The_Spark_Foundation_Task-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science and Business Analytics Internship\n",
    "## GRIP : The Sparks Foundation\n",
    "## Author : Ashlesha Datir\n",
    "## Task 2 : Prediction using Unsupervised ML\n",
    "\n",
    "### Q) From the given 'Iris' dataset, predict the optimum number of clusters and represent it visually.\n",
    "* **Dataset is available at:  https://bit.ly/3kXTdox**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data import successfuly\n"
     ]
    }
   ],
   "source": [
    "# Import the data set from locally saved\n",
    "iris = pd.read_csv(\"iris.csv\")\n",
    "print(\"Data import successfuly\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the first 5 records in the data set\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# More information about the data set\n",
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check null values\n",
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is no null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the data\n",
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = iris.drop(columns=['Id','Species'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: column 'ID' have unique entries and column 'Species' have label data those are not useful so we dropped here**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.038005</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>0.819624</td>\n",
       "      <td>1.447956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.553333</td>\n",
       "      <td>-1.281972</td>\n",
       "      <td>0.705893</td>\n",
       "      <td>0.922064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.795669</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>0.819624</td>\n",
       "      <td>1.053537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.800654</td>\n",
       "      <td>0.933356</td>\n",
       "      <td>1.447956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.068662</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>0.762759</td>\n",
       "      <td>0.790591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "145       1.038005     -0.124958       0.819624      1.447956\n",
       "146       0.553333     -1.281972       0.705893      0.922064\n",
       "147       0.795669     -0.124958       0.819624      1.053537\n",
       "148       0.432165      0.800654       0.933356      1.447956\n",
       "149       0.068662     -0.124958       0.762759      0.790591"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardized data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "std_iris=scaler.fit_transform(iris) #in array form\n",
    "std_iris=pd.DataFrame(std_iris, columns=iris.columns) # make data frame\n",
    "std_iris.tail() # show last 5 records"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AqDrjsAyS4LTPooUtf+WWtF/39azTPSbds9xY0uNN8m3d/eMn39Jj4ZVnGQX1tpOhC33t17aqXXzM48JjmZ4YWLTu61knPaaie4oDCzyopQPcQLbZY5nWN5p1taJWvuW9n91qm3H31nvbS9YsYd7yeQxuGGRwwyDzls9jyZol+aZ1DEoH1ETlY04TWwYHM39Deq0x5Wa/YX0syL9nvfnpkeAA2/Zwso6xJr3FVsdFs2wn7zs90utLgmW7e7bV8q2bJ+3kfXfPaMsNo781rizj2vUuNG5cfO3w6xsXX5v7xcdmx4TLMqZctPx71ps31u7htGuMNet2kso97/vVv8uxWcn6knWlhyZq9WYb9cjz6BnnuZ9Z0pM1zclxgq4ad0+GOhacvuAVD1ZqpNvGtZPAu2VwkI2Lrx1+vbLHmu6tpnut47EH262KGQapDFrV3msUOLIGuloBIe9A3IosF8Eanb5320XELOlpJs3p4zTzbMA6M4yUoyTYNxvowXvieQ7B1Au89S401hrCaNXGxdeydu75rJ17/nDjsXbu+ds0IFJf911gTAwHOmoPKQyP086sfXrf6QuWWRqKRqfv3TadPUt6Wklz5TFPVLt42IpqZaEDkoAMr5wuvuyRZQQCM6bMyG124miD7mjvzYb23c88mrTmsZ9F6uxT9xpduc8j0GUJ6FnTPNqg3yXBoqtVO+bVrge0olpZmLpH06tJB9tWxqQbBeQZU2Ywe7/Z2/SwO/n8j7zuzR7t2HOWYDqatI52P5u5I6aVRqAzd4O0cuU+y7BILfWGZbLK4y6Vdt3pMhblcQxzWk8SbEczJt1oqCTZRt+UPlZvWN3xse9uuDe73jh72mjSWuuzyTBOvSGcrHfEtHo3S+du3Wu20nRgEsQr5BEwRrOOZsfxWxkuGO3n662v3bcN5r0vKTOmzGhpTDotPT5dbWy6UUDPe3w7i8oLllmCWKP1NDN2PbGvj545c4b/b9fYdysNReXPaNPdvWPW1dQKcJVTj1tdppOyTJ+uNaabdZksQzlZrhW0sr7RDkO1Ikt+1VE53JG3pPec/J33cEoRql2wbGXoIPlsEviyfCb92UbbzDJkUnmLYiPpYZxWbh9Mp/vFoSE2/+IXw0E/HchrKVewrmW0QawbZA2SWcfx622jUeCsnBlYq5FrZn2V1xXaNX6fw3BH8ncRGvXOGzUYlePbS9Ys4agm09BK0Kr8v14Qq3XfdvK5VgJfo21mCeiduEUxvc8vPfPMNo1VI8UF63Y/aGc0QaxbVKaviLOBZieONGrksq6vcl+qBfouNNqhjtHK0mBULtNssC46aFWuv113V2TpCTc7pl3ZY29m0lCt9GVtrIoL1ll6ih14clqpdMvZQKNGLkujUm1fcphxOB5kaTBG26gUfXtfpy5Ktno7Xq2zjXSPvXIiUZ5DLtUUOwzSqJI3Mz5apG4ezy7ibCDv/c3aqHT7mU2ORnt7X9nUGo8tUrVebl7pyjKZKOkRt2vIpfMPctpzZucrcTfcadJORexvNxzHLpLH7X1lkwSxCb29bDd5MtA4MNWaDl95l0mjQFxvW62kK/lc1jOCLLcLjnZWaMOedQhhO+ArwKHAn4APmNlDLW+xW423QDPe9rcDOj3e3UlZg1KtHmet4YZq22lm3LfMz+/O0rN+BzDRzI4F/hH4fKEpEpFxpVaPMwnEZQ6wecoSrE8AlgOY2V1Af6EpEhGRV8hygbEH+EPq/5dCCDuY2daC0tT9kotzIiIpyTj7y5s3Dw/NZP2C4UaCmdVfIIQvAHeZ2bXx/yfM7PUVy8wH5sd/ZwCrc0mdiMj4sY+Z9dZ6M0uwfifwNjN7XwjhGODTZnZGzokUEZE6sgTr5G6Q+Ig85plZsTdQiojINhoGaxER6bzOT4oREZGGFKxFREpAwVpEpATGxvOsRaQpIYTDgD8C7wJ+ama3Nfn5KfHPo4AVwAHAsfG1n5nZigaf/yDwVTN7uZntZkjXCcCLwCnAz+Pfe5nZdSGEo6qlK35mHXAu8HMzuzX13inAJuC/Avea2c11tt0PnIHPS/mJmf1nbjtGzsE6hLATftFySwhhRzN7IYQwwcxejO9PwHvzAbDUstssk/xdYxsTzWxLldcnAVuTbQKWnrhTOZGnmW022OcpwI5mti6E8FozWx9C2B3YjBfe+4BNZvZiCGFfM3u02jaT/0MIrwV+D8wB7gRmAoNAHzAJ+BWwC/A74IW4nVnAI8CTwGnAvcCRwAS8oP0MeDnmyfOpbe6BF6yTgXuAl+L6TgYeAp5IvTfZzB4NIRwa03My8HDFNg8E7jGzp1PbeBVeYU4DfoHfh/9r4HAzW16Rl8myx+L36v/OzF7IeBwmxrI0Kb2fFWVyuOyklp8MHAdMBm4FzgSWAltSafmTmd0Rj/Xz8bVfx78nxWO/P/Bb4E3AROBZPGj9MZXGKcCfATvj9WArqboA7AEsiunqwY/3ZmAasBZ4GzAE/BSYih+75/AAMREvDzfjgWW1ma1KpSspi3vHvP1rvAx9EvhECOGuuJ57gMMZKUMrgKNJlaW4T5/Dj//MuM+vBi42sz+GED4LrAghzDSzlal8OxX4JfBGvKx8NB6f7YD/jOnbAMzGy+Kt+F1ovzaz9al8PDjmyU7Aj+P215jZqriNF4ErgH8Ftgc+F0L4Eh6QV8T0pPPtJOBp4NPA38XtJk6Nx+ufgItCCLsyUo5/A/wXYDrwLeBE4DLgvwGzQgjPxXU+EPPvT3i9Wh3TsgtV6kwtud0NEkK4BM/o0+JLu+KF7TvAWcANwPuBVXgGvoRnVA9wI15JbgTOAdbjgeJhvOV+HNgNL9QPAm8FbsEL8nHA7XjmHY1n+g+AC4H/jmfQE8BCYBleoNfjGTsJLzxHADvG9w/GC+Wj+AHdJf59dNyv/WLaNwH74wVtE3At8B7ge8A78cL7S2AfPNDeh1fOSXhFPziuZzu80D2bys5JcT8PAD6GF7wPAJfE9U6Neb0OLzBL4zIr8YJ6SMy754Ev4c9z+THwt8BNcf+3xwvK5Ljs3sSADnwC+AJe8Y+LeTMj7sMb4jY/AXwxtc2ZqfWEuO3b4rF6NO7noXHbO+IV9t64nTcDz+AN1CT8eB4OvBb4NzP7YgjhrLje4+P2jsKP9W/icbgTb7R+ktrPx+L2To7bvCsus31cxwS8w/Ih4OK4zU3xGAzFffgy8B/Agpivz8V9Pid+9g7gfnxS2IPxsy/GvNwP+BHey/sBXg8eiO9Pinm5Kh6HVTEf1uKB8Ky43nfGdLwQ9/dEvDy/ELe/Ag/yO+FB42W8bOwKLE6la6+YB6+P67k95uW5wFV4ANo/HqP7477ejZfdh+L2vghcCXw37udB8f0LYtrfFvfzPLy+z47H4aCYb4aXo48BHzezPw8hfDyu/yN4wz8B73jsEo/HB/HyewNev34L7Av8MKb1Rbzs9AD/DzjdzOaGED6DN2Ynxv04Mn7uw8AxMe+SfJuM16l7Y94tw4PuGjyQ/wZ4VTxmD8TtXgj8H7xeXoGXzfvjfp4Q0zk5rvO6mDc74eV+Vzx2vQa4FJhjZp+mgTzHrCfgreMmvJL9ISZ2BV6JN+KF7HI8czfjB2cV8A38AG0EhszsHXiBmoUX9tcAvfgBvDL+/t94b2ECfirXg1ewlXF96/Bg8R68dfs6HhBfwgPvnXjweU3c1r34QTgorudkvBL14kH2abxF3ID3RvbGD/4ReAVbix+I1+EHdFXMg2fxAP4j/ICtjfvwz3gguQ2vUA8Au8f1viH+PgwYwHt5b8cL3iNxnZPi63OAs/EKPQ1vEA+Ky83AG89dYnpWxbw/Ku7f/nEfXsQLruEVekV87UC8Z/EyXtnX4g3I6Xhv/U945X9rXN9WvEw9FLezNW7zirg/W/BTb+IyK/BnzRwW8+cGvJf0GryB/h3wphDCpXhjvhUvX7vF4/ULvDIdHvN0asV+Ho03IlPifl4Z8zYJwptj/r4dD6z9cV+3xvzoxSve43hj14sHuLfiFW4qXp6S43VF/Ny0+PMIHhR+j5eLdXFf5+A9sr54DI6Pvx+K+3QiHqx/GY/hkXEfL8ADjwFvift1cjxGp+GNUE9My4aYrml4ffh5zLv1cR1PAneb2Tq843MgHuwfxMv6cvyY7w28N753On5WNYCXk2/iwXgOXtdvNrMrgBvM7LPxOOyHB7B3xDw4CW8gfoF7E16mkjPKh4G/wcvVlngMN8b8fntM96vje8/E5U7F6/1+eH0EP4PZHrgeL8tJQ30iHmBPivl6Gh4LfmRmF8fjNRMP2JPxRuFfgCX4GcvvY1pmx+2djZf5X+P1cgjvJK7Ce+63mdkDeGP2vngMevA6eTB+3JMhpbry7Fn3AB/Fe8v34BV5Dd4iPYkXnvfjFXQ3vKV+Hm+BXh1/7wS8O+7I3nhF+DSewQPxMz145uyL91huwQv5Z4B/xzPyWfw0cE0I4Ty8YkzDW/2tMY2b8ODzHH5wHsQDwt145TwrLvdlRk5Hz8AP5s144ZqNV9xb8YP4SNzPD+EV8xP46eYBcZ1X4oH5s8Bf4gU/GbN7EK8MM+O21sf0vAkvcHfE5U7EA/tB+OnZgzGfD8AL+h5xf3eNr6+J+fNk3J/HGAnKlwNPxc+dhPe2ZsXtXoc3Zs/hlfFmvHG8K6ZvZzzwrI3rNjxwBDywbR/zqh/vcfTggWcy3uhNw3vVR+IFeRnemK2O65yB98i/YGbPhhD2wivtx+I+TI95/c3U5w+M6Xo8ptXiZ/rjsmvjZz+JV+Cd8DK1Bi8zO8R1JWcFO8T9OIqRBvl04Gvx+P0KmGpm54cQbsLL4lY8qHwXn/k7K5513ov3kv8OuCbm061x2RXxmBnwBzN7bwjhe3h5WovXh+Nimm+JadoxpuuuuA/b452Wx5O8N7MzQgjL8bKzP7AnXm6fjOvBzD4fQvhGzINT8Lp7Gh4IXxOPwQN4edoQ03GHmV0WG9H9Yzr7gB+a2cK4vgfw4PZLPOD+Cu8M/Squ++W4jr+Px2Fi3MYsvJHeDW80krOES/ChhmfwIHc7HgOewcv+ffG43W1m/xpC+J8xn89h5CznYbxDtQMeXPfF48z6irQ/GNNxL17WX477+QDeeO+Al9XvMdJpOc3MTo1nCi/Effo68D4z++dUeq7BA/qr8Ri0Ltk2DeQZrC8DFuGV6Qg8w3eMCZqGn5J9GM/UN+KF4vXx/S1467IIL1B74q0deM94DV7Q78Mrw654QDwYr1w7443CBjyjrojb+xZeue6L669c5uvx58y4zcvx4PgTvOKsxk+Pb8ED1iN4pdoRH964Hi9Qu+Ct7/V4JX8XI6dOO8fXTsALzjfwM4yX4za34BXiELywPRbz73m8EN3LyBjuc3iwXho/vxkPhIPAp+Jrv63yXpKu+THfJ8Vt/wCvSJvwIHoLIz2PT+O94ENiOrbGfU6GgL4cj8lqvGc4GW/ojojrT5Y5HW9cD8RPUy2m61i8Yfo2HtQuDiH8RXzvXfjxn4D3mr6G97a24gHuFLxyP4z3oJ7Hh9suivmbHKvJMT33MXLMvxbTvCqm//MxLYfGND+Ol8tkWOXy1H4eHvPgqfj/5pg/z+DB5QlGevTE9N+BN/J/gx//U+KxOAIvG8lY7ABecWfjZXRyzIskb9fgPd2kh3h5zKf1eEdhFj5UszfwmJndGEKYDnzIzD4WQjgibnshsHvs7QEQQujFz+pOx4d8doj5+aSZnR1C+B9x374HfMfMjo3rPtbM/m8I4UzgGDO7pMb6/irmyd3x9weAr5jZWSGEM2Nak238DLg8Br4z8SGL31Wk627gKjN7ILXMXRVp+NvUZ4a3lSybzh/gxTpp3wGfwT0/vn5Albz9S7yu3ImXpc2pZZI8StKzR+X+JdtuJM9g/dd44TsKL3hL8VP9B4D/BczFT8seBubhBfhIvEJfh1e+y/DxuuPwHf8tXrBPwyv5w3jgfyJu6814wX4KPx07Gs+smxjpPb8H+Dc8MFQucxZeES+Nr9+Dn6o9F7d9Ez5m9lfAx/FTua/GfezBT91fBLYzs8tDCLvE/ZxmZpeGEC7CK8cewLvM7F9Sry3EK+FE/FTrEDxI/CNwmZnNauU4VJNKV5Lvyf7OTdKV17bykCpLyfG9Cu+BfgovU6/Dy8NC4HxG8u0aPOgmx2og/uzEyDE/AT8dHT4mLaTvn/AG9lEAM7s94/4Ml5VmtymS590gR+G9uP3wnua5eKu/Be+xfRuvfG/Ee6aL8ArzaFzmzfig/Uy8d3Mwfsp2WlzPXvGzK/DK9h28pTP8NKgvLr8e79GcHbe5K37Bo7fKMrvEbc7Cg8Lx+CnKvnG5DfHvC/FhgofjutbH95NhmKTF+zLeOG0JIVyMB+BX4T3C5K6GY+Jr9zPSw01O0zbGfKx5e1CLknSl830D3lBlutuizZKylBzfP8Pz/8OMHKNefIhpD0bybYhtj1VSFtPHfHpcT/qYNGs6HniT9NUN1qn9gZGyItKUPHvWyenD0+nf8VSl6nvNLJP3+vJcpjIPktcq/6+3TLX15aXescl7W3molid5H8/R7Huz68hjmyJ6kJOISAlourmISAkoWIuIlICCtYhICShYi4iUgIK1iEgJ/H8zkC4Ymh0ccQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can visuallize subjective cluster\n",
    "import scipy.cluster.hierarchy as sch\n",
    "dend = sch.dendrogram(sch.linkage(std_iris,method='complete'))\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From above figure we conclude that there are 3 clusters but we will verify from k-mean clustering**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using K-Mean Clustering\n",
    "**First we have to find optimal no. of clusters(value of k) using Elbow Method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "WCSS = [] # within cluster sum of square\n",
    "for i in range(1,10):\n",
    "    Kmean = KMeans(n_clusters=i, init='k-means++', max_iter=30,n_init=10,random_state=0)\n",
    "    Kmean.fit(std_iris)\n",
    "    WCSS.append(Kmean.inertia_)\n",
    "#WCSS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find the values of **WCSS** now we can visualize as below using line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,10),WCSS,'go--',color='red')\n",
    "plt.title('The Elbow Method', size=20)\n",
    "plt.xlabel('No. of Clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**we clearly see why it is called 'The elbow method' from the above graph, the optimum clusters is where the elbow occurs. This is when the within cluster sum of squares (WCSS) doesn't decrease significantly with every iteration.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model,k=(1,10),timings= False)\n",
    "visualizer.fit(iris)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above we see that WCSS does not change drastically after 3 so we can choose the no. of clusters are 3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training successfilly\n"
     ]
    }
   ],
   "source": [
    "# Build cluster algorithm\n",
    "kmean = KMeans(n_clusters=3,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
    "clusters = kmean.fit_predict(std_iris)\n",
    "print('training successfilly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.032057</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.398138</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.284407</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Clusters\n",
       "0      -0.900681      1.032057      -1.341272     -1.312977         1\n",
       "1      -1.143017     -0.124958      -1.341272     -1.312977         1\n",
       "2      -1.385353      0.337848      -1.398138     -1.312977         1\n",
       "3      -1.506521      0.106445      -1.284407     -1.312977         1\n",
       "4      -1.021849      1.263460      -1.341272     -1.312977         1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add a now column(label)\n",
    "std_iris['Clusters'] = kmean.labels_\n",
    "std_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    53\n",
       "1    50\n",
       "2    47\n",
       "Name: Clusters, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can check values in each clusters\n",
    "std_iris['Clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build A Two Dimensions Clusters Again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get centroids\n",
    "centroids = kmean.cluster_centers_\n",
    "cen_x = [i[0] for i in centroids] # find center from x-axis for each clusters(we get 3 values)\n",
    "cen_y = [i[1] for i in centroids]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## add to df\n",
    "std_iris['cen_x'] = std_iris.Clusters.map({0:cen_x[0], 1:cen_x[1], 2:cen_x[2]})\n",
    "std_iris['cen_y'] = std_iris.Clusters.map({0:cen_y[0], 1:cen_y[1], 2:cen_y[2]})\n",
    "# define and map colors\n",
    "colors = ['#DF2020', '#81DF20', '#2095DF']\n",
    "std_iris['c'] = std_iris.Clusters.map({0:colors[0], 1:colors[1], 2:colors[2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Clusters</th>\n",
       "      <th>cen_x</th>\n",
       "      <th>cen_y</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.032057</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.014579</td>\n",
       "      <td>0.842307</td>\n",
       "      <td>#81DF20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.014579</td>\n",
       "      <td>0.842307</td>\n",
       "      <td>#81DF20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.398138</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.014579</td>\n",
       "      <td>0.842307</td>\n",
       "      <td>#81DF20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.284407</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.014579</td>\n",
       "      <td>0.842307</td>\n",
       "      <td>#81DF20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.341272</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.014579</td>\n",
       "      <td>0.842307</td>\n",
       "      <td>#81DF20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Clusters  \\\n",
       "0      -0.900681      1.032057      -1.341272     -1.312977         1   \n",
       "1      -1.143017     -0.124958      -1.341272     -1.312977         1   \n",
       "2      -1.385353      0.337848      -1.398138     -1.312977         1   \n",
       "3      -1.506521      0.106445      -1.284407     -1.312977         1   \n",
       "4      -1.021849      1.263460      -1.341272     -1.312977         1   \n",
       "\n",
       "      cen_x     cen_y        c  \n",
       "0 -1.014579  0.842307  #81DF20  \n",
       "1 -1.014579  0.842307  #81DF20  \n",
       "2 -1.014579  0.842307  #81DF20  \n",
       "3 -1.014579  0.842307  #81DF20  \n",
       "4 -1.014579  0.842307  #81DF20  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters - On the first two columns\n",
    "plt.figure(figsize=(12,6))\n",
    "X=std_iris.iloc[:,:4].values\n",
