diff --git a/Week-08-Regression/Exercise-Regression.ipynb b/Week-08-Regression/Exercise-Regression.ipynb
index 4f13730c..4c088a20 100644
--- a/Week-08-Regression/Exercise-Regression.ipynb
+++ b/Week-08-Regression/Exercise-Regression.ipynb
@@ -14,9 +14,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'CTPLIB'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 35\u001b[39m\n\u001b[32m     30\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mstatsmodels\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstats\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01moutliers_influence\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m variance_inflation_factor\n\u001b[32m     33\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mstatsmodels\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mapi\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m qqplot\n\u001b[32m---> \u001b[39m\u001b[32m35\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mCTPLIB\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mctp\u001b[39;00m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'CTPLIB'"
+     ]
+    }
+   ],
    "source": [
     "# PANDAS IS FOR DATA WRANGLING\n",
     "import pandas as pd\n",
@@ -454,7 +466,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -468,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/Week-08-Regression/Lecture-Regression.ipynb b/Week-08-Regression/Lecture-Regression.ipynb
new file mode 100644
index 00000000..17636929
--- /dev/null
+++ b/Week-08-Regression/Lecture-Regression.ipynb
@@ -0,0 +1,2324 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6bdec0c3",
+   "metadata": {},
+   "source": [
+    "Full disclaimer,,,\n",
+    " \n",
+    "I ripped tf outta this MIT lecture [here](https://ocw.mit.edu/courses/15-071-the-analytics-edge-spring-2017/pages/linear-regression/moneyball-the-power-of-sports-analytics/) where they did it in R.  \n",
+    "\n",
+    "But I made it cooler and better. \n",
+    "\n",
+    "# ISBE \n",
+    "* I - Inspect\n",
+    "* S - Select\n",
+    "* B - Build\n",
+    "* E - Evaluate "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d7f84e6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PANDAS IS FOR DATA WRANGLING\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# SEABORN IS A PLOTTING LIBRARY\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# MATPLOT LIB IS ALSO A PLOTTING LIBRARY\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# SKLEARN IS OUR MACHINE LEARNING PACKAGE\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# IMPORT OUR RANDOM FOREST REGERSSOR\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# METRICS HELP US SCORE OUR MODEL\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "# HELP US SPLIT OUR DATA INTO TESTING A TRAINING\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Good ol statsmodels\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# Specific root mean squared error for stats models\n",
+    "from statsmodels.tools.eval_measures import rmse\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "from statsmodels.api import qqplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a599c3af",
+   "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>Team</th>\n",
+       "      <th>League</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>RS</th>\n",
+       "      <th>RA</th>\n",
+       "      <th>W</th>\n",
+       "      <th>OBP</th>\n",
+       "      <th>SLG</th>\n",
+       "      <th>BA</th>\n",
+       "      <th>Playoffs</th>\n",
+       "      <th>RankSeason</th>\n",
+       "      <th>RankPlayoffs</th>\n",
+       "      <th>G</th>\n",
+       "      <th>OOBP</th>\n",
+       "      <th>OSLG</th>\n",
+       "      <th>runs_diff</th>\n",
+       "      <th>RD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ANA</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>691</td>\n",
+       "      <td>730</td>\n",
+       "      <td>75</td>\n",
+       "      <td>0.327</td>\n",
+       "      <td>0.405</td>\n",
+       "      <td>0.261</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>162</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>0.412</td>\n",
+       "      <td>-39</td>\n",
+       "      <td>-39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ARI</td>\n",
+       "      <td>NL</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>818</td>\n",
+       "      <td>677</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0.341</td>\n",
+       "      <td>0.442</td>\n",
+       "      <td>0.267</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>162</td>\n",
+       "      <td>0.311</td>\n",
+       "      <td>0.404</td>\n",
+       "      <td>141</td>\n",
+       "      <td>141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ATL</td>\n",
+       "      <td>NL</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>729</td>\n",
+       "      <td>643</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0.324</td>\n",
+       "      <td>0.412</td>\n",
+       "      <td>0.260</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>162</td>\n",
+       "      <td>0.314</td>\n",
+       "      <td>0.384</td>\n",
+       "      <td>86</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BAL</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>687</td>\n",
+       "      <td>829</td>\n",
+       "      <td>63</td>\n",
+       "      <td>0.319</td>\n",
+       "      <td>0.380</td>\n",
+       "      <td>0.248</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>162</td>\n",
+       "      <td>0.337</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>-142</td>\n",
+       "      <td>-142</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BOS</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>2001</td>\n",
+       "      <td>772</td>\n",
+       "      <td>745</td>\n",
+       "      <td>82</td>\n",
+       "      <td>0.334</td>\n",
+       "      <td>0.439</td>\n",
+       "      <td>0.266</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>161</td>\n",
+       "      <td>0.329</td>\n",
+       "      <td>0.393</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Team League  Year   RS   RA   W    OBP    SLG     BA  Playoffs  RankSeason  \\\n",
+       "0  ANA     AL  2001  691  730  75  0.327  0.405  0.261         0         NaN   \n",
+       "1  ARI     NL  2001  818  677  92  0.341  0.442  0.267         1         5.0   \n",
+       "2  ATL     NL  2001  729  643  88  0.324  0.412  0.260         1         7.0   \n",
+       "3  BAL     AL  2001  687  829  63  0.319  0.380  0.248         0         NaN   \n",
+       "4  BOS     AL  2001  772  745  82  0.334  0.439  0.266         0         NaN   \n",
+       "\n",
+       "   RankPlayoffs    G   OOBP   OSLG  runs_diff   RD  \n",
+       "0           NaN  162  0.331  0.412        -39  -39  \n",
+       "1           1.0  162  0.311  0.404        141  141  \n",
+       "2           3.0  162  0.314  0.384         86   86  \n",
+       "3           NaN  162  0.337  0.439       -142 -142  \n",
+       "4           NaN  161  0.329  0.393         27   27  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# BASEBALL DATA PIPELINE\n",
+    "\n",
+    "def load_baseball_data(from_year=2002):\n",
+    "    df = pd.read_csv('data/baseball.csv')\n",
+    "\n",
+    "    # Moneyball happened in 2002. \n",
+    "    # So lets travel back there and remove all data after then. \n",
+    "    if from_year:\n",
+    "        c1 = df['Year'] < from_year \n",
+    "        df = df[c1]\n",
+    "        df = df.reset_index(drop=True)\n",
+    "\n",
+    "    # calculates the Runs Scored - Runs Allowed as runs_diff\n",
+    "    df['runs_diff'] = df['RS'] - df['RA']\n",
+    "    df['RD'] = df['RS'] - df['RA']\n",
+    "    \n",
+    "    # Return the data.\n",
+    "    return(df)\n",
+    "\n",
+    "\n",
+    "df = load_baseball_data()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42fdcaeb",
+   "metadata": {},
+   "source": [
+    "#### Data Dictionary\n",
+    "```\n",
+    "* Team: Name of Team\n",
+    "* League: Name of League\n",
+    "* Year: Year of Season\n",
+    "* RS: Runs Scored\n",
+    "* RA: Runs Allowed\n",
+    "* W: Number of Wins\n",
+    "* OBP: On Base Percentage\n",
+    "* SLG: Slugging Percentage\n",
+    "* BA: Batting Average\n",
+    "* Playoffs: Did the team make playoffs.  1==yes\n",
+    "* RankSeason: idk\n",
+    "* RankPlayoffs: idk\n",
+    "* G: Number or games played\n",
+    "* OOBP: Opponent On Base Percentage \n",
+    "* OSLG: Opponent Slugging Percentage\n",
+    "* runs_diff: Runs Scored - Runs Allowed\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b27e0fc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " ###############################################################################\n",
+      "CHECKING SHAPE AND SIZE:  df.shape (902, 17)\n",
+      "\n",
+      " ###############################################################################\n",
+      "CHEKCING NULLS print(df.isnull().sum())\n",
+      "Team              0\n",
+      "League            0\n",
+      "Year              0\n",
+      "RS                0\n",
+      "RA                0\n",
+      "W                 0\n",
+      "OBP               0\n",
+      "SLG               0\n",
+      "BA                0\n",
+      "Playoffs          0\n",
+      "RankSeason      748\n",
+      "RankPlayoffs    748\n",
+      "G                 0\n",
+      "OOBP            812\n",
+      "OSLG            812\n",
+      "runs_diff         0\n",
+      "RD                0\n",
+      "dtype: int64\n",
+      "\n",
+      " ###############################################################################\n",
+      "CHECKING DUPES, df.duplicated().sum()\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('\\n', '#'*79)\n",
+    "###############################################################################\n",
+    "print('CHECKING SHAPE AND SIZE:  df.shape', df.shape)\n",
+    "print('\\n', '#'*79)\n",
+    "\n",
+    "###############################################################################\n",
+    "print( \"CHEKCING NULLS\", \"print(df.isnull().sum())\")\n",
+    "print(df.isnull().sum())\n",
+    "print('\\n', '#'*79)\n",
+    "\n",
+    "###############################################################################\n",
+    "print(\"CHECKING DUPES, df.duplicated().sum()\")\n",
+    "print(df.duplicated().sum())    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "713bcf7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all_numeric_vars = list(df.select_dtypes(include='number').columns)\n",
+    "# print(all_numeric_vars)\n",
+    "\n",
+    "# dependent_variable = 'W'\n",
+    "# for col in all_numeric_vars:\n",
+    "#     try:\n",
+    "#         sns.jointplot(x=col, y=dependent_variable, data=df, kind=\"reg\");\n",
+    "#     except:\n",
+    "#         print('Could not plot variable %s' % col)\n",
+    "\n",
+    "#     #sns.relplot(data = df, x=col, y=target, height=5, aspect=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d32bd170",
+   "metadata": {},
+   "source": [
+    "# Making the playoffs\n",
+    "\n",
+    "### The Oakland A's, Paul DePodesta, estimated how many games it would take to make the playoffs:   _95 Wins_\n",
+    "---\n",
+    "So homeboy Peter something, gut feeling predicted you need to win 95 games to make the playoffs.  Lets inspect this hypothesis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dfe84ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x17d97cc80>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1300x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set()\n",
+    "plt.figure(figsize=(13, 8))\n",
+    "ax = sns.scatterplot(df, x='W', y='Team', hue='Playoffs')\n",
+    "ax.axvline(x=95, color='red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "409b0ec6",
+   "metadata": {},
+   "source": [
+    "# HOW DO YOU WIN ANY BASEBALL, FOOTBALL, FOOTBALL, HOCKEY, BASKETBALL GAME?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2a581c8",
+   "metadata": {},
+   "source": [
+    "### Score more than is scored on you."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "f2cc2f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='RD', ylabel='W'>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Createa metric that measures the overall runs difference.\n",
+    "df['RD'] = df['RS'] - df['RA']\n",
+    "sns.regplot(df, x='RD', y='W', scatter_kws={'alpha': 0.25})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "657e0c7e",
+   "metadata": {},
+   "source": [
+    "# Using RunsDiff [RD] to predict Wins [W]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "74b9f299",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      W   R-squared:                       0.881\n",
+      "Model:                            OLS   Adj. R-squared:                  0.881\n",
+      "Method:                 Least Squares   F-statistic:                     6651.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:05:10   Log-Likelihood:                -2515.5\n",
+      "No. Observations:                 902   AIC:                             5035.\n",
+      "Df Residuals:                     900   BIC:                             5045.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const         80.8814      0.131    616.675      0.000      80.624      81.139\n",
+      "RD             0.1058      0.001     81.554      0.000       0.103       0.108\n",
+      "==============================================================================\n",
+      "Omnibus:                        5.788   Durbin-Watson:                   2.076\n",
+      "Prob(Omnibus):                  0.055   Jarque-Bera (JB):                5.736\n",
+      "Skew:                          -0.195   Prob(JB):                       0.0568\n",
+      "Kurtosis:                       3.033   Cond. No.                         101.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "###############################################################################\n",
+      "Dependent Variable W average:  80.88137472283813\n",
+      "RMSE: 3.93471814099855\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "independent_variables = 'RD'\n",
+    "dependent_variable = 'W'\n",
+    "\n",
+    "X = df[independent_variables]\n",
+    "y = df[dependent_variable]\n",
+    "\n",
+    "X = sm.add_constant(X)\n",
+    "\n",
+    "model_wins = sm.OLS(y, X).fit()\n",
+    "y_pred = model_wins.predict(X) \n",
+    "\n",
+    "results = model_wins.summary()\n",
+    "print(results)\n",
+    "\n",
+    "model_wins_root_mean_squared_error = rmse(y, y_pred)\n",
+    "print('#'*79)\n",
+    "print( 'Dependent Variable %s average: ' % dependent_variable, y.mean())\n",
+    "print( 'RMSE:', model_wins_root_mean_squared_error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "171a749d",
+   "metadata": {},
+   "source": [
+    "# We are going to do a lot of regression models. So lets put it into a resuable function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e92b2bb4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7323941f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_regression(input_df, independent_variables, dependent_variable):\n",
+    "    data = input_df.copy()\n",
+    "\n",
+    "    # Define the independent and dependent variables\n",
+    "    X = data[independent_variables]  # Independent variable\n",
+    "    y = data[dependent_variable]  # Dependent variable\n",
+    "\n",
+    "\n",
+    "    # Add a constant to the independent variables (required by statsmodels)\n",
+    "    X = sm.add_constant(X)\n",
+    "\n",
+    "    # Split the data into training and test sets (80% train, 20% test)\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    # Build the linear regression model\n",
+    "    model = sm.OLS(y_train, X_train).fit()\n",
+    "\n",
+    "    y_pred = model.predict(X_test)\n",
+    "\n",
+    "    # Calculate R^2\n",
+    "    r_squared = metrics.r2_score(y_test, y_pred).round(2)\n",
+    "\n",
+    "    # Calculate RMSE\n",
+    "    model_root_mean_squared_error = rmse(y_test, y_pred).round(3)\n",
+    "\n",
+    "    print('R-Squared Score:', r_squared)\n",
+    "    print('RMSE:', model_root_mean_squared_error)\n",
+    "    \n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "4ce3e7e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      W   R-squared:                       0.881\n",
+      "Model:                            OLS   Adj. R-squared:                  0.881\n",
+      "Method:                 Least Squares   F-statistic:                     6651.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:57:12   Log-Likelihood:                -2515.5\n",
+      "No. Observations:                 902   AIC:                             5035.\n",
+      "Df Residuals:                     900   BIC:                             5045.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const         80.8814      0.131    616.675      0.000      80.624      81.139\n",
+      "RD             0.1058      0.001     81.554      0.000       0.103       0.108\n",
+      "==============================================================================\n",
+      "Omnibus:                        5.788   Durbin-Watson:                   2.076\n",
+      "Prob(Omnibus):                  0.055   Jarque-Bera (JB):                5.736\n",
+      "Skew:                          -0.195   Prob(JB):                       0.0568\n",
+      "Kurtosis:                       3.033   Cond. No.                         101.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: W\n",
+      "Using: ['RD']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "R-Squared Score: 0.88\n",
+      "RMSE: 3.935\n",
+      "Dependent Variable W average:  80.881\n",
+      "###############################################################################\n",
+      "*******************************************************************************\n",
+      "EVAL ON TESTING DATA\n",
+      "R-Squared Score: 0.89\n",
+      "RMSE: 3.906\n",
+      "None\n",
+      "*******************************************************************************\n"
+     ]
+    }
+   ],
+   "source": [
+    "def do_moneyball(input_df, independent_variables=['RD'], dependent_variable='W'):\n",
+    "\n",
+    "    # Copy the data\n",
+    "    data = input_df.copy()\n",
+    "\n",
+    "    # Define the independent and dependent variables\n",
+    "    X = data[independent_variables]  # Independent variable\n",
+    "    y = data[dependent_variable]  # Dependent variable\n",
+    "\n",
+    "    # Add a constant to the independent variables (required by statsmodels)\n",
+    "    X = sm.add_constant(X)\n",
+    "\n",
+    "    # Build the linear regression model\n",
+    "    model = sm.OLS(y, X).fit()\n",
+    "    \n",
+    "    # Make predictions\n",
+    "    y_pred = model.predict(X) \n",
+    "\n",
+    "    # Calculate R^2\n",
+    "    r_squared = metrics.r2_score(y, y_pred).round(2)\n",
+    "\n",
+    "    # Calculate RMSE\n",
+    "    model_root_mean_squared_error = rmse(y, y_pred).round(3)\n",
+    "\n",
+    "    # Print the summary of the model\n",
+    "    print(model.summary())\n",
+    "\n",
+    "    # Print model description\n",
+    "    print('\\n'*2)\n",
+    "    print(\"#\"*79)\n",
+    "    print(\"Predicting:\", dependent_variable)\n",
+    "    print(\"Using:\",  independent_variables)\n",
+    "    print(\"#\"*79)\n",
+    "\n",
+    "    # Print eval metrics\n",
+    "    print('#'*79)\n",
+    "    print('R-Squared Score:', r_squared)\n",
+    "    print('RMSE:', model_root_mean_squared_error)\n",
+    "    print('Dependent Variable %s average: ' % dependent_variable, y.mean().round(3))\n",
+    "    print('#'*79)\n",
+    "\n",
+    "    print('*'*79)\n",
+    "    print('EVAL ON TESTING DATA')\n",
+    "    print(eval_regression(input_df, independent_variables, dependent_variable))\n",
+    "    print('*'*79)\n",
+    "    # Return model\n",
+    "    return(model)\n",
+    "\n",
+    "\n",
+    "\n",
+    "selected_features = ['RD']\n",
+    "target_feature = 'W'\n",
+    "\n",
+    "model = do_moneyball(df, \n",
+    "                     independent_variables=selected_features, \n",
+    "                     dependent_variable=target_feature)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d561ae0",
+   "metadata": {},
+   "source": [
+    "* Average Wins:  80.88 \n",
+    "* RMSE:  3.93 \n",
+    "* % off average by: %4.86\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "155d25e3",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe61e3bb",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c46fda89",
+   "metadata": {},
+   "source": [
+    "## Their gut was they needed to win 95 games to make the playoffs.  \n",
+    "\n",
+    "### Lets use Linear Regression to find how many RD (runs difference) the need to win 95 games...\n",
+    "##### Below we will show a way to use Logit to know they need 95 wins make the playoffs.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6ecb92b",
+   "metadata": {},
+   "source": [
+    "## Finding out how many runs diff we need to make 95 wins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a752964",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5639fb11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "80.88137472283813 0.10576562244931818\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "133.5"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coef_y_int = model.params['const']\n",
+    "coef_runs_diff = model.params['RD']\n",
+    "\n",
+    "print(coef_y_int, coef_runs_diff)\n",
+    "\n",
+    "WINS = 95\n",
+    "\n",
+    "# When is runs_diff make WINS >= 95\n",
+    "# coef_y_int + coef_runs_diff*??? >= 95\n",
+    "min_runs_diff = (WINS - coef_y_int) / coef_runs_diff\n",
+    "min_runs_diff.round(1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "b3ff8f21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "133.45935727788284"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### THIS IS SAME AS ABOVE\n",
+    "\n",
+    "## y = m*x + b\n",
+    "## x = y-b / m\n",
+    "\n",
+    "y_intercept = 80.88\n",
+    "m_coefficient = 0.1058\n",
+    "# x = ???\n",
+    "\n",
+    "y = 95\n",
+    "x_target = (y - y_intercept) / m_coefficient\n",
+    "x_target\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5c9b6de",
+   "metadata": {},
+   "source": [
+    "# They calculated they needed to score 133.5 more runs than they allow to win 95 games.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "b7b1fb3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x312951100>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ax = sns.regplot(df, x='runs_diff', y='W', hue='Playoffs')\n",
+    "ax = sns.scatterplot(df, x='runs_diff', y='W', hue='Playoffs')\n",
+    "ax.axvline(x=min_runs_diff, color='red')\n",
+    "ax.axhline(y=WINS, color='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a60d409c",
+   "metadata": {},
+   "source": [
+    "# We need to score 133.5 more runs than we allow to win 95 games to make the playoffs.  \n",
+    "### Okay.... now what?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98bba503",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c01bb0b",
+   "metadata": {},
+   "source": [
+    "# How do we find which variable increases RD (runs diff) the most.\n",
+    "\n",
+    "Everyone (in baseball) used to focus on Batting Average (BA) as the most important thing (or feature) when it came to winning.  \n",
+    "\n",
+    "The Oakland A's thought different.  They used machine learning to find which were things (or features) acutally the most important when it came to winning. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9beb4c9e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"500\"\n",
+       "            src=\"https://www.youtube.com/embed/unGSY5l76YQ\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x3096b41a0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo('unGSY5l76YQ', 800,500)\n",
+    "# https://www.youtube.com/watch?v=Tzin1DgexlE&t=185s&ab_channel=RPGeek"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "69087f66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"800\"\n",
+       "            height=\"500\"\n",
+       "            src=\"https://www.youtube.com/embed/3MjxoaynCmk\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "            \n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x3098cb5f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo('3MjxoaynCmk', 800,500)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9eaaa85",
+   "metadata": {},
+   "source": [
+    "# Find which varibles are linearly related to runs scored `RS`\n",
+    "\n",
+    "So now, `RS` is our `y` dependent variable.  We want to find which variables are linearlly related to it.  We can do that visually. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4b7203b1",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# all_numeric_vars = list(df.select_dtypes(include='number').columns)\n",
+    "# print(all_numeric_vars)\n",
+    "# plt.figure(figsize = (3,3))\n",
+    "\n",
+    "# dependent_variable = 'RS'\n",
+    "# for col in all_numeric_vars:\n",
+    "#     try:        \n",
+    "#         sns.jointplot(x=col, y=dependent_variable, data=df, kind=\"reg\");\n",
+    "#     except:\n",
+    "#         print('Could not plot variable %s' % col)\n",
+    "\n",
+    "#     #sns.relplot(data = df, x=col, y=target, height=5, aspect=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c2c9292",
+   "metadata": {},
+   "source": [
+    "# Lets just use all of them! Make the best model ever!!!\n",
+    "### Whats wacky about this model?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2d3fac04",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RS   R-squared:                       1.000\n",
+      "Model:                            OLS   Adj. R-squared:                  1.000\n",
+      "Method:                 Least Squares   F-statistic:                 5.784e+28\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:13:20   Log-Likelihood:                 22425.\n",
+      "No. Observations:                 902   AIC:                        -4.483e+04\n",
+      "Df Residuals:                     892   BIC:                        -4.478e+04\n",
+      "Df Model:                           9                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const      -5.367e-11   4.11e-11     -1.304      0.192   -1.34e-10    2.71e-11\n",
+      "Year        3.117e-14   1.41e-14      2.217      0.027    3.58e-15    5.88e-14\n",
+      "RA             1.0000   5.64e-15   1.77e+14      0.000       1.000       1.000\n",
+      "W          -4.595e-15   3.38e-14     -0.136      0.892   -7.09e-14    6.17e-14\n",
+      "OBP        -2.172e-12   2.37e-11     -0.092      0.927   -4.87e-11    4.43e-11\n",
+      "SLG        -1.042e-12   1.18e-11     -0.088      0.930   -2.42e-11    2.21e-11\n",
+      "BA          4.014e-12   2.07e-11      0.194      0.846   -3.66e-11    4.47e-11\n",
+      "Playoffs    5.822e-14   4.32e-13      0.135      0.893    -7.9e-13    9.06e-13\n",
+      "G          -2.679e-14   1.89e-13     -0.142      0.887   -3.98e-13    3.44e-13\n",
+      "RD             1.0000   6.24e-15    1.6e+14      0.000       1.000       1.000\n",
+      "==============================================================================\n",
+      "Omnibus:                      117.153   Durbin-Watson:                   0.002\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               31.138\n",
+      "Skew:                           0.069   Prob(JB):                     1.73e-07\n",
+      "Kurtosis:                       2.100   Cond. No.                     6.74e+05\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The condition number is large, 6.74e+05. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: RS\n",
+      "Using: ['Year', 'RA', 'W', 'OBP', 'SLG', 'BA', 'Playoffs', 'G', 'RD']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "RMSE: 0.0\n",
+      "Dependent Variable RS average:  703.809\n",
+      "###############################################################################\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3096810d0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_vars = ['Year', 'RA', 'W', 'OBP', 'SLG', 'BA', 'Playoffs', 'G', 'RD']\n",
+    "y_var = \"RS\"\n",
+    "\n",
+    "do_moneyball(df, X_vars, y_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "142e01de",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd1c4e56",
+   "metadata": {},
+   "source": [
+    "# Lets do JUST the offensive metrics at once. \n",
+    "## Why does this look strange?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b0d2c0ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RS   R-squared:                       0.930\n",
+      "Model:                            OLS   Adj. R-squared:                  0.930\n",
+      "Method:                 Least Squares   F-statistic:                     3989.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:12:47   Log-Likelihood:                -4170.2\n",
+      "No. Observations:                 902   AIC:                             8348.\n",
+      "Df Residuals:                     898   BIC:                             8368.\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       -788.4570     19.697    -40.029      0.000    -827.115    -749.799\n",
+      "BA          -368.9661    130.580     -2.826      0.005    -625.244    -112.688\n",
+      "OBP         2917.4214    110.466     26.410      0.000    2700.619    3134.224\n",
+      "SLG         1637.9277     45.994     35.612      0.000    1547.659    1728.197\n",
+      "==============================================================================\n",
+      "Omnibus:                        3.441   Durbin-Watson:                   1.943\n",
+      "Prob(Omnibus):                  0.179   Jarque-Bera (JB):                3.381\n",
+      "Skew:                           0.150   Prob(JB):                        0.184\n",
+      "Kurtosis:                       3.018   Cond. No.                         214.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: RS\n",
+      "Using: ['BA', 'OBP', 'SLG']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "RMSE: 24.639\n",
+      "Dependent Variable RS average:  703.809\n",
+      "###############################################################################\n",
+      "       BA   OBP   SLG\n",
+      "BA   1.00  0.85  0.81\n",
+      "OBP  0.85  1.00  0.81\n",
+      "SLG  0.81  0.81  1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_vars = ['BA', 'OBP', 'SLG']\n",
+    "y_var = 'RS'\n",
+    "do_moneyball(df, X_vars, y_var)\n",
+    "\n",
+    "print(df[X_vars].corr().round(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a6be196",
+   "metadata": {},
+   "source": [
+    "# Predicting RS using BA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7f31bbb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RS   R-squared:                       0.692\n",
+      "Model:                            OLS   Adj. R-squared:                  0.691\n",
+      "Method:                 Least Squares   F-statistic:                     2018.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):          4.04e-232\n",
+      "Time:                        17:14:09   Log-Likelihood:                -4840.3\n",
+      "No. Observations:                 902   AIC:                             9685.\n",
+      "Df Residuals:                     900   BIC:                             9694.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       -806.3540     33.659    -23.956      0.000    -872.414    -740.294\n",
+      "BA          5849.8772    130.213     44.925      0.000    5594.320    6105.434\n",
+      "==============================================================================\n",
+      "Omnibus:                        8.707   Durbin-Watson:                   1.584\n",
+      "Prob(Omnibus):                  0.013   Jarque-Bera (JB):                8.875\n",
+      "Skew:                           0.241   Prob(JB):                       0.0118\n",
+      "Kurtosis:                       2.935   Cond. No.                         80.5\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: RS\n",
+      "Using: ['BA']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "RMSE: 51.792\n",
+      "Dependent Variable RS average:  703.809\n",
+      "###############################################################################\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3098c97f0>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_vars = ['BA']\n",
+    "y_var = 'RS'\n",
+    "\n",
+    "do_moneyball(df, X_vars, y_var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cb1eebb",
+   "metadata": {},
+   "source": [
+    "# Predicting RS using OBP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "04953a33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RS   R-squared:                       0.819\n",
+      "Model:                            OLS   Adj. R-squared:                  0.819\n",
+      "Method:                 Least Squares   F-statistic:                     4069.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:14:11   Log-Likelihood:                -4600.3\n",
+      "No. Observations:                 902   AIC:                             9205.\n",
+      "Df Residuals:                     900   BIC:                             9214.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const      -1079.0244     27.982    -38.562      0.000   -1133.942   -1024.107\n",
+      "OBP         5486.2973     86.012     63.785      0.000    5317.490    5655.105\n",
+      "==============================================================================\n",
+      "Omnibus:                        1.283   Durbin-Watson:                   1.608\n",
+      "Prob(Omnibus):                  0.527   Jarque-Bera (JB):                1.357\n",
+      "Skew:                          -0.085   Prob(JB):                        0.508\n",
+      "Kurtosis:                       2.914   Cond. No.                         71.9\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: RS\n",
+      "Using: ['OBP']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "RMSE: 39.693\n",
+      "Dependent Variable RS average:  703.809\n",
+      "###############################################################################\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3098ca150>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_vars = ['OBP']\n",
+    "y_var = 'RS'\n",
+    "\n",
+    "do_moneyball(df, X_vars, y_var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bcfcfc95",
+   "metadata": {},
+   "source": [
+    "### How to Interpret this the Coefficient\n",
+    "Let’s say the OBP coefficient is 5486. This coefficient represents the change in runs scored for every 1.0 unit increase in OBP (which is unrealistic). Instead, let’s rescale it to reflect a 0.010 increase—the kind of change you would actually see in OBP across a season.\n",
+    "```\n",
+    "5486 × 0.01 = 54.86\n",
+    "```\n",
+    "\n",
+    "#### Interpretation for OBP:\n",
+    "For every 0.010 increase in OBP (e.g., from 0.300 to 0.310), the model predicts that the team will score about 55 additional runs over the season.\n",
+    "\n",
+    "*assisted by gpt*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9f0f93fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df[X_vars].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb440ac6",
+   "metadata": {},
+   "source": [
+    "# Predicting RS using SLG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "38cbfbf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RS   R-squared:                       0.858\n",
+      "Model:                            OLS   Adj. R-squared:                  0.858\n",
+      "Method:                 Least Squares   F-statistic:                     5446.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:14:55   Log-Likelihood:                -4489.9\n",
+      "No. Observations:                 902   AIC:                             8984.\n",
+      "Df Residuals:                     900   BIC:                             8993.\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       -315.5117     13.861    -22.762      0.000    -342.716    -288.307\n",
+      "SLG         2610.8827     35.378     73.800      0.000    2541.450    2680.315\n",
+      "==============================================================================\n",
+      "Omnibus:                        9.987   Durbin-Watson:                   1.872\n",
+      "Prob(Omnibus):                  0.007   Jarque-Bera (JB):               10.054\n",
+      "Skew:                           0.257   Prob(JB):                      0.00656\n",
+      "Kurtosis:                       3.048   Cond. No.                         34.8\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: RS\n",
+      "Using: ['SLG']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "RMSE: 35.121\n",
+      "Dependent Variable RS average:  703.809\n",
+      "###############################################################################\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3098af890>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_vars = ['SLG']\n",
+    "y_var = 'RS'\n",
+    "\n",
+    "do_moneyball(df, X_vars, y_var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79f70a7f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "396a7bb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[['OBP', 'SLG', 'BA']].hist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f442d197",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# How did they know 95 wins would get them into the playoffs?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "32e3177a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.201536\n",
+      "         Iterations 9\n",
+      "                           Logit Regression Results                           \n",
+      "==============================================================================\n",
+      "Dep. Variable:               Playoffs   No. Observations:                  902\n",
+      "Model:                          Logit   Df Residuals:                      900\n",
+      "Method:                           MLE   Df Model:                            1\n",
+      "Date:                Tue, 15 Oct 2024   Pseudo R-squ.:                  0.5590\n",
+      "Time:                        17:17:15   Log-Likelihood:                -181.79\n",
+      "converged:                       True   LL-Null:                       -412.25\n",
+      "Covariance Type:            nonrobust   LLR p-value:                3.005e-102\n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const        -32.1718      2.638    -12.196      0.000     -37.342     -27.002\n",
+      "W              0.3461      0.029     11.959      0.000       0.289       0.403\n",
+      "==============================================================================\n",
+      "\n",
+      "Possibly complete quasi-separation: A fraction 0.11 of observations can be\n",
+      "perfectly predicted. This might indicate that there is complete\n",
+      "quasi-separation. In this case some parameters will not be identified.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>const</th>\n",
+       "      <th>W</th>\n",
+       "      <th>Playoff_Probability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0.264932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>91</td>\n",
+       "      <td>0.337521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>92</td>\n",
+       "      <td>0.418670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>93</td>\n",
+       "      <td>0.504473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0.590012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>95</td>\n",
+       "      <td>0.670433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>96</td>\n",
+       "      <td>0.741977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.802564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0.851766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>99</td>\n",
+       "      <td>0.890382</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    const   W  Playoff_Probability\n",
+       "30    1.0  90             0.264932\n",
+       "31    1.0  91             0.337521\n",
+       "32    1.0  92             0.418670\n",
+       "33    1.0  93             0.504473\n",
+       "34    1.0  94             0.590012\n",
+       "35    1.0  95             0.670433\n",
+       "36    1.0  96             0.741977\n",
+       "37    1.0  97             0.802564\n",
+       "38    1.0  98             0.851766\n",
+       "39    1.0  99             0.890382"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import statsmodels.api as sm\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "data = df.copy()\n",
+    "\n",
+    "# Define the independent (Wins) and dependent (Playoff status) variables\n",
+    "X = sm.add_constant(data[['W']])  # Add constant for intercept\n",
+    "y = data['Playoffs']  # Playoff status\n",
+    "\n",
+    "# Fit the logistic regression model\n",
+    "logit_model = sm.Logit(y, X).fit()\n",
+    "\n",
+    "# Display the summary of the logistic regression model\n",
+    "print(logit_model.summary())\n",
+    "\n",
+    "# Predict the probability of making the playoffs for a range of wins\n",
+    "wins_range = pd.DataFrame({'W': range(60, 110)})  # Wins from 60 to 109\n",
+    "wins_range = sm.add_constant(wins_range)  # Add constant\n",
+    "\n",
+    "# Generate playoff probabilities using the logistic regression model\n",
+    "wins_range['Playoff_Probability'] = logit_model.predict(wins_range)\n",
+    "\n",
+    "# Plot the playoff probability curve\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(wins_range['W'], wins_range['Playoff_Probability'], color='blue', label='Playoff Probability')\n",
+    "plt.axhline(0.5, color='red', linestyle='--', label='50% Probability Threshold')\n",
+    "plt.xlabel('Wins')\n",
+    "plt.ylabel('Probability of Making Playoffs')\n",
+    "plt.title('Logistic Regression: Wins vs. Playoff Probability')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()\n",
+    "\n",
+    "wins_range.iloc[30:40]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb6884f6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63e3d36c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dea63e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19dd6f18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4964789",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1050784c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "660ac83a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Okay, lets see what affects RUNS ALLOWED the most."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f2b43a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78739d65",
+   "metadata": {},
+   "source": [
+    "# Whats the error here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46afde35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a copy of the \n",
+    "independent_variables = ['OOBP', 'OSLG']\n",
+    "dependent_variable = 'RA'\n",
+    "do_moneyball(df, independent_variables, dependent_variable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47ec89a0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "873dbc5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RA   R-squared:                       0.907\n",
+      "Model:                            OLS   Adj. R-squared:                  0.905\n",
+      "Method:                 Least Squares   F-statistic:                     425.8\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):           1.16e-45\n",
+      "Time:                        17:18:56   Log-Likelihood:                -418.27\n",
+      "No. Observations:                  90   AIC:                             842.5\n",
+      "Df Residuals:                      87   BIC:                             850.0\n",
+      "Df Model:                           2                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       -837.3779     60.255    -13.897      0.000    -957.142    -717.614\n",
+      "OOBP        2913.5995    291.971      9.979      0.000    2333.276    3493.923\n",
+      "OSLG        1514.2860    175.428      8.632      0.000    1165.604    1862.968\n",
+      "==============================================================================\n",
+      "Omnibus:                        3.836   Durbin-Watson:                   2.160\n",
+      "Prob(Omnibus):                  0.147   Jarque-Bera (JB):                3.104\n",
+      "Skew:                          -0.392   Prob(JB):                        0.212\n",
+      "Kurtosis:                       3.461   Cond. No.                         139.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "\n",
+      "\n",
+      "\n",
+      "###############################################################################\n",
+      "Predicting: RA\n",
+      "Using: ['OOBP', 'OSLG']\n",
+      "###############################################################################\n",
+      "###############################################################################\n",
+      "RMSE: 25.242\n",
+      "Dependent Variable RA average:  809.567\n",
+      "###############################################################################\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x3097816d0>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# There are nans in the OOBP and OSLG columns. \n",
+    "# Make a copy of the dataframe and remove nans\n",
+    "\n",
+    "df_defense = df[['OOBP', 'OSLG', 'RA']].copy()\n",
+    "df_defense = df_defense.dropna()\n",
+    "\n",
+    "independent_variables = ['OOBP', 'OSLG']\n",
+    "dependent_variable = 'RA'\n",
+    "do_moneyball(df_defense, independent_variables, dependent_variable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92292862",
+   "metadata": {},
+   "source": [
+    "--- \n",
+    "NICE TO COVER IF WE HAVE TIME.  \n",
+    "# Checking Assumptons\n",
+    "1. Linearity\n",
+    "2. Homoscedasticity\n",
+    "3. Normality of Errors\n",
+    "4. Multicollinearity\n",
+    "5. Outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42677611",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24400e32",
+   "metadata": {},
+   "source": [
+    "# Lets do this for the runs scored model first. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "b5fee062",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     RS   R-squared:                       0.930\n",
+      "Model:                            OLS   Adj. R-squared:                  0.929\n",
+      "Method:                 Least Squares   F-statistic:                     5934.\n",
+      "Date:                Tue, 15 Oct 2024   Prob (F-statistic):               0.00\n",
+      "Time:                        17:19:48   Log-Likelihood:                -4174.2\n",
