06-Wine-Quality-Prediction/Wine_Quality_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7b44990",
   "metadata": {},
   "source": [
    "# Wine Quality Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f2e969",
   "metadata": {},
   "source": [
    "### Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "462717e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a1711c",
   "metadata": {},
   "source": [
    "### Data collection and preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da4fbf23",
   "metadata": {},
   "outputs": [],
   "source": [
    "wine_dataset = pd.read_csv(\"data/winequality-red.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1cd229af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "wine_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30eb127f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset\n",
    "wine_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "243ac2dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity           0\n",
       "volatile acidity        0\n",
       "citric acid             0\n",
       "residual sugar          0\n",
       "chlorides               0\n",
       "free sulfur dioxide     0\n",
       "total sulfur dioxide    0\n",
       "density                 0\n",
       "pH                      0\n",
       "sulphates               0\n",
       "alcohol                 0\n",
       "quality                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "wine_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2387ec7d",
   "metadata": {},
   "source": [
    "### Data Analysis and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "498f9e41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "      <td>5.636023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "      <td>0.807569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "count    1599.000000       1599.000000  1599.000000     1599.000000   \n",
       "mean        8.319637          0.527821     0.270976        2.538806   \n",
       "std         1.741096          0.179060     0.194801        1.409928   \n",
       "min         4.600000          0.120000     0.000000        0.900000   \n",
       "25%         7.100000          0.390000     0.090000        1.900000   \n",
       "50%         7.900000          0.520000     0.260000        2.200000   \n",
       "75%         9.200000          0.640000     0.420000        2.600000   \n",
       "max        15.900000          1.580000     1.000000       15.500000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide      density  \\\n",
       "count  1599.000000          1599.000000           1599.000000  1599.000000   \n",
       "mean      0.087467            15.874922             46.467792     0.996747   \n",
       "std       0.047065            10.460157             32.895324     0.001887   \n",
       "min       0.012000             1.000000              6.000000     0.990070   \n",
       "25%       0.070000             7.000000             22.000000     0.995600   \n",
       "50%       0.079000            14.000000             38.000000     0.996750   \n",
       "75%       0.090000            21.000000             62.000000     0.997835   \n",
       "max       0.611000            72.000000            289.000000     1.003690   \n",
       "\n",
       "                pH    sulphates      alcohol      quality  \n",
       "count  1599.000000  1599.000000  1599.000000  1599.000000  \n",
       "mean      3.311113     0.658149    10.422983     5.636023  \n",
       "std       0.154386     0.169507     1.065668     0.807569  \n",
       "min       2.740000     0.330000     8.400000     3.000000  \n",
       "25%       3.210000     0.550000     9.500000     5.000000  \n",
       "50%       3.310000     0.620000    10.200000     6.000000  \n",
       "75%       3.400000     0.730000    11.100000     6.000000  \n",
       "max       4.010000     2.000000    14.900000     8.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical measures of the dataset\n",
    "wine_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a798f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x23d3be63b50>"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of values for each quality\n",
    "sns.catplot(x = \"quality\", data = wine_dataset, kind = \"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49b66571",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# volatile acidity vs quality\n",
    "plot = plt.figure(figsize = (5,5))\n",
    "sns.barplot(x = \"quality\", y = \"volatile acidity\", data = wine_dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c79bac38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# citric acid vs quality\n",
    "plot = plt.figure(figsize = (5,5))\n",
    "sns.barplot(x = \"quality\", y = \"citric acid\", data = wine_dataset)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "707620af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation\n",
    "correlation = wine_dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c8bea5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h3MR+Jm0yryZxvt8kTjzal9/bD8btweqUeuJhC0R7a80/7MrRbzfzddNhHJy9khYf5f2+uZ6Uyu6PfmR9P+t83wBU/qgnUV9v4EDj/oR/8hOV8znnnP/wO6s/50j+nnvuOaZNm8bYsWOpXbs2P//8M6tXr6Z8+fJATsXfjb9J2KVLF6ZMmcLMmTOpXr06zzzzDFWrVmXJkiV3LMZinRBOnz6dsWPHEhQURFRUFPv27TNpc8899zBp0iR69+7N+fPniYyMpEePHnz44YfUqFGD7Oxs2rZtS3R0NKtXr+bAgQPUqVOHFi1acOnSJQAWLlzIu+++y/vvv8/+/fsJCAhg1qyCD0BZWVkEBQWxcOFCjh07xjvvvMNbb73FwoV/f4o9e/Zs+vTpw2uvvcaRI0dYvnw5lSpVynN/ycnJtGvXjqpVq3LgwAFGjx7N0KFD8338Ro0aMW3aNDw8PIiKiiIqKoqhQ4fStWtXjh07ZvRc/frrrxw6dIguXboU2Kc7oV7tGviX8S2wzfrN22nRtBE+XqUxGAw827ENqzduMU+At8nd24Py1Suwa+nPABxYsxvf4DJ4B5n28fq162RmZAJg62CHg5MDWVnW82nZpeQ0jkdfpe2fyd2j9wQScSWFiCvJ+W5zJPIS8cnXaFo5wFxhFpq9jwduNUKJWZQzNnErd+NUrgyOwcZjk3EliYS9J8hMKfhDH0uy9/HAvUYo0Yu2ARC7cg9O5crgZNKXZK7uPUlmSv6/n2QtXL09CKgewi9LtwNwdM1eSgf7UirIx6Rt1NHzXA6LBSAjLZ2oY+fxKpf/h0qW4OLtgf99Ify2dAcAJ1fvwzPIF888+nPtajLh+38n3QrH6VJyGsdjrtC2ek5y92jVACKuphBxJSXfbY5EXiY+OY2mla2zYsXX15v776/ON98uBmDJklWEhARTvnzBCewD9WrjV8aXFSvWmyPMQrHz9sTlvgpcWroFgKurd+IQ5IdDkPH7IfXoGa6HxQCQnZZO6rGzOJazvvFx9vbAt3oIJ5fkvG9Or96He7Av7nm8b9KuJBO173cyrPB9A3+dcyoQW8hzTpaV9kMKp3fv3pw7d460tDQOHDjAww///YHL/Pnz2bJli1H7fv36cfToUVJSUoiMjOTrr7+mbNmydyy+Yl0y6unpibu7O7a2tgWWSvbu3ZvVq1fz8ssv59b6DhgwAIDNmzdz5MgRYmNjc1cQmjRpEsuWLWPRokW89tprTJs2ja5du9K9e3cAxo0bx8aNGwucJbS3t2fMmDG510NDQ9m5cycLFy7k2Wefzd3PkCFDcmMBeOCBB/Lc3zfffENmZibz5s3DxcWF++67j/DwcF5/Pe/ZJwcHBzw9PTEYDEbPjZubG61ateKLL77IfawvvviCpk2bUqFChXz7Y0nRMbFGM4hlA/yIjrlowYjy5xXgw5WYS2Rl/p3YxUfE4R3oQ3y4aczeQb70++8blCnvz6+bD7L1u43mDLdA0QkplHFzwu7POneDwYC/hwvRCamULeWa5zbLDp+nXfVy2Nta32dRjoE+pMVchhvG5lpEHE5lfUgLs87XU36cAr25Hn2Z7Dz6cu0u68tfPAO8SYy5bPTeuRoRT6lAH66Ex+W7nZuvJ/e1qc+CLhPNEWaheQR4kRR7xWiMEiLj8Qj05moB/bE20YmpeRwHnIlOSKFsKZc8t1n263na3RdslccBgOCgQCKjYsjMzMy9LSwsgnLBZTl/3rS8/y+vvvoC33yzmIwM6ynrtw/wIT32ktFx7XrkRewDfbkeHpvnNna+pSjVuhFnXh2T5/2W5BboRXKM8fsmKTIe97LeJN5F7xvIOedcjzEem7SIOBzvwnPOHWNFH4IXd9Z5NLaAefPm8euvv3Lw4EHmz5+fuxTsgQMHSEpKwtvbGzc3t9zL2bNnOX065/sQx48fp2HDhkb7u/l6Xj799FPq1auHr68vbm5uzJ07N3fKODY2lsjISFq0aFGo+I8fP06tWrVwcfn7BFyYGPLSo0cPvvvuO65du0Z6ejrffPMNXbt2zbd9WloaCQkJRpe0NHN/kvX30r136Pu2Rebm+PJbdhggPvwio9sMY9ADPbBzsKfu4w/e4ehu002hZ5P/k5+ansG64xF0rFX+Dgf1L9w0OAUMjdW7+YvnhpsH6y5k8t6+RZcc3Zz5z2dD2fbpSqKOnrtTYf1jJmN0t77gTJZTz7/p38eBcnc4qH/HZOGGW4yNs7MTzz7Tnnnzv7uDUf0zt3POsXFzpsLno4ids4TU387c4cj+mdsdG2tmeky7e/sid7diPUN4O3755ReSk5OxsbEhOjo690ubWVlZBAQEmEzlAv9qhc6FCxcyaNAgJk+eTMOGDXF3d+ejjz5iz549ADg7O9/W/opy1aH27dvj6OjI0qVLcXR0JC0tjaeeeirf9uPHjzea7QR4e1h/3nljQD5bFC1/vzJERsfkXo+MjsHfr+AyU3Nq1KkpLbu3A2DP8h2U9vfCxtYmd6bDK9Cb+MiCP9lMS7nG3hU7aPDEQ+xdseOOx1wY/h4uxCamkpGVhZ2NDdnZ2cQkpOLvkfdrd+OJSCr4uJksOmMt0iLjcAzwBlub3E9sHQN9uBZxd33qDHAtMh6nQG8Mtja5n6Q7lvW+6/pSu9NDNO6es5Lyr8t34XnTe8cz0Jsr+bx3HFydeOXL4ZzYeJCdn682W8wFqd6pCfX/7M+x5btw9/cyGiP3AC8SIuMtGeJt83d3Nj0OJKbi75H37ODGE5FU8HY3WXTG0m5cHOaHH5YRVDYAW1vb3FnC4KBALoSZLmD0l6eeaseJE3/kufCMJaVHxeHgb3xcsw/wIT3SdAbKxtWZigtGc3XjXi5+9pOZI81f1aeaULtHzvvm1E+7cAswft+4BXiRGHF3vW/gr3OO103nHG/S7rLjtBQPSgiBS5cu0aVLF0aOHEl0dDQvvfQSBw8exNnZmTp16hAdHY2dnV3uojQ3u/fee9m9ezedO3fOvW337t0FPua2bdto1KgRvXv3zr3trxlHAHd3d0JCQti0aRPNmze/ZR+qVavGV199RWpqam4yeasYHBwcjEpi/mJnZ8crr7zCF198gaOjI88//7zRzOPNRowYweDBg41us0nM/8RZ1B5r1pjOrw+l16sv4l26FAuXraZ1i6Zme/xb2blkKzuXbM29XqPZ/TR88mF2LNpC3dYNiAu/mGe5qG85Py5FxpGZkYmtvR11Wz1I+Inz5gy9QF6ujlT1K8Wq38J4omZ5Np6IJNDTJf9y0V/O07FWiHmDvA3pcQkk/XYWv6cfJuaHLfi0a8C1sNi7snQnPS6BxCNn8X/6IaJ+2EqZdg9yLeziXVcuenjJNg4v2ZZ7vUqzWtR6sgmHFv3Mfa3rcyX8Yp7log4ujrzy5XBO/fwrWz62ngU+fluynd+WbM+9XqFZLao/2Zgji7ZRtc0DXA2Pu6vKReHP40AZT1b9Fs4TNcux8WTUn8eB/MpFL1hllcDXXy/i668X5V5/vFVzXnrxKRZ8tZBOndpy/nx4weWirzzHvC+sb3YwI/4qqUfP4PVkMy4t+h+ebRpxPTzWpFzUxsWJCgtGk7D1EDEzfrBIrPk5uXg7Jxf//b4p37wWVTs15sSP26jY5gESw+PuunJR+Oucc44yTz9MbO455+Jdec65Y4rJKqN3AyWEQK9evQgODubtt9/m+vXr1KlTh6FDh/LJJ5/w6KOP0rBhQzp27MiECROoWrUqkZGRrF69mo4dO1KvXj0GDBjAK6+8Qr169WjSpAnffPMNR48eLfA7d5UqVWLBggWsW7eO0NBQvvrqK/bt20doaGhum9GjR9OrVy/KlClD69atSUxMZMeOHfTrZ7pC2IsvvsjIkSPp1q0bb7/9NufOnWPSpEkF9jskJISkpCQ2bdqUW276V+LXvXt37r33XgB27Ch4RsrR0TH3+5V/Sb9eNAfncZM/YfO2XcRdukz3AW/h4uzEmoXzeGf8NJo3aUDzhxoQXDaAPt3/w8u9hpCdnU39OrXo1L5VkTz+nbDgrTl0ndSHtr07cS0plc+GfJx7X5cPe3F4434Ob9zPPQ3u47Fu7cjOysLG1pbjO4+w/ONFBezZ/Ea1rs07Kw/w+c6TuDnYM7Z9XQDGrDpI08oBNKuSs3hM2OUkjkdfYcYz1vdTEzc6Ney/VJ3eh+D+T5KZlMrJ/jMBqDy5F/Hr9nNp/X4MDnbU3z0Tg6M9du4uPHjwU2IW/cy5D769xd7N68SwuVSb0ZuQAR3JSEzlWP+cha7umdKTuHX7iVt3AIODHY32zMDGwR47DxcaH5pF9KJtnH7f+v6xBfjprc/pNKkXTXs/QVpSKouHzM69r+OHPTix8QAnNh6k4auPE1SrIg4ujlRrVQ+A31btYesn1jPrAbD2rXm0m/wajfp0IC0plZWD5+Te13pCd05tOMgfGw9i62BHr5+nYOdgh6O7C312z+C3JdvZOtE6fkpj1OO1eGfVIT7f9TtujnaMbVsHgDGrD9G0sj/NKv91HEjmeMwVZjxtZaXveXi9z5vM+2wqb77Zj4SERLp2G5h735xPP2LFyvWsXJnzu2IVKpSnTp2aPPFkF8sEewthb82i3OQBlOnzDFlJKVwYPA2A4Al9ubphLwkb9+LbtT2utSpj6+xIqVY5x+krq3cQM/NHC0aet81vzuPRKa9Rr28HrielsnHQ3++b5hO7c3bDQc5tOIiNgx2dt0/JWZTN3YUue2dwcvF2dk2wjvcNwB/D5lBleh+C+3ciMymV3/vn/D9w8znngd2fYONoh627C/UPziF20VarO+fI3c2Qfad+4dBKTJs2jWnTpnHu3Lnc25o1a0bt2rWZNm0aCxYsoHfv3hw6dIjKlSsDOd8bbNSoEUuXLqVNmzYkJiYycuRIFi9ezMWLF/H39+fhhx9m/PjxBAfnrLD4wQcfMHXqVK5du8ZTTz2Fn58f69at4/Dhw3nGlZaWRq9evVi6dCkGg4EXXngBT09P1qxZY7TNnDlzmDp1KmfOnMHHx4enn36aGTNylog3GAwsXbqUjh07Ajkzgr169eL48eNUq1aNUaNG8dRTT3Ho0CFq167Nli1baN68OZcvX84td3399df58ccfiY+P591332X06NG5j/3www8THx/P0aNHb/t5T4+zzu8e/FM9671h6RCKzCdj8l6p9m61b/jpWze6i6RnF5+vdm92ts7fAv2nXIvR2AAMHH1nfs/KEtx7fmPpEIrU/oC6lg6hSG3Psq4S4X+jVrr1rjD9TzwUbV0fNN8odd1MS4eQJ+dWfW/d6C5T7BNC+Weys7O555576Nmzp0k5aGEoIbReSgitmxJC66WE0HopIbRuSgitl1UnhGus8zdynVv3t3QIRU4lo2IiNjaWr776ioiICF599VVLhyMiIiIiIneIEkIx4efnh4+PD//9738pXbq0pcMREREREZE7RAmhmFAVsYiIiIhYlH6Y3myK1xciREREREREpNCUEIqIiIiIiJRQKhkVERERERHroh+mNxvNEIqIiIiIiJRQSghFRERERERKKJWMioiIiIiIddEqo2ajGUIREREREZESSgmhiIiIiIhICaWSURERERERsS5aZdRsNEMoIiIiIiJSQikhFBERERERKaFUMioiIiIiItZFq4yajWYIRURERERESiglhCIiIiIiIiWUSkZFRERERMS6aJVRs9EMoYiIiIiISAmlhFBERERERKSEUsmoiIiIiIhYF60yajZKCOWO6FnvDUuHUKTm7J9o6RCKTMUqT1g6hCI13qmWpUMoUqUyMy0dQpGpkl68ilDKZGRYOoQitfKtCEuHUGSOVaxh6RCK1IepDpYOoUh52haf987S7ARLh1CkNlk6ALEKxetsLSIiIiIiIoWmGUIREREREbEuKhk1G80QioiIiIiIlFBKCEVEREREREoolYyKiIiIiIh1yc62dAQlhmYIRURERERESiglhCIiIiIiIiWUSkZFRERERMS6aJVRs9EMoYiIiIiISAmlhFBERERERKSEUsmoiIiIiIhYF5WMmo1mCEVEREREREooJYQiIiIiIiIllEpGRURERETEumSrZNRcNEMoIiIiIiJSQikhFBERERERKaFUMioiIiIiItZFq4yajWYIRURERERESqhinxAaDAaWLVtWpPs5d+4cBoOBw4cP/+v9/lOFiWHLli0YDAauXLkCwPz58ylVqpRZ4hMREREREetX7BPC2zV69Ghq165tcntUVBStW7c2f0D5CA4OJioqiurVqxd6m+eee47ff/8993p+fRURERERsajsbOu8FEP6DmEh+fv7WzoEI7a2trcdk7OzM87OzncoojurTIg/3Sf3w620OykJycwb+gmRf4SbtKtYpwovj+sBgK2dHaf2H+fb0fPIuJ5h7pDz9MHU2WzZvpvI6FiWfjWbyhVC8my3eMU6Pv96IVlZWTSoV5u3h/TFzs7WvMEWUkiFckyZ9T5eXqVIuJrIkL5vc+rkmTzbVr23MmMnjMCnjDc2BgMT3pvO2pWbzBxx/txD/Wg4vSeOpd25npDCroFzSDgVadKu4gtNqdanPQYbA9Hbj7JvxHyyM63ruw4uof7U/vh17L3cyUhI5pf+n5L0e4RRG+dgH2pNfx2PGiEkn4lmR6uRFoq2cNxD/Wg8rSdOXu5cv5rCjkFzuJrH+ABUer4p1fu2x2AwELX9KHvesq4xcgn1p8bHvXHwcic9IZkj/WeTbDI+vtSY/jruNUJIORPFLisdH7dQPx6Y3gtHL3fSE1LYO3AOiTf1BSDkhabc07cDBhsDsduPcvDNL6xqTP5iXz4Q//FDsS3tQVZCMtFvTeb66QtGbZwfrIXPoFexcXWG7GySNu0ifvqXFor41orLORTAJ8SfFyf3xrW0O6kJyXw3dDYxf5i+3io1vI92w1/A0dWJ7Kxsftuwn9WTfrBAxAUrGxrI8KnD8PTyJCkhiYmDJnH+1IV829s72jNnzSyupabRu21fM0YqxZnVzhDOmTOHsmXLknXTF0o7dOjAK6+8knt99uzZVKxYEQcHB6pWrcpXX31V4H6HDx9OlSpVcHFxoUKFCowaNYr09HQgp6RyzJgx/PLLLxgMBgwGA/PnzwduXXp67Ngx2rRpg5ubG35+frz88svExcXl2z4+Pp4XXniBoKAgXFxcqFGjBt99951Rm6ysLCZMmEClSpVwdHSkXLlyvP/++0DeJaOrV6+mSpUqODs707x5c86dO2e0vxtLRvPra9euXWnXrp3RdhkZGfj7+zNv3rwCntk765UPerL1uw289Uh/1sz5iS4TX8+zXdixc7zX4U1GtxnGO60G4+7lSbMXW5o52vy1bN6EBbMnEehfJt824ZHRzJy7gAWzJ7Fm4Tzi4i+zZOU6M0Z5e8ZPeYdvv1xEs/rt+fTjL5g4Y2ye7Zycnfjs6+lMev9jWjR4gkcbPcneXQfNHG3B6k/oyh9fb2bFQ8M4NmslDSb3MGnjGuxLzWFPs6HjWJY3GoJzmVJUfKGZ+YO9hRqTunPhq01sbTSY0zNXUHPqayZtMhJTOfnhQg6/PtMCEd6+BhO6cuqbzSx7aBi/zV5JozzGB8At2Jfaw55mbcexLG2cM0aVrGyM7pvUnfCvNrGt0SDOzlxBjak9TdpkJKbw+4c/8OvrH1sgwsKrO7EbZ77ezNomQznxyUoeyGNcXIJ9qf7GM2x+YgxrGg7GydeT0BebmT/YQvAb3Z+rC9dwrnV3Ln3+I37jBpm0yUpIInroh5xv35MLT/fD5YGauLdtZv5gC6m4nEMBnv2gO7u+28T4Rwaxec4Knp9o+t4BSE1I5qt+M5jw2FCmdHiLig/eS50Ojc0c7a0N+nAgq75ZzSsPd+WH2T8ydNLgAtt3e+NVjh08bqbopKSw2oTwmWeeIS4ujs2bN+fedvnyZdatW8dLL70EwNKlSxkwYABDhgzht99+o2fPnrz66qtG29zM3d2d+fPnc+zYMaZPn87cuXOZOnUqkFNSOWTIEO677z6ioqKIioriueeeu2WsUVFRNG3alNq1a7N//37Wrl1LTEwMzz77bL7bXLt2jbp167Jy5Up+++03XnvtNV5++WX27NmT22bEiBFMmDCBUaNGcezYMb799lv8/Pzy3F9YWBidOnWiTZs2HD58mO7du/Pmm2/m+/j59bV79+6sXbuWqKio3LarV68mKSmpwP7cSe7eHpSvXoFdS38G4MCa3fgGl8E7yNek7fVr18nMyATA1sEOBycHkw8VLKle7Rr4lzGN+0brN2+nRdNG+HiVxmAw8GzHNqzeuMU8Ad4mbx8vqte6l6ULVwKwevkGgsuVJSg40KRtx6fbcGDfL+zbcwiAzMxMLsVfNmu8BXH09sCrRghnF+8AIGzVPtzK+eIa5GPUrly7+oSv3c+1uAQATi3YREjHhmaPtyAOPh541gghYtF2AKJX7sW5XBmcg437kn4lmct7T5KRcs0SYd4WJ28PvKuHcObP8bmwah9uwabjA1C+bX0u3DBGv3+1idAnrGeMHHw88KgRSuSibQDErNzz5/gYHxvSryRzZe9JMlPSLBFmoTh6e1CqRggXFue81iJW7cW1nC8uN41LULv6RKzZT9qfY3J6wSaCrex9A2Dr5YljtUokrMipXEhavx37sn7YBRqfe9OOnyY9PBqA7OvppJ04jX1wgNnjLYzidA518/YgqHooB5bmvHd+WbMHr+AylM6jLxFHzxEfFgtARlo6EcfO410u/w9jLaGUdykqV6/EhiU5r7efV23DP9gfv6C8/9erUb86ZUPLsmHxRnOGaTlZWdZ5KYasNiH08vLi8ccf59tvv8297ccff8TLy4sWLVoAMGnSJLp06ULv3r2pUqUKgwcPplOnTkyaNCnf/b799ts0atSIkJAQ2rdvz5AhQ1i4cCGQU1Lp5uaGnZ0d/v7++Pv7F6rEcvbs2dSpU4cPPviAe+65h/vvv5958+axefNmo+/s3ahs2bIMHTqU2rVrU6FCBfr160erVq348ccfAUhMTGT69OlMnDiRV155hYoVK9KkSRO6d++ebwwVKlRg6tSpVK1alZdeeokuXbrkG3N+fW3UqJHJTOsXX3zBM888g5ubW577SktLIyEhweiSmZ15y+etsLwCfLgSc4msG0qL4iPi8A40/UcQwDvIl9GrP2LGwXmkJqWw9bu768AZHRNrNINYNsCP6JiLFowof4Fl/YmNvkhm5t/jHRkeRWCQ6T9GlatWJC0tjS++m8marT8yddb7eHmXNme4BXIN9CI15opRCVtyRDyuZb2N25X1Jjn879n/pLCLuNzUxtKcAr25Fn3ZqC/XIuJwLpv3e+Zu4BLoRUohxgfyHqO82lmKU6A3aTeNT2pEHE5WFGNhOZf14tpN45ISEW+SELqU9SblhjFJDo/DxQpfj3b+vmTExsMN/cmIuoh9YP4f5Nn6lMatZROSf95njhBvW3E6h5YK8OZqzGWjvlyOiKN0YMHvHXdfT2q1eZDjmw/d6RBvi2+gL/Ex8Ub9iY2MpUxZ08TVydmJ3qNfZ/pbM8wZopQQVpsQArz00kssXryYtLScT0e/+eYbnn/+eWxtc75Ldfz4cRo3Np7+b9y4MceP5z+VvmjRIpo0aYK/vz9ubm6MGjWKCxfyr9UujAMHDrB582bc3NxyL/fccw8Ap0+fznObzMxM3n//fWrWrIm3tzdubm6sX78+N5bjx4+TlpaWm/zeyvHjx2nQoAEGgyH3toYN/9mnr927d+eLL74AIDY2llWrVtG1a9d8248fPx5PT0+jy69XT/6jx87Pzd/hvbGfN4sPv8joNsMY9EAP7Bzsqfv4g0Uai3n83T9r//5y9k0B5jc2dnZ2NH2kMW8OHkvrps8QGRHNuI+s6ztRhe3Ljc0Kei1alMnrxkrjvB2mB4IC2haynYWYvNbu4vG5uS/5deXGdndVbwt4/di4ulB21hgufb6ItGN/mDGo21OszqGFPE7/xdHNme6fvcH/Pl1O+NFzdzCwf8bk/ZPPu+O1t3vw05fLiYuOv/NBSYlj1YvKtG/fnqysLFatWsUDDzzAtm3bmDJlilGbmw8E2dnZ+R4cdu/ezfPPP8+YMWNo1aoVnp6efP/990yePPlfxZmVlUX79u2ZMGGCyX0BAXmXkEyePJmpU6cybdo0atSogaurKwMHDuT69esAt734i+kB5Z/r3Lkzb775Jrt27WLXrl2EhITw0EMP5dt+xIgRDB5sXPPer8Yr+bQunEadmtKye853Gfcs30Fpfy9sbG1yP0XzCvQmPjL/72gCpKVcY++KHTR44iH2rtjxr+IxJ3+/MkRGx+Rej4yOwd+v4DJTc3rqufZ0790ZgOWL1+Af6IetrW3uLGFAWX8iw6NMtosIj2TXtr3EROWU8CxbtIr5P8wyX+C3kBx5CZcALwy2NrmzHS6BXiRHGJ98kyPicbuh9NI1yIeUCOs6QV+LjMcp0LgvTmW9SY0o+D1jbSo83YRqr+Ws7nx22S6T8XHNY3zgz5nDG8bILcgnz3aWkjM+3ibjc82KYiys1Ii83jfGs4GQM2voekNJrEuQDylW+HrMiL6InZ8P2NrkzhLa+fuQHmlapWFwcabs3HEkbd7NlS+XmDvUAhWnc2i9Tg/RrHtbAA4u34mnv7dRX0oFenM5Mu/3jqOrEz2/HMFvGw+w9fPVZou5II899ShPv/YUAJt/2oxPgK9Rf8oE+hIbEWuyXY369/HgIw/w8sCXcHB0wM3Tjc83/ZduLUy/H15sFNPyTGtk1Qmhs7MznTp14ptvvuGPP/6gSpUq1K1bN/f+e++9l+3bt9O5c+fc23bu3Mm9996b5/527NhB+fLlGTny71mJ8+fPG7VxcHAwKn8rjDp16rB48WJCQkKwsyvcU7pt2zaeeOIJ/vOf/wA5SeWpU6dyY69cuTLOzs5s2rQp3zLRG1WrVs1k0Zvdu3cXuE1+ffX29qZjx4588cUX7Nq1i1dffbXA/Tg6OuLo6Gh0m63h362IuXPJVnYu2Zp7vUaz+2n45MPsWLSFuq0bEBd+kfhw0xO0bzk/LkXGkZmRia29HXVbPUj4ifMm7azZY80a0/n1ofR69UW8S5di4bLVtG7