    "plt.scatter(X[clusters == 0, 0], X[clusters == 0, 1], \n",
    "            s = 100, c = 'red', label = 'Iris-setosa')\n",
    "plt.scatter(X[clusters == 1, 0], X[clusters == 1, 1], \n",
    "            s = 100, c = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(X[clusters == 2, 0], X[clusters == 2, 1],\n",
    "            s = 100, c = 'green', label = 'Iris-virginica')\n",
    "\n",
    "# Plotting the centroids of the clusters\n",
    "plt.scatter(kmean.cluster_centers_[:, 0], kmean.cluster_centers_[:,1], \n",
    "            s = 100, c = 'black', label = 'Centroids') #centroids\n",
    "plt.title('K-Means Clustering\\n', loc='left', fontsize=22)\n",
    "plt.xlabel('Sepal length in cm',fontsize=15)\n",
    "plt.ylabel('Sepal Width in cm',fontsize=15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means Clustering with lines from centroids:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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73/fddx9CCC655BKy2eyEdIG7774bx3EOuXbhQx/60ISOQi9+8YtZs2YNpVJp2jSrQzHb8/rVr34VgI997GMT8thbW1v5h3/4BwC+9rWvNS6fj+fXbIz/REhKSaVS4dZbb8V1XT796U9PeA60tbXx6U9/Gsdx+OY3vzkpBa2jo4M3v/nNjf93dnY2PpG7/PLLJ3w695znPIdkMkk2mz1g+9X+/n7uuusuOjo6+Nu//VtCoVDjupe//OWcf/75NDc3MzQ0xEMPPcQDDzzASSedxDve8Y4Jn6I85znP4dWvfjX5fH7KXebZPpYHcuaZZ3LiiSc2/i+l5Mc//jEDAwOcf/75vPGNb5yQBnfhhRfyxje+Ec/zuOWWW6Y9bl9fH7/4xS9YvXo173znOxvHcByH9773vVPWR8zH661lWSvXYR18P/roo/zud7/joosu4otf/CJCCL7yla/MmIc3F5deeilSykmpJz/60Y9Yu3bthF8Y4/36178GgsBpKmOBzlgKwNq1a/nUpz7FX/3VXzVuo5Ri165d/PCHP2z8kpyqq8pUaxh7wS+VSo3LnvWsZ1GpVLjqqqv4/Oc/zyOPPIIxhvXr1/OqV72KLVu2TLnW8cZ+oc4lX3S+jQUSr33ta/nHf/xH7r//fnzfJ51Oc9111/HsZz/7gMeY6fEJh8OcddZZaK0n5XweffTRhMPhOa13No/P8PAwW7duZe3atZxwwgmTbv+iF71o1vf3vOc9D2DCz8B9993Hscce2/iF/9vf/hYIOgP9/ve/54wzzjioVpxjmpubOeaYYyZdPhbA5nK5gz72dGZzXgcGBtixYwfpdJrjjz9+0u2PO+44urq62LlzZyPono/n14GkUqlJnTQeeeQRKpUKp5566pTB3Pr16zn55JMplUr84Q9/mHDdli1bcN2Jja3Ggvepvu+mpiaAKVNYxhv7ObngggumfN5/9atf5dZbb6W9vb1x27POOmvKOo/9X/PGm+1r2IFM9b2OPden+xkaq9kZu91UxtZ83nnnTTrPoVCI5z73uZO+Zj5eby3LWrkO+1aDz3/+8/nMZz5DOBzmNa95Dd/4xjd417vexQ9+8IMJu0cAP/nJTxq7zeNdcsklXHLJJZMu7+zs5IwzzuD++++np6eHNWvWsGfPHh555BH+5E/+ZNo1jRXUfPjDH56y+G1MX19f49/GGO666y5uv/12tm7dyr59+xrB9tgvMzNF3u/YL9LxxoLk8bf/+7//e/78z/+cxx9/nJtuuombbrqJtrY2LrroIq655ppJO+5TGfuFOF1R22J417vexa5du7jvvvv40pe+xJe+9CVSqRQXXnghV111Feeee+4BjzF23v/oj/5oxtvtXxh1MAHqbB6fsftZs2bNlMdYt27drO/vzDPPJJVKNXa+h4aGeOqpp3j1q1/dCBx/+9vfct1113HPPfeglGoE7Adruo5CY4HKQvTCn815HXucM5lM49Oj6fT29tLR0TEvz68Dmep5NDAwADBje7t169bx0EMPTRrWM9Xxxl4z0un0tNcdyHSfAkxl7Dl88803TyrqHW/8a96Y2b6GHchM53W6n6Gxy2cagDR2jOlmFkz1mM3H661lWSvXYR18H3vssXz2s59tfBz6zne+k3vuuYenn36av/mbv+GLX/zihNtv3bqVO+64Y9JxNm7cOGXwDcHH5/fffz933nknr3/96/nxj38MMGOXk7F+tOedd96MhYnHHnssEAQnb37zm7nrrrsIhUJs2bKFs88+m+7ubs4880w+9rGPTSqim6s1a9Zw22238etf/5qf/exn/OpXv2Lbtm1897vf5Xvf+x7vf//7G90EprNlyxa+/e1vz+qj4HK5zOc+9znOPvtszjvvvAkfWc/WVH19U6kUt9xyCw8//DA//elPuffee3n88cf54Q9/yA9/+ENuuOEG3v3ud8943LFg8LLLLpsxENm/oHI2aSYHY+yThOn6GM8lAHFdl/POO48777yT3bt3Nx6rs846izVr1rB27drGjv7//d//ARzyR+ALdV4O1djj3NbWNqHYdiqJRAKYn+fXgUx1vsYe45mej2Pfz/670Pvvxs6XubxpGnvunnbaaaxfv37a2+2/ITKfZjqv0xlb91w/0RpvqkLl+Xi9tSxr5Tqsg+/W1tYJQV00GuWTn/wkr3zlK7nrrrv4xje+MaE921/8xV/wF3/xF3O6j0svvZR/+Id/mBB8b9q0aUJu4f7GdoivuOIKXvaylx3wPn7wgx9w1113sWXLFv71X/91Uk51Pp+f05qnI6Xk3HPPbeze9ff3841vfIMvf/nLfOpTn+Lqq6+eMUi+6KKLEEJw//33MzIyMuMv0v/5n//hq1/9Kt/73ve45557pr3dWLAx1S/6mdIVTj311MbH1SMjI9x+++186lOf4uabb+b666+fcbJmZ2cn+/bt413vetey6MM8tobpWpDNtTXZ8573PO68807uvfdeHnvsMYQQjamAZ599Nrfddhvbtm3j7rvvZv369VOmjBwOxn4OE4nEnLuuHMrz62B0dnYCzJgfP3bdYtVcjN3PVLvVEHyC0tvby9lnn9041xdeeCF//ud/vijrm42x87p3794prx87pzNtkoz9fE5VcwNM27nkUF9vLctauZbnltQC2rJlC3/2Z38GwCc+8Ykp+8vORUdHB8961rN46KGHuP/++3nsscdm3PWGZ/JG//d//3fK6z/zmc/wspe9jO985ztA0AoL4BWveMWkX6yDg4ON9mYHO+Ft586dXH755ZMK97q6unjnO99JS0sLpVLpgLm5HR0dXH755ZTL5RmDmUKhwBe+8AUg+J5m2pkba8k21ce+Y+dl/HGvvPLKSekira2tvOENb+CEE05Aa90olJxuF3Hs8fnlL3855fU33HAD11xzzZyKvQ7FqlWr2LhxIz09PVM+X6dr0Tad5z73uQghuPfee3nggQc49thjG2+UxvLcb7nlFgYHB2edcjLbVIXlZN26daxevZq9e/eyffv2SdcPDw/zohe9iNe97nUUi8U5P7/m05YtW4jFYvz+97+fMsjbvXs3jz32GKlUasrc5oVwxhlnAEH9wFR1HjfddBPvete76O/vn/CaN9Vu86233spll13G5z//+YNez8E8B8dSrfYvmh8z9knmTCPrzznnHKSU3H333ZOKXY0xjU+QxszX661lWSvXERd8A/zpn/4pp556KrVajbe//e2Uy+VDOt6LXvQijDG8//3vB5i2y8mYl770pXR0dPDDH/6Qb37zmxOu+7//+z+++tWvsnXrVk4++WSARoHVL37xiwk7wP39/bz1rW9t5H4fqEBqOuvXr2dkZIT/+7//43/+538mXHfPPfcwOjrK2rVrD9i7G+Dd73436XSa733ve7znPe+ZFDT39vby53/+5zz99NOsX7++MYhmOmO5uN/61rcmFJR+85vf5KGHHppw22QyiTGGrVu38vWvf33CdU888QTbtm0jHo9z9NFHA898lFwsFicEBNdddx1SSv7pn/5pQlGlMYZ//ud/5u6772bv3r2LFuTAM90vbrzxxgk9xh999NHGG5nZam9v56STTuKee+5h+/btEwKLseD7tttuA2afcjJ2LufrU5jFcv3116O15l3veteEoLZcLvPe976XHTt2kEgkSCQSc35+zadYLMbVV1+N7/u8/e1vn9CJZGRkhLe//e1orbn66qsPKUViLjZt2sRznvMcent7+fjHPz7hten222/nN7/5DUcddVSjd/rxxx/Pgw8+yKc//ekJwfpjjz3Gpz/9aZ566qkD5t7PJBKJAHN7Dr74xS+ms7OTu+++my996UsTXgf+93//l6985SuEQiGuueaaaY/R3t7OS17yEoaGhvi7v/u7xuuUMYbPfOYzk97YzefrrWVZK9NhnXYyHcdx+MQnPsEVV1zB9u3b+ehHP8qHPvShgz7epZdeyoc//GGefvppuru7G7na04nFYnzmM5/hTW96Ex/60Ie45ZZbOO644xotuSAYLDHW2eKKK67ga1/7GnfddReXXnopJ554IplMht/97ndorTnqqKPYsWPHQQ9mcByHD37wg7zlLW/hzW9+M1u2bGHt2rUMDg7y4IMP4jhO443FgXR0dDQGYHz/+9/nhz/8ISeffDKdnZ0MDQ3x8MMP43keRx99NF/60pcOOOzoFa94Bf/2b//Gb3/7Wy699FK2bNnC008/zbZt27j88ssn5ej/3d/9Ha95zWv4h3/4B77zne9w9NFHk8lkeOCBB/B9nw984AON+0yn06TTaTKZDNdeey0nn3wy73vf+zj55JP567/+az72sY/xmte8hhNPPJG1a9fy5JNPsnPnTqLRKJ/97GcXLcgBuPbaa7nrrru4++67eeELX8hZZ51FqVTiN7/5DWvWrCGXy83pI+rnPe95/PM//zMwcVdv9erVrF+/nj179pBIJGbdvWPTpk089thjvOY1r+HYY4/lH//xH+f2DS6R66+/ngcffJA777yTl7zkJZx88skkk0keeughRkZG2LhxIx/84Acbt5/L82u+vf3tb+exxx7jt7/9LS94wQsaj81vfvMbisUi559//oSOSIvhwx/+MK9+9av5+te/zs9//nNOOukk9u3bxyOPPNJ4nRvbkf6nf/onrr/+ev71X/+V//zP/+TEE0+kUChw//33o7XmuuuumzSgaS7Wrl2L67o8/vjjvOENb+DZz35241PO6cRiMT772c/yxje+kX/8x3/ke9/7Hscffzx9fX089NBDuK7LBz7wgSm7DI33N3/zNzz++OONPO6TTz6Zbdu2sW3bNk499dQJn9LN5+utZVkr0xG58w1BsDBWGPXtb3972kmVs9He3t74WPVAKSdjnvWsZ3H77bfzile8glqtxi9/+Ut6enp47nOfy//7f/+P17/+9Y3bdnV18e///u+88IUvpFKp8Mtf/pK+vj4uuugi/v3f/70x3XKu6QfjveAFL+ArX/kKF1xwAXv37uVnP/sZu3fv5pJLLuE73/nOnIrujjnmGO644w7e8Y53cPLJJ7N9+3Z++tOfsnXrVk455RRuvPFG/vM//3PGwqsx69at49Zbb+WFL3wh+Xye//3f/6WpqYkvf/nLU3YjOeWUU/i3f/s3LrnkEkZHR/nZz37G1q1bOe+887j55pt51ate1bitEIJPfOITHHXUUTzyyCMTzt/rXvc6vv71r3PRRRfR09PDL37xC7TWvPzlL+f222+fdnLpQnEch3/5l3/hr/7qr2htbeWXv/wl27Zt4/rrr+cTn/gEwJyCvrF+3+PzvceM7X4/5znPmfUbjA9+8IOccMIJbN++nXvvvXfKCaDLkZSSz3zmM3zkIx/h+OOP59FHH+W+++6jvb2dP/uzP+M//uM/JqR6zeX5Nd+i0Shf+9rXeO9738vGjRv51a9+xf333093dzcf/vCH+fKXv7yobwghKBz83ve+x+te9zogmPK6d+9eXvSiF/Ef//EfEz4dOuaYY7j99tt53eteRyQS4e6772b79u0861nP4qabbuJ973vfIa2lpaWFv//7v2ft2rX85je/4d57753V151xxhl8//vf5+qrr6ZarfKzn/2Mnp4eLrvsMm699VauvvrqAx6jra2Nb33rW7zhDW8Agtdi13W56aabpkzdms/XW8uyVh5h5tIqwbKsJfHEE0/Q0tIyZSHfT3/6U97ylrfwJ3/yJ7zzne9cgtVZlmVZljVbR+zOt2WtJH/7t3/Lc5/73ElFoCMjI430kUP5yN6yLMuyrMVhd74tawX48Y9/zNve9jYgSH1YvXo1+Xye3/3ud5TLZbvrbVmWZVkrhA2+LWuF+N3vfsfXv/51fv/73zM4OEgymeTEE0/k2muvtbvelmVZlrVC2ODbsizLsizLshaJzfm2LMuyLMuyrEVig2/LsizLsizLWiQ2+LYsy7Isy7KsRWKDb8uyLMuyLMtaJDb4tizLsizLsqxFYoNvy7Isy7Isy1okNvi2LMuyLMuyrEVig2/LsizLsizLWiQ2+LYsy7Isy7KsRWKDb8uyLMuyLMtaJDb4tizLsizLsqxFYoNvy7Isy7Isy1okNvi2LMuyLMuyrEVig2/LsizLsizLWiQ2+LYsy7Isy7KsRWKDb8uyLMuyLMtaJDb4tizLsizLsqxFYoNvy7Isy7Isy1okNvi2LMuyLMuyrEVig2/LsizLsizLWiQ2+LYsy7Isy7KsReIu5Z17nsff/M3fsG/fPmq1Gn/2Z3/G85///Mb1P//5z/n85z+P67pceeWVXH311Uu4WsuyLMuyLMs6NEsafP/gBz8gnU7zyU9+ktHRUV7+8pc3gm/P8/joRz/Kd7/7XWKxGNdeey0XXXQRHR0dS7lky7Isy7IsyzpoSxp8v+hFL+LSSy9t/N9xnMa/t2/fzoYNG2hubgbgzDPP5P777+fFL37xtMfTWlMsFgmFQgghFm7hlmVZlmVZ1hHNGIPneSQSCaScfSb3kgbfiUQCgEKhwFvf+lb+6q/+qnFdoVAglUpNuG2hUJjxeMVikSeffHJB1mpZlmVZlmVZ++vu7p4Qsx7IkgbfAL29vbz5zW/mVa96FZdffnnj8mQySbFYbPy/WCwe8BsLhUJAcBLC4fDCLPgI88gjj7Bly5alXsZhYTHOZV71UVJZWtz1hGV8Qe9rJr6uMOLvRgqHVncTUjzzqZYxhozaQ02XSTmdxJ2Wg7qPlfzcVMYnr/qo6iISSdLpJOY0L+maVvL5XI7s+Zw/9lzOL3s+50+tVuPJJ59sxJ+ztaTB99DQEG94wxv427/9W84999wJ1x1zzDHs2rWLTCZDPB7n/vvv54YbbpjxeGOpJuFwmEgksmDrPtLYczl/FvpcZmolwo5LMpxestQrYzRZbxeOhLbQBiL7vQnI+X1oUSEl07SEVk17HK01vbU/EJYxOsLdU95mJT43yypDzu9Bu4qETJF21+KI5bFZsBLP53Jmz+f8sedyftnzOb/m+vt2SYPvL37xi+RyOb7whS/whS98AYBXvOIVlMtlrrnmGt7znvdwww03YIzhyiuvpKuraymXa1nLmqfLKOMTk0sXeAPkVT++qZJw2ojI5ITrKjpHQQ3iijDN7toZj9NTe5gRfydNzhoOhzJrbXyyfg9lnUUgaXbXkHDalnpZlmVZ1iJb0uD7xhtv5MYbb5z2+osvvpiLL754EVdkWStXRecAiMqmJVtDTRcpqCFcEabJmbirrUyNjL8XgaDF3TAhFWV//bUnGPF3EhEp1kVOX+hlL7iKypFRe9FGEZZx0u46XGF3nizLso5ES57zbVnW/KjoPAIxabd5sRijyfh7AUi76xBCjrvOMOrtQRtF2l1LSMamPc6ot4f+2uO4IsJRsXNw5fJIyTgY2ihyfi8lPYpA0OSuIiHbbTcmy7KsI5gNvi3rMKBMDc+UicjkjDvKCymnevFNjaTTTlgmJl1XMyViMk3caZ32GAV/kL3VB5C4bIqcR3iJ3kjMh6oukPH3ooxHSMRIu+sIyehSL8uyLMtaYjb4tqzDwFKnnFR1nqIawRURUs7E2oyyylJUw7giQnqGPO+KyrGzch8AG6JnEXfTC7nkBWOMJqd6KaoRBIKU00nS6bS73ZZlWRZgg2/LOixUdB6AqJx9n9H5oo0i4++r53Kvn5Bu4psqWX8fAkmru3HCdePVdImnK/eg8VkbPo0md2UWV9d0iYy/B9/UcEWEFnf9jCk2lmVZ1pHHBt+WtcJpo6jpIiERW5KWdTm/F2U8Uk7nhEDTGB3keaNocdfjyqkLDLX22Vn+Fb6p0BHaTFv4qMVa+rwxxpBX/RTUIABJp52U0zXtmw3LsizryGWDb8ta4aq6gMEsScpJReco6VFCIkbS6ZxwXVb14JkycaeFmJOe8uu11uys/pqKyZF217M6cuIirHp+ebpMxt+LZyq4IkzaXTcp592yLMuyxtjg27JWuGfyvRc35SToWx2kmwTdTZ7JaS6pUUoqCMqbnTXTHmNv7XcU1ABJp5N14TMWY9nzxhhDQQ1SUAMYDAmnlSZntd3ttizLsmZkg2/LWsGMMVR1HkeEFj23OOv3oIxPk7NqQhcPT1fI+j1IHFpC66cNRnurj5Hx9xAVTWyKnI2UKydo9XWVjL+XminhCJe0u47IEuTbW5ZlWSuPDb4tawWrmSIaRUI2L+r9llWWss4SFnESTnvjcmM0o/5uDJoWd+O0g2SGazsY9LbiiiibYuci5cp5KSqqIXJ+PwZNXLbQ5K5esvaOlmVZ1sqzcn7jWZY1yVK0GFTGI6uCDib7p5tk/H2N0fJRZ+o15fxeemoPI3E5OvocwjK+WEs/JGOdW6q6iBQOLc7Gab9Hy7Isy5qODb4tawWr6jwSh7BYvGE0Wb8HbRTN7uoJHUxKaoSyzhAWcZqc1VN+bcnPsLtyPwCbouesmOC1pEbI+r0YNFHZRNpdixT25dOyLMuaO/vbw7JWKE9X8E2NmGxetAEuJTVKReeIyMSEdBNPl4M8bzGW5z15PTVdYGf1XjQ+6yPPIul2LMqaD4UyHll/H5X6m5xmdx1xp2Wpl2VZlmWtYDb4tqwVarFTTpSpkfN7kTik3XWNy7VR9TxvQ4u7bspe476usaN8H76p0hU+gZbQ+kVZ86Eoq0ywy48iIpOk3bVL0kfdsizLOrzY4NuyVqiqziMQi9ZlI+PvQ6MmBaEZfy++qZF0OqZ8I6C1ZlflV1RNnhZ3A13h4xdlvQcraKHYQ1lnEUia3TUknLalXpZlWZZ1mLDBt2WtQMp41EyJiEwsSqeNohqmqgtEZYq40zru8qFGGkrKmXok/N7qAxT1CCmni7Xh0xd8rYeionNk/X0o4xOWcdLuumk7tliWZVnWwbDBt2WtQFWdBxYn5cQ3VXJ+Xz3neW3j8poukfP76n2up87z7q0+QkbtJSbSbIyctWx7eWujyPm9lPQoAkGTu4qEbF+0XHrLsizryGGDb8taIbaVfklZZTg59bJGvvdCp5wYY8j4ezFo0u56HBECgtSMsTzv8ZePN1TbzqD3FGER56hl3Mu7qgtk/L0o4xESMdLuuglDgxaLMYayzlBUw8SdFpvqYlmWdZhanr8NLcuaQClFX+0xQiKCMZqqLhAS0QVPiSjqIWq6REw2E3PSjcvHgtWU00VETm5zmPX30Vv7A44IsSl2Hu4SBLMHYowmp/ooqmEEgpTTSdLpXJLd7rLKklf9+KaKQCBYnp8QWJZlWYfOBt+WtQLkVA8ajyb3KKq6gMEs+K63r6vk/X6kcGh21zQuL/iDVHSeiEyScjsnfV3JH2F35QFAsjFyDtFlOHa9pktk/D34poYrIrS46wnJ2KKvo6Jy5FU/nqkgEMSdFlJOp+2qYlmWdRizwbdlrQAj/i4A0u6GRWkx+Ey6iaHFWdcYKFPVBXKqD0eEaHEntwsMennfh0GxIfJskm77pNssJWMMedVPQQ0CkHTaSTldCLG4O80VnSPvD+CZMgBx2ULS7bDFnZZlWUcAG3xb1gqQ8/cB0OqsZ1TvwRHugo5lL6hBaqZEXLY0plAq45Hx9yAQtLgbJk149HWFp8v34psqq8MnkQ6tm+rQS8bTZTL+XjxTwRVh0u46wjKxqGuo6jx5f4CaKQEQk82knK4Jk0Ity7Ksw5sNvi1rmVOqRkEPEBMtCCnRSi3olEVPlymoARzh0uQGY+KDnfA9KOPT5K6aFPhr7bOjch81U6TNPZqOcPeCrW+ujDEU1CAFNYDBkHBaaXJWL+pud00Xyak+ajoIuqOyiZTTtSSFnZZlWdbSssG3ZS1zIyroKtLsrqGywC0Gx6ebpN11jR7iBTVAVReJyiaSzuSx8Lur91PWozQ5q1kbPXVB1nYwfF0l4++lZkr1lojrFm0oEQS55XnVT1UXAIjKVD3oXvz8csuyLGt5sMG3ZS1zo16Q790S2khF5xBIImJyh5H5MFb8F3daGkFqRefIq4FGqsb+9lUeJqd6ickWNkSetSDrOhhFNUTO78egick0ze6aRRlIBMGnB3nV33izFJFJUk7XgqYKWZZlWSuDDb4ta5nL+j3B4BfZxbDaSVQ2LUjKRE2XKKhBHBGiyQnSTZSpkfH3jsvznhi8DtaeZNh/mrBIcFT0nGXRy3tszVVdRAqHFmdjI299oXm6Ug+6g6LYsIzT5Kxa9Nxyy7Isa/la+t+UlmVNq6YKlM0oKdmFJyrAwqScGKPJ+HsBGukmxhhGvT1oo2h210xKlch4e+mtPYorIhy9THp5l9QIWb8XgyYqm0i7aycVhi4EX1fJq37KOgtAWMRJuZ2LmuJiWZZlrQw2+LasZWzY3w1Ak7t2XIvB+Q/oxga8JJy2xtCcvOqjZkrEZHrStMWCP8Se6u8QOGyKnEN4ikE7i0kZj6y/j4rOI3FodtctaFHqGN9UKfiDlPQoACERI+V2LmgbSMuyLGtls8G3ZS1jmXrwnXbWUtUFwjI+7zu5VV2goIZwRYQmZxUQDH8Zuyztrp1w+4rOs6t6H6DZEH02cbd1XtczV2WVIev3oFFEZJK0u3bBh9QoUyOvBiirDAZDSERJOV2Llt5iWZZlrVw2+LasZSzn9yAJERYpqqZAVMxvcKeNIlvvIZ521yGExDfVep63pNXdOCG/3NcVdpbvRRmPNeFTaN4vMF9MGsWot5uyziKQNLtrJu3QzzdlPApqgJIaxWBwRYSU00XMaV7Q+7Usy7IOHzb4tqxlqqCGqZoCaWc9niiCmf9877zqwzc1kk4HYRnHGB3keaNIu+smDH/R2mdH+VfUTImO0HG0h4+Z17XMRUXnKDh7KOsYYRkP1rqA0yGDoHuQkhqpB93hYKdbNiOEWLD7tSzLsg4/Nvi2rGVqtBa0GGx2VlPVBVwRmddJiFWdp6hGGikTADnVi2fKxJ2WCTnTWmt2VX9D2WRIO+tYHdkyb+uYC20UOb+Xkh7FoGlyV5GQ7QsWAGvjU1CDFNUIBo0jQqScTmKyxQbdlmVZ1kGxwbdlLVMZFaSDJNxOaqY4r4WW2qhGC8Eg3URQVpl6MB6j2Vkz4fY9tYfJq34SspV1kTPnbR1zUdUFMv5elPEIiRhJtXbKgT/zQRtFUQ1RUEP1oNsl6awiLltt0G1ZlmUdEht8W9YylVP7cEWMkIxQU8V5TTnJ+b0o4zemLQaTIPchcWgJrZ+Q591fe4IRfycRkWJj9FykXLyx7BC0QcypPopqGIEg5XSSdDrZTXbe7ysIuocpqiE0qh50d9WD7sX9vi3LsqzD07L4bfLwww9z3XXXTbr85ptv5qUvfSnXXXcd1113HU8//fQSrM6yFl/W60OZGs3OGio6hxQOITE/0xErKkdJjxIWcZJOB8ZoRvxdGDTN7toJudOj3h76a4/jighHxc7BlQvbRWR/NV1i0HuKohrGFRHaQ8eQcrvmfffZGE3BH2TA20pe9YOAJncVnaHNJJx2G3hblmVZ82bJd76//OUv84Mf/IBYLDbpukcffZSPf/zjbNmyNPmllrVURvwdAMRlO8r4xOcpx1gbn4yamG6S8fY1enyP79pR8AfZW30AicumyHmL2svbGENe9VNQgwAknXZSTte8B8HGaIp6mIIaRBuFxCHldJFw2hZtFL1lWZZ1ZBHGGLOUC7jzzjvZvHkz7373u/nOd74z4boXv/jFHHfccQwODvK85z2PN73pTTMeq1qt8sgjjyzkci1rUewJ/4KKHKSrei5GVonrVXiiiMYjqQ++vV9J9uOJAlHdRsSkqYkcZTmIY6Ik9BoEQYBfo8Bg+CFA0+qdRMwcuIWfJ4qU5AAx3U7YHHx+uqJKyRmgRh5P5omrVTTpjQd9vKkYDDWRoypHMShAEtHNREwzAht0W5ZlWbO3ZcsWIpHZN0RY8p3vSy+9lL1790553Utf+lJe9apXkUwmectb3sJdd93FRRdddMBjzvUkWNN74IEHOPPMpSmwO9zM9lwqpSjlf01YHM/RsRPxTZWO0HEMek8RErGDbvFXVhlG/TBhGafNPRrfVBjytiPooiN8bGMwTU2X2Fb+JQmzgbXh02gLH3XAYxfVMFm/B0Eb7aGjJ42inw1jDAU1SM7vpahTQBNJ2U5zaO2U/bsP5rlpjKGsR8mrAZSJIAh2uZNO+6KMoV/O7M/6/LLnc/7Yczm/7PmcPwe76btsf9sYY7j++utJpYIdtAsvvJDHHntsVsG3Za1kWbUXQ1AM6ZkKUZmiavIYzEEPcwnGr/cgkKTddRg0o/5uDIaW0LpG4K21z87yr/BNhY7Q5lkF3jm/j4IaRAqHNveogwq8g4LPvRT1EEU1Qly2knBaSbvrDup4+wuC7gwFNYBvaggkSaedpNNxxAfdlmVZ1uJatlVEhUKByy67jGKxiDGGX//61zb32zoijPg7AYjJNAARmaKssggEUXlwwXfW34dG0eSuwhURsv6+xnCdsS4qWmt2Vn9NxeRIu+tZHTlxxmMaY8h4eymoQVwRpj10zEEFykU1xIC3lRF/JxWVo9lZQ1toE+2hY+cl8C6rDIPeU402hQmnlc5wN03uaht4W5ZlWYtu2f3mueOOOyiVSlxzzTW87W1v47WvfS3hcJhzzz2XCy+8cKmXZ1kLLtihDgJtg8IlQs2UiMgkjgjN+XglNUJF54nIJAmnjaIaoqyzhGW8MVwHYG/tdxTUAAmnnXXhM2Y8pjaKUX83VV0gLOK0hjbOOZBVpkbG31sfYjNMTLaQdNvnbbe7onLkVT+eqSAQxJ0WUk5nY5ffsizLspbCsgi+161b1yi2vPzyyxuXX3HFFVxxxRVLtCrLWnw1Vaakh4iKNAhNSMSomSIAcdky8xdPQZkaOb8PiUPaXUtNl4L/C4cWd0Ojg0pv9TEy/h6ioomNkbNn7OWtjMeItwvPlInKVP04c/sQraRGGoG3NopmZy1N7iqSTuchd3Wp6Bx5fwDPlIHgvCXdjgUdP29ZlmVZs7Usgm/LsgIZFeRhJ5w2DIaobKKsRxHIgxqyk/H3olFBW0FkI8+71d3Q2EUfru1g0NuKK6Jsip07Yy9v31QZ8Xbimxpxp4VmZ+2cguUg93xfvY3gEDGZJuV2zstud1XnyfsD1EwJCNJ2Uk4nrrRBt2VZlrV82ODbspaREW83QCO3W+LimxoxmZ7z7nJRDVHVwVj6uNPCiLcTZTxSTheRes/unN9PT+1hJC5HR59DWE4/yKemS4z4O9FGkXI6Sbld0952KkG3lT0U1ADK+DQ7a2h21xzybndVF8irfmo6CLqjsqk+uTN60Me0LMuyrIVig2/LWkZyfi8Yh4howhEhfOqpE056TsfxTZWc348UDs3uWgr+YCPvO+V2AlDyM+yu/AaATdFziDrT76xXdI5Rbw8GTdpdS9xpnfVatPHJ+j3kVC8FNUhUpGkJbTjk3W6fMsPe01R1kJYTlal60H3o+eKWZVmWtVBs8G1Zy0RZZamYDDHZgpAQESnKOosUDmEx++mSY11IDJq0syEIxFUfjnBpcdcDUNMFdlbvReOzLnwGSbdj2uOV1AhZvwcQtIY2zin9ZSxoz6s+fFOj2Vl7yLvdni6TV/0UnR6qujl4Q+F0zbhrb1mWZVnLhQ2+LWuZGK2nnMScoLBSCInWioTTNqdAtaiHqJkSMdlMWMYZ8rYhELS4G5DCxdc1dpTvwzdVusIn0Bqefnpk3h8gr4Id9FZ306wDXG0UOb+XrNpH3h8kKptod9fTElp/0DvTnq6QV/1UdA4Ax8RoDx1NWCYO6niWZVmWdSjKvj6or7PBt2UtE6N+PfiWzUgclK7V/5+e9TE8XSHv9+MIlyZnNRl/D8r4NLmrCMsEWmt2VX5F1eRpcTfQFT5+yuMYY8iqfZTUKI4I0eYeNevCxaouMOrtJqd68XSVZnc1aXfdQe92+7pKXvVT1lmARovEXl2zgbdlWZa16IwxDJYU/Tn/oL7eBt+WtUzkVC8giTlpQiJO1RRwRWTWu83GGDL+XgyGZnctJT1SL7hsIukEaSV7qw9Q1COknC7Whk+f5jjB9MuKzhMSMVpDG2fVX9wYTU71kfH3kPcHicgkHaFjD3q32zdV8v4AZZ0BICRiNLldRGRqzseyLMuyrPlQU4Y9OZ+Sp3EOMn3SBt+WtQwU/EE8UyIqnunlbdBz2vUuqEE8UyYuWxAI8moAV4RJu+sA6K0+QkbtJSbSbIycNWUvb218RrxdjaE+QaqKc8D7rukSI94ucqoHT1dodleRdtcf1G63MrVG0G0whESUlNM1Y0GoZVmWZS20TEXRU/