{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing different SVM kernels on a multi-class classification problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wine Data Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 178 (50 in each of three classes)\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- 1) Alcohol\n",
      " \t\t- 2) Malic acid\n",
      " \t\t- 3) Ash\n",
      "\t\t- 4) Alcalinity of ash  \n",
      " \t\t- 5) Magnesium\n",
      "\t\t- 6) Total phenols\n",
      " \t\t- 7) Flavanoids\n",
      " \t\t- 8) Nonflavanoid phenols\n",
      " \t\t- 9) Proanthocyanins\n",
      "\t\t- 10)Color intensity\n",
      " \t\t- 11)Hue\n",
      " \t\t- 12)OD280/OD315 of diluted wines\n",
      " \t\t- 13)Proline\n",
      "        \t- class:\n",
      "                - class_0\n",
      "                - class_1\n",
      "                - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      "References\n",
      "----------\n",
      "(1) \n",
      "S. Aeberhard, D. Coomans and O. de Vel, \n",
      "Comparison of Classifiers in High Dimensional Settings, \n",
      "Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of \n",
      "Mathematics and Statistics, James Cook University of North Queensland. \n",
      "(Also submitted to Technometrics). \n",
      "\n",
      "The data was used with many others for comparing various \n",
      "classifiers. The classes are separable, though only RDA \n",
      "has achieved 100% correct classification. \n",
      "(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "(All results using the leave-one-out technique) \n",
      "\n",
      "(2) \n",
      "S. Aeberhard, D. Coomans and O. de Vel, \n",
      "\"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "Mathematics and Statistics, James Cook University of North Queensland. \n",
      "(Also submitted to Journal of Chemometrics). \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm, datasets\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Load the dataset\n",
    "from sklearn.datasets import load_wine\n",
    "wine = load_wine()\n",
    "\n",
    "print(wine.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['class_0' 'class_1' 'class_2']\n"
     ]
    }
   ],
   "source": [
    "# Consider the 2 features\n",
    "X = wine.data\n",
    "Y = wine.target\n",
    "\n",
    "# Number of unique targets\n",
    "print(wine.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']\n",
      "\n",
      "\n",
      "Total number of features: 13\n"
     ]
    }
   ],
   "source": [
    "# List of features:\n",
    "feature_names = wine.feature_names\n",
    "print(feature_names)\n",
    "\n",
    "print(\"\\n\")\n",
    "print(\"Total number of features: {}\".format(len(feature_names)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick look at the data\n",
    "X = pd.DataFrame(X, columns=feature_names)\n",
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Names :['magnesium' 'flavanoids' 'color_intensity' 'proline']\n"
     ]
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>127.0</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>2.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101.0</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113.0</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118.0</td>\n",
       "      <td>2.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1\n",
       "0  127.0  3.06\n",
       "1  100.0  2.76\n",
       "2  101.0  3.24\n",
       "3  113.0  3.49\n",
       "4  118.0  2.69"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs = SelectKBest(chi2, k=4)\n",
    "X_new = fs.fit_transform(X, Y)\n",
    "selected_features = X.columns.values[fs.get_support()]\n",
    "print(\"Feature Names :{}\".format(selected_features))\n",
    "\n",
    "X_new = pd.DataFrame(X_new[:, 0:2])\n",
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train and test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_new, Y, test_size=0.33, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# SVM Classifier with different kernels\n",
    "C = 1.0 \n",
    "models = (svm.SVC(kernel='linear', C=C, decision_function_shape='ovr', random_state=1),\n",
    "          svm.SVC(kernel='rbf', gamma=0.35, C=C, decision_function_shape='ovr', random_state=1),\n",
    "          svm.SVC(kernel='poly', degree=3, C=C, decision_function_shape='ovr', random_state=1),\n",
    "          svm.SVC(kernel='sigmoid', gamma=2, decision_function_shape='ovr', random_state=1))\n",
    "\n",
    "models = (clf.fit(X_train, y_train) for clf in models)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_meshgrid(x, y, h=.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols, rows = 2, 2\n",
    "#plt.figure(figsize=(20, 40))\n",
    "fig, sub = plt.subplots(rows, cols, figsize=(10, 10))\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "titles = ('SVC with linear kernel',\n",
    "          'SVC with RBF kernel',\n",
    "          'SVC with polynomial(degree 3) kernel', \n",
    "          'SVC with sigmoid kernel')\n",
    "\n",
    "X0, X1 = X_train.iloc[:, 0], X_train.iloc[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
      "  max_iter=-1, probability=False, random_state=1, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.35, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=1, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
      "  max_iter=-1, probability=False, random_state=1, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=2, kernel='sigmoid',\n",
      "  max_iter=-1, probability=False, random_state=1, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAJMCAYAAADnppL9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xd4U9UbwPHvSbr3Li2UWfaWDbJk\nCAqiIrIEBX/gxL0n7o0K7gUoCDgQGYqyZ9kISAVkFiiFDlq60yTn90daQCg0nUnp+3mePHCTe899\nk7Yn7z3n3HOU1hohhBBCCHF5BkcHIIQQQghRGUjSJIQQQghhB0mahBBCCCHsIEmTEEIIIYQdJGkS\nQgghhLCDJE1CCCGEEHaQpKmSUkplKKXqXub1w0qp3naWdYdSaq29ZVcUpVQPpdQxR8cBxfs8hRDO\noyzryook9Z9zkqSpCEqpq5VS65VSaUqpFKXUOqVUO6VUR6VUplLKp5Bjtiul7s//v5tSaqJS6t/8\n/Q8rpb5RStUuTVxaax+t9cH8c0xTSr1amvIuVbYQQtijKtSV+ReYlvxE7IxSaodSasB5r9dWSun8\n1zOUUieVUp8opVzP2+ewUir7vH0ylFKRpXmPouJI0nQZSik/YCEwBQgCqgMvAbla6w3AMeCWC45p\nBjQBZuU/9RNwAzAC8AdaAluBXhXwFioNpZRLZSxbCFHl6soYrbUPEAB8AsxWSgVcsE9A/j7NgU7A\nfRe8PjA/mSt4xJdXsFL/lS1Jmi6vAYDWepbW2qK1ztZa/6m13pn/+nRg9AXHjAZ+01on5zdn9gEG\naa03a63NWus0rfXHWuuvLzyZUmqMUmrBedv/KqV+PG/7qFKqVf7/tVIqWik1HhgJPJF/xbLgvCJb\nKaV25l/5zVFKedjzpgvKzv//NKXUx0qpRUqpdKXURqVUvfP2baSUWpJ/ZblXKXXrea9dn38leSY/\n9onnvVZwRXanUioOWG5HXA8opWKVUjXytwcopf5SSqXmX+G2OG/fw0qpJ5VSO4FMpZRL/nOPXeoz\nuVx5QojLqnJ1pdbaCnwHeAP1L7HPKWAJtuSwVKT+cxJaa3lc4gH4AcnY/uD7A4EXvB4FmIGo/G0D\ntiuqG/O33wRWFeN8dYHU/HIigSPAsfNeOw0Y8rc1EJ3//2nAqxeUdRjYlF9OEPAPcPclznsHsPa8\n7QvLTgbaAy7ATGB2/mvewFFgTP5rrYEkoEn+6z2wXWkZgBbAyfM+m9r55/k2vxzPQuLqcd77fwHY\nBoTmb7cGTgEdACNwe/57dj/v/f+V/zPyLOozsbO83o7+nZSHPJzxURXryvx64j7ABITlP1dQr7nk\nb0cCO4CxF5yvyLpE6j/nfEhL02Vorc8AV2P7I/gSSFRKzVdKhee/fhRYCYzKP6QX4A4syt8OBk4U\n43wHgXSgFdAN+AOIV0o1AroDa7Tt6sZek7XW8VrrFGBBfrkl8YvWepPW2owtaSooZwBwWGs9Vduu\nDLcDPwND8t/PSq31Lq21VduuOGflv4/zTdRaZ2qtsy9xbqWUmgT0BXpqrRPznx8PfK613qhtV7bT\ngVyg4wXv/+gFZV/qM7GnPCFEIapYXdlRKZUK5ADvArdpW4vS+ZLy9zkOZGLrejzfvPwWnVSl1LzL\nnEvqPycjSVMRtNb/aK3v0FrXAJphy9I/OG+X6ZyrCEZha4XJy99OBiKKecpV2K4wuuX/fyW2SqB7\n/nZxJJz3/yzgooGYpSynFtDhvD/+VGzN39UAlFIdlFIrlFKJSqk04G4g5IKyjxZx7gBsf9BvaK3T\nznu+FvDoBeeOwvbzuVzZl3svRZUnhLiEKlRXbtBaBwCBwHygayH7hOTv4wWsw5bUne9GrXVA/uPG\ny5xL6j8nI0lTMWit92Br3m123tNzgRpKqZ7AzdgqhgJLgfYFfdB2KqgIuub/fxVFVwS6GOWXpaPY\nmtQDznv4aK3vyX/9e2yVSpTW2h/4DFAXlFFU7KextWhNVUp1ueDcr11wbi+t9azz9inO52JPeUII\nO1SFulJrnQHcA4xSSrW+xD7Z2D6HjkqpCy8Y7SH1n5ORpOky8gc5P3rewLsoYDiwoWAfrXVB0+tU\n4IjWest5ry3FNgjwF6VUm/zBeL5KqbuVUmMvcdpVQE9s/dDHgDVAP2zN19svccxJbP34FW0h0EAp\nNUop5Zr/aKeUapz/ui+QorXOUUq1x3ZXTLFprVdia8Gam18O2LoA7s5vzVJKKW9lG3juW8L3Utbl\nCVFlVNW6Mr+b6ytsY44uopRyx9aqloCtNa0k51iJ1H9OQ5Kmy0vHNjBuo1IqE1sF8Dfw6AX7TcfW\nvPltIWXcAvwGzAHS8o9vi+3K6iJa631ABrYKoGCswEFgndbacok4vwaa2NE/Xqa01unY+tqHAfHY\nKoa3sI1VALgXeFkplY6tUvmhFOdaAowFFiilrsqvcMcBH2G7GtuPbZBmScsv0/KEqGKqcl35AXDd\nBXebpSqlMrAlaZ2AG7TWJW7lkvrPeahS/ByFEEIIIaoMaWkSQgghhLCDJE1CCCGEEHaQpEkIIYQQ\nwg6SNAkhhBBC2EGSJiGEEEIIO5TL6sfevsE6KDSqPIoWQpSBY4d2JGmtQx0dR1USEuCha0fItDdF\nOZNpJdvl0t8fnuaj+HkX/3q/qHJLytN8buLt8iy/sPdc3PdU0s+uKti6J8muOrFckqag0CgefrXI\nReuFEA7y6MjgI46OoaqpHeHLluk3OzoMp7c4JoPY0CmXfL1J4gT6dSreilArtmaSa9KXLdeZFfae\nS/Kedu7PYZj/48X+/KoC1eELu+pESTmFEEJUKotjMoq1f2VOmApc+J5zTZrZae8Uq4wW0R5lGVKV\nJEmTEEKISqOyJz8lUfCed+7PBc4lUCVJgj6Je7PYSac4p1y654QQQojiWhyTwSdxb9KjDEfbXSkJ\nwuy0dxjG48Qn5gElTx57tPGGxLKMrOxc+LNyxm5ESZqEEEI4jR5tvMu8zCuhdapFtAexVP73cSkF\nCVPBz6pJ4gRWbM2kZzn8PpSGdM8JIYQQVYwztcBdmDCBrWUt1+R8a+NK0iSEEKJSkXE5peOMLW8X\nxlQwXqtgHJezkKRJCCFEpWJvF54kVs7vcj+j2WnvnB3D5SwkaRJCCHHFcsZWFWFT0Ip0qZ+RM06R\nIEmTEEIIISpcfGJeseeacjRJmoQQQogqaMXWTIefu6jWpNjQKU7VzSpJkxBCiCuOsw0gdjaxoVMc\nendaZZ2lXZImIYQQlU5RLRDxiXmV8ku5KihqfUFnJkmTEEIIISpEwazvlZUkTUIIIYQodwUtg8Wd\n9d2ZxjVJ0iSEEEKIclXYrN+VkSRNQgghHK4su21kELhzquwJE0jSJIQQwkmU1WK9lXH+nyuZs3St\nlQVJmoQQQlRal/pCdsbZpKuyK6GVCSRpEkIIUUldKV/EjlIRA6zLqtvVWRZplqRJCCGEEOWmLLpd\ny6rrtrQkaRJCCCFEmXOGlqGyJkmTEEKIK8aV+EVdmV1pXaiSNAkhhLiiXGlf1JXRlTrtgyRNQggh\nhChTV+raf5I0CSGEqLScaYkNUf4c3YIlSZMQQgghnF5s6BTiE/McGoMkTUIIIa4IZbkUiyi5xTEZ\nV+yM7JI0CSGEuGI4y3w+Vd2VOiO7i6MDEMJZWK0WtsfM5XTSUaLqtqZh856ODkkIYQdb69JTjg6j\nUooNnQIxE+jXyafQ1/ccTmXBmiN4uBsZcW00wf5XZjJkL2lpEgKwWq18/+Ew9i99hKZZb/L7N6NY\nueBdR4clhLBDQevSldol5CjrdybQddxcjv67mY0bN9J29I+cTM667DFX+qB8SZqEAA7t3UB6/EbW\nPpvFO8MtbHghmyW/vIsp9/IVhBDCOcSGTrliu4Qc5blP1vPBbWYmj7Yy4x4LA1rk8OHsnUUedyVO\nNVBAkiYhgKzM09QOM+Ca32EdEQDurgZyc67sqyYhhLiUlDO5NIw4t92gmuZ0eo7jAnICkjQJAdSK\nbsuWg5qfN0FSOrw410hgSBQ+fqGODk0IIRyif+faPPOjkWPJsDMOPvjDhf6dazs6LIeSpEkIwC8g\nnNGP/MSjv9SiziMezN3XmlGPzEUp5ejQhBDCIV66qz0NG0TT+nkXrp/kzqOj2nNDt9qODsuhE1zK\n3XNC5KtVvx0PvLHN0WEIIYRTcHM1MuXx7kx5vLtd+y+OySj38UyxoVMgcQItot3L9TyXIi1NQggh\nhBB2kKRJCCGEEMIOkjQJIYQQQthBkiYhhBBClIojB2dXJEmahBBCCFEq8Yl5VWJGdkmahBBCiCrs\nk7g3y2T5k6owI7skTUIIIRyqqnTtOKuCtftE0SRpEkII4VDxiXlX9Hpl4sohSZMQQgghhB0kaRJC\nCCFEiVWl7lVJmoQQQghRYhXdvfpJ3Jus2JpZYec7nyRNQgghhKg0erTxJtekHXJuSZqEEEIIIewg\nSZMQQgghhB0kaRJCCCGEsIMkTUIIIYQQdpCkSQghhBAlsjgmo0qsOVdAkiYhhBBClFhVWHOugCRN\nQgghRBU3O+0dh819VJlI0iSEEEJUcS2iPRw291FlIkmTEEIIIYQdJGkSQgghhLCDJE1CCCGEEHaQ\npEkIIYQQxbY4JqNCF+p1BpI0CSGEEELYQZImIYQQQgg7SNIkhBBCCGEHSZqEEEIIUal8EvcmO/fn\nVvh5yyVpSs+yslJmFhVCCCFEOQjyN5KcZq7w85ZL0lTdK557az5Fk8QJ5VG8EEIIIaowR81g7lIe\nhXq6K/p18rFtxNgSp0/i3qRHG+/yOJ0QQgghKlBVnG4AyilpOl+/Tj7s3J/LvTwFibbnquIHLYQQ\nQojKrUIGgreIdqdfJ5+zrU9NEifImCchhBDCicSGTmFxTIajw3Bq5d7SdCFpeRJCCCFEZeSQKQcu\n1fK0c3+OI8IRQgghhChShbc0Xaig5enV0OeJT8yDRGl5EkIIIYTzcYrJLVtEu59tfQJby9PO/TnS\n8iSEEEIIp+HwlqYLFbQ83e72hG0OBml5EkIIIZxGVR4s7nRJE9hansAdsP1wCibJnJ32Di2iPRwY\nmf1Oxf/LusUfYs49Q6N2Q2ne9npHh1TpWK1WDu2NISc7nZr12uDrH+rokIQQJWS1aj79eTfrdxwn\nMtSHp+9oQ5B/5ajPnUlq8nGOHdqBr38oNaPbopRySBxVtTHDKZOm8xV02a3Ymskw/8crRctT0slD\nfP5Kbx7pm0lkoObF75aTnfkW7buPdHRolYbFYuard0eREH8YD58IMpJjuevJOdSo09LRoQkhSuCh\n99awddd+7uppJma/ga7jj7Bp2hC8PV0dHVqlse/vlUyfPA7/sFZknj5Aw+ZdGH7XZIclTlWR0ydN\nBXrmzyZeGVqetqyeyZ1ds3j+JtsU7w2qZTNq+ruFJk1aa7au/YE9u1bj5x9CzwH3S4sKsGXNbJJP\np9N68O8YDC4k7J3LnK8e49HXljg6NCFEMeWaLHzx614SPtYEeMPorlZ6vZHLnxuPcVOPOhftn5p8\nnJW/fUpmRipNr+pDqw6DHBC185n56QQaXjOFoBpXY8nL5q9fB7FnxzIat+rt6NCqDKcYCF4cBVMV\nuLsphvk/7pTr21ktJrzcrGe3vdzBail8YcEl8yax6KcPyXJpxcFjmXzwwrVkZaZWVKhOKyUxDt+w\n9hgMtrw+oHonUpOPOTgqIURJWK22C0iP8xqVvNzBbLl47bD0tFN88EI/jiRYyDS24JcZr7D6988r\nKlSnZbVayExLIDCyEwBGV0/8wlpLvVjBKl3SVKBnG++zCVSTxAln77hzBi07DmHKUk+mr4Ylu2D0\nF1606jrmov201qxY+BFN+31DZJPhRHeZiEdAE3ZtXuSAqJ1LVN3WJB9ehCkrCa018bu/pUadVo4O\nSwhRAp4eLtzYLYrhnxhZGQtvzlfsOGqgV9vIi/bdvn4uvhFdqNfpWSKbjKBx789YvvBjB0TtXAwG\nI2E1mnJ897cAZJ+JIzluJdVrt3BwZFVLpemeu5x+nXz+M+YJHDvuqXrt5ox8+Cfen/8KptwMGvUY\nRpe+dxe6r9Viwujqc3bb4OqNxWyqqFCdVrM2/Tl6cAcrv78ao6sHQaG1uOPxmY4OSwhRQtNe7M2L\nn2/i+fnxRIZ6s/KzLoUOBDeb/1snGl29sVikTgQY89DXfPH2CI5un4I5L5uBwydSs95Vjg6rSrki\nkiY4N+YJzo17cuSYp7oNO1L38cu3GCmlaN15CHuXP0CN1hPITNnL6aMraXz38xUUpXPrP+Qprhl4\nP7k5mfj6h8lgRyEqMQ93F956oHOR+zVvex3LFvTHO7gJnv51OLLlHdp0GVIBETq/0Gr1ePrdGNLT\nTuLp5Y+bu1eFx7Bzf26Fn9OZXDFJ0/mcreXpcm4Z+za///gW+/56HR+/EO59di6BITUcHVa50Vqz\nbf1PHP53K4HBkVzd93+X/cN39/DB3cPnkq8LIa4soRHR3PXkHBbNeYOTh9Jo0743fW58xNFhlavT\nSceIWTad3NwsWnW4gToNO1xyX4PBgH9gRLnFMjvtHYh5/Oyd6xeKT8xz2u/TinBFJk1w6ZYnwKnu\nuHNxcWPg8KrTsrRw1its37yE0OjBHDi4iZ2bf+f+F37FxcXN0aEJIZxEzXpXcc8zPzo6jApxOukY\n7z/fl6A6A3D1CGbLpNsZcdcHNL2qn0PiaRHtcbaxQVzsik2azlfQ8nR7qMwy7kh5pmzW/Pk5nW7b\ngKtHIFqPZcevN7I/di2NWlxTrLL27lrBvBkTyc48TcPmPbn59tdx9/Au8jghhHAm65ZOJbjODdTr\n/BwA3sGNWDx3UrGTptycTOZOf4a9u1bg5R3EjaMm0qBZj3KIuGqrtHfPFVfPNt5n77gDWd/OEcx5\nJpQy4uLmB4BSBtw8Q8jLzSpWOSeO/sO3k8cT3uIRmvafxdH4FH746rHyCFkIIcqVKTcbF8+Qs9tu\nnqHk5WYXu5w5Xz7CsROpNO0/i7DmDzP9w3EkHNtTlqEKqkhL04Wk5ckxPL39qVmvLf+ueZbIZmNI\nS9hMRtLf1GnYsVjl7N25nNDoGwip1QuA6KtfY/PsbuURshBClKuW7Qew7YM78QluhJtnMAfXv0jb\nTjcUu5zYbb/TYeR6XD0C8PSvReqxAezdtYJqNRqVQ9RVV5VMmqDwMU8gyVN5G/vwVH6a9hQHVt5H\nQHAE9z47Fx+/kKIPPI+7pw+mzBNnt3Mz4nFzPzdo8cj+Lfzy7QtknEkiukkXbhr9qnTdCSGcUr3G\nnRn6v3f445f3yTPl0LbTIPreVPyB724ePuRmxOPqEQCAKfME7h626QjMZhMLZ73C7u1L8PD0ZcCw\nZ2jYvGeZvo+qosomTec7f327JokT+CTuTXq0kS/Z8uDp7c+o+z4tVRmtO93Myt++4J9lD+DhX49T\ne2cxcNgzACSfOsIXbw+nbscXiQhpQty2yXz/2QOMeejrsghfCCHKXPN2A2jebkCpyrh+6DMsnDOW\n8IbDyU7bjzXnGK073QTAvO9eYP+/+6jX/SOyz8Tx7ZS7uPeZn6leu3lZhF+lSNJ0np5tvNm5P5d7\necrppypwNlprUlPisVrMBIZEYTCU33A5D09fHnr5dzau+I7M9BT69/mU6KZdAduClsE1ryG8wY0A\nNOj+NmuntsRqtWAwGMstJiGEuFBuTiapKcfxD6iGh5dfuZ6rQ4/bCAqtyb5dq/Cu35aOPT88O13L\nzk3zaT7wZzz9ovAJbkT6ya3Ebv+j2EnT4piM8gi9UikyaVJKvQ28CmQDi4EWwMNa6xnlHJtDtIh2\np0W0O3Cu205ani7PbDbx7eRxHNgTg1IuhEXWY/zjM8u1kvD08qPH9fdd9LybmyembNvSK0opTNlJ\nGF3cUKrK3PMgKkBVqxdF8e37exXTJ/8PV3c/crNSGHLnu1zVeXC5nrN+027Ub3rx+E7X/HrR0y8K\ngLysU7i5Vy/ROap6Q4I93yR9tdZngAHAYSAaeLw8g3IW/Tr5EBnqyqttnj+7vl15yzNls2HFdyxf\nMJmjB7eX+/nKwoqFH3MqOZMOI2PoMDKGPGN15s96xSGxNGt7PSrvFHtXPETcX5/z92+juHbwEzKb\nuChrVbZedIS4A9tYvmAyG1Z8R56p+HeWVTRTbhbTJ/+PRr0+pu3QVbS84Ud+mvokp5OPOySe/rc8\nwZ6l93Bk+6fsW/00mUlbaNt1mENiqezs6Z4r2Od64EetdVpV+gIqaHVqEe3+n5YnoMxbn/JM2Ux+\naSBmFYiHXx2WLRzOkDFv0qrjjWV6nstZvmAK65fPQCnFNQPupe3Vt5KelohfQBguru6FHnPsyG5C\n6t2AwWh7Paz+YI7/PanCYj6fu4c3D05cxNolX5Oemki3O16hWdvrHBKLuKJV6XqxIm2P+YWfpz1D\naL0byE5bzbql3/LAiwtwdauYSYpTU+L5dspdnE46TlhEPUbd/xlaWwCFr39o4cckH8fFzY/A6rZl\nY3yCG+Eb3JDEE/8SGFyyFp7SaNt1KH6B1YjdvgTP6lF0