Hands on Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9d30d8a6045a8b08aba09e2a3230c666bac821f1"
   },
   "source": [
    "# Housing Price Prediction Lab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "99c0c2513327840d9bd303f4e64a899288ddb54c"
   },
   "source": [
    "## Multiple Linear Regression Hands on Lab\n",
    "\n",
    "\n",
    "In this hands on lab we will use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc.\n",
    "\n",
    "At the end of the lab we will be:\n",
    "\n",
    "\n",
    "- Identifying the variables affecting house prices, e.g. area, number of rooms, bathrooms, etc.\n",
    "\n",
    "- Creating a linear model that quantitatively relates house prices with variables such as number of rooms, area, number of bathrooms, etc.\n",
    "\n",
    "- Calculating the accuracy of the model, i.e. how well these variables can predict house prices.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3436cb2bcf6466380cf2f43733f98c2112e43087"
   },
   "source": [
    "## Reading and Understanding the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "431b6c4e49b75ce7b04e909b83906dfd7b858959"
   },
   "outputs": [],
   "source": [
    "# Import the numpy and pandas package\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Data Visualisation\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "746ef6bc4fafc58e8a37083aaece63eb79bc2308"
   },
   "outputs": [],
   "source": [
    "housing = pd.DataFrame(pd.read_csv(\"https://raw.githubusercontent.com/fenago/pythonml/c4237eba5f11adbb8c6db298209dd7d792abd3b3/Chapter%202/Housing.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "d765ad0cca904b30124809f090959c1540dfe729"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the head of the dataset\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "97cd7a0c1ebdad167b71d99409ccfe9bbb0fd31f"
   },
   "source": [
    "## Data Inspection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "73b846cde866034ab84bfd5e2e3bb35f638b5ec8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "e1e2d0a71e1fa5d41dce0562b2d1aae8ed17e041"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   price             545 non-null    int64 \n",
      " 1   area              545 non-null    int64 \n",
      " 2   bedrooms          545 non-null    int64 \n",
      " 3   bathrooms         545 non-null    int64 \n",
      " 4   stories           545 non-null    int64 \n",
      " 5   mainroad          545 non-null    object\n",
      " 6   guestroom         545 non-null    object\n",
      " 7   basement          545 non-null    object\n",
      " 8   hotwaterheating   545 non-null    object\n",
      " 9   airconditioning   545 non-null    object\n",
      " 10  parking           545 non-null    int64 \n",
      " 11  prefarea          545 non-null    object\n",
      " 12  furnishingstatus  545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "68e9fc5700d7192a76f8f0393dc02b6996db0d43",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0ca4cbb107bdc125987f0b734a1b8834b6d8c0a8"
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "3c78f79ff808df88d1ced4e944158375e2836f45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0.0\n",
       "area                0.0\n",
       "bedrooms            0.0\n",
       "bathrooms           0.0\n",
       "stories             0.0\n",
       "mainroad            0.0\n",
       "guestroom           0.0\n",
       "basement            0.0\n",
       "hotwaterheating     0.0\n",
       "airconditioning     0.0\n",
       "parking             0.0\n",
       "prefarea            0.0\n",
       "furnishingstatus    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Null values\n",
    "housing.isnull().sum()*100/housing.shape[0]\n",
    "# There are no NULL values in the dataset, hence it is clean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "7ec7bfa83693e04422ea2de1fe2041aa75bccf51"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier Analysis\n",
    "fig, axs = plt.subplots(2,3, figsize = (10,5))\n",
    "plt1 = sns.boxplot(housing['price'], ax = axs[0,0])\n",
    "plt2 = sns.boxplot(housing['area'], ax = axs[0,1])\n",
    "plt3 = sns.boxplot(housing['bedrooms'], ax = axs[0,2])\n",
    "plt1 = sns.boxplot(housing['bathrooms'], ax = axs[1,0])\n",
    "plt2 = sns.boxplot(housing['stories'], ax = axs[1,1])\n",
    "plt3 = sns.boxplot(housing['parking'], ax = axs[1,2])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4fbb9b35301a437698b221905aff7a421f6289b6"
   },
   "source": [
    "## Outlier Treatment\n",
    "### Price and area have considerable outliers.\n",
    "### We can drop the outliers as we have sufficient data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "4dbc902628f39c3c5671287fb513966ee9946163"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier treatment for price\n",
    "plt.boxplot(housing.price)\n",
    "Q1 = housing.price.quantile(0.25)\n",
    "Q3 = housing.price.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "housing = housing[(housing.price >= Q1 - 1.5*IQR) & (housing.price <= Q3 + 1.5*IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "74ee9616b156d76319e82a8cbadd649aed6b3fff"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier treatment for area\n",
    "plt.boxplot(housing.area)\n",
    "Q1 = housing.area.quantile(0.25)\n",
    "Q3 = housing.area.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "housing = housing[(housing.area >= Q1 - 1.5*IQR) & (housing.area <= Q3 + 1.5*IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "872f54e4de395aa921844f0b2fae5308a09235ae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlier Analysis\n",
    "fig, axs = plt.subplots(2,3, figsize = (10,5))\n",
    "plt1 = sns.boxplot(housing['price'], ax = axs[0,0])\n",
    "plt2 = sns.boxplot(housing['area'], ax = axs[0,1])\n",
    "plt3 = sns.boxplot(housing['bedrooms'], ax = axs[0,2])\n",
    "plt1 = sns.boxplot(housing['bathrooms'], ax = axs[1,0])\n",
    "plt2 = sns.boxplot(housing['stories'], ax = axs[1,1])\n",
    "plt3 = sns.boxplot(housing['parking'], ax = axs[1,2])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3f5e53ed935216b121bd7266a515654cf9b82b7e"
   },
   "source": [
    "## Exploratory Data Analytics\n",
    "\n",
    "Let's now spend some time doing what is arguably the most important step - **understanding the data**.\n",
    "- If there is some obvious multicollinearity going on, this is the first place to catch it\n",
    "- Here's where you'll also identify if some predictors directly have a strong association with the outcome variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "cf330625f5e0fc1c93f752a8ba90bb2079c450b8"
   },
   "source": [
    "### Visualising Numeric Variables\n",
    "\n",
    "Let's make a pairplot of all the numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "6c7c7195228dbc64fb0bbd2545efc6041bb6024d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(housing)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "82de6addf2af16bd82d64432d0a6017042bc7510"
   },
   "source": [
    "#### Visualising Categorical Variables\n",
    "\n",
    "As you might have noticed, there are a few categorical variables as well. Let's make a boxplot for some of these variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "4c3608492e866165e76eff056ff792bbfd885844"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "plt.subplot(2,3,1)\n",
    "sns.boxplot(x = 'mainroad', y = 'price', data = housing)\n",
    "plt.subplot(2,3,2)\n",
    "sns.boxplot(x = 'guestroom', y = 'price', data = housing)\n",
    "plt.subplot(2,3,3)\n",
    "sns.boxplot(x = 'basement', y = 'price', data = housing)\n",
    "plt.subplot(2,3,4)\n",
    "sns.boxplot(x = 'hotwaterheating', y = 'price', data = housing)\n",
    "plt.subplot(2,3,5)\n",
    "sns.boxplot(x = 'airconditioning', y = 'price', data = housing)\n",
    "plt.subplot(2,3,6)\n",
    "sns.boxplot(x = 'furnishingstatus', y = 'price', data = housing)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6c707cb6e33e92c23ce7bc2e33fe6170cb6d8cd1"
   },
   "source": [
    "We can also visualise some of these categorical features parallely by using the `hue` argument. Below is the plot for `furnishingstatus` with `airconditioning` as the hue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "437223279fb56d4c9c3b5905f0f2ed0f0fd69577"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "sns.boxplot(x = 'furnishingstatus', y = 'price', hue = 'airconditioning', data = housing)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d9889ec8b63eadf25022ac9b928ea02e608e683e"
   },
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "33f46d3bce47c43f1f26910e4c5c95baa7891c0c"
   },
   "source": [
    "- You can see that your dataset has many columns with values as 'Yes' or 'No'.\n",
    "\n",
    "- But in order to fit a regression line, we would need numerical values and not string. Hence, we need to convert them to 1s and 0s, where 1 is a 'Yes' and 0 is a 'No'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "ffa2c206d63c0bbe25355bdbc6b4ff7a78721c20"
   },
   "outputs": [],
   "source": [
    "# List of variables to map\n",
    "\n",
    "varlist =  ['mainroad', 'guestroom', 'basement', 'hotwaterheating', 'airconditioning', 'prefarea']\n",
    "\n",
    "# Defining the map function\n",
    "def binary_map(x):\n",
    "    return x.map({'yes': 1, \"no\": 0})\n",
    "\n",
    "# Applying the function to the housing list\n",
    "housing[varlist] = housing[varlist].apply(binary_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "d754048ba7fe30841f92073ea8b59d7fcad4a6ce"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>9100000</td>\n",
       "      <td>6000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9100000</td>\n",
       "      <td>6600</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8960000</td>\n",
       "      <td>8500</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8890000</td>\n",
       "      <td>4600</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8855000</td>\n",
       "      <td>6420</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "15  9100000  6000         4          1        2         1          0   \n",
       "16  9100000  6600         4          2        2         1          1   \n",
       "17  8960000  8500         3          2        4         1          0   \n",
       "18  8890000  4600         3          2        2         1          1   \n",
       "19  8855000  6420         3          2        2         1          0   \n",
       "\n",
       "    basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "15         1                0                0        2         0   \n",
       "16         1                0                1        1         1   \n",
       "17         0                0                1        2         0   \n",
       "18         0                0                1        2         0   \n",
       "19         0                0                1        1         1   \n",
       "\n",
       "   furnishingstatus  \n",
       "15   semi-furnished  \n",
       "16      unfurnished  \n",
       "17        furnished  \n",
       "18        furnished  \n",
       "19   semi-furnished  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the housing dataframe now\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a0f8c8376ce5a8d6d8c1c783ec9f7a0e627eeefc"
   },
   "source": [
    "### Dummy Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5100d00d83ce73a66afc490280dc00d852cc2f92"
   },
   "source": [
    "The variable `furnishingstatus` has three levels. We need to convert these levels into integer as well. \n",
    "\n",
    "For this, we will use something called `dummy variables`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "e57564544b89f851ca2536d5d0cf8f71b7d2f8af"
   },
   "outputs": [],
   "source": [
    "# Get the dummy variables for the feature 'furnishingstatus' and store it in a new variable - 'status'\n",
    "status = pd.get_dummies(housing['furnishingstatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "3cb5808e2eb979d654827bc727ba28fffcfc5bd6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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      "text/plain": [
       "    furnished  semi-furnished  unfurnished\n",
       "15          0               1            0\n",
       "16          0               0            1\n",
       "17          1               0            0\n",
       "18          1               0            0\n",
       "19          0               1            0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check what the dataset 'status' looks like\n",
    "status.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "87964c68f0849db142b2a0178949ab4ee5dad8a5"
   },
   "source": [
    "Now, you don't need three columns. You can drop the `furnished` column, as the type of furnishing can be identified with just the last two columns where — \n",
    "- `00` will correspond to `furnished`\n",
    "- `01` will correspond to `unfurnished`\n",
    "- `10` will correspond to `semi-furnished`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "2e64521e0eb959791a7e867dec83c6f2a0b5ba38"
   },
   "outputs": [],
   "source": [
    "# Let's drop the first column from status df using 'drop_first = True'\n",
    "\n",
    "status = pd.get_dummies(housing['furnishingstatus'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "1b9198d2b8e96ae31ee346058d91d293e2826e14"
   },
   "outputs": [],
   "source": [
    "# Add the results to the original housing dataframe\n",
    "\n",
    "housing = pd.concat([housing, status], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "202e9fe9bc947dd1ed20afe6a1dfdd3a1a91ed96"
   },
   "outputs": [
    {
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       "      <td>furnished</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>semi-furnished</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "15  9100000  6000         4          1        2         1          0   \n",
       "16  9100000  6600         4          2        2         1          1   \n",
       "17  8960000  8500         3          2        4         1          0   \n",
       "18  8890000  4600         3          2        2         1          1   \n",
       "19  8855000  6420         3          2        2         1          0   \n",
       "\n",
       "    basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "15         1                0                0        2         0   \n",
       "16         1                0                1        1         1   \n",
       "17         0                0                1        2         0   \n",
       "18         0                0                1        2         0   \n",
       "19         0                0                1        1         1   \n",
       "\n",
       "   furnishingstatus  semi-furnished  unfurnished  semi-furnished  unfurnished  \n",
       "15   semi-furnished               1            0               1            0  \n",
       "16      unfurnished               0            1               0            1  \n",
       "17        furnished               0            0               0            0  \n",
       "18        furnished               0            0               0            0  \n",
       "19   semi-furnished               1            0               1            0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's see the head of our dataframe.\n",
    "\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "30d3b04cf9943a6ae45c51d0a4d897dc4a014bf6"
   },
   "outputs": [],
   "source": [
    "# Drop 'furnishingstatus' as we have created the dummies for it\n",
    "\n",
    "housing.drop(['furnishingstatus'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "6387630eca0184a51e3f1ff784dee84fa54bb109"
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "15  9100000  6000         4          1        2         1          0   \n",
       "16  9100000  6600         4          2        2         1          1   \n",
       "17  8960000  8500         3          2        4         1          0   \n",
       "18  8890000  4600         3          2        2         1          1   \n",
       "19  8855000  6420         3          2        2         1          0   \n",
       "\n",
       "    basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "15         1                0                0        2         0   \n",
       "16         1                0                1        1         1   \n",
       "17         0                0                1        2         0   \n",
       "18         0                0                1        2         0   \n",
       "19         0                0                1        1         1   \n",
       "\n",
       "    semi-furnished  unfurnished  semi-furnished  unfurnished  \n",
       "15               1            0               1            0  \n",
       "16               0            1               0            1  \n",
       "17               0            0               0            0  \n",
       "18               0            0               0            0  \n",
       "19               1            0               1            0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "837091e7d8ac08f881d1d1b6ed57ec6d1e806165"
   },
   "source": [
    "### Splitting the Data into Training and Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "5449ca9587bd949ebc3cc166122fcf733b8eb2f9"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# We specify this so that the train and test data set always have the same rows, respectively\n",
    "np.random.seed(0)\n",
    "df_train, df_test = train_test_split(housing, train_size = 0.7, test_size = 0.3, random_state = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0e6c366f2979dc9f6cfa6a0b46dba9a585be18a8"
   },
   "source": [
    "### Rescaling the Features \n",
    "\n",
    "As you saw in the demonstration for Simple Linear Regression, scaling doesn't impact your model. Here we can see that except for `area`, all the columns have small integer values. So it is extremely important to rescale the variables so that they have a comparable scale. If we don't have comparable scales, then some of the coefficients as obtained by fitting the regression model might be very large or very small as compared to the other coefficients. This might become very annoying at the time of model evaluation. So it is advised to use standardization or normalization so that the units of the coefficients obtained are all on the same scale. As you know, there are two common ways of rescaling:\n",
    "\n",
    "1. Min-Max scaling \n",
    "2. Standardisation (mean-0, sigma-1) \n",
    "\n",
    "This time, we will use MinMax scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "facade72a50104e04437fd7bd7191a23713fc8ea"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "3f4c69929aaf26f7aee879139814b027eae01798"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "91c50ab11758f9e3a7905fd689641f8f003860c8"
   },
   "outputs": [],
   "source": [
    "# Apply scaler() to all the columns except the 'yes-no' and 'dummy' variables\n",
    "num_vars = ['area', 'bedrooms', 'bathrooms', 'stories', 'parking','price']\n",
    "\n",
    "df_train[num_vars] = scaler.fit_transform(df_train[num_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "80202e0393e84a01949522f8809bce181a08d84a"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.2</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "        price      area  bedrooms  bathrooms   stories  mainroad  guestroom  \\\n",
       "148  0.523810  0.526907       0.4        0.0  0.666667         1          0   \n",
       "236  0.390476  0.114134       0.2        0.0  0.333333         1          1   \n",
       "356  0.275238  0.072738       0.8        0.5  0.000000         0          0   \n",
       "425  0.219048  0.151390       0.2        0.0  0.000000         1          0   \n",
       "516  0.095238  0.157895       0.2        0.0  0.000000         0          1   \n",
       "\n",
       "     basement  hotwaterheating  airconditioning   parking  prefarea  \\\n",
       "148         0                0                0  0.000000         1   \n",
       "236         1                0                0  0.000000         1   \n",
       "356         1                0                1  0.333333         0   \n",
       "425         1                0                0  0.666667         0   \n",
       "516         0                0                0  0.333333         0   \n",
       "\n",
       "     semi-furnished  unfurnished  semi-furnished  unfurnished  \n",
       "148               1            0               1            0  \n",
       "236               1            0               1            0  \n",
       "356               0            0               0            0  \n",
       "425               0            0               0            0  \n",
       "516               0            1               0            1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "0578a4dfe42b2b1a741e847e00b6bdcc5323156e"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>361.000000</td>\n",
       "      <td>361.000000</td>\n",
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       "      <td>361.000000</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383701</td>\n",
       "      <td>0.350081</td>\n",
       "      <td>0.390582</td>\n",
       "      <td>0.127424</td>\n",
       "      <td>0.268698</td>\n",
       "      <td>0.875346</td>\n",
       "      <td>0.168975</td>\n",
       "      <td>0.349030</td>\n",
       "      <td>0.038781</td>\n",
       "      <td>0.313019</td>\n",
       "      <td>0.218837</td>\n",
       "      <td>0.207756</td>\n",
       "      <td>0.476454</td>\n",
       "      <td>0.304709</td>\n",
       "      <td>0.476454</td>\n",
       "      <td>0.304709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.209712</td>\n",
       "      <td>0.207184</td>\n",
       "      <td>0.149146</td>\n",
       "      <td>0.224465</td>\n",
       "      <td>0.287833</td>\n",
       "      <td>0.330784</td>\n",
       "      <td>0.375250</td>\n",
       "      <td>0.477325</td>\n",
       "      <td>0.193341</td>\n",
       "      <td>0.464366</td>\n",
       "      <td>0.287286</td>\n",
       "      <td>0.406264</td>\n",
       "      <td>0.500138</td>\n",
       "      <td>0.460923</td>\n",
       "      <td>0.500138</td>\n",
       "      <td>0.460923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.237143</td>\n",
       "      <td>0.189829</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.338095</td>\n",
       "      <td>0.295092</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.491425</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            price        area    bedrooms   bathrooms     stories    mainroad  \\\n",
       "count  361.000000  361.000000  361.000000  361.000000  361.000000  361.000000   \n",
       "mean     0.383701    0.350081    0.390582    0.127424    0.268698    0.875346   \n",
       "std      0.209712    0.207184    0.149146    0.224465    0.287833    0.330784   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.237143    0.189829    0.200000    0.000000    0.000000    1.000000   \n",
       "50%      0.338095    0.295092    0.400000    0.000000    0.333333    1.000000   \n",
       "75%      0.514286    0.491425    0.400000    0.000000    0.333333    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "        guestroom    basement  hotwaterheating  airconditioning     parking  \\\n",
