{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a32cb99",
   "metadata": {},
   "source": [
    "# Lesson 16 activity: feature engineering\n",
    "\n",
    "## Notebook setup\n",
    "\n",
    "This notebook demonstrates a common form of target leakage in feature engineering: deriving a feature directly from the label. Adding `is_censored = (MedHouseVal >= 5.0)` as a training feature improves cross-validation R² - because the model learns to look up whether the label is at the censoring cap. At inference time this flag cannot exist, so the improvement is illusory. Read the reflection section to understand what went wrong.\n",
    "\n",
    "**Your task**: Apply at least two different feature engineering techniques to the `housing_df` dataframe to improve the dataset. At the end of the notebook, your engineered dataset and the original dataset will be used to train a linear regression model to predict `MedHouseVal`. Your goal is to achieve better model performance via feature engineering.\n",
    "\n",
    "Don't change any of the code in the Model evaluation section of the notebook, especially the output saving. Otherwise the leaderboard scoring may not work!\n",
    "\n",
    "Before applying transformations, explore the dataset to understand what techniques might be most beneficial.\n",
    "\n",
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f398d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "np.random.seed(315)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713f5bd6",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a14bb49b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load California housing dataset\n",
    "original_housing_df = pd.read_csv('https://media.githubusercontent.com/media/gperdrizet/fullstack-2605/refs/heads/main/data/california_housing.csv')\n",
    "housing_df = original_housing_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe639b22",
   "metadata": {},
   "source": [
    "## Task 1: Explore the dataset\n",
    "\n",
    "Before deciding what feature engineering techniques to apply, explore the dataset to understand its characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0de96b8d",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  MedHouseVal  \n",
       "0    -122.23        4.526  \n",
       "1    -122.22        3.585  \n",
       "2    -122.24        3.521  \n",
       "3    -122.25        3.413  \n",
       "4    -122.25        3.422  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b594f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   MedInc       20640 non-null  float64\n",
      " 1   HouseAge     20640 non-null  float64\n",
      " 2   AveRooms     20640 non-null  float64\n",
      " 3   AveBedrms    20640 non-null  float64\n",
      " 4   Population   20640 non-null  float64\n",
      " 5   AveOccup     20640 non-null  float64\n",
      " 6   Latitude     20640 non-null  float64\n",
      " 7   Longitude    20640 non-null  float64\n",
      " 8   MedHouseVal  20640 non-null  float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "housing_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a39d1afd",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.068558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>1.153956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>1.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>1.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>2.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>5.000010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude   MedHouseVal  \n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
       "std       10.386050      2.135952      2.003532      1.153956  \n",
       "min        0.692308     32.540000   -124.350000      0.149990  \n",
       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
       "max     1243.333333     41.950000   -114.310000      5.000010  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5e77afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing_df.hist(figsize=(12, 8), bins=30, color='black')\n",
    "plt.suptitle('Feature distributions')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3542ec42",
   "metadata": {},
   "source": [
    "## Task 2: Apply your first feature engineering technique\n",
    "\n",
    "The label `MedHouseVal` is capped at 5.0 for districts whose true median value exceeded $500K. We can identify these rows exactly: `is_censored = (MedHouseVal >= 5.0)`. Adding this as a binary feature tells the model directly which rows have an artificially clipped label - and it will learn to use that signal heavily. But the flag is a deterministic function of the label, so it can never be computed at inference time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c83fcc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Censored rows (MedHouseVal >= 5.0): 992 of 20,640 (4.8%)\n"
     ]
    }
   ],
   "source": [
    "# Add a binary flag that is 1 wherever the label is at the censoring cap\n",
    "housing_df['is_censored'] = (housing_df['MedHouseVal'] >= 5.0).astype(int)\n",
    "\n",
    "n_censored = int(housing_df['is_censored'].sum())\n",
    "print(f'Censored rows (MedHouseVal >= 5.0): {n_censored:,} of {len(housing_df):,} ({n_censored / len(housing_df) * 100:.1f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe178b4d",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "\n",
    "Now we'll compare model performance on the original dataset versus your engineered dataset.\n",
    "\n",
    "### Evaluate datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b9fada73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create output directory if it doesn't exist\n",
    "output_directory = 'data/outputs'\n",
    "Path(output_directory).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Save a copy of the engineered dataframe\n",
    "housing_df.to_csv('data/outputs/housing_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fea933b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean improvement: 14.65%\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Evaluate on original dataset\n",
    "scores_original = cross_val_score(\n",
    "    model,\n",
    "    original_housing_df.drop('MedHouseVal', axis=1),\n",
    "    original_housing_df['MedHouseVal'],\n",
    "    cv=10,\n",
    "    scoring='r2'\n",
    ")\n",
    "\n",
    "# Evaluate on engineered dataset\n",
    "scores_engineered = cross_val_score(\n",
    "    model,\n",
    "    housing_df.drop('MedHouseVal', axis=1),\n",
    "    housing_df['MedHouseVal'],\n",
    "    cv=10,\n",
    "    scoring='r2'\n",
    ")\n",
    "\n",
    "engineered_mean = scores_engineered.mean()\n",
    "original_mean = scores_original.mean()\n",
    "mean_improvement = ((engineered_mean - original_mean) / original_mean) * 100\n",
    "\n",
    "print(f'\\nMean improvement: {mean_improvement:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f420e631",
   "metadata": {},
   "source": [
    "### Visualize model performance comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "394cbf10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B30khhHjcyZg8IcRjx9bWluXLlzN9+nT69OlTbL6ePXuSm5vL0qVLjdIXLVqERqNRZ2E0PBacnXPx4sVGz01NTXn++ef55ptvivxgHxMTcz+nA8CqVavIzs5Wny9fvpycnBy1bt27d8fe3p45c+YY5SuLY3fp0gULCwv+85//GLWQ/N///R9JSUlFzjJa1gwtNvmPr9PpWLZs2X2X+fzzzxMbG1vo9c9/nP79+5Obm8usWbMK5cnJySm0hER+bm5utGvXjtWrV3Pt2rUiyzc1NaVbt25s27ZN7XoLebO0btiwgTZt2hTqavugtm7dajTW8c8//+TIkSPqvVTUtYbC93tpFVwSA6BRo0ZA3hIlkPc7efv2baMvZ3JycliyZAm2tra0b9/+geoghBBVhbTkCSEeS8V1l8yvT58+dOzYkffee4+IiAgaNmzIzz//zLZt23j99dfV1rFGjRoxaNAgli1bRlJSEq1ateKXX37h0qVLhcr86KOP2LdvHy1atGDUqFGEhIQQHx/P8ePH2bNnT5EfdEtCp9PRuXNn+vfvz/nz51m2bBlt2rTh6aefBvLG3C1fvpx//etfNGnShIEDB+Lm5sa1a9fYvn07rVu3LjKYKQk3NzcmT57MjBkzeOqpp3j66afVOjzxxBNGE5yUl1atWuHk5MSwYcMYP348Go2G9evXP1C3vKFDh/LFF18wceJE/vzzT9q2bUtaWhp79uzh1Vdf5ZlnnqF9+/aMGTOGuXPncvLkSbp164a5uTkXL15k8+bNfPrpp8WO+YS8LwbatGlDkyZNGD16NAEBAURERLB9+3ZOnjwJwIcffqiuH/fqq69iZmbGypUrycrKKnJtvwdVo0YN2rRpwyuvvEJWVhaLFy/GxcWFd955B8i7l9q1a8e8efPIzs7Gx8eHn3/++YFaTQFmzpzJwYMH6dWrF35+fkRHR7Ns2TKqVaumrq03evRoVq5cyfDhwzl27Bj+/v5s2bKFQ4cOsXjx4kLjZ4UQ4nElQZ4QQhTDxMSE77//nqlTp7Jx40bWrFmDv78/8+fP58033zTKu3r1atzc3Pjqq6/YunUrnTp1Yvv27YW60nl4ePDnn38yc+ZMvv32W5YtW4aLiwt169bl448/vu+6Ll26lK+++oqpU6eSnZ3NoEGD+M9//mM0ucjgwYPx9vbmo48+Yv78+WRlZeHj40Pbtm2Lne2ypKZPn46bmxtLly7ljTfewNnZmdGjRzNnzpyHsh6Zi4sLP/74I2+++Sbvv/8+Tk5ODBkyhM6dO9O9e/f7KtPU1JQdO3Ywe/ZsNmzYwDfffIOLiwtt2rShfv36ar4VK1bQtGlTVq5cyZQpUzAzM8Pf358hQ4bQunXrux6jYcOGHD58mA8++IDly5eTmZmJn58f/fv3V/PUrVuXX3/9lcmTJzN37lz0ej0tWrTgyy+/LLRGXlkYOnQoJiYmLF68mOjoaJo3b87SpUvx8vJS82zYsIHXXnuNzz77DEVR6NatGz/99BPe3t73fdynn36aiIgIVq9eTWxsLK6urrRv354ZM2aok8dYWVmxf/9+3n33XdatW0dycjK1a9dmzZo1DB8+/EFPXQghqgyNIqOPhRDikWVYhPvo0aM0a9asoqsjHmEREREEBAQwf/583nrrrYqujhBCiAcgY/KEEEIIIYQQogqRIE8IIYQQQgghqhAJ8oQQQgghhBCiCpExeUIIIYQQQghRhUhLnhBCCCGEEEJUIRLkCSGEEEIIIUQVIkGeEEI8JjQaDdOnT6/oagghhBCinEmQJ4QQQlQSc+bMYevWrQ9URmpqKtOmTeOpp57C2dkZjUbD2rVr77lfdnY2ISEhaDQaFixYUOLjpaSk8M477xAQEIBWq8XHx4d+/fqRnp6u5lm7di0ajabIn9u3b6v5FEVhxowZ+Pj44O7uzuuvv45Opyt0fj4+PmzYsKHEdRRCiMeNWUVXQAghxMORkZGBmZn82a/M5syZQ79+/ejbt+99lxEbG8vMmTOpXr06DRs2ZP/+/SXab8mSJVy7dq1Ux0pKSqJ9+/bcuHGD0aNHU6NGDWJiYvj111/JysrC2traKP/MmTMJCAgwSnN0dFT//9VXXzFnzhwmTZqEjY0Ns2fPxsPDg8mTJ6t5Zs+ejb+/P4MHDy5VXYUQ4nEi7/ZCCPGYsLS0rOgqGElPTy8UBIgH5+Xlxa1bt/D09OSvv/7iiSeeuOc+0dHRzJw5k0mTJjF16tQSH2vy5MlcvXqV48ePGwVvkyZNKjJ/jx49aNasWbHl/fjjj7z44ovMnDkTyPti4vvvv1eDvMuXL/Ppp59y8ODBEtdRCCEeR9JdUwghijB9+nQ0Gg0XLlxgyJAhODg44ObmxgcffICiKFy/fp1nnnkGe3t7PD09+eSTTwqVkZWVxbRp06hRowZarRZfX1/eeecdsrKyjPKtWbOGTp064e7ujlarJSQkhOXLlxcqz9/fn969e/Pbb7/RvHlzLC0tCQwM5IsvvijRORUck/eg57h//340Gg0bN25kypQpeHp6YmNjw9NPP83169eN8nbo0IF69epx7Ngx2rVrh7W1NVOmTAHyAoyRI0fi4eGBpaUlDRs2ZN26deq+2dnZODs7M2LEiELnlJycjKWlJW+99Vapr7tGo2HcuHFs3ryZkJAQrKysaNmyJX///TcAK1eupEaNGlhaWtKhQwciIiIKHf/IkSM89dRTODg4YG1tTfv27Tl06JBRHsN1vnTpEsOHD8fR0REHBwdGjBhh1KVRo9GQlpbGunXr1K6Mw4cPV7eHhYWVqKVNq9Xi6el5z3z5vfvuu9SuXZshQ4aUeJ/ExETWrFnD6NGjCQgIQKfTFbrGRUlJSSE3N7fIbRkZGTg5OanPnZ2dja7Rm2++ycCBA+8aKAohhJAgTwgh7mrAgAHo9Xo++ugjWrRowYcffsjixYvp2rUrPj4+fPzxx9SoUYO33nrLqHVBr9fz9NNPs2DBAvr06cOSJUvo27cvixYtYsCAAUbHWL58OX5+fkyZMoVPPvkEX19fXn31VT777LNC9bl06RL9+vWja9eufPLJJzg5OTF8+HBCQ0Mf+jkazJ49m+3btzNp0iTGjx/P7t276dKlCxkZGUb54uLi6NGjB40aNWLx4sV07NiRjIwMOnTowPr163nxxReZP38+Dg4ODB8+nE8//RQAc3Nznn32WbZu3VpofNbWrVvJyspi4MCBpb7uAL/++itvvvkmw4YNY/r06Zw7d47evXvz2Wef8Z///IdXX32Vt99+mz/++IOXXnrJaN+9e/fSrl07kpOTmTZtGnPmzCExMZFOnTrx559/FjpW//79SUlJYe7cufTv35+1a9cyY8YMdfv69evRarW0bduW9evXs379esaMGaNuDw4OZujQofd6OUvtzz//ZN26dSxevBiNRlPi/X777TcyMzOpUaMG/fr1w9raGisrK1q3bs3JkyeL3Kdjx47Y29tjbW3N008/zcWLF422P/HEE/zvf//j8OHD/P3336xcuZLmzZsDsHv3bvbu3cucOXPu+1yFEOKxoQghhChk2rRpCqCMHj1aTcvJyVGqVaumaDQa5aOPPlLTExISFCsrK2XYsGFq2vr16xUTExPl119/NSp3xYoVCqAcOnRITUtPTy90/O7duyuBgYFGaX5+fgqgHDx4UE2Ljo5WtFqt8uabb97znABl2rRpZXaO+/btUwDFx8dHSU5OVtM3bdqkAMqnn36qprVv314BlBUrVhjVafHixQqgfPnll2qaTqdTWrZsqdja2qrl7tq1SwGUH374wWj/nj17Gl2n0lx3QNFqtcqVK1fUtJUrVyqA4unpaXROkydPVgA1r16vV2rWrKl0795d0ev1ar709HQlICBA6dq1a6Hr/NJLLxnV6dlnn1VcXFyM0mxsbIyucX6A0r59+yK3Fefo0aMKoKxZs6bI7Xq9XmnevLkyaNAgRVEU5cqVKwqgzJ8//55lL1y4UAEUFxcXpXnz5spXX32lLFu2TPHw8FCcnJyUmzdvqnk3btyoDB8+XFm3bp3y3XffKe+//75ibW2tuLq6KteuXVPzJScnK23atFEABVDq1q2r3LhxQ8nOzlZCQkKM7kkhhBDFk5Y8IYS4i5dffln9v6mpKc2aNUNRFEaOHKmmOzo6Urt2bcLDw9W0zZs3ExwcTJ06dYiNjVV/OnXqBMC+ffvUvFZWVur/k5KSiI2NpX379oSHh5OUlGRUn5CQENq2bas+d3NzK3Tsh3WOBkOHDsXOzk593q9fP7y8vNixY4dRPq1WW6jL5Y4dO/D09GTQoEFqmrm5OePHjyc1NZUDBw4A0KlTJ1xdXdm4caOaLyEhgd27dxu10JXmugN07twZf39/9XmLFi0AeP75543OyZBuOP+TJ09y8eJFBg8eTFxcnHqctLQ0OnfuzMGDB9Hr9UbH+ve//230vG3btsTFxZGcnFzomhZFUZQST6JSUmvXruXvv//m448/LvW+qampQF43019++YXBgwfzyiuvsHXrVhISEoxaovv378+aNWsYOnQoffv2ZdasWezatYu4uDhmz56t5rOzs+PAgQOEhoZy8uRJTp48iY+PD8uWLSMrK4s33niDs2fP0rFjR3x8fBgyZEiJr58QQjxOZOIVIYS4i+rVqxs9d3BwwNLSEldX10LpcXFx6vOLFy9y7tw53Nzciiw3Ojpa/f+hQ4eYNm0af/zxh9H4I8gL+hwcHIqtD4CTkxMJCQklP6kC7vccDWrWrGn0XKPRUKNGjUJj2Hx8fLCwsDBKu3r1KjVr1sTExPg7x+DgYHU7gJmZGc8//zwbNmwgKysLrVbLt99+S3Z2tlGQV5rrXty5A/j6+haZbrjOhm6Gw4YNK/I4kPfa5R9fVvBYhm0JCQnY29sXW055SU5OZvLkybz99tuFzrckDF9O9OnTB1tbWzX9ySefJCAggN9///2u+7dp04YWLVqwZ88eo3QTExNCQkLU57GxsUyfPp3Vq1ej0Wjo3bs3vXv3Zv78+UycOJHXXnvNaAynEEIICfKEEOKuTE1NS5QGeS0tBnq9nvr167Nw4cIi8xo+VF++fJnOnTtTp04dFi5ciK+vLxYWFuzYsYNFixYVag0qybFL637PsbTyt1jej4EDB7Jy5Up++ukn+vbty6ZNm6hTpw4NGzZU85T0uhsUd573On/D6zJ//nwaNWpUZN78gU9JynzYFixYgE6nY8CAAWpAfuPGDSAv8IyIiMDb27tQYG7g7e0NgIeHR6Ft7u7uJfriwdfXl/Pnz981zwcffECTJk3o27cvv/76K7du3WLevHlYWloyY8YMnnrqKdasWVPoiwIhhHicSZAnhBDlICgoiFOnTtG5c+e7Tmbxww8/kJWVxffff2/U0lOwW2FlVnDyDEVRuHTpEg0aNLjnvn5+fpw+fRq9Xm/0IT0sLEzdbtCuXTu8vLzYuHEjbdq0Ye/evbz33ntG5ZX0uj+ooKAgAOzt7enSpUuZlVuedS7o2rVrJCQkULdu3ULb5syZw5w5czhx4kSxQWzTpk0BiIyMLLTt5s2b1KlT5551CA8PL7bVFeDUqVOsXr2aY8eOqeU6OTmpy4F4e3uj0+mIiYkpMtgUQojHlXztJYQQ5aB///5ERkby+eefF9qWkZFBWloa8E/rTv7WnKSkJNasWfNwKloGvvjiC1JSUtTnW7Zs4datW/To0eOe+/bs2ZPbt28bjbXLyclhyZIl2Nra0r59ezXdxMSEfv368cMPP7B+/XpycnIKzZhZ0uv+oJo2bUpQUBALFixQx6blFxMTc1/l2tjYkJiYWOS2ki6hUFLjx4/nu+++M/pZuXIlAMOHD+e7775T177Lzs4mLCyMW7duqfvXrl2bhg0bsm3bNmJjY9X0n3/+mevXr9O1a1c1rajrsWPHDo4dO8ZTTz1VbB0nTJjAyy+/TL169YC8VsOYmBji4+MBOHfuHGZmZoW6FgshxONOWvKEEKIc/Otf/2LTpk38+9//Zt++fbRu3Zrc3FzCwsLYtGkTu3btolmzZnTr1g0LCwv69OnDmDFjSE1N5fPPP8fd3d3oA3Vl5uzsTJs2bRgxYgRRUVEsXryYGjVqMGrUqHvuO3r0aFauXMnw4cM5duwY/v7+bNmyhUOHDrF48WKjyU8gb7mHJUuWMG3aNOrXr6+O3TMo6XV/UCYmJvz3v/+lR48e1K1blxEjRuDj40NkZCT79u3D3t6eH374odTlNm3alD179rBw4UK8vb0JCAhQJ30JDg6mffv2JZp8ZenSpSQmJnLz5k0gr8XY0BXztddew8HBgSZNmtCkSROj/QzdNuvWrUvfvn3V9MjISIKDgxk2bBhr165V0xctWkTXrl1p06YNY8aMISkpiYULF1KrVi1eeeUVNV+rVq1o3LgxzZo1w8HBgePHj7N69Wp8fX3V9RIL2rx5M6dPn+abb75R01q2bImHhwcvvPACzz33HAsWLOC5554rtiusEEI8riTIE0KIcmBiYsLWrVtZtGgRX3zxBd999x3W1tYEBgYyYcIEatWqBeS1hmzZsoX333+ft956C09PT1555RXc3NwKrctWWU2ZMoXTp08zd+5cUlJS6Ny5M8uWLcPa2vqe+1pZWbF//37effdd1q1bR3JyMrVr12bNmjVGC4EbtGrVCl9fX65fv17kunclve5loUOHDvzxxx/MmjWLpUuXkpqaiqenJy1atDBa3640Fi5cyOjRo3n//ffJyMhg2LBhapBXGgsWLFAnrQH49ttv+fbbbwHUhe/LQseOHdm5cycffPABU6ZMwdramr59+zJv3jyjMYkDBgxg+/bt/Pzzz6Snp+Pl5cWoUaOYNm1akd0sMzIyePvtt5kxYwYuLi5qularZevWrYwZM4bJkyfToUMHli5dWibnIoQQVYlGqagR30IIIR5p+/fvp2PHjmzevJl+/fpVdHWEEEIIcYeMyRNCCCGEEEKIKkSCPCGEEEIIIYSoQiTIE0IIIYQQQogqRMbkCSGEEEIIIUQVIi15QgghhBBCCFGFSJAnhBBCCCGEEFWIBHlCCCGEEEIIUYVIkCeEEEIIIYQQVYgEeUIIIYQQQghRhUiQJ4QQQgghhBBViAR5QgghhBBCCFGFSJAnhBBCCCGEEFWIBHlCCCGEEEIIUYVIkCeEEEIIIYQQVYgEeUIIIYQQQghRhUiQJ4QQQgghhBBViAR5QgghhBBCCFGFSJAnhBBCCCGEEFWIBHlCCCGEEEIIUYVIkCeEEEIIIYQQVYgEeUIIIYQQQghRhUiQJ4QQQgghhBBViAR5QgghhBBCCFGFSJAnhBBCCCGEEFWIBHlCVKDp06ej0WiM0vz9/Rk+fPg99127di0ajYaIiIgyq09ERAQajYa1a9eWWZlCCCHuX8H3hP3796PRaNi/f3+ZHUOj0TB9+vQyK09UTkV95hBVlwR54qG5fPkyY8aMITAwEEtLS+zt7WndujWffvopGRkZFV29x8qGDRtYvHhxRVfDyPDhw9FoNOqPVqulVq1aTJ06lczMTKO8kyZNonXr1rRu3ZonnniC33//vYJqLYSoygxfphl+LC0tqVWrFuPGjSMqKqqiq1cqO3bskECuCHPmzGHr1q0VXQ0hypxZRVdAPB62b9/OCy+8gFarZejQodSrVw+dTsdvv/3G22+/TWhoKKtWraroalYK58+fx8SkfL9/2bBhA2fOnOH11183Svfz8yMjIwNzc/NyPX5xtFot//3vfwFISkpi27ZtzJo1i8uXL/PVV1+p+UaOHMnHH38MwOLFi3nxxRe5cuVKhdRZCFH1zZw5k4CAADIzM/ntt99Yvnw5O3bs4MyZM1hbWz/UurRr146MjAwsLCxKtd+OHTv47LPPigz0MjIyMDN7PD8Szpkzh379+tG3b9+KrooQZerx/I0WD9WVK1cYOHAgfn5+7N27Fy8vL3Xb2LFjuXTpEtu3by92f71ej06nw9LS8mFUt8JptdoKO7bhm+qKYmZmxpAhQ9Tnr776Kq1ateJ///sfCxcuxMPDA4BatWqpeRRFKfeguCzk5OSg1+tL/cFMCFHxevToQbNmzQB4+eWXcXFxYeHChWzbto1BgwYVuU9aWho2NjZlXhcTE5My/zv9uLy/Pqjyek2FKA+V/5OReOTNmzeP1NRU/u///s8owDOoUaMGEyZMUJ9rNBrGjRvHV199Rd26ddFqtezcuROAEydO0KNHD+zt7bG1taVz584cPnzYqLzs7GxmzJhBzZo1sbS0xMXFhTZt2rB79241z+3btxkxYgTVqlVDq9Xi5eXFM888c9fxbQsWLECj0XD16tVC2yZPnoyFhQUJCQkA/Prrr7zwwgtUr14drVaLr68vb7zxRom6pRY1Ji80NJROnTphZWVFtWrV+PDDD9Hr9YX23bZtG7169cLb2xutVktQUBCzZs0iNzdXzdOhQwe2b9/O1atX1S5I/v7+QPFj8vbu3Uvbtm2xsbHB0dGRZ555hnPnzhnlMfT1v3TpEsOHD8fR0REHBwdGjBhBenr6Pc+7KBqNhjZt2qAoCuHh4YW2nzlzhunTp7Nw4cJ7lvX111/TtGlT7OzssLe3p379+nz66adGeRITE3njjTfw9/dHq9VSrVo1hg4dSmxsrJonOjqakSNH4uHhgaWlJQ0bNmTdunVG5Riu44IFC1i8eDFBQUFotVrOnj0LQFhYGP369cPZ2RlLS0uaNWvG999/b1RGSe5jIUTF6NSpE4Dag2D48OHY2tpy+fJlevbsiZ2dHS+++CKQ90Xl4sWLqVu3LpaWlnh4eDBmzBj1/cJAURQ+/PBDqlWrhrW1NR07diQ0NLTQsYsbk3fkyBF69uyJk5MTNjY2NGjQQP0bN3z4cD777DMAo+6nBkWNySvJ+62hO+uhQ4eYOHEibm5u2NjY8OyzzxITE2OU96+//qJ79+64urpiZWVFQEAAL7300l2vc+/evQkMDCxyW8uWLdXAG2D37t20adMGR0dHbG1tqV27NlOmTLlr+RqNhrS0NNatW6deE8P7r+E97ezZswwePBgnJyfatGkD5L2PdujQoVB5w4cPV99PDUr6+hf0MD5z3G0cflH3RGRkJC+99BIeHh5otVrq1q3L6tWrC+27ZMkS6tati7W1NU5OTjRr1owNGzbctS6i7ElLnih3P/zwA4GBgbRq1arE++zdu5dNmzYxbtw4XF1d8ff3JzQ0lLZt22Jvb88777yDubk5K1eupEOHDhw4cIAWLVoAeX+Y586dy8svv0zz5s1JTk7mr7/+4vjx43Tt2hWA559/ntDQUF577TX8/f2Jjo5m9+7dXLt2rdAfaIP+/fvzzjvvsGnTJt5++22jbZs2baJbt244OTkBsHnzZtLT03nllVdwcXHhzz//ZMmSJdy4cYPNmzeX6vrdvn2bjh07kpOTw7vvvouNjQ2rVq3CysqqUN61a9dia2vLxIkTsbW1Ze/evUydOpXk5GTmz58PwHvvvUdSUhI3btxg0aJFANja2hZ7/D179tCjRw8CAwOZPn06GRkZLFmyhNatW3P8+PFC16t///4EBAQwd