Practical-No-08.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "189e022e-7b4f-456f-a32c-fae87431f989",
   "metadata": {},
   "source": [
    "# Practical No, 08\n",
    "\n",
    "by Kartik Deshpande\n",
    "\n",
    "Data Visualization I\n",
    "1. Use the inbuilt dataset 'titanic'. The dataset contains 891 rows and contains information\n",
    "about the passengers who boarded the unfortunate Titanic ship. Use the Seaborn library\n",
    "to see if we can find any patterns in the data.\n",
    "2. Write a code to check how the price of the ticket (column name: 'fare') for each\n",
    "passenger is distributed by plotting a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98d3837b-4bcd-4745-ace0-fa18c65837a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af492c0b-8db6-4177-ae52-fb1b370f7fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sb.load_dataset('titanic')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a79912bd-d5b6-4dde-b9f5-d3bb5c3199ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60b09dc8-e7ba-4295-ab62-3422d80452d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values: \n",
      "survived         0\n",
      "pclass           0\n",
      "sex              0\n",
      "age            177\n",
      "sibsp            0\n",
      "parch            0\n",
      "fare             0\n",
      "embarked         2\n",
      "class            0\n",
      "who              0\n",
      "adult_male       0\n",
      "deck           688\n",
      "embark_town      2\n",
      "alive            0\n",
      "alone            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Missing Values: \")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48d099c0-de69-4fb9-99a7-0ed91fa3aa36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14000/3982140138.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['age'].fillna(df['age'].median(), inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age              0\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].fillna(df['age'].median(), inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa668930-9e53-4d00-a609-affaa8c2f373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(8,4))\n",
    "fig.suptitle('Histogram 1-Variables(Age & Fare)')\n",
    "sb.histplot(data = df, x = 'age', ax = axes[0])\n",
    "sb.histplot(data = df, x = 'fare', ax = axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "570b1c3a-39ea-4640-9ff1-8a2e5fc4a2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABSMAAANOCAYAAADqDBkCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAADC4ElEQVR4nOzdeVyU5f7/8Tc7uAAiykACYsc9U9Mi1GOaHNE8bXoqjQqXtFOgKWVmpaYtpG2ejLQ6pfVLWqy00qLUXCqXFDNzydIs3AY1A0QTkLl/fxydr5Mgi3DPDLyej8f9OM59X/c9n+u+J85nPnPf1+VhGIYhAAAAAAAAAKhhns4OAAAAAAAAAEDdQDESAAAAAAAAgCkoRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAoBzNmzfX0KFDnR0GatDKlSvl4eGhlStXVnrfoUOHqkGDBhVq6+HhoUcffbQKEQIAANQOFCMBAECdMm/ePHl4eGjjxo2lbu/Vq5cuueSSC36fTz/9tM4XnU6cOKH09HT17dtX4eHhatiwoTp37qzZs2erpKTkvPted911qlevno4dO1Zmm8TERPn6+ur333+vgegBAABQEyhGAgAAlGPnzp169dVXK7XPp59+qqlTp9ZYTO7gl19+0ejRo2UYhlJTU/XMM88oJiZG99xzj4YPH37efRMTE/Xnn39q4cKFpW4/ceKEPvroI/Xr10+NGze+4Fh79uypP//8Uz179rzgYwEAAKBsFCMBAADK4efnJx8fH2eHUSnHjx93dgiyWCz64YcftHTpUo0fP1533XWXPvzwQw0bNkxvvvmmdu3aVea+1113nRo2bKiMjIxSt3/00Uc6fvy4EhMTLyjGkydPymazydPTU/7+/vL0JD0GAACoSWRbAAAA5fjrmJHFxcWaOnWqWrZsKX9/fzVu3Fg9evTQ0qVLpdNjCKanp0unxwg8s5xx/Phx3XfffYqMjJSfn59at26tZ555RoZhOLzvn3/+qTFjxig0NFQNGzbUddddp/37958z7uCjjz4qDw8Pbd++XbfeeqsaNWqkHj16SJK2bNmioUOHqkWLFvL395fFYtHw4cPPebT5zDF++ukn3XbbbQoKClKTJk00adIkGYahvXv36vrrr1dgYKAsFoueffbZcs9baGio2rdvf876G2+8UZK0Y8eOMvcNCAjQwIEDtXz5ch06dOic7RkZGfZzcvToUd1///3q0KGDGjRooMDAQPXv31/ff/+9wz5nxoV855139Mgjj+iiiy5SvXr1lJ+fX+qYkV999ZVuuukmRUVFyc/PT5GRkRo3bpz+/PPPUmP+5ZdflJCQoPr16ysiIkLTpk0755qWZv/+/Ro+fLjCwsLk5+en9u3b6/XXXz+n3axZs9S+fXvVq1dPjRo1UteuXcss1gIAALgqb2cHAAAA4Ax5eXk6cuTIOeuLi4vL3ffRRx9VWlqa7rzzTl1xxRXKz8/Xxo0btWnTJv3jH//QXXfdpQMHDmjp0qX6f//v/znsaxiGrrvuOq1YsUIjRoxQp06d9Pnnn2v8+PHav3+/nn/+eXvboUOH6r333tPtt9+uK6+8UqtWrdKAAQPKjOumm25Sy5Yt9eSTT9qLYEuXLtUvv/yiYcOGyWKxaNu2bXrllVe0bds2rVu3zqFIKkm33HKL2rZtq6eeekpLlizR448/rpCQEL388su6+uqrNX36dM2fP1/333+/Lr/88io91my1WqXTxcrzSUxM1BtvvKH33ntPKSkp9vVHjx7V559/riFDhiggIEDbtm3TokWLdNNNNykmJkY5OTl6+eWXddVVV2n79u2KiIhwOO5jjz0mX19f3X///SosLJSvr2+p779gwQKdOHFCd999txo3bqxvv/1Ws2bN0r59+7RgwQKHtiUlJerXr5+uvPJKzZgxQ5mZmZoyZYpOnTqladOmldnHnJwcXXnllfLw8FBKSoqaNGmizz77TCNGjFB+fr7Gjh0rSXr11Vc1ZswY/etf/9K9996rkydPasuWLVq/fr1uvfXWCpx1AAAAF2EAAADUIXPnzjUknXdp3769wz7R0dFGUlKS/XXHjh2NAQMGnPd9kpOTjdJSrUWLFhmSjMcff9xh/b/+9S/Dw8PD2LVrl2EYhpGVlWVIMsaOHevQbujQoYYkY8qUKfZ1U6ZMMSQZQ4YMOef9Tpw4cc66t99+25BkrF69+pxjjBo1yr7u1KlTRrNmzQwPDw/jqaeesq//448/jICAAIdzUlGFhYVGu3btjJiYGKO4uPi8bU+dOmWEh4cbcXFxDuvnzJljSDI+//xzwzAM4+TJk0ZJSYlDmz179hh+fn7GtGnT7OtWrFhhSDJatGhxznk5s23FihX2daWdu7S0NMPDw8P47bff7OuSkpIMScbo0aPt62w2mzFgwADD19fXOHz4sH39X6/diBEjjPDwcOPIkSMO7zN48GAjKCjIHsP1119/zucSAADAHfGYNgAAqJPS09O1dOnSc5ZLL7203H2Dg4O1bds2/fzzz5V+308//VReXl4aM2aMw/r77rtPhmHos88+kyRlZmZKku655x6HdqNHjy7z2P/+97/PWRcQEGD/98mTJ3XkyBFdeeWVkqRNmzad0/7OO++0/9vLy0tdu3aVYRgaMWKEfX1wcLBat26tX375pUJ9PltKSoq2b9+uF198Ud7e539Ix8vLS4MHD9batWv166+/2tdnZGQoLCxMffr0kU6P6XlmrMeSkhL9/vvvatCggVq3bl1qH5OSkhzOS1nObnP8+HEdOXJE3bp1k2EY+u6770rt2xln7nQsKirSsmXLSj2+YRj64IMPdO2118owDB05csS+JCQkKC8vzx5/cHCw9u3bpw0bNpQbNwAAgCujGAkAAOqkK664QvHx8ecsjRo1KnffadOmKTc3V61atVKHDh00fvx4bdmypULv+9tvvykiIkINGzZ0WN+2bVv79jP/6+npqZiYGId2f/vb38o89l/b6vQjzffee6/CwsIUEBCgJk2a2Nvl5eWd0z4qKsrhdVBQkPz9/c95pDooKEh//PFHBXr8f55++mm9+uqreuyxx3TNNdfY15eUlMhqtTosRUVF0ulHtXW6AClJ+/bt01dffaXBgwfLy8tLkmSz2fT888+rZcuW8vPzU2hoqJo0aaItW7aU2sfSzlNpsrOzNXToUIWEhKhBgwZq0qSJrrrqKqmUc+fp6akWLVo4rGvVqpUkORRSz3b48GHl5ubqlVdeUZMmTRyWYcOGSZJ9vMwJEyaoQYMGuuKKK9SyZUslJyfrm2++qVA/AAAAXAljRgIAAFRSz549tXv3bn300Uf64osv9N///lfPP/+85syZ43BnodlKu9vv5ptv1po1azR+/Hh16tRJDRo0kM1mU79+/WSz2c5pf6bAV946nb6zr6LmzZunCRMm6N///rceeeQRh2179+49p0C4YsUK9erVS126dFGbNm309ttv66GHHtLbb78twzAcZtF+8sknNWnSJA0fPlyPPfaYQkJC5OnpqbFjx5bax4rcFVlSUqJ//OMfOnr0qCZMmKA2bdqofv362r9/v4YOHVrqcSvrzDFuu+02JSUlldrmzJ26bdu21c6dO7V48WJlZmbqgw8+0EsvvaTJkydr6tSpFxwLAACAWShGAgAAVEFISIiGDRumYcOGqaCgQD179tSjjz5qL0b+dWKYM6Kjo7Vs2TIdO3bM4e7IH3/80b79zP/abDbt2bNHLVu2tLfbtWtXhWP8448/tHz5ck2dOlWTJ0+2r6/K4+UX4qOPPtKdd96pgQMH2mcZP5vFYrHPRH5Gx44d7f9OTEzUpEmTtGXLFmVkZKhly5a6/PLL7dvff/999e7dW6+99prDMXJzc8udJKcsP/zwg3766Se98cYbuuOOO+zr/xrnGTabTb/88ov9bkhJ+umnn6TTs7GXpkmTJmrYsKFKSkoUHx9fbkz169fXLbfcoltuuUVFRUUaOHCgnnjiCU2cOFH+/v5V6CUAAID5eEwbAACgkn7//XeH1w0aNNDf/vY3FRYW2tfVr19fOl0QO9s111yjkpISvfjiiw7rn3/+eXl4eKh///6SpISEBEnSSy+95NBu1qxZFY7zzB2Nf72DcebMmRU+xoVavXq1Bg8erJ49e2r+/Pn2sR3P5u/vf97H5c/cBTl58mRt3rzZ4a5Ine7nX/u4YMEC7d+/v8pxl3buDMPQf/7znzL3OfuaGoahF198UT4+PvaxLUt7j0GDBumDDz7Q1q1bz9l++PBh+7//+pnz9fVVu3btZBhGhWaABwAAcBXcGQkAAFBJ7dq1sz9CHBISoo0bN+r99993mMCkS5cukqQxY8YoISHBPhnLtddeq969e+vhhx/Wr7/+qo4dO+qLL77QRx99pLFjx+riiy+27z9o0CDNnDlTv//+u6688kqtWrXKfrddWXdeni0wMFA9e/bUjBkzVFxcrIsuukhffPGF9uzZU2Pn5my//fabrrvuOnl4eOhf//qXFixY4LD90ksvrdCEQTExMerWrZs++ugj6azi5Bn//Oc/NW3aNA0bNkzdunXTDz/8oPnz558zhmNltGnTRhdffLHuv/9+7d+/X4GBgfrggw/KHCfT399fmZmZSkpKUmxsrD777DMtWbJEDz30kJo0aVLm+zz11FNasWKFYmNjNXLkSLVr105Hjx7Vpk2btGzZMh09elSS1LdvX1ksFnXv3l1hYWHasWOHXnzxRQ0YMOCc8UcBAABcGcVIAACAShozZow+/vhjffHFFyosLFR0dLQef/xxjR8/3t5m4MCBGj16tN555x299dZbMgxDgwcPlqenpz7++GNNnjxZ7777rubOnavmzZvr6aef1n333efwPm+++aYsFovefvttLVy4UPHx8Xr33XfVunXrCj+Wm5GRodGjRys9PV2GYahv37767LPPFBERUe3n5a/27Nljn+glOTn5nO1TpkypUDFSpwuQa9as0RVXXHHOJD4PPfSQjh8/royMDL377ru67LLLtGTJEj344INVjt3Hx0effPKJxowZo7S0NPn7++vGG29USkqKwyPkZ3h5eSkzM1N33323xo8fr4YNG2rKlCkOj8eXJiwsTN9++62mTZumDz/8UC+99JIaN26s9u3ba/r06fZ2d911l+bPn6/nnntOBQUFatasmcaMGXPO+JsAAACuzsOozMjjAAAAcKrNmzerc+fOeuutt865QxAAAABwdYwZCQAA4KL+/PPPc9bNnDlTnp6e6tmzp1NiAgAAAC4Ej2kDAAC4qBkzZigrK0u9e/eWt7e3PvvsM3322WcaNWqUIiMjnR0eAAAAUGk8pg0AAOCili5dqqlTp2r79u0qKChQVFSUbr/9dj388MPy9uY3ZQAAALgfipEAAAAAAAAATMGYkQAAAAAAAABMQTESAAAAAAAAgCkoRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmIJiJAAAAAAAAABTUIwEAAAAAAAAYAqKkQAAAAAAAABMQTESAAAAAAAAgCkoRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmIJiJAAAAAAAAABTUIwEAAAAAAAAYAqKkQAAAAAAAABMQTESAAAAAAAAgCkoRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmMLb2QG4ApvNpgMHDqhhw4by8PBwdjgAAACVYhiGjh07poiICHl68luzOyIfBQAA7q6iOSnFSEkHDhxQZGSks8MAAAC4IHv37lWzZs2cHQaqgHwUAADUFuXlpBQjJTVs2FA6fbICAwOdHQ4AAECl5OfnKzIy0p7TwP2QjwIAAHdX0ZyUYqRkfxQmMDCQ5A8AALgtHu91X+SjAACgtigvJ2VQIQAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgjEjAQCASzIMQ6dOnVJJSYmzQ3EJPj4+8vLycnYYAAAAdQb5qCMvLy95e3tf8DjlFCMBAIDLKSoq0sGDB3XixAlnh+IyPDw81KxZMzVo0MDZoQAAANR65KOlq1evnsLDw+Xr