AI_Class/002/Simple_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple linear regression\n",
    "\n",
    "이 모델은 독립변수 **하나** 에 대해서 1차식의 cost가 가장 작게 나오는 선을 긋는 모델입니다.\n",
    "\n",
    "추가 설명은 같은 폴더 내의 [linear_regression.pdf-클릭하면 바로 볼 수 있습니다.](https://github.com/cpprhtn/Cpprhtn-s_Deep_Learning/blob/master/AI_Class/002/linear_regression.pdf)에서 하겠습니다.\n",
    "\n",
    "* pdf 자료주소: 모두의 머신러닝/딥러닝 웹페이지"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data\n",
    "\n",
    "mpg data : 연료 효율성 예측 데이터\n",
    "\n",
    "mpg : miles per gallon  \n",
    "cylinders : (엔진의) 실린더  \n",
    "displacement : 이동거리  \n",
    "horsepower : 마력  \n",
    "weight : 무게  \n",
    "acceleration : 가속도  \n",
    "model year : 연식  \n",
    "origin : 엔진을 만든 국가  \n",
    "name : 모델명\n",
    "\n",
    "[클릭시 데이터 위치로 이동. *해당 레포를 클론해서 쓰는것을 추천드립니다.](https://github.com/cpprhtn/Cpprhtn-s_Deep_Learning/blob/master/AI_Class/002/auto-mpg.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./auto-mpg.csv', header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1      2      3       4     5   6  7                          8\n",
       "0  18.0  8  307.0  130.0  3504.0  12.0  70  1  chevrolet chevelle malibu\n",
       "1  15.0  8  350.0  165.0  3693.0  11.5  70  1          buick skylark 320\n",
       "2  18.0  8  318.0  150.0  3436.0  11.0  70  1         plymouth satellite\n",
       "3  16.0  8  304.0  150.0  3433.0  12.0  70  1              amc rebel sst\n",
       "4  17.0  8  302.0  140.0  3449.0  10.5  70  1                ford torino"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터의 컬럼명이 없으므로 수정해줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['mpg','cylinders','displacement','horsepower','weight',\n",
    "              'acceleration','model year','origin','name'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 자료형 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           398 non-null    float64\n",
      " 1   cylinders     398 non-null    int64  \n",
      " 2   displacement  398 non-null    float64\n",
      " 3   horsepower    398 non-null    object \n",
      " 4   weight        398 non-null    float64\n",
      " 5   acceleration  398 non-null    float64\n",
      " 6   model year    398 non-null    int64  \n",
      " 7   origin        398 non-null    int64  \n",
      " 8   name          398 non-null    object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 결측치 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg             0\n",
       "cylinders       0\n",
       "displacement    0\n",
       "horsepower      0\n",
       "weight          0\n",
       "acceleration    0\n",
       "model year      0\n",
       "origin          0\n",
       "name            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 통계 요약정보 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>1.572864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>0.802055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement       weight  acceleration  \\\n",
       "count  398.000000  398.000000    398.000000   398.000000    398.000000   \n",
       "mean    23.514573    5.454774    193.425879  2970.424623     15.568090   \n",
       "std      7.815984    1.701004    104.269838   846.841774      2.757689   \n",
       "min      9.000000    3.000000     68.000000  1613.000000      8.000000   \n",
       "25%     17.500000    4.000000    104.250000  2223.750000     13.825000   \n",
       "50%     23.000000    4.000000    148.500000  2803.500000     15.500000   \n",
       "75%     29.000000    8.000000    262.000000  3608.000000     17.175000   \n",
       "max     46.600000    8.000000    455.000000  5140.000000     24.800000   \n",
       "\n",
       "       model year      origin  \n",
       "count  398.000000  398.000000  \n",
       "mean    76.010050    1.572864  \n",
       "std      3.697627    0.802055  \n",
       "min     70.000000    1.000000  \n",
       "25%     73.000000    1.000000  \n",
       "50%     76.000000    1.000000  \n",
       "75%     79.000000    2.000000  \n",
       "max     82.000000    3.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 컬럼 전체의 고유값 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mpg\n",
      "[18.  15.  16.  17.  14.  24.  22.  21.  27.  26.  25.  10.  11.   9.\n",
      " 28.  19.  12.  13.  23.  30.  31.  35.  20.  29.  32.  33.  17.5 15.5\n",
      " 14.5 22.5 24.5 18.5 29.5 26.5 16.5 31.5 36.  25.5 33.5 20.5 30.5 21.5\n",
      " 43.1 36.1 32.8 39.4 19.9 19.4 20.2 19.2 25.1 20.6 20.8 18.6 18.1 17.7\n",
      " 27.5 27.2 30.9 21.1 23.2 23.8 23.9 20.3 21.6 16.2 19.8 22.3 17.6 18.2\n",
      " 16.9 31.9 34.1 35.7 27.4 25.4 34.2 34.5 31.8 37.3 28.4 28.8 26.8 41.5\n",
      " 38.1 32.1 37.2 26.4 24.3 19.1 34.3 29.8 31.3 37.  32.2 46.6 27.9 40.8\n",
      " 44.3 43.4 36.4 44.6 40.9 33.8 32.7 23.7 23.6 32.4 26.6 25.8 23.5 39.1\n",
      " 39.  35.1 32.3 37.7 34.7 34.4 29.9 33.7 32.9 31.6 28.1 30.7 24.2 22.4\n",
      " 34.  38.  44. ]\n",
      "\n",
      "cylinders\n",
      "[8 4 6 3 5]\n",
      "\n",
      "displacement\n",
      "[307.  350.  318.  304.  302.  429.  454.  440.  455.  390.  383.  340.\n",
      " 400.  113.  198.  199.  200.   97.  110.  107.  104.  121.  360.  140.\n",
      "  98.  232.  225.  250.  351.  258.  122.  116.   79.   88.   71.   72.\n",
      "  91.   97.5  70.  120.   96.  108.  155.   68.  114.  156.   76.   83.\n",
      "  90.  231.  262.  134.  119.  171.  115.  101.  305.   85.  130.  168.\n",
      " 111.  260.  151.  146.   80.   78.  105.  131.  163.   89.  267.   86.\n",
      " 183.  141.  173.  135.   81.  100.  145.  112.  181.  144. ]\n",
      "\n",
      "horsepower\n",
      "['130.0' '165.0' '150.0' '140.0' '198.0' '220.0' '215.0' '225.0' '190.0'\n",
      " '170.0' '160.0' '95.00' '97.00' '85.00' '88.00' '46.00' '87.00' '90.00'\n",
      " '113.0' '200.0' '210.0' '193.0' '?' '100.0' '105.0' '175.0' '153.0'\n",
      " '180.0' '110.0' '72.00' '86.00' '70.00' '76.00' '65.00' '69.00' '60.00'\n",
      " '80.00' '54.00' '208.0' '155.0' '112.0' '92.00' '145.0' '137.0' '158.0'\n",
      " '167.0' '94.00' '107.0' '230.0' '49.00' '75.00' '91.00' '122.0' '67.00'\n",
      " '83.00' '78.00' '52.00' '61.00' '93.00' '148.0' '129.0' '96.00' '71.00'\n",
      " '98.00' '115.0' '53.00' '81.00' '79.00' '120.0' '152.0' '102.0' '108.0'\n",
      " '68.00' '58.00' '149.0' '89.00' '63.00' '48.00' '66.00' '139.0' '103.0'\n",
      " '125.0' '133.0' '138.0' '135.0' '142.0' '77.00' '62.00' '132.0' '84.00'\n",
      " '64.00' '74.00' '116.0' '82.00']\n",
      "\n",
      "weight\n",
      "[3504. 3693. 3436. 3433. 3449. 4341. 4354. 4312. 4425. 3850. 3563. 3609.\n",
      " 3761. 3086. 2372. 2833. 2774. 2587. 2130. 1835. 2672. 2430. 2375. 2234.\n",
      " 2648. 4615. 4376. 4382. 4732. 2264. 2228. 2046. 2634. 3439. 3329. 3302.\n",
      " 3288. 4209. 4464. 4154. 4096. 4955. 4746. 5140. 2962. 2408. 3282. 3139.\n",
      " 2220. 2123. 2074. 2065. 1773. 1613. 1834. 1955. 2278. 2126. 2254. 2226.\n",
      " 4274. 4385. 4135. 4129. 3672. 4633. 4502. 4456. 4422. 2330. 3892. 4098.\n",
      " 4294. 4077. 2933. 2511. 2979. 2189. 2395. 2288. 2506. 2164. 2100. 4100.\n",
      " 3988. 4042. 3777. 4952. 4363. 4237. 4735. 4951. 3821. 3121. 3278. 2945.\n",
      " 3021. 2904. 1950. 4997. 4906. 4654. 4499. 2789. 2279. 2401. 2379. 2124.\n",
      " 2310. 2472. 2265. 4082. 4278. 1867. 2158. 2582. 2868. 3399. 2660. 2807.\n",
      " 3664. 3102. 2875. 2901. 3336. 2451. 1836. 2542. 3781. 3632. 3613. 4141.\n",
      " 4699. 4457. 4638. 4257. 2219. 1963. 2300. 1649. 2003. 2125. 2108. 2246.\n",
      " 2489. 2391. 2000. 3264. 3459. 3432. 3158. 4668. 4440. 4498. 4657. 3907.\n",
      " 3897. 3730. 3785. 3039. 3221. 3169. 2171. 2639. 2914. 2592. 2702. 2223.\n",
      " 2545. 2984. 1937. 3211. 2694. 2957. 2671. 1795. 2464. 2572. 2255. 2202.\n",
      " 4215. 4190. 3962. 3233. 3353. 3012. 3085. 2035. 3651. 3574. 3645. 3193.\n",
      " 1825. 1990. 2155. 2565. 3150. 3940. 3270. 2930. 3820. 4380. 4055. 3870.\n",
      " 3755. 2045. 1945. 3880. 4060. 4140. 4295. 3520. 3425. 3630. 3525. 4220.\n",
      " 4165. 4325. 4335. 1940. 2740. 2755. 2051. 2075. 1985. 2190. 2815. 2600.\n",
      " 2720. 1800. 2070. 3365. 3735. 3570. 3535. 3155. 2965. 3430. 3210. 3380.\n",
      " 3070. 3620. 3410. 3445. 3205. 4080. 2560. 2230. 2515. 2745. 2855. 2405.\n",
      " 2830. 3140. 2795. 2135. 3245. 2990. 2890. 3265. 3360. 3840. 3725. 3955.\n",
      " 3830. 4360. 4054. 3605. 1925. 1975. 1915. 2670. 3530. 3900. 3190. 3420.\n",
      " 2200. 2150. 2020. 2595. 2700. 2556. 2144. 1968. 2120. 2019. 2678. 2870.\n",
      " 3003. 3381. 2188. 2711. 2434. 2110. 2800. 2085. 2335. 2950. 3250. 1850.\n",
      " 2145. 1845. 2910. 2420. 2500. 2905. 2290. 2490. 2635. 2620. 2725. 2385.\n",
      " 1755. 1875. 1760. 2050. 2215. 2380. 2320. 2210. 2350. 2615. 3230. 3160.\n",
      " 2900. 3415. 3060. 3465. 2605. 2640. 2575. 2525. 2735. 2865. 3035. 1980.\n",
      " 2025. 1970. 2160. 2205. 2245. 1965. 1995. 3015. 2585. 2835. 2665. 2370.\n",
      " 2790. 2295. 2625.]\n",
      "\n",
      "acceleration\n",
      "[12.  11.5 11.  10.5 10.   9.   8.5  8.   9.5 15.  15.5 16.  14.5 20.5\n",
      " 17.5 12.5 14.  13.5 18.5 19.  13.  19.5 18.  17.  23.5 16.5 21.  16.9\n",
      " 14.9 17.7 15.3 13.9 12.8 15.4 17.6 22.2 22.1 14.2 17.4 16.2 17.8 12.2\n",
      " 16.4 13.6 15.7 13.2 21.9 16.7 12.1 14.8 18.6 16.8 13.7 11.1 11.4 18.2\n",
      " 15.8 15.9 14.1 21.5 14.4 19.4 19.2 17.2 18.7 15.1 13.4 11.2 14.7 16.6\n",
      " 17.3 15.2 14.3 20.1 24.8 11.3 12.9 18.8 18.1 17.9 21.7 23.7 19.9 21.8\n",
      " 13.8 12.6 16.1 20.7 18.3 20.4 19.6 17.1 15.6 24.6 11.6]\n",
      "\n",
      "model year\n",
      "[70 71 72 73 74 75 76 77 78 79 80 81 82]\n",
      "\n",
      "origin\n",
      "[1 3 2]\n",
      "\n",
      "name\n",
      "['chevrolet chevelle malibu' 'buick skylark 320' 'plymouth satellite'\n",
      " 'amc rebel sst' 'ford torino' 'ford galaxie 500' 'chevrolet impala'\n",
      " 'plymouth fury iii' 'pontiac catalina' 'amc ambassador dpl'\n",
      " 'dodge challenger se' \"plymouth 'cuda 340\" 'chevrolet monte carlo'\n",
      " 'buick estate wagon (sw)' 'toyota corona mark ii' 'plymouth duster'\n",
      " 'amc hornet' 'ford maverick' 'datsun pl510'\n",
      " 'volkswagen 1131 deluxe sedan' 'peugeot 504' 'audi 100 ls' 'saab 99e'\n",
      " 'bmw 2002' 'amc gremlin' 'ford f250' 'chevy c20' 'dodge d200' 'hi 1200d'\n",
      " 'chevrolet vega 2300' 'toyota corona' 'ford pinto'\n",
      " 'plymouth satellite custom' 'ford torino 500' 'amc matador'\n",
      " 'pontiac catalina brougham' 'dodge monaco (sw)'\n",
      " 'ford country squire (sw)' 'pontiac safari (sw)'\n",
      " 'amc hornet sportabout (sw)' 'chevrolet vega (sw)' 'pontiac firebird'\n",
      " 'ford mustang' 'mercury capri 2000' 'opel 1900' 'peugeot 304' 'fiat 124b'\n",
      " 'toyota corolla 1200' 'datsun 1200' 'volkswagen model 111'\n",
      " 'plymouth cricket' 'toyota corona hardtop' 'dodge colt hardtop'\n",
      " 'volkswagen type 3' 'chevrolet vega' 'ford pinto runabout'\n",
      " 'amc ambassador sst' 'mercury marquis' 'buick lesabre custom'\n",
      " 'oldsmobile delta 88 royale' 'chrysler newport royal' 'mazda rx2 coupe'\n",
      " 'amc matador (sw)' 'chevrolet chevelle concours (sw)'\n",
      " 'ford gran torino (sw)' 'plymouth satellite custom (sw)'\n",
      " 'volvo 145e (sw)' 'volkswagen 411 (sw)' 'peugeot 504 (sw)'\n",
      " 'renault 12 (sw)' 'ford pinto (sw)' 'datsun 510 (sw)'\n",
      " 'toyouta corona mark ii (sw)' 'dodge colt (sw)'\n",
      " 'toyota corolla 1600 (sw)' 'buick century 350' 'chevrolet malibu'\n",
      " 'ford gran torino' 'dodge coronet custom' 'mercury marquis brougham'\n",
      " 'chevrolet caprice classic' 'ford ltd' 'plymouth fury gran sedan'\n",
      " 'chrysler new yorker brougham' 'buick electra 225 custom'\n",
      " 'amc ambassador brougham' 'plymouth valiant' 'chevrolet nova custom'\n",
      " 'volkswagen super beetle' 'ford country' 'plymouth custom suburb'\n",
      " 'oldsmobile vista cruiser' 'toyota carina' 'datsun 610' 'maxda rx3'\n",
      " 'mercury capri v6' 'fiat 124 sport coupe' 'chevrolet monte carlo s'\n",
      " 'pontiac grand prix' 'fiat 128' 'opel manta' 'audi 100ls' 'volvo 144ea'\n",
      " 'dodge dart custom' 'saab 99le' 'toyota mark ii' 'oldsmobile omega'\n",
      " 'chevrolet nova' 'datsun b210' 'chevrolet chevelle malibu classic'\n",
      " 'plymouth satellite sebring' 'buick century luxus (sw)'\n",
      " 'dodge coronet custom (sw)' 'audi fox' 'volkswagen dasher' 'datsun 710'\n",
      " 'dodge colt' 'fiat 124 tc' 'honda civic' 'subaru' 'fiat x1.9'\n",
      " 'plymouth valiant custom' 'mercury monarch' 'chevrolet bel air'\n",
      " 'plymouth grand fury' 'buick century' 'chevroelt chevelle malibu'\n",
      " 'plymouth fury' 'buick skyhawk' 'chevrolet monza 2+2' 'ford mustang ii'\n",
      " 'toyota corolla' 'pontiac astro' 'volkswagen rabbit' 'amc pacer'\n",
      " 'volvo 244dl' 'honda civic cvcc' 'fiat 131' 'capri ii' 'renault 12tl'\n",
      " 'dodge coronet brougham' 'chevrolet chevette' 'chevrolet woody'\n",
      " 'vw rabbit' 'dodge aspen se' 'ford granada ghia' 'pontiac ventura sj'\n",
      " 'amc pacer d/l' 'datsun b-210' 'volvo 245' 'plymouth volare premier v8'\n",
      " 'mercedes-benz 280s' 'cadillac seville' 'chevy c10' 'ford f108'\n",
      " 'dodge d100' 'honda accord cvcc' 'buick opel isuzu deluxe'\n",
      " 'renault 5 gtl' 'plymouth arrow gs' 'datsun f-10 hatchback'\n",
      " 'oldsmobile cutlass supreme' 'dodge monaco brougham'\n",
      " 'mercury cougar brougham' 'chevrolet concours' 'buick skylark'\n",
      " 'plymouth volare custom' 'ford granada' 'pontiac grand prix lj'\n",
      " 'chevrolet monte carlo landau' 'chrysler cordoba' 'ford thunderbird'\n",
      " 'volkswagen rabbit custom' 'pontiac sunbird coupe'\n",
      " 'toyota corolla liftback' 'ford mustang ii 2+2' 'dodge colt m/m'\n",
      " 'subaru dl' 'datsun 810' 'bmw 320i' 'mazda rx-4'\n",
      " 'volkswagen rabbit custom diesel' 'ford fiesta' 'mazda glc deluxe'\n",
      " 'datsun b210 gx' 'oldsmobile cutlass salon brougham' 'dodge diplomat'\n",
      " 'mercury monarch ghia' 'pontiac phoenix lj' 'ford fairmont (auto)'\n",
      " 'ford fairmont (man)' 'plymouth volare' 'amc concord'\n",
      " 'buick century special' 'mercury zephyr' 'dodge aspen' 'amc concord d/l'\n",
      " 'buick regal sport coupe (turbo)' 'ford futura' 'dodge magnum xe'\n",
      " 'datsun 510' 'dodge omni' 'toyota celica gt liftback' 'plymouth sapporo'\n",
      " 'oldsmobile starfire sx' 'datsun 200-sx' 'audi 5000' 'volvo 264gl'\n",
      " 'saab 99gle' 'peugeot 604sl' 'volkswagen scirocco' 'honda accord lx'\n",
      " 'pontiac lemans v6' 'mercury zephyr 6' 'ford fairmont 4'\n",
      " 'amc concord dl 6' 'dodge aspen 6' 'ford ltd landau'\n",
      " 'mercury grand marquis' 'dodge st. regis' 'chevrolet malibu classic (sw)'\n",
      " 'chrysler lebaron town @ country (sw)' 'vw rabbit custom'\n",
      " 'maxda glc deluxe' 'dodge colt hatchback custom' 'amc spirit dl'\n",
      " 'mercedes benz 300d' 'cadillac eldorado' 'plymouth horizon'\n",
      " 'plymouth horizon tc3' 'datsun 210' 'fiat strada custom'\n",
      " 'buick skylark limited' 'chevrolet citation' 'oldsmobile omega brougham'\n",
      " 'pontiac phoenix' 'toyota corolla tercel' 'datsun 310' 'ford fairmont'\n",
      " 'audi 4000' 'toyota corona liftback' 'mazda 626' 'datsun 510 hatchback'\n",
      " 'mazda glc' 'vw rabbit c (diesel)' 'vw dasher (diesel)'\n",
      " 'audi 5000s (diesel)' 'mercedes-benz 240d' 'honda civic 1500 gl'\n",
      " 'renault lecar deluxe' 'vokswagen rabbit' 'datsun 280-zx' 'mazda rx-7 gs'\n",
      " 'triumph tr7 coupe' 'ford mustang cobra' 'honda accord'\n",
      " 'plymouth reliant' 'dodge aries wagon (sw)' 'toyota starlet'\n",
      " 'plymouth champ' 'honda civic 1300' 'datsun 210 mpg' 'toyota tercel'\n",
      " 'mazda glc 4' 'plymouth horizon 4' 'ford escort 4w' 'ford escort 2h'\n",
      " 'volkswagen jetta' 'renault 18i' 'honda prelude' 'datsun 200sx'\n",
      " 'peugeot 505s turbo diesel' 'volvo diesel' 'toyota cressida'\n",
      " 'datsun 810 maxima' 'oldsmobile cutlass ls' 'ford granada gl'\n",
      " 'chrysler lebaron salon' 'chevrolet cavalier' 'chevrolet cavalier wagon'\n",
      " 'chevrolet cavalier 2-door' 'pontiac j2000 se hatchback' 'dodge aries se'\n",
      " 'ford fairmont futura' 'amc concord dl' 'volkswagen rabbit l'\n",
      " 'mazda glc custom l' 'mazda glc custom' 'plymouth horizon miser'\n",
      " 'mercury lynx l' 'nissan stanza xe' 'honda civic (auto)' 'datsun 310 gx'\n",
      " 'buick century limited' 'oldsmobile cutlass ciera (diesel)'\n",
      " 'chrysler lebaron medallion' 'ford granada l' 'toyota celica gt'\n",
      " 'dodge charger 2.2' 'chevrolet camaro' 'ford mustang gl' 'vw pickup'\n",
      " 'dodge rampage' 'ford ranger' 'chevy s-10']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df.columns.values)):\n",
    "    print(df.columns.values[i])\n",
    "    print(df[df.columns.values[i]].unique())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### '?'을 np.nan으로 변경\n",
    "horsepower 컬럼에서 ?라는 값이 포함되어있다\n",
    "\n",
    "이를 누락된 데이터라는 의미의 nan이라는 값으로 변경해준 후에 누락 데이터가 포함된 행을 지워주도록 하자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['horsepower'].replace('?', np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130.0', '165.0', '150.0', '140.0', '198.0', '220.0', '215.0',\n",
       "       '225.0', '190.0', '170.0', '160.0', '95.00', '97.00', '85.00',\n",
       "       '88.00', '46.00', '87.00', '90.00', '113.0', '200.0', '210.0',\n",
       "       '193.0', nan, '100.0', '105.0', '175.0', '153.0', '180.0', '110.0',\n",
       "       '72.00', '86.00', '70.00', '76.00', '65.00', '69.00', '60.00',\n",
       "       '80.00', '54.00', '208.0', '155.0', '112.0', '92.00', '145.0',\n",
       "       '137.0', '158.0', '167.0', '94.00', '107.0', '230.0', '49.00',\n",
       "       '75.00', '91.00', '122.0', '67.00', '83.00', '78.00', '52.00',\n",
       "       '61.00', '93.00', '148.0', '129.0', '96.00', '71.00', '98.00',\n",
       "       '115.0', '53.00', '81.00', '79.00', '120.0', '152.0', '102.0',\n",
       "       '108.0', '68.00', '58.00', '149.0', '89.00', '63.00', '48.00',\n",
       "       '66.00', '139.0', '103.0', '125.0', '133.0', '138.0', '135.0',\n",
       "       '142.0', '77.00', '62.00', '132.0', '84.00', '64.00', '74.00',\n",
       "       '116.0', '82.00'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['horsepower'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 누락데이터 행을 삭제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset=['horsepower'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['130.0', '165.0', '150.0', '140.0', '198.0', '220.0', '215.0',\n",
       "       '225.0', '190.0', '170.0', '160.0', '95.00', '97.00', '85.00',\n",
       "       '88.00', '46.00', '87.00', '90.00', '113.0', '200.0', '210.0',\n",
       "       '193.0', '100.0', '105.0', '175.0', '153.0', '180.0', '110.0',\n",
       "       '72.00', '86.00', '70.00', '76.00', '65.00', '69.00', '60.00',\n",
       "       '80.00', '54.00', '208.0', '155.0', '112.0', '92.00', '145.0',\n",
       "       '137.0', '158.0', '167.0', '94.00', '107.0', '230.0', '49.00',\n",
       "       '75.00', '91.00', '122.0', '67.00', '83.00', '78.00', '52.00',\n",
       "       '61.00', '93.00', '148.0', '129.0', '96.00', '71.00', '98.00',\n",
       "       '115.0', '53.00', '81.00', '79.00', '120.0', '152.0', '102.0',\n",
       "       '108.0', '68.00', '58.00', '149.0', '89.00', '63.00', '48.00',\n",
       "       '66.00', '139.0', '103.0', '125.0', '133.0', '138.0', '135.0',\n",
       "       '142.0', '77.00', '62.00', '132.0', '84.00', '64.00', '74.00',\n",
       "       '116.0', '82.00'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['horsepower'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 문자열을 실수형으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['horsepower'] = df['horsepower'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 392 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   mpg           392 non-null    float64\n",
      " 1   cylinders     392 non-null    int64  \n",
      " 2   displacement  392 non-null    float64\n",
      " 3   horsepower    392 non-null    float64\n",
      " 4   weight        392 non-null    float64\n",
      " 5   acceleration  392 non-null    float64\n",
      " 6   model year    392 non-null    int64  \n",
      " 7   origin        392 non-null    int64  \n",
      " 8   name          392 non-null    object \n",
      "dtypes: float64(5), int64(3), object(1)\n",
      "memory usage: 30.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>392.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.445918</td>\n",
       "      <td>5.471939</td>\n",
       "      <td>194.411990</td>\n",
       "      <td>104.469388</td>\n",
       "      <td>2977.584184</td>\n",
       "      <td>15.541327</td>\n",
       "      <td>75.979592</td>\n",
       "      <td>1.576531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.805007</td>\n",
       "      <td>1.705783</td>\n",
       "      <td>104.644004</td>\n",
       "      <td>38.491160</td>\n",
       "      <td>849.402560</td>\n",
       "      <td>2.758864</td>\n",
       "      <td>3.683737</td>\n",
       "      <td>0.805518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2225.250000</td>\n",
       "      <td>13.775000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>275.750000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3614.750000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg   cylinders  displacement  horsepower       weight  \\\n",
       "count  392.000000  392.000000    392.000000  392.000000   392.000000   \n",
       "mean    23.445918    5.471939    194.411990  104.469388  2977.584184   \n",
       "std      7.805007    1.705783    104.644004   38.491160   849.402560   \n",
       "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
       "25%     17.000000    4.000000    105.000000   75.000000  2225.250000   \n",
       "50%     22.750000    4.000000    151.000000   93.500000  2803.500000   \n",
       "75%     29.000000    8.000000    275.750000  126.000000  3614.750000   \n",
       "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
       "\n",
       "       acceleration  model year      origin  \n",
       "count    392.000000  392.000000  392.000000  \n",
       "mean      15.541327   75.979592    1.576531  \n",
       "std        2.758864    3.683737    0.805518  \n",
       "min        8.000000   70.000000    1.000000  \n",
       "25%       13.775000   73.000000    1.000000  \n",
       "50%       15.500000   76.000000    1.000000  \n",
       "75%       17.025000   79.000000    2.000000  \n",
       "max       24.800000   82.000000    3.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 종속 변수 Y인 \"연비(mpg)\"와 다른 변수 간의 선형관계를 그래프(산점도)로 확인  \n",
    "#### 중량 대비 연비 산점도"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Matplotlib 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='scatter', x='weight', y='mpg',  c='coral', s=10, figsize=(10, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='scatter', x='horsepower', y='mpg',  c='coral', s=10, figsize=(10, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features.plot(kind='scatter', x='cylinders', y='mpg',  c='coral', s=10, figsize=(10, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Seaborn 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W4JdVhAIGpoJ03m67SLLYEUFYzLqq8Zq3HG/2oltUrClaFldT0SVYXnNw031PUnCcpjoK/egtlbMxUAQMA8NQzGVtDAXJrN30Gt3ewditdWeCILSOfz+X17ECoKioY2134Xm32JFuqzMTNjbryvHqDZm4uqhXU2ZdDHUuylqoWpyxHCZTVkmhHmpHZH73oeW43kBt/CJ8Skr2za5RTjdGgNL5IwjrB/9+Ng3FUMzLQGkgZCg2x87Vsba78Lwb7Igo1Qvdxro6avzIledzx8EXvRqGskKGzb2hUpS1sOYgHDCwHJeJVJ54xDNItSIyPw0fMg1sR6OK0aJRdL5C5jk/ttEa3T7vsNFAXkEQVpdWGonK72fLdtm7JYxSinTerjpsu133eTfYEakzE7qNjjleSqkIcBgIF69zQGv9O0qpLwFvB+aKL/0lrfVT7bimb3T+7PAYmeKx4+beIH3RYCnK+vT9z1bUHAzFwpyey5KzHbTWdTsK/e7DeCTA1LyF63pTtfujAZI5h75ooOk1uqmDsRbS+SMI3clSGol8NDDYG67qAJU3/sRCJkopUnm74vd6zUO1hk+vph2ROjOh2+hkxisPXK21TiulgsDDSqkHi899Qmt9oNmFjo6nuP7uR5vuFHzT6ECpy/DkTIaReKT03h2HKzNOfdEgedshYznMZQt1I7Ly6M12klhVuhobRXWtRIC1jJggCBubVhuJmpF1KH+NqeDFiXkABnsCjM/lANg+EKnZPNSt0hFr5ZRB2Dh0zPHSWmsgXfwzWPxZ2MncFAFDtdwpeOCJU1VrE6plnEIBk8//XP0o0ade9HZTk5+nmQiwW42YIAirz7zlEDCg4JQ1EunFjUR+4JbMFugNm/RHvVmM1Y7byo/kxibSmMVp2lPzBYKGAQom0xa7h2NVj+q69UhvLZ0yCBuDjhbXK6VMpdRTQAL4ptb6e8Wnfk8p9YxS6nalVLiZtdrVKdgp1eJ2d82sle5HQRBWnkaNRMfOJltuIipv/PEbiPzmIf93v4Go2lFdtzYOiVK90G10tLhea+0AlyilBoB/UEpdBHwKGAdCwN3ALcCtC9+rlLoBuAEgOrgFqN8p2MoZfjtqDhbWQkzNW/RFg23LTkldgiAItWjUSGQ5mv4Wm4jKj+T8BiLwnDldvIbfQFTtqK6bj/RWu85MEMpZETkJrfUscAi4Rmt9Rnvkgb8CLq/xnru11vu01vtCsQGgfqdgu7Vi6mWwytuTTQUvJNJMpC1OzWRJ5+22ZKe6Rf9GEITu46Z3XshHr76AnuKAa0PBcOxcI1EoYFRkn4Zi3sGC30RUTdahXPphKBbC0RrH1WzuDZZ+H4qFakpCdIN0hCCsBTrmeCmlhouZLpRSUeCdwFGl1LbiYwp4D/Bso7VcrUnlCnU7Bdt5wzfSffGPAW1Hc3ouh1uMBnO2y8tTGRLJ3LKzU2LEBGH1UEpFlFLfV0o9rZR6Tin1/xQf/5JS6rhS6qnizyUrua/ygPCRsWn+9Bcu40u/9Bbeev5mIsFA6Rhtz0i8InDriwbZ3BuiNxSoedxWfiTnarhguJc9IzEiwUDpd1dT86hOjvQEoTk6edS4DfiyUsrEc/C+prX+ulLqoFJqGK8y4SngVxot5LqagWiID77tNVy5Z2jR88vVijl0NMHnHzzC8SnPUTINRX80ULMQ1T8GPD43X0rB+2jgbCrPZDpPbzjAoaOJlmY2lmvprLb+jSBsYNrWld0sjbqYWx03tpQmouUeyTXbOCTd2sJGppNdjc8Al1Z5/OpW1zp/OMYf/bc3AXByJsvmWKhUp+CzVINx6GiCmw88zWymgFEsUs1YLvmCQzhglq5TnsGKhUxenEiTK7i1lsXR0Bs2W5/ZWPZaqUsQhNWhnV3ZzdCMPWila7BbAzfp1haENaJcb5eN4nG1ZiKVJ2s5bI6FMQ1V552NuevwGOm8jakURnEt5To4mqqFqIeOJpiat0qFp7UwFAzFIjUNY7e2XguC4FHM1j8OXAD8idb6e0qpX8Xryv4M8C3gk8V61WXRjD1YjSYin3ZlqcTuCcIacbxOTGX4p6dP87MXb8MoDklM521yBZeheKiii6YVDh1N8MQrM+RtFwUEMTANRUBBQXuFqMmsxdlknoLrEjQUt33jKH3RIL1hT1QwZ1fPemkNqVyBWDjQlk5Mf7+SoheElaFdXdk7d+5seK1m7MFKdg12qmtburUFYY0MyXa15vaHjvEbX3u64ga1XZfxuRyT6Tx6YbFVA/yUd1EjEI0nRui4GsMwCBoQDpicnM2BgtGBKAVX80Iije24xCNB9myJsyW+WIZMAQFTeZm5NnViyqBXQVgdltuVPTw83PAazdiDTjQRVevc7mTXtnRrC8Iacbz6isd9T5+c4yNfeZz7fvAqjnvO0UpmC5ycyZJbcEPXw095b4lHKCbR0HgCgY6rGegJsXMwyq7NPewZidMXDZUMztnkuZOFkb4IZtF5M4r/NQ2FobyMWbs6MUVQVRBWjnZ2ZTdDM/agnV2D9QK5TnZtS7e2IKyRo8at/RH+98+9kT/65gskUnnuPjzGoR8l+MRPvY7XjsQAL1t1ejbL3/3gVb76vZfJFFx6QyYfufL80vDscvyUtwopdtDD+FwWy9FoYM9IjFuu2btooDbAlniYk7PZUrfQZDqPi+d0RYImvSGTecshb7v0hgI1DWOrxa+SoheEFaVtXdnN0Kw9WE7d1p0PvcA9Dx9n3vIC1P5IgO3FTFN5rVU7urbrDcvuxqJ/QVhJVKtHdKvBGy+5TN//zcNkLJt7vnuc+586jcbLLF1/+Q4+8NbXEAoY/PV/nODLj76MoYpqyyhcDR+9+oJFztf1dz+6qF4iY9mMxCOl1uxarwkaisHeMMfOJknlHXpCBul8MdumYSgeImiabdWwqbaXiVSOjOXQFw1KzZew7lBKPa613rfa+1gu+/bt04899tiq7uHOh17gjoMvlmxj3vbs/pZ4mJE+TzZHa81ctsDoYA/HJ9OcTebrtnFu6w9XtXPlnYvlsxFF00vYSNSzX2viqNGnJxTgpnfs4Y73XcKOwSiOq/nqo69ww18/zrOn5vja4ycxFJiGgVIGplIoNPc8fHzRWgtT3hOpHCdnshxLpEr1DrXS4p/86ddz7w1XsGdLH6ODUUYHezmvP0rINNDAfN5pu5Gptt+JtEVv2GxrzVe7Z04KgrD63PPwcQwFAcPAUEZJOmcifa5swq+1etvuTUykrbpOl9+1Xa3cQcoiBKE+a8Lx8jsZfS7a3s+ff3Af73/rTgwFr0xn+Oh9T5VS6D4az0Ck8/aiNcvrJcbnssxkCmzqDbK1L1JyYoC6NRXlQ2H7okF2D8fYuzVOfzTY9shuYX1HxnIYiYcYikXaZtykgF8Q1h+HjiZI5rygMW87OK7GLNpUV0Mya3HsbIoTU/PMzOd58NlxRuIhIoHa/zz4XdvLHZYtgZ6wEVkTNV5BU7GlL8JU2sJ2PfmGUMDgw1eez9svHOb3//VHvJjwtA5tFxS6pMnlau+mT+YKpSJ9H7/mYOExXnm9w703XFHTiWqmvXthrcPbdm/ikbHpJUlClNd3XHnbwbbXfInGjiCsL/xgylCeLdTaq4cNmgaG8nJaJ2dzBE1V6tw+MTXP6ECEPVviJJI5zqYqZcrKu7ZNQy15WLaIqQoblTWR8QLoDQcYHYzSHw2iyjJgF4zE+OL7L+N//MT5mMWHC67Gclxsx8XV8PNvHmUylWd8LlchxupTK0I7djZZNxqr16Fz6GiCa27/Dh/+yg948pUZTAXHJ9PccfBFTkyll51R6kRbdiuRqiAI3Y8fTG3uDQHeKYDfva2U4ry+cN3O7ZG+SIVIdTNd2812LsqRpLBRWTOOF4BhKDbHwpw3ECFS5iB4RfY7+ctfektJV0trb2xPPGzyz8+e4eN/+zSHjiY4NZutOHo8dDRBMlvgyJkkz52e44en5nj21BxHziSZy9kVx26fOPA019z+nZIjBtWPIsGbk3ZiOgMa8o7Ly9NZEilPbyyZtZdtaKoZt7lsgdmMteS0vWjsCML6wg+mtvZHGY6FSrVdSnlNRxjGomBrSzxMwXVLtiVoKgKGYtfmHnZu6iEcMHBcanZtNyt7IYGesFFZE0eNCwkHTM4biJLMFZiZt0qaXjs29fA3/+Ot/NPTp/nid8awbJe5nEOvC4lUjjsOHiut8XePn+T4ZIp5yyVoKsonAGk8p811NI6rUUrhuJqZTIFU3uaC4VjdAbXX3/0oQVNh2S5lcmOl38udm6UamoVt2b0hE4UXyS41bX/jVbsXDdYVjR1B6E7K5SFqSeeUH/tt7Y+ytT9a6t6+6Z0X8sjY9KJjwYBpsGc4xmBvmJMzGYZ6gpxNW7w6kyFsGvT3BBt2bTcje1HtSHIynSdjOVx520Hp1BbWLWsq47WQvkiQ0cEeYpFzN66hFO++ZDsXDMVKxaHzlsN4Mkeu4HDXd17kjoPHSKRyZAsujusyn68uvKqB8bkc4M1tNBQlR6xetmphJKcULOgPKLGcjNL+vSPce8MVfPeWqxnsDdMXDS4rbd9OgUZBEDqHLw+RLTgEDM+O3HHwRe586IWK15VnxpNZi2OJFCemMsxmrKY6tz/77oswTJPNvSEiARPL0UzPF7jusu3Ltgsr1aktCN3Gmsx4lWMaipF4hL6Iw0QqT6FYwzWVyTM6GCGdd0ik8rgaZjIFZjMFtvaFiQYDFBwXQynsOo3TOdvl2NkUtqsxFITMc75qrWyVH8kppbwzz+Ly/mgihaeZ046Mkl+8//0T04RNxUhfpDTYeynZtHYO1hUEoTOUy0OA171tuy73PHy8IuvlZ8Y//+ARTkxlCRoGowMRLMctZexvvfYNNQVN/Tqs/miE4bi3ZsayeWRsmpuW+RkWZu3LO7WhPc09Mt9W6EbWvOPlEwmajA5GmcsWmMkU2NYXZWo+T1/EywJNpPKk8p6LNZ7MY7uagKGoUmu/CH8Qtqk80UCfWtkq/8guaHo6Yk6xmygSMOgJmdjuOaHC5RiC8q6gSMDAclxOz+Y4bwDikaDUZwnCOmXe8jJd5RiKRZI64Dk4dx0eY5erF4lBN+rc7vTEjE52akvXpNCtrBvHC0ApxUBPiN5wgA+97TX8/r/9iGzBIRI0GOgJEjAVyZyN42om0hZBU+E2UO73s1Sh4msDpmqYrSqPMo9NpAmbBlv6wgRMY8kKztUit/KuoKFYmNNzWTSaRDKHaSipzxKEdUpvyKvBLGs4xNXe49Wo5UD5ndu1MkL1pCHaKZXT6FpLQeRxhG5lzdR4NRLaK3/+g3/xfQZ7Q/zOu36MoViYVM5mc2+YW/7zXn77Z15PT9E4FRxdUVRfjZBp0BsyuXBLnIFosOn6p/17R/jGx97OX3zwLVy6cxBXs+SaqXJhU1PBk6/O8OGvPMZjL0+X5DH6osGSen7e0YzEI1x32XbuOjwm4oSCsM74yJXn42rveNHVbvG/3uPVqNaxPJnOk8o7dQWTq9WAJbMFXp1Kt10qp90DtKVrUuhW1sSsxtdddIne8sHba87+qjcb7KoLh5nJWCRz3s0M8P2xae44eIwzxcL5epgKRgd7CJiqYo5jNTpVT+ALvNqO5vRcFgOFRlNwNKah2D4Qpa8YzfodS+XHnTIvTViLyKzG+vhdjem8J08TDSreuH2wqt2586EX+ONvv0ihLNJUwGBPsDQoG2AynWM+Xzn/Fc7VYcXCASbSedJ5m4Lt4uKVUfiZt2jQZPdwDFg8+7YZfBvq15stJ4vWzDxeQegUa35W40QqX1dor54QX7n2V7gY/Vy+exNb4hG2D0QW1UksxAAMg4aRVyfH7fiR22Q6j4HCMLwfX9jwbCq3KEJcbXFCGQUiCJ3lpndeyJ3vu5TRwR52be5h1+ZYhd3x78E3f/bfFjld4JVQzGULpHIFwBsBNJmyyFhOhQ0DSt3TAz0h+qNBLNst1a6Cd8zp6uVL5ZR3at941W4OPHFqyTa13Rk0QWgXa8Lxshy3bsq4mZRyOGCyfSDKUDyMaSjOJLOlI8dazlc0aOAA/ZEQN//Uhbz9dcM199hJR8c/JvDUpr3HtIZwwGD7QAStWXT8uZppdpn5KAgrQxlFG0kAACAASURBVC278/kHj5TuwVzBXZTp8nF0pWQOyrMrtWzYSknl1PtszdpUkccRupU1UVwfMg2yBadm0WUrRZl9kSC9oQCjA957gqaB7fiDNCrJ224phZ7JO5ycyTIcD1eo5rdbzqEa/rGhaShcVxflKGC46ERetnNwUeq83YWqrSBFrYKwMtQsmk+kGR2M0hMKYC1o3V5o6XK2SzJrkbMdDKUYioUr1iq3YSsllXPoaIInXpnBcV3CAZPheJh4JNiyTRV5HKEbWRMZr+F4uG7KuNWUsmko/tfVF6DxZkBqdOmLKA/e/PT5qzMZ7jh4jH8/Nsnp2SzT8xZa64rMTiRgUHA1p2dzpdR9uxwdP3LbtakHR3uO17b+cN3OxdVMs0tRqyCsDLXGfAGlezBkGtRISpV4ZTpLOOAJpfaVOXILbZhvV7wxQoDyHK5wwGBTT5DecGDZ2SXfrirlCWLbZXZVJHKE9UDHMl5KqQhwGAgXr3NAa/07SqnzgfuATcATwC9qra16a8UjgboifwuF+JrRx9q/d4TPcRF/9p2XeOFskrx9Lg7MWg5KnXO8puYtokGTv370ZS7fvYnZjEXGsvnid15aMTkHP3IrLz71i+irfc6lfCftYjWzbYKwkag15mv3UG/plGAo5o3+qdXBHTRAo+gJmTiurjsyrN1SOfVkcrbEI5yey6I0gGZ8LsdIX0RqtIQ1T8e6GpVSCujVWqeVUkHgYeCjwMeBv9da36eU+jPgaa31F+ut1amuoHIeen6cPz30EqeKGa3hWAjH1SRSVkVq/r9eNsqv7N/NVx95mb965GXAy5INx0JEQwEm03lytsvluzbVdXSambO2VqnXZVruPIqatFAP6WpsjoWdgL5jUn4P/mg8SaGKWLTCy4gFTMXW/ghBQ5VmNDYK1qpdt1Wnq5qdyFg2W/siKKVIZgtMpvNYxSkjd33gzWIrhDVBPfu1InISSqkePMfrV4F/BrZqrW2l1NuA39Va/+d671+O4WrmH/lyAxAyDV6aSGM7muqVX7C5N8j0fGHRcyOxEPGi1le9dmV/zpqhvDZsP7P20asvaJvztdrOTS2j3MgpEwSfjep4Nbp3m723/dcdS6SYTNc+VAiaivP6o8Qj3jHhd2+5urUPuETK5R58Bytvu2i8QHY4Him9NmPZJadQAjZhLVDPfnW0uF4pZQKPAxcAfwK8BMxqre3iS04C2zt1/WZHRiwsBt/WH+Hl6WzNdafmvRou32nymUhbREKBhqnwZuesLZVuGJVRq6hVCu8FoTaN7t1W7m3/75sPPF33moPRIH3RIBnLrlsO0O5gzm8MSGYLJX1C0wDb8SaLAAzFwmQLDnPZAgoouFrG/whrno4W12utHa31JcAocDnw+movq/ZepdQNSqnHlFKPTUxMtHztQ0cT3HTfk5yazTA+lyOdt2u2Iy8sBu+LhipGcSggoLyxQeW4C3augY++4wJ+/IKhunubtypHfUDtOWtLYbU1vOohhfeCUJtG926r9/Zdh8dI520CCw1OEUNBOm83bL7phESM3xhQrk8IikjQZCQeImM5pUL94ViYvmhwxW2a6BEKnWBFuhq11rPAIeAKYEAp5WfaRoHTNd5zt9Z6n9Z63/Bwbf2savhGImM5BIzKrphq/8hX6wwKmQZGUdMmHDAwTQOnznwhQ0FP0ODSnYOcms0yly3UfG1vyFzktNWbs9Yq3ezc1OrCksJ7QWh877Z6b786k8FxdVXHy38kU3CZSOW57rLtNY8smw1iW8HvkMzbLiiNq3VJJmdzb5j+aJDv3nI1995wBam8veI2TfQIhU7RMcdLKTWslBoo/h4F3gkcAb4NXFd82YeA+9t9bT8qDAcM0ApDKZTyBAKr/SNfTXohHgkQK3b5uFrjui4O3hcWDy8+oXU1hIMmL5xNobVmKp3nzFyWgrO4orXVOWut0s3OjahJC0JtGt27rd7bOwZ7MA1VGuvj5ZTOYRqKnqDBcDzMgSdOVZ2B20oQ2wq+TE5PyMQuOofnDXg6iAs/02rYtG4+ORDWNp3MeG0Dvq2Uegb4AfBNrfXXgVuAjyulXgQ2A3/R7gv7UeFQLIyLpuC4WLbLvOWJoL5t9yY+dt8TvPa3/oVdn/xnPvyVx9jeHy4pHIdMg829IUJBk1DAwNUawzAIGorRwQjnDUTZ3BtcpI0zkynwK199gnf98cP8+n1P8Z2jE5yayZLMVWa/bnrnhXz06guIBk1s14vc6hXWt5ru7mbnRtSkBaE2je7dVu/tG6/aTSwcwNEao6i55SfblQKFJ/pcy6loNYhtlf17R7jzfZeyfaCHrf0RYuFA1c+0Gjatm08OhLXNmhiS3WpXUHm3zNm5LBNpTxLCUDASDzObscjZiz/3ey/ZxrsvGa3ZdXfX4bHSurMZi/G5HBpN0DQIBxSz2XMRWcBQ9EWD/OZPvY7Ld28q6umECJit+bq1ugCvu2x73eGxy231FoTVZrW7GtulRbjUrsZa926r9/ahowk+/+ARXpqYxy7WOGggZMBgb5i5bKHUTRg0FH/+wX08c3KWex4+TjJnYyiIh0wytouBAqWxXc32gZ6qQdNSivCb+UwrbdPqDdn29cakw1KoxarLSSyXpRgu31k5M5ulUDQ25/VH6YsG+eGpOaByxpjWXtr98l2b6t5s5ev6ozhG4hFmMhaW7c1StMtOFzf3hvjSL7+F3nAAsziwO1blqLIW1W7+yXSO6fkCo4NRkWQQ1i1d4Hi1RYtwJXQIG7EwgHsxkabguGgNLpUjf8IBheVoTEOVZHUA+sMmtvZGqfWETO5836VVnaObDzxNOm/juN4asXCAP7juTWvONtULeg88cUokcYS61LNfa2JkUKuUH2flHU3INEpOVz0cV9dNLy9c1xu83cNAT4iC41JUh+A1m6JEipO3p+YtfvlLP+CRl6Y8QdZkjrPJHM7C6voaVNvPXKaA4+pFtQe3feOodOAIQpvQHunin8HijwauBg4UH/8y8J5V2F4FjcoRFtYrbe2P4BSdLjh3/GgqyNkaV3tyN8GycUMpy2Frv1dqUc3pAk/RfjZTQLtgKoV2YTZT4PMPHun4Z2w3tcoiHhmbltovYVmsS8cLvJvm3huu4PJdm9jaH2nodIGX8WpUxFltXdPwhFe1pnjsaLJjMMpANIBSMJm2+N//+Cyf++cjzGYs5vM2J2cypPN2tW1UUG0/ecf1ai7KsB2XFxJp6cARhDailDKVUk8BCeCbNKlFuFw5nFZopvtuYQAXjwQrJG0M5R0zLiyFMA1Vcr5cTcOazONTGU8Y2lAo5UlEGMp7vNOfsRP49t7vrty/d0Rqv4Rls24dL59qRZmRgGdxtD73A3DtxVubLuJc+Lr+niAaRV/EOxLM2S7RUICb33khl+4cAODg0QS//KXH+NaRBLbjkkjmSDTIflXbT8AwiEcqjyvPJvNdH4WJJo6w1liqFuFy5HBa5bZvHCWRzPHKdIbjk/M4rl507zeWzDEJFINHn1zBIW87aK0JmJ5t852PlaabOgy7uWtcWBt0VLm+G6g2LPq3f/bHuP+pkzzwzHipDuHai7dy+/suA6g7XNqfsZjKeQGvb6eiQYNrL97KeNLiZDEiSmYsvnDwGFprhmIh0nmbuWyB3/uXI3zr6Fl+/R17oC9CruAyFA/REwpULUxdOCD83W86j688+jLHEimsYkGZqz0DOj6XJWM5WI5LyDSYy1TW/K7WKKFuUNMXhKWitZ5VSh2iTIuwmPWqqUW4Ehw6muCFRBpTedkpX+5hW3+4IgNTbZh2PBIgX3DIFFy0N4kau0yrUOMFpa7WKOAjP9lY7mb3UC/HEmmU1ijlvx/2DPcu63P6KvflrFaWqdZg8m7oGhfWBuve8YLq42v27x3h9vc1/3o4N2NRa10R4poKCo7muy9O8QfXvQnwxnTMzFsl7ZyZeYtYOMDuoRjPn0ny6Ng0//3kY9xw1W7edfE2xudyPPPqHH/4zR8RChiVzsm1b6iY/XjoaALFyziOWyHEWrBdJtIWAQUB08ByXGxXc+hoouVxI+1GRgUJaw2l1DBQKDpdvhbhbZzTIryPDmkRNot/X2nXk4ZQClw0Z5N5Lt05WHpdrQAUvIzZ2OQ8AJGgQX9PANeFiXQeV3v2a3Qw2tQ4s1uu2csnDjxNKmdjOy4Bw2CwJ8gt1+xd1ufcMdizqMlotbJM1b5L6WoUWmFDOF7twp+xuFDA3tEQNhTpvF1KfXtjOgwMQ6G1RmlNtuAQNBSfedeP8ccHjzGTKfCFh45x8GiCm3/qQr70HydQCiIBs5ROr+ac3HV4rDhbzcFwNIahsF2XQnFjLn6HkmJTb7D0/tV0fropYhWEJtkGfLk4c9YAvqa1/rpS6nngPqXU54An6YAWYbO8OpNhSzzM6bkcjqNx3HNBoa9XuDCzf+8NVyzKfN/1gb3s3zvClbcdZCAaRClFNGQykcqTtx3G53Jcc/t3SFtO3Uz5/r0j/J/r3tS0NESzmfduyzLVCs4FoRnE8WqBecshYEAVCTCU8roiT85k0Hi/m0W9CqUUCo3taM6mcux/3TCX7hzgi4de4t+eP8szJ+f4yFceJ2QqtvaFKTguplaYhqpwTnxj9f0T04RNRd7RBIsVsqahzjleGgqOy3DMG71RPm5ktZyfbopYBaEZtNbPAJdWeXwMr95r1fHvq8FosKRXqICAAX966MUKvULH1fzDU2cYn/sPTs3lq2a+/fWc4pGlL7lju5oXJ+bZPhBpmClv5JTUyrxfd3K2pjahZJmE9URTjpdS6ueqPDwH/FBrvWEqpHtDXqTl1y6U4+uA+Y7EZDrvpf9LnUOKoKnYtbm3pOP1yZ/ey9V7R/ijb75AIpXHsuHl6Szb+iJEgt48R8vxnJNyYxUJeMeIrquxgaCpKgr0DeV1V85kChgG7NocA1bX+em2iFXYOKxn++XfV8mcTdBUKBQumvP6o7w87QVUC/UKHzk+w2uHe6tmvv31EskcoEErHBdMw5OHmExb7B6OLStTXi3zPpnO8SeHXmJ0MFqzDEKyTMJ6odmuxg8D9wDvL/78OZ6I4L8rpX6xQ3vrOvwZiwsxFThaEwsHuPGq3RVjOizbKXYHuTha8+Ov3cxIX4Rt/VGCpsHl52/iL39pH++55DwUXq3YKzNZTs9lGZtM8fJUhsl0jv/3X54vGauhWBg4l2Xzjxn9/zMNfEFEzfR8YcnjRtqB38n46fufpSdoeAX/MipIWFnWrf3ytab8UUABUzWlWdhIq7B8PVWsGVWKkmj0cjLlrWgTdlNXtiC0i2aPGl3g9VrrswBKqS3AF4G34o3U+OvObG95tLOD79DRBI+MTWMqsMqU6RVgKEXA9MJKP2r8g+vexKf/4RlOzuUBCJuKTbEQB544xcWjA+zfO8LoYJTZTAGlFDe9Yw9X7x3hs18/wkQ6TzrvtStv7g2SK7icms0yOhCBUKBkVCfTeTKWQ9AwKDiOZyiLkamjdbFdXHPX4TE+ff+z7Bjs4c07+/nW0QnmLYfekMlHrjy/5ney3O9v4ZGCl+Vy+ey7LxKHS1hJ1qT9apb9e0e4bOfgomx2PbIFp2bme+F6YxNpb9SQ9iQoFr6+Vapl3vOOSySw2Bk8lkhx/d2PymgeYV3RbMZrl2+0iiSAC7XW00ChxntWlXYK7vlr/Wg8ieVUHukB9IRNzhuIsrXvXP0DwI7NMV473Msbt/dz4dY+hmKRiihOKcVgb8gb/RMyuWh7P3/94cvZEg+XrjE1XyCVszEVjKfy2I6L1pq+aJCt/RFetyXGUDxMJGAQNBWmMrC1939s1nJI511OTKUZiAY5PpnmgWfG6Q2bvH5rnOF4mANPnKr6nbTj++sm7R1hQ7Pm7FertKJX+LbzBxtmvsvXG4qFcFyNU5TFWW6mvFltwqn5PKmcLaLQwrqjWcfru0qpryulPqSU+hDwAHBYKdULzHZue0unnf/o+2vNZgsoKmsmAJJZu+p1mlU4DpoG2/qjjPRFvChQwc7BSEmdfjZb8ArmbU06b2PZLvP5AgVHM285JJI5bFdTcLxjR8fVWMUZa4YBU+kC6bxNqjjwNpm1G34n7fj+ul3hWQRdNwxrzn61SrXxNn/2gX2895JtmGUNOO+9ZBv33vjjVUfhlGeSytdzNewZiXHBcG9T6vVL2euv7X8toYBZ4YxNzxcY7Al2XeAmdkNYLs0eNf4a8HPAlXina18G/q/2Jmz/ZIf2tiza2cHnr+Vr2vhoXUWyuuw6rRazx8IBeoImOwZ7OJvMsXMwykymwNS8VZKwODOXK9ZCmOy/cIhvPJ/AVOdqMQrlGTlDETQUGphI5bEcF6OsTqPed9KO76+bOxlF0HVDsebs11JoRa+wnlahLxCtihqEAdOgN2TyyZ9+fUkP8K7DY/zG3z1FwdGEAgZ7RuJ1jwGrlS2UaxMCXDw6wOcfPMLR8SROMUM3NW8xM2/h4h1zDsVCFTZopQWhxW4I7aCpjFfRQP07cAj4FvBw8bGupZ1jHfy1jAWZLt8wLXi4dJ2lFLMbhuJ//uQFgFf3MNgbZEtfmEDx4rarMRREgib/+vxZDDzvT6EIFKd0K6AnZBIJeCNA/KLYkGngltVp+HuNhQOLIrh2fH+rUczfLHIMunFYi/ZrNfAFotN5G40nS+Nobw7sixPz3Hzgae586AU+88BzHJ9Mk8zZZAsOc5kCJ6bSNY8BWylbmJy3AFWSySk4mnzRC7MdzanZHL0hs+V124XYDaEdNOV4KaU+AnwfeC+eYvOjSqn/3smNLZd2/qPvrzUQDZbGaJTTFw1UvU6t6faNIqP9e0f47LsvYvtAD5m8w9b+KDsGIgz2BEsCrlPzFrYL2vWUqp1i7ZfXzQhDsTBDsbD3nOvpfcUjAVzt7dffazJbYCKdX2S83rZ707K/v6V+/pWg249BhfaxFu3XauALRC/E0V7DTjpvc8/DxwmayitbKAZ7hqFIZu1lly3cdXiMdN7GVArTMCoCWscXKMOrjW1l3XYidkNoB80eNX4CuFRrPQWglNoM/Afwl53a2HLZv3eE607Ocs/Dx2t28B06muDT//hDTs3l0Bp6gia/8vbdi0ZjlIv3PXd6jnnLwXU9+Yh37B3myJkUx6e8Gy8eCTCXsfilL/2g9H5DeRpgC7NFC9Pkb9u9qaqAoONqrrztIEOxEAPRIKfncuSLMxptoCdg4routusSMM61gUeDJpvtEDOZAj3hAOcPxfiFy71r+CKEQUNRKLZxwzlNn0fGphfNiFxKGr9btXe6+RhUaDtrzn6tBs0IRM87DjuDJpbjlglEexn1pZYtlAtDu64mYCh8t8sPJF0NAcMTmE7n7abW7QRiN4R20KzjdRJIlf2dAl5t/3bax6GjCQ48cYrheJidRcHOcimHQ0cT3HTvEyTz547TMgWHL3zrGEBV56va2Av/vH/PSIxTsxkm05VDqcEzGqm8w9HxuVI9AFBRK3BiKs33T0wzHAsxFAsvqh3YtbmX8WQWy3axHbeoHVbct+WggIFogEgowHsvOY8nX53j5EyG84difH6Bw3RT2d78ESHllGv6dKPT1A5E0HVDsebs12rQjEB01DTIFhxCpoHtnBuEHSo+Xs0BqeesLBSGzlgOBVejlLe2r5voZ78sx11VQWixG0I7aLar8RTwPaXU7yqlfhd4FHhRKfVxpdTHO7a7ZdAoDX3X4THSlud0KXWuXsvVXsp9KdeYy9p1Xz9Xlo5f+N5k1us49IpaF+/3xqt247gwOZ+vehyggZmszehAlPdeNsof/vybOHjzfu694Yq6zlM7a+HWEt18DCq0nTVnv1aDZgSiP3Ll+RQc7ZUtoMnbDpbtkis4nJzJ8rbdmxa9v7zsI5m1OJZIcWIqw2zG4vMPHqkQhvY7xgtlDUDgKedbjksiZZWusZI1pCIELbSTZjNeLxV//Nvy/uLv8U5sqh00SkO/OpOpamTAS7kv5Rq11it/3nbc0jzH8vc26jj0jztv/OrjaHQx9Q6mYeC4LsWTR37w8gz/694nufmnLuS1I3E29YR48pWZmp0/GzmCW88ZPaGCNWe/WqFdnX0Xjw6wrS/MydlcxeMaCBUFoh8Zm+a6y7bzyNg08/kZ0nmv2SccMOjvCVacKvj4tuvzDx7hxFSWoGEwOhDBclxOTM0zOhAFoC8aZAc9jM9lyTu6IuvmaogWtQrvefg4X3v8ZEuC0Mv5jkQIWmg3zTpe/wL8FrCr7D1aa31xJzbVDhqloXcM9nB6NlvVWfK7Zlq9hqEaO1+nZnNcMNzLYG+44r0h0yh1HlbbL1QqSr8ynfH0eTQYKCIBCAdN5rI2R8dT/MpXn+AX3rqTC4dj/Ol3XiISNKq2Py8cPtsbMgmZhqd0f3htKEWvdEu5sOZYc/arWdolb+Cvk6kSdLraCwJ9gegDT5wq1X8utLG1Zjju3zvCXYfH2FVWTwoQNAzOpvL0RUOA53zlbYeZTKFYs6pAK2ytyRcc5ouVHIM9niD0909YjMRD7NzUs6icpF3fUbXZksuZVSkIzR41fhWvEPXngHcVf/5LvTcopXYopb6tlDqilHpOKfXR4uO/q5Q6pZR6qvjzM8v5ALVolIa+8ardxIoOVknZGc95+siV5y/pGv3R+n6sUdaVs/C9fVGv4zAeCdRNm/vvM5XCdb2o0EUzFIvQFwlywXCM0cEotqv5yiMv89v/9BzjcznOzOWYyxSqdv7s3zvCvTdcwWfffRGZgovluGtGKXo1WsqFNUfL9mut0K7OvkYi0bPZwpIFon2qvX5LX3iRnZ7JFNjUGyyOEPJ0CB1Xl4r+FXBmLs9sptCUIPRyvyPpZBTaTbMZrwmt9T+1uLYN/IbW+gmlVBx4XCn1zeJzt2ut/6DF9VpiYSZnYVfe/r0j3Hn9ZU11NZazMLvip91PzmQwUKUunIUYeDfrUCxU6srpCRqlbsjdQ71c/5adFR2H5fv1r/vC2aRXgOq4OBpCJmwfiBIwDSZSXrejZetS8b3vUOYLLqfnsrh4shjVjMbCyM52NIlUjhu/+jiX7RzsykySRKNCEyzFfq0J2tXZV0sk2qc8k79Ugehqrw+YBuf1R5hI5UvHheGAV0PluLrUvV1OsDiwO2+7hAKqoSD0cr+jbutklAz/2qdZx+t3lFL34IkP5v0HtdZ/X+sNWuszwJni7yml1BFg+zL22jKNanj27x3h4U++o+n1qqWs/bT7/U+d5B+eOrPoPbGwyflDsdLfGcsmZBoV3ZDZgsO85XDx6EBVp8+/rmU7JHOe02YoRV/UJGO5zFsO0SCkcg4BUxE0YeGJgQugPQV701Bs7Y8suk65gUpmC5yeyxYbDnTXKjSvRku5sOZo2X6tFdrlFPjrGFU6GoGKhp5ygehW6kOrvT6ZLaChovv8lekM8zM2AdMgZKqK+bgBQ2EaCl0Mb+sJQvuOSTwcqDsUvBHdVAcryvnrg2aPGn8ZuAS4Bi9F/1/w0vVNoZTaBVwKfK/40P9USj2jlPpLpdRg07tdZeqlrB94Zhwo65AsGqp03ll03Km1bin17V93oWihZWtGB6PsGYkzly0QML3nTKMyLV5uNC1HM5Gy+Ok3bGN8LoddFi2WdzhOpvNeBk8pwgGzaxWaN2pXptASy7Jf3Uy7OvsaiUQPRIPLFoiu9vrNvSH6o5XzGNHak8rRXoDpm69A0a66ri7qfdGUIPREOk8yW1jyd9RNHdCinL8+aDbj9Sat9RuXcgGlVAz4v8Cva62TSqkvAp/FOwH7LPCHwCIVaaXUDcANADt37lzKpdvOwuxKMltgMp3nxFQGp05V/Ug8UnF8+On7n20pS+Nft55ooS9+6FNe6B8wKqPGbMHh/zv0IoYJ+y8cYXMsRDwSrIjsLMf1DJ5WDMfDDfe4WnRTNCp0LUu2X91Oo5KKpaxTLhIdj3gi0eNJq2bJRivXWvj6ajqCKIWBJmB6x4jhgIFlu6AU5/VHOJvKYztw4Uicn75oa0NBaICgoRjsDS/5O+qWDmjJ8K8PmnW8HlVK/ZjW+vlWFldKBfGcrr/x0/pa67Nlz/858PVq79Va3w3cDbBv376umKtWntb3j+IAwqYiU/Ry/NmIPqahFg2D3XF4abUR9UQL57KFinmSplK42t+TJmiA40I4aOC4MD1v8bsPPM9VexLc9I497NjUw09cOFymVu8dM27tjxCPeDf61Hye+bzDlbcd7Jragnb9wyOsa5Zkv9YatYzknQ+9wBe/8xLZghdMjQ5Gq0oh1BKJ9uuJ4uEAr06lK6ZyKGBTT5BoOFBhE5qdyuHbNtvRTKbzWI6L42pMA3YPnyvRmEznmM87uBou3VFZb9qMIPRctsA3PlZph9ci3VZvJiwN1cys2GJ91muB43g1EooG7djKG6j1ZWBaa/3rZY9vK9Z/oZT6GPBWrfX76l1/3759+rHHHmvi43SW8vP18blcMSukOG8gwmymwGy2sOg9771kG7e/77Ka65RnaWqlr8trvKb8fmoNQ/EQQdPk1mvfwDMnZ7nj4IsY6ly2q+B44zeU8py0zb0hUBALBemNmPzgxAzgdVL+6ttfy8+8cStD8QixcIA7H3qBPzn0ErbrEjYNIkGTuZxdUtZvtGdBWC5Kqce11vvasE7L9quddNJ+NbIldz70Arc/dGyRU9YfDXDHf7u07r1bvrbtuLw6nWVxqbtHX9hguC9KwdFcd9l2DjxxqrSnqfk8iZRV1XYA3Hzg6VKHInh2S1NsSAqZxCMBQgGzKVtz/d2PVpW4GIlHFgXAa5FW/+0QVo969qvZjNc1S7jufwJ+EfihUuqp4mO/BVyvlLoEL0A7Ady4hLVXjd6QydjkfKnbJmB4BevD8TBaa+aKxe+mobj8NQOMJ61FGaJqcyTfsXeYuw6PefpZVSJEXyjQdjSGoegNmezaHKuI/Lb1hUsdmkZRiT9kRvJImgAAIABJREFUKkb6zmWttNbMzFv8yfvfyr8+f5Y//faLJHM2v/+vP+ILD73Aa4dj/MSeIR58dpzBniCpnE3edsnaBfojASJBk+OT86Vjz9u+cVRueKHbWYr9WhM06uotn8JRysRrT4KhUedv+dpjE2l0rZZtIJl3eU3x2vc8fJzheLi0p/KpHMPxSMUe773hCoZjYdI5G0fr4oBs0HhSOTnbwc5ofm3/zqbszHovPZAM//qgKcdLa/1yqwtrrR+memfyv7S6VjdQHmls7QvzynS2pPtlu5rTszk2x4L82Hn93HvDFXW7T4DSHMlBx+XMXI5/eOoMIVOxrT9SFAacZiQeYnNvuKpQYHmBq3+tcNBk52CUU0XladP06h1Oz+Y4bwDiEU91eefmXnZs6uE9l2znRCLN3z5xEq29wvsj4yl+dDbFYE+QLX1RhuNe9+PzZ+bIFlwyszmU8hxL19W8kEhz6Giioze+tE8Ly2Ep9mut0KjmZ95yqvpKGhrWBZWvbTlu1W7HcsYm0gzFQsxb3iBtn0ZTOVJ5mwtGYiilPAcPUCgcrdm7tY+MZfPI2HTFkWItm7ARHJNuqTcTlk6zXY0bnvLobzJt4XcwFxxd1O7STM8XSpFVve4T/znb0Zyey5UK3y1H88p0lslUvkIYMJWz6woFLtqbUkVV+2KNF5pEMlfRzRMwDY6eSfL3T53CVFQU5rsaZjIF5vM2BcdFa03YNMjbLkr5nUbe/zrdUSMCqYJQm0Zdvb0hs2r069V61a8LKl87VNTOqoftaE7N5giZqmJPIdOoKvtQPkXEf73luBU1rLC4eLyRTSgXhAb49P3Pcv3dj3alzfBnQF5528Gu3aPQfsTxapJy9WLLcQmYBsHit+doTcg0iIfNUiRST+3Yf24ynUcv6IbUgK29I0E/QmwUMS7cmy9nUXA1hvIcvEzBJWioilqAuw6P4WqvED9geJo5vnF1NbwynWEilSdvO8SLqvxae3IYrqtx0WyJhzvaUSPt08JqsNqTN5qlkZxE+RSO8gkdfdFAw+O38rWHYiFUnYyXAaXzjaFYuKWpHOXXCRoKp2hbhmJeN/XC4vFmbMJaCNjWwh6FztBsjdeGp7ybJGQa2K7GUAY9IcXu4VipgLPa633KDUgi5RXnLyxW9csoHBd6Qp5n12iO46K9ORrHLR4NaAiaCkMpMoXKq706kyEc8DslFQaeEr5le8WtGpiat0jmCvRFgmyNh5jJFkqO5lAsQsBUFZ+73Uj7tLBKrOrkjWZpdLTmCzI309UI9Sdz9IYNUvnF5fVGsfQgYHhlGPOWw1BvqKmpHP71pudzZAteQIeCgWiAeCTAqZkMM5kCYxPzvP63H2QoFub0XI5IwGAoFqavaBsW2oS1MIVDpm5sXMTxapLyos2hWKhUR7U1Fq4qyNeoyPMzDzyHaSgKZfpa5Zl8PyrV2tPSmUhbpb8XrlVtb44LpuFl4/z27EQyV1EMv2OwB9txvU5JTyYHx/XEXX9+3yj/+lyCiXSegqOZnrf4iQuGeCGRIhI0iRXVoDtduCrt08Jq0A2TN5qlUc3PTe+8sO4YNJ92TOaYTOdI5Wz6o8GGUznKu7XztvY6sg1FLOJN5BibnCdjOaVO7WzB5dWZLAHDy+z7cj590eAim7AWpnBIULlxkaPGJilXL3Y17BmJccFwL66mpGQMXjvzmz/7b9x035PMZiwmUnnG57IVasf+Wrs2nTMUgWIvtcbrRNwxGGUgGuJYIs1MpsC2eIiBaIi5bIGQadATNEq1C8+cnKU3ZHJyJst4Mk9/xDt2tF2v8N8wFAHT0/Xyi+HBc9hCAZPNvSECpsJ2vczXSCzEt380wY5NUd510VYiAQMNHH5xkvm8g6kUU/N5hmLhjrcxt0uZux1IPcbGZL1M3mhEOyZzTM8XGOwJNlUa0GgiR952CZre5Ay/IkPhzaD19ewn0/mqNmEtTOGQqRsbF8l4tUC9yLLaPMVyra2FqW1/rXK9rGjQoL8nSNA0S1o4o4PRUsZs3nL4r28eLWnkDESDnJjyOiCHYyH2jMSYTOeZSFsEzXPZNNfVuMovhqeUyl54TBELB5hI5wmHAkSDJqdnszz16iybeoL0hE2m5wuk8japvM27Lt7GjW9/LTs3ddZIdEuXksxI25isp8kbjaiXgWl2MsdsxirVZi1co9b1ak3kcFxNIFAMSMsurzWcNxAhkcyRs11G4pFFNmEtTOFY79IXQm3E8WoTfvQ2lfaiN8PwMkzJrM3W/kDNc/ub3nkhF48OLHIsap3/N9LI8TsgF0ruFByXgKk4r2gkfcqdyevvfhTLcUtrp3I2pqHIWJ4ERTwc4EyxC/Prz5zhkbEpPvbOPbzj9VsYioUJmp1JoHZD+7TUY2w81tvkjUYsPNZPZgucTeXqykgsnMxRTcC0Vhan0UQO01C4viZh2fBupTztxLzj0hsKVA3EKgO2xVM4uiGz1C1BpbDyiOPVJpqZp1iLao5FrXmOjTRy/L9d7Y0ysooq0ADn9UfrFsMvjHj9tQquJmgamOEgr9lkMJ7MUXA0U2mLT//jc/zdYyexXU+Y9TWbe9el8ZB6jI1FcfLGXwBHtNZ/VPZ4afIG8F7g2dXYXycoz8DYjluqY90+EGFq3iKddxY5YddevLXmGo2yOP5r45EAU/OWV1ivoa83SMHRXHvxVh54ZhzbPWfTNGBo3zYpekJmzcyzb1f9bLVpqFKNbDJbIGioVR9/1g1BpbDySI1Xm/DP60OmUTJOC+cpLmW9crIFT+W+nkZO+d9bi46WN17CIFA8fqyVyl54zYVrG4Ynavi6rX3c86F9XLZzAICnT85x5EwSR2tOzczz2/c/29b6p26orZJ6jA2HP3nj6gXSEb+vlPqhUuoZ4CeBj63qLttIeR3reDLP/8/eu4dJdpX3ue/al7pX37vnPpoZacSAZAkkwYNiWZYFx0Y4R+BzRIJ8sMEES0nsg5wYIp1zADsCJ3DAxpA4ZGTiALYjQiaOIdjYBuHxgI0M0hghyRpmpBlpNNe+d92r9mXlj7X37l3VVdXV0/eZ9erRMz1du9b69u6uNevyfb+fZQh2DqbpSyfYO5Ijl5xf8JmGaGuHFm9jruo05bZ26m/vSI7+lEpv6M/Y7BnO8fDd1/HJt9/EA3deQ9o2kagxLGmpPLCEabC9Xwk895KvlbENzsxUOTFeouH6SNSCUss4aNYDveO1Qiy2elvquX2nleN7btvLoaNno+/3pS3Gi41IIydeAZlPWQy7CWYqDpmk1TYXIs6t+4b4ncMv4PmSpGVgm4KaS1M1pevDL//ENdyyZ4hPvf3V/Ozv/i1nZqv4Ei4W6mQSJgNpi9/5y+dXZCW3UXKrdD7GlcXl5rzRC3EpCd9XVYZnZ6skTGWJtmc4y1zV4VsP3hld27pj1M4cu5sV2q37hgBIJSz2t+w8HT42zndOTtOXttnen0IIwfHxEklT5WqFx4btBFbjxt4TpXpTleWZmSqZhBH57SZMg75053QQjWal0ROvFSJ+Xu96BRqeJGEZC/wUQw4fG+djf3aM58dLuFJiCrhmNMdDd72yKfH9Y392jBPjJQD2Dme4YedAU07YnuEc9752KNLI2TuS42df1/z3j7b0Hw5Mz5ydpeIoZfqUZWIZMJS1mas41FyPhicYTFtMlx2myw57hzN88KdfFbW1czCD50v2DKUZLzYoNzwqDY+q41Gqe5yZqbClLxXlfvVi/dN6zWylsSFyq3Q+huZyJr7AMQMbtLDCOm6Jtmc413ExdM+Z2abCn+fOz/Gdk1OYAlK2SanmtLFCm/97q61aPJ7nJ8qAqv5uZ4MW7jzHi5WSpsF4oYYvIZuwEAk1jriez2zFJ2EZmIa6v8liA8crrtqz1ZZnmjh64rWC9Hpef/jYOO8/9BTT5QahjJcr4fjFEu879BSfuOfGqJ1yw2uqbPzQV57l4buva0poBZp8zNr9Pd73h77yLMVag2J9/uisEhyjpWyTq8fyFGsOZ2aqlBpetFJsFWAVQnDVcJaLhSo7B9MUqi7jpTqeLynUXP7ZHxzlfT/1Cl69c4C/Oz2z6M5VuwH9xakKOweac9LWK7dK52NoLldaDbFNQ8nROJ4kaQn8wBLt3/5Mb4U/harDXFVVd3tSjWMhharLSC7VZIU2kms2zwaa4xFhxVCzDVqohXj/7fs4fGyc3zn8Ar5UOale4D9rGUpSIhRbjXLFYrm4vpA03IXisMtlo+zYazYWOsdrHTh45CTFmksg0hxp4kigVJ8feFbDLidsMxwUw/EsZLLcAFTVkBEIqnbr+/7b90V6YQMZm619SdKBl9LTZ+d4z+e/x7/75gk+9dhxLIOu99Lpfi8W6k196twqjWZlWYolWic7tHLDi74/Waq3NecGmnwZO1mhXYoN2sEjJ5VYdCCdYwhlgeb6zX2EY57vz9ufgdrdW2m05ZmmHXrHax14eaaC66sEz7jxrLIKkk0ejJ0q6S7l2O7+2/dx/GKBmuNHgoSto2P4/VD7Jm5T1G6nqfUIbtdghvf/5AHqnsdvff04Fwt1PvvtU1iGYFt/kplyg5lKI8qtmKs0mp5L6/1uySc5M1vVuVUazSqyFEu0Tm4SYeFPJmE1TXSgWQ4iZDErtKXaoDVboKnv2Yaq7I5XNNqmQdo2cDwZ9Z9P2U0K/CuFrobWtEPveK0DuwYzWIYRiPrNf1+gqoXiHoydKhsXM1dtZ8D6vkNPUai5TYNi6MkYzv8MoYywTaE0dOJiiJ12mu44MMaj972ebz14J1+8/1beetMO7r5xB1949+v4P16zA4HaEXt5psbZ2SoNx8NATe6KdS+Ku939WqbB/tFcT1VSGo3m0mg1xPZ8iSclI7lEV1PruJvEe27bG30/Pplq3UdSJ4aqEMiX88U78X4WxCNlkw2a40lcX0Y2aKDGj3zKwkdGu1kSNUnbM5SJxo9fuuNq+tIJtvaneMWWPFv7UyQsc1UWc7oaWtMOPfFaB+6/fR/5lKUmOcSEAYFc0lp0gBNCLLp93W6Lu1R31dEmAqNlNAz90Lb3qwnO3pEsAxkbyxSXZNWTTphcuyXPB//hq/j0va9hNJjAScCR4EqVaDGYsaK4O93vQ3e9MprYPXrf6/WkS6NZYZZiifar/+37TBbrnJ6qcGK8FB33vfeN10ZtpG0DIZrFT9X4ZpK0zUWt0D72Z8fwfX9JNmitFmiO5+P6KkdtMJvkbTfvBOBLT54hYxtqx32VF3PrbXm2EaR4NAsRspss8QbhlltukU888cR6h7EoS6leCasaj18sRgn2Gdvkn/74vqhq8eWZCkiV1KpUmk3ec9tevvD4S1TqLo6vci9ySYtizaHuSV63Z4j7b98XCbCK2FnmsQsFpJTsHMwwUaxTbXiEe199KYv33La3ycg2vJ8T40UagW/atVv6FpSEL1al03B9zs1WedNvH6EWS2DNJky29aWouh5//dAbmvrUlYMaIcSTUspb1juO5bJZxq9uxC3RpoI80LglWruJS2uFYX/Gxg0EnfvTdlPqQGiRZptigXhr3fWZKDUwBZENGoBtqlMDYcBrdg3y6H2vbxqzijWXwYzNSC4ZWamFFZRhv2uxe75eY1o8sT/+rPWJwdrQbfzSE68V4lJ+ydu9Z67qIIC+tL1gALJMg0LVYabSQAihSqGDLXfTgJRlsrU/heNJsgmzyf4H4MR4ESTs35KPvhfmbrRWSXaKcTkD2Ns+8ze8PFNhpuJQDyZgphBcNZThv/2zWxnKJpomiporGz3x2jiEVkAX5mq4nows0SxDsLU/1XEMaZ10zJTrOL5sGpcqDZeJYj2qiDw5UcINSg+tIOG94fkYQD028RKo17f3q126bz1454J4w35OTpSifK59o7mo325j32annX3T5X7PG4lu45c+alwhLqV6pdNxYLHmkklYTJYamMEEa7LUIJOwghLswDjWJzKv9X2VjxX2K4M8iPgWdy6pRFWXsu3dGmO8BFwE1UXjxRr3/8GTi25l/9JPXINtGmzJJxjJ2ghUvsbJqTIP/vcf8Oy5ArWWfAiNRrP+hFWGYYUh9GaJFs//fPS+11OKVT6GtFZExqsYG54/b4OGskGLL82UDZqxIGeqtfKyWwXl5Uqn6tPL+Z43C7qqcYW4lOqVdu/xgqRQYN73MTZguL6PEILt/WkmS3Xq3nx5dKhTk7ZN5qoOH37L9U2rzQ/+9KuApYmAdvJvbHg+harDuTllQOtLuUAAsd2x64dRMZ2eLvPKrSmqrs+pyTLfeG6cJ16c4b1vuIbhbILPHH6BU1Pq2Y3mEiAlE2UHgH0jWR580wG9Xa7RrBGLGVr3mizeS0VkWFVJzK4s3K0aySU5N1cFVMViJxu01n4Wq6CE1RM6XS8B1U7PWif2rz964rVCXMovebv3mIYAqdZ04SAXfg1gGQYINcnqS9tNW+it/XYS/FzKh77bADZZqmOgZn1J04gEED/2Z8coN7yOooFh/54vmSzV+eJ3T/PIkZPMVh0e/upz2KbA89TxqQTOBMetplDP58R4ifcfeoqPx4RmNRrN6rFSlmi9WKGN5BJRisXWXDLK8VqKDVov9mfxuFdL6HQ9BVS1zdnGRU+8VohL+SVv955c0kJA8wAkYWtfkkrDVZ6Mwetp22zyZmw3oHSi11VYtwHsYqFOUJyIsFT+WT5lcWK8xM7BdFebn3j/2/vT/Mob9vPYD8d5/OR0lEBroOw8QiRgGgZCSoq19rZB2p5Do1l5lmqJ1ks7rbvucSu0/WM5pJSUG15PNmhxDh8b59DRs032Z65vcPcNW7lQaLTd7e+kxr9ca7LVarcXtM3ZxmXVJl5CiF3AF4CtqOP5R6SUnxJCDAH/FdgDvAj8IynlzGrFsVZcyi95u/e0HgdeM5pFCEGprpIiW19vHZR66bfXVVi3Aey580XOzylFedsQSAnn5qoMuwmArrkFrf1Plet84fGXeP9PXstz5wuRqn580gUx2Q0BrucvOMbV9hwazeqxFEu0xXxoYT4VIcyDXaz9bjZoce9ZLzD4HskmuHpMFRJNlmo8dmyCvrTddkG2lFSRxRZ38dcninW29iWb3r+WeVba5mxjspo7Xi7wq1LKo0KIPPCkEOLrwLuAx6SUHxVCPAQ8BDy4inGsGa2/5KGGSi+7L/EpxmIfljCBPXzPDTsHmmQgFuNjf3aM8UItsgIZzSejQoA7DoxFA8fR0zMIYGt/iqvHlGp1peFyodBgMJtkW7/DVMkJ8j1UMv+FQp2EITh+sRiY7Kr2TUOQS1rc+8jjTe2GRQXg8uj3znBgax9PvjiN26bYVmmeqXJ0y1iYUNttdRm+rnfCNJrVoxcfWmDFFkidvGd9CeMlJXuRSVpMFhtIYPdQpm1/vaaKLLa4a319slTn7GwNIQT5lN2xXc2VxapVNUopz0spjwZfF4HngB3AW4DPB5d9HnjrasWwnrRTju9FXb71mktpd7G4jo+X8KWy0XB9ybnZWrSDFG/flxJfqtcvzlU5OVHi9HSFo6dnODFeZDibZPtACoEyowUlmOj6koYng8mYEkFUMhT1Be0WayphPlwF/ujVw3htJl0hrufjej75lLXgOLVTFc+J8eKynplGo+mNXnxoV9K/sJ33bJzJcoOJYl3loVpGx/56FTpdLPbW17cENksX5mrrIqCq2ZisSY6XEGIP8Brgb4EtUsrzoCZnQojLctuhl7P9Szn/X27OQPh+6SsFeyHAR3KxUOc1uweb2o880qRKbk2EgoVAseZim3VGcikminWSlsqEd3wf2zTwpPKDFEJp7TRcP5K7iIoGhDLjzqfsaBX4nZPTbOlLNml9xXElDKRt/tVPvoLb9o80vdZp1dpwffrT65NnodFcSfTiQyuh7bHeiYuFricEvXrPCuZPA3wJNdfDEKLJ/qz1uK/XVJHwSLJQdZgs1Rd4zrYeWapKc3USMFd1dJ6VBliDiZcQIgf8d+BXpJSFXgUyhRD3AfcB7N69e/UCXCV6yRlYKQmKpeQMvDxTYUs+ybm5GvhEx4SulE2K90BUuu35wUAmAKkEE2uOx3TZIZOwqAcDWzjYCaGOAj1fcmBrH1JKnj1X4MJcLRJCdCWYQN31m1aBH/jyM4zkkowGK0VQu1wXCzUySYszM1Vmqw7//q9eIJOy+LFrR+kLtvA7FTiE4q/tnplOxtdoVo5dgxkmi3U8328ygW31oW1dIE2W6hTr3oJd6U5HeHHv2TjRGBR8LQRkExaZhBnJ7UD7475e8qF2DWY4NVliqtzAQGAKQSOwJjp8bLzt4s8yDW7aPahFSzURqyqgKoSwUZOuP5RS/lHw7YtCiG3B69uAtuc9UspHpJS3SClvGR0dXc0wV4VezFEvxUB1uaaruwYzWKYRCA8KPKlUqPeP5rjjwFhT+31pm+396WgwswzB9oEU+ZSy4cgnTcbyKUzDwDCUtljaNlWlY0yDZ7KkkvAjXTJEpFVmGkaTV1q7+5upNHA8H8fzGcsnMQS8NFXhl//L3/HrX3mW//K3L/KPD36HD3z5GbIJE9sQXJirMlGsR5O6MIb4M+vFbFyj0fROLz607Y71ZioOQ1m75yO8XrxnTUPwL96wn0+//TUkLHNF/BLvv30fMxWVHiGMcG4pGMraHDxyct29GTWbg1WbeAm1tfWfgOeklL8Ve+krwDuDr98JfHm1YlhPevkAXsqHdLkf7PD9linYO5Jl91CGsXyKh+56Zdv2LVOQtAy29SfZN5prShDdv6WPR+97PQffcTNj+RSWKRjJJfCkxPMlI7lENKgOZmwEaldMGGpQFEJw8B03Nxlfh/1PFGucnCjx7Lk5LhTqWKZgNJckkzAZyibY1p9CAn909Cwf/OO/5+REiVxS7b5NlpWl0mg+yda+FNmkyUSpwUSxOc+iF7NxjUbTO3ccGOPj99zItVvy2IYIdr/h2i05PhHo7t1xYIx7btrBRLHOcxeKTBTrJEzBcLZz9V+7/E3PV7mi2wdSpG2z6R+zbNLigTuv4b1vvLbJAHy5pth3HBgjn7KwDYHny2gxOpxNcmamsqJ9aS5fVs2rUQhxG/At4GmIvJj/X1Se15eA3cBp4G1SyulubW1Wr7PIsPViIUo+ByKz6XCytFSdlbj/WS6p9LtKDa/no7JW/7S46XUuYVKqu1wo1PAkWEKwpU+JGPa1GNvGB5R4m9mEGUlg7BzMcGK8SDZhcrFQj/K2hIC4WoSBGpwfuuuV/ODMbGSu6/tgGGAEav19aZty3aE/bXPzVUP8p2+finbk+lJKUPHlmTICwf4teYo1h4liXVkRCcFg2mJ/8OzbGYlLKZmrOk2+b5r1QXs1biyWcizf7lpQY93xiwVKdY+hrB35vZ6ZqZJJGDRcGeVNJS2lYN8X5FRlkyYjufkUhEv1no3HFR/7eh0/tQeiphe0SfY6EuYmOJ7HZLER5TwMZxMkLHNZq6GVcJ+PtxGacvu+RASTHSSM5BM4nmQ0l4wmU0vJhbrrt49wYrwU7HKB4/l4C/PmARjJJRjNJSOD72MXCpiGKgawTMG+0Vw0OfqTB36MOz/+l5QaHjUnMN021DGmAHYOpjk/Vw+SfCWeD9sH0tHz0QPoxkZPvDYOSxlr2l07V3UiW7Mw11Mwn7pwdqbCdMXBNtWxoSrqgcGMxY6BTFAV3WAsn4gma/E2exn/WuOKt5kwDS4W6zie5NqxXFdLspUYdzWXP9okex0JcxMKVRfDEFiGgYEIqgKXd6y1EmXZ8TZCU24plOm2FeRuFaou/WmbgUwiMrtdygATTe6l+r/TpAtUyfnJyXJ0rJAwDaScN8yF+Zy2vpTN1WN5xnIJxnIJhAiPH9Ru2rnZqjLZdX0arpLPiD8fnY+h0fTGUsaaTvlYxZpLJmFFuZ4iqGoGqDkehlCf98CNSHnCujJIG0gxmktQrnvREd4n7rmRj99zY8/Heq1xFWsuhoDpUoNzczWkr2zJTk2Wu+Z6LnacePjYOHf99hFe8YGv8YoPfI03ffKvdN6opgltGbTKhFWIDc9XPozMTyKWq2C83ArH1jbCATG+CboSsZYaHjsGUkyWGtHkqRNKdVpEhrmj+STnZmv4KLHX1snRj149zO8cnsXxfBIG+CjTXAkEm2DRUWTd9Xl5qsxcVSXHLsdSQ1dDaq4kljLWtLvW82W0AIuMto35xVQ9GGP2jeYAOHahoCZesfFiJJdsmwbQ6+euNa6G50eTu4SpFpmh7EVrYn/r57xTBWQoIDtTcaKE/xPjJd79+e8FCf8G+0ayXXfUNJc/euK1yoTlxQlT5Sso+QY1+CxXwXgl3OfjbUQDYkwIZyViDfsIB9Vnz83hdzjhNg3BnqEMFUfJTOSSFsM5m+myQ9o2mkxxQ0ujwYxNseYGq2bJj10zzF+/MNW2D8dXGmSHj40vOoB2mlhpayLNlcZSxpp215qGAKlmIqFMDb6yG6s0XCzDIJ+avz5hGlGu12L9QW8Loda4wj4gJvQajHdx4eWlfM5DAVnTEBhCJeB7wYRTSjCE5MR4ifcfeoqPB8UGmisPfdS4yoTHWX1pC9+XKmEcST5lLftYayWOyuJthBWJQqqEdpXcrmJfTqy37hvizEyV584XODlRIm13/rXLJS0euuuV0Vb+hUKNct0jlzSjpPhWAdrRfIp9ozletb2f3cNZaq7PcDZBqk0/SrzR6nocu5g7wEoqb2s2JkKIXUKIvxRCPCeEeFYI8UDw/SEhxNeFECeCPwfXO9a1YCljTbtrc0mLfEoJF+dTFsPZBIYQZJKqIOaX7ri6SfIhn7LwJfSlrUX769XNo3Ucsk2BL9WRZt31I7HlbMKMvu72OQ8t4W772De595HHOXxsPBKQDSdyrt+8w28aBqahjjlbx4t27WkuT3Ry/RK5lCOmqLpxvEjD9UmYYsEkYrnxLHZUFl53/GIBx5MkLIOUtwr+AAAgAElEQVT9Y/kFlZVhReKZmQrlhte0a5SxTSxTjSjxyszWysbwPu1AAmKq3MAyBXMVh7rnYxkGN+3q5+jLc03q9Dv7k3zkZ25ou7OkRFSVUnTSMsgmLeaC7XyfeU9IKZVKtJSBZVHLyaYAdg1l8Dyfv/5/3tD2GcW9JEP5jHjS/W0f+6auhlwD1jO5PtAY3Bb3mkXZm70LmI55zQ5KKbt6zW6k8Ws5tKtcLtbdjirzH/uzY5ycLAOwdzjDm39kG985Od12rFrq9XHu+u0jnJosN3nPmoaIdsfjlZTZhEnN8ZrGoSdOz+IEVeemAC+0O0KNc+3Gl1zSXFCZ6XiSbMLk1GQ5yFET1BwvSnUwBCQtE4nE9Xy29aej8aJTwv49N+1YctWlZmPQbfzSR41L4FKPmFbTIb6XtsO4G64XKT1XGx4vTpVU/Hdf11TFd/jYOO879BSVRrOQacXxwAEzqHg8NVmK7h+IqjfnKg4IqDpEvm07B9NNRtuzVZctfakFA02ccGfJ8yXn5+qBxyPUHJ+GO2/CmzCV5+SZmSpSSmzLoC9lMVFyFjwLCZyerrC1L8WLU2W29adIWmbTz9YPqiLPzdbYPgD5lN2Uz7ISR7yajU1gaxZamxWFEHGv2TuCyz4PHAa6TrwuF8KxptdxsNzw2DmYjj7fh46eXbQKcv9YjqrjUXF8btg5wHvfeG3XmELvWTMQTA29Z7f1J5uOCpWtkKRU9xhIW4iGR931+e5Ls4zlEyQtkwtzNWrBQtAyBa4nqXuy7fgStjdVckhaJvmUHe3WJW0j8o6MYwYLNSlV4VJ8vGhnBTdZqvE7h19g52BapzRcZuijxiWwWY+YwriLNRcD0VSt2C7+g0dOUqrPG922IiULKjM7VW86nsSIVS+BSso9OVle9FmGookTRSUJ4QbbbxKajLQdT02UXF+VoG/Jp6g0fCyjOf741xcKNd5+8HH+909/m1v/7WP8348epeF6Ua5b6GMZxh2fWOlqyCuLbl6zwBX3L2Av4+ByqyB7HVfD94b/GUG15MVCvemoMCwc8gPfWdeTmIZKpJ8sKp9F01Bi0SlL5boGm/ttx5d2lZlp22Sy3MA2DRKmWDD2qKprH89XR6nx8aKdQOxcxcHz5ab790azOHrHawmsRBXhetBUWSkWr6x8eaaiqpCC62g5jY7LO4TvD41vW6s3AXwpqTQ8jl0okDAN+tLq166Tf2JIuLPU8HzociQuIVqpgtL1OTdXxTINLGM+zrrrIYBb943wrecnuVCocaGgcr5KdY+q45G0zCj5V8h5L8lC1cE2BLd97JvsGsxERwC9HvEudlSgqyQ3Jleq12w3VtqHdjnjajfv2axpNsnSuJ6cl6owwvwutbqcKNajsUsGQ4llGgjfx/VRua+o9vvSNpOl+oLKzDAvbDSfZFt/OopxslRjtuJEaRv7RxdWNbbbRa97Pimr+xip2ZzoHa8lsFyfxPUijDvUxILu1Yq7BjNK7BQWTLogHNya39+pD1OA68d0eTyf8WKD0ay96LMMd5ZMIXBbcrVaSVnzv8qFqhPFISVRmfpVw1luvmqIj/+jG9k/lsMOlrSzwbGAL2GiWCOfstjen8YwBKZhqLYAx5fRlv+ho2e5//Z9XXXNek367fU6zdpyJXvNdmOlfWiXM6528569dktf1O5ILomPjHbxfSmREkayCQBqrhfZACnpGiWrYxgGmYTJga19hEPMsQsFXM/HDazRwspMx5NR6kSc4WySkVySH37kLn74kbv42q/cvmC8aLeL3lrpGX8uOhF/c6MnXktgsx4xhXHnUxY+ctFqxftv30cuOW9024oQLKjM7FS9aRgCAzUx8oPJ2mguQT6dWPRZhkKFe0eybeOII1Em3qaAi8WaqtAMSrlDz0jHk/zTH7+aLX0pSg2Xq4YyDAcDr0RNvKqOT6WhxBzH8ikOvuNmBjIJ+tOdDXw70esRymY9wr6cudK9Zrux0j60yxlXu3nPxtsNKymFUAvA0GNxS3+a4WyCbMIik7QwhGA4m2Brf6rJc3aiWGtKupeADLawwsrMh+++rmmyFzJZqjNXddpOksIJ1Ae+/AzZhIltCC7MVZko1klaBjMVZ4HH7K37hvRCbZOjqxqXSK9VhBuNVt/IeFVjp92aj37tOV6YLKstdSAVq2psV5kZ9vHM2Vkqjq8qC4GRrM3W/vnVa1gB+OG3XN+z5+Rdv32EYxeK7SeCQCZhNlUdjeWTUZsqybfZl61QdcgkDAYySaoNj4uF+cRaQ8BVQ1ne+Moxnj47x/demiFlGYzkklFOR931MA2Dg++4uePP/5aPfJ1K3cXx56utcklrQfXjUqokr6QjyXWuarzivWa70cs4uJSxcjnjarf3tvOlPXT0bEe7n06es3OBV2TSMuePJYWa7H3tV25viiUsZCrWXKqOqgwPrY/i/QFdrZXStslUuc502SEfk9M5eOSktjrbBGivRs2a0VoW/fx4CdeX7BhQBtewcJDoxfvs0984zie/cWLBxMsQ6rihU9ud4or7tA2kE5TqDnNVt6m6MmUbDGcTTJfquMGxpZQSyzSQwZFGJ5uSw8fGuf8PnsSXMvCPVO8fztnsGc41xderZ+SV5hGnvRo3Pu0kZFplZpbT7qUsMFqlc6RUtkPhYjNcgC1lknfbx76JKYjcNxKmgWUICjV3Xi4CuHZLjlduy/Onz1wMThaUJqIhBNv71RgYfraBBZ/7uPF3seYwUaxTcz2yCYtPv/013HFgTMvZbBK0V6NmzWg9NtvarwaYiy3b5fFjhF6O2r5zcpotfUmSQbWQQB0XGEKVfi92RNHaR9z7rVR32TGQ4eG7r+dzv/Babr5KaWLWHJ/zczVSCQvpS9zAB5IgQXdLPtW1UmswoyaD0g+PJyTTZaft0W4vRy36SFKzkQgXAi9OlZirOFQdJVcTysxc6tHXcnIew/eemixRqLnKI7LuUW64zFUcXpwq9ZSf2Uo+aXF2tobrq4VUzfWYi026QG2LHrtY4qtPX2Aoa/Oqbf1YpsA2VYX3ZGm++vHMTKVtJaMXpGkUaw7ngv4sQ1BpeNEz2Ky5xpp59MRLs6K0Dib5lM2OgRRS0tHItt0A1K5KaiSX5NqtfVy/o5/rd/RzYGuegbTdk0luuz5Gckn60zbfevBOvnj/rbz1ph28Zvcgv/W2G8kk1LW+hJmKgwgqNSVKx2x7ULV0Ya7Gd1+cXpC7EcYbT/pNmAb5pLkgvsVMd9vdQ7HmcHKixEtTZY6entH5HZo1p5OETFxmZjntLkdeIpTOCVGJ8p0ldBYjOhkKFl7dLGcdTyotQ4iKfMIqcGBBQVIcM3iOF+ZqOL5Pw/VpuGqyF8a9WXONNfNoOQnNitKuLNoyDW7aPdgx/6AXQdJO1+zf0tdTXkOvoqeZhMUL41O4no8ZqOJLybyytQE7gwHzQqEGQNIUC8QNw/760vaCY9B29CKEG7bpBSKRKlFYaQlpYUXNWtMkUxOTkOkkU7PUduMsRV4iLp0Tr7BeTmylhseOgVR01NgNgZKCABjNJzk3WwsqJY0Fk6T3H3qKszNVXF8p6SdMgTDEAgHWuuvz8lSZuaqjFmqw5Jy4Kyk/dKOjd7w0K8qlrMa6vSes+jkxXuTMTHVBhU+vq7ylxPW73z7FUFABZQtBTKkCz4cXpyuMB3EgYawvtWBVvhqr0rDNC3M1wvPOxY48NZrVYqkyNUttN85S5CXiMYVpUO0kcJYak2Ua7BvNcWBrH0YXOTfLUMr0oUflcM7GEIK0bSzYzZYAApWvJSBhm+SDivJWHF85gRw+Ns4dB8Z49L7X93xcqiVrNhZ6x0uzoixlNRZfgeWDCsS5qhO9B+arfgyg4fpcKNS5UKizcyDFR976I0ta5bXro9Vf7uCRk3z3xWmSpmAok6BUd2l4kpSlLERcqeIAldi/tS8R7aLFV9LdnkM8plxQOTVerHX00AxXqLfuGyJjG9RcHwEkLMH2PpWwK6VsWsUvx1NUr4g1vXD/7fv40FeepS9tMVls4As1+8qn7GUtMsJ2Kw23qYikV3mJD33lWfIpi6lyI/q+ECgJneylxdYaU3/aYqay0BYIoD+T4Odff1WUwL9nOMe//ZmFn6WDR07Sn7abxFYrDZczM1V2DaY5PV2Nvh+ccDKYsTl45OSi1aGtn+F2lkSVhtu1Lc3qoasaNetCLxV6YbVfqeZysViP9HMEKl/jgTuv6erltpQqwPi1F+aUWr5Aaf3kUzbjxSozZYf+tM1MxYnsi2xTsK0vRTph4XgeW/rSXY8+W42/z87W8H2lgG0IJZw2kk/gekqKoz8oK49XYc5VHJyg/9ZKqUfve/0lVT9uxIpJXdW49ix18t1a1dhOZmY5cSwmV/HRrz3HqSm14Ng3ohThgSbpHMfzqbt+pBwPYBuq8CebMBdI2HSq1Nzal+CxYxOUG8ps+7pteY6+PEc9pu68sz/JR37mhgVyFmFbvvQxhNpCL9ZchFALttF8EoDxQo2Ko55jo8W7NmkZ7B/LLahejFdxtjPufvju6/jAl5/RlZBrjDbJ1mw4elmBhfkapybLKk02Zl9kCPjst091nXgtZZUXvza0DJJIxgs1TEMwW3EZzNgMZpP0p2zOF2qUG2pgOz1TJZ+yyCZMfvZ1u6Ly9cViOjlRwhQCT0ikDwnbwJeSQtXFCyS2w9VwseZiCChUXcb6UpybrSGRTJbqWKZoWsVfyupWr4g1vZpfx+klN/FSWKzdw8fGed+hp5itONGx3InxEu8/9BQfv+fGJqma9x16iobbaHq/40tenqliCtg5mI7u9Z4zsxw6ehbH81SCvICqA8+dn+O7L3qM5RPsHlLHmc9PlMmnLHYGi6O4FE3Y94e+8mzUli9lIMLqR9WQMrAlOzOjdreEILA5W7gh0p+yFhyTxn9mnYy7Dx452XOOq2Zt0DlemnWhl0rGMF/DbxmDQvXpcqM5D+RS+mh3bV/aZnt/moRpUPckY/kUuaTJaD6FbRokbZOdgxlGsvMJwMWay1SpwTNnC5yZqfLnT59va+nx8kwF1/M5fqFAueFR93yCVLHo3hqejxuY6YbUXB/XU2KwE8U6gxkb2xDUXH9B3shS7vtSnpXm8mQzyZUcPHKSUt3FFMrWS/2vqhlbzbpLdXfBGBLiS6XNFd7rZ799qm2lZiG28AmfTTGQq+j0vFqrPsO5VGsoXiBVE+6im0Feabh0EyjrtUKtuxRPJ+PuMzMVXQm5wdA7Xpp1oZcVWJhXEW10BSOWKZTBbTbRPFG4lD46XduXtrFMER3fxUVODUNgBwmxlqF2hwo1pVB/8Fsn+avj48xUHVKWQX/Kato5yCVMjl8s0a4uyvV8DEOQMI1oxwuU96QfmJYbAlxfMlNx2oqxLvW+l/MezeXFcqoJ15qXZyp4vvJxDRFCfYZaZWi84LPTDsm8zEPaNik3PHbb5oJKTV+CHZOEAHB9f8HOdvx5xSss6SGlR6BSB87NVbFMI9j5UlWYDU/tkoWK9/c+8jgvz1SYKNbZ2qeOKUMj8Fbj7p2DmUuuhNSsDqu24yWE+D0hxLgQ4pnY935dCHFWCPH94P83r1b/mo1NLyuwUN9q56A6blOiqeoLX8J7btu77D56vbb19VAsciSXYFt/mquG0iSD8sdjF0tMFhs4nsTxJUnLjFbCQoj53a2WGFxfRh6a+ZRFLqmO+yZL9ag6yxCiqxjrUu97Oe/RXF5sJmHOXYOZyBEiREpVTdgqQ2MaYsFnLUSgJiyg7jWbMNtWagqg7kpcT3JyokSx5mAFu2xx4s8rXmHpdpl3xXe7YV77S0o1kds3mmP3UIabdith53h1ogDOztYoVJ3ICDw07p4s1TgzU+X4xQL3PvI4wKKVkL2Yb2uD7uWzasn1QojbgRLwBSnl9cH3fh0oSSk/sZS2NlNyqkbRS5JuOx+10EtxV8vfaw2X6YqDL5XI4N03bOWTb79p0T5h4Sov/F5oKRJSd11cX6ng55IW77ltLzfsHIjaxPeZrrrUXT/abds9pAZZ15d4ns9M1WGyNJ9Pkk9aZJMms5UGDU+t0J3YuUc4bIdFA8O5RFNV40e/9hzHLpYAolwWX6qvswmTp//1m9re+6XYomw0H1KdXL+2bMQCi060y/FSnog2H7/nxqbk9vcdeoqZcoM2aVNRjpdlGjie5J6bdkQ5XpPFBgh1FBimA1hBEYwvIZs0sU0jKoBp5/sY5nidn6t3vR8zkr0QDGbmKyZ3DKSi2B6++7oFPo3FmsOZmSqWKbhmNMdkqc5MxSFpGdRdn8GMzUguuWIFNpvpd2S9WTevRiHEHuCreuJ1ZbESVXVT5TrjxQajuQRJy+DsrBIrbR2IljogxE1sp8oNfF8lvBpifkJjCrGgsjCsQIzHcGamylDWZiSnRFF9X1KsK381P0iaDTFEeEw4f8/hpMsyBaYQvKZFZDauveN6fvTehCmCY0fBwXfcDCw0270cBkM98Vp7Ntrkuxudqho7VT++MFnG8WRkNxZWNZYbXlu5l7ASsdrwMA1BX8qi3PCiY8g9QxkeuuuVXZ9X2NYTL003LfLiCOCqYbWAuzBXQwb3IqVcEFs7n8ZCtcGFQp2xfDK69lKMtHvxjO3VV1az8aoaf1kI8fPAE8CvSiln1iEGzSqyElV1hapKZi3WXIqoyRCBSe2+0dyC9nrtM7xuqqQsRTzUQBxPvg2tReKVhWEFYjyGwYzNdNkhk7BI2yY110NKeOetV/HH3z9H3fWZCaxD/ODowIAovyvs0vEkAzm7ozfjlnyK09PzeSuOJ7FMEWn6ALoiUbMirFaV4mrQa6xLvafw+riuX8IQpBMWW4Iq41CKId52eP0HvvxM0y5/2NYv/v4T0eQrzFu1DKIKRIBc0mKu6vC1X7k9iifebqHq4Pl+tNiDZmeQeMwpy2Akl4yS7euux5mZaiTA2sqJ8SKVuspVTZgGo/kkuaS1IGeulzxArQnYnbWuavwMcDXwauA88JudLhRC3CeEeEII8cTExMRaxadZAVaiqq7h+RhBMmvD8xGi2e+snZdjL32G14Vttm74xq1F4pWF7WIYySXJJ80mj8UPv+V6Hnrzq3j47uvZM5xVmmPB4lSiJl2tHzrbEPzc66/i4JGTTXkTYax9aTuq5AzZ3p9mJJfsaLa7UZOiNZrNQHy3OWkKGp7PubkqhapaSHWSdeimDJ+KWWDE5SRGcsno+4u1m0mYjBcbTJYWOni0i/nsbJWXpyu4vmyyF2vNyzp8bJxiUCBkGgI3sCWbKtcX5MwtlgeoVfIXZ00nXlLKi1JKT0rpA78LvK7LtY9IKW+RUt4yOjq6dkFqls2lJOm2vidhGviBxUc82TSeCLvUASF+XaulSEjcWiSePNsphtArsjVh9Sev38of/fN/wM1XDbK9P8W2vvnB1UdNovYMZdg9lGbPcIZDR88uGKjySSu6p7St8kmSpkEmoSZj3cx2N2pStEazGYjvoI/1pQjT8ydL9baFJ4tJcRw8cpKxvhRXDWXIJlSxTcJUEyHLFB0LWlrbHc2nGM0lKNe9aLEXphS0i9n1pdrNX8Re7OCRkwxm1E6W9OlYwNNLEc5mkiVZL9Z04iWE2Bb7688Az3S6VrN5WYmqur60hS8hn7IYySXwpKrWGckl2rbXa5/hdfmUhR8cM4YyDSHtKgt7iaEVIQS//BPXIITAtgy25BNRXpcv4aXpSqSCL5CkbLNpoJJSRvc0kkvg+RJPLuxfVyRqNCtLfBc5n7LZPpDqqJvXen1Iq7REuHsd+j1euyXPQNpu2jHvpd2RXJL+tL1gsXdivMj52SrHLhQirT8IjjRNwfbgmPTCXI3vvji9QF9wJJdke38ayxR4Uh035pNmUzxhpXmvMRdrDicnSrw0Vebo6Rm96xWwajleQohHgTuAESHEGeDXgDuEEK9G/S68CNy/Wv1r1o525/lhBU6vSbqtOjN7hnPc+9r5yrxrRrMIISjVVSJna3ut788mTBKmofItjjRXM1YaLg3XJ2UJhJgf1KT0EcIgYRnsGc4tqIpcLIZ2z+TEeJFy3aXu+Au0uyQqh61Uc8mnLMaL9QXWJjsHUjSkYLyoJCWEhLOzSuU6YZm879BTjOaS+J7HS7ON6GjUNFRFZPhsFvvZhRWeca9InZOhuVJp1bXLp2xMQ3RMIl9MB6/T6+GOea9xtLYbEh4V+nL+qHCm4pAwBYYh2Deao1B1ODenxo6kKZr0BcN++tI2fUEOV5g038piOXNhW15wXKnSJOaPObs5IVwpaK9GzbLYiOXF7WKaqzoIlDDqascZLyOfLDYCbZ0mx6OeCPPDJDCWT0bVnXFvRz+YaMlAjyKctKl0EsFAxuYTsfL6dnGGFZ6qIeUVaZvmuldF6qpGzXqx1HGt0/X33LSD75yc5sR4kWLNXZK8w1LiuPeRxzk1WWKq3MBATXK84N/2/rRNf9pu60EbTq5CseqVGMejHK9CDV9KRGCSFO6mXSkVkButqlFzGbERPf7axXR2tgoStgbb7asZZ1PlpCHwgkqm1klXOLHqUGWOZP61qLrTEEpXKPB2rPle1HAo9AiB0rahduc63WNrhadhiMgrcmu/pasiNVcsvSq9x3f780krqnYMdQkPHT2LbQoMoOH6XCjUuVCos3MgxUfe+iM97UaHO/SdDMgPHxvn6OkZPH/eMig8KjQEjOaSnJwsU3d9kqZgWzDpgvnj0E73C/Mq+bmESoUYL9aadsfb6S9mbIOa6yuBWkuwvS9NX9pGShkdv16qIfvlUCmpJ16aZbERbUbaxaREEJtnOKsVZ9wqpFVdOyTc/TKDidliu2F110MIodqLvyDbvE/MV2d6vux4j01xinl7lNCmRFdFaq5kejHqjpuKh7tEH37L9dxxYIx7H3kc2xSUai7jpUaT9+L5Qp0fnJntSddwa18qarvdpOtDX3k2Os4jEHfdMZCm7npMlx0ans/+sRzPT5RwW1Z58WPL1vuNx2AKeH6i3LTbXm14/PBCge++OM1oLsFILsmpyRLffXGasXyCjG3g+BI/NgCG/S3VkP1SDNw3MtokW7MsNmJFXbuYzMDwNs5qxblY5WSIEcy2BConqxu2aWAHkzhBzG5ItPydoM1g8mUaouM9tsYJ81Wb6/0z1Gg2OotV74VJ5hOluvp8xj6khoDPfvvUJbfdet2WfCrmZyu5MFdjuuwwmLGjNrYE+VoX5hZKUSwWw2SpgSkEUoDvK2smwxDMVp1Ib1EIZVIemol3qwZdauXj5VYpqSdemmWxESvq2sWUS6oqxbWIM+y/L22pFWJoB8L8n6FFkBQwkAkqOJOdTb/TtsFARinoAxiGMuk1UIN4PB8srNL0AuujTvfYWuHp+n5U0bneP0ONZqOzWCVjuLDxW7akQ02+cqN5cbiUtluv60vbUQ5VOAbkkmaTRlhf2mbHgJqgdapI7BRDXPcw7i0Zun2E2oZx/cVu1aBL1R683LQK9VGjZll0y4VYrzP5djF98KdfBUuIM7w2nttQrLttX2/1ldw1mOGem3bwp0+fZ7LUwPeaE+slYBuQTlhR3sat+4b4mxemOH6xoGxJXNl0hDhdcTENJSshJEyWG7i+UrDvS1pUHZ+q40X9uD4kDCXaGK/s7FQJWq7PUnF8laNScRnKCt536CkqDY+G62EaBnuHlUXKav8Mw5+HPbrnR1a1I41mGSxWcRgmrEef/eADbYZej4nOC61eqxnj14UVifFqxNY26q5P0jIWpCe0GwN3DWY4NVlSwqqexIm9y/V8DENEVmuhtqEhBPUgt+vkRInRfJJtA+kFCfW93t+lXr/R0VWNmlVhI1Y7tqNdnIWq09WjMf56q69kWLEUr6IsVh3GA+Nsy1D6Xr6EB+68hve+8doFMf350+f511/9e+qOy0zVbVoxq+12k76UUrGeqzaYKDkLPC2HMjbTgV1RJ3/Lds8gvF8plfBi2LUpVNzdqiRXgngs3/roOyuNiZeyq9LRGqLHr8uTXk2lP/jlZ3h5phqlFCz2+e+17cWug2YP18lSnYlSg7F8guFssqnyMiwCiLdx8+5+vvKDC9FY0IoBDGRs5mpuNP68PF3Fp9lMvNW4fCn3d6nXbwS6VTXqo0bNqrBZzuTbxVmsuZTqblNug2kIJkuNBa8LIZp8JcM2SnWXYi1oo9xoqja0DKNrjsfnvvMSKdug4qik94Qpovc7nmSuqqqcfF99LZAUqk5zrOUGpqHMt8O4Oz3/drkc4aQr7FeiVuphleRq0VqRup4IIX5PCDEuhHgm9r1fF0KcFUJ8P/j/zesZo2b96EVM9I4DY3zrwTv5l2/cTz5lIVETh26Trl7bXuy61tcqDY+xfIKRXKppTP7st0+1HasfOzbBWD4R9RXPJRWAaQpesbWPB+68hr0jOS4U6iQsg7FcgqRlRsKtw9nEkuJezvPYLKz/6Ka5LNmI1Y7taBen6/uIIDErqviL5THEXw+viec5QHMVpR+TeQg3mLvleIQxKX81QAgsU2mBhSvPi8U6xZqagBnGfN+WYUR6XmGInfwt2z2D8H6jBW5wTtJLleRK0O7nsY58Dvj3wBdavv9JKeUn1j4czVqzWLrEYibZQCRQDGr3Z/9Ynht2DvTUT+su0b2PPB6JHQO4nqThqVSAfSPZjhIQEnXMGKrZh6Rtk3LDY3dMaT401HZ9laoQjllCgBVUVb9iS565qhMdH95wbJz7/+BJXN+nUHOj9g1UWkSvz7Mbm8nAfTH0xEuzKmyWM/l2cYaTF1C5C2EJdpjHEH89/H7D86PXQVUTItVFRsyMO5wMdcvxCGMK+1YrTUHSEoFxt3p/JajclD6B71ugGSZifXbxt2z3DOJ9xvNSeqmSXAna/TzWCynlESHEnvWOQ7M+LEXCoN217z/0lNr1MUQ0Gak2PF6cKjW100s/cbHjQs3F955A9OkAAB4cSURBVGWT/p+UPifGSzzwX/8O2zToT9uRBASodAMh4OxsDSFEpONVdTyyCXV0t0BpHtkkgyElNDxJymqueo5LWgDUXLXQS5gCx5cUa25kFXQ5SUIsB33UqFkVNmK1YzvaxbmYR2P89VZfyXZVlCPZRLSDZAi1Y+ZLeM9te7vGFFYbelIiJfSlLXJJpUI9kk1EBt6+VMn0uaQZxTqUsTt6O3Z7BuH9hpWSUQUTi1dJrgTxWDYwvyyE+EFwFDm43sFoVoelpEt0S1ko1pRAcSjBUKi6Cwy0F+snvCZsqzXlKtQELFQ7p0l0kpN4z217cTwlQREKA6rUgvm243/6gYdsfEcvlLSIbfpHu3KDGZuDR05umvSTtWD9l5Way5JelZ/Xm14qIFs9Gltfb/WVbG2j4frsGkwzWarT8CQZ2+Q9t+3tmOMRj8n1CjQCleh9I3l+8ba9zNUcPv83L2EYFTwfpoIKx4mSg2moHbCZioNpKEuhC4U6e4dVTO2ef+szCO93olRfclVj/Cih5nhMlRrRPwp337CVT779pp5/HghjI45PnwE+jPp36MPAbwLvbr1ICHEfcB/A7t271zI+zQqxlHSJbikLHrKrQHEv/bSKHbfWxIWpABIiv9bWNAnlwajGg1BdPxyTb9g5wP1/8KSquDYFI7kU5+aqWDKwOwslcYK+M/Z8tfSJ8SJb+1KIhFLNF8ynRGzvT5NPWZyZqSBhU6SfrAUbcWDTXCZsljP5TnEuFnvr6+/t4ZrlxhTyv123lalSHc+XfP/lWX7jT55jqtzA89XRhu+rna8tfUkSltlVM6iX/nohfmRSrDrMxXI9PF/yP75/Hjja0+TrjgNjiPtffHpZAa0CUsqL4ddCiN8FvtrhukeAR0BVNa5NdJqVZCnpEt1SFkwh1PG9aC9Q3Es/C9IPYukLMJ8KoCon1SypbZqEaXDT7sEFXol3HBjjpt2DTXGohaJPJmGwbzQHwESxxkzFwfFldFxYrLnYZp2RXIq0beIGlTmWKSJ5i/BeNkP6yVqgjxo1mk1ILmmxYyBNNmnx6l0D7OhPk0+qAc31JT5q9TtdbpAwDYSA/3D4hVWNKX6UEJ90wfyK+Ss/uLCqMaw2Qohtsb/+DPBMp2s1m5ulpEt0S1lYTKC4l35a0w9azTAEanGj0hE6p0l0S/dojaNdCsVMxWEoazcdFw5mbKbLznyfHVIcNkv6yVqgd7w0mk2KZRps6UtRrDmMl2ps7U8y6NicnqkCaru/5qoBLp0wOT1dZrxQYyibwFrMo+gS6KUiMTwG2QwIIR4F7gBGhBBngF8D7hBCvBo1r30RuH/dAtSsKktJl1gsZSGeMrBnONfUTi/9tEs/gIVVjQ++6UDUZ7s0iW7pHq1xtEuhmKs6DGeTTe8bySVxPaVMf2amwv6xHFJKyg1vQZ+bIf1kLdACqhrNZcDbD36H83NVkpaaYDW8ZtHDbMJk70iWT9/7GgwhGMwm6F9h2YZ7H3k8Okp4+uxc02vxqsgX/k1v0lfdBAg3E3r82hysl9PGYnG0umKsRlxhnyfGi1FeJwhsU2AHizTbFDiexDIElYZH3fUjrT/LFFwzqiZcpYZHPql2ySZKdZxgwjmaSyKl5Mxshaqj5HZySatrvmsY20e/9hynplQuWDjB3OgTNi2gqtFc5vzTH78aicDxfIayCeXdiBosQWmGvTRd4a+fn8SXkqlSnbOzVepu99yvpRA/SuhPNW+mh+u7u2/YumL9aTQrRZifOF6sNUkdhDII6xXHqckSn/rm87w4VVq1uMI+X5wqMVNuUGkoDS/Xl1QdpctVrrsUai6e7zNdcagFky5QW7+OJzl+scjzE2XqjsuJ8RLHL5aYrTpUHY+ZcoPjF4v88GKJUt3H89XCsNxw+dQ3n+fT3zjeMbb3HXqK5yfKSKkmayfGS7z/0FNr/rNZSfTES3NFEIoP3vaxb3LvI49v6g8tLLwfgIfvvo6t/WkMIdgznGHPSJahjM2O/jQJ06BYc/ngl5/l4f/590yXG9Qdj//x5Fnu+czf8KMffWzZzyWuLt2XtpWvZPCaAEZyCb730uxl8fw1lxcbReqgNY5iTbliFKruqsUV9lkI7Mla88dATa4MBFXHb/OqwkcVEkyVVUW1BHxfFRn40GRBFlY/ysBku5OLx8EjJynV3UAWwwj+V89lM8tQ6BwvzWXPUoQQNwMd7+fu65qqlQo1h+lSA18qjZ7f+vpxnnhphsPHJzh6eoY3XbeVIycmsE2DbMLiwlx12c+lXXVkq8/aZn/+msuPjeK00RpHO1eMlY4rLlUhCQphOshVxNMXwsuiCsvYNaG0xXwDC5oEMT/x6ubi4fnzchwE/bmev6llKPSOl+ayZ6OsZleKXu+nL2WzYzBNOmGytT/Fx/7PH+HBN72CfMqiUHP50pNnmK04WEH5uW0qacbP/NXKVj9ebs9fc/mxazBD1Wn+x389pA5a40iYBn7MfWI14gr7TJiG2u1qk/YtYpOk0LMxmgvFVJbDa0Jpi2i6JNrspMUmat1cPEyjWbdMBn63m1mGQk+8NJc9L89USNvNH+zNLNy3lPuxTYNt/WmGs0kMw+CnrtvKf37Xa7l9/wig7D1enK4wW2kgpUqCfWmqHP19rePVaNaDjSJ10BpHPqUkHfrS1qrFFfbZl7bUpKnNNQLwkfSn1SGZEZtIqWNI9b8nJcNZ5ZohAMNQQrJG63vk/G7ZYi4euaQVyGL4wf/quWxmGQp91Ki57NksvpG9cin305+xSSdMJkp1hrIJfv3u63j3577H6ekKvoTxUoNC3WUgbbO1L810uUGx5jKaT5Ky269GVzNejWYt6SbpsJbVjq1x7B3J8bOva5Z06BRXvPoxNMcu1t0Fht0vz1SiqsNSw2PXYIabd/fz2LGJqEoxPvmyDUgnLBKmYP+WPm7dN8SfPn2eU1MVhO9jGgaup2zQpJTMVly29iVBSi4WG7iesiAzDYEhwYmdV0oJg2mLLz15hu+cnG4ro/GJe27kA3/8NGfnakgJSVOQtAzed+ipJTtrXAqr8fPXchKay57WHKOq4+F4kofvvm5T5hgt537UwOgwW3X42xem+ORjxynV3KYci5961RZ+9SevjbS+8imboZg35FrFq+UkNOvNRh07WuOaKtcZLzYYzSVIWgZnZ2uAMse2TINC1UEC/Wkb1/ObXq+7PhOlBmP5BJ6njLEBLENZBPkSHrjzmraSD2HV4WzFIRwe/MBSKGmb9LX0N5ixmKkoceWhjM10xWmKs92zjd9r2JaUsilZ3xQq1oGMzSfuuXHFfjbL+fmvi5xEYCA7LoR4Jva9ISHE14UQJ4I/tcGsZtWJV9vNVR3G8ql1HziXwx0Hxrjnph1MFOs8d6HIRLHOPTft6Ol+RKDhtX0ghWUKsrapPN0MolyvP//7i/yzPzzK8YtFAIo1hzMzFYo155LjvZyev+bKYaPmJ7bGVaiq6sdizV1gjh037I7Msw2BKdTr8crJyXIjOg70g1yqS6k6LDU8irWF/U2VnfnYys1xdHq28XsN7y2cdMWPO81AKHY1Kj5X+ue/mkeNnwP+PfCF2PceAh6TUn5UCPFQ8PcHVzEGjQbYPL6RvXD42DiHjp5lNJ9kd7AKO3T0LDfsHOj5Hr/z/BSfeuwEpqGkJ2qOT8PzuWY0x9+8MMULE2X++R8e5R/dsot33noVSdtkolinWHMZySVJWEtbs11Oz19z5bBRqh1bWaz6MW6ODfOG3eG14e51+Hr43ricRHgYdilVh75Ufbb2F1Y8Eqt+jMfR7tnG7zUyCY86Q1VMBm15vlyVis84K/HzX7UdLynlEWC65dtvAT4ffP154K2r1b9Gc7myEquwg0dOkrAM8imbhGWSSVgkTINy3ePf3fsarhrO4Ev44vde5hd//0meenkWgJrjcXa2ynR55ZLvNZqNykapdmylW/VjwjRU8nqsGtIKdqLCa+Ovx98bzyYQsaPDpVYdGiIwCW/pL6x4bP06jLPds43fa9hWFKacjzV0xliNis84K/HzX+uqxi1SyvMAwZ8dl8BCiPuEEE8IIZ6YmJhYswA1mo3OSlQJxtswhLIGySZMLhSqvGp7HwffcTM/f+tVWIbgzEyVf/Glp/jkN45TrrtBnliDMzNVKg13kZ40ms3LRql2XCyuuKF1O3Ps0LC7nZF1vHJyJJuIdpMMoXatLqXqMJcwyacW9jectedjy3Y21O50r+G9hRWSMSULvMCCaDUqPlf6579h5SSklI9IKW+RUt4yOjq63uFoNBuGlViFtbYhhMDxJVcNZbFNg4Rl8K5/sIeDP3czB7bmAfifT53n3Z97gsdPTgHgeD4X5mpcmKvheJ0VrTWazcpGzU9sjWvPcI4H7ryGvSM5fAnXjGbZP6a+Hsun+Pg9N/KJe25kLJ/Cl7B/LMc1o1l8CXtH1Hv3DOdI2ia7BtOkbQOJSijvlFgfxvGJe26MzLiFEOwfy/Hpe2/i4236S9lWFFvSNpvi6PRs4/ca3tu1W/IM5xJkEiaWAZZpcM1odkUT69s955X6+a9qVaMQYg/wVSnl9cHffwjcIaU8L4TYBhyWUr5isXZ0VZBGM89KVFp1a+PHXzHKVLlBoaqS6T1f8kd/d5bf+/Yp6q6aYL3hwBi/9BNXM5BJAEHSfsamP21HuSTLRVc1ajSXH3F5hnayF6sxqY33WXM8pkoNlZBvCO6+YSuffPtNK97nRjLJ/grwzuDrdwJfXuP+NZpNz0qswrq1IYRgJJdkW386yg152807+ew7b+E1uwcAeOzYOL/wuSd47LmLkXntdLnB2dkqNWfljLc1Gs3lQ9wE3BTw/ESZE+MlTMGqGZPH+yxWHSaDSReoReX/+P55/sUXj65on4uxajteQohHgTuAEeAi8GvAHwNfAnYDp4G3SSlbE/AXoFeMGs3ac/jYOP/xr17gpekKW/Ip3v7aXbx27yBfe+YCn/mrFyjX1QTr9fuG+JU37GesLxW9N5+yGc4mMC5R+wv0jpdGc7lx7yOPR2LKJydKuJ7KlLcMwb7RHJWGy1g+1eQ5u5J9Pn12rum1eFL+C//mzSvWp2q78/i1anISUsp7O7z0htXqU6PRrAzxo8jhbIK5WoNPffMED9y5nzf/yDZet3eIT33jBH/9whSPn5zm3Wee4L7b9/EPb9iGIQTFmkO14TGUS5BLaoMMjUbTXhoiLnuxGlId7SQhWvH81Uu5aseGTa7XaDTrR6tkRS5pk7YN/tuTZwAYySV5+C3X8aF/+CoGMzaVhsdvf+ME//JLT/HytBo4Xd9nvKCT7zUajaKdNMRichIr2WcnLtWV41LREy+NRrOAdpIVmYTFeLHGaD6JEVQw3fGKUX7vXa/lJ1+1BYAfnJnjF3//Sb743dPRKrLScDkzU11R422NRrP5aCcNEZe9WA2pjnif/anm3fdwOLr7hq0r2udi6DMAjUazgG7G1vmUTdpWhtvVhkd/2uahuw7whleO8Zt/cZzxYp1HvnWKv/zhBP/qp17B1WO5KPl+pYy3NZorhbU06V5p2sX+8N3XRSbgoQxFqa5yu1rvbSXuPW483nB9bMuIqhoFMJxL8L2XZrn3kcfX7Nlqk2yNRrOAXiUr5qpOk4p9peHy2W+d4svfPxeVa7/9tbv4uddf1WQzlEtZDGeTXbf4dXK95kpno5p098JyY1/te1/t9jeSnIRGo9kE9CpZ0Z+22TGQJhnsYGUSFu99w34+9fZXs2swjedL/vBvT3Pf7z/JM7GKolLNXZbxtkZzJbBRTbp7Ybmxr/a9r+ez1UeNGo2mLb0aWycsgx0DaWYrDWYqDlJKrt/Rz+/+/C38/uMv8eh3T3N6usIDX/w+b3n1dt7zY3vJJCw8XzJRrFOquwxnl268rdFc7mxUk+5eWG7sq33v6/ls9Uin0WhWhIFMgu0DqWgClbAM/slte/mP77iZ/WM5JPDH3z/Huz/3BN89NS/fV21o422Nph0b1aS7F5Yb+2rf+3o+Wz3x0mg0K0bSMtkxkKY/tpK8ZizHf/i/buIXf2wvCctgvFjnoT96mo9+7RhzgS2RNt7WaBayUU26e2G5sa/2va/ns9UTL41Gs6IIIRiOWQ6BSrK/93W7+d2fu5kbdvYD8Bd/f5F3f+57HP7hRLTTFRpvz1Ya6xZ/iBDi94QQ40KIZ2LfGxJCfF0IcSL4c3A9Y9Rc3mxUk+5eWG7sq33v6/lsdVWjRqNZNXxfMlmuU6rN72L5UvLVH5znkSMnqTTUVv+PXj3MA2/cz0guCUBf2mY0n1rXqkYhxO1ACfiClPL64Hv/PzAtpfyoEOIhYFBK+WC3dvT4pbkSWa4UxGaW0QBd1ajRaNYJwxCM5VNs6UtFu1+GENx943b+87tey+v3DQHw1y9M8Quf+x5/8oPzGybPS0p5BGj1kn0L8Png688Db13ToDSaTUDcmHogbS/ZAHu579/o6ImXRqNZdbJJix2DaXIx5ejRfJLfeOv1/H9vPkBfyqJc9/jNrx/nfYd+sJGrtrZIKc8DBH9uniW4RrNGbHQpifVGT7w0Gs2aYMZ2v0LhVCEEb3jlFj73C6/lzuAY4e9Oz/KPDz6+nqEuGyHEfUKIJ4QQT0xMTKx3OBrNmtLOcmypUhLLef9GR0+8NBrNmpJNWuwczJBNzu9+DWQSfOCnX8lvvPV6RnIJ6u6GNdW+KITYBhD82fbsQ0r5iJTyFinlLaOjo2saoEaz3mx0KYn1Rk+8NBrNmmMagi19KcZiu18At149zO+967W87ead6xhdV74CvDP4+p3Al9cxFo1mQ7LRpSTWG61cr9Fo1o1c0iJtm0yW6pTrbvS9B+86wCfWOTYhxKPAHcCIEOIM8GvAR4EvCSH+CXAaeNv6RajRbEzixtRnZirsXGJVYi/v38xVj3ripdFo1pVw96tUd5kq1fH8DVPVeG+Hl96wpoFoNJuQXi3HLuX9cYPreNXjw8H7Njr6qFGj0WwIckmLHQNpMgm9HtRoNJ3Z7FWPeoTTaDQbBss02NqfwvE2bHK9RqNZZzazeTjoHS+NRrMBsU09NGk0mvZs9qpHPbppNBqNRqPZNGz2qsd1OWoUQrwIFAEPcNfTj02j0Wg0Gs3mYblVk+vNeuZ4/YSUcnId+9doNBqNRrMJWW7V5Hqijxo1Go1Go9Fo1oj1mnhJ4C+EEE8KIe5bpxg0Go1Go9Fo1pT1Omr8Ufm/2rv/WKvrOo7jz9cEsYQFaDoSCbC5wlZE5NoQMi1FZNGWf1j/mNlcaat0znBWoz9qZmuwVsulIzTyFxauudWizGCJMEBA1NTLj5bCxI00/SPSfPfH93Ple0/3fi/QOZ/PF+7rsX13Pudzvud83udz7ud9P+f7/dz7jdgr6TRgjaS/RsTa+g5pQnY1wJQpU0rEaGZmZtZVRY54RcTedLsfWA2cO8g+vsismZmZHVeyT7wknSxpXH8ZuAjYkTsOMzMzs9xKnGo8HVgtqb/9uyPidwXiMDMzM8sq+8QrInYBH8zdrpmZmVlp/ncSZmZmZpkoIkrHMCxJLwF/y9jkqUCb/rmr42nmeJodq/G8OyKO+b+sGSR/teHzcAztiKF0+46hd+0Pmb+OiYlXbpI2tekyRo6nmeNp5njapQ3v3zG0I4bS7TuGMu37VKOZmZlZJp54mZmZmWXiidfgflY6gA6Op5njaeZ42qUN798xVErHULp9cAzZ2/caLzMzM7NMfMTLzMzMLJMRMfGSdKakP0l6WtKTkr6W6idKWiPpuXQ7IdVL0o8k9UnaLmlW7bWuSPs/J+mKLsezRNILkrambUHtOTeleJ6RdHGtfn6q65O0+CjjOUnSRknbUjzfSfXTJG1I7/U+SSem+jHpfl96fOpwcXYpnhWSdtf6Z2aq7+nnVXutEyQ9LumhdL9I/wwRS+m+2SPpidT2plRXZHyVIGm5pP2SdtTqso1ntSDHNcSQsx+K5rKG9rOPTxXOV4O0X6IP2pmXIuK434BJwKxUHgc8C8wAbgUWp/rFwPdTeQHwW0DAR4ENqX4isCvdTkjlCV2MZwlwwyD7zwC2AWOAacBO4IS07QSmAyemfWYcRTwCxqbyaGBDet/3A5en+tuAL6fyNcBtqXw5cF9TnF2MZwVw2SD79/TzqrVzPXA38FC6X6R/hoildN/sAU7tqCsyvkpswDxgFrCjVreETOOZFuS4hhhy9kPRXNbQ/goyj08K56tB2i/RB3toYV4aEUe8ImJfRGxJ5VeBp4EzgEXAnWm3O4FPp/Ii4K6oPAaMlzQJuBhYExEHIuIfwBpgfhfjGcoi4N6IOBgRu4E+4Ny09UXEroj4N3Bv2vdI44mIeC3dHZ22AC4AHkj1nf3T328PABdKUkOc3YpnKD39vAAkTQYuBe5I90Wh/umMZRg975th2s4+vkqIiLXAgcPcvevjuQ05rg15rXQua0vuKp2vWp6jiuelETHxqkuHUT9E9U3k9IjYB1XSAE5Lu50B/L32tOdT3VD13YoH4CvpMOfy/kOgOeJJh4W3AvupfrB2Ai9HxBuDvPZb7abHXwFO6WU8EdHfP99N/bNU0pjOeDra7ebntQy4EXgz3T+Fcv3TGUu/Un0D1S+X30vaLOnqVFd8fLVA9vHchhxXMq+VzmUtyV2l81VbclQr89KImnhJGgv8Cvh6RPyzaddB6qKhvlvx/BQ4C5gJ7AN+mCueiPhPRMwEJlN9q3lfw2tnj0fS+4GbgPcCH6E67PuNHPFIWgjsj4jN9eqG1+5ZPEPEAoX6pmZORMwCLgGulTSvYd9cMZWWfTy3IceVzmulc1np3FU6X7UsR7UyL42YiZek0VTJ4JcR8etU/WI6lEi63Z/qnwfOrD19MrC3ob4r8UTEi2nQvgnczqHDuj2Pp19EvAw8QnWOe7ykUYO89lvtpsffQXWapZfxzE+nMiIiDgI/J1//zAE+JWkP1WmPC6i+0ZXon/+JRdLKgn0DQETsTbf7gdWp/WLjqw1yj+c25Lg25bXSuaxg7iqdr1qTo1qbl+L/WCB2rGxUM9a7gGUd9T9g4CK7W1P5UgYustsYhxbZ7aZaYDchlSd2MZ5JtfJ1VOfXAc5h4CLHXVQLUEel8jQOLUI95yjieScwPpXfBqwDFgKrGLgY85pUvpaBizHvb4qzi/FMqvXfMuCWHJ9XR2znc2ixaJH+GSKWYn0DnAyMq5UfpVoDUWR8ldqAqQxcXJ9tPNOCHNcQQ85+KJrLGtovMj4pnK8omKNocV4qnqxybMB5VIcGtwNb07aA6jz2H4Hn0u3E2g/GT6jWBjwBzK691heoFhn2AVd2OZ5fpPa2A79hYMK6OcXzDHBJrX4B1V8P7QRuPsp4PgA8ntrdAXw71U8HNqb3ugoYk+pPSvf70uPTh4uzS/E8nPpnB7CSQ3891NPPqyO28zmUSIr0zxCxFOub1A/b0vZk/88hhcZXiQ24h+o02utU35CvIuN4pgU5riGGnP1QNJc1tF9kfFI4X1EwR9HivOT/XG9mZmaWyYhZ42VmZmZWmideZmZmZpl44mVmZmaWiSdeZmZmZpl44mVmZmaWiSde1jqS7pA0Y5h9Vki6bJD6qZI+17vozMyaOYdZE0+8rHUi4osR8dRRPn0q4KRlZsU4h1kTT7ysZyTdKOmrqbxU0sOpfKGklZIukrRe0hZJq9I13pD0iKTZqXyVpGdT3e2SflxrYp6kRyXtqn1zvAWYK2mrpOsyvl0zO844h1kveOJlvbQWmJvKs4Gx6Vpu51H9Z+BvAp+I6iKmm4Dr60+W9C7gW1SXb/gk1QVW6yal11pIlaygugTEuoiYGRFLu/6OzGwkcQ6zrhs1/C5mR20z8GFJ44CDwBaq5DWX6tIhM4C/SILqmmzrO55/LvDniDgAIGkVcHbt8QejuvDuU5JO7+UbMbMRyTnMus4TL+uZiHg9XaH+SqoLlG4HPg6cRXWh0TUR8dmGl9AwTRw8gn3NzI6Ic5j1gk81Wq+tBW5It+uAL1FdPPcxYI6k9wBIerukszueuxH4mKQJkkYBnzmM9l4FxnUreDMb8ZzDrKs88bJeW0e1jmF9RLwI/Itq/cJLwOeBeyRtp0piA9Y/RMQLwPeADcAfgKeAV4ZpbzvwhqRtXphqZl3gHGZdpYgoHYPZkCSNjYjX0rfF1cDyiFhdOi4zs8PhHGadfMTL2m6JpK3ADqo1FQ8WjsfM7Eg4h9kAPuJlZmZmlomPeJmZmZll4omXmZmZWSaeeJmZmZll4omXmZmZWSaeeJmZmZll4omXmZmZWSb/BWUoef54pR66AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))   \n",
    "ax1 = fig.add_subplot(1, 2, 1)\n",
    "ax2 = fig.add_subplot(1, 2, 2)\n",
    "sns.regplot(x='weight', y='mpg', data=features, ax=ax1)                 # 회귀선 표시\n",
    "sns.regplot(x='weight', y='mpg', data=features, ax=ax2, fit_reg=False)  #회귀선 미표시\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### seaborn 조인트 그래프 - 산점도, 히스토그램"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aISLKG/YptSEspXtBfxmbL6jPNh4n9TtM3DWaosYgFXkpdSLqTqwptcHUNCYAJj77vsj9/CrlEuaV+wIH0JZEItdoAuKNQeK0Q0SUNwxKPleN7cZtO57GtCpKIli3chE2jiwL3Ne2ycy030CljPlHzZkNIquXDuKeXc827Vcpl6xmmYg7BqnoS6kTUfdh853HVWO7cfP2fbN9RNOquHn7Plw1tjtwf9smM9N+G849FQ+uX4O9m87G6NpTcNdD1abBtwv6y6mtJGsq1+qlg5Fz9hERpYFByeO2HU/H2h60rPm8ch8u37Kz4WFuM7+eqd+pf+6c1JIOgsp1/ulDuOuhamrjnWwmqSWi3sXmOw9TFp1pO3CkySwqky2qaa1T/Tv+cq3atC218U7M9iOiKKwpeZQkeH4F03avdjPZ8rKsRJrBkdl+RBSFNSWPdSsX4ebtzaOd161cFHls2MPcZoDq6NpTGmoRQHP/VNB5gGRn/U4z+YHZfkQUhUHJw82ys82+8zI9zI+tlK2arMJSuoNmIK9OTmF06y5A6hO2hp07Dpvg2Cpm+xFRFNGQ/pK8GB4e1vHx8U4XI5S/vwSIHnc0oxpZuwk6bxR3aqNWpTX1kOkepZVdSJRjtnMx481vebtuvPGeNMsSKYW574yfnzWlEGEPZ+97x1bKEEFA4FDjSrJu8kRU7aaV2SDabQ7zJz+4GXPtBikuMkhEURiUDMIyxQA0vGda2G+qNmN1rbDstlYCTJLNYUlnzHGRQSIKw6BkEJUplsRcdl6m4GO7EKArqf4fF5fEIKIsMSXcICxTrJXaS0kEAnN6ual2EzTrgl/YoNx2jE1UjQGRGXNElAbWlAyiMsXiLj8xrYrrP3waAMTKbnMDzBV37AocxNtuUoOJ22xnwow5IkoDa0oGYfPa2dRegrgP+agph/xGVgzh2g8tj700RZSwKX/CEiySbiIkInKxpmRgkynmzb578dUaorLr3b6YB9evid3ElnTmWlQCQ1jzHFO4iSgtXR+UbMfcmPYLW7/IO7DVdrhXO30xSWaumRIYLtuyE5vv24NjK+XArMIhp9kuiRRxIiK/rg5KtunMraY9j01UMbp1F2oz9gOQbfti0hrA6goLjtXJKZRLgnKfNHw2d1kLTqpKRGnp6j4l2wlAW50odPN9e2IFJNu+GDdIprV8BBAdHGvTiqPnzWnq+3rgif2cVJWIUtPVNSXbCUBbnSg0TlPcUIzaThZjg4LmuPObPFhrWNYdAC7fsjNw306liKddoySibHV1Tcl2OYhWl42wbYorieCZySlsvm9PaG3HzYbLYmyQd4E/k6DPl5clNoBsapRElK2uDkrtLlce1dQ2uvYUlPui51WcVo18aHofsCZJP/hHVgzhwfVrcP2HT7P+/K3eqzRwfSai7tPVzXe2adStplu773uXlZg/t4RyqQ8vTtXQJ9I04NXUDBc18WqaD/44nz9Pk6pyfSai7tPVQQmwT6NuNd067Lgl6+8N3B700Ax7kMbpj2pVnM+fl0lVuT4TUfdJLSiJyDwA3wNwlHOdO1X1ahFZAuB2AAsBPAzgo6p6KK1yZMnf6W4a6wOp7zuyYghXje3GbTuehimHL840Qr3W6d/qar3dfE+Iii7NmtJrANao6ssiUgbwAxH5ZwCfBHCdqt4uIl8E8HEAX0ixHG2JM/jWP36nXBL0CeDPGlcFRrfuwtbxfXjwyQPGa8dpsrtqbDdu2b5vNrilOX4oLw/6qKbEpJfdIKL0pRaUtL6k7cvOj2XnSwGsAXCRs/0mABuQ06AU56EW1CdUm1YYJgVHbUZDA1KcJruxiWpDQHKlscRE3h70YU2JXHaDqHhSzb4TkZKI7ATwPID7ATwJYFJVDzu7/AJA4NNBRC4VkXERGd+/f3+axTSKk91l6hNqdbX5OPPjbb5vj7H5L+lO/yJlvDERgorK+/x7adL8x2s3SjUoqeq0qp4G4EQAZwB4S9BuhmNvUNVhVR0eHBxMs5iB4q4lZOpcN62fFCbuMWEP2aQ7/Yv0oM/TmCqiOLzPv2MGFna6OJnKZJySqk4C+C6AMwEMiIjbbHgigGeyKEMcrawlZBq/s27lIpRLzUGmD4BpiNO6lYtildf0kBWnXEkq0oM+T2OqiMhOakFJRAZFZMB5XQHwOwAeB/AAgAuc3T4G4JtplaFVrawl5J0hwTtX3MaRZdh8wXIs6C/P7jtQKePY/nJTAgRQH+e0cWRZrPIGPXwFwMVnLk6876RID3rT74T9SUT5lWb23fEAbhKREurB7w5VvUdEfgzgdhHZCGACwFdSLENLWl1LyNTpHrTdNIbp4CHzAFqTLAe05mnwrI28jKkiIjtpZt89AmBFwPafod6/lFumQZkAZjv0R1YM4eIv/bAhg27VyQtxy5/8p9mfw1KnTddQACv+5l+hCrw4VYs1u0Sch287ad180BNRWkRbTQ/L0PDwsI6Pj2d2PX/as1+lXMKJC+bhJ8+/0vSeG5iCzlEpl2ZrWlHX8F8vyWYnU9nOP30IDzyxvxA1IKKCs85mevNb3q4bb7wnzbJEumjl4qRPafz8XT0ha6uiZtCeqk0HBiQAszWnqNRpm1m6g45Lgqlst2zfxxm3iaijGJQM3Bm04yd019mkTse5RpIp18YxVb6f8zr+iIi6l1WfkoicF7D5RQC7VfX5ZIuUL2H9S60cZ1qjKOoaSaZcx/lMz0xO5WZaISLqfrY1pY8D+DKAi52vL6E+h92DIvLRlMqWC6YU6N94/fzA/VedvDD0ONs1imyOa5UphTzIQH+ZC+kRUWZsg9IMgLeo6vmqej6At6I+4epKAJ9Oq3B5YBrrcv8n3zMbgFze7Ls4Y2T8+y7oL2OgUk5tbE1Q2S4+c3FgEFVFYaYV8nJX8V2y/l6s2rSNQZSoIGxTwk9S1ec8Pz8P4DdV9YCIBKzN0F1MKdDe9O84xwGYXbLCvwjgQKWMqz9wasNx3mXSS87CgXEnbA1qfvMfO/ymhQ0LFr52eNqYHdhKk2ZW8jZpLBHZsw1K3xeRewBsdX6+AMD3RGQ+gMlUStbFrhrbjZu37wt8b3KqhtGtuwAgMHXcDWK2D9q4D+jXDs/Mvg6accIlOLImlP96ne5/4uzgRMVl23z3CQBfA3Aa6gNibwLwCVV9RVVXp1W4bnXbjqdD36/N6GzzWNiURzbNaHFm9Y5akt1LgaZzuAGw0/1PRZo0logaWQUlZ22kB1GfVPU7AH6gRRh1m1P+Jrsg7gM06kHa6vtxl2S3OUdelrUo0qSxRNTIKiiJyB8D+BGAD6LedLddRP4ozYJ1M5ulKdwHaNSDtNX3vdvdPqu4f2X4z52XGkqRJo0loka2fUqjAFao6q8AQEReB+DfAHw1rYJ1s3UrFxn7lACg3CezD9DRtacYpyOyedAGHe89Ls50R1HXjjM2K01FmzSWKO9u3WF+XnklMR2RbVD6BYCXPD+/BCC8Y4SM3KUpTNl3G849kn3nfcBWJ6cgODLzwrxydEXXf3xJpKFJLawfyc30814TqCc5nH96c/ZeVABMWx6SLIioPVYTsorI1wEsw5G1j85FvTnv3wFAVf8hrQIC2U/ImldRk7y2cqwpIAmAvZvOnk1F9xsaqODB9WsCr9OJwNDOvSHqgEJNyGorRk3J+Plta0pPOl9uBPum8/oY2xIUURoPWPec3gd9SQTrVi7CxpFloddsJ9XZdKxbG/I7YaASuSS8v6yrlw7GnmU8qXvMNHCi7mAblP4JwGcAnOQ5RlX17WkUKg/SGIBp6r+ZVsXN2/dh7/6X8fC+F43XbCeRwLTPtGpTjalSLmH10sHQJeEVwOVbds7+lVKdnGroJ7O5X0ne47wkWRBRe2zHKd2MelLDeQDOcb4+kFah8iCN9OaocUAPPnkg9JrtpDqb9nGnMfJPh/TAE/sjkx+iGn6j7leS95hp4ETdwTYo7VfVb6vqXlX9ufuVask6LI2/vFs91j2unVTnuMcmVcMIO0+S97hIaeCcl4/IzLb57moR+TLqA2dfczeq6t2plCoH0khvbmcZDKC9VGfTsQACm9D655bwyqF4aeKmspv6jZK8x0VJA+e8fEThbIPSHwJYCqCM+ozhQL31pmuDUhrpzWFjjoD6LOPePqWga4ZN8hol6NhVm7YFNqG1urihl7dvKughnPQ9bufeZIUJGUThbIPSclVdlmpJciaNv7z9Y4Zcttl3abBdhdbEHcM0ZMi+C3sIuynlea/dJIkJGUThbIPSdhF5q6r+ONXSFNBVY7txy/Z9sw/x+XNL+PwHzWNjwv6aD0qxvvLuR3DZlp0N+/WX+zBVm2l6iAcFNCD8oR+3SXFBfxn9c+dYB5HLfWV3uQ/hItRukpSXWS+I8so2KL0LwMdEZC/qfUoCpoQHLkHxyqFpXOFZeqKd65mmIjpYm2kqE9DcNzR65y5A67OOmz5DVJOiV6VcalrrKQofwo06PesFUd7ZZt+dBeA3ALwP9VRwpoTDvATFtGfpiXauZ8MtU9DxtWmdDUj+/V3uKrRRk8S2ugJukbLishBnRWKiXmRVU+r29O8gNm3/YUtQtLsMRJrH+vcfWTFkbGYDjgSRVh6cRcmKy1KvNVkSxWHbfNdzbJqdTFP0+Pdr53q2xwL2S5QHlS3s+mHZYTaJGXwIE5Et2+a7nmPT7LRu5aLAY0uepSfauZ6t1UsHA48vlwTlvsZmOVPTWdT1g2pjraw02y0DR7vlcxDlDYOSgU3b/8aRZbjkzMUNY3rmzy3h2guXx64ZBF3vkjMXo2KxPMUDT+wPPH7zBcux+cLlVv0XUX1LQbWruNME5WW59HZ1y+cgyiOrpSs6jUtX1C1Zf2/g+CF3mYkk2CwBETTTuU154i6DkVfd8jmoo7h0hUFqfUoisgjA1wG8EfVZIG5Q1X8UkQ0A/gTAfmfXz6jqP6VVjrwK64sZm6jir7+xe3aaHwFw8ZmLQ/u5Whl06z3m2EoZIsDkwRqOrZQxr9yHyYM1nDBQwUmvq+CKO3bhsi07IQD6+gTTM+Y/Zkz9aaaEjOrkFFZt2laYBAgOgCVKT5qJDocBXKGqD4vIMQAeEpH7nfeuU9W/T/HauRY2BgoArti6q+GhrwBu3r4Pq05eiAOvHDIuMxFnPjV/GSanarPvTU7VUCmXcN2HT8P4zw80jJdSIDQghaV7hyVTFGkOOI69IkpPan1Kqvqsqj7svH4JwOMA8v20yUhYX8zm+/YYH/rbf/aC9TITrSwbEXS8aSxWkKgxN1HJFO0uDZIVjr0iSk8mKeEichKAFQB2AFgF4C9E5PcBjKNem3oh4JhLAVwKAIsXW7dTFkKrzT/TqoHp1VFT+bRyLXcf2x5Hm/4U09x/ccvVaRx7RWnzPv+Oe2Nv/btKPftORI4GcBeAy1T11wC+AOBkAKcBeBbAtUHHqeoNqjqsqsODg4NpFzNTpmYeBdAXMrNCnMy4sO1R73n3iZrpwfXKa4etss9GVgzhwfVrMFTwRfncz7F309l4cP0aBiRKlPf5d8zAwk4XJ1OpBiURKaMekG5x115S1edUdVpVZwB8CcAZaZYhj8KascJmiTCNi2qlOSmqKc093nTNo+Y0/tOZnKrFSos2lXn10kGO/yEqiItWLm74SkJqQUlEBMBXADyuqv/g2X68Z7cPAng0rTLklXdMkYn4Xl9y5mJsHAlePaSV+dT8xwxUyljQX2463h2L5daYSiK45MzFOO7oo5rOGbdPyBvYFvSXcf7pQ7jroSrH/xD1sDT7lFYB+CiA3SLidnp8BsA6ETkN9daqpwD8aYplyCVvKnaYpzxjfdwZBGz7MMZ/fiByGYvVS480i84/ao7xnMNvWji7TtIx8+bg3keexQsHa037AeaZH/zXveuhakOixau1Gdyz69nAhI3PffsxNo8R9YjUgpKq/gDBA6R6bkySV9Dg1CDevpWoZTSilr2oTk5hdOsuQOozh5v2CUrJDksdjyq3qeze9adcU7Vp4z154WANYxPVzAJT1gstEtERnGYoYzZLVAjQ0B8UNZ2PzTlrMzobkEyCmt/iLKkR1I/1uW8/1nR8K3OIZJUqzimEiDqLQSljNinPisbaSlQKeZJp1P5z2Z47qB9rbNLx6noAAB90SURBVKJqbOYLsqC/bF2utMSdz4+IksWglDGblGd/AkRUyneSadT+c9mcuyQS2MQV9iD3t+u6q9oOVIIDU1ap4pxCiKizGJQyZpuKHXWMdz/bZS/KpfAxR7bX9ptWDWziCnuQX3zm4sBswQ3nntrR2RJaGfNFRMnhIn8Z888GMNBfhirw4lQtdJE87zH+/dzvG771WGgiwuYLljdlwblZdbbXPrZSxq9frcE/E1LQQoCmOeIGKuXQ9Pawz5q20bWnBM6SzimEiLLBpSu6zIq/+dfAfpwkl1UwLaEBNKexRy2DkUfMvqMMdMXSFW0MmM1+6QpqnWlJCZsH5NUfODX0L/2oJTPCalLuz6aAJM45/DU4/zk337cHl2/ZaV1by1pel29nsKRewJpSzkSNY7KpaZgeXmE1FwBW46eihNXIbMZoFaEm1QlFrXWSEWtKBqwp5YztkhJRUwgFvR+V7txuQALCkxtsxjzZfL5eFPa7472ibsKglDO2S0okee4k053DstRsr8P062ZMVadewZTwnLFdUiLJc58wUEkk5TkqS832GkH7uXP/9ers4UxVp17BoJQzrYxjaufc7vlsxzp5lfskcGZxk9VLByMb0oM+X9ZT/+QxAHK1W+oVbL7LmaBxQXGy7+KcO+h8Ntl3rWR/jU1UcddD1YbMPQHwn09eiKd+NRV6ziz7U6Imv+2UTo/fIsoKg1IOpZmSHHbuNK8bFFgUwFO/moocP5Vlf0qeEwrymqpOlCQGpS4xNlHF5779WMPA2YFKGRvOPbXpQZb2eJexiSo+c/cjOFibAQCIAKaRB9XJKSxZf29oOUwzQ4T1p9h+Rv9+QdcBmFBAlBX2KXWBsYkqRu/c1TSTw+RUDaNbdzX0iaTdPzM2UcUn79g5G5AAc0CafT+iHHH7U2w/Y9B+pj4vJhQQZYNBqQtsvm+Pca2k2ow2zNad9tIMm+/b0zQvni1TOeIu9277GU1NikEzmDOhgCgbbL7rAlFNS973o/pnbJq9wmaMMDV/uYYGKnjGqZnE+Sxx+lNM5/CXzbSfesrJhAKibDEodYGwvhD3/ah9TxioWGWemfYZ//kB3PVQeBNgSWQ2qWHVpm2x+4lsmT6jf24+035JTl5LRPGw+a4LjK49xbhWUrlPGpqewvpnbJq9TPvctuPpyCmE1q1cZFWOdo2uPSWwb0jRuPAgx/4Q5Q9rSl3A/cvfJvsubLzL5Vt2Bp7fpvlvOiSbQQS4eOXihjWU0hx3M7JiCJdZfBaO/SFqz6079kXuE3fSVgalnIqbtu32uXiPm3/UnMDzuQNy/Qb6y4FrMfWJzKZtm/YpiQQGprCmMG8/kVs+d0kLt7bSasAYskwj59gfonxhUMqhVmcViOrvcbd7V6f17vPyq4cDz+sGm+rkFMp9gnJJGrL9KuUSzj99qOEa7nabprCgco9u3QUIZq8Td2YFriBLVEzsU8qhVtO2W+3vcfepWeRy12YU8+fOaUrP3jiyLFbadlS5azPalOY+VZvGFXfsshpTFTeNnIjygTWlHGp1Wp1W+nvi7ON6caqGnVe/r2l7q01hcWZLmFa1rjHZlMdtNqxOTs02QQ6xb4moYxiUcqiVaXXCjjP198Tdx7YccUWltPt556JrZ8okf7Oht5nyiq27cPmWnVDU7826lYsaEjWIKB1svsuhVlOVTcetW7kocjmMoH3c/qO45YgrqNxB1/Z6ZnKq7SmTwlbCnZ7R2QG+06q4efs+XDW22+q