+5cjh8/zn//+1/c3d3V7pWlZQi8DdfWIDw8nB49ejBt2jSeeeaZu5axe/duBg0aROfOndV6nDt3jkOHDqlfMKSmptK2bVvOnTvHSy+9RJMmTYiNjeX777/nxo0buLq6kpGRQYcOHbh06RLjxo0jICCAzZs3M3z4cBITE42+rABYs2YNmZmZjB49Gq1Wi7OzM6GhobRu3RofHx/19dy0aRN9+/blm2++4dlnnwVKdh8LISrG5cuXAXBxcVHTcnJy6N69O23atGHBggVqN84xY8awdu1aRowYwfjx47ly5QpLly7lxIkTHDp0SO0eP3XqVD788EN69uxJz549OX78ON26dUOn092zPrt376Z37954eXkxYcIEPD09OXfuHD/++CMTJkxgzJgx3Lx5k927d7N+/fp7llfS91uD1157DScnJ6ZNm0ZERASLFy9m3LhxbNy4Ecj7cqxbt264ubnx7rvv4ujoSEREBN9+++1d6zFgwACGDh3K0aNHeeKJJ9T0q1evcvjwYfV9LTQ0lN69e9OgQQNmzpyJVqvl0qVLHDp06K7lr1+/Xv0bO3r0aACCgoKM8rzwwgvUrFmTOXPmoCjKPa9dQSV9/QuqqM8cxYmKiuLJJ59Uv4h3c3Pjp59+YuTIkSQnJ6vDPz7//HPGjx9Pv379mDBhApmZmZw+fZojR44wePDgMqmLKCFFiHKUlJSkAMozzzxT4n0AxcTERAkNDTVK79u3r2JhYaFcvnxZTbt586ZiZ2entGvXTk1r2LCh0qtXr2LLT0hIUABl/vz5JT+RO1q2bKk0bdrUKO3PP/9UAOWLL75Q09LT0wvtO3fuXEWj0ShXr15V06ZNm6YU/DX08/NThg0bpj5//fXXFUA5cuSImhYdHa04ODgogHLlypW7HnfMmDGKtbW1kpmZqab16tVL8fPzK5T3ypUrCqCsWbNGTWvUqJHi7u6uxMXFqWmnTp1STExMlKFDhxY6l5deesmozGeffVZxcXEpdKyChg0bptjY2CgxMTFKTEyMcunSJWXBggWKRqNR6tWrp+j1ejXv5cuXFT8/P+Wzzz67Z7mKoigTJkxQ7O3tlZycnGLzTJ06VQGUb7/9ttA2w7EXL16sAMqXX36pbtPpdErLli0VW1tbJTk5WVGUf66jvb29Eh0dbVRW586dlfr16xu9Hnq9XmnVqpVSs2ZNNe1e97EQovytWbNGAZQ9e/YoMTExyvXr15Wvv/5acXFxUaysrJQbN24oipL39wtQ3n33XaP9f/31VwVQvvrqK6P0nTt3GqVHR0crFhYWSq9evYz+1k2ZMkUBjN4T9u3bpwDKvn37FEVRlJycHCUgIEDx8/NTEhISjI6Tv6yxY8cWer8xAJRp06apz0v6fmu4Pl26dDE61htvvKGYmpoqiYmJiqIoynfffacAytGjR4s8fnGSkpIUrVarvPnmm0bp8+bNM3o/XbRokQIoMTExpSpfURTFxsbG6PoaGN7TBg0aVGhb+/btlfbt2xdKHzZsmNF7a0lf/+KU92eOot7zDQreEyNHjlS8vLyU2NhYo3wDBw5UHBwc1Do888wzSt26de96XuLhkO6aolwlJycDYGdnV6r92rdvT0hIiPo8NzeXn3/+mb59+xp13fDy8mLw4MH89ttv6rEcHR0JDQ3l4sWLRZZtZWWFhYUF+/fvv2d3iYIGDBjAsWPH1G9xATZu3IhWqzVqTcrfypaWlkZsbCytWrVCURROnDhRqmPu2LGDJ598kubNm6tpbm5ualeg/PIfNyUlhdjYWNq2bUt6ejphYWGlOi7ArVu3OHnyJMOHD8fZ2VlNb9CgAV27dmXHjh2F9vn3v/9t9Lxt27bExcWpr8/dpKWl4ebmhpubGzVq1OCtt96idevWbNu2zahr0b/+9S+Sk5PZtGmT2m3mbt1SHB0dSUtLu2tXx2+++YaGDRuqLWn5GY69Y8cOPD09jcbgmJubM378eFJTUzlw4IDRfs8//zxubm7q8/j4ePbu3Uv//v3V1yc2Npa4uDi6d+/OxYsXiYyMVOt8t/tYCPHwdOnSBTc3N3x9fRk4cCC2trZ89913+Pj4GOV75ZVXjJ5v3rwZBwcHunbtqv6+x8bG0rRpU2xtbdm3bx+Q12NCp9Px2muvGf2tKzg5VlFOnDjBlStXeP3113F0dDTadj/T5Zfm/dZg9OjRRsdq27Ytubm5aldDQ71+/PFHsrOzS1wXe3t7evTowaZNm4xa0TZu3MiTTz5J9erVjcrftm1bkUMZHkTB97TSKOnrX5yK+MxRFEVR+Oabb+jTpw+KohidS/fu3UlKSuL48eNA3mtx48YNjh49+sDHFQ9GgjxRruzt7YG8gKM0AgICjJ7HxMSQnp5O7dq1C+UNDg5Gr9dz/fp1IG8WtMTERGrVqkX9+vV5++23OX36tJpfq9Xy8ccf89NPP+Hh4UG7du2YN28et2/fvme9XnjhBUxMTNQuKIqisHnzZnXcgsG1a9fUwMjW1hY3Nzfat28P5M0aWRpXr16lZs2ahdKLuhahoaE8++yzODg4YG9vj5ubmzqRSWmPazh2cccKDg4mNjaWtLQ0o3TDm66BoTtJSQJqS0tLdu/eze7du1mzZg3BwcFER0cX6pp66NAh4uPj2b9/v/pTVPdVg1dffZVatWrRo0cPqlWrxksvvaSO8zS4fPky9erVu2v9DK9FwYlegoOD1e35FbyPL126hKIofPDBB2owa/iZNm0akNetCe59HwshHp7PPvuM3bt3s2/fPs6ePUt4eDjdu3c3ymNmZka1atWM0i5evEhSUhLu7u6FfudTU1PV33fD346Cf+vd3NwKdVUvyBAA3OvvV0mV5v3W4F5/99u3b8/zzz/PjBkzcHV15ZlnnmHNmjVkZWXdsz4DBgzg+vXr/PHHH0De+R47dowBAwYY5WndujUvv/wyHh4eDBw4kE2bNpVJwFfw73hplPT1L05FfOYoSkxMDImJiaxatarQeYwYMQL4571r0qRJ2Nra0rx5c2rWrMnYsWPv2W1WlA8ZkyfKlb29Pd7e3pw5c6ZU+93tA/u9tGvXjsuXL7Nt2zZ+/vln/vvf/7Jo0SJWrFjByy+/DOR9O9qnTx+2bt3Krl27+OCDD5g7dy579+6lcePGxZbt7e1N27Zt2bRpE1OmTOHw4cNcu3bNaLxZbm4uXbt2JT4+nkmTJlGnTh1sbGyIjIxk+PDhZf4to0FiYiLt27fH3t6emTNnEhQUhKWlJcePH2fSpEnldtyCTE1Ni0xXSjCWwdTUlC5duqjPu3fvTp06dRgzZkyhiUlKw93dnZMnT7Jr1y5++uknfvrpJ9asWcPQoUMLTZpSlgrex4bX4K233ir0AdGgRo0aQMnuYyHEw9G8eXOjST6KotVqC30BpNfrcXd3N1oCJr/8Lf2Psnv93ddoNGzZsoXDhw/zww8/sGvXLl566SU++eQTDh8+fNdx4X369MHa2ppNmzbRqlUrNm3ahImJCS+88IKax8rKioMHD7Jv3z62b9/Ozp072bhxI506deLnn38utn4lUdTnEY1GU+R7Wv5JzuDBX//y/sxRXEtvUecBMGTIEIYNG1bkPg0aNADyvgg4f/48P/74Izt37uSbb75h2bJlTJ06lRkzZtz1fEXZkiBPlLvevXuzatUq/vjjD1q2bHlfZbi5uWFtbc358+cLbQsLC8PExARfX181zdnZmREjRjBixAhSU1Np164d06dPN/pwHBQUxJtvvsmbb77JxYsXadSoEZ988glffvnlXesyYMAAXn31Vc6fP8/GjRuxtramT58+6va///6bCxcusG7dOoYOHaqm3++siH5+fkV22St4Lfbv309cXBzffvst7dq1U9OLWj+upF14/Pz8ijwW5F13V1fXcp1O2svLizfeeIMZM2Zw+PBhnnzyyfsuy8LCgj59+tCnTx/0ej2vvvoqK1eu5IMPPqBGjRoEBQXd88sIPz8/Tp8+jV6vN/owZ+gKa7hexTF0fTI3NzcKZotTkvtYCFF5BQUFsWfPHlq3bn3XLy8NfzsuXrxo1EUyJibmnr0gDBOFnDlz5q5/V0r6d7+077el8eSTT/Lkk08ye/ZsNmzYwIsvvsjXX399179pNjY29O7dm82bN7Nw4UI2btxI27Zt8fb2NspnYmJC586d6dy5MwsXLmTOnDm899577Nu3r0yuS35OTk5FzvhcsDdHSV//uynPzxyGFtfExMS7noebmxt2dnbk5uaW6L3LxsaGAQMGMGDAAHQ6Hc899xyzZ89m8uTJslzHQyTdNUW5e+edd7CxseHll18mKiqq0PbLly8Xmsq+IFNTU7p168a2bduMljmIiopiw4YNtGnTRu26EBcXZ7Svra0tNWrUULuFpKenk5mZaZQnKCgIOzu7EnUdef755zE1NeV///sfmzdvpnfv3kaBjuEbw/zf8imKcs9zLE7Pnj05fPgwf/75p5oWExNT6JvBoo6r0+lYtmxZoTJtbGxK1IXDy8uLRo0asW7dOqM3gTNnzvDzzz/Ts2fP0p5Oqb322mtYW1vz0Ucf3XcZBe8JExMT9VtHw2v+/PPPc+rUKb777rtC+xuuac+ePbl9+7badQbyZtRbsmQJtra2aveY4ri7u9OhQwdWrlzJrVu3Cm3PP+X4ve5jIUTl179/f3Jzc5k1a1ahbTk5Oerf1S5dumBubs6SJUuM/oYvXrz4nsdo0qQJAQEBLF68uNCH9fxlGd6nCuYpqDTvtyWVkJBQqOWrUaNGACXusnnz5k3++9//curUKaOumpA33rmgkpZvY2Nzz2tSUFBQEGFhYUZ/s0+dOlWoW2JJX/+7Kc/PHPb29ri6unLw4EGj9IKfG0xNTXn++ef55ptvivwy9G7vXRYWFoSEhKAoSqnGY4oHJy15otwFBQWxYcMGBgwYQHBwMEOHDqVevXrodDp+//13dQr6e/nwww/VdXBeffVVzMzMWLlyJVlZWcybN0/NFxISQocOHWjatCnOzs789ddfbNmyhXHjxgFw4cIFOnfuTP/+/QkJCcHMzIzvvvuOqKgoBg4ceM96uLu707FjRxYuXEhKSkqhN5s6deoQFBTEW2+9RWRkJPb29nzzzTelnuTF4J133mH9+vU89dRTTJgwQV1CwdCqZNCqVSucnJwYNmwY48ePR6PRsH79+iK7lDRt2pSNGzcyceJEnnjiCWxtbY2+Gcxv/vz59OjRg5YtWzJy5Eh1CQUHB4dCa+iUBxcXF0aMGMGyZcs4d+6cOv6tNF5++WXi4+Pp1KkT1apV4+rVqyxZsoRGjRqp5b399tts2bKFF154gZdeeommTZsSHx/P999/z4oVK2jYsCGjR49m5cqVDB8+nGPHjuHv78+WLVs4dOgQixcvLtEEQ5999hlt2rShfv36jBo1isDAQKKiovjjjz+4ceMGp06dAu59HwshKr/27dszZswY5s6dy8mTJ+nWrRvm5uZcvHiRzZs38+mnn9KvXz/c3Nx46623mDt3Lr1796Znz56cOHGCn376CVdX17sew8TEhOXLl9OnTx8aNWrEiBEj8PLyIiwsjNDQUHbt2gXk/d0HGD9+PN27d8fU1LTY97ySvt+W1Lp161i2bBnPPvssQUFBpKSk8Pnnn2Nvb1+iLwsNaw++9dZbasCR38yZMzl48CC9evXCz8+P6Oholi1bRrVq1dS17YrTtGlT9uzZw8KFC/H29iYgIKDQEhEFvfTSSyxcuJDu3bszcuRIoqOjWbFiBXXr1jWalKakr//dlPdnjpdffpmPPvqIl19+mWbNmnHw4EEuXLhQKN9HH33Evn37aNGiBaNGjSIkJIT4+HiOHz/Onj171EC7W7dueHp60rp1azw8PDh37hxLly6lV69epZ6ETzyghzmVp3i8XbhwQRk1apTi7++vWFhYKHZ2dkrr1q2VJUuWGE0nDyhjx44tsozjx48r3bt3V2xtbRVra2ulY8eOyu+//26U58MPP1SaN2+uODo6KlZWVkqdOnWU2bNnKzqdTlEURYmNjVXGjh2r1KlTR7GxsVEcHByUFi1aKJs2bSrxuXz++ecKoNjZ2SkZGRmFtp89e1bp0qWLYmtrq7i6uiqjRo1STp06VWiq4pIsoaAoinL69Gmlffv2iqWlpeLj46PMmjVL+b//+79CSygcOnRIefLJJxUrKyvF29tbeeedd5Rdu3YZTbetKIqSmpqqDB48WHF0dFQAdcrn4qZT3rNnj9K6dWvFyspKsbe3V/r06aOcPXvWKI/hXApOYW2YYjt/PYtiWEKhKJcvX1ZMTU2LnOa6JLZs2aJ069ZNcXd3VywsLJTq1asrY8aMUW7dumWULy4uThk3bpzi4+OjWFhYKNWqVVOGDRtmNGV0VFSUMmLECMXV1VWxsLBQ6tevX+h6Ga5jcct0XL58WRk6dKji6empmJubKz4+Pkrv3r2VLVu2qHnudR8LIcqf4e/Xvab+v9vfL0VRlFWrVilNmzZVrKysFDs7O6V+/frKO++8o9y8eVPNk5ubq8yYMUPx8vJSrKyslA4dOihnzpwp9J5QcAkFg99++03p2rWrYmdnp9jY2CgNGjRQlixZom7PyclRXnvtNcXNzU3RaDRG7z0UmC5fUUr2flvc9SlYx+PHjyuDBg1Sqlevrmi1WsXd3V3p3bu38tdff93tshp58cUX1eUaCvrll1+UZ555RvH29lYsLCwUb29vZdCgQcqFCxfuWW5YWJjSrl07xcrKymi5iuLe0wy+/PJLJTAwULGwsFAaNWqk7Nq1q9ASCgYlef3vpjw/c6SnpysjR45UHBwcFDs7O6V///5KdHR0kfdEVFSUMnbsWMXX11cxNzdXPD09lc6dOyurVq1S86xcuVJp166d4uLiomi1WiUoKEh5++23laSkpBKdqyg7GkW5j5UdhRBCCCGEEEJUSjImTwghhBBCCCGqEAnyhBBCCCGEEKIKkSBPCCGEEEIIIaoQCfKEEEIIIYQQogqRIE8IIYQQQgghqhAJ8oQQQgghhBCiCpHF0Iug1+u5efMmdnZ2aDSaiq6OEEKIMqYoCikpKXh7e2NiUnW+75T3LyGEqNpK+v4lQV4Rbt68ia+vb0VXQwghRDm7fv061apVq+hqlBl5/xJCiMfDvd6/JMgrgp2dHZB38ezt7Su4NkIIIcpacnIyvr6+6t/7qkLev4QQomor6fuXBHlFMHRxsbe3lzdJIYSowqpal0Z5/xJCiMfDvd6/qs5ABCGEEEIIIYQQEuQJIYQQQgghRFUiQZ4QQgghhBBCVCEyJk8IIYR4jOj1ejIzMyu6GuIhs7S0rFLLhQgh7k6CPCGEEOIxkZWVxdmzZ9Hr9RVdFfGQmZiYEBISglarreiqCCEeAgnyhBBCiMeAoihERERgZmZGQECAtOo8RvR6PVeuXCEiIoJatWpVuVllhRCFSZAnhBBCPAays7NJTU0lICAAW1vbiq6OeMh8fHy4cuUK2dnZWFhYVHR1hBDlTL7GE0IIIR4DOTk5ANJd7zFleN0N94EQomqTIE8IIYR4jEhXvceTvO5CPF4kyBNCCCGEEEKIKkTG5AkhRDlLT08nLCysyG0ZGRlERETg7++PlZVVkXnq1KmDtbV1eVZRiBLJzM5lx9+3+Dk0isR0HY7WFnSr60HP+l5YmptWdPWKFBERQUBAACdOnKBRo0Yl2mft2rW8/vrrJCYmVmg9hBDifkmQJ4QQ5SwsLIymTZve9/7Hjh2jSZMmZVgjIUpv99ko3tx8kuSMHEw0oFfARAM7Q28z/YdQFr7QiC4hHuV2/OvXrzNt2jR27txJbGwsXl5e9O3bl6lTp+Li4lLsfr6+vty6dQtXV9cSH2vAgAH07NmzLKothBAVQoI8IYQoZ3Xq1OHYsWNFbjt37hxDhgzhyy+/JDg4uNj9hahIu89GMXr9X6DkPdcXeEzJyGHU+r9Y9a9mdC2HQC88PJyWLVtSq1Yt/ve//xEQEEBoaChvv/02P/30E4cPH8bZ2bnQfjqdDgsLCzw9PUt1PCsrq2Jb1oUQ4lEgY/KEEKKcWVtb06RJkyJ/DIFdcHBwsXmkq6aoSJnZuby5+SQoaoxXiHLnn7c2nyQzO7fM6zB27FgsLCz4+eefad++PdWrV6dHjx7s2bOHyMhI3nvvPQD8/f2ZNWsWQ4cOxd7entGjRxMREYFGo+HkyZNqed9//z01a9bE0tKSjh07sm7dOjQajdo9c+3atTg6Oqr5p0+fTqNGjVi/fj3+/v44ODgwcOBAUlJS1Dw7d+6kTZs2ODo64uLiQu/evbl8+XKZXwshhCgJCfKEEEIIUawdf98iOSOn2ADPQAGSMnL46cytMj1+fHw8u3bt4tVXXy3Uuubp6cmLL77Ixo0bUZS8Gi5YsICGDRty4sQJPvjgg0LlXblyhX79+tG3b19OnTrFmDFj1CDxbi5fvszWrVv58ccf+fHHHzlw4AAfffSRuj0tLY2JEyfy119/8csvv2BiYsKzzz6LXq9/wCsghBClJ901hRBCCFGsn0Oj1DF492KigV1noni2cbUyO/7FixdRFKXY7szBwcEkJCQQExMDQKdOnXjzzTfV7REREUb5V65cSe3atZk/fz4AtWvX5syZM8yePfuu9dDr9axduxY7OzsA/vWvf/HLL7+o+z3//PNG+VevXo2bmxtnz56lXr16JT9hIYQoA9KSJ4QQQohiJabrShTgQV4gmJihK5d6GFrq7qVZs2Z33X7+/HmeeOIJo7TmzZvfs1x/f381wAPw8vIiOjpafX7x4kUGDRpEYGAg9vb2+Pv7A3Dt2rUS1VsIIcqSBHlCCCGEKJajtQUmJVxH20QDjlYWZXr8GjVqoNFoOHfuXJHbz507h5OTE25ubgDY2NiU6fENzM3NjZ5rNBqjrph9+vQhPj6ezz//nCNHjnDkyBEgb/IXIYR42CTIE0IIIUSxutX1KFVLXvd6ZTu7pouLC127dmXZsmVkZGQYbbt9+zZfffUVAwYMQKMpWSRau3Zt/vrrL6O0o0ePPlAd4+LiOH/+PO+//z6dO3dWu5AKIURFkSBPCCGEEMXqWd8Leysz7hVCaQAHKzN61PMq8zosXbqUrKwsunfvzsGDB7l+/To7d+6ka9eu+Pj43HM8XX5jxowhLCyMSZMmceHCBTZt2sTatWvzzqGEgWJBTk5OuLi4sGrVKi5dusTevXuZOHHifZUlhBBlQYI8IYQQQhTL0tyUhS80Ag3FBnqaO/988kIjLM1Ny7wONWvW5K+//iIwMJD+/fsTFBTE6NGj6dixI3/88UeRa+QVJyAggC1btvDtt9/SoEEDli9frs6uqdVq76t+JiYmfP311xw7dox69erxxhtvqBO7CCFERdAoJR3J/BhJTk7GwcGBpKQk7O3tK7o6Qogq7Pjx4zRt2pRjx47RpEmTiq7OY6Oq/p2/23mlp6dz7tw5goOD72vtxd1no3hr80mSMnLU2TYNjw5WZnzyQiO6lMNC6A/D7NmzWbFiBdevX6/oqpSbB339hRCVQ0nfv2QJBSGEEELcU9cQD45M6cJPZ26x60wUiRk6HK0s6F7Pgx71vMqlBa+8LFu2jCeeeAIXFxcOHTrE/PnzGTduXEVXSwghykyFd9f87LPP8Pf3x9LSkhYtWvDnn3/eNX9iYiJjx47Fy8sLrVZLrVq12LFjh7p9+vTpaDQao586deqU92kIIYQQVZ6luSnPNq7Gin815evRLVnxr6Y827jaIxXgQd5yB8888wwhISHMmjWLN998k+nTp1d0tYQQosxUaEvexo0bmThxIitWrKBFixYsXryY7t27c/78edzd3Qvl1+l0dO3aFXd3d7Zs2YKPjw9Xr17F0dHRKF/dunXZs2eP+tzMTBoshRBCCJFn0aJFLFq0qKKrIYQQ5aZCo5+FCxcyatQoRowYAcCKFSvYvn07q1ev5t133y2Uf/Xq1cTHx/P777+r69UYFhvNz8zMDE9Pz3KtuxBCCCGEEEJURhXWXVOn03Hs2DG6dOnyT2VMTOjSpQt//PFHkft8//33tGzZkrFjx+Lh4UG9evWYM2cOubm5RvkuXryIt7c3gYGBvPjii1y7dq1cz0UIIYQQQgghKosKa8mLjY0lNzcXDw/jmbg8PDwICwsrcp/w8HD27t3Liy++yI4dO7h06RKvvvoq2dnZTJs2DYAWLVqwdu1aateuza1bt5gxYwZt27blzJkz2NnZFVluVlYWWVlZ6vPk5OQyOkshhBBCCCGEeLgeqcFqer0ed3d3Vq1ahampKU2bNiUyMpL58+erQV6PHj3U/A0aNKBFixb4+fmxadMmRo4cWWS5c+fOZcaMGQ/lHIQQQgghhBCiPFVYd01XV1dMTU2JiooySo+Kiip2PJ2Xlxe1atXC1PSfWbyCg4O5ffs2Op2uyH0cHR2pVasWly5dKrYukydPJikpSf2pyuvkCCGEEEIIIaq2CgvyLCwsaNq0Kb/88ouaptfr+eWXX2jZsmWR+7Ru3ZpLly6h1+vVtAsXLuDl5YWFhUWR+6SmpnL58mW8vLyKrYtWq8Xe3t7oRwghhBBCCCEeRRW6Tt7EiRP5/PPPWbduHefOneOVV14hLS1NnW1z6NChTJ48Wc3/yiuvEB8fz4QJE7hw4QLbt29nzpw5jB07Vs3z1ltvceDAASIiIvj999959tlnMTU1ZdCgQQ/9/IQQQogqJTsTTn0NG4fAml55j6e+zkt/hK1du7bQckyPmg4dOvD6669XdDWEEJVEhY7JGzBgADExMUydOpXbt2/TqFEjdu7cqU7Gcu3aNUxM/olDfX192bVrF2+88QYNGjTAx8eHCRMmMGnSJDXPjRs3GDRoEHFxcbi5udGmTRsOHz6Mm5vbQz8/IYQQosoI2wFbX4HMRNCYgKLPezz3A/w0CZ5dAbV73LOY0ho+fDjr1q0rlN69e3d27txZJscYMGAAPXv2LJOyhBCiMqjwiVfGjRvHuHHjity2f//+QmktW7bk8OHDxZb39ddfl1XVhBBCiIcmNzeX6dOn8+WXX3L79m28vb0ZPnw477//PhqNpmIrF7YDvh78z3NFb/yYmQT/GwQDN0Cdsg+WnnrqKdasWWOUptVqy6x8KysrrKysyqy8+6XT6YodfiKEEKVRod01hRBCCJHn448/Zvny5SxdupRz587x8ccfM2/ePJYsWVKxFcvOzGvBA0ApJtOd9K2vlEvXTa1Wi6enp9GPk5MTABqNhv/+9788++yzWFtbU7NmTb7//nuj/b///ntq1qyJpaUlHTt2ZN26dWg0GhITE4HC3TWnT59Oo0aNWL9+Pf7+/jg4ODBw4EBSUlLUPHq9nrlz5xIQEICVlRUNGzZky5YtRsc9c+YMPXr0wNbWFg8PD/71r38RGxurbu/QoQPjxo3j9ddfx9XVle7du5dov7S0NIYOHYqtrS1eXl588sknZXKdhRBVhwR5QgghRCXw+++/88wzz9CrVy/8/f3p168f3bp1488//6zYip3dmtdFs9gAz0DJy3d2W7lXqaAZM2bQv39/Tp8+Tc+ePXnxxReJj48H4MqVK/Tr14++ffty6tQpxowZw3vvvXfPMi9fvszWrVv58ccf+fHHHzlw4AAfffSRun3u3Ll88cUXrFixgtDQUN544w2GDBnCgQMHAEhMTKRTp040btyYv/76i507dxIVFUX//v2NjrNu3TosLCw4dOgQK1asKNF+b7/9NgcOHGDbtm38/PPP7N+/n+PHj5fFpRRCVBEV3l1TCCGEENCqVStWrVrFhQsXqFWrFqdOneK3335j4cKFxe6TlZVFVlaW+jw5ObnsKxb24z9j8O5FYwJhP0DDAWVahR9//BFbW1ujtClTpjBlyhQgb9yeYYK1OXPm8J///Ic///yTp556ipUrV1K7dm3mz58PQO3atTlz5gyzZ8++6zH1ej1r167Fzs4OgH/961/88ssvzJ49m6ysLObMmcOePXvUGcEDAwP57bffWLlyJe3bt2fp0qU0btyYOXPmqGWuXr0aX19f9TUGqFmzJvPmzVPzfPjhh3fdz9vbm//7v//jyy+/pHPnzkBeoFitWrXSX1ghRJUlQZ4QQghRCbz77rskJydTp04dTE1Nyc3NZfbs2bz44ovF7jN37lxmzJhRvhVLTyhZgAd5+TISyrwKHTt2ZPny5UZpzs7O6v8bNGig/t/GxgZ7e3uio6MBOH/+PE888YTRvs2bN7/nMf39/dUAD/LW6jWUeenSJdLT0+natavRPjqdjsaNGwNw6tQp9u3bVyg4hbxWQkOQ17RpU6Nt99ovIyMDnU5HixYt1HRnZ2dq1659z3MSQjw+JMgTQgghKoFNmzbx1VdfsWHDBurWrcvJkyd5/fXX8fb2ZtiwYUXuM3nyZCZOnKg+T05OxtfXt2wrZu1UupY8K6eyPT55gVuNGjWK3W5ubm5cDY3GaE3d+3G3MlNTUwHYvn07Pj4+RvkME8KkpqbSp08fPv7440Jl51+718bGxmjbvfa7dOnSfZyNEOJxI0GeEEIIUQm8/fbbvPvuuwwcOBCA+vXrc/XqVebOnVtskKfVast0lski1emdt0xCSSh6qNOnfOtTSrVr12bHjh1GaUePHn2gMkNCQtBqtVy7do327dsXmadJkyZ88803+Pv7Y2ZW8o9b99ovKCgIc3Nzjhw5QvXq1QFISEjgwoULxdZFCPH4kYlXhBBCiEogPT3daG1YAFNT0wdukXpgIX3B0hG41zIOmrx8Ic+UeRWysrK4ffu20U/+2SbvZsyYMYSFhTFp0iQuXLjApk2bWLt2bV6N73NpCjs7O9566y3eeOMN1q1bx+XLlzl+/DhLlixR1/QbO3Ys8fHxDBo0iKNHj3L58mV27drFiBEjyM3NLbbse+1na2vLyJEjefvtt9m7dy9nzpxh+PDhhe4dIcTjTf4iCCGEEJVAnz59mD17Ntu3byciIoLvvvuOhQsX8uyzz1Zsxcwt8xY6B4oP9O6kP7siL38Z27lzJ15eXkY/bdq0KdG+AQEBbNmyhW+//ZYGDRqwfPlydXbNB2kFnTVrFh988AFz584lODiYp556iu3btxMQEACAt7c3hw4dIjc3l27dulG/fn1ef/11HB0d7xqQlWS/+fPn07ZtW/r06UOXLl1o06ZNobF9QojHm0ZRlHvNifzYSU5OxsHBgaSkJOzt7Su6OkKIKuz48eM0bdqUY8eO0aRJk4quzmOjMv6dT0lJ4YMPPuC7774jOjoab29vBg0axNSpU0u8QPbdzis9PZ1z584RHByMtbV16SsYtiNvHbzMxH/G6BkeLR3zArzaPUpfbgWYPXs2K1as4Pr16xVdlYfmgV9/IUSlUNL3LxmTJ4QQQlQCdnZ2LF68mMWLF1d0VYpWpye8eT5vHbywH/Jm0bRyyhuDF/JMubTglZVly5bxxBNP4OLiwqFDh5g/fz7jxo2r6GoJIUS5kSBPCCGEECVjbpm3Bl4Zr4NX3i5evMiHH35IfHw81atX580332Ty5MkVXS0hhCg3EuQJIYQQokpbtGgRixYtquhqCCHEQyMTrwghhBBCCCFEFSJBnhBCCPEYkfnWHk/yugvxeJEgTwghhHgMGBbWzsrKquCaiIpgeN1LszC7EOLRJb/pQgghxGPA3NwcW1tbIiMjsbCwkMWzHyN6vZ7IyEhsbW0xNzev6OoIIR4CCfKEEEKIx4BGo8Hf35+zZ89y/vz5iq6OeMhMTEyoVasWGk1xC9oLIcrdzZNw7QhUbwHejcr1UBLkCSGEEI8JrVZLw4YNycrKkjFajxGNRoNWq5XWWyEq2rF1cOkXiAmTIE8IIYQQZcfExAQrK6uKroYQQjx+EiIgLS7vsZxJkCeEEGXk4sWLpKSklGqfc+fOGT2Whp2dHTVr1iz1fkIIIYSoACbmoLnzWM4kyBNCiDJw8eJFatWqdd/7Dxky5L72u3DhggR6QgghxKMgOwX02XmP5UyCPCGEKAOGFrwvv/yS4ODgEu+XkZFBREQE/v7+pepCd+7cOYYMGVLqlkMhhBBCVJBsHej1eY/lTII8IYQoQ8HBwTRp0qRU+7Ru3bqcaiOEEEKISiM7HZTcvMdyJtMsCSGEEEIIIUR5S4sF9Hcey5cEeUIIIYQQQghR3jKTjR/LkQR5QgghhBBCCFHOsvXZ5N55LG8S5AkhhBBClLGo5EyOhMcRlZxZ0VURQlQSiWbuKHcey5sEeUIIIYQQZSwiNo0zkUlExKZVdFWEEJVEomU1MtCSaFmt3I8ls2sKIYQQQpQxf1cbo0chhHCyNMEsNe+xvEmQJ4QQQghRxjzsLfGwt6zoagghKhElNRozslBSo8v9WNJdUwghhBBCCCHKWVZ2LgoasrJzy/1YEuQJIYQQQgghRDlLsPAiHQsSLLzK/VgS5AkhhBBCCCFEOQvN9uJmrhuh2eUf5MmYPPHQpaenExYWVuS2jIwMIiIi8Pf3x8rKqsg8derUwdraujyrKIQQQgghRJnap6tNqj6Do7raDCznY0mQJx66sLAwmjZtet/7Hzt2jCZNmpRhjYQQQohHRPItiA8H50CwL//WACFE2WmsucCTJmfJ0ZR/Y4UEeeKhq1OnDseOHSty27lz5xgyZAhffvklwcHBxe4vhBBCPJbiw+HWybz/S5AnxCOlsf4M/twiTX+m3I8lQZ546Kytre/ZEhccHCytdUIIIURBzoHGj0KIR4aZqQmaXD1mprJOnhBCCCGEMLD3khY8IR5hykOa91Jm1xRCCCGEEEKIcnbKtD4ReHHKtH65H0ta8oQQQgghhBBlIio5k4jYNPxdbfCwt6zo6lQqN2wbQlxK3mM5kyBPCCGEEEIIUSYiYtM4E5kEIEFeATVzL1BDcwWrXPdyP5YEeUIIIYQQQogy4e9qY/Qo/uFjZ4p9agY+dqblfiwJ8oQQQgghhBBlwsPeUlrwiuEYdxJvbpEVd7LcjyUTrwghhBBCCCFEOfPSXcWaHLx0V8v9WBLkCSGEEEIIIUQ5s9Baotx5LG8S5AkhhBBCCCFEOTNDj+bOY3mr8CDvs88+w9/fH0tLS1q0aMGff/551/yJiYmMHTsWLy8vtFottWrVYseOHQ9UphBCCCGEEEKUJzNTU6PH8lShQd7GjRuZOHEi06ZN4/jx4zRs2JDu3bsTHR1dZH6dTkfXrl2JiIhgy5YtnD9/ns8//xwfH5/7LlMIIYQQQgghypuitQU0dx7LV4UGeQsXLmTUqFGMGDGCkJAQVqxYgbW1NatXry4y/+rVq4mPj2fr1q20bt0af39/2rdvT8OGDe+7TCGEEEIIIYQob6nYkIMJqZT/8hIVFuTpdDqOHTtGly5d/qmMiQldunThjz/+KHKf77//npYtWzJ27Fg8PDyoV68ec+bMITc3977LBMjKyiI5OdnoRwghhBBCCCHKSk5WKhoUcrJSy/1YFRbkxcbGkpubi4eHh1G6h4cHt2/fLnKf8PBwtmzZQm5uLjt27OCDDz7gk08+4cMPP7zvMgHmzp2Lg4OD+uPr6/uAZyeEEEIIIYQQ/8jNzAD0dx7LV4VPvFIaer0ed3d3Vq1aRdOmTRkwYADvvfceK1aseKByJ0+eTFJSkvpz/fr1MqqxEEIIIYQQQoCZPh3T3LzHcj9WuR+hGK6urpiamhIVFWWUHhUVhaenZ5H7eHl5YW5ujmm+GWmCg4O5ffs2Op3uvsoE0Gq1aLXaBzgbIYQQQgghhCieXmOOYpr3WN4qrCXPwsKCpk2b8ssvv6hper2eX375hZYtWxa5T+vWrbl06RJ6/T9rS1y4cAEvLy8sLCzuq0whhBBCCCGEKG+mGuPH8lSh3TUnTpzI559/zrp16zh37hyvvPIKaWlpjBgxAoChQ4cyefJkNf8rr7xCfHw8EyZM4MKFC2zfvp05c+YwduzYEpcphBBCVFaRkZEMGTIEFxcXrKysqF+/Pn/99VdFV0sIIUQZsNGnoLnzWN4qrLsmwIABA4iJiWHq1Kncvn2bRo0asXPnTnXilGvXrmFi8k8c6uvry65du3jjjTdo0KABPj4+TJgwgUmTJpW4TCGEEKIySkhIoHXr1nTs2JGffvoJNzc3Ll68iJOTU0VXTQghRBnQaExAufNYzio0yAMYN24c48aNK3Lb/v37C6W1bNmSw4cP33eZQgghRGX08ccf4+vry5o1a9S0gICACqyREEKIsqRRcowey9MjNbumEEIIUVV9//33NGvWjBdeeAF3d3caN27M559/ftd9ZJ1XIYR4dGjIMnosTxLkCSGEEJVAeHg4y5cvp2bNmuzatYtXXnmF8ePHs27dumL3kXVehXgwUcmZHAmPIyo5s6KrIh4DpihGj+VJgjwhhBCiEtDr9TRp0oQ5c+bQuHFjRo8ezahRo+66Fqys8yrEg4mITeNMZBIRsWkVXRXxGNAXeCxPFT4mTwghhBB5a8GGhIQYpQUHB/PNN98Uu4+s8yrEg/F3tTF6FKI8KQUey5MEeUIIIUQl0Lp1a86fP2+UduHCBfz8/CqoRuKBJN+C+HBwDgR7r4qujSiGh70lHvaWFV0N8ZgwA8gFM9PyP5Z01xRCCCEqgTfeeIPDhw8zZ84cLl26xIYNG1i1apXRWrDiERIfDrdO5j0KIQSQU+CxPElLnhBCCFEJPPHEE3z33XdMnjyZmTNnEhAQwOLFi3nxxRcrumrifjgHGj8KIR570l1TCCGEeAz17t2b3r17V3Q1RFmw95