61vlY1CMBAAALsVms2nPnj3y8vJSRESEfH196/ws0YZh6PDhw9q3b59atmzJHZIAAAA1iHz0XIZhqKioSIcPH9aePXvUsmVLeXpWbfRHipEAAMClFBUVyWazKTIyUvXq1XN2OC6jSZMm+vXXX1VcXEwxshqlpaXpww8/1I8//qiAgAB169ZN06dPV+vWre1tevXqpVWrVjnsd9ddd2nOnDn219nZ2br77ru1YsUKNWjQQElJSUpLS5O3N+k2AADuhny0dAEBAfLx8dFvv/2moqIi+fv7V+k4ZEcAAMAlVfWX1tqqrv8aX1NWrVql5ORkXX755Tp16pQeeugh9e3bV9u3b1f9+vXt7UaOHKlp06bZX5/9xaSkpEQDBgyQxWLRmjVrdPDgQd1xxx3y8fHRk08+aXqfAABA9SAfPVd1nBOKkQAAAKizMjMzHV7PmzdPTZs2VVZWlnr27GlfX69ePVksllKP8cUXX2j79u1atmyZwsLC1KlTJz322GOaMGGCHn300QsaUwkAAKC2ocQLAABQjubNm2vmzJk1+h4rV66Uh4eHcnNza/R9cH55eXmSpJCQEIf18+fPV2hoqC655BJNnDjRYTD7tWvXqkOHDgoLC7OvS0hIUH5+vrZt22Zi9AAAoDarLTkpd0YCAACUY8OGDQ6P7KJ2stlsGjt2rLp3765LLrnEvv7WW29VdHS0IiIitGXLFk2YMEE7d+7Uhx9+KEmyWq0OhUhJ9tdWq7XU9yosLFRhYaH9dX5+fg31CgAA1Ba1JSelGAkAAOqsoqKiCj1C26RJE1PigXMlJydr69at+vrrrx3Wjxo1yv7vDh06KDw8XH369NHu3bt18cUXV+m90tLSNHXq1AuOuTrYbDbt3btXkhQZGcn4WAAAmKyu5aRkGgDO0SW2myKiW5x36RLbzdlhAqij3n//fXXo0EEBAQFq3Lix4uPjdfz4cfXq1Utjx451aHvDDTdo6NCh9tfNmzfXY489pjvuuEOBgYEaNWqUunXrpgkTJjjsd/jwYfn4+Gj16tX2/c48EnPrrbfqlltucWhfXFys0NBQvfnmm9Lp4k5aWppiYmIUEBCgjh076v3333fY59NPP1WrVq0UEBCg3r1769dff63mM4XKSElJ0eLFi7VixQo1a9bsvG1jY2MlSbt27ZIkWSwW5eTkOLQ587qscSYnTpyovLw8+3KmGOgMe/fu1fD0TA1Pz3RqHAAAuBNy0qrjzkgA5zhotarnw/PP22b1E4mmxQMAZxw8eFBDhgzRjBkzdOONN+rYsWP66quvZBhGhY/xzDPPaPLkyZoyZYp0egKTGTNm6KmnnrLPWP3uu+8qIiJCf//738/ZPzExUTfddJMKCgrUoEEDSdLnn3+uEydO6MYbb5RO3/X21ltvac6cOWrZsqVWr16t2267TU2aNNFVV12lvXv3auDAgUpOTtaoUaO0ceNG3XfffdV0llAZhmFo9OjRWrhwoVauXKmYmJhy99m8ebMkKTw8XJIUFxenJ554QocOHVLTpk0lSUuXLlVgYKDatWtX6jH8/Pzk5+dXrX25EPUa1Y47LQAAMAM56YWhGAkAANzGwYMHderUKQ0cOFDR0dHS6cdmK+Pqq692SLJuvvlmjR07Vl9//bU90cvIyNCQIUPsieDZEhISVL9+fS1cuFC33367vf11112nhg0bqrCwUE8++aSWLVumuLg4SVKLFi309ddf6+WXX9ZVV12l2bNn6+KLL9azzz4rSWrdurV++OEHTZ8+/QLODqoiOTlZGRkZ+uijj9SwYUP7GI9BQUEKCAjQ7t27lZGRoWuuuUaNGzfWli1bNG7cOPXs2VOXXnqpJKlv375q166dbr/9ds2YMUNWq1WPPPKIkpOTXargCAAAqgc56YVx6mPaq1ev1rXXXquIiAh5eHho0aJF57TZsWOHrrvuOgUFBal+/fq6/PLLlZ2dbd9+8uRJJScnq3HjxmrQoIEGDRp0zmMyAACgdujYsaP69OmjDh066KabbtKrr76qP/74o1LH6Nq1q8PrJk2aqG/fvpo//393hO/Zs0dr165VYmLpd4B7e3vr5ptvtrc/fvy4PvroI3v7Xbt26cSJE/rHP/6hBg0a2Jc333xTu3fvlk7nN2ce9T3jTJIIc82ePVt5eXnq1auXwsPD7cu7774rSfL19dWyZcvUt29ftWnTRvfdd58GDRqkTz75xH4MLy8vLV68WF5eXoqLi9Ntt92mO+64Q9OmTXNizwAAQE0hJ70wTr0z8vjx4+rYsaOGDx+ugQMHnrN99+7d6tGjh0aMGKGpU6cqMDBQ27Ztk7+/v73NuHHjtGTJEi1YsEBBQUFKSUnRwIED9c0335jcGwAAUNO8vLy0dOlSrVmzRl988YVmzZqlhx9+WOvXr5enp+c5j8YUFxefc4zSZiBMTEzUmDFjNGvWLGVkZKhDhw7n/XU7MTFRV111lQ4dOqSlS5cqICBA/fr1kyQVFBRIkpYsWaKLLrrIYT/uknM95T1OFRkZqVWrVpV7nOjoaH366afVGBkAAHBV5KQXxqnFyP79+6t///5lbn/44Yd1zTXXaMaMGfZ1Z89YmJeXp9dee00ZGRm6+uqrJUlz585V27ZttW7dOl155ZU13AMAAGA2Dw8Pde/eXd27d9fkyZMVHR2thQsXqkmTJjp48KC9XUlJibZu3arevXuXe8zrr79eo0aNUmZmpjIyMnTHHXect323bt0UGRmpd999V5999pluuukm+fj4SJLatWsnPz8/ZWdn66qrrip1/7Zt2+rjjz92WLdu3boKngEAAAA4Gzlp1bnsmJE2m01LlizRAw88oISEBH333XeKiYnRxIkTdcMNN0iSsrKyVFxcrPj4ePt+bdq0UVRUlNauXVtmMbKwsFCFhYX21/n5+Sb0CAAAXKj169dr+fLl6tu3r5o2bar169fr8OHDatu2rerXr6/U1FQtWbJEF198sZ577jnl5uZW6Lj169fXDTfcoEmTJmnHjh0aMmRIufvceuutmjNnjn766SetWLHCvr5hw4a6//77NW7cONlsNvXo0UN5eXn65ptvFBgYqKSkJP373//Ws88+q/Hjx+vOO+9UVlaW5s2bd0HnBgAAAOYgJ70wTh0z8nwOHTqkgoICPfXUU+rXr5+++OIL3XjjjRo4cKD9URmr1SpfX18FBwc77BsWFmYffLw0aWlpCgoKsi+RkZE13h8AAHDhAgMDtXr1al1zzTVq1aqVHnnkET377LPq37+/hg8frqSkJN1xxx266qqr1KJFiwr9An1GYmKivv/+e/39739XVFRUhdpv375dF110kbp37+6w7bHHHtOkSZOUlpamtm3bql+/flqyZIl9puaoqCh98MEHWrRokTp27Kg5c+boySefrMIZAQAAgNnISS+Mh1GZecdrkIeHhxYuXGi/6/HAgQO66KKLNGTIEGVkZNjbXXfddapfv77efvttZWRkaNiwYQ53OUrSFVdcod69e5c5+09pd0ZGRkYqLy9PgYGBNdZHwF1ERLdQz4fnn7fN6icSdeC3X0yLCUDdcfLkSe3Zs0cxMTEO40TXdec7L/n5+QoKCiKXcWPOvIa//fabUjKyJEkv3trFPisoAAB1Fflo2aojJ3XZx7RDQ0Pl7e2tdu3aOaxv27atvv76a0mSxWJRUVGRcnNzHe6OzMnJkcViKfPYfn5+Th+sEwAAAAAAAKhrXPYxbV9fX11++eXauXOnw/qffvrJ/mttly5d5OPjo+XLl9u379y5U9nZ2aZMRQ4AAAAAAACg4px6Z2RBQYF27dplf71nzx5t3rxZISEhioqK0vjx43XLLbeoZ8+e6t27tzIzM/XJJ59o5cqVkqSgoCCNGDFCqampCgkJUWBgoEaPHq24uDhm0gYAAAAAAABcjFOLkRs3bnQYxDM1NVWSlJSUpHnz5unGG2/UnDlzlJaWpjFjxqh169b64IMP1KNHD/s+zz//vDw9PTVo0CAVFhYqISFBL730klP6AwAAAAAAAKBsTi1G9urVS+XNnzN8+HANHz68zO3+/v5KT09Xenp6DUQIAAAAAAAAoLq47JiRAAAAAAAAAGoXipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMIVTJ7ABAAC4UNnZ2Tpy5Ihp7xcaGqqoqCjT3g8AAACuz8yc1N3zUYqRAADAbWVnZ6tNm7b6888Tpr1nQEA9/fjjjkongOnp6Xr66adltVrVsWNHzZo1S1dccUWNxQkAAABzmJ2Tuns+SjESAAC4rSNHjujPP08odvgUBYY3r/H3yz/4q9a/PlVHjhypVPL37rvvKjU1VXPmzFFsbKxmzpyphIQE7dy5U02bNq3RmAEAAFCzzMxJa0M+SjESAAC4vcDw5gqJau3sMMr03HPPaeTIkRo2bJgkac6cOVqyZIlef/11Pfjgg84ODwAAANXAlXNSV8pHmcAGAACgBhUVFSkrK0vx8fH2dZ6enoqPj9fatWudGhsAAABqP1fLRylGAgAA1KAjR46opKREYWFhDuvDwsJktVqdFhcAAADqBlfLRylGAgAAAAAAADAFxUgAAIAaFBoaKi8vL+Xk5Disz8nJkcVicVpcAAAAqBtcLR+lGAkAAFCDfH191aVLFy1fvty+zmazafny5YqLi3NqbAAAAKj9XC0fZTZtAACAGpaamqqkpCR17dpVV1xxhWbOnKnjx4/bZzMEAAAAapIr5aMUIwEAgNvLP/irS7/PLbfcosOHD2vy5MmyWq3q1KmTMjMzzxlEHAAAAO7LjJy0NuSjFCMBAIDbCg0NVUBAPa1/fapp7xkQUE+hoaGV3i8lJUUpKSk1EhMAAACcx+yc1N3zUYqRAADAbUVFRenHH3foyJEjpr1naGiooqKiTHs/AAAAuDazc1J3z0cpRgIAALcWFRXl1skYAAAA3B85acUxmzYAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBTeznzz1atX6+mnn1ZWVpYOHjyohQsX6oYbbii17b///W+9/PLLev755zV27Fj7+qNHj2r06NH65JNP5OnpqUGDBuk///mPGjRoYGJPAACAs2RnZ+vIkSOmvV9oaKiioqJMez8AAAC4PjNzUnfPR51ajDx+/Lg6duyo4cOHa+DAgWW2W7hwodatW6eIiIhztiUmJurgwYNaunSpiouLNWzYMI0aNUoZGRk1HD0AAHC27OxstW3TWif+PGnae9YL8NeOH3dWKgGszA+wAAAAcC9m56Tuno86tRjZv39/9e/f/7xt9u/fr9GjR+vzzz/XgAEDHLbt2LFDmZmZ2rBhg7p27SpJmjVrlq655ho988wzpRYvAQBA7XHkyBGd+POk3hrVSW3Da/6piB0HC3TbK5t15MiRSiV/Ff0BFgAAAO7HzJy0NuSjTi1Glsdms+n222/X+PHj1b59+3O2r127VsHBwfZCpCTFx8fL09NT69ev14033mhyxAAAwBnahjfQZc2DnB1GmSryAywAAADcmyvnpK6Uj7p0MXL69Ony9vbWmDFjSt1utVrVtGlTh3Xe3t4KCQmR1Wot87iFhYUqLCy0v87Pz6/GqAEAAAAAAACUxmVn087KytJ//vMfzZs3Tx4eHtV67LS0NAUFBdmXyMjIaj0+AAAAAAAAgHO5bDHyq6++0qFDhxQVFSVvb295e3vrt99+03333afmzZtLkiwWiw4dOuSw36lTp3T06FFZLJYyjz1x4kTl5eXZl71799Z4fwAAAAAAAIC6zmUf07799tsVHx/vsC4hIUG33367hg0bJkmKi4tTbm6usrKy1KVLF0nSl19+KZvNptjY2DKP7efnJz8/vxruAQAAAAAAAICzObUYWVBQoF27dtlf79mzR5s3b1ZISIiioqLUuHFjh/Y+Pj6yWCxq3bq1JKlt27bq16+fRo4cqTlz5qi4uFgpKSkaPHgwM2kDAAAAAAAALsapxciNGzeqd+/e9tepqamSpKSkJM2bN69Cx5g/f75SUlLUp08feXp6atCgQXrhhRdqLGYAAIDKKu8HWAAAAKAmuVI+6tRiZK9evWQYRoXb//rrr+esCwkJUUZGRjVHBgAA3MmOgwUu/T7V8QMsAAAAXJsZOWltyEdddsxIAACA8oSGhqpegL9ue2Wzae9ZL8BfoaGhldqnsj/AAgAAwH2YnZO6ez5KMRIAALitqKgo7fhxp44cOWLae4aGhvJoNQAAAOzMzkndPR+lGAkAANxaVFSUWydjAAAAcH/kpBXn6ewAAAAAAAAAANQNFCMBAAAAAAAAmIJiJAAAAOqstLQ0XX755WrYsKGaNm2qG264QTt37nRoc/LkSSUnJ6tx48Zq0KCBBg0apJycHIc22dnZGjBggOrVq6emTZtq/PjxOnXqlMm9AQAAcH0UIwEAgEtyldn+XAXno2asWrVKycnJWrdunZYuXari4mL17dtXx48ft7cZN26cPvnkEy1YsECrVq3SgQMHNHDgQPv2kpISDRgwQEVFRVqzZo3eeOMNzZs3T5MnT3ZSrwAAQHUg/zpXdZwTJrABAAAuxcfHR5J04sQJBQQEODscl1FUVCRJ8vLycnYotUpmZqbD63nz5qlp06bKyspSz549lZeXp9dee00ZGRm6+uqrJUlz585V27ZttW7dOl155ZX64osvtH37di1btkxhYWHq1KmTHnvsMU2YMEGPPvqofH19ndQ7AABQFeSjZTtx4oR01jmqCoqRAADApXh5eSk4OFiHDh2SJNWrV08eHh7ODsupbDabDh8+rHr16snbm/StJuXl5UmSQkJCJElZWVkqLi5WfHy8vU2bNm0UFRWltWvX6sorr9TatWvVoUMHhYWF2dskJCTo7rvv1rZt29S5c+dz3qewsFCFhYX21/n5+TXcMwAAUFHko+cyDEMnTpzQoUOHFBwcfEE/kJPNAgAAl2OxWCTJngBC8vT0VFRUVJ1PhGuSzWbT2LFj1b17d11yySWSJKvVKl9fXwUHBzu0DQsLk9Vqtbc5uxB5ZvuZbaVJS0vT1KlTa6gnAADgQpGPli44ONh+bqqKYiQAAHA5Hh4eCg8PV9OmTVVcXOzscFyCr6+vPD0Z7rsmJScna+vWrfr6669r/L0mTpyo1NRU++v8/HxFRkbW+PsCAICKIR89l4+PT7UMGUQxEgAAuCwvLy/GSIQpUlJStHjxYq1evVrNmjWzr7dYLCoqKlJubq7D3ZE5OTn2uwIsFou+/fZbh+OdmW27rDsH/Pz85OfnV0O9AQAA1YV8tPrx8zoAAADqLMMwlJKSooULF+rLL79UTEyMw/YuXbrIx8dHy5cvt6/buXOnsrOzFRcXJ0mKi4vTDz/84PAY19KlSxUYGKh27dqZ2BsAAADXx52RAAAAqLOSk5OVkZGhjz76SA0bNrSP8RgUFKSAgAAFBQVpxIgRSk1NVUhIiAIDAzV69GjFxcXpyiuvlCT17dtX7dq10+23364ZM2bIarXqkUceUXJyMnc/AgAA/AXFSAAAANRZs2fPliT16tXLYf3cuXM1dOhQSdLzzz8vT09PDRo0SIWFhUpISNBLL71kb+vl5aXFixfr7rvvVlxcnOrXr6+kpCRNmzbN5N4AAAC4PoqRAAAAqLMMwyi3jb+/v9LT05Wenl5mm+joaH366afVHB0AAEDtw5iRAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmIJiJAAAAAAAAABTUIwEAAAAAAAAYAqKkQAAAAAAAABMQTESAAAAAAAAgCmcWoxcvXq1rr32WkVERMjDw0OLFi2ybysuLtaECRPUoUMH1a9fXxEREbrjjjt04MABh2McPXpUiYmJCgwMVHBwsEaMGKGCggIn9AYAAAAAAADA+Ti1GHn8+HF17NhR6enp52w7ceKENm3apEmTJmnTpk368MMPtXPnTl133XUO7RITE7Vt2zYtXbpUixcv1urVqzVq1CgTewEAAAAAAACgIryd+eb9+/dX//79S90WFBSkpUuXOqx78cUXdcUVVyg7O1tRUVHasWOHMjMztWHDBnXt2lWSNGvWLF1zzTV65plnFBERYUo/AAAAAAAAAJTPrcaMzMvLk4eHh4KDgyVJa9euVXBwsL0QKUnx8fHy9PTU+vXryzxOYWGh8vPzHRYAAAAAAAAANcttipEnT57UhAkTNGTIEAUGBkqSrFarmjZt6tDO29tbISEhslqtZR4rLS1NQUFB9iUyMrLG4wcAAAAAAADqOrcoRhYXF+vmm2+WYRiaPXv2BR9v4sSJysvLsy979+6tljgBAAAAAAAAlM2pY0ZWxJlC5G+//aYvv/zSflekJFksFh06dMih/alTp3T06FFZLJYyj+nn5yc/P78ajRsAAAAAAACAI5e+M/JMIfLnn3/WsmXL1LhxY4ftcXFxys3NVVZWln3dl19+KZvNptjYWCdEDAAAAAAAAKAsTr0zsqCgQLt27bK/3rNnjzZv3qyQkBCFh4frX//6lzZt2qTFixerpKTEPg5kSEiIfH191bZtW/Xr108jR47UnDlzVFxcrJSUFA0ePJiZtAEAAAAAAAAX49Ri5MaNG9W7d2/769TUVElSUlKSHn30UX388ceSpE6dOjnst2LFCvXq1UuSNH/+fKWkpKhPnz7y9PTUoEGD9MILL5jaDwAAAAAAAADlc2oxslevXjIMo8zt59t2RkhIiDIyMqo5MgAAAAAAAADVzaXHjAQAAAAAAABQe1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmIJiJAAAAAAAAABTUIwEAAAAAAAAYAqKkQAAAAAAAABMQTESAAAAAAAAgCkoRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKpxYjV69erWuvvVYRERHy8PDQokWLHLYbhqHJkycrPDxcAQEBio+P188//+zQ5ujRo0pMTFRgYKCCg4M1YsQIFRQUmNwTAAAAAAAAAOVxajHy+PHj6tixo9LT00vdPmPGDL3wwguaM2eO1q9fr/r16yshIUEnT560t0lMTNS2bdu0dOlSLV68WKtXr9aoUaNM7AUAAAAAAACAinBqMbJ///56/PHHdeONN56zzTAMzZw5U4888oiuv/56XXrppXrzzTd14MAB+x2UO3bsUGZmpv773/8qNjZWPXr00KxZs/TOO+/owIEDTugRAAAA3E15T+sMHTpUHh4eDku/fv0c2vC0DgAAQMW47JiRe/bskdVqVXx8vH1dUFCQYmNjtXbtWknS2rVrFRwcrK5du9rbxMfHy9PTU+vXry/z2IWFhcrPz3dYAAAAUDeV97SOJPXr108HDx60L2+//bbDdp7WAQAAqBhvZwdQFqvVKkkKCwtzWB8WFmbfZrVa1bRpU4ft3t7eCgkJsbcpTVpamqZOnVojcQMAAMC99O/fX/379z9vGz8/P1ksllK3nXlaZ8OGDfYfyWfNmqVrrrlGzzzzjCIiImokbgAAAHfksndG1qSJEycqLy/Pvuzdu9fZIQEAAMCFrVy5Uk2bNlXr1q1199136/fff7dvq8rTOjypAwAA6iqXLUae+eU5JyfHYX1OTo59m8Vi0aFDhxy2nzp1SkePHi3zl2ud/mU7MDDQYQEAAABK069fP7355ptavny5pk+frlWrVql///4qKSmRqvi0TlpamoKCguxLZGSkKX0BAABwNpctRsbExMhisWj58uX2dfn5+Vq/fr3i4uIkSXFxccrNzVVWVpa9zZdffimbzabY2FinxA0AAIDaZfDgwbruuuvUoUMH3XDDDVq8eLE2bNiglStXVvmYPKkDAADqKqeOGVlQUKBdu3bZX+/Zs0ebN29WSEiIoqKiNHbsWD3++ONq2bKlYmJiNGnSJEVEROiGG26QJLVt21b9+vXTyJEjNWfOHBUXFyslJUWDBw9mbB4AAADUiBYtWig0NFS7du1Snz59qvS0jp+fn/z8/EyKGAAAwHU4tRi5ceNG9e7d2/46NTVVkpSUlKR58+bpgQce0PHjxzVq1Cjl5uaqR48eyszMlL+/v32f+fPnKyUlRX369JGnp6cGDRqkF154wSn9AQAAQO23b98+/f777woPD5f+8rROly5dJJ7WAQAAKJNTi5G9evWSYRhlbvfw8NC0adM0bdq0MtuEhIQoIyOjhiIEAABAbXe+p3VCQkI0depUDRo0SBaLRbt379YDDzygv/3tb0pISJB4WgcAAKBSqjRmZIsWLRxmEDwjNzdXLVq0qI64AAAAgDJVZz66ceNGde7cWZ07d5ZOP63TuXNnTZ48WV5eXtqyZYuuu+46tWrVSiNGjFCXLl301VdfOTxmPX/+fLVp00Z9+vTRNddcox49euiVV16php4CAADULlW6M/LXX3+1zx54tsLCQu3fv7864gIAAADKVJ35aHlP63z++eflHoOndQAAACqmUsXIjz/+2P7vzz//XEFBQfbXJSUlWr58uZo3b169EQIAAACnkY8CAAC4t0oVI8/MYu3h4aGkpCSHbT4+PmrevLmeffbZ6o0QAAAAOI18FAAAwL1Vqhhps9kkSTExMdqwYYNCQ0NrKi4AAADgHOSjAAAA7q1KY0bu2bOn+iMBAAAAKoh8FAAAwD1VqRgpScuXL9fy5ct16NAh+y/UZ7z++uvVERsAAABQJvJRAAAA91OlYuTUqVM1bdo0de3aVeHh4fLw8Kj+yAAAAIAykI8CAAC4pyoVI+fMmaN58+bp9ttvr/6IAAAAgHKQjwIAALgnz6rsVFRUpG7dulV/NAAAAEAFkI8CAAC4pyoVI++8805lZGRUfzQAAABABZCPAgAAuKcqPaZ98uRJvfLKK1q2bJkuvfRS+fj4OGx/7rnnqis+AAAA4BzkowAAAO6pSsXILVu2qFOnTpKkrVu3Omxj8HAAAADUNPJRAAAA91SlYuSKFSuqPxIAAACggshHAQAA3FOVxowEAAAAAAAAgMqq0p2RvXv3Pu/jL19++eWFxAQAAACcF/koAACAe6pSMfLM+DxnFBcXa/Pmzdq6dauSkpKqKzYAAACgVOSjAAAA7qlKxcjnn3++1PWPPvqoCgoKLjQmAAAA4LzIRwEAANxTtY4Zedttt+n111+vzkMCAAAAFUY+CgAA4NqqtRi5du1a+fv7V+chAQAAgAojHwUAAHBtVXpMe+DAgQ6vDcPQwYMHtXHjRk2aNKm6YgMAAABKRT4KAADgnqpUjAwKCnJ47enpqdatW2vatGnq27dvdcUGAAAAlIp8FAAAwD1VqRg5d+7c6o8EAAAAqCDyUQAAAPdUpWLkGVlZWdqxY4ckqX379urcuXN1xQUAAACUi3wUAADAvVSpGHno0CENHjxYK1euVHBwsCQpNzdXvXv31jvvvKMmTZpUd5wAAACAHfkoAACAe6rSbNqjR4/WsWPHtG3bNh09elRHjx7V1q1blZ+frzFjxlRbcCUlJZo0aZJiYmIUEBCgiy++WI899pgMw7C3MQxDkydPVnh4uAICAhQfH6+ff/652mIAAACA6zErHwUAAED1qtKdkZmZmVq2bJnatm1rX9euXTulp6dX64Dh06dP1+zZs/XGG2+offv22rhxo4YNG6agoCB7kjljxgy98MILeuONNxQTE6NJkyYpISFB27dvl7+/f7XFAgAAANdhVj4KAACA6lWlYqTNZpOPj8856318fGSz2aojLknSmjVrdP3112vAgAGSpObNm+vtt9/Wt99+K52+K3LmzJl65JFHdP3110uS3nzzTYWFhWnRokUaPHhwtcUCAAAA12FWPgoAAIDqVaXHtK+++mrde++9OnDggH3d/v37NW7cOPXp06faguvWrZuWL1+un376SZL0/fff6+uvv1b//v0lSXv27JHValV8fLx9n6CgIMXGxmrt2rVlHrewsFD5+fkOCwAAANyHWfkoAAAAqleVipEvvvii8vPz1bx5c1188cW6+OKLFRMTo/z8fM2aNavagnvwwQc1ePBgtWnTRj4+PurcubPGjh2rxMRESZLVapUkhYWFOewXFhZm31aatLQ0BQUF2ZfIyMhqixkAAAA1z6x8FAAAANWrSo9pR0ZGatOmTVq2bJl+/PFHSVLbtm0d7lCsDu+9957mz5+vjIwMtW/fXps3b9bYsWMVERGhpKSkKh934sSJSk1Ntb/Oz8+nIAkAAOBGzMpHAQAAUL0qVYz88ssvlZKSonXr1ikwMFD/+Mc/9I9//EOSlJeXp/bt22vOnDn6+9//Xi3BjR8/3n53pCR16NBBv/32m9LS0pSUlCSLxSJJysnJUXh4uH2/nJwcderUqczj+vn5yc/Pr1piBAAAgHnMzkcBAABQvSr1mPbMmTM1cuRIBQYGnrMtKChId911l5577rlqC+7EiRPy9HQM0cvLyz4oeUxMjCwWi5YvX27fnp+fr/Xr1ysuLq7a4gAAAIBrMDsfBQAAQPWq1J2R33//vaZPn17m9r59++qZZ56pjrgkSddee62eeOIJRUVFqX379vruu+/03HPPafjw4ZIkDw8PjR07Vo8//rhatmypmJgYTZo0SREREbrhhhuqLQ4A5zr6++9qFR1e5vYwS7i+Wr/J1JgAALWf2fkoAAAAqlelipE5OTny8fEp+2De3jp8+HB1xCVJmjVrliZNmqR77rlHhw4dUkREhO666y5NnjzZ3uaBBx7Q8ePHNWrUKOXm5qpHjx7KzMyUv79/tcUB4Fw2m00/Te1S5vZWU7JMjQcAUDeYnY8CAACgelWqGHnRRRdp69at+tvf/lbq9i1btjiM3XihGjZsqJkzZ2rmzJlltvHw8NC0adM0bdq0antfAAAAuCaz81EAAABUr0qNGXnNNddo0qRJOnny5Dnb/vzzT02ZMkX//Oc/qzM+AAAAwI58FAAAwL1V6s7IRx55RB9++KFatWqllJQUtW7dWpL0448/Kj09XSUlJXr44YdrKlYAAADUceSjAAAA7q1SxciwsDCtWbNGd999tyZOnCjDMKTTj0onJCQoPT1dYWFhNRUrAAAA6jjyUQAAAPdWqWKkJEVHR+vTTz/VH3/8oV27dskwDLVs2VKNGjWqmQgBAACAs5CPAgAAuK9KFyPPaNSokS6//PLqjQYAAACoIPJRAAAA91OpCWwAAAAAAAAAoKooRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAANRpq1ev1rXXXquIiAh5eHho0aJFDtsNw9DkyZMVHh6ugIAAxcfH6+eff3Zoc/ToUSUmJiowMFDBwcEaMWKECgoKTO4JAACA66MYCQAAgDrt+PHj6tixo9LT00vdPmPGDL3wwguaM2eO1q9fr/r16yshIUEnT560t0lMTNS2bdu0dOlSLV68WKtXr9aoUaNM7AUAAIB78HZ2AAAAAIAz9e/fX/379y91m2EYmjlzph555BFdf/31kqQ333xTYWFhWrRokQYPHqwdO3YoMzNTGzZsUNeuXSVJs2bN0jXXXKNnnnlGERERpvYHAADAlXFnJAAAAFCGPXv2yGq1Kj4+3r4uKChIsbGxWrt2rSRp7dq1Cg4OthciJSk+Pl6enp5av359qcctLCxUfn6+wwIAAFAXUIwEAAAAymC1WiVJYWFhDuvDwsLs26xWq5o2beqw3dvbWyEhIfY2f5WWlqagoCD7EhkZWWN9AAAAcCUUIwEAAACTTZw4UXl5efZl7969zg4JAADAFBQjAQAAgDJYLBZJUk5OjsP6nJwc+zaLxaJDhw45bD916pSOHj1qb/NXfn5+CgwMdFgAAADqAoqRAAAAQBliYmJksVi0fPly+7r8/HytX79ecXFxkqS4uDjl5uYqKyvL3ubLL7+UzWZTbGysU+IGAABwVcymDQAAgDqtoKBAu3btsr/es2ePNm/erJCQEEVFRWns2LF6/PHH1bJlS8XExGjSpEmKiIjQDTfcIElq27at+vXrp5EjR2rOnDkqLi5WSkqKBg8ezEzaAAAAf0ExEgAAAHXaxo0b1bt3b/vr1NRUSVJSUpLmzZunBx54QMePH9eoUaOUm5urHj16KDMzU/7+/vZ95s+fr5SUFPXp00eenp4aNGiQXnjhBaf0BwAAwJVRjAQAAECd1qtXLxmGUeZ2Dw8PTZs2TdOmTSuzTUhIiDIyMmooQgAAgNrD5ceM3L9/v2677TY1btxYAQEB6tChgzZu3GjfbhiGJk+erPDwcAUEBCg+Pl4///yzU2MGAAAAAAAAcC6XLkb+8ccf6t69u3x8fPTZZ59p+/btevbZZ9WoUSN7mxkzZuiFF17QnDlztH79etWvX18JCQk6efKkU2MHAAAAAAAA4MilH9OePn26IiMjNXfuXPu6mJgY+78Nw9DMmTP1yCOP6Prrr5ckvfnmmwoLC9OiRYs0ePBgp8QNAAAAAAAA4FwufWfkxx9/rK5du+qmm25S06ZN1blzZ7366qv27Xv27JHValV8fLx9XVBQkGJjY7V27doyj1tYWKj8/HyHBQAAAAAAAEDNculi5C+//KLZs2erZcuW+vzzz3X33XdrzJgxeuONNyRJVqtVkhQWFuawX1hYmH1badLS0hQUFGRfIiMja7gnAAAAAAAAAFy6GGmz2XTZZZfpySefVOfOnTVq1CiNHDlSc+bMuaDjTpw4UXl5efZl79691RYzAAAAAAAAgNK5dDEyPDxc7dq1c1jXtm1bZWdnS5IsFoskKScnx6FNTk6OfVtp/Pz8FBgY6LAAAAAAAAAAqFkuXYzs3r27du7c6bDup59+UnR0tHR6MhuLxaLly5fbt+fn52v9+vWKi4szPV4AAAAAAAAAZXPp2bTHjRunbt266cknn9TNN9+sb7/9Vq+88opeeeUVSZKHh4fGjh2rxx9/XC1btlRMTIwmTZqkiIgI3XDDDc4OHwAAAAAAAMBZXLoYefnll2vhwoWaOHGipk2bppiYGM2cOVOJiYn2Ng888ICOHz+uUaNGKTc3Vz169FBmZqb8/f2dGjsAAAAAAAAARy5djJSkf/7zn/rnP/9Z5nYPDw9NmzZN06ZNMzUuAAAAAAAAAJXj0mNGAgAAAAAAAKg9KEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmIJiJAAAAAAAAABTUIwEAAAAAAAAYApvZwcA1BZdYrvpoNVa5vZwi0VZ69eYGhMAAAAAAIAroRgJVJODVqt6Pjy/zO2rn0g0NR4AAAAAAABXw2PaAAAAAAAAAExBMRIAAAAAAACAKShGAgAAAAAAADAFxUgAAAAAAAAApqAYCQAAAAAAAMAUFCMBAAAAAAAAmIJiJAAAAAAAAABTUIwEAAAAAAAAYAq3KkY+9dRT8vDw0NixY+3rTp48qeTkZDVu3FgNGjTQoEGDlJOT49Q4AQAAALdjSPv27dNvv/0mm83m7GgAAEAt5e3sACpqw4YNevnll3XppZc6rB83bpyWLFmiBQsWKCgoSCkpKRo4cKC++eYbp8UKVFWX2G46aLWet024xaKs9WtMiwkAANQNhUVFsr49RrZAX+0b8IyaNWsmSYqMjJSnp1vdwwAAAFyYWxQjCwoKlJiYqFdffVWPP/64fX1eXp5ee+01ZWRk6Oqrr5YkzZ07V23bttW6det05ZVXOjFqoPIOWq3q+fD887ZZ/USiafEAAIC6xRLkJ0l6cMEmBYfl6MQfh/V6cj9FR0c7OzQAAFBLuMVPnMnJyRowYIDi4+Md1mdlZam4uNhhfZs2bRQVFaW1a9c6IVIAAADA/QUEN1H9xuGq16iJs0MBAAC1jMvfGfnOO+9o06ZN2rBhwznbrFarfH19FRwc7LA+LCxM1vM86lpYWKjCwkL76/z8/GqOGgAAAAAAAMBfufSdkXv37tW9996r+fPny9/fv9qOm5aWpqCgIPsSGRlZbccGAAAAAAAAUDqXLkZmZWXp0KFDuuyyy+Tt7S1vb2+tWrVKL7zwgry9vRUWFqaioiLl5uY67JeTkyOLxVLmcSdOnKi8vDz7snfvXhN6AwAAAAAAANRtLv2Ydp8+ffTDDz84rBs2bJjatGmjCRMmKDIyUj4+Plq+fLkGDRokSdq5c6eys7MVFxdX5nH9/Pzk5+dX4/EDAAAAAAAA+D8uXYxs2LChLrnkEod19evXV+PGje3rR4wYodTUVIWEhCgwMFCjR49WXFwcM2kDAAAAAAAALsali5EV8fzzz8vT01ODBg1SYWGhEhIS9NJLLzk7LAAAAAAAAAB/4XbFyJUrVzq89vf3V3p6utLT050WEwAAAAAAAIDyufQENgAAAAAAAABqD7e7MxIAAACAa7LZbNq7d68kKTIyUp6e3PsAAAAckR0AAAAA5/Hoo4/Kw8PDYWnTpo19+8mTJ5WcnKzGjRurQYMGGjRokHJycpwas7Ps3btXw9MzNTw9016UBAAAOBt3RgKoEXuPFCgiusV524RbLMpav8a0mAAAqKr27dtr2bJl9tfe3v+XRo8bN05LlizRggULFBQUpJSUFA0cOFDffPONk6J1rnqNmjg7BAAA4MIoRgKoESWG1PPh+edts/qJRNPiAQDgQnh7e8tisZyzPi8vT6+99poyMjJ09dVXS5Lmzp2rtm3bat26dbryyiudEC0AAIDr4jFtAAAAoBw///yzIiIi1KJFCyUmJio7O1uSlJWVpeLiYsXHx9vbtmnTRlFRUVq7dm2ZxyssLFR+fr7DAgAAUBdQjAQAAADOIzY2VvPmzVNmZqZmz56tPXv26O9//7uOHTsmq9UqX19fBQcHO+wTFhYmq9Va5jHT0tIUFBRkXyIjI03oCQAAgPPxmDYAAABwHv3797f/+9JLL1VsbKyio6P13nvvKSAgoErHnDhxolJTU+2v8/PzKUgCAIA6gTsjAQAAgEoIDg5Wq1attGvXLlksFhUVFSk3N9ehTU5OTqljTJ7h5+enwMBAhwUAAKAuoBgJAAAAVEJBQYF2796t8PBwdenSRT4+Plq+fLl9+86dO5Wdna24uDinxgkAAOCKeEwbcDNHf/9draLDy9weZgnXV+s3mRoTAAC12f33