bestseller Task2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('bestsellers with categories.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Name</th>\n",
       "      <th>Author</th>\n",
       "      <th>User Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10-Day Green Smoothie Cleanse</td>\n",
       "      <td>JJ Smith</td>\n",
       "      <td>4.7</td>\n",
       "      <td>17350</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>Non Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/22/63: A Novel</td>\n",
       "      <td>Stephen King</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2052</td>\n",
       "      <td>22</td>\n",
       "      <td>2011</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
       "      <td>Jordan B. Peterson</td>\n",
       "      <td>4.7</td>\n",
       "      <td>18979</td>\n",
       "      <td>15</td>\n",
       "      <td>2018</td>\n",
       "      <td>Non Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1984 (Signet Classics)</td>\n",
       "      <td>George Orwell</td>\n",
       "      <td>4.7</td>\n",
       "      <td>21424</td>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5,000 Awesome Facts (About Everything!) (Natio...</td>\n",
       "      <td>National Geographic Kids</td>\n",
       "      <td>4.8</td>\n",
       "      <td>7665</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>Non Fiction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name  \\\n",
       "0                      10-Day Green Smoothie Cleanse   \n",
       "1                                  11/22/63: A Novel   \n",
       "2            12 Rules for Life: An Antidote to Chaos   \n",
       "3                             1984 (Signet Classics)   \n",
       "4  5,000 Awesome Facts (About Everything!) (Natio...   \n",
       "\n",
       "                     Author  User Rating  Reviews  Price  Year        Genre  \n",
       "0                  JJ Smith          4.7    17350      8  2016  Non Fiction  \n",
       "1              Stephen King          4.6     2052     22  2011      Fiction  \n",
       "2        Jordan B. Peterson          4.7    18979     15  2018  Non Fiction  \n",
       "3             George Orwell          4.7    21424      6  2017      Fiction  \n",
       "4  National Geographic Kids          4.8     7665     12  2019  Non Fiction  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name           0\n",
       "Author         0\n",
       "User Rating    0\n",
       "Reviews        0\n",
       "Price          0\n",
       "Year           0\n",
       "Genre          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>550.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.618364</td>\n",
       "      <td>11953.281818</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.226980</td>\n",
       "      <td>11731.132017</td>\n",
       "      <td>10.842262</td>\n",
       "      <td>3.165156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>4058.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>8580.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>17253.250000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.900000</td>\n",
       "      <td>87841.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       User Rating       Reviews       Price         Year\n",
       "count   550.000000    550.000000  550.000000   550.000000\n",
       "mean      4.618364  11953.281818   13.100000  2014.000000\n",
       "std       0.226980  11731.132017   10.842262     3.165156\n",
       "min       3.300000     37.000000    0.000000  2009.000000\n",
       "25%       4.500000   4058.000000    7.000000  2011.000000\n",
       "50%       4.700000   8580.000000   11.000000  2014.000000\n",
       "75%       4.800000  17253.250000   16.000000  2017.000000\n",
       "max       4.900000  87841.000000  105.000000  2019.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      52\n",
       "6      38\n",
       "9      38\n",
       "5      36\n",
       "11     35\n",
       "4      32\n",
       "14     30\n",
       "13     29\n",
       "10     28\n",
       "12     27\n",
       "7      23\n",
       "15     21\n",
       "16     20\n",
       "17     19\n",
       "20     17\n",
       "18     14\n",
       "0      12\n",
       "46     10\n",
       "21      9\n",
       "28      6\n",
       "22      6\n",
       "40      5\n",
       "23      5\n",
       "24      5\n",
       "2       5\n",
       "30      5\n",
       "19      4\n",
       "27      4\n",
       "25      2\n",
       "32      2\n",
       "105     2\n",
       "82      1\n",
       "36      1\n",
       "39      1\n",
       "42      1\n",
       "52      1\n",
       "53      1\n",
       "3       1\n",
       "54      1\n",
       "1       1\n",
       "Name: Price, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Price'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Price</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>User Rating</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001729</td>\n",
       "      <td>-0.133086</td>\n",
       "      <td>0.242383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reviews</th>\n",
       "      <td>-0.001729</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109182</td>\n",
       "      <td>0.263560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>-0.133086</td>\n",
       "      <td>-0.109182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.153979</td>\n",
       "    </tr>\n",
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       "      <th>Year</th>\n",
       "      <td>0.242383</td>\n",
       "      <td>0.263560</td>\n",
       "      <td>-0.153979</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "             User Rating   Reviews     Price      Year\n",
       "User Rating     1.000000 -0.001729 -0.133086  0.242383\n",
       "Reviews        -0.001729  1.000000 -0.109182  0.263560\n",
       "Price          -0.133086 -0.109182  1.000000 -0.153979\n",
       "Year            0.242383  0.263560 -0.153979  1.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IeBUgInZJGpGOWaT9t/W/rfbAiJhPEWAYNuzYTlgdZ2aHiE6YHO+kwLEnIiaWM9KH/ysNyos3L4keBLxUW4+ZWVV0wiNHOnWOoxk/Bj7Xs/pK0qiI+A2wXdLFKU+SJrSzkWZmZd1qPrXLIRs4ImIZsBhYk4a4ZqddlwKfl7QO2ARMa08LzczezJPjLRQRb5qhjoj7gfsblYuIm4CbavZvB6b0SyPNzA6S5zjMzCxLJ6zGceAwM6sQP6vKzMyydMKqKgcOM7MK6e6AwSoHDjOzCvHkuJmZZal+f+MQvo/DzKwTtfo+DklTJG2RtFXSnDr7L5W0PqVVzdwU7R6HmVmF7FPr+hzp6yZuBc4FdgCrJS2OiM2lYtuBj0bEi+nhsPOByb3V6x6HmVmFREZqwiRga0Rsi4i9wO3UPC0jIlZFxItp8xHgmL4qdeAwM6uQnKEqSTMlrSmlmTXVjQaeKW3vSHmNfB74t77a6KEqM7MKyVmOW/4KiAbq3U5Y9w0kfYwicJze1/s6cJiZVUiLV1XtAMaUto8BdtYWkvRB4NvA1Ih4oa9KPVRlZlYhLV5VtRoYJ+k4SUOB6RRPDX+dpGOBu4DPRMTPm6nUPQ4zswrpamGfIyL2SboCWA4MBhZExCZJs9L+ecC1wDuBuenL8fZFxCm91evAYWZWIa2+czwilgJLa/LmlV7/BfAXOXU6cJiZVUh0wL3jDhxmZhXiZ1WZmVkWPx3XzMyyVD9sOHCYmVXKvg4IHQ4cZmYV4snxQ8B1R53Z7iZUxvDq/zwPmOG/d0a7m1AZe3aubHcTDimeHDczsyzucZiZWRb3OMzMLEtXuMdhZmYZfB+HmZll8RyHmZll8RyHmZll8VCVmZll8VCVmZll8aoqMzPL4qEqMzPL4slxMzPL4jkOMzPL4qEqMzPLEp4cNzOzHF3ucZiZWQ4PVZmZWRYPVZmZWRb3OMzMLIuX45qZWRY/csTMzLJ4qMrMzLJ0QuAY1O4GmJnZfhHRdGqGpCmStkjaKmlOnf2SdHPav17Sh/uqs+MCRzrJByVNLeV9UtKydrbLzKwVuommU18kDQZuBaYC44FPSRpfU2wqMC6lmcA/9FVvxwWOKMLsLOBvJQ2TdARwA3D5gdSXLqyZWSVExn9NmARsjYhtEbEXuB2YVlNmGvDdKDwCHCnp6N4q7bjAARARG4F7gGuALwO3AV+UtFrSE5KmAUgaK2mlpJ+ldFrKP0vSCknfAza06zzMzGp1RXfTSdJMSWtKaWZNdaOBZ0rbO1Jebpk36OTJ8a8APwP2AkuA+yLic5KOBB6T9BPgOeDciPgvSeOA7wOnpOMnASdFxPaBb7qZWX05d45HxHxgfi9FVO+wAyjzBh0bOCLiFUl3ALuBTwLnS5qddg8DjgV2ArdImgh0ASeUqnisUdBIUXsmwCdGTWLSiHH9cxJmZjVavKpqBzCmtH0Mxedibpk36MihqpLulARcGBETUzo2Ip4E/jfwH8AEip7G0NKxrzSqNCLmR8QpEXGKg4aZDaQWz3GsBsZJOk7SUGA6sLimzGLgsrTw6A+BlyPi2d4q7fTA0WM5cKUkAUj6UMofCTwbEd3AZwBPhJtZpXVHNJ36EhH7gCsoPiOfBH4QEZskzZI0KxVbCmwDtgL/BPyvvurt2KGqGtcD3wLWp+DxFPCnwFzgTkkXAyvopZdhZlYFrX5WVUQspQgO5bx5pddB5qrUjg4cEXFdafMLdfb/AvhgKev/pPz7gfv7sWlmZgekK7rb3YQ+dXTgMDM71DQzBNVuDhxmZhXix6qbmVkW9zjMzCyLexxmZpalK7ra3YQ+OXCYmVVIziNH2sWBw8ysQjrhi5wcOMzMKsQ9DjMzy+JVVWZmlsWrqszMLIsfOWJmZlk8x2FmZlk8x2FmZlnc4zAzsyy+j8PMzLK4x2FmZlm8qsrMzLJ4ctzMzLJ4qMrMzLL4znEzM8viHoeZmWXphDkOdUJ0M5A0MyLmt7sdVeBrsZ+vxX6+FgNnULsbYE2b2e4GVIivxX6+Fvv5WgwQBw4zM8viwGFmZlkcODqHx27387XYz9diP1+LAeLJcTMzy+Ieh5mZZXHgMDOzLA4cB0DSWEkba/KukzS7H96rS9JaSRsl3SPpyD7KT5R0Xmn7AklzWt2uXLnn0Us9X5V0ToubVyk112qRpMMblFs10G0baCo8KGlqKe+Tkpa1s11vdQ4cFSKp3p38eyJiYkScBOwCLu+jmonA64EjIhZHxE2ta+UByz2PuiLi2oj4SWubVjnla7UXmFXeKWkwQESc1o7GDaQoJmFnAX8raZikI4AbOMCfn55rZwfHgaMfSLpK0mZJ6yXdnvKOkLRA0mpJT0ialvJnpL8q7wF+3EfVDwOj03GTJK1Kda2S9D5JQ4GvApekv1gvSfXfko5ZKOnmVH6bpItS/iBJcyVtkrRE0tKeff2kfB7HS1om6XFJKyWdKGmkpKckDUplDpf0jKQh6Rx62n2ypJ+mY5dLOlrSeyQ9nvZPkBSSjk3bv0x1XZz+ml8n6YF+PM9WWAn8vqSzJK2Q9D1gA4Ck3T2FJF0taUM6p5tS3puubXtO4eBExEbgHuAa4MvAbcAX6/wujU3n+bOUTkv5b7p2dpAiwikzAWOBjTV51wGz0+udwNvS6yPTv18DPt2TB/wcOAKYAewARjV4r93p38HAImBK2v4d4LD0+hzgzvR6BnBL6fjXt4GFqY5BwHhga8q/CFia8n8XeBG4qMXXrNF5/DswLr2eDNyXXv8I+Fh6fQnw7dI5XAQMAVYB7y6VWZBeb0rX5wpgNXAp8F7g4bR/AzC6/P+nSql0rQ5L1+F/AmcBrwDH1Sk3NV2Lw9P2qN6ubSem9LuyJf2/u7HB79LhwLCUPw5Yk16/6do5HVzyQw4PTKM1zD3564F/lXQ3cHfK+2PggtI8yDDg2PT63ojY1aDO4ZLWUgSrx4F7U/5I4F8kjUvvO6TJtt8dEd3AZklHpbzTgUUp/9eSVjRZV443nYekEcBpwCJJPeXelv69gyIYrACmA3Nr6nsfcFKqB4qA9Gzatwr4CHAmRcCeAojir3eAh4CFkn4A3NWqE2yhnmsFRZu/Q3GdHouI7XXKnwP8c0S8ChARu/q4th0nIl6RdAewG/gkcH6d36WdwC2SJgJdwAmlKhpdOzsADhwH5gXgHTV5o4CeH8yPU3xoXQB8SdL7KT64LoyILeWDJE2m+GuokT0RMVHSSGAJxdjuzcD1wIqI+ISkscD9Tbb9t+W3r/m3P9U7j4XASxExsU75xcCNkkYBJwP31ewXsCkiTq1z7ErgDIpexo8ohjgivS8RMStd948DayVNjIgXDvL8WmlP7TVJH/6Nfk7Em/+YGUTja9upulNq9Lt0HfAfwASK8/+v0u7efscsk+c4DkBE7AaelXQ2QPpwmwI8mMblx0TECuBqiq70CGA5cKXSJ4CkD2W+58vAVcBsSUMoehy/SrtnlIr+J/D2zFN6ELgwzXUcRdG17xfl8wD2ANslXQyvr6CZkMrtBh4D/g5YEhFdNVVtAd4t6dR07JAUoAEeAD4N/CL1onZRLBh4KJU9PiIejYhrgeeBMf11vgPkx8DnlFZfSRoVEb+hwbU9BDT6XRoJPJv+n3+Gohdq/cCB48BdBvxVGlK4D/hKRPyS4of1NkkbgCeAb0bESxQ9hCHAehVLea/PfcOIeAJYRzF089cUf5E/xBt/QVYA45Umx5us+k6KeZaNwD8CjwIv57avWTXncSnweUnrKOYmppWK3kERAO6oU8deirmOr6dj11IMzRART6ViPRPfD1L89f1i2v5GmkjemMqsa9nJtUFELKPooa1JP489Qzi9XdtO1uh3aS7wPyQ9QjFM5V5GP/EjRwwASSMiYrekd1L8pf+RiPh1u9tlZtXjOQ7rsUTFTXlDgesdNMysEfc4zMwsi+c4zMwsiwOHmZllceAwM7MsDhxmZpbFgcPMzLL8fy2FtrUDT+t1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Author', 'User Rating', 'Reviews', 'Price', 'Year', 'Genre'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x173943521c8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Price',y='Genre',data=df)# 20 dollar has maximum  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1739443d488>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xgtcbxJ4+Z8zgoaPBDoOBTnNKSP495laa0D8xiRFXAO5ACGYdQ0hg0GkYJn1CrH1wCBAEBrNOjQoTw+gkh8sbhNsXRLVZB38oBBVTYdwbQI1VD38wCKNWh0UOC94dGk9oQyoVS6hDvUWX8Hh6VqUex0Yyay8zabxRqCYehVqv6eLyePFO3OPwxfVm2PL/+D2F5JSjJfVmWDIoV+ppF0q3p3T/lB5PgIJigigo/P4QntnXjzG3H25/KMGIY8u6Ntzz1wM4MuJJeT+v2ojrPtSCrU9MmSfEGyX8/HNnYM/xKR3N6CSphgoB//bgmwnL/AE/btq+G3d/9nRUGNV4p8+TZMrQhuMnJ/DVR4/ERO2f3juYYHbxs389HUOuQOL2NrRjfXsDXtg/lGKAcMFpdXhu38C0jBG83iB27ulLEW1f39FAgfEpImZU8T+f6ECFSYN+lx+DYxOYX2uDSafCSXcwpX3s6hnG6fNqUG/T4ciIB9975p2pdry2Dfe8NNWOb9nYjtcPHU8w54gurzBpcf1v34I3IOCac+ZImspItZeZNN4oVBOPQq3XdHF5vHhGxIxhTbtDMjDOtfmDmOHNtvXt+Fi7QzIwLnXTD6XbU7p/So9nFEqfIIgCovOEEwcGJzDs9qcYcWx9ImyUIPZ+7dLGWEAc/TzeKEGnFjfX0KjUKcsW1ldGyqggCCoRU4ZunNNalyBqn2x2oWaq1O093oU9fU5RA4TuNMtPxRhhT59TVLR9T4GZbhQDYkYV3/zjHqhVKmx9ohtnt9ThwJAbZr1OtH1sPGMutj7RDbVKFQuIo59vfTKxHd+8oyvFnCO6vPO4M7ZMzlRGqr3MpPFGoZp4FGq9pss7acwY3ikw8wcxw5vNO7uwV6ZcqZt+KN2e0v1TejyjUFBMEAVEf8S8Q8ooQey9lMEHAAyNiwuaD0/4UpYNRMwITroDaY0/ooYFUVH05HVOpjFj6E8jrJ7OMONUjBHSfXemou3EFOmMKqK/6+B4uJ2ma1cjE76E9ZM/T27HwxPi3xNvMCNnKiPVXmbSeKNQTTwKtV7TpVjMH6hccZeLQkExQRQQDRHzDimjBKn36dZPNj+Ifl5j0acsq4uYEVSZtbKGBVFR9OR10pkx1KcRVk9nmHEqxgjpvjtT0XZiinS/e/R3jZrMpGtX1RZ9wvrJnye32xqL+PfEP/WXM5WRai8zabxRqCYehVqv6VIs5g9UrrjLRaGgmCAKiI5ZdiyotaDarEsx4tiyLmyUIPb+id292LIu0fwg3ijBHxI31wgKoZRl7/WPRsoIUDFBxJShDX/fP5Agap9sdhHiQur2NrSjo8EuaoDQ1mCbtjFCR4NdVLS9o8BMN4oBMaOK//lEB0KCgC3r2vDq/gEscJjh9vlF28eON49iy7o2hAQB31izOLEdr01sx7dsbMeOJHOO6PKls+2xZXKmMlLtZSaNNwrVxKNQ6zVdFqcxY1hcYOYPYoY329a3Y4lMuVI3/VC6PaX7p/R4RiGdYoKIUCg6xSnqE/4Qqiw6qCNqEx5/CI5k9QmrHjrNlPpErU0PFSLqE2YdKkyp6hOAAGsa9YlZdiMqzVq4PAEYT0F9It5IIao+EZ0JvzRJfSLZAGEmjBGi6hPR/SH1CQAK22Ly75Gp+gQgIJSR+kQQ1RYdgqEgDFotFjmseHdoPKENRdUnonUQU584fjKz9jKTxhuFauJRqPWKQ5FOMalPiEPqE+JkeDzJvAOgoJhIT6EExQQxw1BbJAoFMu8gCgXRtlj2QyhEeXHFdTegb1hcjWDve+/hnAtyXCGCIAiCIAoCCoqJsqJv2Ck6GgwA/q4bclybVIJBAe/0uzDhD4IxIBDkscfMWjUQDAHeQBAWvRbjviB8AQGVZi1G3AFUmbQw6tQY8/hRbdbB5QlhMPJIO5rmAKQX+E824FhYY8aBETeCghBREvCjxqqDzaBBU4UZ3QNho4Vamx4VRjUW1Nig0WQ2TUHM7CPTskRuiP+NHBY9VCqgzqbD8VEfBlw+LGkwYcQdgtsfhFmnwfCEDzUWPTz+IHQaNarMGjg9IQyN++Cw6qFRceg0Gkz4wssaKwxQMWBo3A+9VgWbQYOgAIxO+mA36jA6GUClSQsOAQwqNFTo0WhPTQcoVcMKYopcP7bPdbqG0jQBpfVUelzcHh+6+ydi5drqLTAb5SewKa2nxxPAnn7XVDpc3HVMCjLvIIgSIBgU8HR3H0bdfqhUDOPeYEyreF61EV86vwU/efEArvrAfEwGEo09/vMjC/Hfrx/BNy9aDItejT3HXUmGCu1Y114PvV4jKvD/kUW12LnnRIIBx7YN7aiz6XBizJdgCvKTz56eYgSydX0bTji9OHdBrWxwGwwK2LG7N2Fbt2xsx8ZljRQYFwhiv9GtG9tx7KQHX3usEzetbobHH8T2XUfxqTPmYuuTUwYwW9a24eDgWIrRxi0b26FVM3z9sT2oNOlwzbnz8cNn350ql2ROs2lVKx7edRRfOr8Fu3qGsaKpBk0OD1bMrYkFvaVqWEFMkWvTiFybhSg1qVBaT6XHxe3x4U9dgynlPt5eKxkYK62nxxPAE139KeXWtddLBsZk3kGULVdcdwMuvPhy0dcV1+V/1PdU6e5zYn/EuGNw3Jdg3rF2aSM27wybHoxMphp7/Pgv72Ht0kYEQ4BapRYxVOjCnn5XWoH/zhPOFAOOzY93QadWp5iCaNWpxhxbdnZDzVTozsAoo7svdVs37+jKqCyRG8R+o//a0YUDQxPwBgQsn1uDzTu7cfkHmrH1ycT2sfXJblGjjZt3dOHgkBvegIBPnjE7FhDHyiWZ09z5wv5Yuw+be3QjFGIJZhSlalhBTJFr04hcm4UoNalQWk+lx6W7f0K0XHf/RFbquaffJW7G1O+SLDdd8w4aKSaKFqlUiL7nfpbj2kyfPqc3wawgXoA8as7BmLSxh9sXhMB5WvHykCD+WX8a4X8xU4V0hgyjkwF4/CEsmyO/n6J1cHplyxK5Id1vFG2fUVMXjy+Y1lBDqryc2Uz8e29gygxkeMIHxoBmhyVcDwnDiug6RHFTLOYPVK64y0WhkWKCKBAa7MaYcUc68w5A2tjDbNCkNVSos+nTCvw3pFkuZqqQzpCh0qRFvV0+TyydUUcmZYnckO43imYkRNuRSa8RXc+RxmgjPqMhXRtOfm/QTpmB1Fj0CWYUpWpYQUxRLOYPVK64y0WhoJggCoS2BhtaIsYdDqs+wbzjid292La+DU/s7kWVKdXY4z8/shBPdvZCowJCQkjEUKEdHfW2tAL/HbPsKQYc2za0wx8KpZiCBEKpxhxb17chxAW0ZWCU0dZgS9nWLRvbMypL5Aax3+jWje1ocVhg0Krw9pFhbFvfhgdeOYQtaxPbx5a1bfiLiNHGLRvbscBhhkGrwmNvHMdXL1yUWC7JnGbTqlY82dkbMwPZtr4NajVPMKMoVcMKYopcm0bk2ixEqUmF0noqPS5t9RbRcm310k9klNazo94mbsZUb5MsR+YdpwjpIJYO6XSFgfTawlJl/u/2G/Dhm+7K+LtkUKQNG1WfcPuDQER9YsIXRJVJB40GCIUAXzAIk06LiYj6RIVJi5ORmfqmOPWJcc+U4YaY+kSywH+yAcfCGkuq+oRFB5txSn2i3+mFw6pHhUmZ+kS8UQNNsssaittiVH2ixqKHRgXUplOf0Gow7JZQn7DooVGH1SfcEfWJWXYDVKop9QmrQYOQAIxO+mE3aDHqiVOf4Co0VEqrTxSwYQUxhSKdYlKfmNl6kvoEANIpJojCR6NRoX12RVa3oVIxNDssKTmXGo0Ky+ZUJuT1dkjUZUVTleI6iG2LKCzS/UazKqbazYIc10mMdO2ZKB0qjAasnH/qKTF2heVsCssprac1x/VUelzMRj1Wzs8sDSEepfU0GrVYOb/6lMuZjDpF5QBKnyAIgiAIgiCIwh8pZoypAewC0Ms5X5v02fkAHgdwOLLoD5zzbTmtIEHMIILA0TM8gcEJH1yeICrNWviDAty+ICpNOoz7ArAatKgwquH0COhzejG3ygitBhiZCGLCG0SDXQ9fUIDLG4TdqEWtVY9ZNiP2DbhSzDKSjQ/mVppwdHQy9r7BokdXvwv9kcdQDRV6NFjD60z4AgiFOMYjCgTza8xY4LDQo+sSIrl9NFj0eGdwHC5fEN5ACPWRSS3OyRDGvAFUGLVwevywG3UxU4/o+0AoBK1aHTP5aKzU48SoL9a2AAFqpoZKBagYw6Q/lGLGIWbUAWDGzDuUPuYlsmvIo/QxutLH/Uq3N+HxYm9c+1lSb4Yli2kJStMuvN4g9vQ50e/yod6mR0eDHQaDfDjo94fQecKJfpcXDTYDOmbZodOpZcspPZ65ridQBEExgBsB7AOQLrv65eRgmSCKEUHgeP6dARw9OYkfPvsuKk06XP7+eSkmHb99/Qiu/3Ar7v6//bAbtPjC+QvQMzKJO57fL1rmqxcugsOqx9ce7UwwUljfMQt/eXcwpvM6r9qIG1a1xvRpLz2zASuaHEki6G2YVenGzrd6cU5rLU6MeRO2ddsly3FROxknlALJxhgXLKnB2qWz0TvmSfjNt61vw/ZdR7FqcT3ufGHKbOa681piph7zqo344vkt2JJgKBMut+uIMzZZ842eYZw+tyamfRxvxgEgxajj7s+eDn+Qz4h5h1KTASK7hjxKTRyUmk0o3d6Ex4unRNrPx9odkoGx0noqNf3weoPYuacvpdz6jgbJgNPvD2FH5wlsfjzR4Gnj0lmSAafS45nrekYp6PQJxthsAB8HcF++60IQ2aZnxI09vc6YqcEnz5id1qTj2493Ye3SRlxz3gLs6x+PrSdW5ofPvosDgxMpRgqdJ5wJxgdrlzYmGDaEDROSRdC7oWZqbDxjLg4OuVO29eVHyDihVEg2xviXs+fjwNBEym8eNfGIBsRAuC3Fm3qsXdoYC4iTy0Xfb4mYdCSbgUTNOMSMOjqPO2fMvEOpyQCRXUMepSYOSs0mlG5vb5r2szdLphhKTT/29DnF90/mt+o84YwFmrFyj4evI5LbU3g8c13PKAUdFAP4fwC+BkCQWOf9jLHdjLGnGWNtYiswxq5ljO1ijO0aGhrKRj0JIiOk2uKAy5tgzCFlcBD96/EFMyojJInMeAOphh3JZYfHxUXQhyd8GJ7wpTURGRz3nsIRIfKF3Hkx2Rhj1B1I+5t7/EHJtpSuXXr8wYT3YmYx0TYlZtQxk21wuqL/5YyUIY8c8u2wOMwfiqVcv+Jy4r/xgEv6Ny6WekYp2KCYMbYWwCDn/A2J1d4EMI9zvgzAXQB2iK3EOb+Xc76Cc77C4XDMfGUJIkOk2mKdzZBizJHO4CD616TXZFQm+UmyQatCfRrjgyjpTEBqLHo4LPq0JiJknFAcyJ0Xk40xqszatL+5SSdu4iH33qjTJLx3iJjFRNuUmFHHTLbB6Yr+lzPTMeSRb4fFYf5QLOXqFZZLZ/BUZ5P+jYulnlEKNigGcA6A9YyxHgC/B7CKMfa/8Stwzl2c84nI/08B0DLGanJeU4KYAZqqzWhvtMdMDR5743hak47vbmjHk529+MVLB7G43hpbT6zMVy9chJZaS4qRwtJZ9gTjgyd29yYYNvzxzaMiIuhtCPEQ/vjmUTQ7zCnbuu0SMk4oFZKNMf73tcNY4LCk/OZRE49NqxLNZuJNPZ7Y3YutKYYybXjwlUOx91vXt+GPbx5NMQOJmnGIGXV0zLbPmHmHUpMBIruGPEpNHJSaTSjd3pI07WdJlkwxlJp+dDTYxfdP5rfqmGXHtg2pBk9LZ8mUU3g8c13PKEVh3hFRmfiKiPpEPYABzjlnjK0E8CjCI8dpd4rMO0qHUjTviFefGPcEURFTnwih0qQNq0/otagwhdUn+p1ezKk0QqsFTk4EMe4Lot6qRyAkwOkNosKohSNOfSLZLCPZ+CCqPhF9n6A+YdWjoXJKfcLtCyBI6hPFgKK2CKQaY8SrT/gCAmqtOhh1YfUJpzcAu4j6hMsTgM2gRVAIQaNWY2TCh2qzHo1VYfWJAZcPtRH1CRVTQ52B+kS8UQeAGTPvIPUJ5WRoyKPIvIPUJ8SZrvpEbP9OUdUhqvSyNEfqE1mqZ2mYdzDGrgMAzvk9AC4G8AXGWBCAB8BlUgExQRQ6KhVDc60VzbWZWVKeCmJGDGLGB8nvzxIRQSejhPJArH2cPk+5aUsyjRWn1o7SGXXMlHmHUpMBIruGPEpNHJSaTSjdniXHphhKTT8MBo3oeV0OnU6tyLRJ6fHMdT2BIgmKOecvAngx8v89ccvvBnB3fmpFEARBEARBlApFERQTRDnh94dwZHQco+4QBsZ9mFtlRDAkoM/lQ41FB7UK0KnVAAeCXIA/yDEy4Y94vNug1apxcNiFsckQBsd9aLDrodOoYNFr0VRthiBwdPc5MeL2wWbQwR8UUG+Xfuwcb5rQYDcgJACD49M3SyAKn3RC+JMeP7r6x8PpD1Y9qsxqOD0h9DvD6RDBUAjVFh1cnlDMoMNuVGNehQUnxr0YcfvAwDDi9qHKpIM3GIRJq4VKBdiNuqy0K6WPcYn8otSMIb6Nhs+PVpiMOtlyStMZlG5PqUmFUsMUpeXEzHMy6aNKt6f0uCitJ0BBMUEUFH5/CG8dH8GRES827+zGwloLPvO+edj6xJSZwZZ1bag0aeAPhuD284TPtm1ox7xqPXqGvQlGCVvXt+H5fX34l7ObMDTux10v7MenV8yNactKmR7EmziImYMoNUsgCp90QvgXtTlEjAMSzTi+s64NY54gbtq+O7bODz7Vga4TE7jz+fdS2t+WtW147M39+NQZc/HYm0dx1QcXzGi7UmoiQOQXpWYMkx4/nuwaSPm917bXSQaqSs00lG5PqUmFUsMUpeWSzXwyPfcr3Z7S46K0nlEKWX2CIMqOsMC4GpsjAe015y2IBb1AWG9x6xPdCIaACpM+5bPNj3cBXJ1ilLBlZzf+5ez5GPeEcPOOsPFHvNmClOlBvGmCmDmIUrMEovBJJ4QvbhyQaMbxnSe6IQhIWOfAkBv/9cc9ou1v65Ph8tG/M92ulJoIEPlFqRlDV/+46O/d1T8uWU6pmYbS7Sk1qVBqmKK0nJh5TiZ9VOn2lB4XpfWMkvWgmDH2SZHXasZYbba3TRDFRr/Li8HxKfFxjy/RFAEId3K3P4iT7kBa4wKx5WOTAbgj35fOTEHM9CDeNOFUyhHFT3ohfHFh/WQzDnfce2DKbEPKzCP+70y2KzLnKE5ybRpRLGYaSg1TlJYTM8/JpI8q3Z7S46K0nlFyMVJ8NcI2zf8Sef0CwE0A/s4Y+1wOtk8QRUODzYBa65T4uEkvbopg1mlQZdamNS4QW15h0sJs0CToN4qVTSbZNIEMO8qH9EL44sL6yWYcZl3iY854s4105eP/zmS7InOO4iTXphFFY6ah0DBFaTkx85xM+qjS7Sk9LkrrGSUXQbEA4DTO+ac4558CsASAD8D7AHw9B9sniKKhY5YdQAjbIkYHv3jpILasSzQz2LKuDRo1MDbpS/ls24Z2gIVSjBK2rm/Db147DKtBjVs2tuOJ3b0JZgtSpgfxpgli5iBKzRKIwiedEL64cUCiGcd31rVBpUoMghc4zLj1Ex2i7W/L2nD56N+ZbldKTQSI/KLUjKG93ir6e7fXS8tdKjXTULo9pSYVSg1TlJYTM8/JpI8q3Z7S46K0nlGybt7BGNvDOe+Ie88A7OGctzPG3uKcn57VCiRB5h2lQymadwDp1Sf6XT5UmXXQqKfUJ0JcgC/IMeL2o86aqj4xNO6LjWCYk9QnTrp9sBp0CIQE2Rm68aYJ9baw+sTQxPTNEoicoLgtAumF8KMz7QddPjiselSa1HB5w0oTtdaw+kSVRYfxqPqEVQ+7KVV94qTbj0qTFt5gEEatFhoVYCP1iVJFkXmHUtOIYlOfOFWTigwNU2asnJh5zqmoT5zq9pQelwzrmTfzjpcZY08CeCTy/lMAXmKMmQGM5WD7BFFU6HRqtNZVTOs7FtWnL69SMSybU3lK3ydmmrCglgw8yoF0Qvgmo06RID8ANBtmxmzjVFFqIkDkF6VmDErbqFIzDaXbU2pSodQwRWm5dOY52dqe0uOitJ5AboLiLyEcCJ+DcGT+IIDHIs5zH87B9gmCIAiCIAhCkqwHxZHg99HIiyAIGeKFzmutehh1Kox7w0YcNRYdTDo1fAEBnkAQRq0Go5N+GLVqVJp1WOiw4rjTgwFX+LGRWgWMTvohCMDJST9sBi0mfAE0VZkx32GJPVKajth5lJn4DqJ4CAaFBJOYOls4hWLSxzEZCGHCF0SlSYexST90GhVMOjVqbXqMTgTQ5/JiXrURvgDHycmIiUxAgNmggcvrh06tRp1Nj7lVytsQtcfSQal5h9JypW6moRSl2yuW3wHIQVDMGPskgO8DqEV4pJghHCvT7AaCSCJZ6HzFPDsuXTEvptc4r9qIL57fgp++eCDF/OCmjy7EoWE3vvZoZ2zZVy9chGqzDj/+S6pZwm2XLMdF7fUAMC2xc2D6gulEcREMCnj54CAGXf4Ek5g7Pr0co5MB/Oyv4u2z3qbH1x7bEzOluUdkvU2rWvHwrqO47Ky5aK2zYNWiOkU3aNQeSwOl5h1Ky5W6mYZSlG6vWH6HKLlQn/gBgPWcczvn3MY5t1JATBDiJAudX/6B5gQB87VLG7FlZ7eo+cHtf34PBwYnEpb98Nl3cWjYLbr+lx8JC5pPV+wcmL5gOlFcdPc5oWaqFJOYQIjjO09ItM8hN7yBKVMasfXufGE/1i5txB3P70fncaeiNkTtsXRQat6htFypm2koRen2iuV3iJKLoHiAc74vB9shiKInWeg82bwjanqQzvxASBKTiS6TMt2Yrtg5MH3BdKK46HN6Rc1j5Mxhou3TI7NedLnAoagNUXssHZSadygvV9pmGkpRur1i+R2i5CIo3sUYe5gx9pl4V7scbJcgio5kofN05h3xf+OXJz/Fil+WTtB8umLnwPQF04niosFuFDWPkTOHibbF+HYtth7nU+sraUPUHksHpeYdSsuVupmGUhSbdxTJ7xAlF0GxDcAkgAsArIu81uZguwRRdCQLnT/wyqEEAfMndvdi6/o2UfODmz66EC21loRlX71wEZprzKLr33ZJWNB8umLnwPQF04nioq3BhhAXUkxitCqG76yTaJ8Oc4Ipjdh6m1a14snOXty4uhVLZ9sVtSFqj6WDUvMOxeVK3ExDKUq3Vyy/Q5Ssm3cUGmTeUTqUqnlH/MxZh1UPs04FV0R9otqsg1mfrD4RgEGrQpVJh4W1YfWJwXEvHJaw+sTYpB+hJPWJeVVmNIuoT5yqKHs8M/EdRFaYlnlHOuLVJ4bGw4YdlebM1Cf6XV7MjahPjE6GTWT8QQFmnRrjvgC0M6g+Qe2xoMipeYfScqVupqEUpdsr0N8ht+YdjLGvcc5/wBi7C0BK5M0535StbRNEMaNU6DyKEtHy6Yidz+R3EMWDRqOSNIlJx9wqYNnMVycFao+lg1LzDqXlSt1MQylKt1csvwOQXUm26OQ6GpYlCIIgCIIgCpqsBcWc8yci/05yzh+J/4wxdkm2tksQxY4gcBwbdWPQ5cPwhB8WgwZVZi1aa6aMOZKF0+VE1cnIgJgJ4ttRg92AkBBWh4hvU/HrmHQaePwBgDG4PEHU2w1YUj8lpE/tksiEXJtiFIuZRqmTj+OZC5vnbwJ4JINlBFH2CALHywcG0e/0JZgi3Li6FQeH3LjtuXdxZMSTIJwOSJtvkJEBMRPEt6NKkw6Xv38e7nh+f0KbuuC0Ojy3byChrW1Z24Z7XjoQa7dRIX2VilG7JGTJtSlGsZhplDr5Op5ZU59gjF0UySduZIzdGfe6H0AwW9sliGKmZ8SNcU8oxRThjuf348DgBNYubYwtiwqny4mqk5EBMRPEt6NPnjE7FhADU22qu8+Z0ta2Ptmd0G6jQvrULolMyLUpRrGYaZQ6+Tqe2ZRkO4FwPrEXwBtxr50ALszidgmiaBlweWMGCPHEm3DEL8vEfIOMDIiZIL4dpTPdSCecn9xu+50zYxpDlD65NsUoFjONUidfxzObOcW7AexmjP2Wcx7I1nYIopSosxkw4vbDoFUlnBCiRgahuHNEvHC62PrRz6Ki6+k+J4hMSG5HYm0qKpyfvDxe+TMqpG81aKldErKka1OZmmLkqhydZ2eWfB3PXJh3NDHGHmWM7WWMHYq+crBdgig6mqrNsBrUKaYIN65uRUutBU929saWRYXT5UTVyciAmAni29FjbxzHjatbU9pUW4Mtpa1tWduW0G6jQvrULolMyLUpRrGYaZQ6+TqeuZho92sAWwD8GMCHAVyJDMTkCaIcUakYzm2pxbFRN/73qpUYdvth1mtQZdKi1WFF+yy7qHD6mrZ6LN50ruhnKhWT/JwgMiG5HdXbDLhgST2GJhLbVHSdAZcXJq0a3mAQ3//U0rD6hE2PJXFC+tQuCTk0GhU2LmtEa63llMwtcl2OzrMzS76OZy6CYiPn/HnGGOOcHwHwHcbYywgHygRBJKFSMcyrtmBedapAejrhdDlRdTIyIGYCsXa0oNYiu86pfB9BJJNrU4xiMdModfJxPHMRFHsZYyoA+xlj1wPoBVCbg+0SBEEQBEEQREbkIij+DwAmAJsAfBfAKgCX52C7BFG0RA08Bpw+DLt9aKwwZSwgny9IuH56KDUMyBeCwHH0pBsDLh/c/iDmVZkxr8qEo6OTCW0ACMsrjbh90KlVmPSHUGczYG5l6rrUXoiZROk5Kdfnslz3/WI5Lvkg60Ex5/yfkX8nAFzJGNMA+DSAf2R72wRRjEQNPE6M+bD1iSkDj0wE5PMFCddPD6WGAflCEDheeHcA+wcmEgw8btnYjrte2J9gMKPTMHz3yb349Iq5uPOF8Lrzqo24YVVrwv5SeyFmEqXnpFyfy3Ld94vluOSLbJp32Bhj32SM3c0Yu4CFuR7AAQCXZmu7BFHsRA08ogExkLmAfL4g4frpodQwIF/0jLjRedyZYuBx846uFIOZzuNOrF3aGAuIAWDt0saU/aX2QswkSs9JuT6X5brvF8txyRfZHIJ4CMAiAHsAXAPgOQCXANjIOd+Qxe0SRFEjZeAhJyCfL0i4fnooNQzIFwMuLwQubuCRbNQRNZ2JXzed+Qe1F2KmUHpOyvW5LNd9v1iOS77IZvpEM+e8AwAYY/cBGAYwl3M+nsVtEkTRI2XgIScgny9IuH56KDUMyBd1NgPUTNzAI9moQ8UAgYuvS+2FyBZKz0m5Ppfluu8Xy3HJF9kcKY652HHOQwAOU0BMEPJEDTy2rEs08MhEQD5fkHD99FBqGJAvmqrN6JhtTzHwuGVje4rBzNLZdjyxuxebVk2t+8Tu3pT9pfZCzCRKz0m5Ppfluu8Xy3HJF9kcKV7GGHNF/mcAjJH3DADnnNuyuG2CKFriDTwevHJlWH3CbkTbLHkB+XxBwvXTQ6lhQL5QqRhWLapDi8OCM+ZWYtIfxNyI+sQZcysT2gAANNdYcNLtw8PXnp2gPpG8LrUXYqZQek7K9bks132/WI5LvshaUMw5V2fruwmi1JEy8ChUSLh+eig1DMgXKhVDU40FTTWJv7dYG0jXLqi9ENlE6Tkp1+eyXPf9Yjku+aAwhyEIgiAIgiAIIofkwrxjWjDG1AB2AejlnK9N+owBuAPAxwBMAriCc/5m7mtJEDOPmFC62+fDvv6wYUKdTY/T6s2wGrM/0aEURdv9/hA6TzjR7/KiwWZAxyw7dDp6wKUUQeA4NDSB3rFJ6DRqTPqDqLEY4A+FUG3Wx9rMuMeblzZMFC9KzS1ybVKhtJ65Lpfr/Sum60fBB8UAbgSwD4BYDvJFAFojr/cB+FnkL0EUNWJC6T+6ZBkEgeNrj3XGlm1b346L2h1ZDSpKUbTd7w9hR+cJbH58SjB/24Z2bFw6iwJjBQgCx9Nd/fjBs/sSTDoMWhU2rWrFw7uO4utrTsMHWyrxTNcQNu/symkbJooXpeYWuTapUFrPXJfL9f4V2/Ujq+kTjDE1Y+wv0yg/G8DHAdyXZpUNAB7kYV4DUMEYa1C6PaIwueK6G3DhxZenvPa+916+q5Y1xITSv/LIbhwYmkhYtnlnF/b1Z1c8vRRF2ztPOGMBMRA5lo93ofNEYZplFDo9I258+ZG3U0w6vAEBd76wH2uXNuKm7W/jnX53LCCOfp6LNkwUL0rNLXJtUqG0nrkul+v9K7brR1aD4ogU2yRjTKm2yP8D8DUAQprPGwEci3t/PLIsAcbYtYyxXYyxXUNDQwqrQuSLvmEnKi/4QsrL7w/mu2qnTKZtMZ1QusCRsmzA5ctGVWXrUsyi7f1p9mnAVbz7dKrM5Hkx2kbSmXJElw+4fGmOe3bbMFG4yLVDpeYWuTapUFrPXJfL9f4V2/UjFxPtvAD2MMZ+yRi7M/qSK8QYWwtgkHP+htRqIst4ygLO7+Wcr+Ccr3A4HJnXnCBmmEzbYlQoPZ6oEULysjqbPhtVla1LMYu2N6TZpzpb8e7TqTKT58X4NiJ2XDmfaqvixz27bZgoXOTaYdTcIp5MzC2UnreUllNaz1yXy/X+Fdv1IxdB8Z8AfBvASwDeiHvJcQ6A9YyxHgC/B7CKMfa/SescBxAvYjIbwInpVpgg8o2YUPqPLlmGFoclYdm29e04rT674umlKNreMcuObRsSBfO3bWjH0lmFaZZR6DRVm3HbJctTTDqiOcVPdvbi9kuXY3G9GdvWt+e8DRPFi1Jzi1ybVCitZ67L5Xr/iu36kfWJdpzzBxhjRoQtnt89hXLfBPBNAGCMnQ/gK5zzf01abSeA6xljv0d4gp2Tc943IxUniDySTijd7fPhwatW5nTmfimKtut0amxcOgvNNebYjOilpD6hGJWK4aL2eiyqs6B3bBK/vuIsTPqDqDbrERQErGmvj7WZi9odaKrJbRsmihel5ha5NqlQWs9cl8v1/hXb9SPrQTFjbB2AHwHQAZjPGFsOYBvnfL3C77sOADjn9wB4CmE5tgMIS7JdORN1JohCQEwo3Wo0YOX83AcQpSjartOpsaKpKt/VKBlUKoaWOita6qyS6+WrDRPFi1Jzi1ybVCitZ67L5Xr/iun6kQtJtu8AWAngRQDgnL/NGJt/Kl/AOX8xrvw9ccs5gC/NTDUJgiAIgiCIciUXQXGQc+4M+2zESJkMRxBE5hSTGDqgXPSdKG4yaafUNohypxxMMYqFXATFXYyxzwJQM8ZaAWwC8EoOtksQJUmxiaErFX0niptM2im1DaLcKRdTjGIhF2edGwC0AfAB+B0AF4D/yMF2CaIkKTYxdKWi70Rxk0k7pbZBlDvlYopRLORCfWISwH8B+C/GmBqAmXNemKrNBCHC3u4uXHjx5SnLG2rsuP+eu3JeHykx9EKcyCAl+n6qEzaI4iGTdkptgyh3lPaBYrsOFAu5UJ/4LYDrAIQQ1ie2M8Zu55z/MNvbJoiZwM9VqLzgCynL+577WR5qMyWGHn9CLGQx9Kjoe3J95UTfieImk3ZKbYMod5T2gWK7DhQLuUifWMI5dwHYiLCE2lwAn8vBdgmiJCk2MXSlou9EcZNJO6W2QZQ75WKKUSzkYqKdljGmRTgovptzHmCMkfoEQSik6MTQFYq+E8VNJu2U2gZR7pSLKUaxkIug+OcAegDsBvASY2wewpPtCIJQSDGJoQPKRd+J4iaTdkptgyh3ysEUo1jI+u045/xOznkj5/xjEbONowA+nO3tEgRBEARBEESmZG2kmDF2U9IiDmAYwN8454eztV2CIMoDMn0oDuh3IsoFpWYaZMJROGQzfcIqsqwJYWm273DOf5/FbRMEUcKQ6UNxQL8TUS4oNdMgE47CImtBMed8q9hyxlgVgL8AoKCYIAhFpBO8b621YNmcyjzXjohCvxNRLqQz01i86VzJnF+l5YjskPNbdc75SQB0+0MQhGKkBO+JwoF+J6JckDLTyEY5IjvkPChmjK0CMJrr7RIEUTpEBe/jIdOHwoN+J6JciJppxJOJmYbSckR2yFpQzBjbwxjrTHodB/A9AF/M1nYJgih9yPShOKDfiSgXlJppkAlHYZHNiXZrk95zACOcc3cWt0kQRBlApg/FAf1ORLmg1EyDTDgKi2xOtDuSre8mCIIg04figH4nolxQaqZBJhyFA92uEwRBEARBEGUPBcUEQRAEQRBE2ZPNnGKCOCWuuO4G9A07U5bvfe89nHNBHipEEARBEETZQEExUTD0DTtRecEXUpb7u27IQ20IgiAIgignKH2CIAiCIAiCKHsoKCYIgiAIgiDKHgqKCYIgCIIgiLKHgmKCIAiCIAii7KGgmCAIgiAIgih7KCgmCIIgCIIgyh6SZCOIMkIQOHpG3BhweVFnM6Cp2gyViuW7WkSZQu2RIHIP9bv0UFBMEGWCIHA8092Pm7a/DW9AgEGrwu2XLseatno6IRI5h9ojQeQe6nfSUPoEQZQJPSPu2IkQALwBATdtfxs9I+4814woR6g9EkTuoX4nDQXFBFEmDLi8sRNhFG9AwOC4N081IsoZao8EkXuo30lDQTFBlAl1NgMM2sQub9CqUGs15KlGRDlD7ZEgcg/1O2koKCaIMqGp2ozbL10eOyFGc8maqs15rhlRjlB7JIjcQ/1OGppoRxBlgkrFsKatHos3nYvBcS9qrTTrmMgf1B4JIvdQv5OGgmKCKCNUKoZmhwXNDku+q0IQ1B4JIg9Qv0sPBcVEVrjiuhvQN+xMWd5QY8f999yVhxoRBEEQBEGkh4JiIiv0DTtRecEXUpc/97M81IYgCIIgCEKagp1oxxgzMMZeZ4ztZox1M8a2iqxzPmPMyRh7O/LanI+6EgRBEARBEMVNIY8U+wCs4pxPMMa0AP7GGHuac/5a0novc87X5qF+BEEQBEEQRIlQsEEx55wDmIi81UZePH81IgiCIAiCIEqVgk2fAADGmJox9jaAQQB/5pz/Q2S190dSLJ5mjLWl+Z5rGWO7GGO7hoaGslllgpCE2iJRKFBbJAoBaodEIVHQQTHnPMQ5Xw5gNoCVjLH2pFXeBDCPc74MwF0AdqT5nns55ys45yscDkc2q0wQklBbJAoFaotEIUDtkCgkCjoojsI5HwPwIoA1SctdnPOJyP9PAdAyxmpyXkGCIAiCIAiiqCnYoJgx5mCMVUT+NwL4CIB3ktapZ4yxyP8rEd6fkRxXlSAIgiAIgihyCnaiHYAGAA8wxtQIB7vbOedPMsauAwDO+T0ALgbwBcZYEIAHwGWRCXoEQRAEQRAEkTEFGxRzzjsBnC6y/J64/+8GcHcu60UQBEEQBEGUHgWbPkEQBEEQBEEQuYKCYoIgCIIgCKLsKdj0CSI7XHHdDegbdop+1lBjx/333JXjGhEEQRAEQeQfCorLjL5hJyov+IL4Z8/9LMe1IQiCIAiCKAwofYIgCIIgCIIoeygoJgiCIAiCIMoeCooJgiAIgiCIsoeCYoIgCIIgCKLsoaCYIAiCIAiCKHtIfYKYFukk3va+9x7OuSAPFSpjBIGjZ8SNAZcXdTYDmqrNUKlYvqtFlCnUHolyg9p88UNBMTEt0km8+btuyENtyhdB4Himux83bX8b3oAAg1aF2y9djjVt9XRSJnIOtUei3KA2XxpQ+gRBlAA9I+7YyRgAvAEBN21/Gz0j7jzXjChHqD0S5Qa1+dKARooJWaRc8ChNojAYcHljJ+Mo3oCAwXEvmh2WPNWKKFeoPRLlBrX50oCCYkIWKRc8SpMoDOpsBhi0qoSTskGrQq3VkMdaEeUKtUei3KA2XxpQ+gRBlABN1WbcfulyGLThLh3NZ2uqNue5ZkQ5Qu2RKDeozZcGNFJcopAqRHmhUjGsaavH4k3nYnDci1orzXwm8ge1R6LcoDZfGlBQXKKQKkT5oVIxNDsslL9GFATUHolyg9p88UNBcREgNdGtocaO+++5K8c1IgiCIAiCKC0oKI4jXfA504HnqW5HaqJb33M/m7F6EQRBEARBlCuMc57vOuQUxtgQgCNZ3kwNgOEsb2OmKKa6AsVT32HO+RqpFWTaYr73k7ZfOtsv9rZYKHUACqMexVwHybaYxetzIRyzKFQXcXJdF9G2WHZBcS5gjO3inK/Idz0yoZjqChRffZWS7/2k7Zf39uMphLoUQh0KpR5Uh1OnkOpLdRGnUOpCkmwEQRAEQRBE2UNBMUEQBEEQBFH2UFCcHe7NdwVOgWKqK1B89VVKvveTtl/e24+nEOpSCHUACqMeVIdTp5DqS3URpyDqQjnFBEEQBEEQRNlDI8UEQRAEQRBE2UNBMUEQBEEQBFH2lF1QvGbNGg6AXvTK9ksWaov0ytFLFmqL9MrRSxJqh/TK4UuUsguKh4cLRaeaKHeoLRKFArVFohCgdkjkm7ILigmCIAiCIAgiGQqKCYIgCIIgiLKHgmKCIAiCIAii7NHkuwIEUWgIAkfPiBsDLi/qbAY0VZuhUrF8V4sgihrqVwRB5ILpnGsoKCaIOASB45nufty0/W14AwIMWhVuv3Q51rTV0wWcIBRC/YogiFww3XMNpU8QRBw9I+5YZwIAb0DATdvfRs+IO6/18vl8+Pvf/57y8vl8ea0XQWRCofYrgiBKi+mea2ikmCDiGHB5Y50pijcgYHDci2aHJU+1Anbt2oUbf7oD9saW2DJn7wHc8UXgnHPOyVu9CCITCrVfEQRRWkz3XENBMUHEUWczwKBVJXQqg1aFWqshj7UKY29sgaNlab6rQRCnTCH3K4IgSofpnmsofYIg4miqNuP2S5fDoA13jWg+UlO1Oc81I4jihfoVQRC5YLrnGhopJog4VCqGNW31WLzpXAyOe1FrpVnyBDFdqF8RBJELpnuuoaCYIJJQqRiaHRbKdSSIGYT6FUEQuWA65xpKnyAIgiAIgiDKHgqKCYIgCIIgiLKHgmKCIAiCIAii7KGgmCAIgiAIgih7KCgmCIIgCIIgyp6sBsWMsf9kjHUzxroYY79jjBkYY1WMsT8zxvZH/lbGrf9NxtgBxti7jLEL45afyRjbE/nsTsYYiyzXM8Yejiz/B2OsKZv7QxAEQRAEQZQmWQuKGWONADYBWME5bwegBnAZgG8AeJ5z3grg+ch7MMaWRD5vA7AGwE8ZY+rI1/0MwLUAWiOvNZHlVwMY5Zy3APgxgO9na3/KkWBQwO5jo3imqw+7j40hGBTkCxEEUTYIAsehoQm8enAYh4YmIAg8J2UJglCO0r5XDn022zrFGgBGxlgAgAnACQDfBHB+5PMHALwI4OsANgD4PefcB+AwY+wAgJWMsR4ANs75qwDAGHsQwEYAT0fKfCfyXY8CuJsxxjjnpfdL5ZhgUMCO3b24eUcXvAEBBq0Kt2xsx8ZljdBoKOuGIModQeB4prsfN21/O3aOuP3S5VjTVi8rlD+dsgRBKEdp3yuXPpu16IZz3gvgRwCOAugD4OScPwegjnPeF1mnD0BtpEgjgGNxX3E8sqwx8n/y8oQynPMgACeA6uS6MMauZYztYoztGhoampkdLHG6+5yxgBgAvAEBN+/oQnefM881K26oLRKFwnTbYs+IO3aBBMLniJu2v42eEXdWyxKlBZ0Tc4vSvlcufTab6ROVCI/kzgcwC4CZMfavUkVElnGJ5VJlEhdwfi/nfAXnfIXD4ZCuOAEA6HN6Y40/ijcgoN/pzVONSgNqi0ShMN22OOASP0cMjsufI6ZTligt6JyYW5T2vXLps9l8Dv4RAIc550Oc8wCAPwD4AIABxlgDAET+DkbWPw5gTlz52QinWxyP/J+8PKEMY0wDwA7gZFb2psxosBth0CY2D4NWhXq7IU81IgiikKizGUTPEbVW+XPEdMoSBKEcpX2vXPpsNoPiowDOZoyZImoRqwHsA7ATwOcj63wewOOR/3cCuCyiKDEf4Ql1r0dSLMYZY2dHvufypDLR77oYwAuUTzwztDXYcMvG9lgniOYUtzXY81wzgiAKgaZqM26/dHnCOeL2S5ejqdqc1bIEQShHad8rlz6btYl2nPN/MMYeBfAmgCCAtwDcC8ACYDtj7GqEA+dLIut3M8a2A9gbWf9LnPNQ5Ou+AOB+AEaEJ9g9HVn+SwAPRSblnURYvYKYATQaFTYua0RrrQX9Ti/q7Qa0Ndhpkh1BEAAAlYphTVs9Fm86F4PjXtRaDWiqNmc06WY6ZQmCUI7SvlcufTar6hOc8y0AtiQt9iE8aiy2/q0AbhVZvgtAu8hyLyJBNTHzaDQqLJtTiWVz5NclCKL8UKkYmh0WNDssOS1LEIRylPa9cuizNOxHEARBEARBlD0UFBMEQRAEQRBlDwXFBEEQBEEQRNlDQTFBEARBEARR9lBQTBAEQRAEQZQ9FBQTBEEQBEEQZQ8FxQRBEARBEETZQ0ExQRAEQRAEUfZQUEwQBEEQBEGUPRQUEwRBEARBEGUPBcUEQRAEQRBE2aPJdwUIglCGEAygs7MzZfmKFSug1+vzUCOCIAiCKF4oKCaIIsXVfwR393hRf3jqgY+z9wDu+CJwzjnn5LFmBEEQBFF8UFBMEEWMtWE+HC1L810NgiAIgih6KKeYIAiCIAiCKHsoKCYIgiAIgiDKHgqKCYIgCIIgiLKHgmKCIAiCIAii7KGgmCAIgiAIgih7KCgmCIIgCIIgyh4KigmCIAiCIIiyh4JigiAIgiAIouwh8w6CSCIYFNDd50Sf04sGuxFtDTZoNLm7f/T5fNi1a1fCss7OTnDOclYHglBKvvsPQRClgSBw9Iy4MeDyos5mQFO1GSqV/HVQaTmAgmKCSCAYFLBjdy9u3tEFb0CAQavCLRvbsXFZY84u7Lt27cKNP90Be2NLbFnv2y+jonVFTrZPEEophP5DEETxIwgcz3T346btb8fOJbdfuhxr2uolA1yl5aJk9SzFGKtgjD3KGHuHMbaPMfZ+xlgVY+zPjLH9kb+Vcet/kzF2gDH2LmPswrjlZzLG9kQ+u5MxxiLL9YyxhyPL/8EYa8rm/hClT3efM3ZBBwBvQMDNO7rQ3efMaT3sjS1wtCyNvcw1jTndPkEooVD6D0EQxU3PiDsW2ALhc8lN299Gz4g7K+WiZPvW/Q4Az3DOFwNYBmAfgG8AeJ5z3grg+ch7MMaWALgMQBuANQB+yhhTR77nZwCuBdAaea2JLL8awCjnvAXAjwF8P8v7Q5Q4fU5vrDNF8QYE9Du9eaoRQRQP1H8IgpgJBlzi55LBcelzidJyUbIWFDPGbADOA/BLAOCc+znnYwA2AHggstoDADZG/t8A4Peccx/n/DCAAwBWMsYaANg4569yzjmAB5PKRL/rUQCro6PIBKGEBrsRBm1itzBoVai3G/JUI4IoHqj/EAQxE9TZDKLnklqr9LlEabko2RwpbgYwBODXjLG3GGP3McbMAOo4530AEPlbG1m/EcCxuPLHI8saI/8nL08owzkPAnACqE6uCGPsWsbYLsbYrqGhoZnaP6IEaWuw4ZaN7bFOFc2JbGuwz8j3U1skCoVstMVs9x+i9KBzIiFGU7UZt1+6POFccvuly9FUbc5KuSjZnGinAXAGgBs45/9gjN2BSKpEGsRGeLnEcqkyiQs4vxfAvQCwYsWKlM/LEb8/hM4TTvS7vGiwGdAxyw6dTi1fsMTRaFTYuKwRrbUW9Du9qLcb0NZgn7FJQtQWiUJhpttidMa33aDFvZ87EyadGma9Gq0OUp8g0lNK58RcqyVMR2Wh0FGpGNa01WPxpnMxOO5FrTWz/VNaLko2g+LjAI5zzv8Ref8owkHxAGOsgXPeF0mNGIxbf05c+dkATkSWzxZZHl/mOGNMA8AO4GQ2dqaU8PtD2NF5Apsfn5ohvm1DOzYunUWBMcKB8bI5lVg2R35dgiDCF+enu/rx5UemZnzfuLoVsyuNaHXY8l09gsg6uVZLmK7KQjGgUjE0OyxodlhyUg7IYvoE57wfwDHG2KLIotUA9gLYCeDzkWWfB/B45P+dAC6LKErMR3hC3euRFItxxtjZkXzhy5PKRL/rYgAvRPKOCQk6TzhjATEQTkLf/HgXOk/QDHGCIE6dnhF3LCAGwueUO57fj/2DE6Q8QZQFuVZLmK7KAiFOtnWKbwDwG8aYDsAhAFciHIhvZ4xdDeAogEsAgHPezRjbjnDgHATwJc55KPI9XwBwPwAjgKcjLyA8ie8hxtgBhEeIL8vy/pQE/WlmZw64aIY4QRCnTroZ3wIH+p1eeupClDxSqgdSI5a5LkdIk9WgmHP+NgAxx4HVada/FcCtIst3AWgXWe5FJKgmMqchMjszvkMZtCrU2WiGOEEQp05dmnOKioGUJ4iyIF0fyFQtIVflCGlo9kMZ0jHLjm0bEmeIb9vQjqWzaIY4QRCnTlO1Gbddkjjj+8bVrWittZDyBFEW5FotYboqC4Q4ZPNchuh0amxcOgvNNebYrNWlpD5BEIRCVCqGi9rrsajugzg84oZBq0alSYvFdaQ8QZQHuVZLmK7KAiEOBcVlik6nxoqmqnxXgyCIEkGlYmips6KlzprvqhBEXsi1WsJ0VBYIcU75Fp4xVskYW5qNyhAEQRAEQRBEPsgoKGaMvcgYszHGqgDsRtil7vbsVo0gCIIgCIIgckOmI8V2zrkLwCcB/JpzfiaAj2SvWgRBEARBEASROzINijUR97lLATyZxfoQBEEQBEEQRM7JdKLdNgDPAvgb5/yfjLFmAPuzVy2CyA5ebxB7+pzod/lQb9Ojo8EOg4HmmxKEEgSBo2fEHVOxSZ797veH0HnCiX6XFw02AzpI5YYoYeT6w0yXCwYFdPc50ef0osFuRFsDqb1Ml0yjgSc4549E33DODwH4VHaqRBDZwesNYueePmze2RXzit+2vh3rOxooMCaIU0QQOJ7p7o9ZzUZ1Ute01UOlYvD7Q9jReSJmKR/VQ9+4dBYFxkTJIdcfZrpcMChgx+5e3Lxjqn/dsrEdG5c1UmA8DTI9cl2Msb8zxr7HGPsYY4zU2ImiY0+fMxYQA2FLzM07u7Cnz5mwXjAoYPexUTzT1Yfdx8YQDApiX0cQZU3PiDt2IQfC/emm7W/j2Kgbu4+N4s/vDMCkU2NhrSX2+ebHu9B5win1tQRRlKTrDz0j7qyU6+5zxgLiaLmbd3Shu0++fwkCx6GhCbx6cBiHhiYgCDyTXVRcrpjIaHiMc97CGJsL4FwAawH8lDE2xjlfns3KEcRM0u/yiXrFD7h8sfd0900QmTHg8qb0p4W1Fvzj8GjC6PCWtW3A60fQ2euK9DdvnmpMENlDrD94AwIGx72SOsJKy/U5xcv1O71YNid9PXM9ol1sZCrJNhvAOQgHxacD6AbwcBbrRRAzTr1NH7PEjGLQqlBn08feT+fumyDKiTqbIaU/XXveglhADIT7z9Ynu3HNeQsARPubIed1JYhsI9YfDFoVaq3S7V1puQa7UbRcvV26XK5HtIuNTIe+jgL4DwBPc87fzzn/OOf8f7JXLUIKeryvjI4GO7atb0/wit+2vh0dDVPZQFJ33wRBTNFUbcbtly5P6E8cEO0/Hn8w1t/ayPGOKEHE+sPtly5HU7U5K+XaGmy4ZWPi9eyWje1oa5DObpUamc5GuWIj09lFpwP4IIDPMsa+gbDyxF8557/MWs0IUejxvnIMBg3WdzRgfo0JAy4f6kTUJ6J33/GdP5O7b4IoN1QqhjVt9Vi86VwMjntRazVg3BsU7T9zKk34+efOxG9eOwyVCnS+IkoSnYbh2vOaIXBAxcLvs1VOo1Fh47JGtNZa0O/0ot5uQFuDXbZfRUemk/topiPap1qu2Mg0p3g3Y+wggIMIp1D8K4DzAFBQnGPSPd5vrbVg2ZzKPNeu8DEYNDhrfnXaz6N338k3HXJ33wRRjqhUDM0OSyz3MRgUUvrPlnVt+N7T+9DZ6wIAvLT/JJ2viJKjZ8SN63/7VkrQ+NSmcyVzg5WWA8KB8bI5lZI5xMlER6aTc4MzHdE+1XLFRkZBMWNsFwA9gFcA/A3AeZzzI9msGCGO0uR6IjOU3n0TBJHaf2xGLf7nqamAGKDzFVGa5HqinVLEnvBkooustFyxkWn6xEWc86Gs1oTICHq8n32U3H0TBBEmvv/sPjaG9wYnEj6n8xVRihRTWkLyE55slysmMh3+UjHGfskYexoAGGNLGGNXZ7FeRBqUJtcTBEHkGjpfEeVCrifaEdkh05Hi+wH8GsB/Rd6/h7AkG+UU5xh6vE8QRLFA5yuiXKC0hNIg06C4hnO+nTH2TQDgnAcZY6Es1ouQgB7vEwRRLND5iigXKC2h+Mn0dt3NGKsGwAGAMXY2AHIzIAiCIAiCIEqCTEeKbwKwE8ACxtjfATgAXJy1WhEEQRAEQRBEDslUp/hNxtiHACwCwAC8yzkPZLVmBEEQBEEQBJEjJINixtgqzvkLjLFPJn20kDEGzvkfslg3giAIgiAIgsgJcjnFH4r8XSfyWpvJBhhjasbYW4yxJyPvqxhjf2aM7Y/8rYxb95uMsQOMsXcZYxfGLT+TMbYn8tmdjDEWWa5njD0cWf4PxlhTpjtOEARBEARBEFEkR4o551si/17DOVeqNnEjgH0AbJH33wDwPOf8e4yxb0Tef50xtgTAZQDaAMwC8BfG2MLIdn8G4FoArwF4CsAaAE8DuBrAKOe8hTF2GYDvA/i0wnoSZYAgcPSMuDHg8qLORtI3BDFTUN8iiOKA+mp6Mp1od5gx9gzC2sQvcM55JoUYY7MBfBzArQhP1gOADQDOj/z/AIAXAXw9svz3nHNfZHsHAKxkjPUAsHHOX41854MANiIcFG8A8J3Idz0K4G7GGMu0fkR5IQgcz3T3p3i3r2mrpxMCQUwD6lsEURxQX5UmU0m2RQD+AuBLCAesdzPGPphBuf8H4GsA4o296zjnfQAQ+VsbWd4I4Fjcescjyxoj/ycvTyjDOQ8iLBNXnVwJxti1jLFdjLFdQ0PkVl2u9Iy4YycCIOwvf9P2t9Ez4s5ZHagtEoXCTLbFQuhbRHFC58TcQn1VmoyCYs65h3O+nXP+SQCnI5wK8VepMoyxtQAGOedvZFgXsVsULrFcqkziAs7v5Zyv4JyvcDgcGVaHKDUGXN4Ef3kgfEIYHPfmrA7UFolCYSbbYiH0LaI4oXNibqG+Kk3GXpuMsQ8xxn4K4E0ABgCXyhQ5B8D6SPrD7wGsYoz9L4ABxlhD5DsbAAxG1j8OIN7zaDaAE5Hls0WWJ5RhjGkA2AGczHSfiPKizmaI+ctHMWhVqLUa8lQjgigNqG8RRHFAfVWajIJixthhAP8B4GUA7ZzzSznnj0mV4Zx/k3M+m3PehPAEuhc45/+KsAnI5yOrfR7A45H/dwK4LKIoMR9AK4DXIykW44yxsyOqE5cnlYl+18U4hXxnovQIBgXsPjaKZ7r6sPvYGILBxLvhpmozbr90eeyEEM2laqo256O6BFEySPUtuX5JEIQyBIHj0NAEXj04jENDExAE+fCHroPSZDrRbhnn3DVD2/wegO2MsasBHAVwCQBwzrsZY9sB7AUQBPClOMWLLwC4H4AR4Ql2T0eW/xLAQ5FJeScRDr6JMiQYFLBjdy9u3tEVmzxwy8Z2bFzWCI0m3PlVKoYLTqvDw9eejT6nFw12A9oa7DS5gCCmiUrFsKatHos3nYvBcS9qreEZ7YLAZftlrqAZ90QpIQgcL7w7gM7jTggcUDOgY7YdqxbVybZrnYbh2vOaIXBAxcLviTCZBsX1jLE/IjxJrp0xthTAes75LZkU5py/iLDKBDjnIwBWp1nvVoSVKpKX7wLQLrLci0hQTZQ33X3O2IUXCOdI3byjC621FiybE5bCFgSO5/YN0KxbgsgCKhVDs8OCZocltmxP75hsv8wFNOOeKDWOnnRj/8AE7n3pUKxN37i6FS0OC5pqLGnL9Yy4cf1v30rIKzZoVXhq07kJfbdcyfRW/RcAvgkgAACc807QqCxRQPQ5xScP9DunJg/QrFuCyC2Z9MtcQH2fKDUGXD7c8fz+hDZ9x/P7MeDyyZSjiXZSZDpSbOKcvx4xkosSzEJ9iBkiGBTQ3eeMpAkY0dZgy/njylzSYDfCoFWl3P3W26cmD0idDOgOmSBmnkz6ZS7OVdT3iUJGSWqP2x9EpUmHT54xG9HQ7LE3jmPSLx2aRSfaJfdJmmgXJtOgeJgxtgARuTPG2MUA+rJWK2JaZJJfW2q0Ndhwy8b2lH1ua7DH1qGTAUHkFrl+matzFfV9olBRmtozv9qMy98/LzZaHE2fkJswF51ol7w9mmgXJtOg+EsA7gWwmDHWC+AwgH/JWq2IaZFJfm2podGosHFZI1prLeh3elEfmUQXf2Eth5OBEAygs7MzZfmKFSug1+vzUCOinJHrl7k6V5VD3yeKk3SpPYtlcnwFDtH0iQuW1EtuL92kWMqtD5NRUMw5PwTgI4wxM8J5yB4AnwZwJIt1IxQilce3bE6aQiWARqPCsjmVafdRpWJY1erAQ1etRL/Lh3qbHh0lpj7h6j+Cu3u8qD88dTPg7D2AO74InHPOOXmsGVGqyKU/qFQMVoMWk/4QrAZtQn/L1bmKAoHyRKniSC7LKU3tGRwXLzc04cWCWumUILFJsUQYyaCYMWZDeJS4EWFt4KjV81cA7Abwm2xXkDh1MsnjK0f8/hB2dvVh8+NTj2q3bWjHxqWzoNOp8129GcPaMB+OlqX5rgZRBsilP8g9Gs7luYoCgfJCaVpCrsspTe2hlKDsIJe09RCARQD2APg3AM8hLIG2kXO+Ict1IxQSzeOLF+dOzq8tRzpPOGMBMRC+q978eBc6TzjzXDOCKE7SpT9094X7lJzqA52riGyhVHEk1+WUmmmQCUd2kEufaOacdwAAY+w+AMMA5nLOx7NeM0IxmeTXliP9aR5TDbgSpWjKTbmDIDIluW+4vH7J9Ae5R8N0riKyhdK0hFyXU5raUw4pQblOfwHkg+JA9B/OeYgxdpgC4uJALr82E2bKAapQnKQa0jxuqrMlykO9cWwY4GoEQgK8gRDeODaMM+fU0IWaKGvEUiW+u6EdK+bZsevI1NOW+PSHBrsBm1a3IOo+6/b4cEH7LAyN+/HPwyPoaLDDYNBM+1xFEMnU2QyYV23E2qWNMcmyJ3b3ZpSWkMty8XB5l+YEBIFj3BvA2GQARq0GgsALMihWEgPkOo0lilxQvIwxFrV3ZgCMkfcMAOec22S3QBQlM+UAVUhOUh2z7Ni2oT0lp3jprKlHtUdOunBk2IvNO7un1lnfhhqzCwtqK3JaX4IoJMRSJb79eBd++fkVuPqBXSmSa4LA0X1iPOa4Na/aiC+d34Ir7/9nXN9qx/qOBhgMmQohEURmzK004YZVrSn57nMrTQVVTuk1slikV5Xun1JVDqXlokgeOc65mnNui7ysnHNN3P8UEJcwM+UAVUhOUjqdGuva6vHgVStx12dOx4NXrcS6tvqESXYjE6FYQByt7+ad3RiZCOW8vgRRSKRTiuhzevGDi5fh+5/qwI8uXoazmiqh0ajQM+LGlx+Z6vtrlzaK9K0u7OmjnH5i5jk6Oima7350dLKgyim9Rsrl8xcKSvdPqfPedB37Cud2gigoZsoKcqa+RxA4Dg1N4NWDwzg0NAFBOMXnTAjfWf9pbz8u/9XruOF3b+HyX72OP+3tRzA4Vb+BcZ943vG4tHUmQZQ6UaWIeAxaFXQaNTb97i18/bE9uP53b6EvYuGc3PcZQ5qcfupbxMyT66Aq1+UKxUJdDqX7V2s1iJ5vHBbpdBSl5aJQUEyIEpV7iUeJ3MtMfE/08cvH7nwZn/nFP/CxO1/GM939KYFxMChg97FRPNPVh93HxhKCXSCzO+s6m160vnU2Mr4gyhsxpYgt69pw30sHY+vE9+10fT/5PfUtIhsovfYUS7l0N6mFJr2qdP/UKuDG1a0J55sbV7dCLRO1atTAlnVtKecpTYaqqxQUE6LMlNzLTHxPJo9fgkEBLx8chNMTgDcgwOnx4+WDgwmBcSZ31h31Nmxbn3jh37a+HR31lC1ElDdRpYiHrz0bP//XM/Dwv52NWRV6vDc4ASDcV/7nEx2YFbnYNVWb8f1PLY31pSd294r3LZJfI7JArqXOlJabW2kSlSWUy0U+rc6KbRuS+tOGdpxWV1jXqqZqM+7+7OnYtLoF169qwY2rW3D3Z0+XPS59Ti+e3tMXS8364cXL8PSePvS7pEeY+50+PPbG0YRyj71xFP3OzJ5I0ewGQpSZknuZie/pH0sfzEYT5w8OuzDo8mNL3AS5revbcHDYhUX1FQAyMzUxGrVY0+5AU81KDLh8qLPpsbjeDKNRe0r7TRDFjtcbxJ4+Z4L7Y7JShMcTwC8/vwLHRz0w6TR44JVDCAg8Zogzq8KAqz/YjCqTFvNrzHjlwAB+dcVZGJnwodZqgMOipkl2RNbQaRiuPa8ZAgdULPy+0ModHZ3E718/gh9cvAweXxAmfbgfnTG3UnJi2HGnB3/ZewI//9yZGJsMoMKkxW9eO4wV86TLAbmXOvMHeWzCbfRmQY4GuwEXdTTga4/ujpW7cXUr6m3SI8z+UAjrljbiwOA4BA6oGbBuaSMCoczmBdHZiEjLTDlAZfI9Up1Nq2GiwaxWPdUZxyZDsYAYCAfNW3Z244ErV8bWiT7+TZ6tG28U4PEE8EzXEDbvjFOoWN+Ode31FBgTZYPXG8TOPX0p/SBeKUIQOJ7eN4Bv/XFPbJ3//kQHjg270HnCiTPmVkKnVuOXfzuEn3/uTPz7Q2/AGxBw39+PAQj34QevXInm2nzuKVGq9Iy4cf1v30q5bjyVgXpBLsuNuH1Ytbg+IfjbtKoVJ90+2XLL51TH+lWm5QSB4+mu/tgkWINWhdsuWY6L2rMjdaZUDSIkAHc8vz+h3B3P78cFS+rTlgGASqMOkwFXQhB+4+pWVBh1kuWiUPoEkXfkcoZHPQFsWpWYW7RpVStGPTEZbQymmSA3FDdBTqNR4aIltQnqExctqU2Qr9nT74oFAtHv2LyzC3v6XSCIcmFPn1O8H8Tl3/eMuGMBcXSdb/1xDzrmVIMxAY+9eRybfv9muK+6A2km29AkOyI7FMuEOZ1ahTtfSAz+7nxhP7QyybNKyx0amkhQhfEGBHz5kbdxaGhCstzhYfHg9vCwMhWJZNOsTMvJHc8Jf0g0mHb7MxsppqC4TJkJNYeZQi5nuNKoxcO7juLqDzbj+lUtuPqDzXh411FUxo3czrKLJ/PHp0Z4PAH8qWswUX2iaxCeuOB6wJVGfYJmyBNlRH8G/SDdRevt42MIhRi+/XgXjox48NBrR9JOYC20SUFE6VAsE+Ym/SHRfjQpE8S5fcrKHR5xi5aTk0g7kqbc0ZPS5Uw6jehxMWmlZ77pNSrRcnJB/7hX/Abc5Q2kKZEIBcVlSKZqDrlC7o5Qr1Xhi+e34Jd/O4S7XziAX/7tEL54fktCh+mYZRedxBNvzLGn34W+0XE8cOVK3PWZ5XjwqpXoGx1PGAVOqz5hpRnyRPlQn4EKS7VZJ7pOSAAGxr1YWGvBnZ85Hf+xuhUhLmDr+jbJ/kkQM0mxTLRLF0zXyeTOmvVpgk2ddLBp0KpFy+llglSDVjxI1ckYhbi8fvznRxYmHJf//MhCjPukg9RRj3i5MY9fspzdqBWtZ0WG6Y+UU1yGTNfxZaaJ6gom52JFdQUtei3e6BnGr644C8MTPjgsevzxzaM4q6kqtr5KxTCvRo8HrlwZm9AHFkrIddJrgIZKCz7/69fj8iTboI/rBdVmNbatb0txtKu2ZKjnQhAlQEdD+CYzOac4qhQx6fHjnf5x3Li6Nfao0qBV4dtrl+Delw7igtNOx2feNy8hT/Luz56OB65ciX6XF7PsBnQ02HHc6cm7/TtRmiid5D2dyeFKJtrNrTThuxva8e3HE+3TZZ3wuIAt69qw9Ympa9WWdW3gMl7RNqMaW9e3pUxKtxnkg2Kx7SUHoMmYtBoYtaqE42LUqmDUSoefVSYdTmg9KeWqZHKD62x60f2rzVD6kYLiMkRqZDYfQXFUjzD+4hqvRzi30oT1pzeCAUCkv68/vTHhpLF/yIWeYa9IR3fhtIYKAIAvCFG3ugevmpqMN+4V8F7/GO6/ciWGxr1wWA34S3cvFtbl/rgQRL4wGDRY39GA+TWmmApLVH0CALr6x/G1xzpRadLh6g82g7HwReu0Oitu3dgGX1CAWafGr684C4/88yj+8HYfrv/tW/jF51ZAq1bh9DmVeG7fQEHYvxOlj0ycOCPlpjNB7+7/2x/rR5wDd//ffpw+pwItdda05dSM4Z6/Hkgod89fD+Cuy06XrGcgCDyyKyxZ5vEHY6ox37hoiWS5oICY1Fl8ua+tOU2yHBjwy78fxtqljWAMEHj4vZwChUbN8N9Pv5NyPLf/+9ky+8dF9+/MuZXS9YxuN6O1iJIi+rgmubGdqjHHTNHn9OLBV48kdO4HXz2C0+dWoKnGgr7xSZwY9aWM3vZVT2JOZfhkM+4VV5+ID3gzyRfuHfMiFLcKQ3gWbO+YF8vnhpdNeLzY2++OBQtL6s2wGCk3kigtDAYNzppfLfpZtC/1Ob34yf8dAABc+f45CHKO0ckgaq0avHVkBL/b1Ytt69sAAH94uw/jvgDWtDUU3NMqovRQqpagtNyAy4tKkw6fPGM2WGS1x944jgGX9GDT4RE3/MGp6JuxsITZ4RG3ZFA8POEXLTfilk4vcHn9opJlLq90uUl/ULTcpD8oWW7CG8BVH5iPkUl/rNxVH5iPCZkc3+Fxv+j1enhcup59To+omke/yyN5PKNQUFyGRHOfkjv9qRpzzBR1NgNGJ/2xiyuQGKT3xQXEQNwI75UrY0HxUAbqE9F84eSbgfg8yQW1Jrh9FbgiKcViQW14VHrC48VTIpJtH2t3UGBMlA3JfenK98/BabMqcPmvEvvNZ1aEn8786oqz8FT3AGoseqhUrOCeVhGlh9IbL6XlrAYNLn//vJQnnlYZHW6l5WxGrWg5m0E6d7bKpMe7/RMpkmVVJun0gkqjDvsC4ynlKmXSGWwGLSYDoZRycvWMTrRLvl7L5TBrNeKqHPdfeZZkuSg00a4MieZMPbXpXPz+2vfhqU3n5vWxpdwEhYE0Ae9AXMBblWbST5V5qsPqNcC2lMk+iTnFLk9INAB3ecIzevf2u0Wlqvb2S8/ALQZ8Ph/+/ve/p7x8PlLeIBKpMqkT+tKajkbRfnNhRyO8AQEjEz5sWdsWG1WaKRt5gkhHrqXVJtNIgXlk1CA0KpVoOY1KPjwTKyeX8eH2B0XLyY74pik3IVfOJ35cJnzSx0WtZqI2zxq1dJzi8gRFf79xj3Q9o2RtpJgxNgfAgwDqAQgA7uWc38EYqwLwMIAmAD0ALuWcj0bKfBPA1QBCADZxzp+NLD8TwP0AjACeAnAj55wzxvSRbZwJYATApznnPdnap1Jipow5ZqouUhMb6tOM8NbHjfAatSrcfukyCALg9gVhNmigYki48PqDQN/oRMJkvNcODGB+zdQIuVyKRSlLtu3atQs3/nQH7I0tsWXO3gO444vAOeeck8eaEYWGXqfBAocRD165Eu5AQEIn3BsLdh94ZR++tuY0vHpwGA12w7SfVil11yKKDyW/tdI0QaXlJnziwdiETzoYG3WLpwmMyqRBDI4rC97HvWmCRq90kJqu3IRMObdfvJxcEK5SAWadOmGinVmnjqWmpMNq1Ij+fpYMnTOzmT4RBPBlzvmbjDErgDcYY38GcAWA5znn32OMfQPANwB8nTG2BMBlANoAzALwF8bYQs55CMDPAFwL4DWEg+I1AJ5GOIAe5Zy3MMYuA/B9AJ/O4j4RWUIqSG+vt4nOhG+vn/J4rzJr8d6gG5vjZvBu29COavPUIxqrUS2qPmEzTs26lUuxqLPpcfNFLVg6pwYD4+ETdOfR4YQUjGLG3tgCR8vSfFeDKGDcHh/6Rn0YGPejzmZAlVEDk1bcdbLWasC29W147I0juHTFPDy66wi2v9EXU6P40w3nYmji1O3fBYHjhXcH0HncGctT7Jhtx6pFdRQYlxhKc3yVpgk2VZtx92dPT2lbcuXmVZpE+8AcGRWJ2nRBuMw1pS6NapNc8D6rwiharsEuvT2l5Wqt4tdUh4zMqcNsQI/WjYW11thA16QvgFqL9P7VWw24+eOnYXDcF/v9HFa9rD10lKwFxZzzPgB9kf/HGWP7ADQC2ADg/MhqDwB4EcDXI8t/zzn3ATjMGDsAYCVjrAeAjXP+KgAwxh4EsBHhoHgDgO9EvutRAHczxhiX0yQhcsp0R3T6JnzY1TOEX11xFkYmfKi26LHjzaNY0VSJ5oj24Kg7GAuIgcjj28e78PC/nY25kblCE14Bf9nXl+IVHx+Id6QJwDsiAfjCejN6hidxeUJg3Y6F9fnJx842QjCAzs7OlOUrVqyAXl8aNwJE5rg9PvypazCpf7TBYRWTMmxHhUkNMAPmVs/FgHMSLx8YBRDun9f/9i08telcnN1cc8r1OHrSjf0DqXmRLQ4Lmmry//SLmDmU5vhOR1rNH+QJbUtOKQEANBomKlmmlZFlW+SwiF5zFjmkJ4XVVehE5UPrK6RzfBfWiG9vocz2WqvNouVaZfqbTiMu5SaXGzyn0oQ3jo7iK3ET5m7Z2C57kzGn0gSDVp3w+2VSLkpOJtoxxpoAnA7gHwDqIgEzOOd9jLHayGqNCI8ERzkeWRaI/J+8PFrmWOS7gowxJ4BqAMNJ278W4ZFmzJ07d8b2i5BH6V1+PAMuL7a/0Yftb/QlLP/kmXNjJ8W+NHlg/S4vlkXeD0/48NzeYTy3dzjxe86YahNarVpU71gbETZ/L01OcVPNSqycL38nWmxt0dV/BHf3eFF/eOoERikVpYGSttjdPyHS/sOTXl860RuTMqyzGuDy+nBwyIPvP7MPR0Y8sVngD712BH1ObyTtSNnEugGXTzRP8Yy5lRQUFxly7XA6kzKVpAkqDcL7nT5RibTmmmWYV52+3LtD4/jJi4mSbD95cT8W1VuwbE56GbG+UR9+8uKBpHIHML9mGWZXpN/ee2m2t7jegqUS2+seEC/X7DBjRZxnQDJjkwHR43Lrxo60ZQDgyMlJ3Lwj8Vxz844unD6nEgtq0+/f0VHxcmfMrcyoHWQ9KGaMWQA8BuA/OOculj4hROwDLrFcqkziAs7vBXAvAKxYsYJGkXPITEgvZZLj1WAXf7QTbyPrsOjx3xsWoaWuMpb6cKB/FDWWqRHP7j4nPv+rN1K+5+Frz8ayOZXTzikuxrZobZhPKRUliJK2mLb9j3vx61eP4devHoNBq8KvrjgLbx0bxy//dihlFvjVH2zGT/7vQEbuW+lQmqdIFB5y7TDXEqJKg3C3P4gjI54EFSUAsm2yz+kVLdfv9GLZnPTlht1+0XJykmwn0myvz+nDUont9bvEyw245Ccgih8X6VzkIyfT20pLBcXTVbbJalDMGNMiHBD/hnP+h8jiAcZYQ2SUuAHAYGT5cQDxP8lsACciy2eLLI8vc5wxpgFgB3AyKztDKCLTBiqVYjG30oRbNrbH7v6ij0PizTvaGmyi67Q1TNnI1lhVODikS0p9aIPDOjUK2uf04jMrGnFhRyOGIiPFz+zpjZ2gMpF1I4hSxOMJpG//kQAl5gBp1oAxiPZ9FpkA+801i+EPCnj14HBGaVV+fwidJ5zod3lRZ9XjmnPmYOncGnh8QZj0YYH+uVWlmcZUzuRaQrTOZsC8amPMbAIAntjdKxuEz6syY8U8Oy7/QPMptclMBnTEsBvEJ5TJSrmlKWeRcbSbZRe/OZHL1a0waUXLVZikJdnS1VNu/6Z7E5VN9QkG4JcA9nHOb4/7aCeAzwP4XuTv43HLf8sYux3hiXatAF7nnIcYY+OMsbMRTr+4HMBdSd/1KoCLAbxA+cSFRSYNVC7F4ujoJO56IfGxzV0v7E94HKLRqLBxWSNaay3od3pRbzegrcEOTVze0oBLXG7twatWYl4k77i11giXJ1WnuKXWCCCcUyyaj1XAOcWUF0zMBHv6XbAaVKJ5jCY9w/c/1QGjToMHXzmE+TWn4dzWGtz38qGUvr+ozoobV7ciyDk+F6drLJVW5feHsKPzRGzewIp5dly6ItFGetuGdsypMObykBA5YDq5wUqYW2nCDataJQdhxJhTYcSlZ516m8xkQEcMs14j6gRr0UuHdXaTVrRchVE6SFWrmGg5OYk0k1bcVtqolQ7C9Rq1aC6yXiNdTqltdpRsjhSfA+BzAPYwxt6OLPsWwsHwdsbY1QCOArgEADjn3Yyx7QD2Iqxc8aWI8gQAfAFTkmxPR15AOOh+KDIp7yTC6hVEAZHJXb5cisVAmsc2yaPNKhWD1aDFpD8Eq0GbctLMJPVhxJ0+cF4AoMJowAXtDjTVrIw52i2sN6OigI07KC+YmAnc/iCGx0NoqTPhwStXhlOQrAa8fXQYa+/ak7Buv8uH+TUm3HbJcnz5kbcTLvZ/eOMYVjZX44fPvpvS5xfdcK7oo9HOE86EibSXf6A5FnxEy29+vAuL6qTzMIniJJcSokpzUvcNuEQne8u1SY1GhfUds9BUbUa/y4t6mwFLZyUO6Igx4vbBotckSJZZ9BqcdEun8k36Q6LlPAHpdIZjox5R59m5VSbJ/XN5A/hpUu7zT188gP/5hHRO8ZhHPBf5FplcZKW22VGyqT7xN4jn/ALA6jRlbgVwq8jyXQDaRZZ7EQmqi4Fy1NXM5C6/3ymeYhGdhDMTo81AZqkPmQTOFUZDRpPqCgnKCyami1mnwRf+900srLXgmvMWwOMPgQO4/flDCesZtCrYjBpcfM+r+Nm/no6ffPYM7D4+hpAQfsLzpQ+3otqsPaV8wf6kNCxPGk1YuTxMgpAjXcqf3KTQvjTXsT6ZNikIHH95d/CUJ6NXmw3Y+sTeWJpHSADu+9sh3HnZ6ZL7V2XS4/G394XTPPxBmHThNI8PLFguWW6WXdx5Vi7NY8InnlMsZ97hDYiX88oE74dH3KLl5Gyzo5DNc46YCRWGYiddYks6O0etOnynPBOjzQBgNajxi8vPgEalxtC4Dw6rHkEhBKshA53iOE3FYFBAd58TfU4vGuxGtDXYZO/qCaLYGZkImwx09rqw6XdvAQA+vLAG911+BtRJfepXLx+GNyDA7RVikkpRNj/ehQevWinaz8AYBIGnnBMbkm6MTXrxfEO5CzRByGHSibctuUmhDov4tSN+IrcYSiejtzXYRNM85NIuNGrgU2fOTUjz2LKuDTJZCagwavGtixZj2O2P6f9Wm3WolEm7sBo0ojnacrnBzdVm0XLzZXLJDVq1aDmDTLpGFAqKc8RMqDAUInKj35ncDDi9fmxa1RrzK49KN417w7NoVSqGC06rw8PXnp0QiMZvJ5MJfS5vAH1jvpR8SH1cQFtpUuMHFy/FgcGJWMdfUGtBpTncoYJBATt296aciDYua6TAmChpksX7lzbacP3qBTg46EnRLa6MTKJx+4KoNOnwyTNmxy5Qj71xHJ5AMCXPcMvaNvzkhfcwr8qUck7smGXHtg3tscfTD7xyKDW3f0M7Kkxa0aCaKG6UPmVVUs4fCuErFyzCj557N9a2vnLBIgRCgmQ5rYaJ5s7qZXSKlaolZDKPRox+py+Wpxvd1tYnuvHAlSslpeNGJv3whxL1m79ywSKcnPSjWWJ7Jq0a132oJSU32CQTpM6rNuP6D7em5AbLTbCssWhFt1djlg7eo1BQnCOmKxNSiGQS8GZyM1BtNuDhXXsTcoAe3nU09hhIEDhe3D8Ycxja1+fC0IQ3wb0qkxQLBlXafOEoDpsBu4+Ppwh/OyIzbLv7nKL5Zq21lMtIlDYtNcZYILqw1oKrPjgfwRAT1S1+4MqV+PBp9agya3DlOU24/c/vxfrTTR9dCLtBh82PdyfmC750AGuXNoqeE3U6NTYunYXmGnMswGmrs2JRnQVHRz1gAO596SA2P96V1Sdw5ZgCl2+UPmVVWq7Wqke1RZeQc1tt0ck6sHEBormzd35aOp1BqdoFEA6Ml82pPKWUoUmFcoY6tSp2oxAt86Pn3sXD154tWS7EuWgQ/ttr3idZrmfEHQuIo+W+/XiXbG5wICS+ve3/Ll3PKBQU54hcay3mgkwC3r4xj3ju35gnto7cY6BM3KsySbHIJF/4vX63aNA7typsztHn9IqOfBVbLqOYIkVnZyc4pws8IU53/wS27zqCH1y8DA6LDq8eGoGKsbQ3+199tBM//9yZsYA4+tntf34P91+xUjTvT61C2nOiTqdOMQmwGrX46i9eS6hDtp7AUQpcflD6lFVpuTF3AN/8w56Ua/XD/3Z2TKVIjJOT4rrBo5PSusFK1S6UUmUWT/OoMkk74Q2Oi18/h8alJ/YNR9KukssNT0gfl54RcZ3iHpnc4AFnmuu805co7psGCopzRK61FnNBJqPf2jT5wvGPeFQqBodVhx9dvAxufxBmnQZWozp2ocnEvSqTCX31afKF609hot3sSiO+8KHmhLyqL3yoGY2VxSUFJaZI0fv2y6hoXZHHWhGFzIDLh11HnNh15C3c9ZnTIXDAYRXvUw6rAZUmHVwe8VGpcV9AtNyKeVWndE7M5RO4Uk2BK3SU/sZKy6VzR+2Lc0cVw5wmz92kkw6zpuvAdqr4QyHRdMWAIJ0eYtCqRfdPL5MGYTGIlzPrpcuZ0+gUm2Uk52xpdJFtMrnPUSgJMkdEg7anNp2L31/7Pjy16dyiH2GIjn7Hkzz6PToZwKZVrbH1oh1wdDIQW6dnxI3fvNYDq1EDvUYNm1GD37zWg54RN4DM3auisj1nN9eg2WFJObYdDXZsW9+eUJdt69vRETcxITrRLnmfohPtTFo13P4Q7n3pEO5+4QB+/tIhuP0h2fyoQiSqSBF9mWsa5QsRZUt836g2a6FmwB/fPIpt69tS+pRZp8IPL+6AVs1E+1OFUYvbL12eUO77n1qKDzRXn9I5MZNz0EwhFWQR2SOaXvClD7fg+lXh17xqo+xvrLRtRCfMJZeTmzDnC4bwnx9ZmNCm//MjC+EPSaslDLjCTx/j96/SpJN1igPCTy8ODU3g1YPDODQ0AUGQt2moNuvx8K6juPqDzbh+VQuu/mAzHt51FFVm6f3Ta8M6xfH7d+PqVuhkcqYbbEbRcrPs0gNJ/kBINHbwB6WPp8sTEP0dXN6AZLkoNFKcQ3KptZgLMhn9rjRpYx0wPl/4hxdP3XNP+AJYfVoD/v2hNxImKLh94UY8r9IkmnM1J+nxktPjxbv97ph+8KJ6M+xJ+sGzKnT49RVnYXjChxqLHiEhsYNVm9Wi5gTVlnDQOzThTztq3Vw7M8eVIAqRjnobtq1vx/ZdR2DUqTG/xgyzXoNdPcMJfeqRfx7F5p1duHF1K+ZVm2IC/JUmHS5ZMRtzq0xQqxg+sqgWz/7HuRhw+jDs9qGxwoRe5yQmfEG4fSEMuHyot+nR0WCHIc1M9Vw+gSvFFLhiQGl6QSZOqGJoNWEjmoND7tjTwGaHWXbC3OwKE4KhEB64cmXsaeWk34/GCunt2YwaXP7+eSmmGDYZdQZB4Hjh3YHYXBs1Azpm2xPm2ojRVG3GzR9fgj294XIaFXDzx5fI9hmH2QCrIVHf2GrQoNYi4/RXbcaSBmvik2CDGvNktldh0uPhXd0pscMdMjna1WYd+sY8CfU0alWoNkunh0ShoJhQTCYpC2a9Gl/+6EIciJxgNCrgyx9dCEvcoxNfQIjN2AXCgeaWnd14KDIBTqNh+OL5LSmzerVxJymnx4tnu4ZSnOYubHfEAuM9fU5c8+CbKRe1h65aibPmh5PF+p0B7DsxhvuvXImhcS8cVgOe3dOLxgojFtRmPmpNEKVCvARha50F/3beAtz6p724+eNLYNJp0DbLBgD46qOdCX3jjuf349rzmmHRqcMXeaMW331yb6x//vcnOqBRM3wtUs6gVeHnnzsDA85khZh2rO9oEA2Mc+l2VoopcMWA0vSCo6OT+P3r4Rz4eD1euXIqxjA84U+Yw/LVCxdhgcxglloNDI0HsHnn2wkDKgtkBksmfSHRgZYV86Qnbh8ZcePQkDuhnjd9dCEW1LgxX6KuwaCAsclAQrnvbmhHMChAJyE712A1wGbUYjAuh9hm1KJB5qZQEDiG3QH81x/3xLZ36yc6ZFViQoKA/1i9EIdHpmKH/1i9EBzSo+HBkID/fvqdlOv8g1eeJVkuCgXFxLSQG/026zQpHfBbFy1OyLNKm8cb6XwDLl/aoDkqIfNuv1tkFnwXmmpWxow2+tNspz9uop1Wo4LbH4TAOTgHOOdw+4PQRnKgMx21LmXINrp8EJMgvP3SZfi3cxfg2KgHGhXDyHgAKpUa15wbFmZ67I3jMSMDk06NxkoTBA58NcmB7lt/3INrz2tOWKZTq0UUYrowv8YUu3FNJldP4HJtN0yE6U8zuVnOTGPE7cOqxfUJerybVrVixO2TLOf2hVLcFn/47Lt44MqVacsAQH+c3Ge03Oad3XjwypWYW5V+e+kmoo24pSei9bu8opNYOxrtkkHxnhNOUVWH5hozzkyayBpPd78rdgMbxaBVofHq90mW29vnjAXE0e391x/3oNVhxlIJxSYGwOlJjB2+sWYxZGJiOL3iMpBOT2YDVxQUg2R2sklIQMJdmzcQvos7p8URW6c2zQS42kger9MTED1pjHmmcoQyUZaos+lFA9p4Yw6rQYUVTQ5cdf8/E0aqrIZwUKzRMNFHeVqZR2ulBNlGlw/JEoQLay1weoIxyaN51cbIU5zEwOOh146gsUIPh8WALz+yG9ec2yzaPxsrjGiwh1VdAGAozQz3fpcPXm8wbRpFrii1FLhiwKxTi6YXGGXmcTCwhNQ9IPz4feX89AEckF5bW+5p4ECatjsgo85Qm2ayqlwOs8sTEK3nuEzwl24iYb9MDnM69YlBmf2TGoyS8lj1BkP49SuHE36/X79yGN/7pLTNc6VJi2vOnR+7sYmO9FeQTnFmkMzO9JC7oRgcF++AQxPemJ2rwEOiouech/N97Ubx2aT2uNmkmVg419nU+NL5rSkpFnX2qZPruFcQHXGOahmfdPtFH+X97t/eJynXU2qQbXR5kGxde815C2IjbwCwdmljylOcO1/YjxtXt2JhnRVf+u1UupJY/+wd8+BzZ8/DQ68dQZ/Tm1bNosaiw849fVjXXg9jhrPIleDyePFO3LyExfVm2IyUM5xPJvxB0fSCpbOlndsmfH58esXcFJWFCZ/0hKv6Cr1oEF5nlw5SM7kGiZHO9ENuAlulRStazwqzdFiXToWpVkaHucIsrgZRYZLeXkUaNYgKk3Q/9gZDor+fLyitkqFiTHSkX04XOVY+o7VKmHQyO1HlAyI90RuKj935Mj7zi3/gY3e+jGe6+xNmwGYyA9io1eH5fX34+efOxB2XLcfPP3cmnt/XB4NWG1tfbPZq/Pc6LGqRWfBtcFimAt4BV0g04B1wheLWkR5xTq/VKP2oiyCKkQa7MaGfeXyJOfWMQbQ/NFWbsfv4WOyzx944LjqT/JFdx3HnC/vxyTNmw6BVISiERPtxdPLenj5XRjPsleDyePFM1xAu/9XruOF3b+HyX72OZ7qG4PKQukQ+mfSH0szjkFYhsOp1sYAqWubOF/bDqpcOxtxe8Rxft1d6exadGluT2u7W9W2wyNhD+4M8ZvoRVYP46YsH4A9Kt3M1U4nWU82kwzqtSvx6qlNLlzOq1aLljDL+0Jxz3PTRRDWImz66EJxL759BoxH9/fQy25vuNbrsR4pL0WkuV2Si29lUbcZtlyzHlx+ZGom/7ZLEySltDTZ85n3zwBCeYcoAfOZ982LmHQMuHx589UjCLNQHXz2Cltqp36frxCTsRoYHr1yJgXEv6qwGjE5OouvEJOY7KmLfI5diIadlbEyr1Vj295dECdLWYEuYwW+PG/VZ2mjDWU2Vov0hJHDo4zTK+5xePPTaEVx7XjPmVJrQMzIZGx0GgIV1Fvzicysw4JxEk8MU68fVFj0e/edR/OHtPgDAwLgXPSPurJyb38lgXgKRe2xpnhTaDNLBrcsrnnYnJ80lN8clHQeHJ/GPg8P4VZwSy6P/PAqzToMljRVpyw2Oe9FcbcbK+ZUYdQdQZdZib++YrNSfUonAE04vnt7TlzAB8RcvHcT8GrOkDnP/uPh1uLlGeqLpZCAIvVqVoAahV6vgkbmpmfCKT2qf8Eqnh1jS6EXL6SJHKfugmGR2lJPJDUUwKECjRliOxReE2aCBRo2Ema7+QBBDrkBKWoM/EIRGo4NFr8HopD/BKSjcyKea7/waEw4OTeBvB4chcODA4ASaHWYscExNgGuwy5t36DThu9/kR1K6yEQ7i14j+rlVRlCcIIoNQeA4OjqJBQ4z/vfq92F4IiyRtmVdGx574yg+dcZcbH68S9QI4PjYJOwGDX586XLs63dB4OH8faNWjVG3L6UvV5q0eOvYGPwhASfdASyfW5miZmHQqlBt0WdtwCKTm2YiPfEqJQ12I9oabAkmTUqJ6s7++C9TVuGZ6M6mC44sMufqtHNcZHJ8GysMWDzLnjAf5aaPLsQsu3QsMbfKhDUdqZKkc6ukJ29XW9I408noDdfZ9LiooyFhAuKNq1tl96/KrBO9DlfKOOGZtFr8zzNvpdTzQZmJi9UWnegcoCqLzPYiajfJ12iTzIh9lLK/kpeqzE4uJg9mckOxt9+FIyOTsVmy0RPFXqsLy+eGZ5529Y+nGaExYeX8aniDQdELb7yItz8o4MSYN8UKOl4VIl3AGz/K2+f0pr0bXjYnnP/cXGNK0FzUqhHLfyaIUiBeA1WjUqHZYcYsmwGvHz4JlYrhpgsWxy7+D70W7i9qFXDmvErc9uy7uPjM2dBrNfjPuPPqt9cuwcOvH8Xl72+KnTcMWhW2bWjHtx/vwpERT2w9tUoQ1Qvf8eZRXHf+wqzs86nmhNIE7SnEVEpu2diOjcsapx0Y16TRna2R0Z0VwEXP94KMfIE3EBRte76Q9AhlUBBE1SAeukpaCmzSHxJVV4rOY0lHKBTClrVt2PrkVD23rG1L0d5P3b8Qfv/PxAmIv//nUSyTydHmXPx4ykmkpZso7/RI39QwBlz3oZbYpF6DVoUt69og18UG0oxoz5cZ0Y5S9kFxKcrs5GryYCY3FBO+oOiJ4r7Lp+yE5UZoai06fEfEAOQnn10eW3/cJz4Z497PnRlb58RY+oB3acQTvcqsFb8bjsxcDYYYBly+mKJGVGLOLnO3TBDFxNGTbuwfmEi4yfzBxUthNWix9cm92LquLdbX+pzeWH/54cVLsaa9AcNuP+5NUp357pN7ce15zeh3eXH1B5uh16iwcn4lOo+NYd2yxpiU23ef3Iv7r1yJfSfGYkYI1RY9drx5FOcvbsjagMXiejO2rW9PeWK1uD51ezRBO5FklRJvIDwBubXWgmUSsluZEOJcVHf2f6+WDhrd3pDo+X5xvVWynMOqx4g7kBCEazUqVMuMwI5Nigd/o5MyqhUKn1CoVCo89ubRWBqEUafBg68cwlcuXCxZzh8Sn8DmD0lPYBvzBESPp5x+s0GrEh3xlUs5HPdOqdwA4WOy9YnuhGu6GA6L+Ih2jcwIc5SyD4qB0pPZySTXd9zjxb64Wdan1ZthPcVZ1pncUKQ3u5i6m5UbofEGICqDFj+J2B8QRLfji1tWYdImzOhlDNBpWIInukYlPpqsUYU7sFatEpWY+83Vmc1sJYhiYMDlS7nJPDA4FSSb0jyanl1pxLHRSTTajaL9cX6NGT945l0AwOXvn4crfv3PhAtzNM/Y5QmgZ8SD/3j4bfy/Ty8HB8d15y/M6oCFzWjAmnYHmmpWyqpPZHKOLSeSVUqAiOyW04tlc6b33S6P+DXEJSM9Nq/aLHq+n1slkwPrF/DNP+xJadu/kVEvMOvE+4TcY/t081jkVCu8gZCoDrM3ID1SbNZpRSewPSQzMl1n04sez1qrdLBZZdKJjvjKpV14AuITLJOXJaNmTPQarmaZnTcoKC5B5HJ9xz1ePC3i/nZRu+OUA+Mo6SaSWtKcKBKT3gVRSRqwcJl+lxe3Pfdewh3qbc+9h5s/flrsGyrM8rIvISGE685rSXncJPCpMr1pRpPnVJpwxjxgaCLNzNYJyjskSgexm1mBTylN/OKlgymPbreub8Ntz72DXUecuHF1i2h/NGrU6HN6sWl1S0rQfecL+3H1B5vxy78dgkGrwsYzGvGnzl44rHrJQHMm0xhsRkNGk+pognYiUZWSlPkaMvm0mVCZwbldjEabAV/+6CIcGJqI2SB/+aOL0GiTrlO/RIAvhUYtHoxp1dJtUadhMSv0+KBRzlbarNMk6DBHn6D+6GKp6XLAyKS4WcjJSWl1BjVTiQa3apX0iK83EBId8f1fmSDcalA2Ye5EmhTIuVUmSBtEh6GguASRy/XdN0OzrDN5hKjXpsnjjcsz06jU2N8/Za1cazXgz929aIlcXBxW8TtUR5yu4uhkQDTvON7gQ61Sxy7i0f3e+mQ37o+zf2ywi28reteebjTATBPtiBJiXqUppZ2r2ZTWcGevC3j9CH508TJwhCfF3B4JiAFg+67jov1epWK4flVL2pFktQrYtKoV3368C5edNRf/fl5rQrqExxPAnn5XbCS3vd6KFw+MKEpjmE4wXagTtPOV55ysUhJ9mhdVEJoOXAC2rluCXqc3FtzOshtknc3eGRzHWFKgNzbpxzuD47H5LGKkfXIpo+OrVatg1qkT0i7MOjW0MlJn/S4ffvePIwlpEPe9dBDXr2qVLCeA41/eNy9lvo5cznSVSSe6f5VG6ZHbMU9ANLj9RVwqpBhKTU106rDpRrIJh04mRz3dhMAqmRz0KHQlL0Hkcn1napZ1Zo8QOWZXGhNOFLMrjWBxHVejZuiYU4VX4pQjOuZUxVziDFrx9Il4Df8Ko1b0rvmHcXfN42kew8U7AJl06ohDV+IIWPTu1KIXn9kqp0VJEMWERsRQoKXWkhD4vDc4gWOjk3jw1SP4l/fNRe+YD1/6cEssb/DpPX340cXL4A2EMDThQ4PdgMFxL+57+RCuObdZ9MLcUmvF/zy1D31Ob8qcgEmPH092DaQ84bKbVLjm3OZYTnImaQzTzQkuxAna+cxz1mhU2LisEa21FvQ7vai3G9DWYJ8R9YkQQlCrVQn57bdsbIcAmQllQfHPfWmWR1Exhm9dtBjDbn8sCK8262SPYYhzGLQqLKy1xiZhT/oDCMno8Vr0Grw3OIFNv3srtiyTEVF/QBCVOgvIpBd4/EHRCXqegHQ6ilKJtHSGPA4ZtQuzTg2HRZewfw6LTvZaa9VrREfe5VRHolBQXILI5fpmOstabtQhk0eIeo0GQSHxsVNQ4NBpppqeL41yxKwKIwDAH2S4K/JoNXrBveuF/bjtkuVT3xEIik8eCE51WKtRfJTXapyqi9MTFJ0J/OsrwqPJGjUTHQ3QyDwiK3WEYACdnZ0py1esWAG9XvrkRxQew+N++AKhhHauYgyvHxrCPf96Jsa9QTAAx8cmAQCtdVZRd61qixZjkwxjHj9+/Jf3cNlZc3Htec1YNtuOb69dgu8+uTe2/n9+ZCF6RyfxqTPDs14fe+M4vAEBR0+6MbfKjK4+cZWa+69cifte7kzISZZLY5huTnAhTtDOd56zRqPCsjmV084hToGrRCfxyeXAKv0lRif98ASEhOvRf35kIcY80ukFk74gRieD2LwzsU3X+eR1df/nkx04POyOBeFNNWZYdNLhmV6rxq9eORybwCZw4FevHE4YCBLDqNPgnpe6EwaQ7nnpgGy5dHbUDpkRdI9fXD1KLggf94XwtcdSc7vlVDkW1lox5Pbi3s+didHJACpNWoS4gEW10hMso1BQXKJITR48rd6MH3xqaUKu1QKHBafFzbKOl2SKrtMx245Vi+rigmuD6KzS+EeIE76g6KSFaJAJAB6/uINQdJTI5fXjsyvniehUTp2kTDrxkeKV85fH1nH7g/jmmsUYmZwaAagy6RI87UfT5FuNRh7D9Tl9+NlfD8X85kMC8LO/HsLW9W3omJ3Zb1OKuPqP4O4eL+oPT40MOXsP4I4vAuecc04ea0YowRsM4Zd/T7zg/uDZd/DttUtwcGgi4ZHmplWtUDGI9uHrP9yCR944hm0b2rFuWSPmVplwy5/2obHCiLtfOBDrr20NNgyNexNUXW5c3YoqsxbOyQB6uBsD6SzjI8vjc5Ll0hhmIie40CZol2qe89CE+Dl5eELeocztD6UMtshh0mli15rotn78l/cSrlliGLQa/HlvxBTDF4RJr8EDrxzC8jmnSZbz+IMYGvcl1POmjy5Eg4yt9JgnIDoQJCd1NjLhS3DLYyzsqjciMy+GQ8BNH12Ykq4hl8diNYhfm3986XLJcumd6aTrqdGoMOnj+PIjU7rPt12yPOOnFhQUlyFmvR4qFUvohD+6ZBnMcSN6YpJMN65uRYvDgqaa8Al2llV8IsOsuAvSSXeapH731AltMs0sU48/vMyi14qepOLvGJkqjaZhXD+oNulwcNCdcvKJz6WqSSOIXmOOOtqpRPOVDDLyMuWAtWE+HC1L810NYgbgnItecMERC4iBqQlyP7x4WWxZg90Qu2mcX2PGVR+Yn2BMcNNHF6LClJj395ULFuLu/zuQElTfuLoVdTYDbEZN2jxeR+R8E81JlkpjiOYkB0JCQeYET4dCzXOeLrY0E66sBunwxR/ionq8HY3Sec7pnPDGZcxCvMEgPnXG3AQ1iC1r2+ANSo+IBgXIypaKUWEUV5GQG0l1WPWiT3VqZEZ8T7qD+PXfexKC21//vQdN1W2S5ZzeAK7/cHieQHR7393QLmu+UpdmZLpWpp49I+6Ygy4QPi5ffuRtnNaQ4VMg2TWIkqNnxI2vPLI7odF85ZHd6Blxx9YRk2S64/n9CXnH7wyOo3fMg3tfOoS7XziAn790CL1jHrwzOB5bJ5pPFE9yPtEsm/g69fZwsDqWZvR2bHKqU036Qrjnr4n+8ff89QAmfVP5YyqV+MlHHZeiNOkPT9iL1icaDExGHvVEHe3iPydHO6LUMGg1ohfcoMBF+6JaFe4LDXYDPnf2PPzyb+Fzwpcf2Y3JQCgmvxTrcwz45prFsX7kD4lLKrr9IXzrj3vw1lEnnuvqxbb1bQl9b9v6Njy7pzf2fvXi2lgOrSBwHBqawKsHh3FoaAKTHj+e6OrH5b96HV9/bE9KPz6VnODk7xYEmVlfOSCa56x0nwoVq0GDmz66MGG/bvroQtmgOCiE9XijbfG+lw/h0yvmIihI59w6LOLXIzmdYqNWIzqR26iVruekP92gkJwNsnjw7vbJmGKAiV7bVTIJJ1VmcUnTKrO0CohZp8Hd/7c/4dp89//th0kmPUSjZtiyNrG/b1nbJpuqqNT+OrbdjNYqYBhjawDcAUAN4D7O+ffyXKWCJ5PHbJn4xru8AdHO1TbLFlvHpFOLugPFazcGBYjOMo1qiZsy0H+c9AdxZMSTMIIbXj51Yulzij+O6Xf6Y+YdapVa9FHPLRs7AAABgcOkTcwpNmnVCMpMpiCIYmIozaNLITKZKLkv6jUq/PcnOtAz4k4Jpu94PnxBjPZNb0BAZ68TZl34BtMTCOGseVWi38sjMnCMAff9/RiuOQcxQ486qwGvHhjAr189FgsAOxorYgFx8qSz//5EB37yYrhuUefKa89rRvssO1pqLRnnBBeqcUch5jnPBGMeP2qt+oRzbq1VD6dMjq8xzY1dvNqQGIJC5zalj/uVSs6ZDeLlTDrpcv1prv/9MkGjw6rHFz7Ugu/EPY39zro22ZziQFAQvTYHg9I3J3ajTtSc5AcXL5csN90nJkU9UswYUwP4CYCLACwB8BnG2JL81qrwiTaaeJIbjd2oFV2nIk7ywZuBuPa4N4jtu8IN+/uf6sAPLl6G7buOYjxu8kHvmBf3vXw44U7yvpcPo3cs3EmjXubJo7Mm7VRQHJVKS65vfOBs0oqvY4z7HqtejcvOmhpd+OXfDuGys+bGZrzWWPT41SuHYwF7SAhPbpAbRSCIYqLaohPtK0dGJlP64nc3tKPSqMPwuDet1Fq8br5Bq0JIAH78l/fgCYRg1Krxw2ffwVcuWJTyhOYPbx6PBcdAODAecftQazWgzq7H2uVz8ftr34enNp2bEJSKTTr71h/3YO3Sxlg9+pxe3Pn8AfiCApodloyDx3QT2uKftOWLaJ7z2c01p7RPhYxRq8E3/rAHdz5/AHe/cAB3Pn8A3/jDHhg00mN66Rzm4p8wijHgmrIJjl6PHnz1iKw6U02aPiMnBTbpC4o/nfRLp104PeJPNeXSEurTXP/rZILGYAixgBgIH8vvPNENGTEPNFQYxZ8ERybSp6Op2oyrPrgAX3t0N77+2B587dHduOqDC2SffEz3iUmxjxSvBHCAc34IABhjvwewAcDevNaqwMlETqjWqhdNqo+/K6xOk39bHXcS8PhD2HXEiV1HpuRmgHBAHaXOphfN043qQo5MiHuZt8TlB2UinK7TiK8T/0jIHwqhMUlCrrHSiEDET76p2oyvrzmtoKSYCGKmqbPpU2SNNq1qxYOvHgEA/PDiZfAHQ9Br1BhwedHv8sLlC8Ht94ieE6KxWfR7HnrtCLwBAYvrbfjuk3vR5/RiaMKPr1ywENUWPQ4Pu/HQa0cwOumPrR8tv7jelpAbuKA2NU8w3dOwZMlYg1beOSzT7y72CW2FSrqnFsNu6SC1yiyuxysXpDqs4tcjOQmxCpO4FFilSTrMsujFJ6LFqyuJUW3W4VaRcrfLlOuYZce2De3YHJfju21DO5bOks61Hkw30XXCK9oHoyiVL1T65GO6T0yKPShuBHAs7v1xAClejIyxawFcCwBz587NTc0KmEwazdwqM5od5oTgsNlhTrDIrDbr8I01i/G9Z6ZmjH9jzWJUx3mMz6s2i56Y4r+n0iSeYlFpDo/OVpnFT1KVSSc3Mam0eKwGjeg6lrjctGqTAfe9fAj/cvZ8jE0GUGHS4jevHcbXLlyS8bGTopzaIsm0FTZSbXFOpRl1tgnc+7kzsevIKEICYnJnBq0KdqMWgZAKo5MB/PC5d3HNuc14YncvrvrA/JQbz1s2tqOxwgCgJeV7Kk3aOGUXL2750zuYV23E9z65FDq1CosbbPjuk92x9TO9AU33CHX57IrY8vB5ph0d9TaJb8r8u4t9Qlu+kDsnppMCq8lA5/a7G9pTJnglXxeSEbsh3LKuDXUyahAtNTYcGfHgRxcvi+kUazUMLQ7p9iVAfFKrXLpGR4MdX/pwa0pw2yET3Op0amxcOgvNNeaY3OrSWXboZI+LsnY/nWumUoWX6SjDFHtQLHZUU1oS5/xeAPcCwIoVKyj5E/KNRqViWLWoDs01FsnAeXbVRKIxR5UxIeCdXyN+lzi/ZmqdeZVWnJz0x3IFa60GgIUwrzKsK9hWbxM9ubXHXczSOcrFJ/MvrLXh0PAkhuOUL6oseiyqnfqe+Q4LNiyfmzBb/rZLliccp+l0uHJqi7mQafP5fNi1a1fCMr8//PvqdIk3TRSMJyLVFlUqhvNaavHPIydRZzMk6Al/d0M73ulz4va/7EelSYerP9gMi16N6z7Ugnv+egCXnTU3bDXLgEGXF3ajFh5/CLMrjAk3vptWteLevx5MCUC+eH4Lfv33g9h4+ly01prxg08tw6Q/iLlVZsyvyeximm506uymSjx41cqYI15HvQ1Go3QOZqbfTU+LlCF3Tqyz67FtfXuKaUu9TJA6rypsFpOgVysImFclHcTNqTRjVoU7Ibi1GtWYUyn9+2o0KqxeVIfuPucpGZhUm/WiI8Vr2uslyykNbqNlVzRVya4Xz3TafaHJF0pR7EHxcQDxUuGzAZzIU11KjpkInDO5S9Tp1Di9sRqdJ5xQMQa1imHprOpY5zYYNFjX3oCmatPUxazBDkPcCO/iOhsODbsTAt4Ksw6nxQXOGo0Ka5bUY06lMe1JS6ViuKi9Hqc1lNZklXyRbZm2Xbt24caf7oC9sSW2rPftv0JtqUZ9S3tsGWkmnzoajQrvm1+NepsBv/z8Ckx4Q2iw67G4zoYTLg9qrAZ864978JP/OxC5eVyGuy47HW5/CCadGoGQgCUNNsytNOHo6CRsRh+2X3s2jo568N7AeCw94ooPNuE3V78PfS5vTIbprKaqWL+bV33qF1Kp887K+dXTOi6lOqGtUJldYUaTw4NfX3EWhid8qLHooVZzzK6QDsYsRgPOmleBvf1uqFgQBq0aS+ptsBjlRzbPbalFz4j7lH9fJQYm00nJUxLcKqVc2n2xB8X/BNDKGJsPoBfAZQA+m98qlReZ3AFmso5c5zYYNDhL4mKm0ajwsbYGdFdJ36VnctIqprvaYiMbKRX2xpaEwHvs+AFoKxtkg3GxUebp1qXUUKkY5jssmJ/UF5pqLJhbZcbyORUZXSDj+1N7YwWWNNjwgQXVWb2wZrMf0zkid6hUDCvm1qBnxA3GcEptxmI0YOX8U09ryeXvW0zBZjm0+6IOijnnQcbY9QCeRViS7Vec8+48V4vIE1mzGSVmjEJyvhMbZR49+g7+7UOdWLo0MaDOdqBcjAF6PvL9iPKk1NtMqe9fMVHUQTEAcM6fAvBUvutBEERmJKdU5HNCntgo891/3pvzoF0sQBfbbjEGzwRBEMVC0QfFBFGKOHsThc7dw71Qe70YMptLbllf1yv4n9fGUdnQNbXeSB++/OmPpozYJtPZ2Qln7yHZY+XsPYDOTiGzspbUNB2xoH0mSff9ycs7Oztx28N/hrm6IbbMPdKHB279MuVLEwRBTBPGy8yJizE2BOBIljdTA2A4y9uYKYqprkDx1HeYc75GagWZtpjv/aTtl872i70tFkodgMKoRzHXQbItZvH6XAjHLArVRZxc10W0LZZdUJwLGGO7OOcr8l2PTCimugLFV1+l5Hs/afvlvf14CqEuhVCHQqkH1eHUKaT6Ul3EKZS6FLXNM0EQBEEQBEHMBBQUEwRBEARBEGUPBcXZ4d58V+AUKKa6AsVXX6Xkez9p++W9/XgKoS6FUAegMOpBdTh1Cqm+VBdxCqIulFNMEARBEARBlD00UkwQBEEQBEGUPRQUEwRBEARBEGUPBcUEQRAEQRBE2VN2QfGaNWs4AHrRK9svWagt0itHL1moLdIrRy9JqB3SK4cvUcouKB4eLhTzFqLcobZIFArUFolCgNohkW/KLigmCIIgCIIgiGQoKCYIgiAIgiDKHk2+K0AQmRAMCujuc6LP6UWD3Yi2Bhs0mvK+p6NjQhAEUfwIAkfPiBsDLi/qbAY0VZuhUrGslSPSQ0ExUfAEgwJ27O7FzTu64A0IMGhVuGVjOzYuayzbIJCOCUEQRPEjCBzPdPfjpu1vx87lt1+6HGva6iUDXKXlCGno6kkUPN19zljwBwDegICbd3Shu8+Z55rlDzomRCETCoVw6NCh2CsUCuW7SgRRkPSMuGOBLRA+l9+0/W30jLizUo6QhoJiouDpc3pjHT+KNyCg3+nNU43yDx0TopA5cuQIrvnJ09j0uzdxzU+expEjR/JdJYIoSAZc4ufywXHpc7nScoQ0FBQTBU+D3QiDNrGpGrQq1NsNeapR/qFjQhQ6pqo6WByNMFXV5bsqBFGw1NkMoufyWqv0uVxpOUIaCoqJgqetwYZbNrbHTgDR/Nm2Bnuea5Y/6JgQBEEUP03VZtx+6fKEc/ntly5HU7U5K+UIaWiiHVHwaDQqbFzWiNZaC/qdXtTbDWhrsJf1hDI6JgRBEMWPSsWwpq0eizedi8FxL2qtmalIKC1HSFNQQTFj7FcA1gIY5Jy3R5ZVAXgYQBOAHgCXcs5HI599E8DVAEIANnHOn81DtYkcoNGosGxOJZbNyXdNCgc6JgRBEMWPSsXQ7LCg2WHJSTkiPYU2rHQ/gDVJy74B4HnOeSuA5yPvwRhbAuAyAG2RMj9ljKlzV1WCIAiCIAiiVCiooJhz/hKAk0mLNwB4IPL/AwA2xi3/Pefcxzk/DOAAgJW5qCdBEARBEARRWhRUUJyGOs55HwBE/tZGljcCOBa33vHIshQYY9cyxnYxxnYNDQ1ltbIEIQW1RaJQoLZIFALUDolCohiC4nSIZZNzsRU55/dyzldwzlc4HI4sV4sg0kNtkSgUqC0ShQC1Q6KQKIageIAx1gAAkb+DkeXHAcRPMZoN4ESO60YQBEEQBEGUAMUQFO8E8PnI/58H8Hjc8ssYY3rG2HwArQBez0P9CIIgCIIgiCKn0CTZfgfgfAA1jLHjALYA+B6A7YyxqwEcBXAJAHDOuxlj2wHsBRAE8CXOeSgvFScIgiAIgiCKmoIKijnnn0nz0eo0698K4Nbs1YggCIIgCIIoB4ohfYIgCIIgCIIgsgoFxQRBEARBEETZQ0ExQRAEQRAEUfZQUEwQxP9n78zD26rO/P892jdL3i3FiTfiLFh2QmrCMoW2SYHQZitrl2nKNkxnyiSddG8pmQQ60zUzoTC/DqWl0BUKLSRMS6FJKWXoFmgWm5CFEJsklrfYkizrarvn94esay1XuteyLcvx+3mePLHufc857z331bmvzn3PewiCIAhizkNOMUEQBEEQBDHnIaeYIAiCIAiCmPOQU0wQBEEQBEHMecgpJgiCIAiCIOY85BQTBEEQBEEQcx5yigmCIAiCIIg5DznFBEEQBEEQxJyHnGKCIAiCIAhizkNOMUEQBEEQBDHn0c20AgRBEMTsJBaLoaurS/pcX18PrVY7gxoRBEHkDznFBEEQRF50dXXhjgd/DUt5DUbP9eLhT1yLpqammVaLIAgiL8gpJgiCICZEYoa4u7sb5rIa2KpqZ1olgiCISUNOMTGnEEWOU4MB9PoE1NhNaKiwQqNhM62WIrNVb+L8JDFDHBwegG3ewplWhzjPofGPKBTkFBNzBlHkeK7Tg61PHIAQEWHSa7DzpuVY0+Is6gF2tupNnN9YymtmWgViDkDjH1FIKPsEMWc4NRiQBlYAECIitj5xAKcGAzOsWW5mq94EQRCThcY/opDMGqeYMfavjLFOxlgHY+ynjDETY6ycMfYCY+z42P9lM60nUbz0+gRpYE0gRET0+YUZ0kgds1VvgiCIyULjH1FIZoVTzBirBbAZQDvn3A1AC+CDAD4PYC/nvBnA3rHPBCFLjd0Ekz7V5E16DapLTDOkkTpmq94EQRCThcY/opDMCqd4DB0AM2NMB8AC4CyADQAeHTv/KICNM6MaMRtoqLBi503LpQE2EZvWUGGdYc1yM1v1JgiCmCw0/hGFZFYstOOcn2GMfRNAN4AggOc5588zxmo45z1jMj2MsWq58oyxOwHcCQB1dXWFUpsoMjQahjUtTizZfAX6/AKqSwq/ijkfWywGvYnzDxoXiWJAyQ5p/CMKyayYKR6LFd4AoBHAPABWxtjfqy3POX+Ic97OOW+vqqqaLjWJWYBGw9BUZcOlTZVoqrIVfGDN1xZnWm/i/IPGRaIYUGOHNP4RhWJWOMUA3gvgLc55P+c8AuAXAC4H0MsYcwHA2P99M6gjQRAEQRAEMUuZLU5xN4BLGWMWxhgDsBrAEQC7AXxsTOZjAJ6ZIf0IgiAIgiCIWcxsiSn+M2PsSQCvAYgC+BuAhwDYADzBGLsdccf5xpnTkiAIgiAIgpitzAqnGAA459sAbEs7HEJ81pggCIIgCIIg8mbWOMUEQRBE8cLFGLq7uwEA3d3d4HyGFSIIgpgg5BQTBEEQkyY4PIC7nzyLUtcABk92wjZv4UyrRBAEMSFmy0I7giAIosgxl1XDVlULc2nlTKtCEAQxYcgpJgiCIAiCIOY85BQTBEEQBEEQcx5yigmCIAiCIIg5DznFBEEQBEEQxJyHnGKCIAiCIAhizkNOMUEQBEEQBDHnIaeYIAiCIAiCmPOQU0wQBEEQBEHMecgpJgiCIAiCIOY85BQTBEEQBEEQcx5yigmCIAiCIIg5DznFBEEQBEEQxJyHnGKCIAiCIAhizkNOMUEQBEEQBDHnIaeYIAiCIAiCmPPMGqeYMVbKGHuSMfYGY+wIY+wyxlg5Y+wFxtjxsf/LZlpPgiAIgiAIYvYxa5xiALsAPMc5XwJgGYAjAD4PYC/nvBnA3rHPBEEQBEEQBDEhZoVTzBizA7gSwPcAgHMe5pwPA9gA4NExsUcBbJwJ/QiCIAiCIIjZzaxwigE0AegH8Ahj7G+MsYcZY1YANZzzHgAY+79arjBj7E7G2H7G2P7+/v7CaU0QaZAtEsUC2SJRDJAdEsXEbHGKdQBWAPh/nPOLAAQwgVAJzvlDnPN2znl7VVXVdOlIEIqQLRLFAtkiUQyQHRLFxLQ5xYyxRYyxvYyxjrHPbYyxu/Os7jSA05zzP499fhJxJ7mXMeYaq98FoG+yehMEQRAEQRBzj+mcKf4ugC8AiAAA5/wQgA/mUxHn3APgbcbY4rFDqwG8DmA3gI+NHfsYgGcmozBBEARBEAQxN9FNY90WzvlfGGPJx6KTqO9fAPyYMWYAcBLArYg79U8wxm4H0A3gxknUTxAEQUwxXIyhu7tb+lxfXw+tVjuDGhEEQcgznU7xAGPsAgAcABhjNwDoybcyzvkBAO0yp1bnWydBEAQxvQSHB3D3k2dR6hrA6LlePPyJa9HU1DTTahEEQWQwnU7xJwA8BGAJY+wMgLcA/P00tkcQBUEUOU4NBtDrE1BjN6GhwgqNhikXPE/aJ4iJYi6rhq2qdqbVIAhiEsyFZ8+0OcWc85MA3juWOk3DOfdPV1sEoZbJfqlFkeO5Tg+2PnEAQkSESa/BzpuWY02Lc8oGh1w6FqJ9giCIYmIuOGPFzlx59kybU8wY+3cAXx/bZANjWzB/inOebwYKgpgUar/UuQbgU4MBqTwACBERW584gCWbr0BTlW3adcynfXqgEAQxW5krzlixc2owgK89dwS3v7MJiaViX3vuCJY4S6bk2VcsTGf2iWsTDjEAcM6HALxvGtsjiJxkcyhPDQYkmcQA/L77/4APfffPeN/9f8BznR6IIgcA9PoEqXwCISKizy8URMeJtq90PQRBEMWMmnGbmH4GAyHc3F6H7718Eg/sO4GH/3ASN7fX4VwgNNOqTSnT6RRrGWPGxAfGmBmAMYc8QUwrahxKpQG4xm6CSZ/6tTHpNaguMRVEx4m2Tw8UgiBmM9M9EUGow6DV4P59x1OeJffvOw69drbsAaeO6byaHwHYyxi7nTF2G4AXADw6je0RRE7UOJRKA3BDhRU7b1ou1ZN4lddQYS2IjhNtnx4oBEHMZqZ7IoJQx2g4JvssGQ3HZkij6WE6F9p9nTF2GPGUaQzAvZzz30xXewShRMKhTI9NS3YoEwNw8pc/eQDWaBjWtDixZPMV6PMLqC6Z2hhdJR0n2r7S9RAEQRQzasZtYvrJ9iypsZ9fz5LpTMkGzvmvAfx6OtsgCLWocSjVDMAaDUNTlW1aFheo0XEi7dMDhSCI2cx0T0QQ6pgrz5Ipd4oZYy9zzt/JGPNjbOOOxCkAnHNun+o2CUItSg5lMQzAU+l0F8P1EARBTIbpnIgg1DFXniVT7hRzzt859n/JVNdNEIXgfBuAz7frIQiCIArPXHiWTMtCO8aYhjHWMR11EwRBEARBEMRUMy0xxZxzkTF2kDFWxznvno42iLmFLyjgDU8Avb4QauxGLHFaYTefXwH+k4H6hyCI85V8NyCicnOz3GSYzoV2LgCdjLG/AJCSonLO109jm8R5iC8o4LmOftyzu0MK8N+x3o017ipy/ED9QxDE+Uu+O9pRublZbrJMZ57i7QDWAtgB4FtJ/whiQrzhCUgOHxDPjXjP7g684aENKADqH4Igzl/y3YCIys3NcpNlyp1ixpiJMfZJADcCWALg/zjnv0/8m+r2iPOfXl9INml4r+/82l4yX6h/CII4X8l3AyIqNzfLTZbpmCl+FEA7gMMArgXNDhOTpMZulN3RqMZOu4YD1D8EQZy/5LujHZWbm+Umy3Q4xRdyzv+ec/4/AG4AcMU0tEHMIZY4rdix3p2ytfGO9W4scU5P0nBR5DjZP4I/vjmAk/0jEEWuXGiayaVTofuHIAiiUDRUWPHAhy/C5tULcdeqhdiyeiEe+PBFiptGJDabSB4X1Ww2QeVmd7nJMh0L7SKJPzjnUcbOr8TOROGxm01Y465CQ+XKac+uMFPB/ZPRqZD9QxAEUWjCUY6HXjqZMv4pke9mE1RudpebLNPhFC9jjPnG/mYAzGOfaUc7Im/sZhNWNk6/k5ctuH/J5itmLGG5Gp0K1T8EMRm4GEN393iWzvr6emi12hnUiCh2JjMm57vZBJWb3eUmw3TsaDdtIxxjTAtgP4AznPO1jLFyAI8DaABwCsBNnPOh6WqfmD6CwQgOe3zSTGer0w6zWV9wPXIF9xfyi5ncHwYdKwqdCGKyBIcHcPeTZ1HqGsDouV48/Ilr0dTUNNNqEUXMZMbk2ZJXdzbl8T3fmc48xdPBFgBHACRmmz8PYC/n/KuMsc+Pff7cTClH5EcwGMGeDk9Gnt11bueUO8ZKg0giuD95EC5EcH8y6f2xZfXCGddpKqGBfG5jLquGrap2ptUgZgn5jsmzJa/ubMvje74znXmKpxTG2HwA7wfwcNLhDYhnu8DY/xsLrBYxBRz2+GTz7B72+BRKTozEIPK++/+AD333z3jf/X/Ac52elEVrMxXcn0x6fzyx/zS2rG6eUZ2mCjX3gCAIIkG+Y/Jsyaubb7m3BuTLvTVA+eknw2yaKf4vAJ8FUJJ0rIZz3gMAnPMexli1XEHG2J0A7gSAurq6aVaTmCiFyrOrJjZtuoP71dhien/0eAU89scu/M9H3wGjTlOwBQfTQTHGbM9VaFwkigElO8x3TM437GK2lOs6F5At130ugAuqaSzNl1kxU8wYWwugj3P+aj7lOecPcc7bOeftVVVVU6wdMVkKlWdXbTLwRHD/pU2VaKqyTanzqcYW5fpjaDQMs147LToVkplKyE5kQuMiUQyoscN8xuTZklc333JWg062nMUwm+Y6i49Z4RQD+DsA6xljpwD8DMAqxtiPAPQyxlwAMPZ/38ypSGRDKe9vq9Mum2e31Tm1iUpmKhn4RHE7S/DfH14h5eWsrzBPS3/MBLPlHhAEMbuZLXl18y1XYzdmhNVtWd08rZs2FWMO/6lmVvyk4Jx/AcAXAIAx9m4An+ac/z1j7BsAPgbgq2P/PzNTOhLyqFkMYDbrsc7tREOlZVqzTyQGn3Rdiik2VxQ5XjwxmKLjv3+gFWuWVs9INo6pZjbcA4IgZj+zJa9uvuXqyq1orrHhziubIHJAw4DmGhvqyqdvU6u5sLBvVjjFOfgqgCcYY7cD6AZw4wzrQ6ShNobUbNZjZWPFtOoyU8nAJ4Jcf33xl4exfMEVaDIbZli7yTMb7gFBEOcHsyWvbj7lNBqGVYtr0FRpK8hYOlfWg8w6p5hz/iKAF8f+HgSweib1IXJTLHl/E8xEMvCJUGz9NR0U+z0gCIKYDRRyLJ0LzyZgFjrFxOyiGPL+ziaov4hiJRaLoaurCwDQ3d0Nfv6FExIEkYW58myaLQvtiFlKMeT9nU1QfxHFSldXF+548NfY/NPX8IUf/h7h0NSmTCQIoniZK88mmikmppWpiiFVswva+bBTWrb+AoCT/SN5X9v50DfEzGMpr4Gtqhaj53pnWhWCmDZo2+VM5sp6EHKKiWlnsnFPala9nk8rY9P7a7LXdj71DUEQxHRC2y5nZy6sB6HwCaLoUbMNZr5bZc4GJntt53PfEARBTCWF3q6ZKC5oppgoetSselW7MlbN661oVERnjxc9XgEuhxktLjt0Oo3q81PNYCCE29/ZBDam5lOvnkaPV1C96jdb3/iCYRx8e0i6jsVVNhzt9xfsugiCIIqNXp+AMosB162YnzLmqtmuOZ9yk6HQ4RpzIayEnGKi6FGz6lWNjJrXW9GoiKcPnsHdT3dIMvdtdGPjslrodBrF81ONKHKcHRbwvZdPSu1tXtWMx/d3q171K9c37fUOHO8L4MvPjF/HjvVuPLG/C/u7vNN+XQRBEMWIy2HCpsvqsWvvcWls3LK6GU577vE233L5UuhwjbkSVkJPO6LoUbPqVY2MmtdbnT1eyeFNyNz9dAc6e7yqzk81pwYD+NxTh1Lau3/fcdy7oVX1ql+5vvnU1UskhzhR7z27O7Dp8ibp83ReF0EQRDESEyE5tkB8LNy19zhi4vSUy5dCh2vMlbASmikmih6NhuHqpTV4/M5Lx17tm9DicqT8ylSzMrbXJ+BD7bW4prUW/WMyzx0+k/J6q8crH2rg8QpYtkD5/FSTTWe9lqn+lS3XN8d6/bLXEQxHUz5P13URBEFMN/m8tu/zy4/x/SMCLqjOHgaRb7l89ez1CVhUbcMdV16AYCgKi1GH77705rSFa+S7ecds2/SDnGKi6BFFjueP9Cq+flFaGbugwoSl80pxyyN/SQoZaMH88vHXWy6HWTYMw+kwqTo/lYgih8Osk9V5QcXE2kvvG78Qlb0Os0GX8nk6rosgCGIi5OM05vvavrpEPhSvypZ7LMy3XL56zis14UOX1OOzTx6Uym1b1wLXNI3Z+W7eUWM3ob7CjLVttVKs9Z6DZ1SF/81EDDOFTxBFz1S9fjk7FMI9uzvTQgY6cXZofBOC5koLdqx3p4Qa7FjvRnNlPFShxWXHfRtTz9+30Y0Wl2PS15nOqcEA/EJMUed8WFRplb3OQ90D0ufpui6CIAi1JJzG993/B3zou3/G++7/A57r9EAUc2+pmO9zQ6sBtqxuThkbt6xuhlbBW8q3XL56Dgci2L4n9dmwfU8nhgOR3A3mSb6bd9SVWfAvq5rxvZdP4oF9J/DwH07iX1Y1o67MkrNcvvc933IJaKaYKHqm6vVLry8kW0+vb9zB7PT48cT+Lnz9hmUIhqMwG3R47JWTaKy04OLGCuh0GmxcVovmahs8XgHOsVCO6ViM1usTMDCirHM+dPbKX+dn1yzFxY1V03pdBEEQasnmNC7ZfMW0vLbv8Qp47I9dUsYfzoHH/tiFi+pK0VA59eXy1jNLOY9PwLKspfIn3807