R1NJh5Vr8wwoW/7Ai93qzR5vw5LPtWPTdT7Tp8BjhYRGEh5mu/Lhy6Tqe+08n3NxdSUpMpmmLJhz/Le+SaktIi0/g8m/nCX2qMWcWbiO47QMkh8UZlR4ChK3ay2PL3uHIlKVci0ugcucWnP9pl4Wiztv1uAQSjpyj7NNNCP/hZ/zb1Sc17CKpYdb3D3hBzizazpk/vwcJULZ5LSo81ZjTC7dRru0DJIWbjg/A+dV7eXzpO/w6NWeMqrzcgnPLrWeMrsclkHjkHIFPP0TED1vxa/fgn+NjnaXhBcl535yj3FNNOL/wZ8q2rU9yWJxJQhixai/Nf3qXY1OWkBaXQMXOLQj7qeBzlCVkXrpK2vHTeLRvQcKyDbi1bEJ6ZAwZkTFG7QwuTgTNHUfy9gNcmv1tPnuznOJ0Dt2/ZBv7l2zLvX5vs9rUffIh9i3aSq3WD3Ip/CKX8+iLg4sjPb8cwcmff2HDx9aTsG9YvNHoO4APNH+Axzq1YN2PG3i47UNEh8cQEx5jsl2Px3rl/l2rYU16vv2aVhmVImPVCSHklI22b9+eo0eP5iZPfxk2bBjPPvssderUoUWLFqxYsYIlS5awcWPe9e6VKlXiwoULfP/99zzwwAOsWrWKpUuXGrUJCQnh7NmzHD58mKCgINzd3U2SnZv16dOHuXPn8sILLzBs2DB8fHz4448/+P7775k7d25uievNsSxevJidO3dSunRppkyZQnR0dG5C6OTkxPDhw3njjTdwcHCgcePGXLx4kaNHj9KtWzeT/fXq1YvJkyczePBgevbsyYEDB3JXSM1PQX3t3r077dq1IzMz02hVV0tZ8NYcuk7qQ9venbiWlMpnQ/5eda/Lh704vHE/hzfu554G9/FYt3ZkZ2VhY2vL8Z1HWP7xIgtGbmzc5E/YvG0XcZcu033AW7g4O7Fm4TzeGT+N5k0a0PyhBgSXDaBP9//wcq8hZGdnU79OLTq1b2Xp0PM1YvBYJn8yjr6DupOUmMzgPn9/4DJh+mg2rtnChrVbiIyIZtbUz1i27msyMjKJjorlzUFjLBi5qT3D59Fw6mvc168D6Ump7BowB4AHJ3UnfP1BItYfJOnCRX6dtJiWP70LNgZidhzjj++23mLP5ndk2GfUmtGLigM6kpGYyi/9ZwNQY0oPYtYdJHbdAWwc7Gi2Zxo2DvbYe7jwyKGZRCzazsn3v7dw9Hnb/eY8Gk99jRr9OnA9MZUdA+fk3tfwo+6ErT9I+IacMfpl8mIeX/Zuzk+D7DjGKSsbo6PD5lJjxutUGNCRjMQUjvw5PvdNeY3YdQe4uO4ABgc7mu6Zjo2DPXYeLjQ79AmRi7bxu5WNz4E35lF/Wk/u7Z/zvtnX/1MA6k7qTuT6g0StP0jyhYscnbSYR5a/CzY2xG4/ytlvt1g28HzEvDsD//FD8Or5HFlJKUSPyKki8ntvIEn/203y5t2UfrkjTjWqYnB2wu3RRgAkrdvGpTnWNTZ/KS7nUICFb83lxUmv82jvjqQlpfDtkNm59z334Wv8tvEARzce4OFXW1OuVkUcXByp0eoBAA6v2s3GT5ZZKPK8TR0+neFTh/JivxdITkxhwqCPcu8b8tEgdq7fxa4N1vfhiRQvhuyirDW8AzIzM3N/hP306dNUqFDB6P7Zs2czadIkwsLCCA0N5e233+bll1/Ovd9gMLB06VI6duwIwBtvvMG8efNIS0ujbdu2NGjQgNGjR3PlyhUgZ4GUl156iU2bNnHlyhW++OILunTpYrSfc+fOERoayqFDh3J/2P3UqVMMHz6czZs3k5aWRvny5Xn88ceZMmVKniWsly5domvXrmzatAkXFxdee+01Lly4wNWrV3Nn+rKyshg/fjxz584lMjKSgIAAevXqxYgRI/KMYeXKlQwaNIiwsDDq16/Pq6++SteuXbl8+TKlSpVi/vz5DBw48JZ9hZwS1NDQUO677z5WrVp12+PWNeTp297Gms3ZP9HSIRSZilWesHQIRWq8Uy1Lh1CkSt1mhYI1i7fS3878p8pkWM9vsRWFRJviMz61St195bYF+TA170Xc7lae1j//UGi/ZlyydAhFalP4ekuHkK/Uzwr+CQ5Lce4+5daN7jJWnxCKZaSkpBAYGMi8efPo1KnTbW+vhNB6KSG0bkoIrZcSQuulhNC6KSG0XkoIb19xTAiLzztUikRWVhbR0dFMnjwZT09POnToYOmQRERERETkDlFCKEYuXLhAaGgoQUFBzJ8/v9CL5IiIiIiIFJXsLBUxmov+2xcjISEhRfoTFiIiIiIiYr2s+ofpRURERERE5M7RDKGIiIiIiFgX/TC92WiGUEREREREpIRSQigiIiIiIlJCqWRURERERESsS7ZKRs1FM4QiIiIiIiIllBJCERERERGREkoloyIiIiIiYl30w/RmoxlCERERERGREkoJoYiIiIiISAmlklEREREREbEu+mF6s9EMoYiIiIiISAmlhFBERERERKSEUsmoiIiIiIhYF5WMmo1mCEVEREREREooJYQiIiIiIiIllEpGRURERETEumTrh+nNRQmh3BGfjKlk6RCKVMUqT1g6hCJz+vefLB1CkTpQc6ilQyhSbq7XLR1CkTmbXsrSIRQpN0PxKqp5pE64pUMoMvV2X7F0CEVq571elg6hSJ085WPpEIpMr9K2lg5BpMgVr7ObiIiIiIiIFZk1axahoaE4OTlRt25dtm3bVmD7tLQ0Ro4cSfny5XF0dKRixYrMmzfvjsWnGUIREREREbEuxWSV0R9++IGBAwcya9YsGjduzJw5c2jdujXHjh2jXLlyeW7z7LPPEhMTw+eff06lSpWIjY0lIyPjjsWohFBEREREROQOmDJlCt26daN79+4ATJs2jXXr1jF79mzGjx9v0n7t2rVs3bqVM2fO4OWVUz4eEhJyR2NUyaiIiIiIiEghpKWlkZCQYHRJS0vLs+3169c5cOAALVu2NLq9ZcuW7Ny5M89tli9fTr169Zg4cSJly5alSpUqDB06lNTU1CLvy1+UEIqIiIiIiHXJyrbKy/jx4/H09DS65DXTBxAXF0dmZiZ+fn5Gt/v5+REdHZ3nNmfOnGH79u389ttvLF26lGnTprFo0SL69OlT5E/xX1QyKiIiIiIiUggjRoxg8ODBRrc5OjoWuI3BYDC6np2dbXLbX7KysjAYDHzzzTd4enoCOWWnTz/9NJ988gnOzs7/Ivq8KSEUEREREREpBEdHx1smgH/x8fHB1tbWZDYwNjbWZNbwLwEBAZQtWzY3GQS49957yc7OJjw8nMqVK//z4POhklEREREREbEu2VnWebkNDg4O1K1blw0bNhjdvmHDBho1apTnNo0bNyYyMpKkpKTc237//XdsbGwICgq6/eexEJQQioiIiIiI3AGDBw/ms88+Y968eRw/fpxBgwZx4cIFevXqBeSUoHbu3Dm3/Ysvvoi3tzevvvoqx44d4+eff2bYsGF07dr1jpSLgkpGRURERERE7ojnnnuO+Ph4xo4dS1RUFNWrV2f16tWUL18egKioKC5cuJDb3s3NjQ0bNtCvXz/q1auHt7c3zz77LOPGjbtjMSohFBERERER65KVbekIikzv3r3p3bt3nvfNnz/f5LZ77rnHpMz0TlLJqIiIiIiISAmlhFBERERERKSEUsmoiIiIiIhYleys21vRU/45zRCKiIiIiIiUUEoIRURERERESiiVjIqIiIiIiHUpRquMWjvNEIqIiIiIiJRQSghFRERERERKKCWEReDcuXMYDAYOHz78j7Y3GAwsW7asSGO6HSEhIUybNq3ANpaOUURERERKkOws67wUQ/oOYREIDg4mKioKHx8fALZs2ULz5s25fPkypUqVuuX2UVFRlC5d+g5Hmb99+/bh6upqsccXERERERHLUEJYBGxtbfH397/t7a5fv46Dg8M/2rYo+fr6WvTxb+X8pSRGrTjAldQ03B0dGNuuDhV9PYzarDhyga/2/JF7PTYxlTrB3kx5uoG5w72lkArlmDLrfby8SpFwNZEhfd/m1Mkzebatem9lxk4YgU8Zb2wMBia8N521KzeZOeL8fTB1Nlu27yYyOpalX82mcoWQPNstXrGOz79eSFZWFg3q1ebtIX2xs7M1b7CF4BQaQMXp/bAr7UFmQjKnB35M6qlwozaOQb5UnNYPl+qhXDsbxW+t37BQtAVzCAkk6KNB2Hrl9CVi2FTS/ggzauPasCZ+w17BxtUZsrJJ2Lib2MlfWSjiW/MM8ePRqT1x9nInLSGFjYPncPlUpEk79yAfHp3SE5/7ynP1XDQL275jgWgL5hrqT70ZvXDwcic9IYUDAz4l8fcIk3blX2hG1X4dwMbAxW1HOfzmPLIzresTapvAsrgNfgsbD0+yk5JImjqezLDzeTe2d8BzxlxIu8bVgT3NG+htCKlQjkmfvIeXV2kSEhIZ2ncUf+RxnH6wcT2++H4mZ0//3d9Oj3cm7VqaOcMtkG1QWUqNfBObUp5kJSZx9YMJZJwzHh/7+6rhOXRQzhU7O67/eoSEaR9DeroFIs6fc6g/93zcF3svdzISUjjR/xNSfjc+RjsF+3LP9D641Qgl9UwUB1q9aaFob82+fCABHw7B9s9zTvSIKVw/fcGojcuDtfAZ3AUbVxfIyiLpf7uJm/alhSKW4kolo4WUlZXFhAkTqFSpEo6OjpQrV473338fMC4ZPXfuHM2bNwegdOnSGAwGunTpAkCzZs3o27cvgwcPxsfHh8ceewwwLccMDw/n+eefx8vLC1dXV+rVq8eePXvyjW348OFUqVIFFxcXKlSowKhRo0i/6SC+fPly6tWrh5OTEz4+PnTq1Cn3vptLRk+dOsXDDz+Mk5MT1apVY8OGDf/mqfvXxq05xFP3h7C8V0u6NKjMmNUHTdq0r1GOhd0fyb34uDnRpnqwBaK9tfFT3uHbLxfRrH57Pv34CybOGJtnOydnJz77ejqT3v+YFg2e4NFGT7J3l2nfLall8yYsmD2JQP8y+bYJj4xm5twFLJg9iTUL5xEXf5klK9eZMcrCC53Qi9ivN/DLQ32JnLWMCpP7mLTJSEolbOK3/NFnmvkDvA2B4/pw6fu1nGrRk7j/LqbshwNM2mReTSJswET+aNWb008MxPXB6nh2aGqBaAun+YddOfrtZr5uOoyDs1fS4qMeeba7npTK7o9+ZH2/WWaOsPDu/6gbZ7/+HxsaD+H3T1ZQZ8prJm1cyvlSbfgzbO0wmvUNBuFUxpOQF5uZP9hbcOs7lLS1K7jy2n9IXfwdrgOG59vWpXN3Mk4cNWN0/8z7U0bx3YLFPPJgB+bM+IIJ00fn2/bU72do2+y53Is1JYMAnsMGk7J8JRdf6Ezyt9/j+eYwkzbpf5wmrnsv4l7tQVznrtiW8sTlifYWiLZgVSb1JOqrjextNICwmT9RderrJm0yElM5++H3HHt9ugUivD1+Y/pxZeEazj7eg0ufL8L//YEmbTITkogaMoFz7Xpy/un+OD9QA/d2zcweq0VkZVvnpRhSQlhII0aMYMKECYwaNYpjx47x7bff4ufnZ9IuODiYxYsXA3Dy5EmioqKYPv3vg9KXX36JnZ0dO3bsYM6cOSbbJyUl0bRpUyIjI1m+fDm//PILb7zxBllZ+X8i7O7uzvz58zl27BjTp09n7ty5TJ06Nff+VatW0alTJ9q2bcuhQ4fYtGkT9erVy3NfWVlZdOrUCVtbW3bv3s2nn37K8OH5n9zvtEvJaRyPvkrbP5O7R+8JJOJKChFXkvPd5kjkJeKTr9G0coC5wiw0bx8vqte6l6ULVwKwevkGgsuVJSg40KRtx6fbcGDfL+zbcwiAzMxMLsVfNmu8t1Kvdg38yxQ8w7x+83ZaNG2Ej1fOByTPdmzD6o1bzBPgbbDz9sS1RgUuLt4KwKVVu3AsVwbHIOP+ZV5JInHvCbJSrlkizEKx9fbEuXpFrizbDEDCmh3YB/thX9Y4cb927AzpYTEAZF9P59qxszgEW7ZiIT/O3h74Vg/h5JIdAJxevQ/3YF/cg3xM2qZdSSZq3+9kpFjXP+Z/cfTxoFSNEMIWbQcgcuVeXMv54hJs3Jey7R4kcs0+0uISADizYBNBTzYye7wFMXiWwrZiZdL+l/PB4fUdW7H198emjOnryO6+mtiWDSLtf+vNHeZt8fbxonrNe1i2cBUAa1ZsJLhcWcrmcZy2djalSmFfpQqp63PG59qWn7ENCMDW/6b/X9LSIDMz5297O3B0hAL+77AEex8P3GuEErPoZwAurtyNc7kyOAUbH6MzriRx1cqP0QC2Xp44VatEwvL/AZC0bjv2Zf2wu+k4nXb8NOnh0UDOcTrtxGnsg6zzOC13L5WMFkJiYiLTp09n5syZvPLKKwBUrFiRJk2amLS1tbXFy8sLgDJlyph8h7BSpUpMnDgx38f69ttvuXjxIvv27cvdT6VKlQqM7+233879OyQkhCFDhvDDDz/wxhs5pWzvv/8+zz//PGPGjMltV6tWrTz3tXHjRo4fP865c+cICgoC4IMPPqB169b5Pn5aWhppacb/eGWlZ+Bo/+9fXtEJKZRxc8LOJuezC4PBgL+HC9EJqZQtlff3HpcdPk+76uWwt7W+zzsCy/oTG32RzL9OvEBkeBSBQQGEhxmXvlWuWpG0tDS++G4m/oF+nDj6O++NmmR1SeGtRMfEGs0glg3wIzrmogUjyptjoDfXYy7BDeV41yPicCjrS1q49cVbEPsAH9Jv6kt65EXsA31Jj4jNcxs7n1J4tG7M+a6jzRPkbXIL9CI55opRuWRSZDzuZb1JDI+zYGS3zznQm2sxl436khIRj3NZH1LC/u6LS1lvUm7oW0rYRVzKmibAlmTjW4asS/GQ9fcxLSs2Nuf22Oi/Gzo64fpaXxLHvoVNYJAFIi28gLJ+xNx8nI6IpmyQPxFhpiXKFSqFsOJ/35OZlcmib3/i63kLzRlugWz8ypAZF2d0LMiMicHWz4/M6Bijtrb+fpQePw7bsmVJ27WblOUrzR1ugRwDfUiLNn7fXIuIw7GsD9fC7q5jNIBdgC8ZsTcdp6MuYh9Qhox8jtO2PqVxb9mE8J7WVwYvdzfr+4/ZCh0/fpy0tDRatGjxr/eV38zcXw4fPsz999+fmwwWxqJFi2jSpAn+/v64ubkxatQoLlz4uwb98OHDhY79+PHjlCtXLjcZBGjYsGGB24wfPx5PT0+jy0crdxc6/lsyGF/NJv/p+tT0DNYdj6BjrfJF9/hFLDvbOH6DwZBnOzs7O5o+0pg3B4+lddNniIyIZtxHI80R4h3wdx+zrbna4ubg8hmbu4JJX/JvauPmTLnP3iHuv4u5dvT0nY3rX7j5vXN3j4/x1Xy7cmM7a+1uIcbFtdvrXFu5jKz4uyN5NzlO5fPcH/31OI2qt6T9I8/Tq/MgXuryDG2faHnH4/tX8nmxZUbHEPdqD2Kf6ITB3h6npg+ZObBCsOoTyD9wG8c0G1cXys4ezaXPF5F2zHqP00UqK8s6L8WQEsJCcHZ2LrJ93Wo1z9t9rN27d/P888/TunVrVq5cyaFDhxg5ciTXr1//R/s0+YeL/BOWv4wYMYKrV68aXYa1K5rFXPw9XIhNTCXjzzdgdnY2MQmp+Hvk3aeNJyKp4ONmsuiMJT31XHvWbP2RNVt/pEnTBvgH+mFr+/eCKgFl/YkMjzLZLiI8kl3b9hITlfNJ4bJFq6hVp7rZ4i4q/n5liLzhk+jI6Bj8/axvIaO0yHgcArzhhpllh0BvrkfcfZ88p0fFYR/gY9QX+wBf0iNN+2Lj6kzIF2NJ3LiX+M+XmTHKW6v6VBOeW/s+z619n+Am1XEL8MJwQ5/cArxIjIi3YIT/TGpkPM439cU50JvUCONkKSUi3qiM1CXIl5QI60qosi7GYuPjCzZ/H9NsfH3Jumg8w2FXrQbOL3Sm1LzvcR/+DrYhFfCcNd/M0eav03PtWLXlB1Zt+YHGTRvgH1jG6DgdGOhPRHi0yXZJickkJiYBEB0Zy/Ila3igYR2zxX0rWTGx2PoaHwtsy5QhMyYm322yU6+Ruul/OLd81BwhFlpaZByOgd5G7xunsj6kWdl7orAyoi5i53/Tcdrfh/Qo09lBg6szQZ+9R/L/dnN5/lJzhiklhBLCQqhcuTLOzs5s2lS41R0dHBwAjMpNCqtmzZocPnyYS5cuFar9jh07KF++PCNHjqRevXpUrlyZ8+eNVw+rWbNmoWOvVq0aFy5cIDLy77KYXbt2FbiNo6MjHh4eRpeiKBcF8HJ1pKpfKVb9lrM64sYTkQR6uuRfLvrLeTrWCimSxy4qi39YQeumz9C66TPMnjGPo7+e4Mln2wHQpsNjhIdFmJSLAqxcuo6adarj5p7T16YtmnD8t9/NGntReKxZYzZt3UncpctkZ2ezcNlqWrewvoVLMuKvkvLbWXyfyonNq21D0sIu3nXlogCZ8Ve5dvQMpTrmLHDl0box6eExJuWiNi5OlP9iDInbDnJx5veWCLVAJxdv54fHR/LD4yM5OHslcUfPU7VTYwAqtnmAxPC4u65cFCAtLoErv50j+Omcrx0EtqtPSthFo3JRgIiVewls/QCOPjkfcFXo3ILwZQUfj80t++oVMk+fwvGRnEXSHBo3JTMm2rhcFLjatytXuj7Pla7PkzhhLJnnznC1dxcLRJy3JT+szF0UZs6MLzj26wk6PtsWgNbtHyU8LDLPclFfP5/cD01d3Vx4pOXDHP31hFljL0jWlSukn/oD55Y54+PU7GEyo6NNy0UDA+GvBNjODqemD5NxOu/Vry0lPS6BpCNn8Xv6YQB82zXgWljsXVkuCpB56Sppx0/j0eERANxaNSE9ItakXNTg4kTw3PdI3n6A+NnfWSJUKQH0HcJCcHJyYvjw4bzxxhs4ODjQuHFjLl68yNGjR+nWrZtJ+/Lly2MwGFi5ciVt2rTB2dkZNze3Qj3WCy+8wAcffEDHjh0ZP348AQEBHDp0iMDAwDxLNytVqsSFCxf4/vvveeCBB1i1ahVLlxp/evTuu+/SokULKlasyPPPP09GRgZr1qzJ/Y7hjR599FGqVq1K586dmTx5MgkJCYwcadkyxVGta/POygN8vvMkbg72jG1fF4Axqw7StHIAzarkLB4TdjmJ49FXmPGM9f3UxI1GDB7L5E/G0XdQd5ISkxnc5+/nd8L00Wxcs4UNa7cQGRHNrKmfsWzd12RkZBIdFcubg8YUsGfzGzf5EzZv20Xcpct0H/AWLs5OrFk4j3fGT6N5kwY0f6gBwWUD6NP9P7zcawjZ2dnUr1OLTu1bWTr0PJ0Z/ikVp/YjsN9TZCalcHrAxwBUmNSby+v3cXn9PgwOdtTeORsbRzts3V24f/9c4hZvIWz8NxaO3ljEyJkEfTQI397PkpWUQvjQnIWmAsf3I3HjHhI37cW7SwdcalXBxsUJj5Y5x5eE1du5OMt6vgN1o81vzuPRKa9Rr28HrielsnHQ3wtzNZ/YnbMbDnJuw0FsHOzovH0Ktg52OLi70GXvDE4u3s6uCdbTr0PDPqfu9F5U7f8EGUmp7O8/G4A6k3sQte4AUesPknIhluMfLaLpitEYbGy4uP0o577dYtnA85A0czJug0bg/Ox/yE5JJmnKeABc+w/j+p4dpO/ZaeEIb9/IIe/x0cz36DOwO4lJSQztPSr3vg+nvcvGtVvYuHYrrds/ykuvPktmRga2dnas/mk9P367zHKB5+HqxCmUGjkct84vkZWcwtX3PwTAc/hQrm3fSdqOnTjcXxvX557OKYezteX6gYMkzl9g4chNnRz2X+6Z0YdyA54kMzGV4/1nAlB1Si/i1u0nft1+DA52NNgzE4ODPXYeLjQ89CnRi37m7PvfWjh6U9HvziBg/BC8ez5HVlIKUW9OBsDvvQEk/W83yZv3UPrljjjVqIrB2Qm3R3MWlUpcu51Lc6zvQ7wiV0xX9LRGhuy8agTFRFZWFuPHj2fu3LlERkYSEBBAr169GDFiBOfOnSM0NJRDhw5Ru3ZtAN577z1mzZpFTEwMnTt3Zv78+TRr1ozatWsb/cQD5JRkLl26lI4dOwJw/vx5hgwZwoYNG8jIyKBatWp88skn1K9fP8/Y3njjDebNm0daWhpt27alQYMGjB49mitXruS2WbJkCe+99x7Hjh3Dw8ODhx9+OHc11JCQEAYOHMjAgQMB+P333+nWrRt79+4lJCSEGTNm8PjjjxvFeCupX1rv7/78E1UHrbJ0CEXm9O8/WTqEInWg5lBLh1Ck3Jyu37rRXWJLeilLh1CkAtOL13dHmtY1/d3Du1W93VcsHUKR2nlv4dcRuBucPGVdiyH9GwGlkywdQpGqemKNpUPIV/I7z1s6hDy5ji1+ybgSQrkjlBBaLyWE1k0JofVSQmi9lBBaNyWE1ksJ4e0rjgmhSkZFRERERMS6ZBevD+GsmRaVERERERERKaGUEIqIiIiIiJRQKhkVERERERHrolVGzUYzhCIiIiIiIiWUEkIREREREZESSiWjIiIiIiJiVbKztMqouWiGUEREREREpIRSQigiIiIiIlJCqWRURERERESsi1YZNRvNEIqIiIiIiJRQSghFRERERERKKJWMioiIiIiIdVHJqNlohlBERERERKSEUkIoIiIiIiJSQqlkVERERERErEu2fpjeXDRDKCIiIiIiUkIpIRQRERERESmhVDIqIiIiIiLWRauMmo1mCEVEREREREoozRDKHbFv+GlLh1CkxjvVsnQIReZAzaGWDqFI1f11kqVDKFJrq4+0dAhF5mGnq5YOoUjZ2BevT6uzMywdQdHp61rD0iEUqf+dsXQERcvVUHwWB4lK8LJ0CEWqqqUDEKugGUIREREREZESSjOEIiIiIiJiVbL1HUKz0QyhiIiIiIhICaWEUEREREREpIRSyaiIiIiIiFgXlYyajWYIRURERERESiglhCIiIiIiIiWUSkZFRERERMS6ZBWf36+0dpohFBERERERKaGUEIqIiIiIiJRQKhkVERERERHrolVGzUYzhCIiIiIiIiWUEkIREREREZESSiWjIiIiIiJiXVQyajaaIRQRERERESmhlBCKiIiIiIiUUCoZFRERERERq5KdrZJRc9EMoYiIiIiISAlV4hPCLl260LFjxwLbNGvWjIEDBxbp444ePZratWsX6T5FRERERERuR4kvGZ0+fbqmpEVERERErIlWGTWbuzohvH79Og4ODv9qH56enkUUTclQFM/57XIK9afqjL7Ye7mTkZDC7wM+IeX3cKM2jsG+VJ3eB7fqoaSejeJQqzfNGmNhuYf60XB6TxxLu3M9IYVdA+eQcCrSpF3FF5pSrU97DDYGorcfZd+I+WRnZlkg4oI5hQZQcXo/7Ep7kJmQzOmBH5N66qaxCfKl4rR+uFQP5drZKH5r/YaFoi3YB1Nns2X7biKjY1n61WwqVwjJs93iFev4/OuFZGVl0aBebd4e0hc7O1vzBlsIrqH+1J7xOg5e7qQnJHN4wKck/R5h1MY52Ifa01/Hs3oIyWej2dZqpIWivTWHkACCJw/Kfa2FDZ1G2h9hRm1cG9YkYHhnbFydISubqxv2EDPpKwtFXDCHkECCPhqErVdOfyKGTc2zP37DXsntT8LG3cROtr7+2ASWxX3YW9h4eJKVnETSpPFkXjifd2N7B0p9MpfstGtc7dfTvIHehlIhfrSe0hMnL3fSElJYO2QOl/I4VnsE+fD45J6Uua88l89F8027dywQbcGK03nHNdSfOjN64ejlTnpCCgcHfEriTcc1gPIvNKNyvw4YbAxc3HaUX96cZ3V9AXAL9aPB9F44ls7pz+6Bc0g4ZdqfCi80pVqfDmBjIGb7UfaP+MIq+yN3r7uqZLRZs2b07duXwYMH4+Pjw2OPPQbAsWPHaNOmDW5ubvj5+fHyyy8TFxeXu92iRYuoUaMGzs7OeHt78+ijj5KcnAyYlowmJyfTuXNn3NzcCAgIYPLkySZxGAwGli1bZnRbqVKlmD9/fu714cOHU6VKFVxcXKhQoQKjRo0iPT290H29fPkyL730Er6+vjg7O1O5cmW++OILALZs2YLBYODKlSu57Q8fPozBYODcuXO5t82dO5fg4GBcXFx48sknmTJlCqVKlcq9//Tp0zzxxBP4+fnh5ubGAw88wMaNG43iCAkJYdy4cXTp0gVPT0969OhR6D4UlSof9ST6643sbzyA8E9+osqU103aZCamcu7D7znRe7rZ47sd9Sd05Y+vN7PioWEcm7WSBpNNn0/XYF9qDnuaDR3HsrzREJzLlKLiC83MH2whhE7oRezXG/jlob5EzlpGhcl9TNpkJKUSNvFb/ugzzfwB3oaWzZuwYPYkAv3L5NsmPDKamXMXsGD2JNYsnEdc/GWWrFxnxigLr+ZH3Tn/9SY2Nx7M6U9WUGvKayZtMhJTOfnhQg72nmmBCG9P0Ad9uPTdOk4+0ouLcxYTNLG/SZvMhCQu9PuI3x/rw6kOg3B7sDqlOjS1QLS3FjiuD5e+X8upFj2J++9iyn44wKRN5tUkwgZM5I9WvTn9xEBcH6yOpxX2x23AUK6tXsHlbv8hdeF3uA0anm9b11e7k3H8qBmj+2ceG9+VX7/dzBfNhrHv05W0mpj3ue96Yio7Jv3Iqv6zzBxh4RWn807tj7px/uv/sbHxEE59soL78ziuuZTz5Z7hz7Ctw2g2NBiEYxlPyr/YzPzBFkL9Cd04/fVmVj00lOOzVvJgPmNTY9gzbOw4hpWNBuNUxpMKVjg2cne7qxJCgC+//BI7Ozt27NjBnDlziIqKomnTptSuXZv9+/ezdu1aYmJiePbZZwGIiorihRdeoGvXrhw/fpwtW7bQqVOnfMtEhw0bxubNm1m6dCnr169ny5YtHDhw4LbjdHd3Z/78+Rw7dozp06czd+5cpk6dWujtR40axbFjx1izZg3Hjx9n9uzZ+Pj4FHr7HTt20KtXLwYMGMDhw4d57LHHeP/9943aJCUl0aZNGzZu3MihQ4do1aoV7du358KFC0btPvroI6pXr86BAwcYNWpUoWMoCvY+HrjVCCVm0c8AxK3cjVO5MjgG+xq1y7iSRMLeE2SmXDNrfLfD0dsDrxohnF28A4CwVftwK+eLa5DxuJZrV5/wtfu5FpcAwKkFmwjp2NDs8d6KnbcnrjUqcHHxVgAurdqFY7kyOAYZj03mlSQS954gy4rHBqBe7Rr4l/EtsM36zdtp0bQRPl6lMRgMPNuxDas3bjFPgLfBwccDzxohRCzaDkDUyr24lCuDc7Dxay39SjKX9p606vcNgK23J87VK3J56WYArq7ZiUOwH/ZBxsn7taNnuB4WA0B2Wjqpx87gUM7P7PHeyl/9ubIspz8Ja3ZgH+yHfdmb+nPsDOl/9ed6OteOncUh2N/s8RbE4FkKu0qVSdu0AYDr27di6++PjZ9pnHbVa2ITGETapvXmDvO2OHt7UKZ6CMeW5hyrT63eh2ewLx5Bpufga1eTidj3O+kpaeYOs1CK03nHwceDUjVCCPvzuBa5ci8u5Xxxuem4FtjuQaLW7CPtz76cW7CJoCcbmT3eW3H09qB0jRDOLc7pT9iqvbjmMTbBN43NHws2Ud7KxuaOycq2zksxdNeVjFaqVImJEyfmXn/nnXeoU6cOH3zwQe5t8+bNIzg4mN9//52kpCQyMjLo1KkT5cuXB6BGjRp57jspKYnPP/+cBQsW5M4+fvnllwQFBd12nG+//Xbu3yEhIQwZMoQffviBN94oXLnchQsXuP/++6lXr17uPm7Hxx9/TOvWrRk6dCgAVapUYefOnaxcuTK3Ta1atahVq1bu9XHjxrF06VKWL19O3759c29/5JFHcvdjbo6BPqTFXIYbSiOuRcThVNaHtLCLFonpn3IN9CI15opRmUdyRDyuZb1JDv97Rvvm60lhF3Ep623WWAvDMdCb6zGXjMbmekQcDmV9SQu/u8amsKJjYo1mEMsG+BEdY319dQ705lrMZaPXWmpEHM5lfUgNiytgS+vkEOBD+k2vtfSIizgE+pIeHpvnNna+pfBs05hzXcaYK8xCs8+rP5EXsQ/0JT0in/74lMKjdWPOdx1tniALyca3DFnx8ZCVmXtb5sXYnNtjov9u6OiEW6++JLz7FrZlb/+cak7ugV4kxxofqxMi43EP9CYh/O56/xSn845zoDepJse1eJzL+pByw3HNpaw3KTf0JSXsIs5lC/+Burm45DE2KRHxuJT1MRmbG/uTHBaHqxX2R+5ud90M4V8J0l8OHDjA5s2bcXNzy73cc889QE5JZK1atWjRogU1atTgmWeeYe7cuVy+fDnPfZ8+fZrr16/TsOHfn7x4eXlRtWrV245z0aJFNGnSBH9/f9zc3Bg1apTJzFtBXn/9db7//ntq167NG2+8wc6dO2/r8U+ePEn9+vWNbrv5enJyMm+88QbVqlWjVKlSuLm5ceLECZM4b37Ob5aWlkZCQoLR5Xp2ZoHb3JabZnMNhqLbtbndPDNtyKczNzbLr41VuHmm3ZpjLTJ/99Gq16MyGZq7fGxufq4L6I+NmzMhn73DxU8Xk3r09J2N658yee/k39TGzZlyn71D3H8Xc80q+2MyOCYtXHu8TuryZWTF3x0JVWGP1XeDYnXeufVLzbSdlXYFKPRxwGgMrbk/cte66xJCV1dXo+tZWVm0b9+ew4cPG11OnTrFww8/jK2tLRs2bGDNmjVUq1aNjz/+mKpVq3L27FmTfRd2tVGDwWDS9sbvB+7evZvnn3+e1q1bs3LlSg4dOsTIkSO5fv16ofvZunVrzp8/z8CBA4mMjKRFixa5s3Q2NjYm8d78/cTs7GyTA/rNMQ8bNozFixfz/vvvs23bNg4fPkyNGjVM4rz5Ob/Z+PHj8fT0NLp8k3yi0H0tSFpkHI4B3mD790vVMdCHaxF3xz8VN0qOvIRLgBeGG/riEuhFckS8cbuIeNxuKIFxDfIh5aY21iAtMh6Hm8bGIdCb6xHWN2NWVPz9yhAZHZN7PTI6Bn+/gstMLSE1Mh6nm15rToHepN6F7xuA61Fx2Psbv9bsA324Hmn6WrNxdSb0yzEkbNxD3Oc/mTPMQkuPisM+wMe4PwG+pOfTn5AvxpK4cS/xny8zY5SFk3UxFhsfX7D5e2ElW19fsi4az3Ta31cDl5c6U/rL73Ef8Q52IRUo9d/5Zo42f9WeasLLa97n5TXvU75Jddz9jd8/7gFeJEZa33H4VorTeSc1Mh7nm/rinMdxLSUi3qiM1CXI1yqPfSmRl0z64xLoTcpNsSZHxON6w9dkXIN8SLbC/twJ2VnZVnkpju66hPBmderU4ejRo4SEhFCpUiWjy1+JjMFgoHHjxowZM4ZDhw7h4ODA0qVLTfZVqVIl7O3t2b17d+5tly9f5vfffzdq5+vrS1RUVO71U6dOkZKSknt9x44dlC9fnpEjR1KvXj0qV67M+fP5rLhWAF9fX7p06cLXX3/NtGnT+O9//5t7O2AUw+HDh422veeee9i7d6/Rbfv37ze6vm3bNrp06cKTTz5JjRo18Pf3N1qUprBGjBjB1atXjS4vud5z2/vJS3pcAkm/ncXv6YcB8GnXgGthsXdduShAWnwCl387T+hTjQEIbvsAyWFxRqUhkPM9gqDH6+Hk4wFA5c4tOP/TLrPHeysZ8VdJ+e0svk/lLHLh1bYhaWEXi225KMBjzRqzaetO4i5dJjs7m4XLVtO6hfUt8nE9LoGrv52j7NNNAAhoV5/UsIt3ZbkoQGb8VVKPnaH0k80B8GzdiOvhMSblojYuToR+OZrEnw8S+/EPlgi1UDLjr3Lt6BlKdczpj0frxqSHx5iUi9q4OFH+izEkbjvIxZnfWyLUW8q+eoXMP07h2CLnaxYOTZqSGRNtXC4KXHm9K5dfeZ7LrzxP4vixZJw7w5XXulgg4rwdW7ydr1qP5KvWI9k3eyWxR89T7cmcY3XlNg9wNTzurisXheJ13vnruBb853EtsF19UsIuGpWLQs53CwNaP4Djn30J6dyC8GXW1Rf4a2zOEfJUTn+C29Yv1NhU6tyCCz/tNtmfyL9x132H8GZ9+vRh7ty5vPDCCwwbNgwfHx/++OMPvv/+e+bOncv+/fvZtGkTLVu2pEyZMuzZs4eLFy9y7733muzLzc2Nbt26MWzYMLy9vfHz82PkyJG5M3J/eeSRR5g5cyYNGjQgKyuL4cOHY29vn3t/pUqVuHDhAt9//z0PPPAAq1atyjMBLcg777xD3bp1ue+++0hLS2PlypW5MVeqVIng4GBGjx7NuHHjOHXqlMlqqP369ePhhx9mypQptG/fnv/973+sWbPGaNawUqVKLFmyhPbt22MwGBg1ahRZWbe/jLGjoyOOjo5GtzkYim4Z/lPD/kvV6X0I7v8kmUmpnOyfsyJi5cm9iF+3n0vr92NwsKP+7pkYHO2xc3fhwYOfErPoZ8598G2RxVEU9gyfR8Opr3Ffvw6kJ6Wya8AcAB6c1J3w9QeJWH+QpAsX+XXSYlr+9G7OEtM7jvHHd1stHHnezgz/lIpT+xHY7ykyk1I4PeBjACpM6s3l9fu4vH4fBgc7au+cjY2jHbbuLty/fy5xi7cQNv4bC0dvbNzkT9i8bRdxly7TfcBbuDg7sWbhPN4ZP43mTRrQ/KEGBJcNoE/3//ByryFkZ2dTv04tOrVvZenQ8/TrsM+oPb0Xlft3JCMplUP9ZwNQc3IPYtYdJGb9AWwc7Hhk9zRsHO2xd3fh0YMzCV+0nRMfWF/yEfHWJwRNGkiZ3s+QmZRC2JBpAAR92I+EjXtI2LgXn1c74FKrCjYuTni2yin9v7pqB7GfLLRg5HmLGDmToI8G4dv7WbKSUggfmrPoWOD4fiRu3EPipr14d/m7Px4tc/qTsHo7F2dZV3+SZkzGbcgIXJ7/D1kpySRNGg+A28BhXN+9g+u7b+8rD9Zgw4h5PD75Ner37cD1pFTWDp6Te1/LCd05vfEgpzccxNbBjm7bpmDrYIejuwuv7ZnBsSXb2T7BesaoOJ13Dg/7nDrTe1Gl/xOkJ6Vy8M/jWu3JPYhed4Do9QdJuRDLiY8W8dCK0RhsbLi4/Sjnv91i2cDzsW/4PBpM7Um1P8dm94BPAag/qTsRf45N8oWL/DZpMY/+9C4GGxtidhzl9HdbLBu4FDuG7LvoV9mbNWtG7dq1mTZtmtHtp06dYvjw4WzevJm0tDTKly/P448/zpQpUzhx4gSDBg3i4MGDJCQkUL58efr165e7aEqXLl24cuVK7s9IJCUl8frrr7NkyRLc3d0ZMmQIq1atMnrcyMhIXn31VXbs2EFgYCDTp0/nhRdeYNq0aXTp0gWAN954g3nz5pGWlkbbtm1p0KABo0ePzv2piNGjR7Ns2TKTmb2/jBs3jm+//ZZz587h7OzMQw89xNSpUwkNDQVyZiFff/11Tp06xQMPPED//v155plnOHv2bO4CNHPnzmXMmDFcunSJVq1aUa9ePWbOnJk7s3ju3Dm6du3K7t278fHxYfjw4fz4449GfQ0JCWHgwIEMHDjwtsbqZ/9nbqu9tQuzcbx1o7tERVJu3eguUvfXSZYOoUitrW69vwV4u8o7JVk6hCJlY3PXnC4Lxb9yoqVDKDJfHg+2dAhFyj/D0hEULdd/8GGztUq1KV5f4nsh0ro+nL3R1VdaWDqEPHl+ucnSIRS5uyohlH+nR48enDhxgm3btt3xx1JCaL2UEFo3JYTWSwmh9VJCaN2UEFovJYS3rzgmhHd9yajkb9KkSTz22GO4urqyZs0avvzyS2bNst4fzxUREREREfNSQliM7d27l4kTJ5KYmEiFChWYMWMG3bt3t3RYIiIiIiIFKz4Ty1ZPCWExtnCh9XypXURERERErM9d/7MTIiIiIiIi8s9ohlBERERERKxKcf0ReGukGUIREREREZESSgmhiIiIiIhICaWSURERERERsS4qGTUbzRCKiIiIiIiUUEoIRURERERESiglhCIiIiIiYl2yrPTyD8yaNYvQ0FCcnJyoW7cu27ZtK9R2O3bswM7Ojtq1a/+zBy4kJYQiIiIiIiJ3wA8//MDAgQMZOXIkhw4d4qGHHqJ169ZcuHChwO2uXr1K586dadGixR2PUQmhiIiIiIhIIaSlpZGQkGB0SUtLy7f9lClT6NatG927d+fee+9l2rRpBAcHM3v27AIfp2fPnrz44os0bNiwqLtgQgmhiIiIiIhYleysbKu8jB8/Hk9PT6PL+PHj8+zD9evXOXDgAC1btjS6vWXLluzcuTPfvn/xxRecPn2ad999t0if0/zoZydEREREREQKYcSIEQwePNjoNkdHxzzbxsXFkZmZiZ+fn9Htfn5+REdH57nNqVOnePPNN9m2bRt2duZJ1ZQQioiIiIiIFIKjo2O+CWB+DAaD0fXs7GyT2wAyMzN58cUXGTNmDFWqVPlXcd4OJYQiIiIiImJd/uGKntbEx8cHW1tbk9nA2NhYk1lDgMTERPbv38+hQ4fo27cvAFlZWWRnZ2NnZ8f69et55JFHijxOfYdQRERERESkiDk4OFC3bl02bNhgdPuGDRto1KiRSXsPDw+OHDnC4cOHcy+9evWiatWqHD58mAcffPCOxKkZQhERERERkTtg8ODBvPzyy9SrV4+GDRvy3//+lwsXLtCrVy8g5zuJERERLFiwABsbG6pXr260fZkyZXBycjK5vSgpIRQREREREauSnZVt6RCKxHPPPUd8fDxjx44lKiqK6tWrs3r1asqXLw9AVFTULX+T8E4zZGdnF49nW6zKz/7PWDqEIhVmc3tfHrZmFUmxdAhFqu6vkywdQpFaW32kpUMoMuWdkiwdQpGysSlep0v/yomWDqHIfHk82NIhFCn/DEtHULRcs4rBl8H+lGpjuhDI3eyFyG8sHUK+Lj3Z1NIh5Mlr6VZLh1DkNEMod0R6dvH6emqpzExLh1Bk3FyvWzqEIlWcEiiAx39739IhFJmFNd+xdAhFyq0Y/VMLwClLB1B0frNJtXQIRapL3ThLh1Ck3j3ib+kQioxftv51luJHr2oREREREbEuxewzOGtWvKZxREREREREpNCUEIqIiIiIiJRQKhkVERERERGrkq2SUbPRDKGIiIiIiEgJpYRQRERERESkhFLJqIiIiIiIWBeVjJqNZghFRERERERKKCWEIiIiIiIiJZRKRkVERERExKpolVHz0QyhiIiIiIhICaWEUEREREREpIRSyaiIiIiIiFgXlYyajWYIRURERERESiglhCIiIiIiIiWUSkZFRERERMSqaJVR89EMoYiIiIiISAmlhFBERERERKSEUkJ4B5w7dw6DwcDhw4fzbTN//nxKlSr1rx9ry5YtGAwGrly5cscfS0RERETEHLKzrPNSHCkhvMs1atSIqKgoPD09LR2KiIiIiIjcZbSozF0sPT0dBwcH/P39LR3KHeUc6k+1j3vj4OVOekIKx/vPIvn3CKM2TsG+VJv+Ou41Qkk5E8W+Vm9ZKNqCuYT6U/vj17H3cicjIZlf+n9K0k19cQ72odb01/GoEULymWh2tBppoWhvzSEkkKCPBmHr5UFmQjIRw6aS9keYURvXhjXxG/YKNq7OkJVNwsbdxE7+ykIRF8w11J/aM17/87WWzOEBeY9P7emv41k9hOSz0Wyz0vH5YOpstmzfTWR0LEu/mk3lCiF5tlu8Yh2ff72QrKwsGtSrzdtD+mJnZ2veYAvBPdSPhtN74ljanesJKewaOIeEU5Em7Sq+0JRqfdpjsDEQvf0o+0bMJzvT+j7SdQ31p86MXrnHtUMDPiXxptcaQLkXmlG5XwcMNgYubjvKr2/Os7r+FLfjAIBfSADdJ/fFrbQHKQnJfD50JpF/hJu0q1inCp3HvQaArZ0dp/Yf55vRn5NxPcPcIefLJqAsboPewsbDk+zkJJKmjScz7Hzeje0d8Jw2F9KucXVwT/MGWki+If68PLk3bqXdSUlI4euhs4j+w/S9U6XhfXQY/gKOrs5kZ2VzZMM+Vkz6wQIRF8wrxI8OU3rhUtqdawkpLB86h7hTpv3xDPKhw6Se+N8XwqVz0XzefpQFopXiSjOE/0JWVhYTJkygUqVKODo6Uq5cOd5///3c+8+cOUPz5s1xcXGhVq1a7Nq1q8D9zZ49m4oVK+Lg4EDVqlX56ivjk6XBYODTTz/liSeewNXVlXHjxuVZMjp//nzKlSuHi4sLTz75JPHx8SaPtWLFCurWrYuTkxMVKlRgzJgxZGT8fQIbPXo05cqVw9HRkcDAQPr37/8Pn6V/755JPYj8ahO7Gg3iwszl3Du1l0mbjMQUTn/4A7+9PsMCERZejUndufDVJrY2GszpmSuoOfU1kzYZiamc/HAhh1+faYEIb0/guD5c+n4tp1r0JO6/iyn74QCTNplXkwgbMJE/WvXm9BMDcX2wOp4dmlog2lur+VF3zn+9ic2NB3P6kxXUmpL/+Bzsbd3j07J5ExbMnkSgf5l824RHRjNz7gIWzJ7EmoXziIu/zJKV68wYZeHVn9CVP77ezIqHhnFs1koaTO5h0sY12Jeaw55mQ8exLG80BOcypaj4QjPzB1sItT7qxrmv/8emxkM49ckKaufxWnMp58u9w59he4fRbGwwCMcynpR7sZn5g72F4nYcAHjlg55s+W4jIx7px5o5y3h1Yu8824UdO8fYDsN5t81QRrUahLuXB81ebGnmaAvm1mcoaetWcKXXf0hd/B2u/Yfn29bl5e5knDhqxuhu3/Mf9GDHd5sY+8ggNs5ZzksTTf8nAEhJSOaLfjN4/7EhTOwwgkoPVqNeh8ZmjvbW2ozvxsFvNzOr+VB2zVlJ+4mmxzaAtMRUtkz6kaUDPjFzhJZj6dJQlYxKoYwYMYIJEyYwatQojh07xrfffoufn1/u/SNHjmTo0KEcPnyYKlWq8MILLxglXTdaunQpAwYMYMiQIfz222/07NmTV199lc2bNxu1e/fdd3niiSc4cuQIXbt2NdnPnj176Nq1K7179+bw4cM0b96ccePGGbVZt24d//nPf+jfvz/Hjh1jzpw5zJ8/PzeZXbRoEVOnTmXOnDmcOnWKZcuWUaNGjX/7dP0j9j4euNcIJXrRNgBiV+7BqVwZnIJ9jdplXEnm6t6TZKakWSLMQnHw8cCzRggRi7YDEL1yL87lyuAc7GPULv1KMpf3niQj5Zolwiw0W29PnKtX5MqynNdowpod2Af7YV/WOAG5duwM6WExAGRfT+fasbM4BFvfrPbN4xO1ci8u+YzPpb0nybTy8alXuwb+ZXwLbLN+83ZaNG2Ej1dpDAYDz3Zsw+qNW8wT4G1w9PbAq0YIZxfvACBs1T7cyvniGmQ8NuXa1Sd87X6uxSUAcGrBJkI6NjR7vLfi4ONBqRohhBu91nxNXmuB7R4kas0+0v7sz7kFmwh6spHZ4y1IcTsOALh7e1C+egV2Ld0KwP41u/ENLoN3kOn76fq162RmZAJg62CHvZMD2VnZZo23IAbPUthWrEza5g0AXN+5FVs/f2zKmD73dtVqYhsYRNrm9eYOs9DcvD0Irh7KvqU5/xMcXrMH7+AyeOUxNuFHzxEfFgtARlo64cfO4V3Oz6SdJbl4exBwXwhHluYcC46v3kupIF88bzq2AVy7mkzY/t9Jt+L/c+TupYTwH0pMTGT69OlMnDiRV155hYoVK9KkSRO6d++e22bo0KG0bduWKlWqMGbMGM6fP88ff/yR5/4mTZpEly5d6N27N1WqVGHw4MF06tSJSZMmGbV78cUX6dq1KxUqVKB8+fIm+5k+fTqtWrXizTffpEqVKvTv359WrVoZtXn//fd58803eeWVV6hQoQKPPfYY7733HnPmzAHgwoUL+Pv78+ijj1KuXDnq169Pjx55f2IFkJaWRkJCgtHlenZmoZ/LgjgFenM9+rJRidS1iDicypoeLK2dU6A31/Loi/Nd2BcA+wAf0mMuwQ39SY+8iH1g/kmInU8pPFo3JnHLfnOEeFucA725FmM8Pql38fgURnRMrNEMYtkAP6JjLlowory5BnqRGnPFaGySI+JxLett3K6sN8nhcbnXk8Iu4nJTG2uQ92stHpebXmvOZb1JuaE/qWEXre71WNyOAwBeAT5cjrlE1g19io+IwzufPnkH+TJm9SQ+PvgF15JS2fLdBnOFeks2PmXIuhQPWX+fk7MuxmLje1PlgKMTrj36kjxripkjvD2lA7y5GnPZaGwuRcRROrDg94W7ryf3t2nA0c2H7nSIt8UjwIvEWONj29XIeDxv0R+RoqaE8B86fvw4aWlptGjRIt82NWvWzP07ICAAgNjY2Hz317ixcSlD48aNOX78uNFt9erVu2VcDRsafyJ+8/UDBw4wduxY3Nzcci89evQgKiqKlJQUnnnmGVJTU6lQoQI9evRg6dKl+c5sAowfPx5PT0+jy3fJx/Ntf7uys40/bTVgKLJ9m53JB8d3cV8Abhqbgrpj4+ZMuc/eIe6/i7l29PSdjeufurk7hrt8fArl7z7ePJzWxOQ4kM/Y3NjMmsfP5LnOL9TsQrSxtOJ2HIDbOhbEh1/k3TZDGfhAd+wc7Kn7+IN3OLjbZdIZkxauXV/n2qplZF2KM7nP6hTyWPAXJzdnen02nI2fLif86Nk7Gdk/Y9IfC8VhjbIN1nkphrSozD/k7Ox8yzb29va5f/91wMrKyr/4+OaDWnZ2tsltrq6uBT7mzf805SUrK4sxY8bQqVMnk/ucnJwIDg7m5MmTbNiwgY0bN9K7d28++ugjtm7datSnv4wYMYLBgwcb3bajkmk56z9xLTIep0BvDLY2uZ+gOZb15lrEXXDSuklOX7yM+uJU1pvUu7AvAOlRcdgH+ICtTe7sgH2AL+mRpjNMNq7OhHwxlsSNe4n/fJmZIy2c1Mh4nAJuGp/Au3d8CsPfrwyR0TG51yOjY/D3K7jM1BKSIy/hctPYuAR6kRxh/P3o5Ih43G4ou3QN8iElwvQ71JaWGhmP8039cQ70JuWm11pqRDwuN/THOcjX6l6PxeU40KhTU1p1bw/AnuXbKe3vhY2tTe5MlFegN/F59OlGaSnX2LNiOw2feJi9K3bc8ZgLIysuFhtvX7CxzZ0ltPHxJeui8YfTdvfWwL7ugzg/3xmDgwMGN3c8P5nP1T5dLBC1sfqdHuaR7m0B2L98B6X8vY3GpnSgN5cj835fOLo60fvLERzZuJ//fb7KbDEXpGanJjzYvQ0AR5fvxN3f+FjgEeDN1Xz6I3KnaIbwH6pcuTLOzs5s2rSpSPZ37733sn37dqPbdu7cyb333ntb+6lWrRq7d+82uu3m63Xq1OHkyZNUqlTJ5GJjk/OScHZ2pkOHDsyYMYMtW7awa9cujhw5kudjOjo64uHhYXRxMBTNKoXpcQkkHjmL/9MPAVCm3YNcC7vItTDrK2u7letxCSQcOUfZp5sA4N+uPqlhF0kNuzsP/JnxV7l29AylOjYHwKN1Y9LDY0iPMP5Hw8bFifJfjCFx20EuzvzeEqEWyvW4BK7+9vf4BNzl41MYjzVrzKatO4m7dJns7GwWLltN6xbWt9BHWnwCl387T+hTOVUUwW0fIDkszqg8FCBs1V6CHq+Hk48HAJU7t+D8TwUv5mUJf73Wgm54raXk8VqLXLmXgNYP4Phnf0I6tyBimXX1p7gcB3Yu2cq7bYbybpuhrP50GReOnaXhkznvhXqtGxAXfpH4cNPzjm85P2z/XJXX1t6Ouq0aEHYinxU8LSD76hUyz5zCsfljADg0akpmTDRZsdFG7a7278qV7s9zpfvzJE4cS+a5M1aRDALsXfIzH7YZzodt/pzlO3aWB57M+Z+gdusHiQ+/yKU8xsbBxZHeX77F8Z9/Ze3HS8wddr5+XbKduW3eYm6bt9j56Uqij56jxpM5x4J729TnSngcV8OL73lHrJNmCP8hJycnhg8fzhtvvIGDgwONGzfm4sWLHD16tMAy0vwMGzaMZ599ljp16tCiRQtWrFjBkiVL2Lhx423tp3///jRq1IiJEyfSsWNH1q9fz9q1a43avPPOO7Rr147g4GCeeeYZbGxs+PXXXzly5Ajjxo1j/vz5ZGZm8uCDD+Li4sJXX32Fs7Nznt9ZNIcTw+ZSbUZvQgZ0JCMxlWP9ZwFwz5SexK3bT9y6Axgc7Gi0ZwY2DvbYebjQ+NAsohdt4/T731kk5vwcGfYZtWb0ouKfffml/2wAakzpQcy6g8SuO4CNgx3N9kzDxsEeew8XHjk0k4hF2zn5vvX9ExUxciZBHw3Ct/ezZCWlED50KgCB4/uRuHEPiZv24t2lAy61qmDj4oRHy5zy5YTV27k4a6ElQ8/Tr8M+o/b0XlTu35GMpFQO/Tk+NSfnjE/M+pzxeWT3NGwc7bF3d+HRgzMJX7SdEx9Y1/iMm/wJm7ftIu7SZboPeAsXZyfWLJzHO+On0bxJA5o/1IDgsgH06f4fXu41hOzsbOrXqUWn9q1uvXML2DN8Hg2nvsZ9/TqQnpTKrgE533l+cFJ3wtcfJGL9QZIuXOTXSYtp+dO7YGMgZscx/vhuq4Ujz9svwz7n/um9qNL/CTKSUjn452ut9uQeRK87QPT6g6RciOXER4tosmI0Bhsb4rYf5fy3WywbeB6K23EA4Mu35tBtUl/a9e5EalIKnw35e1XhVz98nUMb93F4437uaVCdlt3akZWVha2tLcd3HmH5xz9aMHJTSZ9Mxm3gCJyf+Q/ZKckkTRsPgGu/YVzfs4P0vTstHOHt+e6tubw8qTetenfkWlIqC4bMyr3vxQ97cmTjfo5sPEDzV9sQUqsiji6O1Gr1AACHVu1m3SdLLRV6nla/NY8Ok3vSpE8H0pJSWT7409z72k3ozu8bDvL7xoPYOtjR9+ep2DrY4eTuwoDdH3NkyXb+N9H6fkqjqBTXFT2tkSG7MDWGkqesrCzGjx/P3LlziYyMJCAggF69evHCCy8QGhrKoUOHqF27NgBXrlyhdOnSbN68mWbNmjF//nwGDhxo9HMRs2fPZtKkSYSFhREaGsrbb7/Nyy+/nHu/wWBg6dKldOzYMfe2LVu20Lx5cy5fvkypUqUAmDdvHu+++y7x8fE8+uijNG3alPfee8/osdatW8fYsWM5dOgQ9vb23HPPPXTv3p0ePXqwbNkyPvzwQ44fP05mZiY1atRg3Lhxt5XobvJ77p88pVbrWjGaTC/vmmjpEIrU2RR3S4dQpB7/7f1bN7pLLKz5jqVDKFJuBZT8340quiRYOoQiMznL0dIhFKlJNYrXDNG7R6xzRdl/wi+7eM2ljDr/jaVDyFf0w80sHUKe/H/eYukQipwSQrkjlBBaLyWE1k0JofVSQmi9lBBaNyWE1ksJ4e0rjglh8XpVi4iIiIjIXS87q3iu6GmNis+0h4iIiIiIiNwWJYQiIiIiIiIllEpGRURERETEqmiVUfPRDKGIiIiIiEgJpYRQRERERESkhFLJqIiIiIiIWJXsbK0yai6aIRQRERERESmhlBCKiIiIiIiUUCoZFRERERERq6JVRs1HM4QiIiIiIiIllBJCERERERGREkoloyIiIiIiYlWys7TKqLlohlBERERERKSEUkIoIiIiIiJSQqlkVERERERErEp2tqUjKDk0QygiIiIiIlJCKSEUEREREREpoVQyKiIiIiIiVkWrjJqPZghFRERERERKKM0Qyh2x2dnW0iEUqSrpxeezk7PppSwdQpF62OmqpUMoUgtrvmPpEIrMs7+OtXQIRSpjxaeWDqFIff92pKVDKDLPZGRYOoQitX1fWUuHUKSq2hefc6i9pQMQuQOUEIqIiIiIiFVRyaj5FJ+PbEREREREROS2KCEUEREREREpoVQyKiIiIiIiVkU/TG8+miEUEREREREpoZQQioiIiIiIlFAqGRUREREREauiVUbNRzOEIiIiIiIiJZQSQhERERERkRJKJaMiIiIiImJVsrNVMmoumiEUEREREREpoZQQioiIiIiIlFAqGRUREREREauSnWXpCEoOzRCKiIiIiIiUUEoIRURERERESiiVjIqIiIiIiFXJ0iqjZqMZQhERERERkRJKCaGIiIiIiEgJpZJRERERERGxKvphevP5VzOE2dnZvPbaa3h5eWEwGDh8+HARhWUeBoOBZcuW5V4/ceIEDRo0wMnJidq1a9/xx9+yZQsGg4ErV64AMH/+fEqVKlVk+z937twtx+XmGEREREREpOT4VzOEa9euZf78+WzZsoUKFSrg4+NTVHFZxLvvvourqysnT57Ezc3N7I//3HPP0aZNmyLbX3BwMFFRUXf9uIiIiIiIyJ3xrxLC06dPExAQQKNGjfJtc/36dRwcHP7Nw5jN6dOnadu2LeXLl//H+8jMzMRgMGBjc/uTr87Ozjg7O//jx76Zra0t/v7+RbY/S/IO8eepyb1wKe3OtYQUFg/9lIt/RJi0q9CwGo8Nfx5HVyeys7I5vuEAGycttEDE+XMP9aPxtJ44eblz/WoKOwbN4eqpyDzbVnq+KdX7tsdgMBC1/Sh73ppPdqZ1/VKrZ4gfj07tibOXO2kJKWwcPIfLefTHPciHR6f0xOe+8lw9F83Ctu9YINqCOYQEEDx5EHalPchMSCZs6DTS/ggzauPasCYBwztj4+oMWdlc3bCHmElfWSji/LmH+tFwek8cS7tzPSGFXQPnkJDHuFR8oSnV+rTHYGMgevtR9o2wvtcYwAdTZ7Nl+24io2NZ+tVsKlcIybPd4hXr+PzrhWRlZdGgXm3eHtIXOztb8wZbCOcvJTFq1UGupFzH3cmesW3vp6KPh1GbFUcu8NW+07nXYxOvUSfYmymd6ps73FvyCPXjoWk9cfzzuLatgONa5eebUrNve/jzuLbLyo5rLqH+1Pq4Nw5e7qQnJPNr/9kk/W58vnEO9qXm9NfxqBFCypkodrQaaaFob8011J86M3r92Z8UDg34lEST/vhQZ3ovPKuHkHw2mq2t3rZQtLfmGeLHY1P/PIcmpLDhFucc3/vKc8VKzzmQ05/mN/RncwH9aT6lJ95/nkOXWGl/ilJ2lkpGzeUfl4x26dKFfv36ceHCBQwGAyEhIQA0a9aMvn37MnjwYHx8fHjssccAOHbsGG3atMHNzQ0/Pz9efvll4uLicveXnZ3NxIkTqVChAs7OztSqVYtFixYVGMOsWbOoXLkyTk5O+Pn58fTTT+feFxISwrRp04za165dm9GjR+e5L4PBwIEDBxg7diwGg4HRo0fnWU55+PBhDAYD586dA/4u81y5ciXVqlXD0dGR8+fP5/kYq1evpkqVKjg7O9O8efPcffwlr5LR2bNnU7FiRRwcHKhatSpfffX3P55du3alZs2apKWlAZCenk7dunV56aWXgLxLRm8VA8DOnTt5+OGHcXZ2Jjg4mP79+5OcnJxnn8zliQ+6se+7/zHtkSFsm7OCJye+lme71IRkFvabyYzH3mB2h7cJffBeanbI/wMLS2gwoSunvtnMsoeG8dvslTSa3CPPdm7BvtQe9jRrO45laeMhOJcpRaUXmpk32EJo/mFXjn67ma+bDuPg7JW0+Cjv/lxPSmX3Rz+yvt8sM0dYeEEf9OHSd+s4+UgvLs5ZTNDE/iZtMhOSuNDvI35/rA+nOgzC7cHqlOrQ1ALRFqz+hK788fVmVjw0jGOzVtIgj9eZa7AvNYc9zYaOY1neKOc1VtEKX2MALZs3YcHsSQT6l8m3TXhkNDPnLmDB7EmsWTiPuPjLLFm5zoxRFt64tb/wVK0Qlvd8lC4PVmbM6sMmbdrXKMfCrs1zLz5ujrSpFmT+YAuh0YSunPxmM0seGsaR2StpUsBxrc6wp1nVcSyL/zyuVbGy11z1Sd258NUmtjYaxJmZK6gxtadJm4zEFH7/8AcOv/6xBSK8PbU+6sa5r//HpsZDOPXJCmpPMT1/ZiSmcvzDHznQe6YFIrw9zT/sym9/nnMO3OXnHICHP+zK8W83833TYRyevZKmBfRn70c/ssnK+yN3p3+cEE6fPp2xY8cSFBREVFQU+/bty73vyy+/xM7Ojh07djBnzhyioqJo2rQptWvXZv/+/axdu5aYmBieffbZ3G3efvttvvjiC2bPns3Ro0cZNGgQ//nPf9i6dWuej79//3769+/P2LFjOXnyJGvXruXhhx/+p90hKiqK++67jyFDhhAVFcXQoUMLvW1KSgrjx4/ns88+4+jRo5QpY/oPS1hYGJ06daJNmzYcPnyY7t278+abbxa436VLlzJgwACGDBnCb7/9Rs+ePXn11VfZvHkzADNmzCA5OTl3P6NGjSIuLo5Zs/I+WBQmhiNHjtCqVSs6derEr7/+yg8//MD27dvp27dvoZ+Poubq7UFA9RB+WbodgKNr9lI62JdSQaalsFFHz3M5LBaAjLR0oo6dx6tc/v9AmpuTtwfe1UM4s3gHABdW7cMt2BfXPPpSvm19Lqzdz7W4BAB+/2oToU80NGu8t+Ls7YFv9RBOLsnpz+nV+3AP9sU9j/6kXUkmat/vZKSkmTvMQrH19sS5ekUuL815f11dsxOHYD/sg4xfP9eOnuF6WAwA2WnppB47g0M5P7PHWxBHbw+8aoRw9s/XWdiqfbiVM32dlWtXn/AbXmOnFmwipKN1vcb+Uq92DfzL+BbYZv3m7bRo2ggfr9IYDAae7diG1Ru3mCfA23ApOY3jMVdoWz0nuXu0agARV1OIuJKS7zZHIi8Tn5xG08rWV/Xh5O2BV/UQTv/5ejv/53HNLY/jQEjb+py/4TV30sqOaw4+HnjWCCVy0TYAolfuwaVcGZyDjV976VeSubz3JJlWejz7y//Zu++wpq43DuDfsHeAsJfgXihucVTc21qtVttq0eLeCrVqa21/rVoFZ121zlqt27oHigO3KE5URFH2kr0h+f2BoiFhKSYRvh+fPI9Jzr15D/fk3Lw5J+dqmRnB2NkR4XsKzp9Rh69Bz8EcuvbSxyY3KR0vrz1S2f75NV2RESyKnHOMSjnn5KpwnXRERjBr6IjHr+rztJT6RKvwOZQ+bu88ZVQoFMLQ0FDutMSaNWti0aJFhffnzp2Lpk2bYv78+YWPbdy4Efb29nj8+DFsbW2xZMkSnDlzBq6uBSeG6tWrw9/fH+vWrUOHDrLfvr948QL6+vro06cPDA0NUa1aNTRp0uRdqwMrKytoaGjAwMCg3NMsc3NzsXr1ajRu3LjYMmvWrEH16tWxdOlSCAQC1KlTB3fv3sXvv/9e7Dbe3t5wd3fH+PHjAQDTp0/HlStX4O3tjY4dO8LAwADbtm1Dhw4dYGhoCB8fH5w+fRpCofCdY1i8eDG+/PJLTJ06FQBQq1YtrFixAh06dMCaNWugo6Mjs9/s7OzCUcrX8iT50BBUzDQtobUIqTGJEL81pSg5IgHGNmZICo8vdjsDcyEa9GqJre6Lii2jaHo2psiISZKaHpUekQB9WxHSi9Sl6GNpYXHQtxUpLNayMLAxRXqR+qRFJsDQVoTUEo6NKtKyNkNuzEvgrbrkRsRBy8YcueGxcrfRMDeGsFdbhLr/rKgwy0TfxhSZZWhn8tqYnoq1sfKIjomVGkG0tbZEdEycEiOSLzo1ExYGOtB49dMCgUAAKyNdRKdkwNZYT+42B+48R58G9tBUV72rRZXU3tLk9GtvP5YaFgcDFWpzOjYiZEUnStUlMyIeOrYiZIapXlsqja6NCFkxReuTAD1bM2SGfVx9NFC5zjlA8fUx+EjrU9EkEmVHUHV8kDNL8+bNpe4HBATAz88PBgYGhbe6desCKPjd3oMHD5CVlYWuXbtKldm6dStCQkLkvQS6du2KatWqoXr16hg2bBj++ecfZGQU/+3qh6SlpYVGjRqVWCYoKAitW7eGQPBmPvTr5Lekbdq2bSv1WNu2bREUFCS1D09PT/zvf//DjBkzShwlLUsMAQEB2Lx5s9Rx6N69O8RiMZ49eyZ3vwsWLIBQKJS6XUp+UGLdykumUyhlWrm2gS6+/ssTF9YeRtT90AqN5b0VrYyghMq8XbSkckokKU99VJ1MOyu+LmoGunD8ay7i1u5F5n35/ZQyFT0ugmLq8nax4sp8XN7UQaU/TBT5W5cUa2ZuHk4ERaB/Y4cPHNS7K1c/oOptrrwnHBVXyapTuc45QPk+ExB9IB/kOoT6+vpS98ViMfr27St3NMza2hr37t0DABw5cgS2trZSz2tra8t9DUNDQ9y8eRNnz57FyZMnMXfuXMybNw/Xr1+HsbEx1NTUZDqN3NzcctXj9cIwb+9H3j50dXVLPanJdGBlVHS/EolE6jGxWIyLFy9CXV0dwcHB7x2DWCzGmDFjMHmy7G+nHBzkfxiZNWsWpk+fLvXYfGf5c+DLymVAe7T16AkAuHPwMoRWplBTVyscJRTaiJAUKf/bMy19HXyzZSYe+t7EpQ1H3yuOilD983aoP7qgLs8OXIaetSkE6mqF3wjq25giPSJBZrv0iATovzWtx8DOTG45RaszsB1cRhXUJ/i/yzAoUh8Da1OkqkCc5ZUTFQ9NKxGgrlY4SqhpY4acSNlRATV9XTht+RkpvlcRv+E/RYdaqvTIlzLtTE9OO0uPSIDBW21M384MGR/hsXvNytICkdExhfcjo2NgZVnyNFNlsDLURWxqJvLEYmi8OlfFpGbCykj+6KDvw0hUFxnKLDqjTDU+b4cGb/Vr+uXo1wyK9GtpKtTmsiIToGMjkqqLrq0IWSoUY3lkRiZAt8ix0bURISPi4xl9qvvWOedxJTjn1B7YDo1e1efJf7LvHQNrU5V6T1DVoJAL0zdt2hR79+6Fo6MjNDRkX/L1YiwvXryQOz20OBoaGujSpQu6dOmCn376CcbGxjhz5gwGDBgAc3NzREVFFZZNSUkpdoSrOObmBR8koqKiYGJiAgDvfK3F+vXrS13zEACuXLlS4jb16tWDv78/hg8fXvjYpUuXUK9evcL7ixcvRlBQEM6dO4fu3btj06ZNGDFixDvH0LRpU9y/fx81a9YsQ60KaGtryyTu7ztdNHDfBQTuu1B4v7ZbYzT+rB1u7TmPBj1bIik8Tu50US09bXyzZSaCz9/B2ZX73yuGivJ0jz+evvr9BgDYdmyM6gPbImTXBTj0boG08HiZ6aIA8PzoNfTYPxd3lu5HVnwKag/rjNCDlxUZulyP9vrj0d439anWsTHqDGiLh7svoEavFkgNj/8op7rkJyQj88FTmHzWEYl7TkPYsw1ywmNkpouq6enAacs8pJ6/idiVO5UUbcmyE1KQeO85nAa2xdNdF2DfuwXSw2TbWdiRa+h6YC7uLiloY7WGd8bz/5Tfxt5VV7e2GD7OE2NHfAmRiTF2HTiKnp1Vb8EfU31t1LEQ4si9cHzayAG+j6JgI9QrYbroC/Rv/O6rX38IIXv8EVKkX6sxsC2e7LqAaq/6taLTRQEg9Og19N4/F4Gv+rU6wzrjmQr0a6/lxKcg5W4obD5vj4id52DVpxUywuI+yumiQEF9ku+Fwu7zdgjbeR7WfVq+qs/H00c/3OuPh5XonPN4rz8ev1Ufh46NUXtAWzzafQHVP8L6fEhcZVRxFPJjhAkTJuDly5cYOnQorl27hqdPn+LkyZMYOXIk8vPzYWhoCE9PT0ybNg1btmxBSEgIbt26hVWrVmHLli1y93n48GGsWLECgYGBeP78ObZu3QqxWIw6deoAADp16oS///4bFy5cwL179/DNN99AXb18SUrNmjVhb2+PefPm4fHjxzhy5Ah8fHze6W8wduxYhISEYPr06Xj06BG2b9+OzZs3l7iNl5cXNm/ejLVr1yI4OBhLlizBvn37Che8CQwMxNy5c7Fhwwa0bdsWy5cvx5QpU/D06dN3jmHmzJm4fPkyJkyYgMDAQAQHB+PgwYOYNGnSO9W7ovw3ewNafNkZU8/44JNx/bD/uz8Ln+u/cBTqdmkKAHAd0QN2jWugfvfmmHB0PiYcnY8OEz5VVthyXfl+I2p/3Qn9LyxGwwl9cWnG+sLnXBd7wK5rQV3SXsThts9e9DjwEz67tARZ8SkI3iF/kSVl8vt+Ixp+1Qlfn1uMZhP64ozXm/p0XOQBx1f1UdPSgPu1FeixdhJEdR3gfm0FXGcOLm63ShExexVMv+yBOmfWwnzc5wj/rmAFQbuFk2DUpWCpf7MR/aDXuDaE3V1R6+hy1Dq6HBYTVKseAHB15kbU/LoT+l5YjAYT++LKq3bWytsDtt3etLE73nvR7b+f0O9yQRt7ooJtDAB+9VmFzv2/RkxcPDymzEbPwSMBAHMXLIPfhYIvtuxtrTHB42sMGzsDPQePhKmJMQb07a7MsIv1Y4/G2BsYin7rfLHpymP81NMFAPDz0Vs4G/zmy8ywxHQExSShe10bJUVaNpe+34g6X3fCgAuL0WhCX1x8q19ru9gD9m/1a7d89qL3gZ/w+at+7bGKtbl7XuvhMLwzOlxaihqT+uHutHUAAOclo2HRvRmAgv6s461VaLJ+KgzrV0PHW6tQZ84QZYZdrNteG+A4rDM6X/RB7Un9EDi94Pzp4jMKVt3e9M/dbq5Ei/VTYFTPAd1urkS92V8oM+xiFT3nnH7rnNNpkQec3jrnjLi2Aj3XToJZXQeMUMFzDgCc+34j6n3VCUPOLYbLhL44+1Z9OizyQLW36vP1tRXo+uoc+vW1FWipgvUh+VavXg0nJyfo6OigWbNmuHDhQrFl9+3bh65du8Lc3BxGRkZwdXXFiRMfdsVsgeRd5zICWLZsGZYtWyZ16QI3Nze4uLjIXPIhODgYM2fOhJ+fH7Kzs1GtWjX06NEDS5YsgUAggEQiwcqVK7F69Wo8ffoUxsbGaNq0KWbPni33d3H+/v744YcfcOfOHWRlZaFWrVqYM2dO4cqlKSkpGDVqFI4fPw6hUIj//e9/WLp0Kfr371946QmBQID9+/ejf//+AAouS/H28wBw8eJFjBs3DsHBwWjRogUmT56MQYMG4dmzZ3B0dMTmzZsxdepUqUtTFOfw4cOYNm0awsLC0LJlS4wYMQIjR45EYmIijI2N5e5rzZo18Pb2RlhYGJycnPDDDz9g2LBhyMrKQrNmzdCuXTusW7eusPyAAQMQExOD8+fPF25z69YtuLi4lCkGALh+/TrmzJmDy5cvQyKRoEaNGvjiiy8we/bsUuv42g+OX5a57Megdq7qLeTwrlIqT1UAAJ+oJys7hAp1N0f+olAfo8F3flF2CBUq79BaZYdQof79Qf51Aj9Glnl5yg6hQuUKKldH/UKz8tRHU9kBVLCxYduUHUKxHtTorewQ5KofcqRc5Xfu3Ilhw4Zh9erVaNu2LdatW4e//voLDx48kPtzrKlTp8LGxgYdO3aEsbExNm3aBG9vb1y9evW9FtAsyXslhETFYUKoupgQqjYmhKqLCaHqYkKo2pgQqi5VTgjvVe+j7BDkavj0cLnKt2rVCk2bNsWaNWsKH6tXrx769++PBQsWlGkfDRo0wBdffIG5c+eW67XLqvK8Q4mIiIiIiD6g7OxspKSkSN2KXn7ttZycHAQEBKBbt25Sj3fr1g2XLl0q0+uJxWKkpqbC1NT0vWMvDhNCIiIiIiKiMpB3ubXiRvri4+ORn58PS0tLqcctLS0RHR1dptfz8fFBenp64c/iPgSFrDJKRERERERUVhKJaq4yKu9ya8VdJu+10i4jV5wdO3Zg3rx5+O+//2BhYVH+YMuICSEREREREVEZyLvcWnHMzMygrq4uMxoYGxsrM2pY1M6dO/Htt99i9+7d6NKlyzvHWxacMkpERERERFTBtLS00KxZM5w6dUrq8VOnTqFNmzbFbrdjxw64u7tj+/bt6N37w6+2yhFCIiIiIiJSKZXlOgjTp0/HsGHD0Lx5c7i6uuLPP//EixcvMHbsWAAFU1AjIiKwdetWAAXJ4PDhw7F8+XK0bt26cHRRV1cXQuGHWYmcCSEREREREdEH8MUXXyAhIQG//PILoqKi0LBhQxw9ehTVqlUDAERFReHFixeF5detW4e8vDxMmDABEyZMKHz8m2++webNmz9IjEwIiYiIiIiIPpDx48dj/Pjxcp8rmuSdPXv2wwdUBBNCIiIiIiJSKWIVXWW0MuKiMkRERERERFUUE0IiIiIiIqIqilNGiYiIiIhIpajqhekrI44QEhERERERVVFMCImIiIiIiKooThklIiIiIiKVUlkuTP8x4AghERERERFRFcWEkIiIiIiIqIrilFEiIiIiIlIpvDC94nCEkIiIiIiIqIpiQkhERERERFRFccoofRD6ksr1XYNFXp6yQ6gwBoLKdWzUNCvXMmQGYrGyQ6gweYfWKjuECqXRd6yyQ6hQlt/PVnYIFSZEq3J9nLHPrTz9AAA8Ua8851BNcBqjovDC9IpTuT4ZEhERERERUZkxISQiIiIiIqqiKtccCyIiIiIi+uhxlVHF4QghERERERFRFcWEkIiIiIiIqIrilFEiIiIiIlIplWsNcdXGEUIiIiIiIqIqigkhERERERFRFcUpo0REREREpFK4yqjicISQiIiIiIioimJCSEREREREVEVxyigREREREakUCaeMKgxHCImIiIiIiKooJoRERERERERVFKeMEhERERGRShErO4AqhCOEREREREREVRQTQiIiIiIioiqKU0aJiIiIiEilSMBVRhVFpUcI3d3d0b9//w+2/3nz5sHFxUXmMUtLSwgEAhw4cOCDvfZrbm5umDp1auF9R0dHLFu2rML2X5a/YdEYiIiIiIioaijXCKGbmxtcXFzKnbC863aKFhQUhJ9//hn79+9H69atYWJiovAYrl+/Dn19/Qrb3/LlyyGRSCpsf0REREREVHlwyuhbQkJCAACffvopBIJ3H6bOycmBlpbWO21rbm7+zq8rj1AorND9KYuJoyX6LBkDPRNDZKVk4LDnOiQER8qUE9qZobf3GFg2qIbE0Ghs7jtXCdEWT8/JCs4rx0PL1BC5Kem4O3kN0h9HSJXRtTeH8/JxMHR2RMbTKFzuPkdJ0ZZO38kKzVeMfVWfDARMWYvUIvUBgGpD3VBnUj9ATYC4C/cR+P1GSPJVb/0wLUcb2C2eBnVTI+SnpCPCaymyn4RJldF3bQRLr2+gpq8LiCVI8b2CWJ+/lRRx8fSdrND0rWNzq5hj4zDUDbUm9YPg1bG5o6LH5vnLNPx45CaSMnJgqKOJX3o3QQ0zI6kyh+6+wN/XQwrvx6Zmoam9CEsGtFR0uCWav3QNzvpfQWR0LPb/vQa1qjvKLbf30Als2LYLYrEYrZu74IcZE6Ghoa7YYMtAz8kKjd/q1+5MXoM0Of1ao+XjYPSqX7uowv0aAAgdLdF16RjomBoiJyUDp6avQ6Kcc46hnRm6LBkD8wbVkBQajV29VeucA7zpC7Rf9QU3S+in3+4LbqtoX2DuaIWvfMZD38QQmSnp+MdzDWKeyNanlmsD9Jk5FDr6OpCIJbh76gaOeO9UQsQlM3O0whCfcYX12em5Vm59aro2QK+ZQ6D9qj73Tt3Ace9dSohYccQcz1CYMk8ZdXd3x7lz57B8+XIIBAIIBAKEhoYCAM6dO4eWLVtCW1sb1tbW+P7775GXl1fidvn5+fj222/h5OQEXV1d1KlTB8uXLy9X8M+fP0ffvn1hYmICfX19NGjQAEePHgUAbN68GcbGxlLlDxw4UGyiN2/ePPTt27fgj6KmVlhO3nTK/v37w93dvfC+o6Mjfv31V7i7u0MoFGLUqFFyXyM9PR3Dhw+HgYEBrK2t4ePjI1Om6JTRFy9e4NNPP4WBgQGMjIwwePBgxMTEAAAePnwIPT09bN++vbD8vn37oKOjg7t37wKQnTJalhhycnLw3XffwdbWFvr6+mjVqhXOnj0rt06K0mPBSARu98O6jl64su4wei+S/zfOTs3Eee/dODhltYIjLJsG3h4I//s0LrSZhmd/HILz0jEyZfJSM/B44U7cGbdSCRGWT5PF3+LZtjM41XYGHq86hKZLRsuU0XMwR/2Zg3Cu3zycbD0NOhZCOH7ppvhgy8Dm1wl4+e9xBHceg/g/98J24RSZMvnJaQibsghPuo9HyKdTod+qIYT9Oigh2pI1XvwtQredwem2MxC86hBcijk29WYOgn+/efBtPQ3aFkI4qOix+fX4bQxs7IiDY7rAvVUt/Hw0UKZMX2cH7BrZsfBmZqCNXvXtFB9sKbp1bIeta7xhY2VRbJnwyGj8sX4rtq7xxrFdGxGfkIh9h08oMMqya+jtgRd/n8a5NtPwtJR+LfAj6NcAoOPCkbi33Q/bOnghYM1hdF4s/5yTk5aJK4t34+Qk1TznAIDL4m/xfNsZ+L7qC5oU0xfUnTkIF/rNw6lXfUE1Fe0LBs/3wKUdp/Fbp2k4ve4Qhi6SbW8AkJGSjq2TVmBBV09495uNmq3qoVm/tgqOtnSfz/fAlR2n8Xun6Ti77hAGLZI9PkBBfbZNWonFXb2wrN8c1GhVD036tVFwtFRZlTkhXL58OVxdXTFq1ChERUUhKioK9vb2iIiIQK9evdCiRQvcvn0ba9aswYYNG/Drr7+WuJ1YLIadnR127dqFBw8eYO7cuZg9ezZ27Sr7tx0TJkxAdnY2zp8/j7t37+L333+HgYFB+f8KADw9PbFp0yYAKIyzPBYvXoyGDRsiICAAP/74o9wyXl5e8PPzw/79+3Hy5EmcPXsWAQEBxe5TIpGgf//+ePnyJc6dO4dTp04hJCQEX3zxBQCgbt268Pb2xvjx4/H8+XNERkZi1KhRWLhwIZydnd85hhEjRuDixYv4999/cefOHQwaNAg9evRAcHBwuf4mFUVPZASrBo64t/8iAODR0esQ2plDaGcmUzYrOR3hNx4jNyNb0WGWSsvMCEbOTojccwEAEHP4KnQdLKBrLz0qnJuUjqRrj5CvgnV4m7aZEYydHRG2xx8AEHn4GvQdzKFnL31cbPu0QuSx68iOTwEAPN16Gnafqd5JTF0khG7DGkg64AcASDl2EZr2ltC0lf7QnvXgKXLDCr6UkeTkIuvBM2jZWyk83pJovTo24a+OTdTha9BzMIdukWNj06cVot46NqEqemxepmcjKCYJvRsWJHdd6lgjIjkDEUkZxW5zNzIRCenZ6FBLtY4NADR3cYaVRcmzQU76+aNzhzYwMzWBQCDA4P69cNT3rGICLActMyMI3+rXog9fhV4x/VriR9CvAYCuyAgWDR3xaF/BOSfk6HUY2ZvDUM45JzspHVHXVfOcA7zpC97up/Xk9NMfS19gIDKCXUMn3Nhf0N5uH7sKkb0FTO1k308R90OREBYLAMjLzkX4g+cQORT/JYwyGIiMYNvQETf3FxyfO8euwdTeAiZy2lrk/VC8fKs+kQ+ew9TBUqHxUuVV5imjQqEQWlpa0NPTg5XVmxPs6tWrYW9vjz/++AMCgQB169ZFZGQkZs6ciblz5xa7nbq6On7++efC+05OTrh06RJ27dqFwYMHlymmFy9eYODAgYXJT/Xq1ctaHRkGBgaFI4pvx1lWnTp1gqenZ7HPp6WlYcOGDdi6dSu6du0KANiyZQvs7Ir/9trX1xd37tzBs2fPYG9vDwD4+++/0aBBA1y/fh0tWrTA+PHjcfToUQwbNgxaWlpo1qwZpkyRHdUoawwhISHYsWMHwsPDYWNjA6AgWT5+/Dg2bdqE+fPnl+8PUwGMrE2RFpskNXUlJTIBRjYiJIfHKzyed6VjI0J2dKJUPTIj4qFjK0JmWJwSI3s3ujYiZMVI1ycjIgG6tmbICHtzXPRsRch46zhlhMVBz1b2ZKdsmtZmyI15CbxVn9zIOGjamCM3IlbuNhpmxjDq2RbPR85TTJBlJO/YZEYkQM/WDJlvHRvdIscmMywOuip4bKJTM2FhoAMNtYLvMAUCAayMdBGdkgFbYz252xy48xx9GthDU12l104rVnRMrNQIoq21JaJjVK+f0LERIasS9WsAYGBjivQY6XNOWmQCDG1FSP2IzjlAQV+QKacvKEs/rYp9gbG1CCkxiRC/VZ/EiHiY2IjwMrz49mZoLoRLr1ZY575QEWGWmVBOfZIi4mFiY4bEEtqaobkQzr1aYYP774oIU2nEXGVUYd77N4RBQUFwdXWVmorZtm1bpKWlITw8HA4ODsVuu3btWvz11194/vw5MjMzkZOTI7PqZ0kmT56McePG4eTJk+jSpQsGDhyIRo0avU913lnz5s1LfD4kJAQ5OTlwdXUtfMzU1BR16tQpdpugoCDY29sXJoMAUL9+fRgbGyMoKAgtWrQAAGzcuBG1a9eGmpoa7t27V+y02LLEcPPmTUgkEtSuXVtq2+zsbIhEIrn7zc7ORna29LejeZJ8aAgq7rcuRRfGeZ/feCqTTD0+9s6uyPz+Yg/L2+VUucpFF2AqIVY1A104/DUX8X/uRdb9kOILKonMWlIf+7Ep0rhKWisrMzcPJ4IisGVY+w8c1If2ps4qvTZYmRvbx0NmMbaP9JwDQKaf/tj7gvJ+HtA20MXov77D6bUHEX4/9ANG9m5km1rp9Rn5lxfOrj2ECBWsD32c3jshlEgkMo339Zu1pEa9a9cuTJs2DT4+PnB1dYWhoSEWL16Mq1evlvm1PTw80L17dxw5cgQnT57EggUL4OPjg0mTJkFNTU2m08jNzS1HzQqUdT+lrQz6Lit9yvvbynv89u3bSE9Ph5qaGqKjowtH9t4lBrFYDHV1dQQEBEBdXTqhK2467oIFC6RGewGgk5Ezuhi/e3LecEA7tPToCQB4cPAyDK1MIVBXK/yW09DaFCmRCe+8f2XIikyAjo1Iqh46tiJkRXxc9XgtMzIButbSx0XXRoTMCOlvNTMiEqSmJ+nZmSMjQvW+Zc+NioemtRmgrlY4SqhpbY7cSNlvndX0deG46Rek+l5DwoYDCo60dMUdm6J/98wix0bXzlzm+KkCK0NdxKZmIk8shsarPjkmNRNWRvJHB30fRqK6yFBm0ZmPiZWlBSKjYwrvR0bHwMqyYhcdqwjy+jXdj7BfqzuwHVxGFZxzHv93GQZF3j8G1qZI/cjqBLxfP60qfUGLAe3h5tEbAHDz4CUYW4mgpq5WOKpmbCNCYjGfB7T1dTBuyyzc9Q3A2Q1HFRZzSZoNaI9PPHoBAAIPXoLQylSqPkIbERIj5f/ttfV1MGrL97jvG4DzKlIfqhzKNZdGS0sL+fn5Uo/Vr18fly5dkko2Ll26BENDQ9ja2ha73YULF9CmTRuMHz8eTZo0Qc2aNQtX+SwPe3t7jB07Fvv27cOMGTOwfv16AAWrdaampiI9Pb2wbGBgYLn3b25uLvV7wvz8fNy7d6/c+6lZsyY0NTVx5cqVwscSExPx+PHjYrepX78+Xrx4gbCwN6scPnjwAMnJyahXrx4A4OXLl3B3d8ecOXMwYsQIfPXVV8jMzHznGJo0aYL8/HzExsaiZs2aUrfiptLOmjULycnJUjc3YYOy/WGKcW+fPzb2moONvebgytrDiLn/HA0/K/gxeJ1eLZAcHv9RTRcFgJz4FKTeDYXN5wWjFpZ9WiEzLO6jnVaVHZ+CpHuhsP+8HQDApk9LZITFSU1DAoCIw9dg07MFtF99OK8+vDPCD1xWeLylyU9IRtb9pzDu3xEAYNSzLXLDY2Smi6rp6aDapp+ReuEm4v74VxmhlionPgXJ90Jh9+rYWL86NplFjk3k4WuwfuvYOA7vjAgVPDam+tqoYyHEkXvhAADfR1GwEeqVMF30Bfo3rqbIECtcV7e2OH3uEuJfJkIikWDXgaPo2Vn1Fi/KiU9Bylv9mlWfVq/a2sfVrz3c649/e8zBvz3m4Oaaw4i7/xx1BhScc2r0aoHU8PiPbroo8KYvKK2fltcXqEo/fX3fBSzu9T0W9/q+YJTvQSiaf1bQ3hr3bIWX4XFyp4tq6Wlj7JZZeHj+Nk6u3KfosIsVsO8ClvaahaW9ZsFv7SFEPghF088Kjk+jni2RGB4nd7qolp42PLZ8j0fn78B35X5Fh60UEghU8lYZlWuE0NHREVevXkVoaCgMDAxgamqK8ePHY9myZZg0aRImTpyIR48e4aeffsL06dOh9ur3HvK2q1mzJrZu3YoTJ07AyckJf//9N65fvw4nJ6cyxzN16lT07NkTtWvXRmJiIs6cOVOYKLVq1Qp6enqYPXs2Jk2ahGvXrmHz5s3lqS6Agt8GTp8+HUeOHEGNGjWwdOlSJCUllXs/BgYG+Pbbb+Hl5QWRSARLS0vMmTOn8G8kT5cuXdCoUSN89dVXWLZsGfLy8jB+/Hh06NChcIrq2LFjYW9vjx9++AE5OTlo2rQpPD09sWrVqneKoXbt2vjqq68wfPhw+Pj4oEmTJoiPj8eZM2fg7OyMXr16yexXW1sb2traUo9V5HRRADg+eyP6+IxGmwn9kJ2WicPT1xU+1/N3DwSfuoknvjehrqWBseeXQENLA9qGephwZQXu7fPHuUWqsTTzfa/1cF4xDtWn9EdeagbuTl4DAGiwZDRiTwQg7kQABFoa6HB1OdS0NKFhpAe3W6sQuecCHv+mesnHLa8NaLZ8LOpM/hR5aZm48ao+TX1GIepEAKJO3kTGi1gELd6DDofmQaCmhjj/+wjdfla5gRcjYs4fsFs8DebjB0OcloFwz6UAAJsFk5DqexWpp69B5N4Peo1rQ01PB0bdCqZfpxz1R9xq1Whjr9322oAmy8ei9qtjc/PVsXHxGYXoEwGIfnVsHi7eg3avjk28/308V9Fj82OPxph75BY2XH4MA20N/NK7KQDg56O30KGWFdxqWQMAwhLTERSThBWft1JmuCX61WcV/C5cRvzLRHhMmQ09XR0c27URcxcsQ8d2rdGxfWvY21pjgsfXGDZ2BiQSCVo2bYwBfbsrO3S57nmtR6MV41DzVb92+1Vbc14yGjEnAhB7IgBqb/VrmkZ66PiqX3ukgv0aAPh9vxFdloxG84n9kJOWCd9pb845nRZ54Nmpm3h26ibUtDTwjf8SqGtpQMtQDyOurcDDvf64/Lvq9AeBXhvQ9FVfkFtKX9D+rX5aVfuCXbPX40vvceg6vj+y0jLwz4w1hc8NWTga93wDcM83AB1G9ES1xjWgpacN5+4FP7EJPHIFp1YdUFLk8u2Z/Re+8B6LzuP7IystE/++VZ9BC0fhvu9NPPANQPsRPeHwqj4Nuxd8Brxz5CpOq1h96OMkkJRjLuPjx4/xzTff4Pbt28jMzMSzZ8/g6OiIc+fOwcvLC7dv34apqSm++eYb/Prrr9DQ0Ch2O2tra4wdOxb79++HQCDA0KFDIRQKcezYscKRPHd3dyQlJeHAgQNy45k0aRKOHTuG8PBwGBkZoUePHli6dGnhb90OHDgALy8vhIeHo0uXLujXrx9Gjx5dOJo5b948HDhwoPD1Dhw4gM8++0xqtDM3NxdTpkzBzp07oaGhgWnTpuHKlSswNjYuTDAdHR0xdepUmctTFJWWloZx48Zh3759MDQ0xIwZM3DkyBG4uLgUXmqi6L5evHiBSZMm4fTp01BTU0OPHj2wcuVKWFpaYuvWrRg/fjxu3bqFWrVqAQACAgLQpk0b7N+/H7169ZL5G5YlhtzcXPz666/YunUrIiIiIBKJ4Orqip9//rnY1UuLWlDt6zKV+1g0ycpTdggVJkPwcS6yUZzaeinKDqFChWR8vNMci+q2QPUu+fA+NPqOVXYIFepUg9nKDqHChGhVrssq2+eq3vX/3oefTuWpj2YlGyHyDt2h7BCKddryC2WHIFfnGNW7nuX7KldCSFRWTAhVFxNC1caEUHUxIVRdTAhVGxNC1aXKCeEpFU0Iu1bChLByfTIkIiIiIiKiMmNCSEREREREVEVVrjkWRERERET00ausK3qqIo4QEhERERERVVFMCImIiIiIiKooThklIiIiIiKVUnnWplV9HCEkIiIiIiKqopgQEhERERERVVGcMkpERERERCqFU0YVhyOEREREREREVRQTQiIiIiIioiqKU0aJiIiIiEil8ML0isMRQiIiIiIioiqKCSEREREREVEVxSmjRERERESkUsScMaowHCEkIiIiIiKqopgQEhERERERVVGcMkpERERERCpFzFVGFYYjhERERERERFUUE0IiIiIiIqIqilNGiYiIiIhIpUiUHUAVwoSQPoip82yUHUKFOjw7QtkhVJhOTcOVHUKFkuQpO4IKFqzsACrOvz9EKjuECmX5/Wxlh1Chut6fr+wQKsyappOVHUKF+kFNS9khVCjfzMpzDu2jW13ZIRBVOE4ZJSIiIiIiqqI4QkhERERERCpFrOwAqhCOEBIREREREVVRTAiJiIiIiIiqKE4ZJSIiIiIilSIW8ML0isIRQiIiIiIioiqKCSEREREREVEVxSmjRERERESkUnhhesXhCCEREREREVEVxYSQiIiIiIioiuKUUSIiIiIiUim8ML3icISQiIiIiIioimJCSEREREREVEVxyigREREREakUMa9LrzAcISQiIiIiIqqimBASERERERFVUUwIiYiIiIhIpYghUMnbu1i9ejWcnJygo6ODZs2a4cKFCyWWP3fuHJo1awYdHR1Ur14da9eufafXLSsmhERERERERB/Azp07MXXqVMyZMwe3bt1C+/bt0bNnT7x48UJu+WfPnqFXr15o3749bt26hdmzZ2Py5MnYu3fvB4uRCaGSubm5YerUqQp5rXnz5sHFxUUhr0VEREREVNlkZ2cjJSVF6padnV1s+SVLluDbb7+Fh4cH6tWrh2XLlsHe3h5r1qyRW37t2rVwcHDAsmXLUK9ePXh4eGDkyJHw9vb+UFViQliVeHp64vTp04X33d3d0b9/f+UFREREREQkh0RFbwsWLIBQKJS6LViwQG4dcnJyEBAQgG7dukk93q1bN1y6dEnuNpcvX5Yp3717d9y4cQO5ubml/dneCS87UYUYGBjAwMBA2WGU2/OXafjxyE0kZeTAUEcTv/RughpmRlJlDt19gb+vhxTej03NQlN7EZYMaKnocEtk4GSJFsvHQtvUELkpGbg2dR1SH0fIlHMc2gF1J/aDQE2AWP/7uPn9JkjyxUqIuGRqNrYwmD4bakZCSNLSkLZ0AfLDnssvrKkF4Yr1QHYWkqeOUWygZaRmYwtDr4L6iNPTkOa9APkviq+P8ar1kGRnIXmS6tVHy9EGdounQd3UCPkp6YjwWorsJ2FSZfRdG8HS6xuo6esCYglSfK8g1udvJUVcOiMnS7RfNgbapobISc7AhWnrkBwcKbdsrSEd0GhiX0AgQJT/fVyevVml3kN6TlZovHI8tEwNkZuSjjuT1yCtSF+ga2+ORsvHwcjZERlPo3Cx+xwlRVuy+UvX4Kz/FURGx2L/32tQq7qj3HJ7D53Ahm27IBaL0bq5C36YMREaGuqKDbaMrB1tMG3JNBiZGiE9JR3LZixFWHCYTLk6Teti/G/jAQAamup4cP0B1v20Dnk5eYoOuVjaTtaosXwSNEwK+oKnU1ciMzhcqoyWnTlqLJsEvYZOyHoWhfs9v1NStKVzcLLH/JVzYWJqjNSUVMyZ/D+EPH4mU65Fm6ZY889SPH/6Zlrel709kJ1V/EiOMogcrfCFzzjomRgiKyUduzzXIvaJ7OeCGq4N0GPmEGjr60AiluDBqRs44b1LCRHTrFmzMH36dKnHtLW15ZaNj49Hfn4+LC0tpR63tLREdHS03G2io6Plls/Ly0N8fDysra3fI3r5OEKoQOnp6Rg+fDgMDAxgbW0NHx8fqedzcnLw3XffwdbWFvr6+mjVqhXOnj1b+PzmzZthbGyMEydOoF69ejAwMECPHj0QFRVVWObs2bNo2bIl9PX1YWxsjLZt2+L584IPtG9PGZ03bx62bNmC//77DwKBAAKBAGfPnkWnTp0wceJEqbgSEhKgra2NM2fOfJg/TCl+PX4bAxs74uCYLnBvVQs/Hw2UKdPX2QG7RnYsvJkZaKNXfTvFB1uKZou+xdNtfjjezhMPVx1GC59RMmX07M3R8LtB8Pv0ZxxznQ4dcyGcvnRTfLBlYDDRE9nHDyFp9NfI3LsD+lNmFltWb7gH8h7eV2B05WcwxRNZRw8h8duvkblrBwymFV8f/REeyAtS3frY/DoBL/89juDOYxD/517YLpwiUyY/OQ1hUxbhSffxCPl0KvRbNYSwXwclRFs2bX4fiUf/+GFfey/cXXMY7eS8fwDAwN4cTb0+x5H+v2Bv2xnQtTBG7aFuig22FA29PfDi79M412Yanv5xCM5LZb9UyEvNwOOFOxE4bqUSIiy7bh3bYesab9hYWRRbJjwyGn+s34qta7xxbNdGxCckYt/hEwqMsnwmLJiAE9uPY6zbGOxduxeTF8m+fwAg9MEzTO87DVN6TsbErhNhZCpEz696Kjjakjn9Phax207hTvuJiFp9AE4+E2TK5KdlImzRdoRMWKb4AMtpnvf32PP3AfRuMwgb/9iGX5YW/0VJyONnGNh5WOFN1ZJBABgw3wNXd5yGd6fpOLfuED5fNFpuucyUdOyYtBJLunphZb85cGpVDy792ig4WgIKkj8jIyOpW3EJ4WsCgfRiNBKJROax0srLe7yiMCFUIC8vL/j5+WH//v04efIkzp49i4CAgMLnR4wYgYsXL+Lff//FnTt3MGjQIPTo0QPBwcGFZTIyMuDt7Y2///4b58+fx4sXL+Dp6QkAyMvLQ//+/dGhQwfcuXMHly9fxujRo+U2Hk9PTwwePLgwoYyKikKbNm3g4eGB7du3S82F/ueff2BjY4OOHTt+wL+OfC/TsxEUk4TeDQuSuy51rBGRnIGIpIxit7kbmYiE9Gx0qGWlqDDLRFtkBGNnR7zY6w8AiDhyDfoO5tCzM5MqZ9enJSKO3UB2fAoAIGTradj3d1V4vKURCI2hXqMWss+cAgDkXDwHdSsrqFnI/t01GjSCuq0dss+cVHSYZSYQGkOjZi1kn35VH/9X9bGUU5+GjaBmY4fs06pZH3WRELoNayDpgB8AIOXYRWjaW0LTVvoDe9aDp8gNiwEASHJykfXgGbTsVet985qOyAimDR0RsvciAOD5keswsDeHQZH3DwA49m6J58dvIOvVe+jR36fh9KnqvIe0zIwgdHZC5J6CVeaiD1+FnoMFdO3NpcrlJqUj8doj5Geo3ofYtzV3cYaVhXmJZU76+aNzhzYwMzWBQCDA4P69cNT3rGICLCehSIgaDWvAb3/B++fS0YuwtLeEhZ1swpudlY38vHwAgIaWBrR1tCAWSxQab0k0RELoO1dH/N5zAICXRy5D28ECWnbSxys/KQ1p1x4iPyNLGWGWmamZCeo518GhPccBACcPn4Gdgw1s7Ct+xEQR9EVGsG3oiFv7Cz4X3D12Dab2FjCR069F3g/Fy7BYAEBedi6iHjyHqYOlTLnKRCxQzVt5mJmZQV1dXWY0MDY2VmYU8DUrKyu55TU0NCASicoXQBkxIVSQtLQ0bNiwAd7e3ujatSucnZ2xZcsW5OcXnEhCQkKwY8cO7N69G+3bt0eNGjXg6emJdu3aYdOmTYX7yc3Nxdq1a9G8eXM0bdoUEydOLPxdYEpKCpKTk9GnTx/UqFED9erVwzfffAMHBweZeAwMDKCrqwttbW1YWVnBysoKWlpaGDhwIAQCAf7777/Csps2bYK7u3ux30rI/XFtbsVMl4lOzYSFgQ401AqaqkAggJWRLqJTik8ID9x5jj4N7KGprlrNW9fWFFkxSVLT1jIiEmQSQj1bETLC4wvvp4fHQ89W9uSgbGrmFhC/TADE+YWPiWNjoWZe5EOTtg70R09E+qolCo6wfNTMLSBOkK5Pfpz8+hiMnYj0lapbH01rM+TGvATeamu5kXHQtCn+Q7uGmTGMerZF6tkbigix3PRtTJFZ5P2THpEAfVvZk6O+rQhpb72HUsPiYCCnnLLo2IiQFZ0oVZfMiHjoqFCMFS06JlZqBNHW2hLRMXFKjKh4ZjZmeBn7EuK3jk9cZBzMi3n/WNhZYPmxFfgncDsy0jJwYvtxRYVaKi0bkUxfkBMRD23bkhN4VWVlY4m46PjCz04AEBURDWtb+R+snWo6YPepLdh5fBOGuA9UVJhlZmwtQkpMolRbS4qIh7FNyed8A3MhnHu1wkO/Wx86RHpPWlpaaNasGU6dOiX1+KlTp9CmjfwRXldXV5nyJ0+eRPPmzaGpqflB4lStT8yVWEhICHJycuDq+uZbalNTU9SpUwcAcPPmTUgkEtSuXbvwt34GBgY4d+4cQkLe/DZOT08PNWrUKLxvbW2N2NjYwv25u7uje/fu6Nu3L5YvXy41nbQstLW18fXXX2Pjxo0AgMDAQNy+fRvu7u7FbiPvx7WLj1wt1+uWSGbYvPiimbl5OBEUgf6NZZNgVSApGnwx3zS9Xe7DTA6oIDL1kY1W/9txyDp8AOKEeJnnVE/RxiWnPqPGIfPgR1CfMrY1AFAz0IXDX3MR/+deZN0PKb6gksm+f0qolOTtYir4LpLpyFQwxgr3po4l9eMqoUiAJTWh2PBYTOk5GcObDYOmliZce6jWNL5yvW8+AkXrIyjmvfPgziN0cumLQV2/weQRMzH4m8/QvV9nRYRYPjJdQcnHR9tAF+5/eeHc2kOIvB/6wcKiijN9+nT89ddf2LhxI4KCgjBt2jS8ePECY8eOBVDwm8Thw4cXlh87diyeP3+O6dOnIygoCBs3bsSGDRsKZwR+CFxURkFkOuQixGIx1NXVERAQAHV16R/Zv70QTNFvBgQCgdS+N23ahMmTJ+P48ePYuXMnfvjhB5w6dQqtW7cuc6weHh5wcXFBeHg4Nm7ciM6dO6NatWrFlpf341rxv/PK/HolsTLURWxqJvLEYmioqUEikSAmNRNWRnpyy/s+jER1kaHMojOqIDPiJfSsTSFQVyscGdCzkR4NBApGDfXfmjqmZ2eGjAjVSz7EcbFQMzMH1NQLR9XUzM0hjouVKqdR3xmazVtBd+hwCLS0IDAwhHD1ZiSPd1dC1MWTVx91OfXRbOAMrRatoPfVm/oY/7kZSaPdlRC1fLlR8dC0NgPU1QpHBjStzZEbKTsio6avC8dNvyDV9xoSNhxQcKQlq/F5OzQYXfB7rGcHLkO/yPtH38YU6REJMtulRyTAwP7NN+wGdmZIk1NOWbIiE6BjI5Kqi66tCFkqFGNFs7K0QGR0TOH9yOgYWFmqzihVx4Gd0N+jPwDg/MFzEFmZQU1drXDkxszaHHFy3j9vy8rIwvmDF+D2mRsuHDr/gSMum5zIBGhZi6T6Ai0bEbIjVHN0Vp5+g3rim7FfAgCO7j8JSxsLqKurF44SWtlaIioiRma79LT0wv/HRMXi6P5TaNbaBScOnpYpq0hNB7RHe49eAIDAg5cgtDKVamvGNiIkRco/52vp6+DbLd8jyDcAFzYcVVjMyqI6y4C9ny+++AIJCQn45ZdfEBUVhYYNG+Lo0aOFn62joqKkrkno5OSEo0ePYtq0aVi1ahVsbGywYsUKDBz44Ua5mRAqSM2aNaGpqYkrV64UTuFMTEzE48eP0aFDBzRp0gT5+fmIjY1F+/bt3+u1mjRpgiZNmmDWrFlwdXXF9u3b5SaEWlpaUtMuXnN2dkbz5s2xfv16bN++HStXlryggba2tsyPaTM1K6Zpmepro46FEEfuhePTRg7wfRQFG6EebI3lJ4QH7rxA/8bFJ6/KlJ2QgsR7oXAY2A7Pd52Hbe+WSA+Ll0kII45cQ8f/fsKDJfuQHZ+CGsM7I+y/K0qKuniS5CTkhwRDu1NXZPseh1bbDsiPiYY4Vnree/LEkYX/13B2gf6341RylVFJchLynwRDu3NXZJ86Dq12r+oTI12fpHFv6qPZyAV6o8ap3Cqj+QnJyLr/FMb9OyJp72kY9WyL3PAY5EZIJ7dqejqotulnpF64ibg//lVStMUL2eOPkD3+hfdtOzZGjYFt8WTXBVTr3QJp4fFSU0NfCz16Db33z0Xg0v3Iik9BnWGd8ezgZUWGXqKc+BSk3A2FzeftEbHzHKz6tEJGWBwywz6eD+nl1dWtLYaP88TYEV9CZGKMXQeOomdn1VnAyG/vGfjtfbNwWjO35uj4WUec3nMabXq1RUx4DGLDY2W2s6pmhbiIOOTn5UNDUwNteroiNEh2xUtlyUtIRsa9ZzAb2AHxu/xg2tsVOWFxyAn/eNrawd3HcHD3scL77Tq5ou/nPXBg5xF069MJEWFRiAyTnQ1lZiFCQtxLSCQS6Onrwa1rW+zdfkiRoct1c98F3Nx3ofB+HTcXNPmsHQL2nIdzz5ZIDI9Dopx+TUtPG99u+R6Pz9/B6ZX7FRkyVYDx48dj/Pjxcp/bvHmzzGMdOnTAzZs3P3BUbzAhVBADAwN8++238PLygkgkgqWlJebMmQO1V7+Nq127Nr766isMHz4cPj4+aNKkCeLj43HmzBk4OzujV69epb7Gs2fP8Oeff6Jfv36wsbHBo0eP8PjxY6lh6Lc5OjrixIkTePToEUQiEYRCYeEIpIeHByZOnAg9PT189tlnFfeHeAc/9miMuUduYcPlxzDQ1sAvvZsCAH4+egsdalnBrVbBj8nDEtMRFJOEFZ+3Uma4JQr4biNaLhuDepP7ITctE9cnrwUANPP2QOTJm4g6eRPpL+Jw33svOh38CVBTQ6z/fTzbfla5gRcj7Q8fGEybBd3BX0OSkY60JQXX4dGf7IWcqxeRe1X+NXZUVdoKHxjMmAW9IV9DnJGONO+C+hhM9ULOlYvIufLx1Cdizh+wWzwN5uMHQ5yWgXDPpQAAmwWTkOp7Famnr0Hk3g96jWtDTU8HRt0KprOnHPVH3GrVXMr80vcb0X7paDSa1A+5qZm4MHVd4XNtF3vgxcmbCDt1E2kv4nDLZy96H/gJAjUBoi4+wOMd55QYuax7XuvRaMU41JzSH3mpGbg9ueACxc5LRiPmRABiTwRATUsDHa4uh5qWJjSN9NDx1ipE7rmAR7+pVvL+q88q+F24jPiXifCYMht6ujo4tmsj5i5Yho7tWqNj+9awt7XGBI+vMWzsDEgkErRs2hgD+nZXdujFWjXrD0z1mYZBEwcjIy0Dy6YvLXxu0u+TcNX3Kq6dugbn1s741KM/xPliqGuo4fbFO/h3hWodn2cz16L60kmwmTQQ+WkZeDql4EteJ+/xSDx5HUknr0OgpQGXS2sg0NaAuqEemtxYj/i9ZxG24B8lRy/rZ6+F+G3FXIya4o601HTMnvzzm+eWzMbZExfgd+ICuvXpiC++GYj8/Hyoq6vjxKHT2L9D+QlhUftm/4XB3mPRcXx/ZKdlYteMNxcrH7hwFB743kSQbwDajugJ+8Y1oKWnjQbdmwMA7hy5Cr9VB5QUOVUmAklpcxmpwqSlpWHcuHHYt28fDA0NMWPGDBw5cgQuLi5YtmwZcnNz8euvv2Lr1q2IiIiASCSCq6srfv75Zzg7O2Pz5s2YOnUqkpKSCvd54MABfPbZZwVTKWNiMHbsWFy9ehUJCQmwtrbGN998g59++glqamqYN28eDhw4gMDAQABAXFwcvvrqK1y+fBlpaWnw8/ODm5tbYayWlpZwd3fHqlWryl3XzE2qew2jd3F4tuw1gT5WnZqGl17oIyJRnct9VYjoYENlh1BhrmcbKzuECmWZV7kaW9f785UdQoUZ0HSyskOoUD/kaSk7hAo1Mr/ynEP76FZXdggV6vfQHcoOoVibbL9WdghyjYjYpuwQKhwTQpIrLCwMjo6OuH79Opo2bVru7ZkQqi4mhKqNCaHqYkKoupgQqjYmhKqLCWH5VcaEkFNGSUpubi6ioqLw/fffo3Xr1u+UDBIRERER0ceBCSFJuXjxIjp27IjatWtjz549yg6HiIiIiKqg8l4Ent4dE0KS4ubmVuolMoiIiIiIqHLghemJiIiIiIiqKI4QEhERERGRSqksF6b/GHCEkIiIiIiIqIpiQkhERERERFRFccooERERERGpFE4ZVRyOEBIREREREVVRTAiJiIiIiIiqKE4ZJSIiIiIilSLhhekVhiOEREREREREVRQTQiIiIiIioiqKU0aJiIiIiEilcJVRxeEIIRERERERURXFhJCIiIiIiKiK4pRRIiIiIiJSKZwyqjgcISQiIiIiIqqimBASERERERFVUZwySkREREREKkWi7ACqECaE9EEYjvlH2SFUqAc1nJUdQoVpfiVJ2SFUqIn6lefYAMA9tUxlh1BhBuXlKTuEChWiVblOmWuaTlZ2CBVm380Vyg6hQn3XfLayQ6hQu9XNlB1ChTmbo67sEIgqHKeMEhERERERVVGV6+tOIiIiIiL66IkFyo6g6uAIIRERERERURXFhJCIiIiIiKiK4pRRIiIiIiJSKbwwveJwhJCIiIiIiKiKYkJIRERERERURXHKKBERERERqRROGVUcjhASERERERFVUUwIiYiIiIiIqihOGSUiIiIiIpUiUXYAVQhHCImIiIiIiKooJoRERERERERVFKeMEhERERGRShELlB1B1cERQiIiIiIioiqKCSEREREREVEVxSmjRERERESkUnhhesXhCCEREREREVEVxYSQiIiIiIioiuKUUSIiIiIiUim8ML3icISQAABubm6YOnWqzOObN2+GsbGxwuMhIiIiIqIPjyOE9FGoWdMJmzYsg8jMFMlJyRjpMQ1BQcEy5b7++nNMnTK68L6drTUu+F/BoMGjFBlusTSr2cBqgSfUTYwgTklH9Gwf5IS8kCqj26oxzKaNgJq+LiCRIO30ZSQs36KkiEvnWN0B3qv+B1NTE6SkpMJz4o948uipTLlWbZtj079/4FnI88LHBvQYjuysbEWGWypjR0v0XDIGOqaGyE7JwPEZ6/AyOFKmnJGdGXr4jIFFg2pIDI3GP33mKiHaklk6WsPDZyIMTIyQkZKODZ5/IPJJuEy5Gk1rY/ivBe8bdQ0NBN8Iwj/zNiAvJ0/RIRdLz8kKjVeOh5apIXJT0nFn8hqkPY6QKqNrb45Gy8fByNkRGU+jcLH7HCVFWzZCR0t0XVrQ1nJSMnBq+jokymlrhnZm6LJkDMwbVENSaDR29Va9tmbtaINpS6bByNQI6SnpWDZjKcKCw2TK1WlaF+N/Gw8A0NBUx4PrD7Dup3Uq1dbmL12Ds/5XEBkdi/1/r0Gt6o5yy+09dAIbtu2CWCxG6+Yu+GHGRGhoqCs22DIyc7TClz7joW9iiMyUdOzwXIOYJxEy5Wq6NkCfmUOhra8DiViCe6du4Kj3TiVELJ+Wow1sFk9/dQ5NQ8R3S5HzRLqd6bk2gqWnO9T0dSGRiJHqexVxPluVFHHphI6W6Lz0zTnnTAn9QKclY2DWoBqSQ6OxRwX7Afp4cYSwinBzc8PEiRMxceJEGBsbQyQS4YcffoBE8nEMyK9Z9TvW//UP6jdoj8U+a7B+nY/cctu27UHzFt0Kb1HRMdi+Y7+Coy2e5bzJSN51DKE9PfByw25Y/jpNpow4JQ3RngvxvO8YvPh8EvRaNIJhbzfFB1tGvy35ETu27kWnVv2wbsUm/L58XrFlgx8/RW+3LwpvqpYMAkDXBSNxZ7sfNrl54fraw+i+SP6XCTmpmbjovRtHJq9WcIRl9838MTi7wxezOk3CsXUHMGLReLnlwh6E4pd+M/FTL0/82H0aDE2N4PZlNwVHW7KG3h548fdpnGszDU//OATnpWNkyuSlZuDxwp0IHLdSCRGWX8eFI3Fvux+2dfBCwJrD6Ly4mLaWlokri3fj5CTVbWsTFkzAie3HMdZtDPau3YvJi6bILRf64Bmm952GKT0nY2LXiTAyFaLnVz0VHG3JunVsh61rvGFjZVFsmfDIaPyxfiu2rvHGsV0bEZ+QiH2HTygwyvIZPN8Dl3ecxoJO0+C37hCGLJJ9/wBAZko6/p60Ar939cSSfrNRo1U9NO3XVsHRFs/614lI/Pc4QrqMRvyfe2GzQLadiZPTED51EUJ6jMOzT6dCv2VDGPXtoIRoy6bDwpG4v90P2zt44daaw+hYQj9wdfFu+KpwP1DRxJCo5K0yYkJYhWzZsgUaGhq4evUqVqxYgaVLl+Kvv/5SdlilMjcXoUmThvhn+14AwL59R+DoaI9q1exK3K5FcxdYWpjj0KGTigizVOqmQmjXr4mUQ6cBAGkn/aFpawkNG0upctlBIcgNjwYASHJykf0wBJr21gqPtyxEZqZo2KguDuw6AgA4dsgX9g62sLW3UXJk70ZXZASLho54sP8iACD46HUI7c1hZGcmUzYrOR0R1x8jN0P1kloAMBQZoVrD6ri8/xwA4MaxKzC3t4DIzlymbE5WDvLz8gEA6loa0NTRgkSsOic9LTMjCJ2dELnnAgAg+vBV6DlYQNdeui65SelIvPYI+Sp6TN72uq092lfQ1kKOXoeRvTkM5bS17KR0RKlwWxOKhKjRsAb89vsBAC4dvQhLe0tY2MkmVNlZ2YVtTUNLA9o6WhCrUFsDgOYuzrCykH2fvO2knz86d2gDM1MTCAQCDO7fC0d9zyomwHIyEBnBrqETAvYXvH9uH7sKU3sLmMjpCyLuhyIhLBYAkJedi4gHzyFyKD4xViR1kRA6DWog+cAZAEDq8YvQsreCpq10fFkPniI37M05NOvBU2g5WCk83rLQFRnBvKEjHr/qB56W0g9Eq3A/QB83JoRViL29PZYuXYo6dergq6++wqRJk7B06dLC51evXg0DAwOp29ixY0vdb3Z2NlJSUqRuFTnyaG9ng8ioGOTn5xc+FhYWAQd72xK3GzFiKP75Zy/y8lRjKpKGlTnyYhOA/DdX1smLioOmTfEfPNTNTGDQrR3Sz19XRIjlZm1riZjoOKljExkRDVs7+Sff6jUdcejMvzjg+w++HjlYUWGWmaGNKdJjkyB56xilRCbA0EakxKjejam1GRJjXkL8Vl0SIuIhKqa9iezM8fNRb6y8uQlZaZk4u+OUokItlY6NCFnRiVLHJTMiHjq2H99xec3AxhTpMdJtLS0yAYYfYZ3MbMzwMla6rcVFxsG8mLZmYWeB5cdW4J/A7chIy8CJ7ccVFWqFiY6JlRpBtLW2RHRMnBIjKp6xtQjJMYlSxycxIh4mpfRrhuZCNO7VCkF+tz50iGWiaW2OvJiXUufQ3MjYUs+hRj3bIe3sDUWEWG7y+oHUyAQYfIT9AH3cmBBWIa1bt4ZAICi87+rqiuDg4MIP81999RUCAwOlbr/88kup+12wYAGEQqHUTSJOrdDYZRLMt+ohj66uDgYP6ouNm3dUaBwVroR6qOnrwXb1z3i5YQ+yHzxRYFDlI5P7F1Ol+3eC0KZhN/TtNARjh0/DV+6D0PtT1ZqWCMi2NUEpbU2lybxtiq9LQngcfurliaktPKChpYlmPVp94ODKqawN7SNS3n5Npcm8b4ovGhseiyk9J2N4s2HQ1NKEa482Hzi4D+VNJVX+1xfl7Ne0DXTh8dd3OLP2IMLvh37AwMqnPO8ZNQNdOKyfi/g/9yDrfsgHjuw9VKZzTgUTq+itMuKiMlRIKBSiZs2aUo9ZWJQ+VWTWrFmYPn261GMmorrvFcvbi8Ps3HkAdrbWUFdXL0xe7e1s8CJM9gfxrw0c2AcPHz6Ru/CMsuRFx0HD0gxQVyv8hlPDygy5kbLfKgv0dGG7/lek+V1B0pZ9ig61RAO+6INvxw0DABzcdxxWNhZSx8bGxgoRr6a8vi0tNb3w/9GRsTi47xhauDbFkf+UO6W3/sB2aOZR8Bumhwcvw9DKFAJ1tcJvbA2tTZEamaDMEMuszYAO6O7RFwBw9aA/TKxMoaauVjgyYGojQoKc9va27IwsXD3kD9dPP8G1Qxc/eMxlkRWZAB0bkdRx0bUVISvi4zgur9Ud2A4uowra2uP/LsPAWrqtGVibIvUjqVPHgZ3Q36M/AOD8wXMQWZlJtTUza3PEldLWsjKycP7gBbh95oYLh85/4IgrlpWlBSKjYwrvR0bHwMqy5GmmitR8QHu4efQGANw8eAlCK5HU8TG2ESGxmH5NW18HY7bMwj3fAJzbcFRhMZcmNyoOmtbS51BNa3O551A1fV04bPofUk9fxcuNBxQcacnqDGyHxq/6geD/LkNfTj+Q9pH0A1R5MCGsQq5cuSJzv1atWlBXf79V0bS1taGtrS312Pt+w7Vt2x5s27an8H6P7h3x1ZcDsfXvXRgwoDeePw/H8+eyqyW+NuKbL7Bxk2qNDua/TEZ2UAiM+nZGyoFTMOjWDrmRMciLjJEqJ9DTgd36X5HuH4CXa7YrKdri7dt5GPt2Hi6879a5LfoP7o29Ow6iZ98uCA+LRESY7App5pZmiI9NgEQigb6BHjp1+wS7th1QYOTyPdjrjwd7/QvvO7k1Rv3P2uL+nguo1asFksPjkRIer8QIy+7SvnO4tO9c4X1ntyZw/awDLu7xQ/OerREfHoeEcNkPT+YOlngZGY/8vHyoa2qgWffWCHv4XKacsuTEpyDlbihsPm+PiJ3nYNWnFTLC4pAZpppT9IrzcK8/Hr7V1qp1bIw6A9ri4e4LqNGrBVLD45H6kbQ1v71n4Lf3TOH9Zm7N0fGzjji95zTa9GqLmPAYxIbHymxnVc0KcRFxyM/Lh4amBtr0dEVo0DNFhl4hurq1xfBxnhg74kuITIyx68BR9OysOguX3Nh3ATf2XSi8X8/NBc0+a4/re86hcc9WeBkeh0Q5fYGWnjbGbJmFR+dv49RK1foyMj8hGVn3QyDs3wnJe31h2KMtcsJjkBsh3c4Eejpw2PQL0s4HIP6Pf5UUbfEe7fXHo7f6AYeOjVF7QFs82n0B1Xu1QMpH1A9Q5SGQfCzLTNJ7cXNzQ0BAAEaNGoUxY8bg5s2bGDVqFHx8fDBmzBi4ubnBxcUFy5Ytk9pu8+bNmDp1KpKSksr1ehpaJf++r7xq166BjX8thamo4NIGI7+digcPHgMA1q1djEOHT+Lw4YLfPFWvXg0B10/CvlpTpKWll7TbMntQw7lC9qPpaAerBTOgbmwIcVoGomf5IOfJc1j+byrSzlxBut8VmI4ZAtGEr5H95M0H8rQTF/ByXcWc2LrHRlXIfl6rXrMaFv/xP5iYGCM1LQ2e439E8KOC6TkLl/0E3+Nn4Xv8HIZ7DMFXIwYjPy8P6hoaOPrfSSxftPa9X3+ifsUcm9dMqlujh89o6JgYICctE8enr0PCq8sbdPvdAyG+NxFy6ibUtTTw7YUlUNfSgLahHjISUvBgnz/8f9/1Xq9/Ty2zIqoBALCqboNvvSfCwNgQmWkZ+GvGH4h8dSmAEQvH4ZbvdQT63kD7wZ3R7ds+EIvFUFdXR9Clu9i5YCvysnPf6/UHZWpWRDUAAPo1rNFoxThomRgiLzUDtyevQdqjcDgvGY2YEwGIPREANS0NdLi6HGpamtA00kN2fDIi91zAo98q5r0TolWx36EaV7dGlyVv2prvtHV4+aqtdVrkgWenbuLZqZtQ09LAN/4FbU3LUA+ZCSl4uNcfl9+zrZ0UJFVALQrYVrfFVJ9pMDQxREZaBpZNX4oXjwsuqTPp90m46nsV105dQ9cvuuJTj/4Q54uhrqGG2xfvYNP8jch9z7a27+aKiqgGAOBXn1Xwu3AZ8S8TYSwUQk9XB8d2bcTcBcvQsV1rdGzfGgCw5+AxbNi2GxKJBC2bNsaPXhOhqVExbeS75rMrZD+vmVe3xpfe46BnbIjstAxsn7EG0cEFX6p+sXA07vkG4L5vALpM6I8eUz8vfA4AAo9cge+qA+/1+qPUK+ZnJFpOtrBZNK3gshNpGYj0WoLs4Bewnj8ZqaevIu30VZiN/wLmk79EdvCbSzqlHPNH/OqKuXzG2RyTCtnPa8bVrdHprX7g9LR1SHzVD7gt8kDoqZsIfdUPfF2kH3i81x9X3rMfGB+2rSKq8UH8Uu0rZYcg19zn/yg7hArHhLCKcHNzQ4MGDSAWi7F9+3aoq6tjzJgxmD9/PgQCgconhMpWUQmhKqjohFDZKjohVLaKTAiVrSITQlVQ0QmhslVkQqhsFZkQqoKKTgiVraISQlVQ0QmhsjEhLL/KmBBWrrMblUhTUxPLli3DmjVrZJ47e/as3G3c3d3h7u7+YQMjIiIiIiKlYEJIREREREQqpbKu6KmKeNkJIiIiIiKiKoojhFVEcVNCiYiIiIio6mJCSEREREREKkX8flcwo3LglFEiIiIiIqIqigkhERERERFRFcUpo0REREREpFLE4KXSFYUjhERERERERFUUE0IiIiIiIqIqilNGiYiIiIhIpXDCqOJwhJCIiIiIiKiKYkJIRERERERURXHKKBERERERqRSxsgOoQjhCSEREREREVEUxISQiIiIiIqqiOGWUiIiIiIhUCi9MrzgcISQiIiIiIqqimBASERERERFVUZwySkREREREKoUTRhWHI4RERERERERVFEcI6YO4Yd1M2SFUqIWZWsoOocJcqmeq7BAq1Jmnyo6gYrk3i1d2CBXG/7qtskOoUPa5leuqWD+oVZ5+7bvms5UdQoVadGO+skOoUOcbzFJ2CBWmh0WsskMgqnBMCImIiIiISKVUrq/gVBunjBIREREREVVRTAiJiIiIiIiqKE4ZJSIiIiIilcIL0ysORwiJiIiIiIiULDExEcOGDYNQKIRQKMSwYcOQlJRUbPnc3FzMnDkTzs7O0NfXh42NDYYPH47IyMhyvS4TQiIiIiIiIiX78ssvERgYiOPHj+P48eMIDAzEsGHDii2fkZGBmzdv4scff8TNmzexb98+PH78GP369SvX63LKKBERERERqZSqNmE0KCgIx48fx5UrV9CqVSsAwPr16+Hq6opHjx6hTp06MtsIhUKcOnVK6rGVK1eiZcuWePHiBRwcHMr02kwIiYiIiIiIyiA7OxvZ2dlSj2lra0NbW/u99nv58mUIhcLCZBAAWrduDaFQiEuXLslNCOVJTk6GQCCAsbFxmV+bU0aJiIiIiIjKYMGCBYW/8Xt9W7BgwXvvNzo6GhYWFjKPW1hYIDo6ukz7yMrKwvfff48vv/wSRkZGZX5tJoRERERERKRSxCp6mzVrFpKTk6Vus2bNKrYe8+bNg0AgKPF248YNAIBAIJDZXiKRyH28qNzcXAwZMgRisRirV68utfzbOGWUiIiIiIioDMo7PXTixIkYMmRIiWUcHR1x584dxMTEyDwXFxcHS0vLErfPzc3F4MGD8ezZM5w5c6Zco4MAE0IiIiIiIqIPwszMDGZmZqWWc3V1RXJyMq5du4aWLVsCAK5evYrk5GS0adOm2O1eJ4PBwcHw8/ODSCQqd4ycMkpERERERCpFoqL/PpR69eqhR48eGDVqFK5cuYIrV65g1KhR6NOnj9SCMnXr1sX+/fsBAHl5efj8889x48YN/PPPP8jPz0d0dDSio6ORk5NT5tdmQkhERERERKRk//zzD5ydndGtWzd069YNjRo1wt9//y1V5tGjR0hOTgYAhIeH4+DBgwgPD4eLiwusra0Lb5cuXSrz63LKKBERERERkZKZmppi27ZtJZaRSN6MUjo6Okrdf1dMCImIiIiISKWIlR1AFcIpo0RERERERFUUE0IiIiIiIqIqigmhgjg6OmLZsmVlLh8aGgqBQIDAwMAPFhMRERERkSoSQ6KSt8qICWEl5+bmhqlTpyo7DCIiIiIiUkFcVIZUnpajNaotmQp1EyPkp6TjhedyZAeHSZUxaNMI1t8Ng7qBLiRiCZJPXUX04pJXaVIWC0crePhMgoGJITJS0rHRcxUin4TLlKvRtDaG/ToKAKCuoYHgG0HYPm8j8nLyFB1ysdTtbGE853uoGQshTk1D8vzfkRf6XKqMZoP6EHpOK7ijoYGcO3eRsmwlkJurhIhLZuhkCdflY6BtYoiclAxcnroOKcGRMuVqDO2A+hP6QqAmQLT/fVyftRmSfNX6+buatS0Mps2GmpEQkvQ0pC1bgPyw5/ILa2pBuGw9kJ2F5OljFBtoGek7WaHpirHQMjVEbkoGbk1Zi9THEVJldO3N0HT5WAgbOiL9WTTOdf9BSdGW7nV9tF/V56ac+gBAtaFuqDWpHwRqAsRduI/b329Uubam7WSNGssnQeNVH/106kpkBkv3aVp25qixbBL0Gjoh61kU7vf8TknRlo2ZoxW+9BkPfRNDZKakY4fnGsQ8kT0+NV0boM/ModDW14FELMG9Uzdw1HunEiKWb/7SNTjrfwWR0bHY//ca1KruKLfc3kMnsGHbLojFYrRu7oIfZkyEhoa6YoMtA10nK9RfOb6wHwiavBrpRd43OvbmqL98HAydnZDxNArXu89WUrSl03CwgcVvXoXn0LgfvJH79IVUGe3G9WD2w2QAgEBDHVm37iN+wWqVPIfSx4sjhOWwZ88eODs7Q1dXFyKRCF26dEF6errcUbj+/fvD3d292H0JBAKsWbMGPXv2hK6uLpycnLB7926Zck+fPkXHjh2hp6eHxo0b4/Lly4XPJSQkYOjQobCzs4Oenh6cnZ2xY8eOwufd3d1x7tw5LF++HAKBAAKBAKGhoQCABw8eoFevXjAwMIClpSWGDRuG+Pj4UuuqDPYLJiBh+wk87DgOsev2wWHRJJky+clpeD7JGw+7TMTjvtNh0KohjD/9RAnRlu6b+WNwbscpzO40GcfW/Qf3RePklgt7EIr/9fse83p5YW736TA0FcLty24KjrZkQq/pyDh4GHFDhyN9+78Qfu8lUyb3SQjiPcYifsQoxA8fCXVjIfQ+7auEaEvX8veReLLND4fae+HB6sNo7TNKpoy+vTkaeX2OU/1/wcE2M6BrYYwaQ90UH2wpDCZ4IvvEISSN/RqZe3dAf/LMYsvqDfNA3sP7Coyu/Bov/hah287gdNsZCF51CC5LRsuUyUvNRNDC3QgY/4cSIiwfl8Xf4vm2M/B9VZ8mcuqj52COujMH4UK/eTjVehq0LYSo9qWb4oMthdPvYxG77RTutJ+IqNUH4OQzQaZMflomwhZtR8iEZYoP8B0Mnu+ByztOY0GnafBbdwhDFsn/oiQzJR1/T1qB37t6Ykm/2ajRqh6a9mur4GiL161jO2xd4w0bK4tiy4RHRuOP9VuxdY03ju3aiPiEROw7fEKBUZZdXe9RiPz7NC63mYYXfxxEvaVjZcrkpWYgZOFO3Bu3QgkRlo/53KlI2XMU4X1HInnTbpj/Ml2mTM6jp4gYOhERg8YhfMAYqJsIYTSotxKiVTyJit4qIyaEZRQVFYWhQ4di5MiRCAoKwtmzZzFgwID3uvbHjz/+iIEDB+L27dv4+uuvMXToUAQFBUmVmTNnDjw9PREYGIjatWtj6NChyMsrGCHKyspCs2bNcPjwYdy7dw+jR4/GsGHDcPXqVQDA8uXL4erqilGjRiEqKgpRUVGwt7dHVFQUOnToABcXF9y4cQPHjx9HTEwMBg8e/MHq+q40RELoNaiOl/vPAgCSj16Clp0ltOykT26Z958iJywGACDJzkXmg2fQdrBSdLilMhQZoVrD6ri8/zwAIODYFZjbW0BkZy5TNicrB/l5+QAAdS0NaOloQSxWnZEBNWNjaNaujcyTpwAAWWfPQ93aGupWltIFs7OB/IJ6QFMD0NYGVKger2mLjGDq7Ihney8CAMKOXIeBgzn07cykyjn0aYnw4zeQFZ8CAAjeehqO/V0VHm9JBEJjqNeohWy/gmOTc+kc1C2toGYh+57QqN8I6jZ2yPY7qegwy0zLzAjGzo4I3+MPAIg6fA16DubQtZc+NrlJ6Xh57RHyMrKVEWaZva5P2Kv6RL6qj16R+tj0aYWoY9eR/aqthW49DbvP2ig83pJoiITQd66O+L3nAAAvj1yGtoMFtIr0aflJaUi79hD5GVnKCLNcDERGsGvohID9FwAAt49dham9BUzk9NMR90OREBYLAMjLzkXEg+cQORSffClacxdnWFnIxv22k37+6NyhDcxMTSAQCDC4fy8c9T2rmADLQdPMCIbOTojeU3BcYg9fhY6DBXTspeuXl5SO5GuPkK/i/YCaqTG06tVE2uHTAID0UxegYWsFDRvpc6gkKxt49VlAoKkBgY42IFG9cyh93JgQllFUVBTy8vIwYMAAODo6wtnZGePHj4eBgcE773PQoEHw8PBA7dq18b///Q/NmzfHypUrpcp4enqid+/eqF27Nn7++Wc8f/4cT548AQDY2trC09MTLi4uqF69OiZNmoTu3bsXjjQKhUJoaWlBT08PVlZWsLKygrq6OtasWYOmTZti/vz5qFu3Lpo0aYKNGzfCz88Pjx8//iB1fVea1mbIjX0JvDVFKicyDpo2xZ/gNMyNYdyzDVL8bigixHIxtTZDUsxLiN+qT0JEPEQ2ZnLLi+zMMe/oYqy4uRGZaRk4t8NXUaGWSs3SAvnx8VLHJj8mBuqWljJl1a0sYbZpPSwP/wdJegYyDh5WZKhlom9jisyYJKnpeOkRCdC3FUmXsxUhPfzNaHpaWBz0ipRRNjUzC4hfJgDi/MLHxHGxUDMv8kFVWwf6oyYiffUSBUdYPro2ImTFJEodm8yIBOjZyn/fqDpdGxEy5dRHt0h99GxFyHirrWWExcmUUTYtGxFyY4r00RHx0LYtOQlRZcbWIiTHJEr104kR8TCxKfl9bmguRONerRDkd+tDh1ihomNipUYQba0tER0Tp8SI5NOxESEnWvp9kxURDx0Ve0+UlYaVOfLjEqTeO3lRsdCwlv1CQcPGEra716DahT0Qp6UjZfdRRYZKVQATwjJq3LgxOnfuDGdnZwwaNAjr169HYmLie+3T1dVV5n7REcJGjRoV/t/a2hoAEBtb8G1kfn4+fvvtNzRq1AgikQgGBgY4efIkXryQnn9eVEBAAPz8/GBgYFB4q1u3LgAgJCSk3HXNzs5GSkqK1C1Hkl9s+fIqOjApEAiKLatmoIvqG35E7Lp9yLz3tMJiqEjlqU9CeBzm9fLCtBajoKGliWY9Wn3g6N5TMXXJj45B/IhRiP10AASamtDp0F7BgZVN0VHw4o7N28VKOn7KJdPQZErojxyHrCMHIH4ZL/OcqpGZoKCqf/ayKmt9JGUoo2Qys0dU9j1RDmXsC17TNtCFx1/f4czagwi/H/oBA/tQ3tRPCZOBykymj1bVN0VZlfG9kxcZg4hB4/Dc7QsItLSg36WdAoJTPmWvJspVRkmGuro6Tp06hWPHjqF+/fpYuXIl6tSpg2fPnkFNTU2mk8p9xx/7Fj3paGpqyjz3etqgj48Pli5diu+++w5nzpxBYGAgunfvjpycnBJfQywWo2/fvggMDJS6BQcH45NPPimxrvIsWLAAQqFQ6rYx+ck71b+o3Kh4aFmJAPU3TVXT2gy5kbLfXqrp66LG1nlI9r2GuL/+q5DXrwhtBnTAvKOLMe/oYtRv1wgmVqZQe6s+pjYiJESW/IE8OyML1w5dROtPVSeREsfEQt3cTOrYqFtYID8mpthtJJlZyDx9BrrduigixHJJj3wJPWtTCN6qj56NKdIjEqTLRSTA4K2pffp2ZsgoUkbZxPGxUBOZA2pvFoVQMzOHOC5WqpxGPWfoDhkO47/+heF3c6HuWB3CVZsVHG3pMiMToFvk2OjaiJARofqJrDzF1SezSH0yIhKkppHq2ZnLlFG2nMgEaFlL99FaNiJkR6jeCFNJmg9oD8+jC+F5dCFqt3OG0Eok1U8b24iQGCn/fa6tr4MxW2bhnm8Azm34+EZurCwtEBn9pt+OjI6BlaXqjfBmRSZAx0Yk9b7RthUhS8XeE2WVFx0HDUtzqfeOhpU58qJii91GkpmFtGNnYdC7kyJCpCqECWE5CAQCtG3bFj///DNu3boFLS0t7N+/H+bm5oiKiiosl5+fj3v37pW6vytXrsjcfz1SVxYXLlzAp59+iq+//hqNGzdG9erVERwcLFVGS0sL+fnSo3VNmzbF/fv34ejoiJo1a0rd9PX1S6yrPLNmzUJycrLUbaSwZpnrUZK8hGRk3n8K08/cAADCXm2QEx6LnHDpDlNNTwfVt85DyrlbiFmhOiu8AcClfecwr5cX5vXywrG1B/DiQShcPytY8KZZz9aID49DQrjshydzB0uov1rlTV1TA826t0L4w2JWiVQCcVIScoOfQLdbVwCAjtsnyI+ORn60dEKobmMDqL9KTDQ0oNPhE+SFqN7obXZCChLvPYfTwIIFIex7t0B6WLzU9FAACDtyDXY9mkPHzAgAUGt4Zzz/77LM/pRJkpyE/KfB0O5YcGy02nRAfkw0xLHRUuWSJ49EkscQJHkMQeqiX5Af+hTJE9yVEHHJcuJTkHwvFHafF3wrbt2nJTLC4pAZ9nF+EHxdH/tX9bF5VZ+MIvWJPHwN1j1bQPtVW3Mc3hnhB1SrreUlJCPj3jOYDewAADDt7YqcsDjkyOnTVNmNfRfg3et7ePf6HmfWHkTEg1A0+6zgC7jGPVvhZXgcEuXUSUtPG2O2zMKj87dxauU+RYddIbq6tcXpc5cQ/zIREokEuw4cRc/OHZQdlozc+BSk3n0Gq88LjotFn1bICotDVtjH1dZeE79MQvbDJzDo0xkAoN+1PfIiYpAXKX0O1bCzBjTenEP1u7RFzmPVO4fSx42XnSijq1ev4vTp0+jWrRssLCxw9epVxMXFoV69etDX18f06dNx5MgR1KhRA0uXLkVSUlKp+9y9ezeaN2+Odu3a4Z9//sG1a9ewYcOGMsdUs2ZN7N27F5cuXYKJiQmWLFmC6Oho1KtXr7CMo6Mjrl69itDQUBgYGMDU1BQTJkzA+vXrMXToUHh5ecHMzAxPnjzBv//+i/Xr1+PGjRvF1lUebW1taGtrSz2mJai45arDZq+Gg88UWEwYBHFaBl5MXwYAsP99IpJPXUOK7zWYj+wL/ca1oK6rDePurQEASUcvIuYP2ZVblW3r7HUY6T0BvccPQFZaJv6a8eZ3o+4LxyLQ9wYCfW+gbusG6PptH0jEYqipqyPo0l0cXLlHiZHLSl60BMZzZsJg+FcQp2cg+beFAADhTE9k+V9C9sVL0GriAv0vPi9YSEZdHTkBN5G6eauSI5fv6syNcF06Gg0m9UNuWiYuT1kHAGjl7YHwkzcRcfIm0l7E4Y73XnT77ydATYCYiw/wZMc5JUcuK22VDwymzoLuoK8hyUhH2rIFAAD9SV7IuXoRudcuKTnC8rnttQFNlo9F7cmfIi8tEzcnrwEAuPiMQvSJAESfvAk1LQ10ubIUatqa0DTUQ7ebKxG2xx9B81XrSyIACPTagKav6pNbTH0yXsTi4eI9aH9oHgRqaojzv4/n288qN3A5ns1ci+pLJ8Fm0kDkp2Xg6ZSCPs3JezwST15H0snrEGhpwOXSGgi0NaBuqIcmN9Yjfu9ZhC34R8nRy7dr9np86T0OXcb3R3ZaBrbPWFP43BcLR+OebwDu+wbgkxE94dC4BrT0tOHcvQUAIPDIFfiuOqCkyKX96rMKfhcuI/5lIjymzIaerg6O7dqIuQuWoWO71ujYvjXsba0xweNrDBs7AxKJBC2bNsaAvt2VHbpcD73Wo/6K8XCc0h95qZl4MHk1AKDukjGIP3ED8ScCINDSQJurK6CmpQkNIz20vbUa0XsuIOS3HaXsXfHif1kO8189YewxFJL0DMTOWQwAMJs3DRlnLyPj7BXotmgM4bABkIjzIVBXR+a1QCStU833TUXj0jmKI5AoY+nIj1BQUBCmTZuGmzdvIiUlBdWqVcOkSZMwceJE5ObmYsqUKdi5cyc0NDQwbdo0XLlyBcbGxti8eTOAgsRs6tSphZenEAgEWLVqFQ4cOIDz58/DysoKCxcuxJAhQwAAoaGhcHJywq1bt+Di4gIASEpKgomJCfz8/ODm5oaXL19i5MiROH36NPT09DB69Gi8ePECycnJOHDgAADg8ePH+Oabb3D79m1kZmbi2bNncHR0RHBwMGbOnAk/Pz9kZ2ejWrVq6NGjB5YsWYKHDx8WW9eyCqzWr6L+9CphhUBL2SFUmN/sVGt64/s689RW2SFUqB7Nwkov9JHwv165jk1lO1laq6n+ip9l9a9W5emjAWDRjfnKDqFCnW8wS9khVBgni/dbP0LVVL+ruitMj3IcpOwQ5FofqnqDDe+LCaGSCAQC7N+/H/3791d2KB8EE0LVxYRQtTEhVF2V7WTJhFB1MSFUXUwIFYcJoeJwyigREREREakUSaX7Gk51cVEZIiIiIiKiKoojhErCmbpERERERKRsTAiJiIiIiEilcJVRxeGUUSIiIiIioiqKCSEREREREVEVxSmjRERERESkUrjKqOJwhJCIiIiIiKiKYkJIRERERERURXHKKBERERERqRSuMqo4HCEkIiIiIiKqopgQEhERERERVVGcMkpERERERCpFLOEqo4rCEUIiIiIiIqIqigkhERERERFRFcUpo0REREREpFI4YVRxOEJIRERERERURTEhJCIiIiIiqqI4ZZSIiIiIiFSKmJNGFYYjhERERERERFUUE0IiIiIiIqIqilNG6YPwFxspO4QKJVTPU3YIFeZRsJmyQ6hQ+gKxskOoUD/dtVJ2CBWmjmbl+s7xSSXqBwDANzNC2SFUmN3qlatfO99glrJDqFCf3F+g7BAqTNa8icoOocqQcMqowlSuszURERERERGVGRNCIiIiIiKiKopTRomIiIiISKVUrh+EqDaOEBIREREREVVRTAiJiIiIiIiqKE4ZJSIiIiIilcIL0ysORwiJiIiIiIiqKCaEREREREREVRSnjBIRERERkUrhhekVhyOEREREREREVRQTQiIiIiIioiqKU0aJiIiIiEil8ML0isMRQiIiIiIioiqKCSEREREREVEVxSmjRERERESkUiQSrjKqKBwhJCIiIiIiqqKYEBIREREREVVRnDJKREREREQqRcwL0ysMRwhVTGhoKAQCAQIDA1Vqf46Ojli2bFmFxERERERERKqBCSEREREREVEVxSmj9FEQOlqiy9Ix0DU1RHZKBnynr0NicKRMOUM7M3RZMgZmDaohOTQau3rPVUK0JTNztMKXPuOhb2KIzJR07PBcg5gnETLlaro2QJ+ZQ6