKGB/44e5zXSqE1v77DKKqL3X8W+vz6353A/g2VAdX8F9Kl7j+LlWFzDpGMt8S5QPHLKA629NpUw1sdVG8tjJ0niLeAd2+B7NyONJBwBLHrc9Jlgu+Lja3T6DD0O40WzNUp8e6jlYJ914VI9AkU7wjFrr14qBH/edd89JvG8EilgS+nBojHlfWbrSjRprv8bZ3rWAI3jF0xuTrTZsS3dp7Ez+9biEJNGlr7R6DLTMM8XQaSkiRhD6fx5a3xlnwnEZF1Fu/uH9cHRqBXT94azksNGnznjVMYHh1Heqq/viXGZENIx/iTQM45hPJrluJS57CHrrGLxOa6x/hSKp5w3D5owS0zs/kxHb3SG3KTr6mQB8Rmc5jwVQLFMnajhbG7F9V5glzrH/GaECQWyD5mowHHPBZpFPj5fQvxnTduXL/xjlm40juMGdkKv7vQjdkofxQUpyEQUxah2LW1FXr84+vHPOTU2RvMvbd5TTnys2RYszifl9WZuelevSFFGgW2PbDIpZqKWp6CFydCM77zxkl+g8jFrt29ID+shdz98dZ4S76TJUsFQ0lYvLt/XB8YwaLpWbEeRsTISEvGHJ0SfzrTjvsqpsd6OIwYIKZjcpQpfhmOEglBWX4WjCPjHnronVNtHnk73AkfABfdLEuWOsYzYTDvPuZowDVmoxgftweUZ+Tr+47b6KTPA2JGcZjwpw6qtxbGEgmB2WrDj971rDTxzD1lfEJQn3kUP76nDD9wiimurSrBE3vO4NWHl7gkxkmII1HOG1IJXK5PlUrw1N7zMGQ7vLdzdUr+fVmyFN++fRZ+8ZdLLkbTE3vE6xIGWxOXER7cDT9v4Tkvf9iEAlUa9q5fhh7TqFdPvzdvSJFGAes4damm8u3bZyEnPQWrFxeAEKAkVwldpgztgxY8/vZZlOSmY8F0VdjCafzx1ogZ4TcVqQEg4RNGYslkrj7Bsaw4G7vqrjGjeIoipmN21iz1y3Dk1sZ0mdRjTV9bocfOT1r4fI7ZWiVeOnSZr0ZRPScXZflZABwxvu6Vq+TJUjyx5wy2fmWRxxjdk/L81bFXe014fM8ZD/tk85rySaUXmVEcJsSOFfTqNBRq0rBpZalLC2MhY1GbIUO/2cpXmuCOcRdNz3LZ7T32hdn8+5SC90Z3Gkf5ts8c3hKVrvaa+H7qztevW2bgd7Vc7DCHLlOGdcsMKM/PQIlWiV7TqEuXHO57e2tPzbzI0UOvkuPpu+fzFUbEjtpm5abjZ/cuwKsfNeGn9y506ewmJKvejtEAeCji333S7JFN7XyS0jFowYLp4Qun8eeYz1dMO4t3Dw6bnaJneBTqSVp9guMzehV+839X0dxrQuEkMgoY/iGmY8xWm2AjLudyqblKGa70DvPrdaEmDa98/SZc6zOjdWCE14un24z8mny6zYjTbUYAwK0zNXxSs7uufe5AA9bfVozm3hGPBlzcSeFapxbS/q7JnUYLmntHXOwTSoH8LNmk0ovMKA4TYkexbQMj2HjHbKwo1bpkZwoZiytKtbx3ivMub7l/IQhxNTKMozbBNpAmq3BTjUAT7aQS8Lta9+/VPmjByx82uRjaYrti1pwj9rT0m/H8hCeY89AK/b0udQ3j5Q+bUFtVgj7TqEuJHSFZnasTvk+uUiYoV0Ld55w91HmZ4T1+CzQ+WagID4t3D47uoVFkyJInXeMOd5KkEny2JBs7Pr2GTSvnxHo4jCgjpmPGbJTvfMl5Um+fnesRxrChuoTPO2ruHcGRpl68UeeoJNFvtvL3e2LVXGw76JmLBIiv4ZZxxzN0mb5PCjfuOon8mqUoy8/yatxy37d90OJSAWPN4vww/Dbjh8mttaIIdxQrS3b8SjlP2Bt1rdi0+zRa+s38tWLGYku/GSvn5WFfbSV21NyMfbWVWDkvz2O3t/tYK2qrSlyeteX+hShUyfnXOJwT59zhhNz9+opCNX8cIvS9nI+Rvb3vy5vIiDzc7v6FDxqx7WAjntlX7yE7tVUlePN4K2+oOhszYrJqs0P07y4kV2Ieaq7hxzxdZli/ty+5BW4Y/HduPYyvvnQUd249jP3nOmB375/OCIi2gRFkT/LQCY7bZuVi16fXMGaz+76YMakQ0jFC4YSbdp9GfadR0KO7enEBfz/7REIz54ldX1WMmuUGGLIVLkaysx4TW8NvNODKdBmjmB4+cKHLp+7zR6dOBpinOExwR7Gah5fgcGOPS1gDABdvra+jXXfvlJC3dmddC3bWLMXImI0/2m3pM3kkSm2oLoGYw0YopnLzmnLcatC4xDoLNU1o6TOh0zgKk3Ucs3PTsX9DJTqMFuRlyGCzA0ev9Iq2p55MQfnxjjfZaR+04Eyb0UVOueM/DjFZ7R628HLRabRAniKF1WbH1V6TYMKnWPe55SU5WFiQhaQwN0Hwp9yfs8HPFc+/0GFEflYayvIzJ9WRYDS5PjACTfrkDp3gyFelQZcpw5/PdWBV+bRYD4cRRYR0jFg4obtji3vducTaO6fasHlNOTbtPu1yUvyZ6SpeX3PVeoRKu7nkIBVmYdwGfNrch9laJb8+pyUnCSYQ2+zweYrr/n2d1/rJlC/EjOIwIpEQ5ChT+SYHHO6GYKBHu0KCv2llqcdxR/ugBa8duRHvo0iRwmanuNAxBDv1rA/rb51grgYxAPSaRnGpcwhXekwuxvdP15RjujoNp1oH+a44hZo0l3jWybqzjGc42dm8v55P0ripUI15ukykpyYLxpTnpKfysW/eNjacXFzoGOLbe3MbK706DTtrlvrsBrdYr4qYIvUV/sAZ/O7F87cfamKx7yFwfWBk0scTO1NdqsWvP7zCjOIpiJCOEdKXusw0wdclxBHne19FAWblKlGad8OAFWsw9Mw9ZVisz4JerRBcwwsy0/Cn8x0u3ek4fQZ4JhBzuR3eQi3dv+9kzhfyq6NdvBJPXW+ck5HGbRSP7zkj2pfc305uzvfMVcoglYCfLELGq3uXnK/fWuRSKcJfoXVPrHKemFyraaHd5k/vXYDv/eGUy+uFmjRs/coiF492gk2ahO3KxP0de02jaO2z4AdvuSrJedOU2Hemw+NkYeH0LN7ILdSk8a3A+S6GuUqU6jJQqJbjXPsgDlzogp2Cr3fM3WdkzMZfy7VejqdOR9x84eKa/U1QjSPisqPdv711BkkSCVbOzwv7veORcbsdG3eexCsPL8H8/PCGASU4Cas7fSFWVclbvpBQabR505Q41jzg0qF285pyfHG+DklJEtEunzXLDZiTl+FhV1zpMeFS5xAudBixq861/vw+p4pQZ9oGcOBCF2x24M3jN/S2vzov2K6lcUZIHe0YXhCaCFwNQbUiNSgPbTAtkJ09yn9/s543iIEbsaCzH63EzFxxoRX7Llvev8gf94h1PhsRSPRr7h3ByJjNpZoBI/I4/x3djT5OFl59eInLyQKlwGtHmjEyZuOvbe4dwfMHG/CHf7oFlzqHXZT303fPx/MHG/jNn3OL5Iy0ZBdjm5PdeEpc4+bLBZHuUZOpIH00aekz4+YZmlgPI2okSSS4Y24e/ufQZTz/1cWxHg4jwvham8XWdqHXr/aaPDrUbtp9Gip5CpYVZ4uGryVJJB6lT331SeD0GVcfuW3AEnTJycncxIMl2oUBoWSkTbtPQ61I5YXQHe4YYqnhRseZI5d70NQ9zO9ChRKcrvaaRMfBTbx9tZUozk0XFNqLnUb+GYF8l1XlNzJMuSYezsiSJfxRu/vrYmEhdjtFU/ewy/dmhAfnv2NqkkRQFszWcfSbrXwS3gsfNKLfbIV7zlBz7whMozYP5f3422d5ueCS9FYvLsB9FQUelSZ8yW4s4OZL9RxtQHLL8E5b/whylFMj0Y6jak4u/naxG20DI74vZiQ0vtZm57VdaP13PpwXMy7rmvvQ0mdCilQiqJuKshUuSetifRK4RD53feZsKzgn9ft7cieW4DcZdCbzFIcB59hErjkBAJfSVkLY7RQtfSYcb3E9Ptly/0Ko5MmCk6XT6LoTEzrGMeSko3toVDCG6dz1IWzcdUrU6+ytTBvgOCL/p+UGj4S+b98+Cy8duoxv3z4Lv/ukmY9f/YxehcERK5q6h1284cF4whn+4yyTpXnC5dMKsuT40T1l+Dcn2XvmnjL0DFmwvqoYwI3uSCbrOFTyFBf53n2s1SVRhJOTGRMK25lAvAj+Nnzx5zpf1zi8JpmsUUeYoJTi+uAIsqdIoh2HIjUJn5uVg+1/u4wf3jU/1sNhRBBvXlLOwSUUVnHwYqdLl8+ygkzMUCsEdXOGLAl1zQN4/uAlj2YZtVUluD5g9qs0G5lwYP34nnLoVa5t153joQNpsmW3U1AKPHvvAjR0DWFXnWONmCw6kxnFYUCbIUOhJg1rK/QuwjszOx0LC+yCmfWcUXihw+gSn6uSp+BChxFLDcLZ+mM2Crudeo1fWjkvD9qMVA/D1TmgXizTVCwJsGKiekD7oAW//ugKfrK6HK8+vARm6ziSkyT4t7fOwDpOoZInCTZp2FnXgk0rS3mjl9Uwjiy5SsffcfXiAlzpGRasStI1bMHWA5f4OPGyaZnoGbZgy19cryvRpmOGRoGv3VLocQ8OLlmkJFeJJIl47WpfjI/b8VFTr0fhe6F4e1+bKn83Xv4mnDJ802eyIkniODWaanyhTIdNu09jfVXJlPOUTyXE1khdpgzvnL4umODW0mdCQ+ewR+c5XYZMUDfr1QrU7nA09uBKtEklQHGuEj9/7wLuWpgvWJrNfUxztEqsW2bAfx24iNRkiaDTKRAHldC17ol/iQ5LtAsDdjvFh409fMFuDlmyBNsfrMCy4mwPYbnaM4w3T7QhPzMN1wZGsPtYKwDwWfAqeQrWVxULGpevfH0JDDnpXoPdizQKfmean5WGlr4RPqCeY0fNzR6xvuPjdrx7tt1jYq8o1aKl3yxoNHCB+9f6RnCpa0gwCY+La+UC8Y9c7sFXXzrq8bv8/f+7GbfMjKv444RMFrnaM4w/ne3AyJiNT4LjvLyUOpIr7qsocOlWWFtd7PG3K9SkYevaRRi12fG1X3/i8XfdUF2C1440uxjMhZo0/NPyYvxw7zlBGRLzRtjt1GNRcZd5Dn8SPbg5xkXlcF7vBEsG8UbcJdqdaOnH9/5wGv85Rb2lr350BdPVCvzbF0tjPZR4ICF1py+EDMNtDyxCilQqaAPsXb8MfSYrPrzc45GQ/F9rF+KH75z30M3f/8IcbNhx0uPZtdXF0GbIYLKMYcW8PBRlizdZevJL8zA4YsWrHzV7TaQLJGlukiTYcbBEu0ghkRAkSYjg8cXp1gFkpiWhLD8LdjvFufZBtA9akJGWjI8au1HXPMgv/nZKeU9z+6AFQ5YxwXbO3DG0r2D3qtlaGLLT0T08iif/eM6n585up3ivvhNb3r/I70wrCtUo1KTh0+Y+aDNkWFKk8TDwJRICs9WGC51D/Bjcx0SI69i8dQPiPOGM4OHK8z12Zykau4b42GEOWbIEKW4FrN0TKPmWoC99jEcqDYJ/1zl5SvzX2oV46JUbBnNz7whePNSIn967AKlSghKtUrC0kLs34lq/CVJC8MMvzYM8NQkvHbrMd71zD73wJft2O8XxlgEXzwx3UjIZkkHilZY+85T2kq4qn4bH3jqDf/ycYco0MJlqCJ0sUQrsOdUmqJPOtRs9NvpcQnKWPFlQN6vlKYLr42cKVfj+7jNoH7SgfHoWbxQDQEoSQc1yA+wUkBDAOmbD7z9pwYNLCz1sB2cCSZqbzAl2HFE3igkh0wG8BkALgALYTil9jhCiBrATQBGAqwDup5T2R3t8waJIFa7nqtcocOBCFwZGrOgesrrU7H1y1TxYx5txus2IrQcb8MMvzXP5/LBIO2exFszu7zvXFPQnZtI5pMG5jWPNcgO2HmgUNGQ4tBkySMmNMTjHWEsljvbChZo0fmxFGgVfqNxZWTyx54yHV5ARONoMGfrNVjyzr14wBvxf/242bG4ZdVwCJSdP7i1BhWStUKRzYXPvCBq7hnD3wnz+VMNbuMz4uB1Hr/Tj3/e4zo/ff9IMqQTIy5DxtZO1GTKP9qXceDj5Esrq3nqwATXLDZMiGSRemepGsSY9FbfO1OCXHzTi3780L9bDYUQI9/rERya8wEI66XL3sIceWn9bMaQSgmf/fAFPrprncqr25Kp5aDeaBWOJ69uNvNfXWY9d7TVh/e9OCJ7Qco6Flz9sEtR9YnaEu8517lYaTGhcohALT/E4gO9QSo8TQpQAjhFC3gfwdQAHKKU/IYR8H8D3AWyKwfiCwmqzCQpxx4AZNjswNGLjDWLAMTl+uPccfnrvAtT+3iHMuixXgdt9rNXDmBFqsexPgtBcnZKPAdarFZiR7Rn/I7YLtFOgPD8DjyyfiSHLGE609GOBWxeyIo0CZQWZuN4/gg3VJdjxaYtHjPXTd8+HVOJQINoMGaZlybx6whnB4ywbLx5qwsO3FuK//34xeoat6BkehXXcjpm56di0cjZMVhukBFgwPRM/v28hvvPGST5pzlkW3eXbWdbE4tC598Vkq9PoCOfpGRrlDWLuvR/uPYdn710ATXoKzrcPecj5/zy4GMeaB1wSV3w9b5ZW6TUZJJCEE4YnV3pMU9ooBoAvLcjH9988jX/83ExoMyaPscAQR5shwzun2jx05H9+eR5+8ZcGl2stY3bkKlPx7388h3XLDHjxUKPLOvjioUZ8d8Uc7Ky7jHXLDEhNclSb6Bgwo0AtR6EmDZtWlrroMW+JdpwuF7MNhOyIbQ8sEtS5K0q1LtcWatLw1F1lvB6fDPoy6kYxpbQdQPvE/w8RQuoB5AO4C8DnJy57FcBfkUBGsUaRip11LS7CzSWXvbL3PL5VXcILrXOVivQUKXSZDq9ehiwZz9xTxnu4+s1WlGjT8e6jlege9ozlDaXeMddMwRnnXaCzl7c8PxMzsxV8Yw7OwL17QT5vGEskBFWztbjWb0LvkNWlAQRwo4SXs9f5pQcrvHrCGcHj3nZ80GLDY2+d5WPKH/vCbHQPjXpsuP5urhalE/Lk3hKUEEfGcUqSBFnyZOROGD/+tAvnEv+EwmXu3HrY45QEcMhMchJBWrIURzp68UilgY/H27jrJLY/WOESHrHl/oX8Z8U8GqV5GXySqrvxC4BVRAmRll4zSvMyYj2MmKJWpODzs3Ow9UADfnRPWayHw4gCRRoFNlTPwnNOictz8jLQZRxBv9nqsuZLJ7rYcUZrc++IS/iEA4ra6lnYeuAS1lboXdben64px4pSrUtOz7iNCuo7OuG9rp6Ti7L8LADw8P6KhYN88fnDLuv3xl0n+dJtc2or0WcaRduAhY+j5vT+tCwZNAL9GRKFmMYUE0KKACwCcBSAdsJgBoAOOMIrhD5TA6AGAPR6fRRG6R9FGgU2rSx1WVCfWDUXL/61Ee2DFihkjvAKlTzFpaUsl6wkT5Ziw84T+MatM7ChugTT1XKU5mXwHl2xhhu+WtmKVXmYu6ESdgqXycEZN7/+8DLuWTzdJclvQ3UJVPIUvhD442+fRUluOhZMV7k8z1Hy7aRoDCqX9GQZs+PxPWc8QigSvaxLPMmnt7bjc3SZLkkhzkrPuUzPtgcWoaFzGNnpqWjtN+NH++rRb7Z6VBThFKWjNbQUVpsdLf1m/m95pdezAsbmNeV4Yo9jAygXCD8q1KTBPGrDV3Z87BGP1z5oQV1zn2g4htgpyoxshehGcbZWOekrokRaPpt7zcjLZJvaVeXT8K9vnMI/fW4mpqvlvj/AiCvdGSgSCcFifRbuWpgPOwVsduCpveeRkkTw7H0LcK3P7KL78r40D4WaNADCp2wNXcNIS5biX1fMwXf/cMqlHObl7mG0Dpj5eOKrvSY8d+Ai34iIq9qztkKPnXUt2HL/Qt4g9rbpdw8H8RY7zF33Dy9/4qIvN+0+zYdqJKpDIWbNOwgh6QB2A/gWpdTo/B51lMQQLItBKd1OKa2glFbk5OREYaT+we229m+oxG/XLcGz9y7A0MgYuoetkCVLYLaM4T/vmu/I+HeK07SM2fHcgQYMjY6juXcEP95/AcOjNnz3jVMgBEELFNcY41LnEB6pNEDntFCp5Ck43jKAO7cexldfOoo7tx7G/nMdAIAVpVqsr5rl0XzhuQM3CoFzr3U4VbIAPA1wWbJncW/nYifNvSPIz5IFXUA8Hok3+eSMQ+5vwW3Wzl8X7+LmjHWc4rkDDfjXP5zG/xxqwoNLC6GSp2DrwQasKs/ni9ZLJARFGgX6zWNYu/1j3Pfix7hz62G8c/o6PrnaA4vVDgKCn927AN//wmzULDdArUhGc6+j2cFLhy7jyVXzXMb5H1+ahx8IxAWvXlwAWbLEo8mI8/i9FacX2yg295n8+p0kMpGUT9PoOIyWMagVU6tGsRAZsmTcMVeLn793KdZDSRjiTXcGil6twJy8DPzqcBNe+KARKUkET91VBnmyFJYxG1Ryx7ywjNnxw3fOYdPKUj7kwlnvbaguwRt1rTBNJK9zjrSXP2zCtoON+J9DTahr7sfVHkfDq17TKKrm5GHjrpPYeqARvzrchPW3laC8QIlXvr7Ep94TaqrkT3MOXyEb8diwyR9i4ikmhCTDYRD/L6X0zYmXOwkhOkppOyFEB6ArFmMLFfc4nCdWzYVxZAwv/O0yXvvGEtGmHJbxG3WK5+Qp8UilAd3Do0EdQfhq+XhfRYFHEhLnEQOA4y39osLOIUuWeHiEnCeJUAzqxjtmwWanfGOId0618V3/JosnLt5wPhpr7jXhxLUBbDvYiDWfKfCZMCHWJWndMgNe+KDRo6KI0PVb3r8oWLf6jbpWfLY4mx/D6TYj8Ekznr13ASQSID8zDX1mq6AcSiXgW4874z5+sVMUMWUulizLwnn842qvCbpMGSQkcTe14eTOMh2+s+sUGjqHUKJVxno4jAjjrGuFQgvc2y43dg3jroX5yM+S4af3LsDVHhMWTs/EponqEoAjvFLIkcaFIs7Jy0BBlszj/Sf2nMXOmqUBVexxxp98JbEQNUq93zveibqnmBBCALwMoJ5SusXprT8CeGji/x8CsCfaYwsVIaPgqb3nMTJmw6aVpZiuUmBmTrqoB1WXKcPXbinEv/7hFLYdbMRDv/4E+891BNz+2FvLR1myBLNylaKTo9No4bNo3cfI2eZcTHGpNsOlTTNXEQBwlAR7/eNm1Cw34JWvV2Dv+mXQZcrw3IEGbDvo2M0+WlXi0WWHEX4447CyOAcVhWrcV1GA9FQpNt4xy8VD4a70fHVJ4uLVvHVVWlWe73HqsPVgA+6rKIBWmerixb7UNQyJhGBFaR7aBi04eW1AUA6rZ+fii/N12LSy1Ov4xRDzgriPZzKE80STKz0m6DLTYj2MuEGekoQ7y3R49r2Lvi9mTAo4XatWpPJhgYBw2+Vxux2yJCl+sv8ian9/Ar/8ayMy05L5GOSstCQUahSiHULtFNi46yR6hoWdB2arzeW1QFszcyXe1lcVo2a5ASlJrptdoVPI2qoSvHm81ee945lYeIo/C+BBAGcIIScnXnsMwE8A7CKErAPQDOD+GIwtJMSMiEXTs/C5Wbn8EbP7DoxrgHBfRQF2fHojWQ8ANu+vx5w8ZUC7LbFxlOdnYF9tJW/MiHnEhLJof7qmHIYcBebpMpCXKUOpNgN/udjlsZPc9sAivjRMv9mKOXkZ+NysXFztNeFf/+CqJB5/+ywW61UJt5NMJLiEsl7TKK4PWFzit/9r7ULsXb8MPaZRwSRNMU+AhICPKfbVVcm5ggWHZcxRBUKvVjiOHN0SRblNnUqe4iGHG6pLkClPRlKSJOgudGJeELHxJHI4TzS53DXMJ18yHKyYp8V3dp3C2bZBzM/PjPVwGFHCl0PhmXvKMF2dhqNNfVjzmQK+c+c8XSafx/Hse5dgGbPj+ytni3pkLWN2Pl/J/X33yieBVKsSK/Hm3KTD2TPeabRgzEbxxJ4zfMm4RHUoxKL6xIcQ73RTHc2xhBsxI2JGtms/9BWlWuybWHhz0mWQSoCF07NgHbdBliT1KOvWZxr1aji6Z9KL1XAt0Sr5BCpvk2PTylJs3l+PdcsMSEt2FAxPkhLIU5Jwx1xHfJJY3dl3H63kv5uzUTEVin7HG/+/vTMPb6M69//3SJYsS7ZlW15jR3YcO7udzYSExinElAYIBRJCWtpAabi+vT8Sh9KFlkK5vXBpUyiUEG4hQMvSAgmENYSwJLQJbQI4++I4dhzbseNVXiVZli2d3x/SjEfSjKzFtiT7fJ4nTyxpNPOOzjtn3nnPuwjDaLjkB8vAUGWRM009yNDGiDZkAcQn0UdvLsC0tFhYBmxYMSfdxWgU235merxkFQgAouXPOF3hVhu4h8QFkxPwp8/OYb7eUbR+uCRTKYar2sLCeQLjXIsRepZU5kJ0lBw3zJ2EP+ypxCvrF4VaHMYYkRavQrYuBisLM3kH1wfHG1Gcl4yb52XigsGIdc4kNS7p+JqZaYiKkmGKLtbFIH35YJ1HkjIXipGti4FGKXepWuV+PxfaB7My4iSrWQnx9X4tnIPtdoq//nBRxDsUWEe7EcSfen8rZqe7KFdOcixONXbBMtiFu4pzATjicrfsq8L20sWSx5TKpBd6bN0vEs4omL6xGPUdJqiVUUiLj3b5bEZ6HB8XxZVW4/YzKyMO55wJAFxGLCdvm9GCxbnJHkbFRCj6HW4Iw2i4+N/CzHisXaTnQxq27R/KEua+IzRS/fHGOkIf0rC9dDGaui3Qxijw13+dF61vnJ2k9tDbR28uwAJ9gkv5tqZuC5753FHCr3RZLq4tyED6CNR+DdSgZkhT3WrEkqm6UIsRdiyfkYrdJ4/jcF0nFmYnDv8FRsSjT1Rj4/J8l2Zdj9w0Bwv1iajvNLsYvZYBR9WGgkxHnfV2Yz/uuTof2UkaGPsHoVLI0Wnux5O3zkNFcw+mpcbhf3dXQBlFsHF5Pm559iAS1UqULsvFtLQ4l6pVYvYBN8/qk6Tn8kDu1+NlTmVG8QjiT70/9zJPdjvFuRajaFta99ggIcKlZs5ArWzuwU3zMrHbrURWrcHkYtRUtogb65xyA54lV+7dcQyly3KhVspx+5Jsl6fXTSX5kgaLP0s3jJHB/Wk/WxeDH1+ZJ+rhn76xWFIffJ3ouDbh7vWKs5Ni8I28xTBbbbyxLRb3fv87J1G6LBeFWVoPXeGuhU6zFd+a6Vmt0demG6w5x+hgc/6umQksptgdhVyGG+dl4rGPz+KN0iWhFocxBtR3mj2adXHhglJe2A5TP2rajahpNUIbo8RP3EIsNdGDKMzSIi8lFk+unYsYhRxrtx3inQdb9lYjWxeDx1bPRV2HCdlJjnur1Dw7Iz1estrTRL5fM6N4hPG13l9Lj+syhLe2tN66IrX0WERrH2frNPhO4SScbe718PSumJ0uWZ5FaKx763A3aKPY+nm1y/ef2luFa2alS/4ugcaAMgJD+LS/83ADHlw5C2ebxUux1XcMrw/DIaZT9+08ge2lrgaxt3AaOwU2vHYUezYV42Vn4xFht0MAONvSi9yUWJfi9b403fB1O4b/NHSaER+jgEohD7UoYcmyacnYdeISDp43MG/6BMBb+IGUF1alkKOqthN9AzY88dlpj3tr6bJcfKdwEuzU0RzMYHJNsMvQqrC2SI/b/zoUlvH4LXO9JulJze8T+X4dsjrFEwW1Mko041OtdL15+NOWlqtBfPB8O9TKKNy+JNujJMv975zE6aZuybqE3i5aDqls1elpcUiJjZY0rrhqFO5VM7gHBi68YiJcYKFEmB3c1G1xjIlEZRG1MmpYfXBHqIdclySxfew924p7dxzHu8casftkkzMhS1y3uOQRR7KGnK/5yRnEKoUM51p6Xepf+lp/0586nQz/qGjqRTaLJ5YkSibDzfMz8YePz4JS/6oJMSIPb5UexKo2PHHrPJj6bXhqbxXsVDw52U4dDgGuv8Bxt+o8qxZ4lm6rau31Os9y87v7XG630wl7v2ZG8Shjtdk8inOXLc/HgFvnAamLiGtLy8F5u7gLY+22g8jQxkh4o/uxfqmjpMqG5Xl8a0nh06r78YQxQ2IX76aSfDy6uwKXuvtEv3/0YpdLQxB/y8kxRg73BhbF+SmixeI3ry5EWny0X+V63PXwui0H+Faj7vtQymVYtzgb2/bXYMPrR3H90wdwwWCULOejUjjaP5e9cUT02nmzvMHFWPflAc+f7Rj+c7apB5MTWeiEN74xNRntxn7881xbqEVhjDJShq+wrbJ7YyGTdZCfn6RKonK5PHdflYcYpRy/WTmL31as0s+OcscKodQ8mxqnEp3LJ/K9m4VPBIlYjCIwlLCkVkZhe/lQmTVKge3l9VgxxzXMQCrTXy4D/9TG7dfd23Wh3Si6HBOrkmNmehxq2k2w2e348bJc/OXfF1yeVsVihoTnND0tDns2FeNCu6PxwysHHcvYO8obPDJiudJynFzjrUVuJOGul0X6JDR0mbGpZBqe2nsO65fmQi4DFugToY1xTAP+xJBdaPfUQ7G23WXL80EI8NTeKpe495MN3Vi1IBMfbixGRXMPzrX08jHDXPvnOkMfX31Cq5JjTlYCLnX14Y4l2S6x674khdjtlF+1YcmeI8+pS92YlREfajHCGpmMYPWCLGzecxbL8lMmjOdtPDJcboJU+AEA1LQZYTD1Qyl3eGxlxDGfapzz087DDfjJ1dPw5GfnXO6t+iQ1nvvneZdQyWxdDP78g4UApTBZbfz8xlUYksuAJI0Sm0ry0Tdg45P0Os1WRxnKRDVONnaN+/b2/kAieSmnqKiIlpeXh+TYdjvFhXYTKpp6UNXaix3lDeg0W7H1tvmwDlJeybJ1MR5ZqJtXF2JSggo6TbTLxcTv02kkvOnc5xO3zsM1M9NQ32nGuZZenLrUg52HG/gl5QytChuW57l0Dbv/2hnoG7B7XFhFOYlIUkfzF6pcBjT3uF60YnGXKXFKrHn2kMtvkKFVYfPqAijkDm/gwRoD7BSYkqxBY5cZxn4brpyWjKIc32LowjQJKmABQqWf7rGznA4+va8KaxZORkpcNDK0KsQoZSiv7YLJaoOcAJfnJkKjUMBg7ke8SgnroB3pWvFx2He2BT96yfPc/rb+MkxKUKO114IYhRxlbxzFDXMzsfNwg0fc+6M3F+CmeZkAHA97nE4aTP0uupahVXl8949r5uHaOemSGdbCWGHu8817KrC2SO9RCUMqpjhM9VFIUMKMpH5+4/f78JNvTWOJdsNAKcVv3j+Ne67Ox8rCSaEWZ7QJ+7kzkGs80NwE93loe3k91iycjHStCu29/YiOkiEuRoGLHWZ8frYV1xZkIFunRoxCjtp2E/LTYmG22lHbbkRGghrm/kG0m/qxo/witqydjy6LFWcu9eKNr+s95jkuUfnRm+cgRilHapwK+kQ1PqlowdnmHmzZW+0h7xull2NxbnLQv3EYIzpYzFMcAN7aKJ9o6OYrSABAnaEPTzvLqpmtNr7AdZ2hz+NikskICAF+9uZxF2/W5j0VGBi04763T3gcr6nbgk6zFSbLANYvzUV2UgwauvrQYxn0SIR74+t6pMWrXOojupdnkao/vL10sYeXrdNsRXldJ1bNz8Txhm7+eJx8HxxvRH5qLBYIPN3+/KYsCSow3FcTVhZm4ul9VS4TZbYuBv/vyjwXT3+6djZ2Hj6H5TPShzUcNRJeV4Vc7lK38r4VM1HZ3CPaqvT+d05i3uQEfnuhV0K4b7FYuZ++eQwzM4r5WDd3r4w+Ue2yWrN5T4WL51kuA5bk6nBZdpKkQcz00Te6zFZHF64RKJU33iGEYM1Ch7f427PToZCzCMZQEeg17kuSurfvrV+ai+3lnobrppJ8bPvkHDrNVjx84xx0mvrx8C6HV3dTST6iFXK09PbDRoGfv3Wc/95Prp6GLy8YkJWkwXUF6VioT+ST7Tj5uKT9bJ2Gl7G23YizzT3I1MZgU0kedpQ3uORuTNQVNHZFBoC3NspiQfJ1hj70DTiy70tfLUedoY//nnuij1SrXM4gdj8eX/h7djqK83VIjovGlr3VsAzaRffjXuHi/ndO4u2jjdhX2YLzrUaca+nFXcW5yNAOXRCWAUfLyEdvLhCN72zp6RdtabmyMBP37TwxbCKT3U5xsrELZ5t7+GOzJKjAcdchQhxjLzQsVxZm4qH3XTOcf/vBadx+Ra6HAXrvjmM41djlkoSRFh+NTSWu8b6bSvL5etfA0BLizfMzkZcS63M8r3s8nlRXPOF3hUkhOToNPqlocYm7X1ukR4ZWxdc93rK3GgdrDKjvNIv+hiwpz3dONHQjNyXsvOhhS2FWAnSaaLz2ZV2oRZnQBHqNB5qbwH1PbD62DDgqTKxakAXLgB0PvncK3RYbmrot/Gc9fQOIlst5Rwb3vSc/O4f0BDU276lAl3kAHWar6D1cmLRvt1Mcqe/Ctv01uO/tk3hufw1uX5LNN/6aKOXXxGCe4gDglJuL2+GaV8RGy9EniOvhUClkSI9XoaWn36UxB6fwwi4xGVoVykrywMW47zzcIGkU6JNisH5pLjITVMhJjkVOcixq2owuhgr3vQytCjPT40SPr1bKUdVidGn2IfREO4L8CaalxWJTST5MVhtfJqvTbHVJEBDKxzWM8Na1zpvXvanbwjreBYAv7Za5sRFiGbCjr198LM+3O7yufVYbZmbEY/m0VOSnxaJ0WS7szri4/LRY6JNcJ1KZjCAnORY2u/fW4u7f4Ty/Z5t7