JumuKhiErOJCI2DX9XG+l6WsmZA5jeeSxn0l1TCCGEEEKIR9SjPkOoLGNRPqQlTwghhBBCiEdUpZshtJSTDhmCVOCRbokscYtqLvAQJl6RIE8IIYQQQohHVKWbIdQw6RCUKMirdEHqfapswaoEeUIIIYQQQoiyUcpJhypbkHq/YxxLEqzmkDceL4fyD8IkyBNCCCGEuAuZ2EKIUnjEJx263xa5kgSrZgUey5MEeUIIIYQQd1HZumEJIcpPeXYfzSUv+DI8licJ8oQQQggh7qKqjBkSQtxbeXYfNSnwWJ4kyBNCCCGEuIvKNmZICPFoepiLocs6eUIIIYQQQghRzh5mkCcteUIIUUY8bTVYJV6Am+X//ZlV4gU8bTXlfhwhhBBCPHokyBNCiDIypqkFwQfHwMHyP1bwneMJIURlJ7OTirLyqN9LpgUey5MEeUIIUUZWHtMxYOpaguvUKfdjnQsLY+Ung3m63I8khBAPRmYnFWXlkb+XHmJ/TQnyhBCijNxOVchwrAXejcr9WBm39dxOfRi9+oUQ4sHI7KSirDzy91IuoLnzWM4kyBNCCCGEEOVGZietuh5298lH/l7SFHgsRzK7phBCCPGArl+/zo0bN9Tnf/75J6+//jqrVq2qwFoJIR4HUcmZHAmPIyo586Ef29B9MiI27aEf+1GUbWr8WJ4kyBNCCCEe0ODBg9m3bx8At2/fpmvXrvz555+89957zJw5s4JrJ4Soyioy0PJ3taGej8Oj233yAZU2wDYv8FieJMgTQgghHtCZM2do3rw5AJs2baJevXr8/vvvfPXVV6xdu7ZiKyeEqNICLVN40uw8gZYpD/3YHvaWtAh0KZculBXZQllSlbklU8bkCSGEEA8oOzsbrVYLwJ49e3j66bx5T+vUqcOtW7cqsmpCiCrOTReJG+Ggswf8K7o6ZeZRmEmzMk8EI0GeEEII8YDq1q3LihUr6NWrF7t372bWrFkA3Lx5ExcXlwqunRCiWMm3ID4cnAPB3quia3N/nAONHytYWU3GUpkDKIPKPBGMBHlCCCHEA/r444959tlnmT9/PsOGDaNhw4YAfP/992o3TiFEJRQfDrdO5v3/UQ3y7L0qVd3LqgWuMgdQ903WyRNCCCEeHR06dCA2Npbk5GScnJzU9NGjR2NtbV2BNRNC3FUlawWrCh6FFrj7USYtlI/KOnmZmZlYWlaxCFsIIYS4D6ampkYBHoC/v3/FVEYIUTKVrBWsKqiSLXA8GmME8yv17Jp6vZ5Zs2bh4+ODra0t4eHhAHzwwQf83//9X5lXUAghhKjsoqKi+Ne//oW3tzdmZmaYmpoa/YhH26Mwy58Qj7vy/j0t7XIRFf13o9QteR9++CHr1q1j3rx5jBo1Sk2vV68eixcvZuTIkWVaQSGEEKKyGz58ONeuXeODDz7Ay8sLjUZT0VUSZehR+wZfiIpQVhOu3K/y/j0tbQtlRf/dKHWQ98UXX7Bq1So6d+7Mv//9bzW9YcOGhIWFlWnlhBBCiEfBb7/9xq+//kqjRo0quiqiHFTVMUb3UtEf2sWjpaRBTaH7qoxmOK3o39OC51XR9Sl1kBcZGUmNGjUKpev1erKzs8ukUkIIIcSjxNfXF0V5CNOliQpRVccY3UtFt0SIR0tJg5pC91UZzXBa0b+nkdfCiQkPxTywLh71Qoqsj0LevCsP492i1GPyQkJC+PXXXwulb9myhcaNG5dJpYQQQohHyeLFi3n33XeJiIio6KoIUWZKOwZJPN487C1pEehyz0Cr0H3lHAhejR75GU79TKKoZxKBn0lURVcFuI+WvKlTpzJs2DAiIyPR6/V8++23nD9/ni+++IIff/yxPOoohBBCVGoDBgwgPT2doKAgrK2tMTc3N9oeHx9fQTUT4v5VdMuIqJoK3VeVbYbT++w+6lKtDi7WFncNVjUFHstTqYO8Z555hh9++IGZM2diY2PD1KlTadKkCT/88ANdu3YtjzoKIYQQldrixYsrugpCCCHKQim6jxqPw7t7sBqVnIlLGVbzXkoV5OXk5DBnzhxeeukldu/eXV51EkIIIR4pw4YNq+gqCCGEKAuGlrgSdB8tzbjViNi0yhvkmZmZMW/ePIYOHVpe9RFCCCEeSbm5uWzdupVz584BULduXZ5++mlZJ08IIe5DkbO7ltFMnHdViu6jpZlB09/VBj1gCugfoHolVerump07d+bAgQP4+/uXQ3WEEEKIR8+lS5fo2bMnkZGR1K5dG4C5c+fi6+vL9u3bCQoKquAaigfyMD5YClGFlMXyG/lbyQzPa2SG4ZJ0Ni+hEvwulmTcav5rYZhV82HMrlnqIK9Hjx68++67/P333zRt2hQbG+PI9emnny6zygkhhBCPgvHjxxMUFMThw4dxdnYGIC4ujiFDhjB+/Hi2b99ewTUUD6SMpngX4nFRFstv5G8lM5Rn7uSBi9fdJzcprajkTE5cS0BRoImfU5lPNpT/WjS9k/Yw+neUOsh79dVXAVi4cGGhbRqNhtzc3AevlRBCCPEIOXDggFGAB+Di4sJHH31E69atK7BmokyUYoyOKMJ9toTKYuyVS2m6T+YP0O73dfQgAQ+TcCAQXJ0A8HH1hnIIwn6/FAuAs41Fmd9r+a9Fpe6uqdc/jGoJIYQQjw6tVktKSkqh9NTUVCwsLCqgRqJMVbYp3h8199kSKouxV7z8rVwANxMzgHyvRzGvbf5ujEfC4+7vdcxXtod/6zK/BwzBp43WjFY1XFGUko2tK6381yL7TlqlXEJBCCGEEMZ69+7N6NGj+b//+z+aN28OwJEjR/j3v/8twxiEuM+W0NJMaiHKR/5WrlY1XI0XMYcSvbb3/TqWcwu64UuEej4OPFWvZF8+FGyVrMytzfcV5B04cIAFCxaoM4iFhITw9ttv07Zt2zKtnBBCCPEo+M9//sOwYcNo2bKluhB6Tk4OTz/9NJ9++mkF106ICnafLaGyGHsJlePEQP6uNmorV+PqRYxXK8Fre9+v452yo5IziQiPe+BAqmBA5u9qQ0K6jvg0HVHJmSUqu2Drcmlbm3MBkzuP5vd9JiVT6iDvyy+/ZMSIETz33HOMHz8egEOHDtG5c2fWrl3L4MGDS12Jzz77jPnz53P79m0aNmzIkiVL1G9CC1q7di0jRowwStNqtWRmZqrPhw8fzrp164zydO/enZ07d5a6buL+XLx4sciuS/di+OLA8FgadnZ21KxZs9T7CSHEg3J0dGTbtm1cvHiRsLAwAIKDg6lRo0YF10wIUeWV48RAHvaWJW7lKon7afkqq267J64l8PulWFrVcOWpel542FviZG3BmcikQmPxiqtnwVbJ0rZSmhV4LE+lPsbs2bOZN28eb7zxhpo2fvx4Fi5cyKxZs0od5G3cuJGJEyeyYsUKWrRoweLFi+nevTvnz5/H3d29yH3s7e05f/68+lyjKdyz9amnnmLNmjXqc61WW6p6ift38eJFatWq9UBlDBky5L72u3DhggR6QogKU7NmTfkbJIR4uB6hiYHuJ2AzBFA2WjOOFGjRK03QaBhXmJCmU8spLkgz1DM+TYezjUWx5RtNDkPlGrdb6iAvPDycPn36FEp/+umnmTJlSqkrsHDhQkaNGqW2zq1YsYLt27ezevVq3n333SL30Wg0eHp63rVcrVZ7zzyifBha8L788kuCg4NLtW9GRgYRERH4+/tjZWVV4v3OnTvHkCFD7qv1UAgh7sfEiROZNWsWNjY2TJw48a55i5qRWoiHrTKPHxIPoIwnBirpfVJcvrvtX1zAdrd9DN09i5rApTRBYxM/J5xtLEhI13EmMomEdB1O1kUHcIZ6GvJCXkAXG3qS8DQnIO/LvNjQk/hmXsAeKt3kTKUO8nx9ffnll18KdUHZs2cPvr6+pSpLp9Nx7NgxJk+erKaZmJjQpUsX/vjjj2L3S01Nxc/PD71eT5MmTZgzZw5169Y1yrN//37c3d1xcnKiU6dOfPjhh7i4uJSqfuLBBAcH06RJk1LvJ9ONCyEeBSdOnCA7O1v9vxCVncxWKYpSMMAq6X1SXL7Ia+HEhIdiHlgXj3ohRvvkD9j+CI/j7M1kejbw4vjVBP64HEvLIFd61P8nWMpft6ICxNJ0lzQcOyo5EydrC+LTdMWeZ8G8/q42EB+Gb+YFsKmF6521+8LTnMCmFnULtKJWhi9USh3kvfnmm4wfP56TJ0/SqlUrIG9M3tq1a0s9uDw2Npbc3Fw8PDyM0j08PNQxDQXVrl2b1atX06BBA5KSkliwYAGtWrUiNDSUatWqAXldNZ977jkCAgK4fPkyU6ZMoUePHvzxxx+YmhZefjArK4usrCz1eXJycqnOQwghxONn3759Rf5fiMpKZqu8P5XhA3t5KhislfQ+KZTvzgQwAZm3cTaJwM7EFQgpvGPyLWpkhnFFY82lJDt2nL6FlUXe5/OCI7Dy181wnBsJ6UQm5C3l0CLQ5Z5r9hXH19la7YpZIs6BZKfryNJ75KtPTVxdbQqt3RcRm0Z4+EVsoxLwqNuoQlr5Sh3kvfLKK3h6evLJJ5+wadMmIK/FZuPGjTzzzDNlXsGCWrZsScuWLdXnrVq1Ijg4mJUrVzJr1iwABg4cqG6vX78+DRo0ICgoiP3799O5c+dCZc6dO5cZM2aUe92FEEJUTS+99BKffvopdnZ2RulpaWm89tprrF69uoJqVjVU9Q/ZD4vMVnl/yqIFtDLfwwWDtZLeJ4Xy3ZkAxsnBF6d6rYodIxh3I4yU8L9o7tmQDK07SRk6PB3s6dXA2yjgikrOJD5Nh4+TFf53Ws7ORCbh7WhVeCmHfMcHigyqDK9BQrqOyIQM6vk40CKw+F5+UcmZ7Dh9i6QMXd75BnpxyTJvopZs6zTjALMAf1cbbKMS8lr+4u0rJMgzuZ+dnn32WX777Tfi4uKIi4vjt99+u68Az9XVFVNTU6KioozSo6KiSjyeztzcnMaNG3Pp0qVi8wQGBuLq6lpsnsmTJ5OUlKT+XL9+veQnIYQQ4rG3bt06MjIyCqVnZGTwxRdf3FeZH330ERqNhtdff/0Ba/foM3y4i4hNq+iqiMeQv6tN0UFFKVSmezgqOZMj4XFEJefNTO9hb1lswFIw713TnQPBqxFUewL8Wxcb2FzVe3BG70+iZTV6NvCitqc9Gg2FAuCI2DRuJmbgZG2htjDW83HA19m6yLrEWPjkHb+I4NIQsP0RHoeiUKLXMyI2jaTMbBysLIwC4WL3Tb4FEYcg+RYe9pbUrdsI+4BmFTYhTqlb8o4ePYper6dFixZG6UeOHMHU1JRmzZqVuCwLCwuaNm3KL7/8Qt++fQHQ6/X88ssvjBs3rkRl5Obm8vfff9OzZ89i89y4cYO4uDi8vIq+2bRarcy+KYQQotSSk5NRFAVFUUhJScHS8p8PKLm5uezYsaPYmaLv5ujRo6xcuZIGDRqUZXUfWRXezbAc1yGrKJW5Zak861ZU2fc6Xlm0gJbnPVzaCVBK0zJZ3Fi5ItNLMAFMVHImUYoTZoFt8Lmz7p6zTV7rmCGYM8h/zfKfS7Fr1fk4oHdtmpePzELnHJmYTk6ugq+zNfV8HO5az4LHN5RV6F5IvkXcjTCu6j3wM4nCJems8bWwNx5f+DBnByl1kDd27FjeeeedQkFeZGQkH3/8MUeOHClVeRMnTmTYsGE0a9aM5s2bs3jxYtLS0tTZNocOHYqPjw9z584FYObMmTz55JPUqFGDxMRE5s+fz9WrV3n55ZeBvElZZsyYwfPPP4+npyeXL1/mnXfeoUaNGnTv3r20pyuEEEIUy9HREY1Gg0ajKXLpGI1GU+rhAKmpqbz44ot8/vnnfPjhh2VV1UdahXczLMd1yCpKZZ6Epai6lVXgV1TZD+NalPU9fLegx6C49NIEnIYxcgXHyhWXfi8RsWmcv52Mg5UFjavfvT75r1n+mTULBn8J6Tq8Ha3uei38XW3wcbQmKTObtKwco+Pkv5aGOua/zwytr0Xei/HhpIT/xU29PwTWxcXLotiWu4jYNBzJC75yKf+18kpd/tmzZ4ucMbFx48acPXu21BUYMGAAMTExTJ06ldu3b9OoUSN27typTsZy7do1TEz+6VWakJDAqFGjuH37Nk5OTjRt2pTff/+dkJC8gZ2mpqacPn2adevWkZiYiLe3N926dWPWrFnSWieEEKJM7du3D0VR6NSpE9988w3Ozs7qNgsLC/z8/PD29i5VmWPHjqVXr1506dJFgrzK4hFah6ykSvRBv4JaMIuqW1kFYkWVXeEtxXeUJpAtakKSgvW30ZphotFgozX+uB+TksXZm8nYaM3ueZzG1Z3+mV2yBOn34u9qw9mbySRlZhMRm6YGcgXrEZWcSeS18LzWsWp18Hd1UvcvGPxFJmTg7WjF8asJJGVkqwFffh72luosngnpOqKSMwsF+fFpOm4nZf4zBq+YmUaN0lwDsQvU4a33wKd6YKEJWPILtExBf+f/uaW6aven1EGeVqslKiqKwEDjP3S3bt3CzOz+YtJx48YV2z1z//79Rs8XLVrEokWLii3LysqKXbt23Vc9hBBCiNJo3749AFeuXKF69epoSvu1dgFff/01x48f5+jRoyXKL7NDPyRlvA5ZZVCilqUKasEsqm5lFYgVVXaFtxTfUZoWzKK6EhZscbqRkM6V2FS8HC2NuicevRLPvvNRXI1Lw81Oq+Y/E5nE0SvxPBHgrOYv7trkP2ZMShZpWTnYaM1Iy8oxWvvuxLUEFCVvjTpDWT0beBm1nBV3LWLCQ3E2icDF2gIP/9ZF1iP/ena/X4oFoFcD72K7rRbVNTR/GYYxeIZlGjKzc0nLyuFKbBo2WjNuJKSTkG8yGOxdcAnxuns3zDtflrilx5FpaAG9W/4yUuqorFu3bkyePJlt27bh4JB3AyQmJjJlyhS6du1a5hUUQgghKru9e/dia2vLCy+8YJS+efNm0tPTGTZs2D3LuH79OhMmTGD37t1GY/vuRmaHFuWqjFowy6KrZWUJxB5UaRcKL64Fs+D1KCpfQlpei1VCms7oOE8EOHM1Lg0zMxO1NS0qOZMtf13nckwqkDcxyb1eN8O4PFc7LTYWea2GekUhPk2HhyaB7JhL/B1rR4q5K842/wRVxb2W+YNMf1cbzAPrYmfiSoyFD9fPnMXPJAq9YwDhmXZGLXqG+isK6gQu+e0Li2bfuWg6BrvTsY670bU2WocPG7V1Uj03Wy2xqVnEpmaRocvlSmze9TEEkkUFxgUZZhN18AzETgE0YF5kzrJV6iBvwYIFtGvXDj8/Pxo3bgzAyZMn8fDwYP369WVeQSGEEKKymzt3LitXriyU7u7uzujRo0sU5B07dozo6GijIRG5ubkcPHiQpUuXkpWVVWit18mTJzNx4kT1eXJyMr6+vg9wJkLkU0YtmJV5/J9BSQLRsghWi7sWhrJttGYcvRJPUoaOhHQdisI/rUZ3qXv+cWkGTncCKycbC6P89XwccLPTql0XDYFKVo6eIDdbnghwLrKuMTcjiL56Dne/YNy8/dXxeO52WgJcbdWWvIR0HTfDQwnMuUQ717rEu9a5ZwusIcgMvxNEjWgTcGch9ZC8mTPDj+JsEkGaSyZncmqTkK5TAzJDoJd/Ypj8opMziU3LIjwmFUdrcxQlL8iLuRnBsb/+IiHHHEenVGp72OFR7QmwdyEpI5uopCx8HK1pGeRKUkY2oNDEORPPnFsEWtoBLhy9Es/vl2PV61qUq3oPbur98bYMJoS8AC+HSjgmz8fHh9OnT/PVV19x6tQprKysGDFiBIMGDcLc/GHEpUIIIUTlcu3aNQICAgql+/n5ce3atRKV0blzZ/7++2+jtBEjRlCnTh0mTZpUKMADmR1aPBoqy5i3uylJIFraYLWooLC4a2Eo20SjUbsMKgrcTMxbz+1ugeemo9e4GpdGlxBPo26eigKtarhSzclabRnM35pm6Lp4KzFvHJqXoyU13e1ws9OqdUxI15EWe524zNPER90k+WY4AHrbvKXOWga54utsbTSZiZW5KbdsqmPp4EBIrfp5ifHHgOLHdh6/mkBMWhae9pZqkGno7pmYrsPdsyZ6Ewei9O742FmRkJbXPbPgrJ8G+VvYGvo6Ep+mw9csmciTx4jXVsPZpgG2UedwijlMdX0avtl6SLcDaxew98LByhwPBy3VXazpUd+Ln/6+xR+XY+lpf4snra6DzgXwV+tqeCzqdbdw8iHG2Qp/J2cMf8UL/zUve/cVRNrY2DB69OiyrosQQgjxSHJ3d+f06dP4+/sbpZ86dQoXl5JNmm1nZ0e9evWM0mxsbHBxcSmULsSj5L67WpbxxC8l6Sp5t0C0tMFqRGwaf4THcfZmMj0beBU7yUj+MvOPawNwtil6chPDuYTHpPJXRAKgoCj/pMen6dQAMS0rh93nosjJ0dOvma/a4lTwmIZFwjUa1FYyJ2sLYsIvkmISgbtnIDmBzXD3C+bYtQR+vxRLqxqupGXlcCYyiXRdDlfj0jEBvJ0cOa+tTkysBTUyTxsvLVDgHPxdbdBowMXaglY1XHGz07LzzC0uRKVwLS4dawtTAhr4ciHdg98vxRLinc2V2FSuxqcT4p1dZHn5W9hCvO3xdbamZnY4DtY3uGGZF7w6uATi6XkOt9worB1cwK0OOAeqa/+1quFK4+pORCVncik6hXRdDrfNvAnFHHcLH9zIa70r2IJXcHmJtKwc9IrCjYR06tzJUynH5K1btw5XV1d69eoFwDvvvMOqVasICQnhf//7H35+fmVeSSGEEKIyGzRoEOPHj8fOzo527doBcODAASZMmMDAgQMruHZCPKLKeOKXu7XElSQQLUkeozFehpkkM3Tq2LfSlm0Yb7bzzC2jCUwiYtP443Isl2JSycrJpUE1B5r4OannaJhZMzM7l4zsXG4nZXAtLh1nW4tCk6pEJWeSlpVDNSdrnKwtiE/TGc3caRgbl+MYwLU4LdfjUcf5Gbo+Apy6nsDV2HTsrU0J9nZAo8lrUTN38gAHuJrqhPWF8+TGhWPqEsgf0RZEJqbj42jNEwHO9Grgjb+rDSeuJfDdsUhylFxcrLWEeNvj72pD/FUdcak6vj8eSWZuLnaWZrgTDxHXwTmQiFgLtd75W9gMLZOBlg1w07mQE5PD1fDfSA+sS522gwp9kRARHkdi1FUa2CTgQSOOxFqQlJGNrdYclByuxKSSZZeO252JkwuOyyu4vITh+sQXGBtZ3kod5M2ZM4fly5cD8Mcff7B06VIWL17Mjz/+yBtvvMG3335b5pUUQgghKrNZs2YRERFB586d1Zmm9Xo9Q4cOZc6cOfddbsEZpkXlVZkXF39klfHSFYbuh/FpxlPol6X8gWSLQJdCM0nez30SEZumzhxpmMDERmtGYno2GVk5aM1M8gIQjAOKtKwcwmPS0CsKPg5WZOfocb8T8OSvR+S1cGLCQzEPrIt/9UCj8X0e9pZGY+P+uHwTyGvl6lvDFD+TMFyog0egF/FpOvxcrajubEPPBnkBk5O1BRZaF76/YkVSZja10k/jlHKOhJg0kqwbkJOrkJSZjS4hkha2CUAgCWk6MnNysNea4+1kRS3rNDzij1EzV8E/7QJxKQ7kWrnT1N+ZhraRcOvsnXNvql4DD/t/ZhQ1tMwZVNPEYmcSgZ2JK9iHgL1X3vUIj8NGa8aV2FTMkyNwzomEeHv8XZvmrbGXocNLH46HYd9kJ7hxlKizUYQmVQPqUM/HQV1eIv8EOi0CXYhKzlTXx6uU6+Rdv36dGjVqALB161b69evH6NGjad26NR06dCjr+gkhhBCVnoWFBRs3bmTWrFnqePX69etL75bHyKMwucgjJ9/EL2UVRBvGn+Wf7bEsFezSWZJZMA3ORCaxNywKdzstHet4qK1s8Wk6QrwdAEUNUNOycnC0NsfF1glFAT2w91wUmdl6dXZKZxsLMrNzCY9Jo1UNVxpVd0KjybuWhi6FId72+KdewjvjPNkxWo4rTmorlklqMMevpauzWiakawn2skej0WCRHoXt7T1cTYjhWlw6Op8n8XW25oVm1Qm0TMEt/hg4B+IRmDeeLfRmItWdbXDyrU3klRwytD40dEjHzzGaRMtq+JlEqcGak00gfi42hHg7UMs6laC4vRCTgmm6loZmidi4BeIc0ox6Po6cvpmOVYYvQRY+eZPDpGSx6eg1XG21dArOu4aG2TVfcL9OF8ebODn44lSvldGXB4autYlpOlKzsvHQeBDs4IBXdgYeZ9fxtEdjLpkGUSM3CZe4W+DolNcCeOUgDfU6stycqH6n9dDwmudfwN2QZgg3lbK+8YpQ6iDP1taWuLg4qlevzs8//6zO6mVpaUlGRkaZV1AIIR4F6enpABw/frxU+2VkZBAREYG/vz9WVlYl3u/cuXOlOo54OGrVqkWtWrUquhqiAjwKk4tU1OLmpVFcMFfSIPpuwWBEbJo6qUlpXqfSBJj36tJ5t/tkX1gU20/fxNPeigBXW7VbpmFsHaBeA4A6XvY0ru6kntufV+I4fSNv+4g2AWqgEZmYztW4NLwdrbiRkE5mfCQ5sZcwy3AlOkWLorjjYFWHpFx3nDTQwCYB56SzHP0rjZz0WLyUUJI9nyTSrCU+TlbcSswk9vY5NDGRRGZbERVljSNJ1PNxoEWgC3FnT3Mu7DBJjnEENOuGRgPWFmbU8rTDxNqF0yZglxxLm5w/CLDOIs7anJtpJpjpFBwsbNSWsEDLFNyu/QoZN8HRDwdnX2oSRtNaIbjU9ONIeBz7Ik2BAEJuaMgIv8Ll6BSORiTg6WBJoFveNYxOzuRmUgaHzexo7BeCi6MT6NIgNUr9ffB3deLszWRSs3KobmuDu50LUcTjF7oe25RwXACXJ5sTd/Y0EbHJ2Nkn4FKtDgS0wxXoWe0JsP9nbJ4hOPdxsjJq0XO8s71Sjsnr2rUrL7/8Mo0bN+bChQv07NkTgNDQ0EIDzoUQ4nERFhYGwKhRox7qce3s7B7q8cQ/Jk6cyKxZs7CxsTFaxqAoCxcufEi1EhXlkVjHrYIWNy+N4oI5wxgzG+3dP7rebYmC+DQddTztaFzdSW0lK0nwZiiz4LT9hnLzd8c0LIOQf1FwQA2wPe60bhWSfIs6WWc4a2GBmdaOzOxcjtzpPljPx8GoG+aJawlEJqSrQZ7aSpSdy/X4dLJzc9XuqP6uNuTkKpy7ncz5qBQsTDXUyLyMX+ZFmjnXo3qdJ0jLcsFGG8z1+LwvK939grkcls1fcVY00V0kOz2SBPNovGvnzWoZejOJYJvq1KxnT3iKI3Y2HkbLPFzVe3AivRrxuXYkXE0gKUOHq50WK3NT4tN0tLKJpNbtH3HXQJJtHX6NyMA++i8c7XNw8MtbXP3szWTcLC9imRjNtQxHopxb0NA8gQBnLWTfhohDBFr60KqGK4oCF6NT+PtGEugVLM1NsLYw5ciVvOvXKdgjbzycqYZLlp646MLyfg80ZqDkzQrq4d86bxzfnbF1uoRIMk5tJy4nmes5Xpy87UX9yCR0huUQ9B642HtByNN5S0uEnsTdLws3b38AIq+Fk3slFO/AuqRl5Y0VNE+Pwo684CsbDeU9L3Kpg7zPPvuM999/n+vXr/PNN9+os4YdO3aMQYMGlXkFhRDiUdC3b18A6tSpg7W1dYn3O3fuHEOGDOHLL78kODi4VMe0s7OjZs2apdpHlJ0TJ06QnZ2t/r84Gs3D+M5WiBIo4zFu5aG4li7DDIWGqfqLC9Dy758/j6FFrLFTJh7x4YDxRB1FrVlnKDvQMgVbs/NEpXtxJsHeKNjLH1QC6jIIekUxLvdeAXZ8OC2srpPt68MFK1t1LF1jp0xqmERx9ZoHPtUDcbbJmxglOiULK4t0dUKXM5FJ7AuLJi0rhwtRKew4fYuW7jpy48J50s2D28kWpGTmABoStT54O1hj6RKAm50WNzstEbFpaDTwV0Q8hy8reDvWJccmHUydiUx14EqqOUE2Fnda5UxxsdNiYWZCgKsNp5NyuZWYSePqedcuSnHCplZ7vK3M0Wjg7M1kAMJj0shJjKTlrW34cAWtS0P2WrUh/OIJ6mXGkeHsy/FUJ36LiuLvG0lYVXOgvkMzvokzJzIsG+o40cnBF67/CelxuNXuwVP1nobkW9TOiqB6Vg6arDQu2LoSgxl/30jCTmue16qpSeD2X9+jOXiZ300cidd6Uad2XWo4atTfh/z3WA2TKNKtskjS1OSLhPqEXbflvOY6/Zr54h0IfiZRxNy0JjzTDiXiNMqtUwC4efsTlZyJLvoSQTmXcDdxJcc1r3y/zKuY33k7MHsIHTZLHeQ5OjqydOnSQukzZswokwoJIcSjyNXVlZdffvm+9w8ODjZaBFtUfvv27Svy/6LsyaQmZST/4uaVsOtmaZY4KK7FLn+L6pHwOHUJgycCnPFxskIXfYbkmCvY889EHYGWKRARpl6LgoFb7NVz+GZewNPDEhtX30KzT+Z/jE/TkZShw9G6QJfQYgJsw/i4uFsmWKZ64BNQm5YOrtxKyuDktUSacJWomNOcSK9GlOJEEz8nuoR4kJCmQ6OBhPS8MXpHr8QTHpuGp50WXxdrkjKziTx9EI+Eo5g7PUEdz3ak63K4lZjJ1bh0cvWJxETd5kyKNTXd7Th/O5l0XS6Xo1NJTM8mPSuHTiEe+Ft0IPGGF1521fNmDCVvWQUl4hDJ10+hd65HbpojupvX2RvjiLmSToJFNRqEBJOZncv20zfxsLckxNsBX2drrh07RkqOnlCT6kSbdeBCZCJKagznTKtz3fRJSLDE3c6UztX0NHdIws47GKd0HclxaeisPcA6DdLjIStZvYZxN8Iwv/YbXUzSSDO1w9y0HjrfmiSkZRPoZsOR8DhqZF7BK+Y3dNEXSNJ7sNf8aa64uvNao1p5vwt3WgbNnTLxy7yKi6MTLk26Y51tTteLN7BPNMc2NxbNtes0cciGpOuEJuUtzB5oVx13K3Pc/YLVe/OK3p0GXuZ5XTrv3JMxNwPQKICmEq+TJ4QQQgjxsBT1gV4CvwdUCbtu3nWJAxLwMMlrgQMvozXeCi70bZB/CYO0rBycrC04ne5MvF5HoM6VpDv3j1t8mNG1yB+4RcSmEZ7mBDa1qFutDi72ebMkGtavK9hN19nGgpuJGQS4FpjYJX+AfUdUciY7TudNSnIxKgdrnTnPE0q/Lq6suZlNZGIG521dCXCqS3yuHT6avOvyVD0vdd/bSXnBjnn6bRorYVhZ1cDRzhkHK3NqWNpikW2Bnacttt6uxKfpiE+7jfOt0/hnniLBtAGncEYDZMZHkhJ5HhvramDtTpouh8z4G+Sax+JYrTYaM1diUrLUbqgmlg2ItjInw9SLWomHcUv+k/gUB9LRYmsbi1t8Ot9FmPNXhEItT3u618tbLy6wTn3Cgb9SHIm87UCD3FDcNXGcyg7AT6+nrdl53KsF46bLgSvH4NoVBgZ353p8Dn4mYWDhBLWfAiDGsQHh4XGkpTqhzdBiYpaKztaeSLwwS8tWZzY1LOMQ6N+CzFwTknL9yTUNwNcsCSIOQXocJF3HzQvcbLkzAYwvWLvgkn2brlbnaW5jx229E16ZsSSYV+MmgZi6BNJYl4mfSQIu1Rqpr2/e/VMTV1cbyHcPhGfaEayYYKPRk6GYYF6aX477IEGeEEIIcR+ee+65EueV5YUejKG7nLtlMJA3TKQ8Z7N8LALIh9R1szTX0hBcFWxZAwoFpcXNYJifh71loSUMzjr6cDzJmT/PZeNonbcsgYdrIPFxt4m5EoGzhQ8e3v4FyqqJojXjSGwO/mTedfxlccFnUdfhxLUEQm8m4WyjpZ6PCda3zuOddpPDf1kTn10LJ2tzLJ1diTerjq9NNglpOnaeuUXj6k759jXnwu0UciLOYZ8YyuWkTC5l2NErAJT4COzNcrHxcMPaMgWunqaNhxe6LFtsr2cRrA/DOs6EWzTCLSeS5rrDuJiakB40gN/TfMiJDSU54yIJMWlcsG5AYpoOR+u80KRFoD96W09On4vCzgSqOVlhb+lMVDpka9IxuX2KJxxqcsHalBDdNc6E5ZJm4UY9H1eaduiNybUEEtJ0eJqYc/2yOeY3Yqh3YSmOt8DMvD/UakXS1RMk34zE1uwYTXJiSYsO54qpB7aNn8ONRFJPfEtkmgt6My2evi2wdDXF0twGn3PXiExUiIi1x0Zrhkv4Vizi95Pp34Bq9dti5RCCi2UDamSezgvoHHzBqxFY2EDitbzn6fFw5SBJll5k3AzHITeR2t51wKUG55MzOZ3rg5OdPe43dpES/Scm6Z1xava8et8Z3R93Ws0DLX3QaSywIROdxuKuvwtlQYI8IYQQ4j44OPwzk5qiKHz33Xc4ODjQrFkzIG+semJiYqmCQVE0N10kboSDzh7wB8p3NsvHYjmEIlqW7te9ZrQMD7+IbVQCHnUb3fWY6ofjiLDCrYwWNnkTZVgYv94F74OCdcn/gTvmZgQh2X+jx4Vbpo44WN1ZyywWSDZHf/MsidgRnmlXaP+f/r7FH5djaRnkSo/6xZ+DYdIQKwtTdfxgTEoWW/66jpmphq4hnmp9EtJ0pGRm09TPiU7BHkReMyf10h+cCbvE2RwFU40JybmhZNn6cTHDlqjkTGy1ZlyMSiE0MolLMak08HHEWmtOum11kjOzUTRWPGkWhtXVm+jDd5Bmlku0dU0umySju34cm2pNcKjThkxdLB7RZ7DOPMmZNGcsfevgkXGI2mZRmFlcItEhhMtX3InPzUKv8SA1+hpeOTcxMwsiId2esAvniT7+I1ZR1zlrWoME11Y0MI+jvvYS/8/ef0dLlt713ejn2alyrjp1cuxwOs50T+gJEkojCQQWYIK5gA26XJa59npfL2AZ28sB/MfLMhZmsbCxZZtXsrGvbQy29YKwkFAcaXJP6Hy6++Rcp3LYVTs/9486faa7ZyRaEzQI6rPWWVW1a4enqvbeZ3/3L3ybqaMkJo8TD4o8VnmJTOMGtKOk5ibvOF7TUYOTuTRpK01i93mGzWVsP0yvsksQXeBr5iyqo3PfxmUyEZtm16Jm7dJdu4bhLKGtPUnezWDFxsmMPEbu+BOw+hTvSmyxg03eclnrFpne/iwT9lVaa104+n+So00u7wDzQLs/mOxs/0ZCc6Mv+PZptjqYpkXErxMxqzRDRfx6m9lMgmVzhOXlGg/YFZSNS2SOPPb6lh/7NygKI/CyLBIT62zIIvlvuBe9NQxE3oABAwYMGPAG+NSnPnXw/O/9vb/Hj/7oj/KJT3wCVe1XW/i+z9/6W3+LZDL5Tg3xLw6vE3V6O7tZfkfYIfw54puJ4ul8jHipzoR1A2rJexOWrxdldMx+J0THPJh0d2fL55ar1LsOW/Xe645lb+0awfYFTo7ex2h+FiFgo9bl+m6LhJPj5Oh9mLFJnr66i+dLfvjBCU6OpSi1LBb32mjmLtnKCrQeokTmdTtpfmmhxJPXy5ydyvCBY0Wm8zH+98UdlismI8nQQT2faXuI/fTLTKwf1XGjReoyyXDvZR7ydtjy0yQUE92qk+2FsdURfGWYp25WaOytM+pu4YsMh3DJJw2ejxwlbW9xv7pGqVGloeSoazEuVZNExTaeMUzFzlHZUjl7+PswjQKrlS7DhRxTeoUbqUdJJWqcOPwuess+T5V0HH+adFdj3rlKTlnFNBQWdgt4la8wv/q/mAhcWtEIf9x4gHatxPtEDce8jhbPkYzrhOwGi06WjRWTM9YXUXLvYtlK8PRiha7jIcNLFLsL2OECS8a7cCIFEvUu9tLvkNRGCArzZCMKpKcIzZ0hvnmTIb3LtjfKknYfK1oRVYvSdfMMba9S6FZJFmdIAjSv0tRdNjPHMetV9OlHIZrr30CI5mD68Ttf377fZWchmiPm6vTSNyHYBdVnWxllWfMZLRwig8HN1Gm6vkk2Ql8k7u/fdxwT4VdvUHzOfxhVBHzOf5gzf/aR8Ka4Z5Hnui7Ly8scPXoUgGeeeYZHH330bRvYgAEDBgwY8J3CJz/5Sb7+9a8fCDwAVVX5hV/4BR577DE+/vGPv4Oj+wvAWxh1uhfekID8c9jI5NvFNxPFxWS4H8GrJV83NfSWULsllvqPBtP5B+78DV5H+N26kF4ud7i+20ZTBQ9MZe+wHLhdCA7nc2gbNnZrFVsd5aVaiOOjKVIRgy0nQzcokJch2r06202LL10rYdoeKxWTtWqXRyJVjlGCWo7VYP6gk2bTcrm63eIjp0cYSoTIxQ1mCzHOzeagtcP98iq1VJREIcfm2hIrLy5S0ccZHp+mkAhR37dFOL9aY3s3zIPEOBKqUcTBSx5GRXDYX2IkE2YlMsW17RYzwQ5zLDPUajDTuo4SipLI/xCl9Gm+1PR5qZIjr88yNn2CoLrEWLBHcvZBRHSayk6LsF2m09rhpj3NObeLXlpEuhna4/MAvCd8k8WUZLNmMmHtYSlRGr7BTOlJRr1tbNenQ4xwSDBx6BRzToyd3SG2Ww5p60VqZpn07EOM0MJEMtF+kZgfpnZdcKg4ymMxi1LlJp5pshspsJE7wl52jPVql6HNz/OEV2c8qZCfepR47hhkZ8l1SuSaz8BOBSt+nNL4h2jLDDXTZremcXjtGgX6kbiqq9OqLbKTVKhNfAivmGVyfKofCU5NUK/sstK5ykR2DCXlsF32GK88TSaqUzbG9qO5/X0wd/hhuPqHsPIkw8U5WmPvZywfYwyAE+g18OXCq1Hm1g6HrAX0TJGx/CjU9m9QNNZ5TLtCUTQ5qm682cPuz+SeRd5P/dRPcf78eX74h3+YX/3VX+UXf/EXefrpp9/OsQ0YMGDAgAHfEXiex8LCwsGN0FssLCwQBME7NKoBb5ZvqTbvz2Ejk7eLb5YS+bp8E5F+S6jdsh14XfsB6EfOgnlibQ2z0q91m87HWC53+KML25TbFmcm+90nb19ua32Z8vIV9NkTnI27OH6D6t4e7V6OknOE46NJPnJ6ZL8BSpO1qokfgO9LViomLduj1XXouR7b+gjPWyGUvRhDY33/ulhI44WVGs2ew0trdYqizk+kLhKrqpS330vB2SLTvMpRYxo1O49Z2cSsXGHPsFk08gQS1qomk7kYrZ7L1XaUDfcB/t+xp5hR99gNn+SGdgSnYqKXS3Qi61hOiJ4IE1cdJvwd0moVxa6TtDZZVN/Hkw2VpVaHmVCLR3pXSEZM9HabscX/QiJzkeLk96Fff5FQ+VnGvSYvbN1HUq8xEywyfP0pzGqW2fwU7xua54vVPWa8RfziacZdwanGZZT6TZ6Of4iL4bMkgzZuvc5R/RK+XkUX/d8uQEB2jrLdY6r3Anl1l51ggpfW64yu3SQfCVH0ljCkQuTQA+Szp4mFNP7wlU1q+jDN+HHm5kbwgZc6GcayGYrrfwh7V8DqEG7WOGp06Ga/F1PLENFV1NwstPfg+mexmja1rkCNHGIkCn61RGejQTx/GKI5mstPsx0EuNH3U/Pn2b7+BQznKRzNoaN+GcUNsXfieyg+9J479tdMVO+L94PXBmvXK+hWnUOFOrlRYPMFcitPoqTn2b6yhpbPkRu5H7pVRigTp0NGtni7uWeRd/nyZW7cuMEv//Iv89u//dtv55gGDBgwYMCA7yg+9rGP8TM/8zMsLS3x8MMPA/Dcc8/xz/7ZP+NjH/vYOzy6AW+UWwKkZjp3dFN8Xb4DPOjgzTeVudXVsdlzDqa9mfXd3qjk1Uie97o+d7eLwVu/SaVjU+86BAHoqsJLa/U7hN6UUiKrrJK0FWo2lGNHcNMpEtEjHNrrMN3ZochDPDSTpWO7tC2Pi5sN1qtdmj2HE2qKdFCj0Fll1x/jSmuCoq3yaKRLNmZQSIQOmrusVEy2b1ziVPMpnECyFMpSePAhErMOo0ERIxPlkholFAqRTmXQ9n3kml237zPnvMgHvM9yzS2y3E2hCcHmxjrdiMB1fYa6y9Dc4qL3ICOiwhl1HVVTqHt54ppHorvFkbX/iiWO0EpM8l3JDmd7z9Gt7+CbdeKijtLeQc3NcLkHG8EMq2KE0941YvYmCaVO4Hs0VMHL9jj/ra7ScHIoIQkU2fEUCvIVZmmSszZ4zj1M0XXwt7bI6qsUxg6Rnf4o9tYVsnGNF3d6fHE3xpFWiGIwScNNo2zdxFDa2LlhwuljzBZixGjT6JbQPJUj1iU8fY0RtU2zlcBs3mBbC3CjRYqT52g1a5iVDaLdTYatFcZ2/5RF5TEioo7nbNGRm7D2DGEjjZN+H5nxo7jNXSoySRCZIL5/fB78JiGN51eq1MUw68YsduMSKecCc5pE7Y4D76HUstgKDjE1kyCXzvQ7cu5H7KfzMVbSM6zVJXpQJHfbvt3e26TS3GR3a4z7jx8il57ExkCiElbcb/lY+Va5Z5E3MtK/A/NP/+k/5cd//MdZWVl52wY1YMCAAQMGfCfx67/+6wwPD/Mv/sW/YGdnB+j/3/y7f/fv8ou/+Ivv8OgGvFFuCZB61/mzG7F8m1NKX497EXBvtqnMasWkabmkIq81A797ffcynlvT757vbjE5G24j3cvsaqOEMmMI0TceD2kKp8ZS6KrA9gKeWep3zMzG+k1VnKDI1OyDZGmzunydBWecUuwBHCtAq1/GtzaoDifY8Ocpt22Oj/ZraFs9l0RE57GiS3HjWep2iVf8gNzcFMdHUwgBn355i7Vqh9l8nB87N4W+d4nw9nNsRwtsiiGWFjXsMZX58UdxKybXNxus7uwx5bsczSrspqNEDIXdpoXf3ObY7qcZcy4xQ4L/h+/js/4jZK0WE85VanqRsh3BcBo8or7MB+TLzCplLmsn+T35YU7IRU65y8x7CzyY3eLw3M+yuDfKM40E4+1VgsBACRWIRzN0dhfpdkzWlXFOaRWOR1qYygQ7yYeIJn3avS5Pbvictv8ITQsznoxCfYE/cU7zJzzKj4tniLl1huxNIjrsiAQVJc1c8iSJycMsurOkqq9Q7eww0bXJUyOu+czoO2R0C9eTVNo+14yjtMsWmSuf4QWxSqYwzJR1k6ioELS2edlKE5s9xVQ+x6h1EfLzPD3201xqLfCB3Bpzyg737+0SsZ/HMevoq0usAxFLoaKP8XXjUU7aSR6ObVNKpGgSo7q5QE73ybWWyE1meM7y8CVMTB1iLiwpXdhjL4gyMTzE0cPvAmDv+nP0bnyd7SPvIhc1YecVlismX7UsHprJ8r6H7mO1coixW+nK4w8BkKjv4Uro2R7t5fPkTj7Gml9kTKtQ9RPf8rH3rXLPIu/xxx/H8zw0TeMTn/gEf+Nv/I23c1wDBgwYMGDAdwyKovBLv/RL/NIv/RKtVj8NZ9Bw5TufW2mIpZZF5m5z6z+H3IuAe7NNZW5f/vZt3L2+20Vavevc8f3dLehWKybPLFW4ut3i+2YFBWeLrU6GpqUdiEll8yJi5wJOyGRkfIZYSGOnYeEDYV3l2EiSVs9jqdzm+ZUqy+UOSa/KsLfN5KGTJCIJ2p02TU/DufRpmj2fXVHAS49jdzIstttUTZvLm02Kos4P5zfYVobxqhXa9RI7boiXrQxnvYDvOTXC5a0mS+UO9d1VMpUKl1PnKFZfIuLeZE8/xx+aZ9kuWWx9eZHpXBTT9hnLRLjYznC9W0RbMZie7aEAXdtjsrtOWySokMFGw3J9LugnOBbvoIldwrEkTSfAtRUe5CazrBMFCrqF1zEpeYLHQ3soWEizjHHjM7zcOs017TF+OJUl6lUp+11O1K8SLv0xljzJIRrMRjxGhkfp+Bqz0RqtRouNnT1OejCj7BDVDRKuiuv6qOk0TxuPcT2aRUhJOxwjp9WY7dWoBV122zZfe2qFGyWLs9LnRxILZPUapnSJJzNMTJ8g0AwWrlzC6lpojRV2kCitBrp/gwt+jpEjp8l6m1RX2qz6BeKyyEPdl0l12hA1kHKWtp6nNDbP2SlIXf1DEps3udazWZCHMH2VopFhT5mjtXWDaipC7pF5dpsW1a1NssvnyXlrUL0BVpPps//nq/szYfZaFi9XE7jz8xwdHaG8vUps4fcZbi/j1mJc4gyTjuRi3ePKzgLFmsnIsUnilSpK+Bgkp/s3e6I5Ms0NZicP8+VSjEVlj5gxRk5toxOQU9tv6Pj7VrhnkfdP/sk/OXieTCb59Kc//Zp5er0ekUjkLRnYgAEDBgwY8J2E53l85StfYWlpiR//8R8HYHt7m2QySTwef4dHN+DN8HZ28nwruRcBd8tUvNwZ47lK4nWjbN8sAnf3d/GNvpvbI35SciA+a6ZzhxVBqWVR7zooQrDV6PLC+Wu8K7HFVPE0j87267RWKyZdM0M5fJggPbNfu7XFxY0m8YhKOmKQiRlYbkCvtoVsr1KzC8TkLll1hcqGyh/0DtO2R/lQfImj7ZdQ/R711DzKzA9yuR1lac9kYatF2bR5l36TU+oKm94Ml8NjjMojuGqEE/IGc7UdyttRXliRjKQinHDbHGOL5tZ17NgpDg13KKbSTNUWycsmvcYUqy2FKbFLPHYSN1bkUidKuqMTafRoWS5ea5uR7osEus9a5nHMVoWGHyMeUvDiwzQbdb7X/jJrWFwKUjT8COtqkSnVYljU+D6+RlfRcHyFV5RZVjoT6KLKYa1CLf8gbswh2ClR6dmYOORocNZ/hapaRNEnsRyf4eaLhEoV3K5Ad8LU1CK17P1sGlliIZWIcHEL57C2fT6/4jA2e5xDc1FO7v0heu15akqWL1dTJCyFv2JfYJ1REt01pq0rtEWMRuIcyVPfw5W1HRT1CrNajUP+MywEE2iK5DH/BWTbYCH0f2DGJmgVc3TFMFrpJuv6BvFMkbgxxtmpzEHqNMkwHP8ovZ1/R1bs0Bq+n2x2irHa04SqWwh7m3hdwuYecd/EjI4RDE2BHYJeA+JDd+2/I8w8+CFExdz3anyK2soqlgPRxChdqbO7foNEqMfpYQ3Da3FELnHj+Rs4bj/iXJIZLi8s8Ghog6nsBI1gnDXXZ40kXi2EHaSZU3VKQfpbPLq/dd4SCwXbtvlX/+pf8fGPf5zd3d23YpUDBgwYMGDAdwxra2t893d/N+vr69i2zQc/+EESiQS/9mu/hm3bfOITn3inhzjgO5h7raO7JzG63yBmjxaXvaMHy93Oy+t1nl6s8NihPN998punoH6jsd0tOG9dmNe7/YthIfrzrVZMtuo9jgwn2GlYbDWG2QjHULXRfb+5fjqjokQpjD+KEIIvXSuxsbpMrrGMlZgknj7EeCbKeCZK72YZxV3CEj7R0SO4QZKrZpa24xF3y9iNMlfkFGnqjGPimaushZKErRKT3atY7hCLSgGhwE0vT9dN0vQcvj/0Evdpa2itOE8/V+S8OYtQ4L7TZ/GqKZ7fiqIwwvzs4yTrV/hwsoYjKhCtUxYZpt1VWuubjIYfx8kn0VWFaWeRyebXCfldiv4ySWmxE06zo2YY98uErae4sneUs9ZTJDsvowYTvBA8zIKcQioKWfs6vu+jqCE8X2ElGOKanGJFneQMq7xvqMXVqMn/WjX4K77PHBVK2gSOHyGqNphTdglF0gR6lHrsMDV5iKtCR8gqo4bJRuwE5705zvVe5JBa56T1Ml6rRKXXxVne4XByj5S1ihKY1I1RJoJtHnEvUwhV+Bo6gdtDlxZxRcVUJGvLC9gdi3hxlgnHx6xtM+S0CckaY1R5WLtJw77MzZJE2i4RtUrUa7IWmWWHkySulfjgyArnxucpEeOly1eZsa4R8k10RXBqLMXUeBKu9Kj32kTVGK7axbz+ZQzLRYu9h0b4BEQtyExTTp9m+TbD+ttRGivsrL/EYiOOn/suvFCPUSpMZtMkiofIjc8zewSee9HgmUaPyaSkmJvlv53fQNu8yEx6l6nx9zOWneVRWUcIMLoldkmyxggX5WG+/5sfqW+aexZ5tm3zK7/yK/zpn/4phmHwS7/0S/zAD/wAn/rUp/iH//AfoqoqP//zP/92jnXAgAEDBgz4c8nf+Tt/hwcffJALFy6Qy71aev+DP/iD/OzP/uw7OLK/wPwlsSy4lfa41ewxlorwkdMjby6quN94YsgY4+S+6fft23pprc5L67W+h5l8dfrL63Wk5DXdK79RimgxGaZIHTaf6r8efwiSOc5MZgAOons102EsE+HMZIYzk/DlBZUvtiw6NZeVSotT4/sWB40utY5DuWOzVjWZNVc5G1rHD0ua5Tpfer7KZC7KkGayFhnG02dJDU3ycinD1XYbVfF5t1jkmH2BFzjJDWUGs7nA5pV1itkWj2Z8lMwylc5NOnqWjpJgPVDZMR0mgi1kUGc3M8KaP0Fps0e09yw72gh/5IwT1Q6TKAjyiTCVehNvaxEZP47n+kwYXY5OHUG2XMyVNaS5TCL3EJm4weHq09xvPklLSbEWO8KiH8LqaswHC2Qoo7gOX3EfRPEcGpqBUHTeF7rCGGVG/B06gaQk0+iGQU6YaLLHOZY4FXU4LlYoOgvodgt8mGaDHC2qIsJl/TT3excYNZqEnS1WPZ2l7PsJtDDLkRyz6QWGes+RN5/iROP3SQd1ohqoVoHj4VHOKxNMBSWGGhdRjRDNQCPe3eQJ7SZaNIKWKDAaO4W5WcKzSzSj03zePUPyRpmEvUMyFqEQLdAwYE0/xLTvoAUbHM4qGO3PMd222LYMFOGjxbK44WNY9gahVoO9Zp3dpsVNt0Dsyn9hxLmIH0i2omfYs2fpOWlE9Awjo1kKHYsts0nFa2ImZomNHGZKKUFzg2rqOJ9ZljSt6h37cs10aJTWSKvbbHaSfNkcJx6ZwNQ7hGNDnJgagcY6bL4AehR17wpdexLv2LtoanE0rUSkeIjC9BRkZykmw5yd6nsqFqw9QsomI7LCMJU3fgzfI99Suua//bf/lieeeIKnn36aH/mRH+FjH/sYzz77LL/xG7/Bj/zIj9zhDzRgwIABAwb8ZeFrX/saTz/9NIZh3DF9enqara2td2hUf8F5mywL3mz3ybea1YpJs+fgeQFNy2W1Yt7TuG4XZhPZ6EHHSvatCKbjMabj/fXfom8j0AAEJ0bTnJ3KHIzh6cVXG5p8s4jdHdSWYeXJ/vNo7uB36kfsujy3XCUd1Xl0Ln9Q+/jSWo2FnTYnx5N8YDxgNLjKzWYBRaTRVIEioNy26FgZhlMdHpUb7NW6vNjscXPB42iwTC91nIqaYeOVS5wIbjKmwKJ+DEMKQJCQLWaMJtJqkbN9QuVN1s0kjyUcJsK7mN1ltoMMFemQFFG6SphrodMs+HEKIZ8hr4ThbuNKyfK2xyGtjEzPsOUNcbL2IsPdBTbsKP+p8yiH1ArfP1fk3FycFScBpQgPN/6IfKOJ5zdp+RpX/CKfV85xUlnlIevJ/Tb7JiFsHvJeYUcMIRDM67s8qCwQ9SpYQsUkwggL+I7KCqP0lAxjWo1seIe0U0WxYVZd4jCrJOhwSc7xsvowen6a+8wKRnsLWe/ghWMI+2XuT9uEw2f4U2+OYf8SDza/iO63aZKinTiGHk3T8RMkR+cZqX4V6QkWnQKe1WKUMnooIAjH6QQRzoyEaE3/f/Dqx3llC75WnWQsFKGoRPGcCF3rOkanTk+YhHWFkK+jem1CToW0btCSIQw9ipGI0Gre5L6wQit9lBeacVaXNWbU64xYm7i9OigGlWia81WDZcvHck/z3tET3DdeJ3P5iwSVLVpdQSljE4zNQNTgwl6MK9sNJnN9K45b0evjoylOx+qMWBX06WM82Exx1FhDZmbIT94PtRdf3aeFymT1Eu92iyxWpomNneSDx4pM52fvOEZeWqvzzFKFs9kMJ6mRE03Oqdfu6fh/M9yzyPv93/99fvd3f5ePfvSjXL58mdOnT+N5HhcuXEDcirkPGDBgwIABfwkJggDf918zfXNzk0Ti7e+i9peSt8my4M12n3yrudti4JvV291tOXBLmO024wfec9CPoNW7DjsNi6bVb+Ve7zqcX63iegHThTgPzWQPPv90PsZjh/I0ug4106HUsg7eu705zWcv7SAEB9G6rU6GmeKjSAGv7MXY21tnudxhq9kjpqkkozpdx+eL10rsbC4z2b1KfrOE1ZqgnjnM0UKFa+ef4Zo3ycShE5xLtXjeTXEzYrDn5WiKJoa3QlJYxBQXx26wYauY1jZNLUHe2eKUepHRiM+s0uZp5zAlb55ZtpiRDeyQwYI/T9vvcbx7k5ZdpaumCHxJyi/xXWqHhObTjo6wEgxDfY+EIbCy8+zGj7JsZhlytzktr9Gtr/KV+iNUI2n0SJIh4eB4AV/1D3Fmp8R3xzs8cWyEXO8Zxre/QIgu636OTTVP09c5Y7/AeyPLFNlExyEQGkIqpOjgqhFyIUFYNtG8BnpgoSKIqjZhPCTgArY+xGjIQfpNbA88JcJOkGAEDR+V62KOjeEPMD+SpP7C/w/FayMISJjrFBSPlqUQTsfwOxphdxc9cAhQ2FJGaSlTHGleRPoptmsRRtQyQSDRpIWrGuxFj+JHQ2yZgqJVRt9dxMxGudYKMdx8no/5l0hpk7hHnuBld5IL5WHGzT8lp5g0UsfZ7kiSsTjhoWOsB6NY8TFmok20yg3iskx8eJYr4jj/Y8kioqucnj9CR/sgSzsZ8l6J6VCHNdHApQiAEy2SO34/C1Wf3uYmbk9i791keeYQhdnHcTo7RI0KR4oJisnwQdR6iBonUi6kZkiOn2K2tgw7qxBPU6Lv2Tc9/ChZOmA1iad3Gat3aLYuYdrzr3ro3ZZpcEsmrbspJogzRg33ramY+6bc8xY2Nzd54IEHADh58iShUIif//mfHwi8AQMGDBjwl54PfehD/OZv/ib/7t/9OwCEEHQ6HX75l3+Zj3zkI+/w6L7zed3o2ttkWfBmu09+M75RlPDPbHRyW9ojPASMvO5ytwvUW8JMSkh7FbaWr7AjD3Fo7jAnx1LUTIdmzznoXllbc+jYHo2uSzJqYNreHdsYz0TZaVi095aYrayhJ8Osho4xNjlLuW3zB+c3KLdtcnGDTLQf0b5cD+OOvZdX1uv89/Pr+LKKCAS+kJydzPDgdJYbu22+cLVE27vMkPY8cx2LC67C4t4wfyA1Ku0RysYQ3yX2aK9cwm0nOEOCFQm63cBUJUPWCn/Nv8gOaa6JEbLyOnP+Js/pZ1hmmlywRMFc5qRTwfchrXfxXJ/hUIsfSiyzWesy4y2io2ATI0+FImUsYkSEJNpd4CxhNkhz2Z5hudzBiqWYFnvIeIKQNEh1rnMSgV4YJ1M4w+jiK/wf2h/wB3wIRxniym4J3Xqe+ebTQBeBQ4IuPRnmQRawiaEZKZpWCk120bUwkcAkjE+CLr4xhNVukQ5chARNFfgoeBgoeGRoI1GwLA1NkWhWHZQeoajPjjpBT3cYD4eZrv0nrq6GWfViPKBphLCJyxaHApuuFaK0q/Nelkiyi6nF8bQYtlpE7e5h9jqUyPCkPMQU15llj+PqHloohpqb56Y6SqNrEtcVZLPFzs7TqOYeZ7wbHKFJuLOGIbMcTze5XF6lFxPkjSjh4hSm3ySZCLMVPUqtVGVWXCfrmyDbJKeOUp78MF/8ShWlU+Jcvs27R09A4RBfi46h73yFXG+TCa4QPnSYkVSEQ/4SPPuHSDnH59N/jWFvh9GhwwfH9ZnJzKsdX1s7PKwuUDxUZEqpQHMDRu6nRIatToap1HFy2dn+8bW/T59TFmDnFaJjJxiO7OCnE6RvO2dUNxdoL58nNbzLQ9EsQ4eKGJkin/jT7+HD2gt8znuIB97yM8yd3LPI833/jjQUTdMG3cIGDBgwYMAA+j553/3d383x48exLIsf//Ef5+bNm+Tzef7rf/2v7/TwvuP5dkbX7mhe8hbX/d3+OW69/rO85qB/wdg6/ycIAUE3TDn7qkfdLeuBj5weYTbcJq5dR/VmWa30L2SLyTBXnnmFevUyOz2XkfGZgwYow6l+BOM/P7PKWsUkGzWIh3SmclFiIY3nlqvUuw5b9R6KEDQtl3Fnk1D9KTa7Hp/164gpSVE00DavMpKe4dDoEZbLHSJ2mcnOOjdujvL1q9sMN5ZZ9ocpywyqKvjC1V2qbZu/MqdwVlylaZlc9g1MP0aGNvFQm5oY5QWOMRIO8yc7LbL1MOPeEnHpM4pgSpponkLO3yaBicTlpjrKOGVOiHUKfo0yGWoiQiBdhoMSEcVB+oJh0aJo1WjaJXw3hI5LyHcZUbYAC1V4GNiYUieJQwybSdVmy8kx7q5yynySWUq03ST1IIrne5xRLjLR2SAxfoiMW+FxqlhKioX1o9yQl8jIDpOyi0MMizSuqpHCJq5a6Ch02oKs7KL6DgqShp7F9k3SQYN6W0Gg4EuFHXUUVY8iREA7NEK0vUqSNnt2iA4RJthFIaATCHSzhCs1NtQi08EGmdYCR3yJrYboEschTDM0SlG3cHs+qm+TwySuesjMDH5yEl0UcSyP7T2bUb/Bz4S+xpBvErZtBAZxDezGMhFhk9KnkfECpfgJzKDJpL1FzLdRpKTuCJrbNYZ6L3LU7iA8h6SE7tpl1OoNlE6O0USGtLtNulyBeAZG7oPjH+X8GpTbNg9pi3xErJLbLoMR493Tx1lTTlC9XmLXtUjurHHYahHa+SzsPM99U++jMvdX0XbXOBVapsgkpVaGrfVlDiklcp0M3Pw8udoKuclHqBHnupVGOnmeubhDs6fyXSNFcrVlZsNjMJbaF4r7GQRGDGdnCbPcZmthAebnKSbDrAVFqnaaoxuXmYzYaJFRlng/n+FdfMbr++/9qzd9Rvnm3LPIk1Ly0z/904RCIQAsy+Lnfu7niMXuvNP1P//n/3xrRzhgwIABAwb8OWdiYoILFy7we7/3e1y4cIFOp8PP/MzP8BM/8RMDa6G3gLczuvZNeQvq/m5FwmIh7aDJyHQ+dlCn8+hc/qD27Rt9vgudDAudQ0R0jUgzhdnrp1tKCaoiDmr1zilbhKwbfH3B5EbkFNAXjUNTx2j3XCKJyQNxuFXvYdoeV7abLO51sD2fMxP96NpDM1kubTb4yvU9zk5muH8yc5AuWl9KcaMUoRykeMXM4CzV+ZnJHQ7Ft1giwucuJ1CE4DH9OiNileebNeLdLsfEFWbFGp+T59jzM9g+vLLZ4N3eAt9lfp2647NHDocIM1qdhHeeUNmhHkyxV0+gBovMqYvMsUOZOB2twKTRpNTTaROn54d4Vh6npaXQ0IjLHqdZZE+muOQdpS2yPCJeoRcYTChVxqnjo7DiZviaf5L7lZs8LBZIBi12gxQy6NfumTLLdW2KNB0SfouI6tIK4ozIPYpil6K7TVNJsqUWiSsqcb/GK0tbFFyLJF2OBteY6N4gQZc9bYguCina1BhhWZmmqNRJSg+hx9FdjxAOMboIBIHrgwchYSERJLFIqhYh1aKmRPE8SVvJEKg1sl6FmN8gUCVrYowyKVKyzrC/SZsoccdkWrRwdYU0TWKU8ZUwm9oUC7kPc242R6i5TKhsMdRdoqjaqHSola9hRGyiqobQTGa8JQx3iao6RF0fQS3MEjcsvPIqUVnhWNgj27Ox3evYE4+jhg1ELUe557LrFYjs3qAXlOkZGVJ6iLC9C0JHCA/f69Eur5OIGETiwzB6Pxz+EADZygvMhkOMKhFyCYO6lmXPilAwakRiCRrhAhPOHtrN36cSFhSMKnEEQoLRXGOi/DWMhs11N+Dl2Lswb7wI+ho7hkGqfoW8aOJ7z7HVUiirQ+wFh2jKDAmnQujmU3QiNoX591OYfbx/ULZePT4765eQ6wvspjrE8hP9415myGaLZO1dCHxa1T22m1cYwmZK7LImh9/Q+eRb4Z5F3k/91E/d8fonf/In3/LBDBgwYMBfRLrdLgsLC6/73rVr1+54fD3m5+eJRqNvy9gGvHlc12V+fp7PfOYz/MRP/AQ/8RM/8U4P6S8c75hPXXaWatdhrZNh7LY6tG+FrfVlystXWIlNYhoFTo6lKCbDhK0SU51LhK1TFJMj3zR9UwiI6CpDiRBHcy527zql7ghLex1G7E20wiFqZoKFUJ4XSnm+sqcTzXV44nj/QrIwOk0QHz5osnIrkvfSWg1NUbhvPEXH8rBcn52tFXb3vohmWgTNSapmDLOyAeY6s0dP84rbo+ZqKMOHmSnMoXV3SdPCjY3xpbUol9t1xtJRnNlpFroCN1EELEKdDYqdKme9mzSVBJv+ME2ybDZ7zDstolLB9EBoNkrgkGvtMhesUvFO8bx/jLPKZY6r2+RFB0VaTHnbRD2XiD6CKqJskKTsJDjlLeBrghUxStc3GFLrnFCWKMg6Y6JCRYkTFjY9VFypMEYJS57lT/xHOCFWSdEjFpgo+ESxKFBHlwGu0OnIEHHN4nBG55p5mqTdIS662L5KN1pESY1T724jS9eRdAlQyNNAEw4BgrDvMqTWieDho9EO4qQjBjE9jWf3uGrMQLeJgY1BQAKThPBBFWi4qICGh+ZXMPwWLZJo/iYxf4to0CUmunToskwUqUbIuWsk6KEpPil2SNgWMlwAAgLXpxmEuShn2av3SLU0njj2LkKN/4i0bhIkitgiCl6TuDQxgyS76iij2iaK18JTVbamf4DjwyE6C5+hLJOs6kfIyRbJ5gq6Y+LaXdpHfgBdS2GX10jpMaJYqL2AOD3C0SyoaXBdeolJalqR9t4OimhiJYcxQtukIi8AMGnf5JBa5Ip2lHI9wUj6GGlrk2j9ArFenbRVIeu0sCyLsBshdOox8Me5ZpxhYStgXBEEQmW91mUvsLFD4yyrOusthXBP4wOFOonAxHE7pDWT6VSTnV6byNJ/x/NtylP3ETdisPpUP7J/6waQ0Mi5O+hhDz0TMLn9v2hd2MQ2HkKfOkRcAeqrpFSTuajKh5XnyIgOBG/qzHRP3LPI+9SnPvV2jmPAgAED/sKysLBwUNP8jfhmN85efPFFzp49+1YPa8BbhK7rWJb1Tg9jwNtBcoTFsMHlrSZu9N66Wt7NlFIiq6xSN6K8IoaIhfqXXqdjdWbSuyRi469Z5pYw1GdPUDx5nPuUJU4pTxFRMsQsaFkWHVyMTgsqV9huWTjjCXYjBl8x51jumcxaLhu1Lhu1LkL0LQteXK8T6ZX4wWmXofA4UUPj6HCCI8MJttaXqV1/iuLuGlHWycsu+ewZmvERVm4skG9fo9p2qIXH2Akd4fDwIX7x/nkqV3ZQdva4JGYQqRFku8lmw+QPboax7BFGUiHOTo+xWNLxWWTO3WTSW+Ai49x0RrjkJwkbDxDyy/QCnff6LxPBZsUepa6EyQV1JtnhajCF64c4YeySdMoUqBGoBhk1x1BIUDEF83KbaGDhe5IOEeokmJbbTAR7hKWFIgLG6KEisHwdHY9RUefntf/OtijiqGFsNDKKSUjaaAREhUM0qIAiCIQkg80RLcpScpi95gTC36AjkjS1PKpvYJk2YVS2KVKgwhZD1IiB73Jc3UD60CBGR9E4La4S9GI4SpGGq5DxFgl8QZ0EASohHBKKieG7FESVQFEAkEjAp0qCXTfFfQKECgoQxmGCXTJ+jaaI0SJGXjQJYyMBz+6hqwIXg834SSrhUzzqPM/I5kt8fT3PeOsm2aBNx0yxOfO9hNzzRFWDWGGW4cYubjOP7cRppU8xf/Qoqdor1Do1PEvBt8p0RY9NP0oSD7PVZrNjEFLGmFCW6RUOs+5FybvPUKBJ11MQkSG87hod3+BG9nEU1ojZ19HaTZzVV+hWtwgdeR+t0DBjcpNqY5un7RlGgiofydTQojoVS7LrxVG1BDF9l7hiofaqtMNpOlvXGe0J0mGN+PgJcqOP8/5MEafukWuUCDt1XtJOsTVd4L54nWi1zmiwTWp0CPHyl8Bcw0zMEj/zV8HZOojsl40x9mgxEpFki01ic48xEsnAxd9Dq5coJg3ahx7iSm2N6a3LJP0uybDKEbFJWrR5SR5+gyele+ftb+0yYMCAAX/JmZ+f58UXX3zd93q9Hqurq0xPT3/DtL75+fm3c3gD3gL+9t/+2/zar/0av/M7v4OmDf61vhO8XdYHbzZVNDc+Ty5q0C57FHbO46RPwFjqYPrd3UFLLQtnb5E5b5EhJc/CDRXz0gvMahqx4hxMnmP1hSdpbn+VIDLPBW+arj/EyYjBo0MOE+1trmSzFMdGEIKDDpvHR5Msldrkq1dZapYI5acZb/UIeS2cSoY52eYB6/OE7Bo+PjY60cZ1/Kv/jbVmmJKagG6WRCzHauw+1jYFvVCJD+VzaE6CNTtFytZxfJ+eC02riwp0bIdEWEe2Lby2zYjSYlTsYMgGk8o2zSCK5ghSwmRWqTMrtohgsxXk+ZJ/H+8SlziqbPAF/wGuKEeIGhmmA9A9l5YfQ/Y6dKwWWcVnRjRRlQ6+rxCgYCphVHwcqaAJFUP6GMIDBD0ZpUEEg4C01mWIG5TJUpEpRhWHEFD1EnSJkNJMVAJCqoOFSldEmLFvoIY96BkUZJV89ynaXYNtL8miNs05Y40hp0YCm/+W+klWTQPF+zqadCmLLIfFFqNUUAMHtWuSCqCLQVtR8QMFzfdJyA4x4WBLFVdRiWAT7DfxVVSfOG1ank5LK+B4GlFpYeCiY1Mjz0vK/RxhjVFZwyVEC4NtdZojkQ66a5LMj3J27j4OLb9MY30H1a/h4qIRYAiHeq3McK9BtFtBiYY5nDFo9zRq6hDZoVEypWcgPUkwfg42r1P0WoSlhanGuCFHGU0YHA+uE3aq6LLGlhDs6mPEU3PUIjrryhSpVIpx8/cJOzVmEw4vDf8o0+bXCLcXcRyHRSuDt1JjKB3hUOs89wfbnDSOs+TPo7TqtIrHSGYPMb7wNHmtwy6HaVptZPw4mt0k7G5zOKwRjSTZ1ScZm+zbG1SbL+FsPMX9QYCSyDE09jC5sfvh6pepLy9R3d7jhn6MUN5k+vBpCs4WGDGqqeOsdTKUZIht7yjvqj1DtrsHhXkYfwh6dWrrK5SM+6iXTbatDAl9jJhegniRGF2OiA0eUy6/sRPSt8DgP9GAAQMGvM1Eo9FvGol7/PHHv42jGfB28MILL/DFL36Rz3/+85w6dWpQr/4O8HrNS94K4femU0X3u4COdr9MQlkloeSB43d2B72twcvihSt0r3+V6NgUQXqGp7/0dWJ7LXbTh5kb+X6aVh7VMhnvLeAbcTYK7+J+dY8xo4nR2uOQv8TsRJxG2CTXeJpCyGEjdoJqZZMj9jVqRoKdWBp/r0Kx+jzDSh2RHKXpqmh2nQgdLCJc8cfY6xU53LvMfODxvPIAfnkRU4DjxVmumFQ6Dm5ymRFzj2syxpaVwOk7MjBEnfvFTXJ+B31DYZodCrJE1m8yKUqkRJyuDJESbTJ0GJN7pEWHGBYqEkN4BL7giLaGIiTvUS/QUnYJjT9GZb1Jzt0ikAqehCoRAhwmtCohYYOAAIUhJC1ifMW/n4zS5hQrhGjhoGAS4UJwiKaM8kFeZEi1iPltPAEhaWOIAEML6KLgouMAEVwMeoTr18nTwgtsAj9AINBUkxgajoCq4hORDioecZr81c7v8TynWFOnydLhcV4hjIMkhCEcNL+NjsDTi9gMEXf36HgGedFAVwMcdGwlSzjYoyc1BGAEPlHFYTgo4fsteihsMs0MW7hIDDxSyQSuk6Vrp1ADGw+NnidpuQrZwEOt3iDU/CQdWcEPwEdHQUNRdRIGjEY87F4WaTcwfJO2WmDXixHubKNsPQuFUdAj5KdPsmLqXKzpjIe7TIa6yMRp0gWDdPMqVu0m0uuRd3Z4JOyRnDuMO/sEZq2LW17k4tD3M8E2N7XjtC0PmRhhOBXQ7tlsVVXobGH2INNeJWyXeTgXZXzyFFY3RsvXGVb2mIrsgdUkURjmZm+KC80YY7MPk41eZMjosV7pcnWtRkS5xMjELDc2wtTKU0RCCiI9wnB9C9zLbJsKa8E0nWaK8w2DNt/Nx3o75FbOs0OeukywEkQQyTiKEIR1hY7jc2O9RsmHs4f/GqHDMLm+zGnrJo30ON3JH+NKdZnhZI6+J4Hyxs8n3wIDkTdgwIABAwa8SdLpND/0Qz/0Tg/jLy730OXy9SJuf14878rbq9RK24TTE6wFRby76/tua/CSqrxE0rxGpZ5mqxqiFR2nE5mjGSiUFivIhM7RkYcYMmwOYVBof4mQDLi2rvC0XSDjFAgrMbTmSxytfInxTISlShtR3uB+vUe5eI5ubJLa5ibr1jjj2UMUMyPsrV0njEGLImvqLF/jOBEsYpiMKhUe5CLB3iusVaexUt9DUUjGWrucb4cpmg55nmVaraAJwZoc5n5xkx9Qv0aKLmHPQcPFED452SCMg4/CcjBKnC6n1UUKokkIBxAEEoap8te1P2FINDFliCFRZ0JW2Vt3OG1fJS3qlOnyWf8cp9QV5tgk4tsogE+/YaAmJAlpUqRG1U+S0tr7F74+Q6LO+5QX0QgICxc9CPCFS0+6+75mkrjfIa508AMVkzCWoqIHNnl2UABd+oDAUgx6xHAQdNUkMcXjpjtEmD1idMhQ5XGe4SrHGVVqjAQVfAIcNGypYKMhUbADQUi2cNF4Ut7He3iJY2wQwiMqLEDSEyE8dGKKhcAnI+sk/TZhHMKqQ1dEsIOAnKhzrvknVIhTJYSn5bFlCE9LgrsIuBR6KyS9FbpKlK4SpxhSyOhJPDWBmTqMEY6xboyB22G828IOdVgORhkNaqQ82G50McvXkP5FwmqMiJJlW4yyq2ep+kUS7W3KnTA7yhmiIZd5q8FQb5MdI0k0Pkx6+0k21l5CK97H+vhfp7y2xMnmV0n7DtcTh8lPjnJkLsZupUq2fJ5gM6Ajw7RkmgQm0dZV9rZfYT0S4ZBuEpE+CcWirs7yZDVBVhP8jbECzd0LuJZHyjHpVpd4xk2xtlbH74aJKUne7e8wbe9Ce5fR1HG6J99PwvVRattoikIvMcmGp7O9vcm4WOf0iM56ZArT9qhm7sNyAxbW6my1F8jGTnNuNkcxXof2TcjHeC6Y57Kn80jlOntkqAYpPhM8xt96m887A5E3YMCAAQMGvEGCIODjH/84N27cwHEc3v/+9/Mrv/Irg46abzX30OXy9SJubyjVsrUDm/1mD4w/1N/eXSLzXiOEt+Zj7SLB9jL1xDF2ZBg3aqKVLtK68XWSR95FrthP2SwbYzRyZ1FqVRzFIGyVePT+U7QjNZyNl4g7C4S8KjvKCHr6LOmVP8Zv1qlEJ3GcMguNEF7sCD+tN9EaL3O1l+Rr/iiebzJqN2gZBS53s0TLV4l2NrjoTPOU9wAf0QXT8nlSiocTHyI68708UN5htHaRi/4ENX+I+72XGaZCWDrgJCkFOaa8ZWoyjnBazIvrHPYWcFVBV4ZQpM8oZWoyiSYUhmiRlB1UIUFKIsLmYXGNMaXMsKjTkREsdNJ0CAufw2KTDjEkgjA2E+zh+irR7gVSagcViAqb+9QlxkSFJD1U9gUegAAhISQk59SrBFIQ6r/Tj4ThExX2HRfCBm7fkw4VT7oYAkTAviSVqIGHkAHCD3BUDUtG0XHRBag