GtrwOJWIJ7p27gqPdOJURcPF0nK9RdORGapobIS8nAw8mrkPE4XKqMjr056i6fAANnJ2Q+jUJA9++VFG3p9J2s0HTFWGibGiI3JQM3p6xF6mPZY1NtqBtqTeoHgZoAcRfu4/b3GyHJV71L6Jo7WmGYz3gYmBgiIyUD2zxXI1pOW6vt2gD9Zg6Ftr4uJGIJ7p66jkMq1taAgn6g69Ix0DE1RE5KBk6V0g+YN6iGJBXtB4CC4/PVW33BP8X0BbVe9QU6r/qCu6du4IiKHR8HJ3vMXzkXJqbGSE1JxZzJ/0PI42cy5Vq0aYo1/yzF86cvCh/7srcHsrOyFRluibQcbWCzeDrUTYwgTklDxHdLkfMkTKqMnmsjWHq6Q01fFxKJGKm+VxHns1VJEZdM18kK9VeOh9arfi1o8mqkF+nXdOzNUX/5OBg6OyHjaRSud5+tpGhLNn/pGpz1v4LI6Fjs/3sNalV3lFtu76ET2LBtF8RiMVo3d8EPMyZCQ0NdscGWgcDcBrrDpkNgIIQkIw1Z25ZAHC3d1tRrOUN33M8Qx745Zhk+M4DcHEWHq3Cqd1atvDhCqATHjx9Hu3btYGxsDJFIhD59+iAkJKTY8vfv30fv3r1hZGQEQ0NDtG/fvrC8WCzGL7/8Ajs7O2hra8PFxQXHjx+X2cfTp0/RsWNH6OnpoXHjxrh8+bLU83v37kWDBg2gra0NR0dH+Pj4VGyl31PHhSNxf7sftnXwws01h9F58Si55XLSMnFl8W6cnLRawRGW3eD5Hri84zQWdJoGv3WHMGTRGLnlMlPS8fekFfi9qyeW9JuNGq3qoWm/tgqOtmS1vccg6m9fXGszBWF//Ic6S8fJlMlLzcSzhf/iwbjlSoiwfFwWf4vn287At+0MBK86hCZLRsuU0XMwR92Zg3Ch3zycaj0N2hZCVPvSTfHBlsGQ+aNwccdp/NJpGnzXHcRXi8bKLZeRko5Nk1bgt64zsKjfLNRsVR/NVaytAQX9wL1X/UDAR94PAAV9waUdp/Fbp2k4ve4QhhbTF2SkpGPrpBVY0NUT3v1mo2aremimYsdnnvf32PP3AfRuMwgb/9iGX5bOKbZsyONnGNh5WOFNlZJBALD+dSIS/z2OkC6jEf/nXtgsmCJTRpychvCpixDSYxyefToV+i0bwqhvByVEW7q63qMQ+fdpXG4zDS/+OIh6S2X7gbzUDIQs3Il741YoIcKy69axHbau8YaNlUWxZcIjo/HH+q3YusYbx3ZtRHxCIvYdPqHAKMtOZ8gk5Fw8jvRfRiHHdw90vpoqt5w4OgwZCycV3qpCMkiKxYRQCdLT0zF9+nRcv34dp0+fhpqaGj777DOIxbLfhUREROCTTz6Bjo4Ozpw5g4CAAIwcORJ5eXkAgOXLl8PHxwfe3t64c+cOunfvjn79+iE4OFhqP3PmzIGnpycCAwNRu3ZtDB06tHAfAQEBGDx4MIYMGYK7d+9i3rx5+PHHH7F58+YP/rcoC12REcwbOuLRvosAgJCj12Fobw5DOzOZstlJ6Yi6/hh5Gar1AeM1A5ER7Bo6IWD/BQDA7WNXYWpvARM7c5myEfdDkRAWCwDIy85FxIPnEDkUfxJUNE0zIxg6OyFmz3kAQNzhK9B1sICOvXRd8pLSkHztIcQZWcoIs8y0zIxg7OyIsD3+AIDIw9eg52AOPXvpdmbTpxWijl1HdnwKACB062nYfdZG4fGWxkBkBPuGTrj+qq0FHrsKkb0FTOW0tfAibS38QShEDpYKjbc0uiIjWBTpB4xK6QdyVbQfAN70BTfe6guKOz5F+4JwFesLTM1MUM+5Dg7tKfgy8uThM7BzsIGNvbWSIys/dZEQOg1qIPnAGQBA6vGL0LK3gqat9N8768FT5IZFAwAkObnIevAUWg5WCo+3NK/76eg9Be0s9vBV6Mjtp9ORfO0R8lX4PQMAzV2cYWUh+x5520k/f3Tu0AZmpiYQCAQY3L8XjvqeVUyA5SAwEELdvgbyrhe0tbzAixCILCEwVZ33NlUdTAiVYODAgRgwYABq1aoFFxcXbNiwAXfv3sWDBw9kyq5atQpCoRD//vsvmjdvjtq1a2PEiBGoU6cOAMDb2xszZ87EkCFDUKdOHfz+++9wcXGRWQDG09MTvXv3Ru3atfHzzz/j+fPnePLkCQBgyZIl6Ny5M3788UfUrl0b7u7umDhxIhYvXlym+mRnZyMlJUXqlivJf78/0lsMbEyRHpMkNSUvLTIBhraiCnsNRTG2FiE5JhHit+qSGBEPE5uS62JoLkTjXq0Q5HfrQ4dYZto2ZsiOTpQ6LlkR8dC2lf2A/jHQtREhM0a6PpkRCdAtUh89WxEywuML72eExcmUUQUmctray4h4mNiUHKuhuRBNerXGfRVqa0Dl6geAgr4g5R37ApderfBAhY6PlY0l4qLjkZ//pt+PioiGta38LxWcajpg96kt2Hl8E4a4D1RUmGWiaW2OvJiXwFvHJTcyFpo2xSch6mYmMOrZDmlnbygixHLRsREhR04/raOCfVZFiY6JlRpBtLW2RHRMnBIjkk9gYg5x8kvgrcEAycs4qJnIJoRqFrbQm7kCel7LoNm+tyLDpCqCCaEShISE4Msvv0T16tVhZGQEJycnAMCLFy9kygYGBqJ9+/bQ1NSUeS4lJQWRkZFo21Z66lDbtm0RFBQk9VijRo0K/29tXfCtbWxswTfOQUFBcvcRHBwsdYIvzoIFCyAUCqVup1Lul7pdeUgkRZYeFggqdP8KVaQuglLqom2gC4+/vsOZtQcRfj/0Awb2Dooel49d0eoUd2gkZSijCsrZ1nQMdDH2r5nwXXsQ4fdlf/+lbJWqH4BsfcrSF4z+6zucVsG+QKYuxbwxHtx5hE4ufTGo6zeYPGImBn/zGbr366yIEMusPO1MzUAXDuvnIv7PPci6X/xPP5SprMemcnlTR9U+TZXe1vLDniDth+HI+H0yMtf/D5rtekGjSXsFxadcEhX9VxlxURkl6Nu3L+zt7bF+/XrY2NhALBajYcOGyMmRnROuq6tb6v6KfoiQSCQyj72dUL5+7vUUVXnlZU6IJZg1axamT58u9diG+vJ/C1NWdQa2g8uongCA4P8uw8DaFAJ1tcJvOQ2sTZEakfBer6EozQe0h5tHwTd6Nw9egtBKBDV1tcKRAWMbERIj5ddFW18HY7bMwj3fAJzbcFRhMZdFdmQ8tG1EUsdFx9YM2RHxpWypmjIjE6BbpJ3p2oiQWaQ+GREJUtNI9ezMZcooS8sBn6DTq7Z24+BFGBdpayY2IiRGyo9VW18H47fMwl3fGziz4YjCYi5J3bf6gccfeT8AAC2K9AVFj09pfcG4LbNw1zcAZ1WgL+g3qCe+GfslAODo/pOwtLGAurp64ZeIVraWiIqIkdkuPS298P8xUbE4uv8UmrV2wYmDpxUTeClyo+KgaW0GqKsVjhJqWpsjN1J2hElNXxcOm/6H1NNX8XLjAQVHWjZZkQnQKdJPa9uKkKUifdaHYGVpgcjoN20vMjoGVpYlTzNVBkliHNSMzQA1tcJRQoGJGcSJsdIFszLfbJOUgLyAc1Cv2QB5ty4oMlyq5JgQKlhCQgKCgoKwbt06tG9f8A2Pv79/seUbNWqELVu2IDc3V2aU0MjICDY2NvD398cnn3xS+PilS5fQsmXLMsdUv359mRguXbqE2rVrQ1299FW5tLW1oa2tLfWYpuD9VvN6tNcfj/a+ialax8aoM6AtHu6+gBq9WiA1PB6p4R/HCe3Gvgu4se9Nx13PzQXNPmuP63vOoXHPVngZHofEcNkPG1p62hizZRYenb+NUyv3KTLkMsmNT0Ha3Wew/PwTRO88C/M+rZEVFousMNWbmlMWOfEpSL4XCvvP2+HFzvOw6dMSGWFxyAiTbmeRh6+h/cGf8MhnH7LjU+A4vDPCD1wuZq+KdW3feVzbd77wfgM3F7T4rD2u7jkHl56tkBAeh5fFtLXxW2Yj6PwdHFehtvZwrz8eVpJ+AACu77uA60X6guaftce1t/qC4o7P2C2z8PD8bZxUkeNzcPcxHNx9rPB+u06u6Pt5DxzYeQTd+nRCRFgUIsOiZLYzsxAhIe4lJBIJ9PT14Na1LfZuP6TI0EuUn5CMrPshEPbvhOS9vjDs0RY54THIjZD+kC7Q04HDpl+Qdj4A8X/8q6RoS5cbn4LUu89g9Xl7RO08B4s+rZAVFvfR9tNl0dWtLYaP88TYEV9CZGKMXQeOomdn1VvwR5KWjPzwEGi06IS8q77QcGkLSUIsJC+LtDUjE0hSkwqGOrV1odGgJXIuq+YiOfTxYkKoYCYmJhCJRPjzzz9hbW2NFy9e4Pvvi1+Gf+LEiVi5ciWGDBmCWbNmQSgU4sqVK2jZsiXq1KkDLy8v/PTTT6hRowZcXFywadMmBAYG4p9//ilzTDNmzECLFi3wv//9D1988QUuX76MP/74A6tXq84KfX7fb0SXJaPRfGI/5KRlwnfausLnOi7ywLNTNxF66ibUtDQw3H8J1LU0oGWoB/drK/Borz8u/75LidFL2zV7Pb70Hocu4/sjOy0D22esKXzui4Wjcc83APd9A/DJiJ5waFwDWnracO7eAgAQeOQKfFcdUFLksh55/Ym6KybAYcpnyE/NRNDkPwAAdZaMRfyJG0g4cQMCLQ20vvoHBFqa0DDSg+uttYjecx7Pftuu5OhlBXptQNPlY1F78qfITcvEzckFx8bFZxSiTwQg+uRNZLyIxcPFe9D+0DwI1NQQ538fz7efVW7gxdgxez2GeY9H9/H9kZWWia0z3rynv1w4Bnd9b+CubwA6jugFx8Y1oK2njcav2tqtI1dwYtV+ZYUuV0n9QKdX/cCzV/3AN2/1AyOurcBDFesHgDd9Qdfx/ZGVloF/3uoLhrzqC+75BqDDiJ6oJqcvOKVCfcHPXgvx24q5GDXFHWmp6Zg9+ec3zy2ZjbMnLsDvxAV069MRX3wzEPn5+VBXV8eJQ6exf4fqJIQAEPXDH7BZNA1m4wZDnJaBSK8lAADr+ZORevoq0k5fhcj9U+g2qg01XR0YdStYVCrlmD/iV6vW5UAA4KHXetRfMR6OU/ojLzUTDyYX9AN1l4xB/IkbiD8RAIGWBtpcXQG1V/1021urEb3nAkJ+26Hk6KX96rMKfhcuI/5lIjymzIaerg6O7dqIuQuWoWO71ujYvjXsba0xweNrDBs7AxKJBC2bNsaAvt2VHbpcWTtWQnfYdGh3/wKSrAxkbS1Y4V37yynIu3sF+XevQsOlbcHvBvPzAXV15N3yR96VU0qOXDHElXR6pioSSMozN5AqhK+vLyZPnoynT5+iTp06WLFiBdzc3LB//364uLjAyckJt27dgouLCwDgzp078PLygr+/P9TV1eHi4oLNmzejevXqEIvF+PXXX/Hnn38iNjYW9evXx8KFC9GjRw8AQGhoqMz+kpKSYGJiAj8/P7i5uQEouOzE3LlzERwcDGtra0yaNAmenp6FMTs6OmLq1KmYOnVqmer4h/3XFfXnUgkh6nnKDqHCfJpZcQv+qIIkgezvaz9mvjqV5/jUya9cx+ZJJeoHAMA3U/V+J/qudhtUrkVSotIMlB1Chfrk/gJlh1BhsuZNVHYIFcrwD+VPQS9OL4deyg5BrqMvVPdv9q6YENIHwYRQdTEhVG1MCFUXE0LVxYRQtTEhVF1MCMuvMiaEnDJKREREREQqhWNWisPLThAREREREVVRTAiJiIiIiIiqKE4ZJSIiIiIilSJWdgBVCEcIiYiIiIiIqigmhERERERERFUUp4wSEREREZFKkfDC9ArDEUIiIiIiIqIqigkhERERERFRFcUpo0REREREpFLEnDKqMBwhJCIiIiIiqqKYEBIREREREVVRnDJKREREREQqRSLhlFFF4QghERERERFRFcWEkIiIiIiISMkSExMxbNgwCIVCCIVCDBs2DElJSWXefsyYMRAIBFi2bFm5XpcJIRERERERqRQxJCp5+5C+/PJLBAYG4vjx4zh+/DgCAwMxbNiwMm174MABXL16FTY2NuV+Xf6GkIiIiIiIqAyys7ORnZ0t9Zi2tja0tbXfa79BQUE4fvw4rly5glatWgEA1q9fD1dXVzx69Ah16tQpdtuIiAhMnDgRJ06cQO/evcv92hwhJCIiIiIiKoMFCxYUTul8fVuwYMF77/fy5csQCoWFySAAtG7dGkKhEJcuXSp2O7FYjGHDhsHLywsNGjR4p9fmCCEREREREakUiYpemH7WrFmYPn261GPvOzoIANHR0bCwsJB53MLCAtHR0cVu9/vvv0NDQwOTJ09+59fmCCEREREREVEZaGtrw8jISOpWUkI4b948CASCEm83btwAAAgEApntJRKJ3McBICAgAMuXL8fmzZuLLVMWHCGkD6JxbpayQ6hQ+yUpyg6hwow1UVd2CBUqKsVU2SFUKEtJ5emWNZUdQAXTxLufbFVRH93qyg6hwpzNEBV4FQAAXy5JREFUqVz9Wg+LWGWHUKGy5k1UdggVRmfeH8oOgT4yEydOxJAhQ0os4+joiDt37iAmJkbmubi4OFhaWsrd7sKFC4iNjYWDg0PhY/n5+ZgxYwaWLVuG0NDQMsVYeT55EBERERFRpSCuJBemNzMzg5mZWanlXF1dkZycjGvXrqFly5YAgKtXryI5ORlt2rSRu82wYcPQpUsXqce6d++OYcOGYcSIEWWOkQkhERERERGREtWrVw89evTAqFGjsG7dOgDA6NGj0adPH6kVRuvWrYsFCxbgs88+g0gkgkgkktqPpqYmrKysSlyVtCj+hpCIiIiIiEjJ/vnnHzg7O6Nbt27o1q0bGjVqhL///luqzKNHj5CcnFyhr8sRQiIiIiIiUimVY8Jo+ZiammLbtm0llpGUMpW2rL8bfBtHCImIiIiIiKooJoRERERERERVFKeMEhERERGRShFXyUmjysERQiIiIiIioiqKCSEREREREVEVxSmjRERERESkUjhlVHE4QkhERERERFRFMSEkIiIiIiKqojhllIiIiIiIVEppF2CnisMRQiIiIiIioiqKCSEREREREVEVxSmjRERERESkUrjKqOJwhJCIiIiIiKiKYkJIRERERERURXHKKBERERERqRQJp4wqDEcIK7F58+bBxcWl8L67uzv69++vtHiIiIiIiEi1cISwClm+fLnUNV3c3Nzg4uKCZcuWKS8oIiIiIiJSGiaEVYhQKFR2CO9Ex8kKdVZMgoapIfJT0vF4yipkPA6XKqNtb47ayyfCoKEjMp9FI7D7TCVFWzpbJxvMXOoFoakQaSlpWDTNG8+DXxRbXlNbE+uOrUZWZjbG956owEhLp1nNBtYLZ0DdxAj5KemInrUEOSHSddFr1Rhm092hpq8HiMVIO3MF8cu2KCnikhk4WaL18rHQNjFEbkoGrkxdh5TgCJly1Yd2QP0J/QA1AWL87+PGrE2Q5IuVEHHJTB0t0W/JWOiZGCIrJQMHPdchXk59hHZm6Oc9BlYNHPEyNBob+v6ohGhLJ3S0RMelY6BjaoiclAz4TV+HxOBImXKGdmbouGQMRA2qITk0Gvt6z1VCtKUzc7TCEJ9x0DcxRGZKOnZ6rkXME9njU9O1AXrNHAJtfR1IxBLcO3UDx713KSHi4okcrfCFz7hXbS0duzzXIlZOXWq4NkCPt+ry4NQNnFCxugAFba3zq7aWnZKBMyW0tU5LxsDsVVvbo4JtTcPBBha/eUHNWAhxahrifvBG7lPpflq7cT2Y/TAZACDQUEfWrfuIX7AayM1VRsjFEpjbQHfYdAgMhJBkpCFr2xKIo8OkyqjXcobuuJ8hjn3T/jJ8ZgC5OYoOt1Tzl67BWf8riIyOxf6/16BWdUe55fYeOoEN23ZBLBajdXMX/DBjIjQ01BUbrBLwwvSKwymjSpKeno7hw4fDwMAA1tbW8PHxgZubG6ZOnQoAEAgEOHDggNQ2xsbG2Lx5c+H9mTNnonbt2tDT00P16tXx448/IreEzvvtKaPu7u44d+4cli9fDoFAAIFAgGfPnqFmzZrw9vaW2u7evXtQU1NDSEhIRVS93GotHoOobacQ0HYywlf9h1pLxsmUyU/NxPOFO/Bw/HIlRFg+0xZOxZF/juKbT0Zi55rd8PSeXmL5b78bgQc3gxQUXflY/jwJSbuO4VmPUXi5YQ+sfpsqUyY/JQ1RM35HaJ8xeP75ZOi2cIZhHzeFx1oWLX//FiHb/HCkvSeCVh9GK59RMmX07c3h7DUIvv1/xuE206FjIUT1oW6KD7YMei34Fje3+2F1R09cXncYfRfJ1gcAslMzcdZ7N/ZPWaXgCMvnk4UjEbTdD/928ELgmsPosFh+fXLSMnFt8W6cnrRawRGWz+fzPXBlx2n83mk6zq47hEGLRsstl5GSjm2TVmJxVy8s6zcHNVrVQ5N+bRQcbckGzPfA1R2n4d1pOs6tO4TPi6lLZko6dkxaiSVdvbCy3xw4taoHFxWrCwB0WDgS97f7YXsHL9xacxgdS2hrVxfvhq8KtzXzuVORsucowvuORPKm3TD/Rfack/PoKSKGTkTEoHEIHzAG6iZCGA3qrYRoS6YzZBJyLh5H+i+jkOO7BzpfTZVbThwdhoyFkwpvqpgMAkC3ju2wdY03bKwsii0THhmNP9ZvxdY13ji2ayPiExKx7/AJBUZJVQETQiXx8vKCn58f9u/fj5MnT+Ls2bMICAgo1z4MDQ2xefNmPHjwAMuXL8f69euxdOnSMm27fPlyuLq6YtSoUYiKikJUVBQcHBwwcuRIbNq0Sarsxo0b0b59e9SoUaNc8VUETTMjGDhXR+ye8wCA+MNXoONgAW17c6lyeUlpSLn2EOKMbIXHWB7GImPUalgTp/adBgCcP3IBVvZWsLSzlFveuWVD2DrZ4tReX0WGWSbqpkLo1K+JlINnAABpJ/yhaWsJDVvpE1t2UAhyw6MBAJKcXGQ/DIGmnZXC4y2NtsgIJs6OCN3rDwAIO3IN+g7m0Lczkypn36clwo/fQFZ8CgDgydbTqNbfVeHxlkZPZATrBo64u7+gPkFHr8HYzhzCIvUBgKzkdITdeIxcFX7/6IiMYNbQEY/3XQQAPD16HUb25jCUU5/spHREX3+MPBWuj4HICLYNHXHz1fG5c+waTO0tYCKnPpH3Q/EyLBYAkJedi8gHz2HqIL/PUAb9V3W59aoud8tRlygVqwsA6IqMYF7Otqaq7x01U2No1auJtMMF55z0UxegYWsFDRvpv7kkKxvIywcACDQ1INDRBiSqNetBYCCEun0N5F0vOOfkBV6EQGQJgWnxyZSqa+7iDCsL8xLLnPTzR+cObWBmagKBQIDB/XvhqO9ZxQRIVQYTQiVIS0vDhg0b4O3tja5du8LZ2RlbtmxBfn5+ufbzww8/oE2bNnB0dETfvn0xY8YM7NpVtqk3QqEQWlpa0NPTg5WVFays/t/enYfVnL5/AH+fNq1aKIqoZItWW7aG7MygzFiypixZosU2ppJ9pNAwGEQYkixjhkn2JZO1ZAmtChVKUqHlnN8fvp1fxwmdpOd8Tvfruua69DmH7/v5djqd+/M8z/00hry8PJydnfHw4UNcu3YNAFBSUoK9e/di8uTJn/y33r9/j/z8fJH/igWSjeVT6hk0RHF2LlBhOd77py9Rr4n4L2Yu0DXQRU52DvgVxvP82XPoNRH/haasoowZS9yw4efg2oxYZQr6uih9Lvq9Kcl8AUX9T/9ylm+oDY3+PVB48XptRJSIqoEO3mbniSz9LHqaA9WPXmtqTRqg6MlL4deFGS+hJoWvx/r6OnjzXHQ8r5/lQNNA+rJWhbqBDgo/+v4UPMuBepMGDFNVn6Z+A+RnvxJ5L8h7+hLaX/j+aOhqwnxwFzw4F/utI1aZ1ifGovWFsahL4ViAyl9rbzj6WlNorIuyFzki79Olmc+hUMn7tIJBIzQ5uBnNL0WAX1CI/IMnajPqF/G0dcF/nQvw/38sgtwXkNMWH4ucXhOoLgiG6rz1UOwpfTOdksjKfi4yg9hEvxGysl8wTFR7+BBI5X+yiApCBpKTk1FcXIyuXf9/VkFHRwetW7eW6N+JiIhAjx490LhxY6irq8PHxwfp6Z/ei1YV+vr6GDJkCEJCQgAA//zzD969e4effvrpk39n1apV0NTUFPlvb+HDr8pRkdgSch6vxv5tFsTXxFc+nqm/TMFfocfwMivn24eqro/H8pnvjZyaKppsXoLcHRF4f5/N8uMvEhvPp54m+OJzpMJH4+H4j45ErzcuEB/O58dTT10Fk7fPw/ktf+PpvbRvF6w6JHyfrqeugknb5+HClr/xTNrGAlTys8Ph11oVf25Kn2Xj6U9ueNxrFHhKSlDr26MWwknqy2Mpy0hCwS8TUPSrO95uWwbFHoOhYN2zlvJ9K/8/TtpWR74FairDQFU2yfJ4PLHnVdwfGBMTg9GjR8Pf3x8DBgyApqYmwsLCEBgY+NX5XF1dMX78eKxbtw47d+7EqFGjoKqq+snnL1q0CJ6eonsSrrec+NU5AOD9s5eop68DyMsJ73DWM2iA909ffuFvSo9+I/rix6kjAADn/jqHhvq6kJOXE95N1zPQxfOnz8X+nnnnduhi3wnj546FUj0lqGuqY8eZP+DSp/K9ObWtNPMFFBo3FPneKDZuiJJM8bHw1FTQdPsyFJ6NwatdR2o7apUUPcuFir4OePJywpkBVYMGKProtVb4NAdqFZYsqzVtiEIpeT1aOPZAF9fBAIB7x65Ao7HoeOrrN8DrZ9KRtSpajegBiymDAABJf/0HtY++P+r6Oih4KsU3TD7SwbEn7P73/Yk7dgWajXVE3gs0DRrg1Se+P/XUlDEldCHunb6JizvYz9zYOPZEz8+MRcugAfI+MRYlNWW4hC5EwumbuCQFYwGA1iN6wPJ/r7VEGXitlSvNegGFRroi79MKjXVRWsn7dDnB23co+Pc81IfYozDyfC0l/TLBqxeQ02oIyMkJZwl52g3Bf/XRWN69/f+/k5eD0psXIG/aDqWxl2ozbo1p3EgPz7KyhV8/y8pG40afX2ZKiKSoIGTA1NQUioqKiImJQbNmzQAAr169wqNHj/Ddd98BAHR1dZGZmSn8O4mJiSgqKhJ+HR0djebNm2Px4sXCa48fP5Yoh5KSUqXLVAcPHgw1NTVs3rwZ//77Ly5evPjZf6devXqoV6+e6L/Nq5nuVyUv81FwNw16P9rh+YHzaPi9Ld5lvMD7DO4slzh16LTIHsBOvTuhn2MfnDx4CnZDeiLrSTayn2SL/b0p/aYL/2zZ1QLTfpkqVV1Gy3Jf431CMuoPtUf+kdNQH9ADJU+fo/Sj4panqgzDbctQePkmcjbvZ5T2y97n5OPV3TQYjeiB1PCLMBzSGYUZL1H4RPRDbcbxa+h71A/3gg7j3ct8mE7og/S/YhilFhV/+DLiD18Wft2ilyXMHXogPuIi2g7ujLwnL/H6CXcKwkeHLuPRof8fT7Pelmjl2B0PD16CyeBOePPkJd5waDw3D1/CzcP//6G0TS8r2Dj0wI2Ii7AY1BmvnrzAq0rGo6RaD66hC/HwYjxO/yYdN1RuHb6EWxXG0rqXFawdeuBmxEWYf2EsLqEL8ehiPM5IyVgA4OGhy3j4mddaPsdea+X4uXl4/yAJ6t/3QcFfp6DWrydKn2aj9Jno7xyFpvoozXr+YR+hggLU+nZH8aMURqkrJyh4jbInyVDoZI/Sq6ehYNUdgpznEOR+9DunvjYEb/I+TKXVU4FCu84o/o+7TVj69eqOCW7emO7shAbaWgg/egKD+nzHOlatoC6jtYcKQgbU1dXh4uKCefPmoUGDBmjUqBEWL14MObn/X8Frb2+PjRs3wtbWFnw+HwsWLICioqLwcVNTU6SnpyMsLAydOnXC8ePHceSIZL9cjYyMcPXqVaSlpUFdXR06OjqQk5ODvLw8Jk2ahEWLFsHU1FRkaSsLSfO2otWGmTB0d0RZwVs8cv8NANAycDpyTt5AbtQN8JQU0ClmE+TqKUBeQxWdb23F84gLSFu5j2n2yqxbsAEL1nnDafYYFL4pwq8eAcLHvAI8cCXqP/x3SjoKjC/J8guG/iovNJg2CvyCImQu/DBD3WjZHBScjUHhuavQHj8cyuatwVNRhnrfD90E30ReRu7WMJbRK3V9QQhs102D2eyhKCl4i5g5WwAAnde64mnULTyNuoXC9Be4u/YQ+v7lB56cHLKj7yF5/3m2wT/hxM8hGBo4DT1mDsX7grc45rlF+Nj3v7ri0albeHT6FuSVFDDr4jrIKylAWUMVc2J+w53Dl3F2zQGG6cVdWBiC3kFTYT1rKIoL3uKcx1bhY9+tcUXaqVt4fOoW5JQU4HQ5CPJKClDSUMW4a8F4dOgyrv0qXccbRPy8HaPWTkefGcPxruAtwrw2Cx/7afUU3Dt9C/dP30RP50FoZtkCSqr10H5ARwBA/PGrOLPpKKPk4g7/vB0j105H7xnD8b7gLcIrjGXE6im4f/oWEk7fRHfnQTD831jaVRjLOSkaC/DhtWYfNBUd/vdaO1Phtdbrf6+1tP+91sZVeK1N+N9rLUaKXmsvl26A7nJvaLmOgaCwCM8Xf/id03CJB4rO/4ei8zFQ6WQJzfGOEPDLwJOXx9trccjb+ifj5OLe7f8NKuM9UW/AKAjeFeHd7g+/c+o5zUHpnRiU3bkKBavuH/YNlpUB8vIojb2M0phTjJNXbnngJpy79B9e5r6C65yfoaqijH/DQ+C7aj1697BF7562MGyij5mu4zB+uhcEAgE621jC8YcBrKMTGcMTUPnNREFBAdzc3HD48GFoaGjAy8sLx48fFx4U/+zZMzg7OyM6OhoGBgbYsGEDxowZg/Xr12PSpEkAgPnz5yMkJATv37/HkCFDYGtriyVLliAvLw8AsGTJEhw9ehRxcXEAPhw1kZeXJzzO4tGjR5g4cSJu376Nt2/fIjU1FUZGRgCAlJQUtGjRAmvWrMG8efMkHt+lxj9+5f9D0mWJQj7rCDXmd3XZOrvoVr4O6wg1Kknxy8/hCl0+h/ddVSJJvmaaZUkLWXonaF4mS6MBBmp9ekknF+l+JzvzD8pLNrKOUKMUG5qwjvBJ1o27s45QqdisaNYRahwVhFKkV69ewoKQtejoaPTq1QtPnjxBo0aStwSnglB6UUEo3agglF5UEEovKgilGxWE0kuaC0LLxtJ3RikA3M66wjpCjZOdn1BSI96/f4+MjAz4+Phg5MiR1SoGCSGEEEIIIdxAx04QEfv370fr1q3x+vVrrFmzhnUcQgghhBBCyDdEM4RS5Pz586wjYNKkScI9ioQQQgghhLAgkNFD4KURzRASQgghhBBCSB1FBSEhhBBCCCGE1FG0ZJQQQgghhBAiVfh0EEKtoRlCQgghhBBCCKmjqCAkhBBCCCGEkDqKlowSQgghhBBCpAp1Ga09NENICCGEEEIIIXUUFYSEEEIIIYQQUkfRklFCCCGEEEKIVKEuo7WHZggJIYQQQgghpI6igpAQQgghhBBC6ihaMkoIIYQQQgiRKtRltPbQDCEhhBBCCCGE1FFUEBJCCCGEEEJIHUVLRgkhhBBCCCFShbqM1h6aISSEEEIIIYSQOooKQkIIIYQQQgipo3gCAc3HEm56//49Vq1ahUWLFqFevXqs43w1WRqPLI0FoPFIM1kaC0DjkWayNBaAxiPNZGksX6OlbgfWESqV+OIm6wg1jgpCwln5+fnQ1NTE69evUb9+fdZxvposjUeWxgLQeKSZLI0FoPFIM1kaC0DjkWayNJavQQVh7aElo4QQQgghhBBSR1GXUUIIIYQQQohUoS6jtYdmCAkhhBBCCCGkjqKCkHBWvXr14OfnJzMbrmVpPLI0FoDGI81kaSwAjUeaydJYABqPNJOlsRBuoKYyhBBCCCGEEKli0tCadYRKpbyMZR2hxtEMISGEEEIIIYTUUVQQEkIIIYQQQkgdRV1GCSGEEEIIIVJFIOCzjlBn0AwhIYQQQgghhNRRVBASzliyZAkeP37MOkaN2bVrF4qKiljHIB8pKSmBs7MzUlJSWEchn1FcXIyHDx+itLSUdRRCCCGE06jLKOGMDh064Pbt2/juu+/g4uICR0dHKCsrs45Vbfr6+igsLMRPP/0EFxcXdOvWjXUkiRw7dqzKzx06dOg3TFLztLS0cOvWLZiYmLCO8k2UlZXhzp07aN68ObS1tVnHkUhRURFmz56N0NBQAMCjR49gYmICd3d3GBgYYOHChYwTSi45ORk7d+5EcnIyNmzYAD09PURGRsLQ0BDt2rVjHU8i58+fR69evVjHIJ/w9u1bCAQCqKqqAgAeP36MI0eOwMzMDP3792ecrm5LTU2FsbEx6xhSpXkDC9YRKvU4J551hBpHBSHhlPj4eOzcuRP79u1DcXExRo8ejcmTJ6NTp06so0msrKwMx48fx65du3D8+HEYGxvD2dkZEydOROPGjVnH+yI5OdEFBjweDxXfTng8nvDPZWVltZarJjg7O8Pc3Byenp6so9SIuXPnwtzcHC4uLigrK8N3332HK1euQFVVFf/88w+nPsDPmTMH0dHRWL9+PQYOHIj4+HiYmJjg2LFj8PPzQ2wst9qBX7hwAYMGDUL37t1x8eJFJCQkwMTEBGvWrMG1a9cQERHBOqJElJWV0aRJE+F7maGhIetIpIL+/fvD0dER06dPR15eHtq0aQNFRUW8fPkSQUFBcHNzYx3xs4KDg6v8XHd392+YpObJy8vDzs4OLi4u+PHHHzl9w7umUEFYe6ggJJxUWlqKv//+Gzt37kRkZCRat24NV1dXTJo0CZqamqzjSez58+fYu3cvdu3ahQcPHmDgwIFwcXHBDz/8IFZ4SaPTp09jwYIFWLlyJbp27Qoej4crV67gl19+wcqVK9GvXz/WESWyYsUKrF27Fn369EGHDh2gpqYm8jjXPmg0bdoUR48eRceOHXH06FHMnDkT586dw+7du3Hu3DlER0ezjlhlzZs3x4EDB2BrawsNDQ3cvn0bJiYmSEpKgo2NDfLz81lHlEjXrl3x008/wdPTU2Q8169fx/Dhw/H06VPWESWSm5srfC+Lj49Hnz594OLiguHDh0NJSYl1PIloa2uL3Nj6lNzc3FpIUzMaNmyICxcuoF27dti+fTt+++03xMbG4tChQ/D19UVCQgLriJ9V1Rk0Ho/HuWX/d+/eRUhICP7880+8f/8eo0aNgouLCzp37sw6GjNUENYeKggJJxUXF+PIkSMICQnB2bNn0a1bN2RnZ+PZs2fYtm0bRo0axTqixK5evYqQkBCEhoZCX18feXl50NLSws6dO6V+Bqd9+/bYsmULevToIXL90qVLmDp1qtR/yPjY5z50cPGDhrKyMpKSktC0aVNMnToVqqqqWL9+PVJTU2FpacmpIkpVVRV3796FiYmJSAF1+/Zt2NnZ4fXr16wjSkRdXR137tyBsbGxyHjS0tLQpk0bvHv3jnXEaouLi0NISAj2798PPp+PsWPHwsXFBZaWlqyjVUn5smQAEAgEcHNzw9KlS6GnpyfyvIkTJ9Z2tGpTVVXFgwcP0KxZM4wcORLt2rWDn58fMjIy0Lp1a9rXLgXKb3jv2rUL//77L1q2bAkXFxeMHz8eurq6rOPVqmY65qwjVCo99w7rCDVO+qceCKng5s2bmDVrFvT19eHh4QFra2skJCTgwoULePDgAfz8/Dg1e5OdnY21a9eiXbt26NWrF/Lz8/HPP/8gNTUVz549g6OjIyc+bCQnJ1c6M6upqYm0tLTaD/SVUlNTP/kf14pBAGjUqBHu37+PsrIyREZGom/fvgA+7MeTl5dnnE4ynTp1wvHjx4Vfl8/gbNu2DV27dmUVq9q0tLSQmZkpdj02NhZNmjRhkKjmWFlZYeHChZg5cyYKCwsREhKCDh06oGfPnrh37x7reF80ceJE4X+TJk2CgoICRowYIXKdC+/PFZmamuLo0aPIyMjAyZMnhfsGnz9/jvr16zNOV30CgQCyMr+hoKAABwcHhIeH49dff0VycjK8vb3RtGlTTJgwodL3C0K+FhWEhDMsLCxga2uL1NRU7NixAxkZGVi9ejVMTU2Fz5kwYQJevHjBMGXV/fDDDzA0NMSuXbswZcoUPH36FPv37xd+WFdRUYGXlxcyMjIYJ/2yTp06Ye7cuSK/qLKysuDl5VWnl7tIC2dnZ4wcORLt27cHj8cTLuG9evUq2rRpwzidZFatWoXFixfDzc0NpaWl2LBhA/r164ddu3ZhxYoVrONJzMnJCQsWLEBWVhZ4PB74fD6io6Ph7e2NCRMmsI5XLSUlJYiIiMDgwYPRvHlznDx5Ehs3bkR2djZSU1NhaGiIn376iXXMOsnX1xfe3t4wMjJC586dhTdRoqKiYG1tzTid5Hbv3g1zc3OoqKhARUUFFhYW2LNnD+tYX+XGjRuYMWMG9PX1ERQUBG9vbyQnJ+Ps2bN4+vQphg0bxjoikUG0ZJRwxrJlyzB58mTO3zUv5+LiAldX18/OaggEAqSnp6N58+a1mExySUlJcHBwwMOHD9GsWTMAQHp6Olq1aoWjR4+KFO1c8eTJExw7dgzp6ekoLi4WeSwoKIhRquqLiIhARkYGfvrpJzRt2hTAhyVxWlpanPuAcefOHaxduxY3b94En8+HjY0NFixYAHNz6Vxe9DklJSWYNGkSwsLCIBAIoKCggLKyMjg5OWHXrl2cm8GdPXs29u/fDwAYN24cXF1d0b59e5HnpKenw8jICHw+tw6drrikl8uysrKQmZkJS0tL4R71a9euoX79+py6QRQUFAQfHx/MmjUL3bt3h0AgQHR0NDZt2oTly5fDw8ODdUSJBAUFYefOnXj48CEGDx4MV1dXDB48WKSPQFJSEtq0aVNnjttpqtP+y09i4EnuXdYRahwVhIQzli5dCm9vb2G77HJv375FQEAAfH19GSWrnt27d2PUqFGoV6+eyPXi4mKEhYVxbnZAIBDg1KlTePDgAQQCAczMzNC3b98qNWWQNmfOnMHQoUNhbGyMhw8fon379khLS4NAIICNjQ3Onj3LOmK1vXv3jrrXSaHk5GTExsaCz+fD2toaLVu2ZB2pWvr06QNXV1eMGDHik01kSktLER0dje+++66W030dWSkIgQ+FRXJyMuzs7KCiogKBQMC592pjY2P4+/uL/a4MDQ3FkiVLkJqayihZ9bRs2RKTJ0+Gs7PzJzuNFxcXY//+/ZxbqlxdVBDWHioICWfIy8sjMzNTbEN/Tk4O9PT0OHe0gayNR5Z07twZAwcOxNKlS4UfAvX09DB27FgMHDhQ6luzf6ysrAwrV67Eli1bkJ2dLTy7z8fHB0ZGRnBxcWEd8bMkaXrD5X1QsuDixYvo1q0bFBQURK6XlpbiypUrsLOzY5RMch8fO7Np0yaMGzdObL80l1YM5OTkYOTIkTh37hx4PB4SExNhYmICFxcXaGlpITAwkHXEKlNWVsbdu3fFVqAkJibC3Nyccw2Z0tLS0KxZM7HO4gKBABkZGcLVN3UJFYS1R+HLTyFEOnzqDubt27eho6PDINHX+dR4njx5womjM4KDgzF16lQoKyt/8WwoLjX6AYCEhAThsjcFBQW8ffsW6urqWLp0KYYNG8a5gnDFihUIDQ3FmjVrMGXKFOF1c3NzrFu3TuoLQi0trSrPXnDhRook51tyqdgAgN69e1d6o+v169fo3bs3J74/5T4+07Jbt25iTaW4Nqvm4eEBRUVFpKeno23btsLro0aNgoeHB6cKQlNTU4SHh+Pnn38WuX7gwAFOzrC3aNGi0p+d3NxcGBsbc+pnp6bQnFXtoYKQSL3ys6B4PB5atWolduB5QUEBpk+fzjChZKytrYXj6dOnj8id9LKyMqSmpmLgwIEME1bNunXrMHbsWCgrK2PdunWffB6Px+NcQaimpob3798DAAwMDJCcnIx27doBAF6+fMkyWrXs3r0bf/zxB/r06SPys2JhYYEHDx4wTFY1586dE/45LS0NCxcuxKRJk4T7b//77z+EhoZi1apVrCJK5ONC4+bNmygrK0Pr1q0BAI8ePYK8vDw6dOjAIt5X+dSNrpycHLHzPKVdxdddufIPqFwrBMtFRUXh5MmTwn3E5Vq2bInHjx8zSlU9/v7+GDVqFC5evIju3buDx+Ph8uXLOHPmDMLDw1nHk9inip+CggJa5k++OSoIidRbv349BAIBJk+eDH9/f5HZMyUlJRgZGXGq3fzw4cMBfDija8CAAVBXVxc+Vj6eESNGMEpXdRX3Z3Btr8aX2NraIjo6GmZmZhgyZAi8vLxw584dHD58GLa2tqzjSezp06eVNvbh8/koKSlhkEgyFfeaLV26FEFBQRgzZozw2tChQ2Fubo4//viDE3trKhYaQUFB0NDQQGhoKLS1tQEAr169grOzM3r27MkqosQcHR0BfCiUJk2aJLI3uqysDPHx8ejWrRureF9tx44dWLduHRITEwF8KKDmzp0LV1dXxskkU1hYKLYPH/hwo+vj/ezSbsSIEbh69SrWrVuHo0ePCveuX7t2jVMdU8tXDPB4PPj6+op8f8rKynD16lVYWVkxSkfqCioIidQr/4BnbGyMbt26QVFRkXGir+Pn5wcAMDIywqhRo+jOnxQKCgpCQUEBAGDJkiUoKCjAgQMHYGpq+tnZUGnVrl07XLp0Saxb7cGDBzn1wQn4MBu4ZcsWsesdO3bk3IdzAAgMDERUVJSwGAQ+rIpYvnw5+vfvDy8vL4bpqq78Rp1AIICGhgZUVFSEjykpKcHW1lZkuTKX+Pj4YN26dZg9e7bIrLSHhwfS0tKwfPlyxgmrzs7ODrt378ayZcsAQHjUSUBAAHr37s04neQ6dOiAvXv3so7xVcpXDAgEAty5c0ekGZOSkhIsLS3h7e3NKh5TfFoyWmuoICRSLT8/X9gkwtraGm/fvsXbt28rfS7XmklwYSajqn788Ud07NgRCxcuFLkeEBCAa9eu4eDBg4ySVU/FLoKqqqr4/fffGab5en5+fhg/fjyePn0KPp+Pw4cP4+HDh9i9ezf++ecf1vEkYmhoiC1btojtddq6dSsMDQ0Zpaq+/Px8ZGdnC5ckl3v+/DnevHnDKJXkdu7cCeDDjS5vb2/OLQ/9nM2bN2Pbtm1is9IWFhaYPXs2pwrCgIAA9OrVCzdu3EBxcTHmz5+Pe/fuITc3F9HR0azjSYzP5yMpKQnPnz8XO8aEKw2MylcMODs7Y8OGDZz7LENkA3UZJVKtYidOOTm5SvdtlO9Z4cKGax0dHTx69AgNGzYU7o38lNzc3FpM9nV0dXVx9uxZsXPg7ty5g759+yI7O5tRMlLu5MmTWLlypcjZfb6+vujfvz/raBI5ceIERowYgRYtWgiX78bExCA5ORmHDh3C4MGDGSeUzIQJE3DhwgUEBgaKjGfevHmws7NDaGgo44REW1sb165dE2tU8ujRI3Tu3Bl5eXlsglVTVlYWNm/eLPJeMHPmTOjr67OOJpGYmBg4OTnh8ePHYvvvuPKZgHyevpYZ6wiVysy7zzpCjaOCkEi1CxcuoHv37lBQUMCFCxc++1wunGkVGhqK0aNHo169eti1a9dnC0IuzSCqqKggLi5O2BSj3IMHD4Qzu1zyqWKdx+NBWVkZpqammDRpEpydnRmkIxkZGdi8ebPImZfTp0/n5AxhUVERvL29ERISItzPqaCgABcXFwQEBHBips3GxgZnzpyBtra2sGnWp9y6dasWk9WM2bNnQ1FRUazjq7e3N96+fYtNmzYxSia59PR0GBoaVvo9Sk9P59TRBlZWVmjVqhX8/f2hr68vNiYudOt2dHTErl27UL9+feE+3E85fPhwLaWSHo212n75SQxk5SWwjlDjaMkokWoVizwuFHxfUrHImzRpErsgNax9+/Y4cOAAfH19Ra6HhYXBzEw67/B9jq+vL1asWIFBgwahc+fOEAgEuH79OiIjIzFz5kykpqbCzc0NpaWlnN0XxWWGhoZYuXIl6xg1onxJckBAAJKTkyEQCGBqasqJQrDcsGHDhA1JyptmyZodO3YgKipKZBY3IyMDEyZMEDlGRNqPCTE2Nv7k+bdcO9ogMTERERERlTbM4gpNTU1hIcuFApbILpohJFItPj6+ys+1sLD4hklqhqwesH3s2DGMGDECTk5OsLe3BwCcOXMG+/fvx8GDBzn3IXHEiBHo16+f2HEmW7duRVRUFA4dOoTffvsNf/zxB+7cucMo5ed9aUlyRdK+PDk+Ph7t27eHnJzcF98TuPA+QLilqs1WeDwezp49+43TfB05OTlkZ2dDV1dX5Prjx49hZmaGwsJCRskkZ29vj/nz53PimCZSPTRDWHuoICRSrXzf4KfOtqqIC3c2P7UPsjJcGE9Fx48fx8qVKxEXFwcVFRVYWFjAz8+PkzO76urqiIuLE7vznJSUBCsrKxQUFCA5ORkWFhZS+wGq4t6znJwcLF++HAMGDBDpknjy5En4+PjAw8ODVcwqkZOTQ1ZWlshe4sp+dXFl35AsLxPLyMgAj8cTnnN37do17Nu3D2ZmZpg6dSrjdHVX+Szmhg0bMGXKlEqPNpCXl5f6xjIVbwglJyfjl19+wbx582Bubi7WgZxuDnFfI802rCNUKvu19J/fKylaMkqkWsXz7WJjY+Ht7Y158+aJfKgNDAzEmjVrWEWUiKwdsF3RkCFDMGTIENYxaoSOjg7+/vtvsULp77//ho6ODoAP53lpaGiwiFclFZcnjxgxAkuXLsWsWbOE19zd3bFx40acPn1a6gvC1NRU4YyGLJx5KcvLxJycnDB16lSMHz8eWVlZ6Nu3L9q3b4+9e/ciKytLbFk5qR2ycrSBlZWV2A2hyZMnC/9c8QYyF24OfWnPbUVc3H9LuINmCAlndO7cGUuWLBHrInjixAn4+Pjg5s2bjJJVT58+feDq6irSyhwA9u3bhz/++APnz59nE4xg27ZtcHNzw+DBg9G5c2fweDxcu3YNJ06cwJYtW+Di4oLAwEBcu3YNBw4cYB33iz4145mYmAhra2vhmYtcV5WVBOTb0tbWRkxMDFq3bo3g4GAcOHAA0dHRiIqKwvTp05GSksI6Yp3G9aMNHj9+XOXnfnzuqjTy9/ev8nPLzzCuS2iGsPZQQUg4Q0VFBbdu3ULbtqJryhMSEmBjY8O5Tpaqqqq4fft2pa3MraysUFRUxCiZ5MrKyrBu3TqEh4cjPT0dxcXFIo9L+x61ykRHR2Pjxo14+PAhBAIB2rRpg9mzZ6Nbt26so0msefPmmDVrFubNmydyPSAgABs3bpToQxZr48ePx+bNm6Guri5yPS0tDePHj8elS5cYJauee/fuiZ1BWC4yMpJz+6PU1dVx9+5dGBkZYejQoejevTsWLFiA9PR0tG7dmnPv04QQdnQ1W3/5SQy8eP3wm/3br169gru7O44dOwbgw5mnv/32G7S0tD779xISErBgwQJcuHABfD4f7dq1Q3h4eJU7B9OSUcIZbdu2xfLly7Fjxw4oKysDAN6/f4/ly5eLFYlcIEsHbPv7+2P79u3w9PSEj48PFi9ejLS0NBw9epSzS8S6d++O7t27s45RI/z9/eHi4oLz588LlyfHxMQgMjIS27dvZ5xOMvfv34e5uTn27t0r/P6EhobC3d0d/fr1Y5xOch07dsSaNWswe/Zs4bX379/Dy8sLO3bs4FwB1a5dO2zZsgVDhgzBqVOnsGzZMgDAs2fP0KBBA8bpCABcv34dBw8erPTmHdf2rCYnJ2P9+vVISEgAj8dD27ZtMWfOHLRo0YJ1NEKqxcnJCU+ePEFkZCQACJfg//3335/8O8nJyejRowdcXFzg7+8PTU1NJCQkCD8rVwXNEBLOuHbtGn744Qfw+XxYWloCAG7fvg0ej4d//vkHnTt3ZpxQMrJ0wHaLFi0QHByMIUOGQENDA3FxccJrMTEx2LdvH+uIEklPT//s41w6q6vc1atXERwcjISEBOHZfe7u7ujSpQvraBIpLS3FL7/8gnXr1sHLywuJiYmIjIzEhg0bRPYSccXhw4cxdepUdOrUCTt37kRWVhacnJwAAHv37oWNjQ3jhJI5f/48HBwckJ+fj4kTJyIkJAQA8PPPP+PBgwecKzhkTVhYGCZMmID+/fvj1KlT6N+/PxITE5GVlQUHBwfs3LmTdcQqO3nyJIYOHQorKyt0794dAoEAV65cwe3bt/H3339z7gaRLK60+Vp1bYYwISEBZmZmiImJEf5ujomJQdeuXfHgwQOxs57LjR49GoqKitizZ0+1/7epICScUlRUhL1794ocSO3k5MSpM7sqkpUDttXU1JCQkIBmzZpBX18fx48fh42NDVJSUmBtbY3Xr1+zjiiRL3WD5UKzAlnn5+eHZcuWQUFBARcuXBDOfHLRs2fPMHHiRMTGxqKwsBDOzs4IDAyEiooK62jVUlZWhvz8fGhrawuvpaWlQVVVVez8O1K7LCwsMG3aNMycORMaGhq4ffs2jI2NMW3aNOjr60u0p401a2trDBgwAKtXrxa5vnDhQkRFRXGuCYuvr+9nV9q4u7uzjljrGtZvxTpCpZ6+uIP379+LXKtXr57wPNbqCgkJgaenJ/Ly8kSua2lpYd26dXB2dhb7O3w+H5qampg/fz4uX76M2NhYGBsbY9GiRRId+UVLRgmnqKqqylTrclk5YLtp06bIzMxEs2bNYGpqiqioKNjY2OD69etf/QbJQnlHvnIlJSWIjY1FUFAQVqxYwSiVZPLz84WNI750/iWXGkyUlJRg4cKF2LRpExYtWoTLly/DwcEBISEhnJpVr6isrAzFxcUoKytDWVkZGjduzMmfm3Ly8vIixSAAGBkZsQlDRCQnJwu7QderVw+FhYXg8Xjw8PCAvb09pwrChIQEhIeHi12fPHky1q9fX/uBvtKff/6Jbdu2YciQIfD398eYMWPQokULWFhYICYmpk4WhNJq1apVYj8rfn5+WLJkyVf9u+XHK31MT08PWVlZlf6d58+fo6CgAKtXr8by5cvx66+/IjIyEo6Ojjh37lyVj/6igpBItWPHjmHQoEFQVFQUbrD9lKFDh9ZSquqT1QO2HRwccObMGXTp0gVz5szBmDFjsGPHDqSnp0v9kQaVKV+SXFHHjh1hYGCAgICAL54dJw20tbWRmZkJPT09aGlpVTrjyaX27OU6duyIoqIinD9/Hra2thAIBFizZg0cHR0xefJk/P7776wjSiQsLAxubm7o2bMnHj16hLi4ODg7O+PkyZPYs2cPTExMWEeUSHZ2Nry9vXHmzBk8f/5c7LxILr3WZJGOjg7evHkDAGjSpAnu3r0Lc3Nz5OXlcaqRGQDo6uoiLi5OrDFbXFwcJ2eis7KyYG5uDuBDc6bylTXff/89fHx8WEYjH1m0aJHwbM9yn7uJt2TJki/ebLl+/ToAfPZ3dWX4fD4AYNiwYcLPW1ZWVrhy5Qq2bNlCBSGRDcOHDxfeMfnc1DdXPtRaWVkJx1PZeUrluDKechWX7Pz4448wNDREdHQ0TE1NOVGoV1WrVq2Eb9rS7uzZs8IzEyuef8l1HTt2RHBwsHCZOI/Hw4IFCzBgwACMGzeOcTrJubi4YO3atXBzcwMA9OvXD/Hx8Zg+fTqsrKy+OLsrbSZNmoT09HT4+PhAX1+fjgGRMj179sSpU6dgbm6OkSNHYs6cOTh79ixOnTqFPn36sI4nkSlTpmDq1KlISUlBt27dwOPxcPnyZfz666/w8vJiHU9isrbSpibwpXRXm6TLQ2fNmoXRo0d/9jlGRkaIj49Hdna22GMvXrxAo0aNKv17DRs2hIKCAszMzESut23bFpcvX65yRtpDSEgtevz4MZo1awYej/fFVv9cOENJVn38IVwgECAzMxNLlizBgwcPEBcXxyZYNZSWlmLFihWYPHky5/amSur9+/ec++D08OHDTzYK2LNnD8aPH1/Lib6OhoYGLl26BCsrK9ZRSCVyc3Px7t07GBgYgM/nY+3atbh8+TJMTU3h4+MjttRXmgkEAqxfvx6BgYF49uwZAMDAwADz5s2Du7s7525GLFy4EPXr18fPP/+MiIgIjBkzBkZGRsKVNh/vlawLdDRafvlJDOS+Sfwm/255U5mrV68KGyVevXoVtra2n20q061bN7Ro0UKkqYyDgwNUVFSq3NSPCkJCCPlIZU1lBAIBDA0NERYWxrkGJhoaGrhz547M7OO6cOEC1q5dK9Jqft68eejZsyfraNV28+ZNkfFwrbtoOTMzM/z555+wtrZmHYXUIeXLYDU0NBgnqTkxMTG4cuWKzK20kURdKwgBYNCgQXj27Bm2bt0K4MOxE82bNxc5dqJNmzZYtWoVHBwcAABHjhzBqFGjsGnTJvTu3RuRkZGYO3cuzp8/jx49elTpf5cKQsIZ7u7uMDU1FdtYvXHjRiQlJXFuE/mqVavQqFEjsVb5ISEhePHiBRYsWMAoGblw4YLI13JyctDV1YWpqSkUFLi30n748OEYPnw4Jk2axDrKV9u7dy+cnZ3h6Ogo0mr+yJEj2LVrl/DIBq54/vw5Ro8ejfPnz0NLSwsCgQCvX79G7969ERYWBl1dXdYRJRIVFYXAwEBs3bpVZm5AyBo+n4+kpCQ8f/5cuP+onJ2dHaNUkktNTUVpaanYHsLExEQoKirS608GaKubso5QqVcFSd/s387NzRU7mH7jxo0iB9PzeDzs3LlT5Hd6SEgIVq1ahSdPnqB169bw9/fHsGHDqvy/SwUh4YwmTZrg2LFj6NChg8j1W7duYejQoXjy5AmjZNVjZGSEffv2oVu3biLXr169itGjRyM1NZVRMiJrtm7diiVLlmDs2LHo0KGD2DEtXLr73LZtW0ydOlWsWVFQUBC2bduGhIQERsmqZ9SoUUhOTsaePXvQtm1bAMD9+/cxceJEmJqaYv/+/YwTSkZbWxtFRUUoLS2FqqoqFBUVRR6vi2epSZOYmBg4OTnh8ePHYvvXubZ3/bvvvsPkyZMxceJEket79+7F9u3bcf78eTbBqmn37t2ffXzChAm1lER61MWCkBUqCAlnKCsr4+7duzA1FX2DSEpKQvv27fHu3TtGyapHWVkZCQkJMDY2FrmekpICMzMzzo1HloSGhqJhw4bC9uzz58/HH3/8ATMzM+zfv59z+zvl5OQ++RjXPgTWq1cP9+7dk5n3AU1NTZw+fRqdOnUSuX7t2jX0799f7DwqaRcaGvrZxz/+8E5ql5WVFVq1agV/f/9Km/5oamoySia5+vXr49atW5W+F3Ts2JFzPzsf798sKSlBUVERlJSUoKqqWidvplBBWHu4t/aJ1FmmpqaIjIzErFmzRK7/+++/nGvNDkDYifPjgjA6OhoGBgaMUlXP9evXwefz0aVLF5HrV69ehby8PDp27MgoWfWsXLkSmzdvBgD8999/2LhxI9avX49//vkHHh4eOHz4MOOEkvl4WRiXGRoa4syZM2IfAs+cOcPJpjl8Pl9sFg0AFBUVOfl9o4JPuiUmJiIiIkLs54eLeDyecO9gRa9fv+bUTa5yr169EruWmJgINzc3zJs3j0Ei9vigOavaQgUh4QxPT0/MmjULL168gL29PYAPHwIDAwM5t38QAFxdXTF37lyUlJSIjGf+/Pmca5k9c+ZMzJ8/X6wgfPr0KX799VdcvXqVUbLqycjIEH5gOnr0KH788UdMnToV3bt3R69evdiGq+O8vLzg7u6OuLg4kVbzu3btwoYNG1jHk5i9vT3mzJmD/fv3C28EPX36FB4eHpw7BqBccnIydu7cieTkZGzYsAF6enqIjIyEoaEh2rVrxzpendalSxckJSXJREHYs2dPrFq1Cvv374e8vDyAD+dcrlq1qsqNNKRdy5YtsXr1aowbNw4PHjxgHYfIMCoICWdMnjwZ79+/x4oVK7Bs2TIAH/bhbd68mZNr6+fPn4/c3FzMmDEDxcXFAD4sI12wYAEWLVrEOJ1k7t+/X2lXRGtra9y/f59Boq+jrq6OnJwcNGvWDFFRUcL9asrKynj79i3jdNUjK5053dzc0LhxYwQGBiI8PBzAh32FBw4ckGgDvbTYuHEjhg0bBiMjIxgaGoLH4yE9PR3m5ubYu3cv63gSu3DhAgYNGoTu3bvj4sWLWLFiBfT09BAfH4/t27cjIiKCdcQ6Jz4+Xvjn2bNnw8vLS3gI+sez0xYWFrUdr9rWrFkDOzs7tG7dWvg+dunSJeTn5+Ps2bOM09UceXl54bEahHwrtIeQcNKLFy+goqICdXV11lG+WkFBARISEqCiooKWLVty7hw1AGjQoAH++ecfseMYrly5giFDhlS6FEaajR07Fg8ePIC1tTX279+P9PR0NGjQAMeOHcPPP/+Mu3fvso4oEVnrzCmLTp06hQcPHkAgEMDMzAx9+/ZlHalaunbtip9++gmenp7Q0NDA7du3YWJiguvXr2P48OF4+vQp64h1TvkxOp/6uFf+GNf2EwPAs2fPsHHjRty+fRsqKiqwsLDArFmzoKOjwzqaxMq7SpYrP/9248aNMDQ0xL///ssoGTv11aRzO1B+YQrrCDWOCkJCyFcbPXo0srKy8NdffwmbEuTl5WH48OHQ09MTzuRwRV5eHn755RdkZGTAzc0NAwcOBAD4+flBSUkJixcvZpxQMrLWmZNIL3V1ddy5cwfGxsYiBWFaWhratGnDuaY/suDx48dVfi7XGmbJko+bf/F4POjq6sLe3h6BgYHQ19dnlIwdKghrDxWEhFMiIiIQHh6O9PR04TLLcrdu3WKUqvquX7+OgwcPVjoeLjUuefr0Kezs7JCTkyM8kDouLg6NGjXCqVOnONnsQ5ZwvTOnjo4OHj16hIYNG0JbW1usM2JFXOjEFxwcjKlTp0JZWRnBwcGffe7H565Ku6ZNmyI8PBzdunUTKQiPHDkCb29vJCcns45IOKzi8tcv4dLyV1I5KghrD+0hJJwRHByMxYsXY+LEifjrr7/g7OyM5ORkXL9+HTNnzmQdT2JhYWGYMGEC+vfvj1OnTqF///5ITExEVlYWHBwcWMeTSJMmTRAfH48///xTuHTH2dkZY8aMqbSDIqldXO/MuW7dOmhoaAAAJxtIfWzdunUYO3YslJWVsW7duk8+j8fjca4gdHJywoIFC3Dw4EHweDzw+XxER0fD29ubk3u9ZdHDhw/x22+/CfcTt2nTBrNnz0br1q1ZR/siKyurzy5/LcfF5a+enp5Vfm5QUNA3TCI9+DRnVWtohpBwRps2beDn54cxY8aI3Hn29fVFbm4uNm7cyDqiRCwsLDBt2jTMnDlTOB5jY2NMmzYN+vr68Pf3Zx2RyIjNmzdj7ty5mDx5cqWdOadNm8Y6IpERJSUlmDRpEsLCwiAQCKCgoIDS0lKMHTsWu3btEnaDJGxERERgzJgx6Nixo3DPd0xMDK5fv459+/bhp59+Ypzw82R5+Wvv3r1x8+ZNlJWVCYvzR48eQV5eXqRpG4/Hk6mmOZ+jrmr85ScxUFCUyjpCjaOCkHCGqqoqEhIS0Lx5c+jp6eHUqVOwtLREYmIibG1tkZOTwzqiRNTU1HDv3j0YGRmhYcOGOHfuHMzNzZGQkAB7e3tkZmayjvhZx44dw6BBg6CoqCi2Gf5jQ4cOraVU5FOOHDmCwMBA4X7B8i6jXOjMmZ+fX+Xn1q9f/xsmIVWVkpKCW7dugc/nw9raGi1btmQdiQAwMTHBuHHjsHTpUpHrfn5+2LNnD1JSuLcU7v79+2LbLng8Hn744QeGqSQXFBSE8+fPIzQ0VHhI/atXr+Ds7IyePXty7jiqmkAFYe2hgpBwhomJCSIiImBjY4NOnTrB1dUV06ZNQ1RUFEaPHs2JvUMVGRoa4sSJEzA3N4elpSUWLlyIMWPG4L///sPAgQPx+vVr1hE/S05ODllZWdDT0xPbDF8RF5fuEOlS3iWxKrjwWpO1pWGyNh5Zpqqqivj4eLHl44mJibC0tERRURGjZJJLSUmBg4MD7ty5I7KMtPy9ggvvBRU1adIEUVFRYmd13r17F/3796+TR0+oqRqxjlCpwqI01hFqHO0hJJxhb2+Pv//+GzY2NnBxcYGHhwciIiJw48YNODo6so4nsZ49e+LUqVMwNzfHyJEjMWfOHJw9exanTp3ixIHUfD6/0j8TUtPOnTsn/HNaWhoWLlyISZMmCZe8/ffffwgNDcWqVatYRZRIbGysyNefWibWoUMHFvEkJmvjkWW9evXCpUuXxArCy5cvc+5M0jlz5sDY2BinT5+GiYkJrl69itzcXHh5eWHt2rWs40ksPz8f2dnZYgXh8+fP8ebNG0apSF1BM4SEM/h8Pvh8PhQUPtzHCA8Px+XLl2Fqaorp06dDSUmJcULJ5Obm4t27dzAwMACfz8fatWuF4/Hx8REuGZF2JSUl6N+/P7Zu3YpWrVqxjlNt1tbWVZ6F4kJH2y9146yIS7Prffr0gaurK8aMGSNyfd++ffjjjz9w/vx5NsGqSdaWicnaeGTNli1b4Ovri5EjR8LW1hbAhz2EBw8ehL+/PwwMDITPlfal/g0bNsTZs2dhYWEBTU1NXLt2Da1bt8bZs2fh5eUldqNC2k2YMAEXLlxAYGCgyPdm3rx5sLOzQ2hoKOOEtY9mCGsPFYSEkK+mq6uLK1eucHqfkCRNfPz8/L5hkpohyYeHiRMnfsMkNUtVVRW3b98We609evQIVlZWnFryBsjeMjFZG4+s+dzy/oq4sNRfW1sbN2/ehImJCVq0aIHt27ejd+/eSE5Ohrm5OefeC4qKiuDt7Y2QkBCUlJQAABQUFODi4oKAgACoqakxTlj7VFSkszHQ27dVb27EFbRklBDy1SZMmIAdO3Zg9erVrKNUGxeKPElwqciThKGhIbZs2YLAwECR61u3buXEERofk7VlYrI2HlkjS8v727dvj/j4eJiYmKBLly5Ys2YNlJSU8Mcff8DERDrPr/scVVVV/P777wgICEBycjIEAgFMTU3rZCFIah8VhISQr1ZcXIzt27fj1KlT6Nixo9gvMGokwR6fz0dSUhKeP38u9qHQzs6OUSrJrVu3DiNGjMDJkydFllUlJyfj0KFDjNNJzsHBAc7OzpUuE+Pi3mhZGw+RXr/88gsKCwsBAMuXL8f333+Pnj17okGDBjhw4ADjdNWnpqYGCwsL1jFIHUNLRgkhX613796ffbxiUxAuKCsrw7p16xAeHi7Wzhzg1p474MMHcicnJzx+/FjsQGcuLA372JMnT7B582YkJCRAIBDAzMwM06dP5+QMoawtE5O18ciC4ODgKj/X3d39Gyb59nJzcyXaP02km7JyM9YRKvXuXTrrCDWOCkJCCPmIr68vtm/fDk9PT/j4+GDx4sVIS0vD0aNH4evry7kPTVZWVmjVqhX8/f2hr68v9mFJU1OTUTJSrrCwUKaWicnaeLjM2LhqZ7nxeDxOnkNIZBcVhLWHCkJCGEtKSkJycjLs7OygoqICgUDAububkydPxoYNG6ChoSFyvbCwELNnz0ZISAijZNXTokULBAcHY8iQIdDQ0EBcXJzwWkxMDPbt28c6okTU1NRw+/ZtsVbzhBBCiLSigrD2UEFIpJqsHQVQUU5ODkaNGoWzZ8+Cx+MhMTERJiYmcHFxgZaWlljTDGkmLy+PzMxM6OnpiVx/+fIlGjdujNLSUkbJqkdNTQ0JCQlo1qwZ9PX1cfz4cdjY2CAlJQXW1tZ4/fo164gSsbe3x/z58zFw4EDWUQghhJAqqacsndsA3r/LYB2hxlFTGSLVhg8fLvzzu3fv8Pvvv8PMzEx4IHVMTAzu3buHGTNmMEpYfR4eHlBQUEB6ejratm0rvD5q1Ch4eHhwoiDMz8+HQCCAQCDAmzdvoKysLHysrKwMJ06cECsSuaBp06bIzMxEs2bNYGpqiqioKNjY2OD69euoV68e63hVEh8fL/zz7Nmz4eXlhaysLJibm0NRUVHkudTAgJC6YfLkyZ99nGurOQghNYMKQiLVKh4F4OrqCnd3dyxbtkzsORkZ3LtbExUVhZMnT6Jp06Yi11u2bInHj7lxxo2WlhZ4PB54PF6lh9LzeDyJzveTFg4ODjhz5gy6dOmCOXPmYMyYMdixYwfS09Ph4eHBOl6VWFlZgcfjiTSRqfhhsPwxLjWVEQgESE9Ph56eHlRUVFjHIYRzXr16JfJ1SUkJ7t69i7y8PNjb2zNKRQhhjQpCwhkHDx7EjRs3xK6PGzcOHTt25NydzcLCQqiqqopdf/nyJWdmoc6dOweBQAB7e3scOnQIOjo6wseUlJTQvHlzGBgYMExYPRXPU/zxxx/RtGlTXLlyBaamphg6dCjDZFWXmprKOkKNEwgEaNmyJe7duyd2MD0XlZSUYOrUqfDx8eHkuWmEe44cOSJ2jc/nY8aMGfQaJFKHdrXVHtpDSDijcePGWLVqFZydnUWu79y5EwsXLkR2djajZNUzZMgQ2NjYYNmyZdDQ0EB8fDyaN2+O0aNHg8/nIyIignXEKnv8+DGaNWvGuWY4dcWqVavQqFEjseViISEhePHiBRYsWMAomeTatWuHHTt2CM+44zotLS3cunWLPowTph4+fIhevXohMzOTdRRChJTqNf3ykxgofv+EdYQaRzOEhDPmzp0LNzc33Lx5U+TA45CQEPj6+jJOJ7mAgAD06tULN27cQHFxMebPn4979+4hNzcX0dHRrON9UXx8PNq3bw85OTm8fv0ad+7c+eRzubZHbffu3Z99fMKECbWUpGZs3bq10s6o7dq1w+jRozlVEK5Zswbz5s3D5s2b0b59e9ZxvpqDgwOOHj0KT09P1lFIHZacnMy55l+EkJpDM4SEU8LDw7FhwwYkJCQAANq2bYs5c+Zg5MiRjJNVT1ZWFjZv3oybN2+Cz+fDxsYGM2fOhL6+PutoXyQnJ4esrCzo6elBTk5ObL9aOS7tUSunra0t8nVJSQmKioqgpKQEVVVVzh1Mr6ysjISEBLHzyFJSUmBmZoZ3794xSiY5bW1tFBUVobS0FEpKSmJ7Cbn2vVmxYgXWrl2LPn36oEOHDmLn9XHtzEsi3T6+8SAQCJCZmYnjx49j4sSJ2LhxI6NkhIhTVGrCOkKlSoqfso5Q46ggJIRUS8Vlol9qgtO8efNaSvXtJCYmws3NDfPmzcOAAQNYx5FIy5Yt4efnh3Hjxolc37NnD/z8/Dh1GHVoaOhnH584cWItJakZnzs0nA4KJzWtd+/eIl/LyclBV1cX9vb2mDx5MhQUaOEYkR5UENYe+sknnJKXl4eIiAikpKTA29sbOjo6uHXrFho1aoQmTaTzjaOiikcBfIm0L7OsWOTJQsH3JS1btsTq1asxbtw4PHjwgHUcibi6umLu3LkoKSkRdhI8c+YM5s+fDy8vL8bpJMO1gu9LZLH5D5Fex48fh0AgEM5Ep6Wl4ejRo2jevDkVg4TUYfTTTzgjPj4effv2haamJtLS0uDq6godHR0cOXIEjx8//uK+L2lQ2VEAleHaMktZalryOfLy8nj27BnrGBKbP38+cnNzMWPGDBQXFwP4sIx0wYIFWLRoEeN0kklPT//s482aNaulJIRwz/Dhw+Ho6Ijp06cjLy8Ptra2UFRUxMuXLxEUFAQ3NzfWEQkRoiWMtYeWjBLO6Nu3L2xsbLBmzRpoaGjg9u3bMDExwZUrV+Dk5IS0tDTWEb9IkvMFuTTrZmRkhH379qFbt24i169evYrRo0dzbhbk2LFjIl+X77PZuHEjDA0N8e+//zJK9nUKCgqQkJAAFRUVtGzZkjPHm1RUvl/1U7h0IwWgg8JJ7WrYsCEuXLiAdu3aYfv27fjtt98QGxuLQ4cOwdfXV7g/nxBpoCClS0ZLackoIexcv34dW7duFbvepEkTZGVlMUgkOS4VeZLIysqqtBGOrq4uJ9uYDx8+XORrHo8n3GcTGBjIJlQNUFdXR6dOnVjH+CqxsbEiX5eUlCA2NhZBQUFYsWIFo1TVRweFk9pUVFQEDQ0NAEBUVBQcHR0hJycHW1tbiW5YEkJkCxWEhDOUlZWRn58vdv3hw4fQ1dVlkEhyx44dw6BBg6CoqCg2C/UxrhyADgCGhoaIjo4Wa5ARHR3NyYPp+Xw+6wjkEywtLcWudezYEQYGBggICICjoyODVNVHB4WT2mRqaoqjR4/CwcEBJ0+ehIeHBwDg+fPnqF+/PuN0hIiSxZk4aUVLRglnTJ06FS9evEB4eDh0dHQQHx8PeXl5DB8+HHZ2dli/fj3riF/08VENn8K1PYS//vorAgICEBAQUGnTEq7tUyPck5iYCCsrKxQWFrKOUiPooHDyLURERMDJyQllZWXo06cPoqKiAHzYB37x4kXOLocnhHwdKggJZ+Tn52Pw4MG4d+8e3rx5AwMDA2RlZaFr1644ceKE2PldpPYIBAIsXLgQwcHBYk1LfH19GaerGkkOBg8KCvqGScjnfLxKoHx/55IlS/DgwQPExcWxCVbDTpw4gYkTJ+LFixesoxAZk