/Pqu+wMWp+db9lVh/dJcPPN5NTK0KqwpykKmNgZtxn7RJVPWgdF3jjd0YcoEvYEGyncvm4w/fFyJm+ZnQRujCLU4ExJv1zhXS10srCLQRlTC5Hape/qUZDU2LM8D4LAnOBLVShiMA2joMot+71JXH9YW6fm6xWL3cK6LaE2bEbUGE+oMJiSqlS6G9+O3zMXkpBgUZCZM2Idc5ikOAK6F47rF2Xjxixps3VeNFw7UIE6lwILsBI+sU66r3R1//Yrfdt3ioacy7mKy2ynONPVi2/6hfd6+JBtF2Ymi2aj1HX3YdaIRCrmM9+LpE9V44tZ5LlUGMrQq3L4kGz9767jo8SclqD2ePoWe6E0l+bhn+zHc8uxBpMRF44PjjXjm82o+3jk7SSNZ9kWlkCElVsWXeznfakRt+1DpF7GELeGxJ+oSTjC4e1o/ON6IuVkJomPk/lodLV5C8EK7Cb0WG57bX4ML7SYcvtiJK/NTsWp+JorzkjFvcgKm6KSNxSnJ0tnY7gjj/DIT1Hj53zWiFTOEXo+aNiO+rjXggxOXsHbbIWzZ66rnlgE75DLw1wLnIbnjL19hz+lmDA7aXUoSSZWMY/roydcXOjCVPSj4RbZOgwX6BDz56blQizJhkarAlB6v8lqNwVtlCW9w3/vgeCNmpseLHlujjMLOww144UAN4lUK3LdiOjYsz8Ovr5uJ37x/SrKk5qSEGA/Ps/A+Kuwiet2WA/jRS+V4bv/Q/Mh9BwQoyEwAAMl7dqBVKcTKvoUjLNEuAOx2ii+q21H6arnH0+KHG4sxJVnjkjjk3tWO29a9q0xNmxHXbfHcbnvpYvz7vMGj9/m+s8347qJslyQ+YVJeh6kfCrnjifYOQYyR8PiqKDkG7RRPiEzO//f9+Tjb7Ej4E8YabS9djL4Bm9fkvLLl+dheXo+Ny/ORoFa4eKG5KhWdZisev2UuNrx+1OPYZSV5XjvujCFhnywiBmdY1hkcVUNOXuzCdYWT8OB7p/iY4h8vy8Nvd53mx+XhG+dg+9d1uHnBZJekTc7jsHphFrbuc7RcfvyWuZiTGS/Zwtxb4pq3YvBiKweP3DQHb3xVh8tzUyCXAUXZSbgiV4eoKJnL9uuX5uLFL2o89Jx7/68/vAwGoxU/e+u4xzbb1hXx1zP3ICtMmA3TmOKQJ9rZ7RRz/+cT/GF1IRLUyqD2NdHo6RvAfTtP4PXSxZg5Pit3hPXcKRVTPD0tTvR+vVsQM+zLXCZ1zJONXTjZ0IWYaAV+7QxnFN4zVxZmurS237K3GmUlediyt1o06fgnV08DAcWjH1V6HO+5HyxAvjNsotZgErUvuBU0of3i/rsI79mBzINhmqPBEu1GCpmMQCEnossYbUYLpqbG+tTVbv7kBHxzWiqvFFLLOWarDVmJMdhUko+UuGikxkXDarPjiqmz8d3nD3nERHEX73DHz05So6nbgtwUjehykE4TjS17j3p8j+tO1tLjMJRzdBqsmJ2OzNLFOFDVDr1Og0tdZqwszESHsd+j3eVTe4eWsrni4sJyXXICfGtWGmZlaMPJAIkouBjbHJ0GfQN2bNtfg8un6lxKA77+lSPprCAzHtPS4pCljUFavAqWQRsfFiEMk+Geny0DdlgGbWjt6fcIUxiuS5J7Qp179jelnm1JH3j3lMeDmFiJQqmQELnMUW7usuwk7K9ucwkp4nSuxzLgspTIddXbPQE7OvlDZUsv4lUKZhAHQHyMArcUZeG+nSfwzv/7BuRMt8YUqbJpX14wDBs6JTaX+XrMgswENHZZ0NBhcpmPuVAHLhzTMmDnwyg5D3FTt8UlWfib+SmgoFDI5VApqlzkztbFIF07dJ+Wsi8IGfJ2T0nWoL7DxM/rgCPUUnjPDqRcW6DJiaGAGcUB4k9ckdS22W43Want0uJVWDg5ER+easKvBE+WUi0c3eMepfari43Gr989hUS10qPm8BO3zuMbOrh/b8BGcedLX2FlYSbkMuCy7CQsydXBbLXh8U9cPc4bludJXoiAo7j4H9fMRX2H2eX409PjMStD63UMGMMjnPjbjP244y+uKwbnWo0uHpClecmo7zDBaBnkkzKF3mLAWQM7OsqlnWggceBi3gMpne4bsImWB3Kf6MX0tWRGKh8jl6PT8A9h7h4X4TlYBuxo7rHw3Zw4ebnOfWFaom3M+Vd1O2ZmxIVajIjlqumpOFRjwPP7a/DjK6eGWpwJh5hxG2jMsD/HXDE7HScbu7F220GP43DOB+HfOw83oGx5Prbsq0JTtwUvflGDJ26dh/n6RL7spLDOPFeGUxhj/Py6ItHzKs5Lxqr5mfyqL5eA5z4vCo11f+b4WoOJT+AXlpIN1xwNZhQHiFjzi82rC6FPHGp1yimEwdTv0dRALAbJW0ONWoOJN4gB1xaOw128UrI+9L7Dg9vUbcErB+tQuiwXhZlapMRGw2q3w2YHtt423yX0YfPqQmzZW+lRTmbz6kLMzdJ6JngRcUOFu9g7zVZMTozBTwVl6LinyMzSxRM64H+kEHqNh2vQwSXGAUDpslxEyRxtvR/96AyfsPHQDbPxu48qPOLXuDCFlFjfbh6c90C4SgDi8HCsLMzkJ+EPjjdK7lN4AxPeOITnJ9Qh7jc429wjGoP32C1zUdnSiw+ON3o0/wjD5b+Q84/KNhTlJIZajIhFRghKi3Px4HunsTQ/GXMymSMg1PgyT3IEU8tcGxOFR28ucHE+cGEKwr8BoKnbgu3l9dj+H4vRN2jzaAbS0mPBrIw4fLixGG1GR414ziAGhporPbhylktoHFcxiJvza9qMHhWqtuxzJOCZ+gexYXmex9woxXBJ9OGao8GM4gCRyQiumZmGbeuKUF7XAZsdeOJTR0zP9XMyIJMRjwYK29YVQSEnohcPd3GlxCn5msbC7cSWPnaUN3hcVGIXr9gykcHUz5eGAxwX3Za91Xh8TSE2vH7UZX97NhXzDT4Mpn5cnpviYVDct/MEPtxY7DGZFGRpPd4TXvhP3DoPJqtN1Du492wrGrssE97wGAmG0y932oz9sNkBO7XjgsGIdYuzERetwKTEGADURXeAoTCFTSX5uGAwYkry8DeHlh6Lh8e2KFuLu6/Mx2/eH4qTf+iG2WjoMonuU3gD424c3q4z7lpwf1DjzqGypRcvHKjBIzfNcXnAjaTlv7Giz2rDkfpO3HFFTqhFiWhS4hzJn//1t8P4YONSFooSYqTCKjiPLGcEZ2hVfuVUcAwO2vHhqSbct/MEEtVKlC7LRV5KLFp7LRiwUaxemAVVlGM1rtNsBeBwJN23YiYKsoYe8L09qIuFgNQZ+tDr7GfAhWy8crAO8/UJvFEsFWJxrrUXW/Y64o7/58Y5yNIO36RHqnQt5zwJ17JvzCgOgvpOs0eyHafomQkxLgpRZ+hD6avlLkvVHFLKffkUndcyMJ1mKxboE3yKexRbJhLz4NZ3mD1u/LvLil2WrqXKybQZLZKtLbn3kjXRsAzaMCVZgwytCrMztKjvNIvKYrNjwhseI4Ev+uW+/aUuC5+0xj3h/9+havz1h4sAiOtOXmocfudsISqWlOLuTUmLV3k09bg8N4U3iIGh+smly3IxOVHjoQfebmBSCMMoxFYwuDjmBfpE/nisRJsn/zzXhvzUWMRGs9tIsFwxNRk17Sb856uH8er6y6GMYoWhQolU/oNwHi0ryXNp1OXLg7LdTvHvGgO/asw5o4RJdRzZuhjJXArA+4O6VAiIsK4CIYAyirh4a6W+Z3O+tAzY8Zv3TmF6WizmTva+QiQ1ZxZmxmN3WXHYhp+F3ZVHCFlBCKkkhFQTQn4Zanm8ITXo5XUdqOswSd5E3fGliLhUGRh9koZvWsB19/IFsf09uHIW3ixv8Cpzjk6Dy7KTJMtVCZsocPJw7y3K0eFcqxG3PHsQP/7bEazddgifVLTwZeSEspQtz8fbRxokfzOG7/hbpL7WYBJtxvLwjQXI0WlEdadseT5+t7vCpfY2MHQjEStvlKPTYFpqnMt1IpUsZ6eQ1AMxnRsOqXN4+0gDf0zh8aTKN4Xj8t9YsevEJSzQs9CJkeK2y/QgBLj7tSMYsNmH/wJjTHGfR8UadQ13v6o1mFBe1yH6vWlpcS7z0X0rZqIgM0FyXhuuzrL7/PbHNfOQlRjjUkZ24/J8lxWx4eZF7hjN3cPfk6XmzPy0OL9slbEmrB7xCSFyAM8A+BaABgBfE0Lep5SeCa1k4nh7qtJEi7fBFbuJ+uKFCsQj5g1uf5mli7H3bCtsdqDXMsAv10jJLJMRLMnV+RQj7Y6Ucba7rNhDlnCPO4ok/PVySm2vkBNe3zhd5Eq+ceMFuOrMcGEHMzPiRa8T99cyghHVA+H1NNw5AP7FGU4EeiwD+EdlG/5469xQizJukMkI/t+VeY4l5pe+xv/9YCHzwocRYvOivwl5LT0WvpKE+/dmpsf7Ve3GW0KgmL0gI8CKpw64zMXuK2Lu34uSyfDTN4/x8yJ3jHTt8HNxpM6Z4eYpXgSgmlJaQym1AngDwI0hlkmSHJ0Gm1cXejxV7TrRiLS4aJ8LfPvqhQrEI+YNrjzMjPR4vPhFDV7+d51H614xmaOiZLihcBJ2lxXjjdLLeaPWlxhSKePMXRbOII6Eiyjc8dfLKbV9Wrzrw1FuSiy+OS0VM9LjXWLfhGM2XEtU96YeHxxvxCM3zXHRwU0l+SjM0o64Hvh6Dty2K2an+63z45V3jjSgIDMe8SrWjW0kUcgd+h6tkOM7T3+BM5d6Qi0Sw4n7vLjzcINP90v3fQgba3Hf27y6EFOS/Vv1Ha6JiLu9wK3iCRHzbAu/Ny8rARvdZH3kpjmY7UNlqEidM8OqeQch5BYAKyildzlfrwNwOaV0g2CbUgClAKDX6xfW1YW2f/zgoB3/rjHwyXa7TjTivhUzsWJ2OgD4VOA71JntwkLk6fEq2OxAm3Hka7NKNScZiaLoo4hfBw83/QT8169Atpcas0DGXJ+oRl2HGfUdJqiVUUiLj4Y+aXT1IAz1zhf8FnAk9NNmp/jmY59j/dIpmJE+LptOhAX7q9rw2pf1WHvZZNx9ZR606oh7AIn4uVOI2Ly49bb5mKKL9fl+ye1j854KvqSpsBlRIDL5Om/5MheLMThox+mmbjR3W5DuzAMKRNYwRPSHijijWEgoO4YJGYkbaoTelP0i1MZ/gIR1VyZf8Ve/RkofI3TMI4WQdLR7/at6/O1QHX593UwQwsZwNOk0W/HW4Ysor+3EqgVZuLVoMmZmxEXK7z4u5k4hkXyvZ3OxBxFhFC8B8N+U0m87X/8KACilvxPbPlwvHIY0EWj8j7uJfayJwDGPFMbcKG439uPbT+7HT741DVMnaNWNUNDW24/PK1tx8Hw7ZIRgyVQd5usTMSM9DlOSNUjSKMPRUGZzZ5jB5mIXIqLN89cA8gkhUwA0AvgugNtCKxJjJAm0PSYjcmFjPj4YsDnaXxfnJzODeIxJiYvGrUWTsWZhFho6+3C2uQf7zrbi1YN1uNTdB+ugHSlx0dBplEhQK6GNUSBeFYX4GAW0MQokqBVIiYtGenwM9Do1S+CboLC5eHjC6sqglA4SQjYA+BiAHMBfKKWnQywWg8FgTGi6+wZQ9vpR2CnFmoWTQy3OhIUQgslJakxOUru832e1oavPip6+QfT2D8Dcb4PZakNbbz/qO8wwWQfRbR5Eh6kfzT0WxEUrMD09DvMmJ2C+PgEL9IlI1LCmIQxGWBnFAEAp3Q1gd6jlYDAYjIlOW28/PjjeiD//owZFOYm4q3jKRF5uDVtilHLEKGPgQ1EA2CmFwdiPWoMZte0mfFHdjqqWXuhiozF/cgLm6RMwMyMeeamx0IVnWAaDMWqEnVHMYDAYjNBxoqEL39n6L5f3irITEa9SYNfxphBJxRgNFHIZpqfFIT81Fg1dfThYY8B7xy/5/P3rCtLx5Np5iI6Sj6KUDMbYEVaJdv5CCGkDEOq6LckA2kMsw1gwUc4TcD3XdkrpikB2wvTTg3CRJVzkAIKTJWDdBKT1U5GcHZ307f+np4MD9sGupn5qtwV3k7ANxkAe1RfUPsaSSJJ3lGWVRcfKo+KSlLIYrUIWrZYTZYycyBUyIlfIiEyGvtpjXW07/6eGDlrFdCQc585wuvaFMLn8J1jZRPUzoo3icIAQUk4pLQq1HKPNRDlPYHydazidS7jIEi5yAOEly2gRaecYSfJGkqzhQLj+Xkwu/xkt2cZFBWYGg8FgMBgMBiMYmFHMYDAYDAaDwZjwMKM4eLaFWoAxYqKcJzC+zjWcziVcZAkXOYDwkmW0iLRzjCR5I0nWcCBcfy8ml/+MimwsppjBYDAYDAaDMeFhnmIGg8FgMBgMxoSHGcUMBoPBYDAYjAlPRBvFK1asoADYP/ZvNP8FDNNP9m+U/wUF00/2b5T/BQzTTfZvDP6JEtFGcXt7uNaUZjCYfjLCG6afjHCF6SYjVES0UcxgMBgMBoPBYIwEzChmMBgMBoPBYEx4okItAGPiYrdT1BpMaOmxIC1ehRydBjIZCbVYDIYoTF8jGzZ+DAZjOJhRzAgJdjvFntPNuHfHMVgG7FApZHji1nlYMTud3agYYQfT18iGjR+DwfAFFj7BCAm1BhN/gwIAy4Ad9+44hlqDKcSSMRieMH2NbNj4MRgMX2BGMSMktPRY+BsUh2XAjtZeS4gkYjCkYfoa2bDxY0Qazd0WfHqmBb2WgVCLMqFgRjEjJKTFq6BSuKqfSiFDapwqRBIxGNIwfY1s2PgxIokj9Z1Y8af9eOqzc1j59Bfs4W0MYUYxIyTk6DR44tZ5/I0qWxeDbeuK0NJjQU2bEXa7ZG1tBmPEsNspatqMOHi+3aveuesrF5Oao9OMpbiMABnr8fNVrxgMd/oHbdj0+lH8aOkU/Pr6WZg/ORG/eOtEqMWaMLBEO0ZIkMkIVsxOx4yyYnSY+tHYZUHpq+UsCYYxZviTfCXU19ZeC1LjWPWCSGIsx48l9TGC4a3DDUiJU+GynCQAwOqFmfjFWydwqMaAxbm6EEs3/mGeYkbIkMkIclNikaSJxn07T7AkGMaY4m/yFaevi3OTkZsSywycCGOsxo8l9TEChVKKv/6rFtfOSeffi5LJcEPhJDzzeXUIJZs4MKOYEXJYEgwjFDC9Y4wGTK8YgXKmqQdGywBmT4p3ef8beck43tCFix3mEEk2cWDhE4wRx1uRfLHPMrQqlJXkgQu723m4AZ1mK1JiWRIMY/RIjxfXuxiFHHY79fAksuYPwRFOv5+ULIHI6P6dDK0jqU9oGLOkPoYvfHiiCYum6ECIq84po2S4Ymoy3j7SgE1XTwuRdBMDZhQzRhRv8XQAPD7bett89A9QbNtfw7+3qSQfGqUcFwxGTElmhgdj5BkctONwfaeH3sVGR6HsjaO4b8VMlxhQFicaHOH0+0nJcs3MNHxS0eKXjFL72nrbfGx47ajLeywpkzEcn55pwQ8WZ4t+tiRXh7/+qxZlJfkeRjNj5GDhE4wRxVs8ndhnJxq68dM3Xd97am8VeiyD2PDaURaHxxgVTjd144F3T3noXXJsNOoMfR4xoCxONDjC6feTkuV0U7ffMkrta4ouFrvLivFG6eXYXVbMHp4Yw9LcbUFLjwV5KbGin+enxsJkHUR1q3GMJZtYjLlRTAiZTAj5nBByhhBymhCyyfn+fxNCGgkhx5z/rhtr2RjB4y2eTuwzO4Xo9pZBO4vDY4waTd3ietrjLJTvrnssTjQ4wun3k5JFSie8ySi1rzajhSVlMvzii+p2zM7USuoKIQQLsxPxyZnmMZZsYhGK8IlBAD+llB4hhMQBOEwI+dT52ZOU0sdDIBPDT6Ri77gi+VLxdO6fyYnneyqFDJSyODzG6JGhjRHVuxhlFP+3UPeG02uGd6TyBkLx+0mNZSCxwEwvGCPFwfPtmJEe53WbBfpE7DpxCXdflT9GUk08xtxTTCltopQecf7dC6ACQOZYy8EIHC6O7rotB/C957/EdVsOYM/pZtjt1GuRfLHPCrK0Hu+VLc/HrhONLA6PMWrMzojHIzfNcdG7h26YjRf2nxeNAdUnqj22f+SmOdAnqkMifyRht1OcaerFtv012LqvGi8cqMHtS7Kx9bb5Ibm+peao2Rmec9FwcxBr6sIYKb660IEZ6fFet5mRHofzrUZ0mKxjJNXEg1Aauk47hJAcAPsBzAFwL4AfAugBUA6HN7lT5DulAEoBQK/XL6yrqxsrcRlOatqMuG7LAQ/vyO6yYuSmxPJeZLEi+e6f6RPVaOgyo6WnH2brIJLUSgzY7UjSRIdLdr9fAjD9DF/cVzeytDGoaOlBc7cF6fEqJGgUaOoWb+xQ02bEnS99hZWFmSAEoBTYdaIRf/3hIuRKxACOAX5fHKHQT6n54sONxZiaGprfTmqOEpuf6jvNXqtReJvvJjhs7vSR1l4Llj/+Tzy3biFkwyTRPfnpOXx/sR43zmO+xCAR/aFDVn2CEBILYCeAeyilPYSQPwN4GAB1/v9HAD9y/x6ldBuAbQBQVFTEemeGAG/xgVz8XG5KrIux4G6QLMpxdOYJl4z0kYLpZ3jireJAnEqBlh4L4uwKLMrRiepeS48FdYY+jwL6nM5HCqHQT29xt6EyisXmKPf3Bwft+HeNAeV1HbBT4IPjjR5VSbzti+EfE3nuPFbfhWlpscMaxAAwOzMe/6hsZUbxKBESo5gQooDDIP47pfRtAKCUtgg+fx7ArlDIxhgef+PopAySWRlxopnbM5weZwZjpBCrErB5TwUGbHa+m6K3hzIWOxo4kfjb2e0UH55qctGNsuX52LynAjPS49j8xBhRjl3swpRk30JuCjMT8OjuClBKWWm2USAU1ScIgBcBVFBKnxC8nyHY7GYAp8ZaNoZv+BtHJ1W2qKWnP2wy0hnjGzFv5crCTJ/bi7PY0cCJxN+u1mDy0I0t+6qwsjCTzU+MEedwXafPD1pp8dGQy8BKs40SofAUfwPAOgAnCSHHnO/dD+B7hJB5cIRP1AL4zxDIxnDDPeyBi7FLiVNie+limK22YTs/SS2fmq2Doh6k9HgVatqMYdH5ihF5iFVGEfNWymXi5QDFQiJkMoJrZqZhR+liNHX3I1YlxyRtzJicT6QjkxGsmJ2OGWXFYx53G2gXPak5KzspBmarDTVtxpDPS+HUIZAROJRSVDT14IdX5Pi0PSEEsybF41/V7chP816tguE/Y24UU0q/gHiA8+6xloXhHfewh2xdDDYuz+ebHnAen8uniMdhckgtn+qTHB4k9w53Z5p6x1WcMWPs8BY77K5rl2Un+bysb7dT/KOqFVUtRjy1t4rppp+EIu42mC56UnNWQ1cffrHzZMjHPpw6BDKCo6GzD4ooGRLUSp+/MytDi/1V7fjhN6aMomQTE9bRjiGJe9jDysJMjy5gvnSlklo+nZKswYrZ6S6dn6boYsOm8xUj8pAK1anvNHvo2pJcnc/L+rUGE040dPMGsXDfTDfDk2C66InNWZtK8vFmeYPf+xoNwqlDICM4Tl/qRq6P8cQcsyfF4+vaDtjsEyofcUwIWfUJRvjjvoRIyPDLzVJLet6WT4UepIPn231e0mYw3BmuMgr3j9NTX8OAWnoskt0XmW6ODsGGBwynC95wn7MICO7ZfgxN3UPxxKEc+2DOjRFenGrsgT7Jv3rnCWolEtVKnL7UjcKshNERbILCjGKGJFJLiFLLzcMt6fmyfBqJmeqM8MEX/ZHSU29hQGnxKsnui0w3R56RCA8Idi4Rzlk1bUZ0ml0bJoRy7Nk8OX44fakbcycn+P29mRlx+Fd1OzOKRxgWPsGQxH0J8YPjjR5dvYTLzSOxpBeJmeqM8MEX/QlET3N0GhRkabGpJJ/p5hgQbnNJuM1L4SYPI3DONvf67SkGHHHF/6o2jIJEExvmKWZIIlxC7DD1QyGXwdRvw/bSJei1WKGQy5EWH81vPxJLeqHMVGdEPr7oT0uPBYlqJVYtyAJX5nPn4Qa09lqQo9OILtnLZATLp6chLyUWC/SJMFsHoU/SYEoy083RgJtLMrQql3HqMPUHNJe09FigVsphtdlRazD5PaeE27wUbvIwAqPHMoAusxVp8f57+GdmxOG5/ecxYLNDIWf+zZGCGcUMr8hkBDk6Dc42u1aE2FSSj1cO1qHTbOWXNUdqSY91iGIEw3D6k6FV4fYl2S5VJDaV5CNDq/K6ZC+TEeQkxyInmenlaJMWr0K2LgZri/TYsm9onPJTY7HATn02/oTz150vfR1UpYZwm5fCTR6G/5xr7sXkJLVPnezciVMpkB6vwomGLizMThoF6SYm7PGCIYndTlHTZsTXtR0eS5lP7a3C/dfNxF3Fudi8p4L3vrAlPUa4Y7PDo4rEG1/Xo63XKrpkf6HdhJo2Iw6eb0dNmxF2lvE96uToNHj4xgLeIAYc43HfzhN8CAU3Pw03Lv6GYvi6XwYjWCpbepGV6H/oBMfMjHj8+zwLoRhJmKeYIYow0eWu4lzRsIjKll68cKAGZcvzcanLjJYeC2ZlxOHDjcVoM1qQHq+CzQ58ecHAisszxgxh1YLUOBXkMqCpeygcorXXNcwnQ6vC2iI99le1iep5RXMPfvbmcVYPdgyRyQgUciIZjpWj0/iciOdPWFcwCX5CvcvQOua+1l7WWIMhTWVzLzITAm8CNCsjHv8814qNy/NHUKqJDfMUM0Rx965w3l8OlUIGSofan3aZB/G957/EiqcOoLKlF0X6JJxp6sX1Tx/A957/EtdtOYA9p5uZ14UxqnBGzXVbHHp3/dMH8NGpZty74zivg6lxKhd9XrUgC1v2VcFOxfX8XEsvqwcbArhwLCFcOJY/3l9v+3En0AQ/od7du+M4PjrVzOY+xrBUNPUgKzFwo3hGRhxOXuqBZcA2glJNbJhRzBBF6F3ZebgBZctds+7Llufj7SNDhexr2k3835v3VOBYQxfONvfgruJcZGhVzJhgjAnuRk2iWom+ARvuKcnnQ33kMriE+XDtnsX0/NGbC/iGDRycl5ExuoiFY21eXQiDqR9tvf1IdOsAJjUu/oR1efMqe0Ood6sWZLEmLwyfqG41BhU+oVZGITtJjSN1nSMo1cSGhU8wRBEmzTV1W/DqoTqULstFYaYWlgE7GrrMWL0wCzsPN6DTbEX/oOMGwC1F/+DFL/nlx7Ll+Xj1UB2aui2suDxjVBEaNRlaFdYtznZJ1Cpbno92Y79LVRWu67ydAntONWH90lzIZUDJjFRoYxRhVZ92IuFePWLARvHgeydRZ+hzSfblGmpw4yLW9MPXSg2BJgsL9c6XJkcMRofJCuugHYlqRVD7mZkehy+q23FFXvIISTaxYZ5ihiju3pVOsxWFWVr0D1L87K3j+P1HlXjhQA1uX5KNB66fidhoOTYsz8P91830SI7Zsq8KqxZk+XRzYUkuDI5AdEG4VM6FRbjrokIu46sStPZa8YMXv8SWvdV44UANVszJwK4TjZiRHo+CzATok1jyaCjhKiykxatQ+mo56gx9AIaSfdcUZQEYGhe5DHj3WCMfPsOFLgCOzpmLc5P5sntiehVosrB7iIav4RqMiUt1qxGTk9QgAVSeEDI7U4v959pGSCoG8xQzeNw9LNfMTMNugXeFUuD6pw94VKF4bt1C/Oerh2EZsKOsJE/US8ItWXu7uYxEFyvG+CBQXeCMmnt3HJP02Jmtjvg7sfjRLfuqsL10MWZnaPlrYXpaHPZsKkZzD6sHGyqkwhrmT07AG6WXIyVWhQsGI3YeacS2/TUeoQszyor59t7Dld0T8yoDQE2bUbLltFDvdh5uwKaSfJeSf+xBiuFOVWsvJgWRZMcxLS0O59tM6DYPQBuk15nBjGKGE283C27J7+D5dtEb0+G6Tv59LlnJffmxZEYqCjITvBoTUkku3A2NMXEIVBc4oyazdDEudvaJ6iJXKF/K0DJbbfikooU9nIURUmEN2ToN34Z5w2tHJSvlcKELvuiVe/1fXx7Q3I3p9HgVrpmVjjYje5BiiFPV0osMbfCrBwq5DDMz4vDv8+24tiBjBCSb2LDwCQYA37KupbK4bYJ7kFiy0hO3zhvWIAYCT3JhjD+C0QWZjMBsteF/P6wQTZzjPHZS+qxWyoNuMcwYWYYLaxDqi7fQhUD0yteKFJwxvTg3GTnJsZia6vg7NyWWGcQMD861GEfEUwwAsydp8U8WQjEiME8xA4BvbVWFS4Scx2Tz6kI88Wmly76iZMCLdxTBOmiXbIUrlgzDdbFaWZjJH/+D440sFm8CkqFVoawkD2qlHJkJalxoN8FmtyNdpB2qlC51mq149VAd1i/NBSGAjAAL9EMPZ0J9TlQrsaYoC9NS49A/aEeiWskncAEsUSoYBgftON3UjaZuCyYlxCAuOgrNEmEIUgzX1ph7wOEeyoXJlULjOZBEupFoOc1guFPTZsKtRZNHZF8FmVr86bNzoJQGHaM80WFGMQOAb21VxW5M+kQ1FHIZb1jcviQbT3zmekOakuwaSye1HHn19FRsXJ6PB949xb//yE1zoA+iZA0j8rDbKc409eK9Y41YW6THz98aapwxPT0e+qQhY0hKl66ZmcYbvM98Xs2/r08a0kVOn2dtKsaR+i7c/85Jjzbm7pUNGP4xOGjHu8cbXa5psRbxvhrGUm2NhQ84XKWcaWlxmJke7/JQLvZgP1y870i1nGYwOMzWQXSYrUiJjR6R/WUlxsBqs+NCu4k9qAUJoTRys/uLiopoeXl5qMUYF9jtFF9Ut6P01XIPL8ruYeI4OU9dW28/7vjrV8N+v6bNiOu2HPDYbnvpYqzddsjv448yAd/xmH4GBqcf65fm4sUvarzqg5Qu7S4r5qsMDFeGS2ofpctysWXvkEEdhjHFQQkzFvp5/GKn6DW9fmku/7AyUtc3Nw8NN96+bifcPtC5cYLD5k4JTjV2Y+PrR/HozQUjts8XDtTgG3nJ+NHSKSO2z3GOqH4yTzEDwPBtVYdLbspNifWI1+OWG8+19AIA9Ilq1HeaXTqECY/T1O17O1bG+IXTI2H1COHSdZuxnzdkhmvhK+VZFDue+z64ygYsUSpwpK5pboVX7Pp2D4fh5g2pyg8c3jzJgWwn3D7QuZHBEON8m3FEkuyEFGRp8VlFCzOKg2TMjWJCyGQArwBIA0ABbKOUPkUISQKwHUAOgFoAt1JKWZuWMcSfeDupOE7u+2KNEx65aQ6e3leFG+Zmih4nQxtY4XzG+ILToxiFDCqFDIlqpYsuvXCghvfccrHHXKlZrpmMPzozXGUDRuBkaGNEf1tugdL9+nYPh8nWxXiEVPnjtRebpwJ5uAm0qQeDIcb5ViPSR9ooztRi2/4amK2DUCuZvzNQQlF9YhDATymlswAsBnA3IWQWgF8C2EspzQew1/maMYb4Wrieu3G5F8jXJ6r574s1Tnjg3VP42TUzsL+yVbRCxewMLWuUwIA+UY1t64qQlRiD36ychTVFnrp0745juNBuwpmmXmzbX4Ot+6r5ZjJbb5vvl84E2rCBMTyzM+LxyE1zXH7bB1fOQpxKjk0leR5j5V7pYWVhJm8QA/5VApGapwJpCMR0hDGSnGs1IkM7MpUnONTKKOSlxuKLqvYR3e9EY8wfJyilTQCanH/3EkIqAGQCuBHAlc7NXgbwDwD3jbV8E5nhMrw5pEoUvXznIr7Rwdlm8RCJqtZerJiTwbfTLcyMR35aHH8cX9uxMsYndjt1qRGcrYvBz6+ZIapLNe1GDz18am8VPtxY7JfOML0bPaKiZLhpbibyU2PR0tOP6CgZfvP+Kb5V8xO3znPZ3j2UJZiWySNZ9zxQHRkpTzVjfFHTZkTxKLRlnpuVgE/PtOCa2ekjvu+JQkjrFBNCcgDMB/AlgDSnwQwAzXCEV4h9p5QQUk4IKW9rY3X5gkGsja6w1qZUfU2pGMwD1e24/ukDONPUi/zUOMmaxlv2VaF4Wipe/KIG+WlxLsfx5fjhDNPP4HA3ZOoMfahq7RXVpf4Bu6gethl9q2st1P9agwk5Ok3E6p2vhEI/o6JkKMhMQFp8NA7Xd+KGuZnI0Kr8qoXu/tqXsAVv8eaBtBD3d24aSU/1RGCizJ12O0V9h3nEPcUAsDA7EXvPtjIdC4KQGcWEkFgAOwHcQyntEX5GHSUxREeVUrqNUlpEKS1KSUkZA0nHJ8FM2Klx4jcuVZSMv9FxbZ2Fy41ly/Px9pEGWAZ8a/sciTD9DA4xQ2ZHeQMeXDnLRZc2leSj3WgR1cOU2OENpolqsIRCP7nfeu22Q9iy1xHmsm5xNm8YCxtnuIcpfHC80SP8wtd5Q8rATolV+T32gRjRvjb9YDiYKHPnpe4+aKKjEKOUj/i+0+JViFdF4ejFrhHf90QhJNHYhBAFHAbx3ymlbzvfbiGEZFBKmwghGQBaQyHbeGG4ZTtflhal9iEjwKaSfDy1dyiJblNJPr9vy4AdzT0Wvt3u3rOtsNmBVw856r762vaZMfEQS2jqNFsxIy0O20sXw2y1QUYI7tl+DLcvyfbQwwdXzsKlLjMIgdel6uH0ny17jxxiv/WWfVV8yT2h11eqFvoCfaLfoS1SNYnlMvgVVmG3U+yrbMGJhm7YKSAnjkz/5dPTvMrBmn4wxKhpMyFzhDrZibFAn4g9p5qxMDtx1I4xnglF9QkC4EUAFZTSJwQfvQ/gDgC/d/7/3ljLNl6QamggzNiWWlps6bHwRoH7PjavLsSkBBW6zAN45eBQpzBKgVcO1mH1wiwAQ8ubMhlBQWYCGrssHrIwg5ghhpQhM1+fyOtLTZsRnWYrTFYbdh5uwPqluYiOkiEvNRab91S4xKsKdV5o6PYN2CSX1nN0mmGvH4bvSM01UqtFYiXT/CmhJtzPNTPTsL10MZq6LcjQxmB2Rjy+ruvwOve5U99hQlWLEdv217g4AfJSYpGTLC0Ta/rBEKOmzSjamXOkKMpJwjOfV+P+62aw7nYBEApP8TcArANwkhByzPne/XAYwzsIIesB1AG4NQSyjQt88QJLlRgasFHeeHDfx307T2D90lzMzIhDp9mKZz6vdvkupY7/H725ADLi6GZV32lGSpyS9/IxrxvDG5whs6N0MZq6+xGrkmOSW+wdZzhXNvfwenj3VXnDen6Fhu6mkjzJElsjmaDFkJ5ruNUiwGEoDOeV99d77560yT3czMqI8zr3ue+zpaefX40AhhI6F+gTvRrFOToNHr6xwKXpBzePFmRqmS5NUKpbjUgbRaM4R6eGzU5xpqkHsydpR+0445UxjymmlH5BKSWU0kJK6Tznv92UUgOltIRSmk8pvZpS2jHWso0XvCWYcOToNNi8utAj5vfB907yNx6povsNnWZsKnEtqfbgyllYoNeidFkuHvu4EiueOoB3jzfizpe+wppnD2HttkPoNA8wg5jhFbud4h9VrfjXeQM2bT+K77/wFa51i/mUyQhmZcRh9iQtH2vsrUoB4PmguKO8wUOHOa+lL9cPw3ekyplxBrEv8b2BxIBLPdzY7PA697ljsg6K6oPZOuj1vIdr+sGYmFS3GTEpYfSMYkIIFk1JwocnmobfmOEBq/A8DpHyzMQo5Dh4vp33skxKULmEQHAxv629Fsl9qKIcHuHU+GiULsuFnQIyAug0SvzPrjOoM/Tx2z/w7im+nSvztjF8odZgwomGbn6pGhgyZnR3LkJKXDRydBo0dVvw0Pun8eNluShdlstXO5FqruBu6DZ1W/DKwTo8f3sRBm126JM0mJLseGBjjRpGFqlyZgBwsrHLJ698IN57qYebNqPF69znvr/sJI2oPuiTfE/2Y7rE4LjQbsJ3L9OP6jEWTUnCtv01+Pm3p7MQCj8JaUk2xugg5pl55KY5KHvjqIuXJUkdjRe/cDQ+eObzaj4Jjrtpue/joZWzkBqrxFN7q/D4x+cAADPS43Dj3EzoYpUuBjHg2s6Ve808JAxvtPRYYKfiXt+jFzvx7rFG7D7ZBE10FJRRBM/ur4HNDjR2mT0qVAjjVcUqEXSarThU04H/+vsRVDpbkQOsUcNo4F7ODHB4iPeebfXJkxqI995b9QmdRnrucyc7Se1RAeORm+YgO0k97HkzXWIIMVsH0WkeQEps9KgeJzdZA+ugHacv9Qy/McMF5ikeh7h7ZmIUcpS9cZQ3Wjkvy55NxaJJTVyIwzUz07BtXRHK6zpgswPP7j+P716mR6JaiaZuC7bsrYZKIcPusmLoNNFe27lyr5mHhOGNtHgV5AQeupSti0GcSoE/febZNvyZzx16uPW2+fhwYzHajJ5VCsQS+MqW5+PVQ3UeXkfWzGP04Ty/dxXn+uRJDcTjKjbmm0ryccFgxJX5qZJznzv1nWY87ayWwXmWn97niCkebtWL6RJDyIV2EzK0qlEff0IIFufq8P7xS5iTyeKK/YEZxeMM92SURTk6fHnB4OHFTVQr0dLTj0S1AttLl2DAZkOSJtplwq7vNLskiQDAU3ur+JAIwGFg1xlMKM5L8bjJcEYLwDwkDN/I0WlQkKX1KLV234qZHsvnD7x7CttLF6NvwOZibExNFS+pxhkn51p6cbKxB68eqgMA3H1VHggB2oz9Q2UHRSogMEYOg6kf65fmQimX4clb5+H3blVD3OcJqaok+kS1ZJIeF3vOhXlxVXI6zVbsLiv2aqwK9UdGCKyD1CWxGIBPXfU4OZguMQBHOTb3xOHRYnFuEv70WRV+uWIGewjzA2YUjyOkSrG5Z1tnaFW4fUk27vjrVy7bLdAnuVw83pLtOFQKGY5e7ELfgB3XzEzD7hGoL8qYuMhkBFfmp0KjjMLjt8wFBVDV2ovqVqOoLvYN2LA417VdqreShJxhcs/2Y0hUK7FucTZfLuuFAzWs9NoYYLdTXOqy4MUvhkqcPbhyFkyWAVyeqxMt1yhVv1isuoRw/LgVLXc4g1bMWBXTn00l+XjloCPuGGCrXozAqGkzIi1+dEMnOPRJakQrZCiv68SiKUljcszxAIspHkd4y7YWxrWtKcryKDEk1mlJKiaPu19xS9Bvljfg3h3HUN9pdokbjIqSIUenQWqcCi09FtQaTOO+YxhDGl+7gtV3mnHnS19jw+tH8ejuCqii5LDZ7T63+x2ukxjndVxTlMUbxGLbMUaHWoMJ9+084fK7P7zrDGZkaL3WL3ePS67vNOPeHY6Hm7uvysNdxbmobO5BfcfwbaO9GbRi+vPU3iqsKRqqw85WvRiBUNVqRPoYeYoJIViSq8PbRxrG5HjjBeYpHkd4y7YWelnMVunGBUKvidSSZVpcNL8cyWVtA57Lid48103drFPYRMFup6jvMOFIfRfuf+ekiy5cMzMN9Z1ml+VvoR43dVvw6qE63L4kG4/cNAcPvHtq2BhQb0lZwphh9xhV9+0YweEewqJPVKO+04xzLb2iv7tCTvyaC1p6LB7efpVChmydBvokx7wiNYd5M2il9Gf+5AS8UXo5W/ViBExNmxGLcsbOa3vF1GQ88O5J/PbG2YiOGvm20uMRZhSPI4TJKFxrUbkMiFFE8V45SgGdJhrZuhiXOGN37wl3Q3OPOdYnqnGwxoAXDtS43DjEvC9SHrvSZbl8kh5brh7fcA9GZ5t7RMusbVtXxMetc/owPS3OQ4/7BmyYlhaHPZuK0dwjHY5jt1OolXKUleTBToGdhxskKwvoNEqftmP4j9gD8SM3zcEbX9XhjityRX9394YGwzXrSItXiXr773/nJOZNThg2aVJq/1JJfdk6DXtYYgQMpRS1BjMyRrHFszspcdHQ69T4/GwrVszJGLPjRjLMKB5HcF6RzXsqXFqLvnesERuX57t42bgkOGFyi4wAB8+3I0OrwpmmXg/vygJ9Euo7TLjQbsT/fX8B2nv70Wbsx47yi7j3W9M9vC9SHhdu1ZzVLh7/CKsMiOlCc3cf/t+VeZiSrEFjlxmVzT2YPSlOVI+37feM+RUaNmJ6W7Y8H9vL63Hfipm8fooZbGLbMQJH7IH46X1VKF02FT9/67jo787NP5xXebh44RydBtNS43z29gsr4XiLO3f3LmfrYvDwjQVo6bHwx2UP8Qx/ae3th1IuQ2z02Jpd35iajDfLG5hR7CPMKB5HcF6RzAQV1m47xN8sVhZm8gYxMJS5/7f1l6Pd2I+MeBW6LFaseOoALAN2lJXkiXr1pm8sxtnmHpisNvy/vx/hbyYP3TAb2UkxHjcKKY+L8ObElqvHN8IHIzFdaOjq41cNypbn491jjcjWafCdwkkeeizUQ0Ic+x60UTzw3knUGfpE9XbLvipsL13sEqsqZrCJbccIHLEH4pWFmXh41xnP3/0/FqPN1M/PPyqFDNvWFQ3brEMmI5iZEe+1VJuU8Ts9LU5y/zk6DaanxeH/vr8A2hgFGjv7PFYz2OoWw1/OtxmRmTh2XmKOy6fo8Pcv69Fu7EfyKNdHHg+wRLsIRSppSSYjHjHDUi1w91e14cd/O4K1zx/CmUu9SFQrHfuWaJ5Q32FCZUuvR5Lebz84Ldo1R6xw/aaSfJfAf7ZcPb7hHox2Hm5A2fJ8D114s9yhC5yBtLIwE/e/cxL1nWbJ2PeK5h6+3e9/vFqOtUV6ZGhVknrbN2DzqaqK+3YcviYIMoYQS3CTy8THp8NsxYbXjrrMKeV1HZIeYCFTkr03x5AK4arrMInuv6XHgg9OXML1Tx/Aj14qxz/PteEXbkmBLBmTEQg1bY4axWNNjFKOhdmJePdo45gfOxJhnuIIxNvSn7eYOPfXNudLLrtaWH9YtMWzQo4omXhyktlq85DTPZ4vJVaFCwYjOs1Wfp8si3t8I1yKfvVQHUqX5WJ6ehwSYhT42Zsn+CRNYKjcH2f8SOmxMFGLM6bXL83lPx8u1t2fRhDDXWsMccQS3OZPThD93dXKKJf4cUIwbNtuDuEc09JjgVoph9VmR63B5JG0yWEZsEMTHSW6fwAulTGkHrTY6hbDX6pbez3i5seK4vxkbP/6ItYvncLaPg8D8xRHIL6WnOIm+Q+ON3q0KS1b7uqx5QwSwJH8sqkk32P7X759AvlpsaIljqQudmEZpampsVg+3VHL+I3Sy/kC+sy4GL9wRsvusmI8uXYuVs3PBAHByYZu/uGIQ6WQQRUl440fsZWGR28u4L3LHJzuiumtt0YQvrTeHe5aY4gjHHfuWv9GbjI2ry70+N3T4h2Jv+sWZ/Otlx//5CwevnGOT2PEVZnoNA9g7bZDWPPsIb6VfWqceEm2tLhoDx3YvLoQJxu6PIxgf0u6MRhiVLeakDFG5djcmZkRj17LIE40dIfk+JEE8xRHIL6WnHIvdM810uDaPgu9dML6w51mK/LTYrH9PxZjb2UrbPah0mu//6gCD66cxccG+uvtZd2dJh7CMa9pM+LeHcewqSTfo2vdppJ8yAlcWo2767GMQNSYpnRIb6VaPQvl8bX17nDXGkMasWv9hsJJKMjUuvzuAPDwjQUu3TPrDH3Y+nmVaMdCMaQeXqRa2euTHGXbhDpgMPWjqtXo4kHmHrSEespWtxiBUNNmxKoFmSE5towQLJuWjNe/qsfcyQkhkSFSYEZxBOLL8q/YDYl7bbdTl7a5wvrBV0zV8TegLy8YPLpB1Rn6EKuU4/nbizBos0OfpMGU5MCzsYcru8QITwIdN87INFlt2Hm4AeuX5oKQoRa8j948B9+clurSqleox3Y79TByNq8uRGaCCqsXZLq0evZF7kU5Oq9y+xNqwRgeqYdihZx4PHzUGfpEOxaKIfbw4ksre3dZPjjeiLLl+XzFk06zFVOSNR4PWgAk20szGO5YBmxoN1pDOm8sy0/Br945id/cMAtqJTP9pGC/TASiT1R7NDJ45KY5yNLG+DRRe/OU5SQP3SCkDAK5TIZ4lQIFmVrROrG+GkvCeM1EtRJrirIwLTUOMzPigzK0GaOL3U6xr7IFJxq6YaeAnAAFWVpcmZ/q0YjDvXRa34ANm0ryoIySodNs5WPYgaFasN7G3Vcvr5geAvA7PjiQ5g8MT6TmBe59GSHDPnx4m1vc5ypfW9kLydFpcN+Kmdi8pwLrl+ZCLgOKspNwRa4OUVEy/kErmDhz5gSYmFxodyTZyUM41rrYaMxIj8OuE024tWhyyOQIdwilkZtJXVRURMvLy0MtxphT02bEnS99hZWFmbyX7cuaNqxbMoVPEgkmIUg4cQtLXnFL3K8crEOn2SpaM1bMWFo+PU1Uhpo2I67bckC0K1UYJTMFLMB41c/adiM+OtXsEfowb7IWh2o6sKO8wUU/AIcxunlPBVYWZkIuAwoztWjqtuB/d1eM+Jh766TIlf3iUClk2D1MnWzuehgu1CIEBCXEWOmn1HhcPT0Vh2o7UF7XgegoGeKio/DoR2dF9UFsH1tvm48pulh+XC4YjHwVC/fyfMDIjTU3bwWy7wmWtMnmTie7TlzC3w/Vo6wkP6RyHKnrxEenm7BrY3FI5QgTRPVzxDzFhBAZgFhKac9I7ZMhTkuPBXWGPhcv291X5blkTVsG7Ni8pwKZCSqYrTafvRKDg3Z8eKrJxbjevLoQCTFROFzfhVcODrV1Ftb1rDWY0GmyoqrFyN+MOGMpLyXWxQMtPA/LgB2rFnh2pWJNPcKXlp5+j7J8T+2twmO3zMVz+2tQtjwfrx6qc6kp7N6IQ6WQ4Xc3F+CtHy9Br2VQ1HsYqDdNKr705TsXBRQfzOLgg0NqPJ79wUL8+G+HeX34ydXTcO/V+chPi0O2TuMy7vUdJpxt7sFdxY4qI/srW1HVMmQEcwYm1/HQl1b2Uno23FgHGmcu9TuweW78U91iRHp86GsEz5ucgJcP1uJUYzfmZGpDLU5YElT1CULIa4SQeEKIBsApAGcIIT8fGdEYHO51UjO0w9cAzdCqsLZIj7XbDuF7z3/JZ2N7q7Fqt1P8u8bgYVzft/MEVIoobNlb7VFCq6XHgj2nm3HdlgO41G0RNZZaevpFj8cteUrVUXavScoID0zWQfGyfP2DfIm0VQuy+JrCLT0WrCzM9Hjw+dU7JxGjiMKSqcl8gijnTePqEPuit+5IGS1m66BoJYH0eBWrQzyKSI3HkfpOF3148rNz6LbYEKOU8/oAOOalI/Vd2LbfUZnihQM1WLtIjze+rvcwMO0UWJybjBydxmvVCDE921fZgvOtw+uBWA1mX+LMvRnTjPFNZUvvmLZ3lkImI1g+IxUv/7s21KKELcGWZJvl9AzfBOAjAFMArAtWKMYQYpP3maZebL1tvks5oStydSgrycOG5Y5/ty/JFvW+eislVWswSRbNlzIo1Eo57/3gjCKx74rBxWvKCSt7FElkJ4kbHG1Gx8OPZcCOOJUcZSV5MPUPQimXSTZuqO9w1cdAS6AJHxzVyihk61xvQCqFDPokz1JsW2+bjzNNvUEZ4QzvSBmRNld1gGXAjuykGMQoolx+/1qDCfe/c9JFJx7edQYrCzM9vs8ZmMOV3XPXs0S1ElUtRlz/9PB6IFUqUEbgVW8CNaYZkU91qxGZYWAUA8A3p6Xgo1PN6DYPhFqUsCRYo1hBCFHAYRS/TykdADDs3YQQ8hdCSCsh5JTgvf8mhDQSQo45/10XpGzjAikjYYoulq8BumdTMQymARdPSnyMgu9Qx8F5dt3hDIpzLb2wUyBbF4O7rxoysLN1MVBEyURvMlabnZet3dQvOunrk8STkrikqVULMvHozQU+1SRljD3uKxXZSWoPXfjJ1dPw9y/rATj0JyXOsVR4sbMPX9d2YFFOoqhuKKNkLoaElDetzmCS9OC5Pziu3XYQG5fn84Yxp09TkjUetXOn6GJZHeJRRsyI/N3NBdCq5NiwPI/v8qVSyFDX0Ye12w66GKTuOpGhVWH90lzok2I8vh+jkOPg+XbUGky4ZqZ0TXT3fa5akOWxyiWlB9y89eHGYmy9bT5Kl+XisY8rseIp7w9U/tTHZowfbHaK+g4zJoWJUZygVmKBPgHby+tDLUpYEmxM8XMAagEcB7CfEJINwJeY4pcAbAXwitv7T1JKHw9SpnGFlJHQZrRgcW4yX/v1p28e8/CklC7LdSmpplLIMGCjsNupy9Ikl/xxV3EuDp1vw4+X5eG3u07zsXq//c5s/OnTSjy+Zh52u2X913eYUFaS50iskxH87JrpePyTSpc4vynJ0pO+3U5x7GI3ntp7ziPje5wmn0QUYjHmT9w6jzc4WnosGLBRPPjeSTR1W/hKKKcae1xiy3/7ndn435vn4NfvDFVM2VSSj9ON3ejuGxy2G+PRi13YsrdaNDlJ7MHxgXdPSda4FcaMHjzfzuoQjzLuXec4feGSd8uW52N7eT3WFunx6qE63iCdvrEYU1NjkRavQrYuBisLMxGnkiNOpXCpk859f8NV+Sh74yi/X05PxMbRXc+8hXCJfV8mIyAE+Nmbx12+5y1G2J/62IzxQ2NnH7QxCqgU8lCLwvOtWen48z+qnfdcpn9CgjKKKaVbAGwRvFVHCLnKh+/tJ4TkBHPs8YrdTlHfYUJLTz9M1kHoNNHDliqSMpzzU2P573I3jwffO4m/3LGILy8kNCh2Hm7AgytneRgYD71/GuuX5qK5Z8gQ52Q909TrYvzcf+0MvHBHEeSE+FSSTRjDLGwxPVwmN2P0kYoxv3fHMX58clNiMThox8M3FqC8rgMxCjkohYfX7aH3T2PbuoUoXZYLOwVkBFAr5Hh2fw06zVbo7lyElLho6BPVHiXQuIonwuMLDQ8p/felxq23OsTuiVj6RLVkyTmGd7gENgAulRu4GPQ/3DIXv9tdwecsCENr6jtMeOiG2fjtB6exsjATf/qsyuP7L9+5CL/YeRx1hj6+XfTZ5h5kJsSIlo50L7XHhXC560FKrHRoQyAJdyxpc+JR1dqLrER1qMVwIS81FvExCnx6phkr5mSEWpywIiijmBCSBuBRAJMopdcSQmYBWALgxQB3uYEQcjuAcgA/pZR2ihyzFEApAOj1+gAPE55wJc2qWoy8UZGti/GoSey+5CZ1Y0+LV7k0R+C60lU09yA7yXGDr27txWO3zEVjlxnGfhuauvpEJ/oYkdg3MQ/dox+dxYdOD89weIthjlRP3XjST1/Hp77TjNJXy/nSeuV1nR7fSVQrIZcRzMqIx4CNoqHTjGf31/BG0IHqdrxwoMbFC93aawEBwT3bj3kkeAqPH0yDDak6xPpEtUf5rEdumoOn91V5eCIjyTAOtX5KGZLVrb0eHTZBCK5/+oDLQ/2gnYp+v7tvACsLMxEdJUN+aix+v6cCdYY+bNtfIzlOyiiCe6/OR0aCGjIC/GntPPzuowqX8pNyLwGGrLHLyBJq3RwtqlqNfIhPOPHt2el47p81zCh2I9iY4pcAfAxgkvP1OQD3BLivPwOYCmAegCYAfxTbiFK6jVJaRCktSklJCfBQ4UmtwYQTDd0uXrY6Qx+e3udodyoWGwc4mnlsXl3oEauWGheNF79wxBk/83k1v7x9qdOMD0814botB1D66hH8/K3jsNsd3ZxyksWTqBZmJ3rEvnkL7fCFlh4L7HR8JdmNJ/30dXzcS+tFR8lcvsM1Ulj/cjl+/DeHvgnLo6sUjjbNnBe4vtOM3JRYLM5NRlp8NNYUZfHx7VzlFeHxg4nV5Ja03WNP6zvNoiEZXHJXpMYeh1o/pZLNZqbHu4zfwzfOwX+/f8rDIzxFYn5SRsnw4hc1eOLTc/jJjmNYW6RHhlYlOU61BhMe3nUGg3bg528dx92vHcU924+hdNlU/PLa6Vi/NBevHKxDs0gOBgeLER5ZQq2bo0Vlc0/YxBMLuSwnCU3dFhyu8/A9TmiCNYqTKaU7ANgBgFI6CMAWyI4opS2UUhul1A7geQCLgpQt4uCMEHdDU9juVFiqCHB4lz+paMETn1Zi/dJcR9H6dUW4ZmYa9EkajwS2suX5sNqox5L4ln1VWFmYif/ZdRq/WTnL4ztRcuLhaQk2mzotXsW3VRUeb/PqQnZjCQN8HZ8MrQplJXnQJ8bgruJcqBVybCoZ+s6aIs8kJq5sG6dfbx9p4D/jKggIw3O4BNLbl2Rj623zXY4vZdj66sHllrSF15fUAx8hrq9ZOa3hESZqyghEDUmtWo4nb52HspI8rF+ai05TP+oMfS77sQzY0dhlFtXHh0QM6FULsvjX7uMkVSbw4V1n0Gux4ZnPq9Fp9t6WN1i9Y0wMzrWET+UJIXIZwYo56fg/Qb8DRvCJdiZCiA7OihOEkMUAugPZESEkg1La5Hx5Mxx1jycUafEqydg2YX1NYZwjpeA9WmIxuQv0CXwcJxdCsaYoS/KGX2foQ5wqChuuyoNl0A5Kge3l9VgxJ91D3mBb4Hprq8puLKHHl/ERiyt/+MY52PbpOT50J1MbI6pvsyfFoXRZLh/WA7jqulh4zlN7q7Brw1IP/RjpWE2ppXF3D3ekrmiMFVKd6HZtWIqLnWaolVFIi4/GoI3iRy99wf/eG5bnif7+vRYbdp1oxLZ1RVDIHXkLBgkDmnuAERuntHiVZJlAQnz3+rIYYYY37HaKC+0mZCWGn1EMAFdOT8E924+hqqUX+WlxoRYnLAjWKL4XwPsAphJC/gUgBcAtw32JEPI6gCsBJBNCGgA8BOBKQsg8OAzsWgD/GaRsEUeOToOCLC02leS7tNDlJmexG8zjt8z1GvOpT9JgRnq8y3cuy06SvOGrFDJUtRoBAFv3VXu9OQSbTc1/Pz2OZWOHIb6Mj5jh2tBpRqfZyj+kSRk409PiARB0mq38e0Jdk/LWnm3p9VgxGWnEHvi4mGIxWRniiOnHhteOYtu6IvzX349IzmM7DzegbHm+SwfEB1fOQkpcNFbNz8SUZFc99DafiY1Tjk4jOQ8W5yVj1fxMNhcxguZSdx9ilHJookesefCIEh0lx4rZ6diyrwpPf29BqMUJC4KtPnGEEPJNANPh6CNd6axVPNz3vifydqDJeeMCzgOsUUahOD8ZRdmJMPYPQp+k4W8ANW1GjxtMVWuvV8+ymOEqluHPlTXiWvQ+evMcvFF6+bCGarCeEuZpCW+GGx8xw3VHeYNLcugHxxtFk0WnJDt0W+qhSspbe66lF7My4kdcZ9xXYYQJf9x1s0CfyB7g/EDqwUaYwCk2jzV1W7C9vB7b/2MxOsxW3qOsT/L8zcUeYDavLoROo8Cy/EVIE2mvK5MRLMnVYfPqQo9yg5flJLFxZYwIVS1GTA6zyhPufGtWGu7dcRwX2k1ey6dOFAIyigkhqyQ+mkYIAaX07SBkmnCIeYDFMqalDJBHby7gOz65e5aFN/lFOUPL3pyhXGcwwdRvQ0OXGSsLM/HqoTp0mq3I1ml8Mjq4YxhM/VDKZTBbbaxcVYQzOGjH6aZuNHVbkKGNweyMeERFeaYf2O0UamWUh+HaabaiKDvRZ4NSyujO0Wk8dJt7aLtiqm5EjWJv16DwOOwBzj+kHmxsdkcs+vcv1yMlNhpatQJ/XDMXP3XW/VUpZLhvxUwUZCUMO4+4P/inxKpwwWDEXa8c9jqfRkXJcEPhJBRkaiUfdNznUDavMfzhXEsvJiWEd4iVWhmFa2alYcveKjy5dl6oxQk5gXqKb/DyGQXAjGI/kOpaN2tTMewU/ITMZd67GyDzJydge+lipxGjwuwMLex2Ktp0gbsxcB7AHJ0Ge04387U//VkW5gyJzXsqsLZI77LU+ejNBVigTxD17DDCE7udos5gQnldJx58b8ir+8hNc3DT3EwXw1g49u7L3E/cOo/vYkipQ7/bevuRFh/t8mA2HDIZEY2J95YAFagRI3UNSjViYPiGlBf31YMXsH7pFJdGPz//9nS8eEcRZIQgNc4R8/vlBYNP4yhc0ahpM2LDa0d9GkvhPFhrMLkcD4BPzgoGQ4ozTT3ITAhvTzEArJiTjnt3HMf5NiOmTvD5LiCjmFJ650gLMpER8wAnqpU4Ut/l4QHeett8fsLn3jvfbnR5b+tt86GUy0WbLrjfGIKJC+YMifVLcz2yuO9/5yRKl+ViRno8u4lEAJyRe7Z5qBMdMFSKLD81FnMnJ/LbC43IVw/V8Yl4JTNSUZCZAMDToNhUko/8tFgsn57msz6IxcRLPbT5uuIiRiCNGBjDIxW+pYtV4q6Xy1307LGPK7GpJB8FWVpUtvQGbIz6O5ZSejMrI449KDGC4lxzLxbqE4ffMMSolVFYMTsdT3xSiWe+vzDU4oSUoKO/CSHXA5gNgHfdUEr/J9j9TiTElhjXFGXxBjEwNCHv2rDUZVlaRoAVTw11iEpUK2Gx2mGwWnFXcS52Hm5w6RIldmOQyQhydBoPr57QyyvmgeNuPlItUu3Ue9tTd9hS5dgg9jtzRu5dxbmiY9nUbYHZ2u4x9oAj/pNLqrtiqsMTXNtuxNnmHtxVnAvAkTj11N4qlC7LRW6yZwiC1Nj789AWjLeXNWIYPdzj0u12ir5+m6ieJamVuNTZh9+8f9rrOHqbK7gSgXZnpZCdhxu8ri5I6c3Ldy4SlbHOYPJpbmLz2cTGZqeoaTeFXTc7Kb49Ox0/ffMYKpp6MDMjPtTihIxgO9o9C0AN4CoAL8BReeKrEZBrQiG2xDgtNU4y8/66ORn8zeHg+XZ+uwytCusWZ+Nnbw3F5XExmFzjDrEbg5inROjVA8SXEaenxbnUCxXL/hYa4t5uEsF4+Ri+wZUHqmjqQVVrL3aUO4yFJ26dh0S1gh8/sbFUyGW4d8dxfntu7MWMSLud4kh9l0uZNk4P7RQeD2bDjb2vyZjBeHuDLS/I8A1urCube0T1RxMdBQrxh2zhPCKlLwA8SgRycxk3lu7zkJTemK2DojIevdiFvgG717mJzWeM+g4zEtUKxCjloRbFJ2KUctwwdxI27zmLl+6ccG0ieIJt3nEFpfR2AJ2U0t/C0eJ5WvBiTSxkMoJrZqZhe+li/OWHC/G39ZcjOS5atDHGuZZel+5MwgYaXEcxocdD2CRB2HRBWFD/ZGOXaD3YEw3dqDWYJD0pcpmjEL9YgweuIYPQUNpzuhnXbTmA7z3/Ja7bcgB7TjfD7nTnSB0j0jqGhSvc73/90wew4fWjeG5/DdYtzkaiWol7dxyDUu7oQrfzcAMeFGne8j+7TuP+62by23NjL9bNq9Zg8ljl2LKvCmuKsiAj8KldeCBjnxon3kwmJXZ4by9rxDA2cGO9o7zBY87YVJIPlVKGpi4zsnUxuPuqPL6TYbYuhp9HxOYrTl+kaltP0TlK+InNQ4M2Kqo3amUUnl9XhGxdDP9e2fJ8vFneMKx+htt8Jpzva9qM/LzLGD0qm3ugT4oMLzHH1TPTUNHUg69rO0ItSsgINnyCaxNkJoRMAtABgDXS9hOuK50wYS1RrfSoVyzMvOeMj5YeC55fV4QH3jspGcaQnRSDbeuK+KYL7l6MspI8yfCH1l4L7/F1/7y5x8LXsTWY+vHynYvQ0NWHOoOJT4j645p5LsvzUkuiLKZzdBH7/bfsq8Jjt8zFo7srcLiugy+b1msZ4BtvcMltTd0WVLX2Yt3ibLx6yNH+lnuQE1ap8NYNTp+kRoJa4XO7cH/G3m6nuGAwelwz935rGi4YjB51bcVg5QFHH26sm7otfCw6IcCMtDjYQfHsP6qxsSQfG5fnu5Twe+SmOcjSxvBx72L60mHqR49lUPSz+g5HuSmx6+CB9056lGbbVJKPe7YfQ6fZikdvLkBrrwW9FptLoxlv+hlO8xnzWoeGiqaesOxk5w2FXIZV87PwyIcVePf/XQFCJp5+BGsUf0AISQDwGIAjcFSeeD5YoSYaYglrTd0WvHKwDqXLcpGpjUF9Zx9vaKbEqjwmuc2rCzFJq8ILB2pcJmOVQobMxBikx8fwE6D7jcFOxZfMhV69bF0MVhZm8l2iDp1vQ4xCzmdrp8RG446/foUfXTEFALB6ocMrKHc6YIa7SbCYztFFKpkzOkqGR2+eA5PVhrZeC974j8Vo6OrDz5ylsTi4Mlpb9jnigtPjVfhHVStONHTDTh03gDajBcunp0mO5bTUWMzJ9CyxNRJjX2swYcNrR5GoVvKGlow44vo2vHaU7/DICC3CseZi0bnmHZnaGPzhlnmgFPjPV494JHvm6DTYvKcC31ukR1lJHtRKOTIT1LjQboJSTtButKKiSTwsgwt5EIYJcdQZ+pCZoMJuZ4nKoxe78MrBIeOXSxp+RtAOdzj9DKf5jFVWCQ1nmnowMz3yYnOX5iXjo1NN+ORMC74927OT7Xgn2PCJswBslNKdAJ4BcAjAu8EKNdGQSlhr6rZgy95qNHb34ZnPq/l4TrkMHpPcfTtPICUu2mNJe1NJPn725glc//RQuIK7gcR1j3L/XmGWFjk6DfSJamxcno8Xv6jB1n3V+OB4I269LBtrtx3ilyCP1Hfhu5fp8bs9Z7FlbzW27qvGlr3VuGe7Y8lQGObBIbxJcDGdYsvxjOBx//0ztCr81zdzcb7NiP/6+xFseO0oNu+pxIV2E+JUcsdStkg4jGXAjmlpcaDUUZh+236HTjy3vwZVLUbUd5gkx1LMIAakx15G4PNyr9AD+cznQ/pnstr4hy9G6BEbay5sYr4+EbkpsWjtFX+A3l/VhnWLc6BRRuG9Y42w24Gfv3UcT3x6Dn/aW4UL7SZ8frZVNJSLC3ng6moLUSlkSNJEIzclFiqFHFv2VvMGMXfsaW75E8PNTeE0n3lzSDBGj7NNvZgcYeETgGPFbO1levxudwUGbPbhvzDOCNZT/CCl9E1CyFIAywE8DuDPAC4PWrIJAJfwISMEKoUMMQqZqHehZHoqrpiq4zPvv7xgcEmuW7UgC4QALT39fBcuMY8H5x1w92IM1z2q1mDilzIBYGVhJn7z3ikXo/z+d07iT7fO4710APjKF629FhTpkzy6mj12SyHsdoqD59tFO4ixbO2RQ5+oxrZ1RTjR0IVsnQbRUTKcutTtUX7tV++cxIcbi5GdpMHC7ER8Ud0Omx0uyZoz0+PR2tuPp/Y6wnw4/bMM2GAwWmGnQEqcEttLF/vUzEVYYaKlxwKNUo6LnX18VRWp5V5hwpRYExEu2ZOtOIQXszLi8PKdi9BjGUBCjAIpcUNzjd1O+RhfsZWKVw/V4ufXzMDPrpmBn7913EV3n9pbhfVLc/HqoTo8dstcVLb0uoT/AECvxeo1oVLKwzszPd6vucmfqimjXaUinLzWEwVT/yDajP3I0EZW+ATH3CwtPjqpwI6vL+L7i7NDLc6YEqxRbHP+fz2A5ymlHxJCHglyn+Ma4QQ4aKN44L2TsA5SbCrJh0Yp94iJ3FSSj/gYBeamDNU65Ca5RLUS6xZn8yEXLxyowebVhbh+TgZaehxeZiGcd2BRjs7jxrBx+TTExyg8Okhx8gonVKnYZavNjhe/cK04sL28HqlxKtR3mvH0vireaNYo5WjqtuDnb3k2F2FLeiMLF7MuHO8HV85ClEwmOo7VrUbIZIBOo0ReSix+4dYAZkqyBnUdJg/9UylkmJykxr1vHkOdoY/f/vIpwzfs4MoCnm3uxcEag4exLlaSSxhClK2L8Xjo4vQvUA8dK6k1sojFtj56cwFS4qL537rWYEJDhxkP3TAbv/3gtMtY7jnVhLVFevzsreOSpQMJcTzkN3aZISOAHY5QLq4s21e1nSjM0uLDjcVoMw4Zq1wCX0uPBc/9YCF+8/4pFx3mYtL9mZt8iVEfi3hfVlll7Dnb3IvJiWrII3S+IIRg7WWT8eRn53DT/ExoooOu3hsxBHumjYSQ5wB8C8BmQkg0gg/JGLeITYBc8twrB+tw/3Uz8ed/VrgkOb1ysA4z0uOQLUisS42Nxot3FKHTNMCXXwOGwigS1Urok2IkvQNctYsX7ihCc7cF9R1m/PGTSj48g5uQhyuf5P66savPI5Fr27oi3rtdZ+jj4/LuviqPN6C57Vmc2+ggFlP48K4z2LZuoeg4nm7qxpa9jljPB66fiRfuKIKcEBfDMDtJgzVFntVOHnj3FNYvdcRf+jumw9VKFiYpuZ9TnaEPT++r4r3TaqUcAzY7VsxJD8iYZclJI8+Fdk89vP+dk7j36nykJ8SgutUIOwXkBMhMUOHJW+ehormHX6lwr64jlQeRoVVBRohLWbYHV85CnCoKFzvMeHjXGfz1h4uwODcZgKOt+bvHG10eqP735gLok2Kg00SP6sPQWMT7BtOgiREYFU09ERk6ISQ3JRbT0+PxwoEabLp64hQVC9aAvRXAxwC+TSntApAE4OfBCjVekaoAsGpBFgCH97TTbOVjIrk44niVwqWM0PVbv8CJhm7UtJtEjYfyug7Y7NIlswCgvtOMry504IF3T/ExdNyEzJUN4m5i7uWTPjjeiEdunOMRF6hWyJGhVbnIopA76sy6x7RKeZtZnNvIIxVTeLapBw/dMNtjHN8sb+C3eeTDCnx1oQNp8SrkpsTyN9LsJDXyU2NF9xstaAfNVQU432rEvrMt+LLGgNp28RhhoZze4s+lzqnO0Ie+ARuWTE3G3MmJKMrRucjsD+FWUms8UNchPl9lJ8eisbPPJT7dYBpAc3cfVFFyvPhFDZq6LZDLhuYMsTyIP66Zh2tmpuH3qwvwxKfnPB4CK5uNeHpfNdYW6dFh6udlON3U7RIeZhmw49fvnIRSLgtYf3xlrOJ9Oa/14tzkUT8nBnD6UjeyEiMzdELImoVZePGLC2g39g+/8TghKE8xpdQM4G3B6yYATcEKNV6RmgCjo2RYtzgbl7o8lw0fumE2FFFEtPbmY7fMlYy9q+8weY3rbOmxwC5Rao3zyHE3MffySUtykxAlJyhdlgs7HfJod5qtvJeQkyUt3jWRjjsPORH39LA4t5FHKqaw22LD37+qxot3FKG91wqZjODR3RUeSUZiDTfqO82IkovHwOckDy3LZuti0NhlwQ9e/MolJEis3TMnJ2fwCMMy3Jd7RztOMpxKao0XNBJx3woZ4UPGANf57dHdjpWzHJ0aKoXcpXLFq4cc1XkKM7WYkhyLCwYjbnnuoNfQCs4Rsb10Mf8Z5xBw376524K5k0fxBwGL9x2vnG7swU3zM0MtRtCkxavwjbxkPL23Cr+9cU6oxRkTWKjDGMAVTu8bsGFTSZ6LN5UzIrbsq8KAjaLLbMVvb5iNp783H5tK8vHsP6vR3msVnbQbu8yijRZ2nWjE0YtdWPPsIazddgid5gGP5bK0eBVvmAoRTsgaQaY2l9X/woEaRMlk6Okb5KtMPPP5kKeZK8Hmbsi4N0e4eX5m2GRnj3dydBpsXl0oWk2iztCHw3VdqGk3orq1F51mq8t3uSVp95t0S48FdQaTaKY/qB0bludhU0kefr9qqP4r4NkYxl3OJ26dh06zlTd4tt42Hx9u9GykEWh2v69NDIarlsLwn7T4aNGqJp3mAdH5zdw/iKZuC178ogaJGiUe/+Ssi751mq3QJ6lx5bRUAMCG1456XWmgVLBvq43/LEMbI7p9unb0xzqcqlQwRgabnaKq1YhsXWSHT3DcOC8T7xxtxMUOc6hFGRMmTvR0iOBiEzfvqcDKwkzIZcBvbpiFP39ejXOtRmxeXYjYaEfSnFIhx+aPK13ija2DFBqVuIel12LD7hNNLrF328vr8d3L9HjlYB0A6Ri1HJ0GBVlaj8Q+10zsaNHEv7T4aCRrlKIyLXerlCE0ZNwTT/RJGhbnNgbIZATXz8lASmw0Dl0weFSTGLTboVMrIZcT0eYXmQkxjqQlO+XHJy1ehT6rDe8ea3SJgd9eXo8b52Vi6z5HTHK2ToNEtdIn77M/sY+BZPcbTP241GXBE59W8tfiZdlJWJKrQ1SUq1HEkpNGFu7hIy81Fk/eOg9Wmx1yGcHOckd2u+j81j/Az4N/P1iL+1bMREOHCX+4ZS7qDSbMydTiCmcSZ0XTUEMPsZUGLneD2ze3ggUAszPiPZI0H7lpDmZnaH06r2CSMVm87/jjQrsRiWoF1MrxYV5pYxS4ZlYaHvu4Elu+Nz/U4ow642PUwpgL7SaXTnXCrOsF+gTokxwJdGuKsvDwrjMe8caP3zIXFusgHr25gG+dyxmnXMhCa48FSrkMFmrHz6+Zgf8VWQIXM0CWT09DXkosFugTYbYOQp+kcen8pU/SID8tlg+TkBEgPy0W+iSHYfDHNfPw0zeHjIY/rpmHwizxWrRisA5iY0dUlAwLshLQ1GPBrwV69JuVs9DdN4Bn99cAAH77ndn445q5sFMgSkZwscOE+g4zfvrmcWy9bT6m6GL5m/eC7ASoFHKPhybhAxnX+EBYCUXK+wz4pxP+ZvevX5qLXScaPa7FzasLcUPhJI8HOGasjAxCx8Bti7Lx5GfnXIzPZ/9ZjZ9cPc3l/YdumA2dRsmXWFu1IMslhCxDq8KaoizYKYVOE41LXWbR0IrsJDUSNUr89oPT/EOg+8NNVJQMN83NRH5qLJq7LUjXqjA7Q+vxoCR1XsEmY7J5cHxx+lIPspPH18PztQUZ+Nmbx3G2uQczIrAhiT8wo3iUqeswYWVhpkeW/v3OerBcKappqXGiS4jnWnuxZW81snUxePLWeahqNUKtlGFykga/XDEDMhnB8/vP40RjDwBgw/I80SVwzgAR82zkJPs/GctkBNfOScfMDGY0RAJ2O8UXF9rRbbbisVvmQhsThbNNPXjjq3oUT0vF6oVZkBPgfJsRm/dUunx3w/I8JKqVqGox8kvUnAFwXUE6/1CljJLhZ2+ekGx84B5TPBZeV2HCHCEQvRbv23kCBZlaD6OEGSsjg7BjJ2f4AkPVSh67ZS6e33/eZcWhu8+KOFUUX6FGmGSXoVV5lAL8ydXT8KsVM/C7PWeRqFZiTVEWpibHQqmQ4Y2vavHwjQVQyAnUSjmsNjsutJsglznCwrh5cO7kRL9iiFmnOIYYJxq6kR3hlSfcUSujsLJwEh7bU4kXf3hZqMUZVZhRPMpolFEuEzqHZcCOiuYe3jM7Mz1OdAmRayhTZ+jDT3Ycw6YSR0jFpjeGjJOy5floM1rR1G3BB8cb8eDKWXh41xlMS41F6bKpoKDotQzCarXhs8pWnz0bXOtcd5m4lrnMaIgcGrpMAAi6+gbQYR7AB8cbcfdV+bhlYRYe/eisS7hEhlbFG7ZcLOaqBVkeyVD37jiG3WXFuCwnCXtON+PYxS7RB7KZ6fH4cGMx6jtMHo1hRhv3hDmpa5El0I0eUh07AcfrqtZerJiT4RLSU7osl2+YUWcwwWS18fPjqgVZ2F5e79Io6LWv6rBm4WS+rju36sbNjw++dxIP31iAtdsOia62BeLhZcmYDDFONHShZEZaqMUYca6emYafvXkMR+s7MV+fOPwXIhSWaDfKpMVHY2ZGvGgiR2uPhU824uI5xZKhOCwDdmQlqj08XVxZN5VCho3L8/HOkYu4/9oZ+P7ibPzsrePY+PoxrN12ELtONWHzngqfy0yx9qDjA6vVhq9ru7DhtSPYsteRLLm2SI9nPq9Cu8nqog9PfHoOa4ocJQKFOuithB7nMXMv3cc9dE1J1mBqaiyumpGGy3N1yEkWLwllt1OfSrf5gzBhbufhBsxMF78WWQLd6CEcA7Hf3maHyxzGtZifkqxBbkosYlVRqG034eEb5+D+a6djgV6L0mVT+bbznD4r5QTGfptoGNrKwkyU13V4JHyuWpAVcLk9lozJcIdSirPNvS4VeMYLyigZbpyXic17zoZalFElJJ5iQshfAKwE0EopneN8LwnAdgA5AGoB3Eop7QyFfCOJPsnR/cu91NqmknwkqpXo6XN41pq6LXjlYB02leQjK9Gx9NLY6ZrtqVLIYLYOihonhZkOr4o+UY0F+kS09/bj9r9+5RGyISyZxr0v5dlg5YIiH7ud4t8XDHwcMTBkKKxf6ogVF2IZsGP+5AT89YdFsAzY8fs9FQCAGWlxKCvJg50Ote/mdIF7eHIv3Vecl4zLcpJ88r6JxWdKlW7zhxydho/Hd1QyOO+RVMUS6EYXLmlx854K/GrFDBjMVr5JR5JaiWedTTayk2Lwyp2LkBrv2vb5UpcFWz+vRqJaiduXZON4g2d78i37qvC39Zej3dgvOj/KZYBSLvN4n/M0B+LhZcmYDHfqDGbEKOTQxihCLcqo8M3pKfjwZBP+fb4dV0xNDrU4o0KowideArAVwCuC934JYC+l9PeEkF86X98XAtlGFJmMoDgvFccvdorW9X31R4uw72wL4qKjcPeVuSAyGX7u7FInXOJTRhH86tqZAIBNJXnYUd7gssSdnxbHT+i5KbE419IreXMQ4s3IZZN+5FNrMOFIfaeoLmhVckxKUGPD8jwA4FvhJmmUsNrs6B+04vuL9NCoFHznRJXC0R3MZBlAtk4DvfMBTpjk9MznjsoTq+Zn+mzMisVnPrW3CqXLcpGbHHiIjkxGsECf4Hrt/bsWpctyMX9yArJ1GhYLP8pwSYuzMuJwpL7LJe5Xo4rCDxbrsaP8Iuo6+qDXqfkcB671MlfSjwvhkapDPGBzjV/nUClkuCwnCXXtRgCOmORVC7IglwH5qXHI0KrQabb6/bDPkjEZ7pxo7B7XoTNRMhlunp+Jxz6uxNv/pQMh40/XQ2IUU0r3E0Jy3N6+EcCVzr9fBvAPRKhRLExmUyujYLXZYDBaXTLwOQ5Ut/MtdZ+8dR5+ImIYvPyjy9DQacE92495GMtcPJy7ocrV3nS/OcxwLh/7YuSyST/y4Zq0uOtCti4GuliVi7HrWKWIQdkbR1Fn6EO2Lga/X1WIO1/62kUnH951BqXLctHVN4B/1bThG7kpQT08cdeLmKEjVrrNX/RJGhRmaXGioRt2AFfNSEVBlhbfnJbKdHkM4Ma3rbcf979zEolqpUei3EMrZ2PnkXqsXpDJf2fP6WacbR4qtSYM4RGb22SEQEbgUamnbHk+Hnj3JDZclY+ibC2Wz0h3OXYwiZ8sr4Ih5PjFLuSMk/rEUnxjajI+OHEJ/zzXhiunp4ZanBEnnBLt0pwd8QCgGYBopDohpBRAKQDo9foxEs13xJaBy5bnI0Yp3v2LW9LjEu/EDAObjXosfz+1twp/uaMI6doYUUNVrPZm2fJ8PPuPaqxfmgu5DCiZkYqCTO8l1Nik7x/hpp/JsdH44HijR93W//7OHPzX3w576NSmknzUGfoAOJI7D9YYJI3Vh94/jcdvmYv6TnPAD0/c9VJvMIleHxqlfETCdayDlF9y54z2ichY66dwPuQ8vKsWZHnkRfx212n8+fsLkKWNQU2bEbUGEyqbe6BVKVz0QqUQ73i4qSQf92w/hk6zFS/98DI8fstcnGvtdanJ/eB7p/C39ZfjBy9+6aH3XCUgRugIt7kzEI5d7MI1s8Zfkp0QmYxg1fwsPPZxJb45LWXceYvDMtGOUkoBiGbYUEq3UUqLKKVFKSkpYyyZd7jlPvdl4C37qjApQe2RSLepJB9ywUTMefSEqBQyGPvF44iNFptkH3uu9ub20sXYtm4hnv3BQmwvr8eJxh68+EUNZqTHD2sQM/wnnPTTbqeoaunFdy/T89n6ZSV5eOa2BbDb7aI6ZXJ2+srQqnD3VXnIT42T7A7m2H4Qrb0W/uFpcW6ypE6KwYVNUED0+pg3OcEvj7NYtzqp0llSiVW+dr2LRMZaPy+0u/72KoVMMmnz9KUeHKrtwHVbDuBHL5Xjuf01yE4emjd3Hm7AppJ8l46HT39vHr9qxnXVvO/tE7CDYsveoW6b3DGkYo7bjIElDwt15XyrEbXt41NvxoJwmjsDwWanqGjqmRAOpEVTkmC2DuKzitZQizLihJOnuIUQkkEpbSKEZACIqF+b84jUtBlFJ90ukxWvHKxzqcX5ysE6rF6YxW8nLKcm9IBoYxSiXjSV0vszTVSUDHMnJ/Ly/fWHi1gYxASi1mDCvW8eR6JaiVULsvikonMtvSjI1EosQbvWgU1UKz263HHdwRye3KigPLlckp7JasPOww0e18e8yb49uHlrpOBP6ayRasjAcPyWYp3m+gdtorrXN2D3qBBxoqEbb5Y3uJRf23BVHvJTY5GfFgeDqR8bXz/mctw6Qx8yJJKEpcLKAtFhqeTQYMq8MSKX6lYjEtVKxEaHk1k1OsgIwc3zs/D4x5UomTG+wtDCyVP8PoA7nH/fAeC9EMriN5w3KlunEfWsxcVEodNsxTOfV2PrPocHo9NsBadLjtqcU7H9q3psuCoPm1cV4LFb5oIQwGa3i3rRktRKn+UL1JPHiEzsdoq23n7cVZyL1Quz8PaRBmzdV40te6vRN2BDglqBJ26d51E+rTBLizVFQ8vbwqooW747D6XLHB3GOs1WPHTDbMTFyINKvBSWtRK7PoTteMXOkfPSnWzsliw36E/pLH+9ygxpag0mVLX28r89V51Eo5Tj4RvneJSf3HWika/LzmGnrnqxeU8ltn5ezScW6zTRomObEhctqt+zM+JF3w9Eh6WSQ4Mp88aIXI5f7EJuysRJQi/KToSNOh4MxxOhKsn2OhxJdcmEkAYADwH4PYAdhJD1AOoA3BoK2QKlpceCRLUSyihH69z/cfP2Gi0DLjG+2boYPHxjAWx2O1790WU4dakHvZYBdFsGICMEDwnKt/3u5gJMT4/F47fMRU27CYN2O7ISY8Z9u0VGYEjFtXPG7PzJCZiRFo9ZGVpMd2uqkZWghowQF09aU7cFm/dU4s0fL0Z2khoz0uOQrIlGmjYakxODW3EQlutyjxP1Zqx4O0fhcnlrrwWLcnTDJgJyyWDnWnpxV3EuX3ZOuJ+JsCw6krT0WPja1dy4dpqtSI5TYVKCEs/ctgDHG7pgswPby+ux4ap8bP+6zmUfYqtnj95cABlxjFmOToOtt813JFE6y7wVZGmhT9JAn6QRjXMfqeRhqRWIYMq8AeJdR5kTI/w5XNeJ3AC6w0YqhBCsmp+Jxz+pxLdnp7uEgkYyoao+8T2Jj0rGVJARJEOrwu1LsrHpjaNIVCtRuiwX+kQ1mnss/HLaR2XF2F1WjA5TPxq7LCh9tdzFcN59ogn3rZjp4X34lbO+8Itf1OCRm+ZgRnocZqTFIyoqnBz9jHBBzIO1Zd9QebNv5CYjKkoGu52isqXXw1iclSFe1kqniUZuSizmYuS6GfFGSrpjKfxv6y9Hh8mKtPhozM7QShoDUucorMPNeYOHM4SGM7BZbe7ASItX8fG/XPiDjAAL9Amw2YH7dn6FlYWZfPvtrZ9X4eEbC1zmxe9epudXz9LjVajvNOOxjyvRabZi8+pCXDsrXTKJcrgkYRpkyK9UHXduv4HoDQvfiVyO1Hfi9iU5oRZjTJk3OQHvH7+ED45fwk3zM0MtzojArKoRwmYHH3fZ1G3Blr3VeOC9U+hzvrYM2NHi9BokqqP52pvA0LLblTNSUd0qHpPMJac88O4pqJVRzCBmSCLlwcpKiMG8yVoolXIAnklQ3JLvoI16LDFvvW0+KMWoJBHJZAQ5Og3aeq34wYtfovTVw1i77RA+qWiRPI7UOXJ1uN29wd7Ch6QMbK7DGqvNHRjcKgAX/vDCAUeCrz5Jg9ZeC+oMfS7hMnWGPijkBLvLivFG6eX4+/rLYbNTLJueimydBk9/XoUte6uHEup2nsDhi51+J1HuOd2M67YcwPee/xLXbTmAPaebA9Jn7vzcw9rePtIQsN6w8J3IxNQ/iIudZmSP83Js7hBCsHpBFv74aSUG3WOfIpTxHxE+RrT2el9K47wG7sknwm0ztTFQR0d59T6wpVzGcEh5sOo6+hAf0ws7ddzQ6zrEawNfdCuxlh6vwpmmXlz/9IFR815JGQMzyor96rZYMiMVV0zV+bUsLmVgc10i2fJ1YHjz0EuNX1q8CrkpjprBH5y4hK2fV3sNj6nvMPucRAn4r2f+nF9KrApyGTBfnxBwWIY/SaGM8OF4QxdydBoo3LtjTQDmZGqREKPEziMNWHtZZJbSEzLxRnCUSItXIVsXg7uvysOG5Y5/2boYUGeZtUdvLkCOTuORfMKhUsjQZuzHo7sdsZXuSShvH2ngX7OlXIY3cnQabF5dKJrIdLKxh/eOxTofwISoFDKolVEunlU7xah7r7wZA1LnKJYwVZCZwHuDAfhUWk0qEY9L5mIGceBIeeilxo/zrNYaTB6raVv2VeH+62Ziw/I8ZGhVvK76mkQJ+K9n/pzf1NRY5CQHl8zsT1IoI3w4UteJvNSJ+9Byy8IsPPlpFfoHbaEWJWiYp3iE0CeqsXF5vkuzjN9+Zzb6B20oXZaLBXpHaSmx5BOVwtE6t6dvAE3dFmwvr8f20sUwW20YsFE8+N5JPraRLeUyhkMmI7h+TgYS1UqU13XwiUxri/R49VAdb9S+9eMlHuXWNpXkIy0+2mV/Y+G9kvIcShkDgcQJS3m3WTvzsWe48ZPSucqWXrxwoAabSvKRGh+Nl/9dg82rC3kDerix81fPxhqmi5HJ17WdmK9PCLUYIWNaWhwyE2Pw2pf1uPMbU0ItTlAwo3iEqO8044F3T7nUhL3U1QeNUo7CLC1sdkc8ploZBWUU8Ug+6ekbgMnqqN9534qZfGMNVl+YEQhRUTIszUtGVmIMzrX0Ahjq7JWhVWHVgiw0dPahMEuLe6/OR0+/DTIC5KfFQp/kegMO1pDwJZs+EGPAWyKVP8vkrJ15aHAfP67EXkuPBZroKNx/7XT09Ds8TzsPN6DTbOWbxjy1twp/unUefrR0Kq6ZmYaCTK1PYxfuRifTxcjDbqc4Wt+JtZdNDrUoIWX1giz88ZNKrL1sMtTKyDUtI1fyMIMrycY1PeAm3MdumYv+AeoSj/nwjXOw9fMqPPN5Nb+0/cbX9fjD6rlYNT/TZRJkbZYZgcLpDgDcs91hBAgbc3D6uHl1IRYkqJCkiR4xg5XDV4/tSBsD/nq32XUWWqQaYXDG8KaSfKgVcjy7vwaAM6lSTnDNjDS/xi4SjE6mi5HF+TYjNNFRSPSjb8B4ZEqyBtPT4/CXLy5gw/L8UIsTMMwoHgHsdgq1Msql6QHgmLirWnv5ckHcew++dwp//eFlOFhj4Je271sxE5flJIXV5MwYH+gT1di2rgjldR3IT43Dz9867qKP9+08gd1eEo2CMST89diOlDGQGhfey+QMV6QaYXAl9p7aW4UNV+XxSXYqhQy5yZ5xu76sSnDVTgDHwxOAsDOMGZFDeV0npqXFhVqMsGD1giw8vOsM1i3OgVatCLU4AcGM4iDhPByb91Rgw1X5Ht4pu3O5Twj3+qZ5mWjttWD1gkw2KTNGBbud4pOKFt7gKCvJ8zs+OJhmAqHIprfbKS4YjB7x0sEuk7OmCqOHL40wrM6ST9xYTkl2jKVwXAZtFA+8dxJ1hj7JVYmxrAXMdGb8c+i8AfkTOMlOyKSEGCzMTsSz/zyP+66dEWpxAoIZxUEirPXa2mvx8E7JCbyWHmJLZIzRxN0DZ6fi+ijlQQ3WgJCKRyYgqGkzjoqRUGswYcNrjiY6wrj9WRlxAR+LNVUYXXxphLF0ajLmT06APkmDKckaPufCW+MVsVUJqdWL6RuLQQhGzIBlOjMx+Lq2A/deMz3UYoQNN8/PxP3vnMSdS3MicmWOlWQLEmGt149PNeOJW+ehrGSoJFtybDTu/dY0ydJDDMZo4u6B23m4waPknzd9FBoQGVoV1i/NxdnmHpxs7Pap4YFUg4N7th8LqnGCN7hzbuq28M0htuytRnNPYGW3ANZUYbQZrhHGppJ8PPLhGRy92IWKph5caDfxXlipxivca/dya1Je6YrmnhFp6sHBdGb8c6mrD2arDZO0kWf8jRa62GgUT0vBls+qQi1KQDBPcZBwtV4T1UqsmJPh4hV4cOUsRIHi//5Vi8dvmYtohQxTnYXpmaeAMRZwdVy5GzNX8u9v6y/HoN0+bHwwZ0C4J+ht21/j4vWSWiYWxiPXGUw4erELrxwcasAQaOMEb4yGd5o1VRhdpBphzEiPw9GLXfiqxoC1i/R4eNcZF69rSpzSp6ZJQqT042KHmV9ZAIDNeyowIz0u4PFlOjP++fKCATMnxYMQdj8X8p3CSfj5W8dRumwq9BHW5Y95ioPAbqfoMDkyo8WS7B7edQap2hgooxzGQaJawZoBMMYUq83m4RleW6QHQH1qMsAZEKsWeOo35/UarnUul0CnUsj5Nr0cwTROkGI0vNOsqcLoI9YIQ6WQ483yBqy5bMggBob0TymXiY4L1zRJbBVETD8evbkAaoUcL35Rg637HC2p1xbp0WHqD/h8mM6Mf/5dbcB0lmTnQXyMAt+alYY/floZalH8hhnFQVBrMOGe7cfwysE6ZGpjRL0Cbb39eHDlbPzli/NI0kRL7InBGB10mmhsL6/H+qW52LA8D+uX5mJ7eb3PusgZEHKZeMJoa6/F52XisTISOK/j7rJi/PWHRShdlst7pwNdwh6uAxtjdEiLV2FNURbONveI6p/ZavMYl82rC3HV9GTsLisWjd8V6scbpZdjd1kxpqXF4nd7znqEYQTTtpfpzPjnYI0BMzPiQy1GWHJdQQb+WdnG18mPFFj4RBAIYxcvdvWJLsm19vajtbcfP1o6lU2GjDEnR6fBfStmBtysgDMgMhNiXEoLAkMGra/LxGPZOIHzOrb0WLBlb/Wwsvmyv3CvbzseydFpMC01DmdbeiUTli+fovN7XNzL/x083y5pdAcK05nxTWNXH4yWQWQlxoRalLBErYzC9YUZeOzjSjx/e1GoxfEZZhQHgTA2befhBvxqxQwYzFbYqaPqRHJsNF74ogZbvjuf71DHYIwlYjdmfaLarzJRMhlBQabWq0HrS0WLUBgJI9nWlzVVGHtkMoKZGfF47JOzKFue79F0htOfXGeuRq3BhC8vGPyuHiGlJ2nxwa1iMJ0Zv/y7uh2zJ8VDxuKJJfnWrDT8dMdxnGzoRkGWNtTi+AQzioMgR6fB1tvm40RDN9RKOZQKOe9NUylkuPdb0/DgylnMIGaEFOGNOdAyUd4MWn88wL4YCSNZ2zXc2/pOBIIdzynJjtWOzXsqsH5pLuQyoCg7CVfk6vj9BFv+jOkJw18OVLVjBgud8Ep0lBw3zJ2Exz6uxCvrF4VaHJ9gRnGQ9A9QbNtfg/VLc/Gnz067xKQ98ek5fLixmBnEjLDBnw5z7kgZtDIZwTUz07C9dDGaui3I0KowO0MbkN6LGTeP3lyABXpHfVp/98mWsEPLSNTq5ccwPU5yDH3R6+GM81kZcXj5zkUwWwddaiEzGO5QSvGv6nY8uHJWqEUJe5bPSMWHJ4/jaH0n5usTQy3OsDCjOAgutJvw0zeP8SWARBPtjBZMZd1uGCFAzAgYjTJR7l3zgmlSIGbc3P/OSWwqycfsSVoo5MRvbyNbwg4d3oxVLtzBFw+y+2qHv3rtzTgHIPoZ1zGPwXCnsqUXyqjgw2smAgq5DN+ZOwl//KQSf7trcajFGRZmFAdBvaBxB+BfpzAGYzSRMgKmp8WNuJ4G4312R8q4SYmNRumr5awzWIQhNZ4tPRacbe71+0EqUL32pqMARkx/GRODf1a2oSAzMmJkw4Erp6Xgg+OXcLiuEwuzw9tbHHYl2QghtYSQk4SQY4SQ8lDL4w2Ns3EHIN4p7NGbC1hMGiMkSBkBchlGvEyUNy+dv0iVbavvNLPOYBGI1HiqlfKAur0FqtfedHQk9ZcxMdh3tjViEsfCgSi5DDfMnYQ/fXYu1KIMS7h6iq+ilLaHWojh6B+08RnRXKewJ26dh+pWI/oH7UiNUzJPFiMkSN3ozzb3YlZGHD7cWIw248jE2I5khQexhKcHV87C1n3Bl1VjjD36RDU2ry7EfTtPuHh2rTZ7QGE8Unrd3GPxGjs+nI6yVT6Grxj7B3GioRv/uWxqqEWJKL45LQXvH2sM+0oU4WoURwSTtGo88O4pPHbLXFS19sJmBx7edQZN3RZHF7D5xaEWkTFBkTICTjb24J7tx0Y0/GAkM/e5pKrpG4tR0dyDcy296LUMoNNsddmOGS3hDxdr/sSnlR5VI+o7zQEZot6MW2+x48PpKKs8wfCVL6raMCM9DjFKeahFiSgUchmuK8zAU3vP4YU7Lgu1OJIQSv1rdzraEEIuAOgEQAE8Rynd5vZ5KYBSANDr9Qvr6urGXkgndjvFrhOX8MdPK7G2SO9SQ/MPqwuxsnAS8xRHPn4NYLjop1jsZdnyfLx6qI5/aNs9gjGTXPLTSFZ44PbZYepHY5fFw9vIYor9001gbPWzps2I67Yc8DBgdzuT7AKpShFMNQtvOjoa+suIzLlzOH664xhio6OwYk5GqEWJOPoHbfjJ9mPY/p9LMC307bFF9TMcjeJMSmkjISQVwKcANlJK94ttW1RURMvLQxt2/HWtAf+obEecSo5JCWrUtpvQP2jHVdOTUZSjC6lsjBEh4DtjqPWTu9Gfa+nFycYevH2kAU3dQ3GSb5RejsW5ySGTzx+Y0SJKUD/AaOvnwfPt+N7zX3q8z+ldoGPKdCFiiNi5UwqbnaLokU/x2+/MRgpbqQqI9441wmy1Ycv35odaFFH9DLvwCUppo/P/VkLIOwAWARA1isMBnSYaL37h2f529YLMEErFYAyVsQKAe7Yfi+iYSVZWLfIYLo430DFlusAIFYfrOpGoUTKDOAi+NSsNP9l+DBc7zJicpA61OB6EVfUJQoiGEBLH/Q3gGgCnQiuVd7hYtZHM5mcwRhKmo4xQwPSOMd748MQlFIV5SbFwR62MwvIZqXj2n+dDLYoo4eYpTgPwDnH0Eo8C8BqldE9oRfIO65jFCHeYjjJCAdM7xnjCbqfYfbIZ9107I9SiRDzfnp2OX+w8gZ98axqSY6NDLY4LYWUUU0prAMwNtRz+wpbzGOEO01FGKGB6xxgvfFXbAU20HJkJMaEWJeJJUCuxOFeHv3xxAb9YEV4PGWEVPsFgMBgMBoMRbrxztBFLprLk+ZHi+oIMvPZlPYz9g6EWxQVmFAeB3U5R02bEwfPtqGkzwm4Pr0oeDMZIwXSdMRIwPWJEIpYBGz462YRvTI2Maj2RQFq8CnMytfj7ofAqvRdW4RORRDD1MhmMSILpOmMkYHrEiFQ+Pt2MqSmx0IVZ/Gukc31hBv74SSXuuCIHKkV4NENhnuIAqTWY+MkdcLQavXfHMdQaTCGWjMEYWZiuM0YCpkeMSOWVg3VYNi0l1GKMO3J0GmTrNHjr8MVQi8LDjOIAaemxuNTfBByTfGuvReIbDEZkwnSdMRIwPWJEIpXNvahtN6Eoh5ViGw2+M3cS/u/z8xiw2YffeAxgRnGAcIXphURaQwQGwxeYrjNGAqZHjEjk+QM1KJmZiigZM5dGg2lpcUiOi8Y7RxpDLQoAZhQHDCtMz5goMF1njARMjxiRRnO3BR+fbkbJjLRQizKuuWleJp7aWxUW3mKWaBcgrDA9Y6LAdJ0xEjA9YkQa//ePanxzWgriYxShFmVcMzMjHrpYJd4sv4jbLs8OqSzMKA4CVpieMVFgus4YCZgeMSKFix1mvHu0EX+4JeL6iUUkaxZm4U+fVeHm+VmIUYauEgULn2AwGAwGg8EQ8D+7zmDFnHRomZd4TMhLjUNuigYvfFETUjmYUcxgMBgMBoPh5LMzLTjV2I3rCyaFWpQJxdoiPV44cAGtPaGrSMOM4gBhnZkYjMiCXbPhBRsPRjhiMPbjV2+fxF1Lp0AZxUyksSRdq8KV01PwP7vOhEwGFlMcAKwzE4MRWbBrNrxg48EIRwZtdtz92lF8I0+HWZO0oRZnQnLTvEz88u0T+Oe5NnwzBA1T2GNQALDOTAxGZMGu2fCCjQcj3LDbKX719kn0D9pwy8LJoRZnwqJSyLF+aS5+8eZxdJmtY358ZhQHAOvMxGBEFuyaDS/YeDDCiUGbHb96+yRONnZj41X5kLPVipBSkKlF0ZQk3Lvj+JiHVTGjOABYZyYGI7Jg12x4wcaDES50mqy486Wvcba5B7/49oyQlgNjDPHdosm41NWHP356bkyPy4ziAGCdmRiMyIJds+EFGw9GOLC3ogXf/tN+JKgV+Nm3pzODOIyIkstwz9XT8O7RRmzbf37sjjtmRxpHsM5MDEZkwa7Z8IKNByOUnGrsxuY9Z1HTZkLpslzMZkl1YYk2RoFfXTsDv/voLNp6rfjltTNGPbSFGcUBwjozMRiRBbtmwws2HoyxxDpox+eVrXj537WobO7FyrkZKC3ORZScLZiHM7rYaPzmhll45vNq3PLsv/G7VQWYkR4/asdjRjGDwWAwGIxxBaUUzT0WfHWhA5+fbcXnlW3ISozBsvwU/PibU6FgxnDEEK9S4L4VM/BZRQu++9whXJ6bhO8u0uOKqTpER41syAszihkMBoPBYEQETd192H2yGTa7HQM2CsuADaZ+G3osA+