uodGZdfhrKgry3l9+d73fMslIKeYKHrUvrZRemVSYzfK1lNjN0qfPb4Q9nd5sb/rbyl1JzuhOp0GyxaUTVusbeI6gpFY1muvKTHKllH7usjjE2Svs98fwvvb5k3tBRGEArFYDF1dXdLn+vp6aLXaGdSIKBbyTXU2mdf9Q6NhPPi7EwUrl4+ehQzlS5DP5h2FjkWe7CQaOcVE0ZN4bZMec5X82kYUOfZ3DyAWY+j3xx3YgUAQ7XWVknPodpZgx3o37tmdmobM7SyR6nGqcJynk/T4sjv+bgF2rG+RQigSMcXzyo0ZZV58owcbV9Sh44wXAyMhtDrtMJv1su24sjnb05Q+iCBy0dXVhTse/DUs5TUYPdeLhz9xLZqammZaLaIIcDlM+Kd3NWEgEIbIAS0D/uldTYqpztQ8N7KVe+DDF+HQaa/UXut8h6owgW/c0IbjfSNSuYXVNsUwgXzbS4TypacHLbaQt8n8OClkuQTkFBNFT2b2ifjGEskzoWe8AYQjIrSa8dmlcETEGW8AC8rivw49I2Ec8wzhB7euRL9fQFWJCb/tPANPQxmazAYAgNtpz+I42wtyrcmvflwOE4wGI6KiiMduW4mh0TBKzQbodRzz7Kmp5l58owftDZW47Qd/hRARcdM7XODvqEefPwSn3YhWlwMm0/jXvXWeAzs2uHFPcp7iDW60zSuuAZWYO1jKa2Crqp1pNYgig3MgEI7hoZfGc7VvWd0MrhAimu/rfgAIR3lKeztvWq5Y5u2hUXiDqfG83mAEbw+NolFhhjKf9nQ6Dda3zkNDhRUenwCX3YTWeepC3gq5gC1fp38yP2ryKZeAnGKi6FGTfWIkGMXZ4VDGjGqF1QiUxevp9Ql4+P/exsP/93ZK/e9tmTeekm0khP2n+vH9Wy7G4EgIFTYjnn6tG+0NZWjKMus6lSRe/bgcJnz00nrcv+940ixAKxbXGFFXnplqbuOKOskhvm65C+0NlfhYSsYKN9a3uiTH2GDQYmPbPDRVWqUBrm2eAwYDvbImCKJ46POHZPP/rqgrU3Q283ndn29Mat+IAL8QzXDe+0aEnHrm254ocvz2aF/BNtOYzCYc+Tj9+f6omcyPIYCyTxAFQBQ5TvaP4I9vDuBk/4jqVaAJ1KzO9WXJ0uATYpJM4rVKMumvVXp9At7wBDAwEsZoKIbBkTDe8ATQ5xckmWhUxMG3h/BcRw8Ovj2MaHTqsrMndLxuxXzJIU5cz91PH0a/P4TucwFEo6LUpxaDDgP+8QV5N1xcJ9MXHTicthGHwaBFe0M53t82D+0N5eQQEwRRdATCUdkY0dGknOpTSa6Y1FyEo1zWeQ9Hcz/v8m2v0JtpzOTmHUpvBdJJ/Bi6tKkSTVW2Ce2cRzPFxLQiihz7jvZmvDpZtbgmxVBzvZZREzjf5w9lWYwxvkBOzWuceaUmfPzdF0Aca44x4OPvvkDK/Tjd2zw3VFjx9evbcKJ/JON6XjraByEq4vAZL/r8ITzfcQYP/9/bqK8w4+s3LJPiqKYrYwVBEEShqS+3ysaI1pUrvw6PRkV09nhTwu6Uxulqm1E2r26lNfe6EiESkx13Q5FYlhJx8o2BzfZc7PUVdgGbmvbkns1K5WZqe2hyiolppftcAMd7RzJeKS2ssklpapSMX01S9Pml8vvO15aOy4gix/BoJEWX+za6IYpc+pKNhqLwBqNS/sdEQvTRUHxWIts2z83VNixbUDYlfWYxauGudcCs16Zcz7Z1Lbj76cPoGgxK4SHXLY/iFwd68ELHGexY78aDLx5HTZb+KtRiQYIgiKmisVI+RrSxMrdTnO8ERozH8M/vXohtSaF429e3QOS5nduGLM57vYLznm8MrMWgk23PovDGL18nPN/2Skw62WdziTG3+znZ1Gr5MivCJxhjCxhjv2OMHWGMdTLGtowdL2eMvcAYOz72/9R4JecB0/mKfyL0+kL42V+7cfs7m3DXqoW444om/Oyv3SmzlqcGA/j+y2/i6zcsw9eua8U3bliG77/8pvR6RasBtl61KCVp+NarFqUkReeQ33c+mWwObWdSWMFwkkOckNm+pxPDwbhT3OON/+r9xHsW4q5V8X9lFgM83tyvutTSfS4Az3AQDpM+43q27+nE2rZa6fODL57ABy+px3f+fgWuWOTEsgUl+Mb1y/DUq13Ysb4lY2OO1hlclVws9kgQxOxCo2F47+Jq/Oj2S/DAhy/Cj2+/BO9dXK04W6hmvJdjMBCVHOJEuW27OzEYyB2uEYrFZDfvCMVyO9P5Xl84FsOnr16c0t6nr16MSCz32JrvJhzhWAybV6Ve3+ZVzYrt+YWo7LPZH8rdn4kZ5vRnrVJYCTC5kM3ZMlMcBfApzvlrjLESAK8yxl4AcAuAvZzzrzLGPg/g8wA+N4N65mQyWw9OhOl+xZ+M0jWFYzHc3F6XsmAs/kUaHyi8wTCuX1GXul3l2hb4gmEAQP9ICEatBnde2QSRAxoGGLUa9I+EpNlmT45XOwl6vFmSnXsFKefwuUBYVmZoNK7L/DKz/Ix0mXmSPRnvy+N9I9Bqtfi/Nwdk9Ui8fnI5TLi5vQ6bvj++mO7Lay9EpdWANzwBXN++AI/euhJ9/hAqbQY4zDqYTLqC2WAyhbRHgiDOL6JREbsPn53w+NHnlw8j6/fnDiM7FwjLvu4/FwjnLHd6KIhfH+6RNkSyGHT47ktvorHSCndt6ZRfX3WJERU2Q8pzscJmQFVJ7jeCmdmc4hs1KT0Hyi1GPL6/O2Vzksf3d+OaFmfOct5gRPY++IK5nWKXQ/7tr1IqvsmGXcwKp5hz3gOgZ+xvP2PsCIBaABsAvHtM7FEAL6JIneJCxsdM1St+JQdKzTXZjfqMBWP37zuOx//hUqmemAg89Vp3fDAJRWEx6vDoKyfx+WsvBAAwMPzfm334yKWNGApEUG7V40d/egstteMznzUl8vurVyW9ElKT7LyqRD5PcaUtPtCYdFrZX71XLa1R3a/ZODUYQGhsRviOK5pk9eA8Plh84X1LcaLPjzuuaMJTr55Gj1fAvc++jv/+yAp85NJ6fOz7+1PCLi6/oBzRqIhXTg5if9c5iBz405v9+MSqRQDnqCu3orFS3kGerCNdiJCT84GZ+MFCEMXO61nGj0XVNrTlGD8cZr3sGGpXyCI0z2HCrX/XgJ0vHJPG0K1XLcI8hU0xakqMuOniBTjR55fWrNx08YKMjZbSyXd8HA5E8IVfHM64vsf/4VLUV2RvTxQ5XjzeJ62tOdLjQ/9IKGOdTzpaDfDBi+synFSlbaznlZpln80uR+5+iYnA3iOeDL/g6gtzO+Fzbkc7xlgDgIsA/BlAzZjDDM55D2OsOkuZOwHcCQB1dXUF0jSVQsbHqJkRVUKNw6vmmgIh+cUHgfD4THEwEpGdKQ5G4jkfRR7D6qUu/OMPX80a46XRAB9/18KMWODkL+ziKptsDuLFVeObd4yGo9i2tgXbn+1M0SWx0rk/yyK2/pEQFtaUQIlcttjrExAIxVdaP/XqafzrexfhP397LOV6nnq1Gx+9tD6lrzavasYP/9SFHq+A0NirvvSwi5/9w6U48HYPPvfUIQgREfUVZnz8yoX4px+9mvX+qrUDJabCHs93ZmJRSTGMiwShZIfZtzMOoS1Hvd5gBJtXNWe8pUzPJZxOKCZKDnGirZ0vHMMPbr04ZzmRc9mUbFwhdUK28bFHYXzMd5tnNet8sukpt4318gW5t7FeUl2CT7ynOSMn/pKa3Ln/fYL8G2S/kHvG3pOlP5UW9iWYVe8uGWM2AE8B+CTn3Ke2HOf8Ic55O+e8vaqqavoUzEG+aVfyITEjmkz6jKg/KOAvbw1iz8Gz+Mtbg/AHU/VQk0ZFzTUZ9RpZXQxJr4VMOp3khCbq2P5sJ0y6xG82jWyMV7L5+gX5WGC/MP6K5mi/X8pBfP8Hl+ORWy7G/lP9ONrvl2SsBh1+d7QH//PRd2DXB5fjfz76DvzuaA+shrguicUG6dejtNggQS5brLGb4LDEZzd6vAJ+8MopfPrqRfh/H1mBO69swk//3IVNlzfJzrxft2I+6ivMsJt1uOOKePx2ImOGEBFx1huUHGIAWNtWm9HncmlypiKdjhp7nOtMRT9PlGIYFwlCyQ6teY65pWY99r0Rn2n82vXxtSr73vCgVGGm2Jfn6/7g2FvD9LeIo2l1pVM1lu0iOXa2vsIsvZ3Mhsthli2nNK72+uTzPitlJ7IYdDDoxn+gMwYYdEzxPpz2BiWHONHePc904LQ3mLNcTISsX6C0HMWok/c59EpT2mPMmplixpgecYf4x5zzX4wd7mWMucZmiV0A+mZOw9xMduvBiaC0/aM/KODXHf0ZM6bXuqtQYo7royZti5pr8gph2V/ryb/2zo1mi+ONjOkiPzvbl/QlDkVEWZlQ0rFgJIKVTVXSJheJfhEi4zMHFqMWmy5vgJZpwAAYtBpsurwBVmP8iy9yUXYmWWk2QA11ZRYc7/Nj+/qWMacf8ddbHh/u33sCAHCs1y97nQ6TFp94TzNuf3R/Sj//8E9dGBoNo9JqTCnHGBTvLzCx9D2CEMXhHi88vtRd9GbLdqQzSb5pkgjifEevZdiyujnjtb1em/sNitmgxabLGlLCGTZd1gCLMbcTZzfrZV/320253aWRkHw+5UAo90I7nYbJZrtQur7FVTbc9Z5mfDlpBvbeDalvPuUYDUexqNqGO668QApL+O5LbyKokPdZ5CI+t2YJRBEIhKKwmnS40LVE8dmX78ztuYD8c18pttsrhPHpqxfjm88flfrl01cvVpxhTjArnGLGGAPwPQBHOOc7k07tBvAxAF8d+/+ZGVBPFZPdenAi6HQabFxWi+ZqGzxeAc6xQPpE0P4RT0ByiIHxzR0aKldiZWPcoVXj8Kq5pgqrCY/vfz0jOH/XzRdJMpVW+Tjecmt862WnXf58coqxqhKDfNq2EoP0Wa/V4e6nX82I3frxHZdIMkYdcHYoc2c851hbGsbwnZdOpFzPd146gfs/OH49+dI9NIotPzuAL75vCb55wzJYDFr8809ey4gvlrvO1vmluHXM2U9c2/37juPOK5uwoMyCGod8Hyr9SFP7Y04Qoth9uCfjh1ZiF71c9kgU9kfzRIjFYujq6gIAdHd3TziJfi64GEN3d/e01E2cP5j1WjgdppQFZU6HCWZ9buc2EhVxZljICBNYUGbJWa7MopN1Ususud2lmizPqeRnkByhWGbI27bdnXjstpU5yx3r90sOcaLcl5/pwOKa3LHWVSVGfOiS1BC8betaFGemzXqNbLrSpsrc47jZoJHtF6PC+F+h4Bdkw+UwwRuMZixAVPtmclY4xQD+DsBHARxmjB0YO/ZFxJ3hJxhjtwPoBnDjzKinzGS3HpwoOp0GyxaUycYkZZt5TX59osbhVXNNBp18rK9RnxS3Ci47m8wRf0o6S43YdfMyRGLjv1D1mvjxBDER+Mw1i/GN34z/OvzMNYuRnC2mX8Vq5HMjMTz44rjTCwAPvngC37h+GVANjIZj6BoM4sHfnUipZzScezZADYnZwif3n8bH370Qg4Ew7riiCS8d7ZP656lXT+PLay/Evc++Ll3nl9deiDPDQenaXA6TtHL64vpyOB161DosKfdzz8Ez0ox0op6vX9+GoUAY+wZ7UT+28C6bHcwrMWH/qXPo8QqothvBANkfWo2VFlzcWJHTHon8fzRP9+K8rq4u3PHgr2Epr8HgyU7Y5i2csrqDwwO4+8mzKHUNTHndxPnDSCiKH//pFDZd3pSy4GrrVYtzlvOH5FOBtXz0HTnLeYOxvJxUzoEvXrsEA4GwNDNdYTVA6dvYn2XjKaUsGWezxiKH0JZjnB0NxWRDDR+7Nff1ZUtXqhRrHRW57Ex/TOFX8Egogi+sWYLB0fH+LLcYEAgpxIRHuKy93P3+lpzlEswKp5hz/jKQ1bZWF1KXyZDPPuxy5LNLTzLZftGmb+5g0LGUX1vJ8UQJRJHDL0QwPBqBWa9L2QgDALoGg/jpn7ukNDVmgw4Pv/Qmyi0LsdRVCiD+i1Au1csad3yV6YA/DG8wmjF7O+gPI/GD+KxXwMN/eCuljof/8Ba+9L6lki42o072tZgtKYn4UDCCD6+sT1ng9q/vXYShscUZ2Wb0ahTSxKihxh7PoLHG7cLWJw6gzGLAje3zcds7G2E36bHrgxfBOxpGqcWAu96zEE67Cd1Do3hg3wnc1D4fJr0GZRYDPnppvfQD4+Exp7l/JIzaUhP+6+blsJt0sBp0GBbCeOy2lQiEojDqNTjRO4LPjsUdJy/0Sv/h47IZM2aFv3FDm+wA7fGF8Mc3BzIctsna8FTVUSzk86O5UIvzLOU1sFXVYvRc75TVmcBcVj1tdRPnB5GYiFVLnBmLi5Xy446G5Rd5BxUmMLLtCDowkttJDYSjCEbElJnpf33vIgQUwhKypR5zKcxs2k3ym2nYTLln0HtV7P4qx8CIfKq6wZHcYQn9/pDsAr2Gitwz9ka9FqFYan9uvWoRjApvCPyhMG54R+oCvX9b1wJ/6DwKnyDGUZvzNdcM0lKnVTYLw1Ln+KzUWwMB3PWTv2V84f73X67ABdU21bq4HGYc6xvB5p/+LaWe5FyD80pM2Lx6Eb70y8NSPV/5QCvmjb06FjkkhxhIzEJ2poQ91NiNGBoNp8zgmvQaVCc5+naTTnbWOjlWrNyix7//pStlpvgnf+nCN2+Ir+dVs1V0vjRUWHHvhlbc+cP9Gc5tYoD9yV+68IVrlyIcE3H32Kszl8MEvTbe9zaDDp9MW7CVSNV22w/+lHLdP/1zF471jeDeDW4wiBgYm5kG4oNdciaRxD9R5PjrqXMZs8KVtmyp7Az40Hf/nOKwiSKfdN7ibLb3/gud6Oz1w+MT4LKb0DrPAYPKRZAzzUR/NM/Ujk8EUUgsBl3KpAkQnzRJjMnZmFcqPyYppQLLtiOoUihTiVEvTaYA8e/jf/72GH6WlH5UDi2T33jq8X+4JGc5q1EnOwNrM+R26xaUKe/+Kke2XWOVUtVV2eSfzUrhGjrGZLOA/Ehhxt6k0+Hf9ryWUu7f9nTihwrlEszOaZU5jJpdehIzSO+7/w/40Hf/jPfd/wc81+mRdnUpMZtwrbsKj922Et/+0EV47LaVKYvsAKDrXED213L3ufHV8NnyR76epEuZVSezu1oLymzjK4C7hkeg1wB3XhnPmnDnlU3Qa+LHgeyB+sm7yIVjMWxPa2f7+paUTUJ8WTJU+JIyVIyGo7i5vQ7fe/kkHth3Ag//4SRubq+TUrIBQDjK8dBL8fP/89JJhKNTEwyp0TCEo/HZjetWzM/IMvGfvz2GtW21+I9fH8EFVTbJIf7opfV44Hcn8K3nj2EkLL/Y48DbwxnXfceVF0CIiHjgd8fBNFrpmh7+w0l89NJ62d2DTg0GZFMIPd9xFjvWuzPu8/MdZ6U2E9kU8t1pKhm5Op7vPIs9HR78/ff+jLt+8jd85Ht/xtOHziI8BaEtxUghM9oUkkSs8cmTJ3Hy5EnEFHYEI85vfEJEdkz2K7xGj8Ygu8OcUvYCnRayzxKFCUoMZlkYNhjIPQPbmyWsr9efe2YzEI5gXqkp5bk5r9SEQEShX0R5Jzym8BjLt9xoOCq7E96owgx6tgX450YVZqZzpE1VA80UY3YlzVeT81XNDJLVaESlLYqYyFFpM8JqTP3VZs26z/m4yaiJaTo7LOCJ/Zkbc9RVWFBXHtfFOxrDZ5/KTEKeiHEqt8ovoksOuNdrtXj11AC+f8vFGBgJocpmxC9f60ZDRb0kk2118EjSdpMGnVY25dkjt1ws9W1iS+rE9Xz/5TexxFkyJbNzRr0WJr0ma3aIunIzPrSyDk6HEQ986CJwACf6/NJrrbfPjcr2VfqbxvhrxPh1r22rlWbpk6/5s9cshtWgw9+6z8UHo0AYlSUGOGUW7ZVYjHjwxeMpr8gefPGEtC11ot4+v4DhUfmURxPJWyz3PfjIpY1SLutEnfc804GmSivaG8rVVTyLKNbFeZMlOdZ49FwvHv7EtWhqappptYgZoiTLBlBKMbBnhwXZHeYWlFuwIkc5nxAbf5b4Q6gqiT9L5pXm3rW0xCS/WUiJMXcKOHOWZ61Z4Q2XTqPFzheOSeGAMRHY+cKx+PqXHOTyIXKR7Ud48q6xchj1WtnwyK9d35azXKnFIBvuWGrJvdAu2wK9CmvumekEc94pnomk+ZNBza5sSumdRJFj39HejBCA5B1tauxG2VczyXHHDkuW1DXmcbPyCVGscc9Lie+Jp0cZd0Sz/1KO/7IrMWozFoVtX98CW1JqHatRg/aGypR0azvWt8BqHH8ZYjfLDz4lSeET2bakTCR89wbD+MglqWl+PnJJg7Ql9WQps+hx38ZWnBsRsHn1QiS2bH/q1dMYGg1jXqkZBi1woi+Qsthu86pmaDTAo690ZSxa3LG+BQ++mLowMD7oxq9bq5F3wJtrrAhFI4iJGvSPPRye2t+Na9vmZdyPpkqb7AJElvQVSjhsZr38fZhI3mK570E0Jqb8WPnuS2/i0Bmf4qAtx2yIVy5kRptCk4g1JoiBPGdgXaUm/P2ldfHX4Tw+Fv39pXVwKaz/4FzERXWpz5Jta1sA5J5iDmTb+CmSe0ZUr4F8yjmF4WZgJCQ75g4o9Eu2NUXV9txOY2WWDE+VttxOKjjHP71rIf4tKWzx39a1gCH3FLOGifjEu5szwjw1LPd9ECJR2YX7gsJ9SDDnneLZFpenJuer0gySmh1t6sqtuHBeCb55wzIEwlFYDTqUmLWoKx9/4JZmidEtTXKKK6wGbP7p31L695vPH8WPbx+Pl8qWci2RBi0qcvz3i6lp0P77xRPYdfNyST4YEmXjjpPjiGxGHb547RL8+6/fkPT94rVLUhbaVdrkv/gVY7PSDAx+IZLSd1+8dgmyrwOdGIuqShCKxqBhwM6kGOstq5th0WvRNTCCCpsJX/hlqs3ev+84vnHDMgyNhvHDP43HRGsYUG034iOX1KdsW/pv61rw8EtvwqTXYPn80oxrrq8wo8xiwOtnR9IGpRb8+tBZrL9oPh766DvgDUbgMOvRPSg/Q534XZnssIkin3Te4vTvQXu9A4OBSMquSdvWtsDwWnfWRZDZHF+1cfszTaEz2hDETFBdki3VWW4nzqDVIMYZPp2Wesyg8B026XXY/mxqTOr2Z5WzT1gNOnznpc6MdJ3fUIh9Nui0sBq0KYvarQYtDLrcM8VVWdZxVCnE6pZbtNixviVj4Xq5JXd75RYd7t3gzsiLXK6Qqs5q1EPDRuO+xFj2qNFQBBZD7hl0zjWyGY2UYoNLTHrZmemdNy7PWS7BnHeKZ1vSfKUcxIDyDJIny442K+rKUrZrDITElAFl503LU3TxCfKpXR5Neq11biRLXFBSAu4Ssxbb1rVkONcl5viXtNcn/4u4zzdehydLmjlPUpq5UjNDqcWQMviUWgwos4w7EeFYTPaLHxHjcY2RWExyqhNt/Puv38APb8udlkYtp71BHHzbi6+PpZZLtLFr73Hc9Z6FqK+w4ojHJ3utbw0EpBmHB393QvqFvG13Jz753kX49gcvwrnRMFwOE4LhGP75PRcgGgOYhuNbNy7Hp35+IMWhDIREmUGpE9+/5WK8fW4Un3vqsJT+rb3ega9d3ybtmmfSa/CtG5ejZV4JLr+gIsVh02jYpPMWp38Pyq0GfPT7f8l4kH3vY+1om5fpbOdyfLPFPDdX27AsR/7PmWCqMtoQRLGiYfKbd2hY7h9/IyH5NSSJULhs5Jt9wjsaln1OeRViYM+NhjNydHMODCmUC0bkZ6aFaO4Z0f6R6HhI41hGqMdeOYkF5UuwsCZ7Ob8QD7lLfn4Gw1H4BaUsINH4hMXu11Pun1JM8WBA3ncYVNi8g3OOD15cl2EvUJiZTjDnneLZGJenlPNVaQYp2zaWyXvCq5lBD2RZ1JVs7IltntP715j0bujUQBB/PZkaw/Xzv3aj3KLHEmcpzAatfB2G8TqypplLmk3weGN47I9vZeQvdNqXoq4iLqNlWkQiITx260r0+uMx5ic8Q9CyePqYc1niYYdG1b2aUaLXJ6DEpJdto8ZuQoVVD73LLnutjZVWnB4axV3vWYhwTMQ7L6jEa28PYd2yWvzX2CK97718Ere/swnfe/kkvn7DMmz+6d9g0mvw5Mcvw6O3rsQfTgyAc8Bh0SIWA75xQxuqS0x47vAZPPLHt+OD0khICr3o8Qr43ssncf2KK9BQYUVrrSPD5pJ/aCWQs+GJxvYn1/FcR49sn/mFqGz2ic4eL76973jKivZv7zuO5mqbqrh9giAKQ68vJBsb3FCZO0woX6eqcmzb5fSwwEqFmFSHRf4to8OcO7zAbtLjiTe78JFLGzEUiKDcqseP/vQW7nDlztvNoMHfujPX0cwvzz1IDQbCODMcwlGPX7q+M8MhxX4ZCUWx++CZjOen0n0IRzn2HvFkrCty1+Z+M+iwyMdoOxS26R4YCcumgLtA5jkkx5x3is/XuLxcM0glZnknsyQpv6GaGfT6cqtsPckhFmaDVvZXfvIigsZKMy5uSovhWteC+sr4woZSs152Jjl5D3ujTv6VvFE//uswEIniX1Y1w6DTop9zVNoM+JdVzSkxX1YjoNMZsOmRv6S8WrIa46OHWa+VHTCTnfzJ4HKYIERisv16anAUX36mA19YswRbr1qUEg6xfX0LHn3lJC5pqoJWAyybX4qv/+YI9nd5pRnjd9TbcWXzSvT5BTxyy8WIijG8Z1ElbnlnI94aGEV9uRnvaq6ERiPizb5gRtjErZcBP91/BtUlJph0Gnz7Q8tRYzdBqxExFAhjvsOcYnPRqIjDZ4ZVxeVONrY/W6x9tlyfg4EQbm6vy4g7OxcIqYrbJwiiMFSV6HFtqytlXcqW1c2oUohlrckSdlGjEHYRjsVkd7RLvC3Mhj8UkY1lHQnnzgbBEcPqpS5pkXCiPa4QO1tm1ePixqqU5+a9G9woU3Aas6VWq1UY3zQaLrueRmE3amizlss9cxuOxGQ37whHc98Hs0GL2lIjFjtLJCe8ttQIk0HdM3rOO8VzMS7PrNNlOFVbr1oEs37cHNTMoDdWyv+gaEz65WjWyW/RaUqKl/IF5cMwErFDnHN85/dpWyv//gTuT4opBrTQaVI3G9FpGIDxdpx2Iw6f9mXEUrXOt0syo2H5nMiJeLJSs142jlppIFJLNMZxvNef8UNi86pm/PBPXRAiIv7juTewZXUz7ryyCfNLzbCb9Xi+owcfWLEgY/HdmeEQerwCTg34UGkzZixauO2KBvzDY69iUbUNH7qkfmyHopWyYROP3roSS+eVQacRcfN3U380HPMM482BADa2zYPBoJ1wXO5kY/vVxNonYzcZZFe0/+j2SyZcF0EQ0whnsuF+ivlqx9ZOpC/wMimEaRm02rx2tLMb5WNZlfIpayDfnlLsbCgiym7znLxeR45sqdUuVihn1etx2OfPWItUr7AJh0mvg8fnm3C5eaUmHE1b+7T1qkWYp5BPudJmwI3tqZt3bF/forwgcIw57xQDcy8ubzQcwyP/dyrly/vI/51Ca9LrDDWbVKj5QeETwqgpMWCewyzJxMQY/IJyPHBi2+mzXvmY4rO+EBLDzWg4hs//IjOtW3L8mD8Yk3d4k2KglbbAjopc1oH/yR25BxS1dA+N4pFXuvDxK5tw55VNuKDShmN9I/jhn7rQM5YyR4iICIRjeGDfCfz3Ry7C9j2v47oV8yWHOCFz/1h4wIO/O4GNK+qkGYXxa+/AI7dcDCEi4o4rL5AGkX5/trcEIbTWluAD/++PKTsbnRkOYk3rfHzq5wfQWGFBVOTQazUTisudbGy/FGNcZcMZbxCVViNqHMasP27DUVG2vUhMVBW3Px3MptSQBFEosq4XUdiB7fRQEL/p6MH/fPQdGB6NoNSixw9efgsOiw6tOdYGDGaJKT6nkNXBJ0Rw2+WN0symTgPcdnmjYj7lvizZl5R2mMtWTikfb7bwsB6FlGzZts1+SGHb7JE8yw2PRmU377hoQWnOcoGQ/DbdajfvIKd4DuJ0mGR3mElfpR+K8JRfad/KsXoz2zbmFoMOx3pHMmZnF9WMOzouhxHt9Y7MWN+xnYdsxizbWSblTM4WP5a8oE8p9RugHJt8Zlh+QDkzLGBFPSaNzajD0GgY33npJK5bMR9lVj2+9/LJDH04H89l+aX3LUVU5LJ6JUI8BrJce2LxSDAph3N1lh2dakqM8IdiuG/9EvSNxFJeEy4ot+C2yxulxW5fu75V/kGWJS53KmL7NRqGM14BW59IXRwqF4LhdOTerlspbn+qmW2pIQmiUGRLBVZhzT3zV1lixGULK1PCErZetUhxJ7XSPGODrUYdtGmxBFotS8ntL0dVntk1SrJt82zM3V62bB7VCu3lu2129nK5w0OUJsumulyC4skvRBSMRBy1ST++w0x6HPVbAwEpGwEQN6pP/fwA3hoY39FOaec8AIjEuOzsbCRpGxy7SYubxl53fO4Xh/GZJw/ipvY62MdinEtMWnzx2iXYvHoh7lq1EFtWLxxLpTYeGjFvbEOJZEz68bRuwLjDmy6TknvZrJXdgc8xlq7GpaKdfBFFjnOBMLavb5F+tPzoT2/hvo2tKfpsXtWMZw+dwb0bWtDrDeHTTx7Eif4RWb0SznPNmBOYfj7xgLAYdbj72oV44s5LodOKsrvT/frwGWz6/l9QbrNkhB7c/XQHBpN2ILKMJaTP6KcscWtqbFKJbCEYpwYDGbJT0d5UMhHd5yqxWEza5W46d7orVDuEOuwmnewOcw5zbudPr5HfJlin8CPTaohnQ0pub9u6FlgUNtMwaTXwC9GU3U79QhQmbW43y2aUby/5+SaHxaDFZ65ZnFLuM9csVtTTYdbi3g2p4/u9G9xwmHOXSzjTKdesV96uOWu5EoWY8CzPa6V8yvmWS0AzxXMQNWEPubZ5vqA6PsurJg5Uza+2c6PyYQ2J1G5aTXzXn4eS8gvfu8GN5DSOWqaRXYyn04x/OXQaLpufUacZd9BPDgRx5OwwfnDrSvT7BVSVmPCbw2dgN+ux2FmKGod8jkdnqcIeoCo4NRjAJx8/gK9d14qv37AMnuFRVJaYsGvvMdz+ziaY9RosryvFyf4ANiyvhdNuxj/8cD+EiIinXj2dscjjvo2tsBk0eHhTO547dEZGbzeiYnxRXygcgt1ilhYY3vF3C8YycIRQXWKUsk8AwIHTw7L3NOm3EL770psZ6YJyxeVORWz/REIwim0twWxLDTkTdHV14Y4Hfw1Lec207nRXqHYIdQyNRvBzmRRi88sW5yzX75d/ezigsH2yNxiBFjwlR/9oKAK/kDsMwpclTKBFIUxgaDQiu2bmPz7QmrOcEImi0paaYrTSZoDSJhWjoRhC0VhKuVA0hlGFGV9AlF1rwRQWBEbFmGy5mMLCRa1GlH3WajW5F+hFYjHZVHVRlT9uySmeoyjFUavZ5lnNg7w6SzhC8quh/myxUWNhDYGw/IKCH9w6Hi/cPRTET//clTJwPvzSm/iHKy/Asrp4/NhISMT+se07B0dCqLAZ8fRr3ahxzJfqqbAasO/YACwmIxgDjvaOYN+xAVzbGt9dq2cohgfTNhJ58MUTaKhYhsmmsE30p0ajwfE+f3zbzt/G46QToS4mvQa3v7MJv3jtNJqrS6Q+6fEK0qYdi2psONY7gm/vO4bPXbME4aiIn+4/g/e1RKVrj2/trcHQaCS+gIRDcogB4OH/exs/+ssZPHbrStz00J9S9BTHZp/T76mGAW21dtxx5QUIhqIotxrw83+8DGeHg6ricicb2z/REIxiWkswG1NDzgSW8pqC7HRXqHYIZXzBKPZ3ebG/628Zx3NhypLK06Qwk6rVMjz4+zelDEOcA88eOoN/V3BSs4UJKDmbfiEqu2bGJ+S+Pp1Gi889lbmORmn760BExH3/eySj3MOb2nOWi8ZYShpLzuNpLJW2lQbX5FUuGtOk5FO2GOJhlZ++ZmnOchqmwe+OpsaS//hPb+GWv7sgt55jkFNMyKJmm+dssadVtvEHealZfovmUktqVghZh2CsrUBIPh9yIDQ+aNTYjTjWN4LNP/1bSh3J+tqMOjRW2VNS2GxZ3YySlJgvEZ9498KMX6cY+zXc65df9NersChCDQnH6MzwKC502fF6j/wmHSUmLTZdVg/GUp3TRM7gr9+wTNLPH4pCp9NI9/IXB3qk676gyoY7f/gqvnZ9KywGbdZ46/R7s+dg/AHxxaQd93betBylFi1cDnPKqt9C7gQ3m9MrzmbdCWI6qcoSU6yUkq3Mopd9hpVZcmcKMmq1sps/mPLcYU4pvCDrmhmF2OD+LAsClRbajWR5no6Ecjvh2Z59igsCs2xH3aegp0+IYNUSZ8rzZPOqZsUZe41GlE1xp5QCLgE5xYQsdeVWNNfYUl6xNNfYUnIQa7Ps2Z4cQnVBpR1nvQIe+ug7MDQaQZlFjxgXcUHleBq0FmcJdqx3Z6QLcztLAMS3bZQbNEpM44NbhVU+rKHCOj6QlZg0qC1NTQ9XW2pCiTnZYdPkTMmWdSHeFMQUN1RY8bXr2/DDP76Fz167VHb7ZZNeg/mlFnz6yYNYVG2TfU308EtvSrILyi2w6rUYDaXuRORymPDAvuMA4vG/uRYYpvfrJ97djFVLK/GrBamhB4dPD2P7nlczYo0LtRNcsYVETITZrDtBTCdWo1Z2l1GrKbeTuqTGjrcGAinj3vwyM5bU2HOWM+o1stsuK+WirywxYMeGFtzzTNIzaEMLqhRiZx1mnexz1K4QM53vgrkqW34LF7M9I5RidfPVs9Ssl02bqZQaL1uKO6VyCcgpJmTRaBhWLa5BU6Ut60O6xyvI7hxzUV2ptIuZTqfBFRdUo7PHi2A4BodZn/EaXafVosquz3Ccddr4oDc0Kp8UfWh0/Bfj6z2jGA4Ex3eiKzHhQPcAXu/R4YLqUgDA6eEQnA4jnHazJAMm4vRwCM1j21tmS8nWNxYD7TBrZJ1vh3nyM6EaDcM1S2oQjXFs+t5fsKjahi+vvTAl9/C9G9wQovHXdIfO+IC/jIeMLCizYOcLb+DQGZ/kIO98/g3cdHE9Lqg0oarEiHOBCCptBvzHr47EyyMe//tv65fKxhxX2LSoKy/HY7etRK8vhBq7EUucVtjNJpSakRJ60JMlnKaQO8EVU0jERJnNuhPEdBFPFZYa4xuMRNHjFbDUlb2cKPKMjX352PFcBCMx2W2XhUjuMIgFZVacGgyk6Fli1mJBWe63Pf0jIVRY9SlOeIVVr7itdDgWk30LG1GInfWHorL5mwMK2y5nay+s0F6+5ZIXbicQIqLi9tdZn+Eq3+aSU0xkRekhXWOXT+2WHgeplN7q0Fkv/ulHf8v4Jfmj2y9Be0M5yi16/LtCUvQauxGfefIghEiqLsm/Dq0GHb7+3JF46rdwDB5/CI+9chKfTYpRUvo1LEQYotFwivN9oncICm90VHO0348vPX1Ycnr7953AnVc2obm6BMf7/PjVobO45Z2Nko6HzvikrZof+NBF+OyapTg1EIBpLKb60BkfOs524Js3LMNdY6El3/7QRTjWNzLe/2d8+PdfvYH/uN4tLa6rsRvR4rTBao5ft7NU2VGjneAIgphq7CYD/vnH8s+HXBw668VnnzyUUW7e7Wa0N5RnLVdhNeL7r7wlxRTHROD7r7yFR25RmKHUMFyxsBqnBgMTettTYTVh+57XU9p78MU3cf8HL8pZzqTTYe+RzNjZO69szlnOatDhyVePZyxc/Oya3LG6Bq1Wtr07rsi9HXW+5cqzpMYrU0iNl20dk9LMdIJZ7xQzxtYA2IX41mUPc86/OsMqzRmmKg7Sk2WGsdcXTybudtrxiXc3y4RXjL8GK7dq8fXr23Cif0TabOSCKltK+MS8MiM2XZa63eSmyxowr3z8y2LRM3zlA634UlK87Fc+0AqLPj6wtbjs6D4XwMtvDkDkwIm+ESystk3ZbmfJixddDhM+ckkdqmxGOMw6LF/gwDMHzuBbvzmaMYO8ZXUzRsMxhKIiPvvU4Yy+5ODSQPHoKyczwlWuX1GHLzzVgZva67HO7YRZYYc+QYjicI8XHl8ITrsRrS4H7QRHEMSUE47Ib7QTjirluRVSNhkCgKdePS09V7LRUGHF59Yszeu5ls/bnhaXHZ++ejGO941Im358+urFiuNm6zwHrn9HHfzBKIRwDH4Nw/XvqEPbPIVyTjs2XdaY9hxsRKszd1hJq9OO61ektbeiTlW5tW3z8WrXkNTe2rb5iuW0jMvOMCtln2h12mXDMZXaSzCrnWLGmBbAgwCuAnAawF8ZY7s556/PrGZzg6mKg3RlWXmf2EihZySE3x45m/FLs72hDE1jzpteq0MkJqZsNvKVjW7otOMm7rRZEBOHU2T+4wOtcNrGt5ssteqhT9suWq9hKLXqpWvWME3GpiZTFftZObZYo8xiwC2XN+A/f3ssxfH90vsvhC8YkVbzajXA8gWlqLAYoNVxxGIa2b5cUGZJCYFocdqw2HkperwCyq0GBMJRfHbNUrQ67aoc4t2HezIGnfWtrhnZCY6Ye3Axhu7ubulzfX09tFrltIixWAxdXV0TLkfMHNYsm1RYFfL4Lig1Y9Nl9RmxurWlZsU2DbrUZ4BBN72x/eFY6kZZX1HIdAHEw0C8wSjuSYq13rHBrRgeYjTqoNWwtGfYMhgVFvbp9Vr4hGhGbLden/s+5NteqcWEvUfezHzu17fkLGc267HO7URDpUV63ql5riWY1U4xgJUATnDOTwIAY+xnADYAIKe4QExFHGTrPAd2bHBnfLkTv3h7fQKef30Az78+kFLutneOp34bDkTwpbRthb/0dAce/4dLUV8Rl+8eGsUXfnk4ReYLvzyMZQtKpXrODofx2acyX7k9dttKzC+L5xKW29RkqeuKKYkF5Ty+eDEYiUkOcaKdXXuP45s3LJNmYpPTtN31noV44Hcn8JtPXiE7W+uel+mcLltgzCvW93CPV3KIE7rds7sDjZUWXNxYUdCd4Ii5SXB4AHc/eRalroEJ5RKmHMSzj3AsJrumJBLLPVNsMepk8wa/d2lNznKnBgO46yeZ4Rq/2jw1Y3w6nT1e6c1kQs8v/fIwFlZZcy5QPtzjlZ6ZiXL3PNOBxor4OJyN+DPsYNoz7CCWuuw5r6+zxyubGnVRTe6F1Pm211hpxcaL6lKySOy8aTkaK5Vn7M1mPVbm6INczHanuBbA20mfTwPICDRijN0J4E4AqKurK4xmhGoMBi02ts1DU6UVvT4BNXYT2uY5YBjLJ6kmh2vWRV4+AYnIYzV5lbMF6Sc2G5nsJgtKtugVInjsj1344vuWyrYTCMun0xGi8VeMPV5h2mdrJ7uNJlEczPZx0VxWnVcuYcpBXFwo2WGF1YjHZdaUrHE7c9Y7kCVl2WAghGaUZC1X6I10erz5LVDOdxzO9/ry1TPf9mYqI89sd4rleifj3QHn/CEADwFAe3u7umR1REExGLRZFz+oiV1Ws8hLjXOtlHJtspssKNlifblVWl0r+8owy6Yqie2cq0tMigsbJ0u2vNJTkZaOKBw0LhLFgJId5hvjm+9YXeiNdPJdoJzvOJzv9eWr52T6cyYy8sz2YL/TAJIf/fMBnJ0hXYhpIvGL8Vebr8DP7rwEv9p8Bda0OFN+MSYWeZn04/u5py/ySjjXyTLpg+uFTit2rE+tZ8d6Ny50WlXXMRkaK+P1P/rKSfzbupaUduJhFVFsvWpRyvHNq5rx7KEzBdvsodXlkO2jVlpQRxDEFKNm/Jcj37F6usf4dNQ8u+TIdxzO9/ry1bPQ/TlZZvtM8V8BNDPGGgGcAfBBAB+eWZWI6UDpF6NOp1EMG1DzOsZmNuF97io0VI4vSrvQaYXNbFJdx2Svc02LE0ucJfAFw/jxHZdgwB+GzaSFw6zH8GgYTZU2PHHnpQiEY7AYtIjERKxxOwu22YPJpMP6VhcakxcyuBwwmWb7cEIUG8kL6rq7uzPyx6bLAIVZPEcL9sYpRF/kM2OY71hd6Nf2ap5dcuQ7Dud7ffnqOds2JprVTzHOeZQxdheA3yCeku37nPPOGVaLmCHUhA2oGVxtZhNWNmZ/tTPdr3QS9RczJpMu52IOgpgKkhfUDZ7shG1eZm7TfBfdTQZasDdOMfdFvmN1oV/b5xvylu84nO/15avnbNqYaFY7xQDAOf8VgF/NtB4EQRDE1JNYUDd6rldRJkHy7GVsbOcsrVabMts80Rnm5Dq7u7thLiu+BXszNYNNixeJ84VZ7xQTBEEQRDLJs5eDJzuhNdtR6lqQMts80Rnm9DrlZq1nmmKetSWI2QA5xQRBEERWEjO0weEBaENhjJiM0t+xoC/j2Ez+PXquN2X2V47gUN94OfP4LlfJscuJa06uL73eRD1q2iwU6XoUQq/0/iKI2QzjcisXzmMYY/0AuhQFJ0clgAFFqeJgNukKzB59Bzjna3IJKNjiTF8ntX/+tD/bbbFYdACKQ4/ZrENOW5zG53Mx9FkC0kWeQusia4tzzikuBIyx/Zzz9pnWQw2zSVdg9umbLzN9ndT+3G4/mWLQpRh0KBY9SIeJU0z6ki7yFIsusz1PMUEQBEEQBEFMGnKKCYIgCIIgiDkPOcXTw0MzrcAEmE26ArNP33yZ6euk9ud2+8kUgy7FoANQHHqQDhOnmPQlXeQpCl0oppggCIIgCIKY89BMMUEQBEEQBDHnIaeYIAiCIAiCmPPMOad4zZo1HAD9o3/T/U8RskX6V6B/ipAt0r8C/csJ2SH9K+A/WeacUzwwUCx5qom5DtkiUSyQLRLFANkhMdPMOaeYIAiCIAiCINIhp5ggCIIgCIKY8+hmWgHi/CcaFdHZ40WPV4DLYUaLyw6dLvX3mChynBoMoNcnoMZuQkOFFRoNk86HwzEcOuuFxyfAZTehdZ4DBoM2pY7RYBgdHj96fSHU2I1wO0tgMRsmLDMcFHDME5BkFjmtKDWbpPPeoICjSecXO61wJJ2fStL7Zb7DjGN9fvhDEcREIBiOocJmQDgmQohEYdTp4BciqLQZEeMcI8EoHBY9OOeIihzhGEcwHIWr1AwdAwYCEQiRGJoqrKivsKJ7aDS1rX4//EIEfiEKs0GHMosei6pK0OMPotcbwkAghJoSEzQaQMM0ELkIDWMYDcdk7+NsQckei4VC6xkMRtDh8aHXH0KlzQCbUQchEkMkxjEYCMNpN8Ji0KJ/JIxgJAaX3YRAOAq/EEWpRQ8tGHr9IZRbDXBYdGAAerwC7CYDwlERTkf2a5gt94QgiNkLOcXEtBKNinj64Bnc/XQHhIgIk16D+za6sXFZreQYiyLHc50ebH3igCSz86blWNPihEbDEA7H8PShs7jnmfE6dmxwY2PbPMkxHg2G8WxHL+7ZnSSz3o217hrJ6VUjMxwU8HxHf4bM1e4qlJpN8AYF/Ebm/DXuqil3jOX65es3tMEXjDupu/YeR5nFgE2X1eNnf+3Gze11uH/fcUn2M9csxsN/eAtDo2H8x3Wt6PeHsPOFY9L57etb8N8vnkDXYBD1FWbc9Z5mfDmpj+/d4EY0FsP2Z49Ix7asbkb/iACPN4ztezql49vWtuB3R3vwnsUubH+2U/Y+zhaU7LFYKLSewWAEezo9Kd/D/7p5GYZGoym2sH19C36+vxtnhkPYdFl9ip3u2jtun9vWtaCqxIBTA6P45vN/y3kNs+WeEAQxu6HwCWJa6ezxSg4xAAgREXc/3YHOHq8kc2owID3sEjJbnziAU4MBAMChs17pQZw4f88zHTh0dryODo9fclQlmd0d6PD4JyRzzBOQlTnmietyNMv5o2PnpxK5fjnRN4I+f0hyLq5bMR+79h7H2rZaySFOyH7jN0dx3Yr5ECIi3hoISA5x4vy23Z1Y21YLAFjbVis5xInzX36mAz2+UMqxXXuPQ8s0khOUOL792U585NJGySFOHE++j7MFJXssFgqt52GPL+N7GI0hwxa27e7EpsubJNtMttMUm9nTCYNWg28+f1TxGmbLPSEIYnZDTjExrfR4BelBlkCIiPB4Belzr09eps8fl/FkOd/rS64jlEUmNKUyauqYKuT6ReTxf4njjMX/Tvyfrhdj4+Vync9WXkxLXCNERJwLRGRlh7McT9zH2YKSPRYLhdZTzvYDoaisDsFwNMWmstnX0Kg6m5kt94QgiNkNOcXEtOJymGHSp5qZSa+B0zEealBjN8nKVJfEZVxZztfYk+swZpExTqmMmjqmCrl+0bL4v+Tjib/l9Ers4p5eJv18tvLpb6ZNeg3KrXpZ2dIsxxP3cbagZI/FQqH1lLN9q0knq4PZoJP+Tj6eLldmUWczs+WeENNL7YI6MMZU/9MZTBOSL+YyxapXMV9L7YK6CdsY4zxrDuPzkvb2dr5///6ZVmPOMIdjihUDHZVssVhjipe4bBRTXARMQM9J2yIw3THFRymmeG6Q82Yp2SFjDDf/zyuqG3v8Hy+fkHwxlylWvfIpU0i9cvi4srZIC+2IaUWn02Djslo0V9vg8QpwOkxocTlSsk9oNAxrWpxYsvkK9PkFVJekriw3GLTY2DYPTZVWaeV5W1r2CYvZgLXuGjRUWrJmllAjU2o24Wp3FRoqV8pmn3CYTbgm7fx0ZZ+Q65fk7BMPb2qXsk9cVFcKIRLDI7dcDL8QRYXNAJFz/PsH3HCY49kn5jlM+O5YGVepETrGcN/GVgiRGBorrGiosOId9WWpbfX78cgt7fALMZgN2pTsE4/duhIDgRCqS0zQaoAL5y0C5xyP33nprM4+oWSPxUKh9TSb9VjX4kRjhQW9/hAqrAaUmHSossXtbjAQhrPECItRC5djEYJhEU6HEcsXOOAPxuCw6vCj21aOZ58w68DG3nz86PZLEImJWW1mttwTgiBmN+QUE9OOTqfBsgVlWLYgu4xGw9BUZUNTlU32vMGgRXtDec52LGYDVjZWTFqm1GzCysbsTq5D4fxUItcv7vml09ZeRlu18m3VV9hQXyF/r84HlOyxWCi0nmazHhcrfH8mylKXOrnZck8Igpi9UEwxQRAEQRAEMechp5ggCIIgCIKY85BTTBAEQRAEQcx5yCkmCIIgCIIg5jzkFBMEQRAEQRBzHnKKCYIgCIIgiDkPOcUEQRAEQRDEnKfgeYoZYwsAPAbACUAE8BDnfBdjrBzA4wAaAJwCcBPnfIgxVgHgSQAXA/gB5/yupLo+BOCLADiAswD+nnM+UMDLmfOIIsepwYC0qYZcQn1BiOJwjxceXwhOuxGtLgdMJt2EZNTUEQxGcNjjkzbVaHXaYTbrU2T8QQFHPAFJZqnTipK0jTeGgwKOJckkb94BAIFgCJ2eEel8i9MGq3nqt3kGxvvXGwwjGuPo9cev36jTYDgYRTAchcWgg1HPwKDBuUAIdrMegVAUNpMeFr0WkZgILWMYDoZh0GkRCEXRWGFFfYUVpwYDeGswAJNeizKzHkI0Bp8QRX25FQtK45t3CJEYIjGOoUAYtWUWlNt06PeGMRKOIhQV0TRWV/fQKAYDIRi0mlm9eQegzq7nCsl9UWHTISCI6PWHYDFoUWLUQasFAiExvnmH3QiHWYe3BoKwm7XQMg36/CFU2owot2oRCIno84dQYtKDcxEGnQ7nAiFUWI3QaAC7yQCtBujxZvZ7Ie4J3XeCmNvMxOYdUQCf4py/xhgrAfAqY+wFALcA2Ms5/ypj7PMAPg/gcwAEAF8G4B77BwBgjOkA7AJwIed8gDH2dQB3Afi3Ql7MXEbN1quCEMXuwz0Z2yKvb3VJTq2SjJo6gsEI9nR4MmTWuZ2SY+wPCvi1zBbN17qrJMdYaZvnQDCE/+3oyzj/fnf1lDvGif79/stv4voVddL2yfUVZvzzuxdi2+7OlC1zv/P7+JbNJr0Gm1c14/H93fjndy9EiUmHrz33RsY20PducOOB3x2Xtnn++LsWpmzXe+8GN4w6IBDmKce/fn0rPL7ULaO/8oFW/PTPp7BqiTOljdm4FS9tKTxOcl9c1liOtctr8aVfHpb65YvXLoHZoEvZHnzL6mb8+nAPrm11pWzrvGO9Gw++eFyy0W1rW/Cdl06kfH7qtW6sXurEY3/swtBoWOp3ANN+T+i+EwRR8PAJznkP5/y1sb/9AI4AqAWwAcCjY2KPAtg4JhPgnL+MuHOcDBv7Z2WMMQB2xGeLiQJxajAgPUAAQIiI2PrEAZwaDEgyh3u8kgOZkLlndwcO93hVy6iqw+OTl/H4JJkjnoCszBHPuL7HssgcG5Pp9IzInu/0jEy6P9NJ9O+my5skhxgA1rbVSg5xQoftezqxtq1W+nz/vuOSnCjGyySc1YTMl5/pkMqsbauVHN/k86UWY8bxE/0BySFOHPvSLw9j0+VNGW2k28NsQI1dzxWS++KWdzZKDjEQ75eBQFhyiBPHdu09jjuuvEByiBPH79ndkWKj25/tzPi86fIm7Np7HNetmJ/S74W4J3TfCYKY0ZhixlgDgIsA/BlADee8B4g7zgCqc5XlnEcA/BOAw4g7wxcC+F6Wdu5kjO1njO3v7++fuguY4/T6BOkBkkCIiOjzj/9+8fhCsjK9vpBqGTV19BZIRk0duZiILSb6NxiKprTJGGR1YCzzsxAREQhHFctkOz8UiGQcF7m8bLqeiePJ9jAbUGPX5wNqbDG5LyZkC2F5W5CzUblyieOJfi/EPZkr973YoOczUUzMmFPMGLMBeArAJznnPiV5mfJ6xJ3iiwDMA3AIwBfkZDnnD3HO2znn7VVVVZPQmkimxm6CSZ9qQia9BtUl4/G3TrtRVqbGblQto6aOmgLJqKkjFxOxxUT/Wow62TbTP3Oe+dmk18Bq0Kkuk36+zKrPOK5l8rLZ9Ey2h9mAGrs+H1Bji8l9UT4RWzDI24KcjSZ/No+VSxxP9Hsh7slcue/FBj2fiWJiRpziMYf2KQA/5pz/YuxwL2PMNXbeBaBPoZrlAMA5f5NzzgE8AeDy6dGYkKOhwoqdNy2XHiSJGLyGCqsk0+pyYMd6d4rMjvVutLocqmVU1eG0y8s47ZLMUqdVVmapc1zfRVlkFo3JtDhtsudbnLZJ92c6if599JWT2La2RWpzz8Ez2L6+JUWHbeta8OyhM9Lnzaua8eyhuJxGEy+zeVVzSpl7N7ilMnsOnsG2dS0Z54dHQxnHL6iyYutVi1KOfeUDrXj0lZMZbaTbw2xAjV3PFZL74pGX38JXPtCa0i8VVgPu3ZD6fdiyuhnffelNbFndnPE9SbbRbWtbMj4/9spJbFndjF+8djql3wtxT+i+EwQxE9knGOJhDkc45zuTTu0G8DEAXx37/xmFqs4AuJAxVsU57wdwFeLxyUSB0GgY1rQ4sWTzFejzC6guyVytbTLpsL7VhcZKy3hWiLTMEUoyauowm/VY53aiIVkmLftEidmEa91VaKhcmTX7RKnZhKvTZJKzT1jNRrzfXZ1yfrqyT0j96yyBNxjGD29biT5/CDUlRhj1Gjy8qR2j4RgsBi2MeoZv3bgcQ4EwbCYdRiNRfP36ZbAYtIiKInbdfBG8wTAeueViBEJRydG4aEGplH2i1KzHj25fCb8QRV1a9olHbrkYQ6NhzHOYUVGiR783jIc++g6EIiIaK8frOhcI4fE7L53V2SfU2PVcIbkven0CKqw6/PSOS9DvD8Nk0MBm1EGnBR655eJ49okSIxwWHS6ossFu1kk2m8g+sevm5ejzh1Fi0oFzEd+6cTnOBcIotxqg0wBfu345tBrgorrSjH6f7ntC950giJnIPvF3AD4K4DBj7MDYsS8i7gw/wRi7HUA3gBsTBRhjpxBfSGdgjG0EcDXn/HXG2HYALzHGIgC6EM9gQRQQjYahqcqGpqrsM6Umkw4XN1bkrEdJRk0dZrMeKxVkSswmrGzM/Tq0VEHGajZiZeP0pGBLJ9G/08XCmhIsrCnJet5dWyp7fEFZ5jElO5hNqLHruUI+fdFc41AWykFDZWZbhbgndN8JYm5TcKd4LJNEtp/eq7OUachy/DsAvjM1mhEEQRAEQRBzFdrRjiAIgiAIgpjzkFNMEARBEARBzHnIKSYIgiAIgiDmPOQUEwRBEARBEHMecooJgiAIgiCIOc9MpGQjigBR5Dg1GECvT5j2fLLhcAyHznrh8Qlw2U1oneeAwaBNkYlGRXT2eNHjFeBymNHiskOnG//NFgxGcNjjy5qDGAB8QQFveAKSzBKnFXZzamo1b1DA0SSZxU4rHBOUCQRD6PSMTHue4sQ96hkOQq/TgEOEFhr4whFY9XoMBkKosZug1zD4hCgC4SgsBh0MWgaDjiEaA0ZC8ZzD9eUWvD00irPeIAKhGCptBmgZw2gkBr8Qhc0Uz1PsE6IYGAljQakZGi3gGQ7BYdHDH4rApNXCatTCqNdgNCRCiMYQioior7CisTK+wUGhbIqYfuTGiFAoijfPBTASiqHfH4LTboTVqIU3GIU3GEG51YDh0QjsZh30WgYNYxgRotBqGax6HcAAvxCFX4ii2m4EQ3wrZafDhPkOM97o9cHjC8Fu1iESFTGv1ILGyrgdpetTV2ZB99DolNpbIcdFgiCKD3KK5yCiyPFcpwdbnzgAISJKOzetaXFO+QMgHI7h6UNncc8zHVJbOza4sbFtnuQYR6Minj54Bnc/PS5z30Y3Ni6rhU6nQTAYwZ4OD+7ZnVTHejfWuZ2SY+wLCniuoz9DZo27SnKMvUEBv5GRucZdJTm9SjKBYAj/29GXcf797uopdYzT79HVF1biqgvn4YHfHcfN7XW4f99xqf1t61rwnd+fQNdgECa9Bl+8dgmMei227e6UZL5+Qxs8XgE7XzgmW6693oEb2+uwbXcnyiwGbLqsHrv2jrexeVUzHt/fjQ9eXIf6CgtODwVT6vrWjcth1DPc9ZO/TbtNEdOP3BjxrRuXwWHW4sxwSLKt+gozPvHuhbgnydYStvLxdy2EXgsEhBj2HDqDj17WgLPDAnbtPS5rYzs2uPHg745Ldrx5VTPufqYDn1uzFFcvrcHzR3pT9Llvoxvf3jcuP1l7K+S4SBBEcULhE3OQU4MBaeAH4jM1W584gFODgSlv69BZr+QQJ9q655kOHDrrlWQ6e7ySQ5yQufvpDnT2xGUOe3ySEyrVsbsDhz0+qY43PAFZmTc849d0NIvM0QnIdHpGZM93ekamsNcy79FHLm3El5/pwNq2WskhTrS/fU8n1rbVSp8HAmHJaUkcO9E3IjmxcuU2Xd4klbluxXzJWUnI3r/vONa21WLX3uMQRWTU9amfH8Ch096C2BQx/ciNEZ/6+UFoNdoU21rbVis5xAm5hK1s39MJk06HwdEwNl3ehDf7A5JdydnYPWP2nV7P1icOoLPHm6HP3U+nyk/W3go5LhIEUZyQUzwH6fUJ0sCfQIiI6PMLU96WJ0tbvb7xtnq88jIerzCmbyhLHSHpc6Fk1NQxFaTfo6FABEJEBGOQbZ8lTWSJPFNG7lhyuWAoKp3P1YYQERFIkk0+L3JkHJsOmyKmn2xjRL8/1f4VbSUchcjj9pVsg2rsOLmebGNEuvxk7K2Q4yJBEMUJOcVzkBq7CSZ96q036TWoLsm9/XE+uLK0VWMfb8vlMMvKOB2mMX2NWeoYD1colIyaOqaC9HtUbtVLn+Xa50kOqZZlysgdSy5nMepSzmeTNek1sJp0sufT3zBPl00R00+2MaKqRN7+0z9LtmLQQcPi9pVug0p2nFyPyyGvT7r8ZOytkOMiQRDFCTnFc5CGCit23rQ8xcnaedNyNFRYp7yt1nkO7NjgTmlrxwY32uY5JJkWlx33bUyVuW+jGy2uuEyr044d69PqWO9Gq9Mu1bHEaZWVWeIcv6bFWWQWT0CmxWmTPd/itE1hr2Xeox/96S3cu8GNPQfPYPOq5pT2t61rwbOHzkifK6wGbF/fkiJzQbUNW69alLXco6+clMo89eppbFmd2sbmVc149tAZbFndDA1DRl3funE52uY7CmJTxPQjN0Z868ZliImxFNvac/AMdqTZWsJWtq1rgRCNosJiwKOvnERTlVWyKzkb27HBnWLHiXp23rQcLS5Hhj73bUyVn6y9FXJcJAiiOKGFdnMQjYZhTYsTSzZfgT6/gOqS6VtlbTBosbFtHpoqrdKK7ra07BM6nQYbl9WiudoGj1eA02FCi8shZZ8wm/VY53aiodKSNfuE3WzCGncVGipXZs0+4TCbcE2aTHpmCSUZq9mI97urU85PR/aJ5HvkGQ5CN5Z94ls3LIM/HMGjt66MZ58oMUGvZfjKxlaMhmMwG7QwaBn0WoYf3rYyI/tEyzx7SvaJr17XFs8+YdSi1KLHD269GIMjYdSWmvHO5gop+8RIKIKvbGyFbSz7RE2JCd/7WDvCURF15ePZJ35VAJsipp9sY0QoFEWpJYAf3LoSA/64/VuNWjxyy8XwBSMosxowHIzgq9e1waBj0IBhJBTFv161GDaDDg0VVrTWOjASiqLKZsRP7rgEoaiIGns8+8SSGpuUfSIaE3FNy0op+0S6PnVlFqyoK5syeyvkuEgQRHFCTvEcRaNhaKqyoalqamc45TAYtGhvKM8po9NpsGxBGZYtkD9vNuuxsrEiZx12swkrG3O/6nRMgYzVbMTKxqlPwZbOVN+jxiobGidYV2vtxNoolE0R04+c/ZnNerhrS6etzbYFZWibgD5TbW+FHBcJgig+KHyCIAiCIAiCmPOQU0wQBEEQBEHMecgpJgiCIAiCIOY85BQTBEEQBEEQcx5yigmCIAiCIIg5DznFBEEQBEEQxJyHnGKCIAiCIAhizlPwPMWMsQUAHgPgBCACeIhzvosxVg7gcQANAE4BuIlzPsQYqwDwJICLAfyAc35XUl0GAA8AePdYXV/inD9VuKuZvUSjIjp7vOjxCnA5zGhx2aXNMhKIIsepwYC06Ua+iezV1KMkEw7HcOisFx6fAJfdhNa0DUAAIBAModMzknNTjeGggGOegCSzyGlFqdk0IZmRoIDXk85f6LTCZp66rWCT+6LaZoQQjeH0cBDlVj000KDPH4LNqIPVqEWJSYOh0Rh8QgR2kx4DIyFU2ozQMBGRGEMgFEV9hRV6LUOvX0A4yhGKxFBdYkIoFkOlxYBzwQh6vALqys2IRDl6fAIWlJphMehwengUVqMOwXAUOo0Gej2DWadDKCoiFI0hFBFRX2GVNliYSabKXmeKYtNfboyIRGJ481wAI6EY+sc277DoNRgKRhEMR1FqiW/eYTfpoNUAeo0GjHEIEWBgJIR5pSZwETjrE1BpM6DUokMwLELDGEbDMbgcJnAO9PlDCISjqC/PbltK/ZVPf06kzuoSE7QaoMdbHPeLIIjJMxObd0QBfIpz/hpjrATAq4yxFwDcAmAv5/yrjLHPA/g8gM8BEAB8GYB77F8yXwLQxzlfxBjTAMi9QwQBIP6we/rgGdz9dAeEiChtmbpxWa3kGIsix3OdHmx94oAks/Om5VjT4pzQwK+mHiWZcDiGpw+dxT3PjOu76XhZdAAAU5FJREFUY4MbG9vmSY5xIBjC/3b04Z7dSTLr3Xi/u1pyjIeDAp7v6M+QudpdJTm9SjIjQQG/kjn/PnfVlDjGcn2xZXUzfn24B2uXzcPOF45Jxz9zzWJU2Yz41gtHcXN7He7fdxxCRER9hRmfePdC3LO7U/r8yfcugscrYNfe41L5T1+9GAYtw7//+g2UWQzYdFk9du09nvJ3QnbzqmY8vr8bH7y4DvUVFpweCqboko9tTCVTZa8zRbHpLzdG3LvBjbpyI04NCtiWZFvJtpZsKx9/10JYDQyhKPDlpO/ultXNeOyPXRgaDWPbuhY4zDp87bk3EI5y/NO7mhAIx1JsT64flPorn/7Mp87ka5lN9kYQhDwFD5/gnPdwzl8b+9sP4AiAWgAbADw6JvYogI1jMgHO+cuIO8fp3AbgP8bkRM75wPRqf37Q2eOVHnYAIERE3P10Bzp7vJLMqcGANPgnZLY+cQCnBgMTaktNPUoyh856JYc4cf6eZzpw6Oy4vp2eEclRlWR2d6DTMyLJHPMEZGWOeQKqZV7Pcv51z8T6ZSL9tWvvcdxx5QWSE5o4/o3fHMWJ/hGsbauVHGIAWNtWKzkpic9vDQQkRyNR/pvPH8VAIAwhIuK6FfOl88l/J2Tv33cca9tqsWvvcYgiMnTJxzamkqmy15mi2PSXGyO+/EwHAK3kEAOZtpZsK9v3dKLUYpQc4sT5XXuP47oV8yFERGzf0wlRjNdz3Yr5GAiEM2xPrh+U+iuf/synzuRrmU32RhCEPDMaU8wYawBwEYA/A6jhnPcAcccZQLVC2dKxP+9ljL3GGPs5Y6wmi+ydjLH9jLH9/f39U6b/bKXHK0gDewIhIsLjHf/d0euTl+nzy/02yY6aepRkPFnO9/qS6whlkQlNqYyaOnKhZIvZ+iIYisoeFznAGFLOyX0WObKWTy+TXj4hmzgeyKLLRG1jKpkqe50pZkL/XLaYbYzo8ws5bS0hlzg+FIhkPZ/4OxCOgrHcdpreD0r9lU9/5ltn8rXMFnsrJuj5TBQTM+YUM8ZsAJ4C8EnOuS+PKnQA5gP4P875CgB/BPBNOUHO+UOc83bOeXtVVVXeOp8vuBxmmPSpt96k18DpGH/9X2M3ycpUl0wsREBNPUoyrizna+zJdRizyBinVEZNHblQssVsfWEx6mSPJ97Uyp1LRsvkZZLf9Cafl5PlPP6/1SSvy0RtYyqZKnudKWZC/1y2mG2MqC6R1zP9c8JWyqz6rOcTf1sNOulzNjtN7wel/sqnP/OtM/laZou9FRP0fCaKiRlxihljesQd4h9zzn8xdriXMeYaO+8C0KdQzSCAUQC/HPv8cwArpkHd844Wlx33bXRLA3wiprjF5ZBkGiqs2HnT8hSZnTctR0OFdUJtqalHSaZ1ngM7NqTqu2ODG23zxvVtcdqwY32azHo3Wpw2SWaR0yors8hpVS1zYZbzFzon1i8T6a8tq5vx3ZfexNarFqUc/8w1i7GwyoY9B89g86pm6dyeg2ewY31LyueGSiu2rG5OKf/pqxej0mqASa/BU6+els4n/52Q3byqGc8eOoMtq5uhYcjQJR/bmEqmyl5nimLTX26MuHeDG0AM29NsK9nWkm1l27oWDI+GcG/ad3fL6mb84rXTMOk12LauBRoN8OyhM3jq1dOosBoybE+uH5T6K5/+zKfO5GuZTfZGEIQ8jCd+5haqQcYY4jHD5zjnn0w6/g0Ag0kL7co5559NOn8LgPa07BM/Qzx7xb6x8+/nnN+Yq/329na+f//+qbykWUliZbnHK8DpMKHF5ciafaLPH19pPdnsE7nqUZJJZJ9IrApvK/7sE4odlc0Wk/ui0mpEKBrDmWEBZVYdNNCgfyQEq0E++8TgSAgVKrJPVJUYEYmJKLcYMBSMwOMVML/MjGiMw+MTUFtqhjUt+4RWo4Fex2DR6xCOihCiMYSjIupyZAgoJFNlrzPFNOqfly3KjRHJ2ScG/CFUj2WfGA5GMRqOodSihzcYgc2og04L6JgGGk08+8RgIASn3QRwoMcnoMJmQKlZNxaCkJl9YjQczWlbSv2VT39OpM4qWzz7hMc3O+1thsjZQUrPZ8YYbv6fV1Q39vg/Xj4h+WIuU6x65VOmkHrl8HFlbXEmsk/8HYCPAjjMGDswduyLAL4K4AnG2O0AugFIzi1j7BQAOwADY2wjgKs5568jnp3ih4yx/wLQD+DWwlzC7Een02DZgjIsW5BdRqNhaKqyoanKll1IBWrqUZIxGLRob8idXMRqNmJlY+4whlKzCSsbc7/iVJKxqahjMsj1RUvt5OttqJz4fWx2lky+4QIxVfY6UxSb/nJjhE6ngbu2dNrbblTRB0r9lU9/5lNnPt8rgiCKk4I7xWOZJLL9WlydpUxDluNdAK6cGs0IgiAIgiCIuQrtaEcQBEEQBEHMecgpJgiCIAiCIOY85BQTBEEQBEEQcx5yigmCIAiCIIg5DznFBEEQBEEQxJxnJlKyzVkSOS4TuXZnMq+lGl0KqW8iJ2qPV4DLYUaLy56SN1kQojjc44XHF4LTbkSrywGTKdV8vUEBR5PyBy92WuFIyx+sRkYpT7EaXaaCcDiGw2e96B8JodSihxCJwaTXQohEYdLroGUMg4Ewyq0GBMNRGPVaBCNRmHRaWA06jISi8IeicDnMWFJdgtPeIAYDIRg0GpwbDcNi0MHpMCIaA86NhmDRazEcjMAvRLGg3AyDVovTQ6OwGXUIjOUpLrPoUWLSweOL5zwWIjE0VVjRWGWbkG3I2RaASdlbMX2/8qHY9JfTJxSK4vjACALhGIZGw6iyGTEajtsjmAgGDfr9IVSVGOETInCY9DBogVA0nnt4XqkJDMDpYQE1diMsBi36/SFYDDrU2I2oK5+8HUz1Nc8mGyIIYnKQU1wgRJHjuU4Ptj5xAEJElHZAWtPiLPigq0aXQuobjYp4+uAZ3P10h9TWfRvd2LisFjqdBoIQxe7DPbhn9/j5HevdWN/qkpxRb1DAbzr6M2SucVdJTq8ameGggOdlZK52V6HUbFKly1QQDsfwzKGz+PIz4+1sXtWMx/d347bLGxGKidj5wrGMcze312HfGx5c/446bN/TOa7jBjee+GsXVi1x4v59x6Xj29a14KlXu7Fh+XyMhKLYtfc4yiwGbLqsHrv2Hs+o/4MX16Gx0oIzwwK+8Zuj0vlv3bgc17rV2YacbT3w4YsQjvK87a2Yvl/5UGz6y+nzrRuXwWLQwOMLp9jW5lXNOPD2IN671IV7disf37K6GY/9sQtDo2FsW9eCn/65C8f6RrBldTMunFeCQEickX4otntAEEThofCJAnFqMCANtgAgROID/6nBQFHqUkh9O3u8kkOcaOvupzvQ2eMFABzu8UpOaOL8Pbs7cHjsPAAc9QRkZY56AhOSOZZF5tiYjBpdpoJDZ72SQ5xo5/59x7G2rRaDo2HJIU4/d/++49h0eZPktEg6PtOBTZc3SQ5x4vj2PZ3YdHkT+kdCkhN83Yr50t/p9e/aexzRGCSHOHH+Uz9XbxtytnXotHdS9lZM3698KDb95fT51M8PQqvRZNjW/fuO4yOXNkqOr9LxXXuP47oV8yX7u+PKC6Tj/mBsxvqh2O4BQRCFh5ziAtHrE6TBNoEQEdHnF4pSl0Lq2+OVb8vjjbfl8YVkz/f6Qkn6FkZGjS5TgSdL/zMGiBxZzwkREcFQVPZ8MJzleCiaUmeinmz1B7LUr9Y25Gwr2zVNps6Z+n7lQ7Hpn02foUBkQseHsxxnbPzvYDgq/T1Z25oMxXYPCIIoPOQUF4gauwkmfWp3m/QaVJdM33bBk9GlkPq6HGbZtpyOeFtOu1H2fI19fEvnmgLJqNFlKnBl6X/OAS1D1nMmvQYWo072vMWQ5bhRl1FnrvqtJvl61NqGnG1lu6bJ1DlT3698KDb9s+lTZtXLHi/Pcrw0y3HOx/82G3TS35O1rclQbPeAIIjCQ05xgWiosGLnTculQTcRr5ZYYFRsuhRS3xaXHfdtdKe0dd9GN1pcDgBAq8uBHetTz+9Y70br2HkAWOy0ysosdlonJLMoi8yiMRk1ukwFrfMcuHdDajubVzXj2UNnUG4xYOtVi2TPbV7VjEdfOYlt61pSddzgxqOvnMTmVc0px7eta8Gjr5xEpc2ILavj55569bT0d3r9W1Y3Q6cBPnPN4pTz37pRvW3I2VbrfMek7K2Yvl/5UGz6y+nzrRuXISaKGba1eVUzfvSnt7BjfebxH8sc37K6Gb947bRkfw+/9KZ0vMSknbF+KLZ7QBBE4aGFdgVCo2FY0+LEks1XoM8voLpk5lY2q9GlkPrqdBpsXFaL5mobPF4BTocJLS6HlH3CZNJhfasLjZUWKSNEesYHh9mEa9xVaKhcmTWzhBqZUrMJV6fJJGefUKPLVGAwaLGhbR6aKq1S9olQJIavXd+GRBaKx25biXOBMCqsBoxGovhafRuEcAzLFzhgM+rw/Y+1YyQUg9NuxJIaO9rry3AuEMLj/3BpSvaJlQ3lGBoNwazXom2+AyNCDPPLTHjv0pqU7BP3bWxFmVmPErMOZVYDvrupHaFIDA0VVjRNIPtENtsCkLe9FdP3Kx+KTf9s+iSyTzxyy8VS9olgJIp31JcBTMRjt61E/0gIVVYj/KEI2hvKYNQCj926En0jIbjsJmgYsKDMguqSePYJ11WLYE7LPjET/VBs94AgiMJDTnEB0WgYmqpsaKqyzbQqqnQppL46nQbLFpRh2QL58yaTDhc3VuSsw2E2YWVj7ledamRKFWTU6DIVGAxavKOhfMrqy30v5Y8315TIHq+vmJxNZLOtydhbMX2/8qHY9JfTx2zWo21B2aTrvqg+9/mZ6odiuwcEQRQWCp8gCIIgCIIg5jx5OcWMMQ1jrGOqlSEIgiAIgiCImSAvp5hzLgI4yBirm2J9CIIgCIIgCKLgTCam2AWgkzH2FwBSdnPO+fpJa0UQBEEQBEEQBWQyTvH2KdOCIAiCIAiCIGaQvJ1izvnvp1IRgiAIgiAIgpgp8s4+wRi7lDH2V8bYCGMszBiLMcZ8U6kcQRAEQRAEQRSCyYRPPADggwB+DqAdwCYAzUqFGGMLADwGwAlABPAQ53wXY6wcwOMAGgCcAnAT53yIMVYB4EkAFwP4Aef8Lpk6dwNo4py7J3E95xWiyHFqMIBen4Aae2YS+mhURGePFz1eAS6HGS0uu7RZRqF1UaOPPyjgiCcgbZix1GlFiTk1l