uX8J3r5BFR7HlcCepGmhUmoPtmboe5RS\noUCVnNyoXycfdu7P5dXQ54lPzCvzqQq2rf+ZPAJoeu03KKUIqTuAX2dMqLCkaem891n225c07vke\nVouJudMn8OuMF3B180ZbTYx+4MtCJ0sLr1aX2D0rCa9/I6A4Hbec0Ig6FRJzYTy8/Og96GGHnV9U\nCVIvVpB5M16gcd8v8A9vjdaa3b/fzvaYubTvPqLcz23KzeLdp3oSWv8Wons8Qfzumbz2cHtQGrSt\nO2z0A19ctKKBX2A18nJOk5EUi09IE3LSj5ORvI+g0FrlHvOlNGjWnQbNujvs/FeKIrvntNZPAZ2B\ntlrrPCALGFTegZXEkRPpfPzjbr76dQ+nz5TPooIFY57OnyRz5dZMVm7NLHXZWZmpePrXOduV5BVY\nj+ys1FKXa681S6bTsOtrBEV1xS+8NcpgoPl10+hw20Ya9vqEbyePJzvrzEXH9Rr0IK7WBLb9dC1/\nzRtI5snVDBo5scLirmyys86QmZ6C1trRoYgSqiz1otaaX1cd5oNZu1i9/UTRBzihnKxUvALqArZF\nzj3865KdWTH14q7Ni8DFh3qdnsY/vDVuXiH4hLWm06gtdBq9mcSULJb++sFFx3l4+jLkzvfYuWgE\nuxbeyva5A+h782OEhJ+7mKyoIR8lVZEL81osZtJOn8BsNlXYOUvqki1NSqmbC3nu/M255RFQSf21\nL4m+9y9gQGsrZ7IVb0zdTMw3txAW5Flu5yxInCLLqOWpfpOuLP11CsF1rsMroB6HNr5Og+bFW16k\nMFprdm9bzOmko0TVbU3t+u0K389qwWxKByA77RAevjXwr9YWgMDqnXDxCCL51GFq1G7xn+PcPby5\n/4X5HDu4HYvVQlSdlri6ld/nXllZLGbmfPEQOzb9ilJG6jTsxB0PfiVzSFUilale1Foz9uVl/PVP\nHFfXt/LB9wbuv7U1j41q7ejQiqVB82s4tOFV6nR4hsyUf0k8MJ/ooT+XutyUxKPs2bEUF1d3WrQb\nUOiNK3l5OVjNuWirBWUwkn5qF1Et78RgtLUshTUYwuH9hf/IW3e6iToN2nPqxH6Cw2oRHFa71DFX\nlNjQKZA44ezwlPJ0aN8mpr5/BxaLBW01M/Lejx227p49LtfSNDD/cSfwNTAy//EVMLb8QyueZz5a\nz6u35PHNOAs/PWDmuua5vDezYgZSX9jyVND6VFw16rRk2PhJHFzzKJtndyfQO5fhd31Yqti01sz4\n+B7mzniDrdt38/WkMaz548tC923auhf71jxP3I4vSTz0J9lph8lJtw1czE47QmbqUTw9fQs91mh0\noVb9dtRt2FESpktYvfhzDh85QufRW+l8+3bOZLuzaM5rjg5LFE+lqRc3xyayemsc618wM+V2K+ue\nNzPxq62kZzr/1fz5Rtw9hQCvHDbN6sbBtY8xbPz7pZ5f6Oihv5j0bC82bNjIyqXzee+5PmSmp1y0\nX/N2A7CaM9m1eBzxsbPISjtEctxKtNZorUmOW0luVtolzxMQXJ0GzbpflDA1SZxw9jujsiiPlqc8\nUzbfTLqdul3eoNPorTTtP43vP72fM6cTyvxcZeWSLU1a6zEASqk/gSZa6xP52xHAtAqJrhiSUrNp\nVuPcdvMoKxtPVtxdFjm5ZlZv28WGXSfw9/FmSK8HCE30KnbLU/O219O87fVlFtfhfZs4sHcLrQcv\nxujiQfUWd7Nwdl869hx10UDKIf97j9zcTHZv+RC0FRc3H7b8fAO+IU1JT/wbd69AsrPTyyy2qubw\n/u2ENRiC0dULgGqNR3Jk9/sOjkoUR2WqF5NSc6gXrvDMH25TPQj8vAykZpjw9Xa7/MFlZP3OBN6e\nvoXs3Dxu7dOIsTc0KvadrJ5efox9eGqZxvXrzJeo1f4pIhrdCsC+VU+xavFnXDfkmf/s5+0TyD1P\n/8g3k25n/4mNKBTJR5aTnrgTra3k5aRSI6peqWJpkjihUtzGH5+YV+Zxnk46hsHFi5DatiWx/MNb\n4xPckITje/ELrFam5yor9kw5EFVQMeQ7CdQsp3hKrHeHmrw8z0hyOhw8BR/+6UKvdhUX5ugXlxIb\nG8tTfZNoFhzHxM//IDMnr1QtT2UhIz0Jr4C6GF1sCZKnXxRGF3dysi8em2QwGBk94UvemnqYp96L\nQWkzTft+QvXmt9O078dY8rLwD4yo6LdwxQgJjeLMiZizY5nSTmzA08uXDSu+Y9/fq2SMU+Xi9PVi\nm0Yh/HVEs2AbZJvg/d/Bz9udyBCvCjn/tj1JDHr0NwY2juf+bom8Oz2GT37aXSHnLkrGmWS8g86t\nyeYV1Ij0tORC961Vvy0vfbqbt6ceoXWnGwmt04c67R+jbocnCanZldCIkidNla21qaz5BoRjykom\nK/UgAKasRDJS9nMibjdb1szmTOpJB0d4MXvunlumlPoDmJW/PRRYWn4hlczE8e2Z8E4ONR88gJur\n4onbWjL82ugKOXd6polF64+S/JnGww36ttBsOGjGxZBGv061HTrLeFSdVqSdfIiUo6sJiOzI8b+n\n4esXhrfv5dd7CwyuTp+bHmFBJDwzAAAgAElEQVTZ/PvxD29B6skdXDf02UveYiuK1nvQQ+x5ZRA7\n59+MwehOVup+rFbIMgdy5uQUmrXuyS1j33J0mMI+Tl8vhgd78cs71zHm5aUc+iCbNg0DWPh+X4zG\nipnodebivTzQ18ydPWzbQd5m7v9+F/cNaVYh57+cBs268u/2yTTsMYm83FQS/vmOTiOfKfK464c+\nw+SXBpKdshutrShrOv3un18BEV+ZPL38uHH0a8z/fggB1VqRdnIXBoOBDeuWYXT15teZL3H/8/MI\nr97Q0aGeVWTSpLW+P3/wY9f8p77QWv9SvmEVn5urkc+f6clnT/eo8IkMDQaFBsxW27bWYDKDUmDK\nszh0lvGA4OqMefAbvv/8Ac6cPk5krVaMf/J7u5Y56Xn9vTRueQ2JJ/YTFvmyU/3iVkae3v489PJi\nDu6JISc7ne8/vY92Q5fg4VsDsymDrT/2oeM1Iy8aaC+cT2WpF7u0rMa+n287O0N+RTIaDORZzm2b\nzGA0KHJyzbi7GR064ezAYc8z56tHWP9tO4wubvQe9BAtOxR986OvfxiPvb6cg3tiUEpRt1En3Nwr\npuXuStWhx0jqNuxAwrE9/L31DxJOG6jf1TbW89jOqfw682XGPzHTwVGeY9fac1rruTjRXSGX44g/\nRG9PV4b3qcug9w8zvoeFNfsMHDipGPrsEixW6Nchghkv98XPx+3sXE/l3fK0bd1PbI35FTd3L3oN\nvI8XJm8vUcVZrUYjqtVoVPSOwi6ubh40bNGTlMQ43Dz98fC1DcRzcfPBO7Ae6amnHByhsJfUi5d3\nx4BGdL/7H/w9zYT6wQs/G1EGE37XTMXPy8iXz/Tgpp51Kyye9LRTLP7pHVKSjlO3QVuGj/+Qkfd8\nDBTv83H38KZxq97lFWaVFBoRTWhENDs2/YZ3SMuzz/uENiPh2K8OjOxil2xuUEqtzf83XSl15rxH\nulLq4gExVdznT/ekX4+r+HFXdY5mReDpamH/e5qMrzVBxgQemrT67L4Fd9udf8fdzv2Fz4t3Ovk4\n22N+Ye/OFVitlkL3udCG5d/y66zXMQT3J9vYjE9fH0zCsT2ylIgT8Q+KxNXVlfjYWWhtJeXYWtKT\ndlO9luO7LsSlSb1ovyZ1A1n28SD+Tq3Lb/uiwODKhN455E7TLH7czF1vrGRfXPHnW7Jarez7eyXb\nY+aSkhhn1zG5ORlMfmkg8ckG3CJuZsum1cz+8iGUUlIvOpH6TTuTEPsdpqxELHlZHN/5OdFNOjs6\nrP+43N1zV+f/W/g95uI/XFwMPD6qNdCaRz9YR5cax6meP0v9UwOtDPyg8InlClqeHgl9xjbXE+da\nng7u3cDX740iILIDOWlxBAUHUT2iBtkZCdRq3IeOvccV2s226o+vqd/tXQIi2wNgyklh06pZ3DDy\npbJ/4xXodNIx4g5sxds3mHqNu1Tqys5odGH8E7OY+sFY9q15Fm/fMO548GunvWNE2Ei9WDwt6gcz\n9cXepGeaqNZ/Og/3tw1baFsXrmmq2BybSIOaAXaXZ7VamPrBWI7H/YtXQF1OT32STj1HkJ4Yi4dX\nMFcPeJzQahcPzP7379UY3MOp1/l5AIKiurFuWmtuGfN2hc+TVpbTDVitFvbHriU7M43aDdpV+ht1\n2ncfSWLCYVbPvBqtrTRvdwPX31r0WLOKZFf3nFKqJef67ldrrXeWX0iVX7VgbzZvMaK1BaVg8wGI\nCL50v3dh69vNTnuHXz55mPpd3yakTh+sVjPb5t5IQ/d13Hu1lbd/30ha8hH6D3/94gI1tpopn1KG\n/LWSipaTdcbWX28wEt2ki9PMuVSwYnhARDuyTh+kVr2mjJ7wpV1js5xVtRqNePrd9ZjNpouWYRDO\nT+pF+3l5uOBiNLD7mIVmUZBjgl1HNeOLeSffrs2LOBF/jFY3LcRgdCPl2FrW/jGOqf/LYv8pAx++\nvJj7Xl5LYEiNi45V6ry6QhlAga2yLFr8kb9JOnmQ8OoNy3xsZ2SoK5Rg2gGLxcxX747iZMJRPH2j\nSPv6Mf73+MxLTl5cGotjMirkBialFAOGPcd1tz6D1laMRrtSlApVZERKqQeBcZzru5+plPpCa+38\nE0uUs137U5gy5y9ycs0M79eI/p1tdxzfe0tTei79lx6vpxMZAMtjFb990M2uMgtanm53e4I56Ufx\nj7DNyG0wuBAY2ZGrG+xiWCfo2SSLWg99Q79hr13U4tKlz2iWzn+CWu2ewJSVRMI/3zJ48Lwiz52W\nEs/klwbi4hWJtuRh1BOZ8OICvHwCi/nJlL1Znz9Iw2umEFTjaqyWXHbMH8zurb/RvN0AR4dWapIw\nVT5SLxbObLYyec5ONu9OoHakP0/d0QZ/HzeMRgOfPtmNXm+u5trmiu1HoE3TKHq2iSxW+Wkp8fiE\ntjw7I7d/tbZYzTmM6AxKWTl+OpvtMT9zzcAH/3NcdNOumGe8wMGNb+EX3paEf2bQrM0A3D2KbvFZ\nMu8DVi3+Ev+wFqSd3E7/IU/SpfeYYsV9OS2i3c/2MhTHtnU/kpySZksgDS4kHvydOV8+wpNvrymT\nuGJDp0CMYybhtF0MO+cFsT1p3J1AB611JoBS6i0gBqjSlcPugylcc+88HutvJigUxr16jPcf6c6Q\n3vXw9nRl9Rc3sWhdHJk5Zt55KpIa4fb/4tlantzp0DyCI399Su0OT5ObEc+p/XPpmD/vpZsLZ2el\nvShp6j0WNzcvtsXMxd3dk7uenE1kzaZFnnf+rFcIqD2QOu0eQ2vN/rXP8ccv73HTqFeL89GUOa01\nGaknCIiwXUEZjO74hLYkNSXeoXGJKk3qxUKMe205R47EMbabmeWxBq65J451Xw3Gw92FEf3q06J+\nMJtjE7kt1Js+HaoXu4u9ZnQblvw6herN7sTDryZx2z+hSU0PlMoCwN1FFzr208PTlwkvLuC3H97k\ndNwMWrZqQ5+bHinyfEknD7Hyt0+46pbFuHuFkX0mjoWzrqd1xxsdfjGZmnwcn9A2GAy2r3H/au34\nd03lXF+wMrEnaVLA+b+FlvznqrQvf9nNhN5mnhxo264RZObV77cxpLetP93D3YXB15TuzpBZL3Xm\n+kcXEfPNNCxWKx6uik0HwGyBVxZ40qH7TYV2TymlaN99OO27Dy/W+VISjxPYaNDZMvyqdSAl8c9S\nvYeyoJQisnZrju74ipqt7yUn/Sgph5dSc/Btjg5NVF1SL14gNT2Xn1YcJuEjK94eMLKLlY4vZbHm\nrwT6dLB1lzWrF0SzekElPkft+u3od8tjzJ9xLSgDXt4BePhY+P0vOHAKvlvvzr0Tbyz0WP/AiGIv\nS5WWEo93YF3cvcIA8PSriYd3KGdSTzo8aaoZ3YY1Sx8lstlo3L2rcWzXN0TVvcqhMVUF9iRNU4GN\nSqmCOUhuxLbmUpVmtljPLk8A4OVue64sRYR4sW36daRlmPDycOHIiXTuen0tP23Ppll0JNa2b5TJ\neU7F/0tKYhwRNepzOHYGARHt0FYLp/bNpn1n57i19vYHvuTLd0ey7q9P0VYzA0e8RC07+u5zczL5\n4avHiN3+O27uPlw/9NliJ5NCFELqxQuYLRqjwdYKDrZhlV5uZV8vduk9ho49R2HKycTd05f1f37K\nE7//grtnIHc+9QIh1Uo/jUFuTiZxB7aSm5NJ5ukDpJ7YTEBEO5KPLMdsSico1PGTvzds3pMe/cbw\nx+weKIMLYZENuePRb+06ds/O5fz4zRNkpp2iVoOO3HbvxzJxsZ3smdxyklJqFdAl/6kxWuuKWQnX\niY3s34hBj+6neqCFIB94eKYLD4wougusJPx9bNlZdJQ/yz619c+t2JpJrulpSCzdPE/LF0xh2YKP\n8AtpTOqpXYRVi2b9tNZobaVFh0H0uO7eMnkPpRUUGsUTb64iOzMVdw8fjC6udh3387SnSUjMpN2w\n1eRmHGfBrP8RFBpFdJOryzlicSWTevFiwf7uXN0inNGfn+KunhZW/KM4etqVq1uW/R2hRqMLnt7+\nAFzd7z6u7ndfmZWddvoEH71yIxj9sJiz8fEN5p8/x6GxjT8c+/A0p5nQ8pqBE+jWbzwmUzaeXv52\ndXcmJhzguyl30bDXFHxDmxO3dTLTPvwfE14ofD6kxTEZzE57hxaSUwF23j0H/AWcKNhfKVVTa23f\nBBlXqE7Nw5n1Wj/e/W4LuSYzD49qzLgbG1fY+XvmzyZecLcdYPvFjva43GH/kZhwgGULPuKqwb/h\n7h1ORvJedsy/hafei8HDwxcPL79yib2klFLFbhLft2slTa+fjZtnEG6eQYQ1HM7eXSslaRJlQerF\n8yilmPNGP577dAPPzz9JrQg/Vn7WucIWBy4rv858Cb8a11Kn/RNobWXvikfo1LY23a8bj7dvMAaD\n0dEh/oeLqzsuru52739wzwaCa/UgqIatDqzT8WnWfNXosnfxFud75Upnz91zE4AXsS1IWdBvr4Eq\nv9ZDr3bV6dWuerGPO5GUxeQfYklJN3Nz9+pc2zGqxDEU3NmwYmsmw/wfh0TsXqIlJTEO3+AGuHuH\nA+AT3BA3jwDyTDkEBF3+fSWfOszMTydw8tgegsPrMOLuyaWeOVxrzV8bfmF/bAz+QeF0u3Z8qRM3\nT+8Ask7vx9PP1pyek7Yfn4btS1VmUU7ExbJx1fcAtO82jEiZsPKKI/Vi4bw9XXn/ka5F71iIHRt/\nZd/fa/ELCKVbv7vOtiRVtKSThwlrPgSwTVMQUL0ryYnr8PUPu+xxWmuWL5zCmsVfYbVa6HjNbfQb\n/GSpp0VJS4lnzZ9fk5OTSYu2/WjQrEepyvPyCSAr9RDaakEZjGSnHcbo4o7ReHHrfVlNM2CxmIlZ\nNo34o3uIqNGAzr3G2N1b4GzsaWl6EGiotS58CegqxGrVTF+0j217TlK3egD9u0Tx7aJ95OSaGdI7\nmk7Nw4ss41RKNq1vX4hH5EBcfGox54XPmTQhi7EDSzf3R8//JEm2JVoKWp7yTNn8+cs7JJ/YQ70m\nvejcZyxKKcIjG3Am6R/Sk3bjG9KUlKOrsZqzikyYzGYTn74+hOD6w2nTaTJJh5fw2Ru38tS76/Dw\nLPmcf4t/fptNa+YR3nAER3btYNv662h2VR9Mplyatelbosrixtte4tsp4wmNvgFTZjzW7KN06DG5\nxDEW5eihv/jsjSFUa3w7KMXm125i/BOzqBXdttzOKRxC6sV8B46d4at5sZjMFm7tHc3euDTblAMR\n/tw3pCke7kV/zcxa8g+/bNhEeKORxMXuZlvMdTzy6h92TQlQUv/GrmXT8q9wdfOm16DHCQ6vDUBU\nnRbE7f3BNp2BNY/E/XPpYMfYzs2rZ7F26SwaXzsNZXBh64oH8fIOoMd195Q4xjOnE5j0XF8Ca/XH\nzTuC7z66j6s6D8JqteIXEErXvuOKfWHZpPW1rPnjG3YtGoF3cFMSDy7iptEXT11TVrTWfDtlPCcS\nEgmq1ZcDKxex7++1jH1keqWcoFhpffnJvZRSK4A+WmuzvYW2bRyqt0y/ubSxOZ37317F5h0HGN7R\nzJLdBtbt1dzZQxPiAx/8aWT6xL7063T5VqN3Z+xg8rLqRHd/H4C0hG2c2jCeo/NuKnV8h+PTOZGc\nRaNaAfz1r5lck8ZssTL+jd9IOmPGNyCSM6eP0bRld0Y/bGsJ+Wvjr8z54iFc3X3RllzueGgq9Rpf\nPG291poViz5m7Z/fYLGYyTPl0mn0lrO/9DvmDWLEuNep07BDiWK3Wi08NaYmHUasxt07HK012+YO\nwsXdj8DqV3MidhqDRjxP265Di132iaP/sHfncjw8fWnV6aZSJXZFmT55PNmuzanR3DaPy/G/v8M1\nezNjH/6m3M5ZEo+ODN6qtZZMroSkXrT5Ny6Nq8fNZUzXPHw94N1FilB/xb29rKzcYyTVHMiSj2/E\n1eXSrS1aa7y6f0vrISvw8LVdsP39+2j6XjeCq7rcUqr48kzZnDgai5u7N+HVG56tr7asmcOPXz+G\nt28weaYszOZcHn5lGWGR0eRkneGr90ZzIi4Wq9VMo5a9uO2+TwudaDH51GFmff4Qp+L3gTIS0WQs\nUa3G2V47soL0g99w//P/Xcf5UjOCFzaB5NJ5k9gZe5gG3WyTGKfGb2TX4nHUajOBrOTdWDL28NDL\nfxR7VnOLOY/tMXNJTztF7QYdqNOg/FrfExMO8OHEG2g3bBVGFw+sllw2z+7B/c/9VOqJQstydnXV\n4Qu76kR7WpoOAiuVUouA3IIntdaTShFfpZOclsO3v/3LsclW/LxgwrVWGj0GN7eFLg2hUaSFN6Zu\nKjJpyswxY3QPObvt5hlMTm7xJza70Atf/sWkWf/gFxBJVno8C97uQc9OEXw57x+S0jXthq3E3Tuc\n1BOb2bFgBFkZp/HyCaRVh0E0btmb9LRT+AdG4OpWeN/1plUzWbPkexr2+hxQ/L14PMd2fkNUyzux\n5GWTk3nyklc8WmtOHt+LOS+XalGNC+03t1otaG3B1cO2pIJSClevMMLqXke1hjfjH9GOxXMfL1HS\nFBHVmIioihlvZsrNwtU3+Oy2q2cwptTsCjm3qFBSLwJT5uzgnmvymDjYth0drvlsmeah/vDAtRba\nvJDG2r8S6Nn20pNYWq0as8WCq8e5qQhcPYLIyyt8PU57JZ86wqevD0YrT0w5p6nXqAOjJ3yBwWBk\n4ayJRLUYQ+32T2C15LH799v46ZuHuPe5hXh4+XHfc7+QlhKPweiCX0DhPQh5phw+ff0WgqOH06Ld\n25w6sIgj2z8jstkojC4eZJ85jKf3f+vEgvGnAKfP5LIvLo0aYd5UDys86TGZsv/7uXgG4eLmS82W\n49Bas/v30ezetpirOg8u1mdjdHEtUV1aEnm52bi4eWMw2sZdKYMbru6+mEyVs160J2mKy3+45T+q\npOwcM57uCp/8nMJogBBfyDLZtsP9ISun6OTnxm61eXfWLLxDW+PpF8WxzS8xrHftUsW28e9TTP7p\nEK1uWYmbVwjJcSu5+ekJnPp9KIfi0/ELbXJ23FJARDuUwZW01ISzg6rdPbxx96gD2PqeTx3fhzIY\nCItscLY/fsem34m66kF8gm3JR73Oz/Lv6ufIy0nmTPxaGrXoXuiYJrPZxKzJw0k4vBEfDyMmQwhj\nnvrtoorIxcWNRi37snfl40S1vJszp3aQFr+Rht1eA8DNK5S8SvBH1qbLTcyb+RpuXqEoZeDI5rcY\nOPQJR4clyp7Ui9jqvEbnrgEJ9wdTftubwQChvpCVc/nGOKPRwICu9di4YgI1rnqE9KTdnD66mgbN\nJpYqth++fpygekOp2foeLOYcdv8+mk0rZ9LxmtGYTHkE1+lvi9PoSlDt6zlz6PuzxyqlCAg+N0wh\nPS2R1ORjBIfVPltvnor/FytuRLW6C4CaLcdx/O9v+WfZg7h7h5N8cCH3PvffViawjUNdsvEYI57/\nk1ohikOnrLz4v7Y0qHXxmnnN213PuqW34hPSDA/f6uxd9TShdfufjdHNK8Tp68Ww6g3wcHfj8OZ3\nCK03kMSDv+FqhIgaFXfjVFmyZ8qByr3KaxmJDPWmXnV/Hp6RyvhrrPyxE3YdhYwc2HIQHpzhwpB+\nDYosp2WDYOa/1Z3HPn6DlIw8buseyet3l66XZG9cKoERbXHzstVewTV78M+fOWRk5TGwa03embWM\n7DNxePrVJDluFQYsdDdO4af9k/5zV0R2ZhrT3h6AKf0IZosmsFozRj06F1c3Tzy9/EhPP3Z235wz\nx6hWI5qGtb0J7XQfrTreVGj/9Lo/vyA4byMbJ2XjaoQn5+Tw24yHGXb/9xfte9t9nzLtw7H8/dtt\nuHl4oxScOfUXnr5RHNrwKi3bDyzV51QRruo8mDxTNqsXv4FG0++mCRV2RScqjtSLNrf0bsC4V47Q\nMMKMryfcM9WWKP19FFbEwj8nDHRqfvkB1AAzJ3Zh8NMb2LzmIXz9w7j76R8LXT+uOE7F/0vja20L\n9BpdPPCvcQ0Jx/cBEBwWRcLen/AJaYbVkkvCvrk0b9Gl0HK2rpnFgu8eo0aIK8eTzQwe/wXN2lyH\nh5cvuVnJmPMycXH1xpKXDdYcmjaOxssnkJZj/iAkvM5F5eWZrYx4fgk/TTDTvTHEJUH7F7fw0t0h\ncMFt/VF1WnHrne8wb8YLmPNycffwxpR5nIzkvWQk/U1K3CrqN32hVJ9TeXNxcePeZ37ip6lPcXD1\ng4RHRjP62Z+KdcefM7Hn7rlQ4AmgKXD2G1ZrfU05xuV0DAbF/EkDeOCdVQz+KJG6kX68cV8dXl8U\nS67JwrC+DXnsttZ2ldWzbSRbpxZvzaXLaVInkJNxK4nMPIm7dziJh/7E19sDHy9XOjWvxtOjmvDG\n9N64egZhNaUy760eeHq4cLvXE+Qm6rN32/35w3N0r7mfL8aYsGoY8tEOVsx/l763PE+fGx/ko5dv\nIDfzBEoZSNo/j/uem0dEzSaXjS35+E5ua5N9dsK7oR3M/PjFP4Xuu37ZNI7H/Uu1JreTmbwLN/dj\npOz5jNycTJq1udahq10nJRzkh2+eIPnkYarXasaQO9++5N00HXrcRoceMlv5lUzqRZt+naJ4+8Gu\nPDljK6Y8K0P7R7P/6GmGfJJIrWq+LP24O0H+Rd+u7uXhwoO3tirTRWFDwuuQsG8udds/iiUvm6SD\nC2lzw50AjHt8Nu8/35eYbxdhseRSvVZTBo167aIyUpOPs2jGY2yamEOjyBw2H4Deb48n+v1YgsNq\n07xtf3YtHEZAjV6kHV9Jk1a9GDB84mUHOJ9KycaorHTPb2ipGQLt6hk4fuoMARfsm5GezPzvX8In\nvBNuXpEk7PmekPAcDqy6D1+/UO56ag5BoSW/+7o0Cu4W3LhyNkajC9cMuJd23YYVuq9fYDXGPjKt\nYgMsJ/Z0z80E5gADgLuB24HE8gzKWYUEePD9a9f+57kJQ5s7KJr/0lYLm2b3xt0ngtysU/i6nZuF\n9+XxrXng1sacSMqiTqQvPl4X3uppu9tu5pHfGXaLCYPBtlTi0Pa5vLdpBwDVajTi4Vf/ZNv6n0Fr\nWo9ebNfMuyE1WvLDlt8Y092WOM3a4EpY9YsTLavVyuIf36Dd0KV4+NZAa82uBUPoc+N9tGjn2Bam\nnKwzfPzqTYQ1voMGzZ/j5J45fP7WcB559U+nm7NFVBipF/MNv7Y+w6+t7+gwLqK15sQ/c0g8sIi8\n3FSMxnO9qH6B4Tz34VaSEg7g4upBUGjNQhOd5FOHqR/pRqNI2/iqdvUg2NdIako81bz8GDrufbav\n/5mE43sJbzee1p1vKfKOsLAgT6wYWLHbQs+mcCQRNh+wMrCPHxkX7Ltx+Xd4hbanYY93AfCPbE/8\n1ld5dtKG0n04ZWDN4i9Yt/xHoru+i8WcxfxZj+Dp7U+zNv0dHVq5sidpCtZaf62UelBrvQpYpZTa\nXN6BVXa/r4/jla82kpmdx+Br6vPs2DYYjeWzanPsodNUq92FyHYvYcpOwtO/NhumtyIjK+/sxHIu\nRoWriwGj4eI/6IK7Dz75MYAfN6bRq6kVq4YfN3sQFHkuKQwOq02fGx8tVmxd+oxn1p6V1Ho4Bi8P\nA1bXcMY89f5F+1kteVitZty8bGOdlFK4+0Riysks1vnKw7FDO3D1qkZUS9tdMXU6PsOmmR05nXSM\n4LBaDo5OOIjUi8WUnmni0Q/Wsn7nCSJDvJn0SLdSrUNXlORTh2k5YAZgxejqxcn9i0g4tvfs60op\njC5uuLi4smpb1tnnz5/jLjisNv/Gm9gbDw0jYeshSDpjISAo8mwZxb3Dz9XFwKxX+zL02T+JDIS4\nJCuv3N2emtX8ib1g35ycDNy8z42t8vCJJNcJ6kSAbRsWULv9M/iF2aYmi2o9ge0b5kvSBBSMbj6h\nlLoeiAfK7zf9CrBh10nueGkJX91pISIAHpyxC4tV89Jd5XNbZ8OaAZw+sY7qyohvSFOS41bi7+Nx\ntkXpi3l7eej9TXh6B4IlgwXv9KBzi4uXNpg+sRs97v6V+o9mkGfRhAZ50/Cqxiz55V2q1WhEs7bX\nF3teDaOLKyMf+oHEE/sxm02ERdYv9O45F1d36jXuxv61zxPV+l7ST+3k9LG11Gv8csk+lDLk6u6J\nKTsFq9WMweCCJS8Dc16W0yylIBxC6sViGvHcnwQZE5g53sqG/Zn0uX8+f824lfDg8vk7CousT0rc\nsrMDwc8cX0G1Zrbuo4z0ZD5/cxipKSew5mXQoUkE7z/UgZPJlv8sTRUQXJ3+I96i3cQnqRnqytEk\nM32HvkLMim9RKFp2vJHA4OJPcNyrXXX2/TyCA8fOUD3Mm2rBXiyOubCdCZq07sv65aMJiGiPu08k\nB9dPpHlb50hK3Nw9MWWda1w1ZZ0iwLd4Ux9URvYkTa8qpfyBR4EpgB/wcLlGVcnNXXGQ+3tbGJi/\n4PQnt5sZ9tm+Mk+aLBYrR09mUq+GHw/cUodJs3rgGxBJdv6UA0op9h5J5bGP/qLFzYvx8q9N0pFl\n3PDEQ5xcdOtFLV+Bfu5s/fYW/j6YgtFg4JHJu1j88yR8qvchZtUb7Nu9jsF3FH+RYKUUYZFFN9/f\n/sCX/PD14+xeNAzfgHD+9/jMUg8GLQtRda+iWvW6xC4ei1/k1aQcXkTrzoNlgcuqTerFYsjKMbNk\n8wnSv9K4ukDLWrB4l2blthMM7XPxXWOlkZ11hqyMFAbf8TpfvDWM5IPz86cc6Ej7HiMBmDv9WVz8\nW9G+31ys5hz2/DmC9TsOct+QpmeXpioY69mu+200bNmH00lHsVjMfDPpdoJr9wNtZdmCXjwwcRGh\n1Yr/HgJ83WnT+PJ1SJ0G7Rn2v3dZOOdlTLmZNGtzHTeMnFiSj6VUtsbXoU3kof88d+1ND/PNpNvJ\nPhOH1ZxF4v6fufXFhRUeW0WzJ2naqLVOA9KAnuUczxXBy8OFpKSCVRUgMR083exd5s8+8YmZ9H5g\nOceTcjGZshk3qBG7vuyr4kUAACAASURBVLuehJRsGtfuSICv7c6E2EOnCazWAi//2gCE1OrFodVw\n6nQOESEXX+G5uBho1SCEfXGpbIw9Rauh63Bx9cacew9bZnXmmgH3lVsi4+ntz+0PfFEuZZeGwWBg\n3GMzWL9sKokJh2k78E7adRvu6LCEY0m9WAwFk1umZkGoH2idXy+6l+2YwNWLv+C3H17DzcMfF6OB\nMQ9PRWuNm7vXfya3jD+ym1qd37Z10bl64l/rJjbv/QGwDVfYuT+Xe/PHesaGTsEvIBy/gHCmTR5H\nZIu7qZnfVX9kW3X+/OV9Rt7zUZnEv3Jr5kVLYDVvN4Dm7QaUSfkl8dJ+W2K3cH8oL0afa1mq17gz\n9zzzE9tjfsFg9GLk6MWF3i14pbHnm3ydUuowtkGPc7XWp8s3pMrvf4Ma02HMblyMeUQEaCYtNjL5\nsXZleo7Rr2wkN+gmrur7BGbTGWYtupmrWyRya+//XvHUq+5H6smtmLIScfMKJS1hGwoLIQGXv6Pl\n9BkTXr4huLja/oBd3P3w8Aok4thTLD7yOYBd69tdKYwurnS9dryjwxDOQ+rFYnB1MfDoiOb0fjOW\nsd3MbDhgJFd707dD2V2AxR3YxpJ5H9J2yB94+NbgxJ4f+O7je3nmvZiL9vXwq0VK3FJ8Q5tjtZrJ\niF9K4xbnLiJbRLvTItr9P61OAFkZqXjVPJcY/J+984yOqurC8HOnpPeQBqlAKAESQgi9iNTQO1j4\nQEURC1YUrKBgQUVFUVBEFAtFpIuCVOkQQi8pBJIQUkgvk2Ru+X5MQsukkYRQ5lmLpXNz7j1nsjJ7\n9tln73db2vuRn3KyRtZvyC2dxmlqroKwphgbPJFlEYtKXff0C8LTL6gOVlR3VJiZrChKE+AtDKW1\n4YIgbBAEwVRPXQ6ebjbs/3Ek5vVaEa9vzq/vhzGyZ82GoI9GXsGt2SMG5Wxze2x9hhB+Nr3UuEB/\nZ155qAnHVvUk8q9BRG0Zx+8zu5bb1gCgRUNHKLpC4ulfKNKlcenEYizVuYQGODMr5G2e8Z52g7qt\nCRP3Eya7WHVmTW7P6090IzKvKa0Cg9m+YNgNfen6dbSplk1JjDuFo1dXLGwNjph701Gkp5xH1BeW\nGjtrjBn5sT9zZl1fTqzqjr9jHC+OLd1Yu19HG+q7aK/avJ5Nsok/8gX5WRfIz4gh4ejXtGhTcV86\nE/cOlTozUhTlIHBQEIQPgLnAT8Avtbmwu4Gs3CL+3B5LoV4irKMXPh7XeppdvJzLkbPJ5OYX0cjL\nngdCPGq0OaFvfTvS43dQP+BRZEmPLnknjXsa78HzzuOBPNzbh/jkPJr7BeJeicRLGyst277qxcMz\nvuXoodk09XVm/fxeNPE23GtsF3Y/RZ5MmDDZRePsPZ7E4TNX8PWwYVBXn6t2r7BI4ujZZI6cTSYl\nPY+UjMZG5E9uHWdXH7KT5iEW5aAxsyXj0h6sbesZFVF0dbQmeuUQjpy9goW5mjZN65VZ3RzY2Pzq\nfxXFn+2JrpxYPwpBEOjS5wk6Pji+UuubGe3CcnvjOkZ3C6N07VlpeaCul1GnVEbc0g4YBowFGgGr\ngdrr7neXkJZVQKcnVtHcrRAnG4V3Fuzn73mDadOsHhHnrjDwpfV8OU7BwwGm/LyfjOxC3n2y5o7o\nfnqrHQ88O4fcCyvR5V0htIkFjw1sXeb4xl72NPayr9IcAQ0dOfpz/zJ/XnL2P8vlbRJT9TdUnZgw\ncS9jsovGmbfsOJ8uPcygYJkfV6tYtdWLJTN6IQgCI17bhJR/mQ+Hwe5zmXR8/A9Or3gY50qIX1aG\nxgFdaRXSm/CVvbF2bETulTOMf6H0kdKO8DwCvMHaUkvXYI8qzdHA1Yx5D6Vy2sW4QG9FPJSzjAwm\n3tK9dc3Y4Im0jFjEu43reiV1S2UiTceANcB7iqKUPhy+T/n8t2P0aKJjweMGEckfdsD0r3fzz9dD\nmfn9YZ7vozC+m2Hsr8/CgE+P14jTJIoyOfl6AvwciVw+lPCzqdha+RIa4ILKiAZTbXP9Lqwk8gRc\nrTwxYeIexWQXbyK/QOSNbw9y6iMZHxcoKJJp9UY8+0+mENTYic2HLpOzCCzM4IEA2HpK5OeNkbz0\ncGC159blZ6M1s2DEhA/o2ONhsrNSqO/dwmiz3We8p13VpqsqgY3NSUzVG03YLo9RuvbcjX137tZ1\n1yaVcZoaKoqi1PpK7hIOnU7haGQaEedS6d/kmup2oBfM325onJicnk/jayd15Bca+g1Vl+/XnGPK\n3P0oCPjWd+DvuT3o1c54ImV8ci7/HryElYWGQV19sLKo2eo9Y5QYopsrT0yYuAcx2cVicvKK2LA7\njpQMHRZa8CmuorcwgyYeAqkZOvJ0IihQoDdcBygU4WJSaW2iKs2dlcoPc8dzOe4EKDJ9hr9Gz8Ev\nYKxJlaIonDu+jYsXo3F39qR1k3pGRlXMrSRst7x0nvrOTUjKiLylOeuKlpfOMzb47oyM1RaVkaiu\nJwjCJ4Ig/CUIwraSf7W+sjuQr1ecYNirG9i3bx+Rscm8v0YgNgWy8uG9NWoeDDX0AOoZ6sV32+D9\n1YYI1ENfg5tT9aIuh8+k8ur84wQN/5uOj51Fcv8fQ6btMjo2/EwqIeNW8u/2PSxavpPOT6wiO7eo\nUvNcySzg17+jWLY5mqzcIhRFITdfT1W+HwIbm191oAJSnycg9Xl2hN8ZKrYmTNQQJruIIU0hdPxK\nlq7exb79hygoknn9dyjUw9/H4GCMQkgzF5wdLLAyFwibAz//B5MXw+lLENapen3Tfv/uRQTrVnR5\n/BTtxu5k1+afOHtsa6lxiqLwx8LH+e/3x8hIDCdsyjqWrD9b6Xn+i7jM4nVnOXgqBYAiUWLbwaoV\nTHbzNhw9TDq0okr3VZVRuvZX/9UGJRIEFRGQ+nytraEuMfWeqyQ5eUVM/+YAJz80hJ5zdNB0qorA\n6QKirDC2lw+zn+kIwFuPB7PlwAUWbM3Ewgzy9Sr+/qhfteY/eCoFJ++eWDkY+r01aPUUu77/BEmS\nSyUwvvrFf8wZo2dCd4Meyrhvc3j5y310be1Bm6b1aNXYuHBxbGI23Z5aTVtfiSIRXv1SjaIopOfo\ncXUwZ8WH/WjfsuKO5SUYizyBKfpk4p7AZBeBT5ZG0KOpjm8fM0TS5/0DH2/Q8NlfIj5ulqz8sCcN\nXA0bxiXv9mT8zK28t9qw0Rz5YCOjkgMBqc9X2kbERR0ieMR7CIIKcxt36jUczIWoQzQL6nnDuPNn\n95IS/S+nPsjHwgzOJULbt3ejVquwttQS1tELyzKi8W/M38fyf87QpSm8s0DB292e8HMZKKwg68QY\nBk/4ErW6cpF8d8cmrMiIZGGlRled8EQ/ZubnolJrkCWxxvOPypIeMMaYrGWQxT2XA2XqPVdJ0rIK\nsbdS4eNiMA62ltDSS82LE3oS1snrhso4C3MNexaNYMeRRPJ0Ip2D3CvURaoIT1dr8q4cQ5YKUanN\nyU4+grOjndGKj6T0fEKKpUQEAUL8ZD5cF0lBegyvz1P46LnOTBjUrNR9b87fx+QeRbwxRCEzD3xf\nEPntOejfGtYcLmDo1L+IWvVIlSteSjRPgFJquyZM3KWY7CKQdCWXLr7XUg/a+hlsVcJfo0tVCw/r\n0ZDWTesRcS6NBi7WRjdg/TraGG0nUhZ2TvXJSjqMa6MBKLJEbuoRHIJHlRqXnZlCc0/V1aPBpvVB\nlGRWrP+P3EIV7y+yYufCYdjZ3NjiKTIuk8XrTnP6YwknG5j6G+yLSidtIcgy9PtsLbs3Nab7wLLF\n4K+PzHTz7saKWjyi25Cfe/U4bVnEImbeJEhZF9wJa6hJKnM8d0OPJUEQgrkPeyx5ulpjbqZl0XbD\nh2XrSYi4qBDctJ5RKQGNRkWvdp4M6e5bbYcJYGAXH7o0V3FyTW/Ob3+c6K1PsPSdTkbHdguuz4fr\n1RQUQUIafPk3zH1E4ZfJIjvflHj+s90UFkml7ktKyyO0oeEY7mwi+LoYHCaAoW3BxVYhKj6rWu+j\nRPekROfJpPVk4i7FZBeBbm28+GarhuQsyCuAjzeq6dbGs0x5Fb/6dgzv4VeliHV5jHniE87veZsz\nWyZxdM1g7G20hHYtXdbv3SiYPWcl9kYa7PdnGw3K5Oteltg2XU8rjxy+WHa81H1JaToaualxKs4b\nPxEP0weDjQXYWcGr/XTEn9te4TormxdU30V7yzbx5mOzusxFmhntgkqtuSfzoe6b3nNbDiTw47qT\nqNUqJo8MNNqwtjw0GhXr5w5kzBt/8/TiHNydzHn/6VCmf72HgkKRh/o1Z0h339pZPKBSCaz6sBvb\nwxNJSdfRrsUAGjawMzr2sxe7MmFmIfZPJqBSQSNXgUe7GHaDTeuDhVYgI6ewlF5Tl9YNmPt3Bh39\nJSy0cD4FUrLA1R4uZ0D8FblSGk8VYSzytCzrEwIb10zpsQkTt4G73i7m5BUx+8fDRF3MoJW/C9PG\nt7lBbLIyPDaoKTEJmfi+eAJZhpE9vLC1MmPE1I34eNjz9sRQHO1K6yTVFD7+oUz9aAexkQexsLLD\nv0U3o0dlzq6+jJi0mEFz/0d2vh47S4HFTyqU+HbtG8qcSMkpdV+Lho5EJyv8fQz6BoJehD2RMCDY\n8PO9UQLWjt419n5KqvNuldvlpFTmCHV04ITbspbbTYWfEEVRSjrw3fE9lv7aE8evm85gbqbm+TGt\nCW5a7+r1J97fwvsjDLk6Q16JZ+1nA6rsOAU0dOTEsoco0kucvZBJz2fX8uYgEUdreH5OIvkF3Xio\nb8WNaW8VQRB4sG3FHbVtrLT88XEYelEmITmXdhP+YH+UTPvGsGi7oVGkq6NlqfveeqItT3+Yi/Ok\n8wCENnci9N0sujSBXedg+oTWRvvVVYd+HW3YHp7HWPupppwnE3cNd4tdzMot4oMfDxN7KZOQ5u68\n/EhrtBoVoigT9sJ6GjpkMKa1zO/7kxg5LYX1cwdUSYRXEARmP9OB959ujywrPPbev+w5dIInuols\nP3OZB55OYP/ikWXmC9UE9k71ad1haIXjAoL7MOK9kfQMteKluf+x7EAM/YIksvLh+50aXh1f2rY6\n21vwx0f9ePjtzSSlF9KgngXnUhSOJ8hIMpy6pOGj5wpIKmPOyiZNV5cd4XlQNRm+CjkeXWD0+tjg\niYyJWMS796mdLvMvWRCEeeXdqCjKlJpfzq2zaut5XvhsBzOHi2TlQ5/n49jy9WBaN6nH/BURfPGI\nxBhDnjayIrHgj+NVdppKMNOq+WHtaV7oLfJimOGau4PEzOURteo0VRWtRoVfAzt+fLcnA2duJVcn\n0bC+Nes+CzOq6WSmVbP4nZ4sfKMHAobo2v4TyZyLy+LFiQ6EBtRMSP1melyX23R95AkwRZ9M3FHc\nTXaxsEii1zNrCPTIZlgLmSW7L3MsKpXfZvUlIvIK6RnZ7HpNRqWCYW0lvF9MJjYxp8wIdnmoVAJZ\nuUWs2RVHyjcylmYwqr1Mp/d0/Hc0iT4dqtZjrqo6SJXheHQBAfYCZlo1c6Z0Ztw7+dhNvIQgwKuP\nBPBImHHb3TXYg/gN4ykoFLEw15CRXcjmAwkIAvTt4MW+E0Vsji4o01aViv5o7XDcvIiMPjUXFdpp\n72s0yjQ2eCIzI24tp2g1XjUTuRLurbym8tz/4cCbgCNwxzejnLf8CAsmiAxsY3hdqBf5fvUp5r/e\nHUlW0F73Ts00IFdTYkVWbnqmGiTpzpRtGdjFh9TNj5FfIGJtqWXLgQTm/ByOuZmaZ0YGEujvfMP4\n6/vSdWjlRodWpQXiaouSyNN4l9coLFJMWk8m7jTuGru493gyipjHookyggBD20p4PBdPaoYOSVLQ\nqLl6PKVSgVolIMu3bsNkRUElQEltiiAYbK0kV02jrrYa1461n0p9F0MRi7Wllj8/6U9BoYhGrUJX\nKPLOgoPEJmbSppk7U8a0QnNTf86So0tHO3PG9L7WS9TcTM9Y+6mVXu/ogNGsOFa5CrS7kW9jbwxG\njG1d+Yq7qnA8uoBgl9sv6Fye05QNbAE2AQ8At391VUCSFMyvK+qyMAOpyPBhfWJIK176YieKIlKo\nh3dWqfl1VunmjBURk5DNuHc2cyQyA3cnc3Lz1bjZSThaw9RlGqaOb1VTb6fGEQQBa0sta3Ze4JmP\ntvL2EIlsHfR8NpZt3wwtU4agLjBFnkzcwdw1dlGSZcw11xwjTbFjJMkKbZrVQ2NmxZSfcxgULPPr\nPhX+3o5VjjIpisKcnyP45JejFIkKHk4WPDy/kKd6SGw/I3A5W0vX1lVrVVKblORSlmBhrqFIL9H7\nubU0cc6id4DMz9sSOBaZyk8zK9eIt0eI9VU7df0G73Ydzd2ueSpLiiTdltyq9CwJ58a1L9p8M+XN\nuADYCjQEwq+7LgBK8fU7hseHtOSZxfuY+7DheO7D9WpWf9IcgFG9GiEI8MPak6hUAktmBNMztOLc\noOuRJJmBL63nya55bHkZNp8o4PFFalYcc0NWFN6c2IzxA0uX8d9pfPFrOAsmSAwOMbwWJZGFf57g\n69e61+3CyqCkv914M1PkycQdwV1jFzu2ciMt34xpy0R6t1RYtFNN+xauuDlZIggCW+YP4c35+/ho\ncwaB/i58PaN9lVsx/f5PND+tO8qBGSK2FjB2fiEpBY58vAV8POzYubBTjTblrQ32Hk+mSJfLT5MM\nEblR7SU8nrvAlcyCSlc+l0gl3Ow43arzUNXjydtdpaZS1f2R2zPe0whsfGvtcKpDmU6ToijzgHmC\nIHyrKMrk27imW+Lxwc1Rq1R8tek0Zlo1v88OoXPQtTDhyJ6NGNmzUTlPKJ+ElDyycwt5ubh/7bBQ\n+GabmikPtaFfR4Oq7eUr+ZyITsfLzZrmfo7Vej+1hV6Usb5us2VtDmJO9Vu81CaG3eGN1XaAqeLO\nxG3nbrKL1pZati8Yxhvz9/L+X1mENHdj0dPtryZ6O9tbsOCN6uWwbzlwkZf6ijQqPsF/f4TEKytF\ndn0/GjBsNvefSEZXKNG2eT1src3KeVrdoBdlrC2uReTMNKBVC4hS1Y8Vb46M3wpVOZ6sjQTwyjA6\nqHaO3O4GKlM9d0cbhusZP7Ap4wc2rZVnO9iak62TuZwBHo6gK4KYZBlne8OX+cbdcUx4719aeQmc\nuSTz1LCWzJx050nIjx/Ugmd/3s+XjxZH5DaoWfnRnR8hK6FEZfz6ijtT5MnE7eZusYse9az48d3K\nHTPdCs72lpxOLAmywalLBmcMDInog1/eQFxiGk42ApezNfw7f0iljgDNzYQqKYNXh46t3EjK1vLO\nH4aI3Pc7VLRpVg83p9IVxhVxfTXw6upJ2lWKnfa+1HduUvsTmbjK7T8QvEuxtzHjzQnBdH7/GIOC\nZf47p6JbG2/aNndBkmT+N+NfNrwi0tEfruRAm7dOMahbQ9o2r7nz5sycQr5cdoKU9Dx6tPViZM+q\nnwQ8ObQ5KgE+/OsMZloVP89sS7fgOyfnoLKU5D2ZIk8mTNQdr44LpuPj50nMLMLOAtYeUbH5a4Po\n7vyVJ9FKVzjxoYRGDXPWi7zw6U7Wfz6owueW5AmVsCM8j532vnTPulDq2OrM0X+JPLoBcysnOvWZ\nhJ1D1QpXbKy07FgwjNfm7ebfPw0RufnPdKyS9MLNa2++fQmq8qSjBQ3Nty/hTI8JtzTH9ZT0tCuL\n6lTQVcTxm6oGy4t81fVxXk1x3zhNRXqJo5FpqFUCQf7OpSojKsO0CSG0a+lOxLk03u5hy9DuvgiC\nQShSlmU6Fles1rOF0EYC5y9l15jTlJuvp/PEVbT3yae1t8xb82OIScjk9fFtqvQcQRCYODSAiUMD\nqr2my1fyiY7Pwq++LZ5ut/9sGYxHnkwtWkyYqBzxyblcSMzB39v+loRr3Z2tOPzzKP7Yep4iUebN\nKT741rcF4PylTHq3MDhMAGFBCkv2Zld5jm9j3Umxl1CpDJGVM7FqJvsZlJEO7fyFXaum8WqYjugU\nDd+88wvPvr8HW/uq2d0Grtb8Oqtvldd2M6IocywqjYRYPWP6PF7muNGBEyqsoKso0jYz2qVOyxBU\nag2rJS8Cr2u5WJ70wb1ynHdfOE1pWQX0fnYt+sJ89JKCWz0HNn4x6JYSFB9s26CUwKSTnTm21mb8\neaiA4aEQnQR7zinMalRzFWmrd8Ti41jA4qcM5+xDQkRaTj/Ca/8LvuUdUXVYvjmaZz/ZSRMPNZGX\nJT56tmONOGK3So8bnCRDc2BT5MmEibL5fvUppn+zv/gzLPPt690Z1avqeZ/O9hZMGl76s9+6iSuL\nV8XwxAMi1uaweJeK1k3qVeqZx6MLgWuVYSVfxLvidpGYdq132441s1j7go52jQBEsgtyCN+9ggcG\nPHvD8wJSn78qN1Bb5ObrCXthHalpWTgrCpu2/EGv94dgYVt1G1TZHnxjW9ddm5LRgRPuGUeoKlQ9\n3HIX8sb8vXT2y+H4B3pOfyTiaZPBh0vCK74RkGWFkzHpHI28gl40nhioUgms+rgfzy81w+8lDSFv\nq5n9TMcyk8EVRSEyLpPDZ1LJLxArtQ5doYSL7bXXzrZQpFeoptzULZGRXcjkj3eybbrE3neK2P+u\nxLT5+4hPrnyjzdqkX0cbzM0ExtpPNfW3M2HCCHFJuUz/Zj8HZhg+w1uniUz6cAdZuUWVuv9KZgEH\nTqZw+Up+mWMeH9yMls188ZqiwnOKmn0X7Zk3tewq3ezcIg6eSuFCYg6JqXqez10J3FgZVnIUVaIF\nVFRUeINddLWV0BfpjD7/ZrmBmmb24sP42mVy+iOR2DkwqFEOx5bur9Yzjalym2xa3XJfRJoiL2bw\nZpihnFQQYFCwxB8n0iq8T1cgMvTVjUTFpWGmARsbG/75avDVRMfrCQ1w5fyaccQn5+LqaFmqW3YJ\nsqzw1Oxt/LX3Aq52KrILNfwzbzD+3uWXQPTt4Mlb3wos2QmtfeD9tWpG9vCscolwTZCQkoe7g4pA\nb0PT38bu0MRDTWxiDl51dEx3M2VpPZkiTyZMQGxiNs3qq2nkZvgMB/mAq72K+ORc7G3Kj5Cv33WB\nx97fhq+LQGyKzMfPGY8yq1QC3735IO8/3QFdoYS3u02Z9urwmVQGv7wRDweFuCsyha0lhJ4qo0nO\n1x/1BHUYwfhFy/j8IR3nU2DxLjMmvtG/qr+OGiHyYjpjgqWruUzD28j8uzH9lp/Xr6MN7JvKsuhr\ndsugag5jspahMq+6cnttUJKrVFbblXuN+8Jpatm4Hr/tzeDBAEO/oOUH1LQOqrglyJylEdgIV4j8\nREKtgheWZjPtq718/9aDAFxKyWPJhnMUFIkM79GQC5dzOHAyGS83WyYOaYa5mbrUM5dtieH42YtE\nfSJhbSHx5d96npy9lR0Lh5e7Fh8PWzZ9OZDX5u3msy06HghpwMfPd761X0g18XG3ITlLYX8UdPCH\nE3FwLlHC36sOal8rgam/nQkTN9LY056ziRIn46GlF+yLgis5Cj7u5W96cvP1THhvG5umirRrZEhF\n6DhzH73aeeFb3xZFUVi1LZbDZ1Lw8bCjfycvfv07ivwCkWEP+BFSRo7nw2/9w5ePFjGqPaTlQMu3\noW3f/tT3rl/uesIe+pAtqywZ+d16LCzteWjKbDy86yZNoGVjF5YfSGJYW4Pj9PM+Nba+lTuOLIuS\niHmJ3Qp2EXC210KWQVm8rrnegV2NV/nJ7/cI94XTNHtyRwa+lIbfy5noJYXgJq68/r/gCu87G5vG\nsJBriYwjQmXeXGOIUMUn59LhsT8YHKzHyVrhwcnHsLUUeOoBmQ3/qlm++Rz9u/ihKxQZ1NX3akL4\n2QsZhLUSsS4OeIxsBx9uqFxtakhzF7Z+O6zqv4Aaxs7GjJ9nGPrZudoJXM6UWfB69xpv5luT3A+R\np+PRBaRnSaWu77T3vf2LMXFH08DVmq+ndqPrrF14OKhIyVb4eUavCnWULqXm4WgtFOcQGaLMLb3U\nRCdk4Vvflre+PcDabad5qIPIqr/UvDl/DyPagbu9QtgLJ3h+TDCFRSL1Xax5YrBhYylJMjGJ+QwP\nNTzT2Rb6tFRzPiGT+q3KdppKIhz9xrxPvzHv19Sv5paZPqENw19Lxm3KZcw0GizcHOjxTIdqPbNH\nWQUt0dV6bI1Tkns2OqjucqxuF7XmNOUXiLw2bw9bD8Xj4mDJx1O60LGWe5idOp/On9tjMTdTMy6s\nydUvcTsbM3YsHEZ0QhZqlYqGDWwrlTwd0LAeqw5fYkwHg+P0038C6dmFNBv5C6IEY9oVMfdRw9gg\nb4V5/yi8NQwuZ0g0m3qFRg5pNHBUGPDiCZa824uwTt4E+Dny6Q4NUweK2FjAigMQ4OtQ478LUZSJ\nTsjGykKDdwW7x1thQBcfzq9+lAuXc/B2t8HBtnbzBWqSG/rbpSo1Um2ny88mM+0Sjs4NsLCq3bB5\neKKf0esbyAUjf0rGkkW/47uaXpaJSrD9cCJvfbuHjOwiwjr58MGzHY1GpGsKUZRZuimK2MRsQpq5\nMLibz1XbN7aPP307eBOfnIuvh22ZKQXX4+lqTUaewr4o6OgPkZch/LyeVz7fhSgpxCTmkfCVoYL4\n9UESAa/BI53ggQCIT5P47o/DTHwANhxVs/LfSDZ/PQStRoV/A2tWHshjbEeDZMvWUwJtepctEHyr\n1VhXMgtISsunYQM7rCxq9uvPwlzDxi8GYr9sEb2bDsfewx6hgvSJW5UdqO2ITniiX6X1n8YGT2TZ\n0UWl7IwiK2QnZSMIArbuhsSzmyUK7kZqzWma9ME2dBnxrJgscTIhn8GvbGT/4pE08qydL5S9x5MY\n8spfTOgqkaWD0GXH2Ld4xNUcG5VKoIl31ZyTqeNaM/zEZRq9koqZRiE7X+GhTgU81UPm6cVwfeTV\n2xkK9Ib/X7QDRrWHRU8asrQ7+ou8s2AfYZ28GdO7ETvC42n0ynlc7FQUiFr++apnTfwKrpKUlk/Y\nlHVkZueTW6Aw4FQVFgAAIABJREFUoLMPP7z9IGp1zX7S7GzMSjX7vVso2cEdjy7kmeJqu1s9sjt6\nYC3LFj2LykaNnCvx6NPf0zIkrErPKCsfYDVeRq8by2eo79ykQs0WE3WHrlBi9PS/WfCYiL87TFt+\njpc/F5n/+gO1Mp8sK4yaton0K8k80Ezkja80HDodwKzJ16IfjnbmONpVfsNjbanl5xk9GTRzK55O\nAjFJEjYWMHdMLkUijJ4HzsV7NI0aGjhCbgGIEizbDzFzoYETyLJEx/cy2bw/gQFdvPltVl8GvbyB\n6WuKSMtU03xgCzxaVqwfV9GXcMnnKsAe5q88wZvfHsTdQUWWTmD1nLAab0YuCAJqZwGHBhV/14wO\nmnhLjXsdNy8Cbe1uzDbk5zK2aeVtyc0Ok16nZ+P7G0mPT0dRFFwauuD+v8aszrlRouBupNacpj+2\nXyR5voKdFbTyhq2nFf7ZH88zI1vUynwzFu5n7sMi47oaXr++rIgvfj/GZy/eet6PhbmGDZ8PJDIu\ni4SUXB59+x8+f8RwXv3GYBi/EEL8wNEanl1ikN+PTYH90dD5Oifdpx7k5Bs8KkEQWDC9B6+PDyEr\nt4hmPg5Xu2fXFFM+2Unf5jl8OEZBVwR958Txw7pzPDWseY3Ocy8Q2NicwMbmhhLn4oqUqkSecrJS\nWbboWfTjdOABJMAvC57k3S9OYWldOser3CiRMYS6LSs2UXNk5RYyrrPEiHaG1989LtHm7dhac5r2\nn0zmzPlkTnwgotXA831FfF88ydRxbbCvRFSpLAZ08SFq1SOcv5TN579F0Kn+BXq2BEUBP1d4cSm8\n2A+2nYaDMSArcDweZBnci30JlQq8nCAn31Ct16ZZPfIm68lO1zC47Whs6lUcHTemE3QzJZuO9on9\nmPXDQY7NlvBxkVgXDiOn/U38hv/ViWRLdalqPpNKrbltCusAB349QJo6DekFCRRIXZ2K615XuHN7\n2leaWnOaLM3VJGeL2BWnuSRnCTUeDr2e7Lwi/K7L7farp3A4tfrZ/CqVQDNfB1wcLcgvVMgpAHsr\n6BMIKPC/BYZx7naQlGdO9w/A1lrLkS06ujaV8HCAl39TM6jrjV+WfvVrb6dwPDqNd59TEASwMocR\nbUVORKcCJqepLK53nkoiT1Bx9Ckt5QI4aAwOE4AnYKNm/5EodnkYjzYZixKpzO3uiMROE7WHSiWQ\nnK0CDHlnydlgZVF7R3PZeXo8nVRoi82usw3YWAjk5BVVy2kCQ4QqxM4FJzsLUor1KgUBnu0N762G\n1YehvgOo1PDqcktEScHHTeCFpYVM7S+zPxp2nVX44s1r/UEFc4GH+5UtCHkzFekEleTZALxyfhMD\nmmrwcTH87geHwP8W6knPLjRaDX2rOG6+M3WLRgdOYEzEIt69TU7TlQtXkFpKV0WNpACJ1JhUk9NU\nHu9ODCHsk3AmPyhyIkHF+XQLRvQwvsuuCQZ29WPa8hP8+JShp9qcvzTMfeWaUFtcUi5/bo9FrRYY\n1bNhldVvne0tGD/An94fx/BQB5Gtp9SYmamQkbG3UpGhN+e/74dQ38UQoVi+OZpJ3x8gr0BkRI+G\nzK5mQmBVaOLtwLpwHS08FfQibDqhZmDPO7OB8J1GifME1xLGSyJPxqJEOklGn5ELaYAzkAL67Fx2\n2PdDhXDbu4+buHNxsrPgQKzCpB8K8HeX+WqLhnefCq21+UIDXDh9CZbshN6tYMFWgfouNldtlKIo\nrN5xgaj4LAIbOxHWybvKczw/JoguT8agK9JjawGf/6NClKF5fQ3RyRIznwzh5UdaAwaR4ckfbqfT\n+8k0cLFi/dwHrnYSaL59SY29b7gW0S35/P12+Xv271BIyQJXe9h5BvSCTOfDv3O252M1Ovf9kAxd\nEY4NHLkSdQXZXwYF1FFqnLydyCO5rpdWbWrNaXphbBB+9e3Zdiiexs2s+OKdlrXa4Xr6hBDydCI9\nP4rETKvi9QltGNrdFzAkiD84eS1D2kgUigIfLQln7w/D8fGwLf+hNzHv1W78tNGdI2eS6dXdgZXD\nAoiMy6JQLxHY2OmGY7YxfRozpk/jmnyLVVhnd3o/t5ZV4XrScxVa+bvx9IjaORa960nZZfSy41GD\n6rCsAPbL2FlcrVIqSmStwrK/JUWLi1C5qJBTZbpO7kqTzqYmmiZuRKMW2P/jSOavPMmlnAK+e8uH\nvh2M56zVBM72Fmz6chBPf7iNaStzCWnqzPq5PVGpBBRFYdIH2zl04gI9AyReWqPmv57N+eDZjlWa\nw9/bnr0/jOCHdWfILBL5e54/vh62RMZl4elqfYONdba3YMVHxqOvSXrxlpwNlbldqZ5mx6ML2EDu\nDZ9VTQOBrCCRltPV+LspRFySUY0SSJYkHDcvIqNPDTg6ZdiS+5EO4zqQ/GYyeQvzUBQFOzs7Qh8K\nJf7cmbpeWrWpVcmBwd18GdzNtzanuIpareLD5zry4XOlP/QzFu7njUF6XuhneP32SomPfgrn22kP\nVGkOQRCYMLApEwY2vXotqMmdlwjtW9+WY7+N5WjkFawsNAQ2di5TVC4xNY/X5u0hJiGT1k1c+ej5\nTtUO3d8u8nR6svP0uDlZVijyWdZONkkvgmDkYyBoGB04oXILCYDc/rlkJ2dj72GPtbOp750J49Rz\nsODdJ9vetvmCmjiz78dRpa6fjs3grz0XODfHIH8yfZBI41dOMWVsUJWj8I087fjgmfY3XHNxtKzW\nuivL6IDRNxzRjdK1ZybnQbgx72d00ERWsAjbNiqOp4toXbWM7WiIMK04tohJh1awMPTa+B/Xn2XJ\n+pNoNSpefDiEgV18KlyL49FI47bkNqLICrpMHVorLVqL2m0bUx7mNuaM+HQEaefTQIB6DeuhuoV+\nr2WxIzyPgKoHRmuE+0KnKT27gGbXyX00qw/rzhqX2q9JcvP1FBRJONub3/ZkQysLDZ0C3csdk18g\n0vOZNQwPzufpkQo/7spmyCvpbPt2WJ0ojVeFD36OYMb3h1CbCTRw0rDtXW+8XQ1GoiRKdDPujqWj\nP+5U3CW8Mti42GDjcmeooZswURHpWYV4OQtX9eKcbaGenZqM7MJbatxbWWRZITVDh4Ot+VWphZrM\nA2p56XyZ+YGjgyayK24Xnj43fubdHZuwIiOShcWvf1x/lg9/2Mu8cSL5RfDkrH/55f1+9AxtUOqZ\nAKTsumpzKr3RqgVyUnLYMHMD+Rn5KEUKIQ+HEDy8Yj3C2kKtVePatGIR6VvhGe9pV5u1327uC6ep\nTwdf3luTSUADkUI9zNmoYcojvrU2n6IoTJ+/j3krTmGmEQhq7MTqT/rjVIMJhzXBodMp2JkXMXt0\niTSCjOeUDOKTc6t8dFnbXB8pyouWSVglojwHeluFmP/0NJx1HtsnindWVYkSmTBxHxLo70xcmsDS\n/