AML01-End-to-End Machine Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing = pd.read_csv('D:\\Data\\housing.csv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             20640 non-null float64\n",
      "latitude              20640 non-null float64\n",
      "housing_median_age    20640 non-null float64\n",
      "total_rooms           20640 non-null float64\n",
      "total_bedrooms        20433 non-null float64\n",
      "population            20640 non-null float64\n",
      "households            20640 non-null float64\n",
      "median_income         20640 non-null float64\n",
      "median_house_value    20640 non-null float64\n",
      "ocean_proximity       20640 non-null object\n",
      "dtypes: float64(9), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1H OCEAN     9136\n",
       "INLAND        6551\n",
       "NEAR OCEAN    2658\n",
       "NEAR BAY      2290\n",
       "ISLAND           5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing[\"ocean_proximity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tuqFn+a4t5w05EkmSJB0Jk0iSJEnLwFxJPEmSpPmyO5skSZIkSZL6MokkSZIk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267416879e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26748d79160>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jid4YhBDrwK8B/y3wn0//+u8Avzj98/8KfAv4L5/kOc7ieDRuKdjoVBiG6Tzv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sJsnVbP48JSq9+GVDE9kGey4jCUs2h5AX+90MSQybbS9FCOSMWcGMwxX+8hoF9kuLvBv9vbOdu3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\nqwEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x267432632e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\",x=\"longitude\",y=\"latitude\",alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26743c5c668>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x7rk2rrxS+dySEpTYVYQLHxP7sJBuvB627VCfKTn5+NctLM4qLDfXkDgjxeT5\nF8Dn1RF2HRl0gNMPugQDbAGYOgaSoqCjs3ubXdXw+h71/PrxkB928WRkwM/vhdIyFcMAZRmMGgm1\ndZCertJ0v9dz9K8BtQ0qRCO8EOVWcZKu9gyGoSwRn0+5zXw+WPW+wZ73vDDcCRE6oRAcroHFD0t+\nfI1gwZWw7Ai0+OG8NLhsOMTFCXx+wfurobFZkDNCoGmC2293Ar37muRmwQ8XQHEZjMyBtD5cWDEx\ncNEJgua+gBIi59CTvCwsTi+sbK4hc8aJSSikLIfEFEHBNCfbdknwR8JhAR7BedPhgnQImRAf7p1l\nmvDGXugar/3XPTAhCfTwxT8zUz1qagza2iA5WeeORUoQpISWFlWTEgxCZKRgaj6UVanX3E644xrV\nURdg9GglUP4ARKbA2s0QEQcff2KCtMEwoXxNQQ3SoSpC8ugqwWEXjM+CWA1WHIHUSJg0UmPuZZID\nz8KkAkFICNrbYdm70NgM86+AnOzuczMyRz2+DE3t8MdP1IyHn1yqBLI/OjHxIYm3/hstTmcsy2RI\nnHFiUl0P7kQoOQAl2CDFhDLABwQlX6yHqgpBehLEtcOM4RATpb43hpo9hRDqxqTXfqtDPP54G1LC\nPfdEkZJio6nJ4MknA1RV6VRVaWRmGixaZGPB1To7DyiL44pZMHcW2HXlpsrOhtIQrN0JJY1w7jT4\nzYsmIFSwQjfBrkGegAQgFhraYfMRMGuhvg68JmRLqHCDF52rroK2FrhyNqz+FHbvVVbGCy/DP/3y\n5DSsa/NBaycYQfjbegh5YXg2jB/DcYO//oaXAwT5O2LQjjuTFhanAZZlMmTOKDHZcwhefAeKq9Ww\nqYwMaGkDUk0o6QSfQaDUSSjOzfSZAm8nbNwOl10IC/Lg1e3KsvnelBNfgKWE9naD//mfBlavVm1H\nDh6MIDlZUFAgWLhQ5+nfQ0MTLHsbXnwFpk6BG2+Ad5rg8zaoMKEiFdzt0Abg0ZSilZqQbMJ+ExKB\nBBtmMrTWwsEG1RyyowVeeQ1mTwanQwXWF1+n5svv3aPEsGuekterjrcr3XkgpFRZbE4npKZ2L89K\nhO9Nh9ffgHXlKjV5/WYYN0oyeQL4fJKUFEFmJkwTLsbisITE4vQl6INaK5trKJxRYnKgWMUmRuXA\n33bAiNFwpEjgbQ2B6QNTEmgN4W2y4bA7lKsp7NpK0MC1VVXNl9thzOze+05JsXH33VFICampNoqK\nAni9Idop2InaAAAgAElEQVTa/DQ0QEyMm/Hj7TidavpbVhrER4MRUhd2nx/aDVjXBjlOJXQftMLh\nRpBRGgwzIaRBQECxhCapgitxIbhMIyVRkJEAUS5IAnaUQ1K8CszXNsLmvUpMZl0IjU3Q3AKTC+C/\n/ktZXFdfBdOmDXwOd+6Cl19R1sbdd6mq+S6MdjD9kJ4iaWyU+Dokjz0iyRsnSE1Vlffjx8P112vE\nOXXLTWBx+uJwQYaVzTUUzigxGTcCNu1Sd9fTJoDdA9fNEbzyYhAzqIGmI6Sd6ABEOmD6JJgWzuja\nvh1aWtVFfuVKmHH+8TUlqanqdHV2mmiaICZGIyIiiNutM3WqjcREwfTp3ac0MhIWLVRFjZnDYcNh\naGiFlDTVvXjiGPjuNPhgNfgF4AeCAhDgATqECuh8bjLiNp2MSIhxQUkTxEUpqwQgGOoOikdFwcJb\n1fMnnlQzVdxu+PDDwYlJc7NJS4uB06nh9fa28+sboLpZ8vqyIOVHAgRbBC6XIOS3Ex+vMWoUrF8P\nq1ZJ8vMFP/zh4Ge6WFh8q7DcXEPmjBKTMbnwd7dAW4eqRv/iEHj9UL3RYG2VicBGMChIT9aYfa5K\n0e0iIUFNL6yogIT47ir2vnjllU4OHzb40Y8SuOKKKNLS7CQl2dm4sYnaWj85Od15tznZSjweWQEt\nXuV22hqA9AyY4IaxLhiXDF/Ua/i8JjShamM0wAA6JcSaTEzX+e6VsPsITJsIew5DUYWyenQg0AIr\nVsM5k1TbFVBuquIi5bIbPmLg81dTE+SzNU14OwyCfkFdXTQjRijTzTRhZwWs2SUpapCYukBK8HYa\ntDQLVq3SaWrT2bUHWhsl8fEqRdsSE4vTlpNoWQshdGAzUCGlnC+EiAeWADlAMXCjlLIpvO4DwB2o\nK8DfSyk/Ci8/B3gOcAPvAz+VUkohhBP4C3AO0ADcJKUsDm+zEPjn8GH8u5Ty+ZP3qXpzRokJQMow\n9QCYFU7lHfavUdx1VxutrSYul42LL7YxZox6LRRSrq3Ro+H7t0JDAxQU9I6ZVFVJyspMCgo0nE7B\n2LF2nE5BSkp3G5NAwOSDD2oAmDQpBoejewctXmj1QnYCRHXAMODqVEiwKTFYOBdqSqCoXEPaTNV9\nEsApIKTj8BmkJcHobPUAuGgKHChRLeaXfwBfbFW1MFu2wt+F55VcfhnEx0EgqAL9x7J7DyxfCemp\ncMVl8PIrzZhIzj/fQTAoeeedVoYPd5CUZKO4GuraITcFjuw0ocMEuw10QbvQaKoVhHZJRmYKdjXA\nhInKyrOwOC05+ZbJT4G9QFefofuBFVLK3woh7g///kshxHhgATABSAM+EUKMllIawJ+AO4ENKDG5\nHPgAJTxNUsqRQogFwO+Am8KC9SBQSPjyIIRY1iVaJ5szTky6aG6GbdvUFMDCQp3nn4/l4EE1WjYv\nTwWpOzvh2WdVa/eICLjjDhg9WtLcbBAM6tjtKoC8ZEmIffskP/yh2teMGU5mzOiu46isbCM62skd\nd2QB9BISgIQISI2B4jpAwPw8GNajVuOHV8EnH0B7DQQ9KtXY9EoICciSJIyxUdfW+/M5HZA3CsrK\nweeFrEy1vKwcamqVReR00m+zxeJi+Ol9EAipb9nSDZI9VW7SEzWmOgIk2AwaGkxqa0MkJdnwB1Qx\n5+WXCvbt0ahoM5G6hu4R+EIa2KGyXkMYkrETYP58FXcpKfGzdm078fF2LrooEpfLCqRYnCacpK+q\nECIDmAf8B/Dz8OJrgFnh588Dq4Ffhpe/KqX0A0VCiEPANCFEMRAtpVwf3udfgGtRYnIN8OvwvpYC\njwk1ne8yYLmUsjG8zXKUAL1ycj5Zb85YMXnjDTh4ULmr7rsPJkxQj9JSeP115QKy26G8HHJzobIS\nPv7YoLq6heZmk6QknTvuiMHj0Sgs1NB1k6ysvr9du3bVkpUVw9ixiX2+btPhjplQ0qAC6GmxvV8X\nAsZlQ0kl7K3VcLokOiaBhhBIQXuRxmufwe3Xq2LLnkRGAGFhFEJVuUcNInNr125o75RER0vK0kz2\noBFKdGHv9LO/0UZon5fKSoOdOyUTJ0JyvLKihC74+c8dvPuWZMsWiHQJOr0qVhM0weaA+36hkZgA\nzc0hnnmmAYdDY8cOH+3tBtdfb7WltzgNCPmg8aRlcz0M3IfqXd5FspSyKvy8GujqOZEOrO+xXnl4\nWTD8/NjlXduUAUgpQ0KIFlRhwdHlfWxz0jljxcTlUn5+h6N7fkeXJaLrsHmzslC8Xti6VbU1kTJA\nIGCSm2unqCjI4cNB8vKcXHCBzvjxGsuXm8yZo5GY2J3yapqSSy8dOCDhssOYfhonahpcMgveWg0I\niHNJqnd0YjZ3AiEC2NhSGccvPU6efri3Cy4uDhbcAG+/q36/6XoV/xmI1BRIjIEie4jGcyRUSSJN\nnUppR5gB4nwG7ugIKqrVZ4yPFiy8Al5dCV6f4LKrneSNNdi5W1JeJvAFVIuW//l3jRE5Ap8PWltN\nQiFIT7fh8WiUlQUGPjALi28DDhekDiqbK1EIsbnHgqeklE91/SKEmA/USim3CCFm9bWHcNxDfrUD\n/uY5Y8XkuusgP191vu0aUevzqWaKWVnqeXw8FJfAjr1gd0I7dtKTBLGxBiCJjOy+ajc1wfbtkkmT\nuvtjbdzYwrJldUyZEs111yUNeEz1DfDCGzBpPFx8Qe/XUlNg7gzYcARKD5mYzV1+rXAkvrmRd5Ym\n8eg4nR/9oHem2cQJ6jEUCgrg1psEa8o0Pkvw40qyMcFmY0+NnWnJOnW1Bvv3mix9y4/LKbnnbg8j\n0uEfb4KqeshIFjS32njuDahvVCnQt10HlaXw8MMqpXjRIhtJSTrFxX6klMyfHzPwgVlYfFsYXMyk\nXkpZeILXZwBXCyGuBFxAtBDiRaBGCJEqpawSQqQCteH1K4DMHttnhJdVhJ8fu7znNuVCCBsQgwrE\nV9DtSuvaZvWgPtWX4JSLyVCyGE4mbreyPHoSF6eaN65dq+7ex4xVbULy8uFIK+yu1nEmR9MpfFx/\nvYfc3O7AxogRgl/9qvd0xXXrWvB4NDZtauHKKxNwuU787atvhKIScDmPF5OYaBXvsLWB2wSVyNH1\n5/EDPlqbgrywNIUOw8E/LO6dcRYMwd4S5S6zD+KvarPBggUwv91Gs03DHxTopmBdGaT5TH5TJ0lO\ntlNbI1mxMsBtt7qIjtZ46yPYvBMuvwhmz4Cf3g7tnapex2aDDZ8r92FNDYDGnXcOo6jIj8ejkZvr\nHOCoLCy+JQhOSsxESvkA8ABA2DL5BynlrUKI3wMLgd+Gf74d3mQZ8LIQ4g+oAPwoYKOU0hBCtAoh\npqMC8LcBj/bYZiGwDrgBWBm2dj4CfiOE6PItX9p1LKeCr8MyGVQWw6k+iPIWWLILsrLhgVmqg29L\nC7g9UNEM5e2gIfBE2xk5wd4rbbiLTdtUOuzscFB7zpw43nuvgUsuiRlQSABGj4B7Fqm05WPRNYiJ\nUNllUW6dw1t0jKCBSu3yAXYMw+Tg1ia25SZwpMzG2B7eteZ2+Nt2SE2AYT1iMsGgmmeflNR3NXpk\nJESiqXsmICdGtcXPToPP1kk0DaorBE1NqsFlW4eyQrqaZNpsENtjDtb8+Woq5KhRXVX3GhMmnKCR\nl4XFt5VTmyvyW+A1IcQdQAlwI4CUcrcQ4jVgD6pX7E/CmVwAd9OdGvxB+AHwNPBCOFjfiMoGQ0rZ\nKIT4N2BTeL1/7QrGnwpOqZgMMYvhlLK+DFr9sKUSZmZDpKbqMa67Fh59XQ1jdEdASS2MyT5++2AQ\nVq4FJFxwrrIKJk6MYuLEAYa690DTlKD0hc2mRGbSBDBNQXL0MD5+v5ayUicQADyYQRder40Nq0PU\n3dFbTIbFwo+v6W6f38Xu3bB0qckPf6iRk9NbUIJB+Pxz1fV4fI/JiiNH2jj/PAfrNgTwhQQTclxo\n4UDNgqtVG/ucftJ+k5PhppsGfUosLL6dGD5oObntVKSUqwm7maSUDcCcftb7D9Q189jlm4GJfSz3\nAd89dnn4tWeAZ77sMQ+FU22ZDCWL4ZQyORV21cKYxO7uwELA7bdAlReKqsBmh4xkVRR4LHY7/Pg2\nVV1/ooLGwRIMSrZs8dPUZJKba2PsWAfzL4X3livR+dFiB+MmpPBP/1SDv9WOslA0QEdIg9IjAYKF\n9qPpy3C8kHTR1KTx1FOC667rXah55Ai8+aayIP7lX7obNjY2C3Y3uClxuuhsA6NKo6FZdU6O8MDo\n4V/uM+/fH6KszGTkSJ2cHKu82OJbjM0FSVY7laFwysTkq2YxCCEWA4sBsrKyvvLxjEiA/zfr+AaO\nDgf87DZYtUVldmXFqir49D4S6PqaAfJlkFLy+usd7NwZwO3WWL3axw03RHDeNCfnTFIiV1oOr7zc\niL/Nj3LgBoF2nPZ4ZEiybl0nIsqJq8BNkx9ydCjZrnpyXXAOTMuHmjpYv1UQlwARbtizp7eYZGSo\nFitpad1CYhjwyP/BSysFHbpAJENbHDz+LPzmV3CgCNJTICfz2M+kkhuczr6nNO7dG+L55wO4XLBy\nZYi77nKSlWUJisW3FKudypA5lZbJULMYehFOr3sKoLCw8KSkzfXVCdgw1UDGKy6AvzwLy1ar1OEf\n/UhlfZ0I05SYJthsQ+uO29Jisnt3gJwcG0IIOjoEn33mY+pU51GrJxSCwweDIHXVml4aCGFw5ZUa\nxSWCnR0h3lgLjQ0Q6AC2Q4wXLi6Al96E+t9BfQXYpWo+nJ8BF13c+zgiIuCWW6CqBv7rcSUIN8yH\nugYwW0B3qynHwVYV03nvE9h9QMWb7vuJslIA6uvhhRehrlaSkir43s29J0ICFBebeDyQkqJRWmpS\nWWlaYmLx7cUSkyFzysTkS2QxfCMsOQQ7GyHVDVXFSkBKS9UFMitL1ap8+BFs/QLGjYVrrlZ38dXV\nBo891k5Dg8n3v+/h/PMHn6mkxEdgmkq4QqHu9OUuKivA7XbQTEBd0ZF4InX0CAfeeIO9tQ7aNIfq\nX68BVdAyDN46DFHV0FYE+CQIaBWCtERYtQEm5avZ9D352zo1rAtgwxcw9yL422poKQdHJOTGweTx\n4IqGvT7IcSlxkRLKq+Gx/5UUHQnQ1h6i6lMb735u5/6fasw+r/s97A6NyioIBk2khIwM6z/V4luO\n1axhSHwTdSZ9ZjF8E5gS9jRCugcqOuD82bB+hXL7jAq7S4uLYc1nkJEOGzfB6FEq5fi11zpZsyaI\nwyF48MF2lixRnXMHQ2SkxqxZTlau9KHrAiHg+ut7l61/+CF4vTEgTJAB0JwYWjwfrZB4ZQCfTUKJ\nX/Wzd5vQjsoqaBW0lWoQMFUuiAkSSW2HRns7lFXCqOG9rbTUZNi6M/w8CWadDxNGw6tLVFua2bNh\n7mxYcRCSRoAzCnwmfLoBnnga1n9sEgyGyBmh0eoX6HUGn6zVmHWuep/DR+CTv9nArjExz+D88zQy\nMqz/VItvOdZXdEh8LWIy2CyGrxtNwOXZsLwMpiXDvFy4dIYKtmuaskq8XjXd0OVUd+JdNDYqF5fd\nDpWVgl/9yuQXv9COilB/hEImNpvG3LkeRo2y094uSUnRSUxUd+rBoERKqKyQdLTr2F2JBENABPiE\nRDOCGJqGbANkCBp0OEdXTRK8Eo4IsEkVr49ENd7yQV0HlFTAs0tgzAj4/g3dcZKZ58KwcNX82JHQ\n2goVlfDPD/SeojgpE/bXQLMffr8K9hwAM6DEpalcUFsLmhvsLpg07ni3YmycxtSpGpnHxFssLL51\nmD7osIZjDYUztgJ+sFyQqh5dOMPeqi/2wdLlsGMd0An798HsOTBunHr90kudHDxoYrdDc7OGaQpK\nSzmhmKxceYT16yu4997peDx2cnLsvV4vLZU8+6yJzaYyp4RUY+GRQtUwBvx4Yg2CUTqBNtQQlFgT\nhtlUh+EtqPb1AogUEBlWv3YYcRlkAdmZcOCwipNkpkMgIHE4BONHdx+HYagOAabZ6/DIiINFM5SQ\nZMdBZQL4cyHP0EhN1PC2mRROCfDzf3QzZqSavvjWWyGyswWLf6C+asNzh/LXsbD4hrC5IN7K5hoK\nZ72Y9EUwCG+uUsHrthbIyITL56r5HF136tOnO7DZBOXlJklJNkBnypQT7zchwUNWVgw2W9/2844d\nKp7Q2QkXXQRvvmlwqETHYTcJuAUCHU0PgSnRHBLTCfg0aBDKCmlHxVA8qJQqv4AAuFJg0oVQ/T6s\neU8N0rqxGZoaQrz6aojZs3UuuaRb2OLjYd7lxx9ffSM8/ybsKwF/AaQlwh1XgN4pePJJFyD5yU/E\n0Uy41asN/vu/ISHB5PXXJX6/4N13VRbZpElD+pNYWHz9WG6uIWGJSR8IEW4Oaai7cyOgAvLJPSpi\nNE0wbZpjUNMLuygoSKGgoP/84rw8jc2bTdxuyM/XeOJFwS13BaguBqcB4y5woAUEh8uCOOM0vLF2\n8OmqVjZXqsmMQdBsgoIxBod2+Wg70IFRZ+O9xz0kT3ARMwnGGvDaB3BZIXR0SHbvk0w5R80+6Ulz\ns6S2VnVVttsFuw+o/mKZdpiWCDMKICNWNcm02wFE+KciJVXD5gihe9SQsldfVuexq1Cy50hgC4tv\nFWdxNpcQ4gJglJTyWSHEMCBSSlk00HaWmACtAfiiCdLcMCoaWoIwayas3QwXXwpOP8QnQPoYePQ1\naGiCyaPhsvNVLOWrUFGhLrAjRkB2tuD++zU0DUqaBD/+bZCqAwJpaoS0EDZh8oNFDg41O1i1H+rK\noL0VWqokxKhRv/Y4QVxdkP0vd9DZVgf4CXZEU1npo2ZtLDHneqhLhMQ6uPFSnXPO1dixV/DoU7Dw\nZlWPkpgIhiF56imT+nplJc2bpzMqF9ZsgrhomFsA8bEqjnS4ArInwvmTlUh0cahK4+Lv2HHYYNlK\nlWggZd91KBYW3zrOQstECNE1TGsM8CzKcf4iqtTjhJz1YrK7Cq79M5QdgMgk+PfboKw5PLsjB74/\nT2V7PbkMPtgA67apWMbuQ+rCeO3sL//eVVXwpz+p1OC0NLjnHnC51JV28xEo/tTE7Awi8GJobkp2\n6GwaDWnZ8Mvr4LUVcLAcUlIFDgkBPxSMlWwvCVHb1ooKtMSB6QA0jEY/jbvdRE4TzJsEKz4VHDoo\n2L1HDdd66GFVO1J4Dsyfp9x9pql+AqQlwy/vUmLQ5e6rqIFlq1RzyaCpAvhdGIZKg7Y5IGSompbN\nm1VBqGWVWHyrOXstk+8Ak4GtAFLKSiHEoHpGndVisqYMvvMyNASARPAfhHsfh3+6E5Lt0BGAV/fC\nvGGq7fr2PbBzJRh+2OkBWxCuntV3MeRgaGxUF9zsbDWkKxDobi2fGY+6aosQUhpgQIxLECNgwVzY\n/gWM8sPYdPjerfDiy3DwkMnW93w01naE3yEKhBs8Noh0KGUwIaIJNmxSF/imNhWj8YVgzBxllXy2\nFmZeIFi8WKO6undSgb13zgAupxISXwBij/nKXXqBGiscCMK1c1XX5jmX9N/2xcLiW4PpA99Zmc0V\n6NmZRAgRMdgNz1oxqemAP26HziDYQwFcaV7aI6Lwt2gcCSoxiXBAgw86A2pK4tZPwWhR24cCsH4L\nLN8NebnQ0Q4Hq6AgE9LD1d/BIHzyCZx7rgpqH8vw4WqscGkpzJnTe0bJeaPgxz/Xeew/3Rhendgo\nB7Pn6MoNFa86+6anK+smwqFcZR8uN+hsBwwXEFQl7DhVSljAB7oDu92gsVgje5zGjoMQEwVOD0Qk\nQEkVbAi3lfnfP8N5cwX1VWEXX5qyoHbvUzPjE8OpxIlx8KOboKEZRuf0/nyx0WrGSXUbrCmF116D\nhgq4cy4smHUy/5oWFicZ3QXRZ2U212tCiCeBWCHEncAi4P8Gs+FZKyb1nWATMDarlaSRq7DHB6kr\nSuZg6ziqXBp+EUNDq05GFIzJgGo/GF03/BowHOonwu93gOcA7CmBBBMSHPDsQkiOUYaFw9G/5eJ2\nq7YtjY2qtUlPdA3+5z4b8wolb7xhx+2WpKQYtLYGeOstmDXLwZo1GoWFKjGgpgECvgD4g6jiEhsY\nQXC6VM8YqZM4zobbadBcBbXtGpquxhd3GFDWBpFjoageLpwG61vh/T9Cezu8sR0evhc6KuGPz8L5\nU2Hhgu5jTU9WD1Cuv9UbobQB/mpIPo7wE4oUGIdtOGp03E3wh4/g/ImQ1feUYwuLb56z1M0lpfxv\nIcRcoBUVN/l/Usrlg9n2rBWTKCekuME5uYimYSYtrZHETmvh3M4GRMjNtk7J+dEJ3DAWYpxwwWQo\ny4ei9UAcMBYcKVDSpoLoJmrSpy0Aa0vhO3kqrjBngPLMjRvhrbfUrJDFi3tbMI2NkhUrDMaNU0H5\n1asDpKSohoreKJNRszSmj4VHH4PSQ2CEbGB2oo7GhFgbWqaGDGmkpAqism0E2iEqQqO+A2LSJNV6\niMoOiEkXVKTqHGwS7Gsx0b0QHyWIjhdsjIcfboYL4mDGLJgR7qp8sAq2FsPoVJico5bV1MNHn8GW\nIGzKhlCWgVFvQwYlfhekJoJXN1i5z2ThDLsVjLf49nIWBuABwuIxKAHpyVkrJlnRcHE2vB90kuiS\nJOkdlBBHhOZiaqrGBKfJvB4XumunQMttsGIaVIbAGYBQKxQdAVMzsacKymIEkzNURhTAzoNwqAym\n50HqsL6PY8MGJSD19VBW1ltMOjpU0aHDoQ7E5RFs3CmprLBh262RlwebPgBvC0RHgtsJnT4b2A1w\n2yHLiWaGiMyw0xptp70FhG7D7YbIaGjsCFHbKTHdGm3JAUpwgUtCB4QE1M6U2IZJIjs0MEE4wCiA\nEVnQ3AEvfAZOG2wrhoRIZWnExcCwDGiWkGpCabmJ7u+koz0SU4PquE6i3Y38w3YXq6t1nrshro+z\nYmHxDXOWWiZCiDaUawPAgcrm6pBSRve/leKsFRP+P3tvHmdJWd/7v5+qOvvap0/v63T3LD37DjPA\nwLCIoCIiKC6v4BZjNIk3+ktifub3SuJN9F6vMVeNuV6iScQlCiiKKCD7AAMMsw+zT0/ve58++15V\nz++P5wzTIMM0MCgy5/161atPV9VTp06d5VvPd/l8gWu7oTPTyb16gcNZF854PZZwsi8N717g4dgE\n3P80BLxw3RZ4yzro08E5BWN9kJgFb3Mc3+qsuvpjdSxpc7G2Gabj8K0fQiELew/C3//pS5/Dpk2q\np0gkogLxc6mvF0SjgpMnLUZHJdt2wnC/CywNPSvIDUG6Ht71TlUImM4J9u92IV0lcAuQBqaARE5D\nA9rrbHJo6AIWdUkeGpJYhg5tAmvSDU1AF6rDTBQwYWwKvAmI1IGrCfKVnm8lE0wb6jyQKVS8a4DL\nCZ95D1yfhYePpXmUwwxkbXJBJ/sHVxBKxyiUPMiyg6fTWeKZEDX+8/QWsMobF1mA8vkXgJdSPp9G\nI4QQqGaGF85n7HltTACW+t14Csv5ehzWenUMIZgswKMDMPgIBH0QS8IdD0GoCwp5SE9BNg36TAlt\ng83sQ3VgSIKLMyxzuoh6Yd8g3PlfkMhBQyPcch10dajsqWQWOhpUTGX9eiXR4nT+ZqaU0yloX+3k\n4f8oYSdtWhp1RvoEhgFaQiJNyZRt0ddnEAgIygVdCQwXgHQevFmod4MusFOS4bxGc4fNik4NWwiM\nIGoKEpQwKCALLEAZkhjKaxpTgpgTRTgYgb9ZAyMxcBpw1XJ46jhc0ANdcwo6hYBuP2TXzlJrCe48\n6GPCPYa/kKQcdxCMxqHsodlj4nNV/VxV3oBobvCflwH455FSSuBnldqTz51t//PemABotoFXqIA8\ngEeH2axKnQ141Q/nyRn1+Xr3YnhwBrLDMFOC7KM+5KgGyyyyhz186QS0+eDLD8GUBpRheNBmzYUm\nb9kKPZsNLJfGx6+Fnhbl2hoaglWrThuTmRml1ltTDztPQDajEY4K/CVBpBYSMZC2JBQqks9L7nnQ\nxt/gIosOHUDOhvEMjJdAawS3Dggsp0RLCDqaoc8JDX6d3EkLa0oDlw19GrRKGBBQrFwcD0QMCJTh\nkgBkZ+C2Z1SCwMevgK3Lznxdawmwm1FGjDKm20ldyxTZoBvPMReX1Dj5swsacDqqxqTKG5TzcMIs\nhLhhzr8aqoCxMJ+xVWMCNLgg4oDRPPgMiBXhhjbo64bn+tSd9uJeOFmAfEFlYdX5YWrGCekyZDVI\ngS0NatrhnoNw9w+BOLBIgssmldf4edyi4cki9SWNycOCj73X4KF7NbJZGBuDmypdnG+7TRmZz3wW\nGsKSkTGLTMwiF9NobxYsW6xRKqke9sf7wB0RRBtgIgfYOkgLKEDJBbahFlNpw6STGsf6bSbrBJql\n01IjyPekMHqTlNIasWQDmJqq2pQC8oIpE+olXNkCh0ZUdlrRhFjm5TOyWqhlpb4Eq5xh37N1uEtu\nevM2rbUGf7wGVlbVg6u8UTlPYybAO+Y8NoEBlKvrrFSNCerG/aOd8PA0pMpweRTW1cCGt8BYRX5+\nIg+33wn9o+A2ACcYjWBOOiBiQ97gpnWC7g1wIAalPCr+4ACOCghAOQCTbpspo8zu7+jcfbvFjde6\nkbZQWmAVNm9WRYwN9dDjLbO0xaSQFTQskOzfrxpzNTbqTE46CIch2Axr1klMh2B6N9ghHYQDakOA\nAKdUHRsBsyDZtR3cQZvmXp0ZT57IsiSzPoNUOKC+REkJJQlCx+mWBLKC0X3w8SNw6+dhJgU+NyyZ\nRxX7aiJ83B/gn05KgmkIFIos6E2RLUS4+zFljFYs4QWvv0qVNwTn4cxESvnhVzu2akwqhJ1wQ4sq\nvssXTkmBgBGGrA0iCcMTIDUoWeCrgRVXQTkB2SENbw6KaTh8FGo7wbMQ8gAZAV6hYhAFHctnI2wN\nIhqJEYvv317g5hs9jIyePpcL54S79u8rs+UiwYkTgsFB8Pls4nENp1MwMwM336wmHZ/6AOxYJfEG\n4OG0+1EAACAASURBVMFdGuWlzRDLQaak/HY2IDRyORvKglwOUk4Lx+VFMjUOUoMezDo3GIKgliIU\nTDBpNVIqeshNgq8AJ2LwzHOwoAk2LgSP8+zXVUPQgUHNJTOYB3x8YeJ2jt97kE/c/35KIYP+3GI+\nuErnPz5jvGolgSpVzjmC88qYCCG+weksrt9ASvlnZztG1ZjMYe9huON+9XhBC2y5Gr47DZaEdWVY\nGYSDCSUE2dMEX34vPLIdnvNB/wBkcxBOgaiBhhUwvhOKScCtgd9GNArsSV0FtssCsCmkNMYnLBYt\n17Ht3yxwDAY1jg6C5dG49HKb6H7J+LjF8rUOHt8BEwn4o1sEAQ8c2G7T4YL3vkNjNOkmNuFkos8k\nHpfg0UBIyhZKW98PJQ1qegtkZzTMvEtJ1gclQrMoJjyYtoHQlKHN5cCohWf6wT0Ck3G4ecuZr6WU\nSkrF54UtCzS+VBOmfYXGv3xlDQ9OLuVwwypoFgQ8cX5ghfnzcVjd8rq8rVWqvAoKIM+rbK6dr/UA\nVWMyh/uegPoIeNywKwbjIzCiQ4sOegQ+dDk8sl9lYn3wchWc36lDfRCma+F4AqIdsKYVolfCQzkY\nG4HRQyACAiMPpZwGSRvyOjgFeGxkAxwswyMH4IpVLzynhSsc3HavjdcF45MaCxtMWtudPHtMY+sV\nIHQNXxjKdhHQ8OkaiQwYLrhqq8avik46umGqJBlOgPCC9EjlfquxyUwE8NakcZkFZMqmFHGSDNdA\nGMjYuI7apHIa3gtAy8EjfRBwq1nXTRerQLxtw9AotDWfdlc9vktdz0/eDK2NsDniZHjHLh762X52\n1dyiZmp7IdftgzpJvwnVFidV3jAIN9J9/mRzSSm/+1qPUTUmc/B5IJuHiQDsaQXNAQkdPAK21EC0\nAa7e8MIxV22CfTEYXQ75EPw8A3fdD2EdPnYN5Mfy7FmS5eAuN7mYn7DTZnpCR5YldAocPTp99Roj\n47D2xAuNyWQKHhl3QIONYdp0d9j86Ud97Dquc2AaGisyKtPlGfYVt5Fa3cLDd2xkfFLgq4E1q2fY\nen2CI0Nt7D3qQuogw7bK1LJAc0lCzjR+VwJrhYYogl3WmI3XkSt6wABnQwIz62Fm1IOR1ci54LJG\nmCjBNx6F1hBEnfDIfXDLe1T6M0A0DC314J2jN6a3LGIsUqPcBwJIgTXjYF2vTle1drHKGwnxfJjx\nvKLSv+SvgKXA899eKeVZ9dGrxmQO734LfP8XsEPA6gZYWQsrgaEyaGf4YDXWQXQLbCjA/YNQyoIM\nwOwE/HTM5LPv3Udz2qL1gE7h8SUM7AlRGJaq9iMg8Neq2YLph0NTL+z3MZFUyVkXXqqxNKrxoStV\nUeB371fSLaPT0LsIfj1W5MnbOxja20BiQFK0wd1ok23q51jMxUEjQdnXoOpZZ1F/F9uEe6fRG/OY\nQUHYn0A6BNmij47ak2SnXCT8EbQ+m/RUCICyzybh1vhZAkQG7jwKOFSH4FsugJo5Vf5Le9Qyl5q6\nALVvDTD+uI3MAgasa9P501Wwcl4i11Wq/HaQQn33zkN+APwYeBvwCeAWYHo+A6vGZA7N9fAXHwFt\nRsUuhABbmoQch8hSJsRydFQ3LNuG3aNKpj5VBq9LZdNqQAmJjc24lWdbYZqukAdXpyScjFE4FuQo\nkpINehEyIxBoLqA7XWRd2gu0qhbWw/JWyBXhpvXKkDzwJAzNQttq+MzNks8fKPP9h+twHgxRyhlY\nFiAkC7tsfr2vBdvIkhl3qtoTD+oEHaCdMCmNu/C0Zwl35HC0WaRdfgJtady1RSLhKcrjBuM7FoIl\nlAESqGTBbKU3fSUAn7Hh0Aw8PQrXVDwDplW5HnNiQE4HXLEOavMa+RhM56Ehp0pjhFBV9EKoup4q\nVX7X2OdRAH4OtVLK7wghPi2lfAx4TAjx7HwGVr+2L0IIeGsAfpCEWQE+/RCr/dso6YJZctRxCQAH\nJuDH+9QPpiMMqYiq+/DlLbS4iV5borlrmISZ5aCZRDQ7gBDBoKTzMkHsrkb8pptFFz/I8o0ncLqb\nqHO8nVO/0EcH4O7HYF0vXH6ZOrcHHoe//RqMpVVSwPERkwfXFrHbdPJPeqC2ktLrlRxtzlFTLjL7\nX/UUDgfABSwENgMG2JMGmaEwmcEQsSNZOi4ZILwkSWI4gnNxCVyC4vEQlCqpxRoqxgPKqGTUcU5l\nvQjjdCqIacE/3wOd9XDTptPXVhdwQys4l0A6B30n4Nrl8NDT0NsD//aw6nXyiSshPO8uClWqvD7Y\n52d6YUUYiXEhxNuAMeAlGmj8JlVj8hIsd8MndRgtg8so43cIBDry+euMyooCHDos8Sjl3KUBeCA0\nyfhsjIxmEGicIFd04PfFGLcbGWxIEGm16VkFjg1xYqk2hltb8Y4JNnY9zkEzyt8ObOaTjQbb90Ey\nAw/ugMvWqzv8546CoUFLGA4dhYlRm9KwE7kCWCARE6B1WdhdEsNhUgg4KHR7VP1qDNgrVZn/1UBY\ng3bgaUFhxM/wM+2sX7CDOGCVNEyHEzsvIGChOSRSF0hLqIwvl4A0iDJIB9QGodUFex+Hvh1w41ug\npxGaXyIOsqlbvYaJONTmYGQSlveoAshEXhmc2UzVmFT53SJFibJ21rbnb0b+QQgRAj4LfAMIAn8+\nn4FVY3IG2hxqsVjOLDkkZSJsfH67OwCFCORtuKALFoZhYxjWFCd5rv0eDhcijHncOPQimoCi5QEh\n6SsZ9ATHCS+SeM0ZjhYWM9K9iKW+B+jhAR7PNPLPo918cJXOcBI2LDntKrrkQsn/vcdkckZQ1DQO\nO020rEAaYLdraIttdCxkzkAzLHz1OVxrRhk/2AFtgA84LKHXhEahyv03AdsEuQkfifEwyy7ej3DZ\npKUfdInhMpFCYNuG6leMBATCUBnGAQNuXgYNMTV5SWbhnsfgI++C+3bDl+6A7ka47gJwO9XMY1Ml\nlnJFL8wmobVBHfWaVWp75xkUlktlKJYgUDU0VV5nJG6ktmQeez7xup/Lb5lnpJRJIAlsfSUDq8bk\nLOi4nndtnWK6CD8aVdXbRRvumoTPBGH3GHzuV4tYsjhCoH2GkN+PaehkpJ+ybaAhyWecTPg99BqH\n8DoLhEiwLbeJA9Zi1nr34jd3cCQTZdoZYWcdZCWsyqrOu7HmBJ2fmcIx4GDoSC35ISdisYWxxqT4\nhAMrr2Fb4PCYeGsz5LI+uteeYObJJsqjTqgRMA7EHBCW4LXBpyn315CGXRQQsnDpJcIkGeqaIj8c\nRDckxYKGbVfSsHJQK6DWDVEN3tsL+3ZDPKVcXIYBzw3BtoPQVgt7+iESgCsrub+WDYMpaPRB55za\nkiuW/+b1H52Eh59R8ZZjQ1AowtUXwZZ1r9c7XqWKwjqfqhZP86QQYgAVhP+plDI+34Hn5dV6rcyW\n1N+QA+pdkDJhJAPf3gF7+3w8+OgVgE1teYoGc5KZfC26lIzPNlPOunEXyuw9uQavneUS/WE2OZ/A\nreVIWgHQE8SsKZ6eKCLLNs8M5/navgRf3WtxcNIi7S9iLcwgthZx/lEWY4OJYRQxokWEkOheC19j\nHGyBESjjq03id2SUvLwEvKhbiKRNqD6Gsz6LvsCEgIkZMUh4arE1A9N00t4wQmBpDr2kYWQkYlyD\nQYFjBlwSJo/CgR3wJ18HyweNtdAchXdcqkSLDU0ZAZ9L9T85xe5J+PpOuOvYy19n24bv/gyGxuCu\nh+DksEqSeOiZ1+VtrVLleSQCiXbW5c2GlHIR8DfAMmCXEOIeIcQH5zO2OjN5FTS4wKnBRAFKNjS5\nVd/0o8dN0nFInFzCj37xQUIdcfJtbnLeAGXToGy56ew4hlaSiLyTI+NL6Kgf5QrxKAk9SE4GmEo3\n4HSNkrZHWeE26NP9NHvGGDMjPDDTyNGTdRSTDjJTQZy9BTSnTbA+iWjKYUU1wEYXEqOuRKgrQWQo\nSSocVDGTHKf1wso6+RkvjVvGybX5sOuhZlEBRzZMtGggyjmy0w7M8RAiqGGldWwE7hBYRZUEID3A\nNBx4Br40BJ+9DhJJ+Nn9cO2Vqm5naEYZlY2LTl+/Wg8EXdA0D3dV2VTHCXohX1aSNisXnX1clSqv\nlfN0ZoKUcgewQwjxReCrwHeB759tXNWYvArCTvjDTtgRV0opF0XhQHGaTNBk4ZoYEfck7UsHyLgC\nWIbOZE2UDEH83jQhI87w8AJGBxdArIzXlSU8kCS73E1eOsgaLpb7nqHHeYDVdbvYXria/uwNzLjT\nHNTbiA3WwLgTBJR2eWm4aIT6RVPkO724iyU0JJZTIB0QKcXZvW89lquSarUS5dICmAK9xSQj/aBD\neH2aukgerRjEOeCjmC0x8uslWEecBBsgpYErotx6chj1ySkBJ0DaMDMLfSth7XI4MQhTU/Bnb1eB\n9kfug4fugQ9XJOS6wvB3F6tMuJdD0+D9b4N7H4crLoR1y5Ubrbv1dXpjq1SpYFOiyNDv+jR+6wgh\ngsC7gJuBbuAumBMsfhmqxuRV0uSBd1ba80okA8YEH7nBJB27myWxnbj0PD/Lv42ZcJQu3yDTVoQL\nHU+gS3hOriEvvZRcDqYGIjSXJsiWguyd2EioYZqII8lyew+RxiRb5S9pMo/y+af+iViPG8YrgfCi\ngIBGadqBKAt8rhwl3UnR1nFpZVwli30PriaRbFAzkTgqL6OEqjdpgeKsC2rB4yyBEOwaXEKzncA1\nUcQzvYLUUBt2CpI66LWq3IQyKpg/C+xHZYoZ4NMhW4DH94NZUobC64IFDXCsUQXO53I2Q3KKhZ1q\nqVLlt4nAhaDn7Du++dgH/Az4gpTyqVcysGpMzgECQRAXZv0sG+O7mAyHse0yW4pPcqi1B4du0mIM\n43DajJuNtIZGmLHqaAsO0GDEGKuNkvV46c8tIN+/jLXL9hBxpjhQXI41pHNr/ycZOt4BbWVAqMLB\nJOCWJDJREv8SpmHlJIFlGQxh4KOO5HAUCg7l2hq0oSDhhA6tKIPgAXuXm3yfm9KGEpblJaTbZEbd\nHB9cxqICxHOgu8Hjg7INdgakCUyo0xBl0HygWbBqOZR16B+DrA6374Bv/EpV9H/xo1B/juVSphJw\nbBxaa1U9S5Uq5xKJwD4/3VxdlQ6LL4kQ4htSypdsQn5eXq3Xgy100C7acDobWOwsUOe0CLbkaPZO\ns9T1HLXOWbLSS8yKUog7uH7ZnWzo3sPsojDj9c2MyHZ8riwl24lLK/HBR3/ALQ/9gO3Ji3hq9BLy\no36KBw2oLYNDqD7tBQHjNqErUohOQdjloHvKzbIaC79h4xZgzJTh0CyMzYIwYQiwUAbJBxTByjmx\nbA8lh5d4m4dEa4ahhKRoQTYJyUkQk+A8CBwCpsARg2Ab6B5o6oQFveCLwiEbjhThi/fC9x+BnUdg\n91kC7a+UTB5ufUClHv/bA+qlValyrrHRzrqcDSGEWwixQwixTwhxUAjx95X1ESHEA0KI45W/NXPG\n/LUQ4oQQ4qgQ4uo569cJIQ5Utn290qMdIYRLCPHjyvpnhBCdc8bcUnmO40KIW852vi9nSCpcdKYN\n1ZnJOSKAi0vpQrZ8kdjMT+m3Z9jtr8OVFWQ8J7Ao0Wd2cmiyl4grSXekHw8FDuWXcdzVga9o0h4d\nwq1lKedcTDqaqdk4w7bkpeTLbiiD/ZQfLi7CVBEmDGgwEV0WyUdriVw7RtfhGkYOOMntFAhDxwrZ\nOFol1joH8rCmquBDEhw2jlgZT1ueXIsfc9pF8YCG321S8hn4WooM6RZWwsAVVjpFhgAjAFoKKIHT\nA+5aqAtDez2MGjAwqupuNF0VMxaz0NMM6xefvk7ZEjxwHLwOuLxb1ZW8UtJ5JTHTUQeD0zCbhuZ5\n1ehWqTI/JJyrmUkRuFxKmRFCOIAnhBD3AjcAD0kp/4cQ4nOoHut/JYRYiopXLAOagQeFEIuklBbw\nf4A/BJ4BfgW8FbgX+CgQl1L2CCFuBv4n8F4hRAT4W1TrXYnKzrr7laT7vhKqxuQcI1x1hFs+Rhvj\nOHIO0nmdae/D1BpPYlpjHA4uJuUPsNO5mqCe5kR8EZGmSdzuAnknbHFvY3KkHveaDNMnm/B5s8iA\nAFOqyvNn3FBvIhpAlh3Y/S50Ad7dPp4ccTJ+wodegOCUgb3JJrDEwmry4clLMDSSLSZiWtDYM4Hp\nE+S9HgjalE2N2FENTQqadDcLtujsKUNZqh/8YgGiadV/3ghCSgefgNomsGpgLAOWAzXXtUC3wdMA\nf/VhpSAMkErDs+OwvRLX7IzAopdp+3sm6kOwrE21EG6OQFfjuXr3qlQ5xblxc1Xu9DOVfx2VRaJa\n4V5WWf9d4FGUWu87gR9JKYtAvxDiBLCxUvsRlFI+DSCEuA24HmVM3gn8XeVYdwL/Upm1XA08IKWc\nrYx5AGWA/us1v7CX4HUzJkIIN7ANdT9sAHdKKf9WCLEa+BZK3tgEPllJRXvTYKDTSytd3gLbvY9y\niAmSLGa1+wTdxr8yI+s4WljIs7MXsDqyg4LtocaRRitbJKZrSJgRNMPGsg3GXY24mvPkGpxwQqh6\nkWlDCS1qgAn2IZt+K0B+oYYMCWQIskWT8BGNplUOtFqVAXXkmA0+G+2YxNJ08kWfkkhxSjDA6tRw\n9sG6oM4g4GqCaAJmTXD5YOt62L8N8jkIByGZV22MU+MgfLB+IaQ1yCehMQjtLvi7W2HLZjDyMHgY\nQk2gt4PbAWH3y1/HM6Hr8P4taobic1Vb/lY599iUyDJ69h3ngRBCB3YBPcA3pZTPCCEapJTjlV0m\nUI5rgBbg6TnDRyrrypXHL15/aswwgJTSFEIkgdq5619izKt+OWfa8HrOTM40vfsC8PdSynuFENcC\nX+a0hX7TkMNmPzMcY5gckjRpCiyla9cxfKkUDT2TXBP5BZFMisbYFCfTnRzr6iWRrqHodFO0XLhb\nskwPNmL5DVhqqsr1E5V4iRtwWOiU0DQopHRsS4AtkZZGXlqYYZM1Ecka6cHpggNTAhwa1l7I17oJ\nrY2RK3vAFuCysFsk7hpBpFHnyDSsWAAD+wEJNT5Y0AbdN8HDD8HIDJTLkC1C0QBtHBI9NhkdikIw\nnhCcVP26eOBeaC1DNAebBPw/N6q6kdCrNCagBDmD3jNvTyRgaho6O8A5j/bCVarMReDCwYL57BoV\nQsztUnirlPLWuTtUXFSrhRBh4C4hxPIXbZdCiLPFKn6rCCG8UsrcS2z62pnGvG7G5GWmdxKVpAoQ\nQqlSvqmwkfyYDIPoDFBPK+OEcODDQ8fRURbtOYGhlXh8+UJGPd0kygJncJyEuYoT8S5o0qFGw9+c\nJHa8HksCtQZsMuEAeGcKOM0STr2A7jMxlxgUAm6KBSfUacicpD46RYA8i2u2kUxcz6GZRlLDUumg\nRCH+n3XEn43CuwXogNNGMwUianPvlED6NYwAbG6AwYPqdiTohv0JsBaBtwnqYzD0BOgG2AGbA2O2\nMnJRjXxWqrThWY3yFEwtguUN0L4E2l7n+EaxCN/6d5iNw8Z1cOP1r+/zVXnzobK55jXlnZFSrp/X\nMaVMCCEeQbmaJoUQTVLKcSFEEzBV2W0UlXx/itbKutHK4xevnztmRAhhoH5XY5X1l71ozKMvd45C\niM3AtwE/0C6EWAX8kZTyk5XX8J9nGjsvYyKEWIQK/jRIKZcLIVYC10kp/+Es415qevffgPuFEF9B\nOWo2z+ccfp8oIpnAZAFesiymliwGGl0EcbSW4aikP+di4XcP0u04SnFRLZnZInLgECc/8BVSnk6c\n4RKUIVAfJzsQxvCWkDUa9hbB9b0/IFTO0pwfo8Y9y57aVTySvYbJJxrQRk0wwCNLNF88yKgjQrit\nD7fDhoZmmBJwiQRdQkzAgyiB6RoNOwr5LkHaIVjkh6gOR224ohvKJXhuAvaPq+r1zQvBvxDuzEHy\nEKScklxKQKdQDUo01AR7O6o97yQs+SxEfwtpvOUyZHOqpXAq/fo/X5U3J/aZPTrzptK5sFwxJB7g\nKlSA/G5U46n/Ufn788qQu4EfCiG+igrALwR2SCktIURKCHEhKgD/ByhVX+Yc6yngRuDhymznfuCL\nczLF3gL89VlO+Z9RsZa7AaSU+4QQW+bzWuc7M/k34C+A/1t5gv1CiB8CL2tMzjC9+zjw51LKnwgh\n3gN8B7jyxWOFEB+v7Et7e/s8T/ONgQeNTbh5igJraOFddONGJ22nuLV7Kw+35ekaOsTlrh3s8TfR\nd/ckyaKHdI9kffoO9obeR9kOkJ/1EuqKYRYcmAk3wi1Z0/4Mi6J9+BvS1MpZNCG5xfouQ9uCzPS8\nHc/GPOSho6YPfyTD6EgbKyJpenwHuP9EI5g6TAhYbKsUYSHU37JQ/dQK0LhJkLWh0QVva4Pr6nIc\ntwrkjnvJmS6WNgjClYLNG6+B1AK4a4dGTkg1z0wDC1DuuMVAGLQJ2DkE757XPdxpHh+CXBmu7p7/\nGL8fbnkf9A/C+jWv7PmqVDnFPGcmZ6MJ+G7lxloDbpdS3iOEeAq4XQjxUWAQeA+AlPKgEOJ2VBK+\nCXyq8jsK8EngP1Flx/dWFlC/od+rBOtnUdlgSClnhRD/HTjV3OoLp4LxL4eUcliIFxhS60z7zmW+\nxsQrpdzxoicw5zn2xdO7W4BPVzbdgZpSvdSYW4FbAdavX/+G8ifOh8vwchGeSv8odd2ePvgE4/4m\nHLlJpKubBv9RjnxzFNqCdHYUSZVnKR15jpXhH5F4/0VYTi+lgovGFeMIE1rkMOtTe6nNTj8vMicR\nOLBY/th9dDz9K7Z/7dOIVhcOo0zKCpDI1zPiyhH2GdRvTjI77MZyupAjmnJCRiWEhYpw1VmEMw7G\nxyVJQ9DvgJkC3DtZojVyiOu7J+jWwnhSFwABUkXIpmEgCQ5DoMXAjgPBSjOtdvF8kkBzA8R1iL/o\nY2naSrvrTEiplldKT7daqlR5NViYpJh4zceRUu4HfuOWRkoZA644w5h/BP7xJdbvBH5DW1tKWQBu\nOsOx/h3491dwysMVV5esxLo/DRyez8D5GpMZIUQ3lWZ6QogbUWLmZ+RlpndjwKUo393lwPF5nsPv\nHY4XTZP1kVlcSzyU/DWIIyfpfzTDwo+B7HUws3ApCXcTkaMaQ8/ZZA4VqF9bIBzMES8F8WXi1J7s\nZ2vffXTEYuz+6CoKbg+1MkZsxI32XJyaTIqLv/FtJv/mKtJ2hICexVs7wDZPK2mXG9meI9SQo5hy\nUDjhxjziUt0TN+rQqYMlGG3No4IoGrm4xoPDoM140dPrGFs3yieue4aT4ikKQ1fh1ATOAly0DJrD\ncN82wUQMTE0gSqCPKuWXukb4wCaIROBoSt1RADwzAvf3wZ9shIjnpa/hEo/FdMZifFyjqensH9fR\nPKRNWOSfv2RLlSovRuDExe+XR+Qc8QlUkL0FFXP5NfCp+QycrzH5FGqWsEQIMQr0A2eTJT7T9C4B\nfK0SKCpQcWWdD1yw+irK9/2QvOHDe1Aj9p4mRm5eRdzfSF66MKWOa2mBBRskw0d0juV6uNx4BPdT\nR+g8cQDvNw/Q8YE8g7M6nh8N4OgNkD5cpP8rszQFQ6R8YazkLLn4DOnGWsKFDKVIkSwdJPt8mEMu\nrCclZE1cmwo4S0VyoyE4YSFWSKRXQsKhdL+mpdJJqZPYIQPbabBteydtHSPcsHkHvW3NOFLL+Y8J\nqPFDzWJY2gl9A1D2woQJ8YKadcxOw55DsHmdUho+Oaj6knj90BoE1xm8CceOlbjtNqVdb9uSm2/2\nsXKl64zXdzgNH7sXMOALl8IFtef8LaxyHnE+yqlIKWeAD7yasfMyJlLKk8CVQggfoEkpzxrWfJnp\n3RPAednayN/UxFs//Fkkkp2JAEOGTtHjY2HpGI25aQrSS0rzMxWJkF47wJ3ZrUw66rjhse8T3TfB\nsncWKQYEE/db7P5emmB2FFcZ7KAHrQ4MUcYZNKn3x1ggtpMuhznm7SY75aU86EL3l7FSOtRpeDw5\nCos8kBGwXiBqTeQRNxysxFBsCW0a5FW6MaaEYcGPv3ghk++w+fyHDhAodyHE6fxctwuWVardZ9Kw\ncxrCXhAZ1ZtkIgULC3Dro2rW0NkOH33fmetEfv3rPOGwRjCokcvZ3Hdf4WWNyVQaMjl1qvniK3tv\n4mm44zGYTcG1F8DKqovsvOYcVsD/XiGE+DIqFp4H7kNpjf+5lPK1SdALIT5zhvUASCm/+kpPtgqk\nmWU2nMS2HPSUT9KSnOKuqetYsPsgrdNH8K0tcomY5NKjO5h0tdEbtWi9royZg+ygQbmxncXD/WQk\nGCY4RB7iY+TqFzNw7WZagzYlF9SIMmXdh1Uw0AMSe1qDss3i9SeobYmR6/Gye3It5moXdp8TdgkV\noHBK6NXU41EbLAtGNDAspC54+vsX8P8Nlbn4SjfxFEyOQk8n1ITU6yubcHAYlrYBQShacOwETB+A\nsEvFT9xuGBxR/U9qIyqdN52F6IvShk/FS6RUuQIvx8oG+OwGKGqSbf4UX+ovcW3U4pP+KA7x8vdN\n9z8Lo9NQE1BGpacFvK+hDqbK7zvnrdDjW6SUfymEeBcwgJJ92cY56GcSqPxdDGygki4GvAN4U1Wt\n/zZJM0NR2kRlnJbsFDti67jpjm+SKRfZW7eU6NcPYNga/rKkqeUQZq3OU5vWMdLTxGbXTi7eO0oh\nCicP6SRcBpq3TGpBG7vf94eMrVyGo3wCv0gzpTVQX9LxRrK4xmfRGmxmImHC0QS5nBcrq2FIG3Ob\nphSFbVTKcK2mjMi0VGnDZQ3aBEgDy2FiDDs58aSPi6+AqBN+fhDiCTBzsH49PDcFkylob1K6lH+4\nAu48CO4i1DXA4KjS74qEwF9pkPXgdnh6L/zVx9W6ZBl6tnrY9uMsyaQNKDfXy+HQ4YZl8Be5Ce4+\nNIUWhy883ErTB8d4j6sd04b9KchZsDwIYcfpsZatZkiOSusX+/cu5aPKueY8NSanbMLbgDukvasC\nOQAAIABJREFUlElxtru4Fw18SaSUpxQutwFrT7m3hBB/B/zy1Z7t+Y6Gjpsy4dIUNbkYN4z+HMfK\nIjlbJ5o+jH2RgLSXset6qJmM454yaZmaxVuy6W9fQGRtEOeGErU22HaIYW87D9dcQcxQFbs+K4vU\nNNy2YMPgMViUoG5TP4WEzuGJRVB2IIcDTDzWij3rUyWkKVlRdNRU7kaTpm4hZkBrNdGCFnZSw047\nSPWAfw/0CjA2Q8AF+0+qWEhoBAwvXL4S6kIwHIOFrXDVJSplN9wCX/sx5DKwaiO4XDA+BU/tgWQK\nUhllTP59DEZ0J5s+pLOsaBGJ6DQ0zC9V87hMY5RMbOHA60xzsmSBC34xAU/H1UvcPgt/2gWeyiHf\nsh6+92uYjMN1m8B/hoSAKucHJiZxpn/Xp/G74B4hxBGUm+uPK4lUhfkMnG8AvgHVVukUJU5ryVR5\nhYRpICyd1NjTRIfiZGehYIMvZBH0QzEiyXsE63YeZKDUTKpkoLuLeK00NSfiZOJedq1czsnadjBA\n1yTd9NEix0mUgxiGhWWG6JopUCtz3KyPsltrIFNvs/X6Pn75L+8j1WcQSbkRpsbMLBQuE6rMvR+l\nAlQPDIOxooTrsrzaJiTFxzyYU26iC+HwGGRr4O0rYPKg0t0KFuHSS+GJEdWyt8UF3/qmclPdeCNs\nPwmhWpgZglt/qdxdT+9V7i6HCx55Gj7wTtVsSxfQVKPTW/PK8v0/5qzni60pMiMWkS6da7xKcGF/\nCjo8ypgM5SFWgtaK0agLw2feo2I72nl5Q1plLhoOfK9Zxur3Dynl5ypxk2SlUDKLEpI8K/M1Jreh\negLfVfn/epTSZZVXgYcAHbIFU0ryo5AYhnIBcELrQhg7KWiVacYmDLSZQaw0JDPgcUNtM3g2N9I4\nMsUeay3DsXZC3TGijklCzjQeZ4FsLkSg7KfNKhFpX02vV9BpH2XWBNu9jLf/YR3f+Y7EAH7VD4WF\nGrhsZUBWAUcq1fEaODcWsLIalAQYEueGAuYdTpJlwcmHBCP3CO47DPUNEA5DNAr9++Gz74eSCb/8\niZqR6Do8+SQElsDOw2CXoX8cPncrODSYKYPhgskiFE1YvwKu64SGV6Gr9XZHmN6W1Yw05+jWHLRq\nymL0+OG5lGq17NZe6OY6RdWQVFGcnzETIcQfzHk8d9NtZxs732yuf6yINF5SWfVhKeWeV3KSVV5I\nnVhGni4KYpxSDtx+yOWhby9k+iXRdrASJiP9MDwImRToDojUwVZ9gja/j8XLj1BXN8G9T12D4+Iy\nVslJsz5GyeWkxV1HQ6SbC+jGiSCoddDhlPicnWhewdDFgif3gtVFRThSU42zijY4UfETu3KyEpXc\nLVEzlAL0b5f0FyXYAs0F47agwycY7FPB9E98TP0wL14MxyuVRFddBb2r4Ju3w8iYykCejEF7RUI+\n5IIjx6ErAI/th6u3gt8JHfUqu2qerlsAunWD7ucl4BTvblJV/VkTNtbAeBH6ktDqgl4P3H5E8shz\nNlEDPrhRVwkEFSyrqk58vnE+GhNUbPwUblRh5W7OlTERQrQDM6jm8s+vk1IOvbLzrHIKIZpxWj3o\nnQcxRhPk8hBqh4n94JBQzEMsDgMnYWykUi0KxOMQDsCS9kl8K1JkF/h4xyU/YW92Iw5vGTSJbUNR\nN7mUJeTJkcMi8KJ+1hM52LwRXBn48l6wk2A/BkwKWIEqWSpC6RkXrsvyz9uR4hMeOIHyW9kCvBLb\nMMkMmxwtmegLDOIjHu57ROPKLXDRRdDcrNxHXV0wMAIdUShmQKZVX5TxJERrlJ6WpwA/2Q7JLDyW\nhRVtsNgNN5Rhc+9ru+ZuHa6oU49P5OA74+DR4BEbjEnJt3+YYeaoC2kKvh2R/Nf/a7BlNfx8O+zp\ng5AX3rcV2qptgt/0vAKhxzcVL27JW5HC+tF8xs7XzfVLTv+eeVDKS0dR3cCqvAqEaEQ33ocWOUHn\ntcNY2VnMEQt7ymYgbqKtdBI7YpLPlZmbWGSWob8PgkMas7qfCbMWzRB0BoawjiaIPTKK62pBX+dh\nStpbeZYnsLBZz2YCc+7Ub7pY9RRZV4Tbj8NgGmwfEBCqHXAKqAVzjwd7xoFWZyJnNazjBuQrwXqA\nch5m8jgXaIQ/kMKMOZgZzPHhz3pZvdTiEx/x8a4r1Zfy0e1w36MwNQAXLgXDCY8dhd5OcDlUam5q\nFg6NQr4W4mWl/Hu8FvoOwdd6oP0lXFOvhpN5NRlrcsFoDu7aVyI+qIOpIQxJbAa++H0Lh0Nn1zHo\naIR0Dm57AP7yvSrrq8qbm3Mh9PgmIAvz0+Kfr5trxdz/hRBrUaJjVV4Dmncr0vEsmsuDI+Akeehp\n5Mg2rIUWTn8Cp8fGqMQMdIdqlZtPQaY+xPFrVjLQshDQCYk0Gjbs7cfeM8psYx3OBSUGOYGGhkSi\nveiLUcrArx9VQfF73wdffxy+d1jNCIhX7hzygB/sKQN7RIeAhP0SoqgdDBuSOcCD3loifzJAccRJ\n6TBM5UwePp7j+N4cFzxYR3OzzlO7VI3JRTqcnITV6+CmRkgVVH2HDUyVodzM80np6ST0YaO50vxi\nxs0tQdfz6cSvhTaXajE8U4K0BQGHREoBEoQGCEmmIJiMg9+riixDPhiaUgWRVWPy5qaMxTSvS3fb\nNzRCiF9weuKgA73A7fMZ+6q+ElLK3UKIC17N2Cpz0JyI6Htg+jawDQIrliLXTlG/YZJCwKStM84B\n2cXJ7k14Fwe4KLYL+64DlN7fSX5RKzNmlB7XADo2Aomsc6MtC+O8IExNaYbh4rfYIDbh8G7A7Qi8\n4KlDIViyBBwO6InAV66Fx38F+0fAtlDao1lYYEBGQtkUFPtt8hmU1FwJmFH1H2BQHDAQwRJGuEQp\nr6YPpu5hOKbxvj8u8r//wcWSHp1ndqsRn7gRplOw44CqSUlFweGFkgNkQbnUpBfQQTNgtt/gsf/U\nGfPAlvVw9cXqdYxPwI9/Bh3t8M63zj+A3uuH9zfAsTxcGYG2pQ72PJYhnzUQOYHbDX/wFkF9EHYe\nB6ehZiZNkWra8PmAgYMQ52U/6K/MeWwCg1LKkTPtPJf5xkzmVsJrwFrehE2tfid4F0HTn0D6adKu\nvYz9TS9FbwuzCHLRGCcnliFiZep8ZcqeLkKf9NEYGmev7sBjFRCVjF4ZL8IPjtC4NUJbaZTwbIp+\nGWYo/wDvLN2Nu+Nj4D+t9O/zwdq1p0/DkipYfuA4amKrATZIGxa3QEsULl2i89xhyd0lGNEkpIWS\nmwfsSYPcTgM0CcWkaibideLzWNhS8OX/Y/MfX9VZ0qN6ynu9cP8PVDvh0hgcGIWrN8O+MajzwGwe\nykXQaqCprNE662PLCpUu/OgOWLwAOltg/yEYm4CJKdh6EYRDp1+TbYNpnrnT4qqAWgBiPp2b3+rn\nyGSR5JTO1Q0OxgY0Rvuhow2cfuhphivWVDO+zgckYJ2HAXgp5WNCiAZOB+LnLcQ735nJ3NtaExVD\n+cl8n6TKWXA2ImvfwSgZnASQ7KXEOMc2fIiO3DQ/Tqxh8eEfM9bUSMIMIVNuLtzzLCvlfg6vX0Im\n4Ke0I0avJ4dvs5+oYy+Bk2nuPfk2pnMNTNT+nJqB/w4Xt0DopaPYfdPqR3N5Lzx3XIVE9BK8/QqY\ntiBZgE1XwVvfIfBuh8xWwb0/cDDyuBsrloVyAGYEGAKcXnz+AtmyJJMRHDhgMjVj8D//Cd75dgic\ncpOhpFfGkxBwg98F5ZySVPEVIeAEocPVLbBzBHbshSWVrK5MpaHomhVKXLKjFUJzkrdicfjuTyGW\nUGnG77zy5Y1Abwu0HtdpbPHiq4fspDqmZcHIMHzmI1Bbc+bxVd58nI/ZXJUeU/8LpeougG8IIf5C\nSnnn2cbO15gcklLe8aInvQnVj6TKOUEg0LGx0OkCmedIyYsJpF1+Blc2E8jO8u7pu9B9JoVFXppz\ns6w4eISSy03skMHQ5W1EXHHKkw7+dfknuC/0NqbjjdwRuZFPxe/gj/Z+B33L/3rJHFtNqB/b910L\nP30EBvuhoQb+9X6wI4ATnvoc/OBz4HArqfe1Nwk664OYx/Ps35MnX9Rp6XHSssLDzicFFCwsoZFM\nQ3trmZ17HDy4DS6+CDweWLoAjo9Aclq5t9IplRY8lIV0AcjCpT0wdFKlG49OwpMHIFoLwXboF+B0\nwB99SE2E5vLw05DKQlsT7NgHq3tVD/sz0VkH/+0ayBXh0GF4MqbWn0oHLpXPwVtc5fcGiXi+Z9B5\nxueBDVLKKXi+lciDwDkzJn/NbxqOl1pX5VUi0GjjUkZ5Eh0/yfxfMp4zwGMS1Sc4Furmq/Fvsbdp\nNXsjvbSICdbk9zGytBG3WWS4s5VDzl5qSHBw0TJ+PngzhaKLS2q2cXniEZKmn38jwieSD4B4Djxv\nA+fi55+/px666+CeZ1Ug3K/DRALsFlQuh2aTnDH51Ld1PvRBjZPfKTCxFy5YbfCFr3jQDC8/exRm\nkyZ33ZvDTFqQORWpFkyPuRgfNJmdybJ/l8n6DS4edvlYsVQQBYZL8MReWNQNfU9DtACYsGdQFTWW\nPZCbBamp2dP34hCdgNUS1tVD9EVxDCFeeVOtiF8txmJ4chcMjys3WXc71L3OfeurvLEoYzFB8nd9\nGr8LtFOGpEIM5mdVz6YafA1wLdAihPj6nE1BXkGnxSrzw08ziysN06aBqCXJlzsIuI/Tag8xpTfy\n6+gmmp2zRIuT2H7wlIvs8q7jbt/1lHCoLKSSj7wwuCZyL+8f/xGmNDBcJulMDalskqDbhOL2FxgT\npwEfugicFjy0Bw7OwFQcCKE+JRkJOoxlbW79dIbJvR5A8tAhm0J/ma98xUFXxGTP41mWNpXIjDhJ\npjVAgNvFxJQOjhjkE6TyJg/fBw5/mKlSE8myRqgGsj6l1HvxZTDwHGQK8P+zd+ZxklXl3f+eu1ZV\nV1d39b5O93TPxuzMxiowyL4MahBBVAQ0EaOiWV4TffPGaN5IEmN8Y2IQoxEEFKKyiKwKOAOzDzPD\nbMze+77WXnXr3vP+cWqGZrbugQEGur6fT32q6t57bp3aznPPeZ7n91QWwe52VQhS+iHlQsqC8ulK\nwv5C62hDAnDx2dDRrQzC2WdCw0koY1SVwx0fh137IOCHBWeAkY/emlQYGIQpf7e78W7wdK52/M9z\nzz8GPDmRhuP9RbqAjcAKYNOY7VHgKyfZyTwnwRI/jLqC3uR06tKP0lZisyU0n2IzQQYfpdowU/vb\n6PFXsllfQkqYSMNAl1lMy6W5YS8VI51kMyYDVimmlqXa6matuJTZWguF0ThFvT+DohlQthSEhqHD\nommw4SDMnQ/JDOzMoOqbBHUo0Clqj9O71UIXEtOSpJKwdSt873suBQVZCi2PSKfkqsscfv+yheH3\n4SDoH85ASwQl/KVq+TqxYXbtK0JrDtIDaEOwfR2YHtgOnFEF02dAZwQyo5CxoKEBmsrgw7NABOCq\n44QJlxTDnZ9WMwvzJHNTpIRXXoENG2HmDDhz9omPd1ylWJzn/cNkrWcipfxLIcQfAeflNt0jpXzk\nRG0OMZ5q8FZgqxDiASllfibyDmIJuLIQIqaPn78ym4PBdmKmH6SOh0dTqg2yGgWpFMmATzmXvSye\np9OZqcOTGnt902kOtDEl1ompZRgMFPN3mTMJ+c5gtr+DL6f2s7B1OzhxqLkYz4NfrYfmKkiUQGUJ\nBDfApj5wfVAiYP48yapn1eDpZZWHzjQlW7a4OE6WGTMM6uqgsU6SPifAnk6TeMCjvzMF2MChkb0A\nkJB08fwuOAJvFLIZSFdBrEzwckLQ1gmFBqSDUF2slrl0G0aegVAhuJflTnUMhDh5QwLQ2garXoK6\nWti8FWZOhzMXHvvY7ij8dCt8aj7Uho59TJ73IpNTmwtASvkr3kSA1XjLXA9LKW8ANgshjlqBllLO\nP9kXzHNyhHwQti/giv3fY9XUAL2EaaAdR1pkCyx0x6UkM8SAVoYUgpi0kQg04dKVraG9vpZk0o8t\nUzyeuYaAlmKmbxueBf9qTuOH6V58Axug5mJAOeJdT9XzaK6HO6+BgRHQLBi0oe2gScevIrS1FgIC\nTfMwDMHevZKMq3GgLcOVV+hEU4UsmauRSEv2taRVlBceyvwUoFZJJcR06PegTFczoD5PWdL5IHsl\nbcMatbrS8Qr6oK4U1r0E1jQleW+acMUlSgTzVCGE6qaXS6M5URRYsQ/Oq4dwPvfkfcdklFMRQnwE\n+EeU7Gvun4CUUo57qTTeMteduftr3lIP87wlLptdzn9v/hqVke8Tt2P06yV0WtU0Ou0g4Mr+p+gp\nq2LEX4ynCUyRJpEuIDti8oq2mEIZZYASEoEgywrWURiLURnqot+upIUKZhmq9K6mwcfPg19vUDpU\nH1oCFUVQU/Z6X/7toRQrrtHYunWYYNBC10N0dbkMRlw0oZHJmjz5XJqmAwk+/Sk/jZUayT7J/EtM\nfv1MCHrjHDYquqnCsHJhvoerJjQIlRSpAUGPEakRDIBdCfUlkParZgMj8NDT8MpeuPZiWLbg6M/O\ny4lVnkxuyJR6uGQ5rN8IZy+DOSdY5vKbcEHDxM+d572B0uaalHIq/wRcK6XcdbINx1vm6s49/LyU\n8qtj9wkh/hH46tGt8pxqiv1wxzI/P9i1mIr+YQKlvYyW1jEsXGwvRoXWz4rNj/Kfsz/PgFVBMukn\nODhCsDBKTbqD4b4yNtYu4crAM8zM7qVRb0d3HGZl9yPE+VB/y+HXaq6EvzzBpUMqBXaBQdNsjYVz\nTYb7TRzHRXoqyRCyZMiyfx/cd7+grCaAZgg62jwq/T56ddQMxBCArmrMF6CUi3e5KiuxTqqpSI8H\nvxol3i9JzrLYdnUBHz5D55Ofh+eeh84RaGxQCZe/fApsQxXbmlqv7rNZ+NF/5VSM/0TVTpkIQsAH\nl6tbnsmJg0sX8Xe7G+8GvW/GkMDEQ4Mv5WjDceUxtuV5m0ikNIZ2X0BdQxtat8Zo5+W8VP4s/XqE\nNl8Fj9ReR3rAAhe8iEG1ZWMPegyXlbLU2cC6xBLcQp1mcYB+qxRD96h3e5lafwEUvH5pvWO/El2c\nNuXY/Vh6YYBv/WsC09Ip9vzc/GFIJCzWb0zRnU6DlsZvZZHZJKP9DtddX0BZsc3Dv0jT1+sqbRRD\ngG6A46lZSZGm0mLbPQgIdcywhP83AFkBIQuv1KBjv8MjBwXVIY0V10geWS3Z0SLwsoKRAZV8r2lQ\nXQG336iW7IaGIOtCJjNxY5Inj4FBGZMnSzW3vAWwUQjxEPAoKogSACnlr8c7x3g+kztQgo5NQohX\nx+wqBF4+6R7nedPs6gQ8Gy0yHYlkr7uTx1supGN6JYlgQKndAprPxZAefQPVDD5Rxm53HgcWNdOw\nv43tC8/Am6FTJoawpZ/ZNGH5Gg+/xkgUfvoblRryjT9RCYFHEsVk+YeKqCyG1n6QNnzrWxrz52vc\nelsMx8miSw3Hk9gGbHjZoajIJZnWkCZq5pGVIDJQICFqw8sSggLO1uBJFzolbE+DkYWygIrL3aYs\nwoubBK+USozeAdKJLKGyACVNJViWQU0V+Cwlc7/xVVVO+Oqr1bJV6C04x/uGYdsBKCmE2mJYvVol\nXV5wgbrP8/5kkvlMrh3zOAFcNua5BN6aMQEeBJ4Cvg381ZjtUSnl0AQ7mecUMBhTyzgAw5m9tO1Z\nzX77RhxpIDw5xmmsYeJgWB7T577G/lUz2N02Gy+tY+0RfG/JXfzVrIc5t94hFLwU9MbDrxEqgMvP\nBp99bEMCUF8GL+8CmRNULS+CbNbj6qtN/vizhdx7b4RMRhIIWJhmAd0tMV7pySA9MDwDV/cjNV3N\nOFKaSnCJo2JHinUoAR5IQ38SXAH9GQjpoHngGiQPJEm2AZ4NjstQJI0nB2laUHk4W90yYdt2OLBX\nOeb/4stv/nOPxOGHj6mqkakMRFuhyq+WzqJRpbqc5/2HREwqbS4p5a0TOU4I8ddSym8fa994PpNR\nYBS4KXeiClT1raAQIpgvjvXO4bhq2Qago+UFSrqjpOsDaCMOmAKpewgNZFogkzqh1BD1VjvGYpdN\nkcXIlEB4kq6OWfy+4Fvsinrcea72hr+LpsElZ5+4HwublE+hcxDOqAcnluQf/7OTRMJl6dJCbr21\nkk2bPH73O4OdOx06O+PqTykEQV8W2+cwNGBg6QZ20CSRglRagpBKBGxIh3ITSj0IC4gaYPvUzKUj\noXRNMgBREGm8pEnLdqisTvLbp/0IoLYOLloGXe1qRmLbb3wPrgeP7oPdw3BFIyyqPP777R9RhqS+\nAoZGYXsfLDwL4nHo6TnprzHPe4hJKqcyHh9FTS6OYqKqwdcC3wVqgD6gAdhFvjjWO0aBrQZBAK8v\nQZEbg4TE6zQRRgatWMMTApkUaJEMZ/TtYn5iO84UA2tDGtezcRI67Xs1Vnowu1Lj1TpY2Hhy/RBC\nGZSFTer5d77TjWUJysp8bNkSYc6cIJ/8ZIhp0+CBB9Lc/4ADmkawQBBL6Nx4TYCODoMFCwRr1sK2\nHR6pJMqLLnJRXlkB+KDAp0oh2kDUgWgWFeKVAKIgw+AquZa9m+NcdI0fiSrCsHAelFSpqpRHLkW1\nR2FjL1QGlFE5s+L4JYErwmrprLUHsh4sPxvaWpXhvfHGk/vs8rx3mGwzk5PguCFuE3XA/z1wNvA7\nKeWZQojlwCdORc/yTIzGMlj1mnocHC0l6HUSHu5n2KpEYuP2SbBAJBxmuHuYae1SqRxpD7MwixVW\nfw3R4+eVtT5eE7B1LVy6CP7uk2ppayx7GGErgzRQyBLKjyqudYhYzKW83EQIga4LUikPXYdFizye\ney7Dxctt1qzNEo97nH+exbe/bXD3PYJd7Rnash6RoKGSWiKeqvCIp5a9HBfKLPBJCErozSktahLK\nAV8FRAwYzgCS6AgsnJnm4P4MbtrisQ02rw2AocGnL1R1SAK5pbsiG3wG9MRhWvjEteULA/C562B3\n26FyyTAwoGTti4vf9NeZ5zQng0fb4Vj1PGM4ruLdRI2JI6UcFEJoQghNSvmCEOJ7p6hzeSbAtCrw\nW5ByoOH8a2m9ewufm/fv/Ff35+kfqoRCgc+LclHyBZqn7Ke3uIpOUU9XchZ+vyTWbxE4I0k07ZGt\ncEjtNdg3JOjqgMgI/M0tUJOTIsrg8hI9hLB4lSEaCFJJ4Jj9uuSSUn772340DYqKDGbOVOno0aiq\nRX/VVT7OPdcjGvWorDQYSQk6hcMfdkTIJD3cLOALKLmWiAfDAoQLxRroDmAqKV+VIwlNFgRN6Mpd\nNdaYkIjiGA7/53tDkNDQvGH63SIuurqIfYPw7dVKAXlBBfzRLAj74E8XQn8CphYd8229gdIiOHdM\nrdGKd6AGfHd3FL/fpLg4H4L2bmCiU0Ve0uAYvOWZyYgQIgisBB4QQvTB5AzCfrcwdPjALHhuG0wp\nDVP3iS/Q/9A9fNn9v0RLi0mFAgSbXJJNPuKmn2BPkn45lcKCEEG/yfZCD2cKuFGJnnIREUFywCDl\ng/9ZB7oLf3YzNNWBjiCAwSgZdAS+E/xMzj+/hClT/MRiWerr/RQWqmOLizUKCgSDgx6hkEZ3r8fA\noMFvvw/9A1BdC2UBneefT2LbQeKDJmQE+Dwoc6B1EEiBHYQZQZBZCLoqEkGWgLDBJ8B0IWyAk1FL\nXiU6XraQVb8dYfb5RexNwbJaqAnB5h6YXwGzypQ45LEEIk8HVq9u5ze/2Y3PZ3DHHUupqDgFdYrz\nnDSTVU5lHI6rFD9RY3IdKj/5K8DNKC3Zb771fuU5GRZPhfX7YSAK4dqptH7lr8h2HaQqk2VGdTM9\nVjtkdlO3o42kCFBldzM4tYnQQAEzZzps13R2DRukD+pkgxJ3mofmhxgaL7ZD1dPw9dtBSI3LmUKn\nFqUcP0Ucp1RhjilTjh6VE0lBVUOQ1asShEMe6ZAPf7Uf0QL1lYIdu31YIoFuB4n22pCSEEpBLAat\nuZrBAG4K9iUQgTJkegCaGiBtQzHKOYIOUQsSSRiKgq8IggZOcQGBRki2Q2dGOfsQyu9xutPSMoxt\nGyQSDoODibwxeRdQGfCTKjQYOFy/5LNAI2Psg5Tyttz9Pxyv7YSMiZRy7Czk3jfVyzxvmaAPPn0B\n/Oh5GIwIloZCJGrnEnIz6EhqtQVgzgbjWTLZOFFRTHqgnOWzdArnejznerBD8KoFWQRGnYMpJP6U\nxaCtsXkP3PsE7GsDXbe4/OxS5h5H4PBEpNPw459DNG5QPz1ExgFfSBIZcBnqk9RWa0yd6md6uUUm\nZbD2oAflaRhJKke7rYMoBNtSmYauxPYkXrCITMaATEr5VHwG1Fgq0XF/GNAgFQPHpv6cQqIp+EAt\nPN+uJjFzSlW9+9Od5cunMjSUoqwsQFPT5EmcO92YpHIqjwGrUAWx3JNpOF7SYpRjO1wmLP6V59RS\nHoI7LoGfr4aOfg2fHAJ3tcrDKFoMvnrijR9gcDCBKAjzkUUG582ADAbt0qNjoUekXdDXopGUYAQE\nyf0w5MCvR+H3/wV/drXyEzz2BygLw4yT1J4aHoWRCEzJ1RBpaZes/U2CA60OmYBJ23aPymaLi+db\n9NUKNNvFiycg7arEEL+qy4IvAPFhcAVmoIxEKK1ikiOx3Cv5oCsEU33gZlBR7Dq4EbQYCN2Hz4WF\nZfCJM1QRrc09MJyCD0yBghNPuN41qqsL+cIXlr3b3ZjUpJG0qBj0yUbgSOmsiTJenknhifbneXcI\nB+GOS6FjCNZtOsjWAyEQBiSHkcF6igpCrLg4xNw6COSitGwEN1s2FzRLflABdr3gZzs1+ixJUtMg\nDFoSomH4xRr48jXgt6Gla+LGJBKDn//S4/77UwwMS665xqauzqClxSXWnabAJ8joGk4W4r0ZvIzO\nnKUGL63M0vNaUsU+a0FIAmYWAio/pabOR0mFQZvnEYnoELdycdIZlfCxz0SVEzNzNeH+K90vAAAg\nAElEQVRMtq2KUfVSMQnDZGoxNC+Gzij8OhcR53pw9Yxjvw/XVTfrNDU2ed5+LDRqCL7b3Xg3eEII\ncZWUckIFscaSrx/3HkUIqC+F+g9UcU3dfSQdgVtxA1aRWg6TO7YiH9uJPP8CRI2aIkQy8MtWQawe\n4i1QVyUoTgo2A1parSx5/TCSe420A+EJzj2lhM//b48HfziEzKrSN11tKf70y8VMqUizV/dw4h6W\nrYOlU2Ar53xLERTPSJHotokMZSHrYBUUYPhskpk0WkIQ6XcRlR4+1yWSTEGhDqMiN2ceUSO/Nwr+\nIHhhcHUgxab745z/wWIivZIHHvEoL5S4to7uF4TGBEmNRNV9ce7S6dlnobUVPve5N/fd7N6dYM2a\nODNn+jjnnPz12HuRyeozQSnFf00IkUZVspvwKtTbFq4ghPAJIdYLIbYKIXYIIf5uzL4vCiFey23/\np7erD5MCfz2+mX9OeO5XKKueRigAIp3E/fXDyD2vkf3No4cP7U1AVwL8ZXDeuVAUgoZSCKI0tmRA\nlcat8+dmJFNgQe7qfece+O3v4LW9x+6GELBtnYPMZhG6AZpOIuZw7gKH888SxGIeyTiUuxmyKY+U\nphNNCeYUwKwqk+YZkmlT4U9utfn6n1ssmSWQPWncVAGxiJ/OliRnztfGXP6kUVnw2VwIsQuZSkin\nINsO2RGGdrby7M920bo/yy//R7Jrs0dlu8tHz4BlNeosPQPwvQfgX++Hrn61beFCuOiiN/d1JJMe\nDz44SE+Pw+OPD9PRkR6/UZ7TEi8nQ3+i23gIIeqFEC8IIXbmxrs7c9tLhBDPCSH25u7DY9r8tRBi\nnxBitxDi8jHbFwshtuX2/ZsQKkNKCGELIR7KbV8nhGgc0+aW3GvsFUK8Lg9+HKSUhVJKTUrpl1KG\ncs8ndEn5ds5M0sDFUsqYEMIEXhJCPAX4UdFhC6SU6ZxES563gn5ELoJlISqrkV0daA2NhzdPDcFV\nU8DSoSEAW4ehoRgWRuFnm2EwBVcug29cqvStaiuUxuKe/XDf/6h66C+th898HJobOYrzzxJs3wSe\nJ0GAZUpKSgQHDkAymaCnJ83AgMX8JZKE52fXjixlIZcbrg4QvMLgd0+mKSoycFIeoYIsYAGeUhcW\nFjv6dQqXlhHdOwpxR5V7zAbBG1HLY46Hqih9yMseIjUywOi+Hnw1NXSnYFdMENkKz/vgU0uU9lYi\nl5sWjQPlUF2tbm8GIUAIgeMovTRNm5RO3Pc8p3BmkgX+XEr5ihCiENgkhHgO+DTweynlXUKIv0Jp\nH35VCDEbuBGlLlID/E4IMUNK6QL/iYq0Woeqy34FSjvxdmBYSjlNCHEjqrjVx4QQJcDfAktQ8/hN\nQojHpTykrHdscoZtOko6S30eUq4c742+bcZESimBQ55SM3eTwB3AXVLKdO64vrerD5MVoesYt34W\nRoah/HVbbWiwvE499jxYUAWbu5Uj+qPzoakEPr0QuoZgJA6BuBJybO9SgVVVFepxV8+xjcnf/Y3J\nmjU+du9KYehw001+Fiww+frXuxkZSWKaAsdJkY4JigvSFLkaboekel6IGZdojJ47iFxdSknWz8KF\nJk88kQAsVUurM8qUq8OMJnWSWY2eiE4q6eJ5KYQUgESS4Q1VHNEBP4M9Cc7/oMCdbnBWsyAchKEE\nPPgKfPkCuP4StUw3/Tiy+yeDz6dx663lbNwYp7nZpqbm3XW8eJ7k6ae76exMcu21NVRVnabJNach\np0JOJVcTqjv3OCqE2AXUoi6oL8oddi/wIqqkx3XAL3Lj40EhxD5gmRCiBQhJKdcCCCHuAz6EMibX\nAd/IneuXwL/nZi2XA88dEuXNGbErgJ8fr79CiM+glrrqgC0o5ZM1wMXjvde31WcihNCBTcA04D+k\nlOuEEDOADwgh/i8qd+UvpJQb3s5+TEaEbUNl1TH3HSTDSi1J0xyT5pIA3VGl9L64Bjbug8fWK+0p\nTYPbPwjTp8LzL0Nrh7rynnqcQbesVPDyi0Vs2x7E75fMnWMQj3u0t6fRdY1wWGN01KWr0+HO621K\nS00cR/L881ECFxr0F8a5+PIiFmDheSZf/WqS7343hudJPvZHBpecm+bu+yIMticxkdghjeFEFunp\nGIYki4syJIdmAy6QprC0hJtv0nhpUAUvAJQEVDpL1oNlc0/tZ9/QYNPQYI9/4DtAZ2eSlSv7sSyN\nF17o46ab8mUhJ0IK2MeEkpLKhBAbxzy/R0p5z7EOzC0/nYmaWVSOKT7YgyoPB8rQrB3TrCO3zck9\nPnL7oTbtAFLKrBBiFCgdu/0YbY7HncBSYK2UcrkQYhZw3NySsbytxiQ3NVsohCgGHhFCzM29ZgnK\n4i0FHhZCNOVmMocRQvwx8McAU6acgkvGPId5kgQeklVaij+ptVkyZjr/zBaoLVHLXENReHE7fPpi\n+Pwt0NOvCk/VHGGjOullkBFm0Ijfb7Ns6es/q0BAY/Zsm5Ur08TjLkJ4lJebdHQIAgEAiWUJFopS\nqghQzaESwoK77qrkrrvUeYaGHP75n7uYXglEJNvbJclkEqRE1wWlpUFGRlzS6Zwh0QRYBpU1hfzq\nwSpKwrBuBOIZNRMbiENFoVLAfz8TDpuEwxajow7NzZMyOulNYSOYIickZTMgpVwy3kE5BZFfAV+W\nUkbEGEE4KaUUQhxX8+odJiWlTAkhEELYUsrXhBAzJ9LwHfkrSSlHhBAvoKZYHcCvc8ZjvRDCA8pQ\nsZ1j29wD3AOwZMmS0+WDfl9Qj8E2MhSjUXCEE1HXlIAvqHsjN9OvrVa3I/GQbOU1UmQIUkATdW/Y\nr2mCf/mXKj7/+R4OHvSYOlXnxhtLuPvuDAMDDtOne9x0UxgLnSnHCcUcGXH4wQ86iUYd+rtSTGso\nYeOaftTlh4brekQiDkVFOsmkg2VrJKxChKnxoZsL+dVjGm4UrvsUrOyD9gEoNOAzZ73FD/I9QDBo\n8qUvTSeRcCktPT1mS+8FJALpnZr4pJzP+FfAA2MqFvYKIaqllN1CiGqUGjtAJ1A/pnldbltn7vGR\n28e26RBCGCiFksHc9ouOaPPiON3tyF38Pwo8J4QYBlon8j7fNmOSS8t3cobEjyr9+48oP8py4IXc\nkpcFDLxd/chzNFdRwCJ8hNHwHbEufN0y+MVLMCKgRUC2ENYPwLIytX80Au3dUByCumrQENS6jawe\nTbDdK0UEYOoRmpDTp/t4+OEGHnvM44orBKGQoKIiQ1WVZNo0g3D4xD/Djo40yaTH/PkFTJ1q84tf\neKhrEcGhgMRk0kHXXWzbxslmsMI6115u09yg0dcNzc2wcCpUBOC/fwEpF7YVwQcvev110hnlhC8u\nVIEHb4ZEwmNw0CMYFITDp0doqd9v4Pe/z6dgpxoJ7ikwJjnfxY+BXVLK747Z9ThwC3BX7v6xMdsf\nFEIcKvkxHVgvpXSFEBEhxNmoZbJPAd8/4lxrgOuB53OznWeAfxgTKXYZ8Ncn6q+U8sO5h9/ITQCK\ngKcn8l7fzl9YNXBvzm+iAQ9LKZ8QQljAT4QQ21Fljm45cokrz9uLgaDuOF/9vAbQ/PAfe2FhMEXG\nTvKTdgPd8NEsTL79C2gBkgZcswiuOQP+0NNATwp6NVg9ADfVwIIjggnDYcEnPqEfHqSvvXbiV8kl\nJSZSSoaHHQ4edLAsGykt1M/HRRkVg1jMoW6qzRnTAvRnPMprDIZG4WMfgiU5v8jqdRAwoKgUtrz6\nujH5/Uq4+z7QLVi6GC5YAq8dgOkNcNaZx5epj0ThyRdhcBim1WfZ8HKMdFoiJaxY4WfZsrzq73sR\nicBzT8nM5Dzgk8A2IcSW3LavoYzIw0KI21FX/jcASCl3CCEeBnaiHIB/mnMXgCqh/lNUROxTuRso\nY/WznLN+CBUNhpRySAjxLeCQT/qbx6uQK4QI5ZbfxgoObcvdB3PnPSHivTCOL1myRG7cuHH8A/Oc\nEjYMwAMdCZxgBwLBaFpnaoFH8WAjP9ljkLQhkQW/AbOnQIUFZ+dqeyRdiLvwtWkn95quC3v3KV2v\nhilH1wrZuTPO+vUROjsFPT02//7vEhhGGRSfUgDAYOkNgtsuMmlpdbjh5nIqynTqxvh42trhx/dB\nNgtXXgY1NbBmM/z93aosb6lPZb43NMKCmTAcgc/coIIQAGIxSWsrlJdDRYXgv/8HDrarnJ0nn0mx\ndGaGilLBjn0wOCD5zj8UMK359JihTBaEEJsm4sc4EWWLFslrV60a97ifBoNv+bVOB4QQT0gprxFC\nHESFQo69fJJSyqbxzpGf++Y5zCguAggYOj1yhGppYQqdWNZkkFGed1K0l0I6KdCikqQpeXVUx2/Y\nzAnqFBpgCsjkgmAcV/lcTlR8ClSY8kO/hFe3Q9b1GBrIcOnFGZZf5KeqSlW0mj27gNmzC3jhhRRf\n/3oMlcgyVVVn9CR4w+A6BKSkvV0ya6bNmXM0Mg48v06F/p53Jkyph7/4EmQyoBvw99+H1VslqQFJ\nTwf0WBAoFXQMCNKDUFUHqVzeYSYj+dGPPPr7wTAkn/+8Tke3oLIMEoksLS1Z9u8zae2wlMNJh6eX\nS+77vuSG647+EFxX4jgePl/e2JxupCTszkyeHCEp5TW5+6lv9hx5Y5KHFFGe4iVexcGiknNCs2go\njtE5GkIXghJ/klhBH/4p7SxMeMSGgwxOKSOjmeBZRE2DDdFSzgkZdKdhrg9+uBVaI1DqhxXNMP0E\n4rc9PbB9BzTUS156OU5bm0cy7rJndz9f/GIFJSWv/0zvuivGmjUZIAOODwxbRW65ISxfL+GMzrnn\nBvjgBwsRQvDSJnjmZWXQXBcuOw8KcwonUsKN18K6jR4duyCbFuCBNyypWibY2gI40JDLlo9GYWBA\n0tio0dICvb2weD78YS2sX+8wMKgRGXQh4KowMc0jnZT86V97nLdMPxzA4HmSVatGWLlymFTKo7bW\n5tpry6mvzy+JnS7YUtAkx88RWvMO9OWdQAix6ET7pZSvjHeOvDGZ5KRJsJr/4mUMQkSQbOEFLcGK\nxnpeS7RQIANEA/t5edQgFi8nnZYEyyMErSivdZ+BSBvYmqSLBFkZ4gPFsLVVDdSNRRDNwH074c5F\nxy9G5XlqsE+lPYaHs4TDJsXFgkxG0tnpHDYmXV1ZXnghC4dCmWUfOEFAI1iYIVSk8/GbS7j66tcj\nANzcuQXKmIxFCFg0T1JugXQFmg6GBV5uuStQBzNr4dBKcDgMM2cKdu+WFBdDYyPMmwdVZZKdO7IU\nFmpEBoAyof5Z5RpEPUbbYf1m+HDOmLz44hDPPjtEXZ2NZWkMDzv8+MedfPGL9ZSWTizJUUrJnj3D\nrFzZQXu7qsq4bFkVS5dWEgpNzB+VzUpWrZL090vOP1+jpmbyXImPjwB3Un0e/5K796Ey5rei/jbz\ngY3AOeOdIG9MJjn9tBJnHx7zsQjikiRCK+ViFoUFafbQQZok5V4T3UaM2dUbsM0EybQfozCNa/so\nLUhS7szkz5tCHBiB1Q405BzwhZaSfN89BGXHSZeqroZpzbBjpyCeMLBMSV1NhlgMQqHXnaD9/R7Z\nLMoKSAuVWhYFNNKOoLoxwNx5Jk/8Nkp7R4ZpTQaz5wTIZk2khAuXvvF1pVQGprocyktgdFQlaloB\nNVtqDMH8eZJIxMO2NXw+wc03awwOQigEPp8abFp6BLXNAfb2ZFXZYVM/JI8HdRrmqKQkNzNzHI9V\nq0aor7cxTfXewmGTeDzN5s1RLrmkdNzvTErJ00+38OKL7YTDPqqrC3AcjxdfbGPDhh4++9l5lJaO\nn+m+aZPkqackhYWwb5/H//pfGoYxqQbQ4yOBU+OAf08gpVwOIIT4NbBISrkt93wur2fXn5C8MZnk\nZEiho9FAN23UItCpJMkAT+FHI4DLFGoI+X1IsQYsh/6eSvasmwVS4GuIk9Zgcc0OIpSja8ceDI0T\n/C91HT5xE+x6TeP8c222bhnB81xWrCh6QyZ5R4fAsgTptESp82govXoXV/PzmlnI7I9KGLIgYWI0\npSm7rY16v8lfz6zFNF/3TcRicO/PoLtL0NQkaJgCvYOQzkgqajTOWAhfuk2yemWCu+/2KCnR+Nzn\nAhQUiKNqwB/shFnTdKbU6dz/rGRXxlWFInUBQrJssc55OUOWTntkMvKwITmE368xOOhM6Ds7eHCU\nlSs7aGwMoeta7jPUqK8P0dub4JFH9vKZz8wf9zyplPrsi4uhr0/ium8+JPp9iTcpDevMQ4YEQEq5\nXQhxxkQa5n86k5wy6olRx2wGaGQHKUL4sEkAcTK0ZZMcpA+XLkqNbkYMHwe2TcP2p/H5UnQdrGFO\n5UGqrEJG5Sh1wVKqC6A9qpa1ImmwDThjnAqHpgnz58H8eRYfWvHG0ToWg/sfhLvvF5Q2BOjeH0W6\nLq/rb+l46MRLNTUaRgAhCd0whFsCXaEIt/+Hn+/0lHHbR9QAsWMndHQoh3xHp8bXvy555hlwXMHs\nOYJbPgkD/S5DQx4NDTqtrS4dHS4zZx79l7lwCTz6vJow/fD/CP7fGoONOyQZF5Y2CH706dcH6UBA\np6TEJBrNUlj4+rmiUZdp0wJHnftYrF3bQzBoHjYkY6mo8HPgQIS+vgQVFSc+3+LFgj17JN3dcN11\nGrY9KQfPY5LyYG/i3e7Fu8KrQoj/Au7PPb8ZeHUiDfPGZJITooyFXM5OVqOhESaMyz5cUnR4GZ72\nZuNk/ZyTWIdreAR0j1AwwkhfMWnXRpgSn53kwQPzWGuU8dl6uGUO/KEddg9Dcxg+WA8TXMY/Js8+\nCy+/CnaJwB62qaoXDHSlcDKOkqAXGoRMtSyRQakcmS5Of5rEMzpGpUl6bZT/vSXARy4poDgEhUG1\nxNXbB34/XHaZ4MorVciw75AfXKqlrdZWF8sSlJcfe3o1e6pH77QEugZLpwX4YbPGnmGBrcPcMqXS\nfAhNE6xYUca993YTjbr4/Rqjo1lqa33MnTsxuZPu7hiFhcf2rQgh0HVBJJIe15gEg4LPfjYfSXYs\nfMD0CaxyrX/be/KOcytKjPfO3POVKLXicckbkzzMYCHl1DHKEBYWMYrYz+/Z45awNz2TO7I/ImyO\nEtd97E83UbushejmuaSSBcxo2o2nucwK+Ai6hfysDf5qJlzTDNecgr5JKdm8OUtLh0SYgupGQTJi\nYoYgkZSMjho4IQPO8EORpnIYBz0YjhH9sQ/IghuAiEaPP8KvHhHcfkuAWbPg+uuhtweWLAY7Z+wM\nQ7k6RkfBNDXuuCNAZ6dLTY1OScmxR5eHHhpm794MngejIw4f+1iYc07gspg+vYAvfKGeDRsiDA05\nXHhhmIULC7Htia3RB4MWkUgan+/Yf1/Pk9h2/q/9ljmpCujvD3K6XHcDT0opd59M2/wvLg8AYcoI\nozRTHCpJEyVBB9OMA1wRf5a0tNhjNLNvsI7heAEFoSHC5f3oVhbpQp8cQRp9ZDLlJF0N8xT5Lteu\nzdDeniY5ZNMrBaYGFyw3mX2myZ6szfodkm0yt7TVB4QAMwXRNGRyJRSRgEQmffz0J0P4FpnsC5hk\ni2F+HfhLVVXJRBr8Fjz5W9j0inLGX/9HGgsWnPjNtLY61NaaZLOS1taJ+T2qqlQ48JvhrLOqeOih\n3RQVHT3di8UyFBXZ1NbmRR3fEhImJhr8/kIIsQL4Z5TM1VQhxEJU5vyK8drmjUmeozDxM4+Pcq54\nhZT4A1lpECJKjduHkcpyZnILRpFHKJzE03XWtX0Az86wU0+w0D9M0Bg/IgkgEoGREZWFfjzH7wsv\npFm0SFBQkGTTDohLwbmzfHziSpt/O6jRnoFt24Cgp64kPZQh0eLKGnjkNgogyerVFpl/G+ZTf1OB\nJuDVEXhiEwT6wNJAy0DvDjh/FmQdePRRmDdXnepIHEf1+6KLgjz7bBQhBNddN8E6x2+B2bNLaWgI\n0dYWpba24LDvJBJJMzCQ4tZb57ylolwDAyoMWp/sK2CT0JigimktIycIKaXcIoSYUCJj3pjkOS7X\na/O4PxshbfmoyAxRIkb4hPcoOxumMayVQEonIgu5oPT3RBKDyKLZLK3SEeIy1IXN8RkchB/8AJJJ\nmDEDbrnl+Jnya9dn+OUjkE27SCn5xj6XFRcZfGqKziv7VG5INqUBHhwEel2QHgid10cEdXIv69K+\nMgEZD92ncWAHrHwNKj1IHpT4syo3JTwgmFkEx6rM2t4B3/1P2HtQ5Zr86S1BvvxlHxkPZFGUFC4+\nJlb7XUqJGE8i4AgsS+eWW2bz3HMqFFgItbRVVubn9tvnMv1EGaLjsHcv/PCHcNVVcPG45ZDex0gm\n5TIXSpx39Ijf5IQ0t/LGJM9xMTWTR3wzWeN8ACl3UDLax46KJr6T+gof6vodCX8BFUXdJIJ+Gkr/\nQLH1IgXGLBJYOMwnQZQiKiig6Khzd3VBIqEG4927lVEJHMNffPbZFt/4+wxOchCVU2LS3V3EN79p\n8YMfBLm1DlrXwvrtwB5NHSIsyIrXsw1V0oe6aZBJgmYI+oZg/V4wsrD/KUn8oEc2A5YfpKtR0Cz4\nxMfeOCtJpeB//S2s2QIZHba1wq5OuO8uAysEXQlJmX/8/57rSh59NMbmzWnmzLG4/vpCTHPiRsXv\nN1mxoplLLpnCyEgaw9AoL/eftGE6kqIimDIFqo5dV23SkHJhb+Td7sW7wg4hxMcBXQgxHfgSsHoi\nDfPGJM8JsfQQF1q1oFeRGX6IVwML6Hy1ieqGbvylEeK+IJFkIWuGl2HbLgu1NIa9jREiaGgIdOZz\nMf4jrtTr69XA1doKCxaoiKpjURQ2cbMZEKOoMGAHZJQtW5SBmlsHF8+Bah1+F4N4C8rhHvejkj3g\n0AK40ARogualfvpjgt9tg8EsODsh2+fhZkAzBU5caXV98h8EzZXwy0dh+06Jz4LzzoXN2wSRNKRT\nygAOxeDJlXDleTCjpAQn4rG3JYNpwvTp5jETAQ8ccNiwIUVjo8mWLWnmzbOZO/fkQ94CAZNAwDzp\ndsejogK+9KVTdrr3LD4Npk/g63gfRnN9Efg6kAYeBJ4BvjWRhnljkufEiALwfQrSv8HyzcLUwphk\n6UhXEuoJUFAS5enRy3mtdTYjO0sIF8X4mxueZG7YIkAREQaJMXyUMSkuhjvvVH6T8vLjL3EF/AKf\nTyMRy80ucnVMpMzS05OlvsrgqoWqqNeUenhtPwy3WQQGS9izXyM9EicWyeJmBbalUz/f5pyvl7Kp\nRc1QSIOTVtHFh5yuQt0RDsGvH4eVL0t+80icWNylotxA2AHiMYErVdsaAc88Da3bAFw8L4rrumQy\nHgsW2Hzyk4Xo+hvfoGEIhBCkUmoWc+T+sSQSHi0tLkJAc7OBZeXzQd52Ju8y1+zczcjdrgNWoGRV\nTkjemOQZH70JAndCzQqu7ruXezZqPJW5hmSXn4OvNjOiFyKlDTUwki3ntsc+xz/88HEuqY8g4ChD\ncgi///gzkkPMmC742I1B7vvvEtxsFDAoLApy3nkwPOxSVWUwtQ6aXBhJwCcvg8W1oGkBNu708Ysn\nHbKJLGfN9Jg3x2D6LJt1EY2vrIfBAfA7kA2BLNZwkx4y7WGVCi6/SlBdDK/tgb7uLKlUlpKwTl+f\nQ32zS5FpEAGMKhBxmD0X6mpg1ao0bW2SSCRKIpFg61aD887zMX36G31IjY0Gl10WYMuWNBde6Kek\nRCeRkGSzgqFhqCiHkQhEYx6PPxpneEjVbamv17nttoJ8guE7weQ0Jg8AfwFs5yRDEPLGJM/E8U+F\nkT/j3FiUVj/sWTedRDyAdExVPqcSKAfSOt/89nIu/ME+ZtNAkDfvENZ1wX9+v4BFC6v4/vd1dM3h\nqittCguhqspgIAX37AJbB6nDk33QXA4lPlgyW2PJbBt443rF4gJYFoC2UkgUQrcJpSUCY0QjlYaq\nWVA+U3DPWohKCIYEug7DQ5JQSHD7rYIXVkE0AlW1sHCamhmBSoTs7PSwrAS2bdHWlqK7O0XTNIt2\n5fenzg8BQ7B8eYDzz/dz//1pvvvdNAODMDhisHELdA1oaKZGSUhSX2Ny68cElgX79mX5zRNZpjSa\nzGg+uu7LewnXVRFxvtNRLHmShgYD/VLK37yZhnljkuekiKYKCWUl7upSkm37VTU6XQe3FKJlSnPe\nhkynn6FUK8X2omNGRJ0Mti34wucL+MxtftavTxGPe5x5po9wWGfLgBqgNVOVpYsloTOhjEnWVati\n5hG/ckODsgBMKwcpYLAKSgJwRrXgpTZwdQgVwNptsH0UIgcM0qYfYWZZsNxk8Xk6i84Ez4WlZ8JA\nP/z0p0qivqbGoqIiQ2+vTiSSprpap26Gjx/sh560+ih0AR+rg9khOHDAY8MGlxdfNGhtlySyAvwa\n2ALXgf4eQSRusmWnZNFcl63bBAMRSXW18tfccTsUF0v275ekUjB1qiAYPLWzlmzW4/e/H8K2NS68\nMPyWnfyghDTvvVeFhi9ZAh/5yPh1b95JUi7sHX63e/Gu8Lc5OZXfo/wmAIypXX9c8sYkz4TJZj22\nbm5hy4YhXM9Ck4W4ngBNgjMIPf1Q2ADhUoxQmmRmM3ilEDg1MaY+n8YFF7wx5MvUYWsMvNxAHU3B\nh7Owfh/89A/geHD9Urh84ettbBOuPRMee0UNYMUFcPtF0BmB1T3QlEuTGR5S4sT7qsBcYOOZNuvT\n8LVn4YPnwgenqsivpib4yldgaAjKyw1aWoLcd58OpPnc54JscCwGMtCQ63rShYc64KszVGjw6tXQ\n1ZW7FPaZEFJ1VTDTkM2SSdls3AwWLpmsYOECA59PVY3cthOiIy4vvSTRNEk4LLjjDuOUGpSOjjTP\nPjuErsO8ecEJy+SfiKeegpXrJFtedbj7J1lWr4Z/+qfAaZPb4tNg+vjCy+9HB/ytwCyUkuqhuZkE\n8sYkz6njqac6aG8d5rJLgmzdLhgchJ5Bj6TfQ4YC4HfhQCtUmNh/OUy5J8BtA2cjpFoAACAASURB\nVCcFThJ8RcfO/svheZL167O0t3vMmqUzb974P09pQiAIyVzg1sxSWBOD9U/AwSHojMK6FphVCw1j\nEs6XNcOUMhhKQmMYAjasa1djOUC/Cy0LYO8IMAccAWRgMAO7ojB3GJ7VYMcQ3DpbJfmFc6t58+cb\nfOc7hUAhnoR7d0LtmIHJr6tCjF0ppfZ7WGZfCNClmi5lMpDohaxE12yqa8o573yBHbLw+Q4dD44j\nWbNG0tgImqbR0uJx8KBk3rxTZ0yqq23OOiuE368TDp+ayLH9rbBxi8vIUJKsp/HDn2S54gqDSy99\n64bqlDB5l7mWSilnvpmGeWOSZ0KMjKRZu7afhoYCNE1QWw1XXQqdQxqP7/DYuzSDFbJIb9JJWfuo\nXaoxx0kTyZYSeumfwEnhlc9i/3Apfdt3EKioYNZ11+EPv+5PeemlLL/9bYaiIsHGjS633cYxVXrH\n4gCzKqBCV2OxpkF7BHpjEHPU/s4o9Iy+0Zjsj8KDHZB2oTEOH29QyY+vxEHPwisSKE5QXDWCTLqk\nuv0kYgU4KYsRQ+cnO2FuKdSEYDQC106Blh7o6IVQAJYvhJoyVQTSp6ulOJ+uDObISIaBOERDEjNt\nc+65Jobh8NpeSCY9pBSqE8IDLMLFDh9eYXDDR02SGdh3UJ23uBgWL4S1L6ucnUBA4nnilPsgbFvj\nhhtObeJJ83QQSKQnMXSJBLq7T7PRe3I64FcLIWZLKXeebMO8MckzIbZtG0YIcZRMR0VY4K0oIWMG\nKOvrxZxjkxp2+cu+nbw8YzmRniEWlxQyx6mk7fmnae0rorD5DEZbW3n1Zz9j2Re/eHgN/sABj7Iy\njaIiQSbj0d7uMfM410h7I3AwDg1BVdUkk3M1dKThvFJoK4F4SmWa1JbAjNxYKJGMuBnu7ZAUGxaV\nPo2WODzdDbtiEAOijkeqtJ+Shi5Er81wdykSDysUx5MSN2XjWjpbR2E4ClsOwh9egt3typjVFcPq\nPfDNm6G0CJaXw2NdoI0m2fnqKAOuRtBx+M49oziZAE62gnmLTSKeh97vMRTV8TQfvmABATvDwkVl\nfPQ6gWnCpz4O+w4odeOmRqX8+/GPazz4oMfgIJx/vqC5+TRyPhyHS5fDE+fprF7tIx53mdUIl132\nFqSlTzWTNzT4bGCLEOIgymeiyrxJmQ8NznNq6OhIEAwe/XPp8Ux2VgVZ2rWRsxesIWjGWLXtAnYM\n6ZxlBuj3xbh7egVLe1qZNnUXxfN1UsERAu3LiO3pI5tMYuZS36dP19i1K0s6rQpgNTYeewF9JAP3\ntqhViNoY3FYHjw9CxIUPFMFlYVh2M/zsZUg6cNNZEA5CO8Nso5t+UvSWQIdjIyMV2E4pO0c0Ui5c\nOR0eHolQ1tBJpDvIcGcJCQK4fRakhfKe6yjPvi5oAwwXtkWh2FZCkb1RiLXAg6vhC1fA0iJ47IkU\n92/OotkhgiMSozvGzsFCEmHBYCZKaGshWsqmskryJ5+C4jKdrp5KZjV7xIbjtLakCS+0sSyYPeuN\nn0dTk87XvqaRzXLMHBQpoW1UzdhcCeUBmFbyRmn8d5q6Grj7XzXWrLORnuTcswXl5aeZETzNJkrv\nEFe82YZ5Y5JnQmiaeF2dZAx96LhRg/ridkbTRWh4VIV7iKdK6GGUl0vLKdu/jWmrHqByU4TRbDE1\nzavZf06WwNDZGGPWZM45x8CyVGjtzJk606Yde7TTBBgCEq4SZ5zqhzvr1L4W9rGRTpqKZvCXV1Uf\nbrOXftbTSgg/dqqIrW0F9MY1igoGgG4KshrJoI/Q/2/vvcPkqO5878+p0Ll7pnt6ctQEZSGBJCSC\nEAiDCMaAbbwEs8b2xeuwXN9N1/a1d/e+e6+fte/62vv6dVqvvQ5re1mwwQaTc7BRAJQ1kkYjTc7T\nYaZzhfP+UQMSICEJJKSB+ujpRz1Vp6pOVXfXt875pXI/qXgev15AFkOUbB1fsoBREJQUr3ODsXHu\n0IpTXN4MQqYIviKEFcfuYhiwaxC6R2GwW3LvN02K0xqluMJkWKFzIois86JeKtCkhQgbVHV7MDMq\n4xWCq9bCOe2QSEi+9S2TgQGVZcuOdDUOfT6eI5gb+tPwm04Yy84UfhRg2k7BsvWtsKrh9HlRVcbh\nA1cL3ra73ymgYEDX+OnuxTuPlLL3rW7rionLcdHUFGTHjgQVFa+diijDQg7pvFSxnPW+RzDyHnpT\nTXxiXh6dNgLKFHN7n6PmYJI+2kh7IuTqR/E1jlK1ogWpOFHtUoJiGqxsnmTl/CCEj559N6LDJ9sk\n20vTmKEEv8WgjhCthDjAPnz42U8n1ThiUsDgZQaoIMhU3sPd+8rw62Os6XiJWl83fpEjbZYxblXR\nV2qkEGlACC+LF2xnfeujBJIFDky38lj/FWwfWYbuLVHM+bBKHucXpEPeBxkTfHknx2QsDFUV8M1/\nh6HdFv2THkr1KrZtQ1IFU4dKsO4XWE1QjHqYMCB0ECIh8GmOl9l4n0plVRmZrCCRgNgxKlYeTl8a\n/vVlCHug5XXxKCUL7t0LORPWHVdO2PcWPhU6jiNX57vQm+st44qJy3GxeHGUBx4YwLLs15SLbVNK\niIxNt2cBP5lqQFMtYi3DzGnT0ChDI4NdH2QqFiCSTDO5oAylSuLx1ZBURrnz+SdJ9O1nYe5Bwj0D\nyF6dbGKapKinfMWFrLj9dtSQj+zkGOGKWvxlzl2xM3CAjbKbcNpgDuXsKy+wT03QRJQCCRppBZxp\nnQHS2EKiobJhMIjPP8IFlffQoXcisfApBTRvCROdHfml1NqtbFPOYshTyy5tIevMZzjH3Ep4YZqG\nuT30ZFsxSjr7di7ExAsqWBEoSaiJgVoCLQpP9ELnkxAOKZj1TlwKUnWmycIaDApnowkVUQHS54xw\ntm6Fxc3w6NNwcDtUVirs3QeDg/DRW+CJJ5y8ZuvXOynwJydNNm0qkMvZzJvnYfFiH7aEu3dDxANl\nRzDIe1RoLoPHD8CiSqg+ReVPnn02w/btBT7+8RjB4EkqcvNO8N61mbxlXDFxOS7CYZ2LL67h8ceH\naG4OvZpLSgj4J28P3xyuxpifIaql+YcFJkktxyU0cDHV7Jx7DRtuMbnwoY0sT23B97JG1lrJI9Y+\nmqvuQqz3sV8vp8KsRikaZB+FrdkA+vd+wObHfkh4USssasCjBLnsuv/KtsYAd1rP4/NlUP0m/Zk8\n19/7DLI5wo5lf8IqfSVhM8bXpzYyoexEVfLEdB2KixmeDjO34QmaxV7MEiws24tPFJAI0naYqJaE\nvM2UEqSbuexSF9IQGeAS7zNUewdpKPXzWP+VbDBWoVYbmEUvFJ3qwdkWOACIBLR4IDniXLu8BmZI\ngZSEMM5NysKJnhRApY30AwlASqZzgp4hKOyA9Dgkk9DWCskUPPIodO4G23ZS98diJt//fhLDkHg8\ngk2bClxzjU39ogCTuTeOSA5HU5w4nZeG4aqOU/O9SSQsxsdNSiV7dokJvFdtJm8ZV0xcjpt162op\nlWyee24Uv1+lstKLqipoJYOPT3eTNxPMvcBHIB7EQBBA52M0k1UbMOsvw1q3Ec+DPybTCoXnO1m9\neIKxpeWMBNtoy/UhTBvpEYQvCxK79GnMtIFZGWX3kiaMJdX4NMGBiQfobehgwK5hygqjYNHiOcgL\na9u5+fn76a7z8e2ySZbI3eRlAHusHNu2KZYnmFR2UGe205/10hSSXBJ6CsW2yeInK4LESDAlIpzt\n3UKf2shQKccUEfZ5OrhSf5jpQoiJUpyzotvYkjvHudmYOKVb/CAs8BiQD0IhDH2NIEOQ7VRm5r6E\nU6PewLG35IElFjRIGBZwcOZmq0D3S6D7IJtzIvn7+qBjLrQ0w55OCAadKa9NmwoYhqS+3on/KC+X\nPPpohotq/GjHUSCrwg+7x0+dmHzgAxGuuCJ8KDbmKNg2PPEUvLzFSYF/7fuPXJLgHcMdmZwwrpi4\nHDeKIrjqqgbOOivKpk3jbN+epFSyiUR01q+vY95Z8+gtGyNPiQXU4Z8pkBVCA8ogtBCpt+JJ5ujP\nDFOoDbG1fCkVdgq9VKIodLAFiq7inR+loTiO9Ym5GBkD84VuphdXMeEPsX2wggm7kYp8itrCIAGj\nQHEyxFOBVWTLNc7VXmBCVlAoCmKVA3i9JpapompJzp7/BDLrwchAd66VusgAQU8OKQRZJUDImqag\n+YgpSaJGgpyMUPD7MC2F4Ww9E2YVFd5JwsoURdPvxAkXnRiVGh3mxuDFNPToICOg1kAhDqRwxKcw\n87+CEwm5RXMm3iXgm1mXd4zlF6+HF16ATBbyRaiIwUUXwcKFTs36cNjJKHy4B5euCywLioZEOQ7L\nuiKcLAGnCkUR+HzH7seevfD4U1BfBzt3QSgI11x96vp1XLgjkxPCFROXE6ahIUhDQ5APfrDlDZUC\nq3gTa25VA+KK2/AMH+T5yXYag4+QIIbPzmOqGh5pISWUSgqJXo0Vawx215dTPp4j02WS3T/JdG+I\nXEeAda1PUl8xSC7gZWvhbLZVLeWi3U9x7dTvGYzWotkFPAETAy+W1NigLWextQu/amBEdYSEF0or\nyU9ezIcjd1EeyDClRMhYOlvyy5iUUa7vvB9N2GxsX86wqOflwfMpmj58EYXkwTlUFlSiPri6A0ZU\nGC9CpmSi+CQ1MYOc7aWUFYgaoEaBIdsRHw8QxwlqSQBxGy1owRSY017CIaiOguKFtWthbAy6+iGR\nhWe3gu6FRc3OJZ03z8OmTXnKyyW6LhgYMGhv91BXrlAaOvZnmSlBdfAtfxVOGtMZ0FTweSESdkoH\nn04KBnQdx/VzOYQrJi5vixNO+tc8D9E8D29nK5u9U6REgHJjjEEZo1zkkCWL4XszBNQM6QM6gd4E\nk2ojBU8aXzpN36rzuaR1I3PYwUveNbycWYFuGdz8u58T/FCeA9FWDDyUedOkjXIwJc+LCwjJFDF1\nCltViMhpCsJDg3ecR2oW8fTYOt4feAAp4b789Ry057C7uIglpU5CcprRTi+/z6+hXRvGEAG27b+K\nuKjnovoMNy7YxNzgHoz8OF3WFGnT4pcjV/O4sZqiNDENFXoU8NhOCFijgCoBA4AC6lUGkeUJ0AVB\nTxrfVklD91yqfXBnF2Rykp4XJbkx0MsFv+8SrFsNc+dI1rZIRod1OjqC9PbmkBKamz0sXBhkYo9F\nZkxghAX6YbVSsmmb8QHwBqCiVjBVFFw//6if1jtGR5szrdXbD0i47gOntz8+FTqOw3PO9eY6hCsm\nLqeF9Wvq+fbPrmTqslE2tzSiFQq07OpH37wFPRRl1TcLhHefRZ3XQ6IBCmub8Tw2SFXEpGNiC2Oh\nOXSNtWNXqDRu2E9wfo7BUBPl09upiqQwbJ2S9FDSPAwadaxUBikpHiIyg4WGKiVFoVIrxnkptIKL\n1GfRpMmEGaek64xqlfy4/lZqCj0UJ226qq7mRTWPXYKIGEP13M8TTS0MK0PInw2R7pIsWTTJostg\n21gzZkhgBlQn0BGgUUJKQFg6hvgBYDF45ueYLkYwp1WSOypQBi2Swsa3QuHg4zB+wMZOCPBC0SPp\n3Q1PTwv21Ut6opK6Cshk/KxZ4+PCCwX/+q+S73xHYlk2/ZMWG2OSVRcqVNRIHv5JkQPbVXwhDY9X\nEKwWvO8GlTlrFXbvzpLJWCxcGDxicCq8tXr1x0ssBnd8BoaGIVoO1dUwMSEZHXVKCFdUnIZYFHea\n64RwxcTltNDeHuSj18zhvvsD5LBRFIFuLKe+9mpuvtFHtKIV1jhfz4XkSDCNtT7PL0d+RWxjggNz\nFzFdEUMiqO8bpPuSdvzd05h5SakJ9JjhBBZqUEJnTFQyny4mRQXlMo2Bhi0UJmQ5UgOvLJEpebE9\nsMtYgomHTDhAcdxiXs0k6xM/4F/Cf0NGixA7K0BLsIfgM5M8ZF5OWS9csnYnk7KJb+38EwrSgzmq\nQRlgCacufVJALeCXcEA49hEvGHgQSBgQMCqwRzQmxwV/jEDtZZB5qUR+r4ouDYrDfmyPExsy0Avv\nmwdVFYKKCsnGjYJtO+CfvimQUsGr2ZSKAn/YYGjnKFPjXkqFKP4Q5MdM1JBGrijpeRTu9qXZvn0S\nVYUNG7x89rMNaNprDea/+90E+/bl+Nzn6gkETk3ofCTivABGRiQ/+IGFaQo0TfLpT6vU1LyDguIa\n4E8YV0xcThvnnhtl4cIwBw7kKBYtKiu9NDX535D/KzDzjzhcFoyz73kPU1YIw9RRMZmMVxAqZpgu\nBIlGpkh6YlQbE4TUKfJ2gHP1DTxprWGZuY2wlmVaCaFKk5QZoUdt432lB3nSvoiuzBwmSzqKnaLO\nHGG52ERD2SQ/2vhZUomPoQZt6hoHWVy2k87MIoLnJRGP+jlrZZLqeIGXUheRtSIkC1GsaRW6FcdG\nUuDV8vWMAjnpGN/TYBU0VN1wkovVAn4gIcnutvEugNoWwVTdCOHKDL0/aEWkvbTVOF5jwoJ0Gvbv\nh849kj++LLAQEMCpQ1yQpJJQETfJ5qoRmkq+pGAaFmqmgJWXbN4M0sywerVOWZlGb69TL6as7LVi\nYtsS05RHzIJwKti7V2JZgqYmQX+/8/c7KibgiskJcsrERAjhA57FKXOnAb+WUv79Yev/CvgGUCml\nPM3mNpfTRSikcdZZR492fz1z9Aasci+KpSBsiWpLupbO5eznN9P5gcUERBGbFMHtOUphg4pWnWrP\nOE1ykN/Jq2i1e6ixxhkV1QyV4ly16+dU37mR0dULMFMBwjUKyyrHiY4PEh4dpm6tJFAumPT4sVRB\nz3QHIxsr+MIF/4cDe5qpbJ/iIk8feycXs+PgeYTKs5QMjzOlpQJTwG6cUYoC9Aknv32NhAkNuUXF\nWgQETSjqoDujFqso6OwqUF8viOrlGBNelrXpNPmLtDeaXHVZkF/fKXn5ZScN/b4eiSUViACvZJGp\nUGGfh2Qqgi+kE4uBImwmJkwM08Yf0VCEydiYl0RiimTSoLXVTzj8xpHHddfFse03r1V/MqmuFpRK\nkslJSank/P1OUihB18A7eshZz6kcmRSBdVLKjBBCB54XQjwkpdwghGgELgf6TuHxXd6FhLRlNNX8\nhtbxQbYuTjOeqiYYs3lk9fs5f+/zdJ/dxsW9z5FP+wmO2kxsnqLxag8LQzu5gkdQMyXsySw9O/2E\njALKcym23i2p3dRDpH4uit8mvMqieZVCqs7PC7nlNDXsoZRSCDRlyBkBpifK+Nq+L/CN+f+NjvRd\nvJRdybP5i8lnQwRjeaQtoFLAGM4vrIjjuVUApkBbU8Ts8sMSiTqZZ37tTjp/sxS7BIw7bQiCMeBj\nKJ1neUeWRdFaqm+EL66DUkklGhX84RmFdeskzz1nkysqjnh5cKZoCjhTagFQcxrRmEo4BF6fIDtd\nwm9DzG8jLUF9vY/PfKaMXM6itfWNI0NwHC3eycJV8+bBDTcI9u+XtLeLo2aPPlX4NOioPHY71wB/\niFMmJlJKifMTAmewr+N8zQG+Bfx34Hen6vgu70581BFceQuXPvQLXsj0UdD9pCZi5P1B7g404x+c\nYu5UN23VByikvewrn4/1xH7q2gqEa02kZZI8AL6BNJ7+PHufk2QzoE0LvL0TRK/2M+9CL3sLC/BS\n4OLyLfzRvp6xkSit/RnWxp/FV5fF1gVBSgQqskxQwXR3GbKoYkoFoVvIgOpMaUVwotxNIAEiYEPA\nyVSpeYqEGqeJNqWxJzQwhbPNK7+UaSjlfEzUHuRH95ZRXqYwJ63zsZt0LAuyWcHwiGTDVhtDFc5G\neSf5JD7AEAS8Ja65MkhbG/zyPwWaLlm21MNAX55CziIUEtx6q4+mpjMo/TuOeK1YIVix4jR2wp3m\nOiFOaX4DIYQqhNiK84z2mJRyoxDiWmBQSrntVB7b5d1LWXAdq6//n/xZIck8ZR9NZQcIadME1ALl\nMfhD/d+RfHopnikv7ZM7CaVGMXalGf6XBN3/kKZ0Txp9e46dD0jGDoAtIV/ygWkT+XCcrvw8tujn\n0MkCpox6lni24rMk/bk6Hk5dynPmhYwcbKJnfzubs6t4rrAGaQqwJLlcEH8w59zMFRz7RRWOvWQm\nWFFUWaBI5DQE66YJVmdRA6YjIDOagAlMAqO2k78rq5N6QfLZP0ty2yfybN5sUVNj88ijFsWYAqrt\nOBxYEgYkjEPchr/8XJBv/3OQv/2yxhf+AuIRm0JOobzcz7Jlfr72tRBXX31mCMme/fD0H3nH7DJv\nyiuVFo/1Og6EEP8mhBgTQuw8bFlMCPGYEKJr5v/oYeu+JITYL4TYK4RYf9jy5UKIHTPrvi1mXOuE\nEF4hxH/OLN8ohGg5bJuPzRyjSwjxsbd8PY6DU2qAl1JawDIhRDlwrxDiLOB/4ExxvSlCiE8BnwJo\namo6ld10mYX41CZuWPRJ1kmLZ6xpeskSQ2GVGqHVA3+YOotc9wr0/U8yvPcgfRMJ8ikFf0RB1RXM\nrEUhXUSoTjp288olJIdHqS4oBJUsZUaKcjON9JrUeQb5eM2/8e/2R4jWpVi6r5PPiO+iFnMUrSA+\nK4fwSvCZlMY8aG0CRTWwGzSYEHAOznxIHmRGQUEi4gbWiJdcfRCmbebfspVdP1npJMwycETFBrXG\nYP+GNthngaVSsn1s2ig5sN/k7OUKQzEJWRUU26nM6BEQsAnU5WmtsxkZi1AsKvh8Tm34W25ROHgQ\n/H5obhZ4vW9uixgZgY0bHffclSvftOryCXPgwBTxuI9IxMmUsHUndB2EC1aCfnKqA789Tt7I5KfA\nd4CfH7bsi8ATUsqvCSG+OPP3F4QQC4EbgUVAHfC4EGLuzL30+8DtwEbgQZzaIw8BnwSSUsp2IcSN\nwNeBPxFCxIC/B1bgyONLQoj7pJTJk3Zmh/GOeHNJKVNCiKeAa4E5wLYZUW0AXhZCnCulHHndNj8E\nfgiwYsWKM+FZxeUMpEKofFArBw7LaBiAxgsvpOeppyibezmtkU6MRA/Z9DiJkQlUTaA3erACEXq3\n9uOdyDDhD2MtUBn5zgHqv9zEPHM3JY+g+7kc8dr9tLcPcqC8jud638fNylfwDE7BCzm0KwUr2jew\ny7+MSHSKqYkyzGkdETYRVQKpKpBU4DycSd+dktLzfrzr8hgDFsmXqthrLWT++l2ID9vsvm8Z9pDP\nGZkEwCrq8LLl3NikhJhGvhwyRcGGwDS0lkHehqyAaUG8fYTYhRMYTQqTAR/PTiX4xJ1xbpgb4ppV\nGpGwQjoNtbVOSpY3wzThJz+BYhHyeSeocMmSk/fZPf/8KEuXxli6tAKAD14FJeMMEZKT6BospXz2\n8NHCDNcCF8+8/xnwNPCFmeV3SimLwEEhxH7gXCFEDxCRUm4AEEL8HLgOR0yuBf7nzL5+DXxnZtSy\nHmdGKDGzzWM4AvQfJ+fMXsup9OaqBIwZIfEDlwFfl1JWHdamB1jhenO5nGxa3/c+hBD0Pf880teM\nrKgk0uJh1ZfWEmquI1Pwg+1l+33/wYs/+Ce0uzczdts6cuUqo389iKqMM9HeQjgYYP59L9D7lfMY\nijdg2wpSVZAeEKaCsr/I6nNe5J5QEqVFwTYVsrof29JQJgz0oIQgiIKFptvkzguAJSikA6hBGy1k\n0be3g8TOGirjozSWDzEyWUNxKgi9FkzazkgFICJgtaC4RGDrgoOGjuYrYaoCJnT0xhwTepxc0k9s\nySSpu8rJ3BdjX1rhcVvyOa/NmtUKleXw/mvg1o+++TW0LKe2fFUVDA1BNntin8HBg9DVBRdfzBEL\nd918c9tr4lk8niO3Ox0UStDVc1xN40KIFw/7+4czD8LHolpKOTzzfgSonnlfD2w4rN3AzDJj5v3r\nl7+yTT+AlNIUQqSBisOXH2Gbk86pHJnUAj8TQqg4s8d3SSl/fwqP5+LyKoqq0nb55TRdeCG5iQlU\nj4dgdfWrEdxlM+2alp5PZ6SSfOceYv/voxTP72B08ULskkVwUzf+3QfJN5YIjJbQOkxMv+DH2Y/z\n5Y6vQlxB9wsyqTCXLnyYzb0XEF0+yehINRkRQdZBIJTFDkjqhofp7F6MVIRT0yQosHwqBIHFkC0F\nyPc2YfzRB2NOxDsBFdKWE/gogTiIFo36dghHBT2bBJF4ikRnBVTYGJZG1ZIuGq5LkPxJJZn7ojCp\nOKogwSzCU0/YLFksuOOOY7tmeb1w7bXw4IPQ0QFnHbMK+Gt55hl48UUnVX5LyxvXvz4w8kzCp0NH\n7bHbbYIJKeXbchOQUkohxKyffTmV3lzbgbOP0ablVB3fxQVADwQoexOb29TAAM0rLyP94h6MZIay\nR7dgPb4Fw3TW2wokihDpS1Nn99Mbn8Pj5iWM5ypZrW8mSZiH1Uu4QjzI2vmPcTDdSnV8GCElialy\nRrJ1TI1UcGDffCzN6wQn6sKZxtIBj4W3LoevNkcp4cEQfse9N4jT5joP1EnHHmIpiDAEyxREOUQb\nJaGJEab6yjANhZqWLqLLCtQmRtn6zCpIqY6QvGoolmDB7k7Jo48KLrzw2DfzFSt4yx5V11zj2Fne\nzOS5Z5/F7k4Lv09wyVrtuDIMvyOc+gj4USFErZRyWAhRi+OkBDAINB7WrmFm2eDM+9cvP3ybASHE\nTIpuJmeWX/y6bZ4+uadxiDP30cDF5R1A1XX88UoCAQWBU+zL5wVVdYzNmuZks53eZnDj9C+pC/dR\nWTfGntr5/KjhNu5vvoJwNE2QIrqw2CaXEqyfIqgk6dq5mPTeOHJCUDR9js1DV5xHuAag2kKPl7Am\ndbI7y8jnAoB9yBPsldonceF0Ii7w1UmamwzCPpuFC0IoCtTOHUDTLNQGQY2cpKezFXtKA1sS1DP4\nPVmIGdDi3CGtkuSee21SqZNzDU1TMjVlYVmvfbiuqIBFi45utP/6/y3ygY8Y3Hq75IO32lx5fZ50\n+gxKiHWSvLmOwn3AK95VH+NQmMR9wI0zHlpzgA5g08yU2JQQYvWMPeRP8M9j6gAAFhtJREFUX7fN\nK/v6MPDkTGjGI8DlQojojLfY5TPLTgluOhWX9zRVixcz/PLLNF9xDVO/+R2ZElT5QQ07D/Vl5aB7\nBK2LhvjZT9bwjT/9S+6LfJC95fPIaj6ydgBtWrKgtJ093rOwbQ29N82BDUvwa1my0RBmSsMwNKSl\nQFA4o46gpKnQS83oKEWfh4OxFjLeMHZcOs+oPpyn40FgPs4MuE+yeNEE/V6DlK3hlZV85MI67n98\nhFLlGL5myBT8pAploEHElwJDokiJEtCxwwIUC2xBT4/k2982ufRShWXLBMHgiY8IpJRs2FDgiSdy\nFAqSYFBw5ZVBli07Qp3g17HpRZN//p7N+JCNZUgQgj9u8vCjnxb5q8/7T7gvJ52TODIRQvwHzggh\nLoQYwPGw+hpwlxDik0Av8BEAKeUuIcRdOHkTTOBzM55cAJ/F8Qzz4xjeH5pZ/mPg32eM9QkcbzCk\nlAkhxP8CNs+0+4dXjPGnAldMXN7TxNrbCdXWonm9LL4qx77Hn2Q8bREOQNAPGXz89K//hYszT3B5\n60Z+9dJV6Jep+O082XyQufoBlkS3MLfrIN9T/py2wW3sf3YRE8lKhj31VFwwjpnT0BQTQ+ig2eAT\ntCQPsHLoRfIeH3rCpH50iGcbLyQ3N4RdNCGhQ8VMWd/tQBXMW2bQJmBiVxm6bXFWuckXLp7mQ3W/\n5fsvLee55BwmvNUERvMoc0qIMYkwLAqWF3sAGDRBCkBQNGE8IXjwQckf/yi5/XaF8vITE5Rt24r8\n9rcZGhp0qqoE+bzNnXdmCIUU2tvf3JL+0JMWk+M2iqeI5oViVkeWTJ551uavPv9WP82TzEkaJEkp\nbzrKqkuP0v6rwFePsPxFYPERlheAG46yr38D/u24O/s2cMXE5T2Nomks+9jH2HnnnZiLVrB4zkKm\n9++lkBkmcGEZdf/tcq6NLsSXu5HMT/+cQPhJDv6hDilfxl+TJz3Hj9nl4QntYgaG2xjo76B9aBup\nUjXSI6jQxinVeylOeMhlywkmU6QCMRYN7yBtCUyfF9sTJjqdpNYeoW9+E6aqExlJUCoJCqoPEdGZ\n12azpNxH//4Q1bV5/FJlqlfjK3e1EfV9nvNaDPw+wUElTf/iAt49JdKVEYLFaQp5nzN/JwEECEGs\nFopS0NwMAwOSBx6wOftslaYmCIWO79o9/XSe6mrt1VgVv1+hvFzyhz/k31RMbFsyPpLFsnUwdSc5\nJSBUJ/38mUChAF37TncvZheumLi85/FGIpxz++1MDw6SGRlBKAplTU0E4vFDjcJQ8an/j9A9vyTw\n5G8ZCAySXVFBuTB5cuoStqgruNS6l8CGNJvN9Uz7y6iZP0DWG6bO6KMp3MeB4rkUxrJYPo05C7Yg\nZJHeXdXkG9qIBwaJhiqYqK8gH/Dj849S3jWF3hRA1laiyTImvQrj0k+p6EUtCPozKi3VEAqUs6cb\nbr0AhsbivLAdvvVluPtheOzBMvZvtTGKwgmulxCOWZy7WiM1k+woEoEf/1iwahVUVsIddxxfrEc6\nbVNZ+VqvMJ9PkEy++SP9li1ZFDNNTU2EkUEd25YIDSpiKrfdembcknwe6DiOWGk3N9chzoxPzsXl\nNDMhdjHZsJuqhmXEmHvENorXS+VNn6Dypk8wQY5Higd5bOBllNRern/+H/D+IceP1n2TSHycBtHP\nWG0dWsHg3P3P4W3zUFc+xPayc6hu3sK0EWGquozQ1dCq7cQriqzMbSI7GaFfbyK9xUtDxxiTqQb8\n3hztlX4qmmHSVEhOKpQMCAchGgH/zGxYWQC81dAdhQVt8M9fgswdCn/5pSLbe3R8QjLYYxMqUxif\nkAizRE+1gsejYlmS5mbo7XXiScqPY4SwYIGHzs4StbWHbiMTExYXXfTmNo+dOwskEpLbP1rk8adK\nDI15iMcEf/slnbUXnSGBJm49kxPGFROX9zw2FqO8hIcgo2wmVmzAfPJOrNQQ+nk3oLS8MWVtnAC3\neBdxc9tChloNdgX+lnuHJSt3/AFZ56fn7AW0pQ5w7uQLJONxTGwqQiN8uCHGjniILeXn4vNnqNWH\nOSjnMGZVkTIrqNLGGA3VUlgeJ99diW2oKIRojYdZ3wijwzAWhbIyaByGXMKJSgsHoLoc/NXQ0QxP\nZ6E/CRcFFf7rpz38j29YTKQFqAqKqtDXZ+GzCtzdL1m7Nsjq1Rp9fbBqlaS3N8fevTbLl4fQtKPb\nUdatC7B/f4m+PoNAQCGbtYnFFM47783FZN8+ld27TRSlwEUXKNx0U4QFCwJveqzTwhnkWDYbcMXE\n5T2PgkqUNpLsJ85iSvd9g8T2XyBG84i++4h/9lGU8JEf1QWCeuGh/qzL6Zv/Ip0Hi/jLppm7f5Cw\nNk1vXRO+aAklZWNWV/NyMMPTapwObSshLYOBjqLYlNlp8EvyAZ0lgc1UVUwi5+hk8jF8psLFkSom\nCxC1oDkMZQI8EVhhgCZgRTv4Z9KjdBbhkSzEVPh5Cr64QOcX39Z4cZdkxx544gGLkQMmUhWMJwUP\nPGBy3XU2X/ualwMHsnz+89solSz++q8X8MEPHj0PezyucscdUbZuLTIyYtLYqHHWWV4CgTePOLj+\n+iCxmONFNm+el4aGMyPR5GtwRyYnjCsmLi5AHRdQw0pUS2Uq+3WUnjya5aGQmsRKHkAJn/PmO/Be\ngFyqUF0xTm5kP1MhD5O1FQQ8BUpFnZQ/Trn3HJ4MjVEt+8ibAcamq7BHoTThIzMRQrUsSjkfRsbH\ngJ2kOdpLRUuBinl9PDxewps8h3PLNF6pT9Wfg3MWQNXrPHFNwCzAwT1gh8GOQyQsWLdasG41VHhg\nYkhjYiJLVZWNxxNkYEBjfFySTJbI5QyklIyPF17d54EDeUZGSrS0+KirO3TzD4cV1qw5MVfeFSs8\nrFhxhkxnHYVC0TXAnyiumLi44IwwVLyggt68iqlzOrG7p7GXtaHUHNmG8tod+AiGL2CyaojqmjD6\n4F4SeRi2qqEUYW7ledytG1SpfeRzPgxDJVpMsGfzYrLjIXJVfupqR2moHGIqX0bfeDNdfQuoSI1z\ntm+Uprp+ptJjlBvVVHpUSjNTMIEj/IKVIRh7BDq7YWkdlJZAIHxo/XmrFTxeH4nxAt/9bopVq3TW\nrvURjdoMDmrcfnsr09MGN91Ug5SwfXuGO+8cRQjI5WzuuKOetrbAybnwZyg+L3S0H7vdpo2nvi+z\nBVdMXFxeh/+CP6eioQPDGCPQfCWq5/h8ZRsiHjonWoiFWqhou4xAIkG9lPjryukVKqa2G2FJ9JBB\nrTVEcjDK0nlb6Wts4MrYo/itHHdbN6BX5Lmg4ykqGWN6LErf2BxUXz1Lq6YZH4+SmPLTnYFlZZAu\nQeiwX/GubvjVA1BZggMTEKqC4OtGLm2tMKdF0tcXoKVF45xzfFRVaYyMGAwOmtx6awM+n4JhwI9+\nBI8+CvG4h2Ryiv7+PN/4hslXv9pOLHYmpPd1OVNwxcTF5fV4fPg7ruVE47DPqoFHup1iW4qmEax6\nNUE2qUKJsG+Cou2lRhsiWMgwr34PrZH9dA8vIFZM8AvjZq5sfJAL/C8QJM8+s43vTt7BpL+KoaSH\nroLKfJ9Bja0yN6ThURR+ehD++wInSwvAnoOOMb61HuoqQdeO7OZ78KDB97+f5E//tIyqKuc2UFOj\nc/PNsVfbJJPQ3Q2mKbjzziLFYorqao0lSyQHD+bfA2LiWuBPBDc3l4vLSaLcD4sqYTL3xnU+ReBV\nS9i2QFUN2vwHWF29CSPrZZ62n+eT57OkcjtXeJ5AlxZJK8JdAzeRMmMUvR5yukJaNdlcMHkgVWBP\npJfeqn1sFxN8bxv0zuTZqquE6awz55+adtyF93Y5tUkOp75e44YbwrS2vtF20dkFm7dCLAbLltl0\ndxdRlCK6rjA2ZlIoQFXVsW0e09M2/f0mtj0bE+K+YoE/1svlFdyRiYvLSeT8JtgxClG/UzTxFVo1\njR2Wn2BgijxB6kODhO1p0vZcglYC06fRrPchsMkQorM4nz35BRQsP3nhR/cUkQJ0TaJ5Lcb1KcZN\njXx8iOcPqtz5VBm/uVxh5SJIZWDXfuhohK4d0LkV1p4PV68/1B+fT2HVqjfaPVJp+Pndjvjc++s8\nTz5RpLvXJp+vxKPG8HpSfOhDVTQ2vnn+rUJB8t3vZkgmba6+2s9FF52BHlvHxBWLE8EVExeXk0hL\nFC5rh0f3Q3M5qDOC4lME7fkm9vt2k7HDGJqGtASeoImatmgK9jFthgiQQ0rJhBUnbwUoCS+eYJFw\nZRokSEPBMENEfTAdKiDSPmTIxoiZ7Eh4WN8IV17gvIZHYO82J+Fwaur4+h8MQGsTjIzY/P6BEr1p\nlZxfh6JC0RQUCyF+/ZxGvw03XwrlRzEnGYYkk5HYNmdWJuDjpFCw6eoqne5uzCpcMXFxOclc3AJF\nE57ugfoI+GZ+ZcvMemzZQ1emwItiBdWRMWLBBHgEc/NdbDWWsLW4hHN82+hRm7HQsKRAVy0kAsvQ\n0DUDnwqVopIsRUqKH29Ywe83ebnMps3SaFedA9bWwIeucURlzfnH13ddh9s/CoYBG54WbJrUIIlz\np9Al5HSeusekcR5s3gOXHaXWSTiscNttAYaHLZYvP7PdgI+EzwcdHccemWxy86m8imszcXE5ySgK\nXDkXPrwQpovQk4JEHhTLw9LkCpaV+ihkfDw/sYbpYojdFe3ElBRLJ3ewJ9PB76fXk5BRgmoabIVi\nxo+R04gGR4mFxqho6mVYzzA8WkZFNkTcCLC6XMXrhX83Sgzbh0YCK86Ga66E8rKj9zeRKHL33X1M\nTxuvLtN1hf/91QAUFVBmkkTqAhRJ3oKpPJQdw8mtvV1nzRrfMYMYz0wkp7qgybsNd2Ti4nKKWNkA\nZ9fC/gQ81+uIiiLCJJRmPKluci1++qxGolqSHq2acGqEBd4s+ZKfPd5KOhZ2sme/xnQ+Qo0+xPzI\nLlqVg+jS4uHhDxLS8ywQAfoUwb60why/RVrAkG1RO1ORKpGwCYUEHs/RU5WUSjaTkyVM85ChPJOx\n2b5LQWQtpDTAEGDbUO8l2KTyi3802PY7wT13qlRUvL00KAMDJt3dBtGowuLFHhTlTEmr4tpMTgRX\nTFxcTiGaCvMrnVeuBEULHir1QPIeHvtNIyKfJieLWOUag7ddzEHvXHIygE6JGnuE5Ys2M56NUx0a\nx6sWMYRO2i6nOtRDwDuAaraiZFuw8JAzJZYOAaFg29DXZ/HDH+ZobVX5L//l6EGGNTV+Pv3pQxF6\nDz1a4P4Hivz+ORs5LwgjAWcOY9qGySK9KR9iSvDssGT5KottWzTKwkfd/VEpFGxeeCHLb3+bIxLx\nUCpJLrzQ4pprzoSASDefyoniiomLyztEwAMBoNKIMT5dYu+NH0LsLlGXGqBj3TBlepazxUvYQsWw\nVOJikjp1iERZBbaq0MkCLFvFVgRGWKdYVDGNFMXJNBNGnNGYZJWqoo4qfPUZSKYE0wWV+UcYlZRK\nNoODJWprPfh8h6ah0lM2//hdSQkfQyEbRjRokNAjIQ9YXtAkUgNMGBqE4QlOWExGR0t86lN72bYt\nT01NiPXr48yZ42PjxiJXXuk/A5I+umJyorhi4uLyDrMkvIqfJe8mGp6kLzif//3DL/K9PX9D7S0J\niIGFim6WqPWOMWA1UOFJMG5XoikmPlEEoKSmSNgtjEwLlLzCgj6VyIRKvFXwnWcEzXGI1isMePxc\n96E39uG++xJs3DjNggV+PvGJmleX6xqUijCWVrB9EhQJL0sozEyBSeGYCvwCsjBvvk1rw4lfg1/9\napz+/hKKIkkkDHp785SVefH7Bap67O1PNY43V/Z0d2NW4YqJi8s7TFzUUbFqEYs3P8viF7axr7+R\nXwdvJfLrLPMW7MBTKTFKOmadl/bafWiWhaUITKGRlGUEFUmlrMYuCBbkY1Rl4yRVhYe2QSILL/VD\n1oTlTaBqgmxWsq/TIB5XaGlx7tTFoo1tO/Eg99yTw+MRvP/9fgIBhc/dpvCru0v4Sza7x1Qo4Bjh\nwamuVXLq2J+9Hjb+Vj+uQlqvR9cF0aiHSERndNTGNBUyGZtbbgkixOkelThFvjo6ju044HpzHcIV\nExeXdxgNnXPFJQwtuRtxV5opEYUmHZqKbH75fEK105TiPs6q2gYGFKSPjB5mQDaSNUPM9VUTtnSU\nHXHmCMdNa3PCSfpYG4FLlsHTWyHmg6tXwP2/LdLdbaNpcMcdPmpqFK6/voKlS4M0NXn52c/y+HyH\nbuC33ujluqt1hsckKy+QTHFYBLsQIMCjwt/9hfqWhATgwx+Ok05b9PQU+Iu/iHLBBeWEw+qrJYBP\nP+4014niiomLy2lgsecigtlR/l6M8xHfd0AVnLfmjzxSvJrMWAjPhMFjyfcxZ103fn+OiXAlUTOJ\nzx+jHJ16PYBiRhjKQCzouOoGfBD2O68LlsNfXQJVZbDhQUl5OaRSkMs5whAIqCxeHATgs599o49v\nOKwQDsOa8y0efkBiWThGeFsCgvZ2wZVr3vr519R4+PKXG9/6Dk45rpicKLPRAdzFZdajIGiLfoQP\nX3wbf6PejygWSPbHWXrliwSXT2HWCvplA/+y9c/YnF3BtCgjqTSzUsZpI8xiLchtawXz6qBkwmXz\nYEEDDKSgLwmXzXeEBODGGz1UVCisW6fR0vLGn7yqClT1yCOCb/yjwrz5Ak2TjpBIQbRG8PPvK3hn\nY4aUE8LNzXUiuCMTF5fTyCXrAnh/14S6d5pt/3kua7/0IIHVeXIrw/jVDD6KTORrEflmFI+N4vVR\ngcpqgoSDcNOFh/Y1kYHhKQh7oaXi0PKWFpVPfeqtWbXnzxM89HuVH/9EsmW7ZNFihU9/EprP5EHF\nSaBQsOjqOs4cNC6AKyYuLqeVsjLB//2Kzkf+1UPK8vLCP1/KvBt3EI0nsUyVZCFOvMxLrTBZmW7g\n4+EYQRQU3jiSiIec18mmqVHw//zdmWLLeGdwDPDHTgPjGuAP4YqJi8tpZk2L4DPrw/x8Q5bhaT/b\n/3MF5W0pypqTtFUL2rMR4oU4N7f6eQuxgS5vGXca60RwxcTF5TTj0+Ara+CiOUHu2Qk94xANBpkb\nqScMLPPCuQ1QPvvyJc5iXAP8ieKKiYvLGUBAhyvmOC+XMwFXTE4UV0xcXFxcjogrJieCKyYuLi4u\nr6NQMOnqmjjd3ZhVuGLi4uLi8jp8PoWOjuAx27neXIdwxcTFxcXlDbg2kxPFFRMXFxeXI+JWUjwR\nhJTy2K1OM0KIcaD3JO+2Ceg7yft8J5nt/YfZfw6zvf8w+8/hSP1vllJWvp2dCiEeBuLH0XRCSnnF\n2znWu4VZISanAiHE+Nv9wp1OZnv/Yfafw2zvP8z+c5jt/X838V5O9Jg63R14m8z2/sPsP4fZ3n+Y\n/ecw2/v/ruG9LCbp092Bt8ls7z/M/nOY7f2H2X8Os73/7xrey2Lyw9PdgbfJbO8/zP5zmO39h9l/\nDrO9/+8a3rM2ExcXFxeXk8d7eWTi4uLi4nKScMXExcXFxeVt44qJi4uLi8vbxhUTFxcXF5e3jSsm\nLi4uLi5vm/8frS7hQm6h/xMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26745515f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\", x=\"longitude\", y =\"latitude\", alpha=0.4, s=housing[\"population\"]/100, label=\"population\", c=\"median_house_value\", cmap=plt.get_cmap(\"jet\"),colorbar=True)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr_matrix = housing.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "median_income         0.688075\n",
       "total_rooms           0.134153\n",
       "housing_median_age    0.105623\n",
       "households            0.065843\n",
       "total_bedrooms        0.049686\n",
       "population           -0.024650\n",
       "longitude            -0.045967\n",
       "latitude             -0.144160\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "population            1.000000\n",
       "households            0.907222\n",
       "total_bedrooms        0.877747\n",
       "total_rooms           0.857126\n",
       "longitude             0.099773\n",
       "median_income         0.004834\n",
       "median_house_value   -0.024650\n",
       "latitude             -0.108785\n",
       "housing_median_age   -0.296244\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"population\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2673e3575c0>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J4qXt3qUP53xRURrY7SYMOxHOw6u7+aUPx3t56cOJopiX+xGLoqJx0EhLL4mCb0ZLRh3P\n8aygaqBsCedaos0UZDFsReK5Ppy1Tb9xwUl9qRWJoJlIggRuHeQJ9HJNImTwMTX2mT6cCNASBj1N\nL45orGNW1KQCojowzto8ZCgKyHWQ1I0NPhsZgW3CPNY9JNi+PXdu3k8g11rJ2uT36PG1ZrVmaEmr\n1XQzgRISIS21h1QIlGj3+BF9OJKHfpySZ/twhKzRz/HhdNLn+3CUjvjsVu/Sh1MiL304SsCrH8GH\no4BBFrR9ax3zwj3dh6PDd5Glkl4akyiYFYZBFiOkp5/GdLP4MR/OsBtxZ7yiaPzjPpx+xktbj/tw\nDgYZwzzmZF5Stj6c1672HvPhfG6vd+nD6WUaJSJ2uim73eDDSSJ16cPJ44hfurF96cN57aD70Idj\nPb/88valDyeJI37z1jYXq4ZcQpzFXNvKKWpHogVCa7qxxjrYHwRTupTikgYpII0Ug0xTGs+rex1i\npShqe+nDWdOW/UHKX/nKl/i7/+z9PpyPK3DgY/XhCCE6gPTez9uf/0/gPwN+Ezh/JGhgy3v/Hwgh\nvgT8fR4GDfxfwGefETTw33rv/5EQ4m8AX34kaOBf9t7/1TZo4FvA19rtfJsQNDB+1n5/3KAB+NmM\nUpuXDXfHS4oqqP6vbPXo5BFVaXjreEqiJXkSYYyj8Z4bWx1iKTGEiJ53z+dMljVpLLnSyTiel9w9\nX9HPI7Y7MVoFE95eJ+bPjqbMlzW3x0tG3YT5yrDXTyhry1/67A47/fx9UWpF2TCrg1/JOci1xgnP\n/fOC3UHCjWHGpLQI6bg17DCvG86XhlEeIZEkWrKoG3qpDhKqEFgf7O0P5iX9LCYViuOiphtHvLLb\nYbFquDcpGOUxXjqqxvPO4ZTpomBaeTqJorKCXqpwzvOVW0NirejGitvjglwrjqdLhv0uUli285Sj\n+YpBGnN4PkdIybwwQeCoLXv9FAl0Oop+lnAwiJksDUpLjqcFD86XVAZu7HfoxwkWx343JRJttCCC\nvX5MVVvuzwreO5lz/2JJN42pXDCf5KkkkoqyMkSRZjvX5Dri2m5GgqKy5mc+Sm2+MDjl2UpSAIbd\nmETKp0apNd5z52zFvDEMkhiDAwOdTPOZrS4ykj+TUWq2cRTOEotgAfk0Ran9TAQNCCFeAf5h+6sG\n/r73/neEENvA/wrcAG4TwqLH7Zj/BPhrBMHm3/fe/257/A0ehkX/LvDvtmHRKfD3gF8ExsBve+/f\nacf8NeA/btf/He/9//i8/f5FGM7PGtbBBpEKviNj3aXD0HrP3fGKTvJQwV1Whpe2ciIl3ze2Npbb\n5yteGmacLmu8c0xLw7RosM6zP0j5zF6XWdHwp3cnLCvLwTBFSUFRW750bcCtrc5jH0rZWG6fLzmZ\nVaxqw/mi5sZ2Rhpp+pnmaFKy02ptB8OMNFKX+3LOcVE01E3wdX3txog80Zdz3j5d8KOzJXu9lKvD\nlJ1ewvG0YrcXc7aoudJPyGON856itpwtK75/f0rRWH50smCYR2SR4hdvDOnnCQe9NJjYvEcIOJ6W\nWO9QQnKln1KbQJTKxnK+qCkbw6wKak8nVlgEo0wzzGOUFBxOCmItqY1jUTUIJHv9mMZ6VrXhtSt9\nkjaSaP1cAO6OV3jv+e7hjERLLlY1WgZmsddL+PxBn508oXKO03lQJ56MTFq/G88iQB/1Hfuo8zxv\nzDqUH94vmD153um85P/94RkCTxwpXt3pkCcRN7byv9A1fVxYR4StTWFPPpMXHR+W4XysJrWW8P/C\nU46fE7Scp435HeB3nnL8m8DrTzleAv/qM+b6O8Df+Wi7/nRg7RTUrTdfKxnyT9Yfexu8sGZGa9vu\n08ZKIULItw4hmm8dzvize1P2BwlfvDYglpJ3Tpf88s0t+lnEP/vBGZVxaCX4wkGfxjjeGy+R7bp7\nvYTjWcnFsqaXajqJoqgN3nsO+mnQ+FqC9Kg4JGUY++07F60DVbGXR5zMK64ryYNJwfmiovGefhqx\nqhtO58HpenWQcWWQ4pxnVhouVg1KChIlaBrHIItRquHaMMM6x5VhivGCexcrvPMMOzHXRzlvn875\nwfGc03lFpCSv7fd4ZafLbjdBKsGrO10q57g3XnH/YsX5smarEzPM42D+AOZluNbGOYx1xFpTNQ7j\nHZOV4d7FimujHC1FcKC7h89MtSZWYyyV1VzpxVRNsPe/fbKkGFqM9XRTfflsj6blJSH+SRG+H2ee\n9RjTOouuDjPyR4Seut3r8+YsG8vhpOD+RcFuN2hWUgrGq4ZbO92fSWbjnOdoWrYCnHrfM/l5wqY9\nwacUz2Mqj/qFKmPeZ9t9cqzzIb/IGsesMIw6mt1+zI3tDsvK0u1HLGuLwbPfz/jqrRHeOeKWWNwd\nF9zcykNEjnUcTouwHxk0KICdXoqxjnnVcLaouTYMuQaVsdwfr7jeal+Rlhz0U5JYXV7LsjKUxrJq\nAiFXUjLqRPzgaM6yCj6e1/b7pFoxXjWkWpLHOuQslA1bnZjxqsZ5QaQVuVTMlwbvSrQSnC4qysZx\n0Ev5/uGcyoQkzFgH5iHwfOfehP1BipaS/UHKze0OSgqujjK0DJrQ2bziomi40k+YlgazahjlCfv9\nhElhyCLNV67nXKxq7pyv2OnGSCG4PymQQjDMIyarhk6sOC4aUiUQQqIV5LGmboNSTuYV/TyEiD0q\naOB4H+E7nBRcG2bP1SiexI9DQJ3zHE4KjLFMK4MxnqNZyVevD0lihfDv39uTc67XVQKSSBKrcM37\n/ZSmdab/LOJ5wp/8SPF7Lz42DOdTig9iKmmkLs1rT5o3njb29WsDzhYVy8qQRpqdbrC1W+cpqmDX\nFq06stNNLhNhPZ5RHvw9a/v4OoLPuRC5BxApwf4gZ7+fopVEKcn9SUFZGx7MSlaNoZfG7PUSlAqf\n6TrypjKWexcrzqYVZ6uQXbOsLNvdhEGm2eklLEpDP43Y7sYsq7b6ghDs9hKUEFzppdwY5cwrw9mi\n5O64wAODPCKSEuM8750vmBU1nSTibFFT1o7pas5WR9OJY7KWwR5OCgTQSR5qGefLOoSwGks/C/ej\nn2qGWcTeICWZlPQ7gcH0sojpqkZKQSd+OMdk1bCVhwTc3V7M+bKhEylm1gUtSAqyWAMVdWNDJYZH\nBI0nCZ9xnvsXRatlqQ+t7fw4BHRZG+6NV4yLGoVgf5hSlZZv37ng2jDDC2iMI+smz5xzvW7SRmm5\nNvzReY96JLLzZw0fZFH4ecKG4byA+LC280eZivBtMqF7mN+j2lycp80dK/k+htRt0+OTSDLKY753\nOKVoHFrA3iDlwawEoGgMV7oxUSu5vnO2ZFmZoC05z6gT89IoxwEns+Br2OslXB/laCHwznN/tiKL\nFPPakmpF0Tj6aZCS9/spZ8uayhjq1kyTRgovCL/PShaV4TN7PXZ7KddGechpALIoRPkIGdYxzjPM\nI+5fFBzPQ5RhP4vZ6lg+c6VDJBVKCiZFg2lDVCdFTaxUm/kN43lDby9uibC8ZKJrc1Egno7dXsLh\ntGC6qkljRS/RnC5qhBScLmu0lnTTKORMyMeZqvOe0/mK796vWnOlQhGS92IpSWPJa1d6OOfZ6ydY\nD/OyuTRdSSnAcUn4pBQcTQsiJeilEc77D23m+agE1DnP2bzCOMt02RApwdm9kl4WXWqrvtVeuqkm\n1uqpcyoRnOcnsxLjPEeTFd1Ms9tNOBimP7PmqQ8S/n6esGE4Lxg+qu1cSkHdPLSNN8aBaJ2yzyh1\n8ay5tZZc38o5mpY0ztHLIq6NFKva04k1nURzMl3xe2+fc6WXEkWSz+x0Q37HOge2zZqMleTWdocb\no0DgIiWprePepKA0jsNJyW43oTGOa6OMog4hqUVtaZzjpWFOpCW3z5ckkaSTaiaThn4ecWUQouPy\nRHNzuwOE9SMlLz98Z1xwUHs4X9Z4YJiFMil1bXHO8c/vzkBCN47Y6UYsKkGqBeOlIZKSsrF8bq9H\n4z1FaXhAiW/9R2lrPrwM2DBBQ9ntJpzP65AVvmq4OkjppBFaCA6nJQc+JDFeHWbcHa+4c75kWVm+\ndzjlZFZSGcdr+z22u5IfnS3JNOzsdJmtGv703pTrw4wvvzQkVpI758ugxYxX7I9CtNj6+uvKUBsf\nKmBIgUS8T6N4lmDzUQmobbWRuNWIQrm98F5GujWNRpLtbvBHNdY/e8424TPWkiuDhH4S8dIoR/+M\nmtPWeJ5F4ecJG4bzAuHHtZ2vx0gpOZ2XeA83tzuPSbXwwTZ0CB/OTifmztmSLAoZ1XjPxaoh1pJ3\nxwWRUnTS8Gp998GMrU7Mrd3OpXY1WdX86HQe8nhanwcQSnII2Mpjrg6Dg//KIMFax8Uq5CksSsNY\nVZwvar58bRAS37SiqBoaY1lWhsOJZZBq7lwUdBPNTi+7JF6pDB9+Yx13L1ZEbR5DJ9YoJbjSTTia\nl2itaJzBeTi8WLLXH3Fzu4MQMK8sozzm68MRe/2E+9Py0l/lfSDIV/opJ/OKyrRZJC2RzJOUbqyZ\n1w07vZhOGnwtnTRiz3t2+gmdSId7LsBYxzunc8rG0kmCtnX3fMm0aDDWk2QJq9pQNJZuGuGF58G0\noG4s756tMNZyURiuj1I+tz/g6jC7vP5ISXT7bJ/UKD5I+FgT0Eejyp6FdSivlIIvXe3zYFaSG0fj\nPINUX2pxWaS53prXnkaUrQ+VNm5sh3dJtlGQ/gWh3WvG/vOMDcN5gfDj2M4fHWOsQ7SZ1NZ7okcd\nyvDcuR/NAwoEOZieamM5rSuUgqrRmMax30taKdZTNpZhqqlqy0XRMC8avn8047M7HQbdhO085mha\nMsoj7l0UJFHQvNahzLlWnK9qEi24aHN7OknEvGg4npVtVQb4zuGMB5OCk1nBr76yy7AThzydyvKV\na+klQ4BW66sdDyYlSRRKC3nhMdbjBczKhqNpxZV+jBShkuiyChUeXt7t0okkQgq6SYSSQRsZZvGl\n9Fo0lkhLrg8zSmPbMN4KrYJWdLqsWZRNYEJS0kkjlmXDyaxCIbhQDaM8QgB73YTbsUJKwd2Lgtpa\nTmY186ImjyOuDVOWVShX1Ik0aaS4O14xXtSM8oiJtWSR4u5FwWt7vUshIokUB8PsqVrKBwk263eh\nMY6TefWB2raUgquDsFZjPfv9lH6q8T5EGi6rh+s/T1O5rLrh/AvnC/lJhaK/6NgwnBcIP47z8dEx\nsq3L5Fun/ZPjnza3aH0BZ/MqlKlxIYFubWePtaIXa4wLBMh6zyDXpJHm3mRF0ibCvne+ItaC03mF\nwPNgXlG3pTr2ugnHM0usQ/UE74PN/+og5fpWjvDw9vmC8aKik0Rh3ShEVQ2ziG/fuWAnj+hEEmsN\nPzyZUxnDdjejtiF6Ln7knqx9CpESxK1kXlZBUq5qy3hR000UqVYY71nVjqJpOJwWzKuGd8+W3NzK\nWZaOL17rs6hsKO/yyH1rTNCgTmYV3gdf0Y1Rxrho8M7RaQMG7k0KtjqGk3nFS6OcbhaYz5/dC8VT\nZ2XDeNVwOivpphE4TxqHKt2vHfSIlGS2rImVwHjP6bxmvKywztE4zem8QgnBvGyorSMSgtIEv9iz\nzDzPE2zW5llrHQ9m5aVJ8IO07TzRfO3GiMNJcVlbcLeXkGn1TI3mSbyovpBPew7OR8GG4bxA+HE+\nuEfHOOMYZjEIKJqnl7o4nBSXDv6tTsydixX3L4Jz+WCYESnB0axkv5cwKQ2rukYpyecPekyWDa/u\ndXjndEVpCrqx4ovX+qEVg2jYH6SczSr6WYJ3oIXgdBa0m1grRnnMD07mGBu0jc9e6V0mQN7c6nC+\nqJkXDXEkGWVRKK6oJd1EYz14GYouCheY5nhZUc8sv3rL8SjHaWzw36yDD1ybEf4L1wdEWjKvGya3\na+5eFCgpGGURzoVxd8cFu50EISVZLHnraM5Xrw8vgxielmcEMC8a3j5bMFk1ZLFmpxfjPVSV5b2y\nYbEyJFqhleR8WeN8KOy6vi/jRUXZWBIt+fXP7NHPNIMsoqgtvUQzrUyoFybg+ijjcFJyPFm1zA62\nOgnH0xK5rvPX+rPip5jCnhRs6lZL8zaEMq81HyngomjI2mi8D9K280Tzym6XZW04m1et8AE7vYRO\n/OFI0YuSFV6VAAAgAElEQVTmC9nk4DyODcN5wfC8D+5ZavuTY+DZpS7Wv3nnOZ6WpJFscx4kp/OK\ng37KINM4YJRFWK/Z7sScL2qyWHEt6zDIIt46WrDVCeXZF7Wl8o6mttTOkSnB3Foq6zAODgYZF6uG\n81XF1UGGsaHcz6xo2O6EMNlIS756fcjRrAwS8tr3Y32IGpOSonFIIblzsWRvkDHoxFwbZZwsK7pZ\ndJn4eDgpOJ4FDWe/n0JwQ4VoLecpG88Xr/aZFZaiabAWPn/QQwvBW87TOM/JrArX70JF3oPWDxW1\n4d+rJhRUXecZRZFEGxEc3Wngft+5c8GkqNkfZFTWcTItMcZxtqhCNWMhuDbKGOUxN7ZySmOIZcgh\nkkqy041pjGe7G/Nn96ZEShBpyV4vpZdofniyIPeeeWW5sROqRLx+tU8aK5z33D5bEmuJ5/GKBI8K\nKbOi4nxZs90NwkfjHFmchCAAraibEJLt8B/avHW+qIl1CDV/MCm4d1FwbZRx9ZGKEs9jKC+SL2ST\ng/M4NgznBcTTPrgPUtufHPPk+LUkFhzbmqI23JsU3NgOmfeurYj97niBd4KDoaabaE4WJX96bxKS\nNUcZ10c5aawpaovoRJcJlkVt+OO7K94+WVAbz2tXOiRa8upOh0Gbl3L/oqCxNgQI5DGHk1Cw9GJZ\nY5wni9Qlg1jn8RxOC7JI8t27M5wIhOqlrYy9XsKtvQ6CUJeqNJZYyktp89owDbXhLgqujzIOhhkQ\natNt5RGFcURSgYjJdQhuOF/WxFq1PoSg6XVizfGsDJUR2uTM8bLmfF5ztqzYMZZIhdYSWitubuWc\nLWvq2nA8DyapQRYTK8ndi4JF1ZBGmv1ewrQwHE9LrgxSBqnmcOrZ6sRMC8N2qvBecH0rQ4vQlCtS\ngjTWOOfZ6qZ8WSvGyzp0xXQOE3kWtWVeheCM2crw6l6HvM3XeVTyTiPF9WHGe+PlZdJubSxH5yXd\nWBNHilEe8WBaUjT2MvjjgwSgh+02QgBLYHCha+nRtGSvl3wov9CLgk0OzuPYMJxPAX4SavuTkpj3\nIYHxbB40CmMclfW8vJVzfSfHGsf/88NTpIA00iSR4GIZJNedTswoj1FSsqoN3nmmRUMv1nz91jYP\nJgWHk4o00ry0lVM2llhJ9gcJx7OKa4OQNzIpKv73Nw8pG99Gkim6iaaTaaQPlZyvDBMaCw5PqiWf\n2+9x97zgbFmxX6dsdTTHiwqhBHhY1A3WhYRVrSTdODj9PSGnxTjHeNVc1ltb1YajaUUehwoJ+4OE\nw4uCXqopG8dLoxACrZWkbixv3p9ycztnf5Dy7umcP3z7jG6ieWm7w+vXgsnu2jBjXtTs9dKQnOmC\nLyyPQofP3X7CRdGQR5rxskbgOZ7VbOUxkZR86aB/SYSrxvKgTSo9mVRsd0L9uf1+yu3GIrVoo8QE\n09Kw0/PkScSyrLlY1USqBzwn0dJ5dBy0tFgrtjsxlQ0RZkpKvnZjRLSuMP3Iu/YsAUi0rTqK2gRT\nJqFcj9aSedFwb2LbZNdPh/npRfU7fVzYMJxPAX4SavuTwQVHswKtII01tTUczyuMdXRShRiHTG8c\npIkiixRF5amtY7Ko6CWa61sZaSTBh0RQJUJH1DRS9LMIBFwdZEjg23cu2B+EIphlE3p6eONx1nN4\nUXFrN0crybunC07nJV+7tcWysixLww9OBIOOpnKeqjRMVg1futbnBycLitLwvVnF5/e6dBJNbSxv\n31tyfZiSROH3aWMQHh60voksjpDAvUnBbh5zb1qglaCyikSFHkO/+fkrFI2hbCxZpC/NZkKGmnPO\ne04XJRb43H6fvX6CczBdGUaZRchQQucLB30uVjXjZY11DiHg+ihofP00+Ge+fD3UomtMCCA4m9e8\ne764bPQ3WTbs9CI+s9vn5lZOZR1X+ymzquH+ZNWGK1t2einz0mBcKBAqpGTYiS4l7sqEJNZHw6LX\npsfxsuZgmKHbSgbPC12GZwtAa+2lMS4UOTWWXqoZ5TH3xitWtUUjuLHbQatPj/npRfM7fZzYMJxP\nCE+2L/gw55fGooQgae3qT5vHuSCFCniMeFjrKCqDUfLS8f7kC7+uzrvOjxnlEWeLiqqxrGrLwSCn\nG2vuT1aXfXhiIRmvKmprieLQItLjKa2lbkLPjsZYBlnMO2dLrPNID51EUdbmsl4aArSQjFc1jbVE\nUpBkURs6a5EIjmdV8A1IQVk77l8EDeTe+YpYS04WFaZxvKb73NrOmaxCp9NZ1fDlawP2egnzBzPe\nvVhROs92HpNpyYNZBYSqAi9tZRh8WzUgXI9SgVFOyoa3jmZcHWT0sphRJ5j5Er3kcFrRSxSz0vLF\nqz12exneeazz3D1fcTgtGC8aDoYpnSSiMqHRz5VBShKFZMdZ2TBZNeSRRMgQSLDdSTltTUrOw3Yn\n4XuHM9JIMl0Zskhyp9Ww5kXNtDQcTiUXK8PXb27RWMefH8/403tT3jtbopWgn2m8nzLMInpp8LnF\nKvTEWTWW985DcMGVfkptHTGhEOo6AOFkXnLnfHVpevyg9/dpAlBZNxxOCrJYkXUTuqlmVjREWnI8\nDf60l7c7HM1KjqbFw1ybT4n56UXyO32c2DCcTwCTVX1ZW0zJUJcsNEl79vl/cueC03mFFILXrnR5\nbb9P2Zps1vN8Zq9L2bjLCgKLqmGyMiyqhtNZIKhJpLi1k9FLYiL9sLoAwO2zJfcmK45nFXgY5TGx\nDuGqkZRt6ZdQxbixDoXgbFVhjCeLNL9wvcO8sdw5X/GDoxl5rHjvHL5/OGNZWb5yfQBSkmnJ2aLk\ndFZyNC1ZNZ7X9nucLkruzwpipZiXls/tdQGBJOxTKUiU5/b5itmyYlEadroxK+NwCHqJYtw4zuYl\nr+536cSaVW0ZJBE3tjuczSuyOMI7f6nFLBvLjWFGEmsaY1mUlkXR8M/vTcN5CnKtSCLJ2aJhWYWq\nB9eV5O55ybwynMxKdroRKynAeX7vR2f82is75GnEbi9mXpq2bYBrE1KToB3GMbEMQoJxjgeTEP0X\nZTEOz3he0401V7oJtu1Fo6TgZF6hJUwKw1YnwlnLsvIsG0seKayDi2XJu+dzNAonHI0N2sO8bDiZ\nVuSJ5rW9HlKIcD2jjJdGObfPl2gVWhvPyobbZ0v2B+ljOVF7bXHMq8PsUnh5Hp7mt1gnZ661wVgr\nsji0VaAN2JAyRELeOV+FaMS2vtvPs0bwacOG4XzMMMbx5v0pqQ4mpbI2vHl/yq++vP1USdEYx5v3\npi1xTTDO8e5ZKN9yMqvJojBPUTX83o/OeOPmKJiLpOX4vOSgn/DdByFnRghBEgm+c3vCF672eWWv\nd1m113vPeFmFUjEmNDE+mq/Y7iT0TMTVQcqdiwJXGWYrw3heB+lcKTqRYKsTk6URSaJZrGpubOVM\ny4ZIC+6fF6yahm/fga/dHHA8b7DWs5UnjPII5wRSet48nIQSN9c6RFry5tGU7Txhf5hyOqsYZjHf\nrT3O1TQ2BAMM8oTxsmJWW7JY0k0Vx/OSSAfzTTdRFMZyPlvROMF2J+JkVmG8pzaOV3Y6eARVE/Ju\n+qnme0cztjoR752teHCxYryqef16n0GaXEr4PzpZ4K3jpa2c03nJt25PQhWDfsIgjUDCQT/lwQw6\nsWZW1OSR5AfHC2aV4UtXB3z2Spe3zxYcTgtqG8xKn93rkkWa82XF9x7MOJ5XxFry+f0er+72OJqV\noWrCeMl4WXM6L9jvZyzrBiVC0ujt8yVRJcmigpd3u0gfqkjHOoS2L2vLdjcOjfRa0+XVYYZznuNZ\nST+LLvOqTqYlHk+sH+YoHU9LrvTT51YTeBRP81tcHWSctGbZR53nqVYhEKM1m2kpuN5GrH2UCtYb\nvBjYMJyPGbVzWOdJ2zyDNNYs66rtzPf+D7h2jsqGTHUlBUoqVrVlWTTU1jLqBM0oam34634xQoq2\nwWDoHigljFcVxmvOljVFYy8ztJeVaasshxDceWVw1hNrEN0Ua0NHzptbObu9UHFZq9AFsmgswyzm\nYBgk5HnZ8K6HRWUYz4OJBOHJYoWxlmlhqBqDUorGWmojMN5RLQ1F49jqxpRNqIBgG88w00E7U7Co\nGq4OU/b7aagC4D2Vc/QyxdGkYqebEGtJrAuWpSHWgan88TtjIi0YdRLeuDXitf0B3jn6iSaJQqHI\ns2WNcY6zuaF2Fgj77ucxs6rh3rjAjUTL9GMmq4osibm5nbftxEPezHhZs6oswoVqzGVteOt4zrww\nDLspB6M0FCsddjhfVHzvcMaiamis53haoAX0Es1bxwuc99zayVEIxsuGm1uOZd0wX/tc8pjSWPq5\nDjkwS8P5PISSdzMJXnD3bIWOJJ04mNmMDeattK3UoESJ8aGC92RVczKvQtfWfhqSgds3an+Qcbao\nsM7TWM9OL/lIxP9pfot9Kd7nPNdavo85HXwITWqTuf9iYsNwPmbEMjCOsjaXGo6SoaXts85PlGJV\n1cQqhLMKoJNFLBt/OU9jLJF6aBX2rZlNydBj5uRsSRwpFKFt9bQInePXEqYgmO4kAmc901UdfENK\nMcqCGUqpMDaYNhKmRY2SEXUT6mc1xvG9BzNWTdjjxapi1RgSpbg6TGkuAwYE3URxb9KggVnR4Lxn\nsqoR/mHW+XY/4eZOlz9454y7FwXjeUWkJVXu2O4k/PF749CvPdd8fr+LEJI0VvTTiHfOFljnOJ2s\nSGKJEALvHN9465SjeYmxcGu7w9VBynjZkEaKLNLsdRXfuTtF+IZ5EUxhV/qhJM101TDMY66PMmIp\n2e3HWOtJdLDHW2eJhcI5x92LJVoHc1VZG4SASArSSJNpTWUcR9OCRW3oZzGVcbx7Mudb7415eS+n\nMZaro5xJ0YQQ6EnBoNW+Iil4eadDURlq5/nsbpeDQcZ750u+92BGJ9aUJpSwqWVoOb6sDNbBvDB0\nU8XpvGanH5z9zji+8dYJ17YydtvWDfcvCna6Mfv9lFgrtBRcHWbUjcV6PnRi5qN40m/xLOf5R3Wq\nbzL3X1xsGM7HDK0lr18btFJxdenDeZbjVWvJ69cH7/Ph3NzucqVvH5vn1z6zQ9m4y1pUr18bMFk1\n7PQS/vTuBUUTNJIv7vfI4ohlZYi14uowo7GOO+OCxnkOJ4Yk0gxiSR4rSuswbRuB98ZL3jyccTqr\niLVg1IkvCz4eTotgAtnKGS8rdnspD6YrdjoRWklev9qjruHWbs7ZomKrDsU1i9qwlSe4PGh/754s\n8M5zY7tD1Er3250QqbWqQhHK83nNbjfm81f74Dx3JyUHg5hOHPHu2ZKzWc2sqji8CL1o+p0E0Wbz\n143n5daUNisMCEL+Slsx+6WtlG++e8F7Z0ukhJdGOd56GuHQMoSIv7Lb5b3xklW5YlYZPnfQIYs0\np/Oae+dLrg4zslijJCSxIiOE+p7PKlZVw04naQM0HNY5pquafh7jnGOnk7Esl8RK8OdHi1CtutVC\nhPfcuShQStJLI3IJcaQY5DFfTiM6icZ5x1tHC7JYkQvNMNX88GTOjZ2cfhJaD9ybFHQihbOeeW2Z\nlg2dlSaPFVudiKKx7HQTbrTVtdeVKaQQXB0Gn9+6n9GjQScfVct4lvP8wzrVN5n7LzY2DOcTwDCP\n+dWXtz90lNowj/n1z+y+L0otjdT75nnyo+/GIdz3N76w97DYoYcr/ZTro/zSLh4rycu7HW7YYLKK\nVDDJXR/lFI1lv59yNCuZtiX0x8uKi2UwBb5+ddBGUYVsc/AcjHK2uwm//OoWiQ79X671M/aGKbOV\noZdFRFoSKyhNCGk+W1Rs5RGdNGJ3kHJ/XHI2qziarhAiZMxrFfwwkRK8eqXPsrJ473HOkSnFsrIU\nlUHJEO5b1BaUoO89s2VDnqq2RpvE+BDxhm2bdylYVQ2Nhd/43C5CwnRZ8/bJgk4U0c1CSZpeopFK\ncH2YcTKTfPFqnwcXBbNV8G/10wiP4J3zJVmbuQ+Ctw8n4AWf2+9hved8UbGqLcfTmnnZ0M80o17C\nwSiln2u+c2eC94JYC/JYcThesTtIuSbBOEuiY6SQIZy8rXJ9Y7vD3fMltXEkWrLXT/DeI0SITkwi\nDQIS3bTVGwx5pMhjTWUMt8+XfPZKNwgig+xSU3hU46it48549ZhGAfxUtIxN5v6LjQ3D+YSgtXyq\nz+Z553efwpienOdJydCL8BHe2Oo+tME3jr1++phdXLYmk8NJQShoE9ohr3vTWB+KdIYwY8OsaJiV\nln4ecbEMpfFvbXcYpm0L5toiBby60w1S6KwiUpIfHC3Y68VIHwIivvHWGe8cz6kay7SydGPFzZ0O\n41nJvUnFl6718V4wK2qMcbyyl7PdyUgiyfm8Io0V3nvyOGKUJ9zqRJR1w8Uqw7QE8t5FwbSoGWUx\ne90O06LhwbTE4YlEKC+zqAzjcai+bJwn6iV8/qDPu6cLpkXD7jAmU5J5aTlbhi6Vy7aN8RcOBhSV\npWosWRIahlXWkmrJ0azi2iDlSj/lYlFxZZjw8m6P42lJEmm+NOzw/aMZHs/n9/vs9BLGi9CQ7aWt\nDtvdiLePF0yWNavaIbQI2l0vxhjPV2/2Lxu7QegrdH2UUxnLrDRY57HWMcw15/Oa+cogpSCPFJJQ\nLDWNNf1c8U+/d0LZOBZlza9/bo+jWcmtSKG1vHyvHm3rrFWIXHwwCd1QEy0/cS3jZylzf+NH+ujY\nMJxPGdYf5IexwaeR4tZ2h91ewtk8MKeyseDhaFZyOq14MC/pRKFvSqwEq8q2H7egm4Z6XZW1aAFK\nh/Is42XNFw56dLKI2+Ml/9/xlMZ6JquGZWkYdeKQa+JqllUgkGeLhlkZovC2OxFl4zidh3DeV/ci\nsljx/cM5SbuXr7+yFaoRrBryVKOVAAdV47k6zOhnEbe2uyzrhq08Zl7VlJXlYllRNl0QMrQfQHFn\ntuT22YJ+FjFfGZaV4fbZirIxdGLFtbqDsZ5FachizbCjuTYMRH5eWTqp5sGkYDyvMd7zlZcGQTMp\nGrQMPjLnIYs1N3Zz4jgUxExjjW6DAb5wtcfv//k533jrlGXVcO+iZLsb0csjXt3pgBDcHOXMCsMw\niy/rwh1Ny+DnE6FytpShokIWK2ZFw6Qw1I0jzRW/dGPE3YsV54uK3789p59GvLKbkMWK3/vhOb94\nYxjMjaP8Ulux3lM0hmVbEVsKQaIliZZ0Huto+uG0jA9LpH9Szd8+Lmz8SD8eNgznU4Ynq0OvbfDP\n+iClDC2GO7GmsY77k6KVXCV24Lg7WVGIEKl0bTtnlEVcGSYUtWNaNOz3E04XFReuhtpxazsmUaEP\nTSINd8crPEHCnouGySoU+cwjRS+LMMazNBatQ/DA8ayhcY7rwwSH4/WrPZIownjL9VHGfj8hUopV\nZUl0MDUeDDJ+74dneKC2jkVtWdaOm1s5n93rsdNL+P6DGRdFgyeEAq9qAwjeuLndlnAB4xyn8wLj\nwTUWRCjXgxRs///svWmMpVma3/U7y7veNW4smZFrLdPL9Ow97cGAB42EWAQWAwhsf0CMhGV/AAlL\nCOEZCQECjAaBEBJICCNsbCyWER/AH7CsATSSJTNjT8+0u3t6r6zcY71x93c7Gx/Ojaisqqzqqu6q\n6qwmH6nUkbfvfeONzDfOOc/z/J/fvxcN2opEcbE2THpxdkatO+ZVR2ccd/ZysiRhkEajuFGhWdSW\nznus8+yUKUWit8Znkts7JcZ7QoBhkjBtoqNpdBj1KClZ1y2LOqOXac7qjhFJ9C/y8HhW0XWWlXF4\nB5vOcm2QReJ147gz6fHK9t/dWE+RaV4/GOB8tHfIs0gOn647FpWJrqhC8HhW8cqkh9aRLD1dd+Ra\nXnHx5lVUD37YLOPZDfLS+rrM9Ls2lw9q/vajyi5e9pF+8Hi54byg8cOk68/+QooQy2yXDonvFbG3\nEVVsmY6LSD9PuLlTslOoiGZpDLPKsFNaJr2UEALTylC3llUTF6xvn6wZ5ymLVcOq0aw6G/1yQlyc\ndnopjXFUNpBIxaAnmBQpbRf43OGAEBwX64b7taXMNPemNYOs49ooKqqEkngRhQDXBjmzyrBpOnId\nCQvGeQ6HGZNBTj9LePO8iqicICgSyfdONxz0M6wNpErw5vmSm+MeRRaBmf7mmIfTiuNFjQhQpopB\npkm05hfv7LBoLFXn2N3KhHfKSFFWStDPM8Z5EjE7Ji7+14bF1uIgZiWt9Yzy2MhvjEMpyeE4jx5D\nSvPaQR9JQGnJdFVzsjJcG9ktmdlx1FnujEvWxvKHD2bMm0jKfv3agFVjyFPJq/0+i9pwvGyunF29\niryzJJF8dn/IN54umVUm0rE7R6YFgcDpts9EgFuTEiUFu72UyjiqrcJyr59yMMyZrru3ZRnwbmHB\ns8/z8aLB+3hQ6YzneNnwhcMhy8ZebS6X+Jvvt5j/KCf3X/aRfvB4ueG8gPFRpOtSiiuzrA9ynUt2\n1qOLmlUTlW6JEoxKzayyTPopaaJIteRiE///752tuXe6orGBVWO4u9tDCxEXawneeZrOU2jP56+N\nOF+1GOeZrTukgERrNFBkmtZ58IGztaFuHdY5xkWJFAEhBCfLmuvDnJujArc1kRvkCYmS/N75mtoE\nRnnCa/t9rA1YB1JLdvsau4V1WifQOiL8UxddO8+WLUWSMCoSlCooU0U/VRAiDaDpPKxalFjzxbs7\n3BgXtCaaxh2vmujHk2iysWS3l10h9wdFQpFqDkfFFdwSYhZyia4JAQ4GGamSuG3Tv5dITlcGvKdz\nnuvDgrrzTPrw7eMlWaIwxjHdxHJdqROkCnzzyYJxPyERUbRwfbSd2G+eM7GvBHf3erzxzVOkiDLv\nL74yoTEB5z39XJMlkax9a6u+6+d6K0AJOB9LtL2Jfl9hwbPPmtv+fS5qgxKCQZGwqFq+/njBqwc9\nCh2J1U/nUWVYpNF6+0VczF+kPtKnLV5uOC9YfBTp+iUj7elVeez9r/Ps98xTSW0EF5uO3X6CsAFr\nHVmiKJTGicA4T+hnmvNVw9GsRmsVESzLhkJLTlYN+72UpMz4pdd2+e7Jiiezil6m+dz1AQS4d7Zm\nkGkm/YzWee6drjladrEM1Vi01igpmG0ibywQDduMC9wYFbyy348CBy05HOX8wt0dvv5kzmK6QSrB\nnd2CSS8Bn0aZthRM1y2PZmuWVYvSKk7O+8CtSU7dejaNjbbbznFRdQQEk16GEjCvDV9+c8Yv3N3h\ncFzw1acLpADrYlagpaDqPNYFlIgzP3miOF213JmUQDz9n63aq5KldZ5HF5EL50Lg1qRgVrdcbBq8\nDxwM8q11gGNRdRgbZ6OWreXe+Zqf2JqZVZ2j7hxlqqiNI/iAFHBjHMULqYyb6yUzL24kJb/y+X3a\nziGlQEnJom5QUm4HahXr2tB5/xZ0M/h39UyeFRa83zOrROwtdcbHUqrzKCXpXDQChC32RjgCvNCL\n+YvSR/o0xssN5wWLHzZdr1rL00WNdZ6zVced3fJd5F3820Gel98zEXHQ885uj4t15Kt98+mSRWW4\nNioiFdpaboxLHl5UHC9aJsMcQVS2LeuOZWNZ1h21CdxE4ILnMwexeT/fGEZFys1JybDQfOPpikk/\n9oAmZcrRskUpgXMOrSOZYNLLOVnW9DLN+aqj84EAV/MiSojI5UriPMnjc8l51aFON9yelHzhxoj7\n0w2t8QQEf+In9njzbEMv1yxbx8/cHNNLE376ZsnXHi/YHWQcjgvmG8uT+YrGGHIVsfw2eB5fbOJi\nKQRFpng43bDpHONCMcoUR6uGV3f7lJneglQti7rjZBkzzfNVx62dt2TNp6uWg0HKunOEAL1M8499\n9oDaOY4uanyI1gvnq466c3xxMqZqA+va8JWHM25OSqxzTIqE3UHG8bK5ok/slAlVa5nVJto5J5rd\nfooPMWsUCNZdJHR/7lqfSW94hblZN4ajZRNVj1JysO0NPa9c9s5nVkpB11qM82TyrddujAuOlw3L\nOvoK7ZUpx67d2hRsN5nt9zpdtS/UYv7OEvePuo/0aY2XG84LFj9Mul61lj94OIvlKiUJwb+LvGus\n58k7DK7S7QCk9Z4QAnVnWDYWLeAbT5YIKbh3XnF3v0AE2Otl9LOosEqkYFAmeGc5WRmOF44i1Zwu\nGxabjl6uuLlT0lmPDx4pJU+mFbd2S3b7HbtlCgROlzVSCZyN+J9Uq1jiCVGw0M9i+WynSFlWhiez\nilf3+pGHlmnuna35/XtzpISfvNbj+rhgvjH8/v0ZSSq4tVsyyjTnVcfNcRHnhwL0i4RFbfjy/TlH\nszWt8RwtNswrQ+cEq6bFB+hsnDla1HEhvjPpMa06jpcNR7OK+z6QpgrnAqM8ZdLryBPFsu748oOL\nKyF76zwXm46bk4JRpvEhsNxmVmWqybVmkGtMFUhTyemi4dowozGOXqZ4PG1QEpQSGONZVJZb44Kb\nk6jMG2YJ10c5Dy8q7p9vQArU1nPmzo7mbNUSfODJIma/N0Y5jfWUmeZgkDPddHQm/ow3Rjm9PGYj\nl5na8xZWEeKC3FmHD3C8qOlsIFGSw/Fbsz1lpvninR2ebvtjSsmrYeXL4eXLUtydRL0wi/l7lbhf\nEqA/fLzccF6weL90/f2EBJdQzsv6uHUeoySN8axqg1aS3V7K8bJ5W5ntaF5HGXGu+cbRkrpznC5r\nGus4WbRU1nLQLwh0nC2i1XBjPb08zrM8nTds5g39THF9WNDawKhIyRLF1x7NqKxGAa9fH/BkVnO2\najnyNZ2zJFJxvGrorGO6NgxTTZCxPzAsE37y+gC97QUJEctnQsaFKmL1NyDg8bSisx6dSnItmTcO\nM4vzRddHOWOVMt9EJdZumSLTOMlkgfNVw+/em7KoYwlNqprjecOqaSnS6Kq5qA3zpuV4qbid9Dia\nRcfMIlN873jNdF1RZim3ck0iJF95MOU7J0uuDfLIelOKYZnw6KKiNoZcSertkOqkTFi3EURqrENL\ngSz9/Z0AACAASURBVAAmPc3ZKlIPDndKdnod3zpaITtLmWvGvYydQtPPEl7d7eGBzjrKLEEqwbKx\n24OKJE8l03XH7W1pr8gUTx81aCXQSnB3UnK8aKNKTUnG/ZQgoLcVN0BU8D0vy77MqFvrOFo0dNbT\ny1TMrLfstGc3qjLTvLbXf9tzPMyTdz3XL8pi/lKR9tHGyw3nBYznpevfT0hgnMd6j1ZvZUcAh9sh\nxPNVy+N5xcWyuzK4MtZzf7qh6SzTynB9kJEmijwRPLmo6ecGvZbcO1vRzzWjQrHXzyi0Yq+fYm2f\nQabpvGe3THk0q2mNuxIgOBdIpcS4wPG8IYTAyXxDbTyLqmWQJby632dQpPzs7RHOB/b6GXjotsDI\n2abj+rjkZFHRyxTGeka53s6jJLHeHzxvnq/JpKC3JWm/cbLmJw+H9NMECFxs3Uj7qUKH6MOzrDrO\nq45hmbDpLIVWPLqIQ6PL2vHaICcRULUe52NpKdGCQqecLFqyROC9Z1hmOB84WjRcLBv6uWZ/4KOs\net3y+rUBDs+qMVSdY68Hh6MiWkL0U771dMWy7ghE36Cny5a67Xh8UUdung+8tt/n524LjINECdat\nJ9eC3UFGZzyNc6Q6cvAUEeCaJhKtBM4FAgHTOUyICrqDYbRKkErwxuma3UEaZ7UELCqDFIJNY5ht\nFWUuxN7Zs8ToZzPqVCv2+gkny5bbk/Lq+XteOVhKcVXWxb84m8vz4qUi7aONlxvOCxrP/hJ+v1PW\n5QJ/tuoIIdApH5u5IbpqPlnUcZ5EwLTq4Bxu7hR89fGCQLiSQV/Uhj0Z3SgPhjn3zjeUWlGnmnGR\nIojN8L1BhjGRan19VEQFm5RYHzhft8xm20yrTGOWIAMPpmuOli3jXFObSLNuTGBYGh5OK5wPXB9n\nfOHakF6RsG6iBLccFayKCMNcNZZVa7k+KpASztaR+Hy6bmi6eLo/ntdXJ/cyTzDecTIzzDYNqZKc\nrhuGWcq41DELAPYHBcsqSr61EpFPJuBk1aGAnV6CQCJkoDMBnUTvIa1Sro0KNl3H0bxBOM/5pkMI\nwcY0fE5pWhtIVLSvNiYgtzLoo3mD3JZPd/spzntma0Mv1dwYCaZrwWsHgv1eRusDy7rDB8F+P0FJ\nSZE5HpxvUBJOVy03xyVCyrhxpppxL0WJONt0tu7QSnBvWhGC52xl0Co+Yz7EZ2lcphwtG/YHGQHY\n7aV87cniajM5KJNYVtuWkp6XUa9a94HKwZ+mocmXirSPNl5uOJ+CeL9TFj6qjjItubNbcryoaYzn\nxijj5k65VY+1DPJoMXBjDPenFfXWA+fObg8B8XS+ifa/T2Y1znlu7OQ8vYjW0f085dYkj/MWQgCB\n4ANVZ3CrWHbRUvLKXh/nPINccbHq2OlnLCvDm+s1TWdwmWJ3kNEYS2MMxTxO2lvreXBeo+SMn709\nRm/9XBIpmVaxlPczt0ZRgedjxpQqQZ5pcLE3kgfPuEwZ55KwU7BfppxuWs4WNc57vviZCW+crVnU\nHTY4dnoJT+YNRSYYlglnq4Zeotgdl6RKcbGuWbUe5x39PCH4aMPQSzJGecrFumPTGGyApjPb+RHJ\noEwwHh7MKq4NM+brqDA7nGQcDkuklJzOa37m5pD+5WLdGHb6KakUfON4GUUEgENwsW7p54ovHA6p\njacxkSP3y5/Z47unG27t9EiUJE8EZ6uWf+iVPtcHOU8XNY1xOB+ojGPTGg6GGUeLFuthf5BSmUAv\nUfRzjZaCo3nNtWHcAA6HOVmqrrLsTfvWyd6FEF1bVSShX/rp7PVTXOBtPZlnS0+fthLVS0XaRxuf\nyIYjhFDA7wNPQgh/UggxAf5X4BXgPvCnQgiz7Xt/A/izgAP+zRDC396+/ovA/wAUwP8J/IUQQhBC\nZMBfB34RmAJ/OoRwf/uZXwP+3e1t/MchhL/2sf+wH0O83ynr2c1Iqzist9h03Nop43yLcW+7Vrot\nh+31U1r3VsnOOh/BniKCImdV9La5Nir4qZsjxkWCkHFCfmMsVetIE8V3jpYMyoSbo1hG6azncFLy\nuUzx1ccLtJLMNx0HgxwhFEIIOuNoWodOYm/haN6w108Zb0tTj6Y1qRKRphwC1nnSRKGlRCcy9piM\nZd54zKLhou547aDP2bJl2XScrTw/fWvEsjUcDDK0Ciw2ERbaGceiNsilQCnJMNPUrScQ50R2+iUg\nOBxlcZAyWC4qy2vDjNYEMg03JgWnywYIzGuDwtO6wKDQIBRVa1nWsS/zyv6EYaZ5NGu41su4u1si\nArQmDtZaF+dtTpYtznuWWxsJAYgQSQOrJg5o3j+v2BvkjAtNkeptZhrIc8WTWY0gUHWOfq4jJXpb\nSrw2zJhVhpkPLGrLuJewrC3rxpJoxasHA5wP+OCvvG8SJVFbG4vL5+PZk70SAucDdeeY1yby23op\nr+z1SZV8T1Dtp7FE9VKR9tHFJ5Xh/AXgm8Bw++dfB/7vEMJvCiF+ffvnvyiE+ALwZ4CfAm4A/5cQ\n4rMhBAf8N8CfA36PuOH808DfIm5OsxDCTwgh/gzwnwJ/erup/fvAl4AAfFkI8TcvN7YXJT4IUeB9\nT1meq83I+sDTeUVjPJlW3JyUpCrKTOd1R+eiCu1wSwXuh8CsNqwqg5SCn789xodYKrmzm7DXS7mo\nDZvWcTDK2R/kSAH3H20YFwlaKvaHGZ0LGG/ppQkemBQ5x6uW2+MeZ1VDnipcJRjlCkfc0NIkTv63\nLjDpxSHJPNXYEJhuGtJEMi6i2dzZuuP6KAMR6c4XG0Mv04yUoOoM3z42FInmzqTgotK02+wgSrsD\nx4sO4WG+bjlbNZyuO26MS1IhqK0jBM/NnZKfuzmOMy4nK778YM4o15ytO/qp4mTR8bN3hzyexlkj\nIaMce5ArJJpxkTBvHYtNlC9nSSxXXVSGzsRs4DunFW+cV/zSqxN2egkPLzaEAKfLhkkv4/ZOyR/V\ni+hYumk52nLPpBD81O0xm84Tlg2+HzeEp4uK03nFvS7KyA8GGUII7p2tybRif9sD01JcATmtj3NC\no1yz28sIIqrJbk1KOuMwLlBskUHf92QfIEskB0mG855JmQHweF6/Z7nsnYenzkRGmwi80PEi95k+\nivikQKQf+4YjhLgF/LPAXwL+re3Lvwr8yvbrvwb8DvAXt6//LyGEFnhTCPE94JeEEPeBYQjhd7fX\n/OvAP0/ccH4V+A+21/rfgP9aCCGAfwr47RDCxfYzv03cpP7nj+lH/dDxYWrZ73XKulwYns5r3jxb\ns2ose4OUs3W0Vf7MwYC7ez2Subz6Jb8xLoBYihsVCYNMx2Y9cLpuOV+35Knm+rjgMNVXhICvPJpz\ntmq25mAJu/2Us03LqrEczwVFqhj3Mq4N44LayxSBwL2zip/Y79EYz8mi5ums5bW9PoNeLKWtWkvV\nWQ6GGcez+PfRzxTGBPp5Qm0s3qd893iFC1F+q4Tk4UWFdY5VaykSjXHgHQQET2c1VRvFEINMUznL\n984rThbRuvraMKeXKhrrKJPY8zAepADjAwSPJ1BuIZXDIsHZaI7zzaMFjQmMyoRCJzTWIrXm1X7O\nG9bTmpbOBEa9lHVrcDb2yV7f68V5pMqwU0bhgQ2ei3U0eitzzeFOwb2TFUWi+OzhgHtHknVneHS+\nAQ9ZorkxLvju6ZpvHS3ZNIbTdcO1YcFeP+XV3R7fOV1TDjV5oki05GTV8tM3RmyayLLb7aUkmWLS\nT69gn8vKMN3E0uXjef2WPPk9TvYuBBIt35Lcb0tuTxc1RaKeWy67XNQu52yWTct0He/n2e/5Mj7Z\n+CR7ap9EhvNfAv8OMHjmtWshhKPt18fAte3XN4HffeZ9j7evme3X73z98jOPAEIIVgixAHafff05\nn/mRxw9Sy36vU9Zlvf3BdMPd3ZLk0p9+2V5NvL+y23vXwvHsYtIYxx88nCEI7PVzpAwcLWp2eylC\nwMUmMraGecKbZs3DyjCvDVVrONt0FIkiE4J6x/GtRLHuHDdGOf1cc2tcIITg5lhv2VwapSR12+GJ\npICLyjLbdCRaI0VgujHYEPjSTkGZSh5MN1wbZqRKcVZZ1k1Ur1Wd5XxZs6w7ZIDpugUV5cDDPOFi\n0zLdNDgX6KfyaoYlhEBrPSeLmAXdv9jQzxLmleGbT5fRJjvTDIqEo3mD9YEQPJNeTqoF1jrePN9w\na5TxxtSSKsGyc3TBUxYJo1ShlOJkWtErEm5MepytO4SAtLUY57i9UyCkoNA186rD+0ChJGerllVn\n6SUKi8OGgPDQegc2cLqqeXyxoZ9HA7UgQAGv7ZckSsU+l/WcrVuMCzydRSFFZyJA9Mao4JW9/lV/\nxvv489+dlKTJu5/F5z1zV15LW9ty6yKQU8CVQu3Zctk7MUt7vZTjpefubnnV/3mRezk/rvFJ99Q+\n1g1HCPEngdMQwpeFEL/yvPds+zA/soRaCPHngT8PcOfOnU/s+37UtWwpo8mYeA/1zPMWjmdfS7S8\nahLfHJecrlsWdUcqBVPjeOOsiU6dvQwQjMuEIlFs2o7OeootxuV003KyaPlHXt8lTzXH85rOBToX\nAY3HixohBPfPVqzbDufhtb2SV/YHIGBcxE1JKUHVOL76aEEQgcWmo+ocoyLh0cWGs2XDtDKsW8s4\nT9FKcL5qkAIKrWi2g5qTYcbJvGHRGSQaHwSN9XzveMm4n3BjWDIZpHzlwZzGrqMqK5GkSnKyjM6q\nzjtMZ3hj3ZLf1PSynIORZmMs40HODRd/rtpEOfWdSQFInHf0ipR1Y2gahxqCFIEH52tGZULnIkst\nnvAb1rVhVls+dzjgjbOKqjGcrFtq49FC4LxHqqhqe3hRMcw1WZKgpcT4sLXIDuSJ4GTZUDSKcZmy\nO0hoOscreyVKSFad5XQdDyOXpAkpBGnywZ/F55XcbowLTlftu3qNIvDuRW3ZIGBr4PfJ9HJe+te8\nOz7pntrHneH8o8A/J4T4Z4AcGAoh/gZwIoQ4DCEcCSEOgdPt+58At5/5/K3ta0+2X7/z9Wc/81gI\noYERUTzwhLfKdpef+Z133mAI4S8DfxngS1/60ie28X3UcsvkOb2ag23z94Pez2WTuMg0E+dj87ux\nnC4aEgVWS85XDZvOcjgeMCw186qjagzGOISAuvHsFo66iyy3RAp2Ss35KiJvhnnCw4s1j2cb2NIN\nvn1ksQS+dGuXXhZ7C8wr6say7gwKwapxTFcd69bw4HyNUJK9fkLdRan0z94ecn2ccbxo2SlSamtZ\nd47ZKvrYeBfQqeTVQZ/aGCSw28sxPmY6RaIQAvqpJk0UZ+sGYx29XNHLclaNo7GeqjVUWcJ5FctT\nv/e9cxCCsJ1FKhNJ1VikklRtxAAdjvPIQKs6ZpWhSBTWwbePljw8l1wb5tzdKxnkmjenG1aN5XzV\nsG46WuPpJYpr/XgYCN4z3VjmVYSA7g8KvPccDFJ+9taIZe24uZPz7ZM1EtgYi/dwUbc01jMpU4II\nlImisZGz9ja0zId4Fi9Lbsb5q2fweX2fIHjXovZJM9M+TVLsTzI+adn3x7rhhBB+A/gNgG2G82+H\nEP4VIcR/Bvwa8Jvb//0/th/5m8D/JIT4L4iigc8Afy+E4IQQSyHEHyeKBv5V4L965jO/Bvy/wL8E\n/D/brOlvA/+JEGJn+75/8vJeXoT4qOWWUorn9mqed73nnfSevZ+mMxyvWm6McqYbw04v42wVeyIX\na4OSgUTATpkiNdTWUxkHQqJlYNoYrPecXDRUnaMx0WpACUGZKU4XNc6DDI480ZhALHmVmnvnG2ZV\nRwggpUQI0FIw6aWcrRsGeUKZaXb6GeeruJgmCqyN0u6H05pm5JhtYm9glGtu7hScrA3eBTadpZdo\neqnEWk8XYkZTZpKLTUOqBT2l48xO51HKUaYJdycZ89qyah2z4yXO+y2CR3K2MQxShVKS3UxxtgrU\nxpFqwbDI+KmbfVaNZb+X8sb5hkQp6i4KNS7WHdYHLqqWv9tNWdaWw3HB7UlJ1aZ0ztNLU1wIVK1D\nb20Ehrlm0Yb4d6sF687xD54sSUR8Dg5HOcY6jo8anI/l1UGm2HSWRAq+c7pGCcG8seyUCQJozdbO\n/EM8i517N5H8nX2fS6zSs4vaJ8lM+7RJsT/J+KRl3z+qOZzfBH5LCPFngQfAnwIIIfyREOK3gG8Q\nySP/xlahBvCv85Ys+m9t/wP474H/cSswuCCq3AghXAgh/iPg72/f9x9eCghelPio5Zbv1at5Nt7v\npHd5P411EW2SaU43HavWsGotu72UO7slozwh0ZLdImGvzDgcZpwuO3yAXCl2cs2jWUW+VZqJMmFe\nWRrnUQqWjcd6S651VLB5KBOF8fDF22OyVGFc4I+ezJHAsrE8nW2Y1YY7Oz2ElCRC8sp+ybpxfOd4\nxZPpmq8erdAK3CxmG9NVSz9LyBPNzZGK9ss+2maP+2XM4CrLyaKhTCWNCZwu4iY5LlJeP+jRmsg+\nW3eWUZbQ2DiXlGvJpjEIJFVr2N2q9nwIpFoy6iX00oR+LjhftYAAKfAIRrnmZNmw10+wPl7rwbRm\nkCV0WzK0EAEfBK/uD1jVljfOVtGSIdPcnUSX1hujPA6W6jh8mSeCeycbFo3lYNv8JwSuj0sIcLEx\nFKljlGfsjxLWxiFE4N7Zhp1+Al7wM7dGDPLkAz2L77eQP5tZv9ei9iwz7YP6Nv0g8WmUYn+S8UnK\nvj+xDSeE8DtsS1ohhCnwj7/H+/4SUdH2ztd/H/jp57zeAP/ye1zrrwB/5Qe9508iPmq55ftd79kF\nQsooSX06r69q+Zefz3WcebHOIyDKggMYF5ito2XzbBPVXwejgkmvwrmA386BXFRRtux9gsDROE+Z\nSjaN53Re008VndMY66mNY6dI0FrxaLpBCjhMi8gbM47HFxWz2rKqOpwPlNclNyY9psuWJ7OaXqrY\nHWhOVgYtYJCleB94MKvRItA6z2euDUAIvPN842hJax3n65bKeMa5xnhLqRXZuCBPBNPKIKTgtf0+\ni5VBIJjXDQ+ma/qpZrefMSgTHl007PdTaus4WTVY57i726M3Khj3UhaVYbl1JvUh/qy5Frx5vuHh\ndMOyTsiUZBHiIjgoNNZ7pJLs9xNWjaWXaR5dVJRptIDIdcKsijYOo17G2bJh0xhc0Dyc1gzySBjo\ntiDWz1wfMsg1RaKYVy2BgAbWjScRjtbFg0c/Tag7x9mqZZAnH+hZ+zALeZ4obo2Lt83nXGbaxkY4\n6MdV7npJC/j+8UnJvl+SBn4M4oM2Qy8XCOvhfNngfKA1nv1B9rZFRsrovPhwuqGznpuTkiJVPF00\nEAKPZg39TPJ02bBpLZ2Nm82ytbgAN/sZTxcVIUgOhin9PMPK6J65NyiY9HNmm5o3t0ibmzslr+/1\nCEJw/3xDsx1efDqvOF827AxyhrmmM4bWwWhbAtrJU5ad4WCQ0xq2FOQNozyhcY4bw5xMSeouAiXz\nXMX5IZGy7Dxny5rpWrLXS+jliqrzFKmmXTZMlxFEeXtSRhvkkCCR3D3osVOmnC87erlk2RkOdwrw\nsN/XXFQWJR33thYI3sPFqiVVglVrWVQd1gZeO+iDjzYD01XDwbBgVCR0zlN3ls7rSIEIgc9dH9IY\nj/GWQZbQdp47uz1CgK8vI79tujFMSs3hTo/XR30SGQ8VBGitjxtbbXh9v0/decY9zXRtIIQrSGea\nSBB84JP/h1nI35lZj8uoCHTOv4tM/X6+TT/IKfwlLeDFiZcbzqc8Pkwz9HIhOF7UkSoAKAnnq5Ze\nqq9+ARvjOF1tFVrOUwfPunXMNx3jXoLAs25hoiVawLqz9LOEzsVy0qIxtB203mLmgVsjgVI5pRYR\nDCnBesWNUcmkn/LFVybslClff7Jg3TjOVcfZqmO6igII5zyZ1gSlKROJCIKdXooxnofnFbWJJmRS\nwLK2EOQWUJmhpSLXEi0Fzsd+k5KC2lmUEvS05MZOwaOLBgmcrmqma8sgk3Qh0FlPmUhGecqwEBQ6\nctVGvYSbk2KbeUiezjY8uqgQCEqtQAZOFzW9PGFTd0wGOYdKoqRiYzv2BxnL2rBsDK3zeNiWLTNs\n7rg+yK84aK1xrGtHbQyns5bDccHP3x7z9+9fsKw7TquWZlGjpOAfFoKf2O+TacWtnQLrPVVnORzm\n7A/2GW9nn843HWXimdUdO1IQgJ0iQUr5gU/+H3Qhv8yslYhZkHOerz9ZcHdSolOFFDCrDUWm3zNL\n+mGb/i9pAS9GvNxwPqL4qCWXz14PeO61rfU8vqjIEvk2i96b40j1fed9SBn96b97sqLeqsp2exnr\n1tAad9XgPdpaGGQ6QSvJ8bzCChiVml6WolXge8cVTSeZVYYb45xUK9JEsqoN1nnyTFEIzcEwI1eK\nz17vE1zgeL7heNUihCTRkl6q2TSOTVOxqC2pBmscD6cbnPM0zjNvOggdo0zT2Dg8OipTms7hIfYx\nhORs3TApEnZ6Cf28x8W6ixtVqch1yqCX0Jo4aLqpW3p5QqoFEokxlkXjuFjXlFmCSjS9RDHOJcYL\n0kSzO8hQMrLDPnswZH+Q8UdPlzyZrWidJwRIteRkZVg00XAuTxWtg1QJvnO8Ylgk1M6xqjsaE7g5\nKfmpwxFewnhrLZBIyaBIaDrLedWgJazbSJNWSvLafp+LquON8w3L2nA4KlEIztdNNDhrO5SKlO5F\nZamN49ogI9PqSoW4TyRT/9Jwwvk6gl3ltpH/LMX5+8UHWchdCNTGsmkjVcD7KHYQUlwZ6HXG433M\nlN+ZJX1UTf8fd1rApyE+1IYjhPgTwGdCCH9VCLEP9EMIb348t/bpiY9acvns9cx2oC7R8m3Xbozj\n8aziybymn79FCngyi26flz72z95HY2LvYlkbEiXZLRPOVy0PzzecLFqCeKt5+9lrQ6yLHicPzjcs\nGkNnPf2sjvDMRctOqZnVhr0i5fO3huyUKY/OVzyYtbStASSpVvSywDhXnK0Nq85hHOyUmlGhEVJy\nsan57klFoiSTXkJnLcfrhkxrholmvrZ0zvPaaz0+fzhkkGkezuptPyWNFstNzL4yFe2ba+v43smS\nfpowKDS3JyXzxkLwrDct55uOVecQomBnkPAz6YivPF5Qm1gGqzpPVTcgCoZ5AiICKW+Mc671U/7Y\nK5NtGTJEIvW8ZtE6BkCiQRMY5JLrw5yu23C6ahikKYMiIZGS00VLYx2DbECRa3qpppcplIB157l3\nvubBecWq7jhft+z1Uka9jGGh2BhH3Rhs58h0hG5aFykJZSIxzvPwYh3xRVlCqiWb1pEoRWs9y8Zc\nTfjPa8uNcUGi5Q/cS/l+C7kIMF135DqazFWtYVFbnPUkmWanTDhaNNTGoaV8V5b0sun/4xMfeMMR\nQlxyyT4H/FUgAf4Gcdbm/7fxUUsu39bYF5LTZYMQXCFEoh99EQnRKmJXgg+crpotgFMw2Nbk34kV\nOV40aCm4Psw5WdX83XsrJv0Uax1BBKyDSS/heNmSIEhSycPpmk1rma7jDEwv1dHSQAn6W4Dkoras\nasduP+H2fp8ugHcpiZbRW6bxHC1ajhZRNZZohUYwry1KSEJQDAtFL0uRBM5qy6axlANFlmtaZ9hJ\nM375sweEIHgw3XC+bqmzmKVJYNN5LqoW76KdQDQyCxS5imq7VUsv6aLkWsL1cU6iJNZ7nkxrhrmG\nAEpIVCpxxjDbOIZFwhdujNBSsG4tO70EJRRn6xYpBUpGYCkIDvoZi9bSNpY0UQipYplNCmyIsu9J\nL+W1V/t87dEFiIxbOyWdc3zjyYKdfmTHHS8qzteGMom/nsY7HLA3yFhUHY8uFnTWMiwTjPcsa4d1\nAaEkSinmG8OTi5raBb50Z8zd/T7OB5QUHA5znszrt034n65abm2HNj8O6XAQ0e6gMo6qsygleX2/\nhwkB11qUlHzxzs57Wli/bPr/+MSHyXD+BeAXgD8ACCE8FUIM3v8jP/7xUZ++nr2ecf7ql+8SIdJa\nS+c9PgSKVLM/yDhbtRHtIqKy6vLE+ex9XF5XbOvls8oS8Oz2EqYrz8kiNq57WcJe3/Nk2TDINK31\nuACdDxRpQmUsRSLxCJJEIoNiVwn6mWIyyNkrNGWiWHeW43lDL4sN/q8/WpBqyaBMoep4Oq/p5Roh\nBOvWYG3AqoBUgaezhkmZorVm3nQsW8/BQLNpI7l51Th2ewn9PGVW1TydV9FCIUswwTOvDZs2qstc\naCAIrPU87TrmVYdDMC4ypJIM0qjIyxPFwbCI/aAmyoWLQrLT0zyZVRRaURlH0/UYFtGq+cF0w9Gi\npjMeKaJxmbGWXEORalwIDPOEqosWAa9MetzeLWmNxwfFMFd89cmcxji897y+30Moye/eO2dedRyO\nCkZ5xiBNSaTg4bSKg6fGIYTg+qjgM/slv/9gRpFodsqY7cxqy6iXotuIH7q7LWNdllnlFjz67PN6\n+Uxppb6vy+eHDSUERarp5zricELAebg1LiKW5/uUoV82/X984sNsON2zGBohRO9juqdPVXzUp69n\nr3fJuRLi7Yj4VMqr9+SJYr8XJ9wTFZvjwNuwIsZ5xJZzdbKIVgDn6wYtJefrlkGecr5qCUAIgSxJ\nKBJPY+Mi1DhPkcSSSwie2caSJjq+3zlcgEJLUuA7x2veON8wKjTz2uC9p7WeYapYdYLPDXImPQ0+\ngBL0MsH9C0tnLBdVy91JjyIRdCph3XT0s+17ic6UiVQoGVBS8XResa5NhIR2cTCyMRZrPY1xjIuE\nVCtmdct81XFjnDHOU6aNpekMO2XCXi9aIkyGGcMiYbFpwbcUWtGFwB8+mnNn3GPUSylTxZPZBr+V\nMT+dR3fQtvMcLWtSLWgtdE7w+KzGeUcI8OpenxvjnGVreDyt0Frw+Wt9Wh9QEv7w4Zymc/zOd04p\nUk0ioZenCAQXVcOm7bZS9lhibYzj20+XdD6q6H75M3scjEruny6pLZRaUWax7LqsOy7WLXf2Y9zU\n9wAAIABJREFU+hxe9va2z46U0SoCuHqmvp/L5w8Sz24YJvirDeOd1gXvFy+b/j8e8WE2nN8SQvy3\nwFgI8eeAfw347z6e2/r0xMdBDLi8ng+enV4KAerOve0X9fI9l8TdnTKhsx4bDJlWV9LTZ1Hx/Vzz\neBYHBSf9lEmZ8q2jBYvacL7p2B8a1p2m1IppZdkfpOz0Uh6cbzhd1LQukEi4Ni55dbfHorGI7QJw\nWnU8nNVsOocIgbN1x3dPlqxrw/6wpMkso17O8bJmWGh0Itg0ljc2LcfLGu/ElUxbakkRJKtG4h0c\njAr6qeJ0FTOffpGQJpLzZUftDKerjuA9xsbB0sYFEg2JUkgCq9rTeUsQJQejjNptmG0MnoZro5zr\no4K745JZbSLwszIoCfNVbHS/eVGTzSukVqyrklGRsWwN3z1eR2fRVKOkpDYGRUApyaSULBtYtYaH\nsw1vTlfcHBR89uaI/WGGcYFHszX3zzY8ndfslSlnqwgZLXMN3nO6ipYPh+OSVAlOVy2DMiEIiXHQ\nusB02TBvLK0VnKxjn43gmfQKOuvJdMLeMFofXPZjro/y7b9pgyVwfZhjtyikZy2j3+ny+cPER7Fh\nvGz6f/rjA284IYT/XAjxTwBLYh/n3wsh/PbHdmefovg4iAHPXg/erVK7HKS7P91wfZgxqw1t5/AB\nfu72mF6qeTyv31aTXzeWw3GO3856/J3vnFG1jtf3exzeLTHOcjhImTeWvX7Kbj/nM9ccf+fbJ7TW\no5SgzFO890yGKWLVcTDK+d7xkqfTmnnTMMxzWmOwzqIQpFqiFFQdDDNLJwQ20ywrw1cfLZlVEVfT\nSxRJorAu8Ppej0fTCq1Aa0E/05HJ5QVIz7eOlpHdJiWv7Pfp2sDxqmZjPAWQpYoy0Rz0oz+Mki2E\nhF4qMc7RuYCSgbuTnCJRrBrD7715Qes8rXV8/tqAVWO4P6tRCJwxdKnC1YZ704qzzVNuTArmdcft\ncYFxjlTBydzQzxRPZxVCKFZ1iwngvSfXmjh2Cava8nhrMvd0UUOAeRuVayLAuJeRpCmFt7xy0GO3\nzPje6YoueOrOUyaK2kQZ+P1Zw7V+yqyKnx1kkd5wsW7YHWX8/J0xrYnk71s7kQCQqshPEwpyKZlu\nWgBe2++/r8vnDxsvN4yX8aFUatsN5uUm85z4uIkBz7t2EICAk2XLsrEIYib0ZFbx+sHgbb0lKQXG\nxSHPrzyc83TRsG4s10cZRabpp4p/8GS9pT53DIuUR7OKddVRt45r44JyiyGpOo/pPForzhc1s8pQ\nu5aucywqs2WutQil2O1nGOvpjKMtNWUuGeaa79YWuQU4eu85rx0HKqU2glVjGRRxEa06y+Opo5+q\n6FNDVDwtNy15rki0IE0lvVSiJfQLTSoU5+uG751XDIuEUZGy09c8mFa01lK1nl6myRLN2apjVIRo\nORBAKMmmcygFIQgEjsqC6CxdgH4mcV4TnGdRdSghsM7RGce6bXFoTAhkOISCwZbObIPDLVr8Yezf\nNMZS27j5pIlilCici2KKMlOEEFjVjkKLLd/OIYJgXXc0XVS/pYlCA0WW0FoXMz8l6WcJvRT+2N0J\n417G+brhjdMO4zx5ElVh5+suCj4aS2c8R4uG68P8fV0+v1+8pDG/jO8XH0altiI6ZwKkRJXaJoQw\nfO9PvYyPMy57PGfLhuHWHdPrwPm649W9WEbrrKOzfqtiiz2aurPkiSBLJI2Fs1XL41lNaxzLxtJa\nz8PzDTfGKcfzBhuIirg8YdNZWmu4Ns5JteYPH1wghER4j3WOh7OGcZ4glCJVse8zyBRaakaZYqdI\nOJ3VLBtL7QAh2diAAjoHr+4VdCZaXHsf6BeKpvO8cbIgSM2dccYwT1jVHdOVwbiKm+Me9dblNDjB\nadvSdjGTGeUqul4qcAgyqbBpzJpq41AicLawXN8pSZTAO+jlgn6ekieC86WjtREQmmnJxjgCLYGC\nV3Z7PJ7XHC9r6iaqrzadZ5ynCAnSwKwxjHJFP0txBL72eMlPHva3A5aBu3vRErw1NkrStaRMFFJK\ncq35+tM1hY79uVs7JeerKKdetJa9RHJ3v89uL+U7p2tsF/syy7qjMZ5vPF1QpJKjecukn1JmCYcj\nyckqAj2nVSQQ5Kmkc4KzTcutcfkDATVf0phfxgeJD1NSu1KkbR01fxX44x/HTb2MDxZSCq4Nc776\neEFtXJyt6ad0NgIQx2XC1x4vOFrUpEryhRtDlISHs4o7kx6v7Q95MF1xumjxwGeuDdFKoqTk8XyF\nktBYy+2dkrVxmFVDYxzXBxm3J+VWkmvIVcBrQWskGsntccGq82SpYrZqo9RYCRaN5dH8gkUVPXS6\nzjIpU1a1QScxU3AhkGvFTqlZNS2LCjItuDfdUJvA01nCuEzZH2X4eUupBbUxEZWysbSdJRA9YboA\n08oQiPNFWmqKTNJ5T+s8p4uafh59fYZFHAoFz/3zmlGmYj9FC5SEzgQa6yk6R1akzKuWzsF80+E9\n9HNFkaZY51g0hld2C64NMx5Oa6RUFKkiTyIrbbYx7A9yppuWk3lHbS17/Yx+IRlmms55vHE0xnFj\nkDMZ5NTGEgIIGq6Pcka5pl+kjMuUs2WL83ET0hb6WcLBMOfNsw2L1nB3p+T6qGDVmAgWzTSDTHGx\n6ZCZxofAwTBHIaKL54csD7+kMb+MDxo/EGkghBCA/307m/PrH+0tfTrjeWSAyyFKEbZy5y0J91kK\nwHsRBSCqyy4/o4Sg7iwbs8Xrb4m+gzzhp28MOVlFQyvrPfv9DO9DnMjPJHaQ0E8TZnXHpEzQQjDf\nRL6XFIFN61g2BhEEPniGRYIxFi1h2MvYG8Dfuz8nJIJhL+XzN4Z8+2jNsm1ZNoa6c2RKsu46fPBM\na0OZJqgAd/ZyeonGBYFxgXVnY1MegdaKznoCgn6WksnAg4s1WkpwjkXrGRXRS6ftQhzIrA3rzlIm\nCVmqyJI47Om9J0kEy9qzqWPTXwjBsvFoJRjmml4mMDZia7yQDAtFqSXXxwWzyqBEnMNZVBYtBMMi\nxQYPtYXgSLTChEDTWp4Yx2t7fdwgxS5bpIBAYN5Ygg+crQ3XhopRmVGkkv1+xriXRsGd91xs4gCu\nVDDQmlQJWuM46ToOpeRgmJN1jtZ7hnnsMx0tapCKg0FBvd2Q7p9v2O0lGAe7/YxFbckzRd05xmUS\nJ/elZLbpOBjkMfvpHJNetDxQEnaKaAKn5FtzMB+0POx9oLGRwF2kkcf3QUYDXpbfXqz4pP49PkxJ\n7V985o+SOATafOR39CmM55EBfAhMNx2DXDNdd7TWsm4dkzLl1k7J3b2oKr/6nPUgoomVsZ7OOU5W\n/x97bx6rWZ7ed31+29ne7a61dFdXL7Nl7LGN5bGxHIHiCALIgBFCSSQQFooIEEQshFAcgRQUoijk\njyBkEFJEpDjsJmKxAlYIDjEEOx4v8TjjWdydnu7aq+72bmf9bfzxO/d2dU/1dNdM18x0z32kW/fW\ne89539+73N9znuf5Lj3LOpEtV13Pl+6tqXvHvDT8wU9f5bMv7VMYxc4k4x/cWzG4kCyMleS3bp3y\ny793RGkUJ/XAxw8ndD7gnefv31oixgG2G/kZRkt+78E68U9yQwjwd187IcQkaCmIiROSa+6vGl5/\n1DAvJKvG0w8DtzqHtR6tJevW0rlALgWzsmLTBx5sepxLSDsJOO/obCKbJovkZIkgZKo21o3jeOM4\nKwasBalgcKntRoxks8hMQu8cEUVlJDFE4sgd6SxYF1EKdqYGRqfPwSYr6J1CMskMs0KzOzU82vas\nW8+9s4bcKLRS9O3AMASECAQBg/doqXAEQpA8XLdY61k1PdEHlJZEH5iWGbkSHEwLlIi8cVzzpe3A\nc4uCmwclJ50nR1APSU6o6x2fvDKltYEHm47by4a7Zw2ZUrxypSIKeOVwgvWBurfcWjbMM01vwSjB\na49qri5yMpHe69cfbJlVGu8jjU3mcl+5bxEk1NoP3txhMcnJlODOsmVRJJTd06Irzz/3fnTwlHAh\nwPn1Zj+X7bfvrPhWvh9PU+H8c4/97IA3SG217+p4kjJAJJE0c5XUj4mRZeu4Os8ZRrMtdXauIyWR\nUnK06YgRXtireLhuebBKFci00Nw53vI7d5bkmeL6TkU7OH7l1SMOZjmv7E157dGWl/YqlJLcOW14\n87RmWQ+URtHZwP5E81u3zpKUTT1wbVEweHi0bDhtBq7vVOxVms4GeuvBhzSQVoKmh7PtcEFOPBaS\nphtwQnBtPmfwA9vB47wn08nCYJJLrs0rpoVkpyzorOe1ozXHmyHZAfhAPSTy37lSwKaPaA25Tq/n\nNJcYDd5C50F6OP8TkAqqjLFSlCihcAis9VifXlPhAlGCkVAaiUCwmOZ03cDgUoV5VA8c1z3zyrBt\nktTNNNe0LvJo23G2tXSDBwnzXLPsHLkWbPrANFccbTpAQAjUA4Q+UBaS/WmJRPDlhxsOJprDeYbp\nHNNcE6Ok0oHTbU8mImWuKBQsG0tuFNs6tdiCEGghkMeSlw+nZEpxbZ5zbxl5/WjL3RCREn7s4wf4\nAKdby6JIKDVisokojebaPENrRdsPydJ6np6Hi5Ftny4AIsnq+mk2mcc/92VmuC7g3qrjGjxRnuZJ\n5z2p/XZZ+Xxr41vdDn2aGc6/9oE/+kcgnqQM4HzEuUiZKVyIaCVg5DYMPkBMhDutJFWeRDeFEAjB\nWCEJfAxAGrwPIWAjTLVCqcRibwdP01pan1jsZZUlDTWj2NaO3gauLUpee7SBqBlscqiM29TumkoY\nbMZ2sAQfULmmzJKOWO8dQ+8ZQhidINMGte5skk7pHGWm2PYD1nl665LNchQMLkF3fYgIJFFEMiUQ\nSOoe+sGhROJyagNapKQzWOgcaBUojcAHSakTzDuLCa3Sh9SezALECNYnEzetAvUQaFwcDdA0OwV0\nIT1OjClpXRUSmWlW3cC27ZhkikWlefV+zWKWIUJg1VvqztGHgI2eRQmZLtgOjm4AKRyZlHidoOVC\nSISSFCYpMhiSuvW0MrR1UkXIlKQyGVIKlm1q3WVaUxjFpnc01nOy3fDKlRkoRRYDgQQq6KwjILh9\n2vDCboEUKTFvWs9hnnPvrEVKwfVFTm4UPqRkuDfNaIeAlOnzsigMLx6U5NrwYNnyaNNzZZZzMM8v\npH+ehm/zToWNaWG4HuH6Tkmh3/1+vp4yx2C/1j30svJ5tvGt1ql7z4QjhPhZ3kKnfU3EGP/kB7qi\nD1m8mzKAVgLrfGL+h7RjDs4TRRrqZEaNkNqUpGKMxJiu2hERJRILe/DJsMoIsM7jPbSDI1OCqjSU\nSl0oGGdaYV0g0yK5ZrrATmVo+zRU362STtpZ07M3SWivvUmBkrBuBmSMTAsJIQlibtcdWmtiDIQY\nWDWeKzOFGZPqqw+3tC5StzCrABFAwvF2YFb0CJJQpHOBbuiZZEntwHoIAZyHrJD0fSACpUpW025s\nhS0mhm7025HJqoXMJJHKTefYDrBuPYczmJc5+Tzw5rIlhkgfFEpIdipNoQUn9cCXHyyRUqFFJACn\n9UBjA26eCJp1Z8kzxeAiuZH0IbDpwTYt1sNbogeRVdOjZbI7MCJlza6LNECe9fTeAwIbAyeN42Aq\nyaRiCAma3o8WEEIKJkajELSDx7qkLUYI1L0nCsnZtmfVOEIIHG0GtFTImOwVABa5Ztsni4OI4Hue\nX7AoDf/waDtewKTfbLqAKgJCJnmZ9LlLm4wdARvvd5N5ksKGUvLrJpt3O+9cEeMSePCtj2+1Tt37\nqXB+45k88kck3k0Z4HyG89LBhJPtkKT7+4TK2quSaCOMMxwX2CnTbty7wF6VM831xQzn+b0p08rw\npXtr7i8Tt+THPnGFl/anaCX51LUZX7m/oR6Ny16cVLTW8du3l4QQePO05YXdkpPa8fLBlDePa5rB\nszcr+L6bOygpOd50nDWW6D2PtgN3T2v6KNLQWSmEhRADLkQKk3TCFAlBRkyij8smogSUhWJRGI62\nA4OPbNuBVZcqHaUEkYAPSVWZEDlXOJlVhkyJ1KKznnYQWASziaIdHNGlSijXkOWKq/OCEOFo27Ns\ne4wy7E9H22UEQgkOK8OjrWWaJWsDowTHazvOkUBIOFt3oCSKQJZp5nni4vgAmyHiHFggGwEgDREp\nYFEm0MWqTc/b5BAdOGfRUjOEgLMQFTxY9WkepjUuCqSEs3pgaz3zwvCxaxO8dTxaddTdQB8E23rA\necE/fLhhb1aitWDwgcFFXtir0EIiSWi8H3xxwU6R0wyOo+3AwTSnd55bpw2DjxfVz1k7cHOvwvnA\nwSy7qLafdpP5RhU23u28KLhUhP42xLdap+49E06M8eeeySN/hOLdlAE+8T5Qau+mKADwyXei1D7j\n2Pap1z8vMlyM3DptCDFybadgb5IxzxN6zcfIp6/M+X9fP+L6vGBa5cQQaGzgD3xyn+s7FZXRaSOK\nEecCrx2t+aUvP+L6XDHJFEIGjteWKwvDGyeOUkM3WA7nJTZEDiY5mYbXHyX75EkuWZSGGCNHm57G\neT774g6vHzdsHm1pbWTHKMoqJd9r84KdMuOsHVi3nud2SpyLrNsaARxMNOs+oGRKDru7it4mgEU/\nJN5K2zsKLWiHQKYiu5OKK1ODA+q2xxiJXydEV7AeKQ1aglKpWlm3DiGAkNB5SsCNvZLfenNF0ydg\nRRi/+pjmTVqA0qRKNIJWqerSAvJCEQS4AKe1Z7dMHjaVEbS955WDCYML7FYmSRSNygCddZzVlqJI\n7qOrxpONcv6zIq2ZCB+7MuHVRzU7RYZSgp1RGTu17jyTwrA/zbm+U6IEnGx7ruYFB9Oc09Zy66Rm\nUSZo+f1ljw+RRWV4+WD61JvMN6qw8aTzQoiXitDfpvhW6tQ9DUrtEPhTwPcAxfntMcY/+AzW9aGL\n91IGeLcX+vHzQohvuz2Xb+9fm6joG0vrErS4t0k+/9x8bd06dspsRD55bi0bTrYWHyKbVZsEGqXg\nxv6UvUn+trVFkkrBndOWOLaNrkynHG9WbHsosoS8Om0GjjctSkp2ypxN7ZkUGZ3rmBaKzgUKJTmq\nO0SErx4ld9Fpodg2SY7FjXMV7xy5zsmMIreBXCkenm0SDyVCPUTWraX1oGN6fZQAG8AO8NqDLWUp\nKLUmU5rWebq+x00MUkRcELRby62z1BITEqzvsR7KTKKySD1EYoAql5S5YN06HqxaOucosjQ3GvUt\niXDhkprLNHPaKw1DsBQKPFBlkmE0XLPDQCsztISYa/rBp3uIgaOt52gzUOaS00c9Wkuycc73+67P\nub9J9t2rxiXIupQ8vzfhaN3x6aszEOBiYJYbtFKICKfbnv2JoTCKVZPM36aloVCK7eC5PstRAqQQ\nFFpxY0+zyDVap1nhNxLfqMLG1/y9XCpCf1vjWyU79DQotf8W+B+BnwD+TeCngKNnsajvxngvaOLj\naBIX4P5py91ly8sHE67M07GttXTOE3zkt+8sGbzjwbJlf5YxyQ2VkbQukEt5UTmd3/e9ZRLnfG63\npFCK1nvuHG04mGbUnWWaKY63PdNC0w2OzgburGoOJwWZkuRGo6Rg8APLzjI4qHLFsrFsekuMScam\nNBIXISsVrYfOBfzg8S7w6oMtW+voHODhdj8kBQJShQEBbVLF6GLa8LtNxBap6ovRc38taO2abe+p\nDIQoqIzCykA7RHpgXsCN3YrTxuLjQG40pUlttDJL1eP1hefBssf71E47DwsEn2Q2+ghrLNNCM8sk\nm86x6SxKa463A0OEs24gYMjVQO1g01qQgqN1l96P3rHtHVMMN/ZLcqM57SzH2wEfBYezgr0qp7ae\ns01HPTgkgijAB8HBLGdWJCO922ctnQt87/U5Wkt2qoz9SZ7UIwZHlUte3KtQUpCbNPuTQtDap5vf\nPKu4VIT+6MfTJJz9GONfEUL8dIzxl4FfFkL8+rNa2HdTvB9o4jmaRErJ8bojNzIBE3xy8VwUmgfr\nHu8Cd1dt8pchbfq3z1quTD1qVrIoDHdXbVIDnuUYnZKP8wGjJS/tT7m3bAghARs+fX3Ga0c1J6ue\nxnoSvVGwOzEUUiFFJIgEPz5rOjYdFBnsVYYsU7S9R0mY5jkxwrr3lJkgIrFjkrXe0bvIWQOlBhlS\nteCBSQ7RMqLnoHep0qhMqlich6ZPCTvPJItSjH4xgbMmUHeeIICxvRkBKZN0f6YSK18K0EojsCzK\nkkVp8CTOz3JUYE4QgMQH8EDtUwvNZAItE98l8X4UpZZYZ5FhbMf5yHbw2AB3lx37M8NJPTDJNXXb\nkUkY3EChp0wzjQ+eK5OMzeCYFopVZ3nxcEJuNFWAVWd55WDCg03Lg1XH/lit3tgp2Z/lF+95ZRQ3\ndivuLRs6Lbk+K9mdZHzx/hrrPKvOsSg0RqsPxIbgg4hLgc+PdjxNwjm/0LsvhPgJ4B6w98Ev6bsv\n3guaGEK8aLcN1tMOnlWb5GGONgOlcaxaycEk46QZONr23D5t+PiVKVfmZbIaLjTXpjkmU8wKQzM4\nfvPNMw5nGWrU33qwaglESiOYlwXzXPFg3ZFpxXHbEUl6ZJXRSQZGRcoyp+ssNiQvmNyARBIjVDrp\np3kkEyNoHGjZ0QfoBpfkbc7JqgaMSLOVwsC6TRu8VlAG6EW6byJs+1T1zIyg6SMDkHlYGMgzzWbd\ns6gyrO2pbfrgGqBQ57wfkdpoJkfpwGY0TgtREmNizId6YJpLBqsQIibHTAEnbeIDTQsoM8XgPb0N\nlNowKTU+OFa9o+sTsq5QoLXCRlhMMpAjdFsJCiMQKr03NkS2Q2AzdFybZTzYJPWCMyxSRF57ENDP\nJVJwZTSbzpFJxRvHNUqCUZrndwucDxzXA84F3jxpOJzlXJ2XHM5ySq24s2zZKTWfe2ON95HTWvKP\nvrT3gdkQXMZlfL14moTz54QQC+DfA34WmAP/7jNZ1Uco3g+RTYzzic46tJQXbTUlBE3vuLdqxzZS\npPVJWLO27kLCpu4dk6h4/bgZNzLNJFOsWsc8S66a00wzELm5UxJC5O5Zw6NNT+scy6ZnWQ80Q2Tb\nO4iRw2lkWmiKRvPifsGbxwkBJYVAKxiCYKfKiC4Znk1zjfcZy25g8IGuDvTWkWdpQ82zghgtvQ90\nNqSrWAGNTWi16MZBfgChBJmMTHKY5IpH1iNlIhQ6lyDUzkIzkh/zCLMcehu5fTIapGlwUTAxsByT\njvWwZ6DIDUopkFBEQSMCiDSgjwjOGouPCusg14reBYSMGBHZnySEmRYSJSRDTNI5JpMMNrI7yVnX\nNUamiirBr3ue2ytRQN15lo1lkWvq3lNmyVVVA+vOksvAZtDc3JvwcNNxtGkxWvGD+xMKrXi47tmf\nCM7ayCv7FbuTnP3KJAvvZUtEkGvJXpXx/E6JjZGbuxVaS6xPHKFt7zmc5UwyQ9M7Nr2jGMEjl9XF\nZTzLeJqE82sxxhWwAn78Ga3nIxXvRzLi/Jh6cLx2Z8u8MJSZ4jPPL+isf5sh1m5l6IZESHQ+8Ppx\nTW4ER+uBVw4mBODqoiQ6R24khZEsJobFVBOjwEhJ3VtWreW3bp1Rtw6lACE52XTc3C8ptESIwG+8\neUamVZqNhEimwQXJNFMICaF3uJBgxrUNdHZgOzgUoJSiKMAGjwue0yZipKJxgbYPLNuQ2k0CfEwJ\nV5tUfYQIUikWE9ifJ3j4/tTRDKP76BBQeqBvU8tLKShymOaaVZdIpUYrBpvUoqWEuR65P8lklJNN\ny0YJrs8KOh9YtZZ5mREQ5Eqy6fo05wJeuTpj3VpO6w479ISgKFWyEGgHixgiQhrq1mOMZHCOIYyo\nNQXEhFhrhqSB19kkJqpKzeGkSLpzNnC4KPAh/UE6kc7PtaQsMnZznapfAdfnObtlxtZ6Mq04rAw3\n9yqGuCZTiZckxUg0Ngpvk2QQJM5FFElvL9dJSDTPFC6GC6vny7iMZxlPk3D+PyHEGyTgwP8cYzx7\nNkv6aMT7mcucH6NEkoS5uVvigevzgrM6+ZcoIZiVSZ/qpB443nQ0gxtNxARN7ym0YlIotl3g0apj\nMTH80M1dzhoHJKb5tUWJdZ7P31lhZOS1h2tsiNSt44WDigfrju1gKbXm0XYgUwmTdbJpqTuHDzHJ\nvzRQFYoQktX1uk12BsEHlEizjUx6XIBFntPbgBGes25gqiV+5LD0pME/gA+wMAIVJIdTjQ+Cw3nG\noshYlAajJZ3z3DquiViEzAh54HTr8OMsZQgRH+GgMhzuTrh/sqF1yeK5qjRd7whpH2YIUPeRVd8S\nk7ADPgzMy2S/UBjF/bpFyAQEuL4oiTHgy4y9quDusqHtHcs28KmrCyal4tZxy6YbKLShykf4tE7z\nmCgiuRTYGBFCcH1eUGWGw3mOUYqjumPTOV46nLJfGb7ycIsRUBpNZx1Fpnh+r2CSGaqdkmok9c5K\nw3M7FdYnm4FXDifcX3VkUuJipHdv59dIKXhuUXJv2ab3ygYWuYaYbr9sp13Gs46nkbb5pBDiR4A/\nCvwHQogvAv9DjPG/eWar+xDH+5GMeOuY1Ear8jRb0UpSjxWElEmhQCvJZjtw1gxcmZbcWbYoKVjW\nlpcPKjKt+cRVw63ThmmmmJU5Lx3MeLjumJUmtcKkYFoo7o8bzptnLZ3zNNazUxpOtparc0lvHVIo\njjcDx+uWVWeTwGgirBN7z26esRzNw6a5oXeBdTckRQWfqpdltHRDEiUVBGyhkDHBirVPm38kec3c\nPJiwbDxSRBrnOd0OtEPkrLXMcs2LBxOmZU/TB7rgWXWezqYKJ4EJ/MUmumcDVZFhMsF62xIGh4pw\npVJsbZLPMTI9n95DPvJoOhuIvmXIC6a5Ro0KzreOtlS5SrcZ2CsVTS6oMhBacrSx5AY6L/n4wYRc\nK24vG4L3hCCY5ZrBC/aMJMTU7toM4aKldk0VBB/Zn2TJkVPCa0c1Sia5oCKTvPaw5vulensqAAAg\nAElEQVRvLLi5N6HKksDppnM0veNkO+DHi5e9KhnXDS4pV1zfeTu0WErB4TTnwbpjnmsOpjkvH06p\n8m9IOP4yLuOp4mkdPz8HfE4I8eeBvwT8HHCZcJ4Q70cy4vyYOJLeusGhRtmRECInW0sIgbtNi3MB\nFyMCweEsp7Ee6wKLSvPi/oRV77hz5hAkj5xrI1T63MhMqmRvcLLuef1oy6MmXfFPc50kUQTslIpc\nS7SW1J3Dhkg3DNgAMkKhU4kgowQVESpdsecaZnlGriJ1b2ktDAP0BAoDisR+bTtPngmq8VPnY0o6\nUsPD1YB1ASmAcd6ltWSaax6sWpatxXvH8cg/UiIyKxInRwsSmVNLGut57XjDTpFxdadiGBxeBJQL\n9DH58hSZZhgCm94TSO2kGNN7UWaGvVnGy4cT/sHtFcu2w0jBtZ05h5OCO+s2Ic58ZAiCOyc1h7MC\nYwxSptfy+Z2STeeIMhKcIDeCykhmWYb1gXXnQY5ov5nhjUeWxjr0acON/ZIsU3z8oOK4HvjYlZ0L\nC4ivPmr4vhu7FEZR954bi5Jby4YX9ysCcH/Z8nDdc32n4Oq8YJLpt1XT1gfuL1vmI/GzHyvATMmL\navqyyrmMZxnvGwcphJgLIX5KCPGLwK8A94EfeWYr+5DHOZHN+jTUtz5+DZHt/Bgf03C8c4FCS9oh\ncVae2ykoc02MoJTgh1/e47lFwcN1z7V5zjQ3fPq5BQhJZz0SwXO7JZMsiTECF2vYdJYH655PXZ1R\n957gPHpMfkZGDiaGjx3O2J3kXF+UCJF0zTzJBdPF1PZTQqCUJDMS6wRnTce2D8mFsrNkRrJbaaZF\nal1ZD0FE5lVGkQsUisYmsAAhIb5mSqBF4sBcmecUJjlnntU9D9YdQiQXzIDEe4uIgd6NyUZCN8BR\nC5s2UKpEya+d4+FZjYuRUhs+fX3BZ67PqIwiVxBFTG3AmBJWdW5XrSS3T1t+5dVj6t4jYsQIePOk\nprE9x/VA3SXRzt1JTmMDm7bHx3TuWWNRRPYmaUbzwv6EeWWYljlFrkY4O+RG01vH7eOEDHx+p0AK\neLTqKbTiY9d30VLylYcbQoQb+xOyTPKle+sL6SRHulDJjKIwihf3J1yd57ywWzEb/ZIgzQlvnTa8\neVJz56zFjRysMtN0zvPGSc3t04Zbpw3dOcv1OzzOE+jjZOnL+M6Pp6lwPg/8r8CfjTH+6jNaz0cq\n3g+R7fFjnl847q5avAs8qgdu7JZcmeX4UU3a2sBpa7lz1vBgKXjlcMKL+5PEmdEwK5KO21lrE5ck\nxov775y/QLpNcgVSErxniIHMJPLfi4cTVrVFAItyzhfvbTGq4LS2nDUuKQ9rhck0u6Xmk1dnfPHe\nhgg8WHf0FrohYHQglzDJGCsWcD5psL2wk67K5dZdKMIOEZ6fFUgUZWYoM8u9ZY+RCSo9BMdrx5ZF\nBkEohujpLVRF8sjpRt5OkpRJtgfee0QuUAhcSMZvTmtu7hc82PRs+x4vQKg0Q4rOc7CoMEaztn2y\nHoiJU2RDkhX4XG8p8wRhli7QEgnOMzjF6aYZoeGebV+Qacm1ec6izDhrUwssRLg6K9idGE5ry7Lp\nmeQSrRTNEDFKcLIdmBeG3lqkEjSNQyuBHytlGwP1YMm0TsoRImmhCSnwLlzIIJ3H47PE3BjOmoH7\ny5YX9ydpLrgdeHG/ItMfHsHMSz+dD288TcJ5ZXT6fGIIIX42xvjvfABr+kjF+yGynUvR/M7dFefi\n0kfrjtNtz3M7ZaosJLx6tCVXkoNZwaJQeASlUXz1uKa1gW2XILSZEsxznfg7Il3Nnqv43nq0JTeK\n/VKxaXrmeVKR3ikzTrcDN/cnTEvNurV8mS0gKcf+vo+wWxkOZgWTQnN1UdH0nuPaJja782zagO2A\n88G5BCUV2kh2qxwbBbnW7EzSRr9tkxHb2WZgWmasOktuBNZbQLNpPUWu2S3AR4mRgSwXxOCSMGZI\nMjM6pmqpD5Hg0+MeVCnj1b2n7j0EGKQges/hqEUWhGDbOzrrWHWBWQzMMsmQK7adZ3DQjiZmxnk2\nMZIrwUv7CdgxLTV11ycrBA9lLlEadicZvQvcXba8sFtxfbfidNuzO8nZnWbko9tpZXTSiguRSa5p\nrWdRam6fNExzxf0VbJohKV9PJCfbnofLnu+7WY5qAoYv3F2l9beOjx1OuLNsLzbhd84Sr8wK3jyp\nWbUDWkn2JxmZ/vAIZl7aWX+442lAA+9Vu/7+b3It37VxLi2jhGBSaO6eNRgtETG5TPoIs1zRnvkk\nvFga5mW6Wo0Clq3jyszQe2h7z+ngOJhmvHlSY5RMHiVGUWWKVx9tcS7SWzicZmilWVQGh+BL9zfJ\nLXPwrFtLmWkisDvNiT5gQ2ReJgn6h5uefjjhqHFsGk+eJcHPVZ3mMEYKlJb0znNllnF1lpLN/bMt\nm96N8ioZQ3BUGoRIiLejbc9hlWDcpZGITBCD56z2FEaRK8UklxSZpusttfV0DjIS50VEyDM4mGbM\nJjkyBgSCaSZBCvZKxWltUSay7hyzPG22uZFEGZE6on2yd9gMCW0nIZm7KQ2DxynJnWWLD5FpZmgH\njyFiSVI9D5eWWWb4oZf2WTYDn74+p8gTDPmstSiTkuCVec6VecGjdUeI8P03dvjHSsNrRxtOasvV\necnHrsy5f9ZyVHe8sFfygzf3WBSGZZMUsJeN5YXdkvurjnlh8CMo43wTfnyW6ELk4bpDjLddmxWJ\nJPohEsz8Vvu3XMYHG88UmiKEKID/B8jHx/rrMcY/I4TYI8GrXyI5h/7hc5i1EOJPA3+M1CX5kzHG\nvzne/kPAXwVK4P8AfjrGGIUQOfDXgB8CToA/EmN8Yzznp4D/cFzOn/tOVb72MY68CXkhFa+lYG+a\n8fxOhXWBa/Ni3NgF685Tj5u2iLBbZZSZJouRaa44rWHZWjKdetyDD3zsYMqqtVyZZeRaEmLg7qoh\n+IgSyRlUKcn9s5Zbpw0uRq4vCia5RgqoB8fQO26fdCijcC6yaSMuOlobCUITY6rEpIAgYZ5LWptc\nNBOfNMm75CpZU2+ajt7BTpXmJ7lUTIYBL5NV86YPtMOA9ckaoHdJJkeIZHVthEDIId2vTxWGAK5O\nNa8cTIhRJhWADOaVwdnI/bXF+4jF4cbHUEpxOCmpB4+zgdYGYojMc8FmiKgANsLgHJ2Fae5Z1gNX\nZjkq05w0PUMI9ENyI922A9suZ1l3RJmEM1dtSkJEQfARI1OCtD7Bmz91bcqVWUk5knbb3lGVmkJr\npJB0LtANgVWTLB8mefLWSYZzSZS1yjTNkKDwg/MJLm0U1xYF95ctd85aMi146WCCloLj8Tk82vQf\nGsHMb7V/y2V8sPGssZA98AdjjFshhAH+7gg6+BeBX4ox/gUhxM8APwP8KSHE95Bg198LPAf8X0KI\nT8YYPfBfAv868GukhPNPA79ISk5nMcaPCyH+KPCfAH9kTGp/BvgsCX37m0KIX/hO4A85FxhCMlbT\nWqKEQEvJbmk4aQbaIQ1ur8/LhFpTktwoPv38nC/f3yBiTDOX/ZLWeg5nhsWoEr1tB756XHNtXpCb\npCL9aN3z/E7iWVydlZw1axaTgmVjaYJjOzgyDdfnE+6c1VyZFmyto9SaTRjobMQQWdUdWxfQNumu\nbQfPLFfk2hNcglJPjKcoNJNcUWSGXCuEUGytRaEolGA7trwCUITkXnl9VtAEz6zICCKikXQ2YGT6\n7tQ5jFqy7RN58qW9CkvBrZOGTdtDTG2/VRt586THx4h3acC/rB1SSoiRIXiGwSX5HAFVETgiKQjM\ncs2s0CilON52aOWoB4e3kcFDkUmUlLSDox4U1yvD3iTn0apJigmkdTbW8pVHW37wxi5CQGkUUgu+\n/+qC3dLwxQcbHqw6ls3ALFO8+rBm23pyo5mXiv1pwZunDb1rmRWpCjVashlSpXey9Xxsf/o1KEcX\nAnfOGpznbdVtas2GC4g8QO8cRssPlWDmpar0hzs+yITzNe/42Ibbjv8141cEfhL4A+PtPwf8HZL1\nwU+SuD098FUhxGvAj4yE03mM8e8BCCH+GvAvkBLOTwL/0Xhffx34z4UQAvingL8VYzwdz/lbpCT1\n339QT/gbiWUz8IW7q6TNJQWfeX7BTpVd/BHtVhmVUUghxivgyE5luLNsCTHy3E7B7iTDucDn7yw5\nqWtCSBul1oKzZuBo3THJNDf2qiQ4GUJi80vJ1UXO7x0JKi24vlPRWUtvA51z3DqtOasHdieG1gaW\n25aH2yRVIxEcbQeMklgCqy6ihWSSK2alYdUMXF1klJlM8imtJ5Npox2sRYlI8JbGRprOEwJICVcW\nmtYGThuLDZFFmbFsLbmCznkyJcgUaCmQQpJnglmuqIqc3YlhcJFlFtAk0qYPkBvBWd2P858059p0\njjJPwpqb3mItRJk4Q7KHwQ7kmeLaVPC9L+wyWE9hJLfPWqQEWUk2bY9RCimgyDS1dQwO9icZR5ue\nXHu0lFzfKUAIKq3Y9o47t1bMslSRTLVBS0nXW4YhcGWes2odxgSKfMLVacbtZcsrhxN6HzhaJwvp\nG7sV1kf6weOqyKLURMHF52aSK442PdYFJrnm5n6JluKitWaUJNPqAiL/eGXwYRPMvFSV/vDGUycc\nIUQVY2ye8Kv/7F2OV8BvAh8H/osY468JIa7GGO+PhzwAro4/Pw/8vcdOvzPeZsef33n7+Tm3AWKM\nTgixAvYfv/0J53xbwrnAF+6uKPQ4gxgcX7i74kdf3n9XEzcR4c6yfduQ9LQeuH/a0tnAlVnB4AO3\njzcsJhmfOJgyyTSndUoUWgomuebeqmV3mvFoZTmcZtxbduxWhswU3D2t0VJy67RJ7bPeUUjJa0cb\n2tGmelpoZn3GdrB4F/ARsizxeIxWzDKNjZEfeWmHe8ue08ayHRx2AOs9OkY2vUsco7EMKDJNrgSZ\nNkgie2VBZhSBSD9YZoUmMxpftww+0ltP7sD5wLQsWLeeo7rnwcYx2GTjbbJ05d710HrIx9Gjc7By\nHkrPMKQKS4jUhms8VCLBowsj2faJUHlW22SeF6DQgDAQI30ASaDrAnWVLJ/nuaC2SfJfiOSuOa8M\nPgRe2a94tOm5sVtytO3IC8Ebxy3XdgsmhWHVWbZtoO0dD4H7Zx0awccPJwiRkG+FUjy3m5BuIUaW\njePesuX6TnnxuXlpb8Lts+ZtkOjz+YZR8iNVGXzYkuRlpHgaA7YfA/4rYArcFEL8APBvxBj/BECM\n8a8+6byxHfaPCCF2gP9FCPGZd/w+CiG+bWB6IcQfB/44wM2bN5/pYw0h4EOkyNLLXmSaekj9f418\noomb9eFrhqRDM9D61A5RUpAhcTHhj41R3Bg3oKZz7O4UlJnm3rLlN988Y15oVo2lMorSJH2zzgUm\nRvG9z8/QUvH33zwhqOQiqoVi01sebnoQEckIQY7JcnqWG2xMkOdgHWdtAhfsVAqjEvLqjVNH23vq\nwdMOCU1mAecd2w72pgYN/L7nKprBkquC4DN2Jhm3jxtOI9RdUohuPDR9YPArDic5kzLnoFS83jgG\nkgioJtkIwKi3NloDQLI3ECJ56UiRngukn1vnOdlaTuozltsBbRQHkwwbHMttT5ZJZJTEGLE2of9O\n64QgEygOpwohJat2SKKdW8tKOaKQPFi37FaGwQ3stYZCS07WA731PFz3ZErw1eOaG7sl91ZNQusp\nwQ+8sEMzBPYmGWeNxblAnktuPKGCUUKkKmYcoL9zvvHNVgbvR4j2Mi7j68XTVDj/KalN9QsAMcbP\nCyH+8fd7coxxKYT4v0ltrYdCiOsxxvtCiOvAo/Gwu8ALj512Y7zt7vjzO29//Jw7QggNLEjggbu8\n1bY7P+fvPGFdfxn4ywCf/exnn2niy2RKEN3gLiocJZMm2bvF+WbRDUlG5tynplSaZWfJlGTwASNJ\n856xVXc4zegLzSTXaCFYWsfRpmPdCual4Y3jBhcih/OcwQcerTtiELy4X3JtMaHKIqC4c9pwVg80\nzjPL0nCfKMhGBNoX7i6ZGMG13RmDSyoASYVNcH9lKTPPyaan66GLCU2mdKo4ugATCTYE+hD59TfO\nmBnBbFrw4l7J972w4Gw7sFtq1o27eE08sK6haXuybLiwgj5Phu6x1287QE0kkzAxsCgNd89sEi0d\nodSQKp5cKRrn2Z9ktEbQe8frxz0+QmdhVyVOUKYUea6Qo15ZDI5pmTxp6t4RRKRQinqwNF1S374y\nL1h1ljunLQ+WLT7AEDwP3+zZmaQZ3NUZ/M6dNTuVwYfI0Wbgi/c3/PM/8FxSlbaee6v2Yl4HvA2h\n9X7mG99oZXDJfbmMDyKeVtrmtng7GuTr0pJHW2o7JpsS+CdJQ/1fIDmG/oXx+/82nvILwH8nhPhL\nJNDAJ4DPxRi9EGIthPhREmjgXyVZJPDYff0q8C8Bf3usmv4m8OeFELvjcX8I+NNP83w/6NBa8pnn\nF3zh7op66C9mOFq/e8IZfMD6wN11DyGyMzXc3JuwV2V8/s6So02PFIIfuLlLbhSrcWM+mBa0g+PW\naUOM8Lv3V9w5a8m14PlFhZbQD45VHVmNTp6F1txeNpxuekaQMT4KfPT4EPHBE4LkcJGnBCAFy62j\nd9DYFVWeuCUuJOO3tvfYUaFTS5CJCsPGpWGeAia5wHnItGDVDlgHUmtOtwP/++cf8PqDNZs+OXWe\nRwQ6IA+gY2RI3TQev1ooSFXUeSLaLQVFLglEjBkVCvrzZwnegc4FznlyLdn0nk0fOM9zZnxk6yDG\nwF5lqG2kc+lPILOeanQOFUJQZhmFkbR94KvHNdFHgoyYUYU6N4LtxjErNDd2qyRX1Fs669ipKgaX\n4Oln9cBXT7ZUxqCU4Hico00Lw2A9YWy7XjzvJ7Rmv1nZmkvuy2V8UPE0Cef22FaLI+Lsp4Evvcc5\n14GfG+c4Evj5GOPfEEL8KvDzQog/BrwJ/GGAGOPvCiF+Hvgi6UL13x5bcgB/grdg0b84fgH8FeC/\nHgEGpySUGzHGUyHEfwycu5L+2XMAwbczdqqMH315/20otXeL8z/0aa7JdyWvH2340v2O09pybVHw\nwy/uwSgrn49Xm9an3pESglePkizKo03L0brjpO6otGLTWure0w8WLSVKS3wMyEIgYmRWGlrrub/s\n0tDeGMDSu0jvAtsmUltPJiW5gX6AdohE7/Aiydb4kBKPv9DGB+V5WyUSgU0bkcrjXPLCmWU5zlnu\nrwLbztH5SPuOuvOiDQa0/bn9dPr/+c/2seMKoHdxtFIGa5PCwCjxhgM6D3bjEAJW9YrOphbe+UNH\n4KyJ6FE1odGe1qbfVlqxP8voB09hAClxPnJ32yCEYFao5IsQYN05Cq3wURGlpFASLQVH6z45oG4H\nRITaJhXwXCuONz3zMnJzf8Jzi4J7q45d6zlrLPuT7G1ET3irivmgqpJL7stlfFAh3pvPOR4oxAEJ\nGPBPkC4K/08SF+bk2S3vWxuf/exn42/8xm98u5dxEdYHbp82lEbx5mnN8aYnxsj1RYkLkavzghf3\nJxdXmY/DrX2MvHlS46znb7/6kHXt+N17a4RMSDkjYNt7jAh4IanyBFlu+sC0ypgXmqO65+Gyo1AQ\nhUwtpBBZN5bN8Jba8ygiTaXSxi2AeQa5FkQSSMD6ZAe9GbOABEoJTXgLSlwA13b0RVU3yQ1EyxvL\nt1cvAMUo2Ok9tONtKS2m0OO6AnCg0+OE5LNGDKlCkkCm0rrEeKwkJSAzfg+8Pcx434uJRCqJRKT5\niYq0nbvgwnjAuYjRyY1TCsn+zFDlGonEyLRxL0rD3VHleTHJqIzgzmkS2CyMYm+ac2O34vqi5IWx\nolhueyIwK82FJI318WusL26dNmNVIp94zPuND/K+LuOjGUKI34wxfva9jnsapYFj4F/+plZ1GU8V\n5yS3dTvwYNly2qSZzbWddBVrfbi4ynwcbm19YHdiONsOnLUDkbRRPL9TMnjHuuvHeUVA6Yy2swgX\nWTeOPNP4CGf1wNkmydy3NuBckpwptSKGtzbm840a0sZ9XlV0FopSsVo5pE6JoX8sawSgHU88x8oH\n4GTjyPNUuWjpad3XJpsceG4uubcODONtGphoLpLb8Ni6GpfW6gEZ3rq/QEqQirfmPp6U+DIDG/tW\nUuWx31c6XQw8NzNEoTjbWPrOs6gUUQqEgLYPKAkhSkqjyDONUcniYNs6Ducle9Oc++ueQklu7E14\nab/iqO7JNz0uBKxX48zP40YC76OzjmbwSCA3Ci3le1hffPNVySX35e1xCZ74xuNp1KL/4qgYbYQQ\nvySEOBJC/CvPcnHf7SGl4Mos52gz4GPiouxUhuNNj/PhApn0ONy6yhW3Tmo+9/opJ9sBARglRptn\nObLpI9YFlq3FWktuFFOT2mkyBO6ctiybYdygHLhArgXEyLJ12JAG8Ea8fYj3+JyljXC0SsgxoxKs\n+J3VwnllZGTa2AXpn4OpIUbYdIFm4GtCAcd1QJCspScifZCHcSBz3q47j2H88ozouHfc3+PPwYzf\nhUyP86T633rYdnC8cVibXiMfAtPcsFOYtI4wvj4xcFx3eOfYr5Lczaeuzfjk9Tn/zA9c58c/fcAr\nV2cczDJcjCy3PVpKrswqjIQ7py298+yUmjunSU7ncJpz0gz8zp0lt88atp19V+sL58+dUt3F7d9I\nnM+GXtiruLlXfdcCBs6Vtz9s6trfKfG+Ew7wh2KMa+CfJcnRfBz495/Foj6M8W5y6c4FmsHhXHji\nMe+87fz/w+BpBocgaZ596tqMm3sVIcKqtUwyxdV5usocQmpBSSl4uO5BwLqznNQdrQ28sFPy8tUJ\nexPDTi65sZvj8OQCBhtoreN0Y9FGUOSCeZa4Hh7IpAAl8D6y7tP8w8ck9SJ59xI5kjZ3TWplfb2/\nSxfeAgL4AP0QmZUyyeSQkoAgVTYTmZLX4NI6+iF5+TigH9tmhYHisU/2OxPMk+K8VXZ+bN+nhHOu\nyXQekqRuHYBl7XmwHrA+tQ03jaXtLU2fnpAHcjnKDyDpXOR0O2Bd5OGq49e/espp7fjsi7tkWrFp\nHL2L7E5y6t4RRbpQ2J1kXN8pWZQaJQSvH9doIXAhUPeWu8uWK7P8idYX287x2sMtt07b9Lny70z7\n7z+kTO3D79ar+sfBE5NcY1SCpV9aJLz/eBrQwPmxPwH8TzHGlfgGr5Y+avFuw9nH21whRPZnGdPc\nXBwDvO28nSqJMq7agdcebZkXBhcCx+uOqjBkSnJ1ltFaTTV63lyTSZvrtB5YbnuO6543TxqklFSZ\n4nQz8NVHW1pr+Z27a6x3iCAYfMRoyU6lkFKxbGzaEOse533abDNN75MzZggwNckh09vEY4H33sw7\nUnvt68XjRUyVJd02IRKay8hUuURSBdWH5IPjGe2iYzKHE4wzmQzKXJNLyXE90I8kz/o99tkEAodc\np4pMjI9p7dsrN0eCdmdqnD2lfIyL8HCd4Na9g50CSiNobXJwRcBuafjK/RU7Vc6qtWwGz9QIXtyr\n+NjhBOcCh/OML95bM680RkmMEtxdttw7a3j1Uc3h1JDpNEdZ18n+OoRRj+8dkSlJpiUv7JXkIz/n\nEl32jccleOKbj6dJOH9DCPFl0oz23xohz92zWdaHJ94NMvrcvLhoc2UmtblWjyw//PIeAPeWLQLI\ndPJDGZznC3dXPD9P2mClUbgQuHfW4CMcZIreen77zpIf/8QV5lWG84F7y5YYIh87mPDaow0P1z3W\nRT5xrcA5z+cfrMiNIoZIDIG6d/RDIMR0xTpYl1Sh4yibYkOCGXvovSNP8mN0dkR3ka7uzzfe8+H6\nB3GNp0kJprWgdMBZaJ5wx0/osqFHxJl3UJSC1rvRdTQlqXcLOX6dz2qmBrSRnK4D5+DwJ4X1qYKr\nH6vcMpJS89SAUAKtNcJ7Pn1twY3dEhsjp61lXhqMVpzVA7dby9Wdit//8UMKrVi2dkTvOaJIFeYr\nh1MmheFgkrNsLS4ENq1lv8opjcL6yPGmf5vDJ6QNMgLVSDSWiMsN8puIS+HQbz6eBjTwM0KIvwis\nRl5MTdIx+66Od7vqab2/UBW4+ICGpOBbZpq6T7XBuZe8FAIfIk6kgeQ8NyybgSjE/8/em8dYlqbp\nXb9vOetdY82M3Kqqu3p6urrdY/fMtAf5D2wsjS3LCxiwLRAeGYMlsLCRwDI2SLYAsy8CLEAjsLDB\nxjb2ADYauzUMGNsyjGkPPdPLdHd1d225RMZ297N+C398NyIjsyIzI6oys3K5jxSKGzfuOfHdiDjf\ne973fd7nQQrop4qoE1O0Fq3vWQIvqpbdSUlpQha13onw3jEtW/YmNd6FyfxOIomUpKgsjQWtwDtP\n6QRISyfWRFpSmwZhPU6AUuF1VXmvqX86ozlmcyU8mTsPA9wuwjnjFs7STzoLjkBIEIAzsDtucf58\npbRjJttx4NEKDmfuzPeTEt5ny/1kiWNYQvYVRZBpTR5JGgt5GrE1yJiWDclSX00pT2ssUgab6q9G\nih++3CPRii9cGzIrW1rn2FvK1yRakSch9VrPM757d46UAuthZ5iFOakHAslqg3yyWJEnPj4uIm3z\ne089Pv2tP/ckF/Si4WEXdabUiapAHIXMBx8UfI9fK+DksfNBIUD7sCEcB6T9ScmidUjhyaOI1jhu\nT0viZTa1qA3zJlgh785q3r47QyuYVAbhPKOyRQjLrSPLvA5ik86HvoeX4PAIAbOqBSnRyhNrRdNa\njIXd6uHZy3EAetJt05Z79OaLwLNkp/lgZxAvg9B5znU8ALo7f/j7PWblnRVsTp9oYWCoBUkcsRML\nvLeMlirga6miNJ7WGarGst1L2B5kSAGHs5pxZbkySMnjMDhbt+E1xnnKxrI/a3FeMMxjrq1l9LMY\n532wIX8gkKw2yCePlXDox8NFSmo/fupxCvxG4Bd5xQPOwy7qOFKnVAVCBrHRCwhqgM0AACAASURB\nVC6QUgiuDDOA+477wtUB46LlxkbOd+/OqFpLJ43IYsfupAZXstlNqFuHIgxreu8ZpJpfujVFeM+w\nE9PUhtG8Jks1nUhSGoi0R1uJFKHsE2uB84LWObwXNI0lij2dOMYS+lLKP75U9jxydAyhp5JwsfWd\nHhQ9K8M5LiMeD65qGQgP7anjDSBsoJUnkWKrkzDMYwZpxBdvDPnB7pzv7c9Y1Ib1TLMzCBYUvTR4\nGkklOZg3bPViUi14bWODWCnePyxIIsmPvr6GBIrGEmtFufRPelggWW2QTx4r4dCPjouU1O6zj16K\ncf7FJ76iFxAPu6gfVBUIJZD7X/PgccG1MePNzS7fP5xzZ1TyzTshmMxbh1i0yL0Zn9rukSWKqm2Y\nVZa2DUGtF2tK7/jOfo2b1SAEiYJPb3bY7id8b3+OsY7Roua9o4puorg8yDiSNUVtubwRESnNd6qG\n+UdJM54j1JzhmfEIHCsVPKw8qIBeFALLuA1Z1DHZwAJdHdhxRkCuFImSzCrLvLJcGQraxvHpS112\nhin784ZbowXGeSId1KVHpWE9j2i0Zd5YNEEOab0bY5cKEMdeNtbDlaXH0eMCyWqDXOF5wcfxw1kA\nbzyphbzoeNhFrbVEn2KfS8QJ9flRXiRJpMi04t2DBQqPjhSZ8+zPKza7gc02KhpujUs28gjrPfPG\nUDUt392fM120JHHo+M9rEKKg8Z5ZbVFScrWfcntcgbHsTxq0lmz0FZd7Ge8eLWiX9aPT0/ufJAT3\nD2ielrF5FC5CZnjc+RwwacM6JKEsudOXeKGIl/M/iRIYYJhFQdnbBkFPgWBWG4YqpjKONzY7XB5k\n9BPFO0cF652Yq2sZzjrGhWGrJ5k2lundOXcnwUDv2MvmWENNiTDQu8IKLwou0sP569y7fhXwOeAv\nP41Fvcw4i0IdqyBF0xrH3qw++V4n1eSJZt5YFpVhq5fQOseoMhwsZmz1Eja7MW9u99joprx7OGe2\nqImkIos11jk8Au8ct0YVdWsZdFImRc3e1JFoDVLgcdQtFMbxC+82TEqLXU74f5Ils9NT/p77SQAX\nnSY5zkI+zmuP1yAImU4USSKpMECa6OBDo2A0bYiVxjhHJ9bkkWJRtsxrQ20sb271WOvENNax3Uvw\nQrDeiQNpxPtlb87STyMQgbFmnKMxLsxXzZszNdRWWOF5x0UynP/o1GMDvOe9v/mwF6/wYZxFoX7v\nYEGsJa1z7I4qrgxTekvKc1nbINg4THj3sMDZYHz22npO1Xq2ejF3JiXOerqZ5vow4/aooPWeojFB\nksaaoIzcQpZImmmBEgrnBdc3O9wZLdifhejS6SoiHFqG5vvjMpuYewOej8JHzZLOyk6O7+fPG3Ae\nlKY57zGPgiOw2cDTSRS1EwyziG4Ws55F9KOI0jhiBEks6eYJG72Efp4gpedgESSHBILdSYmx8Jnt\nLp0kYrKoA7nDeVrn6SYaqQLZ5HI/5da45LWN/ERDbTVXs8KLhIv0cP4vIcQl7pEH3n46S3p58SCF\nGgG3JyWX+wnTyrA/rxgVDb/q2oBOGgGWG+sZX31vhFaSaeP4/JUBsVZI7yhbSxppPhgVVMayKFtG\ni5ZZEXSOZ5UhEmGzTyKYlAbhglxMHMGsKIMIZCqBwCOeGX9fI/xRaDhfj0QQJL7Lx73wHDgdaGLO\nnsk5jeMey1lB55hxdqwLd5zZPI5O7QnDnbEOzLFunvAjN9a41M+41IspWse3d6fUbVB/KCrD/qLh\ns1cGXB1mfP3WhDvTgkgqpIStXsLdacPlvuCgaPnMdpfbk5qysRSN5Y31HL3MghEQ6w8PHrIc/lwR\nA1Z4nnGRktrvAv5DgomZAP4LIcQf8d7/lae0tpcOpynUxnluHhXcnVbMKsMg01TGUTQtX7814c3t\nLqNFi/WeK8OUrU6C8Y6v35rSTTUHbc3uUYFAsNaJsQ6+fzDn3YM5e4XBtyFraX2Ynvc+WAbUTVBO\nXth7wWIQOzoJS521ML1/XpznpcdB4aLZyeNw3kLScXns+P0er1meetxZKhqcp+zmCAFnvaPY7Ces\n5Ql4uDutuD0uubqW0U00WgkEljTWXOon3FjLsXiq1rHVSWgsWOe5O2m4NszZHqSIpXxMlhTsTytq\n6+hmevm6it1phQQ6aXRCwW+N49apUuyqzLbC84qLlNT+deDHvfd7cGKu9r8Dq4BzThxTqG+PS26N\nSpSEK4OUcdFyc1TSSzTeQ9NavnF7whevDpjVllhJJsu5nK1uwuGiYVzU3B5VQNiI3tjMmRYtHk8q\nHaWUNNaeTNGn8T1xTE+gDB8rBDQWMidojT8ZmPwopajHvn+ebE+ofvxLTnDW+xEEplnjl7NJQFcF\n+4LFQ968JohyJhr6SUKWxOzPG7xf0E0U/U5CFmn2bEM3FvTzDp1YcXdWsWgMkZKs5Zo7k4os1sE5\ntbVMipZMLdWfpeDT212uDlJq54iEJI0VWkl2BNyeVFyGpchnwt6sfqnM0VZqzC8vLhJw5HGwWeKQ\ni4l/rkCgUF8dZhjr6KURdWsZLcbMqpa1TsKntxOs99RtUB9eNCEfaJfin2mkUEKQxpokEmipKBrL\n3qxm1gQ7Y4TE2pZOFNQChrlmVhs6S98WVQdhTr2cdnQWFq1HS4g1uPpim/l5cZ7J/4vgIpnSw/pB\nUoZyXyeBebv013lIsJEErbdOFDhza92I9VwzXjQczGoWraKbxYzLhk6sqJ3HOEcSx7y51eXGRodU\nK1rrGBWGamkIt9aN2ewlCHVvpssZh1yyCfdndRgOdp4kUlzqJlwZZksjt+df3+siAWRlZf1y4yIB\n528ubZv/x+XXvxv42Se/pJcfkZLESzHFJFK8ud2lMpZ+ooi1YpBodmdB9Xmrl3BnXNLacAGu5RE3\njwqO5i0CQdE4JkXDwazCGMu4aikqi/MQadgZdhAStIxojKEjBYuqobH3l5ba4BgdNNTO+T6eRhb0\nLNEQNNFigkp1P5GM5g8PYw5IpGCrn6OUQgvB9/eLpaKE441eh9o4jLUY64m15OogPdFBOw4QO4OM\nw1mDE55YSbZ6CVqFG4kokh+yiD4UDfOqZVS0NCb8ba8eZzCO51q+5iIBZGVl/fLjIqSBPyKE+MeB\nX7d86qe99//z01nWy43j0tp7hwv2pmF7/+zlHpGSJ6ZaP7zTY29SgYCNbsxGNyGWkt1ZRWMtTWuD\n9bS03BqbZWNcEolg+9waixCCadnSzzXWOza6MQaYlw2FDewxx9Ijxl18KOtFDjbHOFGhriDGPTZr\nao0nUbDWi9if1UQ62ERLBAeLMGq61omCOngccbBokFLS29R89+6U/XmDFILeUg36+O99Wingwdms\n7V7CP3hvhMCTxIqNPGZvVnMjUk9FvuZJlbQuGkBehGxthY+HC+0x3vu/CvzVp7SWVwqxCmKaN9Yz\n4kjhnKc2jqvDjEVt+KWbY/amFa1xbHZTjPVksaaXaIZ5zNG84f2jgknREAvB9fWM7x0UxHFEmniw\nLuijCeglwa54f9qgZTCyyVXIaFINB1UIPI9jfL3saHj4BSGBTIHWYLwjEp7tXsLRouVgXmMdFLXh\nS6+vsdGLOZg2bHYTdoYpZWP5e28fIKQgjxXrnSBx1E0jrq/lJx4zD9vojzfiSIXnlJL3iXWeVroQ\nS5085/xHChZPsqR10QCyEht9+XERx8/fKYR4WwgxEUJMhRAzIcT0aS7uZca9izH8CY4/O+f51u0p\n88qw2Q1DgbcnBdO6RQjPd3Zn1MYx7CS8daXPeiciiSTTylBULc47hIf1XsIg11zqJmSxoqjDxnh7\n3DAuA0utNrD/CHHOVxEPCzidCHqZXM7SKJRWKAG9VLOexwjpWRjDvA5SNkIKRkXD4aJhb9bQWEuq\nJYlWTCsT1LeXQUFK8VAnSeeC9UCiJXmsiaRkdxII5qc3YimD2vjNcfmR3SiftMHY6QACPDaAHGdr\nrfUsakNr/Ups9CXDRTKc/wD4bd77X3lai3mV0JpQXlBCEEeStSxCyjBrUVtLpCVCCCIl8D4wmfDQ\nOscwi5hXhsNFy6QyRFry/qhi0Vq8czRCMiksDk3jHAezoB5Qt/dcOGGV0ZyFh/Wvyha0cGhlMFbT\nTzQTZ5hXNcNco3UWfG6UYFG2xJEmiYJL26io2OomJ+W6xjisDTcbSohHlp6OPW12hhn7S+pzYzxb\nDzh8Pon+x5MuaX2Uct9KbPTlxkUCzt1VsHkycM6zN6vZGaSMipa6tdxuLF+6sUasJIlSFHVDJAWt\nDVInkZYY54ikREroZZrv7c253M+CRlfa4p0iT2JG84ZZ09JPBNMysM+8tyfWzM+DNtrziOMh0Afz\nAk0gFtQGdOORHZg1jqq1ZJEii2Le3E7ZndbMa0ukFFfXYxIdGISt8WSJpm4dd6clQghe38hPxDdb\n65YlrHs+R867kxKZc6GcdnWYURuL99CJ7790P0qweLCE9zRKWh8lgKzERl9ePDbgCCF+5/LhV4UQ\nfwn4Xzh1I+i9/5mntLaXEs55KmOx1pEnGi2CgnTdWMrW0LSCN7ZyiluhBIZz5JFkf1YxX7pFKiXw\nHo4WNZ1IIfy9AcA7ZY11AtM60n5GbRuatqVt/Ykt9AoPx+niUcqSwQd0YpAR9OLgsHl7XHC5m7DZ\nT2hbx81RxfW1lCtrOVEk2BlkXBvk3BwXbHVjklhRLoPPD13unrhwQig9tdaxN61OejlrnfhEW6+2\ndnmehE4SsTP8cJZw0WDxsF7N0/DPOSuArGZtXk2cJ8P5baceF8BPnvraA6uAc04cX+TGOT4YFRjn\nKBvHtGzYHVcI6ZnXll4a8+mtDr1cc+ug5u29kmnVMkgjht0EYxxaeo7mNXes43BWMSlbihryCNII\nnJR85/Y8DHa2gSK9uqwfjdPBRhNsCqLl1yJSdBJJnkY4K+glEYNOyls7fRyevWnNtfWcy8OU7W7K\n/qyhMJbGeLJEBdvoyrDZi9jspcHsbVnyOv7hx7FBLJv+d6cV3gdlglhKDucNr613Tpr4D27ajwoW\np1/L8mefVX57FiWt1azNq4vHBhzv/e87z4mEEH/Me//vfvwlvZw4XWNPtEYA7x8s2Ogm7E4qjhY1\nSaxRQjCel3zbGEbzCikVWoExnv1ZjZCC/UmNx7GeR/zK7pT9aUukgiHYog0S+l0dOgZyqSxgzSrg\nnAeKcFEcJ4OJgiQWKAHjoqVsDJf7GZd6CYM0pptp+knEeiehEyu0kIyKhs1uzI21nMN5QyzlkhRS\ns6gDqUPrUzpoQKQlNzY6J0SCWdlSG8uisSgh6OdxuDGZVXwq0TTLIPHgpn1WsHhwg9/oxo8svz3N\nktZq1ubVxpNUCvgnn+C5XjqcZqVZH4YC17oJm72YXqrxAiZlzaxqOSgMZWtwS2uBu5OKwhhmdcvu\ntGLRGvbm4Q7aLjVZjIcsgmT5Fy1N+DAOIhXumlcVtcfjWNbHsaRCx5DFKmjSRZo80iRJxO6sZndW\ncPOwpDKWQR5ztGi5PSnZndQY59A6DHUiBJWxSCnoZQov7mdsHZfDnPMnigLHhIKmvVcii7VCeGiX\nm/RZbDK51GI7ndk8+NqDWaiIn5c99iRxFjvTeX8SeFd4ufFxDNgexOr25BE4XWM/vrCVEHjvGS9q\nbo4KpPekcUTRGrqJpKpbSgs4mJQtZeNojQMBk6JlNCtY1AbrwZtwN94sBzgFQaZGeihtaIQ/KdHM\nlxmnvXA0QYkg8h6FQyDpZRHzsqFpNVKAxXN7XOJHJUopBpkmVoK7s5o3tzxZpOnGGiEFm3nM7qym\nbixKBR2042zkwXLYlaXh2t1ZzbRsiLViLY9QckmfPydB4GwygWOrl3A4b55or+Zcv9/VrM0rjScZ\ncFa3KI/A6Rq7846NboKS8PffGTGuWhq7DECNoRMrWuP43JU1vn8w43DWkGhJL41oWkccSRANtZN0\n0hgtDYcLR2FgkICOgtd9Wd9TEogJd++roHM2smVJMo+hqsPwpCRkifXUoiPopR6hJLGCQapJlGR3\nVJAnmklleGtnQGs9eSwZLwIX8LQ2mtaKL91YI9LyQ2Z7lwfpmeWwL91Y4/akRHhQS0WCSMlzb9oP\n2+A7saazru/7ec+ikf80lBFWeHGwynCeIR6cCP/+vmc9j8jjHkoEO+iNfsy1Yc4v35zQyzWf2ury\n1k6QQPnB/pxF7RhkEuEdRWPoJhHOO94/LHAe8DCuDf1Y0ksEVWE5MiHQRITZm9WdwT1IQjDuxJAq\nxaCruWNqhAiq0YmC1oFyoJVG+OCiUxpPPw0DoL0sYtpYRmWLF5BFirU8RkpBckZfxTnPrQcUnm+P\nS64Os/vKYQB5ovnUZvdDgeC8m/bjNvjjjOhZNvJXszavLp5kwPmfnuC5XlocN2RbG+YsKhPuPJNI\nsT+tOJhVHExqKuMCLRrBwaxCCkE/i4kjy+G8YV5bEJJZ4zicLSiM4OpaQqI1s1tjDkqPEkE8+tii\nQEqI3Grg8zRioJcLpIc40sRKEUUSYR0LvyRdCEDDeh6x2Y3RSKbGkGhJrCTDPKG1kGnoxZphHrHe\niU8yjgeb8A+WuYzz3BqVJ32aBzf7s5r4F9m0H/faT6KRv5q1eTVxEQO2LeCfB14/fZz3/p9dfv53\nnvTiXmacbha/uz/HWk9tLFJLprXlci/hW7enbHRiEiVIk5iisXjv6EWStU6MsZaq9cRaE2mIpGRv\nWmF88GwRMmQ2KrR9zm0w9iqhn8OgkzBMFZFSfG6nR29vzjsHBdgWrQRJJImkQMhgC6G84HovIdWa\n1zc6GA+X+jGRVtzYzKja0Ly