7AvKOCNJJklTivsaTJq6vEGBRxNkknf4CMYjOCwxze+eYfTDrNZP1XdBVHkeHso3r43GIHDrId3NAKzQQu9lsGo08AbjMCg08IvRFFm1cGg0SIYjSEmcgRCMdjNOhi0GvT6QphXmv3+Jvp9MBBChdUIvxCFX4hiXqkJZr0WXedGYTNpYdJq0esPweUwo9SiQ58/BINGI20CUmM3Yp7djCO9Ptl7mGwD1SXxDRzeGgzAmrRpw1RskjARu1Zjl3MZQYjicI8XHl8IzrFNajQahhMDfviFGPr9ITgdRhh1GgyNRhAVRZQY9egfCaHSZkRUjMFq0GM0HMO5QBg19vhmHf5QFJEox0goinmlZiyqtOFov1+6Z2VWHXqGQwiEo6gvt6Kxcvy+pN/fUosOZ4fV3T9R5Og+F/9eydWdkCGbyA/qO+J8YFKbd3DOTzDGtJzzGIBHGGOvqCgWBfApzvlrjLESAK8yxl4AcAuAvZzzrzLGPg/g8wA+B0AA8GUA7rF/KTDGrgMwMpnrON8QRY7nOj3Y+sQBCBFR2q50TYsTGg1DNCri6YNncPfTHdL5+za6sXFZ7ZQ7xkq6AFDUxx8U8OuOftyze/z8jvVuXOuukhxaX1DAczIya9xVkmOsph5vUMBvZGSucVfBYTYhGIxgT4cn4/w6t3NKHGNR5PjDiT6cHQ7hO78/gZvb63D/vuNSW1tWN6PCqsfQaBT/+dtjKLMY8E/vagIACFERO184liL72B+7MDQalr2/iX7/9r7juOOdTTjqGcGuvcdRZjFg02X12LX3uGxd/3FdKwZHwvjm80eT+uBCgGlwzzOZ91CjYRk2kFzfltXNaK6xYdXimkk9RCdi12rsci4jCFHsPtyTaucb3KivMOLUgIBtuzul49vXt2DvkR4sX1CRYqv3bXBDqw3jC784DCEior7CjK1XLcLZYSHFtu7d4MYDvzuOrsEg6ivM+Od3L0ypP3FfRJFn3N9t61rw0z934VjfSM77J4oc+4724njvSErbyWXIJvKH+o44X5iMBzTKGDMAOMAY+zpj7F8BKG4Szznv4Zy/Nva3H8ARALUANgB4dEzsUQAbx2QCnPOXEXeOU2CM2QBsBXDfJK7jvOPUYEAanABAiIjY+sQBnBqMJwnp7PFKD5bE+buf7kBnj7fguqjR54gnID2cE+fv2d2BI57xOt7IIvNGkoyaeo5mkTk6JnPY45M9f9gzNZFDpwYD8Adj2L6nE2vbaiUnI9HWrr3HYTHo8Z+/jTu/162Yj4FAGAOBsOQQJ8tet2J+1vub6Pe1bbXoHwlJzsJ1K+ZLf8vV9dZAQHKIE+ctBr3kECeOJdqUs4Hk+nbtPY5Dp70pNpEPE7FrNXY5lznc482082c6AK6VHNbE8W27O/GRSxszbPXuZzrw1kBAOra2rRZv9gcybOvLz8RtMCGTXn/ivsjd3+17OnHHlRco3r9TgwEcOu3NaDu5DNlE/lDfEecLk3GKPzpW/i7EU7ItAHD9RCpgjDUAuAjAnwHUcM57gLjjDKBaRRX3AvgWgFGFdu5kjO1njO3v7++fiIqzkl6fIA1OCYSIiD5//HdFj1f+vMeb8btj2nVRo0+vLyR7vtcXSmqnMDJq6siFki32+gQEQlEIERGMQbatQDgqHWcMEHn8n5wsY+N/p9/fRL8n6kiuM1ddcm0ldE4v4/EKWW0gWTeRI8Um8mEidq3GLs93ctmiJ4ud9/nl+20oEJE9LvLk9pTtNJvt9fmFrPc3GI6myMnR6xOytp0oQzaRP5Ppu7n2fCaKm7ydYs55FwAGwMU5384538o5P6G2/Ngs71MAPsk5n/A0G2NsOYCFnPNfqtD1Ic55O+e8vaqqaqJNzTpq7CaY9Km31qTXoLokHiLgcphlzzsdqbG1hdBFjT41dqPs+Rq7MamdwsioqSMXSrZYYzfBatJJbci1ZTXoUo5rWfyfnCzn43+n39/kfk8vn6suubaSdU4u43SYstpAsm4ahhSbyIeJ2LUauzzfyWWLzix2Xl0i329lVr3s8fQ350p2mvgs1262+2s26FLk5Kixm7K2nShDNpE/k+m7ufZ8JoqbyWSfWAfgAIDnxj4vH1vwpqasHnGH+Mec81+MHe5ljLnGzrsA9ClUcxmAdzDGTgF4GcAixtiLE7yM85KGCit23rQ8xbHaedNyNFTEo1taXHbct9Gdcv6+jW60uBwF10WNPkudVuxYn3p+x3o3ljrH61iSRWZJkoyaehZnkVk8JtPqtMueb3Xap6y/SkxabFvXgj0Hz2DzquaUtrasbsZoOIJ/fe8imPQaPPXqaVRYDaiwGrD1qkUZsr947XTW+5vo9z0Hz6DSZsSW1c1SnYm/5epqqLTi01cvTjk/Gopgxwb5eyhnA8n1bVndjLb5jhSbyIeJ2LUau5zLtLocmXa+wQ2wGLavb0k5vn19C378p7cybPW+DW40VlqlY3sOnkFTlTXDtu7d4Mazh85IMun1J+6L3P3dtq4FD7/0puL9a6iwonW+I6Pt5DJkE/lDfUecL0xmod2/AVgJ4EUA4JwfGAuHyAljjAH4HoAjnPOdSad2A/gYgK+O/f9Mrno45/8PwP8bq7MBwLOc83dP7BLOTzQahjUtTizZfAX6/PHV/skrgXU6DTYuq0VztQ0erwCnw4QWl2Nask8o6aJGnxKzCde6q9BQuTJr1gi72YQ1aTLp2SfU1OMwm3BNmkxy9gmzWY91bicaKi3Tkn1Co2G4YmE13h4K4IKqZfAGI3jstpUp2ScMWg18QgSP3HIxRoQoHBYdjNp49onvfax9PPuERoPGSitcdhNa5mXe3+R+PxcIodxiRGutA/5QFPMcJqxeUh3PPmHUwqTTSnWVWvXo94fw+D9cmpF9YnGN/D1MtoEqWzz7xBJniVR2KrJPTMSu1djlXMZk0mF9qwuNyXY+ln2ixOjHo7euRP9ICDUl8Rlll8OEqCjih7etxMBIPJNJlMezTzxyy8U4FwijusQIq1ELp8OEhzfF7dTlMGJRVQkWJezGbkKZTY9Hb12J0XAUdUkZIjQalnp/x2yxsdKieP80GoZVi2uwsMqGFXVlGXUnZMgm8oP6jjhfmIxTHOWcexmbsNH/HeLxyIcZYwfGjn0RcWf4CcbY7QC6AdyYKDA2G2wHYGCMbQRwNef89Unoft6j0TA0VdnQVGWTPa/TabBsQRmWLZh5XdToU2I2YWVj7ldxdhUyaupxKMiYzXqsbKzIWcdk0GgY6itsqK/I3l9TRaLfs7Eoywx4Nt2y3UM5G2jMYQ/5MhG7VmOXcxmTSYeLZez8wnmlU95W+j2rK1c/bqn9nmg0DA2VNjRUZpcnm8gf6jvifGDCTjFj7FcAPgGggzH2YQBaxlgzgM0AFFOyjWWSyOZJr85SpkGhzlOQSddGEARBEARBEGrI5335DwD8BvENNtwAQgB+AsALYMtUKUYQBEEQBEEQhWLCTjHn/AnE06jZALwf8V3ofgZgCPEZZIIgCIIgCIKYVeQbUxxBPDexEXHnmOcWJwiCIAiCIIjiJZ+Y4jUAdiKeLWIF5zznxhkEQRAEQRAEUezkM1P8JQA3cs47p1oZgiAIgiAIgpgJJuwUc86vmA5FCPWIIsepwQB6fQJq7PL5IKNREZ09XvR4BbgcZrS47Cn5WpXOq5VRg5p6BCGKwz1eeHwhOMdyoppM4+YZCIbQ6RmRcqa2OG2wmlN3kfMHBRzxBLLmIAaA0WAYHR6/JON2lsBiNkyoHjV15Evi3g4GQtCAwR+KwGLQwTsagVGvgd2kQ1Tk6PWFUFtqBuccvlAUoUgMVSVGDAfDMGi0sBp1iHGOPl8IFTYDnA4jIlFIOUS1mvi2yMn2k3yf5peZYdRp8fbQKBxmPTQAzngFuOwmtI7lPH5rIICucwFYx3INzy+1oHtoVNYu1dhsvn2VXCcA1e1Mh07nE3LfWwB4w+PDudEwAA6zXofBQBg2ow5lVj2CYRG9PgHlVgNsRi1EEQhGYojEOKI8BptBD28wDLvZAG8wAodZD6NWg4jIMTASShkfJnt/5VC652QT+UN9R5wPTCZPMTEDiCLHc50ebH3iAISIKO0ctKbFKQ1A0aiIpw+ewd1Pd0gy9210Y+OyWuh0GsXzaupQi5p6BCGK3Yd7cM/ucZkd691Y3+qCyaRDIBjC/3b0ZZx/v7tacoz9QQG/7ujPkLnWXSU5tKPBMJ7t6M2QWeuukZxapXrU1DHZe/u1547g5vY6PL6/Gze31+H+fceltrata8F3fn8C4SjHP72rCYFwDLv2jp//9NWL8VzHWVz/jjps39MpHd++vgX//eIJdA0GpV3kHvtjF4ZGw9h503K8d3E1dh8+i7uf7kCZxYBNl9Wn1Jss//Ub2qBlDJ/6+UHp/NarFsHlMOEzTx7KsEsAijabb18l1/nAhy9COMpVtaPmezSXkfvefuUDrbAZtTjZH8DP/ppqm/UVZnz8XQtTbO7eDW4wxtHvD2fIm/QabF7VjMf3d+Of370wxTbv2+jG+tZ5+O3Rvoz7Y9Ax3PWTv+V1z5TuOdlE/lDfEecLU7+FGTGtnBoMSAMPAAgREVufOIBTgwFJprPHKz3MEjJ3P92Bzh6vqvNqZdSgpp7DPV7JyUzI3LO7A4cT+npGZM93ekakOo54ArIyRzzj/dLh8cvKdHj8qutRU0e+JO7t2rZa3L/vuPR/clvb93RibVstrlsxHwOBsOS4Js5/8/mj2HR5k+ScJI5v2x0vl/i8a+9xXLdivmQ/h86O36frVszPqDdZ/kTfiOQQJ87vfOEYjveNyNqlGpvNt6+S6zx02qu6nenQ6XxC7nv7pV8eRiTKsWtvpm2ubavNsLkvP9MBk04nKy9ERMnG023z7qc7cOis/L08dNqb9z1TuudkE/lDfUecL5BTPMvo9QnSwJNAiIjo8wvS5x6vvIzHK6g6r1ZGDWrq8fhCsjK9vhAAoFfhfCFl1NSRL4l7yxhS/k9vizGAMUDk8ueD4WjWcnKfhYgIT5Jd5WoXyN6umJaDJmGXamx2osjVmU0vuXamQ6fziWzf28CYbaXbSDabySafOJ9s68nHPVnuTzYbU4PSPSebyB/qO+J8gZziWUaN3QSTPvW2mfQaVJeMx7y6HGZZGafDpOq8Whk1qKnHaTfKytTY46ERNQrnCymjpo58Sb636f8nt8XHHAMtkz9vMehylkv/bNJr4Eqzq3zaTX9LmrBLNTY7UeTqzKaXXDvTodP5RLbvrTXJtuTOT1Sec3nbdGa5P9lsTA1K95xsIn+o74jzBXKKZxkNFVbsvGl5yoNm503LpUUoANDisuO+je4Umfs2utHicqg6r1ZGDWrqaXU5sGN9qsyO9W60JvR12mTPtzhtUh1LnVZZmaXO8X5xO0tkZdzOEtX1qKkjXxL3ds/BM9i8qln6P7mtbeta8OyhM3jq1dOosBqwZXXq+U9fvRiPvnIS29a1pBzfvj5eLvF5y+pm/OK105L9tM5zSPfpqVdPZ9SbLH9BtQ3funFZyvmtVy1Cc7VN1i7V2Gy+fZVcZ+t8h+p2pkOn8wm57+1XPtAKvY5hy+pM29xz8EyGzd27wQ0hGpWVT8QUP3voTIZt3rfRjbZ58veybb4j73umdM/JJvKH+o44X2Ccz619N9rb2/n+/ftnWo1JkVjlm8gkkCv7hMcrwOkwocXlkM0+ke28Whk1qKknkX0ikdHhPMg+obi6RM4WZbNP6HXwBpWyT4iotBngEyLQaTQoMcbl+kdCKLcY4CyNZ5/oHxFQZYtnn/D4Uu0n+T7VjmWfOD00CrtJDw2LZ59w2k1oS8o+0X0uAEta9gk5u1RjsxNFrk4AqtuZDp2KlLxsUe57C6RmnzDpdTg3ln2i1KIfCyWSyT4hcsREEVZD3JbtJj28wlj2CZ0GkVgi+8T4+DDZ+yuH0j2fQzYx5ajsu5ydqfR8Zozh5v95RbVOj//j5ROSL+YyxapXPmUKqVcOH1fWFin7xCxEo2FoqrKhqcqWVUan02DZgjIsW5DfebUyalBTj8mkw8WNFVnPW81GrGzMHaJQYjZhZWPu13UWswErc7Sjph41deSLmns7GS6oHq+3oTK1Dbn71FwzPgN+kUxdyfUByKr7dFxXtjrVtjPdfT3byfa9dc8vLUj7k72/E6lT7XkiO9R3xPkAhU8QBEEQBEEQcx5yigmCIAiCIIg5DznFBEEQBEEQxJyHnGKCIAiCIAhizkNOMUEQBEEQBDHnIaeYIAiCIAiCmPOQU0wQBEEQBEHMeQqep5gxtgDAYwCcAEQAD3HOdzHGygE8DqABwCkAN3HOhxhjFQCeBHAxgB9wzu8aq8cC4OcALgAQA7CHc/75fHRKJB3v9QmosRd/wvap0DeRmL/HK8DlMKPFZc9rYw41uqhpS2lDjJGggNeTNtS40GmFLW1jjuGggGNJMoucVpSmyaipR0mXcDiGQ2e98PgEuOwmtM5zwGDQTrjvchGNijjR78NwMIqBkTAqbQYYdAyMMwwHI3CYdYiKgDcY3wDhXCCM6hIjwIHRaBQGrRa9vhCq7UaUmrWoL7XhxOAIhkYjECIx1JSYAMYRjiY2TTBjaU0J3h4OoutcAFaDDk6HEZEoR/fQKGxGHSwGLfxCVLrPAFLufV1ZfPOObJ+n+nslZ3vpOqnZvGO2fO+LhUS/eYNhcA70j4Rg0etgNDCYtFoMByMYDcfgcpjAOYfHF4LFoIXDrIfIOc4FIjAb4ltAx0SOPn8INqMOZoMGGjAMBcOwmwwIRWIw6bXwCmGY9Fq47GYsSLIpl8OEmAj0+QVYDDqEYzFUWI2TuudkEwQxt5mJzTuiAD7FOX+NMVYC4FXG2AsAbgGwl3P+VcbY5wF8HsDnAAgAvgzAPfYvmW9yzn/HGDMA2MsYu5Zz/uuJKCOKHM91erD1iQMQIqK0PeWaFmdRDoZToW80KuLpg2dw99MdUh33bXRj47LaCTnGanRR09ZoMIxnO3pxz+5xmR3r3VjrroHFbMBIUMCvOvozzr/PXSU5tMNBAc/LyFztrpIcYzX1KOkSDsfw9KGzuOeZpPMb3NjYNm/KHONoVMTeo704F4hg+55OqZ1t61qgZRy/PuzB1W4XvvP7E7i5vQ737zs+LrN2KXRaLb6cpN/29S3o94dwtDeAb/zmKISIiPoKM/753QuxbXdnynU88dcu7O/yor7CjI+/a2FK+1tWN+OxP3ZhaDSMBz58EcJRLt37+goz/mVVc8Z9/va+4+gaDE7590rO9tJ1ytXmbPveFwuJfvv+y2/ipvY63LM71T6cDhP+67fHEI5ybLqsHrv2Hs9qb9vWteA7vz8h2ceW1c2oKjFgcCSC//zt3yS5zaua8fj+bnzkknq4HCZ85slDKLMYUupPlvvcmqV53XOyCYIgCh4+wTnv4Zy/Nva3H8ARALUANgB4dEzsUQAbx2QCnPOXEXeOk+sZ5Zz/buzvMIDXAMyfqD6nBgPSIAgAQkTE1icO4NRgII+rm36mQt/OHq/kvCTquPvpDnT2eKdcFzVtdXj8khOakLlndwc6PH4AwOuegOz51z3j7RzLInMsSUZNPUq6HDrrlRxi6fwzHTh0dmJ9l4vOHi8iUS45pIl2tu/phMWgxy3vbMT2PZ1Y21YrOcQJmR5fSHKIE8e27e6EVqOVHGIAWNtWKzkoydex6fIm6Xx6+7v2Hsd1K+ZDiIg4dNqbcu/XttXK3ue1bbXS56n8XsnZXrpOudqcbd/7YiHRb5sub5IcYmDcPt4aCGBtWy2uWzFfclgBeXtL2HByeZNOh//87bEUufv3HcfatlrsfOEYjveNQIiIGfUny+V7z8kmCIKY0ZhixlgD4rvH/hlADee8B4g7zgCqJ1BPKYB1APZmOX8nY2w/Y2x/f39/yrlenyANggmEiIg+f4oPXjRMhb49Xvk6PN6JXbMaXdS01esLycr0+kKqzhdSxpPlmnt96vouly0m6PEKCISisu0EwlEMBSIQIiIYQ4aMyDOPCRERAyOp1yVXVoiICIajOc8zJt+Oknzi81R9r+RsL9u1y7U5277304EaW0wn0W/BLPYp8rgtpNuDWvsIhOXrTZQXuXJ9+d5zsomZIR87JIjpYsacYsaYDcBTAD7JOfdNoh4dgJ8CuJ9zflJOhnP+EOe8nXPeXlVVlXKuxm6CSZ/aDSa9BtUlqXGmxcJU6OtymGXrcDomds1qdFHTVo3dKCtTYzeqOl9IGVeWa66xq+u7XLaYwOUww2rSybZjNehQbtVL59JltCzzmEmvQaVN/rrSP5sNupznOc/dTjb5xOep+l7J2V42neTanG3f++lAjS2mk+g3i1HePjUM0j3Pxz6sBvl6OR+vX6m+fO852cTMkI8dEsR0MSNOMWNMj7hD/GPO+S/GDvcyxlxj510A+lRW9xCA45zz/8pHl4YKK3betDzFydh503Jp0U6xMRX6trjsuG+jO6WO+za60eJyTLkuatpyO0uwY32qzI71bridJQCAC51W2fMXOsfbWZRFZlGSjJp6lHRpnefAjg1p5ze40TZvYn2XixaXHXotw7Z1LSntbFvXgtFwBI+8/Ba2rWvBnoNnsHlVc4qM027EvWn6bV/fgpgYw2euWSwd33PwDLavb8m4jsdeOSmdT29/y+pm/OK10zDpNWid70i593sOnpG9z88eOiN9nsrvlZztpeuUq83Z9r0vFhL99ugrJ7FjfaZ9NFZa8eyhM3jq1dPYsro5p71tW9eSYh9bVjdDiEbxr+9dlCK3eVUznj10BluvWoTmahtMek1G/cly+d5zsgmCIBhP/qleiAYZY4jHDJ/jnH8y6fg3AAwmLbQr55x/Nun8LQDaE9knxo7dB2ApgBs556nvvbLQ3t7O9+/fn3IsseK4zy+guqT4VxxPhb6JjBAerwCnw4QWl2NS2Sdy6aKmrdmYfSKxQr1NPvuE4g2Rs8XkPktknxgcCaPcGs8+oUE8+4TdrEMskX3CpMe50TCqbEYwAMFoFHqtFn2+EKpKjCi1pGafCEViqCoxQsOAkJR9woSlNXa8PRxE97kALEnZJ94eyz5hzpJ9InHvE9kmsn2eruwTyfWn66QmE8Fs+d5PgknZYjqJfvMFwxA5MDAShtmghUHHYNYlZZ+wm8DB0esLw2TQoNSkhwiOoUAERr0GJUYdoiJHvz8Mq1ELi14DxsazT4QjMRj1WviECAw6DeY5xrNP9PkFOO3J2Se0iMRElKvMPpHtns8hm5gpcnamkh0yxnDz/7yiurHH//HyCckXc5li1SufMoXUK4ePK2uLM5F94u8AfBTAYcbYgbFjXwTwVQBPMMZuB9AN4MZEAcbYKQB2AAbG2EYAVwPwAfgSgDcAvBb3tfEA5/zhiSqk0TA0VdnQVGXL85IKy1Toq9NpsGxBGZYtmH5d1LRlMRuwsrEi63mb2YSVjblfY5aqkFFTj5IuBoMW7Q3lOeuYLDqdBktcpVNap7tWub4Lqm24oDr1Xi6sKckqn37vlT5PJdlsT22bs+17Xywk+m2mSL9n6faaC6V7TjZBEHObgjvFY5kksv1aXJ2lTEMWefoJTxAEQRAEQUwa2tGOIAiCIAiCmPOQU0wQBEEQBEHMecgpJgiCIAiCIOY85BQTBEEQBEEQcx5yigmCIAiCIIg5z0ykZCOKgEQ+zkSuXbl8nGpkEjmIe7wCXA4zWlz2jBzEghDF4R4vPL4QnHYjWl0OmEypphcIhtDpGZFyA7c4bbCax3eaCwYjOOzxSedbnXaYzfqUOnxBAW8k5SBe4rTCnkcuYyVd1PTLZBBFju7BEXj8IQyMhOG0G8HA0B8IwWHWIxKNwazXYXg0AotBC4tBi5FwBGa9HoOBEKwGHawGLUbDMdhMeviEMOxGfTwn7EgILocZpRYd+vwhGLQajIZjqLGP5xVOvi4AWa81vR/Sy8vVRzlfZw9ydg4A3efi359wLIYyswHhqIjRSAwiF2E16NE/EoLFoEWF1QAhEoPHF4rn2dYymA1ajAgx9I+EUF1ihN2sxVAgCp8Qgd2sh1GrwVmvgHKbARVWHTTQon8kJGuPLkdynmIdwrEYKlTmKc5mk9P93SYIorghp3gOIoocz3V6sPWJAxAiorRz05oWZ4rDoyQTjYp4+uAZ3P10hyRz30Y3Ni6rlRxjQYhi9+Ee3LN7XGbHejfWt7okxzgQDOF/O/oyZN7vrobVbEQwGMGeDk/G+XVup+QY+4ICnuvoz5BZ466SHOPhoIDnZWSudldJjrGSLmr6ZbL35g/H+3DWG8L2PZ0osxiw6bJ67Np7XGpv29oWfOelTnQNBmHSa/DFa5fAqNdi2+7XJJktq5vx2B+7MDQaxqevXowKmwFf+MVh6fx/XNeKwZEwvvn80ZR79+19x6V6d960HAYdw10/+VvGtQJI6Yf6CjP+ZVVzhi2k1zdV/URML3J2/sCHLwIAHO8dwc/+2o3bLm+EVjsKvxDF3iMe3NReh3uSbHD7+hb894snpPu/fX0LzAYtPvvkoawyW69ahEf+7xSGRsMZ55LtUe57sXlVMx7f343PrVkqa2dK393p/m4TBFH8UPjEHOTUYEAa+AFAiIjY+sQBnBoMTEims8crOUEJmbuf7kBnj1eSOdzjlRzMhMw9uztwOEmm0zMiK9PpGYnX4fHJ1+HxSXW84QnIyrzhGdf3WBaZY0kySrqo6ZfJcGowAL8Qw/Y9nRAiIq5bMV968Cfa2/5sJ9a21UqfBwJhbNvdmSKza+9xXLdiPoSIiG8+fxRvDQRSzr81EJAc4sSxu5/uSKl36xMHcOi0V/Za0/thbVutrC2k1zdV/URML3J2fui0F4dOe7Fr73GsbavF4GgYff4Qdu09jk2XN+GeNBvctjvVTrft7sSJvpGcMjtfOCbZbfq5ZHuU+17cvy+uVzY7U/ruTvd3myCI4oec4jlIr0+QBv4EQkREn1+YkEyPV17G4x2X8fhCsjK9vlBSW7lllM4XUkZNv0yGXp+AQCgqtcEYZNtjSRNXIs8tI0REiGk7XSqVyVYuca3p/aBGz6nsJ2J6kbNzkY/bDWOpn4NJNptArT2lyyTbbbbyuewtm50pfXen+7tNEETxQ07xHKTGboJJn3rrTXoNqktME5JxOcyyMk7HuIzTbpSVqbGPx+jWKMgonS+kjJp+mQw1dhOsJl1KG3LtJW/nrmW5ZUx6DdLf/iqVyVYuca3Z+kGpvqnqJ2J6kbu/WpZqN8mfLUZd3vaULpNst7nKZ2svm50pfXen+7tNEETxQ07xHKShwoqdNy2XHgCJ2LnEQha1Mi0uO+7b6E6RuW+jGy0uhyTT6nJgx/pUmR3r3WhNkmlx2mRlWpy2eB1Ou3wdTrtUxxKnVVZmiXNc30VZZBYlySjpoqZfJkNDhRUlRi22rWuBSa/BU6+expbVzSntbVvbgmcPnZE+V1gN2L6+JUVmy+pm/OK10zDpNfj01YvRWGlNOd9QacWnr16cce+S691503K0zXfIXmt6P+w5eEbWFtLrm6p+IqYXOTtvne9A63wHtqxuxp6DZ1BuMaCqxIgtq5vx6CsnsSPNBrevT7XT7etbsLDallNm61WLJLtNP5dsj3Lfi82rmvHsoTNZ7Uzpuzvd322CIIofWmg3B9FoGNa0OLFk8xXo8wuoLslcZa1GRqfTYOOyWjRX2+DxCnA6TGhxOVKyT5hMOqxvdaGx0jKeOSIt+4TVbMT73dVoqFwpm/HBbNZjnduJhuQ60rJP2M0mrHFXpdSRnn2i1GzC1Wky6dknlHRR0y+TvTdXNFeje3AEP7j1YgyOhFFjN+LHt1+CgUAIdpMekVgM37xhWTz7hFELiz6efeJHt1+Cc4GwlJGirdYBm1EHfyiCEqMeP779knj2CbsJpVY9+v0hPH7npSnZJ1bUlaVcFwD8Ksu1pvdDenm5+mjB0uwgm50DwMIqG1bUlSESi6HUbEA4JqK11gERomRjiewTO29clpJ9wqTX4rHbVqLfP5594mvXtcEnRPH/2zvz8LiOKtH/Tmvr1mpbli15i+xEsSeSF4IRWQjD2JA44NgelpD5ZggQ5vHlPSBm8ngQhmCICTMM8yZDAg9mGLaYwJAQGCfxQEhwwhISErI4ikxiK4tjbEteFFuyZLW2Pu+Pe7vd3bq31Vq61XKf3/f117erTlWdqjpV9/RdqipDhZQUBJg/I8SsMmf1iW9f/QaO9fZ72mNtZZBLz6t1V58oYHA4wrqmWl87G23sZnpsG4aR+5hTnKcEAsKSmnKW1JRPSKawMMDKhTNZudC/rGCwkDcsrk6pT1mohObFJb7xoVARzaPkURkK0rw49a3OGWnIjKZLOu0yEQIBob6mgvqaiozkH+Ws6pH6e9XLr65e7TDab2P64Gfn9bPLqZ+duT59XdLvc0gcB8k6nT0nfV1GG7uZHtuGYeQ29viEYRiGYRiGkfeYU2wYhmEYhmHkPeYUG4ZhGIZhGHmPOcWGYRiGYRhG3mNOsWEYhmEYhpH3mFNsGIZhGIZh5D3mFBuGYRiGYRh5T9bXKRaRhcA2oBaIAN9U1VtFZBZwJ1AP7AOuVNXjIlIN3A28Afieqn40Lq/XA98DQsDPgM2q8RuDGn5EIsq+zl4Od4eZW+m9SH06MkNDEXa3d9HeFaauKkRjXWXC5h0AAwPDtBzqoqM7TF1lkOXzqiguLkiQOdU3QGvHydiGGU21FZSGimPxvX397O7o8dxQI0pXX5g9Hb0xmaW1ZVSFEtckPtEXZm+cTPLmHemWlSkiEWV/Zw89A8OEB4c51uNsxlFRUsiJ8ABFgQIKAlBUEKAiWEBACvjT8VOUFRdSGSrgZHiY7r5BKkNFDAwNUxEsZmB4mJryEoYjcOSkf1/66eNnA8lxi2aWsv/4Kd/fthHC9CLavx0nwhQVCsf7BqkuLY6N36GhCC8c7uZ47yCnBoeYWVpESYGzkUxRoIAjPc4GHX2DQ4SKCunqG2R2WTEBgfbu/phdFxUG6BsY5kTfIFWhIrr6BplTEfScS9KZk9Kpk1/6ieZvGMb0Zio27xgC/reqPi0iFcBTIvIg8AFgp6p+SURuAG4APgWEgc8CTe4nnm8AHwZ+j+MUrwN+npVaTGMiEeX+3R1cf9cuwoOR2Ham6xprExye0WSGhiJsf/YgN25vjcncvKmJTSvnx05mAwPDbG85xJZ7Tsts3djEphXzYo7xqb4BdrQeZsu9cTIbmljfNJfSUDG9ff38d+uREfHvaJoTc1a7+sL8ovXoCJnLmmpijvGJvjAPeMhc2lQTc4zTKSuT/fL7l4/SPxShs3cwoV03r22gtKiA7zz6Cle9YRFlxQVUlRbx/cf28eSrXZxVHeLaPz+Hm+7bHUtz3ZoG7nxyP9dctJj+4Qi3PLjXty/99PGzASAh7qzqEB9b0zDCFr76UBuvdvalXaaRG3j1/XVrGvjik/v5yF80cEVjLb/ce4QDx/u4dWdbXJ83MjhMgh1+bn0jP3m6jStWzOeVY70Jdrh5bQPzZwTZ9tg+1iyr5baH2nznknTmpLHWKT79RPM3DGP6k/XHJ1S1XVWfdo9PAs8D84GNwO2u2O3AJlemV1UfwXGOY4hIHVCpqo+5V4e3RdMYqdnX2Rub+AHCgxGuv2sX+zp7xySzu70r5gRFZW7c3sru9q6YTMuhrphDHJXZck8rLYdOy7R2nIw5oTGZe1tp7TjplNPR4xm/u6Mnlseejl5PmT0dp/Xd6yOzN04mnbIyxb7OXoYiUBAIjGjXW3e20XlqgPUr5nPrzjaO9Q7w0tFerr5oCQDrV8yPOSLRNLc91Mb6FfPpPDUQc0Siccl96aePnw0kx61fMd/TFtavmD+mMo3cwKvvo/a05Z5Wnuvopu1IT8whjsoECwtH2OFNO3Zz9UVLPO3w1p1tvOjacdQhjsYlzyXpzEljrVN8+onmbxjG9GdKnykWkXqcXT0fB+aqajs4jjMwZ5Tk84EDcb8PuGFe5XxYRJ4UkSePHj06Yb2nO4e7w7GJP0p4MMKRk+ExybR3ect0dJ2W6fDJ53B3fFn9PjL9acVnW2YipLLFw91hXusd5LXeQU8dIgoip48jCn0DQ26+eKYRceRG60svUtlAclyq8sdSppE9RrNFv/6Mjgcvu+rtH/JM1zcw5GuHEYU+n3Txc0k6c1IqRks/0fyN8WHnZyOXmDKnWETKgZ8AH1fV7vFk4RHm+Tyxqn5TVVer6uqamppxFHVmMbcySLAoseuDRQHmVATHJFNXFfKUqa2Kk/HJZ25lfFklPjIlacVnW2YipLLFuZVBZpUVMausyFOHgIDq6eOAQKi4MEEmOY0qFIh3XHxfepHKBvzivMofS5lG9hjNFv36MzoevOyqLFjomS5UXOhrhwGB0hLvdPFzSTpzUipGSz/R/I3xYednI5eYEqdYRIpwHOIfqOpP3eDD7iMR0UcjjoySzQFgQdzvBcChydb1TKS+uoxbrlwVOwFEn52rry4bk0xjXSU3b2pKkLl5UxONdVUxmeXzqti6MVFm68YmVsw7LdNUW8HWDUkyG5poqq1wyqkt94xvrC2P5bG0tsxTZmntaX3P9ZE5N04mnbIyRX11GYUBGI5ERrTr5rUNVJcWs6PlIJvXNjC7rJiza8rY9ujLANz37EE+d0VjQprr1jSwo+Ugs0qLuf5t56bsSz99/GwgOe6+Zw962sKOloNjKtPIDbz6PmpPWzc2sby2knPmlLN5bUOCTHhwaIQdfm59I9sefdnTDjevbeCcmjJuf/RlrlvTMMJ+4ueSdOaksdYpPv1E8zcMY/ozFatPCPBt4HlVvSUu6l7g/cCX3O97UuWjqu0iclJELsB5/OJq4KuZ0frMIhAQ1jXWsuy6SzhyMsycipFvWacjU1gYYNPK+TTMKaejK0xtVZDGuqqEN8aLiwvYtGIeS2aXxd7oXpG0+kRpqJj1TXOpn13qufpEWaiEdzTNoX52s++KEFWhIJc11STIJK8+MSMU5NIkmeTVJ9IpK1MEAsIFS2rY39lDTcUwd3yoOWH1ia7wAF/ctJyCABQWBKgMFvCP71zJgeOnKHVXn9h2TXPC6hMXn/06BiMRZpeXsHbZXI72ePelnz6pbCA5btHMUs5fNNP3t73JP32I7/uOrjBFBcKJvkH+5T2rYuP38sY6XjjcTdP8KvoGhplZWkixu/rEtg82c7Snn5qKEsKDQ3zysj+ju99ZveLO//HGhNUniosCfGrdn3GibzBmvzUVJSPmknTmpHTr5JV+ovkbhjH9mYrVJy4G3gc8JyK73LC/x3GG7xKRDwH7gfdEE4jIPqASKBaRTcClqvpH4H9yekm2n2MrT6RNICAsqSlnSY3/FdB0ZAoLA6xcOJOVC/3LKi4uYHX9rJT6lIaKaV5c7RtfFiqheXFqx7QqFKR5cepbnTPSkEmnrEwRCAj1NRVjStMwN335s+eM7Yp3KhvwihvttzF9GG38FxYGaJo/Y1x5r8yQThNNP9H8DcOY3mTdKXZXkvD7673WJ029T/iTjFymzTAMwzAMwzDGhO1oZxiGYRiGYeQ95hQbhmEYhmEYeY85xYZhGIZhGEbeY06xYRiGYRiGkfeYU2wYhmEYhmHkPeYUG4ZhGIZhGHnPVKxTbOQAQ0MRdrd30d4Vpq4qRGNdZcJC+ZOZz8DAMC2HuujoDlNXGWR50uYd6cj09Q3yXEd3bEON5bWVhEJFCXmc6Auzt6PXd2MOgO6+MC/EySyrLaMySaarL8yeOJnkTUAySSSiHOrupeN4P0d7BqgIOptyRCLCyfAQPf1D1FQUU1QoEIGBYaWzZ4B5M0NoRDnc009VsIiT4UHKg4UMDkWYN6OUs2aVsv/4qdgGKl6bEkQiyr7O3pQyRn4RiSj7X3PGQu/AEAtnltI/OMz+46dYXF1Kb/8wx3oHqCotokAUCHD0pLNpR9/AIDNCJfQPRTjS08/cihKKCgJ0dIcpLymkpEgoKSigb3CYwWGld2CIuqoQ59U6c0gkorxyrJdXX+ulrLiQORUliEB71+TYp5e9A2mNgVwdK7mql2FMF8wpzkOGhiJsf/YgN25vJTwYiW2pumnl/DE5xunkMzAwzPaWQ2y557TM1o1NbFoxL+b0jibT1zfIfa0dbLk3Ln5DE1c01cYc4xN9YR5oPTpC5tKmmphj3N0X5n4PmXVNNTHHuKsvzC88ZC5rqsm4YxyJKM8deo29Haf4bFxbfPldy+no7ueWB/cmtHNJYYD/c3dLLOzvL19G32CEf/3labnr1jRw4z2tfGxNA199qI1XO/ti29eua6yNnTAjEeX+3R1cf9euWNpkGSO/iESUh/Ycpu1wD7fubIvZxea1DbR1dHPhOTUxO119VhXvWb2Iz927Oyb36XXL6B/uTbDbzWsb2PbYqxw