8RNQ61pRyqNVU5bDjht+0MHI5DO8+7WbAxa3BzCv3zQYIN5UdwGxtxe+EgYr1jN6mssSdZPW2HU8H1pbSnuCWqJcwKOVUq/0zpuNszhd2bBxxsgf9+wLAa4ePTOY6x5fpBxwZEGwzriqsLHGbDYOaN/O6/hJRUaUWlERkEYCvA3gjgBkAN6jqP4rIQgBbAJwE4CkAH1LVF9IqR7dodSmGtGo2Yee54o5dTQ9wUz9QX0BfljfTMGii2cOquHzLzsBjgfp4q1WbtkWePyhtPEwpYHBXVG2NtSiieNKsKR0GcIWqPiwixwB4SETuB/AHAL6jqptEZD2A9QA+nWI5Cs/2r/GkxzdFHWc6jylhwtsP5E8u8AurwbiHmI598WBtdoBvWFlMM2EcNaevobbm8k6T5D2P6fysRRHFl1qig6o+q6oPO69fAvA4gCEAvwfgJme3mwCMpFWGbtHOUgztjG+Ku5xFWOIAYO4HMjGnOYRrDifBZXG9Wms8ok8Eq05eOFszKongkjMXB/YnmTIRo/q8iChYJn1KInISgBUAdgB4g6o+C9QDl4i83nDMpQAuBYDFi+PNndRtbMctJT2+KW4iQNj+btaeqR8oiLu2UYvLM0WWBTAH5Kd+NYUnr3l/5LnCZo6w6fMiCuJ9/h33xt6qVaeeEi4iRwO4C8Blqvpr2+NU9QZVHVbV4cHBwfQKWABhf423sl+r549i2r8kMjubQtxzumsbCYL7dGyVRAJndmg3IIfNHBF2X8cmqli1aRuWrL8XqzZtS2zlX+oO3uffMQMLO12cTKUalESkjHpAukVV73Y2PycixzvvHw/g+TTL0A1sxy0lPb4p7ngk03mu/dDy2SBg2mdBfznwnO6Erns3nY1rP7Q8dLyVSblPcO2HlmPvprPx4Po1Df05SQTkkRVDs2X0nt/0WVcvHUx1SXqiIksz+04AfAXA46r6D563vgXgYwA2Od+/mVYZuoXtuKU0xjclXU7TPgAiJ1ANOnb10kE88MR+Y6YdABw9b47xs5ia31YvHZzN4Av6HGGr73q3n3/60Gz53P1sx1cR9SLRGHOexTqxyLsAfB/Abhzpe/4M6v1KdwBYDGAfgAtV9UDYuYaHh3V8fDyVchbR2ES1IWMsaN2kJK6RVCpz0LIZLxysNc01B7QXGJesv9fY/9QngH++We91N3zrsdnZ0+fPLeHQ4ZmGCWor5dJss1zQ2CrTTOlA8+/HVE4BsHfT2daflwrNui36zW95u2688Z40y5Iqw3pKxs+fWk1JVX8QcuH3pnXdbjc2UcXonbsaxu9MTtXqSz0gmVTjJFOZ/efyLpvhnWvuyrt345rzlrW1DLlprSegOSB5r3v+6UMNKeCvHGrODvTWZMJmYw+qqU1O1RruX6tzGxL1As59VzCb79sTOKC0NqOJpRonmcpsmwKeRKp0K5V+26XcgSPJD63Mxu79fFyGnciMQalgwrLCkko1TipFPO4x7Zb/xangWlKUuLOjh2UZhnE/H9d6IjLj3HcFEzZfW1LNP0k2L8WZX67d8sedy85ls2yHtyYzuvaUpibUcknw4XcuCuxTcg14MgyTWIadUxhRN2JNqWBG154SuKSDO0lpUtdIqnkp6FxBkmi+CrpWX0R3snHZjpJgoFI212T8MUyB4TctxPmnm4PCy68eTiztu91lO4jyijWlggmary3p7LukUsSDzhWWfddu+U3lHv/5Ady6Y58x+860bIepPJvv29O0dLxNn567TxK/J6aVU7diUCqgJJp+srxGFuUNs3FkWeSqsW4Z3Saxy7fsnJ1J3F/2dvrcbPvNoprmkuz3I8oTBqUO6bb+gLGJasNYnwX9ZVz9AXPtzb+/1/y5JZRLfXhxqhY6cLU6OdVQ21q9dLChT8efyh51z4NS4S/bsnN2oUH3GqZBuu7S6WH9U6Z+M/9YrlcOHZ7tswpKyWdaORXFrTv2zb42jFlqwD6lDui2/oCxiSpGt+5qCDAvHKxh9M5dgZ8paH+vVw5NY3KqFnhvvPcOaBzrdMv2fcYmLZt7bkpfd0NMdXIKN2/fFxp0wt4z9Zv5yzY5VWtK+/enzDOtnLoVg1IHdNuSBkF9LEB9gb6gz2Ta38R7b8LGPZnO+MzklNU9T7PpyzsprZ/tWC5v+ZhWTt2KzXcd0G39AXHHTrUz3qmVY08YqFjd81ZTym3MqM4GDH8zYqsp853uqyNKA2tKHZDUUhF5EVbuoPdaHe/UyrFuk5bNPbdNX2+Fe52gZkSbSdDKJcErrx1ueamLOEtlcFkN6iQGpQ7otv6A0bWnoBwwIKhcCh47ZdrfxD9w1TZwCIDzT6/XJmzuudskZlpGo1VRiwq6ixl6lfsEC/rr46QW9JcBhbGfLUqcPsxu6++k4mFQ6oBu6w8YWTGEzRcux0DlyMN8QX8Zmy9YHviZgvb3mj+3ZBy46r13ABqWLPdTAA88sb/puLB7PrJiCBOffV/swORdRPCSMxcbr2NqRvQuZjg0UMHmC5dj4rPvw95NZ6N/7pymPrg4fZBx+jC7rb+Tiod9Sh3Sbf0BcT9PO58/6Ngl6+8N3NefHGB7zas/cGrT8hRhZlStlp0w9SG5ixkGabcPMs7x3dbfScXDoESF5U0YMI0dshkXZFqMcPznB3DL9n3GrD6b6/ivdWyljHJJGlK+w1LFN9+3J2SNKMHYRDU00I5NVGPdm7jjn7ptvB11HpvvqJD8fR9BD13bcUFB/SZjE1Xc9VC1KSDMn1tq6g8L6w8MGoMExWx/kakZ0T8eK8i0amh/j3uOOPcmTn8n+58oDQxKVEimsT3evp0444L8/Sam8w/0z8XmC5db9wcGnac2o+ifOwd7N52NB9evaWvsUlh/T9g9MpU5Tn8n+58oDWy+o0Iy9XHY9O3Y9JuE7ROnb6qVPpqxiWqs8VLVySksWX9vU/NZ2D0KK7/t5zOdvzo5FdmsSGTCmhIVUjtjvWyOTWosWdzzuE1icQU1n6U9Hi7sPGzGo1YxKFEhtTPWy+bYpMaSxT1PWLNduSSR47uyXHY9bMwYm/GoVWy+o0JqZ82nsGO92WQD/WUcNcc8W3ka5Qxr1tt8wfKGc4XN9dfKteNyz3PZlp2h5SCKg0GJCitO305Q6rJ/XJB/6Qp3EUUAOHjocKyyxU2Vjkr/dgcLe8958NDhhjK6vM1qYffIX8bVSwfxwBP7m5YE8c5G4d/3Gc++YeVo5Z60exwVE4MSdb2gdZL86xMB4U1n7lIc/mPauZ5pf79KuYTVSwebzlnuE+sxTzZlvHn7kXVvvEuCjN65C1DMziph2tdfZm854t6Tdo+j4mKfEnU929TlqOYm01IcrV4vbH+Xm5L9wBP7A1PL58+d09J0VbYp50D9c9ssNRKWjt9q+jjTznsPa0rU9WzTsm2WkWhnyfO42wWYbWK83NBv8+JUDTuvfl9kmWyv2Y6wdPxWpy/itEe9hzUl6nq2qdE2M5AnlXIed7tpn2Mr5dBlJkzLUKSxTErcJUxsytFty7xQNAYl6nq2qdFRS1eYluJo9Xpx9g/ap9wneOXQYeM0P2HTAK1eOhj5OWavY5GKHnVvWk1P77ZlXigam++o68VJjXaz1cYmqtjwrcfqc9WhPlfd1R84te2U81b3D9onKPvO7W8ZWTGUSH+MP/vO1Lw5f+6cyFkioj5jksdRcYkGZM7kzfDwsI6Pj3e6GES5smT9vYEp5AJg76azQ98HEDr7uXuOuNcka9arXL75LW/XjTfek2ZZMnPRysXuS+PnT62mJCJfBXAOgOdV9W3Otg0A/gTAfme3z6jqP6VVBup+SY59AY78RX5spQwRYPJg9MBZ2zL4l7AQqaeau+N84owNOmGggoH+cug4JVPiRp8I5pX78Mohc/ZdUJ9N2DIYQctocHwRtSK1mpKIvBvAywC+7gtKL6vq38c5F2tKFCRofE+lXIpMiw46rlyShrE4fqbz2pYhaiySX1R5gHqfEgRN45Tca8e9Zrvlj7q2ze+mh/RUTclTQ3IZP39qiQ6q+j0AB9I6P1GSY1+ixuK0u3x4nHFBNuUBoscpuYkbQUvFm5jGOtmU3/u5Ob6IWtWJRIe/EJHfBzAO4ApVfSFoJxG5FMClALB4cVOUJUp87Esr17MtQ1rjaqLGKY2sGDKOcfLzjovyi7v0OscXtcf7/Dvujb1Vs8w6JfwLAE4GcBqAZwFca9pRVW9Q1WFVHR4ctE9fpd6R9NiXVq5nW4a0xtW0M27KtF/Q2Ka45+D4ovZ4n3/HDCzsdHEylWlQUtXnVHVaVWcAfAnAGVlen7pLkmNfosbitLt8uM3A3DjlCSuTTRlN5zKNbVq9dND6HKZrcnwR2cg0KInI8Z4fPwjg0SyvT90lztLdUcdtvmB5wzLnA5UyFvSXI89rWwb/fu75Acz2+bjfg8ozNFDBJWcubmmeu6Ayms5l6gt64In91udo53dDlGb23W0A3gPgOADPAbja+fk01IdIPAXgT1X12ahzMfuOgGKkGEelmmdZbv/aUK/VpnGwNgMAqJT7MK9cakhJH4qY+28oIC09aLmLBf1lqNb7u+aV+/Da4RnMaD3orlu5CBtHllmXPWgZjbz9zlvE7DsDDp6lQihCinFgqnlE2naWZcmLS85cHBqYwsqet995GxiUDDj3HRVCEVKMA1PNZ7QhIAHZlDtuCnqWbtvxdOj7YWXP2++cksegRIVQhBTjOGVJu9x5ui9+QTNCeKWV0k/FwKBEhVCEFOM4ZUm73Hm6L35Rg3nTSumnYmBQokIoQoqxaXmJcqnxIZxFueOmoGdp3cpFoe+HlT1vv3NKHoMSFUIRUowDU80vXI7NFyzPvNz+sizoL6O/fOS/e6Xc15SS7k3z9m4Pet+fFu7db0F/GQOVejp9pdwHd7hVSSQyycFfdv/18/Y7p+Qx+46IKHvMvjPgIn9UeEUYv2RiM67JPzbI/Xz+cUju2CDTPv1zSzh4aBqK5MYM+csatgSIqYy298d/Lu+4q4FKGRvOtVuEMQlF/jeXd6wpUaEVYfySSStLaAD1z3f+6UO466GqMXXaZh+gvTFDQWOwbMrvLWPY7yn2ch99gs0XLk/9957QvznWlAzYp0SFVoTxSyatLKEB1D/fbTueDn1Y2+wDtDdmKGgMlk35vWUM+z3FXu5jRjP5vRf531wRMChRoRVh/JJJO2WMGuuT1D6dHE/VyrWz+L0X+d9cETAoUaEVYfySSTtltFm4L4l9OjmeqpVrZ/F7L/K/uSJgUKJCK8L4JZNWltAA6p9v3cpFoeOQbPYB2hszFDQGy6b83jKG/Z5iL/fRJ5n83ov8b64ImH1HheZ2LBcxE8pUdv82U/bd8JsWRmbfefdpJfvOW8ass+/89ycv2XdF/jdXBMy+IyLKHrPvDFhTIqLUmMYZdap24R+39cohb1p3H6457+2s8XQYgxIRpcI/nmdyqjb7nrvMOoBMB7x6y+MNSAAwVZvBJ7fszLRM1IxBiYhSETXOyB3bk1UAsBn3NOPsV+SgFNBUVijMviOiVNiM28lybI/ttTjeqLMYlIgoFTbjdrIc22N7LY436iwGJSJKRdQ4o6zH9tiMe+pz9qPOYZ8SEaUibJxRJ7Lv/OVh9l0+MSgRpawXljkwfUb3K6nztSusPO41L9+ys2t/T0XAoESUIn8acidSodOW9GfsxD3rhd9TUbBPiShFvbDMQdKfsRP3rBd+T0XBoESUol5Y5iDpz9iJe9YLv6eiYFAiSlEvLHOQ9GfsxD3rhd9TUTAoEaWoF5Y5SPozduKe9cLvqSiY6ECUol5Y5iDpz9iJe9YLv6ei4NIVRETZS23pioLMfZf90hUi8lUA5wB4XlXf5mxbCGALgJMAPAXgQ6r6QlplIKL0JTmmqBfGdFG4NPuUbgRwlm/begDfUdXfAPAd52ciKih3fE91cgqKI+N7xiaqHT0XFVdqQUlVvwfggG/z7wG4yXl9E4CRtK5PROlLcnwPxwoRkH323RtU9VkAcL6/3rSjiFwqIuMiMr5///7MCkhE9pIc38OxQkd4n38vTfr/tu9uuU0JV9UbVHVYVYcHBwc7XRwiCpDk+B6OFTrC+/w7ZmBhp4uTqayD0nMicjwAON+fz/j6RJSgJMf3cKwQAdkHpW8B+Jjz+mMAvpnx9YkoQSMrhnDNecswNFCBABgaqOCa85a1lDGX5LmouNJMCb8NwHsAHCcivwBwNYBNAO4QkY8D2AfgwrSuT0TZaHV5inbOxdTx7pVaUFLVdYa33pvWNYmo+3GZie6W20QHIqIgTB3vbpz7jogKpddSxxfOn1uUqYMSwZoSERUKU8e7G4MSERUKU8e7G5vviKhQuMxEd2NQIqLCSTINnfKFzXdERJQbDEpERJQbDEpERJQbDEpERJQbDEpERJQbDEpERJQbDEpERJQbDEpERJQbDEpERJQbDEpERJQboqqdLkMkEdkP4Oe+zccB+GUHimOL5WsPy9e6PJcNYPkA4JeqepbNjiLyL7b7doNCBKUgIjKuqsOdLocJy9celq91eS4bwPJRODbfERFRbjAoERFRbhQ5KN3Q6QJEYPnaw/K1Ls9lA1g+ClHYPiUiIuo+Ra4pERFRl2FQIiKi3MhVUBKRr4rI8yLyqGfbBhGpishO5+v9nveuFJGfisgeEVnr2X6Ws+2nIrI+obItEpEHRORxEXlMRP7S2b5QRO4XkZ843xc420VE/qdThkdE5B2ec33M2f8nIvKxlMuXl/s3T0R+JCK7nPJ9ztm+RER2OPdii4jMdbYf5fz8U+f9k6LKnVL5bhSRvZ77d5qzPdPfr3PekohMiMg9zs+5uHch5cvTvXtKRHY75Rh3tuXi/y75qGpuvgC8G8A7ADzq2bYBwF8F7PtWALsAHAVgCYAnAZScrycBvBnAXGeftyZQtuMBvMN5fQyAf3fK8HcA1jvb1wP4W+f1+wH8MwABcCaAHc72hQB+5nxf4LxekGL58nL/BMDRzusygB3OfbkDwEec7V8E8F+d138O4IvO648A2BJW7hTLdyOACwL2z/T365z7kwBuBXCP83Mu7l1I+fJ0754CcJxvWy7+7/Kr8StXNSVV/R6AA5a7/x6A21X1NVXdC+CnAM5wvn6qqj9T1UMAbnf2bbdsz6rqw87rlwA8DmDIOfdNzm43ARjxlO/rWrcdwICIHA9gLYD7VfWAqr4A4H4AbY/WDimfSdb3T1X1ZefHsvOlANYAuNPZ7r9/7n29E8B7RURCyp1W+Uwy/f2KyIkAzgbwZednQU7uXVD5ImR67yLK0fH/u9QoV0EpxF841eivulVs1B+4T3v2+YWzzbQ9MU5zyArU/5p+g6o+C9QDA4DX56x8QE7un9O8sxPA86j/h34SwKSqHg641mw5nPdfBPC6LMunqu79+7xz/64TkaP85fOVI63yXQ/gUwBmnJ9fhxzdu4DyufJw74D6Hxj/KiIPicilzrbc/d+lYgSlLwA4GcBpAJ4FcK2zXQL21ZDtiRCRowHcBeAyVf112K6GcmRdvtzcP1WdVtXTAJyI+l/obwm5VsfLJyJvA3AlgKUA3ol6s82nsy6fiJwD4HlVfci7OeQ6md47Q/mAHNw7j1Wq+g4AvwvgEyLy7pB9O/J/l+pyH5RU9TnnYTED4Es40tzwCwCLPLueCOCZkO1tE5Ey6g/8W1T1bmfzc07VHs735/NUvjzdP5eqTgL4Lurt9QMiMifgWrPlcN4/FvWm3SzLd5bTLKqq+hqAr6Ez928VgHNF5CnUm1PXoF4zycu9ayqfiNyck3sHAFDVZ5zvzwP4hlOW3PzfJY9OdGSFfQE4CY2JDsd7Xl+Oeps4AJyKxk7bn6HeST/Heb0ERzrqT02gXALg6wCu923fjMbO0r9zXp+Nxs7SHznbFwLYi3pH6QLn9cIUy5eX+zcIYMB5XQHwfQDnANiKxs76P3defwKNnfV3hJU7xfId77m/1wPY1Infr6ec78GRRIJc3LuQ8uXi3gGYD+AYz+t/Q70vKBf/d/nl+311ugC+fzy3od7EVEP9r5KPA/g/AHYDeATAt9D4kP1r1Psl9gD4Xc/296OeffYkgL9OqGzvQr2q/giAnc7X+1Fvq/8OgJ843xc6+wuA/+2UYTeAYc+5/gj1TuafAvjDlMuXl/v3dgATTjkeBfBZZ/ubAfzIuRdbARzlbJ/n/PxT5/03R5U7pfJtc+7fowBuxpEMvUx/v55zvwdHHvq5uHch5cvFvXPu0y7n6zH33zRy8n+XX41fnGaIiIhyI/d9SkRE1DsYlIiIKDcYlIiIKDcYlIiIKDcYlIiIKDcYlKjniciXReStEfvcKCIXBGw/SUQuSq90RL2FQYl6nqr+sar+uMXDTwLAoESUEAYl6hoi8ikR+W/O6+tEZJvz+r0icrOIvE9EfigiD4vIVmeeQIjId0Vk2Hn9cRH5d2fbl0Tkf3ku8W4R+TcR+Zmn1rQJwG856/RcnuHHJepKDErUTb4H4Lec18MAjnbmA3wX6iPzrwLwO1qfmHMc9fV/ZonICQD+O+pTy/wX1CcT9TreOdc5qAcjoD49zfdV9TRVvS7xT0TUY+ZE70JUGA8BOF1EjgHwGoCHUQ9Ov4X6FEtvBfBgfWkhzAXwQ9/xZwD4f6p6AABEZCuA3/S8P6b1iW1/LCJvSPODEPUqBiXqGqpac2aq/kPUJ918BMBq1Jfu2Iv6GknrQk4RtDSB12sx9iWiFrD5jrrN9wD8lfP9+wD+DPXJabcDWCUi/xEARKRfRH7Td+yPAPy2iCxwlnw43+J6L6G+/DwRJYBBibrN91Hv+/mhqj4H4FXU+3z2A/gDALeJyCOoB6mGPiNVrQL4H6iv2Pt/AfwY9VVbwzwC4LCI7GKiA1H7OEs4kYeIHK2qLzs1pW8A+KqqfqPT5SLqFawpETXaICI7UV8DaC+AsQ6Xh6insKZERES5wZoSERHlBoMSERHlBoMSERHlBoMSERHlBoMSERHlxv8HprqGmNNAXdkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='weight', y='mpg', data=features)              # 회귀선 없음\n",
    "sns.jointplot(x='weight', y='mpg', kind='reg', data=features)  # 회귀선 표시\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### seaborn pariplot - 두 변수 간의 모든 경우의 수 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dbg/TNi4tLjCdbWiP2q19n6PNPtPZNrYv2ribBROHmN8/OXMkmxdOkIYfwlkTKl0Jwe6St4wfTGn5O3x5vI0fPb+Lh1+rZsmUQtbPK+H+qy+m5ljQ/kqLC0xnG05GnGubPVGR6MWbdvPg9cPPyVZljk0+JMItCIJJZ+lwx+rcJpHHnofXH6C+xcvkwjxKiwvIctpodPvYtucI144YSF2EM57ltMW0T1Uh5vYsp838HpCldOGMiFVkDcGUu9C0jwUTh5hOtM2icqDBbTa8AVg/r8TU545nu5FOvLFd18+t0Fvm2ORDHG5BEEwMHe7ISvmOzuEO7dzWg5VDejx2q4UPPq/nziuHsvC5D8jNcHD3pKH84PILsFtUvBE5sY1uX0z7NHS7I7c3un3m9/G0jwUhlFjFhs/eOhaPX4tK+8hKt53SNg0NbsMWY9mozaLGtWlDteRsispljk0+ZAYSBMEk0xlbhzuzg3O4IXbnNqFnkeOyM6tkkOls33fVMJa89BFXPPJnbnx6JwFNpzzEHjdV1kTpcz92wwhWvf0Zy0rDNZCXTy+ifNu+U2ofC0IksYoNv6hvjZn20dtpM22ufNu+KBu8ICedC3LSWT69iE2VNVG/Ly8rJjeGTnbF7GL+45Wqc27HLnNsciERbkEQTE64NXw+H+vnleDXdKyqwpHjrZxw2+iV1tWjE7obqqqYbdyXTCk0l+dHFmSxYOIQGlt9DMx28siMESiADuRl2nlhXgmarmNTVTRd55pLB/DSroMsnTqcr+W6qDnWCsAD11xEo9vHw69V8+TMkeDq0v+ukALEKjaM17Zd03QqZhczf20lu2oaWbNjP8//8HIsqoKiKFgUsFoVnHYXS6ZcAuisuXUsJ9w+aps8PL71U34xrSgqEq1pGm9U1UadTwoeUxtxuAVBMFEVSE+z4fZpqAr4AjrpaTYsEhgREoRR3GXkuI4syOK+q4aZzvfkwjx+cm0hfk3ncKObn770Mf/xvUvbI5F/MZfcK8qKGZCVhl/Tefadz82ccIDczPja3YIQimGPkakhsdI+nHYr+dnpprNss6ig69Q2e8OkAx+7YQT9e6fxzYe3mS+TWU4bpcUFaJqGqirkuOxmzraiKEwuzAtzuqXgMfURh1sQBBO7RcXn1/nhc++elAWcNSr4IBGEBGAUd315vI38bGdYIZqhAjHr1ycd6/+68TJ0XafF42fJlEJTs3j+uko2L5xAH6eduyddGObwlJcVk93ufAvCqYhVbHhBTnrcAkTDWf68voV/HGulzaex5KWPwtJB7tnwIb+7vYT53xzEd4sG8qPnPwh7UcxxOaKkAcvLigF4o6pWCh67CeJwC4Jg4vFrUbJrdzz3AevnlXTxyITuilHc1a+Xg4qyYty+k0v6oc43BO3xn9f/laVThzP3mffM4rVHXq9mV00jbl8Arz8QpZW8YF0lG+aPo3+vNMljFU5JvGJDIG4B4tEWD1/Ut7LkpY94dMaImOknrV4/M0sGmS+Pxvb57bYZmSNu2OzPrtPN9JT6Fq8UPqYw4nB3M6Q7pXAudJYsoCCEoqoKfVwOspx2Dh93m8v38aTU0u0W8/vFm3azZEohS7dUsa+2OW6+7aFGN8fdPtHhFk6LUWwYSaxtmqbT6gmYdhdPjURVlCiZS2ifX08hDXiizR+jPbvYcCoi68SCIJgYsoChJEIWUOi5+P0ahxrdfFHfwqFGN37/yaZKqqrgtFtY2a5EYjgvoYTK/UHQMclx2Vk+vYjHt+6N+xlDfcJQetA0nbomDwcbWqlr8qDJS6VwBkTa79FmD/uPtph53rHUSpaVFtHm16hv8caeX9ulASO3K4piOtsjC7JYMqWQFo+fL0+0ib2mIOJwC4Jgkm6PLQuYbk/tqUKcq+TA79f4/FgL1V828eXxNqq/bOLzYy1hTneW007fDDuPzBhB4YDMKBlAQ+7PID/byYDeaTz8WjCtJJ7DU75tn6n0YGgtT1uxnQnL3mLaiu1UH2nqMLsQe+ue+P0ae440cUPFO3x7+TZuqHiHFq+fx7fuJdtlY/n0IuqaPTzyejVLpw7nzXu/zbO3juXt6iO47JaY0oAVs4vJy3BESQOuunk0lvaGTkYh8dItVUwvf4cbKt45pb2K/SUnklIiCIKJ26ux5a8HWD1nDBZVIaDpbHz/H9wyfjDZKSqpFquRhSzLdg3HWr3UNXnMojLDge6VZiOvXXdSVRVyM9I41upj6ZYq5k4YzNrbxqIqCoePt5GVbuXuSUNJt1to9QYo6OPEabdQ1+wBgq3hH3m9mmfmjqWx1Ut9i9fM8TaUHmJpLd/+7PtsXjjhnLtRir11X2qbPVH1AZ8fbaWu2cPzO//BXZO+ztrbxhLQdI42eznR5uP5nf9gwcQhPPTqJ9wyfjBrduxnyZRCclx2cjMdZKZZqG320Cfdxob549B13cwRNyLisWoZ4tmr2F/yIg63IAgmfk2n4n8+p+J/Pg/bPqtkUJeMpyNIpHMlfDW8AY1FG8Mdh0Ubd/NCRFGu1aoyLC+TB68fji+gYVEV/AGdZa/u4YFrLgpz2CtmF9PLYQtTkahr9qAq4HJY+ef1f41Sljh83B0zZ7YjdI7F3rovsdqwP751L+VlxdQ1ebhp1cmCyJEFWdw9aSjzJw7Boiq8UVVLXZPXlASsb/GSlW7jk8Nu05k+6Rw7TfWTVTePpsXjP2N7FftLXsThFgTBxKIqPDjlIq4sHICm66iKwptVh7GkcGQkViMLaSLRNQTiFOVqmo6m6aYOcbpDpb7ZR80xtxnJ/nqei7snDeXe338YrvKwtpIXF45naG4GG+aPwxfQsFlU8jKCnfVClSWy2x2dgK6zes4YHt+6l101jUDH6RyLvXVfYrVhz82008dlp4/Lxuo5Y2jza7jsFgKaxtxngo7v6jljyM92BuUr11YCMLkwj3+bcgkOqxombxnqHBuKKV+eaDPPa+h457jsKIqCpulhkWuxv+RFHG5BEEzS7SrFg/syc9XOkzrcKZ7DHauRhTSR6BqcttjXIs1mMZfBczMcPH7TZVGpJ4/dMIKLB2SwZEohWU4bjW6f6aQEND1Kx9hYRjeierGW2pdPL+Lh16qpa/Z0mM6x2Fv3JS/DQXlZsZlWMrkwj7smXciDL3/ELeMHh0Wql08vIjfDwYEGN49v3cvy6UXm6s7kwjzuvHJo2DwbKm8Z6hyrqkL/Xmmsunk0/++Dmigd78h0EbG/5CV1n6KCIHQ4rV6NOyJyFO9YV0mrVzvNJ5MXY1k2siBJmkh0Pn0zHKyaHXEtZo/GajmpxrBg4hD8mh6VenLPhg/x+HWWbqnixqd3snRLFfddNYzJhXlomh5zGd1QJIHYS+2LNu7myZkj2bxwQofluIq9dV+sVpX87DSemTuWN+/9Nv825RLuWFdJaXFBVI71oo27WTBxCBCsK3j4tWrW3nrycwsj+h0s3hTcP9Q5NoofDx93M6C3g5klg0xn2/hcpJ2L/SUvEuEWBMGkO+pwx2tkIQVEnY+qKgzrH30tQnOqs5y2uKknoTrGhpPy7K1jCeix9w+NFMZbaofY+srn9H8Ue+u2tHgCfOeXfwZg/bySU+rFZ4V0N61r9vBpbTN2i0oflz3m/oaznOOyR63IrJ4zhsw062ntXOwveRGHWxAEE6uqMLkwj9LiAnPZflNlTcrrcMdrZCEkB6HL4I1uHzrEXBZv84XnoR5ocKMokGa1sHrOGNLtFjPVpK7Zg82qxjxH6DETsdQu9pb6hNYUhHabVBQlzFZD9eIjbavVGzC/X1ZaxJod+3ngmouxqErM/c/LcprdUOuaPGEa3INy0s39TpfLLfaXnIjDLUh3SsEk06ly16QLzbQSI4c705na2WexHp4S8el84kmWDc3NYNXNo3nsT9X0SrOS47Lzqx9cxo9fOKkwsnx6Eel2CyMLsgBMZ8OqKtQ1R0sNptstNLf56evSwxQfIs8tS+1CJPHs1GFVeejVT1hWWsTiTbsp37aP5dOL2LbnCCtmjTLTRPKznaycNQpVUdi2aCK6rpNmVfn59cNp8fpZv/MLnpo5KioX23C24eSKjKHBPfu375Kb4WD59CJWb98flTN+NtJ/Mi92Loqup+5ScTxGjx6tv//++wk59tk4p92RHu5wJ2RGSqTdnikHG1q58emdUZGX9fNKGJid3oUjO3tElxZIEputa/IwbcX2KPvavHAC2U4b1bVNzF9bSW6Gg1/ddBn7aluiotaPzBhBQNNNZ2P1nDGmsx16zIe+fykPvPi3MDk0cTBSji6x23h2unTqcOY+815YdLkg20mLN8BDr34StTJ4/9UX09DixW5VsFpUak+cfDEMPUZoZDtyDEumFLJ0S1WY3ODyGSOYs/rdmPfRmUa2ZV5MGHH/eEkV4VYUJQ14G3AQHNtGXdd/pijKYOAFoA/wATBb13Vv/CMJgnA2dMccbtGlTR5OJVnW4Ib5a4MrKwca3Bx3+5n7zHtRxxjQO41Zvz6pd5xut8Q8ps2ixsxvlWsunI54dppuD6Yfhcr7/XnRROqaPLxRVcsbVbVhn3ngmov5zz9+wr9+92KOtXi4ICfdPG7oMbYvvgIIOtmhEpaxNLh31TRS3+yJex+dKfHmxfXzSuRlNEEk2zqxB7hS1/URwGXA1YqilADLgMd0XR8KNAC3deEYBaHbYm3PLQwlP9uZ0jncokubPBh51KEYedRef4DcDAcVs4v50798iz7ptpj7qooSdj2N/NnI/YztiqJIi2vhKxHPTo2c7JEFWVTMLmbjgnFYVcWsOYjcX1UUhuZlkGZTWfLSR+ytbY65n8th4UBDK1/Ut/DRoRP8ZPNu9tY1MzQ3g/OynFGfafUG4t5HZ0q8efFAg5tpK7afsnW8cHYklcOtB2lu/9HW/qUDVwIb27evAb7XBcMThG5PhkNlZVlxmKTUyrJiMhxJNVV8JU7l5Amdy6kky5x2C/dfPYxNlTXUnvDw8z98zLLSonBbnDUKixru3Bh5tKH7LSstYlNlDeVlxTz48kdMWPaWOBHCGRPPTi/ISWdyYR4PXHMRS7dUMb38HW58eid9M+xR8+ay0iL+849V/OjKr3O02cujM0bgslt4cubIsP2evXUsBxvbmPnrvzC9/B2WbqnilvGDeexP1TS4faYGd+hnLshJpzzifOVlxWSHqKKcjnjzYqPbF1NuUDh3EpbDrSjK92NsPg78Tdf12hi/Mz5nASqBrwNPAcuBnbquf7399wXAq7quD4/43DxgHsD5559f/MUXX3TI/yMSyeEOIjncHXSgTrLbM+VAQyv//fFhriwcgK7rKO2dJr9zyQDyJYc7lUkam42XR13b1Mb3V+wIy1kNzXPt7bSRZlPRgRNuv9l8JD/byeo5ozlywoPLYSXDYaXNF+DQ8WB3vmsf/1/z3F81z1XocrrMbuOplBw50caMinei8qc3LhjHhweOhzVlAlj6veFhtvrojBFous55WU6c7Skq31+xI+p4S6YUMvy8XgzMTo8ai0WFBzbtjsoZ/8W0onPK4Q5tvgPBVJdUrd3pQrokh/s2YBzwVvvPE4GdwIWKovy7rutrY31I1/UAcJmiKFnAZuDiWLvF+NzTwNMQLIg459ELQieQbHYb0HRe+vBLBmS7zIn8pQ+/5IqL+3f10M4a0aXtWM7GZkMdBqfdgh49hePza1GaxqF5rhsXjONAg5/8bCePb/2UDfNK8Gk6qqJgtSgsf72a0uICvH7NdEBKiwvCziGpRD2Xr2q38fL9fZoWJ0VNCytuBFg9Z4zpbBv73fv7D1k6dTg2i0pflyNMgz70eMaqT2he94DeTlQ1mCIVK2f8Z9eduW2HzotuX4B9tc1hzrasAnY8iXS4NeBiXdePACiK0g9YCVxOsDAypsNtoOt6o6Io24ASIEtRFKuu634gHziUwHELQo/FaVW5/+phZpc/Q2LNaU3dlBKhawmNpOVmOKLsy1htMJa442ka93HZ+cUrn/DT6wqpa/JS3+LljnYZtvnfHBRTznLLXw+EjeVsnQhRNxEMLEpsDe1Dx9tMuUDDBs8PKZI0ONDgZlDfdJravLT5AmSkWWMeb2BWGkdOeMIi0BVlxQzISsNmVTtEU954qW7EjzwAACAASURBVNA0nRaPn7pmj3kskczseBL5FB1kONvt1AIX6rp+DPDF+oCiKLntkW0URXEC3wE+IRgln96+2y3ASwkbtSD0YAI6US21F23cTaDLY+9nj+HwTVuxXXJ5u4BQNYQFE4dE2ZeRK2rkzW6qrInK3X5q5ihTFrC2ycPdk4aazjbAqEE5prNtHPeOdZXMLBl0zi2uxX6EUJx2S1TNwPLpRSx7dQ+PvF7NkimFbFwwjudvL+FwoztmnnR9s5fb1lTyRX0rVlWJytGumF2MRVWjVETmr6vkw5rjnHD7qJhdfM62bRAa7d6++Ao2L5zQ01LuOoVERrj/R1GULcDv23+eDrytKIoLaIzzmQHAmvY8bhXYoOv6FkVRqoAXFEX5D2AX8JsEjlsQeiy+QOzlUp+mddGIzh2RBexaQtUQ4rXA9voD5kP/F9OK0DSNDfPH4Q1o7K9r4cGXP6au2WN26/s/37047DjxjqsDS6cOJ91uodUbwHEWKzViP0IoWU47uZkO067yejn4l/UfmqkYRgrUm/d+m0ff+DQq6r2stIhfvPKJKTPo9gZiprzFSzVJt1uYs/o9lk8vYunU4Qzu6yLdYaGvy3FODrJIZiaeRDrcPwK+D3yDYBL5GmCTHqzSvCLWB3Rd3w2MjLH9M2Bs4oYqCAKAJU5rd4uSupEOkQXsWiLbtp9qKTzyoV97og2An15XaBZD3v7NIdgtwSX13AwH906+kH690lg9ZwyPb90bloOKfrIlfKPbx0OvfsJ/TLuUvMy0qHHGSxsR+xFCUVWFPi47NceCNmGzqGYqhkF+tpOAprOrppFHXq/mkRkj6N87DV3T+bLdpg2ZQR042uIJc5g1TQ9rIW8wuTCP87KcrJk7ljSbyrEWL/uPtjDy/N7nlPIkKVOdQ8Icbl3XdUVRtgN+gvnc7+ndsa2lIHQjumNr91CHz0AKgjqP0JbqhoRfZA53vKXwPunBaKJReDa5MI+7Jl3Iz//wMU/OHInPr3HPhg/D6g0efq2aumYPy6cXcc/6v5qR8U2VNdwyfjA+v4am6WEOxamUbMR+hEiynHb6907j9mff58bifFaWFUfNmRvf/wcAuZl2XHYLZe3Nmgw7NZro3PX8LuqaPaa9AVQfaeKxP1WHRccnF+Zx55VDuWnVzjDFk5376uib6Qg7/1dRYRIVp84jkbKAPwR+CrxJMML9beDfdV3/bUJOGIK0dk88IgvY8Uhr98QgDxSgi202UqXEr+n4/NoZRdP8fo3aZg/+gIZFVUz7XHfbWB548W9Rtvq720v4e21zVLTbkBv83e0lWFQlrJX2qVrO57jsYj9dR9LOtaE2nZFmobktgF/TsaoKuS47xz0B3F4/3oAesw37C/NKuOv5XWE2unnhBADTFkcWZPGTay8mN9OBL85xVs8Zw9xn3jvrNu+nsn1JMTkrukQWcBEwUtf1egBFUXKAHUDCHW5BEM6O7tjaXWQBu55zyQ+1WlX690qjvsVLq9fPkimFlG/bZ7ZuD+VAgxtfQItqCR8qN9jo9nG0yYPXr5m5r6dKGxH7EWIRadO9w2sjybVb+aLeH7cNu67rLJg4JEy320hTMpzteydfSLbLTkDT4x7HoipxbfdMkJSpziORDvcBoCnk5yagJoHnEwThHDFau0dGO1K5tbuQ/JwqhzRegw5fQItpq4oCL8wroW+GHbtVxWZRafMFsKgq8785CJfdwh0vfRSe0pJhl7QR4Yw505xnu0U127BH5mKfcPtN3W5TftVuIaAFf3/L+MFmOsnqOWMAYs/NFpVX7v4GdotKs8dPbZOHTZU12K2W045T03QCmi6230kkMqXkWeBSTkr4XQ+8C3wKoOv6LxNyYiSlpDOQlJKOJxlSSprb2thf74nKRxyc4yAjLbrQLBXojiklZ1HklLQ2e7rrE2/Je/n0IjLSbGG2+tgNI8h22Zmz+r2Yed0ry4qp3H+UB7fsCTvWy3dOoLHVxxf1raaiyQU56QzKcQF0O/tJIZLObs90PtE0nc+PttDk8dHqDYTVLTx761hu/m10esiLC8fT1+XgQEMrM9tzvgFGFmTxUOlwPH6dhe1ymKFa898a1i9MCaW8rJhheRn8/WjLKcdZ1+ThJ5t3hzn3hizhxf17nZV9SwFm16SU7Gv/Mjz6l9q/z0zgOQVBOAeOuzWe2PopS6YUmkudT2z9lJ9ddwkZqelvdztZt+72AnG66xNvyXtglhOHVeWRGSMY0DsNVVHQ0Zm56i9hx1q0cTdLphQyf20ld6yrbI8W7gk7ls+v4fFrLImIfJ/J+ISexZnaQ32Ll5tXv2s2e/rd7SUcOdFGfYuX425f7HQov4aqKlFpIrtqGmlqC/D02/vMubmPy87y1/dQWlxgOsvGcRasq2T9vJLTjtPrD/BGVS11Td6wOb/vWTrJ3W1u6mgSKT3wR+AyYBowg6AO93Rd13+u6/rPE3heQRDOEr+m80ZVLfPXVnLj0zuZv7aSN6pqUzqHu7vlKMZ74Ne3eLt4ZGfH6a6PoRISivFzjstBb6cNX0DnplU7OdzYFvNYWU6b+b0l4sGfn+0koBP3b9rd7Ec4N87UHoz9dtU0ctOqv3Co0c308neYv7aS2iZPTJs20jhi2XxWuj1sbj7W4uWNqtq4GvQef7wW9CfHaZxnV02jedylW6pQ1bNzDbvb3NTRJNLhXkewQPL7wJT2r+sSeD5BEM4RI4c7lFTP4Y7nsKVqjmIqOYB+v8ahRjdf1LdwqNGN3x/dQCne9XHaLdQ1eQhoGhVl4V31npo5iiaPnyNNbeRl2LFblDCd78hjNbp95veO9rbYxs+rbh6NrscuFvb6A6YecuQxU9V+hHMjlr1OLsxrL+Jt5Yv6Fg43ukmzq2H7hdrm1qojPDVzVLgdzh6NRQ0qRem6HtZJcnJhHk6bysYF46iYXczIgizzePFs3sjNjtweareGZGdHdaxMpbmpK0hkSkmdrut/SODxBUHoYDKdakxN2VTW4Q7VgT4T7edkJ1V0of1+jT1HmkwNbSO39KJ+mVhDOj7Guj7P3jqWIyc85rZX7v4GS6YUkpcZjGg/9OonvFFVa+Zo29ob4ZRv28ejM0Zw7++jtbmN792+AJsXjsftC5BmC6qU1Ld4Y/5NA5rOf7zycVS3wFS2H+HcyHbaKC8rDtOGv//qi9hX1xyWp11RVsyzc8dyc7uU36bKGlbOGsUTb+5l6siBPPXWXpZMKSTHFdSad9pVrn9yu/n5J2eOZN1tl9PU5kMHUw7TKBp+u/qIebxY3SxXvf3Zae22oxV4UmVu6ioSWTQ5CbgJ2AqYbZh0XX8xIScMQYomE48UTXY8yVA0ebChlWd37Gf66POxqAoBTWfj+//g5vGDO1yHuzOLawwtZ19Aw2ZRyctwhDl9qcRZ5kl2us0eanRzQ8U7UQ/fDfPHcV5WeOQt0hZ0dL6/Yof52YrZxSzdUmVqaedmOExJtVZvgD4uG20+jXt//yG5GQ4WX3MRA3qnYVMVDja2oem6Kb1W1+wxtYsjm42E/k0rZhfzq//+lDeqarmhOJ/bv/U1LKqCw6rSLzMtZe0nxUi6udYoNDS68fZx2TnQ4Dbz/w3ys538fv44LKpCmy/AvroWstKt9M1IM5vXhO773A8v559f+GuYLvfqOWPiHvuZuWPplWahxRvA4wtgtViwWxT21bXw6t8OM6mwH+f1TsNpt+KwqqTZOmaO/aqKQj0wh7tLiibnAhcBNoKdJiFYNJlwh1sQhLPDr+lU/M/nVPzP52HbZ5YM6tDzdObErGk6/2hoDVOgaMsJMCjHlZIPgVTRhfYFYueQ+gPRaSWRmsYHG1rDPmsswfsCGrkZDu67alhY5G7lrFFsqjzAkimFDOuXiaLAL16p4oFrLmZGxTtR5zOK0kKLyEL/pk67hTZvgNu+8TVuHjeI3k6b2VykhzoRQjtGoeEbVbUArJ9XQrrdElcTvl+vdA40tjL3mff4w50TCMTpdVDX5GHBxCHMX1tpbgtoGoP6psfcv77Zg6o4qGvymC+TuZl27r/6IqaNGsiijbvJzXBw96ShDO7rQufcg6unm7dTZW7qKhLpcI/Qdf3SBB5f6ELOJtLfw6PiKUFn6XB3pvJDo9vLkRNtYQoUy6cXkZVuo48rNVUmzqWRTGdhpHnE0g0+HaFL0yMLsswl+EVXXcTdk4ZGqTLc8dwHIZ0kL8fj17hp7AVxxxBoLwIOzS81/qaxnIrl04vIzXBwoMFt2uqLC8eTl5mi0j3CWROZNtHo9mGPY2c2i0r1kSa+PN7G5MI8dIJBjVj71rd4zeJeY5vdauF4qy/m/q3eAF/UBx15I43kkderSbdZTGc78sX0XF8Uz2TeToW5qatI5JrYTkVRChN4fEEQOhiHVWXFrPBinhWzRuHo4OXzziyucYdo4BrnWbRxN26vFPIkkrwMB+URxY7lZcXkZUQ/jDVNp67Jw8GGVuqaPGQ7bWYx14KJQ1i8aTdvVNVy/8bdnJ8TO+KX47KzrLSIg41tzFn9HnOfeY8ntu6Nac+r3v7M/DkyvzSWU7Fo424WTBwSdr42X3SkXuj+RBYabqqsoaBP8KUs0tbtNoXbn32fV/92mCVTLsHr12hq80bdF8tKi9hUWUNr+5xkFAY/9OonuH0BHrthRNj+y6cX0cdl4/Gte4GgPS7etJu7Jw3F314AbNw3HakYIkWR50YiI9zfAG5RFGU/wRxuBdB1XS9K4DkFQTgH3L4Af95Ty/O3l6DrOoqi8NIHB/jeqIEdep7OLK4JxFGgCKSu0mFKYLWqXNQvkw3zx+EPaFjj5M7HW6YempvB5oUTaPX6zeu3q6aRw43umLbT22nj/o27+el1hebv9tY2o+s6v7u9BE3XsagKa3fsZ0PlAdMpiix+jOdUGNHHkQVZ3D1pKLoefEmQJfOeRay0iWynjWyXjRfmlaBpOjaLSm6GgyNNbeRmOJg6cqCZtx3MzR7Ncz+8nLomD/UtXtbs2M+8bw3BF9DYvHA8GQ4rLoeFN6pque0bX+M//7iHh75/Kf17p2FRFGwWhbt+dzLfG4I2Oqivi7T2uTUv0xHTjt2+AJqmn5XNSlHkuZFIh/vqBB5bEIQEYLeoXHlxHvtqm8185ysvzsN+BmkAX4XOVA5Ji/OQSEvhordU6eZmtapRBZKRnG6Zuq4pvKX1o298yvLpRWGKEE/NHEWbL8CCiUPo7bSRn+0kN8PBA9dcxFNv/Z3S4gJyXHZyMuzMGHM+s8cPJqDp2FSFQEBDVU86DPGcilZvgJEFWdx/9bCwc0s+d88jVtpEjjUNXOH7Oe0Wls8YwZzV74bZ99xn3mf59CLafBpfz83gp9ddwubKA1w4oBeqonCg1c2lA3uxes4Y+vdOo67ZQ9lv3jWPu3rOGOqaPWHnys92cuREGxf1z+B3t5eY2yLteF9tMy0e/xnbbOhcY7OqYV0yRbHnq5Ewh1vX9S8SdWxB6Il0hpOlKHCizR+V79zROXmdWVxjsRDloC2fXoQlRYMy3U0J4HTL1JEvZ3XNHtLtFh76/qX0cdlxOaz84pUq3qiqZXJhHj+77hLW3DoWVYH/+8dPotpWl5cV8/Bre6hr8nL3pKF4Ahouu5W+GQ5UVYmSfTM+4/YG+OUNI5j923fjvhwIgoGm6Rw54aHF449p37b2IEaTx8eG9w5x7YiBPPnmXvPlsMUb4IujTWS7bFHzVx+XjZWzRnFHSJv3ZaVFvPdZPZkOK/PXVZKb4Yj5Yvrgyx9T1+w5oxqEeHPNy3dOwO1N7pf9ZCSREW5BEDqIznKyvAE9Zr7zC/NKOuwcBp1VXNPiCfDwa9VhrYsffq2aX900khzX6T+fbHS3VuOnW6YOfTlze/3sq2vhwZer2FXTaMoFGsWVt4wfbOoV//e/fDtu2+uHvn8piqLELChrcPv4w18PsHrOGFMac/veWiYPPy+u8orksAqRGPfpkimFMe27f+80ao618vOXq1gwcQhPvrk35svh41s/DWu9rgPZrmCzp2fmBl8sA5rOa387zLUjzjOjzwca3Dz8WjVLpw6noI+TfXUtqApmGsqZ1CCcaq7paJnYnkDqrqkKQg+is1rmxpOsCqRwa3erolDX7AlrV1/X7MGaokGZ7la4dCbd7oyXMx2Y+8x7ptMQ2tY6skjMogSPHetv1b93WsyCsi9PtOELBPhu0UDmPvMeVz76Z+Y+8x5D+/fGokC63SpdJ4UzwrhPy7ftY1lpeEHlstIiGlq83LTqL+yqaSTLaYv7clhaXBDWev0HT+/keKuPL+rdzFn9rmmjV1zcj6a28Gj6rppG5j7zHvXNXpZuqeLQ8TZzDJYzmP+621zT1YjDLQgpQGdNfLY4rd1tKbxkaLWoUQoCy6cXnZE8XTLSHVrVh6qS1Ld4zQLJ7YuvYPPCCXFXbgyZPwgWL/Zx2c2fQ51vgC9PtIX93iDobCgx76dDjW7+dvAEP3r+gzDHZ/Gm3QT0+C8HRkvuuiYPWgq/nAodh3Gf7qpp5JHXgytsGxeMY/WcMazZsR+n3cr6eSVUzC7GF9DivhxG5kfnZzvJSLOa3VSN/RasqyTdHntuaPUGWD69iPJt+8z5z2k//XzRkXNNpBJRT7xPUvOJIwg9jM5yshxWlZURMmorEyAL2Jl4A5qZUrJ+XglLphTy8GvV+GI0YEkFjBzjSAmy7BAN32TGSI+atmI7E5a9xbQV29lb10yOy87A7HRyMx1x06QMqcHJhXncd9Uwlr++x4weNrp9YfdI8BoHomQBV84axdFmb8z7ydBCjuX46Loelt6yffEVvLhwPA5rsCW38X+pPtLUI50JIZzQl7NdNY0s3VKFx6+x/PU93HXlUB5+7RNufHonS7dUkZlmJTfTEdMmc0JeGvOznVSUjQKIHYAJaFHR9PKyYi7sl4FVVXngmotYOnU4/XqlkeU8faHjmaw+nQmx7vmeeJ8krLV7VyKt3ZOTbtT4ptPbDXdWDvehxlZOtPmwqhZUBTQd/FqA3mk2BmSlZs7eoUY3D778kdmKudHtY1NlDQ9eP/y0ChrJSGRraeP/84tppyxuTZoW2XVNHqat2B6V03qmOeh+v8aRpjYzV3tkQRYLJg4hP9uJrhNW7Lhy1iiy0m2AgtYu47fhvRqmjRpIhsMaVXT2yOvVLJg4hE2VNWf09z3X/4twWpLGbs+G0EJ3ONkkZ/nre8xOlRC0mZd+NJ4vj3uYH2K/T80cRb9edj45HFSNanT7cNkt+AJ6zHbvz946lode/cQsvOzjslO+bR97a5tZMHEIOS475/dJBwV8fu2Mih79fo3aZg++gIYtjrTn6ehh90mXtHYXBKGD6CxVD02HzZUHmD76fFAUdF1nc+UBZo8f3KHn6UzSbAp3TbqQO0IdsbJi0mypmSYT2Vra4GfXpUZe5bmmRxkP+1BtbqMd9h/unMDaW8fS7PGT7rCiKvD32hYe37qXumYPy0qL2FvbzE2r/sLIgix+d3sJh9p1vX/+h4/ZVdPI1qoj3HnlUBaGOOPxVhAkx1U4FaGF4QcbWrn28f9l/bySqHvXaKR08YBeZnHwJ1828eDLH/PANRcx95n3zH3Xzyvh0Tf28NTMUWbqk/HCuP7dL1g6dTit3gCqqlBzrJW9tc3mPTKyIIv/+N7wMKf+VIEbTdPZW9d8zoEeuU+CiMMtCClCZ6h6pFlVpowIFoyFRglTWbPa7dV4YuunYSolT2z9lJ9dd0mUbm4qkOrNJ852/KHRQlVRYh7j0PE2eqUFH2uxoteLN+3miZsuI8Nhw2pRUBXISrdhs6jc851hVB1uYlJhP9PZNqLnbb4Atc0e+vdKC3M0Uv1aCJ2HYStG6lOozUwuzENVFGoaWrGoCg6raqrvRO7f6PZR1+zhuZ1f8OytYznW4qW+xcvb1UcoGzeYVl+Az+tbzZfMp2aOQlXg0PE2ejttprMNJ4uFX1w4HgUlLJgDwTqIjlBEkvskSOo+RQVB6HAC2klHBYIT7B3PfUCKpjsDoCpwy/jBLN1SZeZM3jJ+MKlaB9pReZVdxdmMPzQH9M7nd1Hb1BazlfamyhoCWrS05eJNwdbsuRkObBZLUNXhkT9z49M7Odrs5Tf/sw+bVWHp1OEMzcswne37rhrG0i1VTC9/hxsq3uGTwyc41nKy4CvVr4XQeRi2sqmyJizPenJhHndPupAZFe/w7eXb+MHTO/nHsVZWzx0TtOtt+8JsfVNlDStmjWLHZ/Xcu+FDmtr8jCzozfWX5XPTqp1c8cifWfLSR9x31TByMxz86PkPOHS8jaVbqujXK3b3yVZPICy/+vP6FqqPNHGo0d0hkWm5T4JIhFsQUoTOaHzjibP050nhpT9NJ0pua/Gm3axPgLZ4Z9CZTYMSwdmMP1QWc8mUQn70/C5yMxzmqkWrN0B+dhoPXj+cNl/81ux3Txpq5ngb2xdv2s3qOWOYszq4qlMxO1iQGikzeKDBzfx1lTwzdyyNrT4G5bhS/loInYdhK7+YVoSmaWyYPw5d11EUhRsq3gmzs3s2fMgjM0awes4YjrV40XSdFbNG0cdlD3ZItai89KPxtPk0VBXcPi0qcr14026WTClk/tpKsxD486OtMSPN+4+2hN1Pmg6P/ama0uKCDolMy30SRBxuQUgBOqto0qLGXqq3pPDEGFdbPIULxjuraVCi+KrjD80BNZyHAw1uM3cbYPviKwDYW9sc04Z14Ot5GTw6YwSNbh/l2/axq6aRAw1uLOpJmcBPD59gZVkxnjiOe2OrF19AIyvdRh+XI+WvhdB5xLKVgw2tMe3MZbdgbZ93A5qOpuv8oL1Q2IiKL1hXyZIphXElBbOcNjONBeDxrXupKCsOy+GuKCtmzY7Pue+qYWFNd5aVFvHSroMsKy2KahB1NpFpuU8kpUQQUoLOanyjKsRs0pDC/rb5EhGKocUspAZ2q4XJhXlUzC4mJ8PO6jljGFmQZf7eiLql2VX6pNujpC0ryorp47Jx06qdZlrRfVcNY2RBFvnZTgKabu5/9aUDeGLrp/Rud1ZCMaQDF23cjdubuqs+QtcRqUdts6pRdjb/m4NQFIXZv32XG5/eyQMv/g23N0BuRtBhLS0uMFdqspw26ltiy1y2egMsKw3qbwPkZtrJTrfx3A8vZ/viK3j5zgkMyErjmksHxFwFnFTYL0xDfMP8cR0e5OlJSIRbEFKAzqry1nRYs2N/WIHhmh37gwWGKYpFVXh0xgizUUR+tpNHZ4xI6ah9TyPbaTMjesY1XD69iIdfq6au2cOqm0eTlWbl09pm7tnwV3IzHCydOpwLctJRFQWHTWFG+c4oh+KZuWNRFGhy+/j9ghJaPBo2i8LcCYNp9vhYOWtUzOLL4ApJF/9RhJQj1krls7eOZdXsYm5fW2lGr2eWDGLWr/8SZq+LNkaniACmbGVkJLq8LPhy+rOXPmJXTSOTC/O488qh3NAeJc/PdlIxu5i+Ljtfy3VFPV9yMxwM65fJIzNGYLeq2C0KGjpHWzz0dcXXyhfiIw63IKQAnVXlrbQXGEYuLSZibu2MnHQIiqKm2VSWTh1Out1CqzdAmk1NjMCvkBAa3L6o3OtFG4N5+IbtfHmizVwqP9DgZu4z75Gf7eSh719KZlrsZjaNrV5+8conLL7mItp8GjXHWnn1b4eZNmogd/0u3HE/1Ojmkder2VXTSH62kzSbLBALX41YK5U3//ZdXrxjvDk/9XHZqWvyxE0RAcKUS8q37eO+q4aZgZIcl53cTAf9Mxy0+APcNPYC7r/6IkBhzup3w2sS1gZTUuztHVyN340syOL+q4dR9pu/hL3gOu0WVrz1d+75p2ES6T4LksbhVhSlAHgW6A9owNO6rv9KUZQ+wHpgEPA5cIOu6w1dNU5B6AqMKu/IHO6OrvLWOynC3Vk56QB+Teept/5OaXEB6VjwBjSeeuvvKR2174hmFKlEvBUewMwL9QW0MIdhwcQh5GU6yMt0oCiwes4YHt+6l101jUDwhdUX0LjvqmHcF7L68eytY7n5t+9GOe5Lpw43ne1VN4+mjzPoGJ3pC2NnvWAKyUuoHRs2muW04fYFTK3t9fNKYkoHGikicFKpZOFzH7CrppG3q4/wk2sLqWvyUN/i5em39/Hj71xIbobdrMEJaHpYYaRRx5DltPHQq3t4auZIjrX4SLdbyMlwRDnnizbuZunU4ZQWF0RJCdqsKlZVwe0V2z4VSeNwA37gXl3XP1AUJROoVBTlT8AcYKuu6w8pivIA8ACwuAvHKQidTmdVeafb1ail+/KyYtLtHevMxctJT0TnMbUTo/adgd+vsedIU9Q1uqhfZrd1us9khcduUZlcmMfcCYNjdpH83btfcP/Vw8w0lGWlRQQ0nQde/FuYHR5r8cZ07ofkuti++ArsVgvZTttXagjSmS+YQvJi2HFuhiOsSHH1nDGmfcdLEXnshhH4NZ2NC8bRx2XnlQ8PsXTqcL6el4GiYBZUGlQdbuLZW8ey/PU9lBYXcPGATH4+9ZKwhk7LpxehtRePt/k0s3vlxgXjYt4D6XYL6VhMKcHICHhoipfYdjRJMzvrun5Y1/UP2r9vAj4BBgJTgTXtu60Bvtc1IxSErsWo8h6YnU5uZmJy6Fq9Go+3N4lZP6+EJVMKeXzrp7R6O1aIuzM7j8WTBdRSNAe3rtkTlV6xYF0ldc2eLh5Z4jgTHd80u8KdVw6lzadFackv3rSb0uICFm3czS9vCMqtrdmxH5tFjbLDeAVoNotq3nsNbt9XKmLurKJnIbkx7PjuSUPD5qTHt+41tbbLt+1j7oTB5krj1n/5NkunDuc//7iHm1b9henl73Dzb99leH4W/Xun4bCpfHm8LeZ86vYFzB4EnxxuMp1t4/eLNu4moOksmDjErHGB+PdAqzdgRt/3H22JOtaCiUPEtk9BMkW4TRRFGQSMBP4C9NN1xP9AhQAAIABJREFU/TAEnXJFUfLifGYeMA/g/PPP75yBCsI5kmx269f0mG3Df3JtYYeepzM7j/njyQKmqMftDWiM/1oOt3/ra+ZS8aq3P8PXSd2JusJmz2SFp9WjsfC5D3h0xoi4+a8HGtzUNnn4zf9+xl2TLuRokyfKDkOX60Ojd6Hvt/FSA7z+AJqmR70Mx3rBzM1w4PUHONjQKsvwnUAyzLWGHbscljB72FXTyMOvVbP21rHUNnnQdJ2bxw3i/D7poBDW2h3aV1zyMsjPcnL4uNt0kKPmU4tqOvahhZahx3E5rPTvHf7Z8m37oiLsK2eNIivdxrp3PqdidjEn3D7+cOcE0mwWmj1+aps8nNc7zbwfWr1+6poQuw4h6RxuRVEygE3AP+u6fkI5Q+kuXdefBp4GGD16dGo+SYUex1ex287IAbWqCpML8ygtLjDz/DZV1ph6sB1FZ+WkQ/D/1J20xV12C2XjLmDuM++Zf7sVs0aRbu+cNsldNdeeTsc3oOvmknys693HZWdyYR4Ds5389LpLaPMFGJLnorysOCw9564rh6IqCutuu5yGVi+1TR4efq2aX/3gMtOZjpcaEC9VJPIF0yhKuzFEMUKW4RNLsvgIqqrgtFlN+zFe1lq9AVRV4aFX95i1ApsXTgCIac/GnGy3