wxJ0Ut3gyJoa00SBuD0+QwGv7KRzNxCCvUK37zWlXH9DVH76uYBXZ/0XV5BhbENUjfvbqzIuLbR\n+nnDKb58VKRfkOF1cpbEL3+dMeo0Nd++5Gq0uq5tzuZPN5PbJBeliwLZEPFTBG7+buUKhJqoOveF\n0/Tqo61Jyyqg9ZtnUasEpoxpxeODm9XafMu3xLBp11niv1RwtFaYsjSd5+bs5LfZfWttzvJQFIW/\n98VzMSmXkGb1CA0weP9ajYq8AgVZNpQAF4mGf2ba2qvouUoZ5/8OEedQCkAwA0F9LdqlqGyx3KdB\nicwlL1dAaQqU2Pd2oOwR6txomTBxt2BvY8bGLwby+Hv/MnlJLoGNHPjry1619tlXFIXhr/3FtP75\nTOwBZy7BAx/spk0z421VqsLY4ImsOL4EqFqkJzc1l4SIBNRmahSra53PtRoVuYXXjSsowyam7CJJ\nL+LuWE19tJRd4Fr+/YVFEgVFUqnUibTYNE4tO4A+t4D06HSUYQoIgD3ITWVSo1PvLKdJMByhrrQ8\nUNcruWXuC6dJrVYxZ0on5kzpdFvmO3gqmUc7iTgXB2ue6y0z8POU2zL3zSiKwtgZW/nreBxSAwVh\nIcx5sgPPjmxBuwBXnBwdeOTbDPq2lPhtv4a+HRrUeDsUY/lExnKJpDQF5TcFOVuFgkCHSV3wf9Cw\nEz24cBeW56OYMUzip93wWxQgYvgLjgWrO7iFiwkTdyKtm9TjyC9jb8tcOXl64lMMDhNA8wbQI0BF\nxLk0qAE/raobpivnr7D+nfUoDRXQgZyuMMFuGUu6j+WlR0J4bOYWkrMk8grh000atnxdula+JIep\nOg7T6KCJOB5dREafsp8x64dDzF5yFLVKILS5M3I/g4OXnZTNlrfXMWuYHn83GBwN+ligGSCCKkGF\nbdc768RgbOuJtIxYxLt1UyNVI9wXThMY5PP/3hePWiXQv7N3rSY7e7vbsXWXmlf6G4Qwd54Bb7e6\nOX/dfzKFjeFx5D0lghZIh5fn7eOJQU2xMNewad4gPlkawdaLGfTt7sYLYwMrfmgFFWelEDS4OzS8\n4ZKxXKINLyzj1T56XuonczoBunywB+eGLjj6OHJmayTxn8u42EGvlrD9FUj+RoXaRY2SqPBgcRNl\nEyZMVJ5dEZeJis+iVSMn2rWonfwTABsrLeZmag7FiIQ2ghwdhMcqbIzfgcb39udP7l68G313PYQY\nXqvWq1j2VzZLukNYJ29+nx3Gr5vOoNGo2PJ1K1o3ubHxbkkeVm1Ht9fsvMAvG08Q+7mCq53CC0vT\niNigQEeI3RvL2HYSz/cxjP1lIoxZANpGWpQ0hfr+9fHrVHsyP/cr94XTdP5SNt0nrSbUT6JQD28v\nMGPPouG41VLC49PDm7N2ZzQh72Tgbi9wLE5g89fda2WuikhO16F2EQwOE4ATqLQCmblFuJtrsLbU\nMuOpdmXeX16USEyQkVIU1M4CGh9Vtc71xSKRlLgcXiw+wQzwNGjLJEen4ujjiEoQKBINP1OpINRP\nzUXPlrg1c8PV3xUrJ1OkyYSJqjD9672s2HyWrs1gxkJ4YWwwr46rncRhlUrgx7d70H/Wdjr6C5xM\nUBjQtRFLfSLrRNdIl6mD66TzZDcZ+brDgB5t69OjbfnHWsbWXZRfRPyReBRJwTPYEwu76uWx7j+R\nxLhO4lUl9ZfCZJa8Z/h/QXXNJgK08gJ7ay3tR/TAwtYCt+Zud6Xa+Z3OfeE0vfXNPp7pUcT0IYaw\n5iu/SsxefJh5U2unT5eFuYYtXw9hx5FE8nQinYPcqedQN0ngIc3qIcUrcB7wBeEguNoJuOoPQcq1\nD1SZUSJKV525A7aHbYlYHQF+Avod0KJnc9qPM5QdS3qJ8OXhJEUn4eDuQLtH2mFhW/77V2vVWFhr\nOBijp31j0BVB+AVo2tsaQRBoMbAF/eae5o3+IkfjBf6L0TLo+aBqGyUTJu5HIuMy+XH9Gc7MkXC0\nhsQMCHg9nPEDm9WaXMCQ7n4E+jsTcS6NBi7WtG/pyi+bo2plrorwbOVJ5O5IpKES6EATrkHbTarW\nM/Mz8vnztT/R2+tR1AqaHzUM+3gYtm6GI7Lks8lErI1A0ksE9AzAr2PFUSBPNxs2bVUjy4ZTiz3n\nQF2cy9moayNWrz6C9yo9Td0VZqzTEDCkNb7tfav1PkyUz33hNCWl5fFY22uJfm39FP48VUavrxpC\no1HRq51nrc5REZMOrWBXbj5Oo0H3B8jZIHhAxhgB5xOxNw6uQpRIl6nj1+W/Ik+WDcnY+XDy25M0\n69kMOw87/vn4Hy7nXEYKlEiJTSHxjURGfjYSjVnZf26CINDp+R70+XQbXZoLnE5QsG7mTYPWhoqV\n4Efbc9bZlvcjLmJmb03YnBCTw2TCxC2SlKajkZsaR2uDo1DfEdzs1aRk6GpVY8mvvl2ttpCqLB0n\ndEQ3T8fFORcR1AKBIwOJaRpRqXsdNy8yqsd0aNkhdI10KH0M3zXSTol9S/fR59U+pESlsPG9jYjd\nRTCH5AXJSEUSjbuXn9zz5JDmrNoaRfsZmTRwgn1RID1kCC9ZO1sT9vFw1vwRjnisgAbD/fDv2bTM\nZ40NnsjMiBuFQE1UnfvCaeoa7MlnmzLo6C9RJMJXWzQ8NLD2lHhvN+XpnLg7NsE6GJoEG4TPBJVQ\nbV0iXZYOta0a2U42XLAClZOK/Ix8tJZaEk8mIr8sgwbkZjK6xTpSzqZQP7D8cLdve1+cPh1JanQq\nrRytcG/hfjW8LAgCzfu3gP4mZXMTJqpLi4aORCcrbDoK/YJg+X7I16to1KDuHZrbgcZcQ5+pfZAl\nGUElIAgC548drfT9xjaYuem5KF7XNudKA4XcCMPm/NQ/pxA7ilDcNUe0EDm68WiFTpO5mZrNXw9h\n66FL5ObrWdDanebhv139uZ27HR2e61HpdZuoPveF0/Tm4yE8nZyD86TzAEwe7s+zo1rW8arKZ9Kh\nFaWurcjINj64klEiRVEqdcatKIYPfllj7dztEIoEOAUEADFABjh6OSKLMgKl7yt5ZkXYedhh53F/\nGG4TJuoKZ3sL/vioHw+/vZmk9EL8PKxY91m/G1pB1TaOmxcZFZy9nQhVEPGtyH56tvAkeVsyYiMR\n1KA+qMazdfFpgwI3mEWh+Fol0GpU9Ot472zy73buC6fJTKtm8Ts9WfhGDwQMR2d3CuWq4WrtSr0u\nT7itLC4du8TWL7dSkF6Ag58D/V7vh517acdElmQOff8fZ/6NQlBBy4EtaDOuQylDoTHXMODtAfz9\n0d8UrC7AzMaMPtP6YGFngaIouAe4k7Q6CSlQQhWrwgIL3Jq5VXndJkyYqD26BnsQv2E8BYXibXWW\nrqcm1PhvBbFIZPtX27mw9wIqjYrgUcGMHjnRaB+6g6dSGPfOZqIT82npa4vU37i302pQKzIvZxL5\nmSE/1KeLDyFjDOV5Lfq0IHZmLKK54XhOs1VD0ISg2n2TJmqF+8JpKkF7m5ylKkWJqN2u2LlXcvnn\n438Qh4ngA5kHM9kwcwMPzX+o1C7rxB/h2FyIIXGeTJEIfT47w9l6doZjsZtw8Xfh0UWPIhaKaMw1\nNxyj9X29L4eXHSbpdBKOHo60f649mjoyyiZMmCifunKY6pJ9P+0jLiUO5RUFSSdx9Pej2Lvbw02y\nRhnZhQx59S/m/6+IQcHw0+4cXvwVpB4S6psEL1VqFd0nd6frk11RFOWGn7s2dSXszbBrieBPBtCo\nS6Pb8VbvOQJSn6e+S9311bv/Pi01yO2IElWXK9FXEDwFKPl8dgLdHh26LB1WjjeW6V85Fs83g66J\nck4PE5l9LO5qHlFKZArnN59CURQa9m6BWzM3o00hNeYaOozvUOq6CRMmTNwJJBxLQOorgQVgAWKI\nSPzReOh647gTMen4ucDw4lykiQ/Aexu0ZCdl4+jliKSXOLnmGHnxV7D2dKbF0KAyC148Wnjg0cKj\nVt/X/UJgY/M6m9vkNN3EnRQlqgks7C1Q0hTQY9BqygBFVDCzLi3uaeZgxdF46FOsbxkRL6C1NzhW\nSWeS2DHrL94dLCIIMHPWBbpNC8OjpckI3Mnkp+dzas1RxBwdrsE+NOp2F0vxmrhnMGZnbyeW9pbk\npORAcTs5VaoKq4altd5cHCy4kCqRnQ92VpCSBelZEha2hlSEXR9twk9MZlKoxIrweHaeTuTBdwZW\nKVfqfuNOqOJbtjmaLQcu4mxvycuPtK5Sk+r71mm6G6JENYFbMze8mnsR/2M8SgMFIUogdHyo0d1Q\nq0c68MH0JA5dkCgUBXaf19D/k7YARK0/ypxRIpN6GsbaWoh8tj7itjlNiqIQsyuGuGNxWNlbETQ0\nCEv72iuNvhcoyC5g02ureLhNAS18FD7+7SK69FxaDm1d10szcZ+zIiO7TjecnR/rzIZ3NyDHyQgF\nAuaZ5gS+FMiF88dvGNfcz5HRvZrQYWYU3ZsprD4q0XJYEJYOlmQmZJJ1PoUNcyW0GhjXRcL7lVQy\n4jNw8nGq0nqab1/CmR4Tqvw+8tLyOLbuGAU5BfiF+lVK++l+59NfjrLozyO83E/k9CWBjo/HcPjn\nUZW+/55ymsravZQVKbrTo0Q1gSAI9HqlF3GH4shNzaXeqHplJmU7NHBg0BejuXjoIoJKYNAU32ta\nSKKEzXWySLYWhmu1iaIopJ1Poyi/iITjCZzccRKxrYgqXkX0q9GM/mK00YiZCQMxu2N4oHER88YZ\nEle7NxfpMDvC5DSZuO9xaezCyM9HEn8kHo1Wg29HX8ysjNuSz1/uwubOvkTFZ/GL9z6ChhrO6mRR\nxsIMNMWpS2oVWJgJyKJcpbWMDprIimPlbOJvQs5TuHTsEoJG4N9P/6XQvxDFSeHCdxfIS8+j5YA7\nuzK8rpnzcwS73xZp4gGgkJRVxMqt5yt9/13pNFUpSsT94RyVhyAI+LTzqdRYKycrmvdtXuq614Mt\neGVhMnaWIioBXlquocVjATW91KvIksyWT7dw6ewlBBsBfYIengMcQUamaEURsftiadqrbDG3+x1J\nL+Fgea3Sx94SRLGSdc4mTNzj2LraEtCvYhsmCAJ9O3jxom4rGvtrx24OXg5I1jY8tzSbh9vLLD+k\nosDMCkdvx9pb8/ffk79MYIvrFvSJevAHpbiBr+gtEr4y3OQ0VUChXsb+utM4eyuFIn3lHd07xmky\nRYnubHw7+CLpu/HipmOgQPNxgTSsxeqP6B3RXEq4hDjZoHnCLAxJm8Uo5gqSvnYjXXc7vu18Wbny\nMO0bygR4wPRVahp3N1XsmDBxKyTpxRu+d1RqFT1nDGbXj7vZ+Hsatp7O9Hqvc6mqupok7w8FZZBC\nUdMi2AHkXfdDC5Cr8OV/vzIurDGPfhvDzOEipy/B6sMqXn/Wmxfm7q3U/bfdaapqlOhuzie6nsLc\nQjLiM7BytDKqkVTb5KTkkHclDwdPh1tuP9Koa2MadTWeSJyVmMXZLWeRJRn/7v7Ua1TP6LjKknU5\nC9FXvPYX2hxYAfQEkkEVrcLrWZPgW3nYedjRc8Yg5izdS1FuAW7BPoSOLbs5swkTt4OSJuCKopAR\nn4E+X4+zn/NtlyURi0TSL6Sj1qpx8nEyJG9r7YxqNZWFhZ0FnV7oZfRniqxwbus5Us+n4uDhQEBY\nQLUcKlGUUbIA/+ILLYBFgCfgBOrtahqbCj0q5POXuzLze3NeWm5IBP97XicaVkEJv1b+Si/mZTDp\n0ApTlKiYpDNJbJq9CRxATpdpGdbyanNbY2QlZlGYW4ijlyNay+rrUUT8GcGRlUdQO6uR02X6TO2D\nZ3DN9cXLiM9gzbQ16AP1oIUzb58h7M2wapXXOjd0RvOfBrGDaIgw1QPLVEu0W7VY2lnS+b3O2Lra\nVvic+x2Xxi48OHNIXS/DhImrJOlFRrZ8nC2fbCHhZAKCtYBGr2Hwe4Oxr29v9B69Tk9GfAbmNuZl\njqkKeWl5rH1zLYVKIUqhgqufK2FvhDE6YHSV8ovKY8f8HcSei0VsJqLeqebikYsMeGfALVfWaTQq\nBBdQjgJtAHNQm6mxPWGLLMn4tfUj9OHQGln7vYxWo2LW5A7Mmnxrsji14jRlyLAil3smSlRdNs/Z\njH6AHpoCeXDqh1P4hPjgHuB+wzhFUfhv4X9E/ReFyk6FSqdi0MxBVa7EuJ70i+kc+fMI0iQJyU6C\ni7D5k81MWDoBlbpmxD6PrjmKPlQPxeK+ooPIoRWHGDxz8C0/06+jH4mnEzk77ywqSxWWVpaEPBRC\n2oU0LO0tTa1WTJi4i4naHkVCQgLisyJoQb9Xz/b52xk6e2ipsekX0lk/Yz2ypYycLePfzZ+uT3Wt\nVEuostj13S7y/PNQeiggQfLyZE5uPEnQ0JpR6c5PzydmdwzyizKYg9ReImVBCqkxqbj6u97yc21G\nC+iXmSPuEZHzZAKHBqLRaijILcAryKvGbPqdSl0LW0ItOU2Olo4MDxheG4++6xCLRAoyCqCkxZI1\nBmXuS5mlnKaLBy8SfSQa6VkJyUKCI7Bl7hbGfDmmwnlSo1PZuXAn+en5uDdzp/vk7pjbmJOVmIWq\ngcrgMGGYW0GhIKsAK6fytSlkSSZqexQZCYYSWv/u/kZ3SUUFRXB97qMN6Av1Fa65PARBoMvELoSM\nDKEov4i4I3Hs/nk3YmsRdYyas9vPMuLTEUbFNU2YMFE3XJ9+kdG6Cbgab5OSkZCB2NDgMAHQHLIO\nZxkdu3nuZgq7FkIwUADRS6LxOeyDT2j5xS2yKLN/6X5i9sYYBHcf6XC1JD8jIQMlrLgoQg1SY4m0\n+LRKvcfMhEyidkUhCAL+3f2NRr7EQhGVuQrZTL46h2AlIBaKlZqjLNRuAqMXPkJOSg4qtYr176xH\n56FDdpQ588kZujzWhSYP1m0/v9qmLoUtAe5tt/QOQGOmwbKeJZwuvpADxIKTd+noUWZCJlJD6VrC\ncwDkXMqpcI68tDw2vLuB9IB0CsYWEJcfxz8f/wOAg6cDcoIMGcWDo0GtUmNhX35ek6Io/Dv3X/as\n28PxzOPsXrWbbV9tMzq2ademaP7TQCyQYOir1KRLzXxwLR0ssa9vz6FfDyE+JEI3kIZL5FvkE7s3\ntkbmMGHCRPUpyVUaHTTRkBt0NLLMMU4+TmiiNFBouC6cEHDwdjD63NzEXENjcDAkO/vJZMZnVrie\n/Uv3c+bYGXQjdOR0z2Hb19tIOp0EgLO3M8IpwdA0Vw/qc2pcfFwqfOaVmCv8+dqfRMRHEHEhgj+n\n/kl6XHqpcTauNlg7WiP8K0AKCHsF1Hlq6jWsXq4ngFqrxqGBA3GH4yhwKUAeIkM3EEeJ7P91f7Wf\nb6J8TE7TbaDf6/0w22KGdoEW9TdqWg9ujXU9azLiM26oAHP0dkQdowZd8YWTYOdZ8THU5ZOXUXwU\nCAKcQe4vk3wqGbFQxNHLkfYPt0f9vRrtAi3adVr6TutbYRg3MyGThOMJiI8YHBXxUZELBy+QnVQ6\nT823gy+d/9cZ2x222PxtQ6vurbh08hK/PPULa99eS+alig1ceSiKglQoQcmvQgDFVkFfUL1oVkWk\nXUhjz8IySbdtAAAgAElEQVQ9HPjpAHlpeRXfYMLEfcz11WUlaRk3F/6UjPHv7o9vU1/UX6nRfqPF\n6qwV3Sd1Jz0uHV2m7oZ7bBvYwsniFzpQnVdVqqw/Zm8MUj8JXIFGILWViD1g2Gh1faordpfs0MzX\noP5KTQO3BrQYULrHZgmOmxfh7tiEQysOIXYVoTcofRX0HfSErwwvNV6lVjFoxiA8ZU+s/rTCLcWN\n1kNa8+e0P/ntmd848scRFKV68h9igYhsc121nB1IBeVXFI8NnsjM6PKdw1G69qjMjX/vyJLMifUn\n2DV/F9G7oqv9Hu5G7hjJgXuNguwCIrdHoi/Q4xPqwyPfPUL25Wws7CwIXxnOsueWIVgKmGvNGTRz\nEHbudni39abJ0Sac+/ocKjsVar2a3jN6VziXxkKDkFO8axKAfMN/VcUNilsOaEmjzo3Iz8jH1s22\nTBG369Hr9Ag2wrXwuRmoLFXodcYdlaY9m9K0Z1MUWWHV1FVkOmciD5fJj8ln3VvrGPv12FsWohQE\nAe/23iRsSEDqLhl2bucEPJ+suWT2m4k7FMffc/42vH8JTmw6wejPR5tyqUyYMEJJBOl6jIk26qMV\nDp8+jLWTNd0ndyd0TCj6Aj1SkcTaN9cia2SkHInWw1vTdoyhG0HvV3qz4d0NSIcl5GyZpr2a4hVS\nceWs1kKLLltncJoAVa4KMw+DDbJytGLU3FFkJmai0WqwdbetMEeqm3c31urWwvWngvZQeLHQ6Hgr\nRyvCpocBEBcex79f/os4WARzOPrXUdQadbVyqLxCvDjy5xFDlbEzqLeq8elQOT2+W0GRFf54+Q9D\nlM8Gzm47y4WDF+j1qvHqwfJQmdvxbaw7k/2SamGltYvJaaoFCrILWPnySgo9CpFtZI6tPXa1Yu38\n7vNEH41Get5wDCftltj21TaGzh5qyON5sgtBQ4IozCnEwdPBkMi94gjmNuY06dHEaDWdVxsvbFfa\nkrUiC6m+hOaEhqDRQTdEkywdLLF0qHzbEScfJ7R6Lfq9emgOwkkBM7UZDp7GQ+gl5KXlkZWUhfyQ\nbIhjuoIUJZESmVKtir0HX3iQ/777j4TlCVjYWdD1ja41UkXzf/buOz6qKv3j+Oek9wRCTeid0EWQ\nXkRAFLGjq2vvbde1r+vqb21rWde29t4V17YWEJUmiPQivYTeIQnpZWbO7487wYEkZAJJJuX7fr3y\nSmbmlmcyM2ee+5xzzy3L9JemQ0/gNMAFng88TH9uOmf+U2eiiRzpyDmMfBWfwh95/6vkz4PFPRYT\nsiCEtbPWMuHBCQQFB/HhDR9SMLzAqZZnw/I3ltOiRwuapTSjYauGXPTyRWRszyA8NpzI+EhWT1lN\nfmY+ST2SSowNLTbg4gH89PxPuE90OwlTahhdb/594t6gkKBSh0kcTcdBHTnw+QFc8S6wEDIrhI5/\n6Fjueut/Xo9rsAvaObddp7hY9/O640qaEtsmMubOMcx5ew6FOYW06tuKwVcNPubtlSf1l1Sn1+AG\nIBFIhdQPU8k7mFfhS1pNTJnIx0sq5yzF6qakqQqsnLyS/Jb52DO8M7W2dDHnnTlc0OcCDmw+gKuj\n69C4JdvTkv56+mHrxzaJJbZJLBtnb2TGSzPw9PQQlB7Eb5N/49wnncHPHreHRZ8sYuOvGwmNDKXf\nxH5smLWB7F3ZtBzdkhMuOOGYYs/cncnmuZsxQYbRd4xm7ntzObjwIAmtEjj5oZPLnWckJDwEW2Sh\nEOc5esDm2OOegyU0IpST/3TycW2jIoryi5ykyeBUm3pA9uLsatu/SF1QXG3q8uNb5E/DmdU/Hlwe\nF+lvprN9yXZa9GnhjFvq4V0pBmx7S9qWtEMJUUh4CI3aN8JV6OKLe74gMzgTd2M3Id+EHDb4OW1z\nGj+/+TO5abkkd09m0GWDWD9jPaERoQx8aCBRDfy/MGsxay2FKyxL1i6hQesG9BnbhxVfrsAYQ88J\nPf0aeB0aHgq+zUc2lTIvVYs+LbigT/knClWGjG0Z0AwnYQInAQyFzF2Z1XId0BmLckhpVeW7KZeS\npgooyC5g9huz2bthL/HN4hl6zdBS5woqyC7AJvj09TaEolynWys+OZ6QX0NwDfGeObIW4pJK7/L5\n5a1fcJ/vhlbgtm5yJ+Wyfvp6UsalMP/9+axatArXKBdkwPf/+p7gZsF4kj0c+O4AezbsIXNfJiER\nIZx0wUl+VXnSNqfx1d++wt3FDW4I+SyEc54459BknOunr2fK41MA6HlaTzqPKnkJk4i4CDqO7MjG\nDzbi6uoieEswiU0TadL52E+zDYTYRrEcXH3QmTjOA6yGZh1LP6IVqc9inn2NvDmWSSGTSBmVQrfT\nux3W1TWx19XMWD8D7Doobi6DgAQozCkkKDiIyMaR5K3Lgy5AHpgthvgzS1aSU+ekkmWycF/oBgOu\nbi7mvDmHTid3cuZeuu8rioYWQT9Y98M61v60Fk5wuua+fuBr4pPjyc1wzjAefMXgsufB2zsLmgzD\nWsvFD04jd4llYbuFBP8QTJeBXbjktUsAyMvI44enfuDAlgM0bNmQodcMLTUx63VmLzbetZEiVxGE\nQciCEPrfXbsmmm3RpwWLPl8EB4F4YDtQBA3bHPuUOBVxY6t7OHVgTLXs62iUNPnJWsu3D39LWlQa\nnjEesjdm8+W9X3LBcxeUGCPU+sTWrPnXGqevORaCfww+dHpsx+Ed2bJoC9te2IaJc86oOPnB0iso\nRTlFUPx+NOBu4KYwpxCAdTPX4brABY1xBo7HgftSNwSB60QX2/6zDf7oPPb9k98z/v7xZV6ot9iv\nH/xK0ZAi8M67WTi9kK//8TXBYcGER4aTtisN9+nOQMM5780hOCSYDsNLzkA77PphNJ/WnD0b9hA/\nJJ5up3WrdfOHnP730/n0tk8pWlMERRDbIJbhNw8PdFgiNcpPC3aQ87WF8ZARlsH8r+aDocT1z0Z0\nHEFaxzTSfkjDM9AD28FusYcqSWPuGMN3D3+HmWtwp7npfHJnknsll9hfYXYhngYepwIM0BBcuS6s\ntexYusM5IcY7v6On0ANnA13AjZu8z/LIy86DUyBnQQ7ZT2Qz/oHxJfYxsdfVNFj6OuljhrEyNZ2v\n5m6BG8CGWVxDXKx8diXbf9uOCTbkZ+RT0KHA+U5Ync1X933FxGcmlqjIxyfFc+6T57L6h9W4i9x0\n+EeH45qvKRCadmlK93HdWfHCCkgAMmD4jcPr3bQvSpr8lJuWS/rWdDy3OWN1bLLFlepi4YcLDw3i\nLq7IJPdKZsiVQ5j3wTxcBS7aDWrHgEsGkHMgh/CYcE65/RTSt6ZTmFNIwzYNyxyY3aJvC7ZN3YZ7\ntBsOQNBvQSSf5zQkwaE+Z9nl4cyT5MYpAcfiNCrJQBi497vZMHtDuUlTfnb+71P0AyRCzpocOBX4\nHzAS8OZIrlEuVs1YVWrSZIyh06hOdBpVe+cLiWkcwyVvXMKB1APOZRbaNKx1iZ9IVXtz8loYgjNx\nL+Aa6+K3yb/hKfIQ2ySWNgPaHJrbbdy945j27DT2vraXyAaRjPjbCMKiw8hNz6VJ5yZc9PJFpG1J\nIzIhkoTk0sdOJvVMIuijIDydPdAUgqYFkXRCEsYYgsOCIZ/DT4hpiNMmhuIMCM8DWoC7uZtdj++i\nMLfwqCfGpGcWkBvtguJFIsGGWDK6Zjjt7WxgtLM/m2TJfzmf9K3ppV5GKq55HCddWvaVIGqDQVcO\nosfpPcjel018cvwxdXfWdkqa/BQUEoR1WSgCwgEPFB0sYtXyVZidhvkfzueMf5xB447O6ZydRnai\n00gnacjYnsEnt3xCQU4Btsgy6OpBpIwt/+raI28eyfQXprP9le2ERYcx5JYhNO7gbP/EiScy+53Z\nuAc4Z5OxAXgC58NtccqnxR/0PCig9DM8fLXt15aMaRm4El1OgzAdZ5bvlkBTOGwT+RASWrffPiFh\nIeUmmiL1WWR48O9zwAHkQ9b+LOavmk/Q1CCSZycz5s4xGGOIjI/k9PtPB5zK/bx35/HN/d9AqFOJ\nGf/38eVeeqlh64accvsp/PzqzxRkFZDUM4mRN48EoFW/VkR+HEnO1zl4mnmctvpdnLasCOfC3+d7\nN1QI1m3LPRDq2TERkwl2CU5iuBQnIesNZACzvNsPwRm/WWgJCq3bB1exTWOJbVp/L2FVt7/1KlFk\nfCRtB7Vly0dbcHV3YTYarMfiucTjfBiTYc7bc0q9DMA3D35D7om5Ttn4gDNWqUnHJuVOdBYaGcqY\nO8aU+ljnUZ2JTIgk9ddUiqKL2BSyCa4BGgGLgSnAPJyjrCVgBpd/yYHeZ/UmPyufNW+tAcDldmF7\neMdm9QPexzmSMxDyawgn/P3YBpuLSN0wKXmd09ZYnIPJGcA48PT24HF52PHKDvas3lPiDLfUOan8\nNvM37J8tREH6lHR+fPZHznjgjHL32apvKy5+5eIS94dGhHLO4+ew/KvlZKVlsT16O/mt82EcTrv1\nOk6SkwEsAYKceYeOJj4mjJhLDMGT48mamoUJN7j6eS8kngjEgfnAYLtZgtcH06RdExq0LH8OqZps\nZXZ/KPo40GEcZvmGfFKq7mTpClHSVAEjbxnJqsmr2LNhD+lF6aQNSHMSJoAmkLc0r8Q6rkIXuXty\n4UTvHYngaelhy7wtxz07bKu+rWjVtxWzX5oNbXESJnAuN/AtsAvnDLYU/JpuwAQZBl42kIGXDcR6\nLN/84xv2frYXd0c3wWuCSeyYSINQp0Ho+o+uta5PXkQqV3ATw/lPnMeKySsoyClgc9BmPL28iUgI\nmERDflZ+ifXWz1qP7Wmdy0oBnAR7Xttz3PGEx4TT72JnUNOrE191xmcGAVE4F7ldC+zAOTP2B/zq\ncg9uZpj4jDNZ566Vu/juke9wh7jBQnBmMJ1P6ExBbgGJAxLpMaHHcV0TT0p3YfydNWIQOChpqpCg\n4CC6j+9Od7qTOjuVGW/NwNXJBZEQPDOYVn1Kng9prXUSq604k6IVALugsHNhpcUV2SAS5ni3HY5z\nVoMBkoBcCF0ZStdrux5tEyWYIMNp953Gsi+XcWDbARoPbEyPCT3KnXJAROqXBq0aMPS6oViP5eOb\nPyZrThb0BzaB3WEPDSnw5c53w25gME77uMm5JmZlCgoJwrPJ4xxMenCGMHiAVhCyJIT2o9tX+LT/\n5t2aM/7+8az6cRUmyNDtwW6HhmRI/aCk6Ri1G9KOzL2ZLH5rMZ4iD22HtmXAJQNKLBcSFkJwaDDu\nj93O6ev7wLgMLU8of0Zbf/WY0INl3yzD9bzLGey4HRLbJBKfF09oZCi9Hu91TBNBBocGc8L56oIT\nkfKZIMPp95/O1Kemkj4jnchGkYy6dxTRidEllm3VrxU7PtkBr+CcuLIT4ppU7mz7fSb0cS5xsgJn\nmEIOdBzWkaL0IpLGJdFtXNmXTTlk76wSdzXt0lRjHeuxepM0bZ63mRXfr8AEGXqf0bvU01krqvc5\nvel9Tu+jLmOMYfRdo/nhyR+cMzesc622yth/sbCoMC568SLmvDaHzL2ZtDqvFSdMPOHQWSsiIkfK\nyCrgrpfmsWJLGv06NeHRa/sRXda8RX6KaxbHeU+eV+5yKaemsHHeRg5sPwD5TlXolNsqfjmOo+l7\nYV9iGsew+qfVhDUJY8BlA/yfATw07tB185o1qL1nAUvlqxdJ0+ZfN/PTCz/hHuVM2rj7id2Mu2cc\nST2SqmX/rfq2YuJzE9m/cT9RDaJo0rlJpfd7R8RFMOr2UZW6TRGpmwqL3Ay+8Ss2xGdS2NHDkhUH\nWHDbPua8OMHvtum6BZOOef/BocFM+McEdq3YRVF+EU27NK2SWaWLr4lZURNTJh66bt6wVsMqO6wa\nK2XfLYEOoYSUfbfUmPFMUE+SpmXfLsM9xg3eaqzb5WbFlBXVljTB75dGEREJtCVrD7A1K4fCPzgT\nReZ3cLP0+f2k7siifQv/uskmpWeWeb05fwQFB1Vqxb1WMzXnq/jUgTEwNdBR1Fw155WqSuqlEhE5\nxJhSmsXKHYdd6x1PQiiVoyZNNVCsbs/C5dXr9F4ETw2GZcAi50y3HuN6lLueiEhd1KdzI1rHxxD+\nTRCsgYgvg+nbsRHtklUNl5rjwvg7SWpcsy7TUi+SpjYntWH0n0aTvCOZFvtaMO6v42je/egzz4qI\n1FWhIUHMfuFMruzQhSGbmnFT7xS+f+p0zTEUKCFRhwaeV6VJy9+mSXDp08Ys31ByPq1AKo6nZ4fw\nAEdyuPrRPYdzimurfiXnURIRqY/iY8J48bYhgQ5DOHzgeZWyLp6PvZBVpTxUkyaQBCee8LCal8TX\ni0qTiIiI1A7FZ/GN7Ftyjq9AU9IkIiIV0nX624EOQY5DTeuK81WcMNWkqpcvJU0iIlIhu4tcOrus\nljp1YAwXxt952H0p+24