/NcD1bzuXBTNDvNp5KiH/mzc1sWH5PH6550iCPW5e20BZcQHf+PXLHD81MCH79LP34kLhoz98JuUYyNWxkqt6GcZ0wh6fyEN2t3fFHFmA8GCEG7e3sru9a9LzaTnUFXN2ozJb7mml5VD6Ms91dMec1Fj8va0819Edy2NvR6+nzN6O3pjMCz4yL8TJ7PGR2RMnkyn2dfbSP0DM0YiW/+LR045FNOzG7a20HelJCDvWOxBziKNhtz3UxvoV87lxeyvrV8yPhV9/1y72dfYmlB09mfrJGPnFvs5eWg50xRxWcOzi1p1tvPsNixLs9OqLlsQc4qhc56mBEXZ768423nn+AsKDEW55cC+RCCPyv3G7M/aT7fHWnW0c6x2IpZ+IffrZe8uBrlHHQK6OlVzVyzCmE+YU5yHtXeHYxBklPBihoys86fl0dHvLHO5OX+Zwd79PfH/sdzZlMsXh7jCHT45si4jiqVNESUtO5PR3fPiRk6f74LBPH8TLGPnF4e6wr00dO5k4Tvr6h9K226gdhgcj9HqkCw9GfOeEiJKQfrz26WfvyWPKq4xcHSu5qpdhTCfMKc5D6qpCBIsSuz5YFKC2amyPB6STT11l0FNmbmX6MnMrS3ziS2K/symTKeZWBpnr0RYFgqdOyXdE/eRUT3/Hh8+pON0HXuUmyxj5xdzKoK9N1VQkjpPSksK07TZqh8GiAGXBkemCRQFqfewxICSkH699+tl78pjyKiNXx0qu6mUY0wlzivOQxrpKbt7UFJtAo8/xNdZVTXo+y+dVsXVjoszWjU2smJe+zPLaSrZuSIrf0MTy2spYHufWlnnKnFtbFpNZ5iOzLE5mqY/M0jiZTFFfXUZJEXwhqS3Orinj+redO6KdG+aUJ4RVlxXzd29NlLtuTQM7Wg5y86YmdrQcjIXfcuWq2ItF0bJvuXJVQtpkGSO/qK8uY/mCKjavbUiwi81rG/jxH/Yn2Ontj77MTRsaE+RmlRaPsNvNaxv46dMHCBYFuP5t5xIQRuR/8yZn7Cfb4+a1DcwuK46ln4h9+tn7igVVo46BXB0ruaqXYUwn7EW7PKSwMMCmlfNpmFNOR1eY2qogjXVVY159Ip18iosL2LRiHktml8XeiF6RtLLEaDKhUBFXNNVSP7vUd/WJGaEglzbVUD+72Xf1icpQkHVJMsmrT1SFglyWJJOt1ScCAWH5vFlUl5dwxzXNsdUnKkIFLJpVxn9cvZqe/iFmlxVTXCig8P1rmunsHWBeVRBVONzTz+0fbI6tPjE0HOGyxmbOmlXK+YtmcuRkmDkVI99KDwSEdY21LLvuEl8ZI78IBIQ1S+dyTk055y+ayamBIRa4q0+cVV1KfXUpd1zT7Kw+ESqiIKBsu6aZoz391JSV0Dc4RFWomG0fdMLmuKtPLJ5dRnlJIcWFQrCwgOqyYr519WpODQxTW1nCee4csq6xlqUfu4T9r/VSGrf6xLm1FRO2Tz97B/jZKGMgV8dKruplGNMJc4rzlMLCACsXzmTlwsznU1xcwOr6WSnzGU0mFCqieXF1yjxmhII0L07tvFamIVOVhkymCASEBTPKWTCjfNLzXlJTzpIa/3wDARlVxsgvAgGhfnY59bMTbaJpwYyslH32nHLOnpNYdrIuE8nfy97TGQO5OlZyVS/DmC7Y4xOGYRiGYRhG3mNOsWEYhmEYhpH3iKqOLnUGISJHgVczXMxs4FiGy5gsppOuMH30Paaq61IJjGKLU11PK//MKX+622Ku6AC5ocd01iGlLWbw/JwLbRbFdPEm27p42mLeOcXZQESeVNXVU61HOkwnXWH66TteprqeVn5+lx9PLuiSCzrkih6mw9jJJX1NF29yRRd7fMIwDMMwDMPIe8wpNgzDMAzDMPIec4ozwzenWoExMJ10hemn73iZ6npa+fldfjy5oEsu6AC5oYfpMHZySV/TxZuc0MWeKTYMwzAMwzDyHrtSbBiGYRiGYeQ95hQbhmEYhmEYeY85xeNARIIi8oSIPCsiu0XkJg+Zt4hIl4jscj9bpkLXJJ0KROQZEdnhEScicpuIvCgiLSJy/lTomKRTKn1zrn0nAxFZJyJ73H64YYJ5LRSRh0XkeddON7vhs0TkQRFpc79nxqX5tFv2HhG5LC789SLynBt3m4iIG14iIne64Y+LSH2SDgl9mM2yXZkZInK3iLzgtsOF2dJBRP7ObfdWEflPd97Iav0nwmTa4hjK9LPZz4vIwbjx/vYs6LLPbfddIvKkG+bbfxkof2lcfXeJSLeIfDwbbSEi3xGRIyLSGhc2ZtudJF3GNI+JSLUr3yMiX0vKy3MseZTpNxYnRRcRKRWR/xZnXtotIl/y0aNeRPri+vrfMtQuv3LrGi1nzhS1S0WSzR8Tka+MpV0mjKraZ4wfQIBy97gIeBy4IEnmLcCOqdY1SafrgR966QW8Hfi5W7cLgMdzXN+ca99JqG8B8BKwBCgGngXOm0B+dcD57nEFsBc4D/gycIMbfgPwT+7xeW6ZJcBiV5cCN+4J4ELXPn4OXO6G/y/g39zjq4A7U/VhNst2w28H/tY9LgZmZEMHYD7wChByf98FfCDb9c8VW5wEm/088Iksj8d9wOykMM/+y4IuBUAHcFY22gJ4M3A+0Dpa3VPZboZtwk+fMuBNwLXA15Ly8hxLSTKpxuKk6AKUAn/hHhcDv/XRpT6+DzLYLr8CVo/SDxlvF48ynwLePJZ2mejHrhSPA3XocX8WuZ+cfmNRRBYA7wC+5SOyEdjm1u33wAwRqcuagkmkoe+ZSDPwoqq+rKoDwI9w+mVcqGq7qj7tHp8Ensdx1jbiOIu435vc443Aj1S1X1VfAV4Eml07qFTVx9SZkbYlpYnmdTewNnr1xacPs1K2W34lzsn9224bDKjqiSzqUAiERKQQ5yR4KJv1nyCTaovpksJmcwW//ss0a4GXVDXTu7ECoKq/AV5LCh6T7U6iLmOax1S1V1UfAcLx+YwyluLxrc9k6aKqp1T1Yfd4AHgaWDAV7TIGMt4u8YhIAzAH5w9D1jCneJyIc1t4F3AEeFBVH/cQu1CcRyx+LiKN2dVwBF8BPglEfOLnA3+K+32AqT0ZfYXU+kJute9kkLE+EOfW+utw7mrMVdV2cCYznIknVfnz3WMvvWJpVHUI6AKq3bivMLIPs1U2OFc5jwLfFecRjm+JSFk2dFDVg8D/BfYD7UCXqj6Q5fpPhCmfD5JsFuCj4jza9R3J4GMLcSjwgIg8JSIfdsP8+i/TXAX8Z9zvbLcFjN12J5005zE/Uo2lZLlR6zNBXeLzmQFcAez0EVnszl+/FpFLfPKYDF2+6z6K8FmfP9dZbRfgr3DufvldcBy1XcaDOcXjRFWHVXUVzr+7ZhFpShJ5GjhLVVcCXwW2Z1fD04jIeuCIqj6VSswjbEqufqepb8607ySSkT4QkXLgJ8DHVbV7HOWn0sszLs0+zEjZcceFOLeAv6GqrwN6cW7hZVwH11HZiHObcR5QJiJ/k42yU5QxFqZ0PvCw2W8AZwOrcP5k/EsW1LhYVc8HLgc+IiJvzkKZIxCRYmAD8GM3aCraIhVZsZUxzGO+WXiEeek5qtwk6BLNpxDnz85tqvqyh0g7sMidv64HfujeAZtsXf5aVZcDl7if93mp6xGWkXZxSf4jGM+o7TJezCmeIO7t2F8B65LCu6OPWKjqz4AiEZmddQUdLgY2iMg+nNuga0TkjiSZA8DCuN8LcG73TgWj6ptj7TtZTHofiEgRziT1A1X9qRt8OPpojPt9ZJTyD5B4ay9er1gad4Kvwrnt6teH2SibuPgDcXdx7sZxkrOhw1uBV1T1qKoOAj8FLspy/SfClM0HXjarqofdCxER4D+YxNvzfqjqIff7CPBfbpl+/ZdJLgeeVtXDrj5ZbwuXsdrupDHGecyPVGMpWc63PpOkS5RvAm2q+hWvSPdRhU73+Cmc53jPnWxd3Dtb0Ucffoi3TWWtXURkJVDod1FltHaZCOYUjwMRqXFveSAiIZwT4AtJMrVxz1Y247R1Z5ZVBUBVP62qC1S1Huff10OqmnzV6l7ganG4AOd2b3u2dYX09M2l9p1E/gA0iMhi9+rQVTj9Mi7c9vk28Lyq3hIXdS/wfvf4/cA9ceFXibOqwWKgAXjCtYOTInKBm+fVSWmieb0bp680RR9mvOxoJVW1A/iTiCx1g9YCf8ySDvuBC8R5y1zcsp/PZv0nyKTaYrr42awkvt/wl0BrctpJ1qNMRCqix8Clbpl+/ZdJ/oq4K2bZbos4xmS7k1XoOOYxT0YZS/H41meydHHzuhnnj+zHU8jUiEiBe7zE1eXlydRFRAqjF5Rcx3Y93jaVlXZxSbB5D51922XCaAbfYD1TP8AK4BmgBcd4trjh1wLXuscfBXbjvK35e+Ciqdbb1estnF4JIF5fAf4fzj+u5xjlTdQc0Dcn23cS6vt2nDd3XwI+M8G83oRze6sF2OV+3o7z3OlOoM39nhWX5jNu2XuIexsaWO3a+kvA1yC2G2YQ57buizgT5JJR+jDbZa8CnnTbYDswM1s6ADfh/FluBb6P89Z2VuufK7Y4CTb7fZx5qQXnRFuXYT2W4Mwtz+LMM58ZzX4zpEcpzp/9qriwjLcFjkPSDgziXCH80HhsN8M2kUqffTh3TXpc/c9zw/3G0gZg62j1mSxdcK6yKs4f5Wg+f5usC/AuTp/nngauyIAuZTirPLS4Zd3K6VUlstoucXEvA8uS7CCtdpnox7Z5NgzDMAzDMPIee3zCMAzDMAzDyHvMKTYMwzAMwzDyHnOKDcMwDMMwjLzHnGLDMAzDMAwj7zGn2DAMwzAMw8h7zCmeZohIvYi0JoV9XkQ+kYGyhsXZ9rFVRO6Lrs2cQn6ViLw97vcGEUm1g5iRB4zVjlLks1VE3jrJ6hlGjCRb/bGIlPrIPZpt3Yz8wt0z4BERuTwu7EoRuX8q9TrTMafYAGI7YiXTp6qrVLUJZz3Bj4ySzSqctQkBUNV7VfVLk6elMU0Zqx15oqpbVPWXk6uaYSQQb6sDOGujx4huGKCqF02Fckb+oM56udcCt4hI0N1E5ouMc/6M2q6RGnOKzzBE5DoR+aOItIjIj9ywMhH5joj8QUSeEZGNbvgH3Ksh9wEPjJL1Y8B8N12ziDzq5vWoiCx1d73aCrzXvdLyXjf/r7lpvicit7nyL4vIu93wgIh8XUR2i8gOEflZNM44I4m3o7NF5H4ReUpEfisiy0SkSkT2iUjAlSkVkT+JSJFrQ1G7eb2I/NpN+wsRqROROSLylBu/UkRURBa5v19y83qPexXwWRH5zRS1gTE9+C1wjoi8RUQeFpEf4myagYj0RIVE5JMi8pxrU19yw0bY9tRUwZjOqGorcB/wKeBzwB3AZzzO5fWunT3tfi5yw0fYrpEar6uDxvTmBmCxqvbH3ab+DM4WsNe4YU+ISPSK24XAClV9zS9D9x/mWpxtHMHZpevNqjrk3s7+B1V9l4hswdkJ76Nuug8kZVWHs/PNMpydmO4G3gnUA8uBOTg7/HxnnHU3chgPO/omzg6FbSLyRuDrqrpGRJ4F/hx4GLgC+IWqDoqzq3d0K9KvAhtV9aiIvBf4omvfQRGpBC7B2cnuEhF5BDiiqqdcG71MVQ+O9zEO48zHvXN2ORC9Vd0MNKnqK0lylwObgDe69jXLjRph28CarChvnGnchLNr2wCwA+9z+RHgbaoaFpEGnB0JV7vpPW3X8Mac4umH3xaE0fAW4Acish1nW1uAS4ENcvq54yCwyD1+MIVDHBKRXThO61PAg254FXC7O/gUKEpT9+2qGgH+KCJz3bA3AT92wztE5OE08zKmDyPsSETKgYuAH0edXZxtkAHuBN6L4xRfheNQxLMUaHLzASjA2ZIW4FHgYuDNwD8A63C2MP+tG/874Hsichfw08mqoHHGELVVcGzm2zh2+oSPU/FW4LuqegpAVV8bxbYNY0yoaq+I3ImzHfKVwBUe5/JDwNdEZBUwDJwbl4Wf7RoemFM8/egEZiaFzQKiRv8OHIdgA/BZEWnEcQrepap74hO5VzB6U5TVp6qrRKQK5x/qR4DbgC8AD6vqX4pIPfCrNHXvjy8+6ds4c/Gyo+8BJ1R1lYf8vcA/ulfdXg88lBQvwG5VvdAj7W9xrhKfBdyDc9tR3XJR1Wtdu38HsEtEVqlq5wTrZ5w59CXbpOvY+s2TwsgLFQH8bdswxkPE/fidyz8PHAZW4thfOC461TneSMKeKZ5mqGoP0C4iawFcx2Ed8Ij7HOZCVX0Y+CQwAygHfgF8TNzZXUReN8Yyu4DrgE+4t66rgINu9AfiRE8CFWOs0iPAu9xni+cCbxljemOaEG9HQB/wioi8B2JvWq905XqAJ4BbgR2qOpyU1R6gRkQudNMWuX/+AH4D/A3Q5t59eA3n5c/fubJnq+rjqroFOAYszFiFjXzgAeAacVepEJFZqtqNj20bxgTxO5dXAe3unPc+nLtnxjgwp3h6cjVwo3ub7yHgJlV9CWcg3CEizwHPAP+qqidwruwWAS3iLOf2hbEWqKrPAM/i3M7+Ms6VvN+ROPgeBs4T90W7NLP+CXAAaAX+HXgc6Bqrfsb0IMmO/hr4kPsM8W5gY5zonTjO7Z0eeQwA7wb+yU27C+d2Naq6zxWLvkT3CM5Vu+Pu7392X4pqdWWenbTKGXmHqt6Pc2fjSXc+jt7WTmXbhjFe/M7lXwfeLyK/x3l0wq4OjxNxVv0wjKlDRMpVtUdEqnGuEF6sqh1TrZdhGIZhGPmDPVNs5AI73Ddpi4EvmENsGIZhGEa2sSvFhmEYhmEYRt5jzxQbhmEYhmEYeY85xYZhGIZhGEbeY06xYRiGYRiGkfeYU2wYhmEYhmHkPeYUG4ZhGIZhGHnP/wcE5GqvhqDMFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17394b878c8>"
      ]
     },
     "execution_count": 36,
     "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(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre', ylabel='count'>"
      ]
     },
     "execution_count": 37,
     "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.countplot('Genre',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre'>"
      ]
     },
     "execution_count": 63,
     "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": [
    "df.groupby('Genre')['Genre'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Price'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Genre')['Price'].sum().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='User Rating'>"
      ]
     },
     "execution_count": 42,
     "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": [
    "df_nonfiction = df[df['Genre']=='Non Fiction']\n",
    "df_nonfiction.groupby('User Rating')['Author'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='User Rating'>"
      ]
     },
     "execution_count": 43,
     "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": [
    "df_fiction = df[df['Genre']=='Fiction']\n",
    "df_fiction.groupby('User Rating')['Author'].count().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Author'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['Genre'])['Author'].count().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_author = ( \n",
    "    df.groupby('Author', as_index=False)\n",
    "    .agg({'Price':'sum'})\n",
    "    .sort_values('Price', ascending=False)\n",
    "    .rename(columns={'Price': 'sum_price'})[:5]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Author</th>\n",
       "      <th>sum_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>American Psychological Association</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>The College Board</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>American Psychiatric Association</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Gary Chapman</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Gallup</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Author  sum_price\n",
       "11   American Psychological Association        460\n",
       "230                   The College Board        236\n",
       "10     American Psychiatric Association        210\n",
       "92                         Gary Chapman        189\n",
       "90                               Gallup        153"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5_author\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_user_rating = (\n",
    "    df.groupby('Author', as_index=False)\n",
    "    .agg({'User Rating':'sum'})\n",
    "    .sort_values('User Rating', ascending=False)[:5]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Author</th>\n",
       "      <th>User Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Jeff Kinney</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Rick Riordan</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Gary Chapman</td>\n",
       "      <td>52.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>Suzanne Collins</td>\n",
       "      <td>51.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>American Psychological Association</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Author  User Rating\n",
       "120                         Jeff Kinney         57.6\n",
       "197                        Rick Riordan         52.5\n",
       "92                         Gary Chapman         52.1\n",
       "226                     Suzanne Collins         51.3\n",
       "11   American Psychological Association         45.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_5_user_rating\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "The 5 Love Languages: The Secret to Love that Lasts                          5\n",
       "The 5 Love Languages: The Secret to Love That Lasts                          5\n",
       "The Five Love Languages: How to Express Heartfelt Commitment to Your Mate    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('Author == \"Gary Chapman\"').value_counts('Name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Non Fiction    310\n",
       "Fiction        240\n",
       "Name: Genre, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Genre.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "whats_best_cost_genre= (\n",
    "    df.groupby('Genre', as_index=False)\n",
    "    .agg({'Price':'sum'})\n",
    "    .sort_values('Price', ascending=False)\n",
    "    .rename(columns={'Price':'sum_price_genre'})\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Genre</th>\n",
       "      <th>sum_price_genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Non Fiction</td>\n",
       "      <td>4601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fiction</td>\n",
       "      <td>2604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Genre  sum_price_genre\n",
       "1  Non Fiction             4601\n",
       "0      Fiction             2604"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whats_best_cost_genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Author</th>\n",
       "      <th>Name</th>\n",
       "      <th>Price</th>\n",
       "      <th>user_rating_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>The Casual Vacancy</td>\n",
       "      <td>12</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Harper Lee</td>\n",
       "      <td>Go Set a Watchman: A Novel</td>\n",
       "      <td>19</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Veronica Roth</td>\n",
       "      <td>Allegiant</td>\n",
       "      <td>13</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Gillian Flynn</td>\n",
       "      <td>Gone Girl</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Muriel Barbery</td>\n",
       "      <td>The Elegance of the Hedgehog</td>\n",
       "      <td>11</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Author                          Name  Price  user_rating_min\n",
       "154    J.K. Rowling            The Casual Vacancy     12              3.3\n",
       "135      Harper Lee    Go Set a Watchman: A Novel     19              3.6\n",
       "350   Veronica Roth                     Allegiant     13              3.9\n",
       "128   Gillian Flynn                     Gone Girl      9              4.0\n",
       "240  Muriel Barbery  The Elegance of the Hedgehog     11              4.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimum_rating = (\n",
    "    df.groupby(['Author','Name', 'Price'], as_index=False)\n",
    "    .agg({'User Rating':'sum'})\n",
    "    .sort_values('User Rating', ascending=True).rename(columns={'User Rating':'user_rating_min'})[:5]\n",
    ")\n",
    "minimum_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Author</th>\n",
       "      <th>Name</th>\n",
       "      <th>Genre</th>\n",
       "      <th>User Rating</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>American Psychiatric Association</td>\n",
       "      <td>Diagnostic and Statistical Manual of Mental Di...</td>\n",
       "      <td>Non Fiction</td>\n",
       "      <td>4.5</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Stephenie Meyer</td>\n",
       "      <td>The Twilight Saga Collection</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>4.7</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Lin-Manuel Miranda</td>\n",
       "      <td>Hamilton: The Revolution</td>\n",
       "      <td>Non Fiction</td>\n",
       "      <td>4.9</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Bill Simmons</td>\n",
       "      <td>The Book of Basketball: The NBA According to T...</td>\n",
       "      <td>Non Fiction</td>\n",
       "      <td>4.7</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>J. K. Rowling</td>\n",
       "      <td>Harry Potter Paperback Box Set (Books 1-7)</td>\n",
       "      <td>Fiction</td>\n",
       "      <td>4.8</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Author  \\\n",
       "10   American Psychiatric Association   \n",
       "311                   Stephenie Meyer   \n",
       "204                Lin-Manuel Miranda   \n",
       "33                       Bill Simmons   \n",
       "145                     J. K. Rowling   \n",
       "\n",
       "                                                  Name        Genre  \\\n",
       "10   Diagnostic and Statistical Manual of Mental Di...  Non Fiction   \n",
       "311                       The Twilight Saga Collection      Fiction   \n",
       "204                           Hamilton: The Revolution  Non Fiction   \n",
       "33   The Book of Basketball: The NBA According to T...  Non Fiction   \n",
       "145         Harry Potter Paperback Box Set (Books 1-7)      Fiction   \n",
       "\n",
       "     User Rating  Price  \n",
       "10           4.5    105  \n",
       "311          4.7     82  \n",
       "204          4.9     54  \n",
       "33           4.7     53  \n",
       "145          4.8     52  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_price = (\n",
    "    df.groupby(['Author', 'Name', 'Genre', 'User Rating'], as_index=False)\n",
    "    .agg({'Price':'max'}).sort_values('Price', ascending=False)[:5]\n",
    ")\n",
    "max_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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