gw9uNStwUNnX0w9g8CAHSxSsyZpMX6pVOg0ygBABc7zKE8BUaA5KXEYuPyPOyvasd/vFyOQTuFKkqGvLQ4TE6MgS5WiXiVAmqlHMooGRbn6lCYleDXMQilkdtFiBDSBqAuxGIkA2gPsQxjwUQ5T8D1XNsppSsC2QnTTw/CRZZwkQMITpaAdRMYU/0Mp9/bFyJJ3nCWdVTmzoRlt6drl9yaOdw+bH29g/a+ngEIbBxK7VGEyAYDkWk0YXL5D6X2KFm0hkbF6ZRS2/TVHutq3f6AVJaeqH5GtFEcDhBCyimlRaGWY7SZKOcJjK9zDadzCRdZwkUOILxkGS0i7RwjSd5IkjUcCNffi8nlP6MlGwuqYTAYDAaDwWBMeJhRzGAwGAwGg8GY8DCjOHi2hVqAMWKinCcwvs41nM4lXGQJFzmA8JJltIi0c4wkeSNJ1nAgXH8vJpf/jIpsLKaYwWAwGAwGgzHhYZ5iBoPBYDAYDMaEhxnFDAaDwWAwGIwJDzOKg4AQsoIQUkkIqSaE/DLU8gQDIWQyIeRzQsgZQshpQsgm5/tJhJBPCSFVzv8Tne8TQsgW57mfIIQsCO0Z+AchRE4IOUoI2eV8PYUQ8qXzfLYTQpTO96Odr6udn+eEVHA/CJV++qtLYyCPT2M9BnIkEELeIoScJYRUEEKWhOo3GSvCfY4khNQSQk4SQo4RQsqd74XNmBBC/kIIaSWEnBK8Ny7n5NEknPTQnzEdY7nCat4WyKUihHxFCDnulOu3zvdHZR5nRnGAEELkAJ4BcC2AWQC+RwiZFVqpgmIQwE8ppbMALAZwt/N8fglgL6U0H8Be52vAcd75zn+lAP489iIHxSYAFYLXmwE8SSnNA9AJYL3z/fUAOp3vP+ncLuwJsX76q0ujja9jPdo8BWAPpXQGgLlOmUL1m4w6ETRHXkUpnSeoeRpOY/ISAPcGA+N1Th4VwlAPX4LvYzqWhNu8zdEPYDmldC6AeQBWEEIWY7TmcUop+xfAPwBLAHwseP0rAL8KtVwjeH7vAfgWgEoAGc73MgBUOv9+DsD3BNvz24X7PwBZcFzcywHsAkDg6AoV5T62AD4GsMT5d5RzOxLqc/DhHMNGP4fTpXAZ61GWQwvggrvuhOI3GcNxDxsd9CJjLYDkcB4TADkATg0nXyTPyaP8+4WdHvo6piGWMWTztheZ1ACOALh8tOZx5ikOnEwAFwWvG5zvRTzOEIH5AL4EkEYpbXJ+1Awgzfl3JJ//nwD8AoDd+VoHoItSyrWzFJ4Lf57Oz7ud24c7YTE+PurSaPIn+D7Wo8kUAG0A/uoM5XiBEKJBaH6TsSIsdHAYKIBPCCGHCSGlzvfCfUzG45w8mkTC7xJWOhcG87a7PHJCyDEArQA+BXAeozSPM6OY4QIhJBbATgD3UEp7hJ9RxyNZRNfwI4SsBNBKKT0calnGO6HWpTAb6ygACwD8mVI6H4AJbsuQ4+H6ikCWUkoXwLG0fjchZJnww3Afk3CXj+E/oR7TUM/bYlBKbZTSeXCs/C0CMGO0jsWM4sBpBDBZ8DrL+V7EQghRwHEx/J1S+rbz7RZCSIbz8ww4ntSAyD3/bwD4DiGkFsAbcCyrPwUggRAS5dxGeC78eTo/1wIwjKXAARLS8fFTl0YLf8d6NGkA0EAp/dL5+i04jOSx/k3GkrCfIyiljc7/WwG8A8cNN9zHZLzNyaNNJPwuYaFzYTJvS0Ip7QLwORzhEqMyjzOjOHC+BpDvzIBUAvgugPdDLFPAEEIIgBcBVFBKnxB89D6AO5x/3wFHnBH3/u3OjOfFALoFSyxhC6X0V5TSLEppDhxjto9S+n04LrRbnJu5nyd3/rc4t48Ez0zI9DMAXRoVAhjr0ZSlGcBFQsh051slAM5gjH+TMSas50hCiIYQEsf9DeAaAKcQ/mMyrubkMSCs9dBJyHUuXOZtEblSCCEJzr9j4IhzrsBozeOhCpgeD/8AXAfgHBzxLb8OtTxBnstSOJZFTgA45vx3HRwxmHsBVAH4DECSc3sCR0bveQAnARSF+hwCOOcrAexy/p0L4CsA1QDeBBDtfF/lfF3t/Dw31HL7cX4h0U9/dSlcxnoMZJgHoNz5u7wLIDGUv8kYnXPYzpFOPTju/Heaky+cxgTA6wCaAAzAsdqwfjzPyaP4O4aNHvozpmMsV9jN2065CgEcdcp1CsBvnO+PyjzO2jwzGAwGg8FgMCY8LHyCwWAwGAwGgzHhYUYxg8FgMBgMBmPCw4xiBoPBYDAYDMaEhxnFDAaDwWAwGIwJDzOKGQwGg8FgMBgTHmYUj0MIITpCyDHnv2ZCSKPzb0oI+bbbtvcQQv4cKlkZDCGEkCsJIbucf3+HEPLL4b7DYDAYExVCyEuEkFuG35LhC8woHodQSg2U0nnU0RbxWQBPOv/+TzgKlwv5Lhx1ExmMUcPZUMCv+YZS+j6l9PejJRODEY4IunQxGIwxhhnFE4u3AFzv7OoDQkgOgEkADoRSKMb4hBCSQwipJIS8AkfR9RcJIeWEkNOEkN8KtltBCDlLCDkCYJXg/R8SQrYK9rWPEHKCELKXEKIf8xNihB1OvagghDzv1KtPCCExhJCphJA9hJDDhJADhJAZhBA5IeSC8wEtgRBiI4Qsc+5nPyEknxDyTcEq21FCSJxz9WI/IeRDpz4/yz3gEUL+LKHTtYSQPxBCThJCviKE5DnfTyGE7CSEfO389w3n+/9NCHmVEPIvAK+G4KdkhBHObosfEkKOE0JOEULWEkJ+49SZU4SQbc4OdO7fW0gI+adT7z8mQ+2ZywghZ5zz5xtjf0aRAzOKJxCU0g44OsBc63zruwB2UNbBhTF65AP4P0rpbAA/pZQWwdGh6JuEkEJCiArA8wBuALAQQLrEfp4G8DKltBDA3wFsGX3RGRFCPoBnnDrWBWA1gG0ANlJKFwL4GRw6aANQCWAWHN27jgAoJoREA5hMKa1ybnu3c2WtGECf8xiLAGx0fncqhh7efu2u0wK5uimlBQC2AviT872n4Fi5u8wp5wuC7WcBuJpS+r2gfxFGpLMCwCVK6VxK6RwAewBspZRe5nwdA2Cl8AuEEAUc8+QtTr3/C4D/dX78SwDznfPnj8fqJCIRZhRPPF7HUAgFC51gjDZ1lNJDzr9vdXqDjwKYDYcRMAPABUpplfPh7G8S+1kC4DXn36/CYdQwGIBDf445/z4MIAfAFQDeJIQcA/AcgAzn5wcALHP++x0cenQZgK+dn/8LwBOEkDIACZTSQef7X1FKa5yG9esY0j8xneZ4XfD/EuffVwPY6pTrfQDxhJBY52fvU0r7wGA42nR/ixCymRBSTCntBnAVIeRLQshJAMvh0Dch0wHMAfCpU78eAJDl/OwEgL8TQn4AYBAMSVjs0sTjPQBPEkIWAFBTSg+HWiDGuMYEAISQKXB44S6jlHYSQl4CoAqlYIxxQ7/gbxuANABdTm+vO/sB/BccYWO/AfBzAFfCGUJGKf09IeRDANcB+JcgMdl9NY36oNNU5G8ZgMWUUotwZ86VcNNwJ8qYGFBKzznv0dcBeIQQshfA3QCKKKUXCSH/Dc/5kwA4TSldAk+uh+NB8AYAvyaEFAge+BgCmKd4gkEpNQL4HI6lFeYlZowV8XDc9LsJIWkYCuE5CyCHEDLV+Vpq6fjfGFrh+D5YHDxDmh4AFwghawA+yXOu87Ov4PAi252G6TE4EpD3O7edSik9SSndDIf3eIbze4sIIVOcscRrAXwBaZ3mWCv4/6Dz70/gCMOA83jzRuSMGeMKQsgkAGZK6d8APAZggfOjdufKgli1iUoAKYSQJc59KAghs506O5lS+jmA+wBoAcSKfJ8B5imeqLwO4B14VqJgMEYFSulxQshROIzgi3AsU4NSaiGElAL4kBBihsPYjRPZxUYAfyWE/BxAG4A7x0ZyRoTyfQB/JoQ8AEAB4A0Axyml/YSQiwC4kJ4DcDyInXS+vocQchUAO4DTAD6CI/Thazhig/PgcCq8Qym1i+m0gERCyAk4PNncw14ZgGec70fBYYyzGE+GOwUAHiOE2AEMwLG6cRMcCcvNGAr34aGUWomjNNsWQogWDv36E4BzAP7mfI8A2EIp7RqDc4hICMuxYjAYDAZDHELIlQB+RildOcymwu/UwrHU3T5KYjEYjFGAhU8wGAwGg8FgMCY8zFPMYDAYDAaDwZjwME8xg8FgMBgMBmPCw4xiBoPBYDAYDMaEhxnFDAaDwWAwGIwJDzOKGQwGg8FgMBgTHmYUMxgMBoPBYDAmPP8fVCwdrdDjs7sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationships between features\n",
    "sns.pairplot(df,diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introducing SciKit Learn\n",
    "\n",
    "We will work a lot with the scitkit learn library, so get comfortable with its model estimator syntax, as well as exploring its incredibly useful documentation!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('sales',axis=1)\n",
    "y = df['sales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train | Test Split\n",
    "\n",
    "Make sure you have watched the Machine Learning Overview videos on Supervised Learning to understand why we do this step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_state: \n",
    "# https://stackoverflow.com/questions/28064634/random-state-pseudo-random-number-in-scikit-learn\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>193.2</td>\n",
       "      <td>18.4</td>\n",
       "      <td>65.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>287.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>80.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>182.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>222.4</td>\n",
       "      <td>4.3</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>102.7</td>\n",
       "      <td>29.6</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>199.1</td>\n",
       "      <td>30.6</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>239.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>214.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>163.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>52.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper\n",
       "85   193.2   18.4       65.7\n",
       "183  287.6   43.0       71.8\n",
       "127   80.2    0.0        9.2\n",
       "53   182.6   46.2       58.7\n",
       "100  222.4    4.3       49.8\n",
       "..     ...    ...        ...\n",
       "63   102.7   29.6        8.4\n",
       "70   199.1   30.6       38.7\n",
       "81   239.8    4.1       36.9\n",
       "11   214.7   24.0        4.0\n",
       "95   163.3   31.6       52.9\n",
       "\n",
       "[140 rows x 3 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85     15.2\n",
       "183    26.2\n",
       "127     8.8\n",
       "53     21.2\n",
       "100    11.7\n",
       "       ... \n",
       "63     14.0\n",
       "70     18.3\n",
       "81     12.3\n",
       "11     17.4\n",
       "95     16.9\n",
       "Name: sales, Length: 140, dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>74.7</td>\n",
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       "      <td>45.7</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>255.4</td>\n",
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       "      <td>5.5</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>112.9</td>\n",
       "      <td>17.4</td>\n",
       "      <td>38.6</td>\n",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>109.8</td>\n",
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       "      <th>66</th>\n",
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       "      <th>119</th>\n",
       "      <td>19.4</td>\n",
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       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>262.7</td>\n",
       "      <td>28.8</td>\n",
       "      <td>15.9</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>213.4</td>\n",
       "      <td>24.6</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>140.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>220.5</td>\n",
       "      <td>33.2</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>38.0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>175.7</td>\n",
       "      <td>15.4</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>222.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>198.9</td>\n",
       "      <td>49.4</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>193.7</td>\n",
       "      <td>35.4</td>\n",
       "      <td>75.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>44.7</td>\n",
       "      <td>25.8</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>262.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>95.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>228.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>19.6</td>\n",
       "      <td>20.1</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>171.3</td>\n",
       "      <td>39.7</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>276.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>41.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>261.3</td>\n",
       "      <td>42.7</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>69.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>199.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>293.6</td>\n",
       "      <td>27.7</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>59.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>165.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.8</td>\n",
       "      <td>35.1</td>\n",
       "      <td>65.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>219.8</td>\n",
       "      <td>33.5</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>134.3</td>\n",
       "      <td>4.9</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>49.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>177.0</td>\n",
       "      <td>33.4</td>\n",
       "      <td>38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>97.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>87.2</td>\n",
       "      <td>11.8</td>\n",
       "      <td>25.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>11.7</td>\n",
       "      <td>36.9</td>\n",
       "      <td>45.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>156.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>67.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>139.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>96.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>38.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>18.8</td>\n",
       "      <td>21.7</td>\n",
       "      <td>50.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>76.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>69.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>227.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>49.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>195.4</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>206.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>280.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>117.2</td>\n",
       "      <td>14.7</td>\n",
       "      <td>5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>4.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>266.9</td>\n",
       "      <td>43.8</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>265.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>175.1</td>\n",
       "      <td>22.5</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  radio  newspaper\n",
       "37    74.7   49.4       45.7\n",
       "109  255.4   26.9        5.5\n",
       "31   112.9   17.4       38.6\n",
       "89   109.8   47.8       51.4\n",
       "66    31.5   24.6        2.2\n",
       "119   19.4   16.0       22.3\n",
       "54   262.7   28.8       15.9\n",
       "74   213.4   24.6       13.1\n",
       "145  140.3    1.9        9.0\n",
       "142  220.5   33.2       37.9\n",
       "148   38.0   40.3       11.9\n",
       "112  175.7   15.4        2.4\n",
       "174  222.4    3.4       13.1\n",
       "55   198.9   49.4       60.0\n",
       "141  193.7   35.4       75.6\n",
       "149   44.7   25.8       20.6\n",
       "25   262.9    3.5       19.5\n",
       "34    95.7    1.4        7.4\n",
       "170   50.0   11.6       18.4\n",
       "39   228.0   37.7       32.0\n",
       "172   19.6   20.1       17.0\n",
       "153  171.3   39.7       37.7\n",
       "175  276.9   48.9       41.8\n",
       "61   261.3   42.7       54.7\n",
       "65    69.0    9.3        0.9\n",
       "50   199.8    3.1       34.6\n",
       "42   293.6   27.7        1.8\n",
       "129   59.6   12.0       43.1\n",
       "179  165.6   10.0       17.6\n",
       "2     17.2   45.9       69.3\n",
       "12    23.8   35.1       65.9\n",
       "133  219.8   33.5       45.1\n",
       "90   134.3    4.9        9.3\n",
       "22    13.2   15.9       49.6\n",
       "41   177.0   33.4       38.7\n",
       "32    97.2    1.5       30.0\n",
       "125   87.2   11.8       25.9\n",
       "196   94.2    4.9        8.1\n",
       "158   11.7   36.9       45.2\n",
       "180  156.6    2.6        8.3\n",
       "16    67.8   36.6      114.0\n",
       "186  139.5    2.1       26.6\n",
       "144   96.2   14.8       38.9\n",
       "121   18.8   21.7       50.4\n",
       "80    76.4   26.7       22.3\n",
       "18    69.2   20.5       18.3\n",
       "78     5.4   29.9        9.4\n",
       "48   227.2   15.8       49.9\n",
       "4    180.8   10.8       58.4\n",
       "15   195.4   47.7       52.9\n",
       "1     44.5   39.3       45.1\n",
       "43   206.9    8.4       26.4\n",
       "102  280.2   10.1       21.4\n",
       "164  117.2   14.7        5.4\n",
       "9    199.8    2.6       21.2\n",
       "155    4.1   11.6        5.7\n",
       "36   266.9   43.8        5.0\n",
       "190   39.5   41.1        5.8\n",
       "33   265.6   20.0        0.3\n",
       "45   175.1   22.5       31.5"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37     14.7\n",
       "109    19.8\n",
       "31     11.9\n",
       "89     16.7\n",
       "66      9.5\n",
       "119     6.6\n",
       "54     20.2\n",
       "74     17.0\n",
       "145    10.3\n",
       "142    20.1\n",
       "148    10.9\n",
       "112    14.1\n",
       "174    11.5\n",
       "55     23.7\n",
       "141    19.2\n",
       "149    10.1\n",
       "25     12.0\n",
       "34      9.5\n",
       "170     8.4\n",
       "39     21.5\n",
       "172     7.6\n",
       "153    19.0\n",
       "175    27.0\n",
       "61     24.2\n",
       "65      9.3\n",
       "50     11.4\n",
       "42     20.7\n",
       "129     9.7\n",
       "179    12.6\n",
       "2       9.3\n",
       "12      9.2\n",
       "133    19.6\n",
       "90     11.2\n",