       "count  361.000000  361.000000       361.000000       361.000000  361.000000   \n",
       "mean     0.168975    0.349030         0.038781         0.313019    0.218837   \n",
       "std      0.375250    0.477325         0.193341         0.464366    0.287286   \n",
       "min      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "25%      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "50%      0.000000    0.000000         0.000000         0.000000    0.000000   \n",
       "75%      0.000000    1.000000         0.000000         1.000000    0.333333   \n",
       "max      1.000000    1.000000         1.000000         1.000000    1.000000   \n",
       "\n",
       "         prefarea  semi-furnished  unfurnished  semi-furnished  unfurnished  \n",
       "count  361.000000      361.000000   361.000000      361.000000   361.000000  \n",
       "mean     0.207756        0.476454     0.304709        0.476454     0.304709  \n",
       "std      0.406264        0.500138     0.460923        0.500138     0.460923  \n",
       "min      0.000000        0.000000     0.000000        0.000000     0.000000  \n",
       "25%      0.000000        0.000000     0.000000        0.000000     0.000000  \n",
       "50%      0.000000        0.000000     0.000000        0.000000     0.000000  \n",
       "75%      0.000000        1.000000     1.000000        1.000000     1.000000  \n",
       "max      1.000000        1.000000     1.000000        1.000000     1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "daad7f09168cddb4272fd0f10395aac2cc2cecf2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FQbaOM33ZBVXMuXMxNruTeaMTePnPM5h/7zeYjAZGpVh44L1NpOdXcu+547jqxOE881l292QHurpBWFdHlH8srODHwgomxUVw7YQB/PG7LL9nOxjdIen984fy+NJc9ncZYEiAkZfOGM1DP+ZQ3+z0b7hD6Bf3HzuUx5fkdJnXpeHkV1cTEWRiwW/HMjQmlO3lDZ0L+tCh9gWAyfGR/GZQApd96x4whAeamNcvmlM+XUtds4On5gzn5NRYFu8s288r+CBvF8/tL++k2EhOHxjPH39sm4274scMypuasQYF8MJRo9lZ28jGLo5e+0uXx+f3c5rAhOhITukfz7UrMv2aqS1HF8916CAdt7/2RSbeeStBVgvNtbVsevofhCQmYBk2hOF/uJicd95n52eLiRk/FmXy/cd0l/2iQ7ved8oIHv9m2373FQFGxbHD43jy284DOX841LmaH3ZV8MOuCibFR3L9pFQu/7qtP4SYDPxj/kgeW51LQ4uf928c+v7i+10VfL+rgskJkdwwKZXLvnJndmn4zccbCA808uKxoxhiDSGnqnuuN25z6LNkymBg8F3347TZKHj5RZr27iE4KdmP2dy67KIHiP3KUx8xYnwaI8a7B2ebVm8jdUgy979wNcW7K3j4hpcZMT6NkNDg/b+Iz/Xudv7faGPR3WSQ6a3jdrTvcVffXFUX5fc9f53W+psDrUgpdSVwJYDJOhlT2ODDjNp3FFc1khjVNnOZaDFTWt3oVaa+qe2ar5+yinnogglYwwIpqrJRXNVIer77Rj9fr9/DVScN657gHiUNdhJC22ZP4kMCKW03O9TR+tJa+oWZsQSZqLZ337VsxfV2EtvNBCaGB1FS732a29j4cJ4/bRQAUeYAjh4YjcOl+XZHOSaD4qUzRvPJlhK+zvHfwKc1b52dxHazJ4nhwZTUe7fr2MQInj9jjDtvSABHD4px522Xr9buYGVBFfPSov0+yCyx2UkI8e4LZY2d+8IQSwj3TxvCNUuyW69nnJ5gYU99E1V292nKPxRWMD42wq+DzNJG71n3eHMQ5Y2dT30cHBnCPVMGc8OybK/rL8s9s4hV9hZ+2lPBqOjwbh1kljU2ExfcNvMTGxzYmqm9tPAQbh07iNvWbKa2pXu2uSCrlaZ2s4z2qmqCLBavMsFWq9dMpL2ymkBPmSCr+//AiAhiJ46nNj8fy7AhhCYmMP5m97VOtuISKjJ8P2gurm0iKbLdthcRTGldh20vOZLnzx0HgDUkkHlDY3G6XHy7xX0N8bwhsWQV1XY6jd1fijvshxNCgyg9wOnm60tq6B/eth82KcWz80fyRW4p3++q2G89X+qYOf4gmdcV15ASYcYaZKKq3WdHXbOT1UXVzOkX5ddBZoDFSku72XhHVRUBkZbDfh1jSAihQ4dRn53lt8HP1x/+zPefrQZg8Ij+VJRUty6rKKshKiayy3of/OcbaqvrueWxS1ufW/LlWs68aD5KKRL7xxCXFMWenaUMGZXil+x9pZ37chv3Br35NNbuIi3gLUUpNcPz8/nAzwcouxVIUkpNAfBcj2kCvgGuVkoFeJ4fqpQK7VhZa71Aaz1Zaz35f3mACZCxs4rUuDD6RYcQYFScOqU/36cXeZWJiWj7IB6basVgUFTVN1Nea6eoqpGB8e6bY8wcEUdON5wW2V5WRR0DwoNJDgvCZFCclBrLT4Xed7ftH972hW1EVCgBRtWtA0yA9KI6BlpD6B8ZTIBBcdrwOL7bUe5VZva/VzJ7gfvf4u1l3Pv9Nr71lHnyxOHsqLDxyrrCrl7e93n31jLQam7LOyKe7zoMbmf/65fWf4u3lnLvN1v5NqeMKHMAEUHuY2RBJgOzU6PYUen/I/zZFXWkhAeTFOruCycMiGXpbu++kBASxN/mjuSeFdsoqGt3mniDnbEx4QR7rkOZlmAhr8b7YIuvba6sIyXM3Jr3uJRYlu31zhsfEsSTM0dw/+rtFNS3nUIZbDQQYjK2/jw93kJujX8H8R1tramjX6iZBHMQJqWYnxzLLyXe+eOCA3l48nAe3ZTD7gb/Xxu4T/jAATSWlNJYVo7L4aBk9Vpixo/1KhM9fizFK1ahtaYmNw9TSDBBlkicdjuORndWp91OZfYWQpPdXxKba937N+1ysfPzxSTPm+vz7Ol7akiNDqWf1UyAUXHamES+2+p9A6o5f1vKbM+/r7KLuffzza0DTIDTxybyeTedKguQVV7HgEgzyWHu/cVJabEsKfAeLKa03w9HhxFgaNsPPzxnKHk1NhZm7/9SAV/LLKsjNcJMP0/mU9Ji+bHDADel3YG2kZ7MVXYH1uAAwj2XhgQZDcxMtpLn5xsVmQekYi8tobm8DJfDQc36NYSPHXdIdR11dTht7nyu5mbqt24hMCHBb1lPPHs2T79xM0+/cTNT5o5m6Vfr0VqzPWsXIaHBWGMiOtX54bNVbFq1jRsevMjrVM2YeAuZ63IAqK6sY++uUuKTozrV95W+0s59uY1F7yAzmd62AJcopV4GcoB/Add1VVBr3ayUOhd4Xillxn095rHAK0AqsEG5/xJrGfAb/0dvs/D565gzYwQx1nB2rH6Bh//+IQvf/6k7I3hxujQPvLuJhX+Zg8Gg+OCXneQU1XLBXPdpFO8sy+OkSf248Kg0nE5NU4uT6xesbq3/wLsbefbyqQSYDBSUN3Db6+u6N7+Gv67J5aVjR2NUio93lJBbY+N3Q9079g+2F3NcSgynDYrD4dLYnS5uXba1WzO6c2ru+347b5w9HqNBsShzLzkVDVzoucnS2+n7/0I4OTmS345KZEtZPYsvmQLAU8vyWJLvvyP+Tq2577ttvHHeBIxKsShjLznlDVw4wf1l++2N+/8yGBcWxN9PHYXBAAal+GJLCT92GFD7JzM8vi6Xf80f7f6TILnuvnD2EHdf+DCnmCvHpGAJNHHXFPfBI4fWXPj1JrIq6vi+oJx3T5qAU2u2VtXz0Y6iA63OJ3mf3JDLc3NHY1TwWX4JebU2zhrkzvvf3GKuGNmfyKAAbp84qDXvJd+nEx0cwJOzRgJgUvB1QZnfryHtKv+z2Xk8PW2U+xrcwlJ21jdyeoo7/2cFxVwyNIXIgABuHJ3WWudPP/v/T60YjEaG/v5c0v/+HNrlInH2TEKTk9izZBkAyUfPJXrsaCozslh1x70YAwMZ/odLAGiuqSXzhZcA92AyftoUose4zzAoWb2WPT8uBSB24gQSZs/0eXanS3PfF5t545Ip7n3F+t3klNZz4RT3n955e+2BDzQFBxiYPTiGuz7tvssWnBoeXbmDf5/g3vY+zilmR7WNc4clAvD+tiKOS43hjMHxOFyaJqeLm39y/xmsifERnDE4nm2V9fz3jIkAPLs+n2W7fX+9a8fMD63YwSsnuT87PtruznzecHfm97YWcUJqDGcM8WR2uLjxR3fmuJBAHp87DKMBFIqv88s6Hdz0NWU0knTuBex84Vm0y4V1xiyCk5KpXPYTAFFz59FSU0PuE4/gamoEpShf8j1D7n0IR001u994Fe1ygdZETppCxJhDGzgdqYkzR7BxxRau+91jBAYFcM0957Uu++tN/+aqO88hKjaSBU9+RGyClbuvfA6AaUeN4XeXH8/Zlx3Hi4+8x00XPgXA7685lQhLWJfr8oW+2M59rY17AyXzeKiO12H8f6WUSgW+0FqP7u51m1PO71O/hMQTf9vTEQ5b6Ozuv2nJkaot6d676B6xdndJ7CusKf69g64/BAR0zx0bfSUkpG/lBRhh7Z5TQH3p66+6bxbXF0LSOp3g0+s5+94ujtED+9b2d/+EA999uTd6cGPXp432Vn2xjcdGndq3OjJgHfznbvtuX7Xjn72yfWQmUwghhBBCCCF8RK7JlEFmK631TqDbZzGFEEIIIYQQ4n+JDDKFEEIIIYQQwkdkJlPuLiuEEEIIIYQQwodkJlMIIYQQQgghfERmMmUmUwghhBBCCCGED8lMphBCCCGEEEL4iKJX/lWRbiUzmUIIIYQQQgghfEZmMoUQQgghhBDCR+SaTJnJFEIIIYQQQgjhQzKTKYQQQgghhBA+IjOZMpMphBBCCCGEEMKHZJAphBBCCCGEEMJn5HTZXiDxxN/2dITDUvT1Rz0d4bCdeM6fezrCYauOCerpCIcl3uzs6QiHLb+i791iXOueTnB44kP7Xr9IDOl7mWNGh/V0hMPS0NDHOjIQH2/s6QiHLd7c3NMRDktWVd/7WhpvdvR0hMPSF9t4bFRPJzh8crqszGQKIYQQQgghhPChvnc4QwghhBBCCCF6LZnHkxYQQgghhBBCCOEzMpMphBBCCCGEED4i12TKTKYQQgghhBBCCB+SmUwhhBBCCCGE8BGZyZSZTCGEEEIIIYQQPiQzmUIIIYQQQgjhI0rm8aQFhBBCCCGEEEL4jsxkCiGEEEIIIYSPyDWZMpMphBBCCCGEEMKHZCazj5o7Kp77zh2PwaBY9HM+L329zWv5tKGxLLhmJoXlDQB8s2EPz3+5BYBwcwCPXzyJockRaA23L1zHxrzKbn8P7b301J846ZgJlFXUMvm423o0yz712VkUf/gu2uXCOmsOMcef7LXcXlzE3rdeo6mwgNjTziTm2BO8lmuXi/wnHsZksZJy9fXdknlKjIVrR6ZhVPBlYQnv5u3xWt4/1MztYwczJCKM/2zfxaL8va3LfpuayCn941Eovigs5qOdRX7PW5udxe5F76FdLqJnzSHhxJO8ljcVF7Fr4es0FhaQePpviD++rY13vfE6tZkZmMLDGXHfg37Pus/0eAs3jU/DoBSf5ZfwxrbdXstP6B/LRcP6AdDodPLkhlxyatzb4ccnTcbmcOLSGqdLc+mP6d2S9+YJ7ryf5nWRNyWWi/fldTh5ol3eT072znvJD/7PC+5+sdfTL6JmzSG+i35R6OkXCaf/hrh2/aLgjdep8/SLYX7uF1pr1i/8gL0bszEFBTL96ouIGpjSqVx9aTm//ONV7A02olL7M+PaSzCaTOT/vIYtn30HgCkoiClXnId1gPt38em192IyB6MMCoPRyIl/vd2n2WckWLllortffJJXzMIt3v3ixAGxXDKiPwA2h5PH1+0gp9rdL8ICjNw7dSiDIkPQGh5as53Mijqf5uvK7GQrd00fhMGg+HBbMa9kFHotn58SzfWTBuDS4HRpHludy4aSWgKNijdPGUegwYDJoPgmv5wXNu7ye16AqbEWrh+dhkHBlwUlvL3De598XHIsFwxOBtzb398yc8mttQFw+7jBzIy3UmVv4dKlm/yWsSIzm+3vLEK7XCTNnUXqKSd6Lddas/2dRVRkZGEMDGTE5ZcQkeru57/cchfG4GCUwYAyGph6/10A5P73M8o3poNSBEaEM/LySwiyWvySX2vN1y//l5y1mwkICuA3N11I4uD+ncqt+XwZqz5ZSlVRObe++yghkWEAZCxZxy8ffA9AoDmIU645h4S0ZJ9mlDb2fxv3Nkqpno7Q42SQ+SsopYxaa2dPrd+g4MELJnDxM8sprrLxyV3H8H36XnYUeX/Ir80p54oXfulU/75zx7E0u5hrXl5FgFERHNjz3eDND5by0sJveOWZP/d0FMA9QCxa9DYDrruJAIuVvCcfIXzMeIISk1rLGENDSfjd+dSlb+zyNSqXfE9gQiKupqZuyWwAbhiVxq1rsilraualWeNYUVrJrvrG1jJ1LQ6e35zP7Pgor7qpYSGc0j+eq3/JoEW7eHLKKFaVVrHH5r/s2uWi8N13GHzDjQRYrWx77FEix47DnNSujUNC6XfuedRs2tSpfvSMmcTOO5pdr7/qt4wdGYBbJwziuuVZlNqaef2Y8SzfW0F+XVsb77U1cfXSDOpanMxIsHLHpMFc3m4w+eelmdQ0O7ot720TB3HtMnfehcd2kbehiat+ast756TB/KFd3qt/6r684O4Xe959hzRPv8jx9IvgDv0ieT/9ImrGTGLmHU1hN/SLvZuyqSsq47RnH6Bix07WvvIeJzza+SDZpnc+Ydgp80mdOZk1r7xL3o8rGHL8XMJiYzj2vhsJDAth78Zs1ix4x6v+MffeQHBEmM9zGxTcPnkQ1yzJoqTRzhvHjWfZnkryPYMbgL31TVz5QwZ1LQ5mJlq5e8pgLv3O3S9umTiIFUWV3P7LFkwGRbDR/ydFGRTcO3Mwl3+dSUmDnUWnT2BJQQW51W2ZV+2t4seCCgCGWkN5Zv4ITvloHc1OzWWLM7A5XJiU4q1Tx7F8dyXpZf4dGBuAG8ekcdOqbMoam1kwZxw/F3vvk4tsTVy3IpP6FifT4izcOnYwV/2cAcDXhaV8vLOIu8YP8VtG7XKx7c13mXDLDQRFWVn70GPEjB9LWHLb9laRkUVjSSkzHn+I2rx8tr35DlPuvaN1+cTbbyIw3LufDjjpOAaddToAhd/9SP5nXzL8kgv98h52rNtM5Z4yrnvlHvZs28WXL3zAFc/e1Klc/5FpDJ06itdvf8HreWt8NJc+cT3m8BBy1m7mi+fe77L+ryVt7P82Fr2TnC7bBaXUJ0qp9UqpbKXUlZ7n6pVSDymlVgMzlFK/V0qtUUptUkq9rJQyesr9Sym1zlPXL4fRxw2MYldpPYXlDbQ4NV+sLeS4cUkHrwiEBZuYOjSWRT/vBKDFqalrbPFHzMPyy5qtVFbX93SMVo078wmMjSMwJhZlMhE5aSp1GZu8ypjCIzAPGAhGY6f6LVWV1GVlYJ05p5sSw3BLOHttTRQ12nFozY9FZczqMJisbm5hW009Dq29nh8QZmZzdT12lwuXhvTKGuYkRPs1r21nPkFxsQTFxmIwmbBOmUJNhzYOiIggNHUgqos2DhsyFGNIqF8zdjQyKpzd9U3sbXC38XeFZcxN8m6nzIo66lrcx6CyKmqJMwd2a8b2RnXI+21hGXOTD5I3pOfygrtfBLbrF5b99IuQA/QLUzf1iz3rMhg4dxpKKWKGDKTZ1khjVY1XGa01JdnbSZk2AYCBc6dRuM49iIgdlkZgWAgAMUMGYqus7pbco6LCKaxrYk9DEw6X5tuCMo5K9t5XZFTUUdfiPriQWV5HnDkIgFCTkQmxkXyaVwKAw6Wpb/H/MdexseEU1Dayu66JFpdmcV4Z81O8+7LN4Wr9OSTAgO5imcmgCDAovPeA/jHCGs6ehiaKbO7t74e9ZcxO8G7nrKq61vbLrqojNrht+0uvrKXWzwd4avN2Yo6Lwxzn3t7ip06hfGOGV5myjRkkzJyOUorIQWk4bI3Yq2v284puJrO59WenvRn8OKuzdVUWY4+ZglKKfsNTaWpopK6yc77EQf2wxHf+XOs/ciDmcPd22G94KrUV1T7NJ23s/zbujZQydNu/3qrnp7B6pz9orSuVUmZgrVLqIyAUyNJa36eUGgHcDszSWrcopf4JXAi8AdztqWsEflBKjdVaZ+x3Tb9CgsVMUWW7I6HVjYwfGNWp3IS0KL6891hKapp47IMMcopq6R8TSmWdnScvncyIfpFk7armofc30djcYxOzvZKjuooAq7X1sclipXFn3iHXL/7wfeLPPLvbZjEBYoIDKW1qbn1c1tjMCEv4IdXNr7Nx+bABRASYsDtdTIu1sq3Gv4P+5qpqAq1t/TbQYqUhP9+v6zxSceZAShrtrY9LG+2Mitp/G58+MIGVxVVezz03ZzQAH+cV8Ul+iX+CesSaAymxtctrszMq+iB5i7zzPj93NBr4ONf/eQFaOvSLAIsVWy/tF7bKGkKiLa2PQ6Is2CqrMVsjW5+z1zUQEGLG4BkQh0RZaexiMJm7ZAVJ40e1PaEUS/76AigYcsxsBh8722e548xB3v2isZnRB+jHZ6TFs8LTL5LDgqm2t3D/tKEMtYSypbKepzfk0uR07be+TzKHBFHc0Ja5xGZnbGznzMcOiObGyQOJMgdw9bdZrc8bFHx4xkRSIsy8u2UvGX6exQTPPrmx3T65qZmRB9gnn9o/ntWl1X7P1V5TVRXBUW2fdUFRFmpzvbc3e3W1dxmrBXtVNUGWSFCKTU//A5Qied4ckue1HVjN/egTin5ZjSnEzMTbbvTbe6grryYy1tL6OCImkrryGsKjIvdfaT82fruKwZNG+DCdtHFH/mhj0TvJILNr1yulzvT83B8YAjiBjzzPHQNMwj0ABTADpZ5l53hmP01AIjAS8Okgky4OVnWYmCK7oIo5dy7GZncyb3QCL/95BvPv/QaT0cCoFAsPvLeJ9PxK7j13HFedOJxnPsv2acT/SYd4lLAuMx1TeDjmlFQatm/1c6g2XaXTh3i8vqChkfdyd/PU1FE0Op3k1tlw+v1Qfxcr6IOXMOyvmSbFRnJaajxX/tS2+f9xSQblTc1YgwJ4fs5odtY1sqm81m/Zuuyy+wk8KTaS0wfGc+WStrxX/NiW94W5o9lV18hGP+bdb8Be2y86Z+18HU5X78e7TEn2dnKXrOC4B9tOHzvuwZsIibLQVFPHj48+T0RyPHEjfHTaZFefIfspOikukjPSErjCcz2uUSmGWcN4cn0u2ZV13DwhjUtH9uelTP9e49hlV+4i9Pe7Kvh+VwWTEyK5fmIqf/g6EwCXhrM+2UB4oJHnjxnFEGsIOVW2zi/g78z7aekJ0ZGckhLPNb9k+jXTIenYh7tqaE+RyXfdSpDVQnNtLRuf/gchiQlYh7n76aDf/oZBv/0NO7/4mt0//ETamaf5OXj7fIe/08hPz2Hjt6u47Kkb/BCoA2ljPwQSvY0MMjtQSs0DjgVmaK1tSqmfgGCgqd11mApYqLW+s0PdgcAtwBStdZVS6nVP3a7WcyVwJUD07CuJGHHcIWcsrmokMartNIlEi5nS6kavMvVNbafY/JRVzEMXTMAaFkhRlY3iqkbS8903+vl6/R6uOmnYIa/7/wuTxUpLVduMjqO6ioBIyyHVteXtoC4znfrsTFwtLbiamtjz+r9JvvSPfkrrVtbUTFy7U61izYFU2JsPUMPb4t2lLN7tPlZyxdAUypoOve6vEWi10lzVdsOp5uoqAiwWv67zSJU2NhPvOW0Q3DNC5Y2d22lwZAh3TRrMX37O9jrdrdzTplX2Fn7aW8GoqHC/DjJLbc3Eh7TLGxLU5e91cGQId08ezF+WZ3tdf+mVd08FI6PC/T7IDOjQL1p6Wb/Y/s1SdvzovtY9etAAbO1O++o4iwkQFB5Gi60Rl9OJwWjEVlnlVaZq1x5Wv/w28+74M0HtrrkKibIAEBwZTr8p46jYsctng8xSm927X5gDKWs3Q7/P4MgQ7p06hOuXtvWL0kY7pY12sivdM4E/7C7n0hGdbwDiayU2OwmhbZnjQ4Iote1/H7WuuIb+EWYsQSaq7W19uq7ZyZriamYnR/l9kFnW1Ox1unxscGDrNtVeWngIt40bxK2rN1Pb0n3XPwMEW600VbZ91tkrqwnqsL0FdSxT1VZm341mAiMiiJ04ntq8/NYB0D7x06eQ/uyLPh0Arfl8ORu+WQlA0pAUasqqW5fVltcQHh1xWK9Xkr+Hz//xLhc+dBUhEb493V7a2M2fbdwbKbkiUVqgC5FAlWeAORyY3kWZH4CzlVJxAEqpKKXUACACaABqlFLxwEld1AVAa71Aaz1Zaz35cAaYABk7q0iNC6NfdAgBRsWpU/rzfbr3nUBjIto+jMemWjEYFFX1zZTX2imqamRgvPvLzMwRceTs9ffMRN9jHpBKc2kJzeVlaIeDmvVrCBsz7pDqxp/xW4Y++hRDHn6Cfn+4ktBhw/0+wATYWlNHcqiZBHMQJqWYnxjLipJDv2uwJTAAgLjgQOYkRPPD3jJ/RQUgZEAq9tJS7OVluBwOqtauJXLsobVxT9lSVUf/MDOJIe42Pq5/LMuKvNs43hzE4zNG8MDa7RTWt50uHWw0EGIytv48Ld5Crucurv6y2ZM3yZP3+P6xLN/bOe8TM0dw/5rtFPRwXnD3i+Z2/aK6l/WLoSccxclP3MXJT9xFv8njyF+2Gq015Tn5BISYOw0ylVLEjRxKwWr3DcLyl62m3+SxADSUV7L87wuYcc0lRCTFt9ZxNNlpaWxq/bk4YwuR/RN99h42V9bRPzyYpNAgTAbF8SmxLNvToV+EBPHU7JHct3IbBe1uFFXR1EKJzc6AcPeBzqnxFvJq/DtYA8gsq2NAhJnksGACDIqT02JZ4rnJzz4p4W3HdEdGhxFgUFTbHViDAwgPdPflIKOBGUlW8rsh89bqOvqFmkn07JOPSYrll2Lvdo4zB/LIlOE8ujGH3Q3dd3nFPuEDB2ArLaWxrByXw0HJmrXETBjrVSZ2wliKV6xCa01Nbh4mczBBlkicdjsOTz912u1UZm0hrJ/7jqG24rZT68s3ZRCSGI8vTT1tDle9cBtXvXAbw2eMIeOHtWit2b11J0GhwYd1GmdNaSXvP/IqZ95yEdH94nyaE6SNwf9tLHonmcns7GvgKqVUBrANWNWxgNZ6s1LqHuBb5b7itgW4Rmu9Sim1EcgG8oDOt3b1AadL88C7m1j4lzkYDIoPftlJTlEtF8xNA+CdZXmcNKkfFx6VhtOpaWpxcv2C1a31H3h3I89ePpUAk4GC8gZue32dP2IeloXPX8ecGSOIsYazY/ULPPz3D1n4/k89lkcZjSSccwEFLz6LdrmwzJhFcFIylcvdmaLmzMNRU0Pek4/gamoEpahc8j2D7nkIY7uL8buTS8Nz2Xk8OXUUBuCr3aXsrG/ktJQEAD4vKMYaGMDLs8YRYjKigbNTk7h0+UZsDicPThxGREAATq35R3Ye9Q7/XqerjEb6nXsBuc89i3ZpomfOwpyUTPmynwCImTuPlpoatj32CM6mJpRSlP34PSPud7dx/isLqN++HUd9PVl33EriaacTPcu/N1pyanh6Uy7PzRmNQcHnO0vIr7VxZpq7jT/OK+bykf2JDAzgtgmD3HU8f6okKjiAJ2eMBMCo4JvCMlaVVPs971Mbc3luridvfgl5tTbO8uT9b14xV3jy3j6xLe8lP7jzPjWzXd4C/+cFd79IPvcC8p57FlyaqJnuba9jv8jx9AuUovzH7xnm6Re72vWLzXfcSrwf+0XShFHs3ZTN5zc8gDEokOlX/b512ZLHX2TalRcSEmVhwgW/4efnXiXj/c+xpvZn0NEzAMj66Cvs9Q2sffU9gNY/VdJUU8eyvy0AQLucDJg1xft6zSPk1PDU+lyeP2o0RoPiszx3v/jtIHe/+Ci3mD+OSiEyyMTtkwd76mgu/nYT4K778IxhBBgM7Klv5MHVOT7LdqDMj6zcwSsnjsagFP/dXsyOahvnDncPvt/fWsTxA2M4Y3A8LS6N3enipiXuP9sVaw7ksaOGYVRgUIqv88r4qdD/f7bLqeHZrDyenj4Kg4LFhe598ukD3O382a5iLh2SQmRAADeOSWutc+Vy96nJ900cyoToSCIDTXx47GRe21bAl4Wl+13fr2EwGhl24bls/Ntz4HKROGcmYclJ7F6yDIB+R88leuxoyjOyWHn7vRgCAxl5+SUANNfUkvHCSwBop4v46VOIHuPupzs+/ARbcQlKKYKjoxh2yQU+zd3ekCkjyVm7mecvf5iAoEDOuLFtXW/f9xKn33A+4dGRrP50Kb98+AP1VXX865onGDJ5JKf/5XyWvvMNjXUNfPnPD9xtYjBw5XO3+CyftLH/27g36s035OkuSnd1HrjoVmlXftinfglFX3908EK9zImv9o4/jXI4qpv71g4q3tz3bh6VX9G32hi6vnSnN0uN8e8NYfxhlMW/p4r7w+fbgg5eqBdpaOhjHRmIjet8N+PebnR03+rLs+M7n7bd2/1c0re2vb7YxhcMOrHXXpm/P6njH++2ndzOTXf0yvaRmUwhhBBCCCGE8BGZyZRrMoUQQgghhBBC+JDMZAohhBBCCCGEj8jdZWUmUwghhBBCCCGED8lMphBCCCGEEEL4ilyTKTOZQgghhBBCCCF8R2YyhRBCCCGEEMJH5O6yMpMphBBCCCGEEMKHZJAphBBCCCGEED6ilOq2f4eY50Sl1Dal1A6l1B1dLI9USn2ulEpXSmUrpS470jaQQaYQQgghhBBC/A9SShmBF4GTgJHA+UqpkR2KXQNs1lqPA+YBf1NKBR7JeuWaTCGEEEIIIYTwkV72dzKnAju01nkASqn3gDOAze3KaCBcuadGw4BKwHEkK+1VLSCEEEIIIYQQ4tAopa5USq1r9+/KDkWSgcJ2j3d7nmvvBWAEsBfIBG7QWruOJJfMZAohhBBCCCFEH6S1XgAsOECRri7c1B0enwBsAuYDg4DvlFLLtda1vzaXDDJ7gdDZ8T0d4bCceM6fezrCYfv6D//s6QiHLe3Bq3s6wmHJrT6iA1494pK5zT0d4bA1OPrWCSilTcaejnDYmp2HdiOF3iQ+qm9lNsd1/H7T+4WZjujMsR5R29K39hemvtWNAWlj0bVe9idMdgP92z3uh3vGsr3LgMe11hrYoZTKB4YDa37tSntVCwghhBBCCCGE8Jm1wBCl1EDPzXzOAz7rUKYAOAZAKRUPDAPyjmSlMpMphBBCCCGEEL5yiH9apDtorR1KqWuBbwAj8KrWOlspdZVn+UvAw8DrSqlM3KfX3q61Lj+S9cogUwghhBBCCCH+R2mtFwOLOzz3Uruf9wLH+3KdMsgUQgghhBBCCF+RCxKlCYQQQgghhBBC+I7MZAohhBBCCCGEr/SiazJ7isxkCiGEEEIIIYTwGZnJFEIIIYQQQghfkZlMmckUQgghhBBCCOE7MpMphBBCCCGEEL4i03jSBEIIIYQQQgghfKdPzGQqpVKBL7TWo7uzbl8xK8nK7VPSMCrFf3cU85+s3V7Lj+4fxbXjU3FpjdOleWJdHhtLa7s1Y312FsUfvot2ubDOmkPM8Sd7LbcXF7H3rddoKiwg9rQziTn2BK/l2uUi/4mHMVmspFx9fXdG79JLT/2Jk46ZQFlFLZOPu62n47SanWzljmmDMCrFR9uLeSWz0Gv50SnRXDdhAFqDQ2ueWJ3LhtJaEkKDeGzOMKLNgWit+WB7EW9t3tut2Y8aEMUD8wZjNCjeyyrin2sLuiw3Nj6cT8+byDWLN7M4p6xbsmmt2fjGBxRvysYYGMjUqy7COjClU7n60nJWPf8qzfU2rAP7M/XPl2A0majdU8zal9+iamcho885jeGnHguAs7mFJQ89g9PhQDud9Js2gdFnn3rEeUszstn81iK0S9P/qFkMPq3D9qQ1m99aRGl6NsagQMb98WIiU1NorKhk04KF2KtrUQZFyrzZDDxhPgC1BbvJfO0dnHY75phoxl99GQFm8xFnPZiarCwK3n8f7XIRO3s2iSed5LW8saiI/IULsRUUkPyb35B4vE//lrQXrTXpb3xAUXo2psAAJv/p4i77QUNpOateeJWW+gYsqf2Z+udLMZhMB6zf3GBj/b/fpnb3XlAw+cqLiB6SBsCOb5aw47ulGAxGEsaPYuwFZx3xe5kUbeHK4WkYlOLb3SV8sNP7c6NfiJm/jB7C4Igw3sjZxX937Wld9uqcyTQ6nO7PFK35y+r0I85zKOqys9i76D3Q7s+RuBO8+0JTcRG733idpsIC4k//DbHHufu9q6WFvL89iXY40C4nkRMmEX/aGX7JWJWVxc733P01fs5skjv0V601O997n6rMTIyBgQy67FLCBgwAYO9331G6/GdQipDkZAZfdimGgAC2v7yAxuJiAJyNjRjNZsbdf5/PMh9sG9NaU/D++9RkZmIIDGTgpZcS6slc/MMPlC9fjtaa2DlzSDjWvW+zFRay8623cLW0oIxGBlxwAWEDB/osc8d8i1/6L9vXbiYgKICzbr6QpMH9O5Vb9dkyVn6ylMqicu5471FCI8O8lu/etosFNz3DOXdcyug5432aUdrYzZ9t3NtouSazbwwy/UEpZdJaO3o6x5EyKLh72iCu/C6LYpud904ez5LCSvJqbK1lVhVVs6RwAwBDLSE8fdQITv90fbdl1C4XRYveZsB1NxFgsZL35COEjxlPUGJSaxljaCgJvzufuvSNXb5G5ZLvCUxIxNXU1F2xD+jND5by0sJveOWZP/d0lFYGBXdPH8wfv8mkxGbn/dMmsKSggtx2fWH13iqWFFQAMNQayt/mjeC0j9fhcGmeXJvHlop6QkxGPjh9Aiv3VHvV9Xf2R+YP4cL/plNUZ+fzCybxXW45OZW2TuXunJ3G0l2V3ZJrn+JN2dQXl3HS3x+gcsdO1r/6Hsc+3PngQsa7nzD0pPmkzJzMuv+8S/6SFQw+bi6BYaFMuOR37Fnn/WXcEGDiqHuuJyA4GJfDyY8P/o3EcaOIHvLrvyhol4vsN95j2m3XExxl5ef7Hyd+4ljCkxNby5RlZNNQUsq8px6kOjefrNffZdYDt6OMRkae/1siU1NwNDbx832PETN6BOHJiWT85y1GnH8W0cOHUrh0BXlffsews0//1TkP9b3seucdht54I4FWK5v/+lcs48ZhTmrbd5hCQ0k57zyqN3a97/Cl4vRs6opLOfFv7n6w4bX3OOahzv0g8z13P+g/YzIb/vMO+T+tYNCxcw9YP/3ND0gYN5IZf/kjLocDh70ZgNLsbexdn8Fxj92NMSCAppq6I34fBuDqEYO4Z30W5U3NPDN9PKvKKihsaGwtU+dw8PLWPGbERXf5Gneuy6S2pfs+QrXLxd733mHg9TdislrJffxRIsaOI7jd54gpJJSkc86jNn2TV11lMjHwLzdjDA5GOx3kPv0k4aNGE5I2yOcZ8995h5Ge/pr56F+xjhtHSLv+Wp2VRVNpCRMefYT6vHzy336bMXfdhb2qiuIffmTcQw9iDAxk+0svU75mLXGzZjL0T1e21t+56AOMPjy4cyjbWE1WFvaSEsY88ggN+fnsevttRt51F7Y9eyhfvpwRd96JwWRi+z/+gWXMGILj4yn88EOSTj0Vy5gxVGdmsvujjxh+yy0+y91eztrNVOwt4y//uYfdW3fx+Qsf8Kdnb+pULmVkGsOmjeLV217otMzldPHta58zeOJwn+eTNnbzZxuL3qkvnS5rUkotVEplKKU+VEqFKKUmKaWWKqXWK6W+UUolAnieT1dKrQSu2fcCSqlLlVIfKKU+B75VSkUppT7xvOYqpdRYT7n9Pf+AJ8O3SqmdSqmzlFJPKqUylVJfK6UCPOUeV0pt9tR/2p+NMiY6nIK6JnbXN+Fwab7aWcbR/aO8yjQ6XK0/m01GtPZnos4ad+YTGBtHYEwsymQictJU6jI2eZUxhUdgHjAQjMZO9VuqKqnLysA6c043JT64X9ZspbK6vqdjeBkTE05hXSO765tocWkW55VxdIr3F0SbV18wsK8rlDc2s6Wi3lPGSV6NjbjQwO6KzviECHZWN1JQ487++bZSjh8U06ncZeP78dWOMipsLd2WDWDP+gxS50xDKUX0kIG02BpprKrxKqO1pjR7O/2mTQAgdc409qzLACA4MpyoQQNQHfq3UoqA4GAAXE4nLqcLjvDgZ3XuTkLiYgmJi8VgMpE0fTIlG7wHtyUb0kmeNR2lFNbBabTYbDRV1xBsiSQy1T2zZjIHE5aUQFNVNQANRSVEDRsCQMzo4RSv8/+griE/n6C4OIJj3e8lasoUqtK930tARARhqamd2tYf9q7PYIBXP7Dtpx9sI3mqux8MmDudvZ6DC/ur32JrpGzrDlLnzQTAYDIRGBoCQN4Pyxl2+gkYAwIAd186UkMjw9lra6K40Y5Da5YVlzG9w2CyprmFnNp6HN39gbEftp35BMbGEujpC5GTp3QaTJoiIghJHdjldmb0bGfa6UQ7nX6582N9fj7BsW39NWbKFKo2effXyk2biJ0+A6UU4YPScNgaaa6udmdzuXC1tKCdTpzNzQRaIr3qaq2pWLeOmKlTfJb5ULax6k2biJ7hzhyWloaz0Z25qaiI0LQ0jEFBKKOR8KFDqdp3sEcpnJ6Dws7GRgIsFp9l7mjLqizGHzMFpRT9R6TSWN9IXWVNp3JJg/thje/6oMmqz5YxatY4wixHvn11JG3s5s827pVUN/7rpfrSIHMYsEBrPRaoxT14fB44W2s9CXgVeNRT9jXgeq31jC5eZwZwidZ6PvAgsNHzmncBb3jK7O95gEHAKcAZwFvAEq31GKAROEUpFQWcCYzy1H/EJ+9+P+JCgihusLc+LrE1Ex8S1Knc/P7RfHbGJF48ZhT3rdjuz0idOKqrCLBaWx+bLFZaqqsOuX7xh+8Tf+bZcjvog4gPCaLIqy/Yie9ioHhMSjSfnzmZfx03mnt/3tZpeVJYECOiwsgoO/IZk0OVEBbE3rq27EX1duLDvPtxfGggJwyO4a2M7j2NF6CxqgZzlKX1sTnKQqNn8LVPc10DgaFmDJ4vuCHR1k5luuJyufj2zr/y2VW3Ez9mONGDj+x0p6aqaszRbdtbcJS1daDYWqayGnNUhzKV3mVsZRXU7CrEMigVgLB+SZRscA+ai9ZsoLHy0LfhX6u5uprAqLaDZoEWCy1V/l/v/jRWVhPSrm3NUZ1/x831DQSEhrT2g/Z9ZX/1G0rLCQoPY93Lb/L9XX9l3b/fwtHk3h7qikop37qDH+57kp8e/juVuTuP+H1EBwdS3tS2vZU32YkOOvSDShp4eNJo/jF9PCcmxx9xnkPhqK4mwNrWFwKsVlo8g7NDoV0uch59kC233UzYiBGEDEzzecbm6mqC2vdXqwV7h8+65qpqAttte4FWq7ue1UrS8cez4fY7WHfLrZjMZiyjRnnVrcvJISAiAnO879r8ULax5upqAtt9hu9re3NyMnXbt+Oor8dpt1OdlUWzp27Kueey+8MP2XT77RR++CH9zjzTZ5k7qq2oJjLG0vo4MiaS2vLOA6D91i+vZsuKDKacPMsP6aSNwf9tLHqnvnS6bKHW+hfPz2/hHvyNBr5T7sGHEShSSkUCFq31Uk/ZN4H2J79/p7Xed67dbOC3AFrrH5VS0Z76+3se4CutdYtSKtOzzq89z2cCqcAXQBPwilLqS89jv+lq3NXVcecfCyv4sbCCSXERXDthAH/8LsufsQ7uEAeMdZnpmMLDMaek0rB9q59D9XFd9YUuOsMPBRX8UFDBpPhIrpuYyhXfZLYuCzEZePbokTy+JpeGFqcfw3rrqjfoDuEfmDeYx5bn4eqJiZUuZ3O8U+sutjx1CP3cYDBw/GN30dxg45dnFlBTuJfI/kkHrbd/h5K1ixLtijiamlj//MuMvPB3rdddjrviIrLfWkTOp18SP2EsBmM3fHx01e49erDpEH7HB+wrXdd3uVxU7yxk/CXnED14IJveWMTWz79l9O9OQ7uctDTYmP/grVTl7WLV8//hpGceOqS+tT9H2oK3rsmg0t5MZGAAj0waTaGtkewqP1/n30W7Hk4TKIOBIXffj9NmY9fL/6Rpzx6Ck5N9GJCuM3Zq7a77tKOhgcpNm5j42F8xms1sf/llylatInb69NZi5WvW+nQWc3+ZOzXsfsqYExNJPPFEtj3zDIbgYEL69UMZ3HMXpUuX0v+cc4iaNInKdevYuXAhw27qfHqlTxxsh3YQi1/+mOP/cDoGo5/mXaSN/d/GolfqS4PMjl28DsjuOFuplLJ0Uba9hvbF97Oe/T0PYAfQWruUUi267ZuwCzBprR1KqanAMcB5wLXA/I4vppS6ErgSIOnSW4g6+tdd21TSYCchtG3GJz4kkFKbfb/l15fW0i/MjCXIRLW9e66nMVmsXkftHNVVBERaDqmuLW8HdZnp1Gdn4mppwdXUxJ7X/03ypX/0U9q+q6TBTqJXXwii1Na83/LrS2roH97WF0xK8ez8kXyZV8r3uyq6I3Krono7SeFt2RPDgiht8M4+Jj6cF04eCUCUOYCjB0bhcGm+zS33S6acb5eSv8R9XMuaNoDGdjN9jZXVmK3ep7IFhYfR3NCIy+nEYDRiq6giuMPpbgcSGBpC3IghFKVvPqJBZrDVSmNF2/bWVFlFcIes5iiL10xkU2UVQVYLAC6Hk/XPLSB5xlQSp0xoLROWlMC029w33aovKqE03f8HqgKtVpor266/ba6u9uspYV3Z0a4fRKUNwNaubRsrO/+OA8PDaGmwtfaD9n3FHGXtsr5S7t/Jvlns5KkT2fb5N611kqaMRylF1KBUlFI019UTFPHrTzkrb2omJrhte4sJDqLCvv99RUeVnrI1zS2sLK1gWES43weZJquVlqq2vtBSVYXpED9H2jOGhBA6ZCh1m7N8PsgMtFqxt++vVdUEduiv7j5d1a5MFYGRkdRs2UJQTAwB4e7fa/SEidTl5rYOMrXTSeWGDYy55x6fZz7YNhZotbbOnoG77QMi3X06dvZsYmfPBmD3xx+3zsZVrFhByrnnAmCdNIn8N97Al1Z/vpx1X68EIHloCjXl1a3LaspriIiOOOTX2pNTwKLHFwJgq61n+9rNGIwGRs4c65Os0sb+b+NeySBn3/WlQwopSql9A8rzgVVA7L7nlFIBSqlRWutqoEYpNdtT9sIDvOayfcuVUvOAcq117QGePyilVBgQqbVeDPwFGN9VOa31Aq31ZK315F87wATIqqhjQHgwyWFBmAyKk1Jj+anQ+6Yo/cODW38eERVKgFF12wATwDwglebSEprLy9AOBzXr1xA2Ztwh1Y0/47cMffQphjz8BP3+cCWhw4bLAHM/ssrrSIkwkxwWTIBBcXJaLEsKvQeLKe37QnQYAYa2vvDQ7KHkVdtYmL2H7pZeXMdAq5n+Ee7spw2L47s878Hj7FdXM+vVVcx6dRWLc8q458ccvw0wAYYcfxTHP3YXxz92F8mTx7Fz+Wr3NVE5+QSYzZ0GmUop4kYOZfdq9/UyO5evJnnygT9Am2rraG5w39zI0dxMSdY2IpKO7FS4yLQBNJSUYisrx+VwsHfVOuIneOeImzCWPb+sQmtN1Y48TCFmgi2RaK3J+M+bhCUlkHbSsV517LXuXaB2udjx2VcMOHruEeU8FKGpqdhLS7GXu99L5dq1WMcd2r7DVwYffxTHPXYXxz12F0mTx7LrEPpB7Mih7Fnj7ge7lq0iaZK7/ZMmjumyfrAlEnO0lbq9JQCUZm8lwnOjpqRJYynb7D6tva6oBJfDQWC49x0bD9f22jqSQ8zEm4MwKcXchFhWlx7azbSCjAbMnlOBg4wGJkZb2FXfcJBaRy5kgLsvNJeX4XI4qFm3loixh9YXHHV1OG3u7czV3Ez91i0EJST4PGNYaipNpaU0eba98i76a9S4cZStWonWmrrcPIxmM4EWC4FRUdTn5eG029FaU7N1K+aEtpt1VW/ZQnBiAkHtTrX1hUPZxizjxlGx0p25Pq8tM0CLZ79gr6igasMGoqa4Z1oDLBbqtrsvzanbupXguDif5p522hyuefE2rnnxNkbMGMOmH9aitaZwy06CQ4MJjzr0A3w3v34/Ny90/xs1ezynXvM7nw5+pI3938aid+pLM5lbgEuUUi8DObivx/wGeM5zKqsJeBbIBi4DXlVK2Txl9ucB4DWlVAZgAy45yPOHIhz4VCkVjHtG9MbDqHvYnBr+uiaXl44djVEpPt5RQm6Njd8NdX+AfrC9mONSYjhtUBwOl8budHHrsu497VQZjSSccwEFLz6LdrmwzJhFcFIylct/AiBqzjwcNTXkPfkIrqZGUIrKJd8z6J6HfHoXPV9a+Px1zJkxghhrODtWv8DDf/+Qhe//1KOZnBoeXbWDBcePxqAUH+cUk1tt45xh7i8qi7YVcVxqDKcPisfh0jQ5Xdzy0xYAJsZFcMbgeLZV1vPR6RMBeHZDPst3d8/1b06tuffHHN48ayxGpXg/u4jtFTZ+P9Y9o9cT12G2lzh+FEWbsll84wOYggKZ8qffty5b9sSLTLnyQsxWC2PP/w2rnn+VrA8+xzKgPwPnuY+LNVbX8P09T9LS2IRSipyvl3Dik/fQVF3Lmn+9gXa50FrTf/pEkiaOOaKsBqOR0Refx5onn0drF/3mziS8XxK7flwGwID5c4kbN5qy9Cx+uvU+jIGBjL3iYgCqtuey55fVhPdPZvk97kvch/3uDOLGjWbvynXs+t59FULC5PH0m9vVJe++pYxGUs4/n23PPgsuFzGzZmFOSqJ0qTtH3FFH0VJTQ/ajj+JscrdtyfffM+bBB/2y70gYP5riTdl8fdP9GAMDmfyni1qX/fzki0z6o7sfjDn/TFY//x9PP+jXekOfA9WfcPE5rPnna7gcDkLjYpj8J/fvZOC8maxb8Cbf3v4wBpOJKVddckSnygK4NPxray4PTxyNQcF3e0ooaLBxUj/358ZXu4uxBgbw7PTxhJiMuDScMSCJq37ZQGSgibvHu88oMCpYWlTG+orqI8pzKJTRSNJ5F5D//LPg0lhnuj9HKpb9BED03Hm01NSw4/FH3HchV4ryH79n6H0P0VJTw+6Fr4J2oV2ayEmTiTjEA52Hm3HgBeez5dln0dpF3KxZhCQnUfyTZ7uZdxSWMWOoysxi4913YwgMZPCllwIQnpZG9KRJZDzyCMpgJDSlP/Fz2252V7FmLTFTpvol88G2scgxY6jJyiLTk3mgJzPAjpdewtHQ0PonNEyhoQCkXnRR65/sMJhMpF50URdr942hU0ayfe1mnvnDwwQEB3LWjRe0Lnvj3pf4zV/OJyI6kpWfLuXnD36gvqqOF//8BEOnjOQ3fznfb7n2kTb2fxv3SnIfEVTH655E9xvzxvI+9UsYfCSXi/WQr//wz56OcNjSHry6pyMclrpq18EL9TKXzO17f8WowdGXTkCB0ib/3/XV1waE9r1+samy8w3fejOzqe/tL8JMfeqjGgBHH4t8ev/GgxfqZT4r7J0Hw/enL7bxOWkn9rkR25Cj/91tW1/Okj/2yvbpSzOZQgghhBBCCNG79cphX/fqW4fEhRBCCCGEEEL0ajKTKYQQQgghhBC+IneXlZlMIYQQQgghhBC+IzOZQgghhBBCCOErcndZmckUQgghhBBCCOE7MpMphBBCCCGEEL4iE5kykymEEEIIIYQQwndkJlMIIYQQQgghfEXuLiszmUIIIYQQQgghfEdmMoUQQgghhBDCV2QiU2YyhRBCCCGEEEL4jsxk9gK1JS09HeGwVMcE9XSEw5b24NU9HeGw5d3/r56OcFhGP31NT0c4bM0uZ09HOGyZVX1r+7MG9b02rmnpe8df65v71mFzQx/8G3IpoY6ejnDYHK6+1c72PpYXICrQ1dMRDktfbOO+SPfBfZyv9b1PUiGEEEIIIYQQvZYMMoUQQgghhBBC+IycLiuEEEIIIYQQviJ/wkRmMoUQQgghhBBC+I7MZAohhBBCCCGEr8hEpsxkCiGEEEIIIYTwHZnJFEIIIYQQQghfkT9hIjOZQgghhBBCCCF8R2YyhRBCCCGEEMJX5O6yMpMphBBCCCGEEMJ3ZCZTCCGEEEIIIXxFJjJlJlMIIYQQQgghhO/0yplMpVQq8IXWevQhlr8U+FZrvdfzeCcwWWtd7q+MPe2o1CjuP2YIRqV4L6OIf63Z1WW5sQnhfHLhZK79PIvF28tIDA/imZNHEhsaiEtr3knfy2sbdvs975QYC9eOTMOo4MvCEt7N2+O1vH+omdvHDmZIRBj/2b6LRfl7W5f9NjWRU/rHo1B8UVjMRzuL/J4XYHaylTumDcKoFB9tL+aVzEKv5UenRHPdhAFoDQ6teWJ1LhtKa0kIDeKxOcOINgeiteaD7UW8tXnvftbSfV566k+cdMwEyipqmXzcbT0dB4AZCRZuHp+GQSk+zS9h4VbvvnhiSiwXD+8HQKPDyePrc8mpaQAgLMDIPZOHMCgyBA08vDaHzIo6n+QqSc8m880P0C7NgHkzGXr6CV7LtdZkvvkBJZuyMQYFMPHKi7EMTDlg3epdhaS/+i7OFgcGo4Fxl56HdVAqLoeTja+8Rc3OQlwuJymzpzH09BN98j4AJsdY+POINAzAV7tLeD+/87Z3y5jBDI4I47Xtu/hwp7uv9gs1c8+4oa3lEkKCWZhTwMe7/L/91WZnsXfRe2iXi6hZc4g/8SSv5U3FRRQufJ3GwgISTv8Ncce727i5spKC11/FUVsDShE9ey6xxxzrt5zlGdlse2cR2uUiee4sBp7q/XvTWrPt7UWUZ2RhDAxk1BWXEJGa0rbc5WL1A48RZLUw4cZrWp8v+G4JhT/8hDIYiBk3mqHn/tZv72GfqbEWrhuVhkHBlwUlvJPr3U+OTY7lgkHJgHtb/HtmLrl1Nr/nam9itJU/DnPvL77bU8yHO733F/1CzNwwaiiDIsJ4c8dOPt7V9h5emT2FRocTFxqn1ty0epNfMpZmZLP5rUVol6b/UbMYfFrnfcfmtxZRmp6NMSiQcX+8mMjUFBorKtm0YCH26lqUQZEybzYDT5gPQG3BbjJfewen3Y45JprxV19GgNnss8xlGdls9fTjfnNnkdZFP9769iLKPP14jKcfO5tbWPPY07gcDrTTRcKUiQw+87TWzJsXvu3OHB3N2Kv+gMmHmTvm+/blj9ixbjMBQYGcduOFJA7u36nc2s+XsebTn6gqKuemd/5KSGQYAJlL1rLywx8ACAwO5KRrziU+LdmnGaWN/d/GvY7cXbZ3DjJ/hUuBLOCQv8krpUxaa4ffEvmRQcHDxw3jwkUbKa6z89lFk/k+t4ycCluncnfOHcyynRWtzzldmkeW5JBVWk9ogJEvLp7Cz7sqO9X1aV7ghlFp3Lomm7KmZl6aNY4VpZXsqm9sLVPX4uD5zfnMjo/yqpsaFsIp/eO5+pcMWrSLJ6eMYlVpFXtsTX7LC+62u3v6YP74TSYlNjvvnzaBJQUV5Na0tdPqvVUsKXC37VBrKH+bN4LTPl6Hw6V5cm0eWyrqCTEZ+eD0CazcU+1Vtye8+cFSXlr4Da888+cezbGPQcFtEwdx7dIsShqbWXjseJbtrSC/tq1f7G1o4k9LMqhrcTIzwcpdkwdz2Q/pANw8IY2VxVXcsXIrJoMi2OibEzO0y0X6wveZdcf1mKMs/HTfEyRMGktEcmJrmZL0bOqLSzn2bw9QlbuT9Nff46gHbztg3ex3P2b4WacQP24UxZuyyHr3Y+bccyN71mzA5XAw//F7cNib+eH2h0ieMYXQ2Ogjfi8G4LqRady+NpvypmZemDGOlaWVFDR4b3svbs5nVodtb3dDI1etSG99nXePnsIvJZVHnOlgtMvFnnffIe2GGwmwWsl57FEix44jOCmptYwxJJTkc8+jZtMmr7rKaCDp7N8RkjIAZ1MT2//6MOEjRnrV9WXOrW++y8RbbyA4ysrqBx8jdsJYwpLb1lWekYWtpJRZTzxETW4+W954h2n33dG6vODbHwlNSsDR2LY/q9yyjbKN6cx4+B4MAQE019b6PHtHBuAvo9O4eXU2ZY3NvDxnHL+UeO+ji2xNXL8yk/oWJ9NiLdwydjBX/5Lh92ztM141fBD3bsiiosnO36eNZ3VZJYUNbfvVuhYHC7blMn0/287d6zOobfHfx752uch+4z2m3XY9wVFWfr7/ceInjiW83b6jLCObhpJS5j31INW5+WS9/i6zHrgdZTQy8vzfEpmagqOxiZ/ve4yY0SMIT04k4z9vMeL8s4gePpTCpSvI+/I7hp19us8yb3nzXSZ7+vHKBx8jbj/9eI6nH29+4x2m33cHhgATU26/EVNwMC6HkzV/fYqYMaOwDE4j+7U3GXbub4kaPpTdy34hf/F3DPmtbzJ3lLtuM5V7y/jzv+9lz7adfPXiIv7wzM2dyvUfOZAhU0fx5h3Pez1viY/mosevxxwewo51m/ny+fe6rP9rSRv7v41F79SbT5c1KaUWKqUylFIfKqVClFL3KaXWKqWylFILlNvZwGTgbaXUJqXUvsM41ymlNiilMpVSwwGUUg946n0LvKGUGqCU+sGzjh+UUimecvt7/nWl1L+UUkuUUnlKqaOUUq8qpbYopV73lDF6ymV51n2jrxtmfGIEO6tsFNY00eLSfL61lOMGx3Yqd+nEfnyVU0q5raX1udKGZrJK6wFoaHGyo6KB+LAgX0f0MtwSzl5bE0WNdhxa82NRWacvtNXNLWyrqcehtdfzA8LMbK6ux+5y4dKQXlnDnIQj//J9MGNiwimsa2R3vbuNF+eVcXSK93ptDlfrz2aTgX3Jyxub2VJR7ynjJK/GRlxooN8zH8wva7ZSWV3f0zFajYoKp7C+iT0NdhwuzXcFZRyV5N3GGRV11LU4AcisqCXO7G7HUJORCTGRfJpfAoDDpan3lDtSVbk7CYuPJTQuBoPJRL/pkyhen+5Vpnh9Bimzp6GUImrwQFoabDRV1RywrlKKlkb3l3aHrRGzNdL9POCw23E5nbiamzGYTASYg33yXoZ5tr1iz7b3U3EZM7vY9rbXdt722psQbaHI1kRpk90nuQ7EtjOfwLhYgmJjMZhMWKZMoSZjk1eZgIgIQlIHooxG7+cjLYSkDADAGBxMcEIiLdXVfslZk7eTkPg4QuLcOROmTaFso/egq2xjBomzpqOUwjI4DYetEXt1DQBNlVWUp2eSPHeWV53dPy4l9ZQTMAQEABAYEeGX/O2NsISzp6GJIptnH72nrNMBv+yqutZtLLu6jlhz9+7ThkSGU2RroqSxCYfWLCsuY1qsd8aalhZyDtKX/ak6dychcbGtfSJp+mRKNnjvO0o2pJPs6RPWwWm02Gw0VdcQbIkk0jPLbTIHE5aUQFNVNQANRSVEDRsCQMzo4RSv2+izzB37ceK0KZR26MelGzNIatePWzz9WCmFKdi9r9JOJy6ns3X2pqGoBKsnc/SoEZSs3+CzzB1tW5XJmPlTUUrRb/hAmhoaqaus6VQuYVB/LPGdvz/0H5mGOTwEgORhqdRVVPs0n7Sx/9u4V1Kq+/71Ur15kDkMWKC1HgvUAn8GXtBaT/GcRmsGTtVafwisAy7UWo/XWu879FqutZ4I/Au4pd3rTgLO0FpfALwAvOFZx9vAc54y+3sewArMB24EPgeeAUYBY5RS44HxQLLWerTWegzwms9axCMhLIiiurYve0V1dhI6DBTjwwI5YUgsb23a07F6q34RwYyKD2dTkX+PlMcEB1La1Nz6uKyxmZigQxvY5tfZGBsVQUSAiSCDgWmxVmKD/f/lJj4kiKKGtjYusdmJ72KgeExKNJ+fOZl/HTeae3/e1ml5UlgQI6LCyCjzzWmc/0tizYGU2Nq1caP9gF9cz0hLYEVxFQDJYcFU21u4f8oQ3jpuPHdPHuyzmczGqmrMUdbWx8FRVhqrajqXie5YpvqAdcf8/myy3/2Yb66/i6x3/8vIc88AIGnqRExBQXx97Z1885d7GHLysQSGhfrkvcQEBVLW2LbtlTcd+rbX3rzEGJYUlfkk08G0VFUTaG0bPARYrLR4vmwfjubychoLCwkZONCH6drYq6oIave7DrJasFdVdShTTXD7/mC1tA4ctr2ziCHnntXpC0JDcSnV23ew+qHHWfvY36jJ2+mX/O3FmDvso5uaiTHvv5+c0j+e1aXVfs/VXnRQEOX2tv1Fhb2Z6MPsyw9NHMMz08ZzQnKCr+MB0NTFfqGpQ99tquy8j2iq9C5jK6ugZlchlkGpAIT1S6Jkg3tQUrRmA42V3v3syDJXddFHD70fa5eLFfc+wpLrbyV61Agsg9zbW3i/JMo2ugfYJWs30OTDzB3VVdQQEWtpfRwRY6GuovMA6FBs+nYlgyaN8FEyN2ljb/5oY9E79eZBZqHW+hfPz28Bs4GjlVKrlVKZuAd6ow5Q/7+e/9cDqe2e/6zdQHQG8I7n5zc96zjQ8wCfa601kAmUaK0ztdYuINuznjwgTSn1vFLqRNwD5E6UUlcqpdYppdbVr/riAG/j0Gi8j9zeP38ojy/NxbWfA7ohAUZeOmM0D/2YQ32zb2aA9qerYywd8+5PQUMj7+Xu5qmpo3hi6khy62w4u+MgdRehuzo4/kNBBad9vI7rftjMdRNTvZaFmAw8e/RIHl+TS4OPZtn+l3TdL7o2KTaS0wfG80LGTgCMSjHMGsaHuUX8/rtNNDlcXDqin2+CHUr/6qIzKKUOWDf/h+WMvvBsTnjur4y58Gw2/vstAKrydqIMBk58/jGO//vD7Fj8PQ2lvrmc/Ei2vX1MSjEjLoqlxRUHL+wTXeQ7zAO1zqYmdi74F0nnnIvRT9codd2MHYJ22U+gbFMGgRHhRKQO6PyyLhctDTam3ns7Q889i4x//hvt55m5Lpt3P+ucEB3JKf3jeXnLTn9G6uRw9hdduW1tOn9ZvZEHNmRzSv9ERln8MUPcVSJ18BLtijiamlj//MuMvPB3rdddjrviInb9sJTl9/0VR1MTBqMPr3TqcnPrkLmrvuApogwGZj58D0f9/TFq8nZSt9t9YHvUHy6m4IelrLzfD5k76mo7+xUvszN9O5u+XcX8y8448kztSRu38lsb90aGbvzXS/XmazI79mgN/BP3DX0KlVIPAAc6p2zfIU8n3u+z4TDW2dXz+17X1e7nfY9NWusqpdQ44ATgGuAc4A+dXlDrBcACgAFP/XhY3yCK6+0khrcdwU0MD6KkvtmrzNj4cJ4/zT0GjzIHcPTAaBwuzbc7yjEZFC+dMZpPtpTwdY7/ZyfKmpqJazf7GGsOpMLefIAa3hbvLmXx7lIArhiaQlnTodf9tUoa7CSGtrVxfEgQpbb9r3d9SQ39w81YgkxU2x2YlOLZ+SP5Mq+U73d115fzvqW0sZn4kHZtbA6ivLFzGw+ODOGeKYO5YVk2Nc0OT107pY12sivdp//+sLucS4b7ZpBpjrJ4zRQ0VVa1ntraVsZKY4V3mWBLJC6HY791C5avYsxFvwMgadpENr7yNgC7V6wlbuxIDCYjQZHhRA0dRHXeLkLjYo74vZTZm71mh2OCD2/bA5gSa2VHbT3VzS0HL+wDAVYrzVVt1362VFcRYLEccn3tdLBzwb+wTp2GZcJEPyR0C4qyYm/3u7ZXVRNktXQq0352oamqmiCLhZK1GyjbmEF5ehauFgeOpkYyX36VMX/6A8FWC3GTxqOUIjJtoPs067p6AiPC/fZeyho77KODAynvYj+bFh7CrWMHcduazX69trEr5Xa71yx8dFAglfZDP3270tPva1paWFlawdDIcLKrfXsWT7C1i/1Cp31H5/3Lvn7jcjhZ/9wCkmdMJXHKhNYyYUkJTLvtegDqi0ooTc/yXeau+miHftxVmeAO22RAaAhRw4dSnplNeL9kwpISmHzrDQA0FJdQlp7ps8wA675YxsavVwKQODSF2rLq1mW15dWERUfup2bXSvL38MVz73L+Q1cTEuGbM0n2kTZ282cbi96pF49/SVFKzfD8fD7ws+fncqVUGHB2u7J1wK/5BF4BnOf5+cJ269jf8wellIoBDFrrj4B7AZ9/y0kvqmOgNYT+kcEEGBSnDY/jux3eMx+z/72S2Qvc/xZvL+Pe77fxrafMkycOZ0eFjVfWFXb18j63taaO5FAzCeYgTEoxPzGWFYdxAxFLoPvapLjgQOYkRPPDXv8PjLPK60iJMJMc5m7jk9NiWVLoPVhMCW87xjEiOowAg6La7v7i9dDsoeRV21iYvf/Tlf+/21xZR0qYmaTQIEwGxXEpsSzb690v4kOCeHLmCO5fvZ2C+rabo1Q0tVBiszMg3H2kf0q8hfxa39xYyZI2gPriUhpKy3E5HOxetZ6EiWO9yiRMHEPBz6vRWlO5Ix9TiJlga+QB6wZbIynfkgNAefY2QhPc11Gbo6Moz96G1hpHk52qHfmEJcX75L1sq6kjOaRt25uXEMvK0sO7ec/RiTEsKeq+G3WHDEilubQUe3kZLoeD6rVriRw77pDqaq0pfGMhwQmJxB57vF9zRgwcgK2klMYy9++6ePVaYid495PY8WMp+mUVWmuqd+RhMgcTZIlkyO/OZO4zjzPnb39lzNWXEzViOGP+5D4WGTtxPJVb3KfeNxSX4HI6CQgP8+t72VpTR7/2++jk2E43eYoLDuThycN5dFMOuxv8e+O1ruTU1pEUEkx8sDvj3IRY1pQdWl8OMhgwe67fDTIYmBBtZVe972/EFpk2gIaSUmyePrF31TriO/SJuAlj2ePpE1U78tz7DkskWmsy/vMmYUkJpJ3kfUdku+fmT9rlYsdnXzHg6Lk+y7yvH+/LXLR6LXEdM48fy94u+nFzbR0tnhsvOZubqdi8ldDEhE6Z8z5bTH8fZgaYfOpc/vjC7fzxhdsZNn0smT+uQWvN7q35BIcGEx516AOgmtJKPnz0P5xx80VEJ8f5NCdIG4P/21j0Tr15JnMLcIlS6mUgB/e1lVbcp6nuBNa2K/s68JJSqhH3qa6H6nrgVaXUrUAZcNlBnj8UycBrSql9A/g7D6PuIXFqzX3fb+eNs8djNCgWZe4lp6KBC8e571T2dvr+b7I7OTmS345KZEtZPYsvmQLAU8vyWJLvv9k2l4bnsvN4cuooz59RKGVnfSOnpbh3lJ8XFGMNDODlWeMIMRnRwNmpSVy6fCM2h5MHJw4jIiAAp9b8IzuPeof/Tz11anh01Q4WHD8ag1J8nFNMbrWNc4a57xK4aFsRx6XGcPqgeBwuTZPTxS0/bQFgYlwEZwyOZ1tlPR+d7j7G8OyGfJbv9t/1Eodi4fPXMWfGCGKs4exY/QIP//1DFr7/U4/lcWp4ckMuz80djVHBZ/kl5NXaOGuQu1/8N7eYK0b2JzIogNsnDgLcfyrmku/d16A8vTGPh6YNJcBgYE9DEw+t2e6TXAajkbGXnMuKJ19Au1wMOGoGEf2SyP9hGQADj5lL/PjRlKRn893N92MKDGTClRcdsC7AhMsvJOPND9AuF8aAACZcfiEAacfNZcOCN/nxjkdAa1LmziAyxTezsi4NL2zO47HJozAo+GZ3KbvqGzm1v7uNvyh0b3svzvRsexrOSk3iiuUbsTmdBBkMTIq28Gx2rk/yHAplNJJ87gXkPfcsuDRRM2cRnJRM+bKfAIiZO4+WmhpyHnsEZ1MTKEX5j98z7P6HaNyzm6rVqwhOTmbbIw8CkHjGWUSMGePznAajkWG/P5cNTz+HdrlImjOTsOQkCn9095P+8+cSM2405RlZ/HLbvRiDAhl5+SUHfd3kuTPJ/s8brLj7IQwmI6OvuMR9KrYfOTU8m53H09Pc/WRxoXsffbpnH/1ZQTGXDE0hMiCAG0entdb508/pB3pZn3JpeGlbLg9OdO+Tv99bQkGDjRP7uTN+vbsYS2AAz0ybQIjJiEvD6SnJ/HnFeiICA7h7nPsaMKNSLC0uY0OF7/fHBqOR0Refx5onn0drF/3mziS8XxK7PH1iwPy5xI0bTVl6Fj/deh/GwEDGXnExAFXbc9nzy2rC+yez/J5HARj2uzOIGzeavSvXsev7pQAkTB5Pv7mH8zXn4JlH/P5c1nv6cfJ++nFZRhbLPf14tKcf22tqyPz3QrTLBVoTP3UScePdg6fiVWsp+MGdOX7SBJLnzPRZ5o4GTxnJjnXZvHjFQ61/XmOfd+9/iVOvP5/w6EjWfLaUlR9+T31VHQuufZzBk0dy6g0XsPzdr2msbeDrf37gaRMDl//jVp/lkzb2fxv3Sr34hjzdRfn7Wg9xcId7umxPSxvl37vR+kNpSd+7JjLv/n/1dITDMvrpaw5eqJeZP9D/d0v1tQ0VvrnzbHexBvW9bS8uuO9lzqzo+TtYH46IINfBC/UyQyK655RxX3K4+tYX3Smx/r8cxtfWlvWtba8vtvFFg0/oWx0ZGHzO29323X7Hogt7Zfv05plMIYQQQgghhOhbeuWwr3v15msyhRBCCCGEEEL0MTKTKYQQQgghhBA+og0ylSkzmUIIIYQQQgghfEZmMoUQQgghhBDCV+TusjKTKYQQQgghhBDCd2SQKYQQQgghhBC+orrx36HEUepEpdQ2pdQOpdQd+ykzTym1SSmVrZRaevhv2pucLiuEEEIIIYQQ/4OUUkbgReA4YDewVin1mdZ6c7syFuCfwIla6wKlVNyRrlcGmUIIIYQQQgjhK73r7rJTgR1a6zwApdR7wBnA5nZlLgD+q7UuANBalx7pSuV0WSGEEEIIIYT435QMFLZ7vNvzXHtDAatS6iel1Hql1MVHulKZyRRCCCGEEEIIX+nGu8sqpa4Ermz31AKt9YL2Rbqopjs8NgGTgGMAM7BSKbVKa7391+aSQaYQQgghhBBC9EGeAeWCAxTZDfRv97gfsLeLMuVa6wagQSm1DBgHyCCzT3O4ejrBYYk3O3s6wmHLre5bbQww+ulrejrCYcm65cWejnDYUl77c09HOGwz4xp7OsJhmZ/Y3NMRDtvi3UE9HeGwlZf1rf3yzDEtPR3hsNU0970rjEKMHScrerfC+r7XxoEGaWPRhV51SSZrgSFKqYHAHuA83Ndgtvcp8IJSygQEAtOAZ45kpTLIFEIIIYQQQoj/QVprh1LqWuAbwAi8qrXOVkpd5Vn+ktZ6i1LqayADcAGvaK2zjmS9MsgUQgghhBBCiP9RWuvFwOIOz73U4fFTwFO+WqcMMoUQQgghhBDCV3rXnzDpEXJithBCCCGEEEIIn5GZTCGEEEIIIYTwFZnJlJlMIYQQQgghhBC+IzOZQgghhBBCCOEjWiYyZSZTCCGEEEIIIYTvyEymEEIIIYQQQviKXJMpM5lCCCGEEEIIIXxHZjKFEEIIIYQQwleUzGTKTKYQQgghhBBCCJ/5fzuTqZT6C7BAa207zHqvAH/XWm/2S7BDdFRaNPcfOxSjQfHepj38a9WuLsuNTYzgk4uncO0nmSzeVkqQ0cCi308i0GjAZFAs3lbKM8vz/J63NjuL3YveQ7tcRM+aQ8KJJ3ktbyouYtfC12ksLCDx9N8Qf/wJrct2vfE6tZkZmMLDGXHfg37P2pWjBkTxwLzB7vbOKuKfawu6LDc2PpxPz5vINYs3szinrJtTwowECzePT8OgFJ/ml7Bw626v5SemxHLx8H4ANDqcPL4+l5yaBgDCAozcM3kIgyJD0MDDa3PIrKjr7rfg5aWn/sRJx0ygrKKWycfd1qNZ9qnLzqLog3dBu7DOnEPsCSd7LbcXF7H7zddoKiwg/rQziTnO3ZddLS3k//0JtMOBdrmImDCJ+FPP8EtGrTUbFn5A0aZsjIGBTLv6IqIGpnQqV19azornXqW5wYY1tT/Tr7kEo8lE7Z5iVr/8FlX5hYw99zSGn3psa51tXy0h78df0FozaP4shp083y/vof17ef/5j8lctYXA4AAuveN8Bgzt36ncK4+8ya5thRiNRlJHpPD7m8/BZDL6NVtJejaZb36AdmkGzJvJ0NNP8FqutSbzzQ8o2ZSNMSiAiVdejMXze9iw4E2KN2USFBHOMY/f21on653/UrwxE4PJSGhcLBOuvIjA0BCfZ5+dbOWOaYMwKsVH24t5JbPQa/nRKdFcN2EAWoNDa55YncuG0loSQoN4bM4wos2BaK35YHsRb23e69NsxenZZHjaNXXeTIZ10a4Zb3xAcXo2xsAAJv3pYqyedt1f3ewPPqdofTpKGQiKCGPSVRdjtloAqCnYzcb/vEtLYxNKKY5++HaMgQG/KntFZjbb31mEdrlImjuL1FNO7JR9+zuLqMjIwhgYyIjLLyEi1Z39l1vuwhgcjDIYUEYDU++/C4Cc9z+ifFMGBpMJc1wMIy6/hIAQ3/WJ0oxsst5ahHZpUo6axZDTOrd39luLKEnPxhgUyPg/XowlNYXGiko2LliIvboWDIoB82aTdoJ7f7D53Y8o3pSJwWQiNC6G8VdcTIAP+7HWmjWvf8iejdmYggKZdfVFRKd13i/UlZaz7B+vYa+3ET2wP7OvvRijyUTB2gw2LfoClMJgNDDlkrOJHz4IZ3MLXz3wLK4WBy6Xk9RpExh/zilHnFfa2P9t3OvINZn/fweZwF+At4BDHmQqpYxa6yv8lugQGRQ8fPwwLnxvI8W1TXx26VS+zyknp6KhU7k75w1mWX5F63N2p4vz39mArcWJyaD48KLJ/JRbzsa9tX7Lq10uCt99h8E33EiA1cq2xx4lcuw4zElJrWWMIaH0O/c8ajZt6lQ/esZMYucdza7XX/VbxgMxKHhk/hAu/G86RXV2Pr9gEt/llpNTaetU7s7ZaSzdVdljOW+bOIhrl2ZR0tjMwmPHs2xvBfm1ja1l9jY08aclGdS1OJmZYOWuyYO57Id0AG6ekMbK4iruWLkVk0ERbOz5Ex3e/GApLy38hlee+XNPRwHcfXnv+28z8PqbMFms5D3xCOFjxxOc2K4vh4aS+LvzqUvf6FVXmUyk3nALxuBgtNNB3t+eIHzUaEIGDvJ5zqJN2dQXl3HKMw9QsWMn6/7zHsc/0nmQnv7OJww7eT4DZk5m7SvvkrdkBUOOm0tgWCgTL/kde9ale5WvLtxL3o+/cNwjt2EwGVn6+IskTRhNeGKcz9/DPlmrt1Cyu4xH3r6L/M27ePuZD7nrXzd2Kjft2ElcfvfvAXjl4Tf5+ctVzDtjlt9yaZeL9IXvM+uO6zFHWfjpvidImDSWiOTE1jIl6dnUF5dy7N8eoCp3J+mvv8dRD7p/Dylzp5N23FGsf3mh1+vGjRnOyHPPwGA0kv3ex+R8/g2jzjvTp9kNCu6ePpg/fpNJic3O+6dNYElBBbk1bfu01XurWFLg/uwYag3lb/NGcNrH63C4NE+uzWNLRT0hJiMfnD6BlXuqveoeCe1ykf76+8y+092uS+59gsSJY4no17ldj//bA1Tt2Mmm197j6IduO2Ddoaccy6jfnQbAjq+XsPW/i5lw+QW4nE7W/vN1Jl99KZYB/bDX1WP4lQcntMvFtjffZcItNxAUZWXtQ48RM34sYclt+4eKjCwaS0qZ8fhD1Obls+3Nd5hy7x2tyyfefhOB4WFerxs1agSDzv4NBqORHYv+y64vvmbwOWf9qoxdZc584z2m33Y95igry+9/nISJYwlv149LM7KpLyll/lMPUp2bT+br7zLngdtRRiMjz/8tltQUHI1NLLvvMWJHjyA8OZGY0SMYfo478+b3Pybni28Yea7v+vGeTZupKy7jzH/cT3nOTlb95z1OefTWTuXWv/0pI08+moGzJrPy3++S8+NKhh8/h8Qxw+g/eQxKKSp37WHps69y5jP3YggwccJ91xMQHITL4eSr+/9O8viRxA4d+KuzShv7v41F79Tz3yK7gVIqVCn1pVIqXSmVpZS6H0gCliillnjKnK+UyvQsf6Jd3Xql1ENKqdXADKXUT0qpyZ5lxyulViqlNiilPlBKhXmef1wptVkplaGUetrX72d8UiQ7qxoprG6kxaX5fEsJxw2N7VTu0sn9+WpbKeUNzV7P21qcAJgMigCDQvs6YAe2nfkExcUSFBuLwWTCOmUKNRmbvMoEREQQmjoQZez84R42ZCjGkFA/p9y/8QkR7KxupKCmyd3e20o5flBMp3KXje/HVzvKqLC19EBKGBUVTmF9E3sa7Dhcmu8KyjgqKdqrTEZFHXWe339mRS1x5kAAQk1GJsRE8ml+CQAOl6beU64n/bJmK5XV9T0do1XjznyCYuMIjHH35chJU6lL3+RVxhQeQUjqQOjQl5VSGIODAdBOJ9rpBPxzpHPP+gxS50xDKUXMkIG02BpprKrxKqO1piR7O/2nTQBg4Nxp7FmXAUBwZDjRgwZ02h5r9xQTPWQgpqBADEYjsSOGsHut90DU1zb9ksWME6aglCJtVCqN9Y1UV9R0Kjdm+kiUUiilSB2RQlVZtV9zVeXuJCw+ltC4GAwmE/2mT6J4vXdbFK/PIGW2+/cQNXggLQ02mjy/h5jhQwgI67xfixszEoOn3a2DBtJY6fv3MSYmnMK6RnbXu/dpi/PKODrFe19hc7hafzabDK2fE+WNzWypqPeUcZJXYyMuNNBn2SpzdxLaoV2LOrTr3vUZpHj6d9SQgbTYbDRW1RywbkCIubW+025vvV6qNHMLkSnJWAa4z/AICg9DGX7dV6PavJ2Y4+Iwx7n3D/FTp1C+McOrTNnGDBJmTkcpReSgNBy2RuzVnftze9Gj2/pExKCBNFVV/ap8XanK3UloXCyhnsxJ0ydTvKFDP96QTv9Z7szWwWm02Gw0VdcQbInE4pmFNZmDCUtKoKmqGujcj5sqfZcZoHBtBmlzp6KUInboQJobGrF1sY8rzt7OgOnufdygo6ZR6NlfBQQHoTx9wGG3t+6JlVIEBAcB4HI6cTmcR3xtnbSx/9u4VzJ0479e6v/LTOaJwF6t9SkASqlI4DLgaK11uVIqCXgCmARUAd8qpX6jtf4ECAWytNb3eeri+T8GuAc4VmvdoJS6HbhJKfUCcCYwXGutlVIWX7+ZhLAgimqbWh8X1TUxISnSq0x8WBAnDI3j/HfW82TiSK9lBgVfXDaNVKuZN9bvZpMfZzEBmquqCbRGtT4OtFhpyM/36zp9KSEsiL119tbHRfV2xidEeJWJDw3khMExnPfhJsYdF9HxJbpFrDmQEltbzpJGO6Ojwvdb/oy0BFYUuz+UksOCqba3cP+UIQyxhLKlqp6/bcyjyenab/3/j1qqqwiwWlsfm6xWGnce+unm2uUi9/GHaS4rJWru0YQMTPNHTBorawiJtrQ+NkdZaKysxmxt20801zUQGGpu/ZJijrYedEAT2T+JzPc/x15XjzEwkKJN2V2ehutL1WU1WGMtrY+tsRaqy2qwREd2Wd7hcLLq23Wcd51vZ/86aqyqxhzV1heCo6xU5e7sXCbau0xjVTXB1q6zd7Rr2Qr6TZvkk7ztxYcEUdTQbl9hszM2tvO+4piUaP4yaSDR5gCu/i6r0/KksCBGRIWRUea70+qbKr3bzBxlpbJDu3ZVpqmq+qB1sxd9SsHy1QSEmJlz918AqC8qBRQ/P/489rp6+k+fxNDTjv912auqCG7XJ4KiLNTmen/W2aurvctYLdirqgmyRIJSbHr6H6AUyfPmkDxvTqd1FC1fQdzUyb8qX9eZO/fR6g6Zmyq9M5ujrO7nLG392FZWQc2uQiyDUjuto3DZCpJ83I9tVdWEtssdEm3BVllNSLtty17XQGBI2z4uNMqKrbJtkLRrTTob3v2Mppo6jrnjqtbnXS4XX9zxBHXFZQw/YS6xQzq/p8Mhbez/Nha90/+XQWYm8LRnhvILrfVy5X3UZArwk9a6DEAp9TYwF/gEcAIfdfGa04GRwC+e1woEVgK1QBPwilLqS+CLrgIppa4ErgSI+s1fCJt6GOejd3HAR3eYjrz/2KE8viQHVxfTlC4NJ7+6moggEwt+O5ahMaFsL2/oXNBnugjRhw5adRVVd2jwB+YN5rHleV22d3fpMud+yk6KjeT0gfH88Uf3UXajUgyzhvHUxlyyK+u5eXwal47ox0tZXV97Kto79M6sDAYG33U/TpuNgpdfpGnvHoKTkn2eqGP/dK/cO6fucrs88HuJTE5g+OnH8dNfX8AUHIglJRnl59Oqu8qpDpDznWc+ZOjYQQwZ6/vTkL0cyrbexe/hQNnb2/bpVxgMRvrNmnqYwQ7BIXyGAPxQUMEPBRVMio/kuompXPFNZuuyEJOBZ48eyeNrcmnw4VkPXTar6lim6757sLqjzjmDUeecwbZPvyb326WMPPtUXC4nFdtzPddhBvLzX/+BZWAKcaOH//o30SGXd/j9fx5OvutWgqwWmmtr2fj0PwhJTMA6bEhrsfzPF6OMBhJm+LJPdNVqh9BH2xVxNDWx7vmXGX3h7wgwm72Kbf/sK5TRQPJM3/bjrndxHXMf+LvHgKnjGDB1HMWbd7Dp/S85/t7rADAYDJz+5J00N9hY8vS/qSrYizUlqfNrHXraLp6TNvZtG/dC/4uzs4fp/8UgU2u9XSk1CTgZeEwp9W2HIgfqCU1a664+QRXwndb6/E4LlJoKHAOcB1wLdLozhtZ6AbAAYMBj3x/W0KS4zk5iRHDr48TwYErq7V5lxiZG8PwZYwCICgng6EExOFyab9vdjKbW7mBlQRXz0qL9OsgMtFpprmq7TrG5uooAi8Vv6/O1ono7SeFBrY8Tw4Io7XAK8pj4cF442T1jHGUO4OiBUe72zi3vtpyljc3Eh7TljDcHUd7Y3Knc4MgQ7pkymBuWZVPT7PDUtVPaaCe70n0a3A+7y7nEc4Mg0SbAYqWl3alqjqoqAiIth/06xpAQQocOoz47y2eDzJxvl5L74y8ARKUNwFZR3bqs4ywmuE8LbG5oxOV0YjAaaayo6lSmK4OOnsmgo2cCkP7ep4S0O/ruK0s+/pnlX6wEIHW496mvVWXVRMZ0fbbA569/TV11Pb9/+DKfZ+rIPTvc1heaKju3nznKSmOFd5n2MxP7U7BsFcUbs5h15w2HPCg9HCUNdhJD2+0rQoIotXXeV+yzvqSG/uFmLEEmqu0OTErx7PyRfJlXyve7KvZb79cwR1m82qyxsgqz5cDt2uhpV5fDcdC6AP1nTmHF0/9k5NmnYo6yEjN8CEGe6yDjx4+iemfhrxpkBlutXqcs2iurCerwWRfUsUxVW5kgz42IAiMiiJ04ntq8/NZBZtHPKylPz2TirTf6tE8EW7voox36cXCUxStzY2UVwZ6sLoeTdc8tIHnGVBKnTPCqV7h8JaUbM5l+x198knnrN0vZ/sMKAGIGDaChXW5bxX72cba2fVxDZZXXLNw+CSMH88s/y2mqrSc4ou162MDQEOJHDmFP+uYjGgBJG/u/jUXv1IvP5PUdz+mwNq31W8DTwESgDth3ftBq4CilVIxSygicDyw9yMuuAmYppQZ71hGilBrquS4zUmu9GPfNhcb7+v2k761loNVM/8hgAgyK00bE812HO5nO/tcvrf8Wby3l3m+28m1OGVHmACKC3McWgkwGZqdGsaPSNzds2J+QAanYS0uxl5fhcjioWruWyLHj/LpOX0ovrnO3d4SnvYfF8V2e9+Bx9qurmfXqKma9uorFOWXc82NOtw4wATZX1pESZiYpNAiTQXFcSizL9nrfhCg+JIgnZ47g/tXbKahvO+W6oqmFEpudAeHuI6RT4i3k1/q3X/RF5gGp2EtLaPb05Zr1awg/xL7sqKvDaXO3qau5mfqtWwhMSPBZtiHHH8WJj9/FiY/fRb/J49i5fDVaa8pz8gkIMXf6cqCUIn7UUApXu29QlL9sNcmTxh50PU017lMjG8or2b02nQEzfXfq3j5Hnzmb+/5zK/f951bGzx7Nym/WorUmL3sn5lBzl6fKLv9iFdlrt/HH+y7C8CuvqTsclrQB1BeX0lBajsvhYPeq9SRM9G6/hIljKPjZ/Xuo3JGPKcR80FNlS9KzyfniW6bfdBWmIN9d69heVnkdKRFmksPc+7ST02JZUug9WEwJbzuQOSI6jACDotruPij10Oyh5FXbWJi9x+fZrF20a2KHfpk4cQwFnv5dmZNPgNndvw9Ut764tLV+0YYMwhLd21782JHUFO7BYW/G5XRSviWH8ORft12GDxyArbSUxjL3+kvWrCVmgnf22AljKV6xCq01Nbl5mMzBBFkicdrtOBrd+2Sn3U5l1hbC+rkPQFVkZrPzq28Yd/2fMfq4T1jSBtBQUorNk3nvqnUkdMicMGEshb+4M1ftyCMgxEywJRKtNen/eZOwpAQGnXSsV53SjGx2fPktU2682mf9ePgJR3H6k3dy+pN3kjJlLHnL1qC1pmy7ex/XcXCjlCJh5FB2rXLv43KXrqb/ZPd7qy0uaz3joyKvEKfDQVB4KE21dTQ3uPfTjuZmirK2EZkUf0S5pY3938aid1Jdnlb1P0YpdQLwFOACWoCrgRnANUCR1vpopdQFwJ24ZygXa61v89St11qHtXutn4BbtNbrlFLzcV/Lue+Q8D3AWuBTINjzWk9rrb1vH9jB4c5kAhw9KJr7jh2KUSkWZezlhRU7uXCC+wPp7Y3eH/xPnzKSH3eUs3hbKcNjw/j7qaMwGMCgFF9sKeG5Xw7v+sgZ0w9/Z1aTmcmeD95DuzTRM2eRcPIplC/7CYCYufNoqalh22OP4Gxy30LeEBTEiPsfwmg2k//KAuq3b8dRX09ARDiJp51O9KzO16ocyIpNR3Zt4dGpUdw/bzBGpXg/u4gX1hTw+7Huo25vZXjfvv9vxw/nh/yKI/4TJvHJh3+iwcwEKzdNSMOo4LP8El7bspuzBrm/MP03t5i7Jw9mfr8YihvcX2YcWnPJ9+6L9IdaQrl78mACDAb2NDTx0JrtrTcJOhRZt7x42HkPZuHz1zFnxghirOGUltfw8N8/ZOH7P/ns9U9+7fDvWluXlUHRh++jXS6sM2YRd9KpVHr6cpSnL+c+8Qiupkb3rduDghly70O0VJSz+41X0S4XaE3kpCnEnXzaYa9/tNV+0DJaa9a/toii9M2YggKZ9qffEzVoAABLn3iRqX+8EHOUhfqSclY8/yrN9Q1tf8IkIIDG6hq+vfvJ1j/pYAoO4uSn7iEgxMz3D/yd5voGDEYj4y86i4SDzPjMT9z/DNmh0Frz7j8+ImvNVgKDArn09vNIHe6+DvS52xdw8a3nYomJ5Kr5NxOVYCXY7N4dT5w7llMvOeFAL71fi3f/H3v3Hd5Wdf9x/H0kW7I8Je+R4ZE9nJ2QHfaehR+rjFLKahkdrLD3KlBmgVJKgEwoUKAFwsoge3lme2R6W3K8l87vDym2ZTvDiRTb7ff1PHliS+defXR87rk699x7ZT5yIaAwLYvMjz5BO530nzmZwReeTd4PywBIOnWG66s25iykKGMzfiYTY266Bluy6++w7vX3KN2ynYaqKsyhoQz5xbkkzprKd394BGdTI/7Brt1O+IBERt9w1RGzfJXVta/cmN7Hxn0TUzAoxWc7CnknYw//N9h1t8tF2wr49cg+XJASQ5NTU9fs5MV1uWwsPsDY6FA+PHc028qrWk6n+8vGPJbv7dpNRy4YeegbpBWmZZHxYWu9DrnobHK/d9Vr8mmuek1/31WvRpOJcTe31mtnywKs/ss7VBUUgVIERoYz5oarsIRbAdj98xq2fbEYpSBm1HBGXtX5nVsrGo588KI0PZPt8z8Gp5O46VNIOv8c9v7kyt7nZFf2bR8toDwzG4PJxLBfX0doUn9qi0vIeP0tAHSzk5iTJpB0vuvrkVbe+xDOxqaWG0WFpSQx5Lqrj5gFINB45I8XRelZZH/0MVo76TtjCoMuOJv8H12ZE09xZc76YAHFma76Hn3jtViT+1O2bScrn3qRkL4JLbNoQy67kJhRI/jhTw/jbGrC5M5sS0ki9VdHbsfh5qPb52itWfPeIvalb8HP5M/UW39JpLuP+/6ZN5ly81UEhlupLCpl6Sv/oKGqmvDEvky//VqM/v5k/us7cpatwWA04mfyZ9wvLyZmSArlu/ax4s0P0U6n62twJo9l1KVnHzZLef2R70YsdXx8dTx79Om97tzT5Ds+P2EDrNxXL+qR9fM/Mcjs6Y5lkNmdjmWQ2d2Od5DZHY5lkNmdfDHI9LVjGWR2t6MZZPYkxzvI7A5HO8jsSbo6yOxuhxtk9lRHM8jsaY5mkNmTHO0AqCc5mkFmT9Ib61gGmYfXUweZvetTrBBCCCGEEEL0YFpu/PO/cU2mEEIIIYQQQogTQ2YyhRBCCCGEEMJbZBpPqkAIIYQQQgghhPfITKYQQgghhBBCeItBrsmUmUwhhBBCCCGEEF4jM5lCCCGEEEII4S1yd1mZyRRCCCGEEEII4T0ykymEEEIIIYQQ3iLXZMpMphBCCCGEEEII75GZTCGEEEIIIYTwFpnIlJlMIYQQQgghhBDeIzOZPYCtn7m7I3RJXlnvOzxz3YyG7o7QZQ3O5u6O0CX9/nFbd0fosv/86s3ujtBlYXNv7u4IXfJxvrG7I3SZn9LdHaHLRiZ2d4Ku2VjWu/Z7AE7d+/Z9KSG9a983MtzZ3RG6bHd17/oo3RvruDfSck2mzGQKIYQQQgghhPAeGWQKIYQQQgghhPCa3jXHL4QQQgghhBA9mZwuKzOZQgghhBBCCCG8R2YyhRBCCCGEEMJblMxkykymEEIIIYQQQgivkZlMIYQQQgghhPAWmcaTKhBCCCGEEEII4T0ykymEEEIIIYQQ3iLXZMpMphBCCCGEEEII75GZTCGEEEIIIYTwFvmeTJnJFEIIIYQQQgjhPf8zM5lKqfHAtVrrO07gaz4KVGmt/+ztdU+Js3HP+GQMSvHZzkL+sXmvx/PnJEZx/bC+ANQ2NfPU2p1sd1QD8Msh8VycEosGdjiqeWTVdhqc2tsRPZwUY+UPo115v8gr4oNtnnnP7BvFNYP7uPI2N/P8xhx2VLjyfnb2eGqamnFqTbNTc/2P6T7LqbVm0wcfU5iWjdFkYuIt12BL6tehXFVxKatfe4+GqhpsSX2ZeNt1GP38OLCvkHVvf4Q9fw8j/u98hpx3GgDNDY389PjLNDc1oZub6TNpDCMuPe+YMhalZ5P54cdop6b/rCkMuuDMDu8h88OPKUrLxmj2Z+xN12J1v4dDLevYtYf09+bT3NiEwWhg1PVXYEtJxNnUzKZ3P6Iifw9OZzP9pk1i0AVnHVPuzlRmZ1Hw8XzQTmxTphN15jkez9cXFrD3w39Qt2c3MedfTOTprrzOxkbyXnoO3dSEdjoJHTOOmPMu9FquY/XWCzdz9qljKCk7wPjT7+m2HPasLHLnLwKnk5jp0+hzjuffTGtN3vyF2DOzMJhMDLzheoL7u9rIvsXfU/TzzygUgX0SGPir6zD4+1O6fgO7v/iS2oJCUh+4j5DERK9mLs3IZtu8RWink4QZU0k6r2PmbXMXUZqRhdFkYviN1xGa2LptaqeTNY8+g9lmZczvfwtA5a49bJkzj+bGRpTRwNBrryQsOclrmUsystkydxHaqekzcyop53XcFrfMXURJuqs/GfmbawlL7EdzQyNrnn4RZ1MTutlJ7IQxDLzkfAC2LvgnxWmZGIx+BEZHMvLGa/EPCvRa5oMOZGexd9ECtNNJxNTpxJ51tsfzdYUF7JrzPrV7dhN3wUXEnNH63nZ98D4HMjPwCwlh6MOPeT3boYyPtHLLkGSMSvH13iIW5XnuR/oGWfjDiIEMCA1mzo5dfJK/D4A+gRZmjxrcUi42MIAPd+7ms137T0jm24YmYwC+3lvEwrx9HTL/aeQABoQG84/tu/gk35WpT5CFB0cN8sg8Z8duPttV4PWMZZnZ7Ji3CLSTuOlT6X9ux21vx7xFlLv7i6G/vo4Qd3+x6u7ZGAMCUAYDymBg/COzAajcvYftH8zD2diIMhgYdM2VhHpx22uf77t3/knO+s34mU2cf9fVxA7o26Hc+i+Xse6LJdgLSrlr7tMEhgUDULqniH//ZS6FOXuYee15nHTJqV7PKHXs+zrucWQm839nkKm1Xg+sP9rySikFKK2103epjo1Bwf0TUrjlxyyKauqZe9Zolu4tJ/dATUuZfVV1/Pr7DCobmpgab+OhSQO45tt0oi0mrhycwCVfbaC+2cnz04ZwVmIUX+QW+y4vcPeYFG5fnkVxTQPvnzqa5fvLyKusbSmzv6aOW5dmUNnYzORYG/eNG8Cv2wwmb1uaSUVDk88yHlSYlk1VYQlnv/Qo5Tvz2fDeAk57ouNgIWP+5ww6+xT6TRnP+r/PJ++nlQw4fQam4CDGXHcZ+9Z7DoQN/n7MfPAO/AMCcDY18+NjLxI3ajgRA7u2Q9BOJ+lzFjL1vjuwhFtZ8vBzxI5LJTQhrqVMUXo2VYXFnPbio9hz8kl/fwEzH7vnsMtmz/+MIZecS8yo4RSmZZE1/zOmP/h79q3diLOpiVOefZCm+gZ+uPdxEiZPICgq4tgquN172b9wLkl3/AE/q43c554kJHU0AXHxLWWMQUHEXXYllembPJZVfn4k3vknjAEB6OYmcl98jpDhIwhMSjnuXMfjw4+X8tacb3n35du6LYN2OsmdO5/hf7gLk81G+pPPED46lcD41nq1Z2ZRW1zM2KefoCo3j5yP5jLqgfupt9sp+PFHxjz+KEaTia1vvUPJ2nXETJ1CYHw8Q267hZwP5vok89YP5zP27jsJCLex5rFniBqTSnBCa+bSjCxqioqZ+tzjVOTkseWDeUx6+L6W53cv/pGg+FiaautaHtu+6FOSLzqXyNQRlKRnsmPhp4y//49ey5z9wQIm3nMHAeE2Vj76LNFjUglpsy2WZGRTXVjMjOcfw5GTR/ac+Ux55F4M/n5MvO8u/Nz9weqn/kxk6nBsA5KJGD6UQZddhMFoZOvCz8j56luGXH6xVzK3zb5n/jwG3Pl7/G02tj3zFGGpo7C0aSPGwCD6XH4FFWlpHZaPmDyFqFkns+v997ya63AMwG+HpnD/+ixK6xp4bfJoVheXsbu6dT9yoLGJv27JZUq0Z/+0t6aW21altaxn7qyJrCgqOyGZbx+WzL3rsimta+D1yaNYVVzukbmysYk3NucxNSbcM3N1LbesTG9Zz/yTJ7CiqNzrGbXTyfaP5jP6j3diDrex/vFniBydSlCbba88M4vaomImPfM4B3Lz2PbBPMY/1Lrtjb7nD5hCgj3Wm/PxpyRecC4RqSMoy8gk5+NPGXOvd7a99nLWb6Z8fwm3vPMQ+7fl882bi7j+pY6v1WdYEgMmDmfu/a95PG4JCeT0m3/B9tWZPskndez7OhY9U686XVYplaiU2qqUelcplaWUmquUOk0ptUIptUMpNdH9b6VSapP7/8HuZWcppb5y//yoUuo9pdQSpVSuUuqONuvfopR6E9gI9FVKveB+rUyl1OXucsFKqR+UUhvdj1/YJuMDSqltSqnvgcEd3oQXjIgIYU9lHfuq6mhyar7dVcKsvp47qPTSSirdg7KM0kpiAs0tzxmVwmw0YFQQ4GegpKbBFzFbDAsPYW9VHfur62nSmu/2lDAj3vNDQGZZJZWNzQBklR0g2mLyaaZD2bchg8Tpk1BKETEwicaaWmrtFR5ltNYUZ2+nz6QxACROn8S+9RkABISFEJ7SH2U0eiyjlMI/IAAAZ3MzzmYnHMNBLntOPsExUQRFR2Lw86PPSeMo3OA5oC3ckEG/aa73ED4gicbqGursFYddVilFY63rg09TTS0WW5jrcaCpvt6VuaEBg58f/paArgfvRG1+HuaoaEyRURj8/AgbN5HK9DSPMn4hoQQmJkEn9Wl016dubkY3N3NMFeplK9ZupdxR1a0ZKvPyCIiOJiDKVa9RE8dTnubZRsrT0omefBJKKUJSkmmqqaXB4WrnutmJs6ER7f6bm6xWAALj4wiMjfVJ5orcfAJjogmMdmWOnTSBkk0ZHmVKNmUQN9WV2TrAlbnenbmu3E5peiYJM6Z6LKOUahl0NtXWYbZZvZbZkZtPUExUS+a4SeMp3uhZz8Ub00lwZ7YNSKappoY6RwVKKfzatV/lvhNh1MhhGNzt3ZqSRJ3d7rXMB9Xk52GOjsLsbiO2CROoyEjzKOMfGkpQYlKHvgwgeOAgjIFBXs91OIPDQthfU0dhrWs/sqSghMntBpMVDY1sP1BFkz70mTmjI6wU1NRRXFfv68gMtrbLXFjClHaDScdRZB7jw8wHcvOxREdjcbfjmEkTKE3z3PZKN2UQO8XVjsNSPLe9Q1M01bm3vZq6ln7EF7avyWTkKRNRSpEwJIm66lqqyjvmi03pizWm4wHSIGsI8YP6YzD65iOx1LHv67gn0kqdsH89VW+cyRwAXAbcBKwDrgKmARcAs4FrgRla6yal1GnA08AvOlnPEOBkIATYppT6q/vxwcCvtNa3KaV+AYwGRgGRwDql1DKgBLhYa31AKRUJrFZKfQGMBa4AxuCq243ABi+/f6ItZgprWnc2RTUNjIwIOWT5i1Ni+Hm/64NKcW0DH2zZyzcXTaSu2cnqAjurCh3ejtgur4mi2ta8xbX1DA8/dN4LkmJZVej5werV6SMA+Cy3gM/zinwTFKi1V2AJt7b8bgm3Umt3tAy6ABoqqzEFWVo+CAZG2Ki1O464bqfTyfcPPEtVYQkpZ8wkYkDXT2uptTuwhNtafg8It2HPye9YJsKzTK3dcdhlR/7yUlY+/zrZ8z5Fa82MR/4EQPzEsRRszOCb391Pc0MDI6++FFOwdz5cNjrs+Nta8/jZbNTm5x718trpJOfZJ2goKSZ8xskEJiV7JVdv12B3YGpTryabjcrcPM8yDgfm8NYPu2ablXqHnZDERBLOPJ31996Pwd8f6/Bh2IYP83nmersdc5u2abZZOdAuc73dQUDb9muzUmd3YLaGsW3eIgZefonHLCbAoKsuY9OfX2X7wn+C08mEB713CnNd+zzhNhw5eR3LtNsW6+0OAqxhaKeTFY88Q01RCf1OnYk1pWN/sHf5SuImjvNa5oNcbaT172+y2qjOyzvMEt0vIsBESZtBVmldPUOsh96PHMqs2CiWFJZ4M9ohRZpNlNS2HsQtrWtgSNgxZI6L5KcC32Sud9g92vGhtj2P7TPc6nrMGgZKkf7iK6AUCTOnEz9rOgADr7yM9JdeJWfhP9HaybjZvrt8oKqsgtBIa8vvIRFWKssqCA4PO/RCJ5DUsfhf1RsHmXla60wApVQ28IPWWiulMoFEIAyYo5QaCGjA/xDr+bfWuh6oV0oVAzHux3dprVe7f54GzNdaNwNFSqmlwATga+BppdQMwAkkuJefDnymta5x5/viUG9CKXUTroEyfW74ExGnXHDUFdDZQYtDHQMdHxPGRSmx/Gqx6wh7iMmPWX0iOPdf66hsaOKF6UM4JzGK/+SfmJ3uQYfKOy4qjPMTY7hpSetRvt/8lEFpXQM2sz+vTR9BfmUtaaUHfBSss2SeFa47Sa+O4kiSwWDgjGdm01Bdw4qX36Fiz37C+sYfcbl2L34UZQ6R7zDL5v2wnBFXX0rCxDHsW72BTX/7iKn334k9Nx9lMHDWa8/QWF3D8ideJGrEEIKiI7uW+6gd/RE5ZTAwYPYjNNfUsPvtN6jbv4+A+AQf5erdOjTPztoIiqbqasrT0hn/7FMYLYFse+ttiletJnrySb4N2GnbbBe603YNJWkZmEJDCE3sT/mWbR7P7/1xGYOuvIyYCWMpXLueze99yLh77vJS5mPrB1rKGgxMe+IBGqtr2Pjq21Tu3UdIn9b2u/OLrzEYDMRPmeiVuJ46qfCeezAc6DzeYSb/OuWnFCdFh/PejnxvRDqiTjMfVSfeyk8pJkeH8/ftu7wTqmOgjtTR7PNc/4+9/27MNisNBw6Q9udXCIyLxTp4IPt+WsaAKy4jevxYiteuZ+s/PmT03Xd5Pz+us4s6BvTJSx0bqeP/Tf87k7aH1BuroO35Is42vztxDZqfAH7SWo8AzgcOdW5f2/U00zrgrm7z+KE2oauBKGCc1no0UNTmdY5qD6K1fkdrPV5rPb4rA0yAopp6Ytuc/hoTaKKktuNpNAOtgTwyaSB3Ld3ccj3jSbFW9lXVYa9vpElrfthTxuio0C69flcV1zYQY2nNG20xU1rb8RTdAWGBzB43gLtXbuZAm+svS+tcZe31jSzZX3bYWdBjsWPxUhbf/zSL73+aAFsYteWOludqyz1nMQHMIcE0VNfibHad3ltTZifAevRH80xBgUQPHUhB+uYuZ7WEW6ktb53lrSu3d8hnCbdRW+ZZJsAadthldy9fTfyE0QDETxqLPcf1gWbvynVEpw7D4GfEHBZC+KAUHLne+bDjb7XR2OZUwCa7Hf8wa5fXYwwMJGjQYKqys7ySq7cz2aw0tKnXBru9w2lUJpuN+vLW67vq7Q5MViuOLVsxR0biHxKCwc9IxNgxVOYc/ezysTKH26hv0zbr7Y4Op7aaw23UtW2/dgdmqxXHjhxKNmWw/I+zyfzr3ynfspXMt13XChasWEX0eNdp7TETxlGRm++1zAHt85TbXbMObcvYrNS12xbbvy//oEDChwykJKO1P9j78ypK0jIZdcsNXRq4Hi2TzUaDvfXv3+Cw4+/DU+28obSugaiA1v1IZICZsvquXeoxIdLGzgNVOBoavR2vUyX1DUS1ufQjMsDU9cxRvs1stnm243r3dtVWgK3d9lnuaOlTDrZnU2goUWNHc8A9I164chVR41zbXtSEcRzIy/dq7vVfLePd25/j3dufIyQ8jAOljpbnKsschPSgGTapY/G/qjcOMo8kDDh4+7brj3Ndy4DLlVJGpVQUMANY636NYq11o1LqZKB/m/IXK6UsSqkQXINcr8suq6RfSADxQWb8DIoz+0exdK/nDQFiA828OGMYD67cxu42N9gpqK4nNTKEAPd58ZNireRW1OJLW+yV9A22EBdoxk8pTu8bxbICz7wxFjPPTh7Ko+u2s6eq9ZS3AKOBQD9jy8+TYqzkVFTjTQPPmMkZz8zmjGdmkzB+FPnL16C1pmxHHv4WS4dBnFKK6GGD2LvGdTOa/OVrSBifetjXqDtQSUO168ZMTQ0NFGVtIzQ+5rDLdMaa3J+qwmKqi0txNjWxd/UGYsd6vnbs2JHs/tn1Hsp35uEXaCHAFnbYZQNsYZRu2QFAafY2gmKjALBEhFOavQ2tNU119dh35hF8DLk7Y+mfSH1xEQ2lJTibmqjYsJaQ1FFHtWxTZSXNNa76dDY0ULV1CyYfXS/Y24QkJlJbVExdievvXLJ2PeGjPOs1fPQoiletRmtNZU4ufhYLJmsY5vBwKnNzaa5vQGuNY8tWLHG+r9fQpP7UFBVT685cuGYdUWM823XU6FQKVrgyO3bm4mcJwGwNY+BlFzPj5WeZ/uLTjLz114QPHcLIm28AwGy1Yt+6HYDyLdsIjIn2WuawpP5UFxVT485csGY90e0yR49JZZ87s32nq54DrGHUH6ik0d0fNDc0ULZ5K8Hxrnouycgm99+LGXvXrRjNvrk2PbB/IvXFxdS7tz37unWEHeW21122HagkIdBCjMW1H5kVF8Xq4q7dCGdWXBRLfHTaaWe2Vbgyxx7MHBvFqi5mPjkukp8KSn2UEEKS+lPbZtsrWrOOyNGe7ThidCqFK13tuCInF79A17bXXF/fcop6c3095dlbCEpwzcabrVYc21zbnn3LNixe3PYAxp83gxtfu5cbX7uXQZNTyfxxLVpr9m3NwxwY0KNO45Q6Fv+rVKdT4D2UUioR+Mo9S4lS6n33758cfA74DTAH13WTPwLXaK0TlVKzgD9prc9r/9UiSqks4OD3SbRdvwKeB87GNUP5pNZ6ofs6zC9xnYqbBkwFztZa5yulHsB1XeguYC+w+UhfYTJ67vIu/xGmxdu4e5zrK0H+lVPEu9l7uHSg60PKJzsKeXjSQE7rG0FBtWuGs0lrrv4mDYBbR/bjjP5RNGvNVnsVj63eQWMXvsLEbO76kfUpsTZ+PyoZg4Iv84t4f+teLk525f0st5DZ4wZwckIkhTXuztT9VSXxQWaen+y6Jsyo4Ns9Jby/de8hX+dQzkiqO3IhXKeEbHx/EYXprtt0T7j5l4Qnu44hLHvuDSbcdDUWm5WqIvdXmFRXY+3fl0m/vQ6jvz+1jgq+f/B5Gmvr3Df3MHPW8w9SXVrO2r9+gHY60VrT96SxDL/knMNmaXB2Xs+FaVlkfvQJ2umk/8zJDL7wbPJ+WAZA0qkz0FqTMWchRRmb8TOZGHPTNdjc76GzZQHKtu0k48OP0U4nRn9/Rl1/BdakfjTV1bHxnQ+p3FcIWtNvxmQGnnd6p7l2Huj6B+LKrAwKPlmIdjqxTZ5K9NnnUb5sCQDhM2bRWFFBznNP4qyrBaUwmAMY+NDjNJaVsveD99BOJ2hN2LgJRJ/T9WM6//nVm11e5nDmvHY70ycPJdIWQnFpBU+89AlzFi7x6mtcOffmI5Ypz8gkb6HrK0yip06l73nnULBkKQBxs2aitSZ33nwcWdkYTCYG/Oq6lq8k2f2vLyhdtx5lMBLUry8DrrsGg78/ZRs3kTt/AY2VVfhZLAT168vw3995xCyBfkfXt5SkZ7J9nqsNxk+fQvIF57DnR1e77nuKq11v/XABZZnZGM0mhv36OsKS+nuso3zLNnZ9833LV5jYt+9k29xFaGczBn9/hl57JaGJ/Tu8dnt+6ugyF6dnsWWuK3OfGVMYcMHZ7HZn7ufOvPnDBZRkbMZoNpF647WEJfXnwO69ZPxtDjg1WjuJnTiOgRedC8DSux/G2dSEv/vaZ2tKEiOuv+qIWQpru3b1S0VmJvs+XoB2aiKmTCX2nHMpdW97ke5tb9szT9Jc5+rLDGYzQx95HKPFQt6771C1fTtNVVX4h4YQd/4FREyd3qXXtzd0/Rj3hEgbtwxx7UcW7ytifu5ezu3j2o/8e28hNpM/r00eTaCfEa1dX4d1088bqWluxmww8NHMCVy3fD01Tc1dfm0Ap+76vm9ipI1bhyZhUPDt3mLm5e7lvL6uzF/tcWV+Y8ooj8w3Lt/UknnerPFcs2zDMWdOCTnyzGlZRiY75rvacdy0KSSefw77fnK144STXe14x0cLKMtyfRXPkBuuIzSpP7XFJWS+/hbgukY+ZtIEEs937dcc23eyY/4idLNr2xt8zZWEHMW2Nzm66zci1Frz7Vsfk7thC/5mE+fddTVxA11f/7Hwkbc4544rCYkIY90XS1n9z++pslcSZA0mZfwwzr3jKqrsB/jHXS9QX1OHMhgwBZi46a+zMQdajur1VxUfed8ndXx8dXzdwDN73cm5/Z/5/oQNsHbdf1qPrJ9eNcj8b3Usg8zudCyDzO52tIPMnuRQg8ye6lgGmd3N24PME+FoBpk9ydEOMnuSox1k9iRdHWR2t2MZZHa3YxlkdrejGWT2JMcyAOpuRzPI7El6Yx3LIPPweuogs3ftlYQQQgghhBCiJzP0yHHfCdX7DiUKIYQQQgghhOixZCZTCCGEEEIIIbxFZjJlJlMIIYQQQgghhPfITKYQQgghhBBCeItMZMpMphBCCCGEEEL8t1JKnaWU2qaU2qmUuu8w5SYopZqVUpce72vKTKYQQgghhBBCeInuQddkKqWMwBvA6cBeYJ1S6gut9eZOyj0HfOuN15WZTCGEEEIIIYT47zQR2Km1ztVaNwALgAs7KXc78E+g2BsvKoNMIYQQQgghhPAWpU7YP6XUTUqp9W3+3dQuTQKwp83ve92PtYmrEoCLgbe8VQVyuqwQQgghhBBC9EJa63eAdw5TpLNzd3W73/8C3Ku1blbKO6f6yiBTCCGEEEIIIbylB12TiWvmsm+b3/sA+9uVGQ8scA8wI4FzlFJNWuvPj/VFZZAphBBCCCGEEP+d1gEDlVJJwD7gCuCqtgW01kkHf1ZKvQ98dTwDTJBBZo/g79+jjnYckW4/wd4LVDf1vsuPM+3m7o7QJVOia7s7QpeFzb25uyN02fyr3+7uCF2SMPvW7o7QZReNaeruCP/1hlsbujtCl63Zb+ruCF1mCuvuBF3jb+h9HzBMvezjRW+s416pB32011o3KaV+h+uusUbgPa11tlLqFvfzXrsOsy0ZZAohhBBCCCHEfymt9X+A/7R7rNPBpdb6em+8Zi87/iKEEEIIIYQQoieTmUwhhBBCCCGE8BKDTOPJTKYQQgghhBBCCO+RmUwhhBBCCCGE8BIvfdVkryYzmUIIIYQQQgghvEZmMoUQQgghhBDCS2QmU2YyhRBCCCGEEEJ4kcxkCiGEEEIIIYSXKJnKlJlMIYQQQgghhBDeIzOZQgghhBBCCOElMpEpM5lCCCGEEEIIIbzof3ImUyk1W2v9dHfnOB6TY638cXQyBqX4V14Rc7bu9Xj+rH5RXDukDwC1Tc08uyGHHRXVAPzr3PHUNDbj1Jomrbnu+3Sf5z0pxsofx7jz5hbxwTbPvGf2i+Lawa15n9vYmvfzc8ZT0+TK2+zUXPeD7/IWZ2Sz+aNFaKem78ypDDj/TI/ntdZs/mgRxenZGM0mRv3mWsIS+1FbVk7aO3OodxxAGRT9Zk0j6cxTADiwey+Z/5hHc309lsgIRt/6K/wtFp/kHx9p5bahyRiAr/cWsTBvn8fzfYMs/GnkAAaEBvOP7bv4JH8/AH2CLDw4alBLudjAAObs2M1nuwq8nlFrzcY5H1OQlo3RZGLSrdcQntSvQ7mq4lJWvvoeDdU12BL7ctJvr8Po58eBfYWsefsj7Hl7SL38fIacd1rLMtu+/oncH1egtSbllKkMPucUr2S2Z2WRO38ROJ3ETJ9Gn3PO6vCe8uYvxJ6ZhcFkYuAN1xPc3/We9i3+nqKff0ahCOyTwMBfXYfB35/S9RvY/cWX1BYUkvrAfYQkJnola1e99cLNnH3qGErKDjD+9Hu6JUN7M/raeHBqCkalWLSlkLfT9ng8f1piBHdN6I9TQ7NT8+TKHDYUHgBgydUTqW5optndX1z86Saf5SzOyCbL3V/0mzmVgZ30F9kfLaLI3V+M/s21WN39xSZ3f4FB0X/WNJLP9GyrOf/5js0LPuWMN17AHBLs9ewHsrPYu2gB2ukkYup0Ys862+P5usICds15n9o9u4m74CJizmh9b7s+eJ8DmRn4hYQw9OHHvJ6tLV/0yds//YrdS3/GHBICwODLLiR61Aif5J8UbeWuVNe+78tdRXy03XPfd0afKK4e1Lrv+3NaDjsPVBNtMfHQuEGEB5jQWvOv/CI+ztnvk4wlGdlsnbcI7XTSZ8ZUks/r2L9tnbuIkowsjCYTI2+8jtDE1j5bO52sevQZAmxWxv7+twDs+OcXFG9KRymFKTSEETdeR4DN6pP8Wmu+eftTdqzbjL/Zn4v+cDVxA/p2KLf2y2Ws/nwp9oJS7p7/FIFhru0q46f1rPj4ewBMFjPn/vb/iE1O8GpGqWPf13FPIzOZ/6ODTGA20GGQqVxX6SqttfPERzp6BgX3jE3hd0uzKKptYM5po1m2v4y8A7UtZfZX13HzTxlUNjYzJdbG7PED+FWbwdktSzKpaGg6MXlx512WRXGNK+/y/WXkVXrmvWWJK+/kWBv3jxvADT+25r31BOTVTifZHyxg0j13EBBu4+dHniVmbCohCXEtZUoysqkuKmbWC4/hyMkj6/35TH30XpTRyLArf0FYYj+aauv4+eFniBwxlJCEODL+/hFDr7yEiCGD2LN0Jbn//o7Bl17g9fwG4PZhydy7LpvSugZenzyKVcXl7K5urefKxibe2JzH1Jhwj2X3Vtdyy8r0lvXMP3kCK4rKvZ4RoCAtm6rCEs59+VHKduaz/u8LOOPJjoOb9HmfM/icU+g/ZTzr3p1P7k8rGXj6DEzBQYy97jL2rfc82ODYs5/cH1dw+pP3YPAzsvTZN4gfM4KQuOjjyqudTnLnzmf4H+7CZLOR/uQzhI9OJTA+vqWMPTOL2uJixj79BFW5eeR8NJdRD9xPvd1OwY8/MubxRzGaTGx96x1K1q4jZuoUAuPjGXLbLeR8MPe48h2vDz9eyltzvuXdl2/r1hwHGRQ8Om0A132VSWF1PZ9eMoYfdpWx017TUmblXjvf55cBMDg8iFdPH8qZC9e3PP/LL9Ox1/m+v8j8YAEn3XMHlnAbyx95lth2/UVxRjZVRcWc4u4vMt+fz/Q2/YXV3V8se/gZotz9BUBtWTklWVuwRIQf6uWPO/ue+fMYcOfv8bfZ2PbMU4SljsLSpk0bA4Poc/kVVKSldVg+YvIUomadzK733/NJvrY5fdEnAySdeSop55zu0/wG4I+jUrhrRRbFtQ28e/Jofi4oI7/tvq+mjt8td+37Toqxcc+YAdy0NJ1mp+a1zDy2V1QT6Gfk7yePZl2x3WNZb9BOJ1s+nM/4u+8kINzGqseeIXpMKsEJrW2hNCOLmqJipj/3OBU5eWz+YB4nPXxfy/O7Fv9IUHwszbV1LY8lnXM6A3/h2s/t+u5Hcv71b4Zff7VXsx+0c/1myveVcPu7D7Jv2y7+/frH3PiXP3Qo13dYMoMmDuf9e1/3eNwWE8H1z92BJSSQHes289WrCztd/lhJHfu+jkXP1GNPl1VKPaSU2qqU+k4pNV8p9Sel1BKl1Hj385FKqXz3z0al1AtKqXVKqQyl1M3ux+OUUsuUUmlKqSyl1HSl1LOAxf3YXKVUolJqi1LqTWAj0Ne9riylVKZS6nL3utQhHp+llFqqlFqklNqulHpWKXW1Umqtu1yKt+tmeHgIe6rq2FddT5NT893uEmbGR3iUySirpLKxGYDMsgNEW0zejnHUhoeHsLeqjv3V9TRpzeI9JcxI8Myb2SZvVtkBogNPfF5HTj6B0VEERkdh8PMj/qTxFG30HMgUbUwnYepJKKWwDUimsaaGOkcFAdYwwtxHHf0sAQTHx1JndwBQXVBE+OCBAESOGELhet/MrAy2hrC/po7CWlc9LyksYUq7waSjoZHtB6po0vqQ6xkTYaWgpo7iunqf5Ny3IYPE6ZNQShE5MInGmlpq7RUeZbTWFGVvp++kMQAkzZjEvvUZAASEhRCR0h9lNHosc2BfIREDk/AzmzAYjUQNHcjedcc/612Zl0dAdDQBUa52ETVxPOVpnustT0snerKrXYSkJNNUU0uDw/WedLMTZ0MjurkZZ0MDJqsVgMD4OAJjY4873/FasXYr5Y6q7o7RYlR0CLsO1LKnso5Gp+bfOSWclujZX9Q0tR4HDPQ3cOjW7Dv2nHyCoqMIatNfFLbrLwo3ptP3EP2F9RD9BUD2vE8YesUl4KMj4TX5eZijozC727RtwgQqMtI8yviHhhKUmNRhOwMIHjgIY2CQb8K14as++UQZGh7C3uo69te4+uQf9pYwPc6zLWeVt+77sstb99Vl9Y1sd5/NU9PUzK7KGqICzF7PWJGbT2BMdEsdx02aQPGmDI8yxZsyiHfXsXVAMo01tdS7+7e6cjsl6Zn0mTHVYxm/NmfrNNc3+PROm1tXZ5F66gSUUvQZkkhddS2V5RUdysWl9MEaE9Hh8b7DkrCEBALQZ0giB8ocXs0ndez7Ou6JlOHE/eupeuRMpnsg+QtgDK6MG4ENh1nk10CF1nqCUsoMrFBKLQYuAb7VWj+llDICgVrr5Uqp32mtR7tfKxEYDPxKa32bUuoXwGhgFBAJrFNKLQOmHOJx3I8NBcqBXOBdrfVEpdSdwO3AXcdfK62iLCaKaloHAEW19YwIDzlk+QuTY1lZaG/5XWt4feYItIbPcgv4LLfIm/GOmLe4pp7hEYfOe0FSLKsK7B6PvTZjBBr4LKeAz/N8k7fO7sASYWv5PSDchiMnz7NMuQNLuGeZunIHAdawlsdqSsqo2LUHa0oiAMF94inamEHsuFEUrN1Ibbnne/OWSLOJktqGlt9L6xoYEnboej6UWXGR/FRQ4s1oHmrLKwiMsLb8bgm3UlvuwGJrrcOGympMQRYM7g+4lggbteWOw643rG88mQu/pL6yCqPJREFadqen4XZVg92Bydb6NzfZbFTmeraLBocDc3jrgN5ss1LvsBOSmEjCmaez/t77Mfj7Yx0+DNvwYced6b9ZTJCZgqrW/qKwqp5RMR3b8emJEfxpUhIRFn9+83VWy+Naw/vnjkQD8zcXsHBLoU9yHm1/EdCmv7AcRX9RuDGdAJuVsH59fJIbDrbp1vZqstqozss7zBLdw1d9MsCu75ewb8UawhL7MeyqX+Af5P1Bc1SAieLaNvu+2nqG2w7dJ5/XP5bVRR33D7GBZgaGBZFtr/R6xjq73aONBtisONr1b/V2R4cydXYHZmsYW+ctYtDll3jMsB2045PP2b9yDX4WCxPu/b3Xsx9UWeogLMra8ntoZBiVpRWEhIcdeqFD2LR4NQPGDfViOqnj9nxRx6Jn6qnj32nAv7TWtVrrSuDLI5Q/A7hWKZUGrAEigIHAOuBXSqlHgZHudXVml9Z6dZvXnq+1btZaFwFLgQmHeRxgnda6QGtdD+QAi92PZwKJnb2gUuompdR6pdT6ku+/OMLba7dsJ48d6kj+uKgwLkiK4fWM/JbHbvwxg2u+S+PO5dlcOiCeMZGhXXr9rur04NohArfkzcxveezGHzO49vs07lqezWU+zdtZKHXkEm2KNNXVseG1txl29WUt112OuvEadv2wlOUPP01TXR0Go2+O7XRezV2b4/FTisnR4SwtLPNOqE7ozmZRVft6PnKZ9sISYhlyweksefp1lj77OtZ+CSijb7q4DlE6eU8KRVN1NeVp6Yx/9ikm/Pl5nPX1FK9a3aGsaNVpO+6kOXyXX8aZC9dz67ebuWtCYsvjl3+exoX/3MQN/87il8PjmRDX9Q9BR+fI/UWn2vUX6197mxHu/qKpvoEdX3zD4EvO91rKznW2ffn4JY+Jb/rk/qfO4OQ/P8H0J2Zjtoaxed4/vRf5kEkPnRdgbGQY5yXG8GZ2vsfjFqOBpyYO5dXMPGqamr0d8RBNoV0dd9pnQ3FaBqbQEMIS+3e66oGXXsTMl54hbvJEdv+wxAthu+AYZvXy0newafFqTrvBy5ezSB238Fkd90BKnbh/PVWPnMnk0Lu7JloHxgHtyt+utf62w4qUmgGcC3yolHpBa/1BJ+utPorXPtyfse15hc42vzs5RB1rrd8B3gGYsOjnLo0EimsbiAlsPW0mxmKmtM0M1kEDwgJ5cMIA7lyW7XE9Y2mdq6y9vpEl+8oYHhHCptIDXYnQJcU1nnmjA82U1HWe94HxA7hr+eHzDgv3Td4Am43astajyHXldgJsnh9QXbNunmXM7gvtnU3NbHj1HRImTyRuwpiWMsHxsUy65w4AqgqKKE7PwhdK6huIanNadGSAibL6jvV8OBOibOw8UIWjodGr2XYsXkrOjysACE/uT02bU2Xaz2ICmEOCaaiuxdncjMFopLbM3qFMZ1JOnkLKyVMASF/wLwLbHBk+ViablQZ769+8wW5vOeW1tYyN+vLWa1jr7Q5MViuOLVsxR0bi777BSMTYMVTm5BI9+aTjzvXfqrC6nrjg1v4iNthMcc2h2/G6ggr6hVqwBfhhr2tqKVte18h3+WWkRoewrqDjaV3H62j6i4BwK3Vt+ovacnvLjTmcTc2sb9df1BSXUFNSytIHn3Sv08Gyh55m+qP3eszMHS+TzUaDvbW9Njjs+Ldr0z2Br/pkc1jrgcp+s6ax7qU3fJK/uK6BaEubfZ/F3LI/ayslNJD7xgzgj6uyOdBm32dUiqcmDWXx3mKW7vfNgT/XzG+b+rM7WurvcGUCrFaK1m2keFMGJelZOBubaKqrJePt90i9+QaP5eNOmsDGl99gwMXeO3iy9svlbPx2FQDxA/tRUeJoee5AaQUhEV07GF2Ut48vX5nP1Y/fQmCod2e1pY5dfFnHomfqqTOZPwPnK6UClFLBuAaJAPnAOPfPl7Yp/y1wq1LKH0ApNUgpFaSU6g8Ua63/BvwdGOsu33iwbCeWAZe7r/OMAmYAaw/z+Am3ubySfsEW4oPM+BkUp/eLYtl+z5u0xASaeX7KUB5Zs53dVa2nWAQYDQT6GVt+PinGSk5FNb602V5J32AL8YFm/JTijL5RLG+f12LmuSlDeWTt4fNO8mHesOT+VBcVU1NSirOpif2r1xMzJtWjTPSYVPatWI3WGvvOXPwCLQRYw9Bak/H3DwmOjyX57NM8lqk/4BoQa6eTnV98Tf+TZ/gk/7aKShICLcRaXPU8KzaKVcVdu3nPyXGR/FRQ6vVsA8+YyVnPzuasZ2fTZ/wo8pevQWtN6Y48/AMtHQaQSilihg9izxrX9at5y9aQMC61s1V7qKtwnaxQXVrO3nXp9J8y/rizhyQmUltUTJ27XZSsXU/4qFEeZcJHj6J4latdVObk4mexYLKGYQ4PpzI3l+b6BrTWOLZsxRLX/ddh9mQZxZX0D7PQJyQAf4Pi3JQofsj3/IDdP7T1GOPwyGD8jQp7XRMWPwNB/u5TrP0MTOtjZUe5b/oLayf9RWy7/iJ2TCp72vQX/m36i3R3f5HSpr8I7ZvAmW+8wGkvPcVpLz1FQLiVGU/M9uoAEyCwfyL1xcXUl5bgbGrCvm4dYamjjrzgCearPrnO0XrQoXBDGiF94vGFrfZK+gRbiHPv+07tE8XPBR33fU9PGsrjG7azp8rzdMj7xw5kV2UNC3f65q6yAKFJ/alpU8cFa9YR3b6OR6ey313Hjp25+FkCMFvDGHTZxcx6+Vlmvvg0o279NRFDh7QMfqoLWy9rKd6UQVBcjFdzTzx/Ore8fg+3vH4PQyaPJOOHdWit2bs1H3NQQJdO46woLmfhk+9x8Z+uIaLP8d0orjNSx76vY9Ez9ciZTK31OqXUF0A6sAtYD1QAfwYWKaWuAX5ss8i7uE5L3ei+Q2wJcBEwC7hbKdUIVAHXusu/A2QopTYCD7R7+c+Aye7X1sA9WutCpdShHh/ixbd+VJo1PL8xh1dnjMCo4Iu8InIP1HBJiuvD66c5hdw4rC9hZn/uHeu679DBryqJCPDn+amua8L8FHyzu4RVhQ6f531hkyuvQcGXB/Mmu/PmuvOaWvMe/KqS8AB/XpjiymtU8O3uElYX+SavwWhkxLVXsPb519DaSZ8ZUwjpE8+uH12X3vY/ZQbRo0ZQkp7FkrsfxmgykXqjq0nZt+ewb8UaQvomsPzBp4DW2+LvX7WeXd8vBSB2/Gj6zJjsk/xODa9vzuWZ8cMxKPh2bzG7qmo5r6+rnr/aU4jN5M8bU0YR6GdEa7gkMZ4bl2+iprkZs8HAuAgrf8nO8Um+g+LGDGd/WjZf3fUofmYTk27+ZctzS597g4m/uRpLuJVRV17EytfeI3PRl9gS+5J8sqveah0VLH7geRpr61BKse3rnzjnhQfxD7Tw88t/o6GqGoPRyLhf/R+m4MDjzquMRpKvuoLsv7wCTifRU6cSmBBPwRLX3zRu1kxsI0dgz8xk4+wHMZhMDPjVdQCEJCcROW4s6U88iTIYCerXl9gZ0wEo27iJ3PkLaKysYssrrxPUry/Df3/nceftqjmv3c70yUOJtIWwc83rPPHSJ8xZuOSE5zioWcNjP+/kH+eOwKgUH28rZIe9hiuHue4KOn9zAWcmR3LxoBganZr6Jid3frcFgEiLiTfPdPdvBsUXO4tZtsc310Af7C9Wu/uLvu7+It/dXyS6+4vi9Cx+dPcXo939Rfn2HPa6+4ul7v5iyGUXEuOjr9FoTxmN9Ln8KnJe/QvaqYmYMhVLfAKly5YAEDljFo0VFWx75kma61zbWcmP3zP0kccxWizkvfsOVdu301RVRdZ9dxN3/gVETJ3u9Zy+6pO3LviUA7v3glJYIsMZ+Svf3JGzWcPL6Tm8NHUERuCrXUXkVdZwUaKrT/48v5BfDelLqMmfP41y7/u05tdL0kmNCOXsftHsrKjm/ZNHA/D25l2s6uSazeNhMBoZ+svL2fDnV9FOJwnTpxCcEM8edx33PWUGkaNGUJKRxfJ7HsJoNjHi19cdcb3bP/6cmsIiVx1HhDPs+qu8mrutgROGsWPdZl779RP4m01c+PvW15r78FtccOeVhESEseZfS1nxyQ9U2Sv562+fY+D4YVxw15UsnfcttZXV/PvNjwEwGAzc9OqfvJZP6tj3ddwTGXrwaawniur0PPAeQCkVrLWuUkoF4ppFvElrvbG7c/lCV0+X7W49+fzvQ5nWr2unjfYEmXbv30nQl6ZEe/fW+ifCnuoeeZztsOZf/XZ3R+iShNm3dneELrtozIn5eidv2lvTu9pyfGDvq+M1+7vvLu3Halysdy998LWTon1zV3NfWl3cu/bVvbGOr0o5q9d98hz692Un7LP9ll/P6JH105P3Su8opYbhuvZyzn/rAFMIIYQQQgjx36M3Tsh4W48dZGqtfTfvL4QQQgghhBDCJ3rsIFMIIYQQQgghehuZyey5d5cVQgghhBBCCNELyUymEEIIIYQQQniJkqlMmckUQgghhBBCCOE9MpMphBBCCCGEEF6iZBpPZjKFEEIIIYQQQniPzGQKIYQQQgghhJfIJZkykymEEEIIIYQQwotkJlMIIYQQQgghvERmMmUmUwghhBBCCCGEF8lMZg8QGNi7DnfEBDV3d4QuK64zdneELrOZe1c9nxLX0N0Ruuzj/N7XLhJm39rdEbpk39N/7e4IXeb/6Y3dHaHLgv2d3R2hS0p7YZ9cV6e7O0KXGVTvytyse9fnIZA6Fp2TmUyZyRRCCCGEEEII4UUyyBRCCCGEEEII4TVyuqwQQgghhBBCeIlBTpeVmUwhhBBCCCGEEN4jM5lCCCGEEEII4SVy4x+ZyRRCCCGEEEII4UUykymEEEIIIYQQXiIzmTKTKYQQQgghhBDCi2QmUwghhBBCCCG8RMntZWUmUwghhBBCCCGE98hMphBCCCGEEEJ4iVyTKTOZQgghhBBCCCG8qFfMZCqlEoGvtNYjujvL0XJnnqK1nufr15oYZeX24ckYFPx7dxHzcvZ5PH9aQhRXpSQAUNvUzEuZOeRU1vg6locD2VnsX7QA7XQSPnU6MWed7fF8XWEBe+a8T+2e3cRecBHRZ5zZ8tzuD96nMjMDv5AQBj/82AnNfVBFVha7Fy5EO51ETZtG3Nme+WsLCsibM4ea3btJuOgi4s44o1tyHms9N5SXs/v992g6UAFKETFtBlGnnnZCs2utWfjaZ2Su3oIpwJ/r77uS/oP6dij37pMfsmvbHoxGI4lD+/HLP/4ffn5Gn2Yrzchm27xFaKeThBlTSTrvrA7Zt81dRGlGFkaTieE3XkdoYr/W551O1jz6DGablTG//y0Albv2sGXOPJobG1FGA0OvvZKw5CSvZ5/R18aDU1MwKsWiLYW8nbbH4/nTEiO4a0J/nBqanZonV+awofAAAEuunkh1QzPNWtPs1Fz86Sav5zsWb71wM2efOoaSsgOMP/2eE/a6henZZHz4MdqpSZw1hcEXnOnxvNaajA8+pjA9G6PJn3E3X4stqd9hl83++EsKNqSjlAFzaDDjbrkWi83ass6a0nK+u+cJhv7iHAade7rX3kt5Zha58xehtZPY6dPoe07HNp07fyHlmVkYTCYG33A9wf37UVNYyNa3/tZSrq6klP4XnU/C6b7vLxxZWexy98PR06YR364f1lqza+FCHJmZGEwmUq6/nqD+/QEo/OEHipcvB62Jmj6duNNOTP82Oc7Gn8YmY1SKz3MKeX/LXo/nz+4fxXXDXP1cTWMzz6zfyQ5HNf1DLDwzdUhLuYTgAN7K3MX8bfu9nrEkI5stcxehnZo+M6eScl7Hdr1l7iJK0rMxmkyM/M21hCX2o7mhkTVPv4izqQnd7CR2whgGXnI+ADs++4o9S37GFBoCwKBLLyR6lG8+wmmtWfz2P9m5fjP+ZhPn//5q4gZ03Hes+3IZa/+1BHtBKX+Y9zSBYcEAZP60jlWf/ACAKcDE2b+9nJjkBK9mlDr2fR33NDKT2UsGmb1UInAV4NNBpgG4a0Qyf1yTTUltA29PH8WKonJ2VdW2lCmoqeOOVZlUNTYzKcrKn1IHcOuKDF/G8qCdTvbNn0fynb/H32ZjxzNPEZY6ioD4+JYyxsAgEi6/goq0tA7Lh0+eQuSsk9nz/nsnLHNb2ulk17x5DPr97zHZbGx++mmso0ZhaZPfLyiIfldcgWNT930IP556VkYD8ZdeRmC//jTX1bH96ScIGTrMY1lfy1qzhaK9JTw5dzZ5m3cx9+VPmP3X33coN+m0cfz6gV8C8O4TH/Lzv1cz68KpPsulnU62fjifsXffSUC4jTWPPUPUmFSCE1rrpjQji5qiYqY+9zgVOXls+WAekx6+r+X53Yt/JCg+lqbaupbHti/6lOSLziUydQQl6ZnsWPgp4+//o1ezGxQ8Om0A132VSWF1PZ9eMoYfdpWx0956kGnlXjvf55cBMDg8iFdPH8qZC9e3PP/LL9Ox1zV5Ndfx+vDjpbw151veffm2E/aa2ukk/f2FTLv/DizhVn566DnixqYS2ieupUxRejZVhcWc8eKj2Hfmk/aPBZz8+D2HXXbQuacx/DLXh8ad3/zE1k//w5hfX9WyzoyPPiF21DCvv5ecufMZ8ce7MNtspD3xDOGjUwlqs73bM7OoLSpm/NNPUJmbx84P5zL6wfsJjI1l7KMPtaxnzR/vJWLMGK/mO1Tm/HnzGOLuh7Pd/XBgm8wVWVnUFRUx6sknqcrLI2/uXEbMnk3Nvn0UL1/O8Pvvx+Dnx9ZXXsE2ciQBMTE+zWxQcN+4FG77KYui2no+PGM0S/eVk3egdfvbV13Hb77PoLKxiSlxNh6cMIDrvktnV2UtV32zqWU9X184iZ/2lHk9o3Y6yf5gARPvuYOAcBsrH32W6DGphCS0tuuSjGyqC4uZ8fxjOHLyyJ4znymP3IvB34+J992FX0AAzqZmVj/1ZyJTh2MbkAxA4pmnknyO9w6MHErO+s2U7y/htr89xL5t+Xz9xiJueLljX9p3WBIDJw7nw/te83jcGhPBNc/egSUkkJ3rN/Pv1xZ0uvyxkjr2fR2Lnqk3nS7rp5Sao5TKUEp9opQKVEo9rJRap5TKUkq9o5TruIFS6g6l1GZ32QXux4KUUu+5y29SSl3ofvx6pdTnSqkvlVJ5SqnfKaX+4C6zWikV7i6XopT6Rim1QSm1XCk1xP34+0qpV5VSK5VSuUqpS915nwWmK6XSlFIdPyl7yVBrCPuq6yioqadJa37cV8K0mHCPMtn2Sqoam10/OyqJsph8FadTNfl5mKKjMEdFYfDzwzphAhUZaR5l/ENDCUxMQhk7zkgFDxyEX2DQCUrbUXVeHuboaALc+cMnTMCenu5Rxj80lODExE7znyjHU8/+YVYC+7mO+BsDAgiIjaPR4ThByV3SVmQx+cwJKKVIHp5IbVUtjrKKDuVGnjQMpRRKKRKH9sNe4tucFbn5BMZEExjtqtfYSRMo2eR5kKZkUwZxU09CKYV1QDJNNbXUO1zZ68rtlKZnkjDDcyCslGoZdDbV1mFuM3vlLaOiQ9h1oJY9lXU0OjX/zinhtMQIjzI1Tc6WnwP9DWivp/C+FWu3Uu6oOqGvWZ6TT1BMFEHRkRj8/Ohz0jgKNnj2A/s3ZNBv+iSUUoQPTKKxpoZae8Vhl/UPtLQs31xf73H4e//6NIKiIwlpM5D1hsrcPAKio7G4+4qoieMp3+T5XsrS0ome4mrToSmuNt3g8NweHZu3YomOIiDSs035QlWeK/Ph+mF7WhqRkyejlCIkOZnm2loaHA5qCwoITk7GaDajjEZCBw2i/AQcEBweHsKeqjr2VdfR5NQs3l3CrD6e++eM0koqG10HcTJLK4kONHdYz8QYK3uraimsqfd6Rkeuq20e7N/iJo2neKNnvRZvTCfB3b/ZBiTTVFNDnaMCpRR+AQEA6OZmdHMzqhumb7atzmTkKRNRStFnSBJ11bVUlnfcd8Sm9MUa07Gt9h2WjCUkEICEwYlUljm8mk/q2Pd13BMpdeL+9VS9aZA5GHhHa50KHABuA17XWk9wn0ZrAc5zl70PGOMue4v7sQeAH7XWE4CTgReUUgdHLiNwzTpOBJ4CarTWY4BVwLXuMu8At2utxwF/At5sky0OmOZ+/WfbZFiutR6ttX7ZW5XQXqTFRHFdQ8vvJXUNRFo67qQOOrdvDGuKHb6K06lGuwOTrXXH6m+10Wg/sRmOR4PDgSm8Nb/JaqXRbu/GRJ3zVj03lJZSu2cPgUneP3XzcBwlFdiirC2/26KsOEo67sQOampqZvXi9YyYOOSQZbyh3m7HHG5r+d1ss1Lf7u9fb3cQ0KZMgM1Knbvut81bxMDLL+mwJxh01WXsWPhPlv3hfnYs+IQBl17k9ewxQWYKqlo/mBZW1RMT1PEg0+mJEXx7+Xj+dvYI7l+yreVxreH9c0fy+S/GcPnQWK/n603qyh1YIlr/xpZwG7X2iiOWqbM7jrhs9qJ/8fXts9mzch3DLnXtxprq6tn+5XcMveQcr7+XeofDo02bbDbq2x1UarA7MLft92xW6h2e7b5k7TqiJk7wer7OHE0/3OBwYLZ5vq8Gh4PAhAQqt2+nsaqK5vp6HFlZNJyAPjw60ExRm4FhUU0DUYfZP1+UEsPKgo65zugfxbe7SnySsa593+Vusx3KRHiWqXeX0U4nPz/0FD/cfg8Rw4diTWndb+z+YQk/P/AkGe9+QGN1tU/yA1SWVRDaZt8RGmmlspMDlEcjbfEqUsYN9VIyF6ljT76oY9Ez9aZB5h6t9Qr3zx/hGtSdrJRao5TKBE4BhrufzwDmKqV+CRw8z+sM4D6lVBqwBAgADl409ZPWulJrXQJUAF+6H88EEpVSwcAU4GP38m/jGlge9LnW2qm13gwc1fk3SqmblFLrlVLrC77511FXQof1dPag7nwuYkxEGOf2jeHtLfnH/HrHppM8PfjISwed1WePPHR0/PXcXFdH/jt/Jf7/LsdosRx5AS/SneQ/3BHbeS9/wqDUFAampvgyVqfV2qFiO2kjSkFJWgam0BBCE/t3eH7vj8sYdOVlzHjpGQZddRmb3/vQO3kPnfJQUfkuv4wzF67n1m83c9eExJbHL/88jQv/uYkb/p3FL4fHMyEuzOsZe4ujagadboPqiMsO/78LOfu1p+k7ZQI5i5cCsOWfXzHg7FNaZjG86iimq3Wn+5HW0M6mJsrS04kcP857uQ4fqJM4R7MdKixxccSddRZbX36Zba++SmCfPiiD7z/+dLr9HaLs+OgwLkyO5dW0PI/H/QyKmQkRfL+n1Ov5XIG61u92KGswMO2JBzj55aepyM2ncq/rnhD9TpnBzBeeYOoTswmwhrFl/j+9FrmDzt7DMawmP307aYtXc8qvLjz+TG1JHbfwWR33QAZ14v71VL3pmsz2LVzjmk0cr7Xeo5R6FNfAEeBcYAZwAfCQUmo4ru3hF1rrbW1XopSaBLQ9B8XZ5ncnrjoyAA6t9ehDZGu7/FH9ubXW7+CaHWXmVyuO+Qy1ktoGogNaZyaiAkyUtpnZPCg5JJC7U1O4Z+1mDjSe2Our/G02GuzlLb83Ouz4W60nNMPxMNlsNJS35m9wOHpk/uOtZ93cRP47f8U2cRLWMWN9kLCjnz77meVfrQIgcYjnqa/2EgdhkaGdLvfl+99Q6ajil0/8yucZzeE26stbZxfq7Y4Op7aaw23UtSlTZ3dgtlopWreRkk0ZlKZn4Wxsoqmulsy332PkzTdQsGIVg6/+PwBiJoxj83sfeT17YXU9ccGtMyexwWaKazr2DwetK6igX6gFW4Af9rqmlrLldY18l19GanQI6wqO7eh1b2cJt1Jb1vo3ri23Y7GGtStj61AmwBqGs6npiMsC9J0ygZV/fpNhl55HeU4++9ZuImv+ZzTW1IJSGP39STlj1nG/F7PN6tGmG+x2zO36Cle7b9Pvudv0QfbMLIL79cMU1vk26m1H0w+bbDbq7XZCDpax2/EPc9Vz9LRpRE+bBsCezz7D1GbG01eKauqJaXP6a0ygidLajqe8DrAG8tDEgdy+NJuKBs/989Q4G1vLqyiva/RJxoD2fVe5HXO7thlgs1JX1q5Muz7QPyiQ8CEDKcnYTEifBMxt2kWfmdPY8PIbXs29/qtlbPrGte+IG9SPA232HQdKHQRHdO2AWFHePr56dT5XPn4rgaHevTxH6tjFl3UseqbeNJPZTyk12f3zlcDP7p9L3TONlwIopQxAX631T8A9gBUIBr4Fbm9z3eZR36lAa30AyFNKXeZeVimlRh1hsUpo2df5zNaKSvoEWYi1mPFTilMSolhRVO5RJjrAxBPjh/BU2g72VtcdYk2+E9g/kYbiYupLS3A2NeFYt46w1CNVX88RlJhIfXEx9aWlOJuaKF+3Dtuonpf/eOpZa82eD+YQEBtH1Gkn7s64J188jYf/fjcP//1uRk8bwapv17nuapmdjyXIgrWTndjyr1aTvW4bv3n4GgwnYDYiNKk/NUXF1Ja4/v6Fa9YRNSbVo0zU6FQKVqxGa41jZy5+lgDM1jAGXnYxM15+lukvPs3IW39N+NAhjLz5BgDMViv2rdsBKN+yjcCYaK9nzyiupH+YhT4hAfgbFOemRPFDvufNQ/qHts6UDY8Mxt+osNc1YfEzEOTvunbX4mdgWh8rO8p9dzpWT2dL7k9VYTHVxa52sHf1BuLGebaDuLEj2b18DVprynfk4W+xYLGFHXbZqsLiluULNmYQHOc6LXnmw3/krFee5KxXniTlrJMZfOGZXhlgAoQkJVJXVEydu02XrF1P+GjPviJi1CiKV7ra9IGcXIyBFkxtPhgXr1lH1KQTc6osQHBiInXFxdQdph+2jhpF6apVaK2pzM3FaLFgcg9EGw+47phcX1ZG+caNREzwffbN5ZX0DQkgPsiMn0FxRr8olu713D/HBpr587RhPLR6G7srazus48z+0Xzjo1NlAcKS+lNdVEyNuy0UrFlPdLv+LXpMKvvc/Zt9Zy5+FgsB1jDqD1TSWO26iVFzQwNlm7cSHO9qv3Vtrt8t2pBGSB/v3kRu/Hkz+M3r9/Kb1+9l8EmpZP64Fq01e7fmERAUQEj40Q+AKorL+eSpv3PhH68hIsH7/bDUse/rWPRMvWkmcwtwnVLqbWAH8FfAhuuU1nxgnbucEfhIKRWGa1bxZa21Qyn1BPAXIMM90Myn9RrOo3E18Fel1IOAP7AASD9M+QygSSmVDrzvq+symzX8JTuXP08ajkHBf/YUk19VywX9XJ3QF7sLuW5QP8L8/fn9iOSWZW7++XDRvUsZjSRcfhW5r/4FnJrwKVMJiE+gdNkSACJnzKKxooIdzzxJc10dKEXpj98z+JHHMVos7Hr3Haq2b6epqorN991NzPkXEDF1+gnN3+/KK9n2l7+A00nk1KlY4uMpXuo6rS165kwaKyrIfuopmuvqUEpR9P33jHzssRN6yunx1HPtvr3Y16wmICGBbU8+BkDchZcQOnLkCcs/8qRhZK3ZwgNXP4XJbOL6e69oee7Ve9/h2rsvxxoZxtyXPiY81sazt70CwNgZqZx33ZmHWu1xMxiNDP7l5Wz886top5P46VMITohnz4/LAOh7ygwiR42gNCOLFfc8hNFsYtivrzvieof+6pdsm7sI7WzG4O/PsF9d7fXszRoe+3kn/zh3BEal+HhbITvsNVw5zHW2//zNBZyZHMnFg2JodGrqm5zc+d0WwHW995tnuu5q6mdQfLGzmGV7esa1yHNeu53pk4cSaQth55rXeeKlT5izcIlPX9NgNDL6+stZ8dzraKeT/jMnE9onntzvXe0g+bQZxI4eQVFaNov/8AhGk4lxN19z2GUBshZ8TlVBEShFYGQ4Y2646pAZvEUZjaRcfQVZL7+CdjqJmTaVoIR4Cpa4+rS4WTOxpY6gPDOT9fc/iMFkYtANrW26ub4Bx+YtDLz2lz7P2jZzorsf1k4nUVOnEhgfT5G7H46ZORPryJE4srJIf+ABDCYTyddf37L8jrfeorG6GoPRSOJVV+EX5PuZlGYNz6/P4fVZru3vX7lF5B6o4RcDXPvnf+4s5Dcj+hFm9uO+8QNcyzg11yxOAyDAaGBSrJWn1+3wWUaD0ciwa65g3QuvoZ1O+syYQkifeHa7+7d+p8wgatQISjKyWHr3wxjNJlJvdN2qot5RQcbf5oBTu74KZ+I4oke79hnbFn7Kgd17USgskeEM90H/dtCACcPYuT6bN258vOXrNQ6a/8hbnHfHlYREhLH2i6Ws+uR7quyVvPO7Zxkwfhjn3XkVy+d/Q+2Bar5582N3nRj49St3ey2f1LHv67gn6pFXVZ1gqvPrLsSJdDyny3aHmKDm7o7QZSZDr6piABqcvauH+u2QE3u3T2/4OP/EXnfqDd+kd98djI/Fvqf/2t0RuuyuT2/s7ghdVlLXu9pFQ3Pv6t8Asvf3ppO/XKYn+eY0W18ZF9m78gJsKPXv7ghd0hvr+JoBZ/a6DmPqZz+fsA+eKy6e1iPrpzfNZAohhBBCCCFEj6Z63zEpr5MqEEIIIYQQQgjhNTKTKYQQQgghhBBeItdkykymEEIIIYQQQggvkkGmEEIIIYQQQniJUuqE/TvKPGcppbYppXYqpe7r5PmrlVIZ7n8rj+KrGo9IBplCCCGEEEII8V9IKWUE3gDOBoYBVyqlhrUrlgfM1FqnAk8A7xzv68o1mUIIIYQQQgjhJT3smsyJwE6tdS6AUmoBcCGw+WABrfXKNuVXA32O90VlJlMIIYQQQggheiGl1E1KqfVt/t3UrkgCsKfN73vdjx3Kr4GvjzeXzGQKIYQQQgghhJecyJlMrfU7HP701s7S6E4LKnUyrkHmtOPNJYNMIYQQQgghhPjvtBfo2+b3PsD+9oWUUqnAu8DZWuuy431RGWQKIYQQQgghhJf0sGsy1wEDlVJJwD7gCuCqtgWUUv2AT4FrtNbbvfGiMsgUQgghhBBCiP9CWusmpdTvgG8BI/Ce1jpbKXWL+/m3gIeBCOBN99eiNGmtxx/P68ogswcYamvo7ghdEhfY3N0RuqyhuWcdUjoaFY29675c/9lr7u4IXeanOr0koUe7aExTd0foEv9Pb+zuCF32l0ve7e4IXfbm99d3d4QuWVXc+/qLa1NruztCl60r7V31XF7fu/o3gNJ6Y3dH6JLeWMe9kaGHfezUWv8H+E+7x95q8/ONgFd32L3rU6wQQgghhBBCiB5NZjKFEEIIIYQQwkt62kxmd5CZTCGEEEIIIYQQXiODTCGEEEIIIYQQXiOnywohhBBCCCGElxh64Y0FvU1mMoUQQgghhBBCeI3MZAohhBBCCCGEl8iNf2QmUwghhBBCCCGEF8lMphBCCCGEEEJ4icziSR0IIYQQQgghhPAimckUQgghhBBCCC+Ru8vKTKYQQgghhBBCCC864kymUioR+EprPeJoVqiUuh5YrLXefzzBlFIXAdu11pu7uNz7uPJ+cjyv717XbK31021+X6m1nnK86z0WZZnZ7Ji3CLSTuOlT6X/uWR7Pa63ZMW8R5ZlZGEwmhv76OkL69wNg1d2zMQYEoAwGlMHA+EdmA1C1ey/bPpxLc109AZERDLvpBvwsFq9l1lqzYc7H7N+UjZ/ZxEm3XkN4Ur8O5aqKS1nxynvUV9cQntiXyb+7DqOfH3k/r2XLF98B4Gc2M+HGK7D17wPAv373EH6WAJRBYTAaOevpe48rZ/oHH1OQno2fyZ/xN1+LrZOc1cWlrH79PRqrqrEm9mXibddj8PM77PIN1TVs+NtcDuzdDwrG33QNEQOTAdj57U/s/G4pBoOR2NHDSb3qki5nL83IZtu8RWink4QZU0k6r2O72DZ3EaUZWRhNJobfeB2hia3vTTudrHn0Gcw2K2N+/9uWx3d/9xN7fliCMhiIHDWCQZf/osvZDqUoPZvMDz9GOzX9Z01h0AVndsic+eHHFKVlYzT7M/ama7G663PjOx9SmJaJOTSEU599qGWZrHmfUrgpE4OfkaDoKMbcdA2moECvZS7JyGbL3EVop6bPzKmknNcx85a5iyhJz8ZoMjHyN9cSltiP5oZG1jz9Is6mJnSzk9gJYxh4yfkAbF3wT4rTMjEY/QiMjmTkjdfi76XMxRnZZH3kyttv5lQGnt8xb/ZHiyhKz8ZoNjH6N9diTexHbVk5m96ZQ73jABgU/WdNI/nMUzyWzfnPd2xe8ClnvPEC5pDg48pZmJ5NhrstJM6awuBO2kLGBx9TmJ6N0eTPuDbb1qGWzf74Swo2pKOUAXNoMONuuRaLzdqyzprScr675wmG/uIcBp17+nHlP1pvvXAzZ586hpKyA4w//Z4T8ppdpbXm+3f+Sc6GzfibTZx759XEDujbodyGr5ax7oslOApKueOjpwkMO742cCTlmVnsnL8IrZ3ETZ9Gv3M69nE58xdSlunq4wbfcH3Lvq+ppoZt739I9b59KKUYdP21hA1IIe+zf1GWlg5KYQoJYfAN12Nu00aOh9aan//+T3ZtdO33Tv3dL4lK6ViPB4pKWfzS+9RX1RCZ1IfT7rwWo7/rI9m+rB38/N4/cTY3YwkJ5qIn78S+r4jFL/6jzfJlTLziHEadf/JxZ67IymL3woVop5OoadOIO/vsDu9p98KFVGRmYjCZSLr+eoL69weg8IcfKF2+HK01UdOnE3vaaS3LFf34I8U//QQGA9aRI+l76aXHnbVtpuOp502ff8/2Zetd62p2Yt9XyK/+8QwBIUH8+Ppcdq3PwhIWwhWvzPZKXqlj39dxTyN3l/XN6bLXA1nAcQ0ygYuAr4CjHmQqpbz9fmYDLYPM7hpgaqeT7R/NZ/Qf78QcbmP9488QOTqVoIT4ljLlmVnUFhUz6ZnHOZCbx7YP5jH+oftanh99zx8wtftAuPX9D0m5/BfYBg+iYPkKdn/9HcmXXOC13PvTsqksKOH8vzxK2c581r27gDOf6vgBK23e5ww+9xQSp4xn7bvzyf1xJQPPmEFwVCSnPfx7TMGB7N+Uzdp35nksf+pDdxIQevwfcArTs6ksLOasFx+lfGc+G/+xgFMf75gzc8HnDDr7FPpOHs/Gv88jb8lKUk6bcdjl0z/8mNhRw5h8129wNjXRVN8AQHH2NvZvyOD0Zx7A6O9PXUVll3Nrp5OtH85n7N13EhBuY81jzxA1JpXgNu2iNCOLmqJipj73OBU5eWz5YB6THm5tF7sX/0hQfCxNtXUtj5Vv2UbJpnQmP/EgBn9/Gg4c6HK2w2VOn7OQqffdgSXcypKHnyN2XCqhCXEtZYrSs6kqLOa0Fx/FnpNP+vsLmPmYqz77zTiJ5NNnsuHtOR7rjR45hGGXX4jBaCR7wWfs+PJbhl9xsdcyZ3+wgIn33EFAuI2Vjz5L9JhUQtpkLsnIprqwmBnPP4YjJ4/sOfOZ8si9GPz9mHjfXfgFBOBsamb1U38mMnU4tgHJRAwfyqDLLsJgNLJ14WfkfPUtQy4//sza6STzgwWcdM8dWMJtLH/kWWLHeuYtzsimqqiYU15w5c18fz7TH70XZTQy7MpfYE3sR1NtHcsefoaoEUNblq0tK6ckawuWiHCv5Ex/fyHT7ne1hZ8eeo64samE9unYFs548VHsO/NJ+8cCTn78nsMuO+jc0xh+mWsgv/Obn9j66X8Y8+urWtaZ8dEnxI4adtz5u+LDj5fy1pxveffl207o63ZF7obN2PeXcPPbD7F/Wz7f/nUR1734xw7lEoYmkTJhOPNmv+bzTNrpZMfc+aT+8S7MNhsbn3iGiNGpBMV77vtqioqZ+PQTVObmsePDuYx98H4Ads5fSPiI4Qy/7WacTU04G1z9b9+zziDp4gsB2Pv9j+z68t8MuvZqr2TevXEzFQXFXP3GwxRtz2fpOwu59Lk/dSi36sMvGHX+yQycNo4lby1gyw+rGHHWdOqra1j2ziLOe+hWQqLCqXG49g22hBguf8nVdzubncz5zYMkTxp13Hm108muefMY9PvfY7LZ2Pz001hHjcLSpo4rsrKoLypi5JNPUp2Xx665cxk2ezY1+/ZRunw5Q++/H4OfH9tfeQXryJEExMRwYOtWHGlpDH/4YQz+/jR6cT8Cx1/PYy46jTEXuQZr+esySf/yJwJCggAYcvIkRp49gx9e/dArWaWOfV/Homc62tNljUqpvymlspVSi5VSFqXUaKXUaqVUhlLqM6WUTSl1KTAemKuUSlNKzVRKfQqglLpQKVWrlDIppQKUUrnux3+jlFqnlEpXSv1TKRWolJoCXAC84F5PivvfN0qpDUqp5UqpIe7l31dKvaSU+gl4zp13hlJqpVIq150Jd9m73a+VoZR6rM3jn7vXm62Uusn92LOAxf36c92PVbn/n6WUWqKU+kQptVUpNVcppdzPneN+7Gel1KtKqa+O9Y9z0IHcfCzR0ViiozD4+REzaQKlaRkeZUo3ZRA75SSUUoSlJNNUU0u9o+Kw660pLMI6aCAAtuFDKdmw8Xijeti3PoOkGZNQShE5MImGmlpq7Z6ZtNYUZW+n36QxACTNmMSe9a73FjU4GVOwa2YncmASNeUOr+Y7aP+GDPpPd+WMGJhEY01NpzmLs7eRMNGVs/+Mk9i/Pv2wyzfW1FKydSeJs1zHJgx+fi2za7k/LGfwBWdi9PcHICAspMu5K3LzCYyJJtDdLmInTaBkk2e7KNmUQdxUV7uwDvBsF3XldkrTM0mYMdVjmb0/LiXx3DMxuLOZQkO7nO1Q7Dn5BMdEERQdicHPjz4njaNwQ7pHmcINGfSb5qrP8AFJNFbXUOf+e0QOGYh/cFCH9UaPHIbBaATAlpJErRfbiiM3n6CYqJZ6jps0nuKNnpmLN6aT4K5n24BkmmpqqHNUoJTCLyAAAN3cjG5uxt1VENUmszUliTq73St57Tn5BEVHEeTOG3/SeArb5S3cmE7fNnkb3XkDrGFY3TPdfpYAguNjqbM7WpbLnvcJQ6+4BLxwhLY8x1WvbdtCQbu2sH9DBv3c21Z4m23rcMv6B7aejdFcXw+qNez+9WkERUcS0mYgeyKsWLuVckfVCX3NrtqxOpMRp0xEKUXCkCTqq2upKu+4D4lN6Ys1JuKEZDqQm+fa90W52nL0xPGUbfJsI2Vp6S37vtA2+76m2loqtu8gdrqrfzP4+eEX6Op/256x46yv90p7PihvbSaDZ7nqMXZwEg3VtVSXd9yf7MvcTsrk0YDrA3feWlffvWPZepJPGkVIlOtATqC1475hb+Y2wmIiCYk+/oM91Xl5mKOjCXDXcfiECdjTPevYkZZGxOTJKKUITk6mubaWBoeDuoICgpKTMZrNKKORkEGDsG/aBEDx0qXEnnVWy37E34v7ETj+em5rx88bGDh9XMvv8cMHYA7x3pkwUse+r+OeyHAC//VUR5ttIPCG1no44AB+AXwA3Ku1TgUygUfcp6iuB67WWo8GVgBj3OuYjmuGcwIwCVjjfvxTrfUErfUoYAvwa631SuAL4G6t9WitdQ7wDnC71noc8CfgzTb5BgGnaa0PHnaNA6YB5wHPAiilznC/j4nAaGCcUmqGu/wN7vWOB+5QSkVore8Dat2v39khzjHAXcAwIBmYqpQKAN4GztZaTwOijqp2j6DeYScg3Nbyu9lmpb7dB9J6uwNz2zLhVuoPfjhUivQXX2HdY0+zf8nyljJBCfGUprk6uuJ1G6kv986H3INqyisIjLC2/B4Ybu0wUKyvrMY/0NLyYTsw3NbpACHnp5XEjx7e+oBS/PT063x9/7Ps/P7n48pZW+4gMKK17izhNmrbfLAGaKiqxj8osCWnJdzaUuZQy1cXl2IOCWb92x/y/eynWf+3j2iqqwegsqCY0q07+eHh51nyxEuU5+R3OXe93e75Nz9Eu2jbdgJs1pZBw7Z5ixh4+SUeH8IBqguLcWzfyZrHn2XdMy9Skdv1bIdSa3dgaZsn3NZhQF9rd2CJaF/GcdSvsWvZSmJSvTdTVde+DsNtHgOvljLtMh/c/rTTyc8PPcUPt99DxPChWFOSOrzG3uUriRo5vMPjx5q3ff11yFvu+Z4s4Tbq2m13NSVlVOzagzUlEXANTANsVsL69fFOznLPnJZO2kJnZersjiMum73oX3x9+2z2rFzHsEvPA6Cprp7tX37H0EvO8Ur+/zaVZRWERFpbfg+JsFJZdvgDlb7W4Gi3X7PZqHc4PMq49n3hbcpYaXDYqSspxT8khG3vzWHDo0+y7f0PXAcd3PI+/ZzVf7qPotVrSbzIe2fwVJc7CI5szRwUYe3wwbyushpTUOt+LyjCSrW7rh37S6ivquHzh17h4z89z9af1tDezp83enxgPx4NDgemNvVnslppbLcfaXA4MNla35O/zUajw4ElIYHK7dtpqqqiub4eR1YWDe5l64qKqNq5k81PP83WF16gKj/fK3kPOt56PqixvoHdm7aQfNJor+ZrS+rY93UseqajHWTmaa3T3D9vAFIAq9Z6qfuxOcCM9gtprZuAnUqpobgGdy+5y00HDo52RrhnJjOBq4EOn7SUUsHAFOBjpVQaroFc20PRH2utm9v8/rnW2um+njPG/dgZ7n+bgI3AEFyDTnANLNOB1UDfNo8fzlqt9V6ttRNIAxLd68zVWue5y8w/1MJKqZuUUuuVUus3/+sIk52d3aCq3cBAd1LoYJGx99/NhEcfYNTvf8feH5fg2LYDgCE3XMu+H5ey7rGnaa6rQ/l5+2zjzjK1P2TcyZtrV6Yoezs5P61k9FUXtjx2+mN/4Oxn7+Pk+37L9sXLKN6yw7c5dad/hMMu73Q6ceTvIfm06Zz29Gz8zCa2frnYtYSzmcbqGk557G5Sr7qE1a/9Hd3pa3QpNh0OyXeyTqWgJC0DU2gIoYn9O67W6aSxuoaJD93LoMsvIePNv3U9W5cyty9zNO2mc9v+9TUGg5E+Uyd2MZhv8gAog4FpTzzAyS8/TUVuPpV793k8v/OLrzEYDMRP8Vbmw7XVw2hTpKmujvWvvc2Iqy/D32Khqb6BHV98w2D39aS+Stmh+R6ifzjSssP/70LOfu1p+k6ZQM5i125qyz+/YsDZp7TMLIv2Dr0P6TbH2F+AQjubqdy1m/iTZzLu0Qcxmszs/s83LSWSLrmIk/78LDEnTWT/Dz95L3JneTrs9g5dxulspiRnD+c+cAvnPXwbGz75Fsf+4pZizY1N5K/LJGXKmI7rOLbAnWQ5iv2fUlji4og76yy2vfwy2199lcA+fVAGw8E3QlNNDUPvv58+l15Kzttve28/wvHX80H56zKJHZLcchqnT0gd+76OeyCDOnH/eqqjHVXUt/m5GbB24TWWA2cDjcD3wPuAEddsJO7fL9Jap7tvGjSrk3UYAId7drQz1YfJq9r8/4zW+u22BZVSs4DTgMla6xql1BLgaD6FtK8TP47qk5yL1vodXLOz3LLip8P2Cmabjbo2s4z1dgdmq9WjTIDN5jETWV/uwOQuc/CGBqbQUKLGjuZAXh7WwQMJiotl9B/vBFynzpZlZB5t/EPa/u1Sdv64AoCIlP7UlDlanqspd2CxhXm+t5BgGmtqcTY3YzAaqSm3e5Sx79rHmrfnMuu+2zxuMhIY7npPAWEh9JkwirKdu4geejTHBlx2Ll5K3k+unOHJ/akpa6272nI7AVbPnKaQYBqra1py1rZ5L5ZwW6fLK+Wa8YwY4Jq5Spg4lm1fftuyTPyE0a7TAFMSUUrRUFmFOfToT5s1h7f7m9sdHW5eYQ73bDt17rZTtG4jJZsyKE3PwtnYRFNdLZlvv8fIm28gwGYlepwrW1hyEkopGiurMHUh26FYwq3Uts3T7u/tKmOjtsyzTPu/R2d2L1tN4aYspt5/Z5cGgUcS0L4Oy+2Y2+UJsFmpa5e5/d/CPyiQ8CEDKcnYTEifBAD2/ryKkrRMJt57l9cyB9g6qb92dRwQbvV4T7XldgLceZ1Nzax/9R0SJk8kboLrg2xNcQk1JaUsffBJ9zodLHvoaaY/eu9R/W06Ywm3euSsLbdjsR6+LRzctpxNTUdcFqDvlAms/PObDLv0PMpz8tm3dhNZ8z+jsaYWlMLo70/KGbOOKf9/gw3/Xkb6t6sAiBvYj8pSR8tzlWUOgsOP7W/rLSabtV0fZ++w73P1g+Vtyrj2fUq59p2hya7+N3L8WPa0GWQeFD1pIpmvvH5cs5mZXy9j83crXesb0I+q0tbM1WUOgtpvf6HBNFS37veqyxwEues6OMJKQEgw/gFm/APMxA1LoTR/H9b4aAB2b9pMZHJfAq3eOTXSZLPR0Kb+GhwO/NvVsclma5k9A2i02/EPc+WNmjaNqGnTANj72Wcts3H+Nhu2MWNcp38mufYjTVVV+Icc+37Em/V80M6fNzJwmndmhQ9F6tj3dSx6pmM9lbcCsCulprt/vwY4OKtZCbRt4ctwnVa6SmtdAkTgmvHLdj8fAhQopfxxzWTSfj1a6wNAnlLqMgDl0tUr3r8FbnDPiqKUSlBKRQNhgN09wBwCnNRmmUZ3rqO1FUhWrjvyAlzexYydCknqT21RMbUlpTibmihas47I0akeZSJGp1K4cjVaaypycvELDMBsDaO5vr7lpi7N9fWUZ28hKMH1AffgDV2000n+l/8hYVaHyeguG3TmTM55bjbnPDebPuNHkbdsDVprSnfk4R9o6TCgUEoRPWwQu9e4rjHIW7aGPuNd7626tJzlL73D5N9eR2h8TMsyTXX1NLrfU1NdPYUZWwjr27VrrAacMZPTn5nN6c/MJn58KruWu3KW7cjD39J5zqhhg9i31pVz17LVxI9z5YwfO7LT5QOsYVgibFTuLwKgOHtryw1u4selUrJ5GwCVBUU4m5o63JjpSEKT+lPTpl0UrllH1BjPdhE1OpWCFa524diZi5/F1S4GXnYxM15+lukvPs3IW39N+NAhjLz5BtcyY0dTvsWVrbqwCGdzM/7HeRfRg6zJ/akqLKa62JV57+oNxI71zBw7diS7f3bVZ/nOPPwCLR0GSe0VpWez46vFnPSHW/Azm7yS9aCwpP5UFxVT467ngjXriW5Xz9FjUtnnrmf7zlz8LBYCrGHUH6iksboGgOaGBso2byU4PhZw3Swo99+LGXvXrRi9mNma7Jl3/+r1xLbLGzsmlT1t8voHuvJqrUn/+4cEx8eScnbrHQxD+yZw5hsvcNpLT3HaS08REG5lxhOzj3mACWDrpC3EjfPMGTd2JLvd21Z5m23rcMtWFbbO+hRszCA4zlXfMx/+I2e98iRnvfIkKWedzOALz/yfHmACjDt3Bje8ei83vHovA09KJevHta5rrLbmYQ4M6PZBZmhSose+r3jteiJGe+76I0aNatn3HcjJxS/QgtkahiksDHO4jZrCQgAcW7YSGO/qf2uKilqWL0tPJ9DdRo7VyLNncPlL93H5S/eRNDGVbUtc9Vi4LQ9TYECHD91KKRJGDCRnVRoAW39aQ9KEkQAkTkylYEsOzuZmGusbKN6+C1tC6/5vx/INXv3AHpSYSH1xMfWlrjouX7cO2yjPOraOGkXZqlVoranKzcVosbQcxD54s5n6sjLsGzcSPmECALbRo6ncuhVwndbpbG7GL/j49iPerGeA+upa9m/eSdLEkfiS1LHv67gnUkqfsH891fGcH3kd8JZSKhDIBX7lfvx99+O1wGRc117G4BpsAmQAxbp1Hv4hd5lduK7tPDhAXQD8TSl1B3AprgHoX5VSDwL+7uc9r5w+DK31Yvdpu6vcMwZVwC+Bb4BblFIZwDZcp8we9A6QoZTaeIjrMtu/Rq1S6jbgG6VUKbD2aPMdjsFoZNAvLyf9pVfRTidx06YQlBDPvp9cVZpw8gwiUkdQnpHF6vsewmgyMeSG6wBoqDhA5utvufI5ncRMmkCE+9qvojXr2Pej69hA1NgxxE7z7s1z48cMZ39aNl/e+ShGs4mTbvlly3M/PfsGk266msBwK2OuuoifX32PjIVfYkvsS8rJkwHI+ufX1FdVs+69BS31cNbT91JXUcmyF99xv6dm+k+d4Hm9ZhfFjh5BYVo23/zhEYwmE+NvvqbluZ+ff4Nxv7kai83KyCsvZs1rfyfr4y+x9u/TckOfwy0/5tr/Y+2b/8DZ1ERQdCTjb74WgKRZU1j/zocsvvcJDH5+TLjlui7PZBmMRgb/8nI2/tnVLuKnTyE4IZ49P7raRd9TZhA5agSlGVmsuOchjGYTw3593RHXmzBjCtl//4CVDzyOwc/IiBu7nu1wmVOvu5yVz7+OdjrpP3MyoX3iyfvBlTnp1BnEjB5BUXo23/3xEfxMJsbc1Fqf615/j9It22moquKb22cz5BfnkjhrKhlzFuFsamTFs647XoYPSGT0DVd1muFYMg+75grWvfAa2umkz4wphPSJZ7e7nvudMoOoUSMoychi6d0PYzSbSL3R9Xeud1SQ8bc54NRo7SR24jiiR7t2tps/XIizqYl1L7wKuG7+M+L6489sMBoZce0VrH7+NbR20tedN9+dN/GUGUSPGkFxehY/3v0wRpOJ0e685dtz2LtiDSF9E1j64FMADLnsQmJGHdU3WHU55+jrL2fFc55tIfd7V87k02YQO3oERWnZLHZvW+Pc29ahlgXIWvA5VQVFoBSBkeGM8VI7OB5zXrud6ZOHEmkLYeea13nipU+Ys3BJd8fykDJ+GLnrs3n7psfxN5s4587WXd6iR9/i7NuvJCQijPVfLGXNp99TZa/kvTueJXncMM65wzd1rIxGBlx9BZkvv4J2OomdNpWghHj2L3Htt+JnzSQ8dQTlmZmsvf9B91eYtPZxA6+6gi3v/B3d3ExAZGTLc3mffEZNYRHKoDBHhDPoGu/cWRag/7jh7N64mbm3PY6f2Z9Tfte63/vqyb9y8m1XERQexknXXMh3L/2DNfO+IiqpD0NPc+33wvvE0m/MUBb+/lmUUgw9bTIR/V1tu7G+gT3pW5l5yxVey6uMRvpdeSXb/vIXcDqJnDoVS3w8xUtddRw9cyZhI0dSkZVF5gMPtHy9xkE733qLpupqlNFI/6uuwi/IdUpk5NSp5M2ZQ9ajj6KMRpJ/9SuvnmFyvPUMkLcmnb6jhuAfYPZY9+KX/sH+rJ3UVVYx58aHmHDFOQxrs1xXSR37vo5Fz6S8ef62cF0/qrWuct9t9g1gh9b65cMtc6TTZXuauMDmIxfqYRqae/BJ64dQ0diT7xnWUZCfs7sjdFl9L2wXfr0ssr+xV3VvAPzlkne7O0KXvfn99d0doUtWFZuPXKiHGW5r6O4IXbautHfV84TI+iMX6mGkjn3vzuFn9LI9H1z647ITtvP75JQZPbJ+fPE9mf/rfqOUug4w4brJ0NtHKC+EEEIIIYT4L9GTb8hzosgg08vcs5aHnbkUQgghhBBCiP9WMsgUQgghhBBCCC/pXRc8+YbUgRBCCCGEEEIIr5GZTCGEEEIIIYTwEkMP/mqRE0VmMoUQQgghhBBCeI3MZAohhBBCCCGEl8jdZWUmUwghhBBCCCGEF8lMphBCCCGEEEJ4icziSR0IIYQQQgghhPAimckUQgghhBBCCC+RazJlJlMIIYQQQgghhBfJTGYP8M3Xdd0doUsiRwR3d4QuiwnvfYeUqhp6V+bSkubujtBlIxO7O8F/v2B/Z3dH6LI3v7++uyN02W2nvd/dEbrk3Pdv7e4IXbaqOKC7I3TZwNCG7o7QJVEBva+/SApu7O4IXdIb67g3ku/JlJlMIYQQQgghhBBeJDOZQgghhBBCCOElck2mzGQKIYQQQgghhPAiGWQKIYQQQgghhPAaOV1WCCGEEEIIIbxEZvGkDoQQQgghhBBCeJHMZAohhBBCCCGEl8hXmMhMphBCCCGEEEIIL5KZTCGEEEIIIYTwEvkKE5nJFEIIIYQQQgjhRTKTKYQQQgghhBBeIjOZMpMphBBCCCGEEMKLfDaTqZT6D3CV1trhq9do93pLgD9prdcffG33U1dprd90l4kHXtVaX3qMr3FC39PhzBwYycPnDMVoUCzcsJe/LsvttFxqQhif3TyZ3y1M4+vsQpIjg3j98tEtz/e1BfLyDzt4b1W+T/NOjrXxp7HJGJTi89xC5mzZ6/H8Wf2juG5oXwBqmpp5dv1OdjiqAQj2N/LQxEGkhAWiNTy+djuZZZU+zQswLsLKTUNcmRfvLeLjfM/MfQIt3DViIANCg/lgxy4+3bWv5bn3po+ntqkZp9Y0a81da9J9nre9iVFWbh+ejEHBv3cXMS9nn8fzpyVEcVVKAgC1Tc28lJlDTmXNCc04LcHGfZNSMCrFP7cX8m7mHo/nT+4Xwe1j+qM1NGnNc2ty2Fh8gNggM89MH0yExYTWmo+3F/DR5v0nJPOB7Cz2LlqAdjqJmDqd2LPO9ni+rrCAXXPep3bPbuIuuIiYM85seW7XB+9zIDMDv5AQhj78mOQ9hPLMLHLnL0JrJ7HTp9H3nLM8ntdakzt/IeWZWRhMJgbfcD3B/ftRU1jI1rf+1vreSkrpf9H5JJx+2gnLfjDf9+/8k5wNm/E3mzj3zquJHdC3Q7kNXy1j3RdLcBSUcsdHTxMYFnxCcx7OWy/czNmnjqGk7ADjT7+nu+MAUJmdxf5FC0A7sU2dTvSZHdvy3g/ep27PbmIuuIio011tuaG8nL1z3qPpQAUoRfi0GUSecmLaRG/b/rTWbPrgYwrTsjGaTEy85RpsSf06lKsqLmX1a+/RUFWDLakvE2+7DqOfHwf2FbLu7Y+w5+9hxP+dz5DzfF/PWmu+eftTdqzbjL/Zn4v+cDVxnWxva79cxurPl2IvKOXu+U+1bG8ZP61nxcffA2CymDn3t/9HbHKCzzP3pnrujXXc3WQWz4d1oLU+p/1gTLn4vN7bvLYVuK3N4/uPdYDZbr3dyqDg8fOHc/0H6zn91eVcMDKOAVEdP5wYFNx35mCW7ShpeSy3tJpz3ljBOW+s4Lw3V1DX2My3Wwp9nvfe8SncsTSby77ewJn9okgKDfQos7+qjpt+yODKbzby9+zdPDBhQMtzfxqbwsqCci79zwau/HYjeQd8PxAyALcOTeGRjdncumIjM+Ki6Btk8ShT2dTE21tz+TR/X6fruH99JrevTuuWAaYBuGtEMveszea6JZs4NSGK/sGe+Qtq6rhjVSY3LEvjgx17+FPqgM5X5quMCh44aQC3LM7igs/Wc05yFClhnu1izX47l/xrI7/4YiMP/bydx6YOAqDJqXl+XS4XfLaeK79K48oh8R2W9QXtdLJn/jxSfncnQx95HPu6tdTu9xzcGgOD6HP5FUSfdkaH5SMmTyHl9jt9nvOg3pYXXJlz5s5n+O9vZ9wTj1KyZh3V7TLbM7OoLSpm/NNPMPDaX7Lzw7kABMbGMvbRhxj76EOMefgBDCYTEWPGnND8ALkbNmPfX8LNbz/EWb+9nG//uqjTcglDk7jiid8SGh1+ghMe2YcfL+XCa5/t7hgttNPJ/gXzSPrdnQx8+HEq1q2lrsCzXfgFBhH/f1cQ2a4tK6OBuF9cxqBHniDlntmULf2pw7K+ytzbtr/CtGyqCks4+6VHGX/jVWx4b0Gn5TLmf86gs0/hnJcfxT8okLyfVgJgCg5izHWXMfjcU09Y5p3rN1O+r4Tb332Q8++4gn+//nGn5foOS+bap28jrN32ZouJ4Prn7uDWN+9jxhVn8tWrC32eubfVc2+sY9H9vDLgU0p9rpTaoJTKVkrd5H4sXykVqZRKVEptUUq9CWwE+iql7lFKZSql0pVSz7rLj1ZKrVZKZSilPlNK2dyPL1FKPaeUWquU2q6Umu5+3KKUWuAuvxCwtMmTr5SKBJ4FUpRSaUqpF9xZstxlApRS/3Dn2KSUOtn9+PVKqU+VUt8opXYopZ5vv9427+lv7ve8WCllcZeZ4M60yv2aWd6o47ZG97Gyq6yaPfZaGps1X2YWcMbQ6A7lrj8pka+zCymrbuh0PVNTItlVXsM+R523I3oYHh7Cnso69lXX0eTULN5dwswEzw4oo6ySysYmADJLK4m2mAEI8jMyJiqMf+UWAa7BRVVjs0/zAgwKC2F/TR2FtfU0ac2ywhJOio7wKFPR0MiOA1U06Z73XUhDrSHsq66joMaV/8d9JUyL8azzbHtlS11mOyqJsphOaMaRkSHsqaxlb1UdjU7Nf3JLOLmfZx3XNDlbfrb4GThY06W1DWwpq3KXaSa3ooboIN/nr8nPwxwdhTkqCoOfH7YJE6jISPMo4x8aSlBiEspo7LB88MBBGAODfJ7zoN6WF6AyN4+A6Ggs7sxRE8dTvsnzQE1ZWjrRU05CKUVoSjJNNbU0OCo8yjg2b8USHUVApGebOhF2rM5kxCkTUUqRMCSJ+upaqsorOpSLTemLNebE5zsaK9ZupdxR1d0xWtTk52GKisLkbhdh4ydwID3No4xfaCiBnbRl/zArln79ATAGBGCOjaPR4TghmXvb9rdvQwaJ0yehlCJiYBKNNbXU2j3brtaa4uzt9JnkOoCTOH0S+9ZnABAQFkJ4Sv9O34+vbF2dReqpE1BK0WdIInXVtVR2sr3FpfTpdHvrOywJS4jrIGWfIYkcKHP4OnKvq+feWMfdzaD0CfvXU3lrVvEGrfU4YDxwh1KqfQsbDHygtR4DDAMuAiZprUcBBwdxHwD3aq1TgUzgkTbL+2mtJwJ3tXn8VqDGXf4pYFwnue4DcrTWo7XWd7d77rcAWuuRwJXAHKVUgPu50cDlwEjgcqVUx3MCYCDwhtZ6OOAAfuF+/B/ALVrryYBPRkMxoQHsr2gdGBYcqCMmNMCzTIiZM4fFMHft7kOu5/yRcXyR4fujudEWM0U19S2/F9c2tAwiO3NhcgwrC+wAJAQH4Khv5JFJg5h75hgenDCQAKPvT0KICDBRWteaubSungjz0Q9iNPDEuBG8ctJozkqI8UHCw4u0mCiuaz24UFLXQORh6vzcvjGsKXacgGStYgLNFFS31nFRTT0xnQwUT+0XwZcXj+evp4/goZ+3dXg+PtjM0PBgMkp8fwp1g92BydY6WDdZbTTaHT5/3WPV2/IC1DscmMNtLb+bbDbq2w0IGuwOzOFt3pfNSr3D7lGmZO06oiZO8GnWQ6ksqyAk0trye0iElcqyjh/IxNFrcjjwb9OW/W22YxooNpSVUrdnD4GJSV5Md4jX6oXbX629Aku4teV3S7iV2naZGyqrMQVZMLgHOIERtg5lTqTKUgdhUdaW30Mjw6gsPbbtbdPi1QwYN9RLyQ6tt9Vzb6xj0f289Wn9DqVUOrAa6ItrANbWLq31avfPpwH/0FrXAGity5VSYYBVa73UXWYOMKPN8p+6/98AJLp/ngF85F5HBpDRxczTgA/dy28FdgGD3M/9oLWu0FrXAZuB/p0sn6e1TmubSyllBUK01ivdj8871IsrpW5SSq1XSq2v3Ph1l4J3dsMq3W427eFzh/Lst9twHuIAh79RcdqQaP6T5dtTZYFOAx/quMu46DAuTI7ltfQ8AIxKMdgWzCc7Crj6203UNjVz/bDOxvzedbw3Bbt7bQZ3rk7j4Y3ZnNsvnuG2UK/kOlqd5j/EjOuYiDDO7RvD21vyfRmpo87aRScRf9hdxvmfref2HzZz+9hEj+cC/Qz85eRhPLs2h+oTMMPdacvt0XeQ6215OXTn0LZIp2259Y05m5ooS08ncnxnxx5PhI75VE+v956uk795V+u0ua6OXW//lbjLLsdosRx5gePWG7e/w29bALrT9t3D3tgx5MlL38Gmxas57YYLfBConf+Geu7pddzNDOrE/eupjvvGP0qpWbgGjpO11jXuG/AEtCtW3XYRjupjhIeD0x3NeGY+njniw/1Z6tv83P41D1XGcoR1etBavwO8A5D44Nddeh+FB+qID2ut4rjQAIor6z3KpCaE8drlowCwBZqYNSiKZqeTxVuKAZg1MIqsggOUHuJUWm8qrqknJrB1Fi3aYqKktr5DuQFhgTw0cSB3LM2mosF16mxxbT3FtfVkl7tmqX7YW8r1Q30/yCytayAyoDVzZICZsvqjr6tyd9mKhkZWFZcxODSEbPsBr+c8lJLaBqIDWmcFowJMlNZ1zJ8cEsjdqSncs3YzB9ynK58oRdX1xAW11nFMoJnimkPX8YaiCvqGWLCa/XDUN+GnFH85ZRj/zi3m+11lJyIyJpuNBnt5y+8NDjv+VusJee1j0dvyAphtVurLW2clG+x2zO0ym8Nt1Je3eV92h0cZe2YWwf36YQo7cQd3Nvx7GenfrgIgbmA/KksdLc9VljkIDg87YVn+G/nZbDS2acuNdjt+YdajXl43N7H7nb9inTiJsDFjfZCwo96y/e1YvJS8n1YAYEvuT225o+W52nIHFptn2zWHBNNQXYuzuRmD0UhNmZ0A64lt32u/XM5G9/YWP7AfFSWOlucOlFYQEtG1bb8obx9fvjKfqx+/hcBQ35yi3NvquTfWsehZvDGTGQbY3QPMIcBJRyi/GLhBKRUIoJQK11pXAPaD11sC1wBLD7UCt2XA1e51jABSOylTCYQcxfKDgH5Ax3PxukBrbQcqlVIH6+CK41nfoaTvqyAxIog+Ngv+RsX5I+P4bmuxR5npLy5lmvvf19mFPPTl5pYBJsAFqXF8eQJOlQXYXF5J35AA4oPM+BkUZ/SLYtm+co8yMYFmXpg2jIdXbWN3ZW3L42V1jRTV1NM/xHXUeWKMldwK39/4Z/uBShICLcRYzPgpxYzYKNYUlx95QcBsNGBxn95iNhoYG2FlV1X1EZbyrq0VlfQJshDrzn9KQhQrijzzRweYeGL8EJ5K28Heat9el9uZrNJK+oVaSAgOwN+gOCc5ip/2eA4W+4W0HkwZGhGMv0HhqHcNhh+fNohcRw1zsju/8ZIvBPZPpL64mPrSEpxNTdjXrSMsddQJe/2u6m15AUKSEqkrKqaupBRnUxMla9cTPtozc8SoURSvXI3WmgM5uRgDLZjafPgqXrOOqEkn9lTZcefO4IZX7+WGV+9l4EmpZP24Fq01+7bmYQ4MkEHmcTrYlhvcbbli/TpCj7Ita63Z++EczLFxRHVygx1f6S3b38AzZnLGM7M545nZJIwfRf7yNWitKduRh7/F0mHwo5Qietgg9q7ZBED+8jUkjO/sI5jvTDx/Ore8fg+3vH4PQyaPJOOHda6/89Z8zEEBhHRhe6soLuf/2bvv+Kiq9I/jnzOTTCZ9Jr1QktB7703sFXTXhgXbrnXXsmtF7Ap2QXdXZXdVRECKrGIvIEWkQyo9JBAgPZn0Puf3xwzpIIEJSfw9b195mcycO/c7h+femTPn3jtLXvyAqx6+mcBOTa9v4SodrZ87Yh+3J4az+NNeueIrTL4DFWM4UwAAx1lJREFU7lZKxeMYpG06WWOt9XdKqcHANqVUJfANMAO4BXjPOfg8CNz2G+t9F/jQud5YYEsz68pVSm1wXnznW+Cf9e7+l3N9CUA1cKvWusIFhyLcAfxbKVUCrAFcfiJOjV3z9Fe7+PiWERgNiqXbj7A/q5gbRzhm+BZuTTvp8mZ3A+O7BzHjiyRXR2tWjYbXtifzzqT+GA2KlQczOVhYyh+7hQHwWXIGf+7XBX8PNx4b3t25jGb6D7GAY9kXxvTC3WDgaHEZz23e3+qZ7Rre3ZPMC0P7Y1Dw49FMDpeUckknR+Zvj2RgNbkzZ/RgvNyM2DVM7RrB3Rt24G9y48nBfQEwKlibns32s3ySe42GOUkHeX1UPwwKvknLIrW4jCldHPlXHs7glp5d8Hd356H+MbXL3PXL2bsSbo2GlzYdYN6F/TEoxf/2Z5BsK+XaXuEALN2bzgVRQUzpFkq1XVNeY+fhNbsBGBrix9TuoezNK+azKY5ZiTk7Ulh/JP+E63MFZTTS6bobSH57DtquCRw7Ds+ISHLWrQEgaOI5VBUUsHf2i9SUl6OUInv1T/R55nmMnp6k/Gcexfv2UV1cTOLjjxB+xRQCx004+Ur/H+U9nrnbjdeT+NZctN1O6PhxeEdGkL7G8blj+DmTsA7sT15CAtuemInBZKLn7bfULl9TUYlt1256TL+pVXOeTLfhfTm4LYn373wedw8Tlz5wY+19S599j0v+Og3fQH+2rVzL5hU/UZxfxAf3v0zMsL5cev8NJ3nks2f+O39lwpg+BFl9ObD5H7zw5nLmL1nTZnmU0UjE9TeQ8s4csGusY8dhjogk11nLgc5aPvDyi9jLy0Epclb/RM+nn6f86BFsmzdhjoxk/0uOrwIJnfoH/PoPaPXMHW37Cx/cj/TYJL556FncPEyMuKtuO1r3yj8ZceeNeFotDJx2JZve+YDEZV9i6dqZ6HPGAFBmK+Cnma9SVeZ4Pvu/+5mLX52Ju1frHZ7cY0Rf9m/dxTt3vIC7h4mpD9VtQwuffo8pDzi2t81frGXD8lUU5xfx7n2v0GN4X6Y8OI21i76nrKiEr//luGKqwWDgzrcfbrW80PH6uSP2sWh7qvlzW8TpUkr5aK2Lnb8/DoRrrU96DfKWHi7b1oL6t5/vcjtVoQHt+KD1Eyiu7FiZc7LPxjmRrjUgqq0T/P75uNt/u1E7My6k6eH87d2953/U1hFa5LKP7mnrCC3m1rF2yQD08Gv9U2JcqY/l7J624Qq7ba32lfOtoiP28Q3dLu5wW9/Dm1eftff2r486t132T8faMjqGy5RST+Do20PArW0bRwghhBBCCHG2tOcL8pwtMsh0Ma31EkC+ZVYIIYQQQgjx/1J7Pl9UCCGEEEIIIToUpfRZ+zm1POpipdRepdQB5+l8je9XSqm3nffHK6XO+DLcMsgUQgghhBBCiN8hpZQRx8VPLwH6AtOUUn0bNbsE6OH8uRPHBVbPiBwuK4QQQgghhBAu0s7OyRwJHNBaHwRQSn0KTAV21WszFfhYO64Iu0kpZVFKhWut0093pTKTKYQQQgghhBC/T5FA/e83POK8raVtWkRmMoUQQgghhBDCRc7mLJ5S6k4ch7geN09rPa9+k2YWa3wy56m0aREZZAohhBBCCCFEB+QcUM47SZMjQOd6f3cCjp1GmxaRw2WFEEIIIYQQwkUMSp+1n1OwFeihlIpWSpmA64GVjdqsBKY7rzI7Gig4k/MxQWYyhRBCCCGEEOJ3SWtdrZT6C/A9YAQ+0FonKaXudt7/HvANcClwACgFbjvT9cogUwghhBBCCCFcpJ1dXRat9Tc4BpL1b3uv3u8auM+V65TDZYUQQgghhBBCuIzMZLYDXjHebR2hRUpKzuhiU23CM6TjZTaodvYx2G8YO6CqrSO02I5cj7aO0GL9LJVtHaFFcsqNbR2hxTZmdby6uOyje9o6Qot8fesZf8/3WffnJXf+dqN2JrOsY73N62epbusILSZ9LJrT3mYy24LMZAohhBBCCCGEcJmO9fGLEEIIIYQQQrRjHe8YHteTmUwhhBBCCCGEEC4jg0whhBBCCCGEEC4jh8sKIYQQQgghhIsYVMe74KSryUymEEIIIYQQQgiXkZlMIYQQQgghhHAR+QoTmckUQgghhBBCCOFCMpMphBBCCCGEEC4iM5kykymEEEIIIYQQwoVkJlMIIYQQQgghXMQoM5kykymEEEIIIYQQwnVkJrMZSqmPgK+01ssb3f4f4E2t9a42CVbP+EgrT4zuhlEplu/L4D/xaQ3uP7dLIH8d2hWtoVprXt6czI7MQsK8PZg9sRdBnia01izdm84nu46dlbwzRnfDYFAs39t83vuHdcWuocaume3MazIqFlw2CJPBgJtB8X1KDv/YeajV8wIUJSVybOmnoO1Yx00g5KJLGtxfnpHOkY8/ojztMKFTriT4gosAsFdVcfCNV9HV1Wh7Df5DhhF6xdSzknlooJU/94rBoBQ/Hs1geeqRBvd38vLkgX496ebnw4IDqfzv0NHa+/4zfgRl1TXY0dRozd82x7osV0ZcEvELlqHtmqhzxtJrykUN7tdaE//xMjLikjCa3Bl213Ss0V1OumzSsi9J3x6HUgY8/HwYdvd0PK0WAAoOH2HnfxdTVVaOUorJLzyG0eTukucyPMjC3b1jMCrFt0cyWZrSsI87e3vyt/496O7nw/z9h1ie6ujjTl6ezBjUq7ZdmJeZBQcO879Drt/+suKT2PXJUrRd03nSOLpf0bS/d32ylKy4JIweJgb9eTr+UV0oy80jdt58KmyFKIOiyznjib7oXAD2rfiKw2t/wcPXF4Be10wlZFB/l2cHsCUmcmjJErTdTsj48URc0nDb01pzaMkSbAkJGEwmut16K95duwKQsWoVWevXg9YET5hA+Pnnt0pGgLyERA4sXorWdsInjKfLpRc3yZm8eAm5CYkYTSZ63X4rvl0ddV1dWsrejxZQcvQoSil63jod/+7dSPnfF+TGxoFSmHx96XX7rXg469qVTnf/VpmXx5H5H1BdWABKETB+IkHntl4fn6r3XruLS84bQnZuIcMveLTNcmTHJ7Fn0VK03U6nieOIubxpTexZuJTseEdNDPjTLfhFdaGmsoots1/HXl2NrrETNmIo3a+6AoDCQ2nsmr8Ie1UVymigz/RpWGKiXZa5Neo49YsvSV/3C+6+PgBE/+FKAgcOcFnmxvm+fX8F+7fuwt3DnSv/diMR3Ts3abf5y3Vs+nwteek5PLr4Jbz9Hdn2bExg9YKvUQYDBoOBi++6iq79urk0o/Rx6/dxeyPnZMogswml1An7RGv9p7OZ5UQMCmaO6c6fvk8gs6SCJVOG8PPhXJJtpbVtNh3LZ/XhXAB6Wr15c3IfLl+xjWq75tUtB9mdW4yXm5HlU4ew8ZitwbKtkfepsd254ztH3qWnkPetc/tw2WfbqKzR3PZNPKXVdtyU4pPLB7H+SB5x2UWtlhdA2+0c+3QR0fc/hJvVSvLLL+E3cBDm8IjaNm5e3kRcez2FcbENllVubkQ/+HeMZjO6pprk11/Ft19/vGJad4dqAO7u3Y2ndiSSW17Bm6MGszk7j7SSun4uqqpm3t5kRgcHNvsYT26Pp7Cq2qW5tN1O3EdLGP/E/XgGWPj5qVcIHzoQv07htW0y45IozsjiwjeeJf9AKrEffsrk5x896bI9Lzufftc43oQd+O5n9qz4hiF33IC9poat//qI4ffciqVrJyqKijG4GV3yXAzAfX268cS2RHLKK3lnzGA2ZeVyuKSstk1hVTXv7j7I2JCGfXyktIx7N8bWPs7Cc0ayITPXJbnq03Y7SR9/yqhH78ccYOWXZ14mdOhAfCPr+js7PomSzCzOee05bMkpJH60mHHPPoYyGuk77Y/4R3WhuqycX56eTVD/PrXLRl90Ht0uvcDlmRvnT120iN4PPYTJaiVp1iwsgwbhFVG37RUkJlKemcmgF1+kOCWFlIUL6T9jBqVHj5K1fj39nngCg5sbe+bOxTpgAObQ0FbJuX/hYgb+/UE8rFZ2vDCbwMED8a6XMy8hkdLMLEbOeoGigynsX7CQoTOfAODA4iUE9O9Hv3vvwl5djb2yEoDOF19I9FWOD6WO/LSaQ19+Tc/pN7o8+2nv34wGwv94DZ5dulJTXs6B2S/g06dvg2XbwoJla3lv/vf856172yyDttvZvWAxwx95AHOAlY3PzSZkyEB8Iuv6JifeURMTXnmeguQUdn28iNFPP47B3Y0Rjz2Em9mMvbqGLbNeI2hAPyzdY9i3dAXdrryM4IH9yY5LYN+SFYx84u8uy9wadQzQ6YLz6HzxhS7JeTL7t+0i92g29/9nJkf2HuKrfyzjzjl/a9KuS98Yeo7sx0eP/aPB7dGDe3LP6P4opchIOcqy2R/x13lPuiyf9HHr97Fon36Xh8sqpaKUUnuUUvOVUvFKqeVKKS+l1NNKqa1KqUSl1DyllHK2X6OUmqWUWgs80OixXlBKfaSUMjjbDXfeXqyUekkpFaeU2qSUCnXe3s3591al1PNKqWJXP78BQb4cLizjSFE5VXbNtwezObdLwze0pdX22t893Qxo5+85ZZXszi12tqnhoK2UEC+TqyM2MDC4Yd5vfiOvl3td3vr3uRkU7gbV4L7WUpqagik4GFNwMAY3N/yHj2jyZsvNzw+vqGiUseEARimF0WwGQNfUoGtqQLX+R1o9/H1JLy0ns6ycaq1Zl5HNqOCABm0KqqrYX1hMtT4bveiQl5yKd2gw3iFBGNzc6DR6GOnb4xq0ObY9ni4TRqGUIqBHNFWlpZTlF5x0WXcvz9rlayoqavs4K2E3/l0isXTtBICHrw/K4JpdXS9/X46VlpNRVkG11qxJz2ZMo8FkQWUV+36jjwcHWkgvLServMIlueqzJafiFRKMV4ijdiNGDydzR8P+ztwRR+S40SilsHaPoaq0lHJbAWaLP/5Rjk/P3TzN+ESEUZ5vc3nGkylOScEcEoLZue0FjBhBflzD/PmxsQSNGYNSCt+YGGrKyqi02ShLT8cnJgajhwfKaMSvZ0/ydu5slZyFB1PwDAnB05kzZORwcnc2zJkbG0fYWEc/+3WLobq0jApbAdVlZRTs20/YhHEAGNzccPPyAsDNs66u7RUV0Aq7jjPZv7n7W/Ds4pg1NprNeISFU2WzuT5kC23Ysoc8m8tfbluk4GAqXqEhtdte+KgRZO2Mb9Ama2c8Ec5tz9I9hipnTSilcKv3umGv/7qhFNVl5QBUl5W7dGa7ter4bNqzKZHB541AKUXn3lGUl5RRlFfQpF14t05YQ5t+wOrh6YHz7SBV5ZUuf72WPm79Pm6PDEqftZ/26vc8k9kLuENrvUEp9QFwL/APrfXzAEqpBcDlwJfO9hat9STnfR85//8q4A/cprXWquFG4Q1s0lo/6Wz3Z+BFYC4wV2u9WCl1d2s8sVBvDzJK6t6cZpRUMDDYt0m787oG8tCwaAI93bn7h8Qm90f4eNAn0If4Vp4VDPFqmDeztPm853cN5KHh0QR4unNPvbwGBcunDqWLnyeLdx9r9bwA1TYb7ta6AZq71UppSsopL6/tdg7MfoHK7GwCJp2DV3RMa8RsINDDg5yKun7Oraikp1/Tfj6Z54cOQKP57kgG3x/NcEmu8jwbnoHW2r89A6zkJaf+ZpvyfNtvLpu09AsOr9+Mu5cnE558EIDi9CxA8cvL71BRVEzn0cPoeYVrPukNNJvIrjcwzCmvoLelZX0McE5YMGsysl2SqbHy/IZ9Zg6wYktuWLvleTY8Axq2Kc+zYbb4195Wmp1LwaE0LN2iam879NMajm7YjH9UF/re8Efcvb1dnr/SZsMUULftmSwWShpte5U2Gx7Wuvwmq5VKmw2vyEiOfP45VcXFGNzdsSUm1h5G2xo5Per1oYfVSmGjnBX5NjzqPRcPq4VKWz7KYMTd15e9H8ynJO0IPlFd6D7tOoweHgCkrPiczF83YfT0ZNCjTWcLztSZ7t+Oq8zNoTwtDa8o1x262ZGV5+djrr9dWS3YDjaticZtyvNteFj80XY7G5+ZRWlWNp3Pm4Slm6Nfe99wDdtff5t9Sz5D2+2Mmum6w4Fbs46Prl5D5sZN+HbtSsx1V7fK/gKgKMeGX7Cl9m+/IH8KcwrwDfA/8UKN7P41jp8++ooSWzE3PnenS/NJHzu0Zh+L9ul3OZPplKa13uD8/RNgPDBZKbVZKZUAnAv0q9d+SaPln8Ix8LxL62anJCqBr5y/bweinL+PAZY5f190onBKqTuVUtuUUtvy16481efkWPYU2606lMvlK7bxl592cf+wqAb3ebkZmHtuX2ZvTqakqqZF62+p5vI216M/Hcrlss+28defdnH/0Kja2+0a/vD5DiZ/uokBQb70sJ6FT/GaCdiSD96UwUCPJ5+h96xXKUtNpfzo0d9e6Aw1288tWP7RrXE8uHknz+5I4rLO4fSz+LkkV7MZVOM2zbRSJ5i1rrdsv2uncsk7s+g8dgTJP6wFwG6vIXdfMiPuu41JT/+dY9viyErcc5rpTxobaL6WT8ZNKUaHBLAuI8clmZpqLpD67Rb1mlSXl7P9nffpe+M1uDtn1rqeN5HJr7/AhBdm4GHxZ9eiz1wXuUG45mvht9oopfAMDyf84ovZ89Zb7H37bbw6dXLZLHbTnKfSpvl/C22voejQYSImT2LYszMxmjw4/M13tS2i/3Alo19/mdDRIzm26meXRT5ZrpZOLNSUl3Po/XcJv+Y6jPVmX/9fa650G297zda3838GA2NfmMmkN2dTcDCVoiOO14201evoNe0aJr05m943XEPiBwtaNXPTNi2v44hzJjHq5RcZ9sxMTBZ/Di5Z3sxjuEbz+7OWFXSfsYP467wnuf6pO1i94BvXBDtO+hho5T5uhwzq7P20V7/nQWbjbUID/wKu1loPAP4NmOvdX9Ko/VZgmFIqgOZV1Rt81tDCWWGt9Tyt9XCt9XDrpCktWZSMkgrCvD1q/w7z9iCrtPKE7bdnFtDZ1xOLhyOim1LMObcvXyVn8dMh158T1lhmacO8oV4nz7sto4DOfnV5jyuqrGFLho3xkSf6J3EdN6uVqvy82r+r8vNx87e0+HGMXl549+hJ0a6mM8mullNRQZBHXT8HepjIqzj1wzHzKhz/JgVVVWzMyqWnf8tn6JrjGWChLDe/9u+yvHw8Lf6N2libtDFb/E9pWYDOY0dwbOvO2scK6t0DD18f3DxMhA7uhy01rckypyOnvJJgc10fB5k9yK04cS03Z0SQlQOFxdgqq1ySqTGztWFfluflY7Y27m8LZXkN2xw/BM9eXcP2t+cROWYk4SOG1Lbx8PdDGQwog4Eu54zHdjC1VfKbrFYq8+q2vUqbDXeLpUmbivy6/JX5+bj7O55jyPjxDHjqKfo+8ghu3t6tcj6mI4OFinp9WJGfj0ejnB4BVirqPZeKfBsmiwUPqxUPqxU/58VbgoYPpfjQ4SbrCBk1kuztrj/c90z3b7qmmsPz3sUychT+Q4a6PF9H5TgioN52lW9rcmhrc23MjerG3duLgN49yUlIAuDYho2EDndsi6EjhlHgwm2vterYVG9/ET5xPIUprssMsPnL9bz7l1d59y+v4hvgT2G2rfa+wpwCfANP70PSqAHdyUvPoaTAdYdeSx831Bp9LNqn3/Mgs4tSaozz92nAL87fc5RSPsDVv7H8d8DLwNdKqZa8294E/NH5+/UtWO6UJeYU0dXfk0gfM+4GxSUxwfx8uOFgsYtv3fi5T6AP7gaFrcJxQZcXJvTkYEEp85Naf3YNICG7iK5+dXkv/Y28fevltZrd8TU5zgnyMBoYE2ElpaD1LlJ0nFfXKCqysqjMycZeXU3Btq34DRx0SstWFxVRU+rIaK+spHjPbjzCwlozLgD7C4uI8DITavbATSkmhgWzJTvvtxcEPAwGPJ3nXnkYDAwJtHKo2DX9bI3pSnFGFiVZOdirqzmyaTvhwwY2aBM+dACH129Ga03e/hTcPT3xtPqfdNnijKza5dN3xOMT7ujj0IF9KUg7SnVFJfaaGnJ278c30jX9v7ewiEgvT0I9HX18Tngwm7JOrY+POyc8mDXprXOoLIB/TFdKMrMozXb02bFN2wgd0rC/Q4YM5OiGTWityT9wEDcvT8wWf8dVfv+7AJ+IMGIuaXjF0HJb3fk3Gdtj8e3UOhd68YmKojwri/IcR/68rVuxDmq47VkGDSJn40a01hQdPIjR0xOT801bVWEhABW5ueTt2EHgiBGtktMvOoqyzCzKnP2ctWUbgYMb5gwcNIiMXx39XJjs6GcPiz8mf388AqyUZjgOSbft3oNXhOPiSqWZmbXL58bF4RXu+n3HmezftNYcWTAfj7Bwgs9v/QuOdCR+0V0prbftpW/eSkjjbW/wQI45tz3bgYO4eZrxsPhTWVhElfMibTWVleTu2oO389/ew2Ihf88+APJ278U7NMSFmVunjivq7S9ydsTiHena/cWoKyZwzz8e5Z5/PEqfMQOIXbUVrTVpe1Ixe5tbdBhn7rHs2hnmYwfSqKmuwcvPdYedSh+3fh+3R8az+NNe/Z7PydwN3KKUeh/YD7wLWIEEIBXHTOVJaa2XOQeYK5VSl57ieh8EPlFK/R34Gmh6ZvQZqtHw0sYD/Pui/hiU4n/7MzhgK+W6Xo4dz5K96VwQFcTU7qFU2zXlNXb+vmY3AEND/ZjaPZS9ecWsmOr4BHrO9hTWHck/4fpckffFjQf4z8WOvCv2OfP2dubdk86F0Y68VXZNRY2dv/3syBvsaWL2pF4YFRiU4ruD2axJa9mb+tOhjEYirr+BlHfmgF1jHTsOc0QkuevWABA48RyqCgo48PKL2MvLQSlyVv9Ez6efp6qggCPzPwBtR9s1/sOG4zfg1N7AnQm7hvf2JvPcUEc//3Qsk8MlpVzcyfFG5bsjGVhM7rw1aghebkbsGqZ0ieTeX7fjZ3LnyUF9ADAqxdqMbHbkuqYmDEYjg2+9jg2v/ANtt9N10hj8OkVw8Kd1AMScP5Gwwf3JjE3ih789g9FkYthdN590WYDETz+nOD0TlMIrKIAht98AgMnbix6XnMvPT72CUhA6qB/hQ1xzWXe7hn/uTmbWsP4YFPxwNJNDJaVc5uzjr49kYDW5886YwXi5GdEaruwawZ2/7KC0pgYPg4GhgRbm7jrgkjzNMRiN9J9+PVtefQet7XSaOBbfThEcWu3o767nTiRkUH+y4xJZ88jTGE0mBv5pOgD5+5I5umEzvp0jWT/zJaDuq0r2fLqCwsNHQCk8gwIYcJtrr3h6nDIaiZo2jb1z5qDtdoLHjcMrIoLMtY7DoUMnTcIyYAC2xETinnwSg8lEzK231i6//733qCopwWA0EnXDDbi10jlKymik+43Xk/DWXLTdTtj4cXhHRnBsjSNnxDmTCBjYn7yEBLY8MdP5tQS31C7f44br2T3vv+iaGsxBQbX3pSz/H6UZmSiDwiMwgJ43u76fz2T/Vn70CLbNmzBHRrL/pecACJ36B/z6t85XJ5yq+e/8lQlj+hBk9eXA5n/wwpvLmb9kzVnNYDAa6XPTdWx//W203U7khLH4REaQ5tz2Op87kaBB/cmOT2T9o09h9DDR/w7Hv3tFQQEJ/56PtttBa0JHDiNksGOA2u+2m9izcCl2ew1Gd3f6unDba606PrjsM0rS0kApzIGB9Jh+k8syN9ZjRF/2bd3F3DtewN3DxJUP3VB73ydPv8eUB6bhF+jPpi/WsmH5Korzi3j3vlfoMbwvUx+cxq4NccSt2orRzYibyZ1rHr+lxYeCnoz0cev3sWifVPOnG3ZsSqkoHN9z2Tpf4nbydXsBZc4LBV0PTNNan/RLEvt+sK5D/SN0xJLp1wGvS1FR07F2wP2tLTtstD3Ykevx243amX6WjtXPOeXt+XPW5pmNHW8nl1fZsQ5M+vrWd9s6Qov9eUnHu1hJeQd7HTk3vLytI7TY6nTzbzdqRzpiH1/f7eKOVcjAv3b9cNZeSO7te2G77J/f80xmWxkG/MP59Sg24Pa2jSOEEEIIIYQ4W9rzBXnOlt/lIFNrnQqc9VlM57rXA61/bKQQQgghhBBCtEO/y0GmEEIIIYQQQrQFg+p4p124Wsc6iUMIIYQQQgghRLsmM5lCCCGEEEII4SJGOSdTZjKFEEIIIYQQQriOzGQKIYQQQgghhIvI1WVlJlMIIYQQQgghhAvJTKYQQgghhBBCuIjMZMpMphBCCCGEEEIIF5KZTCGEEEIIIYRwEZnJlJlMIYQQQgghhBAuJDOZ7UCNva0TtExoqLGtI7SYj1t1W0dosS7eHStzQWXH+8zKrjveR42bj5naOkKLlJfrto7QYtMHlrV1hBbbmGVu6wgt8ucld7Z1hBb793Xz2jpCiz218va2jtAitg74OhLlU9XWEVqkI/ZxR2RUHe+1z9Wk0oQQQgghhBBCuIzMZAohhBBCCCGEi8gsnvSBEEIIIYQQQggXkkGmEEIIIYQQQgiXkcNlhRBCCCGEEMJF5CtMZCZTCCGEEEIIIYQLyUymEEIIIYQQQriIzGTKTKYQQgghhBBCCBeSmUwhhBBCCCGEcBGj0m0doc3JTKYQQgghhBBCCJeRmUwhhBBCCCGEcBE5J1NmMoUQQgghhBBCuJDMZAJKqQnAe0AVMEZrXdbGkX7ThE5WnhzdDYNSLNubwb/j0xrcf16XQB4Y3hW7hhq7ZtamZLZnFmIyKhZeNgiT0YDRoPg+JYd3dhxq9bwjgy3c3z8Gg4KvD2ey8MDRBvdfEBnMDd0jASirruGNhGSSC0sBeGxQd8aGWsmvqOLWtbGtmjM/MZHUT5eg7XZCJ4wn8pJLGtyvtSb10yXkJyRgNJnodtut+HTtCsCxH38ka/0voBRekZF0v+1WDO7u7Ht/HmUZGQDUlJVh9PRk0DNPuyRvVnwSuz5ZirZrOk8aR/crLmqSd9cnS8mKS8LoYWLQn6fjH9WFstw8YufNp8JWiDIoupwznuiLzgWg8PAREj5cRE1FBZ5BgQy+5zbcPT1dkhcgNyGJfYuWou12IiaOI+qyi5tk3rdoKbnxiRhNJvrccQt+UV0A2PDwDIxmM8pgQBkNjHxmBgD7l3xGTmw8Bjc3PEOC6HPHLbh7ebksc33Dgyzc2ycGA/DtkUyWpDSs5c7enjw8oDvd/Xz4cN8hlqceA6CTtyczB/WsbRfmZWb+/sP871B6q+Q8blSIhQcHxmBQii8PZfLJviMN7r+wUzA39uwEOLa912OTOVBYQoiniaeG9STAbEJrzRepmSxLPtaqWY8bE27l4aExGJXi8+QMPtrdMPMlXYO5pW9nAEqrapi97QD7bSV09fVk9rjete0ifcy8l3CIxXtbJ7fWml/++xmHdiTh5mHivL/cRHC3zk3aFWbm8MObH1FRXEpQdCfOf2A6RnfHy+/RxP388sFn2Gtq8PT14coXHyD/aCY/vPFhveVzGXn9pQy6YrLLshcmJXJk6adou53AcRMIu7jhvq48I51D8z+iLO0w4VOuJPTCun3LoY8/ojAhHjdfX/o8/ZzLMjUnOz6JPc79RaeJ44i5vOn+Ys/CpWQ79xcD/uTYX9RUVrFl9uvYq6vRNXbCRgyl+1VXOJ77oTR2zV+EvaoKZTTQZ/o0LDHRrfo8mvPea3dxyXlDyM4tZPgFj5719R+ntWbLR8s5utNRx+PuuZnAmKZ1XJSVw7q5H1JRXEpgdGfG/2U6Rjc3Dm+NJ3bpV6AUBqOBEbdcTWjvbhQcy2TtnA9qly/OymXwNZfR97Izr2OtNWv+8xkp25Nw9zBx4f03EdrMtleQmcM3r39EeXEpITGduPhBx7ZXUVLGt299TFFOHvYaO8OvPI9+540m72gm37z2Yb3lcxkz7VKGTjmzzNLHrd/H7Y3MZP4/GmQqpYxa65oT3H0j8LrW+sMT3N/4sRSgtNZ2lwVsAYOCp8d257ZvE8gsqWD51CGsPpxLsq20ts3GY/msWpELQK8Ab+ac24dLlm+jskZzyzfxlFbbcVOKRVcMYl1aHnHZRa2XF3hoQAx/25REdlkl8yYM4peMPA4V143l00vL+euvCRRX1TAqxMIjA7tz9y/xAHyXlsX/UtOZMbhHq2UE0HY7KYsW0fehhzBZrSS8NAvroEF4RUTUtrElJlKelcmQl16k+GAKKQsXMmDGDCry88lYtZpBzz+H0WRi33vvk7NlKyHjxtLzrjtrl09dugyjiwZs2m4n6eNPGfXo/ZgDrPzyzMuEDh2Ib2R4bZvs+CRKMrM457XnsCWnkPjRYsY9+xjKaKTvtD/iH9WF6rJyfnl6NkH9++AbGU78fz+hz7Q/ENi7J2lrf+Xg1z/S6+opLsu8d8Fihjz8AB4BVrY+P5ugwQPxiazr49z4RMoysxjz8vMUHkxh74JFjHjq8dr7hz72N0y+Pg0eN6BfH7pdfSUGo5EDS1dw6Kvv6H7tH1ySuT4D8Ne+MTy2NYmc8kr+MWYQG7PyOFxSV8tFVdX8c1cK40IDGix7pKSMu3+Nq32cxZNHsCEzz+UZG+f9+6BuPLghkayySv4zeTC/pOeSWlSX91hpOX9ZH09RVQ2jQ608OqQ7d66No8aueSchhX0FJXi5Gfnv5MFszcpvsGyrZFbw+LBu3PtzIpllFSy4cDBrj+aRUli3fztaUs6ff4qnqKqaseFWZo7ozi0/xnGoqIwbvttZ+zjfTh3Fz2m5rZb18I5dFKRnceM/nyZzXypr5y3h6lcebtJu44KVDLpiMj3GD2PNe5+ye9VG+l88gYqSUtbNW8rlT92Db3AApTbHftgaGcp1bzpq3l5jZ/6fZxIzapDLcmu7nbTFi+j+wEO4W63snf0S/gMH4VlvX2f08qbTdddTEBvbZPnAMWMJPmcyhz76oMl9rqTtdnYvWMzwRx7AHGBl43OzCRnScH+RE59IaWYWE155noLkFHZ9vIjRTz+Owd2NEY89hJvZjL26hi2zXiNoQD8s3WPYt3QF3a68jOCB/cmOS2DfkhWMfOLvrfpcmrNg2Vrem/89/3nr3rO+7vqOxu6iKCObq+Y+Q87+VDb991Mue+mRJu22L/yCvpdOJnrccDb+ezH7V2+k94UTCB/Qi87DB6CUIu/QUdbO+YCr3noK/4hQprz6BAB2u51ldz9Jl5GuqePU7buwpWdx27tPk7EvldXvLWHaa023vfXzVzJ0ymR6TRjGT+9+SuJPGxl0yQTivllHYOcwrpx5F6UFRXx034v0njicgMhQbppTt+39+46ZdB995pmlj1u/j0X787s4XFYpFaWU2qOUmq+UildKLVdKeSmlUpVSTyulfgGuUUpdqJTaqJTaoZRappTyUUr9CbgWeFoptdB52ypnmwSl1NR669itlPoXsAPorJR6RCm11bnO5+rl+VwptV0plaSUurPZ0GdgYLAvhwrLOFJUTpVd8/XBbM7rGtigTWl13fjX082AbuY+N4PCzaBo7etf9bH6crSknPTSCqq1ZtWxbMaHNXwDnphfRHGV4zOApPwigs2m2vvi8goprKxu5ZRQnJKCOTgEc3AwBjc3gkaMID82rkGbvNhYgkePQSmFb7cYqkvLqLTZAMcbIntVFbqmhprKSkwW/wbLaq3J3baNoJEjXJLXlpyKV0gwXiGOvBGjh5O5o2HezB1xRI4bjVIKa/cYqkpLKbcVYLb44++cHXTzNOMTEUZ5vuN5lKRnEtDLMaAP6t+bjG07XZIXoPBgKp4hIXg6M4eOHEHOzvgGbbJ3xhM21pHZ39nHFbaCkz5uYP++GIxGAPy6RVOen++yzPX1svhyrLScjDJHLa/JyGZso8GkrbKKfYXFVOsTb1lDAi2kl5aTVV7RKjmP6xPgy5GSco4d3/aOZDMhvOG+IjGviKLj215eISGejm0vt6KKfQUlAJRW13CoqJRgs0er5gXoF+BLWnE5R0vKqbZrfjiczTmdGvZxfE4RRVWOfUJCThEhXk1zjQy1cKS4jIzS1uvjlC0J9DpnJEopwnpFU1lSRklew1rVWnM0YR/dxgwGoPfkUaRscdT8/nXbiBk9CN9gx/Pzsvg2WceRhL34hwbhGxLQ5L7TVZqagkdIMB7OfZ11xAgK4mMbtHH388M7Khrl3K7q8+nRE6OXt8vynEjBwVS8QkNq93Hho0aQ1Wh/kbUzngjnPs7SPYYq5/5CKYWb2QyArqnBXlMDyjm1oBTVZeUAVJeV42G1tPpzac6GLXvIsxW3ybrrS9saT8xERx0H93TUcWl+0zrOSNpH19FDAOg2aRRpWx2vN+5mD5Szb6srKmhuAic9YS++ocH4BLumjpO3JNDHue2F94qmoqSM4ma2vbSEffQYOxiAvpNHkbzZWT9KUVlWjtaaqvIKzD5eGIwN3xKnxe/FPywIPxdse9LHrd/H7Y1Bnb2f9ur3NJPZC7hDa71BKfUBcPyjwXKt9XilVBCwAjhfa12ilHoM+JvW+nml1HjgK631cqWUG3CV1rrQucwmpdTKeuu4TWt9r1LqQqAHMBJQwEql1ESt9Trgdq11nlLKE9iqlPpMa+2yj9NDvTzIKKl745RZUsHA4KZvTs7vGsjfR0QTYHbnrh8Sa283KFhx5VC6+HmyaNcx4ltxFhMgyGwiq6yy9u/s8kr6NvNm6rjLO4eyOcvWqpmaU2mz4RFQt6MzWS0UpaQ0bJNvwxRgrdfGSqXNhk9UFBEXXsiOxx7H4O6OpW9fLP36NVi2aP9+3P388AwNdUne8nwbnoF1WcwBVmzJDfOW59nwDGjYpjzPhrneALg0O5eCQ2lYukUB4NMpgswd8YQNG0T6lh2U5bluwFaen4+5Xh6PAAuFjTJX2GwN21gtVOTb8LD4g1LEvj4XlCLynAlEnjOhyTrS1/9KyMjhLstcX5CHiex6tZxTXklv/xPX8omcEx7Ez+nZrozWrGCziayyun1FVlkF/awn2fa6hrEps+m/d5iXBz38vUnKb919BUCIlweZ9QaGmaWV9A88ceYru4Xya3rTzBd2Deb7Q63bxyV5NnyC6mrVO9BCSV4B3gF121d5UQkmb8/aD0G8Ay2U5DreqNmOZWOvqeHzp+ZSVVbBgMsm0XvyqAbrOPDLDnpMGObS3JX5NkzWevs6i5WSRvu69qDx/sJstWA72Gh/kW9r0qbcub/Qdjsbn5lFaVY2nc+bhKWb45DY3jdcw/bX32bfks/QdjujZrbdoartQWm+De96ryVegRZK82x4WevquKKoBJNXvToOsFJab8BxaEscOxavpLygiPMev7vJOlJ/3U70ONfVcXGeDd96255PoIXivAJ8Gm17HvW2PV9nG4DBl03ki5fmMe/2mVSVlXPpw7ehDA0HQHt/2UFvF2170set38ei/fldzGQ6pWmtNzh//wQY7/x9ifP/o4G+wAalVCxwC9C1mcdRwCylVDzwExAJHB8VHNJab3L+fqHzZyeOmc3eOAadAPcrpeKATUDnerfXrUSpO5VS25RS22zrVja++6RUM59aNDdn8tOhXC5Zvo37ftrFA8Oiam+3a7jyfzuYtHgTA4N96WFtnXPXjmvuQxZ9gvnTIYH+XNYllPd2p7ZqpmY1M/OkmqRvJrdSVJeUkBcby9DZsxj22qvUVFaQvWlTg2Y5W7a6bBbzhFka5W22Rb0m1eXlbH/nffreeE3teZeD/nQzh1atZf3Ts6guL8dgbOXPohoXdHMzgM4mw2c8wsjnnmTw3/7CkdVryN+7v0GzlC+/QRkNhI0Z2TpRm7ntRLV8Im5KMSYkgLUZrXcY53HN523e0CB/Lo8K5V9JqQ1u9zQaeGlkH95OSKG0+kRnHLhOSzIPD/FnakwYb8c2HHi4GRSTIgP5KS3H5fka5DpJrdZrdMI2dnsN2clpXPbk3Vz+9L1sX/49tmNZtc1qqqpJ3ZpAt7FDXBfaEeqEmdqVZmM22sedpH+VwcDYF2Yy6c3ZFBxMpeiI4/zptNXr6DXtGia9OZveN1xD4gcLXJ28Q2m2C5u80Th5zXQdOYir3nqKyQ/fSeySrxs0q6muJm17AlGjXVjHzb5eN25y4n1z6s7dBEdHcucHL3LTW4/z87xlVJTWnQpQU1VN8pYEeoxzTWbp49bv4/bGqM7eT3v1e5rJbFzpx/8ucf5fAT9qraf9xuPcCAQDw7TWVUqpVMDc6LGOP95srfX79RdWSp0DnI/jAkKlSqk19ZavC6f1PGAeQK//rGvRu9SMkgrCvOsODwv19iCrtPKE7bdlFNDFzxOrhxv5FXWHnRZV1rA53caETgHszy894fJnKru8svYQPHDMruSUN80b4+vFo4O68cjmXRRWtf7hsY2ZrFYq8urOkavMt2GyWJq0qaw3s1eZn4/J35+C3bvxCArC3dcx4xI4ZChFyckEjx4NOA7XytuxgwEzZ7osr9lqpSy3Lkt5Xj7mep+KAngGWBrMRJbn5dceGmavrmH72/OIHDOS8BF1O3mfiDBGPXo/AMXpmWTFJeIqZquV8np5KvJseDTqY4/GbfLr2hzPbvLzI3joYAoPpmB1Htqb/stGcuISGPrIQ828eLtGdkUlwfVqOchsIrfixNtec0YEWzlQWIytssrV8ZrIKq8kxLNuXxHi6dHsttfNz4vHh3Tn7xuTGhyablSKl0b14YcjWaw91vqDYoDM0gpC6x3+GuplIqes6SGv3S1ePDWyB39dm0RBo8Ppx4Vb2ZNXTF656/s44dt17PrxVwBCunehOKeuVktybXg32gbNfj5UlpRhr6nBYDQ62jhnAnwCLZh9fXA3e+Bu9iC8bzdyUo9iiQgB4PDOXQTFdMbL4ufS52CyWqnMr7evs+Xj3mg7bA8cR17U23/l25oc2tpcG3Oj5+Lu7UVA757kJCTh2ymSYxs20vvGawEIHTGMxA8+abXn0F7t+X4t+1Y56jioW1dK6r2WlOba8GxUxx6+PlSW1qvjvPwGs3DHhfXtzoZ/5VBeWIzZz3Hu/NGduwiI7oznGdZx7DfrSPzBkTm0RxeK6m17xfW2q+M8/XyoqLftFeXaamfhdq3axPA/XOA4zDo8GP/QQPKPZBLWMwqA1B27CInpjPcZZJY+bv0+Fu3b72kms4tSaozz92nAL43u3wSMU0p1B3Ces9mTpvyBLOcAczLNz3YCfA/crpTycT5epFIqxLl8vnOA2RvHDKpLJWQXEeXnSScfM+4GxWUxwaw+1PANYBe/unFt30Af3A2K/IpqrGZ3fE2Owxo8jAbGRlo5aGu9ASbAHlsRnbw9Cff0wE0pzosIZkNGwwuehHiaeHFEb17auZ8jJeWtmudEfKKiKM/Kojw7B3t1NTlbt2Id1PBk9IBBg8jetBGtNUXJBzF6emKyWDAFBFB88CA1FRVorSnYswfPsLoL8Nh278YcHoZHvcO6zpR/TFdKMrModeY9tmkboUMGNmgTMmQgRzdsQmtN/oGDuHl5Yrb4o7Um/r8L8IkII+aS8xssU1FYCDjOMT2w8lu6Tp7ossy+0V0pzcqizJk5c8tWghplDh4ykIxfHZkLkg/i5mnGw+JPTUVF7XlUNRUV5CXuxqeT44rEuQlJpH77PYPuvxejh6nJel1lb0ERkV6ehDlr+ZywYDZmteziPZPDg/g5vXVn2I7bk19EJx9Pwr2c216nYH5Jb5g31NODWaP68Pz2faQVN9z2nhjag0NFpSw5cHauKguwK6+Izr5mIrw9cDMoLuwSzNojDTOHeXnw+vi+PLVpL4ebuRDRRV1D+K6VDpUdcMlErnvzca5783GiRw5k75otjnOp9qZg8jI3eROmlCKyfw+SN8YCsOfnzUSPGABA1MiBpO9Oxl5TQ1VFJVn7DmGNrDucfv/67fQY7/pDyby6RlGRlUVFTjb26mryt27Ff2D7u/CGX3RXSuvt49I3byWk8T5u8ECOOfdxtgN1+4vKwiKqShyvbTWVleTu2oN3eBgAHhYL+Xv2AZC3ey/eoSFn94m1A70vmsSUV59gyqtP0GXEQA6uc9Rx9r4U3L08mwxulFKE9e3JoU2Oc/ST126m83DHv0VhRnbtjFbuwTRqqqvx8K07Zzdlwzaix555HQ++dCI3zXmcm+Y8TrdRA9nt3PbS96Zg8jY3OIzzeObOA3qw/9dYAHb9vJluIx3bnm9wAGnxjhoosRWSdzQL/7Cg2mX3rN9O74lnlln6uPX7uD0zKH3Wftqr39NM5m7gFqXU+8B+4F3gr8fv1FpnK6VuBRYrpY5/TD4T2NfocRYCXyqltgGxwJ7mVqa1/kEp1QfY6Jw1KQZuAr4D7nYebrsXx+DWpWo0PP/rAf5zSX+MSvHZvgwO2Eq5vrdjUPPpnnQuigpiao9Qqu2a8mo7D63eDUCIl4mXJ/bCaHAcdvRdSjZr0lr3Cpc1GuYkHuT10f0wKPgmLYvU4jKmdHW84K88lMGtPbrg7+7OQwNiape5c73jhPenh/ZkSKA//iY3lp8/nA/3HubrtKwTru90KaOR6BumsXvOHLS2EzJuHF6REWSsWQtA2DmTsAwYQH5CIjuffBKDyUT3W28FwDcmhsBhw4h/8UWUwYh3l86ETqw7XzB3y1aCRrj2EE6D0Uj/6dez5dV30NpOp4lj8e0UwaHV6wDoeu5EQgb1JzsukTWPPI3RZGLgn6YDkL8vmaMbNuPbOZL1M18CoNc1UwkZ1J9jG7dx6Cfncx4+mE4TxzQf4DQz97rxOna+8TbY7YRPGItPZARHfnZk7jR5IoED+5MTn8jGx57CYDLR945bAKgsKCT+H+8BoGvshI4eQeAAx3mvez/5FHtVNTtfnwuAf7doet9yo8tyH2fX8I9dB5k93FHL3x/J4lBxGZd3dtTyV2kZWE3u/HPsILzcjGgNf4iK4E/rd1JaU4OHwcCwQAtzkpJdnq05NRreikvmzXH9MQJfHcokpaiUK6MceT9PzeC23p3xM7nz8KBuzmU0d6yJY2CgH5d0CeFAQQkfTR4MwPu7DrGxmXM2XZ351W3J/OMcx/7ti4OZHCws5Y/dHZk/O5DBn/t3wd/DjceHd3csY9fc/EMsAGajgVFhFmZt3X+iVbhM12H9OLxjFwvvfR43D3fO/ctNtfd99eK7TL73BrwD/Bl981R+fPNDNi/6iuDoTvQ537FNBXQKo8uQPix56GWUUvQ5fwyBXR1XTq2qqCQtbg+T7r7e5bmV0Uin624g+e05aLsmcOw4PCMiyVm3BoCgiedQVVDA3tkvUlNejlKK7NU/0eeZ5zF6epLyn3kU79tHdXExiY8/QvgVUwgc1/T86DNlMBrpc9N1bH/9bbTdTqRzf5Hm3Md1PnciQYP6kx2fyPpHn8LoYaK/c39RUVBAwr/no+120JrQkcMIGex4w97vtpvYs3ApdnsNRnd3+t7m+n3FqZj/zl+ZMKYPQVZfDmz+By+8uZz5S9ac9RyRQ/pxZGcSKx54DjeTO+Puqavjn2b/i7F33YBXgIVhN05l7dwP2bnkKwKiOtPjXEcdH9ocS/K6zRiMRtxM7kx68PZ6F6mpJD1hD2Pu/K2DyFomelg/Urfv4sO7HdvehffXZf7f8+9ywV9uwCfAn/HTp/LNGx+yYeFXhMR0ot8Fjsyjrr2Y7+d+wsf3zwJgwvSpeDpnBasqKjkct4fz73Hdtid93Pp9LNofdbLjqTsKpVQUjgv39G/rLKejpYfLtrXQ0KZXG2zvYvzP/uG3ZyrAo/XPf3Ol8pp2fGLACewvbL1Zz9ZSWtahdheUl3esvADTB7b7r0puYmNWk7My2rUQc8favwH8+7p5bR2hxZ5aeXtbR2iRAI+Ot7/Iq+hYr30dsY/v7nNhx+pk4Iej35y1jr4w8tJ22T+/p5lMIYQQQgghhGhTv6fzEU/X76IPtNapHXUWUwghhBBCCCHONqVUgFLqR6XUfuf/m1w8RCnVWSn1s1Jqt1IqSSn1wKk89u9ikCmEEEIIIYQQ7YFBnb2fM/Q4sEpr3QNY5fy7sWrg71rrPjguaHqfUqrvb/bBGUcTQgghhBBCCNHRTAXmO3+fD1zZuIHWOl1rvcP5exGOi61G/tYDyzmZQgghhBBCCOEixrN4KR6l1J3AnfVumqe1PtUrlYVqrdPBMZh0fh3jydYVBQwBNv/WA8sgUwghhBBCCCE6IOeA8oSDSqXUT0BYM3c92ZL1KKV8gM+AB7XWhb/VXgaZQgghhBBCCOEiBtV+vipGa33+ie5TSmUqpcKds5jhQLNfRK+UcscxwFyotV5xKuuVczKFEEIIIYQQ4v+flcAtzt9vAb5o3EAppYD/Aru11m+e6gPLIFMIIYQQQgghXKQDXV32ZeACpdR+4ALn3yilIpRS3zjbjANuBs5VSsU6fy79rQeWw2WFEEIIIYQQ4v8ZrXUucF4ztx8DLnX+/gvQ4uGsDDKFEEIIIYQQwkVcMMPY4cnhskIIIYQQQgghXEZmMtuB/tEd6+OOUM/Kto7QYoVVHe/zlGp7x6oLL2P7uZLaqerm2/Fq2eTf1glapj1dYe9Ubc3xaOsILdbDr2PVcmZZx3v78dTK29s6Qou9MOWDto7QImWHn2vrCC3m2eWZto7QIh2xjzuijveu0/WkD4QQQgghhBBCuEzH+yhRCCGEEEIIIdop1bEORmsVMpMphBBCCCGEEMJlZJAphBBCCCGEEMJl5HBZIYQQQgghhHAROVpWZjKFEEIIIYQQQriQzGQKIYQQQgghhIvIhX9kJlMIIYQQQgghhAvJTKYQQgghhBBCuIjM4kkfCCGEEEIIIYRwIZnJFEIIIYQQQggXUUq3dYQ2JzOZQgghhBBCCCFcpl3OZCqlftVaj23m9mDgK8AE3K+1Xn+G64kA3tZaX32C+6OAr7TW/c9gHcVaa5/TXf5EipISSV+2GLQd69gJBF90aYP7KzLSObLgQ8rTDhN6xVUEXXARAPaqKlLefAVdXY222/EbMozQy6e6Oh4AuQlJ7Fu0FG23EzFxHFGXXdzgfq01+xYtJTc+EaPJRJ87bsEvqgsAGx6egdFsRhkMKKOBkc/MACB5xUpydsaBUpj8fOl7xy14WC0uy1yQmMjhJUvQdjvB48cTfsklTTIfXrKEgoQEDCYT0bfeinfXrgBkrFpFzvr1aK0JnjCBsPPPB6A0LY3UTz7BXlWFMhrpesMN+ERHuyRvdnwSe5x93GniOGIub9rHexYuJdvZxwP+5Ojjmsoqtsx+HXt1NbrGTtiIoXS/6goACg8fYdf8hdRUVOAZGMjAu2/HzdPTJXkBsuKTSPxkKdqu6TJpHD2uuKhJ5qRPlpIZl4TRw8TgP0/HEtWFstw8ds6bT4WtEAyKrueMJ+aicwHYtfgzMmITMLi54R0SxOA/Tcfd28tlmXMTkti/aCloO+ETxtG1mVrev2gpeQmJGJy17NvVUcsbH6lXywYDw521XHQ4jX0fL3LUhcFAz5un4RfTtnVRe7/dzsZnZ2O2Whj60H0A7P9sJVk741DOba//n27B7MJtLzs+id0LHXXRadI4ul3etC52L1xKdlySI/Ofp+PvrOXNs96oV8tD6PEHRy3v/99XpK35BZOfLwA9r55KyKDT3p030Rr7C4DM1avJ+vlnMBiwDBhA56ubfYk6I1prdn68jIxYR3+OvPtmrNFdmrQrzsph0zsfUFlcijW6MyPvvQWjmxuFRzPY+v4n5Kem0f/aK+h9+fnNrOXM5SUkcmDxUrS2Ez5hPF0ubVrLyYuXkJvgqOVet99au+1Vl5ay96MFlBw9ilKKnrdOx797N1K/+JL0db/g7ut4aY7+w5UEDhzgkrxaa7Z8tJyjO5Nw8zAx7p6bCYzp3KRdUVYO6+Z+SEVxKYHRnRn/l+kY3dw4vDWe2KVfgVIYjAZG3HI1ob27UXAsk7VzPqhdvjgrl8HXXEbfyya7JPepeu+1u7jkvCFk5xYy/IJHz+q6T0RrzUsvzWPt2u2YzR68/PID9OvXvdl2c+Ys4LvvNmAwGJg27RKmT5/CTz9tYu7chRgMCqPRyIwZf2L48H5t8EwcpI9/H+Tisu10kNncANPpPGCP1vqWU30spZSb1rr6BOs5Brj+1buVabudY0sWEn3/33CzWDn4yov4DhyMOTyito3R25vwa6ZRFLezwbLKzY2oBx7GaDaja6o5+MYr+Pbrj1d0N5dn3LtgMUMefgCPACtbn59N0OCB+ETWZcyNT6QsM4sxLz9P4cEU9i5YxIinHq+9f+hjf8Pk23B83vWSC+j2hykApP24mpSVX9P7lhtdlvnQokX0fOghTFYru2bNwjJoEJ4RdZkLEhOpyMxkwIsvUpKSwqGFC+k7YwalR4+Ss349fZ54AoObG/vmzsUyYADm0FDSli8n4vLLsQwYgC0hgSOffUbvhx92Sd7dCxYz/JEHMAdY2fjcbEKGNOzjnPhESjOzmPDK8xQkp7Dr40WMfvpxDO5ujHjsIdzMZuzVNWyZ9RpBA/ph6R5D0ocL6HXdHwno3ZMj6zaQ8s2P9PjjlDPOezxzwsefMvrR+/EMsLL+mZcJGzoQ38jw2jZZ8UkUZ2Zx7mvPYUtOIeGjxUx49jGU0UjfaX/EEtWF6rJy1j09m+D+ffCNDCeofx96X3slBqORXUv+x/6vvqfvdVe5LPO+TxYz+O+OWt7mrGXvev2cl+Co5VGznbX88SKG16vlwY82reXkZSuImnIZgQP7kxufQPKyFQx57O8uyXu6dXHcoR9W4x0RRk1Zee1t0ZdeUFsHh35cTfIXX9PvVtdte0kff8rIR+/HHGDl12dfJmRIw7rIjk+iJCOLia866iJp/mLGPvMYBnc3Rj7+YG0tb3rpdYIG9sPaPQaAqIvOI+bSC1ySs3Hm1thfFO7Zgy02ln5PP43B3Z2qwkKXZwfIiE2iOCObS958lrwDqWz/4FPOf6HpG9r4xZ/T85Jz6TJ2ONv+u5iUn3+l+wUTMfl4M+SWazi6La5V8oGjj/cvXMzAvz+Ih9XKjhdmEzh4IN4RDbe90swsRs56gaKDKexfsJChM58A4MDiJQT070e/e+/CXl2NvbKydrlOF5xH54svdHnmo7G7KMrI5qq5z5CzP5VN//2Uy156pEm77Qu/oO+lk4keN5yN/17M/tUb6X3hBMIH9KLz8AEopcg7dJS1cz7gqreewj8ilCmvOp6X3W5n2d1P0mXkIJfn/y0Llq3lvfnf85+37j3r6z6Rdeu2k5p6jB9+eJ+4uL08++y7LFv2RpN2K1asIj09h2+/fReDwUBurg2AMWMGcd55o1BKsWdPCg8++ArffffeWX4WdaSPxe/FGR8uq5TyVkp9rZSKU0olKqWuU0oNU0qtVUptV0p9r5QKd7Zdo5R6Sym1Tim1Wyk1Qim1Qim1Xyn1Yr3HLG5mPYOBV4FLlVKxSinP+u2UUlcrpT5y/v6RUupNpdTPwCvOv99WSv2qlDqolLra2S5KKZXo/L2fUmqL87HjlVI9nA9tVEr9WymVpJT6QSnl6WzfTSn1nfM5rldK9XbeHq2U2qiU2qqUeuFM+7c5ZakpeASHYAoKxuDmhv+wkRTFxTZo4+brh1dUNBiNDW5XSmE0mwHQNTXomhpa4/OWwoOpeIaE4BniyBg6cgQ5O+MbtMneGU/Y2NEopfDvFkN1aRkVtoKTPm79GbWaikqXfhFRSUoKHiEhmIMdmQNGjCA/ruEbKFtsLIFjxqCUwicmhpqyMiptNsrT0/GOicHo4YEyGvHt2ZP8nc4BvlLUlDverNeUleFusbgkb8HBVLxCQ/By9nH4qBFkNerjrJ3xRIxz9LGlewxVzj5WSuFWrw7sNTW1fVmSnom1l6P8A/v1IXP7DpfkBchPTsU7JBhvZ+aI0cPJ2NGwjzN2xNHZmdnaPYaq0lLKbQWYLf5YnLNtbp5mfCLCKM+3ARAyoC8GZ61bu0VTnpfvssxNannUCHJiG/ZzzmnUMiiqnXVRXVqOqR3UBUB5Xj7ZcQl0mjiuwTKNtz3lwm3PdjAV79DgepmHk9WoLrJ2xBFZry6qnXXRuJZ1TY1Ls51Ia+0vstauJeziizG4uwPg7ufXKvmPbo8naoLjTV9gj2iqSssoy29Ys1prspL20WnUEACiJozi6DZHLZn9fQno1hXV6DXGlQoPpji2PWcfh4wcTu7Ohn2cGxtXu+351dv2qsvKKNi3n7AJjjo2uLnh5uW6oxtOJG1rPDETR6KUIrhnNJUlZZQ2068ZSfvoOtrRr90mjSJtq+N5uZs9auu3uqKi2Vfn9IS9+IYG4xMc0KrPpTkbtuwhz9bkLVqbWrVqE1deeS5KKQYP7k1hYQlZWXlN2i1e/A333Xc9BoPjrW9goAUAb2/P2j4vK6s4K/uPk5E+/n1Q6uz9tFeumMm8GDimtb4MQCnlD3wLTNVaZyulrgNeAm53tq/UWk9USj0AfAEMA/KAZKXUW1rr3OZWorWOVUo9DQzXWv/Fua6T5eoJnK+1rnEOPsOB8UBvYCWwvFH7u4G5WuuFSikTYARCgR7ANK31n5VSS4E/Ap8A84C7tdb7lVKjgH8B5wJzgXe11h8rpe777e5ruSpbPu5Wa+3fblYrZakHT3l5bbeT/PILVGZnETBxMl7RMS7PWJ6fjzmgLqNHgIXC5JQGbSpstoZtrBYq8m14WPxBKWJfnwtKEXnOBCLPmVDbLvmzz0nfsBk3L0+GPvqQyzJX2myYAupetE0WCyUpKU3b1Ot7d6uVKpsNz8hIjnz+OdXFxSh3d2yJibWHxXW57jr2zZlD2vLloDV9HnvMJXkb97HZasF2sFEf59uatCl39rG229n4zCxKs7LpfN4kLN0ch2r6dooge2ccIUMHk7l1h0sHbOX5NjwD6+UJsGJrVBfleQ0zewZYHbdZ/GtvK83OpeBQGpZuUU3WkbbuVyJGDXNZ5gpbfpM6LWymnz0a1Xv9Wo57w1nLkyYQ4azlHtOuIe7Nt0le8hla2xk2wzWHRZ1pXexZtJSe1/2hwSzmcfuXf86xXzfj5unJiMdct+2VN87TXF3k2zA3qp2KfEddaLudDc/MpjQzmy71ahng8Ko1HNuwGb/oLvSZ9kfcvb1dkrm19hflmZkUHzjA0c8/x+DuTqdrrsEnKsolmesryy/AM8BS+7dngIWyfBue1rrtrLKoBJO3Z+0HOF6BVsqcH+ycDZW2RtuV1UphSnPbXkC9NhYqbfkogxF3X1/2fjCfkrQj+ER1ofu06zB6eABwdPUaMjduwrdrV2Kuu9pldVGab8O7Xp16BVoozbPhVa9fK4pKMHnV9at3gJXSvLqB6KEtcexYvJLygiLOe/zuJutI/XU70eNct4/r6DIzcwkLC6r9OywskMzMXEJCGg7C09Iy+Oab9fz44yYCAvyYOfMuoqIcs+I//riRN96YT15eAe+//8xZzd8RSB+L0+GKC/8kAOcrpV5RSk0AOgP9gR+VUrHATKBTvfYr6y2XpLVO11pXAAedy7rKMq11Tb2/P9da27XWu3AMHhvbCMxQSj0GdNValzlvT9Faxzp/3w5EKaV8gLHAMudzfB/HIBZgHLDY+fuCE4VTSt2plNqmlNqW8tXKEzVrgVP/KEMZDHSf8Qy9XnqNstQUyo8ddcH6T2XFjTLqZq685WwyfMYjjHzuSQb/7S8cWb2G/L37a5t0++OVjH9zNmGjR3Jk1RrX5Ws2z6lkVniGhxN+8cXsfest9r39Nl6dOqGcn+RlrV1L52uvZfArr9Dl2mtJnT/fRXmbidKoDvRJ+lgZDIx9YSaT3pxNwcFUio446qDf7dM5vGotG5+ZRXV5OQajK4+qb+5qa6dQu/WaVJeXs+2d9+l/4zW4NzpXdN/Kb1FGA5FjR55ZzPqajdyon5tpdLzJ0CceYcSzTzLoIUct25y1fPTndXS//hrGvjGbHtdfw54PT7i7OOO8p1oXWbHxmPx88Y/q2uxD97j6Sia9OZvwMSM53MrbXks+6VYGA+NfeJLJb81qUMtdzp3IpNdeYNwLMzBb/Nm9+DOXRW6t/QV2O9WlpfR54gk6XX01ye+/3/y/V2vkb1wnzdb1WfzY/FSe9gmeh7bXUHToMBGTJzHs2ZkYTR4c/uY7ACLOmcSol19k2DMzMVn8Obik8WfOZxC52X/yxn124v0yQNeRg7jqraeY/PCdxC75ukGzmupq0rYnEOWcBRWn2udQWVmFh4eJFSve4tprL2LGjLm1911wwRi+++49/vnPJ5k795PWjNshSR+3nDqLP+3VGQ8ytdb7cMxGJgCzccz0JWmtBzt/Bmit65/4UOH8v73e78f/bvBuVin1kvPw1dgTrb7e7+ZG95U0+rv+upr8m2itFwFTgDLge6XUuc0sV+PMaABs9Z7jYK11nxPkaj641vO01sO11sOjL2/Z+W7uFitV+XWzS9X5+bj7W1r0GABGLy+8e/aiOCmxxcv+FrPV2mAGrCLPhkejwwE9GrfJr2tz/GI+Jj8/gocObjJzBBA6egRZ23c2uf10maxWKvPqDv+otNmaHNpqslqprNf3Vfn5uPs7PqEOHj+efk89RZ9HHsHN2xtzqOOzjNxff8U6dCgA1mHDKE5NdUlec0DD/ivPtzW5CFJzbcyNnpO7txcBvXuSk5AEgE9EGMMfeYAxz80gfPQIPEOCcBWz1UpZbr08efmY633C78hsaZC5LC+/9gIz9uoatr09j8gxIwkf0fBNVtr6jWTtTGDI3be79I3wyeq0NrPVSkWjejedqJadMzEZv24keJjjOQSPGEZhSqpL8p5JXdj2J5O1M561f59B3Lv/JXf3HuLf/4DGwkePIHOb67a9Jnny8h2zwPXbWC2UN6qdxs/LUcs9yI7fBYCHv1/tBZc6TRpPwcFUl2Vurf2Fu9WKdcgQxyG20dEopaguds2hc/t/WMsPT8zihydmYbb6U5Znq72vLK/hLCaAh68PlSVljsPpgdLc/AZHFLQ2k9XScLvKz2/6OhJgpaLev0NFvmPb87Ba8bBaay+mFTR8KMWHDjset15dhE8cf8bb3p7v17Ly0dmsfHQ2XlZ/SurVaWnuCfq1tK5fS/LyG8x0HhfWtztFmTmUF9b9+x/duYuA6M54WlrnMOqOYuHCr5k69X6mTr2fkJAAMjJyau/LyGg6wwYQGhrIhRc6LvlxwQVj2Ls3tUmbESP6c/hwOnn1Zpb/v5I+FmfKFedkRgClWutPgNeBUUCwUmqM8353pdRpXUJKa/3k8UHcCZpkKqX6KKUMwFWns47jlFIxwEGt9ds4ZlsHniRXIZCilLrGuaxSSh0/A38DcL3zd9dcFaMRz65RVGRlUpmTjb26moLtW/AdeGoXAKguKqKmtBQAe2UlxXt2YwoLc3lG3+iulGZlUZadg726mswtWwka0rBLg4cMJOPXTWitKUg+iJunGQ+LPzUVFVQ7D9WrqaggL3E3Pp0iASjNyKxdPic2Hq/w5ialT493VBQVWVlU5Dgy523dinVQw361DBpE7saNaK0pPngQo6dn7WDi+AU6KnJzyd+xg4ARIwBwt1go2rcPgKI9ezCHhLgkr190V0ozsyh19nH65q2ENOrjkMEDObbB0ce2A3V9XFlYRFWJow5qKivJ3bUH73BHHVQ4n4e22zm48hs6T57okrwAlpiulNTLfGzTNsIaZQ4bMpA0Z+b8Awdx9/J0HBKpNXH/XYBPRBjdLml4Jcus+CQOfP0DIx66BzcPk8vygqOWyzLr1fLmrQQNbpg5cHCjWvY6QS0n7cY70lHLHhYLtr2OusjfvRfP0Lavi57XXMU5b73MpDdmMeieOwjs05uBdznOdCipt+1l7YzH24Xbnn90w7pI37ytaeYhAzlary7cPB11UdFMLftEOGq5vN55sZnbY/HtFIGrtNb+wjp4MEV79jjyZ2Zir6nBzcc1FyjvceEkLpw9gwtnzyBy+CBS129Ga03u/hTcPT2bDIaUUoT07cmRzY4PFFLXbyZy+AlfGl3OLzqqwbaXtWUbgYMb9nHgoEG1215h8kHcvDzxsPhj8vfHI8BKaUYGALbde/CKcBxwVP986ZwdsQ0u4nU6el80iSmvPsGUV5+gy4iBHFy3Ba012ftScPfybDKAVEoR1rcnhzY5+jV57WY6O/u1MCO7duY692AaNdXVePjWHcqbsmEb0WPlUNkbb7yML754my++eJvzzx/N55+vRmtNbOwefH29mh0AnX/+aDZtcpxTvGVLYu1hnIcOHavt86SkA1RVVWO1/v8exIP08ZkyqLP301654ji4AcBrSik7UAXcA1QDbzvPz3QD5gBJLlhXY4/j+EqTNCAROJNX4uuAm5RSVUAG8Dxwsi3gRuBdpdRMwB34FIgDHgAWOc85deGxWXWU0UjEdTeQ+o85aLsd65hxmCMiyVu3BoCAiedQVVBA8isvYi8vA6XI+fknejz1PNUFNo58/AHabget8R82Ar8Brr9CncFopNeN17HzjbfBbid8wlh8IiM48vM6ADpNnkjgwP7kxCey8bGnMJhM9L3DcdHgyoJC4v/huOqYrrETOnoEgQMcn1McWP45pRmZKKUwBwbQ65YbXJZZGY10mTaNvXPmgN1O0LhxeEZEkLV2LQAhkybhP2AABYmJJDz5ZO1XEhx34L33qC4pqf2aEjfnOT5RN99c+zUHBjc3om6+2SV5DUYjfW66ju2vv42224l09nHaakcfdz53IkGD+pMdn8j6R5/C6GGiv7OPKwoKSPj3/No6CB05jBDnwClj01YOr3I859BhQ4iccKKLPZ9e5v7Tr2fTq++gtZ3OE8fi2ymCVGfmqHMnEjKoP1lxiax+5GmMJhOD/zQdgLx9yRzZsBnfzpGsnfkSAL2vmUrooP4kfLwEe3U1m159G3Bc/Gfgba6pDYPRSM+briPuTUc/h48fi3dkBEedtRzprOW8+EQ2Pf4URpOJ3rfX1XLC8Vq22wkdVVfLvW65if2Ll6JrajC4u7vsKslnUhcns2+ZY9tDKTwDA+h7q+u2PYPRSN+br2fra+84v3bFUReHnZm7nDuRYGfmtY88jdHDxEBnXVTYCoj/93ywa7S2EzZyGCGDHV9HsXfJCgoPH0Gh8AwKoN9trvvcr7X2F0HjxpEyfz6Jzz6LMhqJue22VjlENXxwP9Jjk/jmoWdx8zAx4q6bau9b98o/GXHnjXhaLQycdiWb3vmAxGVfYunamehzxgBQZivgp5mvUlVWjlKK/d/9zMWvzsTdy3Vfd6SMRrrfeD0Jb81F2+2EjR+Hd2QEx9Y4+jjinEkEDOxPXkICW56Y6fwKk7pa7nHD9eye9190TQ3moKDa+w4u+4yStDRQCnNgID2m39Ts+k9H5JB+HNmZxIoHnsPN5M64e+oe+6fZ/2LsXTfgFWBh2I1TWTv3Q3Yu+YqAqM70ONfRr4c2x5K8bjMGoxE3kzuTHqw7MqO6opL0hD2MuXOay/K21Px3/sqEMX0IsvpyYPM/eOHN5cxfsqbN8gBMmjSctWu3ccEFd+Lp6cGsWQ/U3vfnPz/Liy/+ldDQQO6882oefvgN5s//Ai8vMy+9dD8A33//K198sRo3NzfMZhNvvfVom16YRvpY/F6oVjnXQ7TIH1et71D/CKGezX4jTLtWWOWK04/PrgCTva0jtIjJ0KHKGIDi6o73ImfqYKVsUB2vLnIqWu+Kqa0l2qeqrSO0SGZZu/wGtZOK6mB9DPDClKaHu7dnZYefa+sILebZpWNdxKYj9jH07HAv1on5X521F7/+1svbZf90vL28EEIIIYQQQrRT7XLUd5Z1sM/EhRBCCCGEEEK0ZzKTKYQQQgghhBAuIqecykymEEIIIYQQQggXkplMIYQQQgghhHARmciUmUwhhBBCCCGEEC4kM5lCCCGEEEII4SIykykzmUIIIYQQQgghXEhmMoUQQgghhBDCRQwylSkzmUIIIYQQQgghXEdmMoUQQgghhBDCRWQiU2YyhRBCCCGEEEK4kMxktgPPDClo6wgtkpjf8crGrQN+pFRh71ih04o73mdWAwLsbR2hxdwNuq0jtEiN7lh1DJBXUd3WEVos2NyxarmfpeP1sa2y4+3jyg4/19YRWsSzyzNtHaHFpI9bX9nhxW0docWU6liv1a2h4+0xhRBCCCGEEEK0Wx1vSkoIIYQQQggh2qmOdwyP68lMphBCCCGEEEIIl5GZTCGEEEIIIYRwESVTmTKTKYQQQgghhBDCdWSQKYQQQgghhBDCZeRwWSGEEEIIIYRwEZnFkz4QQgghhBBCCOFCMpMphBBCCCGEEC4iF/6RmUwhhBBCCCGEEC4kM5lCCCGEEEII4SIykSkzmUIIIYQQQgghXKjDzWQqpRYD/YAPtdZvueDxvgFu0FrbTnB/KjBca51zmo+/BnhYa73tdDM2R2vNh299zo5fd+NhNnHfU9cT06tTk3Zzn/mE5D1HcHMz0r1PZ+58/Brc3IyUFJfxzrOLyMnMp6bGzpQbzmHy5SNdGbFJ3u/eX8H+rbtw93Dnyr/dSHj3zk3abflyHZs+X0t+eg6PLH4JL38fAOJ/3saGZT8BYPL04LL7riUsJrLV8h7P/M17K9jnzPyHv99IRDOZN61cx8bP15KXnsPjn76EtzPzcUf2HmLe397i2sdvpf+Ewa2e+Yf3P+PAtl24e5i44qHm+3nrl+vY8sUa8tNz+NuiWbX9nPDzVjYuXwWAyWzikvuuI9TF/ay1ZstHyzm6Mwk3DxPj7rmZwJimGYuyclg390MqiksJjO7M+L9Mx+jmxuGt8cQu/QqUwmA0MOKWqwnt3Y2ayiq+fXYO9qpq7PYaokYNYfC1l7k0+/H8P877jORtu3DzMHHFgzcS1kwfb/tyHVtXOvr4wYV1fZyTlsnXcxaSkZzGpOmXM/oP57k8Y+O8HXHba+91fDznL//9jEM7HLV83l9uIrhb05yFmTn88OZHVBSXEhTdifMfmI7R3Y2dn//EvnWOlwZdYyf/aAa3fTgbs683q/+xkEPbEvH09+X6uTNaJXtHrItvG2Vubp+82Zk5Lz2HRxfX7ZP3bExg9YKvUQYDBoOBi++6iq79urk845r/fEbK9iTcPUxceP9NhDZTEwWZOXzz+keUF5cSEtOJix901ERFSRnfvvUxRTl52GvsDL/yPPqdN5q8o5l889qH9ZbPZcy0Sxk6ZbLL87/00jzWrt2O2ezByy8/QL9+3ZttN2fOAr77bgMGg4Fp0y5h+vQp/PTTJubOXYjBoDAajcyY8SeGD+/n0owt8d5rd3HJeUPIzi1k+AWPtlmOxqSff//knMwONpOplAoDxmqtB57qAFMpddKBtNb60hMNMNuznRv3kJ6WwzvLnuCux6/h369+1my7CRcNY+6nj/HGJw9TWVnFqpWbAfh++QY6RYfy+oKHefaf9zL/7ZVUVVW3Wt4D23aRdzSbv/5nJlfcfz1f/2NZs+06941h+qx78Q8JaHC7NTSQW1+5n3v+9TgTr7+Ir95e0mpZj9u/dRe5x7J58L8zmXr/9Xx5gsxd+sZw6+x7sTTKDGCvsfPDh1/SfWjv1o4LQPK2XeQdy+befz/FpX+9jm//ubTZdp37RnPjS/c16WdLaCA3v3w/d/7zccZPu5iv3/nU5RmPxu6iKCObq+Y+w5g/T2PTf5tfx/aFX9D30sn8Ye4zmLw92b96IwDhA3pxxatPMOXVJxh79038+v4iAAzublz09P1Mee0JprzyBEfjdpG9L8Xl+Y/38d3znuLSv1zHd/9qvo879Y1m2otN+9jT14sL7vojo1p5cHlcR9z2OkIdAxzesYuC9Cxu/OfTnHP39ayd13zfbFywkkFXTObGfz6Nh48Xu1c5annIledz3ZuPc92bjzP6piuI6Nsds683AL0nj+Lyp+5tldzQMeti/7Zd5B7N5n5n5q9Osk+ePqvpPjl6cE/u+edj3POPR5n60DRWznV9XaRu34UtPYvb3n2a8++9ntXvNd8v6+evZOiUydz2rqMmEn9y1ETcN+sI7BzGzXOe4JoX72fth/+jpqqagMhQbprzODfNeZwb3ngUNw93uo8e5PL869ZtJzX1GD/88D4vvHAfzz77brPtVqxYRXp6Dt9++y7ffvsul102EYAxYwaxcuXbfPHF28yadT8zZ77j8owtsWDZWqZOf7lNMzRH+ln8f9Cmg0ylVJRSKrHe3w8rpZ5VSq1RSr2ilNqilNqnlJrgbPIDEKKUilVKTXC2G+5cNsg564hS6lal1DKl1JfAD86/VyilvlNK7VdKvVpvnanOZb2VUl8rpeKUUolKqevqRf2rUmqHUipBKdXbuZy3UuoDpdRWpdROpdRU5+2eSqlPlVLxSqklgGdr9N3WdYlMumQYSil69u9KSXEZ+TmFTdoNHdsHpRRKKbr36UJulu3486astAKtNeVlFfj4eWE0tl457NmUyMDzRqCUolPvKMpLyijKK2jSLrxbJyyhgU1u79w3Gk9fLwA69Y6iMNfWalmP270pkcHOzJ37RFFW3HzmiO6dsDaTGRyznP3GDcLH4tvacQHYuymBAeeOdPZz9An7Oaxb5xP0c0xtP0f2iqKoFfo5bWs8MRMdGYN7RlNZUkZpfsOMWmsykvbRdfQQALpNGkXa1jgA3M0eKOdHhNUVFbXnPSilcDd7AGCvqcFeXdMqHyXu21zXx5HOPi5uQR97W3yJ6NkVQytub/V1xG2vI9QxQMqWBHqd48gZ1stRyyV5TWv5aMI+uo0ZDDgGjylb4ps81v5fttNjwrDavyP6dcfD+RxaQ0esiz3198m/kbm5fbKHZ92+o6q8slX2D8lbEujjrInwXtFUNLN/0FqTlrCPHmMHA9B38iiSNztrQikqy8rRWlNVXoHZx6vJviItfi/+YUH4NfPB5platWoTV155LkopBg/uTWFhCVlZeU3aLV78Dffddz0GgyNbYKAFAG9vz9o+LiurqP29rWzYsoc8W3GbZmiO9PPvnzqLP+1Vez5c1k1rPVIpdSnwDHA+MAX4Sms9GPitjWoMMFBrnaeUuhUYDAwBKoC9Sql3tNZp9dpfDBzTWl/mfGz/evflaK2HKqXuBR4G/gQ8CazWWt+ulLIAW5RSPwF3AaVa64FKqYHAjjPphBPJyy4gMNRS+3dgsD952QVYg/yabV9dXcO677Zz20NXAnDx1eN45dEPuPOK5ygrreChF26u3Ym1hqIcG/7BdXn9gvwpyinAN8D/xAudwM4fNtF9WB8XpmteYa4N/yBL7d/+Qf4UtiBzYY6N3b/Gc9vLf+HovsOtlLKhotwC/Br0s4Wi3NPr59gfNtKtFfq5NN+Gd6C19m+vQAuleTa8rHUZK4pKMHl5YjAaAfAOsFJa743aoS1x7Fi8kvKCIs57/O7a2+12O189/gpFGdn0vmgiwT2iXJ6/OLcAv3p14Rvo6GOf0+jjs6EjbnsdoY4BSvJs+ATV1bJ3oIWSvAK86+UsLyrB5F2vlgMtlOQ2HHRUVVRyeOduJvzpmlbJ2ZwOWRc5tkZ10bJ9MsDuX+P46aOvKLEVc+Nzd7o8Y3GeDd96NeETaKE4r+H+obyoBI96NeHrbAMw+LKJfPHSPObdPpOqsnIuffg2VKPX5r2/7KB3vQ8kXCkzM5ewsKDav8PCAsnMzCWk0YA2LS2Db75Zz48/biIgwI+ZM+8iKioCgB9/3Mgbb8wnL6+A999/plVydnTSz+L/g/Z8uOwK5/+3A1GnsfyPWuv6Hwut0loXaK3LgV1A10btE4DznTOoE7TW9d8FNJflQuBxpVQssAYwA12AicAnAFrreKDpR9aAUupOpdQ2pdS25fO/a/GT080+6Inb/+e1z+gzOIY+g2MAiN28l6gekcz78hlem/93/vvG/ygtKW9xjjNyGp+8pcTtZ+cPmzj/9imtEKiR5jq5BZm/ef9/XHj7lLM2YwWAbhr6dD7lSo3bR+wPmzj3tqlnnqmRZiI284FRc43qfu06chBXvfUUkx++k9glX9febjAYmPLqE1zz7ovkHDhE/uFjrgldP1mzT8Dlq2ld7X7ba/91DKdYC6fQJnVrAmG9Y2oPlW0z7bwumt8ltyxzn7GD+Ou8J7n+qTtYveAb1wSr7xRqt9m6cUrduZvg6Eju/OBFbnrrcX6et4yK0rLa+2uqqknekkCPcUNclbhRtqa3NdfHlZVVeHiYWLHiLa699iJmzJhbe98FF4zhu+/e45//fJK5cz9plZwdnfTz759Bnb2f9qqtZzKraTjQNdf7vcL5/xpOnLP+8uZG95U0+rui3u9NHlNrvU8pNQy4FJitlPpBa/38SbIo4I9a6731H8e5kzjxK0jd+uYB8wDi8776zfYA3y3/hZ+c51R279OZ3Exb7X252QUEBDX/ae6y/35Poa2Yh2ffWnvbz19v5aqbHYdqhHcOIiQigKOpWfTo1+VUopySLV+uZ8f3jvNMInp0oSC7Lm9hTgG+gc3Pup5IZspRvpy7mBufvxsvv9Z5M7b5y/Vs+86RObJnFwpybLX3FeQU4NeCzEf3H2bpy/MBKC0sZt/WXRiMBvqOHejSzNu+WsdOZ+bwnl0obNDPNnwCWzYzkZlylK/eXsy05+9xWT/v+X4t+1b9CkBQt66U5ObX3leaa8PT2jCjh68PlaVl2GtqMBiNlOTlN5jpPC6sb3c2/CuH8sJizH51F1wyeXsR2rcHR+N2Ye0Sccb5t321jth6tVxYry6Kcm2nNfvTmjrittcR6hgg4dt17PrRUcsh3btQnFNXyyW5Nrwb1anZz4fKknq1nGtrMNMJcOCXHfQY3zozU/V1xLrY3Chz4RlmPi5qQHf+9+ZCSgqKm1ysraViv1lH4g+Omgjt0YWiejVR3My/t6efDxX1aqIo11Y707lr1SaG/+EClFJYwoPxDw0k/0gmYT2jAEjdsYuQmM54W07veTdn4cKvWbr0ewAGDOhBRkbddQ4zMprOrgGEhgZy4YVjAcdg54kn5jZpM2JEfw4fTicvr4CAdraPbAvSz+L/m7YeZGbiOMcyECgGLgdaMq2XCgwDtgBXn0kQpVQEkKe1/kQpVQzc+huLfI/jXM2/aq21UmqI1nonsA64EfhZKdUfcNmI4uKrx3Px1eMB2L5hF98t38C4C4awP+kwXt7mZg+VXbVyE7Gb9vL0O/c0OBw2KNRCwrb99Bkcgy2viGOHsgiNdO35HSOvmMDIKxyn0+7bksTWL9fTf9JQju49hIe3uUVvzAuy8ljy4gdc9fDNBHYKcWnO+kZdMYFRzsx7tySx+cv1DJg0lCN7DmFuYea/f1R3+MqKNxbSc2Q/lw8wAYZfPpHhlzsuBrB/SxLbvlpHv0lDObo3tcWZC7LyWP7Sf5n695sJjHRdP/e+aBK9L5oEwJEdiez5fh3RY4eRsz8Vdy/PJgNIpRRhfXtyaNNOoscNJ3ntZjoPd/RdYUY2vqFBKKXIPZhGTXU1Hr7elBcWYTAaMXl7UV1ZSXriXvpPOd8l+ev38YGtjj7uO3Eox/am4uFlbneHynbEba8j1DHAgEsmMuASR87UbYkkfruO7uOHkbkvFZOXucmAQilFZP8eJG+Mpcf4Yez5eTPRIwbU3l9RUsaxXQc4/8HpLs3ZnI5YF/X3yfuc++T+k4ZyZG/L98m5x7IJCHfsO44dSKOmusYlg+PBl05k8KWOmji4LZG4b9bRa8IwMvalYvJuun9QStF5QA/2/xpLrwnD2PXzZrqNdNSEb3AAafH76NSvOyW2QvKOZuFf77DKPeu303uiaz+QuPHGy7jxRseVuNes2conn3zFZZdNJC5uL76+Xs0Ofs4/fzSbNsVz9dUXsGVLYu0hnIcOHaNLl3CUUiQlHaCqqhqr1XUD4o5M+vn/l3Y8wXjWqJMdtnFWAih1P3A/kAIcxTFwPAfn134opYKAbVrrKKVUFI5zMvs7l+0NLMUxQF0N3ORsdyuOrx35i7Nd47+/Al7XWq85/hUlOAarrwF2oAq4x7n+VOeyOc6LDL2utT5HKeUJzAHG4qilVK315c7bPwT6ArFAd+D+k32FyanOZNantea/r68gdvNeTB7u3Dfzerr1cVwmfdbf/s3dT1xLQLA/141/hOAwK2YvxwVRRk0awDV3XEhedgH/fPHT2osFXXnzuUy8+NReuBLzW/7ZhNaab/61nOTtu3H3MDH1oRuI6OmYNV349HtMeWAavoH+bP5iLRuWr6I4vwhviw89hvdlyoPTWDlnMbt/jau9wqHBYODOtx8+5fW7ncbWrrXmq38tZ/+23bibTfzhoRuIdGb++Kn3uPLBafgF+rPxi7X8sqwuc88RfbnywWkNHuv4ILMlX2FSYW95aK013727rLafr3joRiJ6ODIvfuY9Lr/f0c9bVq5l4/KfajN3H96Xyx+4ga/mLmLPhnr9bDRwx9xHTmndacWndliw1prNHyzlaNxu3EzujLvnJoK6OY5e/2n2vxh71w14BVgoysxh7dwPqSwuISCqMxP+Oh2juzsJX/xI8rrNGIxG3EzuDLvpKkJ7dyPv0FE2/GsB2m5H2zVRY4Yy6OpLTpol0tt+Spkb5//+vWUcdPbx5Q/eSLizj5c88x6XOvt468q1bPqsro+7De/LZfffQHF+IR8++BoVpeUogwGT2cSd787Aw+vUrhHmbmjZ7qKtt70a3bHqGCCv4tQya61Z/+9lHN65GzcPd879y02EdHfk/OrFd5l87w14B/hTkJHDj29+SHlxKcHRnTj/QUctA+xZvYnDO3dz4d9va/DYP7z5IccSD1BeVIynvx8jrr+UvuePOWGWYHPLarmt6+J0TiLQWvP1v5ZzwJn5ynr75E+cmf0C/dnUTOapD05j/bKfiFu1FaObY99x4R1TWvQVJrbK306ttebnectI3eGoiQvvv4kwZ0387/l3ueAvN+AT4I8tI4dv3viQ8iLnV5g8NB03d3eK8wr4fu4nlDgvhjbiDxfQ55wRgOPc3f/86Sluf+9ZPLxPbX9xd5+oU35+x/M///x7rF+/A09PD2bNeoABA3oA8Oc/P8uLL/6V0NBACguLefjhN0hPz8bLy8xzz91H797RzJu3nC++WI2bmxtms4lHHrmtRV+t4dnFtecWzn/nr0wY04cgqy9ZOQW88OZy5i9Z49J1lB1+rsXLtGU/u7qPofX7uezw4g43Zksv/fKsDbDCva5ol/3T5oNMcXqDzLZ0OoPMtnY6g8y2djqDzLZ0qoPM9uR0BpltraWDzLZ2OoPMtnaqg8z2pKWDzLbW8fYWpzbIbG9aOshsa60xAGptpzPIbEsds4873iAzo2zlWXuxDvOc0i77p+PtMYUQQgghhBBCnBGlVIBS6kfnVzz+qJSynqSt0fm1jV+dymPLIFMIIYQQQggh/v95HMc3cPQAVjn/PpEHgN2n+sAyyBRCCCGEEEIIF1Fn8ecMTQXmO3+fD1zZ7PNRqhNwGfCfU31gGWQKIYQQQgghRAeklLpTKbWt3s+dLVg8VGudDuD8/4kuFz4HeBTHBVJPSce7gosQQgghhBBCtFPqLF6KR2s9D5h34izqJyCsmbuePJXHV0pdDmRprbcrpc451VwyyBRCCCGEEEKI3yGt9Qm/NFwplamUCtdapyulwoGsZpqNA6YopS4FzICfUuoTrfVNJ1uvHC4rhBBCCCGEEC7Sgc7JXAnc4vz9FuCLxg201k9orTtpraOA64HVvzXABBlkCiGEEEIIIcT/Ry8DFyil9gMXOP9GKRWhlPrmTB5YDpcVQgghhBBCCBfpKLN4Wutc4Lxmbj8GXNrM7WuANafy2B2lD4QQQgghhBBCdAAykymEEEIIIYQQLnI2ry7bXslMphBCCCGEEEIIl5GZzHbguZ3+bR2hRUI9q9s6QosVVnW8z1MCTKf8fbftgsmg2zpCix0u6Xi7QFMHK2WD6nh1kVNhbOsILRbtU9XWEVoks6zjbXtRHayPATy7PNPWEVqk7PBzbR2hxaSPRfNkKrODvV0RQgghhBBCCNGedbyPEoUQQgghhBCinVIykykzmUIIIYQQQgghXEdmMoUQQgghhBDCRZSSeTzpASGEEEIIIYQQLiODTCGEEEIIIYQQLiOHywohhBBCCCGEy8iFf2QmUwghhBBCCCGEy8hMphBCCCGEEEK4iHyFicxkCiGEEEIIIYRwIZnJFEIIIYQQQgiXkZlMmckUQgghhBBCCOEy7XImUyn1q9Z6bDO3BwNfASbgfq31+jNcTwTwttb66hPcHwV8pbXufwbrKNZa+5zu8idSlJRI+rLFoO1Yx04g+KJLG9xfkZHOkQUfUp52mNArriLogosAsFdVkfLmK+jqarTdjt+QYYRePtXV8QDITUhi36KlaLudiInjiLrs4gb3a63Zt2gpufGJGE0m+txxC35RXQDY8PAMjGYzymBAGQ2MfGYGAMkrVpKzMw6UwuTnS987bsHDanFZ5oLERA4vWYK22wkeP57wSy5pkvnwkiUUJCRgMJmIvvVWvLt2BSBj1Spy1q9Ha03whAmEnX8+AKVpaaR+8gn2qiqU0UjXG27AJzraJXmz45PY4+zjThPHEXN50z7es3Ap2c4+HvAnRx/XVFaxZfbr2Kur0TV2wkYMpftVVwBQePgIu+YvpKaiAs/AQAbefTtunp4uyQuQFZ9E4idL0XZNl0nj6HHFRU0yJ32ylMy4JIweJgb/eTqWqC6U5eaxc958KmyFYFB0PWc8MRedC8CuxZ+REZuAwc0N75AgBv9pOu7eXi7LnJuQxP5FS0HbCZ8wjq7N1PL+RUvJS0jE4Kxl366OWt74SL1aNhgY7qzlosNp7Pt4kaMuDAZ63jwNv5i2rYva++12Nj47G7PVwtCH7gNg/2crydoZh3Jue/3/dAtmF2572fFJ7F7oqItOk8bR7fKmdbF74VKy45Icmf88HX9nLW+e9Ua9Wh5Cjz84ann//74ibc0vmPx8Aeh59VRCBp327ryJ1thfAGSuXk3Wzz+DwYBlwAA6X93sS9QZ0Vqz8+NlZMQ6+nPk3Tdjje7SpF1xVg6b3vmAyuJSrNGdGXnvLRjd3Cg8msHW9z8hPzWN/tdeQe/Lz29mLWcuLyGRA4uXorWd8Anj6XJp01pOXryE3ARHLfe6/dbaba+6tJS9Hy2g5OhRlFL0vHU6/t27kfrFl6Sv+wV3X8dLc/QfriRw4ACX5NVas+Wj5RzdmYSbh4lx99xMYEznJu2KsnJYN/dDKopLCYzuzPi/TMfo5sbhrfHELv0KlMJgNDDilqsJ7d2NgmOZrJ3zQe3yxVm5DL7mMvpeNtkluU/Ve6/dxSXnDSE7t5DhFzx6Vtd9IlprXnppHmvXbsds9uDllx+gX7/uzbabM2cB3323AYPBwLRplzB9+hR++mkTc+cuxGBQGI1GZsz4E8OH92uDZ+Igffz7oJTM47XLQWZzA0yn84A9WutbTvWxlFJuWuvqE6znGOD6V+9Wpu12ji1ZSPT9f8PNYuXgKy/iO3Aw5vCI2jZGb2/Cr5lGUdzOBssqNzeiHngYo9mMrqnm4Buv4NuvP17R3Vyece+CxQx5+AE8AqxsfX42QYMH4hNZlzE3PpGyzCzGvPw8hQdT2LtgESOeerz2/qGP/Q2Tb8PxeddLLqDbH6YAkPbjalJWfk3vW250WeZDixbR86GHMFmt7Jo1C8ugQXhG1GUuSEykIjOTAS++SElKCocWLqTvjBmUHj1Kzvr19HniCQxubuybOxfLgAGYQ0NJW76ciMsvxzJgALaEBI589hm9H37YJXl3L1jM8EcewBxgZeNzswkZ0rCPc+ITKc3MYsIrz1OQnMKujxcx+unHMbi7MeKxh3Azm7FX17Bl1msEDeiHpXsMSR8uoNd1fySgd0+OrNtAyjc/0uOPU8447/HMCR9/yuhH78czwMr6Z14mbOhAfCPDa9tkxSdRnJnFua89hy05hYSPFjPh2cdQRiN9p/0RS1QXqsvKWff0bIL798E3Mpyg/n3ofe2VGIxGdi35H/u/+p6+113lssz7PlnM4L87anmbs5a96/VzXoKjlkfNdtbyx4sYXq+WBz/atJaTl60gasplBA7sT258AsnLVjDksb+7JO/p1sVxh35YjXdEGDVl5bW3RV96QW0dHPpxNclffE2/W1237SV9/CkjH70fc4CVX599mZAhDesiOz6JkowsJr7qqIuk+YsZ+8xjGNzdGPn4g7W1vOml1wka2A9r9xgAoi46j5hLL3BJzsaZW2N/UbhnD7bYWPo9/TQGd3eqCgtdnh0gIzaJ4oxsLnnzWfIOpLL9g085/4Wmb2jjF39Oz0vOpcvY4Wz772JSfv6V7hdMxOTjzZBbruHotrhWyQeOPt6/cDED//4gHlYrO16YTeDggXhHNNz2SjOzGDnrBYoOprB/wUKGznwCgAOLlxDQvx/97r0Le3U19srK2uU6XXAenS++0OWZj8buoigjm6vmPkPO/lQ2/fdTLnvpkSbtti/8gr6XTiZ63HA2/nsx+1dvpPeFEwgf0IvOwweglCLv0FHWzvmAq956Cv+IUKa86nhedrudZXc/SZeRg1ye/7csWLaW9+Z/z3/euvesr/tE1q3bTmrqMX744X3i4vby7LPvsmzZG03arVixivT0HL799l0MBgO5uTYAxowZxHnnjUIpxZ49KTz44Ct89917Z/lZ1JE+Fr8XZzzMVkp5K6W+VkrFKaUSlVLXKaWGKaXWKqW2K6W+V0qFO9uuUUq9pZRap5TarZQaoZRaoZTar5R6sd5jFjeznsHAq8ClSqlYpZRn/XZKqauVUh85f/9IKfWmUupn4BXn328rpX5VSh1USl3tbBellEp0/t5PKbXF+djxSqkezoc2KqX+rZRKUkr9oJTydLbvppT6zvkc1yulejtvj1ZKbVRKbVVKvXCm/ducstQUPIJDMAUFY3Bzw3/YSIriYhu0cfP1wysqGozGBrcrpTCazQDomhp0TQ2tcdx44cFUPENC8AxxZAwdOYKcnfEN2mTvjCds7GiUUvh3i6G6tIwKW8FJH7f+jFpNRSUo12UvSUnBIyQEc7Ajc8CIEeTHNXwDZYuNJXDMGJRS+MTEUFNWRqXNRnl6Ot4xMRg9PFBGI749e5K/0znAV4qacseb9ZqyMtwtFpfkLTiYildoCF7OPg4fNYKsRn2ctTOeiHGOPrZ0j6HK2cdKKdzq1YG9pqa2L0vSM7H2cpR/YL8+ZG7f4ZK8APnJqXiHBOPtzBwxejgZOxr2ccaOODo7M1u7x1BVWkq5rQCzxR+Lc7bNzdOMT0QY5fk2AEIG9MXgrHVrt2jK8/JdlrlJLY8aQU5sw37OOY1aBkW1sy6qS8sxtYO6ACjPyyc7LoFOE8c1WKbxtqdcuO3ZDqbiHRpcL/NwshrVRdaOOCLr1UW1sy4a17KuqXFpthNprf1F1tq1hF18MQZ3dwDc/fxaJf/R7fFETXC86QvsEU1VaRll+Q1rVmtNVtI+Oo0aAkDUhFEc3eaoJbO/LwHduqIavca4UuHBFMe25+zjkJHDyd3ZsI9zY+Nqtz2/ettedVkZBfv2EzbBUccGNzfcvFx3dMOJpG2NJ2biSJRSBPeMprKkjNJm+jUjaR9dRzv6tdukUaRtdTwvd7NHbf1WV1Q0++qcnrAX39BgfIIDWvW5NGfDlj3k2Zq8RWtTq1Zt4sorz0UpxeDBvSksLCErK69Ju8WLv+G++67HYHC89Q0MtADg7e1Z2+dlZRVnZf9xMtLHvxfqLP60T66YybwYOKa1vgxAKeUPfAtM1VpnK6WuA14Cbne2r9RaT1RKPQB8AQwD8oBkpdRbWuvc5laitY5VSj0NDNda/8W5rpPl6gmcr7WucQ4+w4HxQG9gJbC8Ufu7gbla64VKKRNgBEKBHsA0rfWflVJLgT8CnwDzgLu11vuVUqOAfwHnAnOBd7XWHyul7vvt7mu5Kls+7lZr7d9uVitlqQdPeXltt5P88gtUZmcRMHEyXtExLs9Ynp+POaAuo0eAhcLklAZtKmy2hm2sFirybXhY/EEpYl+fC0oRec4EIs+ZUNsu+bPPSd+wGTcvT4Y++pDLMlfabJgC6l60TRYLJSkpTdvU63t3q5Uqmw3PyEiOfP451cXFKHd3bImJtYfFdbnuOvbNmUPa8uWgNX0ee8wleRv3sdlqwXawUR/n25q0KXf2sbbb2fjMLEqzsul83iQs3RyHavp2iiB7ZxwhQweTuXWHSwds5fk2PAPr5QmwYmtUF+V5DTN7Blgdt1n8a28rzc6l4FAalm5RTdaRtu5XIkYNc1nmClt+kzotbKafPRrVe/1ajnvDWcuTJhDhrOUe064h7s23SV7yGVrbGTbDNYdFnWld7Fm0lJ7X/aHBLOZx+5d/zrFfN+Pm6cmIx1y37ZU3ztNcXeTbMDeqnYp8R11ou50Nz8ymNDObLvVqGeDwqjUc27AZv+gu9Jn2R9y9vV2SubX2F+WZmRQfOMDRzz/H4O5Op2uuwScqyiWZ6yvLL8AzwFL7t2eAhbJ8G57Wuu2ssqgEk7dn7Qc4XoFWypwf7JwNlbZG25XVSmFKc9teQL02Fipt+SiDEXdfX/Z+MJ+StCP4RHWh+7TrMHp4AHB09RoyN27Ct2tXYq672mV1UZpvw7tenXoFWijNs+FVr18rikowedX1q3eAldK8uoHooS1x7Fi8kvKCIs57/O4m60j9dTvR41y3j+voMjNzCQsLqv07LCyQzMxcQkIaDsLT0jL45pv1/PjjJgIC/Jg58y6iohyz4j/+uJE33phPXl4B77//zFnN3xFIH4vT4YoDhhOA85VSryilJgCdgf7Aj0qpWGAm0Kle+5X1lkvSWqdrrSuAg85lXWWZ1rqm3t+fa63tWutdOAaPjW0EZiilHgO6aq3LnLenaK1jnb9vB6KUUj7AWGCZ8zm+j2MQCzAOWOz8fcGJwiml7lRKbVNKbUv5auWJmrXAqX+SoQwGus94hl4vvUZZagrlx466YP2nsuJGGbVupo3jf8NnPMLI555k8N/+wpHVa8jfu7+2Sbc/Xsn4N2cTNnokR1atcV2+ZvOcSmaFZ3g44RdfzN633mLf22/j1akTyvlJXtbatXS+9loGv/IKXa69ltT5812Ut5kojepAn6SPlcHA2BdmMunN2RQcTKXoiKMO+t0+ncOr1rLxmVlUl5djMLryqPpm8pxK7dZrUl1ezrZ33qf/jdfg3uhc0X0rv0UZDUSOHXlmMetrNnKjfm6m0fEmQ594hBHPPsmghxy1bHPW8tGf19H9+msY+8Zselx/DXs+POHu4ozznmpdZMXGY/LzxT+qa7MP3ePqK5n05mzCx4zkcCtvey35pFsZDIx/4UkmvzWrQS13OXcik157gXEvzMBs8Wf34s9cFrm19hfY7VSXltLniSfodPXVJL//fvP/Xq2Rv3GdNFvXZ/FT81N52id4HtpeQ9Ghw0RMnsSwZ2diNHlw+JvvAIg4ZxKjXn6RYc/MxGTx5+CSxp85n0HkZv/JG/fZiffLAF1HDuKqt55i8sN3Ervk6wbNaqqrSdueQJRzFlScap9DZWUVHh4mVqx4i2uvvYgZM+bW3nfBBWP47rv3+Oc/n2Tu3E9aM26HJH3ccuos/tdenfEgU2u9D8dsZAIwG8dMX9L/tXffcVJV5x/HP99dWJbO0gVBEFFEqoAVscQWC5pEY0GNmsREjS3R/NRo7BpNLGgSlaiE2GKNIvYKFnpHwI4oCrj03vb5/XHvLLN9F2e5947P29e+dufO7PLd453ZOfec8xwz6xN+9DSz9IUPG8LPRWlfp26XeDcr6aZw+uq0iv75tK/zS923ptTt9H+rzP8RM3sMGAysA16VdEg537clzJgDLE/7HfuY2e4V5Co/uNkwM+tvZv07H1Oz9W51mxWwadnW0aXNy5ZRt2mzGv0MgNwGDWi4626s/nBWjb+3KvkFBSVGwDYsXU69UtMB65V+zLKtj0kV88lr0oRWe/YpM3IE0GafASyePLXM8W2VV1DAxqVbp39sXL68zNTWvIICNqa1/aZly6jbNLhC3WrgQPa4+mp2v+wy6jRsSH6b4FrGkg8+oGDPPQEo6NeP1fPmZSRvfvOS7bd+2fIyRZDKe0x+qd+pbsMGNO+2K4UzPwSgUbu29L/sIva97kp22GcA9Vu3JFPyCwpYtyQtz9Jl5Kdd4Q8yNyuRed3SZcUFZoo2b2HS3cNov+9e7DCg5Jusr94dy+KpM+n727Mz+ka4svO0OHNBARtKne95FZ3L4UjMwg/G0qpf8Du0GtCPlV/My0je73NeLP/kMxZPncHoP1zJ9HsfZMmcucy4/yFK22GfASyalLnnXpk8S5cFo8DpjyloxvpS507p3ys4l7vy3YzZANRr2qS44NKOBw5kxefzMpa5tl4v6hYUUNC3bzDFtnNnJLF5dWamzn3y2mheu+JmXrviZvILmrJu6fLi+9YtLTmKCVCvcSM2rlkXTKcH1i5ZVmJGQW3LK2hW8nm1bFnZvyPNC9iQ9v9hw7LguVevoIB6BQXFxbRa9t+T1V/OD35u2nmxw6CB3/u5N/fV0Yz84y2M/OMtNChoypq083Ttkgrade3Wdl2zdFmJkc6Utt13YdWiQtav3Pr/f8HU2TTv3IH6zWpnGnVSPProixx33IUcd9yFtG7dnIULC4vvW7iw7AgbQJs2LTj88KDkx2GH7ctHH80r85gBA3owf/63LE0bWf6h8jZ231cm1mS2A9aa2SPA34C9gVaS9g3vrytpm0pImdmfUp24Ch6ySNLuCko4fa8qH5J2Bj43s7sJRlt7VZJrJfCFpBPD75Wk1Ar894GTw68zUxWjlPo7dWLD4kVsLPyOos2bWTF5Ao17Va8AwOZVq9iydi0ARRs3snruHPLats14xsadd2Lt4sWs+66Qos2bWTRhIi37lmzSVn17sfCDcZgZKz77nDr186nXrClbNmxgczhVb8uGDSydNYdGO7YHYO3CRcXfXzhtBg12KG9Qets07NSJDYsXs6EwyLx04kQKepds12a9e7Nk7FjMjNWff05u/frFnYlUgY4NS5awbMoUmg8YAEDdZs1Y9fHHAKyaO5f81q0zkrdJ551Yu2gxa8M2/nb8RFqXauPWfXrxzftBGy//dGsbb1y5ik1rgvNgy8aNLJk9l4Y7BOfBhvD3sKIiPh/5Eh0OHpSRvADNdt6JNWmZvxk3ibalMrft24uvwszLPv2cug3qB1MizZj+4MM0ateWLj8uWcly8YwP+fTF1xhwybnUqZeXsbwQnMvrFqWdy+Mn0rJPycwt+pQ6lxtUcC5/OIeG7YNzuV6zZiz/KDgvls35iPptoj8vdj3xJxx051848Pab6X3uL2mxezd6/SZY6bAm7bm3eOoMGmbwude0c8nz4tvxk8pm7tuLBWnnRZ36wXmxoZxzuVG74Fxen7YudtHkaTTesR2ZUluvFwV9+rBq7twg/6JFFG3ZQp1GmSlQ3vXwAzn8lis5/JYrad+/N/PeHY+ZseSTL6hbv36ZzpAkWnffla/HBxcU5r07nvb9K/zTmHFNOncq8dxbPGESLfqUbOMWvXsXP/dWfvY5dRrUp16zpuQ1bUq95gWsXbgQgOVz5tKgXTDhKH29dOGUaSWKeG2LbkccyODbrmDwbVfQcUAvPh8zATPju4+/oG6D+mU6kJJo231XvhwXtOtno8fTIWzXlQu/Kx65XvL5V2zZvJl6jbdO5f3i/Ul03s+nyg4ZcjTPP383zz9/N4ceug/PPfcWZsa0aXNp3LhBuR2gQw/dh3HjgjXFEybMKp7G+eWX3xS3+YcffsqmTZspKPhhd+LB2/j78pHMzKzJ7An8VVIRsAk4F9gM3B2uz6wD3AV8mIF/q7TLCbY0+QqYBXyfv8QnAadJ2gQsBK4HKnsGDAHulXQVUBf4LzAduAh4LFxzmsG5WVspN5d2J53KvL/fhRUVUbDv/uS3a8/SMe8A0HzQQWxasYLPbr2RovXrQKLw7TfoevX1bF6xnK//8xBWVARmNO03gCY9M1+hLic3l92GnMTU2++GoiJ2OGA/GrVvx9dvjwFgx4MH0aJXDwpnzGLs/11NTl4e3X8ZFA3euGIlM/4eVB2zLUW02WcALXoG1yk+ffo51i5chCTyWzRnt1+cmrHMys2l4ymn8NFdd0FRES3335/67dqxePRoAFofeCBNe/ZkxaxZzPzTn4q3JEj59L772LxmTfE2JXXCNT6dTj+9eJuDnDp16HT66RnJm5Oby+6nncTkv92NFRXRPmzjr94K2rjDIYNo2bsH382Yxbt/vJrcenn0CNt4w4oVzPzXiOLzoM1e/WgddpwWjpvI/DeD37lNv760P6CiYs/blrnHGScz7rZ7MCuiw6D9aLxjO+aFmTsdMojWvXuwePos3rrsz+Tm5dHnV2cAsPTjz/j6/fE07tCe0VfdBEC3E4+jTe8ezPzPExRt3sy42+4GguI/vc7KzLmRk5vLrqedxPQ7gnbeYeB+NGzfjgXhudw+PJeXzpjFuMuvJjcvj25nbz2XZ6bO5aIi2uy99Vze7Ren8cnjT2JbtpBTt27GqiR/n/OiMh8/FTz3kKjfojndz8zccy8nN5fup5/MxL/eE267EpwX88PMHQ8ZRKsw8+jL/kxuvTx6hefFhuUrmPGvEVBkmBXRdq9+tO4TbEfx0RPPsnL+1whRv2Vz9jgrc9f9auv1ouX++/PFiBHMuvZalJvLzmedVStTVHfoswffTvuQly65ljr18hjwm9OK7xtz6z8YcM4Q6hc0o9cpxzPunoeY9dQLNNupA50P2heAdctX8MZVt7Fp3Xok8ckrb3PkbVdRt0HmtjtSbi67DDmZmXcOxYqKaDtwfxq2b8c37wRt3O6gA2neqwdLZ85kwhVXhVuYbD2Xu556MnOGPYht2UJ+y5bF933+1DOs+eorkMhv0YKuZ5xW7r+/Ldr33YOvp37IsxddR528uux/7taf/cYt/2S/35xKg+bN6DfkOEYPHc7UJ0bRvFMHuh4StOuX46fx2Zjx5OTmUievLgdevHVmxuYNG/l25lz2PeeUjOWtqRH3XMAB++5Oy4LGfDr+79xwx9OMeOKdyPIAHHhgf0aPnsRhh51D/fr1uPnmi4rv+/Wvr+XGGy+gTZsWnHPOCVx66e2MGPE8DRrkc9NNFwLw6qsf8Pzzb1GnTh3y8/O4884/RlqYxtvYZQvVyloPVyM/e/PdRP1PaFO/3B1hYm3lpuTtV9Q8ryjqCDWSl5Oo0xiA1ZuT90cuL2Gnco6Sd14Ubqi9iqm1pXOjTVFHqJFF62K5g1qlOiWsjQFuGFx2unucrZt/XdQRaqx+x2QVsUliG8OuiftjvXrTO9vtj1+jugfFsn0S9nbFOeecc84551yceSfTOeecc84551zGJG++inPOOeecc87FlK859ZFM55xzzjnnnHMZ5COZzjnnnHPOOZcxPpLpI5nOOeecc8455zLGRzKdc84555xzLkPkI5k+kumcc84555xzLnN8JNM555xzzjnnMsbH8bwFnHPOOeecc85ljI9kOuecc84551yG+JpMH8l0zjnnnHPOOZdBPpIZA9f0XRF1hBqZtSx5p02dBF5Q2lCUrNBfrU7eNauezYuijlBjdXMs6gg1ssWSdR4DLN2wOeoINdYqP1nn8h7NktfGyzcm7zVu3fzroo5QI/U7XhN1hBrzNq596+Y/HnWEGpOS97cv05L3iumcc84555xzLraSNyTlnHPOOeecc7HlI5k+kumcc84555xzLmN8JNM555xzzjnnMkQ+juct4JxzzjnnnHMuc7yT6ZxzzjnnnHMuY3y6rHPOOeecc85ljBf+8ZFM55xzzjnnnHMZ4yOZzjnnnHPOOZchko9k+kimc84555xzzrmM8ZFM55xzzjnnnMsYH8n0kUznnHPOOeeccxmTuJFMSY8DewDDzezODPy8l4BTzWx5BffPA/qbWeE2/vx3gEvNbNK2ZiyPmTH8zueY8sEc6uXncf7VJ7PzbjuWedzQax7hs7lfU6dOLrvs3oFzLj+ROnVyWbN6Hfdc+xiFi5axZUsRg089iIOP2SuTEcvkfeX+Z/lk4mzq1qvL8b8fwg67dCjzuAkvjGHcc6NZ9m0hlz1+Ew2aNgJgxtuTeP+pNwDIq1+Po8//OW13bl9reVOZX7rvWT4OM//0D0NoV07mcSPHMPa50Sz9tpDL/3sTDcPMKV9/9CXDfn8nP7/8THoc0KfWM792/zN8Omk2devlcewl5bfzxBfGMOH5d1j2bSG/f+zm4nae+fZExj79JgB5+Xn8+PyTaJPhdjYzJvz7aRZM/ZA69fLY/9zTabFz2YyrFhcyZuhwNqxeS4vOHRj4uzPIrVOH+RNnMO3JUSCRk5vDgF+cQJtuXdiycRMvX3sXRZs2U1S0hU5796XPz4/OaPZU/teHPcNnk2ZTp14ex148hLbltPGkF8YwcWTQxhc/urWNC79axIt3PcrCz77iwDOOYZ+f/ijjGUvnTeJzL+7ncSrnew8+w5dTgnP5R787jVZdyuZcuaiQ1+74NxtWr6Vl5x059KIzyK1bh6nPvcHHY4I/DbaliGULFnLW8FvIb9yQt/7+KF9OmkX9po05eeiVtZI9iefFy6Uyl/eaPD7MvPTbQv74+NbX5LljZ/LWwy+inBxycnI48jc/Yac9umQ84zsPPMMXkz+kbr08Dr/wNNqUc06sWFTIS3/7N+tXr6X1zjty5MXBObFhzTpevvM/rCpcStGWIvof/yP2+NE+LF2wiJf+Ojzt+5ew7ylHsefggzOe/6abhjF69GTy8+vxl79cxB577FLu4+6662FeeeV9cnJyOOWUH3PGGYN5441xDB36KDk5Ijc3lyuv/BX9+++R0Yw1cd9ff8OPf9SX75aspP9hf4wsR2neztlPPo6XrBaQ1BbYz8x6VbeDKanSjrSZHVVRBzPOpo6dy7dfFXLPU1fwm8tP5F+3PVPu4w44oh9D//t/3P7IpWzcuIk3R44H4NWn32fHzm3428OXcu0/zmPE3SPZtGlzreX9dNJsli74jgseuIpjLzyZF//+VLmP69B9Z864+Tyatm5e4nhBmxaceeuFnPvPyxl08hGMuvuJWsua8snE2Sz55jsufvAqjrvwZF6oIHPH7jtz5i3n0axUZoCiLUW8NvwFdtmzW23HBeCzSbNZ+s13nPevqznqgpN4+R9Plvu4Dt07M+Sm88u0c7M2LTj9Lxdyzj8uZ+ApR/LiPf/NeMYF02azauF3/GToNez761MY92D5/8bkR5+n+1EH89Oh15DXsD6fvDUWgB167saxt13B4NuuYL/fnsYH9z8GQE7dOhzx5wsZ/NcrGHzrFSyYPpvvPv4i4/lTbfzbYVdz1O9O4pV/lt/GO3bvzCk3lm3j+o0bcNhvfsbetdy5TEnicy8J5zHA/CmzWfHtYob8488c9NuTGT2s/LYZ+/BIeh97MEP+8WfqNWrAnDeDc7nv8Ydy0h2Xc9Idl7PPacfSrvsu5DduCEC3g/fmmKvPq5XckMzz4pNJs1my4DsuDDOPquQ1+Yyby74md+6zK+f+4/849+9/5LhLTmHk0MyfF/Mmz2b5t4s5694/c+h5J/PWfeW3y7sjRrLn4IM5697gnJj1RnBOTH9pDC06tOX0u67gxBsvZPTw/7Fl02aat2/DaXddzml3Xc6pt/+ROvXqsss+vTOef8yYycyb9w2vvXY/N9xwPtdee2+5j3v22Tf59ttCXn75Xl5++V6OPnoQAPvu25uRI+/m+efv5uabL+Sqq+7JeMaaePip0Rx3xl8izVAeb2f3QxBpJ1NSJ0mz0m5fKulaSe9IulXSBEkfSzogfMhrQGtJ0yQdED6uf/i9LcNRRySdKekpSS8Ar4W3n5X0iqRPJN2W9m/OC7+3oaQXJU2XNEvSSWlRL5A0RdJMSd3C72so6SFJEyVNlXRceLy+pP9KmiHpCaB+bbTdxDGzOPDH/ZDErj12Ys3qdSwrXFnmcXvutzuSkMQuu3dkyeLlqd+bdWs3YGasX7eBRk0akJtbe6fD3HGz6PWjAUhix26dWL9mHauWrijzuB267EizNi3KHO/QvTP1GzcAYMdunVi5ZHmtZU2ZM24WfcLMHXbvxLrV5Wdut8uOFJSTGYJRzj32702jZo1rOy4AH42bSc9D9grbuXOF7dy2S4cK2nnn4nZuv1snVtVCO381cQY7Dwoyttq1MxvXrGPtspIZzYyFH37MTvv0BaDLgXvz1cTpANTNr1dctW3zhg3Fqx4kUTe/HgBFW7ZQtHkL1EJ1t4/Hb23j9mEbr65BGzds1ph2u+5ETi0+39Il8bmXhPMY4IsJM9ntoCBn292Cc3nN0rLn8oKZH9Nl3z5A0Hn8YsKMMj/rk/cm0/WAfsW32+2xC/XC36E2JPG8mJv+mlxF5vJek+vV3/rasWn9xlp5ffhswkx2D8+JHXbrzIZyXh/MjK9mfkzX/foA0P3gvflsfHhOSGxctx4zY9P6DeQ3alDmteKrGR/RtG1LmpRzYfP7evPNcRx//CFIok+fbqxcuYbFi5eWedzjj7/E+eefTE5OkK1Fi2YANGxYv7iN163bEHmFzfcnzGXp8tWRZiiPt/MPgbbjRzzFebpsHTPbS9JRwDXAocBgYJSZ9YEqywPvC/Qys6WSzgT6AH2BDcBHku4xs6/SHn8k8I2ZHR3+7KZp9xWa2Z6SzgMuBX4F/Al4y8zOltQMmCDpDeA3wFoz6yWpFzDl+zRCRZZ+t4IWbZoV327RqilLv1tBQcsm5T5+8+YtjHllMmddcjwAR56wP7f+8SHOOfY61q3dwCU3nF78IlYbVhUup2mrrXmbtGzKqsIVNG7etOJvqsDU18axS7/dM5iufCuXLKdpy2bFt5u2bMrKGmReWbicOR/M4Ky//I4FH8+vpZQlrVqygiYl2rkZq5ZsWztPe20sXWqhndcuW07DFgXFtxu0aMbapctpULA144ZVa8hrUJ+c3FwAGjYvYG3aG7UvJ0xnyuMjWb9iFT+6/LfFx4uKihh1+a2sWvgd3Y4YRKuunTKef/WSFTRJOy8atwjauNE2tPH2kMTnXhLOY4A1S5fTqOXWc7lhi2asWbqChmk5169aQ17DtHO5RTPWLCnZ6di0YSPzp87hgF+dWCs5y5PI86JweanzomavyQBzPpjOG/8exZrlqxly3TkZz7h66XIap50TjVo0Y/XSkq8P61etoV7aOdE4fAxAn6MH8fxNwxh29lVsWreeoy49C5X62/zRe1PolnZBIpMWLVpC27Yti2+3bduCRYuW0LpUh/arrxby0kvv8vrr42jevAlXXfUbOnVqB8Drr4/l9ttHsHTpCu6//5payZl03s7uhyDO02WfDT9PBjptw/e/bmbpl4XeNLMVZrYemA3sVOrxM4FDwxHUA8ws/V1AeVkOBy6XNA14B8gHOgKDgEcAzGwGUPaSNSDpHEmTJE16esQrNf7lrNwfWvHjH/jrM+zeZ2d277MzANPGf0Snru0Z9sI1/HXEH3jw9v+xds36Guf4XrbhytsX0z9h6mvjOPTswbUQqJTyGrkGmV+6/38cfvbg7TZiBYCVDb0t17jmTf+Yaa+N45Czjvv+mUopJ2I5F4zKe9DWL3faqzc/ufNqDr70HKY98WLx8ZycHAbfdgUn3nsjhZ9+ybL532QmdHqycn+BjP8ztSv2z734n8dQzXOhGo+ZN3EmbbvtXDxVNjIxPy/Kf0muWebd9+vNBcP+xMlX/5K3Hn4pM8HSVePcLfe8Cc2bOodWndtzzkM3ctqdl/P2sKfYsHZd8f1bNm3mswkz6bp/30wlLpWt7LHy2njjxk3Uq5fHs8/eyc9/fgRXXjm0+L7DDtuXV165j3/8408MHfpIreRMOm/n7Kft+F9cRT2SuZmSHd38tK83hJ+3UHHO9O/PL3XfmlK3N6R9XeZnmtnHkvoBRwG3SHrNzK6vJIuAn5nZR+k/J3yRqPgvyNZ/bxgwDGDG0lFVPh7glaff441wTeUuu3dgyaLlxfct+W4FzVuWfzX3qQdfZeXy1Vx6y5nFx95+cSI/OT2YqrFDh5a0btecBfMW03WPjtWJUi0TXniXKa8G60zade3Iiu+25l1ZuILGLcofda3Ioi8W8MLQxxly/W9p0KR23oyNf+FdJr0SZG6/a0dWFC4vvm9F4Qqa1CDzgk/m8+RfRgCwduVqPp44m5zcHLrv1yujmSeNGsPUMPMOu3ZkZYl2Xk6jFjUbmVj0xQJG3f04p1x/bsbaee6ro/n4zQ8AaNllJ9YsWVZ839oly6lfUDJjvcaN2Lh2HUVbtpCTm8uapctKjHSmtO2+C+//s5D1K1eT32RrwaW8hg1o070rC6bPpqBju++df9KoMUxLO5dXpp0Xq5Ys36bRn9qUxOdeEs5jgJkvj2H268G53HqXjqwu3Hour1mynIalztP8Jo3YuCbtXF6yvMRIJ8Cn702h68DaGZlKl8TzYnypzCu/Z+aUTj134X93PMqaFavLFGurqWkvjWHWa8E50aZrR1alnROry/n/Xb9JIzaknROrliwvHumc/eY4+v/0MCTRbIdWNG3TgmVfL6Ltrp0AmDdlNq137kDDZtv2e5fn0Udf5MknXwWgZ8+uLFy4tc7hwoVlR9cA2rRpweGH7wcEnZ0rrhha5jEDBvRg/vxvWbp0Bc1j9hoZBW9n90MTdSdzEcEayxbAauAYoCbDevOAfsAE4ITvE0RSO2CpmT0iaTVwZhXf8irBWs0LzMwk9TWzqcAYYAjwtqQeQMZ6FEeeMJAjTxgIwOT3Z/PK0++z/2F9+eTD+TRomF/uVNk3R45j2riP+PM955aYDtuyTTNmTvqE3fvszPKlq/jmy8W0aZ/Z9R17HXsAex0bLKf9eMKHTHzhXXocuCcLPvqSeg3za/TGfMXipTxx40P85NLTabFj64zmTLf3sQewd5j5owkfMv6Fd+l54J58PfdL8muY+Q//3jp95dnbH2XXvfbIeAcToP8xg+h/TFAM4JMJHzJp1Bj2OHBPFnw0r8aZVyxeytM3PchxfzidFu0z187djjiQbkccCMDXU2Yx99UxdN6vH4WfzKNug/plOpCSaNt9V74cN5XO+/fns9Hj6dA/aLuVC7+jcZuWSGLJ51+xZfNm6jVuyPqVq8jJzSWvYQM2b9zIt7M+osfgQzOSP72NP50YtHH3QXvyzUfzqNcgP3ZTZZP43EvCeQzQ88eD6PnjIOe8SbOY9fIYdhnYj0UfzyOvQX6ZDoUk2vfoymdjp9F1YD/mvj2ezgN6Ft+/Yc06vpn9KYdefEZGc5YniedF+mvyx+Frco8D9+Trj2r+mrzkm+9ovkPw2vHNp1+xZfOWjHSO+xw1iD5HBefE55NmMf2lMex2QD8WfjyPvIZlXx8k0aFnVz75YBq7HdCP2W+Pp8tewTnRuFVzvprxMTvusQtrlq9k6YLFNE2bVjn33cl0G5TZCxJDhhzNkCFBJe533pnII4+M4uijBzF9+kc0btyg3M7PoYfuw7hxMzjhhMOYMGFW8RTOL7/8ho4dd0ASH374KZs2baagIHMd4iTzdv5hiXqdbByosmkb2yWAdCFwIfAFsICg43gQ4bYfkloCk8ysk6ROBGsye4Tf2w14kqCD+hZwWvi4Mwm2Hfld+LjSt0cBfzOzd1JblBB0Vv8KFAGbgHPDf39e+L2FYZGhv5nZQZLqA3cB+xGMas4zs2PC48OB7sA0YBfgwsq2MKnuSGY6M+PBvz3LtPEfkVevLudfdTJddg/KpN/8+3/x2yt+TvNWTTlp4GW0altAfoOgIMreB/bkxF8eztLvVvCPG/9bXCzo+NMPYdCR1fvDNWtZza9NmBkv/fNpPps8h7r18jjuklNpt2swavron+9j8EWn0LhFU8Y/P5r3n36T1ctW0bBZI7r2787gi09h5F2PM+eD6cUVDnNycjjn7kur/e/X2Ybnupkx6p9P88mkOdTNz+Onl5xK+zDzf66+j+MvPoUmLZoy9vnRvPfU1sy7DujO8RefUuJnpTqZNdnCZENRzUObGa/c+1RxOx97yRDadQ0yP37NfRxzYdDOE0aOZuzTbxRn3qV/d4656FRGDX2Mue+ntXNuDr8celm1/u2vVldvWrCZMf6hJ1kwfQ518uqy/7mn0bJLMHv9jVv+yX6/OZUGzZuxalEho4cOZ+PqNTTv1IEDLjiD3Lp1mfn863w2Zjw5ubnUyatLv9N+QptuXVj65QLe/+fDWFERVmR02ndPep/w40qztG9YVK3MpfO/et9TfB628TEXD2GHsI2fuOY+jgrbeOLI0Yx7Zmsbd+nfnaMvPJXVy1Yy/OK/smHtepSTQ15+HufceyX1GlSvRljdnJq9XET93NtiyTqPAZZuqF5mM+Pdfz3F/KlzqFOvLof87jRa7xLkHHXjvRx83qk0bN6UFQsLef2O4axfvZZWnXfk0IuDcxlg7lvjmD91Dof/4awSP/u1O4bzzaxPWb9qNfWbNmHAyUfR/dB9K8zSKr9m53LU58W2LCIwM17859N8GmY+Pu01+ZEwc5MWTRlXTubjLj6Fd596g+lvTiS3TvDacfgvB9doC5PlG6tObWa8Pewp5k0JzonDLzyNtuE58b/r7+Ww351Ko+ZNWb6wkJduH876VeEWJpecQZ26dVm9dAWvDn2ENWExtAE/PYzdDxoABGt3H/jV1Zx937XUa1i914vf7t6p2r9fKv/119/Hu+9OoX79etx880X07NkVgF//+lpuvPEC2rRpwcqVq7n00tv59tvvaNAgn+uuO59u3TozbNjTPP/8W9SpU4f8/Dwuu+ysGm2tUb9jZtcWjrjnAg7Yd3daFjRmceEKbrjjaUY88U5G/41186+r8fdE2c6ZbmOo/XZeN//xxPXYttiM7dbBylWvbW4fSc2BJwiWA84Dfm5my8p5XDPgAaAHwYzNs81sbKU/O+pOptu2TmaUtqWTGbVt6WRGbVs6mVGqbiczTralkxm1mnYyo7YtncyoVbeTGSc17WRGLXmvFtXrZMZNTTuZUauNDlBt25ZOZpSS2cZJ7GTO2o6dzB7fp5N5G8FMzr9IuhwoMLP/K+dxI4B3zewBSXlAg6q2gEzeK6ZzzjnnnHPOue/rOGBE+PUI4PjSD5DUhKCw6YMAZraxqg4meCfTOeecc845536I2pjZtwDh5/IW2e8MfAcMlzRV0gOSqlzQ7p1M55xzzjnnnMuQ7bmFSfq2iOFHiU2AJb0haVY5H9Xd36sOsCdwr5n1JdjB4/LqfJNzzjnnnHPOuYRJ3xaxgvsrLLUvaZGkHczsW0k7AIvLedjXwNdmNj68/TTV6GT6SKZzzjnnnHPOZYy248f3MhL4Rfj1L4DnSz/AzBYCX0naLTz0I2B2VT/YO5nOOeecc84598PzF+AwSZ8Ah4W3kdRO0ktpj7sAeFTSDKAPcHNVP9inyzrnnHPOOedchkjJ2HXFzJYQjEyWPv4NcFTa7WlA/5r8bB/JdM4555xzzjmXMT6S6ZxzzjnnnHMZ4+N43gLOOeecc8455zLGRzKdc84555xzLkP0/au+Jp6PZDrnnHPOOeecyxiZWdQZXC2RdE64QWtiJC1z0vKCZ94ekpYXPPP2kLS84Jm3h6TlheRlTlpe8Mwu+XwkM7udE3WAbZC0zEnLC555e0haXvDM20PS8oJn3h6SlheSlzlpecEzu4TzTqZzzjnnnHPOuYzxTqZzzjnnnHPOuYzxTmZ2S+K8+KRlTlpe8MzbQ9LygmfeHpKWFzzz9pC0vJC8zEnLC57ZJZwX/nHOOeecc845lzE+kumcc84555xzLmO8k+mcc84555xzLmO8k+mcc84555xzLmO8k+mcc84555xzLmPqRB3AZZ6knYCuZvaGpPpAHTNbFXWuikjqAXQH8lPHzOw/0SWqnKTbgBuBdcArQG/gYjN7JNJglZB0ETAcWAU8APQFLjez1yINVoEktbGk31d2v5ndsb2y1ISkzsAFQCfS/haY2eCoMlWHpIvMbGhVx+JC0gtA6Qp7K4BJwP1mtn77p3JxIKkA6ErJv31jokuUHSTtWdn9ZjZle2XJZt7OripeXTbLSPo1cA7Q3My6SOoK3GdmP4o4WrkkXQMcRNDJfAn4MfCemZ0QZa7KSJpmZn0k/QQ4HrgEeNvMekebrGKSpptZb0lHAOcDVwPDzazSPxJRSVIbh+cwwG7AAGBkePtYYIyZ/SqSYFWQNB14EJgJFKWOm9noyEJVg6Qppc9bSVPNrG9UmSojaSjQCng8PHQSsBCoDzQxs9OjylYRSTOpuGN8o5kt2f6pKiZpFRXn/YOZfb79U1VO0q+Ai4AdgWnAPsBYMzskylylVdC2xcysyXaMUy2S3g6/zAf6A9MBAb2A8WY2MKpsFfF2dtnIRzKzz/nAXsB4ADP7RFLraCNV6gSCUaqpZnaWpDYEI21xVjf8fBTwuJktlRRlnupIBTyKoHM5XfEOnZg2NrPrACS9BuyZmjUg6VrgqQijVWW9md0ddYjqknQKcCrQWdLItLsaA7Hq9JTS18wGpd1+QdIYMxsk6cPIUlXuZWAL8Fh4++Tw80rg3wQXUOLkDuAbgrwiyNsW+Ah4iOBCZtxcRHBRapyZHSypG3BdxJnKMLPGAJKuJ7g48jBBGw8heO7FjpkdDCDpv8A5ZjYzvN0DuDTKbBXxdnbZyDuZ2WeDmW1MvSGXVIdKro7FwDozK5K0WVITYDGwc9ShqvCCpLkEUznPk9QKiPuUt8lhJ6gzcIWkxqSNXsVQEtu4I7Ax7fZGgqmocTU0HIV9DdiQOhjjKU4fAN8CLYHb046vAmZEkqh6WknqaGbzASR1JPgdoOT5Eif7m9n+abdnSnrfzPaXdFpkqSp2pJntnXZ7mKRxZna9pCsjS1W59Wa2XhKS6pnZXEm7RR2qEkeUauN7JY0HbosqUDV0S3V8AMxslqQ+EeapDm9nlzW8k5l9Rod/VOtLOgw4D3gh4kyVmSSpGfAvYDKwGpgQaaIqmNnlkm4FVprZFklrgOOizlWFXwJ9gM/NbK2kFsBZ0UaqWDltvJb4t/HDwARJ/yO4sPMTILZri4GewOnAIWy94GDh7dgxsy+BL4F9o85SQ38A3pP0GcHIRGeCCycNgRGRJqtYI0l7m9l4AEl7AY3C+zZHF6tCRZJ+Djwd3k5fbhHXi6xfh3/7ngNel7SMYDQ2rrZIGgL8l6BNTyEY7Y6zOZIeAB4hyHwaMCfaSFXydnZZw9dkZhlJOQQdisMJ3tC8CjxgCfgfLakTwRqlOI9KICkXOJqyBVNiWeAlRVIvymZ+NrJAlZDUAPg90NHMzgnXFu9mZqMijlYpSf2A1DqUMWY2Nco8lQlHinuZWVxH08ol6afArUBrgtc4ARbHNUspkuoB3Qiyzo17sR9JAwimmTYiyLwS+BXwIXC0mT0ZYbwyJO0MDCW4AGHAOIJ13AuAfmb2XoTxqiTpQKAp8Epcn4/h3+ehwP4Ebfw+QTG2eRHGqpSkfOBcIDVdfQxwb5yff97OLpt4JzPLhFfH15vZlvB2LlDPzNZGm6x84brAIcDO4dSmjkBbM4vtaKaklwimbpYumBK79TQpkh4iWIz/IWmjVmZ2dnSpKibpCYKR7TPMrEdYJXmsmfWJNlnVwjXQ6dUi50cYp0JhG19gZoujzlITkj4FjjWzxFwpl7QfZS/wxHmUGwBJTQneJyyPOks2kjSQoBL88HBJQCMz+yLqXNkk/NvR0cw+ijpLNvN2duXx6bLZ503gUIJppxBUMHwN2C+yRJX7J0Gn5xDgeoL1Vc8QFESIqx3NrFfUIWpoHzPrHnWIGuhiZieFxV4ws3UxL