WmKmoKyYNYpW78kXvVPdF5/VtYT9bldNI2t27OeFeZej6woev8aXx9088eZefvydC8lMs/Kr//6UW8YPDkvdKi8r5t+nXhK2Tez6JIqeRM0fFEWxAVuA13Vd/2X7tmpgYnt0ewCwTdf1Yac6zujRo/X3338/IWMc9MArCTluT+Dzh67t6iF0FAmZOU5lt52VA9rW5mdvfQt3hDggK8uKGZrjIi2tY9/PO6uI7FhLGwcbPVH/p4FZDvq40jr8fInmYEOr6agZ5Gc7WT+vhIHZ6fE+1uk229nUnmjj+yt3xHSCl5UWsWbHfu68cih2i8LOfUcZPbgvC9ZVkpvhYPmMETS2eqlv8ZoNcfKznaYMW362k0dmjKC308awfpkAVB9p4svjbWbeq4HhKIW+HGiazidfnmB+u/Tb6jljzuhzQve0W03T+by+hSMn2li0MTxP22ZVw5RAgKi537Dne/5pGENzM9hb12x2hizIdpKRZsPt9WGzWMwC4JEFWVH3xYpZo3jyzb3UNXmjfvfUzJEoihK20mOc96axF+ANaCzdUhVlw0unDg+LyPdAu45rs0kT4VaCoezfAJ8YznY7LwO3AA+1//tSFwxPELqUzioyrHd7TcfUOM8d6yqDzlwHO9yd1XnM7dV4oj0v3YjaP7H106BKiSvhp+9w/HHUBvwpmiLTUVgtCo/dMCLYFvv16vYW7EEn2pDzqzrcxO9uL2HC0DyOu30smVJI+bZ91Dd7uPHpneaxjGXxoXkZ7QVtady/8W/UNXvMey5WagDErkVQVYW+Lrt5zXIyojsDSopJz0FVFTLSrNz82/Dakns2fMjSqcN58Prh9O+VZl7/Yf0y2TB/HIca3VH2vHnhBLNlvNsXwOvXONTo5r7ff8j4r+Xw1MxR/Oj5D8yo9fM/vBxFUbAogIKZPmg0uMly2ujXKxiIMIoijfEZ7eJDiydDOdDgJivdFrUt9H7oyWo9SeNwAxOA2cDfFEX5a/u2fyXoaG9QFOU24B/AjC4anyB0GZ1VZBgv3zmVnblAnLz0f+vgvPTOIs2qcv/Vw8IiY8unF5HWTRVKzhS3N8B//nGPmQdrVRWml78Tts+BBjearoel4ywrLcIX0Mwl9ViRwIqyYobmZbCrptG850JTA+LVIoQ6F4qisKmyhjeqaqmYXSwpJj0cn1+LOdem2y3oengdgKoq6Loe0569/oAZvDjY0Ep9swel/XcbKg+wt7bZdKTzs50M6O00j32wodW0w101jeZqzpIpheRlOmKOL8dlp6nNjzfknjHIz3bS22ljZEGWKb85uTAPRVE42NCK027hyAlPj1XrSZoZWtf1/9V1XdF1vUjX9cvav/6o63q9ruuTdF0f2v7vsa4eqyB0NqGtrdfPK6FidjGTC/M6vMjQyHcOJTRfMBWJ29o9Vf9PimI623BSIYAe1qo+skW2026hrtnDp7XN7YoLrTGv++EQRQcjamdRFVMlYsHEIVGqNvPXVXL7t75GfrYzeJ7281pUotRTKmYXo2kax1o8VB9pYtqK7UxY9hY3VLzD3ZMu5JW7v4HLbuGpmSPNz909aWjUNRWlh+5BpJ1q7cELI68/FEMJJNa8Hm9/m1XlWEvw+DrQ6g3Q6g2Y+xqO9L2//xCARrc3qOPf1IZFVXhq5khWzxnD+nklrJ4zhidnjqR82z5q2wuKI8+Xk2En22VjU2UNT80cFWb7y0qLeOjVT7h70lAg6GzfNelCbqh4hwnL3uLDmuM9Wq0nmSLcgiDEIVZr6/KyYrKdttN/+CuQ67Kzsqw4Kt85NwHFjJ2FzaKwes5oDjS0mZ0m87PTsFlS00H1+GKvdngSIKmYrMSrafjd7ZdzsLGNNbeOpa7JE9WavaKsmH/7fx+FHetAgxubRaV3uo0nbxpJtis63eNAgxuLqvDM3DEcOe7h9rXhrblfXDgen19D0+HL42183tZKToYjyrlYsC7Y2W/plioqZhfz0p3jafNqZsFn5DljrWD15CX5VONUtTexCseXTy+iX6+0mMXj8QrNPb4A+9pfunMzHDx4fXDlbvn0orBVsGWlRfz8Dx8zd8JgcjMdPPzaHrKcdmaPu8CsJzDyyCGo1hNaUDy5MM9Uq1IVhbkTBtPHZQtLbXvk9WoAvp6XwZ8XTURVFG5adbLeJN1+ZilY3RVxuAUhBYjV2nrBusoOz+E+7glQuf8oz99egq7rKIrCm1WH6d8rjVx7ak4XqqKENXUwJK6y01PTSVHjqK6oPSjCHaum4bE/VfPj71wY1jWyvKyY3y8Yh9evoek6aTZLlF55cJk9jZ//4WPeqKrlzXu/HVsVwqKaubGh5735t++yeeEEnHYL1V82mU5OvOYhQ3JdPDpjBLUnPORmBO9dixL7mkaqdEkDndTidLU3w/pl8uLC8bT5NCwKOO0WspyxX6BiSWNaVPiw5rg5tx1ocPPgy1X863cvZnBfFy/MK+HL421Red9Gx0ggSrP+ng0f8sK8Erx+Da8/wCMzRjAwK42GVh+zfn2y0Y0hvxlaOGmkRhlOduQ9EE8pxWZVo7q2Gn+/7vRimTQpJYIgxKezcri9/gAPbtnDtx5+i28v38a3Hn6LB7fsSekIhMcf3Qjljuc+wOPvHN3qjsYWkv4AmJExW4o/jL4Kse6H0uICUwUETr6U7j5wnBNuH4ca2/jZSx+xrLQoahlcCSkee+XDQ6wsKw7bZ2VZMS99cMDMjQ3FuA/d3kBYWki85iE1x9zc+PROlrz0EUdOtHHn87v4+R8+ZsWs6OX5yEUYaaCTWpxu3lZVhbzMNM7vk87A7HT6uE7d0Cyy+ZnbG4iKGu+qaWRGxTu0egMcbHAzvfwd5q+tNHOqjTzxLKctbp52XZOHKx/9M8fd/5+9u4+Po7rvxf85M7uzWq1kS5YlA5YBQ8DgcmUsKYSHlPLQELdww49YPAQb85DY2CQhl1ID99fLTVp+/b0Ah0tpiG3sNoCxSTB20rTkhtBCHXoxlEg2dlNjxzFgJAOWLEu2tFrt7syc+8fujPZhRk/e2Sd/3q+XXtLOnDl7dvc7Z45mz3xHx7FIHL/vDmfdNOf+l3dBCJG2r2ROjcrcB9ZuO5DVd61f0orBYd2eenXD6rew7/AAPuoNZy0zS/haIoBnuCmPJptSsYzSCU5avm4Uk88b0uRLuV0IakiJ2pAfz915ERSRuJOmbhr2LZpPBk5xWucyFaQm6EeFX7X/6eoZiOHh6+aiLqRhatCPVb/ai/9x3R/Y9Z176hTHrDYLW2bZc2NTn8e6KCxupF8E53TzEOv211bbVmzaYaceBIAXl14MPXkDo8Gojqhhomcgap/dy+cdWunEed2faj7VMSYba4MwJVzXDcUMVPgV1FUFsGX5JVnpMKsCiaHhUMxAXUiD4dKHTq/S8PJvPsbzd10EyESmoNH2gZ7BKKoCPnz/xnk4raYCQb8PiiJx/dPbs/6JfOT6C7KW/eO3LoNhomTPevMMN1EJGM+trUvpefLJ73LRZKmeEVaFwEBExx3Pvournvg17nj2XQxE9JNqSolTnDZUB1wvQhuM6vbB27qIrG3t2zgWieP2S2fjjT2f2mfqTqsJ4rU93bj7hQ7cvO4d3P1CB17b041ZtUGcUVeZ9rzXzG3AvcmLwjqPpl+kaaVh23z3JXjrwSvx46UX4/FX99lnGoGRfwiAxBn27uPDuH/zLhw+Pox7Nu3A5Y9vSzu753bhXCn/Q1zOvO5P60IazqirzD5rfFsrAj6BaSE/nrxpXta3YWc3hOBTBG5d/w7a1r6NR17Zgz//8hxcM7cBjy1swnA8MVCvDSVSWKZehGlprA3iQE8Yz/zbR+gLx9A9MAwp4bgPPHfnRXhp2cV45PoLYEiJx365F6aZuDZpIOL8T2TmjbzqqwL4tH+4pM96F9WNb3KFN74pL0V4hrsgN2PI18VS5XZRVvexCA4cCWel0Tt7eggNU4NjV1BkPukbwk0ON77ZvOxinHYS3fgmM05rg37s7xlMm9/8zOIWTK/SoJvS9WZBQgC6CRw5PoQZUxPvn1vZU6cGYZoS3YNR6IYJVRF22R8v/QIAZMXZnFOqURPU8OmxCD5Nzqd1u7nOw9clLkpzuqHIz+65DHUhbUJzuMtgXy75uPX6MzBNif5IDJGYAUMCFX4F00OJawP6IlHEdQnDTPyoikBQU6EbEl9dsz0rxp694/NY9au9+O9/ej5+d3gQa7cdwDkNVfjG5bPROxhLi+0nbpyHR3+5Fz2DUby07GJ80j+Ml9s7cf38mY7f6qSmCXxgwfkAgKBfwSf9w7hv83tZbcm8gY7bzaJ+es+laKguqhuYFf+Nb4hodPm6UUy+nidfhg0TP9txCM/e8XmoioBhSqx/8wN8++rPFbppk1JuU2QmyylOMy8qswY3pimzMjw8trAJ33pxZ9oZ57cevBJCIGsqyGMLm2CNkVIH9akXhSkicVvu1Kkoj7+6D6sXzcf7x0buMmnV9/z2D3HnZbPx+Kv77GXf/9U+PPQn57lOG3G6cM5tAMcLLIuD1/2poghMCwUcb+JV53In3UN9Q44xdiwSx3euPhd/9tKutP2i+3gUZ9WH8JOlF8OQEp8eG7YH2+uXtMKnCNz4TCJHuJX3u6E6gOlVAdz745F9bP6sGtx+6Wzc8ey7aQP3p2+dj2+9uDPtH+WgNjIdp7E2iNnTQ45tHooaMEOyJGKaA24iKmsBVcENzTPTbnayqq0JmlqaM+qsvOKZZ3pKNq94DrkNbjIHqkIIvPbbT/B4W5P9T9hb+xMXTZoSeH77h2kD5+e3f4jvfeWCrIsWrYvCuvoi6I/E0TMYtedjA4nPZTBqZF3M+eDW3Xhp2cUYGI7jqa/NR0UyS8OTN18In+r8+VrTRjJfo5XnOXMA3huO4cl/3pf2Op7853346xuaCvYPdRmccS8LbnPLT6tJ3HPhL649H73hGF7fczjrjPXqRc2o1FQ89bX5CPoT3yx1D0bT5oLf/UIHfrLsYvy+ezAtK5BTjvv7X96FR7/6X+w4HYoZqK/SACHwk2UXwzAl/KoCkWxjZps/PBJGZUCFgHD8R7uY4o0DbiIqa4YpHW8Us3nZxQVu2eQoCvD0rfPRF47becVrQ34opfn/Q96kDlTjcQMts6fb/4RZN+i4ed07qK8KZN3J08pF3z0Ydb0ozMrAkJn7OHX+ODBy2/iYYaKqwo+AT0H3QBR3p+Q7Xr2o2c4KMdq8X9OU+Kg3jIO9Q3YsnFFXiTPrQjBNE7dfOjvrTL1pmva2+RyM8Ix78XDL6T0cN7DkRyNnnzfcdZH9GEj0nfds2oFN3/gCTq0OQFWVrM/0sYVNeHPfYcyYEsCfvbQr7dsipwub66sCmFkbRPfxKCSA80+tQtyUiBkGPjoyhL99fT/qqzWsXHBe1v5lTWt58uYL7dvQW6/lnPqqrClmhY43DriJqKzFy2wKhgKBuG5m3axC8Wa6a1nqCcfsmzsBiZSC1uOuvggef3UfHrn+ApxVH0JMN7Gl/WOccvnn4PcpaWfZUi+MlFIiqKmJvMoxA+9/NoDv/2ofll9xtr2N023jf3hrM374r/vtOq30hM/deRE0VSCo+VwHw/2RGA4fH06LhVVtTaip9MOQyDqb+ODW3dh89yUFGfyOlZOa8sctp/dXnn4r7fM5Go65pg30qwr8qpL1mVrf3vzucOLs9vd/NfItS02llrb/WHm7b/v7kUH+6kXNePqN/XhtT7c9gA/4FNz57G9QXxVIOxOu+QR6BqP48Eg4K642331J0cUbz4kQUVlzu119qU7B0E2J+zan33zlvs27SvYfiPHSdROf9EdwsDeMT/oj0E8gj3pmCr+aoD9rYBEzTBimxAdHwnj3o37EdAM+hxzod142GxV+BTNrK1ET1CAgoCoCj7yyBzs7++2z4I21zreN/+aLO+ybkFhe29ON3sHEGb/66vTczKm3Ch/KyP1tfXsTiRmQLnevlFKOms/b7VbkJ4opDYtbXDfTBsLP3NaC6VXOmX+G4wZ0w0Qkrmd9pvVVAUgA06s0/PDWZnua1f0v78JgNJ6Wbz4zb7d1Bt3aH6wB/PSqRL5wK8PQzevewZ3P/QZTKjSsXdyCv319f1obuvoi0DP2cWt5IeONZ7iJqKxV+JWsW3yvWdSMCn9pnm84GS+a1HUTew8P2HdbbaxN3EXyvBnV8Pkm/jlqavqZ6tQ74DmdhV7V1oSgpiISM/D4q/uyLox8+tb5MIMjZ43rqwL219/WWfBN3/gCTJdBcOZ0ESudYWbqyswz02/c/0eO9RlSIuj3uc4FH23w69WZ73LM8V+qnL7heOa2FlwztwE9AzE7/uurAnjypnn2P/jWvlCpqfCpAvs+G3Q8Y31LMnPPNXMbsOGuiwAAB3uH8Jf/uAd/df0f2PtPXZV77vzUx27XrWg+Bd0Dw453j/Vl7OMj2xQu3krziENENE7RuIkfvLEfD183Fy8tuxgPXzcXP3hjP6Lx0r3TZDnlFR+P7sGoPdgGRu4i2Z1xoB0vzZ9+pnprR6d95s3pLPTKLbuhJ/NgW2fsrBzdPYNRaD417azxzs5+e1rKmw9cib++oQkzpwbtW7inaqwNYlpISztrvmZRM+acUoUKLf0QnXlm2iqfWZ9fUUbNAS1c2gHAsztZlmOO/1Ll9A3H3S904H9cOxf3Xn2OHf87O/uhmxKPXH+B3Xc+/uo+fPPFnfj4aAR/+/r+tDu3Zp6xfm1PN5b86F10D0RRV6WhZzCKT44N45FX9uDmde/gQE/YMQ77I/G0xxLS8e66XX1DePzVfVl3j12/pBUNVYGiizee4SaishY3JV7b023PjbX8xbVzC9SiExPQFKxd3JJ1tjegle/5k8wpIMDI18aTMRwzs85Ub3z7oJ0Vwem54rqJU6cGHS82qwsl8mynbrezsx93PvcbvPXglfac0dOmJs4kZqYIfOndg9j49S8AAviwJ4z/+fP/RM9gFGsXt6AmODKlJPPMdDiq44kb5+H+l0fOQD5x4zwownmerjUXXB0l9aFXX8NPJKUhecvtGw5VETi7IT39niJEWj5si0AixlPnaJ8ytcKx3ulVGtb9+gO8cNdFGIzq+OGtzfjmizscLzS25nADsPu2vnAMVQEfHrn+Avvi4OlVGh7Y8h9pbTj/lOq0ax6KLd444CaisuZz+TrSV6IH+uGYifYPj+DFpRdDSgkhBN7Y8ym+9AenOubiLQd+l6+HfZNM7Zh6pjq1vpW+8+y/nb6KHu0g7jZlwp9M+RfTDQQ1FadMDeClZRfjyGAMlZoKwzTR1no6fCpwy7p/T9t++cYO/HTFpZheFUBvOAZDSrx89yWI6gb8qoIKv4pXdh1K+8fh/U8SN9Q52BuGX1XQUBWAz5d+kZiiKK6pDyf6NbzTDYj6InHEdAOhgIqhmIm4Ydpt4QWShWWa0v6Gw+1zdptulVp2KGbYGXdqgn4oQkBxqfdATxjbP+jF1XNn4O4XOjB/Vg0euf4CnN1QhQqfgu/fOA8CiX+spZR4+Lo/wIMLzsenxyIYGI5j5ZbE9JblV5yNSiTa2D+k2/m9d3b245FX9uCnKy6140vXTXQPRu3Yqw36C/7PXfmeEiEiAlAf0uzbdgNIS/NWilRF4JxTpuLW9e/gj1Ztw63r38E5p0wt2YtAx6OhKoC1GZ/h2sUtaKia3OBttOkNY019sNILzqytTLug0W27wWEdN6x+C996cSf2fTaA65/ejm+9uBNDMR2rfrUXveE47nzuNzjUN+x4djAcM3Cofwg3rH4L3/nxe9BNEw/99D/sC8euu7ARWzs6cfO6d7Djo160zJ6Om9clYuOmZ97G3sMDWReY1oU03PelOfZX+4+8sgf3fWnOhL+Gt+YCp95ue+/hAfzFz3bjqX/Zj4+PRnDTM2+P2hbKH+vz+t4//tZxGoZT/KdOt0ot+7mGEB5YkIihR3+5F7pp4pFX/jOr3scWNmFrRyfWLGrG1o5OAEDPYBSnTK3AaVMqMBjVIaXE/S/vwuK/fxfffHEndNPE1f/r11j89+/aU0ZSL76cEvShwq9kTTE5PhyHrpv2NR/FFnu8tfsE8dbu+cdbu9OJisV09IRj0E0JnyJQH9KgaaX5Bd+hviH85T/9Jxa2zLLPTm7t6MR3/+sfYGYZ39rdOmOlGyZ89pnbyZ8zGi0P9WRzVGdul5pq7ZnbWtJu2T5/Vg1W3TjPvute5npg5BbXs6ZV4o//169dy1hpCQHn29JvvvsSnFaTPlfW7TVO5LX3DERxw+q3sp7v4evm4qzpITvP+VhtGUXJx20xSf28rLPTdSENp9UEccqUCsf4F0JAUwVihoSU0o6JI+Eovrp6e1bsWvWeMqUC06u0xP0BpIDfoY7ecAw3rH7LPnttpfs7c3qlnSoQSOwr9159DmZNC6LzaMS+4H04bqJSU9EfiWPttgPoGYxi892XAABueubtE429yeKt3Yno5KVpPsws0QF2JiHgeEMTUb4nuAEAPp+S04PlaLfcnuztuDO3S72FdmbqwZ2d/ehNuZFO6k10UufmP/wPv8UTN81zrAMYSfU3s7YSB3vDjuud5rqPdlfO8b52t7nANUE/VEXkdN49nbjUz8tKsQcAbz14Zdo/VeOJgdQ0gqlxmVnvqVNdTwLY7bEu2rT807cuS7tOJXFhsoKVLyey/ryUvGmZ09xy3TAh4XwtQqFjrzyOQEREJwkpgaAPeGnZxfYZ+8PHhlCGX1aWnHjcSJyFT34ulYGRueeZc2Hnz6pBXVXAviX27z49jlOmVmDj178AnypwbCiO6VUaVt04D6oi8MxtLTClxDVzG9K+3fi0LwwAONgbhk8R+N515+GycxrsW9Zvaf/Yca57Lu40qfnUrPZs7ehEfyRuZ17J1bx7OnGZ1xlYZ44Nmci9PpEYSK3LKbbvvfocmFLik/5I2hlyv0+BqgDhqAFVCNz9h2ei+cy6tPgJaj4E/QLP3XkR+odi6A3H8P1f7cPOzsT1CTOmVMCQ0jG+VEW4zlFXFYHDxyLw+xTUBMf/WnN1V1YOuImISkhdUMPRobg9dcCak14XLM056eUiHjewt3vQvmOl9bn8eOkX8LX1/56WkcG6fbw1ncS6tfztKbfVXrO4BT6fwB1rR5b9zc0X4s+/PAd3Pdeetl1mLKz61V77Tn2J2PCntTVXd5qsDfpx79XnpmXMWbO4Ba+814XX9xzOukX9icy7pxOXekt3KwZTM4RMJAZS63KKbafMI1ZMrmprwuOv7kN9tYZvX31u2j6zelEzXv7NQbS1zoIQAnHDtKerNNYm7qp730vvob46cW1O2v62qBkbtn+IO744O2vd6uS6K86bkZiGUhHHmXWhMV9rLu/KyjncE8Q53PnHOdxEIw71DTnO031p2cVlPYe72I32uWg+FZG4gcPHhhHVDZxRF8LX1o+UdZub/eOlF+MPH//XtGWPXH+B/VW623YPXzfX/oreKTbc5l5P9LbXbvVYc8qtLCUnMO+ecZtjqWdrneJ1IjGQWldQU6GbEnHddKw3MyYfvi6RltUtfh95ZQ+2Lr8E0eQdXwUEPjs+jMd+udfOTnLN3Aas/PJ5OBqO2WfHF7bMgqYqeOdADxZdMht6cvv1b36AzR1d9j4EABfMnDrma53EvsI53E44eCaiUnMy3mmyFIz2ucysDuBQ3xBufOZtAMi6Q6Tb3Gwz44RYV18ElZo65naZd+rLjI1c3WbdrR5rTjkA1LhP4aUCsOZnp15fYJloDDjN9XarNzMmrcduZbv6IhjWTXx2bBg3r3sHLy27GLeseyet7Gt7uvH1L56Fm1OWf/2LZwEAnvm3j3DV+aekrbPqt/ah8bzWXO0rANMCEhGVFCuveKpSziteLsb6XKw5rwBgmDKtrDUHNnNbJeNK2MbaIOqqApg/qwbzZ9Wk3aEytYx1p775s2rw7B2fBwB80h+x06KltiV1O82nwjQT83kP9Q2hZyAKc5R/5EarB8CE6qL8Guuzy3W9mXeP7I/EXePeWu5TBIZiRtoyp7LzZ9XgmdtasGX5JZgW0ux84G7bDMUMDMUMO967jw8nLzgewuFjEfQPjcRqLt8nDriJiEpIXdA5rzjncBdWQ1XA8XOx5iyn5jde/+YHabmNt3Z0Om5rSjMrp/Hjr76P731lLh76k/Ow6ld7s/Ier1ncgq0dnZg/qwYPLJiDh3/+26xcxG45w2uD/qy82vsOD7gOlEfLWe6Uo3u0uii/xso3n8t6V6fk4LbmcK/ddsAxx7eVt9ua739GXSXew+pOAAAgAElEQVRWtSWWZcb66kXN2PFRL/78y4l84G1r38adz/0GQU3FhrsuctxmVVsTpoX8OKOuMhHvnw3gq2u2449WbcMt697BB0fC6B6I4lD/EExT5vR9OqnncHNKSWngHG6iEYf6hnD42BBmTK1My1IyY2ol53AXWGaWkoaqAPz+kTNhqXNeM+c3T6vwozcykiu+OqgiEjWhmxKfHhtGbziGtdsOYGdnP5694/N4+Oe/zcqnfOrUCkyv1HBkKAYJ4JZRcnI7ZV6w8iJPZF6vWwaHHM0TZ9x6KFfZN0arNzOP90iWEhOqAEIBFTEjMfcbABQBSAh7vr9pSvRHYojEDCiKgJSAlBKKEDgajmFK0J92PQSQiLOf3nMpBARM04QhAcM0IYSAXxF2lhK3eLdy308N+lFfHZjo+8Q53ERE5UA3Jb669t+zlv965RX5bwyl8fvV0f7pyZrzmjm/eWZF+iF5SkXiH6y2tW+nLa/UVNe8x4GADzMDvjFzcjvNv53MfFW3nM25nPtK3phsvvlc1FsXGn9d00IBIKP8ob4hXPuD/4OXll3sGGdx3Rx1XwTcY7RSU6GIkTneuXqfOOAmIiohPkU45j7mHO7S5nYWLTN3MgB7XmvmmbnUeaV+VXGOk1HyYDs9V+ac7NQzl6oAFEVxPOM3Vl1UPrw6Uz7acwHp870z48zvU9AzEB21TW4x2jAlAJ+iIKjlNlY5h5uIqITUBRO5ax95ZQ9uXvcOHnllD7599bmcw13CRpvv7DSH9Iy6yjHnldaHnOOkfpS5pxOZk33TM2/j9z1h/MXPdjvOzfZqjjAVl3zO1U99rm+9uNN1bvf6Ja0YHNbHbFNdSMP621qz5nj/2Uu78LX17+Dw8dxe6Ms53FT0OIebaATzcJefseY7O51BBDDqWcXJzqGe6JxsK2eyU705OPPJuC1yucrpPpnnsu5oed6p1fbcbs2nQlWArzw9vjaZpsSRwSiG4wYO9ITxt6/vt/N8T/J1uMZsUZ3hFkL8SAjRLYT4bcqyaUKIfxZC7E/+ri1kG4mICol5uMvPWPOdrTmkM2srUV8dgKIIx2UTqdONogjUhTRoPhUx3UBvOAbTlK71WTmTIzE9K/XfWG2k0pfPufqZz7Wzsx93PvcbmKbEaTVBO84isfG3SVEEGqZUQFUE7nzuN/Zg24vXUVQDbgDPAViQsewhAK9LKc8B8HryMRHRSYl5uMuPFzmRJ1un2xQBv08ZNWfy+58NMPXfScirfN5O3GLQn3L3UtOUWXnux9OmfLyOohpwSynfBHA0Y/H1AJ5P/v08gP8nr40iIioiVQHFMWdzVaCounOaAC/mO0+2zt5wDEs3tNtnCLv6Ili6oR0+RWTVZ+VMfmxhIq+yVda6qI3KXz7n6vsUgVVt2Xm1U0829IZj+P9+sSdrXvczt7WM2qZ8vI5SyFIyQ0r5KQBIKT8VQjQ4FRJCLAOwDABOP/30PDaPaPIYtzRR/RED7x/qx0+WXQzDlFAVge37ezC1Yjqm5uE22ozZ3FMUgTkzqvGzey7LWaaHydbpNkUgEjPS6hNCQEBiYcssfP9X++yv4os19R/j1htexK6bSMzA46/uw8PXzbUz7zz+6j48fet8O21gTDfw2p5u9AzE0spNH6NN+XgdpTDgHhcp5ToA64DEBREFbg7RuDBuaaL8qoJ/2duNqaGAfTD5l73d+OK5jucico4x6w0vciKPp87MCxuDmns6v8z6egaieOSVPSWR+o9x6x2v8nln0nwqegajdt55IDverKkhqfnpr5nbgP8ycyoO9Q2NOpD2+nWUwneQh4UQpwJA8nd3gdtDRFQwk0n3RuTEab724eNRbLjronF9tc7Uf5RP44m3zDLXzG3AvVefi5ueedvztIVjKYUz3P8I4HYAjyZ//7ywzaFSMJmUj0WYfpAoS/+wjhUbO9Lm2K7Y2JFIX+UvvjOLVLzc5mv/9J5Lx/XVej6nExCNJ94yywghcNMzb2fFuBdpC8dSVANuIcSPAVwBYLoQogvAd5EYaG8WQnwdwMcAbixcC4mICou3zKZccYul8dwW25Kv6QREwPjiLbXMob6houkvi2rALaX8msuqq/PaECKiIsVbZlOuMJao3BVTjJfCHG4iIkrivFnKFcYSlbtiivGiOsNN5GQy87GJyhXnzVKuMJao3BVTjHPATURUYjhvlnKFsUTlrlhinFNKiIiIiIg8xAE3EREREZGHOOAmIiIiIvIQB9xERERERB7igJuIiIiIyEMccBMREREReUhIKQvdhpwTQvQAODiJTacDOJLj5kwW2+KsGNpyREq5INeVnkDceqUY3utcK7fXNN7XU0wxW2yfAdvjrtBtKaa4nYhCv29uirFd5dYm15gtywH3ZAkh2qWUrYVuB8C2uCmmtpS7cnyvy+01leLrKbY2sz3uiqktpaRY37dibNfJ1CZOKSEiIiIi8hAH3EREREREHuKAO926QjcgBdvirJjaUu7K8b0ut9dUiq+n2NrM9rgrpraUkmJ934qxXSdNmziHm4iIiIjIQzzDTURERETkIQ64iYiIiIg8xAE3EREREZGHOOAmIiIiIvJQWQ64FyxYIAHwhz9e/XiCccsfD388wZjlj8c/nmDc8sfDH1dlOeA+cqTY7hJKNDbGLZUaxiyVIsYtFUJZDriJiIiIiIoFB9xERERERB7igJuIiIiIyEMccBMREREReahkBtxCiPuEEP8phPitEOLHQoiKQreJiIiIiGgsvkI3YDyEEDMB3AtgrpQyIoTYDOAWAM9Npj7TlOgNxxDTDWg+FTUVPvSEY4gbJmqCKgajJnRTwqcI1AU19EZi0E2JaSEVxyPp645F44ibEooApAQqNAXDsUQZvyKgKAJSSpgSMEwJVRHwqwJSAnFTwjAlKnwqhAAicQM+RSDgUzAUM+BXFdRXBeDzZf9flPoagpoK3ZSI6yY0n4q6kAZFEWO+brdyRABw5kO/mPA2Hz16rQctIZqcifR5qWX9qgIpJYZ1Ez5FoKEqAL9fdawz9fjhVxRoPoFwzLCPEcfjOqJxA4oQUASgCAE92ff7FAEIAMljx1DMhGFK+FUFPkVgOG5AVQRURcAwJYQAhBBQABhSQkogqCmIxJyPWVODKsIpx7PplRoCgcRhPx430D0YtdcFfAokBGoqfDgSjiFmmFAVgaCmoDYYgKKItNcuhIAqAJ8qENUl4kbivaoPaTgWNRDTDVRVqBgcNuznSF3HY1BhZI4dJGTamKVCU1ET1AAAR8JRABJxXabFiaIk4jimS8SMkZgFJCr86fFYVaFgcNiESI6RTCmhCGE/nhJUMBAxEU+OjzRVgSklYnoi/kRy/whqCmK6tMdNflVAFQJGsj7TTLRRTcbZsGlgMGLY9foVYefs8/tEWkwfGYza4y+fIhAzTAT8KqaHsuP+ROK2JAbcST4AQSFEHEAlgE8mU4lpSuw7PIClG9rR1RdBY20Qaxa34Aev/w5/fF4Dzp9ZgxUbO7LW3XXZmTg+rGWtm17lw1/90x7cfuls7P/sGFpmT08r8+RN8+D3KfjWizvtZT+6oxXHhuK4b/Mue9mqtiY8/uo+9AxGsXpRMza+fRDbP+jF2sUtOG9GddqgO/U11FcF8MCCOVi5Zbdd1/olrZgzozotIJxet1M5IqJyMJE+z6lsap+8ZnELzmuogqoqaeXu/sMzcd2FjWl9vtV/90di+PbV56ate/rW+YjrZlrf/8SN8/DG+5/h2nkzcc+mHY7P/9jCJjy//UPcfulsPL/9Q3zzys9hOG7i/U/6s445axa3oOPDI6jUfI7Hs3Onh6AoAnu7B7PaveOj3qz6VrU1YcYUHafXVmJ/z2Dae+T0eqznHxg2cMX5M+y6rpnbkPV+8BiUX5ljh+99ZS6GYkba+OHJm+bhlKkVGBw28A87O7Pics2iZsyYGsDxSBw9A9GMsUcLjoaB5Rkx98p7Xbh8zgw8uHWkrBXT3776XPzg9d/htT3ddrxVaiq+94977Njf/9kxXHZuA45kPN9Tt1xoD9C/mTLGeu7Oz2M4bqa1Y1VbE6ZU+KCbiWH3sYpkTHcPYukLzvv9+iWtOKe+KivuJxu3JTGlREp5CMD3AXwM4FMAx6SUr02mrt5wzH7jAKCrL4IVGzuwsGUWLj2n3u4MMtfNmhZyXGeYAgtbZuHBrbtx1dxTs8rct3kX+sLxtGWH+obtDspatnLLbiy/4mx09UVwz6YdWHr5Wejqi2D5xg50D0ZdX8PyK862A9Cqa+mGdvSGY2O+bqdyRETlYCJ9nlPZ1D55RbIfzizX1np6Vp9v9d8LW2ZlresLx7P6/vtf3oW21tPtQY3T8z+4dbd9nFnYMgtHw3Hc//Iux2POio0duGruqa7HsyNDMXQPRh3b7VTfyi27cbB3CN2D0az3yOn1WM9/fXNjWl1O7wePQfmVOXY4Go5njR/u27wLUV1i6QvtjnG5YtMOxHSJzqORrG0/Oxa1B7l2+Y0daGs93R5sW8utWLbGWNbylVt242g4nhb7V809FV0Oz/edn7yHI4MxHM0YY3UejWS1Y+WW3egeSJQ9Go6PxPQL7vv90g3tjnE/2bgtiQG3EKIWwPUAZgM4DUBICLE4o8wyIUS7EKK9p6fHta6YbthvnKWrL4KaoB+GKV3X6S7rDFOiJuhHV18EpnQuU6mpacsqNdX1eay/1eR/Tl19EeiG6foarOfOrCumG+N63ZnlKL/GG7dExaJUYnYifd5oxwXrb92UWeVURThupyrCsW926/vd6kl9fqu+mqDfrsftmCOldD2e6cmv3t22czuGxQ1z3K8nMY0yva7xHqu8Uipx66XMsYPb56eI0ePSMKXjtpOJ79Q4t5ZXampa7JvS+fmssuMdY1llrR+nmM7a713KTCZuS2LADeCPAXwopeyRUsYB/BTApakFpJTrpJStUsrW+vp614o0n4rG2mDassbaIPojcaiKcF3nc1mnKgL9kTgaa4NQhHOZoVj6BzMUM1yfx/rbSH7t0VgbhE9N/5hSX4P13Jl1aT7VdZvRylF+jTduiYpFqcTsRPq80Y4L1t8+RWSVM0zpuJ1hSse+2a3vd6sn9fmt+vojcbset2OOEML1eGbNU3Xbzu0Y5leVcb8eIURW28Z7rPJKqcStlzLHDm6fnylHj0tVEY7bTia+U+PcWj4UM9JiXxHOz2eVHe8Yyypr/TjFdNZ+71JmMnFbKgPujwFcLISoFEIIAFcDeH8yFdWFNKxf0mq/gdYco60dndi+vwdrFrc4rus8GnZcpyoSWzs68djCJryx59OsMk/eNA+1IX/aspm1FXjypnlpy1a1NWHttgP2XLr1b36Axtog1i5uQUNVwPU1rN12AKvamtLqWr+kFXUhbczX7VSOiKgcTKTPcyqb2ievSfbDmeW2tH+c1edb/ffWjs6sdbUhf1bf/8SN87Cl/WOsXtTs+vyPLWyyjzNbOzoxLeRPzP12OOasWdyCN/Z86no8m16poaEq4Nhup/pWtTXhjLpKNFQFst4jp9djPf/Pd3Sl1eX0fvAYlF+ZY4dpIX/W+OHJm+Yh4BNYf1urY1yuWdQMzScwa1owa9tTpgaw1iHmtrR/jMcWppe1YtkaY1nLV7U1YVrInxb7b+z5FI0Oz/fULRdiepWGaRljrFnTglntWNXWhIbqRNlpIf9ITN/mvt+vX9LqGPeTjVshpRy7VBEQQvwlgJsB6AB2AviGlDLqVLa1tVW2t7e71uV2lblumJg6ySwlqgDMlCwl1hXoaVlKpIQq0rOUmKZEgFlKSo0nb9pYcZtvzFJSVk6KmM2UzywlumHC55qlxExmKJlglhLdgCpGspQoAkCespTEDRNK4bOUnJRx6yW3LCVWPGZnKQHiujlqlhLTlPCpCgQkArnKUmKYUIVzlpLE8yWylJhSQuQgS4mVESiRpSTxOiaZpcR1RclkKZFSfhfAd3NRl6II1FennzU+rWbkK4OplenlZ1aMvE3VGdm/Kyoc3sLQCTdxTE6vwYttiIhK1UT6vPGWHev4AQDTU/52PEa4mDbZY0fGdqnHrJqM45nF71cxs9Z55akZr8cy3veoXht5/qlB93WUf46foUvcNWQOeMYro77MGMg02acZjQYfpoyjXkURaBijYK7GTqUypYSIiIiIqCRxwE1ERERE5CEOuImIiIiIPMQBNxERERGRhzjgJiIiIiLyEAfcREREREQe4oCbiIiIiMhDHHATEREREXmIA24iIiIiIg9xwE1ERERE5CEOuImIiIiIPMQBNxERERGRhzjgJiIiIiLyEAfcREREREQe4oCbiIiIiMhDHHATEREREXmIA24iIiIiIg9xwE1ERERE5CEOuImIiIiIPMQBNxERERGRh0piwC2EmCOEeC/l57gQ4r8Vul1ERERERGPxFboB4yGl3AfgQgAQQqgADgH4WUEbRUREREQ0DiVxhjvD1QAOSCkPFrohRERERERjKcUB9y0AflzoRhARERERjUdJDbiFEBqArwB42WHdMiFEuxCivaenJ/+NI5oExi2VGsYslSLGLRVaSQ24AfwJgB1SysOZK6SU66SUrVLK1vr6+gI0jWjiGLdUahizVIoYt1RopTbg/ho4nYSIiIiISkjJDLiFEJUAvgTgp4VuCxERERHReJVEWkAAkFIOAagrdDuIiIiIiCaiZM5wExERERGVIg64iYiIiIg8xAE3EREREZGHOOAmIiIiIvIQB9xERERERB7igJuIiIiIyEMccBMREREReYgDbiIiIiIiD3HATURERETkIQ64iYiIiIg8xAE3EREREZGHOOAmIiIiIvIQB9xERERERB7igJuIiIiIyEMccBMREREReYgDbiIiIiIiD3HATURERETkIQ64iYiIiIg8lNcBtxDiO0KIKSLh74UQO4QQ1+SzDURERERE+ZTvM9x3SSmPA7gGQD2AOwE8muc2EBERERHljS/PzyeSv/8UwLNSyl1CCDHaBvaGQtQA+DsAFwCQSAze3/ammRMXi+noCcegmxI+RaAuqOFoJIZ48rFPETAhYZqAYUooyWUCgBBA3JAwpESFqkA3pb2dIgBTAj5FQAIwk+tURUBTFfhUYDguoYhEvYYpUeFXAQBxw4QQAqoAFEVBXUgDAPSGY4jpBjSfirqQBkVJfASmKR3XuS0fbZvJyGVdRFRedN1E92AUccNEMNnHDesGVCHgVxVIKVEZEDgeMe1+2O8TiOsSQgBSAkFNweCwAVUR8Ksi0e8m+9O6oIbeSKIPr6lUMThs2ttJmeinhQBUkeiLo7oJnyIQCigYiklIKe1+CwCOhKMYjifaF9RU1ARz05+xnyx/mZ9xTYUvOb4wk/EuICWgm4lxg6Yq0HwCkVgi9jVVgV8V0E0J3UyMD6QEDCmhCgEhAEgg4FMQ0U0YyW0UAQwn47qqQkE0JmECWXX4FQVCJPaBoF+Fbpj2mKXCr0BVBKJxEwAQTxmXCACRuAG/qqChKgCf7+Sa1ZzvAXeHEOI1ALMB/HchRDUAc5zbPgXgVSllmxBCA1DpVSMnKhbTsa8njBUbO9DVF8E1cxtw79XnYnnycWNtED+8dT6G4ybuf3mXvWxVWxOmhfyI6RIrNu1AfVUADyyYg5VbdttlHlvYhOe3f4hvXvk5xA2J//bSe2nbT68OYMtvPsblc2bgwa27R63jvi/NQcCnYMmP3rXXrV/SijkzqgEA+w4PYOmG9rR159RXYX/PYNby0baZM6N6wgcA05Q5q4uIyouum9h7eADLN3agviqA//dPz8N9m9P70s81hPBRb9Tuhxtrg1i9qBm/2HUIl8+Zgee3f4hvX30utr1/GP/xyTF866pzcM+mHXaf/e2rz8WKjR347nXn45SaSvzg9d/h9ktn48GtI33pEzfOQ4VfwTdf3GkvW7O4BT94/Xd4bU83GmuD2HDXRYjGTSx9oT2tfTOmVODMutAJ9WfsJ8tf5mecGpvW4z//8hz0Dsbs43xmmcbaINYsaoYpJX74r7/PimNrTPCtq87B02/st2N3VVsTHn91H+qrNaxccB6GojqGYgaefevDrDqeuuVCvPRuJ25onpk23li9qBlTgz6YUuKzY9G0dU/eNA/////ei57BKNYubsF5M6pPqkF33l5p8kz2/wTwEIDPSymHAGhITCsZa9spAC4H8PcAIKWMSSn7PWzuhPSEY3agA8DClln2YBsAuvoiOBqO24Nta9nKLbuhKipWJDv95VecbQenVebBrbuxsGUWjobj9mA7dfuuoxG0tZ5u7wij1bF0QzsO9g6lrVu6oR294Rh6wzF7B09d1z0YdVw+2ja94diE38Nc1kVE5aV7MGr3qcuvONsebAMjfWFMl2n9cFdfBPds2mH3jwtbZmHFxg5c39yIhS2z7ME2AHtdV18Ec0+bihUbO7CwZZbdr1r13f/yLhwNx9OWWWWtxwd7h+zBdmr7DvYOnXB/xn6y/GV+xqmxaT0+1DecdpzPLNPVF8GKTTtwNBx3jGNrf7hn04602F25ZTeWX3E2FrbMQtfRxLhl5ZbdjnV85yfvYenlZ2WNN+7ZtANxA/Apata6+zbvwvIrzk7sxxs70D0YzdO7WhzydoZbSimFEP8gpWxJWdYLoHccm58FoAfAs0KIeQA6AHxHShm2CgghlgFYBgCnn356Tts+Ft2UdlABQE3Qn/YYACo1NWtZV18EioC93Gm7rr4IaoJ+++/MdZWaClUR46rDKp+5LqYbrvXHDdNx+WjbWOsmIqYbOaurlBQybokmoxAxm9oPufVxmf2wtdzqH63tpJRZdaQ+tupxex6nPtTqowH3vr5SU0+4PztZ+8lcKJW+NvMzdopVAKOWsdZXaioq4RyP1japsZv5OLNs5vLUsUfqckUkpp6MNZ7RjfFOcCgP+T6X/44Q4vOT2M4HoBnAGinlfABhJM6U26SU66SUrVLK1vr6+hw0dQKNUwQaa4P24/5IPO0xAAzFjKxljbVBmBL2cqftGmuD6I/EXbcfihkwTDmuOqzymes0nwrNpzpu51cVx+WjbaP50g9I45HLukpJIeOWaDIKEbOp/ZBbH5fZD1vLrf7R2k4IkVVH6mOrHrfncepD+yNx+/FoffWJ9mcnaz+ZC6XS12Z+xk6xmhljo8XqWGOC1Ni1HlvPYT2PWx2pY4/U5aZMXOvg9rzW3z715JlOAuR/wH0lEoPuA0KI3UKI/xBC7B7Hdl0AuqSU/558vAWJAXhRqA9pWLO4xQ6urR2dWJvyuLE2iGkhP564cV7aslVtTTBMA2sWNaOxNoi12w5gVVtTWpnHFjZha0cnpoX8+JubL8zavnFaEFvaP8ZjC5vGrGP9klacUVeZtm79klbUhTTUhTSsX9Kata6hKuC4fLRtrIuGJiKXdRFReWmoCth96tptB/DkTdl9qeYTaf2wNZ/U6h+3dnRizeIW/HxHF7Z2dGJ1st8FYK9rrA1izyfHsGZxC7Z2dNr9qlXfEzfOw7SQP22ZVdZ6fEZdJdbf1prVvjPqKk+4P2M/Wf4yP+PU2LQez6ytSDvOZ5ax5nBPC/kd49jaH1Yvak6L3VVtTVi77QC2dnSicVpi3LKqrcmxjqduuRDr3/wga7yxelEz/Cqgm0bWuidvmoe12w4k9uPFLWioCuTpXS0OQkqZvycT4gyn5VLKg+PY9t8AfENKuU8I8T0AISnlSqeyra2tsr29/YTaOlG5zlKiJ6+cd8pSoie3T81SoorkFcvMUpIPnjSqEHE7mjMf+sWEt/no0Ws9aAnlQMnHrJWlRDdMu48b1k2oAhPLUhI17EwPVr+rCq+ylCTaxywlk1bycTsZuc5SoibHEaaUUByylJimhH+MLCWpdfiSWUpiemJfHCtLiWlKBJJZSobjBnzlnaXENWbzmqVESnlQCPFFAOdIKZ8VQtQDqBrn5t8GsCmZoeQDjONiy3zSNB9maulv52kV+U4CMz711c7/VSqKcFzntnysdROVy7qIqLz4fApOqwmOWa66YvT100Lu62am9NlTx34qW41DzqyGsRoySewny5/TZzye2McosT0pE9gHaGx5HREKIb4LoBXAHADPAvAD2AjgsrG2lVK+l9yWiIiIiKhk5Pt8/g0AvoLERY+QUn4CoDrPbSAiIiIiypt8D7hjMjFpXAKAECLXX4AQERERERWVfA+4NwshngFQI4RYCuBfAKzPcxuIiIiIiPIm3xdNfl8I8SUAx5GYx/0/pZT/nM82EBERERHlU97TaCQH2BxkExEREdFJIS8DbiHEAJLztp1IKafkox1ERERERPmWlwG3lLIaAIQQfwXgMwAvIJEcfBGYpYSIiIiIyli+L5r8spRytZRyQEp5XEq5BsDCPLeBiIiIiChv8j3gNoQQi4QQqhBCEUIsAmDkuQ1ERERERHmT7wH3rQBuAnA4+XNjchkRERERUVnKd1rAjwBcn8/nJCIqBWc+9IsJb/PRo9d60BIiIsq1vA64hRD1AJYCODP1uaWUd+WzHURERERE+ZLvPNw/B/BvSNxhknO3iYiIiKjs5XvAXSmlfDDPz0lEREREVDD5vmjyFSHEn+b5OYmIiIiICibfA+7vIDHojgghjgshBoQQx/PcBiIiIiKivMl3lhLeVZKIiIiITip5GXALIc6TUu4VQjQ7rZdS7shHO4iIiIiI8i1fZ7jvRyId4BMO6ySAq/LUDiIiIiKivMrLgFtKuTT5+8rJ1iGE+AjAABLpBHUpZWtuWkdERERE5J18TSn56mjrpZQ/HWdVV0opj+SgSUXNNCV6wzHEdAOaT0VdSIOiiILVk2vF2i4iKh6j9RO57EPYH1E5cIpjADmPbe4vk5evKSX/dZR1EsB4B9xlzzQl9h0ewNIN7ejqi6CxNoj1S1oxZ0b1hII6V/XkWrG2i4iKx2j9BICc9SHsj6gcOMXxhrsuQgZ6EPAAACAASURBVFQ3cxrb3F9OTF7SAkop75RS3gngG9bfKT/jva27BPCaEKJDCLHMw+YWVG84ZgczAHT1RbB0Qzt6w7GC1JNrxdouIioeo/UTuexD2B9ROXCK44O9QzmPbe4vJybfebh/L4RYJYSYO4ltL5NSNgP4EwDfFEJcnrpSCLFMCNEuhGjv6enJSWMLIaYbdjBbuvoiiOlGQerJtWJtV6GUS9zSySMfMTtaP5HLPoT90cmjnPtapziu1NScxzb3lxOT7wF3E4DfAfg7IcQ7yR1gyng2lFJ+kvzdDeBnAC7KWL9OStkqpWytr6/PdbvzRvOpaKwNpi1rrA1C86kFqSfXirVdhVIucUsnj3zE7Gj9RC77EPZHJ49y7mud4ngoZuQ8trm/nJh83/hmAMB6AOuTZ6h/DOBJIcQWAI9IKX/vtJ0QIgRAkVIOJP++BsBf5avd+VQX0rB+SWvWHCnrAoh815NrxdouOjmc+dAvJrzNR49e60FLaDRj9RO56kPYH1E5cIrjM+oqcx7b3F9OTF4H3EIIFcC1AO4EcCYSebk3AfhDAP8bwLkum84A8DMhBJBo84tSyle9bm8hKIrAnBnV+Nk9l53QVcC5qifXirVdRFQ8xuonctWHsD+icuAWxwByGtvcX05MXgfcAPYD+FcAq6SU21OWb8mck51KSvkBgHleN65YKIpAfXWgaOrJtWJtFxEVj9H6iVz2IeyPqBy4xXGuY5v7y+Tlew73ewDutwbbQohaIcSPAEBKeW+e20JERERE5Ll8D7hnSyn7rQdSyj4A8/PcBiIiIiKivMn3gFsRQtRaD4QQ05D/aS1ERERERHmT78HuEwC2J7OSSAA3AfjrPLeBiIiIiChv8p0WcIMQoh3AVQAEgK9KKffksw1ERERERPmU9+kcyQE2B9lEREREdFLI9xxuIiIiIqKTCgfcREREREQe4oCbiIiIiMhDHHATEREREXmIA24iIiIiIg9xwE1ERERE5CEOuImIiIiIPMQBNxERERGRhzjgJiIiIiLyEAfcREREREQe4oCbiIiIiMhDHHATEREREXmIA24iIiIiIg9xwE1ERERE5CFfoRswEUIIFUA7gENSyusK3Z7JMk2J3nAMMd1AVYWKwWEDuinhUwTqghp6IzH7caWmIKZLxE0Jw5QI+hToUkI3JHyqAkUAigCkBHRTQjclVEXArwgIALqUUIWwt68KqBiOm3b9U4IKjkcSj/2KgE9VMBw34FcVqAKI6CZURaA6oGAwOrJddYWCgeGRx0FNweCwYf89FBtZp/kUmKaEKSVCgfTtqgIK+iMG/IpAKOM5qgIKjg0bUETi9fh9CmqCGgDY758QAqoAFEVBXUiDogiYpkR/JIZIzICiAFIKGKYJRQgoCqAqCqaHAlAUkfV5aD7VroeIToyum+gejCLoF3afUOFTYEogZpjwKQI1QRXhmETMMBH0KxiOm4m+qkJFJKUfqfAriMZNVCT7F8OUCPpVmDLRt0gJGKaEkuz/EssBvyLsfqzCp0A3JaJ64rl9ikDMSPQNVRUqwlETccOEX1UQ8AkMRg27H7ba71cVNFQF4PPxfFW5Sj2GGFKiwq+itsKPnnAM8WTcBnwKYoZpx93UoIrh+MixWksenwN+gaGoiXgyjgM+BcO6aR+3jeRyVREYTsappggE/MKuTxWAKUeO9XFTIpDcj3QjsY0vub1fVeBLHrsz97XU4x+QOI6apgkhBHQj0UZr/GAdb3kszJ2SGnAD+A6A9wFMKXRDJss0JfYdHsDSDe24uaURV5w/Ays2dqCrL4LG2iDWLG7BD17/HV7b041r5jbggQXnoWcgipVbdqO+KoAHFszByi277fJP3XIh6qo09ByP4r7Nu+zlq9qaMC3kR0yXGIzqWLllNy49qw6LLzkD92zaga6+CK6Z24BvX31u2vOvamvC46/uQ89g1P77+nmnoGX2dNd2Wo+3vX8Yg9E4rruwMb3somZoPoFpIQ0Hj0az6nn/UD+GYrrjc2x7/zBe6ujCqrYmVGoqwiEdg1EDSze02+UeW9iE57d/iPu+NAfn1Ffh474hHD4+jGff+hC3XzobD27d7Vh2zoxqALA/D6vM+iWtmDOjmh0N0QnQdRN7Dw/gw57jOLN+ClZs7HDsw9YuboFhmth58KjdB7j1jR0fHkHL7On4weu/Q89ADA8smOO4n69qa0JQU7H6X3+POy+bbfdpqxc14+k39tv9Vmp/l9mnrV7UjI1vH0R/JJbVT65d3ILzZlRz0F2GTFPio94wDh8ftuP07j880/G4BgArNu3AvVeejflnTsOR5LHaKvOjO1oRG5RYnrLd6kXN+MWuQ7h8zoysmE099s6sDaJnIIpnfn0At186G89v/xBf/+JZuP/lXY77Ueb2P9txCDc0z0wrYx///ngOAn4Fj/7yfdxz5ecQ182s8UOlpqK/Io4z60I8FuZIyfQWQohGANcC+LtCt+VE9IZj9uDu+uaRHRgAuvoiWLGxAwtbZgEAFrbMQufRiL3DLL/ibPtvq/x3fvIedAP2zmItX7llN1RFxZHBmL3N0svPsgfbVv2Zz79yy24sv+LstL+vmnvqqO20Hl/f3Ii21tOzy27aAVVREdOlYz2XnlPv+hzXNzfabTkajiOqS/v9s8o9uHU3FrbMwtIN7egejOJg7xBWbkksszo0p7K94Vja52GVsdYR0eR1D0axfGMH5p9RZ+/bTn3Y8o0dOBqOp/UBbn2jVWZhyyy7Lqf9fOWW3egLx7GwZVZan3bPph1p/Vbqusw+7Z5NO7D08rMc+8nlGzvQPRjN91tKedAbjtnHEOszdzuuHRmMoasvgkvPqUdXyrHaKnOob9gebFvL7tm0A22tpzvGbOqxN6ZLfOcn79nxvbBlFu5/eZfrfpS5/dLLz8oqYx//XmjHwd4hLGyZhb5w3HH8cDQcx8HeIR4Lc6iUznD/DYAHAFQ7rRRCLAOwDABOP/30PDZrYmK6YQe2KaX9t6WrL4KaoB8A7N9WmZqg37G8IuC6vFJT7XWqItLKudWX+rw1Qf+Y7bQeSymzniO1LbrpXI9hSkg4vwYppf13paa6vlbrteiGab/m0V5fV18EMd2wl2WWsdZ5rVTilsgy3piNG6a9f4/Vh1Vqalo/49bnyOTyzD7Krc5KqI7lU8uNtk5VhGv9umGO8U5RMRlv3MZ0I+24CWQfO4GRGAMS00IytwHguMyKq7GOvdaxzoq/1Dgcz7F7tOdI3T+s5W6vLV/HwpNBSZzhFkJcB6BbStnhVkZKuU5K2SqlbK2vr89j6yZG86lorA0CABQh7L8tjbVB9EfiAID+SBxDMcMu0x+JO5Y3JVyXp25vmDKtnFt91vNbf4/VTuuxECLrOVLb4lOc61EV4focQgj776GY4fpardfiUxX7NY/2+hprg9B8atrnkVpG86nIh1KJWyLLeGPWryr2/j1WHzYUM9L6gNH6A2sftuoarc7UMtbyzH5rtHWGKV3r96klcfikpPHGreZT046bQPaxExiJMSAxIM/cBoDjMiuuxjr2Wsc6pzgfz7F7tOdI3T/c2jgUMzAUM/J2LDwZlEqPcRmArwghPgLwEwBXCSE2FrZJk1MX0rB+SSsaa4P4+Y4urFncYge7NU9xa0cnAGBrRydmTUvMp2qsDWLttgP231b5p265ED4VePKmeWnLV7U1wTANTK/S7G3Wv/kBVi9qtstt7ejMev5VbU1Yu+1A2t9v7Pl01HZaj3++owtb2j/OLruoGYZpQPMJx3q27+9xfY6f7+hKm5Me8An7/bPKPbawCVs7OrF+SSsaqgI4o64Sq9oSyx5b2ORati6kpX0eVhlrHRFNXkNVAGsXt2DnwV5733bqw9YubsG0kD+tD3DrG60yWzs67bqc9vNVbU2oDfmxtaMzrU9bvag5rd9KXZfZp61e1Iz1b37g2E+uXdyChqpAvt9SyoO6kGYfQ6zP3O24Nr1KQ2NtENv396Ax5VhtlZlZW4G1GdutXtSMLe0fO8Zs6rFX8wk8dcuFdnxv7ejEEzfOc92PMrdf/+YHWWXs499trTijrhJbOzpRG/I7jh+mhfw4o66Sx8IcEtZX9qVCCHEFgD8fLUtJa2urbG9vz1+jJmiyWUrM5BX+o2UpSb1KXwAwpISSzFJimhKh8WQp0Q34lXLJUiJGshcIQEleDX6CWUo8uYKk2OL2zId+MeFtPnr0Wg9akjvF/Jo8bltBYnbSWUpkIqPSaFlKTDORPSKRoSSRkcQtS8mwbkJJyVIS00cyO2RmKdENEz5mKSkWBYnb9CwlQIVfccxSEjdMO+5Ss5SYyTgZLUuJlXnEkBI+kTguRZNxOlaWEt2UieNqRpaSqJ6I3cwsJfFkmdTjH5CdpURP2X+YpWTSXN+wUprDXTYURaC+euTsyNT0b3MwsyK/H0t1xfjKTa1Mfzwlo93TQiN/14bgKnO71HoznyPzsSX1/cukKALTQgFglDZklh+tPiKaHJ9PwWk1iR1+tD4h5NYHuWwzbZz79kRl9sV1VSN/j9Z+Ki9uxxArlt1MdVle43IcG4tbfbnC415+ldyAW0q5DcC2AjeDiIiIiGhc+J0YEREREZGHOOAmIiIiIvIQB9xERERERB7igJuIiIiIyEMccBMREREReYgDbiIiIiIiD3HATURERETkIQ64iYiIiIg8xAE3EREREZGHOOAmIiIiIvIQB9xERERERB7igJuIiIiIyEMccBMREREReYgDbiIiIiIiD3HATURERETkIQ64iYiIiIg8xAE3EREREZGHOOAmIiIiIvIQB9xERERERB4qiQG3EKJCCPGuEGKXEOI/hRB/Weg2ERERERGNh6/QDRinKICrpJSDQgg/gP8jhPillPKdQjdsMkxTojccQ0w34Pcp8CkCkZgBzaeiLqRBUYRreacyY60nIjoZOfWNANAbjsE0TRgSkFJm9ZvsUylXJnL8DmoqdFMirptjxh1jtPSUxIBbSikBDCYf+pM/snAtmjzTlNh3eABLN7Sjqy+CxtogVrU14fFX96FnMIr1S1oxZ0Z1WsefWT61zFjriYhORm59Y8Cn4NFfvo/bL52NB7fuzuo3AbBPpZyYyPG7viqABxbMwcot2THpdBKOMVp6SmJKCQAIIVQhxHsAugH8s5Ty3wvdpsnoDcfsnQQAuvoiWLllN5ZfcTa6+iJYuqEdveHYqOVTy4y1nojoZOTWNx7sHcLClln2YDt1XW84xj6VcmYix+/lV5xtD7adyk6kXipOJTPgllIaUsoLATQCuEgIcUHqeiHEMiFEuxCivaenpzCNHIeYbtg7iaWrL4KaoN/+O6YbY5a3yoy1nopbqcQtkaVUYtatb6zUVNQE/a79JvvU8lSIuJ3I8Xu0mJxovVScSmbAbZFS9gPYBmBBxvJ1UspWKWVrfX19Qdo2HppPRWNtMG1ZY20Q/ZG4/bfmU8csb5UZaz0Vt1KJWyJLqcSsW984FDPQH4m79pvsU8tTIeJ2Isfv0WJyovVScSqJAbcQol4IUZP8OwjgjwHsLWyrJqcupGH9klZ7Z7HmcK/ddsCeh2Vd2ONWPrXMWOuJiE5Gbn3jGXWV2NrRiccWNjn2m+xTKVcmcvxeu+0AVrU5x+RE66XiJBLXIxY3IUQTgOcBqEj8k7BZSvlXbuVbW1tle3t7vpo3YcxSUvI8eXOLLW7PfOgXE97mo0ev9aAluVPMr8njtp0UMZuJWUpKXsnHLbOUnHRcP4RSyVKyG8D8QrcjVxRFoL46kL4wNMHyE1hPRHQycusbx+ov2adSrnh1/GaMlp6SmFJCRERERFSqOOAmIiIiIvJQSUwpIaIRxTwPmYiIiLJxwE1EBTWZfyAA/hNBRESlg1NKiIiIiIg8VBJpASdKCNED4OAkNp0O4EiOmzNZbIuzYmjLESnlgrGLTcwJxK1XiuG9zrVye03jfT3FFLPF9hmwPe4K3ZZiituJKPT75qYY21VubXKN2bIccE+WEKJdStla6HYAbIubYmpLuSvH97rcXlMpvp5iazPb466Y2lJKivV9K8Z2nUxt4pQSIiIiIiIPccBNREREROQhDrjTrSt0A1KwLc6KqS3lrhzf63J7TaX4eoqtzWyPu2JqSykp1vetGNt10rSJc7iJiIiIiDzEM9xERERERB7igJuIiIiIyEMccBMREREReYgDbiIiIiIiD5XlgHvBggUSAH/449WPJxi3/PHwxxOMWf54/OMJxi1/PPxxVZYD7iNHiu0uoURjY9xSqWHMUili3FIhlOWAm4iIiIioWHDATURERETkIQ64iYiIiIg8xAE3EREREZGHOOAmIiIiIvIQB9xERERERB7yFboB5co0JXrDMcR0A5pPRV1Ig6KIQjeLiMoA+xd3Zz70iwlv89Gj13rQEiI6UeXU13HA7QHTlNh3eABLN7Sjqy+Cxtog1i9pxZwZ1SUbKERUHNi/ENHJoNz6Ok4p8UBvOGYHCAB09UWwdEM7esOxAreMiEod+xciOhmUW1/HAbcHYrphB8j/Ze/c46Oqzr3/XXtumSRAQkgQIValiEQbC0Hkco6inKJWLC9NxFaCglZAjnJOa1F7PBxtedsjUl/fWuXmqYB4KQr1VWmrtli05aIYUYopiIgaEEkICeQymdte7x+TvZnLntwmmWSS9f18+JDZs/faa5I9az97rd/zewyO1HjwBYLd1COFQtFbUOOLQqHoC/S2sU5JSlqhJf1QIKBTWe/FH9Rx2DTyMl3Y7RpOu41h2e6IC2VYthun3dZdH0OhUPQS1PiiUCg6Sippolsa6zrzcyTrd6JmuFvA0A/NWLGdScv+wowV2zlwvA5dlwQCOvuP1zFz9U6uWL6Nmat3sv94HYGATk6GkydvHsuwbDeAqTvKyXB28ydSKBSpzgCXjZWlRRHjy8rSIga4VMCtUCji01JM0xOJF0tlux2d9jmS+TsRUvbMX3QijB07Vr733nsJt1NV52XGiu0xT1cvLZyEP6gzc/XOmPdemD+Bs7PcKfUUqWg3XfKH7KzrVtG7OVrTyE9f/Yjionyy3A5qPX42l1XwwPUXMTQ7Pd5hfeaaVS4lvYo+c90mg5Zimtx+rm7sWXysYqnqBl+nfY4u+J3EvWaVpKQFWtIPBXRp+V4gqAOgaaLHXsAKhSJ1CeiSN8oreaO8MmL7/dcVdFOPFApFKpCKmmirWKozP0cyfydKUtIChn4oHEM/5LBplu/ZbepXqlAoug67JqzHHrWCplAoWqClmCYV0HVJVZ0XoNM+RzJ/Jyo6bIGWtNi5GU5LHWWu0mkrFIouRI09CoWiI6Ryflm41vrO5/awvKSwUz5HMn8nSlLSApomGDm4Hy8tnBSjxa5uCLDlgyOsnXMpNk0Q1CWb3vuCsy7/OrmO1HhaVCgUqccpb5Cywyd47vbxSCkRQvBm+THO6p9GrlMN6QqFwpqWYpqeTrgn95EaDw+/doCl0y9meF4mbkfHP0cyfydqdKZlS5hw/VD4fgA1jQE+PdFgJi69+1ktN0/suVoohUKR+vgCQeqaQuOMJJShU9cU7NE6TIVC0TNI1fwyXddZMq3AjLdWbTvE3HW72X7vlTGfJzqmy3Y7qPH44wbUyfqd9PmAu62lQ632WzFrDI+/eZA3yisZlu1meUkhbqea3VYoFF1HZpqNyaMGc9OTu8yxaGVpEZlpauxRKBS9D12XnGjwsXRLuTnmLSsuZP2OwzFa6+hYbWpBHoumXMCCZ8q6vTx8n9dwt7V0qNV+C599n+KifPP14k17CfRQP0uFQtE7qG8KckfzzQNCY88dz5RR36RmuBUKRe+jusHH/A2RY969m/fyn9cVxGito2O14qJ8M9g2ju2u8vB9PuBuqyVMvP2y3I6I1/6A3nWdVSgUfZ64lqTqYV+hUPRC4sVfNk3EzFJH75vldvQYK8Q+H3BHW8KMzs9i7ZxLCcqQ/YxRbSiedUytxx/xOlXsdRQKRWqibAEVCkVfIjz+Gp2fxerZRWxaMAEhRExFyOhYrdbj7zFWiH0+4A63hBmdn8U914xkycv7uPzhbRElPq2sY1aVFrG5rMJ8nSr2OgqFInVRtoAKhaIvYcRfUwvy+PHVI1m6pZySVTuZuXpnTBn26Fhtc1kFq6LGy+6K1VRpdyIzWq1KJv98RiG5/VztznxV9FpUuWFFt1FV5+V/3v6EkrHnRFiS/uDyr7eUad9nrllV2r1X0WeuW0XL6Lrkq9NNzFy9s9Uy7LouqfX48PiCBKUkw2UjqIM/oCcjVlOl3VvCsIQ5fsrDLRPP497NeyMyYXU9frn2VLTXUSgUqYuu61w+cjBz1+22HKcUCoWit6FpAimt81es9NjHT3tbdZ9LNn1eUhJOUGIG23AmEzbY+xYBFApFiqLGKYVC0Rdpaxn2trrPJRsVcIcR7+mpN8puFApFaqLGKYVC0Rdpaxn2trrPJRslKQnDeHqK1gcp5xGFQtFTUOOUQqHoi7S1DHtPHSOTPsMthMgXQvxFCPEPIcRHQoh/a94+UAjxJyHEweb/s5u3CyHEY0KIT4QQe4UQY7qqb219elIoFIruQo1TCoWir2Lk0g3NTie3n8tSk91Tx8jumOEOAHdLKd8XQvQDyoQQfwLmAFullA8JIe4D7gPuBa4FRjT/uwxY2fx/pxHuPjK4v4vfLZyYrGxWhUKhaBeaJhiRm8kL8ycQCOrYbRp5mdY3HoVCoehNRLvFWcVobZ0JTzZJD7illMeAY80/1wkh/gEMBaYDk5t3Ww9sIxRwTweeliGB4i4hRJYQYkhzOwmj65IDx+t6XDarQqFQWKHrkoNV9WrMUigUfYr2xGtWrnLdTbcmTQohzgVGA+8Ag40guvn/vObdhgIVYYcdad7WKbSWzarroYqTR2saIypPRtPW/RQKhSIRqht8PPqnAyyZVsDGeeNZMq2AR/90oNsz8BUKhaIrOdHg7RL3kWTFb92WNCmEyAQ2A/8upTwtRNyZGas3Yn4bQoh5wDyAc845p839aCmbta1PU2qWXNFROnrdKvouuq63WC+gq1HXrCIVUddtaqPrkkZv57uPJDN+65YZbiGEg1Cw/ayU8nfNm48LIYY0vz8EqGzefgTIDzt8GPBldJtSyjVSyrFSyrG5ublt7ktLvo5t9XLsqZ6Pip5PR69bRd8loEtLH+5AklbV1DWrSEXUdZvaVDf4OHyioU0+3O1tN1nxW3e4lAjgN8A/pJT/J+ytV4Bbmn++BXg5bPvNzW4l44FTieq3w5cPJJKnbx1nmc3aVi/Hnur5qFAoeh9B3dqHO6hkbAqFopfiCwR5bOtBlhUXRsRrq0uLWnUfaUkyksz4rTskJZOA2cDfhRAfNG/7D+Ah4AUhxG3AF8ANze/9Afg28AnQCMxN5OTxlg9euXMSHl9kNmtbvRx7quejQqHofThsmuV447CpOmYKhaJ34rTbqKr38svXQ/krWW4Hjb4gQ7LSWpR+tCYZSWb8lvQRWkr5NymlkFIWSim/2fzvD1LKainlFCnliOb/TzbvL6WU/yqlHC6l/IaU8r1Ezh9v+SCoE+Pr2FYvx57q+ahQKHofTodgeUnkLM/ykkKcDpUvolAoeidGnFVV72X+hjLufvFDzhqQRpa75TirNclIMuO3Pldpsj3LB9Fejg67hl0THDvliZgJD/fFBYmUoazO6gZfj/B+VCgUvYcmn857h0/y3O3j0aVEE4KX3z/COQPTIaO7e6dQKBRnaItvdlvJyXTy3O2XYROCDJcNX1DGxGPRtBbzJdOzu88F3O1dPjC8HFtalgA4WFXPo386EOMeoNxKFApFZ5Lu0rjiwjxuenKXOc6smDWGdJeSlCgUip5DZzmAWLWzqrSIx7Z+zBvllS2225aYL1me3SJUT6Z3MXbsWPnee9bKk45eAFV1Xmas2B7zR3tp4SQAZqzYzpJpBSzdUm65T08zYFckRJc8PbV03SoUBkdrGrlxza6YcWbjvPEMzU6Pd1jKXbPn3vf7LmnXis8eui5p51K0i5S7bhVnaCluak9MFK+dJdMKmL+hrMV2u8G2OW6jfW6Gu6PLB60tSxyp8ZDldii3EoVC0aUE4riUJMsWUKFQKNpCZzmAxGsny+1otd2eVOa9T65BGssH0UmSLdGSX7fxXq3H3+kekQqFQhGOXROW44xdydYUCkUPoqW4qTPaqfX429RuR2K+rqBPBtwdoaVMVuO9zWUVMR6Ryq1EoVB0JnmZLlaWFkWMMytLi8jLVLI1hULRc+gsBxCrdlaVFrG5rCKhdpNNn9NwJ0JL2bbGe7quE5QgpezWpQtFl6J0hYpuxe8PUlnvJaBL7JogL9OFw9HirFHKXbNKw60gBa9bRSSd5VIS3U6220GNx9/tMhELlIa7M2gpkzVZWa4KhULhcNhaSpBUKBSKHkFnxUZW7aRazKUC7iiin6Ky0uxUNfjwB3UcNo28TBd2e+tKHF2XnGjw0uQPYhMCt9NGlrvHPIEpFIoUxusNcKLRZ85wD0p34nKp4VyhUCSfzvTabq1Nq+1Awufvis8QjRqhw7Cyj1lZWsSvw7weV5UWceHgfi0G3VbtLC8pZHD/NM7NyVBBt0Kh6DBeb4CPTzRwxzNlEePUBYMyVNCtUCiSSlfY7sVrc0RuJger6mO2u+waNz/1bofPnyzrQJU0GYZVCdA7nimjuCjffL3gmTIq673tbmfxpr18Xt1olhNVKBSKjnCi0WcG23BmnDrRqMYWhUKRXFornd6ZbVbWey23f17dmND5u+IzWKGmQ8Joq9ejP6hztKaRQelOqj3+GLlJvHbSnTblya1QKBJC+XArFIqeQkte2+2RaYTvG5RxxrigHje2sjp/Z3yG6L4lIjdRM9xhtNXrcf9Xddy4Zhcfn2hg/fZPuWL5Nmau3sn+43UEAnrcdhp9QeXJrVAoEkL5cCsUip5CzjvWcAAAIABJREFUvHjHYdc4cLyOGSu2M2nZX5ixYjsHjtehW0wMGJIOY99DlQ3WY5xNixtbRW9rT6zVkl94dN9a+hytoQLuMKy8HldGeT0uKy5k1bZD5jJuydhzgEi5iVU7y0sK+VpOeo/3iVQoFD2bDJdm6cOd4VLDuUKhSC7xvLbtmmizTCNa0vHY1oMsL4mtaZLu1GK2PzrzEs7OSkvI67slv/DOlJsoSUkYViVAs9LsPPidi7n/Op39X9Xxy9cPsKeiFgj94m1hs0rGkofRzu8WTqTJr2MTKJcShULRKZzyBPnH0Vp+O288QV1i0wQ7DlbRP20QWcopUKFQJJF4pdOPnfK0uax7tKRjT0UtD792gI3zxgNEtPnwawdYMq2ALLeDWo+fX/xhPz+bfpG5bVi2myED3O2KtVoq/95Z5elBBdwxWHk9np3l5mhNI5vLKlgwebj5h95cVkEwbFlhWLYbl13jy1oPupRoQuCyCTRNU8G2QqHoFOyaoNEXQJcgAV1Coy+gJCUKhSJptKZrNmQa4cFqPKlH+L6j87NYMHk4ORlOhBCc1T/NbNdpt1FV72X+hrKINr881cT8DWUMy3bz0sJJpn1grceHxxfShKfZbdhtAo/Pur/x/MLb8zlao8MBtxDCBiySUj7a0TZSiUHpTu6ackGMFdf2g5VA6A/w1JyxVNR4+PeNH5j7LCsuZP2Ow/zwWyM73WJGoVD0PXLcTorOG8RNT+6KGIty3EquplAoup622OgZMo3ofaykHsa+j/7pALdMPI97N++1bNeqzeUlhTz82oGI9nVd8ll1A8dPN7F405m2Hp15Cb/4w36q6r1ttv1rz+dojYRKuwshtkkpJ3e4gS6iK8q2flnrYebqnTFPORvnjSegS/xBSW2jzwy2w/dZMq2ApVvKeWnhpJSrjKSwRJUbVnQbR2sauXHNLsuxqIXqkyl3zarS7gpS8LrtC1TVeZmxYnvMGBQd47TXpeSr002WcVZ4u+FtOuwadi121rqqzsu+o6dY8vI+y3gsfDa8LTFZO11Kuqy0+3YhxOPARqDB2CilfD/BdnsMxi/aH8eOJthc6e2K5dvYOG98XFvBjmp+FAqFIhxlC6hQKLqTtuqa21PWXdMEMo4dYHi7lm1mxPYv3Wlr0ea5PdaFnVWePtGAe2Lz/z8L2yaBqxJst0cQvmyyZFqBpY7HpgmCUrJ2zqX4g7rlPrUef4TFTFeXD1UoFJH0pu+dYQsYPc4oDbdCoegKosdPt9Na1yyE4GhNY4fH2Hh6aaBd7TrtNhp9wbjxmPGz22lLSoVJg4R8pKSUV1r86xXBNkRa1azadohlxZF2NCtLi/jTR8e4/OFtLHl5H/3S7DxywyUR+ywrLmRzWQVP3jyWbLej0/wcFQpF2+hMH9WegLIFVCgUycJq/Dx+2svTt46LGINWlRbx4Cv7Ehpjrez5Vswaw09f/ahd7eZkOPlaTrqlheCqbYfMwDqgy6RUmDRIVMM9GPgFcLaU8lohRAEwQUr5m87qYEfoLH3W0ZpGJi37i/nayJ4dkZfJwcp6NpdVUFyUb2bMDst289vbL8Pj1/EHdQa4Hdi1kEuJ4efYFt2TosejdIUpRFv1hqnC59UNvLLnKNPHDENKiRCCl98/wvTRQzknJyPeYSl3zSoNt4IUvG57G/HGz98tnIggZJsnhODBV/bxRnllxD4dGWMNLfeXtR4GuB0sf31/h9qNdCmBNLsW41Jy7JQnIsYz2H7vlS3lw7RGl2m41wFrgfubX39MSM/drQF3ZxG9vLGnopalW8pN0T3Abf90vrn/kRoPR2ubuHHNLiD0Rxs84Ez1os70c1QoFG2jt33vbJpgY9kRHvnzQXPbsGw3M4qGdWOvFApFbyTe+OkP6GZQerSmMSIoNvbpyBhraLlLVu1k47zxHW5X0wQDM1wx+u7w151p+dcWEl2DHCSlfAHQAaSUASA172IWWC1vPHJDaEnCeB1d9j1cHxT9R2upfKhCoegaetv3zqEJHp15ScxSqUNpuBUKRSfTWtnzqjqvmcc2Oj8rZp9Ezmnkv1mdGzDPf7Smkao6b6dIWDpq+dcWEp3hbhBC5BBKlEQIMR44lXCvehAuu8bS6ReT7gyJ8NMcoWcUQzf5660fm69XzBrDMzs/j/tH60w/R4VC0TZ62/cuK83B6QxnxLiUleEkK83R3V1TKBS9jHjjp5GTZuWJbfhcd3SMDfflXlZcGOPLbXhtJ5rw2FKFya4gUQ13EfAYcDGwD8gFSqSUezunex2js/RZ0dql0flZLJoygvNzM7Brgj99dIwh2RkRlScfuP6iFv9ovcktoQ+jdIUpRm/63n1Z6+HBV/ZRXJQfMfY8+J2LOTvLHe+wlLtmlYZbQQpet70Rq/EzXk7axnnjO2WMNc6p6zpBCVJK3E5bqO5JQO9U3Xgn0zUabillmRDiCmBk80kOSCn9rRyWMoRrl0bnZ/Hjq0dGPGktKy7kl68fYE9FrXnMA9df1OIfu7P8HBUKRdvpXd87GVONbVlxIYLUdF1RKBQ9G6vxM562G+iUsTb6nFYz2suKC6mq85kxWE/PzUko4BZC/BV4G/grsL03BdsQKahfMHm4eYOD0B/23s17IxIo42mWetPsmkKh6F6khPU7DrNkWoE5w71+x2EeuP6i7u6aQqHoIyQ74bC6wcejfzoQM+4tmDw8IgYTQqDrskfGWIlquG8B/gkoBpYLIbzAX6WUP0y4Zz2AcO2SUS0ynCM1Hs7qnwbEF9t3hs5IoVAoDGyasJzhtqnxRKFQJIlk58boum457mWlh3JXjNcPvrKPH35rZI+MsRKVlHwqhPAAvuZ/VwKjOqNjPYFwQb03YF216Kz+Lv5275UEdYnbEftkF148B84Yq/cAnZFCoUhBgrq0XG3bOG98N/dMoVD0Fboi4VDXJScavDT5g7hsGroEXcqQI4ok7ri3acEEqht8psS3/Fhdj4yxEpWUHAJOAM8R8t6+S0qpt3LMU8A0oFJKeXHztgeB24Gq5t3+Q0r5h+b3fgLcRshucJGU8vVE+txeDB3R8VOemGzZx28aTWW9jzueKTvzhDd7LCPPOvNk1ds8gBUKRfcSlNJyTAkmkACvUCgU7aUzc2PC1QC5mS7uuWYkizedibeeue0y63FPD3l2R2/viTFWopKSxwhJSr4PjAbeEkK8LaU81MIx64DHgaejtj8qpfxl+IbmypXfAy4Czgb+LIS4QErZZb/JeHprTdMIBgNsnDeegC6xawKHXfDdFTsjZ683vMfGeePJcTup9viQYDkz7rBpHK1pJCglaQ4bgzJcPW75Q6FQ9DxsQjC1IC/GpcQm1PihUCg6DyMe8gaCCCA0xAjyMl3Y7ZFlXAIBncp6L/6gjtthQ5cSb0DHpgncTo0BLic1Hj+C0PaALnHYNPIyQ7HPV6ebaPAGWDKtgJwMJ/++8YOI2OrwiQbLWMqmCf56z2QkglMeP1/WethcVhHh1d3WHLquzrdLVFLyK+BXQohMYC7wIDAMiKual1K+LYQ4t42nmA78VkrpBQ4LIT4BxgE7Wz6sY7Skt+7vtJGd6ebGNbvM91aWFjHx/BxeKDtitnGkxsMpj59Gf5AjJz0MynSyYtYYFj77vnnc2rmXUlHTyL/99gOl61YoFO0i3aVx15QLIlbWVpYWkeFKtI6ZQqFQhIjnCrJ+x2EWTbmACwf3M4PuQEBn//E6FjxTRm6mi//49oX88IUPI/y5c/u5+NvHlYw5NyciHlpVWsQAt53vP/kOR2o8TC3I4yffHhUzm/3Hvx9jVWkRC6LGvad3HObykYMj1AerSovISrNTWddEozfI4RMNPLb1oOkPbhVrJSPfLqERWgjxiBDiHeAd4JvAfwEjOtjcnUKIvUKIp4QQ2c3bhgIVYfscad7WJcTTW1c3+DjReEY6Yrx3xzNlzLtieEQbw7LdZGc4OVHnZcnL+7j+8e08/uZBnr51HG8tnsySaQVU1/vMYDv6PAqFQtESHq9uORY1eltU8ykUCkWbsYqH7t28l+KifBY8U0Zlvdfct7LeawbCCyYPN4Nt47jFm/ZScdLDVQVDzGDbeG/BM2V4A2dkcsVF+Xx2ojGiwuTo/CxmjBnKY1s/Zsm0AjYtmMCzP7iMLR8cYcy5OTHa7se2fszHVfV8d8UOJv9yG0te3sePrx5JbqYrbqzVUvzXWSQ6JbIL+I6U8iIp5W1SyvVSyk870M5KYDihoP0Y8EjzdqvHCkuhohBinhDiPSHEe1VVVVa7tEpLeuuAbq2bdNhERFnQ1aVFBHVpao8A3iiv5Oan3kUTgvkbyhAQ9zyKvkVnXLeKvoU/zljkb2dZ446irllFKqKu2/YRLx4yHNsCwTMP+P6gbu4bz9Et3RmSmVi9Fz6BnOV28NjWgywrLjRjq0VTRrB4017eKK9k/oYySlbtZNb/vMOYc3Msz1dclM/8DWUxDwsLJg+PG2slI98uUUnJi0KI7wghLm/e9JaU8tUOtHPc+FkI8SSwpfnlESA/bNdhwJdx2lgDrIFQFan29gFa8ZWM41IS0GWEL2RuPycev275h9OlZPXsIvL6uyzbglB1S+XT3XfojOtW0bewaSKuljEZqGtWkYqo67Z9GPFQbqaLBZOHk+V20OgLokvJsGw3dtuZ+VqHTTPHpFqPP2Z8mlqQR06mC7uwHrucNo0//+hy0uwaCMH9142iyR9keUkhmhDkZLosY6qv52aiidg8uZwMZ9yHhXhe4cnwFU9UUvLfwL8B5c3/FjVva287Q8JeziBUJh7gFeB7QgiXEOI8QnKVdxPpc0sYvpLhM9aGr2SO28nK0qKI91aWFvGHD79k/oYyblyzi81lFRw75eXTqoaI5RBjf11Klm4p50cbP2R5SWFEW8tLCrnzuT3MWLGdA8fr0JM0W6VQKFKLdKfGilljIsaPFbPGkO5UGm6FQtE55GQ4efrWcdxzzUiWbinnxjW7WPLyPuyaYN3cS8nLPONOkpfpYlVzfLRq2yEenXmJOT5NLcjjzqtGMGftu9z1/J6Y2GdlaRE/2/IRi1/cS0WNh++t2UXJqp3c97u/o0v4zd8+NYPqcIZlu/niZCM/eiE2nsrr57Lcv9EXjOsV3lL811kImYCVlBBiL/BNwwpQCGED9kgpC1s45nlgMjAIOA480Pz6m4TkIp8B86WUx5r3vx+4FQgA/y6l/GNr/Ro7dqx87733OvSZ4mWpflnr4cFX9sU4A3x/3NeYu243w7LdPH3rOB764z+4ecK5DMly80V1oynUXz27iF/9+WPeKK8EQpqkRVNGMDw3g0NVIUG/UZ50WLa7R3pIKky6ZCoxketW0Xf4sqaR9TsOUzL2HGyaIKhLNr33BbdMPI+zs9PjHZZy1+y59/2+S9q14rOHrkvauRTtIuWu295EZV0T312xI2bW93d3TCSvueifgeFSEgjqpDW7lPiaXUoMswk4E/ucn5uBEIJndx5mzLk5DM/NoOKkJyYW2jhvPC67xlenvREJk8tLCnn4tZDvthlP5WXgdtjJdjs4WFUfkQC5urSIIVlpZLm73KUk7gGJ2gICZAEnm38e0NrOUsrvW2z+TQv7/xz4ece61n7i+Ur6gzpvlFeaAbPBf15XwFuLJ7P/qzp8gWBMJaSVs8aQ19xe+LF7KmqZu243by2ezNx1uyPaVHpuhUIRFwGXjxzM3HW7I9wDuiY0USgUfRV/wFoe6w/GJmjb7RpnZ7ljth+taYxow4h9Ns4bT7rTFuMwsqy40CxgYxw3qF8aAzNcZpEdgDuf22MG5kab2++90ozfOlKUpzN9xS3bT/D4/wb2CCHWCSHWA2XALxLvVs/D0CiFY+gmbZpg6ZZybJoWky17x7PvE9AlugwtrayeXcRLCyfypx9ezu8X/RN2LeSpG90uhPTcSlqiUCjCkXEqrqm6NwqFor3ouqSqzsvRmsaYmMPQNYczLNuNw67FPS56u9tp3Uatx0+aw2Y5lt099QJzPwjNtNd6zsw8O+waVWEuKca+nam37goSTZp8XgixDbi0edO9UsqvEu5VDyQ3I6Thjva+/emrH1FV52N5SSGNvjhZrkEdu6ax+JoLmbs2clbqV3/+mEVTQhfXG+WVEXruljwjFQpF30RKa5cjFXArFIr20Jr3tKFrDn9/eUkhXn/Q8rgRuZkxUo4nbx7L07eO4+an3jV9tu+/roDaRj+aEJZj2ZAsN1ML8pg76TwzFjIkJFX1Xp6+dVxMv8L11snw1O4InZFlM4GQBvuK5p97JbVNAbZ8cIS1cy7lzbuv4Lnbx/PrrSFN9p6KWh5+7QAZLrvlk9zHx+v5/pO7OFHnJbc50SDa0/Kea0bx5x9dztLpF5u6JOXPrVAooomXQKSeyRUKRXtozXta0wSD+7tYOv1iNs4bz5JpBTz82gE+qWywPK6y3mu5PTPNzksLJ/HOT67i3/7lAmb9zztMf2I7X5xstBzLvqhu5J5rRkXEQos3nbH1u/mpdxncPyQx2X7vlby0cFJEMJ0MT+2OkKhLyQpgAfB3Qs4i84UQT3RGx3oavkCQ1X/9jG89+jZXPfIW1fXeGE324hc/ZHWUk8my4kJWbTsUccEYhHtaVtd7qa73MXfdblOXZOyj9NwKhcJA04Sly5FaBVMoFO2hLd7THl+Quet2c+OaXczfUMaeilrSnTbL4wLBOJrvgE5uPxeapkX4Yz+29WDMWLasuJDHth6kut4bEwtluR3mzx5fkNx+LoZmpze3fWb8S4andkdINGnyCuBi2Wx10qzj/nvCveqBRHs0ZjbPZof/UavqveT2c/HC/Ak0+YMcrKw3xf9w5oIZnZ/FgsnDyclwMsDtYGpBHo2+IL6g3uU+kAqFIrXxBnReev8oa+dcarqUPPn2p9x51de7u2sKhSKFaIv3tNU+jT7ruiT2MD9ug6kFeQghOFrTCEBumKe2oQ7YcNs4Kk97qfX4+eXrIdlIoy8yODZ031Z97Mjn6g4SlZQcAM4Je50P7E2wzR5JtEejL6hHVEIynsyklJzVPw2HTWPplvKIJ7TQsq/gx1eHfC1LVu1k7rrdLJpyAV/Py2BzWUVMm53tA6lQ9EVaSgxKNdKdNmaMGcrcdbu56pG3mLtuNzPGDMXtVA/mCoWi7bTFezrb7eC5H1zGpgUTWD27iKkFeXwtJ93yuLxMV8T2qQV5LJpyATNX72TSsr9w45pd3HPNSEbnZ5ntV9V7kRLufvFD5m8oo6reyyM3XMLADEfMKt6qbYfaFBclw1O7IyTqw/0WoYRJoxjNpcBOoBFASvmdRDvYEbrKYzPco1EIYenL/fMZheRkOPmsuoHjp5vMEu/Dst2snl1EttvBzDBPSmj2tVw4EYFA13WCEqSUifhAJo1O8q1MNZQ3bAqh65LPqhv4vLqRdKeNRl+Qr+Wkc25ORkpeq8dqPdywemfMGPLi/AkMsbDlaiblrlnlw60gBa/bnkB77sst7WuVfLh6dhEj80J6aavjouOkmRZj1dLpF5v1S5aXFPLS+0eZUjCYvH4u+qU5yHDZcNk1ArrEH9Bx2DXsmsDja3uc0Y2xSZf5cP9XgsenFLou8Qd1ArrEaRPcd+2oiMzb/7yuAF8gyFenm7j5qXfJzXSxZFpB2EWk4Q/KuBqnofGLVvRIemomsEIRTq3Hx/HTTSx5eV9Epn1WuoOBGalXXEqX1mOIrmxKFIo+T3vvyy15T1slH87fUMbGeePjBrHh7UV7cBttDM/LYPu9V+Kwa9Q3BdjxaTUvlB0xC9QMzHBEFKgxguf20NWe2h0hUVvAt4QQXwNGSCn/LIRwA3YpZV3ndK/nEAjo7D9eF1HpaHVpES/fOZFAQHKiwcdN//MOR2o8bFowgSM1Ho7UeFi17RA/vnokdz2/h9xMF8tvuMRSW+Swp15Z5niZwKpKpqIn4fEFzZUmwExg3jhvPGR0c+c6gF2L1UkOy3Zj11JvDFEoFJ1Le+7Lrc0Cx0s+PFLj4e4XP7QM5KNnuK3GKoFgyAA3miYYlCH53cKJNHqDHD7RwH/+v30RlshAr5nYS9Sl5HZgE7C6edMw4P8l2qmeSFX9mbKi0Pyk90wZPr+MybytbvCZ2qEFk4dz7+a95Ga6+PHVI3n4tX/E6LSXlxRiT7ELB3puJrBCEU4wzoxwMEUnhG0arJg1JmIMWTFrDDYVbysUfZ623peNmfAZK7YzadlfmLFiOweO17Wp8E2tx29ptRfd5oOv7IsZq5aXFPLVqaYI60GBoPQ375gubeFt91SLv46QqKTkX4FxwDsAUsqDQoi8lg9JTXzx7G6CeswS76pth1hWXMi9m/eatn9LphWYFZWq6nwsmVZgupTcs2kvj980OuVm23pqJrBCEU6aw/o6TXOkZoTaFNB5/M2DLJlWYOaPPP7mQf7r+ou6u2sKhaKbaet9uS0z4VaFb4zS68Yx4YF8dJuGdfLaOZdyssFHrcfPw68d4L5rL4w4rrWHhN4ysZfoHccrpTQfM4QQdiBF541axqYJ62ITmoh5CtxTUcv6HYd5Yf4EhmW7GZbtNgNv4/35G8ooWbWTkw0+cvs5Tductjoo9ATXhZ6aCaxQhDMow8WTs6Ou09ljGZSC+m2AoC7Jcjs5f1AGuf1cnD8ogyy3M6WdVxQKRefQ1vuyrussmVbAxnnjWT27iNH5WTGBrKYJRg7ux0sLJ/H2PVeydPrFEVbH4ZZ/oTgkdmLyjfJKTjb4eOiP+wG4/7pR5GS6IlyV4s2kO+22Ft9LNRKd4X5LCPEfgFsI8S1gIfBq4t3qebidGstLCiNcR5aXFOJ2amS7Y58Cf/itkZzVPw1dl6wqLaKqzmv51KkJYdrmtFWf1FOSFcO/jH3MpUSRYrgcGkunX2y6lLhSdHYboF+ajdIJX2Puut3m93/FrDFkpqXeDUihUHQubbkv63oo72zplvKImev1Ow7HBLJG8qGuSxq8AarqvUCk5V+4g8nUgryIooDhdsjGKr8RsxiJkVYz6eEPCS29l0okaguoAbcBUwlZobwO/I9MpNFOoCssf1qzFouXfFBV5+X+l/Yyd9J5ZLrs3PHs+xFJlwMznJYWXy0lHlbVeZmxYnu7jlF0KsqqKoXobd+XL2s9llZbL8yfwNnKFrBDKFvAHkvKXbepQLwx8bkfXMaw7PQ2WQjGs/x77geXmQYSZwJrR6txTmv2hClkP9w1toBSSh14EnhSCDEQGNbdwXZXoWmCc3My6JfmsPyjx7Og8QWCvFFeyRvllYzOzzJ1l8Oy3QwZ4ObYKY+lPsnjC1BVh+WFpZIVFYq209u+L/44+SSBoN5NPVIoFN1JewPSeGOiTRMtHtcWyz+bJmJm1+PFOdHylXgTID3R4q8jJBRwCyG2Ad9pbucDoEoI8ZaU8ked0LceR1v+6OEXvttpi7DFMbTbxmzUsVOeuLY5//iqjqVbyi2lIlZJEeFaqhR4AlQokobDrjG1IC+mSFUqWnECOCzKJxtllRUKRd8insR0RG4mNR6/ZRDeHsODeMF8S21Ez1ob70XvC6HZ9r4SryQ6Qg+QUp4GvguslVIWAf+SeLdSk3BLnDuf28OBr+p48JV9MTaAq0qLePCVfaZtzqrSopgS8au2HYprfxOdFBFdPtXK3keh6KukOQR3TbmApVvKuXHNLpZuKeeuKReQ5kjNAT43w8nKqDFjZWkRuSmoaVQoFIlh5Tby6J8OcKAyvuVf2xMr41sHttZGdDy0vCTWDvnO5/b0qXgl0aRJuxBiCDATuL8T+pPShF/4S6YVmAmW4TaAQwak8dNXPzKTCoz/X5g/gUBQ5x9f1UVkAVstfUcnRURrqVQBGoXiDHWeIHdEeejf8UyoWlr/tG7uXAeobQqw5YMjrJ1zKTZNENQlm977grMu/zq5DpU4qVD0JazkIcVF+RG1QaJjgrYaHrRmHdhSG+HHHqnx8PBrB1g6/WKG52ZwqKqBh187E+f0lXgl0YD7p4QSJf8mpdwthDgfOJh4t1KT8AvfygYQ4K3FkyMyeCEUdD9wvcTttJtZwwbxlnnaoqXyBYJU1XlTJdFAoegSArp14ZtAis6o+AJBVv/1M1b/9bOI7TdPPK97OqRQKLoNK2nHWf3TEtJMG7SW/9JSG9HH7qmoZe663by9eDJz1+1usW+9lQ4H3EIIG5AvpSw0tkkpPwWKO6NjqUj4hV/r8VuXcI+jvzQCYiv7m6w0O1/WhorsOGwaeZku7GH603haqoAuubE5E7ktuq4UywRWdBGBgE5lvTfu9ZZqxPvOOVJU86wKTikUfYvwe7PDruG0CRq8QYJSkma38fztl/FJZQPpThuaEGSlO9o9Rljd/xMZa+Ida28hBurtdPiOI6UMEkqYVDQTrmlate1QjGbpyZvHkpfpiqt7Cl/m2X7vlby0cBJfH5TBgcp6Zq7eyRXLtzFz9U72H68jENAtz2u0uWLWGH7++/KYpaAvT3ks9VhtKfOq6P0EAjr7j9e1eL2lGk6bsNQPOm2p+TCpCk4pFH2H6Hvzf770d7446eHGNbu4/OFtfHflDk55Ajz/7ufcuGYX9d4AP/99eUzu2OrZRXHHiHj3/2y3o8NjTbxxqqUYqLeTqA/3z4EBwEagwdgupXw/8a51nO702Ix2KQnoEn9Aj5gxNmYQA80ziE6HoMmnW84qh3vujs7PYsHk4aYWfMgAd8zstMcf5FBlPYMynVz/+PaY/m1aMIGSVTvN14YXJtCrvIq7mF7rDdtBj+cezefVDTzx5ifcfvn5pub5ybc/5c6rvs45ORnd3b0O0YHVqJS7ZpUPt4IUvG5bo73f3WjP7NWziyylp8tLCjndFGBEXiYHK+vZWn6cKQWDTWembw4bwOAB1mN4S7UKcjKcHV75jvdZe/lqetf4cAMTm///Wdg2CVyVYLspS2u6KF2XHKyq5/an3yM308U914yUAZlMAAAgAElEQVSMqF4ZbQNoeO6Ozs+yrNRk7Guc92hNI3PX7Wb17CLLZZtox5Nw7VRv8ipWdIx4Hs/+FPZ4dtk1ZowZGlGZcXlJIc4Ulsn0Fl9ahaIv0ZEq0dFa6PD8MIPcTBeZLntELLGsuNA0YDAK0ui6tDxPS1rtRMaaeMf21fEr0cI3V3ZWR1Kd6Ce2bLfD1EpnuGw0+nRAIiVIKXnou9/g/NwMvAGddXMv5atTTbzywZec8vipqGkkza5hqDnWzrkUh02YwTackYj87o6JCCFMtxK7JphakEeG08b6W8fxRXUjj209SFW9lyduGsMTf4nMaQ3XTvVVXVWqkIxZAbtm7QtvT+HZB12X5o0IQt+dxZv28sK88d3cs47j9wdDq2S6xK4J8jJdOJRDiULRo2nN9cOKaC20kR+Wm+liweThZLkd5GS6mLP23Yh27928l4e++w38Qcm5g9I52eAjzWHDH4xdTXfYY3XVUwvyAPiytjEUtwCuqNjGabeRlWbnRIMPX1DHpgncDg0hBB5f22a1e/lsdwSJFr4ZADwAXN686S3gZ1LKU4l2LJWwempdVVrEY1s/JsvtpHTC13j8zYPcMvE8M2ieWpDHnVeNYGFYqfeVpUX8euvHVNX5Yma+1986zvIJtMEX5L//UM4b5ZWhNmaN4a6rRkSUkF9VWoQ/GOTZXZ9zy8TzKD9WF/F0bWinrBI2+4KuKhXoyMxIR0h3aqwsLTJt9IzrMt2ZurPBurRevUnV9AS/P8j+yvqYv9GFeZkq6FYoejAdqXobbaawuayCtXPGcqLeZ8YImxZMsGx3aHY6s39zpsz68pJCHn7tAFX1XvP+AVDfFGB5SaHZ3tSCPO6acgE/ffWjiLglPLZ5o7zS3C98LFpeUki608aDr5Sb5xmRm2mu7EebOFht7+z7Wk8hUUnJU8A+Qj7cALOBtYQK4fQZrJ5aFzxTxpJpBZw/KIO563azZFpBxAx1cVG+GWwbx9zRfAwQMyP3RXWj5czjZycaKC7K543ySo7UeDhR72PJy/ss+/JC2REOVtaHvDDzMnE7Ip8m2+LLqegeOjIz0hEafTq/3voxS6YVmNq/X2/9mAeuv4js1JQ7I4T16o1I0Uu7st4b11d8aHZ6N/dO0RId0aUrfXnvoa2uH9Gzvnn9nSydfjHpThu1Hj8Omy0iRqhu8MWND6JX9pZMK2D+hjLz/gFw81PvkpvpMsf9s7PcfP/JXTFxS3g88UZ5JcVF+TFj0eJNe1k6/WIWTB5unueF+RMs71/xtvfW3LFEA+7hUspwG8CfCiE+SLDNlCPeU2uW24FNE+bPremwjP2Mn8N5bOtBVpUWsSDsSdLQaN137YXmfulOm2W7xkx1Vb2XswakMSzLHRNM91VdVSrQkZmRjhDQJW+UV8Z4xd9/XUGnnieZCAHLigsjZmmWFRembMDd23zFFYq+Qjzr3/CVZKvVzGduuyzCu/rNu6+IGANWbTsUM8atnDWG/3r5o4jzR8cY4flbR2o8Zr0Qo/3W4pR476c7baRjM18HWsgN6ku5Y4kG3B4hxD9JKf8GIISYBHhaOabXEe+pdWCG0/QAjvbljufTPTDDyfHTTTHvVdV76Zdmj3jK/eXroaWhWo/f3K/RF7Rs9+wsN+/85CqCzRry6gZfjBZLzWj3XJLlveywacz/53MpGXtORBXDVPWsBpAS1u84HDFrv37HYR64/qLu7lqHMPI0iovyzc+zuawipXX2CkVfoC0VHq1WMw+faIgY/4O6jHi9p6KW9TsOs3HeeIJSYhOCL042UlXvjTi/EYsYPwMIEZu3o0ss4xbjOH9QZ/XsIvL6uSy13zmZLkCyenZRaGyyaZZjVkt1SXojid5F7wCeEEJ8JoT4DHgcmJ9wr1IMK7/JlaVFLH99P4ue38PykkI2l1VE+GJuLqtgZWlRxDHLigtZ/vp+hmansSrqvRWzxvDKnqOkOTTufvFD5m8oo6rey4pZY9hcVmHuNyjTyaMzL7H0vjzZ6Gfm6p2mz+b+43Xc/9Je5budAiTLe3lQuoNp3xzG3HW7ueqRt5i7bjfTvjmMQemOTj0PhGZyquq8HK1ppKrO22XXXpZb464pF7B0Szk3rtnF0i3l3DXlArLcqfkQkZvhtPw8uSrfQqHo8RgryUOz080y6+FYrWY+tvUgq8Nigk3vfRETI8yddB53PreHm558h9pGPweOnYrx4l4xawyrth0ytdZ3PreHB1/ZF9NWhktjVWlRTNxiaLiz0x0s3VLOj174MKLGgZGbNmftu/zL/3mbpVvKWTTlAgalO1gUNWYtah6z+pInd6I+3C6gBBgOZAGnACml/FmLB3Yx3eGxGa65Avjpqx+Zy/Kj87NYNGUEI8/KREo45fFzssFHbj8nGS4HX51qorrBx6pth0LHTr+Ix988SHFRPjkZTgZmONn47ueUTjgPXYaClKx0BxUnPfzx78dMr81GX5CCIf1w2LUY/+/qBp+lz6ah5zJe91btVCfTLd6wycjmTpYPd7KSQAGO1jRyqPI0w/P6m64exutU1Dy35Jnbwnc35fyMk+nD3RE6oq1WGu52k3LXbaLE+36/cuckgjoRjmQffXma/IFuKk56eGzrQfZU1Jr7P/uDy3hu12eMOTeHnAwnuf1c9HfbqGvSOVRZHxE7SOD8QRmmg4lEhuKSeh+DMp2kOWzUewPUNvoZnpfBTU++Y/bPiG/OG5SBTRN8/8ldlvcPq/tKoj7fPZQu8+F+GagF3geOtrk3QjwFTAMqpZQXN28bSKiAzrnAZ8BMKWWNEEIAvwK+DTQCc7q7sI4V4frnz6sbIjSweypqmbtuN28vnowEvv3Y38z3Xlo4MaIQzerZRWYypdGGERh/WRuqLgWwcd54U9P1QtkR8/jt917J4IzYm25LOvPw171VO9UbSIbGPp6mLtDJPtzJSgKFkOb55rVlMdvfWjy5U8+TLJKl51coFMknns47yx0ZiBo1N8JjAYMjNR6q6rzcMul8pJQRgWyjN2QVHF3XY3VpEaOG9EfTBEdrGvH4gjHtQqx+3IhvNjbbrMa7f8Qbs/pS7liiAfcwKeU1HThuHSH5ydNh2+4DtkopHxJC3Nf8+l7gWmBE87/LgJXN//cowmcf43kZa5pAEOmY0OANRLyOl4SQk+GkrilgbgvXVoVXoBRCWJrbx9MAh+u/O6qd6ks+mt1Jsny4rbR2tk4+TzKDxt7mLZ4sPb9CoUguxhjfP83OC/MnYBOgaZopsaisa6LJH8QmRNz8MDhT5C63nwu7JhCE2s3JcOK021g0ZUSM+8j8ZqcjCCWax8sHczTrscMnFY08MePn6GNsccbgvjZmJSpi3CGE+EZ7D5JSvg2cjNo8HVjf/PN64H+FbX9ahtgFZAkhhnS0w12BsTw+Y8V2Ji37C0/vOByjz15ZWsRLZUc4fqqRlaVFTC3I45nbxvG1nHRWzBpj7is5k8xgMCzbTV4/F/kD3TEa8KkFefz46pEs3VJOyaqdzFy901KLbaUBNjRaxuuOaKeiP7vSgncNyfo9u53Wemd3J/twG0FjOF01AOe4nZbfxxx3auoEk6XnVygUySN6jJ+5eicnG/3m9/rA8Tq+u2IHlz+8jRvX7OLz6gbWzb2UzWUVPHHTmIjxYFlxKG+s4mQjHx+vp84b4GhNI59VN5DtdnDeoAzLCY8jNaFV9HpvgLx+sflgy4oLWbrlI+6acoFZGMfQgy96fg9Lt3wUEc8Yxzy943CMTrwvjlkd0nALIf5OKDa0E5p5/hTwEtKuSCllYRvaOBfYEiYpqZVSZoW9XyOlzBZCbAEeCnNC2QrcK6WMK8BKtj4rWnNlZOZGzxIuvvpCXA4bH35RzXm5/U2Lv6kFedx37ShOefzkZDo52uxlaSz1rJg1hqFZaax561CEe8T2g5V866Ih3LgmVjP1uzsmktc/LaKf4TOkQgicNoEvKGOWnBL57Mb5e7kWPOm6wmT9nnujhvvLmkYefPWjmO/jg9dfxNkpqOGGDq12pJwWVmm4O36eXkTKXbdtJfp+DFiOvcass9V9/pc3XEJ+thuPP4jDplFV56W6wcfmsgrmXT4cu01w53N7ImKJcwa68QUl312xI25OlxFEA+QPTI/IMzNKxa+dcymnPH4GZjhZte2QKW2dWpDHPdeMQgCfVNWbx0wtyOPB71ycULyRInS6hntaB4/rCFadj3lKEELMA+YBnHPOOV3dpwiil8ez3A5LL+P7rh2FTYOLhmZz81NnyrC+UV5J+bE6lkwrwBfQefi1AxEWZg+8/BG/+t43uXzkYOau2x3hJRyM48nb6A/i9wepbQpE3JRzMpxxAp1YX+6WMAaLRl+AJdMKzC+VcX6lJ20bbb1ukyXBCOp6RAGEWo+fVdsOEdQ7V8PdFnusziKet/h/prC3eHfqHrtzrFUoOkpPum6jJxymFuTxk2+PshzjmwI6NQ2+iCRFo6T72Vkhucaatz7lpvHnUNcUIMvt4Pvjvka/NDuPvHEgQjay8Nn3efYHlzF0gDtGJ27U9TD21YTgxjW72DhvvJk7Ft6vkw0+blyzywzUjYD7jfJKbvun8wFMQwZj+wPXy5RMVO8sOhRwSyk/7+yOAMeFEEOklMeaJSPG3fEIkB+23zDgS4s+rQHWQOjptQv6F5doTWU8TZVNE+ajQrwExkZfkKp6r3mhGhnAEvAFQsGQsfRz7+a9bJw33vJcui75uLKe+WGFcp68eSw5mU7LZLXfLZxIXr/IGfF4WM1OGl9W4+m3r2mzOkpbr1srr9Rh2W5zZqSzcNo07rlmZMQKy/KSQpxd4MOdrKAxnn6ws3XpfYXuHGsVio7SU65bXZccP90UcR8uLsrnsxPW1aRtQpDXP41h2W5yM10xyY4rZo3h3/5lhOUMuFER0sBIpnTYNHIznWycN56ALtGEoMkfYMHk4azadoiqei/+5kT5ePGMkf8Vbb4wLNtNoy+I22lj9eyiiFXFvh4X9CQj2leAW5p/voWQA4qx/WYRYjxwSkp5rDs6GI9oTaWVx/ZTc8ZSVeflxjW7OFhZb6lfbfQFOT8vg5XNGqjR+Vncc81Ilry8jyuWb2PJy/v48dUjGZ0fUt4cqfFAcxW9aM2U3SbMYNvY9/an38Pjs54pbfQG26wHtnKYuHfzXhZMHt5ntVldjS3O39nWyTFjQJcRJYOPNMubUrmKocMmYnSFK2aNwdHZvzyFQqFoAWOyyuOPXRV/bOvBmDF+5awx/HDjB/zvZm20VbLjwmffR5fWk3jR92EjmbKm0ceB4/XcuGYXVyzfxvef3EVlXUiKcs81I3niptH0S7MzOj8rbs0Qw8bYiF2Mn5eXFPK1gekMynTG+G5nuzu/nkMqkahLSYcQQjwPTAYGCSGOAA8ADwEvCCFuA74Abmje/Q+ELAE/IWQLODfpHW4Fq+XxrOYs40BQx27TEAJuXRcKUq3KsK4qLaKuyU+9J8Cv3zzIkmkFXJCXyeww6YkR2IbrrJr8Out3HGZ5SSFn9U8jKCUn6n04bJrpYHLPNSPN9wwXiugM48MnGnDaNc7qn9bqsn48ecOos/qZvpptkQYod5O2o2maZbXEn89oNV2iXcQrGx5M4YDbH5Q83vydMn53j795MGUrTSoUitTEmKx6/vbIlelaj5+qei8v7znK2jmXYtMEuoQGr589FbWMzs9C1yXn51onO/oCuqVr2VkD0sz7vREor99xmMVXXxgTuBuxxeJNe3nou9/gvt/9nd/OG48mBLkZTjO+CeqS//37cnM1+8mbx5LX38nb91yJTYDbGZrF/s7j2yPaX/BMWW/P7WqVbgm4pZTfj/PWFIt9JfCvXdujxLFaHg9PMjta02hefHsqavnl6yGd9gWDM0lz2JC6JNNlx2ET3H9dAScbfACmhMTAWL4xZun+8OGX/GjqBfgC0gzOjQD+wWkX8s2vDcTjC5rvTS3I4/7rCvjJt0cRCIYSLydfeBZBXRLUJcdPe8jNDAXd8YLheLZkac1ftGOnPK0G0MlMmusN5GQ4ue/aUXxe3QiEpB/3XTuq01cSXHbrErxOe09aDGsfAV3yjbMHMGpIf3QpGZLl5htnD0jphwj1sKpQpAaBgE5ls0TDrgkmnp+D3QbLSwrN1cTNZRU8NWcs1fW+iDytR264hLv/ZQRXXJjHwmffZ8m0gjj2ehrP3z6eZ3Ye5vKRg81g2rjf/8e3C9A0gccX4D++XYBNOzMjbgToZw9IIyvdya+/P5ost4PcTBcOTSA0wbG6JmxC4Hba6O9y8PMZhTxwfeTYk5Nx5jOHxzsGKrermwLuvkh0kLqnopalW8rZOG88R0428sMXPozQzD782gGq6r3mz+EVpPL6h5LaHn/zIAuv/Douu40frH835mnyt/PGc/B4PUte3mc++d4y8Txm/c875rlWlhbx/Dufsfqvn5nn9uuSBm8wbjAcz5i/vilgJoO2FkAns/BJb8Eb0M2/pfH77WzsmuDOq0aYxZeMB7tU9awG6JdmY/KowdzUXAHNuO4z01JTT6geVhWK1CAQ0Nl/vM50JDPkpQ3eIOlOG0unX0y600ajL0iawxYj57v7xQ95/vbxZvVGq9Vxw5Kvqt7Lsz+4zLy/W93vjRnuO68awdSCPKrqfPz46pGs33GYWyaexx1h4/4TN42musHHvA1lEeca3D+Nc3MyWhxrVK0Aa1TAnSSsgtQnbhpDVZ3XDLYhNKPd5NdZfkMhh6oaWLv9MIumjGDuut3ml+Bo8yx3cVE+TX6dQNAX52lSJ91pM99bMHl4zDLSHc+U8dzt47lq1FnUevys3X6Ye64Z1WIwbCWhsWlnlpCMJ+YGb4Djp5twOTQ8vtB+2W4HNR6/cjdpJ8l6QGkK6GawbZxn4bPv89tma6pUxOPT+fXWjyMkJb/e+nFIUpLR+vE9DfWwqlCkBlX1XhY8Uxbh/KQJjYdf+wdVdT4WTB5OOjbyB6bj8QUjxqit5ceZUjCYoDwj8wtfHb/wrH58WtUQMSFXVedt8X5/7+a9pp3fkmkX8dWpJn74wgcsmVYQs+/JBr85wWNsW7xpL0unX0y/NEeLY028Sbm+ntulAu4kYQSpG+eNp7LOS6bLTmaajUbfmZKno/OzYjKQlxUXcv6gdP78o8tJd9qoOOnhvt/93Xx//a3j+KQ5CTP6aTKoy4hqUfGqWAZ13bT3WVZciMsuWl0OipbQGEtIVp/BmKXP7edk0ZQLIp72lbtJ20ieLaC1hjuVCxkJAbdMPC/me9XJBi9JQ5V2Vyh6ProuaQoELZ1FjPue4UY2s2gYN088l6VbyiNWFh9/8yDFRfmWq+MbbhsXU3q9usHX6v3+lMdPyaqdpvQ0N9NluW/4ZF348elOW6tjTTJtX1OJ1BVmpjBBXfLpiQZ8AckX1Y1m9m+8J1JdQsVJD1LC3S9+GPH+F9WNcStNbXrvC87OSmN5SaFp4zO1II/Vs4vYOG88q2eHKlXaNC3ifCBiXFSmFuQhhOBoTSNVdd6YAMxYQrL6DIs3hRxMiovyzWA7/HyGu8nTt45DIuOeoy+TrMqMjuak2uhrJJUlJVLC2weOs3bOpbx59xWsnXMpbx84TgdqfvUIklmlU6FQdIzqBh+fnWi0dBYx7nsG135jSMy9ceGz71NclM+qbYdM5zLA1HZ/darJ3DY6P4vVs4s4q38az/7gMqYW5Jl2fuEMy3YzwO1gdH6WKT1dNGWE5b7GZJ3B6Pws1s65lJxMF0KIVu/PxqTc0Ox0c2W8r6NmuJOEle5yZWkRf/z7sVBixIsfxn0irazzsuTlfWy4bVzM+49tPcjS/3Uxr35whKdvHcfJBh/VDT5Tk/Wbvx5m0ZSvs+HWcWS4bJyd5eaOsBnmlaVFSBny2zSkILqUPPeDy3h212eMOTeHYdlupDxTBcsIjjPT7PgDuikVefLmsTR4A5afwfDptCqqMuqsfrxy5ySOn/Zyc3P1K6VLjSRZS3R2m2Dx1SM5UtMEhJIzF189Ers9df8GDrvgukuGRiQjrZg1BkeKfia1XKtQ9Hx8gSCPbT3IL2deYnlPHJbtZvXsItNNZHlJIZoQ5n1xT0Uto4b04/9+75vYNFg3dxx1TX5qG/24HBrP7frC1GRHr+A9cdMY0p0aT9w0hn997owue1lxIctf38+Prx5priyfl5vBL35fHqMNz85w8H9v/Cb/vvEDcjNdMfUZ1P25/aiAO0lY6S7veKaMpdMv5qE/7mf9rePQBJbSkMpmXZaVMX5VvRePL8iYc3No8AYY4HbgsGncc80oFr/4IXsqaplSMJjNZRUsmXaRGWyH92HtnEstpSArS4v49daPKS7KN5e6IBQ0Hz/dxM1PRX75vj4og8p6b1yT/P5pdsuiKplpdoI6SpfaAklbopNwot4XkZy5vKSQAWmp65/qD0hLXfrGFNWlq+VahaLn43baWDRlBDaLomVTC/JAEiEhWV5SyEN/3E9VvZdlxYW8feA4tY3+iAT2ZcWFPLb1IFX1XpZMK2D9jsM8cP1FEUVvjtR4+Nfn3jdzpNbNHUdtY2gizgiyjcrWS7eUI4DFV19IhstmBvWVdV5++ko5uf2cbLhtHA6bxveizqHuz+1HSUqSRDzd5Tk56VTVezlR5+VHGz+0NL43DOYf23owZmlp5awx/OIP/2D+hjKuf3w733r0baY/sR0hQsE4hArxGFaDVn1o9AXjJlQa9nC5mS5TZvBwSSFrtx+O+fIdO93ET1/9KOYzLC8JmeQLIeIWVVG61NZJxhKdT5es3R7y+944bzxLphWwdvthfCks74nnLZ7KxXzUcq1C0XMJVZMMrUz/cOMHpqwTQvfE+64dFVOYzpBeGpKT0gnnxUwUGFIUIwD/ybdH4dclS6YVmAXxjH2z3A6q6r3YNChZtZP5G8oiDApyMpwsKy7ktMfP3HW7afLr+AM6dz2/h/kbyqiq9zJ30nlICQLrwjrq/tw+1Ax3kohnk1NVF3pSzevnoqrea2YgG2Xe672BMxnI9V7qvQHWzrmURl/IVsjt0MzAOrxdl11j/a3jqG8KkJXuQBPw1ekmyz5kZzjJSreWs2S5HehSxsxMLysupKrOF/EFrqzz8kZ5JVV1PpZMKyAnw8nQLDcuh8bjN42OyLYOP4chS1E2Qt2PFifBMJXjOXuc0u6prEtXKBQ9AytP/PAV7SM1Hh5+7QBLp1/M+bkZ+AI6DXEqPhvSyyM1HgJBPe4+UwvykBJm/+bdiHE63IDg7Cw3z98+nuo4q84D3A6Wv76fB66/iFfunERAl7jsOi/Mn4AQoc/ldtrIcjsjkjHD21D35/ahZriTRHT5d7NCUz8XS7eU86MXPuTRmZdQVe9l/oYy7n7xQ3Iynazdftjcf2VpEWu3H+Zkg4/pT4Rms+96/gPLhMmfvfoRn1TWM/2J7fz89+WAYHNZRczs89o5l/JlrQd/UDelJQaGFCRoUe47OuljWHaoZCyEsqjnbyijZNVOdCkZmOFiyAA3SOIme8X7/ShdanKREssEn1RNMATo79ZiShOvLC2iv1sNfwqFouMYuVkzVmxn0rK/MGPFdg4cr0PXI4PlPRW1zF23m69ONTF33W4ynNaJz7Uev/lz0OJ+ObUgj5xMF0umXdSiAcGy4kKWbvkIbyCIwyZiZtgfueESlr++nx9+aySD+6Vx/LSX767YwaRlf2Hm6p3UNvoZMsDNwAxXRO0NdX9ODCFT+U4ah7Fjx8r33nuvu7sRQ/iTsBACmwChCeyawOML4nbaCAQlTQEdm4B0p4bHL80KVbkZTk55g/gCQX766kem3GNIVhr/OFYXkYi4p6KWjfPG89Af95vG9v965Qie+EvIZignw8nQbHeLRXdWzhrDr988yG3/dD43rtkV83k2LZhg2gutnl3Er/78sVkyfnR+FoumjGB4XgZuhx2bBvdt3hsze7p6dhGjzuqPpolUqp7XJZ3qCddtxckG/vnhbTHb/3rPZPIHpqBpNSHLyoqTDeQPzCCgS+yaMF8PzU7v7u4li5S7Zs+97/dd0m5n8dlD17X7mI58po6cpxfRo6/bqjovM1Zsj5n5fWH+BNNkwGD+P5/LrAnnEQjq2G2CU43+iEIz0QXvzs5yU3m6ybw/Ty3I464pF3DHM2U8csMllvfktxdPxm7TsAnQNI1st4PqhlCVS4nAHwwZJDR4AwzMcDJkQGiizOozROuzo+/PRk2NFLhfJ5u4vwQlKUkixpNie6rEZYf9rOsSvEFcds384h2p8bB2zqURSY0Q+sLoUvJwSSGnPH6Ki/J5+0Al9107CiEEgaBOICgjiu4YOrLnbx/PJ5X1OOyCeZcP56wBaZbLSWdnudl+75Xml++H3xpJ+bE6y4zm1aVFVNX5IiQztR4/g8K+pNHe3q2RQgF6yqBZJPgMy3ajdYFpdbL+fm6nDbfTzsfH682qbgMzHLidajlUoVB0nHi5R/6gzpOzx3L7htB9/sFpFzL2vEER1W7Xzr2U526/jOp6H96Aznk56fzqe98kqEu+Ot3EE29+wowxQ3nmtssQImQnbFRyNmz8osdpt9Mecw/VNI3vrdppGVAbn+GRGy6JmKyz0mcb92ddl5xo8HKk1sPhEw1mEqdyLWkdtaaaZOJViTPkGPEIX7oq+6I2wm3ksa0HY5aMnrhpNJoQzF23m5JVO1m6pZzJF+ZxyuNDSsncdbvxxhksvIEgc9ft5gfry8jt56K6+Yk7vP3Vs4twO0NfLI8/wEmPjxG5mbxy5yQev2k0DptmJnIcqfEwv9nvMxynTcPx/9k79/ioymvv/56955qZQEIuXEwUShGMNBiCyOW8FuWIWlGq3CqEmxdAtJ7XeoHTNtW3sX0F9HhaFQJUuUoLgr5YrGCrpT0HRDCmUBqNHECbyCVDSGAmmft+3j9m9mb2zN7JJGSSmWR9P598YPbM3vMk82pJZ2IAACAASURBVMx61l7PWr9laN8U1N/K6347Np0JY4hJO0pEDndnfn4coQZQpbuOYebagyjddQzNviA4aK4QBNF+