       "22      5.6\n",
       "41     17.1\n",
       "32      9.6\n",
       "125    10.6\n",
       "196     9.7\n",
       "158     7.3\n",
       "180    10.5\n",
       "16     12.5\n",
       "186    10.3\n",
       "144    11.4\n",
       "121     7.0\n",
       "80     11.8\n",
       "18     11.3\n",
       "78      5.3\n",
       "48     14.8\n",
       "4      12.9\n",
       "15     22.4\n",
       "1      10.4\n",
       "43     12.9\n",
       "102    14.8\n",
       "164    11.9\n",
       "9      10.6\n",
       "155     3.2\n",
       "36     25.4\n",
       "190    10.8\n",
       "33     17.4\n",
       "45     14.9\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Model (Estimator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import a model class from a model family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create an instance of the model with parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class LinearRegression in module sklearn.linear_model._base:\n",
      "\n",
      "class LinearRegression(sklearn.base.MultiOutputMixin, sklearn.base.RegressorMixin, LinearModel)\n",
      " |  LinearRegression(*, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)\n",
      " |  \n",
      " |  Ordinary least squares Linear Regression.\n",
      " |  \n",
      " |  LinearRegression fits a linear model with coefficients w = (w1, ..., wp)\n",
      " |  to minimize the residual sum of squares between the observed targets in\n",
      " |  the dataset, and the targets predicted by the linear approximation.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  fit_intercept : bool, default=True\n",
      " |      Whether to calculate the intercept for this model. If set\n",
      " |      to False, no intercept will be used in calculations\n",
      " |      (i.e. data is expected to be centered).\n",
      " |  \n",
      " |  normalize : bool, default=False\n",
      " |      This parameter is ignored when ``fit_intercept`` is set to False.\n",
      " |      If True, the regressors X will be normalized before regression by\n",
      " |      subtracting the mean and dividing by the l2-norm.\n",
      " |      If you wish to standardize, please use\n",
      " |      :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on\n",
      " |      an estimator with ``normalize=False``.\n",
      " |  \n",
      " |  copy_X : bool, default=True\n",
      " |      If True, X will be copied; else, it may be overwritten.\n",
      " |  \n",
      " |  n_jobs : int, default=None\n",
      " |      The number of jobs to use for the computation. This will only provide\n",
      " |      speedup for n_targets > 1 and sufficient large problems.\n",
      " |      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
      " |      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
      " |      for more details.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  coef_ : array of shape (n_features, ) or (n_targets, n_features)\n",
      " |      Estimated coefficients for the linear regression problem.\n",
      " |      If multiple targets are passed during the fit (y 2D), this\n",
      " |      is a 2D array of shape (n_targets, n_features), while if only\n",
      " |      one target is passed, this is a 1D array of length n_features.\n",
      " |  \n",
      " |  rank_ : int\n",
      " |      Rank of matrix `X`. Only available when `X` is dense.\n",
      " |  \n",
      " |  singular_ : array of shape (min(X, y),)\n",
      " |      Singular values of `X`. Only available when `X` is dense.\n",
      " |  \n",
      " |  intercept_ : float or array of shape (n_targets,)\n",
      " |      Independent term in the linear model. Set to 0.0 if\n",
      " |      `fit_intercept = False`.\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  sklearn.linear_model.Ridge : Ridge regression addresses some of the\n",
      " |      problems of Ordinary Least Squares by imposing a penalty on the\n",
      " |      size of the coefficients with l2 regularization.\n",
      " |  sklearn.linear_model.Lasso : The Lasso is a linear model that estimates\n",
      " |      sparse coefficients with l1 regularization.\n",
      " |  sklearn.linear_model.ElasticNet : Elastic-Net is a linear regression\n",
      " |      model trained with both l1 and l2 -norm regularization of the\n",
      " |      coefficients.\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  From the implementation point of view, this is just plain Ordinary\n",
      " |  Least Squares (scipy.linalg.lstsq) wrapped as a predictor object.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> import numpy as np\n",
      " |  >>> from sklearn.linear_model import LinearRegression\n",
      " |  >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
      " |  >>> # y = 1 * x_0 + 2 * x_1 + 3\n",
      " |  >>> y = np.dot(X, np.array([1, 2])) + 3\n",
      " |  >>> reg = LinearRegression().fit(X, y)\n",
      " |  >>> reg.score(X, y)\n",
      " |  1.0\n",
      " |  >>> reg.coef_\n",
      " |  array([1., 2.])\n",
      " |  >>> reg.intercept_\n",
      " |  3.0000...\n",
      " |  >>> reg.predict(np.array([[3, 5]]))\n",
      " |  array([16.])\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      LinearRegression\n",
      " |      sklearn.base.MultiOutputMixin\n",
      " |      sklearn.base.RegressorMixin\n",
      " |      LinearModel\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, *, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y, sample_weight=None)\n",
      " |      Fit linear model.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix} of shape (n_samples, n_features)\n",
      " |          Training data\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_targets)\n",
      " |          Target values. Will be cast to X's dtype if necessary\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Individual weights for each sample\n",
      " |      \n",
      " |          .. versionadded:: 0.17\n",
      " |             parameter *sample_weight* support to LinearRegression.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : returns an instance of self.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.MultiOutputMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.RegressorMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the coefficient of determination R^2 of the prediction.\n",
      " |      \n",
      " |      The coefficient R^2 is defined as (1 - u/v), where u is the residual\n",
      " |      sum of squares ((y_true - y_pred) ** 2).sum() and v is the total\n",
      " |      sum of squares ((y_true - y_true.mean()) ** 2).sum().\n",
      " |      The best possible score is 1.0 and it can be negative (because the\n",
      " |      model can be arbitrarily worse). A constant model that always\n",
      " |      predicts the expected value of y, disregarding the input features,\n",
      " |      would get a R^2 score of 0.0.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples. For some estimators this may be a\n",
      " |          precomputed kernel matrix or a list of generic objects instead,\n",
      " |          shape = (n_samples, n_samples_fitted),\n",
      " |          where n_samples_fitted is the number of\n",
      " |          samples used in the fitting for the estimator.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True values for X.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          R^2 of self.predict(X) wrt. y.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The R2 score used when calling ``score`` on a regressor uses\n",
      " |      ``multioutput='uniform_average'`` from version 0.23 to keep consistent\n",
      " |      with default value of :func:`~sklearn.metrics.r2_score`.\n",
      " |      This influences the ``score`` method of all the multioutput\n",
      " |      regressors (except for\n",
      " |      :class:`~sklearn.multioutput.MultiOutputRegressor`).\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from LinearModel:\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Predict using the linear model.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array_like or sparse matrix, shape (n_samples, n_features)\n",
      " |          Samples.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      C : array, shape (n_samples,)\n",
      " |          Returns predicted values.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(LinearRegression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit/Train the Model on the training data\n",
    "\n",
    "**Make sure you only fit to the training data, in order to fairly evaluate your model's performance on future data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding and utilizing the Model\n",
    "\n",
    "-----\n",
    "\n",
    "## Evaluation on the Test Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics\n",
    "\n",
    "Make sure you've viewed the video on these metrics!\n",
    "The three most common evaluation metrics for regression problems:\n",
    "\n",
    "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$\n",
    "\n",
    "**Mean Squared Error** (MSE) is the mean of the squared errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$\n",
    "\n",
    "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n",
    "\n",
    "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$\n",
    "\n",
    "Comparing these metrics:\n",
    "\n",
    "- **MAE** is the easiest to understand, because it's the average error.\n",
    "- **MSE** is more popular than MAE, because MSE \"punishes\" larger errors, which tends to be useful in the real world.\n",
    "- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \"y\" units.\n",
    "\n",
    "All of these are **loss functions**, because we want to minimize them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Performance on Test Set\n",
    "\n",
    "We want to fairly evaluate our model, so we get performance metrics on the test set (data the model has never seen before)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We only pass in test features\n",
    "# The model predicts its own y hat\n",
    "# We can then compare these results to the true y test label value\n",
    "test_predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15.74131332, 19.61062568, 11.44888935, 17.00819787,  9.17285676,\n",
       "        7.01248287, 20.28992463, 17.29953992,  9.77584467, 19.22194224,\n",
       "       12.40503154, 13.89234998, 13.72541098, 21.28794031, 18.42456638,\n",
       "        9.98198406, 15.55228966,  7.68913693,  7.55614992, 20.40311209,\n",
       "        7.79215204, 18.24214098, 24.68631904, 22.82199068,  7.97962085,\n",
       "       12.65207264, 21.46925937,  8.05228573, 12.42315981, 12.50719678,\n",
       "       10.77757812, 19.24460093, 10.070269  ,  6.70779999, 17.31492147,\n",
       "        7.76764327,  9.25393336,  8.27834697, 10.58105585, 10.63591128,\n",
       "       13.01002595,  9.77192057, 10.21469861,  8.04572042, 11.5671075 ,\n",
       "       10.08368001,  8.99806574, 16.25388914, 13.23942315, 20.81493419,\n",
       "       12.49727439, 13.96615898, 17.56285075, 11.14537013, 12.56261468,\n",
       "        5.50870279, 23.29465134, 12.62409688, 18.77399978, 15.18785675])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAE = mean_absolute_error(y_test,test_predictions)\n",
    "MSE = mean_squared_error(y_test,test_predictions)\n",
    "RMSE = np.sqrt(MSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.213745773614481"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2987166978863796"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5161519375993884"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0225"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sales'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Review our video to understand whether these values are \"good enough\".**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Residuals\n",
    "\n",
    "Revisiting Anscombe's Quartet: https://en.wikipedia.org/wiki/Anscombe%27s_quartet\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Anscombe%27s_quartet_3.svg/850px-Anscombe%27s_quartet_3.svg.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table class=\"wikitable\">\n",
    "<tbody><tr>\n",
    "<th>Property\n",
    "</th>\n",
    "<th>Value\n",
    "</th>\n",
    "<th>Accuracy\n",
    "</th></tr>\n",
    "<tr>\n",
    "<td><a href=\"/wiki/Mean\" title=\"Mean\">Mean</a> of <i>x</i>\n",
    "</td>\n",
    "<td>9\n",
    "</td>\n",
    "<td>exact\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>Sample <a href=\"/wiki/Variance\" title=\"Variance\">variance</a> of <i>x</i>  :  <span class=\"mwe-math-element\"><span class=\"mwe-math-mathml-inline mwe-math-mathml-a11y\" style=\"display: none;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"  alttext=\"{\\displaystyle \\sigma ^{2}}\">\n",
    "  <semantics>\n",
    "    <mrow class=\"MJX-TeXAtom-ORD\">\n",
    "      <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n",
    "        <msup>\n",
    "          <mi>&#x03C3;<!-- σ --></mi>\n",
    "          <mrow class=\"MJX-TeXAtom-ORD\">\n",
    "            <mn>2</mn>\n",
    "          </mrow>\n",
    "        </msup>\n",
    "      </mstyle>\n",
    "    </mrow>\n",
    "    <annotation encoding=\"application/x-tex\">{\\displaystyle \\sigma ^{2}}</annotation>\n",
    "  </semantics>\n",
    "</math></span><img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/53a5c55e536acf250c1d3e0f754be5692b843ef5\" class=\"mwe-math-fallback-image-inline\" aria-hidden=\"true\" style=\"vertical-align: -0.338ex; width:2.385ex; height:2.676ex;\" alt=\"\\sigma ^{2}\"/></span>\n",
    "</td>\n",
    "<td>11\n",
    "</td>\n",
    "<td>exact\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>Mean of <i>y</i>\n",
    "</td>\n",
    "<td>7.50\n",
    "</td>\n",
    "<td>to 2 decimal places\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td>Sample variance of <i>y</i>  :  <span class=\"mwe-math-element\"><span class=\"mwe-math-mathml-inline mwe-math-mathml-a11y\" style=\"display: none;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"  alttext=\"{\\displaystyle \\sigma ^{2}}\">\n",
    "  <semantics>\n",
    "    <mrow class=\"MJX-TeXAtom-ORD\">\n",
    "      <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n",
    "        <msup>\n",
    "          <mi>&#x03C3;<!-- σ --></mi>\n",
    "          <mrow class=\"MJX-TeXAtom-ORD\">\n",
    "            <mn>2</mn>\n",
    "          </mrow>\n",
    "        </msup>\n",
    "      </mstyle>\n",
    "    </mrow>\n",
    "    <annotation encoding=\"application/x-tex\">{\\displaystyle \\sigma ^{2}}</annotation>\n",