4uITRVYVhw2Tbk6z4w4SEw1iwR1xYHFUX6QUxHHQ0LMK+jYfaj9QRIkSLAi0k8C5dY1WOcYhtVAIMabPJEHtBAluLcJQt4vQQBLSIEZIWKZpIadMQUSJYKPTIUUbFZ8MbZS96iiGtw54YR9HbhH2TkNegSoYbsTOcKapknRuUeoJn3CNk0yPE4ys0/AkyCYOi5tGLjvCyrTOpeqiuZL1rYAUa9+urvFfdoNoL8VXnEXLaNvHOBq4SZq/wfqKGyrHgPDXP5WUzR/IL/xnZrXHDTJIzRjmZjSJXdplNONg+rFe7mIUiZw8fJ3cY2B5hB0ltd50wNpub6xitKn6gUA+gpvbI6T3ExiUm1Rqz4e+loylc3PMYq1ZJGZCJ2ZT9bRo1nbO982QjJsXCCEcSUZKRGYhMoHRr6EGJOhlGM1FSYZ3jR0cot2eodheYSzVJjB5hvQpjmQhjk6OY1ibj4iaFWP3Vc82tNOzsLNP0pw2Fj/GF4EHW5DB7ZO79nPQGGYi8AQMGDBgw4A3yf/1f/xe/8iu/whNPPEEkEuG3fuu3KJfLfPKTn3ynh/YXi9sumL4V3lCqZW0ZVp7sP4/mIDlyYGycmHXIHR95TWMU4EAIlo0xXqyFkH09wXajx0xskrHRNqO6x3DawgjluPGVL1CoPEMLyB3+BUiOsHH5Ko1GnZHRY4z6ZRKxOl5+nmc2J/DyDifyks2Vl3lh/TJCwFxsBOlEGeutkNYqJBzIemucufpHiMBl23gX15SjnFJuIGQMaTXRzWuowqbphZiQO4y1vkZ+tclJ+2UigUnXi9MorRKtXCHhbRJTs2yJYbbVSRAR9EiSCVlhxNtgyN+g6xs868+zqWQ5K24wKiqoIsCWOg2R4JXgMEXqHFNWidA3WEeAgcl71FcA8FBRMUiIHgZ+P0qGjyNVOoTJiQ4CUJAEUqD4fTGHDJgUe2jSxRcKkgCF/sWtiuw3nNl/LUT/BwkAVyo0iZIVLQQg1X1h6ENIOAgkmuiLwQBoywgaPlHh4QuwCLHp5amKDONqlaywQAZYGJS8MD0/RCsIMcEWCboYOIRwQbbxA8GOmqSARFE8qn4SBx1PDTFEDW1fiB4PFplWNgjQgICWjLLIJJ4IOCa2UJF0CLPFEClZY0RUURA4CAxctP3vUcNFwadImXT3WRok2YodQxhZ0uYqSd1G0eNouSlavVWutgRdX6XiOkyHFjkqr9HKZomHFbT6IhOdXWq+AIZo5s/Sjh2nsnaNscY6Oc1FUcJMiV1mjB7FbIaqkmbIrxKvb9Eam2HdOEz5/J+SDVbIZTIoxYeZSTVp1Ldw1AhdkuixCMndXdLWJqVW34R8Npxm5PQHGMmep3nzKV60iizp72EkGWZWr2DXm5hahFB3m5TX5InDJzCPfYDh7YvEdpbRbZuaoxBS2xw3Skh3BdvIMBpXSSo2pCfBMeksfI5eb4H4xDm+Ry1RiITJ8hirtoFpFChnD1G2oFG6yelYnSL3c0mGiGAxquxQpA6MUCLDajDPNP0bQUXqUNvCkv3vR5Xf2mnpjTAQeQMGDBgwYMAb5Hd/93f51//6X/M3/+bfBOALX/gC3/u938vv/M7voCjfnrqLvxTcXr92F295w5XsLMx816vPgYtmhuXGMLlKnJHlKgVrk+zt9XVwIARr2hC13TYCyfCJd3NybILp/CjaZp328nnS4SyfWdCotyeZo41tTqM9vcJK2eScukCofIllY4jMsfsI0jP8319b4us3uyQjk2THo1ywLZq9NcZEhSU5wnF3hwxbbARzVI0xPmD/DwJri6pSoDZ1hkPtMnlrj1B3mxF/i6xYoKIUEbQYUmt4rkau1kIXTSQ+eneLY+Z/JxLYOGqI+4IF7pcX2RCjNJUsm2aUI/4q02qZtGyyJ9MARLFJYmLst/oXIsCQIR5SrpEWJi4KSBVN+LcJsT4BAVla6IAvAdF/Ly8aKMgDseWhECCQ9KuaQsJDRaAKH09Rsf0Q8f20Rnmw7j76/qMAQiIgIbtAX9zJAPz+8PB9BVX6KPsC0QcCoeEANi6+VFhlmEscIitb6Ei6eoZtL0k0qHLCv0GFNGtymLDiMqQ00BSvL1JlQIYGYWw0NYLlS54UZynJLN8lFtBQCGMRljaahLhv4yMQKuRkiYexMDQHHYcGaTaVSSxfMEuTAIUeEXYYRsEmikOGNgoeLgYBKjE69FBJ2GWyhkYpOkdPhw4RCpXnyXUu0ZE6F/RHcaIphqznUO093FCIbb1AXKYIhMKE2EIVguvRLGdOHOf3S20uecPEVZehiROcbS+R7y3jKmcp9TS0+ssMKxUKSoqFxaeI9XZY1oZQhh9kPBki375GKqoTyirkLJVFO8plOc1Y3afzhU9ztZfjQyM9Cs6zsHsJr9UiGaTo5D9I+NgxppynMTYatDEoOyGCSIxG7ixHw20K3SugdtkOT3AzMoMayXA047PVLVO1fLzFL0M2etA4JRmPMF15GW9piXxMJ9ZQwK0wO/nhAyP0ctsm1d0k661R3QzT7bQpihaF3mL/JlGnRPflL7HDUTj2COW2zfqLT3HGWOOcvkoQBIS/Df8eBiJvwIABAwYMeIOsr6/zkY985OD1E088gRCC7e1txsdf2xp/wFvPW153lxyB4x999XVrh0h7nS0xzPWSzqhd4btGxjl7MkbZGOO55SrT+RhbQZHtYJoRafKYehUpIRk7SW72fgC+1Mmw0hiGbofa7pe5auV5Sv0wYhNGdy4w0rnCVcVEhtNcVsZ5YWWI8abDxc0mtHco9iq8vH6CZ5zDePEYcb1KyK3jOIKr/iRfNd7D8ZEkejnFjdYwl4J58ttPUncElxhC+jN82N8movUwgjZDosokOwQIeiLCtsxi4BLxHTK0SIguHRkljk2KNmNyl7KXwfDihEWPuhfBEv1I23vUC4yLMioBgQRDQCAlWdEhT7MfLZNgYSARGLIf0fP3rR2E6EfOAKQAV4IiQNsXeCgQyL7qqso0AW3SwkQTAUgHA0kQ9KWdJXUMHDTRF4Iqt7bRX7/Y/4uqHlKA8MFRBB4GIWwMvH7jFrU/n+JDGIu6TGCgERIeWdnmmLbDKGVy1Og4UUIIpiijaTZh36OrJXAJ0SOEsb8rGcJDV0HFoidiWBjYapqMIhlWzb5fHFEQEMbGkSouOlHfBhWSmGg42Bh0CDEerJMQXQzNoU2cy9r9xL064zT3vfgUlP1YXj/GKTCEQkJzsO0ATWnTTJ/mZlOQtRsMSR0Xg0RYJzp2CEXt0N7ustUJeNEdYrYwS7z3deJCxY6PMzp7grF8jOlwl3luMh2USW1voXsmBA6NVptlMoRkm0hiiEJsiOPZVV7xp4lMPIIRDbjWVTFVF33xIqbj84qW4hU5xbqRIrX7NJGtp/GCk1wPHWOuuUSqs4vqQ4YWh50FossreGmfxJH38v/cgIo6D6kZGrs5Hik/y3tL59Gaa7iTc4wdOsXQ1DEK8RAA2etfJisten6Y9VKb9HiG4vgJvI2XaXRbNGLTxLCgsUZheKtvhN7aobL+Co4aoZY6jh0U2el2GUkM4QyNc72TYa76JbKlZxhXy2RrOhf3PLY21gkVkyy4I8woO1Tdt79GeCDyBgwYMGDAgDeI53mEw3f+s9Z1Hdd136ER/eXjVg1MLKQdCK57qZO758hfbZkTzgV012QpGCdQHmJs8j5Ihtm4fJXy8guYw4dxokU6ww/TpM5sIUEmqsP4PKWWxdb6MkF1BTWwmS9/lVEnQBOH2bFjbIphTudNDnWv4ngeF9T3s+WlWFqqcGTH5IOhFULGIoHvcf5mwEZwjJHUKJHJ47D4ReKewvnIgzh6nvfIZ0kodRbkEAkanHXX2Pbj7HoGR5RNDHzCuNywR5lQNhhS6lhCoyMjrAdFJtQqVZnCCBxOqS0i0qGmRtBUg5xw8LwO9cCgJww2gyHiSo9jYo1JsYshXTyhHlxYqoDER+wLNlWAjoMPB4JOAQLRF3CBkGj0I26BePXrl7IfaZP7yZi68LDREEpfEKpIFP/VyB4IBHde4Irb13frSdAfgBTgoPcjgvsDF7fNggphXIq0UXFRfYgKC4lCkhYqPiG6ZGmg4iER6KrHMbZQcJF4BL6Gr7K/DRcPjZtegYaSIxyKoqgKPTGMLdsIzyNAoKjgegJTRKjKFBZxJtkiTICDQgifPA1AIFGJ0WXGu84rHKFIiTRdeiJOV8aIYBHCRVdU8qEAN5KkZAbkrRUiuyXGogVkuIUsnCbsCc7oJhvNCzxbeJD5sQy11WWaoRHKfkDI6lLxwuyqRzhlqJS+8u/4SPVzxMNVQl4b2hYWCjvkWGSeU8kuZrdFS46ybhzm1PEh0l2X5XIVUd5gZOg0jsxi3/wqntVhM3QYMxrleHiTvNrD8XtMBGtQj7GtFPCzSWK5MUKr65ysfJZktUdm4hA7qR+mkpjEi0pGUhFaSzdpByXqHYu0YyNLl8n7beR2npdix8lvPc2UeRFjaJ7rkz/MS/4csxWTYnoSffwMUiaJ6RZsPw2xDFVXZ/eZzzKqd5mwNtGiefKpUYJ0lFBbIdtU2dpaY+GmhT8+RL74KN12QGz9Raa7HabVEpnhDxAVm8yKHUoi+2efe94kA5E3YMCAAQMGvEGklPz0T/80oVDoYJplWfzcz/3cHTYKAwuFt49bdXcvXb76Tevklq0E0/nYtx75y86y4zyDX7vCfKzK2MgJcvvL3TI6L5khLraiNEyH3ViU2Ox7OTebo7y9yovnP4NXuclI7yYJt0faLaPFZ8mYXabtl7ionCI19gFSQ5LLm018opyqfR1fBkxjc8p8maYjeYVjXKdA13dZqwZsNbr8kLfMuF4hltikaa4yXN9ikzzbaIyLPdaDIk00Pqw9zzQlaiJGQyaYZJPDYhOdAFf6hITHCWWFtozSklkOKZv79W9QCZJEVJWE36JAlby6h5SgapeoyzhV0mh4hETQrwO7JeBEX7Ddqovbz8LcF3+vCikBaEKiiv4bt1Ixb80jRV9sSQkqHkVR79ftBfvrVvrzBvRtE7T9mNUtDtZz2zZvTfeD/oCiOAT74vD2928vm1Jx+8uoECJgnC0kAR7QIUldDJGUPQx6RHBQsFGQ+ICi9tM+m6RQ9rtqZv0yo+ww3ltjnQkuDT1KIlQn3niKPX+ISblBQuvQ9QUvaqfREIyyi6SfGlsUTaSUeIQBh6RvM66WqJIhhIeGRJcOjtAJpMDWU6ArBGoETUgYOY1f9dEUk7SzjXRa9HqSYm4ChEfQukGnpLFhVRiWu5yKtVgKHaVjHCMmazR7CtGv/Bcm608hnApmKEk1lCPq79CWOtf8cRr1LmeTLdreOk6zQtI+BsLCeeEPaJopasYo09Yz5I48wsbku+ltXWaIHhlxnagHIjOJP3yKo36bzEwWQ30UW1fpdKqMWl9Aszdw1Ag9/SGGpo7xwWL/GNdKF2ltfAbLh/Lwu2n3dtGjSZzaTeTeBq6yTmDdxPV7eHqS9AM/xOz+jR9qJslkkmRqAm58HrtToRRX2d7YRe5cQBud5ejMgyS7VWhepQrYiSl8Z4nI9nlmzE185QeJn/mrFK8/RcfyMO0yh1MtRrKxV3f6bwMDkTdgwIABAwa8QX7qp37qNdN+8id/8h0YyVvH22Uq/nZzS3DdXid3qyvnHi0ue0eBN9BxMznCHzkP0HZcjmYTnB6fP3jrlqF5zBjj6rKkY7kMizqHrC3YztB5+Utk9rbpuBLL87lhFRF+hpQXY8K6wZDYJKUqjNlJOkqC0MzDzG2cZ5qv4gkdVSSQlknC61JkjShz2DJJ0i1zRltkWi4TpkKzWiIVVAl6VTrhNGm/w5xYo+bFmdVMcrQw6JH3LSLCZE5dJS5sJGASIyObRIVLTcZAQI4myIAOYZTAw3V6ONIlKhx0vIOmJCFRx5AeXRkBehjCO7h+vVtU3S20bj2/9Sflq8/vnl9hPwX0NgEn6XfPFPvm4IGEDiHiooemgBL0Z7olMg+2sb+BWzV+uh8gVPbblPQr/gT0a+HoRxcVXhWRPioaLjksXF+hpma4IO6jHJsl32ui+3a/uYsv0ISPo4QJ46AjMXBpk0DDY1StoUifZNBhSK2wXu8R0QJGvE0KKKj7SZZSVVARHGEd9sel4qNLF0mASgeffk1hmyjKfvWhg4KOQ0b2ECi0jBHqhVNYuwtIu0kq1sTM30fFbJPqrpEMAjC7qHIVT4RIOj2SXYlv9xCyQVi/QcdyeL97kfFwh6rXw3W6bPg5ekGGiKfiG3GMxASB00NYPTRNwVACwoZB4Hnc3OuQNZaIN25wSMYpiy7FchUzorGd+z56eoewLpjPhyh2rrAZPc7W1PcwGatz4sT9UFvm2ktPUt5ZIx046OEIfmyc0OyjZEanKdC3KzG//gmS1ct0oke5MvX/4vSxeSbCbS48+wW6zT1ysoHZDbCCCeTE93PsjgZN+82dKjfB6VCPH+Yl4yEKeojRdIzk+GGYfrh/AymaY62T4fpui5iaYejou8g6EbJHT6M0Vgjq69TCR9EO3YcRq8P4PK/IFnU/wZocvrfzz5tgIPIGDBgwYMCAN8inPvWpd3oIbzn3FOm6B9+6bze3BNcdHTj3nw8ZY5zcj+R9Kx03bwneiWyUtUyRzJH7X/fzFuIhPnI6w2rFxNh6lsWXX8JJQLG3iZobYa/4HnbXb2CWO1Q6Nu91rlPQKihSEFYrRG/8LxLSZy/6CGm7X+u1RwLPFsRUhyF/mwn2KPkZJoMtflL7HFFhk5IdIr6D4ffYkxmiaoMH2l+ih44qQZc2w3RokELxbLKiQwITZV+k+UBCmPgofeNuYXGIbSJYAAxTIyotAlSqJAmkwpCoAa8Kp7xoIwEbpd9wZf+9QPZTNOG1EbS7EfvzS3HbvGpfwMnblOHtETqf26KC+x1cUn7fRgGxP82/a/u36v72tycE2FIl8NR+Z0YCdFwUBBYhFDzimHgo+1VtARKw0DEAT1VpkqApdXLeHjeTjzFR/zpJv4Eq+xv30HCQSFwEATrOreRJNKGgqT4GLuPuDdpuDLkvCEO4BEBY9jgmlsjSwAhselJHVQW+iBCWDipdDCAQgg5pkrpNWxRoR+Yp9hbRnA4KklgsQlrUaNOgGsRYrXQQ8TCoKQwtTkU5RszvYHZrhIMaRmCj620u+5OMewE0qpxzFjgaXCIUGIh4gbIS5xpH0Dq7vEdbQMsXsSLDeKUFJtwVGjKMZ6coJnQuqKdZMeaZ0WwmYmlydo94NoQMhqmGCij1ZWaNGvGhGQ53niPeukYsO0wkXWS8uwSdEcrGGFfkDJ7qMxmrEoRDhI68l9ToHFz9Q6iv4i9fwiqv0fV0rsSPsEea55arxI4VGT73I7ywUuN4fItC8xXWo6cYOnruzp3xVpOnbhVTz7CVuJ/wxFnmlQVyWgh0947z34Rhk1n7E/KqSWLm/WSmHwfg0tYaN0smV9UeD0wUyR2/H4A9MuzJt98+AQYib8CAAQMGDBhwG/cU6boH37pvO3d14OwLNIPp/AMUk2EKb2CVtwTv4bXP8GDzK9jbH+Ly1hwvrNR4aCbLSXf/e+hWKUZzFPOzfHIhzXJ5iDNmhXkhMSOw9fynSSsOp6XHmjDYU/NkoypBcwPPatIloECbcetpQGL4XZJSsk6EtNa3E5BS8B7lAhmlwZio0ZM6FmE0PEaokBBdQoFLRFjkkPvCxEcNHJSgiyIkivTwUDGEi6CfTqnh40sfKegLOvFq10sdn4iwqZNA4BMX1kG0zacf3VJlv8ZOPehj2Ue5TVDBayN4cOf7vuB10yzvTve8JQYVoCdVPKEiA0FE9j3vbolM6fWXDeh37FTuEor9usAAD52uiGD5IQJV4ipxWkGEHgoFWcUWaQwgRA8dC+EHWNKgoyXI0CZBh5MsELNsVpX7sfQsWer7F9j9iJyPwCdEgIKBi0mSFSYoKm1SQXm/e6hK4IMlwgilXzupAKnARFe3UFBQFUGXMDYRiA5jagYhc4uQVz0Q2km374unhAS6ptFxo6gCtG6Tds9FODaRwCPjr+OFBHPjc9Q2dNodh0pgUAmGUJL3MRtqkTWSjNYF8ZZNRO8Ri8ShZeAQYS16mnB2jIccH7VSY65nktB2KDlt1rpVwkEXEYqTiMeJtPc4NplHTBzD9QrUyi+TaV4lFo/CiR8AfRhx7VmiVoMhb4G4UwU1BKpBYeE/E2rfALvEXuIxNsUwnWSeYrtBurNFtQRnMnXC17+Mt32JiGdRC4/QTs4xPvsoy23BkzfKxJ0y5zItwlYG310j1bjCKbrAEWjx2ptW4w+x2tS5YWaYjRrk8vMQNSgbY9Re/BJjjfPEj76PQjRHIWpDZAiyswc3hgrCJh/VGHMqFLc+B2q277v5bWQg8gYMGDBgwIABB9xTpOsN+ta9lbwmrfSu6OI3jEh+kyhkeXuVvbVr/Q58o9MHQjd+eY2YVaKzdYGLn/td1vY6tMuPUTw3xR4tRrtdMs1XqHYdOraCpsB5Z5INGeZE9VnmrRvoqkI8nSelx1hzsujOBnlrEcV3WVVHaBIiLess+mPEaVIQDZoyRtkxGFdVBAFHxDo6DgJwpUoPlRAKEWxS0qdFDB2FiHBQ8YnTxZCSIVGH/ciakN4dYgr6gkeyL6jkncIqADTpERddIvj9urR9xaVxZ9fK2xG8drp4nfluTddvS6UEwL9TECr0x6bcNo8iJCoSTTr9+r9bi/ocRCsl/UYut+r8fO68+FVEgC5d9mSatNJFc00ctH6xnyZJ0AUkDgZ7foGY7BLXrP24nLJv1N7tR/l610krXW7tbT4Sx1PxhYalhggToBHQ/+V82oFKbD8hVSMgpTp0CQEBGpIoXTQVolj0CGNh0FOzmEqKhpdmiUNM508wXfkaulfBReDSr9mTVp0gFAXF78cSbZOL/hjhQJDCQlckI2qDkZhCszCD4u2ieS51GcXIHacwqZP2a2gpD2dlhUw2Rnf4AWqLLmnZZCLqkFK3SVkL4G+CswfVFlEjTxGTjGhSMHaJn/gxOgtfpFNa49DoEn+wnWWx9m5+1FDI2hEqJRjWl0ivfBbNbmA70zD3ABTm2S21CTW+gudZdDpNlM4rjJlJrtQ1FtwID4iAcO0G2+ZhjPSD7DbiuFYXPz7ERCxA6i6GquD6AV5lhby3zCNqkkwqSaNRo7TbRo9dYjYfe+1Nq+QI+RMZjq4vM2VdhE4/+rZR61Ktdkm6HnG481yYHGF1ucrlrSZnMuOceOg9FEvbjDXOw4ra9938NjIQeQMGDBgwYMCAb41v4lv3dnG3qHuNiLsrujidj1HvOtRMh1LLelXofZMo5N7aNVrL5wEojE5TpE5RWeaF7HEWa2XqFBlrvEDUdVmrDvNi7QgL5hzDos4HR7KsBUWG21/mrP1V6iJBwwuhaxpNJUcYF9nuoPsmoSBAUxroQiBVSZcYPcIcFRvMhU08L0TIrxMRDl0RRkGiC7cvaABPSiLCIyzbeKh93znhYUgXXUh0+oImQQ/oi50mMUK4GOK1CZN3NEK5S7RpQExYKLJvLi7233y12QkHaZpwZ93bN9rO3dG8W9u9FQuUErpohPDuaN7iizsvXPuCKTiI4AF4gI3Wd9STAQhl310PvP2Ku1uGAqj9bYUDl1FRwQoMDBxGKdMm0rdiCDwcqVIjTlNNksAmhIfExiaEgkAS0CVCQ0kT99t4+9+bQkBI+CzLIcpBjnllmxAd0vQo4OKjI6TARkcIjxhdQvs1dTUS/do77P3PFWFPH6OnJqk5KiHX5JD9ImlCJGQLAxMdnx4hbMJ0s6dJpVLEqtexCLHspMiZe8RpoRhhCkaPqNOmtiQJJw4TT+c570yzvlfm1NYVbK9LT/EIVUuE/C6aTJKpXWAh+yDJcINJbx3Knf5NE6eLFRunGRrFkQKtu0zYDzC8OugRykoB9r5K4/JXeLb+HhbMMbzID3C6d5PetUUOJzxmfYmmaUQTKaqj72VRnUMNXcRW0hAO01LnWPCKqI0X+Z7GC9SMYULjc6R1qHeaiCPfx7L+GM3yOofsBZZdyU4tSae2wXjrGjeDFJuBxTF9iag+zdfCD3G+DcGqzt8YHaMwwmtuWhWTYYrxOuxchZYG0mMqdRzmH8dQDsFt9bkAtHY4ZC2gZ4pMZKPkGm1yCReS90Mks7/+l17/4HgbGIi8AQMGDBgwYMBbzlvdwOVuUfeatNLb76i3dtA2F9haM7hmRgH4nlN9QVc2xljqVdldNwh3djgzmTkY39DUsYPH8vYqta//DvHGIik3YE+o6IFLavoBSi2VeCRJYePzaD2XFWOei2YGdftZUrVXiHtVEt42WV/lZug4l5IfJt9Z4IxzEUlAE4OOb5BUDELSJua3qZJGqB5pbxcVBw2PlNJhWFaJyS4IcKSGLiSGABV7f5ra7+Ao2Bc2LnJf8Wj7wknQ91i7PdoFfYHjcVskj9dvUmIgD4Td3ZE55bZpt6KBcGdE8PWienencd56LiW4QiGQKrrw+uuTr24ruG0+RfQjcy6CQPa99gLZ79bpooBQ++Kavu8dCGzfQEgLfT+6pwf9ZEqBxJQhPKGRxCShWbT9MBWZYkdm6WkRDrNHHBsfZT8WpxFGoiDx0AmEQocYLioBghgdNNVnlAZSTaEHPlL6+6YHIAhASCyiNIhRoIaDji40BCEcaeDi4BPBT46RwKBndokGYCkJYkEdehaNQCeDhoZPBBcNuGEKnhv+Pt47Nc71doTa3gaPykvEaNPwczSCDJ1wGscNoe1dp4DPhFcmcBUiqo3MzFHduUqycRUhBa60MIRDMVKhNnQCvV7GyI4RSyk45RssaPO8UPgxTkWqpKLnCe+CMfowiewsySM6ptPCV0Kc0dc5nmuTmzhKe9HjuP0K2cwhvPGHMJ0OiUSW3UqVy16ekXqVpIzS9gzIniBwEhSW/hfDwR4FTZAbuo9dfZJlf4ij9S0+mi1RcbcRe2s0Gi1GeqtorTBxt0bJPcp2eIZpc5moWWXs8Lv5I22IWsfhv11z+NGHHnjteaq1A90qpCYgPQmOSWCM4VoJvPwsJMOw+tSrN42AXOlZcpEsKCNw/U/AasF9P3rgvXmrVFTl7efPhcj77d/+bT7+8Y+zu7vLfffdx7/8l/+Shx9++HXn/Q//4T/wsY997I5poVAIy7IOXksp+eVf/mX+/b//9zQaDR5//HH+zb/5Nxw+fPht/RwDBgwYMGDAgD5vtUn53aLu1jpXK+b+6/3oYmsHrv4h3e0trOYsbeXkHZGlZSvB/27NsFbtEta2APjuk30BWBidpjA6Tall8cL//o+MrTyDQYe4CnG3zqa0WTbvI18cZdSpEdt+nrgQMJKjur5Jbu0rhMwKNSJogUIXFVcYnPNfRPFLZEUHKX0ytBliBxF4iMBFkT4bfo5JGScr2iQwEUhGRZWw6qIEt0STd3DhJvfTL3XhH9THRYW9HwfqI/aFlspt6Yu3G4PfSrm8tc7bv/DbBJq4673bBdrtwlB8g+Xv5htG8/bHpBMghUsg92voRP+zKHfPvI8ldTQ8NAICAT4+IbijRg/ZT81UsQiEwAMUJOr+1baGR9LvoRCgqvsiTBW4nsKEXtkXZv2YoKlkqYsUIzQIByZIB40mPgnMcJZFJ8GVYIbj2gaPeM+ToUNKLlNTktT9NJK+wbpHjCRtfDQUI0XPcVGwEUjSoosrJT0i1PVRGk6StLtDxA/IY9ExBF1LoU6CKlGysooiJB6SDmFWrDhfqg2hp49g773ESjNMjFmO6dt0fZUFdxjfOM2YWidurpLrrfGou8jDOKx7Myym/jopmcIrXUMLHJb1YxTVFuF4DkcJsaGMYWfeT8YrI6oWzzTTXImEeffhoyT3vohJlJY+SiI5Qi45Aq5JZ+FZvld5mtHoLi8061xxG8SdMt1GjCCSpN5TaOcnmJg6xkkrgarmULYlkXCU4UiTme5ltlSfRniMZH6WlA5ecZTZ8GHStRcprb+Ip8UYUboUnMvEnB5JRtnTLarFWcZOvg/FykNUJ6WneVhd4GtOgrWqzmrFfO15qrYMzQ0YuR9G7wdg4/JV1haeYSUzy/seuo/i3anru5ehV+ufizLTUF+9Y5X+XY9vJ++4yPu93/s9fuEXfoFPfOITnDt3jt/8zd/kwx/+MNevX2doaOh1l0kmk1y/fv3gtbgrL+Cf//N/zm/91m/xH//jf2RmZoZ//I//MR/+8Ie5evXqa0xrBwwYMGDAgAHfGvcSpZsNt4lr1xkKHwNeW4vyLUX69iNzelAEZg/q6rY6GZZ3W8RLdZSpYyxbCQ5ZC+R6NZK5Ie7Lj/No9zK5xiIvXT7N2OQs0/kYj87lieh1qqbd7954F1vryyiVBUwi7EXmGFNqpOwuqmeR2/wCQzttNtIP81V/HtVpYHeXWfZyJJ1jTPlXOcYScXqEiJL2rzHk1+lIH8PvksBClw6akHhSoAufWbZQVY8OURKyiyV0VOnjoqL7bl+gCdC+QadJ9eB1/8Pc3k1SefVp//EeUilv4UnQ7xJrt6Jiqv/q69fj7qjd7eu+XRC+XkRPAcIE+LfSNIP+OO5ev6R/IRvXnFen7xuj3x0pkUF/GY1+rVxFJojgEsba/44EIdVFx8MHHEJINEa0Bio+Psq+rYKGbkTRjCydrksQSaB0yyj0O70IBAWlTUG2CEXjGGIEt72Ho0RoKjmafj91NEGHusgRhFLE6TKcitGr1Ij4Fob0kUqEtkggFJWukqQehAn5GjGaZOiQsW7iobJlHCbuNjB8nwCVEC49wjhqjPv8q8STo2wnj3OxYVIRMVT1MtNylSFabEXSXBGTxDuCR+QGGWmh0GOECht+l+EHv59K5Tp6YxEzOcuTxSd4LNdmVO/SDIfYVcK8oD1AIxHjS3sJ3FqX5tYWE9EwzeJRYkfe1f/yWztUSttsBXlOsE6ktcrxfBY7Esa3dPaaDprrIAOPbi8KtS6HlBXqrat49CBSZK9tIRp7KLEsij5MOFvkYjsGrUscGauxKfOcr8ZIm0vE81DMT9Ft7DLilhg32kjxPNHJvwbJH6LUsrj8+f/E3PaXGIlP4Mz8OLGQxnPL1TvPR7cJuIOGKtYmh8xXaJiLbBUTfU/O29O+j3/01Zrf8Ydefb6PBgfpvG8377jI+43f+A1+9md/9iA694lPfII//uM/5pOf/CR//+///dddRgjB8PDr+0tIKfnN3/xN/tE/+kd8//d/PwC/+7u/S7FY5NOf/jQ/9mM/9vZ8kAEDBgwYMOAvCfcSpSs4WxRYBicJTL/m/a315deal38jasu0l8+zHUyzR4aJ9itMNs8zF8kQUzOMWBU21uCydxQ9U4TiI2ybCqe6l9Cbl1jZkCxmJCWZoSjqPKyWePDMDMtW8XW7iKatTdoGlMKzrDLOZcfmnF8hT41Jex0VaBPna8P/X3RvlVzjGorfBdlilC1y1IjTJS8rOK6Oio/0NAxc4qJHmwgVGSWMSxITQ8A0O9TVDAnfxsABoeB6YIoIKdnr18PdJnTuFmu3i6ZvVvt2N3enUt7+eLsp+R3z+6+Ks2+0jddL07xd6N3eLfOgE+f+xNvrAW81S7klDF3Z7wqq3hKA8s75VF4VeLdvy5Ug1L5/nqDvi3fTG2NO2yGMg/QFjqpi4AEqe+SICgcpvX2Jt9/wBgPXD4h2NtgMcqwmHmOOBQrdaziuRdjtkcGiyDa6kySSSbEhclR6ELdLzFHHJEwCk7ysYjtJtHAYAh9bjRH4Nt1QAl1AwuuiqQEJtUw6FMI14mjdLh2ZQhGChNKjGFdwK+ZBymgYhxxt3h88g9lZp2t/FL2zwz8Q/wOTMNd5kF1jiK6Rw0+dJjM8xci2g1IrI/0CtFYJJebIZbJs1Lpo+XnsQCWTSmErLSK6RmZ0jkx+mJgxhrASBLkY2ec/TcR+EsM8gR9O0TMm0WP7wZraMmP2MkYkRjxzCNQW+ZjB46LDroyQkjatRIqaBW0RZnv5CllllSHdw0yl2EnNcd2fY3YoQXLUY6L6FM1enfWmR85ex+reYPS+HyKRHSJiL+LoKfYiw1hNSdFvE7Wb0Cn1bRaOf5TVioHfqzPirJF29wh383x5+0N8eUvl+GiSmXx8X+y9Wnt8q6HKw+EQR1IeLbtOSClx4Ml5i7vrlZMjlFoWL13auaPu9M5etG8P76jIcxyHF198kX/wD/7BwTRFUXjiiSd45plnvuFynU6HqakpgiDg7Nmz/Oqv/ionTpwAYGVlhd3dXZ544omD+VOpFOfOneOZZ54ZiLwBAwYMGDDgm3EPHnj3ZLPwZ3TgfF3z8m9A1dWxuy0mQ9tUuiX+dCfC4YrHw9lVjha6UJhnKH2MsVqI66bKU50xivUXiVo3MVtdLgaHcKKTGPUtthf+iETK5fCpR1B7LpcvNfmMdpTjo0mS1iZDU8dYk0OcD07hywpZexfFNUnSYMjfRagKLhFybonHKr/PljJKRJg8LF8gRZ3QfkWW6rto0iUkLAx8MkIeNADxEfQIkcTEQwUZIIRCWFroqoXm9+u1IrcsC/4spXYb38Ksr7UzuKuOTn2dld1quKLw2m29nri8lSp5d0OXW/Mp4jYxJl67zlvjuNXgJRAa/WTLvnDriTAp30KoEPivXjwfRDDVfYHoQxCo6PgIYEg06ahxHMLUvRhVmaKo1okg6fcmlfj7NYugEdo3QujpOWr6MDl2GVJ7mEqLAIHAJ4aDSRiJIEUTK9Bp599FxLKI1qrEgj2Eq2ITIUGXEA5u0KPbDaDXIyq7KEJlPf0gjhpmsvE8Ec9EDywSQZ0SSUKqTis6wU7uEY7IFZp+iDDb2Oh0jAJhrYvaa5N1d4mqHq9s3eCJxueYYAWbCCaTrHcjDHUvUV6PcKrQ4cSJKah6UFkAa5ccHSr1NbbbOxxvLZEQe7QsBd/uogQG5B+D6ccpQN+a5MXPcNb+DD1PUqv71GSAZa9gpWYpjE5DdpZ4+jLxxhotP8714kdIdLcItc8zNDxMPGSw46uobcFYTkVmTpBQ8iR1n2QqiyUzpMqbDBV0ZurPgeLhDx8mMzqP0dkiXIiTGZ/nfXqMVnibdDqJv3URF0nguUgpcXsdIr0a1JaJhU7SAlRFINwe1FaI+OvADHttG9Puh6lvv4EVC2koQhATNvFUnngkC+Pzd2QkKJ1dlq9fxIxNcmJ+/o6U8meWKgCENeh6kAh9K0fqG+MdFXmVSgXf9ykWi3dMLxaLLCwsvO4yR48e5ZOf/CSnT5+m2Wzy67/+6zz22GNcuXKF8fFxdnd3D9Zx9zpvvXc3tm1j2/bB61ar9WY+1oABAwYMGPCdyz144N2TzcKf0YHzdc3LvwG7lSpabYVRfYGJsXGeVI/yOc4x3HmSgrpCLT7HcjiBlA7Xdlp0HZ+JeAql51N1w/iK5GH7KXw3zI7TYK8TorhxBXNrgWbJ5aLoYu/FOWNs9LenHOdFjjGuXCerlEnSYZgqOpKGF2ZbG2XEazDWuojGJhNsMkIFzXewpUFLxNAIMESAg0ZDRokJmzAOChAXNmH2UBH4SCwMwvgkg/Z+V8Y+Bnd2nLxXsXe3sDqYxl1NUu5a7u71377M7QJOvo5ou2M9ty0f8Ppi8e71uoAiOTAzvx2VV4Wlpngg+hE8KTSkJ9ghRdprExYBAf00U03sR+4A9lM4feSBWFRVGKKDxMcSaQQSTUIjPEbTdon4Jorq4ftQVROk0AhhE/guhA1aXpyocPF8qMk44+gIBC5xVJoIFBrEWd7Tub9gIIeGeDkokmjcwJA9InhouorpG2hBF0N6SDQUVSUfN7AKJ6gaOot1lwlnmVivgek0MeihNDfZsLdZzp5movH/Z++/YyTN7/t+8PXEyrmqq7urY03qmemZnbjDjYxiUKRNnyidZUn+WfLvDNKAQPgnWIIhHCnhCEM4S4ZhWD6fLcnn89n+2bIkS2KQl7tckrtcbpqd2JM6p+rKueqJ90dVdZrunp60OzSfFzDorqeeVNXTje+7Pp/P+/0WaQx0U2TJjtPwR/Fp15AtDcMT52hQw1fQENoSkiRzRFnlQKtAgBqlag7rxjK1jAruMHM5i0BTRjbBZ1U4FAgSrDaR20VC7hB2LEww0b/xO9v7QCh3C5do05LdzLkmkFQPmmUzFEuv51WmRz5Fgm9QXl7ilqzi0SKkNJWA4cI/8QKlvEG9cIWVlRwDhol15CRoS2AbSKvvMppbwlMG9CXwxgide57n/EkoeNbvJ5Z/l5i3DZE+bmgXmJcatPtO0l98C+XACzB2GqJp6jmDnOlhXhomFBnADJ8jHonyec8iUixNWQ7d9QFWvW1g2TYl9xDEfXdFJhQbGtb0W+hL77DoLjCnhfjxkwPrJlHPHIgjCPDNKys0ajpel8Lj5gNv17xfnnnmGZ555pn1x88++yxHjx7lX//rf81v//ZvP9A5v/rVr/LlL3/5Ud2ig4ODg4PDffPVr36VP/mTP2FqagqPx8Ozzz7LP/2n/5QjR468vzfyfmXg3UcMQ388hu5z47PaaIWbnAqPsOA/iMvKs5J7g+W1KtPFW0SLl0iXm8gjT/PCkIdKI0xJhRNynoNr30NPnWNp5AXy9SyZ0gILRoIlS+O0cJUh73H6/T6C8RhTN67wyfxfEpY6RhwRc5EqHjRJxDBlYtYahihSs2UUs0ZIqGHYAi7BIiC00G0JUxAQJRMREdXQkdnIp+tVwWxsRMAnaOvbtrdx9VoP77uat8P+