9DTxTzqakJFmwOYHR+PAsltwy3X9sHhLBcZ9Kwt/fOJmbH5gNCwGERaDgGy7CQaBockXhCgw/N/3P8f96z7B9opavPPZNxAFFupLEVEDJbd4jyfFQy8dJNNqRPU5J2auPYiZaw+ibHcVnrp9KIryM3Tzs2W7fd+qA5jw4j6U7jqGp24fihy7OS4/pqdDEe5Opr1qHJGOerRed2VNI1bsqcbmB0ej7pIXHECW3Rwj4/PIm5+hbMpwCIxhzZxinHdpF0KcvehRzhEYw6NbK1X62aEIoQkNTQG88P7n+KCqTnHCzQYB89df1juOLOS4tq8dP/7eMFUKy7o5o5BhbXtkk9pbJwaBMWw8cEq1C7HxwCn8fMrwDn2fzvz8PD5JUx1n28IxKdnaPRVJ9vQQgmgPWTYT1pQUK4ojkwpysezO63DR7Yc3ELrRv9jsR0DimFmchwnD+qr6AayePRICA/73tr8px8pLigEAGVYTSsZeg/vDUfH1829U1mu5xbtciGk1ici2aasV6cmIatngpTtDrdv79bZoOu9655ROLsCizRWkWtIKFOHuIAIBCacb3fi6vgmnG90IBCTN17WlS5wkcTicXnzT0AxfuEUsAM2uUA6XF1+ec2Hm2oP4wdqDOHvRo+nYp5lEPLq1Etk2E76VbYspJntp+gi89MGXymP5rlouhJy59iAWbDiMf9Y3Y+4bhzBv3KDLUezNFai54NYt5BAYi0lheXjzpzjfpFZZiQeSEUwMAmNYMH4QynZXKVGPBeMHdXhKSWd+fnrqOEEKcBMEcQUIAkP/DAvKpgzH7x8bj0dvGYK5bxzCvasOoOT1T3DJ7Ucfuwn+oITvj8zDI29G9cF48zPUOX2qY4u3VODZu6/H4xOHqGQB3//7GayKkAV2uLzITjdjQC8LctMtLQattGRE9Wzw4Fx7TGqI7Is0+/Qb25FqSeuQw90BBAISvjjnxIw1H+O7K/dhxpqP8cU5p6bTHW+1b/SW+8y1B/HMHaHtHq3tpOVTCxW97qL8DPQOfwEikaugc+xmNPuDaPIFEAgG8cJ938E7S8Zh0wOj8fp/n1RkhVbPHgmjENvmXb5OtFqJ7NBHIut9rps7Cn5JW+bI49e+OWkJam+dGNz+IFbsqVbpcK/YUw2Pv2Md4c78/Cw672VpZzoTQRA9m8hgmMCAwTk22C0GpasjcHknjYGBsVDBol4QLPrYmYseBKICBRML+uLVj46rbPMrH36JRk+gXeMPSlzbLhqFGGdb9kW+OOvUPKfZFyTVkjiglJIOoM7ljZHoWbylAtsXjcWADPXkjLdLnNbWzdM7Qts9CzYcxsYDp7DpgVAHKIEB/+f3/0BlTaPSMXLl3i9iWrUun1qIXZXf4Jk7hsbod/7891UAgMcnDsFP7irASUcTfrbrH8hJN6G8pFj5/SLTRORxydqh8hdPbhEvp5/kZ1phNAhw+4KaKSxiC8FTvcI6am+dGESBISdd/TfMSe/4YsbO/vxenjFClcr08owRCXkfgiC6N7ID+vCmT1UCAS9N127hbhBD6xwPy/zl2M2q9TF691CW2M1IM6nWywyrER9U1SlKYDLP3t32YEh9kw/Pv1eFl6aPwJNvqVXKXJ4Asm1csfmRvogc7FMpjZUUo3+GpV2poT0Ncrg7AL0710BQO3IbjxqH3nZPfh8rti0cg0a3H09uP4JXZxWhb7oFj0+8FlVnnKqOkQ6nDy/c9x0MyLDi6/pmvLi3Go9PHKKpqS23ec2ym/CL96pUX+pBWWn47cNjIHGOk44mJScbuBztltNReqfFtm9/afoIvP7fJ/H894dj1eyRqla1q2aPhMUo4JuG5pibj0jDpqXoQu2tO540k1oBR9asTjN1bDS4Mz8/tz+IX/7hC1Ve+i//8AV+9YMbOvy9CIJIfVpSUIp0QEsnFyhrnZZyyKSCXNS7/Fi8pQI5djNem1WEZl9QtT6uLinGpIJcpRZKDmgNybWrgl3NOgGr9uwK+gJBfFBVF0ofnDIcaSYRjW6/IksYWUsT6YvIueOlkwtwXb90WE0GWnfbADncHYBRFLQ72Yntd1L0Oi+ecDRh0eYK5bHJIMJgEDCsbzq2Lxqrcv4raxpR8vohFOVn4NVZRfjJXdfpdpQc1i8dZVOGgwFwOH1YM6c4VIxhFBGUOMwiQ5AzDMxOw+MTh+D9v5/Bnd/pj4HZaTCJAl6ecQN++YfPsfTOYTEO/ZNvHUHp5AKcPN+MN/77pMrxefWj4/jpXQX4/KwTZxqa8K/X90dQ4jCKAtJMQouFdW2RESQJwfhw+yRUnDqPreEbLIExfFR1Bn2u75+yBYZGUVAaSslc6feTIIjuSWuBHtkBLcrPwLW5dkVSTwDHjsVjEQina4jhHhvTwpJ8tQ1uOD0BLHv77+o87i0VWD//Riy8eTB6W414ZsdRVNY0wuHy4qnbhyoOscR5TES6tV1BvXVP9i9kJTMAqp1pXyAISeKq10b6FWW7q0igoB2Qw90B5NrNMWkX5SXFyLW3fzJqbbmXlxTj1x9eLmiM/LIZDAIGZIRaxUc76g6XF6cbPfAFJdRccGundQihL95vH74Jz9wxFOv3n1Ka1ETrakcK8EdGql+ZVYSgpF2glptuhi8gaW6JPfgv38JnX9Vj8g15irKKfOc/7ltZ2F5Rq7pWWwvrWjOgxGUsRgHFUXqxq0uKYTF2rHPamZ9JIr6fBEF0T1pTULKaRLy1aCwsRgFzwrrYz00ehvHX5uLU+SZV9Lq8pBg5drNyLaMoaK6PF91+mI0iVu79QqmhKi8pRqbViH69Lcp4JhXkYutDN0EMO8ItBY5asrGyf3H2okdJc3nq9qGqVJHo11L65pVDnSY7iEBAQp3LG+4iJSDXbobhCouy2tPZSetLJnewAoBn7hgKu9mg6nAlf3nuW30AL9z3HSx7++9YOa0QHr+ENJOIPjaTImUEAGvmFGs22imbMhzfzrUrMkaRz216YDS+rm9G6a5jMc+VTi7At7JtqveQn9uwYDT+9T/+ojr27mPjEZQQd7RarxuYbEDbEf1O6u5nV8I3Dc2YuTb289u2cEyHdmVs7TPpaBLx/UwxunTOdkdZQOo02Sl0+rz9pqEZ45f/Oeb4/qW3oG+6BdV1TtRd8qrWsr8+cwtO1Lk01ze57grQXzvfWjQWosDg9oe0uE2igD5pJhgMQrt3Z+NZ9xrdPpxp9KDO6dUc+xWskT0Z6jSZaOQIc0eilTLRmjMSnRvLAfxwa6WSc33/uk9QlJ+B3y0cAwYoXx5J4igvKYbHH5IftJsNeHpHyCnfsXis6osYrQMORFZbx257rZxWCIlzDOlrw+rZxYo0UmS+2ksztAtOjCLD+vk3Kt2sNj0wGucuedsUGW1Jgo6i32oCOjsUgQ5uSNPZso6J+H4SBNH90EvnNBoEnL4Ykr+VCyTlNAzOOdJMoqZNuyYrTbnezooarC4pVu0Or5szCm5/UOkiKa9B2eEduLZ2YJZpzcYKAkMfmxkZVhPSrc2tvpbSR64ccri7IZFfjjqnBw6XWufa4fLCZBCQm25RnTOsbzrOOT14fOIQJQIOhLbYIg2QXlvZZl8Q3kAo71erEOPNh26CxCWUTRmOQdk2nDp/uQCTh68Rfc2AxFG665hSCR2QOOauOqC73aeFngE1GURqoBOFQdBu7W7o4JuPlj4TgiCIrkIvhcIgMNQ5vahtcEPiXEkreeTNz/C7hWN0ixoj18NmXxAmEXjhvu/AYhSRm26GzSJiyqttW9PiIV4bKwgMVqOB7HEnQA53NyfbZtY0Htm22C+ywSCgf28r/FFRzmgpoJ0VNVg9e6QqLWXltELkpJthMjD0sZswfc3HMdd3OL0ISBwmg4AmXwAmg6DcDHh8QU0FE5cnpPe9aEsF3lkyHv6AtiJMS5HRlnLQzlx0t/l63Zk0kxATgUmESgnlBRKpTndMkyH0FZTOXHSjvsmHSQW5EBiDyxvAE9tDaRhBSUJen9A6GJ3D/cs/qFW/5DTKktcPIS/Tiq0P35SQNagtNpbscedADnc3QyvXKtJ4GA0CDALDmYtuzVwsIZw/Fl2VvPHAKfxu4Rh4/EHUXHBj88df44X7voN+vS0QBQaLUYTVKEDiwEWdCHh9kw/D+qXD5Qmgl8WIX/3pS5ROLsDgHBsAhrV/OYH182+EKDAEJY51fz2JqcV5AC4boPZERluSoKNIq5pLniD2fX4OWx8eA845GGPY9VktphRdhcwOVCkhWUeCIJIVrRQKq0lEb6tR6VORbTcp68YXZ10409CE278zAL9bOEZRKZE4jxEJqG243LuitsENkWnvKl7pGtQWG0v2uHPoUVVD3Z3o7pT3rtqP6nNOAKHc7/69rah3+XDPq+rnpaj8XJEhppPlvHGD0NDkw9NvHYXFKODAyXqUvH4I89cfxulGD57ddQy+IEeG1QTOoWpBK+dq76yowUlHEy55/Hjlw+N4+H99C2W7q1Dv8uGEw4UDJ+tx28t/xa0v/QW3vfxXHDhZj0a3X7mG0SDE3akzGq3WtkD8nT97CkZRwLaKWty84s/47sp9uHnFn7GtojYhEnp6nwlBEEQyIUkc5y568dRbR/DdlftQuusYOEI620BoF3hIv96YXv4xGppCrdp/sPYgPj+j3Zkxcl2zmsSErUFtsbFkjxMPqZR0I1qrSo5XGcLh9OIn7xzF1OJ8RS97Z0UNphbno2x3FTYsuBFnL3pgFAU0uv0o33cClTWN2L/0FlyVmQZJ4rjk8eGiOwCH04v6Jh92VtRg3rhBeHFvKJ+7dHIByvedwNI7hyEv04qf//4figxhZErJs7v+AYfLi5XTCtG3lwUDs0Jh1o6smCaVksucd3pw9pI3RkKvXy8zsiNy/omUg1RKUhRSKel42mpr65we3BdROwSE1s7ND4xWpAEnFeRi2Z3XQRQYfvFeFaYW52NAbwskDqXle6RqmMPlVQr0gY5d04guhVRKegKtVSXHqwyRZTPhiduGqvK51swpRrbNhHeWjIcoAPPXx0r4yVtggsCQkWZGL4sJhnB6yNTifFWHygyrEZU1jfjB2oP4+N9vxb9NvBa/+jCUYpJlMyEn3QyXJ4Bldw7T7IDVkQWNVIF9GV9QgkEENiwYDYEBEgcCUhB+na6pBEEQ3R2PX3vt5ADKpgzHwGwbvmloxpPbjwCAStN6UkEuNj8wGo1uP5q8AQDAsnCgqX9vq+JY0xrU/SGHO0XRisq2lo/clqrllvK5JInHVWAh50jLEoGR7xm5pSZJHP0zLLh/9DVIM4WUQ8wGAXe/uj/m9+6pxYydhcSB//jgy5jdjZ/dfX1XD40gCKJL0MuzNoZT7QQGVQfJF/dWo2zKcHwrxwajKOC5d4/FFE5uXzRWt5aK6J5QDncKopernWk1tpgL1pZ85ZbyuSId8v1Lb8HuH45Hb6sRNQ3NON3oRiBwORqq9Z4rpxWifN8J5f9Wk4gMqwn9elvw5FtHsGhzBc67fJq5bz21mLGzMIkCFowfhLLdVZi59iDKdldhwfhBMFEbdIIgeihWk4iV0wpj1rGzFz0wGQRsPfgV1swpVp53uLzI7WXGVb2t6NfLgiduG6o6t7ykGM+9e6zFWiqi+0E53ClIS7nYWTZTi7lgLeUrt+e5QEDCVxeaUHPBreiM5vexYmAfm9LJL/JciQNnL3ogcY5mXxDXZKVhYJYNgsBUr7OaxDY3uNEiQR2ykiKvMBFc8njR2ByAL8CVlBKTgSEjzYBeFtryTGEohztF6ayOlu19rwTTZfM2ej1qbPbj6/pmZZ3Ly7TgvMuH9ftP4Yl/HYohuXZccPvg8UsQWagAnXMOo0FAIMjhCQQhMgajKOCn/+/vMRHvntr7oRuSOjncjLGvADgBBAEEOOejGGN9AGwDMBDAVwBmcM4bumqMXU1Ludit5SPrPd9Sx0UAus9daPbBEdEWVr7z72UxIreXJeY9JYnDYhQ1HeDosWVYTVckU0RdJNuO389x0R2I0eG2GUWAaiYJotOhm5XOR2vt2PTAaFzb144zFz0wigznXT4IjOH+0dcgyx5am+pdPtU5r84qgj8g4YntlzsvrykphsPpU71fT+790JNI1n3iWzjnN3DOR4UfLwPwIed8CIAPw497LHIudiRXmm6h13GxvsnX4nO+oKQI/cvPPb3jKHw6RXadKVPU0rgJbTwBSXG2gdDf7JEtFfAEqGiSIIiegdbaMfeNQwhIHNPKP0bJ64dw/7pPMHPtQSzYcBhufxDnm7wx5zQ0+RVnWz62aEsFHp84RPV+lC7ZM0hWhzuaKQA2hv+/EcD3u3AsXU4itKNbipq39Fwwqiul/FxX5qNJEseFJi+afQHqItlGAjqfZ4DyCwmC6CHorXkGgWkGu/xBjmav+pyi/Azk97FqXmdQtk25zqSCXGx96Cb4AkE4nF7K5e7GJF1KCQAO4APGGAewhnO+FkBfzvkZAOCcn2GM5XbpCLuYRHSFYjpV2Cycc6anbmLkXPM5i7Fr7tYlieOr+iacu+SBxy9pji0ocUgSp7QSDaK7jALhz5qKJgmC6CHoKXrZzCLKS4pVfQpWzR6JtX85gfuK85RzivIz8NTtQ1Fzwa15nTSziHeWjIckSTjf5MOs33xCaY89gGRcRcdzzkcCuBPAo4yxm+M5iTG2kDH2KWPsU4fDkdgRJgFtSbcIBCScbnTj6/qmGBURGa3uksunFkJkLUfUs+1mrJsT9dycUci2d03xR32TD1/XN+PpHUfx6w+Pa/5Oz79XlTRpJck2bwUGzWp8sv2ETLLNWYKIh7bMW701r5fFhGF907Ft4Rh89OR3sX7+jdjy8dfYXlELzrliOxdPGIylO7XXoHVzRyHbZg6v2wIWbVan8FHaY/cl6SLcnPPT4X/rGGPvABgN4BxjrH84ut0fQJ3GeWsBrAVCFcidOeZkJhCQ8MU5Z0znwGF90xUVEQAQBAEbD5xC6eQCRX9544FT+MW9ha1G1If269ho+5XgCwSRZhJR2+BGbYMbL+6tVn6n3HQzfrT9CCprGvHs3cmRVpJs89YTkLBiT7VqHqzYU43//MENXT00IklItjlLEPHQlnnb2poXlDjmhjtMyhhFAb9473OUTi7AkFy75hoU3ewm3mZ0RPcgqRxuxpgNgMA5d4b/PwnAzwG8C2AegBfC/+7qulGmFnWuy226gdCXefGWCmxfNBYDMi7noml1l4zMC29J/SSZOjWaDLJkU2gbr7KmEYs2VyAv04rSyQWorGmkApUWMAgMDpcXizZXKMfyMq0wUIibIIgehN66Vt/kw/PvVWH51EKlm2RephV9bCbFdsqa3NFr0DtLxquCUfE2oyO6B0nlcAPoC+AdxhgQGttWzvkexthhANsZYw8C+CeA6V04xpTCH5S0i+CiVEQSkRfeFWTZTLgmKw0rpxUq6ilyWsSKPdUdUmDanbEYBawuKY6RBbQYkzH7jCCIrqY9soVJqPcdN75AEB9U1cHh9CnR7ON1LpTvO6E44eX7TsSsQVrrjpy60lrXZqJ7kFQON+f8JIARGsfrAUzs/BGlPnoFjwaNIrhkilS3F0FgGJhlQ0aaEdsWjkGQAxaDAIPI8OqsopS9kegsvAGOilPnsfXhMeCcgzGGj6rOIGv4gK4eGkEQRJcjR6XlyPWaOcUo212F2gY3jte5UDq5AFk2E67uk4a3l4yDPyDprjvdJdBFxAeFrbo5uXYzykuKVUUb5SXFyO2iosbOQBAY+thCBaVX90lDbi+L8rg9et49iVy7GaMGZWPWuoP47sp9mLXuIEYNyu7W84UgCCJeogsqd1bUKGtsZU0jynZXwWY2INtuRm66pdV150r7TRCpQ1JFuImOx2AQMKxvOrYvGotAUIJBFJBrN6sKJglChuYLQRCEPlpR6UyrkaLURKuQw90DMBgEVYEkQbQEzReC6JlQG/n40Eq/TPV0TCLxUNiKIAiCIAiCIBIIOdwEQRAEQRAEkUDI4SYIgiAIgiCIBEION0EQBEEQBEEkEHK4CYIgCIIgCCKBMM55V4+hw2GMOQB83Y5TswGc7+DhtBcaizbJMJbznPM7OvqiVzBvE0Uy/K07mu72O8X7+yTTnE22z4DGo09XjyWZ5m1b6Oq/mx7JOK7uNibdOdstHe72whj7lHM+qqvHAdBY9EimsXR3uuPfurv9Tqn4+yTbmGk8+iTTWFKJZP27JeO4etKYKKWEIAiCIAiCIBIIOdwEQRAEQRAEkUDI4VaztqsHEAGNRZtkGkt3pzv+rbvb75SKv0+yjZnGo08yjSWVSNa/WzKOq8eMiXK4CYIgCIIgCCKBUISbIAiCIAiCIBIIOdwEQRAEQRAEkUDI4SYIgiAIgiCIBEION0EQBEEQBEEkkG7pcN9xxx0cAP3QT6J+EgLNW/pJ4E9CoDlLPwn+SQg0b+kngT+6dEuH+/z5ZOsSShCtQ/OWSDVozhKpCM1boivolg43QRAEQRAEQSQL5HATBEEQBEEQRAIhh5sgCIIgCIIgEgg53ARBEARBEASRQMjhJgiCIAiCIIgEYujqAaQSksRR3+SDLxCEySAiy2aCILCuHhZBED0MskWEHjQ3CCI5IYc7TiSJo/qcEw9v+hS1DW7kZVqxbu4oDO2bTsaMIIhOg2wRoQfNja5n4LL32nzOVy/clYCREMkGpZTESX2TTzFiAFDb4MbDmz5FfZOvi0dGEERPgmwRoQfNDYJIXsjhjhNfIKgYMZnaBjd8gWAXjYggiJ4I2SJCD5obBJG8kMMdJyaDiLxMq+pYXqYVJoPYRSMiCKInQraI0IPmBkEkL+Rwx0mWzYR1c0cpxkzOjcuymbp4ZARB9CTIFhF60NwgiOQloUWTjLGvADgBBAEEOOejGGN9AGwDMBDAVwBmcM4bGGMMwK8AfA9AM4D5nPPPwteZB+Cn4cs+zznfmKgx61V4CwLD0L7peGfJeKr+JgiiyxAEhiE5dmxfNBb+oASjKCDXbiZblOJ0hLoIrVMEkbx0hkrJLZzz8xGPlwH4kHP+AmNsWfjxUgB3AhgS/rkJwGoAN4Ud9GcBjALAAVQwxt7lnDd09EBbq/AWBIacdHNHvy1BEETcSBLHcYeLlCi6ER2pLkLrFEEkJ12RUjIFgByh3gjg+xHHN/EQBwFkMMb6A7gdwB855xfCTvYfAdyRiIHpVXifuejG1/VN+KahGX4/FZ8QBNF1kBJF9yNRn6kkcTicXnzT0AyH0wtJ4gk5pzOuRRCpTqIj3BzAB4wxDmAN53wtgL6c8zMAwDk/wxjLDb/2KgA1EefWho/pHVfBGFsIYCEAXH311e0arF6F95mLHkwr/xh5mVasLinGsFw7jEYqQiGunI6Yt0TPwqtjp7ydpERBc7bjSYS6SHui5h0ZaU82TXCat0RXk+gI93jO+UiE0kUeZYzd3MJrtb6BvIXj6gOcr+Wcj+Kcj8rJyWnXYLUqvCcV5KK31YhtC8egdHIBXvnwS9S5vO26PkFE0xHzluhZMITs0po5xdi2cAzWzCnGpIJcTUOZCGjOdjztURdpLXrcnqh5R0bak20nhuYt0dUkNMLNOT8d/reOMfYOgNEAzjHG+oej2/0B1IVfXgsgP+L0PACnw8cnRB3fl4jxyhXespGYVJCLx24dggUbDit36MunFoJRmiRBEF2EUWR47NYhWPLmZ4pdWjV7JIwiGaZUJXrtaU1dJJ7ocXui5h0ZaSdNcIJQk7AIN2PMxhhLl/8PYBKAYwDeBTAv/LJ5AHaF//8ugLksxBgAF8OpJ3sBTGKMZTLGMsPX2ZuIMUdWeO9feguevft6ZVEDQsZi6c6j4JSGRhBEF+EP8hi7tOTNz+APkmFKVaLXnneWjG8x9SKe6HF7ouYdqeNNmuAEoSaRKSV9Afw3Y+wIgEMA3uOc7wHwAoDbGGPHAdwWfgwAfwBwEsD/AFgHYAkAcM4vACgDcDj88/PwsYQgV3hflZkGDmjeodOyRhBEV0F2qXsSufbkpLcs8xhP9Lg9mtwdqeNNmuAEoSZhKSWc85MARmgcrwcwUeM4B/CozrXeAPBGR4+xNczhO/RIw5aXaYWZ7tAJgugiyC4RJp05EBk9bo8md0fqeJMmOEGooU6TLUB36ARBJBtkl4h450BbouZXck5nXIsgUp3OaHyTskTfoRsNAgwCw5mLbrpbJwiiS4jsNBkISjBQp8keR/TaxBiDyEK53bQuEURy0iMd7ra00JXv0CWJ46v6Jnxd34w0k4hmXxDXZKVhYJaNjBtBEJ2GJHH8s6FZZYs8WUGyRT0MQWDIspmuWOv6SlvKd0RLeoLoCfQ4h7u9YvyNbh+cHr/qmNPjR6Pbhz42aqNLEETn0OD2atqiBrcXWTZLF42KuFLa47jqqZW8s2R8XO3dr7Q5TbI1tyGIZKbH5XC3V4zfH5DQ7AuidNcxzFx7EKW7jqHZF4Q/IHXGsAmCIAAA/gDXsUWkU5KqyI7rvav2Y/zyP+PeVftRfc7Zaiv0K9W6vtLmNMnW3IYgkpkeF+Fur4HySxxP7ziqMizr95/Cs3dfj28ammMiErTNRhBEIghIHPu+OIf182+EKDAEJY4dn/4T+eMGdfXQiHYgSRxnL3nQ5A2gdHIByvedQGVNY1yRapNBxKL/NRDTRl2tmgvxal1fqcNOzW0IIn56nMPdmpySnqMsca46pyg/Aw/+y7cwc+3BmK00ALTNRhBEQjCJDJNHXKXqgLt69kiYDGRbkp3o9SXTasRxh0u1ViyfWogX91ajsqaxVcc1w2LA5Bvy1HOhpBgZlviW9njkBVsav9EgtOl8gujJ9LiUEj05JVEAzl104/OzlzS39QyCoOqa9eSka/HkW0dittLOm9QP9wAAIABJREFUN3lpm40giIThD3I8EtVp8pE3P6OUkiRHK23k9EV3zFqxdOdRLJ4wOC7H1dHkwyNbKtRzYUsFHHGuNW2RmNQav8sTwLo5JFFJEPHQ4yLcWlJ/Lk8A97y6H6WTC1C2u0qzAEVkwPKphVi6M5RW0q+3VXMrzeOXIDLtTnC0zUYQxJUSkLimfQm0ku9LdC1agZg6p1fzs5Qd4dYcV39Q0p4Lwfhqi9rSnEZr/HPfOIS3Fo1B2ZThimKO2dDj4ngEERc9zuEGLkv9AYDD6cXcNw6htsGNDKtR11E2GURsPHAKpZMLkGE1wqyzlSaytm/TEQRBxItBYJr2xUDpakmNVr5zfZNP87MckGFFv16WVlMQjaL2OmQQ43d6I9fDto6/tsGNbxo9WLDhsOr941VJIYieRI+/FY00Io1uvyptBLjsKGfZTHjitqEo212FmWsP4uxFD1ZOK1Rtpa2cVgirSaROcARBJAyLQcCq2SNV9mXV7JGwUGQxqZEDMZHsrKjBmjnFMWtFPM42AOTazSgvUZ9fXlKMXHvHO7ta48/LtMakStJuLkFo0yMj3JFERqPL951QpY3kZVqx6YHR4OA4czG0zff2I+Pg8QdhMYpweQKqrbS+vSzIsJpUneD8QQlG6gRHEEQHIYoMOekm/PbhMZA4h8AYDGLoOJG8yIGYyALJJ24biiE59piOkeebvDAIDG5fy2keBoOAYX3TY7qOGsI3Xx2plqU1/jVzivGrP32peh3t5hKENj3e4Y40IpU1jdh44BS2PnQTRIHBahJx7pIXc1cdUAzMymmFWLGnGg6XF5seGI3rr+oFf0BSK5pIPKbynFRKCILoCNIMImobPFgcLpaTo5rX5tAOWjLTUr60VsfIyLWmpfXDYBAwIMMac7yjm9JojT/TasQTtw1F1Rmn6j1oN5cgYmGcd79Cm1GjRvFPP/007tfrRQEcTi/uXbU/Jj9u8wOj8WWdC599VY+54wYhIHElim0wCLrnUV5btyEhd01tnbdEz+SbhmZFjlQmL9OKbQvH4KrMNL3TaM4mMXprRunkAizaXIFJBbl47p7h4Jy3GqmOXM+05klHr0MJ7jmRcvN24LL32nzOVy/clYCREF2E7pzt8RFuQL9oRK9IpM7pxc6KGjx26xDFoE0qyMVP7iqAqKHZLZ9HeW0EQVwpQR2VkiCplKQUkY6q3mc6IMOKovwMzBs3CDPWfNxqpDoyqv3S9BGdsg61VHRJDeAI4jJUZdMCjDHNIpFGtx9Ti/OxJKyFKxvE2b/5BP+y/M84UdekW3xJEARxJcgqJZGQSklqEa1pfcKhvWb0thqxeMJgpa4IaLmvQ6R0X0siAPIYHE4vvmlohsPpbbWN/JX+jvG2qyeI7gpFuKG+C7eaRAQkDn9AgiAgpohS7gK27M5higGMNoi//vA4Vk4rxPr9pzC1OB9ZNhNy083ItBq78tckCKIbIAgMr80qwoUmv1Kw3cdmpMhhChGtaS2vGU/vUK81DBxZNlPckWpfIIgcuxmlkwswoLcFr88bhdONHmWeXJOVhiybqdX87tYi0/FErvUawFFqJdFT6fEOd6ThybGb8cwdQxWjt37+jfjtoa9ROrkAQ/um4+wlDzjnWHbnMPSxmRR1k2j97sqaRrzz2Tf4t4nXYtGWCuTYzXh84hBIHEgzi8i2kWIJQRDtQ1YmiURgDN2xHqe7Ep2uWFnTiBV7qrH5gdGoc3ohcQ7GGCQO9O9twXuP/4uipFVZ06i7Y2o1icoalmM348ffG4bSXcdUTjXQsjOsVcAZ7YzH46w3+wKUWkkQEfT4lJJIw7N4wmDF2QZCUYcF4wehbHcV/MEg0kwint5xFDPXHsTKvV9gdVj/VGvrbvqofNQ5vVg9eySeu+d6lO46hgkv7sN9qw7QthpBEO1GZAwefxClu45h5tqDKN11DB5/MMYJJ5KXaE3rovwMPD5xCII8dENlEBieeusIvrtyH2auPYjGZj92VtTgqduHYlJBrq4SSEDiyhq2eMJgPLH9iGYqil59ki8QxPkmr6YzLqew6Dnr9U0+VRrJF2edlFpJEBH0eIc70vBoRapX7KnGtoVjkGYyKDnbAPBBVR1e+fBLbH14DAb0tuC1WaFGFEX5GXhr0VhYTSJKdx3D6YsePLr1M13jRRAE0RYCEo9xpJ7YfoRau6cQkc3RivIz8MwdQ1G66xj+9T/+Ao9fivl8l+48iqnF+Vi68yieu2e4rrSfPyDprmfyteQ0kEkFuVgzpxjbFo7BmjnFmFSQC6NBQLNX3xkH9MUE3L4Azl7y4OU/Vqv6WlADOIII0eNTSiIb38iR6khj4nB5YTKImkbmg6o6PDLh27h31QHMKM7D1odvwiV3AA6nF09s/1ur7eIJgiDaSoBUSpKWtuQ+9+1lxttLxsEfkFTyfWkmUfPzldcSzrluSmJr65kcYc60GvH4xGtjtNxNIsM/zjfpnhf9HpHPf37WibLdVVg+tRAOpw+VNY14cW81SicX4Lp+6bCaDMi0Gkm1hOix9PgId2SkoXzfiZh27fIdudEgxGyPTSrIRW+rEb9/bDxKxl6DE3VNWLylQmUwW6sUJwiCaAuijkoJOS5dS2uqHNHP3/PqftS7Qjudkc6r3pohHzeGez1oqYvEu541uP2Ksy2//+ItFWjyBvHrD4/HRKbXlBQrkenI95CfXz61EOX7TijR+MUTBgMI7RKX7a6C1WRAls2E4w4XqZYQPZYeH+GO7p5lNYlK1CHyDlwUoKoin1SQix/eOgRz3ziE0skFKNtdpeieRkYWtNrF07YaQRDtxSCwGEWLldMKSRawi2lNlUPv+e2LxqoixrKjHK1YsvHAKaybOwouTwBz3zikWbAY73qmlxYS5KFdXTkynWE1otkXRP8Mi3JDF/kebl8An5914sW91aisaVSuI69vkesdqZYQPZ0e73ADLQv3yzR5g1ix57IRykk3Y+4bh5BjN2Nwjk3laEc62XK7+M0PjIYoMhgEASILGWfaTiMIoj2kmUSUTRmuyL2lmWjHrKtpqRBRfl6W7MuwGtHo9qN83wmIDFgzpxiLNocizg6XF9l2EzY/OBoGgUEMrxm/uLcQogDc8+r+Fp3WyPVMTmGJRi8txGIUsG7uKDy86VMs2lyhOMwZVnWASH4PhxMo210Vc50BGVbsX3pLXE4+pVcSPQVyuONEZAwOlxeLNlcAAP70o5uRYzfjqduHouaCO8bRfnFvNcqmDMfVWWlwOL1wB4IwcQGz1n/SarcwgiAIPYwig9UkAk1+5ZjVJMIokh3pSvScWDl9MFKyL3JnwmgQ0MdmxH/OvAFZdhO+Ot+MZ3b8HQ6XF+UlxSjo30tZI75paI7baW1Jvk9OC4l+LttmRrbNrETIW8uz1rtOv16WmHNa+/sQRHenx+dwx4vVJKry4USB4fGJQ7B051El583h8mJX5Tf47cNj8NKMEaHct30n8IO1B7FwUwXOu3ykVkIQxBXh9UvY+WkN8jKtyEk3Iy/Tip2f1sDrl7p6aD0ardzmyPTBSMk+ILQGPL3jKAIShwCGPjYT5rx+CAs2HEZlTaOSV32+yau8h1b340kFuQAQk9PdknxfZFrI/qW34J0l41VpKTnpZlyVmYac9JZ7RrR0nbb+fQiiu0MR7jjJsJrQt5dF2cYVBYars9JQ2+BGbYMbL+6txspphbCbDbh/3UFV7t3xOhcqaxqRbVcbFtpOIwiizTDg5qF9sWDDYZWdAQW4u5To/Ono6HCkZB8Q0t5ePGEw3L4gDAKD268ntxeEJIWUSUSm7n48qSAXj906RFE5iYxit5bCEU8qZby/dzzXae3vQxDdHYpwx4kgMAzMsmH4Vb2Rl2lFmjG0hSvfrVfWNCIocTzyplpzW67Yzsu0QowyLLSdRhBEW+EcisMFXLYz1Giy62kpOhypdFWUn4Gnbh+Kst1VSnObQJAr0WqZvEwrghJHo9sXvr6AjQdOoXRyAbYtHIOnbx+m6g8RGcWObq4jX68r15y2RM8JortBEe42IEkc/qAUajARkHDknxfw2qyRSmObfr2tmhGFLJsJK6cVwmYSsX7+jUqh0zVZabSdRhBEmwhKXLP4jnS4u56WdLgNAsNrs4pwocmP/D5W1FxwI8duVnZJH936GTY9MBpVZ5xKtPq1WSPxwvuf49m7rwdsobSMJ24bqqSK7Fg8Vjsq7g/CLDJVMSalcBBE10IOd5wEAhK+utCEmgtuxWEe1r83ghLHmw/dFG7HK2BSQS6mFucrC+HOihr062WBKAJ1Th9Kdx1TGT+CIIi2YDIIeO6eAlwIF02axNBjk4E2LDuK1hrY6J2jV6QoCAz+gASPX8JvD32NqcX5yLKZ8NKMESjfdwLbK2pR2+CG0xNA6eQCZNlMyLKZsHrfCXxQVYefTr4eQGxahpzTHV2IeKLOhQUbDmNSQS62PnQTRIFRCgdBdDHkcMfJhWYfHE6vymF+ecYI9O1tQTDIMeeNQ3htVhEeu3WIssWXl2nFqtkjYRQZqs44lXMB0iAlCKJ9CIyh2RdU2aKV0wohMHKkOoLWHGc99IoU331sPIJSqGjy9f8+iXnjBqn6Mrw2aySO17ngcHmRZTfB4w/C6QnAIDIcr3Mpcn0y0bJ/0SohK6cVYsWeagChbshVZ5y0zhBEEpDwkAhjTGSMVTLGdocfD2KMfcIYO84Y28YYM4WPm8OP/yf8/MCIa/x7+Hg1Y+z2RI9ZC19Qiqkwf2L7EZyoa0Kd06tEJ6Lz6Za8+RlO1TfrtuulokmCINqCli16esdR+IKkUtIRtKTu0RJaRYo5djPONHpw76r9qHN6MbU4Pyb//tGtn+HxiUOwclohwDlmrj2IBRsO47GtlXh84hBFrk+LaJWQbQvHYMWey01o5PegdYYgup7O2IP8NwCfRzxeDuBlzvkQAA0AHgwffxBAA+f82wBeDr8OjLECAD8AcD2AOwCsYox1etVHUOKaDnOaSbzcclcUNF/DQC3eCYLoGPRsEeVwdwztbdCiVaT4+MQhWBRuoV7n9CLLZtK8dn4fK1bsqYYnIKmOD86xtRpZjyxENBlEOFxe1fO0zhBEcpBQh5sxlgfgLgC/CT9mAG4FsCP8ko0Avh/+/5TwY4Sfnxh+/RQAv+OceznnpwD8D4DRiRy3FlajdsW3XLC0fGohmn1Bzdfk9jLDZhLx6qwi0iAlCOKKMAqxWsx5mVYYKTe3Q2hN3UOSOBxOb4zutZbO9KDsUBfiovwM2Ewi+vayaF77hKMJDpcXjc1+1XGLSWxTzjVpXRNE8pLoHO7/BPAMgPTw4ywAjZzzQPhxLYCrwv+/CkANAHDOA4yxi+HXXwXgYMQ1I8/pNAwiw8szRuCJ7UdUOdy//MMXqKxpxIt7q/H894djdUkxHtlSocqn+9G2I3C4vHhp+gi8POMG5KSbkW4VEZSAMxfdVMxCEETc2C0C1i+4EbURBdx5faywW6hosiPQ656YZTO1mt8drTPNEZL6k/O2c+xmrJxWqOo2uXxqITYeOIW1c4vRy2LER09+FxIHzAamm0qiB2ldE0TykjCHmzE2GUAd57yCMTZBPqzxUt7Kcy2dE/l+CwEsBICrr766zeNtDX9AgtEgKI1vmn1B9E4zIic9FDlwuLxItxjw/HtVKJ1cgME5NtRccKvy6Z586wi2LRyDXLsZ/3O+qc1FOUT3I9HzlgjRHtWJZMXj42hs8sUUcPc2G9DL2vr5V0p3n7OCwDAkx47ti8YiEJRgEAXk2kOa0Q6nVzO/Wy5KjG4CI0kcP72rALN+84ki/7diTzXKpgzH1VlpkCQOjz+IRyYMRjDI8YOIBjblJcXo34u3eZ52VEOb7kZ3n7dE8pPICPd4APcwxr4HwAKgF0IR7wzGmCEc5c4DcDr8+loA+QBqGWMGAL0BXIg4LhN5jgLnfC2AtQAwatSoDk9mDHLgsa2VMfJL2xaOwbN3Xw+jQYDXH8QHVXX4oKoO2xaOwYINh1XXqG1w48xFD4ISb9Fox0N3ciB6Momet0Tou/JVfRO+Dhcvyxr4A7NsKfmd8Ukc6/7rpEqHe91/ncTP7r6+U96/u87ZSJsalDief68KH1TVtdq9Mcduhi8QxDcNzTG2WBAYRIGpzqmsacSCDYexY/FYTCv/GADwxyduVjqHAlDaum9fNBYDMjrhLqoH0F3nLZE6JMzh5pz/O4B/B4BwhPspzvlsxthbAKYB+B2AeQB2hU95N/z44/DzH3HOOWPsXQBbGWP/AWAAgCEADiVq3Hpwrt1sAgD697ai+pwTZy96FE1UuUgy2kHvbTUioFP0FG8leXtlqwiiJ9Lo9uHcJU+MjF5GmhF92rhlnwwIDDHScsunFoK++u1Hy6Yun1oIh9OHyppGJSAi53fL9rsoPwPP3DFU1Vp9zZxiZNtMEAQBWTZTzDlAaC3o28uiNEIzGbQL7gOkPEMQ3YauSPpbCuBHjLH/QShH+/Xw8dcBZIWP/wjAMgDgnP8DwHYAVQD2AHiUc97pGkdWk4hn7gi14p259iDKdlfhmTuGwmoSFRmpX394HMunFoa2A/edwMppharilfKSYjQ2+3G60X1FiiXtla0iiJ6I2xfUlNFz+1JTKo1au3c8WjZ16c6jWDxhsPLYFwjGFCU+PnFIzNxatLkCzb4gjn1zEV/VNyHTatQsZPQHJZTuOoaZaw/ipKNJc00QE3gXpVf8SRBEYuiUxjec830A9oX/fxIaKiOccw+A6Trn/wLALxI3wtYJSFxz0X57yTj4A5KSn/fi3mpsfmA06pxeWIwCtjw4GhzAV+ebUfr/jsHh8uI384qxavZIVYOctlSSt1e2iiB6IkGd3algivoXQa4jC0ged7vRs6kZViOAywGRyPxufzj6rHVeXbhJ2muziuBwedHLYsD2RWMhMkAQBIgCcM+r+5Vzf/3h8ZhiylWzR8Lcju6h8aQb0i4pQXQ+1GkyTmSnOpLaBjf8AUnVXreyphFf1rmws6IG88YNwoUmv6rDZFF+Bhqa/Fi//5TSwjc33YwBva1xGzq9LUrSWiWIWGxmET/+3rAYhSGbOTVVPQw67bwN1GmyRVpyRPVsqpwaGKlSctzhUhzV9fNv1D0vx25Gsy+I6Ws+jnFqz1x0x+R1r9hTjd8+PAb+oISgxLHj03/ioZu/3ebfMR5HWm+XlDpSdg0Dl73X5nO+euGuBIyESCSpueJ0AbJTHUlephWMMYgMWD17JNbPvxHbFo5BnzQTfnJXAZbuPBrTYXLxhMF4esdRfFBVh0WbKzCt/GPM+s0nuOCOPx2EtFYJIn6CQSjONnC5S2wwRTeEBIHFpKutnFZIkckWkB3Re1ftx/jlf8a9q/aj+pwTF5pCKRWigBibumZOMW7I6413loxXHNZoR1WOTEeet3xqIcr3nVBsvVbqn5bWt8PlhTcgwRHuXDxz9DVttunxphvSLilBdD4U4Y4TkQGvzipCQ5NfUTrItBkhMsBsDN23yJHsSQW5+PH3CjSLJzOsRk1D5/WHDG08qiOktUoQ8ePRcS48KepceAMSVuypVqXIrNhTjV/94IauHlrSoueIlk0ZjgUbDiMv04pND4xWUgT1bGq0oypHprctHIMg5zhR14QX94akYPVsvdsfhMiArQ/dpFJCKS8pRkOEYyxxDklqmyxgvI407ZISROdDDnecGA0C/AEpRvvWaBDQ7JPwykfHlQWwj82Es5c8SvHk8qmFStODLLtZ09BJnOPeVfvjzqdrj9YqSQkSPRFBJwVDSNEUDJMoKPr/MjnpJhhF2rDUQ88RTTOJyv9feP9zPHfPcAAAB0ej2we3T20rtRxVh8urvCYQ5Hh84hCkmURdW3+izqU4+WvmFKNsynAEOeBwevDE9r+plHR6WYzI7WWJ+/eM15FuqbkPQRCJgSx0nAQkrrktHZA4AI554wYpCiYLNhyGxSjgpekj4HB58eLearw84waUfX84Vuz5XFEyAaAUx/zivaqEqo7obalSZTrR3THppGCYUvRm02xk+OHEa1WKST+ceC3MxtT8fToDvXbtje5QK/Wi/AzMGzcIM9Z8jPHL/4z7Vh1A9VknHttaqbKVraXzeQOXlUdW7Pkc5SXFMfPu1x8eB3BZ0UQQBEic49GIPg9yUb5PQxawJXWReNMNI3dJ9y+9RZU2QxBEYogrws0Y+zfO+a9aO9Yd0IsC6xVNevwSRBYr0/XY1kqsnFaoRL0zbSbMX38ItQ1uOJw+pWAyI80Eq1HAB1V1MdfuyHw6KpIheiyMIc0kqrrEpplEIEUj3G6fhEe2VKi+y49sqcC2hWMAWxcPLknRiuiunFaIFXuqAYRqa6Jt+NM7jqJ0cgEWba5Q2Uq9dL7oLpSyTd++aCx4WEHmsa2VSudh+X3kRjta60t0QKS1osjodEOrSURA4jhz0a3ZlIdsP0F0HvFGuOdpHJvfgeNIClqKAutFSE7UuVAXLnKJpLbBDaMoYNHmCjz51hEwdlk+qrKmUSmYNIqsxYLMjopAU5EM0VPxByX8tdqBwbl29O1tweBcO/5a7VBk3VINvcZZAdqt0iU6ovv2knHIy7Ti8YlDsG3hGAzOsWv+TYfk2rFmTrHSTVK+Vk66GVdlpint3AFtG/tBVR0457gqMw0mgwiHy6t6Xk73sBi11xezUZ0KEk9RpDy+/r2tOHfJi/tWHaBdTYJIAlp0uBlj9zPGfg9gEGPs3YifPwOo75whdh4tGTOtrTp5e7DO6dU0lv17W/CXpydgw4LRaGjyab4mIHF4/EG8+dBNmFSQqxxfPrUQz717rMMMpN4NAxXJEN0dm1nE3UUD4AtIkCQOX0DC3UUDYDOn5tw3CNo36AZKB2iRSEc522aGP0qIXTPogVDO/HP3FMBqanm+tGZjW0r3YABenjFC9dzLM0Yg+hNtS+CEGqQRRHLRWkrJAQBnAGQDeCniuBPA0UQNqqtoyZhFb9UBl7cHIwsjI5sW/J/f/0OpQH91VhFemj4CT751WQt49eyRWP7+53A4fXh84hD8+/euw8/uvh4Opwc///3nqKxpRNUZZ4ekfVCRDNFTCUocl9wBVaOpVbNHIt2cmjXjRpFhdUmxklaSl2nF6pJiGEVyuOOl0e3DuUselbJUdDOyldMK8aPtR+BwebFyWqGSFhJNZBpitPJIpI1tSV3K7Q/il3/4QqU888s/fKEoz8jvEeQc6+ffiF9/eFxJTdELnNCuJkEkFy2uOJzzrwF8DWBs5wyna2mtwjsy563O6VG2BytrGvHi3mqUTRmOb+XYIDCGst3/UHL4InO618+/EQaRIRDkWLn3CzicPjx1+1CVs15eUoyfT7kepy96UL7vRIcYSJISJHoqHr+kOFJA6Pu45M3PQjnPKQjngChAlZMuCuhxrd2vRHXJ7QuqNLJlW/3bh8dA4hwnHU1YsadacWqf3nEUv9OYL1o51bLyiCAIqjG1NF6jKMDh8mLR5grl2nmZVhhEQfM95Pxzh8uLTQ+MBgfHNw3NrSqq0K4mQXQdceVwM8buY4wdZ4xdZIxdYow5GWOXEj24zqYtDWUMUcoHDleolfvpRjfOXfJoFkEKjMEXlMA5cNHtxwdVdZrFOou3VOD0RQ/KdlfhmTuGtrqVGS96uYcE0Z3pbjnPHMDCTRVYsOGwooq0cFMFUvO3aR9XqroU5LFz4oOqOngDQfiDEhZsOBxT3Kh1ba20DVl5JNLGtjbeXLs5RtGkvKQYuXaz5ns8veMoXp1VhHcfGw9vQNLM06YGaQSRXMS7p7oCwN2c888TOZiupi1RYLcvqNl8Ytmdw1AfzteOjiwMzEpDQOI47/KhX28L8jKtus0R5ONP7ziKt5eMa9fvQ7rbRDLTWfNTznmOaYWeot8Fr45ikjeQmkWg7aHR7cPZix68NH0EGt1+lO87EbfqkiRxGAQBOxaPRX2TD+X7TqCyphGTCnLDnYO154uWznm8aRutqUQZDAKG9U3H9kVjEQhKMIgCcu2h43rvAQBBCS1el3Y1uy/UDj71iNfhPtfdnW2ZeKWS5Irz6C1A2fhH53RvemA0LjT78fCmT5FjN+O5ewqwclohmn1BTeMuKyjUNrjhb8dC2pp8FEF0JZ05P+0WQTPn2W5JzTYEos4NhNhDvteSxHGm0aNqQrZ8aiFe3Fvdavqd1rxbPrUQf60+h8k35GHeG4eQYzdj5bRCJeVEni/ZGpFhvbQNAHA4vYqDG49jbjAIGJChLrps6T1MBrHV65L0H0EkD62plNzHGLsPwKeMsW1h1ZL7Io73WLS265ZPLVSiJRsPnML2RWOVpgJ2iwEv/zEUEV85vRAXmvx457Nv8K0cG1bPHhmjfiIvnu3NuaMKdSKZ6cz56fJI2P23WqyffyM+evK7WD//Ruz+Wy1cntSMCJtEQbuRTw/pNFnf5MOiKB3ypTuP4vGJQ2A1ibpNYeRzo+fd0p1HMXfcIOWGTG7XXjZlOP70o5tROrkAr3z4JRo9gZix6KlXRTfMuRKVqJZSQ0h9iiBSh9Yi3HdH/L8ZwKSIxxzA2x0+ohQhOv0kKHE8/14VKmsakZdpxbI7r4NBZPAHODg4gpKEeeMGqaLer80aCV8giJ/t+odmasqV5NxRhTqRzHTm/GQMuPW6fliw4bDy3Xtp+ohU7XsDk4EhO92sKprMTjfDZEjRX6iN6M2da/vace6St8VdE71zo/P8K2sasWDDYWxbOEbZxXz27ti5GbkOuP1BnKhzqYot5fQOLZWoNXOKkWk1tvr7ttTMxmgQsOmB0Zj7xiFSnyKIJKc1lZIFnTWQVCRyuy4QkPDcPcPxk7skmEQBbl8Q9606oBjB3z48JqY48tGtn2HLgzdh9MAM1XVHD8xAXqYVbz50E0zhKvWWKt0BxByjCnUimenU+cmhyHECoe/ek28dwfYUVSnx+iXYzSKG9LUjKHG9dlCnAAAgAElEQVSIAoMohI53Z2TbB0Bz7jDGNHdN5E6PJoMIq+nyvCvKz8DiCYORZTPBIDBMKshVit2L8jPw+MQhyLKbsGZOMXZW1OjOTXkd+KahGQs2HFY9FykrOyTHjq0P3YQ6pxf1TT68W1mL2WMGQgwrimRYDHA0+eAPSjBG5HBHvodeKta7j42H20d52gSRzMTb2v3XGocvAviUc76rY4eUekgSx3GHSx29KBmJF+77DoyigEa3H06PXzOyct7lxawxAzH7N5+o8gUjNbzLS4oxLByl0TK2ZoMQE+EYkmMn3W0iaelMXfiAhiJFbYMbgRTV0TMYGE43ePFIhGb06tkjMSDT0tVDSxiRjqZWjvW6uaPAdT7n041uTCv/WHndpgdG44X3P4/ZcVxdUgwAcDh9eOaOoarrl5dcjkbrFftG30TKTnuQczicXogCMCts54vyM/DU7UOVx/J7uH1BSJyj2RdEc58ABvaxKU430HLx5VWZaZ3xURAE0U7iLZq0ABgG4K3w46kA/gHgQcbYLZzz/52IwaUK55u8KiOYYzfjkieAZW//XVU0qRWVqW/ywWoSVSklr3z4JeaOHYipxfnIsBrhcHqRm27WjeCUTRmuaYCpQp1IVjpTF15PdUJM0ZwSj09SnG0g9J1/RNYVt3Xx4BJEpKNZ2+BWcqwH59phNYbmjp46VG+rEdsWjkGj24+X/1iN5+/9Dp67ZzhmrPlY9Td85cMv8dPJ14MBuH/dwRipVjk1RK/YN/ImMsdujnHa15SEWsTXNrh15WDLpgzHgg2HlVzwXhYjcntdvpGiVEGCSF3irbL5NoBbOeevcM5fAfCvAK4DcC/Ued09Eo9fbQQXTxisaqpQ2+DGC+9/HqOzunxqIT77qh6BIEfZ7irMXHsQZburMG/cIAzOtamKoC55/PDqGNu0KJ3uyG1M0t0mkpXOmp8CA5ZPLYz57qXq16G76YrHQ7SjKedYiwzK3NEqLlw1eyRW7v1CZVu5xGOi4UX5GZg3bhBmrTuI041uXae2pWLfyJvIV2cVxawBi7ZU4PGJQwBAVw5WtuWyJKwvqE4ToiJJgkhd4nW4r4I6dmIDMIBzHgTg7fBRpRhyBE1Gy5h+UFWHzDQj3nzoJuxYPBalkwuw8cAp3H/TQDy6VR2tWrrzKHwBjtJdxzBz7UGU7joGh9MLBmga22ZfMOYYGWCCCCFxYOOBUyidXIBtC8co371U9U9lXfFIUllXPB7icTQjHd79S2/BtoVj8OpHx1Udf5fuPIogj71eZMTZH5Q030tgrcv7Rdb1aL1uULZNkY/Veo9Gt1/1+miVFWpmQxCpS7wO9woAf2OMrWeMbQBQCeBFxpgNwJ8SNbhUwWYWsSpC2k/W1o4kL9OKY6cvoe6SF05PABlWI6YW58PpDWgaZofTq3LCn95xFBw8JlK3Zk4xrslKIwNMEDowBswbNyhmFylV/VOjyFT2Ro7kGruxSkm8jqbs8PbvbYXEuWbHX845MiwGrI7YccyymRR7K0Z1EZbTOwQWf4RZ73VpZhHvLBmPG/J6Y82c4pj3KN93QvV6i1F93eibCjl1kHYvCSL5iSuHm3P+OmPsDwBGA2AAfsw5Px1++ulEDS5V8AU5Xv3ouJKHLXGOl2eMwBPbj6hkyF54/wsAwI+/N0x5bv38G3VzuyOpbXDD45fw4l51d8tsmwk56RbK1SY6jO7WoTQywi1/bzYeOIWf3X19Vw+tfTCG9458g/Xzb4QoMAQljh2f/hMP3jy4q0eWMNqS8y9JHF/VN0Hi2momAFDn8mL332qVOdG3l0V5rcAYXnj/ixip1l/dX4S89PiKffWKgrNtl1OnIu220SDA5Q7A4fIq41w3ZxSy7bFNa6iZDUGkJi063IyxYZzzLxhjI8OHasL/9mOM9eOcf5bY4aUG/oCED6rqVNGUovwMbH5wNOouedHsC8JsvLyZYDQIKJsyHP16W9DbaohxzlfNHolXPzqueo/QlrGAxRMGK8118jKteGfJ+IQZ4O7meBGtIzsrX9c3KxrP12SlYWCWLWU/e5MoYMH4QaoCtlRuFJNtM+P7I/NVuuKyM9ed0LI/8di5RrcP5y55sO+Lc1g1eySWRKi5rJo9Er/603EcOFmvdKesrGnEbx++SVE+aXT7NbsIGwSGMxfdyLKbWpXhi+cGIdpuZ9s4BU4IohvTWoT7RwAWAnhJ4zkO4NYOH1EKoqUp7HB58eU5l2K08zKtKJ1cAAB4bGslahvc+Oszt2DGmoPIsZtROrkA386145/1zdjy8deYN24Qqs44VQ7CE9v+BofLi+VTC7HxwCk8cdvQK04d0XOqqTV8z6TR7YPT41cdc3r8aHT70CeFHbo0k6hqFBNdaJxKyJrO2xeNRSAowRDWbO5O38u22J9oGyZJEp7ecRSlkwtUO4+Nbj9e/eg4phbnY3tFLTYeOIUV0wpxockHiXP0shhQNmU4su2mGEe9vKQYz717TJFqjccWtjUQQpFrgujetNb4ZmH431s6Zzipidb2oRw9kaltcGNAbwvsFgNWzx4Ji1EE5xylkwtQvu8EyvedwMrphUrjhON1LmWhyO1lxo+2HVG6ly3deRQbFoxudw6qvEBJkoTzTT4s2lwRs6i1pPdKi0L3xR+Q0OwLonTXMdXNnj+Quk1V3P4gnnu3CosnDEYaRPiCEp57twq/+sENXT20diFJHP9saFbtQniygim9CxGNnv3ZtnBMq4GB8rD8XobVGLPzCADL7rwOWx4cDbvFoNoleGP+KAztZwfnIWWbF6ePAAOQZTdjxZ7PVcWX7bGFtGNIED2beBvfpCEU7b6ac76QMTYEwFDO+e6Eji7J0DOY0duHjDE89+4xxUEGgEkFuRAYw//9Q6jhQmTTipemj4DZKKDmgluJlFfWNGLR5golMh55rdoGN+pdXjz51pF2GX15gSqdXICy3VWaTjXpvfZM/BKPkTN7esdR/C5FuzICoSI4rRSBVHV2Gt0+1Lu8qpuil2eMQEaaMaV3ISLRsz9yl9CWAgOynrWsNhKdw3260Y1lb/8dK6cVKrrYtQ1uPLDhskN/76r9ynnbFo7RLL7UsoW0Y0gQhB7xJjGuB+ADMC78uBbA8wkZUZIiG8x7V+3H+OV/xr2r9qP6nDNGtgkADCLDsjuvU1Wg//SuAizaUoGpxfkxDQ+efOsIGpr8+PWHx2NUSMpLQm2FI5Hlo9rjAEcuUHpasPJiIY+jKD8Da+YUY8fisWCMaf7ORPcgqKPxnMqfuVFgeG1WEdbPvxHbFo7B+vk34rVZRTCmqKPj8QWVmg8g9Pk8sf0IPL7uczOsp/Ih272X/1iNs5c8aPYFUDq5AEX5GcrrZPk9s0HUVBsZmJWG0skFWL//FJ65YyjWzClW5CJFIVb6T0/CL1qZpKU1oiX9boIgegbxdpoczDmfyRi7HwA4527GUrRNWztpKcVCr/uYXFhjNYlw+4J4afoI5KablaiKjNzwoLKmUaVCkptuxu8OfY0fTrxWlc8tp6u0R287cjGRF5LoCJAcmVk3dxRe/mN1TAtkisx0X4yCdlfGVNZ4ZgwQBEEVEV5dUoxUtWD+HtD4pqU0PblJjdwpMvI5uZg8zSQit5cZ//t3f4tRG1l25zCU7a7CS9NHoI/NGNMNMttuUn0Hyved0GwlL9fPyA612x/A2YseVdScdgwJgpCJN8LtY4xZESqUBGNsMHpYw5uWDKaeMx6UgP69rTh3yYuZaw9i5tqDmPPGITxzx1BVRCayeY2cSvLkW0fwZZ0La/7rK7zy4ZfYvmgs9j01AWVThuPFvdVwuLzt0tuOjByV7zsRE1GXrymnyTx3z/CYiDxFZrovBlHAyzNGqObEyzNGwJCiih4AEAhyPLKlQjWHH9lSgUAwNR1UvcY3YgrfFEUTmab312duUexeZc3/b+/Mw6Msr8Z9n9knGwkhwYWoqLikFIQom61VaWlVWj4LaFVQcQFcamvVahda+6Hfp1BrXaqgdUVRELT6069IS0VbFZeAUI2islhQNCEkkGUy6/P7412YycxAApmZJDz3deVieOddzrzzzHnOe56zNKZsi37j0nXMPPUoW4c1ByOEo8oOJTr3gVXMWFBNXXPQ9pJf98xatja0JXWD/KS2OcEzXtccpH+Rj2evHJNU+zreq33KnJXMev59rv/ubv2easXQQjco02gOLDrq4f4tsAyoEJEngZOBizMlVHckVSWSASV+RITWUOrmNemM8RuWrGP2hMFMe/Qd26Bxuxz2+S2PzfNrPmf+1CqK/W6UUgwo9lPkd3Hv+cOIKpXUFKEjxHuO1mxp5LE3NrHwspE4HZKUyONwSFIL5PjPpul9KKXsspVWQp7b5UCpnmmcQu/zCPs9ziSP69xJQ/D34MorqbCqdsRiiiazVB8kNqmx2NoQ4PiDCnn2yjGg4D87WgF45OITmfZo6mR2a2Wx/XncTge3/fUjZk8YzFHlBfjd6RMcU+n3G5caFVKsHJz4FcO91e/ubehEUY1mNx01uC8EXgKWABuBnyiltmdMqm5IKoVplYqaWFWRNjQjnWd8YL98nrtyDC3BCJGY4tBCL4umjyKmFMGIIhCOMvPUo7jtrx8mlKLyuhxc+PDb+xze0ZkGEpD+QUN7ZnonDoeD+175lIlVFXZFj/te+ZRbzx6Sa9H2GadDGFdZzsSqCju0YGn1lh478Rd53ZQVehMeisoKvRR53bkWLSM4HMLBxT778/bxu1PqJJ/HSX2zYQCXFXi5ZuwgSgs8LLh0BE6HsKG2xfaSW8e0tot7t+LE12xpZNqj7/D6jaftMSk9nX4vNmVsv2J4INXZ1omiGk0inUma9AE/AO4G5ovITzImVTekfUvdxTNGc/eKj1leU7vH0Ix0S4nhaIxQJEZLKMojr28CBK/LQWNrmIsfeZvv3/MvLnz4bS4aM5BhFcV2KMdn9a37Hd5heY4OLcmjrHDP9Xs72lJZ0zsozfdw7XeOTWiD3hX13nOJz+Xg6tMHJXymq08fhM/VM8NkGgJh5iz7iFDUKNUYisaYs+wjGgLhvRzZcyn2ezioj4/rnlnLz5esS0qGfPDCE3E5xDa2r//uscx6/n1O+/2rTH3obbY1tlGS707o5HjH5KFU9PUnnOf2iUZ79WEVxTxy8UlElaKuKZg2aTidfreaksUbl53Ru70BnSiq0STS0dbu/xCRV4GTgNOAmcDXgLvSHSMiPuA1wGteZ4lS6rciMhB4GugLrAamKqVCIuIFHgeqgHrgXKXUZvNcvwAuBaLANUqpl/fhs+438Y0JPm9otUtFxSc7Hn9QIX6Py/ZepPKM3z+lirkvf2R7ru+fUkVZvoe6lpBdLhCSlyfTLYGGIlFiMUVjIEQgFLXDTeLbCO/PZ86WZ0YvP+ae+KYq4WgMdy9oqhKMxFI2QPltD23tHopEU9aX/u33e2+YV3s95Pc4efbKMYQjMdwuBy6H0NQWoazAy5xJQ9gZCNs9DtZsaeS6Z9by2CUjmDtpCP2LfHxS28xD/9rIbROH2OeMxhS3vFQDwM+/d2xSkmQqz2y6UJGD+/TcspNdhU4U7T0ccdNLnT5m821nZUCSnk1H63CvAPKBN4F/AicppWr3fBRB4HSlVLOIuIF/ichfMep536mUelpE5mEY0veb/zYopY4WkR8BtwPnikgl8CMMA/8Q4O8icoxSKqe/2vahFmu2NDL7xZqkutipanQ/9vpGJlZVcOk3jqQxEOaeFR9z69lDiKWJly72G0vF6ZZA3S4Hm+tb+GpXW4cmic6SjQ5oevmxexCLKT6pa+5V34MISZV2bp84ZJ8bR+WaAzXMK5UeitcbcycN4ZdnHpfQzCa+ekmD6VndGQjhcTr45ZmVRGLKdkzEYopbzx5CKBLl3AdWpaxI1f76B2KoSEc5UMepRpOOjq6prsOowz0YGAIMNquWpEUZNJv/dZt/Vjv4Jeb2x4D/Ml9PMP+P+f5Ys/TgBOBppVRQKbUJ+BQY0UG5M0ZnQi3ilxKdAqcc2z9hefuiMQMRFLGYSlt7dkCJnz+ee0LSEuj8KVWg4LP6VtvYHlZRzKzxlbQEI3y5qy1hOTQWM5ZIP29o3eNSaSZJJYNefuwebG8JcuffjNUaqzbxnX9bz/aWri9KlK2xqBQpq1r00JzJAybMqyPjI15vuJ2OpPrkVvv2JTNH08fv5pHXN1FW6APgq11tfPD5LjZub2aHOb4tg7qswGvX554/1ehcmc4ze6CFinSUA2WcajQdpaMhJdcCiEgBMA0jpvsgjHCRtIiIE6gGjgb+BGwAGpVSEXOXrcCh5utDgS3m9SIishMoNbevijtt/DHx15oOTAc47LDDOvKx9ot99WxE203+hiKP0RaJcctLNdw+cUiCJ27+lCqcDrjth1/H7RTmLPuIWeMrKc330Dffw7yVGzjnpAr6FXiYNb6SQ/r4iCm4auHqJA8lkHMvcjpPdpHPdUAuP2Z73O6NaCyW0hsc7WLrNJsrGtE0K0fRHlp5Jdde1WyM2Y6Oj/iwhZK8xOolVr3u9h5vpVRCTfa5k4bQ0BKmwBfk2P6F5Hkd/G7C17gyrhvw3ElDcDsdttGvQ9/2Tq7HaXu6m67VHHh0yMMtIleLyCLgPQyP9MPAGXs7TikVVUqdAAzA8Eofn2o36zJp3ku3vf21HlBKnaiUOrGsrGxvonUJ++LZiERj9qQwrKLYTu75vCHA8ppaOxbc8i4i8NmOAOGo4qqFa1heU8uMBdVMmvcmFz78NmMr+xOOxlDA7Bdr+GJnm21sw25P8Ze72tjeEuxyL3JnvZTpPNkiqWsL9/blx1yM2z0Ri6XxBnexwZ3NFQ1nmrHl7Kmdb8itVzUbY7aj4yM+adFjlla1SFevOxhRCdtuWLKOg/v4+MvqLTQGQuwKRG1jO36f5mCEbTsDfPjlrr12HO4OK4ndge7k/e9uulZz4NHRsoB+4A9AdZx3usMopRpFZCUwCigWEZd5ngHAF+ZuW4EKYKuIuIA+wI647Rbxx/Q4HHGd/OInBCtsxGp8A4ZRMGt8Jcf0L7Bbbg+rKGbmqUfZyV+H9PGxMxC2J4h07dq/aAxQWpC6fu2+epH3xUuZLpHGKRyQdWq7G5E0Nau72sOdzYQqEZJWjnpyDPeBQEfHR3zSoqAS6pOnq9fd1BZO2haMxDhr6KG0hWPUNQVTHrejJUR9S4jZL9bsMb5b56NoNJpUdMjDrZSaq5R6qzPGtoiUiUix+doPfBv4EHgFmGTudhHwvPn6BfP/mO//QxndNl4AfiQiXrPCySDg7Y7K0d1wOcQuaRVvHM9buYE/nT88qUTV0uotRKKKLTsCjKss5/rvJpZsiynok+exYw7LC70pvXn1LSE2b2/tUi/yvngp05XRcjgcCWUX25fU0mQHt9OR8vvp6k6T2ey8F1Pw2BubElaOHntjEz200eQBQUfHR3zYgtvpoMjn4t7zhvHK9d+iOM+T8hzBSCxp27adAa58cjXhaIz6llBaHZrOoRH/IKDzUTQaTSo66uHeFw4GHjPjuB3AYqXUiyJSAzwtIrcAa4CHzP0fAhaIyKcYnu0fASilPhCRxUANEAGuynWFkv3B7RLyPE5mTxjMIcV+29u9ZksjT676jMcvGUFTW4Q8j9F05KYzjqfA66S0oA/HHfQ1PvqymTsmD6UxEGbeyg1ctXA1z8wcbZexKivwJnWhi++udv8Fw+3Sg/vrRd4XL+WeOq5loxqKZs94ncJ9FwxPiF+974LheJ1d++CTzc57Lodw1WlHs6PF8Gx6nA6uOu1oXPphrtvSmfFh6Y1IJEZTMEKR38l5D66irMDLHZOHmi3cd8dil+a7GVdZvrss6wXD2RkIc8fkobgcwurN9faKiNVA57DSPLY1BogptdfKG7ocnqa7si/l/TRdR8YMbqXUOmBYiu0bSVFlRCnVBkxOc65bgVu7WsZcUOL3stMXYUdLGJdTEozjNzbWc+k3j0CEhESfR6edREmem/qWcEKyj2VIhyIx+xxbGwLMWbae2RMGc3hpHp/UNttlsQaU+GkORux6xBVmd7ZtOwP7lNCyL2WfulsijSaRQJqa1b/JQM1qb7sW8t4MNaJxOQSHSMJv5/4LhmuDuxuzL3qiIRDm4kfe4fFLRti6MKaUPcYaA2HmLFtPXXOQRdNHcdMZx+N0CHVNQW569t+2cf2jkYfT1BbhnvNOwOVwJDgo7pg8lHvPH8bVC9ckJbbHYgqHQ3Q5PI1Gk5JMerg1KXA4hIpiP16XA6UUftPbnedxogC/x82lj+2uAVtW4KWuKciWHQHbYIDdCUCzJwwmHJeICdhtiVdef6odb2gZ6M+t/pyxlf3xuZ2EY4qbl65LaB3fmTCOffVSak9298VI9kpc+q5rCmUkafLCh99OMkpS1TreXyIxldRQ6oonV7Nk5uguvY6ma+msnrA8y06H8MjFJ5HncVJe6OVni9fa7dwttu1sY9K8N3nk4pOY9fz7dnfK+Dj/Jy8byQV/fith3Fz3zFp+P3koT142EoCNdS38+i/vU9cctPVnNldvNBpNz0Eb3FkmEomxvraZmU9UM2t8JUurtzCxqoI8DA9Oc1s4wQiZeepR3LBkHXdMHppymfKIfvl8tastpUclz+Nk8YzRRMwqJgtXbWbCsEOTksfqmkKs2dKYtrlDOrS3uvfhdTmSuuzNnTSky73P2Vx2D0Ziaa4VS3OEpificTkZV1lOXVMwqezfnGXrbaPbiscGyPM42doQYNb4yqSKJumSJ/sVeLj1pRrOG3E40x59x34vXn9qvajRdJ7e3tEyM2u4mrTUNgeZ+YTRpn3eyg1cNGagnQS5MxBmizlJWFhJOlYVk3gGlPjxuRwc1tdYIk9oiDO1il1tYc6Z/yanzF3JrS/VMDWuvjLs9pLPPPUo+/+dNXi6U9knzf4TiSnb2IbdJdEiXezhzmbSpNORuiygHqu9i9J8D78+q5KfLnovafxeM3YQsDsZfd7KDQC2Xm2fDDmsopg+fnfKcbOhroXlNbXkeRLHarz+1HpRo9G0RxvcWSY+/GPNlka77varN5zKIcV+llZv4faJQ2xF3xqKMqDEz7yVGxK2W3GotU1tnDN/Fb95/gNmTxjMK9efyr3nDSMSjbFlR4A7Jg9l/tQq6ppC1DeHUnpsyk2PdrzB076ObCQS03Vlc0w2avtGYypll72uLguYzS50Ppcj6YH0/guG48tQzLgm+1jdahUwa3wlwyqK7fe2NgSo6OtnyczRPHnZSF5b/5Xt7Z63cgNzJw2x9Szs7o8w9+WPUurcIp+LcZXltIYSnRM6Tluj0ewJHVKSZayya/FG9+wXa1g8YzQHFfm49jvH2q21S/M9HFriZ96UKmY+Uc3vXzaSIY/ol4dDhNpdQa5d/J6dIDTt0XcYUOLnth9+nT5+d1KCZZHflTL0pMDrsr3iJX53Uh3ZcZXlXDP2GNszr+vKZp9s1fbNVkhJNsORwlHFPe0SQe/5xyf87geDu/xamuyT6rdhJZRbyeIb6lqYsaCaASV+Fl4+kpfe/8oIG2kOmvrPbVdwiu+PUNcUsnVxcZ6HG55ZS11zkHlTqugTp091nLZGo9kb2sWTZcoLvMybUpXgNZk3pYryAq9thNx69hAGH1LEYaV5oODuFR8za3wlN51xHKFojP/9vw8BiKVpWX1QH19SktiNS9fhcTqSPDa3TxyCz+1g1vhK7vr7x6yvbWJ7c2JHyolVFbaxbZ3P6l6pPd7ZIVu1fbMVUgLZW3YPR2N2h9ZzH1jFjAXVLK+pJRzTMdy9gVQddK1QufYhJGUFXgTh0Wkj+OfPT+O+C4bTHIzw1a4gv3n+A2aNr2RQeUGCQ8Tq7FvfHGTNlka2NgSY+UQ1XrdT9w3QaDQdRnu4s4zL5eC4/oV2MqPb6cDjFr5qarO9fGWFXttr0xKMsLymluU1tQnn+cWZx9vLoO091k6RlIY47G4AYnn6Xlv/FZNOPIxiv5uJVRXc9feP+dVZlQnH76l75aR5b2qPdxbIVpJhNEudJrOJFcOd6nei6VlYoSPWqkixz0UglPq3cdxBhcyeMNj2dA+rKObn3zuW8x5cZXul/3T+cA4vzUMpqGsOMmNBNfOnVqUcL42BcML5w5EYh5bkZe2zazSano32cOcAl8thNr3JozEQZsK9b3Dy7a9w9n2vs/6rJntSsTyYqRJ3vtzZRkm+2+5aaW2fN6WK7c27jxlWUcz8qVU8f9XJuJ3Cj08fZCdpLq3ewvgTBjDt0XfszpWXfuNInA4SrpkuYdPyrlre1m07A9rbnSGylWToylKnyWzidjqSfidzJw3B3YM/04GI5YQ4+77XOfn2V/jVc+v4qLaZjXUtKcfstp1teFwO6pqDAFwzdlDS6s1VC1cTjcGWhgCPTjvJzpdJNV4sL7m1TfQDm0aj6QTaw51DUoUJ3Pm39dz8A6O29qzxlXy8bVdS57/7p1Tx4ntbeXtzIz//3rEsuGQEMQUOB0SiMZraYtx/wXDu+ccnXDRmII+9sYmJVRXke120hKLMnTQEhwh98z12gx3r+tc9s5bF00cxf0oVM8wwkqXVW+w48lTdK61jreO1t7vryVZtX4eQ1KnUGC9depmsopSiyOdKaLJT5HOhlH4w7Em015cTqyq44olqygq8dmfI+MZGzcEIz6/5nLmThnBQHx+ONCt/XzQGuP6Ztdx5zlCevWIMbZEoLofw+8lDEUAB/Qo8tuFuNcDp4uarmm6I7syo6Uq0wZ1D2ocJDKso5qIxAzln/pv2xPH4JSO47a8fJiZ8rTDCPl56/y3Oe/Ate4IpLfCAgkde38S0kwcya/zXmP3iB1wUVw7QMpbnLPuIm844LuUEFIjEuGvFxyy8bCROh+D3OGlsDdsGS2mBlznLPkxoJmEtuVrebisJVBvdXUO2kgyDkRhzlq1PGGA3OEUAACAASURBVG9zlq3nrh+d0KXXySYxFOF2qy7hmMKoTq/pKbTXl1ao29aGgF3tqdjvprzIy88WGcmN954/jGA4xtSH3mbW+Mq0oSJbGwJcu3gti6aPwuVwcPML7zOxqoJivxulFMFwNOGBzed24NZVbjQaTSfQBncOad8COD47Hgzjd0dLKGUM91WnDUowil5c+zlTRg8kEovx47HHcMUT1dwxeSgTqypS1t6eNb7SDhVJakHsdDCxqoJbXqrh1rOHEI2R0BXQioWs2daU0uNteY12BsLa092FZKNDp9shdiyrxYASf49ugx6LYbfithhQ4mfR9FE5lErTGWIxRTSmEvRVvP6ykhsHlPhZcMkIZp56FPNWbqChJWxXa7JKq7Z3PsTrragCJypB586fWpUQigLG+Hn2yjHZvxEaTQ9Brw4kow3uHNI+TKA035PkcQ5HY3ab4sZAmHkrN1DXHOTLXW22UWTVjT3vwVXMGl9pt3NvDIRTnnNrQ4Biv5uH/rUxKVxl7qQhXPPUGuqag9w+cQixWIxIu0S6NVsambNsPQsuHQEIX+1q4/a/fpTUye2ni97jhatPJhpDd1zrIYjAHZOHct0za+0xccfkofTkcNXemAh6oFHfEuKWl2oSxubS6i0p9dfPFq+19ZfVSRIS+x4cd1AhG+ta7IRKMBuJuR0IiUm26ZLGw7pTqUaj6QTa4M4h7cMERBIV/bCKYlwOSWpTXFrgSYifjveMx08O81Zu4I5zhqb0Yh9a7OfX4yuJxhSzJwymoq+fLTsCCS2Qb1y6jsUzRid54sHI6I/GoF+Bi7awOyG+0fIalRV42dbYZseC62om3Z+Ygof+tTFh9eShf23kt9//Wq5F22dc7WrfQ89PBD3QCEWiLK+pZdrJA+3QjsZAmCfe/IzZEwYzsF8+m7a3JOmvR6eNSNn34N7zhlHaLi77wQtPpF++sYIU7whJVw1KN7nRaDSdQRvcOSY+TCAWUwmK/pqxg7h28dqkmshPXDqSaScPtEM64r3Y7ZdZ563ckJx0eUEV//3iByyvqWVcZTk3nXE8IjDt0XcSZNvaEEApRWm+JyGJ0jKqb3hmLfeeP4xjzTKHXzQGqG8J2V6jRy4+yT7GOt/lj7/Lc1eenPHQCM2+4XCQMubf0YNtU6v2fXzSr1X7XtMzsB76HSJJempx9VZeuf5bKfWXzy12Qxvru7/vguH89vkPKCv0sPDyUWxrDHBoiZ9D+hhVSepbQvTNc7N4xmiUUvg9zqwkLGs0mt6NNri7Ee093u1DOcCYRGJKJSS2lRf5bCO7fZziGxvruWDUYTx52UgiMYVSMGfZh3Z84vKaWmq2NfHU5aPSenEcDuHgYl+CZ+n3L6+nrjlov39QkY+dgTA/XfSePSkN7Je/x9rR7Wvq6nCT3BOLJddqf+yNTT3aw+1wCHkeZ0LSW57HqcdaD8IKv/tyZ1vavJNU20MRZTe0Oe6gQhwi/GX1VtsLfsWpR3PdM2vteP5U3VwP7uOn2O/JSldUjUbTe9EGdzfGqi/bfhKJxlRCYturN3yLpy4fRTgaIxpTvP5JLY9fMgIR2Ly9FYCfPv0eAHMnD0lKwNzaEKAlGE5KKIr34hT7PRzUx5fWy5OqioZCpTXis9WqvDeRjQcUkTQe7h78ldS3hBKSfsEYh3qlpefgcAhH98unNN+TtFpx3wXDaQ1HUyZEtgQjdhiJld9yx+ShPHV5X4r8bop8bjMXBRoDqbu5WuNEjxWNRrM/aIO7GxFvhN5z3jDuXvFJ0iQyb0oVfo+DJy8bya0v1VDs97CrLcoVT7ydEDLy2vqv+PbXDmZQ/wLu/vsnzDz1KLPEFSmN4M92GN7xWeMrOf6gQnweJ04HbG1sxSlGacBBZQV79PJY4TGWYRiOxFh42UhueamG5TW1CUZ6ulbl2ghKTTYfUHY0B3h6+iiiMYXTIaz5rJ6B/XpuR71sdenUdB2pOkqur21m5hPVjDmylEenjcDtFJwi3L3iE8ZW9mf15noeufgknObvoTkYwe9x8sjFJ1GS7+Z3L9TYvQLuPOcEGlvDzFiw23CfP6WKsgJvwljR40Sj0XQV2uDuRsQboQVeF3XNwYT6sq2hKIFQlPH3/Ms2vkvy3Jz7wKoEw/WKJ6t56vJR/HjhGn555vGcPfxQu6zVuMrypJhuK8nR8gS9cPXJfLUrmGDczZ00hP5FPo4ozd+jgZfKMJw/tYrZEwbjcDgo8bupbwnRGoowa3wl81ZusJd39eSWnmw9oPjdDo47pJhPvmq2wy+OO6QYv7vnBnGnSvrVSW/dl5Q6ZEoVd6/4mK0NARZXb2Vx9Va7tOMbG+sBuOyUgUbieJ6bPn439/7jE/tB/47JQ+3zb20I0K/Aw9S4VY+tDQFmPFHN7AmDE2LB9TjRaDRdhTa4c0y8JwewPSxtcUukVn3ZuZOG8D//9yFgTBAzn6jmyctGpvTeRaIx1mxpJBiJctOz/7b3scJJrISgaExxy0s1rNnSaHtNIzGVZNzdsGQdsycMptDnTkjybB/ikMownLGgmueuPJnSfE/SRBpv7Hfl5Nbb4sOz5aUNRRTbm4JJlXGKvD1XVWSrS6cmPZ35PabUIU9UM2t8JctrahlWUWyv2AE8fskI6ptD1DeHEsbt7ROHUNcUYs2WRq57Zi2zxlfaujSqUufHDOyXbz+c6XGi0Wi6kp47i/YCYjHF5voWPqtvtb2JN/+gkptfqOGLnW0srd6yOzGy0MvPFq9N6O64tSGAyyEpvXcRs8aw2+lImliW19Ty2+8rDi3JIxZT3Hr2EH77/d0T4badgZSTUZ7HmZDwmCrEocjnSmsYpppIrSY8s1+s6bLJrTfGh7tdqZPCurrbXTimEpp8WA9bT/fgJjHZ6tKpSU1nf4/pHi5L8z12z4H2uSaHFPuSVvos3WKFjRT73fYDZLrkyzyvU48TjUaTEXruOnEvoDEQ4qtdbcx6/n3OfWAVs55/n1Akxl3nncAhfXxcffogZr9Yw7kPrGJzfatdM9bCatRw/5QqBpT47W33T6li2b+3MayimL75HpbMHM38qVUMqygGYFxlOSLC5w2tbG8J0r4csbUE3/5araGo7YFuDIT4cmcbd0weyvypRuzj5Y+/a9cSb3+sx+VMO5Eef5BhDHWVQZwu/KK+JbTf584VLocwd9KQhO957qQhXd4BMhpTlBV4mT+1ikXTR9nfbayHN4mx8gsOLcmjrNCrjags0tnfYzr907/Ix9zJQ5M6517++LtE0oxbyws+oMRPeaGXP557AqUFHh5/czO3T0z8PVl1uPU40Wg0mUB7uHNIIBRN8iZeu3itHUc4rrKchZeNxOkQfB5HUnb+/VOqUAoO6ePl6ctHETKrlCx59z+cOeQQRhxZyrRH36GswMs1Ywfxh3OH4nYKja0Rzpn/ZkLIwJxlRpm/By88kUFlBUlL8FYMd2m+h1hMsa2xLWn59vcvr8cppF2+r28JpfQq+T2uLo1D7o1JcoFQNKEUZGMgzJxl67n3/GGQ33XX8bkc/Px7x9rj0vruvV3sSdccOKT7PQbCUT5vaE3yJKcLAQpHYjS2hlKey5Nm3MaUsnXl029/xhlDDiEQinLeiMMp8rl4dNoInA7wOh0c1MevDWyNRpMxtMGdQ9LFEeZ5DC+yVSPbiH/20sfrYfGM0USiMRwOYcEbm5j/z80JRrMVcjLqqDJmPf8+ZQXehCXYRy4+yTaUrevdsGT30uvlj7/Ls1eO4dj+hTx75RjawjGcAj6P0fJ4284ADpGkhjY3LjVivKMKBpUVsHjGaMLRGG6ng/ICw1OUrVja3pgk53E5E0pBQmY+UzRNSMniHhxSoskt6X6PG2qbmfboO0khJu1DgPweJ8FwjM8bA/TxuxlXWZ5Q2tSotx1LGwo1e8JgFryxmenfOootO1oT9J91/OIZo1Ma270tF0Sj0eQObXDnEJ879UTUGAjb/9/aECAQilDXZHh+Din2U9cU5Oz7Xk9rNJ9TNcBuOjNrfGXCEmyex5nSyC/2u+1kpEAoSj0h+uUbhnL7GMy//+xbKc9xWGkePrewvrYpqdzWwcU+iv0e2xiPRGO44ozxrqQ3Jsll6zOla7YUUT07pESTO1KNXctBAIkVd6yVMMvA7V/o45O65oRj759SxeXfPIqYUrSGopQXeuzzxLO1IcCXO9vsqiMTqwak1X8qxfjujbkgGo0md2iDO4f0y/fucSICwwD/8MsmO6nw2P6FaZdoi/1uzqkawJTRh7Npu9E0p9jvTtg3vvV7/DXC0VjKZKRj+xeyvSWYEIPplNS1vD1OB01tUdvYtuSyym0dXppHMBLL+ATWG5PksvWZnGmScJ3Sc++dJre0H7sAVy9ck5QAHovFkgzchZeNTIr/viKufJ/Rd2A4bmfqcdsW3h1G1hgIp+1ImWqlSPcK0Gg0XYkOzMwh8RPR6zeexrNXjqF/kc9OjrRio+et3JCQaJQqMXFcZTmlBV6uHXcM9c0h+hV4+NP5w2kNRRP2nbdyQ1Ly3bwpVRxZlo/TTMxbNH0Us8ZX8taGOr7c1UYglGjgf7mrjTvPGZqUwLe1oZW6pmDaMJnP6luzlszYG5PksvGZREhKJuvpnSY1uSd+7FrhUfGMqyxPWY60dg/6xHp9xZOrEZGk5PF5U6o4vDTPThift3IDffPdSfov3UpRb8wF0Wg0uUN7uHOMNRFZ9MtXPHflyQRCET78ssmuUQ27lb3LIQkdKMdVlvPj0wcxZ9mHXDP2GDtGcVxlOb8ZX5mQbFnXHMTvcXLbD79OvtdFH7+b2/76od0gYu6kISyt3sr5Iw/jmP4FrP+yCZ870Ss0Z9l6bv5BJb+fPJSDinxs2t7CnGXruemM49ImRjYGwpQXelNOYG1pkqf2RkfiK3UMZueIKXht/Vd2xz4rCffw0oG5Fk3TSyjxu1l42Uhqm4LUt4RYvbme758wgG0725L0w570icXWhgBtoSj3rPiYpy4fRSQWAwX/G6fXbp84hMfe2ES+10V5kZenLh9FOBrD73bSv8iXUif0xlwQjUaTO7TBnQP2ZARaBnhdE8x+sSatsn/sjU12xYq++R7mvvwRN3z3OKY9+o59jJV0ed8Fw+1W3R992cTvXjAa3cyfWsWPn1qT4FF65PVNXHXaIK5auLsT5R2Th3Lv+cO4euEa22gvyfdQ5nAQjsbsGMnGQJil1VuS2tH/6fzh3PzCB8ydPDTlBBaMxPj2H15lXGU5vz6rEqdD9mgcx2KK7S1BWoNRNm1v4e4Vn9gVVuLDUzoag9lTjPJsyOlzOThr6KH2OBpQ4ue+C4bj01VKNF1ALKaSYrLnT6nirhUfM7GqgnGV5UysqrAr8azeXJ/UGTdV2F0kplheU8tNZxzPZ/WtSYnhNy5dx6Lpo/jbB9u4+cWPbF2wp865vTEXRKPR5A5tcGeZjhqBe1P2137nWPu9l675BheNGcjOQDilB7nQ56K+OUiRz51gxLeP7waYWFVhG9vW8dc9s5bbfvh1Zo2vZFB5AfXNIZraIsxYYHR/s4zoeSs3cP13j7UfBkrzPfTN9zBv5QbDs+52JBnjt08cQiQaZVhFMReNGcj5f35rr8Zxum6V7eMr08VgPnvlGMoLfZ36PnJNtuQMRmK2cQPGPbvyydUs0lVKNPtB/MNiui6SK2q+4urTByUY1w9ffCLhqGL2hMHkeZwooLTAkxB2d/8FVTz42kajg2RMpU2M3LazjaqB/Xj3V2NRyF4fWHtjLohGo8kd2uDOMh1NxNmbsm+fhNTe+LUYUOLH6RCuWriGMUeWJniLrPju+P1L8z0pJyu308HsF//N7AmDOaosn/P//BZlBV6KfC7uv2A4Vzy5mjVbGnnsjU3cdMbx7AyEaQlG+GpXGzNPPYqZpx5FKBpL8Mw3BsI89sYmbvjuccw89aiUDS2evXIMgtj3QJEc5xnfUS4+vjJdDGZrMEosX+FwSI9JjMqWnOmqlER7eOMbTe6If1i8Y/LQlOOrNN/D2Mr+SQ97nze0JZXxG1dZzsLLRxExy47+ZfVW3thYz7wpVbSGIpQXeVPqwfqWELNfrGHxjNEcUuzrkOztQ/40Gk334oibXur0MZtvOysDkuwdbXBnmc4k4uxJ2ce/93lDq+1hTuVBvnfFpyyaPgoF/Pf/+8A2eA8t9nHH5KFc98xae/+ywtSTVWsoyrwpVZQXeglGonZ97xuWrKOswMvsCYM5ol8+X+1q47rFawGYeepRlOZ7cDkd3LviEy46+QguGjMwSb62cDRtfHdrMMqUh3Z7vZ+4dGTaCi3t4yvTxWBu2t5CvtdottNTEqOyJacrXZUS7dXT7CPxD4vpqiSVm7qs/RhP5a1eXlPLr86q5IvGAE6HMOLIUgYPKKYkz825D6yirMDL3ElDEprgWKtgWxsCRKKxzH9ojUajaUfGDG4RqQAeBw4CYsADSqm7RKQvsAg4AtgMnKOUahARAe4CzgRagYuVUqvNc10E/No89S1KqccyJXemyUQijnXONVsa+f3L6+1wjj5+Nz9fso665iA3uI4jGImyvKbWbhoxf2oVS6u3MGt8JeWFRhvkhas2Jxnt86ZUEQhFmfWX96lrDvLEpSO5Zuwge5+tDQG7RNfjl4ygrNCTZFjPnTSEIp+bu/7+cZKHe9b4ryFpSg1u2t6S4PGyyh2meiBoH19Zmu9h/pQqu0lP/MR77/nDMvZ9ZIJsyelyCHeeM5RrF+9+CLvznKFd3kJec+AQ/7CYyikwb0oVBxf5cLuSS/alWoWzHgBvevbfCXHgv/t/H9j6aM6y9cyeMJjD+ubxaV2znXw+oMSPy6nzETQaTfaRVAX/u+TEIgcDByulVotIIVAN/BdwMbBDKXWbiNwElCilbhSRM4EfYxjcI4G7lFIjTQP9XeBEQJnnqVJKNaS79oknnqjefffdjHyu/SUTsbipztm+Xfux/QupbwklNMwZVlFst0OeNb7Sju+2GuAcVOSjtMBjx4bPW7mBQeUFXHbKQJwOB2PveDVJln9c9y0cAlMeejtpknz+6jHU7gqllLOs0MP13z2WzxvayPM4aQ1Fqejr54Zn1iXU6x1WUcwt/zU4wYiOb6xjNeqx4kXdTgcffLGLPI+TxkDYjie3QjH28fvIiPW5p3GbrRjuHS1tNAWjhCMKhxhVS9wuodDrpG9+x5bhNd2SrI9Zi/aNuoZVFHPN2EEcWZZPKBJjybv/4bJTjkYpxYa6ZtszPa6ynF+PryQSVXxW32onR8+fUkVJvpuWYJRQNEaB10UkGmPsH15LuvaK677FRQ+/nWDcH9e/EJdOAu4p5Gzcwr6FK2i6PxkOKUk7ZjPm4VZKbQO2ma+bRORD4FBgAnCqudtjwErgRnP748p4AlglIsWm0X4q8Del1A4AEfkb8D3gqUzJnkkykYjT/pxulwOXQ7j3/GF4XE5K/G7bAF142UhueamG5TW11DUH6V/k45kZowlFY/aEuGZLo50AaSVQjqss585zT0AENta1cHAfL49cfFKSIVvfHKJfYeo48EhE4XU5mD1hMBV9/WzZEbDb0Q+rKKapLWLHaw4o8TN/ahVlhYkVAeqagxxc7Et7/9obpuMqy7lm7DF2WcT2yac9JTEqW3KGI4qdreGExLX7LhiOT3sFNftIid+dVJrU53bw06ffo645yO0ThyAoWsNR5ixbz4JLRtAcjOAQ4fwH30rQB9GY0V3yrHv+ZRvQb23YzvAjSlN6wrc1BuxwN5/LQVmBVxvbGo0mJ2QlhltEjgCGAW8B/U1jHKXUNhEpN3c7FNgSd9hWc1u67T2WTCTipDxnvmmAftnE5QveTZi4Zk8YjMPhsI22uqYgM755BJNOPAynQ3A5HZz/4Crb433RmIF2LLVV9zveOJ47aQh+j5NIVLF5e2vKyS+q4ELT27Ro+ii7nCDAdeOO4SdPv5dYvWBBNU9PH0XNtqYEY9nyZKeifXKhFT6zeMZolFIpDdWekhiVDTljipRVSp6ZMTqj19X0TmIxRW1zkLtXGKFkR5XlJzxog1Hi9Cdjj6G2KUhdc5CPa5vpX+jliqdWJ+mDhZePZGdr2N4284lqFlw6Ap/LwYNTT0zSc/3yPQl6TqPRaHJFxg1uESkAlgI/VUrtkvQtolO9ofawvf11pgPTAQ477LB9E7YXsr05aE9CsHvievaKMQnGWx+vk/EnDLDrLy+ZOdo+pn0FkYlVFVzRzii7Yck6bvvh1zmoj49bX/owZZxmTO2ugNE+eeqgPr6UXvFYTHXKq5squXB5TS2//b7i0JK8/bmVGaG7jdv4lQ6LrQ0BwjrRTGPS0TFrrTa1BCN27kj7B20w9MmMJ6opK/DaDWp+dVZlan2gwOd2JmxrbA3T2BpmYFk+i2eMxiloI1uTRHfTtZoDj4yurYmIG8PYflIp9ay5+SszVMSK8641t28FKuIOHwB8sYftCSilHlBKnaiUOrGsrKxrP0gPJhBOXd2iLZxY3aKuJcQV5pIv7O7wBsn1ulPV77ZKBzpFqGsO2smbi6aPYvaEwZQXeIjFlH1OK3nK+r/TkdyufkCJv9PtzK3kwvbn6W5JkBbdbdzu6XvQaKDjY9ZabYrXJdaDdjxWKVIr6fuXZ1amHYdOEZqDkYRtffxuZj3/PqfOXck5899kR2tYG9uaJLqbrtUceGTM4DarjjwEfKiU+kPcWy8AF5mvLwKej9t+oRiMAnaaoScvA+NEpERESoBx5jZNB+ioAdW+/nK8Qdx+kkw1aVr7bW8OMXfSEOqag8xYUM11z6zF53aACLe8VGOf06rZ/fglI3juyjE4RJg7abcBboWpuMxwl1gH60BbDYPiz6O7w3UctyP19+DWxoumk1irTfNWbuAOs8vsvJUbksZXuVmKFIz8ka0NrcSUSjkOFYpGM6TE6mJ7218/TKpPX98SysEn1mg0mvRkMqTkZGAq8G8Rec/c9kvgNmCxiFwK/AeYbL73fxgVSj7FKAs4DUAptUNEZgPWOuR/WwmUmr3jcTqSatJaBlRdU9D2BKWqv+x0CE9eNhKvy8H9U6psD/jS6i1J7ZbnTalCBHYFwhR4XXZnuNZQlP5FPsLRmJGo2RSyyxD28bu57a8fsrymlnGV5dzwveMSjsvzOLniidUp27anI11yIRjVErpzYmR3wO8R+hV6E76HfoVe/B59rzSdI361yet22GPKIcJTl4/CIdhJ3VZX3TFHllJe5CUQitCvwJMwDksLPIQiMXxuB4umj6K8yEtzW8TO07DojnX0NRqNJpNVSv5F+vIoY1Psr4Cr0pzrYeDhrpPuwKHE704yoEoLPNyx/GPe2FhvG7IFPqdtVJcVePn5947l+riGOI9OO4nfTx5KvwIPW3YEeOLNz+x62tY5G1vDdiOca8YOon+Rjzyvk375XntZec2WRmYsqAaMjnE3/2Awv/2+kcxY7HOR7zFKfG2oa+HmF2rsxKrOdFVsn1zYU9q3dwdagoqPvmhk2OGlRGMKp0NY81k9BUf0o8i/9+M1GgurOkldU5CrF65JSqK2fs+RSIxiv4unp48kGoMLzC62N/+gkkH9C+xx6HTAncs/YXH1VgaU+Fl42Ui2N4d6RB19jUaj0Z0mezmNbRGWvPMfJp14GG6Xg0hU8cCrG1hcvRXYbciGIlFWfvgVT08fhVJwnlmhBAyP0cWPvGMnJJUXenljY7098f3x3BMIR2P08btYNH0UQJIX2Qr1iDd6rxl7DCKKg4p2h7gcUuzn84bWpMSq/fFa9ZT27d2BUDTGj59el7T9tRtOzb4wmh5NQyDM3Ss+5uffOy7BID6nagCXn3IkraEItbsU25uD/PHvH3PDd4+za/5vbQgw4U9v2Mcsmj6K655Zy6zxlXYb94OLfBT4XEnNrXQImUaj6Y5og7uXE4pEmf/Pzcz/52YWTR/FuQ+sSnjfMmQ9LieLqrdyzMFFdhJT+/0iMYUS6Fvgtr3bTodQ5Hcl1Ms1mkv4Empj17eEKPA6eXr6KHtSvXvFx1x9+iD8bifFebsN367uqthT2rd3B5wOYVxlOROrKuxuoEurt+iVAE2nCZmdbSdWVdi/53OqBjBl9OF2NaRHLj6Jp97+jCtPOxqXUyjOc/PIxSdx94pP7NUtKz9ka0OAY8oLmDW+krtXfMytZw+hrNBLsd/T7evoazQaje4A0MuJj6NMl+xoTVKWZyi+qkD8fhtqmzn59lf4aFszs1+s4dwHVtHH7+aSRxO9xzOfqKa2OQjsDuc4+77X+eaclfzogVU0thqNcpbX1HLlk6tpCSYavqkSH+dPraLE797ve9D+c2sS8bkc/Pj0Qfb3O/vFGn58+iB8ulmIppNYv7v4BOzLTzkyoc57nsfJVacdjUOEqQ+9zbf/8Bqznn+fn3/vWIZVFNuJkfNWbmBAiZ9ITDFjQTXLa2rtB+bOVjLSaDSaXKBn0V5OvPGaqkKAZWRbyYaHFPtZWr0loWSf1W3w7hWfAHD3ik/s8zgdktobbtZtThXOcePSdcw89ajd+7arQOJwCIPKClh42UiWzBzNrPGV3PX3j/mkrrnD1UrS3YP2n1uTSDiqkmqsX/HkasLRzt93zYGN9buzyoTOnjAYj8uRoC9iSlGc50lqtnTDknX84dyhPH7JCJ5c9ZndkdIqZ6ofmDUaTU9Dh5T0ctpX7fB7nDx75RjCkVjS8qvDIRxU5OPa7xzLnX8z6miX5nsoK/Typ398ai/xrtnSyJxl6+147VThHy6zFXi6cI5i01s9oMSPO0Xb8IZAmPP//FbCsTXbmvYp7rqntG/vDujGN5quItXvLhyNJeiLaExR1xRMOeYcInyxM8DEqgGMrezPY29sssNT9AOzRqPpaWiD+wCgMy3BrUny1rOH2JOk0wFvbKxP2K+uOWiX9Jo3pYqZcUlL86ZUUV5gXC9dPLYV3hK/bzxdi08OjQAAERhJREFUHXfdU9q35xpnivKQuvGNZl9p/7uLRGIJ+sLndtohbO3HnFIklDOdP6WKfgVGvLZ+YNZoND0NbXBrkkhVVu/xS0bwWX2rXVrw8NI8e9I7rn8hi2eMJhKN4XI6KC/w4jJjflNVJ5k/pYq++W4WzxidsG88XZ04qekYfk/quu1+j44+0+w/LpeDY8sLWDR9FJGYwuUQHnjNiPG+cWmicf3UW5vtVbbyQi+H9PGn1BUajUbTE9AGt6ZDBCMxZj3/fkLpLQuXy8EhxemLNPcv8rJo+iiiCnxuB/3y957YlMpQ18vImaeP1wghiq/bXlbopY9X33fN/hOLKT7d3mL/rsdVlnPN2GO4e8XHCcb1wUU+LjvlaDsMLhJTfNXUpsPBNBpNj0Ub3Jq9sq91rNM1nOmX37HmNTruOvvsCISYs+wjJlZVkIeTUDTGnGUfccvZX6e80Jdr8TQ9nPa6xOoSefMPBqOUSvidl7mdummVRqPpNej1Oc1e2dd46nSGen1LqEPX1eW+sk9b2KidPGNBNec+sMouwdYW1kmTmv0nlS5ZXlOLUirl73x/dYhGo9F0F7SHW7NX9jWeWjec6Xk4JXXSpFM/62i6gM7qEq1DNBpNV3PETS/t03Gbbztrv66rPdyavbKvdax1w5meh9/jTKrVbiRN6u9Ms/90VpdoHaLRaHoL2sOt2Sv7Gk+tEx97HsV+D/2LfAlJk/2LfBT79Xem2X86q0u0DtFoNL0FbXBrOsS+1LHWiY89D4dDOKI0n0KfW39nmoywL30BtA7RaDQ9HW1wazKKbjjT89DfmaY7ocejRqPpDegYbo1Go9FoNBqNJoNog1uj0Wg0Go1Go8kg2uDWaDQajUaj0WgyiDa4NRqNRqPRaDSaDKINbo1Go9FoNBqNJoNog1uj0Wg0Go1Go8kgopTKtQxdjojUAZ/tw6H9gO1dLM6+omVJTXeQZbtS6ntdfdL9GLeZojvc666mt32mjn6e7jRmu9t3oOVJT65l6U7jtjPk+r6lozvK1dtkSjtme6XBva+IyLtKqRNzLQdoWdLRnWTp7fTGe93bPlNP/DzdTWYtT3q6kyw9ie5637qjXAeSTDqkRKPRaDQajUajySDa4NZoNBqNRqPRaDKINrgTeSDXAsShZUlNd5Klt9Mb73Vv+0w98fN0N5m1POnpTrL0JLrrfeuOch0wMukYbo1Go9FoNBqNJoNoD7dGo9FoNBqNRpNBDliDW0QeFpFaEXk/bltfEfmbiHxi/luSBTkqROQVEflQRD4QkZ/kUBafiLwtImtNWX5nbh8oIm+ZsiwSEU+mZYmTySkia0TkxVzLciDR/r73dESkWESWiMhH5m9tdK5l2l9E5Frzd/q+iDwlIr5cy5QKEdksIv8WkfdE5F1zW9b0W2d0vRjcLSKfisg6ERmeBVluFpHPzfvznoicGffeL0xZ1ovId7tSFvP8nZp/Mn1/uitdeZ9E5CJz/09E5KL9lKtTc7aIeM3/f2q+f0Tcubp0rHV07s6yTB3WRRn5DpVSB+QfcAowHHg/btsc4Cbz9U3A7VmQ42BguPm6EPgYqMyRLAIUmK/dwFvAKGAx8CNz+zzgiix+Tz8DFgIvmv/PmSwH0l/7+97T/4DHgMvM1x6gONcy7efnORTYBPjN/y8GLs61XGlk3Qz0a7cta/qtM7oeOBP4q6kLRwFvZUGWm4HrU+xbCawFvMBAYAPg7GJ5OjX/ZPr+dNe/rrpPQF9go/lvifm6ZD/k6tScDVwJzDNf/whYlKmx1n4O6SYybaaDuigT32HOB3Iu/4AjSFR864GDzdcHA+tzINPzwHdyLQuQB6wGRmIUgHeZ20cDL2dJhgHACuB04EVz4OdElgPpr/19z7U8XfB5ijCMU8m1LF34mQ4FtphK32X+PsblWq40sqaa5LKq3zqq64H5wHmp9sugLDeT2uD+BfCLuP+/DIzO8H3a4/yTjfvTE/729T4B5wHz47Yn7LefMu11zo4fQ6be2G7Oq1061jozd2dLJvMcHdZFmfgOD9iQkjT0V0ptAzD/Lc/mxc2llGEYT6k5kcVcBnoPqAX+hvFU2aiUipi7bMWY7LPBH4GfAzHz/6U5lOVAov197+kcCdQBj5hLnH8WkfxcC7U/KKU+B34P/AfYBuxUSi3PrVRpUcByEakWkenmtpzq2j1c33qQsciWjrnaXLZ+WHaH12RVlg7OP7m6P92G/bxPXX7/Ojln29c339+JMa92tVydmbuzJRN0Thd1+XeoDe5ugogUAEuBnyqlduVKDqVUVCl1AsYT6gjg+FS7ZVoOERkP1CqlquM350KWA4k0972n48JYxr9fKTUMaMFYOuyxmEbZBIyl1kOAfBGZklup0nKyUmo4cAZwlYickmuB9kAudMz9wFHACRgPT3dkW5ZOzD8HtA7ugvvU5fevk3N2xuXah7k7a/eKzumiLpdLG9yJfCUiBwOY/9Zm46Ii4sb4ET+plHo2l7JYKKUagZUYsUvFIuIy3xoAfJEFEU4GfiAim4GnMZam/pgjWQ4kku67iDyRW5H2m63AVqXUW+b/l2AY4D2ZbwOblFJ1Sqkw8CwwJscypUQp9YX5by3wHIZRkFP9tofrbwUq4vbLuI5RSn1lGk0x4EGM+5M1WTo5/2T9/nQXuug+Zez+dXDOtq9vvt8H2NHFcnV27s6GTECndVGXf4fa4E7kBcDKOL0II04ro4iIAA8BHyql/pBjWcpEpNh87ceY1D8EXgEmZVMWpdQvlFIDlFJHYCRS/EMpdUEuZDmQSHPfu6vntEMopb4EtojIseamsUBNDkXqCv4DjBKRPFOHjMX4rXYrRCRfRAqt18A44H1yoN/ake76LwAXmhUKRmGE6mzLpCDWZG9yNsb9sWT5kVnFYSAwCHi7i6/d2fkn6/enO9CF9+llYJyIlJirVOPMbfsqV2fn7Hh5J2Hod0UXjrV9mLszLhPsky7q+u9wfwLQe/If8BTG8l0Y44nlUoy4oRXAJ+a/fbMgxzcwliPWAe+Zf2fmSJYhwBpTlveB35jbj8QY6J8CzwDeLH9Xp7I70zmnshxIf/H3vaf/YSzXv2uO7b+wH5UBussf8DvgI/O3uqA7/hbM3+ta8+8D4Ffm9qzpt87oeozl4j9hxMH+GzgxC7IsMK+1DmOSPzhu/1+ZsqwHzsjAvenU/JPp+9Nd/7ryPgGXmPPXp8C0/ZSrU3M24DP//6n5/pGZHGt0YO7Olkx0Uhdl4jvUnSY1Go1Go9FoNJoMokNKNBqNRqPRaDSaDKINbo1Go9FoNBqNJoNog1uj0Wg0Go1Go8kg2uDWaDQajUaj0WgyiDa4NRqNRqPRaDSaDKINbo1G0y0QkUdFZJL5+s8iUtnJ45szI5mmtyIiR4jI+3vfU6M5sOiIDo7X2e22HyEi52dOup6JNrg1Gk23Qyl1mVIqY81pzGYGWv9p9pm4rnndmp4ip6Z7sZ86+AhAG9zt0BNOL8Z8yvzIfFJ9X0SeFJFvi8jrIvKJiIwQkZtFZIGI/MPcdrl5rENE7hORD0TkRRH5v1RPshrN3hCRC0VknYisFZHnRGST2SYZESkSkc3W/+OOWSkiJ5qvm0XkVvP4VSLS39w+UETeFJF3RGR2u+NvMLevE5HfmduOEJEPReQ+YDVQYXpo3heRf4vItdm4H5puh1NEHjR13XIR8YvICeZYW2eO2RKwx+X/iMirwE9EZLI5ftaKyGvmPk4RmRs3/maY208VkdfM89WIyDzroU9EzjPH4Psicru57RwR+YP5+icistF8fZSI/Mt8XSUir4pItYi8LLtbVCfImd3bqelOiMjPReQa8/WdIvIP8/VYEXlCRMaZenS1iDwjIgXm+/E6+FIR+djc9qCI3Bt3iVNE5A0R2RhnI9wGfFNE3tN6dTfa4O79HA3chdGR6jiMp85vANcDvzT3GQKcBYwGfiMihwA/xHhK/TpwmfmeRtMpRORrGN3CTldKDcXorLcSY7yB0fp3qVIqvIfT5AOrzONfAy43t98F3K+UOgn4Mu6a4zDaAI/A6DBZJSKnmG8fCzyulBoG9AMOVUoNVkp9HXhkfz+vpkcyCPiTUuprQCMwEXgcuFEpNQSjy9xv4/YvVkp9Syl1B/Ab4Lvm2PyB+f6lGG2gTwJOAi4XozU1GGPyOgy9ehTwQ1Pf3g6cjjFeTxKR/8IY6980j/smUC8ih2Lo73+aD6n3AJOUUlXAw8CtaeTUHLjEj6MTgQJz7HwDY2z/Gvi2Umo4Rjfen8UfbI7PWcAo4DsYdkQ8B5vnGo9haAPcBPxTKXWCUurOLv9EPRRtcPd+Niml/q2UimG0M12hjPai/8YwqAGeV0oFlFLbgVcwJoVvAM8opWJKqS/N7RpNZzkdWGKOLZRSO4A/A9PM96exd0M3BLxovq5m97g9GaNVNhjtsS3GmX9rMDzZx2EYVQCfKaVWma83AkeKyD0i8j1gV6c+maa3sEkp9Z75uhrDEC5WSr1qbnsMOCVu/0Vxr18HHjVXBp3mtnHAhSLyHvAWRutoa/y9rZTaqJSKYozdb2AY5SuVUnVKqQjwJHCKqXcLRKQQqAAWmnJ8E/gnxsPjYOBv5rV+DQxII6fmwKUaw+lQCASBNzEM728CAaASeN0cQxcBh7c7fgTwqlJqh+kYeabd+38x7YQaoH8GP0ePR8d29X6Cca9jcf+Psfv7V+2OUYBkWC7NgYHQbnwppV43wzu+BTiVUntLWgubD4kAURL1Vvuxa13zf5VS8xM2ihwBtMTJ0SAiQ4HvAlcB5wCX7PUTaXob8ToyChTvZf/4MTRTREZirNi8JyInYIy/HyulXo4/SEROpfO69k2Mh9L1GEb2JRirjdcBhwEfKKXSrT62pNmuOYBQSoVFZDPGOHoDWAechvFguQn4m1LqvD2cYm+2QPzvR9sNe0B7uDUAE0TEJyKlwKnAO8C/gIlixHL3N7drNJ1lBXCOObYQkb7m9scxPHz7E8bxOkZICsAFcdtfBi6Ji0U8VETK2x8sIv0Ah1JqKcaS6fD9kEXTe9gJNIiItQw/FXg11Y4icpRS6i2l1G+A7Rie6JeBK2R3nsIxIpJvHjLCzD1wAOdi6Nm3gG+JSD8RcQLnxV3vNYzwv9cwVmxOA4JKqZ0YRniZiIw2r+M2Q7g0mvbEj6N/AjOB94BVwMkicjSAiOSJyDHtjn0bY3yWiJGAO7ED12sCCrtK+N6C9nBrwPhBvYThMZmtlPpCRJYCY4H3gY8xJoWduRNR0xNRSn0gIrcCr4pIFMNouBhj2fwWdoeE7As/ARaKyE+ApXHXXC4ixwNvighAMzAFw3sZz6HAI7K7Wskv9kMWTe/iImCeiORhhB5NS7PfXBEZhOHZWwGsxfAgHgGsFmMA1gH/Ze7/Jkac69cxjJ/nlFIxEfkFRtieAP+nlHre3P+fGEb8a0qpqIhsAT4CUEqFzCS1u0WkD8Z8/keM0EGNJp5/YuTSvKmUahGRNowY6zoRuRh4SkS85r6/xpjzAVBKfS4i/4NhA3wB1LB3W2AdEBGRtcCjOo7bQHav1GoORETkZqBZKfX7FO8VKKWaTe/k28DJZlyhRrNfmIbCBKXU1FzLotFkAzOk5Hql1Phcy6LRdIY4W8AFPAc8rJR6Ltdy9TS0h1uzJ14UkWLAg+H51sa2Zr8RkXuAM4Azcy2LRqPRaPbKzSLybcAHLAf+kmN5eiTaw63RaDQajUaj0WQQnTSp0Wg0Go1Go9FkEG1wazQajUaj0Wg0GUQb3BqNRqPRaDQaTQbRBrdGo9FoNBqNRpNBtMGt0Wg0Go1Go9FkEG1wazQajUaj0Wg0GeT/A6Is+XZJeFkPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(features)  \n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3433.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>3449.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.00</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.00</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84.00</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.00</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement horsepower  weight  acceleration  \\\n",
       "0    18.0          8         307.0      130.0  3504.0          12.0   \n",
       "1    15.0          8         350.0      165.0  3693.0          11.5   \n",
       "2    18.0          8         318.0      150.0  3436.0          11.0   \n",
       "3    16.0          8         304.0      150.0  3433.0          12.0   \n",
       "4    17.0          8         302.0      140.0  3449.0          10.5   \n",
       "..    ...        ...           ...        ...     ...           ...   \n",
       "393  27.0          4         140.0      86.00  2790.0          15.6   \n",
       "394  44.0          4          97.0      52.00  2130.0          24.6   \n",
       "395  32.0          4         135.0      84.00  2295.0          11.6   \n",
       "396  28.0          4         120.0      79.00  2625.0          18.6   \n",
       "397  31.0          4         119.0      82.00  2720.0          19.4   \n",
       "\n",
       "     model year  origin                       name  \n",
       "0            70       1  chevrolet chevelle malibu  \n",
       "1            70       1          buick skylark 320  \n",
       "2            70       1         plymouth satellite  \n",
       "3            70       1              amc rebel sst  \n",
       "4            70       1                ford torino  \n",
       "..          ...     ...                        ...  \n",
       "393          82       1            ford mustang gl  \n",
       "394          82       2                  vw pickup  \n",
       "395          82       1              dodge rampage  \n",
       "396          82       1                ford ranger  \n",
       "397          82       1                 chevy s-10  \n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['weight']]  #독립 변수 X\n",
    "y=df['mpg']       #종속 변수 Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>2790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>2295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2720.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     weight\n",
       "0    3504.0\n",
       "1    3693.0\n",
       "2    3436.0\n",
       "3    3433.0\n",
       "4    3449.0\n",
       "..      ...\n",
       "393  2790.0\n",
       "394  2130.0\n",
       "395  2295.0\n",
       "396  2625.0\n",
       "397  2720.0\n",
       "\n",
       "[398 rows x 1 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      18.0\n",
       "1      15.0\n",
       "2      18.0\n",
       "3      16.0\n",
       "4      17.0\n",
       "       ... \n",
       "393    27.0\n",
       "394    44.0\n",
       "395    32.0\n",
       "396    28.0\n",
       "397    31.0\n",
       "Name: mpg, Length: 398, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train data 와 test data로 구분(7:3 비율)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,               #독립 변수 \n",
    "                                                    y,               #종속 변수\n",
    "                                                    test_size=0.3,   #검증 30%\n",
    "                                                    random_state=10) #랜덤 추출 값 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data 개수:  278\n",
      "test data 개수:  120\n"
     ]
    }
   ],
   "source": [
    "print('train data 개수: ', len(X_train))\n",
    "print('test data 개수: ', len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 단순회귀분석 모형 객체 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train data를 가지고 모형 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 학습을 마친 모형에 test data를 적용하여 결정계수(R-제곱) 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6893638093152089\n"
     ]
    }
   ],
   "source": [
    "r_square = lr.score(X_test, y_test)\n",
    "print(r_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "기울기 W:  [-0.00775343]\n"
     ]
    }
   ],
   "source": [
    "print('기울기 W: ', lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y절편 b 46.710366257280086\n"
     ]
    }
   ],
   "source": [
    "print('y절편 b', lr.intercept_)"
   ]
  }
 ],
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