/OS7PzBROl7mkDDbTkRL00gjn/bk3+4ts2o967Uo0c4VnhYtkOP8r\n8HcIpmurfetjolm6fk7Klso4NjopSSyDiZoUTCoTNLaMoxPFrHc0Sik+OFywO2/RErZ7Hb67N6OX\naA4WLXVjmRYVAqgs+DaITUYaTBtKaopXO8NJCBlfRchuWgMKzzCNkFrRWsHOoMNRYWhbSxxpsljQ\nS2J6uebKIOPXvLbO1WHG3qxid1xzfZBweZhxfS1nb14TSXHCvjoreJwuczW1oTGeGxv5SVA4vdk/\ni77KqpG/wrPCRQJO7r3/oxc5uRDiOvDngEuE1sFPe+//MyHEOvCXCNnSu8Dv8t6Plsf8MeD3E4La\nH/Lef2X5/I8C/x3BL+tngT/svfdCiGT5M36UYAr3u7337y6P+Sng31gu59/23v/Zi6z/aeG4hNFJ\nNL/m+pDv7SmMh3UZYcyCg0WDMZbGCiIR0pPGecbzirJ1pFqSxRmjomZStFwZpiitmRQ1xVK3RgFC\ng3OhlCZloEZfUM/xpYMgBNwYSKIQjKeV5aA0JMrTmpLP7vS4sZaTRgJnLL0soZNqXtvo8PmrQ75w\nbYixntI4rA86Z69vdAMZYFwyaQNdXUlBHqkzM4XjMldrHZEKLp9wP0X5WfVVVo38FZ4VLjKH878J\nIX7LBc9vgH/Fe/8W8BPAHxRCvAX8a8DPe+8/A/z88muW3/s9wOeB3wz8l0KI4yvsvyKU9D6z/PjN\ny+d/PzDy3r8J/KfAv7881zrwJ4BfC3wZ+BNCiLULrv8jwzkfNLLOUNo9LmEkWtFJI966OmC7l3B1\nGGY2hrkmjTUbHUUeCcrWgIc3NnL6aRT6P9YyKQyDTCOEZD1TDDLFtUHKRq4QKtgmWweVgVhBJkIg\nepVREe5kBOF3YxwsaoPAgwhMtaNFyxdvDPnilSHX1rtcGWZcW8u5vtYh0ZqmtcGSwLqllppib1bj\nredw0eCdJ4813oWvxcMcRJekgp1h9iGFZOChszZPA8cB8Pp6fqI48KTwqGthhVcLF8lw/jDwx4UQ\nNYFpKwDvve8/7ADv/R3gzvLxTAjxK8BV4HcAv375sj8L/C3gjy6f/4ve+xp4RwjxPeDLQoh3gb73\n/v8BEEL8OeAfBf7G8pg/uTzXXwH+tBBCAL8J+Dnv/dHymJ8jBKljx9KnhsfdmZ4M+nl/4ui53knY\n6sbEWvP33zlC0bI3r/HeE5ugMLy/qEkixZVhhncgKFFSMq1qbKSZVR6tBGXh6ERQtCGjaQnyNqvL\n/R5qILLBD0jr8PfII8UbWz0O5sE91VjPII/ZGWa8sdWjNQ5B8C3aHVfcnZX00oijRcuVYcJmN2aj\nG7OoLUVjEMAgi7DeP/JCO6upfyzo+Sz7Kk+jkb+SsVnhNM6d4Xjve9576b3PvPf95dcPDTYPQgjx\nOvBrgF8ALi2DEcAuoeQGIRh9cOqwm8vnri4fP/j8fcd47w0wATYeca6nivN4ipz2/bDOc6mf8qXX\n1vjURpfDWcO1tYxOGqFFaFh/4VqfPInwHnZ6KdPKMCtrPIJ52bKoDIuqJVWSxkNlPbMa6lOy0Ktg\ncz+Ofx/OQS+OEVLy+mZOmkga57gzrSmMCwrPk4oPDhd4EcpnVwcZRWO43EsZ5jGjRcNX3x1xc1wg\nheBSL2GjE9Nax7houTUuH+tN86BCwOm+ivOeomoxzj00WzqN5yWjeNL+Oiu8+LgQLXpZkvoMQUgX\nAO/93z7HcV2CU+i/7L2filPNyGUf5hP7DxRC/AHgDwDcuHHjY5/vvIyfs+5q74wL3jtacFS0jIoa\nCVzqZWil2JvW7E0r8iuSG+spb+8tsNZRWUuiFOOyhaXqcKJCsDkmBwhWAecsxBpQQb7GWc/houb7\n+wXDVDIrW3ppxGjRBDVn53lzs0sniRAy2EhbF8pesZakcYxwPjinGsftSUWsBVfXMrQUF6YZn9iQ\nHyy4OS44WjSs5zHewWubnYdmCc9TRrFiv63wIC5Ci/7nCGW1a8DXCD2Z/xv4Rx5zXEQINn/+lJXB\nXSHEjvf+jhBiB9hbPn8LuH7q8GvL524tHz/4/OljbgohNDAgkAduca9sd3zM33pwfd77nwZ+GuDH\nfuzHPva+fBHGz+kShjGOb+/OQMCVQUKqBe8czNmfVdzY6KAlDPOYuvGMihphg6tkaxzT2uKtw3hH\nN5K0Lty5n7zHj/umXkJooJNIeqmmlyZcX8+51EtRvmB3UaMQVK0nTzQGT54o1DIIaCFII40UHmMj\njHMczGr2iwbvBJ+70sN6Ty+LkMu/+0fZaOMlmSBWgtc2crSQjMuGaCx5faPzXMzTPAor9tsKD+Ii\npIE/DPw48J73/jcQymPjRx2w7KX8t8CveO//k1Pf+mvATy0f/xSBcn38/O8RQiRCiDcI2dTfX5bf\npkKIn1ie8/c+cMzxuf4J4P/w3nvgK8BPCiHWlpnZTy6fe6o4vjOtjWM8D2Kca3kQuW8ay2hRsyhb\njHHUraVuLc55GucwznGpn3FUtDQ23B0669kdz9mb1gw6mjgKel5CwcE8SKMYAwjPooSDmaOsXm3q\n83mggE4k0VJirGXY0RjvcErQtpZIK8rWMCsrsI71TkKkl4KTWvKFqwOcE4wWDd/dndFNI6ZFS2st\no1lzIsIJ5xPHPKsMZr2naC2jwnC0aDlYNCeGfGeJXT4ojCmFoDGW1n4ygkYry+gVHsRFSmqV974S\nQiCESLz33xZCfPYxx/w64J8Bvi6E+NryuT8O/HvAXxZC/H7gPeB3AXjvvymE+MvAtwgMtz/ovT8u\nfv+L3KNF/43lB4SA9t8vCQZHBJYb3vsjIcS/Bfy/y9f9m8cEgmeBurV8cLRgXBpujSrSWPCDuwum\ndYvznitrGYMsJpaSjW7MVjeIKd48WpBoQWs83nl2pyXvHMyojefbu5rXt3J6UcR7e3OKuqaqHVka\nIZHMhKVYpTPnQg2MC8OnthMipbg9rljvpKwlMbdFyWhe4RFkacRnBsH3Jo3UScYwzGO+/Po6eSRI\nYkWqJfuzhm6qUFLy1pU+i9qei2b8sDKY8MESQYqQ7RxnUjuD9LG6acZ57oxL2qWV9ZVh9omU1lYy\nNiucxkUCzk0hxJDg+PlzQogRIVg8FN77v8vDNdZ+40OO+VPAnzrj+a8CXzjj+YqHWCN47/8M8Gce\ntcYnDbe80MdFg3EwzCPmVcMv/GBCvPQ82Z1UfO2dET/y2pA8jtifV3y1NsRScLio2Z/WjBYNn97K\n+d5BgXHQevB1yy+9N6aXCJSSKCFx0lFULVKG16xwPuQK1joR/Twh1RLnwFvLpHX0M4UQMVt5zKQ2\n9JKIVCukFDS1CX0yGWwG8jTi2jDjYFFzbS2jai1aBbO1G2s5XoDwQR7n2D7gNB5VBvMCNrsxUsLh\nIiiK9tKI7f6jddPujEtujkpiLbixkX+kHtJHwcOGVFcyNisc49wBx3v/jy0f/kkhxP9J6JX8zaey\nqhcY1vuTEoaUAiEEd2clB/OGQRbR2OCF44DRoiHVmsNZzXd2p8RasZnHZEoxb1pGdcu8MmSxJMXT\nGsesailauLHWQUhJLART4/F25XfzMEQEiZ/jVFkQ1KHtcg+8tpEzLlrmtaWbaWatJtECoSV9paid\n53BRURtPYzyRCtJCR4uG/VmDsYa69Ujh8AKuDHP8MsjYZUB5WBP/UY11JQRZrLmRam6s5xjr8ASl\n54chXdLmW+s+dg/pInieyAorPL94bA9HCNFffl4//gC+DvxdoPuU1/fCQYlAbwWwNsxrxFqRJ0EK\nfrSoKRqLXdbhx2XLuGixLrCbCmM5KCqs9WgvyGPNom6oWsfhoqVxUDdwa1xRtY5OEt3n4bLC/VAE\nNtrprS8GkGBbz/605Lu3Z0wrQxwr+llMJ1b0EkUn1gzTmLJq+TvfOWBRtycZwzduTYhUyCDSSOO8\nZ5BqPn9lQLy0DxD+4cObxz0b4Tkpg8H9/Z7jjMU6aK1HiFAae1yWEilJrNWFekgfByv68wrnxXky\nnL8A/FbgHxAIT6f/az3wqaewrhcWUgp2hhmNdcyrllndstlN+OK1Ad++M+H7ezOkCDMfZeNYNBUe\nz/X1jN1JzbRsMSbIzwvgxnrGr+w2LGqD97DdjWlaw6SySMLmkiZQFJ/0MxrwbQAAIABJREFUO38+\nEQswJgScTAS/GyPAWDDK4qxiWhviWLKex3zh6gAlBF+/Oca4ku1+wq+6PqCoHaNFizUOFSusC3fy\nsZa8vtWlm2oSrfCek+a4F2cbpS0aw+G8OckGhnnEuGjP7Pd8lB7Is5aqWdGfVzgvHhtwvPe/dfn5\njae/nJcDaaT4zHbQ49ruT5kULUdly1s7feaN5fX1DKk0VW341p0plYX1TkzrYVQ0LBpDJ43pZ5ob\nax1+6FLOz31rj9vjMmQ5xmOXKY2bOWZuRX1+GLwPcjVrnVDGXDSOqgqp/XZXsz3IEUJgLLTOUbWW\nSdGw3omZli3ewy+9N+byMOdQN/zy7QlvXe6hZFCKgHCH30tjrg0zvOA+35sHacEC2J/VJDqYRpSN\n4XDuuD7MMXhiKdH64zu/P8tm/Yr+vMJ58diAI4T40qO+773/xSe3nJcHx5PjkZJ46alay7wOFNXC\nQFlUvLbe4UeuD3nvaMGkbOkmmm6s2OmndBLNeFHz/lFJoixla0n0UnzTBZZVFyhWweaRaAElQjDR\nHlIlEJFHSyitYFEbhp2EYR7RGs97RwVaQieNUEpStR7jHM46kkjRtpab45IvXh0ybyyL+l4GcRwo\njstlZ1lHb/YS9mc1i9rwnbszrPOUjWVyuWWYxx/qf3yc3sizatavxD9XOC/OU1L7j5efU+DHgF8i\nlNW+CHwV+IeeztJebFSt5eZRwe1xybwyHM5ruokGBGVtOVw07PQzjoqGWEnq1hKUecLdqFKhxHNU\nNNRNuINEKMrGohTEBnQUbAdWeDgsUFiggq2uppdojA2SNVIEooWxjkGq+cL1IdeGGd/Zm3PrqKS7\nnvHdOzMUgjRSXB4klHXovR3Ma64MM7SW92UlZwWI05kGwJ6v+NbtKXmikEIwWtS8d1hwqZeC4IRR\nBjxXg5yPwor+vMJ5cJ6S2m8AEEL8DPAl7/3Xl19/gXuimSucwkkTVQsq61AqbFjWw/60xjpPXVve\n3ptijEdriSf0A3anNZGUSNFweRDz3gcFEkdpLEVpMQ6qpaHafBVszgUPWLtsPnqIVYR1BiFA4nHe\nkUSaa2sd8liz3on55q0p+7MKLaGbxDTWMi1bNjspi9Zwe1ryzTtTfvhyj04ScXmQEit5ZoC4NsxO\nOp9SCtY6MY11pD5YF2z0EpwLWVgW65P+B5zdA3peeyMr+vMKj8NF5nA+exxsALz33xBCfO4prOmF\nRFUZ5sbQ1RoVBfMt4TyRlLTOLllIntc2OlxdT5iWlh/cndPJgjYXzmOBnX5M2cDepGBRNXjX8v64\nBmtZ2EDv9YQm+IoGfX5YwHiPay3WtSxqyyCPQQRGl1Ri2VcJd+tvbnf55Q/GJ2WwfhoFJptq2e4l\nzGpLJCXz2tDPInYnFTuD9EMBYlrVvHu4CJvxMuPpJxE7g4xYQRJrPjgswIf/lQf7H6veyAovEy4S\ncH5ZCPHfAP/D8ut/GvjlJ7+kFw/vHsz5yjfv0BhHrCU/+bnLHBU1v/jeiNGiDU3lXIfa/d0Z39uL\nubHZwQvQAnbHJW/vL2hbQ5oo3tzskESKw3nF125OOVpY7AP20Csa9PkggUEC1sCiteRxmNoXqacb\nR6xnEXkSUVaWsjEkkWZaWnqZ5up6Th4rtnoJUsI7eyWxlmz3E8q2JY01nhAUWh9ozQLCeXTIcA7n\nDa9t5MT6/pLYr7o24Bu3JlRFSyfRbPVi6mVAOd3/WPVGVniZcJGA8/uAf4GgqQbwtwmmaK80qsrw\nlW/eIY8iLvU1s9LwN75xhzxRJFqzM9S8vTvh7qwiUYKm9UyFYW8SyjXvHS4oaot3lso4Wu/53v6C\nWdVwd1pTtZZYwmQVYT4SHOAs9yb+PeSxZlQ68kSx0Y3pd1K884wWDVpb8IF1Zu2CvWnF4bzCmnCO\n8m7D0bQmTzWbHegm+qRfY52nMY69WQ3ARidioxMT6w+XxIZ5zE+8sUHjHLEMtgRn9T9WvZEVXiZc\nRGmgEkL818DPeu+/8xTX9EJhbgyNcVzqh19lL9PcnRbEWvHp7WAh7IGvvX9ElmiUAuddkD5Zz2iN\np2w9V4YZo7LhvYOCojEoKdECGgPtqlfzkRERrLWFAOsDQaC2nkEWEalAk5ai4fp6h/VOglaCUdFy\ncDRnWrWUtaFsHTvDlEEWk8eKg3nN56/2mdeOqnFh7qafsjer6aaafh7RtJbWhSDxsJKY1hLpxEm/\n5nhg+EGseiMrvCw4N+FfCPHbCbYEf3P59a8WQvy1p7WwFwVdrYm1ZFaGjsqsNOSxJkskxZIyW1QN\nVWMwxlG1htZZhICytUgJ67nm2jDHGE8aSzqRQkuY1y1VG2yRV/hocEDVghTQTxWdJAYcG1lC3XoW\njUEq2B4kzBvLUdGSx5Ik0kTSUzZLszvv6SSKNJZs9GJe2+jw42+sc3094+owI9LyRKlZCkEa65P+\nz8PUkqvW8v5RwQdHBe8fFY81aVthhRcdFymp/Qngyyw9Zbz3X1taCLzSSFPNb/r8Dl/55h3GZU2s\nJb/li1cA+Mo371C1lvGi4TOX+9ydVoyKBgl018PMzVonZrZoOCwahJRsduKTaXWE4JMRln95MFAg\nNWSJYpBnTIqGunUwgF99dch6njAuWr51a8qX31zHWviFHxyC8xwuWq6v54wrg7WeSWkYELGeJeRp\nFBhvWp1kJmc1+DuxprOuP1QSe968a1ZY4VngIgGn9d5PxP0MmdXMIfD6Zpef+rVvnLDU4ljx/lHB\nT37uMtOq4dt3ZoyKhutrHTpaMi4NRW35xp0p1nmuDHI2ujHWebwLJZ9bo5JuolnPLGUbmtGzFS3t\n3FBAJCDvSOrG44RACU+kBT0dkWjNII+5ut6hcXP2phXv7i8YZjF1a2ltEOk8mDdorYgU9FLF9bWU\nnWG+nOM5f4P/wZLYSg5mhVcRFwk43xRC/FOAEkJ8BvhDwN97Ost68ZCmmnT562yXPvRZojkoGjpZ\nzKKxbOcRt8cFSsKtScXlbvBiGc8rjIv43NUuH+yXlMv5j51BTjeK+MHBnKJdWUWfF5LQu8ljuNzL\nWLTBDfVw0bDWidgZpFwZ5tw5qhhkCRAcPCMp+P7BgvVOwt1phZISIWBnkBDJYC3xxlaXa8OcSMuP\n1eBfycGs8CriIqJN/xLweYKqyl8AJtxjrK1wCsebSdNavIfNToT3nqN5Qx5rvBDLYRpBN1U4IZjX\nlt1xTRyHmZBLg5hJ1QYnUAPOh7uD6JN+c88xYsLvRwPdBC4NMzppzFY34s3NLlu9lJ1+hzSKGWaa\nxjqOFjX9TLPdTzDeI5bW0JeHKduDlKIx3J0GgdUr/ZQsUuxOH66EfCxp9Liy2MoNc4VXERfJcN5a\nfujlx+8AfjtB4maFUzjeTG6PS+rWESnBT7yxwTd2p5StZVG2dBLFpGxYVC2H8zpMqw8TDiYN7+4v\nGBUNwkNjLXkGvgo6aqu28ocRETTTpIAkhku9hE6s2egmNM5zY6PDwVKW4XBeMehEvH3X8Ws/s86l\nXk4aS6QXVMaSRRVSBh+bWWX43NU+gyyitZ7vHy7Yqiy1cRjriLU6t7bZWeZkK8rzCq8aLhJw/jzw\nrwLfgFUv+3GIleTqMGMtj7gzLjHOsdmJSbRCKQEHnq++OyKNBN00WEx/9+aUaWUYVy3z2tCaFiUB\nIcgjz3RlsnYmNBApSKNALXbOI2QoV42LlvVco6VkkGpmjcU56KSSbhzz5RvrHJQhuEsp+OzlPt/f\nn9NJWt4/LHhzq8PtScVWN2Qfe/MK7z0/FHeR4nxOmo8S4HxVKc8Pcwdd4eXGRQLOvvf+rz+1lbxE\nqFrLnXHJvDbcHC04WrQY52law7QyNI3h7f0CIT11C409dpNsWDSetjEclRZnoQH8snOz6t98GBJQ\nEQwSRdFaBmnwt2mdozIJ4HjnoODGekZlHHhPYyydWDMuam5PS17f7BJpifBwc1zy6c0uHtjoBCLH\nG5tdyiZoqc0qwyCPuD0uuTzIcN4/stG/YqN9GCt30FcXF6JFL6Vtfp7QxwHAe/8zT3xVLzCc87x3\nuOBoXnNnWvHO3pxhJyLRiklpqFvD7UmFcy2xFIwqSxwJVGNwznA4N+BByhBg3KqG9kh4gmJ2rSxS\nQDfTHC1alJDs9CKQmpuHM2aVwXtPEkmcE0xqRzprOCxq1Ejyme0elsAcy5YWztfXO7x7sMB7Ty/T\n5JEii1oiHWwndiclW730kY3+FRvtfqwC8KuNi0rb/DD3LOIhXO+rgHMKrXXsTWuyWKJlmCZf1BYt\nFZEWTAtHayxSabAGIyzSQuUtb2z22F+MwAafG7FMaVYW0mdDcI9EYR10Mk3Tht6IUJ5F44gjR+08\n+7OGfq5pjaNsPeNZxed3+qRasTeteX2jE5r9p5hjWgpurOesd2LuTivuTCo2eglAyFiNX+qsrdho\n58UqAD+feFYlzosEnB/33n/2qa3kJYMk3MF57zEOWmO4PaoYFRXT0lDVLd4LYhlKbV5J3t6fEiuB\njgSudlgByq2CzWmo5YcHsjiYq210NEoqennMMIuw1rM7r/lBM2XYybjUT5AINjspSsGktJSt4Qf7\nM4adiEEagshZRmI7w4w0UnSWygFJJNFSUpvAQOzEZ19Cpy/glQDnPawC8POHZ1nivEjA+XtCiLe8\n9996Kit5SRApyXYvYVw29NOI7W7CwaJmVBoaa1BSsZYLhITRokZ6gRQSrSKKpmWzm+CdozUNZRv+\nQKuAE5CIMFvT2jDU2c0jNjJNmiZEQjBvWu7OGnp5hPWeojVoCdfWOigpGOQxB9MKIT3dJKKbRBwt\nGgZpfLLhPYw5prXk2nrO7qSitXYZjM4OHI8zYTt9zKvWPF+5gz5feNYlzosEnJ8AviaEeIfQwxGA\n996vaNGnIKXgtc0O0Tjcve0MUjqJ4v97f8SntjoczWruzmqGteHaIOWwaOhEinnruHm4YFE75mVD\nGimGHUeLZ2+2YqdpliVGAZf7MZu9ZCkBDalS9FJFGgt2pxVJHGPL4JiqtGKQxSxqQx4rtvsZl0WG\nloK1bsowj9jup/hzXFvnoTE/6gJ+UJzzVW2er+jgzw+edYnzIgHnNz/xn/6SIo0Ur290Ti6o1jo6\ncYRxnso61vKYWAsEepn5QFVZnPPMqqC15oVj0XrmzSrYSEBJSFWwF1jLIzpJzOVhwuG8oRsr3ro6\npG4NX/nWPk1r6cca4xxV69mfV9wY5gzzGJUJKmPY7uds92KEEHQTfZLhnA4CAFvLmZ4TiZrH0JjP\newG/6s3zV5UO/rzhWZc4L2JP8N5TWcFLitMXVITk0iClaC2TsmVSNMwrw6e3u1zqJxzOa/YWFY2z\nKCmx1lE1HiVDv+JVhATyZULQODAOag+XhqFXs9VP6CURmVTcuNTh131qi+8dzPjM/oJR0TIqGprG\ncamXsd1JuLyW8auvDdnsp+xPKsalwXrBdi9hZ5ghpbgvCBgHu5OSW6OSa2vZSS/ncTjvBbxqnq/w\nPOBZlzgvkuGs8BEhpeDqIOP7+zNSLZl6yJYCn5sdTS+LuLbWYX9ekujglbNoLLZ+NbObnoJIghXQ\ntCH4SCBPYZDG1Mbx7t6cPNXsDFKqxrI/rzmct3TTMDvTSSL25iWekFXuzWu+fnPKj9yQdLOY1za7\npJG6T4bmOAhIKTmYViRaIYTD47l5VPD6RgetH60Gdd4LeNU8X+F5wbMsca4CzhOCMe7EvfF4U3LO\n01pH1VreO5jzvd0pk6LBeMe0MEzrBuszYhx4RzdRzH04RhJ8cF7FgOMFZClUDVQ+MNISCelyY96f\nVVgn6BrHZifhBwcFG3nCdj9BCcEHo4J39+cMs4hMK670cyprSWJB0Vr6WcSoaLmxHt13cZ3WwLPO\no1Qohx7OG4rGgoBra/ljM53zXMCvevP8RSdLvOjrfxDPqsS5CjhPAOOi4Ru3JmGTkoK3dvpEWnLz\nqGBvVrM3rZiWDe+PaqZlzf6sxjrDvLKMiwYpJG3bMmsc/UwjvadoX71go4AsgkzBrIJYQZaAFmEQ\n1grBwkAnDvI1znvK1rKeKn7xgzFf/vQGs6rl9Y2crW7CvG6YVIY4CoZosQpzOFIIWu9OylcPUphv\nj0vq2lIpg/QSoaCTaBIlz91nOc8F/Ko2z190ssSLvv5PEquA8zFhjOMbtyakWpLGmmlZ8/Pfvstm\nHjOpDJd6MZESvHO4wFlLoiTeOY4WDXiP9ZqmrSkaRyxhXllmr2gpTRLozrMmBJlBVyFlkKzpRpKF\ncWx1I7wXSO/opTEOz+64YlEbOlrS78SMFqFk6b1gLUu4Msg4Kg37s4rNfkxjLBDKV8ebh3EOPFwZ\nZlzup5RLleiDeejhXF3LiSPFon6yfZZXrXn+opMlXvT1f9JYBZyPicY5rPOkscb5MN3unQcJkRJM\nG4sTntZ40khhnaNsbbjT9uCEozaOtgWhoDL2ldNMS4BeDpEQIASVdUQaZq1HE7TP+mnEII5ASC71\nYm6OS2ZlQ1W3gOfSMKV2jt1RSW0dv/6Ht7k8SClqy51ZzSDVOCeZFgZjKrb7CVVr2ZvVOOeYlC1N\nGzaPjW5MP4vopholFxjnkAKqJtwGrPosHx0vOlniRV//J41VwPmYiKVESUHVGLSSlLUliRWxVMy9\nZVG3GO+xznF7WhIryVY3CaQA02Iai10ysLBBM6j5pN/UM4QErgwVSimEh9Y7utYR6WC/fTApkVLS\nSzWf2uwxXgpzXl/LybRiVht2ZxXOC8rGoaSgn8cM84hRabiylrHRjdnoxOxPG65uZGRRuDm4PSlx\nPtgQKCHoZRGjRcUHRwWDTkRZWw4WNfPScmdcsdlNubae0VhHKlcllI+CF50s8aKv/5PGRQzYLgwh\nxJ8RQuwJIb5x6rl1IcTPCSHeXn5eO/W9PyaE+J4Q4jtCiN906vkfFUJ8ffm9/1wsfa6FEIkQ4i8t\nn/8FIcTrp475qeXPeFsI8VNP6z1qLfnC1QFV6ziYV7TWcWM9AwnzJthLW+P4h39om51hRmuhmyi6\nscYJQdEGxWgLFD40yV819LOYLI4Y5IrLw4ydfoqXirJxSCXZ6Sf0sxgv/v/2zjzIlvuq75/Tv17v\nfmd5s75FlmQZIVuyETKEhCKxcUxCYaoCiU0CTqBwIARDCpIyoSoEKIMpCCSVheACx04wUOBAcKUC\nxmXiOJWyjSWBJNuyjW1Z0tPbZ79rbyd/dN/RaDTz3jzNcufN+32qbk3f33T3PXfm3v727/zOYlhs\nRbzuq2Z5y2vP8sYH5jk9VeWOyRrDNOPi2oArG30qnovvmrJddE6uguM4TDUCqkERKOCaojp0nilx\n8vzFw3cNVztDLiz3eHa5x0Y/ZZBkLLZCGpFLxTeF/36XBmyW63OrN5671e0fN4cqOMB7eXHC6DuA\nj6jq3RSVp98BICL3Am+m6Cr6RuA/icjoNvJXge8H7i4fo3N+H7CiqncBvwL8QnmuCeCngNcCD1FU\nut4UtoMm9AyzrZD5ZsQ9szVWuykOwrnJGg+dm+DsVI2zkxXqvsdcw2e6UeH0ZISjkKYQ38bXrqYp\n2gBEvmGtl7HaTemnEEqOkFENXMLAp1XxcclYaFeZbUQ0Ih8Ph+l6QMVzCIwgUrg16pHhK0td+nHG\nTDPkgTMtXjZVI/Jd0qyoO5tmOcY4LLaLRfv1fkyaK43QRQSMMeSAZwyKEgUeCjgimy0JLC+NUbDE\n6YkKZyZuHPV33LjV7R8nhyo4qvoxYHnb8JuA95Xb7wO+fcv476jqUFWfAr4IPCQic0BDVT+hqgr8\n123HjM71AeB15eznbwIfVtVlVV0BPswhVUoYLSJGnmGyHlILizvoxYmIxXaFWuSx1o35+JeusTYY\n0okzOsO4CBqgcJ/dDt3sdvtKRqFhqVsUM634hqmqT7viYYwh9AwzjQrtWkAvzggDj5dNVTk9UcF1\nHJJMGSY5WQ6N0CdwC9fbpfUhwzhDUGbrIfXQw3WdHe9Ma5HHa860OdUIaUcexnE41QhZaIWcqgdM\n1jx81zBIUgQYphmCXcfZL3ttxX1cudXtHxfjWMOZUdWL5fYlYKbcXgA+sWW/8+VYUm5vHx8d8yyA\nqqYisgZMbh3f4ZgXICJvA94GcObMmZt+M9sXEQPX4IjQG6ZsDDM6g4RHnlkhdBzSHHpxxqX1jH6v\nT5rrbSE2sHMBUqGo3ByYLt0Map5hfZgRuoIiGGNohi6TVRfHMXztuUlOT1XwxCFVZaYZkEuD7iBj\nrRczVXUIPEOaZCQKxilqq70sKErT7BaGXAlcXjZVI1NFyryflW5MPfS4tjFkvhXQj3P6KIMk51Q9\nsOs4FstLYKxBA6qqIjJW34Sqvht4N8CDDz5407ZsJgumxSU1zXImqh6XN4Z4jkPkGxbbAU9e6BB5\nglQCrm6s8OXljNt9GUAp+v5c2iiep2GG50Cnn1OJfLIs4+L6EN83LLR9EHj82TXiJKMWuZxpV7ln\npgEKTy91eOpaD3GEmUYIKGuDlMVtHTl3C0PeOn52sopvHJKy+OpULeBqZ4jnCL5nNme1NhTWYrk5\nxiE4l0VkTlUvlu6yK+X4c8DpLfstlmPPldvbx7cec15EXKAJLJXj37TtmI8e7NsocByhVfF4+Kml\nIj7fdXj5TI1mxaXiGtaHMU9f6fK5SyusdFMGSUqnp+Rq2w7A8w3UAg/yBFK36NwpZBgTkmlOYAzn\nJmus9mNWuwmdOCHcMKysx9y72CAyDu2qT5rlDFIlTnPqoUczdBHnhRFEe8kQDz3D2S3FVzNVnG5M\nWPa+cYzYUFiL5SUwDsH5IPBW4F3lzz/cMv5bIvLLwDxFcMCfqWomIusi8nXAJ4HvAf79tnN9HPgO\n4E/LWdOHgJ/bEijwBuAnDuPNFC2lO3z5Wg8RJVV46mqP82s9HIGnrnS4vDHg4uqA7kDJckj19kzs\n3AnPFP1tMgUR8KUo1mkcw0TVpxoY7pmv4Rnh2kbMIEmp+C5Zrnzu8hpfvLZBI/JpRC5R4NGsOLjG\nYbLq47mG+Wa0KSw3kyH+gplQjg2FtVgOgEMVHBH5bYqZxpSInKeIHHsX8Lsi8n3A08DfBVDVz4jI\n7wKfpbge/5CqjiYB/4Qi4i0C/qh8APwG8N9E5IsUwQlvLs+1LCI/C3yq3O9nVHV78MKBMEwyPntx\nA98V6qHPIE55+Okl5poRjqM8u9xnudvHccBzIb5NqwhcD18gTcD3ACm6aFZ8FwFm6hHdOGej3ysu\n8sZhrZfw1LUNKr7LV8+3mG0USZ93TdUIA4NBEBHmWxGVoPiI7ydD/Have2axHBSHKjiq+pZdfvW6\nXfZ/J/DOHcYfBu7bYXwAfOcu53oP8J49G/sSyVRJs6LCcz9JSTPoxxmB6zBIU/pxSmeQMswKH5rn\ngJMXHewsReCA50PFwGyzykTFpxa5GMchzZXuMGOyBqcnK1xYH3BxuY84QmBc6qFPnOZUI4+sn+B5\nDndM1nBKN9pWQdhvhvjtWvfMYjlIbKWBfeI7RY/7yDMM05zBMC762Bjl889skKQ5uRYX1l5aCI1L\nESZ8u6/hGKAZgW9cotBlsR0yVYuohi7LnSGVwOX0RJWXz9ZY6aXcNVWjFbh0hilJltOMPKqhy2CY\nghadP3cLVT2IDPHbre6ZxXLQWMHZJ2KEe+cbfGWpS5xmOK2AB860eOSZVS5tDKkEDkhAN04REvwU\nKAMGBtntkYMjFPXStuYcGaDugzgulchlIgoJXY9emtHfyIg8w52n6tw1XaMW+aTZgBxlvl3FceDB\nc5N84UqH5W7MRpxy71yDheu4x6xbzGIZP1Zw9okRoRn5vGrOJRXFyeC59QGvOdMiS3OubQy5sjEg\n8oSe75BmRVHP5Y0BSfZ8QmQy1ndxOIy6dlZ8cIyDb3KurUMQQDvyUAc843LffItaxaM7yJhrRrxi\nro7rgOcYHOOw3o9xjcMDiy1U4NrGEAW+9qxPs+pR9VwCz9xQPF6KW+yk9T2xWMaJFZx9MgqLHvXD\nEYVaZHBd4Y5TNYxxuNob4roeFcCERcmUOMtZHcQoxT8hoxCfkyA8o/IVATDb8pms+YSeh2hO5A9o\nV4ros/VhSt13OTtZZb5dZX0Qc3qiwl1TdXKBZ5Z6NAMX4zrMN58PAKj67p5FYLtg3IxbzPY9sVgO\nFis4+yTPldVewtnJSrFGkOd87sIGq/0ha72YeJhwqh4wVQ04v9rjaieh1xtCnqMULqb+6FxjfB8H\nwUhoQoFqAHdMV1lsV+klGRO1gDtPNWiEhqeWejyztEEU+Cy0QppVH8+F7iAjz5XLnSHtisd8M2Sm\nGRK65gWtnfcqGvsRDNv3xGI5eKzg7JNR9FPgmCLiSYSlboxnBM9zcTxDpLA4USFHWVofsj6MiZNC\ncE5K4ICh+DC5BioevHyuiW8cunHOA2fb1AOPM5NVzk5WSbKrxHHGVD1gkCqXN4Ysd2LOTlXoxjnG\nSdgYJMw2Qq5sDF/S7GInwbi42me+Fe2pBta4+55YV57lJGIFZ58YKfreX1kf4DhCL07I85yoUpTG\nv3u2wbNLPS6sxix1hlze6LLWy9H0ZOXjjITTdYpEzn6SMlGtsNCq4HsuzShgqZswU0u52hmSISAO\nril7zdQDjGMo6rMqIFR8Q1hWeL7Z2cV2wUhz5fxKn6RsQXAjARtn3xPryrOcVA67PcHtQZklD+AZ\nBxGI4wzXLe6F65HLM8sbDBIl8ANC72SJzYhRuLfjQC8uOqGuDlPSLKdZKaoDdNOM7iClERocgdVu\nwvnlPkmSgUDFd7naiSFXfO/52cXNtgTYKhh5rlxc7RfJuZGHZ+SGPW3G1fdk68ysGrh7stViuVWw\nM5x9kqniuQ5nJqvkueI4QuQZ/vJyh/VejOcaZqo+gWsIXCFyR3fJt+4FxKH44MQUAtN2oa/QiATU\noRq6eI4QeA7ksNJNEIGvXmiw0Iy4//SQC6tdzq/0yfKcyXrIuakuuzhjAAATUElEQVQ6q92YPHJB\nYaoZFH9PIy85Z2YUBh2nhWCcKdfZ9loLbRzJnuN25Vksh4kVnH2y9U569HOiGvIt99V5/PwKf3F+\njaXOkLV+kYWSpBn9WBGK/JRbSXbqpqiU4HswzKEu0Kr4LDRCurEShoIRQy0wxKkyXQ3ppjn3zNaY\nbUScLqsw33WqRi1yqEc+vhEGaU4tdBmkOY3Q467pkLOTVa5sDPeVMzMSjKR0i7nl8TcjYEed7Glb\nGFtOMlZw9skoLPqJ82vEWYZvDK9cbFLxXRSH+xcbLPdSVDM+/dw6jhReTFdgeIuojQMEDkw1XBpR\nwGIrZJArHjBZi+jFKacDQyfOaIQ+uSr3zDRoV31Cz2FxosIdU7XNSLOzU1WMETQXfNdhqh5wbWPI\nMM1ZaFdYbBddFM94Zt+zC8cRAscw34puiaRPm6BqOclYwdknea5cXh/gOuCWDbkurw9wWxGDJCPJ\nMs4vd7m8njBR8dgYpCxO+EWZm+X42K3leBQBAFsrAvhAM3KYawS8fK7NHdM16pHLfCPkaidmY5jR\njFxW+wmDOGehHVL1PIwRcoWzE9VNsclzxTjCuYkqM41wM4lzrhkxVQ+o+u7mxfUgZxe3Ui20W8lW\ni+VmsIKzT4oItSH10N10gVxZHzLfCFnqDOklKVc2hix3+nSSrFxPcFjrDsZt+ibu6OFCmoOrRbuA\nwINm6NCs+kzXK9R8w/xEhDEOFdclziFOlcmKz9VuzFQ1YMOk1DyPDJitBSy0K5sJmztFX23tO3PY\nF9ZbqRbarWSrxbJXrOAcEgo0Ky5rSwlxktGJc9Z7McNMGSYpvgvOmEtGB0C7BnHZGkDVoR66ICAK\n/STn3tk6tYrPZDVgphFSDT1mmwGR52EEVvsJtdAjKwMmAtewOFUhTnLOTFYJykiz6yVSeuZwgiVt\nLovFcrywgrNPPONwqh6w2o+JsxxV5VQ9wDMOVd9jrqU4Tg6qPLsinF/uMUyVYTKepE9v9HAh8EHE\nZa7pMd+OcI1wbSNhmGW4juGOKY+JqkeaC444hVtMhTMTNVZ7CQI0I59enJDmOYM4Y7IeYBB817xA\nSI46+srmslgsxw8rOPvEcYSzU1XcFWGYZASeYaFdwRWhGblcWO2RZrDai+kOExxHCD1BgCQ9OtFp\n+8WMK/RcKr5hpZeQZkot9Dg3XeWumRquEa6s9HFEGKoy14yoBi4LExWavkeGEqeKbxymagGX1vq0\nKh6+cTjdrtBLciaqPpnyooXuo4y+smVpLJbjiRWcA0JEcI2DiDBIMlZ7CcZxuNoZogqVwMdIQjOC\nXA1rvZg8T4mHhxsa7QPzLZe7Zls0Q5dWNWCu6fO5CxugOWHgc+epOr1hwuWNAZc6KacaAfcvNJif\niLi8MuTemSbGdTAirPZjhkmO4wiTtYC7Z+rUPBcxgiho6Y5TYTMvCY42+srmslgsxxMrOPtkdDcd\nuA7VwCVOMz793BpnJyo0Io/pWkh3mPCy6YjlzoDVQU7VM3T6CYEPMw5c6x9M5YFRbk9OsT4z04CJ\nWsTX3znD6YkqX77WoRa65DnUoyIZ9eUzdbpJwvmVPgvtiIVmROR7pBlEnotjEtIsx7jFrCTyXBZb\nEf0049rGkNVewrqkmy6r67myjir6yuayWCzHEys4+2T73bQjUrQpcATNlUrgEgWGYeIz3eiz1I0R\n32G6EbLSN3gORGHOpdWY/h6mOobnBSUG2l4ZwmwoSuxoIV6nWyHtWkSz4jLV8GlXXe6vtKj6Lr7n\nUI/6ZFmG44KXGaYbIQ+em2CpE7PWT+kME3pxyrmpiAvrQ3IdYBzhvoUmjiMsdWJ819m8oF9aG7BY\n5rpcz5V1FNFXNpfFYjmeWMHZJ9vvpnMt8kw0Vzzj0Ipclrsxea40Qo+FVojrujhSBBes9VKmGuA7\nylI/IY3BuBAYQBwW2hGrvYxOf0AnhjyDMDJUTUaiDvecqqGOIfAKl9b51R7DOKdZDZmuBzgUIjTf\njFgdZJydqOC6DovNCk8v95isejiOMF0PcB2H2VZElvcwDsw1IkCYaRgcEXItWjFEntnRZRXn+Q1d\nWUcVOWZzWSyW44cVnH2y0930fQtNVnsJSZIx04xoV/0iGdQ0mKz5pDkM0hxB2RimuCKcqgUkCiud\nmMh3aYQuC80KjcilG6c89vQqy/2Y9V7CRD1gEGfUQ5fZdsRdMw2WOkM6w5SJesgwKS6ySFG9OfQ8\nmtWAQTbg0vqAs5NVxHVYbEWb/WbmW5XNJnIT1YB75uo0Qo/nVvv47vPRXd1h4fzbyWXlO851XVlH\nHTlmc1ksluOFFZwDYKe76UbobT7PcyXyXfIs5zHW+NLlDoM04/REhXPTNbIsx/OaeMZhquoRuS6n\nJ6sExnB+tceF1T53ztR58sIG3cGAq+spc62QzjAjU7i0NuCBs00mKj79JOeRr6wSeMJgmFMJDVFg\nygt8xDNLPTYGSRGooHBlo0gGmq4HPHR2ghTFd4oQ6DzXHQXEM86OLivX3XncccRGjlksFis4B8X2\nu+ntz0dlXF4xWydJCqGYqgckaY46TpEo6nskqfDK+Qa1yAPgDreGI0LoGSq+y6W1AZ3hMpXQY7oZ\ncPepBv0447V3TLI6SOkOY5oVlytrA/pJTuAJaabkeVHVeqEdMdcIubheBDqkuXJprc9zK30W2xFz\nrWizDM311kJCZ2eX1W6uLBs5ZrFYrOAcMr1hyoXVPkjhhjo3VeNsu8qjz66Q5TmDpJhFBJ6hVQvo\nDBIubQx4WVDUFPOMg+8aBKj4Hqfbwmqvxkw9JPBdIs/gOA610MP3DI+u9LjzVA1RqFVcfONyqurz\nbCko860Is2VG8dxqD08EzxVEeNGs43prIbu5rHYat5FjFovFCs4h0humPPrMCkYE33NoRx5LnZhT\n9YCZZsjF1T79JMMVKVpQ51qIi7J55791llENDEtJxv2nW5xfGVB1YJgp9y00cV0HFZhrhBgjVD2X\nyHcZpBkLrYh+nDHfigg8Q54rSZbzlWsdvnS1i+cIjYrHbD0kR1806ziItRAbOWaxWKzgHBJ5rlxY\n6+MI1COPNMtZ6SfUQ5cLa30aoUdrzmeuGfKZCxv045TQc2lXPIzjvODOf+ss4+7pIqny1Yv6gvUW\nKGYRxjgYAc8t1km8MnJu+2wiz5W1foJrBNcpjrm41me2GR3arMNGjlkstze2xfQBMZo1jFoBZ6qI\ngl9e+F3jECc5eVaMu8bBcYSJWshXzdWZqgW0o0Jsrnfnv+lm84s1nZHYjCii4JRqYBikOUbg2eU+\nSZ5zfrXPIMmKC74jzLUi7p1rMF0PUIRhmjNVDw5VCEb2jwIJtv7NLBbLycbOcA6AncJ9feNgjEO7\n4rHSS+jFMbnCYrvCtW78grWMauCx2IpQYcc7/72EE2/dB4rXuXOyxjOrPSarAb5nXpCg6ZrCSRa4\nhum6Qy9Oi9pp/tF8JGxxTYvl9sPOcPbJ1nDfauDiGeHSWtHrZrYZYpxi7eZUI+Q1Z9rUIo/ZZkiS\nKd1hSpLpZkjx6M5/L+ffOivYvk/gOix1YrQMVPC95yPDclVUYL4V0Yp8NgYp3WHGdD1koX00Icp7\neU8Wi+XkYWc4++R64b67rVnczFrGXsKJd9sHdk7QNCJ4nsPdM3XOTVUBdhS7w8KGSFsstyd2hrNP\ntob7Ai8K9926ZrGV3cZv9vzX22eUoLl9NrW1rlngGQLPHOkC/l7ek8ViOXmceMERkTeKyOdF5Isi\n8o6DPv8o3He3i/pRnP96+4xmU6cnKpyZqByLdZLD/ptZLJbjyYl2qYmIAf4j8M3AeeBTIvJBVf3s\nQb7OYYf77uX8LyVBc5zYEGmL5fbjRAsO8BDwRVX9MoCI/A7wJuBABQcO/6K+l/MfR2G5HreavRaL\nZX+cdJfaAvDslufnyzGLxWKxHDEnXXBuiIi8TUQeFpGHr169Om5zLBaL5cRy0gXnOeD0lueL5dgm\nqvpuVX1QVR+cnp4+UuMsFovlduKkC86ngLtF5A4R8YE3Ax8cs00Wi8VyW3KigwZUNRWRfwp8CDDA\ne1T1M2M2y2KxWG5LRNWWExkhIleBp7cNTwHXxmDOzWLtPFisnQeLtfNgOW52nlXVG65JWMG5ASLy\nsKo+OG47boS182Cxdh4s1s6D5VaxczsnfQ3HYrFYLMcEKzgWi8ViORKs4NyYd4/bgD1i7TxYrJ0H\ni7XzYLlV7HwBdg3HYrFYLEeCneFYLBaL5UiwgnMdDru1wUEgIqdF5H+LyGdF5DMi8iPjtmk3RMSI\nyJ+LyP8cty3XQ0RaIvIBEfmciDwpIl8/bpu2IyL/rPx/f1pEfltEwnHbNEJE3iMiV0Tk01vGJkTk\nwyLyl+XP9jhtLG3ayc5fLP/vj4vIH4hIa5w2lja9yM4tv/sxEVERmRqHbTeLFZxd2NLa4FuAe4G3\niMi947VqR1Lgx1T1XuDrgB86pnYC/Ajw5LiN2AP/DvhjVX0FcD/HzGYRWQDeDjyoqvdRJDW/ebxW\nvYD3Am/cNvYO4COqejfwkfL5uHkvL7bzw8B9qvoq4AvATxy1UTvwXl5sJyJyGngD8MxRG/RSsYKz\nO5utDVQ1BkatDY4VqnpRVR8ttzcoLo7HriK2iCwCfxv49XHbcj1EpAl8I/AbAKoaq+rqeK3aEReI\nRMQFKsCFMduziap+DFjeNvwm4H3l9vuAbz9So3ZgJztV9U9UNS2ffoKi/uJY2eXvCfArwL8AbpmF\neCs4u3PLtTYQkXPAq4FPjteSHfm3FF+OfNyG3IA7gKvAfyndf78uItVxG7UVVX0O+CWKO9uLwJqq\n/sl4rbohM6p6sdy+BMyM05g98r3AH43biJ0QkTcBz6nqY+O25WawgnNCEJEa8N+BH1XV9XHbsxUR\n+Vbgiqo+Mm5b9oALvAb4VVV9NdDleLh/NinXP95EIY7zQFVE/sF4rdo7WoTGHuu7chH5SQp39fvH\nbct2RKQC/EvgX43blpvFCs7u3LC1wXFBRDwKsXm/qv7+uO3ZgW8Avk1EvkLhmvwbIvKb4zVpV84D\n51V1NEv8AIUAHSdeDzylqldVNQF+H/grY7bpRlwWkTmA8ueVMduzKyLyD4FvBf6+Hs+8kTspbjYe\nK79Ti8CjIjI7Vqv2gBWc3bklWhuIiFCsNzypqr88bnt2QlV/QlUXVfUcxd/xT1X1WN6Rq+ol4FkR\nuacceh2H0JJ8nzwDfJ2IVMr//+s4ZoENO/BB4K3l9luBPxyjLbsiIm+kcP1+m6r2xm3PTqjqE6p6\nSlXPld+p88Brys/uscYKzi6UC4ej1gZPAr97TFsbfAPw3RSzhr8oH39r3Ebd4vww8H4ReRx4APi5\nMdvzAsrZ1weAR4EnKL7HxybzXER+G/g4cI+InBeR7wPeBXyziPwlxQztXeO0EXa18z8AdeDD5Xfp\nP4/VSHa185bEVhqwWCwWy5FgZzgWi8ViORKs4FgsFovlSLCCY7FYLJYjwQqOxWKxWI4EKzgWi8Vi\nORKs4FgsFovlSLCCY7EcEiLyURF5sNz+XwdZ6l5EfkBEvuegzmexHAXuuA2wWG4HVPVAk3FVdewJ\niRbLzWJnOBbLFkTkXNmA670i8gUReb+IvF5E/l/ZPOwhEamWTbH+rKwo/aby2EhEfqds2vYHQLTl\nvF8ZNckSkf8hIo+UDdTetmWfjoi8U0QeE5FPiMiuFZVF5F+LyI+X2x8VkV8o7fmCiPy1ctyIyC+V\nTdoeF5EfLsdfV9r9RPk+gi02/nyZYf+wiLxGRD4kIl8SkR/Y8tr/XEQ+VZ7zpw/0H2A50VjBsVhe\nzF3AvwFeUT6+C/irwI9TVOn9SYp6cA8Bfx34xbKFwQ8CPVX9KuCngK/Z5fzfq6pfAzwIvF1EJsvx\nKvAJVb0f+Bjw/Tdhs1va86PlawO8DTgHPFA2FHu/FJ1B3wv8PVV9JYWX4we3nOcZVX0A+L/lft9B\n0djvpwFE5A3A3RT9oh4AvkZEvvEm7LTcxljBsVhezFNlgcQc+AxFp0qlqFt2jqLL4jtE5C+AjwIh\ncIaicdtvAqjq48Dju5z/7SLyGEWDr9MUF3CAGBi1336kfK29MqoSvvW41wO/NmoopqrLwD3l+/tC\nuc/7SrtHjArUPgF8UlU3VPUqMCzXoN5QPv6copbbK7bYb7FcF7uGY7G8mOGW7XzL85ziO5MBf0dV\nP7/1oKJw8/URkW+iEIKvV9WeiHyUQrAAki3l8DNu7vs5svFmj9vtPFvf9+i5Cwjw86r6a/t4Dctt\nip3hWCw3z4eAHy5bAyAiry7HP0bhfkNE7gNetcOxTWClFJtXULirDosPA/+4bEONiEwAnwfOichd\n5T7fDfyfmzjnh4DvLRv+ISILInLqAG22nGCs4FgsN8/PAh7wuIh8pnwO8KtATUSeBH6Gwr21nT8G\n3HKfd1G41Q6LX6fonfN46cL7LlUdAP8I+D0ReYJi5rLniLeylfVvAR8vj/8ARTl/i+WG2PYEFovF\nYjkS7AzHYrFYLEeCDRqwWI4xIvKTwHduG/49VX3nOOyxWPaDdalZLBaL5UiwLjWLxWKxHAlWcCwW\ni8VyJFjBsVgsFsuRYAXHYrFYLEeCFRyLxWKxHAn/H5etV8aPHjs0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26743c35f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind=\"scatter\",x=\"median_income\",y=\"median_house_value\",alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"rooms_per_household\"] = housing[\"total_rooms\"]/housing[\"households\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>6.984127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>6.238137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>8.288136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.817352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>6.281853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "   rooms_per_household  \n",