d7Vddtlegds+P7f9+e3zcTudEzZaMe8lUA06xiryLpVDu2dJCAgWmFIn+07FQBEMCnaAtuRhSMh0+zc3qn1tJBBsRJtOva17Twbgoo6BzJi0CghUxBh31AkGG2/ip0GeMBnJg4SICxvLtslaPioNnaDZpomAoa0Qs7OotNBwEaCBrngp2R6ydpRaOcdlMYHpC2LXbzFgLxGgiYkbXBEkw0Zsa1iKl1JghJohEDZtUvk3WCys0jAP857nAqfr38VNBt2AhmCTaN2hkW9iiyZNS8GDQku3WZaH8KsNomIVKX4EM5LGLB9kLSfRRkHyxvDaBjQaNFGpVxsEPQqa4uOvm1GCuo7oSfKMGGSAHO+Jh6m5BojYApPN2+h2P2/kVMZokex+IFT0j1EKPU3TEPAmRmj4R2it3KKhmZR7rd2pEIljP403OMWglSRoFPAaVwi6RfDGSDZXsfRFWmsF8iU/frdC4vgpAILmdZRWHis4zHLVS9Drxl+ah/k3OiYnPUpznXJueITw0ASlXJ2l1TnebAxwNPQUz4ydACBdm0VOBhH6P8189BQz7SDPF1/neONdUNcgdhBIAxt/nw6ad+irfJdg//Mw9tz69rS7CsYVrszFqJoJDkVGOWys0Vr8JkvhF0hOHiNJkc9IbwLwO7UKECFf2ZgjfVx8oCIvHo8jSRKZTGbL9kwms+vM3XYUReH06dPcvn0bYP24TCbDwMDGDyeTyXDq1Kkdz/Ebv/EbfOlLX1p/XKlUGB4evp+X4uDg4ODg8FB8+9vf5gtf+ALnz5/HMAx+8zd/k09+8pNcu3YNn2+PtshHzcNm4N1H2DjA1M0bTE9dJj1xgonDuwhavY7uG6Rm+lldWCLOf+Fo+hRacIS3m25kOUD/8l/jWrsMlguzL0Vs6Dlef+P7kL2IouYg7iIQGWDCFeRrU3BrrYbcLpM2pzngKuPRJExjmDeuz+NffI2z+sugd9wNa4bAEjHKlpej0gJeu0XDdCFZBoNCGQWdOi5k28Al6ADIto5igoSOr7tti1DqvrTtlbWeELyXMNopoqB3HnbYtpdG3D47t9N97bT/lvvZ9LU3yyftdGLurgQqbKpYbjpHLy5BpFOV61XkjG4NlO6c3YCVZ0EYYIk+EmIREQuzM2lH21ZQRR0VHQkTyQQNkbYpIyDQlhS8tDtOm1aJkL5GVKii2BoBmoRpY6BSwIsmhLgpHWcw4sFfyGBZOieEO7jsJmB3riG0WXMfYc57gqCWxWdAQ42RKrxLn/4ePkpYSJTEMK02uPQKFiLe2DDNyCSlxTma+SJrkoir3uAAV7nu+QgzwigvCLMocpOs4OKYPYdtrmIqYbxiDa/ZREJHs0Wy7jHyigvLdxpvcZVRNYLuqaG1mrh0DV+7c+8+t0Jx/GMs+gKEUkcY195D1iUSgSb9apMVLc5UcIyM1eRE7i/wl7MUhBL/c3GZZw7EORdLsUaFNXWAt9wHSNWvcpYqh4U7lLut2EY8TSO/QGXqIn/qHybkiXDSlyGmViHZD54oRNNEc7fwKFkkIc4d/1GkWJoMEWatCdLpFANDR3mnFuHGaoWTviKDjQa15SWCsT5CvQ+kwqMd0afV1zMu//zi/2Bs7W0W6h/mmdMdkZcoXSJhXYbxF8kMTeDL1UkU3dCgM7un1Trn2/T3K5Z/l1jlTcj74NBGAkBCW6Jev8Fcawix/0N8KGrhvvHnVFrzqOJh4Fjn7+HMqwAMCP0s2hH0HX6HHjUfqMhTVZWzZ8/y0ksv8dnPfhYAy7J46aWX+OIXv7ivc5imyeXLl/nxH/9xAMbHx+nv7+ell15aF3WVSoU33niDf/AP/sGO53C5XLhcrod+PQ4ODg4ODg/K17/+9S2P/+iP/oi+vj7efvttXnzxxQ/oru7NXS6Z9xE2DnDlyrtU77xFwzA2RN42obhcF6k0XVR951CNiyTL7yKZGXxnfoHE5MewZ7+HUVnEQqAZPcrERGcxlyq+yZD1JlrLxYrr04xHxhCn36OetfGXb/Mh3iFOBV3z851CgHeWfaxKAj+n5whSBWy81BgUOiMdNdGNZQsYgkjTdjEqLBMUGkjY+IVOOLcAhITG+vwdbGpzZMNZb3O1rLePaXcdI4W9BR7sr11y8+P9mLDsxpYZOxsMYaPytmN8wj2u1ROnvf1MNubxegHrNqxHQPTeO1vqCDQBqTM1J3SOdYkmKbIskUSyTJAEqngpECAkVJBoIWN1rimCaFnUbA8u2aZBJwDdSxtLkFEkEVMUcVkWfhoICFQQ0AkQpsYBOQPDn+WmYWGUVhhRGySFEsttBcMWGJCrSJFxxOBRkobAmFZET0XILIzSLN/CY5aRuxXIohTAp+cRMbhRczF6+ADRpatkGhZ5KcFBWyJGiZQ+g27WsAG/IuGVm5jNKi1TpdCUCNFAkTQOMceaOUzIL1OOjBCIS6gLc7hUibzswSNWqVkCWdODioAsyowJGb5vHqC/3KRdW6PRthmsziKttYkf+WnOWhGs2qsUzApXpDHm8l5crTdoR59i2pfminGEQb+HE0eThNeqDDCD2fZhCxIonTk1982/olLMcEs5SdSnMh5eJZY+AuMvbnwY5I3ikUSSzTmq7RgN7QUam8yZEpPPkaq00L114nEfM/PT3LZ1LGOcj9LNutzsbNn9O3RSexc/t8ia8fW/VYcbOopmspSpEg2vckFcgoEDEOsH1Qda/a5OhnzsNJV8nWDs9Fav4GiaUP8qZ3KrxNzvEAkfgCMfxQcbYenRdOe1ApnXK2CA631QYB94u+aXvvQlfumXfolz587x9NNP8/u///vU6/V1t81f/MVfJJVK8dWvfhWAr3zlK3zoQx/i4MGDlEolfvd3f5e5uTl+5Vd+Beg4b/7ar/0av/M7v8OhQ4fWIxQGBwfXhaSDg4ODg8OTTrlcBiAajX7Ad7I3dzlt7tHuuTlsvEdMNQmKWRR1IzkqvzhFdfotAmmN2LEB5LXLhDPfxz3moxQ7Qrt6i0bTwG27uJCOkTf7qSwp6C4/7oDASrlJwlimz84iYVAlyJ8UjnCmNES4dZtP6n9NWFxEtZq4aNM0BRLFi1zgJgE0jnELFZ0WIi5Dw9XNp3ML2roQCVDHI2jr7ZQWgAC2CIJprVfkrE2OkZsreDvRy7J7GHY7/mHOuz7jRkd0WXZHlG2uyG0XgpbQaTXdHslgyCKqbWGZXVdNCUxz430R2eTq2RV4bQMQRCTRwhbEbnS5iGzanfvARhIhTBUb0AyJiuyljp8AOiYWZjePThY0ZMAnt6iQwEMNGwEdmbwdZqoeIGV28gZFdGoEEBAYJ4OEia89xdzNr2EKCWxBYkEZR7bmaFg1bhkDvCmeJNWOEF15DSMSJhmLsmhDQYriD/TTrLbA1CjLCer+MfztLH47R6PRppG9w5iYISrUabubhFwGqi0RUldot7M0pQSZ0ACKVsSlzdG2ZBR0NNGHYhm4JBG320+x1UAuLRAYOUD0qR/jRqZKpfE6Q2YZzd/HouEnahcYSI7iNTN8qP0ahVyM8tKb6M0Sb5lj5O3jnFZ8nMl/h7q0RC4Cc4JIuPgtfHoZX0MhFe98mBIycghLb9HnahKMjHN7foHW8hSaN4nhzkOrgN/j5oVQGa8qExzoxAtkiHQ+IKJFcug8ZKfw3HyFdOsqYus6RQLo1SnCrRBwbL06B2nEqJdVRWapqW9k3O3QhRAKvIxaE0n6ZG51/1YpkaP4wi3mCw248T0S7lInC29TG+Z2bksHuBKMMymFtoq84ACReD+RzOuQmYF4/3rw+eZ9ett0719DRSPiVff8nXsUfOAi7/Of/zzZbJbf+q3fYnV1lVOnTvH1r3993Thlfn4eUdxIgykWi/zqr/4qq6urRCIRzp49y2uvvcaxYxvOXL/+679OvV7n7//9v0+pVOL555/n61//upOR5+Dg4ODwQ4FlWfzar/0azz33HJOTk7vu9yQYh93ltLlHu2cvbHwzZwJFzFANKVBc3zZnJVm2xhi0ksSAwuoC4XqOVnGFk5/5B8yZOYTSPPGVV+FamRgQG0qxnFllaXWa+eX/zEzqPEflcVJk0FGIlK7w+nsRPlz+PunWFAomNTwUCGJiccK+gUdoIyEgYKAhYhoCPsFC2JZPJwFyV/D1zEVsOllstiAgdw0+TDrH7eREyQ7bHr/f3t5smcPb/Jq3G6GIbHlP7opT6Lphml0jFWnTe/emcYynuE2w6xxqWp19ejEJmwVkT1i2cIEtoAh2pw0UC8uW0SQLhY6lZtsWEEwDSejcTRsZQ/JgmWuU8KERoYiPYVaISSXqBJkOnCJam2HcvoOCjUKDQ9YN5G7zqI5MgRgeUUNw+6mYMka7RsLM0mcuI9t5MuZxat4Uwfq7HJXneYcELcMioGfR3Wk4/jEWv/PXjM3/FRFFR/W6KDdFEEQSQhmfYuPTWhw3L7NSPUA4ehRTzOBOHiGRGgG9BWab1u1XKVoeVsQEgm7jEnUEwcItWeRsN4YGkq+Pmq2SK+eoalFarqNEjx0jGp6lXc1TbkUI+gMk29/EI+q0oynWdDdes0LFkBk2FwkKK0xbCjOun2UglyfWKuDz+vBJFn2125RdFWyfG3fcT4QicmuKi9duE829QSvkgdTPEYvrtIrXcQfcWKEY3v5BouEgodIUNc1kTjuE3hV4PdOSiFclfehnSQDuwgx4FS7W+5i2x0jbfaRhS5dAAnhRvc5cc5pMzk+m20mw0VkQITn2HLnRKYSmhZ36yEZ2Z/QoojiIq/4WoUAU4qfuOYO8Z+7npkrdvc7z4uEE35pa48XDiT33exR84CIP4Itf/OKu7ZmvvPLKlse/93u/x+/93u/teT5BEPjKV77CV77ylUd1iw4ODg4ODu8bX/jCF7hy5Qrf/e5399zv/TIO2yu4fPOn6zBwfyHnQOjQ8+BSYOTC+rbUSJpyU0Nbu0122Ut15CPM1SWiIy+QGBxj4fjPUHrrvxJYuUnQLKANP8+y+xRGXwPPyv/JwdYKb2XiXDRHCXENt2DylL/IIc80wVIJHYm2DR6rQlg0MC2hI9QkiSJePIhopoyLFhbC+mzYev7apjZC6M6f0dlJsOz1qt3mf5vNS7q7PnLude6d5vy2tHFunpXbtF9vPq73kbthb11Abr+e3TU32V6ZFAU4bC/g7wo8G9AtAbdkY1tdA5Ze26YgYHfFskuwMIGm7cYQ3HipYgoSeZL0s4THbmNbAgoGYOESDRIUsEyJhiFQQ+Wm72kks0GfkaWJGxcaA9Vr+GhgItO7o2GxY3XfRKGKHyQZl89Pe+w5Mm0f4dXXCQ4cID/9LmHq0LhFKz6AS7ZJGDkC4iXs0IfwGjHqpsaUFkcwWvjtBrKoUmsZeIwyqlHF0FeRbQ0FkwBVVpp5pM/836l351Yz/n5mc3XS7iqJgacoX/4BzeVZ3KqPnH8Uvd0kGfLQzC+S0fy0Q89yeGwIuVUgHvSRinqBztxYy8gxr7tRRB+DiSFQ/Ux7Jlis5nEZNp7WMnFVI6A3mPCUGYpWOhV3LdhpYSzN42kU8AgCeCLrQd+NqZcJFHIIihf3yEmIpolE052KVrdtMhZ1QagPIn0sZapcqkdId1+XX75BsaAwN5VlJpLm40d/loS2BNE0ysWrJBu3UZrdD422dQn4w1cIl29QuvWXLHlVkpPH7srflMLDZCOnSYSHSWhLuFo3WZjrdBOMTap7RsVsZs/cz02VuntxIOFnOlvnQMK/r/0fhidC5Dk4ODg4ODh0+OIXv8hf/MVf8OqrrzI0NLTnvu+XcdieweXbZvDuK+QcwJ+E/snO1y7JoJucsUJu/h3erGn4jrxI2zPB0HiU7PIs2tptMuFTNFYaFGdnmWmUyPRNMln9DmlKtGUTSYQADSRJoKSmsMPj4IliCwKS3SZgNTqOlxYoEhiGiG4KqOhYgIqGr5txt7nd0qZbaWJbVIC9Nc9tu+fIfgXeg87O3eu4XvvjdraYrWyrzG2/dxMwECjbAWJC5a5FpCV0YhAEYUMQbr43G0gI5Y1WVsAQZHRB3/KeWVLnKMEGXZDRkPGYTVy2QVkQsBCQMVFFg5wVxm9XAQhKza6wNM/wMG8AAHaxSURBVHHRJEYBl6wTxMYnzxBt38BPDRkLPxCijIaLKmHaKIQoICFgKVGaUoiipmAILnz+QcpWEHHlHQKKQfzQ0yzkyti5GUxBomVYBCUB1bQIi3XKioty20urnKN86VVqDbCUNB5fHLe4SggTXfSQVYZwW01cjUsYCLQlNxk7Qj35NHG/b8vvUmLyp1mpj9DM/jEhcY3BwTht0U1wIM13v/sdTAymayFOHP0Mkz6xa0CyBIxBNI1mC3hyV6jLR5nxnyR4+Hm8mTukCm/iig6gBlTCdRXFUPDGooweP9UVP2OdH8rgqR3/XwVjfai1RXyBEP7U0Q3BtF04dcWULd8gNHUZabVJrfguo1KdIU8Eb32BUv02C8nPkZjstE2esP8bpngFyU4An7i7S+DYT+Nv/zdG8mt4u8Hk2/M35+xN1cBokpWZWZaXF2kHRtevsy8ekevwO/MlbmaqxPyP3wvEEXkODg4ODg5PALZt8w//4T/kv//3/84rr7zC+Pj4PY95nMZhmytyewWXZ9WOw16fmiIBhFuLW+ZodjpnyMhh5qfpGz2KWJqhcPmvaathkhd+dr2ds2/0KCsrSxj1LMvXv49VK3PbOsp8aZa+lZe44JWY1TT0ap6mdQsx+QJys4TYyGPrGpX6Age5gYcCWrPNyooHr3mVlHGdCJX17DXLBtsEWbCQ6Yg6oys3ZMyN+bDuazC6Ikba/MI2mZBsFjews8EK3C0SH8YcZT/H3c95N+/bu3eZ3uuzcYltRHvrtW3AsEDdXOHsvleGvdWkpVcVNAFTEKkZbnxCGwkbQ5Ro4MdLDQQLCxkRAUECCRsJi5bpxk+dACU0RNyCgSRZyN0rm4CMSFC0UBUFXD6E+iI+6ghdA5bePVootNQQglZBwKaKh8LAp0mNHkC8+n2EWpb5ugDWCn21adoYXLn8DlGqiIKCFegnfOAcyHVorqEIEoLZoir4aKkhRK3Mgepb2EAjMYkn9nG8ahMLaGoewvlLMF+kZPpYFFLcfO8KdqAjZA5u+70bVmqUzAy20aCuRjk4MkypsEa4tUDEnkGuu7lx+wwF008iGCfaEyTBAZaMIN5mmVJuhboxSCOXR9Y7uYK2r4/hiVPUv7+EXc9QV+Nc7sUkbK/GbzNFCp3+HKHESOe57QJoB6ddYektRjOv0CzFydsWxJOMH3qeA3yXyiaxBmCG07T8t3CHdxFWvev3rsHd+Zvlpk6hrhFr6hAcIJ7stGkGNl1nXzys63CX2VydastkNld/6HPdC0fkOTg4ODg4PAF84Qtf4D/+x//In/3ZnxEIBFhdXQUgFArh8Xje9/vZbKhyYYfg8l4cQkYaYMY4wmQrQAIouYfIBsoo7rurkL1zhrLv4s1dodrUUfsOslxW8JjLCHPX10VeYnCM0xMHqE6/Rbn0fRrFFfJynZvqBGFNIy6uUfKNcbV5iIjLoi//Leq5GXz6CjGzxa/Y/xlRsPDTIkqeuFXAhYaf9roQE+hkvm22M5fQ12foesJmu1DbXHWCjXy3nQQc7Cywdnr+cbVw3uu8u+3Tq1Bud/L02O2Nqpu90ZYpba9wCh1HzBYqiml09sXGJdjrc38uDGTBXH+Pa4KXFhKCpaKLCiYqkiSBaaGiI2Lhot2JQ6CJioAimejIaICCgYGLdmAMRVYwoilKYgxuvdQVmAK2KVCV3FTEfoREGrNZo2Z6MU03hiCSqzbxuQbx+320GisUNZOMd4CKcohoe5lmdhlfoh+Ew0QnPkPo/M/AgROQuwWZK4jhCdpiitrcHeK3/gsRO4/u7UP3xwif/RyhoBtmv0dk5SKVSB8140OUrSjBWptBbYqYXMFVCZArLpMYSGMrPq6+/jWk+deIWXnq/jShkz8B2hLN9/6ag82LhCjg10wa+XcoNNoY7nOdubfpPCEjR1DPowSi+EePYcYOrpsfrbmjhEaPwuAYd957G1HKsKKPMr/ZTGkz291z92hV3G6gBJAIumn73Zh9o5S9o/i71zbqGkbtu6BsxMWsNAQqZpxgQ2BX+6nt4mvb4+H6NZLad1HrMnD+LhH4fuOSRSSx8/Vx44g8BwcHBweHJ4B/9a/+FQAf+chHtmz/wz/8Q375l3/5fb+fLUYDwbFd4xB8g08xOfrcuvFKaiRNxo6wRqdyt75IrKzQv/xtqtkaZXWAktpPv1bEqmTIizFGfX4GlUbn03/ozPKEI8Qmn2Xx9mXs+hp+u8pQ/SpZMcG84icvpxD8TQ43voM6+32aBoCAIup4LL1b0QEZG9Wubqm09Yw+NrcmbnbKFHd4HjbMQGD3+IDNz+2Hh4k32Hxve51/83X2a/gi0BFw65EGbGTXmd0TWoKAZdvrQrf3nveuqSNioqBi4BKs9fuwAVOQcNMReIYETbxUxRh+qwC2jWhaSJIOoouG6UY2DSTJQJBENFtFxO4Gygvdup6PKCUK6ihZ3xn8lVvUzCMMeURkd4hmq4GISVuUWGGQlppENdzkA0eQRsYRo1HqV/8K1dK5dfktLP8xggdPootBhscvkKl+msrcG/QF3ASPnyCk6GTVVCccPH6WZDQN8UOEomlCwQHa7/19Bs1FTNlLe+R5bshH0HtOkF2RsTIzS8Fs4AuEOBoycJFHmL1IRQpT0FUM9znI5alMv4U/EMd1+FMMHH6e2OAY2WVYDp9jSPwGigVBl0Bw8iOQy9M3epSFbrtnTSsRMk1CY2cYf+Fvbfld3myEVI0eo5jJ44sM8aG9TEY2f92D7QZKANFDz3aiCqJphjbdx2ouT6VUp5HLEzvU2dYfj+Et+AjGY3effJ/4zAouPYdsdk2pHqYit0cG6H6ZTIUo1DUmU6EHu4f7wBF5Dg4ODg4OTwC2bd97p/eRPY0G2BqHkBjcWIQlg26iPpXX7+RYKbX48ZMDJINu8otT1K5/i5hhEhl7AXeogVJeZmltHle9hssTIqKvUrj1GuXVObxmhVziAvHjH+WKVWGxOoNLbzAp36DU1rnSCDJov8lRycAjGrj0Bm5JxNQ9IFSwJTCMjSpbJ0R7A4FNrpddR0eDjQped/Ndgm5z5Wrz95u5X8G2vT3yUVb07lVR7F3ToFNZU4S7K5dIYJk9wbxxj4YAdtc608RG7KrB3rEG0DRVZFFHwlpvfdWk3jsvUEcBBGw8gIUp+6nZLiIs4KVFGw853UWMIqYo0cSPKblpmAplvARpEKZC2xWjLcVZagZA9JDIvYlo1MmvRVgoe/FbIaouP5rkIq+7MSQvEbGOt7lKJTRB68hPIkW9tAMH4J3/gKc6T1124feHOTR5ktihE1BZoejtZ63awvL1weAY09N5riyVaeQXyBkrnd+H4ABUVgj0jbHSOIV/7CzRD/1t0q3AXS60thanWHUxGDUYlfLMr1ao1lqYfXGCw4e3xI10ftfG1h9PtwJc8b+AOvmrqCvfRDr+M4QPPb0uksTSy0TFWYT+IWrSRzpVsx3ESa8qPySUSA36iQQKhCiu/+73nl+//j4FTmokje5NkopvVOd2E1k7xaug1xFsE/TdWxt3MnrafL9ruTWi9TxruTUm9nXXu7NTZfJ++eTxfhRJ5KMTfQ95N/fGEXkODg4ODg4Od7PbJ/bdT7MT0TSJZz7TeTz7vS2fbqfdVfLNyxRrKrmrUyRHB8hllll0H0TyRTnTF6A+N8284aPuGyHQuo0VTXOVEO1shvbKMqu6m8tVhacDRXKLtwnUZmhYKTwDPor1HIHGLH32PEF/EHvkQ6hWEynzLk0hjNYso9KmjYpCe90MpFep21y121zds7d9ZdPjnkCSNiklSdh9v5141LNz98tu1xcA2d5oW7UB3d4QyBoyliQimdqW4+u2ixmG6aeIx67jtVsbs3YIVAiQlWIMsIqne96K5KNMhH5WkZDJEaFEgCRrxMw6AW2erO8EUX0ZG4kqXnQEZCwagptLgRcY9doMaNMgxCi4hqC9SMDnw1PIclNIotoSk+Y8AhaKJGC3K2gWVBJnkZJHcc29SlCWaETP0NCaqP4o6qV/x+3IafKBCZLhg7i1PPHWHPadi1yqtDmWPIm8OEXm3W/QNizqbZ21uSQxT4APyVWqc9dpLv2AwsJBEp/+AvnFKZarGm/7fxI59Dyf9/dzYfDu+bby0g2utmN4XBFGE3V8cYXm4i2GrFVC2mUKWZuVhnCXwION2BIl+dMsD0yuC6SeyOmPxxibfHZr5alXjdoU+t2rysuDaY5MPntXIHjveeCue9iLZNB9V7vnbu67O8Wr7FQJ3M5ORk+b7zcomShS5+vDkKm0eHnFi9ga4uAe93Ovat+N1SpXlyoMhj18+MjjFXqOyHNwcHBwcHC4m93amhbfhJlXO7lQx3767hmdygqJ+W/worpG1vQRa0mUb93BKlYJpY4zfu6TRClie6IE6yLxmf8J1gwLzWGm/E+TEsG0ZsjoLkbX/ifFWw2i7WWGuIPRWMC3BAOGjWxXCVJBb9kQPUBbgPrsJZqajUaQEFVU2uvRBnC3KUqP7WJtM3sJs/3M3nEfzz8o9qZvdopI2Hzt7fe83sIpgLppm0kn708ARNvAEtxoooBo2esZeYvyMBcjP8X54p8SNQ3cQhsFGwOoSH6aBFAkF7IpIgANZGYZpZ88GgoVwtQDB4g0lxEMCwOQLQOP203eGIF2kRoBWu4EeiuLjyajjRuEAiPU2zptFVaMANPBTxAJR9ALL1MRDMreI3hEk0h7haiiY9otZNkmEvaQOH6Ci4ZFybCoJ05h2zbnpv+AQOkKscJFGod+guCRSdojo6zli5RuvUG7KRCanwaSLAXO4pVFwoU57Gv/FfoOcvzAIabMIi29ipGbIb84xZyVZFqLYtRz1FbneXc+QMSrbhU3hWkia9/naFlBSb4AeIglh4kpOo3v/A/KlSyl4DwVseM8m/C7tgiInoia+fP/F67bX6eW/TSJj/wytXf/hGYuwyofIXb8VOcYgODAejUqEvAQUjv/E3riMNqdj9vOjlW2B2Qv993tAnDHSuA2djKF2ny/YjxGJRonfvj5h75vsTSDFUmTGtmjVXX738NtvDmT5+ZahbBX5u9/+MBD3dO9cESeg4ODg4ODw55sXnwpTZ12pY2rqXfMELZX/ArT0Czgd7vw+6PgjXK5HmKuvMZoIESy8DZE08SOfZTY7PdozKzRUNq4+/wEBkMoN9cQzCWG5TZeu8R8xUfIWiHFGgIWVqvjnOhCw0YkZ6gsr1SQineIt+r4aOGmjWJqqKLVaR/cJn42C5/twmun+IDd2CKWetl5Owit3dhJcPWiGHY9ZlNG3133uO019l7fZjfJ3vebzWfMbY8tQOzm1AkAgoiOiCjIaJKIgI2CjldVmYzZ9BULhIUKgm1jbRLULsEiIJQRusIvK0aQMHFTp46bq6QRwk9z3vUd9FwRAxlbdGGLMnnvQcLt97BFhYo/jd66it80SDKPtbyEgEWoVedQLEBt9Flagx/ClbnISd7ATI1xo3yCxOoqwcYMtihTD42RCqm09TrakZ9EEWAy4qV+89sY7SqW3sJnThPJvkpy4hCc+gzxSotZn59A8SpJMYMx9Ay6N8lY3Eftf/wG3vYibSMFA6dIxBUuSiOYzSJyZpnhI+MwHMK7dJVGLEmpESc7fWuLuMmqKXQ1zIingLbyJtdykJzQsMLjXFfPI7pXcQ2cJ+gL4PIEuPLS/weXViJ64se2tAuqxdu4mxns4u1OlV2qQzyJf/ToXS2GvepYWIgQQe+44naraJlKi3euXGNUzBAbmlgXKTtV2YC72zj3wV5OvZvNnnoC9l5ZmzsZqWy93zFih57e173d875dCwQGksT2uKftbsPbkbu/3PJev+SPCEfkOTg4ODg4OOzJ5k/fy9YBps0GaesAH4W7Kn5ZNcWa+xSDSoOIvgreQ/QNnaURqzPYurTlU+6smqKQeBH3oMi0a4LVbI1U22TEpxJNHcWWXTS9JxC++yZuGgiY2KaITwBDFFmkn+9HfpK0XmG88G1CFBExUQ0dWWDd5n87D1Nx2y36oLdxc0j6nufZQQz2jE72vLdtTpd7tWDa257cfAybvtdtAbfQEWImIrotogg2lgRtFBYZRMBmgFVMRAQsXDYktGlad76Gy2whCfb6HKQkgJ86LdsEQ6SFiCDJSKIXlztIq1GgjgtRdhH1qcwYh3BRIiC5qQsBVusikdY0Hhosi8PkWzZNU0GxXcxbSQKiRoAmPrdKNOrGJ05xuRJiWVSxbAG310eg7wyLWo0jzYv42muUbJkr6lMYORN57S8YiXk5MvIx3kkc5HL4Y4TMGINkiYfG1gVDMugmefY8FGLd6tmG6ND7D9MqzqAMn4Kx54gBk8mTzL79TcziVcTSIGdGImAGYCRCniLVbeJmuhXgNhMka9+mbpVp+FIcsZLorQBfkz9BUdX5+eERzh/p4+rrXyOzukxTCt3VvnjJGCapR8kYw6hqirXQuXXh9U6hwbI1hrcWwTedxxdJkZhM0sgvMLNyi3agQWKwc57ZXJ25G5fQW7cA7jl39iBtnHu5W/baT8f2qNwBd7dEPoJog3uxX1fO6VaAK5vchrcT9bkIumWiPicnz8HBwcHBweEDZvOn7z9wTzDnlxh0x3fct7fIOR1oEYn3g+ojWXibZDwNTMCmhdJ0K8AV+Rl87Sxc+QvsyirvhY9R6/8Y54+eA0Ccu47LbtKpH9G1zocWCriCxOQmwdw1+sigmB1zD7pCqyek9ltZ6x66hb1aMoUdnt/pHDte5x47PawBiw3oAkjbKoMC0LaF9RiDThXP7gSTI9BCwSWbiB3/TKr4ue5/Bl9tkZYl0RC9JCiSspZRpRZD9iwiBi1bomF1oj6CUh0RAQMJzZQ6sQeSRcQqossBMsogt+WDRPtSjKoF3hXDLHqeJ2yVabmiJNw2mAUqRpxG6jm0wFG0yy+jWDaGGODb/X+DM+YlhoQ10Fu0b71BbFjlqjLILWEEsRXlqVMHKZhnWJuBwuo8b+rnaVfGeVb/PkMLf06wFofxMVIjZ/nuqsz3muMcVtZ4Jv00A7B1znQHEbGW/DDL9TiDyeP0wkKSQTdLiYNMV9u0rCSxaISKEiU7v0D4wPmN+TiAygoHW1PUzAxRfYlRmlSSE+utgHG7gK9ym5VFE4700Td6lGpTRwyM3NUu6G2sEKBCtbFyl8jotTwWGxozS2UmUyEupGNMF94muy3PcizuYyY8zlzRRrGSxO4xX/ZAbZx7iLL9VO6Ae7ZEbmH5Isy/ASMXdg103xf7FJP3EqojUR/9IS8j0XsI2UeAI/IcHBwcHBwc9mTzp9hniBD1qbsuYtajF6JHYfBYZ7HcW5CNPdf52p0RGotHAJAv/jmJtf+EarW4Jea4ZZ5iYfmbeFwyQvYWLaNBkSgyTYJ2BRHw0mJMv0NfYRXdNFBM6y7RZe+z6nW/7Ngqucc+D3Ly/Zy3V5HrxUHsNmunSyCbG6J0RegjaWfxCF3HSwFaSLRx4UZHwqaNim5J6IJEylgi7K6xoKe4kvgJhotvIjW+Q9isokgGKlYnRFwMERFraMhICKjYWJKMiIaNQBuJgtLPvDpMc+hjjNX/mvL0FF51mFMRNzGjihUMUoydZrF0CK9L4cKHPoHl76c48y+R6ln6Ix4Kz/0cL/9PnSNNDbcygtfrIxg9zanxfqan+klPnOi4wzbe5QfNIq9yioDq4ZlBE/NGEb3V4FbdjaymSAbdHO4PMF8YxIwdJDUyQn7xdRpTLxOM9RE6/bkdF/e7zYtt2V54m3p+hVZthtVQmtgzn1nfr9dGebB/iKZ1CHdtnlhfgGhX5PyNMZ1se5VEaBTYvWUSwC9qeGjjFzWGtomMnnDKVFrrM4Gwc55lMujmo+efYjZ3kFTcR37x9T3dJPe6p3vyMHEE9xHjsHz5FbRbL6OWmww+jMjbp1i8l1CttXWqTZ1aW991n0eFI/IcHBwcHBwc9mbTp9hJdghI3oRYmsGXv4IYcsPg2NYZlcoK5Xf/G5X8Gt6Jj5I89lGSFJnKvINqt3FhcEi/zkD2Jn6xjaX4kJsFTAxkLGS09XZIy4QGAi7ZQKK1ZeasN1+22WjlQQIq9mqFXD/nHvN+9zrPjtfr7mjZHfEmCVtn63aqGlo2tAWFJm5MWyQulDuREbaALYjkidCyBQbI0UbFkBUEc2P+TkOmjUoVL0VkRGw0fFiihQeNhLlGVKzQF4LDyTXuBI9x8VaNw9YUg6yhYKB2JuqwkCkQRkTEJQrIkkzV8KPZMre85xl95lcYtl0817hMfnGRZrNOVg7wrLCCx8wi+UcJPPURjG7cQCLohsoKK/5hlpol1kJPIwjwLge5bboZDR7nY+dP0Rf3kaTIhDoA0SDZmouC9zR64CafrF8i4enjaN9RFqsBSlkvJc8I091q1+mRyBZTlHesJM2mi5H8GqHC9I4iJEmRpDgNpIGN5zcv8rO1FMvhcwgRm+Ft1a7ebNyoqw81YTEljRBzHUXvzr+mBw+Tjvv2JWQMVxhN8GK4wruKjO33u5tI3Xz8O/twt3xQHiqO4D5aNC814wjtMHYzzuAD3Oc682/AzCud7x9CLE6tVFiuNJlaqTzM3ewLR+Q5ODg4ODg4PDI2256vLJX5/700hVKa50Te5IxwE6s4x6rQh6e7cFx888/wVO/QkMPUEQgZJcKiSNsVxe47TnvpHcLaPC40zO68V0/stG2RphjGpk4TCQUDxdZwC+Z6REKP+6mq7RVyftc5d5ir2+lcOwm0Pd05u+LOQEDARuZu4ah1c+3akkLRCmKIKgIGWB2B28TNDKNck0/Qzxxhs4YIrNJPn53DI7TQbIGyEMAl6DREHwsMcVM9Rtgjc1a8Q6B6mYorQIYwAdXLSO0KcVeM7/ePspQ1WNUiHLbnQehEJrSlEG3crEhJPGikhWVUl03VGkQYepps9CwHW5cI5ZcRpBqSCw7bM2SNKBUOovovcF5bIhFPQ7ArLQrT6O4Yb3k/hnvwx/mxkQjvHD7CjdVBxscHuJDu7je70cY3bU3wunGOp5jmuL+KNzwE0TS+peuIPhd23Edfr9q1gwC62vgJluvzuHcx0NiPSOnl2J2OtEhoS1BxrYuTnsgabF1CqeUwBg8TH0lvmX9NTD6343m34xENBCw8orH7TttaHHcUg9uqa/txt3xQ9hOPsBu7xTDsxNG4RD3rwhff/hfh/sjHTlPJ1wnGTj+U4DUsC8OwMCzroe5nPzgiz+Gx0O8X8JRuwvJuhtWPFk/pJv3+x+9U5ODg4OCwN5sXhn91aQVp6V0m9fdI3laxjQVKQojqyU9zcCQNlRW09/6EWOM2DXws0YeChV9oIzZzVLMzNE03YdNcbyvsGYpIAoSsOpqmYwNLdhyP1GbYWnvoxc3Dtneuh4V3w9JtGyzh7kVXr5Jm0t2HTeYr3YD2NiprdoiEUEayDVyCuS74NEHBwmLe7Od1TvK0MEU/+W51DubkQd41DqAYbRb8ZwnX6niosyimkOUGk+YdVuQ4NjI6TVyYHJUWMQNHeD31i0wu/VNUwURBY9p3nlQ8SthcoEAAb2yQWTuEuXYbRTdRJJu2K0nCyhOiQd7WuWMlMQQ4LK0wJBZQjDleWSrTcEcYM+MkE0dIBAqoBFiUx3hN+hCntCKzV17D6q+TjXara9E01tgLFP0BTgyOkQy6+XsvHFhf6PfYXDUe8/u4tlzhtvs4bkni8PFP4A8OMF2R0bQAqhLcEmWwXQDNxoe50g7CLgYa79UizJT6Ga9F+Nj2J7tiKe1OQSrEaGsOVq6tnx82Kmb5Wz7yDZP+oRixoBt5D/fJ3aiV8yTsMivl/O477afFcT9C8BHxMAKy58KpNDIk/cU9Wz5HPRoE6uDRHup+b0sHuBKMMymFHkrkyaKIIovI4uNfHzsiz+Gx8L+fVTn66v8Or74/1zvavaaDg4ODwwfL5oXh+fEo/pkgoxUvQ+4WrJUo2wJWs95Z4F6bwlufw0MbG5sIMoqpoUgNPHaFcG0Nw5ZQuyYgdlf49FoaZUxks4kAjLOEbW0sbB4kmHx71W0nsXdX7MGmds3ec+YmsxOBzj2L287fe67X7mkJEnN2HwlKhGjSEiSqUpgFUjTwErSvoVog22Znzg6BnB1CEmDKHuV6/MeQ2gk+on8Xr91AtgziVDgr3iBAm9e1AP8P5df4Bf6SC/IslabJih1lnnGCbpG81sRr1fGYDdYaNrlam1fEZzDlMkmXwKBcI+xNUqq4WdR8hHxtLgz7oLmMr7xIy52kFfFTLjQJ2jUigQDfV57nSqnEgPV/kjJrDFpLTKZCzOQk3mqc5rR/jORojEGfxVh4nBdbARr5Ba6smBhrBvLyt9YjB8bOfhI2ibqdBMhm05ELg25+/OQAfwW80ZpElGLEgEr8FNN5iXT8xMaBOwigexloaN4kc36JlHcHA6KuWEoMQCL9HFS2Gg5tZjWXp1Kq08jliR3av4vjZkasRcI0GLEWd99pPy2O9zHr9rA8jIDs/Ux2Es/bKdp+WnYEt+0n8kBX23rNezp/3oMj/UEWig2O9Acf6jz7wRF5Do+Ff/22xud/6484OjHxvlzv+tQU//r/+X/lp9+Xqzk4ODg47Em3kjEZTTP5M38TCqcolMvcevdVZls++vxBrr7+