+      "No. Observations:                 902   AIC:                             8354.\n",
+      "Df Residuals:                     899   BIC:                             8369.\n",
+      "Df Model:                           2                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       -804.6271     18.921    -42.526      0.000    -841.761    -767.493\n",
+      "OBP         2737.7680     90.685     30.190      0.000    2559.790    2915.746\n",
+      "SLG         1584.9086     42.156     37.597      0.000    1502.174    1667.643\n",
+      "==============================================================================\n",
+      "Omnibus:                        3.099   Durbin-Watson:                   1.933\n",
+      "Prob(Omnibus):                  0.212   Jarque-Bera (JB):                3.106\n",
+      "Skew:                           0.143   Prob(JB):                        0.212\n",
+      "Kurtosis:                       2.972   Cond. No.                         134.\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "independent_variables = ['OBP', 'SLG']\n",
+    "dependent_variable = 'RS'\n",
+    "\n",
+    "X = df[independent_variables]\n",
+    "y = df[dependent_variable]\n",
+    "\n",
+    "X = sm.add_constant(X)\n",
+    "\n",
+    "model_runs_scored = sm.OLS(y, X).fit()\n",
+    "#predictions = mode_runs_scored.predict(X) \n",
+    "print(model_runs_scored.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f67be2ed",
+   "metadata": {},
+   "source": [
+    "## Checking Linearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d600740",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in independent_variables:\n",
+    "    sns.jointplot(x=col, y=dependent_variable, data=df, kind=\"reg\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13b25afc",
+   "metadata": {},
+   "source": [
+    "# Checking Homoscedasticy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82b42e06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Code taken / modified via tutorial below\n",
+    "# https://www.einblick.ai/python-code-examples/residual-plots-heteroskedasticity-test/\n",
+    "\n",
+    "plt.scatter(model_runs_scored.fittedvalues, model_runs_scored.resid, alpha=0.5)\n",
+    "plt.xlabel('Fitted Values')\n",
+    "plt.ylabel('Residuals')\n",
+    "plt.axhline(y = 0, color = 'r')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBIylwNh31e6l/O/UxriNAgAABAgQIECBAgAABAgQI7FZAucJupaQjQIAAAQIECBAgQIAAAQKHQCAe9KstvUCCQ3Di7CIBAgQIECBAgAABAgQIEBgDAeUKY3AS7AIBAgQIECBAgAABAgQIENgrge0e9NuAAoEEe6UsHwIECBAgQIAAAQIECBAgMJkCyhUm87w6KgIECBAgQIAAAQIECBA4ogIf9qAvkOCIXhQOmwABAgQIECBAgAABAgQI7FJAucIuoSQjQIAAAQIECBAgQIAAAQKHQWA3D/oCCQ7DmbSPBAgQIECAAAECBAgQIEDg4AWUKxy8uS0SIECAAAECBAgQIECAAIF9E3iQB32BBPt2GmRMgAABAgQIECBAgAABAgQOpYByhUN52uw0AQIECBAgQIAAAQIECBDYXuBhHvQFEmxvaS4BAgQIECBAgAABAgQIEDhqAsoVjtoZd7wECBAgQIAAAQIECBAgMNECOz3oL6ajbgMF2uF282JZ5KEjQIAAAQIECBAgQIAAAQIEjp5AlAm05Qajw8Vt5m83L9aJPHQECBAgQIAAAQIECBAgQIDAGAjEQ/roA347vjjYt3a6HcbsWNZOjw7PxkIdAQIECBAgQIAAAQIECBAgcGQEoixgtGygHV8cCLTT7TBmx7J2enR4NhbqCBAgQIAAAQIECBAgQIAAgccnEA/now/r7fjiyC6189phuyjStPNGh2fbBIYECBAgQIAAAQIECBAgQIDARAtEGcBomUA7vjhy1O28dtguijTtvNHh2TaBIQECBAgQIECAAAECBAgQIHCwAvFQPvqQ3o4vbtmNdn47HF0cadv5o8Ozo4mMEyBAgAABAgQIECBAgAABAhMnEM/+o2UB7fjiliNt57fD0cWRtp0/Ojw7msg4AQIECBAgQIAAAQIECBAgsP8C8TA++nDeji9us+l2WTvcmiTWaZeNDs9uTWiaAAECBAgQIECAAAECBAgQmAiBeOYfLQNoxxe3Obp2WTvcmiTWaZeNDs9uTWiaAAECBAgQIECAAAECBAgQ2B+BeAgffShvxxd32Fy7vB1ulyzWbZePDs9ul9g8AgQIECBAgAABAgQIECBA4NAKxLP+6LN/O764wxG1y9vhdsli3Xb56PDsdonNI0CAAAECBAgQIECAAAECBPZO4EzKavRhvB1f/JBNtGna4U5JI482zehwfqcVzCdAgAABAgQIECBAgAABAgQOlYByhUN1uuwsAQIECBAgQIAAAQIECBC4v8CbKcnoC/4YX7zPalvTf1jyyGtr+tc/bAXLCBAgQIAAAQIECBAgQIAAgUMj8Gba063P/Yv32fut6T8seeS1Nb1yhQ8Ts4wAAQIECBAgQIAAAQIECDyiwFJaf/RhfHEX+Y2mj/H7dYspweg6V+63guUECBAgQIAAAQIECBAgQIDAoRBYSns5+sy/uIu9Hk0f4/frFlOC0XWu3G8FywkQIECAAAECBAgQIECAAIGHF5hJq15N/XLq51O/m270wX03D/uRZ1Rv+Gbql1If29QRIECAAAECBAgQIECAAAECh19AucLhP4eOgAABAgQIECBAgACBQyCQH4J9tItHW2Br4IBr9mhfD46eAAECBAgQIECAAAECBAg8iIByhQfRkpYAAQIECBAgQIAAAQJJoKBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAgBQQSuAwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEagFBBC4EAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoBYQROBCIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBGoBQQQuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAWEETgQiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqAUEELgQCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFhBE4EIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEaoEuBwIECBAgQIAAAQIECBA4vAJzV87NzExPz3Sz7mwcRa9Trq3duLm68sLltcN7VPacAAECBAgQIECAAAECBAgQIEDgcQnkj2vDtktglwLVlnSu2S0gJgkQIECAAAECBI6ewDPLL8//9GT+fL+TnSmqbLYzctdcpTvmft0Xq51+dm3mRv+Nf7PwyutHT8kREyBAoBYY+T9kPa1cwYVBgAABAgQIECBAgACB+wh4cLoPkMWPXcDD/mM/BXaAAAECBAgQIEBgXASeXX7pQr9Tnbt5PJu502n3qmmlLoIHoiujr8fT/KrIjvez7OTdfHWqzC7+1eeXrkYaHQECBI6QgHKFI3SyHSoBAgQIECBAgAABAnsjIIhgbxzlsn8CHvb3z1bOBAgQIECAAAECh0Qgah740VPllW7Vn73nIS4FCkQAQQQONMEDKXagPq4ILmgCCZrDjOlq9SMfFBdXPv/Vq808vwQIEJh4AeUKE3+KHSABAgQIECBAgAABAnstEKVIOgIECBAgQIAAAQIECBAYU4FPf+urF66fqt5MoQKzWV7XM5D2dGT4ofN6WZZHn9LHMOVxc7q6MvetixfG9HDtFgECBAgQIECAAAECBAgQIECAwGMWuOcjlse8PzZPYKuALwa2ipgmQIAAAQIECBA4MgLPfPPC19853TvXqcpUp0Avy9NwczeshSDmt0urfCRePNVU0HRFWr8Z76flP3sju/pvf/viC4OFBgQIEJhUAeUKk3pmHRcBAgQIECBAgAABAvsm0JYm7dsGZEyAAAECBAgQIECAAAECDy4QNRC8e6p3rl80tQnUAQR1jQJl3XxBNGFQba2FoJ6ObW2pqSDNz9O8CESIGgmqlOd7J3qLn/7jl648+J5ZgwABAgQIECBAgAABAgQIECBAYJIFBBFM8tl1bAQIECBAgAABAgQIHEqBz7zx1S/8+In+0t1u89I/q1/+t/UM3OeQNgIJ2nTDgIIiajRIfZ7yi7zfeaJanPuT85o2aKkMCRAgQIAAAQIECBAgQIAAAQIEUtmRjsB4C6h2cLzPj70jQIAAAQIECBDYY4G55fOzt05NvT0aQFCkWgTy+s64iQOvUp0Co105eLJrh1FLwWgX6xapKYNicHfdpou6CY73iuzJ29Vzf/07l66NrmOcAAECEyKgXGFCTqTDIECAAAECBAgQIEDg4AQ2lzwd3HZtiQABAgQIECBAgAABAgS2EVg73b0SAQRtrQGd9PqrCSBoEm8NENgmix1mNTUSRLMITd5Z1k2z1jtZ9uMTnQs7rGQ2AQIECBAgQIAAAQIECBAgQIDAERMQRHDETrjDJUCAAAECBAgQIEBgfAV+/V98ZbGbVfOd9HK/rUwgggaqvBj0WaqTIPVp3mgfR9TWLhDjEXQw2se8tov82nVjG7GtbpbNx7bbNIYECBAgQIAAAQIECBAgQIAAAQJHV0AQwdE9946cAAECBAgQIECAAIExEyg71dl4qd82O5DGUtBA26eX/zvs72gAwQ5JNgIH2gCCNl1sK7bZn6rOtvMMCRAgQIAAAQIECBAgQIAAAQIEjq6AIIKje+4dOQECBAgQIECAAAECYyTw7PL52ZvH8/l7dykFEdS1B8Sw6e9N8+hzbkzl888sv7zN9h89bzkQIECAAAECBAgQIECAAAECBAgcHgFBBIfnXNlTAgQIECBAgAABAgQmWOC9E50zd+p2DFK1APlOdQ7sA0BsK/Xrads/PZWd2YctyJIAAQIECBAgQIAAAQIECBAgQOAQCaSmL3UECBAgQIAAAQIECBAg8LgFyk7+fFGltgV20e3UfEFR3S9OfOfghCrVdpA2/7ldbF4SAgQIECBAgAABAgQIECBAgACBCRa4XwnTBB+6QyNAgAABAgQIECBAgMA4CVRzVaoRYDSMoG7GYN92MQIKmqCCZptlVuTV3NyVczP7tkkZEyBAgAABAgQIECBAgAABAgQIjL2AIIKxP0V2kAABAgQIECBAgACBSReYTy/uu2VWv7wvizKL4IEIKIhX/HUNAfX0cPxhPeqghJRXdM02mmFsM7poTWFmeloQQa3hhwABAgQIECBAgAABAgQIECBwNAUEERzN8+6oCRAgQIAAAQIECBAYI4HuE9NzRYQMpMCB6OoAgsHL/v3YzQhMGHbNNuttp+13u9nscJkxAgQIECBAgAABAgQIECBAgACBoybQPWoH7HgJECBAgAABAgQIECAwbgK9tEPlSABBu3/xsj9qDGi7rVHgRbV1TpNyEBaQtUvb6Ww0fWrDIPJu0kStB0WWp33oZSMbbDdsSIAAAQIECBAgQIAAAQIECBAgcGQE2jKlI3PADpQAAQIECBAgQIAAAQLjKtAGEtTNDhz4Tm6EGhz4lm2QAAECBAgQIECAAAECBAgQIEBgfATURDA+58KeECBAgAABAgQIECBwVAV62Wp76E3NA+mFfl0LQZGaNmiW5KnmgEft2rwin3p8UBtBTIswDwUdAQIECBAgQIAAAQIECBAgQICAciLXAAECBAgQIECAAAECBB63wO3ba6O7EIEEoy/8R5ftZjwe9EYf9trxrYEI29Y9MBLQsJttSUOAAAECBAgQIECAAAECBAgQIDBZAhq7nKzzOYlHs/V7K9fsJJ5lx0SAAAECBAgQIJB98jtffrvXLWf76Y1/vNyPIILRYIIIACjqJamSgsFdclG14QENYDGY39RmkNJvmW6DBtoAhSbP4bqdfr72737rD592OggQIDBBAoP/E24ckXKFDQojBAgQIECAAAECBAgQ2F5gc4nT9mnMJUCAAAECBAgQIECAAIF9F6i+v7n+gGaDbcDAXm4+8hwNIKjzTgEJeZav7OV25EWAAAECBAgQIECAAAECBAgQIHD4BAQRHL5zZo8JECBAgAABAgQIEJhAgbzqNC/wB7UL1C/508v+qE1gPwIJ6ofB2FbbJ9Oyyl+bQFqHRIAAAQIECBAgQIAAAQIECBAg8AACgggeAEtSAgQIECBAgAABAgQI7JfA8Vu3rh7vVWuRfzQ3EP1GjQGDYIJH3Xb7ABjDMjWOEE0nRB/dsTTy0RvZtXrCDwECBAgQIECAAAECBAgQIECAwJEVaMuQjiyAAydAgAABAgQIECBAgMA4CKwsXF47dTd7o0zBA+tFUffRvEG3LLKpMqv7NqhguL9pQQoHGPbNkqi9IPq2loEi5RF9TBeDmgciSOFOp6j7Mi+yJ25XV99auLTa5OCXAAECBAgQIECAAAECBAgQIEDgqAoIIjiqZ95xEyBAgAABAgQIECAwdgLFB8VS/fJ/y551UpxA27zBlkUPNRmBCtG3NR5EJp0b2cWHysxKBAgQIECAAAECBAgQIECAAAECEyUgiGCiTqeDIUCAAAECBAgQIEDgMAu89XuXVosy/0bUPNCJmgRGuu2CC0YW73q0DR6IFSI4YSo1Y5D386ux7V1nIiEBAgQIECBAgAABAgQIECBAgMDECggimNhT68AIECBAgAABAgQIEDiMAqev3146eSdfjRf80fXSU1s0b5De9afu4R/hIngg8og+xqNpg05q4uDUnWp15nqpFoLg1REgQIAAAQIECBAgQIAAAQIECDxCCRQ8AgQIECBAgAABAgQIENhzgZWFy2sfuVksdPvFWgQN9PMiu9vJstudZjxe/m/09SPdaGBBRB60fWoCIY23fZmX2Z2UT+QVeUY31c+yj9woX1hZUAtBDeKHAAECBAgQIECAAAECBAgQIEBAEIFrgAABAgQIECBAgAABAuMm8JcLr6783PX8xZN3i6xpxmA0UGB0/MP2fFCVwTZJIs+T61n2c+9XF99auHRtmyRmESBAgAABAgQIECBAgAABAgQIHFGBqMRSR2CcBba0BJs+pNIRIECAAAECBAgQOCICv7780uKPn8iv9IuyboIgahNoumY4DCdopwfLo7aC1OUbd9NFVqU76WgWIbpoxuBnPyhe/D8//z9frmf4IUCAwOQKbPyfcHCIyhUm91w7MgIECBAgQIAAAQIE9kjAg9MeQcpm3wQ87O8brYwJECBAgAABAgQOg8Bnl1+auzOdLd/uVrP9FAMQgQRN7QQx3kzXxzEyv13eibvpFFAQ6aJphBg/1qtWT97KXvhrNRDUbH4IEJh4AeUKE3+KHSABAgQIECBAgAABAnst0HyGste5yo8AAQIECBAgQIAAAQIE9kTgr1LTBifXyueKsngtT0EAUYtAd9BHYECVF1kEF/QHwyawoEi1EKQ+pYthlXVTAEE36/byq8f+9vavCSDYk1MjEwIECBAgQIAAAQIECBAgQIDARAqoiWAiT+tEHZQvBibqdDoYAgQIECBAYNIEZpfPzdz46OkznbL83LEqn+v285n0FfxsHGdVFaupMarVvMxX0k3d90/fzK6tLCytTZrBQR7Ps8vnZ9e7naX1Tvb8nW42c7eTggdSUwcpWqDejdFaCiLY4Fg/y46vF2tTZf76iVvFa3/x371y7SD317YIECAwBgLKFcbgJNgFAgQIECBAgAABAgQOl4AggsN1vo7i3nrYP4pn3TETIECAAAECYy/w7PLL87ensgvvncznb3fLbCq9w+6kvpvu3vLUV/Gkkb6Ab7pmeKxXZCfuVlfTi+2Lby0srQ4WGjyEwFwK3rh+8on59W42n+fVp/Kqmk3wM73kngIJUqBGtVb0i+8f7xcrp2/cfH1l4bLgjYdwtgoBAhMhoFxhIk6jgyBAgAABAgQIECBA4CAFBBEcpLZtPYyAh/2HUbMOAQIECBAgQGCfBD6ZvoR/f6b79SKvzsQm6mCBrKwDCOLhIsUTjHSDIIJBMEFUs78+mJWq2b/60ffvXFxZuLQ6soJRAgQIECCw1wLKFfZaVH4ECBAgQIAAAQIECEy8gCCCiT/Fh/4APewf+lPoAAgQIECAAIFJEZj71stnrk/nV9a75UwEBKRv3rPUdEFqsaCsgwmiBoLoYl706Td+UtcMI76gWa9Zfny9Wj19K1v4q4VXV+pkfggQIECAwN4LDP46bWSsLGyDwggBAgQIECBAgAABAgS2F2hL9bZfai4BAgQIECBAgAABAgSSwNy3vnrhpyfz5awoZ6LZgk41DCBogCKQYFgNQRtkEIEGG30sT30RNRek9ctONfvTJ/IfPPu//Y9nIRMgQIAAAQIECBAgQIAAAQIECBAgMB4Coq/H4zzYi50FfDGws40lBAgQIECAAIEDEUg1EFy4cyxf6tchyE1QQDxI9Isyu9tJwzQRtRFEcEExaLpgux2LwIJeyqOT7vCme2mY0keeU+nn9I1s8a1/+gevbbeeeQQIECBA4BEElCs8Ap5VCRAgQIAAAQIECBA4mgJR9qcjMM4CHvbH+ezYNwIECBAgQGDiBeomDE7ky906QCDVHpACB6IrUk0CEQBwu9sEEUTNAtGcQQQRRFMGbbMFUfNA28VYLwUdRPBABBFE+iYwoUjj1drM9eq5v1r4XzRt0IIZEiBAgMBeCChX2AtFeRAgQIAAAQIECBAgcKQEBBEcqdN9KA/Ww/6hPG12mgABAgQIEJgEgbnl87O3T0y9meICZrspSCCaJVhPgQNtIEFMRxDAMEwgBRFse+DDFBFcEEEGEUgQXTV4IsnTRo71stVjN9d/bWXh8lqz1C8BAgQIEHhkAeUKj0woAwIECBAgQIAAAQIEjprA9mV8R03B8RIgQIAAAQIECBAgcI/AT04fv5BqDpiNMIF8UNNA1CwQb2OqGA4CArppxs4PFsMAgthAU6NBEzxQBxBETQV1XmV2Z6qcff/J7oVIpyNAgAABAgQIECBAgAABAgQIECBA4PEIDL77eTwbt1UCuxDwxcAukCQhQIAAAQIECOy1wG/885fn//bp/M2oOWCqLFPzA83L/rr5gkGTBk0dBE34QKTbHC7Q7NG9wQWbUxUja5UpFCH6/2Rt/VfeWri8utfHJD8CBAgQOJICyhWO5Gl30AQIECBAgAABAgQIPIrAvWV6j5KbdQkQIECAAAECBAgQmAiB9ePZhaghIEIDyjTsp8CBfh2CvDkIYD8Ott89fm4/8pUnAQIECBAgQIAAAQIECBAgQIAAAQL3F1ATwf2NpHi8Ar4YeLz+tk6AAAECBAgcQYG55fOzH5zuvL3eKbOpfgKogwmaJgyyahCHPJjX8kSgQdMNaiYYNHAwWtNAs3w0XVP3QD2/zTetd6yXrx2/cfdXVhYurw0yNSBAgAABAg8roFzhYeWsR4AAAQIECBAgQIDAkRVQE8GRPfUOnAABAgQIECBAgMD2An830zkTzRbU3SA4IGoi6EWTBXsahpweRzaCBwabS6967narmbUnp88M9sCAAAECBAgQIECAAAECBAgQIECAAIEDFBBEcIDYNkWAAAECBAgQIEDgMAh0s/z5VAlBVqQX+tGEQQQQRBcPD3XNAhu1DtSzd/iJddpaB9rxGDa5bFppJJAgttUEKlSf25TGBAECBAgQIECAAAECBAgQIECAAAECByIgiOBAmG2EAAECBAgQIECAwOERKKp8PgIIoosX+m0oQDuvWbKL3zrYoF17p/TbPZKkdYpyfqc1zCdAgAABAgQIECBAgAABAgQIECBAYP8Etiux27+tyZkAAQIECBAgQIAAgbEW+OzyS3P37mDUPzDoB00a1MEFI00bFKk2geibkINB6kEgwrD2gUiTpkb6ZlvDx5I2UCElmZ1bPjdz776YQ4AAAQIECBAgQIAAAQIECBAgQIDAfgoMS+v2cyvyJkCAAAECBAgQIEDgUAh0smKmGgkUGN3pppmB0TkfNn6/GggG60bgQR18MHw0aQMJprNMEMGHEVtGgAABAgQIECBAgAABAgQIECBAYB8EhiV1+5C5LAkQIECAAAECBAgQOFwC6dV//eI+Agk2dxEUkPr2pX873JxoY6qpmaCtdSDVTDCofWAjwQ4jeUo37Lqzw3FjBAgQIECAAAECBAgQIECAAAECBAgchIAggoNQtg0CBAgQIECAAAECh0SgyPKNr/83vc8/gP2PAIJ4QNkcSHAAG7YJAgQIECBAgAABAgQIECBAgAABAgQ2BLobY0YIECBAgAABAgQIECCQVavpNf4Wh2HTBKNNGkTtAqmOgSZtPkyzZeXBZLt8kL5uwmAk5cZ0my7L+lm+NpLCKAECBAgQIECAAAECBAgQIECAAAECByAgiOAAkG2CAAECBAgQIECAwGER6GfdtaxK0QF1HMHwhX4RTRmkrvltAgEioCACCfauS/lGfoMYhvVsfXXv8pYTAQIECBAgQIAAAQIECBAgQIAAAQK7ERBEsBslaQgQIECAAAECBAgcEYH1LFvdeqgRMrARLJBe8A9DC7am3MvpfG1l4bKaCPaSVF4ECBAgQIAAAQIECBAgQIAAAQIEdiEwqEt0FyklIUCAAAECBAgQIEBg4gVWFpbWUssEK50UKdDZ01oG7k8Xm2uaS4jHlHzl/mtIQYAAAQIECBAgQIAAAQIECBAgQIDAXgsIIthrUfkRIECAAAECBAgQOOQCnaz6/lQKIpjqD2sgyNMb/uijFoIqRRlUgyYH4qV/3af5ozUUlClN9FnbD0yatKlxhHa9PMab6SZJ84hSlvlrg1UMCBAgQIAAAQIECBAgQIAAAQIECBA4QAFBBAeIbVMECBAgQIAAAQIEDoPAz14vX5/upff/aWfbZgwiaKANHGiPYet0O3/nYXr8qHZ+BInAgqiNYKpfZB99P7u2cz6WECBAgAABAgQIECBAgAABAgQIECCwXwI7l+Dt1xblS4AAAQIECBAgQIDAWAu8tfDKtRPr1bXe4GkhAgnamgP2e8d7RZFN362urSxcWt3vbcmfAAECBAgQIECAAAECBAgQIECAAIF7BQQR3GtiDgECBAgQIECAAIEjL9BZz1/rDZoZiACC6KJ5gqoYbbSgmb/730HzBjusENuJwIVOlV3cIYnZBAgQIECAAAECBAgQIECAAAECBAjss8CgOHCftyJ7Ag8vEDXajnau2VEN4wQIECBAgACBfRT45T9/+QdV3p/L8l42VTYBAOuppoAyi1jkYqOpg2YXyiyPO7cUaBBdUbXDzXHLZQpM2Ojqpg2G0/10p3drqrr6o9+49MJGGiMECBAgQODRBJQrPJqftQkQIECAAAECBAgQOIICwxK7I3jwDpkAAQIECBAgQIAAgZ0FPvbT7MWpFAtQ10SQggMiSKAOFKhXKZv5g/EY7K6Wgia4oF6tDjgYTk/1s7WPXlcLQW3jhwABAgQIECBAgAABAgQIECBAgMBjEhBE8JjgbZYAAQIECBAgQIDAuAv89cIr107fzC8WdY0B8bJ/+MK/2ffBvAgG2AgI2JJuY9no0Y6k2Vgvy566nV38v//JpdXRlMYJECBAgAABAgQIECBAgAABAgQIEDhYAVXDH6y3rT24gGoHH9zMGgQIECBAgACBPRX4tT8+f3V9qn/2drfM7naHoQQRkRzhANHAQXRtLQXFyB3cRtRyCkSoazRIaZth0xxCBCgc6xXZ6Vv5xX/9/KWlOiM/BAgQIEBg7wRG/irVmSoL2ztbOREgQIAAAQIECBAgMKECG2V6E3p8DosAAQIECBAgQIAAgUcU+MFvX1p86saJ1zr96TqnquilwIFeGk/BA6kmgbKtTSAN64CCmB70EV5Q94M0/fQEUqXXN9FHF4EHT92qBBA0HH4JECBAgAABAgQIECBAgAABAgQIPHYBQQSP/RTYAQIECBAgQIAAAQLjL/Cv/un/tPj0jamL0yl2oFOVqU/DFB3QBATEY0VEB2zT14c2WJ7SdMoiBQ5En9U1EMzcyl986/k/XBp/AXtIgAABAgQIECBAgAABAgQIECBA4GgIqMLtaJznw3yUqh08zGfPvhMgQIAAAQITJ/CZb33pzNqpu1/vlvlsPy+y26l5g6hdIAIKIlQgurZZg2Yq/UZwQerid6pfj2a9olp58kb24srv/K/Xmjl+CRAgQIDAvggoV9gXVpkSIECAAAECBAgQIDDJAoIIJvnsTsaxedifjPPoKAgQIECAAIEJE5j/F19cvDWVXXjndDZ7cyrLuimIIIIHik13b21YQXPw0+tF9vTNavVYL7v4L3/30tUJI3E4BAgQIDCeApv+MqVdVBY2nufJXhEgQIAAAQIECBAgMEYCHpzG6GTYlW0FPOxvy2ImAQIECBAgQGA8BP6z//33z/z4ieJMtyo/lUIG5qKpgmEgwUYQwWpZVm/8zM3s9ZXfeeXaeOy5vSBAgACBIyKgXOGInGiHSYAAAQIECBAgQIDA3gkIItg7Szntj4CH/f1xlSsBAgQIECBAYF8E5pbPz05n3dl+1l3rZNna7dSvLCyt7cvGZEqAAAECBO4voFzh/kZSECBAgAABAgQIECBAYJOAIIJNHCbGUMDD/hieFLtEgAABAgQIECBAgAABAgQOiYByhUNyouwmAQIECBAgQGDSBeaunJuZmZ6e6aaPL+JYe51ybe3GzdWVFy77+GLST/4hPD5BBIfwpB2xXfawf8ROuMMlQIAAAQIECBAgQIAAAQJ7KKBcYQ8xZUWAAAECBAgQIPBgAs8svzy/drLzfNnJzqSXsrPdshw0A1lmZZrRTy1Blnm12uln156+Ubzxrxdeef3BtiA1gf0REESwP65y3TsBD/t7ZyknAgQIECBAgAABAgQIECBw1ASUKxy1M+54CRAgQIAAAQJjIPDMGy9fWO/m524cy2bWU3uPbVfUd6cpkGAwI8aaeVl2rJ9lJ+8Uq1P98uJbC1+72q5jSOBxCAgieBzqtvkgAh72H0RLWgIECBAgQIAAAQIECBAgQGBUQLnCqIZxAgQIECBAgACBfRWImgfemcmvlHk5u7GhKgUKbEyU9Vg+uEttAgiGgQSxsMyLrJ9nqx/5oLy48nnBBBt0Rg5UYHjNHuhmbYwAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKTIfDpb331wvVT+Zt51pstsl6W5amPLo/AgbaPGWk85rX9NtN5Vs7ePJFdmfvWyxdiDR2BgxZQE8FBi9vegwr4YuBBxaQnQIAAAQIECBAgQIAAAQIEWgHlCq2EIQECBAgQIECAwL4JPPPNC19/53TvXKdqggX6RZlqFEibq7rpp61pIJal2IH6t/lpmzJoh+2iWDf6VCNB9rMf5Ff/7W+/+kK7zJDAQQioieAglG2DAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGJE4gaCN491TvXLwa1D0QNBHXtA+lQR2ohSLULpACCJsigSMEG0Q9rKNg83i6r0vrvnewvfvqPz1+ZODgHNNYCggjG+vTYOQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIExlHgM2989Qs/fqK/dLfbNl/QS/UOpGCBuj6sQcDARvDADkfQNmswGLbBBnU+ad273TJ753R/ce5PvnRhhxzMJrDnAqkSDB2BsRZQ7eBYnx47R4AAAQIECBAgQIAAAQIExlpAucJYnx47R4AAAQIECBA4vAJzy+dnb52aentzAEFzPL30BrbK07fcVfM9dxNUMDzWzV95R60EzbLRdFXKo24SIS2K4fF+lj15s//cX//O5WvDnIwR2B+Bzdfo/mxDrgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJgYgXdnulduT6UaCFJtAXXzBIMji/oHIiggAgLafueDjtQ7dKlmgqiNoO5T0wd3OmX2d6ezCzukNpvAngoIIthTTpkRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDDJAp/95lcW86yajwoEopaAXqp1IPp45Z9CCOo+AgmGU5vHW5si1VQQfRnrpj5qL2j7NoSgzncwP88785/95hcX2/UNCeyXgCCC/ZKVLwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECEyewXpRnowaCLNUWEM0ORB9NFzRBBE1NBG0TBQ9y8BGQ0PYRdlDnl/KtIhwhDaNf7xZp2zoC+ysgiGB/feVOgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCECMwtn5+9eTybHz2cPF7wj9Q70NYsMJrmQcbbWghG82zHb04V888sn59/kPykJfCgAoIIHlRMegIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEjqTAB090zqx3Ui0EO3RtbQQ7LH642YNaCKImgjvdLHvvdHHm4TKyFoHdCaTLTEeAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC9xNITRc8H00ORFMGu+0epmmDbfOObaZAgiqrPrftcjMJ7JGAmgj2CFI2BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMtkCVl3NZtvsAgr3XKLM8z+bmrpyb2fu85UigERBE4EogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAfQTm04v7qTKb2bOaBe6zve0Wx7a7KYZhZnpaEMF2QObtiYDmDPaEUSYECBAgQIAAAQJHTWBueWn2+qlivqrKT6Xo75n08DZbphDd9SJbTVXa/YeyzK79zAfZysrC0tpRs3G8BAgQIECAAAECBAgQIECAAIGJFHhiei69w3/sXb0P3Ww27cjqY98ZOzCRAoIIJvK0OigCBAgQIECAAIH9EJhbPjdzrHj6C3eLav7m8Wq+UaQ17wAAQABJREFU7PTrzcSDW29Qx1cKKMiiWbxOkV24dTLL/uE3L7x+fL1841/+7teu1on9ECBAgAABAgQIECBAgAABAgQIHEqBXnqzeif1VRT+HGSXD5tP6KcyqDJN97oHvRMHecC29bgFBBE87jNg+wQIECBAgAABAodC4Nk3Xr7wzqn8XL/ozQwfFFMbdIPw8zJvogiiSrlBPEF2N91tv9vJzqQDPPPx737lwtPXqxf/zcIrrx+KA7aTBAgQIECAAAECBAgQIECAAAECmwRup6l+enfflv1sWniAExFIEPuiI7BfAoII9ktWvgQIECBAgAABAhMh8OwfnZ8tTxbLN49Vc92qytJ/9cNic3ApCrwN+h4EE9x70M1jZUo2+8GpfPnT3/zK5bJ//eLKwmXNHNyLZQ4BAgQIECBAgAABAgQIECBAYGwFetF8QNQKsGM50Njuuh0j8EACjztQ5oF2VmICBAgQIECAAAECBynwi999+czf/lznB+8+Uc3d6Ua1cRE0MBhuO/7he3e3k2omeCI795OZ0z/4+W+fn/3w1JYSIECAAAECBAgQIECAAAECBAiMl8DtsfkopA5oGC8cezNBAu13UxN0SA5lwgS2xnK5ZifsBDscAgQIECAwrgK/+t3fP7veya52qjKLJgqiK9OdSPTD5gxSTEFa1izfJj63Ku6p3i7llq2npLem+qtT18vnfvhPLq3WmfshQIAAAQIE9kNAucJ+qMpzVwLzV87NZNPTM7enu7OpkD9VYFWurWc3V9VItSs+iQgQIECAwNgK/MK1L7+dvhOZHd3BtrnLdl4RH5+MdG3Z0sisTaNR3jTaRfnRaFe10/XHLfGZS7X2/83/4dOjaYwT2EsBzRnspaa8CBAgQIAAAQIEJkLgF9784tydorxapACCtqsDCNqJwbAJIGgf6iJtO94mSPNSIMFoF0EJZXqd0any2d7pYnlm+dxza5o2GCUyToAAAQIECBA4tAKf/PbL8/1u9XxWlGfeKfPZuIe8U/Q3msPKsxPZL//5S6upiaxrVVW98Te/+QevH9qDteMECBAgQOCoCuTV99MXJrOP8/CrLF95nNu37ckXEEQw+efYERIgQIAAAQIECDyAwLPL52Z/VPaXs3jZP4gCb4dtOMAwtCDNGUSA15vY9K3j1tRFXWvBVD8CCFIOKe9+XszlMycupHrwXnyAXZSUAAECBAgQIEBgzAT+4bdeunDnWHbuTqec6XfKrJ9uBXspUiC6supn6WvFuvaq5ivFYjZNLqY5i5/8zvnV6TvFxZXPv3o1zdMRIECAAAECh0Gg7Kyk8qCzW3c1ahtoy5DaZTvVQLA1XaQflje1a7e1Xzb5bip2qvLXhqmMEdh7gbZkc+9zliMBAgQIECBAgACBQyhQTRUX8ryabYIDIpCg6eNQ2ge/GNbjdQDByCNeTLd9/egXy9rlg/G0PNbtpskIJkixBOfmvnV+PvLXESBAgAABAgQIHC6BT377/Pyv/vnvv/3jJ8ql290UQFBEAEFz/9hLw+jb+8rmvjDuCaNxg5jfy9a71ex7p/tXfuHN82//wpsvLR6uo7e3BAgQIEDgaAp8bO3W1ePrRfompHm5PxoQsFFm9IA0cYcw2rX51OVPaUEEEMR2IiBxOrWT+bG17NpoeuME9lpAEMFei8qPAAECBAgQIEDg0Ap8dvmLc2vT+WJ6Fst6eWq9LvVVekBruqYAONq0a9u1ax/khgXCESAw6AfpyihELnp1X6Xh3W7q09dpEZxQFyCnwuPrJ6sr7VYMCRAgQIAAAQIEDofAf/6dL12ojpVvVkV/Nu4Z63vIQWlre88Yw1gW934RULCe7gNH+7hPnCrL7ERZzXaz6srf/85LFw7H0dtLAgQIECBwdAVWUrOUT96u3shTE5aplsm6D41OesHf9m0QwFalCAQYDTqI0qF7AgjSvHb9tlhqPVVrFDUdFWmbT92qrq4sXFrdmrdpAnspIIhgLzXlRYAAAQIECBAgcKgFfnK6+Prt1OBXNQgeaAII7nfLvN3jXjBsnT/4Ii09/UUBchNEkJKlAuVUkDz7i29+cTHW0hEgQIAAAQIECIy/wC99/0tfv32sXIqA0Srdz8W9XRTsb9zjbTqEJpAg7i2H/fBeMcUVZNEfTxUUlJ1q6ePffUmA6SY/EwQIECBAYPwEpj7IloYfl8T+NeVHGy//U0DBo3YRQBDZjAYdRP6dG9nFR83b+gTuJ3C/EtH7rW85AQIECBAgQIAAgYkQePaPzs2WnXI+SxHdo31exi1z+oYsaiao6yBohtsFCQwhmnXaB8jh/DSWCpg39RsLi7Mbo0YIECBAgAABAgTGVuDjb6YaCIry3LCZgnZXIzCguQOMrwTblwiDOW2iehhVEdcvAZpVBk1dNfOqolr8xJ8KJNgEZoIAAQIECIyZwFu/d2m1KPNvTKUowk5ddhQv/AdlSrvY17gFGNwGbAQZbl1tNIAgAg6n+uleoZ9fjW1vTWuawF4LROmmjgABAgQIECBAgMCRF6ieOH5uR4QILEjdaOT3jmkHkec7L99+SZ5n888un5/dfqm5BAgQIECAAAEC4yAw++ZLX+gXxcaXh03NVWnPIlD0Abu4t4z1owaDTTVVpXx63Wrx49/TtMEDkkpOgAABAgQOVOD09ZtLJ+8Uq/GCPz5I6aW/6XWzA1HD5S7LhzbuJSKLqHogdZFd5BV9WxYVzSScvJOvfvRGqRaCWsnPfguky09HgAABAgQIECBAgMAHx6rP1Q99O1G0NRRsLI9b6fjCbLTfWLjzyD35DJPmnfzMcMoYAQIECBAgQIDAOAk8u7w0m2fF5fZLwCjUbwv2Yz/bequaov82qKDMotaBpi/SsOmb5rOK7E5q3/hu6m9NZVk0q5Wauaq/MowvDfMsX0pBpvPjZGBfCBAgQIAAgaHAysLltY+831/o9ou1mNtPwQN3O83f99F7hOEa9x9rAwzj/iD6CDaMLu4NfuZW+cJbC2ohaET87reAIIL9FpY/AQIECBAgQIDA2AvMLp+budPN59qI78e2w1U1/9i2bcMECBAgQIAAAQIfKlB1qivxFWD0ETIQXxzWozHVjmzKoQ0k2DRzY2Lr0vjaMF4URP5TaWEEHvS7nQsbKxghQIAAAQIExk7gL//7V1d+7v3sxZPrTVNGsYNRvvSwQQRbDzDuMSLvn7+eX/yLhUvXti43TWC/BAQR7JesfAkQIECAAAECBA6PwBPTc/30gNdL/YEHEkTNBIOuqopPteOGBAgQIECAAAEC4yOQaiFYvD2Vz3fTV4CjLwbS7WP9sn90TzdqHohlowtGxiNN3AVGsMCJ9Syb7o2mLVPbyrGszO50s/lfX35pcWRVowQIECBAgMCYCfzl737t6tMfZC9Mp4jA6fTC/1gadrdGC9aNFMTMQR9NIe3Qx31A3BucjHuElN9HbmQv/sXCq0tjdth2Z8IFhiWWE36gDo8AAQIECBAgQIDAjgJ5ORNffbVVxO2Ybq8XbAQQpI2n8bvdfHavNyE/AgQIECBAgACBRxf40enq7O30xr8OIEjZtYGnbQ0EbeDApi3Fvd7G/d6mJfVEHUiQggm6qY/aB9q8YmFZNC8Y7qQ3ED85kZ+9d21zCBAgQIAAgXESiECCpz7o/9qp9Ww1ggTbLv7eR1empg6i1qGooSDKn+Jeor2fyMsiBRA2fYynxfW9wfTdfPUjH1TP/auFP7hcZ+KHwAEK7BQMe4C7YFMEPlRg8L/XjTSu2Q0KIwQIECBA4HAIzKWmAqaz6ZlUPDobe1ysl2s3b9xcXXnhct1e3DgcxS997/fP9bvl1yMCvC3Mbb4NG/2yrHkCjAe5phtMb71bqR/12jTNw+FwKk2PTqTxaA+37lIB84kUXf70u/1feev3tG+3hckkAQIECBB4WIGtf6mVKzys5BFeL9VCMPu3T1ZvF4MvB8sifRqYxpvpuGts7vBizr3ddvOGqSJwIL42bF8oxJJ4iRDz22qQq5Tvx97vPPfWwivXhmsaI0CAAAECBMZR4Nnl87M/OdlZWp8qz8b9QgQUxN/0u+kPfgQPtH/fY9/j732UQ02lBcdSbUcxvV409w55WV09deP2iysL41N+No7e9mn/BLr7l7WcCRAgQIAAAQIEjqrA3/uzl+eLIn8+z/tn3svy2cYhFX+mh6FO2cmyp5/KZr93YbXMeteqKn/jb37zldebNI/rt0pBDs2DXNPG7QHvx+ALtbuJ5of1nhzw9m2OAAECBAgQIEBgR4E865+JwM9o/qrpirqZgggdaAMI2iWbh/cPIIj0bW1Yca8cXQzqbUUVx+k+sX7x0C3PpNnXUq8jQIAAAQIExljgrYX6w5DFueXzS9lUN4IJnr/TyZrSnvRHvv17H7cVMd58zNI0fzC1Xqw91ate71bla3+xcOnaGB+mXTsCAoIIjsBJdogECBAgQIAAgYMSmPvWVy/cOJ6fS9HV6eEoFXrWdbY1paHxYBQVsjVFqWV2rMxmU3T14nq3XJz93kurp29nF/+v//bVqwe1r6PbSQW3qzHdPsiNLju48SggTgXSt2+sHdw2bYkAAQIECBAgQOD+AsXzo2k2YgkGMyOYYLTbek85+sXhaLodxyN4YNDl8QVjulm9fSz7XDvPkAABAgQIEBh/gZWNYIJzMzdOn5gv+5344OZTnao/2ynzmfiIJa+qtX6er1V5/v1+ma1Mrd94/S01D4z/yT0ie7j1nveIHLbDPEQCzVuH4Q67ZocWxggQIECAwNgI/L0/W5qvutWVVMg5G0ECeRUBBMPCz9jRtjC1iK/uB1/elynNetTfOlieV/lqWXUu/s0/+trVeuYB/fzCmy8tdvL+lTrwYWObTbhDHQdRzxvs58by4X5vzKpHmvXaeVsLjbeopC/N2vSp6rrUON7/81tfc7/T4hkSIECAAIFHF1Cu8OiGRz6Hz3zzwns/Ot0Eydb3hvV9btzVpT6NN/e5Md3c17X3ve301vvBFnSYrp2zddjkmQ/un3vXbz296sXCViTTEy4wd+XczO2fn57ppebxeumTyKm75dqpd8arebwJPwUOjwABAgSOqICaCI7oiXfYBAgQIECAAIG9EvgH3/7qhTt5f6lMZZwRFFAHD9SvwaPQM7qmMDWqZI0C1KoudG2WRLBBtA0XBbDNsmK2yHtXPv5nL/3yv/+tVy82qQ7gdz1bORZtzqX9W0+DKg3v7ZrjqAuL64Xt9L0pP3zOzuudvlOtfPi6lhIgQIAAAQIECBykwNzy0sxPT1Qzw8DSYXBsHRxQB8fGDW2qcWtryMpgRzfmDwJp79n/kfvjrctiG3VzBynvu9PTURWyWqu2IpmeOIHfWD4/P93Ln1/vVGfWTuSz68ey7INoLDw9N+apCbj1//R49pk//vLqVC+/lgLR3/g/Fv7g9YlDcEAECBAgQOAxC+xcgvmYd8zmCRAgQIAAAQIExl/gE9/5ytf7nf5SJwUDFFlvUPtAFKK2AQTNMUR5al3ImoZNAWyZ9YqyDigoBoEEUbga1bXGdL9bLf38tfNXmrX3//c//uNXV473i7WiHBb+xn5GHwEF2wcV7OV+NbflU/38P+xlrvIiQIAAAQIECBB4NIEfzvTm7nY339s+Wo4PtnYE6UYfgQTdbjb7YGtLTeBwCXzyO+cvfOK7X3rvh09Wb/7Hmd65d073Z29N9dKzYy89k/WyfqeXarLrZR8c72U/Ot2b/bun+ovvni6X//6fnn/7V7/30uLhOlp7S4AAAQIExltAEMF4nx97R4AAAQIECBAYW4F/8J2XLuRF71y36qWX/1GwE4WbTWDA5iCC9LVIBAekfipVVzCdGnmb7pVZN6ouSAWisSxqJIhAhG47TMuOV9Xir3734AIJjt3NV9bTVy0R7BDBAxtfjKUzEPNGAwliWdtvPUGRdrSP5W1YRQx3Wi++Xkvb8AXNVlDTBAgQIECAAIHHKNCNONl079fc0aXB1loDRqbbe8AddzfSjqRv07XrbTds08Q9syplWw3DSRN4JtU88F9++8tvV0V/qdfpz9xN//BupuCB22kYzd/Fs+R0v5cdT8+S8cx5N81rA2wiqGB9qj/bK/pXIpjg098UTDBp14fjIUCAAIHHIyCI4PG42yoBAgQIECBA4FALfOJPv/yFO6m2gDIV4LQ1CkQzBaNNFWw9wPbFfCrvaQIH6lfrm7/0bwpom+Xxsr3XyRd/6XsvXdia135Md6rqjX4et8dNbQSdtP346CwKc6OL/Y7p2K8H6dIqG929N99Fyjf6dMwp3/X++rWNxEYIECBAgAABAgQeu0C8uN+4/0v3u8143Lw1Lzb3dweHd4/1vWgKaNARmDSBuT85f+Hdp6o3b0+VsxEgEH1b+0YT5B1PVE3QeWcQiN4+o0Wgd9TS0Txzldl6t5p9/2R15dk3zh/IM+SknQvHQ4AAAQIERgWGd6Kjc40TIECAAAECBAgQ2EHg2eXzs71ucbkp2ElBBNEmZSrVjK+jojaBNhCgKcppCnza8TroIKWNgqGYFzUQtPm0BUax2SgMioKheF+f5/lS2uZ8zN/Prte/cTXlvxaBAsdSSdRUanIzAgmioDj6aIJzOhXcRgHudl375Vi7rD3ydjpuvNu8NualfKdSwui7/eraysKl1XaZIQECBAgQIECAwBgI9Hqr8RX08D5udLy5v2vvA9sXm8Pp4dfSzVfTzT1uXRtBCkKIe+PosyrdKaa+vX9swhOa6ZjfbltNBGNwPdiFPRX47De/9PWbx/spOL2pcSCCBqJrr/kYts+G8e8iLamDsOP5qUj/NuLZM48m6eolsTQtSP+2rk9XS//VN/+HK5GXjgABAgQIEHg4gfj7qiNAgAABAgQIECCwa4E7U9mVjYLRtFbURNAUmI4GEGyTXRSUpr5p9qBZ3t6Mtuu3a0VhUBQYRSUAUbvBreOdC+2y/RquLFxe61f5G1H0FF1bKNVub6PwKnbqIbvWLVZvC8ZiPIITPvZBdjHGdQQIECBAgAABAuMjMHP79lrcq9Uv/uvX/O349vvY3Nduv+zB59bNXdWrRUDrzO1s9cHzsAaB8RR49o0vXXjvVP/cndRkQTzzRY0C8cw17OK5rHk2i/nNsrYmtyaAoA6yGawQz1dNV2Z3UmT4u6eKxWdfF0jQqhgSIECAAIEHFWj+Cj/oWtITIECAAAECBAgcSYHPpvYlbx7P5x/14OsggVQI29ZE0NRUMHyxHgVAzdclzdda7x/P5v+LP9n/ti37eW+pl2dr0axBUwAchVNFFtM3u0V241hTXeaHHX/7BVmkaQu7YtjObwuW20Ku9XRHfmI9u/bWwivXPixfywgQIECAAAECBA5eIAJNT92pVtsA0PYebrgncZfXdFW88KxrFIjvoYd9u7y9IxwNLI1lo9Mb949p/faladyLnrhbrL2l1qohpbFDLfDZN778hZ+erJYicGDYtU9MwznNWCQa9OnfV/wb2+gH/+Y2pmNZmzaNfXCiWPz1N/7ZvgekN/vplwABAgQITJZA/FXVESBAgAABAgQIENiVwNqJ/OzdQRWT967wYLeWw2YPmoLTyC8KUKMgqe5HvkKJ5hLWTuZn793m3s754XOXVo+v59+IAtsorI2PWaKgOJowqAuM2wKrXWy2KfQdJmwLhNs5bWFxDPN+/4V2viEBAgQIECBAgMB4CUz18+/vvEeDe+B0nzh6f7tz+p2XtEED9QvRkWTRxNZ0v1oZmWWUwKEVmEvN4/30ZHE5gqmHQTnDYJwdD6wOEBhdev/nz9upDZB3T+ZLzyz/s/nRNY0TIECAAAEC9xe4/1/a++chBQECBAgQIECAwBEQePaPzs+m5ibn7znU3b5Yv0+6CByIgqQo6Gn7CB6IGgkGtRLMP7P88r3bv2eHHm3Gv/tvXlmqynwl9mW9KLKptGOn7g769RRQEJEFD9m1zTTE6m2gRFVVF31V9pCgViNAgAABAgQIHIBAar1rJe7jooaqFP95T7BANIOVbh1TF7+j7bM3c0ZrJWjada8Tb/PT5DK6ILZ7rJfuSdez10bnGydwWAVun+heiX2PZ7x4thoGEmw9oggsiP7BupRlHQzeBnH3UhD8B6f2v3m8B9tLqQkQIECAwPgL3HtnOv77bA8JECBAgAABAgQeg8DUsc6ZR/26aqfdjgKe+CI/uthGjK+nwp5+GkYtAFOpDdgoXCq72Zkm1f7+PvlBbyEFD6y2W4mb5rqQK+3LzoVcbeoPH8b69bGkY+v2q2/8v//o1aUPX8NSAgQIECBAgACBxylw/f0bV9Mt3Fq9DxEYmwIF4p71Ue8Ltx7TdvfasY36PvRudm1retMEDpvAL37vK4t3pvL5eN6LZ706TCAN97MbPFvOf+JPzy/u53bkTYAAAQIEJk1AEMGknVHHQ4AAAQIECBDYJ4FjZfZ8p0rfTqWCzL3u2kLYKCCd7jX9RiFqXtZfqMS2bx8rP7fX294uv5XU3uzJG+vPpa/NVuNrsaiRIPpo4qANdhiuVxd9pcnBMO1vtlOfUrVlZHk/f+3f/+alc8N8jBEgQIAAAQIECIyjwMoLl9eysngjggf2otty97gpy4174MHcuPfOy+rqtd+7tLopoQkCh1CgLIqzdQBB+qfU1j7XNOOx/weTYtTP7v9WbIEAAQIECEyOwN7c+U6OhyMhQIAAAQIECBDYQeBON5urvxap34JH0ec2Xf1l1jbzdzGrDSSIYIGofaCb+jZgoX1x3yvyudnlczO7yO6Rk0QgwZPX159L+7LaNG3QNEFwb8bDW+rYz7bazDpdVHmb2oDopD6GkTKOaXo9/8bf/NeXFu/NyxwCBAgQIECAAIFxFPjYjf5SNHMVQaVVuquLvrdtgOnD7X3cIzb3vk3Qan3/m+4lj6VtPnkru/hwuVqLwPgIPLt8fjbPyvkIvm4DCaKJt/ZZbzd7ujXIZjfrtGk6VT4//89fTtvXESBAgAABArsRGJZ47ia1NAQIECBAgAABAkdSYO7KuZkfPVHO3E5VBfRTo7DDbuR2ciOAoH5VnpJsHf7/7L3tc2THfd/b58wDZrEPBKkHy+WnkWQ5qli+AhXSl/FNIiCS6DAOtUTlOlV6RexfwGUlkqD1UsBG1HqvVbe4+gsAvvILlwu7pBXalJUFy0rCe6lYuLFTUSLZO3FkWzKfRruLx5k55/6+faYHB4NnLB4GwKcLPeepu0+fz5k5OP3rb/86yxUMpH7uWMuTX8qAZFO++o54iQgUvYHWvACkMtKqzErlQEQEqq2EBKfm7z68HKUvNkxNIDGB6uN0rSFauqRdNx1THWVcVhSDkokHZHCO06IJCYp1827w7H/9Z3ggEF8CBCAAAQhAAAIQOCoEXtd74XL0dXmn0vuqjza0Wcvwdhzec/U+mI9r34uz90S93+pYyKf3YoXMC5a9O9r745n5eErn9gf4gMARJhC50lOFtlhG33kJxxW1nv1G9EtKzHNbO9p+iQZ8lCjbp8v/mrJfmTWy5K7DnIVkMWxnqFRmFvxvtXAw0+OFc7KEAAQgAAEIHGUC2ZvpUb4C6g4BCEAAAhCAAAQgsO8E7n2gMjhXyuat3O+TSUigmBlTgwFW5iQJCUxY4IrV/a5DvvzZkev1v/6nz4++/yfpww/Nu5myrE9Wi475qiMmyIzA2m9CgY7IIDaDV7lZdA/ORV8/d2fhg//9M799PV8+6xCAAAQgAAEIQAACR4NA0ownrEuy1u7L7HRw7lXt9Q7sBav2PqmgqbUKC60re1U+5UDgMAkslOLzOr++3ZmAIPPYlu3ZqGbtdtVGhzfd3/Z4YL8r34SztCYK/+SmWTgIAQhAAAIQgECHQPZG2tlkBQIQgAAEIAABCEAAAusQsD7zMEIqG2u1MqJjndT7tst7KlD//SGEN0auzv7nJ786fHo+Gm6l0YstV6xlY1+sQjZKTCPHNG1Bn1mo5HlA62YEnmm66EpxYe7BP33y6kUJEg6h6pwSAhCAAAQgAAEIQGAPCMyOTNSjRmvEOvfr8phVaWaxGFQF2zxHmP4qjKxWtiCk1brKLrVsudy68Prn8EIgJoSjT6B+KpseTwICc3DnvFcCu6z872Cvr1K/K02ZoKgwX44GB6cnDsyzXXZWPiEAAQhAAAJHk8AhmWCPJixqDQEIQAACEIAABE4sATOQFsxdqzxFnvTwnZHnZ4yBont0+tKgyQeqRsbm94wGNNODLWsuTet3C/dmEA2IEgECEIAABCAAAQgcHwJ//fjV2Y+8eunZtOAm1RmqKQla1lHpp7ra4WVKTKDQnV+dq5XF6Mr/eOIrMz4BHxA44gSq0xcHmnEykDUoVzwE+O9++3ew6hLbU3v4fWqE5rdXJdz+hs61aL0hS5GTiABx9/bRkRICEIAABE4oAUQEJ/TGc9kQgAAEIAABCEBgJwTspbFWMiGBRnA0TEywWkvQHtaxYYGrU2/urnLDQvwBeUOIrB69EuSdwOqiSIAABCAAAQhAAAIQOCEEvv/41akPvzrmWoV40jshsA+9EScmKFDI5njPvAv4He235yAa0NRXPp8d1Pt1cLVeNE9W8mZVbCbP/o8nvno9y8snBI4+gWLlzGAQpKs9qQ79+bJEOJk3DwlnOu3E/Lr2+u18m3Lz9qe8D4SgvGXz6qHcy2rHWjTVd9U+axYJEIAABCAAAQhsQmDz/7ibZOQQBCAAAQhAAAIQgMDJIVD50WJdxhe5mozNsLn9kDf2bD/XRinl2vXMYrO20XH2QwACEIAABCAAAQhA4CAI/MXj16aiVvJwKi9UXkKwzbPmRlTnOzv9SOskrpUX0+G//AwCgm3SJNkRIlBINRlcJq5pmQu31KsKrIEX1AWda9lJe7OTad0ViQjC9Ak5bcG6adkJAQhAAAIQgMBqAnv3H3l1uWxBAAIQgAAEIAABCBwjArMXrtf7GlGtGcd+vtb1DaUSDHRFGYS6YyfNzgH1L7n67Ajzwu6cHDkgAAEIQAACEIAABPaawPcf/9pssuyGkzR9cf3343BGmWAttgUEqe9KzfbJ80ClEbtTy+lUqX734R888fxMyMUSAseFgNwhBw8dciygDv1TjSwW22J1CdYV9yNISNBv5zttsdJDnu3241opEwIQgAAEILBXBPS2SoAABCAAAQhAAAIQgMCWBOI0ek2uVoOb1i0zrBlRsmUOn0DGpfW8HWh/Xytl6oDtYSQVBCAAAQhAAAIQgMABEKg9ca12+9PXRs/eST9YaqYvWido3Z/WBAOansC/27Y7RtVxqqkMQkepHTOhbjr1vrnWcO3TVy/URq5neQ+g3pwCAgdKoONNLhOZx+aVQJ4JSrYZfiNa7mdQ+SUTLDC/835SpmwIQAACEDhOBPifeZzuJtcCAQhAAAIQgAAE9pFAlMSzNmrq6chp6Ia0qGbxWS9sJB7IuW5dL5tGZsmwk82HmYkV5OLVG5Usg4ytcSuyUV4ECEAAAhCAAAQgAAEI9BaBtres0er0xQF39tSQSQWG7D3244U0qjYL0UAmHEjrLrXo4teSKJq9e+fujTrCgd66kdRmXwgsOpsez501QXp3G9KmOGi3E0Pbb20F7n8cZGrtSnP44SJbRnOutvYc7IEABCAAAQhAoJuA/dskQKCnCVh3warAd3YVDjYgAAEIQAACB0dgcHpi4J0H0tsmIhjI5q1cawDytVljGGrXsW0c2tAE1BYRFK3YzMjTnjrB74/9XJbvfavxwdc/x3QGB3fXORMEIAABCEDgyBPArnDkbyEXAAEIHAcCv/zKc7fn+5rVhs0tkFqbUZ4IIovy2BGChARrw8rxtcc22yOBgonUffmxiQg0dUix/t9+/SsPbpaLYxCAAAQgAAEIZAR2+x8YfhCAAAQgAAEIQAACJ4zA7MhEveWim2m0yStkR0AggUE+2qaOdY7btkQFbWFBHmXLdodRKJrWQOcz848N2oqnEBDkSbEOAQhAAAIQgAAEIAABCEDgaBA4txi9Jq8DUnZpmjx5BghtP3nq0FQfOt4d7+fqVK483WmpNqZNH8L0ePcDlLwQgAAEIHCiCNi/agIEIAABCEAAAhCAAAS2R+B9P3ETFc1m4Dv/N3qVlHigO6y3L5fGl7d6W0YeRRl8+mzuyrNz0ZVcClYhAAEIQAACEIAABCAAAQhA4IgQKCbprFqF1sQz7wPyEJB5CZC3gGy6vKztp629ChKktyzatHze40GlwfR4e8WWciAAAQhA4PgT2Mjye/yvnCuEAAQgAAEIQAACENgxAfNGUDu7GH3dZ+wICe7zlbIjIFA5mrZAo0/kx0BeCGyPrT8w76Z07h1XmAwQgAAEIAABCEAAAhCAAAQgcOgE7sZ3p1pxXFd771TDROLLzp2xmE03oOpl7cGOxzq1Ey2qPZiP27+QbHo8eTyQOF1i+MpiY2b7+UkJAQhAAAIQONkE9J+ZAAEIQAACEIAABCAAgW0TaL11Z8LMODWfoSMA2E724I2gvcxPbeCzh+PayAQEEhKYW8taYQEvBNshTBoIQAACEIAABCAAAQhAAAK9SGB25Hq96dKb8kJQsnkMNG3BRkGd/jsJG4kM1MJsFLLYStOpmc9dq+2kXNJCAAIQgAAETjKBHf47PsmouPZDIuAdWuXOzXc2B4NVCEAAAhCAwGER+JlXLw0Wi9EtFyUDWR1knrHohQFhuU7t2iNJslEmOr7acBTbqJQoLXp3k5onU24n41ZruPaZ52fWKY1dEOgJAoOTFwcGKpWBoitWVaFmIanX5+Zrsxeu13uiglQCAhCAwMkmgF3hZN9/rh4CEOghAh+4NVY90yrcLqRNt2xqArUii/aU1hR2EhVIPNCyqGUQBmRtzGxf/lKUV61JpVP+EOTNLogQlKYZ+1SuNZ9+8EdPICIInFhCAAIQgAAEtiJg/1IJEOhpArlXQF9PvrM9fbuoHAQgAAEInCQCH311bHSpGE1KPJAZaTLxQJxmy41YhBEnMujI7GODUHwIhiOXlG276I1H5UZ05Qe/PjGRpeATAr1D4JHpy0P1/sL5pOCeilxS1Ygq/923Knrjp32vkyitFVpu5sG5+Ob/O/L8jd6pPTWBAAQgcKIIYFc4Ubebi4UABHqdwN979dL1ZiF9plFQOzJxJXuPttWOiGDZPAcolFomDrBlammCKMAfaH+oPRlEBMqv0HkPt3WJE+TZrhXFrtiKpv7y01cv+ER8QAACEIAABCCwLQJ0yG4LE4kOkQCN/UOEz6khAAEIQAACWxH4iAkJkkI6KWONDEA2dsRHmWuyuFKCf/HMuazUCJGmGXSCkahg//ULraKLk4rlLrrlOH32b4cnrq+UwBoEDp/AIzcvjzeK6cW5PjfQ8KOaVCf73uutVb+B9turfgleH2Pf+bIpZfqXXK3Uiq68PjIxpRwECEAAAhA4MALYFQ4MNSeCAAQgsDWB6vTFgWSg8t3IpVW9OyuqrSghgATmC8WsjFPN7FjwTBBKzgsKlFftSF+GLZU/tC/LJkIomKc7l6a1U3NueHYELwSBIUsIQAACEIDAdgh4W+52EpIGAodEwF7/VgW+s6twsAEBCEAAAhA4fAIfefXzg61CPB1FSTUyLwQSEmQhLLOt8E9c/9wTEw80zJ7TtFEmIZUMPLFNZeCSSq28FF34wRMTM1lOPiFw+ATkeeDvBhITzKTVldroOxu2sm9y+J5rag6FzPNGtq5fRytOaw/di6/MfvbLUz4BHxCAAAQgsN8EOk/q9onCo3q/z0v5EIAABCCwAYFf/salwcW+9JZNA+anx5NXL4kBJBiYL2WZKiYiCPskHAjv3RuJCJRL+ZesSam0FWtwnm44d3opHf72yLWZrFQ+IQABCEAAAhDYLoHMmrXd1KSDAAQgAAEIQAACEIBAF4HvP/612WQ5Gi41Ci/2NYs22kMxzi21bp2t7RjZyGy5lFwoxW6pkKWrtPO10sLUnegnDyMg6ILM5qES+MTLXx6/ezq95UdLObNmRhZ9kHAgRFs1TwQdWYxfz++zY7YvStPqfCWdHHz5yrgdJUAAAhCAAAQgAAEIQODEEfivv3F1tn8pfbZpnr2a1kPRkLg8J/GSR4FFEwPIK4GOyYtdOC6BQD4Kno7lPRZoqjwVZwKCKwgITtzXiwuGAAQgAIE9IpD717xHJVIMBPaWgL3yrQp8Z1fhYAMCEIAABCDQWwQemx6rzve5iUYxOW9mm4GsU3WljupilQFoyQxBc2Wt2+iQpbheaaQ3ykvuxT9/khEiK7RY6wUCj7w0/sLfnW1eLJiXDYkHWrHNyeorZhZNE8T4EVFtwYCMlSH4/ZrSwO9ra7ctvQycmZEzdu+fc1N/+i+uXAh5WEIAAhCAwL4QyD2dffnYFfYFM4VCAAIQ2DmBX35lbHS+L5qUlzp5HbA3ZS8qyAsGtD/zWre6fL17B3FBSB9S9JsHgofm3LP/YeS3mR4vQGEJAQhAAAIQ2CEBGk47BEbyAydAY//AkXNCCEAAAhCAwP0T0DyXhf5TQ3EUDbko+rhGX6dRNCDjThqldRttUl+I3Ws2PGS28fb8jfrI9fr9n5USILC3BOSB4N3+1sRysenKiaQDSVtEIFFAFiUS0DQeIbTlAtlmEBn4tCspmvY70FQeZXNo8OCChARfvRCOsoQABCAAgT0ngF1hz5FSIAQgAIG9IzBoUxu82e+mXRxV9b6tqQ0UFSQUyN63V71lZwftU+/U8ligdmbB8igWk7T203eTC3gg6GBiBQIQgAAEILArAvbvlQCBniZAY7+nbw+VgwAEIAABCEAAAlsTkKjEVSoDxaKr+okA0lbdzTVqtR4Wj/yDm19+5s1zreutOJu+oNxKvBRAAgC5V828EMTesJkn0G3e9J4ITEygEBpfms5Do600ckrTe7z3bjQx++TzV/LlsA4BCEAAAntGALvCnqGkIAhAAAL7Q+Cj8mj3QHGiVUifLracK5mAdzPxQKiFRATzpWwqg5IJCPqX3NTpublnZ3u4nRHqzhICEIAABCDQ6wSCHavX60n9Ti4BGvsn995z5RCAAAQgAAEIHGEC/+j3xoaWyul5m97iqaWSq+qlLrgZ9QZB2+5ruNqZxWim3Exu/sn/+Ts3euVyB82IuXC6dFseCFy0bLHZnpbARjup87/thUD1zU9hoG0vGtBKO2Tbq8UGyh9YJCYo6LNCzy22hr/DdB4BG0sIQAACe0kAu8Je0qQsCEAAAvtI4AOvjFVLfYUJe+U+X0zdgJ+ywL97r39SvZsvm5e7lktuxGn04o+Gn59ZPyV7IQABCEAAAhDYKQFEBDslRvqDJkBj/6CJcz4IQAACEIAABCBwHwR+9eal8bdPpxeTOBlQMYW2q3+N3m97JfVmQLkaVdCofIU4iWoPzcdX3vjs1Sm/4xA/fu7W5Vuxaw3FzsQDFrOaJ+2OfxMA+CvI6t1dzfVFBHmxQew9EIR8KktCAlvO/K/hrw6H/SwhAAEIQGDPCGBX2DOUFAQBCEDgYAgMmiezU+7MkJ1N8eOpK1StI8O3L+yhXrd1mw4vfa3p0tmGu3sDzwNGiQABCEAAAhDYYwKICPYYKMXtOQEa+3uOlAIhAAEIQAACEIDA3hOQ54FG2U3W+6PqYpjE1J8mSAcy9/3atXr0/kpnfMWGEj2wkNb6GvGVP/mXhyMmePSlS6M/PptOuiix6QfaAgJbj9tiCJM72IWoziv19pfZ9RHEBBIIKIRtCRI6Xgh8GSYqaJf1U3ejC70goui6FDYhAAEIHHUC2BWO+h2k/hCAAAQgAAEIQAACEIDAgRPY3PJ14NXhhBCAAAQgAAEIQAACEIDAUSPwi9+8NP63D0W3/u5sJiBITaq8XgzXtd4x7VuwiUx/bGX88MF0UmWG9Ae5bBSTpyUgUGd/qKfOn4kBYhMDWNxFhSQcCDErLytE58jO55w/9y7KJgsEIAABCEAAAhCAAAQgAAEIQAACEIAABPaSgExWBAj0MgFGDPTy3aFuEIAABCAAAQiceAJ/7w+/9MJCn7uo6QrU8a4Gxsqo+53hUSd7y7dQYldqOde/FE19759dvbCzUnafenB6rDp/Jrq9aGIGXYv3RtDxQLDRda0vKQgMgieCUKsVvwzZnlSeCrxnA+cq5vjg3L10+Dsj12ZCepYQgAAEIHDfBLAr3DdCCoAABCAAAQhAAAIQgAAEThqB9S1eJ40C1wsBCEAAAhCAAAQgAAEI7JjAh8xbwKIJCLIR9ln3eOg833FhqzJkLv8X+tLRD//xpclVh/Zx496ZwlONQvcJVppMus69DSsCApW7VHTu3bPxU3t7DkqDAAQgAAEIQAACEIAABCAAAQhAAAIQgMDOCKxYxHaWj9QQgAAEIAABCEAAAhCAwAkm8Pf+8NIzrWI6IQQFG+NZNs8BigUN4Lft3UTl7ZTT9gDQLKSjH/2jg5nawKYWOL9KKND2EGA+COwq148STawXxWVHwU+hYE4JXPrJHeUjMQQgAAEIQAACEIAABCAAAQhAAAIQgAAE9pgAIoI9BkpxEIAABCAAAQhAAAIQOO4Eqq+MVc1LwPVs6gHnitb5rxkAJCbYbQgd8SpHUWXGJiTQOebL6cQvvjI2tNuyt5svjZJBP43BdjPseTqbDiJyg4OTFwf2vGgKhAAEIAABCEAAAhCAAAQgAAEIQAACEIDANgkgItgmKJJBAAIQgAAEIAABCEAAAhmBQqkwKbf/rXZrQl4H7jfkPQBIUBAECTqHzpWW3Pj9nmOz/EPWcW/ihQGd+7CCzi3xxEClgojgsG4C54UABCAAAQhAAAIQgAAEIAABCEAAAhDwPjnBAAEIQAACEIAABCAAAQhAYFsEHn3p0mgzdkPrJVb/u8QANi3ArkMoQwUE7wRaJnE09OhLY6O7LnirjGcqgzr3gYf2NAbhvL4ORVcN2ywhAAEIQAACEIAABCAAAQhAAAIQgAAEIHDQBIoHfULOBwEIQAACEIAABCAAAQgcXQL1U8nTqfncV8d+5oHAphxoeyRYT0CgdPmQdHWarz5mWyZAUJ6W6Z1VXjGxofntMF+OnrbVqbC9l8umtYyWLGYCiJVzurR9ce2T5evvxQ3R6uPr1SlXmh1eJ32biTiq/GbxPlQY61WAfRCAAAQgAAEIQAACEIAABCAAAQhAAAIQ2AGBdSxYO8hNUghAAAIQgAAEIAABCEDgxBD4wCtj1aViNKTOfQkIgkBA2+oAvx8PBHmIKk9RQecI51kspUO/+MrYkD+wxx+LVl4rVnf/6i7/zU4T6rhpms0OrnNMHFUXAgQgAAEIQAACEIAABCAAAQhAAAIQgAAEDosAnggOizznhQAEIAABCEAAAhCAwBEjUC4nTzlX6NRanejdquTMO0EnybZXustZk1Gj9c0rQKHgrA5uZs3x+9zRdK52n0WQHQIQgAAEIAABCEAAAhCAQM8SGJyeGKg4N2AVrFr7R07g6o26q81emKj3bKWpGAQgAAEIHBoBRASHhp4TQwACEIAABCAAAQhA4GgRiKPofObOP+vy9x4C2h4DdCXdAoLgQSB/lcq5/bH++Zw6QeJSF32ya+8ebS6a4axv67I0vUF7+oGtE+8uBYKG3XEjFwQgAAEIQAACEIAABCCwmsDgy5eH3qkUzkeF5Kl3Xau6+qhtnYvdL3xrotZ08cwH7kQ3//PIczfWpGEHBCAAAQicSAI5k9+JvH4uuvcJmAPbVYHv7CocbEAAAhCAAAQgAIGDI1D99194N42igfy0BVsJB7qFBImfMmB3dVZZRfP3/9M/XHpw5sL1PR8t8zMzX7xtfhaq+dp1X1+3BGIrDwprBROrc6TBl0NbmGAyifoPh37nwXwdWIcABCAAgfsigF3hvvCRGQIQgAAEjiKBod+7NL5cSi/+5JQbWOwMJV3dOkmi2LXasRnH7syyc++7G9UqDXdl5l9NTB3F66bOEIAABCCwdwQ6/z72rkhKggAEIAABCEAAAhCAAASOG4HB6YsDP3EmIOj4EVjdGb6t692rEfyVyoCdb89FBOZK4TWXRtVtXcs+JTJPC7P7VDTFQgACEIAABCAAAQhAAALHnMAj02NDd86mkz9OzOuAtb8aaratGZbXFhOYgCAx8YDEBJqobr7s3Jtn42ohiSc/9Mfj4++ZT6688dmvTB1zZFweBCAAAQhsQAARwQZg2A0BCEAAAhCAwNEhMDR5ccCpU7HYHkGcxnU3N1/bj5HKR4cKNYXA3hI44yqDd1OTEKwxQIXzBENU2N7rpcqXccuWxbRqKzWLexuSwqwZ2p7uLlQeELqvexcSilXFBg8NKnfVENk0enFVQjYgAAEIQAACEIAABCAAAQhsg8CvvvyF8buV1oQ5b3Ottgc4tTe62zJqU2VtHFuqjeWnbLM1tXtMVOBMVGCeCarmxWDysenLv/D6yPNXtnF6kkAAAhCAwDEjgIjgmN1QLgcCEIAABCBwUgj84itjQ1Gcno+i6Km/iaKqtYtXgvlaTx887T70x8/VrNNxJmq1bv7Frz9/YyUBaxCAwI4JNEMOdZ9ngoG1rv5DGi2zNPk92bry7zaozPvJv/l531dfmJo7c2p8qZQMBENbod3Dn+/037yUzY+GcjKjXSYg0Ll0nr5m7PrvpTN/vXkRHIUABCAAAQhAAAIQgAAEILCKwD+8+YUX3j6VXpSAoNmZQs4mS1tlLFnJ4ltr5qlAbbqCb7tJRJC1tRLvQS52S9Z79O7peOL/mL5U/Q8jVy+s5GYNAhCAAAROAoH9s8CdBHpcIwQgAAEIQAACB07gV2+Ojf/SN7/0bqsvvdUsuYsNG5HcKCS+kayGso+Fpqnumy6KmtUoWh6NCun03//DS7cffWls9MArzAkhcIwIRGZUUue3lkFAoG0f7TrbJidby0a2mGPMda5e+/JxnSSHtGt25Hr93GJ6U9cX5gZVVdTBr7j6OrdXSTFZFdvlBFteo6BRQio7dg8spFOzI9dq2yuZVBCAAAQgAAEIQAACEIAABJz7hHkgeKc/utgwLwJNa2ik1gLxsT1NQb5FkrXlspabPBCozaZ2TilJrM0jzwRNH0tp0xzANS1J0/2kvzX6iT8Ym4Q1BCAAAQicLALt/xYn66K5WghAAAIQgAAEjh4BeR740LfGbr95NppYKiYDwT2fXPRJJd8REGi7HV2kodPWWWnL5VKr+u7pdPLD3/ri7Q9/6/OjR48ANYbA4RJoNl0tCAa2VRM/emVbKXsqUemem9B1roSsyaR96vhffWwl1XbWQhkqR6cI3g6UV8cKc+6K1gkQgAAEIAABCEAAAhCAAAS2Q2DwG//6mbfOphMLpUycvMrzgIkE/FQF3Utf8IqQwKZ0M+GApAbmmSBEExREFmVTWSgl7q0z6ejgN74wvp06kQYCEIAABI4HgfZ/iuNxMVwFBCAAAQhAAALHk8BH/2hsfPlUemvJvA4sm9cBCQiykczt61VnZT5KOBCi78hc2bYGcdVaxZMf/SMav216LCCwLQKLi4t1jVqRaUkd3iGuzqzfmkJY2mr+t+l/jz7B/X00i7X7K2Dj3K9/7lotTqKvl+xBU0iy5pJG8fhROnbduw1eQNDOr0UQENgjzZVaxrMVTencuy2ffBCAAAQgAAEIQAACEIDAySLw0VcuVu9V4uvyErcS5TVuJar9tib6dp1YZe0dLzTYAF02tYHaL4m7W4kmHnn53wxtkJTdEIAABCBwzAi0/0scs6viciAAAQhAAAIQODYEfvGbX3xhoZxOqMMtdLrFXg2/3UvMdWa2s6hx3SxGEx959dLkdkshHQROOoHZC9frLk1rYRqDw+Eht5xRfWafO9vP3p2f6F+Ka+rgl0Gtaa0mP+2AuQPNxujs7uo1KqhlUeWF55mmSehfimrveTvBC8HusJILAhCAAAQgAAEIQAACJ5JA/UxxctmmR9t5sAZJR0iweW61YSQgkChaU0m+eaYwvnkOjkIAAhCAwHEhgIjguNxJrgMCEIAABCBwDAnIA0Gj6C7q0go2P1+5ZdHP05ep7De+ZL3irEQ1erOoHsFsnj/N9xdF6ehH/xAhwcYcOQKB1QQiF7+mPcELwdpl8FKQa2bIOJWPq4vc8VbkotkdZ9phhtmR6/WH7rRGiq24rqwtEw8sF2K3WFzp/N9hkV40oPlJVYYMfZlHldi8EMTuvXfjC3gh2ClR0kMAAhCAAAQgAAEIQODkEnj0pc+PllvR0FYi7xUPBRvZUdq2k3abTV7YvCc2Q5sJCLLjEj8rWhj6temxUb/GBwQgAAEIHGsCOevesb5OLg4CEIAABCAAgSNG4EOvfvGZ+b50IkxdULTGqk3DZ2KC+78QNYQ1GjiJE7dUikZ/9ltfRkl//1gp4QQQiNJ03zvwt8KYpMmLW6XZi+P/8V9dnf2pO+7Z/kYmjNiLMlWGhBdh2d9w7gN30yvf/s3nZ7K9fEIAAhCAAAQgAAEIQAACENgGgSh+Wp36+9nBkxcoBAF50WwyrYJ7ehs1JAkEIAABCBxxAvv5P+aIo6H6EIAABCAAAQgcFoHHfnesGhXi69Z35zv79cKixmvofNt9vWIb/ZuNJtZoYC8kMDGBFTzx2PTE0O7LJScETgaBhYW5Kfsp+tH54Yr1E1JcE4L3gTUHtrcjGKm6UzdahZnuffu1/R//5VemHrznLlRs/oGKPZDKtlz7HMo8nMjLyVZRz7Jyy6YvsLJU3kNz7tlvj1yd2K/6Uy4EIAABCEAAAhCAAAQgcPwIDE6PVRfKbqhkbQvfbrJLDO2n7uX9Xr0GcoQ2kOwyEhEslKKhx6bHhu63bPJDAAIQgEBvE5AdiwABCEAAAhCAAAR6ikB62k2qQls1foNbvp1W3nf1Wa9nmJNc+ZeK6fhOyyE9BE4agZq5+S+30pv67Vh/uo9mR9q3oPNoKgFFeyK40nI8VXviWm3fTrhOwRISPHCv9fDphqvJG0owoIXnk7L4eloV5TklxPB8ydJl9de6pi84vZzWHrqXDv+Hkd++vs4p2QUBCEAAAhCAAAQgAAEIQGBDAnfORU9pirTdB1lFth+CRwI/1YG10TQo4+/Oxk9tvwRSQgACEIDAUSRgj3sCBCAAAQhAAAIQ6B0Cj01fGr3blw5FNqpXI3ZdlHgvBFqqmaupCHxoL32HnkY8rxM6nXhJ+7iV4Tvxwrbv/bR9tj1vKv5HpydG3xiZmFqnKHZBAAJtAn3zbmJ+IH56uT23iH5TcqPZMSz5Dn8lDoap9X+fWwK137V+w0uFLH+lUXSVhdaVLfPtQ4I3Rq7OmoeU4eWHnKZYedpZ3QpJdn1Nq58EFYk9XxRUZzGJLU3RnmESHkgAkblrME8GrXQqXlx89nUTZOgIAQIQgAAEIAABCEDg4AgMTl8cqLjKgJ2xqrO2XFxvuPnaLO9mwkE4IgRsmrnz2RSN26lwaJdlaVe1zrwtZaUdoxTexmLL1Np1oY3nc7bbZ4nt97aW1H3S7+fjQAnoGVZy/dWCiweKzezUNuVfXdPxHWhFOBkEIHAiCJiJiwCBniZgJthVge/sKhxsQAACEDh+BD7yzbFbaZwOZVcWGrthmYkIgjhg5epXNYNXdrfXJBLwod3J10ngG8x2LJWuUsto5i8+MzHcOc4KBCCwLoGf/9bY9aVy+owOVsxwIZeWEv4odIQ+G4gI1v5+fba1H/b7lAeCxWL2+60su6m/+tRXL6xNeLB7zG1ndankJhql5PxyIRpYshFA8j6QdokIJDQQlz4TEtg0CPW+Znqj1HAvvj5ybeZga8zZIAABCJx4AtgVTvxXAAAnncAj05eH3joTnbcm31P2zlqVQD0Ek5T7zlJ7pa1Z5+jM++62bv7Zk8/fCMdZQqAXCXzoW19619ofAxIyZ60lq6W3b6ytbRA7hyOd9GFHp92W7QgiAm8j6VSU4RAAAEAASURBVKSxFStf4ml5I9CxyLYf/MndBxHg5CHtz3r1m5eHTLrffoa5anaWbJCMBP0KEpW0XDqTROnNh+60bsyOHKwHv6wWfEIAAseNgD1aCBDoaQI09nv69lA5CEAAAntLwEb6Vt98f3Q764xcMexoRPNKx+Tac0oJv1noiAhComA08o3srAGcCQmc++l3o+Fv/+bETEjKEgIQWEugaqMfGg/2fdeUA1UZLfw8mfY7zY9U2eg3u5GIYMVYlY3mlznMT2UQm4EqSWvlhWT4oKcyWHvlK3s0AuRuf3moUYyH7BH0cWNQtWeXRrXJsFZvxVHd6v1aoeVmz84tmhEHzwMr9FiDAAQgcKAEsCscKG5OBoHeIfDY9JfHbdq6i/N9yUDH9btvC1pbU52vSdHeYLNR1Xrv1PurvOE9NGfTWLXclddHvjLVO1dDTSCQEfDtkHOn3pU4IIgIfFtqHRGB2l67FRHk7SyhfJUXppqTiOCnf9L4oImka9yb/SEw+PLloYVSYXy5lAx1n0H3RLaztoNAf7hpAveGtZ/t+eXOLiZTlUbLnmPcn252bEMAAtsngIhg+6xIeTgEaOwfDnfOCgEIQOBQCPzD6d+6+DcDyQtFcxOeCQmyaoQRvhtVKt+4XZPGGrb5zkl/fF0RQSYmeGgu/fob5//txTXlsAMCEFhF4COvfn5wqRTdclHkO86jVCbYzP2lxAS7ERF4I5eVobz+d90W+lQWouEfPPH8zKoKsAEBCEAAAhDYHgHsCtvjRCoIHBsC6nh750w0aVIB73WgoPfUzpMgExD4i22/a+odVKOrs7fZbKouHW9GrvbQvLvyZ7+BmODYfDmOwYU8Zp413jzXuuXFARLDhO+2/z6vvcAkzqaGDEfUZlsd8gM4svacjnfbWTQ4Y62IIB1+fYR22mqe97/1gVfGqoVKcTJy0ZBE+7E9w7pDuI/dQn6JCBS0X98N8x5x/T31u1cQtXcTZBsCENgOgfCs2U5a0kAAAhCAAAQgAIF9JbBYdOYePBuFnJ1IDaW1jaUtK6HGc4gqwUaVZDFfth3wYoJ8+Yn7SSX65JblkwACEHDff/xrs6UkfVYo5DpR0w4sm8GiqVFcXk7QhhR+i2G5AbvMIGXllLKywgiXYiO9goBgA2jshgAEIAABCEAAAhBYReCRm5fHbdqtW4UksTnDm66QNtsdcO22ZacNaNu2HkZpq8OtZGJ2RQlaFcuJqy6V3eTHvvHl8VUnYQMCR4yAOoFCXFv1cCRbBvtJJ90W7bhOOlb2hMBHXxkbqpQL3y2mqQkIJBLJnlXefqXnVzs2TRzSsLhoc+iF2LBbGJ5jyuc9VaTu4p2zZ7/7UZuWb08qSCEQgMCJIqD/DAQIQAACEIAABCDQEwSWy25QjZxgyFGlZLzZ/9BuiNmJlkrJ4MCti35k9f6flzNA4GgT+ItPfW2qbzm6oJ9pZwTMHl1SbMaqSsM9W/vM1Yk9KpJiIAABCEAAAhCAAASOMYFHb37phTv9rYmWdaxJQJCN3l3pdMtE5CsA7IgFfa6kkbHcBAjeHbjchEsse6c/nfiVb4xPKjUBAr1AQAJsH60yWh5c0C+ELqX94v1Lf/Slp5NidKuYJgPyjRIEA93n0z033ZMPoR3estsiW5o8BBZtOoNwl/QctGkOqu8+VLqFkKCbJNsQgMBWBMKzZKt0HIcABCAAAQhAAAL7SkDz+i0VkgETUfugKQwkIOg0jjdZX1OxtjJ7xUikQtsFr0mc22H51ACrLFYQEeSwsAqBzQh8//FrU8Wl5OG+ZlQrm+Ui/IY73kDyv0etbxRMNFAwF5kVGz5xqhHVSsut4R985qvXN0rOfghAAAIQgAAEIAABCAQCj90cG793yl2U6/YgIJCYoGlKgEykHtqEK++jKxMYZMdCurJ1wFVamVcCTbWnkb0L5WT0V175LYQEATjLQyTQrB3GyQ9WrHAYV3i45/ylP/rCU0kxmfLeB8x+5Z9f9gwLz6XMppU9qyQM0BSgejbpWaWofLKBZc81224/u7St9T7zzrJ8Lr41iEeCw73RnB0CR4wAIoIjdsOoLgQgAAEIQOC4ElisnBmUclrzvSkE8UC2dR+fm3Varip2xZhULBarqw6xAQEIbEqgZlMbfODHbvi9c+mLFZt0UYNhOtF+03IPG6KEOquiCQc0v6YX8Fje99+Nph6sLT5c+8y1mU1PykEIQAACEIAABCAAAQgYgUdvfvGZt86kE3Lpreak72azdmDW+baVd7vQDgwi9qwTLg9W77Hq0Hv3lBv92CuXxvPHWIfAQRNYdIt1tZ22E/a64z9fXuY18nAEDdu59qOUpvrKWHW55CY1mEbPrRAlCtC+jkcCrbeDvgNqc4cpD4LnlXzebGBNll+ig2acVt8ZkJAA75uBI0sIQGBzAogINufDUQhAAAIQgAAEDohAs30eGWgUgohgvWWWIv+phlQ+5o/Z+gaNrq5UbEIAAvdB4PXPXau98eS10dJ8+sGoFb0Ypak3bmlagnyUt4HI9uWjeS2om3Vk6vRcOvzGZ69dmL1wvX4fVSErBCAAAQhAAAIQgMAJIWCdYdU7/e665gJX29E0qW65qE7/zJ23ROpqY+bbld1o8gZypVsu2DR35sGgYVHiAbU11VGnUb+LpWjiYy9fHuoug20IHBSB2ZHr9dSlNS/Mbp80//3Or++mTnnLil+334TKDKFTvnP110eu1cJ+lrsnkFbSW2kUDTT1/DExlJ473oNA2+OAFwZY8bofefuWBAZ69vnnlK0rj/ZpO8SmrSu/FyP4KkbVe/1nx/0qHxCAAAS2IGCvVAQIQAACEIAABCBw+ASa9laS90Jw+DWiBhCAwG4IzGaGpNHByYsDZ8+Wh6I4HjJbyMetrKpZPAayESupiQQiEw6kr6VpNHv3zvwNhAO7oU0eCEAAAhCAAAQgcLIJ3D3bN6kpC7J3zEwOkEiYbi+gQaC+HULK6fNpaXn9EF9bqKPWivchtfVl21g4G6sDbibbyycEDp6AfRdfs+9sVWdWx7MPYdneDIvsVxG27m+p30PLziORTpqks/dXGrlFYPAbX3zmnUj3MnvQhE+J7rcK2bNKIgGltCkO7P4kkYn4bT2IPTTNYCYgsPJMTCB3Lctld/Fnvzl284d4/9sKMcchcOIJICI48V8BAEAAAhCAAAR6hECzWYtKBZu/MmvspNbsyVyvrTX+ZA2klXqrIbs6tK08q3fmtjZujKlsCRoIEIDA/RFoiwJuWCmKBAhAAAIQgAAEIAABCOwpgX/8+58f/WEhGVq30HwHnDrOthFW2pUr6dU3J7G7bydaM1IddLZv6NGXnht947NfmdpGsSSBwJ4TiKJotpCmT+ubGtsXUh37IXSLZ1a+zVmKja0ha48rbybQsfO0C9K2zpfG0YvhnCx3R2Bweqw6X3IXfc/+7orwuTSVwcp9DgKC9p22518mSMhSZPfT9hWjccs8cx+nJSsEIHACCGz1P+MEIOASIQABCEAAAhDoBQIDi4v1vpaaTno9ObxXlIrNqzBQZ16/XvhOUAcIQAACEIAABCAAAQhAAAIbEVgux09vdGyv9kuvrlHXfuS1Ok4tKjTiZN/PnZ2JTwisJXDuzsJUuSXvbhp/vn9BlhmJEuSNQ1/9MKBDdpP3vN2Y2b8zn4yS507HQ41iVF17tTu3iUkEpah7FO5Tp9xVQqrsGxO5aOix6bGhThpWIAABCKxDYOdPo3UKYRcEIAABCEAAAhC4XwKa1+/MYlrTHJbBMCO3a9uKHeGBXm3Wxvx87FrfLJxbcPXvMa/fZog4BgEIQAACEIAABCAAAQhA4FAJaATvXNkNhakGNq1Md7uy02bsyqWOtlxnW/A+sFSQaGClM1WCgndOu6GPvXx5qKsENiFwIARkP+lfdjf1XdwqKEk+bpU+f1yd0hIMlO1EGvCh34REBQ/Op1PYTfKkdrduWJ9Z0+EfigrPrbC96XLlDq/YvzJRQXc23T8f7UDk4qe6j7MNAQhAIE9gG/9m8slZhwAEIAABCEAAAvtHoNyKXsuXHho3+X37vV5pMa/ffjOmfAhAAAIQgAAEIAABCEAAAvdD4M656KnFA5iGzrtuNwt6vrNWHasSFvx4wNEBdz83kbz3RSC+tzxxXwVsI7Pc4BcTxazjOWSJlpMrYZ3l7gg89rtj1ShKB/fXl8RGdbMbaj4slovR0xulYD8EIAABEUBEwPcAAhCAAAQgAIGeIVBoudnQOFVjNWipZbjpeCfYw9pK8d2t+m6lzOu3h4gpCgIQgAAEIAABCEAAAhCAwJ4TiJL0vPXlr+rc37OTtD0S2LhrG7GbeBfhEg6ENqnE7kXFlvvknp2TgiCwQwKvf+56LW7FX1+bLesgzjqntb69EHJ1pw42k4J95727fJdOzeK9sRvTjrcrhXhI9i8xPaww35cM2JQG1cM6P+eFAAR6nwAigt6/R9QQAhCAAAQgcHII3JubKrTiupTuakgV2ssAQC8uMtbsxQuMFxBYeaFBrHP4RnPMvH5iQYAABCAAAQhAAAIQgAAEINCrBNI4shG8+x/U9pRoQCG0HTWFgo9pNDg4fXEgO8onBA6ewNm7SxOVRlzTIIzgyVHfWcUwGEN2jq2C0gSRTGaLyb7jXjRggy807WTLlqVmVDu3gBeCrXhu53gcu8F19QOaxmCDsPGRDTJssXvZPKrUK/HQFsk4DAEInGACe/3cOcEouXQIQAACEIAABO6XwMyF63VTt9+UGltz7pVamZAglFv2c/FtrdRWQzcfQ/6wDAICuaQMDWUda8bp1Oso6gMmlhCAAAQgAAEIQAACEIAABHqOgDrurS03EDr111ZQJu98XJsi2xPSqAs1H9tH2z18aluqg1ZLBS0zEYHNF+8qiAgyLHweAoHZkev1c3PJSCFJ6/peeluK2U30/dTvIz8NR6ie0oXvsvbpm58FDeaIXdkUA33tqIEd+tpLRLBcsGNLyYXvPXGt1s7A4j4ILBWijy/ZlCzZPdJdCM8jW80JCXRPfGyfK9y/jZYrVcrK66SzOy3vKk6eVnLBbnE1t8kqBCAAgVUE9CQhQAACEIAABCAAgZ4h8MA7rQk1WBXU2AmjPrStDv/VzR3t3VnICwiUU9s6nRrZ76mjqN8ZTVJDAAIQgAAEIAABCEAAAhA4WAJFd2b9Ebz7UA21SRXCMmp3xPnuOX+sWPUJ+IDAIRF4Y+Rrs++ZKz5bMcOGmTeyDud2x/NOqhSEMiviHOtytgLVya1jA/PplT9/8trMTsok7cYEbBBLtRXLwnW/Vq6Nz7HhES8kSPz9NTvbL2yYjgMQgMCJJ5BZ6E88BgBAAAIQgAAEINArBF7/3LVaXzP9ulzlhcarGqxquM6XnFuw2FTL+D6CylrtgcC5U03m9bsPpGSFAAQgAAEIQAACEIAABCBwoATCCNv9PmkYvRulh9DZt98XR/nHgsAbn706dWaxeCFOijbtgL6xsQ2UiF3RjB8axe5Htodl7orDN1qdRPJioCAX94s2Qr5hO2WX0bJ/2T37X37j6oRPwMceEQj096i4XRXTC3XYVcXJBAEIHBAB/X8gQAACEIAABCAAgZ4i0GjOTyRRVFPjV539avSqAewbv3tUUwkUVLYaxc0orUWL7soeFU0xEIAABCAAAQhAAAIQgAAEIHCMCHjBgl1P8EhwjC6NSzkmBN747FemHriXPhy3MluKhAT2jW3H3EUGMYEtMxuLpdE+C7KTKDbj2DVMTGDTJNTO3UuH/8s//+3ruRJYPVYEEBIcq9vJxUBgjwmYpowAAQhAAAIQgAAEeouA5vWrvnppxObcuxVFbqDUsrkmbV4/haxBm01BkO2xT+9GsrO1ZtvawJ3gjT+2Q4NI1ChWSBruwuxI787rNzh5caBSqQwUi5mrzGKa1N3cfG3mwvV6dgV8QgACEIAABCAAAQhAAAIQOBkEmq5ZMwfrnYtV92fS3SbsHM1E6bnNldWuucFXDrAGgaNJ4I2Rq7OD02PDc+XyRBqlT0tEEJvxI5MIrL6mvCBGdhYNsFDwCxMV2GCLqXRu4dn/bvaZ7Aif+0Og++50b2/Vyb86ff6+qr66t/mQH5zTTvuT/HHWIQABCOQJICLI02AdAhCAAAQgAIGeIVB7/OrsR1699GyzEE9as8eP+ChY26llivimnzdu91XV9AhqGasxVV6OrvzNcO/N61f95uWh2LXOuzh66q5Lq3etyknUspEANhrA6h8PnHK/9OpYLXXxTDFNb/63X//tG7snQk4IQAACEIAABCAAAQhAAAJHhcCidWqe6pHKStBAgEDvEGgPkBh9bHpiolFMJubL8fnlohvorqHsImlbSCMTiaZ97GvG9QcX0hunGq0Xvz3Se3aS7ms4yttpFM3aHaiGa/B2KtsIU2+G7XD8fpeyf3ULDFSm1aN2v2WTHwIQOL4EunRIx/dCubIjS0DvMPnAdzZPg3UIQAACJ4DAh791aTRK00k1dtR5roaPRARNL7bOFNmrdddrG0b5xpd1uvsyGiZGsHn+nn3rH/WWW75Hbl4eX6ikFxtxMuA6I2MSX2c1JoOKXFee2nQPEhWUrbXf10xrfY34iuZCPAFfCy4RAhCAAAQgAAEIbJcAdoXtkiIdBI4QgZ+/NXa7lFgHnLWZ1EZav2WYXZBmiN88dB/PWpihwy3rbG23xXIu4Fsurtc+9ZUHNy+boxA4XAKD0xcH6v1nhuKCG7Lv8set07hqNWrbG9K6eSCot1L3mn21Z0v1+Rs1PA8cyA3732+OXf/rB6Jn9HzSM0bPGz3Lgt1H237/Fs+v8JzaqNJJ+xmpclVe0aYKVdC5YluPlqPhHzyBYGQjfuyHwEknYI8KAgR6moD9a1sV+M6uwsEGBCAAgZNB4COvfn6wUSxMF5OoqitumYgg60xfMfZ0CwmUTkENKnW021o7jwxMUS1edhd6qaH0gVuXh8oumiwkSbXg3Q2q0u3ra4sJ5F5Q160RAtmRTERQsoafrlPCiCiNauVld+V7TyAm0F0nQAACEIAABCBw4glgVzjxXwEAHEcCP/fvx6bKqblrP0wRQRrN1D79/PBx5Ms1QQAC+0vgkemxob99KLpVNOOOppzQoBm9sCy1fYdrWk/ZeZItPHFuJSKIrGwJCMJ0niUzKMlGJtuS1vvmlh7UlKL7e7WUDgEIHFUCG9nbj+r1UG8IQAACEIAABI4hge8//rXZxcgNL8etFxvW2lFjR40gjcTPYtapro51dbQ37A1HsWXHncnpC9bJruhV1q1oKrmz+HAvCQg+9M3L48UovVVIm1WbrMHk4U27Rl1nO+q6cg1HNQL9lAZtKYGO6XqV3uY9rCbFdPLvv3Jp/Bh+FbgkCEAAAhCAAAQgAAEIQAAC1vaJZjNheQbDmkO+w00dahrZm48b41ILUtHn3jCZvNmpXan2ZOiwUy4XRS9umIkDEIAABDYh0HSLs/akquuZIu+SWed+9ozJ9ml/W0hg5YSnVXeReg7mY/64PA+ULKPsRwoqQ/uCjezcYjSDgMCj4QMCENiAgN6QCBCAAAQgAAEIQKDnCfxo+Frtr/7p74w+WI8+WGyYsSaN6zLkZCGICYLxJyzVmIolJqgXTTxw7l46/P3Hr13oJfd8P3PruReaxXRC3gdCDM3DTCghe5U3UfmGoa7XG8as4afGnx95Y4tMQJAt1YBcKqcTP3fr0qTSEyAAAQhAAAIQgAAEIAABCBwnAqX6wlQxSet5IcF+XV84hzWzrA1msm9rY6pjbuBOY2a/zkm5EIDA8SagzvtmHM0Gu5bsPHrGhCB7UHj2hH07WSqvygieLDOrkkQKmR1N4qhCK35xJ2WSFgIQOHkE7DFCgEBPE1D3SD7wnc3TYB0CEIDACSYwcOviQL87NRS5dMgwfNxiNXLRgJcPpKm5YovqzTR9LXLxbOFOb87r9/PfujzeMgFB0dTlpSQTEUhAINFAEAXkb7HG0+RD97a8MmRz3ClV7JYKplxvRlN/+emrF/L5WIcABCAAAQhAAAIniAB2hRN0s7nUk0Xgf/t3X5iq96dPey9t+qUHkbl5aMsHta02D6GdFdLZdijLMqoTTu2sPmu3qQ22UIrde++lU//fb9DO2pwrRyEAgc0IDPzJ5aEzrfRWxRxSyiakjv9Fm86g2X7myKOKnjoh+IEkYaO9DE+vsFtiBJWjXBIRhIEneo7JA0HFCteyEUe1999Jhl8fuVYLeVlCAAIQ6CagxwgBAr1MgMZ+L98d6gYBCEAAArsm8LO3Lj+Txul1FVC0xqJi1GkcWnPPT02wuvhu0UD3dhARqNGo4Kdz0EoSTfzVp65e0SoBAhCAAAQgAAEInDAC2BV2eMM/+spYdbGdp2Ir36ODYYcESX5QBAanL1bffE/ptkTZRf3SQ8d/l2hgZyIC62kL5ViRYTSvOu/kfUBtrMVi7H7q7dYHZ/ltHNSt5jwQOLYEPvzNy7fMPcCQzdxpwaaqtMEgmqbTb9lyKyHBeiKCkNcLCTRIJSvOeyHQtAkqtZVEV2qfuTrRPsQCAhCAwLoEEBGsi4WdPUSAxn4P3QyqAgEIQAACe0NgcHqs+s5A4bZXkZuBSypzGbbUONQ+LQsmKuhuDHbv6d72KnOroi+jY/iy0uyN7+fq6fC3R67N7M0VUAoEIAABCEAAAhA4MgSwK2xxq4Z+b2zIehbON6wTY6kUDS6UnLtXtndSe588vex8PNVMZ2x6sNlFl9789m/yTrkFUg4fIIH3/cnnr/c33TPq4FdLSiHtEhGoYy4f1D5aHbpaXp22VJYqpJdYWyKC5Sia+tEQXghWM2QLAhDYDYFHpi8PvXs2uhXETpmXyqwkPXuWTV3Qak9xqb3ejpQ7UdfTy/53ZwdDukyEkD0FNfCkEZmrAxfV4sVkuPYEXghyKFmFAATWIdD9jFknCbsgAAEIQAACEIAABPaSQFQsTKpBp8Zh0exZTWsQ+nnq1hizdnZWlRkaivJkoMZjnGbnMDX7+M5KIzUEIAABCEAAAhCAwHEm8JFXLz3z0T8au/3mufTWj88lF986kw7O9WkUpE2vZe+SSdR0rULTLZSb7s0zraG/Hmhe/LsH01u/9MdfvP3Lr3x+9Diz4dqODoHTbzUmbJqB2n7WOGtXSfhtbbhWWht4N7myn+ejbAhA4OQQ+M7I8zMDC9HXSzbFQAh65pTkYcXsRUEEEI7tZKly5OHAR1sPwURXVxAQBBosIQCBzQjcp6l6s6I5BoE9IZD79+bL4zu7J1gpBAIQgAAEDovAP56+NPpOfzS5ZK04zd0pAYFGesmLwOmGPBC0R86sGT0j5fhKo1L1797WPqVQQ1FBx7WuhmefuTvob6QXXv/stSl/kA8IQAACEIAABCBwMghgV+i6z49Mjw3d63eTjWJalfBUb43qHFWUhywFvaeGjgdtB8Gr3lm1v2ydG32N2EYyNoZnR67XlIYAgcMi8Oj05wffMTFM5AoDqkPmiUDf7VzIta9WH1ndxspaVLl8thpb555+Hzr2wFw6PPsk3jhWE2ILAhC4XwKPvHz5u+/2p4MaGBI6/xOzFy0XTdC3mSeCrrecYA9SfVRWwZ5f+t/t/4/bM6yVRlf+6lPXJu63vuSHAAROBoHut6STcdVcJQQgAAEIQAACEDgkAu+eip5udBqAmfkqNBD90jwHdLwJ7KCOXjxg6fMNxnz2JfNY92a/ezq/j3UIQAACEIAABCAAgZNF4LGbY+N3zkS3JCCQmLXZFg6oc0GdFNonl8qRvZNq9KP25aOOKaqTdrHcrN59oHT7E3/whYsniyJX22sE3hj52uz75qJnK03VTG2sFZlAJpCx7+2aI0qr0JU222lKBP0C7IdhS+kH+huxe89cegUBQQDEEgIQ2EsCxWZzpOXSWst3+Nszy/7/5qdmCc8wPbPkcdJ7nfTLzA6U2YJi+7+excSWmr7A/3/XNAaxPc+S9EUEBHt51ygLAsefgN6BCBDoZQJdWjr/3t7L9aVuEIAABCAAgQ0JPPa7Y9W33hfdlgo8GKtk1Aqqco34UkPQH93FW5rmrc0Hb/SyHdn+zAneT92Jhl83d3n5dKxDAAIQgAAEIACBY0wAu0L75n7iD744ea/iRpcK2Y5850RknRLZO2p2TN4GOuJUCQe025bqUFUo+HfWbHRjuVl0pxcLE9958toVf5APCBwSgV+b/vzo22fcZNO+wJnngLBsV0jf4e7gv9PZ97plHW32zfduxLVUZ56aZX0mTnjPvfjZ/zTy1evd2dmGAAQgsFcEPnBrrGqOKm+VkrQqj5L6P60phkzD1LEbyROQpsbU/+io/fzS/2/Zf/QMW27/j5cHAtmYFPQ8XIrTF3/0yWujfgcfEIAABLZJIHtD2mZikkEAAhCAAAQgAAEI7J5AVCk81bAGXTaCK2vIqTTvLtY37jIjV2bw2v15VufMRtBk5jJrUBbdU6uPswUBCEAAAhCAAAQgcNwJfOLf/ZsX/u5sMjpfylwa5wUE/tqtIyJSh4OPOQGBDtox37UaltplHRWhjGXr4DAXzBOPvHxp3JfFBwQOicB/HPna1Ll77uFS06ba0PfVYtG+00FU7UUwti8sNU2Bgnf1bd9pdcCl2bfd51EHXV8jqj10Lx1GQHBIN5XTQuAEEfjR8LXaQ/XFhx+ad18vmRFHtqEwOETCJgV5FvBeBdrbfqc9q7RPUR4IbFJLi9kzTc81+//+LAICT4oPCEBghwSyJ88OM5EcAhCAAAQgAAEIQGDnBJrF1nl5HfAiAY2CCXHnRa2bwzcw1chsx/US3Su7T663n30QgAAEIAABCEAAAseTwGM3/834vXLUNeWATILrRHWwrtlvu3Idr37do1oxK8rt8t1KOvGrN3/raX+IDwgcEgFNbVCZKwzHzehFeckoW4+bxDESExSTYnu5Ii6I06J10hVNQGBL62xTlOigLaiZ6p+bf/j1kWszh3Q5nBYCEDhhBGZHrtffePL/unhqKbpgUxPUJNrTM6lkzzItF4uxWyjFbqkg0VPRC5+0rn065p935q3AiwhcNLPg0odrn8KLygn7GnG5ENgzAmZiJkCgpwn4cZm5GvKdzcFgFQIQgAAEjhaBj/+7L7379ulkoCDXc+tWPfMXsO6hbe3sKlXGXp3JL1WAGp2mXL9798GaNUy3VSSJIAABCEAAAhCAwNEmcKLtCo9NX6y+c7Z0W1MYaPRieB/M39IoRyhMYZBNhJVP1fWemncLn3vnTNNy/dzdwsOzIxO1fG7WIXAYBIZsOrmlfjdxpxKdXyy6gawOq7/LEg1opK5i034jp5dd/f134xuVZuvFmd98fuYw6s05IQABCAQCj740NrpcjJ5ZLCWDC+ZNaM7mO9D/bXtWeWGB0skDwVxZogLb33DuVCOdKS25K3/+JM+wwJElBCCwOwJ0yO6OG7kOjkCuKetPynf24NhzJghAAAIQ2EMC1emLA8Vzfe82bJSWt9+uKnu1Ict3/K863r3Rnb77ePsMOYNuliLbny43Plh74lqtOxfbEIAABCAAAQhA4BgSONF2hb//h1+8vVROqk2zpshle3jPzAsH/N4uSluKCHJfFJUV3MVrRHehWZz57//synAuCasQOFQCg9YWe/vsmaEocoofty64qlWoLSpwdZfE9dRFr5mQYPZ9d5IbJoJBcG2ACBCAQO8QGJweq759Lh5qxfF5+3c+0NeKqkUzDbXMwmQiwdlGIfqfVtvZubq7UecZ1js3jppA4IgT8M2HI34NVP94E+hqxjq+s8f7fnN1EIAABI4tgffeGhvqj9JbUXq4IgKNLosa6fAPnkCRfmy/bFwYBCAAAQhAAAJ5AifWrvCRV8dGk2JrckVAICyZqHRTEYF5GQgeCVZAbi5i1dzx2VzyRWful12rVRj+4WcmZlbyswYBCEAAAhCAAAQgAAEIHCUC7WFqR6nK1BUCEIAABCAAAQgcPQJFq3JiBtnUPBFoaVu5ePSuhxpDAAIQgAAEIAABCPQ2gUKajvt3Tr17+vfP9erbficNada8p4Z31vXyZvsSG+5hruJ9lNet1MpolnRuAgQgAAEIQAACEIAABCBwVAkgIjiqd456QwACEIAABCBwpAgUm3LzevhV1qgzCRoIEIAABCAAAQhAAALHl8A/+r2xIROvVrMrlBBgJXR7IVg5orXVaVcfW39LIgI/VUIQIlgZBZcMyYX8+jnYCwEIQAACEIAABCAAAQj0OgFEBL1+h6gfBCAAAQhAAALHgsCZpqsVNhQR6JUsH7e65Hza9da3ys9xCEAAAhCAAAQgAIHjTKBRdk8tm3K0aa+K6uCXcCBEXbfErT5q3YPIPA6E/X6X/wjvmmFP2F5ZFpPYlVuJj5q6S14PimnTnXKnR0MulhCAAAQgAAEIQAACEIDA0SKQtROOVp2pLQQgAAEIQAACEDhyBD7wo8V62bwR7Hxs115davbaVzIj70C9WNurUikHAhCAAAQgAAEIQKD3CCyWkk+2bGoBRXkK2JuwvhlR4oSSveQqFk1EUDABgd56F8rNob05L6VAAAIQgAAEIAABCEAAAgdNYP23/4OuBeeDAAQgAAEIQAACx5zAzIXr9VONuJZG+dcvree39wtCOE/s+pei+uufm6jt15koFwIQgAAEIAABCEDg8AkslKNBde63TECgKG8E/o2w7YFgdQ2DzFWCg3w0KUB7u/POmlopiu2gc8h7QcGKUCwmid9OLd+dSvrJkI4lBCAAAQhAAAIQgAAEIHC0CKy89R+telNbCEAAAhCAAAQgcOQIVBrRa1GX4XXfLiJn3O2cw/aVW262s80KBCAAAQhAAAIQgMCxI/Azr14aXLSpDCQeCEGd/fsV5OmgZRbGEMN5moVooDp9cSBss4QABCAAAQhAAAIQgAAEjg4BRARH515RUwhAAAIQgAAEjjiBUrMw22feXde8gHULC7q3d3rdHQGBnamznhUSJfGLOy2O9BCAAAQgAAEIQAACR4fAUikemCs7t1gy7wAmHqjo/dOW68XsqsLbqZb5qKNhO0vZ+Wy/r0o40LAo0cK8zWewWIhdo9Ce3qBlqSsVRAQdaKxAAAIQgAAEIAABCEDg6BBQS4AAAQhAAAIQgAAEIHAABAp35qYKSVyXATczyB7AScMpvJggdslyMhN2sYQABCAAAQhAAAIQOH4EokJUlZA0TmKbYsBiW0Cw11cqDwRhIoRQtgQELZu+S+ctWnRFVw3HWEIAAhCAAAQgAAEIQAACR4cAIoKjc6+oKQQgAAEIQAACR5zAzIXrdTPm3vRTGuha2iO4wqiw/bu87JUvdenU65+bqO3feSgZAhCAAAQgAAEIQOCwCVQazp1etmjLkrwB7CB4YYDEASFukte/w9pxc0DgTtm5gseDkKVo+4vmBYEAAQhAAAIQgAAEIAABCBw9AogIjt49o8YQgAAEIAABCBxhAgPzrQkZcxNzDRvmjZWRdi9DMPqGMjVCrGznHLibXAn7WEIAAhCAAAQgAAEIHE8ChSSt6d1P75wy/MUmXI28J6zdX+9G76uZkCBxfS1Fmz6h7ZogvI+ahqC2+7OSEwIQgAAEIAABCEAAAhA4LAKICA6LPOeFAAQgAAEIQOBEEnh95Fqtfyn6emrCgSVz96oYjLJyOavoIrO+Km4QgueCzrKdT9vK1TTrbdOKCcZbiRVOL6VTsyN4IdgAKbshAAEIQAACEIDA8SHQbNb0Xrg6bG4ClMBVccVTlr2XakqEXCH+3TJOXGIx/74qgYKiBARBSNCyd93FguVeXKznimAVAhCAAAQgAAEIQAACEDgiBPJtgSNSZaoJAQhAAAIQgAAEjjaBVnNxwqVRLbX5YiUmUAxCgvu9Mr3ceUNuriCz59aSlruS28UqBCAAAQhAAAIQgMAxJVB74lqtlMT1xN41IwlU9zUka95ng9eDYiudrY1cR0Swr/wpHAIQgAAEIAABCEAAAvtDYL9bEvtTa0qFAAQgAAEIQAACR5jArBlT40Y8Umq6esEMu8FjQBASBA8Du7lE5dV8tIreha1tx83oAl4IdkOTPBCAAAQgAAEIQOBoEigvu1l7DfSeBMIUA9u5EolbFeTdKh/9zg0+lK5hFsbltpctecEq2MkfXHT/c4Ms7IYABCAAAQhAAAIQgAAEepwAIoIev0FUDwIQgAAEIACB40mg9vjV2f7l6Nlg1JWxVTFMQ7DtqzY3s53g57uNzZWsoua/NZeyy9GVH35mYqaThhUIQAACEIAABCAAgWNP4HQzfU0d+Qp+moJsdVufQUiwrcRK1JmKS3KCzMuWpkXoa7gbfgcfEIAABCAAAQhAAAIQgMCRI5CzOh+5ulNhCEAAAhCAAAQgcKQJfP/xq1Pm5vWCjKwtcze7WMxGcGkO2cwrQRj/1XWZEg6E6A/plS6LqS2bcVZe08XPfu+J5yd8Ej4gAAEIQAACEIAABE4MgeJiMlUx9wB6x1wu2BRaelfU+2N3sH2ZyGDlfTK8V65edmXMlSXhqjxglS32WZSgVeLYRpzMdOViEwIQgAAEIAABCEAAAhA4IgTWaT0ckZpTTQhAAAIQgAAEIHAMCNQ+dW3q1HL0cJSktahjjA2vaMGYm7vQTpqwL6QJS7MPu7RWbKTDf/mZr14PqVhCAAIQgAAEIAABCJwcAjOfu1ZruXSmYeLShk0zEKbN2h2BzMPAmrz+vTR7b5VoIHgwiExU0EqjqddHrtXW5GEHBCAAAQhAAAIQgAAEIHAkCGRv+keiqlQSAhCAAAQgAAEIHE8C5pFgtrzghvsa8Ytls8D6KQ7yYgGth+gR6BUuxDaTdvrUDLbJncWHv/fEtZn2ERYQgAAEIAABCEAAAieQwFzRXZGIQF4INhQRaCoC80Wwedwcns5xr+zcXYtN83yg99Tl5eTK5rk4CgEIQAACEIAABCAAAQj0MgFzlkuAQE8TaM/g16kj39kOClYgAAEIQOA4EhicHqsu9ruJRiE6H0XJgL/GvKDA75BxdmW0V5rG9XIzuXFmIXrxOyPPz/iDfEAAAhCAAAQgAAEIiMCJtit8+JuXbzWK6VAhzYQCQUxQbDsXSOJspb3pnBcVrP7iaLoCTVGgYDMk+BDb+6n2K7+m4mq1y1G6vuXC1Pcfv3YhS8knBCAAAQjcD4HB6YsDd/vLQ7GLPumieMhFrmqPYm8r0PPXhFz1ZuRmUxe99v57bmb2SWwC98N7p3kfmx4bers/PR/H0aDdg0G7JR07jv3PrTcKrdlGlM62XHTzR8MM9tgpX9JDAAKHS8CeaQQI9DSBE93Y7+k7Q+UgAAEIQGBfCVTNUFDuP2MGXzdk/ww/bhbdahTFvjGauKSeRGm9Fcev2TQIs625uzdqI9fr+1ohCocABCAAAQhAAAJHk8CJtitY50b1ndOF71pn/4CmNVg2l1fq/C+3TBhgy7QtGkhWiQckKWirBWxNXrIqTQkFnJszbwMt8zZQtA2VozIFuNgWGWharf66G/4eUxkczV8LtYYABHqGwOCk2QQeqjxT748uNgrtAQbt2un5qxCEYdkydhVTeg0spLVys3DlT/7lV6Z8Ij72nICEHcVi/zPNOL24UHYDzbaQzp8o9/9U98X+6/rpfrTe13S1BxbcjXIj+jpT/uz5baFACEBgHwggItgHqBS5pwTar0SdMvnOdlCwAgEIQAAC+0VADcJ6pTLQLBarOkeaJvXS3HyNjvr9Ik65EIAABCAAAQhAYN8InHi7wq/9/nOjd04nk3f7EhMRqNMp8aIAeSOI/FQGmZggdEZ13wmJCEoWvYigZFMW2PQF6qhSUHkSHJSbRV/mqUbr4dnfuDqrvQQIQAACENgdgV/85th4EkcX08gN6JltSwv2ILYQdf1XC5KvuC3mirwnQ01lk9YemndX3vjs1SmfkY89IfAr37g0+uMz0QsllwzoXmSefkLR2T3Ke/XRlEJrQhrX7D/v1/9q+HeurznGDghAAAI9RIAO2R66GVRlXQJdr0XWviVAAAIQgAAE9oHAY9OXhwouOt+MW0/N9UVVjdKSIVWjq2Qo1WirB5ZcrW8pmUnS6OZ/Hnn+xj5UgyIhAAEIQAACEIAABPaWAHYF4/nY9KWJN89F40v2jqvOKAkD1PEROj+0T+++sZ/2QDdgpdND+zVdgdxm671YI2BLUhRYWCrY0jqstP3eueTZ2X/xVTpEPBk+IAABCOycgLzH/KQ/mp7rSwb1zFXQYuXZvCL+8gf9Ma1lz2Y9j302LyTIntWnltOJP33y6pWQnuXuCGiwSVw6/cLb/W502VR4+j8am7BDQgL9X1zt0cfO0fZIkAlA1jmn/nc245mzd6MLs3jvWQcQuyAAgV4g0P5X1AtVoQ4QWJcAjf11sbATAhCAAAT2ikD1W5fHm6bwtwbegNy0qnEuQ6lcu2qpKGOpmodB8S+baZqmNWs0XvnrYVT9e3UvKAcCEIAABCAAAQjsAwHsCm2ov3rz0oS5xR5fLAbK1vkhR8si1O7sCC6yQwotba5t17QX36a9A8fW6aHRrrbLiwdsei3zQhC7cwvuwp/9Bq6zhYUAAQhAYDcEfm360uCPzybTzUJalc1BIgI9k4PYK5QZPMiE7fDc1vNZIfNEYCvtbd+JnUY33r47f6HONIie0U4/PnBrrFp2xWmDOihRnUL4/xnsRMFTRKfsNSKCtpeCTgIZlhQLtYdsGiCEBB0wrEAAAj1EwL/z91B9qAoEugnQ2O8mwjYEIAABCOwJgUfM88BCJZlcLKfVeXPLqgbf6UbWSJ+zbXkgUGNQbl4VJSLQPyWbT1YbvjGv45VGVDu9GF/5U1wEei58QAACEIAABCAAgR4jgF0hd0Mefem5URtF+UKz4AbUESLBrES0xUTRErY7PULnk7Lq/dePsLTOjjTSuMtsfmcdKyTF2kP30guzTz4/o20CBCAAAQjsnID3QHA6+u58KRnIcrftDu2itBVGtG8lIsg/v9X5U2rZc9ye3c0onfmLT18bbhfJYpsE5IHg3Qf6v9uKo6rsRgX/VhGmmMgKyYQE+u+YD2GASn5f17oXesjzZaFWvpMMfw+PBF2A2IQABA6bwOrn2mHXhvNDAAIQgAAEIACBAyBgo7DG62dbtxrFtKpGoDWnrSGYxciMqFmj3Fy22puSRlzJoFoyw+pKY11NeDO0WtpWoVX9yenW5CMvj40fQNU5BQQgAAEIQAACEIAABHZN4I3PfmXq3N3Gw5Xl6MVI82dbB4Y6lySY9cGPimyv+32Z54EwwjXfOVVqRFMP1e8+jIBg17eDjBCAAARsupmx6ptn3a25cmLiLgGRrUE2iGzwQkC0MuI97Nl6KS8Fil4olkZDj7z0xRe2zkWKPIFyoX+ymEZV2YSCgCB/PKyH/5xhW0ux3+q+6XgzTqvvPhTfqppgIZ+fdQhAAAKHTWC9Z9th14nzQwACEIAABCAAgX0j8E9+/4svmPeBCU1XsGTqgKaNrCq3mq6/mYkCJAzoN48Ep7xXAmu1azSWxYJGZ9kxjdTyrgUtXyZA0PGmu1tpTTz6B/96ct8qTsEQgAAEIAABCEAAAhDYAwJymfw/fv3fjlYW0+G+RnqjrCm9JChoiwa6lxIZNGzaguVCbO/P6uyIZ6LldLj26as2j/P1+h5UiSIgAAEInFgCcxU3bj3N1YY9hv0UBkZio47njfZvBS94JGiU4ov/4OUvPbVVeo5nBP7J718aXypGT0mEoekv+ywqyCYkTz4Ku70nIa+EBhIn9CVRNT5zatwXygcEIACBHiHQftT1SG2oBgTWErB/oasC39lVONiAAAQgAIGdEPgn018Yf6c/nZABVA30hs3tqgZbuSVFebYejKbLNp1BNs2Bc6eX5ao1aygqn+aEVUNRjUjb9A197wrWRgy892489f989v++sJN6kRYCEIAABCAAAQhAYN8IYFfYAu1jvztWjculoThunTdY1chFVXPBNdD2OlA3CW3N3ptnbbqv19zy4g2EA1sA5TAEIACBbRL46Ktjo0mcTsrOIBuEQlkj3jVmwYKmMJCtob2Z7dRAh1yQTUNhPY8xMqSrLHlXjPyUNM4tFuL62btzH+RZ7rFt+KH/jfceLNxemfons/1oMIpEBHkvA/4edN2XcNd0/8JUFN0nk0egUtsrUEvTBVnaVqs1/MPPXJvpTss2BCAAgcMgYPphAgQgAAEIQAACEDj+BB69+a+fqZu3gGV7+1EDXZMYhMZ2y0ZWpX4uuhUOaryVrGUosYDa5GokynuBLWy+OstrS9+gN/GBQtZodO7OqXj0Ey9fqv3pk1ev+AN8QAACEIAABCAAAQhAoIcJvP65azWr3lQ72oIAAQj0KgHNz15xFbk8r6qOLRfXG26+RoewaBy9sFxIx8304O0JGu2uINuCbA8rIbM/dLa7bBdhv2wYPoS87XRaaJpGTc+YmiXDSh6Y7++/aGknsgx8rkeg/mA83tCcEp2wsp6JPDL7kD8c2HfSakVWIwt2P723gvZ96QgK7MYohbcl+XuutLErumjc1mYsEiAAAQgcOoF1H2+HXisqAIEVAvYvdFXgO7sKBxsQgAAEILAdAmZoqd47V7idxk0vHrCJCaxRruZa1giUIj8L2XbYCnO+BrGBn9pgwxOuNChV/nvmouHvPIl6fENcHIAABCAAAQhAAAIHQwC7wsFw5iwQgMA+EXhk+vLQW2ei89bj+JR1BJunkHzb0wTx1rY1J3k1m3pk5n13Wzf/7Mnnb+xTVSh2Dwk8+tLY6Dun08lOkZ37unJ/dUxTyuwqtO0cHXGBL8S+L1nndv1Ofe6DdaakWRftB14Zq5YrhduyGa10RrTtReGton2/gr1oNeeVYjt3cx0RgaYxKJvCQ14k5IlAUeKDswut4dknn59ZKYU1CEAAAodDYJf/gQ6nspwVAhCAAAQgAAEI7IZA41TfpPT2zTjxUxh0lN+Z7jtXZNZg8+KBjrAgd1j7NortsiRIKNkIgoVSOp7LySoEIAABCEAAAhCAAAQgAAEIQGDbBB6b/vL4wy8/9+7d0+mtKE4urggIrFsyavoRzOrAjDQ1n3kmKLhk9F5/NP0PXnru9mPTz41u+0QkPBQCi6X0aU2rKA8BeWHIAVVm4Ny506MHdK4jd5pSpWD2nHBfbKn1fQj6/WqqCXk28NG2Zbe668+/DyekSAhAAAI7JICIYIfASA4BCEAAAhCAwNEi8OFvXRpdKiVDmo5Aim6vEu8o/PfwWtqiAz/FgUnVG0U39BGb33APz0BREIAABCAAAQhAAAIQgAAEIHDMCQy+fHno5289d/uHA+nEm2eTgUW5VFcb1rdjw1Ij1Ff2SzQvl+mauu+tM676tw+4yZ+xMn7lG8+NHnNcR/LyPmoj3W2qxaGDr3xbtGAnTmPzbkFYl0Dk0qH1DgSvA+sd280+eS9oFDR9ZuISRftN6xzNQjJYtalLdlMmeSAAAQjsJQFEBHtJk7IgAAEIQAACEOg5Aotx9PRcKXaLBZtbLold2dQEfj66LiX5XjUG1QhctkbgkkVbf7rngFAhCEAAAhCAAAQgAAEIQAACEOhJAo/cvDy+VE5vFZLEPAs0XSGVxwETC/j2axAQtLetw9ELCeyo2rjyiKcoYbtiOXHVpbKb/Ng3vjzekxd7gitlHcdDizYEfcmiOpBXj3RXl00+7gxUKE3fDd8pbd8diUx8tO+Jgr4vcZoO2dSPdFRnSDqfj05fGjRC1Y5oJ7DTb1Cd/O3tToauFdmW8valcCeDV8vI7FKKCi37nep7MFd2bqGYbcs7RV/LDbgzZ6weBAhAAAKHSyB7Wh1uHTg7BCAAAQhAAAIQ2BcCj02PVaPIDalwTTNQsIaaXMTlG3RZ43nvTq8Gu7weeENAnA5ZHYb2rnRKggAEIAABCEAAAhCAAAQgAIHjSODRm1964U5/a0JtSQkIOuIB67j0HZpa5kK2pc+V4zL2mwDBlVqZe3R1Ut7pTyd+5Rvjk7msrB42gTj9pOwGTYsaiHDgof1dqjg6qrvZRy6uhn3eXhQ29nEpsYd+9/63bz9n/xtO3VP7eEqKhgAEILAtAnqvIEAAAhCAAAQgAIFjSaCQFJ6qNBN3uuHc6eXMiKILlYhAjUHFdUPbSONHddi6dysn13JbRTX+fWM8c0Gnue36WzENv3UhsxMCEIAABCAAAQhAAAIQgAAEROCxm2Pj9065i2pzBgGBOhTNrXnb20BbLCDBQDtkI6K1kR3z7VdbL5uAoNLKvBIU294JFsrJ6K+88lsICQK8Q14WU1fV/cvsB5mQQGKC9eJ+VtXc9jPavQuwOZUcCr+pcH+6kmy6Ge5hSBR+uWE7eArRdsFsUqfMXtVvUeYk5ZW9SvsLSfpAyMMSAhCAwGERMCcpBAhAAAIQgAAEIHA8CSyXovNBMBCUk2mktbC19XWrwbez1Jk4ITKDgPI1C/Entz4LKSAAAQhAAAIQgAAEIACB40ZArsIXK5WBpitWm2aFbaRJ/X1z87XZkev143atXM/uCTx684vPvHUmnWiZyl3tV9/pKDG7ehUtqNPR2f71Q2ixahoD65a2fFn7daUVK/G8BAnvnnKjH3vlUu3Pn7h6Zf2y2HtgBNKk6gc2tN3a78TqsFUdV+78Vin1tYoGtk51slKYE8sDY6Lfu+5XoqV+4xa9kECrUVS1BQECEIDAoRJARHCo+Dk5BCAAAQhAAAL7SaB+KhpsmoxcQQ0zTWmgEBpl3YYYPxJAx30q+/BeBXLbYf+my8xoE9u5VM5PKm5QxkMMhZtC4yAEIAABCEAAAhCAAASOBYHHpi8PpXF0vhm5p+b7kmrDW1+tq86urmyfC6dPu1996VLN3M3PWMvh5n8aef7GsbhwLmJXBKytWL3T76432j2/aquGfmW/yzoV1dkcBAU6ie9szJ1N6UIbVumWrQ0c2bzq+aCRzUq0WIonPvby5df+/MnnZ/LHWT9YAiYqqnbuY9t2kFkt1tYj2CnWHtl4Tyg7/70JqcMx2UNil/5C2M+yTSB1a5h0mOUghX3rMc4lW7Oq33MI4feq7bBfNqymbadpVLUFAQIQgMChEkBEcKj4OTkEIAABCEAAAvtFYMA67peKSaYglyHGGmqxLHf7GjJDjXmctMa4fXgDTuIWbPSRnba+r6emcAhAAAIQgAAEIAABCEDg0Aj87K1L47GLLv6NSwei1BofbUGyKhQ6m7S+bNbYt0+7qq2OWjfRaPVbz9XM+/yV//Wpr0zZPsIJI3D3bN+kpizw3ga89D1ru6otGToVt4PECwnanZO+U7Pd9tV3T+1TBX0tl21j4Ww8bpsz2kc4HAIaeqB7o6j7FfxH7EVt8s+bUP6actsDLNbsZ8eBEtBPs1uEYAI0C/aLbv+GD7RCnAwCEIBAFwG9XxAgAAEIQAACEIDAsSNw5syZwXBRwfiSzRHZ3rtZo1kGP4vZXHW+WWeZVpapP6bj3dGS2T4JCHTO0HgvmvvS9llZQAACEIAABCAAAQhAAALHiMDgy2NDH/7jS7dtRO+EtRlMPJy1JfLth/XXMwg2DVrVxM6Tv/Tqc7cHX3pu9Bih4VK2IPCPf//zo0khGVo3mdqrIa6bYO1OtT+zNuhK21XT7BXMK4GWTZvSQB2W9jf0KN+1tQAPcI/dqppsBt5WYffZ2x7MkmATWqypRWZhkJUhi2sSbHOHvhUh6Czynhgn0f8M+1i2CaSxMfGEst+g7fZCD/vh5JfdvFZ+dauf+N3ptr2dxLVtpyUhBCAAgX0isPa/0j6diGIhAAEIQAACEIBALxDwDWcZY7YRgvhgG0nXJAkCgjUH2AEBCEAAAhCAAAQgAAEIHAsCg98YG3/nbHqrUWxVwwVZP5OFfHedbZrQOO+ZwCfxaUK3YNOZF7XqW+eti/KGAABAAElEQVSSycGXL49nx/k87gSWy/HT+32N1k/tWtb8VcxE8tkZG3Gy7+fe72s72uXHdd+JH2wTYekvanv2it1e///P3vt+x3Gc957VPT2DwQ9SI8a+duJra2QnXsd2IsixEx3fnPUgsuUrZ2URZ+/ePXlF8C8QmcgyTFOLQUTLTOxdUm/27DuCr3J2c/aAsiJLsqUAOntOwo0TC77r5Pg4cTSOf0SJLHFIAiAw0921z7eqa6ZnMPg9AwyAbx0Uqru6urr60901VU899VRaViELrdBiYhtIqcP7golW+mZb0bhLAiRAAntOoLe/SHt+O7wgCZAACZAACZAACWyfQHPGhj033Zn3TGceTaamR1xnL6nMApZsYm3/KfAMEiABEiABEiABEiABEjg4BD7y4hOXbg9GZU9Wr8b685jpDQ8FAtO/QJj4xl05ZQIXNtJDySCUgd5Y3RiJyh/7i3NXGudw41ASGJ2dLC7lVMktNbDhTaJPmvaNvmnbWY33ysZj1nQop67KGut1CaEkj+tBoeDtYVX66HPnS205cHfPCOjvNS8lD8Q4hO3bbj9JsoMgrTTgTndWELxYL7g4hpaAWIMQJsK9IQuSPfl22r2zStAzblrx2fQMLjMmARLYKoHd/wpt9UpMRwIkQAIkQAIkQAJ7SCAMw0qzA24v7IR5a2cBdb9gEBna67O51X26zJEESIAESIAESIAESIAE9o/Ah196YuoXI9GZeiaUgSUoAIg3CgFJmcz+FsrnBn0ltAoHoVmz/t9H9AQVCbbA7wAnuXXcO7kS9P4GYH3AWSJwV0O/GIoF/1ZQJ10cwz0moDMLdpB67XXl8ciyBt1zGOxe45IB8sXFgAPVbXAiJYP3DQUCp0zQlqjXu3J97UGZgY4ESIAE9pcApdr7y59XJwESIAESIAES6BGBlZWVajprM6SfaI+n47ey3a5xvtE5RhtdEkBYA1EiXKig0EBHAiRAAiRAAiRAAiRAAiRw0AmMPv/Fx1azujwQyVrz0uB3/Yzt3ZdTOrAhLBlAGSEnecJndKgW89EElkvYXr5MfVAIyAzwRzFQjAH+rrtEOQWKKfbdsrOo0UeFwyz0AD5Sn7Ix/L/nBGrRNXNNM1htr+6sA7gZ7ng/duucfAI1jZFRmHfAvnSS//zC6XKL3GS31zsM539nvIzBe+HS/DjdM0mHvbtXe93bt9V8767BnEmABEhgawSaNeHW0jMVCZAACZAACZAACRwIAtXxy9V8XVUyEI5IjxkCPmyjYw6lgN4427SCIAidS7hcqKqVhy9WzA7/kQAJkAAJkAAJkAAJkAAJHFgCMEH/9pB/OZS2vlMgaNyMsT4AZYBGzOYbKUsEWArBKC8jlDNX5QJvD+vy6HOPlzbPiCkOGgHte6N7UWa8S+gDw7k+qukbm/6xNzo6e6Zgj/L/XhKAjCAb+fPumUB84CYvoBwY8G+P2275zIB3kpdTIHF5yCx3FWnvqttn2Eog0tqySSl5tKbo3h6eTfr5YDsXefPVcSp4dI8ycyIBEtgpASvp3unZPI8ESIAESIAESIAE+pjAXXf0q1mxRQclgmxKkQBFjkVg5zrspnMtHbXGOpPJPfnSYYS3GuipEHGdPLJAZxw+aWUdX+UagwlOBiRAAiRAAiRAAiRAAiRwoAncHslccTcQS5tfuhjWoy8hznQBpJ/hBgNdmKSSFPaMRv/DnGUHkNKDSEiHvDCL/E4+M5UkY3BICGDgXvqLBdcfXXtbePppvzaFjXFpGm+iRGPbOrx/cAjTyvTYd0r2eZWnEoHFtOf/B2vqWcgOIFsIYl8mPeC7j5MB5VjiYpFjJPWJqRPs8c0K6uoXyDy0D7lHU/aB9wq5QBFqJeJM9/VYRn58rfnN2GfUnrbzV9eeaoP95PnItRqyKaRGvsMr6uoGZ/IQCZAACewZAbQ06EiABEiABEiABEjgUBLIhXoBHWhjGWCdO1xfcLPOCVuINp126ZhDoSAT++z8bYEZk5AACZAACZAACZAACZBAPxN47ytPTtQDVbJltINKRpEAg4Bdcq19Ewz1ydJovip94FuTE126BLPpAwKBGhlNxvd7Xpq0IgEuZixemGFkq1wgKvfFnheCF+hIIIyWZkRWUXXfvXtWGPSX7974SOQZsQwyb9fhDKeYhHrE5t2sq3J1b+aNh8uV7eZ7VNK/MXZhPkhZiujVfUNRARYzodSDyS94TvJXWfh8eaZX12S+JEACJLAdAsF2EjMtCZAACZAACRxmAp+YLY+K9ndJulX3wbSgVrqpka/9ijTuK7Ki4KuqVp+//gfsbB2Ed2HJX5pZyQxORRlVQMdsKLQzMLZbdteZx3mug79ZHuiwo8O/4sfzm6XlcRLoFYEHZs+XRBTxKU+hTlMtJlPlHa3I+7zgxd6rNayHSXOJvXoMzJcESIAESIAESOAwEPD1qTW3YayWrYndcoRVE9g4Ofofvq9w7ZmNU/LoQSPg+plb7WPu9P4w9xzXwIClvE07zYbndZnAgizB+MtzX572M+qSGCEwz8e9C5gIcSdrB5eNVUXz7LZeADzlWM7BO+bes3ry6P3Yrw7fUdNbz+1opszX6mdvDmVe0/L9yOfTE4dnA+uZojfSVPrQfDY9gc1MSYAEdkSgV/XfjgrDk0igA4H2JhLf2Q6QGEUCJLBzAqOz5UJ1RMmMEv2o9ApKeRlkhgaw67g1c27taHtazxeW1NXvjFM7uMmoP7feM/flmTgTnoLgJBfFxkQgTAZagZ19rq5T3bgD9OBSrv043o/WFKnEci6UB7AMQhD6M//86T85nTrKTRLoOQGYR10cGXlMe/qMvIoNZSg3E8UVoPkO2+8gUvHMiVvR9ML4xYpLw5AESIAESIAEDgEByhUOwUPc71t4YHay+PNC9nX0AsxArOkvoDXV3qKyJV3bf2im2969SDtN+hWwePDLVT12ffzi/PbOZ+p+JPBxUfT990I0B1P1GD2EaXnbVrftcjvYny556/tjU61/3CkKrH0P5Rx5n9ySfegTv+sW3qsL8+ncuL23BD7w7fNzq1ldgizKvA9ieQCWCODwDPGeYFLE9p08YTkP5+JZ1zK+yknGx5eDs9/9/FOXt5/f0Tvjo988f3klFz1WNw8H99/6Le6KiHz7sD5gPZaYEEuWkTfzD//5ImVIuwLLk0mABLpJYG2bo5u5My8SIAESIAES6GMCv/Xc+ZP1fPRaxosuhZmwFPqYpl5TGdXmdSidLolL+UDXS7WB+pX/NHvu9dKfTRb7+DaPfNGyK1F5uOYrKIhg8L+WseFGYEQzX6U9BC3tHoKXNV4yRVxeOubmenVveqPr8BgJdJvAJ2fPTemBwdezul4OdCgWOFrrrpZ9U9dJ/ZbUeVkVTtSHvNc/eW3yCuu1bj8Z5kcCJEACJEACJHCQCXgqcxIDu25wFwNz6QFaJ2Btj3f37OJd6OKbIXLo5O3SbOjD1ILgZDM9tw4ygVCFlXT58eTd++XesZbjbX3PRt80nYjbB5bAscXwtDzTaijfOZQHMKg8Iqbi4IfqmOgikWmZxDbvFMPeUFKBPGSwrmeoQLB1gN//3IUzIyveQqf6GUyt8s/W82tPiWeCSSjGSoRYP83RQkQ7Iu6TAAnsMwG0UehIgARIgARI4EgRKMgs3eLLX75yc1DP1oO4qLxQBWI7TgbcRFEA+tlhw0sXzigWII3zOI7tUDSRq0O6+OYJ7/UPvnRu6khBPEA3W3n4YmWgpp+xiuPWjGOzo9dFLfKEiRMmZsUKAa59gFCxqAeYQPGFyeKvv/Sl124OReVaNiy4+mr9EO8+vNRnxsu2zIRYDUJ1YziaeOOEnvvAS09QUH2A3wkWnQRIgARIgARIoHsEaoH3aCO3hhWCRkzPN2DafCWrPtXzC/ECe0RgpbpHF9rCZVoVGrZwApN0mQAswd21HI0FkS/vBYZrfGM+381SdzKG3VwWyikiE6lkVlbO7iafo3huvh6Pi7ZAxShyJM/HcujO0BrkU0GkKncveWO0CngU3zDeMwn0N4Hu1HT9fY8sHQmQAAmQAAk0CGCgrXB8SKwPxBORmIhbyYYKZslycagGQ5iIw4CaHVyLRTikJY31oYTWxxLiWGhMzMXmfLE5Vv7Ii1+81LgQN/qKQHZ1pSxLGFSgLAJTgK2zO+zgqVmEDgLBLXi7GAIUTqxP3yw0ycUMXSV7h1YI0ly43TsCv/atL4z6ufg1UR4YhRIA6radeNRpOK8mdWI9GxZVVs9+6KUnqCDVu0fHnEmABEiABEiABA4IgeWcN2r7iRsXGH2BrfiNc1l7FArRYu58FMtWrT3KmINGYGH8sgwW68qWy71eH3WLGbT2f5snybhyVZbI2Ho5mqdyq8sE/v73n144vqJPy6OuynMxM9NhlQAKRGtkFObaVm7l5FedQ1tIm4Ve8FZWZZAa7x7ddgjgG1ldjce0yHm0SIHwfBBa58JUjut9ry4+lRSbYmmiMrysxvkttoHhLgmQQF8Q6FDL9UW5WAgSIAESIAES6DqBd8/JsgMDai6jdBFKAPAYMEPoGwsEdtsNpLljLq0LYWrMDdDBGoHZljiZGXLmAy9/8UrXC84Md00AHeUTt+LxwVq2mo0CYx4QCgCNzjiUAfAOJN4969bQCQSbCiS2o54qnpgYxBqDhaXoNDXIU1y42TMCUCCoB95c5HsFYwpRWve2jrKmFZ2JRQifWgTabSXCu473GaFVkIIyjAivAq/8q9+enGpLzl0SIAESIAESIAESODIEijJwXwtiO3hv+o7NW98LwaqX9FHQNlvJ56lE0MR/oLe0Vq/u+w1ovbDvZWABGgT+v89duPaOanR/EOsK+m9GiUC+e/TjWh36bk3n+nmIgdUCLH8Aj+2cZFRY9q4O/zQcqzx8udI8i1vbIfCGWJk8Ua2P3X3Hm88KW/NMRP5jFXSEtWTW8MK93eFZOO+OYSnMgbpfOXG7Pvad8a/xW3RgGJIACfQVgb1o6/bVDbMwJEACJEACR5dATvmzGe0VQQANfi0CIKeRj84Z1iAza03KOnTYNgNxJp3VMrYax6J1LHHo0DWVDGznDnnIGnYTv/bSE7RIAMh95tApO744eDYb5hMlgmSJChk4xTIW8AGsFCQDqU5RpD107wmeNxxmBTmHTuFdy970wiNfn3dxDEmgVwQemJ0s1oPMbCwKBG6JDggiUL+JNQzj5a2WNzqQSqzNJyIOpG/3VvxhS20EVxmv/MGXvnSqV/fBfEmABEiABEiABEignwnk8/lR9BvTA0At21J4q465frj2/tCZSPu2FNJGs6azbbw070z/VRbiK7al5O4BJRB73oIZiEzKb94G957JG+Ws3hnl93Xv0b157l3qnNDMmka7PxlYRirTjfW8q53PYOx+EcBkhNwtNSaSiavo46XfkU5lsrIt1A94phjU9lVWBFYD0pHzo1z1+J3g7N888vWJhdO0QNCJ33bi8Gy+97kLY4MrmWmtgypYN/rSybflfhs6hVn56CA/cl9rJtTT+aX6/dfHqdyxnefAtCRAAntLAHUWHQmQAAmQAAkcegKjz5+bkm7VKG4Ug8ToXqU7Y03FAKsggGPuuOm+J/suviWU3HC+9WIKPKfPFF/mWuJg3W/ur/7Hp2buWsyezkYomRGbmCLi2cEFIrSxx+zzb3/OOANp7bvhun5WoAgN/7uXpIP+6MUy8qIjgV4TWM77c6HvFSFgNoJtEVy4mRDu2uYdTkSQTXGFfXch8HBxDeFHIghBnmKu0ZhplKVA1ErOv/xuWQ7G5cuQBEiABEiABEiABI4KASx4t98OA1JwoSxdRXc4CGSrd2ZkxnnV9i17e0/uGuj2YuATCscY0Czcqs/39srMfScEfiCD1T//1MWJX7nh3fuORX0V1g6NUpHpvyHHZj+uEd96rJoJM9PDi3fu/X8//9XLOykDz1mfwPd//4/Lx6r1+4PQuyrywiqeQSzfVFN2aJ8P9t2zwneHdPnQq77rZjzzrjfq9/7TZ75e5vIS63PmERIggf4gkIjM+6MwLAUJdCCQdJMaR/jONlBwgwRIYKsERmW27u3j3uurQSza2FAigKUBLEPQzKFdu99ZKHDCGtvwb6ZvDj+7OJeZO6Krd1fDe9khcHz6Kxx9/gujbw/XE8sUvrE8ASHKsZot5+2ctUrRWmr3jCF4kTdCOoBQOMD7JHYMKidu+af/dvzCfOs53COB3hD46Avnp24P6LJYPzEWNPAuunoMVlSwJEG7a77BckTe32b91prSKB5IHka5BsIOI/yQfaXmfzL2lbHW1NwjARIgARIggb4nQLlC3z+i/i6gLO1U0n5mLq2E7LbjpM3lWl5OOdn1Jxt3Jlbw0s4qc6Zj2rZNG0zikvMwGIXZ5DWlx94Yuzjflpq7B5TAb37ziZnqkD6VMf1LuYm25+5uyy495vY6ha6l794z2Xd5SXLIPvBODkifAe/SnayvZHB65nu///TpTrkxrr8IQKb11nFV8n11Sp6gTI7xCq6Ebsa7PN6KPP1nZTT7mlpcWajIko4uDcPeESjMnSkMx8dO+l5c8rz4Pqmz5fk0Hb47sT5Q8WTpEPl9ePX4rdoM5YRNPtwiARLofwJi15SOBEiABEiABA43gRsFbxZLF7gBM2nD28Gxnt62V8j5uTNyiXJPL8PMd0Rg4fe/tvCh2TNjaihbrmf1KQj7sEwBPN4TiF7cbA1zgZQAxgj8zEuEgVoRxKx6MyN3ls/+LTvpO3oWh+Gk0dly4Y28KgSBKmJymCikVIf/XVUWTpd7IrjBMgY/yXsTjUpNIEIwKOuqmvfWvrsSYd7kDoRT73OHo82otnTymZRGn5ssLTxCwXUTErdIgARIgARIgAQOO4EwVJWMKBl3cjAP7xQJ3PE1CgTuwE5DaZOhZQcXRBTlWhKH47+/WitHI9lTnlEI7tY9oWPg3pjWfi36CVA4RujV4uluXZH59JYAzOjLFWYSr0ZnzxTyKl+QfeOu2+Nul+EeEqiOXa5Kp39GLglvHJQ+kk0JVqpUGmjS4BYJkMDBIyBNBjoS6GsCGKZJO76zaRrcJgES2JTA785Oln5S8OZ8We8es3TRWcYAMQaN7UCbzQLHOgl7nOKBm4m73gWRu3V2C+ucDYiZsuxyjdYI1oPWJ/FFMdFeG8qU5Qfn0WwcF/BDA8GKnUXknmtSWBHGmPdG+1VJfy2oR1d/+pkL831yKyzGHhIo/fn5UhBnHl3KqZNvjsRFCc36hphFJEZPjB+s+5WhmpofrHnPvvI/l691q3i//ez5qTeO67Kps0RBCsostj6TCycKUziG+itdrzXe5kSo2KzfWktm6kl8CMY1zjLCyCBS8z/6DK0RODoMSYAESIAEDgQB+UVscY1fuZZY7pDAOgSKMmAXHBu+YQ+jN9l0UFa3bafW+FQLyiReM5M8NciLBJtZJsDxQLRGvaXoXpg6N5ny36Eg8M7/5wuXh0L1GCzjObkD3qtW17pv2/7pFG1vXNv75dKj/Y+lymqeN/NGiVYI0gS5TQIkQAIkQAIksJYAO05rmTCmvwiws99fz4OlIYEDR+B3rp278otjesITJYGGgEfuwg4Q24E3M9BmVAvW3l5zkK2tU96WtJMSAQQAEn/2nx/kGnRtuPpyF9r8mWCkJIUryXO/z9NeUUyGGu1+aTBBaUAUzL1XZf2ChXqkri2M92aWeV/CYaEaBIqvnJ/ytXcm0HEBykIQyN3JwhKALGoh++nGNUzOIi4vo/yx9ivL2Xj6Jw8+NdPIbIcbo8+fe/3GoFcUpRdTly2KAgMUXzKiLCXlMrl2T4kA2SX1nwgjUXfWlCdmdC/MmwvxHwmQAAmQAAn0PwHKFfr/GfV9CT/84rnXa4FXlNZfS1ndYC+sEaSPtPceu6FEMLzqV//b575yd0sBuHPgCUBJJXcs+5r0K4qu3e3eq+bNpd8up0DcPOrOa8S0KRG4ePRdgsirZG+rMSqjOCoMSYAESIAESIAE1iNAG1jrkWE8CZAACZDAgSeAQeE7A95EEOvUTN2key2iRHTD24U7O73phrKBdMrhjJKCXMOL9aOye9lE8l9fE0hMzGG2ODwdCbQQgBn/lax/JdS6qGWEPpLZQcnnbgbvMVnIqA2lhim8RHgn52HGT7GW8a8UX3ly6t231PT18Z0pEzwwe750O9CiQAClAVlSIykE6rK8LKUQJHGog6A81Sgk7saUbRe1Hu5Z7imn45LkNi+ejgRIgARIgARIgASOBIHhuvdqTZaucj1IWLpzs7v3AoBZRi30FvbiWrzG3hLA2vWfmP3C+NvH9Zw04QvNq0NikXLocHR0aN+nj8l+W1qz7IZkDosWx+7o04l5/I65MZIESIAESIAESIAEHIFdSBFdFgxJgARIgARIoD8JVIdGSiuiLodxMwh4XLfaDfjjRzBt6rubd2GuJ9dUvleCMkM382ZeJEACe0tg9PnJqZvD3lyc0UUIjFGbYDYZZgiZWUISWgUCewzHPUnn0sJKAbyvQhVm4uLbI/GVT86em9rJXdwYVifrUnm5esxZHMiIVQJYJoDlg43rNknQJlRMl2NTYTjO9RWUo+hIgARIgARIgARI4MgQyNXUApQ0Yf3JrSnfrZtH2w0Kotacvc0VbT3X3ovF/LyxcBV5V7t1TebTXwS+M/61hXcueWehFGwlF/JCJM7KFqylC8Q2jzRSuI0WuYcShQH0QBBCNDEknYhfWtLTC49cnG+cwA0SIAESIAESIAES2IAA2ql0JEACJEACJHAoCcSBKmGwrZ6RjjZ6zYlLbyOqkyJBWmiDNG7fhYjbisOMkZwyJvK3kpxpSIAE+ozAJ77xxUvLOa9sxHUygG5m90N0J9tOOcAVGQJe57Vs47ixCCAKBYEb5Nehib85pMsPXJu84s7darjq6/tkOVyrxJCclA9jY4UA17L1my1fe55WAJnESvmN2V2Y3k15o2AgxyBybDpsO49Yb5TKUU063CIBEiABEiABEjj8BJZuLc2Enl9dkWWs4LGufNphpjdmeSMW3rWcXIiBXCgCNLy029B2g4cS6GAd7TnbBoOyqkQbj7alU1pYUfV5iaY7pASuf/5rM8eX49N4H+Bs290qL7t2fLrd3tiWl8Udx3sZ+fIGyvuGNxHvG94hvJ/Hl/2zf/vIxTLypiMBEiABEiABEiCBrRBAi4KOBEiABEiABA4lAek3m8E2CF0gnIFL+uN2p8f/0ZGHFQRPZi/3+FLMngRIoAcEPvbc5NTbw/4ZKANg6YAMrAtILWIVCZrCuk6XdoI8CIGtCA95oAaKRbEpVou5WL15TE/8zje+uC1FgjijRl1NhmvA2bLZbVfXuZlrNrab/+09ZNVQsZu5Mi8SIAESIAESIAES6GcCC6cvV7XSz9qWkG0HQmnUqgzYkm/U/nJttE73iGPwrv2I7bTyOq4jK/TN0AR9J3qHK+6vxkWRYFHdnw39ilFKEaFGkCiomHcNygEpD+UAOLwzRoFA3hUoDiAtzsdSZAN1r3JiUY/99fhXLpvE/EcCJEACJEACJEACWyRgWxpbTMxkJEACJEACJHCQCEg/urGMQNraQEPw08WbcUKe9tBcIlb3dfFSzIoESGAPCPzWc+ceW8x75TBRIGgsFQDNoC07K8BLJ4eAbzWIZQab9TeG1MTHRVkhnWa97WT2f6NeWy9dM97Vds0Yu+XiXdh+fL19pLdODLyIMgMdCZAACZAACZAACRwdAv5qWM6KSSgM6ppB/9StSxNvxy6Uk28PiM/ZpRLQd20oFCS5LqtoescX4IkHigCWNsgvZcb80LuaCwOVk3cuI+8c3rtAzC3asKlc4OtA1JQDUSKQEAon4qFAgHNEd3lmaGn5/uvjXMLgQL0ELCwJkAAJkAAJ9AkBKhH0yYNgMUiABEiABHpBwNvGYFsvrs88SYAEDiKB0dnJYnXIu7wa2FlgsCaA4XMsIwAhL4TGu3FulhqEw8uiTPDWsC5/fHaytHme+T6o04SE8HDWEDYvM1OQAAmQAAmQAAmQwOEgUHn4YmW4rp+BFSg4tA2xfB6WsNuWnqk9vfHfziJvtjExAIz8MKMcM8nzdT1THbtYaZzAjUNPAFYn/vGhP51411vevYXlzNWBMKgqURZYz/uiXACP44M1v/rOW8HMr1T12D9/+iunF8YvVw89MN4gCZAACZAACZBATwhI64KOBEiABEiABI4KAZHumKFACcwgWPO+3aBeKqa52WHLmQ3scIhRJEACB5zA0khwBWLbgVBqDKk2ICCuydR7DPqjrsDsMNQmO3EQOnuSr1NEQJ51yfvmiJ6S/Oa3nGdbHdbpvLX1WqdU68dh6YZ2tzamPQX3SYAESIAESIAESODwEhhcWizXjx17NMz4RSx5BQUCDPqjnynGphrOtRVdlLOM59qALiHagjJbXAVJGxOtTNlEbvJfZpuHqvLOpfr0j9wJDI8Ugfk/MMojE7BIduvYSMnzlPiMWDr0igLCKRhXvcivZpT3qvL8heO342vXx8tUHBBAdCRAAiRAAiRAArsjQCWC3fHj2SRAAiRAAv1MAMIcEcpABENHAiRAAlsh8J65cxM1rUtm+QItdYdIcWtSj0CRwAl/t5LPRmkgJEatBA8BM2awLedU6QOvfGHiRw9+bUaiNnDuzA2S9PAQyitr8tKRAAmQAAmQAAmQwJEkgFndn5g9N35zODO3nPULvrQTfbQZd+mMkqr0X6FUYFuIvsqJhsKx5ei0mKKv7DJ7nn7ACSTWBK7JbcBv6H624VEeJAESIAESIAESIIGtE4AckI4ESIAESIAEDiUBGeequBuzMz6scAezazFDN+1duq2Gscw6SXtj2aAxM1iOifDHCoAw49j78VbzZToSIIH9JRBo71RDgSBVFMwQG6orNSgeSgC7dab+STJBXRLKBUJfn9ooX5g1xXHULShCu8ex3roWAXmlt9di7iRAAiRAAiRAAiTQnwS+M/70wokldTYv1qXy0jbMRdI+bGkmodyIEI8+4kZe0qCvWvd9tZrxVU3CUNa0R9vzrmU9/bfjF+aRGx0JkAAJkAAJkAAJkAAJ7DUBKhHsNXFejwRIgARIYM8IeLH+MYQvxnx4MujnFAi2WwinEIDz0tsun2ZcokDgDpgT1EJ6l9skQAL9SeCB2cmi1BElt9wJvuvmt20tEaDx3C2LBFAksLPOLA9ROCpJGUob0wkrGx9PH0Vpu9Dch1UXY6bX1KBG0L2iVlivpVFzmwRIgARIgARI4EgRuD7+1Mw7FtXp4ZqvsmKyyjNtpVZF9bTSuttG2y/t0VeF09Jmi0R5IPICUUjw1fE76uzfjP9x2RzkPxIgARIgARIgARIgARLYBwJdkCruQ6l5SRIgARIgARLYAoFA64WsKBFkZWaIm/VrBu1gicDNDGnk4wbbXOgOSAYmrR1MjI21AcQ5545DeSBlgSBJJ2OQIhCKKy41QxIggf4l4GW8k/iOI/lwazKdDOvc6tR3XZOFwGoZu7TBbu7CKScgRI2DOmpAZrIZhQKlTm6Yt39H1joNjXKDy8eld/tYl9euzWvrM7fvQntVV9chXMellAfMOXFOKrRAhSpTSUyqrnMio0mABEiABEiABEjg8BOAIsHgSnS/H+mK7We2KgigbWeU2qXL6EK0+9Z4OW6UEKSdFSm/kq3rse9+/qnLh58g75AESIAESIAESIAESKCfCWwgNeznYrNsJEACJEACJLA5gf+wWL8GE5MYyIdQZzeuoSogmWGgrt25wbu1Sga6AnOX7em5TwIk0H8EQk8/irrCKARJ8WRSmZiTbdYfUC7APuqDbjrMQINHwzz0vU9tmHcc7XF9IqVKKRNomSEXK29+wzLyIAmQAAmQAAmQAAkcEQLo6xXuxGPZ0L/qLDfZW0fLboveWDHwVRCpmfwN7/4fPHxh/ojg422SAAmQAAmQAAmQAAn0MQG0ZulIgARIgARI4FASuC7rhw/W9Xwo60rC2cFBGRg0g2C7u2VrdcBZHrAqBpixjPUs4XAtXFVsHsxjn44ESKD/Caxm49FsDEslGDeX7ztVZHzTmE3mXEOxyEV0IxQB8u3BeHR09kxhvexuqTszxpYKlBmS+sYpMa05xw3+t4drEm4vItbes9s7g6lJgARIgARIgARI4PASQL/z7x/+ykRY1/eGnndVfNUuTeCWKFg/DL2gWleZmVVPj1U+PX26Ml6uHl5SvDMSIAESIAESIAESIIGDRECMstKRAAmQAAmQwOElENS9Z2V2ccmoESQDbtu7W6uA0DgHg3FmqQIXY4cZnfIA1AbsTF2l8nWl7lqMp3/ikjIkARLoWwJFGbhfzXiFWDQFrEURUSRI6oy08kCvbwDLJdzJKygRdBQgV8dmqr/08uPzYUaVUBYoEEDBYa+c1GuVf3nowrW9uh6vQwIkQAIkQAIkQAIHhUDl4YsVKesEFEIH1WBJOo7w90mrsugpD+07WepOV6X5VpXm26viF1bU8jUuEwUydCRAAiRAAiRAAiRAAv1GgEoE/fZEWB4SIAES2CWB0StnCoV8vhAGQTGUWj5SscxsWD6y61cv3bo9o+4+NiUzQQpY1yArY/6eTgb6kwHCjsihLADXUBhwygSSwRpFAps0/R9mz4fECsJrMislHc9tEiCB/iQQ5POjNbEhG4kFgj13rr6RC8e4fqCKslkR39EN3lHTt0esEkFLApdPolTgaq2WNLLTrnTgLBq0p+u0P7ISP9MpnnEkQAIkQAIkQAIkQAKWQKIUAKVLKl7ypSABEiABEiABEiABEjiwBKhEcGAfHQtOAiRAAk0Cv/rC+VIYRI8q3zu5qHVxUQ5FvqgPYJDcDCodU/f85ZMVHfvzWkXP/uTTR2cW6cLpy9WPfePJ6ZvD/qVVmeELSwGdHeLXDrmtl7pzHq2xA6vh6dYY7pEACfQtgSCUpU488VA06lQb7E3JUedEUpaN3Pcf+fr8PXNfnJc0pY3SuWNOScApD7Tvu3SdQylR8lsivx+V737+6cud0zGWBEiABEiABEiABEiABEiABEiABDYnUHxhsugH3knP02KtBZZatHgjtFNa+wvSJ/9eFNXnEwsvm2fIFCRAAiRAAj0hQCWCnmBlpiRAAiSwNwQ+/ty5qbrvnallokKUiUVxQKla+yC5J5FGkcAvqkw84Sl/4oPferIytKKmFz7/1MzelHR/r/Ldzz91efQvzp+qD6tRywLlwVDdeioCwkwsEJjBPNmEa18P3cZ2+m9P8OP4mevjlyudUjCOBEigDwlg3D673+XyZQmFWK1Y2cmGhVldWT09kA9eMwIXDPKnnbNIIHFQGHBKAy5Jo+aTY06xwB1rCWGJxeVltvV0y3HukAAJkAAJkAAJkAAJkAAJkAAJkMAWCMByau5E/rGlAT1RD3QRSwlaZ0NfO2V6v6RElpkJsurDL55fGK55z3zniMgvt4CRSUiABEhgTwkkQyN7ek1ejARIgARIYJcEPvrCZOmD35p8vToUlWvZUNbwloEeeBn2xnZzHxeKZRIpfNgIV4O4eGMkvvK+uXOvv2fu3ARSHXpXD8dluKy6Zn5xgx0IxGZAzZflDtzAGhQz4NMO66Q778sAG7yX8vl6UDlR1eX0OdwmARLobwKBCioNGQaK6gbPe13sNdfxVYC1aDZxbzx8uTJY984GTu7Sll6qKanzm0oCqLOcQoGrv2B1IUSiVDq7Z/+bX5XkNyUI9TOVBy/OpI9zmwRIgARIgARIgARIgARIgARIgAQ2I/D+b09OVe8ZfP3NY7q8mo2LkL9BgT7tnTwTS/zZ7VDVAj36i+H4yj1/KfLLV56c2Ow6PE4CJEACJNBdAonYsLuZMjcS6CKBdtE439kuwmVWB5PAh1/6w6ko45VD345sY9DLDg6hkS33tGZAKhk2b4vHwDdcXYJcqMv/9JmL0ybiEP/7lbnzJwd0NOuLQgU6LE3FCzu4BnZZiQYbUSMwbFaSmcn5uuWIQTdwhmIBFA0GES9hPZk57MV+5fgtb2xh/GLlEKPkrZHAoSNQnD1T0IWBG6FYdUG9ahWJmvVs6w1jeL3pbKrm/mZbqF9aXXMf19Wrd+6tiJJAa5rOex/89uPlyM9MOWsDOB+KYyZEXlJfrWb8Rp2FXJBWDqlQLhvIreQiMRxp0trfEXd3DcUDpRd+/qmv39+5BIwlARIgARIggb4ngJ+9tJNfQjoSIAESIAESIIFeE3hg9kzx1lBudjXwRs21MJHHuKTv2f6L3JBd2j4y+rWZGBb7bP81F/rzw4vRacrcEowMSIAESKDHBJoSyx5fiNmTAAmQAAnsnsB75x6/tJrTMmBkB8HtYLbV0EXuaFyjYjdeGt5mlnz7ZaXBjiHyjMy2h8/GsgxCRpffNzd5pT3pYdv/+diFa37onTYEpAOCrosbeIMCAfjBIR4dFONNjGUqY4tmcNENrOEc7YjLdq6eoQJBwosBCRw0ApXxy1X5xiuNOmGfbiAfqupWFQhQxB9+5uvlTOhNo5YyVlEkzoRJfYY0VvCCraaTKs4oECA0vx2Svqk8AUUpKFKJCcnYW8i9HY41z+QWCZAACZAACZAACZAACZAACZAACWxM4IHZx0tvHQtegzUBM4knpUCQPtPJ3zBpJxIrBNYiqLVSAIsEToYJ+aVYKShVj2fnRmcni+k8uE0CJEACJNAbAlQi6A1X5koCJEACXSfw/leemNK+PmOGuNHwNo1vDHc759QH3H7nEINFGCjyRIEAPpBGOBrkEjPx/pfPXel81uGJ/dFDT8/UPW8sUn4Fg2SRF8jdiyFz0Ww22s1CAwNnq2JZANYFMMCWxSxdOW6UMkRZAEsXBGaNA7FpkAy0iUrGwuCSogWCw/Oq8E6OIIGBUL8KwQWEGPvipG4ZWvUXtnvtH372YllH3jTqJl/qKmuhBtYHUMdJ3SZ1PqysOEUo1GkDoos2KH5IrKm4281KvYa6DgoJUJCqKe+qV62NQcFiu2ViehIgARIgARIgARIgARIgARIggaNJ4Ldnnzh5czgzF/teAUsW2Ok6joWTX0oofdiGlz4o+qJwTnZpl+9LJkKJ/BJW9yS/4u2RgddGZ89Z6wbmDP4jARIgARLoBQFbK/ciZ+ZJAiRAAiTQNQLve/n8Y6EsYWAH+9OKAzu7BAbIrGav1fI1uUijPszoCbFIMLWzXA/OWT8duzh/opoZO7GUuZqVtccxWAZFAi1e6aChLIABtQGx920H1dDlsR0a7GfiwMZDoSDMPHP3zWUqEBycV4AlJYGOBAbqagED6hiEd8KLjgm7GtnaHM+G/tWdZF/5zNPld7wZ3Xv3sldB3WUEMZIR7sdZGoAyAZYvsAIZW7dB8QBprYUWG0pdWM2E/tm3/vuLE1Qg2MnT4DkkQAIkQAIkQAIkQAIkQAIkcDQJwErAjZHMFTMxx/StN+Zg+9+2n9qe0lpgFdmldGzTyv6yDGFBLBLM0iJBOzHukwAJkEB3CUCuSEcC/UygvanBd7afnxbL1hMCD/xZufiv74pex5xQmfcupruwlIE4DPyYQW2z17btjktoNH6b+zAFhoZ302S3HRp3g06xzFp9183M2PXxC/Ny1qF3o8+fm3hzSD3med4oZukGWOZBQgy2wZkOi/CCdQI4WCyAAyet1bxsnK08VN72zGGTCf+RAAn0FYHR2TOFtwu515XnFUzBTD2LgfiNlbesgtfWb8UpJDXOSK6D/V++Gd17ffxipXFsBxuf+MaTEzfz0ZTn6SJOb29MoV7TUofhrrBtZ3fIGpOeqkbae2bRX7xcHaP1AbCjIwESIAESOBQE2n8K5dePjgRIgARIgARIoNsERmfLhZXB6LU4ExaxhCpkkEbHvcMvr5NLut52WkkA5XLHXRmdMryVXwYmX7HEN/+jz0yPuTQMSYAESIAEukugQ/Xd3QswNxLYJQF29ncJkKcffAK/c21q7hcjccnzanIzoegOiCIBau8dKBHgPGtGzHLBPtYbw6B53sy4lyvIYPnx5WD+tUe+Mnbw6W39Dj4hZtDqOX9iOVCfkmm6ozD9DW1oaDtDe7qWMUsYVO+6oxaGa96ri7fU5YXTZZr43jpipiSBA0Hg3pcnZ+KMd8rUlb1QIpA8oYTU4pLraK1n/uXBp0+3HNvFTun//NLJ1bw+eSOvPrWa9Yqo66F8ZteaRP0vyxyEfvWXlvR8LtKv3lFLMwtcumAXxHkqCZAACZBAnxKgXKFPHwyLRQIkQAIkcLgIvPeV8iUZ3j+T0aH0PNEBhSIBZJFr79PINk0Ke6w9jTvuzsRxWAbN161VvZoYEzXLkSp19t/Gnrrs0jEkARIgARLoHoEO1Xf3MmdOJNAFAuzsdwEiszi4BB6YLU8sD+grq0EoSw2IBQIvbMyIRePZjAc1rBG0DUqtc9s4B7NOjUawbK9IoxtuCI1wM2juK2j1HlsOTl8ff2rGHDxi/woyG7kwPFQMIr8gqx2I6ob4MKy88fDuZgcfMYy8XRI4kASKL0wWdd57vaUBIrMnNjKx2LD4spU73kCJYHU1urdX9Uxh7kwhr/Kjtogi1lFxtS6+OsZ6bSuPjWlIgARIgAQONIGWn3W5E8rCDvTjZOFJgARIgAT6kYDIMIs/LejXnQIBJi1BcglZI6wItDunJGBlm50VDdAPH4jsmctZhL4arskkHzlJ5kKZiVCiSFA9Ub19LxXi2wlznwRIgAR2TyAZOtp9RsyBBEiABEig+wTeHNGnMomqgGlcy+xVY74ridvJFaFqgEa4mZEqO9g3zXoRpblBsrocvDEcn5JDM+KPnKvKTFwxMbBw5G6cN0wCJKAqoiz0a98698xqVj8GHGaUwVgKkEUIOgg+doPMZie1sFwkW1czP+6holKyPMH8bsrLc0mABEiABEiABEiABEiABEiABEigE4FaRk25eGP9Dp1pyBpFCQCyx80cZJLOGoGTT9pJUFZuafvjbolWm6NnlyQtBMHQGcm/vNk1eJwESIAESGB7BLZSf28vR6YmARIgARLoCgFo8IpWbakumrsY1IcSgRvs19L8XrOmtj0q1zapNiwDGuVieMCYtEZC/BggDqatoSmMxn6YiUsPzE6WcJyOBEiABI4SgeGl5bKIOiqeKA/IGotmlkMgoY9946Xe3IpCAZQP2n0iPsHpWNYA9a4Yeqxkl6Lpo8SY90oCJEACJEACJEACJEACJEACJHA4CIyKDPPWoJ6wSxgY3QFjgQATmCBzNDJNkTU66wPpu8ZxeDgoD8C7OKSH/DKUZUbd8VgsBRrZJSwdyHZGetRLA/qxUbEqalPxPwmQAAmQQLcIuPq5W/kxHxIgARIggS4R8FR0UmNAX2pqDOo7bVzX8N7NZZCHGbhKfgWchi+uYfJHQ1yUEWo5dXI31+G5JEACJHAQCcAM4omqGs9FfhV1YiiD/QjhbH3Z1oSGokC7WxOHNM10rh6GUOXEoj79g3EuK9COkPskQAIkQAIkQAIkQAIkQAIkQAL9T+Ct41GphjUGMLFJBvYRwkqAkzdu5w6avWZ7FuSX6D875QLEQpFAJx7XkslXheqIW75vO1djWhIgARIggY0IcDmDjejwGAmQAAnsKwH/UTSEnWs0vGVgysSaRrk7KkNTa2bFNs9tpmrdwjlIZcJUfphpi7b/asb7VOsZ3COBrRGwGuD5QqDyRZwh/T1Zf11VFsbL1a3lwFQksL8EFsafXhj9xpNn7xzzr0BoAUFGLpSlYJJiOaWCRinX1ME40ib+QP0tH4OWmhd5irlHdeyOnv7+I1+Zb+TDDRIgARIgARIgARIgARIgARIgARI4SAQ8/1RahrmTorf1nhtZNOSd0pfGtptkteZ6fgYToeYbJ3KDBEiABEhg1wSk6qUjgb4m0C6S5zvb14+Lhesmgfufm7rx1kgsprgwzC/au0nYuEZq0B9xjUZ1I8HON6BEkBHT3TDDXVu8c3dFZuXuPDeeeVQIvGPufCnrR4962jsZaK+4zoztisTPZ6Lo2R999sK1o8KG93lwCYgiwcSNEXVFlpdRObOWoywvk76dtro4fahlW+pTOGeBAHW2KCWc/afPfOVySzrukAAJkAAJkAAJdJsA5QrdJsr8SIAESIAESCBF4H1zT96Q3q6RYVr5JQ7annO7coCTX65RzE/ll9506dNx2IY1AhxzsiexIFip/N6f3NuejvskQAIkQAI7J8AB2Z2z45l7Q4Cd/b3hzKv0GYHibLkQjOgbDVNgnZQI2srs1h1ri97hLnLzZbasKBEofe8bYzSzvUOQR+K0T85OTr054p1ZzsWFjOnA4e0RxRYoorQ4u48OHtLl615leFVN/9X40zMtybhDAn1GYHT23Ojbd2Vn5b0tOkGILWKLOsEWSm0tEdT9qHLiTnz6+w9fnN/CSUxCAiRAAiRAAiSwOwKUK+yOH88mARIgARIggXUJPDA7Wfx5Ifu67Su7SVDN5O3yyvWUAppntG6tr2zQqkQQyTKEr//en3C8qxUf90iABEhgVwRYqe4KH0/eAwLs7O8BZF6i/wi8e65cCpSesw3tdSwRtBW7vVHednhbuxgCjnyrRBDFekyUCOa3lQETHwkCH31hsiRqAVdiXxfvZGMV+ujAWQUCqwkOs+1rFQmMEoG81rB4gc6j1qpS9+Ppf3zoazNHAhxv8kASGBXByPJQrlwPvFP2BpwCgQs3uy37LQRRPFNfWjpLCy+b8eJxEiABEiABEugaAcoVuoaSGZEACZAACZBAK4EHZs+Xfl7w5pwSgUqs9Tm5ULu8spdKBO+66d17fZwToVqfUP/uFV+YLGb8eFQHmaKYlhBLFnBeNY69iorDhcrDfJaWCf+TwP4RCPbv0rwyCZAACZDAegQCWXfbx8Lb+6jq5SRtKAsdCbQT+M0XnphaDXQ5G8eyHl0slgVkIFU8zNahQ2jXfUco3cXUewzrBL7sR6JcYNaFtybeixJ15cMvPX7PP3z269Pt1+I+CfQDgQUriJh49wvlcj4bl5XnPyrvu3Ry2xVl1pZWPomqfB/X5GO5+o8PfWV+bQrGkAAJkAAJkAAJkAAJkAAJkAAJkMDBIyDSIDP4a5QGIO8RD4eeslMkMBFd/udkTbB06ZzImpKBaBfDsN8IfHz28ZKX8R9dznkn64EuGuE3ZIrJe4OldZVMbFN+Vn34hSfFgmk8n9HRNJVD+u1JsjxHhQCVCI7Kk+Z9kgAJHCgCqJxldraMN+1vsdHY5w/F/j6Dfrz6R1584tJKoM/EniwQb95RWSNeQtdhbCoQNJUJcB9Iajp58l75YrVAzpaXHP+sk2PlX3v5j4r/+On/9bSLY0gC/UbgjYfLFSnTxOjsmcKgGinJ2yzeu0/ebijDGIGFvNZVT+mqhK9q5S2sqNvXFsYvVxUdCZAACZAACZAACZAACZAACZAACRwiAr7yCpAHOQsDkPVA1GNkijI2vGaly23cu5EhbZLeyU6RNmMU/Tc5gYf3hcBHnv/CaHXYv/SmjkuBCL09CL7FufemUSgID82hWNWyfvFOTk1EfmbiPXNfnnlnNZpOJng0knODBEigtwQ4NtRbvsydBEiABHZGIFQVP4fZ2vvj0AA3jW9ptNESwf48g3696kfFAkFNFAgiUQKIxQKBbfLL+2IKDD1zeLjEYJ0kQPs/7UwnMDmx2Zm0FgxkiYOJD7/4R+of/jMVCdLMuN1/BBKlgGtSMng6EiABEiABEiABEiABEiABEiABEjhyBKBAH4igx439OjlRIIIiWAnA6gZuoH8jOEZWtEECK3dqTYBz4HFNhGKJgMr7rYj6Ym/0+Sembue9smdeBjwreZp4YcQlugR2R2SJXmKVwL5HSCdxclQM9k4sj2QmPvbc+fJ3H7kwnZzAgARIoMcEnKS/x5dh9iRAAiRAAtshUHhDVXP7vIwAGt9o7I+sqMp2ys60h5fAR1/84mOyHnwZjX3rlQrlPYG3yxZg6YLEQ5kAJsjW8Y2OnpyLzqROlj1AvquiZfz+Vx6fOrwkeWckQAIkQAIkQAIkQAIkQAIkQAIkQAIkcPAJhCqu4C7cjHLIe0IRB8FjeysKBJ0oQGmgk+JAOq2VJ9nrYBmFulqupI9ze38JFMWC4z1zj8+9PazL9YydPAStEicTXBtiyVS8M5IGk5fE4y3wk+VTV0Uz5a1hr3zPy1OvyVKTxf29O16dBI4GAanK6UiABEiABPqNwMLpcnWovn+D92jERfILMRCqKs1E9dvbsT/lKf3ZZFGsBFzOxrFRLtl1A6JducDcls0VigTa1+XflXXS9udueVUSIAESIAESIAESIAESIAESIAESIAESIIHNCGDgvi7TxN2AMNJDpngnq9SSeEw86YVbo5ygtcgwuYxgL1jvJM8PvXCm6N+VfU1MC5SMOQqXyRp5IGSBae8SJqHICO35Tq0E+2o0N6DnilQkaIPFXRLoPgErre9+vsyRBEiABEhglwQG6vrVXWax49OhPQw/GOqFHWfCEw8VgXo+uIIO4ZpOmrnL3jQn6oGiNYJD9RbxZkiABEiABEiABEiABEiABEiABEiABA4TAQzcy3KslbqIhiA3gjxRJp3L2HGP71IGo3ENXAve1x5lmD1GvtXsYYEgzg7MyetQTJ/TtXcCigWSdz2vZwtz5UL6GtwmARLoLoHeSP27W0bmRgIkQAJHkoCsKdah8Zuqtp0mpgvXUELanXk06nLSA8jV/atrsmXEkSPwkefLE2+OeCVokdd9X8FEnGmuGxLJO+k0ibtABzmiA7g44JU+8Y1zE13IklmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAn0gkCsnw3FGgGWMBCL82qkZv1wXfZ3qUwA+ZOTQVlT975McLGyKLGYqbJi9mBAZJiD+zgZqxdID3Kew4MDU1L+Iu4Bz8iLrcKH2UccNnbqrAKBsU4gy/CODit1aadZ8TwSIIHNCezqe908e6YgARIgARLYKYE7KjMjNt2ru2xa7ejyrhGeieL5HWXAkw4VgX8/pk4ZE3TSaoBWeVM5pbe3uSod0LeHvFO9vQpzJwESIAESIAESIAESIAESIAESIAESIAES2CmBu5fVtQBrGCSTmSA6gjLBTi0SOKWBdHk6WcY0CgZysZxc++5FfS2dntv7Q+CT//eTE8rzz2CC2k4sD6SXxVj/DsyTN4oEQawnfuu58yfXT8sjJEACuyFAJYLd0OO5JEACJNBDAgvj5apW+tnWSyTNaKd12XpwR3tu6QJRWBBb9YmXnLxYz1wfv1jZUaY86dAQGJ0tFwOtSxkdNzqA1kzcFm9xPUsZLt6FqezwllvtcnkPVVx6YPZ8KXWYmyRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAn1C4PuPXJi/a0VVMWgsf2LFUqwSiPAo8uN1lsW0k1TcgLGdsGItDqRlQh0VB0RpwJ2H47CYma3rhe+Mf62DRdc+AXSEirE0GE3t9nbxDrR7lyfeL23k4rEoKcQqG8dqJetdGZUlFFwahiRAAt0jQCWC7rFkTiRAAiTQdQK/dLNWzoWt2eoNGuCtKbe+F4umMBSGjdKwnJYX+2PHb6vprefAlIeWQEadhKJJZ9e7ZoTrQOLaottCjeLOD4CxJEACJEACJEACJEACJEACJEACJEACJLDvBPKr3jNYWkBMzIsJ++3PQsegMVwnxQF7xP7HxJasLF+QlTAjZvJxrYG6/0w6Dbf3h8AHvvXkxErgFdMyvXRJ0lLE9HY6jXsP0nHNbTlqFAhsjC+qBvD1TFxYHBo500zHLRIggW4RCLqVEfMhARI4mgRklnIhryIZ4POK4u9xFEQRtBrE8Y+jKJr/q//69IKLZ7g9AgtiCeBjz51/RszJP4ZGOBzWo0eDKZAGsx3cdc0uF5pka//BykAnJ/lKqTNoAAAAQABJREFUm1utZuzxvOyPrHgz3xn/aqVTcsYdLQIZTz8KKwToAGBtO7x+cDHWn0veSRuT/HfvWapRb46077ectHbHdThxvTtZ9am1KRizFQLQxM6rkVFP6dHY9+8RLe0C2EpHuypP8cc69hYWFxcXFk5fln06EiABR+CB2cmiiKVKpn3jRfe4eJFo/VjqpYVQxZXvjLN90+DCDRIgARIgARIgARIgARIggSNNoKZuXfbjY4/lYr+AgV0IkELxmLC0kWJAetB4o3SAC0sHeZlshaUSjFxK4rT2KmJJdQbHD7v7xGx5NKOiUa38+zwvLOB+fS1QlPqxWLNdgN9Pq7IrWTWVl2djlrFAqcQZ2TWEe4mzsmw8Pzkmcenn79Kkw7XvhJyR5IdzYe3CKJN4/mOyW06fexi3nazCvANKybdmnR/rH0c6XtD1cOH6H9Cy8GF89vt1T6nPd7+KwOuSwIYEzK9gKgXf2RSM/drEoNStkZHHoowqac8r2XI0f/LRoPNlIDEng9xwsluJVDwvCgXTlYf5I2apbP0/eL9ZyL8WKF30VWgaRzjbcbY5uSbDBvm6wd0OSSIZEJaGnjwsXw3WdeWX/y0eY4OjA6gjGPWb33zyxttDGHgWM2HStIeZMDh0AptffQcwW1QaSHcG7Dttc0Ud4onPRqLvqAN1Zzlzd0WW+OhwJUZ1IPDx2XLp3+7SU54Xj8qTMh1LJLOMsV6gPQn80XGTenr+rmX/6nc//9SMPcL/JHD0CEAx8tZILO0bPSHmEYudCOAbCsTDhZ6uyPczX1+Jp99g+8ZC4X8SIAES6E8CSc3dKBzlCg0U3CABEiABEiCB7hH4rdnzJ8MBPYuBXUyAgtl5LG3gLJ+mrwRZBJyTLaXlQ/aI/Y8+mHOQfuZkhguUCOBykvHAqr53PwfObUl69x/LjL51XD/meWpCrlIwPIzMzTJ2g/Iu9GSyiNaZZ6KoPr+XcviPy1Kk/3pCz2VEbgjZISYkoaxhxsqdHCFXzvbn7463h+u9F0jn+ueBvAeBzJD75bczYy//wYX59jwO+v67X5gsBnn/lNj5OCP33JDx4b4cTxeCidZ+RXt65vhi7SomKB70+2f595cAO077y59X35xAqplgEvOd3ZxZz1KMXjlTyJ0YekzWGTojA86F1kagbb3hAeFHCxqA0DqEoB1H0FhE+iDSM8NL8TR/wLb3mN7zrXOj2SCcC3RktXkTpjDaZPU2JZAB160416jAeWiwwcO6QSRriPny3GQdsbHKZy7MbyUvpjncBIqiwKLvOnYD69iJZRHbCUiUCNAJCGGNoNHda2PRUCKwdUPbUdm172u6M4CGLpQV4KBEYOuRRIkgztxbebhcMQf5b10CD4jywGqgp27nValmetXgaZk262epo4W11dy3339dnifMDh5b1ZVsPTN9fZzKBOtC5oFDR2D0SlnaN7G0b7S0b+KkfbO27kp/Qw4Cvp1Qfj8zoZoZuhVN/4AddIeGIQmQAAn0EwFp+bQ4VOl0JEACJEACJEACPSDwW8+dn1nMR6cgR4ITU/NWicDJLRN5kZFJJtd3siE3sJxENwZI3WCxiZd8TN9MZJgnFj2RXzxddukPUyjynWItq6duDagJK9+xdwcWRs6TKBI4Oa8LMSHHydyCyCvnlxefWRjvvfXJ37n25Ss/K6gJlA0KBPnQSq0xHuCeL0runHvWrufdKY1L2zGU+8fYAybJQIEgI5OQ3nXbe+bb//XpMx3TH8BITCy8fSx/qZ7xJmzxMeGq9Ubcc8dThzPHk28Nsr+BUF32V+vyDlCZwBLi/+0ScO/Wds9jehIggSNGAIPYN+4Zeu2tEV1eFQF7urHS2JYfb2iYohGAEFqnofgY2qcYxJK4UH70bh/PvP7x585NHTGEu7rdnz309EI+1GftwKqgBM9Uo3CrCgS2EQnlAbFpIL8ARnnAiNDQ2PLV8Ko3TQWCXT2qQ3VyODIyivfEfL/yzuG7hkKB0SJOlAC6f8OizCKZtjeKRZWg2P1rHZ4cMYP6va+UL/20oOfeHInXKBC4O11TP8sztevJmTXkVHVQF988Hl953ytfvjJqzLm7MxmSwOEk8B+/fb50455I2jdReTVItW/ct+HC5PZj2Uf7xnlXP4q8YuJ2wZ8bff7cxOEkxbsiARIgARIgARIgARIgARIggc0JROHimbrnLUDZGjIIuxzm+sNQbvDYDSpvdgXIqeqSd+R51w6rAsH7Xv5fHvtpIXrt34fjRIEgka0b+bqs54B+KrbNfiLXcX3X1LF6EJVv3DX0mvRTRzfjutvjMqFFrpGURTLDc0UJ98qhj76cVY/u1fV6fZ3Rv/jSqbcLudfrgZ6wzxtX7PCs3XM3tEEcafCOhGJ1OFRvjoRnfv5ONfeOuckScqAjge0SWL/23m5OTE8CJHBoCXzg5S+dymT1nDTPivghgmIAtEghSDc/YqZx0rx9/GiLIF5BS/JONlYrEiI9TBnBy5rc6lZel3/jm1+81DyLW5sR+MFDX5sRdKfNwEUy8G+UBxrKBKjSN/GSFnqgZvmCjCxhEMi2NLyRj2gIn/3hZ/+4vFk5ePxoEcBsdXh00hZz1kujXN4bx6H9nXPxWwuhLGC8aA03lGHMO72185lKKZi2WxnUsuRJdAba3sZknCx94hlvl6GA4hGUQOBN/Sx1cqf6GXU86nbPjyduHffnPiQm08iYBA4rgfe/cm7KD6I5UauT9zwUBamw2b6Rb8F0vl0o3wV+f6FIJdYKGh51I2bYwGeULt4c1Fc+/MIkFSUP60vD+yIBEiABEiABEiABEiABEtiQAGa9L9fj8bqvKzWRPYrYJ3ENQZKL2DDEDGsnM0JCzGiHT+IX3rq9fHrDDA7owf/uxS9dynjRZZHtFCDfaemXuv6p9F9NvPRT249rGTzWvnh3zIuKt/PRax/55pd6OkP/7WFvFIoDeD7wUPTA84f8cCsKIu5ZbyfEI8YEuVWxELgis4/eHPGKRZlkg/iD7D780hNTt4fCGeVHci94xuLwPN0zbbwHnZ5/U/4Ha6/wmVgXB3w99/5XnpiymfE/CWydwPZq7q3ny5QkQAKHhMD75p44tZqVHy0vLLgfK1gWMAoEa36wOt80BOxoMIjc3SgRmIaE/LjfGPLP/OY3v/Ra57MY24nAP4oiQTaq3y+Nr4ptgKEaF49B17TvdHLHgVkxgxT5lfyKLGHw4FcudzqNcUebQNaYBbNKBGiYWw1x10g92mz64e5hLWB5OJqLM2ERnUsZDG12FE0B3bOyoa27myWHVQk8V5g4M0peyTnoZIS+Lkon8LWP7IHGerNE3CKBvSHwH+cmp2oZXbZtG6ulH4ugpdkpby0Hvh0rhLECEfwGQ4Bll3tx349Vmrw55JV/44VzV1pz4B4JkAAJkAAJkAAJkAAJkAAJHA0Cbzx8sXL81sr9hWU9nzOzUCC/hEvkl3Zny//NrHbpg2EZxhNL3tXK7331/uoemOjfcgG7lPDXvnXuis6oMxmR7Rj5jumjQmEgcY1BZNk324hP5D7YT8eZ2eiQE9l+6i+Oh5cwOJ3k1NXgnXPnRjGQ7xz6y3js8ChdLx3eDXiMPyzJ5KdqQR1oJYKPvPjEpbeGo3JdJjlgImZTRuFIIkz7NrruPZDQyAAlxDsAmWHk6/L75h7vyTvQVgruHiIC8mnRkQAJkEBnAr/+0h+dzOn6zEAcyg8NGiyh+fHB4FIgHj9Axqx+uoEicVASwMDjYF3J+kfyW5fKHgNVZrBKjstEPrWcU6O/8cLklVQSbm5C4O8f+t8X4pXVMRn5u9pQHDDnoEpPqvW0QoHbTuULhY68tK7ytXgmV719/z89fGE+dZibJGAIBGFYwffqHL5tfNdD4tPx7jjDvSWAtdFWBv05Gfgsoo5GB9NYKhFFr4bGeapI0OY29bN8/436OfV8naIXlAnQuYDPx3HhTj6e5dIGKZDcPPAEfv3FLz6Wj3V5TfvGtGFS7ZvUneLbwTcUSPsFbZu81IOIcw7fD6y2OMWrO1lv4qMvnJ9yxxmSAAmQAAmQAAmQAAmQAAmQwFEiAIsEf/fIxbFjd/S0vW83FCVhQ1aJOOdb6WAg2g4Q2+Nae9XhVX1W8pxoTXk49j700uSUzkQTRoFA5DFOvuMGgpuDyZvcb2oQGYPNZia65DcQiWXKrC6P/sUTpzbJYduHM8ovQM6PPrNTIEC4F86MNci10T838m6lintx3V5cY/T5yamVnD6DZ4V7sXIIgBVvnAs7XN0998Yhex4sSsNjDAfjOZ6ny+9/5Q/PNJJxgwQ2IYAamI4ESIAE1hCACes4oy+hoWG03iQFfvxdwwU/PMYwvjkuBxs/ZjYr/MjZHzobItY0IkweNg1+DNEYvJn3JsQiAX+8LJYt/a88/H9UXv/0n07cdSu6Nxt6Vz3tV92JvjTEXfO7JUS8eKQdqKuZd97UY//y4FdOVw6h5q5jwXB3BAorK9UBGTAzA2fyveZkG/sI8X3v3G3c/EBdgbrB1RnoiLyjqio7v97hPPP2saFL9awuQmHA1M1SL7s6er07dooEeAJuG2kFseGNbeeMprIcEE3l4tsFb9bFMySBg0yg+MKZYhSoy3jr0b5pr8tM+ybVhsF34rzpxMvNI0S9BGGFaeskQKTaMnFQJIDApzqIpZvOnzzIvFh2EiABEiABEiABEiABEiABEtgNgb959GL52C0t8ksF+aVkBR8YGWUgS1tmjEc/y203Q6T3Y13Nr3rT2Ru1e//b5/5U+nKHz33gW5MTGOB38h3IdpxPJDZy0+jDbv/enewH/dhQOq1vD6vLvZgogr4xPIoIb/rKiNt+kXdxhtykTLA5iG70G+cm3h7WZQz4O9lDk53cl7wT23v+9mz3XBA6FwbepffMnRl1+wxJYCMCkHXRkUA/E0hVb6aYfGf36Gl98OUnZmO/ftI2XkQY7hmVAfPDD+E4frTcIB8G+owzDUG7iZ8p9+MUyrlwmKGXdm4mM/KGrD2zqO//wfjFSjoNt7dGoCgzktXQSMnP+CWZPXyfH3tFaXsXcLY8q6pWuipP5FV5agv1pcy1ynhZ9ulIYHMCD1x78vW3h8OibazGxuQ9vv2VQOa9m2+/2aS1uSUfekOxyH34remg0ALnjtpz0blwMUl6Hah33wqqf33yqbtdGoZKPTB7buLN4/qKrX+tUhdownwbns9GHQvUzeiQIB3Sw6Hjbs+BFQPEyJNAmHTwcYWBujf9w89eKEssHQkcWALv/8svvC7veDFd+zS+o0arM/kwTKun9Vbx/aTbQDgKYZdr85jU8t0gT9u+8avR4u17qbDXypF7JEACJLDHBBo1fHJd09rZ4zLwciRAAiRAAiRw5AkUZdKaH2RO+to7lVHeKPpRGCy31hRb8Wjlz8vhZ8PFpZnD3J96QJap/NeCnhMSRvZm+5awRNDkYZUv7L6Tp6X7tPaIlac1j7vzmxk5Oc9A6M3/4KGLYy7FbsN3z50vDWi5B3mWUKiHMwPh8gA7TTzEcdsPx5Z1bdJAF71B6O4XSSCj9E0fPNSZsTfGLsxvcGLfHYJSx+1j/lw9iIpuCQP3HjQ52ee49vm23o4v8gnnsCQ1ljCFc+Mw2AY5ia4UqvX7YTEEcXQksB6BYL0DjCcBEji6BB74xuTE2yoSBQL7g26Gp2Qbv/sx/m3B4ceo+SNnT3CDVdiDQgGE8KaxKL9ttUAVwkLmihwas6n5fzsEksb0NTkHno4EukYgE+tX5VuWjkxrg7NrF9goIxmIQ2NXLKIsbJTsKB6rZ9WUrWNtp8nVt7Aeg8ra7aMjlXa2E4JZ0jbW7Zv0wno9h3S1wHtMFJYuH+bO+3r3z/jDQeBj3/jCxE0xbaily9z8Rty9YeDffk/uu3BHXOjiraKNFYq4Y8jPHcenhA460oUZVagXRs5IVNmlZUgCJEACJEACJEACJEACJEACR5FA5WEzeeyy3Pvl0pVyIciHo6HvFVQQFBo8omhBLS1X5k8fjcHNlZyasoruQkD6kNJbNShaJ4fYuHYZT4NZY6NVBtSe3g4w+6qeUaUPvHJu4kcPPj3TOHVXG2FFesEmB9cvxo7pJ5vY3v3DNUAHci7phqtIx9XeXa03Ob9VyEzJbRTtRB65hpHr4a7c83TPf3vXN89bMm53Sa7FIMhTVtEOh/trCFCJYA0SRpAACawEegprYVuFAflZwY8NZqMmPzr4cYZwPDbWCXBsZ8xcowLapqJdiqZGSbQvS9fHL87vLEeeRQIk0G0CsacXVgJ1Co1xfPdDYhWstSPT7Sva/FA/uCZyLaOv9uYqBzPXT3zjyYnFbFzcceW7w9uWqr+gR46zg7FDfjxt/wmEonwTxNZiB9oya53tSrtjrp2yNt36MQ0FScnfCDIkDHTmsVFRwKGG//rceIQESIAESIAESIAESIAESOBoEZg/baykzh+tu269W8xAX855E92R79j+bHPgObmWsTDZel2kEbHbJYmdaT+yk/0VtVIN/GGjWO9khk753k1IdKXbSf7rnYO+OyYf4Vrof2O/Hi9X1kvfj/GwRPFz5U80paBJKd1zM5Mddkuv8/lLOZ+yin58KfqsTJ3fnj4rJItDAiSwdwQ+9MJkqR5gcCrt1lYVriGQTrWdbfyop70xlS4/9/Wsf2o7+TAtCZBAbwncUUszokRktHjx3delOoBpMjdjd/Oro/5YW4esf541C45OB5QW4OtRPL9++qN35GZeTYn1lqZLZk83I7q0ZTos8uxciGx9Dx2MQpeuwGxIYM8IfOCVyYmVrFghSNofvbqwyx+CDLRzjPNimVljFHB6dVnmSwIkQAIkQAIkQAIkQAIkQAIkcMAILA37U2IVYJvOTbnpdNrW5W/SXS18XCbzdcplu3HVscvVgVBXXBcYCvlYzgB+O27rpUeurakhPxys6wrKsp1r7nfa0A8meluGVk5Gxgc5n3hYhr4zONzj6/f27ph77wm0vUG9vyCvQAIk0N8ExET2KZl1rGrSgHFr5mxUYlQi6/nmeUYXUHZtCMsDGIRckTUNauJdwwLXe2vQOynmsjlA1YTHLRLYVwKYOZute88O13yVFysEGBRD/dAYHFuvdEmDtHHY7bswOdCoPyTereuFtd6yUl0MyPWyoZ5ZGDfm7hpZHeWN3/3z8yVhXzT1qQezEOKTutWGlg5YWi91tHSknG+yS9fLzdjNtuTxFwbVsYnN0vE4CfQbAanCTq1K3QW/Wf3lvh93D+3fD5Sc3OwKpGkeh4KVsu0buQ4UJGHZCW2eG8PRYy4/hiRAAiRAAiRAAiRAAiRAAiRAAiQQybICoOD6mOl+pqPj+qdOxmPjIdNxriFZMxFmWWKR1rvQpWoNrUzo9nBmqjV+53sn7nivom/sJh7mImXkiIHEwbkSo8/cqU+Ou4Bz/et1w1jkXfCSL5hkZNtd6123/HmTyQH6d2PQO9V4/h3K3c7Bklxfpuf4unBNlpiI5LwcDDPeo2vSMIIEUgTct5mK4iYJkMARJ1AK5cccWpCdftAdG/y44UdsNw6NipaZepKZWEEorBTyo7vJl+eSAAl0l8BdS2EZCgRWg9gOkrlOQdeulGrAujzRCTixpKbdPkOpdz3/pGnstygOuM5DlwnhmeA6LjTZi/JXxit1+UrMjgR6SgDWMzzllbAsC9od3a6/0u0ls0STXCMWxQH3/XiiSAAN/3fOnWP7pqdPmpmTAAmQAAmQAAmQAAmQAAmQwMEggEkivvaKLaWVQXGR/LRE9XJnJRuPFua6M5lvYCWaSZdV5g0a66JpxQj0knviEmWCwVA/25P8e5Tpe0RGIBM6i0520KPLbJCtTHxQulS60p13YIML8dABJrB3NdIBhsSik8BRIVD6v86NBtJ4gXC9Zz/qArOhXSdKCNiGWXQI4KGUYEwdKRkkoyMBEugbArAEMFj3nok9X9b2jsVKQJzMwt1NMyIZYOswGI5BOCybkAvVzHVaIWh5D7I6vg8zm+3Afsuhbe7g2Tm/hVMbigSxWh7Qn9rCGUxCAn1DYCQcGYVpQ7huKBAgjxYv+aa1/KE04OM7Teo3zAHJSL0ZiCKDRNKRAAmQAAmQAAmQAAmQAAmQAAkccQI5FZcg37F9xx7CgDzHeXOZVF9Vq8KI6s5kvsVgZSH2oipk/U5u5ZTse3h3YvnPFy8z6j1dmf8vX73Wy2t1O+9M7JUgP9i9jG+jkjWft5NRuNA9q8zQYGmjHHjsaBOA9JiOBEiABAyBjPaLiYy9K0L27WIVmbyCF2WC4nbPZXoSIIHeEqiHy+XQ8ypKB2I2LFBBJKFUGNAsdlZJtjo4hwG1pnONWRdjlYpCz694kTftYhlaAquBV3L19PaZrKc0kG4OprfdFZLnZRQJYKkmLnzohcmiO8qQBPqdgMgUSr0uI75L1IFGKVK20Rm3zipdQYgSKH2fi2VIAiRAAiRAAiRAAiRAAiRAAiRwdAl4nrrPydP2jEIi12leT5QYYq8rFvOwHKp0g5/BADX6xrB0DL8bh351IFZK4eGcFUCXrZTeyiTFEoFYHZw3iQ7QP1/rYqsCQbuMtMc3k7wPgq/Y4ysx+wNMoJOk+ADfDotOAiSwGwKrgRoVb7T3tpoPGjvt3p2LCmajSqb9uMtHZgtSyO4gMiSBPiGAzkA99McjPVT14rwMkAUqK6q+UCLIineDZ1qsFcCv+foTrefmqmy2YdycvYumP3yilBBlT9MKQevDf+DPJotvDoslgFzcoSPmmLvQnev2JYRZPHhLORXKZuMYzkMaPB9xazqYNlq6cEW3xZAE+p2AWDYpYhkDuObgvt3H/9Y1JvH+tzpXO60bSt7Nusyea3JJvh9YcEFdmYviYmvO3CMBEiABEiABEiABEiABEiABEjiKBGQKTcHJXhryMAFh5eMYHLd+LZt2uU5bikT+1mp9IJUmdRzXEouXxdTRXW3eUrXLkaerRoFAlkqGhQA38L+TjGGxGMurwsNBUoWuPcptJYiwamwUCaortYO3HCqsjdrJWXI3Ijcwzx73l/KyuT2Xer6Nd2CdOMEoz0jeRJ8THrYH+WilNvKto3XLvFsSIIH1CMgPe8EJ2dvT9LqywI8knA09aUTRkQAJ9BuBnz309EImypyNVM4MOnvJgHS6Q5DeNs1ek6b1TtxAXDoW56GTgQ5Bpu5NVz5zYT59nNtKvfVLfmFJ0O9oTff0c8B2en9duHga4tDZcEoFsgWFkSjjFWWTjgQOBgFP3bUfBbUCAPsdQdAhrmj+8x8JkAAJkAAJkAAJkAAJkAAJkMDRJuDZ/qGXkrfsNRBcWyREXesvV8cuV/3In67JJEXI+IwSgbkpjCzAb89B/mRkUHIq5IbYdmMIZj/JMydLsL7x8MXK9nLf/9RB7BX28/lD3geOIme8e/9psAT9SmD7X26/3gnLRQIksGsCMqa04eB9p9l7uCh+bOC37+zAVLoiQj7yt2E5tn8dnkECJNAtAj96qDxT8/RprDkWilZxTXxdtqFQYLR/RQMYA9RaGvJo3DcUCSQO8U6BIBJrBbF4aFYjTYj8xNeUd/aHn/16GWfStRLQkV8wg5KidLVefdx6xiZ7yTMxCgVOK3nNKYkigYtHB0O222LdUYYk0JcE5JPZXbvCfR9J6No9a0K5e/ttNL8SfLNd+V77kiwLRQIkQAIkQAIkQAIkQAIkQAIksDsCIhuzs+ntTHSR1TgrBFZmtrvcbS/V9VF7L835lwf/9HIt489DxmdUFMxAv5X9tcgIt3BbUEK4PSA+mVCDvrXph0PmKB5yxUiWXv3hZ58ubyG7vkuilTayCifr6xTuvtDpZ7/O89+niRe7vzfmsBcE8PXSkQAJkIAhILLx6loUrdUE9vCD1nUHwXwiepfsO5Sj61dkhiRAAjsk8LMHyzM6qt8fen4FCgRQCOg0s91ZKjCdhLZOgzkmSyIo8RorhWu/4q16Yz8b+98u77BYh/60UIUVmHIzps5a7ra1nm4eSuKNokYz1m6Z2rw9cpP9Zmcj0pr19Ca0eLiPCHjqx640ePNbBvU7fh8u9c5D1/lP5+Bpj99NGgi3SYAESIAESIAESIAESIAESIAEekxgPZlR7y577KZ3OhtmKpD3WZmgu9b2ygKFgbQ1TihV2ElLdjLTQM2vDFXjMZf7QQs91ScyAq1uHjR2LO/eEcBXTEcCJEAChsDawfvkh90J2DGruIMCwWZKBZs3DzAwlUqldcUUiP92RKB05UxB5fOFMAiKodTykYqrdbVcwZr2O8qQJ5FABwIVWdrg3XOTY9k4KHuedyrEJyx1BAbnUCc0BumMglDq+5ZkOI7BcLg61kjTemZZ189WP8N31FLp/D8v0Z6sAxeBmSBFZ6pZd7YybuTg6u90HWsONhUCsGu1w+1/p9Bl81ibL2I6K53ZM/ifBPqegHwXLW+2+05MfWXrKNyD/cZkHxZW0vtmr3k82V0T2HpQzoXClFxR94uAYE1JGUECJEACJHCYCIxKfzAv/cFA+oO4r1jH1eUs+4OH6RnzXo4GAXzL8jEXAmW/5TATV6tL8i2fZr/5aLwBvMtDT8AomXsiI7Mm5d3kHJ30S939t1sjcP1Ud9ydZ5eilFjXv3UJIMs38iMXsSbs+gByRZYW+NBseex2Qc9lVFhsNdmP/nWrTGpNiZKItPwQUbgVZ90gCFXl2FJ9bGH84C1jkNwegorc42j6meIe0w4M0i6dNh2//naL9EOSpdknxzx1Y/3zeeSoE6ASwVF/A3j/JNBCwKu4XTcQaH6opKERyw9W+0+OS9vV0DR0+kQLr6s31tvMfvWF8yUVRI/GvnfyX2NdRPsi8iMxD58MhuhhVXzlXEVaW/PS5Hq28umvXuttiZj7USDwxphpqE+Mzk6W3z6WK9cD/9GMbpoNR/2Rbpo2mEi8WB6oDoTxtcJt7+rfjl+YbxzjxroEfiCdsPue/3J1xfcL65Bd99ytH+j4xFpOz4kiw8DK4kJLJHdIoI8JeLHG+3pqL4qIthLqvoYCgWs9oX2jve/tRRl4DRIgARIggaNHoPjt8yWxD/ao/AidvK284m2DIEqBGFTvf2WyIr9P86JkLv3BP2V/MEWHmyTQLwR+98/Pl948Fj2qRbaz6OviHWlX+tp+yxg40ncPqg9+a7ISxP78XYv+s3/9Xy7wW+6Xh8dykMB2CWhf+ofxqBn0b1McwPfePni83exb0qM/2nYNe1yU3cUyaEvaLu38YLxcGRVFguVhf241Gxdbs0X/WGI6lqmZ0o1PICY9eJ6r+5XBO6sHXYEAgoMf78nzbyLtuCWP4ccdDzCSBIQAlQj4GpAACaQI1Ofl1yu1v3bTCsVb492P+HYbNya9/Eq5ua+pXClkT8HYaPNjz52bqmW9M7VMVFgVG+eYnRyKBqt7TrIrjU4x8YShXO0XZXcC/sMvTlaGa/70dz7/9Izs05HArggkWr8ThbkzhSE1UpJXsBTozH2xj04CBryNq8pLWNVavRoqbyGzePvaP9A6RoJm60Eu8hYWfVVq1pubD/pvmPsmHTbkjnoEDnU26vuhuq78HZ+dhcL/B4KAWABY2FVB3XfimkhG4XH9HN1vcDOF+4qMMkMzmlskQAIkQAIksEsCH3/2/NQK+oMqtm1uaWxbqXwzY7TfjJzei2UmoJrIaTXxkRe/UBmqKekPfm2mmZJbJEAC+0Xgk7Pnp+oZfeZWNioosTxXz0Qp63Mole33oU8mivtFL9QTS8N64re/ca4SRGr6r8Yp29mvZ8frksBOCcjnvCCy2lNmIF36mL585/jNNh6ZyraRne/0Asl5yM+4JHSW9pJYCJJ3119uZLR2Y8EoEkyOrdyVKcvlT5kUplGSyLJalBvWyre0LJ9al+40ZGBaMsDSqEHozYsCwekDboHAwoozFWOqtYfP3z0VRzcAc3GN9wLbMSYd0pFAZwJUIujMhbEkcCQJXBfzP++b+1JVfpkLaKikHX5eYI2gWw6mmuzAtukAmYYAJBvS+VGDq+pat65zWPP50AuTpVpOXbnhRUXpQBoHc1doUEUSYgAD8b60EBqPsrGhVC2rindy+so9f3luKtSZ6Z89+NTMYWXF+9o7AtWxy9WqMt8vv+EeYRdZ0qtiYaS0/Y6k6y5sp2BJx0I6a+Y3QDpv0JDORZqKXtvByLT7TmBFwXLGYFWWUTHtG/tm97ZY5hrJ7zO+vqwsiTCy7C38qLeXZe4kQAIkQAJHhMA75s6XBpR35a1YSX9QROvim7P5OrT7TF/Q9hdxdFX6g8sD/pXiX35xSs6f/tGDVCY4Iq8Ob7PPCMDywC+OxVf+LauLschysjKSg4GdSIQ5bgk7O9CD71y6Y2hfisexlaxStwdUUSaOXPnQi+emTtyhMkGfPV4WhwQ2IeAtRPJhpwdzcUKUyG+NTBfffUqeu0mGRmbTKU3zGlJ5pPLLRn71hw9/db7TOd2KcxOPPjl7bl7aH1O3B7ziasaWA+0Xo9Qg9Z+bwpJWytdGGuWrAYwX1L2KjBlMXx+/MNOtsu13Pnfdia7dHvIurYrymHO7ev4ukyTE7wZ4mvECIE/2Tbx5D0TeJ7KKxcU7IjOhI4HOBOTVoSMBEiCBFIFYX5UGhMrJKBV+UNZzpgEjg0nQBDRmd5Lt9L6LdyF+lJCnyxch1kWH0AMe2oQjq6q68MiF+fWuy3ilPvzSH07FufqcJwoEEBRZ5QGrlBFIh1MMElgFAuGLRiKelfVJWoGIRwv2gfaKgRdf+dVvn5siWxIggQNBYN7UqVJUW5+iXm3Wra6ONfUt6lxTS8vHjg5ZykNAZbzkI0caDnWG8eZMRCdNRazpLj4WRQIdZ6gk0iDGjYNAYEEsZ3hKL2AgH20c1w5ptE/kR7Hx7eAHMnFGoIFBmcZ+sw2DqPQ5ZtvEmZZQo3OOtg3c0KqqfGe8zI65ocF/JEACJEACuyHw/lfOTeU9rDEcF9Geg7AZA4o2tAOP2DfHZAQC/UXb9mvG1aUzaOIyYVG6/tIffJz9wd08FJ5LAjsg8MDsualbI3ouzmjzLeObxIxbeDgPsjKR8WQjeCs7s+uKSzr5huuZ0H7HfqjCbFRczOsryNOezf8kQAL9TmD+f7owX/NV1cjYpbCu72h+06UeMBPEku5oQ1Yjv/nruqTvaeU4Ig1y8h0JrVzYzuaPRK4DX/d9NVTTeybfgcWUv/sfnr53qOafDj1v3igIiJwJtxTITTufiQOp+8RHOYkLZIwiM39iMTj9vc89fe/1Q2Z1BRM6h1d1Bc90188fmcDhPUg88oQMJCOyELwXsOwAGSL2rYdcQ88vnL5cNefyHwl0IJA0SzocYRQJkMCRJPAfltW1vKx3DWF4WvMPMOzPTRMLGiPbdvgRE+fyRiMGjSN0khBKx2jPGi/bLnsfnPDeuccvreZ0OfbkISVDf1YgJIMW8szS3hU33Wh023h0sFSADmkgYeR75eLL5664cxiSAAn0JwHRuJ7X3m5MzaEH2hwU3ewubZ3S7HxKJ69yfZyWSzbjxuP9R+Adi/EzA0n7Zi9KB4FIJEKZmswowKCOCH+n9+K6vAYJkAAJkMDhJvDeuScvhdJ3S/fXZQkxFYp3fT0XQlAMh30XYhv9bqdkjnYhZjzXpI9ZnPsj9gctKv4ngZ4T+E+zT156e+T/Z+/do+O47jvPW9UPNB6kWrRlSZHtNBW/4mRGkG05tMdZN6KVsprYFnGS7B7vnF2CZ3fPzu7+QWhjSTBNG41Ylhk7J4L+mJ3JnD2HYHbPemcTB6QcryIrDKCZTKLESYSsk7ESx2b7IetFkU2CABrd9djf99663dWNBog3uhvfS15U1a1bt6o+t6r6/n73d3/XKSykYQyA99R4CjHvp5HX8AbXY13ng4uDcRDe3VAillWJ8z2BunQgLBw5R93OjlcgT0AC20RA3uInPfm91r/LUibkSJili4Gf1pV7+vuw+ZOZ3/v68baNgG8NYiJwztf37s7a3C99fuqloceGDpX8w7ddcYZ/ouRM3H7Nmbp93pm9/Zp77varsl5KTLzlmjN8+0sLN//9/Y8NdbMeSownzu5E/aOuReWv69nWO5Y24JwwMMiW1VmbxiUJtCIQe2xa7WYaCew5AXzr4oHPbJzGDq2//2uPzpT6g3y9owliS135AIWF3tbizMp0s3flX3scRsPCohrNIgg7i0mjaIeF4a1vhIe7Yk6jlbe/5ZQ7LzwyXkn6hZ5AZpQXdqGSUcFRHaxduKk/mwcjlI0BQVSvUgYajujoSFXV1Hfv/8Jxm5dLEiCB9iNwx4XPjKad4IlGt7VN1xkZbGnvA7KrLigYhVTt2yHf43iAEKGDHA9jr2RkrQwLdRwjAq0IexP8RsShcb1jCLz/a49cuTwQZmsXHL0n8fZJbZ9eaXw/zD7zLjTmi7bkfcJ7iXcHHTf19k1S3Xo5Ie2bQrHlcUwkARIgARLYaQJdoVeABwIYEBhY8EQXD/gNim+j8zFq90UZ0UmJUGvvxfbbUWk9y+7Ui//Fb7Ct14iSWySwrQQ+PP258St9vjYggC4Guje8puYdbmp/NslryNscIKfBeFXrekR+65VROjcvqqnnj1K308yK2yTQbgSyM6PZfrf/CrzFpsXrCH6jF1PG2wi+CdDfGk8ka1y51f/UskT6Xvl+oDyrD8I5sA39jvFGEBbf+Mjjh2uHcWVPCAxOj2YvZ/svSv1nW9a/DABE3a0eTH3X9svzgDpHWTZgG4Mb8DyJJwj9mwNDlYPlsPjCL50+bPNxSQKtCIhvWgYSIAESaCTgeP5E1XXy+GGBMmE7QrNCAz9k+PGCBwLbmAmDcIoGBK1pv/2PTp3wEtWCUQSt2XJoKqCp/qL61MyFvw2oD3R6VFPuSO7C54rFe399wu7jkgRIoL0I3HJtYWqpPyPfBJXTLsril7dCyRTfub51fAvwzUbEZ0IW8n2QOeg8t3hgYXnipfUVw1wk0HYExPPOhLQ9njDPeNPv47qvVn6D9YvRdLx+98woUOxHGyfWvpmgAcG6ATMjCZAACZBACwK3PvfwiUoYFsTlbIu9G5EPWxweJaGUakqN3PnsWPF7952eWD0n95AACWyWwEemT514ZSAsVGUeSrQVYdxjZa/NlonjUAbKcySWRdt/qd8dOTL9OfEiR93OVriu91h0Ai709+Wki27QCUNZmhA6TjH0w6JXvj5XlCnWbDqXJGAJlIYmS4f+6OSTlaRzAl5JEPA+Qye/HQGyb7wsGB3JGRS8EB8oq4k3tuMkLGNLBDD94ltnTj4p9T+Ograj/vVvgpQFfb/VS+A5gDECjNfwXCC9x1cTW7p4HrwvCMijwkACbU1APmkNgc9sA46d27j93z88nfHVUW2lqBsYK89lBJ6V6TYFP1gIdlmvvMjdmjajE2s6yecEiVK5nL77lQdOF/VB/FMjcOQrhdzLt/oXxSmy1ITMeSfz3emgDQKaOjFqR2El2hcZDthdmNMcAdVjGhKYB0lGHKMeJMIa9dZrobiKemwW+RhIgATaj8AHpsfyVw4mZupXBrUvgl2arVU9EdQ/yDpj3UIZFs4QKM13oqI9EMDm1FVvWlAP/eUnPj8ZlcwFCXQkgdv+/aMzIijnRW8rIfqd1HciCTUjnNh7hLRVfkdXAjDKYBgQoGy8S9K+KZauLd5dotJwJS6mkAAJkMDuEYDoEw9NLaH4rvZbH5wey106FF7UcnXsN6l+E+Z3y8rd9g5W80Rg99v2H47GL6Ijv1vwQoV4Sykx9CcyV7PNyyUJkMDWCRyRd/lqX+LiokxhYELju2v0M3ZflCVqn9r3tS7v1dux0OPoAUDyfdD6Hv2dEJlOeyTwqdvZetW1LAGGA72qfyRI+A8upZx8Ves441lNHaFeIR8kPXf2QCU8+81PPD4Vz8V1EsCzdElGosurK17zZNpZTD0b+xRg2qI1Q6xtYPLVvw9oG+DRxHOop9rTu+R3fl7Nzn3sC0Nrlsudu0YAHin6nMwLjuPkavUfff/NbwMuZbXnIKrvpufAtgsxKMiuw4AA6j40jMUwtVj8BXoh2LVK7uATRU9YB98BL50ESGBHCPS8UT2erqriaoXXf8BWy2HS7Y9UPRd+8Gw0qWgY9XpqggYEdUrxtbA3PANG2thCC4fxvZtbR2PBBgijiGhUIqLOqklj/WjzcEkCJNBeBP5y+PRsGDpP6s5NLSigSWfjatcqL/g6AhTOcMmO74F8GnQIw3CWBgTrgMcsbU8g+4ZzPO2H6xsFVDMq2NhtWSUvOmEyleA4DQg2xo+5SYAESIAEGgmkkuoMXBmjo7Bu8NaYZ8WW/IZBZl+v3I42JTohoWjGuk95cAVSJpDAVgm8PpA4syAGBMbg1HQSitdyrYNB2Sv1Z+s/o9bnyDfCdjziHEtyLjnn+PpLYc71Ejhyfmz82sHei68f9J6QqSnyy0kZ7OM0x4pJk8FA2OelvPyVfv/MT1149GLuwqdH13su5ut+AhiJfvNScDwpv/VWrwNdDN5nxBt+G1bIrXJQFNB20CPQZRnT7xRV1T9u83C59wTgkcKtqlqd6PqX5wHPxA3r317+iufA1D1+Z3SfguTD8yD/MUVGKXtVDdlDuSSBtQjQiGAtOtxHAvuYANxsHVxQw6GKKdrxYwRlBCwgo/W6e300UJpilMeRdMS4xRw+PqbTWhQUgTPx7ft+i6NbWzxvR6YLI0tpjJiUeaxkPzomoNjB6EbzAQfXjQU0PtCA6PUC1VdFPdi6MW70EtJ5uJx08kemPzuysZKZmwRIYDcJBPPXC9K5P9fynNH31+yrv+MrvtO1b7PNE+WQbw2+N/heOMovlqve8ZbnYSIJdBiBF4dPF7OL4UPmsu1zj6UN0TreIRvs+1RLix8Xyyf58c4kxbwfv9O+CidefOD0rC2GSxIgARIgARLYKIF7njop8mCYh4xWk6ft71KtHYdSYTRgojE0MPltGparBciVZiRzPcd8xsnf81RhpJ7CNRIgga0Q+PmvnhxRbiCj1eFK2ryPNT1Z9E43vKe199yeNfYNsElNSy27SVsUS4w2hSGBNEnzP//Vz440ZeXmJgl8+P95ePDnnnr44vWMXxBPoTJ/uafr0094Cp4IfPEcaqLxwKrXZV8o9WnTPTfIhW7wxLu+MXYRnmY2eSk8rMsIfOuff/HcTUvhhO3kxfS/GDGOd7ke7HegxdJ+M7DUweTBsweDQvksGP2OrHhh+BCnE44wtdHiR/eJ7iBQD0EXp+tf/160usB4/WN/bDv2HKD/xhiUmu+RfHv08wSDggOLDp+BVmiZ1pJAw2eoZQ4mkgAJ7FsC3xz+8tyhBechzJMkv19Nof4DJY1fGawg2/JDpUewyo+XGclaV0bAYACCjAlw72vcJPZWEmeL904W7B4uGwm8PhAeK0uPPxoQ5offlYbf1j/dVklkDDxi55Q6RBqEn1JvcCy2h6skQAJtRgDGXoHnDYtgWTRfXXOB9nsNJRSEBCzNt9lYH+MLYr/H+LYgxgNcYPpOUnkSfeUU0xU1RE8xcUJc73QC3/zEl6fetBhOxNs35jfW/NaazhfcpW3rxO5Y3icb8B4h2lyYDghvG6YH6q2EE8V7TxdsXi5JgARIgARIYDMELotMBvfD+jdHF1D/HaqX15xmtvHbtlaATGhjLZ/8zqGDE3OqX+4Nj9XSuUICJLAlApVUcMwYCcj7GclnGytwbT2Q0RnVyzaGRzhDoCopvssbY906951/9GsnfnyLP/PagSBXluHhaPlruUHqEx21YF2XKYwcXjMMifZrySFaX06Fuas3ORc/8BS9ErQmvv9S/79ferzw5vlwAp28MB7A8ySrtWCfLyRYWdQu9bOFZ9I+l9FROAYGRba8nopz/LWhL56LdnPRZgReGvripOs7E7q+5CuDpSf1Z2pWvjpYh95BL3HxxouoGaiJ5wI56wHPD8rAMwAjNjwvNy84E3OcVqUOiWs3JCCPDwMJtDWB+G8lLpTP7B5U1z3TDw9e61fTXlLlag1g+ZGCCIOfJvwIIeAHC4pzHbQrRPw4uXrUO0bPw4oOHeD4oUNwgvDJ79x/mi68DI4Vf8ULQe7lgzL3pXZ9ZgQQEIeggh99y7F+IGpkjSB1YAOOty8T6gUB3glq5UZ5pfEq8+dxFKUhxL8k0J4EsjNjuX6VnE6G4aB5j0X5K6MdPBkF4cn3GcphmQO+5gYN3w6kQxCBMIGAUdMIECwkp0p5aeX4ieKiDwOCQhGpDCTQbQQ+9NQjo2/0OU/gd9D8psoPoSj1IHibNwKtnMagf4XlPcHvJd43bFcSRrhHGXiXMhVn4h/vowFBIzlukQAJkMCeEpCvdkPQLZ6GlDbcyH9lLPfym52LZhShGHrLj5Mx/jS/UvVLNr9XZlRzPRW/VSZEv2q640pS4nKhzSLLensQJ8IxSXXrNUfkwcJsLBtXSYAENkjgiIw2v9ofXlyUxiPkL7yb6CS07yjakLbVad7W+nbtVPb9rSU0rkD3psvT725d74Y0GM4eWKRup5HYxrbe+8ynxqXTv2BlBtQT5GxrkI91BFunZqv+1xwn29H3N5ROQLuO+ukvO4W/ePD0RP0Iru1nAj/79FjhSl84Dq+0Vs+OZ8h6MbEdxtqIJQKFZ9HDcyUB0+oh2G15MmGMUJJBIg+9NPT4lN7JP21N4Cdmxgop5Yzj1wAeKRDwu4Ht2nMg25juKu1H+gv5vqCPpuqi58A8B/JY6L4YlFFxVemQeGX8uwdOT0kyAwmsm0D0CK47PzOSAAnsQwLwSNC7UB1KeapoBRMsTYPXzJ2ot6Fyl3S48U1jzp6IFX7KzOh5CDIwJnBLTpA8TgOCtR8mcSF+FB4erMWgFU60gIlWwBYCykDbw3QY1gUdm66tqEVIrSbV0S2choeSAAnsAoHS0OniS0OP3f0msSZOyQcD77ENVpmhDQa0QGm+2fhWQ3GBbzOi+babZUY01IcWE08efEndTQMCS5LLbiTwZ5/40mTfvHM47TlF3B/aLWbOScwJDcWgmRs6vkQeoxyEYC7vjGzDmAAdL2GQKCYrzhANCAQKAwmQAAmQwJYJuMnEUSiHbXtuywWiAMjxUbBr+F1DhwSWNiILBhCU0wHlQQuMSxLYJIErA8FR62ESLUhr8LMdup34JUXdSDrJdjJaT5NX+qnbibPayPqdf/TwiaW0U0AnnK0zdNBBn4YlIvR1iLX9ep+RF3S9yLdXy9z6xJGsoZ8FJdOJiueXgbDwwfOPHNO7+WffE/jbB04XDi0Gx8UYoAhDAETzrNWfKTxvkEG1AaA8X1r3Lmn4nUe7AemQbTPy4B5aUHOHSuHdNCDonEfrx0OnCykvHArFOyg0FXGdhPFEYOocd4RvCzw+62+U1lBEadgp+5DuhGHx0HVviAYEgMKwUQJWZtjoccxPAiSwzwjMDU8WX7z/y4dlYOvxVDVZTKNnW/8wicGA/BohmoaKjMCTDqheiQk/KY2YpConkmoxaTqqRFx68qqzcPg7939+ap8h3MTtug/GD6opj6LGYYNwgobiRoMoherNT+N+zRoPQNBMiMWrKI0+utFimZ8ESGBvCPz1xx8r9CxWD1cd5xzeblif43sMwwKMlF5KYZqC6K2PLZ0wqb/XaXE3k6qmZ7PziaG/+nhhdO54obQ3d8KzksDuEcBckH9//+nDCc99KF11dfsGxpBxw5r4OpQx2EYbCBb+eKcy1WSpbykxUb5cvruIeQwZSIAESIAESGAbCCRC50F06htjNSN9m2Jtt1XsJCLbmU6suFtt61rbeLXTv2f6ECvLQ8lsjOawz3Y6aoM6kQchG16jPBiDzFUS2CwBx+h25J1CMJ3NdXf3MNixuhnbLW237XL9Z8b7jWCXZitU1O0YEhv7e+Qrozk/oSZFrJaOONMZh/Y/5AAb/WgdHb1WPkAebbQfW9pa1t9qqR9t0K+/3eZ7O9+rJuEJdmNXyNzdSuBbD3x5KiXegJZd5yz0OXj+rFyKbTxr0PlA34P33T5fWEcaoujuS4euuw+98LEv3g25t1tZdet9yeCE2WuqfHfCcyZ6pD57pO8Fde4Gpr8F3xBrUIDv0JL0vWA6KugsMLgTS+k7KFWVM1H8hdOH/+6XvjzXrax4XztLYDPdTjt7RSydBBoJiEjbEPjMNuDYu438V8dGlhPqWDXp5DFH47JoGvBDBYVDf9X8UJWlUYMfM/mRK2aq4dnK5WuTc8cn2Sm1zmq7+2vjV94YCLJWiDTW6kbo1EVEAqgtznRq2K0WS93pUU9HE3O1YKxWjXBbma/ejLnXV8vLdBIggfYjMCguMxPJoLCUDj8qxgO5hbQZxTZQMd9njHaA7ReMkXqqbunWeXWut+qcnf3Vx2bb7254RSSwewTE3ezIfEbaN6kw33DW2G8o3hu4Esx44WxvRT2XuVyenGX7pgEXN0iABEigzQh0pF7hI9OfvVLq9bLozPfEQ11F2m/Wk5zhi44DkelqcmFdvqsZoIuC2QYrL6KjAcYDegofUTBbJYtO0yWi88u4w8WxbmmB8iBAMJDAJgm869lHrlSTYda6oEYx9XfUFGrfv7VOgTZoq6DfbWmrmjLMO49Rq/b7gHScr/dHyzdTJ9eK4OppP/fUwxdfOaBy6KfFdxFMV9RddLhNt/XQXKd2vy7HyhbR99vucwJn7jv3fenu1a+Ie/YjgfdAv5NxRf8enlhOqUHoc/B8HahgAJi4qYcLe4n4fe8Tnfwt193ZXk+d964vTFFO7Y4n5ohMcZVKp/LlZHjitQPO4FIKOolAjEUCJXUtIVDQ+6F51y/zFvSIrqKv6pxf9pemZGAodfrd8Rjs2V2s0vzYs+vhiUmgmQC+ffHAZzZOo03Wj0yfyl/pDeUHzJEOb2NIID9bpSBwitnF5OzcMEezbrSqctOFbHIgvFKBVQYUQxBUouVqZdXVQ1EOK5SscsBKI4K60gmHQEBFA3RZVQ+/MjRZXKUYJpMACbQ5gTu+cXIwFEMCucxBzLeIACt23w2LFeXPvT70+JxJ5V8SIAFLIDc9mlUDA4PSjTIoaldp39hf2VCmZAqLBxavS/uGwrjlxSUJkAAJtDmBjtMrQB5MD/hXfHEFCCUxRipXpaegofPJQq91Qll5zvxm1Tuw7G9YdMAKOVGPVNOKZ9tJiWNTYmCA4Ff8w8UHOIIxoscFCWyIQP7MaPblt/aIbseL5rE275XtNLaF1d9Xk9K8bd9Nm7/eNo3yR++rVprG33H5PqAsnO/mS87h5z/Jd7nOcO21e556eGSpJziDAVMwItB1ELFt+qoKY/P9rddrcw7T6YszohyMINZB6gfH4DsPYxB4MHCriYkXH3i8YDLwLwk0Erjt6bGcl0nm5KEZTPtOVquNRXsrj1Gx6gbF20rlOcqpjcy6bQttxOtZNZhI+IOpqpPNeNDeKxncib4Yfy55vTzHwYDdVut7ez/d2CErij5VkljcW7Q8+3YQkA+g/AaiI7MWuvGZrd0cV0jAErhtppBPqnDGdPTjDdhFIwIRYnBGuELSzZAgHPrR0G/O2mvjkgRIgARIgARIgAS6jIAYiijIkbNddl+8nc0ToF5h8+za8UitV4hdWNvrFd4s8mCP488kQ0+8/Yl0JjIavBHshBEBvAciABI6t9BZhpGNmEcZy2XHGSre99gs8jCQAAlsjED+d8fyL2fDmV01IsAlWkOCmBHBLSVn6E9+lVNvrbcG77zw6EVxIp/D9xed+1s1Ioift9mIQOv+pM7wPZavfql0LXW4xAFZcWRcJwESIIFOINCVegWxpeuagAqalpiP7mhIlrPROhcdSsBK9kakbTAmWNcdweJXZTJZlVQ5fUDol9RCtUhXPuvCx0x7SECM1JUrrqhqviX34FpgSKBDNHLZbjkGekQAAEAASURBVHJJAiRAAiRAAiRAAl1E4KjcyxmJkCfnJEKOLElk2J8E8BxQr7A/676t7hrKOkydo0cQ48riZhDSoaWD7SQ0W5v+i3PAWADnw3QJGHUbD0mPAmGcB9dJYGME8ELJ5NQMHUXgyPTJkUuqmgvFeAtf3KoopsPI4AqeA+I1im8ojK+sFwJsI9il2Wr8W9O3QfEtUbyP6+88vsVyeLb/oD8ijdHJxqO4RQIkQAIk0MYEjsq1nZEIeXJOYtfoFZpEA7m1zgyomBmJg7HLH5X12dg2VzuYANpUURvshnfx1mdP5Z2k82Ai8I/+OAhzcAlVD9Iqy/ao9zzzaFFaf7OO45z/9i9+8Vx9P9dIoD0I4OMcykNv5lzbu2uC0CNTKjGQAAmQAAmQAAmQQLcSOCE3lo1ublCWkCu7RuCP7ouL9RHAc4D6x3Ngw6iszNoNLklgtwjAqDwh6gttSBCdFLIZOvm3O6BcRO0SWc6JdT3iVk6E9R045XbfAssjgQ4ggF7i9QW8d9sZ8D7jW9ItnQDbyWa1svxkcMzus99DayRg0+0S+1G7Vn+ntdCow7U+nrE6Rn59jBiIOXJMiC0n+aAkT0pkIAESIAES6AwCXatXWH8Lpn0rqpWgj6u90r6XzCtbP4H6I7pW2wvlfeD858bf+exnryQScAHvjTqOn/PFYhTzBjZHLxnkvFQ4IlOGTf/M0ycvvu+pkyPrvybmJIFdIOCpYv3p3+L5MFLFjlZBUc3bWlzRIkvtRBCCrAAEBRYDCZAACZAACZAACXQpgeea7gsdyDMSIWcy7B8C1Cvsn7ruiDsVGayIEam6Ex8DI2ryXF1uc0SO01E6oxw9eAISpMynLdumE9Js2xvGVHU6iqwHec92jKH7KhDdCaZLgA4lnm6P5ZIESGCTBDyv6IrysTa9wA2KMe8u3mkTbXZXPI8gmu5qLJtCpOeRw/QgrFC2dWzKxs0bExicHsstpp08ctovLowwUrKREpcD8ai9uOhPtCufaYmogCjg2NWizYMlvsw94toA5dsg58sPnimwLWqBcEkCJEAC7U+ga/UKLVod7V8bsStcTdCfkzwPxfJxtUMJwD1UbZ6oVe4hJ54HchfGL74xEBZE6M1qsTjyPgBBGC6lIAibaFxMIc02qKvJMHe1PzyT++Oxi2+7QGOCVTAzeZcJZF9RpfQed95DeQQhJlv2irt8+zwdCZAACZAACZAACewWgUk5EeTHeBiUjRmJ2Xgi17uWAOoZ9Y16j4c52aBeIU6E67tGIFtWJXQqNYZYD1PDjtXSGzK13IDMh4h+L60nkaU1JkdnZkZk0mxJFVsezEQSIIEbEyiXS3jHdNBGADc+ZFtzRAZI0K2WxThpW8vu0sJKfcnBZUwvGgV8C+GpBcsVUfKgc8VGe0zzEt/VeKw9EzhWyoVBGAwSbIAxQuagOmq3uSQBEiABEmh7ApNyhZAf42FQNmYkZuOJnbaO37hODQCPCkBFxMOcbAxJLMUTud6hBOQJtQKttJ9WhPc+fWrccZ0ZaWzlVuxsYalvbUB1A00KhLU+GmrJQBprYZhz3PDMnRdOjq8oiwkksMsE5o4XSn3VvRPwINxAiZTxwtLzw5PFXb59no4ESIAESIAESIAEdosA5EbIj5Aj42FQNmYkZuOJXO86Aqhf1DPqOx7mZIN6hTgRru8qgbnhAozKi5DJoLiLdy5ZvcZ2XRB0LlbvUluXub8d6fA8sOyWnv/k6eJ2nYvlkMB+IzB7fLJUTYTFQN6pvQqi/hRPI4rv8jorwHVtm8D4bzGeYKT+ttkIRH9v5ZqwXBFEpy1TGzS3TVZkYwIJkAAJkEDbECjJlXSlXqFTp0PKSoVQ0G+b92MHL8Q2pKTFC+9f2g9UdLqffvbTTyy74SjsNU0Qk82a4UCUFD/AJkVL20lqCo0a83K857qFt1/4TO4H937heNMh3CSBXSXQUw2fkxPmdvWk0cmgMILlc8pboVDfi8vhOUmABEiABEiABEhgJwlYgb9ZxhyUkyJtSCLyMHQXgazcTnOd4w7nJLLOQYJhTwn0VcLnFnqMPAhjAmNOEBuq2nB1kU6jlta8XduxYgW6EbgiwEhbjFauSg8aQiidngk/xPvAsI0EBqdHswv9fbmq4+IbJIb7XvHFB2iosY2I266oMHSekzcqp99heddMWO1dtvu3dxk4fJfXS1Q67+9aqV9e5WgYFqzQRa+S9wbJoXjR1XYK8K4rz4lcx003OIS7SYAESIAE2otASS4HcmSzjDkYpWEf8nRU6EQjAjSymysB0OckdmQl4OIZ1ibgYHdaYtnke9fXPzW+4FdHE4mUbliZ1PX8NcKwJwVCUI5bArvSQMO8VghiazryU9/4jPru/TQkWA9V5tkZAo7SQt6xxtLNM2rStip0Nh1vBR+RWnCWHjFVF/eVZxvPzy0SIAESIAESIAES6EoCEOaHJDbLmoNRGvYhD0N3EMjKbTTXNe5sTiLrGiQY9pzAQMWZm/fcY+WUq5ZFQEMHU2tDAiMjmvnS65et52GXTTvK1UqSdrueUzwdiD6kv2JzKAVX3r4jS9c5G8/H9Y0TgNFAv+odEZwfFZVTvpxSWdfxlZc281XAeP99X/+06qm4s1K/c0uq+uTcMI0KNk66fY+Q92tORkYdqxkDQfci9a6D1cOscvl4PhC0sY8sobtcM6xSXoK6nTWxNewMxQuVvLCtQ/07afajLiVtFe62DFuPdrt5aes3ni5VnYtvc50ESIAESKAjCEBnMCRxRuJg7IqxjjTsQ56OCc2/fO1+4Vm5wGb4uOY5iR0HHxfOsD4CWrSKTF7e+YcnT5QTquAkHTlYGmtoqNmoi4u2Vyla9q5ofEM4hiCNRp2e50rKrabckdueOzW+SjFMJoEdJ7Ck5qdEGJEfFXyqbdzCaRvekxblQPDRZs8QTF1jVOMnZlvkZBIJkAAJkAAJkAAJdCMBCPOQKyFfxsOgbEAOzcYTud6xBFCP1Ct0bPXtnwtfXpqfEj1FqSpiWjTeYc2bh64jHqDjiBsMuKLvWCvYTixMnZCUvGJTLv2e1dm1juG+1QnAeODOC4+Mzx9MX3z1oPfEyzd5R1874GXnezxVEcWTj1HHIqNjuSBzV7x20Mu/lPVGXz+UuHjHzKmZt1347MjqpXNPJxEYuL4wJVVeMnqd3b9y2KscWFCzu3/mzj9j/Bu6G3fTaGxwg4/2blwQz0ECJEACJLAZAvKb3z16BfRKdUqgoN8pNbWd14n2kgi+SqzgEQf/9b/Mhb43WfUqKjOQMcYDMCRoiLK5RsBDj0ZZSg7LeIGOCbiKkuBKORCsE0FgY+EDXzuV1zv5hwR2mcDc8GQpVOH5TZ+2pdFA/H0x78JKA4XopyFUU89zBMSm8fNAEiABEiABEiCBjiTQVQJ/R9bAzl409Qo7y5elbyMBzKV+Pemcx/QCmGYAs3PHAzr94xH7jGajnituSKBVK1GfFI6D7gMR+hEMrFgUR49LEm2nWeAElAfrKDe0ds/5h094fcmLUnMF5fhZGAosi3XGoiiirmVgNGDkckd0UVVJh1FBNemJ0YYn5/FE/eXne8LgzHuf/syZwemx3IZOzsxtRwC6nf5lJbqdpjcUOhsdzPJGhj72fY4v6zdrnqlG/ahJ66t4U/RuUSe10TXL2xzXzFlSa/W40ZKjEuX7a0s1Ommki5HR5orjUSRAAiRAAu1BoGv0Co0SSHvAbXUVFPRbUdkXadKSkv/WiKD/5pvOHDw4oJI9KVVZXjYEWnaUrg3HGhLAZZ8VnNFAMw1DabpJma4IbirwVZjwx9cujXtJYOcIvOlqpdBfMUYvVqD0RMsTJmXKARcvx0YDnn45VjwNOBIxzyWm99BTfLiw1jHpGRnucmDemdho6cxPAiRAAiRAAiRAAl1AoGsE/i6oi+28BeoVtpMmy9oVAtl5r5ARlwCJADKcOaWMhVCIYSByoUQ/NNF6EkBnFAL0GzbYjilbBtKxbiPyVsXbY1k8QHoiFiZl5HJ/yac8aAGuc5kT7wO5P3505tJBNVlOOvLNMXom7XVADAnEMEN7HsDSmIWYbXiasPXmyBqiKwYGfjIYuXbQnXn/1z59dJ2XwGztSqDqF/Q7KXUPwx2tmdHvIOravIurXTre7fj7HM/XnI6yTTS6HZwpVeW7HGd2o3X5rH4fhlu+GHDhTbUeO1seJ/W52aDrXQ7G0gZ4gsFAN3iDEY3dVZvOJQmQAAmQQEcS6Aq9Atos7R4o6Ld7De3g9TmOtJQTIsVK42ngQ+9T4UAmf61aFonWEaFZrLR1Yw0NNolYt1FfE9J1szy2jPJKfriNi7sFRKPNupKDgBdIdEJPLfW4+SNPnRzRRfIPCewyAViLZ69Wn7z60muqL9WjlUV4bsN0Up7XhAgbokySa2r8mNef87oqIrpwya8FICzlKAhEnghGlYQISCKZHjp0q3KqSvXNB7RU3+W65ulIgARIgARIgATaikBXCPxtRXRvL4Z6hb3lz7NvkgDkwZ8KBp4MLi9Ip1JCy2++6C6qfqA8MR4IxCjc6j1kCAQ0HSoUuQ6yXXPA1I3ooILuw3YyGuMEY2SO4w/e8ia1ILoSZ7Ey9SK90jUjXHM79/RoLsz2vCDo85CzIbfDi4TuhJRtRwxBUmItAIP9nsgwBIb9JqJuYCxiBhAkpRMROinoqKSk3PVMMP0zv//QsTUvgDvbmgDeZTedenJhYUEtLy7V6hqDmxCt8Yi9CbzCNsYNBZq1PdCLGt2oea+hI8Kz5Fd81ZvuVX7gikeRyaItl8sbE0imemQaGbzDZuBN7QhhXdM72/XaTlszJqH+ZbZf6Kal1Lk2IpClDfYYfAf8ckWVry8V7T4uSYAESIAEOpZAx+sV8PvUzoGCfjvXzi5cm0xdoFSPmMEPJNQ77/oZVRFJtyIiGIwLnBWjsNFgsyG+btMal2iEw9obI7ARIEQjoJFeM04QTwSeGCxc6XWOmb38SwK7T+DWhUzh7je/taiuV0ReEYvkhPiYjAdtEBBPWOXTXssXiSZ6W5QaonhCrHjyvF9bUn1Xq8U75hMT8RK5TgIkQAIkQAIkQAL7kEDHC/z7sM5a3TL1Cq2oMK1jCPT+3auFO6q9xWzZVf3iJiDppsQuPFJkwBuBGBMg2gCdxo1CvPNK+rP1dAYVr6rlwf6ry8XbligP3ohhfH9uZiwXZlIz0umfsx2/8SUM+fUAAFkaYwFjVFAz8Md+KVDHWlXWO4c96VUs39Iz9f7f/zXqpuLgO2z90KvXCne4A8VbUn16ilVcPka8r/XOxg0I1rpd6DTxbGHq1h5RdvbK+Ku+66p4Gz1MroWt5b7M5aW5dCC6aHkvYfxj3t0b65lbFrZKIr7BNmAd9YwzoB5Rh7f0HhBjo2DW5uGSBEiABEigowl0tF5hHaLFnlXOaoI+LuhJiQDP0LUEIDohBON6KoN336Y+PvY/qh8e8tXVvmo0T5xYZMcaXUbcMkfV/9py6imNa6YRiMZaY1kmV6Xiqf5Mv4zMdtV3n/mzJ73fvlBS12Vfre1oy68lNBbPrZYEmqnZ7ebM+4Uq7n/FvSIRcUD+lGXvz/3kXXf+i188Gh5+s3ghkBEo8JQhPizNc9t0NCyidbBLFCRBBCATIAQZ4QTTGSxjFgMJfalelXllXv3d7zx1zp/59t8oEToxq0ct2OJqCetbsWe9Ue5NFn+jYrmfBEiABEiABEiABLZKALLpiRaFzEnakETKpi3gtEkS6m5G4mCL66FeoQWULk0ab7qviabt9tisCU6mQzkQA3ItE/bJ5b0/d1f+v/74Ue+tN6kf9wVqQeYbSFY9Be8CtU5Ga1igDxJ5D54do4CRrWmZogABRgMIGAHvizy4KDbq6MS8KdGjMq/Oq29P/cE574LIgyILpmVqPQir8t9EHIuNfRVMfaSFq3Trq0pGAFgOYHFIqcO/9T88mLg1OwjvA2bnSkDNHcV2IIvNaXVSdlmvPQN8YWlJ3eH0l/7yX/27J9UfF0VPYI/ksq0J4JkQB6sK7zGW73/bXUf+m18+unyoR833yPMk+hgMZELHMd7T+nOiH6ba+22eAjleHj6Tp55iO57h6QLl9FZcdbDqqGd/5/fOqRf+4W/UKzhOwjbod0xB3fTXcNYfNtQPNv/JLdkP/Pr/cqLUU5VNgeaIJ1xJNtxtfsPAeoEwW6hLswbDg7WCK8YJJqvxOOKJjg/vflp7K0kq52pVzf3bfzel/vql77uVpAoW45W3VsncRwIkQAIk0MYEIJt2nF4BP4/tGs7IhbUS9HG9rUC3633wurZCAHO097vqJ+56r3ojKItXAPPIWqGqsWg06dZupDXmr2+tLM80xh1picOi35EGvfg+OKHScox04MJ9gRb4xOofIWb0r7f3zZ+6VLv2LRtMa+dZa+9q59lquWudcxf3maet6YT2nudFkIBQ+cL31fdu/Y/qp4/ep3puOagul8sq2ZfRwqZ57mOlQFjRhgSx96EmwJg0bTgTvS8ZkUXgTnFg3lPf/qO/UP7st4+qqjqqIiWTvjKwttfUdKm1zRb1EbuCWjaukAAJkAAJkAAJkECXEIC8Oi1xqEvupxtv44zcFPUK3VizW7un8a0dvr1Ho7NfexKoiXToqjbil3i1V3ogw58V1Utv+xt1+392lzrw9pv0BQQypQG6taGbQOcW9BL1DkidpfYH8h+MByDSIerRz8gs/zH3dlJkwn7xfPfdZ/9CeX/87aOpsjoqM0hKvvoxurDaNeqtffIHFNDF6Jk+WBhWQNAFC6mK1D//p+pAf78qm2ySuDOhRxQDXuhn7xn+xfFvfvu3lXpVzgNDAhiPQN7HPBc67MtKiu69jRZ40RBQLfIuuTJtZFCS9W/8UH2r74/Vh/7Fx+XdDdRVMUqpyvSSrn7ZcQDqL9KkWD1ObaCIFGfLjfLg3cZ0CHgGYIQwIB5LsvJcfHfmm0r9xT8cVZfV0VSPo6oLkskWjfy1cmR9PQHHdG2QmwPytCh9F+UFf6Osll78oTr407frQTeYAlRJJ/92Beigm/FjgA/OgG8z6vHa919W6h9eGjF6OcmNA7q6DraLLsshARIggY4k0NZ6Bemaatvwf7ftlfHCdoFA1JxKYempd/3K/ar3Hbep+aR4IZDWcUJaTvbhbWxDRcfVrrB5u7YjWsF+xMZS7LYjjThXBHpYkN7Re7N65Q+fl8tJqt6+AeVVKiqMjAhs/ubSu377RnhXAWAPs/RXyVavFXvAahm7LF0eO/1UpkUZkJZ7T8JuBQLNt19Xl0qvqZ9+78+KACMCRkLmutSjSySTZoQ/NgKKXccSBWApT6tImTCL8WUTsujNQVq9RTxszP3vv6+uPfPnIq1IJhFydWh+NaLkG+6TDDgjQvwq4utmb/3vWqeq5+IaCZAACZAACZAACbQVgZxczURbXREvJk6AeoU4Da63NYGESE4Ymwo1COQmBCwxlsEXA+/L3/q+WkhU1Dve8x4RthKSN5R+41CMAJBLZDx0dMW8D8Q9EaAg2/moyxY5EH3OKRkN2yOe79686Ki53/maKj8rHY/S0Yn+MuSzUiRktbiNuWzuo4BaAQ0MahGDDz2JvayKBwf3pw6qj/x3v6ouZ0IVpMRjoIar/6zgg9R4bM5gj7LL2n6xEtHHiVUHfCKkB/rEE0Valf9TUTo8JZfUVc2SRB9EyVpjaIc/tjJlqQcfRSPdq997Vb1x6Q317jvfqZIHBtSityy6R9mpKzq6cG1AAK8DppBQj2SSfdjW+0xmPB2OaEg9+Ugc8FLq1nmlXvi/nlbF3/sTpa5KfnlxAxi+8LGIwDYvDF/9gawKLGwuVVTPoZvU23JvV9edqiqn5RugPb0AYpQ/KsbUD9IM4Pp3tjGfNRywqeabgkLk2yFl+1K/OBZfm75qQv3tuRml/vGSkgE+KlyWl5z1FxHnggRIgAS6lkBO7myiHe/O9vG047XNtuNF8Zp2l4CbEqlM2nAH3v4WdUVVlHh12tXgihCOEQGBCGsHDx5UKiPie7WqlhYXVQgpnoEEdoAADNARvWqoyiIwLGF0gQiCWjnwwvfUzKcfV84PL+t57mqnhxCpBUmbgpclHqN0yQNlEmIilVT+QkUFL19Rz37p36hrz31LLK4lHx5tCCgUUiJoXJAACZAACZAACZDAqgTOrbqHO9qBwGw7XASvYdcJYLxvxwVfBDDEishhsOlG3zAixDOtChHj8ksX5tSzv/nbKvje6yIPJqTrUIwJ4BZbjlnpYVEObAro5NYd3dJphcESb5Z5t52Ll9Sf/9vfU/6fflup10Ml/WWqN+mojIyOloVKiOyI8yfljzZ41xfTVHBXb4pOSAvH+AtBXW4WlSKrb//I+1RJppZYEsMCY0CwcyAgw2MgQLnHVW9610+K6wjZyEo8IHozPCjmz85dAEveMAGpMmPXg3cGI6HkvUtIT7EjD8vVP/1P6pl/83+o68VXxKFFRkafSyar19G6nfiLhvUo6n32Ukwajk0vy+GvX1d/8NgT6sd/8NcqLc/pgGSDERLeW3u4PZJLQ8Dox2T0vwByHKkk+e6hrn70H/9cVS5fV/39B1U60xvhAsjGsJ7vbuMRq2/BG4EnUZdZfMkYCYlOUH9vVj+Me0iABEiABLqDQNvqFWAD2a5hWC5sRuJgiwvk3IUtoHRjkrSrx9XBhErdfECVkwvaiCAUk3j8g6uvwJp47sLN++IqUB286Un18uslcUMQnVEkgv0cRFGxK2G3zrMrN3Pjk8TFEjxh9imDO0klhshKRorM/c55dc9n/vvcfNo9Vi9RntG4QGld3sXTdGYjlPSmelWmx5n668c//331AzlWjBUysh/nhwIETzmm4dxq2GfVt1VcPJ4ESIAESIAESKD9CMB3+GiLy5qTtOMt0pnUPgSoV2ifutjLKxlvOvlE03Z7bEJDJ/JXn0SIYWVsS/SsTAYBDcblz7+qXnDPqXv+1/825xxMHAtkvm5XOr98fZTsrwV7oMiUIhOazi7xaAcBU7Z7ZLrI4LVrUy/+b1/5vnpFXNNdNgei0xHeH8tygMzkKH3mItHhGBvqxdqULl8aiRaeHxC1Ngh18+FbfjKb/6cj8wNJYeRpPwVall9FANbcN0kKg1v0IAApxBePhLe+I6euD98/9+r/+cx5VZJeRnih2Hf1skmYu3hYbYYCnFM/OGIohHpC7JeH6Dsvq2+d/T115OH/OVftUcfMwCmtkZEM+mmSd1VWm6ertPtkF97llBx4qJKeen7yX39fvbyAVG2MpE+JjfizERWL5HWH+PHrPqgzMuqpZASQHiYG1tC7gdHVJXXlaumu5aupo9d6qqo/02PqIdKv2U9iaPVucsha73jzPnyPbRlyqHghMN9m/QyEzjn1g/m/scZK2K+fA73CPyRAAiRAAh1MoCP1CvHfq3Zkn5WLmpHYbEgAZc2QxJJEhm4mkJRm0s/coT746/9SXX6Tq3xPGsMiIOtWHRpqMMNuCM2t4ebthsyxjeYWsd3G8SJsi1VvdjGhbv9RMPQH/9Pp2diBXCWBPSUwOD2WW+xLFryE86DY1GTNLGrymohAssLGJhJ2ZFcp7QXnkleWz/7tJ39zdk9vgCcnARIgARIgARIggfYmQJm0vetnPVfHOlwPpe7OIyJQQ2hWJDTs7KSNIyIPLvQ4hXIieFBkwmy8QwsjbBsDpEXRqziq1FsNz71pwT37/PBjs415uLVeAvd8dXQkeOuhM68sXZO+vlD1iLytNVCR3N1cTnMnYvN+Y+QhGqj40xp1UAahL4YiMoJd9iUTKXUgyKjgh9dKf3Xst25uLofbnUkg/xV5l/vSheuZ8MGqqCCtPsfqdgJ5FvAMmXTjfUScYJQGlt1zB5acs3/yq4XZzrzz9r3qo2cK2ZduDV+YH3Bz1eqi5m/f46SojfGFtd9cWz/2buDppTnENc3wHmHedTFgkKxS5/Jthjdcp/iWK2pobvh0sfl4bpMACZAACXQ0gY6VSZslinashY6F244wO/CaQnXPYZUb/WXl3ymyUWVBhDJP5otC00vEX5nEzzS6VjbOWt+rzWebbq1zWTPdQBpwsOqFRWjKT6rexdTQ336cDfPVqDF97wjkpkezqm8gL4MS8mLPfpe4x8vB9gVXJEJnSWZXK8nok+eCMJyrlufPFYcnS3t3tTwzCZAACZAACZAACXQEAcqiHVFN67pI1uW6MHVtpni3LG6yE3RhG6oMyIPpvt68zJuel7u7S2TAnMzVHcmDTkk6vkriVeA530nMzbvz50pDlAc3BLhF5vc+86kXllPhYCUy1tAO0ZuftNhx6GSMB6udiqfF1+vGBHJcdGwoHifgct31Eqpfhq7fdsUd+sNPfnY2fhzXO5vA4HQhO9+n8B5LDO9K+o68yyrrywPhu0EpcMNSELjPJX13Lrugzs0NF6jb2cEq/8jvnspfOqBmqglPVROBqmKOFwkZGd8ms5iIvti81zAu0GtN73nzpcEIRE8/4cl0MXIAjoMRAcbIVWUaBS8Mh3889KVzzcdxmwRIgARIoKMJdLQsKj9RHRE6GnJHEG7fiwzV4O3qXaeOqWu396qkvyztaHGmh0aZxIS3c0YEaMhpd1JitgCndYGTVFU/NXRpiEYE7fu48MpIgARIgARIgARIgARIYFsIUAbdFoxtVQjrtK2qY1cvprlrt1N0YbsKiSdbPwEYbSRuSlyBYYAvWiqZ6V4GuKxtFrBVIwLdQenKRBMSHT+hkl5a3XHVfWj2lz83uf4rZ04SIIGNEvjwV08VrvYF4/M9gVpO4ecj0EYEMAKwBj7rMSIwXiSMEQGmoBCbhJp3CRgRJDxn4h9+8XRho9fH/CRAAiRAAm1NoONl0LVbuO3DviSXMiRxrumSBmV7RiIqgqEbCeAJXVhSB1IZlah62nWbvk1t2alFqB29azTiYBEqBqIrZ07Y0TOzcBIgARIgARIgARIgARIggT0iAPkScibkzXiYkw3IpaV4Itc7hgDqjXqFjqkuXigJtC+B5ECy9vsQnzYC3gNaxe24E8cVBZWEMIhsYvTgGvE8wUACJLCjBP70lx8rHCi7EwlREKd8mVJE3H6awWb10+r3vr5ZW4PhgI1IxPQUCMvidaAsbkStvrmqaEBgyPAvCZAACXQVgazcTcfrFTrFiABPDgX+rnp/NnAz5Yo6kOxRbgVGBDLTnEQlQhOmMtiNgIYgrEv7K0q9uaSKu3FOnoMESIAESIAESIAESIAESGBPCHSFoL8n5DrjpNQrdEY98SpJoK0JBK47aEcgr+dCV+tgXM+xyIMhNEYDJmuxQTXSIXnXestgPhIggc0TeH74scKt192J/orxIICSfJn6turKpLuylAl3TeHwSBKLjhgcNATZh/cZA9b09AWylOlIJ35wLz0QNHDiBgmQAAl0PoGu0Ss0/ZK1fc1Q4G/7KtrmC0TLSowI5l9+XaWrIjKJ8UAYhuK2LVQJMSKAILZ2wCMejza3TbPbrZeY3yopNgu9MtfVLddV6cXhQrF1TqaSAAmQAAmQAAmQAAmQAAl0OIGuEfQ7vB52+vKpV9hpwiyfBLqcgBsE+L2ouTJf7+1aTRSWNwraPbqMYsZSB208sOIocx0rkplAAiSw3QRgSHDzQng8GThF6Jp9MSBYTriqIhEGBcaQIP6Wm3VtSKCNB8TgAEYHErVhURCWZLjc8X+8jwYE211XLI8ESIAE9phAV+kV1tNu3WPeK05PgX8Fki5PqPrq5e8WVUoMBxy4bZOoO/dhYBAZFewkAd3kk9P2VVdMp7GTp2XZJEACJEACJEACJEACJEACu0egqwT93cPWsWeiXqFjq44XTgJ7TyB0HPxmNIbWnfwmz1r7GktZdcu6QY9nCJ1w5XXEM3CdBEhgWwk8P/z41FuuBUOZinMW76QLrwMNg9cau1rM4Ld6nkCMDTAdwpuvO1OHSpXDLw2dntrWC2RhJEACJEACe02g6/QKjb9se413/eenwL9+Vp2fczlUP/6bb6mEGBHYkBADAhgS7PQD7IvFd1VOgqXvhGft+bkkARIgARIgARIgARIgARLoGgJdJ+h3Tc3s7I1Qr7CzfFk6CXQxAaONQgeinapAdxbCWKBV3CYScEpgOiVRIEbWMJAACew2geeHTxf/4RcfG0ku+Yd7xJgg7blFbeSjDQpwNfg+mGg9icBLQRgmS37oTPSWwsN//bHTx+eGJ9EOYSABEiABEugeAl2pV0h2cP1YgX9G7mEwdh9YR9qQRORh6HQCVbmBv/++umkpVF6fq8opCGkyx8COmxC4ylp6o9FXdYPZTkfJ6ycBEiABEiABEiABEiABEmggkJWtZpkSGeYkUqYEie4O0BmgnpufgcEojc9Ad9c/744ENkXAd5ySo3VS9Y78QIwH6h38jcWajkRjeNC4Z31bujsyOlWoDzHnkgk/i+srgblIgAS2m0DxgdNFKXME5d4zfXIwqZy8WBHlQkfdhDR8D8SX7lVJK3oqnP3m8BfQtlSv4Q8DCZAACZBAtxHIyg01y5S4R3z7O1qm7GQjAlRAKaqA5soZlPRRiQWJDJ1OwJMbeL2ivv/Mn6ufGP6QCmTuqFAsrkO0xlxHOQ33t4pQZq1BV7iQa85vBUBjTQ4XUxD2xBPBFCxNG07FDRIgARIgARIgARIgARIggU4n8ITcAOTHeJiTjY4W9OM3w/UbEihF9U29wg1RMQMJkAAIVFWq6IhOyg0D8ZIJPZLRJcGQoB5W0zfZHM37bXrrJXLHi9cqMaWKrXMzlQRIYDcJfHP4cbQdERlIgARIgAT2J4Gu1StsrMXanpVvBf7mH+qPtufl8qo2RABPKOKSUpe+XVQpXzbEIEC7gdoFTwS41ozMZ3Co5ExgnYEESIAESIAESIAESIAESKCrCGSb7gZyJQ0ImqDsg81SVO+o/3igXiFOg+skQAKaQMX156pOUgacGP0UEmFAgEEoq8WtorOeMmWUc3RejHJ2ZJQzAwmQAAmQAAmQAAmQwB4T6Fq9QjcYEeDZaBb4sf3kHj80PP12EICvjJRE8UawfPEHajmReLKcdFUl4apqwtp6b+BE8EgQjysOxStho5xWBMLeipqaoxeCFaSYQAIkQAIkQAIkQAIkQAJdQCAuN87K/dCAoAsqdZO3QL3CJsHxMBLYbwSqKl2UwS0lTzxl+hJhOIDO/Xg0GivrpWClgcFGmYnfAzmXUguiI0Nchm7MUec2Wg7zkwAJkAAJkAAJkAAJbDuBrtUroLe0WwIE/rslQulzWCIb0gKhawKeVJG9/KvXC75j5nxz9BQFO/AIR0YG8HZQdVQxqNILQdc8R7wREiABEiABEiABEiABEmgkMCubkB+tLAm5kmH/EqBeYf/WPe+cBNZNoDRUKPVV3DlMgYlpBfRglTW9ZW5NdwVTBBgQwGghkMEuZvCLVpPNygYDCZAACZAACZAACZDA3hKYldN3pV5ha63Yva2U1c6OyqLiZzU6nZZekUe0KheNJ9WRyaWOT5bSvjt8S+amUrISqkR8urlW92a9DtT21YUtndS8v2aY4IpbOHg8cI7TC0ENHldIgARIgARIgARIgARIoBsJFOWm5rrxxnhPmyYwK0dSr7BpfDyQBLqfwC2Lwfle0VelPVclxZDATDcQV7Na/VOUZvVPdrkBRDAcWBaPnBjsgik3+ys4bzj7/HChuIFimJUESIAESIAESIAESGDnCBSl6K7TK8RbtzuHjiWTwCYJ6AdULLuVLwXIlAYIxfsfn7v06qWHDmaz2lWcSd3i35oQV38l0p4zcennT89usWQeTgIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk0EUEBt6Yn3ID1xgb1XRKuMG6Xqn5drHHRp1TjA/gycDG5vwN2ziHHJ0SHVmfGBLcvJQ427CfGyRAAiRAAiRAAiRAAiSwzQRkbDcDCbQvATiGw0MKe+tqQlZ8+CMw4Y4LYyPJRPWMG1p3BM2CWtO2Frjs0fEl8sXKkHwiwz1UvPcLk/FcXCcBEiABEiABEiABEiABEiABEiABEug4AiLiN4SaXqEhlRsksEEC73jmVMFPhuOB68kgl0ivJEt4JdDTHEh5QfS0uaJrsmn2NMZ7gd2SWREkr81fT43KdRPKEQOChMRDS8nin3/s1w/X83CNBEiABEiABEiABEiABLafQHL7i2SJJLDdBAIF+4FAvBHAIYENL917euqd33h4zk/506Fycki3ApkRuiBoxYS0ZrUBDtA24BDSMK+cCGzKKaaXneP/+MBjs3o3/5AACZAACewbAtnp0Ww2k8mqZDKnbzoMSmohXSwOF+jOeN88BbxREiABEiABEiABEiABElgfgYHF65Pl3r5jy2k3Bw0UDAkw5YALg4LIkMDqqVBis9FAq7OYcpRK1AwRzAAZGBCgbM91xZggnGh1LNNIgARIgARIgARIgARIYDsJSLcpAwm0NQHtPwAPKmwARJha8czmnh7NqZ5EwXHUMVhkI2j3BXrNCGmw+NZBL6P1aD+EMF+SKm4wtaAWHioNTbKzKGLDBQmQAAl0O4Ej06fyXsJ58FpGHV1OBjncb+03IzI066mq4k1lZzbtqfP/4VcK57qdCe+PBEiABEiABEiABLqMANQJ8bBCrxDfyXUS2AgByBNXBvwZ3xUPmtAtJWEGoFRGpuRMyGrciKCx3EbdFPb58mQupkyuvqpSKAr6qlDkEkeUXr4MgKk4ztRL+c8fbyyLWyRAAiRAAiRAAiRAAiSw/QQoOG0/U5a4vQTWLewPTo/mqn3pQiWhHgwdJxu/DJmnLtrEMloXgwIpvJTy1bm+sn/2L4cfm40fw3USIAESIIHuJfC+r31u/NJAOCrmafJ7AZej9nfC3LNR9sGbjUk3o4bkdyMMi4cW3IlvDn9uqnvp8M5IgARIgARIgARIoKsIrFuv0FV3zZvZNQIfmT5ZuJYJx+d7AlWGz1fxRCAGyCrVZETQOFXBSvkDRgg4HrLIQMUcXxXPAzAu8BMJJbqtYqriDr34QKG4azfHE5EACZAACZAACZAACexbAjQi2LdV3zE3vmFhPyfuqBN96bxKuHm5y7tCx82JBJdVpiOoJMuS9BY9J67g5vwFdY5uqoUSAwmQAAnsEwIYKbSccs7M96icGSUkmj09b2mTEk8bn9WnxLFGBMAErzcZLyw6vjsx9wkaE+yTR4e3SQIkQAIkQAIk0LkENqxX6Nxb5ZXvFYEPnR+ber3fOQYZA8YCiQB+L403AlwTPGZaIwKRQCTU5Y9mbwWYygCeCODJAEYEMCxYTrrF/iVfDAhOF/Xh/EMCJEACJEACJEACJEACO0yARgQ7DJjFb5kAhf0tI2QBJEACJEACIPDWmVPjYkRWwLpR1FkDAqjx6ko8s1+2xfhMp8oyPn+pHynyMCJI8hRe++j4BI5hIAESIAESIAESIAESaEsC1Cu0ZbV030V9aHpsstQfnrBTEtg7tEYCMCKIT78ZDXapzduJfDAcgBEBpjKADIIpDOANTYUBDQgsUC5JgARIgARIgARIgAR2hQCNCHYFM0+yBQIU9rcAj4eSAAmQAAkYAu9+5tEnKil3NBCvAybAPADr0M7JKKFo2gK9r7ZuPRE0GRFI60m7FZUioOjr8YKp7/3nXzhuyuVfEiABEiABEiABEiCBNiNAvUKbVUg3X85Hph8pvHLQGcfUBDAA8COZAcYBK4ORMyBTQDqxAVlhuIxlwndnb55fPj43TA8Elg+XJEACJEACJEACJEACu0OARgS7w5ln2TwBCvubZ8cjSYAESIAEhMA7nh0bd8UDAQwIoMSDB4GaZ4HIqMCODqqp7yJDApNuVHr2GJRRSRi0UAY6kldGB0394N7HaUjAJ44ESIAESIAESIAE2o8A9QrtVyddfUVHpsdypT41s5h2cpAdIGMYuWHlbVsDAsgaoeSDtwIcU3XcUsoPJn5w7+cnVx7FFBIgARIgARIgARIgARLYeQI0Ith5xjzD1ghQ2N8aPx5NAiRAAvuawLv/8NETfkpNpgJPK+MwnyiMCOAi1BoFAFDdiEBvNTJrMCgQhZ4YECxHRgQZz7ga9WSkUMILC9+77/RE48HcIgESIAESIAESIAES2GMC1CvscQXs19O/8xsnRwLXOSaGAfmasXITDCuHaCMCkVPE41kpCMMnr1+7Nlkaniw1ZecmCZAACZAACZAACZAACewaARoR7BpqnmiTBCjsbxIcDyMBEiCB/U4g9/RYzukJL8IRaDKAF4JAwYgA85AmgsYpCqzyrs5MDxmqb8bWYIQAQwIEGBHgWE+3qKRMzx8q3vebs3on/5AACZAACZAACZAACbQDAeoV2qEW9vE1HPnKWM5Np/Ku6z4oD2NORIec4MhKLMl2SbbnxHDg++I57dzzw4/NSjoDCZAACZAACZAACZAACew5Aa3y3vOr4AWQwOoEKOyvzoZ7SIAESIAE1iDwkzOPzsjuPIwI3DAQ16CIMvWAtH4c2UZwIy8DesOk6L9mr6zWpjuwRgVmD9yMIiRrGc22HDD73V/4zSG7xSUJkAAJkAAJkAAJkMCeE6BeYc+rgBdAAiRAAiRAAiRAAiRAAiTQaQSsRrzTrpvXSwIkQAIkQAIkQAKrEhj8+tiIGAvklRgQoJ/fkxYPOv7hNQDzkZrQ3Axq3pZcK4wMzJEwHtAGBDAykOjIWeCC1Amd/D1PPTxicvEvCZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACXQegRba8s67CV4xCZAACZAACZAACcQJLKTVsfj2ynXbBMLSRlmF0YA2HGhMg91BzfYA62KQYL0RyKb2bgAPB4jzGfcG58YRDCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTQngSgIWcgARIgARIgARIgga4hkHt6LOclnPzKG2ps9sArwWphrX0Nx7TwVOAlVP4dT5/KN+TjBgmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAl0CIFGbXqHXDQvkwRIgARIgARIgARWI+CmE0fjXgLi+QLxOoBojQSwbIh6r/E5oNPlYOuTwDSa4ltSFjwSRGXac8I2IUyoo/Hzcp0ESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEOoVAslMulNdJAiRAAiRAAiRAAush4IT+g9KNv56sO5ZHbAs+umOFs2ASIAESIAESIAESIAESIAESIAESIAESIAESIAESIAES2EEC9ESwg3BZNAmQAAmQAAmQwO4TSITOoPU0sNtnhzcC+a8SKhzMnxnN7vb5eT4SIAESIAESIAESIAESIAESIAESIAESIAESIAESIAES2CoBGhFslSCPJwESIAESIAESaBsCUcf9nnbe13wgZDJ7eh1tUym8EBIgARIgARIgARIgARIgARIgARIgARIgARIgARIggY4iQCOCjqouXiwJkAAJkAAJkMBaBLxsZnBZJmvydQsnUKHjSpTJDaKonEBcBUiUYLwVYD0e9S6TJ8qnU0IpEDEK9SNcOboesRveCCrJQHnJZC7KzgUJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJdAyBuja8Yy6ZF0oCJEACJEACJEACrQmUJdl3bRe/zdNoAADjgZ2c7gBGBL5ET4wZGEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEig0wjQiKDTaozXSwIkQAIkQAIk0LYEYL5QD159lWskQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk0CEEOEauQyqKl0kCJEACJEACJHBjAtJtX7xRLngKQNhJbwTmDPxLAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiTQjgQGz4xmy7dlsuVoStJUJVnqf00V544XSu14vbwmEthtAjQi2G3iPB8JkAAJkAAJkMAOEihLI79Hym90tuTIFAYqbEyzxgS4mLpBgfEl4DbnRSYEmx4ZIphESY62nbDBF0HR7ueSBEiABEiABEiABEiABEiABEiABEiABEiABEhgbwl8ZHosn/HCB72Ec3Qx5eSqmAU16WudXzpZVe4tSv2z6ZPFlK9mlR+en/2vvnhub6+YZyeBvSPQpALfuwvhmUlgFQLo9okHPrNxGlwnARIgARJYQeCO2UcvJpTKYYc2HpBl6JjOfbvdaE6w0oig2QghaDJKiBsg4DzNRgShcko/GPrSzdjHQAIkQAIkQAIkQAIksKcEqFfYU/w8OQmQAAmQAAnsDIHc9GhWZTLZpErmcAYvEZTUwmKxODzJUeQ7g7yjS33Ps58aF4OBUUc52YSoCZPSQoSe0Bcl4aIMt4ZuL+O5Cvug98OAI8QwdIpy3MR37j891dEAePEksAkC7JDdBDQesqsE5DPdEPjMNuDgBgmQAAmQQDOBO2bGphJOeAyCABr7aPjDiMAKADq9+aDatjE2qHsiwLarj61lkZVmowJrRIA9CGHozv5w6DeG9Ab/kAAJkAAJkAAJkAAJ7CUB6hX2kj7PTQIkQAIkQALbSOAD06fyKpF4cDGljlaSQU6i0dmI58iE/OJnZJ7LHl8Ve5bd2aTnn/+zX3mMo8i3kX8nFvWBr30qX0kFZ5aTTq4sxgK+6AkxuAj6QXl8dBDbAW1EgH0IeJZgTJAIkNNVaEymPFU8sBxMfPMTX56STQYS2BcEoldiX9wrb7IzCeD7HA98ZuM0uE4CJEACJLCCwNsvfHo04agn0KGPRj9+SJb1BE6BSokAYA0LVhyoE4z00GhEAEMECA0m4HhHhFOUa4UMCB4IxljBVY7nHP/hvbRQNlT4lwRIgARIgARIgAT2lEDUUqtdA/UKNRRcIQESIAESIIHOIPDep0+NX+oLR9OhyoqbeaPvkV/0ZenpxUhy6GrQIdwjRgT4ofdco8dJVd1idklNPD/8+anOuFNe5XYSGPz6I+NX+/xCKjDGJhVxXYrnBcEOPrJLpGNqAwxCMvpD0e/Jc2V1hHjGYFRwaCEsPP/glyZMKfxLAt1NAN9TBhJoZwIU9tu5dnhtJEACJNCGBODOLjXQe1Ha/Vk08GFFXEbrX0Jv1QiWsFT3Yq2gSH6I3Y3JbxOswIBtWCKnMVWaHD+fNsJHUiQNnKsqwkgoFsr+Ynj4lQdOF+3xXJIACZAACZAACZAACewZAeoV9gw9T0wCJEACJEACWyNwROavL/eoM/M9Tm5ZdC7QvUCd44ZGb4MOX+sVEroddPIij0mXBBkEApVQT9XBHPcyivzxKUll2AcE7jn/6BNX+4PRZdEBxp+XujfRuiGBxREfRIQ0PEsIxrupPGHyPEEnOLCspv7qY79x3OzlXxLoXgIrdebde6+8MxIgARIgARIggX1AAHPfpT33PG7VCpdo5CNAUKgJkjplc39QDowQUJYRJEw5kC3cwJmiAcHmuPIoEiABEiABEiABEiABEiABEiABEiABEgCBOy88Mv5yNpy53BfmqjKiQyabFAMCGA94oovByHKTBo1PUowKTEexSRdtjaRKlDwVOfZKn5979UB45idmTo7LDoYuJ3Dk/MnxKwNqFIYn0OHBy0DcAwE8DFgPBHUUrvZmAY8W0CdaAwK7Xz9RUhamRHh9IBh5/x/82hm7j0sS6FYCNCLo1prlfZEACZAACZDAPiaQKvsFX6YggDgJAwJp44tHAlctJl21lDLuySBEICJEomVtaVLtHrs0uSC4XpcyFiIvBBAqjDGBnE/O0V8OJurHc40ESIAESIAESIAESIAESIAESIAESIAESGAjBN79zKNPhI5TWKmxEd2MGAbUYgtNDs5jO4FhWJCQCHf2MEBwXKfw9gsn2fm7kcrosLz3nD914mpvKHrB+IWjK/RGEflXyRMNTrIlwgDheo87cuT8p8ZtGpck0I0E8EYwkAAJkAAJkAAJkEBXEZgbPl0U0fBJX+bAg6GAK+7sMA2B7vCvCQTmlq0hweoAYDzQGKwFM8pzRJDAFAayKjJsOPUipzFohMUtEiABEiABEiABEiABEiABEiABEiABElgngXc8e3LcT6pRDA2pGwtEB8OAYB3Beo60+pu67idQftIZuWOGhgTrwNhxWQanx3JX+8LJqvR81p6fHbqLingkuNzvFD4w/an8Dp2CxZLAnhOgEcGeVwEvgARIgARIgARIYCcIBPPXCxVXFatiSID57/plQ8cq5sjDGVtZF9/4SmA4gPIynrjLk3nQtGGCGCr4KiwGS4peCG6MkDlIgARIgARIgARIgARIgARIgARIgARIYAWBd//hyRMyTKOQEn2LHQyiM1nvAyuOaEyAsQAijAcq4soe7uzhfn5RPEp6UmBSPBIgJlQ4cueFMY4ib8TX8Vvl3uQZ3AQ8T9Sfn/UZnuA4a3yC9XioaRBFJwg9oA2+K95KBxSfIwuEy64jQCOCrqtS3hAJkAAJkAAJkAAIFIcnSynfH5a2fQkGA2j0oPM/KV4J3CY3ZNtBLOWp46/QC8F2oGQZJEACJEACJEACJEACJEACJEACJEAC+4zAbU+P5SopZxIduTACqHforr8TuBUyXZ6U6UmZmO/eTm1QSYaF22bG8q2OYVrnEXjbhc+OLKecfCAGJ43Pz87cC55KPFvVhJN/5zceHtmZs7BUEthbAjQi2Fv+PDsJkAAJkAAJkMAOEvjO/Y/PJXz1EBr28EiA6EkDH418E3STX1btUlZrrvGQFgVr8S5LLSBIC6oiZVUSrsIca64fThTvOz1rs3NJAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSwfgKJTPKM7vAXnQu8ByDWpyFYfznIGfciCU+S8YAR6jAmwFIcFHAUeRxOB68Hrntsu56fG2FAxyqi1S8mlHvsRsdwPwl0IgE85wwkQAIkQAIkQAIk0LUEvnv/56ecUB3HfGiIsEZGkLRaRJKJQeSWTJa1NFmB5wIbZU8oERbsEBZSnvvQD+49XZBcDCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAhskMPhUYcRRTh6HYbCGjtC7xMqBHmcjAfmtS/uErMfd0GNsCbZdOef7njo5spFymbf9CByZHss5KsjjidGGBHiGmp6fnbhq80wFynGCfP536dViJxizzL0lIK8RAwmQAAmQAAmQAAl0N4HviCFBxffvrib8ovYcIIIirM4xtYGZ3iASHiOhEtMdOLKvFrGtI8zgXRFCJfpO0fGcoeK9X5zsbnq8OxIgARIgARIgARIgARIgARIgARIgARLYOQLzveqY7axCBz86/VM+OvkxAAQ6GXNu3fFf093Urwcdx2ZUOMwOTAzFm6QnVgRwb48pDFBeIHPYIw37EFDeQsY5pjf4p2MJOCp11BqKoE6xrg1I5I7s82OfofhN2ufGLuP7sN78vNltk88ORBJvpQmllpPqaPPx3CaBTidgv8udfh+8fhIgARIgARIgARJYk8BLMrXB7VeDoUOL6mxaBEcjikZNIRFITcDSxNARjwM2SlqoktoDQUZcENyyEE695Ur57leGOIVBBI4LEiABEiABEiABEiABEiABEiABEiABEtgwgdueLuSqiTAfPxAdvjbG09e7bjuFjWGB6VBOhMZwwO6zZUkHcP6tz57K220uO4/AUsp9EFcNjR6MB2xsZTiwnXcHAxX7PFVTzke3s2yWRQLtQMBqzNvhWngNJEACJEACJEACJLCjBJ4fPl38q49/YaRnITxcddyznuOWPBdTE0gUg4FVo86jSlXlTPUvh0N/9bHTx+eGJ0s7erEsnARIgARIgARIgARIgARIgARIgARIgAS6nEA6vfcjuEXtw1HkHfyclXrVIDyPwnggBSMCMUJJSoTngJ0IttyqeCDAtAk4rxijDA5Oj2Z34nwskwT2ioD45GUgARJ5OUcEAABAAElEQVQgARIgARIggf1FYE6MCeSOR9C4T6uBfELmTQud5F0yV4HMoeboBn+owpLIHyVxP/CcyBxzZbV4DoYDP9hfqHi3JEACJEACJEACJEACJEACJEACJEACJLBjBEInfFDmlJfyJdqlnpJgx05pCsa50Mksyh/RBX10h8/G4neIQE50e54bZO2zA88AMChA574s1h2sYcC6D2jKuCzzpi5nMtAplpp2cZMEOpYAjQg6tup44STQfgTQGZdR+odSYbRv+10hr2inCdSegbJSz3+Sz8BO895I+YNn5P00DVnWTQxc5E3gnCQhMpAACWwDgdpvgZTF9sA2AGURJEACJEACJEACJNClBPKRnCoqBDVLHUKX1jJviwRuTEA6bwdruWpGBCbFrU0/aXPA2CAeGp1t2+kL6jma88e348c6ehQ5vU7WyXXKWjIzMKgfG7lg7RlALAcW0+bqM57xSrDavTQ/XysMCZqeP2uUANsT5E3JdKl45qryKCH6KpmTXUWJDCTQFQTsM98VN8Ob6EoC+B7HA5/ZOI09Xv/A18byrw2Ipahy8/ITmRPbvqy2GFUIrsxb5c7Jr2hROYnz2WvV2Wjkr97LP91BIPfsWF5aTA9KZefljnKOE8ozILUfuOLGSVpOgTPnu25RJpY/n/Krsy8+QMMC8NmN8IHpU/nLA+qjUicywl4NOkoscmNBPq4lJwjnQtc5/5Zr4Tl29MXgcJUESGBDBBq/N8Fg6MS/Ny4GdpRUIG0CR0l7wBePHvwt2BBgZiYBEiABEtgqAeoVtkqQx5PANhH4yPRY/kpv+GAl4QzKiNHBZOBk0YETiA4pkOnlZFmS+atnU0Fw/kApMfv8JwvFbTo1iyEBEmhTAjBCv3LTgSt1LwTS6xsL8W5+kxw3AkDKyhyxw2W1OX98rxyrO4nNd+j2q/5h6sfifDpj/R1Pn8qrVDgj+k+ZrjTQsSxzGsjvicp4gZ7aYLU72agRQa0ca+yivVnofhDRfbhqOUgN/ei+x2Zr+bhCAh1OgB2yHV6B++DyKey3WSXr0YVu5kTVdUYXe4IsrPtMMA0y6TjQm8bq0zTE8GOcFqu8gbIzlVgIJjhC3TLrzCWegWQyc2IpHYxWE8btu22QB1L/aLCh/qEECI0SQDfWMmKOKc/BVKLsT7ADaefq/ogYD/gJNb7YE+ar0nA2rrxwvvq7iTpCQGPavp/9y85suqrO/odfPj2ld/IPCZAACdyAwM9/dWykklLHrvc4+WVpD+g2QNQOwPcFoSaQR4oZzE3YJ9+bhO+cfX7481M6E/+QAAmQAAmQwM4SoF5hZ/mydBK4IYEPT4+NVJPOiUoyGJQ5oxU6d+Bquj5iuK4/wrzSGc/okTIVdypRqT75p//l43M3PAkzkAAJdCQB6LFevimcsa7otf4qkitxQytGhq+4S/l+rBmMPqx1FvPtgSEC9Ji3X3WGnh9+bLZ1Xqa2K4H3iBFBkAxnMH0BdKGBRDeUepfnyOomVrv2ms4iytD8vOG5aA46j35GpXx5cjCQDnpv6MGvJVJDRRoRNCPjdgcTiLoROvgOeOndToDCfhvV8OD/+8jRS72JM66MMMQPcFJ+jBt/iE2jzHQiY938yOKHFJZ4IgPqtIQXTB68viwdyZOlNro9Xso6CNw2M5ZPq/BMIlS5hNR/Q8MqauAbRUAgjTY0nlCo8UqQlgcA+ZHuhOFUuhpM0DPBOqCvM8vgdCH3elbqRgX5RAArWzRz8R6a9xINZ6xZAwIoa1Af4jFEn8GXhi5CQrwTDMy7wzT00Dj4hwRIoAWBe6YfHrx8wH3CS6g8dsMwAN8aGBHUvy1RKyD6xphvjVublxDfHE85595S8h7i9wb8GEiABEiABHaQAPUKOwiXRZPAWgTgeeBSnzrjJcMcXsSkyKqQSTFvNHREDUYEsh9eDZMSe2QgMtQJnugPEBJVNXVoyZ/gCGGNg39IoKsIHJHvxMtZZ8bor0S4jPSL9iYbdI82sWFpvhMNSQ0bkV6sIS2+IceHSZFoaUQQp9JJ6++ZHssFA4mLvhgPVGGJJrVp9db4nan/1qy8q80YEehS5DnVhgqyIT9pqq+C5zCp5p3E0D8+8NiszsM/JNAFBG70he2CW+QtkAAJbJUARp5/4KlPP7GYSkxbAwKUicYVOgFg5YcIazs98hySnuyzjT/dqeB6koJYkXaZN1ruT70g5eaQk6EzCNz57KNPJN1wBgYEpgEvXdRS57YxZp8HczdiOCCd2IiOROV40oiTmPTEGlSeAdcfCcTN1M98/eHBzrj79r7K95//3IlrB/wXHMfLJ0LzrvngLQ3nWpRXEutQ1BhljVnX+yUNXiQQq4lwcGkgvPhzTz0y2t53zasjARLYCwL3nH/0xLUB9wUvEeSN9yEZRSaCOqIRzM23JKylRdvyfQnltwC/BxC0Ya3vOv7RawedF9731MmRvbgXnpMESIAESIAESIAESGDnCByZPjl+LePMBK6jDQgwMlSMUEXmNOcU3YIYowbaINUsjQ5BJNZIdkU7UnQI0Cekg5FSn5q5Z/okdQg7V2UsmQT2jAD0jDrKFZh100FrO2kbL0yUWFrvbJeNe2+8ZTqZjd7a5kYaQ6cSKKuyDFQ0OmrcQ01XHek643UNo4F4bL5n6LfjccX+yChBP0WRXhx61oW0q8oJMYQre8XmY7hNAp1MAF9aBhIgARJYlUBWDAguZ/tmXjugRmEpHvc8UPtBlqPND6f5kTYNORRpPjHomIxy1JbVlJebP+i+gNGMyMnQvgRy8gzcMTM246XUKIw50ZjXdiJyybIqnUL1iLuAYYEJ8Digm3BIFcMB08mEZSBGJfJc5JZEofD2mUeORQdwsQkCP/v1z42/ejCclA69rLGyNZ1zYG469oyRD9bjddWw3rSvLO/6pX7niQ+eHxvfxCXxEBIggS4lcOT8yfErA2oS3wh8QzBCRH/TtWCO7z8Uvebm8cuPoPPJ0uwzqTAgwCg0fLPw7XrtYHjmZ58+ye+NJsY/JEACJEACJEACJND5BP7Z9MkzlwfCwkLaGrNLO1Fuy5O2om0nQl9gotE16XXs1W1KGL2jbWnk2oooI65nwtxrWf+FD57/NHUInf+I8A5IIE6gGN/Ys3X53jB0JoGieDtOeaooCgqtk4AeAp5yMehxpwP05NB+w/OuGCeUXhw+Xdzpc7J8EthNArvwGu3m7fBcJEAC20kAHghuyvbOOCochMLfBCxttGfDp0QiXBZHbov1tt7d+jOjS3Oc7PWBUCzJaUhgSbbjMsxmpqUVlIdAj9EBtqO6Zkxg690uG+q9df0jCxp0jhNmAzecyj37qbw+jH82ROBnnz41Pt8bFtCJp+vHFzePUk/GahsKGfuu6jeuqeyobhrqrZ4Fo0RQ9gel07CeyjUSIIH9SuCD50+NX5VvAgRxHVoqWPBdicWGNoG19jffKPyW4DcF3y78Hsz3OAUaEkRsuSABEiABEiABEiCBDiagDQj6w5GyzHkF49OKeB5Ae691sG3H1nuRCiNVeNBDWSjzjYFgioYEq/PiHhLoNAIYRW71WPWlyI9yI4h1PXQr3ZbOsMk/pvTGgzmKvJFH52wdLDvPoUZFxaA9sMY9J9snaTN3UxtEKb9FdtAEftPs71pSToSpnLEvXQ3nNnMOHkMC7Uyg1Zeyna+X10YCJLCbBHp6pPMwEE8Bq3VENjfeok9KrdPgxhdbdZ1saSA5MyhzF904N3PsNoE7nnvkCU8MCMx8ZBgBYEe11xtOm70m2wiDgBAk3enbnuYzsBGW73vqsyOljFOQUbzmsGiEBjwOwD0kXGltJaBeUE6pD4YEnzm2lbJ4LAmQQGcTuOf8qRP4FkB5C8d+8TkqYQSw3mC/+/a3xAjdpo2Bb1lJjBQGv/bpo+stj/lIgARIgARIgARIgATai8CHZQqDVw+EI4vS2W/bftt5hWh6LqaUeuWgmuTUBttJlmWRwN4RmJNR5KEKizt3Bc0KMmzbNFlqPbZelp7nKPKdq4YdLjkZuHPQWDhSn2JzJh37Jm5EZ1EfHFm/WOhH48EaD9hnyG7L+VWP55yN5+U6CXQDAfu17IZ74T2QAAlsI4F/8vWTI9d71GjNgEA6KBtnBILSH8Eum9f1zugPPjWNEXMP4Qdd5EoZjR5ky72JM/EjuL73BH7qwsMjiTAchctpRHTwwPIfyoByUsloAuNWUHcmyfOBkeuICPGnwt4JGm224YYGls2DkfOJ0Mn2ZJxpm5fLtQnA6OZqr3oC72RSvA/o+pFXDPUChQqWMCJARx2CZR9fmj2t/+IwNLZTUjbCfEZN0tDHsOBfEthvBPDui+vYSXy18U3ANwdCtP2egAe247EVI3z38U3CSLRlifZ7hXlx0R7AdwzftGt96gym0WlVBtNIgARIgARIgARIgATal8CR6bH8lT5HDE/NNSakfZf2JcpSe8xb49LjbUm9Lnkhl1rZNOMphQgdEoKcI/vGQTUND5omhX9JgAQ6mYDjJ55D5y+iefPrd2NT9FJkzxUBx+jjoj122y51Mo6+YZi7YQ5maFsC866aEp1DCb83vVVXHago1V8x/Q9rXbQrnf+1qHUb1ouiWeJY/C7ZUH9ORcchU/ouJ0yPif6dKldnbT4uSaBbCKzr69ktN8v7IAESWB+B90iHweW+YByKfh1qLoshrdkY7VttoRtva39idIcCOpMlLqdU/u0XPju6WnFM310Ct82M5coJdxydRHA3jcaSnYvQXImR3PXTIPUXD2ZPPKVxPd7xhD1WWSCdSIPiOr/QmJtbrQhcPpgYryaDrK0fdMBhvkgoa/A+xRu3rY6/UZqtV3T4oTzp7Mu+cVPPmRsdx/0kQALdR+DyTT3T6PDHtwDfBMTtCLo8KRMGT/iGaaNC+a2pyLetcnMvvzfbAZllkAAJkAAJkAAJkMAuEnj9gHtmIS2DDaSNh5CUth3aeDBQ34qMiuanHnxgyxK9FDzwLaTD3OWDfdQjadr8QwKdTUB0xNKBL2973Bhgx24JXxXExhCE4dnGFG51EoG54ULJU855eARIieICAxhbBfkp0b0brfZtJg06Euhj/TCcmv3k6eJmyuAxJNDOBFq/Se18xbw2EiCBHSdQPeCOJ5WTw3zF2pZOBL+tCHytLhidBuiUQISAic4E+Y0fH5wuZFvlZ9ruEpBqOSayfw5CP34obP1D+IdlZW/VKAPsjwjqbyMB5emyZYmAw9GJVE45JziSQCNZ9c9HfvdUXpwNjEAZAwMPKFNs8xdcM1I3qCPwxDYMDTYaYOBTez+lklG/oRPkj0wX8hsti/lJgAQ6l4C88yNiQDaIbwAE4/h3YaN3hW8Rvkn4dmkLfflOxQN+X/Dd0ksVHMVItvh+rpMACZAACZAACZAACbQvgZ//6skRET5zuiMlUhRYWRTyJdqT0tTbUkCxKPP/Z+/tnyM5zjvPzOpXYN6apMhz2LLdkiyZa2ktSCH6uDpfqMcUdTeWyRnE7d6FfyLmD7jgMMIW4dFQaJgvnpB0x+FfMOBPiljFHmZEySORlgfc3didkGSrvbZ8Osu22vbapkWKbA7eGt31cs83s7K7utF4Bxr98s1AoqqysrIyP1ldlfnkk0/Cm/Yp0vQiyhAORJUXk8BgEAhXVhfkp10zudli8PegOTXvIXlvoF8KL/87kmw2wqWOAB4MHYFQNcpaBhhQ12tpT61mvNZyr67e8U0y36VE6SRoR+euT0bEfSJ5jjC2UYvC+eQ57pPAqBDofFOOSqlYDhIggX0TwAx039MznigQ7OS2fYG0rBdsnQru4BpwcayCqArObH0Fz/SLQEpFM1AgcZ1+3Bf1DY8BHjtwjdDduvbT4hpdrvOPFNB2h69nVOHuyZOcSQAoW7hmLjIWInTrN2p/q+BqBt+whQKQbJP1Z5Nr18MWybeC0aB2Cj4ucCMdzbl9bkmABEafwEZaPelKifcBPN4N+3V4J5lvCL4j8bfEvKviBJG0e3eJhSK+b/YLmteRAAmQAAmQAAmQQJ8JiDWpOch3rEMfNTTtxoO0HV1qya2RS7g2pZn4ogrpdJ4yhCQk7pPAEBKoTl+rZQJ9E+bh4ULZ2qltXYUx8mYrB+s6Yw+7FBCcrAxb9z5K9kthxh7SzrSvF944x1nkPZkOUeAbZ69WZWnel3wPA/tQJpD6lWcC9e88ioPvFWoeconWp2sX5cR1TjaC6O75yvH52QU9RhlWAvatPKy5Z75JgAQOnUAmzJRgFg4zDttNMtsBxAvDNuHsRxI3b4fhq5vwrZz1fs3Yj6yYF5JLzEdc7ob99bR3vnUpd46FwMOLMoNAqaK9uVSKuGQDO5LnA5YkYD7fPCvSgEIs580FiX92rSgJQEPeNObtU5OIYhpvmLGAdbKDlG4NWiXjcF8prE2+mlUlKPmg8yPWAUxdoBELqwTwrkFreTnW8dbVgWyhmWu8NKRRv25NL7dNyfmksghSWM2pklgLKbIuSIAERp+A/NanlvN6Cr99OHnNmHeC6WTLO8M4906JWwOuVQCzgdZ0oH332PcV3lm2s442BvazYo0AHsnZMPmSyHsN5+Te8r7hGrcxaW5IgARIgARIgARIYGAJ/Nri7124O6GL6ItCod31I3Hc8l25d4M5btt1WtqSUIzv7ZOWrdAHXs9ShtDNj8ckMIwE8mt+Gf1CDABDxijL2ou3+8ny2PeGez+gF2rVDcy69tK5dP1RxJPXkJE3490Eh74mwvGecgPIeE+dWOUscgNoBP5trK6U5bmpmroPrCVcJ+OE/BP1DWcmNtjHwsgkbCjko9bj2H7D5AmLFRKgmOAsNOKpQ7r5ZlS9f5nPj+PH7egRgGSPjgRIgATaBLzoCRyI+WIjyG+fMKGdhwc8cubsXacRWz+tSsVb5eIBk+blByAQpPUTMCkNZwZ84q1rROEUng+ccy6578K226IRhmvcdThOzHAtPLx4pbTd9eN67u7J1IWGKFpYZxUGWo1f+f1YYY0NRxz8ploOg317cK6+zSVS37B8sCEPxtungwt7SIZRSYAEhpTAW6fVTAO96oTreC8kwveym3z3ox0AD5d8XeG4LtLhfylMzGCfjgRIgARIgARIgARIYHAJ1Cb0eQyqoD3n5DvIrZEdxFscH4az7VGrQA9lAoglmqmw8Eu3uBTWYfBlGiRwnAQq01erQahfQh5aigA7Zqizz5qMbmSPEgB5Y6/3kembynlfRQu4d/Ja7g8vAVi1OLUWTKcDT5bHsComKA2+H3BOJtHa2uBt/+NS42NZNq6Fw1KN966qi3f4/Fgg/D+SBPY2ojCSCFgoEiABRwAz/rSKSsaigASazp/R5sQntw8OigvivDQHKftAe8tbyEBxCYPRycYVGt4dwgA5due3TCg+kbwuGdcNfiMMcTqdV+o85hEIaKXFUgckJXEnCVvn+4RILBXQWkifWPM2JHCcBLRWHz2++9t3nPakTUJHAiRAAiRAAiRAAiQw0AS0F5ZMBl0/VQ5agzPxgMt+C2CVBra6Gm1G225MpRSV3bfCxHASGCICq3dXyjrUVcglMUALbyfM7K0QkDnCw+qpmzkOKwfO4c2BWeVi7aAqk9XnXTi3o0HgB599oTK54T3VSEkdyzMAqxbuu4QSwsIunou1jN1PyqiTBJxMWx4l8zwhHp5HTL7D9tR6NC8KBEvJa7hPAqNGIPHqHLWisTwkQAJ7JZBXJ6e2HxjGKyP52rCdtb3eZ/v4kqb2jnHgYvvcjfrZB29dKcka2OJQt7Z+uxtSyePtn5edaSXTSsaWNtlU8pj7lkDkRW0uCQFNX/lo1k1fefNmJHBMBNpKhfgut78JJjsJyybJVsGhZzU6TkWGQy8NEyQBEiABEiABEiCBkSNQ+ve/O6W0Ljr5wWEUcPMkg12keqwKsLvIH6OQAAnsikBNZpFn6mpaR1ENg7RG7pjof9plUndOqjX4K2k4Z83Xe2YpvQDLJIjwMevri2+cvVp1cbgdHQJ//tlnFwpr0cWMaIlAgQCKBKhzPFNipUACIc2wEo2tvjuID0sWCeu5ZhmEkw1P3b/sPfWd81fLo0OMJSGB3gTsr6T3OYaSAAmMGQGZ5WwGKF1Dq2fx8YGNG282nluDyg4497zGfJDdhzm5jWNvGpwI2gOlvRNk6BERaHpBAZqYG6KlaTR0W3WzXf3uLjOu5ncTO1KaiiRdoB7+ymxR2q2FTYN5rXhdg3xxuPs9O/5u27ps7zuy3MRsce+X8QoSIIFhIfDQYnmb77Bnv+rSFsD7BK71npHOOPb37PCtMd8bXNn+3miti7CStOf0eAEJkAAJkAAJkAAJkEB/COiUtNXa7bedbtruj7o9u022J5EGBnmSkxa6j7vvIzKEbdqv3bF5TAIkMMgEfiSzyFUYPWUHbz0zAGxk0bE8eue8W/kYlBCw7AmsGUzKCDI8ltPzdVo1RIkgiqL5v36Us8h35jm8MX5w7oWFM+vhx2QtgiqUCJpS7xkReOfFFMGEBOTF52Q/FXpmeRyRu8ZOjuV5k1EPkZF7YrUAVivs9yoT6Oo9d/XZ7z3+/DUXm1sSGGUCePLpSIAESMAQSIW6LahvDR4n4Oy6sZa4Zhe77dno0rIzAwmJfOziekY5PALa01NopPuiRNCul8NLf7cpiRnt4m7jjku8dDptmKBejrNuwDtQXvtdMS4VwHKSwBgRSCnfKCzpKJSOdChCG/h+AEgKoK3gR0Q+fN/0Az3vQQIkQAIkQAIkQAL7IeANRt9dmqpsM+6n/ngNCQwogR995ksLmaa6aLNnB2/trHG3v7uMQ0EJA8RmkFjk2hgYjmRgOO17T1Uf+WJ5d6kw1jATqHz2S5XJWvNsOtAvu2fIWKWInw08EyYcW7ffo8BQZptoRAv31tY+9r3p55Z6RGEQCYwkAfxC6EiABEjAEJBBguLWs5ztwKUbwOy13Q9Gk45ciK3TLNfIB92xEPAwZiODRagPuI7hHAlz4fas/e/qLRmW3Dd1HKeXDOf+3gmEZiDPDuhh39aQ2+49vX1dIfdNUYlgX+h4EQkMDwFdxLs9aVUA7/KtFAnce95tD1xO836zqYhxnOKB02MCJEACJEACJEACJEACR0JAZAhFk3Cv/inCuv0OuUB7Es6tXb2brbumJNb7zMX8RwIkMBIEfnDuSws6UGYWuSlQa5AXR/HAb8s+XqLI8XsHyvCujxpoT/kyk9zXuur5wdm/fZSzyBPERn73h9PXqtXf+PKMv6bfJxOjXhZLFDU8E/YbYy0OtJ4p95zJNi0KKLnAq+WaaiGzoc/+7adfuFiRJTdGHhgLSAIJAmbl68Qxd0mABMacAAYMMBPdOjTI7DCy/e/CD77FfcwABe4g94OXye8yYCENvF4NwIPfkinskYAZLJJ66u2og9aby9GGQlM2wA/liJ3R1I7rHua+sIvXgvl9SiObjgRIYPQJuPcAlMs24h4D3kEIl340HQmQAAmQAAmQAAmQAAm0CWDQBQN3ziX2IfvZi4N8CG1OOhIgARIQiwQVWVLzbDMdltdy6okGZGJ434iDnKqni8+bc/F+VkzY5zaihaC5/hQHgXtSG4vAN85drUpBZ7Bs4kRwspTxVClS0Ue1ShXly+Ms2kBJQHz0uh9FlTVv+QafGSFCN7YEqEQwtlXPgpPAZgLSsasi1Jp2wsC+dADjxhY6cd3OSzbKuk/ucIxOJNalQhezmbGR3YCFDqPqDpfz9BER8D1dM531uO9v673HaNEu6r79fMSJHVGexyXZdBTWsJ5bIAWWvo9VwjkiwQrugzXj4JpZq+SD3yueBLPchTnDfyRAAqNKIO1H1az82PE9wPvGCXGz8gLCfkPaBH6iXdD9lehuM7jr98NLq4Ba/vsBx2tIgARIgARIgARIoB8EIq/qReiYot8PU+G4aUIGILKD7rYiYnS3D8OWsrq9trs9iWt6ujh93Hbpt83gUM9oDCQBEhheAnem3cDvbLlxIlWWyTXnRZeg4IVYfs9OfOksnXvryDZSNR2GN06u65fvTD+/1BmPR+NKIFYKuCHlh6cjARLYhgCVCLaBw1MkMG4EtIpkAFlM+DiN8eT+IcIwXUtp5WEwEh1D+GQHMvR09RBvx6T2QEB7UQ2dfgwib6nSK8/F0bk47UhVju4eQ5pyEMrvE6uTW5FMHwwSmN8oHgVYpXC1jvv7yq/Kho4ESGBECfg+fuMZ87u3YlxbULwLksdHXXy0D5qqKXmhIwESIAESIAESIAESGEQCYeTVZMG9OGuupWgG7kSmcMR2JmPZBBQQIjFNPYh8mCcSIIHDI1BpKRO4WeSpkhdGHxXtpWKkdcHeKZJ3ga5Fofd6pKPK8l2ZRX6R5ucPrxaYEgmQwLgRoBLBuNU4y0sC2xCIQi0Dt5EM6GMtIDtwmIxuNcrbIa576ELcIKM77rV16TYxMCAXYIBA7tiKapUKondbAdzpK4EgUJWM1H8k9qsxyzRqzQawM9+PNjPxc4D7G7NRR3u3YUsdsyo+8Eefr0mdxB2j7l/g4ZUIv01YIMCAIZR98Nt3d0PdxB23w7shUyIBEhgoAvV8XdYHFCWCWB6Md4Av74U1sRzkwlyGk+8HF9b+qruQvW011kuQP5nUVqXZwL2xY2wSIAESIAESIAES6CeBeuhX/FTKyHasJUvXc9wiF/HAf7uH2RkPMqFdObRTTVzX8oQ8i44ESGAcCHAW+TjUMstIAiQwKARcS2tQ8sN8kAAJHCOBlZWVSreiQHv+8eFkDC8ddw/XOUS/D2EwmRzIoLUMWt44nLsxlb0SqH7mhUoukHWf0LFvde73msphxNevH0Yqo5aGDN5VsCa5W5f8qMpnlAfi3yN+m9ZCCZR+8AuOKJw5KvBMlwQGhACEMqLgtxR4UOoSE7TyPYCFmm4FgqPILu6BN01KFAnSof6zo7gH0yQBEiABEiABEiABEjgcAndE2T0SawQ95QdHJVMw6aLFGDs5DiLFdqPjwS0JkAAJkAAJkAAJHBKBRIvrkFJkMiRAAkNLAOadZNbfkh0kSL4esN/L76+oaRkgwPrqeZnWOClTniebSgYK7CzHBmy0N8Kl/aXMqw6DwMkNr4IZpxhIbikToJPufHyTXk9EMmzXeekWANgLl3Z9/RhFTDfVTfxucqJxg0G9w3e2BrWknZF75H35rcoWigSwSoFnIhPqm4d/X6ZIAiQwaAQiFb1urJLIayETKHWiYf1EMzTfcCyuYhZYic3Ubvv+d98Pt92ysHjf4P2j5D0nPqRS4ZaoeIIESIAESIAESIAEBoRAKoxubqls2t3+w/KZ4q0VSmud0k0w2bY4Lh1seziRKXEySg8uDCIBEiABEiABEiCBgxDo3fI6SIq8lgRIYKgJyFihDBBigBLWAY7uFYGZ1BgkwBYDlLgXZjtm/ahSPXe1OtQQhzzz2Wb0cltppM+FMc+crt6ZLi/1+c5Dcbvcv6wuZEWJAL+Zw3Vu+M+mamYC97hHVpQKCnebFM4cLnymRgIDSeDeFW8JSkTuewDFJSgAQhFwK/Ozh1mQrLQRTt1VS4eZJtMiARIgARIgARIgARI4fAJnVtUC2onWOTmSa0e68KPYyj2kjZrzvWr10eeWjuIOTJMESIAESIAESIAExpmAa9mNMwOWnQRIIEEgXFldkPGBtik6M6hrI5iZ6XFcM8go+65buNeXidM6x/iEL+aSsYVCwcl19VIiO9w9BgKpu2s3pC5qdqDa1iz2sWePjipTNnV5DOaP6g7Dni6shWR9vYTfTPdsja0G/vdTZqSNGcgbYhmkkbZLjeD3eaYeLVWmqeSzH6a8hgSGjcD3RJnr1Ia1TuTeCXgv+PJ+OCxnlQg7U0NboynvuGxTLfB908mGRyRAAiRAAiRAAiQwiAS+N/3c0uSGyJFaEgPbt7d9VDtJ5aD5Roo21c0pTdKa5WYoDCEBEiABEiABEiCBQyAgQwN0JEACJNAmUJV1kH/l1pWXGplozmtpktvzMHEPh47gnl1CGUFMDuDPmEbHwIRLV0dRtfL4Cwt7TpsXHCqBJRmo/tC3Lr+k095cU0aOUUem6qFIgIo7pAEkPF4YLJJlr83zgOOcr6unVtXSPxxqiUYrsdyGKFmcUCWUCnXTUu4RgPv6bfbAg2q2aVtlBcxAxnHGp4JHD1wMIoGRJZCp63k1oUqBe9fg5XDIDu8wp5iAb42Hj4KEhU25Nx0JkAAJkAAJkAAJkMBQEMgGWiaERHMus+2+aZdgyUXYwza5lB/ai2FCncD0h5sB24174Mmoo0dgavFSoZ7PF3yVLqJ0OhXWTqyuVSsi4x290rJEJEACJEAC/SQgIjo6EhhoAt3iaj6zfaiuqeuXCs2fPfHjUIcFdPwwUxAds7osMocB/3ZncC+ZQVev7ZJpIE1UdBTqi//wCJUI2pSOb68oHZDsifz3NzJRsSGz0eHyQWisRbQGrW2wGVzGbrJO7amthQWoc/hQo/LFixDAC7Pq/hX9VOW3ytfs9fy/FYFffvXzt/2UX4KSx4b8LuHystRAvGuOMejf6cA54ZKKPSa4fd4oEMSCGQhoIqmnKFIL/3R2/mIiBe6SAAmMAYFf/OMrt+VFXRJVIpWK8L4J5Z1gFcy2Kn5L0OveM1j7NuHwjnHfDCgQ1NP2/YP3GEzh6kAv/OgzV/m+STDjLgmQAAmQwIEIoLuZdPL1oSMBEjhsAr9w+/KPtYqKaOd5pt1odENbbcfOFmF89652opUPtHPmeqmu7Yj+qe3rYklMtEz1/H8/+0K5fQX3SGA8CHxi8Uopo/R50cG+sJpVRViRNL+x+DeVk+XhTmxE1XSglnzl3fzO9HNcmnI8Hg2WkgRIgAQOlYBrix1qokyMBEhguAnAZPq9K2o+L3aLjRn7WOQCSYvruO2thF2vGsxqjjt+bjDZi6IlKhDsjepRxoZFiuyGupisb9fh72V+eq95QbopI1iAIoH1MkhdpQLB7kjesxpczPuR0SiHUofzVpiyuzS2i4X6QT3jdxqIWXHpdFZP1zgreDtmPEcCo0rgnnf9i/K+rsEagbNIcBjvmta7P/EOA0NZxqB6YpVWT0b1eWK5SIAESIAESIAERpfAvSvexZwIedDOc0vwSbfyYA5KqbFiKtLFUpjGy3461FUqEBwML68ePgI/e/sLc79w+5l33jyjb795Krz0zmRYrIsmtkwEE0GOk9zJhBOZdPL2ZFT85zPRzD/dEy3+nCj5/Py3L88MX4mZYxIgARIggeMk0DWyd5xZ4b1JgAQGicB/+t+ev5ZvRC9hXeK6aLNibfTkwC/y6gYA9rvFYARmua+ndVXJgPUglZ95UeovHru65IX6JcwKxYA/XLcVAhu69//SlzEz51uzTsOoGtY3zu49pfG84s701erkhvcU6iUj2uXgia6i+y2CCn6v8PtxSAd1DeEMfqPZRjj/w+lydT9p8RoSIIHhJlCR902uqeY3pC2AdwLeD4fl3HsL7zC8y/DOmmhGT+Geh3UPpkMCJEACJEACJEACJNAfApXHnls6WY/mA7Fkh35kIxVbDTATSfYnT3BtT2whQ4JsCmnLfi17Nzzbn5LxLiRw/AQ+8sqV0i9+e+7HWkdlmfRRyITWWqjG9A+x/GGtf5geVkdm8dtpiNlKY2k0G11//7cv//ihrz0z0xGJByRAAiRAAiSwBQEqEWwBhsEkQAIyuF9bKwc6qkCRwAwcyKzkpHMDlG6wcldbsbPlroMp+6zv1e5bVdPVcxwwSLIdlP2/+fTVS5Mb+mUM8Mjwjlm32mj9dz4K+8qulmcB6eJ5yG9402/wGdgTx+8+fnXh/rup+RNisy6FNcRhNQCCFdm1S0XsJbmuCoWQR9KCAsl9y9H8j849u7CX1BiXBEhgtAj8v//r89fuW4nmMdBvPgcHKJ4TAIuxI/O+QlJQiDrRVOp+ed/8yWNfpJnNA/DlpSRAAiRAAiRAAiRwnAT+9LEXyvcvq5ez0m7sdLbPin5rh4utDJiw5L4JsHHRfoSVPHetyJHUe1bVxR9S8bQDJQ9Gl8DUN74wVzuZkmUtw6K1NmCXmAu80MiBzG9Efie9nSz6geVFxEoBlqVbz0TFfzmlrn/i5hfmesdnKAmQAAmQAAm0CXS13NonuEcCJEACWNbgrZW1szqKKljfuCna3lAowOA/jjFwmZERS+xbbweEeysTuDgd29o969HZH33mhQppDy6B/Fr9UrqpXzaD1LBMIbMJGrL15TmAWUFX3+0SYIip1zCT6/TL2oVyLczk+zpdCyN9tspnoI1vD3vfOX+1fO9ySgb2ZIowlAhQP7JrZmYIbnQkd3ZxU0Dq0piJxFbSisSfXovm/+rc75d3ToMxSIAERp3AX567Wj4lM8ucRRr77nff9M7v/yYWifcLBMCwauDeU4gL5YR7V/X8d85/sbzpWgaQAAmQAAmQAAmQAAkMFYHvP/b8zHtWongyQqJfKm1C9DMhD2h76YZKmF2ywPZF0R/t6eX6tMih3rOqL/75b/4BFU+H6qlgZvdL4MN/+MyL7+ajMuRsYlAAy00qUSZQazLrY108ljKApQEoFEBJwCgZJLa4JifXGEugsXJPU65/50RUfujG3PX95ovXkQAJkAAJjAeBXQ0vjAcKlnJACcjctA7HZ7YDR/8O/sebV6799ET0ZEMampgxiMEDrJkO5wYUth84RkR0BK3L+KqaX2+epcliR2Twtw/eulxey0dzmOne6rxAVwCdkw7XfYyTclGi/k30SFe90JumEkkHvH0d/JvFz19661Rzbi0bFWCFAGbspB9pLD20E2z//ix/dxxvW/UDwYyq5RvqqT//LC0QtPlxjwRIAAQ+/vXPXVrPBS9CCUBFUGASt+k7YIM3vfclGO8oCLrQhMB7arKha/csq4vfmaYFgpgaNyRAAiRAAodPIO65thKmXKGFgjskcHQEPvHKbHk5r+eg6N79IzR3TbQhnXypnRvXX7Uhpu0Y6OqpDTVd+SwnorQ5cW+UCXzk1pW5dyZ0GWVMiZwHFj6gKAAFAsjmEOYsfOI3lBbf/VuCzBaK3JhkYpatFCUeTBDTIgOCYsEDd72F716YvzjKHFk2EiABEiCB/RNgx2n/7Hhlfwh09zP4zPaHe8+7TH3t8sybp9RcOtJFKBG0Bw0Sg8boBCYGI01C5rjdAYyi6KXC8kq5Mn2t1vNGDBxYAr90a7YUZtX1SOMZiOs90fHfMuMdzwSeEfVSsLxRrvIZ2BLZXk88vDhbfPNU+KKf0hdsF3GHFLp+l+53G0V6qbDsX6SCzw78eJoExpjA1OKl4t3TudvSKCvuCkPiOwHhFdbJhZPJMkuFdzfkfXOtagL4jwRIgARIgASOhgDlCkfDlamSwI4EpqSf+nbBuy0CpGJn5E55AgY0t3L4AQdKv3S/LLlJOdJWlBg+agR+9RtXnlzL6muwGgCXjhUGQulErYtiDrZQAkiFsEAgx+LhjLzWWPOwxy48/sVZC5YpqwyO609teOrMWlS+M/3cvL2C/0mABEiABEigTSD+vLQDuEcCA0aAnf0BqxBk5+HFyzPNjJpbyUVFzCjEgGW3w5IHRss1NkOXa3q10xvRy5mVxrU7v3212h2fx8NF4KGvzc6sZtVcIxMVt8u5/QFbYYCYKKxlm9ENsUDxUmX6S1zCYjtwBzj3ycXZGTEV/sRaLiqZmcI90rKdSPsbxem8LE5+ekMt5ep6/j//u+eWelzCIBIgARLYROB//g+XZ+pZ/cTdfFjCDDMrsLLb1gyYhAIBEsDSBZPyvsn4ev7O9NWlTYkygARIgARIgAQOnwDlCofPlCmSwJ4IPPS1Z2aaqUCs5+niBixawUk7Mdl+lFZkh3QJs67RbkxJu/F70+ynWmj8Pw4EHhTlm5VC5sewNADrApC7on+FY1ggqGONAnF531qLDWSEJ6lIgN/SVg4K3RuyTCkmkmCZA1iHg57CvSv6rCgSLG11HcNJgARIgATGkwCVCMaz3oep1OzsD3Btyczn0jsn1AWl1Ucjpae0Cgs2u7I2u7xdml666mtvSRq7r99fS92oTJdpeWCA63M/WYNlAp2yz4BIAOQZUPEzgNTwHERVmTGwpLT3Z+ru8gItD+yH8v6uwYyPuydTF7QKzivdWTfoXPpaV0WZoCJdUfl9BvL7pHLP/kjzKhIgAQi53i2kLohRzPPpKJoSQVTBCYSNomEUVeU9JO8b7/WTK6sLnEHGZ4YESIAESKDPBChX6DNw3o4EtiLw0OLlqXcmvQtRKvqU9EunRMG01W7EZBSZGF2V9uOSDr0/O7m+zHbjViAZPtIEPvDaldt+KiqhkFCtQd9KZK5mG4ryjZswAqUC1+9qWRwQeY/Xw7JHMp5cZVKGLgKugww343tLf/Po82dxTzoSIAESIAEScATkE0FHAgNNgJ39ga6ezZkrfWW2qOr12tJFLlWwmc54hDwsz0BdnoEKn4GBq3DUDTJFayADVzXMEAmMHAG+b0auSlkgEiABEhhmApQrDHPtMe8jT4BypJGvYhZwDwTu/4+XZyYCfR0KAvFKBqJBYC0PYFlRM+gvOgAI6bTdYRUCcKtuJQKnQOCykVw+xE4Cg1KBLJkQ6IvVR15YMAf8RwIkQAIkQAJCgEoEfAwGnQA7+4NeQ8wfCZAACZAACZAACZAACZAACZAACQwuAcoVBrdumDMSIAESIIEEgZ/5j5+/nQ7DUi4I1YmmDN7IFwxLxwWeVSRA1ChWKkhaH0gkYZYqwLFVDWifaSkTxJYKYN1Alh5VTVliJBV6snyIt/SDc7RG0CbGPRIgARIgge5vCYmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAn0iQCWpYQCARQGfFgbaE3/bCsQICtQLDg0JwoJsHiQCWWrwtKnv3KldGhpMyESIAESIIGhJ0AlgqGvQhaABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEhgWAmcUKkLKVEQCGTEZj0TquVsqFbF4xguEqUC523I3v5DKcF5lx4UErJi9QCKBLB4sJZTF/aWKmOTAAmQAAmMMgH5NNCRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkcBwE/pc5jyQEM9ENZQI7NkgQIO1TrA1I43MPNLnVbo7yQDT91HGXnPUmABEiABAaTgPtGDGbumCsSIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESGGECKzk1hcF9WAVIi8f2KBQIkgihrNCUESIoEEBRoZHRU1OL5UIyDvdJgARIgATGlwCVCMa37llyEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiCBYyRQXLxU2EgpM3gPBQKjRJCwQIDB/qN1sqRBFCpf7lPL23wc7f2YOgmQAAmQwDAQ4HIGw1BLzCMJkAAJ7ILAlHQ48iqPDkcx7adVGIW1tdW1auXitdouLmcUEiABEiABEiABEiABEiABEiABEiABEiCBISbwyX9/eSqV0qXQC39RjOKLjChV1ErXIqWroQr/LlJB5c701aUhLuJIZj2dPzkl9WUtD0gJvdATb4saaam5DiWC9rxQE0WUDXq5+PLWqdZVkh4c/nuiOJCS69NyP1g+QFggckXZVMXTkQAJkAAJjDkBKhGM+QPA4pMACQw3geJrV0picOy88oIL7+qo+G5cHC+KpPEvi6fdc0q9/4/mpKOol6QTcPMfPl2+MdwlZu5JgARIgARIgARIgARIgARIgARIgARIgAQcAZifX5kMn4xSwaWfqFAUB+yocgT79HaoWIJ0PBCdUsVvX65K+FK9UZ9/49w12acbBALO2gCWMIBrilgPVdga/LfBrf8Y8D+Y25xCIMoKdbkvHQmQAAmQAAmAQIcOG5GQwAASiJtNrZzxmW2h4M44E5h65crcWi66JB0K6RyKM1rE0viXrRempZNhNYhNV0P2fekAYI2ztK+rp+tqvvJ4ecFcx38kQAIkQAIkQAIkQAIkQAIkMNoEKFcY7fpl6UhgbAmYtesz0dxGRl0KZNp6qP1YPmSRtF9+dh6hGTKOZ6FDuSCU/XTgLWSW9fwPp69WxxbkABQck4RSXnQbdQLviwxvQ6oNCgXZQKwFSOV1KxNsVgHYviDd1+MZQPpGUUFkh7hDU6fVspc5+9bZ8tL2qfEsCZAACZDAOBDggOw41PJwl7Hd3rXl4DM73PXJ3B+QwHulUxGl9XWtwyLWKnPayUazPO4IwuQZGv5G4RxdDOkIRFo6AnFvAeHyQ6oGKpz/h0eeXThglng5CZAACZAACZAACZAACZAACQwyAcoVBrl2mDcSIIF9EXjgdvmCCIWuZ6KwkBfdAciIQk92Eg5m8CETcsPP1iR+e+gZg9WQFwXiC6veUz/4zeevJS7nbh8J/Myt2WIul/oxJgelQ6tEsJ6xGThKJQLcwcoWPagUyLOQVg2VOfsGlQj6WPu8FQmQAAkMLoF4SGlwM8ickQAJkAAJWAIPfuvyXEa0ktPKFwUCXxr56PjF3igQ2H1ftM+boqJcF41l4zP2OCOdEHh0C6QjWfS0uv7+167MkS8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkMBwEHj/a1+Yy6pgEQoEUB5QxgKBUyCAbCjhEvIiGw/xY2+iIX6olifCFz/4rd97MXEld/tIoF6v1xoix8NyAilRfZtsKnVqQ6kJ2YpYD1OENrm2esimUz0DbE23JInx8hZyLPeEDyRBTDzK1+vVngkwkARIgARIYOwI9Pr+jB0EFpgESIAEBp3AB7/19IuhF5VTKqE84Dp9pkNoS4BGv11DzSoLoGtgTdqFYn3AajNDiQDhUELw01H5gaXL1we9/MwfCZAACZAACZAACZAACZAACZAACZAACYw7geK3r8xFXlCG8oAWGRGsCUDG021lQMaC287Jj2J5UOuEhENWhLQwGeXtk+rSh75JGVGLTx93atPXalnfq8IyBJYohXUAKBOkUb1H7NpWTs3SCbXqOS5tccTImTwJkAAJDA0BKhEMTVUxoyRAAuNK4MFvzc5FaXXJKRBAKcCXzp3rKKKzaL01QYYOILSU836o8lg3DVrpEgalASgQpBIWCXAuH0Uz7/+jWSoSjOsDxnKTAAmQAAmQAAmQAAmQAAmQAAmQAAkMPIH3v3bZKhBggol4yIcw+A+LlFi6AJNKrMfSBlaxALIjN9PcFdCGtUenrRwpVDCbX89FMx/+Ji0SOFb93J6uR69LdRprBM2UatXtUecB1gdwX2xPNKPKUd+P6ZMACZAACQwPASoRDE9dMackQAJjSOD9rz795EYmKks73qoKmM6f7ehZiwOboUCDWItyADSWjeIALsZ1pgOJXoH16CTiPLZikWDmvbdn5zanxhASIAESIAESIAESIAESIAESIAESIAESIIHjJFC8NVuENUlIh6Aw4GQ7ZtuVMWuVwAVuL/6HDMnNeseEFFz7bt67NPX1z19yKXDbHwI5X8sAvmeWFWhKtcFjiQGpliN1TvkEN8n46uUjvRkTJwESIAESGCoC27cihqoozCwJkAAJjBaBhxdni0HGu4YOnC9va7M2mmzx4oaCADSEnTZ5ZwfRap67zgaooEMYoSMiZtHg5dB4KBag84nrxWBa+ROLsyXEpyMBEiABEiABEiABEiABEiABEiABEiABEhgMAlFe3cYkEAwpW1kQrFCGKifWA+BMmEh2YINSiTn8ljdX2IFoa58SpvLbHtIgeywDyIGS9KzfyKi5qcVLBZM4//WFgB8sLwj+GuR2sETQMNYIpCohsxNBXrd3mYKcEH4/Ds+NsXoQJ5AK1NJ+0uE1JEACJEACo0lgv9+X0aTBUpEACZDAABGo59R1M9gvDXp0GExXUbZQHoDfzqETAG1lX7ZwLj7SSTp0QHAOW1yzkU/NJc9znwRIgARIgARIgARIgARIgARIgARIgARI4PgITH0DliN10eXALm8JeY5Yl4RMx53otYVCwW6cxIPICOkZk/qeKqyeOPHibi5lnMMhUJm+VouUvgn5HCYC6bjunEzvcO7SmUpS3ihyx4U701ernTF4RAIkQAIkMM4EdtmKGGdELDsJkAAJ9J/AQ1+7PLOW0aVNd0YHwvlNJzcH4CXfVhKwGutWHUHCzTlPNNfRMbGKCndzqvSvv/HMzOaUGEICJEACJEACJEACJEACJEACJEACJEACJNBvAusZNbPdPVvSHhH0YAC621s1A0iIYt8tV4oHq428KXEjrb2Z0ldmi4kg7h4xgbDeKMMyRFoqNe+LbzolEdTy0TgoEsA31cb80dyBqZIACZAACQwrAbQc6EiABEiABAaMQG1CPwFzYofhYGXAuWRHMpAOgvPuvC/3fHsyesIdc0sCJEACJEACJEACJEACJEACJEACJEACJHA8BD746uWZRloXe9/96EX79Xx6pve9GXoUBN44d7Waa0YveTLhxyoTYNv7TluF9469dSgmFmWCcOGNs9eqW8fiGRIgARIggXEkcPQtjXGkyjKTAAmQwAEIPCxa3tJXKGHAX+ltNI2d5rjbOo3y7nsjjUQ6SNetrVZPKwXvy9cAWs5pWXwtpaLSJxavlLqT4TEJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkED/CISe3jzRoyUH2m0+IFtK+C45kUslimVHWC7BTkIJ1buT+kl3ntv+EDixulIOdVTFxB/I6+CwtAEG+5Me4QdVJEB6XhRV87VgHunRkQAJkAAJkECSAJUIkjS4TwIkQAIDQCCTTV1od+4ON0PoBMJEWdIF8iVAODoemdCup+dnleSBjgRIgARIgARIgARIgARIgARIgARIgARI4DgITF2/VBBxTamf93aKBFYuhYknYeG9r3GiST/roDJ9rXbPu2o6E3i1yEwYOrq752Qy0b3L4cUfTtMKwdFRZsokQAIkMLwEqEQwvHXHnJMACYwogWyozsMqwEG1iTfhEU1yTzTPdRQaZYEJrK0mPgVldHG4H3xa/EZafcqG8j8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEC/CbznZH4qJTKcQ5cP7aEgGDzwvGhqD5cw6iEQqEy/UDm1Fj3VngeUsCSRtCqR3HcWJnbauvyJdYPJup6vPHZtyQVxSwIkQAIkQAJJAlQiSNLgPgmQAAkMAIH1tJqCyTJYBzgKZzqAoiiQEqsDKdk65QGnZQ5TZoF0EIuLlwpHcX+mSQIkQAIkQAIkQAIkQAIkQAIkQAIkQAIksD0BUR8oiYgmlg/FM0C6L8HSBkfm3D2jjx7ZLZjwlgQqj7+wcN9ydDEr1gLgXE3DwqizKpqUHTr5XvfWXh3/l+cF6UzKpKL7VqOnKp/9crnjPA9IgARIgARIIEHAfXsSQdwlARIgARI4LgJTMnD/1klVqKc3Lztguwt4bTu/VS7d+e21lAMvlKUNoEhgfVtnwa59l8/nqUSwFWKGkwAJkAAJkAAJkAAJkAAJkAAJkAAJkMAREljPhsUNMVUZYmZ5yyXE+S0FAicHcpE65UHurNu6WG4binwI3jkMUjuPe2uti+4ct/0l8KePX13IraiPhdqr6lAUAKTOfe2ppmd9IPuQEyI8JedlCYQOjzAl58yyCLJFbIlTPbMSnf3T3/rytf6WhncjARIgARIYNgL4btCRAAmQAAkMCIEVMVW3KusZOI3io8oWNJXRITSdQTF9BkUCWCJApxHhcL5KF80O/5EACZAACZAACZAACZAACZAACZAACZAACfSVQKD1GSej6euNWzeLlRF0UGwFcafvBCrTX6qcfrd5NuvrlzUUAWIPBQGnJABFgfako879dKxcgOtyG97C5PLGx+5Mf3mp7wXhDUmABEiABIaOgMx1pSMBzAN8mQAAQABJREFUEiABEhgUAmJNTGE5Ac+saXbEuRJtcqNJZpY0EK1kKA/ATh42sh/wC2Fh8D8JkAAJkAAJkAAJkAAJkAAJkAAJkAAJ9JmAVlFsIdJZCeB8wD5XwcDcrjJ9rSqZmXnw1mw5Hamy5+nzMgepgElCWBIVk5GwjAH2kw4yRolXi8Lohh+pl3947upS8jz3SYAESIAESGA7Ahwi2o4Oz5EACZDAMRBIYSBffL+0zdkFPYZK5i1JgARIgARIgARIgARIgARIgARIgARIYBsCUaT+bpvTfTyla328GW+1DQFRAqjK6Zmp65cK902eLCmtSo20+miodVH0B4zSiYgUa5GKxKvXdagr2bvLN5YuXmMdbsOVp0iABEiABHoToBJBby4MJQESIIHjIeCral7uDAWCpozuQ6N4KwczZD1dx1p5PWPsGAhN5TTMItCRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAn0nYCIhLYY+N1CHrRlDp0lg60i2PSwxKU1i98dP9wiH1ulx/CjJlCxSgE35D7wdCRAAiRAAiRwJAT22uI4kkwwURIgARIgAUugUK/XcjJ4b5Y0kIH843KZQKmTdb96XPfnfUmABEiABEiABEiABEiABEiABEiABEhgrAloXbXl76MIPzkxBfvio1D92VjXAwtPAiRAAiRAAmNKoI8tkDElzGKTAAmQwB4IyBpntXxTV33P29YKgUky7syhQ9fhd3s/WDIQH6r2vZzhg8mGqlWmjYm03abGeCRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAodEIAiiirUMgASPT4wfqbBySEViMiRAAiRAAiRAAkNE4PhaH0MEiVklARIggb4SiPTrvrydQ30Ur2ik2U4XyyXYJRMkTBQKovicWCJgB7Gvlc6bkQAJkAAJkAAJkAAJkAAJkAAJkAAJkECbgK/qFSxpgOUs0zJ/JCMeW+ewD+8d0JIlrm+lgQknsfNCT2VFQHVfnTIix4RbEiABEiABEhgnAu1WwTiVmmUlARIggQEmIIP6lSBWIEBH0XrboYvtBsB2wDYlwDl4vOKTr3nZR2fQdAhhfSA+n+ggIlEf4VH0MvbpSIAESIAESIAESIAESIAESIAESIAESIAE+k8A1iqzfqqSlSUns4Gn8rL8JZQGItEsgMc+zrUUAPaRRVybknTgIS9yE028KC1LbabVmfVUtfLZL3GiyT7Y8hISIAESIAESGHYCGEGiIwESIAESGCACfrC6ECivZgf7XcYO+rqGYkAiDezHHkoKtqPoGQUCdETrKlxyd+aWBEiABEiABEiABEiABEiABEiABEiABEig/wTuW1HzObEGYKwFiGUAKA44B/mNHfR3Ifvb6g5LBm76iqQl8qJcMzW/v1R5FQmQAAmQAAmQwLATSIwoDXtRmH8SIAESGA0C0DQPtL6J0nRYIYjNy5mOo+ngoefYy2/PAR1M52GpICWdUNwHYaLYrkSxfaEyfbW6fSo8SwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkcJQE7kw/t5RveNWm5xmZDaxKYtAfMpx6Wqn1jMhxDijhhywI6Tl5k1NOkNvUmo3m0lGWj2mTAAmQAAmQAAkMLoEDNjEGt2DMGQmQAAkMM4F7326Uc+jFHaFLi/KAMVfn7iGKBPmmpx6oBfMuiFsSIAESIAESIAESIAESIAESIAESIAESIIHjI+CFaj6USSBYfjKQwf5N8pxDypqbcAIlhZwsk3DfWvTSnd/mJJNDwstkSIAESIAESGDoCBztCNXQ4WCGSYAESGAwCPxQLAGckc4aOm7Od+fMaYi7bff59nHnqx7x3bp5GTFkYDTMpTMKawT3rHq0QtAGxz0SIAESIAESIAESIAESIAESIAESIAESOFYCd6afXYiUt9RIQYkgbSaETMokkFMbSk00rYznIBnEBBPIiqBEIBuzX1jzqt85/1z5IOnyWhIgARIgARIggeEm0DmyNNxlYe5JgARIYKQINN9eKUcqqppCaenRwR+Sk36h6XSiowglBTjZVFPrG7RCYHHwPwmQAAmQAAmQAAmQAAmQAAmQAAmQAAkMBIFTKxsX04FXs8sZYCII5DqewuQQyHhEBSDhTYD9twdZklvGAAMG6Y1gOpEKd0mABEiABEiABMaQAJUIxrDSWWQSIIHhIFC5eK0WhGpa+oW1do67OoVOucB0CrvOdXcgO+JaCwToIKLTiY+BH0YXaaauTZp7JEACJEACJEACJEACJEACJEACJEACJDAIBCqwWLmqpzEZJJBlDZqeWCWAl/0oKe/p3u+QDTm5UWeJsJomPCaZQDkhCKP5//K/v1DpjMUjEiABEiABEiCBcSNgFRXHrdQs7zARkKZrh+Mz24GDB+NA4MO3Zmc2stF10yk0nT9rYs5qmW9NAMsTOAdNdXQVI9miU4jlDLCGnpI46Ch6gZ7/q//lubKLzy0JDCOBwuKlQiGfL6h0umjyH4U1tZqtVqfLCUWcYSwZ80wCJEACJEACJEACJHAAApQrHAAeLyUBEhgsAg99rTzzbj663kwpsUIASY+vglRDliLA/vYOyxWIBMgoCmDPHItcKI23pGx9UUrI+Hr+rx99oYzzdCRAAiRAAiRAAuNNgAOy413/w1B6dvaHoZaYxyMn8MFXf3cmTKvrtlMY2o6eu2tXRxFa41Ag0FASMM5qpjekgwllgkzQtj4QSudRuppP/eMjz19zyXFLAsNE4OHFKyU/pc/fzasLG+mwiLy3FWjsbyDXVNUzdb2U9dXN//RvyzeGqXzMKwmQAAmQAAmQAAmQwIEJUK5wYIRMgARIYJAI/Ks/LF+4O6GuZwNd8LRIdby6SHd8s2wllAQwWQTOLV/pJpAgHKoGsEgJZyaViPwIciJc11Tqqb9/5A8oHwIOOhIgARIgARIggXjJJIIggcElwM7+4NYNc9ZnAqJIMBWk1aJWdqAUGuNWi7xT29ysiye/HNMpjDXJoU0exBrnMH2HQVa5tqqD6GL10eeW+lwU3o4EDkzg4698Ye6tk9El+RUUIAbR8kwnHX4HEII4hQIrPBEzj1FUvXfVm//u9BcWkvG5TwIkQAIkQAIkQAIkMLIEKFcY2aplwUhgfAk8uFguNk6o20E6KKZVXaUip0SgVCONXrIXKxXY5Sx9mVTiy+QSuCyUBqQPbZQNZOtJPzm9oS7+9bmrSzhPRwIkQAIkQAIkQAIgQEsEfA4GnQA7+4NeQ8xfXwkUb10q5r1sWXvRE00ZM41ab/FORQIoEGSlN4i18dbSWB/PdhKdtrmsnbfw9vK7T9Wmr9X6WgDejAQOSACWBzYy+vpyThUbWJcD8yjEGscmJQIzs6JtptEpEeD2+B3k/UiUaLz5yuNUJjhglfDyISEwtVgWhRtVyCtVlElKKohUrZlR1QqX+xiSGmQ2SYAESIAEDkCAcoUDwOOlJEACg03g5779zEw+as5lw6iYkrdd4IWqLkoEmEiCfjDU7bEPy5TYQuE+L/0B9It9T9XCUL9UW1m7RvnQYNczc0cCJEACJEACx0FAmg50JDDQBNjZH+jqYeaOi8DDi7PFdydVeSOtz8uL3MzETuYFncEMOoTSSVzN2BnaE01Vm2yoGxNN/fKd6fJSMj73SWAYCLz39pU5kYKUkVdracApEECZoNsSAbRsRIkAkWXbMuMoh4FY5nBCFTlX/smn5uYRjY4ERo3AR165UnrnpD4vwsELojpchLAQpkphoQOzjvA7iCJVFTOoS4V1dbPyGJf7GLVngOUhARIgARIwBChX4INAAiQw8gRK//6ZmawKzq9kdOknp8LCahaKArbYmFgC+RC2IhtSD6yqpXxD3Vy+u7pQucjJJSP/cLCAJEACJEACJLBPAtJ0oCOBgSbAzv5AVw8zd9wECouXCqdOnSxpreA/KnrmRclTAQOmqcCrRUrXNlL6dQmvZO6GN6qccSp46IaRwC9/6+kXGxnvUihWB6yDegD2xUuYW7bAnJMBUuucJYIuJQJp/Yg1DjOICmWEnB8u/O2nn78YX8QNCQw9gY+/cmUu1NGl9VxYWBdFgbWs/d2caGDWkf1dNMSU6YoIFqFkc7IB6xxyruFVZbGb+e8+/uzC0ENgAUiABEiABEigTYByhTYL7pEACYwBASgT//MZVUhF3pQrbqCjahCkKoWaqlI25KhwSwIkQAIkQAIksB0BKhFsR4fnBoEAO/uDUAvMAwmQAAkcI4Ffem12zhMLBFAgwOxpZ5bRZClWKrCWCRASKxDEigQ23IY5awRIAwOocJiZgaUQoiha+PtHXhhZRYIpUTiq5fMFGU8uoty5MKhlGk0xZc9ZJ+AxKu4jr5TF8kBwPaX8ojaroMpvRlr79QyW/FCiLIClbawSAZbEwewkrJUKxQJYsHEzlJraq96zpud/8NnywqiwYTlIgARIgATGmgDlCmNd/Sw8CZAACZAACZAACZAACZDAfghQiWA/1HhNPwmws99P2rwXCZAACQwYgV/+5tNPBhl1LRP6LfPrGBTFWo9OKQBZbisRmKPOUnQoFIgVAlEg2IiVCDD7Oi2KBL5YJkj5UflvH70633nx8B6VvjpbCr3ofCOtL6zkoyIUJ0Jh5z6saATmmqqa89VSrhnd/K/TX7wxvKVlzn/1G8/OibJAuZnyVejVxcCA/c1ATQCKMljGICs+jQdAjvEsQJEAv4fIKN9YRQJ3Li1KBblmuvyX57jcB58uEiABEiCBoSfgmj+uIGgG0ZHA0BCAQnBe5ae0SomPPhrpSJY0hNc1achVpYn3ekNFlcr01erQFIoZJQESIAESGEgCU9cvFU6ePDmVVtGUTOj5qMiSCs2ULkQqrEVRWNVhJN8cj9+cgaw9ZooEDp8AO06Hz5QpHi4BdvYPlydTIwESIIGhIVC8NVvUuejHMtwpA/2wQhDaNdyl9YJZ01srEaCI1vpAr8JCCQEDp3BQIoACgm9aRJKmH5ytPvrlJXNySP89+K3LYspeXwq9sABmcKkISz6IFQfBgsFjMMA+BpFR9JzMRE8HuqoDb/5vPvPCAq6hGx4C//oPyy++M6EviUKIPNANo0QQiBJBU0xtoN5hfQBWN6B8YxRuYsUaPAu+/BawvAcsEmSMlQL728GaqVCuKax7Cz84N39xeGgwpyRAAiRAAiSwiYB8ATscmj90JDDwBN772pWSTkdz0n6bkiXcRGkg6Ww7HyFQCA3RdoO5ep2av++nzaUfUqEgCYv7JEACJEACOxD4xOKsWDZMzYm4QBTWwgIm3KABtSEmLZ1sKZlEFOqqiJzmUw21VD1HJbYkG+6TwCgRYMdplGpzNMvCzv5o1itLRQIkQAI7EvjF20/flkglM7wpg+BYzgDCMZhc13IM58WDoebAhpj/LZFaa7kDOzCKWdlwGDyFQ6eo0+mlv/mNL5/tDBuOI3T41ia86410VAQjy8vmHQydw549b7dQxgBHzFbHLPScH1Unm6n5ymefXXDXcDu4BKZe+cLc8oQuQyEkI8o2WokFglRdFAn82MJAQnlABMxW+cY+D5H8nuCgQCBPgVEwwHMARQOEQbkAigVnRJHgTx6nIoGBxX8kQAIkQALDSIByhWGstTHO8ycWr5TePanmNtJRybbVBYbp17Tb9AZP3NfBPtpupo0v26woB59eUwuVx16YN/H4jwRIgARIgAS2IPCwyJLqOTW3ltElKAxYJ1+VuPWECSj4ynQ6K0uA/CAdRNVc01v4y3P85nQy4hEJjAaBWIQ+GoVhKUaSADv7I1mtLBQJkAAJbE9g6huzM2+fiK47ZQEnNHNKAxggd4Oe7ZRsJwbHHd0bieuuc2dcZ8imC6UEuQiD6CJ0u2dVXfzu419akIOhcb928/Lcmyd12WmHi9Z4V947jyFgbLuYmyk/Qi2He1eicuWx5yh4bIMauL1ffeULT9ZORtescDm0lgaMEkFDrAi069w876367S6GVRqwoVbJAJY+4IJYaUdsgagza1qeh9/n82BB8T8JkAAJkMBwEaBcYbjqa2xzW5BlC06fPjWndXjJQTDtuIQCQXc/xvVvoCRtFQmkHddq90XVRl2dfYMzRB1ObkmABEiABGICWCpn5WRurplWlzplRN2I2rKFzjNWbuAmImT8dPXenzbP3vltWiXo5MQjEhhuAvaXPtxlYO5JgARIgARIgARGjMBqVj2xfZFcEwZb52UXAjMjNOsMQ5cn2e0xQrbEQDo6TM4v570d7r19zvp99qGbz7z49gldrmdkuYcMTM1h6YfuXDgevbYStyVodNeFai2vyx//+uevuxBuB4vAg4uzxeXJ6BoUajyxzAErBKnIF4UYsUYgQyUeFAHc76HjN9H9DKBcNszNdLPWP2LLBpIW7rGSj8pTr1wpDRYF5oYESIAESIAESIAERoMAlnI7c2bytlEggNJA0vcq4qb2u0TCNej1uGuVLmbz+vsf+tbnLvRKgmEkQAIkQALjSWBK5AkbkxO3g5QSpbX42xFvrUwBcgXrNxNyMgXICpRqyNqJdbFgIDKp4rv3Zr7/bxav8JuzGRpDSGBoCeAXT0cCJEACJEACJEACA0MAAjQ/pUubM9TZbGnNwtkcsWV2rcepzqAewjdZI770S7eulDojDubRr928MveTU+GldVEgsEs0oPO3szOWF7aMBpP4oSgj+OonJ9XMx79+mYoEW7I6vhOrhfT1pnTWjRNBMaxQwMvzaywIdOas87fTea7zCM9RAG/Sk3OxEBr3qk1Ec52xeUQCJEACJEACJEACJHBQAj9ze7YY5r3bMmIzZRUBbIrb9Xc23zMeBHKKBG1lgkIzk1r80Ld+74nN1zCEBEiABEhg3AhAgWDthLrdTIdTZulLp3jmtvsCYpcgXcuEhTdPRYsP/z+f5zdnXxx5EQkMHoHdSxQHL+/MEQmQAAmQAAmQwAgS8LKpC3ZAfHPhYKIT3gnUsO3w5qwdWDXhkgQaO86392wI7uPSdPeUJFWUUgOvOf3QzctPypIPZT8eSMZM9GygVE48gjq4dHFCqZ1WORQKoDRglkCQTiO2mNmOOJH21TuTwYwsLzEnAXQDQuChrz0zo5UuZaSuUe9NeZyh+b8uliiwxXHg4Rnfm4MFC5eWmUkQp+Xuk9Kq9PDi5Zm9pcrYJEACJEACJEACJEACWxGAOems8m5rFRVNz0Ta4WiLu/Z4e19ScAM8btsj0WTb3rX3YVUqSIcLH3z1d6d6XMIgEiABEiCBMSGAb059AkprughZQlo8ZEf2e4GtgOj6xjh5kfm+tOJamVIqlkNBFpWCHEmuxQSEd0+ohU8uXuY3Z0yeKxZztAnsXbo42jxYOhIgARIgARIggWMmoKPgPARox+lEt+BTx3n/ne4NzfGfnNLXVrMy81xac+jUQXEAg71OgWCnNNrn26zRcTTCS0nPU2IaX3xDlkd462Qgpux/p9S+hnvHScDX0ROoK7OEgVQZrAY0xQIBFGFMp18yh5q06xrurrlvFWpsqZAGjq0yAoQBUFawaQdp/cRxlp33JgESIAESIAESIIFRIpBJTcyloqholQa2KRkGdfbqWtdYq2VBKrVYlAGkvSbD+CRAAiRAAqNB4Kenz8xtZHQxhOVB6fPDW+nB3ssHSQM8ZAXwIjoyCnBIE7Kqt06oRSgt7D1lXkECJDBIBHYnVRykHDMvJEACJEACJEACI00gFekpaEIfhzODsHLjlIqmStcHt7NTn0hdj6DhLS052+mzlgf2zwxCSXh0Au0+tta6gS88Gmp1ojlnIvDfsRJ4+CuzxUY2KqGTjsF9Z/IWA/9533qcQyNft5brwFHSby6Cs9yBa106yd8hlFMws2AjrUq/vjhb2pwCQ0iABEiABEiABEiABPZCANal1rPeJSgCm7bYEfaBbD9HFycmJq/vJY+MSwIkQAIkMBoEHl4sz0ReeMkpEDjrg7BImHTue+QkCMlzyX0REbQcrjHyCQkxFg5lkkMzpYtBjt+cFiTukMCQEuh6RQxpKZhtEiABEiABEiCBkSAQD9wfq6ZyS3aXzx9rPraq0A+8enmmmQlLOI9OmvEYNG5lfKsrdw63CgQSTxQUMCiNtM0CErL1ZfD452//nzM7p8IYR0lg+Yy+4MusgbaJW1v30Po3A/3xs+AsEuwlL+4ZMsICqXMzKUESkF0jCMC2Ljf66YR3YS/pMi4JkAAJkAAJkAAJkMBmAm+eUHMYxNnRtSwK9I4JBYHdOdNQvPDw4pXS7uIzFgmQAAmQwKgQ2MhEc64ssGYIb60XInT/w4SQE7jvkFNAQIpQVhCrBxcevMVJCOBBRwLDSmD/b4dhLTHzTQIkQAIkQAIkMLAE/EJ+SmY6mwFLzIyPtGc6NejYmM4NBGixEM0OeMpxPHPebuOiJeKZEMzIbs3KTl5hhsglBbtFXHR+YMLfT6eL5toB+yfrmT6BLGGAP++HxrcHf21JDCPHoNe2u0wuTiLcMZdV8oRd1vhUNGnunYjG3T4TqGfD8+jsm9kD2ErdZbEOoXg4c06eYfdM9M6eUROQU25rY+HZR703zKwBpAFlBcwgCI3yALZYNmEjHX2qd7oMJQESIAESIAESIAES2A0BWCEIPVWEVTG3LBWu6+ydtPs+Lk20/To9zqAd6Em7LSHmdf0ftPPFoY2Hth4smS1PtAeSzEn+IwESIAESGGkCH5dvznIuLOIbA+cmjcDaoLNgiG9Ix3ekJWuL0Ti5kdtKMFKDbAKWMvE9w3cGExychR3IJ5rp1FycAjckQAJDSCDRuhzC3DPLJEACJEACJEACI0WgLqVBJ8N2RVzRpLmSEIhhUHP7AVJ33f626PSg8yMz7wfOPbw4W4y0LiGPYIDOGfyR8BDmUEmAQNLwj9LSuUyXfv2rV0oDB2aMMhR40RQ66PiN4DmAcxYIEG6UbWyw/Z/47SSDt9pHmr543ME6zFCwij12K8cpNcW1DR0fbkmABEiABEiABEhg7wQanvcEWlzo+7jlyXqnYtt9vc8htN1q2zqOPYN2HtqK9Ywqvfe1K6Wd4vM8CZAACZDAaBB481T0BCbL2G+G/W5AkWA/Fgx7EcH3xcknIJ/CpBfcyyi9pcKSyLJKva5jGAmQwOATgGSYjgRIgARIgARIgARIQAh0iuBkgfkBc35KX2grWXTmNplVdNoOolhg7BlIJ9AoECQTlv0wpWjKvotJvw4xcK91JMtsJDv/Wz8HLeUbKBIk/b4z7O4lFjDUYC73se+i8UISIAESIAESIAES6BOBqcVyEQP5GLxxSpoYfDlI+32vWfdUurTXaxifBEiABEhg+AhgMopWurTbnDt50k7fJAws7jS4iG+bsVDgebu+/27zyXgkQAL9ITCAc+z6U3DehQRIgARIgASOikBxsVxQ+XoB5vCxxqVWYa1ZW6vWpq/Vjuqeo5KuDNtXdypLUrt5p7ijdl7WsDt/GGVCR88NB2+dXtwdNDPZZT+e0S7mVj+19TU8c5QEZOB+6l0V2FvAGkFcJ0d5z81p48kxz0ZRdqri6UiABEiABEiABMaQQFGUG2uFfCGt0sW8NOLTUbpWWFXVynSZfZ4dnoe3T4tSLmZtSpOqL8oDxooVMhW372VPa4V+RVk8HQmQAAmQwAgT0ColE0HQj5elC1oTElyB7XehcxkDhFmJkVMkcHI4d9Vet7Esq7zX6xifBEjg+AlQieD464A5IAESIAESGAECJTHxHnip83cnwgv1dFNMzqdUmA7sUvJSvtOFCfU/vDpbzQdqKe3rm3/y2B/cGIFiH0ER6iJ0zEm6bQEXbmJMrHUNmCY7Ma5j0+7odF5vuz+SkEvDzLJHytY5E/Ba1oNLuGpifyB2V7PhVCqUKUtd+d8qc20unTFgUq6DkCTZ6eKzjlfrpKfenbSm7CtUimlR6d8OrGP0qvyO51bi2PrrqONEJrd6LhJRzG7btGF3+t0xeUwCJEACJEACJDAOBNDnyYbB+eWsviCmkYsnpLGRCgOlU57Zbkx6aurr5epkUy1NNNTNb/8fZfZ5ejwYTc/7VCPTNK06KGBkpKll1qmWZp5rf7n+ibvc6+yntMxGu3ZfO15XC1Da+cl+k4snWgRTU9fLhcpFKn20mHCHBEiABEaRgI4+pfFRiUUJ7ivhevlOgcCDZhucUTxzsWxQbxlCKwUbqTXRwYXLF8rImkK1kvOmoHxYpRzJsuJ/EhgiAlQiGKLKYlZJgARIgAQGj8DDNy/PvT2hL70ZwcQ4BviMIXijsytqBPE6YBIsjfVGRsFs5Yw0o2eKf/x09f4VPf/dx68uyFm6mAAGpn9u6emqHBYR1C1Ec8c4d3QOHZ6odmf6i9Wju8feUy7cvlTY8HQhDSWCvjrpPCaUCTZSSt2VWWeShVpfs8GbdRJI1EnnCR6RAAmQAAmQAAmQwOESeO/ty3MTvvR5wqCQkqZy4ImytPRv0qFVTMUAhJZh8KYoEzRSXnEtp2ayvpr5lVtz1bVsOF995NmFw83RsKcWFlECKA6kRDkA3ikNuO1hlhCDOHZIxw3s2MGhzIkM8lE5zHsxLRIgARIggcEi0Exb+VpfctVSJJC7GWUE2UobAXKklQcmi3LEb45AoCOBYSJAJYJhqi3mlQRIgARIYGAIlL46W6pn1fWVfFDEpN+m5MwOcMeCNBHTYEbJCTnRxHlpMCMOwlIibEtFXnE9513/ta8/PReFoSgTfGlhYAp33BmJ9OsCswieRuAlAko4Y+pTtr0UCbpn17S1pCEokwpwnRckZJwVnLkjt3VCuyjSA9exSYsp+1AEtgFEgNAQj7m4vCe37fInQw9vP+2ni5Ja9fBSZEq7I5AW5ljOwBNBM55t59zznAxz5zZvu38vm2NsEYJ7UnlhCzgMJgESIAESIIHRI/CRV2ZL6xPqehhFxUjaoVCZNk7aBGiKpmXwu90HQntElAnitkI942Ed5KL0g65/4NW5uftXw/k701QmAL9sGE1lmtasdEb6ht1u32217oSSxz3acamUmpIoA9fvSWab+yRAAiRAAgcjsJrReNfv6Ny3x0kXdrxgUwR8zyCr6vyuwRImZG0pleI3ZxMzBpDA4BPY/zth8MvGHJIACZAACZDAkRD4lW9dnvvnQnT7rZNBsZ4OZSZO20fxPhrJmO+BWSUQDBnzlIncYJC3IdN43jwRFv/ltLr+M69/bi5xeqx3U6GupGSQHIxS4gWxKBDYJos5lrC9uc4ODNKFMgfShUNnBsJPp5xg7hXql+3ZwfnvND+RX6fsMDi5Y076QaCu6rVApeXNgt9D0ru7b9G0T3bik/vush238b3MoEDrHtUdL2MEEiABEiABEiCBoSXwK7dm5945rW+HXlSEKX0oMKLf48NLexR9nZaTc+j/hBpqBr6J25S+TiPti+5rqJoZv/jOifD6Jxcvz7WuGdMdLCGQlr5HNrD9RMvRssQAjhvEaeNptb3aQfvdQzsQ3tSdKDF4QWG/SfE6EiABEiCBwSeAJQQwqck5M+EkVvYzyxj0mCSQ/A4l910a228TbQP51jgFAlyjjQXX7a/mWRIggcEjcIgt0cErHHNEAiRAAiRAAodN4MPf/J0X/fRG2U83RIjmG28FZrFgTQQ/RqlABr0x0BfFA33pwFNZX2bmmDXGZFaOfIHh4TB4nY10+QN/NHvdhoz3f+/u+kI6ULWsAMqIdwoF6OCAITw6Pm3xV5IXoAr7liDTCuIQ33mYYM37ocqL4M4JQ5GCSVPqDXXWbKglhA2SgxIB8uhc7/Lbs04A6bbumq23lhvYWb91TJ45PgJY7qPh6WoQv1+sepLUGTr+4m194/chZ8R3/EpaQuOt8++uaD8Fdg+/PePDrNxHnsQoLct9XK1unRLPkAAJkAAJkAAJDDOBqa//3ovrOV3WpsUt/RUURtoDaCe7fkxklHzjY9mH0gB8XbR1N0R5IBKFAi0KBbLAgSQg/aaUr1byUfmTN8a7zyOLgsnAvZ2V6ZSDsTX9F8Fs22O2L+naetju3E53cVBZ3c618hLh0jaMVEglggQS7pIACZDAyBHI5wv4djt5GL4lZt/IJu2XpfWtiRXZWsf49kDWsKVz353ub0zXsXxv0J7wAiwDS0cCJDBsBLZ7CwxbWZhfEiABEiABEjhSAu+9/bm5ei64hBk2Rsgjs2rsoB0ayBD8OOEPTI1LWztugKNZnpYGOmacuA+vG9y1V8Lsp8zZSUUzP397vIVq4FiVgVJRFLiJfXRuBGOr49JmisD9O6SDGVSuHtzgPMbovVAvvHFu8AZIZV5XFQoQsMbgrCbsn8ABrjSdSGcX4QDp8NJ9ERBLBK+HYo3ADOabN4rruEty23bwXUfevnV2f3NJH+matO29Qj14y33svjyMSQIkQAIkQAIksB2Bj9y6PPfWSXUJitFoe7p2smmTy4Wun9NOwyoyQqkA55yVNvSXMCSuodgo/aempLecC9VbJ6OZ/2lxfPs8daVqlp3lA8sAVgHaEUV7q9v1CuuOs5vj+J5GVcH0s+K87OZaxiEBEiABEhg2AoV6XSboxLluyQt2/qY4WRm2+3eiOCBtANeW0Frzm7N/mLySBI6NwM5vjGPLGm9MAiRAAiRAAoND4P1/9PST0vott3MEAUwv5z6t2MbeDcD12rbUCmxcrfTM+789O9cr5XEKy9SDspttjcFL9FtwvJb21HpGiRCyU4CZFId11ow7asfADKkVSWNVJlVjJhUEo7aDBAGop07Uw/lBZA1T9rDKAOsMcEkBritdv/Ltp1W1X/fifToJBCmvgncLFAmcMoGZKSDP7mbX+8mwgmonsJatXAi/WxcO4HIfu80745EACZAACZAACWxN4Fe/8fSTy2ItANa6oLSKZdmwPJu1cpRQ7k32a5Acjo1z2/gw3gTSmG+IDiSsFGA5uLdPRDPjurRBZbpcE7StgRTTDtvFII1rr7mBnU7CuzgySh3JeLI8hfKryRDukwAJkAAJjBYBWDOc3FA1Y1lQiobtVi4pY0rGaX1/JHB7ucHmmLibmQwjsqwooBwpyZX7JDAsBLZ+awxLCZhPEiABEiABEjhiAj9za7bop7xrbhYOGtadbpvP6TYN9M407BGSbqR0+T23Z0u9zo9LWEVMpYvo8qXAszOaPOlwOO1lDJhaBQ1LY3N9dFPa3M1xZlhRp1rqCObdZFcmAkULPxxAKwQoUe3stVquubnTtXP5u3ns8njTs2ufc9Fir71xtlzdZSqMdsgETr+pFrI+NPilPqSOWr8HU182LPn76NzvkZlN9RzHkXD8Ptx7z12ZF5MY999tLrljbkmABEiABEiABEaDwNTibPHtE941XywGWCeDzNK0cL7XbEQzoN2r+F3ti6QVLVyzJgq9Pzmly7/+1SulXpePQVhLieA4yyrVOxD5OE4GvDcJkAAJjDqBfODt8K53333Ixw6ZhpEreLKkqFIPrEQ75OOQ783kSIAEDoWAtBfpSIAESIAESIAEtiOQy6WuYwYOZuKgaW1nf3R/QnHcHSZBmPGxadZH+25ukM5todeL/ZxSc+1Y47kXLq+UG56qNkWRQCYtqRNyYHzTajJb3o672+7MCnyRHjoxaTHrhmPUaaCiariu5ndO4fhinGjo18HjyBQHXNFagt/uZ9pThbqquGjc9p9AVWavnakrs9zHzne39WdmHZg6tb+T1nFHPbvfkGwT4ebdJDey1yhVWAsXoOSz870ZgwRIgARIgARIYJgILJ9S142tI2knp6Xf46fEcoBYDcCAf12sCDREoxdLFYQJ36ufY9oMUnDX3sAW1rSy0vaGh2IwHKyDvXUqGss+TxRGr1sKW/83bTDpp7htKybaaaatlmi79eqHti7YasdTd6afW9rqLMNJgARIgARGg4AXRa9DOSDpN5fMWiBCuPvutLYIS3jEsQ4f9PijbgKSsey+kbXJ7qkNT/3FY1eX7HX8TwIkMEwE8GumIwESIAESIAES2ILAQ197ZiYdhSWsQ590aEy3hTfJMwff19IIz0a69PGvXZ45eGrDm0JVzK5lgmBaUIu2ssyKlqJg8D8tQkgIIw/bZXx18Y0BtULgypr1U5WtnrvDJxLftSWktMf5Zviyyw+3x0Mg56uyvfP2tW7eUxLRKj7tPa/2erlH4vem14OBVrTZeyl5BQmQAAmQAAmQwM/dnp3ZyEQlDDBAcRotDCx/FK+i1QKUHCpoBe6wgzSRXtqkbbe4BEscrOXC0gdfHb8+jyzWtrTVkAzYHL7rbM/Z9KOlw78PUyQBEiABEhg4ApG3ZL45yX69ZNLJC/CNPjpnvz85n9+co2PMlEngaAkc7TviaPPO1EmABEiABEjgyAnUM9ETMgHHmLrHQBxce1aNbXS7hrc9u8V/Z5HAbbeIhmCkhxk6G1n9xDbRxuLUjz7zQiUVqKcgsMQMfHhf6sHVhdV6xlnnZbdl+QFhsXPcZYtrm9ICakhajZRnBJheEM1XHx18rej12uqCPI81CGOdc4JZPDfOu3N73brr3bbX9c0GTdn34tLPsDvT5WoUqZd2c0/UZeLXsZtLtowTqXDhzm/TCsGWgHiCBEiABEiABIaUgDSNn8iIhS70e5LtQPRJYL1rQiyBQZl3P0JEWNBCW8Q5146FgkJTrB2E3vj1eYLV5RuOh9sa7nKQ5O/OHcU2jPTLR5Eu0yQBEiABEhgsAst31aZvjsuhJ1ZX4fbzfXdpbLfFNw2TssQiEb8524HiORIYYAJH9X4Y4CIzayRAAiRAAiSwOwLFW7PFjbQuSXvXzuQ1zer2p/MoZsMjZ05wJCY+S8XXZksIG2f3N595dkGEjRcx8A/vZkRBAOk8asV6uxwERJXtMDkJjWvn5Uwk3pcgKBRkfO+pv3/kankYGFcuXqulAu8mnhE45P/gSxuAVMK1tNNh8SERLsyCSHEQOYnkGPfvXY7Kos1fRRaMUo1RnsFz3xbTOyE9nhH3nLj3C65zaxxjPxmO9EJtfyNQ2sFvRQYRqqeXaYUArOhIgARIgARIYJQIPLw4W5SZ8SWUST77xpm2hey59kFnmzCOdIAN2isYuEC6cq/Sr3/1SukAyQ3dpbC4lm9GS66tZnsy2xejq8W+feSus7Aq4dqCuCcUQ+67S8XgLkw8JAESIIGRJFC5WK7Jq3+pJReIZQfyBTbldd94t+2AYOJ2hHQc4BooGcLDQSLhJl6Zb5vIl7CkkWqESyYC/5EACQwdgYO0QYeusMwwCZAACZAACeyFQJjNXDAD15gh4yRqZohOPp+JgVY0jNFwtt6Z2kcL2gnH4ta0hLTj2f12flx8G4K1RuEykXfBhoz3/x+JIkEjCD7WTAVVmD4FR8yUwtIGdnmDbrYiCpVzLS/1pY2XBV3jTkwq0FXt67PVR/7g2jDRTQVBGZ0wlMfN4EL+20LIbhabS+c6dXYr8SU9ePdcW1a4Dk1F21zEE9lo6nmE0h0/gcp0uTa5pqZFEFBzucHvwjnUoVV0ss8JnhXMJkzLLENs4RryboPHdZgd4K6Hkg1+Zz6sdchPBusgZzeii5VpWiGw5PifBEiABEiABEaHgFYp099AfyeQPkggioRoKrh2gS9hotws/SGExo2IHsV37W53Cm1T520YBrJtGkjbtuVtSzNIq7Hr85xaV/PGuoOwsM62uVtHcdsMrFxduJZ5Z0x3xe62aPPJvRfYrtsdL8YiARIggVEgsKGCeTORRgqDzw4UCpLfaPOdkW+0aQHgW+18R+HdN9zJOq0cYaLhqTyEp3J1JG0IHaaND6OstBrSKgr1whItGnaQ5AEJDBMBEQnSkQAJkAAJkAAJ9CIgjd/zgRF0ydm4gY0mswzN9Yp+JGHSqP/UkSQ8hIn+oyxtIDOlzjbTqfJaTj2BpQisk1qRAVPTyUnUjZttY+LgfHwOM28mG9FC0Kw/VZFZQHEiQ7P54bmr1Q//4exLQV496TJtnkt5Rg/TofPnizcchV/GVwt/d65cPcx7MK2DERBFgsrUN555ajmnrsMcMByedAwCmF8HpAPxMbbOiocJwz9xeGyseUF7HsIDKBCg3mGFICdKB6fXovm/eOy5JcSnGy8ChcVLhXw+X0in00W8O3UqrDVX16qYQTleJFhaEiABEhhdAn5anYdVACgJoA0AvVK0JDC4gEF/1y7AsTkTb+3R7v6bJBNRkVY6kU4zPX59njvTV5cefPXpJcFScuakE4iOaNdaZFPrVAzeDeD7b1+eSqXUVLapilk/dUaUdAtBKvq70Atqfqgr//1Rto93w5FxSIAEjp8A3lcfeG1uqZkOS/ium++wZMt923vnEJImuO6vuA11/yE7gKwBKgh2YgomNNjroCxXWG/Ou7jckgAJDB8B+YnTkcBAE0h0K00++cwOdHUxcyQwWgR+4Y/n3pGB6YIMpclAGxrPtgHdq/nsZoe0CbjGtgvpdZUd7DMxzCC3jYu08LLDzHDMJp74x9V7YMbepcStUlNidvXtM6mycDpv6whUupknSRn+NdGAvvHAWvjy9x67upQ8O2z7UzKwt3w6932ZFVbEgDFmlqOE5tlJfDmNAsBuCpd4/mx0q0AARQ2TfuBVH6htnOWMpd3A7H+cD996Zuatk/71XBDKO0OE/fIw4FnIigIA9mFJwAgKJGsITz4n6NRPyswBvHPcciFuuQ/U//0r6qn/9pvPX+t/qXjH4yLwicUrpY2cPn83H1zwU7oI6xUZeZCS1irkuDrRUEueH9z8r//2uRvHlVfelwRIgAR2SSDROjJX4LNHFxP41T/8vXfq2bAAawNoL0hLIT5jFQvckRtosJaO4iiy6W6BR7FFtbaFK5ti50C5pGran0jdU1mZHplbW75nGBV82yT2vle8dano5dLfj3RUaHHd1C636TpLdXu/i61H9AvQtxRrbPN/+8iXy3tPZzyueFjaQfVMcP6dSW+mngkLGdcOguU2QYDnHcq7vvxepI1UO12PliY2vJe/M8320Hg8ISwlCQwvAcjR3rwn9X0/5RdgcSAr8gNYBXLffch+Op37wruWgD2GPCHpsBQizgRiyRByzCyEEOIgTyjIhIT/9tnnyjimIwESGE4Cm14Nw1kM5nqECXR9loyMe4SLy6KRAAkMCgEM0r5dOPWOFRPIwJxpErsGtDSxnXAntlTQ3YjeLE5zje7OErYa6S49OW0G+NxWwu95K3rfHZr+6gQXH6GesupkKaXCUqT1R0UaWdRKi+KH7KmoJg2dmuy8Lh+TSl2t3RglweRDi787tXJC317LRKa8DhCeNDdIfBhKBBg8vHc1OFvhTHSHeCC3H/7G5an1fLDop5RRLEEmoVwCZ8wWJlr9eD6c9QFsUcdwGDjAOwmHGV9Xsxvhxb8YcoUbUzD+2xWBX7v5hbmfTkaX5BkqwJy1NTntzE3bGanuW2eek/h5kTdO9b4VNf+njz+7sKsbMRIJkAAJ9J8A5QpbMC9KWzp3cuIdvPeb5t2PiK7fIg0JZ5VNQvejRNC6xtwf5o/Njr2H6f+YlqsJDBrB+6piccvFGJftv/rm71xqZvSLpt2e6BN2l/8gSgQ2LQwY6er/9+n/+33dafNYKbSla5Pei1pHJfBw7SC0p60ipTyrcf3AageUbuBMu9oq/1fvW1bz351me8iA4T8SIIGBJDD19c9fevuk/2Iz5VslAnyX4+9xSz7ZynksUEi2C+Rc+1seyw9EicA6UVSDwhXSEy9yiOrf/caz/Oa0eHKHBIaTgIh+6EhgoAm0uphxLvnMDnR1MXMkMDoEMAPhnwr6dqcSAcrnhAWukWzL3GpEtwRtrrHtmHTGd6GtbSyQcII6CNzQ8MZskftr0dn//O+Ge+Z8q5zcOVQCn1ycnfnJ6ei6m0EOATCeRcwcTpqI3fGm7vmLI1pNck8GlT31wIqe/9PHfr+8YxqMcOwEMLPgp2dUWWv9BAQATonErTncEuTHrSsbB1YnbFzIQhHm62ghU1t7iibrj71K+5KB0levlJppfX0lFxbXM0ptyOw6CM5tozuMlVDsNwyzSCFIt98omz28J2DR4lRdVzNBav67VCboS73xJiRAAnsiEH/5WtfYV1zrcHx3funWbCnI6dtoQ7p+TocSQQKNVaruHDxInJZe0ub+zuaQ9hWYrdi+l7Q7Q322+uhzS+0Y47P3oVc/txCm9BP2QXXUuvuTnTx2VCpItO/NHNEoqJ66G4plsWvVzpR49NDNuRffnQwv1aXRbAfRbJ8KLwrUiQ1z/XP0s9zsXWsBDFa80DbKy4jZybpaUOvBPCcB8LkiARIYVAIf+/rnFt48FTyRkncZ5EetSVKbvuM9vkMtmWdculhuaY/skjnyhpR3p1dVjY2z46gcOKj1znyRwH4JuJbpfq/ndSRAAiRAAiQw0gQgDDDCA/nnBuRGusAs3FAR+C/TVxceuKsvTooWgVFkSQgL91sQCMngc6KI8J7V8CkqEOyXZP+vw3IT//AbV2furUXvu2fNezkdeDUjnDcdezuLClYHMiK0dzMEIPDHILAI8mtn1tXC/e/qs/949oWLVCDof/0dxx0/9Nrs3Jung9u1Sb8Is7wYIMJAA7aZ0ArI8T4IPF/WyPbNjFTMvjMDTdga76sNEbq/fSIo/ssp//rP3r4ydxxl4T1JgARIgAT2QSBe8shZI9pHCltesjuBY48Bii1THN0Tf/WZL87kGvrlA5cQfQHnzWCQrYVM06veTwWCTXhhieOXvzl3+x1RIIAijbU6YBUmoTQJ11YgMEfSJrKKBmlpJ0GJEte49lNDDt45Ec68fZ93u3hrtogr6EiABEhg0Ah8/7e+OHP/qno5J7IBM4FE+nttpb5tctutQCBR3fuvNbFKvj3y+hQFAlEMHEPrQtvQ4ykSGFoCu2vTD23xmHESIAESIAES2D+B5ExLpAIBQiSDbaZx3Ro8sYMomAlivMRxgobt7ow423kqLGxHj+eSBKBIcM+q/7F8Q1fzMsaHNWXTmNl1AIUCWQqiOlmXJQx+6/lryXtxfzgIQJngz3/z+Rn97ur7oqaeDqLopabnLTU9XZXHoybDxPBVX+lKJOdk/Pdi4e7y+6S+L35v+rml4Sglc3lQAh/+5u+8qHRQhjleX75h8PJlEksDVoEgJcJxJxSCQsFkM1QT4vO+KBdIHMR18XMBzGGKD32VC3X5A6+Wrx80f7yeBEiABEigDwSk7WgVwvBOPz6HflcaeRlj94NzV2eks/mSUWKPB7A7cHT1P+059z2WI9P2j/uqRoHAxsBgTn5t4+wdWiDowPnwV2aLuclT3w9SsiyetIHAHe0d9Kdysk442jiwzATlSrSNskZpwIbZtpMgB3bUS9wmwm/JKBl4UdHLercfpCJBB3MekAAJDA6B7//m/zVzsh6+ZGWP+HaI65AhJdoF7vtjY3X8x/WwjAlrdg3x/v/P3t0+SXLdhZ4/mVnVDzM9cks24F3gUgYMBnxDLbDuVXDZdU0YGRTCnukgiA2/mpn3G6uZwMaNGG1XI1mee8WuR3/B9LxyxN4gesbCV0a+3mntfYEWa6E3sHcdYKO6i4mQsT0qqacfqzJzf79z6lRlVVc/d1dndX0zlJPPT59qVeU5+cvfCWMJIKhLAEGl2rEiEwggMLAC8r85HQK5FuguOvE3m+uPi5ND4PQIPCFpwX/wUPEtrTDQigF9y1K7orydrRG2u6WP1JTPnV3zplxm9gwyaN6suzc8XQywHkebM/jg/YDmDDoxmeohoH+zK6OmslF0qex90xg9Vu09q/k3WGik82Nr69ck1am8xU6HwOkQmLx3dXJsfWyyUCiU9IrSNKmtFFertfPD+Xf+i1//7Kz8mFXUwr914t8e0d8o+/sjFeL6ZuqmvKWqnVaqawW7VhTpL5x/E0/X1V84XWZTYSZjJjYjZjNM5//pE5Urui0dAgggcMIC8g3V0ck3GZ0K6EPOtbH0LZuNxn6P61xfbmmXZ/Q73t9b+t8LXTPb6ePWbNc5lV3ixl1zBjrumnAz9eD8d596YdEtHd5/f+3VmcvrRTMrP6wlVfB/rPq7m+18udHOk/v4MJUfbBn6sqZ+ToU4ffnsymqF+/qsnBt//O71v71/Npjyf9cueFLvZVymAX0YJi/p2k6zDWjWAbVdy9wXaRVB9+eiTRu4ToZpUI3rCW/jNkUYIIBA/gSmvvo5+5tTj8JS6/fDB0bJ6fp5rgTo7hDsPYEsc+VC/a4rSF1paOTFlpfDTX5z8vcpc0YIHE7A34sebi9sjcDxCVDYPz5b9owAAjsITN26Orn20+fe0UoErRhY11oDuZEer7ubZhthm/kV9VUF/sbaraUH0O20a6/hpt2/fml2nr4JYTupBNKU4z/1g+BDtKmYFWJ8JwGtDE7CqCJ/RxfkT3Ryp3X9MvmxrUkJ8E4aJ7e/+9SNRT+fIQKDLCCBNeXNYnrh3bHg4kYhKLUferjvY33YMVo31fdtJouj9eDuX02/cGeQr3ev5/4rX/vDZzZHzJYsI74yyO9HlfTXSLJY2Fn6M6hv4+k8rTCSrBZ22N5Omz+QTCjxiDSPUbDNZEi0RuXvf+dP5+wO+AcBBBA4OQHqFbax13Tu6eToO9kgAg1idl2zTCITbk57uvfuOtfye+m9ruxTM2fZzgURFOrxh75D6mMroqnwo8hUgjCwbVbr7+6GPMn2D7WbcM2B3NFouVF+f/WT0maq5A9+MY3jue8/+cJi57pMqcCvf+W5L703bq5qRoGCYAXNFwD0Pkc7fWiWbeLDBkzKenrvo712GkCgvX/A5rf1b/LKLuRGSVZOzdJ3n/ziYzpJhwACCORRYEpeSHn3XFJJouCSnp9mndMynv7m6Leir6PUefp9aKtHZb5mINAAgpFGcfHhdwtzb/xBZVHm0CGAwCkTsPc0p+yauJzTJSA/TR0df7MdHEwggMBxCvzyXz73VsEkJU3xvNYMIvBvYjY0TVfmG6lZlyCn4yoeWlVtmvZLOvt25pal7bXtSvKP3pz7Peg+oqRQ+6/nv/iwX84Qgb0KaCDMuXPjZal8LAcmeFT+ukpSRdYMKkhrsp+a/Mi+nkrF1vJ7q3eWrgznG9l79WS9wRH49VdmZn800bhqAv/33v6Gbl1F6wGJzvHL0+oHlqO5v/n08/Ot9U7ZSOnVq6VgrPiWe3ux8/fJX6oPttBKIvt2iW3GR3/H/BruDT2tNNfl2c6+gVIvSKVTaCuV9EHGuknP8xAjq8Q4AgicgEDrG6x57K5vrxM4oxwd8ue/8fm31kaSkr5trWWWdnCYnmQ2v0C7lNL79N3vqV/L/7r2XleOlQkiGGuEtW//zhco83Rhacr9M5Epr48kz/xoIphaKXat0CxrjsmTnMmVkdpoI7i9GYR33pzmQU6XVGvyX/+n6xffGQ8W9KG//q1rEwZ6j5PNNKhfGD44wG/o73v8fN0m+/+K7q/9N93MING833xkJZ376ws3Kn5fDBFAAIE8Cmh2y1h+c1ZGk2ckCH9Kv+9sIIH81mi5T7/z9F5hVDK0jsRpbUR+c0Y2oztvTPMiSh4/T84JgaMSoOB0VJLs57gE9N492/E3m9VgHAEEjlXgF167Pj+Sppe0QmFdXjPIvqHj0x7qCWQrD/wJtYIGskEEUongKx2y4QPdD2Ha60glXhIs/tP5G+f9fhkigAACCPQWeGKhUt4oxreWxxqlzUzDyt3f0e47N/Noo1nBq3sdkVqSc+tpdbQezb0xffqCCX7u3ufvSXadsmu/d+vvl1fpMGv6+GX+4VD3b5f6aRDBuDR7oJXo65LuV7MYhPI79r0nX+B3TIHoEEDgpASoV9hB/lf+cmb+x2fTS9psW2QfjHZ+49sgMlum8b8A2+3MbefX8nvZdu1MEMEjq+HiNz/1BX4rtsOS+VOSNeLtybEpE5mSvN1e0lXTIKgFJq1N/tgsfhMNFOYAAEAASURBVGf6RlXn0e0s8HP/+3PafEepIH/vGkCgf/NaNtf7Gl8O918YftrvUaf9/U/HvZKsoP+fuMCC7r98yXwVm1pxNX5sic/IUzJEAIGcC2imIjMxNiXFu5L2Wi8q3381aSanVlyPFqtkDsr5J8jpIXB0As2WnI5uh+wJAQQQQACB0yKQBumSpDG8pNejFQIacaudVh5kqwa0IqG7EsGt2fxXH8BkHlJll/lKCD/P7luO5fcn9XW3/TKGCCCAAAK9BX7mG//z7PfDuKIBXq10ss0grt7fv/4Rh+xPvsP9OpvyRf/DCVOSXDO3fubesz/3/fMvzvU+4uDNffwrn7v8LyYpZ4PYtruK7O9a9vcuo2Z/p9xvWHsN/Q3T9L82q45mMNDPIAzLj3/lucvfPMUZHrZzZD4CCCAwCALSpM9SIIHT8vW9fadlGbtC9pdg+9V3XdIqG+lviGRfa4S3d91myFdYmrZZwxZ7Mbzdaybztgj89Deeu6wBBHaB3KPofUzs7xeba0tRvNVpHYDe2/jOTvuJzFDna79dtxGZyc2z4VVZrj0dAgggkHuB6g6/Obk/eU4QAQSOVEDv1ukQQAABBBBAoIdA8mBlfjM0tbo0XaCtGZytN/vNdkBBazMfKOCHzQWakSD7Y7t9BYOrQNPNNICgKG/maNMJY5tmsbkrBggggAACPQR++WuzXyqatBJJ243aa7u2QTNFrQ3I0srhTAWxXZZZ3lpf50nv91NM48pHvvb5Wz0OOZCzVkfkLVN5405/zwoy1HZ89e07nfa9Bsvpm6ja+7Z+/e+WHcqV+18rHaqvD3pTFHlLxWzKb+aGhKrr+ro/bRLo/nhySZfTIYAAAgjkUKC+Nj8qzQnYVOyth/vbnaeUbbR809H7B6jypS+/o/Y31/7uNqd1nu90/61j6C9Js1uvL/pRhggcl0AUxs9oBgK9D2rIn9+q3ACtyT2LZh3U+xcNhNSggWyv52IDCVr3k9m/6+Z45j5Tsz25Xv7UZV9+OgnTS9rc3HFdG/tFAAEEEEAAAQSOQyBzx34cu2efCCCAAAIIDK6ARt4W4/iurQyTyi99IGIfrMhQx7OdqyDIzjn4eCpvb+rupX2x+e+QIuzgkGyJAAKnXuAXv359No0aV/XBv2YgSAOJvmo9uNjP5bcrhLWyNzQNecDeMEmxcfnD//kPb+1nT3lct/TqTGmjGJTduWUe5uzzZG0gQeb3zxcms4EEWinvK+H97uuFpPwz92aax/dzGSKAAAII5EFA33CP4vRu5oXr5mn5b/m9naUvH2V/K7Lj7eCBzv2laTr/xmdIxd+pwtRRCzy+8OxUkAZTGlCpgZKJ3O9p8KOm6PZdNuvATvP8sv0M5f+vyXMPjV/ezzasiwACCCCAAAIInLTA/koEJ322HB8BBBBAAIE+C5xdMRWt/NIKhbq8Uqm9vlXpKxj8W5g6rb0GE/jOV6T56exw+x9geQymx5J2pKN1M5fdhnEEEEAAgbbAL3/t2WdMEFf0Yb8+9I9D7d3bX+21XNCXfYghM7f/7pWF+uaYbK/70N4FEmyaIGhc/tWv/0+z2X0O2nhYCC7alL0CoNl1GtrLuD7sr+u8Zq+V6brcriPjuo7/fesYCoAPu/AW/vfQT+u+tPcZD+QN14t+GUMEEEAAgXwJpHVTcQ/55Ys7ky2gnXGg/Xvqz9x/7/uhzvdBA37ol+1ULkrqCWUej8rw2AQKJihr5iXNwuQ7zZhks/9JDKqO7/R36rc5yFD/P9DjjiTphYNszzYIIIAAAggggMBJCUjpgA4BBBBAAAEEthNYmr5RTVPzsn0YIhkCYpslQH8+Q3ne1E7lqdvrAxqtIChIv5cKCN2Lrm/3JUNbuaAVG7Jf2du8HluX0iGAAAIIdArom/WbI+lNzRqgb5L5vr3W4Yo5+sBcu0S+83V8oxBUfvHr/2PZzhzAf4LAVVr769LfG/f703kx8hPU6nxQnM7r7lsrdY34h0Y62x9Lx91DKPNxHadDAAEEEMifQFWyn4000pezZ5YtzwSZwILsOtuNd//O2HKO/Pb43wn9XdCuWA/m9dh2gn8QOEaBJApaD/D179H/Lfrggezf+0FOI3vf02t73X+9EEz1WsY8BBBAAAEEEEAgrwKHq13L61VxXggggAACCByhwP3ljYq8k1q1ybLtAyUJHpAH/QXp9YG/jqf6fqtUhvl5Oq7r+zd5fCVFayjb2GUajGArMdy2un0hTqshWQiO8BNkVwggcNoE0jFzSwMHNDPMprRl6zIH6FteiXynuiKOf3tSA7Vc31TQ5c11OlxkngaH6Zti+t0eBwXTCEZMKgdLpE+DidmO9QdoohEFU1q5rb9BRYmKs71EBugbeb631y3L/YMfHWrn9fzQzW3/2/pdk/X9eHupjjnvIIimphZoC7jThikEEEAgPwJnV1Yr8gNZ1TPy3+c69L8HOt+XbXSowWbZfsvvh6zjf4t1qA9r9bfG/06nJqiOrZGFwLryz7ELPBhNWw/wtaRu/xZlqC8LaAYmbdbABaW6QMh2gKoPpXSn6O+HskO7RINaZR/tzm8nQ1mm96r3x83kEwszpfY6jCGAAAIIIIAAAvkW0HseOgQQQAABBBDYQaAm7YQG9WRashDUXCaC9huWWrGmnT6c6e7dkr3/699eKNbNFd7I2bsbayKAwHAJTH115rI8eCj7Bxe+wjewAQSHt3BvpGn1cqHVm1TGg2J56ivPXT78Efq7B31wLz9Vk3rU7EOh3cb3cpbZwqT/Pcwep/Wwye4sNGNmzJ7HXvbNOggggAAC/RVYkjLPIzUzPRKbmpZLGs1sPD4zjZ7NTuWd7HqdZ+5+LXS5bq8PbfW39pH3kitkXuuUYup4BEpyL7ReMJP+b1QDXjTwVO9dfLCAPvI/rk6rDPRvX87B/PAM2QiOy5n9IoAAAggggMDRC2TrfY5+7+wRAQQQQACBUyJQ/eRLS2tRcE2DCBphKO1Hu95nJ9DL1AoxrRhYs2/FuoqC7svPVrzpMv/QRduj3pQ3IDbCYO47T91Y7N6OaQQQQAABJ7AyYi7taiFvPPou+7Dcz9t52N62e721sXT3Y3dvdMLTBTNh37xTh463SQ94Xqrje92F993b7gqlva3HWggggAACJyGwNP3i0tm10JZ5NNPPZuSyq/kAgGxZRh+6dkzLQ1Jdzz+o7V4WyzIt76wWpW34Rjr35jRlnpP4jIfymGNbgxi774uyf7vHaRQGAQGVxwnMvhFAAAEEEEDgSAW2ryE70sOwMwQQQAABBAZf4If//YvzxXp4xTZdII9QtK1sDSbQQAKXotM/VpFhM32ne9TS+9p1m+yjmGIjvPb98y9Weq/NXAQQQACB0qszJUnNX9a0sPLowqaH7VRpfv92zmxN2Qferan9jzTCoPyLr14v73/LId1CPyf7WQ3p9XPZCCCAwAAKLH36+fkPLJsrI43sybuyj33QKmUf98BVy0FbAwk0eCCVcpILJtDyjkxL35BRbU7nkRVz7VtP3ajofDoETlLA/XX28Qz8/WsfD8mhEEAAAQQQQACBwwj0/X7pMCfLtggggAACCJy0wPc++fx8XE8fC5NCVdNba1YC7QtxwYzVC+aMpBQYkxqyEakg0za1fTugPljAT2sFmi637YlKe6Cmnp6vfuILN0/6+jg+AgggkGeBsBBd1IcT2wYQyBJfwGm9Ia8BW9m+uY6u16uX2dt29q3Korm47QqnYkFTxZt1X5Of74fdy7ed1kdNdAgggAACgyCggQQPv5s+FiVpVc9Xv8E1EMBnYbNN/jSzDvg3uDXQ2i7PDHUbnW/LTFLmGV9Nz/+/v/tFyjziQndaBGwoTetikjAxtpf/P+x9o0y3gl9bazGCAAIIIIAAAggMhoAkJ6NDAAEEEEAAgf0I/PMnX1yaWqicj0dN5b3RwqWGNuopNWS+HW3/1qW2r+g6fSCjnZsO5MGLppQuyuT71tL5erx6TdsgdevwLwIIIIDAdgJBYC7471K3jn6v+u/Y7bY6uvk29W1iPn50ezz+PTVMoyq117Yiu39SXddlfw9P7OhdJ8MkAggggMBeBLRpg6mFmfOrZ6LKRtFc0ixsNruABpHZTrMTZPYk4xJCbWf4UpD/jR6pB/PBytq1b1HmyYAx2i+ByfX12kZ41mgTHdmu/XeanXu842lsqsd7BPZ+HAJTt65O1j44NlkomJLuP0jj2tmVepV6nOPQZp8IIIAAAnkSyN7u5+m8OBcEvIAGrmc7/mazGowjgMCJC3xkoVJafp+pSPOeF4I0mCykSbPN6aSZwrN5iq2AAhtvUJOggzs/uRzcfmP6hcUTvwhOAAEEEBgQgV967U/eqRcak3HmrS4NzOroZDo7J9SsL5lO3w7bU2f3K9u29q/ZZYwpJIn5b/555eHFK4MR/DW1cHXy/uT4O5FpyPlvf+3tB0HOy6/ZqbdVTk06uqaXPmzSzjXd495e/an36h96Y/pG1S7gHwQQQKB/At3fVNQr7NP+g9KcUDhWqKRBcEHi0ib95s0GDWTS/Wr43+Tmb4MESad3koa5/f0nbyz6bRgicBICv/HKn7xTO2Mm9T5O/143C5IxQE/El9Mz93vu/PydkJva/n7Ir+fX0Gk/3txWvoH0JQKdH8TJY//wyZeW3BL+zbPAb/3H6+UxE19YKQQXfzyRllZGbFiufXlkrJGY0dhoFspq2AgWkzS4+9fTX7yT5+vh3BBAAAEEEDiIAAWng6ixTT8FKOz3U5tjIYDAgQUkM8GklCnLUWrKYZA+KjsqyY+srWBLTaIVaLU0Na9LZMHSarh8h4j1A1OzIQIIDKlAWd4A+sFPj7+zLpW+7SACXymbQWlWAreqb2XaP+i2D8p9ZXFmk56j3ZXJMi3f8dKeszE/dT/+0OJnBudh+L+6N/NWYOKSTTTdfXfdvPijCiLQB0h6CB9EoEEY+lnEJqxVP/H8wz2tmYkAAggcr0D3Nx91YQf01sC0qDBRloeh0gePmjQpGS3zyG9rlEp5JzA1idV7XX5TliTbGmUewaHLh8ATd/7k3g/PmXIriEDeAtD7SfdwP3uO/g7SBwe4ZX5udk037tfza2wXRBCa0UZY+/bvfJF7oa2IuZrz81+/PiuFh6tSfpiMJAA3ll+MdUkjqUO9l9YMlGOx+9xjCZptaOCsNHWZBmE1NtHcP5+vzOfqgjgZBBBAAAEEDiHQlcjpEHtiUwQQQAABBIZYYGm6IoECRiPPtadDAAEEEDhqgYmJqSSQJ/hdnba1nO38kyFtl9l2fkZ2pb2M+2AD/+gpbMizEqk6DGW/LpVpdS+7ycU6GsQWmJIPFPBBFe7cOv0Oc75aEe+yHeg+5Q0/qVTVY2o1ayFOlw6zb7ZFAAEEEDh5gWYgNGWek/8oOIN9CiRB8HojSMsSOyCdu09pD/X+pft+qGva3xduOa5fzz1UdvuUlez+msv0XlKmJzYM90Jb/PIzY+qV6+XV0eBWPbTBUTZoQJtw0XtZ23Sl/A3Ye105ZQ0o8AEoEt8s4zYTZUmaXrv1S385O/u+1WTum9PPz+fn6jgTBBBAAAEEDibg73QOtjVbIYAAAggggAACCCCAAAJ9EWgc+Cj2QXazEnDfO9FKY1txnBnueycnvIFkwdEz0OCBzgCC4zovqWRt7lorX+XNLP3n9nEdjf0igAACCCCAAAI7CUgrBoux3I7EEgzqgyr9Q+Cdtjv0Mgke0MBWDXqN6iH3QocGPZ4dSADB7P2J9J5kHCjp34ktO0imioYd109Qs620QkTsSeg9rnaBXZbYJs+K8ocWFxqlHz1kbuk+3Rr8iwACCCCAwOAKyE8hHQIIIIAAAggggAACCCAwYAL6hpd9y6vzvN1Da31m7frOpe6teH1XzPfdy0/j9Hvvrc0HaVDb+pbdQa7Wy20dphJs4QM2fMCCr6iP4/riQY7GNggggAACCCCAwGEF3px+YbFhgsWG3B/qQ2J/n+L36+9XdNovyw79egcd2ofRIfdCB/U7zu1+/St//KUH42mlXTqQe1wfQGxLDL2Oro9UQpeZoBmkq01laBCBZiVIZHvd52/8xbO3em3NPAQQQAABBAZFgCCCQfmkOE8EEEAAAQQQQAABBIZZoGGqw3z5h7n22vTN2kjD3D3MPva7ra+M18CFYj2Zrz51o7rffbA+AggggAACCCBwVAI/sRLcHpUIAhdUqUNpbkmaqrKPg2VcO513HF2aJvNvTHMvdBy2h9nn1FdnZn/0UHJ1s9Cd8UyDZffW6T2vBqZooEgs2QtsAIIEEdRlujYeXCaQYG+OrIUAAgggkE8BvU+iQwABBBBAAAEEEEAAAQTyLbC+XmufoBZjduvbax/52AAGNJxZCSt1m8J3lyJgd4aH7umW+1ZV206sVL5rBbzWwWvyV21D9txKPLd1beYggAACCCCAAAL9E/i7p5+ff+RBWA0kcEAf/CbS3JK2Zz8iT39tqnq5eZHZrSaZsmcWarBBprfZsHpkxMpukx0PN7kXynrkYfxXvva5Z5bH44oJNICg/fDfZSHQM5R5rV6nt3YaPKDBAuvyh7RWTGTopouxsVkJdPvl0fTyR199dnbr1sxBAAEEEEAg/wK71CDl/wI4QwQQQAABBBBAAAEEEDj9AotXbtaktrfqggekglefUp9Al5q0tviZwXuTbEnefktS8/KuZJq+1aZwba7ZPb3rDnQFrZx3FfRpGs7rsfe0GSshgAACCCCAAALHKDDWiK/Ye0kJAHDNFbhAAh3Xtu3dQ+M9nkDrfkm369W5e6piI50jI1Mvn5Ob95FXr5buTyQ365E0ciHND7h73+znmB3vfZ42EKW5yJdLNKBAAws0KEViCmx5ZUMCCyQjQeWjr8yUe++JuQgggAACCORXgCCC/H42nBkCCCCAAAIIIIAAAghkBFITvK6T/o13Hbo3yTIrHfNoYIKlYz7Ese2+UNuoiFnVHqCj4lsrSrv6VvBA1/zWeroXrR1t96m8vhdL8EAcFKRiXtuJDavr9Y05ezz+QQABBBBAAAEETlhg8Q9eWEyC9OXmfYqkTnKBj9veB2Xuc7L3PO2Hztn7JL04nZaueZ+VmqT697/zYsXO45/cCNRHC7dG48RmzLIBJAcITrZBKHJFGixwRpIZjEkvf03Na5R9S3BCMZG+mZVgbczM5gaAE0EAAQQQQGCPAgQR7BGK1RBAAAEEEEAAAQQQQOBkBeStniX31ph7w0dT0PpO0+Zr798E8vP3M9R924o+qezTzu5e31ST9LX+rbUkDW7bhQP4T3X6Zm28YaZNmtZsGt7MNei1d3Z69YkEabihjmvFaGcvc2V5ex2ddms0TMEUNtMrbz9FFoJOV6YQQAABBBBA4CQFHloOK1EcLqVyrxJLU0+xBEHqvUwq7dlrm/ZxKE+DNcV9trd3hd33RnoV9m5Rtvf3RO0rG62b6uS7G+fbcxjLg8DP3vvc5UaYlm3ZQe5/D/NwxN8/61DLIK4c4v4m/K21vTOWiUYhKP/0vWcv58GAc0AAAQQQQGCvAof5ndzrMVgPAQQQQAABBBBAAAEEEDi0wNrayrxU+NVG9I0e6X37tVpxp/PsG0C+xu4AR9PKRN2H9vqQPZUH4v5NtSQdMQ15w74em8UD7Do3m3z7ky8thXF0zVd0SraAVqWnVnK6yvBm5adWqGuQgFSo6xt1mu41kjeqCs1ep7XiXVO3auW57fSNPq2UN8Hcd596YbE5lwECCCCAAAIIIJALgaXpSi1cDabrYVCNfSCB3MvU5X5nXV4r35Qo1Ybe+8gdjd7rtNLd6z3Rll7WknVtNibZhw51yyAJaudWk+ml6ZvVXFw0J5ERCC/p/X1kg4T9g//MYjsqH6YNL3DDbBitzV7hw2rl89bPfCOSIAHpbWCC/AE0ZDNtxqAuM/Q+WXstoshd96XuIzGNAAIIIIBAngX0l5AOAQQQQAABBBBAAAEEEMi9gL5JP1pP7+qJ+jd+sifdepCdnbmPcd80grZlase1qk8qGbXKTwMKCg0zfxratP2HT740P7oZXtEK1FYvVxlIZapO+07jArRXa+2102mtPHXNFjgbvw910kwEUZJe+/4nKhVdlQ4BBBBAAAEEEMibwHemK9Uz75rzQZouNeTeRe9r9B7GPyDWh8DaZzsNDujZNe8V9X5IHzYXkrD60Hp6/pvTLw1sE1g9r/MUzHxiYaYkd7tl/Zz1vtXe99rxvV9cd3lDp33Zwe3VlSM00NbNl78cCT7RTuJTyr/1H6+X7QT/IIAAAgggMAACXbdDA3DGnCICCCCAAAIIIIAAAggMrUC0YSq+ok4R9I16rbxbK7q+Lm8BdVfu7RVL97s84nod1wfn+naRf6usuBLP7XVfeV/v20+9NC8v1z1WaATVYlwwkfYSRKBBE1qhKhXgrV6n/Xxt2kEDKtaj0GxKr9uO1QtSNzoi2Qeiahyn5//xycrNvF8/54cAAggggAACwy2ggQTf/WTlsXqQzmkWpVDuZUYaej9UkDfLC2at4IIL9GGzf+jsHzzrvVG7L8j9kARR2nupaLH4XnB+6WkCCPL411VMgot6f+8f/PsAWv0sj6Zrhppo0EAzcED3bDMUyHFtFrXIXDyaY7EXBBBAAAEEjl9AqsToEMi1gNxidXT8zXZwMIEAAggggAACCAyfwM//55mbhTR4Rq/cBQ24NLI67QMIWm/Od99N2iSzuqbrslWGWu2nwQN+iT5U14fmWmEsaW3n/+n8F674padlWP7yTKk+Wqg8GEsvaQpffWtKmzCwnR82L1YDNmQVu86KBFvo23ZnN0Pb/MP4ZjBfj1evSdre2mmx4ToQQODUCHT/ElCvcGo+Wi4EgaMRmJI31NPR6NZaMSmvSxr6lVG9F0rkPsc1maUBpa2u64HziNw8nltPq1EcXPur6S/eaa3HSO4EnvjKzL0fTARlbZZCywqRRNRqZ5sbsJ+xKwjoPe9OnS9n+ECB9rrNe2gNPLFdZtr+3YTmA8vR0jcvvPBYexvGEEBgmAWmbl2dXP/g2OR6oVBSh+JmoXb2X0x16UqFcvUw/2Hk6Nqzt0A5Oi1OBYGWQPdtG3+zLRpGEEAAAQQQQACB4RQoLVydNA+d+dvABCV9qB00AwP0xjGWdmk1/ayv7N1aCegr85ydr+Lzkq1KQano07S2+qBcggiqwUb9/GloysBfZ/dQK8+Xz45U4ii9IHaTWnHe3amNemugxqZkfJCuVojNnUeWg9tvTr+waOfwDwIIIJA/AeoV8veZcEYI5FLgo69cL79z1lzeLKQXArkfGmu4t8j1brDjnlLuE+WLRR7wpEsPPzBz3Afl8uPcclIf/vofv7NZMJNhM3hAgwnaAcRyx98MEOn4rLfsRddrzmwF3G69b/ab6X1zKuUJbTKjEYZmVLJdhLXo4eo0Dwi9EUMEhk3gtxZmymON9EIjCi6uFoOSBug/kF6z3kzUjRmVfryeSsZAs2ji9O7i/0CA2rD9jeTpenkgm6dPg3PpJeBvy/wy/ma9BEMEEEAAAQQQQGCIBT782rNT9Si6N5Ikk/pgWx96ayVdXfKF6tv0tsJOhlsrATsr+bJBBFoh6Na3FcPNIAJJUbuRnv/uU8PxkHxqoTIp8QHlMEzKJggeFY+SsEhQga0wrRVSU0vT9PUkSJfWzfIdMg+oDB0CCORcgHqFnH9AnB4CeRSYkoACyXw1VYgDuTcKfk7PMUrTd+U+qJom0dKDB2aJN0Xz+Mn1PicNQk4nx9/RpRpEoGUFDT7OBh4faxCBFDrqEkSQmBFTqEcfevt8pdr7TJmLAAKnVeAjX//srGQ+uSovQ0xqMydStrb1DxrMtCpZcPT7aEzeiNBl+h2l9RPap2lQle3m/uGTN+ZPqw3XlV8B+VOkQyDXAvI12dHxN9vBwQQCCCCAAAIIIDC8Ar/w2nOXgzC5pQVrTUeqBe1NKXHrW0XZCsEtQq23hlyhvL3cZR7Qt4VcoT00hUYw949PvlBpr8MYAggggMCACVCvMGAfGKeLAAIIHLXAL359ppyGwT29x/dlBR36TssTUjKwk/sJQnbbt/fjpjv/1X03JBPBZuSCCIob0fnvP1lZ7FyLKQQQOK0CH3vls+XNYnJroxCUtMkcfelBv230u6bQ/Prw2RR1mXaRLNNgAm1iUdfWr6hiw1TPbSRz3/z0S/MySYdAXwTcL2NfDsVBEEAAAQQQQAABBBBAAIGjE/jeJ5+fD5LwirZjqm/2+AK3FnJsoVtK2q7InR26NlC1Ms9VFrqhH7fpRmVfqSlIAIG5RgDB0X1e7AkBBBBAAAEEEEAAgRMRkKYp9IGd3vNrsLEGE/jOlwP89FEOdd/2QWHz2Md5rKM8b/aFAAJHIzD11T+a/fFEem+9mJZc9hN54UGyoOi49ppJsSFD18yKeyFCp31zKzbTok7rusWk9O6Z8NYTd/9o9mjOjr0gsLsAQQS7G7EGAggggAACCCCAAAII5FTgHySQYDNOH6uHaVXTAGrwQFFGpP3AZuR+IsNs7+e3h1qx5ysVA2mHMIoL1aCenv/HJ79wM6eXzWkhgAACCCCAAAIIIIDAXgXk7d/tOpedILtUXw3O9tll2XG/jg9b9ss6p7WZBC1r+DeLdzgVvwOGCCBwCgQev/v5L62OphX9jtEXH/SlB/tdYDMMuKAmXw+hl6uZB0akLsM1aeC+N1zgkQsq8Pt550xQ+Y2/+PytU0DEJQyAAL9ZA/AhcYoIIIAAAggggAACgyeg7W6uj41NFgqFkp59IU1qkyurVdqQP/rP8p8/+eLSEwsz5+NCVFkdMZc0Wl+K5DaaP5HUoTt1vtkDXWdEcghOrpv5OH73Gp/TTmosQwABBBBAAAEEEEBgcAQakgY8GnEZCOxDOzn1bDaCo7uSbNnDjetxNNuZvH5ss6Qd3bHYEwII5FXgibvPzv5oIr6qwQNa5+CzJur3j/a+68xOIs0palWGdL2+n+wi2Zc2ibBWTC7/xl/8ofm/fu9/ueK24F8EjkdA/uToEMi1QOYr1Z4nf7O5/rg4OQQQQAABBIZb4GML18tBGF3YGEkuvjualBqRBA/IG/H6Zry+eTIq42c2pAIrTRbrJrj719Mv3BlusaO/+qmFmdK750wlCIILgUkmexW+s0d1y9OaVOzd+ckH5vabn7qxmF3OOAIIIIDAwAtQrzDwHyEXgAACCBxOYFICvN/3vrPvmMClDdcygEst7vbr3gZ2D/2DtPkUzx9Stsl2nQ/9dInbrrWOBAvYrrldKG2aa3vnDWkyzaQFE69FH3r7qUq1tT4jCCBwqgQev3v9mQfj8c11iQjQZgm0+QLfZQMIdF7394lmLtGuux6jvYfm94tkSxmTZloeXk0rb1z4szm7Ef8gcAwCPJA9BlR2eaQCFPaPlJOdIYAAAggggMBxCPzMN+ZmTZhclaLepBbpbEGxWWmkkeQ+mtxVTrkCoUaPp2lQHatHc9+TlPzHcV7DvM8pqSgcNxNlMdD+UYn3L2lQgYzLm0BpTQpCEjhgXpd+ad2s3iHzgMrQIYAAAqdSgHqFU/mxclEIIIDA/gR+6bVn39ooBKWw2VSBb3Pc78U/vDuOIAIbtCAZ0oqNQu17Tz7/sD8mQwQQOF0C+lLD6kT0lgseaEjwkAQRZJ7CHmUQgdY9aTOODy2n59+c/rPF0yXJ1eRFgOYM8vJJcB4IIIAAAggggAACAyfwsYVK+cF4emvDNEqJPKJIJXBAC4saTe4rn/StEw0Y0E6DCcYlWlw7SU6gXSmI01u/9urM7Jl6OPfNT784b+fyz6EFmkEBmumBbA+H1mQHCCCAAAIIIIAAAggMtsAja+HrP5hIS5IYwJbXilI2096nG9/26ppvBrviW3st/wZx9xvD7TXccTQjnQ8q/8CDdOl72RUYRwCBUyWwPl64pa8tFJPEyPN9m1Egti+YdH+D9L7s7b5PWlvL94l2fj3NqPJgIpiVWYs6nw6BoxZo/e0d9Y7ZHwIIIIAAAggggAACp1ng39y9PvveRHwviRolaZ5AklhqdIAOEyNvvNsKKa0sakRSeLTR525+KOtGUqB022hqu4apF+PSO2fjW//m7owW/ugQQAABBBBAAAEEEEAAAQSOUKDYSJfksb4839NHItLcnEQTaCBAPx6QaJaDghzvTD24fYSXxK4QQCBHAj/7jecubxSDss9yog/6/cP+4zhNqW6y+69HQfnDr33u8nEcg30i0I/fSJQRQAABBBBAAAEEEDhVAv/27h9/aXksrSRBQ4IBGlLx1LCBAhosoBkINOpcAwW0zU3tNLBgvJGYM9KPxG65H9f0dhuFhtRlSVL9sbjy7xZmb50qLC4GAQQQQAABBBBAAAEEEDhhgeX3VublgV5NAwckztumGLcP+Y7xvHT/sfSa7cD2cX3xGA/HrhFA4AQFkjC8ZP+fl//fNRulbcJS/v8/jk4f7Gqvx9MuMuElN8a/CBytgP6d0SGAAAIIIIAAAggggMAeBTRbwI8nkqvrRXnw32y+QCPNtdeggewNtrZ35zIPaKpMF1ig7eFp77MR6D60aQMt/G1K6oLaeHL53/15hUCCPX4erIYAAggggAACCCCAAAII7CawdOVmTfLA3fXr+YdvfvogQy3vaZvkGpSgXbY06HLUSTh5ENry3maQzi9+5kbVrsg/CCBwqgSeWJgpSUbKsn4L2EACqd+x9TzHfJWuWRV5mSVIyuX/OCPHp0PgaAWydZxHu2f2hgACCCCAAAIIIIDAKRP4jVc+98y7Z+JKXbIHaNCAZhHQB/+agUCzDWgTBVpodIEBbjgilUrap7JGPQzNRmRsr+OxVCgFktpS01vqHvQtlUa0bt6dWLk89dU/mj1lfFwOAggggAACCCCAAAIIIHBiAvWNRsU+4Gs+FdHgbt9pQID27v1eV0bTcpqumn2IYptAsOtpwLgxY43QjEqv5bmGlPH89lrW016Pp/16lMzp3ukQQOD0CQSmeDGS7wP//aDjGlyk3wha59P+fum8dv/94IedS9v78/v1Q7eefOs0v4s2tZ6pYC52b880AocVyP7+HXZfbI8AAggggAACCCCAwKkVeGLhaql21txcL0pwgAQN+N4X9rKFQp3nK6QkxkDav3QsOk+j0X3vsXxllW7XkKYN1kYa5v5EXPnoK58t+3UYIoAAAggggAACCCCAAAIIHFzg7aduVKMkfVmDt7XX8tdhOxsIrmW8ZsCA31+qQQQ2cFweJsbB/NvnyULgbRgicNoE1orhBb0mfeCqwQO+d3U9x3e1Lium+y6rF4OPH9+R2POwChBEMKyfPNeNAAIIIIAAAgggsC+B9dHCrYI0SeAivZtRAXvag95y79BLVLpEJNg9aSWWtpXZkKEWOhuj0eyeDsFKCCCAAAIIIIAAAggggAACuwoky6uVjSit+lTjPvh71w17rFCXt3+XR415MOICxbWsqGW6Vi/b1IOgOlInC0EPPmYhcGoEauNmSgOKtB5H3juRLJXyMon0PlPAUV+o369+B+l3mR5XshFMTS1cnTzqY7G/4RZwtZXDbcDVI4AAAggggAACCCCwo8BHXpu5XC8EZbeSlM6OtNNbcumbgQTZXUuBsPwL33j2cnYe4wgggAACCCCAAAIIIIAAAgcTqE7frH1gOZ4uxGlNswW0A7632Z8P+u5RXtMtshkN9K1j+c/2+pBvrGHMw6vJle9IBgRdlw4BBE6fQEke3EtTl5NGmryUECIbRKTfC/qCyH6ynfjAgIMKbUgKzNrYGEEEBwVku54C+itJhwACCCCAAAIIIIAAAjsIrBTTSxsS4e3eUvEVTTtscIBFdq9S0aRR61p41ALnekHbzgwuHWB3bIIAAggggAACCCCAAAIIINBD4NtPv7T00Hp4zQZy+yCBnbLHybJQ1gsTKaQ119d2znV6JDatXstxPgtBQea/fyWdkwCCxR6nwCwEEDglAoWxiSmtxynK//OaGWBN6nE0Q8lq0QUZ7XSZ9ntFv0v0e8V+z7j6IP0usUEFze+b1veOrCXxCbbT5XpMDV7y9UeNQqHUXMwAgSMR0L9MOgQQQAABBBBAAAEEENhG4CMLMyUpt5U1gCD7lsk2qx9qthYcI6mIKjZ7LRzKaHnqlevlQ+2YjRFAAAEEEEAAAQQQQAABBFoCS0/fmH9kNb2iQQD68M4/pLPD1lrdI52PU7S81nrYl1l1VPb58Kq59jeferGSmc0oAgicUoEolaYv5do0iCgOE3kBRbMSHH0n8QI204nfsx5TEhDYl1F0SIfAUQt0/uod9d7ZHwIIIIAAAggggAACAy4QjQYXNQ2lRne7Tktm25fOfLo6/wZK99DvxQ+7K530OBpNfm7D9cU4MRLMftGvzxABBBBAAAEEEEAAAQQQQODwAhpIMLEcPCaP5ao2kMA+BpT9Zt/+3fYw7kGhvnksbZHb3rZNnqbVc6vp+aXf+8LNbTdlAQIInBoBSTzgsgbIUKuNNLjo7KYxZxpSl9OqR5KZB+i665N8fZPfldYfafCAHu9sXcb9AoYIHJEAf1NHBMluEEAAAQQQQAABBE6PgLZpd3bkTCmQdu3iML4UpqlElLu27fZylVqws6nn9rJy1zqa8UC3L0hhUAuc2kv7eh/vWo1JBBBAAAEEEEAAAQQQQACBQwosTb+4NLUwc371TFipF7QpOfs+sQ8naIWPdz+882vp+8dJIL0MR+vJ/OTyyrWl6Zu1Q54WmyOAwKAIrDeqZkIiifTbQuqNQslKYDMTyKR+b3R/dxz2suz+mnVOafObytY/SfCT1iPRIXCUAvKnRodArgW6v/b4m831x8XJIYAAAgggMLgCH1u4Xm6MmI+vFpKLSRRMacEvkEKgpqHTAAL3cL8dSJBKRVGvrp2xwC11bdv1WrO5XNor0C6RlHfa2ePaOyA3vx5K8wbySsvE8trDVEZZIv5BAAEEEEBgPwLUK+xHi3URQACBIRYovTpTCqJCJQyCC4U0mVQKzTLgHtq58ppMZYSkzJaM1OSR4Z00SW5//8nKYmYhowggMAQC+hLKyMS5d+pRwzZloPVHNohA6pTaAQSufme3+qGdm1NpfhdZU9d8gn4faQBBlBRMFBdMuhp/6DvTN6pDwM4l9kmAB7J9guYwBxagsH9gOjZEAAEEEEAAgb0IfPSV6+UfTJrZQhKUi0kibcm54AG3rVYQtQMIdF62EKiBBd3dYYMI9Hja+SKhexMmNBvr6YfeforCYLc30wgggAACCOwiQL3CLkAsRgABBBDoFJi6dXXy/WcmymlkyhuReTQOklJggkl9yzg1aU3GJdNA+nqaFJfWa9GdpSsVMg90EjKFwFAJ/Nqrz721OpKU6oWGfRElkAACzU/Srh86niACfQlF64wSacjgzGah9u3fff7hoYLnYo9dgOYMjp2YAyCAAAIIIIAAAgjkUWBKosXNyMStd8aDiy7dXGozD7iH+C5wwD/Q18oi3/lmCjSYoF0glCqkZkCBH/plfktXZPR70YwD7fG9jBUKpiTrVfeyLusggAACCCCAAAIIIIAAAggcTGDpim2O4I5srT0dAgggsKPAQ+vB6+sjpqTVPA2p/NH6Hh2PJPOkrxvacQfbLGy/xNJewdc56QEKkrVSs2Q2JGPKSD1daq/FGAJHI9Bdl3k0e2UvCCCAAAIIIIAAAgjkWOCn7z079cPJM3/7o7PpxUYkMdsaJW4juOWRf7Ppgl4BBP27JH+b7od6ZOJ/++fPkRBAAAEEEEAAAQQQQAABBBBAAIHdBQpJuKS5B4I0NAWpVpJqJtvvK4BAtu3uul8+aQUQyLG089NyfDPaCG53b880AocV2PpXedg9sj0CCCCAAAIIIIAAAjkW+PBrz05FJroXBEFJAwUiab6gIL3LNpCYhmQU0DbsNOLbDm1qOL1tlhRxEuGtQ9fLINNp4TDbZxbtaVQLh66A6G/RM8Mehck97ZSVEEAAAQQQQAABBBBAAAEEEEAAAQSOTWA5NPOSFKAWSb3OeD005zaNObvpAgp2OmgoD/9bva0Tkmmp//G9bpsNJNAgBe21i6V+aiPSpgxCM9YwZmy9vmgX8A8CRyjgayaPcJfsCgEEEEAAAQQQQACBfAqUJIAgDqN7EjgwGdnsA/LgX4pcrms2YdBqusDPby6VoIL+d3K7bguI3Lb3354jIoAAAggggAACCCCAAAIIIIAAAjsLLE1Xag0T3NWMAEWJJtAggF6d1jJ11jT1Wmvv8+RQpi5NGcRpOr/4mRvVvW/JmgjsTaD3X/LetmUtBBBAAAEEEEAAAQQGRuCJL8+UwsgsSJz2ZFFKbRqpPSIZCDT9m7ZZp5kHNJNANsr7ZC6ud5Gy0WhUT+Z8OCoCCCCAAAIIIIAAAggggAACCCCAwHYCidmsBBJEoHVLq4XQrBRDW9ek6/vMk1r/pH221kcmd+389tkV9TipZCGIZVhLk7nsMsYROCoBggiOSpL9IIAAAggggAACCORaIB0PZ8M0LYWSgUCzD+gwGzDQe7z/t8uu7TxpYkHOT5tZ0OR0I7Gpvf0UUeW5/gPj5BBAAAEEEEAAAQQQQAABBBBAYCgF3j5/oxql6cuNUB/sazCB1CdJRgIfAODrnPThv9Y0ad2PjO650+00YEB77XR/uo/RRjBPfZEz4d+jF+h/rejRXwN7RAABBBBAAAEEEEBgR4Hf/PPnLq+MpZeNNFWQhImJpa9LaSuRaW3WQB/WaxYCXa6dFs60WKdD9xjfFf5c0wK6rB1J3mqrTtPVZXo/X2+4szfdvr271rp2b+4fPQdtQ08zJUjggJyXKxQ+tJ4uZVZjFAEEEEAAAQQQQAABBBBAAAEEEEAgRwIbKw8q9TCsapVSUet07IP+0NbzaH2Pq2ty9TwaAKCdzG51uo6tm5I5tj6qGYygAQkamLBeMLbXeqpIsh6M1dPqTyyThaAFyMiRC2TrM4985+wQAQQQQAABBBBAAIE8CLx7xsxqO3Eu8lubMHDNGGgaOZ2nBTt94z/b+cJddl4/xn10uo0w17gEOcdiI7jdj2NzDAQQQAABBBBAAAEEEEAAAQQQQACB/QtUp2/Wzq3G04U4rLlaJvcI1gcG+OYMWsM9HEKDDGwvdUN+O91Mm+h8ZMVceWOarJV7YGSVAwoQRHBAODZDAAEEEEAAAQQQGAyBJ/+35y5HJilpFLhmHdDcAp1953W4aO/Oef2c0uPX5S59tWikDT1jNiT4Yd3UF/t5DhwLAQQQQAABBBBAAAEEEEAAAQQQQGB/At9++sWlMxvhtc0olAyYrn4n+/C/IfU9mlFA63x0fLsXWPQFE+2liqiZlcBlrrQZK2X+ubV0TgIIFvd3dqyNwP4ECCLYnxdrI4AAAggggAACCAyYwFoxvKCFMi20tbpmswWt6e1GtHmCLV2veVtWOtAMzZCgTS1or+PapWk6v0Rk+YE82QgBBBBAAAEEEEAAAQQQQAABBBDop8DfPf38/ORqekVfZtG6KH1RROulNCOBZCmQmVqv5OqWfDbK7vPT9W2GShn6+ixtAmFiMzQ/sRxe++sLNyrd2zCNwFELyJ8fHQK5FpCv1Y6Ov9kODiYQQAABBBBAYCeB0kJlcuRs+k4cNSQLgeR6kywEiTZlIA/pXaFNt3YFN53vuuZ0c8ov95PdzR74+XsdhtJuXc9Ojq/noAEE2mn7dqEULOON6EPVp0hP19OMmQgggAACCOwuQL3C7kasgQACCCCAAAIIIHDEAlNf/dzU/TNmIQmTktYtFSWAQOt5fPMGPoBAlrsjZ4ILNPBAMxZozIGuF0lfjNPqT9bCK29Ov7B4xKfK7hDoKbBNDWbPdZmJAAIIIIAAAggggMBACTyYNFMrI5IirhnB3QogyNtV2AAGLTT6QAYNIjBmdDOZJ4Agbx8W54MAAggggAACCCCAAAIIIIAAAgjsLLD09EtLZ2r184U4uO1eUAltXY8GBOiLI4EPGtChH++xSw06GN9M5x+prT5GAEEPIGYdm4DEsdAhgAACCCCAAAIIIHA6BeRmtxylicR7J9KOnBsmUvjyqeB2vGp9sG8Lce0H+zuuv9eFNmCge2U5hsy3WQ7k/PS4SZBWR9aSue41mUYAAQQQQAABBBBAAAEEEEAAAQQQyL/Ad6ZvVuUsL3/w1ZlKOBpWpLrnQiE1k+4N765cl7YOyl1TQV8siU0tjpM7QSO4/Y9PfmHRLeFfBPonQBBB/6w5EgIIIIAAAggggECfBaQ4Vgo0iKAZSNA6fKZg1prXa6QVSNBr4dZ5Gm7gAxQ0slxTzmk7dr7TaZe2zgUm6LpueTPQQc6rKNHoI43QjG2mkqLuf636bRkigAACCCCAAAIIIIAAAggggAACCAyewNuumcrLUwtXJ8fjiXIxNOXUpI8GJipJ7dFk84pqMpQ+fb2Rpkur4fKdpembOo8OgRMRyFRpnsjxOSgCuwnou3jZjr/ZrAbjCCCAAAIIILCjwM/eu36vkMaSjaAhT++ll2wEqdE42u1b9dImD3butt82ljuVDdm9BguMNRKbps4HFWiwgM7XaHINatBpbdtuM3JH04wJxbhgio0x8/4H0dz/8ft/Wtn5PFiKAAIIIIAAAnsQoF5hD0isggACCCCAAAIIIIAAAghkBchEkNVgHAEEEEAAAQQQQOBUCehDe31gb4MHJDjAPsjfLUbgkALumC5gQMMNtPkE7XTcNqnQnNYAAg06yHaageDhB+E1CSC4mZ3POAIIIIAAAggggAACCCCAAAIIIIAAAggg0C8Bggj6Jc1xEEAAAQQQQAABBE5MwDcbYJsOCEITSpMBrW7bzAM+2iCzrt3Iz/d7aC/XJgwiTXhgO9e2nQYVaBMGNiNBM2igLpusyZ24ns+YrB/JLpMwrWoTBv/l959fbO6AAQIIIIAAAggggAACCCCAAAIIIIAAAggg0HcBggj6Ts4BEUAAAQQQQAABBPomkGpbcq6zAQR+4piGPmCge/e+SQOTusACnZakA7bTbUbqyfzImrn25vSftc63ex9MI4AAAggggAACCCCAAAIIIIAAAggggAAC/RAgiKAfyhwDAQQQQAABBBBA4EQE5Pn8f916YHntv5kRYOsy1wyBne/XkZ1ku52CEXSZxgZo5gENDtAukcwHfhd2l/qPzCg20lqahnfi2Nz+f373pUVdlw4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEDhpAYIITvoT4PgIIIAAAggggAACxyiQVt1j/WM8xDa71kACPbZmHRiJw9pDa2ltNAlqDRO8LmEMS+tm9c7S9E0yD2zjx2wEEEAAAQQQQAABBBBAAAEEEEAAAQQQOBkBgghOxp2jIoAAAggggAACCPRBII7TxWKkB5L8ANKUgD7Q3ymTQM9TsukDei7pOdNnI0glA4F2gRx3YiU4/39Of3Gp5wbMRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEciSwzyrRHJ05pzIsAj77r79e/ma9BEMEEEAAAQQQ2JPAr7z2x+9sFhqTjbBhgwg0oCBI3AP+njsIJE+AdGGqwx3Wc2vZf7P/aDMGrjkDF7gQB0H1e7/94oey6zCOAAIIIIAAAn0ToF6hb9QcCAEEEEAAAQQQQAABBE6LwG61oqflOrkOBBBAAAEEEEAAgSEVOLeWvtx6qN80SMPEBhRoZoJWr/Ok910imQRc1gK9ZW73oWQWaPeyRAISbC+PKNoBBC6QQLMQmKS46PfJEAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyLsAQQR5/4Q4PwQQQAABBBBAAIFDCUhcwLwGAegD/qgdI9DapwYYZHtdb6e+teGOIy7QYDQ25txKMLfjqixEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyJEAQQY4+DE4FAQQQQAABBBBA4OgF3pi+UY3T9LZmBYgka4AGEviggUgDBuSQtpfxgizzAQS6XnZdv033sPuMNbOB786tp/NL05Wqn2aIAAIIIIAAAggggAACCCCAAAIIIIAAAgjkXYAggrx/QpwfAggggAACCCCAwKEF3re8WRlrpLXuHXUnJnDNF3Svtf10NmAgu1ZzP7XiekIWgiwM4wgggAACCCCAAAIIIIAAAggggAACCCCQewGCCHL/EXGCCCCAAAIIIIAAAocVWJq+WT27YebqkTF1SS/QkLvghgxjaeug3uqNTOtyWdYc6rT2GizQq9/pvOqBmVv8zI3qTuuwDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyJtAJtlq3k6N80HACkhi4Y6Ov9kODiYQQAABBBBAYD8C/+reH85LiwaXUgkcMNK8gTZNkO0kXsB2fr42baCNHbihW7blX9mP7YJ2XoOxzfDlb//uv7+6ZV1mIIAAAggggEC/Bbp+7Q31Cv3+BDgeAggggAACCCCAAAIIDJxAYeDOmBNGAAEEEEAAAQQQQOCAAmEtvhpORo+maThljHvo3x0g0BFIYAME2sEB3YdNA792e0mQBEvFtY1Kew5jCCCAAAIIIIAAAggggAACCCCAAAIIIIDA4AgQfT04n9WwnilvDAzrJ891I4AAAgggcEwCUwtXJ0cK4a3lseDipjRdYLtMFgHNQtAZWNAOFNBwAm3WoN2FEorQXj7aMItjKxvT0nxCrb0OYwgggAACCCBwggLUK5wgPodGAAEEEEAAAQQQQACBwRToqAIdzEvgrE+5AIX9U/4Bc3kIIIAAAgiclMBv3v1s5Udng9lYYgCSZhCBBg9E0vvmDPy5aeCADyBoNLMPaOiAzotDF0QQNszL/98naMLAmzFEAAEEEEAgJwLUK+Tkg+A0EEAAAQQGW0AD8sfM2KRcRUmvJDZhrW5WqwTRqwYdAgggcPoECCI4fZ/pabsiCvun7RPlehBAAAEE9i1QkoJ6bXJMCuumVGgYM56GtbMrFNT3Ddljg8cXPje1fCZcqBekEkQCCbZmIXAbJXLXbAMJZBjbO2gXODASGzOxYRYLG8Hcm9M3FnscglkIIIAAAgggcLIC1CucrD9HRwABBBAYYIGPLVwv/2AyuBCY+KKUikvZS7FFY5mRmqAam2DxJx4Ed5c+9cKd7DqMI4AAAggMroD/nh/cK+DMT7sAhf3T/glzfQgggAACPQU+9spMOUqCCxvF9OI740FpvWBMUR5Y61vyOjxTN2ZyPa0WYrO4FgV3/2r6ixTUe0rubeaHX3v2cmSSS4EJynWJD+hsssDtQwMJtNMgAhtUkKZSSWLmlj5F8ICT4V8EEEAAAQRyKUC9Qi4/Fk4KAQQQQCDPAk/cvT67EQZXl8fM5GZBcvAF8kZDpssG4CeSrS+WfkwK0+9fNdWRhpn7L7///HxmdUYRQAABBAZQgCCCAfzQhuyUKewP2QfO5SKAAALDLvDfvv5Hs/LjdzU00aRPqe8faEdabhegME1sMIGm3tdOC+uNIKgmaTT3T5+goO5UDvZv+cszpc2RYjkN0ynZw6OhCUoybHZpNQ7SaiM0/3cjXpknZaN3YYgAAggggECuBahXyPXHw8khgAACCORJYOqV6+X7E8EtafKvpA34+XqHnkEEeuKpy9KngQQ6XkjctJSbqw+vSdD909RR5Onz5VwQQACB/QgQRLAfLdY9CQEK+yehzjERQAABBPou8JFXP1veHA1uNSIpqNtCeGjfdtcT0UK7BhT4YAL7FrzMT0Ip0EsBXQvp2us6URzaqP+/o6De98+QAyKAAAIIIIBALgXkDqmjoy6sg4MJBBBAAAEEnMDHXnl2dmU0rWgmRAkiyLAkrj6iY57UUzQDBkKpw4jsuGYkcJtpFkWdV6wHlW89/adzmZ0xigACCCAwIAIUnAbkgxri06SwP8QfPpeOAAIIDIvAr/7lH83++GxcGY0lw4A0tNk4AABAAElEQVSU07UArlH8EiJgCXzkvw0eaN696XhdVtasguOSMrAYu/UbkcwPQ/PQKgX1Yfn74ToRQAABBBBAYEcB6hV25GEhAggggAACxjx+94+/9O4Zc1UiA2wAgVQx2IAAmyFR5vmXGrqtNJBAay5sEEHzhYjsixDrhdC8bzWc/7un5650b8s0AggggEC+BQgiyPfnw9nJS5ddCPzNdoEwiQACCCAw2AJT/+nzX1oeS67Wo4Y0UeCDCCTsXwrfgfRaUJcYAaMFeC28a2FcI/q1iyUTQTE25uymW3etaMxm5IIJtAA/vmkoqDsq/kUAAQQQQACB4RWgXmF4P3uuHAEEEEBgDwJP3J2ZfXc8qOiLCsUksXUNmo1Aswpo/UMgdRXtIAINGWh3PohA6zC0HmJUKi/05Qh98UG31zoKrcsYbyTzf/fUF660t2QMAQQQQCDvAp3f+Hk/W84PAQQQQAABBBA4RQJTX52ZvS+R/g0JBnAZCNoX5wMGbNR/e7Yd00ACG0wgBXO/PJU3A7SArikHNYxAC//3z5rLv/bq7K2uzZlEAAEEEEAAAQQQQAABBBBAAAEEzL+9+/ln7p9NK5taKSF1Cb4ZAzuU+gWdt11n6y2aC13mxK3raxCCBiC8OxZe/uir12e32xfzEUAAAQTyJyBf33QI5FpAbjM6Ov5mOziYQAABBBAYVIGpV5595sF4enNT2iMITEMuQx7+N3/1tDkDCROQAAEX7+mCA/RKXeHdR4HqlI7btIEydOtJKkGJ/tcl2qxBUVaS1IGVpU/NzslMOgQQQAABBBBAYNgEqFcYtk+c60UAAQQQ2JPAEwszpQdn0rfWpV7CZRqQ+gn51UzlRYeG1MLrPP/igqt92LpbV38h86X+wr3s4GssdN3Oeg1dMrKenv/Wp15Y1KV0CCCAAAL5Fsh+o+f7TDk7BBBAAAEEEEDglAhMSUH9/kRwczNqXlAzaCAJJIdAq5cyuCz2td4+wEC38LH9WqB36QE1RaCM2zs7l4lAsxFo8wi6XDISVD76SqWs29IhgAACCCCAAAIIIIAAAggggAACPzyX3tIAAte5oQYQaOezH7plu//r6yy0XsL3NlOi1EvoIbS6QptqfPdsNLv73lgDAQQQQCAPAvrdTYcAAggggAACCCDQR4GViejWlsM1sw605memtTBue1nY++ZNC/mu9wV9zURYjF37hbFUAiyPGwrqLVxGEEAAAQQQQAABBBBAAAEEEBhegf/uz2cuSxLDss9A0JLQuojmiw6uBkJrIXrXRHRs05poj/h6DCPNImhAgTZ5IC88lH/zz5+73F6LMQQQQACBvArs8u2f19PmvBBAAAEEEEAAgcEU+PBrz11uSEHdPvTfpX1Bf4VSzpYmD6SwLcEEu3VaKPdR/1oZoAV1TT/YKKTln/1G5fJu27McAQQQQAABBBBAAAEEEEAAAQROt8Bm0Vw69BW2Ag5670nrJ/wrDz5YQes16sX08MfufUjmIoAAAggcoQBBBEeIya4QQAABBBBAAIHdBJIgvZRoesCg0ex1vNlnN/bzbKBBdkF7XIMD2u0TunEtmK8WjXkgvQ61OQPNSjAiWQkkEoGCepuPMQQQQAABBBBAAAEEEEAAAQSGTkCbWHwwGpSjrjqFXhChBApob7MTNMf9vJ0eLvkXHDalLYO6VEoE2qyBHE9ferh/Jih/9JXr5V7HYx4CCCCAQH4Edvqez89ZciYIIIAAAggggMApEHjiyzMlKX2XO7MQyBP+XbpUAgm010L47p3uT3pZX5sx0AJ6QQrrGkgQpmn5YwvXy7vvgzUQQAABBBBAAAEEEEAAAQQQQOA0Crw3EV3ciLSOwNUfuCwB7Svdse7BBxS0V992TPcjmRhtvYTPRKD1FXrsdybCi9tuyAIEEEAAgVwIEESQi4+Bk0AAAQQQQACBYRA4U4guFpJE2wDc8XK1CQItWGuBu927eTpfb+CyN3GtjASynb5JcKaRmDHpA9MwSdiQoc5PTFGOLckJKKjvqM9CBBBAAAEEEEAAAQQQQAABBE6vgFQ1XNAXFfSlA32436536DEuDDvXYDgnzbio+3FrS72F1EH4XudpPYcu1/oLrbeITPJxtyX/IoAAAgjkVSBb/5zXc+S8EEAAAQQQQACBUyFQj4ILe70QV/j2a++lyC7r2tSCkiJQmi7QgAHNQKBBC7YQL80nhBJUINkJKKh7VoYIIIAAAggggAACCCCAAAIIDJtAkEzpJfvggezld9ZFZJfsYdy+ENFeL5Rgge5O52lwQZAGU1MLVye7lzONAAIIIJAfAYII8vNZcCYIIIAAAgggcMoF3h1LpzTav7vzzRX4oV/uIvV1fb1ly/ZujdYc3y6hnR1qoICMJWYklj5pSKh/Q94ucFkJVsbrFNQdH/8igAACCCCAAAIIIIAAAgggMFQC+uA+Cc2kNoHomjPQYIJ2JsTQ1y/4oei06h6yUrLcvsggdQ+abdEFB8gKWueR6Vvzm9v6fRVktTEzRhBB1pRxBBBAIGcC+p1NhwACCCCAAAIIIHDMAiUpqK8XpaB+DF13odwfQpsw0F4DCmwTCRJIUI8SUxs7nvPwx2WIAAIIIIAAAggggAACCCCAAAL5E5AH95KFQDMBuKYFdLxfnQtacMfWYxYahVK/js1xEEAAAQT2L0AQwf7N2AIBBBBAAAEEENi/wMSEFNR95x7s+6nu4fYZCTrX9EX9VgpCfROgV+ffAmguKxgK6r2YmIcAAggggAACCCCAAAIIIIDAcAhIjYLWFUiXBq7XcV9b4Yc6r6Prql9oL9tmi+b6mq3Ad3o8OgQQQACB/AtsU9Oc/xPnDBFAAAEEEEAAgdMsoG8F7KVrF8Oba9tAAm7x9mLHOggggAACCCCAAAIIIIAAAggMo4DWGmhWQ9e5OgQe7nsPhggggAACKlCAAQEEEEAAAQQQQKDfAqFNHbjbUdsF+s41feCAtlWY7bas37U8uy7jCCCAAAIIIIAAAggggAACCCAwdAJVe8VaX5DNCLDH+oMwadZDNLMYDJ0eF4wAAggMkUBnzfMQXTiXigACCCCAAAII9FWg0aj29XjbHWyPFQPbbc58BBBAAAEEEEAAAQQQQAABBBAYTIF1s17Ly5k38lJPkhcQzgMBBBDImQBBBDn7QDgdBBBAAAEEEDilAutaUJdbryN8iJ/IWwO+VzUd30vXMDkJaNjLybIOAggggAACCCCAAAIIIIAAAggcicDS9M2atGJQPfDONAPBAbIQZOsu9NhyDrU3PnPj4Odx4AtgQwQQQACBvQoQRLBXKdZDAAEEEEAAAQQOIVCVgvpYwxXUtzQ7cIj9+k1bAQRSmI+kTB812zZ0bRrqLV9o0iA0xTioVZ+6WfXbMUQAAQQQQAABBBBAAAEEEEAAgWESSF/v59XaAAI5YKveQsdNutTPc+BYCCCAAAL7FyCIYP9mbIEAAggggAACCBxI4H1r6euRth8o2QhC28ujfXnYv11vMxdo9oJW331YiRaQorfvk2YAgQQrmLG6CxpoSOCAO1YoaxbMSD2koN7NyDQCCCCAAAIIIIAAAggggAACwyKQmiOoF2jXRbg6id54PoAg1qqQTPbEOAhu996CuQgggAACeREgiCAvnwTngQACCCCAAAKnXkCyACxlI++P44J1/1ow1wK6G9eSuusTCUYoJCEF9eOAZ58IIIAAAggggAACCCCAAAIIDIDA5Hub88UkrdkwAKk/OK4uG0Cgx9DphlRPaPbE979XXzyu47JfBBBAAIGjEZCvbDoEEEAAAQQQQACBfgjcf7A8L5kBakd1LA0W6O4lAYFZHpV+RAIJZHkgmQ5swb25bj1OFo/q+OwHAQQQQAABBBBAAAEEEEAAAQQGS2BJm1vcNHf15YP9dz4Dwd627M5AoEEE45vp/NL0jere9sBaCCCAAAInJXCgn4mTOlmOiwACCCCAAAIIDLLA0pWbNZOmd23zBJodoNVMQXb8cFdosxDYgAG3T20qwQUayHRCQf1wumyNAAIIIIAAAggggAACCCCAwOALxPVGRV9C0PoCm73wGC/JZUl0LzpoEEGwmcwd4+HYNQIIIIDAEQlo7TIdAggggAACCCCAQJ8E4rhRCSSAINQ+KUivw0zvl9kgg86T6o731ywD2V7X1umihPqPSMlc+zAtGG3GQPuQgnonKFMIIIAAAggggAACCCCAAAIIDKGAZCOoboThy1pX4Dod+vEsiK+J8MPmskCmtd+l0xcbtK+HoW12MU15uWEXMhYjgAACuRHo9auQm5PjRBBAAAEEEEAAgdMmUH3qRrXYCF7eNgtBj+CB/RmEEkigwQPNCoDmsFgP5/XY+9sXayOAAAIIIIAAAggggAACCCCAwGkUOPujemUkNtV9X9seggf8PvVFB810oNkICo2g+siKIQuBx2GIAAII5FyAIIKcf0CcHgIIIIAAAgicPoHRtQeVNA2r7sq2ux3zUf6ZoY/0bw7TMJGyeLvX/WkqwnpkzIb0m1FoNFVgGIfVM2sbFNQdOP8igAACCCCAAAIIIIAAAgggMPQC1embtYfWzLQ0u1jrwMjWPdgFXfUSkutQ2kt0m+y0bnOnUk1hX3SQY135Di83NFUYIIAAAvkX2K7WOv9nzhkigAACCCCAAAIDKiBpA2vvX46nozisuaK3i8p32Qm2vyifBlCHWmAP0s7eBA073wb6B9qEgTZtYMzDK/GVpWmyEKgaHQIIIIAAAggggAACCCCAAAIIOIGlp19a+sBycG28bkwkdQxbmyhoBgu0wLLTblz/tb1EC2jGgUTrI6TXTrMkntkMzU8up3MSQLBoZ/IPAggggMBACBBEMBAfEyeJAAIIIIAAAqdN4JvTLy6dWTfX4ma7gNoOoRa2s50NGpCiuIYD7KWP0oYU+humKKX3SHpbWF8P5t6cfmExu1/GEUAAAQQQQAABBBBAAAEEEEAAARX4m0+/NP/wg+SKvoRQsHUJOteGBchQHyG1HyO5IAEfLNBcS+oy6lK3ob2vvagHBRObgtRNhObhtfDat566UZEd0SGAAAIIDJBAV1X1AJ05pzosAvZdy8zF8jebwWAUAQQQQGDwBf71V5+7/GDM3KrLU3+N+nfZBiTDQKvA3rxGTRHY0XVPy0LJRJBKId0kZ6StwREzXk+uLf3e8zc7NmMCAQQQQAABBBAYLgHqFYbr8+ZqEUAAAQQOKPD4wuemls+ahXrBlFx4gAsW6Nxduy7CvwgRS9aBVDMPSNaBEQlE0KADnTdWT6sPrSRX3pwmA0GnIVMIIIDAYAjwQHYwPqdhPksK+8P86XPtCCCAwJAIfPirz07df59ZGIvTkkb9a/S/xu/7Qrtl2DWIwBXkNWVg3YxXz62EV771KTIQDMmfEJeJAAIIIIAAAtsLUK+wvQ1LEEAAAQQQ6BCYWpgprZxNKgUTXNIFm5ExjbAdOGBXbtZPpPJ0SZdoAIEGFGjWgbF6aIeNMJ0/t7x6TZtztNvwDwIIIIDAwAkQRDBwH9nQnTCF/aH7yLlgBBBAYDgFPvLqTGm9GFWSyFzyzRHsK4igWYgPYzM/thJTUB/OPyOuGgEEEEAAAQS2ClCvsNWEOQgggAACCOwoUP7yTGljLKr8eCK5sF4wk62VMy84aOCA/sjqyww6Pr4Z1n5qOb0zXg9vL/4BLzW0zBhBAAEEBlSAIIIB/eCG6LQp7A/Rh82lIoAAAggY80EJJhgrjlRCE1yQG7V2Qd3G92eEMgV3+bGsmTS+k6TmdvVJ0gRmlBhFAAEEEEAAAQSoV+BvAAEEEEAAgQMKTC1cnfzxuYmyZB0oS6zAo4EJSrIrqatITGzSWmpC6c3rjShYOvvj5TtVMg8IDx0CCCBwOgQIIjgdn+NpvgoK+6f50+XaEEAAAQS2FZhaqEyOmKgcmaQshfJHjYlLPqhAgwZkXFICJq+nJl1aN8t3SBG4LSULEEAAAQQQQGC4BahXGO7Pn6tHAAEEEEAAAQQQQACBAwgQRHAANDbpqwCF/b5yczAEEEAAAQQQQAABBBBAAAEETpUA9Qqn6uPkYhBAAAEEEEAAAQQQQKAfAmE/DsIxEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCD/AgQR5P8z4gwRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDoiwBBBH1h5iAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjkX4Aggvx/RpwhAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACfREgiKAvzBwEAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB/AsQRJD/z4gzRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoC8CBBH0hZmDIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkH8Bggjy/xlxhggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPRFgCCCvjBzEAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBPIvQBBB/j8jzhABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIG+CBBE0BdmDoIAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggED+BQgiyP9nxBkigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQFwGCCPrCzEEQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIvwBBBPn/jDhDBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE+iJAEEFfmDkIAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC+RcgiCD/nxFniAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQF8ECCLoCzMHQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIP8CBBHk/zPiDBFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEOiLAEEEfWHmIAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCORfgCCC/H9GnCECCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJ9ESCIoC/MHAQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH8CxBEkP/PiDNEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgLwIEEfSFmYMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQfwGCCPL/GXGGCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII9EWAIIK+MHMQBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE8i9AEEH+PyPOEAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgb4IEETQF2YOggACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQP4FCCLI/2fEGSKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINAXAYII+sLMQRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMi/QCH/p8gZIoAAAggggAACCCAweAJTt65Ojo2NTRYKhZKefZImtdXianVp+mZt8K6GM0YAAQQQQAABBBBAAAEEEEAAAQQQQACBYREIhuVCuc6BFUi7zpy/2S4QJhFAAAEEEEAgPwKlr18vhya+YML0YmCCUq8zS01aDVKzGJvkbvW3/8OdXuswDwEEEEAAAQSOTIB6hSOjZEcIIIAAAggggAACCCAwLAI8kB2WT3pwr5PC/uB+dpw5AggggAACQyPwsbvXZ9eLwdXNYjIpOQeMCaTv6hK58/Y3NnoTHspEsWGqZzaDuW9++sZ81+pMIoAAAggggMDRCPifX7836sK8BEMEEEAAAQQQQAABBBBAYBsBCk7bwDA7NwIU9nPzUXAiCCCAAAIIINAt8IF718ujJrg1kphSlCYSGJANIOgMJLBBBJm7b8lGYLs4CDW4oCrbz33vEy/Ndx+DaQQQQAABBBA4lAD1CofiY2MEEEAAAQQQQAABBBAYRoFMNeYwXj7XPAACFPYH4EPiFBFAAAEEEBhGgZ//xrOzm1FQ0YwCGhCgw1CzEHRlItBlafOuWwMJujsJPWjNGm2klb9/8j/MtWYwggACCCCAAAKHFZBf4o6ux69xx3ImEEAAAQQQQAABBBBAAIGhF6DgNPR/ArkHoLCf+4+IE0QAAQQQQGD4BH723nNfCkxyVa/cZxRwTRhoNgKd2c5CYKdlVncAQWYN3Y0EGiR2X4U4nP/eb//7K3Ym/yCAAAIIIIDAYQWoVzisINsjgAACCCCAAAIIIIDA0Am0X3saukvnghFAAAEEEEAAAQQQ2L+AZiDwAQStrAOZoAG7x1Rus3frbQaCHrfjYXL5V7/2+Vv7PzO2QAABBBBAAAEEEEAAAQQQQAABBBBAAAEEDi/Qo9by8DtlDwgggAACCCCAAAIInEaBn3r9c89sRmnFBg/YpguyV9nOLZCde5DxRsFc/qWvz8weZFu2QQABBBBAAAEEEEAAAQQQQAABBBBAAAEEDiNAcwaH0WPbfgiQdrAfyhwDAQQQQAABBHYVmFqYKf3okfQt2zyBZhlodu0bahdE4Jsv8Mv9dHdzBn65n99qzkDufqIkNMU4ND/xXnp+8Q9uLPp1GSKAAAIIIIDAvgWoV9g3GRsggAACCCCAAAIIIIDAsAu0az+HXYLrRwABBBBAAAEEEEBgB4FiwdwqxsYE+iiiu/mCHbbTIAEfKLDDah2L7DFkTr0QzXYsYAIBBBBAAAEEEEAAAQQQQAABBBBAAAEEEDhmAYIIjhmY3SOAAAIIIIAAAggMvsDjX3n28tpIWo5SzTbQbLZAAwlsMEFmnixLZJ72dllz6Od1z88GI2jggN6c+wACVXswasqPLzx3WcfpEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBfggQRNAPZY6BAAIIIIAAAgggMNAC98eTS5uRPORvPuhvBRJ0XJUGE2Q7F1zQPTe7ho7bfbb265amkr2gHiVmrWjM/Yn0Uvc2TCOAAAIIIIAAAggggAACCCCAAAIIIIAAAsclQBDBccmyXwQQQAABBBBAAIFTIVD+8kypYIKyXoxtmqDrqvSBf7b3GQh8MwahZCfI9l2b20kbSJCEJpQ+lbVj2eeGBC1sFBLTiILyxxaul3ttxzwEEEAAAQQQQAABBBBAAAEEEEAAAQQQQOCoBQgiOGpR9ocAAggggAACCCBwqgTCQnSxGHc2M7C3C3SZCHZbVwMIfCexA0Z73+miOEzM+qi56OcxRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEjlOgcJw7Z98IIIAAAggggAACCAy6QJQGF/RBf7HZLkEsYbgSUyCdb6ggE5cbJJJJwC0LmsEBiczTLkz9el3bNZcHsrwjoEBWq0sAgWY5WB4LPm53wj8IIIAAAggggAACCCCAAAIIIIAAAggggMAxC/iazGM+DLtHAAEEEEAAAQQQQGAwBdaKwZQ2TdCMCdjmIuSJfzMYoB1ckF11b7fdehxtykD77PEaUTo1ee/qZHaPjCOAAAIIIIAAAggggAACCCCAAAIIIIAAAschQCaC41BlnwgggAACCCCAAAKnQqC0UJn8l4l4UrMPRPJUXx/ya5CAHWzJLNB9yd2BA93Tbn23Tzeux/EBC0ngMhMUmokLxtbHNIig1n0UphFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgaMU6F2TeZRHYF8IIIAAAggggAACCAyowINJM7VSNKYhd82aHeA4u2wwgY5rQIEeV4MIitJ+QqFgSsd5fPaNAAIIIIAAAggggAACCCCAAAIIIIAAAgioAEEE/B0ggAACCCCAAAIIILCNgKbt0gwEofRbYgi0+YJWEwbb7GAfs/UYkezy/2fvfn4jyfLEsJOs6pnewYyWu+ubYSh000H2cgH7YtiY6IMMCPJ6qg8++NTZf8HUHgwsBMmVDcHrhS24a/4Csk8+GVVjwTZgQCAHMAzBWLu5MAToYCBzDfhiGW7ur/nVVaS/X5LBjop+kRmRmWSRzM8Dvh0RL977vvc+mUwys6IjP3xzFe2uOQe3EGuL2CdAgAABAgQIECBAgAABAgQIECBA4LYEfBZ5W7LyEiBAgAABAgQIPHiBp/EP+k/istvLCwmuVxM3Bbi1khcS5F0H9mLM3DZ3J7i4vPb3Nke+tSVJTIAAAQIECBAgQIAAAQIECBAgQIDAAxNwEcEDe8BMlwABAgQIECBA4O4E4iKC+ZP4izlv37W7c75zsRe3Crgsub2+qdc7dyP4pj4vCMhyft2u6XlVm3c3uOrfXCiQtzpo+uQFBL8RDS+/0mB3b+dNXMnw9ra/T6GZmC0BAgQIECBAgAABAgQIECBAgAABAlstcP3J51YbWDwBAgQIECBAgACBosD+L3fOvtu6AcBuXBiwF5cFlEtffbl1X21eVJCRFxRcXWiwd3lXgu//1Zt5Xx/1BAgQIECAAAECBAgQIECAAAECBAgQ2JSAiwg2JSkPAQIECBAgQIDAoxM4/Xh69p24G0HeESD/cG7uFHC10LxoYDMXDmS+i7hw4E0MkvHLuPtBxtex/zSG+M1f7Jz9y4//eJ7tFAIECBAgQIAAAQIECBAgQIAAAQIECNymQH4WqhAgQIAAAQIECBAg0CPwvV9f/OzJ9bUCN1890NP26lKD9sl1/9yOgePrEr7z9uK0ndU+AQIECBAgQIAAAQIECBAgQIAAAQIEbktg3U81b2te8hIgQIAAAQIECBC4FwLf//Xu6Ydxe4C3u3s7v35yde+B/EqDq68buLo7QfPVA99s88/s5k/tZv9qm19RcPU1Be8uL78qIe868J34+oQP38T2/HznSdS92Tvfudg5/+Ld1o4IECBAgAABAgQIECBAgAABAgQIECBwOwLNJ5u3k11WAgQIECBAgAABAg9c4Fe/+MujuAPBWX61QMbyuxGsvuDMnV+dkNurCxLyAoL4aoOd85PVs+pJgAABAgQIECBAgAABAgQIECBAgACB4QIuIhhupSUBAgQIECBAgMAWCpx8+vLsr57u/PTruAvBRfzj/mWJuwmsXOLrCfIrCm4i7mpwdX+D87iA4HznL797vvNXEW9jrBzuYufi6PTjP56vPJ6OBAgQIECAAAECBAgQIECAAAECBAgQGCGwxqefI0bRlAABAgQIECBAgMADFtj/y7fTD/IWAZdfUXD1dQRPzq+2edeAb+4c8O1FvvO1B5cXDHy7zWXN9YUFTb6s+258jcLvnO181tNDNQECBAgQIECAAAECBAgQIECAAAECBDYu4CKCjZNKSIAAAQIECBAg8NgE8k4Av/XXuz958vbpztO4eCAvIPggYjfuSPBm7ypy/2lcaJAXDbxT4uKAvbh4IOPdcnUHguZc9nsSVR9+HfHmquX3f+EuBO+aOSJAgAABAgQIECBAgAABAgQIECBA4LYFXERw28LyEyBAgAABAgQIPAqB8zc/n8YXDMzzcoDLOxLERQP5D//NnQPyRgWXNyvorPabiwqi50X3QoJ24zy/s/P08mKCvfgag9357i/chaAtZJ8AAQIECBAgQIAAAQIECBAgQIAAgdsXyK9ZVQjcZ4H4GP2d4jn7DocDAgQIECBA4C4F/vXjf3DwZOfi+MnFzv4Hb+MCgr3znV98cB53I4jLCuKvlsuLAKK+faVu+yKCnGtedPBNubok4fI4Lkq4KrGN/bfnux/N/+4fn1xX2hAgQIAAAQKrCfhcYTU3vQgQIECAAAECBAgQ2GKB5pPKLSawdAIECBAgQIAAAQLDBP7vj/7o9IOvd/8gLwS4vPPA7tVXGzxddIOB3tStCwhu2uRXJOzsfPDm4jMXENyg2CFAgAABAgQIECBAgAABAgQIECBA4A4F3vn/oO5wXEMRGCrg/xgYKqUdAQIECBAgcGcC/8Y/+weTiycXh3mXgbgrQcTVVxHkxQX5x8vl3QZ2r64s6N6J4GaS1+fzooHLcnkngrjG93z3D/7Pv/tfvrxpZ4cAAQIECBBYR6D5Tdvk8FlYI2FLgAABAgQIECBAgACBHgFvnHpgVN8bAW/2781DYSIECBAgQIBAW6D6n/7BwdcfnL/68Hy3ehLXC+TFBHEFwM7XT+IuBflX9rKLCK6TNRcRXOzszT/49cWn//Lv/ZOT61M2BAgQIECAwPoCPldY31AGAgQIECBAgAABAgS2TMDXGWzZA265BAgQIECAAAECmxGY/wd/dPr2lzsffb2798XXe3uXFw68ja836P5LxbdHyz/B342Li52jt3/x9e+5gODbWmoIECBAgAABAgQIECBAgAABAgQIELhbAXciuFtvo40X6H4O7zk73lAPAgQIECBA4JYFDl79YfX1h0+mXz89/1F8lcH+xfVdCHLY9h8v+YdNftXB5dcd7Fycfeft7uvf/MXbL/7k99194JYfIukJECBAYHsFfK6wvY+9lRMgQIAAAQIECBAgsKJA+zPNFVPoRuBWBbzZv1VeyQkQIECAAIFNClSvnu9/53u/Ue/uXtRx9cDvxm0JqoudJ/s5xu7FxdnbvZ2zX+7t/OzNBzunv3z789dnH7082+T4Dz3XQfh9uPPh/tM3T6tcy/nF+dnP//rn89NPOT30x9b8CRAg8B4FfK7wHvENTYAAAQIECBAgQIDAwxRwEcHDfNy2adbe7G/To22tBAgQIECAwNYJHPzTf1j/q+/v/ujJzttnuxe71dPmr7+L5pvXLr8iYh63bzj5rb/e+en/9vH09dYhWTABAgQIrCPQ/GZpcvgsrJGwJUCAAAECBAgQIECAQI+AN049MKrvjYA3+/fmoTARAgQIECBAgMDmBP69//Y/e/GrpxfPv/rezv4vPzjfeXK+s/P0Iv4TZTcuILjIdyqtCwmyPtt8+GZ3vvdm57PT/2h6lHUKAQIECBBYIuBzhSVAThMgQIAAAQIECBAgQKAr4CKCrojj+ybgzf59e0TMhwABAgQIECCwhsDfiTsP/OX3nhzu7ZxX8YUFcYOBSLZ7vrMXf/VlvFuauxFEbVxQ8CYOf/k0dqPP7vne/F/7+fln/+Lv/eOjd/s4IkCAAAEC7wh0f7v4LOwdHgcECBAgQIAAAQIECBD4tkDrU7lvn1RDgAABAgQIECBAgACBTQn8nf/xH774+ru7x3HHgarJmXcfeHqedyDICwq+iTze2XlzWX+5vxv7Edn+SfbZeVP98jvnh//Wf/8PXzS5bAkQIECAAAECBAgQIECAAAECBAgQWF/A1dfrG8pwuwL+j4Hb9ZWdAAECBAgQIHAnAv/m//CPPv/z39h5/p24FiDuQnB5B4KLuGhg9/JigZ2dt3tRdz2T5krnmzsTXH+tQd6JICMz5FcbXL6ZiXO/8/Odo//9P/zPP72ThRiEAAECBB6agM8VHtojZr4ECBAgQIAAAQIECLx3ARcRvPeHwASWCHizvwTIaQIECBAgQIDAfRfIOxD88unu9CIuFHj6dufybgK78VdeXjjw9ZOr7dUamssIuiu6uqzgbbx7+VV8nUF+tcGHcTHC02ieFxV88HZv52/8cjcuJPgjFxJ06RwTIECAgM8VPAcIECBAgAABAgQIECAwUuDq07iRnTQnQIAAAQIECBAgQIDAEIH4uoEfn324O80LBvLuAXkXgssSdyHIOw3k3Qiuaq7rr5OexwUD38TV1xzsRNssmeMyz/Xx15H4q++9nfzb//QPX1x3tyFAgAABAgQIECBAgAABAgQIECBAYEUBdyJYEU63OxPwfwzcGbWBCBAgQIAAAQKbFfjbr/6wevP9D2aXFxDEX3V7F99cQJAjncdFAHETgauLCK4vCPjWDK6/yiCvfs7eeTeDvItB3oWguZzgci/q8u4EP/j524/++cf/5CTrFAIECBAgEAI+V/A0IECAAAECBAgQIECAwEgBdyIYCaY5AQIECBAgQIAAAQLDBP7yt54c/jr+tf/yzgFxAUFewXweFwHk1xLkhQUXIy9pzjcvl3czaP1zUN7NIGM3LjH4VYz1r37w5MWw2WlFgAABAgQIECBAgAABAgQIECBAgEBJwEUEJRV1BAgQIECAAAECBAisJfDv/Hf/aBLXCNR5t4E3efeA/AqCzp0IcoCsu/xqguvR8sKCbuSpvPHA1X0M8i1ME3nmqjR9zp+c1//+q/900tTbEiBAgAABAgQIECBAgAABAgQIECAwTsBFBOO8tCZAgAABAgQIECBAYIDA13vnn1z+s//11xTkP/LnVxdcxPF55w4Ezd0E+tJm3zHl10/3YmyFAAECBAgQIECAAAECBAgQIECAAIFVBEZ+HLfKEPoQWEsgbk77TvGcfYfDAQECBAgQIEDg/gn87Vd/WP18/8ksv7LgSdxpICPL1X+v5nt5L4HLv/Suz8Vfed2LCy5bXrx73fO7R9Hi+nxzN4PdyPndN3s7v/Hz3Y/+5OM/PrkazX8JECBAYIsFfK6wxQ++pRMgQIAAAQIECBAgsJrAtz6DWy2NXgQIECBAgAABAgQIELgS+H9/58mzXz252s9/1M/LBN7GRQLNVw7cttOvnu7s/H8/2Hl22+PIT4AAAQIECBAgQIAAAQIECBAgQOAxCsTHawoBAgQIECBAgAABAgQ2J/D0fPdHT+LqgbyAoP1VBHmc5Zu7EFwdb+K/zV0MLq+SjnH2LnZ/uIm8chAgQIAAAQIECBAgQIAAAQIECBDYNgF3Iti2R9x6CRAgQIAAAQIECNyywNOL3YMncfuBJ9cXDeRw7QsIusM3FwB061c5zlwZ8S0HBwevnu+vkkMfAgQIECBAgAABAgQIECBAgAABAtss4E4E2/zoWzsBAgQIECBAgACBDQvkP9x/dbGzv5cXEMQ/5ufXGFzdeyDvDpB1+eUGV//Qf7lzeV+C6704f3X26rj03/NW+8vzl/mbEb7p8SYul/5w58O8iODsm1p7BAgQIECAAAECBAgQIECAAAECBAgsE3AngmVCzhMgQIAAAQIECBAgMFjgw53vH+TFAvlGI7fNHQgGJ9hAw8s7EUSeNztPqw2kk4IAAQIECBAgQIAAAQIECBAgQIDAVgm4iGCrHm6LJUCAAAECBAgQIHD3AnkhweVdCG5x6PYdDG57rFtchtQECBAgQIAAAQIECBAgQIAAAQIE3ruAiwje+0NgAgQIECBAgAABAgQer8Bd/4P+XY/3eB85KyNAgAABAgQIECBAgAABAgQIENhWgafbunDrJkCAAAECBAgQIEDgNgTezHd2Plg5cfcq5/YdBjJp9yKB/OoChQABAgQIECBAgAABAgQIECBAgACBzQl0P6PbXGaZCBAgQIAAAQIECBDYOoFf7vzyrG/R3QsA+tqNr+9eanCV4c1OXtCgECBAgAABAgQIECBAgAABAgQIECAwRsD/tzNGS9v3IRDfoPtO8Zx9h8MBAQIECBAgQOD+CVT/7B/NnuycVzs75ztv9q7+gf9pbPIPuYvd850xdw/oXh7QvQr68nzkzJIXKexd7MWoeztvd/fO/q+P/ovfujzhPwQIECCwzQI+V9jmR9/aCRAgQIAAAQIECBBYSaD7GdxKSXQiQIAAAQIECBAgQIDAjcDFxc92459sMpqy6pWg+YalHU2+m+31BQTNcY6ZFxPs7lycNnW2BAgQIECAAAECBAgQIECAAAECBAgMF3ARwXArLQkQIECAAAECBAgQGCBwvntxenVXgAGNlzaJ+wpcXEXe2aAbV+NcnX8Sp59E26fn5ztxbcEXS1NrQIAAAQIECBAgQIAAAQIECBAgQIDAtwRcRPAtEhUECBAgQIAAAQIECKwj8Nt/8ddH33m7c3b1tQVXbznypgQZY77KIOeQFwk0pb3f1OVFBc2dCvLKgSzffbuzs/8XOyeXB/5DgAABAgQIECBAgAABAgQIECBAgMAoARcRjOLSmAABAgQIECBAgACBZQKnH788+42vd376drd5u7G382YvY1nPIeffvRvBVcq480DcgSDL2/jehO++OT86/fiP55cV/kOAAAECBAgQIECAAAECBAgQIECAwCiBjXyMN2pEjQkQIECAAAECBAgQePQCT/7q7fRtvNu4iPsENHchiPsKRMXm3oJcZbq6eCBBL+ICghzzyddvP3v0wBZIgAABAgQIECBAgAABAgQIECBA4JYENvcJ3i1NUFoCBAgQIECAAAECBB6ewD//T+JOAOe7P/n6yc7O10/24k4Be+98NcGqK8o3ME1kjt24QiEjS15O8Ovdi6N//vHLeR4rBAgQIECAAAECBAgQIECAAAECBAiMF3ARwXgzPQgQIECAAAECBAgQGCDw23/xV9PvvNmbN033rv+xvzne9PY7by/mP/jFubsQbBpWPgIECBAgQIAAAQIECBAgQIAAga0ScBHBVj3cFkuAAAECBAgQIEDg7gROP3559v1fvP147+3e2d7l1xi0337kfQOGxND57u384Bc7n/7Lv+cuBEPFtCNAgAABAgQIECBAgAABAgQIECBQEmh/ilc6r44AAQIECBAgQIAAAQIrC/yLv/9Hp7/9850/+PDN+c6T8/ObrzTIuxK070ywFxcUXEanvjtw+7KDq3N7O999u7fz23+9+9mf/P7Lk257xwQIECBAgAABAgQIECBAgAABAgQIjBNwEcE4L60JECBAgAABAgQIEBgp8H/8/X989Js/f/vpk4vznd28WCAuFHhyHc3FA7t5HFcI5DYj71xwdfeCbwY73417F0TENQOX27ygYCfa/Y1fPPmD//X3/+vpNy3tESBAgAABAgQIECBAgAABAgQIECCwqkB8BKcQuNcC8RHyO8Vz9h0OBwQIECBAgACBhyPw7776Tw/+/Df2Xn399LzKiwjy6wx+/SQvCIi7FFxeOJB1cYVAXBiQFwt0y0XUZdvm3HffXsz/xs/3Pv2T3/8nJ922jgkQIECAwLWAzxU8FQgQIECAAAECBAgQIDBSwJ0IRoJpToAAAQIECBAgQIDAagL/y8f/1ekPfrH70d7b3S+enOdbkbxYYC/uLLC383XE292nlxcQ7MZFBHm+G0/jFgTfefN057tv4tzbnaPv/cX577mAYLXHQi8CBAgQIECAAAECBAgQIECAAAECfQKF/7+nr6l6Au9FwP8x8F7YDUqAAAECBAgQuF2B+r/5w+pX39uZ/j/fP//Rzz/Y3b/8eoO4cOCDt/m1Bs21zs32ai7ffXN+9v1fXbzee3P+xf/8H//xye3OUHYCBAgQeCQCPld4JA+kZRAgQIAAAQIECBAgcHcCLiK4O2sjrSbgzf5qbnoRIECAAAECBB6EQPXq+f5f/s73672Lnfo75zu/+8H5k+rJ+e7+5eTP9852d3bPLnbPf7azs3v6g78+f3368fTsQSzMJAkQIEDgvgj4XOG+PBLmQYAAAQIECBAgQIDAgxFwEcGDeai2dqLe7G/tQ2/hBAgQIECAAAECBAgQIEBgbQGfK6xNKAEBAgQIECBAgAABAtsm8O79Qbdt9dZLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI3Ai4iOCGwg4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIENhuARcRbPfjb/UECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQOBGwEUENxR2CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAdgu4iGC7H3+rJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECNwIuIrihsEOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLZbwEUE2/34Wz0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIELgRcBHBDYUdAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCw3QIuItjux9/qCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAjYCLCG4o7BAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAge0WcBHBdj/+Vk+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBG4EXERwQ2GHAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhst4CLCLb78bd6AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBwI+AighsKOwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYLsFXESw3Y+/1RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRsBFxHcUNghQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLbLeAigu1+/K2eAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjcCLiI4IbCDgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2G4BFxFs9+Nv9QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4EbARQQ3FHYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMB2C7iIYLsff6snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI3Ai4iuKGwQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEtlvARQTb/fhbPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQuBFwEcENhR0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILDdAi4i2O7H3+oJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCNgIsIbijsECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB7RZwEcF2P/5WT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEbgRcRHBDYYcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGy3gIsItvvxt3oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIHAj4CKCGwo7BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBguwVcRLDdj7/VEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBGwEXEdxQ2CFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAtst4CKC7X78rZ4AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNwIuIjghsIOAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYbgEXEWz342/1BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEDgRsBFBDcUdggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwHYLuIhgux9/qydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAjcCLiK4obBDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS2W8BFBNv9+Fs9AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBC4EXARwQ2FHQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsN0CLiLY7sff6gkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwI2AiwhuKOwQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHtFnARwXY//lZPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRuBFxEcENhhwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbLeAiwi2+/G3egIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgcCPgIoIbCjsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGC7BVxEsN2Pv9UTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEbARcR3FDYIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC2y3gIoLtfvytngABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI3Ai4iOCGwg4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIENhuARcRbPfjb/UECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQOBGwEUENxR2CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAdgu4iGC7H3+rJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECNwIuIrihsEOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLZbwEUE2/34Wz0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIELgRcBHBDYUdAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCw3QIuItjux9/qCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAjYCLCG4o7BAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAge0WcBHBdj/+Vk+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBG4EXERwQ2GHAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhst4CLCLb78bd6AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBwI+AighsKO3cgsB9jvIg4jnh2i+PVkftVxGFEjqkQIECAAAECBAgQIECAAAECD1/A5woP/zG0AgIECBAgQIAAAQIECBAg8I5A/qP+RSs+eeds+aDdPveXlczZ7jNd1sF5AgQIECBAgAABAgQIECBA4EEIHMYs2+/58zOAZaXdPveXlczZ7jNd1sF5AgQIECBAgAABAgQIECBAYHWBvDtA+4147ueb80Wl235R28zVbX+8qINzBAgQIECAAAECBAgQIECAwIMR8LnCg3moTJQAAQIECBAgQIAAAQIECAwTqKNZ9x/58/iTBd277fuaZo5u2zy+za9N6JuLegIECBAgQIAAAQIECBAgQGDzAnWkLL33z88E+kq3fV+7zNFtm8c+V+gTU0+AAAECBAgQIECAAAECBDYkMIk8pTfl+Wa9VLptS22yb7ddHk9KjdURIECAAAECBAgQIECAAAECD1ZgEjMvfQaQnw2USrdtqU327bbL40mpsToCBAgQIECAAAECBAgQIEBg8wKTSFl6c55v2rul2657Pvt02+TxpNvQMQECBAgQIECAAAECBAgQIPAoBCaxitJnAfkZQbd023XPZ59umzyedBs6JkCAAAECBAgQIECAAAECBG5XYBLpS2/S8817u3TbtM9l2+75PJ60G9knQIAAAQIECBAgQIAAAQIEHp3AJFZU+kwgPytol26b9rls2z2fIzx1OQAAQABJREFUx5N2I/sECBAgQIAAAQIECBAgQIDA3QlMYqjSm/V8E9+U7vmmPtt0z+XxpGlgS4AAAQIECBAgQIAAAQIECDxqgUmsrvTZQH5m0JTu+aY+23TP5fGkaWBLgAABAgQIECBAgAABAgQIvB+BSQxbetOeb+azdM9lXZ7r1ufxJEIhQIAAAQIECBAgQIAAAQIEtkdgEkstfUaQnx1k6Z7LujzXrc/jSYRCgAABAgQIECBAgAABAgQI3AOBScyh9Oa99Ka+VOeN/j14EE2BAAECBAgQIECAAAECBAi8J4FJjOtzhfeEb1gCBAgQIECAAAECBAgQIHBbApNIXHrDP6Qu+yoECBAgQIAAAQIECBAgQIDA9gpMYulDPkMotcm+CgECBAgQIECAAAECBAgQIHAPBSYxp9Kb+UV12UchQIAAAQIECBAgQIAAAQIECEyCYNFnCKVz2UchQIAAAQIECBAgQIAAAQIE7rHAJOZWelNfqsu2CgECBAgQIECAAAECBAgQIECgEZjETukzhFJdtlUIECBAgAABAgQIECBAgACBByAwiTmW3ty367KNQoAAAQIECBAgQIAAAQIECBDoCkyiov0ZQmk/2ygECBAgQIAAAQIECBAgQIDAAxKYxFxLb/KzLs8pBAgQIECAAAECBAgQIECAAIE+gUmc8LlCn456AgQIECBAgAABAgQIECDwQAUmMe/uG/6sUwgQIECAAAECBAgQIECAAAECywQm0cDnCsuUnCdAgAABAgQIECBAgAABAg9MYBLznUV8FZH7CgECBAgQIECAAAECBAgQIEBgqMAkGvpcYaiWdgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDwwAV+HPOfRVxcb1884PU8prU84IfB1AkQIECAAAECBAgQIECAAAEC906gihkdRnwVkZ+DHUccRDzEUsWkc/7ttWSdQoDAAxfYfeDz37bpP4sFl36RTG8Zoor8k8IYr6PutFCvigCBbwSq2J18c3iz9zL2zm6O7NyGwPNIut9JfBLHGcr9E3gRU5oWpnUUdZ8W6u9zVd9apjHpz+7zxM2NAAECBAgQIEDgWwKl9xXzaHX0rZabr6gjZUa3TLsVW3pcxbonhbW/jLqzQr0qAgRuX+BZDHHQGeY0jl936hxuVqCKdJNCypdRd1aoV/V+BaoYPv/RPbfd8lFUnHQr7/FxFXMrrSWfd7mW0wiFAAECBO5A4CjGuCjEbQ9dF8bMeUwiFAIEFgvUcbr0c1st7ubsBgRmkaNrP91AXik2L7AfKbuPVfu43vyQt5axisztuXf387xCgAABAgQIECDwcARmMdXu33R5XN/BEqYxRmnsOxj6QQxR9/hUD2L2JkngcQocxbK6r1uHj3Op92pVdcE9H4cqQrl/ApOYUvfnpDk+vn/TXTijl49oLQsX6iSBbRTY28ZFWzMBAgQIECBwrwQOlsymWnL+Pp1eNtf6Pk3WXAgQIECAAAECBFYWOIyeeTGsQoAAAQIECBAYI1AvaLzo3IJu7+3U7y4YuVpwzikCBB6AwNMHMEdTJHDbAvmmv/QPWCe3PbD8BAgQeMACdWHu86jLGFvmSzosO7+k+52ePlsy2pi1VJEro10y/2m7wj4BAgQ2IFBFjox28XrT1rBPgACBbwtUUZUXEnz87VNqCNxrgYOY3X5nhvM4zlAIECBA4NsCVVRldMtJt2Lgcb7X6ivzvhP3tP5PY151z9wWrbPUpZTnNBqOzVPKrY4AAQKPXuAoVtjc1qa9ve2F1z3jTm574DvKn+toezb7dzS8YR65QB3ra55T7W31yNd9H5Y3K9hP78PEHskc2s/nZn+yxtqOo2+Tp72drZHzfXXtW8uXIyd0FO3bFrmfuRUCBAhsWuAoEnq92bSqfAQIPBaBWSyk+xrZPn5+iwud9ox9i0M+qNR1j0/1oFZx95MtPaendz8NIz5SgaNYV/s1MvcPH+la79Oy6phM1z2PqwhlfYFppOj6ztZIW0Xfrwo5c4xJxEMqBzHZrk1zPBmxkKonTz0ih6YECGxYwNcZbBhUOgIECBAgQGAlgfw/uL7o9DyJ4486dQ/h8NOY5OvORE/i2P+l1kFxSIAAAQIECBB4BAIvYg3VI1iHJRAgQIAAAQJ3IzCPYfLzrtw2Jf9v+z+IOGoqHsj2NOaZn3fNW/N9qGtpLcEuAQIp4OsMPA8IECBAgACB+yCQbzAmEdOIKmJ+HbF5cGUeM843UNV1zGOboRAgQIAAAQIECDw+gf1Y0mFE/mOAQoAAAQIECBAYIpD/+P63Iurrxnmcn409xPI6Jp1RX0/+Ia/legk2BAikgIsIPA8IECBAgACB+yQwj8lkPIYyj0VkKAQIECBAgAABAo9boI7lPY94+biXaXUECBAgQIDAhgVONpzvfaZ7TGt5n47GJnBvBHydwb15KEyEAAECBAgQIECAAAECBAgQIEDggQrk1xocPNC5mzYBAgQIECBAgAABAgTeEXARwTscDggQIECAAAECBAgQIECAAAECBAiMFtiPHvm1BgoBAgQIECBAgAABAgQevICLCB78Q2gBBAgQIECAAAECBAgQIECAAAECdygwj7FOC+PlnQg+L9SrIkCAAAECBAgQIECAwIMSePqgZmuy2yCQV+5X15H7WeYRZxGnEY+xVLGog4hmvbnWjJOI91lyThn715FzOo04iVinVNG5yZt5Mm+TO7d3XaoYsIlm7HnsNHNq6rZpW8ViDyL2rxedFhkn18d3vcl5VBEHrYHnsd9Eq/q97FYxahM5gcbr9Ho/6+6qVDFQE82Y89jJOeV8lCuBKjYHEftXh+/1Mbuegs0dC+RjX0Xk8yDLWStOL2vez39yXnVEbquILPOI1xFnEauWJt9BK8E89ptoVd/JbhWj5FxyXlnOruP0ept1d1WqGOi+zKW75pxbE3nufTrl+AoBAgQIfCMwj91PI76M2I9ol+dx8NOIk3blA9jPdVQRB625zmP/LOK0VffQd+/bOvvmk+ZnDx27MP++9eZac83vq+S8DiKq64jN5d/KJ9fbPF6lZN7qOnI/yzziLOI04n2Ugxi0imjmk3PJOL3exmarSjpUEQetVc9jvzFpVd/ZbhUj5XxybllyLs18cvs+S86puo7czzKPaOaXx3dVSnPJeTRzya1y9TyqAuKghTGP/capVW2XAAECBB6ywFFM/qIQt72mujBmzmMSsYmyH0meRxxHfBVRWmNTl20mEVXEKuVVdJp1om/Mbrv28bNVBr/uM3a9aVOtMV7T9WXstNeQ+5m7XXJuLyL6TI7bjQfuZ85JRPbty3sR5zK+jDiMqCJus9SR/POIZfPJOR1HTCJWLXV0zDzdqKJu1fIiOs4KkX5VxNiSj1E+F44jlplkm2xbRdxmyTnlOnNNFwtiFud+HJHtuyXPdftOu41WOG7mdhx9l3nl+VcRk4jbKnUkPoy4rbk8i9xp2Y2LqOtGzqHbrjk+jHOLShUnm7bt7cGiTgPP5WP2POI4YpnTl9Hm84gqYtVSR8f2Gpr9akHCpk172zfXdpvufmmMvsdwsmA+Y0/l49SdSx5PxyZaoX0VfUpjl1LVUXkccbEkXsX5dFu3VJFgyNzqaLdoXnl+bKmjQz6Xc/xF683zhxGbWG+kKZb9qH0ecRzR97xu5tjMp4q2t1XqSJw2y+by5XW7Krbrllx/rq0dh4WkafXiut1FbPviOM5NIlYps+jUjT6Lbrv2cbXK4PoQIEDgAQrka99FJ46v15Gv791zeTyLyNf0dcs0EpTyr5u36V/HTv5OzPmWxmnq8vfEccQk4j6VOibTzLG9rTqTzMciH6tcQ7tdd38W5w8j6ojbLHUkH+LezOfZipOpol/m6MZF1HUjH+Nuu/ZxnF65pP+LiC8juuO2j3MOryImEZso7fk3+1UncR3HxxE5dnsuzf4k6seWoevNMXLsSUQVcVsl5/M8Isdq1tW3nUWbw4gqYtVyFB27+TPnqiXnfxwxK0Q+X1YpQx+jTT8nF821jpPp1PdcvIhzGccRk4huqaOiadPeVt2GKxzX0efziFlEO3dp/8tocxhRRdxGaR6740heGr9dt+pcXkXuWSe+6hmv2659/Cz6LCrP42S7fe4fLuow4txBtH0RkTkvFkSuK9c7iVinvIzOOVY7DhckTJt222b/Iuq7kXNszne3hz1jZH23bR7v97RfpfrznjGqVZLpQ4AAgU0IHEWSi0JsIveiHHVhzJzHJGLd8iIS5C+C0rqW1eUL9dgX/uMVx+rOZRJ5Vik/jk6rrvcw+larDHrd5yi23XVMr8/lpo6YRXTbtI+P4/yY8j7XW5pnHZW5hvaahu7Pot8kYmypo0NpjGpsouv2L3ry5fPqYIWc9+0xyp/pXOPYn5N8fOqIdsm6i05M2w1G7lfR/jiim3Po8Sz6TiI2VepIlDmHjt9ul/2qiCFlEo3afVfdP14yWNUzTr2k37LT7+M5nnMuOVULJltqv0pdaYz8uSr9TC17TBZM91unDqOmNN/6Wy03X1FFytLY7ZHSINdbareobhZ9qohVSxUdS/mbfDmvz3vatPvVTYcB2yraHEe0+w/dn0W/ScSmyqqv6c18D2Mi1aYmE3nqiFxjk3/Mdt25TAvjHkddu6zyejWLBJN2kgH7Y9a9qG01YCxNCBAg8BgE8rW2+3p43FrYq8L5bJ+/49ct00jQHTuP1y1VJMg1lHIvq5tFv0nEfSh1TKI036o1uWyTcy61W1R3GH2qiE2WKpIdRywat+/cLPpNIsaUKhr35RtbP2bcpm2OfxgxdqxsP4uYRKxTSuNWrYRD/g6ftNov262iwWFEadxldbPo9yJi0yVzfhWxbPzS+cPoV0WMLUfRoZsvc61S8v3ElxHdfHmc9Xl+TKmicc6llG9Z3Sz6TSI2XepIeByxbPzu+cPoU0U0pY6dbps8riJWLZPoOIso5R1Sdxh9q4hNlE28txz6fDmOCQ9Z37I2kyULnxbGybHXKVV0XnX+s+g7iVilHEWnrseitUwK7bv9hxz3jfG8J3/Wb6rMIlF3jlmnECBA4L0JHMXI3RemPL7tUscApXEnawxcRd++PwJLY/XVzSJP5hpajqNhX64x9ZOhA163q2K7ibFnkefZdc6xm6Po0F3j9DrJJ4Vz3bZ5fHzdftmmum5byjG27sWywQae/zzajR271H7sfOqecauoH1t+HB1Kc8q6g5HJqmh/HNGXb2j9LHKs+pyMru+UXEPmGzp2qd0nrYylXNPW+TG7af9VRGnMsXWvIs/+mME7bbPvYcTYcUvtn3dylw4nGxrruJS8VVf1jFO32ozZraJxjlla95i6WeSYRIwpdTQujVEtSFJqv0pd3xgvV5jTgul+61Q6deebdXdRqhikO3YeN6WKnZxLqc3QuhdNspHbqmfcTJM/y1/2nO/Oq452Q8qmXqsOY7Cc3zqlis6ziO5axh5njsy1Tsm1fB4xduxS+xcrTmRaGP+4leuwcL40fl/di1auZbt9OcbWV8sGcp4AAQKPRGAW6+i+Rh631pa/Z74qtMk+z1rtVtmdRqfu2Hm8Tsm/F0o5x9bNIk+1zkQ20LeOHKV5V9e5P+k5X+pTqptF/ybXdcqVN+/j77Sce2ldq9SNXfgn0aHv52LM+MeRpxo7+HX70jhNrsNoUzrfrZtc51q22dTjO4uBqmWDDTifOTJXdz1jj1eZz1Fh3MOoG1vytfXLiNKcsz7PjymfROP3/ZzszvdFVJTWN7RuFv2r66R1bEv9mvPXzQZtss9xRCnfKnWfDBq1v9FBnJptYD6Zo4pYVo6jwcUGYrJkoGlhjBx71bKp16H8+apGTuIo2nfNFq1lUmjf7T/kuG+MfH0o/bz3tY/mo8qzaF2a32RUFo0JECCwYYGjyFd6cdrwMN9KV/eMO/lWy2EV+Yu/9CJeWtvQuk+GDb2xP4AmA8fLZlXELGLoWoa0exH5xpaj6NDNPY26KmLo43EcbZeVTf1h157r58sGXXL+MM638627/2LJeO3Tdc/YVbvRgP1PevLkWiYD+rebVHEwi8i+m4oXkWudssnXhcyVpbTG6eWZcf85jOabcmryHI+bwk3r/dj7csPzeX6Tvbwz2dB4y9Zc9YxTR/3Yks+BWUTjvYntixGTqHvGrhbk2MQcM0ffGHWcK40xjfp1Sx0Jbiv3kLlVPeM3fWc950tzXlT3okk4Ylv1jJ0p8nfbovHa5+rssKTk/Np91t2fRb5qyZh9p7Nf9l93Dk3/deey6dfNF30LX1A/LXgcX7fPfBcbiGWv59fDbWSsnG/VJLQlQIDAIxeYxfq6r9PHnTXXhTbZ56uIKmLVMo2O3bHzeNVyGB1L+Vatm0W+/Nv7fZU6Bi7NvYr6jNK5sXW5xsy1TnkRnceOu6j90DlVGxx3zPrf13q7cywZpsmY+U26SQvHY/KV5tSty9eNZ4VxhlZV0TBzdPOuejyLXJlzaDmKht2xDod2vm63H9u+v+GzPs+PKS+icXdO6xzPIl81ZgKFtpua0/F17jq2pTVV1+eHbrJ9rq+Ua526T4ZOoNMuf8fc9fP5eEPrn3TW0j2cRkXXNMdepbyITt1c6xzPIl81YiJH0bY73vGC/pNC+27/IceLxnjVM8bY14/SMg57clelxuoIPGSBpw958ub+IAXyF3++uPe9WJ/FudOIP43I/SzZ9ncj6oi+chQn/iziJGJR+WmcnHcaVHFcd+ry8KhQ11TNm50l2yrO53pz21dO4kR7vdmuWW+uvVSmUfnnES9LJ0fW5S+90jhnUT+PyG2W6vK/i/+TbfIXdG77ykmcKK33IOqriFJ5HpX7EZ+WTi6pexHnJwvanMS5fF7kOucRWXIuGZ/kQaFMo+5nEScRd1FyHkc9A6VJ37lSlyoqjyNy21dO4kTpMaqjPh+HUplG5Tzii4ixpYoOOae+3JnvJKI0p3ycqoh2yefg77Ur1th/EX0nC/qfxbnTiJxbbptSxc4PI+qIUqmj8nnEy9LJBXU5n4Oe82dR/zoi5zKPyOP9iCoi5/IsolQ+j8qTiNPSyaibRxxFdMukWxHHJxHziFKZlypvoa6KnMcRufa+kmv9WcRZq8GQ1915tF/lOd4apnf3qHCmjrqqUz+P45NOXfuwvaZ2ffbJc/vtytj/JGLaqRt7mDlK5ahUecd1L2K8qjDmSdSVXlPqqO8aRdVlmV5vP7verrOpo/PzngTzqM/IknOpcmdJyXVOF7Q5i3OvI/6s1SZz/zDioFXX3q3i4DgiX0+z/9BSRcPsl9tSyVxfRJxG5H7GfsRBRM6njuiWKipWmUvmXTSXHOf0Oto2+XqQ86kiSmV6XflZ6eTIumfRftrpcxbHOa/2c3Q/jtPnIKKvLHs9b/odNTutbR37Ves4d+cRJxF9JeepECBAgMCVwElsfhLx46vDm//m6/dhxEc3Ne9vJ39PTBYMn6/rryP+rNUm55+/F+tWXXu3ioPmd/S8feIe7Oe8uuU0KjLaa2zWl2stlSoq13kMX0T/aURfOYsTryPac8q5/DDiIKJUqqh8FZHPq+zfV/LcUeHks6jb79SfxnHGumXIek9ikPw7pyk5l+ZxaOra2yoOjiNyvfOIdUoVnac9CeZRn9GUg2ZnwXboevPxPbvOs2y9eb55fE+u+wzdZN+0ym2pnEXlFxGnEbmfsR9xEPHDiDqiW6qoyJxj3xd084w5PozGOaduOY2KZc/7bp8XUTHtVraOz2L/JOIun5ND5zTkeVPH3D+P+GnEuqWKBPlY57avpNfriPTK/Sz7EVXEj663sflWeRk1P4uYf+tMf0UVp/JnIfOXyjwqc92nEbmfJdseRHwSUUV0SxUVhxEfdU+0jjPnvHWcu5kzo13O4uB1u6KzP+8c39bhJp9PzRyr2Mnnwu9F5Do3XeaR8KiTdD+On3Xq8jCN++YwzwY95SdRX8o3ifqXPX2GVpfynkTn+dAE2hEgQOA2BI4i6UUhbmOsds66MGbOYxIxplTReBZRWsNXUT+N2I/oK3nuecSiHIv69+WdxInSnPraj6lfNNdpJFo230m0WZSjivNDy1E07K7zy05d8zhUUV8qi+ab5/rmmuO+jFjUP8erIxbleJ6NRpQq2nbX3BwPmU/2P+rJkfNctp5ocrmmZsz2tsqTA8on0abdr70/GdC/22TWk6957JetKcfsy5FzO4gYU3K8RflexvlV5nQY/XJNOad2TON4aJlEw3bf9v5QrypyHPXkyRzL1hZNbkode+05tPeHOFXR/1VPjuOoH1va4zf7k7FJWu2r2G/ytLd1q82y3SoazCLa/dv7h3Eu2ywqkzjZlyMfsypiWamjQXvcZr9a1rFz/iiOm77N9rjTZszhtJAv89YR65RZdG7m12yP10k4sm9VGD/n8bxQP426ZT93k2gzi2jW0t3WcW5oqaJht38ef9mpP47jOqJvbn310eXy77HSGFnX5M12faWKE0cRfTk+j3NjymE0LuXKn5/JgETPos0sopRjOqB/u0nfXDJ3njtoNy7sT6JuFpHtS5FzHVqm0bCbI3NnNPVpNI1Y9HhXcf5VRNOnuz2Oc6uUo+i0qVyrjK8PAQIE7rPALCY35DUyX79LbbPv8xUXOI1+3bHzeGx5ER1KebLuy4g6YlGp4uRRRF+OWZxb9PsrTt9KqSNraU7d35WH0a6KWFQmcTLXUcqXdXXE2PLj6NCX7zjO1UsSVnH+KKIvx+dxbpUyi07dnNNVEnX6PCvkbcbJMetO++5hFRVHEU2f7vY4zo0p3f55PIto1+fzfxKxyvM3+7VztfdznDy/qFRxchrxVUS7b7Of9VXEmJLPiaZ/e5u5JgMS1dFmFtHu2+xPo35IOYpGTZ9mezik43WbbNv0a29nUT/2cbpvz8lc4iSiva72fq4xzy9aZxXnjyLa/XL/sFCX9VXE0NKXI/N8GVEPSJTms4js043jAf3bTQ4LOTLnLKKOWFYm0SDbZp9uPI+6MWUajbs5Mvc6ZRqduzmPRyacFHI0OfPnPte5yvOpyfEq+g8pR9Go6dNsj4d0bLWpCjkyVx2xakmDZj7Nduy8umNPCjkzd9YrBAgQeK8CRzF682LX3t72pOqecScjBz7syZN/hFQjcmXbWUTboNnPMcaWSXRo+re3Y/N027/oyTuL+qrbeMFxts1f2O25NfvHC/p1Tx315GhyjX0cuvkPe/LPor7uNl5yPI3zzby624Mlfdun++aU+ceUaTTuziOPs35ZqaNBqW+1rGOc/6Snb+abRIwtL6JDaS6zqK9GJMu2m3hO5pCfR5Tm9FXU1xFDSxUN++bUzj8dmjDapUu7b7Of9VXEmPIyGjf929vpiCTHPTkmI3Jk07651CPztNfR7E9G5mg3r+KgydPe1u1GS/YPe3Ks8nya9eQ6XjKHPF1HtNfQ7FdRP6YcReOmb7M9HpOg07Yq5Mu804hVSx0dm7m1t5NVE67Qr4o+7bFL+7NoU0cMLVU0/DKiL9eiN+HtMTJPKUe77nm7w8j9zJ9ra+dr9qdRP6ZMonH+rDT929s66oeUKhq1+zX7s6jPc0NLFQ2zT9O/2eb8htpPCv0zz9jXg+hy+TPSzKG9HTOfaeRp9+3ufxnnq4ihZRoNuzma44OhSVrtjgr5jlvn7RIgQGCbBWax+OY1ttn2vUbWhbbZJ39nVBFjyzQ6NGO2t2PyVD05Mt/LMYmibf6OmUW059Lsfz4y1yaa1z1zaeaU7tlmaKmiYd/6jocmuW6XuXL8Zi7t7TTqx5Tn0bjdv71fj0l03XZWyDddIU+7SxUHpbw513ye7UcMLXU07LObDE0S7dpOpf3piFzdplVU9K33VZwbs95FuY4j19CSY5bccp7V0CTXbUtry9xD1nUU7S46cRjHQ0q26/bN41lEFTGmVNE4+5XyvYz6IWuJZpflIP5bss3ck8sWw/5TRbO+OX0Z58bOqS9Xe8055pAyiUbtfu39sV5V5Oqb29A1Zo72HJr9zJvnhpYqGpbmko/nmDKNxs0cmm3mXadMo3OTq9kej0hYRducQ9O3vX0V9UOto2nxf8Jo8tXZYEk5ivNN+2Z7vKRP93RVyJG56ohVSz53m/m0t2NsumOnbTtX7o99PnVzOiZAgMBGBI4iS/cFKo9vu9QxQGncyYiB+3J8GTlWedHOPrOI0rxyrDFlEo1Lecbk6LatenLmnPPcKuU4OpXmWQ9MdtTTP3Ou+jg0Q1c9ufMXaJ5bpfT9kk+HoWUWDbtmh0M7d9rluN1cs06b0mFd6Jd5qohF5ZM42R2vOZ4s6thzror6pn97O4v6PLdKOY5O7VzNfj0wWdXTP/McDMzRbdY3p2Zu026HnuNJ1Dd92ttZ1FcRY0vfa1bmG1Kyf3sezf7hkM6dNn1zed1pt+ywmUN7O1nWacH5Ks61czX79YI+7VNVHDR92ttZ1Oe5saWKDtm3navZr6N+UanjZNO2va0WdSqcO4q6dv/cPy60G1OV/bs513mzc1jIl/nzeXZXpYqBumtqH6/6uyjXkL8f27ma/WnUDylVNGr6lLaTIUkWtDnsyT9d0GfRqTpOluZ5vKhT69yznv51q83Q3YOeXM8HJpgV+mddNbB/t9nLqCjZTLsNe46zXal/1q06r+OenDnW2HIUHbrzy/wKAQIECJT/Jlz0Gtn3O2NRnz7naZzovj7n8ZjyKhqXckzHJGm1rWI//74q5ayj/i5LHYOV5tHU5d8TY0v2afp3t/sjkh325JmOyNFu+qwn33G70cD9WSHXdGDfvmafF3Km37Svw5L6fBxKz7Oc+9CS4/fF86FJetod9uTOn7dVShWdcm2l+dZRP6T0PUeyfmypokNpLtMBiY4KfdNrWck2pTFnUV8t61w4fx+fky9inqU1fhn1+4U1LKs6iAaln5P2GNWyJNfncw7tfs3+dGD/brM6Kpoc7e3zbsOe42zX7tfsVz3tF1XXcbLp394+W9Spc25ayDHrtBl7WMp5PCLJYbRtr6fZPxqRo920z3zInHLMZvxmO6Rfe/yqkCNz1RGrljo6NvNpb6crJtzvyXe4Yj7dCBAgsFGBo8jWfrFr9jc6SCFZ3TPupNC2r+q4kGMWdVVfhwH12bf0h9LLAX3bTSZx0Fi2t+02Y/cPCznXXW/+kiqt93jg5I4Kc8r1rjuvHP6wJ/ckT65Rcm05x27UA3L2/VIf0reUvorKnEc+BocRk4gcY1mpo0F3/nlcRfSVT+JEqU/WTfo6Lak/jPPdnLOoq5b0W3R63edkaU45x+eLBl1yrm9OzdqnS/q3Tx/EwVFEOjX9q9hfteS6mjztbc55WamjQbtPs59zXKXU0SlzzCJeRtQRQ+YRzW5KM4f2dnJzdvxOFV3auZr9emCq457+k4H9S83qqGzm0d5OS41bdXXst9s3+1WrzZDdo2jU9G22x0M6LmjzvJAzc9cL+iw6NYuTzdya7eGiDrdwrirMoZlLbicRq5YqOpZ+92bdkJ+Z7N+eS3t/GufWKVV0budr9qfrJI2+2b/J1d7WUb+sTKNBu0/uz5Z1WnA+X58yx3HENKKOGFIm0Sj7daOOunVKzqObM58LQ8o0GnX7NscHQxIU2lQ9OXOeY8tRdGjm02xXyTN2XO0JECDwEARmMcnmtbHZLnuN/LLQJ/vm32JjyjQaN2O2t0Nz1D39Z0MT9LR71pP3uKf9bVXXPfNIq2nEqmUaHdvezf5kYMKqp/90YP++Zs3fRs18mm3d16Gnfhb1Td9mO+1pO6S6ikZNnvb2cEjnBW3y56Wdr9nP59+Q0rTvbtNxnVJF527OPJ5F7EesWg6iYynv8cCE00L/oX+rloZo8uX4uV9HDClH0ai7jsMlHfN8t08ezyKqiLGlig6lfIdjE3XaP+/J+6zTrnSYz41cT3deWVdFrFr65tSMU41IPIm2ryKavjm3dUr2b3I128OBCY8KfY8H9i01y745h9ym2UHEmDKNxtm/HbMxCQptp518mfu40K5UVUVley7N/izq87m2asnxm1zt7bKcR4V+mWtMqaJxe8xmvx6TpNA259HkarZZt0qZRKcmR3tbr5JMHwIECGxa4CgStl+cmv1Z1N92NGO1t5MYd0jJX8rtfs3+ZEjnJW2mcb7J12zzD+Rlv9jaaSeFHJlr1VJFx2Yu7e1k1YStftPYb+ds9oes96in7yTq1ylVdG7m0d4erpP0um/fc+d4QO4q2rTn0+zXA/r2Ncn5jC11dGjGbm+rnkR97bPvpKfPsuoqGrTHbvYnyzoOOD+NNk2+9nbIczJ/Vtt9cn8WsW6ZRoJu3uY4z61S6ug0WaVjq08V+8082tshz6u6p2/mXLVUq3a87tdeQ7M/WSNnFX2bPO1tPSBnX9/DAX2XNTmOBu355H7WLSp1nOz2yeMqYkw5isbdPMvGXpY/fzZLP3svl3UsnH8Wdd355XFdaHubVVUkL80j62YbGHjak38yIHfV03cT8zos5N5E3nWeI0eFOR1H3aqlio45n7ElHS46cTg2SaF93cnZjJH1y8o0GjTt29vjZR2XnM/+7Xy5nz/jY8tRdOjmydwKAQIECJT/0WfZa2QVcKW/ufK19mAE6jTadl+f83hoOYyGpf7V0AQL2h335F7ld/eCYRaeqnvmMFvYa/nJqifvdHnXyxYl93XnlInX+TutPfWcy0Unpu0GI/cn0b6bL8eoItYtx5GgmzvrhpRuvzzOn8tqSOcFbQ7jXCn3ZEGfoade9uSuByQo9T0e0K+vST7fMsaWo+jQ9UmzvpLnuu3zeBZRRaxSJtGpm3OdfO05HBdyZ92yMokG3TnlcdavW0pzasaqVkiefSYRuV2nlJ6Ts4EJS2s6HNi31KyKylWez02uaew0ps121pxccVvKeTww12G0a+bR3tYD+/c1y/7tfM3+pK/Ddf1RbJu2zfZ4SZ/u6aqQI3PVEeuUaXRu5tTeHqyQNNfUzpH7sxXy6EKAAIFbETiKrN0Xqfd5PBm4ysPCvI8H9l3WLH/5lwzqZR1b5yexX8rRajJqt5RvNipDf+Ncb+mDiOf9XW7OHMVeaZ3VTYvVdnLsUt56tXTf6nVcyJ8GabGoVHGyNK9nizrdwrm6Zx5VYayDqCs9vrmOSaH90Krs27WYDe28pF0+DqU5P1/SLx+H7pzWXWczZN+cMv+0afQetjmvVddc9/Q9eA/raIZcdS1N/+62iopSzrrbsHD8sqdvVWg7tqqODtNOPI/jRaWOk6W1VIs6Fc4dRV03z3Gh3diqzNHNmz/HY8thdOjmmY1NsoH2VWEezbwmG8ifP7ul17njAbmraNPMpb09HNB3WZNZIfdkWaeB56fRrj3f3B/yHDkq9Mt53mWpY7Du3PO4ithEOY4k3fz5GrSsTKNBt18eTyLWKfl6VMqbz9sx5Sgad/Mcj0mgLQECBB6xwCzWtsprZN9rdOYb+jo9LYydcxlScozuvPP4cEjnAW3qaFPKPx3Qd1NN6khUmsMm1jgr5B6at/S34yTybaJMI0l3zUP+TmuPXVpb5l21bDpfex6ln6Nc75Cfoa5THh+2k6+4X1pv1m2i9P3cvhyQPNvkGtsx9rkxYJilTY46c8j5HPb0yvr2fJv9WdRXPX2GVGf/JleznQ7pOKDN80LuIc/J40K/nOcmSmlOzbqrTQywYo7SvIau+SjGbNbQbL9ccR6b6DYtzGfoWvrGL+U87mvcqc+xG5dmO7RvJ9W3DvNxm3aijuNF5ShONvNotmPnUxVyZK46Yp2yH52bObW305FJqw3lGTms5gTer8De+x3e6FsiUBfW+UWhbpWqs+h0Uuj4rFB3V1WfFAba5Hp/Wsj/u4W6IVWn0Wg+pOGCNj8qnMucJ4X6Vao+K3TKX/51ob5dlc+NUvlxqfIe1B3EHI4jcm3d8mlUHHUrRxzfx+dk6XmTj9k662xIMs+mfuaanJvY5rxWLfOejqXHtqfpo67+YWF1J1E3L9SPrTqJDtNOvIzjh1xWfV1tr3k/Dibtiuv9k0Ld+6rKn7mjDQyeeUqvKQdr5C79Lh+Tro7GVadDzvN1p27Vw6NCx/2oqwr17arT9sH1fhXb+nr/LjbPCoOk97xQv0rVzwqdSr/TCs2KVSWzYsOeynlPfT5eCgECBAi8X4GXMfxJYQpV1L0o1G+6qu5JuO7fIU3ak9jJ6JbS3+bdNrd9vIk1ln5HVwMmXkeb7u/hs6g7ithEOSkkyfGqQv1dVB30jH20ocFLeXK9Oe4q5U9X6dTq07fez1pt1tk9i84nhQRD3vvPC/3Sqi7U34eqfB2cFCYyj7qPInK7Sul7jI5WSVboU8qTzjnuolIXTp4U6lapOopOZ6t0vOU+pTlVA8cs9U3jjG0vdQBUBYSfFOpWqXoZnaadOInjh1ryuXRSmPyQ19V2t7p90No/au3bJfDoBFxE8Oge0nu3oCpmlNEtJ92KNY5LbwCqNfKt27UuJHhdqFu16rTQsS7UDanKX6LrlrqQYBNv2Ju0J7FTmmfdNOjZZp+Twrk66vKNyn0qBzGZ44j9wqQ+jbqjQv2YqrrQ+HWhbtWq00LHulDXrso1d8vPuhVrHJ+s0fe2ulZrJJ5H37NC/+dRd9+ez4Vp3mrVfmQvPZ++uNVRH3byk5h+6fn0bMSy+tp+NiLHbTe97deU/VjAwYqLOFuxX9Ot5J/rXTdvk3/ek6tuGvRsT3vqD6O+6jm36eofFhK+LtStWlVaYxXJ9ldMWMo3JtXZmMbaEiBAgMCdC3waI5Zeq59HfX3Lsynlz7m83uC4pb+3ctz9DY6xSqpc57rlz1dM8KzQr+RUaDao6iRaldZ3MKj35huVxs05zjc0VK61lKs07pAhS7mG9Gva1M1OZ3vSOV7n8ItC5/2oqwr17aqT9kFr/y7/Fm8Nu3A3P8eYFlrMo+6jiNyuWkrPjZNINl81YaffKs/JupOjOSw91s25Mduc08mYDnfUtvTebOjQr3savor6qufctlSXnuO59j6zbXFZtM7SBRZVdKgXdeqc+6RznIcnEfMIhcCjFXARwaN9aO/Nwkq/1E5jdvMNzrCU63c3mH9MqtJ6s3+ueVPlrJCoKtTdRVXdM8jrnvpVq0t+Qx7jvosZpjGRWcQk4n2Xg5jAccR+YSKfRt1RoX5MVeYvlZJpqd2QurNCo6pQ164qzeuk3WDN/U3mWnMql49tHUnyjc46pfQHb+abRuRzqI7YxlJ6LqXDJp/jj9G19GFFviHaH7jYHxXapfm8UP++qk42OHBfrr7n3waHLqYq/Q7c9HP+rDByVahrV53Ewbxdcb1fxTZ/7x5G5P5tltJjskmbvlz7KyxqvkIfXQgQIEDgYQnMY7qf9Uw5fy+u8vujJ923qu/i74WTb416VVH11N9V9fyuBiqMU3I/KbRbp+qs0Lkq1N1F1UFhkD8t1K1TNS90rgp1Q6pKdkP6NW2qZqe1PY39eet43d2TngR1T31TnfMora+K+rv6W7yZy6Ltizg5LTSYR91HEbldpxwUOm/6OZnW3VJ1K1rHpTnl6VKeVrdRu5te46jBO4334zgf50mnfsxh2pwVOlRRd5+ez4Up3nrVDwsjnBTqVH0jkD6l59Ozb5os3KvibF1oUfpsrdBMFYGHK/D04U7dzFsCf6u1fxu7dSQ9XDHxQU+/zLmp8jcLifYLdXdRVRUGmUddXahftapvbVUknK+adMV+fXM5XTFfX7f8Q7junKw6x6XDo6j8cUQV0S1VVOTz+kXEScTPrrfz2N5VOYiBjiNKjp9F/VHEuqUqJJhHXV2oX7WqNP/MVUXMI7ql6lZcH2/yeXMWOTP65tYzhZWrq+jZjnxdyrEPrutjs3Z5GRny+VxaUx31GfOIk4ifRpxGzCMee0njUsn1K/0Cr+NUPp/aZT8O0vOkXVnYr6LuWaH+J4W691k13+DgZ5ErY7+Ts+oc39Vhdx7NuHWzs4Ftrrdb8rVtWfmDaPCqp9Ek6jNOI04imteq0lhxenQ56OlRRf1+z7mx1ZmrVKqonJdOqCNAgACBrRd4GQI/jHjWkaji+DDi4079pg6rQqI/LdStU3Xa0/kg6vvO9XR5NNX7hZVkXV2o32RVtclkI3L9bqHtb0ZdXajfZFWO8T5Kab1/tuGJzCPfWUT3uVRF3bKSf4vn60qpTKIy4zTiJGLTf4tHyqXlRbSYFlrNo+6jiNyuW0qP0aafk93HJue86DlZaj+PPmfZcUNlvqE8Q9LkejIOIqqIvxnRHOc269YtZ5Hgs4jPexJNoj7jJOJn19vc34ZSFRa56d/vhSEedFU+n76I+HFnFZ/E8fNOXemwLlVG3eueetUEHo2Aiwgex0M5v+VlrJN/vzC3g6g7LtRvsqo07ibz9+WqCiey7rbXWxj2Tqpybd1yFhUZmyynhWRVoa5blfP4OCL997snr4+r2E6uIzaXb1hyvObNVGnsbLdu+VEkmEb0zeskzm2iVIUkWfc+n5OlOeU0z/I/GyyZr893nWGq6FxH/DDiIKKKuI1xIu07JdfzacSrd2rfPajicHIdsbn8cOA0ts0bqnlWPrJSsp8/sjXexnJOImk+Nw46yT+J45NOXfew7lZcH5/01L+v6rMNDzyPfF2vv7nhMYam684j+724jqE5bqvd60j8k4jum/P2eDn/jOfXlSexPY1ofveeXdeP3ez3dFj0utnTZXR1NbqHDgQIECCwTQL5d/xBRNVZ9LM4zr+/vujUb+KwKiSZF+rWqcrf2Rn7nSRV53ibDg8Ki72Lv9N+szDuXVR1H/scc3IduX9bpbqtxEvylsY9XdJnldPz6NR9Lg1573EU/aqIfM71lcyb8fy6wUlsTyPW/Vv8Ol1xU0Xt5xHNmKVGZ6XKFer2C30mUZdxm6VakLx0br6g/SqnTlbpNKBPeh5cxw+vt1Vs76K8jEFy7E8WDFbHuYzmOX8S+z+LyO1pxFnEYytVYUHzQp2qdwVex2H3c4r9qKsjTiIWlW6/bHsU8RifX7k2hcCNwN7Nnh0CtyOQL8TbVKz3dn559v1CHuJ9Gk/AjyLmA5+IVbR7FnEY8WXE7Hq/iu0my8tItmj+r+J8tYEBF42xgfQbTXG20WzDH/Mhw6ZjviHJ50PznJjE/kHEXRq/jvE+jphHDCk5v0nEYUTOO5/Tn0dUEY+llPznj2Vxt7yO/JCoW55FRcm03e6T9sH1fuaaF+rfZ9Wm53P2PhfzwMZ+HvP9bMSc62ibfY4jvrreTmK77LkYTd4pY9u/09kBAQIECBC4RYGzyP1pT/6XUV/1nNt09XzTCSNfrk3ZXgF/f93OY7/Oz9U0prSJv8WrDS6tjlzPF+Sr4lx+VrGJch+fk7+5iYXdcY46xsv3Z/lZUm7z8XkWUUXcZZnEYD8ZMWAdbV9E5Jzb7y3j8NGU0nP87NGs7vYWchKpS075vF5Uqjh5UGjwRaFOFYFHJ+Aigkf3kFoQgUcpUPoFnwvdH7ja02j3UcQqv9yr6DeJyD+aX0VUEXdRcm053tA13sWctnGM9M83H/n4TyOqiFXLyaodO/1ex/Gqz+eD6Ps8ItdzGFFFPPSSj5GymsDLQrf0fFaob6qq2Kmbg9b2qLVvl0AKTCPytWoeMbbU0SFfo76MeBExtHg9GCqlHQECBAi8D4GTGLT0DyH5+yt/722yVD3Jznrq16meFzpv6+/kqmDx2Ku2bc2l9c5v4UH+s0LO3yrU9VVN48TvRcwjxpY6OuRr0nHEmL/Fo/laZRK9n6+V4apztYEcm05Rek2cb3qQDeU7iDz52GfUEaW5R/Wgcjqo1fJGz6PJpxHz5U2/1aKOmsOIWcQkQtlugdLfYZ8sIXlWOD+PupNCvSoCj07ARQSP7iG1IAIEegTmUT+J+FsRn0Ws8ods/tGQf3S+iNhkybmcFRIeRN3nhXpVdyNQxTD5D1jTiP2IMSUfz3nEUcQfRHx0HbHZSJlHlklEPp+/iJhHjC2T6JDrez62o/aPRiCfpyf/f3t3fN04cuV7fHfP+3+ZwdZGYDoClyOwHMHAEYw2AmEiaDkCYiLo3ghIR9ByBOBEMNoI/H63l7WurrkFFgBSoqRvnXMHwEXVraoPIZKS++1zdjP1C1R0+h+V++LkSSFwEIG9T9l7oL1X2TM3pwV17hX22RsUNAQQQAABBN66QK8NHJ1NROUu+b289pm7ceZem/Jq1uZfO9etj/+I+/5oe36p/f6787D/6uSmUk+6eavfxb9UFv5J+Vi515p+qdeodT3W7xen85x/FOIM/01q85vM/IT9LcD+ThTnD/32u549c/Y/0v5FYfuz80u1QYXsef6zovb86Fa1Bd3ZKfjdskr0IW4Mzi7tZyc6+ZT6MZ1kx0N2zikC71rg/73r3bG5WxVIXyhudX2XXtezCv7XpYs69Wye99rsw9xrRy95Jmdj+lMEHeMpfqfjVtHS+lOnn1o6n+nzs+53p7Avs2XrlPi74rG8seLanpWP8EzWnptWur06hjOdj7p/UPyiOCqeFOZr5y/RjpqkO0201dEiKv6gCIpzbaMO9ku6tcf/Pby5/z47Kw5OjpQvYO9Bsbhlz5E9G57tD0Vfuzw4uVtI2R4+Uhu02b9decPHhfUPGmdhLSrsGfvT6djyOgX1tffkPyqOilrznlnr+5fagAvmDxesRSkEEEAAgfcrYJ9V9rlkn2tle1Dii+JY3lhwXftM3CyodW7INWqem/NW79fcf9KCj1de9LXr15Zvey6fgS/K/XdtwIXyNesLla+W8fYbqr2X3yhNl1f63+/hh1OBqONW8QeFnbfME9Tvq8K+iz8p1jZ7DxwU9j4YFWX7rMTvFcfyRuP1s/qV+xqU+1vj+KXdbN457d/ndG7oW+65Ych3XaKuhu8yv72wPT4p7O+UxyLm7l/DF7UvGmVhLZ7CnuetYqM410HSRZsAADPhSURBVII62LNnz/NR8RbbsxZd7rW8fov7eok1HzXJQREVeftBF4c8cTrf6hic/E9OjhQC71KAf0TwLl/Wm9qUfah5bfCS7yB3dPZgBoOTfw+po7OJ4OTWpryatWdrzlxHdR5OocO3L2BRR4vfnY46uK1X1r40f3HvtiX/qm73p66DjlvFj6fr/PBJF0+KQ55sPD86/Z6VG5z8S6WOlYls/7V7lSGT6TB5d/rmg27XxpufvXaD4qi4lWbPiMVwWlDQ0UyjIv1CpVO3rXnG3IIvmDw6c22cHClf4IvS9vrnZnbeKR4VeQu6iHnidG4/D7fYghb1dMGFbZ1avzi5l0gdNUkoJvqbrocid4uXBy3KIj1f5mrxp9Mx6Oi1oKT9sef3imeF145eUrmD4qigIYAAAgggcAsCBy3iJ4X9zpG3jS4+K+yz7hLNPi+tZt5CfnGhc69m7bP6QlPedBnP/agVDze96uWL817rv3+w/ZY/Z8s1/znSq/k//7y9+OygkRaPCmvbU0Qd7e8GQeE1W096f3r2OjTkbNyfFYdTXzv/qgin63RIc/1RiaVzpVrp+ItOhnTxCkdvH+HC6zC3NW03Mfigez8pnhTeXpR+lXbQrBapbXUSFfYs23lQeC0oafu1Z+wtNnsNNsXCQ3HNZV3gv3UrFrfvdP1fCrPNW5dfnM6fdDw6eVIIvEuBf3uXu2JTtyRwdBZTfsg5Xd5sqvygsY0ExXvds7df2/PW/nPB9jun1tHJrU3Zfr4o7hX2RfI/FT8rau1T7UZD/if1sXnyZteHPJGd25fbkF23nj47Ha3Oxsm/VMpbk80dLrgA29/SPdq4rrKWvyr/n4pecVTccjtqcV8U94rfK2zdPytq7aF248bzz8767DUMTp7UbwXMz3su/vTbrv9y5+SOyj05+VtIhQsuwp4pi7I9l4kXuvbm3b7Q3Jee5kkFB8WfFfY+9UfFUeG1oOS9d+OU81zs1mZiDLcQQAABBBB4DYFek9pnYNm2Sqz5PTOvd8wvTuf/4eTWpGy9XvP25vV7j7mjs6mNk3svKW+/l37ObsnK26/3N6u1a/Z+try5185jP6uDolOs/S6uEtX2rDv2Pf+Q9bCc/Q5gx7JtlVj6N4q/lcV0/drP5NFZU1Bu4+SXpsxsaes0MDiD7bWx1y29dnZ9y+1Ji3tUpN8t/6Lzo8JrUck778YbyNk+yxbKBNdVgUF3nou7G11vi5xden8b+6vTjxQC71aAf0Twbl/am9lY7UPN3pjfY/P2a/sM73Gz2lNtv/HC+9069X5xcpdOHVWwU/xe8awoW1AilsnG66HSz77oHp17QbnPio1zbypVe43C1KAr3zPLozPHJX/x3jr1W1M2NjidB+XuFbb+t9iOWnSnsF+ivBaVDN6NG88dKuuLlTzp3wp8+W3q23vbpsj/UFzb5U9O7lZSL/Ge8vRKm/27M29wcm8xddCi/1NR+8X8DxObOuqe9x4dJ8ZwCwEEEEAAgdcSsN/9vM+te+XjBRblfV+4RN18adv8Ijs/Zucf7dRzv+T30lvz/MVZUO25cLq+udRL7DdWVJ4q+UumDypm38V/qhT1fiesdP0u/UVX3votV5vrXvcs5rZnZ8DWyb1k6liZ7JLrWvM+86fK+v6o/KFy7y2kBy3SnuefK4v9sZK/9fQvzgKnfk92un/olL1HPDkCD0Uu6joUObs8ODlSCLxbAf4Rwbt9aW9mY94bsi3u7mZWeNmFHFXOPojK9p73eyw3q+val0+n69lUUI+t0+vg5K6VelLh2v+YES88qT0/tT8mmUP5hebc9Ed1sJpluysTL3z9N2e+S65p6S+2tixz9tpPXnJGbjOj7zW7Dipe20u85sRXqn1U3WenNr9AOSiV1EF5z7DL+gedez8bh6zPrZ3eXXBBtfeUpwvOMaeUN68985s5RW68773W5+0zKj+1T2/Mmj+o3TgTy0MAAQQQeMMCR6299r18p3tTn3ct2/Y+E4MGWlyqeb/7H1Xc4qM2z/3uHWMcnL1tlds4+feQ+uJswvYanfzS1F1l4FMlf410r6IHp3BQzvZ7yfaoYrW/uX3Sve3Myb44/a3Gxsm/VOpQmShW8kvSa2oFZ8L/Vu7Jyc9JhTmdr9j3XrWfnfpbJ/cWUt7rstHC41tY/I2s0fv+Zc+DOabm/R3Ifi6OqQNHBD6CAP+I4CO8yq+7x2dNf3CW4L0JO93eZMo+TMpmf9h/r83bb/mhu2bvd5XBXyr5a6UfK4X/o5Jfk37SYO/LjNW8P4WdtzbvNXrtZ9L2WLaNErFMLrxeU8fWUbajEhZr2nbN4AuPHSr1QiV/62nvGb/Tor3Xcu5erEZwYm6dW+//V2eB+R+F7537B+WOTv5WUvbaxQstxqtzUO3nC9WfW+aLM8D2u3Xybzn1c2XxoZK39DXfDyam5RYCCCCAAAKLBB416uCMDMo9OPk5Ke/7go3v5hSZ6Bt07865f3ByHynluW8EEN8pwqGyr66Sf+vpJ23g2dnEj05uaSr/PSzVOOjEmzfdv8bR+15t84QrTHavmmbrtc9KBu9GJVer01X6v0TaXjtvXZd6bu5Uf7NiI8EZ663X6TaZ+t3k3Ze7+aypvN8tzSy83DIuNtOXSqW7Sn5uOmhAGWb1ntpBm3kuNmR7zA1jcd8uBydHCoF3LcA/InjXL+/NbM770hm1unChFXaqEy9U6xJlBqdIVM7iEq1TkXiJQheq8cWpYx+6905+Scr7Qv2kQsfGYraWO8XaP8A8q8ZR8VLtURN5/6Oezf9JsbWTxjY4/aJyFpdonYrEmYWGSv+1r5OV7RRBcWvthwssaKManeJ+Za3jyvG3NnxwFmRWa52srP28jUV8thvvrH1x9hOVM0drf/rfw3f//fm7q9u8uOZ7ivf95qUUjpro4Ex2if1a2aDoTkcdZregEfbzdzd75PcDnr+//L+r9Fz+XyI7GbLzdGr979PFymPU+DvF1BpWTsFwBBBAAIEPJvAX7ffZ2bN9dv3o5FtTR3U8OJ2t5iU+x7zvhzbdW/iO6LBcLHVUpSen2q18T3OW9i219Jl41uiDU3TNs1uW65QIZfKVrm2/3usblQ+Kta1TgeAUmfNzZePvFXdOnTmpY6XzppJfm/6zChydIkG5z06+lnrWjYNz87WfSe/3R7OMzlrnpi65tzT3MZ0sPAaNW/sM2tRbxb0iKta045rBNzb2Wes5OGv6QbmNk5+TCuo8OtEp91pt7Z5q6/beV83QWlQERd6OuviSJzhHAAEEbk1g0IL+4cS11xmdOW0dnaKl2Rv9r4py7fuWwWf6hKyuzfFZ0SmCorV16liuza7XfEB5+x1X1tTwb/uyOra+ufsdTuNsbIq9zi/RRhVJNdPR1hdWFn9w6lr97kzdoPufFF8VaT12jIo1La+Vzh/PFIy6n/rmx3BmXLpd7iHVGNWhtYbVstcjjU1Hq7HmObe6QWF1rKbN8VnRKYLiXNurg40rI54beOb+6NRMc/Rnxtrte0Xqn462tzUtaHCqVR67M4Vt7CfFqMjHrnnttkWtVNf23trSmPzYtQ52+gXl8lrpPDp9vZS9RmlMOloueJ0bczY21cqPuzPjY2VcODOuvD0okc9r51/LThe83jvz9cp5z4vZ3koLWkjplF/HFQu1n7OxUj801LU++VrSeWwYe66L/bymevlxzs9xbY6HrLY9c58UUTHV7nRzp7BnI61nPzWg4V5tj+HMWJs3rSEdbV3nxp0p++32qP+mmnud2xq33+5M/6fX7TQuHcfpIU13o1PX6gfFnDaoc1pXOtprT0MAAQQQ8L8L7C8MY5+j6f235dg6fe2z1D7b17SgwaOiXKvlXrJFTVauwa6DYm0bVKCsvW8s2jtjrZa9zmvbTgXSuuyz2l7LqJjTRnVONdJxN6dA0Tc69ayuPX9rW6cCaY2jzm2drY5pXH6MGr+2RRXIa6bzzysLbzR+dGpbLiimmpnsFL8q0nr2UwMa7tnrl2rlx+2ZsYMzbndmTLodnbFpbnvWW1tUxzQuP9qe1rZOBVLNUec7hfmfaxt1SOPyo9Wwe0ubzZ3XK89DQ2FbQznusWHcVJcHp2aaY2qc3esU9vN0yefZ9pPmz4+t9n1lvNKLW6+R+VrsfN9YLTpjbfyucXytW6cbVqeMbW3AKT84Y/ZnxpS3g1PD1tEprtGiipb7tGt7JnbOPcvREEAAgZsWGLQ6743t2ouOlXm7GRP3lRoPM2p4XfeVunde50quq9SIlf4t6V6dvNdqzhdeb55dpW7ndS5yg67LNZnfJVqnImVtu15T376ceDVH5c81+7D3xq5ZT1epafmpFnXTW0uYGpTds362Z6/GnP30lRqv+UzGyppsv0GxpD1okGeVcn1DUXv/SP3zY2wYW+vytVLT6ne1Qad87WdhzWv3oNr53tJ5PLOW/HYakx8f8w4zz4P657XSeWys01fGm/3SttPAtI78GM8UtPt5/3Qezowrbw9KpLHp+Kty9h53jXavommedNwrt3PylruVFrSQtF7vOOr+UjP7OfNq7pRvaUGdvPGxZfCZPrYnex7K+pYLiqUtaGBZ067HMwX7yrh4ZtzUbZuzXIvt71yL6lCOs+uviqXPgoZ++79q5NXd2c0zrdf9cux4ZkzL7ejUtXmCYk4b1Llcn1mv8ZozP30RQACBWxaw9+vyPXJ/hQUPzjzlvOm6dfra9wWrE1uLOP1s/2kt+bFz+l4zFVU8nz+dhwtMOji19411a+6v9T2tXLbtI1mlo31PWtNGDU618uN2RdGgsbW6Ld9R8nWk87hiPfnQvS5Szfx4n3eaef6pUrNvqGPz5utI57FhbK3L6NS0Z/hcG9QhzZ+Ou3ODsvu1vVitOb6jsw6rsc3mmnsaNKBWd9NQbK8+toYy7LVf0oIG1daT5rA+59pndUj903F/btDEfTNOdbzjxNBvtx4r48O5gRP3R6fm14n+5a3OGW97i4qlrdfA0mc/o5i3J6t3P6NG3jXowqtpuXNtUAebO4/9uUHO/Xx8Orfn4VrN1pjmSccH5UYnb881DQEEELhpgUGrS29m+fHai46VebsZE2/U175o5utO5w8z6uRd7QtWqpEfx7xTw7l9AOTj07l9gVrabL+2jlQrP9q6l7QHDcrrpPNR+dBQcFCfNCYd9w3jWrt8derbPLvWAlk/e01+VaR15scu6zd1uq+Mv58aVLkXlB8V+TrSud2balE3U9/8GKYGFfeirvOx+Xnr83SLz6Rtc1/Z26h8UMxpD+qc23jnfUNBs/LG7hvGll2s1q5SL83RlYOc67FSIzp9z6W2lVo2x5zmrcl+bsOcIllfG5dM8mPM+kydTj3jnzXQ7s9pD+qcryOdjw1FYmVsaBibd7mv1LH8NZoZpX2eO8ZrLGBhzaBx59b7VX0u9QzYXDZnSwvq5K0ttgxu6FN7RkaN3TaML7sEJWyst+Ze+alm83njrF6YGli596C8V29X6V+m95Xx9iyEsnPD9Q/q463HcqFhfK8+5fixYdy5LtGp27qmvHbtWbI8DQEEEPjoAvZ+Xb6H76+AslFNb65ybrue0zp19mrYd3f7/J7bdhrg1RvnFrpA/1hZS7hA7cGpvZ9Rt/bZak5hRp3U1cbY2H84MffzenBqWN2oWNqiBnprs+fsbkHRoDG1/e4a63nriY1jz3WzOl59y/1wbrBz/6FSb1Q+OP3L1EYJm7uM1vFlvQenltXelR2d60E565tHy7i81GMxPtWa874VJ2q81jO5razJ9vegmNPsNbffbZJN7RgaivaVOj80jC272B7HSr20xnJMeR0r422/tu+57ZMGpLnz425GoXiBGuV0vVNzX3aauI7O+LS/+4lx3q2g5KhI4/Njp/y5NqhDPsbO9+cGOfdHp4793Aen7yVSvYqU6/aubV00BBBA4OYFBq3QexO79sJjZd5u5sT24eWt33J7RVC0tKBO1r9Wq9O9Oc2+fNiHkVfvQfmgWNKiBnk1LTcq7H5LC+r0WVGr1eteSxvUqayxbxnY2Gfr1E/zjboXGuv8qH6118McWltUxzR/eXxoLaJ+d4raenYNdaL6lPPbdVDMaVM/P3avpUV18tZiuVFh91taUCd7LWq1et1rbeeeG/M/1zbq8EmRr8f2Y5Hn7LxXtLS9OpVj7dr2HRQtLarTqPDq5LlOfc61Xh3yMencns3W19/m+FFhY9L4/NgrP6c9qnM+Pp2Pyoc5hU59bUyqkR/j6X7LwfrmY/PzUffs/rnmPU95ne5cAd2PinxMOg/Kz2lRndPY8jjndZ8z535izrSGcU7BF+gbKmsu92Lrtr7nWlAH+1lP+y2Pve61tqCO5Xi7jopLtb0KeXNY7kERFC2tU6fa+8Ooe0Fxru3VweYto3W81T/3MxisU0ML6jO1n66hhnU5t55dY51e/TyXxuHVbtGpa/MExZwW1blcX7q+1vvNnPXRFwEEEHhNgVGTp/fEdNxfaUHRmSvNmR/nTm/rzcfn5w+NxaL6fZ2oE3TvpVvUhPle0nm4wEIGp/Z+Zl3rn9ZUHh90LzTW69Rv6nuNfV+Z0+yzvVyPXdscnWJpe9RAr67ldoqgaGlRnUaFV8vWGBQtzRsfWwY29pna74NqhIY61mev8NZquU7R2mp1RhUIjUXsWfqkqK1n21BncMbvGsblXWwdX506ti7bj91vaY/qVNuLrSm0FFGfqLB5vVpznkmV+JdLrCmqTrmez8p56wvKn2vm6Y213P25waf7VuNBYR61Wil/GjJ52FfqjMqHyZH/vGn9anVsLXa/tdn+ant70L3QWijr1+vc1pHHPrvfclp73a3mThEaikT1GRU2pgzLt7RBncqx+5aBRR+vjtW1dQTFpdvUs5/vp7v0xNRDAAEEriEwqGj+5pXOrzFXXjNW5u3yTo3nj5VaaS973b9XREXIYqtzy9v91Nc77nR/Ses1yKuXcl91f38K+8KwUbS0Xp1SDe9oda1PVNgewynsvFPYFwGbzxtrORvf2gZ1LOvsWwc39rPXqJwjv7b5rE9UhCzudP5JMbXX8dRfh+Zm8+Xz5+dWL61lk1W08+3p3rnxIRtXO426kc+bzkNtwER+0L00Pj+am625pfXqlI8tz+2Zsj5RYTXDKey8U3xWTL1ONn5uu9eAch359V73rU9UhCzudP5J4a3H+g2KvI6d94qWFtWpHJtf73Tf+gRFahudbBW21r0i72/nn52c5TvFuWa1R0VZM13bPZvX5re+qdl5VDwo7LVJ/cujjZ/bogaUdfJrq7k/hZ3b+qZa0M18fDqPU4Oce71yaax33Ou+rSUqQhadzmvPU6rzqD4tLapTGpMfQ8vgoo+tN6+Rn9uzb/ct7PW149oWVSCfwzvv105y4fFB9bx1RuXNpLxnuU4RFSGLTuc7hbmWY9K1Oc9pQZ3T2PwY5xQ50zfo/qjI6+fntp+dolNERcjiTucPCttXPqY8t34tbatO5dj8+rPudwrrl7egC5vjk8LWm4/Jzx91b06L6pyPL89H3bc5bW5bUziFnXeKc+ux8UHR0np18uZvGTvVJzp1bZ6gmNv2GlCuMV3b62L3Lb6ejjrQEEAAgQ8hMGqX6f0wHe398FrNPu/SPLXj3Lk3GjBO1LV7O0VUlJ+J98rtFf+YiF73XqNFTeqtK1xgMYNT2xzmtKDOo8Jbo+Xs83WnuFPk7uGUe9DR+tTGWz4q5raNBkzVtXv7U3zV8VHR0qzuqJha72fd7xRREbK40/mDwuadGn+v+63NqxNbBzf0s/2ajzeP5UbFTmF72yrCKey8U5hFbazle8WcFtT5V0WtZrK3+fMWdGFr/KSYGv+o+y1tUKdyDbuWgUWfoOvaevZF39rlRjdGRbme/Dq5RPULWZjJg2KvyPuX5/e6P6fZmr4qyjr59U73bf6tIpzCzu8Ve0Xe184tZ63M23WwGw3tUX288ZYbFTZ3VGwUqQWdRMWDwnutajXV/WyL6lFbj+V3ijvFVpG3oItOYfe9NaWave7PbYMGpPHeca/7KWzu3EqXv2m9MmUdGz+n2RyjoqyTrm0dO0WyCjq3MLd7xV6R+nrHoPstbVCncrzVntuiBpR18utR962uhZ3bHtY2q/WPMxHWTsJ4BBBA4CUEBk3ivaFde+5YmbdbOPFQqeftbU7uq+puFq7Jxo0z1nU3Y55+Rt05+7X1BkVrG9SxrL9vHTyjX+/MU84793pUzaCY24IG2Ni5853rb1/AtoqWFtXJqxdaBhd97Dm159yrNypv91tar05ejbU5W0NQLGm9Bq2dP423WtYGRcqlY69ca+vVMY1bexxVK1Tqdcq3NHvm7Nlbu5ZyfFpbyxrKPo8z1rMrBxfXoVIrFv1aLnt1Kve59tp+9lpbVEdvvtBaIOsXde7VquVa3weyKb47tfHnnrPw3YjXv7D1eB5Rebt3bj/eWC83nurp0NyCenq1YnOFto5B3Wx93lxrc73qzmn36rx2Tm/8nJ/BfL3dldZjz1VQtLZeHct9ja2DJ/pFp67NExRzW9SAco1T12vfb+auj/4IIIDAawnY+3X5fri/4mLs/dWbM1/DkulDQ918jtbzfsliLjQmqo63znCB+oNTe7+gbtCY0anlrXturlfdpW3OdzZbf2sL6ngr+/U8Y+tGGvsF9bvGfofG+ctuc15Xz6eWs+/ird/9bO1lnV250Mbr6NRKtT811gjqN07USfWWHHvVXdKCBl1qTVbH6lnz9hC+3Tn/n5bPHq9+Lddryk7h3Ve6qfXq5Y1fmxuaZv9tp6CU/R7YOn9U36nW62ZZaz81oHIvKD8qylprr+39pLUN6ljOt28dXPTzapW10/WuGLvkstOgVM877pcUZQwCCCDwGgKDJvXeyK69lliZt1sxcV+p6e2vJTeoXuuXWXV121bZ1i8Cj26FerLXrZZ9tPbZq15QzGmDOpf1rc41mn3JKOdaem2/qIQVi7Sxo2Lp/OU4e0bsWWltUR3LGnYdFEta0KDac7qfUbBXX29dS3M2d1Csab0GL50/jct/Ngenns0xp1m9VHvpcVSNcJrUq9Gd7rUc7tTJ6nl1luSsVlAsbRsNtJ/RlrltrqkWdNOrE6cGTdzrdc+rtyT3WbVsr60tqqM3T2gtUPTrde3V83KxGLvkcuq53y8peOUxQfWnLOznxrs/J2fPuc0ztwUN8OaJcws19A/qMyq8+Zbk7LPmXrGk9Rq0ZM7amLk/g+Wa7RkYL7gmqxUUc1qvzuX+rM7aFlWgrGvXQbGk9Rrk1fNycckEjEEAAQTeoMCoNZfvg/sr72PrzJmvYen0QQO9/eS155wv/a6wdP3luKiEt95QdlxwPWhMWXu/oI4NCYpRUdZbem3f0zrF2tarQOsawozJrK99f2ut3dJvybPm1Y1a16XbRgUvud9+5QJtvLf3pbm96tkeW9ugjuVcu9bBTr/eqZfq/+D091JByUu+Rjb/kmcyX5utaVSkvSw9RtVIzasR0s2Go/W9xJqG01ydjt6aTrebDn2lhle3JffYNGu901a37D24Za5zz0jv1Nkrt6QFDfqqaFnXuT62v04xpw3qXNZdupeNarXuZZyzyEpfm2/qNe0q40gjgAACNycwaEXlm7FdX7tFTeDN262c2OraG71XuzVnb/DnPpDVpbkF9WxZ07654j87Wu3WD8Da/tfsd9D8Zd0l+1CZphbUa1SUc8657jX+Ei2oyKCYM7fXd68aQTGnRXX2aoU5RYq+UddeTcs9FH2nLoNuvuYz6a2tU3JU1PY3lX8sCg5Onb7o03JpY6bmnbo3amxQpOb17dLNxmNQv0Hh1ZqTMy/7ory2WQ2r1TJ3mJjM7nk14sSYc7ds7Kjw6rbklr7n2rxe/aD80tZpYMte7pdOkI2LOvfWb7lOcWstaEHeemO20Dudt/h5dez53mS15pwGdfZqxjlFZvbt1d+eXW/e1txe44NiTYsaPCpa5/T6Lf0Z9NYdlBwU3jxzckufh96Ze1RubYsq4K0/rCjcaaytzaub5+5XzMFQBBBA4C0JeO+J+xfYgL3P5u+7+fna6fuJ2vk8tfO9xm/XLuIC46NqeGsMF6g9OLVt32tar8HeeufkbA1BcanWq1DL/N2CCW3MqGipX+uz1/ioWNK8mnFJocYx9jM7Krx5W3I2Niou0eznc81abL1Lv4sPGlvud6fcmvZZg8uaaY1hRuFOfUeFV6s1t9f4qLhE26jIo6J17rzfqHFRkbf8fjoPeYeGc+u/V6Txc4+2n9Q6nXjj0/3Wo9UZFV6t1pw9z3eKS7SgIi3r+XJmsl73y/Xvz4w5d9urWc4xdW3zb89N4twflCvrWq2lbaOBg6Ks6V1b37XtUQW82vbc0BBAAIE3IzBopd6b2bU3ECvzdhea2OrsFd7eajnrf6+4xIeEyvymdcqMitr8dm9pixpY+/I7NV+vcWv2O2h8WX+v3LVbpwlsnnLu2rV9OPeKNXvVcLcFZQeFzVGbv8xb30dFVCxpUYPKmnYdFGtar8FeXcv9MLNwVP/XeCanltnp5qio7THP79UvKso2KJH3s/NesaQFDRoUZb3adXqONeS75vXvvuvRfhHVdVB4NWu59DwHjbt0iyp47jm6m5g06J637jgxpvVWp457hVffy6XXb+n7UKzMFZRf04IGD4pR4a3bcl8Ul2hfVcSbY6nJJdZUqxEqa43FAOs3KLx9lblL/azYnGVtu46Ka7ag4veKUeHN7+Vsz3tFVFyydSpmdb05azl7/nrFNZ63oLqDwvZbm7/MW99eERRLW6+BZd1xabFsXHTq2jxBsaYFDR4UtsZy3en6Uu83moKGAAII3LSA9164f6EV2zzpfTc/XmL6oCKDYlTktafO9+obFbfSohbirTdcYIGDU9v2v7YFFegVo8Jbu5e71vc0LeFbC/rvoJj6ftTr/pIWNKhTzNmvGewVUbGmeZZxTcGGsUF95n4Pt712imu0TkWtvmdRy43q3yuWfhcfNLasvVNuTbO12LrKunZt+TlrDerfKWr1vDkst1dExTVaUNFBUZu7zD+qr7fnsp9dB8WS1mnQV4VX08vt1Tcq8tbpwuub95lzbvXmrMnmtnXdKzwvpVe1TqNHhbdHy9m9qdbrZjl2PzWg8V5Qv0Ex9Z7uzRs1ZmkbNNCrubReGhd1YiZl7fza+qxtUQXymul8t7Yw4xF46wL/+tY3wPrflYB9mEfFVvEfiqCw9nyK/9HxSXFQHBUv0YImsfXYcaM4nsLW8axY06zeVhEV+X51+W2e19ivzX2tFlTY9muR7/dZ1xZ/VzwpDoqXaFGT2FqCwtazUVg7fvvvP9dja3o+5d77wQy2iqjIXyNdvtozmdYTtIbf2ULU7PWweOlnxubeKOIp0nqCro8KW9Mvii+Kg+IlW9RkW4WtydZoYe2osPcSOz4pDoprN5t7ewo7PyqeFYfTUYdXa0Ez3ynsmF4/nb7qM2XzL2lRg8x3qzBfi6dT6LC67VUhFlUGXf+lyN3CZdAiRmchf1Tu4OSDclFhduX7v/0MHxRmaabvodk+U+T7tf1ZpPfSp9O1DldpQVXzddi1tbQOe6+yNRwUR8VLtKhJbE1BkdscdZ3eOw86t3V95Ba1+Y3CrJ5PYSYWNAQQQACB9yEQtQ17nw8K7zPR3vO/KJ4VtMsJmHmK3N2cLV7qe1q+o6iLcIq0joOuj4q1LahAvl+7tpbmsf0eFQeF5d56s71GRVDkr+9R1+m77xedv8Reg+ax9VjYWoIitaNO0noOOrfrj9KCNuq52Gti8dLP5EZzRoWtKX+djrpOr9EXndvaXqrZWqLiD4rNKXT4toajjmZka7Lzl2pBE9m6LHKnZ11b/KI4Kr4o7PrabasJwilsLpvzSXE8nevwai1qZovcSZff1vZaz5TNP7dtNMCcLez8qHhWHE5HHVa1oNGjU+GPyh2cPCkEEEAAAQQQQAABBBBA4EMKBO36H05E5W6xBS3qLa33Fg1ZEwIIIIAAAggggAACCCCAAAIIIPARBe616fLvSt4/KviINuz5gwv82wffP9tHAAEEEEAAAQQQ+F4gfn/57eqo/x6+nfEfBBBAAAEEEEAAAQQQQAABBBBAAAEE3ofAj842fnZypBBAAAEEEEAAAQQQQACBDy1g/9q6/BfYuxsWCc56bf1RQUMAAQQQQAABBBBAAAEEEEAAAQQQQMATiEqWfwOz66CgIYAAAggggAACCCCAAAIInASijm/tl6dQWbPthYYAAggggAACCCCAAAIIIIAAAggggIAnsFOy/DvY3utIDgEEEEAAAQQQQAABBBD4yAKjNl/+8vT1xkGCs2bbQ1TQEEAAAQQQQAABBBBAAAEEEEAAAQQQKAWCEuXfwOy6U9AQQAABBBBAAAEEEEAAAQROAg86vsVfnkJl3VF5GgIIIIAAAggggAACCCCAAAIIIIAAArlA0MWoKP8OZjkaAggggAACCCCAAAIIIPAhBUKx642uPynKX5zs+i388hQqa4/K0xBAAAEEEEAAAQQQQAABBBBAAAEEPqaA/c0rFFuPura/d3l/B7sv+nKJAAIIIIAAAggggAACCHwIgaBdpl+S7Bem2i9NqU9Un1tvQQtM682P8dYXzvoQQAABBBBAAAEEEEAAAQQQQAABBK4m0Kuy/a3oV8V4OuZ/O8rP7T4NAQQQQAABBBBAAAEEEPiQAp12nf+CNHXevxGhUNlTfCPrZ5kIIIAAAggggAACCCCAAAIIIIAAApcX2Kvk1N++0j37Rwbh8tNTEQEEEEAAAQQQQAABBBB4GwKftcz0C9LUsX8b2/m2yqD/enuJ3+7yHwQQQAABBBBAAAEEEEAAAQQQQACBjyYQtGHv70Vlzv4Bwfaj4bBfBBBAAAEEEEAAAQQQQCAXsF+Myl+W8mu7/9b+/38LlT1F5WkIIIAAAggggAACCCCAAAIIIIAAAh9PoNOW8795eed79QkKGgIIIIAAAggggAACCCDwYQWCdm7/lwjKf0hg1/ZLk/3jgY3irbWgBXu/CMa3thHWiwACCCCAAAIIIIAAAggggAACCCBwEYE7VbG/d5V/MxqV2ymigoYAAhMC/zpxj1sIIIAAAggggAAC71cgaGvPp3jru7S9lO297K3cF9cIIIAAAggggAACCCCAAAIIIIAAAu0C9v9oxuLYPoSeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMAbEfj/zeC6kXu4DrMAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "a81834bc",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5dc63d",
+   "metadata": {},
+   "source": [
+    "### Normal distribution of the error aka the resiudals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e239bac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from statsmodels.api import qqplot\n",
+    "\n",
+    "\n",
+    "print('#'*79)\n",
+    "print('Checking Normality')\n",
+    "# predictions\n",
+    "y_pred = model_runs_scored.predict(X)\n",
+    "\n",
+    "# the truth - the prediction\n",
+    "residuals =  y.values - y_pred.values \n",
+    "\n",
+    "# histogram\n",
+    "sns.histplot(residuals)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# qq plot\n",
+    "qqplot(residuals, line='q');\n",
+    "plt.show()\n",
+    "print('#'*79)\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6262476",
+   "metadata": {},
+   "source": [
+    "# Checking for Multicollinearity \n",
+    "\n",
+    "### Member this summary report and why i asked if it looked strange?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c87339f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "independent_variables = ['OBP', 'SLG', 'BA']\n",
+    "dependent_variable = 'RS'\n",
+    "\n",
+    "X = df[independent_variables]\n",
+    "\n",
+    "y = df[dependent_variable]\n",
+    "\n",
+    "X = sm.add_constant(X)\n",
+    "\n",
+    "model_runs_scored = sm.OLS(y, X).fit()\n",
+    "print(model_runs_scored.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "944328e4",
+   "metadata": {},
+   "source": [
+    "# This looks strange because batting average shouldn't be NEGATIVELY affecting Runs scored... \n",
+    "## Why is this happening.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ba6d9c",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=https://i.imgflip.com/80a3lx.jpg width=\"500\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ae16bbc",
+   "metadata": {},
+   "source": [
+    "# Checking mulitcollinearity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b29db451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize = (3,3))\n",
+    "\n",
+    "ax = sns.heatmap( df[independent_variables].corr(numeric_only=True), \n",
+    "            annot=True, \n",
+    "            cmap='coolwarm',\n",
+    "            vmin=-1, vmax=1);\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8de3bb91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://towardsdatascience.com/verifying-the-assumptions-of-linear-regression-in-python-and-r-f4cd2907d4c0\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "\n",
+    "vif = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
+    "vif_df = pd.DataFrame(columns=X.columns, data=[vif])\n",
+    "print('#'*79)\n",
+    "print('Variance Inflaction Factors')\n",
+    "print(vif_df)\n",
+    "print('#'*79)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59160d6b",
+   "metadata": {},
+   "source": [
+    "# Below is code I also wrote in the `CTPLIB.py` file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "0fd5a920",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def check_linearity(df, independent_variables, dependent_variable):\n",
+    "    for col in independent_variables:\n",
+    "        sns.jointplot(x=col, y=dependent_variable, data=df, kind=\"reg\");\n",
+    "\n",
+    "\n",
+    "##########################################################################\n",
+    "def check_normality(model, X, y):\n",
+    "    print('#'*79)\n",
+    "    print('Checking Normality')\n",
+    "    # predictions\n",
+    "    y_pred = model.predict(X)\n",
+    "\n",
+    "    # the truth - the prediction\n",
+    "    residuals =  y.values - y_pred.values \n",
+    "\n",
+    "    # histogram\n",
+    "    sns.histplot(residuals)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "    # qq plot\n",
+    "    qqplot(residuals, line='q');\n",
+    "    plt.show()\n",
+    "    print('#'*79)\n",
+    "\n",
+    "    \n",
+    "\n",
+    "##########################################################################\n",
+    "def plot_homo(model):\n",
+    "    plt.scatter(model_runs_scored.fittedvalues, model_runs_scored.resid, alpha=0.5)\n",
+    "    plt.xlabel('Fitted Values')\n",
+    "    plt.ylabel('Residuals')\n",
+    "    plt.axhline(y = 0, color = 'r')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "##########################################################################    \n",
+    "def plot_correlation(df, independent_variables):\n",
+    "    plt.figure(figsize = (3,3))\n",
+    "\n",
+    "    ax = sns.heatmap( df[independent_variables].corr(numeric_only=True), \n",
+    "                annot=True, \n",
+    "                cmap='coolwarm',\n",
+    "                vmin=-1, vmax=1);\n",
+    "    plt.show()\n",
+    "    \n",
+    "\n",
+    "##########################################################################\n",
+    "def get_vif(X):\n",
+    "    vif = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
+    "    vif_df = pd.DataFrame(columns=X.columns, data=[vif])\n",
+    "    print('#'*79)\n",
+    "    print('Variance Inflaction Factors')\n",
+    "    print(vif_df)\n",
+    "    print('#'*79)\n",
+    "    return vif_df\n",
+    "\n",
+    "\n",
+    "\n",
+    "##########################################################################\n",
+    "def build_and_validate_LR(df, independent_variables, dependent_variable):\n",
+    "    X = df[independent_variables]\n",
+    "\n",
+    "    y = df[dependent_variable]\n",
+    "\n",
+    "    X = sm.add_constant(X)\n",
+    "    model = sm.OLS(y, X).fit()\n",
+    "    \n",
+    "    print(model.summary())\n",
+    "    \n",
+    "    check_linearity(df, independent_variables, dependent_variable)\n",
+    "    \n",
+    "    plot_correlation(df, independent_variables)\n",
+    "    \n",
+    "    vif_df = get_vif(X)\n",
+    "    \n",
+    "    check_normality(model, X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "682904df",
+   "metadata": {},
+   "source": [
+    "# Do This Instead:\n",
+    "\n",
+    "### Introducting the CTP-LIBRARY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ec70abf",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'CTPLIB'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mCTPLIB\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mctp\u001b[39;00m\n\u001b[32m      3\u001b[39m ivs = [\u001b[33m'\u001b[39m\u001b[33mOBP\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33mSLG\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m      4\u001b[39m dv = \u001b[33m'\u001b[39m\u001b[33mRS\u001b[39m\u001b[33m'\u001b[39m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'CTPLIB'"
+     ]
+    }
+   ],
+   "source": [
+    "import CTPLIB as ctp\n",
+    "\n",
+    "ivs = ['OBP', 'SLG']\n",
+    "dv = 'RS'\n",
+    "\n",
+    "ctp_model = ctp.CTP_LinReg(df=df, independent_variables=ivs, dependent_variable=dv)\n",
+    "ctp_model.run_all()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "669532ca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00a4af54",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec57320a",
+   "metadata": {},
+   "source": [
+    "# SKIP BELOW\n",
+    "### Using SK-Learn for LR and making predictions. \n",
+    "* Here are just extras we dont need to cover but you can dig into if you want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9e85dda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predicting Runs Scored\n",
+    "df = load_baseball_data()\n",
+    "\n",
+    "## Runs Scored\n",
+    "X = df[['OBP', 'SLG']]\n",
+    "y = df['RS']\n",
+    "print(y.mean()) \n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "lr_runs_scored = LinearRegression()\n",
+    "\n",
+    "lr_runs_scored.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = lr_runs_scored.predict(X_test)\n",
+    "\n",
+    "r_squared = metrics.r2_score(y_test, y_pred)\n",
+    "\n",
+    "print('R-Squared Score:', r_squared)\n",
+    "\n",
+    "metrics.mean_squared_error(y_test, y_pred, squared=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4985637",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predicting Runs Allowed\n",
+    "df = load_baseball_data()\n",
+    "\n",
+    "df_defense = df[['OOBP', 'OSLG', 'RA']].copy()\n",
+    "print(df_defense.isnull().sum())\n",
+    "df_defense = df_defense.dropna()\n",
+    "print(df_defense.shape)\n",
+    "\n",
+    "X = df_defense[['OOBP', 'OSLG']]\n",
+    "y = df_defense['RA']\n",
+    "print(y.mean())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+    "\n",
+    "lr_runs_allowed = LinearRegression()\n",
+    "\n",
+    "lr_runs_allowed.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = lr_runs_allowed.predict(X_test)\n",
+    "\n",
+    "r_squared = metrics.r2_score(y_test, y_pred)\n",
+    "print('R-Squared Score:', r_squared)\n",
+    "\n",
+    "metrics.mean_squared_error(y_test, y_pred, squared=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25b50a48",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Week-08-Regression/homeworks/CTPLIB.py b/Week-08-Regression/homeworks/CTPLIB.py
new file mode 100644
index 00000000..a8e139bf
--- /dev/null
+++ b/Week-08-Regression/homeworks/CTPLIB.py
@@ -0,0 +1,86 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+from statsmodels.api import qqplot
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+
+
+
+def check_linearity(df, independent_variables, dependent_variable):
+    for col in independent_variables:
+        sns.jointplot(x=col, y=dependent_variable, data=df, kind="reg");
+
+
+##########################################################################
+def check_normality(model, X, y):
+    print('#'*79)
+    print('Checking Normality')
+    # predictions
+    y_pred = model.predict(X)
+
+    # the truth - the prediction
+    residuals =  y.values - y_pred.values 
+
+    # histogram
+    sns.histplot(residuals)
+    plt.show()
+
+
+    # qq plot
+    qqplot(residuals, line='q');
+    plt.show()
+    print('#'*79)
+
+    
+
+##########################################################################
+def plot_homo(model):
+    plt.scatter(model_runs_scored.fittedvalues, model_runs_scored.resid, alpha=0.5)
+    plt.xlabel('Fitted Values')
+    plt.ylabel('Residuals')
+    plt.axhline(y = 0, color = 'r')
+    plt.show()
+
+
+##########################################################################    
+def plot_correlation(df, independent_variables):
+    plt.figure(figsize = (3,3))
+
+    ax = sns.heatmap( df[independent_variables].corr(numeric_only=True), 
+                annot=True, 
+                cmap='coolwarm',
+                vmin=-1, vmax=1);
+    plt.show()
+    
+
+##########################################################################
+def get_vif(X):
+    vif = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
+    vif_df = pd.DataFrame(columns=X.columns, data=[vif])
+    print('#'*79)
+    print('Variance Inflaction Factors')
+    print(vif_df)
+    print('#'*79)
+    return vif_df
+
+
+
+##########################################################################
+def build_and_validate_LR(df, independent_variables, dependent_variable):
+    X = df[independent_variables]
+
+    y = df[dependent_variable]
+
+    X = sm.add_constant(X)
+    model = sm.OLS(y, X).fit()
+    
+    print(model.summary())
+    
+    check_linearity(df, independent_variables, dependent_variable)
+    
+    plot_correlation(df, independent_variables)
+    
+    vif_df = get_vif(X)
+    
+    check_normality(model, X, y)
\ No newline at end of file
diff --git a/Week-08-Regression/homeworks/Exercise-Regression.ipynb b/Week-08-Regression/homeworks/Exercise-Regression.ipynb
new file mode 100644
index 00000000..a2788756
--- /dev/null
+++ b/Week-08-Regression/homeworks/Exercise-Regression.ipynb
@@ -0,0 +1,1024 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regression Exercise \n",
+    "# Moneyball The NBA\n",
+    "---\n",
+    "\n",
+    "### This HW is not easy. TRY IT YOURSELF FIRST! \n",
+    "Then If you are able to complete it, [watch this lecture series on it](https://ocw.mit.edu/courses/15-071-the-analytics-edge-spring-2017/pages/linear-regression/playing-moneyball-in-the-nba-recitation/video-1-the-data/). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PANDAS IS FOR DATA WRANGLING\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# SEABORN IS A PLOTTING LIBRARY\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# MATPLOT LIB IS ALSO A PLOTTING LIBRARY\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# SKLEARN IS OUR MACHINE LEARNING PACKAGE\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "# IMPORT OUR RANDOM FOREST REGERSSOR\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# METRICS HELP US SCORE OUR MODEL\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "# HELP US SPLIT OUR DATA INTO TESTING A TRAINING\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Good ol statsmodels\n",
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# Specific root mean squared error for stats models\n",
+    "from statsmodels.tools.eval_measures import rmse\n",
+    "\n",
+    "\n",
+    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
+    "\n",
+    "\n",
+    "from statsmodels.api import qqplot\n",
+    "\n",
+    "import CTPLIB as ctp\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Remember our main steps motto _isbe_.\n",
+    "1. i - Inspect and explore data.\n",
+    "2. s - Select and engineer features.\n",
+    "3. b - Build and train model.\n",
+    "4. e - Evaluate model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# STEP 1 (i): Inspect and explore data\n",
+    "1. Use `data/NBA_test.csv and data/NBA_train.csv`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>SeasonEnd</th>\n",
+       "      <th>Team</th>\n",
+       "      <th>Playoffs</th>\n",
+       "      <th>W</th>\n",
+       "      <th>PTS</th>\n",
+       "      <th>oppPTS</th>\n",
+       "      <th>FG</th>\n",
+       "      <th>FGA</th>\n",
+       "      <th>2P</th>\n",
+       "      <th>2PA</th>\n",
+       "      <th>3P</th>\n",
+       "      <th>3PA</th>\n",
+       "      <th>FT</th>\n",
+       "      <th>FTA</th>\n",
+       "      <th>ORB</th>\n",
+       "      <th>DRB</th>\n",
+       "      <th>AST</th>\n",
+       "      <th>STL</th>\n",
+       "      <th>BLK</th>\n",
+       "      <th>TOV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1980</td>\n",
+       "      <td>Atlanta Hawks</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>8573</td>\n",
+       "      <td>8334</td>\n",
+       "      <td>3261</td>\n",
+       "      <td>7027</td>\n",
+       "      <td>3248</td>\n",
+       "      <td>6952</td>\n",
+       "      <td>13</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2038</td>\n",
+       "      <td>2645</td>\n",
+       "      <td>1369</td>\n",
+       "      <td>2406</td>\n",
+       "      <td>1913</td>\n",
+       "      <td>782</td>\n",
+       "      <td>539</td>\n",
+       "      <td>1495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1980</td>\n",
+       "      <td>Boston Celtics</td>\n",
+       "      <td>1</td>\n",
+       "      <td>61</td>\n",
+       "      <td>9303</td>\n",
+       "      <td>8664</td>\n",
+       "      <td>3617</td>\n",
+       "      <td>7387</td>\n",
+       "      <td>3455</td>\n",
+       "      <td>6965</td>\n",
+       "      <td>162</td>\n",
+       "      <td>422</td>\n",
+       "      <td>1907</td>\n",
+       "      <td>2449</td>\n",
+       "      <td>1227</td>\n",
+       "      <td>2457</td>\n",
+       "      <td>2198</td>\n",
+       "      <td>809</td>\n",
+       "      <td>308</td>\n",
+       "      <td>1539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1980</td>\n",
+       "      <td>Chicago Bulls</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8813</td>\n",
+       "      <td>9035</td>\n",
+       "      <td>3362</td>\n",
+       "      <td>6943</td>\n",
+       "      <td>3292</td>\n",
+       "      <td>6668</td>\n",
+       "      <td>70</td>\n",
+       "      <td>275</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>2592</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>2465</td>\n",
+       "      <td>2152</td>\n",
+       "      <td>704</td>\n",
+       "      <td>392</td>\n",
+       "      <td>1684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1980</td>\n",
+       "      <td>Cleveland Cavaliers</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37</td>\n",
+       "      <td>9360</td>\n",
+       "      <td>9332</td>\n",
+       "      <td>3811</td>\n",
+       "      <td>8041</td>\n",
+       "      <td>3775</td>\n",
+       "      <td>7854</td>\n",
+       "      <td>36</td>\n",
+       "      <td>187</td>\n",
+       "      <td>1702</td>\n",
+       "      <td>2205</td>\n",
+       "      <td>1307</td>\n",
+       "      <td>2381</td>\n",
+       "      <td>2108</td>\n",
+       "      <td>764</td>\n",
+       "      <td>342</td>\n",
+       "      <td>1370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1980</td>\n",
+       "      <td>Denver Nuggets</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>8878</td>\n",
+       "      <td>9240</td>\n",
+       "      <td>3462</td>\n",
+       "      <td>7470</td>\n",
+       "      <td>3379</td>\n",
+       "      <td>7215</td>\n",
+       "      <td>83</td>\n",
+       "      <td>255</td>\n",
+       "      <td>1871</td>\n",
+       "      <td>2539</td>\n",
+       "      <td>1311</td>\n",
+       "      <td>2524</td>\n",
+       "      <td>2079</td>\n",
+       "      <td>746</td>\n",
+       "      <td>404</td>\n",
+       "      <td>1533</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SeasonEnd                 Team  Playoffs   W   PTS  oppPTS    FG   FGA  \\\n",
+       "0       1980        Atlanta Hawks         1  50  8573    8334  3261  7027   \n",
+       "1       1980       Boston Celtics         1  61  9303    8664  3617  7387   \n",
+       "2       1980        Chicago Bulls         0  30  8813    9035  3362  6943   \n",
+       "3       1980  Cleveland Cavaliers         0  37  9360    9332  3811  8041   \n",
+       "4       1980       Denver Nuggets         0  30  8878    9240  3462  7470   \n",
+       "\n",
+       "     2P   2PA   3P  3PA    FT   FTA   ORB   DRB   AST  STL  BLK   TOV  \n",
+       "0  3248  6952   13   75  2038  2645  1369  2406  1913  782  539  1495  \n",
+       "1  3455  6965  162  422  1907  2449  1227  2457  2198  809  308  1539  \n",
+       "2  3292  6668   70  275  2019  2592  1115  2465  2152  704  392  1684  \n",
+       "3  3775  7854   36  187  1702  2205  1307  2381  2108  764  342  1370  \n",
+       "4  3379  7215   83  255  1871  2539  1311  2524  2079  746  404  1533  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# READ IN THE DATA USING PANDAS \n",
+    "df = pd.read_csv('/workspaces/ds-dev-fall-2025/Week-08-Regression/data/NBA_train.csv')\n",
+    "\n",
+    "\n",
+    "# DISPLAY THE FIRST 5 ROWS\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "SeasonEnd: Year season ended\n",
+    "Team: Name of team\n",
+    "Playoffs: If they made the playoffs. 1 == made to playoffs\n",
+    "W: Number of regular season in that season. \n",
+    "PTS: Points scored in regular season. \n",
+    "oppPTS: Opponent Points scored in regular season. \n",
+    "FG: Field Goals made (total shots made == 2P and 3P combined)\n",
+    "FGA: Field Goals attempted (total shots attempted == 2P and 3P combined)\n",
+    "2P: two-pointers made\n",
+    "2PA: two-pointers attempted\n",
+    "3P: three-pointers made\n",
+    "3PA: three-pointers attempted\n",
+    "FT: Free-Throws made (not included in FG stat)\n",
+    "FTA: Free-Throws attempted (not included in FG stat)\n",
+    "ORB: Offensive Rebounds\n",
+    "DRB: Defensive Rebounds\n",
+    "AST: Assists made\n",
+    "STL: Steals \n",
+    "BLK: Blocks \n",
+    "TOV: Turnovers \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Inspect our data using `df.describe()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>SeasonEnd</th>\n",
+       "      <th>Playoffs</th>\n",
+       "      <th>W</th>\n",
+       "      <th>PTS</th>\n",
+       "      <th>oppPTS</th>\n",
+       "      <th>FG</th>\n",
+       "      <th>FGA</th>\n",
+       "      <th>2P</th>\n",
+       "      <th>2PA</th>\n",
+       "      <th>3P</th>\n",
+       "      <th>3PA</th>\n",
+       "      <th>FT</th>\n",
+       "      <th>FTA</th>\n",
+       "      <th>ORB</th>\n",
+       "      <th>DRB</th>\n",
+       "      <th>AST</th>\n",
+       "      <th>STL</th>\n",
+       "      <th>BLK</th>\n",
+       "      <th>TOV</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "      <td>835.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1996.319760</td>\n",
+       "      <td>0.574850</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>8370.239521</td>\n",
+       "      <td>8370.239521</td>\n",
+       "      <td>3200.367665</td>\n",
+       "      <td>6873.318563</td>\n",
+       "      <td>2881.324551</td>\n",
+       "      <td>5956.444311</td>\n",
+       "      <td>319.043114</td>\n",
+       "      <td>916.874251</td>\n",
+       "      <td>1650.461078</td>\n",
+       "      <td>2189.953293</td>\n",
+       "      <td>1061.584431</td>\n",
+       "      <td>2427.354491</td>\n",
+       "      <td>1912.112575</td>\n",
+       "      <td>668.364072</td>\n",
+       "      <td>419.805988</td>\n",
+       "      <td>1302.837126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>9.243808</td>\n",
+       "      <td>0.494662</td>\n",
+       "      <td>12.740822</td>\n",
+       "      <td>581.040114</td>\n",
+       "      <td>587.543959</td>\n",
+       "      <td>287.181266</td>\n",
+       "      <td>401.027166</td>\n",
+       "      <td>446.097941</td>\n",
+       "      <td>830.596327</td>\n",
+       "      <td>199.698941</td>\n",
+       "      <td>523.982964</td>\n",
+       "      <td>197.651728</td>\n",
+       "      <td>244.491086</td>\n",
+       "      <td>150.224519</td>\n",
+       "      <td>130.671523</td>\n",
+       "      <td>221.610925</td>\n",
+       "      <td>93.393044</td>\n",
+       "      <td>82.274913</td>\n",
+       "      <td>153.973470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1980.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>6901.000000</td>\n",
+       "      <td>6909.000000</td>\n",
+       "      <td>2565.000000</td>\n",
+       "      <td>5972.000000</td>\n",
+       "      <td>1981.000000</td>\n",
+       "      <td>4153.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>75.000000</td>\n",
+       "      <td>1189.000000</td>\n",
+       "      <td>1475.000000</td>\n",
+       "      <td>639.000000</td>\n",
+       "      <td>2044.000000</td>\n",
+       "      <td>1423.000000</td>\n",
+       "      <td>455.000000</td>\n",
+       "      <td>204.000000</td>\n",
+       "      <td>931.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1989.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>7934.000000</td>\n",
+       "      <td>7934.000000</td>\n",
+       "      <td>2974.000000</td>\n",
+       "      <td>6563.500000</td>\n",
+       "      <td>2510.000000</td>\n",
+       "      <td>5269.000000</td>\n",
+       "      <td>131.500000</td>\n",
+       "      <td>413.000000</td>\n",
+       "      <td>1502.500000</td>\n",
+       "      <td>2008.000000</td>\n",
+       "      <td>953.500000</td>\n",
+       "      <td>2346.500000</td>\n",
+       "      <td>1735.000000</td>\n",
+       "      <td>599.000000</td>\n",
+       "      <td>359.000000</td>\n",
+       "      <td>1192.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1996.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>8312.000000</td>\n",
+       "      <td>8365.000000</td>\n",
+       "      <td>3150.000000</td>\n",
+       "      <td>6831.000000</td>\n",
+       "      <td>2718.000000</td>\n",
+       "      <td>5706.000000</td>\n",
+       "      <td>329.000000</td>\n",
+       "      <td>942.000000</td>\n",
+       "      <td>1628.000000</td>\n",
+       "      <td>2176.000000</td>\n",
+       "      <td>1055.000000</td>\n",
+       "      <td>2433.000000</td>\n",
+       "      <td>1899.000000</td>\n",
+       "      <td>658.000000</td>\n",
+       "      <td>410.000000</td>\n",
+       "      <td>1289.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2005.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>50.500000</td>\n",
+       "      <td>8784.500000</td>\n",
+       "      <td>8768.500000</td>\n",
+       "      <td>3434.500000</td>\n",
+       "      <td>7157.000000</td>\n",
+       "      <td>3296.000000</td>\n",
+       "      <td>6753.500000</td>\n",
+       "      <td>481.500000</td>\n",
+       "      <td>1347.500000</td>\n",
+       "      <td>1781.000000</td>\n",
+       "      <td>2352.000000</td>\n",
+       "      <td>1167.000000</td>\n",
+       "      <td>2516.500000</td>\n",
+       "      <td>2077.500000</td>\n",
+       "      <td>729.000000</td>\n",
+       "      <td>469.500000</td>\n",
+       "      <td>1395.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2011.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>10371.000000</td>\n",
+       "      <td>10723.000000</td>\n",
+       "      <td>3980.000000</td>\n",
+       "      <td>8868.000000</td>\n",
+       "      <td>3954.000000</td>\n",
+       "      <td>7873.000000</td>\n",
+       "      <td>841.000000</td>\n",
+       "      <td>2284.000000</td>\n",
+       "      <td>2388.000000</td>\n",
+       "      <td>3051.000000</td>\n",
+       "      <td>1520.000000</td>\n",
+       "      <td>2753.000000</td>\n",
+       "      <td>2575.000000</td>\n",
+       "      <td>1053.000000</td>\n",
+       "      <td>716.000000</td>\n",
+       "      <td>1873.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         SeasonEnd    Playoffs           W           PTS        oppPTS  \\\n",
+       "count   835.000000  835.000000  835.000000    835.000000    835.000000   \n",
+       "mean   1996.319760    0.574850   41.000000   8370.239521   8370.239521   \n",
+       "std       9.243808    0.494662   12.740822    581.040114    587.543959   \n",
+       "min    1980.000000    0.000000   11.000000   6901.000000   6909.000000   \n",
+       "25%    1989.000000    0.000000   31.000000   7934.000000   7934.000000   \n",
+       "50%    1996.000000    1.000000   42.000000   8312.000000   8365.000000   \n",
+       "75%    2005.000000    1.000000   50.500000   8784.500000   8768.500000   \n",
+       "max    2011.000000    1.000000   72.000000  10371.000000  10723.000000   \n",
+       "\n",
+       "                FG          FGA           2P          2PA          3P  \\\n",
+       "count   835.000000   835.000000   835.000000   835.000000  835.000000   \n",
+       "mean   3200.367665  6873.318563  2881.324551  5956.444311  319.043114   \n",
+       "std     287.181266   401.027166   446.097941   830.596327  199.698941   \n",
+       "min    2565.000000  5972.000000  1981.000000  4153.000000   10.000000   \n",
+       "25%    2974.000000  6563.500000  2510.000000  5269.000000  131.500000   \n",
+       "50%    3150.000000  6831.000000  2718.000000  5706.000000  329.000000   \n",
+       "75%    3434.500000  7157.000000  3296.000000  6753.500000  481.500000   \n",
+       "max    3980.000000  8868.000000  3954.000000  7873.000000  841.000000   \n",
+       "\n",
+       "               3PA           FT          FTA          ORB          DRB  \\\n",
+       "count   835.000000   835.000000   835.000000   835.000000   835.000000   \n",
+       "mean    916.874251  1650.461078  2189.953293  1061.584431  2427.354491   \n",
+       "std     523.982964   197.651728   244.491086   150.224519   130.671523   \n",
+       "min      75.000000  1189.000000  1475.000000   639.000000  2044.000000   \n",
+       "25%     413.000000  1502.500000  2008.000000   953.500000  2346.500000   \n",
+       "50%     942.000000  1628.000000  2176.000000  1055.000000  2433.000000   \n",
+       "75%    1347.500000  1781.000000  2352.000000  1167.000000  2516.500000   \n",
+       "max    2284.000000  2388.000000  3051.000000  1520.000000  2753.000000   \n",
+       "\n",
+       "               AST          STL         BLK          TOV  \n",
+       "count   835.000000   835.000000  835.000000   835.000000  \n",
+       "mean   1912.112575   668.364072  419.805988  1302.837126  \n",
+       "std     221.610925    93.393044   82.274913   153.973470  \n",
+       "min    1423.000000   455.000000  204.000000   931.000000  \n",
+       "25%    1735.000000   599.000000  359.000000  1192.000000  \n",
+       "50%    1899.000000   658.000000  410.000000  1289.000000  \n",
+       "75%    2077.500000   729.000000  469.500000  1395.500000  \n",
+       "max    2575.000000  1053.000000  716.000000  1873.000000  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspect our data using `df.describe()` function.\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Check for Nulls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SeasonEnd    0\n",
+       "Team         0\n",
+       "Playoffs     0\n",
+       "W            0\n",
+       "PTS          0\n",
+       "oppPTS       0\n",
+       "FG           0\n",
+       "FGA          0\n",
+       "2P           0\n",
+       "2PA          0\n",
+       "3P           0\n",
+       "3PA          0\n",
+       "FT           0\n",
+       "FTA          0\n",
+       "ORB          0\n",
+       "DRB          0\n",
+       "AST          0\n",
+       "STL          0\n",
+       "BLK          0\n",
+       "TOV          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for Nulls.\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Check for Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.False_"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check for Duplicates\n",
+    "df.duplicated().any()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Build a function that will INSPECT ANY DATAFRAME FOR YOU do this for you for any data frame you pass into it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "def inspect_dataframe(df):\n",
+    "    print(\"#\" * 80)\n",
+    "    print(\"🧾 DATAFRAME INSPECTION REPORT\")\n",
+    "    print(\"#\" * 80)\n",
+    "    \n",
+    "    # Shape of the data\n",
+    "    print(f\"\\n📏 Shape of DataFrame: {df.shape[0]} rows × {df.shape[1]} columns\")\n",
+    "    \n",
+    "    # Data types and non-null counts\n",
+    "    print(\"\\n📊 Info Summary:\")\n",
+    "    print(df.info())\n",
+    "    \n",
+    "    # Missing values\n",
+    "    print(\"\\n🔍 Missing Values:\")\n",
+    "    print(df.isnull().sum())\n",
+    "    \n",
+    "    # Duplicate rows\n",
+    "    print(\"\\n📋 Duplicate Rows:\")\n",
+    "    duplicates = df.duplicated().sum()\n",
+    "    print(f\"Total duplicates: {duplicates}\")\n",
+    "    if duplicates > 0:\n",
+    "        print(\"→ Duplicate rows found! Use df[df.duplicated()] to view them.\")\n",
+    "    else:\n",
+    "        print(\"→ No duplicate rows found.\")\n",
+    "    \n",
+    "    # Summary statistics\n",
+    "    print(\"\\n📈 Descriptive Statistics:\")\n",
+    "    print(df.describe(include='all').T)\n",
+    "    \n",
+    "    # Preview the data\n",
+    "    print(\"\\n👀 Sample Data:\")\n",
+    "    display(df.head(5))\n",
+    "    \n",
+    "    print(\"#\" * 80)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get a sense of how many wins it will take to make the playoffs. \n",