    "  </semantics>\n",
    "</math></span><img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/53a5c55e536acf250c1d3e0f754be5692b843ef5\" class=\"mwe-math-fallback-image-inline\" aria-hidden=\"true\" style=\"vertical-align: -0.338ex; width:2.385ex; height:2.676ex;\" alt=\"\\sigma ^{2}\"/></span>\n",
    "</td>\n",
    "<td>4.125\n",
    "</td>\n",
    "<td>±0.003\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td><a href=\"/wiki/Correlation\" class=\"mw-redirect\" title=\"Correlation\">Correlation</a> between <i>x</i> and <i>y</i>\n",
    "</td>\n",
    "<td>0.816\n",
    "</td>\n",
    "<td>to 3 decimal places\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td><a href=\"/wiki/Linear_regression\" title=\"Linear regression\">Linear regression</a> line\n",
    "</td>\n",
    "<td><i>y</i>&#160;=&#160;3.00&#160;+&#160;0.500<i>x</i>\n",
    "</td>\n",
    "<td>to 2 and 3 decimal places, respectively\n",
    "</td></tr>\n",
    "<tr>\n",
    "<td><a href=\"/wiki/Coefficient_of_determination\" title=\"Coefficient of determination\">Coefficient of determination</a> of the linear regression  :  <span class=\"mwe-math-element\"><span class=\"mwe-math-mathml-inline mwe-math-mathml-a11y\" style=\"display: none;\"><math xmlns=\"http://www.w3.org/1998/Math/MathML\"  alttext=\"{\\displaystyle R^{2}}\">\n",
    "  <semantics>\n",
    "    <mrow class=\"MJX-TeXAtom-ORD\">\n",
    "      <mstyle displaystyle=\"true\" scriptlevel=\"0\">\n",
    "        <msup>\n",
    "          <mi>R</mi>\n",
    "          <mrow class=\"MJX-TeXAtom-ORD\">\n",
    "            <mn>2</mn>\n",
    "          </mrow>\n",
    "        </msup>\n",
    "      </mstyle>\n",
    "    </mrow>\n",
    "    <annotation encoding=\"application/x-tex\">{\\displaystyle R^{2}}</annotation>\n",
    "  </semantics>\n",
    "</math></span><img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/5ce07e278be3e058a6303de8359f8b4a4288264a\" class=\"mwe-math-fallback-image-inline\" aria-hidden=\"true\" style=\"vertical-align: -0.338ex; width:2.818ex; height:2.676ex;\" alt=\"R^{2}\"/></span>\n",
    "</td>\n",
    "<td>0.67\n",
    "</td>\n",
    "<td>to 2 decimal places\n",
    "</td></tr></tbody></table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "quartet = pd.read_csv('anscombes_quartet1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x21603321888>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y = 3.00 + 0.500x\n",
    "quartet['pred_y'] = 3 + 0.5 * quartet['x']\n",
    "quartet['residual'] = quartet['y'] - quartet['pred_y']\n",
    "\n",
    "sns.scatterplot(data=quartet,x='x',y='y')\n",
    "sns.lineplot(data=quartet,x='x',y='pred_y',color='red')\n",
    "plt.vlines(quartet['x'],quartet['y'],quartet['y']-quartet['residual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='residual', ylabel='Density'>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wrf4GqjpPVQ/f478QSHI/PwOYq6rFqroPmAtM9mBW4yO+2biXnN1lXDWqp1/NOd2awkMCufPsDLJ3l/Lywq1OxzEO8mSBSATy673e7l52JDOBT1qyr4hcKyJZIpJVWFh4nHGNL3ju283ER4VyzkndnI7Srp0xoAtj0uN49LMcCksrnY5jHOIVg9QichmQCTzSkv1U9RlVzVTVzPh4a8Tm77J3lfJ1TiEzTu5BaFCg03HaNRHhnqkDqKip5WHr0+S3PFkgCoD61xgmuZf9gIicBvwBmKqqlS3Z15j6Xvh2M2HBAVwywganW0Pv+EiuHt2Tt5ZuZ9m2fU7HMQ7wZIFYAqSLSE8RCQGmA7PrbyAig4GncRWHPfVWzQEmiUgn9+D0JPcyYxpVWFrJuysK+OmQJGI7hDgdx2fcMjGdLtGh3P3+WmqtT5Pf8ViBUNUa4GZcP9jXA2+q6loRuU9Epro3ewSIBN4SkRUiMtu9bzFwP64iswS4z73MmEa9snArVTV1zBzd0+koPiUyNIg7zuzP6oIS3szKb3oH41NE1Td+K8jMzNSsrCynYxgHlFfVMPrP8xic3JHnrxzmdByfo6pc9MxCNu4uZd5vxtMxwo7QfImILFXVzMbWecUgtTHH4/XF+RQfrOKG8b2djuKTRIR7pw6g5FA1f/0sx+k4pg1ZgTDtWmVNLc98vYmRvWLJTI11Oo7P6t8tmitOTuXVRVtZU1DidBzTRqxAmHbt7aXb2X2gkpsnpDsdxef98vQ+dIoI4e7Za/GVU9Pm6KxAmHaruraOf87fxEnJHRmV1tnpOD4vJjyY303uy9Kt+3h/xQ6n45g2YAXCtFuzV+xg+75D3DIhzdpqtJELhiZzYlIMD36ynoOVNU7HMR7WrAIhIu+IyFkiYgXFeIXaOuUf83Pp1zWKU/vbjHFtJSBAuPucAew+UMlT83KdjmM8rLk/8P8BXAJsFJGHRKSvBzMZ06R3lxewqfAgPz813Y4e2tjQHp04b0giz32zmS17Dzodx3hQswqEqn6uqpcCQ4AtwOci8r2IXCUiwZ4MaExDlTW1PDY3hxMSY5gysKvTcfzS7ZP7ERwo/Omj9U5HMR7U7FNGItIZuBK4Btc8Do/jKhhzPZLMmCN4fdE2CvYf4rdn9LWjB4ckRIdxy6npfL5+N1/lWCdlX9XcMYh3gW+ACOAcVZ2qqm+o6i24WmUY0ybKq2p4cl4uI3vFMiY9zuk4fu2qUan0jOvAvR+sparGJhbyRc09gnhWVTNU9UFV3QkgIqEAR7pF2xhP+Pd3W9hbVsVvz+hnRw8OCw0K5K6zM8grPMhLC7Y4Hcd4QHMLxJ8aWbagNYMY05Tig1X866tNnNY/gaE9bAZabzChXwIT+yXw+OcbbWIhH3TUAiEiXUVkKBAuIoNFZIj7MR7X6SZj2syjc7Mpr6rld5P7OR3F1HPn2Rk2sZCPCmpi/Rm4BqaTgEfrLS8F7vBQJmN+ZMOuA7y2aBuXj+xBny5RTscx9fSM68DVo3ryzDd5zDgllYGJMU5HMq3kqEcQqvofVZ0AXKmqE+o9pqrqO22U0fg5VeW+D9YRHR7ML0/v43Qc04ibJqbRMTyYBz9Zb32afEhTp5gucz9NFZFfNXy0QT5j+Gzdbr7fVMQvT+tjcxF4qeiwYG49NZ3vcouYb5e9+oymBqk7uL9GAlGNPIzxqIrqWh74eD3pCZFcOiLF6TjmKC4Z0YPUzhE8+PF6amrtsldfcNQxCFV92v313raJY8wP/WNeLluLynll5giCAq0VmDcLCQrgtsn9uOHVZby9dDvTh1tBb++ae6PcwyISLSLBIvKFiBTWO/1kjEfk7inln19t4ieDujPaboprFyYP7MrQHp14dG6OdXv1Ac39lWySqh4AzsbViykN+K2nQhlTV6fc8c4aIkKC+OPZGU7HMc0kItxxZn/2lFby7Dd5Tscxx6m5BeLwqaizgLdU1eYcNB711tJ8Fm8p5o4z+xEXGep0HNMCQ3t04swTuvLM13nsKa1wOo45Ds0tEB+KyAZgKPCFiMQD9i9vPKKwtJIHPt7A8NRYLhia7HQccwxum9yP6to6Hv98o9NRzHFobrvv24FTgExVrQYOAtM8Gcz4J1XljndXc6i6lgfOO4GAAOu31B716NyB6cNSeGNJPtuKyp2OY45RSy4L6QdcJCJXAOcDkzwTyfiz91YUMHfdbn4zqQ9pCdYouD27eWIagQHC377IcTqKOUbNvYrpZeAvwGhgmPthXVxNq9pVUsHd768ls0cnZo7u5XQcc5y6RIcx45RU3lteQO6eUqfjmGPQ3COITGCUqt6oqre4Hz9vaicRmSwi2SKSKyK3N7J+rIgsE5EaETm/wbpaEVnhfsxuZk7TTqkqv39nFVW1dTxywUkE2qkln3D9uN6EBwfy2Fwbi2iPmlsg1gAtmttRRAKBp4ApQAZwsYg0vF5xG65mgK818haHVHWQ+zG1JZ9t2p9XF21jXnYht03uR8+4Dk3vYNqF2A4hzBzdk49W72RNgV382N40t0DEAetEZI6IzD78aGKf4UCuquapahUwiwYD26q6RVVXAXZfvh/L3lXK/R+uY1yfeGacnOp0HNPKZo7pRUx4MI/OtbGI9qapdt+H3XMM750I5Nd7vR0Y0YL9w0QkC6gBHlLV9xpuICLXAtcCpKTYbf3t0aGqWm55fRlRYcH85YKT7KolHxQTHsy1Y3vxyJxslm7dZ5M9tSPNvcz1K1x3UAe7ny8BlnkwF0AP93SmlwB/E5HejeR6RlUzVTUzPj7ew3GMJ9z/0Tpydpfx6IUnER9lN8T5qqtGpRIXGcJfP8t2OoppgeZexfQz4G3gafeiROC9JnYrAOrf5ZTkXtYsqlrg/poHzAcGN3df0z58snonry3axnXjejG2jxV4XxYREsSN49P4flMRC/OKnI5jmqm5YxA3AaOAAwCquhFIaGKfJUC6iPQUkRBgOtCsq5FEpJOIhLqfx7k/e10zs5p2oGD/IW777ypOSorh16f3dTqOaQOXjEghLjKUJ760K5rai+YWiEr3QDMAIhIEHHXaKFWtAW4G5gDrgTdVda2I3CciU93vM0xEtgMXAE+LyFr37v2BLBFZCczDNQZhBcJH1NTWcevry6lT+PvFgwkJsjbe/iAsOJDrxvbiu9wilm7d53Qc0wzNHaT+SkTuAMJF5HTgRuCDpnZS1Y+Bjxssu6ve8yW4Tj013O974IRmZjPtzN+/zCVr6z4enz6IHp3tklZ/cunIFP751Sae+HIjL1413Ok4pgnN/dXtdqAQWA1ch+uH/h89Fcr4roV5RTz55UZ+OiSJaYMSnY5j2lhESBAzR/dkfnYhq7bvdzqOaUJzr2KqwzUofaOqnq+qz6rNTG5aaN/BKn4xawU9OnfgvmkDnI5jHHLFyT2ICQ/miS9znY5imnDUAiEu94jIXiAbyHbPJnfX0fYzpiFV5Xf/XUXRwUqeuHgwHUKbe3bT+JqosGCuGpXK3HW7Wb/zgNNxzFE0dQTxS1xXEA1T1VhVjcV1s9soEfmlx9MZn/HKwq3MXbeb2yb3Y2BijNNxjMOuOqUnkaFBPGlHEV6tqQJxOXCxqm4+vMB9X8JlwBWeDGZ8x/qdB7j/o/WM7xvP1aN6Oh3HeIGYiGBmnNKDj9fstE6vXqypAhGsqnsbLlTVQiDYM5GML3G10lhOTLi10jA/NHN0L8KDA+0owos1VSCqjnGdMQDc9+E6NhWW8diFg2xuafMDsR1CuGR4Ch+s2kl+sc06542aKhAniciBRh6l2H0KpgmfrtnF64u3ce3YXoxOj3M6jvFCM8f0JEDguW/ynI5iGnHUAqGqgaoa3cgjSlXtFJM5ot0HKrj9nVUMTIy2VhrmiLrFhDNtUCJvZOVTVFbpdBzTgPU4MK2urk75zVsrqaiu5fHp1krDHN3143pRUV3HfxZsdTqKacC+c02re+G7zXyzcS93np1B7/hIp+MYL5eWEMVp/bvw0oItlFfVOB3H1GMFwrSq9TsP8PCn2ZzWvwuXDLdJnEzz3DC+F/vLq5m1OL/pjU2bsQJhWk1FdS23zlpOdHgwf/7pCYjYJa2meYb2iGVYaiee/3Yz1bU2A7G3sAJhWs0jc7LJ2V3GXy44kc52SatpoevH9aZg/yE+WLnD6SjGzQqEaRVLthTzwnebuWxkCuP7NjWXlDE/NqFvAn26RPL0V3lYL1DvYAXCHLdDVbX89q2VJHYM5/dT+jsdx7RTAQHCdWN7k727lHnZe5yOY7ACYVrBI3Oy2VJUzsM/PdG6tJrjMnVQd7rHhPGv+XbjnDewAmGOy5Itxfz7e9eppVPS7G5pc3yCAwOYOaYXi7cU27SkXsAKhDlmdmrJeML0YcnEhAfzr682OR3F71mBMMfMTi0ZT+gQGsSMk3swd91ucveUOR3Hr1mBMMdk2bZ9dmrJeMwVp6QSGhTAs1/bWISTrECYFquureOOd1bTJSqM2yb3czqO8UFxkaFckJnEu8sL2HOgwuk4fssKhGmxF77dzIZdpdwzdQBRYdbU13jGNaN7UVNXx7+/3+J0FL9lBcK0SH5xOY99nsPpGV2YPLCr03GMD0uN68CUgd14ZeFWSiuqnY7jl6xAmGZTVe56fw0BItw7dYDTcYwfuHZsL0oraqyJn0M8WiBEZLKIZItIrojc3sj6sSKyTERqROT8ButmiMhG92OGJ3Oa5vl49S7mZRfy60l96d4x3Ok4xg+clNyRk3t15vlvN1NVY0382prHCoSIBAJPAVOADOBiEclosNk24ErgtQb7xgJ3AyOA4cDdItLJU1lN0w5UVHPPB2sZmBjNjJN7OB3H+JHrxvVi14EKZlsTvzbnySOI4UCuquapahUwC5hWfwNV3aKqq4CGvxqcAcxV1WJV3QfMBSZ7MKtpwl/nZFNUVsmD555IUKCdmTRtZ1yfePp1jeKZrzdRV2dN/NqSJ7/TE4H6Jw63u5e12r4icq2IZIlIVmFh4TEHNUe3fucBXl64lctH9uCEpBin4xg/IyJcO7YXObvLmJ9jTfzaUrv+VVBVn1HVTFXNjI+PdzqOT1JV7pm9lpjwYH51el+n4xg/dc5J7iZ+X9mNc23JkwWiAEiu9zrJvczT+5pW9NHqnSzaXMxvzuhLTITd82CcERwYwNWje7J4czHLt1kTv7biyQKxBEgXkZ4iEgJMB2Y3c985wCQR6eQenJ7kXmbaUHlVDQ98tJ6MbtFMH2bzSxtnTR+eQnRYEM9Y+40247ECoao1wM24frCvB95U1bUicp+ITAUQkWEish24AHhaRNa69y0G7sdVZJYA97mXmTb0r/mb2FFSwb3TBhAYYPNLG2dFhgZx+ck9+HTtLjbvPeh0HL8gvjK1X2ZmpmZlZTkdw2fkF5dz6qNfMWVgVx6fPtjpOMYAsKe0gtF/nsf5Q5N44NwTnI7jE0RkqapmNrauXQ9SG8/500frCAoQm+fBeJWEqDB+OiSJt5dup7C00uk4Ps8KhPmRbzYWMmftbm6akEbXmDCn4xjzAz8b05Pq2jr+Y038PM4KhPmB6to67v1gHT06RzBzdE+n4xjzI73iIzkjoysvLdjCwcoap+P4NCsQ5gdeWrCV3D1l3HlWBmHBgU7HMaZR143rxYGKGmYtsSZ+nmQFwvzP3rJK/jY3h3F94jm1f4LTcYw5osEpnRieGsvz3+RRXWtN/DzFCoT5n0c+zeZQdS13nZOBiF3WarzbDeN7s6OkgneX2T20nmIFwgCwavt+3lyaz9Wje9I7PtLpOMY0aXzfeAYmRvOP+bnU2FGER1iBMNTVufotde4Qyi0T05yOY0yziAg3T0hnS1E5H67a6XQcn2QFwvDeigKWbdvP7VP62RzTpl2ZlNGFvl2ieHJerrUC9wArEH6urLKGBz/ZwKDkjpw3uLnd2I3xDgEBwk0T08jdU8ana3c5HcfnWIHwc098uZHC0krunTqAAOu3ZNqhs07oRq+4DjzxZS6+0jrIW1iB8GN5hWW88O1mLsxM4qTkjk7HMeaYBAYIN05IY/3OA3yx3iYUak1WIPzY/R+uIywokN+e0c/pKMYcl2mDupMcG84T8+woojVZgfBTX27YzbzsQm49LZ34qFCn4xhzXIIDA7hxfBor8/fzzca9TsfxGVYg/FBlTS33fbCOtIRIZpyS6nQcY1rFeUMS6R4TxmOf59hRRCuxAuGHnv06jy1F5dx9TgbBgfZfwPiG0KBAbp6YzvJt+5mXbWMRrcF+OviZgv2HeHJeLlMGdmVMerzTcYxpVRdkJpESG8FfP8ux+yJagRUIP/OnD9chCH88O8PpKMa0uuDAAH5xWjprdxyw+yJagRUIP/LNxkI+WbOLmyemkdgx3Ok4xnjEtEGJpCVE8ujcHGrtKOK4WIHwE1U1ddw9ey2pnSO4ZoxNBGR8V2CA8KvT+5C7p4z3V1in1+NhBcJPPP/tZvIKD3L31AGEBtlEQMa3TR7QlQHdo/nrZzlUVNc6HafdsgLhB3aWHOKJLzdyekYXJvS1iYCM7wsIEO44sz8F+w/x0oItTsdpt6xA+IF7Z6+jtk65ywamjR8ZlRbH+L7xPPFlLvsOVjkdp12yAuHj5qzdxadrd3Hraekkx0Y4HceYNvX7Kf05WFnDE1/mOh2lXbIC4cMOVFRz1/tr6N8tmp+N6eV0HGPaXN+uUVyYmczLC7ewteig03HaHY8WCBGZLCLZIpIrIrc3sj5URN5wr18kIqnu5akickhEVrgf//JkTl/18Kcb2FNayUPnnWB3TBu/9avT+xAUEMBDn2xwOkq747GfGiISCDwFTAEygItFpOFJ8JnAPlVNAx4D/lxv3SZVHeR+XO+pnL4qa0sxryzcxpWnpForb+PXEqLDuHF8bz5Zs4tvrZFfi3jy18rhQK6q5qlqFTALmNZgm2nAf9zP3wZOFRGbteY4VdbUcvs7q0nsGM5vJvV1Oo4xjvvZ2F706BzBXbPXUFVT53ScdsOTBSIRyK/3ert7WaPbqGoNUAJ0dq/rKSLLReQrERnT2AeIyLUikiUiWYWFha2bvh371/w8cveU8aefDKRDaJDTcYxxXFhwIPecM4C8woO88N1mp+O0G956YnonkKKqg4FfAa+JSHTDjVT1GVXNVNXM+HhrPAeQvauUp+blcs5J3ZnQz+55MOawCf0SOK1/F/7+xUZ2lhxyOk674MkCUQAk13ud5F7W6DYiEgTEAEWqWqmqRQCquhTYBPTxYFafUFVTxy/fWEFUWBB3n2P3PBjT0N3nZFBbp/zpw/VOR2kXPFkglgDpItJTREKA6cDsBtvMBma4n58PfKmqKiLx7kFuRKQXkA7keTCrT/j7FxtZt/MAD553AnGRNkucMQ0lx0Zwy8Q0Plq9k0/XWLfXpnisQLjHFG4G5gDrgTdVda2I3CciU92bPQ90FpFcXKeSDl8KOxZYJSIrcA1eX6+qxZ7K6guWbdvHP+bncv7QJCYN6Op0HGO81nXjepPRLZo731/D/nK7w/poxFem5svMzNSsrCynYzjiYGUNZz/xLVU1dXzyizFEhwU7HckYr7Z2RwnTnvyOqYO68+iFg5yO4ygRWaqqmY2t89ZBatMCd76/hi1FB3nkghOtOBjTDAO6x3DD+N68s6yAeRtsetIjsQLRzr2Vlc87ywq4ZWI6p/SOczqOMe3GzRPTSE+I5PZ3VlFszfwaZQWiHdu4u5S73l/LyF6x3HpqutNxjGlXQoMCeeyiQew7WM3v3l6Jr5xub01WINqp8qoabnptGREhgTw+fTCBAXYDujEtNTAxhtun9OPz9Xt48fstTsfxOlYg2iFV5TdvrWTjnjIeu2gQXaLDnI5kTLt11ahUTu2XwIMfb2BNQYnTcbyKFYh26Ikvc/l49S5un9yPsX3sDnJjjoeI8MgFJ9GpQzC3vL6ckvJqpyN5DSsQ7cyna3bx6NwczhucyLVjbY4HY1pDbIcQnrh4CPnF5dwyazk1tdbQD6xAtCtrCkr41ZsrGJTckQfOOwFrfGtM6xneM5b7fzKQr3MKedDmjgDAWn22E3mFZcx4YTGdIkJ45vKhhAUHOh3JGJ9z8fAUsneV8vy3m+nbJYoLhyU3vZMPswLRDuwqqeDy5xcD8PLM4STYoLQxHvPHs/qTu6eMP7y3mvjoUCb09d+uyHaKycvtO1jFFS8souRQNS9eNZxe8ZFORzLGpwUFBvCPy4bQt2sU17+8lEV5RU5HcowVCC9WWFrJxc8uZEtROc9cPpQTkmKcjmSMX4gOC+Y/Vw0nqVM4M/+Txart+52O5AgrEF5qZ8khLnp6AVuLyvn3lcM4Jc3aaBjTljpHhvLqNSPpGBHMFS8sZvm2fU5HanNWILzQlr0HufDpBewpreSlmcMZZcXBGEd0jQnjtWtGEh0WzKXPLeKbjf41tbEVCC/z/aa9/OQf31FaUcOr14xgWGqs05GM8WspnSN4+/qTSYmN4OoXl/DByh1OR2ozViC8yGuLtnHF84uJiwzlvRtHcVJyR6cjGWOAhOgw3rjuZAYnd+KW15fz6Nwcaut8v7mfFQgvcLCyhtveXsUd765mVFoc79x4CqlxHZyOZYypJyY8mJdmDuf8oUn8/YuNXP3iEp+fkc4KhMNW5u/n7Ce+5c2l+dw4vjfPz8i0SX+M8VJhwYE8cv6J/L9zB7JgUxFnP/EtCzb57mWwViAccqiqlr9+ls1P//k9ldW1vP6zkfxucj+CAu2fxBhvJiJcOqIHb15/MoEBwsXPLuTO99ZQVlnjdLRWZ3dStzFV5bN1u7nvg3UU7D/EeYMTufucAcRE2FGDMe3JoOSOfHrrWP7yWTYvfLeZLzfs4fYp/TjrhG4E+Mj8LOIrsyhlZmZqVlaW0zGOasGmIv72eQ6LNhfTr2sU904dwIhenZ2OZYw5Tku37uMP765mw65STkqK4fYp/Tm5d/v43haRpaqa2eg6KxCeVVunfJ1TyD+/2sTizcUkRIVy04Q0Lh2RYqeTjPEhtXXKe8sL+Otn2ewoqSCzRyeuGdOT0zO6evWMj1YgHLDnQAVvLd3O64u3sX3fIRKiQrlxfG+mD0+xTqzG+LCK6lpeX7yNF77bTH7xIVJiI7hgaBI/GZxIcmyE0/F+xApEG9m+r5y563bzyepdLNlajCqc3Kszl45MYVJGV0KC7IjBGH9RW6fMWbuLF7/fwuLNxQAMT41l0oAuTOyXQM+4Dl4xp4sVCA+oqa0jb+9B1u4oYfHmYr7LLWJbcTkA/bpGMWVgN84+qRu9rfuqMX4vv7ic91cUMHvlDnJ2lwGQEhvByF6xZPaIZWhqJ3p27uDI4LZjBUJEJgOPA4HAc6r6UIP1ocBLwFCgCLhIVbe41/0emAnUAj9X1TlH+yxPFYiyyhryi8vJLy5nW3E5W4oOsnbHAdbvPEBFtWtawqjQIEb06syotM6M7RNvRcEYc0T5xeXMzynkq+w9LNmyj5JDrjmww4MDSUuIJL1LJH26RJEWH0n3juF0iwmjY0Swx442HCkQIhII5ACnA9uBJcDFqrqu3jY3Aieq6vUiMh04V1UvEpEM4HVgONAd+Bzoo6q1R/q8Yy0QZZU1vLZoK8UHq9l3sIqig1XsK6/63/PD/3iHRYUFkdEtmgHdYxiY6PraO76DDTgbY1qsrk7J21vG0q372LCrlI27y9i4p5TdByp/sF1YcADdYsKJiwwhJjyEmPBgYsKD6Rjh+prUKZxT+3c5pgxHKxCevA9iOJCrqnnuELOAacC6ettMA+5xP38beFJcZXIaMEtVK4HNIpLrfr8FrR2ypraOBz7eQHCgENshhE4RIcR2CCGjezSxHULoFhNOcmw4KbERJHeK8GglN8b4l4AAIS0hirSEqB8sLymvJm9vGbtKKthRUsGukkPsKKmguKyKgv2HWL/zACWHqv93c97glI7HXCCOxpMFIhHIr/d6OzDiSNuoao2IlACd3csXNtg3seEHiMi1wLXul2Uikt060YkD9rbSe7Umb8zljZnAO3N5YybwzlzemAm8M1fcVtgrNx3z/j2OtKJd30mtqs8Az7T2+4pI1pEOuZzkjbm8MRN4Zy5vzATemcsbM4F35vJkJk+eOC8Akuu9TnIva3QbEQkCYnANVjdnX2OMMR7kyQKxBEgXkZ4iEgJMB2Y32GY2MMP9/HzgS3WNms8GpotIqIj0BNKBxR7MaowxpgGPnWJyjyncDMzBdZnrC6q6VkTuA7JUdTbwPPCyexC6GFcRwb3dm7gGtGuAm452BZMHtPppq1bijbm8MRN4Zy5vzATemcsbM4F35vJYJp+5Uc4YY0zrsov3jTHGNMoKhDHGmEZZgWiCiPxaRFRE4pzOAiAi94vIKhFZISKfiUh3L8j0iIhscOd6V0Q6Op0JQEQuEJG1IlInIo5emigik0UkW0RyReR2J7McJiIviMgeEVnjdJbDRCRZROaJyDr3v92tXpApTEQWi8hKd6Z7nc5Un4gEishyEfmwtd/bCsRRiEgyMAnY5nSWeh5R1RNVdRDwIXCXw3kA5gIDVfVEXO1Vfu9wnsPWAOcBXzsZwt125ilgCpABXOxuJ+O0F4HJTodooAb4tapmACOBm7zg76oSmKiqJwGDgMkiMtLZSD9wK7DeE29sBeLoHgN+B3jNSL6qHqj3sgNekE1VP1PVwxPyLsR134rjVHW9qrbW3fXH439tZ1S1CjjcdsZRqvo1rqsHvYaq7lTVZe7npbh+8P2oi0IbZ1JVLXO/DHY/HP++AxCRJOAs4DlPvL8ViCMQkWlAgaqudDpLQyLy/0QkH7gU7ziCqO9q4BOnQ3iZxtrOOPpDrz0QkVRgMLDI4SiHT+OsAPYAc1XV8Uxuf8P1S2ydJ968XbfaOF4i8jnQtZFVfwDuwHV6qc0dLZeqvq+qfwD+4G6JfjNwt9OZ3Nv8Adcpglc9nacluUz7IyKRwH+BXzQ4anaE+z6sQe7xtXdFZKCqOjp2IyJnA3tUdamIjPfEZ/h1gVDV0xpbLiInAD2Ble7OrUnAMhEZrqq7nMrViFeBj2mDAtFUJhG5EjgbOFXb8OaaFvxdOclax7SAiATjKg6vquo7TuepT1X3i8g8XGM3Tg/ujwKmisiZQBgQLSKvqOplrfUBdoqpEaq6WlUTVDVVVVNxnRIY0hbFoSkikl7v5TRgg1NZDnNPDPU7YKqqljudxws1p+2MAdzt/p8H1qvqo07nARCR+MNX5olIOK45bhz/vlPV36tqkvtn1HRcrYparTiAFYj26CERWSMiq3CdAnP8MkDgSSAKmOu+/PZfTgcCEJFzRWQ7cDLwkYgcdVZCT3EP4B9uO7MeeFNV1zqRpT4ReR3XHCt9RWS7iMx0OhOu34ovBya6/y+tcP+G7KRuwDz399wSXGMQrX5JqTeyVhvGGGMaZUcQxhhjGmUFwhhjTKOsQBhjjGmUFQhjjDGNsgJhjDGmUVYgjPEAEXmusSZzInKliDx5HO9b1vRWxrQOv76T2pjmct/AJararJ43qnqNhyMZ43F2BGHMEYhIqnsOh5dwtVW4U0SWuOe9uNe9TQcR+cg9V8AaEbnIvXz+4TkoROQqEckRkcW4bgQ7/P4visj59V6Xub9GisgXIrJMRFa7G0ca0+bsCMKYo0sHZgDRwPm4WncLMFtExgLxwA5VPQtARGLq7ywi3YB7gaFACTAPWN7EZ1YA56rqAXFNVLVQRGa3ZY8rY8COIIxpylZVXYirrckkXD/clwH9cBWP1cDpIvJnERmjqiUN9h8BzFfVQvdcEG804zMFeMDd2uFzXK3Bu7TOH8eY5rMjCGOO7qD7qwAPqurTDTcQkSHAmcCfROQLVb2vme9dg/uXNBEJAELcyy/FdWQyVFWrRWQLrm6dxrQpO4IwpnnmAFe75ylARBJFJEFcc4KXq+orwCPAkAb7LQLGiUhndxvrC+qt24Lr1BPAVFwzlQHE4OrzXy0iE4AeHvkTGdMEO4IwphlU9TMR6Q8scM8RUgZcBqQBj4hIHVAN3NBgv50icg+urqn7gRX1Vj8LvC8iK4FP+b+jlVeBD0RkNZCFF7SWNv7JurkaY4xplJ1iMsYY0ygrEMYYYxplBcIYY0yjrEAYY4xplBUIY4wxjbICYYwxplFWIIwxxjTq/wM6175005k8VAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(quartet['residual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x216032d8a08>"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sjTvrKSJC0jkUn/u0FzgG+ExE3F7p4szMLHvlTo+9H3gmIjwl1syswZQbFG8A/rOkx0gGtAEi4viKVGVmZjWj3KB4e0WrMDOzmlVWUETEY9N5UkmvAm4A2oEeYHVE/L5Eu5eAB5LV30XE2aPbmJlZZWX1vKaLgTsjYilwZ7JeygsRsTz5ckiYmWUgq6BYBVyXLF8HvDOjOszMbBxZBcWCiHgyWd4NLBijXbOkLkm/kPTOtANK6kzadvX19U1nrWZmDa3cwewJk3QHsLDErktHriT3acQYh3lNROyStAT4iaQHIuK3pRpGxHpgPUBHR8dYxzMzswmqWFBExBlj7ZO0R9IREfFk8sjy3jGOsSv5vkPST4ETgJJBYWZmlZFV19NG4Pxk+Xzg5tENJL0yeUESkuYBbwIeqlqFZmYGZBcUlwFvk/QoxXdwXwYgqUPS1UmbPwa6JP0KuAu4LCIcFGZmVVaxrqc0EdEPnF5iexfwl8nyz4A/qXJpZmY2it97bWZmqRwUZmaWykFhZmapHBRmZpYqk8HsWlQoBD39A+zZm2fB3GbaW1vI5ZR1WWZmmXNQUAyJzVt3s3ZDN/nBAs1NOdatXs6KZQsdFmbW8Nz1BPT0DwyHBEB+sMDaDd309A+M85NmZjOfgwLYszc/HBJD8oMFevflM6rIzKx2OCiABXObaW468KNobsoxf05zRhWZmdUOBwXQ3trCutXLh8NiaIyivbUl48rMzLLnwWwglxMrli3k2DWn0rsvz/w5nvVkZjbEQZHI5cSSttksaZuddSlmZjXFXU9mZpbKQWFmZqkcFGZmlspBYWZmqRwUZmaWKpOgkHSOpK2SCpI6UtqtkPSwpO2SLq5mjWZmVpTVFcWDwLuBu8dqIGkW8DXgTOA44DxJx1WnPDMzG5LVO7O3AUipN7SdBGyPiB1J2+8Bq4CHKl6gmZkNq+UxiiOBx0es70y2mZlZFVXsikLSHcDCErsujYibK3C+TqATYPHixdN9eDOzhlWxoIiIM6Z4iF3AUSPWFyXbxjrfemA9QEdHR0zx3GZmlqjlrqf7gKWSjpZ0KHAusDHjmszMGk5W02PfJWkncApwi6Rbk+2vlrQJICL2AxcCtwLbgA0RsTWLes3MGllWs55uAm4qsf0JYOWI9U3ApiqWZmZmo9Ry15OZmdUAB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZKgeFmZmlclCYmVkqB4WZmaVyUJiZWSoHhZmZpXJQmJlZqqxehXqOpK2SCpI6Utr1SHpAUrekrmrWaGZmRZm8ChV4EHg38PUy2r41Ip6qcD1mZjaGrN6ZvQ1AUhanNzOzCaj1MYoAbpO0RVJn1sWYmTWiil1RSLoDWFhi16URcXOZh3lzROySNB+4XdJvIuLuMc7XCXQCLF68eFI1m5nZwSoWFBFxxjQcY1fyvVfSTcBJQMmgiIj1wHqAjo6OmOq5zcysqGa7niS1SJoztAz8GcVBcDMzq6Kspse+S9JO4BTgFkm3JttfLWlT0mwB8G+SfgX8X+CWiNicRb1mZo0sq1lPNwE3ldj+BLAyWd4BvK7KpZmZ2Sg12/VkZma1Iasb7hpWoRD09A+wZ2+eBXObaW9tIZfz/SRmVrscFFVUKASbt+5m7YZu8oMFmptyrFu9nBXLFjoszKxmueupinr6B4ZDAiA/WGDthm56+gcyrszMbGwOiiraszc/HBJD8oMFevflM6rIzGx8DooqWjC3meamAz/y5qYc8+c0Z1SRmdn4HBRV1N7awrrVy4fDYmiMor21JePKzMzG5sHsKsrlxIplCzl2zan07sszf45nPZlZ7XNQVFkuJ5a0zWZJ2+ysSzEzK4u7nszMLJWDwszMUjkozMwslYPCzMxSOSjMzCyVZz2ZVYkfCGn1ykFhVgV+IKTVM3c9mVWBHwhp9SyrV6H+T0m/kfRrSTdJOnyMdiskPSxpu6SLq1ym2bTxAyGtnmV1RXE78NqIOB54BLhkdANJs4CvAWcCxwHnSTquqlWaTRM/ENLqWSZBERG3RcT+ZPUXwKISzU4CtkfEjoh4EfgesKpaNZpNJz8Q0upZLQxm/xfghhLbjwQeH7G+E3hDVSoym2Z+IKTVs4oFhaQ7gIUldl0aETcnbS4F9gPfnYbzdQKdAIsXL57q4cymnR8IafWqYkEREWek7Zd0AXAWcHpERIkmu4CjRqwvSraNdb71wHqAjo6OUsczM7NJyGrW0wrgE8DZEfH8GM3uA5ZKOlrSocC5wMZq1WhmZkVZzXq6ApgD3C6pW9JVAJJeLWkTQDLYfSFwK7AN2BARWzOq18ysYWUymB0RfzjG9ieAlSPWNwGbqlWXmZkdzHdmm5lZKpUeR65vkvqAx5LVecBTGZYzWa67+uq1dtddXfVaN6TX/pqIaCu1Y0YGxUiSuiKiI+s6Jsp1V1+91u66q6te64bJ1+6uJzMzS+WgMDOzVI0QFOuzLmCSXHf11Wvtrru66rVumGTtM36MwszMpqYRrijMzGwKZnRQSJol6ZeSfpR1LRMhqUfSA8ld611Z11MuSYdLujF5KdU2SadkXdN4JB2TfM5DX3slXZR1XeWQ9DeStkp6UNL1kuri5RaSPpbUvLXWP2tJ10jqlfTgiG2vknS7pEeT76/MssZSxqj7nOQzL0ia0MynGR0UwMcoPv6jHr01IpbX2TS8/wVsjohjgddRB599RDycfM7LgdcDzwM3ZVvV+CQdCawBOiLitcAsis9Dq2mSXgv8FcX3zbwOOEtSySc11IhrgRWjtl0M3BkRS4E7k/Vacy0H1/0g8G7g7okebMYGhaRFwJ8DV2ddSyOQ9ArgLcA3ASLixYh4JtOiJu504LcR8di4LWvDIcDLJR0CHAY8kXE95fhj4N6IeD55ntu/UvzHqyZFxN3A06M2rwKuS5avA95ZzZrKUaruiNgWEQ9P5ngzNiiAr1B8Qm1hnHa1KIDbJG1J3rNRD44G+oD/nXT3XS2p3l7fdi5wfdZFlCMidgFfBn4HPAk8GxG3ZVtVWR4ETpXUKukwis92O2qcn6k1CyLiyWR5N7Agy2KqYUYGhaSzgN6I2JJ1LZP05og4keL7wj8q6S1ZF1SGQ4ATgSsj4gRggNq8JC8peZT92cD/ybqWciT94qsoBvSrgRZJ78+2qvFFxDbgS8BtwGagG3gpy5qmInmXzoyfOjojgwJ4E3C2pB6K79r+U0nfybak8iX/WyQiein2l5+UbUVl2QnsjIh7k/UbKQZHvTgTuD8i9mRdSJnOAP49IvoiYhD4AfDGjGsqS0R8MyJeHxFvAX4PPJJ1TRO0R9IRAMn33ozrqbgZGRQRcUlELIqIdordCT+JiJr/3xaApBZJc4aWgT+jeLle0yJiN/C4pGOSTacDD2VY0kSdR510OyV+B5ws6TBJovh51/zkAQBJ85PviymOT/xzthVN2Ebg/GT5fODmDGupikzeR2GpFgA3Ff/2OQT454jYnG1JZftvwHeTbpwdwAczrqcsSSC/Dfhw1rWUKyLulXQjcD/F987/kvq5Y/j7klqBQeCjtTzpQdL1wGnAPEk7gc8ClwEbJH2I4lOqV2dXYWlj1P008FWgDbhFUndEvL2s4/nObDMzSzMju57MzGz6OCjMzCyVg8LMzFI5KMzMLJWDwszMUjkozMwslYPCzMxSOSjMKkzS50a+d0HSP0j6WIYlmU2Ib7gzqzBJ7cAPIuJESTngUeCkiOjPtjKz8vgRHmYVFhE9kvolnUDxES2/dEhYPXFQmFXH1cAFwELgmmxLMZsYdz2ZVUHyoMQHgCZgaUTU7TsYrPH4isKsCiLiRUl3Ac84JKzeOCjMqiAZxD4ZOCfrWswmytNjzSpM0nHAduDOiHg063rMJspjFGZmlspXFGZmlspBYWZmqRwUZmaWykFhZmapHBRmZpbKQWFmZqn+PwsjxBcL2IlzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=quartet,x='y',y='residual')\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "quartet = pd.read_csv('anscombes_quartet2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "quartet.columns = ['x','y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x21603475dc8>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y = 3.00 + 0.500x\n",
    "quartet['pred_y'] = 3 + 0.5 * quartet['x']\n",
    "quartet['residual'] = quartet['y'] - quartet['pred_y']\n",
    "\n",
    "sns.scatterplot(data=quartet,x='x',y='y')\n",
    "sns.lineplot(data=quartet,x='x',y='pred_y',color='red')\n",
    "plt.vlines(quartet['x'],quartet['y'],quartet['y']-quartet['residual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='residual', ylabel='Density'>"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(quartet['residual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x21603410fc8>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=quartet,x='y',y='residual')\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "quartet = pd.read_csv('anscombes_quartet4.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.0</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       x      y\n",
       "0    8.0   6.58\n",
       "1    8.0   5.76\n",
       "2    8.0   7.71\n",
       "3    8.0   8.84\n",
       "4    8.0   8.47\n",
       "5    8.0   7.04\n",
       "6    8.0   5.25\n",
       "7   19.0  12.50\n",
       "8    8.0   5.56\n",
       "9    8.0   7.91\n",
       "10   8.0   6.89"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = 3.00 + 0.500x\n",
    "quartet['pred_y'] = 3 + 0.5 * quartet['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "quartet['residual'] = quartet['y'] - quartet['pred_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x216035bf808>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=quartet,x='x',y='y')\n",
    "sns.lineplot(data=quartet,x='x',y='pred_y',color='red')\n",
    "plt.vlines(quartet['x'],quartet['y'],quartet['y']-quartet['residual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='residual', ylabel='Density'>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(quartet['residual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x21603641688>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=quartet,x='y',y='residual')\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Residuals\n",
    "\n",
    "It's also important to plot out residuals and check for normal distribution, this helps us understand if Linear Regression was a valid model choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions on training and testing sets\n",
    "# Doing residuals separately will alert us to any issue with the split call\n",
    "test_predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If our model was perfect, these would all be zeros\n",
    "test_res = y_test - test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x216036b5308>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=y_test,y=test_res)\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2160370e708>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(test_res,bins=25,kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still unsure if normality is a reasonable approximation? We can check against the [normal probability plot.](https://en.wikipedia.org/wiki/Normal_probability_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure and axis to plot on\n",
    "fig, ax = plt.subplots(figsize=(6,8),dpi=100)\n",
    "# probplot returns the raw values if needed\n",
    "# we just want to see the plot, so we assign these values to _\n",
    "_ = sp.stats.probplot(test_res,plot=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------\n",
    "\n",
    "## Retraining Model on Full Data\n",
    "\n",
    "If we're satisfied with the performance on the test data, before deploying our model to the real world, we should retrain on all our data. (If we were not satisfied, we could update parameters or choose another model, something we'll discuss later on)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how it may not really make sense to recalulate RMSE metrics here, since the model has already seen all the data, its not a fair judgement of performance to calculate RMSE on data its already seen, thus the purpose of the previous examination of test performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deployment, Predictions, and Model Attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model Fit\n",
    "\n",
    "Note, we can only do this since we only have 3 features, for any more it becomes unreasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = final_model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(nrows=1,ncols=3,figsize=(16,6))\n",
    "\n",
    "axes[0].plot(df['TV'],df['sales'],'o')\n",
    "axes[0].plot(df['TV'],y_hat,'o',color='red')\n",
    "axes[0].set_ylabel(\"Sales\")\n",
    "axes[0].set_title(\"TV Spend\")\n",
    "\n",
    "axes[1].plot(df['radio'],df['sales'],'o')\n",
    "axes[1].plot(df['radio'],y_hat,'o',color='red')\n",
    "axes[1].set_title(\"Radio Spend\")\n",
    "axes[1].set_ylabel(\"Sales\")\n",
    "\n",
    "axes[2].plot(df['newspaper'],df['sales'],'o')\n",
    "axes[2].plot(df['radio'],y_hat,'o',color='red')\n",
    "axes[2].set_title(\"Newspaper Spend\");\n",
    "axes[2].set_ylabel(\"Sales\")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residuals\n",
    "\n",
    "Should be normally distributed as discussed in the video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals = y_hat - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x216039d7ac8>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=y,y=residuals)\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.04576465,  0.18853002, -0.00103749])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "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>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>0.045765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radio</th>\n",
       "      <td>0.188530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newspaper</th>\n",
       "      <td>-0.001037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Coefficient\n",
       "TV            0.045765\n",
       "radio         0.188530\n",
       "newspaper    -0.001037"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(final_model.coef_,X.columns,columns=['Coefficient'])\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the coefficients:\n",
    "\n",
    "---\n",
    "* Holding all other features fixed, a 1 unit (A thousand dollars) increase in TV Spend is associated with an increase in sales of  0.045 \"sales units\", in this case 1000s of units . \n",
    "* This basically means that for every $1000 dollars spend on TV Ads, we could expect 45 more units sold.\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "* Holding all other features fixed, a 1 unit (A thousand dollars) increase in Radio Spend is associated with an increase in sales of  0.188 \"sales units\", in this case 1000s of units . \n",
    "* This basically means that for every $1000 dollars spend on Radio Ads, we could expect 188 more units sold.\n",
    "----\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Holding all other features fixed, a 1 unit (A thousand dollars) increase in Newspaper Spend is associated with a **decrease** in sales of  0.001 \"sales units\", in this case 1000s of units . \n",
    "* This basically means that for every $1000 dollars spend on Newspaper Ads, we could actually expect to sell 1 less unit. Being so close to 0, this heavily implies that newspaper spend has no real effect on sales.\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note! In this case all our units were the same for each feature (1 unit = $1000 of ad spend). But in other datasets, units may not be the same, such as a housing dataset could try to predict a sale price with both a feature for number of bedrooms and a feature of total area like square footage. In this case it would make more sense to *normalize* the data, in order to clearly compare features and results. We will cover normalization later on.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "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>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     radio  newspaper     sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "radio      0.054809  1.000000   0.354104  0.576223\n",
       "newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction on New Data\n",
    "\n",
    "Recall , X_test data set looks *exactly* the same as brand new data, so we simply need to call .predict() just as before to predict sales for a new advertising campaign.\n",
    "\n",
    "**Our next ad campaign will have a total spend of 149k on TV, 22k on Radio, and 12k on Newspaper Ads, how many units could we expect to sell as a result of this?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "campaign = [[149,22,12]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.893032])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model.predict(campaign)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How accurate is this prediction? No real way to know! We only know truly know our model's performance on the test data, that is why we had to be satisfied by it first, before training our full model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "## Model Persistence (Saving and Loading a Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sales_model.joblib']"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(final_model, 'sales_model.joblib') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_model = load('sales_model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.893032])"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(campaign)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Up next...\n",
    "### Is this the best possible performance? Its a simple model still, let's expand on the linear regresion model by taking a further look a regularization!\n",
    "\n",
    "-------\n",
    "--------"
   ]
  }
 ],
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