JeFfY8g35NT5hAiVNIiIi9V4gu8JmLMo5NIapJidMUI8GgouIiIhjxqIc\nRvSNrhGzgN/Y6p4anywVU9IkIlKPdZ3+NruLXACkj1GXW33gJCj3wD7f24FRE7oGK0LdcyIi9dR1\nCyYdNj6pOuYKkmPTdfrblXq5lVMHxhz6CZTiAek18Sy5sihpEhGppyalZ0Koc605Deyu+ZoltAt0\nCJVi0vK3WZHcrsbOxXQ06p4TqSXcRW6WfraU9dPWk5OWQ3TDaDqM6ECf8/sQHOrM8pu1J4uPrvmo\nxLrthrbjlDtPqe6QpRaYmDLx8Dv2zoImwwITjNQLHreLv++YyYXxdx5XlamwyM1j7yzl3cnr2bEv\nh+TG0Vw8tgP3Xt6H8LDfZz4/mF3Irf/+hS9nbcbjsYwf0prnbh9EYnxEhfeppEmklpj/7nxWTV5F\nvz/2o1G7RuzfuJ8FHyygMKeQQdcMOmzZAVcMoGnXpoduR8RVvHGQuq3B1NdLdveExtFg6TrSxyhp\nkqpVGTOQ3/PCfF7+fBUPX9+PPp0asXjtfu57ZQEZWYU8e/vvbeLEe39k3daDvH7vMIKCDHf/Zz5n\n3TmVn1+dUOF9KmkSqSU2zNxAyrgUep7VE4CknknkpOWwYcaGEklTfIt4mnZpWtpmRA6Z2PPyw29X\n1+U8RCrBh99v4IZzU7jtIqdNHHliEjv25fDBlA2Hkqa5v+1h6rztzHz5DIb1ca45m9w4mpOu/JIf\n52/nlP4tKrRPjWkSqSU8bg9h0WGH3RcWHYbFBigiEakuu4tcDGtVNyqAQUGVc8ZekctD/BFtYkLM\n4W3i5F+20bRh5KGECaB/tya0TYpl8txtFd6nKk0itUSX0V1YPWU1yT2TSWybyP7U/ayavIpup3cr\nsezMZ2dSkF1ARHwEHYZ1oN8f+xESro+7HL/KPotL5FhdfWYXXvlyNaP6JdOrYyJL1u7npc9XcfN5\nv7eJa7Zk0KVNQol1u7ZJYM3mjArvU+98kVqi/2X9cRW6+N89/zt0X8ppKfS9sO+h28GhwaSclkKL\nPi0Iiwpj5287Wfb5MjJ3ZTL2vrGBCFtqoAZTXz901lxF6RIqUlM8dlN/8gpcDLn29zbxxnNTuP/q\n39vE9KwCEmLCSqzbIC6c1B1ZFd6nkiaRWmLZ58uc8UvXDiKxTSIHNh9g4QcLiYiN4MSLTwQgqmEU\nQ64fcmidpB5JRCVEMfvl2RzYdIDEtomBCl9qmBJnzfm4bsEkXulX9uNSuSYtf7vKJxadtOx10nt3\nqtJ9VLcn31/G+1M28Pwdg+jZIZFl6w/w91cWkhgfwYPXnVgl+9SYJpFaID8zn4UfLKT/Zf3pPr47\nzbs3p/v47vS/rD9L/ruEvIy8MtdtO7gtAPs27KuucKUWm9jramf+Jql76tBUEvsz8rnv5YU8flN/\nbj6/O8P6NOeWid15/Kb+/POdJexNc9rEBrHhHMwuLLF+emYBDWJLVqDKo6RJpBbI3J2Jx+Uhsd3h\nlaJG7Rph3ZasfWWXmY0xh/0WEQmUyjo7M3VHJkUuD707Hd4m9uncCJfbsmW30yZ2aZ3Ami0lxy6V\nNdapPEqaRGqBmMbOmSb7N+4/7P79G5zbsU1iy1w3dU4qAI3aN6qi6ETkuFhXoCOoVpXRFdm6mdMm\nLl5zeJu4yHu7TXOnTRw3qCW7D+Qxe+nuQ8ssXL2P1B1ZjBvYssL71ZgmkRpq3bR1zHxuJhe+eiGx\nTWJpM6AN89+Zj7vQTWKbRPZv2s+ijxbRbnA7IuMjAVj44UKK8opo1rUZoVGh7F65m2VfLKPNwDYa\nzyQitdq7363jyodnsvGzC2ndPJazhrfh7hfmk1/opmeHRJau38//vbaI80e1o3EDp00c2KMpY05q\nwaX/mM6//jTAO7nlPIb0albhOZpASZNIjWWtxXosxVOOjLh1BIs/XsyKb1aQm5ZLdMNouo7tygkX\nnHBonYQWCSz/cjlrfliDu9BNTKMYep3diz4T+wToWUiNs3dWoCMQOSYej8Xttlhvm/jO/SN48I3F\nPDdpBTv355LcOJrrzu7K36884bD1PnlkFH95ei5XPjzTexmVVjx3++BjikFJk0gN1XlUZzqP6nzo\ndlhUGAOuHMCAKweUuU6HYR3oMKxDdYQntVSDpes0ZYDUSpeP78zl439vE+NiwvjXnwfwrz+X3SYC\nJMSG89b9I3irEmLQmCYRERE5zKytqkiWRkmTiIiIHJI+5mp2p6+r9O1OWjWp0rdZ3ZQ0iYiI1HDN\nQkNqf/WnKLPKJ/GsakqaRERERPygpElERKQOm7Ts9Vpf4akplDSJiIiI+EFJk4iISA23ulu7Khmc\nXV0mLX870CFUCiVNIiIiNV1tv9iuddWJLkIlTSIiIiJ+MLZ4PvLK3Kgx+4Atlb5hEaksra21jQMd\nRH2idlGkRvOrTaySpElERESkrlH3nIiIiIgflDSJiIiI+EFJk1QqY0ySMea/gY5DRKQmUJtYt2hM\nk4iIiIgfVGmqhYwxbYwxa4wxbxtj1hljPjDGnGKMmWOMWW+M6e/9mWuMWWKM+cUY09m7bpQxZpIx\nZpUx5gtjzDxjzInex7KNMY8YY5YZY341xjT13t/YGPOZMWaB92ew9/7hxpil3p8lxphYb2wrvI9f\nboz5j0/c3xhjRvjs60ljzEpjzI/eeGcYY1KNMROq+V8qIrWY2kSpLkqaaq8OwFNAF+/PRcAQ4A7g\nXmANMNRa2we4H3jUu96NQLq1NgX4O9DXZ5vRwK/W2l7ALOAa7/3PAk9ba/sB5wKve++/A7jJWtsb\nGArkVSD+aGCatbYbkAU8DIwGzgYerMB2RERAbaJUg5BAByDHbJO19jcAY8xK4CdrrTXG/Aa0AeKB\nd4wxHQELhHrXG4Lzgcdau8IYs9xnm4XAN96/F+F8YAFOAVKMMcXLxRljYoA5wL+NMR8An1trt/ss\nU55CYIr379+AAmttkU/8IiIVoTZRqpySptqrwOdvj89tD87r+hAw3Vp7tjGmDTDDj20W2d8Hubn5\n/f0RBAyw1uYfsfxjxphvgdOAOcaYsYDvMi4Or2ZGlLGvQ/Fbaz3GGL0vRaSi1CZKlVP3XN0VD+zw\n/n25z/1zgIkAxpgUoIcf25oK3FJ8wxjT2/u7vbX2N2vt48ACnJK4r81Ab2NMkDGmJdC/4k9DRKRS\nqE2U46akqe56AvinMWYJh1cUXwQaG2NW4fSZrwQOlrOtPwEnGmOWe9e73nv/rcaY4nJ2ETD5iPXm\nAJuAVcBzwOLjeUIiIsdBbaIcN005UM8YY4KBUGttvjGmPfAj0NlaWxjg0EREqp3aRKkI9ZPWP1HA\ndGNMKGCAG9U4iEg9pjZR/KZKk4iIiIgfNKZJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJ\nRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8\noKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRE\nRET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9K\nmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERE\nxA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJ\nRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8\noKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRERET8oKRJRERExA9KmkRE\nRET8oKRJRERExA/1NmkyxmQbY9od5fHNxphTqiGO/zPGvF/V+6kIY8zFxpipfi5bIn5jzBxjTJ8y\nlh9hjNleGXEGkjHmKWPMDUd5vI0xxhpjQqozrjJimWGMuTrQcYhUhupqu8vbT1U52n6NMZcbY2aX\n8ZjanGpQ5UmTMWaIMeYXY8xBY0ya9wu1nzFmgDEmxxgTU8o6S4wxN3v/DvN+Ma/3Lr/ZGPOmMabN\n8cRlrY2x1qZ69/G2Mebh49leXWKt/cBaO+ZY1jXGnAFkWWuXVHJY1coYk2KMWWiMSff+/GiMSfFZ\n5F/AvcaYsEDFKFKV6nvb7buf6hSo/Yp/qjRpMsbEAd8AzwMNgWTgH0CBtfZXYDtw3hHrdAdSgI+8\nd/0XmABcBMQDvYBFwKiqjF2O2fXAe9W90yo4utqJ895sCDQC/gd8XPygtXYXsAbnvVlljKPeVoQl\nMNR2119qc46uqv8xnQCstR9Za93W2jxr7VRr7XLv4+8Alx6xzqXAd9baA94S62jgTGvtAmuty1p7\n0Fr7grX2jSN3Zoy5whjztc/t9caYT31ubzPG9Pb+bY0xHYwx1wIXA3d5y6Jf+2yytzFmufdI6xNj\nTAyYTagAACAASURBVERpT9JbMp1jjPmPd9k1xphRPo8nGWP+5z1a22CMuaaM7XxrjLnliPuWG2PO\n9on5eu/zyjDGvGCMMd7Hgowx9xljthhj9hpj3jXGxHsfKy7bXuH9H6R7t9PPu/0MY8x/jng+s31u\nP+tdL9MYs8gYM7SM+MOAk4GZPvdFeo8G040xq4B+R6yTZIz5zBizzxizyRjzpyPWfce77mpjzF3G\np2vPe+R6tzFmOZBjjAkpZ3tBxph7jDEbjTEHjDGTjDENS3su1toMa+1ma60FDOAGOhyx2Azg9NLW\nL+V/c6433u7e2wOMcxSfYYxZZowZ4bPsDGPMI8aYOUAu0M5730Pe91mWMWaqMaaRzzplbk/kGNSX\ntruDMWamd7n9xphPfB6zxpgO3r8TjTFfe9vABcaYh49oI60x5kZv3Fnez2p772cy09vWhPksf41x\nvgvSjPPdkHSU/f7Pu435QPvSnkcZz01tTlWw1lbZDxAHHMD5gI0DGhzxeEvABbT03g7COYI5y3v7\nMWBmBfbXDsjwbicJ2AJs93ksHQjy3rZAB+/fbwMPH7GtzcB873YaAquB68vY7+Xe5/EXIBS4ADgI\nNPQ+Pgt4EYgAegP7gJO9j/0f8L7374nAPJ/t9vL+/8J8Yv4GSABaebdzqvexK4EN3ucZA3wOvOd9\nrI133Ze9MYwB8oEvgSY4R5F7geE+z2e2Txx/BBKBEOB2YDcQUUr83YCcI/43jwE/e/+HLYEVPq9J\nEM6R5/1AmDf2VGCs7+sPNABaAMuL1/V5jZZ6txvpx/b+DPzq3VY48ArwUTnvqQzva+sB7jvisXOA\nxWWsV/w/DwGu8L42xe+3ZO/repo35tHe2429j88Atnr/nyE476kZwEacL7NI7+3HKrC9q6vys66f\nuvVD/Wm7PwL+5t1vBDDE5zHf/Xzs/YnCqaZt4/A20gJfef9v3YAC4Cdv7PHAKuAy77InA/uBE7zt\n0PPArKPsdxIQDXQHdvju94jn0ga1OVX+U6WVJmttJjAE54V8DdjnzZqbeh/f5v3nXuJdZRTOm+hb\n7+1EYFcF9pcKZOEkJsOA74GdxpguwHDgZ2utpwJP4Tlr7U5rbRrwtXe7ZdkLPGOtLbLWfgKsBU43\nxrQEBgN3W2vzrbVLgdcpeZQGThdQJ2NMR+/tS4BPrLWFPss8Zp0qyFZguk9MFwP/ttamWmuzgb8C\nF5rDu60e8sYwFcjBSRj2Wmt34CQ2pQ7etta+b609YJ2jxadwXqPOpSyagPP/9zUReMRam+Z9vZ/z\neawfzofsQWttoff1ew240GfdR6216dba7UesW+w5a+02a22eH9u7HvibtXa7tbYAJ+E7zxyla89a\nm4DT6N0MHDlOK8v7nI/mVuBOYIS1doP3vj/iHJF/Z631WGt/ABbiNEDF3rbWrvT+z4u8971lrV3n\nfa6T+P2192d7In6rR213EdAaSPK2jSUGWRtjgoFzgQestbnW2lU4yeSRnrDWZlprV+IcHE71tscH\ngcn83r5eDLxprV3sbYf+Cgw0R4z18tnv/dbaHGvtijL2eyS1OVWoyvstrbWrrbWXW2tb4GTKScAz\nPou8w+8fvEuAj31esANA8wruciYwAueDNxPngz3c+zOzzLVKt9vn71ycCk5ZdljrpNheW3CeaxKQ\nZq3NOuKx5CM3YK3NBz4B/micPuU/UHJ8UFkxFR+d+e4jBGjqc98en7/zSrld6vMzxtxhnO6xg8aY\nDJwkolEpi6YDsUfcl4RzVOYbV7HWQJK3vJvh3fa9PjEfua7v36XdV972WgNf+Dy2GqfbrSlHYa3N\nwanSvWuMaeLzUCzO0fHR3Am84E36fOM8/4g4h3D4e72051rWa+/P9kQqpJ603XfhdL/PN8asNMZc\nWcoyjXHa0vLaIn/b18Paau9B7gFKfieUtt8tlE9tThWq1sFe1to1OOXU7j53fw60MMaMxOnu8M2k\nfwT6G2NaVGA3xR+8od6/Z1L+B8+WcX9FJBvjjC/yaoUzmHgn0NAYE3vEYzvK2M47OEcio4Bca+1c\nP/e/E+eN7LsPF4d/cCvMOOOX7sKp+jTwVl4O4jQ0R9rgrGJ8P/y7cEr5vnEV2wZsstYm+PzEWmtP\n81nX97X33U4x39euvO1tA8Yd8XiEt9JWniCc0rzvc+sKLCtnvTHAfcaYc4+I870j4oi21j5WxvMq\njz/bEzlmdbXtttbuttZeY61NAq4DXiweT+RjH05bWl5b5K/D2mpjTDROZe7Idqh4v2W1n2VRm1OF\nqvrsuS7GmNuLPzjerqo/4IwrAQ4dxf8XeAvYYq1d6PPYj8APONWBvsYZ6BtrnEHMpR0RgPPhGglE\nejPtn4FTcd6UZZ0Gvwen7/l4NAH+ZIwJNcacj/OF+p23jP0L8E9jTIQxpidwFVDq3EzeJMkDPEXF\nzkL7CPiLMaatcU4FfhSna8917E8JcKopLpwPcIgx5n6cfvsSrNON+CNOI1dsEvBXY0wD7/vAd6D7\nfCDLOIO5I40xwcaY7saYfqWsm4zTRXY05W3vZeARY0xrAGNMY2PMmaVtyBgz2hjTx7uNOODfOJW0\n1T6LDccpux/NSpz33wvGmOIz7d4HzjDGjPVuP8I481dV5AvGV2VvT+q5+tJ2G2PO9/mcpOMkDod1\nA1pr3TgJ4v8ZY6K8XYalDa/w10fAFcaY3saYcJy2ep61dnM5+00BLvNj+2pzqlBVV5qygJOAecaY\nHJwP3AqcwcS+3sHJvN8tZRvnAd/hdFsd9K5/Is6XcwnW2nVANs4HrrhvPhWY430TluYNIMVbZvzS\n72d3uHlAR5wBfo8A51lrD3gf+wPOIL2dwBc4feOlxu/1LtCDMhKrMryJk2TNAjbhDPS+5ahr+Od7\nYAqwDqc0nE/pZdxir/B7yR6c05S3eGOaik8i6H09xuP0k2/C+d+9jtP9B/AgzuDSTTiv939xBliW\nyo/tPYszbmyqMSYL5/14UhmbS8Bp3A7iDIZsjzPoPh/AGNMcZ0Boue8Xa+0yb1yvGWPGeRPpM3G6\nDvfh/D/v5Bg/j5W9PRHqT9vdD+c5ZuO0DX+2pc+RdDNOO7Ibpw37iKO0RUfjbfv/DnyGU01vz+/j\nLkvbb4x3v2/jJKj+7ENtThUxhw/DkWNhjLkc50yBIZW0vUuBaytre9XNOKet3mwreYJL48zAfaG1\ndni5C1cxY8xTwEZr7YuBjkVEqpcx5nGgmbXWn8qP1CEBn25dDmeMiQJuxJmioFay1g6ujO14qznt\ngLk4Vbzbgf8cdaVqYq098ohbROoob5dcGPAbTnXqKqBOXiZEjq7Ol9JqE2PMWJxS5x7gwwCHUxOE\n4XT3ZQHTcOZBqbXJpIjUWrE444tycLobn8Jpj6SeUfeciIiIiB9UaRIRERHxg5ImERERET9UyUDw\nRgkRtk3zIyeGFpFAycxxpp7JC3Hmydu+adl+a23jQMZU36hdFKm5Fq3Z71ebWCVJU5vmsSx855yq\n2LSIVMD0RTkUFDrjFlc1fv7Q/bdfnOjP5RikEqldFKm5zEmv+tUmasoBkTpoytzsQ39/fPBJenaI\nCGA0IiJ1g5ImkTpk+YYCdu5zrplaXFnqqU44EZFKoaRJpI4ori69uPUxRvSNDnA0IiJ1j5ImkVrM\nt7IETnVphCpLIiJVQkmTSC2lypKISPVS0iRSiyzf4FxY3XfckipLIiLVQ0mTSC2hypKISGApaRKp\n4Y48I06VJRGRwFDSJFJD+c61pOqSiEjgKWkSqWFKm2tJ1SURkcBT0iRSg2jckohIzaWkSSTAfCtL\nxZc8UWVJRKTmUdIkEkBHVpZ0yRMRkZpLSZNINdNcSyIitZOSJpFqpDFLIiK1l5ImkWpSnDCpsiQi\nUjspaRKpQtMX5QBQUGhVXRIRqeWUNIlUkeLKks6IExGpG5Q0iVSy6YtyKCi0gNMVpzPiRETqBiVN\nIpXE97InxdUlERGpO5Q0iRynIytLgKpLIiJ1kJImkeNw5LglERGpu5Q0iVSQ7xlxoHFLIiL1hZIm\nkQrwrSwBqi6JiNQjSppE/KAz4kREREmTSDk0bklEREBJk0ipfCtLoOqSiIgoaRIpQZUlEREpjZIm\nEa9pC7NZsGoPe9NyMD2fUMIkIvXe7gO5/LRgBxFhwYwb1IqoiPqdNtTvZy/iNfmXLF7676/8tmEP\n/TvAtMljGXX+o/QfcWmgQxMRCYgVG9M45aavGNLJkpYDD70excxXzyE+JizQoQWMkiapt5ZvKGDn\nviIAHp1/IVvWf8Xqf+YSHQHrdkHv++7mhMEXEBIaHuBIRUSq353P/sz/nVXE9aeAtXD5K9k889Ey\nHrimX6BDCxglTVIvHTluqUPTb4hqEUy0t0euU3MIDzXk5mQQl9A0gJGKiATGzn059G/v/G0M9G/n\n4bd92UdfqY4LCnQAItVp+YaCQwnTqsbPHxq3lNymF/PXu/hlnXNE9dJPhsjohsTE6ZQ5EamfhvZJ\n5olvgykogr0H4dUZIQzpnRzosAJKlSapN4qTpRe3PsaIvtGHPdawcUvOve5Nxv37GvLy8mjWLJlL\nbv+EoCAdV4hI/fT4LYO49IEc4q7egTFw58XduHhcx0CHFVBKmqRO8x23tKrx8wCMKKN4lNJnDPe/\ntJmiwjzCwqOqK0QRkRopOjKUz544jYJCNyHBhuBgHUQqaZI662iVpbIYY5QwiYj4CA8LDnQINYaS\nJqlzjqwulVVZEhERqQglTVKnHEt1SURExB9KmqTWW76hAEDVJRERqVJKmqRWU2VJRESqi5ImqZU0\nbklERKqbkiapdVRdEhGRQFDSJLWGqksiIhJISpqkVlB1SUREAk1Jk9RoxckSqLokIiKBpaRJaqTp\ni3IoKLSqLImISI2hpElqnOLqkipLIiJSkyhpkhpD1SUREanJlDRJjVBcXfr44JOM6BsR4GhERERK\nUtIkAVVcXQKnO66nuuNERKSGUtIkAeNbXerZQdUlERGp2ZQ0SbVTdUlERGojJU1SrVRdEhGR2kpJ\nk1QLVZdERKS2U9IkVUqXPxERkbpCSZNUCV1cV0RE6holTVLpNG5JRETqIiVNUmmOrC5p3JKIiNQl\nSpqkUqi6JCIidZ2SJjkuqi6JiEh9oaRJjpmqSyIiUp8oaZJjUpwwqbokIiL1hZImqbApc7NVXRIR\nkXpHSZP4rXj8kjNRpRImERGpX5Q0iV98xy8pYRIRkfpISZMclc6OExERcShpkjLpunEiIiK/U9Ik\nJei6cSIiIiUpaZLDqLokIiJSOiVNAqi6JCIiUh4lTcL0RTkUFFpVl0RERI5CSVM9t3xDAQWFVtUl\nERGRcgQFOgAJnClzs9m5r4iPDz4Z6FBERERqPFWaqsjuA7m89uVqcvIKOXN4Owb2aBrokA6pLXMv\nWWvZvyeV/LwsmiV3JjQsMtAhichx+Hz6JuYu30VSkxiuPzuFyAh9BVVUVk4ha7Zk0KRBJK2bxwY6\nnHpH79gqsGt/Lidd8V/G9yygeYLl7DtW8erfRjFhWJtAh1Zrzo6z1vLxq7eycvFUwqMSwZPL9X/9\nlMbN2gc6NBE5Bg+9vpCPJi/n0sEuZs0J5r8/rmP6y2cTFhoc6NBqjYWr9zH21p8IjWxCduYebjqv\nM4/feEKgw6pXlDRVgVe/WMUZvQp44XILQL92bu577dcyk6Yf52/nxwW7aNownGvO7EpMVGiVxLV8\nQwFQO86OWzL3czasW06/C2cSHBrF9uVv8NErf+FPD/wv0KGJSAUVuTw8+s4SNj1taZYA1roZ9GAW\nU+dtZ/yQ1iWWz8gq4PWv1nAgs5BxA1swrE/zAERd85x778807/8YTdqfTlF+Oq99NY7TBjRj+AlJ\ngQ6t3tCYpiqQk1dEUoI9dDu5AWTnuUpd9uXPV3Pe3xcwafUInvo2jv5XTyY3v/Rlj8f0RTns3FfE\nqsbPV/q2q8KeHWtJSB5JcGgUAI3bnc6+XesCHJWIHIvCIjcAjby9ScZAUgJk5xaVWPZgdiEnXP4d\nz/7QmE9WDuOMu+bwwZQN1RlujeRyedi26wCN254KQGhEAxKSBrF6c0aAI6tflDRVgQnD2vKfH4P5\n4TdYuR1ueS+Yc0aW3q109wuL6Tz2Q9qceCsdR73KQU9bPpuWWqnxTJmbfegMudqiaXJnMrZPw1WU\nA8De1G9o3LxTgKMSkWMRHRnKsF5NuPHtINbtgndmwZz1lFoheW/yOoqi+tBx5Au06XcbHUa9wV0v\nLAtA1DVLSEgQLZsnsjf1OwAK89LI2PkLXdskBDiy+kXdc1VgSO9mvHjPydz12q/k5Lk49+QOPHjd\nSSWWs9aSl19AeGwyAMYYwqKTyczZVWmxFI9hqk0JE0CfgeewbsVsFnw0jPCoRIwt4Ia/fhrosETk\nGH3yz1O55cmZjHtqN0mNovnumeE0bxRVYrmsnCJColoeuh0Rm0x2XkF1hlpjff7oUMb+5W/sX/Fv\nsg/u5c8XdFXXXDVT0lRFzh7RlrNHtD3qMsYYTh3UnqWz7yK5791kH1jDgc3fM/qkcZUSw5S52Xx8\n8El6doiolO1VJ2MMF177NKPP+jN5uZk0TepEaFjtex4i4kiIDee9B8eUu9ypA1vyz/c+JS55GFHx\nbdk2/x9MGNqm6gOsBfp2bczmz89h7ZYMmjSMpGXTmECHVO8oaQqwD/5vINc8voBpUyaQGB/JV48P\np1Or4yu3Fs/wDdTIhGn9ylls3biYhMRk+gw8h6Cgss+eSWzSpvoCE5GA69O5EZ88NIg/PXM3u7ML\nOG1QC168o3+gw6pSaQfz+fiHjeTmuxg/pDVdjtLlFhMVSt+uNfxMnjpMSVOAxUaH8fGDgytte8Xd\ncTW1wjT92xeY/u1rJLYdR/a+KSz+5Suuuv1dgoI0vE5EHOMGtWL9oFaBDqNa7M/Ip89l30J8P4LD\nG/HgW1/x3VMjGdK7WaBDk1IoaapDpi9yBk3X1AkrXUUFTPn0UfpdOJ2ImCQ87iKWfnE6G1fPpmO3\nYRXa1vZNy/jm40fJyU6na++RjD37DoJDqmaqBhGRqvLsJysJajSa9kMfByC66QBufe5JFr5ZsaSp\nyOXh/teW8e0vu0mMD+Pft/SiT+dGVRFyvabD+zqi+Ay5mnxJlIL8HExQKOHRzpwrQcGhRMa1Jje7\nYqfMHti7mZf/eT5BiafQtNc9LF30C1++f39VhCwiUqX2ZhQRFtfx0O2ohHakZVZ84PtN/1rA29MN\nISlPsyvqKkbc9AObdmZWZqiCkqY64bAKUw3skisWFdOARs3as3nBUxTlZ7Bv01QO7l5Em44nVmg7\nKxdNIbHtqSSl/IGEpP50HvkMi2ZPqqKoRUSqzoTBSexb/SrZB1ZTkLOHHQsfZfygik/m+cGUdbQb\n8QIJzfuRlHIxDVqP4+uft1ZBxPWbuudqudo0pYAxhmvueI/3X7yZ+R8NITYhiStvf5f4hhU7ZTYo\nOAS3d/4mAHdhNkHBv7+V9+3awDefPEpmxn46dR/MmLNuU9ediNRIpw9pxaNX5/DA6xeSX1jE+aPa\n8eTNFR/4HhISjLswB7yzOHiKsggNceoiHo/lXx+u5LOZu2gQG8qj13bnhC7qujsWSppqMd8KU20R\n3zCJm+77/Li20XvAWfz0v+fZ+MtDRCZ0YOeK1xk5/iYAMjP28PyDE2je7SoadOnG0kUvkZWxl4lX\nP1UZ4YuIVLobz+vKjed1Pa5t3H1Jd5769DIap9xAwcF1FKX9yvmjzgDgvleX8Pr3uTTv8w82ZW5h\n5E2Ps/Ct0+nYKr4ywq9X1D1XS9XEWb5dRQUUFeZV+X5i4hpx60NTaN08lCjXUiZccCejzvgTAKuX\n/kBc8wG07H0dDVsMocspL7Lw54/weDxVHpeIiC9rLZnZhVhry1/4OP310h48d3N7eke8xxmdF7P4\n7dNplOAM13jtqw20G/4Cia2Gk9z9Uhq0P49JP1XulSfqi3IrTcaYJ4CHgTxgCtAT+Iu19v0qjk3K\nUNMqTB6Ph8/fuZf5M94FIOWE07j4hv9U6WSU8Q2ac9YlD5e435ggrLvw99jcBRgThDGmymKR+kft\nopRn8Zr9jL9zOgcycomKCOXTR4ZxSv8WVbY/YwwXn9qBi0/tUOKx4KAgPD7tovUUEBKsNvFY+FNp\nGmOtzQTGA5uBDsCdVRlUfeZ2e/h29lbe+24dqTtKnvlQEytMc354gzWrFjPw0gUMvmI5e/bnMPnT\nxwISS/e+48hLX0nqr4+yZ92XrPr+KoaMuVZJk1Q2tYvVKHVHJu99t45vZ2/F7a75VeOCQjdj//IT\nDXs9zOCr1tFmxOuc89dZ7DmQG5B47ry4CxunXcPutZ+zZeG/yd72LRePLZlcSfn8GdNUvMzpwKfW\n2oP6AqoaLpeHMbdOY+X2UCLj25D21Ld88dgwRvVzrk23fINzGmpVJkxLf/2CGZNfwwQFMfasv9Cl\n1yhcrkJCQsLKXGfjmvk06/JHQsOd/vGk7lewceUzVRbj0UTFNODWf0xm6pdPk5kxjZPHXcKgUVcE\nJBap09QuVpMf5m3n3Ht/pmGLQeQdTKV7y3V8//TJhIRUz+iSg9mFXPPPuazakkPfznG8fOcAQkOC\nMAaCg0uPYcvuLDxE0qTDeAAaJA8kLrE9K1LTaZpY8np7Ve32i7rTpEE4/53xKg2bh3DfPafRQpdg\nOSb+JE3fGGPW4JShbzDGNAbyqzas+unjHzayenccKRM+xwQFE791Jlc8chtbvzwHgJ37ipyZvqto\n4spfp7/PF+/dT4dB92M9Rbzx1KVERieQm72PhMRWXPbn12nZtneJ9RIaNmPT9kU06+zEmblnCQkN\nAzebbVyDZpx3xeMB27/UC2oXq8kVj8yj3YiXaNhyKB6Pi1XfncOkn1K5qBoqJUUuD10u/BqbMITG\n7U7jm+Wf0PyMz8jNdYZIXHlmN164vV+J5KlxQiS5uRnkZW4jMq4lRfkZZKZtJalRpyqPuSyXjOvI\nJeM6lr+gHFW5qbq19h5gEHCitbYIyAXOrOrAjkXawXy+mLGJ7+ZsJb/AFehwKmzX/lwiEntjvNdi\ni2/ah71pWcDvUwtU5TxM33/+NJ2HPUrzLufRpP3pBIWE03bQgwy/diNJve/gtScvpiA/p8R6o8/6\nC3n75/Pbtxex6vur2Lf2AyZcpMkmy2Kt1cD0Wq42tYvzV+5l0o8bWbO5YpPI1hT707OIa+ocrAUF\nhRCZ2Ied+0q2Q1Xhq5mbyciLoOuoZ2nc7lTimvbBRrZnwKVLOOnSxXw5D576aFWJ9RrEhfP4TSey\n6usz2DTjWlZ8OYbrzmpP17YNqiXu2sjjqfrB8pWhzEqTMeacUu7zvXl8541Xso3bMxl5wxd0S/KQ\nmQf3vxLNtBfPIi6m7G6lmmZgj6Y88t43NE25gsi4Vmxf+jwndU+ulLmYtqUuIX3/NpJadadRs3al\nLuNyFYL3Nc5J30B4dHMatx0LQJMO49m2+Bn27d5IizY9D1svOrYhtz/yI+t+m47