5WVhczMTFhaWgpvTF67dg3169dHmzZtGKcjhLBABSHhnLNnz+LWrVvCg9z79u3LOlK17N69G6NGjRJr7FFcXIywsDBMmDCBUbLq43LTko/P57p58ybKysrQunVrAMCjR48gLy+PDh064OzZsywiElTeVEYgEMDQ0BBhYWHo2rUro2TVQweFE0IIYY0KQsIZ9+7dQ7t27Sp9LDIyEgMHDqzlRF9HXl4emZmZ0NPTE7mek5MDPT09Ti0ZlTVBQUE4f/48QkNDoa2tDeBD8w9nZ2f07NmTc2f3yZILFy6IfF1+sLapqSknz1Hr1auXSIFLB4UTQgipbVQQEs5QUVHBmjVrMHv2bOG19+/fw8vLCzt27MDbt28ZppOcnJwcsrOzxRri3L59G71790Zubi6jZKRJkyaIiooSuwFx9+5d9O/fn5NnEXKZjY0Nzpw5A21tbSxduhTe3t5QVVVlHavaKjaXIoQQQlijW4+EM/78809MnToVJ06cwM6dO5GVlQUnJycAH7pZcoW1tTV4PB54PB769OkjMgNQVlaG1NRUzs12ypr8/HxkZ2eLFYTPnz/HmzdvGKWquxISElBYWAhtbW34+/vDzc2N0wWhg4MDsrKyoKur+8mVAoQQQkhtoYKQcIajoyNsbW0xceJEtG/fHoWFhXB2dkZgYCBUVFRYx6uy8jPu4uLiMGDAAKirqwsfU1JSgpGREUaMGMEoHQE+fGAvf23Z2toCAGJiYjBv3jzOHWsgC6ysrODs7IwePXpAIBAgICBA5OemIi40MdLV1UVMTAx++OEHCAQCsT2RhBBCSG2igpBwSllZGYqLi1FWVoaysjI0btyYU41LAMDPzw8AYGRkhFGjRkFZWZlxIvKxLVu2wNvbG+PGjUNJSQkAQEFBAS4uLggICGCcru7ZtWsX/Pz88M8//4DH4+Hff/+tdG8dj8fjREE4ffp0DBs2TLhSoHHjxp98Lu0lJoQQ8q3RHkLCGWFhYXBzc0PPnj2xY8cOxMXFwdnZGc2bN8eePXvoEGdS4woLC5GcnAyBQABTU1M62kQKVDzLk8sePHiApKQkDB06FDt37oSWllalzxs2bFjtBiOEEFLnUEFIOENNTQ1r166Fm5ub8NqrV68wbdo0REZGip1PJu3Kysqwbt06hIeHIz09XXh+XzlqKkOI7PP398e8efM4vSeSEEIIt1FBSDjj4cOHwjPhPrZnzx6MHz++lhN9HV9fX2zfvh2enp7w8fHB4sWLkZaWhqNHj8LX1xfu7u6sI9Ypjo6O2LVrF+rXr//FfYKHDx+upVQE+NCVs6qGDh36DZMQQgghsof2EBLO+FQxCIBzxSDwoWvqtm3bMGTIEPj7+2PMmDFo0aIFLCwsEBMTQwVhLdPU1BQ299DU1GSchlRU3ojpS3g8Hif23JV3Gq6KW7dufeM0hBBC6joqCIlU8/T0xLJly6CmpgZPT8/PPjcoKKiWUtWMrKwsmJubAwDU1dXx+vVrAMD3338PHx8fltHqpJ07d1b6Z8Ien89nHaFGVbXAJYQQQmoDFYREqsXGxgq7PN66deuTd9W52La9adOmyMzMRLNmzWBqaoqoqCjY2Njg+vXrnOucKmvevn0LgUAg3Nf1+PFjHDlyBGZmZujfvz/jdITryjsNE0IIIdKA9hASqRYfH4/27dtDTk6OdZQat3DhQtSvXx8///wzIiIiMGbMGBgZGSE9PR0eHh5YvXo164h1Vv/+/eHo6Ijp06cjLy8PrVu3hpKSEl6+fImgoCCRxkakdi1duvSzj3Ph2AlCCCFEmlBBSKSavLw8MjMzoaenBxMTE1y/fh0NGjRgHeubiImJwZUrV2BqakqNMRhr2LAhLly4gHbt2mH79u347bffEBsbi0OHDsHX1xcJCQmsI9ZZ1tbWIl+XlJQgNTUVCgoKaNGiBef23MnJyX12hQMX9kQSQgjhNloySqSalpYWUlNToaenh7S0NJnbS1SRra0tbG1tWccgAIqKiqChoQEAiIqKgqOjI+Tk5GBra4vHjx8zTle3xcbGil3Lz8/HpEmT4ODgwCDR1zly5IjI1yUlJYiNjUVoaCj8/f0ZpSKEEFKX0AwhkWpTp07F7t27oa+vj/T0dDRt2hTy8vKVPjclJaWW00mO2udzg4WFBVxdXeHg4ID27dsjMjISXbt2xc2bNzFkyBBkZWWxjkg+cvfuXXz//fdIS0tjHaVG7Nu3DwcOHMBff/3FOgohhBAZRzOERKr98ccfcHR0RFJSEtzd3TFlyhThzA0XyVr7fFnl6+sLJycneHh4wN7eHl27dgXwYbbw4yWLRDrk5eUJO/XKgi5dumDKlCmsYxBCCKkDaIaQcIazszOCg4M5XRAS7sjKykJmZiYsLS2FTY2uXbuG+vXro02bNozT1V3BwcEiXwsEAmRmZmLPnj2ws7PD/v37GSWrOW/fvsWiRYvw77//4uHDh6zjEEIIkXFUEBJCyCckJSUhOTkZdnZ2UFFRgUAg4OQRJ7LE2NhY5Gs5OTno6urC3t4eixYt4twNI21tbZHXlEAgwJs3b6Cqqoq9e/fS0nFCCCHfHBWEhDB04cIFrF27FgkJCeDxeGjbti3mzZuHnj17so5Wp+Xk5GDkyJE4d+4ceDweEhMTYWJiAhcXF2hpaSEwMJB1RCIjdu3aJVIQlhe4Xbp0gba2NsNkhBBC6goqCAlhZO/evXB2doajoyO6d+8OgUCAK1eu4MiRI9i1axecnJxYR6yzJkyYgOfPn2P79u1o27Ytbt++DRMTE0RFRcHDwwP37t1jHZH8T35+Ps6ePYvWrVujbdu2rOMQQgghnEMFISGMtG3bFlOnToWHh4fI9aCgIGzbto3OumOocePGOHnyJCwtLaGhoSEsCFNTU2Fubo6CggLWEeuskSNHws7ODrNmzcLbt29haWmJtLQ0CAQChIWFYcSIEawjSiQyMhLq6uro0aMHAGDTpk3Ytm0bzMzMsGnTJpolJIQQ8s3JsQ5ASF2VkpKCH374Qez60KFDkZqayiARKVdYWAhVVVWx6y9fvkS9evUYJCLlLl68KFxSfeTIEQgEAuTl5SE4OBjLly9nnE5y8+bNQ35+PgDgzp078PT0xODBg5GSkgJPT0/G6QghhNQFVBASwoihoSHOnDkjdv3MmTMwNDRkkIiUs7Ozw+7du4Vf83g88Pl8BAQEoHfv3gyTkdevX0NHRwfAh9m1ESNGQFVVFUOGDEFiYiLjdJJLTU2FmZkZAODQoUP44YcfsHLlSvz+++/4999/GacjhBBSF9A5hIQw4uXlBXd3d8TFxaFbt27g8Xi4fPkydu3ahQ0bNrCOV6cFBASgV69euHHjBoqLizF//nzcu3cPubm5iI6OZh2vTjM0NMR///0HHR0dREZGIiwsDADw6tUrKCsrM04nOSUlJRQVFQEATp8+jQkTJgAAdHR0hDOHhBBCyLdEBSEhjLi5uaFx48YIDAxEeHg4gA/7Cg8cOIBhw4YxTle3mZmZIT4+Hps3b4a8vDwKCwvh6OiImTNnQl9fn3W8Om3u3LkYO3Ys1NXV0bx5c/Tq1QvAh6Wk5ubmbMNVQ48ePeDp6Ynu3bvj2rVrOHDgAADg0aNHaNq0KeN0hBBC6gJqKkMIIRWUlJSgf//+2Lp1K1q1asU6DqnEzZs3kZ6ejn79+kFdXR0AcPz4cWhpaaF79+6M00kmPT0dM2bMQEZGBtzd3eHi4gIA8PDwQFlZGYKDgxknJIQQIuuoICSEEWdnZ4wbNw729vZ02LmU0dXVxZUrV9CyZUvWUQghhBBCvilqKkMIIzk5ORgyZAiaNm0KLy8vxMbGso5E/mfChAnYsWMH6xiEEEIIId8czRASwlBeXh7Cw8Oxb98+XLp0Ca1bt8a4cePg5OQEIyMj1vHqrNmzZ2P37t0wNTVFx44doaamJvJ4UFAQo2SEEEIIITWLCkJCpMSTJ0+wf/9+hISEIDExEaWlpawj1VmfO1qCx+Ph7NmztZiGEEIIIeTboS6jhEiBkpIS3LhxA1evXkVaWhoaNWrEOlKddu7cOdYRCCGEEEJqBe0hJIShc+fOYcqUKWjUqBEmTpwIDQ0N/P3338jIyGAdjRCpdenSJYwbNw5du3bF06dPAQB79uzB5cuXGScjhBBCuIdmCAlhpGnTpsjJycGAAQOwdetW/PDDD5w8WJuQ2nTo0CGMHz8eY8eORWxsLN6/fw8AePPmDVauXIkTJ04wTvhljo6OVX7u4cOHv2ESQgghhApCQpjx9fXFTz/9BG1tbdZRCOGM5cuXY8uWLZgwYQLCwsKE17t164alS5cyTFZ1mpqarCMQQgghQtRUhhBCCGeoqqri/v37MDIygoaGBm7fvg0TExOkpKTAzMwM7969Yx2REEII4RTaQ0gIIYQz9PX1kZSUJHb98uXLMDExYZCIEEII4TZaMkoIIYQzpk2bhjlz5iAkJAQ8Hg/Pnj3Df//9B29vb/j6+rKOVy0REREIDw9Heno6iouLRR67desWo1SEEELqCpohJIQQwhnz58/H8OHD0bt3bxQUFMDOzg6urq6YNm0aZs2axTqexIKDg+Hs7Aw9PT3Exsaic+fOaNCgAVJSUjBo0CDW8QghhNQBtIeQEEII5xQVFeH+/fvg8/kwMzODuro660jV0qZNG/j5+WHMmDEieyJ9fX2Rm5uLjRs3so5ICCFExtEMISGEEM559uwZcnJyYG5uDnV1dXD13mZ6ejq6desGAFBRUcGbN28AAOPHj8f+/ftZRiOEEFJHUEFICCGEM3JyctCnTx+0atUKgwcPRmZmJgDA1dUVXl5ejNNJrnHjxsjJyQEANG/eHDExMQCA1NRUzha5hBBCuIUKQkIIIZzh4eEBRUVFpKenQ1VVVXh91KhRiIyMZJiseuzt7fH3338DAFxcXODh4YF+/fph1KhRcHBwYJyOEEJIXUB7CAkhhHBG48aNcfLkSVhaWorsuUtNTYW5uTkKCgpYR5QIn88Hn8+HgsKHpt/h4eG4fPkyTE1NMX36dCgpKTFOSAghRNbRsROEEEI4o7CwUGRmsNzLly9Rr149Bom+zpMnT2BoaCj8euTIkRg5ciQEAgEyMjLQrFkzhukIIYTUBbRklBBCCGfY2dlh9+7dwq95PB74fD4CAgLQu3dvhsmqx9jYGC9evBC7npubC2NjYwaJCCGE1DU0Q0gIIYQzAgIC0KtXL9y4cQPFxcWYP38+7t27h9zcXERHR7OOJzGBQAAejyd2vaCgAMrKygwSEUIIqWuoICSEEMIZZmZmiI+Px+bNmyEvL4/CwkI4Ojpi5syZ0NfXZx2vyjw9PQF8mOH08fERWQZbVlaGq1evwsrKilE6QgghdQkVhIQQQjihpKQE/fv3x9atW+Hv7886zleJjY0F8GGG8M6dOyLNY5SUlGBpaQlvb29W8QghhNQhVBASQgjhBEVFRdy9e7fSJZZcc+7cOQCAs7MzNmzYgPr16zNORAghpK6iYycIIYRwhpeXFxQVFbF69WrWUWrckydPwOPx0KRJE9ZRCCGE1CE0Q0gIIYQziouLsX37dpw6dQodO3aEmpqayONBQUGMklUPn8/H8uXLERgYKDxDUUNDA15eXli8eDHk5KgZOCGEkG+LCkJCCCGccffuXdjY2AAAHj16JPIYF5eSLl68GDt27MDq1avRvXt3CAQCREdHY8mSJXj37h1WrFjBOiIhhBAZR0tGCSGESLX4+Hi0b99eJmfLDAwMsGXLFgwdOlTk+l9//YUZM2bg6dOnjJIRQgipK2TvtyshhBCZYm1tjZcvXwIATExMkJOTwzhRzcnNzUWbNm3Errdp0wa5ubkMEhFCCKlrqCAkhBAi1bS0tJCamgoASEtLA5/PZ5yo5lhaWmLjxo1i1zdu3AhLS0sGiQghhNQ1tIeQEEKIVBsxYgS+++476Ovrg8fjoWPHjpCXl6/0uSkpKbWc7uusWbMGQ4YMwenTp9G1a1fweDxcuXIFGRkZOHHiBOt4hBBC6gDaQ0gIIUTqRUZGIikpCe7u7li6dCk0NDQqfd6cOXNqOdnXe/bsGTZt2oQHDx5AIBDAzMwMM2bMgIGBAetohBBC6gAqCAkhhHCGs7MzgoODP1kQck16ejoMDQ0r7ZCanp6OZs2aMUhFCCGkLqGCkBBCCGFEXl4emZmZ0NPTE7mek5MDPT09lJWVMUpGCCGkrqCmMoQQQggjAoGg0tnBgoICKCsrM0hECCGkrqGmMoQQQkgt8/T0BADweDz4+PhAVVVV+FhZWRmuXr0KKysrRukIIYTUJVQQEkIIIbUsNjYWwIcZwjt37kBJSUn4mJKSEiwtLeHt7c0qHiGEkDqE9hASQgghjDg7O2PDhg2oX78+6yiEEELqKCoICSGEEEIIIaSOoqYyhBBCCCGEEFJHUUFICCGEEEIIIXUUFYSEEEIIIYQQUkdRQUgIIYQQQgghdRQVhIQQQgghhBBSR1FBSAghhBBCCCF1FBWEhBBCCCGEEFJH/R/iFw6fyXylLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap for correlation\n",
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(correlation, cbar = True, square = True, fmt = '.1f', annot = True, annot_kws = {\"size\": 8})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0017e016",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate data and label\n",
    "X = wine_dataset.drop(\"quality\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "79e5b766",
   "metadata": {},
   "outputs": [],
   "source": [
    "# label binarization or label encoding\n",
    "Y = wine_dataset[\"quality\"].apply(lambda y_value: 1 if y_value >= 7 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "48548b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
      "0               7.4             0.700         0.00             1.9      0.076   \n",
      "1               7.8             0.880         0.00             2.6      0.098   \n",
      "2               7.8             0.760         0.04             2.3      0.092   \n",
      "3              11.2             0.280         0.56             1.9      0.075   \n",
      "4               7.4             0.700         0.00             1.9      0.076   \n",
      "...             ...               ...          ...             ...        ...   \n",
      "1594            6.2             0.600         0.08             2.0      0.090   \n",
      "1595            5.9             0.550         0.10             2.2      0.062   \n",
      "1596            6.3             0.510         0.13             2.3      0.076   \n",
      "1597            5.9             0.645         0.12             2.0      0.075   \n",
      "1598            6.0             0.310         0.47             3.6      0.067   \n",
      "\n",
      "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
      "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
      "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
      "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
      "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
      "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
      "...                   ...                   ...      ...   ...        ...   \n",
      "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
      "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
      "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
      "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
      "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
      "\n",
      "      alcohol  \n",
      "0         9.4  \n",
      "1         9.8  \n",
      "2         9.8  \n",
      "3         9.8  \n",
      "4         9.4  \n",
      "...       ...  \n",
      "1594     10.5  \n",
      "1595     11.2  \n",
      "1596     11.0  \n",
      "1597     10.2  \n",
      "1598     11.0  \n",
      "\n",
      "[1599 rows x 11 columns]\n",
      "0       0\n",
      "1       0\n",
      "2       0\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1594    0\n",
      "1595    0\n",
      "1596    0\n",
      "1597    0\n",
      "1598    0\n",
      "Name: quality, Length: 1599, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ccd53e",
   "metadata": {},
   "source": [
    "### Split Train and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e8a0f978",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify = Y, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e570d474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total data:  (1599, 11)\n",
      "Train data:  (1279, 11)\n",
      "Test data:  (320, 11)\n"
     ]
    }
   ],
   "source": [
    "print(\"Total data: \", X.shape)\n",
    "print(\"Train data: \", X_train.shape)\n",
    "print(\"Test data: \", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f60598f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
      "1031            7.3             0.550         0.01             1.8      0.093   \n",
      "1364            7.2             0.605         0.02             1.9      0.096   \n",
      "649             6.7             0.420         0.27             8.6      0.068   \n",
      "276             6.9             0.540         0.04             3.0      0.077   \n",
      "792             7.1             0.610         0.02             2.5      0.081   \n",
      "...             ...               ...          ...             ...        ...   \n",
      "524             9.2             0.430         0.49             2.4      0.086   \n",
      "1024            7.7             0.580         0.01             1.8      0.088   \n",
      "1445            7.4             0.785         0.19             5.2      0.094   \n",
      "1035           10.1             0.370         0.34             2.4      0.085   \n",
      "141             8.3             0.715         0.15             1.8      0.089   \n",
      "\n",
      "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
      "1031                  9.0                  15.0  0.99514  3.35       0.58   \n",
      "1364                 10.0                  31.0  0.99500  3.46       0.53   \n",
      "649                  24.0                 148.0  0.99480  3.16       0.57   \n",
      "276                   7.0                  27.0  0.99870  3.69       0.91   \n",
      "792                  17.0                  87.0  0.99745  3.48       0.60   \n",
      "...                   ...                   ...      ...   ...        ...   \n",
      "524                  23.0                 116.0  0.99760  3.23       0.64   \n",
      "1024                 12.0                  18.0  0.99568  3.32       0.56   \n",
      "1445                 19.0                  98.0  0.99713  3.16       0.52   \n",
      "1035                  5.0                  17.0  0.99683  3.17       0.65   \n",
      "141                  10.0                  52.0  0.99680  3.23       0.77   \n",
      "\n",
      "      alcohol  \n",
      "1031     11.0  \n",
      "1364     11.8  \n",
      "649      11.3  \n",
      "276       9.4  \n",
      "792       9.7  \n",
      "...       ...  \n",
      "524       9.5  \n",
      "1024     10.5  \n",
      "1445      9.6  \n",
      "1035     10.6  \n",
      "141       9.5  \n",
      "\n",
      "[1279 rows x 11 columns]\n",
      "1031    1\n",
      "1364    0\n",
      "649     0\n",
      "276     0\n",
      "792     0\n",
      "       ..\n",
      "524     0\n",
      "1024    1\n",
      "1445    0\n",
      "1035    1\n",
      "141     0\n",
      "Name: quality, Length: 1279, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceda3797",
   "metadata": {},
   "source": [
    "### Model Training using Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b4705c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d00b4965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f1e429",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b89cebd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.9992181391712275\n"
     ]
    }
   ],
   "source": [
    "# accuracy score on the training data\n",
    "X_train_prediciton = model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(X_train_prediciton, Y_train)\n",
    "print(\"Accuracy on training data: \", training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a1bf2d69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data:  0.934375\n"
     ]
    }
   ],
   "source": [
    "# accuracy score on the test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
    "print(\"Accuracy on test data: \", test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c2b8dee",
   "metadata": {},
   "source": [
    "### Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "6b92a4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.iloc[311]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2b682c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fixed acidity            8.90000\n",
       "volatile acidity         0.35000\n",
       "citric acid              0.40000\n",
       "residual sugar           3.60000\n",
       "chlorides                0.11000\n",
       "free sulfur dioxide     12.00000\n",
       "total sulfur dioxide    24.00000\n",
       "density                  0.99549\n",
       "pH                       3.23000\n",
       "sulphates                0.70000\n",
       "alcohol                 12.00000\n",
       "Name: 1008, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.iloc[311]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d11e9208",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = X_test.iloc[311].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "d856b036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 8.9      0.35     0.4      3.6      0.11    12.      24.       0.99549\n",
      "  3.23     0.7     12.     ]\n"
     ]
    }
   ],
   "source": [
    "print(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "225bebcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TanveerKader\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data = (sample)\n",
    "\n",
    "# changing the input data to numpy array\n",
    "input_data_as_numpy_array = np.asarray(input_data)\n",
    "\n",
    "# reshape data\n",
    "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "abcee185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good Quality Wine.\n"
     ]
    }
   ],
   "source": [
    "if(prediction[0] == 0):\n",
    "    print(\"Poor Quality Wine.\")\n",
    "else:\n",
    "    print(\"Good Quality Wine.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1601a732",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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