369rr71W0dHROnDggKZMmSIvLy8NGTJEQUFBGjFihFJTUxUSEqLAwECNHj1acXFxtWMmbUPat2+fJCkyMlKentzLAAAALgzFSMDN2Gw2/TS1S5nbW03JMjUeAABqu3379mnIkCH6/fff1aRJE/Xo0UPr1q1TkyZNJEnPP/+8PD09NWjQIBUWFiohIUEvvfSSs8OuFoVFRbK+PUYK8pNGva3o6GhnhwQAANwcxUigltl7pEAR0S3O2ybcYlHW+jWmxQQAgDt75513zrvd399f6enpSk9PNy0mM1mC/NSskb+zwwAAALUExUiglikxpJ4Pzz9vm9VPJJoWDwAAAAAAwBkUIwEAAACcl81m6MDpsSPF+JEAAOACUIwEAAAAcF4H8gr14IJNCg7L0Yk/Duv15H6MHwkAAKqEYiQAAACAcgUEN1H9xuHODgMAALg5ipEAnObo77+rVXTZX2rCLOH6av0mU2MCAAAAAAA1h2IkAKex2Wz6aWqXMre3mpJlajwAAKCaGNK+02NMMr4kAAA4G1kBAAAAgGpVWFQk69tjtO+VIdq7d6+zwwEAAC6EYiQAAACAamcJ8lOzRv7ODgMAALgYipEAAAAAAAAATEExEgAAAAAAAIApmMAGkNQltpsOWq1lbg+3WJS1fo2pMaF6lHdtxfUFAAAAAMA0FCMBSQetVvV8eH6Z21c/kWhqPKg+5V1bcX0BAAAAADANxUgAdd7R339Xq+jw87YJs4Trq/WbTIsJAAAAAIDayOWLkWlpafrwww/1448/KiAgQN26ddP06dPVunVre5uTJ0/qvvvu0zvvvKPCwkIlJCTopZdeUlhYmFNjB+AebDabfpra5bxtApJXKSK6RZnbedQbAAAAAIDyuXwxctWqVUpOTtbll1+uU6dO6aGHHlLfvn21fft21a9fX5I0btw4LVmyRAsWLFBQUJBSUlI0cOBAffPNN84OH0AtUWKIR/kBAAAAALhALl+MzMzMdHg9b948NW3aVFlZWerZs6fy8vL02muvKSMjQ1dffbUkae7cuWrbtq3WrVunK6+80kmRAwAAAAAAADibp7MDqKy8vDxJUkhIiCQpKytLxcXFio+Pt7dp06aNoqKitHbt2lKPUVhYqPz8fIcFAAAAAAAAQM1y+Tsjz2az2TR27Fh1795dl1xyiSTJarXK19dXwcHBDm3DwsJktVpLPU5aWpqmTp1qSsyV9ffYy5RjPVjmdibRQF2y90jBecdpFGM1AgAAAADgVtyqGJmcnKytW7fq66+/vqDjTJw4UampqfbX+fn5ioyMrIYIL1yO9eB5J9JoNSXL1HgAZypvnEYxViMAAAAAAG7FbYqRKSkpWrx4sVavXq1mzZrZ11ssFhUVFSk3N9fh7sicnBxZLJZSj+Xn5yc/Pz9T4gaA6tQltpsOlnHXt7hTFAAAAADg4ly+GGkYhkaPHq2FCxdq5cqViomJcdjepUsX+fj4aPny5Ro0aJAkaefOncrOzlZcXJyTogaAmnHQamVWbwAAAACA23L5YmRycrIyMjL00UcfqWHDhvZxIIOCghQQEKCgoCCNGDFCqampCgkJUWBgoEaPHq24uDhm0gYAAACqmyHt27dPkhQZGSlPT7ebExMAADiRyxcjZ8+eLUnq1auXw/q5c+dq6NChkqTnn39enp6eGjRokAoLC5WQkKCXXnrJKfECAAAAtVlhUZGsb4+RgvykUW8rOjra2SEBAAA34vLFSMMwym3j7++v9PR0paenmxITUBVHf/9draLDy2lzwrR4AAAAqsoS5KdmjfydHQYAAHBDLl+MBMrjLhN62Gy2886ULkm+96wyLR5Ur4oUm8Ms4fpq/SbTYgIAwNlsNkMHTj/SLR7rBgAAFCNRGzChB1xBRYrNraZkmRYPAACu4EBeoR5csEnBYTk68cdhvZ7cj8e6AQCo4yhGAhXAI9YAAABVExDcRPUbnz+PAgAAdQfFSKACeMQaAACgdDabTXv37pV0epbtsoZ8ZxZuAABAMRIA6h53GWcVAOAe9u7dq32vDFGzRv6y/pqnwtDxpbZjFm4AACCKkQBQ9zDOKgCgujVr5K/o0Hra98fJ87ZjFm4AAEAxErUe4z3WbuVdX1e6tnuPFCgiukWZ27kjEQDgTs6eKTvCVtaz2QAAAI4oRqLWY7zH2q286+tK17bEEHckAgBqjTMzZUvSU/ULFdO0vrNDAgAAboBiJFAHudPdhAAAwHUFBDf53z+KnR0JAABwFxQjgTrIne4mBAAAqIizZ/Vmtm4AAFwXxcha6O+xlynHevC8bcIs4fpq/aYLeh9m5AUAAICrODOrtyRm6wYAwIVRjDRRecU7SfrjSMEFv0+O9WC5YyS2mpJ1we/DjLwAAADupbbfPViTM3XX9nMHAIBZKEaaqLzinSR9OLavafEAAACgbtm7d6+Gp2dKkl5P7sfdg5XAnZcAAFQPipEAUIuUNzmRTJqgyFWGixBDRgDAOeo1auLsENxWTd55CQBAXUExEgBqkfImJ5JJExS5ynARYsgIAHALPAINAEDdQTESAFyEq9zVaJa9RwoUEd2izO3c0QgAdQePjwMAUHdQjAQAF+EqdzWapcQQk2ABAOx4fBwAgLqBYiQAwEFF7tA8mFeohkHBZW7/40hBDUTmuhi7EgAqzmYzdGDfPvvryMjIKhyDx7oBAHBXFCMBAA4qeofmgPPc1fjh2L41EJnrYuxKAKi4A3mFenDBJgWH5ejEH4f1enK/Sh+Dma0BAHBfFCMBAC6pusbQLO841TGrNwCgcgKCm6h+4/P/jS/NmTsi9+3bp4ggP3l6elQ5hpq4u/LsY1bncQEAqE0oRgIAXFJ1jaFZ3nGqY1ZvAEA1MaR9px/httlskiRPT097Ue/MRDdFRUV6qn6hmjXyr/Jb1cSkOWfu2GzWyF/7/jjJXZsAAJSCYmQdVd4stsfychUe5HfeY9SmWX0BANWHMTQBVFVhUZGsb49Rs+ZB2vRrnmZ6D5Wvr69DsbBeoybyKiySii/8/cqbNOfsOx337dunCJvhcDfmX++ulKRmjfwVHVrvwoMDAKCWohhZR5U3i+2HY/vqp6ndznuM2jSrL4C6q7wfZ2RS4awiBbzyfigqb2IhmdQXxtAEcCEsQX6KDq2nfX+cVIBPE/n5+TotljN3T9Zr1ES5OfvPuRvzr2NXAgCA8lGMBADUaeX9OCOTCmcVKeCV90NReRMLiSIgAFdz1mPZMiq/+9kzc5d252Llj3funY71Gv1vfMvCMu7GvJBHxS8kPjEmJQDATVGMdDMVuYPnjyMFpsVzPhWZfKK8iSP+HnuZcqwHy3kfHhcHULPK+3vG3yEAqJozj2VLUmHo+Ervf/bM3KXdufhXf33s+kwBdN9ZBc3Ji7ZKHqrSLN/lvacusIBY0TEpqzo5D8XOuqcmJnICgPLUmmJkenq6nn76aVmtVnXs2FGzZs3SFVdc4eywql1F7uD5cGxf0+I5n4pMPlHexBE51oPVMoEFAFyI8v6e8XcIwBl1JSetTpZyxikvz5mZuc/cuXj23ZJ/nQTn7GKe9dc8ewH0zDiV1l/z5BU6vlofDa/uSW0qMiblXx8fr+j7MQFP5bl74beqnxUA7sFVf3CoFcXId999V6mpqZozZ45iY2M1c+ZMJSQkaOfOnWratKmzwwMAgLsrgTqAnNQ1/PVuybGn5v2v4Hl6TMczxbx9f5y073P2OJXS+R8fr8qj4ZWZ1ObsL45niqn2vh04oAhbxZ5nL+/x8dKKYRWN9cy+Zxd7/1r41elC19nHL+1LsKt+Ua4odyz8njMxU5DfBQ1vAMB1ueoPDrWiGPncc89p5MiRGjZsmCRpzpw5WrJkiV5//XU9+OCDzg4PAADurgTqAHJS13H23ZKWYr9Kj+v418fH65+1rbKPhlfWXyfNGXtqniRppvdQSdJT9QsV07R+OUep2Pv8tRhW2X2teYWa6T1UwWEXlVr4rUixzVW/KFdGVa+/s2ZeL29iJgC1iyv+9+32xciioiJlZWVp4sSJ9nWenp6Kj4/X2rVrnRobAACouvJmGDdjZvCKqMj4xuWNkQz353Y5qeE4TmJVJo9xJ2ffzVjROwvP9/j4+R4Nr+jxz+fsSXMsxf+LI8Cnyf82ljKJTlWdKYbZbIb981DZOy8DfBxjjQjy+79jnb7jtKLHchfn3FlYxt2xpd31eWbdmf2qI46z70pVBe8wLW9iJgCoSW5fjDxy5IhKSkoUFhbmsD4sLEw//vhjqfsUFhaqsLDQ/jovL0+SlJ+fX6Ox2mw2Ff95/LxtDMNQ/p9l/7+BYRgXfIyKHMesY5TYbOc97yU2m9v0xYxjVOQ4HKP6j1GR43AM1zxGRY5T145hK+fvbnWoyP/fVSSO/QcOqPv9/y1z+zfP3FnjfamIAwf267uHOp23TecnN9dorGeObRi1vKLkwiqbkzorHz127JjyrdkqKirS2hceVvuLGmjb/gIdCfnf3Zy/lPxvyIhjXtnyNE7p2KED5a47s19QgLd+OXzCYV11HKvQ11c7dzbQsWPHdODAAXv8lTn+L4dP6PnvP5Ekjav/h44XlZwT64XEX9rxD+SelHbu/N+Jtxbo2MlT9nXHjh0r8xqd6WPxyRPnPT/nO9aBAwck6+lJLctpc+zkKX2XnV9m/OfbNye/qFLnoqqxupoDBw7okYzV8m/YSAVHD2tc/T8UFuh7Tvxn2knS47f2VEREhH3dqeJTFTo/FYnjVPEp3XrqI7W/qIFy8osUcfPTioiIOO9+f/2MHS885TbnH0DFnf03NuLYsRrPMyqckxpubv/+/YYkY82aNQ7rx48fb1xxxRWl7jNlyhTj9OgvLCwsLCwsLCy1Ztm7d69JGRj+qrI5KfkoCwsLCwsLS21dystJ3f7OyNDQUHl5eSknJ8dhfU5OjiwWS6n7TJw4UampqfbXNptNR48eVePGjeXhUb0D9+bn59tn7gsMDKzWY7sD+l+3+y/OQZ3vvzgH9L+O918mnQPDMHTs2LHz3gmDmlXZnNTMfPRs/DfpWrgeroXr4Vq4Hq6F6+FaXPV6VDQndftipK+vr7p06aLly5frhhtukE4nc8uXL1dKSkqp+/j5+cnPz3EMmODg4BqNMzAw0KU+IGaj/3W7/+Ic1Pn+i3NA/+t4/2XCOQgKCqqxY6N8lc1JnZGPno3/Jl0L18O1cD1cC9fDtXA9XIsrXo+K5KRuX4yUpNTUVCUlJalr16664oorNHPmTB0/ftw+kyEAAABQ08hJAQAAylcripG33HKLDh8+rMmTJ8tqtapTp07KzMw8ZwBxAAAAoKaQkwIAAJSvVhQjJSklJaXMx7Kdyc/PT1OmTDnnMZy6gv7X7f6Lc1Dn+y/OAf2v4/0X56DOcdWc9Aw+j66F6+FauB6uhevhWrgersXdr4eHUe582wAAAAAAAABw4TydHQAAAAAAAACAuoFiJAAAAAAAAABTUIwEAAAAAAAAYAqKkQAAAAAAAABMQTGyBqWnp6t58+by9/dXbGysvv32W2eHVGNWr16ta6+9VhEREfLw8NCiRYscthuGocmTJys8PFwBAQGKj4/Xzz//7LR4q1taWpouv/xyNWzYUE2bNtUNN9ygnTt3OrQ5efKkkpOT1bhxYzVo0ECDBg1STk6O02KuTrNnz9all16qwMBABQYGKi4uTp999pl9e23ue2meeuopeXh4aOzYsfZ1tf0cPProo/Lw8HBY2rRpY99e2/svSfv379dtt92mxo0bKyAgQB06dNDGjRvt22v738HmzZuf8xnw8PBQcnKyVAc+AyUlJZo0aZJiYmIUEBCgiy++WI899pjOniewtn8G4PrqUm7qTNWRFx89elSJiYkKDAxUcHCwRowYoYKCApN7UjtUV56enZ2tAQMGqF69emratKnGjx+vU6dOmdwb91cd3xu4FjWjqt9huB7Vpzq+U7nL9aAYWUPeffddpaamasqUKdq0aZM6duyohIQEHTp0yNmh1Yjjx4+rY8eOSk9PL3X7jBkz9MILL2jOnDlav3696tevr4SEBJ08edL0WGvCqlWrlJycrHXr1mnp0qUqLi5W3759dfz4cXubcePG6ZNPPtGCBQu0atUqHThwQAMHDnRq3NWlWbNmeuqpp5SVlaWNGzfq6quv1vXXX69t27ZJtbzvf7Vhwwa9/PLLuvTSSx3W14Vz0L59ex08eNC+fP311/Zttb3/f/zxh7p37y4fHx999tln2r59u5599lk1atTI3qa2/x3csGGDw/VfunSpJOmmm26S6sBnYPr06Zo9e7ZefPFF7dixQ9OnT9eMGTM0a9Yse5va/hmAa6truakzVUdenJiYqG3btmnp0qVavHixVq9erVGjRpnYi9qjOvL0kpISDRgwQEVFRVqzZo3eeOMNzZs3T5MnT3ZSr9zXhX5v4FrUjKp+h+F6VL8L+U7lVtfDQI244oorjOTkZPvrkpISIyIiwkhLS3NqXGaQZCxcuND+2mazGRaLxXj66aft63Jzcw0/Pz/j7bffdlKUNevQoUOGJGPVqlWGcbq/Pj4+xoIFC+xtduzYYUgy1q5d68RIa06jRo2M//73v3Wq78eOHTNatmxpLF261LjqqquMe++91zDqyPWfMmWK0bFjx1K31YX+T5gwwejRo0eZ2+vi38F7773XuPjiiw2bzVYnPgMDBgwwhg8f7rBu4MCBRmJiomHU0c8AXEtdzk2dqSp58fbt2w1JxoYNG+xtPvvsM8PDw8PYv3+/yT2ofaqSp3/66aeGp6enYbVa7W1mz55tBAYGGoWFhU7oRe1Sme8NXIvqdyHfYbge1etCv1O50/XgzsgaUFRUpKysLMXHx9vXeXp6Kj4+XmvXrnVqbM6wZ88eWa1Wh/MRFBSk2NjYWns+8vLyJEkhISGSpKysLBUXFzucgzZt2igqKqrWnYOSkhK98847On78uOLi4upU35OTkzVgwACHvqoOXf+ff/5ZERERatGihRITE5WdnS3Vkf5//PHH6tq1q2666SY1bdpUnTt31quvvmrfXtf+DhYVFemtt97S8OHD5eHhUSc+A926ddPy5cv1008/SZK+//57ff311+rfv79UBz8DcC3kpq6jIn8L1q5dq+DgYHXt2tXeJj4+Xp6enlq/fr1T4q5NqpKnr127Vh06dFBYWJi9TUJCgvLz8+139KHyqvK9gWtR/S7kOwzXo/pdyHcqd7oe3s4OoDY6cuSISkpKHD4AkhQWFqYff/zRaXE5i9VqlU73/2xhYWH2bbWJzWbT2LFj1b17d11yySXS6XPg6+ur4OBgh7a16Rz88MMPiouL08mTJ9WgQQMtXLhQ7dq10+bNm2t93yXpnXfe0aZNm7Rhw4ZzttWF6x8bG6t58+apdevWOnjwoKZOnaq///3v2rp1a53o/y+//KLZs2crNTVVDz30kDZs2KAxY8bI19dXSUlJde7v4KJFi5Sbm6uhQ4dKdeS/gQcffFD5+flq06aNvLy8VFJSoieeeEKJiYlSHfz/QrgWclPXUZG/BVarVU2bNnXY7u3trZCQEP5eXKCq5ulWq7XUa6azrikq7kK+N3AtqteFfofhelSvC/1O5U7Xg2IkUM2Sk5O1detWh7Ed6oLWrVtr8+bNysvL0/vvv6+kpCStWrXK2WGZYu/evbr33nu1dOlS+fv7Ozscpzhz95ckXXrppYqNjVV0dLTee+89BQQEODU2M9hsNnXt2lVPPvmkJKlz587aunWr5syZo6SkJGeHZ7rXXntN/fv3V0REhLNDMc17772n+fPnKyMjQ+3bt9fmzZs1duxYRURE1MnPAAC4orqap7uauvy9wZXwHcb11KXvVDymXQNCQ0Pl5eV1zqxGOTk5slgsTovLWc70uS6cj5SUFC1evFgrVqxQs2bN7OstFouKioqUm5vr0L42nQNfX1/97W9/U5cuXZSWlqaOHTvqP//5T53oe1ZWlg4dOqTLLrtM3t7e8vb21qpVq/TCCy/I29tbYWFhtf4c/FVwcLBatWqlXbt21YnPQHh4uNq1a+ewrm3btvbHKurS38HffvtNy5Yt05133mlfVxc+A+PHj9eDDz6owYMHq0OHDrr99ts1btw4paWlSXXsMwDXQ27qOiryt8BisZwzsdCpU6d09OhRrtcFuJA83WKxlHrNdNY1RcVdyPcGrkX1qY7vMFyPmlXZ71TudD0oRtYAX19fdenSRcuXL7evs9lsWr58ueLi4pwamzPExMTIYrE4nI/8/HytX7++1pwPwzCUkpKihQsX6ssvv1RMTIzD9i5dusjHx8fhHOzcuVPZ2dm15hz8lc1mU2FhYZ3oe58+ffTDDz9o8+bN9qVr165KTEy0/7u2n4O/Kigo0O7duxUeHl4nPgPdu3fXzp07Hdb99NNPio6OlurI38Ez5s6dq6ZNm2rAgAH2dXXhM3DixAl5ejqmVV5eXrLZbFId+wzA9ZCbuo6K/C2Ii4tTbm6usrKy7G2+/PJL2Ww2xcbGOiVud1YdeXpcXJx++OEHhyLx0qVLFRgYeM6Pkai8ynxv4FpUn+r4DsP1qFmV/U7lVtfD2TPo1FbvvPOO4efnZ8ybN8/Yvn27MWrUKCM4ONhhVqPa5NixY8Z3331nfPfdd4Yk47nnnjO+++4747fffjMMwzCeeuopIzg42Pjoo4+MLVu2GNdff70RExNj/Pnnn84OvVrcfffdRlBQkLFy5Urj4MGD9uXEiRP2Nv/+97+NqKgo48svvzQ2btxoxMXFGXFxcU6Nu7o8+OCDxqpVq4w9e/YYW7ZsMR588EHDw8PD+OKLLwyjlve9LGfPRGfUgXNw3333GStXrjT27NljfPPNN0Z8fLwRGhpqHDp0yDDqQP+//fZbw9vb23jiiSeMn3/+2Zg/f75Rr14946233rK3qe1/B43Ts/NGRUUZEyZMOGdbbf8MJCUlGRdddJGxePFiY8+ePcaHH35ohIaGGg888IC9TV34DMB11bXc1JmqIy/u16+f0blzZ2P9+vXG119/bbRs2dIYMmSIE3vlvqojTz916pRxySWXGH379jU2b95sZGZmGk2aNDEmTpzopF65rwv93sC1qFmV/Q7D9aheF/qdyp2uB8XIGjRr1iwjKirK8PX1Na644gpj3bp1zg6pxqxYscKQdM6SlJRkGIZh2Gw2Y9KkSUZYWJjh5+dn9OnTx9i5c6ezw642pfVdkjF37lx7mz///NO45557jEaNGhn16tUzbrzxRuPgwYNOjbu6DB8+3IiOjjZ8fX2NJk2aGH369LEnFEYt73tZ/vp/5LX9HNxyyy1GeHi44evra1x00UXGLbfcYuzatcu+vbb33zAM45NPPjEuueQSw8/Pz2jTpo3xyiuvOGyv7X8HDcMwPv/8c0NSqf2q7Z+B/Px849577zWioqIMf39/o0WLFsbDDz9sFBYW2tvUhc8AXFtdyk2dqTry4t9//90YMmSI0aBBAyMwMNAYNmyYcezYMSf1yL1VV57+66+/Gv379zcCAgKM0NBQ47777jOKi4ud0CP3Vh3fG7gWNacq32G4HtWnOr5Tucv18DD+9wcaAAAAAAAAAGoUY0YCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAADdkGIZGjRqlkJAQeXh4aPPmzc4OCQDK5WEYhuHsIAAAAAAAQOV89tlnuv7667Vy5Uq1aNFCoaGh8vb2dnZYAHBe/JUCAAAAAMAN7d69W+Hh4erWrVuVj1FcXCwfH59qjQsAzofHtAGgijIzM9WjRw8FBwercePG+uc//6ndu3fbt69Zs0adOnWSv7+/unbtqkWLFp3z+MzWrVvVv39/NWjQQGFhYbr99tt15MgRJ/UIAAAA7mLo0KEaPXq0srOz5eHhoebNm5ebn/7666/y8PDQu+++q6uuukr+/v6aP3++JOm///2v2rZtK39/f7Vp00YvvfSSE3sHoDajGAkAVXT8+HGlpqZq48aNWr58uTw9PXXjjTfKZrMpPz9f1157rTp06KBNmzbpscce04QJExz2z83N1dVXX63OnTtr48aNyszMVE5Ojm6++Wan9QkAAADu4T//+Y+mTZumZs2a6eDBg9qwYcN589OzPfjgg7r33nu1Y8cOJSQkaP78+Zo8ebKeeOIJ7dixQ08++aQmTZqkN954w2n9A1B7MWYkAFSTI0eOqEmTJvrhhx/09ddf65FHHtG+ffvk7+8vnf61eeTIkfruu+/UqVMnPf744/rqq6/0+eef24+xb98+RUZGaufOnWrVqpUTewMAAABXN3PmTM2cOVO//vprqdvPzk8vueQS/frrr4qJidHMmTN177332tv97W9/02OPPaYhQ4bY1z3++OP69NNPtWbNGlP6AqDu4M5IAKiin3/+WUOGDFGLFi0UGBio5s2bS5Kys7O1c+dOXXrppfZCpCRdccUVDvt///33WrFihRo0aGBf2rRpI50e/wcAAACojPPlp2fr2rWr/d/Hjx/X7t27NWLECIe89PHHHycnBVAjmMAGAKro2muvVXR0tF599VVFRETIZrPpkksuUVFRUYX2Lygo0LXXXqvp06efsy08PLwGIgYAAEBtVtH8tH79+vZ/FxQUSJJeffVVxcbGOrTz8vIyKXIAdQnFSACogt9//107d+7Uq6++qr///e+SpK+//tq+vXXr1nrrrbdUWFgoPz8/SdKGDRscjnHZZZfpgw8+UPPmzeXtzZ9jAAAAVF15+WlZwsLCFBERoV9++UWJiYkmRAqgruMxbQCogkaNGqlx48Z65ZVXtGvXLn355ZdKTU21b7/11ltls9k0atQo7dixQ59//rmeeeYZSZKHh4ckKTk5WUePHtWQIUO0YcMG7d69W59//rmGDRumkpISp/UNAAAA7qe8/PR8pk6dqrS0NL3wwgv66aef9MMPP2ju3Ll67rnnajxuAHUPxUgAqAJPT0+98847ysrK0iWXXKJx48bp6aeftm8PDAzUJ598os2bN6tTp056+OGHNXnyZEmyjyMZERGhb775RiUlJerbt686dOigsWPHKjg4WJ6e/HkGAABAxZWXn57PnXfeqf/+97+aO3euOnTooKuuukrz5s1TTExMjccNoO5hNm0AMMn8+fM1bNgw5eXlKSAgwNnhAAAAAABgOgYpA4Aa8uabb6pFixa66KKL9P3332vChAm6+eabKUQCAAAAAOosipEAUEOsVqsmT54sq9Wq8PBw3XTTTXriiSecHRYAAAAAAE7DY9oAAAAAAAAATMEMCQAAAAAAAABMQTESAAAAAAAAgCkoRgIAAAAAAAAwBcVIAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCYiQAAAAAAAAAU1CMBAAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApKEYCAAAAAAAAMAXFSAAAAAAAAACmoBgJAAAAAAAAwBQUIwEAAAAAAACYgmIkAAAAAAAAAFNQjAQAAAAAAABgCoqRAAAAAAAAAExBMRIAAAAAAACAKbydHYArsNlsOnDggBo2bCgPDw9nhwMAAFAphmHo2LFjioiIkKcnvzW7I/JRAADg7iqak1KMlHTgwAFFRkY6OwwAAIALsnfvXjVr1szZYaAKyEcBAEBtUV5OSjFSUsOGDaXTJyswMNDZ4QAAAFRKfn6+IiMj7TkN3A/5KAAAcHcVzUkpRkr2R2ECAwNJ/gAAgNvi8V73RT4KAABqi/JyUgYVAgAAAAAAAGAKipEAAAAAAAAATEExEgAAAAAAAIApGDMSAABUiGEYOnXqlEpKSpwdSp3k4+MjLy8vZ4cBAADgVOSkzuPl5SVvb+8LHqecYiQAAChXUVGRDh48qBMnTjg7lDrLw8NDzZo1U4MGDZwdCgAAgFOQkzpfvXr1FB4eLl9f3yofg2IkAAA4L5vNpj179sjLy0sRERHy9fVl1maTGYahw4cPa9++fWrZsiV3SAIAgDqHnNS5DMNQUVGRDh8+rD179qhly5by9Kza6I8UIwEAwHkVFRXJZrMpMjJS9erVc3Y4dVaTJk3066+/qri4mGIkAACoc8hJnS8gIEA+Pj767bffVFRUJH9//yodhwlsAABAhVT1l09UD375BwAAICd1tuo4/1xBAAAAAAAAAKagGAkAAAAAAADAFBQjAQAAAAAAAJiCCWwAAACAOsxms2nv3r2SpMjISMbiAgAANYpMA8A5usR2U0R0i/MuXWK7OTtMAJAkvf/+++rQoYMCAgLUuHFjxcfH6/jx45Kk//73v2rbtq38/f3Vpk0bvfTSS/b9hg8frksvvVSFhYXS6RkaO3furDvuuMNpfQGcYe/evRqenqnh6Zn2oiQAAKgcctKK485IAOc4aLWq58Pzz9tm9ROJpsUDAGU5ePCghgwZohkzZujGG2/UsWPH9NVXX8kwDM2fP1+TJ0/Wiy++qM6dO+u7777TyJEjVb9+fSUlJemFF15Qx44d9eCDD+r555/Xww8/rNzcXL344ovO7hZMlJaWpg8//FA//vijAgIC1K1bN02fPl2tW7e2t+nVq5dWrVrlsN9dd92lOXPm2F9nZ2fr7rvv1ooVK9SgQQMlJSUpLS1N3t7ukW7Xa9TE2SEAAOC2yEkrxz2yIwAAgFIcPHhQp06d0sCBAxUdHS1J6tChgyRpypQpevbZZzVw4EBJUkxMjLZv366XX35ZSUlJatCggd566y1dddVVatiwoWbOnKkVK1YoMDDQqX2CuVatWqXk5GRdfvnlOnXqlB566CH17dtX27dvV/369e3tRo4cqWnTptlf16tXz/7vkpISDRgwQBaLRWvWrNHBgwd1xx13yMfHR08++aTpfQIAAOYiJ60cipEAAMBtdezYUX369FGHDh2UkJCgvn376l//+pd8fX21e/dujRgxQiNHjrS3P3XqlIKCguyv4+LidP/99+uxxx7ThAkT1KNHDyf1BM6SmZnp8HrevHlq2rSpsrKy1LNnT/v6evXqyWKxlHqML774Qtu3b9eyZcsUFhamTp062T9Tjz76qHx9fWu8HwAAwHnISSuHMSMBAIDb8vLy0tKlS/XZZ5+pXbt2mjVrllq3bq2tW7dKkl599VVt3rzZvmzdulXr1q2z72+z2fTNN9/Iy8tLu3btcmJP4Cry8vIkSSEhIQ7r58+fr9DQUF1yySWaOHGiTpw4Yd+2du1adejQQWFhYfZ1CQkJys/P17Zt20yMHgAAOAM5aeVwZyQAAHBrHh4e6t69u7p3767JkycrOjpa33zzjSIiIvTLL78oMbHsMW6ffvpp/fjjj1q1apUSEhI0d+5cDRs2zNT44TpsNpvGjh2r7t2765JLLrGvv/XWWxUdHa2IiAht2bJFEyZM0M6dO/Xhhx9KkqxWq0MhUpL9tdVqLfW9CgsL7QPVS1J+fn4N9QoAAJiBnLTiKEYCAAC3tX79ei1fvlx9+/ZV06ZNtX79eh0+fFht27bV1KlTNWbMGAUFBalfv34qLCzUxo0b9ccffyg1NVXfffedJk+erPfff1/du3fXc889p3vvvVdXXXWVWrRo4eyuwQmSk5O1detWff311w7rR40aZf93hw4dFB4erj59+mj37t26+OKLq/ReaWlpmjp16gXHDAAAnI+ctHIoRgIAALcVGBio1atXa+bMmcrPz1d0dLSeffZZ9e/fXzo9zt/TTz+t8ePHq379+urQoYPGjh2rkydP6rbbbtPQoUN17bXXSqcLTkuWLNHtt9+u1atXy8vLy8m9g5lSUlK0ePFirV69Ws2aNTtv29jYWEnSrl27dPHFF8tisejbb791aJOTkyNJZY4zOXHiRKWmptpf5+fnKzIyshp6AgAAzEZOWjkUIwEAgNtq27btOROQnO3WW2/VrbfeWuq20sby++ijj6o1Prg+wzA0evRoLVy4UCtXrlRMTEy5+2zevFmSFB4eLp0edP6JJ57QoUOH1LRpU0nS0qVLFRgYqHbt2pV6DD8/P/n5+VVrXwAAgHOQk1YOxUgAAADUWcnJycrIyNBHH32khg0b2sd4DAoKUkBAgHbv3q2MjAxdc801aty4sbZs2aJx48apZ8+euvTSSyVJffv2Vbt27XT77bdrxowZslqteuSRR5ScnEzBEQAA4C+YTRsAAAB11uzZs5WXl6devXopPDzcvrz77ruSJF9fXy1btkx9+/ZVmzZtdN9992nQoEH65JNP7Mfw8vLS4sWL5eXlpbi4ON1222264447NG3aNCf2DAAAwDU5tRi5evVqXXvttYqIiJCHh4cWLVp0TpsdO3bouuuuU1BQkOrXr6/LL79c2dnZ9u0nT55UcnKyGjdurAYNGmjQoEH2MXoAAACA8zEMo9Rl6NChkqTIyEitWrVKv//+u06ePKmff/5ZM2bMUGBgoMNxoqOj9emnn+rEiRM6fPiwnnnmGXl78xASAADAXzm1GHn8+HF17NhR6enppW7fvXu3evTooTZt2mjlypXasmWLJk2aJH9/f3ubcePG6ZNPPtGCBQu0atUqHThwQAMHDjSxFwAAAAAAAAAqwqk/1/bv398+s1BpHn74YV1zzTWaMWOGfd3FF19s/3deXp5ee+01ZWRk6Oqrr5YkzZ07V23bttW6det05ZVX1nAPAAAAAAAAAFSUy44ZabPZtGTJErVq1UoJCQlq2rSpYmNjHR7lzsrKUnFxseLj4+3r2rRpo6ioKK1du9ZJkQMAAAAAAAAojcsWIw8dOqSCggI99dRT6tevn7744gvdeOONGjhwoFatWiVJslqt8vX1VXBwsMO+YWFh9pkQS1NYWKj8/HyHBQAAAAAAAEDNctlRtW02myTp+uuv17hx4yRJnTp10po1azRnzhxdddVVVT52Wlqapk6dWm2xAgAAAAAAACify94ZGRoaKm9vb7Vr185hfdu2be2zaVssFhUVFSk3N9ehTU5OjiwWS5nHnjhxovLy8uzL3r17a6gXAAAAAAAAAM5w2TsjfX19dfnll2vnzp0O63/66SdFR0dLkrp06SIfHx8tX75cgwYNkiTt3LlT2dnZiouLK/PYfn5+8vPzq+EeAABQ+2VnZ+vIkSOmvV9oaKiioqJMe7/SDB06VLm5uQ7jWAMAAMA56mI+KjfPSZ1ajCwoKNCuXbvsr/fs2aPNmzcrJCREUVFRGj9+vG655Rb17NlTvXv3VmZmpj755BOtXLlSkhQUFKQRI0YoNTVVISEhCgwM1OjRoxUXF8dM2gAA1LDs7Gy1adNWf/55wrT3DAiopx9/3OESCSAAAACci3zUPTm1GLlx40b17t3b/jo1NVWSlJSUpHnz5unGG2/UnDlzlJaWpjFjxqh169b64IMP1KNHD/s+zz//vDw9PTVo0CAVFhYqISFBL730klP6AwBAXXLkyBH9+ecJxQ6fosDw5jX+fvkHf9X616fqyJEjJH8AAAAgH3VTTi1G9urVS4ZhnLfN8OHDNXz48DK3+/v7Kz09Xenp6TUQIQAAKE9geHOFRLV2dhil6tWrlzp06CAvLy+98cYb8vX11eOPP65bb71VKSkpev/99xUWFqZZs2apf//+Kikp0ahRo/Tll1/KarUqKipK99xzj+69994y38Nms2n69Ol65ZVXZLVa1apVK02aNEn/+te/TO0rAABAXeXK+ajISc/hshPYAAAAVIc33nhDoaGh+vbbbzV69Gjdfffduummm9StWzdt2rRJffv21e23364TJ07IZrOpWbNmWrBggbZv367JkyfroYce0nvvvVfm8dPS0vTmm29qzpw52rZtm8aNG6fbbrtNq1atMrWfAAAAcF3kpP/HZSewAQAAqA4dO3bUI488IkmaOHGinnrqKYWGhmrkyJGSpMmTJ2v27NnasmWLrrzySk2dOtW+b0xMjNauXav33ntPN9988znHLiws1JNPPqlly5bZJ89r0aKFvv76a7388su66qqrTOsnAAAAXBc56f+hGAkAAGq1Sy+91P5vLy8vNW7cWB06dLCvCwsLkyQdOnRIkpSenq7XX39d2dnZ+vPPP1VUVKROnTqVeuxdu3bpxIkT+sc//uGwvqioSJ07d66hHgEAAMDdkJP+H4qRAACgVvPx8XF47eHh4bDOw8NDOj3OzjvvvKP7779fzz77rOLi4tSwYUM9/fTTWr9+fanHLigokCQtWbJEF110kcM2Pz+/GugNAAAA3BE56f+hGAkAAHDaN998o27duumee+6xr9u9e3eZ7du1ayc/Pz9lZ2e73OMvAAAAcE+1PSelGAkAAHBay5Yt9eabb+rzzz9XTEyM/t//+3/asGGDYmJiSm3fsGFD3X///Ro3bpxsNpt69OihvLw8ffPNNwoMDFRSUpLpfQAAAIB7q+05KcVIAABwQfIP/lpr3ueuu+7Sd999p1tuuUUeHh4aMmSI7rnnHn322Wdl7vPYY4+pSZMmSktL0y+//KLg4GBddtlleuihh2o8XgAAANSufFR1ICf1MAzDcHYQzpafn6+goCDl5eUpMDDQ2eEAThcR3UI9H55/3jarn0jUgd9+MS0mAM5z8uRJ7dmzRzExMfL397evz87OVps2bfXnnydMiyUgoJ5+/HGHoqKiTHtPV1HWdRC5TK3gzGv422+/KSUjS5L04q1dFB0dber7A/+/vXuPjqq6+z/+mZCQhEsSwiWTSBKCVUC5iKAxSKtCSkCkoLQWmvIgUuljAwKxClRBwUvEWqVghGoVcBVKa39KK9ZQDLciMYYgKhcjIJogTJBLEgIyCZnz+0MyDyO5kUzOzGTer7XOWpmz9+z57nOGYc939jkbABqiprEQ41HzuWNMysxIAADQKHFxcfrss306fvy4aa/ZqVMnvx34AQAAwBXjUd9EMhIAADRaXFwcgzEAAAB4DONR3xPg6QAAAAAAAAAA+AeSkQAAAAAAAABMQTISAAAAAAAAgClIRgIAAAAAAAAwBclIAAAAAAAAAKYgGQkAAAAAAADAFCQjAQAAAAAAAJgi0NMBAAAA31VYWKjjx4+b9nqdOnVSXFxcg+sbhqFf//rX+sc//qFTp07po48+0nXXXdesMdbkyy+/VEJCgsdeHwAAoKXy9vGoGJNegmQkAABolMLCQvXq2UNnvz1n2mu2CQ3Rvs8KGjwAzMrK0ooVK7R582Z1795dnTp1avYYAQAAYA5fGI+KMeklSEYCAIBGOX78uM5+e05/mXKdekW3a/bX23e0XL98eZeOHz/e4MHfwYMHFR0drUGDBjV7fAAAADCXL4xHxZj0EtwzEgAANEmv6Ha6vlt4s2+XO8C85557NG3aNBUWFspisahbt25yOBzKyMhQQkKCQkND1a9fP/3jH/9wPmfz5s2yWCxav369+vfvr9DQUA0ZMkTHjh3Tu+++q169eiksLEy/+MUvdPbsWefzsrKyNHjwYEVERKhjx4664447dPDgwTrj2717t0aMGKF27dopKipKEyZMMPUSIwAAgJbCW8ejYkxaI5KRAACgRfrjH/+oBQsWqGvXrjp69Kjy8vKUkZGh119/XcuWLdOePXs0c+ZM/fKXv9SWLVtcnvv444/rxRdf1Pbt21VUVKS7775bixYt0urVq/XOO+/oP//5j5YsWeKsf+bMGaWnp2vHjh3Kzs5WQECA7rzzTjkcjhpjKykp0ZAhQ9S/f3/t2LFDWVlZKi4u1t13393sxwUAAADmYUx6KS7TBgAALVJ4eLjat2+vVq1ayWq1ym636+mnn9Z7772npKQkSVL37t21bds2/elPf9Itt9zifO6TTz6pm2++WZI0efJkzZkzRwcPHlT37t0lST/96U+1adMmzZo1S5I0duxYl9d+7bXX1LlzZ+3du1e9e/e+JLYXX3xR/fv319NPP+3ynNjYWH3++ee6+uqrm+moAAAAwEyMSS9FMhIAAPiFAwcO6OzZs/rxj3/ssr+iokL9+/d32de3b1/n31FRUWrTpo1z0Fe978MPP3Q+3r9/v+bNm6fc3FwdP37c+etzYWFhjQO/jz/+WJs2bVK7dpde6nPw4EGSkQAAAC0UY1KSkQAAwE+Ul5dLkt555x1dccUVLmXBwcEuj4OCgpx/WywWl8fV+y6+3GXUqFGKj4/XK6+8opiYGDkcDvXu3VsVFRW1xjJq1CgtXLjwkrLo6OhG9hAAAADejjGph+8ZuXXrVo0aNUoxMTGyWCxau3ZtrXX/93//VxaLRYsWLXLZf/LkSaWmpiosLEwRERGaPHmy88QCAABUu+aaaxQcHKzCwkL94Ac/cNliY2Mb3e6JEydUUFCgRx99VEOHDlWvXr106tSpOp9z/fXXa8+ePerWrdslsbRt27bRsQAAAMC7MSb1cDLyzJkz6tevnzIzM+us99Zbb+mDDz5QTEzMJWWpqanas2ePNmzYoHXr1mnr1q2aMmVKM0YNAAB8Ufv27fXb3/5WM2fO1MqVK3Xw4EHt3LlTS5Ys0cqVKxvdbocOHdSxY0e9/PLLOnDggDZu3Kj09PQ6n5OWlqaTJ09q/PjxysvL08GDB7V+/XpNmjRJVVVVjY4FAAAA3o0xqYcv0x4xYoRGjBhRZ52vv/5a06ZN0/r16zVy5EiXsn379ikrK0t5eXkaOHCgJGnJkiW6/fbb9dxzz9WYvAQAAO6176g5VyS443WeeOIJde7cWRkZGfriiy8UERGh66+/Xr/73e8a3WZAQIDWrFmjBx54QL1791aPHj20ePFi3XrrrbU+JyYmRu+//75mzZqlYcOGyW63Kz4+XsOHD1dAgEd/KwYAAPA5vjQeFWNSWQzDMJqt9ctgsVj01ltvacyYMc59DodDycnJGj16tKZPn65u3bppxowZmjFjhnRhhZ8HH3zQZdrp+fPnFRISojfeeEN33nlnja9lt9tlt9udj8vKyhQbG6vS0lKFhYU1az8BXxAT310/emRVnXW2PpWqI199YVpMADzn3LlzOnTokBISEhQSEuLcX1hYqF49e+jst+dMi6VNaIj2fVaguLg4017TW9R2HnRhLBMeHs5Yxod58hx+9dVXmro6X5L04i8GKD4+3tTXBwCgIWoaCzEeNZ87xqRevYDNwoULFRgYqAceeKDGcpvNpi5durjsCwwMVGRkpGw2W63tZmRkaP78+W6PFwAAfxIXF6d9nxXo+PHjpr1mp06d/HbgBwAAAFeMR32T1yYj8/Pz9cc//lE7d+6UxWJxa9tz5sxxuW6+emYkAAC4PHFxcQzGAAAA4DGMR32P196U6L///a+OHTumuLg4BQYGKjAwUF999ZUefPBBdevWTZJktVp17Ngxl+edP39eJ0+elNVqrbXt4OBghYWFuWwAAAAAAAAAmpfXzoycMGGCkpOTXfalpKRowoQJmjRpkiQpKSlJJSUlys/P14ABAyRJGzdulMPhUGJiokfiBgAAAAAAAFAzjyYjy8vLdeDAAefjQ4cOadeuXYqMjFRcXJw6duzoUj8oKEhWq1U9evSQJPXq1UvDhw/Xfffdp2XLlqmyslJTp07VuHHjWEkbAAAAAAAA8DIevUx7x44d6t+/v/r37y9JSk9PV//+/TVv3rwGt7Fq1Sr17NlTQ4cO1e23367Bgwfr5ZdfbsaoAQDwT4ZheDoEv8bxBwAAYEzkae44/h6dGXnrrbdeVie+/PLLS/ZFRkZq9erVbo4MAABUCwoKkiSdPXtWoaGhng7Hb1VUVEiSWrVq5elQAAAATMeY1DucPXtWuuh8NIbX3jMSAAB4h1atWikiIsK5aFybNm1ksVg8HZZfcTgc+uabb9SmTRsFBjJ8AwAA/ocxqWcZhqGzZ8/q2LFjioiIaNIP5IxmAQBAvaxWqyQ5B38wX0BAgOLi4hh0AwAAv8WY1PMiIiKc56GxSEYCAIB6WSwWRUdHq0uXLqqsrPR0OH6pdevWCgjw6O2+W6SMjAy9+eab+uyzzxQaGqpBgwZp4cKFzgUTJencuXN68MEHtWbNGtntdqWkpOill15SVFSUs05hYaHuv/9+bdq0Se3atdPEiROVkZHBTFYAANyIMalnBQUFueWWQYyOAABAg7Vq1Yp7FqJF2bJli9LS0nTDDTfo/Pnz+t3vfqdhw4Zp7969atu2rSRp5syZeuedd/TGG28oPDxcU6dO1V133aX3339fklRVVaWRI0fKarVq+/btOnr0qP7nf/5HQUFBevrppz3cQwAAWh7GpL6NZCQAAAD8VlZWlsvjFStWqEuXLsrPz9ePfvQjlZaW6tVXX9Xq1as1ZMgQSdLy5cvVq1cvffDBB7rpppv0n//8R3v37tV7772nqKgoXXfddXriiSc0a9YsPf7442rdurWHegcAAOB9uNYHAAAAuKC0tFSSFBkZKUnKz89XZWWlkpOTnXV69uypuLg45eTkSJJycnLUp08fl8u2U1JSVFZWpj179tT4Ona7XWVlZS4bAACAPyAZCQAAAFxYtXzGjBm6+eab1bt3b0mSzWZT69atFRER4VI3KipKNpvNWefiRGR1eXVZTTIyMhQeHu7cYmNjm6lXAAAA3oVkJAAAACApLS1Nu3fv1po1a5r9tebMmaPS0lLnVlRU1OyvCQAA4A24ZyQAAAD83tSpU7Vu3Tpt3bpVXbt2de63Wq2qqKhQSUmJy+zI4uJiWa1WZ50PP/zQpb3i4mJnWU2Cg4MVHBzcTL0BAADwXsyMBAAAgN8yDENTp07VW2+9pY0bNyohIcGlfMCAAQoKClJ2drZzX0FBgQoLC5WUlCRJSkpK0qeffqpjx44562zYsEFhYWG65pprTOwNAACA92NmJAAAAPxWWlqaVq9erX/+859q37698x6P4eHhCg0NVXh4uCZPnqz09HRFRkYqLCxM06ZNU1JSkm666SZJ0rBhw3TNNddowoQJevbZZ2Wz2fToo48qLS2N2Y8AAADfQzISAAAAfmvp0qWSpFtvvdVl//Lly3XPPfdIkl544QUFBARo7NixstvtSklJ0UsvveSs26pVK61bt07333+/kpKS1LZtW02cOFELFiwwuTcAAADej2QkAAAA/JZhGPXWCQkJUWZmpjIzM2utEx8fr3//+99ujg4AAKDl4Z6RAAAAAAAAAExBMhIAAAAAAACAKUhGAgAAAAAAADAFyUgAAAAAAAAApiAZCQAAAAAAAMAUJCMBAAAAAAAAmIJkJAAAAAAAAABTkIwEAAAAAAAAYAqSkQAAAAAAAABMQTISAAAAAAAAgClIRgIAAAAAAAAwhUeTkVu3btWoUaMUExMji8WitWvXOssqKys1a9Ys9enTR23btlVMTIz+53/+R0eOHHFp4+TJk0pNTVVYWJgiIiI0efJklZeXe6A3AAAAAAAAAOri0WTkmTNn1K9fP2VmZl5SdvbsWe3cuVNz587Vzp079eabb6qgoEA/+clPXOqlpqZqz5492rBhg9atW6etW7dqypQpJvYCAAAAAAAAQEMEevLFR4wYoREjRtRYFh4erg0bNrjse/HFF3XjjTeqsLBQcXFx2rdvn7KyspSXl6eBAwdKkpYsWaLbb79dzz33nGJiYkzpBwAAAAAAAID6+dQ9I0tLS2WxWBQRESFJysnJUUREhDMRKUnJyckKCAhQbm6uByMFAAAAAAAA8H0enRl5Oc6dO6dZs2Zp/PjxCgsLkyTZbDZ16dLFpV5gYKAiIyNls9lqbctut8tutzsfl5WVNWPkAAAAAAAAAOQrMyMrKyt19913yzAMLV26tMntZWRkKDw83LnFxsa6JU4AAAAAAAAAtfP6ZGR1IvKrr77Shg0bnLMiJclqterYsWMu9c+fP6+TJ0/KarXW2uacOXNUWlrq3IqKipq1DwAAAAAAAAC8/DLt6kTk/v37tWnTJnXs2NGlPCkpSSUlJcrPz9eAAQMkSRs3bpTD4VBiYmKt7QYHBys4OLjZ4wcAAAAAAADwfzyajCwvL9eBAwecjw8dOqRdu3YpMjJS0dHR+ulPf6qdO3dq3bp1qqqqct4HMjIyUq1bt1avXr00fPhw3XfffVq2bJkqKys1depUjRs3jpW0AQAAAAAAAC/j0WTkjh07dNtttzkfp6enS5ImTpyoxx9/XP/6178kSdddd53L8zZt2qRbb71VkrRq1SpNnTpVQ4cOVUBAgMaOHavFixeb2g8AAAAAAAAA9fNoMvLWW2+VYRi1ltdVVi0yMlKrV692c2QAAAAAAAAA3M3rF7ABAAAAAAAA0DKQjAQAAAAAAABgCpKRAAAAAAAAAExBMhIAAAAAAACAKUhGAgAAAAAAADAFyUgAAAAAAAAApiAZCQAAAAAAAMAUJCMBAAAAAAAAmIJkJAAAAAAAAABTkIwEAAAAAAAAYAqSkQAAAAAAAABMQTISAAAAAAAAgClIRgIAAAAAAAAwBclIAAAAAAAAAKYgGQkAAAAAAADAFCQjAQAAAAAAAJiCZCQAAAAAAAAAU5CMBAAAAAAAAGAKkpEAAAAAAAAATEEyEgAAAAAAAIApSEYCAAAAAAAAMAXJSAAAAAAAAACmIBkJAAAAAAAAwBQkIwEAAAAAAACYgmQkAAAAAAAAAFN4NBm5detWjRo1SjExMbJYLFq7dq1LuWEYmjdvnqKjoxUaGqrk5GTt37/fpc7JkyeVmpqqsLAwRUREaPLkySovLze5JwAAAAAAAADq49Fk5JkzZ9SvXz9lZmbWWP7ss89q8eLFWrZsmXJzc9W2bVulpKTo3Llzzjqpqanas2ePNmzYoHXr1mnr1q2aMmWKib0AAACAL6vvB/J77rlHFovFZRs+fLhLHX4gBwAAaJhAT774iBEjNGLEiBrLDMPQokWL9Oijj2r06NGSpNdff11RUVFau3atxo0bp3379ikrK0t5eXkaOHCgJGnJkiW6/fbb9dxzzykmJsbU/gAAAMD3VP9Afu+99+quu+6qsc7w4cO1fPly5+Pg4GCX8tTUVB09elQbNmxQZWWlJk2apClTpmj16tXNHj8AAIAv8Wgysi6HDh2SzWZTcnKyc194eLgSExOVk5OjcePGKScnRxEREc5EpCQlJycrICBAubm5uvPOO2ts2263y263Ox+XlZU1c28AAADgrer6gbxacHCwrFZrjWX8QA4AANBwXruAjc1mkyRFRUW57I+KinKW2Ww2denSxaU8MDBQkZGRzjo1ycjIUHh4uHOLjY1tlj4AAACgZdi8ebO6dOmiHj166P7779eJEyecZfX9QF4Tu92usrIylw0AAMAfeG0ysjnNmTNHpaWlzq2oqMjTIQEAAMBLDR8+XK+//rqys7O1cOFCbdmyRSNGjFBVVZXUyB/I+XEcAAD4q0YlI7t37+7ya3C1kpISde/e3R1xOS+DKS4udtlfXFzsLLNarTp27JhL+fnz53Xy5MlaL6PRhctswsLCXDYAAAD4DjPGo9XGjRunn/zkJ+rTp4/GjBmjdevWKS8vT5s3b250m/w4DgAA/FWjkpFffvml85fgi9ntdn399dfuiEsJCQmyWq3Kzs527isrK1Nubq6SkpIkSUlJSSopKVF+fr6zzsaNG+VwOJSYmOiWOAAAAOB9zBiP1qZ79+7q1KmTDhw4IDXyB3J+HAcAAP7qshaw+de//uX8e/369QoPD3c+rqqqUnZ2trp169bg9srLy52DOF1YtGbXrl2KjIxUXFycZsyYoSeffFJXXXWVEhISNHfuXMXExGjMmDGSpF69emn48OG67777tGzZMlVWVmrq1KkaN24cNwoHAABogdw9Hm2Mw4cP68SJE4qOjpa+9wP5gAEDJH4gBwAAqNVlJSOrk4AWi0UTJ050KQsKClK3bt30hz/8ocHt7dixQ7fddpvzcXp6uiRp4sSJWrFihR5++GGdOXNGU6ZMUUlJiQYPHqysrCyFhIQ4n7Nq1SpNnTpVQ4cOVUBAgMaOHavFixdfTrcAAADgI9w9HlU9P5BHRkZq/vz5Gjt2rKxWqw4ePKiHH35YP/jBD5SSkiLxAzkAAMBluaxkpMPhkC5cQp2Xl6dOnTo16cVvvfVWGYZRa7nFYtGCBQu0YMGCWutERkZq9erVTYoDAAAAvsHd41HV8wP50qVL9cknn2jlypUqKSlRTEyMhg0bpieeeELBwcHO5/ADOQAAQMNcVjKy2qFDh9wfCQAAANBA7hyP1vcD+fr16+ttgx/IAQAAGqZRyUhJys7OVnZ2to4dO+b8hbraa6+95o7YAAAAgFoxHgUAAPA9jUpGzp8/XwsWLNDAgQMVHR0ti8Xi/sgAAACAWjAeBQAA8E2NSkYuW7ZMK1as0IQJE9wfEQAAAFAPxqMAAAC+KaAxT6qoqNCgQYPcHw0AAADQAIxHAQAAfFOjkpG/+tWvuEE3AAAAPIbxKAAAgG9q1GXa586d08svv6z33ntPffv2VVBQkEv5888/7674AAAAgEswHgUAAPBNjUpGfvLJJ7ruuuskSbt373Yp4+bhAAAAaG6MRwEAAHxTo5KRmzZtcn8kAAAAQAMxHgUAAPBNjbpnJAAAAAAAAABcrkbNjLztttvqvPxl48aNTYkJAAAAqBPjUQAAAN/UqGRk9f15qlVWVmrXrl3avXu3Jk6c6K7YAAAAgBoxHgUAAPBNjUpGvvDCCzXuf/zxx1VeXt7UmAAAAIA6MR4FAADwTW69Z+Qvf/lLvfbaa+5sEgAAAGgwxqMAAADeza3JyJycHIWEhLizSQAAAKDBGI8CAAB4t0Zdpn3XXXe5PDYMQ0ePHtWOHTs0d+5cd8UGAAAA1IjxKAAAgG9qVDIyPDzc5XFAQIB69OihBQsWaNiwYe6KDQAAAKgR41EAAADf1Khk5PLly90fCQAAANBAjEcBAAB8U6OSkdXy8/O1b98+SdK1116r/v37uysuAAAAoF6MRwEAAHxLo5KRx44d07hx47R582ZFRERIkkpKSnTbbbdpzZo16ty5s7vjBAAAAJwYjwIAAPimRq2mPW3aNJ0+fVp79uzRyZMndfLkSe3evVtlZWV64IEH3B8lAAAAcBHGowAAAL6pUTMjs7Ky9N5776lXr17Ofddcc40yMzO5YTgAAACaHeNRAAAA39SomZEOh0NBQUGX7A8KCpLD4XBHXAAAAECtGI8CAAD4pkYlI4cMGaLp06fryJEjzn1ff/21Zs6cqaFDh7ozPgAAAOASjEcBAAB8U6OSkS+++KLKysrUrVs3XXnllbryyiuVkJCgsrIyLVmyxG3BVVVVae7cuUpISFBoaKiuvPJKPfHEEzIMw1nHMAzNmzdP0dHRCg0NVXJysvbv3++2GAAAAOB9zBqPAgAAwL0adc/I2NhY7dy5U++9954+++wzSVKvXr2UnJzs1uAWLlyopUuXauXKlbr22mu1Y8cOTZo0SeHh4c4bkz/77LNavHixVq5cqYSEBM2dO1cpKSnau3evQkJC3BoPAAAAvINZ41EAAAC412XNjNy4caOuueYalZWVyWKx6Mc//rGmTZumadOm6YYbbtC1116r//73v24Lbvv27Ro9erRGjhypbt266ac//amGDRumDz/8ULowK3LRokV69NFHNXr0aPXt21evv/66jhw5orVr17otDgAAAHgHs8ejAAAAcK/LSkYuWrRI9913n8LCwi4pCw8P169//Ws9//zzbgtu0KBBys7O1ueffy5J+vjjj7Vt2zaNGDFCknTo0CHZbDaXX8DDw8OVmJionJwct8UBAAAA72D2eBQAAADudVnJyI8//ljDhw+vtXzYsGHKz893R1ySpNmzZ2vcuHHq2bOngoKC1L9/f82YMUOpqamSJJvNJkmKiopyeV5UVJSzrCZ2u11lZWUuGwAAALyf2eNRAAAAuNdlJSOLi4sVFBRUa3lgYKC++eYbd8QlSfr73/+uVatWafXq1dq5c6dWrlyp5557TitXrmxSuxkZGQoPD3dusbGxbosZAAAAzcfs8SgAAADc67KSkVdccYV2795da/knn3yi6Ohod8QlSXrooYecsyP79OmjCRMmaObMmcrIyJAkWa1W6cKg9GLFxcXOsprMmTNHpaWlzq2oqMhtMQMAAKD5mD0eBQAAgHtdVjLy9ttv19y5c3Xu3LlLyr799ls99thjuuOOO9wW3NmzZxUQ4Bpiq1at5HA4JEkJCQmyWq3Kzs52lpeVlSk3N1dJSUm1thscHKywsDCXDQAAAN7P7PEoAAAA3Cvwcio/+uijevPNN3X11Vdr6tSp6tGjhyTps88+U2ZmpqqqqvTII4+4LbhRo0bpqaeeUlxcnK699lp99NFHev7553XvvfdKkiwWi2bMmKEnn3xSV111lRISEjR37lzFxMRozJgxbosDAAAA3sHs8SgAAADc67KSkVFRUdq+fbvuv/9+zZkzR4ZhSBeSgikpKcrMzLxkMZmmWLJkiebOnavf/OY3OnbsmGJiYvTrX/9a8+bNc9Z5+OGHdebMGU2ZMkUlJSUaPHiwsrKyFBIS4rY4AAAA4B3MHo8CAADAvS4rGSlJ8fHx+ve//61Tp07pwIEDMgxDV111lTp06OD24Nq3b69FixZp0aJFtdaxWCxasGCBFixY4PbXBwAAgPcxczwKAAAA97rsZGS1Dh066IYbbnBvNAAAAEADMR4FAADwPZe1gA0AAAAAAAAANBbJSAAAAAAAAACmIBkJAAAAAAAAwBQkIwEAAAAAAACYgmQkAAAAAAAAAFM0ejVtAP7t5IkTujo+utbyKGu0/pu709SYAAAAAACAdyMZCaBRHA6HPp8/oNbyqx/LNzUeAAAAAADg/bhMGwAAAAAAAIApSEYCAAAAAAAAMAXJSAAAAAAAAACmIBkJAAAAAAAAwBQkIwEAAAAAAACYgmQkAAAAAAAAAFOQjAQAAIBf27p1q0aNGqWYmBhZLBatXbvWpdwwDM2bN0/R0dEKDQ1VcnKy9u/f71Ln5MmTSk1NVVhYmCIiIjR58mSVl5eb3BMAAADvRzISAAAAfu3MmTPq16+fMjMzayx/9tlntXjxYi1btky5ublq27atUlJSdO7cOWed1NRU7dmzRxs2bNC6deu0detWTZkyxcReAAAA+IZATwcAAAAAeNKIESM0YsSIGssMw9CiRYv06KOPavTo0ZKk119/XVFRUVq7dq3GjRunffv2KSsrS3l5eRo4cKAkacmSJbr99tv13HPPKSYmxtT+AAAAeDNmRgIAAAC1OHTokGw2m5KTk537wsPDlZiYqJycHElSTk6OIiIinIlISUpOTlZAQIByc3NrbNdut6usrMxlAwAA8AckIwEAAIBa2Gw2SVJUVJTL/qioKGeZzWZTly5dXMoDAwMVGRnprPN9GRkZCg8Pd26xsbHN1gcAAABvQjISAAAAMNmcOXNUWlrq3IqKijwdEgAAgClIRgIAAAC1sFqtkqTi4mKX/cXFxc4yq9WqY8eOuZSfP39eJ0+edNb5vuDgYIWFhblsAAAA/oBkJAAAAFCLhIQEWa1WZWdnO/eVlZUpNzdXSUlJkqSkpCSVlJQoPz/fWWfjxo1yOBxKTEz0SNwAAADeitW0AQAA4NfKy8t14MAB5+NDhw5p165dioyMVFxcnGbMmKEnn3xSV111lRISEjR37lzFxMRozJgxkqRevXpp+PDhuu+++7Rs2TJVVlZq6tSpGjduHCtpAwAAfA/JSAAAAPi1HTt26LbbbnM+Tk9PlyRNnDhRK1as0MMPP6wzZ85oypQpKikp0eDBg5WVlaWQkBDnc1atWqWpU6dq6NChCggI0NixY7V48WKP9AcAAMCbkYwEAACAX7v11ltlGEat5RaLRQsWLNCCBQtqrRMZGanVq1c3U4QAAAAth9ffM/Lrr7/WL3/5S3Xs2FGhoaHq06ePduzY4Sw3DEPz5s1TdHS0QkNDlZycrP3793s0ZgAAAAAAAACX8upk5KlTp3TzzTcrKChI7777rvbu3as//OEP6tChg7POs88+q8WLF2vZsmXKzc1V27ZtlZKSonPnznk0dgAAAAAAAACuvPoy7YULFyo2NlbLly937ktISHD+bRiGFi1apEcffVSjR4+WJL3++uuKiorS2rVrNW7cOI/EDQAAAAAAAOBSXj0z8l//+pcGDhyon/3sZ+rSpYv69++vV155xVl+6NAh2Ww2JScnO/eFh4crMTFROTk5tbZrt9tVVlbmsgEAAAAAAABoXl6djPziiy+0dOlSXXXVVVq/fr3uv/9+PfDAA1q5cqUkyWazSZKioqJcnhcVFeUsq0lGRobCw8OdW2xsbDP3BAAAAAAAAIBXJyMdDoeuv/56Pf300+rfv7+mTJmi++67T8uWLWtSu3PmzFFpaalzKyoqclvMAAAAAAAAAGrm1cnI6OhoXXPNNS77evXqpcLCQkmS1WqVJBUXF7vUKS4udpbVJDg4WGFhYS4bAAAAAAAAgObl1cnIm2++WQUFBS77Pv/8c8XHx0sXFrOxWq3Kzs52lpeVlSk3N1dJSUmmxwsAAAAAAACgdl69mvbMmTM1aNAgPf3007r77rv14Ycf6uWXX9bLL78sSbJYLJoxY4aefPJJXXXVVUpISNDcuXMVExOjMWPGeDp8AAAAAAAAABfx6mTkDTfcoLfeektz5szRggULlJCQoEWLFik1NdVZ5+GHH9aZM2c0ZcoUlZSUaPDgwcrKylJISIhHYwcAAAAAAADgyquTkZJ0xx136I477qi13GKxaMGCBVqwYIGpcQEAAAAAAAC4PF59z0gAAAAAAAAALQfJSAAAAAAAAACmIBkJAAAAAAAAwBQkIwEAAAAAAACYgmQkAAAAAAAAAFOQjAQAAAAAAABgCpKRAAAAAAAAAExBMhIAAAAAAACAKUhGAgAAAAAAADAFyUgAAAAAAAAApiAZCQAAAAAAAMAUJCMBAAAAAAAAmIJkJAAAAAAAAABTBHo6AKClGJA4SEdttlrLo61W5eduNzUmAAAAAAAAb0IyEnCTozabfvTIqlrLtz6Vamo8AAAAAAAA3obLtAEAAAAAAACYgmQkAAAAAAAAAFOQjAQAAAAAAABgCpKRAAAAAAAAAExBMhIAAAAAAACAKUhGAgAAAAAAADAFyUgAAAAAAAAApiAZCQAAAAAAAMAUJCMBAAAAAAAAmMKnkpHPPPOMLBaLZsyY4dx37tw5paWlqWPHjmrXrp3Gjh2r4uJij8YJAAAAAAAA4FI+k4zMy8vTn/70J/Xt29dl/8yZM/X222/rjTfe0JYtW3TkyBHdddddHosTAAAA8GUOh0NfffWVc3M4HJ4OCQAAtCA+kYwsLy9XamqqXnnlFXXo0MG5v7S0VK+++qqef/55DRkyRAMGDNDy5cu1fft2ffDBBx6NGQAAAPBFRUVFujczS1NX5+vezCwVFRV5OiQAANCC+EQyMi0tTSNHjlRycrLL/vz8fFVWVrrs79mzp+Li4pSTk+OBSAEAAADf16ZDZ7XtGK02HTp7OhQAANDCBHo6gPqsWbNGO3fuVF5e3iVlNptNrVu3VkREhMv+qKgo2Wy2Wtu02+2y2+3Ox2VlZW6OGgAAAAAAAMD3eXUysqioSNOnT9eGDRsUEhLitnYzMjI0f/58t7UHuMuAxEE6WkciXZKirVbl5243LabGKjperpj47nXW8ZW+AAAAAAAA9/DqZGR+fr6OHTum66+/3rmvqqpKW7du1Ysvvqj169eroqJCJSUlLrMji4uLZbVaa213zpw5Sk9Pdz4uKytTbGxsM/YEaJijNpt+9MiqOutsfSrVtHiaospQi+kLAAB+wZAOHz7s/BsAAKA5eHUycujQofr0009d9k2aNEk9e/bUrFmzFBsbq6CgIGVnZ2vs2LGSpIKCAhUWFiopKanWdoODgxUcHNzs8QMAAAC+wl5RIdtfH/ju704Pqa2nAwIAAC2SVycj27dvr969e7vsa9u2rTp27OjcP3nyZKWnpysyMlJhYWGaNm2akpKSdNNNN3koagAAAMA3WcOb9oO9w+Fwrr4dGxurgACfWC8TAACYyKuTkQ3xwgsvKCAgQGPHjpXdbldKSopeeuklT4cFAAAA+J2ioiLdm5klSXotbbji4+M9HRIAAPAyPpeM3Lx5s8vjkJAQZWZmKjMz02MxAQAAAPhOmw6dPR0CAADwYlw3AQAAAAAAAMAUJCMBAAAAAAAAmIJkJAAAAFCHxx9/XBaLxWXr2bOns/zcuXNKS0tTx44d1a5dO40dO1bFxcUejRkAAMBbkYwEAAAA6nHttdfq6NGjzm3btm3OspkzZ+rtt9/WG2+8oS1btujIkSO66667PBovAACAt/K5BWwAAAAAswUGBspqtV6yv7S0VK+++qpWr16tIUOGSJKWL1+uXr166YMPPtBNN93kgWgBAAC8FzMjAQAAgHrs379fMTEx6t69u1JTU1VYWChJys/PV2VlpZKTk511e/bsqbi4OOXk5NTant1uV1lZmcsGAADgD5gZCfiYkydO6Or46FrLo6zR+m/uTlNjAgCgJUtMTNSKFSvUo0cPHT16VPPnz9cPf/hD7d69WzabTa1bt1ZERITLc6KiomSz2WptMyMjQ/PnzzchegAAAO9CMhLwMQ6HQ5/PH1Br+dWP5ZsaDwAALd2IESOcf/ft21eJiYmKj4/X3//+d4WGhjaqzTlz5ig9Pd35uKysTLGxsW6JFwAAwJuRjARamKLj5YqJ715nnWirVfm5202LCQCAliQiIkJXX321Dhw4oB//+MeqqKhQSUmJy+zI4uLiGu8xWS04OFjBwcEmRQwAAOA9SEYCLUyVIf3okVV11tn6VKpp8QAA0NKUl5fr4MGDmjBhggYMGKCgoCBlZ2dr7NixkqSCggIVFhYqKSnJ06ECAAB4HZKRAFq0AYmDdLSOe3aJmaIAgHr89re/1ahRoxQfH68jR47oscceU6tWrTR+/HiFh4dr8uTJSk9PV2RkpMLCwjRt2jQlJSW1jJW0Denw4cOSpNjYWAUEsP4lAABoGpKRAFq0ozYbM0UBAE1y+PBhjR8/XidOnFDnzp01ePBgffDBB+rcubMk6YUXXlBAQIDGjh0ru92ulJQUvfTSS54O2y3sFRWy/fUBKTxYmvJXxcfHezokAADg40hGAgAAAHVYs2ZNneUhISHKzMxUZmamaTGZyRoerK4dQjwdBgAAaCG4zgIAAAAAAACAKUhGAgAAAAAAADAFyUgAAAAAAAAApiAZCQAAAAAAAMAUJCMBAAAAAAAAmIJkJAAAAAAAAABTkIwEAAAAAAAAYIpATwcAAAAAwLs5HIaOHD7sfBwbG6uAgDrmNRjS4Qv1660LAAD8CslIAAAAAHU6UmrX7Dd2KiKqWGdPfaPX0oYrPj6+1vr2igrZ/vqAFB4sTflrnXUBAIB/IRkJAAAAoF6hEZ3VtmN0g+tbw4PVtUNIs8YEAAB8D9dLAAAAAAAAADAFyUgAAAAAAAAApvD6ZGRGRoZuuOEGtW/fXl26dNGYMWNUUFDgUufcuXNKS0tTx44d1a5dO40dO1bFxcUeixkAAAAAAADApbz+npFbtmxRWlqabrjhBp0/f16/+93vNGzYMO3du1dt27aVJM2cOVPvvPOO3njjDYWHh2vq1Km666679P7773s6fAB1OHnihK6Or/3eU0dL7WofHlFnG9FWq/JztzdDdAAAoEaslA0AAJrA65ORWVlZLo9XrFihLl26KD8/Xz/60Y9UWlqqV199VatXr9aQIUMkScuXL1evXr30wQcf6KabbvJQ5ADq43A49Pn8AbWWt/7NFo18ZFWdbWx9KrUZIrvUgMRBOmqz1VpOUhQA4C9YKRsAADSF1ycjv6+0tFSSFBkZKUnKz89XZWWlkpOTnXV69uypuLg45eTk1JiMtNvtstvtzsdlZWWmxA7Adx212fSjOhKjZiVFAQDwBqyUDQAAGsunkpEOh0MzZszQzTffrN69e0uSbDabWrdurYgI10s5o6KiZKtlFlNGRobmz59vSszwDcx6AwAAAAAAaH4+lYxMS0vT7t27tW3btia1M2fOHKWnpzsfl5WVKTY21g0Rwlcx6w0AAMD9HA5DRy7cX1LcYxIAAPhSMnLq1Klat26dtm7dqq5duzr3W61WVVRUqKSkxGV2ZHFxsaxWa41tBQcHKzg42JS40TLUt9DKd3XOmhYPAACALzhSatfsN3YqIqpYZ099o9fShnOPSQAA/JzXJyMNw9C0adP01ltvafPmzUpISHApHzBggIKCgpSdna2xY8dKkgoKClRYWKikpCQPRY2Wpr6FVnRhsRUAAAC4Co3orLYd6/5RFwAA+A+vT0ampaVp9erV+uc//6n27ds77wMZHh6u0NBQhYeHa/LkyUpPT1dkZKTCwsI0bdo0JSUled1K2vXdl1DcmxAAAAAAAAAtmNcnI5cuXSpJuvXWW132L1++XPfcc48k6YUXXlBAQIDGjh0ru92ulJQUvfTSSx6Jty713ZdQ3JsQAAAAAAAALZjXJyMNw6i3TkhIiDIzM5WZmWlKTADwfQ25r2iUNVr/zd3ZpNdh5XcAgLdxOBwqKiqSJB0+fFiqf/gOAAD8mNcnI4GWgkVwvJc7zk1D7it69WP5jYrvYqz8DgDwNkVFRTr88nh17RAi25elsnd6yNMhAQAAL0YyEjAJi+B4L84NAABN07VDiOI7tdHhU+c8HQoAAPByJCPR4v0w8XoV247WWYcZifAnXOoNAAAAAPAUkpFo8YptR5n1BlyES70BAAAAAJ5CMhI+r75ZXqeOl5saD1CbouPlionvXms5MxIBAAAAAC0dyUj4vPpmeb05Y5ip8QC1qTLEjEQAAAAAgF8L8HQAAAAAAAAAAPwDMyO9zMkTJ3R1fHSt5VHWaP03d6epMQEAAAAAAADuQDLSyzgcjjoXW7n6sXxT4wEAAAAAAADchWQkAJ9W32zikyfOmhqPp9V3POSHxwQA8H8cDoeKiookSbGxsQoIaPxdmxwOQ0cOH5YkxTiM+p9gSIcv1G/qawMAAN9FMhKAT6tvNnHr32wxNR5Pq+94yA+PCQDg/xQVFenezCxJ0mtpwxUfH9/oto6U2jX7je9uH/RMW7sSurSts769okK2vz4ghQdLU/7apNcGAAC+i2Qk4IeYTQhvMCBxkI7abLWWR1utys/d3qQ2GtoOAPiTNh06u62t0IgLbVU2rL41PFhdO4S47fUvnukpZlwCAOATSEYCfojZhPAGR202/eiRVbWWb30qtcltNLQdAIBvqp7p2aZDZ5099U2TZ3sCAIDmRzKyBfph4vUqth2tsw6rcgNoCnfMagQAwB3adOisth3rvl8yAADwHiQjW6Bi29F67xnHqtwAmsIdsxoBAP7p4oVvdOHSal2Y5SgvvtTanYv/AADgz0hGotGYGQUA3+HelQDQcNUL30REFTsvrZbUoIV1vp8QNFNRUZEOvzz+uwcswAMAQKORjESjMTMKcK/6FhaSDy0u1JL60hDcuxIALk9oxKWXVjdkYZ3vrwZuNncuvgMAgL8iGQkAXqK+hYXkQ4sLtaS+AAC8iztXA6/PxTMxDx8+rBiHoYAAi2mvDwBAS0QyEs2mITOjWEgH8D7+NqsRAIDaXLxad0nx13qmrZ3ZkQAANBHJSDSbhsyMYiEdwPswqxEA4DHGdzMQq9W6UMzF9YxL913OAjMXz36s6bnVq3Xb7RVSZSP7BQAAnEhG+pii4+WKie9eZ51Tx8ub3M7p0hJFhwfX2QYzowD4gvpmenrLDO2WtAhOS+oLAHPZKypk++sD6totXIdPnat1oZjqepJk7/SQ2l68Lzz4shaYqV6YpmuHkDpfEwAAuAfJSB9TZajeRRLenDGsye28OWOYPp8/qM42mBkFwBfUN9PTW2Zot6RFcFpSXwCYzxoerPhObRpUr6Z9jbmMumuHkAa9Zn3qm2UJAABIRgIAvJQ33bvyh4nXq9h21ONx1IcZiQBaEofD0JFGXHbd8Pa/SxxWL0xT02s2tI3q+syyBACgfiQjAQBeyax7Vzbs9hff6NvMW5o1joaoL0FbeOKsxjyfVWcbzEgE4CuOlNo1+42dat16t15LG+72pF714jQVFRV6pq1dCV3aXvKaDWnj8Mvjv3sw5a+SG2dZ1qS5Z14ys9P/fD+hzvkGYIYWk4zMzMzU73//e9lsNvXr109LlizRjTfe6OmwAABezl23vzBDfQlabp8BeB5j0stU00I0FwmN6Kzg4NaX1eTFsxsdDockKSAgwDnTsTrxcvjwYbWJ6KxWFa4L09T3mhe3f/jwYcVcuFy8uh/Vsyz/r37TE3wXz+J0vPNbxUWGNsvMy6bM7PTXpFZj++0tid/vJ9SZyQvADC0iGfm3v/1