1SEpMEEawXbEawt7gjMBfaIMlcl2gBzJU2k5JrMWG9hAixKWAfzYaALQQXR1LQ3I8ZTqcOR158RdoxTTz0zuz7CWBUKO2i/pmxHPpYX0aC4KnV/gqrUwwmKnT1CMIIVO5J2Be4F2oQX/noBg83sxoijVSh8Tf4rkEdQMKwPcH2cX+O8nV028U5m9sk3s1QHEzNbHU49jKu9zWxPSVMBzGyZpLyoQ1XhZUmHW0z3mKzAWEndzWx21EGqaWN4ZdQAJHUhrSMUUzcQbEPwhpn1lXQwwXqauLqm6ofE0qRwFPY5SnaOYzn1m6Aj0T0JSxbSPE+wBchk4v+8gyDvu8AbxH/9WspPCPYrngJgZt+EBdni6l/AZcD9AGY2Q9JjBPsZx9U1BNX23wEws2nhdNQ483Z2WcM7mdlnjaQ9UxUiwzVi6yLOVJlN4ZTeVGeiFfGuegrBep//hetfN5GANWEEBUbGSlpI8KYxlTmuI7LXAK8AHSQ9SnB1/8xIE1Vtk5ktkZQjKcfM3g6LF8WSmY1WsGVQatbAhIRMnW0CrCVYd55iQFw7mbMIttP4NuogNbCjmR0ZdYgaaGBm/xd1iBraaGYmKfW3r2HUgarQwMwmlJpQEsciUOk2m9mKmE+CKc3b2WUN72Rmn4uBpySlqtTtQLD5d1zdDfwPaC3pJoKqgFdFG6lKtxMUmJiZoNGJhwgqiZZYRxpXZva6pCkEI4MCLjKzwohjVWW5pEYEIyqPSlpMjN8chNU4/0pw9VnAPZIuM7OnK/3GiJlZbKsiV6AlMFvSBJIzLfkDST3TtyWIuVGSjjKzl6IOUgNPSrofaCbp18DZBKNYcVUYzihJdYpPIP4XTmZJOhXIDYvHXUiwFVKceTu7rOGFf7KQpLoE6zxSlQw3RRypXOFI4D7AUuBHBHnfjPt6K0mvAj82s9h31lIkvWVmsdyaIp2kbuF+cXuWd3+M93AsLrrF1g20mwKPmtmSSINVQNJ04LDU6GU4i+ANM+sdbbLKJW3NUlg5tAwzG729s1SXpNnALsAXJGDmg6RVQEOCrLGfXRKuL9+RoOJwcSV4M3s90mCVCCv4DiOo77CM4Nw4LeZVTxsAf6Jktf0b4lz11NvZZRPvZGYJSYeY2Vthef8y4rpeSdJYM0vUvneS/g3sDLxMyZGJ2G5hIumfQDOCPVNju45N0rBwy5K3y7nb4t5RTtL0U0kzzaxn2u0cYHr6sTiSNJpwzZKZ9Q2PzTKzHtEmyx6SdirvuAV7lboMkDTZzPpFnaOmwotpOWa2Kuos2czb2WUDny6bPQ4E3gKOLee+OK9Xek3Sz4BnEzT19IvwIy/8SIL6BJ3LWK9jCzuYOcBVZvZ+1HlqIoHTT18JR+UfD2+fBCRhumEi1ixJes/MBoajbOmvbbEdZZPUxMxWElT5jr0kz3wAxkkaYGYTow5SHUmrOAzFsx4upWzV4dherPR2dtnERzKzSPjm/ASL2UbZlUmb5rSZrVMNY/kGrLSwEqClV/N1mZHQEe7ETT8NZz4MJHjejTGz/0UcqUqSXgZ+BzwVVqY+Afilmf044miJJ2mUmR0j6QuCjnF6T97MbOeIopUryTMfwinJuwJfAmuI/5TkV9hacbi4gq+Z3R5ZqCqEr8n3UTbz5MhCVcHb2WUT72RmGUljzGxQ1DlqQlJzoCslN7CP83qlHsDDQPPwUCFwhpl9GF2qyknaEbiHoEqrAe8RFNP5OtJgFZB0HTCDBI1wJ3H6aTi9dy+CcyLW03tTkrZmSdLDZnZ6VcfctpOk0q8TkvLjvCYsaVOSkzglPYlTkr2dXTbx6bLZ53VJlwJPEFwdBcDMlkYXqWKSfgVcRFAEYRpBIaAPCAoBxdUw4Pdm9jaApIMIqgLuF2GmqgwHHgNODG+fFh47LLJElfs9wQj3FknrSMYId6KmnyZwei8AZvY5cGiC1iztkX5DUh0g9m/IJLUHdqLk9Lcx0SWq1IME1VmB4vVsI4nx35FUZ1JSa9IusMZYYioOhxeuAV6QdB5BBfv0WgSxfD8U8nZ2WcNHMrNM2jSnEuI2zSlF0kyCQinjzKyPpG7AdWYW221XJE0vPQWyvGNxImmamfWp6pjbNmnVIgeQkOmnSZzeCyCpGXAGZdf/XBhRpHJJugK4kmA99NrUYWAjMMzMrogqW1UU7O96EjCbrdPfzGK67YqkG4CWZnaupALgReBfZjY84mgVkjSYYDusdsBigg79HDPbo9Jv3M7Cv9FG8FzrCnxOzCsOVzDdOyV2077B29llJx/JzD7dgfMI3ugawZ5990WaqHLrzWy9JCTVC4s47BZ1qCp8LulqgimzEIwKfhFhnuoolHQaW0fZTgFiubVGSvgmLDX1+x0zGxVlnsqYmUl6LpwyFKtiSpXIKTU9dgmQE1WYGngJGEfM93w1s1uAWyTdEucOZQWOB3Yzsw1VPTAOzOxqSbdKuo9glPgvZvZM1LmqcAPBzJ03zKyvpIMJXpfj5pioA9SUmXWOOsM28HZ2WScJbyhczYwAdgfuJliDt3t4LK6+DkcmniOY6vs88E2kiap2NtCKoDPxv/DruG8Qfzbwc2Bh+HECadPL4kbSXwimUc8OPy4Kj8XZOEkDqn5YbLwi6VVJZ0o6k2D0J7bTe9Pkm9nvzWy4mY1IfUQdqiJmdoWkAkl7SRqU+og6VxU+B+pGHaIqkn6a+gAmEHTapgKmCrbzipFNFuyhmyMpJ1x+0SfiTGWY2Zfh1N46wMLw687AcQQFamJL0olhgT4kXSXpWUl9o85VHm9nl418umyWSeJUzhQFm5Y3BV4xs41R56mKpCZAkVeXzTxJM4A+ZlYU3s4FpsZxylBKkqpFJnF6b4qkS4DVwCgSsP6ngnXnY2Ne+fQZoDfwJiXbOG5TkiubDmtmFucLaW8QjBjfArQkmDI7wMxiubZf0jSgP8E09VcJ1rzuZmZHRRirUpJmmFkvSQMJ2vlvwJVmtnfE0Srk7eyyiU+XzT5TJe1jZuMAJO0NJGK/wThXlE0nqSfwH8LqspIKgV+Y2axIg1UiadVlQ82AVMehaYQ5qisxW2gkdHpvykaCgkV/Yuv6cwPiuv7nIrauOz84te484kxVGRl+xJqZnRVegLrQzO6MOk91hMtCNhCMUK0HLgGGELzGxXYvRIILqpvDEeK7zOweSVOjDlWF1Hrio4F7zex5SddGmKc6vJ1d1vBOZvbZGzhD0vzwdkdgTmpReRxHVRLofspWl01tqRBXSasuewvBBZO3CUbZBhEUUYktM/syfMPbhmS8tiZqM/g0vwd2MbPCqINUU+LWnZvZCEl5BCPzAB+Z2aYoM1XEzLaE67cT0ckExgJ7AvfZ1m1sYjvdO80mSacQFN06NjwW9ynVCyTdDxwK3CqpHvFfJubt7LKGT5fNMqpg760Ui+keXEmSxCnJSawuK2kHghEgAePNbGHEkSol6QLgGmARWwvSxPbCTpKm96aTNBI42czWVvngGJD0P4I12xcDhxDs7Vk35tPfDiLo+MwjOC86EMzWiOUWJpJuIhgJLL1115TIQlVA0iyCkfg/A5eVvt/MYjmzQFJ34LcEU70fl9QZOMnMYrtWXlID4Ehgppl9Ev5N6Wlmr0UcrULezi6beCfTuRoK3zROoWR12f5mdnxkoaoQrv/5NyWry55lZrHcR07Sm6WzlXcsTiR9CuwdFvOIvYouSMX9QlT4/NsDeJsYrxcsT1LWnUuaDJxqZh+Ft3cFHreYbrgezngozeK47jVctzaEoBBb6SnJsV5HmhSSmpjZSm3dx7GEuK7fThpvZ1eVJEzpci5uziZYU5W64jyGZFSX/TvBlDIDPiCG1WUl5QMNgJYK9rtL7b/VhGA/uTj7iphXASzlxrTpegBIehg4vYLHx8Vz4UdihB2LrmY2XMF+pO2J97ZHdVMdTAAz+1hSbKfsmdnBUWeoLjN7D3hP0iQzezDqPFWR9KSZ/Vxb93EsIaYzHx4j2BJkMmX3cYzl+m1vZ5eNfCTTuRoI19y9amaHRp2lusLMI8zstKizVEXSRQTTCtsBCwincAKrCDaw/0d06Son6UFgN4KtQNJH2O6ILFQlJE0xsz3TbucSTHfqHmGsapFUH+iY3hGKK0nXEFSL3M3MdpXUDnjKzPaPOFqFJD1E8LxLzdYYAtQxs9heTJN0NMEId37qmJnFtpCOpIYERX86mtk5kroSnCOx2g9Y0g5m9m1SZz4khbezy0Y+kulcDYRFJtZKampmiRi1CjO3kpQX5yl6AGY2FBgq6c8ElfVWSrqaoFDG2GjTVWl++JEXfsSSpCsIiijVl7QydZigauuwyIJVk6RjCUrk5wGdJfUBrjezwZEGq9hPgL4EU+wxs28U7ikXY+cC5wMXEm5vA/wz0kSVkHQfwQyIg4EHCPYBnhBpqKo9RDAClCoY9zXwFMHWPLFhZt+GnxPZyZHUHtiJtPe7cVxb7O3sspGPZDpXQ5KeJNjr7nVKFpmI7ZqwsPLbngRrgNIzx3WULX3frZuB2/F9tzJK0i1mdkXUOWoqXC94CPCOmfUNj800s57RJiufpAlmtldq5DgcwRob0+lviZT2epH63Ah41swOjzpbRcLpsv0lTU07j2NbQC7cUuNWoDXBhYdUobAmkQarhKRbgZOA2WzdZsNifEHK29llFR/JdK7mXgw/kuSb8CMHiPsoCpTcd+u+OO+7JekuM7tY0guUv5Ymrn9oR0lqaGZrJJ1GcBFiaAKupG82sxVS+vKfsu0eI0+GF3maSfo1wVrof0WcqVwJXRcGsC78vDacjrwE6BxhnurYGE77NgBJXUibZh9DtwHHmtmcqIPUwPEEU5Dj3K6leTu7rOGdTOdqyMySsKdZCWYW983fS0vSvlupdWt/izRFzd0L9JbUG/gj8CDwH+DASFNVbZakU4HccB3bhQSFrGLJzP4m6TBgJcGa3T+b2esRx6rIReHnYyJNUXOjJDUjeIM+OTz2QHRxquUa4BWgg6RHgf2BMyNNVLlFCev4AHxOsMdkkjo/3s4ua/h0WeeqqaKr+ykxvsqf2oLgUqATJddMxK7EP/i+W9tD2vTNPwMLzOzB0sWA4ig8N/4EHE4wlexV4AYzWx9psCwi6VYz+7+qjsVFOCJ4LnAAwWv0u8C9cT0nJOUQrBt9k2DphYBxZlYYabBKSBoKtCWo7Jxe2CyW+3oCSHoG6E3QzonY7sjb2WUT72Q6V01pVd/ODz+nV15cG/NKhtOB+wiu8qemomJmkyv8Jlcj4ajaLUB3Sla4jGUZd0mjCUZSziZ4c/4dMC2uaxvLE1bEbWhmK6t8cEQSusaqzMWG1HrHqDJVJlwnvwp4JDx0CtDMzH4eXarKSRpjZoOizlFdkoaXczjW+3pK+kV5x+M8G8nb2WUT72Q6V0OS3i+9/UB5x+JE0mSL6Ubq2ULSewRT4O4EjiXYO1Vmdk2kwSogqS1wKjDRzN6V1BE4yMz+E3G0Skl6DPgtwcWSyUBT4A4z+2ukwSog6VMSssZK0rnAeQT7232Wdldj4P24boNUXsGcOBfRAQirZq8DnqBkMbbYbWAfXsz5i5ldFnWW6krwdmPezi5rxHWNk3Nx1jCsegqApP2AhhHmqZCk5pKaAy9IOk/SDqlj4XGXOfXN7E2CjuWXZnYtQRXUWDKzhcAzQL3wUCHwv+gSVVv3cOTyeOAloCNweqSJKpekNVaPEVwgGRl+Tn30i2sHMzRV0j6pG5L2Bt6PME91nE3QoR8NTEr7iB0z20JQGCwxwsxrJTWNOkt1eTu7bOOFf5yruV8CD4UvqgasIHjDEEeTCTKmSnGmXyE1ghELlxnrw7VWn0j6HbCAYIpkLIWVTs8BmgNdgPYEU6p/FGWuaqgrqS5BJ/PvZrZJUuym5ITTZAEmSXqCZKyxMjObJ+n80ndIah7HUbbQ3sAZkuaHtzsCc1Lr6GM6zbc7QSdzIFvXkd4XaaLKTZM0kmAvz/SR1ziexynrgZmSErPdGN7OLot4J9O5GgrXMfaW1IRg1GpF1JkqYmadASTlly6CISm//O9y2+higg3hLwRuINgY/owoA1XhfGAvYDxAWGAptp3iNPcD84DpwJhwrXQc12Qem/b1WoJCRSkGxPFN42MElWVLX5yCeF+UOjLqANtgBMF5e3d4+5TwWFzXkTYn2BomfXZGXM/jlCRuN+bt7LKGr8l0roYktQFuBtqZ2Y8ldQf2NbMHI45WoQoKecS+kmiSSOpPUPV0J4Jy7hDfURQkjTezvVObwUuqA0yJa97KSKpjZpujzlFaEtdYue0jietInXOuJnwk07ma+zcwnKBDAfAxQfGG2HUyw+Iu7YH6kvqydWSiCcGom8ucRwmmI88EiiLOUh2jJV1JcG4cRjB174WIM1WLpKOBPUir4gvErrqzmW2RlJgLOVVlNbMp2yvLD8BUSfuY2TiI/zrSsOppmVGJmFc9/YLyM8d1RN7b2WUV72Q6V3MtzexJSVcAmNlmSVuq+qaIHEGwwfeOwB1px1cBV0YRKIt9Z2Yjow5RA5cTrC+eCfyGoIhO3DewR9J9BBdIDibIewIwIdJQlUvSGqvbw8/5QH+CKckCehFMqx5Ywfe5mkvaOtJRaV/nAz8BvokoS3X1T/s6HziRYDpqnHk7u6zh02WdqyFJ7wA/A14PN7PfB7jVzA6MNlnFJP3MzJ6JOkc2k/QjgnVVpTekjmNnIrFS+zWmfW4EPGtmh1f5zRFI6L53/wVuMrOZ4e0ewKVmdmakwbJI2r7L5TKzL7dXlm0RFjl7w8xiW0G7PJLeM7PEXCzxdnZJ5iOZztXc7wlK/O8s6X2gFcFoSmyZ2TPlTTE0s9hNMUyws4BuBOsxU9NlY1uwIcFTnNaFn9dKakdQJKNzhHkqZWZnRZ1hG3RLdTABzGyWpD4R5sk6ce9EVkNXgtHX2Co1/TuHYMStcURxtpW3s0ss72Q6V3OzCfYTXEsw7fQ5gnWZsZXAKYZJ1NvMekYdogaSOsVplKRmwG0EVVAhxtN8Je0K3Au0MbMeknoBg83sxoijVWaOpAeARwguRJwGJGWvT1cLJK2i5EWphcD/RRSnum5P+3oz8AXxrd4LeDu77OLTZZ2rIUlPEpSefzQ8dApQYGYnRpeqckmbYphEkv4F3Glms6POsq2SMMVJUn3gXOAAtu4veG/pLXriQtJogoJQ95tZ3/DYLDPrEW2yioXbG50LDAoPjSHGbexqj6T9zez98rbBiitJF5nZUEkDzey9qPNUh7ezy0beyXSuhpJYej5tu4pxwE8JphjOMrOuEUfLGpLmAF0IruJuICiYEscCHkCFU5zOjfN5DMUXeVYRjLJBcJGnmZnF8sq5pIlmNiC1VUx4bJqZ9Yk4mnNVkjTZzPolacur1PMrYZm9nV3W8emyztVcokrPhxI1xTChkrYh/O1snZa1GZhHMGU27nYr1RF+W9L0yNJUrVBSF8K2lnQC8G20kSonaX/gWoI9X4vfJyRgva7LvE1h8aodJd1d+k4zuzCCTFWZI2ke0ErSjLTjcb7w5+3sso53Mp2rplRpeYLCLqnS80bwRizuUyT/xtYphmMJpxhGmijLJLCQxyiC8ze1d6oBB0hqYGbTIktVtaRd5DkfGAZ0k7SAYKR7SLSRqvQgcAnBBam4bs/kto9jgEOBQ9h6gTLWzOyUcI/oV4HBUeepJm9nl3V8uqxz1ZTkkvNJm2Loap+kxwimyI4k6GgeDUwkqJD7lJndFmG8Mkpd5NkNKHGRJ65rHCV1NrMvJDUEcsxsVepY1NkqkppeH3UOFx+SeptZnGcMZAVvZ5dNvJPp3A9AEteRutol6VXgZ2a2OrzdCHiaYPPvyWbWPcp8pSX1Ik9565VS66+iylQVSX8Bcgm230nf83VKZKFcbCRlDV45075T0zgTMe3b29klnU+Xde6HIWlTDF3t6whsTLu9CdjJzNZJ2lDB90Qmrp3IikjqRrAvbVNJP027qwlpe9XGVGoUM32bGyOYyuecqn5ILCR92re3s0s072Q6l8USvo7U1a7HgHGSng9vHws8Hk7r9HPj+9uNYJ1VM4K2TVkF/DqKQNVlZgdHncHF2otRB6imFWb2ctQhvgdvZ5doPl3WuSyW1CmGbvuQ1A8YSHDF/D0zmxRxpKwjaV8zGxt1jpqQ1BS4hq37ZI4GrjezFdGlcq5mfNr39uHt7CrinUznnHOulkjKB35JMHW2eJqsmZ0dWagqSHoGmAWMCA+dDvQ2s59W/F0uG0l6z8wGSlrF1i2PYOu6uyYRRauSpLfDL1O5U5ljN+3b29llI58u65xzztWeh4G5wBHA9QTbl8yJNFHVupjZz9JuXydpWlRhXHTMbGD4uXHUWbbBO+Uci+XIirezy0beyXTOOedqzy5mdqKk48xsRLh1zKtRh6rCOkkDzew9KK4euS7iTC5ikgqADqS9d4z5lMjVaV/nE6yRjvsFHm9nlzV8uqxzzjlXSyRNMLO9JI0BzgMWAhPiXN5fUh+CqbJNw0PLgDN9/74fLkk3AGcCnwNF4eFETYmUVA8YaWZHRJ2lIt7OLpv4SKZzzjlXe4aFIxNXAyOBRuHXsWVm04DekpqEt1dGm8jFwM8JplFvrPKR8dUAiO3FnZC3s8sa3sl0zjnnaomZPRB+OZqEvPGSdDNwm5ktD28XAH8ws6siDeaiNItgO57FEeeotrQtvCCoftqKYF10nHk7u6zh02Wdc865WiLpM2Ac8C4wxsxivweppKlm1rfUsSlmtmdUmVy0JPUHnifoBKVvUzE4slBVKLWF12ZgkZltjipPdXg7u2ziI5nOOedc7ekO7A0cAPxNUjdgupn9JNpYlcqVVM/MNgBIqg/UiziTi9YI4FZgJlvXCsZaQveB9nZ2WcM7mc4551zt2QJsCj8XAYuI/1S4R4A3JQ0nmAZ3Nlv3zHQ/TIVmdnfUIX4AvJ1d1vDpss4551wtkbSWYFTiDuANM1sScaRKSRKwI7AHcCjBxuqvmVnct11xtUjSHQTTN0dSchpnnLfWSBxvZ5dNvJPpnHPO1RJJxwEDgb2AjcAHBGsz34w0WCUkTTazflHncPEh6e1yDidqa40k8HZ22cQ7mc4551wtC9di/hi4GGhtZvWjTVQxSf8A/m1mE6PO4pxzLplyog7gnHPOZStJz4QVZocCDYEzgIJoU1XpYGCspM8kzZA0U9KMqEO56EhqI+lBSS+Ht7tL+mXUubKNt7PLJj6S6ZxzztUSSYOA981sS9qx4sqtcVRqS4JiXkXyhyvs9AwH/mRmvSXVAaaaWc+Io2UVb2eXTXwk0znnnKs9d6V3MENjI0lSBUlNwi9XVfDhfrhamtmThNtqhPsglj6v3ffn7eyyhm9h4pxzzmWYpLZAe6C+pL4EVVoBmgANIgtWuceAY4DJBFuXpCi8vXMUoVwsrJHUgvC8kLQPsCLaSFnJ29llDe9kOuecc5l3BHAmwXYgd6QdXwVcGUWgqpjZMeHnzpKaA12B/GhTuZj4PcG2Gl0kvQ+0Ak6INlJW8nZ2WcM7mc4551yGmdkIYISkn5nZM1HnqQlJvwIuIuggTwP2Idh65UcRxnLR6kJQHbkD8DNgb/w9ZG3wdnZZw9dkOuecc7XnTUl3SJoUftwuqWnUoapwETAA+NLMDgb6AoXRRnIRu9rMVhJURj4UGAbcG22krOTt7LKGdzKdc8652vMgwRTZn4cfKwmqR8bZejNbD8WVcOcCu0WcyUUrVXzmaOA+M3seyIswT7bydnZZw4fgnXPOudrTxcx+lnb7OknTogpTTV9LagY8B7wuaRnwTaSJXNQWSLqfYHTtVkn18IGK2uDt7LKG75PpnHPO1RJJY4HLzOy98Pb+wN/MbN9ok1WPpAOBpsArZrYx6jwuGpIaAEcCM83sE0k7AD3N7LWIo2UVb2eXTbyT6ZxzztUSSX2AEQQdNYBlwC/MbEZkoZxzzrla5p1M55xzrpaE091OIKga2Yxgzzszs+ujzOWcc87VJl+T6ZxzztWe54HlwBRgQbRRnHPOue3DRzKdc865WiJplpn1iDqHc845tz15xSrnnHOu9nwgqWfUIZxzzrntyUcynXPOuQyTNBMwgmUpXYHPgQ2ACNZk9oownnPOOVervJPpnHPOZZiknSq738y+3F5ZnHPOue3NO5nOOeecc8455zLG12Q655xzzjnnnMsY72Q655xzzjnnnMsY72Q655xzzjnnnMsY72Q655xzzjnnnMsY72Q655xzzjnnnMuY/wd3WVfSEMLl0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check the correlation coefficients to see which variables are highly correlated\n",
    "\n",
    "plt.figure(figsize = (16, 10))\n",
    "sns.heatmap(df_train.corr(), annot = True, cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "262156ecb629391512238b83b03a89c8ebdb504f"
   },
   "source": [
    "As you might have noticed, `area` seems to the correlated to `price` the most. Let's see a pairplot for `area` vs `price`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8b7a573b3de7738aca7829dbf4c1b41fa666d47e"
   },
   "source": [
    "### Dividing into X and Y sets for the model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "8d461aa6a96a85e680fa73c706d0e440c9147322"
   },
   "outputs": [],
   "source": [
    "y_train = df_train.pop('price')\n",
    "X_train = df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "cde8d33f5a71cab0d31cccd80c91542783fb7090"
   },
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fdfe691aa611e8138f3b157273139777bd2b11f8"
   },
   "source": [
    "This time, we will be using the **LinearRegression function from SciKit Learn** for its compatibility with RFE (which is a utility from sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1c8b65d8f5bdb8fffd1312bd8fe765f62e18e5f0"
   },
   "source": [
    "### RFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1fd1e50c0155a1a90fc55fcfa3a6046c9fd61485"
   },
   "source": [
    "Recursive feature elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "15f69354ac60bbc21b8765f713c6bac720909a69"
   },
   "outputs": [],
   "source": [
    "# Importing RFE and LinearRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "0855894d7a3314cdc687f1e6071b9561bd42a8bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running RFE with the output number of the variable equal to 10\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "b90e694cc030855fad26f5254a3b6b2115ecfb72"
   },
   "outputs": [],
   "source": [
    "rfe = RFE(lm)             # running RFE\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "35bcd6f28ff5e9e597cc0c9bbdda32460f6f7d3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('area', True, 1),\n",
       " ('bedrooms', False, 9),\n",
       " ('bathrooms', True, 1),\n",
       " ('stories', True, 1),\n",
       " ('mainroad', False, 7),\n",
       " ('guestroom', False, 8),\n",
       " ('basement', False, 3),\n",
       " ('hotwaterheating', True, 1),\n",
       " ('airconditioning', True, 1),\n",
       " ('parking', True, 1),\n",
       " ('prefarea', True, 1),\n",
       " ('semi-furnished', False, 5),\n",
       " ('unfurnished', False, 2),\n",
       " ('semi-furnished', False, 6),\n",
       " ('unfurnished', False, 4)]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns,rfe.support_,rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "0e0c0316b3e26b0cb0a0dfa047364b5db008d172"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area', 'bathrooms', 'stories', 'hotwaterheating', 'airconditioning',\n",
       "       'parking', 'prefarea'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = X_train.columns[rfe.support_]\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "6ed23a57c9d3fd16c47987c9efc868cb26e46f7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bedrooms', 'mainroad', 'guestroom', 'basement', 'semi-furnished',\n",
       "       'unfurnished', 'semi-furnished', 'unfurnished'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[~rfe.support_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9dd20434f604066af48b392196e3c3fa98a1444e"
   },
   "source": [
    "### Building model using statsmodel, for the detailed statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "9a0392e0e603fc5be49e59cc6cf9c49b22fd6636"
   },
   "outputs": [],
   "source": [
    "# Creating X_test dataframe with RFE selected variables\n",
    "X_train_rfe = X_train[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in /opt/anaconda3/lib/python3.9/site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy>=1.15 in /opt/anaconda3/lib/python3.9/site-packages (from statsmodels) (1.20.3)\n",
      "Requirement already satisfied: scipy>=1.1 in /opt/anaconda3/lib/python3.9/site-packages (from statsmodels) (1.7.1)\n",
      "Requirement already satisfied: patsy>=0.5 in /opt/anaconda3/lib/python3.9/site-packages (from statsmodels) (0.5.2)\n",
      "Requirement already satisfied: pandas>=0.21 in /opt/anaconda3/lib/python3.9/site-packages (from statsmodels) (1.3.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.21->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.21->statsmodels) (2021.3)\n",
      "Requirement already satisfied: six in /opt/anaconda3/lib/python3.9/site-packages (from patsy>=0.5->statsmodels) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "e97c23ab5f5c3cadbdc003bb0ae6516b80ee8eec"
   },
   "outputs": [],
   "source": [
    "# Adding a constant variable \n",
    "import statsmodels.api as sm  \n",
    "X_train_rfe = sm.add_constant(X_train_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "8ad33909ce0fe7cbadc46d09ab8d32cad45f9e4a"
   },
   "outputs": [],
   "source": [
    "lm = sm.OLS(y_train,X_train_rfe).fit()   # Running the linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "f117337580f3eaf3e3bd6b3e8947d023499f56c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  price   R-squared:                       0.618\n",
      "Model:                            OLS   Adj. R-squared:                  0.611\n",
      "Method:                 Least Squares   F-statistic:                     81.72\n",
      "Date:                Fri, 08 Apr 2022   Prob (F-statistic):           5.53e-70\n",
      "Time:                        11:32:11   Log-Likelihood:                 226.04\n",
      "No. Observations:                 361   AIC:                            -436.1\n",
      "Df Residuals:                     353   BIC:                            -405.0\n",
      "Df Model:                           7                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "const               0.1058      0.015      7.196      0.000       0.077       0.135\n",
      "area                0.3519      0.037      9.479      0.000       0.279       0.425\n",
      "bathrooms           0.1969      0.033      6.042      0.000       0.133       0.261\n",
      "stories             0.1857      0.026      7.165      0.000       0.135       0.237\n",
      "hotwaterheating     0.0918      0.036      2.541      0.011       0.021       0.163\n",
      "airconditioning     0.1014      0.016      6.193      0.000       0.069       0.134\n",
      "parking             0.0960      0.026      3.742      0.000       0.046       0.146\n",
      "prefarea            0.1127      0.017      6.467      0.000       0.078       0.147\n",
      "==============================================================================\n",
      "Omnibus:                       43.622   Durbin-Watson:                   2.072\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               89.024\n",
      "Skew:                           0.659   Prob(JB):                     4.66e-20\n",
      "Kurtosis:                       5.045   Cond. No.                         7.01\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Let's see the summary of our linear model\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "e311b4572715c8c90511b0b67d63618933ee003b"
   },
   "outputs": [],
   "source": [
    "# Calculate the VIFs for the model\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "77684badbfb8b6d35a1af6a7f6e6a90f694b9cde"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>4.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>area</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>airconditioning</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>stories</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>parking</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bathrooms</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>prefarea</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hotwaterheating</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Features   VIF\n",
       "0            const  4.56\n",
       "1             area  1.24\n",
       "5  airconditioning  1.22\n",
       "3          stories  1.17\n",
       "6          parking  1.14\n",
       "2        bathrooms  1.13\n",
       "7         prefarea  1.05\n",
       "4  hotwaterheating  1.03"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "X = X_train_rfe\n",
    "vif['Features'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "929bfe56bbc1c23f9c4069acce30593044af093d"
   },
   "source": [
    "## Residual Analysis of the train data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "848c69f31ac4b52bf81ce2f4fdafbf9b4b4fba37"
   },
   "source": [
    "So, now to check if the error terms are also normally distributed (which is infact, one of the major assumptions of linear regression), let us plot the histogram of the error terms and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "ea322f903d893af55c8ce74af23ad9da69297296"
   },
   "outputs": [],
   "source": [
    "y_train_price = lm.predict(X_train_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "0dfe7cfd174b675cc86f03f19cebf5c3aee750b7"
   },
   "outputs": [],
   "source": [
    "res = (y_train_price - y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "ab7df18b1e1e01bb58d7d5322b9be3429ca58ae1"
   },
   "outputs": [],
   "source": [
    "# Importing the required libraries for plots.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "6c88bec7ff0f3264d1fcfdce138b657fbc87732a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Errors')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histogram of the error terms\n",
    "fig = plt.figure()\n",
    "sns.distplot((y_train - y_train_price), bins = 20)\n",
    "fig.suptitle('Error Terms', fontsize = 20)                  # Plot heading \n",
    "plt.xlabel('Errors', fontsize = 18)                         # X-label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "fc5970cce08cf1588b50049536ed59add70ab46e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "833b7f1da0f80b0f5b8e8acd8c1c58ae672a25e3"
   },
   "outputs": [],
   "source": [
    "# There may be some relation in the error terms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6ab0d38a9e8f4e1ac3522265272378670317e2a0"
   },
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "45b3cacf9af65798f6aa2043c9938ac7be66cc2d"
   },
   "source": [
    "#### Applying the scaling on the test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "18dc79e154426089b57bc592cbf9c7c9d4f9f925"
   },
   "outputs": [],
   "source": [
    "num_vars = ['area','stories', 'bathrooms', 'airconditioning', 'prefarea','parking','price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "369940e6dcf9b0a2e37418d68b0b03474c2a23df"
   },
   "outputs": [],
   "source": [
    "df_test[num_vars] = scaler.fit_transform(df_test[num_vars])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a4878645a8b2dea09fef010e81dc3ad1f5024aa6"
   },
   "source": [
    "#### Dividing into X_test and y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "7374561e97f330101662ada491c40ea47addd7cc"
   },
   "outputs": [],
   "source": [
    "y_test = df_test.pop('price')\n",
    "X_test = df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "1d36fa795be13df3abd528745d2003f15165793e"
   },
   "outputs": [],
   "source": [
    "# Adding constant variable to test dataframe\n",
    "X_test = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "2adbc1f44001b942ed5110e4323e8cf00c7ea139"
   },
   "outputs": [],
   "source": [
    "# Now let's use our model to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "cf0bfd37ba6988b7bbf450bdf9ea7072d0fe0e10"
   },
   "outputs": [],
   "source": [
    "# Creating X_test_new dataframe by dropping variables from X_test\n",
    "X_test_rfe = X_test[X_train_rfe.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_uuid": "a7273e142e5a42104f7b5a0fff4ace465f7ea0b3"
   },
   "outputs": [],
   "source": [
    "# Making predictions\n",
    "y_pred = lm.predict(X_test_rfe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_uuid": "9b8d11e9ed91c84d950ee8584cbd747cfb189457"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5889706572454201"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score \n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "22f7ec00c2e3bfe9d8c00f38dd94fc17e4efe389",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_pred')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting y_test and y_pred to understand the spread.\n",
    "fig = plt.figure()\n",
    "plt.scatter(y_test,y_pred)\n",
    "fig.suptitle('y_test vs y_pred', fontsize=20)              # Plot heading \n",
    "plt.xlabel('y_test', fontsize=18)                          # X-label\n",
    "plt.ylabel('y_pred', fontsize=16)                          # Y-label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e3ca975a2eef6d3706f69042c0f07f459b7b0459"
   },
   "source": [
    "\n",
    "We can see that the equation of our best fitted line is:\n",
    "\n",
    "$ price = 0.35  \\times  area + 0.20  \\times  bathrooms + 0.19 \\times stories+ 0.10 \\times airconditioning + 0.10 \\times parking + 0.11 \\times prefarea $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}