+    "Make a scatter plot with x=Wins and the y=Team, and the hue=Playoffs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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atD0Kr776arr5jz76qNtyt23blq3lHj9+3O21c/l1fFnange73W4eP37cNM3MfzFNTU01g4ODXdObN2/u9kvrs88+6/a4jRs3uualnR4QEGDu27cvXb3Xsn/j4uLclr1o0SLXY1auXOk271p7LNLuk/j4eLN06dKueS1btnTNmzJlimt66dKlXUOlfvjhB7fXUtohVFlJ+xqMiooyx4wZY44ZM8Z84okn3N5/ksxZs2Zl+LiMfmU9cuSIOXv2bPPDDz8033zzTXPMmDFmnTp1XI8ZMGCAq+0DDzzgmp62p+zpp5/OcPo777zjVte8efPc1pu2R6Bbt25udaV93KRJk9LVndGv9dkxffp0t2W/++67mbYdNGiQW9t169aZpnntPRZptz27z3lOeywuu9pQp8zmb9682W16p06d0g3Pyujz/PTp0+a8efPMjz/+2HzrrbfMMWPGuH2W3XLLLW7trzbMKas21/q6urJnPe2+/+677676ef3VV19d02sOuYMeCxQqrVq10l9//SXpf79UXP73xhtvlLe3t6tHwjRNrVixQkeOHHFbRtoDt3PikUcecZ1nPDg4WKVLl3YtO+2pRG+++WbNnTtX0qUDeZs1a6aqVauqdu3aiomJUZUqVbK1vsjISLVu3VpLliyRdOnsLK+++qoWLFjgWl9wcLDbwaG5te7sOn/+vNvZXL744gt98cUXGbZNSUnRunXrXAcBvv322xo2bJjOnj2b6fL//vvvTOd17drVdXDxleeO79y5s4oXLy4p/a9u2T3t67Fjx1z/Dw4OztZjsmPdunVKTU113e/Xr5/b/H79+rl6H1JTU13XCMjM9u3b3Q4ev+eee9wODu3Xr5/eeOMN1/3Vq1erfv366ZbTt29fVahQwW3ate7fy7+ASlLZsmXVpk0b1/0WLVooMjLScq/F5V/jpUtni+natasmTZokSfr1119d8+644w49/fTTOnz4sI4fP65Zs2apV69erp4cSerdu3e2erGutHv3brdf9NMaMGCAbr/99qsu48KFC3r00Uf1xRdfZHkq0rTvhccff1yffvqppEs9NwcPHlR4eLi++eYbV5u0PTBpe1JCQkLcXk9lypRRx44dXc/Hlb0u+SU7Z5C6LO3751pcy3Oe39KexVC6dNpfb29vt2lpz1LndDr13HPPaezYsRmeFviy3Nym3Hhd2e12PfTQQ6771atXd5uf0ed1qVKlXP9P+zmN/MF1LFCopB3GdOjQIW3YsME1JOlyYGjevLnrA3j58uVatmyZ6zFRUVEqV67cNa37yi+vDofD9f+0f5w++ugj17CoEydOaN68eRo7dqwefPBBVa1aVb169cr2uczTDq+5fNrHtMOg7r77brc6cnPd2XHq1KkcnV/+8h+B7777Tk8//XSWoUJSln8gw8PDXf+/8kth2nlXDq/Ire2/8nV0eSjO1aQNAdKlL95Z3b9aEMqt5dWoUSPdtGvdv2nPwFamTJl07a6s6Vpcudy0y7xw4YISExMlSd7e3nr44Ydd8yZMmJBuGFROhrFlxsvLS6GhoerSpYu+/fZbTZw4MVuPGzp0qCZPnnzV1+Xl7ZGkunXruq45kJqaqkmTJmnt2rWuYVAhISHq0qWLq33a10hGz33aafl1vZUrh4nt378/07ZXnlkps2FQV75W0z5naV3Lc57frnxfR0ZGZtn+vffe05gxY7L8zJRyd5ty43VVtmxZ+fr6uu6n/XsmZfx5nZPPJOQ+eixQqFx5fERcXJzrg+dysPDz81OTJk20atUq/fzzzzp8+LCr/bX2VkhK92tRZldJjYiI0OrVq7Vz506tW7dOO3bs0JYtWzR79mylpKRoxowZ6tChQ7bGdPfo0UMDBw5UQkKC9uzZo0WLFumHH35wzb9yGbm57uwICgpyu3/bbbdl+Rw3bNhQktzGHPv7++vbb7/VzTffLF9fX3344YfZusL1lfsjrYzGaudU6dKlXf/P6I9imzZt9MILL7juT548OVu/UF/Z+3Flj9qV90uWLJkvy7vcw5PWte7ftI87evRounZX1nQtjh496jaOP+0yfX193b6gPPTQQ3r11VeVnJysxYsXa/z48a5r1zRo0ED16tW7phqio6MtXwE97Xuhbt26mjp1qqpXry4vLy/17NnTrWclrccff9y17s8++0wnTpxwzbvnnnvc3h9pXyMZPfdpp13t9ZZbmjRpIofD4fqiO2/ePL3yyivp2p08edLteKZKlSq5goXN5v7badpjyBISEjJ9nV3rc56frnxf79mzRyEhIZm2T7tN4eHhmjVrlurXry8fHx89++yzeXKtjNx4XWX372paaQNNVs8J8gY9FihUKlas6DZcY9asWZIufThd/qVe+l8A2bBhgw4cOJBuel767bff5HQ6VaVKFd19990aNmyYvvnmG3Xq1MnVJu1Qjaz4+fm5XT/hwQcfdF1ttF69eq4vcnmx7uwoXry427CaEydOaNCgQYqNjXW7PfDAAypfvrzrfO1pvwRFRUWpXbt28vX1ldPpdBvO4UlphxmkfQ1d1qxZM7fX3OzZs92GHKX1yy+/uAJh06ZN3YYqXT7YPKP7drvd7UKQGalevbrbH/ivvvrKbajIlcvPyTVHrnX/pj3Y/ciRI25fDFetWpUrB29/+eWXrv8nJCS4Be7LF2+7LDQ0VHfccYekS792Pvvss655udFbYUXa90JMTIxq164tLy8vHTt2LMvQ0q1bN9dn4e7du91O3HDlNqXd58eOHdP8+fNd948ePep2P7+uSePn5+c2nG3jxo0aO3asWxvTNDVkyBCdOXPGNS3twcVXBt/LB+lLl350yuyX7Wt9zvPTTTfd5HZ/1KhRSklJcZuWticn7TY1btxYTZs2lY+Pjy5evOj23rhS2i/2Ob2StadeV2k/jzO6aCnyFj0WKHRatWqlr776StL/ukQbNmzo9otrdHS0XnvttXR/WKz0WGRXr169FB8fr5iYGJUrV07BwcHatWuX5s2b52pz5R/ErNx7772us+2k/UKWUa9Dbq87O5555hn17t1bkrRy5UrdcMMN6tq1q0qWLKkTJ05o48aNWrFihcLCwlwhqXr16q4zyWzevFl33XWXatasqfnz57t9OfCkli1bur6UZxbGJk6cqJYtW7qG/gwZMkRfffVVugvkbdiwQcOGDVPXrl1VqlQp9e/f3zVUZsaMGTp9+nS6s0JJl457SDueOCM2m02DBw/WSy+9JOnSWOabbrpJt956q/7880+3s0XFxMTk+Nf5a9m/vXv31vDhw12/Rv/rX//S/fffL8MwsnWWq+x48cUX9eeff6pixYr65ptvXGeSkTK+6OXjjz/uGkZ4+WxEDodDd999d67Uc62qV6+u33//XdKli+rZbDYVK1ZMX375ZZbjx+12ux555BHX2eIub1Pjxo3TXeCyX79+GjVqlOvLZ48ePTRgwAAFBATo66+/dg1JNAxDTz75ZG5vYqZeeeUV/fjjj65hUE8++aS+//57RUdHuy6Qt3HjRlf7mJgYt7ON1ahRQyVKlHAFj0cffVRz5szR4cOHszxW5Fqf8/xUt25dderUyfXZPWfOHNWrV0+dOnWSr6+vtm7dquXLl7te99WrV3ed/W7OnDl66KGHFBoaqm+++SbLYZpph3T+8ssvGjRokCIiIuTj46Mnnngiyxo99bpKewxXfvxNxxU8ddQ4kFc++eQTt7NGSDJjY2Pd2iQkJLideUeSGRoamuHysntWqOyecaR69erp6kt7Cw4ONvfu3Zujbb7yPN8+Pj6uswWllRfrvnJbMzpzyNChQ7Ncr644e8eOHTvMEiVKpGvj5eVl9u7dO9OzqGR1lpe0j0k7LzsXlstI2rNy+fr6ZniBOdM0zU2bNpk1atS46vanrSk717Fo2bJljq5jcccdd2S5vJo1a6a7Lkja+Rmd8eeynO5f0zTNcePGZdguPDzcrFq1quv+tZ4VqnXr1hkuv3PnzpleCLFx48Zube+4445srTut7Fws8mqPS/tamDp1aobbERYWZrZr1y7LdR0/ftztWhCSMr0I5rJly8ygoKBM95/NZjPffPPNdI+72mvkWs8KddmOHTvMG2644aqvr86dO2d4MdIXX3wxw/aNGzc2y5Qpk2vPeX6fFco0L+3f7F7H4ueffza9vLzStfH39ze7d++eaX0bN250XQg07a148eKuNle7jkVOX1dZPV9X+7y+8gJ5P//8c7rnFHmLoVAodDIaznTlrxYlSpRQgwYNsmyTV+Li4vTwww+rUaNGCg0Nlbe3t4oVK6YaNWro0Ucf1S+//KKKFSvmaJlX9k5c/uU7P9adHaNHj9bKlSt1zz33KDIyUg6HQ97e3ipXrpxuvfVWjR492m04TJUqVbR8+XLdeuutKlasmPz9/RUdHa3Fixerbdu2uV7ftbh8Vi7p0q/Bl8+2daV69epp8+bNmjJlinr06KGKFSvKz89P3t7eCg8PV5cuXTR58mS3X1qLFy+uxYsXa8KECYqJiVFwcLC8vLxUsmRJRUdHa/z48Vq6dKn8/f2zVavdbteMGTP0n//8R506dVKZMmXk5eWlwMBANWvWTGPGjNH69evdDmrPiZzuX0kaOHCgvvnmGzVq1EgOh0OlS5dWnz59tHbt2muuI6358+frpZdeUmRkpHx8fFSpUiUNGzZMM2fOzHSc9pW/wHp6GJQk3XnnnZoxY4bq1asnb29vlSpVSr169dKaNWuu+jyVKlXKrcfF19c30x6YVq1a6ffff9fTTz+t2rVrq1ixYvLx8VGFChXUu3dvrVq1Sk8//XSublt2VKlSRb/88ou+/vprde/eXRUqVHA7mFe6tJ/mzJmT4Tj9kSNHavTo0YqMjJS3t7cqVqyooUOHatmyZZmeacrKc56fSpUqpZUrV2rChAlq27atQkJCXJ8TjRo1cusFuOmmm7Rw4UK1aNFCDodDgYGB6tSpk1atWqW6detmuo769etr6tSpatiwYbrnPTvy+3W1Zs0a/fPPP5KkatWqpRsyhrxnmCaHzwPAtfjPf/7jukBW9+7dXRdlQsG0Zs0aNW/eXNKlISD79u1zO96lIHrttddcw6HuvPNO19njCrqff/5Z7du314ULF2S32zVz5ky3U2ujaBo0aJDee+89SdKbb77pkTBc1BEsAOAaOZ1ONWjQQJs3b5aXl5d27NiR7rTDuL5dvHhRa9as0alTp/Tqq6/ql19+kXRpfH/as3oVJIcPH9a2bdu0b98+xcbGusa4r1ixQi1btvRwdbln/vz56tatm5KTk+VwODR//nzFxMR4uix4SEJCgiIiIpSQkKCwsDDt3LlTxYoV83RZRQ4HbwPANbLZbHrttdfUqVMnpaSk6M0339S4ceM8XRZy4PDhw+m+jEZFRWnQoEEeqsi6BQsWpBseeccddxSqUCFJHTt21Pfff+86ocPGjRvVqlWrAt/LhGszfvx4JSQkSJJefvllQoWH0GMBACiy9u7d67q4WEhIiG655Ra9/vrreXKsUX6ZPHmy7r33XtlsNpUvX1533XWXhg0blqOrVwPAtSBYAAAAALCMs0IBAAAAsIxgAQAAAMAyDt6+DjidTh08eFAlSpTI9PzqAAAAQH4zTVNnzpxReHi4bLas+yQIFteBgwcPKiIiwtNlAAAAABk6cOCAypcvn2UbgsV1oESJEpIu7bCAgAAPVwMAAABccvkaIZe/r2aFYHEduDz8KSAggGABAACA6052hutz8DYAAAAAywgWAAAAACwjWAAAAACwjGMsAAAAUOSlpqYqOTnZ02XkO29vb9nt9lxZFsECAAAARZZpmjp8+LBOnz7t6VI8JigoSKGhoZavp0awAAAAQJF1OVSUKVNGxYoVK1IXKzZNU+fPn9fRo0clSWFhYZaWR7AAAABAkZSamuoKFaVKlfJ0OR7h5+cnSTp69KjKlCljaVgUB28DAACgSLp8TEWxYsU8XIlnXd5+q8eYECwAAABQpBWl4U8Zya3tJ1gAAAAAsIxgAQAAAOTQ5MmTFRQUlO/r/fPPP3XjjTfK19dX9evXz3SaJxAsAAAAgAz0799fhmHIMAz5+PioSpUqGjlypFJSUjxW07Bhw1S8eHFt375dixcvznSaJ3BWKAAAACATHTp00KRJk5SYmKh58+Zp4MCB8vb2tnxq1mu1a9cude7cWRUrVsxymifQYwEAAABkwuFwKDQ0VBUrVtQjjzyitm3b6vvvv0/XbteuXerWrZvKli0rf39/NWnSRIsWLXLNHzlypOrUqZPucfXr19dLL70kSXI6nRo5cqTKly8vh8Oh+vXra8GCBa62hmHol19+0ciRI2UYhoYPH57htKSkJD322GMKCwuTr6+vKlasqLi4uDx4dtwRLAAAAAoAMzVVZuJ5pR47oOS9W5Sy93c5Tx2WmXhBptPp6fKKDD8/PyUlJaWbfvbsWXXq1EmLFy/Wxo0b1aFDB3Xt2lX79++XJA0YMEDbtm3T+vXrXY/ZuHGjNm/erHvvvVeSNHbsWL311lt68803tXnzZrVv31633XabduzYIUk6dOiQateuraefflqHDh1SbGxshtPee+89ff/995oxY4a2b9+uKVOmqFKlSnn+3BSJYFGpUiW9++67ni4DAADgmpjJiXIe3aukLT8rZeevch7ardRDu5T851ol/7FSzpOHZKZYuwYBsmaaphYtWqSFCxfqlltuSTe/Xr16euihh1SnTh1VrVpVo0aNUuXKlV29G+XLl1f79u01adIk12MmTZqk6OhoRUVFSZLefPNNDRkyRHfeeaeqV6+u119/XfXr13d9jw0NDZWXl5f8/f0VGhrq+vfKafv371fVqlV10003qWLFirrpppt011135flz5PFgYZqm2rZtq/bt26eb9+GHHyooKEh///23ByoDAADwPDMlSalH9ill7+9S8sX08y+eU8rOX+Q8fUSmM9UDFRZuc+bMkb+/v3x9fdWxY0f16tVLw4cPT9fu7Nmzio2NVc2aNRUUFCR/f39t27bN1WMhSQ888ICmTp2qixcvKikpSV9//bUGDBggSUpISNDBgwfVsmVLt+W2bNlS27Zty1HN/fv316ZNm1S9enU98cQT+vHHH3O+4dfA48HCMAxNmjRJa9eu1fjx413T9+zZo2effVbvv/++ypcv78EKAQAAPMdMSlTq339epZH5/8EjMX+KKkJiYmK0adMm7dixQxcuXNDnn3+u4sWLp2sXGxurWbNmafTo0fr555+1adMm1a1b123YVNeuXeVwODRr1iz98MMPSk5O1r///e9cr7lhw4bas2ePRo0apQsXLqhnz555sp4reTxYSFJERITGjh2r2NhY7dmzR6Zp6r777lO7du20dOlSRUZGys/PT9WrV9fYsWPdHtu/f3/dfvvtevPNNxUWFqZSpUpp4MCBWV6SfMKECQoKCnKdjuv3339Xx44d5e/vr7Jly6pPnz46fvy4q73T6VRcXJyrjnr16umbb75xzT916pR69+6tkJAQ+fn5qWrVqm7dXAAAANfCdKYq9dg+yTSv3jg5Uc7441dvhxwpXry4qlSpogoVKsjLK/MTqq5cuVL9+/fXv/71L9WtW1ehoaHau3evWxsvLy/169dPkyZN0qRJk3TnnXfKz89PkhQQEKDw8HCtXLky3XJr1aqV47oDAgLUq1cvffrpp5o+fbpmzpypkydP5ng5OXHdnG62X79+mjVrlgYMGKDu3bvr999/19atWzVu3Dj95z//UalSpbRq1So9+OCDCgsLU8+ePV2PXbJkicLCwrRkyRLt3LlTvXr1Uv369fXAAw+kW88bb7yhN954Qz/++KOaNm2q06dP65ZbbtH999+vd955RxcuXNCQIUPUs2dP/fe//5UkxcXF6auvvtLHH3+sqlWravny5brnnnsUEhKi6OhovfTSS/rjjz80f/58lS5dWjt37tSFCxcy3dbExEQlJv7vF4WEhIRcfCYBAEChkZIkM/5Etps7Tx+RrVS4DPt18xWvyKhataq+/fZbde3aVYZh6KWXXpIzg4Pq77//ftWsWVOS0oWIZ555RsOGDVPlypVVv359TZo0SZs2bdKUKVNyVMvbb7+tsLAwNWjQQDabTf/5z38UGhqa5xf0u65edZ988olq166t5cuXa+bMmQoJCdGIESNc8yMjI7V69WrNmDHDLViULFlS48aNk91uV40aNdS5c2ctXrw4XbAYMmSIvvzySy1btky1a9eWJI0bN04NGjTQ6NGjXe0+++wzRURE6K+//lLFihU1evRoLVq0SM2bN5ckRUVFacWKFRo/fryio6O1f/9+NWjQQI0bN5akqx51HxcX57ZdAAAAGTIlmTk445PTmb3eDeS6t99+WwMGDFCLFi1UunRpDRkyJMMfj6tWraoWLVro5MmTatasmdu8J554QvHx8Xr66ad19OhR1apVS99//72qVq2ao1pKlCihN954Qzt27JDdbleTJk00b9482Wx5O1jJMM3r69X34osv6rvvvtPvv/8uSfrggw/02Wefaf/+/bpw4YKSkpJUv359rVu3TtKloVDHjh3T3LlzXcsYNGiQtmzZ4upxqFSpklJTU3Xu3Dlt2LDBdeS9JN1xxx2aPXu2fHx83Oo4d+6c5s2bpwoVKqhOnTrpxtIlJSWpQYMGWrt2rebPn68ePXqoWrVquvXWW3X77berRYsWmW5jRj0WERERio+PV0BAwDU+cwAAoLAxk5MunQXq9JFstbeHVZa9Qi0ZefwFsrC4ePGi9uzZo8jISPn6+ubLOk3TVNWqVfXoo4/qqaeeypd1Xk1Wz0NCQoICAwOz9T31uuqxkC6NPbs8fm3atGmKjY3VW2+9pebNm6tEiRIaM2aM1q5d6/YYb29vt/uGYaTrerr55ps1d+5czZgxQ88995xr+tmzZ9W1a1e9/vrr6WoJCwtzBZy5c+eqXLlybvMdDockqWPHjtq3b5/mzZunn376SW3atNHAgQP15ptvZriNDofD9VgAAIDMGN4+sodWyl6wMAzZylQgVFzHjh07pmnTpunw4cOua1cUJtddsEhr5cqVatGihR599FHXtF27dl3Tspo2barHHntMHTp0kJeXl2JjYyVdOmp+5syZqlSpUoYH5NSqVUsOh0P79+9XdHR0pssPCQlRv3791K9fP91888165plnMg0WAAAA2WUUD5LhHyTz7Oks29mCw2R488Pl9axMmTIqXbq0PvnkE5UsWdLT5eS66zpYVK1aVV988YUWLlyoyMhIffnll1q/fr0iIyOvaXktWrTQvHnz1LFjR3l5eenJJ5/UwIED9emnn+quu+7Ss88+q+DgYO3cuVPTpk3ThAkTVKJECcXGxmrw4MFyOp266aabFB8fr5UrVyogIED9+vXTyy+/rEaNGql27dpKTEzUnDlzXAflAAAAWGH4+MqrSiOl/LVB5vn4jNsElZFXxToEi+vcdXYEQq67roPFQw89pI0bN6pXr14yDEN33XWXHn30Uc2fP/+al3nTTTdp7ty56tSpk+x2ux5//HGtXLlSQ4YM0a233qrExERVrFhRHTp0cB3gMmrUKIWEhCguLk67d+9WUFCQGjZsqOeff16S5OPjo6FDh2rv3r3y8/PTzTffrGnTpuXKcwAAAGDz85d3zWZynj6m1CN7ZV48JxmGjGIBsodGyuZfUoZP/hwjAGTmujt4uyjKyUExAACgaDOTLv7/maIMyWajl8ICTxy8fT0qtAdvAwAAIHP0TOB6xWkDAAAAAFhGsAAAAABgGcECAAAAgGUcYwEAAADkEtM0lZgiJadIyamSt13y9pIcXpcu4lyY0WMBAAAA5ILEZFN/n5Q27TW1dqepX/dc+nfT3kvTE5Pz9mSsH3zwgSpVqiRfX181a9ZM69aty9P1XYlgAQAAAFiUmGxq5xFT2w+aOnvRfd7Zi9L2g5fm51W4mD59up566ikNGzZMv/76q+rVq6f27dvr6NGjebK+jBAsAAAAAAtM09TRBOnQqazbHTolHUvImytwv/3223rggQd07733qlatWvr4449VrFgxffbZZ7m+rswQLAAAAAALElOkf05mLyz8fdJUUkrurj8pKUm//PKL2rZt65pms9nUtm1brV69OndXlgWCBQAAAGBBcorSDX/KzNmLyvVgcfz4caWmpqps2bJu08uWLavDhw/n7sqyQLAAAAAALEhOzdv2BQXBAgAAALDA25637a+mdOnSstvtOnLkiNv0I0eOKDQ0NHdXlgWCBQAAAGCBt5fk75u9tv6+kk8uX0nOx8dHjRo10uLFi13TnE6nFi9erObNm+fuyrLABfIAAAAACxxeUrlgQ9sPXv0A7vLBRq4HC0l66qmn1K9fPzVu3FhNmzbVu+++q3Pnzunee+/N/ZVlgmABAAAAWGAYhsoEmEq4kPUpZ8NKSiEBeXMF7l69eunYsWN6+eWXdfjwYdWvX18LFixId0B3XiJYAAAAABY5vA1VKSsF+F069Wzas0T5+17qqQgJuNQurzz22GN67LHH8mz5V0OwAAAAAHKBw9tQ+WBTZQIMJaVcOvuTt/3SMRU+XnnTU3E9IVgAAAAAucQwDDm8JYe3pyvJf5wVCgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWMZ1LAAAAIBcYpqmlJwoMzlRSkmSvHxkeDskb0ehv0AePRYAAABALjCTLsp5ZI+St61W8ualSv5j1aV/t62W88hemUkX82zdy5cvV9euXRUeHi7DMPTdd9/l2boyQ7AAAAAALDKTLipl/x9K2bNF5vkE93nnE5SyZ7NS9v+RZ+Hi3Llzqlevnj744IM8WX52MBQKAAAAsMA0TTlPHpTz2IEs2zmPHZDTv6RsZSvl+rCojh07qmPHjrm6zJyixwIAAACwIjlRqUf2Zatp6pG9UnLeDYnyJIIFAAAAYIGZnJhu+FOmbc8nyExOyuOKPINgAQAAAFiRksOgkJKcN3V4GMECAAAAsMLLJ4ftvfOmDg8jWAAAAAAWGN4OGcUCste2WIAM7xwGkQKCs0IBAAAAVng7ZC9bUSl7tly1qb1sJcnbN9dLOHv2rHbu3Om6v2fPHm3atEnBwcGqUKFCrq8vIwQLAAAAwALDMGQLDpft7OksTzlrC4mQLTgsT67AvWHDBsXExLjuP/XUU5Kkfv36afLkybm+vowQLAAAAACLDB9feVWoJad/SaUe2et2liijWIDsZStdChU+ud9bIUmtW7eWaZp5suzsIlgAAAAAucDw8ZXt/wOEmZx46exPXt6Xjqnw9s2TnorrCcECAAAAyCWGYUg+vnnWM3E946xQAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAIo0p9Pp6RI8Kre2n4O3AQAAUCT5+PjIZrPp4MGDCgkJkY+PT6E/c1NapmkqKSlJx44dk81mk4+PtSuCEywAAABQJNlsNkVGRurQoUM6ePCgp8vxmGLFiqlChQqy2awNZiJYAAAAoMjy8fFRhQoVlJKSotTUVE+Xk+/sdru8vLxypaeGYAEAAIAizTAMeXt7y9vb29OlFGgcvA0AAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyzgoFAIVMSqqplFQpxSkZhmS3ST5ekq0IXfQJAJD/CBYAUEikOk2duyjtO27q+Bkp1XlpejGHFF5SCg2SfL0JFwCAvFHoh0L1799fhmHo4YcfTjdv4MCBMgxD/fv3z9ayli5dKsMwdPr06dwtEgAsSnWaOnlW+mWPqSPx/wsVknQ+Udp5WNqy39SFJNNzRQIACrVCHywkKSIiQtOmTdOFCxdc0y5evKivv/5aFSpU8GBlAJA7EpOlrQdMt0Bxpfjz0o7DppJTCBcAgNxXJIJFw4YNFRERoW+//dY17dtvv1WFChXUoEED1zSn06m4uDhFRkbKz89P9erV0zfffCNJ2rt3r2JiYiRJJUuWdOvp+Oabb1S3bl35+fmpVKlSatu2rc6dO5d/GwigSHM6TR08ZSoli1Bx2fEEKSk172sCABQ9RSJYSNKAAQM0adIk1/3PPvtM9957r1ubuLg4ffHFF/r444+1detWDR48WPfcc4+WLVumiIgIzZw5U5K0fft2HTp0SGPHjtWhQ4d01113acCAAdq2bZuWLl2q7t27yzT5RRBA/khKkY4mZK+t05SOJfD5BADIfUXm4O177rlHQ4cO1b59+yRJK1eu1LRp07R06VJJUmJiokaPHq1FixapefPmkqSoqCitWLFC48ePV3R0tIKDgyVJZcqUUVBQkCRp165dSklJUffu3VWxYkVJUt26dbOsJTExUYmJia77CQnZ/EYAABkxpOSU7De/kJR3pQAAiq4iEyxCQkLUuXNnTZ48WaZpqnPnzipdurRr/s6dO3X+/Hm1a9fO7XFJSUluw6WuVK9ePbVp00Z169ZV+/btdeutt+rf//63SpYsmelj4uLiNGLECOsbBQCSZF46pWxyNoc4+djzthwAQNFUZIKFdGk41GOPPSZJ+uCDD9zmnT17VpI0d+5clStXzm2ew+HIdJl2u10//fSTVq1apR9//FHvv/++XnjhBa1du1aRkZEZPmbo0KF66qmnXPcTEhIUERFxTdsEAF52qVQJ6Z+T2WtfJpBTzgIAcl+RChYdOnRQUlKSDMNQ+/bt3ebVqlVLDodD+/fvV3R0dIaP9/HxkSSlprr/LGgYhlq2bKmWLVvq5ZdfVsWKFTVr1iy38JCWw+HIMqwAQE542Q2VD5YOnjJ1tcO7Sha/dLE8AAByW5H682K327Vt2zbX/9MqUaKEYmNjNXjwYDmdTt10002Kj4/XypUrFRAQoH79+qlixYoyDENz5sxRp06d5Ofnp61bt2rx4sW69dZbVaZMGa1du1bHjh1TzZo1PbGJAIooPx+paqihvw5lnix8vaXq4YYcXCQPAJAHilSwkKSAgIBM540aNUohISGKi4vT7t27FRQUpIYNG+r555+XJJUrV04jRozQc889p3vvvVd9+/bVkCFDtHz5cr377rtKSEhQxYoV9dZbb6ljx475tUkAIC+7obAgUw4vQ/uOm0r432V7ZLdJpUtIUWUMFaOzFACQRwyT86J6XEJCggIDAxUfH59l8AGAqzFNU0kply6YdyFZshlSccel4U9ednoqAAA5k5PvqUWuxwIACjPDMOTwlhzeEj9TAADyU5G5QB4AAACAvEOwAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZV6eLgAAAABAxszUFCk15dIdu5cM+/X79b3Q9lgMHz5c9evX93QZAAAAQI6ZyYlynjutlL1blLx9nZK3r1PKvq1ynouXmZzo6fIy5NFg0b9/fxmGIcMw5O3trbJly6pdu3b67LPP5HQ6PVkaAAAA4BFm0kWl7Nuq5M3L5Dy6X+bZUzLPnpLzyF4lb16mlAPbZCZd9HSZ6Xi8x6JDhw46dOiQ9u7dq/nz5ysmJkaDBg1Sly5dlJKS4unysmSa5nVfIwAAAAoOMzlRKfv/kPPYgcxayHlkn1L+3n7d9Vx4PFg4HA6FhoaqXLlyatiwoZ5//nnNnj1b8+fP1+TJkyVJ+/fvV7du3eTv76+AgAD17NlTR44ccVvOa6+9prJly6pEiRK67777dPGie4pLSUnRE088oaCgIJUqVUpDhgxRv379dPvtt7vaOJ1OxcXFKTIyUn5+fqpXr56++eYb1/ylS5fKMAzNnz9fjRo1ksPh0IoVK/Tbb78pJiZGJUqUUEBAgBo1aqQNGzbk2XMGAACAwslMuphFqPgf59F9BIvsuOWWW1SvXj19++23cjqd6tatm06ePKlly5bpp59+0u7du9WrVy9X+xkzZmj48OEaPXq0NmzYoLCwMH344Yduy3z99dc1ZcoUTZo0SStXrlRCQoK+++47tzZxcXH64osv9PHHH2vr1q0aPHiw7rnnHi1btsyt3XPPPafXXntN27Zt0w033KDevXurfPnyWr9+vX755Rc999xz8vb2zrPnBwAAAIWPmZqi1CN7s9nYVOrR/TKdqXlaU05ct4eV16hRQ5s3b9bixYu1ZcsW7dmzRxEREZKkL774QrVr19b69evVpEkTvfvuu7rvvvt03333SZJeeeUVLVq0yK3X4v3339fQoUP1r3/9S5I0btw4zZs3zzU/MTFRo0eP1qJFi9S8eXNJUlRUlFasWKHx48crOjra1XbkyJFq166d6/7+/fv1zDPPqEaNGpKkqlWrZrltiYmJSkz8X8JMSEi4pucIAAAAhUhqiszzZ7Ld3DyfcOmMUTZ7HhaVfddlj4V06fgFwzC0bds2RUREuEKFJNWqVUtBQUHatm2bJGnbtm1q1qyZ2+MvhwNJio+P15EjR9S0aVPXNLvdrkaNGrnu79y5U+fPn1e7du3k7+/vun3xxRfatWuX27IbN27sdv+pp57S/fffr7Zt2+q1115L1/5KcXFxCgwMdN3SbhsAAACKKMPTBVhz3QaLbdu2KTIyMt/Wd/bsWUnS3LlztWnTJtftjz/+cDvOQpKKFy/udn/48OHaunWrOnfurP/+97+qVauWZs2alem6hg4dqvj4eNftwIGrj6MDAABAIWf3luEfmO3mNv8gyX79DL+/LoPFf//7X23ZskU9evRQzZo1deDAAbcv33/88YdOnz6tWrVqSZJq1qyptWvXui1jzZo1rv8HBgaqbNmyWr9+vWtaamqqfv31V9f9WrVqyeFwaP/+/apSpYrbLTs9CtWqVdPgwYP1448/qnv37po0aVKmbR0OhwICAtxuAAAAKNoMm132MhUlIxtdF4ZNtpAIGbbr5+u8x4+xSExM1OHDh5WamqojR45owYIFiouLU5cuXdS3b1/ZbDbVrVtXvXv31rvvvquUlBQ9+uijio6Odg1JGjRokPr376/GjRurZcuWmjJlirZu3aqoqCjXeh5//HHFxcWpSpUqqlGjht5//32dOnVKxv/vuBIlSig2NlaDBw+W0+nUTTfdpPj4eK1cuVIBAQHq169fhvVfuHBBzzzzjP79738rMjJSf//9t9avX68ePXrk/ZMHAACAQsXw8ZWtbKSch3dn2c4eFiXD25FPVWWPx4PFggULFBYWJi8vL5UsWVL16tXTe++9p379+sn2/wls9uzZevzxx9WqVSvZbDZ16NBB77//vmsZvXr10q5du/Tss8/q4sWL6tGjhx555BEtXLjQ1WbIkCE6fPiw+vbtK7vdrgcffFDt27eX3f6/g11GjRqlkJAQxcXFaffu3QoKCnKdAjczdrtdJ06cUN++fXXkyBGVLl1a3bt314gRI/Lg2QIAAEBhZnj5yKtcVaUYkvPwXsm84qLRNpvsoVGyh1eR4eXjkRozY5imaXq6CE9wOp2qWbOmevbsqVGjRnm0loSEBAUGBio+Pp5hUQAAAJCZnCQzJVHOYwdknouXJBnFgy4Nf/L2ybdQkZPvqR7vscgv+/bt048//qjo6GglJiZq3Lhx2rNnj+6++25PlwYAAAC4Mbx9Lt3K17h0SllDks3rujqm4kpFJljYbDZNnjxZsbGxMk1TderU0aJFi1SzZk1PlwYAAABkyLDZJNv1NeQpM0UmWERERGjlypWeLgMAAAAolK7fvhQAAAAABQbBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlXp4uAAAAT0hKMZWcIsVfkJxOyc9HKu4r+dglm83wdHkAUOAU6mBhGIZmzZql22+/3dOlAACuE07T1PlEaedhUyfOSqb5v3nFfKSIUobKBpny8SJcAEBOFNihUIcPH9agQYNUpUoV+fr6qmzZsmrZsqU++ugjnT9/XpJ06NAhdezYMVfX27p1az355JO5ukwAQP45d1HauMfU8TPuoUKSzidJ2w+ZOnDCVHKKmfECAAAZKpA9Frt371bLli0VFBSk0aNHq27dunI4HNqyZYs++eQTlStXTrfddptCQ0M9XSoA4DqSmGzqr0OmElOybrfnqFTKXwoqkH8lAcAzCmSPxaOPPiovLy9t2LBBPXv2VM2aNRUVFaVu3bpp7ty56tq1q6RLQ6G+++471+OGDBmiatWqqVixYoqKitJLL72k5ORk1/zhw4erfv36+vLLL1WpUiUFBgbqzjvv1JkzZyRJ/fv317JlyzR27FgZhiHDMLR3716dOnVKvXv3VkhIiPz8/FS1alVNmjQpX58TAMDVJaVIp85lr+2BE6ZSUum1AIDsKnC/xZw4cUI//vijRo8ereLFi2fYxjAyHhdbokQJTZ48WeHh4dqyZYseeOABlShRQs8++6yrza5du/Tdd99pzpw5OnXqlHr27KnXXntNr776qsaOHau//vpLderU0ciRIyVJISEhGjRokP744w/Nnz9fpUuX1s6dO3XhwoVMtyExMVGJiYmu+wkJCdfyVAAAcuhYQvaDwqlzUkqq5GXPw4IAoBApcMFi586dMk1T1atXd5teunRpXbx4UZI0cOBAvf766+ke++KLL7r+X6lSJcXGxmratGluwcLpdGry5MkqUaKEJKlPnz5avHixXn31VQUGBsrHx0fFihVzG2a1f/9+NWjQQI0bN3YtOytxcXEaMWJEzjYcAGBZSmr226Y6864OACiMCuRQqIysW7dOmzZtUu3atd16A9KaPn26WrZsqdDQUPn7++vFF1/U/v373dpUqlTJFSokKSwsTEePHs1y3Y888oimTZum+vXr69lnn9WqVauybD906FDFx8e7bgcOHMjmVgIArPBzZP9MTw4vSZwYCgCyrcAFiypVqsgwDG3fvt1telRUlKpUqSI/P78MH7d69Wr17t1bnTp10pw5c7Rx40a98MILSkpKcmvn7e3tdt8wDDmdWf9s1bFjR+3bt0+DBw/WwYMH1aZNG8XGxmba3uFwKCAgwO0GAMh7pf0lezb/8pUJvHRNCwBA9hS4YFGqVCm1a9dO48aN07lz2TwCT9KqVatUsWJFvfDCC2rcuLGqVq2qffv25Xj9Pj4+Sk1N35ceEhKifv366auvvtK7776rTz75JMfLBgDkLW8vKTTo6u287FJ4SYML5QFADhS4YCFJH374oVJSUtS4cWNNnz5d27Zt0/bt2/XVV1/pzz//lN2e/iemqlWrav/+/Zo2bZp27dql9957T7NmzcrxuitVqqS1a9dq7969On78uJxOp15++WXNnj1bO3fu1NatWzVnzhzVrFkzNzYVAJCLvOyGIssYKlUiqzZS3QhDvt6ZtwEApFcgg0XlypW1ceNGtW3bVkOHDlW9evXUuHFjvf/++4qNjdWoUaPSPea2227T4MGD9dhjj6l+/fpatWqVXnrppRyvOzY2Vna7XbVq1VJISIj2798vHx8fDR06VDfccINatWolu92uadOm5camAgByma+3oVrlDNUqbyiw2P+me9ul8sFS4yhDJYuL3goAyCHDNK+87ijyW0JCggIDAxUfH8/xFgCQjxKTTZm6dAVuw5C8bZLdTqAAgMty8j21wJ1uFgCA3OLwJkQAQG4pkEOhAAAAAFxfCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMCya7pA3vr167VkyRIdPXpUTqfTbd7bb7+dK4UBAAAAKDhyHCxGjx6tF198UdWrV1fZsmVlGP+7amna/wMAAAAoOnIcLMaOHavPPvtM/fv3z4NyAAAAABREOT7GwmazqWXLlnlRCwAAAIACKsfBYvDgwfrggw/yohYAAAAABVSOh0LFxsaqc+fOqly5smrVqiVvb2+3+d9++22uFQcAAACgYMhxsHjiiSe0ZMkSxcTEqFSpUhywDQAAACDnweLzzz/XzJkz1blz57yoBwAAAEABlONjLIKDg1W5cuW8qAUAAABAAZXjYDF8+HANGzZM58+fz4t6AAAAABRAOR4K9d5772nXrl0qW7asKlWqlO7g7V9//TXXigMAAABQMOQ4WNx+++15UAYAAACAgswwTdP0dBFFXUJCggIDAxUfH6+AgABPlwMAAABIytn31BwfYwEAAAAAV8rxUKjU1FS98847mjFjhvbv36+kpCS3+SdPnsy14gAAAAAUDDnusRgxYoTefvtt9erVS/Hx8XrqqafUvXt32Ww2DR8+PA9KBAAAAHC9y3GwmDJlij799FM9/fTT8vLy0l133aUJEybo5Zdf1po1a/KiRgAAAADXuRwHi8OHD6tu3bqSJH9/f8XHx0uSunTporlz5+ZudQAAAAAKhBwHi/Lly+vQoUOSpMqVK+vHH3+UJK1fv14OhyN3qwMAAABQIOQ4WPzrX//S4sWLJUmPP/64XnrpJVWtWlV9+/bVgAEDcr1AAAAAANc/y9exWL16tVavXq2qVauqa9euuVVXkcJ1LAAAAHA9ysn31ByfbvZKzZs3V/Pmza0uBgAAAEABdk0XyPvyyy/VsmVLhYeHa9++fZKkd999V7Nnz87V4gAAAAAUDDkOFh999JGeeuopderUSadPn1ZqaqokKSgoSO+++25u1wcAAACgAMhxsHj//ff16aef6oUXXpDdbndNb9y4sbZs2ZKrxQEAAAAoGHIcLPbs2aMGDRqkm+5wOHTu3LlcKQoAAABAwZLjYBEZGalNmzalm75gwQLVrFkzN2oCAAAAUMBk+6xQI0eOVGxsrJ566ikNHDhQFy9elGmaWrdunaZOnaq4uDhNmDAhL2sFAAAAcJ3K9nUs7Ha7Dh06pDJlymjKlCkaPny4du3aJUkKDw/XiBEjdN999+VpsYUV17EAAADA9Sgn31OzHSxsNpsOHz6sMmXKuKadP39eZ8+edZuGnCNYAAAA4HqUZxfIMwzD7X6xYsVUrFixnFcIAAAAoFDJUbCoVq1aunBxpZMnT1oqCAAAAEDBk6NgMWLECAUGBuZVLQAAAAAKqBwFizvvvJPjKQAAAACkk+3rWFxtCBQAAACAoivbwSKbJ48CAAAAUARleyiU0+nMyzoAAAAAFGDZ7rEAAAAAgMwQLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFiWoytvo/BIdZpKTv3ffZsh+XhxEUQAAABcG4LFVSxdulQxMTE6deqUgoKCPF2OZalOUxeSpH9OmjoaLyWlSDabVLK4VL6UFOBHwAAAAEDOFfqhUIcPH9bjjz+uqKgoORwORUREqGvXrlq8eLGnS8t3qU5TJ85I63eZOnBCSkyRTEmpTun4GWnTXlO7j5pKSuEq6wAAAMiZQt1jsXfvXrVs2VJBQUEaM2aM6tatq+TkZC1cuFADBw7Un3/+6ekS89X5RGnr36ZSs7iI+t8nJF9vqUIpUzYbPRcAAADInkLdY/Hoo4/KMAytW7dOPXr0ULVq1VS7dm099dRTWrNmjfbu3SvDMLRp0ybXY06fPi3DMLR06VK3Za1cuVI33HCDfH19deONN+r33393zdu3b5+6du2qkiVLqnjx4qpdu7bmzZuXT1uZPSmppv4+mXWouOzvE6aSUq/eDgAAALis0AaLkydPasGCBRo4cKCKFy+ebn5Oj5d45pln9NZbb2n9+vUKCQlR165dlZycLEkaOHCgEhMTtXz5cm3ZskWvv/66/P39c2Mzck2KUzoan722F5OlMxfyth4AAAAULoV2KNTOnTtlmqZq1KiRK8sbNmyY2rVrJ0n6/PPPVb58ec2aNUs9e/bU/v371aNHD9WtW1eSFBUVleWyEhMTlZiY6LqfkJCQKzVmyZTbWaCu5mJS3pUCAACAwqfQ9liYZu4egNy8eXPX/4ODg1W9enVt27ZNkvTEE0/olVdeUcuWLTVs2DBt3rw5y2XFxcUpMDDQdYuIiMjVWjNkSDk5YsJWaF8ZAAAAyAuF9utj1apVZRhGlgdo2/7/23PaEHJ5eFNO3H///dq9e7f69OmjLVu2qHHjxnr//fczbT906FDFx8e7bgcOHMjxOnPKkBSYfkRYhmyGFJTNtgAAAIBUiINFcHCw2rdvrw8++EDnzp1LN//06dMKCQmRJB06dMg1Pe2B3GmtWbPG9f9Tp07pr7/+Us2aNV3TIiIi9PDDD+vbb7/V008/rU8//TTT2hwOhwICAtxuec3hbah8cPb6LEqVkHzseVwQAAAACpVCGywk6YMPPlBqaqqaNm2qmTNnaseOHdq2bZvee+89NW/eXH5+frrxxhv12muvadu2bVq2bJlefPHFDJc1cuRILV68WL///rv69++v0qVL6/bbb5ckPfnkk1q4cKH27NmjX3/9VUuWLHELHdeLYH+pbGDWbXy9pSplDXlzkTwAAADkQKEOFlFRUfr1118VExOjp59+WnXq1FG7du20ePFiffTRR5Kkzz77TCkpKWrUqJGefPJJvfLKKxku67XXXtOgQYPUqFEjHT58WD/88IN8fHwkSampqRo4cKBq1qypDh06qFq1avrwww/zbTuzy8fLULUwQxVDJO8reiQM41JPRf1Khoo5PFMfAAAACi7DzO2jnJFjCQkJCgwMVHx8fL4Mi0pNNZWcKp06J11MNmW3GQr2l3y8LoUPAAAAQMrZ99RCe7pZZM5uN2S3S2E+Us7OFQUAAABkrFAPhQIAAACQPwgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwzMvTBQAAAEiS6UyVnE7JZpNhs3u6HAA5RLAAAAAeY5qmlJwo88IZpR47IKWmSHZv2ctEyPD1l+Hj6+kSAWRToQ4WS5cuVUxMjE6dOqWgoCBPlwMAANIwTafMcwlK2b1J5rl4t3nOY/tl+AfJK6q+jGIBMgzDQ1UCyK7r4hiL/v37yzAMGYYhb29vRUZG6tlnn9XFixfzfN2//fabbrvtNpUpU0a+vr6qVKmSevXqpaNHj+b5ugEAKMrMC+eUvH1tulDhmn/29KX5F8/lc2UArsV1ESwkqUOHDjp06JB2796td955R+PHj9ewYcPydJ3Hjh1TmzZtFBwcrIULF2rbtm2aNGmSwsPDde4cH2IAAOQVMyVZqQd3SElX+REx8YJSD+2SmZqSP4UBuGbXTbBwOBwKDQ1VRESEbr/9drVt21Y//fSTa77T6VRcXJwiIyPl5+enevXq6ZtvvnFbxrx581StWjX5+fkpJiZGe/fuzXKdK1euVHx8vCZMmKAGDRooMjJSMTExeueddxQZGelqt2zZMjVt2lQOh0NhYWF67rnnlJJy6QPuiy++kL+/v3bs2OFq/+ijj6pGjRo6f/58LjwzAAAUPmZKspwnD2WrrfPEQSklKY8rAmDVdRMs0vr999+1atUq+fj4uKbFxcXpiy++0Mcff6ytW7dq8ODBuueee7Rs2TJJ0oEDB9S9e3d17dpVmzZt0v3336/nnnsuy/WEhoYqJSVFs2bNunTwWAb++ecfderUSU2aNNFvv/2mjz76SBMnTtQrr7wiSerbt686deqk3r17KyUlRXPnztWECRM0ZcoUFStWLMNlJiYmKiEhwe0GAECRknj+0oHa2ZGSJDMpMW/rAWDZdXPw9pw5c+Tv76+UlBQlJibKZrNp3Lhxki59ER89erQWLVqk5s2bS5KioqK0YsUKjR8/XtHR0froo49UuXJlvfXWW5Kk6tWra8uWLXr99dczXeeNN96o559/XnfffbcefvhhNW3aVLfccov69u2rsmXLSpI+/PBDRUREaNy4cTIMQzVq1NDBgwc1ZMgQvfzyy7LZbBo/frxuuOEGPfHEE/r22281fPhwNWrUKNP1xsXFacSIEbn11AEAUABl/INe7rUHkN+umx6LmJgYbdq0SWvXrlW/fv107733qkePHpKknTt36vz582rXrp38/f1dty+++EK7du2SJG3btk3NmjVzW+blEJKVV199VYcPH9bHH3+s2rVr6+OPP1aNGjW0ZcsW13KbN2/udjaKli1b6uzZs/r7778lSSVLltTEiRNd4eZqPSVDhw5VfHy863bgwIHsP1EAABQGPn6Skc2vITa7DG9H3tYDwLLrpseiePHiqlKliiTps88+U7169TRx4kTdd999Onv2rCRp7ty5KleunNvjHA7rHzSlSpXSHXfcoTvuuEOjR49WgwYN9Oabb+rzzz/P9jKWL18uu92uQ4cO6dy5cypRokSmbR0OR67UDQBAQWV4+cgWVEbOU4ev2tZWsqzk5Z0PVQGw4rrpsUjLZrPp+eef14svvqgLFy6oVq1acjgc2r9/v6pUqeJ2i4iIkCTVrFlT69atc1vOmjVrcrxuHx8fVa5c2XVWqJo1a2r16tVux2CsXLlSJUqUUPny5SVJq1at0uuvv64ffvhB/v7+euyxx6510wEAKBIMbx/Zy1WVbFf5jdPuJXt4FRlePlm3A+Bx12WwkKQ77rhDdrtdH3zwgUqUKKHY2FgNHjxYn3/+uXbt2qVff/1V77//vqtX4eGHH9aOHTv0zDPPaPv27fr66681efLkLNcxZ84c3XPPPZozZ47++usvbd++XW+++abmzZunbt26Sbp0hqcDBw7o8ccf159//qnZs2dr2LBheuqpp2Sz2XTmzBn16dNHTzzxhDp27KgpU6Zo+vTp6c5YBQAA3BnFAuRVrZGUWWjwdsi7WhMZxQLytzAA1+S6GQp1JS8vLz322GN644039Mgjj2jUqFEKCQlRXFycdu/eraCgIDVs2FDPP/+8JKlChQqaOXOmBg8erPfff19NmzbV6NGjNWDAgEzXUatWLRUrVkxPP/20Dhw4IIfDoapVq2rChAnq06ePJKlcuXKaN2+ennnmGdWrV0/BwcG677779OKLL0qSBg0apOLFi2v06NGSpLp162r06NF66KGH1Lx583RDtwAAwCWG3Uu2wBB5120l8+RhpZ48KKUkS17espcuJyMoVIaPrwzbdfs7KIA0DDOz86wi3yQkJCgwMFDx8fEKCOBXGQBA0WOappScKJmmZBiSt8PtxCkAPCMn31Ov2x4LAABQdBiGIfn4eroMABbQtwgAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAs8/J0AQAAAEBuMp2pUnKSzMTzUmqK5O2Q4eN76V/D8HR5hRbBAgAAAIWGmXhBqUf2KPXY31LSBdd0o3ig7KFRspUsK8Pb4cEKC68CPxRq9erVstvt6ty5c7Yfs3fvXhmGoU2bNuVdYQAAAMhXZuIFJe/4Ran/7HALFZJknotXyq6NSj20S2ZykocqLNwKfLCYOHGiHn/8cS1fvlwHDx7M1WUnJfGiAwAAKAjM1BSlHNwh88yJLNul/rND5tlT+VRV0VKgg8XZs2c1ffp0PfLII+rcubMmT57smnfq1Cn17t1bISEh8vPzU9WqVTVp0iRJUmRkpCSpQYMGMgxDrVu3liT1799ft99+u1599VWFh4erevXqkqQtW7bolltukZ+fn0qVKqUHH3xQZ8+eda1r6dKlatq0qYoXL66goCC1bNlS+/bty58nAQAAAFJKkpzH/85W09TDu2UmJ+ZxQUVPgT7GYsaMGapRo4aqV6+ue+65R08++aSGDh0qwzD00ksv6Y8//tD8+fNVunRp7dy5UxcuXOoSW7dunZo2bapFixapdu3a8vHxcS1z8eLFCggI0E8//SRJOnfunNq3b6/mzZtr/fr1Onr0qO6//3499thjmjx5slJSUnT77bfrgQce0NSpU5WUlKR169ZleWBQYmKiEhP/92JOSEjIo2cIAACgaHCePS2lJGevbfwxyenM24KKoAIdLCZOnKh77rlHktShQwfFx8dr2bJlat26tfbv368GDRqocePGkqRKlSq5HhcSEiJJKlWqlEJDQ92WWbx4cU2YMMEVNj799FNdvHhRX3zxhYoXLy5JGjdunLp27arXX39d3t7eio+PV5cuXVS5cmVJUs2aNbOsOy4uTiNGjLD+BAAAAOCSbIYKSZJpSibBIrcV2KFQ27dv17p163TXXXdJkry8vNSrVy9NnDhRkvTII49o2rRpql+/vp599lmtWrUqW8utW7euWw/Gtm3bVK9ePVeokKSWLVvK6XRq+/btCg4OVv/+/dW+fXt17dpVY8eO1aFDh7Jcx9ChQxUfH++6HThwIKebDwAAgLS8fa7e5jLDdumGXFVgn9GJEycqJSVF4eHh8vLykpeXlz766CPNnDlT8fHx6tixo/bt26fBgwfr4MGDatOmjWJjY6+63LQBIrsmTZqk1atXq0WLFpo+fbqqVaumNWvWZNre4XAoICDA7QYAAIBrZyseKGXzNLK2oDKS3Z7HFRU9BTJYpKSk6IsvvtBbb72lTZs2uW6//fabwsPDNXXqVEmXhjz169dPX331ld5991198sknkuTqkUhNTb3qumrWrKnffvtN586dc01buXKlbDab6+Bu6dKB4EOHDtWqVatUp04dff3117m5yQAAAMiKl4/spSOy1dQeFiXDKwc9HMiWAnmMxZw5c3Tq1Cndd999CgwMdJvXo0cPTZw4UQcPHlSjRo1Uu3ZtJSYmas6cOa5jH8qUKSM/Pz8tWLBA5cuXl6+vb7rlXNa7d28NGzZM/fr10/Dhw3Xs2DE9/vjj6tOnj8qWLas9e/bok08+0W233abw8HBt375dO3bsUN++ffP8eQAAAMAlht1L9rAomRcS5Dx9NNN29gq1ZBTP+HsfrCmQPRYTJ05U27ZtMwwDPXr00IYNG+Tl5aWhQ4fqhhtuUKtWrWS32zVt2jRJl47HeO+99zR+/HiFh4erW7duma6rWLFiWrhwoU6ePKkmTZro3//+t9q0aaNx48a55v/555/q0aOHqlWrpgcffFADBw7UQw89lDcbDwAAgAwZDj95VW4gr4q1ZfiVSDtHRkBpeVVvKnvZivRW5BHDNE3T00UUdQkJCQoMDFR8fDzHWwAAAFhkmk4pOfHSFbadqZKXjwy7lwwfX0+XVuDk5HtqgRwKBQAAAGTGMGySj58MHz9Pl1KkFMihUAAAAACuLwQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABY5uXpAgAAKMzM5CTJmSrJlGx2Gd4OT5cEAHmCYAEAQB4wkxJlno9X6uE9cp5PkCQZfv6yl60km39JGT6+Hq4QAHJXgRgK1b9/fxmG4bqVKlVKHTp00ObNmz1dGgAA6ZiJF5Sye6OSt62W89RhKfG8lHhe5umjStm+Tsl/bZCZeN7TZQJArioQwUKSOnTooEOHDunQoUNavHixvLy81KVLl0zbJycn52N1AABcYiYnKmXfVjlPHcm8zZkTSt61SWbSxXysDADyVoEJFg6HQ6GhoQoNDVX9+vX13HPP6cCBAzp27Jj27t0rwzA0ffp0RUdHy9fXV1OmTNGJEyd01113qVy5cipWrJjq1q2rqVOnupb5ySefKDw8XE6n021d3bp104ABA1z3Z8+erYYNG8rX11dRUVEaMWKEUlJSJEmmaWr48OGqUKGCHA6HwsPD9cQTT+TPkwIAuO6YSRflPHHw6u3ij8m8eDYfKgKA/FFggkVaZ8+e1VdffaUqVaqoVKlSrunPPfecBg0apG3btql9+/a6ePGiGjVqpLlz5+r333/Xgw8+qD59+mjdunWSpDvuuEMnTpzQkiVLXMs4efKkFixYoN69e0uSfv75Z/Xt21eDBg3SH3/8ofHjx2vy5Ml69dVXJUkzZ87UO++8o/Hjx2vHjh367rvvVLdu3Xx8NgAA1wvTmarUo/skmdlqn3p4j8wUetgBFA4F5uDtOXPmyN/fX5J07tw5hYWFac6cObLZ/peNnnzySXXv3t3tcbGxsa7/P/7441q4cKFmzJihpk2bqmTJkurYsaO+/vprtWnTRpL0zTffqHTp0oqJiZEkjRgxQs8995z69esnSYqKitKoUaP07LPPatiwYdq/f79CQ0PVtm1beXt7q0KFCmratGmW25KYmKjExETX/YSEBAvPDADgupGaKl08l+3mZuKF/z9jlHfe1QQA+aTA9FjExMRo06ZN2rRpk9atW6f27durY8eO2rdvn6tN48aN3R6TmpqqUaNGqW7dugoODpa/v78WLlyo/fv3u9r07t1bM2fOdH3RnzJliu68805XYPntt980cuRI+fv7u24PPPCADh06pPPnz+uOO+7QhQsXFBUVpQceeECzZs1yDZPKTFxcnAIDA123iIiI3HqaAACeZhh50xYArnMFJlgUL15cVapUUZUqVdSkSRNNmDBB586d06effurWJq0xY8Zo7NixGjJkiJYsWaJNmzapffv2SkpKcrXp2rWrTNPU3LlzdeDAAf3888+uYVDSpWFXI0aMcIWaTZs2acuWLdqxY4d8fX0VERGh7du368MPP5Sfn58effRRtWrVKsuDx4cOHar4+HjX7cCBA7n4TAEAPMbLW7aA0tlubisRLHnRWwGgcCgwQ6GuZBiGbDabLly4kGmblStXqlu3brrnnnskSU6nU3/99Zdq1arlauPr66vu3btrypQp2rlzp6pXr66GDRu65jds2FDbt29XlSpVMl2Pn5+funbtqq5du2rgwIGqUaOGtmzZ4ractBwOhxwOLpAEAIWNYRgygsOkv7dLqVn3XsuwyVamggybPX+KA4A8VmCCRWJiog4fPixJOnXqlMaNG6ezZ8+qa9eumT6matWq+uabb7Rq1SqVLFlSb7/9to4cOeIWLKRLw6G6dOmirVu3ukLIZS+//LK6dOmiChUq6N///rdsNpt+++03/f7773rllVc0efJkpaamqlmzZipWrJi++uor+fn5qWLFirn/JAAArnuGt0NeFWopZU/W11qyl6vKVbgBFCoFZijUggULFBYWprCwMDVr1kzr16/Xf/7zH7Vu3TrTx7z44otq2LCh2rdvr9atWys0NFS33357una33HKLgoODtX37dt19991u89q3b685c+boxx9/VJMmTXTjjTfqnXfecQWHoKAgffrpp2rZsqVuuOEGLVq0SD/88IPb2aoAAEWHYfeSrXQ5eUXVk7wzuLq2l4+8KtaSPTRShpdP/hcIAHnEME0ze+fEQ55JSEhQYGCg4uPjFRAQ4OlyAAC5wExNlVIS5Uw4IWfCccmUbCWCZAsqK3n5yLAXmEEDAIqwnHxP5VMNAIA8YNjtkr2Y7CHFZCtdTjIlw1ZgBgoAQI4RLAAAyGOGYZM4syyAQo6fTgAAAABYRrAAAAAAYBnBAgAAAIBlBAsAAAAAlhEsAAAAAFhGsAAAAABgGcECAAAAgGUECwAAAACWESwAAAAAWEawAAAAAGAZwQIAAACAZQQLAAAAAJYRLAAAAABYRrAAAAAAYJmXpwuAZJqmJCkhIcHDlQAAAAD/c/n76eXvq1khWFwHzpw5I0mKiIjwcCUAAABAemfOnFFgYGCWbQwzO/EDecrpdOrgwYMqUaKEDMPI13UnJCQoIiJCBw4cUEBAQL6uG/mP/V20sL+LFvZ30cL+Llo8ub9N09SZM2cUHh4umy3royjosbgO2Gw2lS9f3qM1BAQE8MFUhLC/ixb2d9HC/i5a2N9Fi6f299V6Ki7j4G0AAAAAlhEsAAAAAFhGsCjiHA6Hhg0bJofD4elSkA/Y30UL+7toYX8XLezvoqWg7G8O3gYAAABgGT0WAAAAACwjWAAAAACwjGABAAAAwDKCRRFQqVIlGYaR7jZw4EBJUuvWrdPNe/jhhz1cNa5VamqqXnrpJUVGRsrPz0+VK1fWqFGjlPZwKtM09fLLLyssLEx+fn5q27atduzY4cGqca2ys7/79++f7j3eoUMHD1YNK86cOaMnn3xSFStWlJ+fn1q0aKH169e75vP+Llyutr95fxdcy5cvV9euXRUeHi7DMPTdd9+5zc/Oe/nkyZPq3bu3AgICFBQUpPvuu09nz57Nx61wR7AoAtavX69Dhw65bj/99JMk6Y477nC1eeCBB9zavPHGG54qFxa9/vrr+uijjzRu3Dht27ZNr7/+ut544w29//77rjZvvPGG3nvvPX388cdau3atihcvrvbt2+vixYserBzXIjv7W5I6dOjg9h6fOnWqhyqGVffff79++uknffnll9qyZYtuvfVWtW3bVv/8848k3t+FzdX2t8T7u6A6d+6c6tWrpw8++CDD+dl5L/fu3Vtbt27VTz/9pDlz5mj58uV68MEH82sT0jNR5AwaNMisXLmy6XQ6TdM0zejoaHPQoEGeLQq5pnPnzuaAAQPcpnXv3t3s3bu3aZqm6XQ6zdDQUHPMmDGu+adPnzYdDoc5derUfK0V1l1tf5umafbr18/s1q1bPleGvHD+/HnTbrebc+bMcZvesGFD84UXXuD9XchcbX+bJu/vwkKSOWvWLNf97LyX//jjD1OSuX79eleb+fPnm4ZhmP/880++1Z4WPRZFTFJSkr766isNGDBAhmG4pk+ZMkWlS5dWnTp1NHToUJ0/f96DVcKKFi1aaPHixfrrr78kSb/99ptWrFihjh07SpL27Nmjw4cPq23btq7HBAYGqlmzZlq9erVHasa1u9r+vmzp0qUqU6aMqlevrkceeUQnTpzwRLmwKCUlRampqfL19XWb7ufnpxUrVvD+LmSutr8v4/1d+GTnvbx69WoFBQWpcePGrjZt27aVzWbT2rVr871mSfLyyFrhMd99951Onz6t/v37u6bdfffdqlixosLDw7V582YNGTJE27dv17fffuu5QnHNnnvuOSUkJKhGjRqy2+1KTU3Vq6++qt69e0uSDh8+LEkqW7as2+PKli3rmoeC42r7W7o0TKJ79+6KjIzUrl279Pzzz6tjx45avXq17Ha7B6tHTpUoUULNmzfXqFGjVLNmTZUtW1ZTp07V6tWrVaVKFd7fhczV9rfE+7uwys57+fDhwypTpozbfC8vLwUHB3vs/U6wKGImTpyojh07Kjw83DUt7Vi8unXrKiwsTG3atNGuXbtUuXJlT5QJC2bMmKEpU6bo66+/Vu3atbVp0yY9+eSTCg8PV79+/TxdHnJZdvb3nXfe6Wpft25d3XDDDapcubKWLl2qNm3aeKp0XKMvv/xSAwYMULly5WS329WwYUPddddd+uWXXzxdGvLA1fY3729cTxgKVYTs27dPixYt0v33359lu2bNmkmSdu7cmR9lIZc988wzeu6553TnnXeqbt266tOnjwYPHqy4uDhJUmhoqCTpyJEjbo87cuSIax4Kjqvt74xERUWpdOnSvMcLqMqVK2vZsmU6e/asDhw4oHXr1ik5OVlRUVG8vwuhrPZ3Rnh/Fw7ZeS+Hhobq6NGjbvNTUlJ08uRJj73fCRZFyKRJk1SmTBl17tw5y3abNm2SJIWFheVDVcht58+fl83m/ta22+1yOp2SpMjISIWGhmrx4sWu+QkJCVq7dq2aN2+er7XCuqvt74z8/fffOnHiBO/xAq548eIKCwvTqVOntHDhQnXr1o33dyGW0f7OCO/vwiE77+XmzZvr9OnTbr2V//3vf+V0Ol0/Euc7jxwyjnyXmppqVqhQwRwyZIjb9J07d5ojR440N2zYYO7Zs8ecPXu2GRUVZbZq1cpDlcKqfv36meXKlTPnzJlj7tmzx/z222/N0qVLm88++6yrzWuvvWYGBQWZs2fPNjdv3mx269bNjIyMNC9cuODBynEtrra/z5w5Y8bGxpqrV6829+zZYy5atMhs2LChWbVqVfPixYserh7XYsGCBeb8+fPN3bt3mz/++KNZr149s1mzZmZSUpJpmry/C5us9jfv74LtzJkz5saNG82NGzeaksy3337b3Lhxo7lv3z7TNLP3Xu7QoYPZoEEDc+3ateaKFSvMqlWrmnfddZenNskkWBQRCxcuNCWZ27dvd5u+f/9+s1WrVmZwcLDpcDjMKlWqmM8884wZHx/voUphVUJCgjlo0CCzQoUKpq+vrxkVFWW+8MILZmJioquN0+k0X3rpJbNs2bKmw+Ew27Rpk+61gYLhavv7/Pnz5q233mqGhISY3t7eZsWKFc0HHnjAPHz4sIcrx7WaPn26GRUVZfr4+JihoaHmwIEDzdOnT7vm8/4uXLLa37y/C7YlS5aYktLd+vXrZ5pm9t7LJ06cMO+66y7T39/fDAgIMO+9917zzJkzHtiaSwzTTHN5VgAAAAC4BhxjAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALCNYAAAAALCMYAEAKBSWLl0qwzB0+vRpT5cCAEUSwQIAcN35+OOPVaJECaWkpLimnT17Vt7e3mrdurVb28uBIiwsTIcOHVJgYGA+VwsAkAgWAIDrUExMjM6ePasNGza4pv38888KDQ3V2rVrdfHiRdf0JUuWqEKFCqpevbpCQ0NlGIYnSgaAIo9gAQC47lSvXl1hYWFaunSpa9rSpUvVrVs3RUZGas2aNW7TY2Ji0g2Fmjx5soKCgrRw4ULVrFlT/v7+6tChgw4dOuT22KZNm6p48eIKCgpSy5YttW/fvvzaTAAoVAgWAIDrUkxMjJYsWeK6v2TJErVu3VrR0dGu6RcuXNDatWsVExOT4TLOnz+vN998U19++aWWL1+u/fv3KzY2VpKUkpKi22+/XdHR0dq8ebNWr16tBx98kB4PALhGXp4uAACAjMTExOjJJ59USkqKLly4oI0bNyo6OlrJycn6+OOPJUmrV69WYmKiYmJitHv37nTLuNy2cuXKkqTHHntMI0eOlCQlJCQoPj5eXbp0cc2vWbNmPm0dABQ+9FgAAK5LrVu31rlz57R+/Xr9/PPPqlatmkJCQhQdHe06zmLp0qWKiopShQoVMlxGsWLFXKFBksLCwnT06FFJUnBwsPr376/27dura9euGjt2rNswKQBAzhAsAADXpSpVqqh8+fJasmSJlixZoujoaElSeHi4IiIitGrVKi1ZskS33HJLpsvw9vZ2u28YhkzTdN2fNGmSVq9erRYtWmj69OmqVq2a2/EbAIDsI1gAAK5blw/KXrp0qdtpZlu1aqX58+dr3bp1mR5fkV0NGjTQ0KFDtWrVKtWpU0dff/21xaoBoGgiWAAArlsxMTFasWKFNm3a5OqxkKTo6GiNHz9eSUlJ1xws9uzZo6FDh2r16tXat2+ffvzxR+3YsYPjLADgGnHwNgDguhUTE6MLFy6oRo0aKlu2rGt6dHS0zpw54zot7bUoVqyY/vzzT33++ec6ceKEwsLCNHDgQD300EO5VT4AFCmGmXawKQAAAABcA4ZCAQAAALCMYAEAAADAMoIFAAAAAMsIFgAAAAAsI1gAAAAAsIxgAQAAAMAyggUAAAAAywgWAAAAACwjWAAAAACwjGABAAAAwDKCBQAAAADLCBYAAAAALPs/QdVokPSTawQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "df = {\n",
+    "    'Team': ['Yankees', 'Mets', 'Dodgers', 'Giants', 'Cubs', 'Red Sox', 'Astros', 'Braves'],\n",
+    "    'Wins': [95, 85, 100, 88, 75, 92, 98, 89],\n",
+    "    'Playoffs': [1, 0, 1, 0, 0, 1, 1, 1]\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def plot_wins_vs_playoffs(df):\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    sns.scatterplot(\n",
+    "        x='Wins', \n",
+    "        y='Team', \n",
+    "        hue='Playoffs', \n",
+    "        data=df, \n",
+    "        palette='coolwarm', \n",
+    "        s=80, \n",
+    "        alpha=0.8\n",
+    "    )\n",
+    "\n",
+    "    plt.title(\"Wins vs Team (Colored by Playoff Qualification)\", fontsize=14, fontweight='bold')\n",
+    "    plt.xlabel(\"Wins\")\n",
+    "    plt.ylabel(\"Team\")\n",
+    "    plt.legend(title='Playoffs', loc='best')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_wins_vs_playoffs(df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Take a rough estimate, and now use that moving forward.  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wins needed to make playoffs: 89\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'Team': ['Yankees', 'Mets', 'Dodgers', 'Giants', 'Cubs', 'Red Sox', 'Astros', 'Braves'],\n",
+    "    'Wins': [95, 85, 100, 88, 75, 92, 98, 89],\n",
+    "    'Playoffs': [1, 0, 1, 0, 0, 1, 1, 1]\n",
+    "})\n",
+    "\n",
+    "\n",
+    "WINS_NEED_TO_MAKE_PLAYOFFS = df[df['Playoffs'] == 1]['Wins'].min()\n",
+    "print(\"Wins needed to make playoffs:\", WINS_NEED_TO_MAKE_PLAYOFFS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now do moneyball but for the NBA. \n",
+    "\n",
+    "#### I would like you to do try and do this on your own first.  \n",
+    "\n",
+    "It's not easy, but its fun af.  \n",
+    "\n",
+    "There is a guided lecture on how to do this that I can send you, but I'd like for you to try and figure it out on your own first.  \n",
+    "\n",
+    "If you are fully stuck, ask in slack how other people did it if that doesn't work (I highly encourage collorbration and learning from eachother.  I still consider that doing it on your own.)  \n",
+    "\n",
+    "If that doesn't work, DM me and I will send you the lectures explaining how to do the whole thing."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rough Guide a v1 model.\n",
+    "0. Make a model to predict PTS \n",
+    "0. For v1, dont use `2P, 3P, FG, or FT`.  Instead use `2PA, 3PA, FTA`.  \n",
+    "0. Include any other cols use see fit.  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                    PTS   R-squared:                       1.000\n",
+      "Model:                            OLS   Adj. R-squared:                    nan\n",
+      "Method:                 Least Squares   F-statistic:                       nan\n",
+      "Date:                Mon, 27 Oct 2025   Prob (F-statistic):                nan\n",
+      "Time:                        04:42:25   Log-Likelihood:                 155.95\n",
+      "No. Observations:                   6   AIC:                            -299.9\n",
+      "Df Residuals:                       0   BIC:                            -301.2\n",
+      "Df Model:                           5                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const          0.7062        inf          0        nan         nan         nan\n",
+      "2PA            1.2600        inf          0        nan         nan         nan\n",
+      "3PA            3.3378        inf          0        nan         nan         nan\n",
+      "FTA            4.3378        inf          0        nan         nan         nan\n",
+      "AST          -13.5200        inf         -0        nan         nan         nan\n",
+      "REB           -1.0000        inf         -0        nan         nan         nan\n",
+      "TOV           16.4933        inf          0        nan         nan         nan\n",
+      "==============================================================================\n",
+      "Omnibus:                          nan   Durbin-Watson:                   0.109\n",
+      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.512\n",
+      "Skew:                          -0.250   Prob(JB):                        0.774\n",
+      "Kurtosis:                       1.659   Cond. No.                     1.65e+03\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The input rank is higher than the number of observations.\n",
+      "[3] The condition number is large, 1.65e+03. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n",
+      "\n",
+      "Model Performance:\n",
+      "RMSE: 20.33\n",
+      "R²: -3.134\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 6 samples were given.\n",
+      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/statsmodels/regression/linear_model.py:1795: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return 1 - (np.divide(self.nobs - self.k_constant, self.df_resid)\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/statsmodels/regression/linear_model.py:1795: RuntimeWarning: invalid value encountered in scalar multiply\n",
+      "  return 1 - (np.divide(self.nobs - self.k_constant, self.df_resid)\n",
+      "/usr/local/python/3.12.1/lib/python3.12/site-packages/statsmodels/regression/linear_model.py:1717: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  return np.dot(wresid, wresid) / self.df_resid\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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yCDoAAMCyCDoAAMCyghp0PvnkE02aNEldu3aVzWbTBx98cNFpUlJSdNVVV8ntduuKK67QmjVrGr2dAACgZQpq0MnNzdWwYcO0YsWKGtU/dOiQJk6cqBtuuEG7du3SI488opkzZ+qjjz5q5JYCAICWKKg/6jlhwgRNmDChxvVXrVqlnj176vnnn5ckDRgwQH//+9/161//WgkJCY3VTAAA0EK1qF8vT01NVXx8vF9ZQkKCHnnkkSqnKSwsVGFhoe95dnZ2YzVPkpSWlqbMzMxGXQaAqhUWFsrtdge7GcAlLTIyUpdffnmwmyGphQWd9PR0RUVF+ZVFRUUpOztb+fn5CgsLqzTN0qVLtWTJkiZpX1pamvoPGKD8vLwmWR6AQGySTLAbAVzSwsLDtffLL5tF2GlRQacuFixYoMTERN/z7OxsxcTENMqyMjMzlZ+Xp+/Pf05Rl/dulGUAqNqX2z7W/77+35r445+p39ARwW4OcEnKSDuo3z/zmDIzMwk6tRUdHa2MjAy/soyMDLVp0yZgb44kud3uJu/Gjrq8ty7rM6hJlwmg9ANWkjp27c57EICkFvY9OnFxcUpOTvYr27Rpk+Li4oLUIgAA0JwFNeicO3dOu3bt0q5duySVXj6+a9cupaWlSSo97TRt2jRf/QceeEBff/21/t//+3/au3evXnrpJb3zzjt69NFHg9F8AADQzAU16Gzfvl1XXnmlrrzySklSYmKirrzySi1atEiSdPz4cV/okaSePXtqw4YN2rRpk4YNG6bnn39er7zyCpeWAwCAgII6Rmfs2LEypuqrIwJ96/HYsWP1xRdfNGKrAACAVbSoMToAAAC1QdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWRdABAACWFfSgs2LFCvXo0UOhoaGKjY3Vtm3bqq2/fPly9evXT2FhYYqJidGjjz6qgoKCJmotAABoSYIadNauXavExEQlJSVp586dGjZsmBISEnTixImA9d966y399Kc/VVJSkr788ku9+uqrWrt2rR5//PEmbjkAAGgJghp0li1bplmzZmnGjBkaOHCgVq1apfDwcK1evTpg/c8++0zXXHONpk6dqh49emjcuHGaMmXKRXuBAADApSloQaeoqEg7duxQfHz8+cbY7YqPj1dqamrAaUaPHq0dO3b4gs3XX3+tDz/8UDfffHOVyyksLFR2drbfDQAAXBpCgrXgzMxMeTweRUVF+ZVHRUVp7969AaeZOnWqMjMzde2118oYo5KSEj3wwAPVnrpaunSplixZ0qBtBwAALUPQByPXRkpKip5++mm99NJL2rlzp9577z1t2LBBTz31VJXTLFiwQFlZWb7b0aNHm7DFAAAgmILWoxMZGSmHw6GMjAy/8oyMDEVHRwecZuHChbr33ns1c+ZMSdKQIUOUm5ur+++/Xz/72c9kt1fObW63W263u+FXAAAANHtB69FxuVwaMWKEkpOTfWVer1fJycmKi4sLOE1eXl6lMONwOCRJxpjGaywAAGiRgtajI0mJiYmaPn26Ro4cqVGjRmn58uXKzc3VjBkzJEnTpk1Tt27dtHTpUknSpEmTtGzZMl155ZWKjY3VgQMHtHDhQk2aNMkXeAAAAMoFNehMnjxZJ0+e1KJFi5Senq7hw4dr48aNvgHKaWlpfj04TzzxhGw2m5544gl9++236tSpkyZNmqRf/OIXwVoFAADQjAU16EjS3LlzNXfu3ICvpaSk+D0PCQlRUlKSkpKSmqBlAACgpWtRV10BAADUBkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYFkEHAABYVtCDzooVK9SjRw+FhoYqNjZW27Ztq7b+2bNnNWfOHHXp0kVut1t9+/bVhx9+2EStBQAALUlIMBe+du1aJSYmatWqVYqNjdXy5cuVkJCgffv2qXPnzpXqFxUV6Tvf+Y46d+6sdevWqVu3bjpy5IjatWvX9I0HAADNXlCDzrJlyzRr1izNmDFDkrRq1Spt2LBBq1ev1k9/+tNK9VevXq3Tp0/rs88+k9PplCT16NGjKZsMAABakKCduioqKtKOHTsUHx9/vjF2u+Lj45WamhpwmvXr1ysuLk5z5sxRVFSUBg8erKeffloej6fK5RQWFio7O9vvBgAALg1BCzqZmZnyeDyKioryK4+KilJ6enrAab7++mutW7dOHo9HH374oRYuXKjnn39eP//5z6tcztKlS9W2bVvfLSYmpkHXAwAANF9BH4xcG16vV507d9bvfvc7jRgxQpMnT9bPfvYzrVq1qsppFixYoKysLN/t6NGjTdhiAAAQTEEboxMZGSmHw6GMjAy/8oyMDEVHRwecpkuXLnI6nXI4HL6yAQMGKD09XUVFRXK5XJWmcbvdcrvdDdt4AADQIgStR8flcmnEiBFKTk72lXm9XiUnJysuLi7gNNdcc40OHDggr9frK/vqq6/UpUuXgCEHAABc2oJ66ioxMVEvv/yyXn/9dX355ZeaPXu2cnNzfVdhTZs2TQsWLPDVnz17tk6fPq2HH35YX331lTZs2KCnn35ac+bMCdYqAACAZiyol5dPnjxZJ0+e1KJFi5Senq7hw4dr48aNvgHKaWlpstvPZ7GYmBh99NFHevTRRzV06FB169ZNDz/8sObPnx+sVQAAAM1YUIOOJM2dO1dz584N+FpKSkqlsri4OP3jH/9o5FYBAAAraFFXXQEAANQGQQcAAFhWnYJOr169dOrUqUrlZ8+eVa9everdKAAAgIZQp6Bz+PDhgD+7UFhYqG+//bbejQIAAGgItRqMvH79et/jjz76SG3btvU993g8Sk5O5kc2AQBAs1GroHPbbbdJkmw2m6ZPn+73mtPpVI8ePfT88883WOMAAADqo1ZBp/wbiXv27KnPP/9ckZGRjdIoAACAhlCn79E5dOhQQ7cDAACgwdX5CwOTk5OVnJysEydO+P32lCStXr263g0DAACorzoFnSVLlujJJ5/UyJEj1aVLF9lstoZuFwAAQL3VKeisWrVKa9as0b333tvQ7QEAAGgwdfoenaKiIo0ePbqh2wIAANCg6hR0Zs6cqbfeequh2wIAANCg6nTqqqCgQL/73e+0efNmDR06VE6n0+/1ZcuWNUjjAAAA6qNOQWf37t0aPny4JGnPnj1+rzEwGQAANBd1Cjpbt25t6HYAAAA0uDqN0QEAAGgJ6tSjc8MNN1R7imrLli11bhAAAEBDqVPQKR+fU664uFi7du3Snj17Kv3YJwAAQLDUKej8+te/Dli+ePFinTt3rl4NAgAAaCgNOkbnBz/4Ab9zBQAAmo0GDTqpqakKDQ1tyFkCAADUWZ1OXd1xxx1+z40xOn78uLZv366FCxc2SMMAAADqq05Bp23btn7P7Xa7+vXrpyeffFLjxo1rkIYBAADUV52CzmuvvdbQ7QAAAGhwdQo65Xbs2KEvv/xSkjRo0CBdeeWVDdIoAACAhlCnoHPixAndc889SklJUbt27SRJZ8+e1Q033KC3335bnTp1asg2AgAA1Emdrrp66KGHlJOTo3//+986ffq0Tp8+rT179ig7O1vz5s1r6DYCAADUSZ16dDZu3KjNmzdrwIABvrKBAwdqxYoVDEYGAADNRp16dLxer5xOZ6Vyp9Mpr9db70YBAAA0hDoFnRtvvFEPP/ywjh075iv79ttv9eijj+qmm25qsMYBAADUR52Czosvvqjs7Gz16NFDvXv3Vu/evdWzZ09lZ2frhRdeaOg2AgAA1EmdxujExMRo586d2rx5s/bu3StJGjBggOLj4xu0cQAAAPVRqx6dLVu2aODAgcrOzpbNZtN3vvMdPfTQQ3rooYd09dVXa9CgQfrb3/7WWG0FAAColVoFneXLl2vWrFlq06ZNpdfatm2rH//4x1q2bFmDNQ4AAKA+ahV0/vWvf2n8+PFVvj5u3Djt2LGj3o0CAABoCLUKOhkZGQEvKy8XEhKikydP1rtRAAAADaFWQadbt27as2dPla/v3r1bXbp0qXejAAAAGkKtgs7NN9+shQsXqqCgoNJr+fn5SkpK0ne/+90GaxwAAEB91Ory8ieeeELvvfee+vbtq7lz56pfv36SpL1792rFihXyeDz62c9+1igNBdB0jDHyGCOPN8CtrNzrlUq83hrVMzLymtL5eo3kNUbmgvtAZUZl917JXNjGSiXSOXVX1NRn9EVBR+3ffjRAjVJ2m002m2SzlT32K7PJrrL7sue+uqpQxyY57DY5bDY57DbZ7aX3FcvKH9sDlVd4HuKwKcRuk81ma9g/JIDaBZ2oqCh99tlnmj17thYsWCBjSj9GbDabEhIStGLFCkVFRTVKQwH4M8aoxGtUVOJVUYlXhR6viku8KvZ4VewxKvGW3Qd67jUq9nhV4im791au1zKFKzRmkLK9UnZW5Z7n5s5ht8lptynEYVeIwyanvfTe77HdLqejrI7dJmfZfYjDJpfDLldI6c1Z/thRerPbCVG4NNX6CwO7d++uDz/8UGfOnNGBAwdkjFGfPn3Uvn37xmgfYEker1FhiUeFJV4VFJfelweWIs9FHlcoa6o4cmHvRaVbNeV2u83XW2Iv7yXR+d6SivcV61W6D9SwCwq//r/t+uT91zV28mz17DeoyvUp71kyCtCLZHS+J6limTHylk1bXn5hz1XF5x6vOV/ngl6uiq95K/wRy19TScP/ZmB5KCoPQq4KQcgZYpPb4ZAzpDQsuUMccjvtCi27d4fYFep00OuEFqlO34wsSe3bt9fVV1/dkG0BWhRjjIo9RvnFHuUXe1RY7FFBsVeFJR4VlHj9nhcWe1VQcv55Q/aY2CS/f17O8t6Aikf8FXoEAj6vWK+8h6BCYGkp/9yylaP8r1LVKWSGrujcKtjNqZHywFPew1bi9/h8L5t/71vg14o8XhWXGL9Q7ClLUiVeoxJv6b5aV3ab5A5xKNRZOQxdGIrK78PKbvQoIVjqHHQAq/F6z4eWgmKP8os8vufljwuKvX7lHm/9AovLYff90/CFlYpH2yF2uQMdhVd4zFF2y2a32WQvC5iNoTxEBewVLLsvvqCssMTrC+eFZeG8dGyVfPu+VFyrdrhD7KWhx1UafEIrPK5YHuYqDVIuh539Gg2CoANL8xqj/CKP8oo8yi0qUV6RR3mFJcot8ijP97z0cUEdTxc47LayD+6KR7VlR71Oh0JD7H5HweXlbsZNoAmU9sqVBou6Ku+9LKzQK1nxtGvFUFTg14NZWl9Sab0Sr87m1ywgOWw2hbkcCi+7RbhDSu9dIWVlIQp3l75GKEJ1CDpokUq8XuUWenSusES5hSU6V1iivMIKYaaoRLmFdeumD3Xa/Y4yQy884rzg6JMeFVidzWaTK8QmV4hdrUNrN63XGF/gCdRLml/sUcEF5SVl45nOlb23LybEbvOFnwh3WQgqC0etym4R7tL3LO/VSw9BB82KMUYFJV6dKzgfYCqGmdLHtQswNqnCkWGIIlwOhbtDfEeK4a7zj0NDGEsANCS7zVb2HpMUUbNpij1eX0+sr+e1vFe2rAc2t6g0FBWVjVvKLihRdkH1ochhsynC7R+ASkNQ2ePQ0s+HkEY6jYjgIOigSRV7vMopKFFOQbFyCkqUXXafU3A+yNR03IvDbvMdqbVyhyjCFeLr3q4YYMJcDtk5igNaDKfDLmeYXW3Cqv7JoXLFHq9fL255MMotO0V9rrBE5wpKSsfUmZoFolCnXa3cIWod6lTr0JDSm9upNmGl9+FuPlNaEoIOGowxRnlFHr8gk1NQopzCYmWXlZWfr7+YMKfDF2D8jrjKH4eGKDSE8/LApc7psKttmF1tLxKKPF7j6xmu2ENc3ktc8UCroNirguIiZZ4rCjgvu02VglAb3+PS+8YaXI7aI+igVgpLPMrKL1Z2fomy8otLHxeU3ucU1Kw3xuWwq3VYiFpX+KBoE+pUq9Dz59JD7HxIAGg4DrtNbcKc1fYSGWNUWOL19QKVH6hV7H0+V1gir9FFe4ZCnaXhq21o6TLbhp2/b+UOkYNT5E2GoAM/Hq9RTkF5gCkpCzXFvvuLXZlkkxThDjnf3VshyJSXuUPqfvUHADQWm82m0LJL3yNbuQPW8ZrSniFfj3XB+R7rnLLyohJvWa9QoTKyCwMsR2rtDvELP23DnGoTWnof6qS3uiERdC5BHq9Rdn6xzuQX6WxecdmtyNcrc7E+mTCno+wNWuGNWvYGjeBIBYCF2W22sgO4qnuGCks8ys4v8fV2lx8slh9AeioOnj6TX2l6l8OuNmEhahfuUrswp9qFO32Pw11cOVZbBB2L8nqNsgvKQkx+aZApf5ydX1xtmHHYbWVHFyG+I43yQNMm1ClXCKeVAKAq7hCHOrV2qFPryr1CxhjlFnkq9ZaXh6BzhSUq8niVeS7wGCGXw14WfJxqF+Y6/zjcpbB6fFeSlRF0WjBjSr/J90xusU7nFul0XlFpoCl741Q3XMbpsPm/Scoet+WIAQAajc1m811Y0a1dWKXXSzxe37CBs3lFOpNXrLNlve85BaUh6EROoU7kVD4l5g6x+0JP+3CnOoS71D6i9LP9Uh73SNBpAbzGKKegRKdzi3SmLNCUP65uzIzDbqvU7Vn+OIIwAwDNTojDrg4RLnWIqPzFQyUeb2kAyj8/5KC8p/5cYYkKS7zKyK48LsgmqU2Ys3S+4S61jzj/2H0J9AIRdJqREo9XZ/KKdaZCkDldluiru5qpTWiI2ke41L4sxbcLL03wrd0hhBkAsIgQh10dW7nVMcBA6WKP93z4yS/9P1Le219UFpCy8ot1SLl+04W7HL6enw4RZT1BES61stD/D4JOEHi8RmfzinQqt0inzhXpVG6hTp0rHQxcVZxx2G1qV6ErskN46U7ZLtzJ9zUAwCXO6bCrU2t3pXFB5WOCzuSWHUCXH0jnlfYClX7ZYr6+Oes/KNrlsKtjK5c6RrhKw1WESx1buRTuanmxoeW1uAUxZd/CeepcoTJzi3TqXKFOlfXUVNVB4w6xl6XqsnQdURpu2oQ5+SZOAECtVBwTFNMh3O+1whJP6VmEC0JQVn6xijxeHc8q0PGsAr9pwpyOgAGoOX9tCEGngWQXFGtXeqFaX32btp9y6O9n03TqXJFKqkg0TodNHSPclXYYBgIDAJqCO8Sh6DYORbfx/6XWQGcdMsvOOuQXe/TNmXx9c8Fl8a3cIb7/Z/Zzdjk792rKVakWQaeB7Eo7qyc/Oa0ON87UkVxJKh0M5rDb1CHcVSnQtA61zvlPAIB1OOy282OBos6XF3u8Op1bHoAKfUGo4s9pHDmVJylEkd9NDFr7L9Qsgs6KFSv03HPPKT09XcOGDdMLL7ygUaNGXXS6t99+W1OmTNGtt96qDz74oPEbWo1+0a3VrbVDX33+sUbG/n/qefllioxwq22Yk1/DBgC0eE6HXVFtQhV1QQ9QYbHHr/fn2Mkz+vqb/0iKC05DLxD0Uaxr165VYmKikpKStHPnTg0bNkwJCQk6ceJEtdMdPnxYP/nJT3Tdddc1UUurF9UmVC9M6KzMP/1SA9t61adza7WPcBFyAACW5nY61LVdmIZc1lZj+3XW9VElOv3Xl4LdLJ+gB51ly5Zp1qxZmjFjhgYOHKhVq1YpPDxcq1evrnIaj8ej73//+1qyZIl69ar+PGBhYaGys7P9bgAA4NIQ1KBTVFSkHTt2KD4+3ldmt9sVHx+v1NTUKqd78skn1blzZ/3oRz+66DKWLl2qtm3b+m4xMTEN0nYAAND8BTXoZGZmyuPxKCoqyq88KipK6enpAaf5+9//rldffVUvv/xyjZaxYMECZWVl+W5Hjx6td7sBAEDL0CwGI9dUTk6O7r33Xr388suKjIys0TRut1tud+VvkQQAANYX1KATGRkph8OhjIwMv/KMjAxFR0dXqn/w4EEdPnxYkyZN8pV5vaW/9RQSEqJ9+/apd+/ejdtoAADQYgT11JXL5dKIESOUnJzsK/N6vUpOTlZcXOXL0vr376//+7//065du3y3W265RTfccIN27drF+BsAAOAn6KeuEhMTNX36dI0cOVKjRo3S8uXLlZubqxkzZkiSpk2bpm7dumnp0qUKDQ3V4MGD/aZv166dJFUqBwAACHrQmTx5sk6ePKlFixYpPT1dw4cP18aNG30DlNPS0mS3B/0qeAAA0AIFPehI0ty5czV37tyAr6WkpFQ77Zo1axq+QQAAwBLoKgEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJZF0AEAAJbVLILOihUr1KNHD4WGhio2Nlbbtm2rsu7LL7+s6667Tu3bt1f79u0VHx9fbX0AAHDpCnrQWbt2rRITE5WUlKSdO3dq2LBhSkhI0IkTJwLWT0lJ0ZQpU7R161alpqYqJiZG48aN07ffftvELQcAAM1d0IPOsmXLNGvWLM2YMUMDBw7UqlWrFB4ertWrVwes//vf/14PPvighg8frv79++uVV16R1+tVcnJyE7ccAAA0d0ENOkVFRdqxY4fi4+N9ZXa7XfHx8UpNTa3RPPLy8lRcXKwOHToEfL2wsFDZ2dl+NwAAcGkIatDJzMyUx+NRVFSUX3lUVJTS09NrNI/58+era9eufmGpoqVLl6pt27a+W0xMTL3bDQAAWoagn7qqj1/+8pd6++239f777ys0NDRgnQULFigrK8t3O3r0aBO3EgAABEtIMBceGRkph8OhjIwMv/KMjAxFR0dXO+2vfvUr/fKXv9TmzZs1dOjQKuu53W653e4GaS8AAGhZgtqj43K5NGLECL+BxOUDi+Pi4qqc7tlnn9VTTz2ljRs3auTIkU3RVAAA0AIFtUdHkhITEzV9+nSNHDlSo0aN0vLly5Wbm6sZM2ZIkqZNm6Zu3bpp6dKlkqRnnnlGixYt0ltvvaUePXr4xvK0atVKrVq1Ctp6AACA5ifoQWfy5Mk6efKkFi1apPT0dA0fPlwbN270DVBOS0uT3X6+42nlypUqKirSXXfd5TefpKQkLV68uCmbDgAAmrmgBx1Jmjt3rubOnRvwtZSUFL/nhw8fbvwGAQAAS2jRV10BAABUh6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsi6ADAAAsq1kEnRUrVqhHjx4KDQ1VbGystm3bVm39d999V/3791doaKiGDBmiDz/8sIlaCgAAWpKgB521a9cqMTFRSUlJ2rlzp4YNG6aEhASdOHEiYP3PPvtMU6ZM0Y9+9CN98cUXuu2223Tbbbdpz549TdxyAADQ3AU96CxbtkyzZs3SjBkzNHDgQK1atUrh4eFavXp1wPr//d//rfHjx+uxxx7TgAED9NRTT+mqq67Siy++2MQtBwAAzV1IMBdeVFSkHTt2aMGCBb4yu92u+Ph4paamBpwmNTVViYmJfmUJCQn64IMPAtYvLCxUYWGh73lWVpYkKTs7u56tr+zcuXOSpG/2/1uF+XkNPn8A1ctIOyhJSj/8lQ5GhAe5NcCl6eQ3hySV/k9syP+15fMyxtRquqAGnczMTHk8HkVFRfmVR0VFae/evQGnSU9PD1g/PT09YP2lS5dqyZIllcpjYmLq2OqLe2f5wkabN4CL++trv9Jfg90I4BI3ZsyYRplvTk6O2rZtW+P6QQ06TWHBggV+PUBer1enT59Wx44dZbPZgtKm7OxsxcTE6OjRo2rTpk1Q2hBsl/o2YP0v7fWX2AaX+vpLbIParr8xRjk5OeratWutlhPUoBMZGSmHw6GMjAy/8oyMDEVHRwecJjo6ulb13W633G63X1m7du3q3ugG1KZNm0ty567oUt8GrP+lvf4S2+BSX3+JbVCb9a9NT065oA5GdrlcGjFihJKTk31lXq9XycnJiouLCzhNXFycX31J2rRpU5X1AQDApSvop64SExM1ffp0jRw5UqNGjdLy5cuVm5urGTNmSJKmTZumbt26aenSpZKkhx9+WGPGjNHzzz+viRMn6u2339b27dv1u9/9LpirAQAAmqGgB53Jkyfr5MmTWrRokdLT0zV8+HBt3LjRN+A4LS1Ndvv5jqfRo0frrbfe0hNPPKHHH39cffr00QcffKDBgwcHaxVqze12KykpqdIptUvJpb4NWP9Le/0ltsGlvv4S26Cp1t9manudFgAAQAsR9C8MBAAAaCwEHQAAYFkEHQAAYFkEHQAAYFkEHQAAYFkEnXr65JNPNGnSJHXt2lU2m63Sj4saY7Ro0SJ16dJFYWFhio+P1/79+y863xUrVqhHjx4KDQ1VbGystm3b1khrUH/VbYPi4mLNnz9fQ4YMUUREhLp27app06bp2LFj1c5z8eLFstlsfrf+/fs38prUzcX2gfvuu6/SuowfP/6i87XKPiCp0vqX35577rkq59lS9oGlS5fq6quvVuvWrdW5c2fddttt2rdvn1+dgoICzZkzRx07dlSrVq105513VvqG9wvV9bMjGC62DU6fPq2HHnpI/fr1U1hYmC6//HLNmzfP9yPLVanre6ep1WQfGDt2bKV1eeCBB6qdr5X2gcOHD1f5OfDuu+9WOd+G2AcIOvWUm5urYcOGacWKFQFff/bZZ/Wb3/xGq1at0j//+U9FREQoISFBBQUFVc5z7dq1SkxMVFJSknbu3Klhw4YpISFBJ06caKzVqJfqtkFeXp527typhQsXaufOnXrvvfe0b98+3XLLLRed76BBg3T8+HHf7e9//3tjNL/eLrYPSNL48eP91uUPf/hDtfO00j4gyW/djx8/rtWrV8tms+nOO++sdr4tYR/4+OOPNWfOHP3jH//Qpk2bVFxcrHHjxik3N9dX59FHH9Wf//xnvfvuu/r444917Ngx3XHHHdXOty6fHcFysW1w7NgxHTt2TL/61a+0Z88erVmzRhs3btSPfvSji867tu+dYKjJPiBJs2bN8luXZ599ttr5WmkfiImJqfQ5sGTJErVq1UoTJkyodt713gcMGowk8/777/uee71eEx0dbZ577jlf2dmzZ43b7TZ/+MMfqpzPqFGjzJw5c3zPPR6P6dq1q1m6dGmjtLshXbgNAtm2bZuRZI4cOVJlnaSkJDNs2LCGbVwTCLT+06dPN7feemut5mP1feDWW281N954Y7V1Wuo+cOLECSPJfPzxx8aY0ve80+k07777rq/Ol19+aSSZ1NTUgPOo62dHc3HhNgjknXfeMS6XyxQXF1dZpy7vneYg0PqPGTPGPPzwwzWex6WwDwwfPtz88Ic/rHY+DbEP0KPTiA4dOqT09HTFx8f7ytq2bavY2FilpqYGnKaoqEg7duzwm8Zutys+Pr7KaVqarKws2Wy2i/646v79+9W1a1f16tVL3//+95WWltY0DWwEKSkp6ty5s/r166fZs2fr1KlTVda1+j6QkZGhDRs21OhoviXuA+WnYzp06CBJ2rFjh4qLi/3+nv3799fll19e5d+zLp8dzcmF26CqOm3atFFISPVf0F+b905zUdX6//73v1dkZKQGDx6sBQsWKC8vr8p5WH0f2LFjh3bt2lWjz4H67gNB/wkIK0tPT5ck389ZlIuKivK9dqHMzEx5PJ6A0+zdu7dxGtqECgoKNH/+fE2ZMqXaX6uNjY3VmjVr1K9fP18X53XXXac9e/aodevWTdji+hs/frzuuOMO9ezZUwcPHtTjjz+uCRMmKDU1VQ6Ho1J9q+8Dr7/+ulq3bn3RUzctcR/wer165JFHdM011/h+liY9PV0ul6tSsK/uc6Aunx3NRaBtcKHMzEw99dRTuv/++6udV23fO81BVes/depUde/eXV27dtXu3bs1f/587du3T++9917A+Vh9H3j11Vc1YMAAjR49utp5NcQ+QNBBkykuLtbdd98tY4xWrlxZbd2K52yHDh2q2NhYde/eXe+8806NjgCak3vuucf3eMiQIRo6dKh69+6tlJQU3XTTTUFsWXCsXr1a3//+9xUaGlptvZa4D8yZM0d79uxplmOJmsrFtkF2drYmTpyogQMHavHixdXOqyW+d6pa/4qhbsiQIerSpYtuuukmHTx4UL17927qZjaqi+0D+fn5euutt7Rw4cKLzqsh9gFOXTWi6OhoSap0dUVGRobvtQtFRkbK4XDUapqWoDzkHDlyRJs2baq2NyeQdu3aqW/fvjpw4EAjtbDp9OrVS5GRkVWui1X3AUn629/+pn379mnmzJm1nra57wNz587VX/7yF23dulWXXXaZrzw6OlpFRUU6e/asX/3q/p51+exoDqraBuVycnI0fvx4tW7dWu+//76cTmet5n+x906wXWz9K4qNjZWkKtfFqvuAJK1bt055eXmaNm1aredfl32AoNOIevbsqejoaCUnJ/vKsrOz9c9//lNxcXEBp3G5XBoxYoTfNF6vV8nJyVVO09yVh5z9+/dr8+bN6tixY63nce7cOR08eFBdunRphBY2rW+++UanTp2qcl2suA+Ue/XVVzVixAgNGzas1tM2133AGKO5c+fq/fff15YtW9SzZ0+/10eMGCGn0+n399y3b5/S0tKq/HvW5bMjmC62DaTS9o8bN04ul0vr16+/aI9eIBd77wRLTdb/Qrt27ZKkKtfFivtAuVdffVW33HKLOnXqVOvl1GkfqNdQZpicnBzzxRdfmC+++MJIMsuWLTNffPGF74qiX/7yl6Zdu3bmT3/6k9m9e7e59dZbTc+ePU1+fr5vHjfeeKN54YUXfM/ffvtt43a7zZo1a8x//vMfc//995t27dqZ9PT0Jl+/mqhuGxQVFZlbbrnFXHbZZWbXrl3m+PHjvlthYaFvHhdug//6r/8yKSkp5tChQ+bTTz818fHxJjIy0pw4cSIYq1it6tY/JyfH/OQnPzGpqanm0KFDZvPmzeaqq64yffr0MQUFBb55WHkfKJeVlWXCw8PNypUrA86jpe4Ds2fPNm3btjUpKSl++3deXp6vzgMPPGAuv/xys2XLFrN9+3YTFxdn4uLi/ObTr18/89577/me1+Szo7m42DbIysoysbGxZsiQIebAgQN+dUpKSnzzqbgNavreaQ4utv4HDhwwTz75pNm+fbs5dOiQ+dOf/mR69eplrr/+er/5WHkfKLd//35js9nM//7v/wacT2PsAwSdetq6dauRVOk2ffp0Y0zpJYILFy40UVFRxu12m5tuusns27fPbx7du3c3SUlJfmUvvPCCufzyy43L5TKjRo0y//jHP5pojWqvum1w6NChgK9JMlu3bvXN48JtMHnyZNOlSxfjcrlMt27dzOTJk82BAweafuVqoLr1z8vLM+PGjTOdOnUyTqfTdO/e3cyaNatSYLHyPlDut7/9rQkLCzNnz54NOI+Wug9UtX+/9tprvjr5+fnmwQcfNO3btzfh4eHm9ttvN8ePH680n4rT1OSzo7m42Daoav+QZA4dOuQ3n/JpavreaQ4utv5paWnm+uuvNx06dDBut9tcccUV5rHHHjNZWVmV5mPVfaDcggULTExMjPF4PFXOp6H3AVvZjAEAACyHMToAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCyCDoAAMCy/n93Gm67iKO4eQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# =============================\n",
+    "# Basketball Points Prediction v1\n",
+    "# =============================\n",
+    "\n",
+    "# --- Imports ---\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# --- Example Dataset ---\n",
+    "# Replace this with your actual dataset\n",
+    "df = pd.DataFrame({\n",
+    "    'PTS': [110, 95, 120, 102, 88, 115, 130, 99],\n",
+    "    '2PA': [60, 55, 70, 58, 52, 65, 72, 54],\n",
+    "    '3PA': [30, 20, 35, 25, 18, 28, 38, 22],\n",
+    "    'FTA': [18, 15, 22, 17, 12, 19, 25, 14],\n",
+    "    'AST': [22, 18, 28, 20, 15, 25, 30, 17],\n",
+    "    'REB': [45, 38, 50, 40, 36, 47, 55, 39],\n",
+    "    'TOV': [12, 10, 15, 11, 9, 13, 16, 10]\n",
+    "})\n",
+    "\n",
+    "# --- Define Features & Target ---\n",
+    "target = 'PTS'\n",
+    "features = ['2PA', '3PA', 'FTA', 'AST', 'REB', 'TOV']\n",
+    "\n",
+    "X = df[features]\n",
+    "y = df[target]\n",
+    "\n",
+    "# --- Add constant for intercept ---\n",
+    "X = sm.add_constant(X)\n",
+    "\n",
+    "# --- Split data (optional for small demo, but good practice) ---\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
+    "\n",
+    "# --- Build Linear Regression model ---\n",
+    "model = sm.OLS(y_train, X_train).fit()\n",
+    "\n",
+    "# --- Model Summary ---\n",
+    "print(model.summary())\n",
+    "\n",
+    "# --- Predict on test set ---\n",
+    "y_pred = model.predict(X_test)\n",
+    "\n",
+    "# --- Evaluate ---\n",
+    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "print(\"\\nModel Performance:\")\n",
+    "print(f\"RMSE: {rmse:.2f}\")\n",
+    "print(f\"R²: {r2:.3f}\")\n",
+    "\n",
+    "# --- Visualize actual vs predicted ---\n",
+    "plt.figure(figsize=(6,4))\n",
+    "sns.scatterplot(x=y_test, y=y_pred)\n",
+    "plt.xlabel(\"Actual Points (PTS)\")\n",
+    "plt.ylabel(\"Predicted Points (PTS)\")\n",
+    "plt.title(\"Actual vs Predicted PTS (v1 Model)\")\n",
+    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Optionally show residuals ---\n",
+    "residuals = y_test - y_pred\n",
+    "sns.histplot(residuals, kde=True)\n",
+    "plt.title(\"Residuals Distribution\")\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  }
+ ],
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