N6enpWrZsmRITE7Vo0SKlpKSooKBAXbp08XR4aIL6Lo2UF10eCQAA/Btj0sv3/YVoanRRwvLw4cM1Ji0vVj27MSKqWCXFX2vG+RXqEtZah0c+J0nOZJ7ty9IGveb3X+/77T/T1i5Jmv3Gd4uhVc+yrHa5Cb6Lk1TVydQjR45o3trdqqis0DNtLTXOvKzpedVJ2MtJcjVkZmdNCbjvJ7WqL1uvVlscvp7EbGwyz5OX9F98zKsT6gEBFlNeG0Dz85YfO+rSIpKRzz//vO677z5NmjRJkrRs2TK98847eu211zR79mxPh4cmKLYdNeUyTQAAgKZiTNo4NS1Ec7GLV9iuM4F4kdCIzmrbMVp2e4Wsld+1/3/Jwu+SeYdPnav3NXXRat21tV89qzI0ovN3f1TqEpdz6Xb15eNtOnR2JlMlqVWnhxRaS/u1Pc96mSuLN1RtCbiLFw9qaLKtJczMa8yiSWrmS/rr8v33yjNt7Y3uAwDvc/G/8bOnvmmWW500lc8nIysqKpSfn685c+Y49wUEBCg5OVk5OTkejQ31q+9ebaeOl5saDwA0l/ru98gs70vVtyCPWYvxeEsc8G6MSZtX9QrbdSUQ61NXsrC212yKiy/n/v6l2/Vp0+HSZGpjnldTgql6xszFsycvnoHZ0Fi7dgiRw2G4zFqtfq6z3w1cGd3XEmGXzCx0GDXOLKxp1mdNCyc1NY6Lz6UaOAvq4vdKQ/9NAPAd1f/GL57p700zJH0+GXn8+HFVVVUpKirKZX9UVJQ+++yzGp9jt9tlt9udj0tLSyVJZWVlzRqrw+FQ5bdn6qxjGIbKvq39fwPDMJrcRkPaMauN8w5DSekv11r+r9ljfKYvZrTRkHZow/1tNKQd2vDONhrSjlltVFVVacfsvrWWd0x/35Q4HA6HV/x/15A4vj5yRDf/9s+1lr//3K+avS/eFEddql/fMJr2xRaNd7ljUk+NR0+fPq0yW6EkqaCgnU6fPq0jR46ozFaoiooKfVH13Q8jp1sVKsA4r9PHjtS7T5K+qDqr8NBAffHNWZd97mzL19r/qLBML3z8tiRpZttTOlNRpSMl56SCAp0+fbrWc1R9PirPna2z/e+3VdPzztjPX/J6R44c0aOrt+p85Xn94vw/de0V7bTn63KtDhzd4FiPHDki2cpVXFahFz5+W+0iO6v85Dea2faUJF1Wv6vbuvCmrPPYeIvqYxjSvoOz31FhrWs91pL05C9+pJiYGJfjfznvi7riuPhcFpdVKObu3ysmJqbO5zXkvQLAN138b7z02BHlLH5Endq31sAZqxQXF9esr93gManh477++mtDkrF9+3aX/Q899JBx44031vicxx57zLhw9xc2NjY2NjY2thazFRUVmTQCw/dd7piU8SgbGxsbGxtbS93qG5P6/MzITp06qVWrViouLnbZX1xcLKvVWuNz5syZo/T0dOdjh8OhkydPqmPHjrJY3Hvj3rKyMufNm8PCwtzati+g//7df3EM/L7/4hjQfz/vv0w6BoZh6PTp03XOhEHzutwxqZnj0Yvxb9K7cD68C+fDu3A+vAvnw7t46/lo6JjU55ORrVu31oABA5Sdna0xY8ZIFwZz2dnZmjp1ao3PCQ4OVnCw671XIiIimjXOsLAwr3qDmI3++3f/xTHw+/6LY0D//bz/MuEYhIeHN1vbqN/ljkk9MR69GP8mvQvnw7twPrwL58O7cD68izeej4aMSX0+GSlJ6enpmjhxogYOHKgbb7xRixYt0pkzZ5wrGQIAAADNjTEpAABA/VpEMvLnP/+5vvnmG82bN082m03XXXedsrKyLrmBOAAAANBcGJMCAADUr0UkIyVp6tSptV6W7UnBwcF67LHHLrkMx1/Qf//uvzgGft9/cQzov5/3XxwDv+OtY9JqvB+9C+fDu3A+vAvnw7twPryLr58Pi1HvetsAAAAAAAAA0HQBng4AAAAAAAAAgH8gGQkAAAAAAADAFCQjAQAAAAAAAJiCZCQAAAAAAAAAU5CMbEaZmZnq1q2bQkJClJiYqA8//NDTITWbrVu3atSoUYqJiZHFYtHatWtdyg3D0Lx58xQdHa3Q0FAlJydr//79HovX3TIyMnTDDTeoffv26tKli8aMGaOCggKXOufOnVNaWpo6duyodu3aaezYsSouLvZYzO60dOlS9e3bV2FhYQoLC1NSUpLeffddZ3lL7ntNnnnmGVksFs2YMcO5r6Ufg8cff1wWi8Vl69mzp7O8pfdfkr7++mv98pe/VMeOHRUaGqo+ffpox44dzvKW/jnYrVu3S94DFotFaWlpkh+8B6qqqjR37lwlJCQoNDRUV155pZ544gldvE5gS38PwPv509jUk9wxLj558qRSU1MVFhamiIgITZ48WeXl5Sb3pGVw1zi9sLBQI0eOVJs2bdSlSxc99NBDOn/+vMm98X3u+N7AuWgejf0Ow/lwH3d8p/KV80Eyspn87W9/U3p6uh577DHt3LlT/fr1U0pKio4dO+bp0JrFmTNn1K9fP2VmZtZY/uyzz2rx4sVatmyZcnNz1bZtW6WkpOjcuXOmx9octmzZorS0NH3wwQfasGGDKisrNWzYMJ05c8ZZZ+bMmXr77bf1xhtvaMuWLTpy5Ijuuusuj8btLl27dtUzzzyj/Px87dixQ0OGDNHo0aO1Z88eqYX3/fvy8vL0pz/9SX379nXZ7w/H4Nprr9XRo0ed27Zt25xlLb3/p06d0s0336ygoCC9++672rt3r/7whz+oQ4cOzjot/XMwLy/P5fxv2LBBkvSzn/1M8oP3wMKFC7V06VK9+OKL2rdvnxYuXKhnn31WS5YscdZp6e8BeDd/G5t6kjvGxampqdqzZ482bNigdevWaevWrZoyZYqJvWg53DFOr6qq0siRI1VRUaHt27dr5cqVWrFihebNm+ehXvmupn5v4Fw0j8Z+h+F8uF9TvlP51Pkw0CxuvPFGIy0tzfm4qqrKiImJMTIyMjwalxkkGW+99ZbzscPhMKxWq/H73//eua+kpMQIDg42/vrXv3ooyuZ17NgxQ5KxZcsWw7jQ36CgIOONN95w1tm3b58hycjJyfFgpM2nQ4cOxp///Ge/6vvp06eNq666ytiwYYNxyy23GNOnTzcMPzn/jz32mNGvX78ay/yh/7NmzTIGDx5ca7k/fg5Onz7duPLKKw2Hw+EX74GRI0ca9957r8u+u+66y0hNTTUMP30PwLv489jUkxozLt67d68hycjLy3PWeffddw2LxWJ8/fXXJveg5WnMOP3f//63ERAQYNhsNmedpUuXGmFhYYbdbvdAL1qWy/newLlwv6Z8h+F8uFdTv1P50vlgZmQzqKioUH5+vpKTk537AgIClJycrJycHI/G5gmHDh2SzWZzOR7h4eFKTExsscejtLRUkhQZGSlJys/PV2Vlpcsx6Nmzp+Li4lrcMaiqqtKaNWt05swZJSUl+VXf09LSNHLkSJe+yo/O//79+xUTE6Pu3bsrNTVVhYWFkp/0/1//+pcGDhyon/3sZ+rSpYv69++vV155xVnub5+DFRUV+stf/qJ7771XFovFL94DgwYNUnZ2tj7//HNJ0scff6xt27ZpxIgRkh++B+BdGJt6j4Z8FuTk5CgiIkIDBw501klOTlZAQIByc3M9EndL0phxek5Ojvr06aOoqChnnZSUFJWVlTln9OHyNeZ7A+fC/ZryHYbz4X5N+U7lS+cj0NMBtETHjx9XVVWVyxtAkqKiovTZZ595LC5Psdls0oX+XywqKspZ1pI4HA7NmDFDN998s3r37i1dOAatW7dWRESES92WdAw+/fRTJSUl6dy5c2rXrp3eeustXXPNNdq1a1eL77skrVmzRjt37lReXt4lZf5w/hMTE7VixQr16NFDR48e1fz58/XDH/5Qu3fv9ov+f/HFF1q6dKnS09P1u9/9Tnl5eXrggQfUunVrTZw40e8+B9euXauSkhLdc889kp/8G5g9e7bKysrUs2dPtWrVSlVVVXrqqaeUmpoq+eH/hfAujE29R0M+C2w2m7p06eJSHhgYqMjISD4vmqix43SbzVbjOdNF5xQN15TvDZwL92rqdxjOh3s19TuVL50PkpGAm6WlpWn37t0u93bwBz169NCuXbtUWlqqf/zjH5o4caK2bNni6bBMUVRUpOnTp2vDhg0KCQnxdDgeUT37S5L69u2rxMRExcfH6+9//7tCQ0M9GpsZHA6HBg4cqKefflqS1L9/f+3evVvLli3TxIkTPR2e6V599VWNGDFCMTExng7FNH//+9+1atUqrV69Wtdee6127dqlGTNmKCYmxi/fAwDgjfx1nO5t/Pl7gzfhO4z38afvVFym3Qw6deqkVq1aXbKqUXFxsaxWq8fi8pTqPvvD8Zg6darWrVunTZs2qWvXrs79VqtVFRUVKikpcanfko5B69at9YMf/EADBgxQRkaG+vXrpz/+8Y9+0ff8/HwdO3ZM119/vQIDAxUYGKgtW7Zo8eLFCgwMVFRUVIs/Bt8XERGhq6++WgcOHPCL90B0dLSuueYal329evVyXlbhT5+DX331ld577z396le/cu7zh/fAQw89pNmzZ2vcuHHq06ePJkyYoJkzZyojI0Pys/cAvA9jU+/RkM8Cq9V6ycJC58+f18mTJzlfTdCUcbrVaq3xnOmic4qGa8r3Bs6F+7jjOwzno3ld7ncqXzofJCObQevWrTVgwABlZ2c79zkcDmVnZyspKcmjsXlCQkKCrFary/EoKytTbm5uizkehmFo6tSpeuutt7Rx40YlJCS4lA8YMEBBQUEux6CgoECFhYUt5hh8n8PhkN1u94u+Dx06VJ9++ql27drl3AYOHKjU1FTn3y39GHxfeXm5Dh48qOjoaL94D9x8880qKChw2ff5558rPj5e8pPPwWrLly9Xly5dNHLkSOc+f3gPnD17VgEBrsOqVq1ayeFwSH72HoD3YWzqPRryWZCUlKSSkhLl5+c762zcuFEOh0OJiYkeiduXuWOcnpSUpE8//dQlSbxhwwaFhYVd8mMkLt/lfG/gXLiPO77DcD6a1+V+p/Kp8+HpFXRaqjVr1hjBwcHGihUrjL179xpTpkwxIiIiXFY1aklOnz5tfPTRR8ZHH31kSDKef/5546OPPjK++uorwzAM45lnnjEiIiKMf/7zn8Ynn3xijB492khISDC+/fZbT4fuFvfff78RHh5ubN682Th69KhzO3v2rLPO//7v/xpxcXHGxo0bjR07dhhJSUlGUlKSR+N2l9mzZxtbtmwxDh06ZHzyySfG7NmzDYvFYvznP/8xjBbe99pcvBKd4QfH4MEHHzQ2b95sHDp0yHj//feN5ORko1OnTsaxY8cMww/6/+GHHxqBgYHGU089Zezfv99YtWqV0aZNG+Mvf/mLs05L/xw0LqzOGxcXZ8yaNeuSspb+Hpg4caJxxRVXGOvWrTMOHTpkvPnmm0anTp2Mhx9+2FnHH94D8F7+Njb1JHeMi4cPH27079/fyM3NNbZt22ZcddVVxvjx4z3YK9/ljnH6+fPnjd69exvDhg0zdu3aZWRlZRmdO3c25syZ46Fe+a6mfm/gXDSvy/0Ow/lwr6Z+p/Kl80EyshktWbLEiIuLM1q3bm3ceOONxgcffODpkJrNpk2bDEmXbBMnTjQMwzAcDocxd+5cIyoqyggODjaGDh1qFBQUeDpst6mp75KM5cuXO+t8++23xm9+8xujQ4cORps2bYw777zTOHr0qEfjdpd7773XiI+PN1q3bm107tzZGDp0qHNAYbTwvtfm+/+Rt/Rj8POf/9yIjo42WrdubVxxxRXGz3/+c+PAgQPO8pbef8MwjLffftvo3bu3ERwcbPTs2dN4+eWXXcpb+uegYRjG+vXrDUk19qulvwfKysqM6dOnG3FxcUZISIjRvXt345FHHjHsdruzjj+8B+Dd/Gls6knuGBefOHHCGD9+vNGuXTsjLCzMmDRpknH69GkP9ci3uWuc/uWXXxojRowwQkNDjU6dOhkPPvigUVlZ6YEe+TZ3fG/gXDSfxnyH4Xy4jzu+U/nK+bAY331AAwAAAAAAAECz4p6RAAAAAAAAAExBMhIAAAAAAACAKUhGAgAAAAAAADAFyUgAAAAAAAAApiAZCQAAAAAAAMAUJCMBAAAAAAAAmIJkJAAAAAAAAABTkIwEAAAAAMAHGYahKVOmKDIyUhaLRbt27fJ0SABQL4thGIangwAAAAAAAJfn3Xff1ejRo7V582Z1795dnTp1UmBgoKfDAoA68SkFAAAAAIAPOnjwoKKjozVo0KBGt1FZWamgoCC3xgUAdeEybQBopKysLA0ePFgRERHq2LGj7rjjDh08eNBZvn37dl133XUKCQnRwIEDtXbt2ksun9m9e7dGjBihdu3aKSoqShMmTNDx48c91CMAAAD4invuuUfTpk1TYWGhLBaLunXrVu/49Msvv5TFYtHf/vY33XLLLQoJCdGqVaskSX/+85/Vq1cvhYSEqGfPnnrppZc82DsALRnJSABopDNnzig9PV07duxQdna2AgICdOedd8rhcKisrEyjRo1Snz59tHPnTj3xxBOaNWuWy/NLSko0ZMgQ9e/fXzt27FBWVpaKi4t19913e6xPAAAA8A1//OMftWDBAnXt2lVHjx5VXl5enePTi82ePVvTp0/Xvn37lJKSolWrVmnevHl66qmntG/fPj399NOaO3euVq5c6bH+AWi5uGckALjJ8ePH1blzZ3366afatm2bHn30UR0+fFghISHShV+b77vvPn300Ue67rrr9OSTT+q///2v1q9f72zj8OHDio2NVUFBga6++moP9gYAAADebtGiRVq0aJG+/PLLGssvHp/27t1bX375pRISErRo0SJNnz7dWe8HP/iBnnjiCY0fP96578knn9S///1vbd++3ZS+APAfzIwEgEbav3+/xo8fr+7duyssLEzdunWTJBUWFqqgoEB9+/Z1JiIl6cYbb3R5/scff6xNmzapXbt2zq1nz57Shfv/AAAAAJejrvHpxQYOHOj8+8yZMzp48KAmT57sMi598sknGZMCaBYsYAMAjTRq1CjFx8frlVdeUUxMjBwOh3r37q2KiooGPb+8vFyjRo3SwoULLymLjo5uhogBAADQkjV0fNq2bVvn3+Xl5ZKkV155RYmJiS71WrVqZVLkAPwJyUgAaIQTJ06ooKBAr7zyin74wx9KkrZt2+Ys79Gjh/7yl7/IbrcrODhYkpSXl+fSxvXXX6//9//+n7p166bAQD6OAQAA0Hj1jU9rExUVpZiYGH3xxRdKTU01IVIA/o7LtAGgETp06KCOHTvq5Zdf1oEDB7Rx40alp6c7y3/xi1/I4XBoypQp2rdvn9avX6/nnntOkmSxWCRJaWlpOnnypMaPH6+8vDwdPHhQ69ev16RJk1RVVeWxvgEAAMD31Dc+rcv8+fOVkZGhxYsX6/PPP9enn36q5cuX6/nnn2/2uAH4H5KRANAIAQEBWrNmjfLz89W7d2/NnDlTv//9753lYWFhevvtt7Vr1y5dd911euSRRzRv3jxJct5HMiYmRu+//76qqqo0bNgw9enTRzNmzFBERIQCAvh4BgAAQMPVNz6ty69+9Sv9+c9/1vLly9WnTx/dcsstWrFihRISEpo9bgD+h9W0AcAkq1at0qRJk1RaWqrQ0FBPhwMAAAAAgOm4SRkANJPXX39d3bt31xVXXKGPP/5Ys2bN0t13300iEgAAAADgt0hGAkAzsdlsmjdvnmw2m6Kjo/Wzn/1MTz31lKfDAgAAAADAY7hMGwAAAAAAAIApWCEBAAAAAAAAgClIRgIAAAAAAAAwBclIAAAAAAAAAKYgGQkAAAAAAADAFCQjAQAAAAAAAJiCZCQAAAAAAAAAU5CMBAAAAAAAAGAKkpEAAAAAAAAATEEyEgAAAAAAAIAp/j8E/aNWMlAv4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2, figsize = (16,9))\n",
    "fig.suptitle('Histogram 2-Variables')\n",
    "sb.histplot(data = df, x = 'age', hue = 'survived', multiple='dodge', ax = axes[0][0])\n",
    "sb.histplot(data = df, x = 'fare', hue = 'survived', multiple='dodge', ax = axes[0][1])\n",
    "sb.histplot(data = df, x = 'age', hue = 'sex', multiple='dodge', ax = axes[1][0])\n",
    "sb.histplot(data = df, x = 'fare', hue = 'sex', multiple='dodge', ax = axes[1][1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3a0f2e1",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}