       "0             6.984127  \n",
       "1             6.238137  \n",
       "2             8.288136  \n",
       "3             5.817352  \n",
       "4             6.281853  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"bedrooms_per_room\"] = housing[\"total_bedrooms\"]/housing[\"total_rooms\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "population             1.000000\n",
       "households             0.907222\n",
       "total_bedrooms         0.877747\n",
       "total_rooms            0.857126\n",
       "longitude              0.099773\n",
       "bedrooms_per_room      0.035319\n",
       "median_income          0.004834\n",
       "median_house_value    -0.024650\n",
       "rooms_per_household   -0.072213\n",
       "latitude              -0.108785\n",
       "housing_median_age    -0.296244\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = housing.corr()\n",
    "corr_matrix[\"population\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare Data for MLA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         129.0\n",
       "1        1106.0\n",
       "2         190.0\n",
       "3         235.0\n",
       "4         280.0\n",
       "5         213.0\n",
       "6         489.0\n",
       "7         687.0\n",
       "8         665.0\n",
       "9         707.0\n",
       "10        434.0\n",
       "11        752.0\n",
       "12        474.0\n",
       "13        191.0\n",
       "14        626.0\n",
       "15        283.0\n",
       "16        347.0\n",
       "17        293.0\n",
       "18        455.0\n",
       "19        298.0\n",
       "20        184.0\n",
       "21        367.0\n",
       "22        541.0\n",
       "23        337.0\n",
       "24        437.0\n",
       "25        123.0\n",
       "26        244.0\n",
       "27        421.0\n",
       "28        492.0\n",
       "29        160.0\n",
       "          ...  \n",
       "20610     484.0\n",
       "20611     441.0\n",
       "20612     289.0\n",
       "20613     365.0\n",
       "20614     460.0\n",
       "20615     216.0\n",
       "20616     441.0\n",
       "20617     109.0\n",
       "20618     247.0\n",
       "20619     340.0\n",
       "20620      41.0\n",
       "20621     247.0\n",
       "20622     147.0\n",
       "20623     244.0\n",
       "20624     300.0\n",
       "20625      17.0\n",
       "20626     184.0\n",
       "20627      65.0\n",
       "20628     421.0\n",
       "20629    1856.0\n",
       "20630     505.0\n",
       "20631     493.0\n",
       "20632     416.0\n",
       "20633     412.0\n",
       "20634     395.0\n",
       "20635     374.0\n",
       "20636     150.0\n",
       "20637     485.0\n",
       "20638     409.0\n",
       "20639     616.0\n",
       "Name: total_bedrooms, dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.dropna(subset=[\"total_bedrooms\"])\n",
    "median = housing[\"total_bedrooms\"].median()\n",
    "housing[\"total_bedrooms\"].fillna(median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling Text & Categorical Attributes\n",
    "* This is a transformer\n",
    "* fit is to be used for training estimators\n",
    "* fit_transform to be used for transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = LabelEncoder()\n",
    "housing_cat = housing[\"ocean_proximity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_cat_encoded = encoder.fit_transform(housing_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'], dtype=object)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing[\"OPN\"] = housing_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>OPN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "   rooms_per_household  bedrooms_per_room  OPN  \n",
       "0             6.984127           0.146591    3  \n",
       "1             6.238137           0.155797    3  \n",
       "2             8.288136           0.129516    3  \n",
       "3             5.817352           0.184458    3  \n",
       "4             6.281853           0.172096    3  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([880.0, 7099.0, 1467.0, ..., 2254.0, 1860.0, 2785.0], dtype=object)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.values[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape((-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [3],\n",
       "       [3],\n",
       "       ..., \n",
       "       [1],\n",
       "       [1],\n",
       "       [1]], dtype=int64)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoded.reshape((-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_1hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseEstimator.get_params of OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,\n",
       "       handle_unknown='error', n_values='auto', sparse=True)>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.get_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Custom Transformers\n",
    "* Custom cleanup operation\n",
    "* Combining spefic attributes\n",
    "* Transformer should have a fit_transform() function implemented\n",
    "* The transformer class should extend base class TransformerMixin\n",
    "* If you want set_params() & get_params(), extend BaseExtimator class\n",
    "* np_c - This adds more columns to dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rooms_ix, bedrooms_ix, population_ix, househiold_ix = 3,4,5,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "class CombinedAttributesAddr(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:,rooms_ix] / X[:, househiold_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, househiold_ix]\n",
    "        \n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix] \n",
    "            if bedrooms_per_room > 5:\n",
    "                bedrooms_per_room += 10\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]\n",
    "        \n",
    "attr_adder = CombinedAttributesAddr(add_bedrooms_per_room=False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 15)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 13)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on CClass in module numpy.lib.index_tricks object:\n",
      "\n",
      "class CClass(AxisConcatenator)\n",
      " |  Translates slice objects to concatenation along the second axis.\n",
      " |  \n",
      " |  This is short-hand for ``np.r_['-1,2,0', index expression]``, which is\n",
      " |  useful because of its common occurrence. In particular, arrays will be\n",
      " |  stacked along their last axis after being upgraded to at least 2-D with\n",
      " |  1's post-pended to the shape (column vectors made out of 1-D arrays).\n",
      " |  \n",
      " |  For detailed documentation, see `r_`.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]\n",
      " |  array([[1, 2, 3, 0, 0, 4, 5, 6]])\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      CClass\n",
      " |      AxisConcatenator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from AxisConcatenator:\n",
      " |  \n",
      " |  __getitem__(self, key)\n",
      " |  \n",
      " |  __getslice__(self, i, j)\n",
      " |  \n",
      " |  __len__(self)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from AxisConcatenator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.c_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ZekeLabs\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: RuntimeWarning: invalid value encountered in greater\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m-----------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-165-3120a44bb7f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mattr_adder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCombinedAttributesAddr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madd_bedrooms_per_room\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhousing_extra_attribs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mattr_adder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-161-3eb87c5fd26a>\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_bedrooms_per_room\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0mbedrooms_per_room\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbedrooms_ix\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrooms_ix\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mbedrooms_per_room\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m                 \u001b[0mbedrooms_per_room\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m             return np.c_[X, rooms_per_household, population_per_household,\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "attr_adder = CombinedAttributesAddr(add_bedrooms_per_room=True)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_extra_attribs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Select & Train a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformation Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,Imputer, LabelBinarizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some estimators (such as an imputer) can also transform a dataset; these are called transformers. Once again, the API is quite simple: the transformation is performed by the transform() method with the dataset to transform as a parameter. It returns the transformed dataset. This transformation generally relies on the learned parameters, as is the case for an imputer.\n",
    "\n",
    "Scikit-Learn provides a handy class to take care of missing values: Imputer. Here is how to use it. First, you need to create an Imputer instance, specifying that you want to replace each attribute’s missing values with the median of that attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_pipeline = Pipeline([\n",
    "        ('imputer', Imputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAddr()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_num_tr = num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_num_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values\n",
    "    \n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', DataFrameSelector(cat_attribs)),\n",
    "        ('label_binarizer', LabelBinarizer()),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = LabelBinarizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_cat_1hot = encoder.fit_transform(housing_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    (\"num_pipeline\", num_pipeline), \n",
    "    (\"cat_pipeline\", cat_pipeline),])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " cat_pipeline.fit_transform(housing).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline.fit_transform(housing_num).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(10).reshape(2,5)a\n",
    "b = np.arange(6).reshape(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.hstack([a,b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_prepared = np.hstack([cat_pipeline.fit_transform(housing),num_pipeline.fit_transform(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg.fit(housing_prepared, housing['median_house_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "housing_prediction = lin_reg.predict(housing_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lin_mse = mean_squared_error(housing['median_house_value'], housing_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sqrt(lin_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Hyperparameter tuning\n",
    "# Every model requires some initial parameters, how do you find the best params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_grid = [\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest_reg = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.fit(housing_prepared,housing['median_house_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class StandardScaler in module sklearn.preprocessing.data:\n",
      "\n",
      "class StandardScaler(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin)\n",
      " |  Standardize features by removing the mean and scaling to unit variance\n",
      " |  \n",
      " |  Centering and scaling happen independently on each feature by computing\n",
      " |  the relevant statistics on the samples in the training set. Mean and\n",
      " |  standard deviation are then stored to be used on later data using the\n",
      " |  `transform` method.\n",
      " |  \n",
      " |  Standardization of a dataset is a common requirement for many\n",
      " |  machine learning estimators: they might behave badly if the\n",
      " |  individual feature do not more or less look like standard normally\n",
      " |  distributed data (e.g. Gaussian with 0 mean and unit variance).\n",
      " |  \n",
      " |  For instance many elements used in the objective function of\n",
      " |  a learning algorithm (such as the RBF kernel of Support Vector\n",
      " |  Machines or the L1 and L2 regularizers of linear models) assume that\n",
      " |  all features are centered around 0 and have variance in the same\n",
      " |  order. If a feature has a variance that is orders of magnitude larger\n",
      " |  that others, it might dominate the objective function and make the\n",
      " |  estimator unable to learn from other features correctly as expected.\n",
      " |  \n",
      " |  This scaler can also be applied to sparse CSR or CSC matrices by passing\n",
      " |  `with_mean=False` to avoid breaking the sparsity structure of the data.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <preprocessing_scaler>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  with_mean : boolean, True by default\n",
      " |      If True, center the data before scaling.\n",
      " |      This does not work (and will raise an exception) when attempted on\n",
      " |      sparse matrices, because centering them entails building a dense\n",
      " |      matrix which in common use cases is likely to be too large to fit in\n",
      " |      memory.\n",
      " |  \n",
      " |  with_std : boolean, True by default\n",
      " |      If True, scale the data to unit variance (or equivalently,\n",
      " |      unit standard deviation).\n",
      " |  \n",
      " |  copy : boolean, optional, default True\n",
      " |      If False, try to avoid a copy and do inplace scaling instead.\n",
      " |      This is not guaranteed to always work inplace; e.g. if the data is\n",
      " |      not a NumPy array or scipy.sparse CSR matrix, a copy may still be\n",
      " |      returned.\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  scale_ : ndarray, shape (n_features,)\n",
      " |      Per feature relative scaling of the data.\n",
      " |  \n",
      " |      .. versionadded:: 0.17\n",
      " |         *scale_* is recommended instead of deprecated *std_*.\n",
      " |  \n",
      " |  mean_ : array of floats with shape [n_features]\n",
      " |      The mean value for each feature in the training set.\n",
      " |  \n",
      " |  var_ : array of floats with shape [n_features]\n",
      " |      The variance for each feature in the training set. Used to compute\n",
      " |      `scale_`\n",
      " |  \n",
      " |  n_samples_seen_ : int\n",
      " |      The number of samples processed by the estimator. Will be reset on\n",
      " |      new calls to fit, but increments across ``partial_fit`` calls.\n",
      " |  \n",
      " |  See also\n",
      " |  --------\n",
      " |  scale: Equivalent function without the object oriented API.\n",
      " |  \n",
      " |  :class:`sklearn.decomposition.PCA`\n",
      " |      Further removes the linear correlation across features with 'whiten=True'.\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      StandardScaler\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.TransformerMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, copy=True, with_mean=True, with_std=True)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y=None)\n",
      " |      Compute the mean and std to be used for later scaling.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape [n_samples, n_features]\n",
      " |          The data used to compute the mean and standard deviation\n",
      " |          used for later scaling along the features axis.\n",
      " |      \n",
      " |      y: Passthrough for ``Pipeline`` compatibility.\n",
      " |  \n",
      " |  inverse_transform(self, X, copy=None)\n",
      " |      Scale back the data to the original representation\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape [n_samples, n_features]\n",
      " |          The data used to scale along the features axis.\n",
      " |  \n",
      " |  partial_fit(self, X, y=None)\n",
      " |      Online computation of mean and std on X for later scaling.\n",
      " |      All of X is processed as a single batch. This is intended for cases\n",
      " |      when `fit` is not feasible due to very large number of `n_samples`\n",
      " |      or because X is read from a continuous stream.\n",
      " |      \n",
      " |      The algorithm for incremental mean and std is given in Equation 1.5a,b\n",
      " |      in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. \"Algorithms\n",
      " |      for computing the sample variance: Analysis and recommendations.\"\n",
      " |      The American Statistician 37.3 (1983): 242-247:\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : {array-like, sparse matrix}, shape [n_samples, n_features]\n",
      " |          The data used to compute the mean and standard deviation\n",
      " |          used for later scaling along the features axis.\n",
      " |      \n",
      " |      y: Passthrough for ``Pipeline`` compatibility.\n",
      " |  \n",
      " |  transform(self, X, y=None, copy=None)\n",
      " |      Perform standardization by centering and scaling\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape [n_samples, n_features]\n",
      " |          The data used to scale along the features axis.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  std_\n",
      " |      DEPRECATED: Attribute ``std_`` will be removed in 0.19. Use ``scale_`` instead\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : boolean, optional\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.TransformerMixin:\n",
      " |  \n",
      " |  fit_transform(self, X, y=None, **fit_params)\n",
      " |      Fit to data, then transform it.\n",
      " |      \n",
      " |      Fits transformer to X and y with optional parameters fit_params\n",
      " |      and returns a transformed version of X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : numpy array of shape [n_samples, n_features]\n",
      " |          Training set.\n",
      " |      \n",
      " |      y : numpy array of shape [n_samples]\n",
      " |          Target values.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : numpy array of shape [n_samples, n_features_new]\n",
      " |          Transformed array.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(StandardScaler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "* Try replacing GridSearchCV with RandomizedSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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