H46Zjt2FExahx\nOJK1lu8mPcqsKS9jPR5OGDKR8694QnNI1SK1rV2894W5fDB5NSe2M9yy1sMTtwzmsvFdAh1WhfTr\nnsz2Jc/Tuv895GVu5cCmrxl00/FfrzPtYD6zl+0mIiyEEX2bExZa8sLh+w/mY3yui3lw1wJa972Z\nkPA4ABqlXM/UBf/mrj+W3P6fJqYwvHdTftuYRvsWAxjYo+lxx1wXrUpN5+y//sz6LXto3qQBnz48\nhEE9a+51947WPXeG93cTnCOqad7bI4FfqGGNw53P/swtowq4czxYC5e/ksm/3l/Kg9fXnqtjD+nd\njH9e15Xbnh2Dy+2hZ6dmPHDTQAqLji9h+uqDB1g45wviGnUjY/cizrviMfoMLNH2065TX9bOug/r\ncZGfvZv8rG0U5acTGtGAwtx9ZB/cTkRE6f3g4RHR9Og3/phjrA9+nf4ui+f9QP8LZxAUEsGaH2/i\n+8//xWkT/xro0MR/taZdXL7+AO9+u5rf/umiQTSs2Qn9H5jN+ad0ICqi9kzRN+mhwYy/8ztmv/4m\nIcFBPH1r/+P+Ul23NYMh108lPL4zRfkHSYpfzs8vjSY68vADmAlDW3PL00tYO/OvNGo7mvysbWTu\nWUqj1qMAyNy7lGhX2dOs9OqUSK9OiccVa11WWOTmlD//SHzKXQwbO5G0rTM47fZb2fDp2YemS6hp\nyvzkWGuvADDGTAVSrLW7vLebA29XS3QVsG1PFned7PxtDAzp5GHu7uq77o7HY3nh0xXMW7GTlk3j\nuPuyE0iIDa/wdm44tyvXnd2F/EI3UREhTJmbfXwVpk1LWTjnC044dzKh4fFkH1jDpNfPo0e/8SUG\nd19y8yu89Oh5rJt1H9Z6CAoOZ+FnE0hIOomMHXMJi4gvtXtO/LP2t9k073Yl4dHOEWeL3jeydsUz\nSppqkdrULm7bk0P3loYG0c7tLkkQGxHE/ox8WjWrnkHAazZn8NzHS8nNL+L8Uzpz+pBWFd5G80ZR\nLHprHLn5LiLCggkKOv4B9zf+azENutxMcs9rsNayYdoN/PujFfz9yj6HLZfUOJrPHhnMBX//H/tT\nv8V6XOxc9SG5aeuw1kPm3qW07hJ93PHUV5t3ZVHgCqd514sAaNTmFNJWtWPZ+gOHToCqafw5/aBl\nccPgtQeo+Du/ip3UvTnP/xBMkQsycuDNn0M4qXvFr99zrG5+YiaTvlvA6LZb2Ld9JSOv/5K8/GMb\nVxUUZA4lTMcrY/924hp1PXRmW0xiF4KCw8jNSiuxbEhoOLc88DUPv7KK2x7+nuBgQ8ch/yC+aV86\nDnkIjzufmDgdNR2ruITG5BxYeeh29v5VRETGsGrJVHZvXxPAyOQY1Ph2sWfHhixKtczf6Nz+ZC4E\nh4TQvFH1nL21bmsGw679gqTgdQxsvonrH/2Rj75ff8zbi4oIqZSECWDz7hzimg8AnO7VqKaDuWzw\nTQAAIABJREFU2Liz9IrRhGFtyJ1xEfu+O4ezR7Qmuev5NGo7lsbtx9G80xm0S1LSdKwS4yPIyz1I\nQc4eAFyF2WRlbGfr7ix+mLedrJzCcrZQ/fyp0f5kjPke+Mh7+wLgx6oL6dg8dvMg/vC3LBpctxu3\nx3LNhA5cc1ZKtew7N9/FW9+uZ88LlrgouHSoh6EP5zJt4c5jOrKCyrumXFLr7mTsWkzW/pXENurG\n7nVfEBYeRUx82afghYZF0qxFF04acTGL5z5IfNJAdiybw9AxV1f4OnHyu1PO/DPP3H8qq37YTlBw\nBGnbZmI9Hr7+r4us/asZdurVjDn79kCHKf6p8e1iy6YxvHHfyZz60DSsx0N8TBhfPnnaoeuRVbU3\nvlrN1cOLuO9s53b7pi7++sFi/jA28GdwDeyeyKxVbxI99EncRTmkb/yIIZeXfS02YwzxMWE8dkMv\nTrr6QzyNB2Ctm4IDC3n4wdOqMfK6JTE+gnuv6MVTH48nocVwMnfPIzzEcs/rOwkJiyKocB5zXhlb\nbZVRf5SbNFlrb/YOfhzqvetVa+0XVRtWxcVEhfL10+PJzC4kJCSoWvvsXS4PQQYivL1dxkB0OLiO\ncxK2ypiPKbFJG86/+l988uqFGBNMRGQM19zxPkFBJQc9HmnCRQ/QpecI9u5cT7MzL6BDypDjjqc+\ni0toyh3/nM6Khd9RWJDLN5t/pPeZnxGT2IXC3H3M+uw0evY7nWYtatdA3fqotrSLE4a3Yd+Qy0nP\nKiQxPrxaJ3ktcnmI9xmWUhltYmX5z20ncsadM5j3dnc8HjdXnZnCVRM6l7teu+Q4Vn4wga9nb8EA\nE4ZNqLFjb2qL+y7vyYjeTVi2fidzlocza3M3Op78IsYEsXXhv7n531P43xPDAx3mIX5lFtbaz6lB\nAxyPJhBny8XFhDG6fxKXvLSbm0e7+XmtYekWuOXJmVz9yDTOHdGWZ+8YRnhY+YkKOFWmF7c+xojj\nmI9pw8qfWTLva8LDIxk8+koefmUduTkZRMcmEhTk/5Fmp+7D6dS95rxha7uo6AT6D7+IA3u38P0X\nzxKT6CRIYVGNiW3UmfT925Q01RK1pV0MDg4KyBf7hWM6Mv4va2nTyEWTePjLB8HYIGg46k2SGkXw\n4j0jGdan+oZQZOcW8fTHK0jdmc+wXg2Z+eJo0jMLCA8LLjEA/GiaNIzkqgn6jFamIb2bMaR3M2Yu\nSyc2aSTGON9R8S2GsfG3zwIc3eHK/PY0xsz2/s4yxmT6/GQZY6pvhHUt8eHDY0lq3Ym7P0/gpw2N\nCDYePv9TAUsfdrF9Wyp3P/+LX9spnvV7RF+nnzwvN5PUNXPZtW213xd9XL7ga95+7jr25TVn8y43\nz9x/KgfTdxEb37hCCZNUnfiGzTHWxf7NPwHO+KaDe1fQNFmNcU2mdtF//bs14ZNHT+WdBU148NuG\n5LvDOTUli7VPunj0nGzOvXsyW3cf27jNVanpzFqyi/TMAr+WLyh0M+i673l1RiPmZJzP3W/s4dZn\nFtIwPqJCCZNUrYHdEkhP/Rh3US7W42bf2vfp37VmTV9ztLPnhnh/x1ZfOLVXVEQIT9/mVOrveOYX\nRnfcx4ne6ZCeuMDN2c9v4Znbhx5lC46d+4oOVZl2bl3Jy49NJDw6ifzsXXTqNpRmSW3Jy9pNm5T/\nZ++8o6Mqmzj83N3Npmx6b6SQkBB6Sei9I1WKFBso5ZOqCIqIgIogKFJEURQQEQQLCEiV3gOEEjoB\nUiC9t832+/2xCGoCSUhCAuxzDudwk73vnd2TnTt33pnftKdeWK8i19i1cRE12n6Gk48xQyQadBzb\nu5qegz8onzdbSRQoc0i6fQVrWydcPJ7suUkymZzhk1az6otXuXlEgk6r5IURX+DoUq2yTTPxEEx+\nsXS0D/WkfWhf8pRaXLr+wPwhIJFA78bQ/pjA0fNJ+LiX/LssiiKj54WzYe8dFHYeqHIO8tGIOkTd\nzsTexoIx/WsXOaZkz8l4UpWO1Oy5DEEQcAvsw7KfQpk3piEW5k+O/MJ/EUWRqzFZZOaqqRvgiI3i\nydElLIoJL9Ti1NVjbPqpEVKpGfVrOLD4zfaVbda/KNFfiyAI9bm/d39IFMXIijPpycfB1oKoaxLA\nuH8flQT2pdg2/DvLtG7ZBKo1moxHzYHotQVE/N4T68yNPN9Yx9INv5KZfIO2Pd8qdL5Oq8LM4n50\nLrNwQKsp2UOwXqcl8fZlBEGCh0+tEtU+PQ5u3zrL8vlDsbD2RJkTT+OW/Xj+lU+e6EG8/kFNmLH0\nPDmZSdjYuWAmt6xsk0yUApNfLDkWcimCIHA7XcTXBfQGuJkiYm9Tupv89qO32Xg0h/oDDyOTW5Ny\n40/eXjqFT/oruRUrodlrVzn5wwBcHP79XVJpdJhZ2N3zF1K5NRJBgkZrwKIEyjAJqfnEJOYS6G2H\nq2PV+J6KosgrHx1ny9FErKyd0auS2fdlJ+oEOFa2aY+MVCph3YetSMsKRaPV4+FsVeV8fLF7NYIg\nTATWYhRzcwXWCoIwvqINexKIS8pj1vJTTF16gtNXUu/9/H/9a3EoyoIhX0mZ9JPAiBUy5owrXsH2\nvxID6Sm3cPbrBIDUzBInn/Z0q6tj0nOw9x0lezZ9VuSWXWjL/tw8Op3spDOkRu8m4eJKGjQrfsKD\nMi+ThTO6sWLhKJZ/PoyvPn6+yugyrflqDP7NZlK/72bCBu/nQsQ+rkXuK/7EKo5MJsfRxccUMD1h\nmPxi0YiiyM+7bvD2oqMs2XARtUYPgEwm4dMxTWg7R8a7P0OnT2W4ODvTpal3qdaPup2NrUdLZHJj\nN5WTXyd0WhVvPwdfDzfQpoaaNTsKyxq0beRJQcZF4i+sIDf1ArcOTaJFfa8S1cB+t/k6QYM2M+DD\n6wQM2Mive2+VyuaK4te9t9h1VkuDgUcI6b0bp7rv8uKHJyrbrHLB2d4CTxdFlQuYoGQ6Ta8DTUVR\nnCGK4gygGTCyYs2q+sQm5tLstd/Iij+HZX4kz03cwp6TdwBjG+XJHwbSonlTXP3D2Lesb4mFuv7Z\nMefmHULSdWMRnFaVRXrMdurdVTBwtQWtTocoFu5G6djnTZq37k386ZlkRX3PS2OW4h9UvDL61p8/\nRmpbl0YD9xD6wj5UOLFn86IS2V2RiKJIRspNnP07AyCT22Dn2YzUxBuVbJmJZxiTXyyCqV8eZ97K\nQ7iJl9i1P5yeb21FpzP6qAmD67FyZldsvUN5tV9Lti7sWWjQbXHUDXQk6/Z+NAXpACRd/Y3q7hZ/\nT3/CzU5EWaAtdJ6zvQWHl3XFU72WzFMjaV/9JpvntSn2ereT83hr8Wnq9P6Tmr12UbP7Lwz/5Bg5\neZWvH3Q9LhuFR3ukZsbtSCe/rty6U1h/z0T5UpLtOQHQ/+NYf/dnzzRLf7nAy801zBtiPK5TTc/s\n78Pp1MT45ORga874F+qU6RovjfmKb+a+QPKVNaiUGUjRkZkHF+Jg5h/mNAhrX+T2mUQioVPfN+nU\n981SXS8p/gZONccYo3tBiqNPZ5Lid5fpPZQHgiDg4lmT5Ot/4BEyCE1BOpl3DuHeq/AoGBMmHhMm\nv/gfcvM1LP3tMnGLDTjZwNs99DSansmR80m0a2zUd+sQ6kWH0EdXeu4Y5sWYvil88XMrrBT2qAty\nqOup4lwM3EqFVYek7PnKr8hza/rZs39px1Jd71Z8DnZOfljZ+wNg41IHSytHbqfkUdu6crfB6gQ4\nkrtpF9oGYzAztyM1aiM1/R6sNWWifChJ0LQKCBcE4W8Nkr7Aiooz6ckgr0BDzX8U9Xs6QF4RTzhl\nwdnNn6mfHyU9JQZLKzuy0uOZu24SeVvT8K/Vjn5D55fLdfJzM8jOTMTNszoJN7fi4NUCUTSQHr2d\nunXLFviVF6+OX843nw4i4cJyVPmptOk2ihq1i39S1Ou0bNvwCZGnd2BuYU3PQe8R0qDTY7DYxFOO\nyS/+B+OYk/tjW6QScLcvf784e3RDxg8IJiNHjbergtkrTvPi8mjsrOWsn9OiXGa96fUGom7nIJNK\nyEmPIT/jOgrHILKTz6JSZuLjVvlii33a+LL3dCor17fASmGPhbSAbUtL5ttOX0nlf59FkJReQLuG\nriybEvbEF5E/LoSStLELgtAY+Lso57Aoimcf9vrQEBfx9OqnOwuw+8QdXv94N2tGG4dhjl4lo3eH\n+kx7rfEjr1nWOXOPwqnDG9j4w3tYWLuhVqZg5+CJMj8Pg0GLZ7WajJi8psrU22g1KtKSo1FYO2Dr\nULKBnZvWTOfq5Uj8m81AlRdP1MEpjH53PT4BjSrY2qrN2y86RYiiGFrZdjzJmPzivxFFkVYjfifM\nO4sxnQzsvwwfbZYT+fNgnOyeHAHIzBw1HSfsJTpJg16vw89Nzq34XBS2rhTkpbJ2Vkt6tfatbDPv\nEZ+ST1aemkBvuxJpAd5OzqPOi1vxCvsQG9d6JJ5bTC3HK+xc2OExWFt1EZouL5FPLGmv5Tkg8e/X\nC4LgI4piXBnse+Lp0syb+RNa8+bq06g0eoZ0DWLqsEe/EVdGwJSReps/fpxOg74bUTgEkpUQzuW/\nRvPGe79hbqHA2T2gSuk6mckt8KgWUqpzIsO3EtJtDVb21bF2qkluzaFcjNj5zAdNJsoFk1/8B4Ig\nsHlBT8bNP0D3BSn4utuwe2nbJypgAnhryRkypC1pMOhTRIOOqD2vMeGFfAZ28MPf0+aRBrFXJF6u\nCrxcSz7/bu+peBy8W+Ie3B+A6m0WsGdFCBqtHrlZ1eiWrsoUGzTd7QiZiXEg5d/79iJQr2JNq/oM\n6VrjkeYo5eRpWLMjipx8DV2bVaNRzcrZh05NuoGNc00UDkadFHvPppjJbbCwssHFPeCh5+Zmp/L7\n6mkk3r6Cq3t1+g+bi71T2adS37h8hJjr4djYu9G41QvIZGVLGZvJrVArU7CyN4pmaQpSMDcPKrOd\nDyMzPZ7zJ/5ARKR+kz4m7aWnEJNfLBpnewvWz+n2SOcePpvIoXOJuDla8XL3GiWeoFDenIvKxjG4\nP4IgQZDKsfN7nitx39EwuHg/vWZHFPPX3UCvFxnf35//9atZ5g6w7DwNa3ZcJzdfS/cW1WgQVLb7\nhZWFDI0yFVEUEQQBTUE6UqkUWSmL8kuDKIpsOhDD5ehMavra07+Df5XsjCsJJck0TQSCRVFMr2hj\nngR2nbjNmatp+Hva0CHUi1/23kSl1tO7jS9BPvbFnp+Tp6HR8O2ozeshsw5h7k+/snZmM8xkZd+H\nfxAGg55Th34mOf4aNWq3IaSBsQvN2c2f3PRrFOTcxtK2GrmpF9Cqc7C1f/jWl16v45u5LyB3aopf\ni89Jj/2Lrz7pxzufHijTVt6R3SvYtWkRLgF9yE8/RPjBDTRq3geNWklw3fZ4+dUt9ZrdB07h9x8m\n4FH7VdR5CeQlHaPJuA8f2cbiSE28wZIPe+Lg0wVBENi7pRPjZ2zBzav4uVYmnihMfvEuKRkFbNhz\nE43WQJ82vkQn5HL6Siq+HjYM6lS9RB1y3/1xjclfX8QxYACazEiWb9nNkW+6VGjm40p0Jj/tjMLa\nUsao52vdy4iF+NkQHrsDO48mIBrIubOTuq2Lz+Rs3B/N+EWX8Gu9EJlExvsr3sZcLuW1Xo/+kJaZ\no6bRsG3oFI2RWXkzd82vzHytFlqdATdHS17sFljqz6hXK18+WnWZqH3/w8KxIRlRa5jxWkMkkooL\nYv43/yQbj+Zh7dWZvM172X4imZXvN6+w61UkxdY0CYKwH+gsiqKupIs+rXv3H39/ijV/XqBvYz0H\nrki5lWKgaz1wVMD6cAlbF/SgWV23h66xeP0FPt9mS42OywHIuHOEnLOT+GNeNxJStWXaolOr8sjP\nzcTOwR2pzDgawGAwsHRGW+7E3cDcQoFGU0DzdoPpO2wBAId3r2DHr3NR2PujzIpm8KjF1A3rUeT6\nEUd/5dCuVei0KjLT7tD8lYh7TwtnN/bg5Tc+x69G2CPZbjAYmDbCj0b9t2Nl54coGjj9a3ekZpbY\nuTUi5cYfvPjGUmo17FLqtW9cPsLFiJ1YWtnQotNwbOxcH8nGkvDT12PI1vnh22gMALfPfYuVeJ1X\nx39bYdd8FEw1TWXD5BeNxKfk0/z132kXrMXWUmTtUVCYCwxtYeDIdSlenh5smNvtoTdkURSx6fAT\ntXtvReFQw6hyva0vS95wZmDH6mWyz2AQSUjNx8pChuM/tgl3Ho+jz7uHEAQJer0WS3Mp19Y/j4eL\ngpSMAlq/sZvMAkv0Og01q5mxZ3FHLIsYAp+SUcC4LyK4cDObHKUWReBYvOsOAyAt+i9sk+dy5JvS\ndez9k8/WnGfJHldqtF8KQHrsfq7snYBXrRdQZ0biY5PE4W+6YCYrXZYoT6ll6a+XuJOmpmNjV55v\n5//INhZHTEIudV/eRsNBx5DJbdBp8zm7oQVnV3UlsJpdhV23tJRnTdMt4IAgCNuAe4N+RFH8ogz2\nPXFk5aqZ/9N5biww4GYHaq2OGm/DxK7QJACaBhiYvuwYe75+/qHrZOSokdncHxtgZefP7XwV9QLN\nSUh99C6TY3t+YMu6mZjJrZGZmTFyylo8fWpz9vhG4u/E0HjgDhQOgaRG7+Longl0GzQLC0sbWnd5\nnTqNu5GZdhsX9+oPDCjOh29h89rZBLT6BBDI2Pc2Sdc34RHcD4Nei06di8zswXv9yvwsdFo1Nnau\nRaZlDXotep0GCxujZIMgSLC088fJtxMeNQfg4NOBzWtnPVLQFFirFYG1WpX6vEdBmZeNpZvfvWML\nO3+Ud04+lmubeKyY/CLwxbpzvBCm5vOhxofvRr6w7hjMHwIanY760xI5FplMqwYPzl4bDCIqlQZL\nW2NxtSAIWNj6kZVbtiReSkYBnSfuIzoxH61Wzeu9g/lyUhiCIDB89nHcAnsR0PpT9LoCLmwZwPDZ\nB9i5uAeujpZErulJ5I10ZFIJ9QIdi8yWaXUG2o39C5Vtdxwa9EF5cytx577Fs9ZQJFI5WnUmluYP\nDmZ0OgOJ6Uqc7SyKDMgA0nM0mP3jfmFp749EpsC/2QeIooGr2/qy+WAMA0oZXFpbmTH11QalOudR\nycxVY6lwRCY3Th6SmSlQWDuRmVuyuYFVjZKEp3HAX4AcsPnHv2eK7DwNtpYSXG2Nx+Zm4OsMGXdF\nvIM8ICO7+D+Crs28Sb++juyk06jzk4kLn0X3ZqVTxf0vCXGX2P7rPBr3307Tl8LxajCJlV+8iiiK\nJN2+jJ1bvXt1Sy7+XREEKRmpsffOd3DyonpwM2zsXBFFkfzcDArys/91jZOHf8U3dApOPu1w8mlL\njVaziDn1OXcuruby7lF4+gTh6VNYnsBgMPDHyrHMnVCTRe82ZMWcLoXWBpCZmeNboxm3js9Go0wj\nLXYvGXeOYO9pFOW0svOnID+rTJ/T46B2w47cOfclyqxbKLOiuXN2CXUamSQOnkJMfhHIyC4g2P3+\nbkWQB+SpjP+Xy8DfVUJGjuqha0ilElo18iXm+Aw0yjTS4w6SHruXto08ymTba3NOkqvoRuMXzxE6\nNJxfDhewbpdREDdbKeIW8jKCIEFmpsA1eAixyfcFK83lUsJqudIw2BmpVIJGqychNf+eUCcYt/eS\nc6T4NpuJnVtDqjf/AAGIOjKT2DNfcefUR8wYVvQA7tNXUvHrs4amw37Bvftq1u8uWqi3e3Mv0q+t\nJjv5LOr8ZKIOfYCTr3EWmyBIsLD1J6OEQ4sri5q+9sgM2cRfXIWmIIOES2sQdBnU8q9ag3hLSrGZ\nJlEUK64A5AnC21WBg60l87bmMaqDyF8X4GwMWMkhPgPe/1VKtxbFt6G2qOfOd+82YtKXo8jNV9Gj\nlR/fvntfrbtW6vhSb9ElxF7AwbsFlnbG67sFPc/1w++hVuVRo3ZrDu/+AU1BOnJLJ3JSziOKOlw9\nCu+zazUFrF/6IlGXjyOKInVDu9N/1HdIpTLMzMxRa3LvvVanycXB0RUH6U1qtmhHq64ji+y0C9+/\nGlXcFpKWarEyh5ErLrJj3RT6jVxe6LXDJn7PyoXDCF/XCrm5NRJBaixSlFkRHT6H4HpVvyW2Rafh\n5OdmcGTbYERRpGWnYbTqMqKyzTJRzpj8opFuzf2YsSyOlkE6bC1h8jpQmBsfJvdfhohokSa1i98O\n3zinFS99dILDv7XGyd6a3+e0LlGN6MOIuJqGb6eXEQQBM3M7bHz7cfLydl7sBh5OFmTE7cPGpS6i\nQU96zB661yn6Jr7jWBwvzdiDTCoikUj5ZU5XWjf0wFwuRadVIRq0CFI5okGHmVRLW58I7K0v8dob\nnQgNcSm0nl5v4Pkp21k0VM2Apkax4o6fHiKslisB3rb/em3bRp4sGFeHyV++iFqjw1phhdTeE60q\nk9y0S6TF7qVto+5l+pwqGksLGfu/6szQWSs4FzGfGj6ObPqyEwpLs8o27ZEoSfecC/AOUBu4tyks\nimLVv4OVI1KphD8X9mT4R38xd2sm/h4KxgzwZeg311BpDAzpEsCHo5uWaK1BnQMY1Llwd1q35taF\n5s+VBEdXP7ISTqNT5yAztyU76TRmZhaYW1gTVLc9DZr1Jvzndlja+lGQdYP+w+YjMyvclbbnt4/w\nk54g/BsNOj0898VfHN6+hHa9JtH+udEs/2woOm0eAgLxkd8yYvJP+Ac//D0n3jrOa62U2NytDx/T\nUcML358u8rVXzu0hJeEmHjX7k5d2CXs5RO0bi0adR0iDrgwYPq/Un015kZudwpZ1H5GaFEM1/7r0\nGDQNC8vCiQVBEOjSbzJd+k2uBCtNPC5MftHIoC6BJKTl03n+WTQ6A33a+HErPgv/tzLxdbdi8+cd\ncHeyKnYdRzsLti9oV662VXO3JuP2Aazs/TEYdOTc2U9gS2NB9/YF7QgdvpyUm9vQaXLxtNfx3Xs9\nC62RnK7klVl72DpJR4sg2Hlez8D3dnJz00sE+djRoo4D5/8ahq1PL3Jvb6NpiC0/zWzz0BqupPQC\ntFodA+66zro+0CRA4OLNjEJBU55Sy4L111E418NB4UlazC78DIc5s34zTvbW/PZJK4J9yxZcloU1\nO6JY9kcsZjIJ016uQddmRXcKB/vaE7Gqagd3JaUkNU1rgQ1AT+B/wKtA6kPPeErx87Rh/zf/LuSc\nP6H8OwBKm22yUtij1eQSvqETCocgclPOY2llfa92aPCoRbR77n9kZSTg7hX8QGmAhFvHWdxXjVxm\nTK2PblvAwtPHAfCtEcb/3vuVE/vWIiLSc+p6fAKKF/K0c6nB7svmjOqgRiKBPZck2DkXzsiJosjG\n1VNp0GcjCscgDAYd5//oywsjPn+kOqbyRKspYOnHfVG4tcUpZDwxUb/x/YJXGPv+xie2bdZEmTH5\nxbu8NbQ+bw2tX9lmFMLWUsLZUwtJvbUTjTIVvToDRxtjHU+wrz0JWwdw8nIqFnIpTWu7IiuimPpq\nbBbBHhJa3E3Md6sPdpYQm5hHreoObJ7XlkXrL3Huxhrqdbdm0pB2xXahOdtboNLCuRho4AfpuXAu\nVsTXo7DK+PI/rpArq09Qh28RBAFrr50ob35E/oGXy/rxlJnV26/z5pfXqNbkY/S6AgZO/4At86T3\nRuY8rZQkaHISRXGFIAgTRVE8CBwUBOFURRv2pHPqcgrzfjhNfoGGfh2CGNE3pEQ32L+zTQci8mnX\nuGSCZXeiz+Hi1x7PuqPQ5CejcAzh5IZ2qApy72VDXD2DsHP0LDI78jd2zv7suXyFTnV0iCLsvSLH\nxul+gWE1/wZUe710xYOtu49l1bzt1J1+CzsrgRup5ox8f2Gh1+m0anRaFVZ3a68kEhkKh0DyctJK\ndb2KIO7mWfSiBf7NpiEIAvaezTjxU1Oy0uNxcC5bPZqJJxaTXywlGq2eOasiOHY+Hk8XGz5+oxnV\nKnAcyaXoHOr3/AmtKhOpmRVZiac4eXUnL95NeNgo5DSr44qZTFJkwARQzc2aawl6EjPBwwFuJkNy\ntgF3J2PqXG4m5Z2XSyfNZS6X8t20dnSed5CwAAmRcQZe6127SP2lpAw1crva9+4d1k4hREcUlOp6\nFcXXG2Op1uwTnH2N3YFadSbLt2wwBU3A3y1diYIg9AASgMqdVFjFuXgzg+fe/JPZ/XV42MN7azNQ\nqrVMHFyypzFPFzPGMBVSKVHGycG5mjG7ZOeHjXNtcpLPYWZmibmF0SFdPL2ddd+MR69TY2PvwYjJ\na3D3Llyg2HnQHL6bfYqD13LR6CBd40rnQZ0JP7AWd68gfB9BTkBubsWIabuJvh6OTqumR40wLK1s\nC73OTG6Bp18DYk5/gW+j8eSkRJIedxD/oKmlvmZ5I5FIMejvF1uKog5R1FUptXQTjx2TXywlI2bv\nIy0pjrc66zlxM402oxI5+9MLFaaw7eNuQ3baRTxrDUU06EmOXEz1Fsatwjylln7vHeJARByIMH5Q\nXT4f37jQg211L1umvNyQxjPO0dhf4ORNAx+ODGXniTsIQPcW1R7J/oGdAgir5cqFmxn4ultTr0bR\nOn2dwzxY/udPuAQ8h7m1J3fOzKNDaNkK5MsLqVTAoLvvFw06NTLp0595L4lOU0/gMFAN+BKwBT4U\nRXHLg855GvVISsO0r8ORZJ1n9gvG45M34bWVCi5ueLFU6+w8nsfXcZ8+NOOk06oRJDJ+/+FdLp3Z\nh7VTEFmJEQz53xLqNO5ORmocC6Z1pHa3Vdi6NSDxygaSLn7F+wtPF3nTV6vyuHX1BBKJlKjLRzl5\n+DfsPZqQmXCCNl2G07nvW6V6D6UhOzOR1UtGc/tGOFa2rgwasaDSt+bAOPR38aweiHJf7L3bknrz\nD1ydrBn+1qondnvOpNNUNkx+sXSo1DrsO64i41sRq7sxxnOfm/HawLalbpcvDlEUUan13LiTQ7ux\nu1E41UaVn0qgm5b9SzsZBSc/Oc6e675Ub7MInSaXazsH8dlIT4b1LFqI8uLNDG7cycGSVLcMAAAg\nAElEQVROIWfIzCPIHeojGgwYci9xamV3PF1KPsaktHz92xWmLjuDSqWmW4sA1n3YAmuryi+i3nIo\nhpc/PoVHw3fQawtIjlzIvqVFF78/CZSnTlO4KIrZQDbQvsyWPQMIgP5+Zyo6PY90c31YxkmZn8Xq\nJSO5deUIEomUrgPe47U3vyU7Kwkv33k4uRrrhuJjIrFzb4Stm3FbzSNkENEnPyUvJxVb+8JCnOYW\n1oQ06ER6SiyrvxxF6At7kVs6olamsO+XzjRtO6TEw3JLi52DBxNmbrkn719VkMrMGPP+7+zZvIiU\nxH00DmtJ+55jq5SNJh47Jr9YCv7+rhT2i+V7nZ9332Tk3GOoNVqC/Vz5a3FHbifnY23lSNuGHve2\n4Q6dT8MtbC4SqRlyS0ccarzEwXO/MqxwLTgAdQIcqRPgyOAZR1H4v4Jv6NsAxIR/wvTlxytU3XrM\ngBDGDAipcn6xdxs/fv1YxvKta5BbCkx6ggOm0lCSoOmoIAgxGIseN4qimFmxJj35vNIjmNYjL+Fk\no8PTHmZslPHOsNILidULNKdeoDk7j+dRK3U867M/o16gsVHnl+8noxLdaT3iCpr8ZA5uG4KHdxD1\nwnr9aw07Rw/yMq6h0+Qhk1uTnxmFQa/BSvHwjou87BSs7KohtzTuOJhbuWJp7UZOdkqFBU1/U5Uc\nw99YWNrQc/AHlW2GiaqDyS+WAnO5lFefC6T3wmjGdNRxPEogNlNOl6blVxN48WYGo+efplaPjSic\nQrhzbinDZq8j8qfCkZC3qxWJyRFGyQFRRJV6Er+axQ8Wjk9VoXC/78utnBsSl3Kg3N7Dw6iKfrFL\nM2+6lFFn8Emj2KIMURSDgOkYW2sjBEH4UxCElyrcsieYYF979n7dl4uZ1dl02ZvZY9sw6vlaj7xe\nt+bWeLqYMdhuCrVSxwMQfT0c7wZjkEhkWNh44RLYn1tXTxQ61yegMfUad+Xsxh5c2zeOyK2D6T9s\n3kPVu8FYOK7OSyAtZg+iKJJyczs6dVaxg3xNmHgWMPnF0vP1u23p0aERP5/1RKsI4vDyftgoyjaQ\n+5+EX0rByact1s61EAQB7wZjuXwzGbVGX9iWSY1Iu7CAG3te4eq2PthoI5g0pHax1+jY2InUS9+g\n0+SiVWeTdmU5nUMrbm6oiapHSTJNiKJ4EjgpCMIc4AtgNfBTRRr2JKDW6DlwJgGVWk/rBu7/mm0k\nkwrIZAIymQSzcpge/d+sk9zSxVj8bVvNqOKdHoltSNEzjvoP/5RGLU6QmX4HL9+pRRaB/xdLhR2v\nTfqRH78cxcVdo7Fz9GLE5J8wt6i4vXsTJp4kTH6xaKLisjkflY6vhzVhtf4tbGkmE4zdalJJuQ+I\n9XCyIj/9Mga9BolUTl7aJayszJGbFfa/tao7cOXnPuyPSMBC7kbXZo2xMC/+djh9WD1iEk+wdrUx\n2zSsZwiThxYfbJl4eiiJuKUt8DwwGAgANgFNHnrSM0CeUkunsX8gavNwUMC4zyTs+7ovNXzsiIrL\npsXrvzOlhwEvT5jw2W2SM5SMGVh4zEhp6dbc2BH34YtuvPv1u2THbkGZn4HCApq1L1q7QxAEqtcs\n/Z67f1ATZn55Dq1GhZm8+NS1CRPPCia/WDTrdkbx5heHaBkk4WysyKDONZk3oQUAr3+8j0tXb/FW\ndzh4FcJeiebC+iHlVtTcrXk1mteM5tjmblg71SQt7jCrprV44LaWq6NlkSLDD0Mmk/DDBy347r1m\nCHePTTxblCTTdB74A/hIFMXjFWzPE8Oin8/jb5/DujF6BAG+2A5vLzrMli968sG3J3m9rYFpfYyv\nDXATGbT0ZLkETX8zbqAfvVs7s2TDLawsLAir5cnvcQKgulf3VF6YAiYTJgph8ov/Qa3R88a8Qxyd\noadONT1Z+VBv2lUGdQmidnUH1u6+Rfo3YGsFQ1tCsxkqftx+nTEDyidTI5EIbJrbht3hd0hMy6NZ\nna6EVNB8MzNTsPTMUpKgqbpYnC7BM0RMQi6XbmVy7noqnYL197o/2tSEH0/k3n1NDjX+0blqYQYF\n6sL76qVl6+FYRn56kozsPFrUr8avs1vy+USjsNr+iHxetXoHtUaEVMhTatidPQi53BLfGmFIpSXa\niTVhwkTJMPnFu2h1Bo5FJpGQqsTcTKTO3Uka9gqo5yMQl5yHt6sViGD2DzekMIdrsYWHd5eG3HwN\nL846zq4T0VhbWbBgQiOG9ShaNgDg/PV07qTkU6+GY4UKa5p4einJndRZEIRnfsYSwPrdNxj/2UEa\nV5dw5paeiEsCg5uLWFvA0r8kNKltbOFvWsedxVvS8XMBT3t4ex042JZNwO3SrQxenHWcwE4rCXKu\nRezp+fSbdpDDyzoD0P4fWk5Rcdm0GbWVQLfNpOeKKC3q0Xrob0hl5sVmoTRqJXE3IhAkUnxrhCKT\nlV+hpgkTTxEmv4ixTKHLuM0UKHOxsQCV2sCXu2B8V+NA8/AbIl/WcMTJzgJLOQxcDG92h+NRcOoW\njH2lbEKNw+eEcy41mKYv/UZBThwTF71MgKcNrRsWXnfql8f4acdV6lQTiLglsnJGR3q1Ln7IOsD1\nuCxuxecS7GuHv2dhcV4Tzw6m2XMlRKnS8b9PD3J4up66PnpSsqHWOwLuY0Euk9Ciriu/TGwFwMej\nw/jzSDQfb1Iik0JqrsD2RZ3LdP3D55Jw8uuEvYdRldu3yfsc+X4Ner0B6X8KzSd+fpB3e6h5s7tR\nF6XPF+fRnehBzQB33LDDyc6oivtf7afc7BS+m90ZV8tsNHqRbJ0zZjIpyUl38PT2p9/oVbh5Pvgp\nzoSJZwiTXwQWrD2Hn102a98zZt0/3Cjw4SaBWZsE9AZY8X67e0HGZxNb8t5Xx7i6UqRAI9Cyvjt9\n2/qV6fp7T8UT0nsVMnNbbFzq4Bg4mL2nwwsFTeEXU1i/+yoX5+qwVxgFh7vN2ssvc7uisJTRpJZL\nIT/6N4t/Ps+cH05Tz0fC2Wg9rRp4cSwyCUGAsQPq8MGI0CopB2CiYjDNnishyRlKbC0F6voYj13t\noHGAGaOHtKF9Y69/ZZJsreVErhvE7/ujyS/Q0aWpd6Hp1aXFyc6CgqwoRNGAIEhQZt5AYWVR5Bf9\nVkIOne8KD0sl0Km2ng83XiEl6SaRsQZWTO+AXO58T74AYH32Z5zaPJWBDZJY+KKO3ALwnZjHvCEw\noAmsO36Nj+b3ZtL8s5jJLcv0XkyYeAow+UXg1p1MOtW+X6bQpa7Itou2/LmoNw625v+q/RndrzaN\naroQfikFLxcFfdr4lbmDzsHGEmVmFBbWHoiiiDbnKs72hbP60Qk5hFUXsL+bkG8SACqNjvcW7yJf\nBZ5ujvy5sGehDrqYhFxmrzzNmdl6qjnpeX8DbI64zckPjQ+kA5ZcwM3JitH9TB10zwolqWb714wl\nQRAa8gzOWPJyUaATJWw9Yzw+Hwtnog00rulS5NabwtKMV54L4o3+tcocMAH0betHoHMuV7f3J+bY\n+1zdNYSlk4tu1mlc04Vv90kwGCBbCd8fgI8GwN6pGrZN1jHs4310DLOiW3Pre/9edXkHy7xD9Kiv\nA+ByPHg7wsj24KCAsZ1E7MxVpCbeLPN7MWHiKcDkF4HGIe78eFRGvsoYRCzfL6FxiBuujpZFFkuH\n1XJl3MA6PN/Ov1wkB76e3IibB8YQffQ9ru8aip14heE9gwu9rn4NJw5dNXAtwXj88zGwtoCTs3Rc\nmKvDWkhnyYYLhc6LTcol2FNKtbtSTGdiYO5g8HOBADd4v7eOnceiy/w+TDw5lCTTNFsQBDvgbe7P\nWKq4AWQVxNlraazdcR2pVGB4rxBq+j1cEfu/yM2k/D6vO/3f3cEbPxjIU4l88kYTvt10GZVax8BO\ngTSvW3gsSXlhJpOw/6tO/LLnJskZN2j9ZrtCGih/s2RKW3pP+hP3cdnkqwx4OcK4Lsaa1SYBIJOI\npGWp8HC2undO+8YK2jT0YOXBHNqF6LGSw50MyC0AG0vIyoe0rHzqqb/ANfXfWk3rsz97oN3l3cln\nAiJvqEp9zmC7KRVgyTPNE+8XtToDy367RNTtTOoGuvB67+AHblE9iLED63Dueiqe46Mxkwo0CHJi\nQDNXxn92CF8PO8YNrF0i/aNHpVvzahxf3pU9J6Oxs5YzqHN3rCwKXy/E34F541oSNvMINhYScgt0\nfPuacYyLVIAudfScv1NY1D3Y156rCQZO34LQ6sbA8Eo89Gpk/P21RAFHO1Pm/Vmi2IG9j0JlDaY8\ndTmFX/66gblcyut9Qu7tpR89n0TfyduZ0EWHWgvfHpCx7+u+1A0s/YOhVmcgITWf/AItHcZs5pUW\nWhwUsGi3lNWzutCtebXyfluPhCiKJKYpiU/Np8ebWzk8XU+wJ2yJgDdWmxO39eVCDjK/QEv/d3YQ\ncTUVvUHEzdESc4mazrX17IiU0rVlEAveavWvc/ZH5D/QBrWmdH9b/62xehr555ZoWTCXl/4p/Z/N\nAiUdTmmi/KgMv6jW6Plm42Wi47NoFOLGy91rIAgCBoNI38nb0OQl062unt9Pywis7sOqmZ0e6Trp\n2So0WgPzV0dw+HQUL7XQcfCalEytA3991bfKtOgrVTrSslS8/9Ux7Ilj8csGCjTQ/XMZQ3o25Y0i\n5A82H4xm+Ef7sbGEXJVxe6ZfmAG9AbZHyjjyXb9y2U0wUbmU1Cc+MGgSBGHJw04URXHCg35XGc5h\n76l4hry/i/GddeQUwJpjZhz5rh+B1ezoM2krfWsnMryt8bXzt8K1vABWfFC0gnZJmLzoKPL8S8wZ\nZDzedAoW7nPk0HcDyuHdlC8/bL3KxC+O4mgtoNZL2DT/OZrWKTpLJYoiCalKpFIBN0dLNh+K5WpM\nFrWrO9CzlU+FFTzuPJ5XIetWRf4WKK1MTEHTo/Ek+UW93kD3iVsx06XRIUTPzydktGwcyOLJbTh7\nLY0BU7Zwdb4OMxko1eAzUcrZtYMeuRU/J0+DZ48fubPEgL0CDAZoPMOMBZO70CHUq5zfXdlIz1bR\n660/iUnMpkAt8nw7f757v/0DM21KlY6kdCWezlakZqn4fV80ggADO1bH08U0JeFpoKQ+8WF5037A\n+4ADUOWHUc5ZGc7SV3S80Mx4bGGmZekvkSx6uzVKlQ7XfzwIuNnB2VRt0QuVEKVKi/d/1ixQ68q0\nZkUxrFdN+rWvTkpmAd6uCq7EZDHt63DMzaQM71UTH/f7TlIQBLxc7zuBvm39oG3F21gVAgkTJkrA\nE+MXj0Umk5CUzvk5eqQSeL2djmoTrzNrVFOUKh2O1sI93SRLOdhYCihVj+7DVBo9chnY3t2tkkjA\nxQYKVGXXqCtvnOwsOPJ9f+KS8rAwl+Joa863m64QHZ9N4xBXBnUO+NcDopWFjOpeRodfzc2aN4fU\nrSzTTVQyDwuacoC/gB1AO6BK91QqVTrc7O4fu9tBcqYxMHqhczDvrM3EQWHcnpu1ScaCt0rfOp+c\nrmTs/AOcuZqGnbU5m9KkhHjpcVTAhDUyXuheddvxba3l2FrLORCRwMCpO/lfBx0ZBQJNh1/k2Ip+\nJu0REyZKxhPjF5VqHS62An8nT2wtwVIuUKDW0TDYmSyVGXM26+jdSGTNUQEHOwUBXqX3Az9uu8bn\nP0Wg1uhxtrdg7GoVYzoa2H8ZriRKaF636Kx2ZSORCPh52qDXG+g1aRv6ghTa19Tz6QoZpy8n8fmb\nrYpfxMQzx8OCpm+AvUB1IOIfPxcA8e7PqwwDOgYxad0Zlr+mI1sJn/4p47sPagAwom8IKo2eMT9d\nRCoRmDW6If06lM58g0Gk16Q/aRuQzdwpIrsjVczYJOP9TbZodQYGdwlm8ksNK+KtlSufrAhnycs6\nhrQAEFGYa1iyPpKFk0wOwoSJEvDE+MWmtV25mSphyU7oXBe+3Sch0NsOD2crBEFgz1d9mPD5Qdac\nyKJeoBM7Frct9Sy1P4/E8sGyo/z0Px22ljBsuZSz8bYMWqbF18OaPV+1/dcg86rIiYspxMancf4T\nPTIpjO6ow2fiVaa/Hoa9TdlEiU08fTwwaBJFcQmwRBCEZaIovvEYbXok3hpaH61Oz6vfX0UukzJ/\nYuN7RdmCIDB+UF3GD3r0lGp8aj5xSbnMny4iCFDDHX47LfDeqFZ0aeYNGLNdt+Jz8HC2wqmKOoq8\nAi1e/xjH5O0IZ9M1lWeQCRNPEE+SX7S3MWfPV30YP/8AS/fl0rimC1u+aHdv28nXw4bNC3qW6Rqb\n9t/gvZ46Wtc0Hi96Sc97m+Dyry8CxhrJuKQ8lCodgd62VXLAbV6BFjc7kEmNx/ZWYGUukF+gMwVN\nJgpRbC9oVXcMfyORCEwd1pipwxpXyPoKCxlKtUi20jhTSaeH5GwRayvjR3j8QjL93tmBvZVIYqaB\nuWOa8saA8hvQW170bRfIlPXn+O51Y0Zu3jYZX08NrGyzTJh4onhS/GKQjz27lvatsPWtLeXE/6Oy\nKz7D+DMwFqK/9tE+dhyPxdpCwN5WwY7FvXBzsnrAapVDk1quXE+S8M0e6FQHvt0n4Odh+y9JFhMm\n/sY0xbWEONpZ8HrvYDrMjWJQUx37rkjx83ahaW1XDAaRAe/u5LvhGno2gphUaP7hSVo39KROQPnp\n3anUOtbsiCIls4A2DTyKnK9UHFNebohao2fA0qvIzSR8/EYoPVr5lJuNJkyYeHaYOKQ+LV6/QZ5a\nh42FyLK9Mn6bZxTd/X7zVaJj44hZqMdSDu+uz2HC5wfZMLd7udoQcSWVXeF3sLeW83L3GtgoSjcv\n08HWnN1f9mbsvP18uv1uRm5Bu3IR3zTx9PHMBE2iKBKfko9UKsHdyfKRWue/eKsVP+/24MyVFPp0\nsWNk3xCkUglpWSqUKi097wqe+blAiyCBy9GZ5RY0qTV6Oo3djI00m/rVdAz5VcZH/2vOa71DSrWO\nRCIwY2QYM0aGldmmApWOhDQlHs5WRQrKmTBhomqjVOlITFPi5WL1SCKU1b1sObFyAD/8eRWNzsDu\npQE0DHYG4MKNVPqH6rC6u8P1ckuRQcsyytN8Nh+MYdScvbzS0kBEqoRlv0Vy9Pv+2FqXLnCqVd2B\n/d+WXQ5CFEWS0gvQ6w14uSpMM+meQp6JO11+gZZ+U7Zz9noaegN0aOzJ2tldkJtJS7WOIAgM7RrI\n0K7/3s5ysJEjk0k5dMVAmxBIzjZO955eze4BK5WePw7GINNns/1dHYIAL7fS0Xr2cYb3qlkpX8w9\nJ+8wZPpfKMxFcpSw8oP29G3n/9jtMGHCxKOxaf8tXp99AFsrUGoEfp7dhY5hpddT8vO0Ydaowg9h\nNXwc2bFPyphOesxksOWMQJBP+flEgKlLj7J+jJ72tQH0DFyi5Idt15kw6PGXRmi0eoZO383+iASk\nEmgU7MLv87ujsDR77LaYqDiqXlVeBfDBshM4y9NI+FJPwpd6lNkJfL7mXInPT89WkZyu5EFCoFKp\nhLUfdab/lzJafiSn7ntSxgyod++Jqyhy8jTEp+RjMJRMNTszV02Aq3hvMGagG+QV6Et8fnmSm69h\nyPTd/DZeS8xCHbvf1THik/0kpysfuy0mTJgoPUnpSkbOOcBfU3XELNSxYYyWIe/vJk9ZMv06rc5A\nXFIeqodo073RvxZmCjdqviMjdIYZq49bsnjyg0Xf9HoDt5PzyC8ouYZeZq6GQPf7xzXc9GTlqkt8\nfnky/8ezqHIS7t1nHGWpzPgmvFJsMVFxPBOZprPXUni/u7GdVCaFl1vq+e1CUrHn6XQGXvt4L38c\njEUmNbbw/vppd6ytCj85dGnmzeUNQ7gSk4WXi+KBsvqiKDLjm3AWrr+IwkLA29WarV/0LFZVtn1j\nTz5YJrArEhr6wozfJXQOdSv1rKjyICYxDxcbgbZ3dwZDq0OQh4RrcdlVrsjThAkThbkWm0VNTwmN\n7yaH29cGR2uIScwttqTgyLkkBr63EwkGlBqRVR90KDLLLDeTsnlBD85HpVOg1tEgyPmB2/jXYrPo\nNelPcvPV5KlE5o5pyrgXiu92fq6FD2+vi2HxS3pupcCqw1I2feZd/AdQAZy9mswrLfWY3709DGut\n59PdKZVii4mK45nINFX3tmdnpIAogijCrgsSAqo5FHveovWRJMTHkfSVgZSvDTjKUnn/6+P3fp+d\np2HV1mss+/0yMQm53LyTw7nr6UTeSH9gBmjLoVh+33OZ6IUGkpbq6V4rh5Gz9xZrS7CvPetmd+HN\nnxWEvCsjTe/Jmo86l/xDKEe8XKxIzDJw9e7E8NhUuJ6ox9fdpOptwsSTgK+7DdcS9MSlGY+vxENy\ntgGvYh7eClQ6BkzdwcrXNcR/qeOvd/WM+GQ/8Sn3Z1AePZ/Ekg0X2XwwhjylhvNR6Zy7nk7SQzLR\ng6ft5M1OShKX6rk418CnP5wi/GLxAceXU9qgcPSl3vsyXv3ekiWT29GsAgenP4zq3vbsuiC5d5/Z\nGSkhwLt0g+FNVH2eiUzTnDHN6TgmhUOzVGh0YG5pzeIZxY/diricxLBW+nuFjCPb6Zm+ORkwbtk1\nf+03anlocLASmfbVMcxkAgObiPy4WcK6HR70bhuAUqWjc1PvexL8EVdTGRimw+VuIup/HURCZ6aX\n6H10burNlbv6J5WJo50Fiye1ovXHR6jvKyUyTs+skU3w9bCpbNNMmDBRAvw8bZgxIozQGaeo5yPl\nfKyeJW+3wsH24bpEccl5WJtD9wbG49DqUKeahCsxmXi5KliyPpLP15ymZ0MDKzdKGJ0lElYdXG1h\n5vJwPp/QknyVDk8XBb1b+yKRCOj1BiJv5TB6pnFNXxfoVk/k7PW0B87I/BuFpdkjDxkub6a/Hkbn\nsQk0+iAPMykodRbsW9a8ss0yUc5UWNCk0xmY9+MZ9p6Mw8XBkg9HN6emX8VG3fEp+ew8cRtzMym9\nW/ve66Bwc7Li1OqBhF9KQSoRaFrHtURF4H6eduy9LOHFlgYEAXachxyllvajfycrT0ebQCXfjzS+\ntmUNWHMEvhoGqTkGgt6+TXZGAt6OMP0bgS0LetC8rht+Hjb8eEKGVmcclLnvMvhVUIYmJ0+DuVyK\nubx0Be8l4ZUewbRp6Mm1uCwCvGwJLMei9ycRg0EkK1eNvY25qVXZxAOJjErno+/CycxV0b2FP5Ne\nbFChfy+iKLI7/A7RCbk0DHL+VxAyflA9urfw5WZ8DsE+9vh5Fv/Q4+5kRVquMctc0xOSsuB8jI5P\nV53k01WnOHoxjWufifg4g0pjIHgyTOsNzWvAqO9h0hcHGdBMysoYgXU7PFg/pxtSqQQvZ3P2X1bT\nqQ7kq+DEDYEBfcr/IUyvN5Cdp8HB1rzcG2jsrOUc+b4f4RdT0BtEmtZ2xfIZ7yrOL9AiCMJT1V1d\nYe9k0sIjRF6OYlovPRduQ/s3kon4cWCFTYS+eDODTmM307GWSE4BfLziJEe/74+zvVGZ29JCRrvG\nnqVa871hjek45jZNZuZjYSZyJd5ApzpKRrTL492foZHf/dfW94GvC4z/X3kQOteBDRP0CAKsPw6T\nFx3i6IqBvPJcEFsO3aTetGSqOQlcuCOwY3H7cvoUjGTnaXhh6g6ORKZgEOGtwXX4ZEyzcncSfp42\nJXK0TzsHIhLoO203BWodCgsZW+d1o2V99+JPNPFModbq6TR2CzP6agl0g1mbssjKUzP7jWYVcj1R\nFHlj7gEOn4mmRQ2RT1YITHk5lAmD6917TWA1u1I98NhZy1nydivazD5CaHUp4Td0CIgMa5qGVgcn\nL4PP3f4XC7lxckJqDugNsO4YRMyGYE89Gh2Ezkhk76l4Ojf15oeZnRg0bReh/gLXEkU6NfWja7Py\nrU36dc9NRnxyABBxd7Lkj8+eI8S/+DKN0iA3kz6Sft7ThkarZ8iHe9lyIBaAAZ2rs+b99lVSEb60\nVFjQtPLP60QvNOBiC13qwbk4PVsPxzK6X60Kud57S48yo4+WMXfLfMasMrDgp3PMHffoDsnWWs6R\n7/tzNDKJ5PQCxn92kDX/M7bPvtsL3vkZutUHRwVM+8U4EFOlgdO3IKw69zrd6vtASqYKAJlMwsb5\nz3H8QjLZeRqa1Ha9F9iVF28uOEQ161SyvxPJUkLHuVeoE+hSSCrBRNnJzFHTa+ou8vpoIQA01zU8\n984O7mx8sdQieyaebjJzNAxsomdcF+NxDXcdrWZfqbCg6czVNHYdj+biXB0KC4hLg9pTT/Ja75Ai\nm1lKyis9gmlZ34PL0ZnYbL9Cc884XmoFBgN8/Ad89ieM7wIHrsDxKPiwPyRmgVYPQXfjCbkMQjwF\nUjKNT5odQr04v3YQZ66l4eZoSWiIS7k+5EXFZTN2/kEOTddT3xe+359Pn8nbuPbbiyYtpQpg5ooI\ndty+je4dEUTY8mssc9ec5YPhFTOx43FSYWGfRCKg+Uc3qkYvVGgaOjldSUO/+8cNfQ0kZ+SVeV1z\nuZQOoV50CPVEbxDRG4w/H9gUNDpoOA08x0FmPlxPlmIzQuDQdTOW7ZNyI8mYav5wk3GNv5FIBFrW\nd+e5lj7lHjABnLiYxJtdDcik4GwDw1rrOBGZUO7XMQFXY7OQOggQcPcHQYAV3IzPqUyzTFRBBIF/\n+0QdSCrwhp2cUUCQhwTFXRfj4wy2lhIycsrekh/gbUuv1r442lnwt+qARAJTexmDJpsRMGYVyCTQ\nY4GUGm8LONmaMfsP433h0BXYd0mkWZ37RdueLgp6tvIlrJZruQcyZ66l0aamQH1f4/GI9pCcoSKz\nHD4LE4XZH5lAQSM9mAFyUDbUse8puQdVWKZpwgu16b3wMpO764i8LXD8poyvZlWc+GG7UG/mbMlj\n3Rg9uSpYukfG269Wu/f7jGwVe07FI5UIdGnqXeosgJuTFV2aevP84nhebaVn90UJGj0Ee0pwtIZL\nCVIOLO9DjWp2CILA4vXnCZ1xmgK1gefbevHFW63L+y0/EG9Xa45eV1KnmrGL40QrzQ4AACAASURB\nVFiUlLDQoiUQTJQNLxcF6nQ95AI2QDaoswy4m6QXTPwHR1tztkVqmfW7hhruMHerjImD61fY9RoG\nO3M2xsDuSOhYB5bvAytLOV4u9/82T1xIJup2NrWrO9Ko5oN15R7EqOfr0GXcLcxlOmwtYeZGCWo9\n9Gwk4WwsjO4fzPwJLRFFkdvJ+QydvpMPN2bg7mjOjx+2f6A0S3nj7argXKxIngqsLSAyDkDArpTK\n4SZKhr+bDRG3U9EFGbvIze5ICHB/Ou5BwoMEG8tCaIiLeOqH5/l242X2nYrF2d6KacND8XaruJZ0\ntUbP6Dn7+fmvaGRSgSkv1WPmyDAEQSA2MZc2ozZR19u4lx6XKefwd/1wcbAs1TU0Wj2frznHmatJ\nVPd2YPJLDTlzLQ2VRk+bBu442v07aySKIqLIYy8Mvngzg87jthDmL5KSAxK5DXu+7vtUFeNVJT5a\nFcG89eeR+gjoY0VmDWvMlKEVdzMsD4SmyyNEUSy+hdREuREa4iL+MqcT81ZHkHW3EPzVnsEVuj10\nICKBV2ft4U6ainrVbVk/pxvBvsaGnJnfhrP6z0u0qAEHr8A7rzRm4pDS/92evpLK0g3n0Gj1vNi9\nFjV8bDkflYGvuzVNahfufjMYxMfuE0VRZNz8g+w6dosGvgKHrhpYOqUtL3Q2lSxUBAmp+YSO3ESe\njRZEsFPKifi+H66OpbvnPk5K6hMrLGg6vbrsc3weBb3egEQi/MsRvfTBboJtY/ngeeN7nfijBMEu\nmEVvP77sz+MmJaOAw+cSsbKQ0THM64Hdgtl5Gj5ZeZobcRk0rOnOO680rJBuu4pAFEUMBrFSBD7/\ny9lraVyLzSLEz4H6QU6VbU6xmIKmx09l+8V/fk+i4rJpNfI3Ln2qx9kGbqdD3fek3Nj4YoWUDFQF\nRFHkWGQyd1LyaRjsRJDPg7u5dxyL48c/LyOTSRj3QoNipQ+qEkXdAyuDnDwN+yISEICOYV5lqqN7\nHJTUJz51qYeibqAJqXm82uh+cNgs0MDGS7kVbktlZZoAXB0t6d+h+kNfo9Hq6TJuM7VdsxnS0MCa\no8kMvprCxs+6V/oXrjh+2HaNcV8cpaBAT7MGrmye07VSnX3DYOeHjs0xYaIy+a9fTExXEugmxdlG\nD0A1J3Czk5Ccoazw75EoipXiXwRBKFFX6x8HYxj76V5mD9CTr4aeb91h26KeRWbNqhKpmQX0eX8X\n4edSsbKSsnRSK159LqjS7LG1ltO3rV+lXb+iqPxH9MdAi/qeLN4tRamGbCUs2yejZYPSD6YsDUvW\nR+LYaSWWbb5nyLRdpZqn9Lg4dTkVVUEeK0YaGNgUfh2v5/C5RBJSq/YMuRMXkhm7+Mj/27vvsKiu\n9IHj3zMzDL03BVQsiGBFsfduNBpjXGP2l2QTN3U3m7ZJNk3T3WTTq8aYHmNioqbYYotGEQsWxIIV\nCyIiRdowzNzy++NiCyhIEZXzeR4emeGWM+Dcee8573kPxbcpaE/pbHI5yV+eW1bfzZKkq0Zsc3/2\nZWqs2GE8nrsRikoFLcLqLu/k6Iki+t41F5den9Dk+i9ZvO5InZ2rJj78fisf3K5yZ394YBg8NVrh\nk/k76rtZlRr/3HKSrNloT+kU/Z/C/e+sYeNOuYxLbWsQQdPkv3fFP7gJAfcKQu4XtI9pyb+qsK5R\ndS1Ye5h3v01i0wsqOdN1dFs6j769ps7OVxVb92Tz8+pDpGWcndWlajrnjtqZTWA2G89faXRd5/Nf\nU7l18m88/n4ijlgNQgELOPtrJG47Ud9NlKSrRpCfG99PHcGtH1vxmCT493du/PTGyDotxjju8UUM\nj87F9jnMureU259bzoH0+ptleqqwlEUJR1iZdAynop15/s/XRasFVE2r4Aj170hmEQ++/ge3TfmN\nhC2ZOPtrxvhRI1BjddZsq3yNVenSXHPDcxVxtZr5+sVhfPKMghCiznN2fk9K556BypnVt5+7UeWG\nd4/V6Tkv5t8fJDJ9wW4sjQXKUZ3Pn+rPhMEt6RYbjEN349FZKtd10PhyrZlOUUE0Ca2bAqSVOZ5t\n4/bnlrJ2+0kaBbgy7cmBjOhpzIB8cWYS85al8Mhwhe8yQT0KaBhhfyb4+V18+YeG7N5Nc+q7CdIV\naGB8GBmL/kZhsRNvT5c6HTIrLHaw81A+GyfrCAH9YmBQW8GGnVmXbQbdufYfzafn/T/j8FPRSiDK\nz4e1H96Ah5uFu8a2518frsGhKhTb4aWfzfzwatvL3sbTZv60i2enb6DQpjK2XxNmPDMIT3cXMnNs\n9Pr7XG7v5SCumc7ssmshkYAGLlkmQvyvzfy0+tQggiYAm11h/Y4TmE2Cnu1Dq7SMSnWF+HuQkmzC\n+FQ3prcG19N/3i2p2UxfsBvb3Qp4AMfhjpdXMbZfJG6uFlZ8NJZnPkrktWWn6NQ6hBn3da+3fKab\nn1pMr2a5zL9fJ+mgnb9MWca6T2+iVRMf3vp2OzteVWkSCH/tDY0eAtvnJkyhAvbAZy/0r5c21wf/\npTMvfSeXa2O6r1S79h3N50B6AW0i/Wheh0NzHm4WzCbBvkyd1o2NGlW7M3Tuqqc8xHveXENunB2t\nF6DB7nmneHv2dp65szN/HRGF2SSYuWAnFrOJWS/F0a+eqnwv35jOi5+sZ9kTCk0C4b7P03nojT+Y\nOXkws5fuZ3g7J1NvNkYGbKXwr2/BrYMZc46grV8AE4fJ2YG1rUEETSdybAy8/yd8rKU4FDBbPVn+\n0dg6q9Fx/01t6bMkleteLyHMT+eXrYKf36ifmXqHMwuxNBZGwATQGHQT5BaU0ijQg2B/d2Y8M6ha\nxz6UUcjeI/m0CPeu8dpz9lKF9bty+f1xHbMJBsQa1dYTtmfSqokPiqrjUfbnslpgVDsT7oGt6dIm\niH6PNa715RCqozo9OnPyqjc8MaHjXdXa77QZzKjR/tLV793ZyUz9IomOTU1sPaTx5sN9uH1UdJ2c\ny2w28e6jvRkwNZExnXU2pwmimzdmcNe6zS29kLTjBWinMzRMYG+qsjcj/8zPbx7WipurEXA4nCob\nd55E1TS6xdZ87bkVm9K5e4BC+6bG41f+ojL4tXQAnIp25poIMLITPP+ThZcH9yDA142x/SNxuQaW\nLbnSNIig6emPErm+fTH/u8WYzXbXzAKmfp7Ea//qVSfn8/GykvjZeOb9nkaxXeGZByNoEV4/d/od\nWgXiPKrBCYwcoB3g7e5CcA3v8D5fsId/vrMWayMzjkyV/97TjYfK8sQ0TWf6/F2s232CmAh/Hp3Y\nvtKLh6vVjJvVxL5MlTZhxlpVqRkwwdcNIQS3j2zFxA8P8uwNCtuPwNIdZjZ/1bnWa39VqxfnXJfa\no+Piw4TYCTU7pyRdokMZhbz8WRJbXlZpEqiyJwO6P7+W0X0j8fepm6HuSWNi6BAVxIYdWQwf6sEN\n/SLrbXHrHrGhZGyx4WikgQM8dlrofUvN1ovML3LQ+x8/c6SoCMwQZHJjw/SxZ+oB7jl8ivfn7sDu\nVLlzRHSVZvIF+XmwaZ8ZMGY57kyHwLK/z7gBzen59RbaN9GICoXJcy1MGt2mzpYqkwx1EjQVFGss\nSbzwEiZfOxfg5V71CPjjrjX7UDmYfopbrjO6MIWAoe005u44VaNjVsbT3YXb6nG652ktI3yY+UR/\n/j51NcIFPK0uLHlzZI1qG+Xm2/nHm2ux36lSEqzCKXjy442M7RtJs8be3DH1d+amHMLWVsHtDzM/\nJaSROG3sRRdrFELQ+UYrfV8pYXxXSDoEp9x0frGuZ8GmDWgDdPLtZu6YreHqZaLfP9146cgiqGAC\nTnV7b06raS+OJF3pDmcWEh1mpkmg8WEcHQaN/ExkZBfXWdAEEB8TTHxMcJ0dv6qmPdqHg48VkPxm\nDpoCE0e25K4b2tTomJNnbmKfZz6OiUZahn2ZysMfJDJr8iB2p+XR7Z6fKI5zolvh28f2M+/FYWdy\nNi/k7hva8NXCXYx+00aTAI0fNpqY899+gLHY8m/vjeH5jxPJ21jKqIHNeeL2uBq9BqlydRI0lVia\n8LVzARtLS848l6WqcM5NhUmt4h25s4A51bz7zxtmfPjFRYfy2epTDIhRUVT4aq2ZPt1DK9n72vHX\nYa0Y1z+SnPxSQgPca7zSdOTPX2P30OH0tc8P7H4q7X79DlMwFCzV4VHAFexdVJKmZ+P/2adYIiu5\nq2wDiq8XXxxWMMWbcGnnwo+20/sI6O9ufGF0nHGhuFz23kjSRUU38yM1QyPpIMS3gJU7IbcIIht7\n13fTLgs/b1fWTx9LVm4JrlYzft41DxR3Hz2Fo4V25nPO2UIjNcW4OX9zznaKuzjRy1IvS/xUJn+x\nqdKgycfLSsLMm5iz/ABFJQpr7o6gTeTZopyd2wTxy9uja9x2qerqJGjKKE1mgc1EWODZnpYwoF/T\nfnVxugrNSZ55ZqhFb62jb9Xx/wdoKlhaqaxrtIXXl269bO35swn+PjXuEakvJn+w2C0ohxRjpkYG\nWPIsjO03EU3V+N76PaqLWrYxuHi60K/pUCI6RlR+8Ct79RFJuiY0CvTg02cHMuzF3/FyA7si+G7q\nMDzdr+yqzbVJCEFoLa4R2TMmlIS1mZS0MToI3FLM9GhjFMS0lSro564g4g4lpWqVjuvl4cKkMTXr\nBZNqT50ETQEeQYyLq5/lAk778xCLHq9jy7UhTAIP//pdTHXOrjnMKeKy9YhkH8hm1bRVFJ0sIrhV\nMAMfGFjh70DXdbb/sIWDy3YiTILWozsRe33F9azSH09n6etLwQrYYeBDxjF1Xcc/3J/cxblocRri\noMBSZCEk+squpitJDc0N/ZtzbGETjufYCA/2vGqWT6oNiqLxxLQNfL10H65WMy9PiueOCyTB7zl8\nivumrmRfegHtW/rz8dODadqofC7lM7fHsWlPFqveOQ4C4qOD+N/93QGYNCKanycfwuangit4LrNw\n9y0yELoa1cnac8FRwfq4t+o3aJIMJfklfP/A9zgGOiASxCaBX5Yf498YX660wK5ft5O3LInv7lMo\nVWD8hxZa3NKXVgOiKjy2UqpQnFOMR4AHLm5n71BLi0pZ88kasvZn4dvIl75398XnGlnh+loxY4xc\ne+5yq8+156TzPf3xRt79fQe2kQqUgMd8M3OfK59jVFjsoN3E73j8Ojuj4+DrtYLZmzzZ9u3ECmem\n6brO8WwbqqYTEeJ53jV23u9pTPkyCYeicu+oGB6d2OGKX66qIWmwa89J5zuRegI9VIdOxmN9iE7B\n6wXYC+y4+56/4nTmhv289xeFdmXXjRdvUHh10/4zQVN+Rj77V+8DXadlvyj8IvzwDStfasDVy5Uh\njwyp09clSZJUXd+vOoBtsHImL9PWXeWH1QfLBU3J+3Jp5KvywDDj8TNjdT5fa+fgsQKim/mhaTrf\nLNnHroO5tIn05/aRrQkLrrg48LiBzRk3sHldvizpMpBFHK5xVg8rFHK6ziYUg+bUsLiWj5fN7q4c\nyTn7+FAOmN2MBMncw7ksfmIe/fK30L9wK4ufnEf2wey6fwFSjThsDrb/vJ2krzeSsSOjvpsjSVcE\nX08rnC3LhDlf4O9Zvm6fj6cLJ/J17A7jcWEJ5BXpeHu4oOs6d728kmmz1+Jt384ncxK444Xl1MXo\njVS7Vm3O4NlpG3l79nYKix2XtK/sabrGNYptRGBoINnfZqNEKFh2W2h3Y7vzhtNOi53Qjcdf+JW9\nJxTsTsE3GyyMeLUzALt/TGLKaCf/Hmls2zRAYcbcJPo8PuKyvZaMlAyOJR/D3c+d6CHRFb4G6SyH\nzcGSJ+bSK8xGuzCVaW/soO1tvWk9uG4KGErS1eKN+3ow+snfKMlUsNhN+Bx04ZGnO5Tbrn2rAHq2\nD2fIq8cY3l7hpy0W/jq8JWHBnhxIL2BRwiEOvKni6QaPjlRo9e8j7D2ST3QzvwrOWvsKihx89use\ncgtLGdEjgl4dalZrqiH44tdUJk9fx9/7KaxPNfPFr7tImHlTlfeXQdM1zmQ2cf2U60ldnkphViGh\nfw8lskdkhdsGRwVz3avjWL1mP8IkGPVGa7xDjSnISomDpoFnt20aCMquS4vQq8OWa8Nhc5CRksH6\n2etROiiYU8zsWr6Lca+Nq7DHTDIcWHOATsElzP2XMUtnTGeFoW+vl0GT1OANig8n4aMxzF2VhrvV\nwp3PR9M4qPzkGCEE37w0lK8W7WPf0VM8fmcgNw9tCUChzUGgtwlPN+P95W6FIB8ThTZnnba91KFy\n8FgBblYzgx9ZyHFfG6V+Km/+uJ1PH+snl06pxDPT1rPwUYVOkQAqo9+08f3yA1XeX37iNAAmi4nY\nEVWrEusX4UeXW8rnwjXu0Yon52bRIkTBJOA/P1oIu6Hu3py6rrP2k7XsWbEHk4cJpUCBu4BQUHWV\nom+LSFuXRtTAipPUJXDanDQPPLs6e7MgKC1R6rFFknTl6NQ6iE6tgyrdzmw2cefo8jcaMZH+KFh5\n9ReFm3vo/LhRYHO60LYOl3RK2Z/LoIcXYBcq9jwFWoIyrmztuVYKD3+YKIOmShTYFJqe82dvFqRR\nWFz1QFfmNElVEjU4msbXdWHURx6M+MCd4KGdaT0sps7Ol7YujX2b96E9qKE8oBirCJy+FgnQfXUc\ntrrv6bqaRXSOYFaiYPE2SMuCe74wE9n14sX0JEmqGlermd/eG8OqtBD6T3Vl2b5gfnt/TI3Xm7uY\nMU8vIbuXnaJ/OFG66yh+5+RP+UPxJXz4N1Rj+jThH1+YOXQSFm2D79ebGNq96msgyp6my0RTNIpz\ninH1djWSsy8zp91JyakSPAM9Mbtcej0WIQRtx3Sk7ZiKq0/a8mwcTDiIruo0696sxiUGctJyUFor\ncHqCX0vgF2AocAJEqiB8Uv0s9nm1CGgWQJ/HhnPX52uwFzoIj4ug+z2Xr8CsJFUmN99OUYlCRIjn\nZV+HTtd1jmUV42o1n1kf7lJFhnmz5P0bLvjzJYlHSd6XQ8twH24a1LxGJQZUVePwkSL4W9kTscBX\nQBQQCG7LzYzq06zax28oZjwziAff+IO+r6QT6GPl25f70rZFQJX3l0HTZZB7JJeFzy/EqTjR7Brd\nb+9O+wsUjQQjgddhc+AZ4ImohQvJnpV7WPvxWoSbwKybuW7ydYRE1V6xyaKTRcx9fC5KUwXdRSdp\nThJjXh5DYPPAyne+AN8wXyzrLSh9FHABWoDLRhfEZwJXX1f6PtEXv4jLk2x5NYuIiyAi7pb6boYk\nnUfXdR58dx0z5u/G7CpoFuLFyndGV5hXBEbAcDzbhq+XFe8KZrldqtx8O0MeXUjqkVOoTp0JQ1rw\n5dMDazVw+8+0DXy4aCelUSquP5uZuzaNb6cMqnbgZDabCAlx58S+EogGAsDV1YTvUisOp8bInk34\n5In+tdb+a5WnuwufTh5c7f1l0HQZLJm6hJLeJRAH5MGmLzbROKYxQS3Lj6dv/mEzW+dsRVgFHr4e\nXP/c9WeSsasjPyOftTPXok5SIRiUXQqLX1nMbZ/ehqkGi/aea8uPW3DEOtCHGF3FWrBG4teJXD/l\n+mofM2pAFGlJaRybdgyTtwlRKOh7T1+Ks4tx83WjcdvGtdJ2SZIuvx9WHOTzP/bgeEgDN9i/soDb\npq5k+VvlrxlpGQUMfGgBWfklKHadKZM68+wdnWt0/vveWstOjzwcD2vghHmzD9Hrp93cP65quZ+V\nOZlXwjvfp+B4QANPUJwKv0w7TPK+nCrlUV3IvJeHMuKxxZg2CBw5KrcNiaJnbCj5RQ4Gdw3How6H\nBiWD/A3XMcWhUHyi+ExxSfyBFpB9MLtc0JS+LZ3kRcloD2jgBUUJRSx9ayk3vVb5dMiC4wUkfJFA\nUU4RYbFhdL+1OxarhdzDuZiamFCDy9Y5igVlkYI9345HwMWXk9F1nfQt6Zw6dgr/pv5EdKp47biS\nwhL0oHPG1oPAfsBeaZsvRpgEwx4fRu6hXBzFDgoyC1j10Sq0WA3TSRM7luxgzEtjqjXUKElS/dqU\nepLi6LPD70pnna2zcircdtzkZRyNLkbrrUMB/PfLbfRu14iB8WEXPYeu67w5ezvfrNiHl7sLr97V\nnT6dGpWdPwvHMM3I6nUFW6xC4q7MKgVNmTk2fl59CCEEY/tHEhJQfmjvVKEDFw8TDs+yiRgu4OJv\nIregtNLjX0yvDo1I++EWUvbn4utl5bZXfmdWyn4Ufw3zTBNznh/CqD5Na3QO6eJkIngdM7uYcfFy\ngUNlT9iBdCrM+ck+kI0arYI3RrJzvE7ewbxKz2EvsDP/qfkc9TxKXq88UveksuLtFQB4h3qjZ+hQ\nXLZxBqCBm49bpcdN+DSBZdOWsSFlA0vfW8r6L9dXuF1kl0gsiRbIBvLB8oeFyPjISo9fGSEEgc0D\nadyuMYlfJqJMVNCu01BuVcgrySNtXVqNzyFJ0uXXKswHjyNmY4IHIA4Y+UEV2bk3Dy2+7KbMB5yt\nNTannqz0HC9/sYXn5m4mOS6XhPATDH9sEcl7jcCsZZgPpoNlw2QauB02E9Ok8llv+4/mE/N/c3jk\nt0QeXryOmFvncPh4YbntIsO88XO1YkoESoDtQA50iqp+ysJpgb5uDOgSxsadWaSJQopvUSi9TsN2\no8I9b/1R4+NLFyeDpjomhGDIv4dgmWvBOsuKZbqFqG5RhESHUFp0/l2Hd6g35qNmOD0r/CB4BFe+\nuPCx5GNooRr0ASJBHadyZMMRFIdCUIsg2g5vi/ljMy6zXLB8a2HQQ4MwVbBu0rkKjhew5/c9KHeW\nBSqTFHYu2UlRdlG5bVsPak2n4Z1w+coFyycWotpGUXKqhLlPzmX528ux5dmq+NuqmK7rOIuccDoN\nywR6sI69sGa9WZUpzilmx4Id7F62W87Uk6RaNGlMG3oEh+I5w4LPNy74J7ry5ZP9yc2341S087Zt\nHOoOB8seOMGabqL5BQKsc328YDe2Uca0fDqBLU5h1rL9AHzyeD+Ctrvh87ULXp+40N4SwMM3XzjP\n9LQnPt5AQWcHJWNVSm5UOdWulGdmbiq3nYvFxOr3x9A+PQDXd0202ubDq/d048YpS+nxj5/4ctHe\nSs9Vmex8O6VBKpxOkQqB/IK6vU7pus7ChCO8/k0yidsz6/RcVyo5PFdHlFKFwxsP47Q7Ce8YzsQP\nJpKdlo1ngCeHkg7x+S2fI0wC/0h/Rj4zEnc/d1r0asH+dfs5Nv0Ywl/ACRj8bOUJa8Is4NyZpmVB\n1+mEw+63dieqXxRFJ4sIaBqAV3D5Fbr/rCS/BJOvCdW97FbQA0w+JuwFdryCzt9fCEHn8Z3pPL4z\nuq6z4PkFZKlZqF1UctNyyXoqiwnvTqh2IUohBKHtQ8lanoU2SIMsELsFjf+v7vKasvZk8fPkn9Gd\nOuiQ+Hkit3x0C+5+1ZtlI0kSJO/NYeOuLMKCPPntjZFs2n2SwmInIQFu3PD0UtIzi0GH9x/tzT1j\njZIm3z03hOH/XoRpm0DJ1RjaMYIbB1S+hpvZbIJzYgiTE6xleZzNw3zY/93NbNiZhZvVQo92IVgq\nuZEEOJ5rQ2t99rEWChnpxRVu2zLCh22fjQcgITmTYY8tMta7c4WUD3NRVZ1JFdR/qqpB8eFMnb0N\nW6wCgWBdaap0yLImdF1n0L8WsGrrcSNQE/DI+Pa89VDPOjvnlUgGTXXAWeJk3pPzsJlt6F46fAHX\nP3c9Tbs05UjSEZIXJ6P/S0f30sldnsvK91YyasqoM3k8J/eexF5oJ7hVMM4SJ3uW78HVy5Wm8U0r\n7CFqEtcE169cUReqaOEali0WooZHnZfvE9A0gICmVZ9W6d/UH1EkjG7lGGAHmEpN+IVffMaaLddG\n1t4s1EdVMIPeUqf081JOpJ4gvGP1SwQMe2wYy95cRuYbmVi9rfR7oF+NZudV5rc3fkNvocNNgAOU\nLxVWvL2C61+ofnK7JDVkXy7aw/1vr0VECUyZMLhNOPNfGYYQgtjb5nAoqhDtb0AOPPJRIl2ig+gS\nE0zP9qHsm30zm1OzCfR1o0ubQBasPUz2KTu9Oza64JIlU27rzIPT1mHrpWAqAs9dLkx6/GyQ4u1p\nZUi3ivM0L2R0j2ZsX5iLLVwBDTzWWxg9vvJp/tN/3YWtpwJlFVtsFoV35qfUKGjq2T6U6Y/05cF3\nEyguVhjYPYxvnh1U7eNVZmHCEVZtOw53ABFACrz9QwpTJnXGz9u1zs57pZFBUx3YuWgnRV5FqOPK\nuk63wx+f/MH418dzIvUESlsFylKa9O46WZ9lndlXCEFItDEOdSz5GL+99hu0AnIh4JcARr8wGrOL\nGV3X2b10N/vX7cfqYaX/P/uzc+FOCncW0qRHE7r+tWu12m4vsJO+NR1hFgz/z3B+//B3in8qxivC\ni+HPD6+0t0iYBLqmGwsEmwEdUKlx6QQ3HzdGvzC6Rse4FKXFpXA9xmtwB7pC/pb8SvaSJKkiqqpx\n3//WYr9TNYbZFVjx6TFWJmUwoHNjUvedQp+Icb0MAj1KJ2n3SbrEBAMQGujByN5NURSNIY8uZHPG\nSfRg0N/jvOTnI5lFPDF9PUezixneJYK37urOzIV78PFw4d1pvWgRXr36cQnJmew7ms/gzmEcyy5m\n5vupCAH3jm/DQ3+pfFjPYjKdyd8C4/Wba6G8wW3XRXHbdZdnVYSk3SehMUbABNAeWAi7D52iZ/vQ\ny9KGK4EMmi6B4lDY/P1mTuw/gW8jX7rf2h037/IJ1cV5xaih54w1N4aShBIAvIK9sGyxoGiKkVF2\nFDwCK85bWjVtFcoNilG8TIPcb3PZv3o/0UOiSZ6fzJYlW1D6K3AKjr50FLOPGcIgf3E+jiIHeVl5\nuLi6ED8+vsLyBn9WkFnA/CfnozZSQQFrvpVx/xuHu587QgjSt6Wzfup6dF2nw3UdaNK5fHVpD38P\nIuIiOPbDMdT2KqY0E55WT0LbXF1vKndfd4oPF0MTjMAvDQLCq95TJ0kNzIPx6QAAE59JREFUxbrt\nmUz9dit2p8r9o2K5aVCLctvY7AqKqkFw2RMWECGCzBwbZrMJP38reUcd0Axwgvm4ICKkfBrB3N/T\nSDpxkuI7y66fh+COV1dxcsHt5Obbib9rHrmxpaitdDb/mo2ao+HW2gwn4S/PLadL62AOZxcxqEMY\nz97RGZcqDMk99sF6pi/eBU1ATzN6sOyr/w5AcYnCI+8nsuVANp2aBzD1nm4V1pF68KZ2zPnnQWxm\nY3jO4w8Lz/4n7hJ+y/WvT8fGMAsjsd0dY+KPE9q1qLtlY65EMmiqIl3XWfraUo4XHUftqJJ1MIvj\nTx9n/JvjsVjP/zWGtwtnzyd7UNop4AmmtSbC2hljza0HtWbv2r3kfpoLvsBRGDhlYIXntJ+yw+kh\nahOooSq2XCOpOmVRCsqNihH5HwTdQ0e5u6wQZCbs+mQXjAVskDE5g7H/HUtAs4t/6Cd+lYijkwO9\nnzFTRV2isvDFhbj5ueHm7saR5COog4xgMPOtTIY9MowmXcoHTsMeG8bWeVs5se8Efs38iH88/qor\nDTDyqZHMfXwu2h4NHOCquDLog7rr+pakq9GmXVkMfXQRtv5GMJD4ehYOReOWP61/5u1ppXkTbw6s\nK0DrCRwD9aBO97ZGr/qsyYMYP2U55kiBlqUztEMEI3uXv7Zk5thQQrWzU5jCIC+3FF3XWbTuKCUh\nCupA4/pVukaF66CoowI6pM7OZ+/OfLQesHn1SXYfOcWcF4dc9PXtPXKKj37eScl9KngABTD5oySW\nbU7HajVxML2Qw15F2NuobNqTRcKDJ9g040Yjn+occdFBrH5/NK9/l4y9WOXeKTGM7H11lQYY0i2c\nm3pHMveDQxAK4hi8fG98rRQbvZrIoKmKSk6VkLEzA+1RDSygRWuUfFZCys8p+Ib7EtYu7Mw0/sge\nkcRlxLF52mZ0RScsPoy+d/dFUzXMLmZGPz+ajO0ZOIodhMaE4hnoWeE5Q2NCyVyTiTZUg1Ng3mmm\n0Sijzgjn9uwWAqEYAZNW9j1Aa8AVFJvCnhV76Dnp4gl7xbnF6F3O1lvSw3Xy9udBF2AFMAijQCeg\nCpXkRckVBk0mi4kuE7pc9FxXOv+m/tz6ya0c234Ms9VMRKeIcsGxJDV0H8zfia2HAmVrfNtcFV74\nZjMCaNbY+7xhm6VvjGLUk4vZveIU3t4ufPPcIFpG+KCqGtf1asqOr8azcedJQgPc6d+5cYWVs3t1\nCMX0mTCuSUFgWSPo0iEYIQRCYPQKn1YEhGNcEwXQFLRioA2UtFCZ/3oaxU858XR3ueDry8wpwRpo\npsSjbGzNB5wuGitcM4xzHQf+DZigtLXGvmn57DiQR8fW5fMt42OC+f6FiwdpV7ofXxnGhh1ZHEjP\np1PrIGIbWC8TyKDpkgjOfxM7S51s+W0LpgATpo9N3PDKDWeW9ogbF0enGzuhazq2PBu/PPcLufty\nsXpZGfDAACJ7RFZ6viGPDGHJq0s4OfUkwiLo8fceNG5nzBhrP7I9W37agtJPgSxgH/A/jK5Tf4xa\nT6dz83SjAGVlwtuGk7chD7WJaoy/r8O4OMUCKVX4BV1j3HzcaNmnZX03Q5KubOL87/ceyeeeH9ag\nZejcMaQ1HzzSBzBqF+38agJORcPFYuKd71PwGPgZTofG4F7h/PD8EG4eevH3W9fYED56qA/3v74W\nh0OjfUwA818bBsDIXk3x+NBCyUoVtbGO0EH/FjiF8UlnBsacPZaq6xWc4XxtW/ij5uiwH6N0wU6M\na2MvIA/Ycv72lR/x6te9XQjd29XeMlxXGxk0VZG7nzuNYhuROS8TtaNqvImKQX1IRbWqsB7WzFzD\n6OfPJisLIRBmwcKXFpLfJB8mgOO4g+XvLGf8G+MrXTvNzceNsVPHojpVTBbTeXdeHcd2xNXLlf2J\n+0GD4+I4TMS4s1oDJGK8wYuBDSB6V550GD8xnsKThaT9L81497sbieoAdAZ+5MxUU8sKCx0e6XAJ\nv0FJkq41/7ihLT88fJASV9W4SVsEem8o7OMEO3w+Yy93jog+k9ANRg2jJYlHeeLTDTjv0sAHlv2S\nzu1Tf+enqcMrPecdo6L528jWOJwartazw/7+Pq5snjmOJ2ds5MihIvZ7FnDc1wb3Y/TGf4YxG9gE\nbKJKVQoDfd1Y+L8RjH16KQUFDsxWgaOHZvTqBwGuYJoHWntw3WMmKsSXdi0bXu9LQyKLW1aREILh\nTw6nbUxbQnaF4H3C27jbOD2c24QKCz8qDoX8Q/kwAONOJwK0phppiVWvZm12MZfrqhZCEDM0htFT\nRhPQOABaAE3LztEfo1bTVuAw0BZcfSqfEmp2MTPk0SFM+n4Sd353J/6N/TEtNMFeMO0y4RPiQ9ix\nMMLSwxjyyBCadrm6xuQlSapd3duFsPSNUQzPj6DnoRBcdGEU2QVwA0sjQXpW+TpGn/6airOzBoGA\nC+gDYfH6o1U+rxDivIDptIhQL76ZPIg/3hvDiVwbDDaOTwDQHcjFCJjCwKRDFTqb6BfXmJxFt1Ow\n4k5+fHEo7lvMRs97CriXmrg+qBk994UwKSqa1e+NLpfPJF1bZE/TJbBYLfT4Ww8AUpelsm7+OpQ4\nIwHStNFEozaNyu2j67oRmp7EyDVSgWyjeGRtcfFwMY6vYPxFs8t+0AWwgeV3C61vb33B/f/sdNL2\nmBfHsP6b9eSk5BAcGUy3/3TD6tGwkv4kSbq4Pp0asaTTSFRVI2zsLLKSS4x6RMdAOarTsYKlQ3JO\n2Y2cIx2j9zoTVK12B7dMZoGWqRs9QjqQjpHM3R3ct5gZ2jcCL48L5zOdSwiBh5uF0X2b8f2zQ3hr\n7naEEDzxYkdG9Cyf1yldu2TQVE3RQ6LJPpTN7rd2gwlC24XSe1LvcttZrBajdtFXOkQDmYAdwjtU\nv9Djn7Uf057tC7ejzdCM2XapxvIrXqleRsmBKfEEtbj0lbWtnlb63duv1topSdK1y2w2seytkQx/\nbBG5S0qxmEx8M2VghWvKDesRwaovj6N/hTGLOBXCgipfMupS3D2iDdN+3g17gQIQx2FgXBgFuxwM\n6BbGy3dVr5bd6L7NGN238oKW0rWpwQRNJ1JPsHvFbkwmE21HtK1xNWkhBH3u7kOPv/VAU7QL9sAI\nIej/QH/WzFiDlq0hHIJGbRrRNL72hrbcfd2Z8M4EVr6zkuLMYsL6hNHv/n5X3TR/SZIuH3upwpuz\nU0g5nEvXqGAemtCuSkuJXEyHqEAyfrqVvIJSfL2sFxyqemB8Oz5bvIcjtiI0TcdsNtV6NesPH+9D\nSIA7Xy7Zi6e7hQ/f7kP/LnW3zIjUMAi9KoO6lyg4Klgf99a4Wj9udWWkZLD4v4tRexqzwiwbLYx5\naUyVCj7WluwD2ZxIPYGHvwfNujfDJMe9pXo0Y8yMzbqux9d3OxqS+JhgPenLK+O6qKoa/R/8lS22\nbEpaqHikmhncJJyf/zu8wqn+dcFmV5j3expFJU6GdA2nVRPfy3JeSaqI6F61a2KD6GlKmpuEOlQ9\ns+6PYlHY9ss2hjxy+WpmBLUMuqxBmiRJ0oVs25tD8pEcSu411oi0dVRZ9t4xjmQW0axx+eG0uuDh\nZuHWy7QEiCTVlgbR3aE61bM1iwDcjFltkiRJDVGpU8VkFcZsWwALmFwEpU71ovtJUkPXIIKmmEEx\nWFZY4CCwFyx/WIgZFFPfzZIkSaoXca2D8BNWLL8LSAfrbyaaB3vTspoL2kpSQ9EghufaDG6Druqk\n/JaCEIK4u+No1lXOfpAkqWFyd7Ow7qOx3PfWGnatzKNzVBDTX+wrawxJUiUaRNAEEDMshphhsndJ\nkiQJIDzEk19fHVHfzZCkq4q8rZAkSZIkSaoCGTRJkiRJkiRVgQyaJEmSJEmSqkAGTZIkSZIkSVUg\ngyZJkiRJkqQqkEGTJEmSJElSFcigSZIkSZIkqQpk0CRJkiRJklQFDaa4pSRd7VSnyra529i3ch/F\nucV4BnjSakAr4v4Sh9nFWESs8EQhs++eXW7fFn1bMOTxy7dAtSRJUl17bkYS81alcfh4EToQ3dSX\nx2/tyM1DW57Z5vlPknhh5pYK9596f1eeuiPuks4pgyZJukps/GojuxbvouutXQlqEUT2gWw2zdqE\no9hBr7t7nbdtjzt7EBoTeuaxm4/b5W6uJElSnSoodnDHqGhim/thNpn4ceVBJj67ArNJMH5wCwDu\nGtOGET2anLffT38c4rWvkrmuV5OKDntRMmiSpKvE/tX7ib0ulg5jOwAQ1iGM4txi9q/aXy5o8o3w\nJbRNaEWHkSRJuia8/cj5171hPSLYmZbHV4v3ngmaIkK9iAj1Om+7lz7bQptIPzq1Drrkc8qcJkm6\nSmiqhtXTet5zVk8rOno9tUiSJOnKEujrisOpXfDnOfl2lm08xi3nDOFdCtnTJElXiTZD27B7yW7C\nO4QT2DyQ7IPZ7Fq8i7aj2pbbdvW7qyktKsXN141W/VrR9dauWFzl212SpGuPomgUlThZmHCEpRuO\n8d3Lgy+47dyVaTgVjVuGtarWueRVVJKuEt3+1g3FofDLk7+ceS52ZCxdJnY589jsYiZ2ZCwRcRFY\nPaxkpGSQPC+ZguMFDH92eH00W5Ikqc6sTzlBz7t+BsBiFnzwWG/G9o+84PbfLTtA5+ggopr6Vut8\nMmiSpKtE8rxkI3/pnl4ERgaScyiHpFlJuHm7Ef9/8QB4BHjQ574+Z/YJax+Gh58Ha6evJScth8Dm\ngfXVfEmSpFrXvlUAm764kVOFpSxMOMIDbyTg42nlluHle5KOZ9tYvfU4r/2zW7XPJ4MmSboK2Avs\nJM1Kove9vYkZHgNA43aNMVlMJHycQNtRbXH3c69w3+a9m7N2+lpO7j8pgyZJkq4pnu4uxMcEAzCk\nWwT5RQ7+8+GGCoOmOcsPoOv6eSUJLpVMBJekq0BBZgGaohHY4vygJ6hFELqqU3iy8IL7CiHO+1eS\nJOla1blNEEdPFKMo5ZPBv1t2gD4dG9HkT7PpLoUMmiTpKuAVbLzJsw9kn/d89n7jsXeI9wX3PZhw\nEICglpc+vVaSJOlqkpB8gogQTyyW88ObQxmFrN+RVe0E8NPk8JwkXaH2rtzL6vdWM3HGRLxDvIns\nEcnGLzeiOlQCIwPJTstm8+zNtOjdAndfY2gu6dsknCVOGsU0wsXDhcydmSTPTyayZ6QcmpMk6ar2\n1aK9THp5NQfmTgRg0surmTi0JS0jfCiyOZm/6hDfLTvAtP/0Kbfvd8sOYDEL/lJWv6m6ZNAkSVco\nXdfRNZ3TZZgGPDyALd9tYceCHdhybXgGeBIzPIbON3c+s49fhB/bf9pO6rJUVIeKV5AXHW/sSNyE\nS1sqQJIk6UqjaTqqqqPr4O/jSliQB1O/2MbxHBt+XlZim/uz8K0RjOzdtNy+3y07wOCu4QT51Wx1\nBKHrtV8YLzgqWB/31rhaP64kSbVjxpgZm3Vdj6/vdjQk8THBetKX8rooSVci0b1q10SZ0yRJkiRJ\nklQFMmiSJEmSJEmqAhk0SZIkSZIkVYEMmiRJkiRJkqpABk2SJEmSJElVIIMmSZIkSZKkKpBBkyRJ\nkiRJUhXIoEmSJEmSJKkKZNAkSZIkSZJUBXVSEVwIcRI4XOsHliSptjTTdT24vhvRkMjroiRd0ap0\nTayToEmSJEmSJOlaI4fnJEmSJEmSqkAGTZIkSZIkSVUggyapVgkhwoQQP9Z3OyRJkq4E8pp4bZE5\nTZIkSZIkSVUge5quQkKISCFEqhDiCyHEXiHELCHEECFEghBinxCiW9lXohBiqxBinRAiumxfDyHE\nHCHELiHEfCHEBiFEfNnPioQQrwghkoUQ64UQoWXPBwsh5gohNpV99S57vr8QYlvZ11YhhHdZ23aU\n/fwOIcQH57R7gRBiwDnnel0IsVMIsbysvauEEAeFEGMu869UkqSrmLwmSpeLDJquXq2AN4E2ZV9/\nBfoAjwFPA6lAX13X44ApwNSy/f4B5Om6HgtMBrqcc0xPYL2u6x2BP4C7y55/F3hb1/WuwE3AzLLn\nHwP+qet6J6AvUHIJ7fcEVuq63hYoBF4GhgI3Ai9ewnEkSZJAXhOly8BS3w2Qqi1N1/UUACHETmCF\nruu6ECIFiAR8gS+FEFGADriU7dcH4w2Prus7hBDbzzmmA1hQ9v1mjDcswBAgVghxejsfIYQXkAC8\nJYSYBczTdT39nG0q4wCWlH2fApTquu48p/2SJEmXQl4TpTong6arV+k532vnPNYw/q4vAb/run6j\nECISWFWFYzr1s0luKmf/f5iAHrqu2/+0/atCiIXASCBBCDEcOHcbhfN7M90ucK4z7dd1XRNCyP+X\nkiRdKnlNlOqcHJ67dvkCx8q+v+Oc5xOACQBCiFigfRWOtRT41+kHQohOZf+21HU9Rdf114BNGF3i\n5zoEdBJCmIQQTYBul/4yJEmSaoW8Jko1JoOma9f/gP8KIbZyfo/iR0CwEGIXxpj5TiC/kmM9CMQL\nIbaX7Xdf2fMPCyFOd2c7gcV/2i8BSAN2Ae8BW2rygiRJkmpAXhOlGpMlBxoYIYQZcNF13S6EaAks\nB6J1XXfUc9MkSZIuO3lNlC6FHCdteDyA34UQLoAA/iEvDpIkNWDymihVmexpkiRJkiRJqgKZ0yRJ\nkiRJklQFMmiSJEmSJEmqAhk0SZIkSZIkVYEMmiRJkiRJkqpABk2SJEmSJElVIIMmSZIkSZKkKvh/\nfD1zob4Uiu8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8d2c44898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cm_bright = ListedColormap(['royalblue', 'orange', 'green'])\n",
    "for clf, title, ax in zip(models, titles, sub.flatten()):\n",
    "    score = f1_score(y_test, clf.predict(X_test), average='micro')\n",
    "    print(clf)\n",
    "    plot_contours(ax, clf, xx, yy,\n",
    "                  cmap=cm_bright, alpha=0.6)\n",
    "    ax.scatter(X0, X1, c=y_train, cmap=cm_bright, s=20, edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel(selected_features[0])\n",
    "    ax.set_ylabel(selected_features[1])\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'), \n",
    "            size=15, horizontalalignment='right')\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}