BRGpojkvawy+P75n4cG3xb1jIw4u0CIO40IxBogo+yYs7sxsl7ukxBDeCIEAmnjWF/x7g8b4eh2nYGvacIizhHxc5Fysk/+kEP7134pkpMpoPe4AcInI0x5FU/o/4u8v7AuN3uDXPe7VamRkENUlKjGAyuiJByumZ9DizLsixrISht6Cn4ZCoaKWBdk0tL1KFatTnflrViZfw9aKOJyRYcEcYzZcIiPqupjBPSTZw1ZP2eYGqku4aQjJH199Fb+wOOCLEpdh7uFP2vPV1mxN+JMj5Jp4Mmd9UB79fTFUa8nWTVPmq6TJOzipbQRpJOx5wH7wRB90g96A4HO92y+ZCnXVqWZVnWoSh6mj05H08Z4iHBuqYQEefQfjfZ4NuylphSipzqQ+ISc9KAAaj/+8Dyqh/PVEg4rdRMsd7pJE3CaaPkj7C78gAg2Rg5h+gU+dJVnWfU24NG0eyuJuG0z3h/xhgKapBhfwcFf4CwTNAZOm7Ou93a+BTUIEU1gkHjiFCw0y3TNui2LMuylpQxhoGSYqCoAOhMOHTGnXn5/WSDb8taYjnVg8YjWk+v0EYjEMTkgdMtarpIQQ3iijBhEoyqPbgiQrO7pt7L+z4Mig2RZ5N0JwfVZZUh4+8FoMXdcMAUD19XGfF3kfH3UNVFmpxVtIY2zWm3Owi6hyiq4UbQnXRWEZetNui2LMuyllxVGfbmPEqeIeQI1je5JEJzmzI9Ext8W1PSxkdw4EI7a3raKATygAFlxt+NMYaYSCOQaHyiMoUUM/94GqMbgXPS6SDr9yCQtLgb0Mbj6fK9+KbK6vBJpEPrJn19wR+s77g7tIY2HrBtX1ENMeRtJ+/3ExIxOkObaQ1tnPUYd20URTVMUQ2hUTjCJel01YPu+XtRsyzLspaGNsEnt3IFb6SMVhS99aLKdFSyJuniyPn9fmzwbU2ijMdAbStSOFTEKMp4s2o1Zz3DGMNAbSsxp5lmd+2Mt834e/FNlSZnNYageCMmW2b8GoBcfWpkQrZSVCNoFGl3HQ4u2yt3UzNF2tyj6Qh3T1pbTvVSVMM4IkSru2nGAFqZGiPebka9XVRNkZTTSVvo6FnvdhujKaphCmoQjUIKhyZnFQnZZoNuy7Ksw4SvDdtHPRwBx7aGl3o5cza+qNKRzxRVLgQbfFuTSFwSTjslNUJVjjBQ20pUNpFw2uxQk1kzaBS+qc14K6VqFPQArogRcmJoo3BFeMrc7PGqukBRDeOKCAYdtBh0Wog7Lews30dZj9LkrGZt9NSJq6rvlpd1lpCI1nt4T/8iWVIjDHrbyPt9uCJKR6ibttDMwfr4+yrqetBtFJJ60O3YoNuyLOtwoo1hZ8ajpgxdiZX3qflCFFXOxAbf1iRCCJrcVaScTp7WfbgiQllnGwFb3GklJtOz6v98pBoLLsd2sqczonYHXU5EM9p4SBEiKptmDE61UWT8vQgEEZGiqIcIiSjNzhr2VR4mp3qJyRY2RJ416etG/J3UdImwjNPqbpr2MVTGY9TbzYi3k6opkHQ6aA8dO6vdbmMMpXrQrYyPxCHldJFw2uxzxrIs6zBjjGFX1qfsG1piDp2JlRNaLmRR5UxWzhmyFp0QkohppiN8HDVdpKhGqOgsWb+HPP3EnDQJ2TbrseNHGoHE1PPfpjM2/j0Z6sQ0upzMXPSYU70o4xETzZT0CBKHFncDQ97TDPtPExYJjoqeM6GXd5A6sgvPVIjJ5noP76kD/LLKMOA9Sd7vwxGR+m73UQfc7TbGUNaj5NUAyngIJEmng6TTfsD8dcuyLGtl2pf3KdQ0qYhkbXLlbLAsdFHlTOxvRGtWwjJBWCZQZhUlNUpJD9eL54aJyARx2VbfsV25RRbzTSAYaxs4nYy3F2VqpGQXxmgc6RIWyWlvX9E5SmoUV0TwTAWDJu1uoKAG6a39AVdE2BQ7d0Ivb09XGPF3oIxPwmmj2V0z5bG18Rn19jDsPU3F5EnKDjrCxx1wtzsIujMU1AC+qdWD7naSTocNui3Lsg5jfQWf0YomHhJsaHJXTAwwWlH05H20WbiiypnY34zWnDgiRMrtJGk6qOo8RT1EVRep6iKOcInLNuJOiy3QJEjfmSntpKYKlM0oIRlHCx9XRGfsca2NT9bfh0AgcahRIuG0oYzHnurvEDhs2q+Xd1UXGPV2o1E0uatIOh1THruicwzUniTn9yBFiM5QN22ho2fc7TbGUNFZ8moA31QRCBJOG0mnwz7+lmVZh7nhsmKwpAg7go3NoRXR4URpw768T7YaFFWuT7mkF6iociY2+LYOihCCqNNE1GnC11WKepiyypBX/RTUgC3QpJ52MkPwPezvxjc+KdmOQSOEJCbT094+6/eijE9IRKiZEmERJyySPF35XwyKjdGziLutjduXVZaMvweAFnf9lEN7xvLHh2rbqJg8CdlGR6iblNs54w5GWWXJq/5xQXdrPeheeRXulmVZ1tzkqsHOsSMFm9Ih3EXcNT5YhZpmb36sqFKyvsklvIBFlTOxwbd1yFwZoVmuoclZRUmPUlIjkwo047LliOtwIZBoo6a9PuPvRukqyfCq+lj1yLQTIssqS1lnkDh4uoYUDimngx2VX6GMx5rwKRNaGhbVEFm/N8gHD60nMuVkywIDta313XSXjtBxtIeOmXG3u6JyjYmaAHHZQtLtwBU279+yLOtIUPQ0u3M+UsBRze6CdgWZD8YY+ovBLr0Q0JVw6FiEosqZ2ODbmjdCSBJOGwmnrV6gOUxF547YAs0D7Xzn/B4MmpCJEhJx4tP09tbGJ6v2Mda+EAFNzmp2Vx6gZkq0h46hPXzMuOP2UVCDOMKt9/CeGNAH7Qb3MVh7korJE5ctdIaOn3G3u6rz5P0BaqYEQEymSTmdR8xjaVmWZUHV1+zKegBsaA4RW6QCxYNVVYY9WY+ybwjXiyrjy2DNNvi2FsQzBZrelAWaCdlORKZWTHHGwRBCTNvtpKxGKet8sCMtDVI403Y5yfj70EZhjEEIQVy00e89TtlkaHLWsCZyChC8uw96eGdwRYS20KZJaSA1XaK/9nh9t9uh3T2WjvCx0+52V3WBvOqnpseC7maSTuesp1palmVZh4eaMuzIBpMf1zW5pMJLH8TOZKQcTKrUBlqiktWLXFQ5Ext8WwtqfIFmReco6eEjqEAz+CE3Rk9KuRmq7cA3FdLuWkAQlvEp86XLKkNF51CmhiPChGWcvN9HXvWTkK1siDwbCHK3R/3dVHWBsIjTGto4odOIMYas38NA7QkqJkdMtrAqdALJaXa7a7pIXvVT1UUAorKJlNM5bVqMZVmWdfhS2rAr6+Epw6qks2CTH+eDrw09i1BUWfGDwTwHwwbf1qIQQhBzmok5zdMUaDaTcFoPqwJNwTODdsb+PSaj9uHrKlHRTEQmp0w5UaZG1u/BNzUkEikcarrCqNpFRKTYGD0XKSXKePUe3mWisokWd/2EYN/TFfq9x8l4ewBZ3+0+bsrd65ou1YPuAgARmSTldBGW8Xk8M5ZlWdZKoY1hV86n4hvaYg4d8eUbOhZqQUDs64UrqlQ6yCEfqSi0N/Mgveks3zNoHbbGCjRTThdlnakXaGYo68xhVaA5PvjeX9bfhxCCkIzWx8lPTjnJ+PtQpoY2Pq6MgxEMeVtxRYSjYufgyjC+rjLs70AZj4TTSpOzprGTbYwh5/fRX3usvtvdTFfoRFJu16Tdbk+Xyat+KjoPQEQm6kH34fNmyLIsy5q7vTmfYk3THJGsXqZDdPYvqlyVdGiPzW9RpTGG0Yqmr6hQOsghb0+57Oyd+7Fs8G0tGSmcRoFmVRcoqZHDqkBzfBDMuJ//rNdHTeeJijRShIjI1KSx6yU1QlUXqJoCUdmMxGXQ24rEZVPkPMIySU2XGPF3oo0i5XSRcjsbX+/rKv3eE4x6uzBAm3s0neHNk3a7PV2hoAYo6ywAYRkn5XQRkdMP+rEsy7KODGPpG/GQZN0yHaJTrad/LGRRZdHT9OaD+5DjgvtabeZBetOxwbe1LERkkohMHlYFms/sfE/84Rzxd+DpKjG3lbCIT+rt7ZsqWb+XisoFO88GhrxtIGBD9FnE3TQVlWPU3wMY0u5a4s4z/b3zfj+91UeomBwRkWJ1eMuk3W5fV8mrAco6A0BYxEm5nVO2JLQsy7KOPIMlxXBZEXUFm5rdZTlEZ6GLKj1l6Cv6ZCrBJ9gtUUlXwiV0iKksNvi2lpWZCzRDxGXriinQFGMFl/ulneT8HnyqRGWSsEhMmEgJQUqKZ4qNtJtRfzda+KwNn0aTu5qSGiHj70MgaQltICqbgCBHvL/6BCP+TgzQ6m6iK3zChN1u31Qp+IOU9CgAIREj5XY2jmFZlmVZmYqir+DjymB65XLpEjLGr0+qzNWLKjekXJrnsahSG8NwWTFQVGgDMVewJjV/O+o2+LaWpfEFmp6uUNLDlFZYgeZUOd9KKUa9PUhc4k4bMbd5Qm57UQ1RVlkqukBCtJLxe1DU6Ahtpi18FHm/n7waQAqHVndToxCy4A/QU32EiskSEUlWhbfQ5K5q7HYrUwt2ulUGgyEkoiSdzmnbG1qWZVlHpnx9EqQj4aj00k2BnE6+ptlbL6pMhCXrUvO7xlxV01vwqSmDIwVrkw6tsfnNdbfBt7XshWSUZrmWlLOKss5QVMMTCjQTThsxmV52BZpTBd9ZtZeaKRGRScIyMSHlxNdVsn4fRT1EnDayah+KGml3PavCJ5Dx91JSo7giTGtoE66IoIzHQHUrw/7TGAyt7ka6wic2druV8SioQUpqpDFFM+V0EpXNKyqFx7Isy1p4ZU+ze2yITlOIqLt8fq8aY+grKoYWqKiy6mt6CopCTSMEtMUcuhLOguz62+DbWjGmK9DM+PvI0bfsCjSrJo+vaxMG7Yz4O/FNhbhoJSabG4WNYwNySmqYsEiS1T1oPBJOO2tDpzHq76pPrQzTFjoaR4Qo+kPsq/6eiskSFglWh7fQ5K5GCIE2PgU1SFGNYNC4IhzsdMt044VKG4VvqkdMG8Gx71cg7ZAgy7Ks/VSVYWc2yJ/e0OSSXEZDdPYvqtzQ5M7bdE2lDQP1/HZjIBmWrEk6RBbwjYcNvq0VaWKB5gglPTKuQDNJQrYtaYGm1pod5XsROHSMG/0+6u/GM2Va3HUTiiSLaoiiHkKZGlVdwDdV4jLN+vCZjKpdjPq7GfaeJuWsYnX4JPqqjzHkbUOjaXE2sDqyBVdG0MYn7w9RVMMYdJBD76wmJlsa5yJ44zJKRWcxGDpD3cvmDct8UKaGb6qT/igTDEMQCFaFT1x2n5RYlmUtFV8bdmY8fG1YnZzf/OlDNVwO8s+1gZaYw5qkM2/Fn6MVRV9B4WtDyBGsSTo0RRb+e18WwffDDz/Mpz71Kb7xjW9MuPznP/85n//853FdlyuvvJKrr756iVZoLVdBgWYXSdM5rkCzQFUXlrRAU0qJQOKbaiPtRFEl5/XiECEWammknHi6QsbfS0ENIY1Tby/YxLromWTUHgZqT5JXAyRkKy3ueraX76asRwmJGOvCJ9PsrsGgyfv9FNUwGoUjXJLOKuKyFSEEyniU/QwlPYJvagC4IkLCaV2Rgbc2CkWVssrsF2TXpuyr7ogQEZnEFREiImkDb8uyrDptgumVNWXoiDu0x5dH4L2QRZUlL8jrLnlB68CuRPB9L1ZHlyUPvr/85S/zgx/8gFhs4thqz/P46Ec/yne/+11isRjXXnstF110ER0dHUu0Ums5W44FmhIXbUqNYLAo+6mZIhEZJy5bCclo0LTf201O9SGMoKCGiDnNrA+fQdbbQ0/tEXxTJeV0kpDtQV9vNGlnPavDW3BlmKIaoqAG0SikcGhyVpGQbYCgZgqU/FEqOofBIJDEZHrZF6uOmWkXu+DsZdR/plOMQOKKyBR/wjbYtizLmoIxht25IAhNRyWrkkseFgILV1TpKUN/0We03jqwORJ8z4tdVLrkZ3nDhg187nOf493vfveEy7dv386GDRtobg66MZx55pncf//9vPjFLz7gMR955JEFWeuR6oEHHljqJRwUg6ImCtRkDk2w2yuJENFNhExy0sj3+bYvvI+yGELXHiJqWii6PQwM7iblHcXTfg/7TImKGCHr7sQzRTwnR9ikaPaa6JU/JeNux8HFVUkkJbR8CmnCNPlHUzWaveIeqjKDQSFwCOtmIqYZQ46aeJiazGHw6993mLBuImySCCrAwIJ+73Nh0Gg8lKih8dDimX/D5AEGAhfHhAnTzM5HB3FMGEkIiQtUFn39h5OV+rO+XNnzOX/suZxfY+dzsCrJ+YK4Y6hGNANLXIdvDIzUJBk/aNbbGtakQ4bReThu1heM1iQaCAtojyhqDgzOw7rnasmD70svvZS9e/dOurxQKJBKPbOrlUgkKBQKszrmli1biERW3kfpy9EDDzzAmWeeudTLOGTjCzQNBomz4AWaseIAQ95TbImfRHN4Nd9+9Ps0tzZxcvw8jklegDI+PdU/kPDSlLUg5qynI3QcFZ2lr7aPFnEMISeOMBIENDurWR06GU+UKKhBlIkhWU3CaScuW/FMiZIeqY+ITyBYTcxpJi5bl0VR5cRd7Nq4XWwPmFgAOfUudhhXRBq72IfLc3O5sOdzftnzOX/suZxfY+ezv+gTLiqOdQVHpZe+l3fV1+zO+UR8wyZXsD41P0WV+VqQYhLxDauloCvh0BqV81ITVq1WD2rDd8mD7+kkk0mKxWLj/8VicUIwbllzsRQFmmERpFIFuclZqmKEuGymObIWicuA92SQ662HiDutNDuryPr7GPafJkyQlywQOCLE6vAWwjLBqN6NMh4CScoJJlJWdI4h/6lGQWFYxIk7LcFYerG4uXvG6EnB9difmXOxwxMCbUeEF3XdlmVZR5KR+gCZsLM8huiML6psjTmsnoeiyqoy9BWCnHEIjtuVcHCXwcCgZRt8H3PMMezatYtMJkM8Huf+++/nhhtuWOplWSvc/gWaRT00oUAzIduIOy1Iceg/Gg7BjrpHlbK3C0+USDjHkHK6yPm9DHu7yPo9xGSaKE1k1D4K/iAIByklIRkjJVfR6m6kQo6KyiGQJGQrrohQMTnyXpA+IgnaMI7lki80ZbxpcrG9SbcNdrHDU+Zj21xsy7KsxVX0BfvyPo4MxsYf6qj0Q+Frw968T76qcaRgY9OhdxvRxjBQVAzVWwfGQ5K1KWdZ9SxfdsH3HXfcQalU4pprruE973kPN9xwA8YYrrzySrq6upZ6edZhYroCzZzqI6/656VA0xVB8O3rCkPedhQ1WkKbkLj0e08wVNtGiDiuiJBTvVRMDmMUKWc1UdFEa2gjDiGKZhiBICKTSBzKJovWCoCITBCXrURl07wHssEu9tQFj1PvYrtEZGJSgG13sS3LspaHkqfpr0raBGxqdhe0l/WBjC+qTNaLKg/1jUCmougd1zpwdcJZVm0TxyyL4HvdunV85zvfAeDyyy9vXH7xxRdz8cUXL9WyrCPExAmaoxTVyLgJmjESTutBTdAM1XPJfVNlwHsSIQSrQycwUHuSvupjGDRCGor+IBWTQxKiPbSRiEyTcoM3msrUkMJBCElVBzUPUjgknQ7ismVe8tXntostptzBdkR40VNcLMuyrNmr+pqd2SA9cX1TiPg8DamZK20MfYVgqE0wqdKl4xDbG5a9YDplyQumU3YmHDoWsXXgXC2L4NuyloNggmY7Caedqi5QVMNUdb4xQTPutBB3Whs72gfiiiD9I+8PUFRDhHUKZXz2Vn9HWWdwiFDys1TMKClnNSm3k7jbTkK2oaiBEUjhotFg9CHlpk+1iz1WAKlRk24/1S62I8I4hO1YesuyrBXGU4YdWR+lDe1hTVNkaQLvSn1SZcU3ROahqNLXhv6iYqQc/B5rqrcOjCxhKs1s2ODbsqbwTIFmjZIapaRHKKghCmpo1kFwqB58D9e2U9MlQrqZfbWHGKntQqORsorWVVpCm0g67bS4m1B4lHWWsIwHwa4IEZct9UFBB07fGL+LvX93kf2N7WJHRBJnv5xsu4ttWZZ1eFA6GKLjKUNXwqEWmtzCdTEMlRR9RR8zD0WVxhiGy5qBko/SEHEFq5MuqfDyyeueiQ2+LWsGjgiPK9DMUtxvguaMBZpGMFB9iqzfT9xpQhtNb+0PlFUWR0YI6TCtoU00h9YQIUnN5Ik5LUFALFPEnRYiYnKAb4yZdvjMVLvYUjiEZXxyqojdxbYsyzqsGWPYlfMp+4aWmENnwmXPIq/BU0FRZaEWFFWua3IPaee9UG8dWPENjoTVSZe22Py0DlwsNvi2rFkICjTTxJz0DAWabYRlHGMMv8h8mt/nbyerg5c56YWIRNup1lYREkmSxEm7G2lyVxMWCWJOK1GZJC5biTlpHBFCGx/PlPD1/kH21LvYjggTFpMLHu0utmVZ1pFpb96nWAvSTNYmF/93Qa6q2ZsP0l0OtaiyVm8dmK23DmyJOaxaJq0D58oG35Y1Rwcq0Hww/23uy3+V8dMZNR5lt5dhr8JRsXPpCB9Pe+goEk4bMScdpJkQwjdVRr3ddhfbsizLOiS9BZ9MRRMPCdY3uYv6O0IbQ28hyMUWItidbj/IokptDEMlxWBJoQ3EQ4I1yfkZwLNUbPBtWQdpqgLNghri0dIPmWosOgKqFOgKn0R7+CjishlHhPBMGU+Vx91sul3s8Lz0H7csy7IOb0MlxVBJEXGDITqL2fWjUp9UWa0XVW5ocg+6x3a2ougtKjxlcKVgTcqhZRm2Dpwr+5vcsvZjjMZQ/2PMM/9Go8dd5+kKnikHaSFU8E2FbcVfkld90x8bj72VBwiLGCU5TEgkCMs4UZEkJOOERIyQiOFIF4mLJNRIG9Em6Hoi5cp9t29ZlmUtrGxF0VvwcaVgU3NoUdMyxhdVtsUcVh1kUWXFD1oHFmtB68COeNA6cKkncc4XG3xbK85YcKxRk4Ljyf+f6TrTOI7SPspU8aigtIfCa3QO0fgo46FNcJnCQ+Oz/+62MZoh/+kDrr+kRxj2t9e7mdSDbPFMoC1xEMigvzdy4r+Fg4OLI0L1r6nfXjiNr3UIIYSsHze4TIpQ/W8XKSSC4GuD3fT6bXGR0r4kWJZlrVSFmmZP3seRwRCd8CK13Juvoko11jqwEkynTEUkqxPOkg4DWgj2N601r2YMhscHwtMEwxp1wGB5NrT28amh8fF1DUU9kDb1QJp6MI1C42OMRiAIkj7G/pYIQSMABhotAF0RISRiSFw0PtooqrqA51bYxi8x+FOuK0wTz2n+cxzh4JkyygRrUzpYEwgM1APwYLiOFAJt/GCdukbNaEBjRD34r/8VBNz1QBz5zNcj5zQgSOAghQyCeMaCd4kYC/YbfztBEF+/bSPYF049mHeQsh70N4L7w+sF1LIsa7kI0j2C4WgbmkKLlhOdqyr25hVKG1IRydrk3IsqjTGMVjR9xeA4YSdoHbhU/cgXmg2+rSmVVQbf1KjIYbJ+zwzBsNrvuvnrHxoEv0EQLHHrgWgQCHu6Rs7vQeOhjMLg4RuvHqQGawq+VgASGiG1xBEOLi7IOI4I4RLFFSEcEQn+X8+vdkUsCLSJ4MowLlGklGijqOgcFZ0NAm5TYbS2m57aw/TVHiNGmhJDU35PmxPP54TkCwHq3UyCdBXPVPB1Fc9U6udToetvDIJhO8GOdkgGawoC/iDoHyvMNNpHo4OdehTGKAwKXX98QDAWqQtE441FiDhaBOdNGzXhvrWpBY+xmZ/HNXhEJgbo43ftBc64TwOCy1LOauJuel7u31qeshVF2BHLtoCqpgxlL+gYMR9Fa8YYslVNMixXZKcGa/mpKcOOTNDzen2TS3IR+l3PV1Fl0dP05oN2iFLAqqRDe8w5rJsI2ODbmkQZj1E/aJFXFRmKanjK2wWBUz04rgfFY/9v/BHj/73/dWOBtTP5+gPs1PZUf0/ZjE5cj5CEZAqXMI4MpjG6IowjIoQmjUQP48rorM9JsLOdo+xlqOoCBoNvqhTVEKPeXvqqvyer+giLBKenrubJ4s8Y1FsbXx8TrTRVjuWs1dfj6TIhGUMKl4hIEiHZuJ0xBkUNT48Lyk2l0V5Qo+gIbZywNmN0Y/dd4dUD8meC8/0v2/8NUmeo+4Bj6rWu3wf1IF174wJ01Th+cF8awzNvDMauM8ZHm3q6ECr4Pk3wRmMmBT3EMe4Fs3qcrJWnqgy7c8EnRVFXkI5KmiPOon1cPhtDpWAUtisFHXGH1pg8pAK2gmfYk/NxpKAr4dAaXVk9iq3lxdeGnVkPXxtWJV3Si1CQWPaC9Jaqb4i6QTeVuRZVesrQVww6sgC0RCVdiYNvRbiS2ODbmsQRIdpDx2BQJFSWjtCx+wXHS/+OtDN0PEmnA0kIV0Yau9PzKQi485R1lqrOPxO0GqiZAgU1xED1Sfprj6HwaHOP4rj48zH49HuP43phFD7t4aO5pO39PPb7rQgkI/5O2kPHTDmxUggRfC9OBGhuXG6MxjOVKXt2CyFxCOMICBGb1fc1lsYCHDDwBpBSIgkD9TXP86aK1v4zwb3R+KZWD9J9YrJlfu/MWlYijmBTOsRoWZGrafoKir6CIhGWtEQlTWG55EVWXQkHIWCkHBSyDZYE7XGHtoMMwlPhYAT2YMmnJ+8zWhasTrkklunOv7V8aWPYlQ2C4PZ6UeJCGywp+g+hqNIYw1BZMVAMWgfGXMGalEv8CHr+2+DbmlJYxgFwiRGSBw7oFpsrwzTJ1fN+3OkC7pCI4hChZorkdA+Z2l76vCcoqREcGWFd5AxOiL0E31R5tPRfVHWRlvBGmpxVdEa6aXK7CJm9NLmryPl9jHi7aAsdPesBOEJIwiI+L9/jWF42HDjoXiyy3t3FOjKlwpJUWKJ0kI4xWtEUa8EfKaApEuyGp8JiSd74OzLIP+2IOwyVgmKwvrEgPCZpi829C0NH3CEdkfQXfUYrmqdHPdJRyaojZOfPOnTGBJ+glDxNc0SyOrmwr6HjiypdKVh7EEWVuWownbKmDI4UrE06tMZWfuvAubK/7awj3kwBd1Q24xKmqEcY8XdQVMOM+rvJ+vuo6QpJp51N0bM5OnY+PjX6Ko9S1AOERJRWZxPSCfKYxySdDpSpUVQjZPw9tLgbl/xTBMtaLhwpaI0Fv4yrypCtKDJVTaYS/HGkIB0JdsSXIj/clYJV9SB8uKwYKiv6i8HfbTGHttjcpu2FHMG6phCtMU1PPvj4PVet0Zk4/HNerUPXU1DkqppEWLK+aWHDuf2LKtel3Dk916v11oGFeuvAtphDV+LwaR04Vzb4to5IxmgqOlcPuAuNLipjAXdMNmPQ5Px+BtWTFNUwNVVgyN9NTefRRtEV3sym6DmsjmzBoBit7mKw9iSeqpJwWmkJbyKveycE3wBNzpqgmFXnyakemt21S3EKLGtZiziCzoRLZwJKXj0Ar2qGy0H+dcQVtCxRfrgjg7W1xRxGKorBkmagGAw1aYs5cy46i4ckx7aGGSkr+opB2s1IWR/W3R6sQzNY8hkpK6KuYOMCTq/UxtBTUIyWFVLAmlTwvJ8tpQ0D9ZoJYyAZlqxJHn6tA+fKBt/WESMIuPOUdWZCwO2KCDHZTFQ2E5JRPF0mp/rI+b0U1TAGTV4NUvD6qZkiroiwIXImq6NbaA8dAwgGvZ0Mq51orVHGI+Z00O4cS173TEotEULQ4m5g2HuaohrBFRESTvsSnBHLWhniIUk8JFmdNORrhkxlYn54PCTrgfji5oc7UtARd2mLGYbLujECe7isGK5KPGXmlELSGnNojshGn+NdWS/oc5x0idhUFKtutBI870NOMERnoZ7zh1pUObZOXwc/B2uSDk2RIy/FZCo2+LYOa2MBd0Vnqej8tAE3gKfLjHi7KKgBCmqo8fUj/i4qKotnKqTddayKnEhnaDNN7iqEEGS8vdRUiZzfS40icdlM2l1HNJQAz0w5El4Kh9bQRoa87WT9XhwRJiqbFu/EWNYKJISgKSJoijyTH56pBrnhJU/TU4CmsCQdDXLIFyttQ4qgC0pbTDJa0QyWFBlfsHWkRks0KIKb7e68I4Pis9aYpKegyFc1hVqN9phDZ+LgpgVah498TbOvPkTnqOaFqw8YLPn0F4Pd6va4w6rE7NOgSl6Q113ygtaBXYng0yD73H2GDb6tw85cAm4Igu68GqCohiipURQeYZEk7/eS8fdR1XlcEWVt5DhaQ0fREeom5gSdSMoqS0mPkvV7UEYhDDgiTGtoE269m4lk6nf6jgjT6m5iyHuaUW8P7aGjl2Vxq2UtR+Pzw2vKkK0qRiuabDX440hBcz0/fLG6KEghaIsFrQN7w5qQFIyUFaMVRboehM92BzvqSo5Oy2BUeDHYUR+taFYnnUVpJWctPyVPszvrIYCNzaEFSd2o1Ysqi/WiynXNLqlZ9gz3lGkUEAM0R4KuPsupbehyMavg+9FHH+VDH/oQTz75JJ7nTbr+kUcemfeFWdZczDXghmeC7rIapaRH8U2FqGhBUWWwtpWyzuDrKs3uelpCa0i762kLHd04jjIeWbUPbTzyagBjNFJEQCjaQhsZm0szUxePkIzR4q5nxN81YwtCy7KmF3aC9I+OePBR+Wg9P3ykHAwACTv1/PDo7IPfQyGEoClk6G4NkakGO+GjZUWmokhHZBCEzzJwao46pCKSwXpKy56cz3BZszblzLmvsrVyVZVhZ9ZHG9jYvDBtKbMVxb5CMKinKSJZO8uiyqB1oGawFHxt1A26Ay3GoJ+ValbB9/ve9z5CoRDvfve7iUZnP5jEshbS9AF3mJhMTxlwwzNBd0VlKJssnq7UiyxbGPV2MervoaYLRGSKtvAmUqEu0u560u76CfnbWX8f2iiKahSPEo4I48gaCWcVEZmkpkoAB2wnGHWaaOLgWhBaljVRLBR0QhnLD89Wg44Q/cWgM0k8JOuDfBZ+uqQQgpZo0FIwOxaEV4I3B80RSWdidgG0FIKuRDA8pa/gk6tqnhrRtNY7RtgpmYc3Xxt2ZjyUNqxJufOeN30oRZX5WpBiUvVNPWXKDo2ajVkF3zt27OC73/0uxx133EKvx7JmZIymqguUdZaKzk0IuIMAOj1lwA3jgm6do6rz1EyJiEgRd1qpUqKn+jBFPYIxirbQMUScOEmnixZ3PSm3a8KximqYis4jkeT8fTiEEDgoU6MjdCwRmaSicsCBg2+wLQgta77tnx+eqz3TPzzISQ36i7csQn64EIJ0NEgXyVWD4SJj6TFNEUln3JlV68SII9jYHGoEPCPl4Dh2SubhS5sg8K4pQ2fCmVOnkdkoeZo9uaDvdqxeVDmbT2WqyjTeCAL2jeAczSr4PvHEE+np6bHBt7UkDhxwN8+YKz0+6PZMiaouERZxmpxVuCLKUG0bg/52fF0lJptJh9bgiDApt4sWdwNRZ2IhpK+r5Pw+JA5Ff5SyyZF2NjDq7yAs4sSdNGGRQBOMTZ/t8JiJLQh7aXbXHOQZsyxrPEcGO9At0Wfyw4Oe2sEfR0JzxCEdlQs+ZbIpEnR8yFWDj+nH1pCqB+GzyU9PhSXJlhBDZc1A0U7JPFyZ+vTKsm8ao9fn89hjfernUlSpjQnaatZbB8ZD0qZAHYRZPZJ///d/z5vf/Gb+8Ic/sH79eqSceJIvv/zyBVmcdeQ61IAbJgbdytSo6SKOiJB02omIJBVTYEf5Hko6g0TSFdqMI8NEZIKE006ru3HS6HVjDBl/LwZNVKbZ7f+WMFFcE8JgSLmrCcsEQsjG+HZnlsH3xBaEw7gibFsQWtY8G58fXvHr+eGVifnh6agkvcD54U0RSVMkTL4W9AjPVzX5+sCUzrhzwHxZUe+wYqdkHr721adJpur51/PF07AjO/eiykxF0TuudeDqhEOzLf49KLN6NH/yk5+wa9cu/vmf/3nSdUIIG3xb82J8wF3V+cbO8VwCbpgYdGvj45sKAoeY00JYxgmLOPuqDzHoPY0xmoRspS10FJ4pE3daSTjtpN11U6aLFNQgNVMiJtOMeruomAKrQycy6u1GG48292giMgmA0kFxsphDUyHbgtCyFk/UlaxOSlYlDAUvmKiZrQbB8EBREQ8F6SILmR+eCgdpL0UvuN9CTbOjpomHgpzwAwVFY1MyW2Ka3glTMl3aYzYVZaUae0MVDwk2zOMQnWxFsbfskKjpWRdVlr1gOmXJC6ZTdiaCzj22deDBm1VUcMstt/BXf/VXXH/99cRithWaNX+MMRNGu48F3I4IkZStsw64YSzo7qei82A0nqkBEJbJYJCNbCOn+the+T+qJo9DmLbQUYSdBNp4pJwuUu4qUm7nlMev6RIFNYAjQjiEGfKeJkyclNtFr/cHojJN2IkRESmA+s63QIq5fRzniDAt7kaGvR22BaFlLQIhBKmwIBWWrDGGXDXYES/UNCXPb+SHpyOSVEQuSNCRCEmOSktKnmagFOyE78wEwVdH/MCTLhMhyTEtIUYruj4l02e0EnSdmG2rOGt5GC4Hb/7C9Rz/+Xi+KW3oLQQBvQHWplxaD5A/7msTDHwqB7+Xm+qtA+3Ap0M3q+Bba81LX/pSG3hb88IYQ9XkqahcsDs9IeBuqRdNzv65NjHoNmh8DIaQjOIIl6TTiVKKndX7GPZ2InFIyi7a3E31LiUucdlCOrRu2l1mYzQZfw8GQ9pdx57K/VRMnrXhk8n5fQCk3C4c4T7TihAfgUQw9198YRnfrwXhsTgiNOfjWJY1N3JccaSnDJmqJltRE/LDmyIOLQuUHx4PSTY1S8r1IDxX1ezKesTcIM1kpo/5hRATpmQOlxU7M54NmlaQXFXRk/dxpGBTOjQvn7jsX1S5LqZmDLyNCSa2DtRbB0Zc+yZuvs0q+L7qqqu49dZbede73rXQ67EOUwcKuKOymbCMz+mYE4JuwGAAgxQhBDLI7ZYpBmvb2Ft5gCpFIiJFi7uBuJOmonNEZTMRmaQltAFXRKa9r5zqxTc1kk47VZ1nxNtDVKToCB/Ho8X/whhDWq4lLJKNr9FGBcG3PLgXrP1bELaHjkbMcRfdsqyDF3KCgLcj7lDxg9zw0YpmtBy0ZQs5gnR9kM98DzyJhSQbmyUVvz4xs6LZnfOJllQQhEemTynZf0pmrqrJ12qN78WmCyxPRS94jKUIplce6pslYwyDJcVAKSiO7IgHHUmyMzxVC/VOOhXf4EhYnXRps+lL825WwXehUOD222/nv/7rv1i/fj2h0MQduK997WsLsjhrZVuIgBsmB90Sp1GQiRDEnRaSspOiGuHJ0s8Z8XfhEqbV2UizuwaDaeR3x+qj4GcKais6R1GNEBJRUk4XTxbvompyrIucTlWV8EyJpNOB44Qb+d4wFnwHe98HK+l04JsqJTXKqL/btiC0rCUSdSWrkpKuhKHoGTL1/PCx4Tcxd2zHfH7zw6OuZH2TpDNeD8KrwS5mvyPoTAQFl9O9Juw/JXOgWJ+SaQvllp2qH3zCAbChOTSr1pMzqSnDnpxPyQuKKtc3zzz0plZvHZittw5siQXdT2zrwIUxq+Db930uu+yyhV6LdRhYqIAbJgfdrghhjEAR5HZHZYqUswqDpq/2KPsqD1GjRFy20uKuJ+Y04+kyQkgiIkWTu4qk0zHjfWrjk/X3IRCk3fVk/H3kdC9x2UJraBM91d8DQZtAoBF8G2Mw+PVfiof2ItrsrEUZj4rOk1d9NLmrD+l4lmUdPCEEybAgOS4/PFPVjR3DviIkw5K8L9DGTLnLXPM120Y92mIOXcnZFWRHXMm6JkmHMvWdcMXesSA8HqTBTBeE7z8lc3fOJ1HRrEmu3BZxWmu2jnisbwqt+EmKnjLsyAYpHuuaDj29I1NR9NQnVTZHJGtmKKrUxjBUf15oA/GQYE3SPeTg35rZrH7qP/rRjwJQq9UIh4PR1z09PaxZY/sQWwsbcMPkoDskoggEnqkEud0iRpO7irCIk/F62Ff7HSP+LsIiQbvTTcptR+Dg6QquiODIEC3ueiIydcD7zvj7UManyV2FQ5i+6mNUdYH1kdOJ0cqIvxOXKDHRSkhE98vLFkhCiEPY+YaJLQgrOkcTNvi2rOVg//zwbFWTqQTFkgNVyRPDNZrCQdvC8QHiaCUIHMEjERZsaAqxqdmdVSAccQTrUi6dcYehkmKkotiX9xkoCdpjDq2xqQtCx0/J7C345KuabaOa1miQiuCssB3Oog9PDHtkK5pz1q3cejSlDTuzHp4yrEoGvegP5VhjRZVSHLiocuwTEU8Z3Pp0ykO5f2v2ZhV8Dw4O8pd/+Zc861nP4u1vfzsAV155Jcceeyyf/exnaW1tXdBFWsuPMYaaqffhVuMDbpeEbCMm04cUcMNUQXccV7hUTRFtFI4I0eSsIuakqeo8uysP0Fd9BI8Kzc4aWtwNuDKKMRqNT0hGCYkYraENOCJ8wPsvqVEqOkdYxknIdoa9pynoIRJOK+nQOkbVDpSp0eoehXTEhJQTIQSt7iYqOofk0F/MpHBoDx37THqNZVnLSsgRtMcd2uMOVV/TGwp2vcfGyY/lh6ejkq6ky/nrozyd8egtKB4fqvHEcI1VcYeN6RCrZ7EbHnaCvO6OuMNQvUd5b8FnsBSso22aIDziCDY1h8hVg5364XKQyrIqMfPu+XKTCkuawoK+UtCecSXufhtj2JUL8qtbYw4d8YPv5T2+qDIeCtpPTpczXtXwdMajWAtaB47VAqy0N2Ar2awe6Q9/+MMIIfjjP/7jxmX/9m//xt/+7d/ysY99jE984hMLtkBr+ZgQcOsc2uwfcDcTlolDvp/9g+6wjBMiRtXkKesSEocmZxUJpw2DZri2i321h8j6uwmLFF3OCSTddowwYEAgcESImEyTdtfOqmjRN1Vyfi8ShxZ3PcYoBrwnqekCXZEzSTod7C7fD0DaWQcwIfiGIAAP/szPLwUhBGIeAnnLshZWxJW0hTXHt4Up1IK0lFxVTcgPb446nNEVRXUadmd9duc8eovBTmS8PuZ7U3OI+Cz6fK9O1oPw+k54Xz0I74hLWqNTB1VNEUkqHGKo3tZuX95npBwE9LOZsrkcbEqH+P1AjaczHqd0Tl8wv1ztyQWDbpojkjXJg3ttn66ocqo3UareOnBf2SFVH96z2nbBWRKzCr5/9atf8c1vfpNNmzY1LjvmmGN4//vfz+te97qDvnOtNX/3d3/H1q1bCYfDfPjDH2bjxo2N62+++Wa++93vNnbWP/jBD3L00Ucf9P1Zc2d4pg/3pIDbmb+AG57poz0+6I6IFFWdp2iGEQgSThsppxMpXMoqQ391K321R1B4pN31tIWOQSAwGCQSjUYISZO7atbTIsemWGoUaXcdjgjT7z1BSY2QdDppclehtCKj9hERTUScJL6pEhYTz8PYLvXBtBq0LOvwkAxLkmGJTjrkq5rRen54ueDTVwiu70w4HNsaYqSs2FHfDd864vHkqEdn3GFjc4jVCTlpuvR4rhSsSrq0xx2Gy0Gbwb5CEOy3xRzaY5OD8GBKpks64tBXDAb0bB/1GqPMl/uUzI3NLo8Pe+zO+ZzYHsI9yM5SS6EnHxQ3xkOSdQc5RGd8UWWono401ScAxphG/3elDa6Ajc2hA/aOtxbOrIJvIQTlcnnS5UopPM876Dv/n//5H2q1Gt/+9rd56KGH+NjHPsa//Mu/NK5/9NFH+fjHP86WLVsO+j6suRu/w513djHsBTsKCxFww9RBd0ykqZoCedUPQEw2k3K7cEUEZWoMVbfT4z1C1t9DRDbR5ZxIwm3Dp9pYqzI+Ujq0uBsm7UrPpKiGqOkSMdlM3GnB1zWGvG14ukRX5HiSbgf91ccx+LS6G/FMhYhMTtrhtsG3ZVljpAh2u5ujDr42ZCqabDVImSjUghzd5ojk+PYIp3QY9hQUuzIe/UVFf1ERHbcbPlOKhSuD3O72mMNIRTFYCiZnDpcVrdEgLWb/4ruQI1jfFKK1PiVztKLJ1Wp0xJf3lExXSjY0uWwf9diT8zkqfeB0wuVgsBQ8HhFXsKnZPajWj/sXVa5NuVN+wlH0gse07BukgFVJh2pM2cB7ic0q+D7//PP5yEc+wj/90z81iix7e3v52Mc+xnOe85yDvvMHHniACy64AIDTTjuNRx55ZML1jz76KF/60pcYHBzkec97Hm9605sO+r6smQUBd5GyzkzY4QYWJOCGqYPuhGyjZorkVC8GQ1jGaXJWE5ZxjDEU1RAD1Sfprz2OFoq0u56u8IkYfHxTxRVhDBplfMIiTkto/azyu8eMpbw4wqXZDZ7rg96TlFWWlNNF0u3EFREGalsBaAltpGaKUwb3z+Rn2xc5y7Ke4cqJ+eGZ6rge4pWgNVw6KnnO+hglT7Mj69OT93lqxOOpEY/2mMNRaZc1SWfa3XBHBrvabbFgYMpYR4vhcjBgpT3mTNrZHpuSOVLR9K+QKZlHp0NsH/V4etRbEcF3pp4W5Mog936uedZKG3oKwacUUgTdUaYqkvSUaXyaAUz4NGP38nwvdUQRxhhzoBsNDw/z+te/nqeeeqqRAjIyMsKJJ57IF77wBbq6ug7qzt/3vvdxySWXcOGFFwLwvOc9j//5n//BdYP3BP/8z//Mq171KpLJJG95y1u49tprueiii6Y9XrVanRTAW9MzGBQVPFnAE0VMvWhS4BAyCUI6iUP0kLt17M+nQlWO4osSAI6JEtFplKhRlRlAIwkR1W2ETBDwK6oU5QB5ZwdlZ5CQTpDUG4mpVjxZCFatY2hZxaAImRQx3T6nXWeDpuDsQ1MjrlcRMgl8avSHf01VZGnxu2lSG1F47Ir+mLBO0+GfiifyJNU6HCbmHGadHRSc3bR6W4iZtnk6e5ZlHa7KCgq+pOCLxlv3sIBUSBMVhmFP0l+R5FXwuhYShs6IZk1UcaBaPW0g7wsynsQ3IICUa0iHNFOleCsDIzVJzg9e/xOOoS089W2X2h+yLiOe5NSmGss5/i75gr5qMPlhTUwx183nsoKBqoNvICqDx37/x8MYyHiCUU9igIiE9rDCNjFZWFu2bCESmX3dwax2vtva2vj+97/Pvffey1NPPYXruhxzzDGcd955h/RxVDKZpFgsNv6vtW4E3sYYrr/+elKpoB3chRdeyGOPPTZj8D1mrifhSDK2w13RWco6izYRoBkpHGKyOWgLKBKNx/WBBx7gzDPPnJf7nrjT3URYxknKTjQ+edUXpImINaScLuKyFSEExhgKaoCh2jZUbZioSJFyjmNt5BSU8aiZEo4IERZxKjoHQJO7moQz92A35/dSUFESTivN7loA9lYexK11EXO6WR3eQktoAzvKv8KrrmFT5DwiTvDmoCt8wqTj9VTDDHkOR0VPJeV2zeu5tOb3uWnZ8znfDuV8GmPI14Jd8HxNM7ZFtiYsuSAq0VqzO6fYm/fxNQwBrTHJpmaX9Sl3xtzwsfzfgVLQYk4ISEeDbhdTFd5VfM2+vKLkTeyMsZhTMg90LtcVfO7dVyGZcjhzzfJsO1j2NDuyHi2GA6YO7c8Yw0ApKIxtBjoTDp3xyUWVYx1sIsqwRgpWJZwpWw3an/X5c7CbvrPua+M4DhdccEEjTWQ+nHHGGdx111285CUv4aGHHqK7u7txXaFQ4LLLLuNHP/oR8XicX//611x55ZXzdt9HkskBd7DDLYVDwmmdFHDPt/3TSyIyQdLpBAw5vw/PVOrj4DtIOh1I4dS/rshobQ/9/hPk/B5iMk1HuJsWZyNFPYhGERUpDFDWWRzh0uJuOKj0mKouUFBDuCJCk7O6fv8FRv1dKGo0y7WNgTyDtacQCNrcTWR1LzGZnvqg9d+YY9+PZVnWbAghaIo4NEWC/PBsPS1lfH54e9xhU9olW9HsyvmMlDUj5Rp/GPRYn3I4qjlE0xTbnUIIWmNBW8FMfULnaDkY2pOOyCAIH9dvPOpKjmmRZCqK3sLynJLZlXRJhQW9BUWppg/YIWaxVZVhZ32IzoammSdNTvW1e3MeJc/Uc/NdEvttd1d9TU8hqB8Q9edGp20duKwdfFPJefDCF76Qe+65h1e+8pUYY/jIRz7CHXfcQalU4pprruFtb3sbr33tawmHw5x77rmN9BTrwMYH3BWdQxkfWLyAG6YPuiUuOdVLVRcAiMsWUm5nIzdbG0Ve9TFc20l/7Yn6kJmNrIuchhGGvOprBOtVncczlXp+94b9htzMjjaKjL+3PsXymVHzfdXHqekyTc5qYm4zIRkj6/VRMRnSznqMNKAntxhsHBcN85yyY1nWkcWVgraYQ1vMoaoM2UoQ/GYqmkwluP64ljBOq6GvqNiTVzyd8Xk649MSlWxsdlnf5E7qBCKEoCUajKfPjgXh9ZzzdDQIwscP/UlHHZoikoGiYqi8/KZkbkqH+MNAjaezHls6ls8n37427Mp6+NqwOunO6Q3LaCXo3a40pKOSNcmJRZVKBzviw+WgzWAyHLQsjCyDx8Oa2ZIG31JKPvShD0247Jhjjmn8+4orruCKK65Y5FWtXMYYPFNqFE0uRcAN0wfdrgiT9wco6dH65UmanNWEZLTxtRWdI1Pbw4C/lbzfT8xpoSN0HO3uceR1H76uEhJR4rKVvOpHo0g4rTQ5aw76+8r6PSjjkXK6GoOBSn6GjNqLQdHkrq7v1MOA9zgAHeHNjTcP03dSGcvatAG4ZVmHLuIIOhMunYlgqMpoRZOtaobLwaeZsZDk2ascijXDvkIQJI9WajwyWGNNyuWYtEs6OvHXvhg3pTNXDXa2M/Xgviki6Yw7jVHjUgTtDFtiy29K5qZml8eHauzO+pzYFpox9WaxaBME3lXf0FEvsJ2N8UWVjoT1TcFk0vFGK0ErSV8HO+JrksEnJdbKsKTBtzU/arpY78OdnRBwx52WYNLkIgTcwTqmDrpDIkZBDTKihjFoQiJKk7tqwnh3bXyyfi8j3k4GaluRwiXtbmR99Ewc4ZLxd2MwJJw2JA5Z1VPfqV5L3Dn4CatllaWsM4RFvJFWAtDvPYavqzS5a4jIZCMoH/K2IwnR4RzNoNo+xUj5ZxgMAjnvBauWZVnxkCQekqxJBvnhmYomV9MMlIJ0t7VNLhubXUbKip6CCgb5ZH3S0WCc/cbmybvhY6kuuapmsOSTq2py1WAYS2fcaQzfmWlK5kzjzBfSWNvBpzM+e/I+G5uXtvLSGMPunE/JM6SjklWzmFoKQWvAPTkfb5pJlSUvOOclL2gd2JUIgvrFzMG3Dp0NvleomQPuZsIiuWi9WacLusMiQUkPM+rvro+Dd0k5a4jJ9IS1ldQoWW8vA96TFNQgcaeF9tCxrA6dRE73U1A5pHBodlYHA3f0cD2/e+MhjbBXxiPr70Mg6+kmwZoK/hB51Y8QgqTTQaq+6z1UexrPlGgPHYeSHkbpGfuHa2N3vi3LWljj88NVPT98tKIp1oLXn5AjOLHdpaKotxvUZCo1Hq3vhm9qdmnfr1VKU0TSFAmTrwU9wvNVTb6qGwOBxnKOp56SqZZsSubRLeF6yo235MH3voJqnLN1qQOHWuOLKiEIqjvGFVV6ytBfDHqwQ9Dbe1XSJbzMByFZU5tV8F0qlfj617/OQw89hOd57N+d8Gtf+9qCLM6aaDkF3MF6pg66IzJJReUY9J/EN7UJ4+DHD6LxTZWs30PG28tAbSuuDNPqbmJt9HSiMsmwvxNlvPpxu8j6+4JJkjJOi3tw+d3jjU2xbHbX4MpncgT7a4/imxrN9f7iYwWcA17Q27szNJuUEwBtd74ty1o0jgyKKVtjDjVlyFSCHel8Lfid3RpzWZWAXFXRXwp2WPfkfFJhwaZ0iA0pl/C4fOFUWJIKS4peEISPFXzGQ0EQngrLZTUlM1V/czBQVIyUfVpjS7O/OFD0GS0rYq5gwyymV85UVGmMYagcfBKhNETdoO/6XIo2reVnVs/MD3zgA9x5551ccMEFtLS0LPSarHGmDLhZuoA7WNP0QXdNlxjytlPTpfo4+FZSThdSPPNUM8ZQ1EPkvB4GvW0U9fC43e4tlMwIQ97TCAQpJ5hqOertqud3t9HkrD7k77mohqjqAlGZmtCWMOf3UtQjOLjEnJbGrrdSNUa8nbgiRouzkREVrG//kfLjabQNvC3LWhLh/fLDM5VgmE/ZN4QcyVHNgqoSZCuKkarmDwM1HhussTrpsCkdomPcbngiJDkqLSl5QYvCfFWzM6OJh4LAuykiJ0zJ7FnCKZlHp0MMFBXbR70lCb5HysFE0rAj2DiLITqjFUVP3kebyUWV+VqQYlL1DY4UrEk5tEaX78RRa/Zm9cy85557+MQnPsGLXvSihV6PRRDcBkWTyyfgHlvXdEG3b6qMersp6ywAUdlEk7Nqwo4ygKcrZPy95PweBr2ncEWEVncj6yJnEHOaGfV3U9NB7+4Wdz1VXWTU341A0uKuJ+akD/n78HWVnN8fpLLU+3mP6as+hm9qpN11hEW8kZc+5G9H49HpdoPUeP7UI+XHM41uJ/aF0rKspTOWH746acjXgh3xfL1lYUvMoS3uUKgphsuavXnF3rwiEQ5ywzc1u41uJvGQZFOzpFwPwnNVza6sR8wVdCaC1JdESHLsEk7JXJ10SYQF+wqKk329qJ1YclVNT8HHkcHY+Jl2/ZU27Mv7ZKv1osrUM0WVVWXoKwQ59wCtsaCg1bWtAw8bswq+lVIcf/zxC72WI1pNlxp9uJXxgHrALVuIOUsXcI+tLa/6x6VaPBN0B4WSPZTUSDAOXsRpcldN6rVtjCavBsj7/Qx6T1HWWWKymY7QcawOn0TNlBisbUOjiMlmUs4qcqqXis7hiBCt7kZC8tCHJxhjGPX3YNC0OBsnpK6MenuomBwhEScqm0i64wowa08A0BU+iZou1s/DTCknAAYhhN39tixrWQjywwVNEYnShlztmfzweMghEXao+oZsNQisHx+q8cRwfTe8KURXvWgwFpJsbJZU/CAdJVvV7Mr6RF1FR9yhOSJpiwV/9xcVI2XFzoxHU0SyehHylDc1hXh0qMbTox4nLlLbwZKn2ZPzEASdV2Zq91eoafbmx4oqJeubgnOijWm0cjQmeLOzNrU8Wjla82tWwffll1/ON77xDW688Ub7ccc8mingjjpNRERqSc93TZcoyl6GvKBwZXzQbYym4A9SUMGwG1eESTmriDnNk45T1QWyfg95v49BbxshEaPFXc+6yOkknDZyqpeiGqkXPq4lLBKM+DvxTZWITNDibpiQtnIoCmoAz5Qb53iM1pr+2mMoo2hxVxMSMaIyuL6mymTVXqIiTXNoFRlvb3A+RGrK+4AgyDdjaSf2R8ayrGXGkUGf75ZokB+erQZ9vo2BjrhLazQIznNVRU8++BN3BRuaXTY2hYiHJVFXsqFZUvWDPuGZapBHPuAKOuJBD/G1KZfWqKSnEAT0hVqN9gWekrmp2eWJ4Ro7sz7HL0Lbwaqv2Zn1McCGptC0xabGGPqLisGSQoiJRZVjQ4zGWgcupyFG1vybVURTLpf5wQ9+wE9/+lM2bNhAODyxitgWXM5NWWXJqd5lGXBDsEs96u+honP4okREJuo9sIPd7LLKkFN9KOM1upDEZdukdWujyKleSmqUIe9pSmqYmGwOcrvDJ6HwGfK245kKIRGlxd2Ab6oMetswaJJOOyln1bydj5ouklcDOCJEk7t6wnUj/g5qpkRUJgjLRCPXG6C/9jgGQ0co6EFfNXkc4U7oTz7FWQQDNvK2Djdju52OFISdYMhLSApCMuisYVueLY2H+ysAtMddOmJyQuHkgYSdIHe7Ix6MQc9UNXuywej6jriLMppC1VDwNE8Me2wd8eiMOxyVDtEVl0RcybomSYcyQRBeUezN+Qw4QRDeEpUc0xJatCmZYTfYTd6Z9dmXV6xvnt25GCwpQpJJPbVn4inDjqyP0oa1qSD/fUymoqj4hlVJl6ofvDEp+4ZwvagyHgpSeHoKPiUvmE7ZmZj6jYkxQSebQk0veiHrbFSVoeRpip4h6ohZ9zQ/Us0q+NZac9llly30Wo4YNVPEGL2sAu7xDJqaKRKRCRJqDW2ho4FgBzsYB19GICaNgx+vonJk1T6U8QmJKBGZICISrI2cRtJtp6RGyPq9GHRQlClXUdDBTnqQ371hyl30gzU2xRKgxV0/Yc1a+wx4WzHGkHA6CInohF3xsS4nqyIn4ukKyvjTj5RvEEgRwrVFl9ZhJkhL0NNe74wLxF0pCE/xbxugzy+tg/HiFd/wdCaoE0pHBW0xl464Q0dcTurpPZ1YSBILSXxl6ClCvhocNx6SdEVMUKRZVvTmffqLiqgr2Njksqk52A1fl3LpjDv1iZlB+8GBkqA95tAaC7qnDJYWfkrmMekQO7M+2zMe65sP3BlLmyDPOhmWsw6+VX16pacMXeN6nBtj6C0EkyeDN6c+fUWFNtASDVJvDDRaM0LQtnFV0p3Q0xuCCZmjFcVQSeNrgxDQFjNLGnwbYyj7hqIXBNwlz+DrZ7rgJcLSBt8HMKvg+6Mf/ehCr+OI0uyuodlds9TLmJYULqvCJwKwk1E8/f+z9+ZBkmT3fd/nvZdZd3VX390zPdfOXgAWwB7ALi4aAk/zkHkJoECKlCGQksygJTNkWqbpoGgSAHWYIRJByRE0KQVFyRRIKcwIBG1LskURBEEKi13sYoE9557p+6r7yMz3nv94WdXV53T3dM90z+QnomO6q6uysrOra775y+/7ftvU9HxvoWVWlhjwJnp18P1oG1KN5miZSi+tpKDGGEs9AjgR3F2YKVFxVneetegGHVPHEymGvHO3mSrvn6qeI7IBBTW2xY++FF0ish0ycgBPpjeU7bT0Gg2zRFFOkFVD1PUScHu/txCCUf8hanqRZPqdcD8xmFG8PSUJjSXQThyExhJqS2iIb7e0I7vjNpQEXzpB7isnyjd/fi/bEk8aUkq+7UKWlaZhqaVZji0g5XbI5bUQIaCUdoJoLKsY2YMYnx5wTZaVjmGtpakEhnpgCbRhKOsyxVsaKm3N66Fx0/C84tyAx6mC4nQswpdbzvM9V49YarqJ6HheHXlL5kBGMZp1z19uR1uaPTfTil+vGW9vz98t0WlFlqGsYjzvth8Zd3sjMHgSfAmzdY2ScDaejK+0DItxdGDa234xaicyLLcM5bYT7VLASLw4drNAP2q06RPakaUVGvq0Np4UDKbdot68L/Z8DB9kdnw1fv7zn+c7vuM7SKVSfP7zn99xA0KIZCp+n6JtSFMusRS+BTjPt6uD337hY1OvUo3mMWhSMkdJTW9IOwlMg7XoJtqGcVb3GYw1LIeXiGxARhYpbZpKHwZtXaWp1/BFhqKa2PC9yAQsh5fAQk4N44kUGbk+cZ/vvArAqO9OHvaW772RZPKdcL+hYnG8m57RXVFu3KX5yFiCvs9Dc3uB3rO0qHiaHgv2lHKfJwJ9HU9KJgqytygyMoalpvNir7Rc9N9a2/AWTowPZSRjWcVYXjGSkVt80UIICilBISU5VVDUQ0ul7RJRKh1DQxuEhdGcpB1CMzLcrDpLUlo5b/j5QZ+pgpu+LzfdJHg+FuFjOcmZokcjY4+sJfOhIVcAdGkt4j1TtxHfoXstZvcoHG/VIuqBYSAtOV1w+9uOvd+htngStHFivLuoMtCWS2sh7ciipEtmGdkUw1gPDMstF+cI7krReNZFDN6t13vXQtKMBffmv9OMJ3pCO+fLpOjnAOz4avyZn/kZPvCBDzAyMsLP/MzP7LiBRHzffxiraehl6nqZUFTxxUWK3kRvAeJmXFnODB3TQKIY9E5tyM621lLXS9T1Ihbbm4a3TZVydAuLoajGKajxQ7ffGBtR1rfiKvozW7a/FL6JtiF5OYqS/pZ9WAzfQiCYSL0Naw2Bae5aKd+PpfuGlbwxJTx49AT6LvfRZl2UR/H0vP/z0Fg6uwh0Kba3t/h9fvQHNZ7Nk5KpgrM4QJogMiy1DMvNiOWmZrVlWG25ibUSMJhxFfJjOSf0+sW4EIJiSlBMSU4VLfW4zn655ZJODK7J0RpoaksjdIU8b62GjGYV5wc9ThddDfpKqyvC3cLD0aziYsljrWN6LZlrbc1U4c5bMqfykpwnmK1FtMd2jx1sR07s7kV8z9ddmVDOd95tIVxe+q1ahLYWgSAyIARM5hUDKcl8PerZtYayism+6EBrLeWOYbmpe0I35zurzkD6aHO9rXUnwd3JdmOThUQKZyPpCu2cl5z0HgY7iu/XX399288T7l+stTTNKnW9iLYRSnhkzRij/sPb/vFba2noZWp6EYshI4sMeqc22FG0DSlHN+mYRi+72xc5anqeul5GIBn2zm3wWB8m5egWxmoGvMktVhZXCHQFgSIjiyjhb/ByV4IZOrbKkDpHSmXpmBqW3SvlN7KzaEhISFgX6LuFwRm70dLS/3kUi/VOZGjs8Hgptlpa3CR9ozf9fiflSU4XXfoIOLHp6uadGO6K8ddXnBgfyihGc5KJvEst6Ypx2Vdnf7roUQsMa23NYtNQ72giIONJWhHUA02to1lsRKR9yZmi4sKgz2PDKVbbrup+IY7WG864BZwrLb2lJfOgSCk5V/J5bbmbfLJz5XwrskjBrhGBQO+YpT1XoiOFYKERsdjQPdHqSbeI9XRR0QzdtNtYJ6hPFTyy8UlFZCyrLXc1ofvYwdgedKcnHjuhjaUZuX1uhHuzkBynNWn3C/emezXh2NE2VarRPJHtIJDxdHqUW7a57R9eaFqUoxlC20IKRUltXSDZ1lXK2onfjBygFBfarEZX6ZhGr2BncxHPYdHUq7RNLY5IHNvy/cXgDSyaopxESEVBjW34WRdCd9I5nnoU2L/lpDv5TmwnCQkHRwpB2tuDQN9kaen50WOBHgQ7C3QhtlpaNn9+3NIl7pRMnFAyPeCu4rUjw1JDsxhPppfjj9dXQjzpil5GsorxnOw1RyopKGUUpYzizICl2nFCfKHhSnyaUrgrnx1DuaOptDWX1kJGc24a/vCQotJhw0nAcFZxZtBjKU5EqQYBldBt5yAi8KFBjzdXAq6WQx4d8raNHTTx9Pd2grfSdt51TwrOD/oI4HolpNoxNEPTO6kbyiryPtyq6diC4toph7olOvFViErs51YSRnPu+B62hSPoSyHpWkjmOpJSwy307LeQZH151/3kDyqJ+H7AcQU683Ti4pi8Gqagxne0VbiynAUaegWLJSeHGPAmN9XHG6p6noZeQSB6NpTQtFiNrqNtGIvx6UP3d3dxVpg5JIqSN73l+21TYy26jkealCyghEdODve+r7VmObqCxGfUWxfft6uU357kzSwh4SiRQpBW7CocrN1qaQn1xmm687lu/3gh2GJp2fy5JzmxU8KMJzkzKHvJIM3AsNSKWGoaVpouHnCxoXkNd7VgOCMZzSrG85JSxsOTguGs82ufHXCJOMtNzWJTk/E0zcjQDmG5FbHaipitRRTTitMFxblBhbEuIWU5zsAeyrhklNW2ZjmQvLUWcqrgUdhnS2bKk5wqetysRszWNdMDWx/ftXrsZjmpB4abtQglXY64Ba6UQxqhi/8r+JKUJxjJSBqRZa3logPHYiuPkoJa4I5JPXD2k5QSjGRdFONhWDn2aiEp+ZZzgz55P7GQ3CsS8f2AEtkOtWiRlikDkJFFBtTUrlNoV5YzQ2QDPJFi0DvVq1/vbdd0WItuENo2nkgz5J3Flxmaeo1KNBv7uycoeuM7PMudY62lHDoveck7s20qy2LndSw29nizZeq9pq8T2Raj/iMopdA2JLS3r5TftCdAMvlOSDgOiH6BvsOSDWu3sbdsmqbvVaAvtGVvSrrRj34yBHouJTmXSnEuvqBZDwxLsV98ueXsIgsNzTeW3c82mpGM5hXjWeWSRnLu46G4wGcxLpepBZJ6YGiEbjHoUkPw1qpLQDk7oBjJSlbbltVYvBZTkqx03v+r5ZDBOJJvPxPih4ec+L5cDnuT/n6a3cWW/vbbbEeGG1X3Sz874BNZuLkWUAvcfpXSLp4xpQRLLVdUVIzbPH0J5bZhpdXv55aM5dzJxZ28FpyFZF1ob2chGeizkGRjC8laymzII0+4+yTi+wHD2Ii6XupNrn2RZcCb3NVK0V+WA8TlNxNbROjm7O4BNQUIKtEsDb3iogX9czsu3Dws6nqJwDbJykGyqrTl+82oTFnfIkWOlMwhhNgw9QZXrAMw6bvIxb1Xyq/TW3B5/P+fTUhIwInilHITydsJ9J0WiIYGWpGhrgXLTb3tNraztKQkeH1e9OMm0AspSSGV4kLJfV0LDIuNiOWWZqVpmGto5mIrQ1oJRnLdybgXF/i4oplyx7h0k6azpqy2DHONkLl6xPWyYDCrmI5bMUMjqHYMbeOEY2ig0jHUgoCxWNzvJTO+lPEYzkpWW4ZqWzOwKce7u9gyt83kO9CWq2UXC3hmwHO2jXrISss4i0nG+aLDOF4zpVx0YNYTrMbpMDrO5y7FVwuyB/Rz91tIWqHpxSN2SW9KIUksJMeXRHw/IFhraZhl6pGrg1fCZ0BNbitO+wlFPU4EcWU5JW96S9SgsZpKNNPL7i7FBTnahqxFNwhM88j93b39NS3qehElvB2z1BdCFx9Y9CcxaAbU5IYTCa0D1qIb+CLHcOoc0Of33qVSfgvJesuEhPuOvQr0RlZzccjfcbFoK7I07c5vEt4mS0svbrEv3eVeCvRiSlJMpbg45L6utp1ffLmhWW6bXiU9BGQ8wWg2zhnPeTw5kaEVml4Jz2rbfb7S0iyshNysRgylJRN5yWjOHeSu0JQCQg0LDc1qXNAzkL69ffFiyWe11eGtcsgzkxvv311suXmaHhnLtUpIZFyJTj1wE/+VlqaUkaSkQCBoR26/JguKgi9YaWluVN0EXElnPRnep5+730LSipzgDvVGC0m/0M75D8bC4fuFPYnv1dVVfuVXfoWvf/3rtNvtLd//d//u3x36jiUcHhvq4FEMeJPk5eiub9zahlSiWZpyAWNHGFCT5NXWxwSmyVp0Y0N2txKp+PbrcRvkIIPe6SPzd3ex1rAW3cRiYz/51pd3PVqiphfIyAEUPgjIy5EN91mMLmEImfAf793WsTWkUPsq/0kWXCYkPJgI4fzfOV/uKNCB3rQ82lRY1P28FVnsbQT6BkG+aZruS+5am+hAxtlNHo7FeLkdsdhwcYSrbcOtmuZWLMZznmAkbt58x2gaK2C15SrpV1uahaabot+oaoYyEbWGYrTp2icznkQIS1u7POrrFUsh5UT4bkklrkVTMFONeOeoIRXf11hLR1uy3kYLiLGW65XI2UoyklpgmKtr6oGOc8gFQroZy1DG2U6qHcN83U3/U3HF+lBG7ul3YKzt5Wp3BbfuK5JVO1hIEk4mexLfP/dzP8crr7zCd37ndzI4eHiV3wlHS8fUqUULBLYZ18GPxnXwu//aG3qFWrTgJuQ2y1jqETyxdWLdFbIuu3s9p9vZT2axWAbUJAVva9LIUVDVLq0lr0a2eNG7zAXfAGBAncIQUZRb7TOLseVkKvUEwD4q5XcieYNMSEjYiq9un6Ky2dKy+fOOtlvsB/0oudHSst00/SgEeinjUcrAo/HXq62IxaaJYw01N6sRN6sAAfmUW3h4uqB4ZNin2jHM1CMW65q5RsTNpsLMdhjJSoYzksGMYjijsDgbSjtyHnKXxrJ9S6aUkvODHq+vhFyrRDwaxw62I4u1GxdbWmu5WY1ohoaUcs8xU4sQWPIpiY1jA9OeIOcJmpFlrR0BcbV6nM+9G+HmFBLt9qNL2nNiO7GQ3J/sSXz/2Z/9Gb/5m7/Je97znqPen4RDIDIdqnpuQx180RvfVkBvflxZ3yIwzdg+cpqCCbY8bmN2t0fJO0NaFrDWUolmaOhVJIph/8yOIviw6ZgaDb2CJ9IMqMlt7+Nq79fIy1EQFmnVhjIggEDXqepZsmKod9LQse447sfv7Ugm3wkJCXdGtyho+15hR7TJ0rLZj74Xgd6doG/OP+8uFr1TgT6c9RiOfwhjDKttE6ebOMvJjSDiRsV9v5hyaSrvGE/xqPZJlyNSBcViw03Pi6mIYkrEvm8PbQWLDU0zdOU/k4X1WL9+LpR83lgNXexgLL67x6V/seVsXVPtGCJrabYtCw3tTlqUpODL3vGKDKy1TS+dZWQHP7eNp+v9KST9FhIhIOt1hbYT24mF5P5mT+K7UCgwPDx8+zsm3FO0DanrRZp6DYslJXMMqClSMrfr4zY3UGbkQFyW4wNXN9y320rpsruLPXtHv7/bFxmG/LO3FfuHhbER5ci1WA55Z3ZMI+lWxQ96pwhsk6Ia32KFmQ9cCspo6uHebQeplIek4TIhIeHusBeBrjfnn28S7EG8YHAnlNxmgeimz/caWyelZDQnGc05CWKMYaXtcsaXWppK21ALNOAsHNrCQyWfswM+lU7ETF0z39DM1g05FVLMuDhBbaXLBu9oRnNqS0tmxnNV8Ldqzmt+uuiti+948r3UjFhpRlQDl4ldbrvpd873yPsCa917e2gESsJ43k3h+69g7N1Cst4amVhIHiz2JL5/5Ed+hF/7tV/jH/7Df0g6fXcEVcLesdZQ18vU9RIW05v+7qU1MjBNKtEMoW27RYrq9LaPs9b2tVIKBr0p8mo03kYj9n07a0bJO72POL47pxLNom3EgJrcshi0y1p4k7atMiCn0AQI5JapN8BS+BYAkymXcrLfSvmNJCsuExISjgfdNtHMLv/rdwX6dvaWrmC/nUDfskB00+c7WULGcpKxTWJ8oeFyv68ZwXLLqVeLYLrgMZ5TrDY1lY5mtu4Wd6Y8mMi5qfdqy03Wpwf8DVXuF4d8btU01yohp4se7dC4ZkslegtA52qajo7zu9OKwcz6PvvK2U1Gs4pS7OcOtaXS1rtbSFLrSSS3a9FMuP/Z8c/w27/923tnYtZabty4wR/90R8xPj6OUhunhcmCy3uDtZaWWaOmF9A2QgrFgDpFTg7f9iy6W5ZT18sA5NQQA2pq20WRLrv7JqFtbcjuBucPr0ZzAAx4k9s2SR4lLV2mZSqkZK53MrAZYwwLwasIBEV/io6pbut9r+sVGmaJoprqtXUGtrHPSvl1kgWXCQkJJ4meQN/lPrq7QHSXuMXOLgJdiq2Wls2FRd4mMZ5dDDh/OsNSS7PUjKh0XGvkaE6Q9iX5wHnJm6Hl8loIRGR8wVhOMlfXnC4qHiqlnGc86zGUkbww3+GPrzUoB/DB6TRnB32ulkMurQa0IktoLUNpRUpCSso4alHGLZTQCC0ztYhm6K4adHEWko2Rf4mFJGEzO4rv/+q/+q/u5n4k7JO2qVKLFghtO66DHyevRveUKNIxNcrRDNqGcVnO6R3FZSBqLIWXsBhyaohBdQohJNYaKnqWpl5DCsWQd/ZAAvVO0DagEs0ikJS86R1POFaiKwS2yZA6S2RbO069FzpuoeW4/0jvtoNaTjaSvPEmJCTcH3QF+m7XwI21O0Ysdj/vRIbGDo+XYqOlpRJJPCU4O+BzseQjhGWtbeNK+ohy2zKR99zkuh4560ldM1+H15YCShm3kPOdY2nOD3r89itVvnSrTVw0yR+8UeeJsSr/xZksVgh8gbPFZN3UO+crsp5AW7hVC7dYSIp9CyOz3p374xPuf3YU3z/1Uz/V+/z555/nySefxPc3XnYPgoA//uM/Prq9S9hCaFpU9VyvDj6nhiiqiT1ZIoyNqEbzNM1anH4yRlGNb2sRcdnds7TkIoLxXnY3ONG7Ft4ksE18kWXYP7tti+RRU45uYdCUvNM7+suNiVgK30SgKHqTNM0qeTWy7fFaDi8h8BjzH+vd1jGNA1bKQ2/BZfJGnJCQ8AAhhbNm3Fagmx3iFuNpehA4gV4OBbO1aMPjRWwVOTeY4mzRUAsslY5iMu9RbWtWO5obtYiFumalHfDWasAXrreYrUe8Wd5YfhRa+OpiyHLL8AOP5jlV9CllFVnf5Xh3ovVpfkoJBjKJhSThztiT5/vHfuzH+NM//dMtiy5nZmb4O3/n7/C1r33tSHYuYR1tA6rRwoY6+KKa3HPudEuXqehZjNX4IkvJO72jP9paw3J4mch2UDbDaOpiT9x2TJ216AbGanJyiEHv1F31d3dp6lU6pkFGDpBTOy8GXgrfIrIdRryLBLbeO+nYTCWYoWOrDKlzpJQ7Lq5SvrXPSvl1bOL5TkhIuEtcr4R0tO1dZxM4gbr+1frXvfts/rrv9vWHih3vs9PXW553D4/xtxQHCay1RBbmfcNwVjqRrt0EvR5o/u8bbYLIoJTAQ+B7oIQAa+low1BK4uctS23rvOOVkGu1nY/hbE0TWNdG2Y40rVDgd7PThdt+PRQsNSwaiLQltKC1xeBOHCINBou27vPBtOR7H7s7qV8JJ4cdxff/8X/8H/zWb/0W4LzFP/iDP4iUGwVItVrlwoULR7uHDzjG6rgOfnnPdfD9dK0ZbVNDIPdUsGOxSBQFNUbedHrCu6GXqUbzgEsM2c66cfcQpESOknd6x3tEJmApvITEY0BNUjML5NXwtlNvpTJkRInp9NO92w5SKb8Bm/i9ExISjh5rLZFx0XfdK27dBX/xV+zS1XPsWQtdNXw/1op4AShEocVYg7GgrftZu/9GBiySrC9YbukdnsGhgf90rcXiqCbrSdI+pOKEFyX2l0giBHgSfGXRWm9ZK5fwYLOj+P6BH/gBqtUqxhg++9nP8j3f8z3kcuuRdUII8vk83/7t335XdvRBw1pL06xQ04sY6+rgi2qCnBra8zacWF7oLRgc9E7tKf5PCsVo6iIAghmsNZSjGVqmjBIeQ95ZUvIgNozDI6eGbnssFsPXMURM+I/TsuUdp94ABTXCs4M/tuG29Ur5g4lvN/lOxHdCQsLRIoTg4tDe0pi6jZm29zVbvu59zsY77fiY7R67/uDbPqb79eb7dD9b8A0TebXpMYpPvGvQBQ+ELmmkFVoaoaGloRm41JFOZOhEmls1Q6Wt6MYX7kSlE7HcVJSyBikU0usuEoWMhEx3MWVKkleSbEpQSEmKKUHGk+R8yPp7MhUkPMDs+ArJZDL8zb/5NwGYmpriu7/7u0ml7r6v90GkpSvU9DyRDVwdvJokr0b2bH2ITIdydIvAurKcQW96X6K9H0PIcniF0LZIiRxD/tkDRO7dfQLTZCW8hifSFOQ4FTNLTg7ty5u+Xim/W3ru7iST74SEhOOE2GQj2dtb1L19Hyt6lkJKEmiXLBLGaSuB7pbVuKl0LgW5lJswtyLDfD3iRscwW7fUQst43uPymqazw1WAlISzgykQlkgL2tqS8SW+EuQ9t+hTCoisoBnnd9OBrqCX8bQ7pQRpJcl6Lls847k6+IznPOK+XM9mT3gw2VF8P//8873Pp6enefnll3fcyHvf+97D3asHlMA0qEbzvTr4vBqJi2D2dha9uSwnKwcZ8KYOLJY7pkZdzRDafJx0cvrELB5cCF7Dohnz307TrgLsq+b+zivlIcn5TkhISLg9xtqesA60E9VBXPwTasv1liK3Fm55nK+61etO8DYCw41KyPVaxGw9ohG4shuwjGQkQxnJw8Oab6xEW7algI89nueJ8RTXK5rVlkZbSzOw2HhfhrOKYsajmLJklEJI6I7ELAJtXQ56qN00fsXC5kl7V6B7UuAJlwG+WaBnNgl0f4MXPuF+YEdV96M/+qMI4RY89P/Su5es+m977bXXjnAX739cHfw8bVMFICsHKXoT+2qIDEyTcnTLLZLcpSxnr9T1EtVoHouh5J3edVHjcaNtapSjm6REjrwcpaxvkpWlfR3Pg1fKr2Od6TshISHhgcZaN6kOdb/IjjPBtcsO3w4lXaJJQVlGc4qUEu4jni4LIViqR7y5FnC1EjJb07QiQ0dDWgpKacW7RgRaCGqB5VYt4qnJDI8Oad5YDXhtVWOBiyWPj7+9yCefHHRivxJyaS1gsaEptw2RsXjCUukYap0ATwpyvqbgK0ZyksGUIu25CMacL0j39o9erGIzcv+2IifQgzjRpdU2RMZsGNVIAUqAp5xA96T7ufvFeTZ+vq5I7/6715bRhHvLjuK7P0LwC1/4Av/7//6/83M/93O9yMFXXnmFT3/603ziE5+4Kzt6P2JsRE0v7LsOfuM2NDW9QEOvAJBXwxTV5J7yvrfD+btv0TIVlPDI61MnSniDq5G3WMb9x2kYVyJUVOP72sbh5HvbxHaSkJDwQBCZraK6+3Vktl/wKYRLEsmm5BZhnVLrQrKSMUwV1uXKTC3gzdWI65WQlZahFRkCbRlIS04VfMazktG8hxKWmbpGG1hrBWQ9yfmSx/c8XKDW0XzhZovVluY9pzK8d8rZC30leHg4xblBn5laxNVKyGpL09YWEf8QGmgGloqOqIeSjIpIKUkhJSimnB9cxQPKlHKCfDQryfkemTgHfHPU4gaBHtrYr26J4ml6IzAsWeiep4iuQI8n6J5yi0LTan2anvXAlxJfbZyi6+Si7D1nR/E9MTHR+/w3fuM3+PSnP82zzz7bu+25557jF37hF/jpn/5pvv/7v/9AT26M4Rd+4Rd44403SKVSfOpTn+LcuXO97//H//gf+Sf/5J/geR4/+IM/yMc+9rEDPc9xw1pDQ69Q10sYNJ5IMaCm9j2pbptqXK0e4ok0Je/0HS2EjGyHtfAGoW2TkjmGvLPc4mTFSDajMlU9S0YMkPeGWQmvkZWDeHLvU+87q5TfTCK+ExISTj7r1hA2eq/jz3cYXuNJQdaTpNS6qE6puIJ+j3YKreHKWsDltZDr1Yh64CbIgTEMpRVnhnxGMgLf88j7gqzvPNm3ak54l9sRvieYyHt810N5CinJSkszVfAZTEt0PJ3uz+z2leB8yed00WO+HnGtGropuIWUgKm8E7HN0FXLB8YQGmd98WKPeCElCTxBR0MZl9YiBGSUIOtLcr4g5wkKKYUzvmzE2vVyoiielHciQzuytCNDW0NHG8L46kFgLfXAJd90BbaKF4sqIWIBDleqHsy24ym6+31420zRE8vL0bEnM/Hy8jKlUmnL7alUinq9fuAn/3//3/+XIAj43Oc+x0svvcTf//t/n//tf/vfAAjDkF/+5V/m3/ybf0M2m+XjH/84H/nIRxgbu7v15YeJq4Mvx3XwIVIoBtUUOTmyrxe3sRGVaI6WcQkeRTVOQY3dUd5221Qph660Jq9GGFBTJ/IPbj78BgAT6cep6UWAfVfeB7Z54Er5fmwy+U5ISDghdIXedsK6W36zHUpuFdVuwaETsAdtewy05mpZc2k14D/PpxkRjZ4HfDAjeajkM1WQeFKhrVss6SlB0RfM1DRzDU1kLEqANoKJvOTbL+QYyKh4+xZfQR7pRHRkSW+jiHwlODPoM1nwWGxE3KxFrLWcAM77gmJeuqm8tZQ7lnJHE0TQjgz10MaLLKGYUr32y7a2tCLNamv9GGY9J8aznjtZ8WNbTfekhd4caKNI78ZMbi4qCrShFe9HW7vM8ci4+7a0YKWpiaxFm/VYxK7g3vB5XGaU9sS2wtzrE+5Js+fe2ZP4fu9738unP/1p/uE//Ie9ifiNGzf4pV/6Jb7pm77pwE/+wgsv9B7/5JNP8vWvf733vcuXL3P27FkGB12r4jPPPMNXvvIVvvM7v/PAz3cv6Zga1Wi+VwdfUGMU1Ni+7SFNvUZVz2GsJiVyDHqn91y0sxO1aJGaXkAgKN1BMsq9ph65xaZZOURGDtIIV8nI4r7TSg7HcgIkUYMJCQnHiKhvEWMQW0PCPrG9mzWkkHKCMK1c9nVKufr3w0zsaAWaS+WQK2shMzVNZN0+N7TkrC94pORxqqDIpjws60knaQmjWYW1lpcWApqRE70ZKXh1NWQwI/nw2RyjuXXJE2hL1hOEAlqRm2AP7fJfqa8Epwd8xvIeS03NbD1krWVotAxDWUVGCaaKitNFj4621AJNrWNdBGIUT98l5H1JKePsKUpKpHC/h3pgqAcbny/nrU/Id6qtFyIuAlK7/x66zaGhAeYj3jGV7n3djqfpgbbo+JgHkaVpDdq4qbqhu1h03ebiiXXfuZJxGZHcNEXfZHlJUl4cexLfv/ALv8AnP/lJPvKRjzA0NIS1lrW1Nd7xjnfw8z//8wd+8nq9TqGwLnCUUkRRhOd51Ot1isX1Vqh8Pr/nKXu/iL/XaDq05SqRaALg2yIZM4SkDczseTuGkJZcjrcjyJgRUnYAQfnA+2bRNOUSkWgg8MjrSWZdbtIGXnjhhQM/x91k0XuRUNYYCYrMyj9CixZ5fRqPlX1tp6ZuYQgY0O5E6aBU1TUEipt6/XV7Uo7lSSE5nodLcjwPl7t9PI2FyEJoRCxcBWHf12aHxykBvrB4Any58V9PQOcItVI9hJm2Yq6tWA5kL+9bAEXPMJLWPDcCqnoN0bTMLFpaWmJwM+BB3zDgW15rSm60PCwwmtLULLzZ8Mkpw7mBiPmOYT5+ztDAjZbCE5bQCGqRYC1tmc/ungHejzZgQkktEMxGgo6RFD1DTrqJusQSWHfc21oQaEEQn9xE1l0TTStLTlpKKUNOuWZNLLSNoKPFlkTylICMsqSlJa0sKdHfJrp3Bn249frWBDvhgmEQRrh/LWCFKy0ygrbBNXpa0Ii41Ai0dZ+bvowBJZwgV8KiAE9Yt5BUuvso4W5TYv1zGb/eVHy7d8Cf7ySwJ/F96tQpPv/5z/Onf/qnXLp0CSEEb3vb23juuee2tF7uh0KhQKPR6H1tjMHzvG2/12g0Nojx3XjiiSdIp/fu8T0KtA2oRYs0zRpQJC0LDKjJfU9hu2U7riwnT1oWKHmn95VXvR2R6bAaXSeyWdIyz5B3dttIwxdeeIFnnnnmjp7rblCJZvDboxTU25lOP8lyeIW0LDDi76+BVduQhSBFWuYZ8R+6o32a72RRwmcs9Qhwco7lSSE5nodLcjwPl6M4nv0e4P7UkK4PeydriBRssYW4D3f73bYLrLQ0b64GXCuHLBkDefDyloc8wXBGMpL3GMkohIBiSnLltVd4/B3vZLXtWiyVFIzlFCNZSTu0PD/fRrYMjynB4yM+5Y7hxfk2bxuXPDOV4fGRjf9f1gNDvhwymBZUOpZaYBhIS94+mtr3sQi0ZampWW5FVNqGZmgppl3pTjFeSBoa95zGWFqR88d3tEUbt7Ay0BB5grwvGEwrRnKKou+uLITa0oxcZGI7Mhu89VIQF/usT8lTt5mAw529NjcvFO23unQn6aE2TpQbetaWyNhYqHd/AIHAouL20PW0FmeB6Vo2t0t0OU6Wl06nc6CB755rmDzP48Mf/jAf/vCH9/0kO/H000/zR3/0R3zXd30XL730Eo8++mjvexcvXuT69euUy2VyuRxf+cpX+OQnP3loz31UrNfBr2Ax+CIT18Hv7cShn9C0qUQzrixHKErqDFlVuuN9bOsq5cj5uwtqlKKaPJH+7n7mOy7uctJ/B3W9BOw/4QT6KuXF/n9f23Oyj2tCQsLdRfcL6z4P9l6sIflY7KUVG0T2cbjMP18PubQacrUSstZe/yGG0oKRnGI4rcin3TAv4wlKGUXeE1QCw3IgWW4ZPCmYyCuGsxIpBDO1iK8udAi1ZTyveGTIZ7Ye8rWFDjlf8o7R1BbhDU74uueRVDqalHTT9nZkyfn7O1YpJThd9BjLKZaamtVWRD2wLDY0tcBSTAlynmQy7yym1cAllxSAwFgEYGIR3o4s1yohN6uha9H0BSNZyUBaMVVQZJQiMM4i04wsrdDQDA3Nvgh0L447zHYtK97hxg/KbqLKLiJ/u4WiYXxyGBr3vW4CjjaWKP43jE9OtOk+13pbRtfyonqedJB9/7+6KfvJsbzsKL6feOIJvvCFLzA8PMw73vGOXcXZQW0e3/Zt38af/umf8pf/8l/GWstnPvMZPv/5z9NsNvmhH/oh/sf/8X/kk5/8JNZafvAHf3BDAstxw02oV6nphbgO3qOoTpGVpX0LW2tNXJazFJfllBj0pvZctrMbtWiBml5EIBnyDkfM32tWg+t0bI0BdQpfpimHNdIyf6Dkl8PzeycLLhMSErZi7Q7COvZh6x28IUo6QbV5eu3H8XzHcYBysxLw5mrI9UpEPex2hMBUQTKWUwxlJDIOClASBtPuNl8Klpqaq40IY53wmip4PdEdGcPLCx2uVSKEgHeOpxjPKa6VA15ZDEh7koeHfN49sb2JO4jFd1do+8rtQzO05A4YcLVZhK+1Nc3QsNY21KWlGRl8KRnNSSbzPo04JaUVuR8wpSylDAgraGu3WHO1bVhoarIqIudLBtJuMl5MSyZyCl956FiwtuKIwmZkqXYM1b7mzbTX5x/3xLYncIfJ9gtFN7LTQtFw0+fdfTXdCbq1BBE0jfsfVmL7vCmul6bnS99mIi7EDlP0LZ8f7d/Ujmrul37pl3p+7F/6pV86kp2QUvKLv/iLG267ePFi7/Nv/uZv5pu/+ZsP/XkPm7auUtXzRLaDQFJUExTU6IHSRwLToBzNxGU5PoPeKTLy4GU5XYzVlKObtE0NT6QY8s7d8ULN44AxhsXwdQSCyfTbD5xw0uUwKuXXScJUExIeREJtaWlYa+vegsbOHqwhfpwJnZIbbSH3whpyELTWXK24RZM3K5ETljjv7rkBxVTBo5QWhLFHGKCQkgxnJMW0JDKw3NSstjXWuuMxmVN0sprRnJscl9sRX5nr9KbK75lKIxDcqEa8uhLiScHZQZ9nJne2nvZPvruFNtq6SvrtIv/2w3YiPIgs1Y7Fk5rQGJRwk/0zg36ckmIot11WuY33q5gSSOleS5WOoRFaVisaT2ryvmv2HEy7OMNiSjKaVYice42E2onx7oS8HRnW2pa1tjuzu9pUDK0F6wkrvtx1kn0UHGShaFeoh/HJa/fz/j8pa23P8tKKrCu7w12DFl1TvXW3eHJ9Yr4de7G8mAOeyewovtPpNPV6neHhYX7gB37gQBu/3wlMk6qeIzDdOvhhimriQBNqV5YzT0O7KnRXLT9x4LKcfkLTZi26TmQD0rLAkHfmUKbox4GV6AqBbTLsnUfh0zZVUiJ3YJvPnVfKr5M0XCYk3J90rSH9ZTL9PmxrYbatGKxurDH3VWwN2Sae73Yi5LgSaM3ltYhLayG3ahFhvEow5QkeGfY5U/QYzEAjdP7ljnE/+3BWMph2rZWBtszVnVDtiu7xeDIuhOBafGgurQW8uhSgLZwd9Hj3eIpaYLlZjbi05qJCJguK506ld12PFmgb2xfcSY2MNVkzPLyBSb8IX2xqym0XfdiKLBKLtpbVlmYgLRnJKibyHs3QifBKx7gTBB2fgBQ8JG6haLmtaYaWlZZmoRGR9Zw9Je8LCmlFwXfCfSCtGOg7/3AlPs47npJOmDZDzcq2cYfu3+Ng0egK3t3GYbrPztK1t/SL83DDFSWx4XGRgdAaQMQD9PiKtQBhLO34ETsdCxuF295+259rp2/8nb/zdxBC8Nhjj/H+97+fD37wg7znPe+55wsZjwOR7VCLFmiZCgAZOcCAmtxXkUs/bV2lomfQNorLcqb31XK5Gy1doRzdwmIoqDGKauJYXp48CMZELIVvIlCMpx6j3p16ewefesPhWE66JLaThISTR7cOfYMlpM+HvXMduiATC+qSbzld9Jwt5BhbQw5CPdBcWgu5Wg6ZiyMBwdk4Hi55XCi5CXc1dKUz5TZIYRnKOkGd950w7mhX+V6ORXdKCcbzilJabjhWgYEv3Wqx0ND4SvD0eIrpAZ/VlmamFnGzGhJoGM4q3n86i7eL8O7afrKe274n3UQ150tqnbhK/hBFZ0oJpose430i3MRCUWKpdDTVjiHrCUZzzts9VVA0QjcRr3ZcZCE4+8hUQSGlcNP0wE3Em6FhtW3w6rrnFc/63am4IO9L0p4k7cFQBuazmrePpmjHYrw7JV+PO9S9fc/5oreoc6e4w3uNir3gu13L38tCUXdVauvPZ+z6FSsh3EJRIZwh3R725PvLX/4yX/nKV3jhhRf4yle+wu/8zu8ghOCpp57iAx/4AB/4wAd44okn7ps3k73g6uAXaepVVwcvcgx4kwduldQ2pBrN0TKVuCxnIi7LufNjaq2lpheo66XY332WrBq84+0eJ5bCt4hshzH/ESSKlqngi+yBbTqH6vfuW9GdkJBw/Oh6rDcL667Y3o51a4jcUCbTnV73L2xbShmGs3d+5fK4UGlr3opbJhebpufFHUgLLpR8Hh7yKGU81tpOMM41XG5dPiUZyjiLRFe4dbRlqRFR7rjtpD2XXrJZdAMs1CO+suYzPaAZzkqemcxQSEmWm5q5esRCI4otKJIPnM6S8Xa3ewaGntAH5wnWxpJVUMNNvwfSh/++vZ0It9YJOymgGRluVi1zUjCakwxlFNMpD1tQ1AI3Ea8FhpVo3a8+kVN4UtCKXGJLI3S+76WmxqDJeu61momLewbSgkJ84iOFE9bO4+5ep5FZF+OtWJiX2wZ2aOfMeoJ0XAZ03DnchaLxNpyX5UD7s6P4LhaLfOQjH+EjH/kI4OJUXnrpJV544QW+/OUv85u/+ZtIKXnuuef47Gc/e6AnPylsVwdfVJN3JGabepVqNI9Bk5I5Smr6wJPzzRgbsRbdpGPq95W/u5/IBCyFl5B4jPmP9qbeB0k4gcOulAd6PrPj/6aUkHA/0r0U3fVbd8V2V1zvVIfeFdc9Ud2bXJ9ca8hBWapHvLkWcL0astxcP2CjOcG5AZ9Hh1KUsopyR7PWNqy23CV4XwlG8pJSRm0QO53IxD5oJ+bSnrOXDG4juo0xfH055PJaSGRdhOBjwz5SSpaaEfN15w0vtw05X/CB6TSF1O3XWXUXW3b3q6vVU54EDK3IxQ4eFV0R3vWEd0W4FG6RYGQs83XNYkNTyihGcyq2kCi0cdPuctsJ7Wbs8cmn3ALWM8qjES+4bISadgSN0Pm9lXDWlKwnuNZQjFdDiik3He9O+j0pGEiLDT9/R3dTVdZF+eZ2zoznFnJ2LSsn9e/kIAtFmy29j8aWdfZs/E2n0zz99NN4noeUknw+z5/8yZ/wpS996QBPe7JYDi8T2vaB6+A3U43mqOtlBJJB7xR5NXJo+9rv787IIiXvzKH4xo8bK+FlDBETqbeBsLRMGV9kyKiDTb1D2zqUSvkuNllseSiEN26A1njT0wj/ME6KDpeoXKbzZ39G+skn8aamDmWbwaVLyMFBvLGD2aeOmvDWLYJvfIPsN30TMnfn9jjT6RBdv46anEQN3PnichtFXHntOjWVRo1uPYZKOuG1QVjHtpDUCZni3Q3+w5UGr604MS0ETOQlFwZ9HhlOMZhxFo3VtmF+xXmtpYBSxgnu4jYiuNoxXK+47WW6ojuz/f9NtcDwlbkW5bYl5wneNRDwtlE3nFpsRCw0NNpYVpqalBK873SGUmZvcqYTT467k+/u1Yru14fp+96N9DYiPDTuZCCtBIGB1Zbe4AsvpNxEfCijiIxbiFluGxqB++jmoo9mJWcGFM3QCfF66BZzNkPLckuzHEhuVCLyvrtac6roMbLDVZq0EqSVohTP76x1kYi9qMPIxs+//piBtOTc4PF7vz4MNi8UTaOORnxfvXqVL37xi3zxi1/ky1/+MmEY8s53vpMPfOAD/NW/+ld58sknD/C0J4uUzJGmeKA6+O3wRJqcHKLojd9xWU4/LV2mHM1gMRTVOEXv+EYz3ikZNUDJTDPmPUxVz2OxB044AVAiRUYOkJPDh7J/UihyaugQ88IfTIRS6EqF8K238M6cQe6xaOtuIZXCdjp0vvpV5MgIMnUIf89BgFlawo6OHkshGF6+jG02D2Vb1lqiW7ewnc7h2O2iiPDaNfxOm0IpRzarNgjrlDrczOP7maGs5NyA4lzJ5+Ehn0JK0Yq9xXMrQW8BW853yR2ltNz12HoSimmXbDKQ3vn/0euVgK8tBkQGThUVT42neaXsvjdfj1iKBfepQY9mZLlQ8hnL7T1AYPPkW/U5CMZy6q5PbbcT4Y3QklJQSksC040OXPeFD6bdtHokqxjJKgLtvOPltundV0kYSEmGsorpoqIZuZOaasewnDJkPEE1cL/EXLxg83aWHXDiM+sLsj4QC/b+uMNmaMl4yd/Y7djxFfvzP//zfPGLX2Rubo6zZ8/ywQ9+kH/0j/4R73vf+zZUwj8IDHqnD3V7OTVMTh2OyIOuv3ueul5Gohjyzh14AnxSGPROM+idRtuAli7jiTQZeXAbkBI+w/65Q9xDKHnTh7q9BxHv9GlEJkM0N0d47RpqfBw1Pn5sRKksFvEfe4zw9dfpvPwy2fe+9863OTiIXl3FNhqIY/Zea9ptzOoqolQ6nKn3ygq22UQODt7xiZXVmvDaNWyrxampYbzTh3Ml4kHlPVNZ3jPlbBBrbcNcPaDdjQ6UgrGc83Kn9yDYAHK+5PzgLgkkkeHlxQ63ahol4MmJFBdK6yezs7WIlZYT3hdKPikl+MD0/iNhuzGDqU22E21hsnDvUsA2iPDYD1/uOJE8mpWEBiodw81qtMEX7sVrD8ZyHmM5aEduGl7uuIzxtbZBScFgWlLKSKYKHqtZzdlBn1rQLemxvLUa4ivXyjmQdos297q4UklBISX2ZPtJcOz4Svu93/s9pqam+Ht/7+/xfd/3fWQy95dn+H7B+btv0DENPJFm2Dt3aN7xk0C3iOiwFqomHD/UyAgilyO6cQO9uIhtNPDOnLnXu9XDf+wxotlZ9K1bRKdO4Z2+s5P1rvg2lQrymInvaMZdYPUPwWJjOx2ihQWEUninTt3ZtvqEtxoauuPfwYOOtW4B31q8yM/GPSZOwCmKqcO15yw3I16Y69CMLKWM4D1T2Q3WlaWOJNXSpD3BhUH/jqbTgba9pkRYL2HZKcHmbpNWgukBn7G+RantyE2TJ/OK0Lj8+M2+8O4kP+NJJguSyQK96MJyx/QsLL4S1CKXgjKW84mMSzmpxR/d+0lBL0O8mDq5Pu7jyq4lO1/60pf41V/9VT796U/z1FNP8aEPfaiXcpJw7wlNi9XoOtqGZOQAJW/6vvR374S2IU29hhL+oWVzJxxPZDaL//DDRLduYapVwkuXoNW617sFuLKw9DPP0P5P/4nOyy8jx8buyH4i8nmEUphKBXvq1LE6qdSzswCHIm6jmRkwBnXmDMI7+MTRak107ZqboA8N4U0nV5wOiitjcR9dMZqNq95LGXkkuc+vLXd4YzXEWrg45PPEqN/L6LbWMlOLqEaChz3B+ZJ/R/vQjRnM+evCvuuQ2CHg5p6xWYSvtQ3zkSbjCabyCo1gJRbKm33hXXK+JOdLpgqWetyoWQ0M5VDw1mpIxhOUYhtQKaOw1tIIbU+Idy0sbluiJ8SzfjLhvlN2fMf76Ec/ykc/+lGstbzyyit86Utf4k/+5E/47Gc/S7FY5P3vfz8f+MAH+NCHPsTk5OTd3OcEoKnXqEQzWCwDavLA2dYnmYZexmIpquNjQ0g4OoRS+OfOoZeXiebnUbOzRIuLeOMHS7g5TLxSCf/RRwnfeOOO7SdCiHXrSb2OOCY+dxME6JUVxMDAHVtE9OoqptFAFouoUunA27HGEF27hmk2kaVSMvE+ANq4POm1lu41UqrYTzyUOTqh1QwML8x3WG5p0krw1GSaqT7bh7XdDHBDRjpv95369Xt+7z5PspTHa/K9mZ4Iz6+L8Jm6E+GTOZcSs9zS2/rCu/8vCiEoppx4NtaylHaJLrXAMF93U/Sc76rrSxlJIeUxhbPoVDumz56ie1nrXSFeSB3P7O/jzm3HDUII3vWud/Gud72Lv/k3/ybNZpPnn3+e//Sf/hOf+cxnaLfbvPrqq3djXxNwb0hVPUtDrzp/tz99KPXzJw1jIxp6NZ56D93r3Um4i6jRUUQuB2++iV5Y6NlQ7mR6ehj4jz9ONDd3KPaTDdaTYyK+o7k5sPaOU11sEBDNz8Md2k02CO/BQZeIk4iAPWGtm4SutdwktJvZXUxLhtLO83uUx3KmFvHVhQ6htoznFc9Mpjcs9rPWcqMaUe0Ycr5kMmMOZaFsJ27f7F/v2dXhkdl6/+PEdiL8Zs2J8Im8834vN/WOvvAuUggKnuXcoL8hurAeGJphxHwD8r7zhw/EEYZjORdzWEvsKYfGnv+3unLlCl/96ld58cUXefnll7ly5QrT09O8733vO8r9S+hD25C16EYvj3rIP4snHhx/dz91vRy3dt4/jZ0Je0fmcujpaWSxiKnVXBrK2bPI/MEKrw5lnw7RfiLyeYTnYapVrLXH4jWuY7/3ndo6otlZFx95+jTigMfHGkN0/bqbng8OupOvY3CMjjsd7QT3Wtv0GvvSnmAoIymljz7pIzKGVxYDrlUihIB3jKZ4dGTja8DEwrvWMeRTkvODHuVD2q3eYss+MdoV9eaATYV3m54Iz5m4rMdwvRL1RPhEwWOlqbf6wrNbF8cqKXrRhaF20YWVjo6bLg0yji5003AZ24+cPaUZrU/F++0pWc/lhCf2lN3ZUXw///zzvPjii3z1q1/lpZdeolKpMDExwXPPPccnP/lJ3v/+9yd2k7tIYJqsRdfRNiIrByl50wjxYL6wjdU09SpKeIcWDZhwAlEK//x5oqUl9MIC4dWrqImJe5qP3W8/Cb72NTLvec+BttOznqysHAvriYki9PIyIp+/oyxuXS5jajVkPo8aPtjfbk941+vIgYFEeN8GHedBr7WddQBc1vlQVjGckRv8z0dJta358lybWmDJpwTPTqW3ZHMba7leiagHhkJKcm7QO1RLw3a2k+75RnQytHePtCc5MyAZ3yTCs55gPK+YyKdYbZttfeHb4StnVxnNKTra+cPLHRML8vXowsGMouC7yvq8L3v2lFrHUI3tKa2Gs6e40p7EnrIdO4rvH/3RH2VoaIhnn32Wv/23/zbve9/7uHDhwt3ct4SYpl6lEs06f7c3eUd51vcDDb2CQTOgJh/YE5CEdbyxMWQ3DWV+3tlQpqfvmQ2laz+Jbt4kOn36wDaNrvg+DtYTPTfnptV3YhOJIrdgU8oDW3KstUQ3bjjhXSzinT2bCO8dqAeGctvZELp25kJc9T7QV/V+N7i0FvDqUoC2cHbQ493jKTy58b1bG8u1SkQzdH7kswPeof9uN8cMgpv+CkEvt/ykcTsR/tiw77K9+3zht1qKi229bbMouOn6eN5jPL99dKHXF13o2mAF6Vi46770lGpgN9hT8ilnZUnsKbuI7z/4gz/g8ccfv5v7krAJay0VPUNTryGFYtg7Q1oeD//nvcJYTUMvI4UiLw+vGTThZCPzefxHHiG6edPZUC5dcjaUQ8ii3ve+9NtPXnrpwOU7Ipc7NtaTKE45UXcgvqPZWazWeJOTiPT+7XI94V2rIQuFRHhvQ6At5bazlQR9QnMobp5M3WXB044ML8534ikovHcyzfTA1uZDJ7xDmqFlMC05cwTCG9zx8dXWCawUAn1CbCc7cTsRfnEoRTM0LDc1HcOuvvB++qMLG3F0YaXjJurd3PVSRlJKO1uLkoLBjGsv7dpTulPxWsd9gLOnFNNOjD+I9pQdxXcivO8t2oashTcIbBNfZBn2zx5qG+ZJpalXMWiKciKZeidsQHge/oULRIuLzoZy5QrexATqHthQDsN+clysJyaK0AsLiFwO74BWEV2tYioVRC6HHB3d9+N7wrtadcL73DmETP7+wVk1qnHVeyNuLJSCnuC+V8UnC/WIF+Y7dLRlOCt5ZjKz7b5ExnKtHNKKLEMZyeni0QhvYy2htuS32QdPntzJ92Z2FuGa8bzH2UGf2axmNKf25Avvp2s1OVWw1ALXqlntGBYbbhtZLxbeaUlKuSz4fGxRmWTdnlILDI3YnrIY21O6QvxBsafc23iAhG0JTIO16Ebs7y5R8k4nQhOw1lA3S0gUeZVMvRO2xxsfdzaUmzeJ5ucxzaazoai7m4F/GPaT42A9MQsLoDXqgGt8rNZusaYQB0oksda6KxrVKjKfT4R3TDOueq92dF/Vu7OVDN6m6v0oMcbw6krIW6shQsBjwz6Pj6xnd/cTajfxbkeWoazidEEd2dWMzbXy/SgBwQmffG9mexEekvUEgRFMFTzGc2pHX/huJ21CCAbSzs/dPfmrxKK6VY+Yr7vXYil+LXan6rvZU9ZamrU+e0o3PeVuX625WyTi+5jR0MtUo3kABr0p8mr/U6L7lYZZwVhNUY0/UGVCCftHFgqulCcWbeFbb+GdO4fM7r+O+sD7ICXpp56i/YUvHNh+IuPUE12tou6R9SS8w2IdPTeHjSLUxARyn3YTa60rVqpUkLkc3vnzD7TwDrWl3HG2ks7mqves2lZY3k1qgeErcy3KbdfI+N6pNKO57WVGqC1XKyGdyDKcVZwuHq0c6R6v7cSckgJrLcba+27q2i/CFxpuDcB8R3JpNWA87+rsR7Nyiy98u7zw7ZBC9FJQIuOEeLnjrsI0Q8Nc3a01KKU3rjXYyZ5S28GeUkzdvcXBd4NEfB8TrDVUolmaxvm7h7yzpOXxqpa+l1hraOhlBDKZeifsCeH7eBcuoBcX0YuLhJcv401NoUbu3uvHGx7Gf/hhwrfeOrD9pGc9qdUQd5A0chCMMej5eUQ6jXcAu4ip1dBra4hMZt/2H2st0cwMplx+oIW3jSeLax0X/9Zf9T6UdckTx8H7fr0S8LXFgMjAqaLiqfE0qR0sDIG2XC2HBNoymlMbynWOisDsPvkGl/Wduk/nOmlPcnZQ0o4MN5WlFdneJHw87/WEcNcXfru88O3wpGA4qxjOqvhE0VBp656YlgIG0pLBtKKYWn/d7t+eIiik7u6i4cNmx1f85z//+T1v5C/+xb94KDvzoKJtwGp4g9C2SIkcQ/6ZxN+9iaZZQ9uIghpDiuScMWFvCCHwJibWbSizs5hGw2VM3yUbiv/2txPNzx/YftIV37paRd5t8b2wAFGEOkC2tzXGVcgf0G6iZ2Ywa2vOa37+/F23Dd1rWqFLlih3Nla9D2UVpXtoK9lMEBleXuxwq6ZRAp6cSHGhtPP/X51YeHcLdibyd+f9fLuCnS5K9Gd9H4/jelRkPMlExvDIsM9iPAm/XgnJ+YKxnOeSZgYlHe2SSvbrC+/iK9Er6OlE7nVcbq9/KImrtU/LLRaXzfaURhhniffsKe4EtHCC7Sk7vup/5md+Zk8bEEIk4vsO6Jg6a9ENjNXk1BCD6lTi796EtZa6XkIgKSQ2nIQDIItFl4Zy4wamUiFstVwayl2woUgpST/99IHtJzKfR/j+PUk96aacHCRiUM/PY8MQNTa27+Mc3brlJubZLP4DJLwjYym3DWttTbuv6n0056reM3sQPXeT1VbE87MdmpFlICV49nSW4i5e4U5kuFKOiIxlsqAY28GSchTsbjtx/x73lsvDJNM3Cd9JhO/kCy+mJaO38YX3k/YkE55kIu9OKrtCvOvz9uR6Ysrm5BMlBQNpxUB81tSMhfhme0qmr9znJNhTdnzlv/7663dzPx5I6nqJWrQAwKB3KrFT7EDLrKFtSF6NJFPvhAMjfB/voYfQCwvopSVnQzl16sBlL/vhTu0ncnAQvbx8V60nxhiXcuL7yH1aRkyjgV5ZQaTTqPHxfT02mpl5oIS3tS45Yq2tqfXZSgbSbvFkMXW0Ve8H5fWVgNdXAqyFi0M+T4xuv6iySzsyXO0Jb+c1vpsE2vYSODbTnXx3rzA8SOwmwsfzHsW4Yr7fF94VvVlPMJJz0+u9vkazvhPYk3lLI3TWlGpHs9x0H2lPUEq7hZrbTdhzvhPXk7jfabdhsxGup66cBHvKHSmZIAh45ZVXeOaZZw5rfx4IrDWUoxlapowSHkPeWVLy3tViH2fWp97igS8XSrhzhBB4k5POhnLrlvMUd20oB/QT73UavVf7yXbb64rvu2k9MSsr2E4H78yZXUXVZnp2E9jzce3+zNHsLHp1FZHJOOF9wKIkGydXHKZoPeyrDp3IpZWU+6reM55bvDaUkbf11x71/u1EMzC8MN9huaVJK8FTk+nberZboeFqJSIyhtNFf8eWxaNCG0tk7I6T2u7kWz942rvHdiL8WnmjCN/OF36rGjG/D194FyEEhVgc24KiFri/hVrgFoYu9EUXltLbl/KklGAkqxjJKox16SnVwPnF++0peV/2puLHxZ6yp3e2V155hZ//+Z/nzTffxJit12Vee+21Q9+x+5mKnqVlyqRkjiHvLEpsLR1IcLRNhcgG5NVwcpwSDg05MOAm0TdvYsrldRtKJrOv7Zhmc8954nu1n4Svv+5q0/vSRWQud9etJ9GtW8D+U0704iK200GNjCDztx8q6GqV6OZNUArC0AnvCxcOLLxNtUo0M4MoFPDPnDnQNjbvn15YgDDEf9vbDuXYN0PD5bUQcMJvOK5632/ZiDUGU61iymVMvY4aG8ObmLjj/duJmVrEVxc6Pb/2M5PpXa0wNgioV+pcWaijm01O+RHDTzwG3P2pN2y/2BL6Jt/3WdzgQegX4QsNl36yWYTnfHefQFtW7sAX3sVFFzprSTe6sBwvMG7VIxYb8OhwatdWTNnbBlB0f2PdqXg9cB/u5xMUU06MZ717t1h5T+9un/nMZ0in0/ziL/4i/8v/8r/wcz/3c9y6dYt/8S/+Bf/gH/yDo97H+46MHECRoqDGjuXlxOOCtZaaXkym3glHgkil8B96CD0/j15eXrehDA3taxvC84jm50EI1G0SQTbYT155hcx2Vw09D726ihod3dAEebetJ3p+HjwPuQ8xZ1ot9PIywvf3lAtu6nWiGzfQKyvIQsF58w848bbGoOfm0Kur7ndRuLO0KFOvEy0sYJtNANTIyKG9X3cndnnfXR7fz2Vxay22VkNXKphqFeKBmMhmkXf4M+9EZAyvLAZcq0QIAe8YTfHoyNYTRxtFmEYDW69j6nXqrYAbLYkBpvOS4eGSO8m6y2xXK99PVyfeL0U7h0HGk5wblLRClxO+nQhPqZ3zwvfrC++yObqw0jGE2rLf5Q5de8pEft2eUotFeLupWWpq5ydPOa943r+7i5j39A732muv8S//5b/kiSee4Pd+7/e4cOECP/RDP8T4+Di/+7u/y3/5X/6XR72f9xUZOUBG3t3UgpNI21SJbIecGkrSXxKOBCEE3tQUIp93NpRbt5wN5dSpPdkluq2a4ZUrRHNzIOVtPeQ9+8mNG0TT01smlWp83AnSpSW8vpSRu2k9iZaXse026tSpPVtOupncWLsnu4lpNgmvX3fCO5NxwvvCBYS//ytcptUiunkT2+m4WMQDXMXo35aen8fU64C7SqImJ/edUb4bnhSc2meutWk03IS7UsFqF90hUilkqeQ+DnH/+qm2NV+ea1MLLPmU4NmpNKWM23drDLbRcPtWr2Nbrd7j6kZySxQRo1kujBcZGswdyf7thd7k29tp8u3+jZLB9xay/k4iXDKeVxRTTrQepi+8iyfFoViUdrWntN1H157SnYoftT1lT3/91lqG4/9Qzp07x5tvvsmzzz7LRz7yEX7913/9SHcw4cGlrhcBkql3wpGjBgaQFy86G8ra2roNZQ+CRqTTeBcuEF254rzOUqJKpR3vv8F+8uKLyG/5lg32EzkwgEin0eUyanwcEX9vg/XEmCPNvNYHKNbRS0tOsA8N3baN07TbhNeu9RZlylLpwMI7WlpythBrUSMjqMnJAx0b0+mgFxYwlQrgUmZUvD7gXmHabSe4y2Vs6GwqwvOcpadUOvJ9u7QW8OpSgLZwdtDjXWM+qtMhWlrD1mqYZhO6Vg0h3Gu0WKThZ5kNfZQQnB3wGUjf2/SJ202+uxNP8wAuuNwr24tws0GECyEO1Rd+FOxkT6n12VPm6uv2lGJakjsCe8qexPcjjzzCH//xH/Pxj3+chx9+mBdffJG/8lf+CisrK9t6wBMS7pS2rhLaNllZwhNHM9FJSOhHpNP4Fy8668LKCuGlS3inT+8qpLvITAbvwgXCq1fd9FdK1C7T6d3sJ0II1NiYi9tbXt4Q89edfpt6fdft3ynR3BwotedKedPpoBcXnTC8TY657XSIrl519hTPQx1QeNsg6F2pEJ6HNz19W9G/03b04iK6XAZrEdmsy4Y/wLYOAxsEzlJSLmPbbXejUsihIdTgIKJQOHK7YjsyvDjfYaGhUTrimVzAVKeFeaOBiafuEFtd8nlkoYDI5xFSUu1oblYjpICzg/6u0YN3i0BbhICddiWZfO+d3UT4RH7dZnLYvvCjYid7SiMwLG2ypxRTLpP8MOwpexLfP/ETP8FP//RPo5Tiu7/7u/n1X/91fvInf5LXX3+d55577o53IiFhM7V46l1U+4spS0i4E4QQznKSzxPNzDgrQ72O2oMNRcbReOHVq0Q3biDOndtVwO1mP5GlEmJhAb22hhob6wnTnviuVI5MfEflMrbZRE1NIffgve63m6jblBfZIHDHZ2kJEVt0/Ice6k3394quVNxVBq17i1P36xO3UYReWkKvrDjRnU6jJieP9KRmt30xseA2scccIZADA27CXSzetXbP+XKLF29UaTY6lHSLp/MBeQ2G2OYyMIAoFl3+/KZjXmlrbtYiBHBu0N+33/eo6OwSMwjxCa98MKMGD0q/CF9oOpvJ1W1E+FH4wo+KrfYUSzWeim9nTynewRWdPb1bfcd3fAef+9zn8H2f06dP8xu/8Rv87u/+Lh/+8If5W3/rbx34yRMStqNjaoS2RVYO4slk6p1w91GDg8hMhvDmTfTaGqbVwj97dsMCyO2QuRz+uXOE168T3riBf+7cjovgtthPvu3bemK3N/2enXXT73iafDesJzpOObndBLuLWV7GNpvIUmlX4WrDkPDaNaKlJbf90VE38d6H8LZaE83NYdbWQEp3ZWKfOe3WGOedX14Grd3i0IkJd8JzFxfAW60xtRpmbQ3TaPTsGzKfRw4NOZF7FxYnWq1dLnu1yjfmm7xV1kgBF3OGt5UEamDQLYbN53d9/a+1NbeqEUo64Z0/JkUnkbFoAzl/99+tFOKBjho8KFlfcn4PIvyofOFHhbOnuMWYFF1c5ub0lLk6+DY80Pb3PCp45zvfCUC5XObtb387v/Zrv3agJ0xIuB21xOudcAwQ6bRLQ4kTNILLl53YGxzc9XGyUMA/e9YJ8OvX8S9c2NGb68WT3/DyZYKXX95gP5HDw4jFRZd8MjbWmzLKUgm9tHRk1pNucstOOeT92E6HaHERodSu97daO+G9sABa401MOOG9j0WCptFwVyLC0FXOT0/va5GhtRazsuKiELVGKOWm+8PDd22qbK11grtcxtRq60kluZw74RscPJDvfV/7YAy22cTU6y6ZpNmkEcFXqoqyluTyGd47nWNsbHDPi1ZXW5qZmhPeFwb9fUcmHiW3ixns4ol1b3jC/tlJhOdTkvHcugg/Cb7w7eiWA43nIeyW+wSGWvNgr5k9i+/f+I3f4F/+y3/JUjy1mJqa4sd//Mf54R/+4QM9cULCdnRMncA0ycgivjz66u+EhN0Q8XS1Z0O5cQM7MoKamtp1QiOLRbyzZ4lu3CC8ds0J8B0q1v0nntjWftKbfsce9O7tcnDQie8jsJ7oahVbq6HGx7fNIN9MNDMDxqDOnNnR9mGNWRfeQYB36hTeQw/tWXhba50ne9GdlKvxcbcQdY8TMmstplxGLyy4RYtKoSYmXHTgXYq9M/W624dqFbpJJfFCUzU4uK+TkP1ircW22y6NpF53tpbuWi0huGlzfF1n0ENpzo3keGoiQ2ofXtzlpmauHqGk4KGSt2vu973gdostu0gpMJG9azn69ytdEd6MPeG1juFqsFWEw8nxhW/GV8Jl82cVrbTm1dn9b2NP4vuf/JN/wm/91m/xX//X/zXvfve7Mcbw0ksv8Y/+0T/CWsuP/MiP7P+ZExK2YT3hJPF6JxwfVKmEyGZ7mdSm2cQ/c2ZX0aQGBmB62jVaXr2K99BD204Se/aTP/kTV77zLd/Ss5/0pt8rKy73WylkNotIpY7EetJtplR9izx3Qq+uYhoNZLG446JUay3R9evo+Xlsu40fC++9Tqxtp0N46xa22UT4vmvb3ENxT28fq1X33J1OL4e9/yrCkdLpOItMuYyNIsAllcjRUefj3uFk7DCwnc76ZLte70UTAohMBpnPY3I5Xqr7zDQsKgPvHk9xobQ/7/1SM2K+7uq8Hyp5x1Io7WfyDRAZ8O9+FPl9R24XET6RVxtsSSfJF76Zg1bX7+kd6F//63/Npz71Kb7ru76rd9tHPvIRHnroIT772c8eWHy3221+5md+hpWVFfL5PP/gH/yDXqRhl0996lO8+OKL5OM33H/6T/8pxXu0Cj3haAlMg45pkJYFUvLexXslJGyHjNNQotlZzNqas6FMT+86fValEsR16z0Bvo3w9EZH8S9edPaTV14h89RTgJu8y7ExVwS0uooXt2j2pt+HbD2JZmf3ZDmxQeDsKUrtGEdorXXT/NlZl50+Pb0v4a1XV13qijHIoSGXx77HSfWWgpyhoQ2xjUeF7XR6SSXq1i10segm7UNDzlOezx/JVNWG4YZym24sIYDwfbdws+vb9n1WWxHPz3ZoRpaBlOC9UxkGMvtTnIuNiIWGxleCCyX/tuL2XtGJ9j75BjDWAsfzZzmJbCfCr+wgwk+aL/xO2JP4rtfrPPbYY1tuf9e73sXq6uqBn/x3f/d3efTRR/lv/9v/lj/8wz/kn/7Tf8r//D//zxvu841vfIPf/M3f3CLKE+4/6tpZmpKEk4TjipASf3oanc8Tzc4SXb+OHR112dI7/IeghoedAJ+bI7p6dcd0j176ybVrRKdO9WwmanjYCe2lJezIiBPkR2E9abWw1SpqdPS2Xt9odtZ5t0+f3tGjHM3MEM3MYGo1vDNn8HeY/G/GRpF7XLXqxP3Zs7f12XfZUpAzOOgWUx6lrSMMMZUKulLpiX2kxBYKeHHizWGLBau1K7fpTre7kYTgYgkHB9cjADf97K+vBLy+EmAtPFTyeOdYas9FSl3m6xFLTU0qFt5HXUhyJwTaIsXtxbfXFzeYLPM/fPpF+EJDUw92FuEn1Re+H4S19rZu8Z//+Z+n2Wzyy7/8y/h9b7Sf/vSnabVafOpTnzrQk//UT/0UP/7jP86TTz5JrVbjL//lv8wf/uEf9r5vjOFDH/oQTz/9NMvLy/ylv/SX+Et/6S/tuL1Op8PXv/71A+1Lwr1F06GubqFsloK5/SXvhIR7TqeDWlyEIMBmMpjxcdhlsZxYW0OuroLvo0+dgu2sD+UyqZdfxmYyhM8807uPWF1Frq1hRkawscVD3bgBUYQ+fx4OwXoib97Eu3KF6OJFTF+z5pafo1ZDLi5is1nMDvYUubSEnJ9HNBqY8XH06dOwl8KiZhO5uAhau+2Pje16THsEAXJtDRGLbpvLYYaH9/ScB0JrRLOJqNcRXcEdP68tFLC53OHWqFsL7Tai1XIfnc6GchubzWIzGfe8qRRsI/bbEbzR8CiHEl9YHitEjKT3v1hsuSOpRAJfwFRGc4zWVm7LlYbCF3Amp3e9XzkUrASSybQh7yULL4+atoa1UNLU7rWalZahlGGnQsvQQDWUVCPhYi+BomcZ9M2O+e13kyeeeIL0Pt5v9jT5DoKAf/fv/h3PP/887373u/E8j1dffZXr16/z9NNP89f+2l/r3fef/bN/tu02fv/3f5/f/u3f3nDbyMhIz0KSz+ep1Wobvt9sNvkrf+Wv8IlPfAKtNT/2Yz/GE088weOPP77r/u73ICTszAsvvMAzfQkMR8VqeJ22KTLiXyAtt49mO+ncrWP5oHAcjqeNLSWmXHaJH9PTu1a/RwsLrowmTlLZznvcGRsjvHwZL5Xq2U9sFBG88YabvD/2GEJKotOnXQX9PibDu/Hyiy9y4cIFct/2bTums9gwJHzrLezYGKmHH97W8x7NzxMGAdoY/Olp/Icfvm0TozXGWWtWVmBoyC2IHB297cS4V5CztgaFgktBmZjYMd7xTrDG9BZOmmoVslkYGdkxqeROX5+m3XYtknF9O+k0xL9nkcutT7Zzudv6/mdqEV9d6DBUsjyWVzwzmT7QwsiZWkSqpbngCS4M+vh3aeJ90GMZaktqJWAgLTk3uPtJXDexZXrAY2ifFpyTxnF47+zSPwkHKKRcY+ZOUZXa2J4vPIz9/PfSF37Qoe+exLcQgu/5nu/ZcNtTTz3FU/F/DHvhox/9KB/96Ec33PZTP/VTNBoNABqNBgOb/tPKZrP82I/9GNl4Ycr73vc+Xn/99duK74STRWjatE2VlMjdt8I74f5ESIl/5kzPhhJev44aG0NNTGwrHL2JCYhzpsOrV13c3iYBvp39pFsprpeWMGtrrl6833pyh+LbNJvIWg35yCO7CuVobg6rtfNfbye8l5YIr15Fr6zgnzmzJ+Ft2m0XIdhuI9Jpt6jyNosR71ZBjrXW2TvuQlKJ7XSc0I5TSTYskkynndDu+rb3OFWPjOGVxYBrlQgh4B2jKR4d2b/v3VrLTC1ire28t+dL/om45B+YvS22BOiei+iktPuukvMlF0qSRmhYjEV4PTA7ivD7xRe+J/H9y7/8y0fy5E8//TR//Md/zLve9S6+8IUvbDkTu3btGj/90z/N//l//p8YY3jxxRf5/u///iPZl4R7Ry/hxEtyvRNOJmp4eD0NZWkJ22jgnT27rR/am5pyAnx11eWAnz+/QUxJz9s2/USNjKBXVtBLS8jh4fXUk1rtjlNPolmXleXtknKiq1VMpeKmriMjW7+/skJ4+bIT3nuceOulJRdBaC1qeNhFOO7yc1it3TE44oIc02y6CXelsp5U4vvuuJdKe86/3g0bRc6vHU+3bRD0vic8zxXtdKfbB8j+rrY1X55rUwss+ZTg2ak0pcz+U16stdyqRZTbhpwvOD/oH0q99t1gr4stYT21Qt/eiZtwBOR3EeETeUVukwg/6b7wPf8lVioVPve5z3H16lX++//+v+f555/nkUce4eLFiwd+8o9//OP83b/7d/n4xz+O7/v8yq/8CgD//J//c86ePcu3fMu38Bf/4l/kYx/7GL7v873f+7088sgjB36+hONHZDq0TAVfZMnIu1/rnJBwWMhsFv/hh50NpVIhfOstN8XdJp3JO33a5U+vrRFdu4Z34cIG0emNjuI99BDRlSu99BPh+6ihIRd1WC73EjT04iKmVruj6beem3PPu4P4tlqjZ2ZcEsr09Bahq8tlgkuX0IuLbnHlww/vGglow5Do5k1Mo7Enu86OBTkjI4cmuk2n4wR3udwTwkIpVFdw7yPicDusMRsXSbZa699UyiWS5POuuv0Op+mX1wK+sRSgLZwZ8HhyIoV3gJMzay03qhHVjokXzHknRnhDX8ygt/fJd5RMvu8p+xXhcDLzwvckvq9evcqP/MiPUCwWmZmZ4Sd/8if59//+3/OzP/uz/NZv/RZPP/30gZ48m83y2c9+dsvtn/jEJ3qf/8RP/AQ/8RM/caDtJxx/koSThPsJoRT+2bPolRWiuTnCa9d2LIXxTp8mMgZTqRBdv4537twGAZ56xzvQCwvOfjI9jTc2hhodRa+uohcXnSAcHHTi+w6sJ6bddoK+WNzRKx3NzWGjCDU5uUUYmmqV4M030QsLeKdPk3rkkV0917pScXniWrsyounpnQt6jrggx4ah236lsi6GpVy3lNxJUom1Pb+2rdUwrdaGRZI9oZ3PI7LZQzmJaEeGF+c7LDQ0noT3TKQ5cxuv806YWHjXOi6R4vygd+BM43tFr2BnDycM3Z/NJJPvY0G/CF/YowjvzwtfaztLynHNC9+z7eQ7vuM7+Ht/7+/1fN7/6//6v/JzP/dz/Mqv/Ar/6l/9qyPdyYT7k8h2aJkyvsiQUcnUO+FkYjudLb5fFS/Ei27ccNPaRgPvzJkN9gEhBN6ZM0TWYqpVohs38M6dcyIwk9loP3nxRWc/SaVQpRJ6bc0J7lIJkU7vaj2xxoAxOwrcaG7OCcUd4lxNrYZZW0Nks6jR0Y3fq9fpvP46en4eb2qK1KOP7ii8rdaudGZtDaTEO3UKtY19pcudFOSYZhM8b9uWTqs1Js7iNvGaI4RAFovuhGZgYMtxtFHkvOW3sX+YdruXta2uXSPse12IbLaXtU02604+DjEYYKkZ8ZW5Du3IMpSRvGcqc2ChoY3h+nKLuhYUsz7nTqDwBjf5VpI9LQxNJt/Hk7wveWifIlxJwWhOMdLzhZstvvB7vah2T+L75Zdf5u/+3b+74TYpJX/9r/91fuAHfuBIdizh/qeul7BYCirxeiecTEy9Tnj1qkvZmJra4HHu2VBu3cJUq4SXLjkbSp847Qnw69cxtRrB9esEL7+MLBTIfvjD29pP1NgYulxGLy2h+qffO1hPohs3sO02qR0WquvY723Gt159slq7KbUQLtO7T4CZZtMJ77k5vMlJUo89tq3Fpnvf6OZNbBAgsll3HHYQngctyDHGoGdmCK9cwayuosbHyX7wg+7nMMadRJTLmFqtN4GWuVzvCsJmUW/abXdSU606MS8lqbe/fcMxsEGwcZFk7A93O+4sKyLO2rZBgG00iBYX3QmWtfgXLhxKMosxhj+71cYAjw37PD7i7zm723Y6mE4H2+lg222idodrax2aEQzmU5x/18PHfvHaTgTa7rn8RwqBFGCSwfexpCvC64Er6+mK8GLa1dZvJ8J384VbC8M75RreBfbs+e50OltuW1lZIXXEjWEJ9yfaBrR0GU+kycg7j0lLSLgXiFi8mXKZ8PJl18Q4MdGbkAql8M+dQy8vuwi+q1dRExN4fUJXSIl37hzR1auYWg3p+9hqlc6Xv0z6fe/b1n4iSyXM2hq6Wr2t9cRqvVEU9mGCAL20hCgWYZvFkXp+HhuGqLGxDQkkpt2m89pr6Fu3UFNTpN72tm0929ZaFwW4tOQWVe6SBGOaTVeQE0+j91qQo6tVl7By82av2VEODeE98gimVnONk/1JJZnMuq2k7/+vnie7WnWiu68lsivS0Rrd3yS5eZFkqdRbIGniJBa9sOCm9707CjcFj+0mh4GUkneOpyimJKO5HSw8QYBttzcIbdvpgFkf9WoL1zuKlpemNJjm3OTAiRbexu5tsWWXMwM+x8wanLCJQkpSSK2L8O5EezcRDht94dWOYSB9b3/RexLf3/zN38yv/uqv8o//8T/u3Xbz5k0+85nP8Bf+wl84qn1LuI/pn3qf1Df3hIRu1KAZHu5ZKoJKxfm8+3Kq1ejoug1lYWHdhtIt0ZES7/x5wmvXkKdPE125gl5YIHj5ZTJPPUX6qadof/GLPfuJGhtz4ntxsZe3vZP1ROBE8HZ0LSfeqVPQvwAQMI0GenXVxfj1nSzYToegK7zHx3cW3p0O4a1b2GYT4ftu2r3NokXT6TjRXa0CIAsF5y3fRZiaKCK6edNdMVhbcz+n7+M99JAT98ZglpcJu4J7h6QSG4ZuIl6ruVbMrhCNWyJFoYAQwtXGr631UmF69ykWEfk8eB5C694JhI0i5OIiemTEecgLBUQ+j8zl9pTLfRAulNyJRFdkb55o94tsAKREpNPuI5PB+ClmOopI+IxnJKeL3ol+b97PYssu91qQJeydfhG+0NgowidyiuwOIjylnCXlXrMn8f2zP/uz/MRP/ATPPfccURTx0Y9+lEqlwrvf/e4tdpSEhNuhbUhTr+GJFFlZute7k5Bwx8h8Hv/iRSeI5+edmFxddVaUWJjKXG7dhlKruTSUs2d7glQohX/+PFy9CufOEV26RHTtGkE+T+rRR9ftJ9/4Bpl3vxs5OOi8y7Xa+vS7WkXFDZg9dhFQ3ZQTdeoUXL7cu90aQ3TrFoBbEBmLRRsEdF57jejGDdTICKl3vGPbbG29uuqEvTHIUgnv1KktCyRtEBAtLq6L5z0U5ETlMtHly04Ex9N8NTqKmpqCfB6qVczSUu94bpdUYprNnuDuTxwR6bQT277vTlbqdWfJ6V8kmctBnLNtjYFm0031+zO5Pc/9bkZH8R9+GJHJHImItWHoRPYmod2/L9397grs7r8ynYZUqrdfobZcr4S0hWUoq5gu7j+S8LjRW2x5l4qAEu4NBxXh95o9/YUNDAzwuc99ji996Uu89tpr+L7PI488wvvf//6j3r+E+5CGXk6m3gn3HUIIJ/YGB9ELC70cb1kooE6dQqbTCM/DP3+eaGkJvbCwbkMZc+seegL8yhU4d47w8mWCb3wDkcut20+uXCE6dQo1Po6pVNCLi6jTp9etJzuIb2vtRs92FLm2zVwOb9Nj9OIiNghcmU9sR7FRROf114muX0eWSqSeeGKLzcVGkYtarFZBKbwzZ7bsj42fV6+u7qkgxwQB0c2bhDduYMtl9yOl06h4waYNQyeiW61tk0qs1s56Evu3e+U1Qjihn067+wUBplzeKKSzWSdYlcICtFqY5eV1QR7vixwYWJ9sxzYZu7R027KgvWDDcN0m0ie0dxTZhcIGoS36RPZ2hNpytRLSiSwjWcWp+0B4Q9/kOxHfDwQnTYTv66/sAx/4AM8++yxvvPEGI7usUk9I2AljIxp6FSV8snLoXu9OQsKhI5TCO3UKOTKCnp11uc5vvYUaGXELB5Vyvu2uDWV+3tlQ4sg94Xn4Fy44gXfuHMHVq3RefJHMBz6wwX6S/ZZvQRaLbqFlFK1bT+Ic7C1Yu2EKrufnIW6r7Me0Ws4HnkqhJifdQ7UmeOMNoqtXkQMDpN/97i2i2tRqRLduOctFLudsNf2eaq3Ry8uuIMeY2xbkRMvLzuc+O9sTmnJkBDk05LzSrZZbPCmEy8gulZwNREqX2b287CbczWZPLAvPc4tCpcQCNvZv9353qRQim8Uq5ew6cfb3+h2Emxzn8+tiew/pK3vBRtEGgd37dzuRnUr1FnKKTGZPIns7Am25Wg4JtGU0p5gq3B/CG5LJ94PKTiJ8IPaEHxcRvutf2h/8wR/wL/7Fv+DXf/3XOXXqFJcuXeKv//W/ztzcHEIIvv/7v59f/MVfRB1C3mrCg0FdL2MxFNT2i64SEu4XZDqNvHDBRebNzaGXl101/OSkm5Dn8/iPPOLKZmo1l4Zy9qwTdL7fE+B+7J3u/PmfuwSUPvtJqruoMM791gsLToz3C+Pu39km33c0MwOAmp7u3WatXbebnD6NkBJrDOEbb7gFpYUC6Sef3LB9a4yb9C8vu0jAyckNfndrjCvIWVq6bUGOCQKia9fclLtWczemUk6kF4vOt2wttFqu/TGOBkQpbKPh7D612saFkOk0KOWOQxQ5wd79nlKITAa6HuwwdN5vcJNuKTcK7Xz+jv3aNoq2TLJtu71VZONOBmQ+v3GSHU/q75ROLLxDbRnPKyby94/whm7MoDi2DYcJR8tmEV7tmN5Cy+Mgwnf8a/u//q//i5/92Z/le7/3e8nGl87+h//hf6Ber/Obv/mbFItF/qf/6X/it3/7t/lrf+2v3bUdTji5GBvR1Kso4ZGT22cKJyTcb6iBAWSh4GrRFxeJZmbQXT94Po9/4QLR4qKzoVy5gjcx4fKsU6meALftNnp1ldaf/RnpD30IPT9PdOWKm7AXCi5XeshdSdpiPdlGfBtjnOUkk8Hry/fWS0vYdtu1ZxYKWGsJ33qL4NIlZ3158sne84BLPYlu3sS224h02i2qjP+/6DZ46sXFjQU5o6NbBGy0tER47ZrzoGuNBWSx6J4rlUKAq5PPZlGDg8jBQRDCxRLGVxfos5PgeT1B3Z80YoVwVxcAK6UTvO32+qHqNk12hfadlN9o7Qp2NgvtbZJneiK7X2Cn00eyMBOgExmulCMiY5ksKMZ2SEg5qVhrCbQlu4/Flgn3J10RXgtcY+ZxEeE7/sX9zu/8Dv/df/ff8Tf+xt8A4PXXX+fVV1/lJ3/yJ/lgnJ36t//23+bXfu3XEvGdsCcaehWDZkBNIsTxuPSTkHA3EFK6hspSiWh+3kUTXrniFiNOTuKNjzsbys2b7vvNprOhpNN4Dz3kSnBiO0jw/PP4Tz5J8KUv0fnqV0k/+yzU69hKxaVWbLaebBfrNzcHUbRh6k2n4wS556GmptaF9xtvINJp0k8+iddnN+zGJ2ItangYNTXVE4u6UlmP2JNy24Ic026vT7m7ZTdSulSSYnE9rjGVchPuUgnivO7w5s1eDjjEaS5SOlENiFjgWmOccJfSHQdrIYronoaIVArRJ7YPUnpjtd5oE4mFtrp2jXCT51v4vvvZNk+yj0hkb0c7MlztCW+PsWOQ/HDYBPEFksTvndClmJIUeyI8uucifEfx/cYbb/CpT32q9/WXvvQlhBB85CMf6d322GOPcePGjaPdw4T7AmM1Db2MFIq8TNYLJDyYCN/fGE1YLhNUqy6acGTEpaHcvLleynP2LDKbxbtwAQt0vvpVolu33CLJ8+edJ/r6dVShgKnVEIODEJfD9Kbf20y+ozjlxIvFt7UWubwMxaKzmyhFcOUKweuvI1Ip0k89hRe3W9owdIkt9brzr09P9xJdTK3mCnJaLWdB2aYgJ5qbI7x+3XnOrcWGIaJYdFcI4u0IpZwXfGAAjMFWq0RXrvSmxr0M7lhwC6XctuImSotbAIuU65YSa5HZ7LrQju09e8Uas0Vg205nQx54/+/Z5nJuyt8vtO+iyN6OVmi4WonQxnKq6DFyD0tGjpJOlPi9E7bHifDUtiJ8Iq/I3KWg9x3Ft7V2Q4HO888/T7FY5Iknnujd1m63SR9iPW7C/UsznnoX5UQy9U544JH5PKmHH0avrm6IJlRTU3gXLrg0kMVFwsuX8aamnDA/fx4bhrS/+lXCt97Cf+IJlx1+5QryqafchmOv8wbrySbxbYxBz8+7qXqcsmKWlxHtds9DHV67RvCNb4DvO+Ed53zrahV96xZWa2RXqPv+bQtyTLNJeP26a9tsNnsiWpZKeMPDTpx3LR/ZrBO69Tr62rWeqLZBsC5efd8lkAQBJop6k22h1LqglhKVzW7M197D+iRrzLaT7G1Ftue5DO/Nk2ylMI3GlsWs95JmaLhWCdEGThe9e9rud9QEyWLLhNtwr0X4juL74Ycf5oUXXuDMmTPU63X+/M//nL/wF/7CBv/bv//3/55HHnnkSHcw4eRjraFulpAo8iqZeickdOlFEy4uoldWXIxfN5qwa0OZnXVC7vRpUhcvQhTRefFFwm98A//RR9E3bhC++Sbe2bNu4izl9qknXfG9tIQNQ7yzZ93NnQ7R4qKLBpyacos7X3kFpCTz1FN4ExNuUeXsLHptDaTEi2P+TKfjim62KcgxxhDNzLgp9+LiuoDO5533e2jICeY4tQNrsfU60dqay9ButZwnWyn3kcm4ApkgQMTWEZFK9aIQUWp9qr0Hv3ZPZG8W2n2LNbsIz1tf+NgvtE9I2EAjNFwrh1jgzIBHKXMy9vugJDGDCXtlJxE+mJaMH6EI31F8/8iP/Aif+tSneOONN/jqV79Ku93mr/7Vvwq4WvnPf/7z/MZv/Aa/+Iu/eCQ7lnD/0DArGKspqnGkuL/f9BMS9ouIRa8cHt4YTTg8jPfQQ+iZGUylQthq4Z09S+rRR7FhSOerXyW8dAk1OoqtVtErK6iBAWfjUMoV7gwN0ZUfFtd22UszOXUKiFNPjMGMjqIXF+m89BII4YT35CSm2XSLKoMAkc26CEEhCG/d2rYgx9TrdL7+dWefaTaxrRZWStTQEP5DDyF83yWMeB4IgW02nXhvtzHttvNtK+Wm4XG7JACdDjKT6eV3C9/fkELS31zZj7V2+0n2diJbqY0LH7tC+5DiBO8F9cBwvRIL76LH4H0uvCGJGUzYP10RXu0YlpoRlY6hcoQifMd3lO/7vu+j0+nwuc99DqUU//gf/2OefPJJAH7913+d3//93+fHf/zH+b7v+75D3aGE+wtrDQ29jEAmU++EhF3oRhOaapVobg69soIpl5ETE8hcztWlX76Md+oUqbe/3dW8f/3rLsHD92Fx0QnSuH3RVCq96XI/emHBLfybmHDP0Wg4r/X8PO0XXwRjSD/9NGpqyqWwLC66RZVjY8iREczS0npBTibj6twLBfTsrPOkx4kpttNBFgp4Fy64Ah0pnW1Eyp74tY2Gs6AIAb7vIgO7iSBhiMxmEYODCCldvna/X7vPFgl9Inu7SfammEXRnZL3T7EzmRMtsrej2jHcqDq7zNkB/4GpTw+0xZMClcQMJuyTgbRkIH30InzXd5of+qEf4od+6Ie23P43/sbf4G/9rb/FUF/kVELCdjTNGtpGFNQYUtxf/7ElJBwFcmAAv1h0hTSLi+jZWRexNzaGWV0lmplxDZPvfCe20yF86y0YGMCkUtiZGfwLF9xiyFTK2Tb6PN/R8jK200FNTyOiiHBhwVk6Uim8116Dc+dIPfUU3tgY0ZUrmGbTleFMTWHbbcI331wvyJmcBCndos9bt9CViksg8TyX9T025qbunufSSOLJtmk2EdZiYzFujXHbtNb5skdGnDju92vHle7ux7DO612pbBTanc4WkU13O5sn2ftYaHlSqXY0N6oRAjg76FNMPRjCuxszmDsmZSoJJ5N+Eb7YOHwRfiA1NBm3niUk7Ia1lrpeQiApqNF7vTsJCScGIcR6NOHCgsvLbrUQuZwrgimXEa0WqXe+00UQ3rqFkRI5MkJ08yayVHICtVrdKL7jYh3/1Klec6TI5ei8+CLCGFLvfjeqWCS4fNl9r1hEZrPomRnnIfc8xOgoptWi8/LLbpFls+kE/ego6pFHkKmUm75HkYvhW1vDtNtuUt6taTcGYiHcrWTfMNXOZt1+B4Frq1xZ2SC0txPZIptFdvOxu0L7ARDZ21Fpa27WnPA+N+hTeECEN/T5vZOM74RD4HYi/KCvsmQUmXBktMwa2obk1Ugy9U5IOADC9/Gnp3vRhLbZ7Ilp02ohwpD0E0/QbreJFhbQN29ix8bw83lMuYyJs78BsNbF+3keNpPBrK669so334QoIjp/HiGlq4iPmx1pt9G1mhO3uRy6XEa//jqmWnW17dksanTUJX54niuWaTZ7grxrM0EIZCbjptC5nBPYhUJPcAPOhtLpoFdX1yfZxmw8IFK6hZTp9EahvcmC8iCz1tbcqkYo6YR3/gGbAHd6iy3v8Y4k3FfsJMILcmtp1l5IFFHCkbA+9RYU1Ni93p2EhBONzOVIXbyIXltDz8+7qXIUYdttpDH4jz6KbTZ7Hm2hNWpsDF0uo+I4wWh1FdtsIicmsAsLbiFlfF/vwgXEpUuYSsVNvIVwfuw4EUSvrWHix9goQpVKrgTI87CAqdex9bo7MYgj/1Sh4IR2NosYGEDl8y4i0PM2bNfOz28vsvui+2QisvfEakszU3PC+8Kgf88rtO8FnbjoNEk6STgKNovwWtvc/kHbkIjvhCOhZcpENiCvhlHiwbz0e9yxcVsgQmwsJJFyw9f3uhjkOGOtxdZq7jj5vqtCP2gd+R5QQ0PIgQG3CHJlBQvolRW3sPHhhzGdDnpmhsgY1xaZz2NaLVe602oBbrGhrteJrl8Hz0OOjbnfcTyxFtksplLBVCouWrDRcB7y2Mohh4fdFL1SgThju1uKI/N5J7bjhkqhFFZK0Nrdf5PItlq7x3uea7bM5Vw2+BEfx7uFtdbV3hvT87ajtbsdXNvlIf2cy03NXD1CScFDJe+ulYUcN5Kkk4S7QVeEr9Qirs3t//GJ+E44dJKp98nAlMs9D/Bt6QpypbYV6Nt+vdv3tvv6BGIbDcLr1zfcJnx/XYj7vqsw7//6Dk9mNkQTzs2B77uSHMA7c8a1TM7MYNttkJLg1VexjQbZb/1Wl0ASW016bY+ZjBPzxrjK+KUllxfeaGDDEJtKOQuKEK5oplKBVApZKPRq4EWhgPR9rFJu+q21K7+JJ/R0F352v9894fM896/WSM9zqSj3kC0CeSfh3H/7NvfBGNSVKwS3mdT7588jisU7329rmW9EeLHwTj+gwhuSgp2Eu0shdTB/UyK+Ew6dtqkS2Q45NYQSyWXi44oslfBi4dMvKDDGfd0VE5u/DkNnG9i86O1OiMV4v8C/rWAvl13k3R4E/lFN70U+j3fmjPMoBwEEATYMnR+62dz+MXG6SFeU94R59+s9FrfIdBp5/jyqWiXKZNArK4hmE+/UKfTaGtHNm3RefZXwyhXodGi98QbZd7yDVL3ubCUAUhLeuIGp1fC/9jXaw8POa621m3TncshYIPcWReZyyHzeiXIp3WQ7CNDNpluUGW+3ezVApFJucWXfCZZQCuLUke59ZLeRcx/sJH4PJKKN2bqQcz90X8NSOl99Ou1OWrqv5+5rUyl3LOKTlsNACMGFkk9KCvwHXHR2IouvBPKEntAnPBgk4jvh0KnrRYBk6n3MEXHxyUHZVaBv9/Ve76t1LyljN+TKyt4n93Bk03tZLMLg4AZxaa11JymxGLdh2BPmNghcakdsA9lCXJG+7dQ8ldqSRd2NJlRDQ0TZLEiJPzFB5f/7/2BxcX2fqlWab75J8PrrZL/ne5CNBsFrr6HLZZepvbSEjiJUsehyv7NZZCoFuRyqm0ASC0fTaDibiu+7dsp4Eo7vO6HueVtPMHzfTbrjBZibha8pl/cmkPvF9Z0gRO/3K3x//fe9k1jey+19mFoN/6GH7mwf98GDtrByO4y1RMaSf4DSXRJOJon4TjhU2rpKaNtkZQlPpO/17iQcIT3hEU9qj2LOtEF8bRLsZm0N78yZjQL+OEzvdxPsXbHazbfWeuPJRmzVMF1h3n18VyjC+nY2T81zOfxHHkEMDdH+sz/bILz7iV57jUYY4pVKWGPcYsZ83ons4WHkwIAT35kMMt5f4XnuueLmSdk9KVDKTbGV6k18EcK1VBqDjafhvWN/J/QdWxGL/O0E8W0Fcv+VlYT7iqRWPuGkkIjvhEOlFk+9i2r8Hu9Jwv1A1xcMW8W9zedRB7AqbNjGUU7vY4F/gB96vSAnijY0PlpjnEC31n3eFZH9FhshaL/00q5PYWZmMJkMcnQUNTKCHBzENBquOj4W3DKTcdPvrvj2PPe76ArffrR2+xu6NkUL68I3nnZvK4h3mSpveyKTkLALnSjxeyecDBLxnXBodEyN0LbIykE8mUy9E44/d2V6f0Bxf7v7264wD0NMGEK7TVQuYxYWXIHObmSz5L7zO/EmJ93CyVyO8Pp1su98p/NndyfZ+7VddG8/Io99QsJuBCaZfCecDBLxnXBo1Hpe72TqnZDQpSdKORxx3833tu02emWFcGGB6NYtzMKCywGv1W67DTU05BZSKoU3MYE3NobRmtTb3vbAtkImnHy6k++kYCfhuJOI74RDoWPqBKZJRhbxZeZe705CwonHWrteqd5uoxsN9OIiZmXFNU2urGBrNZeyAi7ez1rnAR8exq6u7rjtzJNPgpTo+Xn0jRuQzeLV67R9HzU8vF7zns87z3di+Ug4AXQLdhLbScJxJxHfCYdCPZl6JyQcGBuG2HYbEwtt225jqlV0uYytVNC1mquDj33fwhhX7T456SIBPc8J71qNzssv44+MEKytwcrKludK/xf/Bfnv/35kLgdCuFzw2VnE/Dydr3/dFeYMD+ONj7uFl0q54pyuIM/lEltJwrGkoy0pJZKTxYRjTyK+E+6YwDTomAZpWSAlc/d6dxISji3WmJ647v8wUYRtNNDlMqZadc2jYeiSQoxBaI0oFPAGB5GlEqJYRPo+ptPpVbvbtTU6r7wCQYCamKD0Hd+BXl4mePll14KZyZB67DGy3/3dTpyEISKbxT93jvQzzxD9x/9IenQUvbiIbbcJr14FXJShLJVQQ0N0wx9Ft6Anl3P/JrXvCfcYbSzaWLJJzGDCCSAR3wl3TJJwkpCwFdvpYPpsI7bddgU2uFr1boW7bTQw7bZLLIm/ByByOVQsttXQEHJoaD39pNVCr6257VlLVC4Tfv3rCECdO0fqne8kNTGB//jj7r7Ly4hCgeCll1ym9/Cwyx6XsrcPdnCQ7Pveh4ki9Pw80cyMa76MIvTSEnpxETU4iBgYcGkrrVZvsi58f12I5/NuGp9MHxPuIknMYMJJIhHfCXdEaFp0TJ20zJOS+Xu9OwkJdx2r9cYpdvxvf661abfdNLvVwrRa2HYb4XmuHVLrXomOLBSQg4PIgQFXbDMw4GIHm00ntoMAs7bmmiTTaWwUEd28Sfj1ryPTabwLF/Df/naklKiJCVSpRBQ3bXpTU4RXrmArFdRjj7l2UEAODWHKZeTcHNHsLGpyEn96Gn96GmMMZm6OcHYWs7CAqdehXncNjqUSangYcjn3c8UnE26jcqtVZY/NnQkJB6GT1MonnCAS8Z1wRyQJJwkPCtZa104Zi+feR5xt3X8/ggDTamGaTWyzCVHkxHZcBCSyWVdWk806sZ3J9MS3GBhw9e1xmomen8ca48S7tYjBQUSrhQ1DwjffJLx2DTk0ROqJJ0hdvIipVpFDQ3inTrkd6p4ECIH/8MMEL75ItLiIf+4c0cwMtl7HO3sWLl1Cr6xg6nW8M2fcvkmJPH0a7/RpJ8TjGEO9sIBZXsYsL4PnoSYnUaOjiEIB0elgmk3XgtlorFtVMpneZFxms65sKCHhkOhNvr1EfCccf46F+P4P/+E/8P/8P/8Pv/Irv7Lle7/3e7/Hv/7X/xrP8/hv/pv/ho985CP3YA8TtiM0bdqmSkrkSMvCvd6dhIRDo7sA0rbbG6wjbGrE7Nao207H2Udi0Sm0Xr+PEIhi0VWw+z6iUEDGTZAyl0MWi87DnclgrcWUy4RXrzqRby10OqC1W/zYV97T+vM/x6ytoSYnyTz7LN7wMKbZRA0N4Y2P98St7dsP78wZwldfJbp5k9Tb3oaamEAvLLiPyUnUyIiLL7x82U3OR0d79hEpJXJqCm9qygnxlRWiW7dcYsqtW+hbt0Ap1Pg43unTqLNnEUHQOwExrRa63YZ44i48b12I5/PuhCSxqiQckGTynXCSuOfi+1Of+hRf/OIXedvb3rble0tLS/zO7/wO//bf/ls6nQ4//MM/zAc/+EFSyeKeY0Ev4cQbu8d7kpBwMKwxG+L8bLvtRG+feAZciUwmg8hknM2k0XALHatVZ8XoivJYUItMxi1CTKddYU0sKkUq5awlxaKbEsepITYMiRYWMCsr688thJuSZzLucyGw1hIuLxO++CK22cS/cIH0N30T0hgsbrosslnk6Oj6vvdNvqWU+BcuELz2GuHVq6Tf9jYIQ/TqKnJpCfXss8hisSeqbbWKNz29ZUotpUSOjeGNub/9aHkZPTtLNDeHjj9QCjU6ipqawpuexvN9d2wbDWe9aTS2t6r0e8cTq0rCHgm0RQhI1lsmnATuufh++umn+dZv/VY+97nPbfne1772NZ566ilSqRSpVIqzZ8/y+uuv8653vWvXbX79618/qt19IHnhhRe23KYJqKubSNIUdXAP9upkst2xTDg4+zqeYYgIAggCRPxBGG6ZZuP72FQKm0q59sYgQDYaiHodUau5x3VRCpPPY3M5rO+7dsdKBcpl930hsNms+34uB0HgPNPz8+77nQ6yXEY0Gm4/pMR6HiKKnGgWApvJ9PZV3riBunEDqTXRuXOEZ84gv/ENt59x+6WenoauoAXk/Dyi0UC3Wu5+YUjqxg3srVuE9boT5YuLiFaLl/7v/xszMQHGIJeXEfU6vPgiZnQUOzBw+2M8Ogq1mhPy8/OIS5d6x8EMDmJGRzFjY9AdoIQhot3ufRBsei9JpbCZTO+DE1YAlPy9Hx63O5ZXGwpPQCend71fgiN5bd5b7pr4/v3f/31++7d/e8Ntn/nMZ/iu7/ou/vN//s/bPqZer1MsFntf5/N56vX6bZ/riSeeIJ34CQ+FF154gWeeeWbL7eXwFk1TYNg7R0bt4T/lhB2PZcLB2Ol4Wq17iSD91hFSKcj3LQpWCplO9ybaIpPBADYusDGrq5hq1S2IlBIGBhDj48hSCTk0hMxkQClss7ledAOIdBpZLDr/dj6/JRPbWusyvJeXnR+8WHRFNrlcb/IOOG+3lJhyGdNqEbz5JmGrhZicJP3e95J5z3vQc3Nu+4WCW0g5MrLu9Y4Jr17F1Ouk3vGO3r60s1miK1dIjY6SunABawwv/eEf8vazZ1HDw3inTwOgy2VXVR/bXrzTp53VZo/oapXo1i2iuTls91jOz7sc8dOn8U6dcnnj/b+7ZtNZVeIJef/CVaHUxkWcudyxtaokf++Hx+2OZWQsqeWAYlpyfvBknaDdC5LX5uHR6XQONPC9a+L7ox/9KB/96Ef39ZhCoUCj0eh93Wg0NojxhHtDZDu0TBlfZBLhnXDvsHZrlF+7vUEIdxHptLN5ZDJucWMmA57nRPDqKmZ21qWJNJvrk3Apkfk8cnjYpXoUi4iu5aTRcAsgu/cbGFi3k+xgi7NRhF5bc9aSeJFm1+9t63XM2tr6baWSW9DYaqGrjLZfjwAAQXBJREFUVYJLl9CXLyMHBsh++MP473gH+to1d/+JCSfCfR81ObntcXIHYV2kpi5eJLp6lfDyZVIXLjiRPzGByGRcCorv442Po0olZC7n6uurVcJGA2962vnP94AaGEC9/e2k3/52V+YzM0M4N4dZXSVYXSV45RW3OHRy0m23UHD+9/h93lrrfqf9VpVq1Z0UxT9TzzPetavs4+Qg4f4giRlMOGkc63epd73rXfzqr/4qnU6HIAi4fPkyjz766L3erQeeul7CYimoxOudcHewUbQlzk9dvUq46QqXUArZXbzXnWin072Jr6lWCWdmnOAulzfaHHwfOTKCGh5Gjow4gRlF2FoNU6thu2KbOLmjf7q9y/TVtNuY5WV0peKmuFKi4u2bchk9O+u2mcvhTU5iWy2iW7dcwkmlQuf119E3buCdOkX2W7+V1PnzhJcvg9Z4Z8+6xBFr8U6d2r550loQG1v/ZKGAmprq+bS9qSlQCj/etl5YQHieq5pPpfAfegi9tES0sEB4/TpqeBg1NbWvpktZLJJ6/HFSjz+OqdeJ4uc2q6sEa2sEr72GGBjAO3XKLdgcGHCLVbNZ1+bZ/XH6F3HGH8RxikDvKoIaHt4wVU+4f0kWWyacNI6l+P7n//yfc/bsWb7lW76FH/3RH+WHf/iHsdby0z/904md5B6jbUBLl/FEmowcvNe7k3CfYa1dXwDZH+kXRRvvKIRLDxka2mgd6fMEW2udQFtZ6dkYosVFN2EWwgnoU6dQIyM9MWzDsCe0o1jUAs6iMji4Lrhv4z221mJqNTe9jq/eiVQKNTKCKBYxq6uE16+DMYh02kX1DQy4BZXXroG16LU1gq99Db2ygv/oo+S/9VtR4+MuCSUM3ZQ7ijDNZi8bfFuU2nYa7D/yCHp2lvDSJSe+cWU53oULRJcvE83MuBjBeLtqbAxRLBLdvOlOXrqRhAcQuLJQIPXoo6QefRTTbrtCn9lZl7Ty+uuEr7+OKBbxJidR09N4pVLvsSKVQqVSEN9mjdliVbFraxBFyPPn971vCSePZPKdcNI4FuL7ueee47nnnut9/YlPfKL3+cc+9jE+9rGP3YvdStiG/qn3cfVaJpwMbBhiW62N1pG4sbEf4fvOitEV2HFGtu508Ken17cXRehq1bU1xtF2/dvqCkvx8MPI4WFkNgta9ywken5+g8gXudy6lWSPMXhWa8zaGnplpWd/kfk8cnQUkc9jVleJ4qm18H3UqVOuLr6bhiIEanKS1vPPE77yCjYMST/5JLlv/maXQnL9OrbVQg0NoUolgrfeAqV64nk7vDNnNvime7cPDyOHh9HLy0TdBaLgynrOnye8epXoxg3EhQvI2CsvMxn8hx928YLLy4RXrqDGxlDj4wd+P5CZDKmLF+HiRUwQrAvxpSXCt94ifOstd1Vgagp16hRef5ILIKR08Y0FF3faPYFL7CcPDp0omXwnnCySd6eEPaNtSFOv4YkUWVm617uTcEKwxmz1Zbfb28f5ZbNukt1vG9kpbi4I3AQ2tiB0q9u7iGy2tzBP5nI9L7ZpNjH1OuHcnPN4d++vlJukx0JuP+LNdjquEGdtbd1aMjTkRHc6jVlbI3rzTWwUIZRCTU4iR0a22DaimRmazz9P+OqryHSazLPPkvnQh5CZDNHMDKZWc5aR06eJbt501pNTp3adxAulXMrJNqQefpj2l79M+NZbblFpjMzl8M+eJbx+nfD6dfyHHnILTInzwqem1iMJFxfdFHx6GnmHVyZlKkXqwgW4cMEJ8W504eIi4eXLhJcvIzIZVOwR70Ydbvh546saCQ8OgbZIkYjvhJNDIr4T9kxDLydT74RdsZ3Oxkl2q7X9Asg477orsGU6vWvjoTXGTclja4FtNlE3bxLF006U6vmve0kYfRnapl7HLCxga7UNOdoyl+st8JPZ7L5/XlOruWSU2A8uPA85Po4aGkJ4HrpSIbpxw50YSOmmxGNjW04orNYEb75J56WXCC9dQo2NkX76adLveQ9SSqKlJfTqKiKbxTt71vnQKxVkLocaGdn3fnfxTp9G5PPomRnYJGRlsYg3PU108ybR1av4Fy9uWEwqCwX8Rx5xvu21NWdfiYt6DgOZSpE6dw7OncNEEXp+3k3FFxeJrl0junbNWXYmJpzwHxtD7sODnnD/0NE2Ed4JJ4pEfCfsCWMjGnoVJXyycuhe707CPcZqvcGX3RXcm+0NvQWQfXF+IpO57UI9G3uZu62RdhsLiS0U3NQ3n3eL7OITwq7XW9frzr8dR/eBE8dqaMgJ7kLhQCUu1ph1a0k8bRe5HGp0FBkvEjT1OuHCgpusC+EWKI6PbzuhNp0OnZdeovONbxDNzOCfPUvm2WddAQ6gKxX0/DzC9/HPnQMhXPyfEKg4EvBO8C9eJPja11C3bm35niqVIAyJ5ucJr13Df+ihDVcEhFL409PoYpFoZoZodhZTqzlRf4iZ3NLzkNPT+NPTrl1zbs5NxefniW7ccPaYOO3Fm5pCTk0lQvwBIdQWYxO/d8LJIhHfCXuirpexGApqIpl6P0BsWADZ/xFH5fUQwgng/kl2NrtnAWb66tlto7FxWt5dHBm3HspcDuH7mEajN2W1QeDEdvxB/3Q7n99Q4X7gYxEE69YSrd22SyW3WDNedGhaLaL5ebcPgBwcxJuY2HGqH62t0XnxRcI33kBXq6Qef5zs+96Hf+aM216j4ewlSuGdP4/wfaLZWbfgcnz8jn6eLt65c4SvvYa6cQMTRchNdhs1Nub89MvLzoISRxNuuM/g4HokYa1G+NZbqOnp3mLNw0RKiTx9Gu/0aSfEl5ac/WVhwU3pb950C0W7NfeTk1t+poT7hyTpJOEkkrwjJdwWN/VeQQmPnBy+17uTcETYMNxQStP9fMsCSM9bt4x0vdl9k+fbPk83naKb29xsrotl2NVC0tuGtdBqOctDve72s7t/vo8cHFyfbt/hBNQ0Gs5aErdGCqWctWR4uHdyYTsdVw8f30cWCk707WBlsdYS3bhB62tfI7p0CSEl6be/ncyHPoQ3PLy+zevXAfDPnEFmMi73e2XFJX5s43c+CNLz8M6fhzffdMU728S5elNT7mpEuUx048b/396dR8dVX3cA/75tZjSSRqu1WYu1eQEjbJmwlAAthOOUQABjgy2wMfgYCEmJDYcAPQ3QQh3aUweIiRsoxBhSTjBQoIWcAi0ETjiUE2MWg/GiZbTv20gjaea99/v1j5/mSWMtluTRbLqfc3yOpdHM/PT0bN133/3dC7WoaMLPW9I0aMXFYgNnezuM+nqwtDSoubnzNiZelmXI2dlQs7MBAEYgEG9rE5s2W1rEmPusLDHmPjcX8hR92Els8lPwTWIQBd/klLxmNzgYEpUsSBLdyo11nLHgbHagpd90GyDHl4zMMovIdT2oL/OEEhKbTQTKgcz2FNlc7vOJzPbAgMh6t7TATEkRQ24CXUmSk0970x8w2ipwdNKlNXEyIUFkuUcnTwa+N7OjQ2TDORc12dnZ1pCYqY6H/+hR+I4dg1FXB9nlglZWhoTzzhvr2GEY0N1ucNOEungx5ORkEbA3NwMYrdUOYVmFVlYGvPce9Lo6qGVlk5ZsqPn5MAzDGpYzvtPMeEpmptWSkPX2jg3mGT9ddJ6oixZZmzCNnh6Yo9M1zdE/fkkSdfejvcQpEI99gcy3XaXgm8QOCr7JtDhMeM1uyJKCRDk0G6lI+HC/3xpKE9TO7ySSzTaxNttmm1OJEQtMJAx0ITm5hOTkLiRTlKZwxsTkx9E/49ct2e3gKSnQliyZdIT7XHFdFx1UurutixE5JUUE3eOCR26aMLu6YHZ1iV7dNpvo1Z0yfe97NjQkNlXW18NoaoKSlQVbeTns555rlUZwxqDX14P7/SJjO5oJZ11d4MPDVkeWUJIdDrCsLFEr39ICeZLAWpIkUaJSVye6t6gq1MkmakK0K9RKS8WFSUfHWEvC7PCVranp6VDT02GvqIDR1ycC8bY2a03+L78UPd4DgTh1SIlJVuZbpuCbxA4Kvsm0fJIHDAlwyTmU9Y5i1gbI8VMgfb7gcg5AlHQ4ncE9s+32OZcFTDbgZEIJSXKyyGgnJExaQjIe8/msiZJsaGhsA+f4Ee5JSaJ938DAtBnm2WDDwzC7ukTZCOeiVCEzUwzFGZcd5YyBdXfD7OwEN02xgTMnB3J6+imDSqOzE74vv4TR1ASjqwu2wkLYVqyAvaIi+OuamsCHhiCnplrlFNzng9HRAUlRpgx4T5eRnw/09ECvrp4yqy3JMrSiIjEFs7NTfP8n9d22vlaSxF2ApCRRCtLZOTaYJ8zD0tTUVKipqbCvXAnT47E2hwYuoPxffQU5PR1qbq7I0tNkzJjhNzkUGdCo7ITEEAq+yZQ4Z/DL/ZCRB6dCtd7RgHNuZbODykZO3gCJ0THbSUnBpSOneZvdKiEJZLZPqgmXbDZILtdYCckpgiw+OuTGGuE+7vuYzQj3OX0vnIN5PGIK5Wi/b8luF1nutLSgiwSrDKW9XaxxtFe3Mkmv7gnvwxj0ujr4jx4VZSM+H2zFxbBXVoqe1uMYra1WC0F1XABstLQAjEEpKJi/4TFJSVBUVbTy6+qaMMwmQFJVaMXF0GtqYLS2is2N4yZQnkxOTBSDeVpbYQZaEmZnTxm0zzfF5YLicsG+YoUYcz+aEWc9PfD39MD/zTeQUlOh5eZaJT8kevlMDgcF3iTGUPBNpuRl3eAwkahkQJbmZ8MUmRo3jLEs9viSkcna+Y3rmW1tgDzNUgyr08n4LiTjg/zxJSSBzPYMupuwkRFrhDsbGpo4wj1Qvx3CVnVB35dhwOztFaUlo9+PnJwsgu5JAi3T4xFB98iIaO+XmSl6dc8gCOY+H3zffgu/2w2ztVVkrouL4fjOd6ystvU+3d0wu7og2e1BGxrNvj6wwUGxiXOaIDcUtLIyUSZSXT1l8A2Iiyy1uBh6bS2MpiZxDk4TpEqKAjU/H5LLZdVhs4EB8bl5+jnPhJyUBNvy5bAtXw42NASjpUUMM+rpgb+vD/5vv4XkclkZ8fno3kLmzm9ycE713iT2UPBNJsU5g9fsAiAhUaFa7/lkBbmBjY+BP+NGnQOwWu5ZrfwCgXaIghdumsFdSE4qIZHGl5A4naJkZQYBPjdNa6MkHxgIHuGekDCW3XY657UemI2MgHV1wezvH5tCmZEBOSNj0gw983phtrVZWXElLU306p7h3QPm8WDk8GFR3tDRAdnlgpqdjYQLLpgQqDKPB0ZLi+ibXVRkBfbcMETHDlmGmpd3mkfg1NTsbBEgjwbH0wXUssMhpmC63dAbGsQUzFMMKlJcLsjl5da0Tv+JE6Id4Clq5cNBdjphKyuDrawMbGRkbMx9dzd0jwf6sWOQEhOh5uVByc+HOs8XQuTUqM0giVUUfJNJDbFemNyAnadAlug0CRVrA+T4biM+38R2fpomAt3x7fzmuAFyurVM6EIyfg12+6xKSMZjw8PB2e3AayoK5NTUsYB7nvsvc85F/XhXF5jXK9Zgs0FJTxd12pPUurORERF0j06tlF0uUdc9w++fcw6jrQ3+r7+G2dMDc7SEQ8nJgeOCCyZ02GDDw9AbG0WAvWRJUE9ws60N3DSh5ORMOwE0lLSyMvgPHYK/uhqO1aun/Vo5KQlqQYEYdBOYgnmKdUqaBm3JEpg9PTBaW2E0NIClpoqBSfPUknC2ZIcDttJSoLRUjLlvbh4bc3/iBPQTJyA5nVBzc8WGzQiV0Cx0gc2WNGCHxBqKqsgEnHMMmp2QIMPGUiO9nJjEGZuwAVKpq4P/5KxpoJ3fyRMgQxyEcM7FOkYz2pOVkASCbKsLySwCY24YwdntcRnz0x3hPhfcNMemUI52W5ETEyFnZoqLmkkuYrjfD6OjA6y311q3kps7q8133DCg19TAX10N1t8vNhhmZ0MrLoZt9eoJLfy43w/D7QYYg1pUFPRezOuF2dsLyeEIa320WlAA/cgR0SrwzDNP2Y5PSUkB8vJgtLSMTcGcwd0YJT0dcmIi9KYmsL6+sZaEIe7kcrpkm03U5hcXizH3ra3WmHu9pgZ6TY34GeXkiPVnZNB0zTDxGZT5JrGJgm8ywTDrhcl1UeuNkVM/YYHjPp/IZI8vGxnfXi9AVUWP6PGlI/OUzbRKSMZ3IRlXKy4piugeEhhiM8uSj8AIdzZPI9znivt8Y1MoA6UlaWki6J6qf7hhiPZzPT2iV7fDIbp0zLK+lw0Pw3/kiAjMPB4xhTIzE/aVKycdXMNNU7QUNAyRQR33fhN6eodxqqwsy9CKi+H/9lvoNTXWmPvpKBkZ1nG0pmDO4Ocu2e3QSkpgdnaK59bViZr6nJyonKQrqyrkggJoBQUiEG9rGxtz73bDcLvFpt3sbEjd3WCMUSA+j/yMgm8Smyj4JkHGst4SkpRFAFojvaSoMX4D5PjSkUk3QJ7cM9vhgOn3QyssnJ+1BUpIxnchGb8muz04sz2HoJ/ruijhmGKEu5XdjkC/ZDYwIKZQjpaKSKoqplCmpU2ZweeMjfXqNk1ImiY6mMyhltfs6YHvm29ETfngICRJgpKRAceaNVAXL5743pzDqK8Xd0QyMiZkts2ODnCfL2h0fTipJSXwnzghSkmWLZtRAKlmZwO6DrO3F0Z9PdTi4hkF0JIkQc3KEi0JGxtFy8dAS8Io7r0tqyrk/Hxo+flizH17u7jwamuD0dAAra4Ow34/5OxsaHl5kHNzKRAPMZ/BocgSVOrxTWIMBd8kyDDrg8H9SFTSoUiR60IQSdYGyJPKRia085MkURcdyGSHeAPktOsbP8jG6w3enCnLY3Xagaz2HGqrOefiPQLZ7alGuCcmRqZWlzGR5e7utgbwSE4nlMxMyC7XlIEf51z06u7oEL26FUWUl2RkzDrbyhmD0dQE/7FjIts9OAjZ4YDscsF+wQVTbsozmpvBvF5RT56bG/xt+XxjPbRP6ogSLrLNBrWwEEZtLYz6+gktEaeiLF4sSn48HhiNjbO62JSdTmjl5aK2erTfuJqdDWV0YmU0k2UZ8uj4esYYWGcnzOFhQJZhNjXBbGoSLRmzssQG05wca6gSmRvOOXTGkUCdTkgMon/9xDIx671wcNMUHR6GhyffAKmqYxsgx7fzC+OtcebxiKzgdCUkiYlig+Yc18X9/uDsduB9JCnkI9znivv9MHt6oNTXw3A4xNpSU2eUJWZDQzAaG0VZkCxDyc4WvbrncPHA/X746+qgV1eD6TowNCTqyjMyxKj4KdYSqCuXnE6oBQUTflZmczPAOZTFiyO6AdFWWgqjrg56Tc2Mg29JksQGzLo6sP5+GC0ts+rSIsmy1VvbaG4W/bcDLQljZBS8LMuQs7NhLluGxDVrYHR2io434/5AUaBkZcG+alVUZ/ejmZ+J/6ZpsyWJRRR8E8sI88DgPjiVNChSbPyiCxXu94s64cAGyPGt/ByOee/KMROmxwPm9Yose0LCWGb7NAJhzpjIbo8G3CePcLcC7hCOcJ8r5vWK0hKPR/zWlSRr/PpM7zawwUFRi52RIdoGzvHnygYGxIbEwF2HwUHRhm7xYtjXrJkyq2n29sJsbxcdPwoLJxzTwF0NOTU14j2l5aQkKLm5MFtaYLS2Qj0pQz8VabRri15bC7O7G9A0qLPMXssuFzSnU9wh8Hjgr64WLf5isL2fumiR+P7PPhtGT8/YmPvWVrDSUgq+T4MkAU6NSnlI7Il8REGixqDZAQALLusNAHJCAmxnnCGC7yjc6AWIjXfIzT3tbOi0I9wDddujI9wjzZos2d1tbeqUEhKgZGTAHBmZMKjmVNSsLCiZmad1IWF0dsJsaxurdZckSImJ0JYtg/2MM6Z8HhscFJsoRwftTHbBIEkStOXLI36hE6CVl8NsaRElIDMMvgFY/cr12lqYbW3WJtzZkFQVWlGRqCFvaREXOx6PaEkYBRfDc6Gmp0NNT4e9ogLM7z9lJxkyNbsi4YxMG+Qo/f+akOnE5v9gJORGTA90PoIEORWqFPmgKxKipcfwVCRJAuZSHsGYyPgGstvjOrFIDkdwdjtKfpFxXYfZ0yOmUI5u7JRdLlHPnZgovmiOAepcA1tumjCamsA8HkiaJvo/nzgBSBJslZWwFRVN+Vzm80FvaAAAaIWF05btREvgDYhgUU5Lg9nVBaOvb1aDZQJTMI2aGhjNzVZ51GwpaWmQnU5x7Pv7x1oSxvjYdwq8Tx8F3iRWUfBNAAADo1nvZCUrwishocBGRoKz25ONcE9Kiro6WjY8LOra+/vFmhVFtJ7LyIjoWtnIiOhO4vcDCQmifKSpCZLdDvu55047ZIXrOoy6OsA0RQePKOtjfSpaWRl8f/4z9BMnoH7nO7N6rmy3ixKUujrojY3Qiovn1L1FstuhlpSI7jTt7dDdblE6lJMTVRcrhBAyExR8E/jYAHQ+jAQ5Baq8MLPesS4wwj0QcEdqhPtccM7BPB4xhXJ0GqZkt4sNlGlpEQ+uzL4+US7CGKSUFBhuN1h3N6TkZCScf/60wTRnTPTy1nWxuTMGa5a1/Hz4jxyB2dwshu7MMniWnU5oBQXQGxpguN1QS0vntGFXkiSoixaNtSTs7h5rSRim4U2EEBIKFHwTK+udRFnvmBI0wn142MpuWyPcA9nteW59OFfcMGD29orSktE2jnJysgi6o6CkgHMu2t51d4s7BhkZ8H/9NbjXKzpVfOc705YOcM5hNDSADw9DSUuDmhW7/760khL4Dx+GXlsL+8qVs36+7HJBXbwYRlOT6B1eUjLnOxlyQgK0sjKYbW0wu7uh19SIC5vMzKi7sCSEkMlQ8L3A+dgg/GwIDtkFTaZd99HMGuE+OAju8QSNcJeczrHs9mm0GgwHNjIihtH0949NoczIEGO5o2CTJzBaKtLQADY0BMnhABIS4D90CFzXoZaUwHbWWaccmGK2toINDIiuIZMM2okl6pIl0I8eheF2Q1u+fE49qpW0NHHB1dYmpmCWlMx5n4Uky1Dz8kRLwqYmmG1t4B6PaEkYJecQIYRMhYLvBW4hdziJdpxz8NHsNhscBB8tyQBG+46npYmAOzExJro/WH3KvV4AYkOekp4OOT09qja7ssFB0QvcMCCnpIDpOvSDBwEAtooK2EpLT/kaZmcnzO5uMaq+sDCqL4ZmQlZVEYCfOAHD7YatrGxOr6MuWiSmYHZ3ixKU4uLTKiuSk5OhlZfDaGkB6+8XLQlzc6Gkp8/5NQkhZL5F/29sMm/8zAsf88IuJ8Emh3+ENZmI67o1UTLaRrjPBTdNsN5e0SpwtMuKnJgIOTNTdFiJsqDU7OyE0d4OAFBzc2G0tECvqQFUFY5zz51Ra0PT44Ex2l5PKyqKqguL06GVlUGvroZeUwO1pGTOo9LVvDxxF6e/H0Zj42lfnEiqCq2wUNTmt7SI3uADA1AXL46Ji1JCyMJD/zMtYNThJPKifoT7HHGfT4x+7+sTFxCyLFrGZWZG5YUDN00RtPX3i7sKeXnwf/ONGIjjdMJxwQUzGnoTmKCJ0UEz0dZN5nTIDgfU/Hyx2bGlBXJ+/pxfSy0ogGEY4m5IczPU03itACU1dawloccDfWhITMuM8LAiQgg5GQXfC5SfDcHHBmGXE2GTEyO9nAUlaIS71xuc3Q5sknS5oqb+eTbYwIDoQjEwAGC0PCYnB0paWtRmIZnPJ9oI+nyQnU7ImZkYOXgQ3OOBnJ4Ox3nnzeiCgft8MNxugHPRyzsOO3BoS5fCaGyEXl0N7TQCZkmSoBYVQa+rE5NlNW3WA5MmfV2bDVpJiXUHQ6+vh5KeDiU3N+JdcwghJCA6fxuSeTdodgKgDifhEBjhLnd3w3/8ePAId5vN6kwiJSXFZIDAGRsrLRn93iSnU2yiTEmJutKS8cz+fhhNTQBjUDIywO12jHz8MbjPB7WgALbKyhmVV3DDEC0FTVNsBIzTbKvickHJyoLZ0QGjq2va/uanEjQFs6NDTMHMyAjNOhctgpScLLL0PT1jLQnn0GOcEEJCjYLvBUhnIxhhHtgkJ+xybA38iBXc5wvObjMGqa8PXNejboT7XHG/H2ZPD8yeHpG9lyTIqaki6I7yIIdzLlrVdXWJEpGCArDBQfg+/hgwTdhWrIBt+fKZvRZjVuZcWbQoZAFktNLKymB2dIiR86cRfAOitEpbsgR6TQ2MlhZAVaGkpIRknbLDMdaSsKsLem2t+PlkZUX1BSEhJP5R8L0AWR1OVOpwEiqcMfBxGyUnG+HOcnNhO+OMmP/Fz7xeUVri8QCci77iWVlQ0tOjtqf4eNwwRBtBr1eMQB8tf9CPHgUUBfbvfGdWJRVGczPY0BDklBSoOTnzuPLooGZnQ3K5xlopnmZPdsluh1pcDL22FkZjozifQjQFVJIkqLm5Yy0JOzpEFjw/PybLuggh8YGC7wXGYD4Ms35oUgIccnzeGg+XaUe4u1xj2e3RTXe8pSVmA2/OOVhfnygtGR4GICZnWqUlMVIuw4aGRJbaMMTPKC8Pvi++EKPiHQ7YzzsP6iza1BltbWB9fZCdzpBsGowVWlkZ/IcOwV9dDcfq1af9enJCArTCQuj19dAbGqCVlIR0Y66clCRaEra2gvX2iqx9Tk7c36UghESnqAi+33vvPfz3f/83du/ePeGxRx99FIcOHUJiotgUuHfvXiRHwfS7WEUdTuaOmyaY1wvu8Yjs9uhURmB0hHtSkmifF4Uj3OeK67qome3psUbWyy4XlMxMyImxtVHX7O6G0doKcA4lJwdycjJGPv4YrLcXUmoqEs47b1blMmZPD8zOTit7HisXIKGgFhRAP3IERmOjGDkfgq4ucnKy1U3FqKuDVloa0m4xkqJAy8+HmZwMo7lZ9AYPtCSMgTs2hJD4EfHg+9FHH8Wf/vQnrFixYtLHv/nmGzz77LNIp6EJp83gPgyzPmiSAw6Fst4zwYaHrYmSsTjCfa7Y8LAoLenrE9+zokDJzISSkRFz7fM4YyLQ6u2FpChQCwvBGcPwhx+CDw1Byc2F/ZxzZjW1kQ0MwGhpEQHdkiVR28llvsiyDK24GP5vv4VeUwP7FP9/z5aSmgoYBozWVuhut5iCGeJjq6SkjLUkHBiAfuIElPz8GbWSJISQUIj4b4zKykp873vfw8svvzzhMcYY6uvr8eCDD6Krqwvr16/H+vXrI7DK+EAdTk4taIT7wICV7QVia4T7XHDOwTwesK4uUUYDUY+rZGRATkuLycwu9/mgNzSAj4xAcjqhFRTA7O7GyMGDgGFAKy+HfeXKWb0mGxmB3tAAACLjvUBrh9WSEviPHxdZ6mXL5jx052RKZqYYQ9/ZKcbQn+YUzMlImgatuBhmVxeM9nYY9fVgaWlQc3Njqp8+ISQ2SZwHClXn1yuvvIL9+/cHfW7Xrl2oqKjAp59+it///vd4/PHHgx4fHBzECy+8gFtuuQWmaWLLli3YtWsXlk/RhcDn8+Hrr7+et+8hljHoGFAaIUNDkpkPCfEVOJ42w4Dc3g5p3JAbKAq40wmekADudALx+kvZNCF5PJA9HmD0YoM7neApKeL7jlGS1wu5owNgDNzlAsvMhNzZCfXoUQCAUV4Olps7uxfVdSjNzYBpgmVng4doY2CsUo4fh9LaCmPp0tkfy1OQOzogDQyAO51gOTnAfF3s+v1QOjoAnw/QNJiLFgFx2KOdEDJ/Vq5cCfssEjFhy3xv2LABGzZsmNVzEhISsGXLFiSM/kd4/vnn4+jRo1MG3wGzPQgLQb/RDK+ZiFQ1H04lbcbP++yzz7BmzZp5XFl0YD4fjJoaSHa7GHCTlBTyISnRdizZyAhYVxfM/n4RbOTmiimBmZkx0QliquPJOYfZ0QGzowPIyICalwclTZzz/uPHocsy7JWVUBfNrtsPN03otbXgiYlis94snx/t5nJ+smXLMPTee5AcDiSG+NzmnIuM9MAA5NRUaAUFIX39k9/LOmcg+oQr2dmndXcr2v69xzI6lqFFxzN05pr0jer7yG63G1VVVTBNE7qu49ChQzjzzDMjvayYY3IdQ2YvVMmGBDk10suJSrLdDtsZZ0ArLYW6aFFcTicMYB4P9Npa6CdOwAzUQefkwLZ8uRjHHQOB91S4YcBwu8XQFk2DVlJiBd4AYFu6FIlr184+8OYcxmj5ipKeHneB91zJSUlQcnPBBwZgtLeH9LUlSYJaWAjJ6QTr6xObZeeJJElQs7NFjbnNJkpeamrAxg3EIoSQUIl4zfdk9u3bh8LCQlx22WW46qqrcP3110PTNFx99dUoLy+P9PJijtfsAgdHkrIo7uqUycxw0xybQjnag1xOTBRZ7uTkuDgv2PCwaCOo65CTkqAWFIRss57Z3Aw2OAg5ORlKXl5IXjNeaGVlMFtbRfu+EIyIH0+S5bEpmF1dYgrmPF74yImJ1vdjBloSZmdDOc1hQoQQMl5UBN/nnXcezjvvPOvjW265xfr79u3bsX379kgsKy4wbsBr9kCRNCTIMy83IfGB+3wwu7th9vVZUyiVtDQRdIewj3Kkmb29MJqbRRvBrKyQBoFGZ6e4Q5CQIDKxcXChEkpqZibktDRRtuHxhLxriKSqY1Mw29oATRNdUeaJpChQ8/PFIKGmJtEbfGBAfC7OuhoRQiIjqstOyOkbNLvAwZCkZFLQsICwgQHobjf8x4/D7O6GJElQAqUl+flxE3hzzmE0NcFoagJGs6ShDLzNvj6YbW2ihGWB9fKeDa2sDACgHz8+L68v2WxQi4sBRbFaBM43xeWCVl4OOTkZbHAQ/hMnxP4IQgg5TfSbJI6JrHc3FEmFU6Y+6fGOMwazpwf+Eyegu91gAwOQnE6oBQXQli+HumhRfPWj1nVRjtDbC8nhgK20FHIIs67M6xVBvaJAXbKEsp7T0PLzIQV6Z4+2qQw12eGAVlQEANAbGubtfcaTNA3akiVQ8/KA0bp/vbER3DTn/b0JIfGLgu845jW7wcGQqGRCkuhHHa+43w+jrQ360aMwmpvBfT7RHaK0FLbSUiipqXF314MNDEBpbgYfGoKcliamIYZwoyjz+aDX1wMAtMLCuLlTMJ+00lKAc+i1tfP2HnJiItTCQoAxGG532DZEKhkZ0MrKrM2f+okTYIODYXlvQkj8oYgsTjFuwmt2Q5YUJMoZkV4OmQfM64Xe0CBKSzrFACUlKwu2ZcugFRTMalR6LDE6O6G73QBjUPPyRNY1hOUggY4pME3R/WWB9/KeqcDdAcPtBhs3nCrUFJcL6uLF4KYJo64OXNfn7b3Gk+120T0nOxvcMKDX1cFobUWYRmUQQuJIHN2DJuMNmT1gMOGScyjrHUc452B9faJryfAwAEByOKBkZkJOSYnrmmRumqKsweOBpGkw8/KgZIT2wpIzBr2+Htzvh5KVFdSmkExPVlWoS5ZAP3EChtsN22gd+HxQ0tPFFMz29rEx9GEYgiVJEtSsLMhJSTAaG2F2dYENDkItKKC7I4SQGYvf39QLGOcMg6wTMhQ4Far1jgdc12G0t4vSkqYm8OFhyC4XtOJi2MrLocTo+PeZYiMj0KurwTweqx0cQhzscM5hNDZapSyhbpu3EGglJYAkQa+pmff3UrOyRBA+MiJaTDI27+8ZIDud0MrLrffXq6utu0+EEHIqlPmOQ17WDcZNJCtZkKU4HYm+QLDhYZjd3WB9fQDngKJAycyEkpEByWaL9PLCwuzrE20EGQvJ5MEp36etzQru1cWLQ/76C4HsdELNz4fR2Ai9qQlafv68vp+SlwduGGAeD4ymJtHbPUz7GyRZFmVJyckwmpthtLWNtSRcIP82CSFzQ8F3nOGcwWt2QYKMRIVqvWMR5xzM4wHr6rI6Okh2O5SMDMhxnuEej3Muhp10d4uOI0VFIe8hHWB2dYkhLnY71KKiuNugGk5qWZkIvqur5z34liQJakEBjLo6sP5+mIoS9gsn2eWC5nTCaG4G83jgr64W3VEIIWQKFHzHmSHWC5MbSFIWQZboxxtLuGHA7O0F6+62NpHJycki6E5OjvDqwovrOozRdnKBgHi+xt6bHg+M1lZrmEs4aofjmZqaCiUrC2ZHB4yuLqjzPB1SkmVRa15bC7OnB9A0qFlZ8/qeE9agqtCKisSwp5YWGI2NkNvbwQ0jvtp7EkJCgv5XiCOccwyanZAgI0mhccixgo2MgHV1iQEejAGyDCU9XUyhnKeAM5oxrxdGQwO4YUBOSRG38ecp28+Gh2E0NgKjARyVC4SGVlYGs6NDjGcPw2h2SVGsKZhme7sYQ58e/v0uSloa5NF+59LgIPQTJ8RQqwV28UwImR4F33FkmPXC5DqSlEzKescA5vGIeu7RfsGSpkHJyoKcnr5gs69mZyeM9nYAgJqbC2UeAzfu94uWgoyJzHpCwry910KjZmdDSk4WdfSDg2Fp1yhpGtTiYhg1NWKPgKrOW5nStOuw26GWlIDV1YGbJnS3G0pGBpScnAVTMkYImR79TxAnxrLeEhIp6x21uGnC7OqC/9gx6PX1IjBJTIRaVARt2TIoixYtyMCbMwa9oQFGW5vIYhYXz2/gPRoUccMQQX4EgrR4Fxi64w9D55MA2W6HumQJIMswGhvBvN6wvfd4kiSBjxv+ZHZ3i249o+1BCSELGwXfcWKY9cHgfjiVNCgSjcGONtzngzwadButreC6DiUtDVp5uRjc4XIt2E1+zOcTgUl/v2jhVlYGOTFx3t6Pcy5a0/l8onNMGMoiFiK1qAiS3Q6jvh7M7w/b+8pOJ7SCAjFts74ebGQkbO89YS0JCdDKyqBkZID7fNBramB0dtJgHkIWOAq+48D4rHeSsijSyyHjsIEB6G43/MePQ+rvhyRJULKzYVu+XNSCLvDBHKbHA72mRgTCGRlQS0ogafN78Wg0N4N5vZBdLqi5ufP6XguZLMtQi4sB04QxjyPnJ33v0SmYME0Ybjd4GIP/k0myLCaxjm7mNdvaYNTWgvt8EVsTISSyKPiOAz42AIP7kKCkQpFow1i00GtqoLvdYAMDkJxOsKwsaMuXQ83KWvAdEDjnMNraYNTXA5xDLSiAmpc379l/s7sbrLcXktMJtaBgXt+LjJaeKAr0ujqwMA7BAcTmRzUnB1zXrRKjSJKTk6GVl0NOSQEbGoK/uhqMAnBCFiQKvuOALKmwyU4kK+Ftr0VOQVUhp6ZCKy2FrbQUPDl5wZaWjMcNA0ZdHczOTkg2G7TSUiipqeF5c0WBnJQEraiINr+FgWyzQS0oEFMoGxvD/v7KokVQMjNFyUeYp2BORlJVaIWFNI6ekAVuYaff4oRNdiJTLo30MshJtKKiSC8h6rChIdFGUNdFaUB+flg3mCqpqeEL9AkAwFZWBsPthl5dDVsE/k2oubliCmZfH4yGhqgYokTnISELG6V+CCFhYXZ3Q6+tFZtNc3JE9nkBdnZZaOTkZCi5ueAej9VGMtzU/HzISUlgAwMwm5sjsgZCCAmg4JsQMq84Y9CbmmC0tECSZWhLlkBdRBuDFxKtrAwAoFdXR+T9JUmCWlgIKSFBTKFsa4vIOgghBKDgmxAyj7jPB722VmxyHG27RtP+Fh41MxNyWhrMjg6YHk9E1hCYginZbDA7O2F2dUVkHYQQQsE3IWReMI8H/poa8OFh0dO8pITGty9gVvb7+PGIrUFSVRGAqyqM1laY/f0RWwshZOGi4JsQEnJGezv0+noxun3xYrGxkrqLLGhKXh4kpxNGUxPY0FDE1iEFpmAqipiCOTgYsbUQQhYm+m1ICAkZbhjQ3W6YHR2QNE20EUxPj/SySBSQZdkaOa+HeejOhLUkJEArLAQA6A0NNPadEBJWFHwTQkKCDQ+LMfEDA6KXdlkZ5ISESC+LRBF1yRJImgbD7QaL9NCbpCQxaCkwBZMG3hBCwoSCb0LIaTN7e8faCGZliSBrgU/xJBPJqgp1yRJwXYfhdkd6OVBSUqw+4NEwBZMQsjBQ8E0ImTPOOYzmZhhNTYAkQSsqgpqdHfEhJiR6aSUlgCRFvPQkQMnMhJKVBe73iwDcNCO9JEJInKPgmxAyJ9zvh15bC7OnB5LDAVtpKWSXK9LLIlFOdjqhLF4M7vXCiJKBN2p2NpS0NPDhYTGBlfNIL4kQEsco+CaEzBobHIReXQ0+NAQ5LQ1aaSkkuz3SyyIxQisvBwD4IzR0ZzLK4sWQk5PBBgfFnRxCCJknFHwTQmbF6OyEXlcHzhjUvDxo1EaQzJKamgolMxOspwdGT0+klwNgbAqm7HSC9fXBaGmJ9JIIIXGKfmMSQmaEmyb0+nqYbW1iWElJCZSMjEgvi8Qoa+jOiRMRXskYSZahFhVBstthdnfD6OyM9JIIIXGIgm9CyCmxkRHoNTVgHg/kxERo5eWQnc5IL4vEMDU3F1JyMszW1qgadGNNwdQ0mG1tMHt7I70kQkicoeCbEDIts68Pek0NuM8HJTMTanExtREkIREYuuOvqYn0UoJINtvYFMzmZjCPJ9JLIoTEEQq+CSGT4pzDaGmB0dgIAFALC0W2ktoIkhAJlHgYDQ1gfn+klxNEdjigFRWJtoiNjWBDQ5FeEiEkTkQ0+B4YGMAdd9yBm266CTfccAM+//zzCV9z4MABrFu3Dtdffz0++OCDCKySkIWH6zqM2lqY3d2Q7HZoZWVQUlIivSwSZ2RZhlpcDBiGdZEXTeTERDEFkzExBZNaEBJCQiCi94737duH888/H1u3bkVtbS3uuecevP7669bjnZ2dePHFF/Haa6/B5/OhqqoKF154IWw2WwRXTUh8Y16v6HVsGJBTUqAuXgxJUSK9LBKntNJScK8XclpapJcyKcXlAgoLwb1euutDCAmJiAbfW7dutQJp0zRhP6lP8FdffYXVq1fDZrPBZrOhsLAQR48eRUVFxaSvF8hK+KPs9mWs8/l8kV5C3Ij2Y2n29sIc7fCgZGYC6enwGwYQpWO3o/14xppIHU/prLNgAjCj9efpcAAOx6zXR+dn6NCxDC06nqERiDdne1csbMH3K6+8gv379wd9bteuXaioqEBnZyfuvfde/O3f/m3Q44ODg0hOTrY+TkxMxOA0u+J1XQcAHD9+PIQrJ19//XWklxA3YupYtrSIP1Espo5nDKDjGVp0PEOHjmVo0fEMLV3X4XA4Zvz1YQu+N2zYgA0bNkz4/LFjx3D33XfjZz/7Gc4999ygx5KSkuD1eq2PvV5vUDB+ssTERCxduhSaptHtQUIIIYQQMm8459B1HYmJibN6XkTLTqqrq/HTn/4UTzzxBJYvXz7h8YqKCjzxxBPw+Xzw+/2oqanB0qVLp3w9WZanDc4JIYQQQggJldlkvAMkHsHt2z/60Y9w7NgxLF68GIDIdP/rv/4r9u3bh8LCQlx22WU4cOAAXn75ZXDOcfvtt2Pt2rWRWi4hhBBCCCGnJaLBNyGEEEIIIQsJDdkhhBBCCCEkTCj4JoQQQgghJEwo+CaEEEIIISRMYj74phH1offee+/hnnvumfSxRx99FOvWrcPmzZuxefNmDAwMhHl1sWe640nn5syNjIzgb/7mb1BVVYXt27ejp6dnwtfQ+Tk9xhgefPBB3HDDDdi8eTPq6+uDHn///fdx3XXX4YYbbsCBAwcitMrYcarjuW/fPvzgBz+wzsfa2toIrTR2fPnll9i8efOEz9O5OTdTHU86N2dH13Xce++9qKqqwvr16/G///u/QY/P+vzkMe7JJ5/k+/bt45xzXlNTw6+55pqgxzs6OviVV17JfT4f93g81t/J5B555BG+du1avmPHjkkf37hxI+/u7g7zqmLXdMeTzs3Z+e1vf8t/9atfcc45f+utt/gjjzwy4Wvo/JzeO++8w++77z7OOeeff/45v+OOO6zH/H4//973vsf7+vq4z+fj69at4x0dHZFaakyY7nhyzvk999zDDx8+HImlxaRnnnmGX3nllXzDhg1Bn6dzc26mOp6c07k5W6+++ip/9NFHOeec9/T08EsuucR6bC7nZ8xnvrdu3YqNGzcCOPWI+uTkZGtEPZlcZWUlHn744UkfY4yhvr4eDz74IDZu3IhXX301vIuLQdMdTzo3Z+ezzz7DRRddBAC4+OKL8cknnwQ9TufnqY0/hqtWrQqacldTU4PCwkKkpKTAZrNhzZo1OHjwYKSWGhOmO54A8M033+CZZ57Bpk2b8PTTT0diiTGlsLAQe/bsmfB5OjfnZqrjCdC5OVvf//738dOf/tT6WFEU6+9zOT8jOmRntsIxon6hmOpYXnHFFfj0008nfc7Q0BBuuukm3HLLLTBNE1u2bMHKlSsnHZC00MzleNK5ObXJjmdGRoZ1vBITEyeUlND5eWqDg4NISkqyPlYUBYZhQFVVOh/nYLrjCQA/+MEPUFVVhaSkJPzkJz/BBx98gL/6q7+K1HKj3tq1a9HU1DTh83Ruzs1UxxOgc3O2AhMsBwcHcdddd2HHjh3WY3M5P2Mq+A7HiPqFYqpjOZ2EhARs2bIFCQkJAIDzzz8fR48epeAGczuedG5ObbLj+ZOf/MQ6Xl6vFy6XK+hxOj9P7eRzjjFmBYp0Ps7edMeTc46bb77ZOoaXXHIJjhw5QgHOHNC5GVp0bs5Na2srfvzjH6OqqgpXXXWV9fm5nJ8xX3YSGFG/e/duXHLJJRMer6iowGeffQafz4eBgYFTjqgnU3O73aiqqoJpmtB1HYcOHcKZZ54Z6WXFLDo3Z6eyshIffvghAOCjjz7CmjVrgh6n8/PUKisr8dFHHwEAvvjii6DzrbS0FPX19ejr64Pf78fBgwexevXqSC01Jkx3PAcHB3HllVfC6/WCc45PP/0UK1eujNRSYxqdm6FF5+bsdXV14dZbb8W9996L9evXBz02l/MzpjLfk9m9ezf8fj/+8R//EcDkI+o3b96MqqoqcM6xc+fOCXXhZHrjj+VVV12F66+/Hpqm4eqrr0Z5eXmklxdz6Nycm02bNuG+++7Dpk2boGkadu/eDYDOz9m4/PLL8fHHH2Pjxo3gnGPXrl34r//6LwwNDeGGG27A/fffj23btoFzjuuuuw7Z2dmRXnJUO9Xx3LlzJ7Zs2QKbzYYLLrhg0gQRmRqdm6FF5+bc/eY3v4HH48HevXuxd+9eAOIO7fDw8JzOTxovTwghhBBCSJjEfNkJIYQQQgghsYKCb0IIIYQQQsKEgm9CCCGEEELChIJvQgghhBBCwoSCb0IIIYQQQsKEgm9CCJnEG2+8gfXr12PVqlVYvXo1Nm7ciD/84Q9hX8eyZcvw5ptvTvrYnj17cPnll4d1PdXV1fjjH/9ofXzppZdarbdm4j/+4z9wxhlnzMPKCCEkNlDwTQghJ3n55ZfxD//wD6iqqsKbb76JV155BX/5l3+Ju+++G6+//nqklxdRd955Jw4fPjzn519xxRXWYBpCCFmIYn7IDiGEhNrLL7+M66+/HuvWrbM+V1ZWhrq6Orzwwgu49tprI7i6yDrd0RAOhwMOhyNEqyGEkNhDmW9CCDmJLMs4dOgQBgYGgj5/3333Yc+ePdbH/f39eOCBB3Deeefh3HPPxfbt21FbW2s9fv/99+O+++7Dz3/+c6xevRrf/e538dRTTwUFsO+88w6uu+46VFRU4Oyzz8bGjRvx1VdfzWndra2tuOuuu1BZWYm/+Iu/wM6dO9He3m49vnnzZuzevRv33nsvKisrcfHFF+ORRx6BYRjW13z44Yf44Q9/iLPOOgvr1q3D888/j2XLllnPb2howFNPPYVLL73Uek57ezvuuOMOnH322fjud7+L3/zmN1Ou8eSyk2XLluHVV1/FjTfeiIqKCnz/+9/Hyy+/PO33+eGHH2LDhg04++yzcemll+LZZ58Ner233noLmzZtwllnnYUrrrgCX3zxBV566SVccsklWLNmDe6++274/f6ZH1hCCAkhCr4JIeQk27Ztw1dffYWLLroId9xxB5577jl8++23SE9PR35+PgCRAb7tttvQ0dGBZ599Fi+99BLy8vJQVVWF3t5e67XefvtteL1evPLKK7j//vvx3HPP4ZlnngEAfPXVV9ixYwfWrVuHP/zhD3jxxRcBAD//+c9nveahoSFs3rwZdrsdv//97/Hcc89B13XcfPPNQYHmvn37UFxcjNdeew233347/v3f/x1vv/02AODIkSP40Y9+hEsvvRT/+Z//iU2bNuHxxx+3nrtnzx4sXrwYt956K1599VXr86+99houueQSvPXWW9iyZQsef/xx/PnPf57x2v/lX/4FN954I15//XWcc845ePjhh9Hc3Dzp137++ee44447cOGFF+KNN97AAw88gF//+tc4cOCA9TW/+MUvsH37drz55ptISkrCbbfdhvfffx//9m//hl27duHdd98NWj8hhIQTBd+EEHKSv/7rv7YypQcPHsQ///M/45prrsG1116LEydOAAA++eQTHD58GE8++STOOusslJWV4e///u+RkpISFAimpaXhscceQ1lZGa688kps3boVL774Ijjn0DQNDz30EG688Ubk5+ejoqICGzZswPHjx2e95rfffhvDw8N47LHHsHTpUqxYsQK//OUv0d7ejnfffdf6uhUrVuDOO+9EcXExbrzxRixbtgxffPEFAGD//v1YvXo1duzYgeLiYmzYsAFVVVXWc1NTU6EoCpxOJ9LT063Pr127Fps2bUJBQQFuu+02JCcn4+uvv57x2q+77jpcccUVKC0txc9+9jMwxqbM/r/44os455xzrDVefvnleOihh5CQkGB9zfr163HppZeipKQEV199Nfr7+/HQQw9h6dKlWLt2LVasWGH9HAkhJNyo5psQQiZRWVmJyspKmKaJb775Bu+//z5+97vfYfv27Xj33Xdx5MgRmKaJiy66KOh5Pp8PNTU11sdnn302bDab9fGqVauwd+9e9Pb2YsWKFUhOTsbTTz+N6upq1NfX49tvvwVjbNbrPXLkCHp6enDOOecEfX54eDhoPUuWLAl63OVyQdd16zUuvvjioMfXrFmD3/72t9O+d3Fx8YTXHBkZmfHax6/J5XIBgLWmkx0/fnzCGq+55pqgjwsLC62/JyQkQJZl644FIOrOqeyEEBIpFHwTQsg4ra2tePrpp/HjH/8YixYtgqIoqKioQEVFBc455xxs27YNx44dg6ZpSE1NDcpyBzidTuvvqhr836xpmgBEXfn//d//Yfv27bjssstQWVmJ6667Dm63Gw899NCs161pGsrKyvDUU09NeCw5Odn6+/gLgYBADbqiKHMK/GV54k3U2WzMnG5NJzv5eM7kayRJgiRJM14PIYTMJyo7IYSQcex2O1599VW89dZbEx5zuVyQJAkZGRkoLy9HX18fAKCoqAhFRUXIz8/HE088EVTvfHIm+8svv0ReXh5SU1Px0ksv4cILL8QTTzyBLVu24Pzzz7dqnWfbVaS8vBxNTU1ITU211pORkYFf/OIXMy5jWbZs2YRyjy+//DLo40gHsaWlpRNKWh5//HHceeedEVoRIYTMDgXfhBAyTnp6OrZt24bdu3djz549OHbsGOrr6/Hee+/hgQcewLXXXou8vDxccMEFWLVqFXbs2IGDBw+irq4Of/d3f4cPPvgAS5cutV7P7XZj165dqK2txZtvvokXXngB27Zts97r2LFj+OKLL9DY2IgXX3wR+/fvB4BZl0VcddVVSEtLw44dO3D48GEcP34c99xzD7788kuUl5fP6DVuueUWHDp0CHv27IHb7cYbb7xhbQINSExMhNvtDuqiEk633nor/vznP2Pv3r2or6/HO++8gxdeeCGo+wohhEQzKjshhJCT7Ny5E0VFRThw4ACef/55+Hw+FBYW4tprr8XWrVsBiAzwr3/9a/zTP/0T7rzzTvj9fqxYsQLPPvssysrKrNeqrKzE0NAQ1q1bh/T0dOzcuRM33XQTAOCuu+5CR0cHtm3bBkVRsGzZMjz22GPYuXMnDh8+PKF+ezoOhwP79u3DY489hptvvhmSJGHVqlXYv38/MjIyZvQay5cvx5NPPolf/vKXePrpp7FixQps3LgRv/vd76yv2bp1Kx599FH86U9/wieffDLj9YXKmWeeiT179uBXv/oV9u7di5ycHOzcuRPr168P+1oIIWQuJH66ExMIIYRM6v7770dbWxuef/75SC9lRr766ivYbDYsX77c+twzzzyDAwcO4H/+538iuDJCCIkfVHZCCCEEgOh2cvPNN+Ojjz5CS0sL/vjHP2L//v344Q9/GOmlEUJI3KCyE0IIIQCA66+/Hh0dHXj44YfR0dGBrKwsVFVV4fbbb4/00gghJG5Q2QkhhBBCCCFhQmUnhBBCCCGEhAkF34QQQgghhIQJBd+EEEIIIYSECQXfhBBCCCGEhAkF34QQQgghhITJ/wMvDTgKyqxnzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "fig, ax = plt.subplots(1, figsize=(12,6))\n",
    "# Plot data\n",
    "plt.scatter(iris.SepalLengthCm, iris.SepalWidthCm, c=std_iris.c, alpha = 0.6, s=10)\n",
    "#Plot centroids\n",
    "plt.scatter(cen_x, cen_y, c=colors, s=70)\n",
    "# plot lines\n",
    "for idx, val in std_iris.iterrows():\n",
    "    x = [val.SepalLengthCm, val.cen_x,]\n",
    "    y = [val.SepalWidthCm, val.cen_y]\n",
    "    plt.plot(x, y, c=val.c, alpha=0.2)\n",
    "plt.xlim(-2,2)\n",
    "plt.ylim(-2,2)\n",
    "plt.title('K-Means Clustering with lines from centroids\\n', loc='left', fontsize=22)\n",
    "plt.xlabel('Sepal length in cm',fontsize=15)\n",
    "plt.ylabel('Sepal Width in cm',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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