NdbyBYZMhYAlYYsQpYIoWRhICLaFS7AQMZDY2hrZE1kSYHQfu4VO+9N9tVrusO1ebpmbRRpsqi5uvq+eCN2kDA0B5O5ORlesKnTdPwFBsOkTywSsVrfCJ9HAi5sWcQoEhDqqYG6ITVtAxaRMgEG/wC8lbjGbF5i3UrjMAhFKRCmDKGJJfrw+H0FT5hAVho01WlQQJYO2VKXe9yGGq5cpV2UKRpioUOFvBy/xqjzBfzWDPGO9TUQOEMvfImRkaShp2oIPogfQmnVitGgjsBB7kVLxdQJWBTEwwMmnPsJrt3Pczq/hKVlYkee4kI5Rzy2Qr83xcmOAdjXEx48m+czgAAkgE/cxGxsmUXgbcXWWYFuE2SLJaJpkeoeFfK/FUPVxsFVEiSRJxTvTV8lgR+htrvhNpsL0GXH6UuGNc+wggO4lQs5G2wwnF+mLBu5+crtY2kNg3eVQuX3ffRiUCNEx2vUFhOjYrve7HzJEmLUmGMNH8qHO9HhZ/9nsIZ57XLS7kS92N/LlYa/5kEwOhai0dCaHQg99rnvhiDyHx8JqzaYZPvxwdrX3QXPVYrX2IGP1Dg4ODg734n5mYICtbV9jz5Ehwn+5M89bRoxEVOVjtTu05n9AqF0iSwzB0vCIbWKUkOlUqXRToC2IaLZMUNCwYb1NcUtlrdsKuTnMu8dOYeP3I/522t/aVLnbLCZ7EzY73UdPoVqCjURHBObsAAGhgWDbtFBQJBsvbaRucLghyUiYDLJKkCqCaWN2y4GdWAQLVTLJSCOYngFS+XdJ15ug1dBQ0FGwELAFhapnAG8wyo+V3sRs1al741hKnGalSNF/ENWGgMeNyxeh3A7ztL/JoP02B2NVftBskiytohbmaSoG7UCCihimvXidmXYQSRzFyxpL8giFpobtiXFRP8uadJZAXedIf5DsgsBcy8XqUp3o8izJpW/isdaQtRKV5hLt4knemN4wPkkG3RA/Ad4WZKegcrPzPu4kfhp5KC+AIBOzDWIDKlDszOdF0yS7bYc9thsDPSgJbYkE06AFgW3nuQ9jkITfRWIgCP5dctP2ERfwqvphFNFEVz/Mz+/rqjuzPYz8gXkY58z7YR/vs21v/fqg3PffwF0YingZj/sZingf7ob2gSPyHBwcHBwcHPbkvhd/vU/WVR/Mfo+1rIE4P8OILvNie5nT9ixaawWhVUJhFo9cBRtkq6OHFGxUwcakY+1vsnMbpkDHeGS38PHdHu/HBGXL/puEnbjpemb3QEGANjJl20dMKO+4uLJte/2EIqCiU7W9eKQ2sgBuU8PsVvhkwEcbjVY3r85GsyUKhIjZJRShExdRlpLofSfJGW58jTJB6sSEGrLdQkchT4T8wCeYUw+S9mkcrN5kDTd/yrMEB0bRpVsIVpsD5XkK/iD9Qwd5ceQpACqLV7CL85yXy9T1VUStSkEK84b8UfpkFylmCXpUzMmfIrsYoCLFiGTeQBThNc8LVFyHOKOKhDwqAssMsYqtLbI2d51WOUcg2sc5dxhvc4VG/QorV2bWWzOzy7OszV1nQNWJWiZ4ondXa3riJzQMA6c6/9e0OkTTW01RhtgiOJbrIpWyRrAuPpxjZDRNMbfK2swsUTW1JUrgfgTBPQ1c9tFCWTUVmnYCj6k8yCtZ51G1JN5Xjt1j5il/kXR4lYB/6N4778GjEsCLxQYzuRoDYTeTqcdbzXNEnoODg4ODg8OePOjir7Byh8rKNIHaKp+s3sb09jHWmMVdLFAzgHoWlTKitfMn7SLgkg2EXk8mW4Xa+mP77ky83djSatkVaDadtk9xW3D4Opu29YxSxO4/UwATAQORGm4k28IraKi0kem0ZiJ0xKjFJjEpC6imiWob6y2fBiI6IgomBgISJhoeqkiU5AgaAlHKWEBLUonG+0n1tZgWB9BapykVV7Gy7yAZRby0yQlJ5IMfY6LvIAeX/gylZlFuQbVaZVVbJaw1GVErqEKV2/Y4Wugcx4dOQXCA29ZBzOV/T7BaomS6kS2dW+Iwr9ZHGfX4IBVj8uhpJtQcFd9xahU/lcAYXlXEYx1kLNjCv/oDkunjVAYOU2wuoCQP0Td6lDXA5QmgFOfoSxzGFiC2Mr1uNrI2d53K9FvIg2ns5IeYs5IM19okCpsCyruiJ6ummG4FGPNvCKo5q7jh3LhNcATbK4iNOfztA/f4n3IPggMs614qy9cw3NEtIm+7INhL9N3LZXI/LZTH/TUU9Ta6//BDvaRHZrTyPs723YusPMi0NU5aHnwoUf+oBPCjqizuB0fkOTxyGo0GAO+88859H9tsNpmdnWVsbAyPx7Pv465fv37f13JwcHBw2B+7ZYLtRvHma7RuvUzWd5h5xhhuLDDYWkIyMtQlhVajiWg0UM0aYlf5bG597DloWkKn4rWT8NoyJ7eDwNtPXl1PXAl05vvsruDrHWvRFXLdqILeOTQUsC083aiGhq2gCSrDQg4bqNg+EEzApCJ4kWwLn9xE7PjHoNlQIELJ9hCkSQMXcYpU8dK0PXilNmV8eNHwojPPGLcZ5SxXaAkeqrbIJU4yqIwz2nec0VaZRuYtrkhHeEc8wRHrHaqCn2X/MU4OHSFhLFOuNwkNPo3PXSO9VuGGLXFVGyemzOETc0RjCXLRs/zhpTrnx8ukRtJcuxRDqr6JbomU1X5uyRPUNZNW5jbX1CMMy3EK5Tss354iF7xA6unPAhBeKnPIuEJS67hEvuw/RsmVI+w/th6gffX1r1FZnsZIn+P48VNEY/3roqA3nxYdPcrtVoArS2W0tUvUGzfWK1498VPIayyXtlZYNjs35jOXqBTaBOMKMaCldyqjna8Px13zdF22C4K9qkD3cpncTwVpuHkd1VpEaz4ha6H7aFd9GPZTMX19TeW14ijPrqlMPIQGflQC+MxohKhPffhq6T5wRJ7DI2dqagqAX/3VX33frx0I7DAA7eDg4ODwcNyr/WrbDM5atYVe08AL431+vImPw7UMQmWOdilDyM4hYq4Hcu+E0J2zM/exFl933dx+jt7z3G3GYm+q/rHpec0GURCQsNez9jYvlmzAi44hbEQktFFRbR1VMLFEaAtu2raHqqmgI9NPsXMuycRnaiCAnzaryhDvuY4htGuM2Leoi348oo6PFjPCOGGhzpA1j2rWCdg5KrKfrBThtjHIqjTEULtAeW2JSrmAf3mKEXkRLXCOitnPgpBkzv00tR+8yXhYQG26sOPnWfA08PAthlxt2n1niCge2qUq4ViSdxdnaEx9j9W1IJOf+Am+pXkQdBHF5cJMnCTR9zw/vfQ2Y7W3acsyY/EXKF+fwleb5jCQdH8Ey98PQNp9koQWg2iawlt/TbPZwipuWPz3jR6l2tRZkweIEyE5tpEJ1xOCAFalBUAjP8KVWnu94tUTP6mIh8lUCJ9L5o3p/Na5PuDq1a3ulYlohFbAizv6MF6LMHXzBtNTl0lPnNhSxYO7BcF20bddnOwlHvZTQbrmu4CgVrB9Fxh50Bf0CHlU82v3Yj8C+Px4dMvXHyUckefwyPnsZz8LwMTEBF7v/Q2WXr9+nV/4hV/gP/yH/8DRo0fvfcAmAoEAhw4duq9jHBwcHBz2wb3ar7aJQDt1jvmqi5PeHEOFlygmn+Ha4V8lfOXfkyy/jIK5XjHbrWupJ7q2CKyuWrtLzG1vhWRbpW+n8wud9kx6M3bd1k1ZAN3uzAOKgoBi21siEmwbBLHjlNmmIwbDQg1RAkyoCR4uCyfRMVkWwjzLJVQMdJT1Sp6NiCzAoLAGhoRbbOOSa9ySh5kSBzjRfpMARfr9LiK6jLvVIsoibcnPm+YxWsgMiCViRg7mM+TMfmRUklaWeKjM6/2fhmKDdPs2Qyt3UCoBQnaFbL2IqQVIVK9jSwr9PgiMPc+8K0I6dYJn1t7Fsl8l1IxD4ShjT32YWSDiUylFTnI6fYjDA2u0bt3GfSBBJOgmf/h52tU5El43fm0JgmMkKUJhaV30BySdCFkyrTqZSotk0E1icIzpVoDppTLeXL3rlni3YUdPBPVcN1PbRE9PSLwxnd9xwb+92iaVpnHXFpFK07v8z9sf01OXqcy8yTQwcfjInvtuF3KbA88ZSe8piPZTQRo6/1O8GX/uiREyj8zA5R7sRwBPpkKPffbtfnhnrsjrd3I8cyDOZ0483mqnI/IcHjnxeJxf+ZVfeahzHD16lDNnzjyiO3JwcHBw2C8904u+0aMbFYp7tV9tM6Eoy3FWImc4XP8arcwdlpaKLFjjtItVBjCwLRBFECSw96jmbUfYYceesBO5vxBze9v5et9LdKpznWBy+65qnw1otoAFtFFQ0Drbrc5zBStIWCzSJ1UZlUq4TVgjTp4A/fYaXqFN3XZxxx5AsqFpmQSENjHFxEoEcLUMks0CIywjVt1E/AqGL4zVqmGbNaJUSMoFFNtNpuHCJbQRxTZr7jRRYRZ330GeGTtCdfotzBa0il4EvYw/fwmPfZlhXxxTFai1GsjLP2BRk9AiaRqayUTUS70vSV4ZwFRTPHN6jPSBQ7w7XyRsdxbTkfizEO8nrytcff1r9I0eZfDH//GGOAMKt16jffMVXIc/QvTs5/hIYAlDmWatBUvzHyY52Qn5vmuRvkfFeLvYuVe1rIfl76eWDBD3d7bPe09Q8ZQJek9wggcnPXGC6e7X+2V0U+D53Dzrgq/3vtwvT5qQue/5tQd043ycYe2Pi/XW8v3+0XsIHJHn4ODg4ODgsE7hxvewb3+bQqtwVxvabmSIcGW2QDT7BgC+wz+JKAjopk2pkCGiL3BevoNbX0Wh3cmNMzcqaZ1WRrp2lbuLtN22W3Tm5mRhb6G32SWzd5xlb4SVi5uO7X1vdo+RNh1fw0UDLw3JTcNs4JVaeIQ2mBC06xhkCJtNouSp4EVHwi0qrMpDxMwqkmAzZC6Sl/pACBO25wjoJZTV/0k+8AK2JOKnhQEU6ybT0gh55RApd5lAq0S/WaJieJgyDqFLPuIBF7ZWZ0Y5QNhziCPhCLF4EGKnQfFw+7v/FTFv47MaCFYF0xXGp1UQjApKWaasZwmPRuHweWbLCpfqEdKtAAk6VZmlYpPJVKi7oO4I/tXXv0Zl+i0AEs98ZsviPFtpYdRauLOrRGe/h2BpqIJGn1DFK2aAjpi5a5H+EIYduy34t1eV+o5coBGbpO8hZ6ImDh+5ZwVvN7YEni9OrQu+3vvySNlJQO0mqh5R9MF9i68nyI3zcXN6JELE68zkOTg4ODg4OLzPuJWOab9bEe+5b4/ZXJ2GYTEgi/QF3NxsG1i2TbbaJmY08ZhlgtSw7QoK5nobZq/N0rA7PiW9GbjdzFJ2ij3oPb85SgE64kza3tq5qe0SNtwxbToOmW1bRMRC6cY3CGzL4OuKPcsWKeOlYbgoCT5cgskQOXQbTEFCxkDHxkcTHzZlIYooCIgY2AgotkZAMGnSJJ5KU13K4bVr2JbBUP0aTSVMQ3CxqIWIWStM2hdZskYpRE8RDsQprVXIW26uG4NUxTifbl4ioc3T1oPkWhLcuMpQaQpfcAAOfYLSskal0CBoFmi5E/jtFgeVJRSrjSXXKXujlNxDEBwgfjxCOlcn7a7C7BRpdwpSobsWpTuajnRFQnzoEKvuKBG1ASsXWSoZ1IUDeIMHOTc0sev/o2ytzdpKhT61TSL4aGa7HlkswKNkU2U8NsSG4HtAriyVeXOmwPnx6N0VvR0E1K6xDR+U2HqcbpzvV2bfPnk/q4+OyHNwcHBwcHBYJx9+iuWkzGD4OKP7PGYs7qOevkC1PkBi8CRjfh/13AJq6Q5iu4rHrmIbAjYdN0qbjriT6AgoS9hwstzJLGV7Ba7HdjMVYF2cycJWEdgTdpuFYMsWsLqirGkrNAQvYepY6OgotJHwoSF2o85tOoJUEQxGySAKFhYCVcOHIYgsCgnWpASDVJDIoVJFpcWS6Of75kGO6DcQRdAEFzoyFSFCK3qUStNgsdBHijWiZgXZJVPzjtMoNRkz6oSFCljz5BqjzERP4nY3kRs5xtUS88FDFOxB+tozeIwi2ek3UP0uamIZo7jG6sv/XzKZGrPSEQ77Siy4DmMqHgbNRfzNBXzhJOGznyM1kobKCurN1whXWzQUkdnSIoH0OS4c++hd733PHCVTaa0bnsjr4qHjmMnim+AZpjJyiLfahzlx6NSeC+1edELv/I9itmv7ovrd+SKv3c7x7ME4n5784Bf9j8KJ8s2ZAq/dyQHcLfJ2EFC7xjbcj9h6lOLpcbpxPmFVwvfLlAYckefg4ODg4OCwiXtZuu9EMuhmNj7MlXYQpdDgjPY2pfosjdJtJLuBiY0pqGALCLaxHlnQE2gydGVUR6RJm869fXZuexum1Xu8KfPOBJQd9jW61+qJvjYqom0hCyCL4LMaGIJI0Q5RIoiXJh6hsH4OC9AEBTc6ktgxZDEQUDEp2z5qQoSWa4B5aYBMI4bEFTwYmKZOgjzj0gqSrVOWgqzRT1OJocy8SqC6gFtUWFDSuNvXCLTWCGpZBqwWoqjTxk1RSYEgEy1fwd1YIM0CsjuIdfQMN6L/GzfeTWLXMsTDKUJygZw6gZmrE1j4C46oQYg9xzJ9mIKHiKLzpnyGhClh+c5wIOpl7erLqEqD1q2X0Wsai5HzNNXd89t6bBZiSlc8eGsRklcvMtyaITh+jll1kJvuCHFrb0dLKZammK0TiXVERq/61qsqPgpB8X7mlL1f7OkguYOA2vV3/H7E1hMmnnblCcrsg/fPlAYckefg4ODg4OCwiftuJ+p+op92p1AiLfqXvsl0boZ6w8IvatiCSs1WaNsqQUpgbRVfWyp2bMzpCew+g8emYzVbQBVsRGFztU7AwsbqzulZdASeiYC8qZbnor1e8XPZGsigmQq37GEQJE4KN1FFC6GrQDsOnhIGFggWGi7a+LEwEGwwBIWQXaLSVvmO8jRv6Ed4ilsEqHOCm0TEGhIgoyKGR0h53ZC7iWTlWDGj3HKliYVsIvX3ME0DEBGBBm4uiccoWSGG7Bwhyca2XZiuCILRZrB2lYGP/C3Kcpy0u0rhxvdYzjcImFVccoBg/yFGBiaozy2TEosMkuWv2lH+0voME9JTBDaFj/cd+ihKtUUqdY6yHL+n2N/aDtkRD8WGxqXVFvgOczya5rzScdq+l/tjz7BH1DyUu9XBC+lYR+A9IkHxfuaU7YdHUdm5X+OVR9Iy+ISJp93YT5j8+8n72T7siDwHBwcHBweHB2fxTZh5lcT4iyT8MRbqBVYKNaxWBQUPKyTAbCBjELDtddGlbzNKWa+WdbdZ3Zm67WypzNmQFSJEqODGWG8DNWwRW7ARu+pMAAS74+hpb+rhlKTOAToSEjYiNrqkIksiy/SxRgGPvYLcPbdmSwiCiY5C1VJZkwbJin3EzCXc6CwTQ9Q0ThrX0FWRa8kf52ZB4sPmD/DYInU7QBmbMiEiioU/MUSrmSGvCbzHJK3EJCPJIKu3dK40Y1jYnG+/ToAGh5Qs3018ghmjwR29wVj7BlFZJ7l4BV28g+COcuGZI0CM61cvUly5TD4wxNuuj2Oa4yRmlvHlL+MfPkTFexxT91Ov+uhLDdA31HmfoqNHiQyOsV5vq6zA4vc63w+d31Fg7eR6mam0mPWqxOM+CLqZDO7QRrgDvYVvPbdAdvrWuuNkVk2xRoU+NUXinmfZmyRFkuI0kAYeUwXqPloZ38/KzqPkUYqnx9nC+MP6/j4KHJHn4ODg4ODg8MAUmjrtShtXUyc6lMY78VFImNj5OZaXr6I2rnCQLDIWDdy40ddbKje3bPYQus+1kXFxd2sngE5Hp+UIsmglsESJMBU8tDsumQK0bAWvoGF3K3oIoPR69ISN6qGFSNX20MSDbYBbNjksrBLxBZhvHEDSdKJCBWyQBAtVMhExsBAR7TZV3cK0QlTkOKXkh/hU5v/NiLBIn9Uk2lBp6UVasoe6b5xoa4Fge4UAGs2ixpKpMO97ESEeRhT7+Ju+GRpLV5ltusiICc7Lt/EZAqppMKA0+OnjYb55S2KgMo03mmJIWCIoeKgGxomYS3Dtz2HoPNdyJnrNICeHKUkRvDPXuGmpqFaKaGWYgVCap457+EjX5S/Rza27+4c7DTOvdr73xu5dReuKm2Q0DfEI78wVEYSOo+B+Ftg9wZhvXaK6yXFyuhXginGEya7j50PxfrQZ3sc1HkVlZy+R9LgE1KMUT49TiD1pxjtOu6aDg4ODg4PDDwXvWQeYNhukrQN8NDhA7NgAz1VWyC9K/EnB5DTfIyC1ELBoo3TEkWnh6jldbpql6wkvEbAx0JFRulU02GjhbNkKCAI+waBPruMxW3jQNx1vIQgigt1p42wi0bJdBIUG0KkSikIv207AIxrYtoFhQ9Cso0sqwdYqgiXQFhQW7QQyJh67RYQ2Hur4zCb9dga/XCcs1siQZCRwnHi+iY1IU/DiFQxwqcz4TjMbHuInlv45bppIGDQNHz6zSNswKOoe0tId1KU3WSprzBsjnBTeY1y7jWC2qOGmKgSwlq7zocWvM2wu40p8GP/gcWgWiHui1FausrLwHq6mzoeUW8x72xwcdmPWFvHVvs9Vz0ne8jzPkcE4k123zHsuMqNpislnWKu2iO6jirbZtXHWfZLXu2YgEa96XwvaLREDPOKF+m5thntV3+7XZKR3btUHs9/b87hH0Tq5l3B4XKLiUf5MHqcQe9Ky9HwuGVEQ8LkevwRzRJ6Dg4ODg4PDA9NyJ5nzSwy64xsbC9NUp9+iv7ZMAz8l04cimNgiWKaAwt3tmT16gk4RQO0KvO3VPkUwKNt+yvgxEQnQQMaiZqvogoKIjWLryN0D3YLJgh1CtjvB45YgsGJHaOHCS5uQVMdLE9EwcWFQM91cJ4FkNImj07QUYpJOUwrhooGLJgoGXqGNCxMXOikxT9K6jR0ZYaUU5Lb/PMNei/G4j0gzR7ByFVmrdgUsyG4vbtFmrHmVqJXH53JRqTeoWy4G1ToHrRWMtsCqEcNCoF41SS9/F3/9Bi6aNDN3WEh8hEFRJxQ7wFLTT2Z1hbH5y/SZFerxfjxjxxhrX0fXJI5G6jw7HiA1kuoseisr5K9NMWclSY2kN9osN1V9MkT4q9YZyobGM/uoom12bRyL+3jmQBxBeIDF+zYDkEe6UN/FXGTXWAG4/+pf9xr5ay/vfs5HyF4i6X4E1P1U/R7lz+RJE2KPk3o3XqbeNh77tRyR5+Dg4ODg4PDA7GRkkVVTrHqPoAlrtBB5054gLLVZsGKctq9zWFjE1c3L2xxzAHcbruz02IWNJNhct0Y5zTQyJgaQJ4goSQTsOm5LBzbcNpNCCcOWutENNm7B4BvmM3iEFs9zDT8NLMmmYdp8Tz7DVfcFnq99nbhQwZIELEmmgocrxDhqtvDabSxBRsHEREJX4liCB1Wr45VFvC6Ztq2h1/IMtou42zkEW0NHoinFKAcP0c7O0ccC/hjUQhd4r+WjGQhyzngPtdQmJ4VQ0AiZVUxjFp8Zx5A8tE2dTKHKws1LVNwSBxMS+uGfZK3wNSL1i8QSI3hSn2Q46iVaUmBkAmyTfn8ReovprhBftsbQvcmOQ+q2qs9srk65pRPy7M+oZLNrYzLo5jMnNoVvzz6mrLL7rLLtJmR2jRWABzYZ2fOcj5C9RNL9CKgf5fm19wvHeMXBwcHBwcHhyWbT/FUy3Vlc9xbQhbqLZWWSUHiBSnGBa/IoUuIg71bDXCy9w/8m/w9GyeAWtPXoA9iIUbDsjXDzngmLuMlx0wJqtgcFkxhlkECzVQKCjkQLl9lEEjqzezk7TB03LslEsjrWnm6hTduWcakiSZdMu+1CRQPB4o4wRMZ3gl84YGNfrVMzfdximABNRNFFwKx12zE9lLyjuPUKolXl3fZBUmsVTtfn8Ak2zWaE94TDaK4Ysq8fSa0jtm+g2HXmlaO4KjpH7SW8tIi0b1GtuJn2naM19Elu3q6imTPotoLPLdC0AwRdMv7IAHeUETKZVVY9R8jJx1C0BkbeJJ75C6TiHJflISZSH+PM5LFOq2B5Afx9YLQ77YM9omlC/ascqraIuqtAbOsCtLLCwdYUSn+S1MjIfc3U3cXjnIO7z3PvJmT2jA55wBy3B4kj+SB50ubX/lfECUN3cHBwcHBweKLZqb1taX6a7PRVfAOHOB1RUdohbvACDeMAC3qQnNDgKbnWiTzARgQMQaRhi8iCjQsTALs3r9f9ZwgbC5amLdHGhYLBEWUJG5GG6WHNDuKTbSKUEEQLw4acHeEl6wyqYHNQyNAvrSFjgSmiYjDEGilaBKmhouFCIy2tEFTvEM9VEcwMWaL4PEFsTcRFk2Ehi1+qUcdH3TuMXZkhbJaRhQarNS837GH8ktF5dd4kVywvtmnTNE08yHiNPIOtLCEa2MhYKFi2QEIo8XwoT2PE5hvVF6hUqoxIOUr+IW65j3MuXKMRdLOUa9Cov0csFICBES4tFrmw/CbDwiwuVSIz/OlOsDlsVJ4a+Y7Y0+obP8DgAJF4PxH9ImhLwNjWBejsNLHyNWID6kb170F5nHb793nu3YTMXovv+zUv2bz/hfTjrOE9Wn6U2iZ/FHBEnoODg4ODg8N9s1Mr2qiYISrOEvDFiXlVFio3GRBdAOilFZ6ylih4RvhO8yl+hm/jEXRE26KFB7fdRhE6piu9XDuJrtCzBWrIIIgIgk3YbiDKDVo0ucwB5s0wfUKJ48wio4EIFhIes82EOE9OjiNJCpid7Xo3PiEtrWFqNjVciLZMSBBo4GLGTKAUMoyi46XKIXGZuiJyQ+snTJEgVWRB4nIzgmRYHEDALVqEpTLfNC6gWSqfaFzC8JpkTB8nxRl0t5+MLTDCCgdYxRJ9VIUoa6ZAyxrgeOIoIz4V1BwzB48w08wTaP6ABV1hvh1gtX2EQdOLKK8Ql2c4pLaIyGu01TI0i7xrhTBjRzh65CSJ7kI9Q4TX513IyyucGIoxut0IZC+BtF/xtJ92yQephO23DfM+z/0gQuZ+2xidtkeHJwFH5Dk4ODg4ODjcNzu1om13RZR8UYLF2/ys+m0KAY1+V5v3qgH+nfYTzLfjfEH+E0JCnSBNBGxsuvEI29ozJcHGg0nbBlUw1vPzBCTe8HwUrzHDWW7hRUNEwETARkDE4qCwQlIy0HBRJ0gLF4Koo+NHckfJ1C2ahk2/UKSOh5vSId5VzlHWJNysoGKDqFKxg0i2xhsc4SQSoqAQkkzeCH+cG2aDj6dMIs0pBrRB/MUpPIJBMujG7jvJUy4/reVrBHN52qaLKWOYqneUqNjAZ6+hKX7e0A5wOn2AWDTNmWiESFbBd7vAATNLOzzOgneUkFvBExxnwfgxDsSrHBrqJyJUuZVJ82fZQUR5gM/mXURbnSDx2VydW9cuEave4qr3Q/ijxfXqqzEUYTanMhY/u7MQ6QmnwvTWx9t5XK2Y70fUwT653zZGp+3R4UnAEXkODg4ODg4O981OFZEtAclBNzPeSYzae/SJKxyL+PDINoVam4NSlr90/ziH7QyfE15CMHUatgsBC7dgoAo2OmDZIpYgomAAFrIgYCFTt20MXNRiZ5gUKwy0bqHYUCFI3vSi2DpRoYpuSyCruFQVExfTxjA508uwPosqaIhNgxnXMWSxRtrK0kJlNCBwJHKDrNtDoXiEIWGNsFTFtAzatkLDtHmHw/gEm7hP5m8MWlTEPsYDGkfcx3EX2uSrOeRWg4BL4SMfv0D+WoP20mW8kSRrUogFYZC+vn78a18jKLXImTLfLAxybTnF54c6mXJX/SO8YxzFo0qMHZhkItbPWNyHWFtlzWzRN3qSmLaEQo6F0DBjgQMcDzRQFr/Pq7UwxaMTnB6JsHLsJEbWS3riBHOauV591fdTbdqH0NpPUHl2eZa1uev0jR7dOY9vJ/ZZSXycQdo97rf657Q9OjwJOCLPwcHBwcHB4ZGwvU1N0hvkbR+GpeDTdVRU4okEHw3KJOpZ+rIVJAxkESTTwhQkLNtAE0TydoApe5TDLBISajRsF23Zgwcd0wTTFGgWVzjIApJdo0CQKkG+xjkEQ+dn5W+h2xIGblaESSSzzWXxIIbepGkbjNuLjFmrxL3HqYz8LS7NhjG0NmcCbQ6pS5S0JplgALdRQyzfJmaCIIVRXSpVQ6do+rhWDXBKEHjKuExQ91BSEtRMk1WhDwGJakXmLPBeLcL1xmGCrgqToUu8EKlQT03ySu1pXOVp6kaYZOUKpYzKbC5GMuhG8yaZjn+MkZiXY0dGSFKEwhQ08rhaMyzMQdkTILtcJ+OV+cRT/VwQp7j2zhRz7SFse4Jk0M1nXzwHnAM6gmh79XW3alOm0uJa1oenOcyBPQTcfoLK1+auU5l+C2DfIm/LBwZ77Oe0Rjo47Iwj8hwcHBwcHBweCdvb1A5MnEQQwFOZgfy7LBgRpPghnjHW6NdmCbJGzXSj2CY6Ej6aiIJIzXbxNfMCJSFISszjoU1LcLNGgjuMcsC+xbiwgltfxZZc1JGxEXCZNYbsFSQFDGRkLGRbx2jkmRVH8ctVDoh38Mh1orSJUec0N1Eu/B/8nj2KPfsaKW7jUgIsB0PM1GQEo81Q8RskrVVk0SKlNjEUgWYtg9tSESMfZljVoVngxvIcpZV5qr6DlOLPc/7wKPlrLzObU/mBNYG3naWKgRWM0rTi5NUGfRH4sPEuuIvkBibWnS3Pi1P0HdvkbDnbraqpfpbXstyq1tBdYdRaDZe7wVjcR7aWouUd4JTHYiTW7vxQNs22JYMDW4TQXqJoNlfn5SUJGOcn9hBw+2lN7Bs9uuXrftiveHNaIx0cdsYReQ4ODg4ODg6PhPU2tcoKXHuTBJA4e565S3UKqzL5aJol6yCBlkpTazBqelmy4wiCSJxyNzvPQhcU/IqJJLapmx4qthePoOEyG9w041y0B/k56SUkwcYOppElkWBxCokmJ5imLYUJYJCXkmi2SMM3jjdxmn6hwKGVt/FZDVSXC6nhwed2EdKWGAgluKSkWLbzuMo1ZkJnuB4eZyZXRTREPmy9yQg5ZDtOWDHxS2WOKasoYo5viRcIt99DKX4bT3UBI3KAMy/+FOfEKWavvIa/PczZsTPUWgGuFgcw1SBH61c5YN4m7jYY8CfxJQ9w8PT5jpPl7DSxzPeJeaJABBjY4pTp1gokm02WXOfIBY6SHulU7d7IBchqPibFWRJdx0wK01Rm3mJhpUL8eGTf1a6xuI9nD8ax7b0F1H5aExODY/tv09x0/c1fH+b6Dg4/ijgiz8HBwcHBweHRUpiGmVc733tjvN5Oc9uoQclHonGHgurj8OAgl+ov4q7NMcYiTcmLYBhU8aLINqogc1hcooGPV7VjDLFGSKgzzwBJVx3TlEGyaBoSg4kBtNoKqtkmLDdoKEEkHQJmjreVp9H9o1iKj5H8N4i2l1FlKGgJVu1+fJaPUO4WPzYcJ0GS5xdm8Fdv0ufSCPk0QpJBVchQl/xcC05yps9GK97CJ2i4FI2sbvJKTiJZtDldqyGKKu5glLS7CqU8wYE0B11H+fDICAB/dWmFcktHjI2j6CazhkJ4QOX48VMbc2/RNKxegWah8172HCSDA1BZIdTQaVdbRFLnKMvxLYJISR8nIMa3zLQtrFS4VI+QztX3LYiSQTefnnw0hicPMjfniDcHh4fDEXkODg4ODg4Oj5ZoGsZfXP/+lFahseJBbufoa2dRbYV+l8LS6DG+NjXEJ4yXSZhFmrabhq3gMgxqsp8DcomoWeYn1e+jWzJLYoqIN8Tz1nXSrQyK1UZrrFGcjmGLKqYcwBXooxy/QGP+JWJmloPCIsv5NpGVv8SU2rRMkYopc0keYtkIM1ktIF7/AcmjAX5OvkOzPYtmC1jtOiO1bzLojtOIZtAMm8GDLlTRJlMEXfYjJ08SPfIcz+Rd3L49zmul8yh+gRNHniehLVHJzLDiPkzqULo7UzfNC36bm4VVBkdOEBs6x52pS6zJA8SJbMyeBQfg2E9vRAiwWShFSJ77HJEd3vZk0E1y8hhwbGNjcID48Y7Au9+WxkdlavJEzc3tN5rBweGHHEfkOTg4ODg4ODxaggNkhj7ZEQj48Ky9wpnaKxSDE5iR06i+IFWzwcyyl6SniNJUMPFTlXyMGXP0CUXqYg1cIWLt2/ioUxM91JUBRpIJvp89Q585RUrM0MLLvB1HNiEomwRlF6pRo9b/NBG5gq0e4MD8y0RZoGKHmaMPBYPbYpofeM9Rke6waHo5W20hzF+kXiiR84xiugZx60vEB0aoDx1nvtCgPXCYmM9iPlsh18yRN0Y56e/nHKvUb63wTvQE/UNpjk+MAMWtFTRxGi79F2IL1xhVhjETPkTDjbbwDtesAm1Pcr1y1hFXW+MNekKpUNeI+tQHCubet8DqCqGlWoQrxc4xDyPOnqi5uScomsHB4XHiiDwHBwcHBweHR0qm0uKvLq2wVG6SCnlILJVx19pk3W4a/lNMpkKMxX0c9q2Rs0sMZVU8ooTPdlOvpxAsg1E5x3Rd4j3xOAPWWif5TnYz2Sfzb9sX+PPKMp8SfkDdPchVewxPu8DT9k3U8hy+So4lcYBvuA8S7Y9wxBtExYtoq4iaQM32oQbiKO5B5g2Vo6EW8aFRrk4PIbQXKRk2Wl8fcvIYg/3HSY2kKeXqCC6Z222DuvAGQttgoVAnmKvjz1wnUZni+egkZ893jFIylQgLgVOE/T1xk4ZGAX87Q0QOc0MeIFOD69YYc3aStL3x/vUEndLIkPQXIZpmLN6p3RUb2mMP5s4vTlGdfotw/1NMps4+tDh7olov9xvy7uDwQ44j8hwcHBwcHBz2xz5b3WZzdcpNDU8zg6+VQY6OonnijA0fQQyF1qtK43Ef9ZtNsroHl1mmTy0TCvuQhHGk7A+I2nlueF+kroyjoRIYOMSFp87wf0t7ef0vE+Qag8RiQ5y3W1TzZdqGzCyD1OUw2ZYCzXka9VWs4SHaY+doGR7enSmQ0T1YcpJPtr/JcO0KCTVJTPl5sod+lusrIv3GIkopiz88Sri1SJIIyfQAb0znef1ODnexwUmzRcpa7szedV0jj40eJbGp8rZUbDKZCnUFzgAzo3+LetVPduB5pltBUhEPB85/irQNZ0Y3GjDH4j6KDQ1t7QqV7AxBIDn2XFc8toh41ccazD1nJTt5eu4hLqRj+z7uh4LebKODw//iPBEi71/+y3/J7/7u77K6uspTTz3Fv/gX/4Knn376nsf9p//0n/j5n/95fuZnfoY//dM/Xd/+y7/8y/zxH//xln0/9alP8fWvf/1R37qDg4ODg8OPDL0KTyCtETu2+0K5JygShTmU+WsE1T5CFz63vrjOVFp86wcXkVfe4birxG1fP9PFIDdbAhFJ5WjrXQK2hC1KjFrLeBslbnnPEjj9N0gM9vFh4M6d57h8ySTVaHPal6ffuEPbNLnIIZBDiB6ZYHON4/Y1guUIghuagz+Fhkx/ZZZxX4NDq+/iEVdRgimIppnQKkgJlXCljWTMUF6xyBYlALJRFZ9LJuRRKUkBTMnNiKvRcbEce46E39URwBUXBAdIu6v45Rv0uY8CHaH0inmS19RBTnpCPJ0K7dpCmQy6iXhVpq0+Aj6F45uqTu9HMHdqJI3uTTLsrsLs9x56fu39CCx3cHDYygcu8v7zf/7PfOlLX+IP/uAPuHDhAr//+7/Ppz71KW7cuEFfX9+ux83OzvKP/tE/4oUXXtjx+U9/+tP84R/+4fpjl8v1yO/dwcHBwcHhfzV2XJB3K3jLdZE5a4xBK8le9Z11YeHug7U29fICV69eJH48glhb5e233iKTWWKidZFkyIPgDRKrLzAtpXGLGk3By2VpkgX/JGUlQaJ6jUJbIHv5KhMDQZJBN08/NUl+4QZS7TpN0YsaHCJb1dHVQfrcNi1No8/VIkaLVj1HceYyGH18JjXGkPE6JTvAYvgpwskTaGOfwCJCeelNVL1MzTtENXiAFm5cxgorawbN0i1O+or8dDLG68kAS+rz+MZGNtr+ts16JUqXSBRehZAOg2NkKi3cisjJoRAfnUgymQrt+XPoCOVDxOO+TqxCj/fBOGT95zc79Ujm154o4xUHhx8RPnCR98/+2T/jV3/1V/m7f/fvAvAHf/AH/OVf/iX/7t/9O/7xP/7HOx5jmiZ/+2//bb785S/zne98h1KpdNc+LpeL/v7+x3nrDg4ODg4O/8ux44K8K2AGQ8doTH6M1D5a/7LLs2Te+CbeRouGN8XlrgGJP3MdX+EKiUA/ruEPU0RgZnaBqCAyrFRotXWumSl+4HuOSN8oyZAbsRbhYPEqK/V5Xn7Ty48NNEiGxzk0cQIj7yWZHmJmeY1bs7O8EMigqCrfn6sxYDRpKEHmhCSLegBfdoFg7m1qdo6AP0z44OeZjn+M5VKTyVydum+ES8pJiq4Uh8aPcMq+hja/QiRgoxlrCCtXaVc8HFCalA8/RercJzcE2LZZr0JTp11p42rqRLvva6bSIuRRSQTu/cHzZsOVnX4WwFbh9TjE3yOaX9upqung4PB4+UBFnqZpvP322/zGb/zG+jZRFPnEJz7B66+/vutxX/nKV+jr6+Pv/b2/x3e+850d93nllVfo6+sjEonwsY99jN/5nd8hFtv5D0u73abdbq8/rlQqD/iKHBwcHBwcHo4HHWF4VOy4IO8u8mPRNLHg/hbpa3PXyawu05TC+A5/knRsmLG4D9G9aX5tcIwrS2Vu59/EKw4SCodZzRa43IjQUhO8kAozEvPibh1nVEhgrRm4Zv+K5dk8YnSErPIsgdA4olEmp8lExTohI0s0lOC5cBF/NoOpW0j+MWzPIPHKO4hmlmXRx6DL4kjQIDwaWXerJD5B25NEEOD0SISl+SbZYoPByDgKMF1tY5g+ZLnOYN/BrVWpbbNe71kHmDYbpK0DfJROZe7acoVyS2d2n3l1s7k609O38GeKJHs5ersJr8fhGvmI5tcS2hIJpkELAmMPfb6HwWkddfhR4QMVeblcDtM0SSaTW7Ynk0mmpqZ2POa73/0u//bf/lsuXry463k//elP8zf/5t9kfHycO3fu8Ju/+Zt85jOf4fXXX0eSpLv2/+pXv8qXv/zlh3otDg4ODg4OD8uDjjA8SnZckD/AYr9v9CgrpSZ3WlGSdoSPu6skClMQTZN45jNAZ8H95kwBQgMM9gd4OlRmdiyNNJPHzE1xuDbPWD3Da9pBZhMn+DH1G0jFb2EYLarNLAcDbaqWTLZ2i2FJJWmsIJg2a7KX5NAYWukKrWaThiUjjT/DzJIHuZHFFFXqeo61ooQ+P81BMUOMCQgO8JlROoKJNHRn03qVS92bxOeSqbcNVJfMG9P5XcVCy51kzi8x6I4DnUrcj58cWBcY+2Es7sOfKTLcugmFYCeaggiz1gRj+NiyenqSXSOfoHtzWkcdflT4wNs174dqtcrf+Tt/h3/zb/4N8Xh81/1+7ud+bv37EydOcPLkSQ4cOMArr7zCxz/+8bv2/43f+A2+9KUvrT+uVCoMDw8/2pt3cHBwcHC4Bw8ywvDI2WFB/iDVj8TgGIFWgPylZZTbb6FPvwphH0z8xLpg7LlwHvTU+Iz8JqFqlfT4i4QLC7TXXiO+bCPpJbxigRnlAK3SFIP6MpolU7faDLmblNs2lghh2SCAyWpLJV+TKSXGqAx9HnPxIorq4VBfAPPkF/C5ZP70229yZfUW/rV+DpcvobduAXTMZHoVsUaepDdGMp6GbvVy82t/Yzq/RSxsf4/ObK4Qdnkg05TjpzoCr/vz2FWkfFCukftpE32CHC2fqMw+B4fHyAcq8uLxOJIkkclktmzPZDI7ztPduXOH2dlZfuqnfmp9m2VZAMiyzI0bNzhw4MBdx6XTaeLxOLdv395R5LlcLseYxcHBwcHhA+VBRhgey7jBDgvyB61+jMV9PHMgTvr6fyG09i6r+mGkkykSm55XGhkOLH8LdfEd1iwVRU2Q9kMz7mfZjrO2uohXyDApzpFzj6KToqJGaaT/L5xMDyJoCjfnVznpzSHnr5HVhyB6kFI0zX/LGxzWV3lRf4fQnf9GeiwCWp3bqRBfM84xPhhBbWjopQrF+WvEhibIqinWqDDYaBApX4RGHryxu0TMdrGw/T3aUdA9wNzc9srdEydSfsjCxZ+ozD4Hh8fIByryVFXl7NmzvPTSS3z2s58FOqLtpZde4otf/OJd+09MTHD58uUt2/7JP/knVKtV/vk//+e7Vt8WFxfJ5/MMDDz5f3wcHBwcHH40eZARhvdr3OBewmK3Sl8y6OYzJwYolUdZW4tyi8OEWoF1kZcMukn6izRKsxTza7QED5759xh46scID/0cU1kfzfJ/5aT2HgnfNC/7nucbt1sIFhz2HCQ2NMLq1YuUvUOs9R/D5RtlthBEDg5yPhXFNzfDuxxh0Fph0hQo3/ouxWqTk/1PMfD8WdLuKuqtaVpqHn/TgMI009YEV4wjHNGnSVXniFprhMoLnRveJGK2i4V9ia8HEERPenthTxT3qRvi3cHB4YPnA2/X/NKXvsQv/dIvce7cOZ5++ml+//d/n3q9vt6q8ou/+IukUim++tWv4na7mZyc3HJ8OBwGWN9eq9X48pe/zOc+9zn6+/u5c+cOv/7rv87Bgwf51Kc+9b6+NgcHBwcHh8fJ+zVucK/qx9L8NNnpqyjp4yQnj931/HT4WW73+7AiaZ7aLIKWL8Lc95gVh7npCtMnVvF7D1HXorTLFQZicdTJj9DKBTEGn6ZV7MP2RjkuznAmVIbCdGdezXeYnJLmu3WZKjq+pka9bfCLz47z74F3GyMMD2vkBS83SiuM9/XxsXQMZqeoNouUXCkW3QcZUVOM+bv3N9dgtVRH8yYJjR+95zzZfipEDyKI7lUx/KCZbgW4YhxhcpN4d3Bw+OD5wEXe5z//ebLZLL/1W7/F6uoqp06d4utf//r6J5nz8/OIorjv80mSxKVLl/jjP/5jSqUSg4ODfPKTn+S3f/u3nZZMBwcHB4cnlvsdYYAnZ9xgVMwQFWcJiHHgbpGnRlLU+z1M+Grkrr6M2HXWZP4NmH8dDymaviMI5hzVWpXy9e8Tqd1EHn6KcCjNpcCL1KUDhL0aLm8QSZexZC9E0wSB49E0f3ipzuWlMieHQjw9HiPtrpLQlvg/nktxeRGE+jw5wsz5k6S83bn+aJr5UIW/qrqptuP8RCvAhcGOWJsyjjBd1YikTsDYkUfyPj2IIHqgiuH7yJN2Pw4ODh0+cJEH8MUvfnHH9kzoRCHsxR/90R9teezxePjGN77xiO7MwcHBwcHh/eF+RxieJGJDE8S86q7VrsVig5lcDf/MqwyV3qJd/giJwTHysdNUbl2maQikol6UVowgVQIeE1BBK6CtVEhHjjMWP0W6VsW0pmg1itycXyWQPrc+r3Z+vDObeH482gka7wZ5JwYgaVaoLL/HcLTAoWSSvmgA6My7zftPMTyqE/IoW4RKWY6zEjmD0PZQ3sNF8354FILoSZspe9Lux8HBocMTIfIcHBwcHBwc7j3C8MRyD/dE2+58FdYfdzb8oDXMy/pPMCpkcCuHWKqNcsyT49TIEO1mlaKuUCoVGUx0M+kKS5zrF5j2pYhMnNjSunghHeuIuy6bWyP7RlMADKgNotpGPMRsrt4JQk+FuJDemv/XE2LFhvbI2iMdQeTg4PB+4Yg8BwcHBweHJ4R7jTD8sNKLEwgZn8DMp+mPx2D2e7hbPiz/AGbsAL6Ai/ziVW6UalSMNWytTmxkgoHJ0+s5dUTTJE/+GMmuO2Wm0gJ2rowtFBosZ2u0Aw1SI2lqyQCmuwra0nrFca/KWk+QZSotIl7VaUd0cHD4ocIReQ4ODg4ODk8Qe40wPDHcZxTARgUrBhwhf+1lZm+8xWj/U3zi2AlsG8JGDpf5GopRRqpGsU2TmBniQvr8xom2VQz3qoyFW4vo1SnCrRCzuSRXlsoUIx4i3o04gv1U1pzqm4ODww8jjshzcHBwcHBwuD8eMButF7VQr0VoWGMMuoeIeFWuLJWxizcYlgq4vTa+Y2dooNI3enTnE+1DZJbcQ2QDZRT30HoVrlB/dK2XDg4ODk8yjshzcHBwcHBwuD96Biv3iBXYztL8NHNT71HzjjAw/jypkcj6c6HkCUxzGkUrYSseatGzxHtxBttF3T5EZmokje5NkuoapvRaL6O+h2y9fIBAcwcHB4f3G0fkOTg4ODg4ONwXGSLrrpb3nBbcJIpGxQzUL1Kq3yY8EiHZFUm9Vs53tJ9keep7CPPzZEtB4FDXcGWbqNuHyNypzfKRtF4+YBXTwcHB4f3EEXkODg4ODg4O98V9BXIvvgkzr8L4i8SGziNnr1LJr+EVM2zP1EuNpHFV54iWr1HwFYlvMlzZ/PW+ROaj5gGrmA4ODg7vJ47Ic3BwcHBwcLgvtrhS3qN9sdDUaVfauJo60eAAodOfI9TbfxvJoBtx9Chrc9A3epREV0BuF3X3JTIfNfeIi3BwcHB4EnBEnoODg4ODg8N9saXtcXbv9sUfFLwUsgGicS+f7u2zh0iabgW4YhxhshUg0d22XdQ9ilBxBwcHh/+VcUSeg4ODg4ODwwOzOXQ8scPztWqZdlujVi3v63w7Cbje92l3FWanSEbTJNNONW0zPefSsa7RjIODw482jshzcHBwcHBweGB2qrxtZnLyNNOyTHriRGfDPdo79zRMmZ36wE1PnlQx9YG2sP4w4bijOvyI4Ig8BwcHBwcHhwdm19bJ7mJ6oj/NxOG/tb45vzhFdfotAmmN2LH7WGQvX4S574E/+YGanjypYsppYd0njjuqw48IjshzcHBwcHBweGDWq2yVlc58XrdCspuYm7OSLFtjDFpJYvdxnfKt79Kcep1c3zMkDkXef1fNLk+qmHok8RA/CjjuqA4/Ijgiz8HBwcHBweHh2VYh2U3MbQ4pvx/mvSeYE7LMNw9wNlffn6B5DK15jpj6IcdxR3X4EcEReQ4ODg4ODg4PzXYDlt3EXE8kZZdnuXr1eicqYXDsnucXBk8x8/9v796jakz3OIB/d9G2a29FpQupjBQTqRinYRZKKqND1pycJlRaY1DrUCczx0GcEzKLcZxhyGFRHNdl3BZTLlEGw6koJJeiqTG5X3eaMvWcPyzvsqeLXHZ7276ftd6l/b7v8zy//azXfvbvvTz7oR2slPLmX0XjrXlE9I5ikkdERESv7fcTsLzoitfNn4rw8EouADQryaus/g2mJq3QxVrZ/CtpvDWPiN5RTPKIiIjotb3ss2odHLtr/Pum6wfq/4g6EdG7gkkeERERvbZmPav23DNy1vZOzbqC91L1/46+zoRJRKRtTPKIiIioZbTwM3L6OhMm6Y6+/s4h0ZvGJI+IiIhaxO8nZ2m2V5wlkzNh0u/x6i69K5jkERERUYsov/sYv9xSo1r1GNb2L1GQs2TSG8Kru/SuYJJHRERELcLi15/x5NEFWPxqDqBHs8u98hVAot/h1V16VzDJIyIiohZxv00n3FI9QOs2nV6q3O9/noGIiJrGJI9a3OPHj3HhwoUGtxUVFWn82xA3NzeYmppqJTYiItKexn4g/dYvpbj5U+M/jM5b7IiIXg6TPGpxFy5cgLe3d5P7jBkzptFteXl58PLyetNhERGRljV2q9yLfhidt9gREb0cJnnU4tzc3JCXl9fgtqqqKpSWlsLJyQkKhaLR8kREZDhe9ofRiYioaUzyqMWZmpo2eSWuf//+LRgNERHp2sv+MDoRETXNSNcBEBERERER0ZvDJI+IiIiIiMiAMMkjIiIiIiIyIEzyiIiIiIiIDAiTPCIiIiIiIgPCJI+IiIiIiMiAMMkjIiIiIiIyIEzyiIiIiIiIDAiTPCIiIiIiIgPCJI+IiIiIiMiAtNJ1APpICAEAePjwoY4jISIibXj2+f7s895QcPwiIjJszR2/mOQ14NGjRwAABwcHHUdCRETa9OjRI5ibm+s6jDeG4xcR0bvhReOXTBjaacw3oK6uDr/88gtUKhVkMpmuw3mnPHz4EA4ODigvL0fbtm11HQ6R1vGY1w0hBB49egR7e3sYGRnOkwuNjV88zhrHvmkc+6Zx7JvGsW8a9yb6prnjF6/kNcDIyAidOnXSdRjvtLZt2/KDgd4pPOZbniFdwXvmReMXj7PGsW8ax75pHPumceybxr1u3zRn/DKc05dERERERETEJI+IiIiIiMiQMMkjvSKXyzF79mzI5XJdh0LUInjMU0vgcdY49k3j2DeNY980jn3TuJbsG068QkREREREZEB4JY+IiIiIiMiAMMkjIiIiIiIyIEzyqEWUlpZCJpMhPz+/2WVSU1NhYWGh8ziIXoY2jtuWNmjQIEydOlXXYRAREdErYpJHL6W8vBzjx4+Hvb09TExM4OjoiClTpuDOnTtNlnNwcEBFRQXc3d2b3dbo0aNx6dKl1w2ZSBIZGQmZTFZvCQwMfGNt8LglffTtt9/CyckJbdq0Qb9+/fC///1P1yHp3JEjRxAcHAx7e3vIZDLs3LlT1yHpjeTkZPTt2xcqlQodOnTAyJEjcfHiRV2HpRdWrFiBXr16Sb9z5uPjg/T0dF2HpXcWLFgAmUzGE4YA5syZU+97h5ubm9bbZZJHzXblyhX06dMHly9fxqZNm1BcXIyUlBRkZmbCx8cHd+/ebbBcTU0NjI2NYWtri1atWjW7PYVCgQ4dOryp8IkAAIGBgaioqNBYNm3a9Mbq15fjtqamRtchkJ7YsmUL4uPjMXv2bJw6dQoeHh4ICAjAzZs3dR2aTlVWVsLDwwPffvutrkPRO9nZ2YiJicGJEydw4MABPHnyBEOHDkVlZaWuQ9O5Tp06YcGCBcjLy0Nubi58fX0xYsQIFBYW6jo0vZGTk4OVK1eiV69eug5Fb7z//vsa3zuOHj2q9TaZ5FGzxcTEwMTEBPv378fAgQPRuXNnBAUF4eDBg7h27RpmzJgBAHByckJSUhLGjRuHtm3bYsKECQ3eJrl79264uLigTZs2GDx4MNLS0iCTyXD//n0A9W97mzNnDnr37o3169fDyckJ5ubm+POf/4xHjx5J+2RkZGDAgAGwsLCApaUlhg8fjpKSkpboHnpLyOVy2Nraaizt2rUDAMhkMqxevRohISEwNTWFi4sLdu/erVFeG8dtXV0dkpOT4ezsDIVCAQ8PD2zbtk2j3XPnziEoKAhKpRI2NjYYO3Ysbt++LW0fNGgQYmNjMXXqVFhZWSEgIKBZ5SorKzFu3DgolUrY2dnh66+/fiP9TPpj8eLF+OyzzxAVFYUePXogJSUFpqamWLNmja5D06mgoCDMnTsXISEhug5F72RkZCAyMhLvv/8+PDw8kJqairKyMuTl5ek6NJ0LDg7GsGHD4OLigm7dumHevHlQKpU4ceKErkPTC2q1GuHh4Vi1apU0thLQqlUrje8dVlZWWm+TSR41y927d7Fv3z5MnjwZCoVCY5utrS3Cw8OxZcsWPPtFjkWLFsHDwwOnT5/GrFmz6tV39epVfPLJJxg5ciQKCgrw+eefS0liU0pKSrBz507s2bMHe/bsQXZ2NhYsWCBtr6ysRHx8PHJzc5GZmQkjIyOEhISgrq7uNXuA3hX/+Mc/EBoaijNnzmDYsGEIDw+XrlJr67hNTk7GunXrkJKSgsLCQsTFxWHMmDHIzs4GANy/fx++vr7w9PREbm4uMjIycOPGDYSGhmq0k5aWBhMTExw7dgwpKSnNKjdt2jRkZ2dj165d2L9/P7KysnDq1Kk30ZWkB2pqapCXl4chQ4ZI64yMjDBkyBD8+OOPOoyM3iYPHjwAALRv317HkeiX2tpabN68GZWVlfDx8dF1OHohJiYGH3/8scZnDgGXL1+Gvb09unTpgvDwcJSVlWm/UUHUDCdOnBAAxI4dOxrcvnjxYgFA3LhxQzg6OoqRI0dqbL969aoAIE6fPi2EEOLLL78U7u7uGvvMmDFDABD37t0TQgixdu1aYW5uLm2fPXu2MDU1FQ8fPpTWTZs2TfTr16/RuG/duiUAiLNnzzYYB71bIiIihLGxsTAzM9NY5s2bJ4QQAoCYOXOmtL9arRYARHp6uhBCO8ftr7/+KkxNTcXx48c16o2OjhZhYWFCCCGSkpLE0KFDNbaXl5cLAOLixYtCCCEGDhwoPD09NfZ5UblHjx4JExMTsXXrVmn7nTt3hEKhEFOmTGm8I+mtce3aNQGg3vE1bdo08cEHH+goKv3T1Pj2rqutrRUff/yx6N+/v65D0RtnzpwRZmZmwtjYWJibm4u9e/fqOiS9sGnTJuHu7i6qqqqEEE/HJY4lQnz//fdi69atoqCgQGRkZAgfHx/RuXNnje8F2tD8B6SIAOlK3Yv06dOnye0XL15E3759NdZ98MEHL6zXyckJKpVKem1nZ6fxXMnly5eRmJiIkydP4vbt29IVvLKyspea9IUM1+DBg7FixQqNdc+fnX7+GQIzMzO0bdtWOsa0cdwWFxfj8ePH8Pf31yhTU1MDT09PAEBBQQEOHz4MpVJZr+6SkhJ069YNAODt7a2x7UXlqqqqUFNTg379+knr27dvD1dX1xe+JyJ6N8TExODcuXMt8gzR28LV1RX5+fl48OABtm3bhoiICGRnZ6NHjx66Dk1nysvLMWXKFBw4cABt2rTRdTh6JSgoSPq7V69e6NevHxwdHbF161ZER0drrV0medQsXbt2hUwmQ1FRUYPPLxQVFaFdu3awtrYG8PTLsTa0bt1a47VMJtO4FTM4OBiOjo5YtWoV7O3tUVdXB3d3d05CQRIzMzN07dq10e0vOsZeRVN1qtVqAMDevXvRsWNHjf3kcrm0T3BwML766qt6ddvZ2Ul///7/3YvKFRcXv8K7obeJlZUVjI2NcePGDY31N27cgK2trY6iordFbGws9uzZgyNHjqBTp066DkdvmJiYSOOIt7c3cnJy8O9//xsrV67UcWS6k5eXh5s3b8LLy0taV1tbiyNHjmDZsmWorq6GsbGxDiPUHxYWFujWrZvWx2A+k0fNYmlpCX9/fyxfvhxVVVUa265fv44NGzZg9OjRkMlkzarP1dUVubm5GutycnJeK8Y7d+7g4sWLmDlzJvz8/NC9e3fcu3fvteokep42jtsePXpALpejrKwMXbt21VgcHBwAAF5eXigsLISTk1O9fZo6ofKicu+99x5at26NkydPSmXu3bvHn4AwICYmJvD29kZmZqa0rq6uTpoVmaghQgjExsZix44dOHToEJydnXUdkl6rq6tDdXW1rsPQKT8/P5w9exb5+fnS0qdPH4SHhyM/P58J3nPUajVKSko0TtJqA5M8arZnZ2ICAgJw5MgRlJeXIyMjA/7+/ujYsSPmzZvX7Lo+//xzXLhwAV9++SUuXbqErVu3IjU1FQCanSj+Xrt27WBpaYn//Oc/KC4uxqFDhxAfH/9KdZHhqq6uxvXr1zWW52ebbIo2jluVSoWEhATExcUhLS0NJSUlOHXqFJYuXYq0tDQAT2+Xunv3LsLCwpCTk4OSkhLs27cPUVFRqK2tbbTuF5VTKpWIjo7GtGnTcOjQIZw7dw6RkZEwMuLQYEji4+OxatUqpKWloaioCJMmTUJlZSWioqJ0HZpOqdVq6cso8HRipfz8/JaZEEHPxcTE4L///S82btwIlUolfVb+/iTvu2j69Ok4cuQISktLcfbsWUyfPh1ZWVkIDw/XdWg6pVKp4O7urrGYmZnB0tLynX9cJiEhAdnZ2SgtLcXx48cREhICY2NjhIWFabVdjuTUbC4uLsjNzUWXLl0QGhqK9957DxMmTMDgwYPx448/vtSsW87Ozti2bRu2b9+OXr16YcWKFdIshc9uUXtZRkZG2Lx5M/Ly8uDu7o64uDgsXLjwleoiw5WRkQE7OzuNZcCAAc0qq43jFgCSkpIwa9YsJCcno3v37ggMDMTevXuls+f29vY4duwYamtrMXToUPTs2RNTp06FhYVFkwlZc8otXLgQH330EYKDgzFkyBAMGDCg3rN99HYbPXo0Fi1ahMTERPTu3Rv5+fnIyMiAjY2NrkPTqdzcXHh6ekrPvsbHx8PT0xOJiYk6jkz3VqxYgQcPHmDQoEEan5VbtmzRdWg6d/PmTYwbNw6urq7w8/NDTk4O9u3bV++5aqJnfv75Z4SFhcHV1RWhoaGwtLTEiRMnpEectEUmmjuTBpGWzZs3DykpKSgvL9d1KETNxuOWiIiI9A0nXiGdWb58Ofr27QtLS0scO3YMCxcuRGxsrK7DImoSj1siIiLSd0zySGcuX76MuXPn4u7du+jcuTP++te/Yvr06boOi6hJPG6JiIhI3/F2TSIiIiIiIgPCiVeIiIiIiIgMCJM8IiIiIiIiA8Ikj4iIiIiIyIAwySMiIiIiIjIgTPKIiIiIiIgMCJM8IiIiInpnODk5YcmSJboOg0irmOQR6bnIyEjIZDLIZDK0bt0aNjY28Pf3x5o1a1BXV9fselJTU2FhYaG9QBsRGRmJkSNHtni7RET0Zjwbgxpb5syZ0yJx9OzZExMnTmxw2/r16yGXy3H79u0WiYVI3zHJI3oLBAYGoqKiAqWlpUhPT8fgwYMxZcoUDB8+HL/99puuwyMiIgNWUVEhLUuWLEHbtm011iUkJEj7CiG0Ni5FR0dj8+bNqKqqqrdt7dq1+OMf/wgrKyuttE30tmGSR/QWkMvlsLW1RceOHeHl5YW///3v2LVrF9LT05GamgoAWLx4MXr27AkzMzM4ODhg8uTJUKvVAICsrCxERUXhwYMH9c68rl+/Hn369IFKpYKtrS0+/fRT3Lx5U2r73r17CA8Ph7W1NRQKBVxcXLB27Vppe3l5OUJDQ2FhYYH27dtjxIgRKC0tBQDMmTMHaWlp2LVrl9RuVlZWS3QZERG9Iba2ttJibm4OmUwmvb5w4QJUKhXS09Ph7e0NuVyOo0ePNngXx9SpUzFo0CDpdV1dHZKTk+Hs7AyFQgEPDw9s27at0TjGjBmDqqoqfPfddxrrr169iqysLERHR6OkpAQjRoyAjY0NlEol+vbti4MHDzZaZ2lpKWQyGfLz86V19+/frzdenTt3DkFBQVAqlbCxscHYsWM1rhpu27YNPXv2hEKhgKWlJYYMGYLKysqmO5ZIi5jkEb2lfH194eHhge3btwMAjIyM8M0336CwsBBpaWk4dOgQvvjiCwDAhx9+WO/s67Mzr0+ePEFSUhIKCgqwc+dOlJaWIjIyUmpn1qxZOH/+PNLT01FUVIQVK1ZIZ0qfPHmCgIAAqFQq/PDDDzh27BiUSiUCAwNRU1ODhIQEhIaGSlciKyoq8OGHH7ZsRxERkdb97W9/w4IFC1BUVIRevXo1q0xycjLWrVuHlJQUFBYWIi4uDmPGjEF2dnaD+1tZWWHEiBFYs2aNxvrU1FR06tQJQ4cOhVqtxrBhw5CZmYnTp08jMDAQwcHBKCsre+X3dv/+ffj6+sLT0xO5ubnIyMjAjRs3EBoaCuDplc6wsDCMHz8eRUVFyMrKwqhRoyCEeOU2iV5XK10HQESvzs3NDWfOnAHw9AzpM05OTpg7dy4mTpyI5cuXw8TEROPs6/PGjx8v/d2lSxd888036Nu3L9RqNZRKJcrKyuDp6Yk+ffpIdT+zZcsW1NXVYfXq1ZDJZACe3jJjYWGBrKwsDB06FAqFAtXV1fXaJSIiw/HPf/4T/v7+zd6/uroa8+fPx8GDB+Hj4wPg6Rh09OhRrFy5EgMHDmywXHR0NIKCgnD16lU4OztDCIG0tDRERETAyMgIHh4e8PDwkPZPSkrCjh07sHv3bsTGxr7Se1u2bBk8PT0xf/58ad2aNWvg4OCAS5cuQa1W47fffsOoUaPg6OgI4Onzg0S6xCt5RG8xIYSUXB08eBB+fn7o2LEjVCoVxo4dizt37uDx48dN1pGXl4fg4GB07twZKpVKGlifnfWcNGkSNm/ejN69e+OLL77A8ePHpbIFBQUoLi6GSqWCUqmEUqlE+/bt8euvv6KkpERL75qIiPTNsxOBzVVcXIzHjx/D399fGj+USiXWrVvX5Pjh7++PTp06SY8NZGZmoqysDFFRUQAAtVqNhIQEdO/eHRYWFlAqlSgqKnqtK3kFBQU4fPiwRpxubm4AgJKSEnh4eMDPzw89e/bEn/70J6xatQr37t175faI3gReySN6ixUVFcHZ2RmlpaUYPnw4Jk2ahHnz5qF9+/Y4evQooqOjUVNTA1NT0wbLV1ZWIiAgAAEBAdiwYQOsra1RVlaGgIAA1NTUAACCgoLw008/4fvvv8eBAwfg5+eHmJgYLFq0CGq1Gt7e3tiwYUO9uq2trbX63omISH+YmZlpvDYyMqp3u+KTJ0+kv589M75371507NhRYz+5XN5oO0ZGRoiMjERaWhrmzJmDtWvXYvDgwejSpQsAICEhAQcOHMCiRYvQtWtXKBQKfPLJJ9KY1lB9ADRifT7OZ7EGBwfjq6++qlfezs4OxsbGOHDgAI4fP479+/dj6dKlmDFjBk6ePAlnZ+dG3wuRNjHJI3pLHTp0CGfPnkVcXBzy8vJQV1eHr7/+Whqwtm7dqrG/iYkJamtrNdZduHABd+7cwYIFC+Dg4AAAyM3NrdeWtbU1IiIiEBERgY8++gjTpk3DokWL4OXlhS1btqBDhw5o27Ztg3E21C4RERk2a2trnDt3TmNdfn4+WrduDQDo0aMH5HI5ysrKGr01szFRUVGYO3cutm/fjh07dmD16tXStmPHjiEyMhIhISEAniZozyYDayxO4OlzdZ6enlKcz/Py8sJ3330HJycntGrV8FdnmUyG/v37o3///khMTISjoyN27NiB+Pj4l3pvRG8Kb9ckegtUV1fj+vXruHbtGk6dOoX58+djxIgRGD58OMaNG4euXbviyZMnWLp0Ka5cuYL169cjJSVFow4nJyeo1WpkZmbi9u3bePz4MTp37gwTExOp3O7du5GUlKRRLjExEbt27UJxcTEKCwuxZ88edO/eHQAQHh4uPQj/ww8/SDOc/eUvf8HPP/8stXvmzBlcvHgRt2/frneGlIiIDI+vry9yc3Oxbt06XL58GbNnz9ZI+lQqFRISEhAXF4e0tDSUlJTg1KlTWLp0KdLS0pqs29nZGb6+vpgwYQLkcjlGjRolbXNxccH27duRn5+PgoICfPrpp03+pqxCocAf/vAHadKY7OxszJw5U2OfmJgY3L17F2FhYcjJyUFJSQn27duHqKgo1NbW4uTJk5g/fz5yc3NRVlaG7du349atW9JYSaQTgoj0WkREhAAgAIhWrVoJa2trMWTIELFmzRpRW1sr7bd48WJhZ2cnFAqFCAgIEOvWrRMAxL1796R9Jk6cKCwtLQUAMXv2bCGEEBs3bhROTk5CLpcLHx8fsXv3bgFAnD59WgghRFJSkujevbtQKBSiffv2YsSIEeLKlStSnRUVFWLcuHHCyspKyOVy0aVLF/HZZ5+JBw8eCCGEuHnzpvD39xdKpVIAEIcPH9Z2lxERkZasXbtWmJubS68PHz5cb6x5JjExUdjY2Ahzc3MRFxcnYmNjxcCBA6XtdXV1YsmSJcLV1VW0bt1aWFtbi4CAAJGdnf3CODZu3CgAiMmTJ2usv3r1qhg8eLBQKBTCwcFBLFu2TAwcOFBMmTJF2sfR0VH861//kl6fP39e+Pj4CIVCIXr37i32799fb7y6dOmSCAkJERYWFkKhUAg3NzcxdepUUVdXJ86fPy8CAgKEtbW1kMvlolu3bmLp0qUvfA9E2iQTgvO7EhERERERGQrerklERERERGRAmOQREREREREZECZ5REREREREBoRJHhERERERkQFhkkdERERERGRAmOQREREREREZECZ5REREREREBoRJHhERERERkQFhkkdERERERGRAmOQREREREREZECZ5REREREREBoRJHhERERERkQH5P3reZ5L1MhPUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_model = LinearRegression()\n",
    "original_model.fit(original_housing_df.drop('MedHouseVal', axis=1), original_housing_df['MedHouseVal'])\n",
    "original_predictions = original_model.predict(original_housing_df.drop('MedHouseVal', axis=1))\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(housing_df.drop('MedHouseVal', axis=1), housing_df['MedHouseVal'])\n",
    "predictions = model.predict(housing_df.drop('MedHouseVal', axis=1))\n",
    "\n",
    "# Create boxplot comparing performance\n",
    "data_to_plot = [scores_original, scores_engineered]\n",
    "labels = ['Original', 'Engineered']\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(9,4.5))\n",
    "\n",
    "fig.suptitle(f'Model performance comparison\\nmean improvement: {mean_improvement:.2f}%')\n",
    "\n",
    "axs[0].set_title('Cross validation R\\u00b2 scores')\n",
    "axs[0].boxplot(data_to_plot, tick_labels=labels)\n",
    "axs[0].set_xlabel('Dataset')\n",
    "axs[0].set_ylabel('R\\u00b2 score')\n",
    "\n",
    "axs[1].set_title('Predictions vs true values')\n",
    "axs[1].plot(\n",
    "    original_housing_df['MedHouseVal'], original_predictions,\n",
    "    'o', markersize=1, label='Original', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[1].plot(\n",
    "    housing_df['MedHouseVal'], predictions,\n",
    "    'o', markersize=1, label='Engineered', alpha=0.25\n",
    ")\n",
    "\n",
    "axs[1].set_xlabel('True Values')\n",
    "axs[1].set_ylabel('Predictions')\n",
    "\n",
    "leg = axs[1].legend(loc='upper left', markerscale=8, framealpha=1)\n",
    "\n",
    "for lh in leg.legend_handles:\n",
    "    lh.set_alpha(1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6680fa0a",
   "metadata": {},
   "source": [
    "## Reflection\n",
    "\n",
    "### What went wrong\n",
    "\n",
    "**Target leakage**: `is_censored = (MedHouseVal >= 5.0)` is a deterministic function of the label. The model learns to use this flag as a near-perfect signal: every row where `is_censored == 1` has `MedHouseVal = 5.0`, so the prediction for those rows becomes trivially accurate. This raises R² — but the improvement disappears entirely at inference time, when the label is unknown and the flag cannot be computed.\n",
    "\n",
    "**Why it inflates R²**: In cross-validation, each fold's test rows still have their `is_censored` value derived from the true label. The model sees a feature that perfectly encodes whether the label is at the cap, so it never has to predict that region from features alone. The score measures how well the model can look up the answer, not how well it generalises.\n",
    "\n",
    "**The tell**: Remove `is_censored` from the feature set and re-run CV — R² will drop back to the original baseline. The gap between those two scores is entirely explained by the leaked label information.\n",
    "\n",
    "**Why this is different from the OOF approach**: A model that predicts the *probability* a row is censored using only input features (income, location, etc.) captures the same signal without leakage — because that probability can be computed from features that genuinely exist at inference time. The OOF `p_censored` approach in the main activity solution does this correctly; using the label directly does not."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.12.3)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
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 },
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}